"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zui4ITyyAjFY",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"# @title Functions for building transition matrices and affordance grids.\n",
"\n",
"def build_simple_grid_stochastic_states(\n",
" size=5, terminal_states=[], p_success=1):\n",
" \"\"\"\n",
" Builds a simple grid where an agent can move LEFT, RIGHT, UP or DOWN\n",
" and actions success with probability p_success.\n",
" A terminal state is added if len(terminal_states) > 0 and will return matrix\n",
" of size (|S|+1)x|A|x(|S|+1). Moving into walls does nothing.\n",
"\n",
" Note that this is a function modified from emdp package to customise mdp\n",
" transitions.\n",
"\n",
" Args:\n",
" size: size of the grid world\n",
" terminal_state: the location of terminal states: a list of (x, y) tuples\n",
" p_success: the probabilty that an action will be successful.\n",
"\n",
" Returns:\n",
" P: transition matrix of size (|S|+1)x|A|x(|S|+1).\n",
" \"\"\"\n",
" n_actions = 4\n",
" p_fail = 1 - p_success\n",
"\n",
" n_states = size * size\n",
" # The number of entries in the state vector corresponding to grid itself.\n",
" grid_states = n_states\n",
" # Add an entry to state vector for terminal state.\n",
" if len(terminal_states) > 0:\n",
" n_states += 1\n",
" terminal_states = list(\n",
" map(lambda tupl: int(size * tupl[0] + tupl[1]), terminal_states))\n",
"\n",
" # this helper function creates the state transition list for\n",
" # taking an action in a state\n",
" def create_state_list_for_action(state_idx, action, p_success):\n",
" p_success = p_success\n",
" p_fail = 1 - p_success\n",
" transition_probs = np.zeros(n_states)\n",
" if state_idx in terminal_states:\n",
" # no matter what action you take you should go to the absorbing state\n",
" transition_probs[-1] = 1\n",
" elif state_idx == n_states - 1 and len(terminal_states) > 0:\n",
" # absorbing state, you should just transition back here whatever action you take.\n",
" transition_probs[-1] = 1\n",
"\n",
" elif action in [actions.LEFT, actions.RIGHT, actions.UP, actions.DOWN]:\n",
" # valid action, now see if we can actually execute this action\n",
" # in this state:\n",
" # TODO: distinguish between capability of slipping and taking wrong action vs failing to execute action.\n",
" if check_can_take_action(action, state_idx, size):\n",
" # yes we can\n",
" possible_actions = get_possible_actions(state_idx, size)\n",
" if action in possible_actions:\n",
" transition_probs[get_state_after_executing_action(\n",
" action, state_idx, size)] = p_success\n",
" possible_actions.remove(action)\n",
" for other_action in possible_actions:\n",
" transition_probs[get_state_after_executing_action(\n",
" other_action, state_idx, size)] = p_fail / len(possible_actions)\n",
"\n",
" else:\n",
" possible_actions = get_possible_actions(state_idx, size)\n",
" transition_probs[\n",
" state_idx] = p_success # cant take action, stay in same place\n",
" for other_action in possible_actions:\n",
" transition_probs[get_state_after_executing_action(\n",
" other_action, state_idx, size)] = p_fail / len(possible_actions)\n",
"\n",
" else:\n",
" raise InvalidActionError(\n",
" 'Invalid action {} in the 2D gridworld'.format(action))\n",
" return transition_probs\n",
"\n",
" P = np.zeros((n_states, n_actions, n_states))\n",
" for s in range(n_states):\n",
" for a in range(n_actions):\n",
" # MDP states are considered stochastic such that the probability\n",
" # of success of a s,a pair is different for diff states,\n",
" # in particular between the range of [0.1, 1.0]\n",
" p_success = random.uniform(0.10, 1.0)\n",
" P[s, a, :] = create_state_list_for_action(s, a, p_success)\n",
"\n",
" return P\n",
"\n",
"def _unit_test_P(P):\n",
" assert np.allclose(P.sum(axis=2), 1), 'P matrix is not stochastic!'\n",
"\n",
"\n",
"def build_affordance_grid(\n",
" affordances, size=13, p_success=1.0, terminal_states=()):\n",
" \"\"\"Builds a backbone for intent induced mdp transition matrix\n",
"\n",
" Args:\n",
" affordances: AF of shape |S| * |A|\n",
" size: The grid size of the mdp.\n",
" p_success: The probability of success for a transition.\n",
" mdp: Environment specified as mdp.\n",
" terminal_states: List of terminal states.\n",
"\n",
" Returns:\n",
" Returns model dynamics array of shape |S| x |A| x |S|\n",
" \"\"\"\n",
" p_fail = 1 - p_success\n",
"\n",
" n_states = size * size\n",
" grid_states = n_states # the number of entries of the state vector\n",
" # corresponding to the grid itself.\n",
" if len(terminal_states) > 0:\n",
" n_states += 1 # add an entry to state vector for terminal state\n",
" terminal_states = list(\n",
" map(lambda tupl: int(size * tupl[0] + tupl[1]), terminal_states))\n",
"\n",
" def create_state_list_for_action(state_idx, action):\n",
" transition_probs = np.zeros(n_states)\n",
" if state_idx in terminal_states:\n",
" # no matter what action you take you should go to the absorbing state\n",
" transition_probs[-1] = 1\n",
" elif state_idx == n_states - 1 and len(terminal_states) > 0:\n",
" # absorbing state, you should just transition back here whatever action you take.\n",
" transition_probs[-1] = 1\n",
"\n",
" elif action in [actions.LEFT, actions.RIGHT, actions.UP, actions.DOWN]:\n",
" # valid action, now see if we can actually execute this action\n",
" # in this state:\n",
" if check_can_take_action(action, state_idx, size):\n",
" # yes we can\n",
" possible_actions = get_possible_actions(state_idx, size)\n",
" if action in possible_actions:\n",
" transition_probs[get_state_after_executing_action(\n",
" action, state_idx, size)] = p_success\n",
" possible_actions.remove(action)\n",
" for other_action in possible_actions:\n",
" transition_probs[get_state_after_executing_action(\n",
" other_action, state_idx, size)] = p_fail / len(possible_actions)\n",
"\n",
" else:\n",
" possible_actions = get_possible_actions(state_idx, size)\n",
" transition_probs[\n",
" state_idx] = p_success # cant take action, stay in same place\n",
" for other_action in possible_actions:\n",
" transition_probs[get_state_after_executing_action(\n",
" other_action, state_idx, size)] = p_fail / len(possible_actions)\n",
" else:\n",
" raise InvalidActionError(\n",
" 'Invalid action {} in the 2D gridworld'.format(action))\n",
" return transition_probs\n",
"\n",
" n_states = size * size\n",
" n_actions = 4\n",
" if len(terminal_states) > 0:\n",
" n_states += 1 # add an entry to state vector for terminal state\n",
"\n",
" P = np.zeros((n_states, n_actions, n_states))\n",
" for s in range(n_states):\n",
" for a in range(n_actions):\n",
" if affordances[s, a] != 0.:\n",
" P[s, a, :] = create_state_list_for_action(s, a)\n",
" else:\n",
" P[s, a, s] = 1.0\n",
" return P\n",
"\n",
"\n",
"def _construct_dynamics(mdp, affordances, size, p_success=1.0, wall_locs=None):\n",
" \"\"\"\n",
" Function to construct transition dynamics P\n",
" Args:\n",
" mdp: The mdp.\n",
" affordances: The wall locations of the mdp.\n",
" size: The grid size of the mdp.\n",
" p_success: Probability of success.\n",
" wall_locs: The location of the walls in the mdp.\n",
"\n",
" Returns:\n",
" P: initialized transition matrix of shape |S| x |A| x |S|\n",
" \"\"\"\n",
"\n",
" if wall_locs is None:\n",
" raise ValueError('Please give me wall locations.')\n",
"\n",
" grid_size = mdp.size\n",
"\n",
" assert len(mdp.terminal_states) == 1, 'Only one terminal state supported.'\n",
" goal_loc = mdp.unflatten_state(\n",
" convert_int_rep_to_onehot(mdp.terminal_states[0], mdp.state_space))\n",
" # Attempt to make the desired gridworld.\n",
" reward_spec = {(goal_loc[0], goal_loc[1]): +1}\n",
"\n",
" tmb = TransitionMatrixBuilder(grid_size, has_terminal_state=True)\n",
"\n",
" # For the purposes of constructing the dynamics matrix\n",
" # and to match the way the library deals with MDPs, we set the\n",
" # walls to have affordable actions.\n",
" # affordances = affordances.copy()\n",
" # affordances[wall_locs, :] = 1\n",
" terminal_states = reward_spec.keys()\n",
" basic_affordance_grid = build_affordance_grid(\n",
" affordances,\n",
" size=mdp.size,\n",
" p_success=p_success,\n",
" terminal_states=terminal_states)\n",
"\n",
" tmb._P = basic_affordance_grid\n",
" for (r, c) in wall_locs:\n",
" tmb.add_wall_at((r, c))\n",
" P = tmb.P\n",
" _unit_test_P(P)\n",
" _checking_P(P)\n",
" return P\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "cT39F8t1VHLQ",
"colab_type": "text"
},
"source": [
"## Affordances"
]
},
{
"cell_type": "code",
"metadata": {
"id": "A4sm9YlzrSP3",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Function to compute intent completion.\n",
"def _get_intent_completed(\n",
" mdp, state, action, statet,\n",
" threshold=0.0,\n",
" intent_name='collection',\n",
" P_AF=None):\n",
" \"\"\"Determines if a transition completed an intent.\n",
"\n",
" Args:\n",
" mdp: The MDP to evaluate the intent on.\n",
" state: The one hot representation of the current state\n",
" action: The integer representation of the action (currently unused).\n",
" statet: The state after taking the action.\n",
" threshold: The threshold to select actions with\n",
" intent_name: The name of the intent to calculate. Three intents are\n",
" currently supported \"collection\", \"up\" and \"left\". Collection is the union\n",
" over all intents.\n",
" P_AF: A probability transition matrix for the affordances.\n",
"\n",
" Returns:\n",
" An integer that represents if the intent is completed.\n",
" \"\"\"\n",
" x_t, y_t = mdp.unflatten_state(state)\n",
" x_tp1, y_tp1 = mdp.unflatten_state(statet)\n",
"\n",
" delta_x = x_t - x_tp1\n",
" delta_y = y_t - y_tp1\n",
"\n",
" state_int = convert_onehot_to_int(state)\n",
" next_state_int = convert_onehot_to_int(statet)\n",
"\n",
" # Select affordable actions based on the threshold.\n",
" # Default value is True for default threshold=0.0\n",
" prob_gt_threshold = True\n",
" if P_AF is not None:\n",
" prob_of_going_there = P_AF[state_int, action, next_state_int]\n",
" else:\n",
" prob_of_going_there = mdp.P[state_int, action, next_state_int]\n",
" prob_gt_threshold = prob_of_going_there >= threshold\n",
"\n",
" if intent_name == 'collection':\n",
" # If the agent has moved in any direction return a 1.0.\n",
" if (int(delta_x) or int(delta_y)) and prob_gt_threshold:\n",
" return 1.0\n",
" else:\n",
" return 0.0\n",
" elif intent_name == 'up':\n",
" if not(_is_absorbing(next_state_int, mdp.size)):\n",
" if (x_tp1 < x_t) and prob_gt_threshold:\n",
" return 1.0\n",
" else:\n",
" return 0.0\n",
" else:\n",
" if action==actions.UP:\n",
" return 1.0\n",
" else:\n",
" return 0.0\n",
" elif intent_name == 'left':\n",
" if not(_is_absorbing(next_state_int, mdp.size)):\n",
" if (y_tp1 < y_t) and prob_gt_threshold:\n",
" return 1.0\n",
" else:\n",
" return 0.0\n",
" else:\n",
" if action==actions.LEFT:\n",
" return 1.0\n",
" else:\n",
" return 0.0\n",
" else:\n",
" print(\"Not a valid Intent, See _get_intent_completed\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "nCjcsWdWTJmp",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Function to compute the affordances from an intent completion function.\n",
"def _compute_affordances(\n",
" mdp, n_states, n_actions, intent_name,\n",
" threshold, mdp_wall_locs):\n",
" '''\n",
" Args:\n",
" n_states: number of states in mdp\n",
" n_actions: number of actions in mdp \n",
" intent_name: name of the intent collection, up, etc.\n",
" threshold: float value between 0-1\n",
" mdp_wall_locs: list of mdp wall locations\n",
"\n",
" Returns:\n",
" Affordances in the form of a |S| * |A| array. The array has entries of\n",
" 1.0 or 0.0 based on the intent I_a(s') is true or not respectively.\n",
" '''\n",
" affordances = np.zeros((n_states, n_actions))\n",
" for s in range(n_states):\n",
" x_t, y_t = mdp.unflatten_state(\n",
" convert_int_rep_to_onehot(s, mdp.state_space))\n",
" if (x_t, y_t) in mdp_wall_locs:\n",
" # You're in a wall nothing is affordable.\n",
" continue\n",
" for a in range(n_actions):\n",
" if not(_is_absorbing(s, mdp.size)):\n",
" s_next = get_state_after_executing_action(a, s, mdp.size)\n",
" x, y = mdp.unflatten_state(\n",
" convert_int_rep_to_onehot(s_next, mdp.state_space))\n",
" if (x,y) in mdp_wall_locs:\n",
" s_next = s\n",
" intent = _get_intent_completed(\n",
" mdp, state=convert_int_rep_to_onehot(s, mdp.state_space),\n",
" action=a, statet=convert_int_rep_to_onehot(\n",
" s_next, mdp.state_space),\n",
" threshold=threshold, intent_name=intent_name)\n",
" affordances[s, a] = intent\n",
"\n",
" # Hard code terminal states to be affordable since all actions are possible.\n",
" if s in mdp.terminal_states:\n",
" affordances[s, :] = 1.0\n",
" # Hard code absorbing state to be affordable (Environmental quirk).\n",
" affordances[-1, :] = 1.0\n",
" return affordances"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "kUZ6lanOJcTV",
"colab_type": "text"
},
"source": [
"## Value Iteration and Policy Evaluation algorithms"
]
},
{
"cell_type": "code",
"metadata": {
"id": "E5delmlK9xFf",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Code for value iteration.\n",
"# We build this function on the following base code:\n",
"# https://github.com/andrecianflone/policy_value_iteration\n",
"def value_iteration(\n",
" r, p, theta=0.0001, gamma=0.99, max_iteration=100,\n",
" AF=None, seed=None, min_mask_value=-10, mdp_wall_locs=None):\n",
" \"\"\"Value iteration computes value & policy for a reward and transiton matrix.\n",
"\n",
" Args:\n",
" r: Rewards, array of shape |S| x |A|.\n",
" p: State transition probabilities, array of shape |S| x |A| x |S|.\n",
" theta: Stop if the change in value fn is less than this value.\n",
" gamma: Discount factor.\n",
" max_iteration: Maximum number of iterations to run VI.\n",
" AF: Affordances of shape |S| x |A|.\n",
" seed: Seed value for randomness.\n",
" min_mask_value: An optional check for values to not be negative.\n",
" mdp_wall_locs: Wall locations in the mdp.\n",
"\n",
" Returns:\n",
" pi: Policy, |S| x |A|.\n",
" v: State values, |S|.\n",
" it: Number of iterations.\n",
" seconds: Planning time in seconds.\n",
" v_log: Log of value functions from init to convergence.\n",
" \"\"\"\n",
" if AF is not None:\n",
" AF = AF.copy()\n",
" assert mdp_wall_locs is not None, 'If AF is given, wall locs must also be given.'\n",
" if seed is not None:\n",
" np.random.seed(seed)\n",
" random.seed(seed)\n",
" t1 = datetime.now()\n",
" n_states, n_actions = p.shape[:2]\n",
" v = np.zeros(n_states)\n",
" v_log = np.zeros((max_iteration + 1, n_states))\n",
"\n",
" if mdp_wall_locs:\n",
" wall_states_idx = list(\n",
" map(convert_onehot_to_int, map(mdp.flatten_state, mdp_wall_locs)))\n",
"\n",
" # Mask out walls to be zero. But uncovered states should be -inf.\n",
" if AF is not None:\n",
" # Accounting for emdp environment behaviour.\n",
" AF[wall_states_idx] = 1\n",
" mask = np.logical_not(AF).astype(np.float32)\n",
" for it in range(max_iteration+1):\n",
" q = r + gamma * np.einsum('ijk, k->ij', p, v)\n",
" minimum_v = np.min(q)\n",
"\n",
" if AF is not None:\n",
" q = AF * q + mask * minimum_v\n",
" \n",
" v_new = np.max(q, axis=1)\n",
" if np.all(np.absolute(v-v_new) < theta):\n",
" v = v_new\n",
" v_log[it, :] = v\n",
" v_iters = v_log[:it, :]\n",
" break\n",
" v = v_new\n",
" v_log[it, :] = v\n",
" v_iters = v_log[:it, :]\n",
"\n",
" # Greedy policy extraction.\n",
" q_values = r + gamma * np.einsum('ijk, k->ij', p, v_new)\n",
" if AF is not None:\n",
" q_values = q_values * AF + mask * minimum_v\n",
"\n",
" # Use \"random\" argmax with stochastic tie-breaking:\n",
" rargmax = lambda arr: np.random.choice(np.flatnonzero(arr))\n",
" best_actions = np.apply_along_axis(\n",
" rargmax, 1, np.isclose(q_values, q_values.max(-1, keepdims=True)))\n",
" pi = np.eye(r.shape[1])[best_actions]\n",
" assert pi.shape == r.shape\n",
" \n",
" t2 = datetime.now()\n",
" seconds = (t2 - t1).total_seconds()\n",
" return pi, v, it+1, seconds, v_log"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "72u1xS4tKM7R",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Code for policy evaluation.\n",
"\n",
"def _policy_evaluation_exact(pi, r, p, gamma=0.99):\n",
" \"\"\"\n",
" Evaluate policy by taking the inverse\n",
" Args:\n",
" pi: Policy, array of shape |S| x |A|.\n",
" r: Rewards, array of shape |S| x |A|.\n",
" p: State transition probabilities, array of shape |S| x |A| x |S|.\n",
" Return:\n",
" v: 1D array with updated state values\n",
" \"\"\"\n",
" # Rewards according to policy: Hadamard product and row-wise sum\n",
" r_pi = np.einsum('ij,ij->i', pi, r)\n",
"\n",
" # Policy-weighted transitions:\n",
" # multiply p by pi by broadcasting pi, then sum second axis\n",
" # result is an array of shape |S| x |S|\n",
" p_pi = np.einsum('ijk, ij->ik', p, pi)\n",
" v = np.dot(inv((np.eye(p_pi.shape[0]) - gamma*p_pi)), r_pi)\n",
" return v"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "xVH35aHyLfj9",
"colab_type": "text"
},
"source": [
"#Sec 6.1 Experiment 1: Planning with Intents\n",
"Evaluating the impact of intents and affordances on planning. $||V^{\\pi^{*}_{I}}_{M} - V^{\\pi^{*}}_{M}||_{n}$"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LG2pxLBcSqC2",
"colab_type": "code",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 329
},
"outputId": "6af0376f-9a4d-4488-bd61-cdd0850ee95e"
},
"source": [
"#@title Evaluate policy obtained from MDP M vs I in original MDP M\n",
"#------------------------------------------------------------------------#\n",
"# 1. Compute optimal value function and optimal policy in I \n",
"# 2. Policy Evaluation in MDP M\n",
"# 3. Report planning time in M vs I \n",
"# 4. Report L2 value loss \n",
"#------------------------------------------------------------------------#\n",
"\n",
"p_success_probs = [1.0, 0.75, 0.50, 0.40, 0.30, 0.25]\n",
"thresholds = [0, 0.15, 0.25, 0.35, 0.45, 0.65, 0.75, 1.0]\n",
"max_iterations = 10000\n",
"\n",
"# Initialize empty arrays to store results.\n",
"valuefn_I = np.zeros(\n",
" (len(p_success_probs), len(thresholds), max_iterations+1, mdp.state_space))\n",
"v_pi_star_I_M_thresholds = np.zeros(\n",
" (len(p_success_probs), len(thresholds), mdp.state_space))\n",
"V_star_I_plan_time = np.zeros((len(p_success_probs), len(thresholds)))\n",
"V_star_M_plan_time = np.zeros(len(p_success_probs))\n",
"v_pi_star_M_M = np.zeros((len(p_success_probs), mdp.state_space))\n",
"\n",
"# Stores the ratio of |AF|/(|S| x |A|)\n",
"AF_SA_size_ratio = np.zeros((len(p_success_probs), len(thresholds)))\n",
"\n",
"# Iterate over the thresholds and success probs to create data for the plot.\n",
"for idx_p, p_success in enumerate(p_success_probs):\n",
" for ind, k in enumerate(thresholds):\n",
" # Create an mdp with a success prob of p_success\n",
" mdp, mdp_wall_locs = build_one_room_example(p_success=p_success)\n",
"\n",
" # |S| x |A| size in base MDP M.\n",
" state_action_space = mdp.state_space * mdp.action_space\n",
"\n",
" # Compute affordance matrix.\n",
" AF = _compute_affordances(\n",
" mdp=mdp,\n",
" n_states=mdp.state_space,\n",
" n_actions=mdp.action_space,\n",
" intent_name=\"collection\",\n",
" threshold=k,\n",
" mdp_wall_locs=mdp_wall_locs)\n",
" \n",
" # |S| x |A| size in intended MDP M_I\n",
" AF_size = np.count_nonzero(AF)\n",
" AF_SA_size_ratio[idx_p, ind] = AF_size/state_action_space\n",
" \n",
" #construct P_I with a determinsitic probability\n",
" P_affordances = _construct_dynamics(\n",
" mdp, affordances=AF, size=mdp.size, p_success=1.0,\n",
" wall_locs=mdp_wall_locs)\n",
"\n",
" _checking_P(P_affordances)\n",
"\n",
" try:\n",
" # Compute optimal value function in MDP I with affordances\n",
" (policy_star_I, V_star_I, _,\n",
" V_star_I_seconds, V_star_I_iters) = value_iteration(\n",
" mdp.R, P_affordances, max_iteration=max_iterations,\n",
" AF=AF, mdp_wall_locs=mdp_wall_locs)\n",
" except RuntimeError:\n",
" print(f'No affordances found for threshold={k}, p_success={p_success}.')\n",
" continue\n",
" \n",
" V_star_I_plan_time[idx_p, ind] = V_star_I_seconds\n",
" \n",
" # Evaluate the optimal policies from MDP I in the original mdp M.\n",
" v_pi_star_I_M_thresholds[idx_p, ind, :] = _policy_evaluation_exact(\n",
" pi=policy_star_I, \n",
" r=mdp.R, p=mdp.P, \n",
" gamma=mdp.gamma)\n",
" \n",
" # Compute optimal value function in original mdp.P\n",
" V_star_M = np.zeros(mdp.state_space)\n",
" (policy_star_M, V_star_M, _,\n",
" V_star_M_seconds, V_star_M_iters) = value_iteration(\n",
" mdp.R, mdp.P, max_iteration=max_iterations)\n",
"\n",
" V_star_M_plan_time[idx_p] = V_star_M_seconds\n",
"\n",
" # Evaluate the optimal policies in M in the original environment M \n",
" v_pi_star_M_M[idx_p] = _policy_evaluation_exact(\n",
" pi=policy_star_M, \n",
" r=mdp.R, \n",
" p=mdp.P, \n",
" gamma=mdp.gamma)\n",
"\n",
"# Compute the absolute error wrto the ground mdp.\n",
"value_loss_to_plot = np.zeros((len(p_success_probs), len(thresholds)))\n",
"for p_id in range(len(p_success_probs)):\n",
" for _thresh in range(len(thresholds)):\n",
" value_loss_to_plot[p_id, _thresh] = np.linalg.norm(\n",
" abs(v_pi_star_M_M[p_id, :] - \n",
" v_pi_star_I_M_thresholds[p_id, _thresh, :])\n",
" )\n",
"\n",
"fig, ax = plt.subplots()\n",
"x_axis = np.linspace(0, 1, len(thresholds))\n",
"for i in range(value_loss_to_plot.shape[0]):\n",
" ax.plot(\n",
" x_axis, value_loss_to_plot[i, :], color=colors[i],\n",
" label='p={}'.format(p_success_probs[i]), linewidth=3.00,\n",
" marker=markers[i], markersize=14)\n",
"\n",
"ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.19),\n",
" fancybox=True, shadow=True, ncol=7,\n",
" facecolor='w', fontsize=10)\n",
"ax.set_xlabel(\"Threshold $\\kappa$\", fontsize=18)\n",
"ax.set_ylabel(\"$ ||V^{\\pi^{*}_{I}}_M - V^{*}_M||_2$\", fontsize=18) \n",
"plt.title(\"Planning with Intents\")\n",
"matplotlib.rc('axes', edgecolor='black')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wWgD4QVJoNR3",
"colab_type": "text"
},
"source": [
"# Sec 6.2 Experiment 2: Planning time with & w/o Affordances"
]
},
{
"cell_type": "code",
"metadata": {
"id": "WlY_AIqwGiqf",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Utility to create One-room and Pachinko Environments\n",
"\n",
"# One-Room gridsize_dict\n",
"ONE_ROOM_DIFF_GRIDSIZES = {\n",
" 7: \"\"\"#######\n",
"# #\n",
"# g #\n",
"# # #\n",
"# ### #\n",
"#s #\n",
"#######\"\"\".split('\\n'),\n",
" 9: \"\"\"#########\n",
"# #\n",
"# g #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"#s #\n",
"#########\"\"\".split('\\n'),\n",
" 13: \"\"\"#############\n",
"# #\n",
"# g #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# ####### #\n",
"# #\n",
"# #\n",
"# #\n",
"#s #\n",
"#############\"\"\".split('\\n'),\n",
" 15: \"\"\"###############\n",
"# #\n",
"# g #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"#s #\n",
"###############\"\"\".split('\\n'),\n",
" 17: \"\"\"#################\n",
"# #\n",
"# g #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# ########### #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"#s #\n",
"#################\"\"\".split('\\n'),\n",
" 19: \"\"\"###################\n",
"# #\n",
"# g #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# ############# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"#s #\n",
"###################\"\"\".split('\\n'),\n",
" 25: \"\"\"#########################\n",
"# #\n",
"# g #\n",
"# #\n",
"# #\n",
"# #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# # #\n",
"# ################# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"# #\n",
"#s #\n",
"#########################\"\"\".split('\\n')\n",
"}\n",
"\n",
"def build_one_room_gridsize(gamma=0.99, seed=2017,p_success=1.0, grid_size=13):\n",
" _ONE_ROOM_TXT = ONE_ROOM_DIFF_GRIDSIZES[grid_size]\n",
" char_matrix = get_char_matrix(_ONE_ROOM_TXT)\n",
" return build_gridworld_from_char_matrix(\n",
" char_matrix, p_success=p_success, seed=seed, gamma=gamma)\n",
"\n",
"\n",
"# pachinko_gridsize_dict\n",
"PACHINKO_DIFF_GRIDSIZES = {\n",
" 7: \"\"\"#######\n",
"# #\n",
"# # # #\n",
"# g #\n",
"# # # #\n",
"# s #\n",
"#######\"\"\".split('\\n'),\n",
" 9: \"\"\"#########\n",
"# #\n",
"# # # # #\n",
"# g #\n",
"# # # # #\n",
"# #\n",
"# # # # #\n",
"# s #\n",
"#########\"\"\".split('\\n'),\n",
" 13: \"\"\"#############\n",
"# #\n",
"# # # # # # #\n",
"# g #\n",
"# # # # # # #\n",
"# #\n",
"# # # # # # #\n",
"# #\n",
"# # # # # # #\n",
"# #\n",
"# # # # # # #\n",
"# s #\n",
"#############\"\"\".split('\\n'),\n",
" 15: \"\"\"###############\n",
"# #\n",
"# # # # # # # #\n",
"# g #\n",
"# # # # # # # #\n",
"# #\n",
"# # # # # # # #\n",
"# #\n",
"# # # # # # # #\n",
"# #\n",
"# # # # # # # #\n",
"# #\n",
"# # # # # # # #\n",
"# s #\n",
"###############\"\"\".split('\\n'),\n",
" 17: \"\"\"#################\n",
"# #\n",
"# # # # # # # # #\n",
"# g #\n",
"# # # # # # # # #\n",
"# #\n",
"# # # # # # # # #\n",
"# #\n",
"# # # # # # # # #\n",
"# #\n",
"# # # # # # # # #\n",
"# #\n",
"# # # # # # # # #\n",
"# #\n",
"# # # # # # # # #\n",
"# s #\n",
"#################\"\"\".split('\\n'),\n",
" 19: \"\"\"###################\n",
"# #\n",
"# # # # # # # # # #\n",
"# g #\n",
"# # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # #\n",
"# s #\n",
"###################\"\"\".split('\\n'),\n",
" 25: \"\"\"#########################\n",
"# #\n",
"# # # # # # # # # # # # #\n",
"# g #\n",
"# # # # # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # # # # #\n",
"# #\n",
"# # # # # # # # # # # # #\n",
"# s #\n",
"#########################\"\"\".split('\\n')\n",
"}\n",
"\n",
"def build_pachinko_gridsize(gamma=0.99, seed=2017,p_success=1.0, grid_size=13):\n",
" _PACHINKO_ROOMS_TXT = PACHINKO_DIFF_GRIDSIZES[grid_size]\n",
" char_matrix = get_char_matrix(_PACHINKO_ROOMS_TXT)\n",
" return build_gridworld_from_char_matrix(\n",
" char_matrix, p_success=p_success, seed=seed, gamma=gamma)\n",
"\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VYFvBmjloZ85",
"colab_type": "code",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 245
},
"outputId": "bad5093c-0733-4244-89d8-57dfa6d87cd5"
},
"source": [
"#@title Planning Time with Increasing Grid Size: One-Room\n",
"#------------------------------------------------------------------------#\n",
"# 1. MDP's will have p_success 0.5\n",
"# 2. Choose a threshold of 0.5 for AF computation\n",
"# 3. For different factors in range(factors):\n",
"# Compute V_star_M, V_star_M_I\n",
"# Planning time for each\n",
"# Return V_star_M, V_star_M_I plan times\n",
"# 4. Multiple runs to account for randomness\n",
"#------------------------------------------------------------------------#\n",
"\n",
"seed = 10000 \n",
"p_success = 0.5\n",
"threshold = 0.5\n",
"max_iterations = 10000\n",
"nruns = 10\n",
"grid_sizes = [7, 9, 13, 15, 17, 19, 25] \n",
"\n",
"V_star_I_plan_time = np.zeros((len(grid_sizes), nruns))\n",
"V_star_M_plan_time = np.zeros((len(grid_sizes), nruns))\n",
"\n",
"# Run VI for MDP M\n",
"for size_idx, size_val in enumerate(grid_sizes):\n",
" for run_id in range(nruns):\n",
" # Create an mdp with grid size size_val, fixed p\n",
" _mdp, _mdp_walls = build_one_room_gridsize(\n",
" grid_size=size_val, p_success=p_success)\n",
"\n",
" # Compute V* in original _mdp.P\n",
" (policy_star_M, V_star_M, _,\n",
" V_star_M_seconds, V_star_M_iters) = value_iteration(\n",
" _mdp.R, _mdp.P, max_iteration=max_iterations, \n",
" seed=seed+run_id)\n",
" \n",
" V_star_M_plan_time[size_idx, run_id] = V_star_M_seconds\n",
"\n",
" # Compute Affordances AF based on intent I\n",
" _AF = _compute_affordances(mdp=_mdp,\n",
" n_states=_mdp.state_space,\n",
" n_actions=_mdp.action_space,\n",
" intent_name=\"collection\",\n",
" threshold=threshold,\n",
" mdp_wall_locs=_mdp_walls)\n",
"\n",
" #construct P_I with a determinsitic probability\n",
" _P_affordances = _construct_dynamics(_mdp,\n",
" affordances=_AF,\n",
" size=_mdp.size,\n",
" p_success=1.0,\n",
" wall_locs=_mdp_walls)\n",
" _checking_P(_P_affordances)\n",
"\n",
" # Compute V*_I in mdp M_I with AF\n",
" (policy_star_I, V_star_I, _,\n",
" V_star_I_seconds, V_star_I_iters) = value_iteration(\n",
" _mdp.R, _P_affordances, max_iteration=max_iterations,\n",
" seed=seed+run_id)\n",
" \n",
" V_star_I_plan_time[size_idx, run_id] = V_star_I_seconds\n",
"\n",
"V_star_I_plan_time_avg = np.mean(V_star_I_plan_time, axis=1)\n",
"V_star_I_plan_time_std = np.std(V_star_I_plan_time, axis=1)\n",
"V_star_I_plan_time_CI = V_star_I_plan_time_std/np.sqrt(nruns)\n",
"\n",
"\n",
"V_star_M_plan_time_avg = np.mean(V_star_M_plan_time, axis=1)\n",
"V_star_M_plan_time_std = np.std(V_star_M_plan_time, axis=1)\n",
"V_star_M_plan_time_CI = V_star_M_plan_time_std/np.sqrt(nruns) \n",
"\n",
"#@title Plot Planning Time: TwoRooms\n",
"fig = plt.figure(figsize=(4,3.5))\n",
"ax1 = fig.add_subplot(1,1,1)\n",
"# sns.set_context(\"paper\")\n",
"sns.set_style('white')\n",
"sns.set_context(\"paper\", font_scale=1.85)\n",
"\n",
"fig.patch.set_facecolor('1.0')\n",
"\n",
"plt.grid(False)\n",
"x_axis = grid_sizes\n",
"xi = list(range(len(x_axis)))\n",
"\n",
"ax1.plot(xi, V_star_I_plan_time_avg, color = colors[1], label = '$V^{*}_{M_I}$', \n",
" linewidth=4.00, marker=markers[1], markersize=10)\n",
"ax1.fill_between(xi, V_star_I_plan_time_avg-V_star_I_plan_time_CI,\n",
" V_star_I_plan_time_avg+V_star_I_plan_time_CI,\n",
" facecolor=colors[1], edgecolor=colors[1], alpha=0.5)\n",
"\n",
"ax1.plot(xi, V_star_M_plan_time_avg, color = colors[3], label = '$V^{*}_{M}$',\n",
" linewidth=4.00, marker=markers[3], markersize=10)\n",
"ax1.fill_between(xi, V_star_M_plan_time_avg-V_star_M_plan_time_CI,\n",
" V_star_M_plan_time_avg+V_star_M_plan_time_CI,\n",
" facecolor=colors[3], edgecolor=colors[3], alpha=0.5)\n",
"\n",
"ax1.legend(loc='upper left', bbox_to_anchor=(0.05, 0.9),\n",
" fancybox=True, shadow=True, ncol=1,\n",
" facecolor='w', fontsize=15)\n",
"ax1.set_xlabel(\"Grid size\", fontsize=16)\n",
"ax1.set_ylabel(\"Planning Time\\n(seconds)\", fontsize=16) \n",
"xticks_pos = [0, 3, 6]\n",
"ax1.set_xticks(xticks_pos)\n",
"ax1.set_xticklabels([grid_sizes[i] for i in xticks_pos])\n",
"fig.tight_layout()\n",
"\n",
"plt.show()\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "CyIEYxUubTNw",
"colab_type": "code",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 245
},
"outputId": "e68a7f32-bc80-4d00-9a45-25e6bc673fa9"
},
"source": [
"#@title Planning Time with Increasing Grid Size: Pachinko\n",
"#------------------------------------------------------------------------#\n",
"# 1. MDP's will have p_success 0.5\n",
"# 2. Choose a threshold of 0.5 for AF computation\n",
"# 3. For different factors in range(factors):\n",
"# Compute V_star_M, V_star_M_I\n",
"# Planning time for each\n",
"# Return V_star_M, V_star_M_I plan times\n",
"#------------------------------------------------------------------------#\n",
"\n",
"seed = 10000\n",
"p_success = 0.5\n",
"threshold = 0.5\n",
"max_iterations = 20000\n",
"grid_sizes = [7, 9, 13, 15, 17, 19, 25] \n",
"nruns = 10\n",
"\n",
"outer_dir = os.path.dirname('/content/')\n",
"dir_name = \"planning_valueloss\"\n",
"dir_name = os.path.join(outer_dir, dir_name)\n",
"if not os.path.exists(dir_name):\n",
" os.makedirs(dir_name)\n",
"\n",
"V_star_I_plan_time = np.zeros((len(grid_sizes), nruns))\n",
"V_star_M_plan_time = np.zeros((len(grid_sizes), nruns))\n",
"\n",
"\n",
"# Run VI for MDP M\n",
"for size_idx, size_val in enumerate(grid_sizes):\n",
" for run_id in range(nruns):\n",
" # Create an mdp with grid size size_val, fixed p\n",
" _mdp, _mdp_walls = build_pachinko_gridsize(\n",
" grid_size=size_val, p_success=p_success)\n",
"\n",
" # Compute V* in original _mdp.P\n",
" (policy_star_M, V_star_M, _,\n",
" V_star_M_seconds, V_star_M_iters) = value_iteration(\n",
" _mdp.R, _mdp.P, max_iteration=max_iterations,\n",
" seed=seed+run_id)\n",
" \n",
" V_star_M_plan_time[size_idx, run_id] = V_star_M_seconds\n",
"\n",
" # Compute Affordances AF based on intent I\n",
" _AF = _compute_affordances(mdp=_mdp,\n",
" n_states=_mdp.state_space,\n",
" n_actions=_mdp.action_space,\n",
" intent_name=\"collection\",\n",
" threshold=threshold,\n",
" mdp_wall_locs=_mdp_walls)\n",
" \n",
" \n",
" #construct P_I with a determinsitic probability\n",
" _P_affordances = _construct_dynamics(_mdp,\n",
" affordances=_AF,\n",
" size=_mdp.size,\n",
" p_success=1.0,\n",
" wall_locs=_mdp_walls)\n",
" _checking_P(_P_affordances)\n",
"\n",
" # Compute V*_I in mdp M_I with AF\n",
" (policy_star_I, V_star_I, _,\n",
" V_star_I_seconds, V_star_I_iters) = value_iteration(\n",
" _mdp.R, _P_affordances, max_iteration=max_iterations,\n",
" seed=seed+run_id)\n",
" \n",
" V_star_I_plan_time[size_idx, run_id] = V_star_I_seconds\n",
"\n",
"\n",
"V_star_I_plan_time_avg = np.mean(V_star_I_plan_time, axis=1)\n",
"V_star_I_plan_time_std = np.std(V_star_I_plan_time, axis=1)\n",
"V_star_I_plan_time_CI = V_star_I_plan_time_std/np.sqrt(nruns)\n",
"\n",
"\n",
"V_star_M_plan_time_avg = np.mean(V_star_M_plan_time, axis=1)\n",
"V_star_M_plan_time_std = np.std(V_star_M_plan_time, axis=1)\n",
"V_star_M_plan_time_CI = V_star_M_plan_time_std/np.sqrt(nruns) \n",
"\n",
"\n",
"#@title Plot Planning Time: Pachinko\n",
"fig = plt.figure(figsize=(4,3.5))\n",
"ax1 = fig.add_subplot(1,1,1)\n",
"sns.set_style('white')\n",
"sns.set_context(\"paper\", font_scale=1.85)\n",
"fig.patch.set_facecolor('1.0')\n",
"plt.grid(False)\n",
"x_axis = grid_sizes\n",
"xi = list(range(len(x_axis)))\n",
"\n",
"ax1.plot(xi, V_star_I_plan_time_avg, color = colors[1], label = '$V^{*}_{M_I}$', \n",
" linewidth=4.00, marker=markers[1], markersize=10)\n",
"ax1.fill_between(xi, V_star_I_plan_time_avg-V_star_I_plan_time_CI,\n",
" V_star_I_plan_time_avg+V_star_I_plan_time_CI,\n",
" facecolor=colors[1], edgecolor=colors[1], alpha=0.5)\n",
"\n",
"ax1.plot(xi, V_star_M_plan_time_avg, color = colors[3], label = '$V^{*}_{M}$',\n",
" linewidth=4.00, marker=markers[3], markersize=10)\n",
"ax1.fill_between(xi, V_star_M_plan_time_avg-V_star_M_plan_time_CI,\n",
" V_star_M_plan_time_avg+V_star_M_plan_time_CI,\n",
" facecolor=colors[3], edgecolor=colors[3], alpha=0.5)\n",
"\n",
"ax1.legend(loc='upper left', bbox_to_anchor=(0.05, 0.9),\n",
" fancybox=True, shadow=True, ncol=1,\n",
" facecolor='w', fontsize=15)\n",
"ax1.set_xlabel(\"Grid size\", fontsize=16)\n",
"ax1.set_ylabel(\"Planning Time\\n(seconds)\", fontsize=16) \n",
"xticks_pos = [0, 3, 6]\n",
"ax1.set_xticks(xticks_pos)\n",
"ax1.set_xticklabels([grid_sizes[i] for i in xticks_pos])\n",
"fig.tight_layout()\n",
"\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r05HA2UWhKz6",
"colab_type": "text"
},
"source": [
"# Sec 6.3 Experiment 3: Planning Loss Bound\n",
"$\\Big|\\Big|V^*_M - V^{\\pi^{*}_{\\hat{M}_{{{\\mathcal{A} \\mathcal{F}}_{\\cal I}}}}}_M \\Big|\\Big|_{\\infty}$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SAxhAKT3emQW",
"colab_type": "text"
},
"source": [
"## Model Learning"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CZQ7dOYZfJZD",
"colab_type": "text"
},
"source": [
"To learn the model from the experience-data, we consider the count based approach:\n",
"\n",
"Transition Dynamics computation $\\hat{P_{\\cal I}}(s' \\mid s,a) = \\frac{1}{n} \\sum \\text{COUNT}(s,a, s')$\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "fa0XlSG4co_v",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Helper functions to collect data and create transition matrices.\n",
"class Actions(enum.IntEnum):\n",
" LEFT = actions.LEFT\n",
" RIGHT = actions.RIGHT\n",
" UP = actions.UP\n",
" DOWN = actions.DOWN\n",
" \n",
"def get_randomized_state(mdp):\n",
" \"\"\"Generates a random state and sets mdp current state to that.\"\"\"\n",
" state_id = np.random.randint(0, mdp.state_space-1)\n",
" x, y = mdp.unflatten_state(convert_int_rep_to_onehot(state_id, mdp.state_space))\n",
" while (x,y) in mdp_wall_locs:\n",
" state_id = np.random.randint(0, mdp.state_space-1)\n",
" x, y = mdp.unflatten_state(\n",
" convert_int_rep_to_onehot(state_id, mdp.state_space))\n",
" mdp.set_current_state_to((x,y))\n",
" state_onehot = mdp.current_state\n",
" return state_onehot\n",
"\n",
"def get_trajectories_transitions(\n",
" mdp, num_trajectories=500, max_trajectory_length=50, policy=None,\n",
" intent_name='collection', random_starts=False, seed=None):\n",
" \"\"\"Takes transitions samples from an environment.\n",
"\n",
" Args:\n",
" mdp: The MDP to evaluate the intent on.\n",
" num_trajectories: The total number of trajectories to sample.\n",
" max_trajectory_length: The maximum length of the trajectory.\n",
" policy: The policy to sample using. If none is given a random policy\n",
" is used. The policy must take a single argument, the one hot\n",
" representation of the state. \n",
" intent_name: Name of the intent to be considered\n",
" random_starts: Data collection including random starts\n",
" seed: seed for randomness \n",
" Returns:\n",
" The trajectories collected from the environment:\n",
" This is a 4-tuple containing the batch of state, action, state' \n",
" and reward\n",
" Human Readable transitions:\n",
" A set containing the unique transitions in the trajectory batch.\n",
" \"\"\"\n",
" if seed is not None:\n",
" np.random.seed(seed)\n",
" random.seed(seed)\n",
" print(f'seed set to {seed}')\n",
"\n",
" trajectory = []\n",
" if random_starts:\n",
" s_t = get_randomized_state(mdp)\n",
" else:\n",
" s_t = mdp.reset()\n",
" trajectory_length = 0\n",
" human_readable = set()\n",
" if policy is None:\n",
" def policy(_):\n",
" return np.random.randint(mdp.action_space)\n",
"\n",
" for _ in range(num_trajectories):\n",
" action = policy(s_t)\n",
" s_tp1, reward, done, _ = mdp.step(action)\n",
" state_int = get_current_state_integer(s_t)\n",
" intent = _get_intent_completed(\n",
" mdp, s_t, action, s_tp1, intent_name=intent_name)\n",
"\n",
" # Human readable vesion:\n",
" human_readable.add((\n",
" mdp.unflatten_state(s_t),\n",
" Actions(action),\n",
" mdp.unflatten_state(s_tp1),\n",
" reward))\n",
"\n",
" trajectory.append((\n",
" convert_onehot_to_int(s_t), action,\n",
" convert_onehot_to_int(s_tp1), reward)\n",
" )\n",
" trajectory_length += 1\n",
" if done or trajectory_length > max_trajectory_length:\n",
" if random_starts:\n",
" s_t = get_randomized_state(mdp)\n",
" else:\n",
" s_t = mdp.reset()\n",
" else:\n",
" s_t = s_tp1\n",
"\n",
" return trajectory, human_readable\n",
"\n",
"def get_stochastic_states_P(mdp, wall_locs):\n",
" \"\"\"\n",
" Function to initialize P with non-uniform probability\n",
" of transitioning to the neighborhood states.\n",
" Introduces stochasticity in actions.\n",
"\n",
" Args:\n",
" mdp: The mdp to get the affordances from.\n",
" wall_locs: The wall locations of the mdp.\n",
"\n",
" Returns:\n",
" P: initialized transition matrix\n",
" \"\"\"\n",
" grid_size = mdp.size\n",
"\n",
" assert len(mdp.terminal_states) == 1, 'only one terminal state supported.'\n",
" goal_loc = mdp.unflatten_state(\n",
" convert_int_rep_to_onehot(\n",
" mdp.terminal_states[0], mdp.state_space))\n",
"\n",
" # Attempt to make the desired gridworld.\n",
" reward_spec = {(goal_loc[0], goal_loc[1]): +1}\n",
"\n",
" tmb = TransitionMatrixBuilder(grid_size, has_terminal_state=True)\n",
" terminal_state = mdp.unflatten_state(\n",
" convert_int_rep_to_onehot(mdp.terminal_states, mdp.state_space))\n",
" stochastic_P = build_simple_grid_stochastic_states(\n",
" size=grid_size,\n",
" terminal_states=[terminal_state])\n",
" tmb._P = stochastic_P\n",
" for (r, c) in wall_locs:\n",
" tmb.add_wall_at((r, c))\n",
" P = tmb.P\n",
" _unit_test_P(P)\n",
" _checking_P(P)\n",
" return P\n",
"\n",
"def get_uniform_phat(mdp, wall_locs):\n",
" \"\"\"\n",
" Function to initialize P with uniform probability\n",
" of transitioning to the neighborhood states\n",
"\n",
" Args:\n",
" mdp: The to get a uniform starting state for.\n",
" wall_locs: The locations of the walls.\n",
"\n",
" Returns:\n",
" P: initialized transition matrix\n",
" \"\"\"\n",
" grid_size = mdp.size\n",
"\n",
" assert len(mdp.terminal_states) == 1, 'only one terminal state supported.'\n",
" goal_loc = mdp.unflatten_state(\n",
" convert_int_rep_to_onehot(\n",
" mdp.terminal_states[0], mdp.state_space))\n",
" # Attempt to make the desired gridworld.\n",
" reward_spec = {(goal_loc[0], goal_loc[1]): +1}\n",
"\n",
" tmb = TransitionMatrixBuilder(grid_size, has_terminal_state=True)\n",
" terminal_state_idx = mdp.unflatten_state(\n",
" convert_int_rep_to_onehot(mdp.terminal_states, mdp.state_space))\n",
" uniform_P = build_simple_grid(\n",
" size=grid_size, \n",
" terminal_states=[terminal_state_idx],\n",
" p_success=0.25)\n",
" tmb._P = uniform_P\n",
" for (r, c) in wall_locs:\n",
" tmb.add_wall_at((r, c))\n",
" P = tmb.P\n",
" _unit_test_P(P)\n",
" _checking_P(P)\n",
" return P\n",
"\n"
],
"execution_count": 35,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9NV8NBmVeoj1",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Function: Code to learn a count based model from data.\n",
"\n",
"def learn_model_from_data(\n",
" mdp, mdp_wall_locs,\n",
" num_trajectories=50, \n",
" max_trajectory_length=10,\n",
" random_starts=False,\n",
" affordances=None,\n",
" policy=None,\n",
" seed=None,\n",
" trajectories=None):\n",
" \"\"\"Learns a transition dynamics from sampled data.\n",
"\n",
" Args:\n",
" mdp: The MDP to sample from.\n",
" mdp_wall_locs: The wall locations in that mdp.\n",
" num_trajectories: The total number of trajectories to sample.\n",
" max_trajectory_length: The maximum length of the trajectory.\n",
" random_starts: rollouts are performed with random starting state.\n",
" affordances: The affordances of shape |S| * |A|\n",
" policy: The policy to sample using. If none is given a random policy\n",
" is used. The policy must take a single argument, the one hot\n",
" representation of the state. \n",
" seed: seed for randomness in any sampling used.\n",
" passing_data: set True if previously collected data is being passed\n",
" trajectories: tf trajectories from previously collected data\n",
" Returns:\n",
" The transition model P_hat\n",
" \"\"\"\n",
" if seed is not None:\n",
" np.random.seed(seed)\n",
" random.seed(seed)\n",
"\n",
" Mhat = np.zeros((mdp.state_space, mdp.action_space, mdp.state_space))\n",
" Phat = np.zeros((mdp.state_space, mdp.action_space, mdp.state_space))\n",
"\n",
" if trajectories is None:\n",
" trajectories, _ = get_trajectories_transitions(\n",
" mdp=mdp, num_trajectories=num_trajectories,\n",
" max_trajectory_length=max_trajectory_length,\n",
" random_starts=random_starts)\n",
" else:\n",
" trajectories = trajectories\n",
"\n",
" for (s,a,s_dash, r) in trajectories:\n",
" Mhat[s,a,s_dash] += 1\n",
"\n",
" # Use get_random_phat instead of a naive initialization to take into account\n",
" # the wall locations and the fact that we cannot transition into walls.\n",
" Phat_init_uniform = get_uniform_phat(mdp, mdp_wall_locs)\n",
" \n",
" for (s, a, s_dash, r) in trajectories:\n",
" if affordances is None or affordances[s,a] !=0:\n",
" Phat[s,a,s_dash] = Mhat[s,a,s_dash]/np.sum(Mhat[s,a,:])\n",
"\n",
" # Copy over \"initialized uniform state transition\" here.\n",
" # i.e. if you have never seen a transition (s, a), \n",
" # P (s,a, s') = 1/4 (except walls)\n",
" P_visited_mask = np.sum(Phat, 2) == 0\n",
" for s in range(mdp.state_space):\n",
" for a in range(mdp.action_space):\n",
" if P_visited_mask[s, a]:\n",
" if affordances is None or affordances[s,a] >= 0.0:\n",
" Phat[s, a] = Phat_init_uniform[s, a]\n",
"\n",
" return Phat"
],
"execution_count": 36,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fv5ilraeV7gD",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Planning Value Loss Evaluation\n",
"#------------------------------------------------------------------------#\n",
"# 1. Computationally build affordances\n",
"# 2. Learn a model for those affordances from the data experience\n",
"# 3. Compute optimal value function and optimal policy in M_M and M_hat_I_M\n",
"# 4. Policy Evaluation in MDP M \n",
"# 5. Compare loss for different number of samples \n",
"# 6. Hypothesis: the planning value loss shrinks with more and more data\n",
"# for low data regime, there would be an intermediate value of |AF_I| which\n",
"# yields the optimal planning value loss.\n",
"#------------------------------------------------------------------------#\n",
"\n",
"n_trajectories = [250, 400, 500, 750, 2000, 10000]\n",
"thresholds = [0.0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.9, 1.0]\n",
"\n",
"p_success = 0.70 #@param {type:\"slider\", min:0, max:1, step:0.1}\n",
"max_iterations = 10000 #@param {type:\"integer\"}\n",
"max_trajectory_length = 10#@param {type:\"integer\"}\n",
"seed = 10000 #@param {type:\"integer\"}\n",
"nruns = 10\n",
"\n",
"v_pi_star_Mhat_I_M_thresh_nsamples = np.zeros(\n",
" (nruns, len(n_trajectories), len(thresholds), mdp.state_space))\n",
"\n",
"# Create an mdp M\n",
"mdp, mdp_wall_locs = build_pachinko_gridsize(grid_size=19)\n",
"\n",
"mdp.P = get_stochastic_states_P(mdp, mdp_wall_locs)\n",
"\n",
"AF_sizes = np.zeros((len(thresholds)))\n",
"\n",
"for n_traj_idx, n_traj_val in enumerate(n_trajectories):\n",
" for ind, k in enumerate(thresholds):\n",
" for run_id in range(nruns):\n",
" # ------------Step 1: Compute Affordances ------------\n",
" # Compute Affordances AF based on intent I\n",
" AF = _compute_affordances(mdp=mdp,\n",
" n_states=mdp.state_space,\n",
" n_actions=mdp.action_space,\n",
" intent_name=\"collection\",\n",
" threshold=k,\n",
" mdp_wall_locs=mdp_wall_locs)\n",
" \n",
" \n",
" # ------------Step 2: Learn a Model for AF ------------\n",
" # Learn P_hat_I from data: M_hat_I - Model with AF\n",
" Phat_I = learn_model_from_data(\n",
" mdp, mdp_wall_locs,\n",
" num_trajectories=n_traj_val,\n",
" max_trajectory_length=max_trajectory_length,\n",
" random_starts=True,\n",
" affordances=AF,\n",
" policy=None,\n",
" seed=seed+run_id)\n",
"\n",
" # ------------Step 3: Value Iteration------------ \n",
" # Value iteration - pi*M_hat_I\n",
" (policy_star_Mhat_I, V_star_Mhat_I, _, \n",
" V_star_Mhat_I_seconds, V_star_Mhat_I_iters) = value_iteration(\n",
" mdp.R, Phat_I, max_iteration=max_iterations, seed=seed+run_id,\n",
" AF=AF, mdp_wall_locs=mdp_wall_locs)\n",
"\n",
" # # ------------Step 3: Policy Evaluation------------\n",
" # pi*M_hat_I evaluated in M\n",
" v_pi_star_Mhat_I_M_thresh_nsamples[run_id, n_traj_idx, ind, :] = _policy_evaluation_exact(pi=policy_star_Mhat_I,\n",
" r=mdp.R,\n",
" p=mdp.P,\n",
" gamma=mdp.gamma)\n",
"\n",
" AF_sizes[ind] = np.count_nonzero(AF) \n",
" \n",
"(policy_star_M, V_star_M, _, \n",
"V_star_M_seconds, V_star_M_iters) = value_iteration(mdp.R,\n",
" mdp.P,\n",
" max_iteration=max_iterations,\n",
" seed=seed) \n",
"# pi*M evaluated in M\n",
"v_pi_star_M_M = _policy_evaluation_exact(pi=policy_star_M,\n",
" r=mdp.R,\n",
" p=mdp.P,\n",
" gamma=mdp.gamma)\n",
"\n",
"value_loss_to_plot = np.zeros((len(n_trajectories), len(thresholds)))\n",
"value_loss_to_plot_std = np.zeros((len(n_trajectories), len(thresholds)))\n",
"value_loss_to_plot_CI = np.zeros((len(n_trajectories), len(thresholds)))\n",
"for n_id in range(len(n_trajectories)):\n",
" for _thresh in range(len(thresholds)):\n",
" value_loss_raw_values = [] \n",
" for run_id in range(nruns):\n",
" sliced_result = v_pi_star_Mhat_I_M_thresh_nsamples[run_id, n_id, _thresh, :]\n",
" value_loss_raw_values.append(np.linalg.norm(\n",
" abs(v_pi_star_M_M - sliced_result)))\n",
" value_loss_to_plot[n_id, _thresh] = np.mean(value_loss_raw_values)\n",
" value_loss_to_plot_std[n_id, _thresh] = np.std(value_loss_raw_values)\n",
" value_loss_to_plot_CI[n_id, _thresh] = value_loss_to_plot_std[n_id, _thresh]/np.sqrt(nruns)"
],
"execution_count": 38,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "OwWVblmnYqv0",
"colab_type": "code",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 333
},
"outputId": "d2bf4025-a399-479f-ae64-764fc2c3b645"
},
"source": [
"#@title Plotting Planning Value Loss Evaluation\n",
"\n",
"fig, ax1 = plt.subplots()\n",
"x_axis = thresholds\n",
"for i in range(value_loss_to_plot.shape[0]):\n",
" plt.plot(x_axis, value_loss_to_plot[i, :], color = colors[i], label = 'n={}'.format(int(n_trajectories[i]/max_trajectory_length)),\n",
" linewidth = 3.00, marker=markers[i], markersize=10)\n",
" plt.fill_between(x_axis, value_loss_to_plot[i, :]-value_loss_to_plot_CI[i,:],\n",
" value_loss_to_plot[i, :]+value_loss_to_plot_CI[i,:],\n",
" facecolor=colors[i], edgecolor=colors[i], alpha=0.25)\n",
"\n",
"ax1.set_xticks([0, 0.25, 0.5, 0.75, 1.0])\n",
"ax1.legend(loc='upper center', bbox_to_anchor=(0.5, -0.19),\n",
" fancybox=True, shadow=True, ncol=7,\n",
" facecolor='w', fontsize=10)\n",
"ax1.set_xlabel(\"Threshold ($k$)\", fontsize=16)\n",
"ax1.set_ylabel(\"$||V^*_M - V^{\\pi^{*}_{\\hat{M}_{{{\\mathcal{A} \\mathcal{F}}_{\\cal I}}}}}_M ||_2$\", fontsize=18) \n",
"plt.title(\"Model Learning: Value Loss Analysis\")\n",
"matplotlib.rc('axes', edgecolor='black')\n",
"plt.show()"
],
"execution_count": 39,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "t3lzaMwFLi4D",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Helper function to plot affordance arrows.\n",
"\n",
"def plot_affordances_camera_ready(\n",
" nstates, nactions, affordances, mdp, mdp_wall_locs, ax=None,\n",
" headwidth=2.7, linewidths=1, scale=1.9, headlength=2,\n",
" wall_color=(0, 0, 0, 1), grid_kwargs=None, figsize=(14, 8)):\n",
" \"\"\"\n",
" Plots the environment with walls.\n",
" Args:\n",
" nstates: Number of state in the mdp\n",
" nactions: Number of actions in the mdp\n",
" affordances: Affordances of shape |S| * |A|\n",
" mdp: The mdp to plot\n",
" mdp_wall_locs:Locations of the walls for plotting them in a different color\n",
" ax: The axes to plot this on\n",
" headwidth: quiver arguments for arrows\n",
" linewidths: quiver arguments for arrows\n",
" scale: quiver arguments for arrows\n",
" headlength: quiver arguments for arrows\n",
" wall_color: RGB color of the walls\n",
" grid_kwargs: grid argrument specification.\n",
" figsize: Dimensions of the figure.\n",
"\n",
" Returns:\n",
" Visualization of the environment\n",
" \"\"\"\n",
" grid_kwargs = grid_kwargs or {}\n",
" if ax is None:\n",
" fig = plt.figure(figsize=figsize)\n",
" ax = fig.add_subplot(111)\n",
" plot_environment(\n",
" mdp, ax,\n",
" wall_locs=mdp_wall_locs,\n",
" plot_grid=True,\n",
" grid_kwargs=grid_kwargs,\n",
" wall_color=wall_color)\n",
" action_symbols = []\n",
" for s in range(nstates):\n",
" for a in range(nactions):\n",
" one_hot_state = convert_int_rep_to_onehot(s, nstates)\n",
" y_pos, x_pos = mdp.unflatten_state(one_hot_state)\n",
" if (y_pos, x_pos) not in mdp_wall_locs:\n",
" if affordances[s, a] == 1.0:\n",
" left_arrow = (-0.8, 0)\n",
" right_arrow = (0.8, 0)\n",
" up_arrow = (0, -0.8)\n",
" down_arrow = (0, 0.8)\n",
" if a == actions.LEFT: # Left\n",
" ax.quiver(\n",
" x_pos,y_pos,*left_arrow, color=DEFAULT_ARROW_COLOR, alpha=1.0,\n",
" angles='xy', scale_units='xy', scale=scale,\n",
" headwidth=headwidth, linewidths=linewidths,\n",
" headlength=headlength) #L\n",
" if a == actions.RIGHT: #Right\n",
" ax.quiver(\n",
" x_pos,y_pos,*right_arrow, color=DEFAULT_ARROW_COLOR, alpha=1.0,\n",
" angles='xy', scale_units='xy', scale=scale,\n",
" headwidth=headwidth, linewidths=linewidths,\n",
" headlength=headlength) #R\n",
" if a == actions.UP: #Up\n",
" ax.quiver(\n",
" x_pos,y_pos,*up_arrow, color=DEFAULT_ARROW_COLOR, alpha=1.0,\n",
" angles='xy', scale_units='xy', scale=scale,\n",
" headwidth=headwidth, linewidths=linewidths,\n",
" headlength=headlength) #U\n",
" if a == actions.DOWN: #Down\n",
" ax.quiver(\n",
" x_pos,y_pos,*down_arrow,color=DEFAULT_ARROW_COLOR, alpha=1.0,\n",
" angles='xy', scale_units='xy', scale=scale,\n",
" headwidth=headwidth, linewidths=linewidths,\n",
" headlength=headlength) #D\n",
"\n",
" return ax\n"
],
"execution_count": 40,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0bM1DormLHHr",
"colab_type": "code",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 390
},
"outputId": "7e2c289a-03bc-4602-e066-373e317d2d8d"
},
"source": [
"#@title Plot affordances computed from transition matrix.\n",
"intent_name = 'collection' #@param [\"collection\", \"up\", \"left\"]\n",
"world_name = 'pachinko' #@param [\"pachinko\", \"one_room\", \"four_room\"]\n",
"grid_size = 7#@param {type:'integer'}\n",
"headwidth = 5#@param {type:'number'}\n",
"\n",
"if world_name == 'one_room':\n",
" mdp, mdp_wall_locs = build_one_room_gridsize(grid_size=grid_size)\n",
"elif world_name == 'pachinko':\n",
" mdp, mdp_wall_locs = build_pachinko_gridsize(grid_size=grid_size)\n",
"elif world_name == 'four_room':\n",
" mdp, mdp_wall_locs = build_four_rooms_example()\n",
"else:\n",
" raise ValueError('Unknown environment!')\n",
"\n",
"AF = _compute_affordances(\n",
" mdp,\n",
" mdp.state_space,\n",
" mdp.action_space,\n",
" intent_name=intent_name,\n",
" threshold=0.0,\n",
" mdp_wall_locs=mdp_wall_locs)\n",
"\n",
"if intent_name == 'up':\n",
" AF[mdp.terminal_states[0], :] = 0\n",
" AF[mdp.terminal_states[0], actions.LEFT] = 1\n",
"\n",
"plot_affordances_camera_ready(\n",
" mdp.state_space, \n",
" mdp.action_space, \n",
" affordances=AF, mdp=mdp,\n",
" mdp_wall_locs=mdp_wall_locs,\n",
" linewidths=0,\n",
" headwidth=headwidth,\n",
" headlength=4,\n",
" wall_color=(220, 220, 220, 0.5),\n",
" figsize=(5,5),\n",
" grid_kwargs={'color':(220 / 255, 220 / 255, 220 / 255, 0.8)}\n",
" )\n",
"plt.axis('off')\n",
"plt.tight_layout()"
],
"execution_count": 43,
"outputs": [
{
"output_type": "stream",
"text": [
"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HbXIGuRCX35O",
"colab_type": "text"
},
"source": [
"# Sec 7.1 Learn Affordances"
]
},
{
"cell_type": "code",
"metadata": {
"id": "K3p1iqV8waWh",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Helper function to collect data from environment.\n",
"class Intent(enum.IntEnum):\n",
" completed = 1.0\n",
" incomplete = 0.0\n",
"\n",
"def get_trajectories(\n",
" mdp, num_rollouts=500, max_trajectory_length=50, policy=None,\n",
" intent_name='collection', random_starts=False, seed=None):\n",
" \"\"\"Takes trajectory samples from an environment.\n",
"\n",
" Args:\n",
" mdp: The MDP to evaluate the intent on.\n",
" num_rollouts: The total number of trajectories to sample.\n",
" max_trajectory_length: The maximum length of the trajectory.\n",
" policy: The policy to sample using. If none is given a random policy\n",
" is used. The policy must take a single argument, the one hot\n",
" representation of the state. If using a tensorflow function make sure to\n",
" handle batching within the policy itself.\n",
" intent_name: The name of the intent to evalaute.\n",
" random_starts: randomly sampled start state per rollout\n",
" seed: set a specific seed\n",
"\n",
" Returns:\n",
" The trajectories collected from the environment:\n",
" This is a 4-tuple containing the batch of state, action, state' and intent\n",
" target.\n",
" Human Readable transitions:\n",
" A set containing the unique transitions in the trajectory batch and if the\n",
" intent was completed.\n",
" \"\"\"\n",
" if seed is not None:\n",
" np.random.seed(seed)\n",
" random.seed(seed)\n",
" mdp.set_seed(seed)\n",
"\n",
" trajectory = []\n",
" if random_starts:\n",
" s_t = get_randomized_state(mdp)\n",
" else:\n",
" s_t = mdp.reset()\n",
" trajectory_length = 0\n",
" human_readable = set()\n",
" if policy is None:\n",
" def policy(_):\n",
" return np.random.randint(mdp.action_space)\n",
"\n",
" for _ in range(num_rollouts):\n",
" action = policy(s_t)\n",
" s_tp1, reward, done, _ = mdp.step(action)\n",
" state_int = get_current_state_integer(s_t)\n",
" intent = _get_intent_completed(\n",
" mdp, s_t, action, s_tp1, intent_name=intent_name)\n",
"\n",
" # Human readable vesion:\n",
" human_readable.add((\n",
" mdp.unflatten_state(s_t),\n",
" Actions(action),\n",
" mdp.unflatten_state(s_tp1),\n",
" Intent(intent)))\n",
"\n",
" # Prepare things for tensorflow:\n",
" s_tf = tf.constant(s_t.astype(np.float32))\n",
" s_tp1_tf = tf.constant(s_tp1.astype(np.float32))\n",
" a_tf = tf.one_hot(action, mdp.action_space)\n",
" # The mask will only consider the action that was actually taken.\n",
" mask_tf = tf.cast(a_tf, tf.float32)\n",
" # Computing targets\n",
" if intent > 0.0:\n",
" # if a completed the intent I_a(s')\n",
" targets = tf.one_hot(action, mdp.action_space)\n",
" else:\n",
" # vector of zeros if action a completed no intent.\n",
" targets = tf.zeros_like(a_tf)\n",
"\n",
" trajectory.append((\n",
" s_tf, a_tf, s_tp1_tf, targets)\n",
" )\n",
" trajectory_length += 1\n",
" if done or trajectory_length > max_trajectory_length:\n",
" if random_starts:\n",
" s_t = get_randomized_state(mdp)\n",
" else:\n",
" s_t = mdp.reset()\n",
" else:\n",
" s_t = s_tp1\n",
"\n",
" trajectory = list(map(tf.stack, zip(*trajectory)))\n",
" \n",
" return trajectory, human_readable"
],
"execution_count": 44,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4GMq1t77eigL",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Function: Populate Learned Affordances\n",
"def get_learned_affordances(mdp, affordnet, classification_threshold=0.55):\n",
" '''Gets the learned affordances from the environment.\n",
" Args:\n",
" mdp: The mdp to get the affordances from.\n",
" affordnet: The affordance network.\n",
" classification_threshold: accuracy of the classifier\n",
"\n",
" Returns:\n",
" affordances in the form of a |S| * |A|\n",
" '''\n",
" n_states, n_actions = mdp.state_space, mdp.action_space\n",
" affordances = np.zeros((n_states, n_actions))\n",
" for s in range(n_states):\n",
" action_prob_predictions = affordnet(tf.eye(n_states))[s]\n",
" actions_affordable = tf.where(\n",
" tf.greater_equal(\n",
" action_prob_predictions, tf.constant(classification_threshold)))[:,-1]\n",
" for ind, a in enumerate(actions_affordable.numpy()):\n",
" affordances[s,a] = 1.0\n",
" return affordances"
],
"execution_count": 45,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "t789yH3IkGjR",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Function: Affordance training code.\n",
"def train_afford_net(\n",
" mdp, network, optimizer,\n",
" intent_name='collection',\n",
" num_train_steps=10,\n",
" fresh_data=True,\n",
" num_rollouts=1,\n",
" max_trajectory_length=100,\n",
" optimize_performance=False,\n",
" debug=False,\n",
" print_losses=False,\n",
" random_starts=False,\n",
" passing_data=False,\n",
" trajectories=None,\n",
" unique_transitions=None):\n",
" \"\"\"Trains an affordance network.\n",
"\n",
" Args:\n",
" mdp: The mdp to collect training data from.\n",
" network: The affordance network.\n",
" optimizer: The optimizer to use for training.\n",
" intent_name: The name of the intent to train affordances for.\n",
" num_train_steps: The total number of training steps.\n",
" fresh_data: Use fresh data at every before completing a training step.\n",
" num_rollouts: The number of rollout trajectories per training step.\n",
" max_trajectory_length: The maximum length of each trajectory\n",
" optimizer_performance: Use tf.function to speed up training. (Right now\n",
" there are no apparent speed benefits of this function...?)\n",
" debug: Debug mode prints out the human readable transitions.\n",
" print_losses: Prints out the losses at every training step.\n",
" random_starts: randomly sampled start state per rollout\n",
" passing_data: set True if previously collected data is being passed\n",
" trajectories: tf trajectories from previously collected data\n",
" unique_transitions: tf unique_transitions from previously collected data\n",
" \"\"\" \n",
"\n",
" def _train_step(trajectory):\n",
" with tf.GradientTape() as tape:\n",
" s_t, a_t, s_tp1, intent_target = trajectory\n",
" preds = network(s_t)\n",
" mask = tf.cast(a_t, tf.float32)\n",
"\n",
" intent_target = tf.reshape(intent_target, (-1, 1))\n",
" preds = tf.reshape(preds, (-1, 1))\n",
" mask = tf.reshape(mask, (-1,))\n",
"\n",
" loss = tf.keras.losses.binary_crossentropy(intent_target, preds)\n",
" masked_loss = (loss * mask)\n",
"\n",
" total_loss = tf.reduce_sum(masked_loss) / tf.reduce_sum(1-mask)\n",
" grads = tape.gradient(total_loss, network.trainable_variables)\n",
" optimizer.apply_gradients(zip(grads, network.trainable_variables))\n",
"\n",
" return total_loss\n",
"\n",
" if optimize_performance and not debug:\n",
" print('Training step has been optimized.')\n",
" _train_step = tf.function(_train_step)\n",
"\n",
" initial_data_collected = False\n",
" for i in range(num_train_steps):\n",
" if not initial_data_collected or fresh_data:\n",
" initial_data_collected = True\n",
" running_time = time.time()\n",
" if not passing_data:\n",
" trajectories, unique_transitions = get_trajectories(\n",
" mdp, num_rollouts=num_rollouts,\n",
" max_trajectory_length=max_trajectory_length,\n",
" intent_name=intent_name,\n",
" random_starts=random_starts)\n",
" else:\n",
" trajectories = trajectories\n",
" unique_transitions = unique_transitions\n",
" collection_running_time = time.time() - running_time\n",
" if debug: print('unique_transitions:', unique_transitions)\n",
" running_time = time.time()\n",
" loss = _train_step(trajectories)\n",
" if debug or print_losses: \n",
" print(\n",
" 'loss:', loss.numpy().item(),\n",
" 'collection_loop_time', collection_running_time,\n",
" 'train_loop_time', time.time() - running_time)\n"
],
"execution_count": 46,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bNMt4bEJKFJz",
"colab_type": "code",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 407
},
"outputId": "873aedbb-2205-4261-e513-b64a69753349"
},
"source": [
"intent_name = 'collection' #@param {type:'string'}\n",
"world_name = 'pachinko' #@param {type:'string'}\n",
"grid_size = 9#@param {type:'integer'}\n",
"headwidth = 5#@param {type:'number'}\n",
"classification_threshold = 0.95#@param {type:'number'}\n",
"num_transitions = 2000#@param {type:'number'}\n",
"max_trajectory_length = 10#@param {type:'number'}\n",
"\n",
"if world_name == 'one_room':\n",
" mdp, mdp_wall_locs = build_one_room_gridsize(grid_size=grid_size)\n",
"elif world_name == 'pachinko':\n",
" mdp, mdp_wall_locs = build_pachinko_gridsize(grid_size=grid_size)\n",
"elif world_name == 'four_room':\n",
" mdp, mdp_wall_locs = build_four_rooms_example()\n",
"\n",
"\n",
"network = tf.keras.layers.Dense(\n",
" mdp.action_space,\n",
" activation=tf.keras.activations.sigmoid)\n",
"\n",
"sgd = tf.keras.optimizers.Adam(learning_rate=0.1)\n",
"\n",
"train_afford_net(\n",
" mdp, \n",
" network,\n",
" sgd,\n",
" intent_name=intent_name,\n",
" num_train_steps=5000,\n",
" num_rollouts=num_transitions,\n",
" fresh_data=False,\n",
" max_trajectory_length=max_trajectory_length,\n",
" debug=False,\n",
" print_losses=False,\n",
" optimize_performance=True,\n",
" random_starts=True)\n",
"\n",
"AF_learned = get_learned_affordances(\n",
" mdp,\n",
" affordnet=network, \n",
" classification_threshold=0.95)\n",
"\n",
"plot_affordances_camera_ready(\n",
" mdp.state_space, \n",
" mdp.action_space, \n",
" affordances=AF_learned, mdp=mdp,\n",
" mdp_wall_locs=mdp_wall_locs,\n",
" linewidths=0,\n",
" headwidth=headwidth,\n",
" headlength=4,\n",
" wall_color=(220,220,220,0.5),\n",
" figsize=(5,5),\n",
" grid_kwargs={'color':(220/255,220/255,220/255,0.8)}\n",
" )\n",
"plt.axis('off')\n",
"plt.tight_layout()"
],
"execution_count": 47,
"outputs": [
{
"output_type": "stream",
"text": [
"Training step has been optimized.\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
================================================
FILE: affordances_theory/README.md
================================================
# Code for "What can I do here? A theory of affordances in reinforcement Learning.
This repository contains code that accompanies the paper "What can I do here? A
theory of affordances in reinforcement learning".
The iPython notebook AffordancesInDiscreteEnvironment.ipynb covers the
experiments in Section 6 and Learning affordances in discrete environments.
The iPython notebook AffordancesInContinuousEnvironment.ipynb covers the
experiments in Section 7.
================================================
FILE: affordances_theory/requirements.txt
================================================
tensorflow==2.1.0
tensorflow_probability=0.7.0
matplotlib==3.1.2
numpy==1.17.5
================================================
FILE: alphafold_casp13/README.md
================================================
# AlphaFold 1
This package provides an implementation of the contact prediction network used
in AlphaFold 1, associated model weights and CASP13 dataset as used for CASP13
(2018) and published in Nature. **This is completely different code from that
used in AlphaFold 2 which was used in CASP14 (2020). You can find AlphaFold 2 at
https://github.com/deepmind/alphafold.**
Any publication that discloses findings arising from using this source code must
cite *Improved protein structure prediction using potentials from deep learning*
by Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent
Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex
Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan,
Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis.
The paper abstract can be found on Nature's site
[10.1038/s41586-019-1923-7](https://www.nature.com/articles/s41586-019-1923-7)
and the full text can be accessed directly at https://rdcu.be/b0mtx.
## Setup
**This code can't be used to predict structure of an arbitrary protein sequence.
It can be used to predict structure only on the CASP13 dataset (links below).**
The feature generation code is tightly coupled to our internal infrastructure as
well as external tools, hence we are unable to open-source it. We give guide as
to the features used for those accustomed to computing them below. See also
[issue #18](https://github.com/deepmind/deepmind-research/issues/28) for more
details.
This code works on Linux, we don't support other operating systems.
### Dependencies
* Python 3.6+.
* [Abseil 0.8.0](https://github.com/abseil/abseil-py)
* [Numpy 1.16](https://numpy.org)
* [Six 1.12](https://pypi.org/project/six/)
* [Setuptools 41.0.0](https://setuptools.readthedocs.io/en/latest/)
* [Sonnet 1.35](https://github.com/deepmind/sonnet)
* [TensorFlow 1.14](https://tensorflow.org). Not compatible with TensorFlow
2.0+.
* [TensorFlow Probability 0.7.0](https://www.tensorflow.org/probability)
You can set up Python virtual environment (you might need to install the
`python3-venv` package first) with all needed dependencies inside the forked
`deepmind_research` repository using:
```shell
python3 -m venv alphafold_venv
source alphafold_venv/bin/activate
pip install wheel
pip install -r alphafold_casp13/requirements.txt
```
Alternatively, you can just use the `run_eval.sh` script provided which will run
these commands for you. See the section on running the system below for more
details.
## Data
While the code is licensed under the Apache 2.0 License, the AlphaFold weights
and data are made available for non-commercial use only under the terms of the
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
license. You can find details at:
https://creativecommons.org/licenses/by-nc/4.0/legalcode
You can download the data from:
* http://bit.ly/alphafold-casp13-data-license: The data license file.
* http://bit.ly/alphafold-casp13-data: The dataset to reproduce AlphaFold's
CASP13 results.
* http://bit.ly/alphafold-casp13-weights: The model checkpoints.
### Input data
The dataset to reproduce AlphaFold's CASP13 results can be downloaded from
http://bit.ly/alphafold-casp13-data. The dataset is in a single zip file called
`casp13_data.zip` which has about **43.5 GB**.
The zip file contains 1 directory for each CASP13 target and a `LICENSE.txt`
file. Each target directory contains the following files:
1. `TARGET.tfrec` file. This is a
[TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) file
with serialized tf.train.Example protocol buffers that contain the features
needed to run the model.
1. `contacts/TARGET.pickle` file(s) with the predicted distogram. These pickles
were pickled using Python 2, so to unpickle them in Python 3 you will need
to set the `encoding='latin1'` optional argument for `pickle.load()`.
1. `contacts/TARGET.rr` file(s) with the contact map derived from the predicted
distogram. The RR format is described on the
[CASP website](http://predictioncenter.org/casp13/index.cgi?page=format#RR).
Note that for **T0999** the target was manually split based on hits in HHSearch
into 5 sub-targets, hence there are 5 distograms
(`contacts/T0999s{1,2,3,4,5}.pickle`) and 5 RR files
(`contacts/T0999s{1,2,3,4,5}.rr`).
The `contacts/` folder is not needed to run the model, these files are included
only for convenience so that you don't need to run the inference for CASP13
targets to get the contact map.
### Model checkpoints
The model checkpoints can be downloaded from
http://bit.ly/alphafold-casp13-weights. The model checkpoints are in a zip file
called `alphafold_casp13_weights.zip` which has about **210 MB**.
The zip file contains:
1. A directory `873731`. This contains the weights for the distogram model.
1. A directory `916425`. This contains the weights for the background distogram
model.
1. A directory `941521`. This contains the weights for the torsion model.
1. `LICENSE.txt`. The model checkpoints have a non-commercial license which is
defined in this file.
Each directory with model weights contains a number of different model
configurations. Each model has a config file and associated weights. There is
only one torsion model. Each model directory also contains a stats file that is
used for feature normalization specific to that model.
## Distogram prediction
### Running the system
You can use the `run_eval.sh` script to run the entire Distogram prediction
system. There are a few steps you need to start with:
1. Download the input data as described above. Unpack the data in the directory
with the code.
1. Download the model checkpoints as described above. Unpack the data.
1. In `run_eval.sh` set the following:
* `DISTOGRAM_MODEL` to the path to the directory with the distogram model.
* `BACKGROUND_MODEL` to the path to the directory with the background
model.
* `TORSION_MODEL` to the path to the directory with the torsion model.
* `TARGET` to the name of the target.
* `TARGET_PATH` to the path to the directory with the target input data.
* `OUTPUT_DIR` is by default set to a new directory with a timestamp
within your home directory.
Then run `alphafold_casp13/run_eval.sh` from the `deepmind_research` parent
directory (you will get errors if you try running `run_eval.sh` directly from
the `alphafold_casp13` directory).
The contact prediction works in the following way:
1. 4 replicas (by *replica* we mean a configuration file describing the network
architecture and a snapshot with the network weights), each with slightly
different model configuration, are launched to predict the distogram.
1. 4 replicas, each with slightly different model configuration are launched to
predict the background distogram.
1. 1 replica is launched to predict the torsions.
1. The predictions from the different replicas are averaged together using
`ensemble_contact_maps.py`.
1. The predictions for the 64 × 64, 128 × 128 and 256 × 256 distogram crops are
pasted together using `paste_contact_maps.py`.
When running `run_eval.sh` the output has the following directory structure:
* **distogram/**: Contains 4 subfolders, one for each replica. Each of these
contain the predicted ASA, secondary structure and a pickle file with the
distogram for each crop (see below for more details). It also contains an
`ensemble` directory with the ensembled distograms.
* **background_distogram/**: Contains 4 subfolders, one for each replica. Each
of these contain a pickle file with the background distogram for each crop.
It also contains an `ensemble` directory with the ensembled background
distograms.
* **torsion/**: Contains 1 subfolder as there was only a single replica. This
folder contains contains the predicted ASA, secondary structure, backbone
torsions and a pickle file with the distogram for each crop. It also
contains an `ensemble` directory, which contains a copy of the predicted
output as there is only a single replica in this case.
* **pasted/**: Contains distograms obtained from the ensembled distograms by
pasting. An RR contact map file is computed from this pasted distogram.
**This is the final distogram that was used in the subsequent AlphaFold
folding pipeline in CASP13.**
### Distogram output format
The distogram is a Python pickle file with a dictionary containing the following
fields:
* `min_range`: The minimum range in Angstroms to consider in distograms.
* `max_range`: The range in Angstroms to consider in distograms, see
`num_bins` below for clarification. The upper end of the distogram is
`min_range + max_range`.
* `num_bins`: The number of bins in the distance histogram being predicted. We
divide the interval from `min_range` to `min_range + max_range` into this
many bins. The distograms were trained so that distances lower than
`min_range` were counted in the lowest bin and distances higher than
`min_range + max_range` were added to the final bin. The `num_bins - 1`
boundaries between bins are thus `np.linspace(0, max_range, num_bins + 1,
endpoint=True)[1:-1] + min_range`.
* `sequence`: The target sequence of amino acids of length `L`.
* `target`: The name of the target.
* `domain`: The name of the target including the domain name.
* `probs`: The distogram as a Numpy array of shape `[L, L, num_bins]`.
## Data splits
We used a version of [PDB](https://www.rcsb.org/) downloaded on 2018-03-15. The
train/test split can be found in the `train_domains.txt` and `test_domains.txt`
files in this repository. The split is based on the
[CATH 2018-03-16](https://www.cathdb.info/) database.
## Features
There is currently no plan to open source the feature generation code as it is
tightly coupled to our internal infrastructure as well as external tools which
we cannot open source.
Some features are needed only as placeholders to construct the model. These can
be set to all zeros when running the inference. Such features are marked in the
table below as not needed and you can just fill them with zeros when running
inference.
The table below provides an overview of the features we used to make it possible
to reconstruct our feature generation code. Some features that require more
thorough explanation are explained in the section below the table. Note that
`NR` stands for number of residues, i.e. the length of the amino acid sequence:
| Name | Needed | TF DType | Shape | Description |
|-----------------------------------|:------:|----------|-----------------|------------------------------------------------------------------------------------------------------------------------------|
| `aatype` | ✔️ | float32 | `(NR, 21)` | One hot encoding of amino acid types. The mapping is `ARNDCQEGHILKMFPSTWYVX -> range(21)`. See below. |
| `alpha_mask` | ❌ | int64 | `(NR, 1)` | Mask for `alpha_positions`. |
| `alpha_positions` | ❌ | float32 | `(NR, 3)` | `(x, y, z)` Carbon Alpha coordinates. |
| `beta_mask` | ❌ | int64 | `(NR, 1)` | Mask for `beta_positions`. |
| `beta_positions` | ❌ | float32 | `(NR, 3)` | `(x, y, z)` Carbon Beta coordinates. |
| `between_segment_residues` | ❌ | int64 | `(NR, 1)` | The number of between segment residues (BSR) at the next position. E.g. `ABCXXD` (`XX` is BSR) would be `[0,0,2,0]`. |
| `chain_name` | ❌ | string | `(1)` | The chain name. E.g. 'A', 'B', ... |
| `deletion_probability` | ✔️ | float32 | `(NR, 1)` | The fraction of sequences that had a deletion at this position. See below. |
| `domain_name` | ❌ | string | `(1)` | The domain name. |
| `gap_matrix` | ✔️ | float32 | `(NR, NR, 1)` | Covariation signal from the gapped states, this gives an indication of the variance induced due to gapped states. See below. |
| `hhblits_profile` | ❌ | float32 | `(NR, 22)` | A profile (probability distribution over amino acid types) computed using HHBlits MSA. Encoding: 20 amino acids + 'X' + '-'. |
| `hmm_profile` | ✔️ | float32 | `(NR, 30)` | The HHBlits HHM profile (from the `-ohhm` HHBlits output file). Asterisks in the output are replaced by 0.0. See below. |
| `key` | ❌ | string | `(1)` | The unique id of the protein. |
| `mutual_information` | ❌ | float32 | `(NR, NR, 1)` | The average product corrected mutual information. See https://doi.org/10.1093/bioinformatics/btm604. |
| `non_gapped_profile` | ✔️ | float32 | `(NR, 21)` | A profile from amino acids only (discounting gaps). See below. |
| `num_alignments` | ✔️ | int64 | `(NR, 1)` | The number of HHBlits multiple sequence alignments. Has to be repeated `NR` times. See below. |
| `num_effective_alignments` | ❌ | float32 | `(1)` | The number of effective alignments (neff at 62 % sequence similarity). |
| `phi_angles` | ❌ | float32 | `(NR, 1)` | The phi angles. |
| `phi_mask` | ❌ | int64 | `(NR, 1)` | Mask for `phi_angles`. |
| `profile` | ❌ | float32 | `(NR, 21)` | A profile (probability distribution over amino acid types) computed using PSI-BLAST. Equivalent to the output of ChkParse. |
| `profile_with_prior` | ✔️ | float32 | `(NR, 22)` | A profile computed using HHBlits which takes into account priors and Blosum matrix. See equation 5 in https://doi.org/10.1093/nar/25.17.3389. |
| `profile_with_prior_without_gaps` | ✔️ | float32 | `(NR, 21)` | Same as `profile_with_prior` but without gaps included. |
| `pseudo_bias` | ✔️ | float32 | `(NR, 22)` | The bias computed in the MSA pseudolikelihood computation. |
| `pseudo_frob` | ✔️ | float32 | `(NR, NR, 1)` | Frobenius norm of `pseudolikelihood` (gaps not included). Similar to the output of CCMPred. |
| `pseudolikelihood` | ✔️ | float32 | `(NR, NR, 484)` | The weights computed in the MSA pseudolikelihood computation. |
| `psi_angles` | ❌ | float32 | `(NR, 1)` | The psi angles. |
| `psi_mask` | ❌ | int64 | `(NR, 1)` | Mask for `psi_angles`. |
| `residue_index` | ✔️ | int64 | `(NR, 1)` | Index of each residue giong from 0 to `NR - 1`. See below. |
| `resolution` | ❌ | float32 | `(1)` | The protein structure resolution. |
| `reweighted_profile` | ✔️ | float32 | `(NR, 22)` | Profile where sequences are reweighted to weight rarer sequences higher. See below. |
| `sec_structure` | ❌ | int64 | `(NR, 8)` | Secondary structure generated by DSSP and one-hot encoded by the mapping `-HETSGBI -> range(8)`. |
| `sec_structure_mask` | ❌ | int64 | `(NR, 1)` | Mask for `sec_structure_mask`. |
| `seq_length` | ✔️ | int64 | `(NR, 1)` | The length of the amino acid sequence. Has to be repeated `NR` times. See below. |
| `sequence` | ✔️ | string | `(1)` | The amino acid sequence (1-letter amino acid encoding). See below. |
| `solv_surf` | ❌ | float32 | `(NR, 1)` | Relative solvent accessible area computed using DSSP and then normalized by amino acid maximum accessibility. |
| `solv_surf_mask` | ❌ | int64 | `(NR, 1)` | Mask for `solv_surf`. |
| `superfamily` | ❌ | string | `(1)` | The superfamily CATH code. |
### More details on needed features
#### `aatype`
One hot encoding of amino acid types. The following code converts an amino acid
string into the one-hot encoding:
```python
def sequence_to_onehot(sequence):
"""Maps the given sequence into a one-hot encoded matrix."""
mapping = {aa: i for i, aa in enumerate('ARNDCQEGHILKMFPSTWYVX')}
num_entries = max(mapping.values()) + 1
one_hot_arr = np.zeros((len(sequence), num_entries), dtype=np.int32)
for aa_index, aa_type in enumerate(sequence):
aa_id = mapping[aa_type]
one_hot_arr[aa_index, aa_id] = 1
return one_hot_arr
```
#### `deletion_probability`
The fraction of sequences that had an insert state (denoted by a lowercase
letter in the A3M format) at this position. We used the following code to
compute it from the HHBlits MSA in the A3M format:
```python
deletion_matrix = []
for msa_sequence in hhblits_a3m_sequences:
deletion_vec = []
deletion_count = 0
for j in msa_sequence:
if j.islower():
deletion_count += 1
else:
deletion_vec.append(deletion_count)
deletion_count = 0
deletion_matrix.append(deletion_vec)
deletion_matrix = np.array(deletion_matrix)
deletion_matrix[deletion_matrix != 0] = 1.0
deletion_probability = deletion_matrix.sum(axis=0) / len(deletion_matrix)
```
#### `gap_matrix`
Covariation signal from the gapped states, this gives an indication of the
variance induced due to gapped states. Example:
```
MSA = A A C D B D F J G B M A
- - C D B D F J G B M A
A A C D B - - J G B M A
gap_count = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0]]
gap_matrix = np.matmul(gap_count.T, gap_count)
```
#### `hmm_profile`
The HHBlits HHM profile (from the `-ohhm` HHBlits output file). Asterisks in the
output are replaced by 0.0. The following code parses the HHM file:
```python
def extract_hmm_profile(hhm_file, sequence, asterisks_replace=0.0):
"""Extracts information from the hmm file and replaces asterisks."""
profile_part = hhm_file.split('#')[-1]
profile_part = profile_part.split('\n')
whole_profile = [i.split() for i in profile_part]
# This part strips away the header and the footer.
whole_profile = whole_profile[5:-2]
gap_profile = np.zeros((len(sequence), 10))
aa_profile = np.zeros((len(sequence), 20))
count_aa = 0
count_gap = 0
for line_values in whole_profile:
if len(line_values) == 23:
# The first and the last values in line_values are metadata, skip them.
for j, t in enumerate(line_values[2:-1]):
aa_profile[count_aa, j] = (
2**(-float(t) / 1000.0) if t != '*' else asterisks_replace)
count_aa += 1
elif len(line_values) == 10:
for j, t in enumerate(line_values):
if j <= 6:
gap_profile[count_gap, j] = (
2**(-float(t) / 1000.0) if t != '*' else asterisks_replace)
else:
# Neff_M, Neff_I, and Neff_D are given in units of 0.001.
gap_profile[count_gap, j] = float(t) / 1000.0
count_gap += 1
elif not line_values:
pass
else:
raise ValueError('Wrong length of line %s hhm file. Expected 0, 10 or 23'
'got %d'%(line_values, len(line_values)))
hmm_profile = np.hstack([aa_profile, gap_profile])
assert len(hmm_profile) == len(sequence)
return hmm_profile
```
#### `non_gapped_profile`
A profile from amino acids only (discounting gaps).
```python
def non_gapped_profile(amino_acids):
"""Computes a profile from only amino acids and discounting gaps."""
profile = np.zeros(21)
for aa in amino_acids:
if aa != 21: # Ignore gaps.
profile[aa] += 1.
return profile / np.sum(profile)
```
#### `num_alignments`
The number of HHBlits multiple sequence alignments. Has to be repeated `NR`
times. For example, if there are 10 alignments for a sequence of length 8, then
`num_alignments = [[10], [10], [10], [10], [10], [10], [10], [10]]`.
#### `pseudo_frob`
This feature collapses the 484 channels of pseudolikelihood into one by taking
the Frobenius norm of the 484 channels and then subtracting the Average Product
Correction of the computed Frobenius norm. The Frobenius norm does not take into
account the 22nd gap state.
#### `pseudolikelihood`
Parameters of a Potts Model coupling the amino acid types of particular residues
estimated by pseudolikelihood. See https://doi.org/10.1103/PhysRevE.87.012707
for more details.
#### `residue_index`
Index of each residue giong from 0 to `NR - 1`. For example, the sequence `AACR`
has `residue_index = [[0], [1], [2], [3]]`.
#### `reweighted_profile`
Profile where sequences are reweighted to weight rarer sequences higher. The
sequence weights are calculated like this:
```python
def sequence_weights(sequence_matrix):
"""Compute sequence reweighting to weight rarer sequences higher."""
num_rows, num_res = sequence_matrix.shape
cutoff = 0.62 * num_res
weights = np.ones(num_rows, dtype=np.float32)
for i in range(num_rows):
for j in range(i + 1, num_rows):
similarity = (sequence_matrix[i] == sequence_matrix[j]).sum()
if similarity > cutoff:
weights[i] += 1
weights[j] += 1
return 1.0 / weights
```
#### `seq_length`
The length of the amino acid sequence. Has to be repeated `NR` times. For
example, the sequence `AACR` would have `seq_length = [[4], [4], [4], [4]]`.
#### `sequence`
The amino acid sequence (1-letter amino acid encoding). For example, a protein
with Alanine, Lysine, Arginine has `sequence = 'AKR'`.
# Disclaimer
This is not an official Google product.
================================================
FILE: alphafold_casp13/asa_output.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Class for predicting Accessible Surface Area."""
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
class ASAOutputLayer(object):
"""An output layer to predict Accessible Surface Area."""
def __init__(self, name='asa'):
self.name = name
def compute_asa_output(self, activations):
"""Just compute the logits and outputs given activations."""
asa_logits = tf.contrib.layers.linear(
activations, 1,
weights_initializer=tf.random_uniform_initializer(-0.01, 0.01),
scope='ASALogits')
self.asa_output = tf.nn.relu(asa_logits, name='ASA_output_relu')
return asa_logits
================================================
FILE: alphafold_casp13/config_dict.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities for storing configuration flags."""
import json
class ConfigDict(dict):
"""Configuration dictionary with convenient dot element access."""
def __init__(self, *args, **kwargs):
super(ConfigDict, self).__init__(*args, **kwargs)
for arg in args:
if isinstance(arg, dict):
for key, value in arg.items():
self._add(key, value)
for key, value in kwargs.items():
self._add(key, value)
def _add(self, key, value):
if isinstance(value, dict):
self[key] = ConfigDict(value)
else:
self[key] = value
def __getattr__(self, attr):
try:
return self[attr]
except KeyError as e:
raise AttributeError(e)
def __setattr__(self, key, value):
self.__setitem__(key, value)
def __setitem__(self, key, value):
super(ConfigDict, self).__setitem__(key, value)
self.__dict__.update({key: value})
def __delattr__(self, item):
self.__delitem__(item)
def __delitem__(self, key):
super(ConfigDict, self).__delitem__(key)
del self.__dict__[key]
def to_json(self):
return json.dumps(self)
@classmethod
def from_json(cls, json_string):
return cls(json.loads(json_string))
================================================
FILE: alphafold_casp13/contacts.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Code to run distogram inference."""
import collections
import os
import time
from absl import app
from absl import flags
from absl import logging
import numpy as np
import six
import sonnet as snt
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import config_dict
from alphafold_casp13 import contacts_experiment
from alphafold_casp13 import distogram_io
from alphafold_casp13 import secstruct
flags.DEFINE_string('config_path', None, 'Path of the JSON config file.')
flags.DEFINE_string('checkpoint_path', None, 'Checkpoint path for evaluation.')
flags.DEFINE_boolean('cpu', False, 'Force onto CPU.')
flags.DEFINE_string('output_path', None,
'Base path where all output files will be saved to.')
flags.DEFINE_string('eval_sstable', None,
'Path of the SSTable to read the input tf.Examples from.')
flags.DEFINE_string('stats_file', None,
'Path of the statistics file to use for normalization.')
FLAGS = flags.FLAGS
# A named tuple to store the outputs of a single prediction run.
Prediction = collections.namedtuple(
'Prediction', [
'single_message', # A debugging message.
'num_crops_local', # The number of crops used to make this prediction.
'sequence', # The amino acid sequence.
'filebase', # The chain name. All output files will use this name.
'softmax_probs', # Softmax of the distogram.
'ss', # Secondary structure prediction.
'asa', # ASA prediction.
'torsions', # Torsion prediction.
])
def evaluate(crop_size_x, crop_size_y, feature_normalization, checkpoint_path,
normalization_exclusion, eval_config, network_config):
"""Main evaluation loop."""
experiment = contacts_experiment.Contacts(
tfrecord=eval_config.eval_sstable,
stats_file=eval_config.stats_file,
network_config=network_config,
crop_size_x=crop_size_x,
crop_size_y=crop_size_y,
feature_normalization=feature_normalization,
normalization_exclusion=normalization_exclusion)
checkpoint = snt.get_saver(experiment.model, collections=[
tf.GraphKeys.GLOBAL_VARIABLES,
tf.GraphKeys.MOVING_AVERAGE_VARIABLES])
with tf.train.SingularMonitoredSession(hooks=[]) as sess:
logging.info('Restoring from checkpoint %s', checkpoint_path)
checkpoint.restore(sess, checkpoint_path)
logging.info('Writing output to %s', eval_config.output_path)
eval_begin_time = time.time()
_run_evaluation(sess=sess,
experiment=experiment,
eval_config=eval_config,
output_dir=eval_config.output_path,
min_range=network_config.min_range,
max_range=network_config.max_range,
num_bins=network_config.num_bins,
torsion_bins=network_config.torsion_bins)
logging.info('Finished eval %.1fs', (time.time() - eval_begin_time))
def _run_evaluation(
sess, experiment, eval_config, output_dir, min_range, max_range, num_bins,
torsion_bins):
"""Evaluate a contact map by aggregating crops.
Args:
sess: A tf.train.Session.
experiment: An experiment class.
eval_config: A config dict of eval parameters.
output_dir: Directory to save the predictions to.
min_range: The minimum range in Angstroms to consider in distograms.
max_range: The maximum range in Angstroms to consider in distograms, see
num_bins below for clarification.
num_bins: The number of bins in the distance histogram being predicted.
We divide the min_range--(min_range + max_range) Angstrom range into this
many bins.
torsion_bins: The number of bins the torsion angles are discretised into.
"""
tf.io.gfile.makedirs(os.path.join(output_dir, 'pickle_files'))
logging.info('Eval config is %s\nnum_bins: %d', eval_config, num_bins)
num_examples = 0
num_crops = 0
start_all_time = time.time()
# Either do the whole test set, or up to a specified limit.
max_examples = experiment.num_eval_examples
if eval_config.max_num_examples > 0:
max_examples = min(max_examples, eval_config.max_num_examples)
while num_examples < max_examples:
one_prediction = compute_one_prediction(
num_examples, experiment, sess, eval_config, num_bins, torsion_bins)
single_message = one_prediction.single_message
num_crops_local = one_prediction.num_crops_local
sequence = one_prediction.sequence
filebase = one_prediction.filebase
softmax_probs = one_prediction.softmax_probs
ss = one_prediction.ss
asa = one_prediction.asa
torsions = one_prediction.torsions
num_examples += 1
num_crops += num_crops_local
# Save the output files.
filename = os.path.join(output_dir,
'pickle_files', '%s.pickle' % filebase)
distogram_io.save_distance_histogram(
filename, softmax_probs, filebase, sequence,
min_range=min_range, max_range=max_range, num_bins=num_bins)
if experiment.model.torsion_multiplier > 0:
torsions_dir = os.path.join(output_dir, 'torsions')
tf.io.gfile.makedirs(torsions_dir)
distogram_io.save_torsions(torsions_dir, filebase, sequence, torsions)
if experiment.model.secstruct_multiplier > 0:
ss_dir = os.path.join(output_dir, 'secstruct')
tf.io.gfile.makedirs(ss_dir)
secstruct.save_secstructs(ss_dir, filebase, None, sequence, ss)
if experiment.model.asa_multiplier > 0:
asa_dir = os.path.join(output_dir, 'asa')
tf.io.gfile.makedirs(asa_dir)
secstruct.save_secstructs(asa_dir, filebase, None, sequence,
np.expand_dims(asa, 1), label='Deepmind 2D ASA')
time_spent = time.time() - start_all_time
logging.info(
'Evaluate %d examples, %d crops %.1f crops/ex. '
'Took %.1fs, %.3f s/example %.3f crops/s\n%s',
num_examples, num_crops, num_crops / float(num_examples), time_spent,
time_spent / num_examples, num_crops / time_spent, single_message)
logging.info('Tested on %d', num_examples)
def compute_one_prediction(
num_examples, experiment, sess, eval_config, num_bins, torsion_bins):
"""Find the contact map for a single domain."""
num_crops_local = 0
debug_steps = 0
start = time.time()
output_fetches = {'probs': experiment.eval_probs}
output_fetches['softmax_probs'] = experiment.eval_probs_softmax
# Add the auxiliary outputs if present.
experiment.model.update_crop_fetches(output_fetches)
# Get data.
batch = experiment.get_one_example(sess)
length = batch['sequence_lengths'][0]
batch_size = batch['sequence_lengths'].shape[0]
domain = batch['domain_name'][0][0].decode('utf-8')
chain = batch['chain_name'][0][0].decode('utf-8')
filebase = domain or chain
sequence = six.ensure_str(batch['sequences'][0][0])
logging.info('SepWorking on %d %s %s %d', num_examples, domain, chain, length)
inputs_1d = batch['inputs_1d']
if 'residue_index' in batch:
logging.info('Getting residue_index from features')
residue_index = np.squeeze(
batch['residue_index'], axis=2).astype(np.int32)
else:
logging.info('Generating residue_index')
residue_index = np.tile(np.expand_dims(
np.arange(length, dtype=np.int32), 0), [batch_size, 1])
assert batch_size == 1
num_examples += batch_size
# Crops.
prob_accum = np.zeros((length, length, 2))
ss_accum = np.zeros((length, 8))
torsions_accum = np.zeros((length, torsion_bins**2))
asa_accum = np.zeros((length,))
weights_1d_accum = np.zeros((length,))
softmax_prob_accum = np.zeros((length, length, num_bins), dtype=np.float32)
crop_size_x = experiment.crop_size_x
crop_step_x = crop_size_x // eval_config.crop_shingle_x
crop_size_y = experiment.crop_size_y
crop_step_y = crop_size_y // eval_config.crop_shingle_y
prob_weights = 1
if eval_config.pyramid_weights > 0:
sx = np.expand_dims(np.linspace(1.0 / crop_size_x, 1, crop_size_x), 1)
sy = np.expand_dims(np.linspace(1.0 / crop_size_y, 1, crop_size_y), 0)
prob_weights = np.minimum(np.minimum(sx, np.flipud(sx)),
np.minimum(sy, np.fliplr(sy)))
prob_weights /= np.max(prob_weights)
prob_weights = np.minimum(prob_weights, eval_config.pyramid_weights)
logging.log_first_n(logging.INFO, 'Crop: %dx%d step %d,%d pyr %.2f',
debug_steps,
crop_size_x, crop_size_y,
crop_step_x, crop_step_y, eval_config.pyramid_weights)
# Accumulate all crops, starting and ending half off the square.
for i in range(-crop_size_x // 2, length - crop_size_x // 2, crop_step_x):
for j in range(-crop_size_y // 2, length - crop_size_y // 2, crop_step_y):
# The ideal crop.
patch = compute_one_patch(
sess, experiment, output_fetches, inputs_1d, residue_index,
prob_weights, batch, length, i, j, crop_size_x, crop_size_y)
# Assemble the crops into a final complete prediction.
ic = max(0, i)
jc = max(0, j)
ic_to = ic + patch['prob'].shape[1]
jc_to = jc + patch['prob'].shape[0]
prob_accum[jc:jc_to, ic:ic_to, 0] += patch['prob'] * patch['weight']
prob_accum[jc:jc_to, ic:ic_to, 1] += patch['weight']
softmax_prob_accum[jc:jc_to, ic:ic_to, :] += (
patch['softmax'] * np.expand_dims(patch['weight'], 2))
weights_1d_accum[jc:jc_to] += 1
weights_1d_accum[ic:ic_to] += 1
if 'asa_x' in patch:
asa_accum[ic:ic + patch['asa_x'].shape[0]] += np.squeeze(
patch['asa_x'], axis=1)
asa_accum[jc:jc + patch['asa_y'].shape[0]] += np.squeeze(
patch['asa_y'], axis=1)
if 'ss_x' in patch:
ss_accum[ic:ic + patch['ss_x'].shape[0]] += patch['ss_x']
ss_accum[jc:jc + patch['ss_y'].shape[0]] += patch['ss_y']
if 'torsions_x' in patch:
torsions_accum[
ic:ic + patch['torsions_x'].shape[0]] += patch['torsions_x']
torsions_accum[
jc:jc + patch['torsions_y'].shape[0]] += patch['torsions_y']
num_crops_local += 1
single_message = (
'Constructed %s len %d from %d chunks [%d, %d x %d, %d] '
'in %5.1fs' % (
filebase, length, num_crops_local,
crop_size_x, crop_step_x, crop_size_y, crop_step_y,
time.time() - start))
logging.info(single_message)
logging.info('prob_accum[:, :, 1]: %s', prob_accum[:, :, 1])
assert (prob_accum[:, :, 1] > 0.0).all()
probs = prob_accum[:, :, 0] / prob_accum[:, :, 1]
softmax_probs = softmax_prob_accum[:, :, :] / prob_accum[:, :, 1:2]
asa_accum /= weights_1d_accum
ss_accum /= np.expand_dims(weights_1d_accum, 1)
torsions_accum /= np.expand_dims(weights_1d_accum, 1)
# The probs are symmetrical.
probs = (probs + probs.transpose()) / 2
if num_bins > 1:
softmax_probs = (softmax_probs + np.transpose(
softmax_probs, axes=[1, 0, 2])) / 2
return Prediction(
single_message=single_message,
num_crops_local=num_crops_local,
sequence=sequence,
filebase=filebase,
softmax_probs=softmax_probs,
ss=ss_accum,
asa=asa_accum,
torsions=torsions_accum)
def compute_one_patch(sess, experiment, output_fetches, inputs_1d,
residue_index, prob_weights, batch, length, i, j,
crop_size_x, crop_size_y):
"""Compute the output predictions for a single crop."""
# Note that these are allowed to go off the end of the protein.
end_x = i + crop_size_x
end_y = j + crop_size_y
crop_limits = np.array([[i, end_x, j, end_y]], dtype=np.int32)
ic = max(0, i)
jc = max(0, j)
end_x_cropped = min(length, end_x)
end_y_cropped = min(length, end_y)
prepad_x = max(0, -i)
prepad_y = max(0, -j)
postpad_x = end_x - end_x_cropped
postpad_y = end_y - end_y_cropped
# Precrop the 2D features:
inputs_2d = np.pad(batch['inputs_2d'][
:, jc:end_y, ic:end_x, :],
[[0, 0],
[prepad_y, postpad_y],
[prepad_x, postpad_x],
[0, 0]], mode='constant')
assert inputs_2d.shape[1] == crop_size_y
assert inputs_2d.shape[2] == crop_size_x
# Generate the corresponding crop, but it might be truncated.
cxx = batch['inputs_2d'][:, ic:end_x, ic:end_x, :]
cyy = batch['inputs_2d'][:, jc:end_y, jc:end_y, :]
if cxx.shape[1] < inputs_2d.shape[1]:
cxx = np.pad(cxx, [[0, 0],
[prepad_x, max(0, i + crop_size_y - length)],
[prepad_x, postpad_x],
[0, 0]], mode='constant')
assert cxx.shape[1] == crop_size_y
assert cxx.shape[2] == crop_size_x
if cyy.shape[2] < inputs_2d.shape[2]:
cyy = np.pad(cyy, [[0, 0],
[prepad_y, postpad_y],
[prepad_y, max(0, j + crop_size_x - length)],
[0, 0]], mode='constant')
assert cyy.shape[1] == crop_size_y
assert cyy.shape[2] == crop_size_x
inputs_2d = np.concatenate([inputs_2d, cxx, cyy], 3)
output_results = sess.run(output_fetches, feed_dict={
experiment.inputs_1d_placeholder: inputs_1d,
experiment.residue_index_placeholder: residue_index,
experiment.inputs_2d_placeholder: inputs_2d,
experiment.crop_placeholder: crop_limits,
})
# Crop out the "live" region of the probs.
prob_patch = output_results['probs'][
0, prepad_y:crop_size_y - postpad_y,
prepad_x:crop_size_x - postpad_x]
weight_patch = prob_weights[prepad_y:crop_size_y - postpad_y,
prepad_x:crop_size_x - postpad_x]
patch = {'prob': prob_patch, 'weight': weight_patch}
if 'softmax_probs' in output_results:
patch['softmax'] = output_results['softmax_probs'][
0, prepad_y:crop_size_y - postpad_y,
prepad_x:crop_size_x - postpad_x]
if 'secstruct_probs' in output_results:
patch['ss_x'] = output_results['secstruct_probs'][
0, prepad_x:crop_size_x - postpad_x]
patch['ss_y'] = output_results['secstruct_probs'][
0, crop_size_x + prepad_y:crop_size_x + crop_size_y - postpad_y]
if 'torsion_probs' in output_results:
patch['torsions_x'] = output_results['torsion_probs'][
0, prepad_x:crop_size_x - postpad_x]
patch['torsions_y'] = output_results['torsion_probs'][
0, crop_size_x + prepad_y:crop_size_x + crop_size_y - postpad_y]
if 'asa_output' in output_results:
patch['asa_x'] = output_results['asa_output'][
0, prepad_x:crop_size_x - postpad_x]
patch['asa_y'] = output_results['asa_output'][
0, crop_size_x + prepad_y:crop_size_x + crop_size_y - postpad_y]
return patch
def main(argv):
del argv # Unused.
logging.info('Loading a JSON config from: %s', FLAGS.config_path)
with tf.io.gfile.GFile(FLAGS.config_path, 'r') as f:
config = config_dict.ConfigDict.from_json(f.read())
# Redefine the relevant output fields.
if FLAGS.eval_sstable:
config.eval_config.eval_sstable = FLAGS.eval_sstable
if FLAGS.stats_file:
config.eval_config.stats_file = FLAGS.stats_file
if FLAGS.output_path:
config.eval_config.output_path = FLAGS.output_path
with tf.device('/cpu:0' if FLAGS.cpu else None):
evaluate(checkpoint_path=FLAGS.checkpoint_path, **config)
if __name__ == '__main__':
app.run(main)
================================================
FILE: alphafold_casp13/contacts_dataset.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TF wrapper for protein tf.Example datasets."""
import collections
import enum
import json
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
_ProteinDescription = collections.namedtuple(
'_ProteinDescription', (
'sequence_lengths', 'key', 'sequences', 'inputs_1d', 'inputs_2d',
'inputs_2d_diagonal', 'crops', 'scalars', 'targets'))
class FeatureType(enum.Enum):
ZERO_DIM = 0 # Shape [x]
ONE_DIM = 1 # Shape [num_res, x]
TWO_DIM = 2 # Shape [num_res, num_res, x]
# Placeholder values that will be replaced with their true value at runtime.
NUM_RES = 'num residues placeholder'
# Sizes of the protein features. NUM_RES is allowed as a placeholder to be
# replaced with the number of residues.
FEATURES = {
'aatype': (tf.float32, [NUM_RES, 21]),
'alpha_mask': (tf.int64, [NUM_RES, 1]),
'alpha_positions': (tf.float32, [NUM_RES, 3]),
'beta_mask': (tf.int64, [NUM_RES, 1]),
'beta_positions': (tf.float32, [NUM_RES, 3]),
'between_segment_residues': (tf.int64, [NUM_RES, 1]),
'chain_name': (tf.string, [1]),
'deletion_probability': (tf.float32, [NUM_RES, 1]),
'domain_name': (tf.string, [1]),
'gap_matrix': (tf.float32, [NUM_RES, NUM_RES, 1]),
'hhblits_profile': (tf.float32, [NUM_RES, 22]),
'hmm_profile': (tf.float32, [NUM_RES, 30]),
'key': (tf.string, [1]),
'mutual_information': (tf.float32, [NUM_RES, NUM_RES, 1]),
'non_gapped_profile': (tf.float32, [NUM_RES, 21]),
'num_alignments': (tf.int64, [NUM_RES, 1]),
'num_effective_alignments': (tf.float32, [1]),
'phi_angles': (tf.float32, [NUM_RES, 1]),
'phi_mask': (tf.int64, [NUM_RES, 1]),
'profile': (tf.float32, [NUM_RES, 21]),
'profile_with_prior': (tf.float32, [NUM_RES, 22]),
'profile_with_prior_without_gaps': (tf.float32, [NUM_RES, 21]),
'pseudo_bias': (tf.float32, [NUM_RES, 22]),
'pseudo_frob': (tf.float32, [NUM_RES, NUM_RES, 1]),
'pseudolikelihood': (tf.float32, [NUM_RES, NUM_RES, 484]),
'psi_angles': (tf.float32, [NUM_RES, 1]),
'psi_mask': (tf.int64, [NUM_RES, 1]),
'residue_index': (tf.int64, [NUM_RES, 1]),
'resolution': (tf.float32, [1]),
'reweighted_profile': (tf.float32, [NUM_RES, 22]),
'sec_structure': (tf.int64, [NUM_RES, 8]),
'sec_structure_mask': (tf.int64, [NUM_RES, 1]),
'seq_length': (tf.int64, [NUM_RES, 1]),
'sequence': (tf.string, [1]),
'solv_surf': (tf.float32, [NUM_RES, 1]),
'solv_surf_mask': (tf.int64, [NUM_RES, 1]),
'superfamily': (tf.string, [1]),
}
FEATURE_TYPES = {k: v[0] for k, v in FEATURES.items()}
FEATURE_SIZES = {k: v[1] for k, v in FEATURES.items()}
def shape(feature_name, num_residues, features=None):
"""Get the shape for the given feature name.
Args:
feature_name: String identifier for the feature. If the feature name ends
with "_unnormalized", theis suffix is stripped off.
num_residues: The number of residues in the current domain - some elements
of the shape can be dynamic and will be replaced by this value.
features: A feature_name to (tf_dtype, shape) lookup; defaults to FEATURES.
Returns:
List of ints representation the tensor size.
"""
features = features or FEATURES
if feature_name.endswith('_unnormalized'):
feature_name = feature_name[:-13]
unused_dtype, raw_sizes = features[feature_name]
replacements = {NUM_RES: num_residues}
sizes = [replacements.get(dimension, dimension) for dimension in raw_sizes]
return sizes
def dim(feature_name):
"""Determine the type of feature.
Args:
feature_name: String identifier for the feature to lookup. If the feature
name ends with "_unnormalized", theis suffix is stripped off.
Returns:
A FeatureType enum describing whether the feature is of size num_res or
num_res * num_res.
Raises:
ValueError: If the feature is of an unknown type.
"""
if feature_name.endswith('_unnormalized'):
feature_name = feature_name[:-13]
num_dims = len(FEATURE_SIZES[feature_name])
if num_dims == 1:
return FeatureType.ZERO_DIM
elif num_dims == 2 and FEATURE_SIZES[feature_name][0] == NUM_RES:
return FeatureType.ONE_DIM
elif num_dims == 3 and FEATURE_SIZES[feature_name][0] == NUM_RES:
return FeatureType.TWO_DIM
else:
raise ValueError('Expect feature sizes to be 2 or 3, got %i' %
len(FEATURE_SIZES[feature_name]))
def _concat_or_zeros(tensor_list, axis, tensor_shape, name):
"""Concatenates the tensors if given, otherwise returns a tensor of zeros."""
if tensor_list:
return tf.concat(tensor_list, axis=axis, name=name)
return tf.zeros(tensor_shape, name=name + '_zeros')
def parse_tfexample(raw_data, features):
"""Read a single TF Example proto and return a subset of its features.
Args:
raw_data: A serialized tf.Example proto.
features: A dictionary of features, mapping string feature names to a tuple
(dtype, shape). This dictionary should be a subset of
protein_features.FEATURES (or the dictionary itself for all features).
Returns:
A dictionary of features mapping feature names to features. Only the given
features are returned, all other ones are filtered out.
"""
feature_map = {
k: tf.io.FixedLenSequenceFeature(shape=(), dtype=v[0], allow_missing=True)
for k, v in features.items()
}
parsed_features = tf.io.parse_single_example(raw_data, feature_map)
# Find out what is the number of sequences and the number of alignments.
num_residues = tf.cast(parsed_features['seq_length'][0], dtype=tf.int32)
# Reshape the tensors according to the sequence length and num alignments.
for k, v in parsed_features.items():
new_shape = shape(feature_name=k, num_residues=num_residues)
# Make sure the feature we are reshaping is not empty.
assert_non_empty = tf.assert_greater(
tf.size(v), 0, name='assert_%s_non_empty' % k,
message='The feature %s is not set in the tf.Example. Either do not '
'request the feature or use a tf.Example that has the feature set.' % k)
with tf.control_dependencies([assert_non_empty]):
parsed_features[k] = tf.reshape(v, new_shape, name='reshape_%s' % k)
return parsed_features
def create_tf_dataset(tf_record_filename, features):
"""Creates an instance of tf.data.Dataset backed by a protein dataset SSTable.
Args:
tf_record_filename: A string with filename of the TFRecord file.
features: A list of strings of feature names to be returned in the dataset.
Returns:
A tf.data.Dataset object. Its items are dictionaries from feature names to
feature values.
"""
# Make sure these features are always read.
required_features = ['aatype', 'sequence', 'seq_length']
features = list(set(features) | set(required_features))
features = {name: FEATURES[name] for name in features}
tf_dataset = tf.data.TFRecordDataset(filenames=[tf_record_filename])
tf_dataset = tf_dataset.map(lambda raw: parse_tfexample(raw, features))
return tf_dataset
def normalize_from_stats_file(
features, stats_file_path, feature_normalization, copy_unnormalized=None):
"""Normalizes the features set in the feature_normalization by the norm stats.
Args:
features: A dictionary mapping feature names to feature tensors.
stats_file_path: A string with the path of the statistics JSON file.
feature_normalization: A dictionary specifying the normalization type for
each input feature. Acceptable values are 'std' and 'none'. If not
specified default to 'none'. Any extra features that are not present in
features will be ignored.
copy_unnormalized: A list of features whose unnormalized copy should be
added. For any feature F in this list a feature F + "_unnormalized" will
be added in the output dictionary containing the unnormalized feature.
This is useful if you have a feature you want to have both in
desired_features (normalized) and also in desired_targets (unnormalized).
See convert_to_legacy_proteins_dataset_format for more details.
Returns:
A dictionary mapping features names to feature tensors. The ones that were
specified in feature_normalization will be normalized.
Raises:
ValueError: If an unknown normalization mode is used.
"""
with tf.io.gfile.GFile(stats_file_path, 'r') as f:
norm_stats = json.loads(f.read())
if not copy_unnormalized:
copy_unnormalized = []
# We need this unnormalized in convert_to_legacy_proteins_dataset_format.
copy_unnormalized.append('num_alignments')
for feature in copy_unnormalized:
if feature in features:
features[feature + '_unnormalized'] = features[feature]
range_epsilon = 1e-12
for key, value in features.items():
if key not in feature_normalization or feature_normalization[key] == 'none':
pass
elif feature_normalization[key] == 'std':
value = tf.cast(value, dtype=tf.float32)
train_mean = tf.cast(norm_stats['mean'][key], dtype=tf.float32)
train_range = tf.sqrt(tf.cast(norm_stats['var'][key], dtype=tf.float32))
value -= train_mean
value = tf.where(
train_range > range_epsilon, value / train_range, value)
features[key] = value
else:
raise ValueError('Unknown normalization mode %s for feature %s.'
% (feature_normalization[key], key))
return features
def convert_to_legacy_proteins_dataset_format(
features, desired_features, desired_scalars, desired_targets):
"""Converts the output of tf.Dataset to the legacy format.
Args:
features: A dictionary mapping feature names to feature tensors.
desired_features: A list with the names of the desired features. These will
be filtered out of features and returned in one of the inputs_1d or
inputs_2d. The features concatenated in `inputs_1d`, `inputs_2d` will be
concatenated in the same order as they were given in `desired_features`.
desired_scalars: A list naming the desired scalars. These will
be filtered out of features and returned in scalars. If features contain
an unnormalized version of a desired scalar, it will be used.
desired_targets: A list naming the desired targets. These will
be filtered out of features and returned in targets. If features contain
an unnormalized version of a desired target, it will be used.
Returns:
A _ProteinDescription namedtuple consisting of:
sequence_length: A scalar int32 tensor with the sequence length.
key: A string tensor with the sequence key or empty if not set features.
sequences: A string tensor with the protein sequence.
inputs_1d: All 1D features in a single tensor of shape
[num_res, 1d_channels].
inputs_2d: All 2D features in a single tensor of shape
[num_res, num_res, 2d_channels].
inputs_2d_diagonal: All 2D diagonal features in a single tensor of shape
[num_res, num_res, 2d_diagonal_channels]. If no diagonal features found
in features, the tensor will be set to inputs_2d.
crops: A int32 tensor with the crop poisitions. If not set in features,
it will be set to [0, num_res, 0, num_res].
scalars: All requested scalar tensors in a list.
targets: All requested target tensors in a list.
Raises:
ValueError: If the feature size is invalid.
"""
tensors_1d = []
tensors_2d = []
tensors_2d_diagonal = []
for key in desired_features:
# Determine if the feature is 1D or 2D.
feature_dim = dim(key)
if feature_dim == FeatureType.ONE_DIM:
tensors_1d.append(tf.cast(features[key], dtype=tf.float32))
elif feature_dim == FeatureType.TWO_DIM:
if key not in features:
if not(key + '_cropped' in features and key + '_diagonal' in features):
raise ValueError(
'The 2D feature %s is not in the features dictionary and neither '
'are its cropped and diagonal versions.' % key)
else:
tensors_2d.append(
tf.cast(features[key + '_cropped'], dtype=tf.float32))
tensors_2d_diagonal.append(
tf.cast(features[key + '_diagonal'], dtype=tf.float32))
else:
tensors_2d.append(tf.cast(features[key], dtype=tf.float32))
else:
raise ValueError('Unexpected FeatureType returned: %s' % str(feature_dim))
# Determine num_res from the sequence as seq_length was possibly normalized.
num_res = tf.strings.length(features['sequence'])[0]
# Concatenate feature tensors into a single tensor
inputs_1d = _concat_or_zeros(
tensors_1d, axis=1, tensor_shape=[num_res, 0],
name='inputs_1d_concat')
inputs_2d = _concat_or_zeros(
tensors_2d, axis=2, tensor_shape=[num_res, num_res, 0],
name='inputs_2d_concat')
if tensors_2d_diagonal:
# The legacy dataset outputs the two diagonal crops stacked
# A1, B1, C1, A2, B2, C2. So convert the A1, A2, B1, B2, C1, C2 format.
diagonal_crops1 = [t[:, :, :(t.shape[2] // 2)] for t in tensors_2d_diagonal]
diagonal_crops2 = [t[:, :, (t.shape[2] // 2):] for t in tensors_2d_diagonal]
inputs_2d_diagonal = tf.concat(diagonal_crops1 + diagonal_crops2, axis=2)
else:
inputs_2d_diagonal = inputs_2d
sequence = features['sequence']
sequence_key = features.get('key', tf.constant(['']))[0]
if 'crops' in features:
crops = features['crops']
else:
crops = tf.stack([0, tf.shape(sequence)[0], 0, tf.shape(sequence)[0]])
scalar_tensors = []
for key in desired_scalars:
scalar_tensors.append(features.get(key + '_unnormalized', features[key]))
target_tensors = []
for key in desired_targets:
target_tensors.append(features.get(key + '_unnormalized', features[key]))
scalar_class = collections.namedtuple('_ScalarClass', desired_scalars)
target_class = collections.namedtuple('_TargetClass', desired_targets)
return _ProteinDescription(
sequence_lengths=num_res,
key=sequence_key,
sequences=sequence,
inputs_1d=inputs_1d,
inputs_2d=inputs_2d,
inputs_2d_diagonal=inputs_2d_diagonal,
crops=crops,
scalars=scalar_class(*scalar_tensors),
targets=target_class(*target_tensors))
================================================
FILE: alphafold_casp13/contacts_experiment.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contact prediction convnet experiment example."""
from absl import logging
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import contacts_dataset
from alphafold_casp13 import contacts_network
def _int_ph(shape, name):
return tf.placeholder(
dtype=tf.int32, shape=shape, name=('%s_placeholder' % name))
def _float_ph(shape, name):
return tf.placeholder(
dtype=tf.float32, shape=shape, name=('%s_placeholder' % name))
class Contacts(object):
"""Contact prediction experiment."""
def __init__(
self, tfrecord, stats_file, network_config, crop_size_x, crop_size_y,
feature_normalization, normalization_exclusion):
"""Builds the TensorFlow graph."""
self.network_config = network_config
self.crop_size_x = crop_size_x
self.crop_size_y = crop_size_y
self._feature_normalization = feature_normalization
self._normalization_exclusion = normalization_exclusion
self._model = contacts_network.ContactsNet(**network_config)
self._features = network_config.features
self._scalars = network_config.scalars
self._targets = network_config.targets
# Add extra targets we need.
required_targets = ['domain_name', 'resolution', 'chain_name']
if self.model.torsion_multiplier > 0:
required_targets.extend([
'phi_angles', 'phi_mask', 'psi_angles', 'psi_mask'])
if self.model.secstruct_multiplier > 0:
required_targets.extend(['sec_structure', 'sec_structure_mask'])
if self.model.asa_multiplier > 0:
required_targets.extend(['solv_surf', 'solv_surf_mask'])
extra_targets = [t for t in required_targets if t not in self._targets]
if extra_targets:
targets = list(self._targets)
targets.extend(extra_targets)
self._targets = tuple(targets)
logging.info('Targets %s %s extra %s',
type(self._targets), self._targets, extra_targets)
logging.info('Evaluating on %s, stats: %s', tfrecord, stats_file)
self._build_evaluation_graph(tfrecord=tfrecord, stats_file=stats_file)
@property
def model(self):
return self._model
def _get_feature_normalization(self, features):
return {key: self._feature_normalization
for key in features
if key not in list(self._normalization_exclusion)}
def _build_evaluation_graph(self, tfrecord, stats_file):
"""Constructs the graph in pieces so it can be fed."""
with tf.name_scope('competitionsep'):
# Construct the dataset and mapping ops.
dataset = contacts_dataset.create_tf_dataset(
tf_record_filename=tfrecord,
features=tuple(self._features) + tuple(
self._scalars) + tuple(self._targets))
def normalize(data):
return contacts_dataset.normalize_from_stats_file(
features=data,
stats_file_path=stats_file,
feature_normalization=self._get_feature_normalization(
self._features),
copy_unnormalized=list(set(self._features) & set(self._targets)))
def convert_to_legacy(features):
return contacts_dataset.convert_to_legacy_proteins_dataset_format(
features, self._features, self._scalars, self._targets)
dataset = dataset.map(normalize)
dataset = dataset.map(convert_to_legacy)
dataset = dataset.batch(1)
# Get a batch of tensors in the legacy ProteinsDataset format.
iterator = tf.data.make_one_shot_iterator(dataset)
self._input_batch = iterator.get_next()
self.num_eval_examples = sum(
1 for _ in tf.python_io.tf_record_iterator(tfrecord))
logging.info('Eval batch:\n%s', self._input_batch)
feature_dim_1d = self._input_batch.inputs_1d.shape.as_list()[-1]
feature_dim_2d = self._input_batch.inputs_2d.shape.as_list()[-1]
feature_dim_2d *= 3 # The diagonals will be stacked before feeding.
# Now placeholders for the graph to compute the outputs for one crop.
self.inputs_1d_placeholder = _float_ph(
shape=[None, None, feature_dim_1d], name='inputs_1d')
self.residue_index_placeholder = _int_ph(
shape=[None, None], name='residue_index')
self.inputs_2d_placeholder = _float_ph(
shape=[None, None, None, feature_dim_2d], name='inputs_2d')
# 4 ints: x_start, x_end, y_start, y_end.
self.crop_placeholder = _int_ph(shape=[None, 4], name='crop')
# Finally placeholders for the graph to score the complete contact map.
self.probs_placeholder = _float_ph(shape=[None, None, None], name='probs')
self.softmax_probs_placeholder = _float_ph(
shape=[None, None, None, self.network_config.num_bins],
name='softmax_probs')
self.cb_placeholder = _float_ph(shape=[None, None, 3], name='cb')
self.cb_mask_placeholder = _float_ph(shape=[None, None], name='cb_mask')
self.lengths_placeholder = _int_ph(shape=[None], name='lengths')
if self.model.secstruct_multiplier > 0:
self.sec_structure_placeholder = _float_ph(
shape=[None, None, 8], name='sec_structure')
self.sec_structure_logits_placeholder = _float_ph(
shape=[None, None, 8], name='sec_structure_logits')
self.sec_structure_mask_placeholder = _float_ph(
shape=[None, None, 1], name='sec_structure_mask')
if self.model.asa_multiplier > 0:
self.solv_surf_placeholder = _float_ph(
shape=[None, None, 1], name='solv_surf')
self.solv_surf_logits_placeholder = _float_ph(
shape=[None, None, 1], name='solv_surf_logits')
self.solv_surf_mask_placeholder = _float_ph(
shape=[None, None, 1], name='solv_surf_mask')
if self.model.torsion_multiplier > 0:
self.torsions_truth_placeholder = _float_ph(
shape=[None, None, 2], name='torsions_truth')
self.torsions_mask_placeholder = _float_ph(
shape=[None, None, 1], name='torsions_mask')
self.torsion_logits_placeholder = _float_ph(
shape=[None, None, self.network_config.torsion_bins ** 2],
name='torsion_logits')
# Build a dict to pass all the placeholders into build.
placeholders = {
'inputs_1d_placeholder': self.inputs_1d_placeholder,
'residue_index_placeholder': self.residue_index_placeholder,
'inputs_2d_placeholder': self.inputs_2d_placeholder,
'crop_placeholder': self.crop_placeholder,
'probs_placeholder': self.probs_placeholder,
'softmax_probs_placeholder': self.softmax_probs_placeholder,
'cb_placeholder': self.cb_placeholder,
'cb_mask_placeholder': self.cb_mask_placeholder,
'lengths_placeholder': self.lengths_placeholder,
}
if self.model.secstruct_multiplier > 0:
placeholders.update({
'sec_structure': self.sec_structure_placeholder,
'sec_structure_logits_placeholder':
self.sec_structure_logits_placeholder,
'sec_structure_mask': self.sec_structure_mask_placeholder,})
if self.model.asa_multiplier > 0:
placeholders.update({
'solv_surf': self.solv_surf_placeholder,
'solv_surf_logits_placeholder': self.solv_surf_logits_placeholder,
'solv_surf_mask': self.solv_surf_mask_placeholder,})
if self.model.torsion_multiplier > 0:
placeholders.update({
'torsions_truth': self.torsions_truth_placeholder,
'torsion_logits_placeholder': self.torsion_logits_placeholder,
'torsions_truth_mask': self.torsions_mask_placeholder,})
activations = self._model(
crop_size_x=self.crop_size_x,
crop_size_y=self.crop_size_y,
placeholders=placeholders)
self.eval_probs_softmax = tf.nn.softmax(
activations[:, :, :, :self.network_config.num_bins])
self.eval_probs = tf.reduce_sum(
self.eval_probs_softmax[:, :, :, :self._model.quant_threshold()],
axis=3)
def get_one_example(self, sess):
"""Pull one example off the queue so we can feed it for evaluation."""
request_dict = {
'inputs_1d': self._input_batch.inputs_1d,
'inputs_2d': self._input_batch.inputs_2d,
'sequence_lengths': self._input_batch.sequence_lengths,
'beta_positions': self._input_batch.targets.beta_positions,
'beta_mask': self._input_batch.targets.beta_mask,
'domain_name': self._input_batch.targets.domain_name,
'chain_name': self._input_batch.targets.chain_name,
'sequences': self._input_batch.sequences,
}
if hasattr(self._input_batch.targets, 'residue_index'):
request_dict.update(
{'residue_index': self._input_batch.targets.residue_index})
if hasattr(self._input_batch.targets, 'phi_angles'):
request_dict.update(
{'phi_angles': self._input_batch.targets.phi_angles,
'psi_angles': self._input_batch.targets.psi_angles,
'phi_mask': self._input_batch.targets.phi_mask,
'psi_mask': self._input_batch.targets.psi_mask})
if hasattr(self._input_batch.targets, 'sec_structure'):
request_dict.update(
{'sec_structure': self._input_batch.targets.sec_structure,
'sec_structure_mask': self._input_batch.targets.sec_structure_mask,})
if hasattr(self._input_batch.targets, 'solv_surf'):
request_dict.update(
{'solv_surf': self._input_batch.targets.solv_surf,
'solv_surf_mask': self._input_batch.targets.solv_surf_mask,})
if hasattr(self._input_batch.targets, 'alpha_positions'):
request_dict.update(
{'alpha_positions': self._input_batch.targets.alpha_positions,
'alpha_mask': self._input_batch.targets.alpha_mask,})
batch = sess.run(request_dict)
return batch
================================================
FILE: alphafold_casp13/contacts_network.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Network for predicting C-beta contacts."""
from absl import logging
import sonnet
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import asa_output
from alphafold_casp13 import secstruct
from alphafold_casp13 import two_dim_convnet
from alphafold_casp13 import two_dim_resnet
def call_on_tuple(f):
"""Unpacks a tuple input parameter into arguments for a function f.
Mimics tuple unpacking in lambdas, which existed in Python 2 but has been
removed in Python 3.
Args:
f: A function taking multiple arguments.
Returns:
A function equivalent to f accepting a tuple, which is then unpacked.
"""
return lambda args: f(*args)
class ContactsNet(sonnet.AbstractModule):
"""A network to go from sequence to distance histograms."""
def __init__(self,
binary_code_bits,
data_format,
distance_multiplier,
features,
features_forward,
max_range,
min_range,
num_bins,
reshape_layer,
resolution_noise_scale,
scalars,
targets,
network_2d_deep,
torsion_bins=None,
skip_connect=0,
position_specific_bias_size=0,
filters_1d=(),
collapsed_batch_norm=False,
is_ca_feature=False,
asa_multiplier=0.0,
secstruct_multiplier=0.0,
torsion_multiplier=0.0,
name='contacts_net'):
"""Construct position prediction network."""
super(ContactsNet, self).__init__(name=name)
self._filters_1d = filters_1d
self._collapsed_batch_norm = collapsed_batch_norm
self._is_ca_feature = is_ca_feature
self._binary_code_bits = binary_code_bits
self._data_format = data_format
self._distance_multiplier = distance_multiplier
self._features = features
self._features_forward = features_forward
self._max_range = max_range
self._min_range = min_range
self._num_bins = num_bins
self._position_specific_bias_size = position_specific_bias_size
self._reshape_layer = reshape_layer
self._resolution_noise_scale = resolution_noise_scale
self._scalars = scalars
self._torsion_bins = torsion_bins
self._skip_connect = skip_connect
self._targets = targets
self._network_2d_deep = network_2d_deep
self.asa_multiplier = asa_multiplier
self.secstruct_multiplier = secstruct_multiplier
self.torsion_multiplier = torsion_multiplier
with self._enter_variable_scope():
if self.secstruct_multiplier > 0:
self._secstruct = secstruct.Secstruct()
if self.asa_multiplier > 0:
self._asa = asa_output.ASAOutputLayer()
if self._position_specific_bias_size:
self._position_specific_bias = tf.get_variable(
'position_specific_bias',
[self._position_specific_bias_size, self._num_bins or 1],
initializer=tf.zeros_initializer())
def quant_threshold(self, threshold=8.0):
"""Find the bin that is 8A+: we sum mass below this bin gives contact prob.
Args:
threshold: The distance threshold.
Returns:
Index of bin.
"""
# Note that this misuses the max_range as the range.
return int(
(threshold - self._min_range) * self._num_bins / float(self._max_range))
def _build(self, crop_size_x=0, crop_size_y=0, placeholders=None):
"""Puts the network into the graph.
Args:
crop_size_x: Crop a chunk out in one dimension. 0 means no cropping.
crop_size_y: Crop a chunk out in one dimension. 0 means no cropping.
placeholders: A dict containing the placeholders needed.
Returns:
A Tensor with logits of size [batch_size, num_residues, 3].
"""
crop_placeholder = placeholders['crop_placeholder']
inputs_1d = placeholders['inputs_1d_placeholder']
if self._is_ca_feature and 'aatype' in self._features:
logging.info('Collapsing aatype to is_ca_feature %s',
inputs_1d.shape.as_list()[-1])
assert inputs_1d.shape.as_list()[-1] <= 21 + (
1 if 'seq_length' in self._features else 0)
inputs_1d = inputs_1d[:, :, 7:8]
logits = self.compute_outputs(
inputs_1d=inputs_1d,
residue_index=placeholders['residue_index_placeholder'],
inputs_2d=placeholders['inputs_2d_placeholder'],
crop_x=crop_placeholder[:, 0:2],
crop_y=crop_placeholder[:, 2:4],
use_on_the_fly_stats=True,
crop_size_x=crop_size_x,
crop_size_y=crop_size_y,
data_format='NHWC', # Force NHWC for evals.
)
return logits
def compute_outputs(self, inputs_1d, residue_index, inputs_2d, crop_x, crop_y,
use_on_the_fly_stats, crop_size_x, crop_size_y,
data_format='NHWC'):
"""Given the inputs for a block, compute the network outputs."""
hidden_1d = inputs_1d
hidden_1d_list = [hidden_1d]
if len(hidden_1d_list) != 1:
hidden_1d = tf.concat(hidden_1d_list, 2)
output_dimension = self._num_bins or 1
if self._distance_multiplier > 0:
output_dimension += 1
logits, activations = self._build_2d_embedding(
hidden_1d=hidden_1d,
residue_index=residue_index,
inputs_2d=inputs_2d,
output_dimension=output_dimension,
use_on_the_fly_stats=use_on_the_fly_stats,
crop_x=crop_x,
crop_y=crop_y,
crop_size_x=crop_size_x, crop_size_y=crop_size_y,
data_format=data_format)
logits = tf.debugging.check_numerics(
logits, 'NaN in resnet activations', name='resnet_activations')
if (self.secstruct_multiplier > 0 or
self.asa_multiplier > 0 or
self.torsion_multiplier > 0):
# Make a 1d embedding by reducing the 2D activations.
# We do this in the x direction and the y direction separately.
collapse_dim = 1
join_dim = -1
embedding_1d = tf.concat(
# First targets are crop_x (axis 2) which we must reduce on axis 1
[tf.concat([tf.reduce_max(activations, axis=collapse_dim),
tf.reduce_mean(activations, axis=collapse_dim)],
axis=join_dim),
# Next targets are crop_y (axis 1) which we must reduce on axis 2
tf.concat([tf.reduce_max(activations, axis=collapse_dim+1),
tf.reduce_mean(activations, axis=collapse_dim+1)],
axis=join_dim)],
axis=collapse_dim) # Join the two crops together.
if self._collapsed_batch_norm:
embedding_1d = tf.contrib.layers.batch_norm(
embedding_1d, is_training=use_on_the_fly_stats,
fused=True, decay=0.999, scope='collapsed_batch_norm',
data_format='NHWC')
for i, nfil in enumerate(self._filters_1d):
embedding_1d = tf.contrib.layers.fully_connected(
embedding_1d,
num_outputs=nfil,
normalizer_fn=(
tf.contrib.layers.batch_norm if self._collapsed_batch_norm
else None),
normalizer_params={'is_training': use_on_the_fly_stats,
'updates_collections': None},
scope='collapsed_embed_%d' % i)
if self.torsion_multiplier > 0:
self.torsion_logits = tf.contrib.layers.fully_connected(
embedding_1d,
num_outputs=self._torsion_bins * self._torsion_bins,
activation_fn=None,
scope='torsion_logits')
self.torsion_output = tf.nn.softmax(self.torsion_logits)
if self.secstruct_multiplier > 0:
self._secstruct.make_layer_new(embedding_1d)
if self.asa_multiplier > 0:
self.asa_logits = self._asa.compute_asa_output(embedding_1d)
return logits
@staticmethod
def _concatenate_2d(hidden_1d, residue_index, hidden_2d, crop_x, crop_y,
binary_code_bits, crop_size_x, crop_size_y):
# Form the pairwise expansion of the 1D embedding
# And the residue offsets and (one) absolute position.
with tf.name_scope('Features2D'):
range_scale = 100.0 # Crude normalization factor.
n = tf.shape(hidden_1d)[1]
# pylint: disable=g-long-lambda
hidden_1d_cropped_y = tf.map_fn(
call_on_tuple(lambda c, h: tf.pad(
h[tf.maximum(0, c[0]):c[1]],
[[tf.maximum(0, -c[0]),
tf.maximum(0, crop_size_y -(n - c[0]))], [0, 0]])),
elems=(crop_y, hidden_1d), dtype=tf.float32,
back_prop=True)
range_n_y = tf.map_fn(
call_on_tuple(lambda ri, c: tf.pad(
ri[tf.maximum(0, c[0]):c[1]],
[[tf.maximum(0, -c[0]),
tf.maximum(0, crop_size_y -(n - c[0]))]])),
elems=(residue_index, crop_y), dtype=tf.int32,
back_prop=False)
hidden_1d_cropped_x = tf.map_fn(
call_on_tuple(lambda c, h: tf.pad(
h[tf.maximum(0, c[0]):c[1]],
[[tf.maximum(0, -c[0]),
tf.maximum(0, crop_size_x -(n - c[0]))], [0, 0]])),
elems=(crop_x, hidden_1d), dtype=tf.float32,
back_prop=True)
range_n_x = tf.map_fn(
call_on_tuple(lambda ri, c: tf.pad(
ri[tf.maximum(0, c[0]):c[1]],
[[tf.maximum(0, -c[0]),
tf.maximum(0, crop_size_x -(n - c[0]))]])),
elems=(residue_index, crop_x), dtype=tf.int32,
back_prop=False)
# pylint: enable=g-long-lambda
n_x = crop_size_x
n_y = crop_size_y
offset = (tf.expand_dims(tf.cast(range_n_x, tf.float32), 1) -
tf.expand_dims(tf.cast(range_n_y, tf.float32), 2)) / range_scale
position_features = [
tf.tile(
tf.reshape(
(tf.cast(range_n_y, tf.float32) - range_scale) / range_scale,
[-1, n_y, 1, 1]), [1, 1, n_x, 1],
name='TileRange'),
tf.tile(
tf.reshape(offset, [-1, n_y, n_x, 1]), [1, 1, 1, 1],
name='TileOffset')
]
channels = 2
if binary_code_bits:
# Binary coding of position.
exp_range_n_y = tf.expand_dims(range_n_y, 2)
bin_y = tf.stop_gradient(
tf.concat([tf.math.floormod(exp_range_n_y // (1 << i), 2)
for i in range(binary_code_bits)], 2))
exp_range_n_x = tf.expand_dims(range_n_x, 2)
bin_x = tf.stop_gradient(
tf.concat([tf.math.floormod(exp_range_n_x // (1 << i), 2)
for i in range(binary_code_bits)], 2))
position_features += [
tf.tile(
tf.expand_dims(tf.cast(bin_y, tf.float32), 2), [1, 1, n_x, 1],
name='TileBinRangey'),
tf.tile(
tf.expand_dims(tf.cast(bin_x, tf.float32), 1), [1, n_y, 1, 1],
name='TileBinRangex')
]
channels += 2 * binary_code_bits
augmentation_features = position_features + [
tf.tile(tf.expand_dims(hidden_1d_cropped_x, 1),
[1, n_y, 1, 1], name='Tile1Dx'),
tf.tile(tf.expand_dims(hidden_1d_cropped_y, 2),
[1, 1, n_x, 1], name='Tile1Dy')]
channels += 2 * hidden_1d.shape.as_list()[-1]
channels += hidden_2d.shape.as_list()[-1]
hidden_2d = tf.concat(
[hidden_2d] + augmentation_features, 3, name='Stack2Dfeatures')
logging.info('2d stacked features are depth %d %s', channels, hidden_2d)
hidden_2d.set_shape([None, None, None, channels])
return hidden_2d
def _build_2d_embedding(self, hidden_1d, residue_index, inputs_2d,
output_dimension, use_on_the_fly_stats, crop_x,
crop_y, crop_size_x, crop_size_y, data_format):
"""Returns NHWC logits and NHWC preactivations."""
logging.info('2d %s %s', inputs_2d, data_format)
# Stack with diagonal has already happened.
inputs_2d_cropped = inputs_2d
features_forward = None
hidden_2d = inputs_2d_cropped
hidden_2d = self._concatenate_2d(
hidden_1d, residue_index, hidden_2d, crop_x, crop_y,
self._binary_code_bits, crop_size_x, crop_size_y)
config_2d_deep = self._network_2d_deep
num_features = hidden_2d.shape.as_list()[3]
if data_format == 'NCHW':
logging.info('NCHW shape deep pre %s', hidden_2d)
hidden_2d = tf.transpose(hidden_2d, perm=[0, 3, 1, 2])
hidden_2d.set_shape([None, num_features, None, None])
logging.info('NCHW shape deep post %s', hidden_2d)
layers_forward = None
if config_2d_deep.extra_blocks:
# Optionally put some extra double-size blocks at the beginning.
with tf.variable_scope('Deep2DExtra'):
hidden_2d = two_dim_resnet.make_two_dim_resnet(
input_node=hidden_2d,
num_residues=None, # Unused
num_features=num_features,
num_predictions=2 * config_2d_deep.num_filters,
num_channels=2 * config_2d_deep.num_filters,
num_layers=config_2d_deep.extra_blocks *
config_2d_deep.num_layers_per_block,
filter_size=3,
batch_norm=config_2d_deep.use_batch_norm,
is_training=use_on_the_fly_stats,
fancy=True,
final_non_linearity=True,
atrou_rates=[1, 2, 4, 8],
data_format=data_format,
dropout_keep_prob=1.0
)
num_features = 2 * config_2d_deep.num_filters
if self._skip_connect:
layers_forward = hidden_2d
if features_forward is not None:
hidden_2d = tf.concat([hidden_2d, features_forward], 1
if data_format == 'NCHW' else 3)
with tf.variable_scope('Deep2D'):
logging.info('2d hidden shape is %s', str(hidden_2d.shape.as_list()))
contact_pre_logits = two_dim_resnet.make_two_dim_resnet(
input_node=hidden_2d,
num_residues=None, # Unused
num_features=num_features,
num_predictions=(config_2d_deep.num_filters
if self._reshape_layer else output_dimension),
num_channels=config_2d_deep.num_filters,
num_layers=config_2d_deep.num_blocks *
config_2d_deep.num_layers_per_block,
filter_size=3,
batch_norm=config_2d_deep.use_batch_norm,
is_training=use_on_the_fly_stats,
fancy=True,
final_non_linearity=self._reshape_layer,
atrou_rates=[1, 2, 4, 8],
data_format=data_format,
dropout_keep_prob=1.0
)
contact_logits = self._output_from_pre_logits(
contact_pre_logits, features_forward, layers_forward,
output_dimension, data_format, crop_x, crop_y, use_on_the_fly_stats)
if data_format == 'NCHW':
contact_pre_logits = tf.transpose(contact_pre_logits, perm=[0, 2, 3, 1])
# Both of these will be NHWC
return contact_logits, contact_pre_logits
def _output_from_pre_logits(self, contact_pre_logits, features_forward,
layers_forward, output_dimension, data_format,
crop_x, crop_y, use_on_the_fly_stats):
"""Given pre-logits, compute the final distogram/contact activations."""
config_2d_deep = self._network_2d_deep
if self._reshape_layer:
in_channels = config_2d_deep.num_filters
concat_features = [contact_pre_logits]
if features_forward is not None:
concat_features.append(features_forward)
in_channels += self._features_forward
if layers_forward is not None:
concat_features.append(layers_forward)
in_channels += 2 * config_2d_deep.num_filters
if len(concat_features) > 1:
contact_pre_logits = tf.concat(concat_features,
1 if data_format == 'NCHW' else 3)
contact_logits = two_dim_convnet.make_conv_layer(
contact_pre_logits,
in_channels=in_channels,
out_channels=output_dimension,
layer_name='output_reshape_1x1h',
filter_size=1,
filter_size_2=1,
non_linearity=False,
batch_norm=config_2d_deep.use_batch_norm,
is_training=use_on_the_fly_stats,
data_format=data_format)
else:
contact_logits = contact_pre_logits
if data_format == 'NCHW':
contact_logits = tf.transpose(contact_logits, perm=[0, 2, 3, 1])
if self._position_specific_bias_size:
# Make 2D pos-specific biases: NHWC.
biases = build_crops_biases(
self._position_specific_bias_size,
self._position_specific_bias, crop_x, crop_y, back_prop=True)
contact_logits += biases
# Will be NHWC.
return contact_logits
def update_crop_fetches(self, fetches):
"""Add auxiliary outputs for a crop to the fetches."""
if self.secstruct_multiplier > 0:
fetches['secstruct_probs'] = self._secstruct.get_q8_probs()
if self.asa_multiplier > 0:
fetches['asa_output'] = self._asa.asa_output
if self.torsion_multiplier > 0:
fetches['torsion_probs'] = self.torsion_output
def build_crops_biases(bias_size, raw_biases, crop_x, crop_y, back_prop):
"""Take the offset-specific biases and reshape them to match current crops.
Args:
bias_size: how many bias variables we're storing.
raw_biases: the bias variable
crop_x: B x 2 array of start/end for the batch
crop_y: B x 2 array of start/end for the batch
back_prop: whether to backprop through the map_fn.
Returns:
Reshaped biases.
"""
# First pad the biases with a copy of the final value to the maximum length.
max_off_diag = tf.reduce_max(
tf.maximum(tf.abs(crop_x[:, 1] - crop_y[:, 0]),
tf.abs(crop_y[:, 1] - crop_x[:, 0])))
padded_bias_size = tf.maximum(bias_size, max_off_diag)
biases = tf.concat(
[raw_biases,
tf.tile(raw_biases[-1:, :],
[padded_bias_size - bias_size, 1])], axis=0)
# Now prepend a mirror image (excluding 0th elt) for below-diagonal.
biases = tf.concat([tf.reverse(biases[1:, :], axis=[0]), biases], axis=0)
# Which diagonal of the full matrix each crop starts on (top left):
start_diag = crop_x[:, 0:1] - crop_y[:, 0:1] # B x 1
crop_size_x = tf.reduce_max(crop_x[:, 1] - crop_x[:, 0])
crop_size_y = tf.reduce_max(crop_y[:, 1] - crop_y[:, 0])
# Relative offset of each row within a crop:
# (off-diagonal decreases as y increases)
increment = tf.expand_dims(-tf.range(0, crop_size_y), 0) # 1 x crop_size_y
# Index of diagonal of first element of each row, flattened.
row_offsets = tf.reshape(start_diag + increment, [-1]) # B*crop_size_y
logging.info('row_offsets %s', row_offsets)
# Make it relative to the start of the biases array. (0-th diagonal is in
# the middle at position padded_bias_size - 1)
row_offsets += padded_bias_size - 1
# Map_fn to build the individual rows.
# B*cropsizey x cropsizex x num_bins
cropped_biases = tf.map_fn(lambda i: biases[i:i+crop_size_x, :],
elems=row_offsets, dtype=tf.float32,
back_prop=back_prop)
logging.info('cropped_biases %s', cropped_biases)
return tf.reshape(
cropped_biases, [-1, crop_size_y, crop_size_x, tf.shape(raw_biases)[-1]])
================================================
FILE: alphafold_casp13/distogram_io.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Write contact map predictions to a tf.io.gfile.
Either write a binary contact map as an RR format text file, or a
histogram prediction as a pickle of a dict containing a numpy array.
"""
import os
import numpy as np
import six.moves.cPickle as pickle
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
RR_FORMAT = """PFRMAT RR
TARGET {}
AUTHOR DM-ORIGAMI-TEAM
METHOD {}
MODEL 1
{}
"""
def save_rr_file(filename, probs, domain, sequence,
method='dm-contacts-resnet'):
"""Save a contact probability matrix as an RR file."""
assert len(sequence) == probs.shape[0]
assert len(sequence) == probs.shape[1]
with tf.io.gfile.GFile(filename, 'w') as f:
f.write(RR_FORMAT.format(domain, method, sequence))
for i in range(probs.shape[0]):
for j in range(i + 1, probs.shape[1]):
f.write('{:d} {:d} {:d} {:d} {:f}\n'.format(
i + 1, j + 1, 0, 8, probs[j, i]))
f.write('END\n')
def save_torsions(torsions_dir, filebase, sequence, torsions_probs):
"""Save Torsions to a file as pickle of a dict."""
filename = os.path.join(torsions_dir, filebase + '.torsions')
t_dict = dict(probs=torsions_probs, sequence=sequence)
with tf.io.gfile.GFile(filename, 'w') as fh:
pickle.dump(t_dict, fh, protocol=2)
def save_distance_histogram(
filename, probs, domain, sequence, min_range, max_range, num_bins):
"""Save a distance histogram prediction matrix as a pickle file."""
dh_dict = {
'min_range': min_range,
'max_range': max_range,
'num_bins': num_bins,
'domain': domain,
'sequence': sequence,
'probs': probs.astype(np.float32)}
save_distance_histogram_from_dict(filename, dh_dict)
def save_distance_histogram_from_dict(filename, dh_dict):
"""Save a distance histogram prediction matrix as a pickle file."""
fields = ['min_range', 'max_range', 'num_bins', 'domain', 'sequence', 'probs']
missing_fields = [f for f in fields if f not in dh_dict]
assert not missing_fields, 'Fields {} missing from dictionary'.format(
missing_fields)
assert len(dh_dict['sequence']) == dh_dict['probs'].shape[0]
assert len(dh_dict['sequence']) == dh_dict['probs'].shape[1]
assert dh_dict['num_bins'] == dh_dict['probs'].shape[2]
assert dh_dict['min_range'] >= 0.0
assert dh_dict['max_range'] > 0.0
with tf.io.gfile.GFile(filename, 'wb') as fw:
pickle.dump(dh_dict, fw, protocol=2)
def contact_map_from_distogram(distogram_dict):
"""Split the boundary bin."""
num_bins = distogram_dict['probs'].shape[-1]
bin_size_angstrom = distogram_dict['max_range'] / num_bins
threshold_cts = (8.0 - distogram_dict['min_range']) / bin_size_angstrom
threshold_bin = int(threshold_cts) # Round down
pred_contacts = np.sum(distogram_dict['probs'][:, :, :threshold_bin], axis=-1)
if threshold_bin < threshold_cts: # Add on the fraction of the boundary bin.
pred_contacts += distogram_dict['probs'][:, :, threshold_bin] * (
threshold_cts - threshold_bin)
return pred_contacts
================================================
FILE: alphafold_casp13/ensemble_contact_maps.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Form a weighted average of several distograms.
Can also/instead form a weighted average of a set of distance histogram pickle
files, so long as they have identical hyperparameters.
"""
import os
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import distogram_io
from alphafold_casp13 import parsers
flags.DEFINE_list(
'pickle_dirs', [],
'Comma separated list of directories with pickle files to ensemble.')
flags.DEFINE_list(
'weights', [],
'Comma separated list of weights for the pickle files from different dirs.')
flags.DEFINE_string(
'output_dir', None, 'Directory where to save results of the evaluation.')
FLAGS = flags.FLAGS
def ensemble_distance_histograms(pickle_dirs, weights, output_dir):
"""Find all the contact maps in the first dir, then ensemble across dirs."""
if len(pickle_dirs) <= 1:
logging.warning('Pointless to ensemble %d pickle_dirs %s',
len(pickle_dirs), pickle_dirs)
# Carry on if there's one dir, otherwise do nothing.
if not pickle_dirs:
return
tf.io.gfile.makedirs(output_dir)
one_dir_pickle_files = tf.io.gfile.glob(
os.path.join(pickle_dirs[0], '*.pickle'))
assert one_dir_pickle_files, pickle_dirs[0]
original_files = len(one_dir_pickle_files)
logging.info('Found %d files %d in first of %d dirs',
original_files, len(one_dir_pickle_files), len(pickle_dirs))
targets = [os.path.splitext(os.path.basename(f))[0]
for f in one_dir_pickle_files]
skipped = 0
wrote = 0
for t in targets:
dump_file = os.path.join(output_dir, t + '.pickle')
pickle_files = [os.path.join(pickle_dir, t + '.pickle')
for pickle_dir in pickle_dirs]
_, new_dict = ensemble_one_distance_histogram(pickle_files, weights)
if new_dict is not None:
wrote += 1
distogram_io.save_distance_histogram_from_dict(dump_file, new_dict)
msg = 'Distograms Wrote %s %d / %d Skipped %d %s' % (
t, wrote, len(one_dir_pickle_files), skipped, dump_file)
logging.info(msg)
def ensemble_one_distance_histogram(pickle_files, weights):
"""Average the given pickle_files and dump."""
dicts = []
sequence = None
max_dim = None
for picklefile in pickle_files:
if not tf.io.gfile.exists(picklefile):
logging.warning('missing %s', picklefile)
break
logging.info('loading pickle file %s', picklefile)
distance_histogram_dict = parsers.parse_distance_histogram_dict(picklefile)
if sequence is None:
sequence = distance_histogram_dict['sequence']
else:
assert sequence == distance_histogram_dict['sequence'], '%s vs %s' % (
sequence, distance_histogram_dict['sequence'])
dicts.append(distance_histogram_dict)
assert dicts[-1]['probs'].shape[0] == dicts[-1]['probs'].shape[1], (
'%d vs %d' % (dicts[-1]['probs'].shape[0], dicts[-1]['probs'].shape[1]))
assert (dicts[0]['probs'].shape[0:2] == dicts[-1]['probs'].shape[0:2]
), ('%d vs %d' % (dicts[0]['probs'].shape, dicts[-1]['probs'].shape))
if max_dim is None or max_dim < dicts[-1]['probs'].shape[2]:
max_dim = dicts[-1]['probs'].shape[2]
if len(dicts) != len(pickle_files):
logging.warning('length mismatch\n%s\nVS\n%s', dicts, pickle_files)
return sequence, None
ensemble_hist = (
sum(w * c['probs'] for w, c in zip(weights, dicts)) / sum(weights))
new_dict = dict(dicts[0])
new_dict['probs'] = ensemble_hist
return sequence, new_dict
def main(argv):
del argv # Unused.
num_dirs = len(FLAGS.pickle_dirs)
if FLAGS.weights:
assert len(FLAGS.weights) == num_dirs, (
'Supply as many weights as pickle_dirs, or no weights')
weights = [float(w) for w in FLAGS.weights]
else:
weights = [1.0 for w in range(num_dirs)]
ensemble_distance_histograms(
pickle_dirs=FLAGS.pickle_dirs,
weights=weights,
output_dir=FLAGS.output_dir)
if __name__ == '__main__':
app.run(main)
================================================
FILE: alphafold_casp13/parsers.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Parsers for various standard biology or AlphaFold-specific formats."""
import pickle
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
def distance_histogram_dict(f):
"""Parses distance histogram dict pickle.
Distance histograms are stored as pickles of dicts.
Write one of these with contacts/write_rr_file.write_pickle_file()
Args:
f: File-like handle to distance histogram dict pickle.
Returns:
Dict with fields:
probs: (an L x L x num_bins) histogram.
num_bins: number of bins for each residue pair
min_range: left hand edge of the distance histogram
max_range: the extent of the histogram NOT the right hand edge.
"""
contact_dict = pickle.load(f, encoding='latin1')
num_res = len(contact_dict['sequence'])
if not all(key in contact_dict.keys()
for key in ['probs', 'num_bins', 'min_range', 'max_range']):
raise ValueError('The pickled contact dict doesn\'t contain all required '
'keys: probs, num_bins, min_range, max_range but %s.' %
contact_dict.keys())
if contact_dict['probs'].ndim != 3:
raise ValueError(
'Probs is not rank 3 but %d' % contact_dict['probs'].ndim)
if contact_dict['num_bins'] != contact_dict['probs'].shape[2]:
raise ValueError(
'The probs shape doesn\'t match num_bins in the third dimension. '
'Expected %d got %d.' % (contact_dict['num_bins'],
contact_dict['probs'].shape[2]))
if contact_dict['probs'].shape[:2] != (num_res, num_res):
raise ValueError(
'The first two probs dims (%i, %i) aren\'t equal to len(sequence) %i'
% (contact_dict['probs'].shape[0], contact_dict['probs'].shape[1],
num_res))
return contact_dict
def parse_distance_histogram_dict(filepath):
"""Parses distance histogram piclkle from filepath."""
with tf.io.gfile.GFile(filepath, 'rb') as f:
return distance_histogram_dict(f)
================================================
FILE: alphafold_casp13/paste_contact_maps.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Combines predictions by pasting."""
import os
from absl import app
from absl import flags
from absl import logging
import numpy as np
import six
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import distogram_io
from alphafold_casp13 import parsers
flags.DEFINE_string("pickle_input_dir", None,
"Directory to read pickle distance histogram files from.")
flags.DEFINE_string("output_dir", None, "Directory to write chain RR files to.")
flags.DEFINE_string("tfrecord_path", "",
"If provided, construct the average weighted by number of "
"alignments.")
flags.DEFINE_string("crop_sizes", "64,128,256", "The crop sizes to use.")
flags.DEFINE_integer("crop_step", 32, "The step size for cropping.")
FLAGS = flags.FLAGS
def generate_domains(target, sequence, crop_sizes, crop_step):
"""Take fasta files and generate a domain definition for data generation."""
logging.info("Generating crop domains for target %s", target)
windows = [int(x) for x in crop_sizes.split(",")]
num_residues = len(sequence)
domains = []
domains.append({"name": target, "description": (1, num_residues)})
for window in windows:
starts = list(range(0, num_residues - window, crop_step))
# Append a last crop to ensure we get all the way to the end of the
# sequence, even when num_residues - window is not divisible by crop_step.
if num_residues >= window:
starts += [num_residues - window]
for start in starts:
name = "%s-l%i_s%i" % (target, window, start)
domains.append({"name": name, "description": (start + 1, start + window)})
return domains
def get_weights(path):
"""Fetch all the weights from a TFRecord."""
if not path:
return {}
logging.info("Getting weights from %s", path)
weights = {}
record_iterator = tf.python_io.tf_record_iterator(path=path)
for serialized_tfexample in record_iterator:
example = tf.train.Example()
example.ParseFromString(serialized_tfexample)
domain_name = six.ensure_str(
example.features.feature["domain_name"].bytes_list.value[0])
weights[domain_name] = float(
example.features.feature["num_alignments"].int64_list.value[0])
logging.info("Weight %s: %d", domain_name, weights[domain_name])
logging.info("Loaded %d weights", len(weights))
return weights
def paste_distance_histograms(
input_dir, output_dir, weights, crop_sizes, crop_step):
"""Paste together distograms for given domains of given targets and write.
Domains distance histograms are 'pasted', meaning they are substituted
directly into the contact map. The order is determined by the order in the
domain definition file.
Args:
input_dir: String, path to directory containing chain and domain-level
distogram files.
output_dir: String, path to directory to write out chain-level distrogram
files.
weights: A dictionary with weights.
crop_sizes: The crop sizes.
crop_step: The step size for cropping.
Raises:
ValueError: if histogram parameters don't match.
"""
tf.io.gfile.makedirs(output_dir)
targets = tf.io.gfile.glob(os.path.join(input_dir, "*.pickle"))
targets = [os.path.splitext(os.path.basename(t))[0] for t in targets]
targets = set([t.split("-")[0] for t in targets])
logging.info("Pasting distance histograms for %d targets", len(targets))
for target in sorted(targets):
logging.info("%s as chain", target)
chain_pickle_path = os.path.join(input_dir, "%s.pickle" % target)
distance_histogram_dict = parsers.parse_distance_histogram_dict(
chain_pickle_path)
combined_cmap = np.array(distance_histogram_dict["probs"])
# Make the counter map 1-deep but still rank 3.
counter_map = np.ones_like(combined_cmap[:, :, 0:1])
sequence = distance_histogram_dict["sequence"]
target_domains = generate_domains(
target=target, sequence=sequence, crop_sizes=crop_sizes,
crop_step=crop_step)
# Paste in each domain.
for domain in sorted(target_domains, key=lambda x: x["name"]):
if domain["name"] == target:
logging.info("Skipping %s as domain", target)
continue
if "," in domain["description"]:
logging.info("Skipping multisegment domain %s",
domain["name"])
continue
crop_start, crop_end = domain["description"]
domain_pickle_path = os.path.join(input_dir, "%s.pickle" % domain["name"])
weight = weights.get(domain["name"], 1e9)
logging.info("Pasting %s: %d-%d. weight: %f", domain_pickle_path,
crop_start, crop_end, weight)
domain_distance_histogram_dict = parsers.parse_distance_histogram_dict(
domain_pickle_path)
for field in ["num_bins", "min_range", "max_range"]:
if domain_distance_histogram_dict[field] != distance_histogram_dict[
field]:
raise ValueError("Field {} does not match {} {}".format(
field,
domain_distance_histogram_dict[field],
distance_histogram_dict[field]))
weight_matrix_size = crop_end - crop_start + 1
weight_matrix = np.ones(
(weight_matrix_size, weight_matrix_size), dtype=np.float32) * weight
combined_cmap[crop_start - 1:crop_end, crop_start - 1:crop_end, :] += (
domain_distance_histogram_dict["probs"] *
np.expand_dims(weight_matrix, 2))
counter_map[crop_start - 1:crop_end,
crop_start - 1:crop_end, 0] += weight_matrix
# Broadcast across the histogram bins.
combined_cmap /= counter_map
# Write out full-chain cmap for folding.
output_chain_pickle_path = os.path.join(output_dir,
"{}.pickle".format(target))
logging.info("Writing to %s", output_chain_pickle_path)
distance_histogram_dict["probs"] = combined_cmap
distance_histogram_dict["target"] = target
# Save the distogram pickle file.
distogram_io.save_distance_histogram_from_dict(
output_chain_pickle_path, distance_histogram_dict)
# Compute the contact map and save it as an RR file.
contact_probs = distogram_io.contact_map_from_distogram(
distance_histogram_dict)
rr_path = os.path.join(output_dir, "%s.rr" % target)
distogram_io.save_rr_file(
filename=rr_path,
probs=contact_probs,
domain=target,
sequence=distance_histogram_dict["sequence"])
def main(argv):
del argv # Unused.
flags.mark_flag_as_required("pickle_input_dir")
weights = get_weights(FLAGS.tfrecord_path)
paste_distance_histograms(
FLAGS.pickle_input_dir, FLAGS.output_dir, weights, FLAGS.crop_sizes,
FLAGS.crop_step)
if __name__ == "__main__":
app.run(main)
================================================
FILE: alphafold_casp13/requirements.txt
================================================
setuptools==41.0.0
absl-py==0.8.1
numpy==1.16
six==1.12
dm-sonnet==1.35
tensorflow==1.14
tensorflow-probability==0.7.0
================================================
FILE: alphafold_casp13/run_eval.sh
================================================
#!/bin/bash
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
# We assume the script is being run from the deepmind_research parent directory.
DISTOGRAM_MODEL="alphafold_casp13/873731" # Path to the directory with the distogram model.
BACKGROUND_MODEL="alphafold_casp13/916425" # Path to the directory with the background model.
TORSION_MODEL="alphafold_casp13/941521" # Path to the directory with the torsion model.
TARGET="T1019s2" # The name of the target.
TARGET_PATH="alphafold_casp13/${TARGET}" # Path to the directory with the target input data.
# Set up the virtual environment and install dependencies.
python3 -m venv alphafold_venv
source alphafold_venv/bin/activate
pip install wheel
pip install -r alphafold_casp13/requirements.txt
# Create the output directory.
OUTPUT_DIR="${HOME}/contacts_${TARGET}_$(date +%Y_%m_%d_%H_%M_%S)"
mkdir -p "${OUTPUT_DIR}"
echo "Saving output to ${OUTPUT_DIR}/"
# Run contact prediction over 4 replicas.
for replica in 0 1 2 3; do
echo "Launching all models for replica ${replica}"
# Run the distogram model.
python3 -m alphafold_casp13.contacts \
--logtostderr \
--cpu=true \
--config_path="${DISTOGRAM_MODEL}/${replica}/config.json" \
--checkpoint_path="${DISTOGRAM_MODEL}/${replica}/tf_graph_data/tf_graph_data.ckpt" \
--output_path="${OUTPUT_DIR}/distogram/${replica}" \
--eval_sstable="${TARGET_PATH}/${TARGET}.tfrec" \
--stats_file="${DISTOGRAM_MODEL}/stats_train_s35.json" &
# Run the background model.
python3 -m alphafold_casp13.contacts \
--logtostderr \
--cpu=true \
--config_path="${BACKGROUND_MODEL}/${replica}/config.json" \
--checkpoint_path="${BACKGROUND_MODEL}/${replica}/tf_graph_data/tf_graph_data.ckpt" \
--output_path="${OUTPUT_DIR}/background_distogram/${replica}" \
--eval_sstable="${TARGET_PATH}/${TARGET}.tfrec" \
--stats_file="${BACKGROUND_MODEL}/stats_train_s35.json" &
done
# Run the torsion model, but only 1 replica.
python3 -m alphafold_casp13.contacts \
--logtostderr \
--cpu=true \
--config_path="${TORSION_MODEL}/0/config.json" \
--checkpoint_path="${TORSION_MODEL}/0/tf_graph_data/tf_graph_data.ckpt" \
--output_path="${OUTPUT_DIR}/torsion/0" \
--eval_sstable="${TARGET_PATH}/${TARGET}.tfrec" \
--stats_file="${TORSION_MODEL}/stats_train_s35.json" &
echo "All models running, waiting for them to complete"
wait
echo "Ensembling all replica outputs"
# Run the ensembling jobs for distograms, background distograms.
for output_dir in "${OUTPUT_DIR}/distogram" "${OUTPUT_DIR}/background_distogram"; do
pickle_dirs="${output_dir}/0/pickle_files/,${output_dir}/1/pickle_files/,${output_dir}/2/pickle_files/,${output_dir}/3/pickle_files/"
# Ensemble distograms.
python3 -m alphafold_casp13.ensemble_contact_maps \
--logtostderr \
--pickle_dirs="${pickle_dirs}" \
--output_dir="${output_dir}/ensemble/"
done
# Only ensemble single replica distogram for torsions.
python3 -m alphafold_casp13.ensemble_contact_maps \
--logtostderr \
--pickle_dirs="${OUTPUT_DIR}/torsion/0/pickle_files/" \
--output_dir="${OUTPUT_DIR}/torsion/ensemble/"
echo "Pasting contact maps"
python3 -m alphafold_casp13.paste_contact_maps \
--logtostderr \
--pickle_input_dir="${OUTPUT_DIR}/distogram/ensemble/" \
--output_dir="${OUTPUT_DIR}/pasted/" \
--tfrecord_path="${TARGET_PATH}/${TARGET}.tfrec"
echo "Done"
================================================
FILE: alphafold_casp13/secstruct.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Layer for modelling and scoring secondary structure."""
import os
from absl import logging
import numpy as np
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
# 8-class classes (Q8)
SECONDARY_STRUCTURES = '-HETSGBI'
# Equivalence classes for 3-class (Q3) from Li & Yu 2016.
# See http://www.cmbi.ru.nl/dssp.html for letter explanations.
Q3_MAP = ['-TSGIB', 'H', 'E']
def make_q3_matrices():
"""Generate mapping matrices for secstruct Q8:Q3 equivalence classes."""
dimension = len(SECONDARY_STRUCTURES)
q3_map_matrix = np.zeros((dimension, len(Q3_MAP)))
q3_lookup = np.zeros((dimension,), dtype=np.int32)
for i, eclass in enumerate(Q3_MAP): # equivalence classes
for m in eclass: # Members of the class.
ss_type = SECONDARY_STRUCTURES.index(m)
q3_map_matrix[ss_type, i] = 1.0
q3_lookup[ss_type] = i
return q3_map_matrix, q3_lookup
class Secstruct(object):
"""Make a layer that computes hierarchical secstruct."""
# Build static, shared structures:
q3_map_matrix, q3_lookup = make_q3_matrices()
static_dimension = len(SECONDARY_STRUCTURES)
def __init__(self, name='secstruct'):
self.name = name
self._dimension = Secstruct.static_dimension
def make_layer_new(self, activations):
"""Make the layer."""
with tf.variable_scope(self.name, reuse=tf.AUTO_REUSE):
logging.info('Creating secstruct %s', activations)
self.logits = tf.contrib.layers.linear(activations, self._dimension)
self.ss_q8_probs = tf.nn.softmax(self.logits)
self.ss_q3_probs = tf.matmul(
self.ss_q8_probs, tf.constant(self.q3_map_matrix, dtype=tf.float32))
def get_q8_probs(self):
return self.ss_q8_probs
def save_secstructs(dump_dir_path, name, index, sequence, probs,
label='Deepmind secstruct'):
"""Write secstruct prob distributions to an ss2 file.
Can be overloaded to write out asa values too.
Args:
dump_dir_path: directory where to write files.
name: name of domain
index: index number of multiple samples. (or None for no index)
sequence: string of L residue labels
probs: L x D matrix of probabilities. L is length of sequence,
D is probability dimension (usually 3).
label: A label for the file.
"""
filename = os.path.join(dump_dir_path, '%s.ss2' % name)
if index is not None:
filename = os.path.join(dump_dir_path, '%s_%04d.ss2' % (name, index))
with tf.io.gfile.GFile(filename, 'w') as gf:
logging.info('Saving secstruct to %s', filename)
gf.write('# %s CLASSES [%s] %s sample %s\n\n' % (
label, ''.join(SECONDARY_STRUCTURES[:probs.shape[1]]), name, index))
for l in range(probs.shape[0]):
ss = SECONDARY_STRUCTURES[np.argmax(probs[l, :])]
gf.write('%4d %1s %1s %s\n' % (l + 1, sequence[l], ss, ''.join(
[('%6.3f' % p) for p in probs[l, :]])))
================================================
FILE: alphafold_casp13/test_domains.txt
================================================
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================================================
FILE: alphafold_casp13/train_domains.txt
================================================
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================================================
FILE: alphafold_casp13/two_dim_convnet.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Two dimensional convolutional neural net layers."""
from absl import logging
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
def weight_variable(shape, stddev=0.01):
"""Returns the weight variable."""
logging.vlog(1, 'weight init for shape %s', str(shape))
return tf.get_variable(
'w', shape, initializer=tf.random_normal_initializer(stddev=stddev))
def bias_variable(shape):
return tf.get_variable(
'b', shape, initializer=tf.zeros_initializer())
def conv2d(x, w, atrou_rate=1, data_format='NHWC'):
if atrou_rate > 1:
return tf.nn.convolution(
x,
w,
dilation_rate=[atrou_rate] * 2,
padding='SAME',
data_format=data_format)
else:
return tf.nn.conv2d(
x, w, strides=[1, 1, 1, 1], padding='SAME', data_format=data_format)
def make_conv_sep2d_layer(input_node,
in_channels,
channel_multiplier,
out_channels,
layer_name,
filter_size,
filter_size_2=None,
batch_norm=False,
is_training=True,
atrou_rate=1,
data_format='NHWC',
stddev=0.01):
"""Use separable convolutions."""
if filter_size_2 is None:
filter_size_2 = filter_size
logging.vlog(1, 'layer %s in %d out %d chan mult %d', layer_name, in_channels,
out_channels, channel_multiplier)
with tf.variable_scope(layer_name):
with tf.variable_scope('depthwise'):
w_depthwise = weight_variable(
[filter_size, filter_size_2, in_channels, channel_multiplier],
stddev=stddev)
with tf.variable_scope('pointwise'):
w_pointwise = weight_variable(
[1, 1, in_channels * channel_multiplier, out_channels], stddev=stddev)
h_conv = tf.nn.separable_conv2d(
input_node,
w_depthwise,
w_pointwise,
padding='SAME',
strides=[1, 1, 1, 1],
rate=[atrou_rate, atrou_rate],
data_format=data_format)
if batch_norm:
h_conv = batch_norm_layer(
h_conv, layer_name=layer_name, is_training=is_training,
data_format=data_format)
else:
b_conv = bias_variable([out_channels])
h_conv = tf.nn.bias_add(h_conv, b_conv, data_format=data_format)
return h_conv
def batch_norm_layer(h_conv, layer_name, is_training=True, data_format='NCHW'):
"""Batch norm layer."""
logging.vlog(1, 'batch norm for layer %s', layer_name)
return tf.contrib.layers.batch_norm(
h_conv,
is_training=is_training,
fused=True,
decay=0.999,
scope=layer_name,
data_format=data_format)
def make_conv_layer(input_node,
in_channels,
out_channels,
layer_name,
filter_size,
filter_size_2=None,
non_linearity=True,
batch_norm=False,
is_training=True,
atrou_rate=1,
data_format='NHWC',
stddev=0.01):
"""Creates a convolution layer."""
if filter_size_2 is None:
filter_size_2 = filter_size
logging.vlog(
1, 'layer %s in %d out %d', layer_name, in_channels, out_channels)
with tf.variable_scope(layer_name):
w_conv = weight_variable(
[filter_size, filter_size_2, in_channels, out_channels], stddev=stddev)
h_conv = conv2d(
input_node, w_conv, atrou_rate=atrou_rate, data_format=data_format)
if batch_norm:
h_conv = batch_norm_layer(
h_conv, layer_name=layer_name, is_training=is_training,
data_format=data_format)
else:
b_conv = bias_variable([out_channels])
h_conv = tf.nn.bias_add(h_conv, b_conv, data_format=data_format)
if non_linearity:
h_conv = tf.nn.elu(h_conv)
return h_conv
================================================
FILE: alphafold_casp13/two_dim_resnet.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""2D Resnet."""
from absl import logging
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import two_dim_convnet
def make_sep_res_layer(
input_node,
in_channels,
out_channels,
layer_name,
filter_size,
filter_size_2=None,
batch_norm=False,
is_training=True,
divide_channels_by=2,
atrou_rate=1,
channel_multiplier=0,
data_format='NHWC',
stddev=0.01,
dropout_keep_prob=1.0):
"""A separable resnet block."""
with tf.name_scope(layer_name):
input_times_almost_1 = input_node
h_conv = input_times_almost_1
if batch_norm:
h_conv = two_dim_convnet.batch_norm_layer(
h_conv, layer_name=layer_name, is_training=is_training,
data_format=data_format)
h_conv = tf.nn.elu(h_conv)
if filter_size_2 is None:
filter_size_2 = filter_size
# 1x1 with half size
h_conv = two_dim_convnet.make_conv_layer(
h_conv,
in_channels=in_channels,
out_channels=in_channels / divide_channels_by,
layer_name=layer_name + '_1x1h',
filter_size=1,
filter_size_2=1,
non_linearity=True,
batch_norm=batch_norm,
is_training=is_training,
data_format=data_format,
stddev=stddev)
# 3x3 with half size
if channel_multiplier == 0:
h_conv = two_dim_convnet.make_conv_layer(
h_conv,
in_channels=in_channels / divide_channels_by,
out_channels=in_channels / divide_channels_by,
layer_name=layer_name + '_%dx%dh' % (filter_size, filter_size_2),
filter_size=filter_size,
filter_size_2=filter_size_2,
non_linearity=True,
batch_norm=batch_norm,
is_training=is_training,
atrou_rate=atrou_rate,
data_format=data_format,
stddev=stddev)
else:
# We use separable convolution for 3x3
h_conv = two_dim_convnet.make_conv_sep2d_layer(
h_conv,
in_channels=in_channels / divide_channels_by,
channel_multiplier=channel_multiplier,
out_channels=in_channels / divide_channels_by,
layer_name=layer_name + '_sep%dx%dh' % (filter_size, filter_size_2),
filter_size=filter_size,
filter_size_2=filter_size_2,
batch_norm=batch_norm,
is_training=is_training,
atrou_rate=atrou_rate,
data_format=data_format,
stddev=stddev)
# 1x1 back to normal size without relu
h_conv = two_dim_convnet.make_conv_layer(
h_conv,
in_channels=in_channels / divide_channels_by,
out_channels=out_channels,
layer_name=layer_name + '_1x1',
filter_size=1,
filter_size_2=1,
non_linearity=False,
batch_norm=False,
is_training=is_training,
data_format=data_format,
stddev=stddev)
if dropout_keep_prob < 1.0:
logging.info('dropout keep prob %f', dropout_keep_prob)
h_conv = tf.nn.dropout(h_conv, keep_prob=dropout_keep_prob)
return h_conv + input_times_almost_1
def make_two_dim_resnet(
input_node,
num_residues=50,
num_features=40,
num_predictions=1,
num_channels=32,
num_layers=2,
filter_size=3,
filter_size_2=None,
final_non_linearity=False,
name_prefix='',
fancy=True,
batch_norm=False,
is_training=False,
atrou_rates=None,
channel_multiplier=0,
divide_channels_by=2,
resize_features_with_1x1=False,
data_format='NHWC',
stddev=0.01,
dropout_keep_prob=1.0):
"""Two dim resnet towers."""
del num_residues # Unused.
if atrou_rates is None:
atrou_rates = [1]
if not fancy:
raise ValueError('non fancy deprecated')
logging.info('atrou rates %s', atrou_rates)
logging.info('name prefix %s', name_prefix)
x_image = input_node
previous_layer = x_image
non_linearity = True
for i_layer in range(num_layers):
in_channels = num_channels
out_channels = num_channels
curr_atrou_rate = atrou_rates[i_layer % len(atrou_rates)]
if i_layer == 0:
in_channels = num_features
if i_layer == num_layers - 1:
out_channels = num_predictions
non_linearity = final_non_linearity
if i_layer == 0 or i_layer == num_layers - 1:
layer_name = name_prefix + 'conv%d' % (i_layer + 1)
initial_filter_size = filter_size
if resize_features_with_1x1:
initial_filter_size = 1
previous_layer = two_dim_convnet.make_conv_layer(
input_node=previous_layer,
in_channels=in_channels,
out_channels=out_channels,
layer_name=layer_name,
filter_size=initial_filter_size,
filter_size_2=filter_size_2,
non_linearity=non_linearity,
atrou_rate=curr_atrou_rate,
data_format=data_format,
stddev=stddev)
else:
layer_name = name_prefix + 'res%d' % (i_layer + 1)
previous_layer = make_sep_res_layer(
input_node=previous_layer,
in_channels=in_channels,
out_channels=out_channels,
layer_name=layer_name,
filter_size=filter_size,
filter_size_2=filter_size_2,
batch_norm=batch_norm,
is_training=is_training,
atrou_rate=curr_atrou_rate,
channel_multiplier=channel_multiplier,
divide_channels_by=divide_channels_by,
data_format=data_format,
stddev=stddev,
dropout_keep_prob=dropout_keep_prob)
y = previous_layer
return y
================================================
FILE: avae/README.md
================================================
# The Autoencoding Variational Autoencoder
This is the code for the models in NeurIPS Submission [AVAE](https://papers.nips.cc/paper/2020/file/ac10ff1941c540cd87c107330996f4f6-Paper.pdf)
Folder contains code to train AVAE model in JAX, and we will be uploading
evaluation setup soon.
Code files in the folder
- checkpointer.py: Checkpointing abstraction
- data_iterators.py: Datasets to be used
- decoders.py: VAE decoder network architectures
- encoders.py: VAE encoder network architectures
- kl.py: KL computation between 2 gaussians
- train.py: Function to train given ELBO, network and data
- train_main.py: Main file to train AVAE
- vae.py: VAE model defining various ELBOs
## Setup
To set up a Python3 virtual environment with the required dependencies, run:
```shell
python -m venv avae_env
source avae_env/bin/activate
pip install --upgrade pip
pip install -r avae/requirements.txt
```
## Running AVAE training
Following command will run AVAE training for ColorMnist dataset using MLP
network architectures.
```shell
python -m avae.train_main \
--dataset='color_mnist' \
--latent_dim=64 \
--checkpoint_dir='/tmp/avae_checkpoints' \
--checkpoint_filename='color_mnist_mlp_avae' \
--rho=0.975 \
--encoder='color_mnist_mlp_encoder' \
--decoder='color_mnist_mlp_decoder'
```
## References
### Citing our work
If you use that code for your research, please consider citing our paper:
```bibtex
@article{cemgil2020autoencoding,
title={The Autoencoding Variational Autoencoder},
author={Cemgil, Taylan and Ghaisas, Sumedh and Dvijotham, Krishnamurthy and Gowal, Sven and Kohli, Pushmeet},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
}
```
================================================
FILE: avae/checkpointer.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Checkpointing functionality."""
import os
from typing import Any, Mapping, Optional
from absl import logging
import dill
import jax
import jax.numpy as jnp
class Checkpointer:
"""A checkpoint saving and loading class."""
def __init__(self, checkpoint_dir: str, filename: str):
"""Class initializer.
Args:
checkpoint_dir: Checkpoint directory.
filename: Filename of checkpoint in checkpoint directory.
"""
self._checkpoint_dir = checkpoint_dir
if not os.path.isdir(self._checkpoint_dir):
os.mkdir(self._checkpoint_dir)
self._checkpoint_path = os.path.join(self._checkpoint_dir, filename)
def save_checkpoint(
self,
experiment_state: Mapping[str, jnp.ndarray],
opt_state: Mapping[str, jnp.ndarray],
step: int,
extra_checkpoint_info: Optional[Mapping[str, Any]] = None) -> None:
"""Save checkpoint with experiment state and step information.
Args:
experiment_state: Experiment params to be stored.
opt_state: Optimizer state to be stored.
step: Training iteration step.
extra_checkpoint_info: Extra information to be stored.
"""
if jax.host_id() != 0:
return
checkpoint_data = dict(
experiment_state=jax.tree_map(jax.device_get, experiment_state),
opt_state=jax.tree_map(jax.device_get, opt_state),
step=step)
if extra_checkpoint_info is not None:
for key in extra_checkpoint_info:
checkpoint_data[key] = extra_checkpoint_info[key]
with open(self._checkpoint_path, 'wb') as checkpoint_file:
dill.dump(checkpoint_data, checkpoint_file, protocol=2)
def load_checkpoint(
self) -> Optional[Mapping[str, Mapping[str, jnp.ndarray]]]:
"""Load and return checkpoint data.
Returns:
Loaded checkpoint if it exists else returns None.
"""
if os.path.exists(self._checkpoint_path):
with open(self._checkpoint_path, 'rb') as checkpoint_file:
checkpoint_data = dill.load(checkpoint_file)
logging.info('Loading checkpoint from %s, saved at step %d',
self._checkpoint_path, checkpoint_data['step'])
return checkpoint_data
else:
logging.warning('No pre-saved checkpoint found at %s',
self._checkpoint_path)
return None
================================================
FILE: avae/data_iterators.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Dataset iterators Mnist, ColorMnist."""
import enum
import jax.numpy as jnp
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from avae import types
class Dataset(enum.Enum):
color_mnist = enum.auto()
class MnistDataIterator(object):
"""Mnist data iterator class.
Data is obtained as dataclass, types.LabelledData.
"""
def __init__(self, subset: str, batch_size: int):
"""Class initializer.
Args:
subset: Subset of dataset.
batch_size: Batch size of the returned dataset iterator.
"""
datasets = tfds.load('mnist')
train_dataset = datasets[subset]
def _map_fun(x):
return {'data': tf.cast(x['image'], tf.float32) / 255.0,
'label': x['label']}
train_dataset = train_dataset.map(_map_fun)
train_dataset = train_dataset.batch(batch_size,
drop_remainder=True).repeat()
self._iterator = iter(tfds.as_numpy(train_dataset))
self._batch_size = batch_size
def __iter__(self):
return self
def __next__(self) -> types.LabelledData:
return types.LabelledData(**next(self._iterator))
class ColorMnistDataIterator(MnistDataIterator):
"""Color Mnist data iterator.
Each ColorMnist image is of shape (28, 28, 3). ColorMnist digit can have 7
different colors by permution of RGB channels (turning on and off RGB
channels, except for all channels off permutation).
Data is obtained as dataclass, util_dataclasses.LabelledData.
"""
def __next__(self) -> types.LabelledData:
mnist_batch = next(self._iterator)
mnist_image = mnist_batch['data']
# Colors are supported by turning off and on RGB channels.
# Thus possible colors are
# [black (excluded), red, green, yellow, blue, magenta, cyan, white]
# color_id takes value from [1,8)
color_id = np.random.randint(7, size=self._batch_size) + 1
red_channel_bool = np.mod(color_id, 2)
red_channel_bool = jnp.reshape(red_channel_bool, [-1, 1, 1, 1])
blue_channel_bool = np.floor_divide(color_id, 4)
blue_channel_bool = jnp.reshape(blue_channel_bool, [-1, 1, 1, 1])
green_channel_bool = np.mod(np.floor_divide(color_id, 2), 2)
green_channel_bool = jnp.reshape(green_channel_bool, [-1, 1, 1, 1])
color_mnist_image = jnp.stack([
jnp.multiply(red_channel_bool, mnist_image),
jnp.multiply(blue_channel_bool, mnist_image),
jnp.multiply(green_channel_bool, mnist_image)], axis=3)
color_mnist_image = jnp.reshape(color_mnist_image, [-1, 28, 28, 3])
# Color id takes value [1, 8)
# Although to make classification code easier `color` label attached to data
# takes value in [0, 7) (by subtracting 1 from color id)
return types.LabelledData(
data=color_mnist_image, label=mnist_batch['label'])
================================================
FILE: avae/decoders.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Decoder architectures to be used with VAE."""
import abc
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
class DecoderBase(hk.Module):
"""Base class for decoder network classes."""
def __init__(self, obs_var: float):
"""Class initializer.
Args:
obs_var: oversation variance of the dataset.
"""
super().__init__()
self._obs_var = obs_var
@abc.abstractmethod
def __call__(self, z: jnp.ndarray) -> jnp.ndarray:
"""Reconstruct from a given latent sample.
Args:
z: latent samples of shape (batch_size, latent_dim)
Returns:
Reconstruction with shape (batch_size, ...).
"""
def data_fidelity(
self,
input_data: jnp.ndarray,
recons: jnp.ndarray,
) -> jnp.ndarray:
"""Compute Data fidelity (recons loss) for given input and recons.
Args:
input_data: Input batch of shape (batch_size, ...).
recons: Reconstruction of the input data. An array with the same shape as
`input_data.data`.
Returns:
Computed data fidelity term across batch of data. An array of shape
`(batch_size,)`.
"""
error = (input_data - recons).reshape(input_data.shape[0], -1)
return -0.5 * jnp.sum(jnp.square(error), axis=1) / self._obs_var
class ColorMnistMLPDecoder(DecoderBase):
"""MLP decoder for Color Mnist."""
_hidden_units = (200, 200, 200, 200)
_image_dims = (28, 28, 3) # Dimensions of a single MNIST image.
def __call__(self, z: jnp.ndarray) -> jnp.ndarray:
"""Reconstruct with given latent sample.
Args:
z: latent samples of shape (batch_size, latent_dim)
Returns:
Reconstructions data of shape (batch_size, 28, 28, 3).
"""
out = z
for units in self._hidden_units:
out = hk.Linear(units)(out)
out = jax.nn.relu(out)
out = hk.Linear(np.product(self._image_dims))(out)
out = jax.nn.sigmoid(out)
return jnp.reshape(out, (-1,) + self._image_dims)
================================================
FILE: avae/encoders.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Encoder architectures to be used with VAE."""
import abc
from typing import Generic, TypeVar
import haiku as hk
import jax
import jax.numpy as jnp
from avae import types
_Params = TypeVar('_Params')
class EncoderBase(hk.Module, Generic[_Params]):
"""Abstract class for encoder architectures."""
def __init__(self, latent_dim: int):
"""Class initializer.
Args:
latent_dim: Latent dimensions of the model.
"""
super().__init__()
self._latent_dim = latent_dim
@abc.abstractmethod
def __call__(self, input_data: jnp.ndarray) -> _Params:
"""Return posterior distribution over latents.
Args:
input_data: Input batch of shape (batch_size, ...).
Returns:
Parameters of the posterior distribution over the latents.
"""
@abc.abstractmethod
def sample(self, posterior: _Params, key: jnp.ndarray) -> jnp.ndarray:
"""Sample from the given posterior distribution.
Args:
posterior: Parameters of posterior distribution over the latents.
key: Random number generator key.
Returns:
Sample from the posterior distribution over latents,
shape[batch_size, latent_dim]
"""
class ColorMnistMLPEncoder(EncoderBase[types.NormalParams]):
"""MLP encoder for ColorMnist."""
_hidden_units = (200, 200, 200, 200)
def __call__(
self, input_data: jnp.ndarray) -> types.NormalParams:
"""Return posterior distribution over latents.
Args:
input_data: Input batch of shape (batch_size, ...).
Returns:
Posterior distribution over the latents.
"""
out = hk.Flatten()(input_data)
for units in self._hidden_units:
out = hk.Linear(units)(out)
out = jax.nn.relu(out)
out = hk.Linear(2 * self._latent_dim)(out)
return _normal_params_from_logits(out)
def sample(
self,
posterior: types.NormalParams,
key: jnp.ndarray,
) -> jnp.ndarray:
"""Sample from the given normal posterior (mean, var) distribution.
Args:
posterior: Posterior over the latents.
key: Random number generator key.
Returns:
Sample from the posterior distribution over latents,
shape[batch_size, latent_dim]
"""
eps = jax.random.normal(
key, shape=(posterior.mean.shape[0], self._latent_dim))
return posterior.mean + eps * posterior.variance
def _normal_params_from_logits(
logits: jnp.ndarray) -> types.NormalParams:
"""Construct mean and variance of normal distribution from given logits."""
mean, log_variance = jnp.split(logits, 2, axis=1)
variance = jnp.exp(log_variance)
return types.NormalParams(mean=mean, variance=variance)
================================================
FILE: avae/kl.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Various KL implementations in JAX."""
import jax.numpy as jnp
def kl_p_with_uniform_normal(mean: jnp.ndarray,
variance: jnp.ndarray) -> jnp.ndarray:
r"""KL between p_dist with uniform normal prior.
Args:
mean: Mean of the gaussian distribution, shape (latent_dims,)
variance: Variance of the gaussian distribution, shape (latent_dims,)
Returns:
KL divergence KL(P||N(0, 1)) shape ()
"""
if len(variance.shape) == 2:
# If `variance` is a full covariance matrix
variance_trace = jnp.trace(variance)
_, ldet1 = jnp.linalg.slogdet(variance)
else:
variance_trace = jnp.sum(variance)
ldet1 = jnp.sum(jnp.log(variance))
mean_contribution = jnp.sum(jnp.square(mean))
res = -ldet1
res += variance_trace + mean_contribution - mean.shape[0]
return res * 0.5
================================================
FILE: avae/requirements.txt
================================================
absl-py>0.9
dill==0.3.2
dm-haiku
jax
optax
tensorflow
tensorflow_datasets
================================================
FILE: avae/run.sh
================================================
#!/bin/bash
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
python -m venv avae_env
source avae_env/bin/activate
pip install --upgrade pip
pip install -r avae/requirements.txt
# Start training AVAE model.
python -m avae.train_main \
--dataset='color_mnist' \
--latent_dim=64 \
--checkpoint_dir='/tmp/avae_checkpoints' \
--checkpoint_filename='color_mnist_mlp_avae' \
--rho=0.975 \
--encoder=color_mnist_mlp_encoder \
--decoder=color_mnist_mlp_decoder \
--iterations=1
================================================
FILE: avae/train.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""VAE style training."""
from typing import Any, Callable, Iterator, Sequence, Mapping, Tuple, Optional
import haiku as hk
import jax
import jax.numpy as jnp
import optax
from avae import checkpointer
from avae import types
def train(
train_data_iterator: Iterator[types.LabelledData],
test_data_iterator: Iterator[types.LabelledData],
elbo_fun: hk.Transformed,
learning_rate: float,
checkpoint_dir: str,
checkpoint_filename: str,
checkpoint_every: int,
test_every: int,
iterations: int,
rng_seed: int,
test_functions: Optional[Sequence[Callable[[Mapping[str, jnp.ndarray]],
Tuple[str, float]]]] = None,
extra_checkpoint_info: Optional[Mapping[str, Any]] = None):
"""Train VAE with given data iterator and elbo definition.
Args:
train_data_iterator: Iterator of batched training data.
test_data_iterator: Iterator of batched testing data.
elbo_fun: Haiku transfomed function returning elbo.
learning_rate: Learning rate to be used with optimizer.
checkpoint_dir: Path of the checkpoint directory.
checkpoint_filename: Filename of the checkpoint.
checkpoint_every: Checkpoint every N iterations.
test_every: Test and log results every N iterations.
iterations: Number of training iterations to perform.
rng_seed: Seed for random number generator.
test_functions: Test function iterable, each function takes test data and
outputs extra info to print at test and log time.
extra_checkpoint_info: Extra info to put inside saved checkpoint.
"""
rng_seq = hk.PRNGSequence(jax.random.PRNGKey(rng_seed))
opt_init, opt_update = optax.chain(
# Set the parameters of Adam. Note the learning_rate is not here.
optax.scale_by_adam(b1=0.9, b2=0.999, eps=1e-8),
# Put a minus sign to *minimise* the loss.
optax.scale(-learning_rate))
@jax.jit
def loss(params, key, data):
elbo_outputs = elbo_fun.apply(params, key, data)
return -jnp.mean(elbo_outputs.elbo)
@jax.jit
def loss_test(params, key, data):
elbo_output = elbo_fun.apply(params, key, data)
return (-jnp.mean(elbo_output.elbo), jnp.mean(elbo_output.data_fidelity),
jnp.mean(elbo_output.kl))
@jax.jit
def update_step(params, key, data, opt_state):
grads = jax.grad(loss, has_aux=False)(params, key, data)
updates, opt_state = opt_update(grads, opt_state, params)
params = optax.apply_updates(params, updates)
return params, opt_state
exp_checkpointer = checkpointer.Checkpointer(
checkpoint_dir, checkpoint_filename)
experiment_data = exp_checkpointer.load_checkpoint()
if experiment_data is not None:
start = experiment_data['step']
params = experiment_data['experiment_state']
opt_state = experiment_data['opt_state']
else:
start = 0
params = elbo_fun.init(
next(rng_seq), next(train_data_iterator).data)
opt_state = opt_init(params)
for step in range(start, iterations, 1):
if step % test_every == 0:
test_loss, ll, kl = loss_test(params, next(rng_seq),
next(test_data_iterator).data)
output_message = (f'Step {step} elbo {-test_loss:0.2f} LL {ll:0.2f} '
f'KL {kl:0.2f}')
if test_functions:
for test_function in test_functions:
name, test_output = test_function(params)
output_message += f' {name}: {test_output:0.2f}'
print(output_message)
params, opt_state = update_step(params, next(rng_seq),
next(train_data_iterator).data, opt_state)
if step % checkpoint_every == 0:
exp_checkpointer.save_checkpoint(
params, opt_state, step, extra_checkpoint_info)
================================================
FILE: avae/train_main.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Main file for training VAE/AVAE."""
import enum
from absl import app
from absl import flags
import haiku as hk
from avae import data_iterators
from avae import decoders
from avae import encoders
from avae import train
from avae import vae
class Model(enum.Enum):
vae = enum.auto()
avae = enum.auto()
class EncoderArch(enum.Enum):
color_mnist_mlp_encoder = 'ColorMnistMLPEncoder'
class DecoderArch(enum.Enum):
color_mnist_mlp_decoder = 'ColorMnistMLPDecoder'
_DATASET = flags.DEFINE_enum_class(
'dataset', data_iterators.Dataset.color_mnist, data_iterators.Dataset,
'Dataset to train on')
_LATENT_DIM = flags.DEFINE_integer('latent_dim', 32,
'Number of latent dimensions.')
_TRAIN_BATCH_SIZE = flags.DEFINE_integer('train_batch_size', 64,
'Train batch size.')
_TEST_BATCH_SIZE = flags.DEFINE_integer('test_batch_size', 64,
'Testing batch size.')
_TEST_EVERY = flags.DEFINE_integer('test_every', 1000,
'Test every N iterations.')
_ITERATIONS = flags.DEFINE_integer('iterations', 102000,
'Number of training iterations.')
_OBS_VAR = flags.DEFINE_float('obs_var', 0.5,
'Observation variance of the data. (Default 0.5)')
_MODEL = flags.DEFINE_enum_class('model', Model.avae, Model,
'Model used for training.')
_RHO = flags.DEFINE_float('rho', 0.8, 'Rho parameter used with AVAE or SE.')
_LEARNING_RATE = flags.DEFINE_float('learning_rate', 1e-4, 'Learning rate.')
_RNG_SEED = flags.DEFINE_integer('rng_seed', 0,
'Seed for random number generator.')
_CHECKPOINT_DIR = flags.DEFINE_string('checkpoint_dir', '/tmp/',
'Directory for checkpointing.')
_CHECKPOINT_FILENAME = flags.DEFINE_string(
'checkpoint_filename', 'color_mnist_avae_mlp', 'Checkpoint filename.')
_CHECKPOINT_EVERY = flags.DEFINE_integer(
'checkpoint_every', 1000, 'Checkpoint every N steps.')
_ENCODER = flags.DEFINE_enum_class(
'encoder', EncoderArch.color_mnist_mlp_encoder, EncoderArch,
'Encoder class name.')
_DECODER = flags.DEFINE_enum_class(
'decoder', DecoderArch.color_mnist_mlp_decoder, DecoderArch,
'Decoder class name.')
def main(_):
if _DATASET.value is data_iterators.Dataset.color_mnist:
train_data_iterator = iter(
data_iterators.ColorMnistDataIterator('train', _TRAIN_BATCH_SIZE.value))
test_data_iterator = iter(
data_iterators.ColorMnistDataIterator('test', _TEST_BATCH_SIZE.value))
def _elbo_fun(input_data):
if _ENCODER.value is EncoderArch.color_mnist_mlp_encoder:
encoder = encoders.ColorMnistMLPEncoder(_LATENT_DIM.value)
if _DECODER.value is DecoderArch.color_mnist_mlp_decoder:
decoder = decoders.ColorMnistMLPDecoder(_OBS_VAR.value)
vae_obj = vae.VAE(encoder, decoder, _RHO.value)
if _MODEL.value is Model.vae:
return vae_obj.vae_elbo(input_data, hk.next_rng_key())
else:
return vae_obj.avae_elbo(input_data, hk.next_rng_key())
elbo_fun = hk.transform(_elbo_fun)
extra_checkpoint_info = {
'dataset': _DATASET.value.name,
'encoder': _ENCODER.value.name,
'decoder': _DECODER.value.name,
'obs_var': _OBS_VAR.value,
'rho': _RHO.value,
'latent_dim': _LATENT_DIM.value,
}
train.train(
train_data_iterator=train_data_iterator,
test_data_iterator=test_data_iterator,
elbo_fun=elbo_fun,
learning_rate=_LEARNING_RATE.value,
checkpoint_dir=_CHECKPOINT_DIR.value,
checkpoint_filename=_CHECKPOINT_FILENAME.value,
checkpoint_every=_CHECKPOINT_EVERY.value,
test_every=_TEST_EVERY.value,
iterations=_ITERATIONS.value,
rng_seed=_RNG_SEED.value,
extra_checkpoint_info=extra_checkpoint_info)
if __name__ == '__main__':
app.run(main)
================================================
FILE: avae/types.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Useful dataclasses types used across the code."""
from typing import Optional
import dataclasses
import jax.numpy as jnp
import numpy as np
@dataclasses.dataclass(frozen=True)
class ELBOOutputs:
elbo: jnp.ndarray
data_fidelity: jnp.ndarray
kl: jnp.ndarray
@dataclasses.dataclass(frozen=True)
class LabelledData:
"""A batch of labelled examples.
Attributes:
data: Array of shape (batch_size, ...).
label: Array of shape (batch_size, ...).
"""
data: np.ndarray
label: Optional[np.ndarray]
@dataclasses.dataclass(frozen=True)
class NormalParams:
"""Parameters of a normal distribution.
Attributes:
mean: Array of shape (batch_size, latent_dim).
variance: Array of shape (batch_size, latent_dim).
"""
mean: jnp.ndarray
variance: jnp.ndarray
================================================
FILE: avae/vae.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Standard VAE class."""
from typing import Optional
import jax
import jax.numpy as jnp
from avae import decoders
from avae import encoders
from avae import kl
from avae import types
class VAE:
"""VAE class.
This class defines the ELBO used in training VAE models. It also adds function
for forward passing data through VAE.
"""
def __init__(self, encoder: encoders.EncoderBase,
decoder: decoders.DecoderBase, rho: Optional[float] = None):
"""Class initializer.
Args:
encoder: Encoder network architecture.
decoder: Decoder network architecture.
rho: Rho parameter used in AVAE training.
"""
self._encoder = encoder
self._decoder = decoder
self._rho = rho
def vae_elbo(
self, input_data: jnp.ndarray,
key: jnp.ndarray) -> types.ELBOOutputs:
"""ELBO for training VAE.
Args:
input_data: Input batch of shape (batch_size, ...).
key: Key for random number generator.
Returns:
Computed VAE Elbo as type util_dataclasses.ELBOOutputs
"""
posterior = self._encoder(input_data)
samples = self._encoder.sample(posterior, key)
kls = jax.vmap(kl.kl_p_with_uniform_normal, [0])(
posterior.mean, posterior.variance)
recons = self._decoder(samples)
data_fidelity = self._decoder.data_fidelity(input_data, recons)
elbo = data_fidelity - kls
return types.ELBOOutputs(elbo, data_fidelity, kls)
def avae_elbo(
self, input_data: jnp.ndarray,
key: jnp.ndarray) -> types.ELBOOutputs:
"""ELBO for training AVAE model.
Args:
input_data: Input batch of shape (batch_size, ...).
key: Key for random number generator.
Returns:
Computed AVAE Elbo in nested tuple (Elbo, (data_fidelity, KL)). All arrays
have batch dimension intact.
"""
aux_images = jax.lax.stop_gradient(self(input_data, key))
posterior = self._encoder(input_data)
samples = self._encoder.sample(posterior, key)
kls = jax.vmap(kl.kl_p_with_uniform_normal, [0, 0])(
posterior.mean, posterior.variance)
recons = self._decoder(samples)
data_fidelity = self._decoder.data_fidelity(input_data, recons)
elbo = data_fidelity - kls
aux_posterior = self._encoder(aux_images)
latent_mean = posterior.mean
latent_var = posterior.variance
aux_latent_mean = aux_posterior.mean
aux_latent_var = aux_posterior.variance
latent_dim = latent_mean.shape[1]
def _reduce(x):
return jnp.mean(jnp.sum(x, axis=1))
# Computation of .
expected_log_conditional = (
aux_latent_var + jnp.square(self._rho) * latent_var +
jnp.square(aux_latent_mean - self._rho * latent_mean))
expected_log_conditional = _reduce(expected_log_conditional)
expected_log_conditional /= 2.0 * (1.0 - jnp.square(self._rho))
expected_log_conditional = (latent_dim *
jnp.log(1.0 / (2 * jnp.pi)) -
expected_log_conditional)
elbo += expected_log_conditional
# Entropy of Z_aux
elbo += _reduce(0.5 * jnp.log(2 * jnp.pi * jnp.e * aux_latent_var))
return types.ELBOOutputs(elbo, data_fidelity, kls)
def __call__(
self, input_data: jnp.ndarray,
key: jnp.ndarray) -> jnp.ndarray:
"""Reconstruction of the input data.
Args:
input_data: Input batch of shape (batch_size, ...).
key: Key for random number generator.
Returns:
Reconstruction of the input data as jnp.ndarray of shape
[batch_dim, observation_dims].
"""
posterior = self._encoder(input_data)
samples = self._encoder.sample(posterior, key)
recons = self._decoder(samples)
return recons
================================================
FILE: bigbigan/README.md
================================================
# Pretrained BigBiGAN models
We have released pretrained BigBiGAN models used for unsupervised image
generation and representation learning, as described in our July 2019
tech report,
["Large Scale Adversarial Representation Learning"](https://arxiv.org/abs/1907.02544) [1].
The pretrained models are available for use via [TF Hub](https://tfhub.dev/s?publisher=deepmind&q=bigbigan).
The release includes two BigBiGAN models with different encoder architectures:
* Small encoder (ResNet-50): [https://tfhub.dev/deepmind/bigbigan-resnet50/1](https://tfhub.dev/deepmind/bigbigan-resnet50/1)
* Large encoder (RevNet-50 x4): [https://tfhub.dev/deepmind/bigbigan-revnet50x4/1](https://tfhub.dev/deepmind/bigbigan-revnet50x4/1)
See the TF Hub pages linked above for documentation and example usage of each module.
## Demo (Colab)
A Google Colab-based demo with example usage of the model functionality and sample visualization is available [here](//colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/bigbigan_with_tf_hub.ipynb).
## Example use
The snippet below demonstrates the use of the released TF Hub modules for
image generation/reconstruction and encoder feature computation.
(The [Colab demo](//colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/bigbigan_with_tf_hub.ipynb)
includes more extensive documentation and visualizations.)
```python
# Load BigBiGAN module.
module = hub.Module('https://tfhub.dev/deepmind/bigbigan-resnet50/1') # small encoder
# module = hub.Module('https://tfhub.dev/deepmind/bigbigan-revnet50x4/1') # large encoder
# Sample a batch of 8 random latent vectors (z) from the Gaussian prior. Then
# call the generator on the latent samples to generate a batch of images with
# shape [8, 128, 128, 3] and range [-1, 1].
z = tf.random.normal([8, 120]) # latent samples
gen_samples = module(z, signature='generate')
# Given a batch of 256x256 RGB images in range [-1, 1], call the encoder to
# compute predicted latents z and other features (e.g. for use in downstream
# recognition tasks).
images = tf.placeholder(tf.float32, shape=[None, 256, 256, 3])
features = module(images, signature='encode', as_dict=True)
# Get the predicted latent sample `z_sample` from the dict of features.
# Other available features include `avepool_feat` and `bn_crelu_feat`, used in
# the representation learning results.
z_sample = features['z_sample'] # shape [?, 120]
# Compute reconstructions of the input `images` by passing the encoder's output
# `z_sample` back through the generator. Note that raw generator outputs are
# half the resolution of encoder inputs (128x128). To get upsampled generator
# outputs matching the encoder input resolution (256x256), instead use:
# recons = module(z_sample, signature='generate', as_dict=True)['upsampled']
recons = module(z_sample, signature='generate') # shape [?, 128, 128, 3]
```
## References
[1] Jeff Donahue and Karen Simonyan.
[Large Scale Adversarial Representation Learning](https://arxiv.org/abs/1907.02544).
*arxiv:1907.02544*, 2019.
================================================
FILE: box_arrangement/README.md
================================================
# Predicate tasks.
This package contains tasks associated with "Behavior Priors for Efficient
Reiforcement Learning" (https://arxiv.org/abs/2010.14274), "Exploiting Hierarchy
for Learning and Transfer in KL-Regularized RL" (https://arxiv.org/abs/2010.14274)
and "Information asymmetry in KL-regularized RL"
(https://arxiv.org/abs/1905.01240).
This is research code, and has dependencies on more stable code that is
available as part of [`dm_control`], in particular upon components in
[`dm_control.locomotion`] and [`dm_control.manipulation`].
To get access to preconfigured python environments for the tasks, see the
`task_examples.py` file. To use the MuJoCo interactive viewer (from dm_control)
to load the environments, see `explore.py`.
/pretrain.pkl`. To monitor the progress of
the pretraining, you can run a second worker (using `--worker_mode=eval`) with
the same `checkpoint_root` setting. This worker will regularly load the
checkpoint and evaluate the performance of a linear classifier (trained by the
pretraining `train` worker) on the `TEST` set.
Note that the default settings are set for large-scale training on Cloud TPUs,
with a total batch size of 4096. To avoid the need to re-run the full
experiment, we provide the following pre-trained checkpoints:
- [ResNet-50 1x](https://storage.googleapis.com/deepmind-byol/checkpoints/pretrain_res50x1.pkl) (570 MB): should evaluate to ~74.4% top-1 accuracy.
- [ResNet-200 2x](https://storage.googleapis.com/deepmind-byol/checkpoints/pretrain_res200x2.pkl) (4.6GB): should evaluate to ~79.6% top-1 accuracy.
### Linear evaluation
Setting `--experiment_mode=linear-eval` will configure the main loop for
linear evaluation; we provide presets for 80 epochs.
Use `--worker_mode=train` for a training job, which will load the encoder
weights from an existing checkpoint (form a pretrain experiment) located at
`/pretrain.pkl`, and train a linear classifier on top of this
encoder. The weights from the linear classifier trained in the pretraining phase
will be discarded.
The training job will regularly save checkpoints under
`/linear-eval.pkl`. You can run a second worker
(using `--worker_mode=eval`) with the same `checkpoint_root` setting to
regularly load the checkpoint and evaluate the performance of the classifier
(trained by the linear-eval `train` worker) on the test set.
Note that the above will run a simplified version of the linear evaluation
pipeline described in the paper, with a single value of the base learning rate
and without using a validation set. To fully reproduce the results from the
paper, one should run 5 instances of the linear-eval `train` worker (using only
the `TRAIN` subset, and each using a different `checkpoint_root`), run the
`eval` worker (using only the `VALID` subset) for each checkpoint, then run a
final `eval` worker on the `TEST` set.
### Note on batch normalization
We found that using [Goyal et al.'s](https://arxiv.org/abs/1706.02677)
initialization for the batch-normalization (i.e., initializing the scaling
coefficient gamma to 0 in the last batchnorm of each residual block) led to
more stable training, but slightly harms BYOL's performance for very large
networks (e.g., `ResNet-50 (3x)`, `ResNet-200 (2x)`). We didn't observe any
change in performance for smaller networks (`ResNet-50 (1x)` and `(2x)`).
Results in the paper were obtained *without* this modified initialization, i.e.
using Haiku's default of $\gamma = 1$. To fully reproduce, please remove the
`scale_init` argument in Haiku's ResNet [BlockV1](https://github.com/deepmind/dm-haiku/blob/0673817149470d19d4c03de4a45e6409f214b61d/haiku/_src/nets/resnet.py#L99).
## Running on GCP
Notice: we currently do not recommend running the full experiment on public
Cloud TPUs. We provide an alternative small-scale GPU setup in the next section.
The experiments from the paper were run on Google's internal infrastructure.
Unfortunately, training JAX models on publicly available Cloud TPUs is currently
in its early stages; in particular, we found the learning to be heavily
bottlenecked by the data loading pipeline, resulting in a significantly slower
training. We will update the code and instructions below once the limitation is
addressed.
To train the model on TPU using Google Compute Engine, please follow the
instructions at https://cloud.google.com/tpu/docs/imagenet-setup to first
download and preprocess the ImageNet dataset and upload it to a
Cloud Storage Bucket. From a GCE VM, you can check that `tensorflow_datasets`
can correctly load the dataset by running:
```python
import tensorflow_datasets as tfds
tfds.load('imagenet2012', data_dir='gs://')
```
To learn how to use JAX with Cloud TPUs, please follow the instructions here:
https://github.com/google/jax/tree/master/cloud_tpu_colabs.
## Setup for fast iteration
In order to make reproduction easier, it is possible to change the training
setup to use the smaller [imagenette](https://github.com/fastai/imagenette)
dataset (9469 training images with 10 classes). The following setup and
hyperparameters can be used on a machine with a single V100 GPU:
- in `utils/dataset.py`:
- update `Split.num_examples` with the figures from
[tfds](https://www.tensorflow.org/datasets/catalog/imagenette)
(with `Split.VALID: 0`)
- use `imagenette/160px-v2` in the call to `tfds.load`
- use 128x128 px images (_i.e._, replace all instances of `224` by `128`)
- it doesn't seem necessary to change the color normalization (make sure to
not replace the value `0.224` by mistake in the previous step).
- in `configs/byol.py`, use:
- `num_classes`: `10`
- `network_config.encoder`: `ResNet18`
- `optimizer_config.weight_decay`: `1e-6`
- `lr_schedule_config.base_learning_rate`: `2.0`
- `evaluation_config.batch_size`: `25`
- other parameters unchanged.
You can then run:
```shell
mkdir /tmp/byol_checkpoints
python -m byol.main_loop \
--experiment_mode='pretrain' \
--worker_mode='train' \
--checkpoint_root='/tmp/byol_checkpoints' \
--batch_size=256 \
--pretrain_epochs=1000
```
With these settings, BYOL should achieve ~92.3% top-1 accuracy (for the
*online* classifier) in roughly 4 hours. Note that the above parameters were not
finely tuned and may not be optimal.
## Additional checkpoints
Alongside with the pretrained ResNet-50 and ResNet-200 2x, we provide the
following checkpoints from our ablation study. They all correspond to a
ResNet-50 1x pre-trained over 300 epochs and were randomly selected within the
three seeds; file size is roughly 640MB each.
- [Baseline](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_baseline.pkl)
- Smaller batch sizes (figure 3a):
- [Batch size 2048](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_batchsize_2048.pkl)
- [Batch size 1024](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_batchsize_1024.pkl)
- [Batch size 512](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_batchsize_512.pkl)
- [Batch size 256](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_batchsize_256.pkl)
- [Batch size 128](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_batchsize_128.pkl)
- [Batch size 64](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_batchsize_64.pkl)
- Ablation on transformations (figure 3b):
- [Remove grayscale](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_no_grayscale.pkl)
- [Remove color](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_no_color.pkl)
- [Crop and blur only](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_crop_and_blur_only.pkl)
- [Crop only](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_crop_only.pkl)
- (from Table 18) [Crop and color only](https://storage.googleapis.com/deepmind-byol/checkpoints/ablations/res50x1_crop_and_color_only.pkl)
## License
While the code is licensed under the Apache 2.0 License, the checkpoints weights
are made available for non-commercial use only under the terms of the
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
license. You can find details at:
https://creativecommons.org/licenses/by-nc/4.0/legalcode.
================================================
FILE: byol/byol_experiment.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BYOL pre-training implementation.
Use this experiment to pre-train a self-supervised representation.
"""
import functools
from typing import Any, Generator, Mapping, NamedTuple, Text, Tuple, Union
from absl import logging
from acme.jax import utils as acme_utils
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import optax
from byol.utils import augmentations
from byol.utils import checkpointing
from byol.utils import dataset
from byol.utils import helpers
from byol.utils import networks
from byol.utils import optimizers
from byol.utils import schedules
# Type declarations.
LogsDict = Mapping[Text, jnp.ndarray]
class _ByolExperimentState(NamedTuple):
"""Byol's model and optimization parameters and state."""
online_params: hk.Params
target_params: hk.Params
online_state: hk.State
target_state: hk.State
opt_state: optimizers.LarsState
class ByolExperiment:
"""Byol's training and evaluation component definition."""
def __init__(
self,
random_seed: int,
num_classes: int,
batch_size: int,
max_steps: int,
enable_double_transpose: bool,
base_target_ema: float,
network_config: Mapping[Text, Any],
optimizer_config: Mapping[Text, Any],
lr_schedule_config: Mapping[Text, Any],
evaluation_config: Mapping[Text, Any],
checkpointing_config: Mapping[Text, Any]):
"""Constructs the experiment.
Args:
random_seed: the random seed to use when initializing network weights.
num_classes: the number of classes; used for the online evaluation.
batch_size: the total batch size; should be a multiple of the number of
available accelerators.
max_steps: the number of training steps; used for the lr/target network
ema schedules.
enable_double_transpose: see dataset.py; only has effect on TPU.
base_target_ema: the initial value for the ema decay rate of the target
network.
network_config: the configuration for the network.
optimizer_config: the configuration for the optimizer.
lr_schedule_config: the configuration for the learning rate schedule.
evaluation_config: the evaluation configuration.
checkpointing_config: the configuration for checkpointing.
"""
self._random_seed = random_seed
self._enable_double_transpose = enable_double_transpose
self._num_classes = num_classes
self._lr_schedule_config = lr_schedule_config
self._batch_size = batch_size
self._max_steps = max_steps
self._base_target_ema = base_target_ema
self._optimizer_config = optimizer_config
self._evaluation_config = evaluation_config
# Checkpointed experiment state.
self._byol_state = None
# Input pipelines.
self._train_input = None
self._eval_input = None
# build the transformed ops
forward_fn = functools.partial(self._forward, **network_config)
self.forward = hk.without_apply_rng(hk.transform_with_state(forward_fn))
# training can handle multiple devices, thus the pmap
self.update_pmap = jax.pmap(self._update_fn, axis_name='i')
# evaluation can only handle single device
self.eval_batch_jit = jax.jit(self._eval_batch)
self._checkpointer = checkpointing.Checkpointer(**checkpointing_config)
def _forward(
self,
inputs: dataset.Batch,
projector_hidden_size: int,
projector_output_size: int,
predictor_hidden_size: int,
encoder_class: Text,
encoder_config: Mapping[Text, Any],
bn_config: Mapping[Text, Any],
is_training: bool,
) -> Mapping[Text, jnp.ndarray]:
"""Forward application of byol's architecture.
Args:
inputs: A batch of data, i.e. a dictionary, with either two keys,
(`images` and `labels`) or three keys (`view1`, `view2`, `labels`).
projector_hidden_size: hidden size of the projector MLP.
projector_output_size: output size of the projector and predictor MLPs.
predictor_hidden_size: hidden size of the predictor MLP.
encoder_class: type of the encoder (should match a class in
utils/networks).
encoder_config: passed to the encoder constructor.
bn_config: passed to the hk.BatchNorm constructors.
is_training: Training or evaluating the model? When True, inputs must
contain keys `view1` and `view2`. When False, inputs must contain key
`images`.
Returns:
All outputs of the model, i.e. a dictionary with projection, prediction
and logits keys, for either the two views, or the image.
"""
encoder = getattr(networks, encoder_class)
net = encoder(
num_classes=None, # Don't build the final linear layer
bn_config=bn_config,
**encoder_config)
projector = networks.MLP(
name='projector',
hidden_size=projector_hidden_size,
output_size=projector_output_size,
bn_config=bn_config)
predictor = networks.MLP(
name='predictor',
hidden_size=predictor_hidden_size,
output_size=projector_output_size,
bn_config=bn_config)
classifier = hk.Linear(
output_size=self._num_classes, name='classifier')
def apply_once_fn(images: jnp.ndarray, suffix: Text = ''):
images = dataset.normalize_images(images)
embedding = net(images, is_training=is_training)
proj_out = projector(embedding, is_training)
pred_out = predictor(proj_out, is_training)
# Note the stop_gradient: label information is not leaked into the
# main network.
classif_out = classifier(jax.lax.stop_gradient(embedding))
outputs = {}
outputs['projection' + suffix] = proj_out
outputs['prediction' + suffix] = pred_out
outputs['logits' + suffix] = classif_out
return outputs
if is_training:
outputs_view1 = apply_once_fn(inputs['view1'], '_view1') # pytype: disable=wrong-arg-types # jax-ndarray
outputs_view2 = apply_once_fn(inputs['view2'], '_view2') # pytype: disable=wrong-arg-types # jax-ndarray
return {**outputs_view1, **outputs_view2}
else:
return apply_once_fn(inputs['images'], '') # pytype: disable=wrong-arg-types # jax-ndarray
def _optimizer(self, learning_rate: float) -> optax.GradientTransformation:
"""Build optimizer from config."""
return optimizers.lars(
learning_rate,
weight_decay_filter=optimizers.exclude_bias_and_norm,
lars_adaptation_filter=optimizers.exclude_bias_and_norm,
**self._optimizer_config)
def loss_fn(
self,
online_params: hk.Params,
target_params: hk.Params,
online_state: hk.State,
target_state: hk.Params,
rng: jnp.ndarray,
inputs: dataset.Batch,
) -> Tuple[jnp.ndarray, Tuple[Mapping[Text, hk.State], LogsDict]]:
"""Compute BYOL's loss function.
Args:
online_params: parameters of the online network (the loss is later
differentiated with respect to the online parameters).
target_params: parameters of the target network.
online_state: internal state of online network.
target_state: internal state of target network.
rng: random number generator state.
inputs: inputs, containing two batches of crops from the same images,
view1 and view2 and labels
Returns:
BYOL's loss, a mapping containing the online and target networks updated
states after processing inputs, and various logs.
"""
if self._should_transpose_images():
inputs = dataset.transpose_images(inputs)
inputs = augmentations.postprocess(inputs, rng)
labels = inputs['labels']
online_network_out, online_state = self.forward.apply(
params=online_params,
state=online_state,
inputs=inputs,
is_training=True)
target_network_out, target_state = self.forward.apply(
params=target_params,
state=target_state,
inputs=inputs,
is_training=True)
# Representation loss
# The stop_gradient is not necessary as we explicitly take the gradient with
# respect to online parameters only in `optax.apply_updates`. We leave it to
# indicate that gradients are not backpropagated through the target network.
repr_loss = helpers.regression_loss(
online_network_out['prediction_view1'],
jax.lax.stop_gradient(target_network_out['projection_view2']))
repr_loss = repr_loss + helpers.regression_loss(
online_network_out['prediction_view2'],
jax.lax.stop_gradient(target_network_out['projection_view1']))
repr_loss = jnp.mean(repr_loss)
# Classification loss (with gradient flows stopped from flowing into the
# ResNet). This is used to provide an evaluation of the representation
# quality during training.
classif_loss = helpers.softmax_cross_entropy(
logits=online_network_out['logits_view1'],
labels=jax.nn.one_hot(labels, self._num_classes))
top1_correct = helpers.topk_accuracy(
online_network_out['logits_view1'],
inputs['labels'],
topk=1,
)
top5_correct = helpers.topk_accuracy(
online_network_out['logits_view1'],
inputs['labels'],
topk=5,
)
top1_acc = jnp.mean(top1_correct)
top5_acc = jnp.mean(top5_correct)
classif_loss = jnp.mean(classif_loss)
loss = repr_loss + classif_loss
logs = dict(
loss=loss,
repr_loss=repr_loss,
classif_loss=classif_loss,
top1_accuracy=top1_acc,
top5_accuracy=top5_acc,
)
return loss, (dict(online_state=online_state,
target_state=target_state), logs)
def _should_transpose_images(self):
"""Should we transpose images (saves host-to-device time on TPUs)."""
return (self._enable_double_transpose and
jax.local_devices()[0].platform == 'tpu')
def _update_fn(
self,
byol_state: _ByolExperimentState,
global_step: jnp.ndarray,
rng: jnp.ndarray,
inputs: dataset.Batch,
) -> Tuple[_ByolExperimentState, LogsDict]:
"""Update online and target parameters.
Args:
byol_state: current BYOL state.
global_step: current training step.
rng: current random number generator
inputs: inputs, containing two batches of crops from the same images,
view1 and view2 and labels
Returns:
Tuple containing the updated Byol state after processing the inputs, and
various logs.
"""
online_params = byol_state.online_params
target_params = byol_state.target_params
online_state = byol_state.online_state
target_state = byol_state.target_state
opt_state = byol_state.opt_state
# update online network
grad_fn = jax.grad(self.loss_fn, argnums=0, has_aux=True)
grads, (net_states, logs) = grad_fn(online_params, target_params,
online_state, target_state, rng, inputs)
# cross-device grad and logs reductions
grads = jax.tree_map(lambda v: jax.lax.pmean(v, axis_name='i'), grads)
logs = jax.tree_map(lambda x: jax.lax.pmean(x, axis_name='i'), logs)
learning_rate = schedules.learning_schedule(
global_step,
batch_size=self._batch_size,
total_steps=self._max_steps,
**self._lr_schedule_config)
updates, opt_state = self._optimizer(learning_rate).update(
grads, opt_state, online_params)
online_params = optax.apply_updates(online_params, updates)
# update target network
tau = schedules.target_ema(
global_step,
base_ema=self._base_target_ema,
max_steps=self._max_steps)
target_params = jax.tree_map(lambda x, y: x + (1 - tau) * (y - x),
target_params, online_params)
logs['tau'] = tau
logs['learning_rate'] = learning_rate
return _ByolExperimentState(
online_params=online_params,
target_params=target_params,
online_state=net_states['online_state'],
target_state=net_states['target_state'],
opt_state=opt_state), logs
def _make_initial_state(
self,
rng: jnp.ndarray,
dummy_input: dataset.Batch,
) -> _ByolExperimentState:
"""BYOL's _ByolExperimentState initialization.
Args:
rng: random number generator used to initialize parameters. If working in
a multi device setup, this need to be a ShardedArray.
dummy_input: a dummy image, used to compute intermediate outputs shapes.
Returns:
Initial Byol state.
"""
rng_online, rng_target = jax.random.split(rng)
if self._should_transpose_images():
dummy_input = dataset.transpose_images(dummy_input)
# Online and target parameters are initialized using different rngs,
# in our experiments we did not notice a significant different with using
# the same rng for both.
online_params, online_state = self.forward.init(
rng_online,
dummy_input,
is_training=True,
)
target_params, target_state = self.forward.init(
rng_target,
dummy_input,
is_training=True,
)
opt_state = self._optimizer(0).init(online_params)
return _ByolExperimentState(
online_params=online_params,
target_params=target_params,
opt_state=opt_state,
online_state=online_state,
target_state=target_state,
)
def step(self, *,
global_step: jnp.ndarray,
rng: jnp.ndarray) -> Mapping[Text, np.ndarray]:
"""Performs a single training step."""
if self._train_input is None:
self._initialize_train()
inputs = next(self._train_input)
self._byol_state, scalars = self.update_pmap(
self._byol_state,
global_step=global_step,
rng=rng,
inputs=inputs,
)
return helpers.get_first(scalars)
def save_checkpoint(self, step: int, rng: jnp.ndarray):
self._checkpointer.maybe_save_checkpoint(
self._byol_state, step=step, rng=rng, is_final=step >= self._max_steps)
def load_checkpoint(self) -> Union[Tuple[int, jnp.ndarray], None]:
checkpoint_data = self._checkpointer.maybe_load_checkpoint()
if checkpoint_data is None:
return None
self._byol_state, step, rng = checkpoint_data
return step, rng
def _initialize_train(self):
"""Initialize train.
This includes initializing the input pipeline and Byol's state.
"""
self._train_input = acme_utils.prefetch(self._build_train_input())
# Check we haven't already restored params
if self._byol_state is None:
logging.info(
'Initializing parameters rather than restoring from checkpoint.')
# initialize Byol and setup optimizer state
inputs = next(self._train_input)
init_byol = jax.pmap(self._make_initial_state, axis_name='i')
# Init uses the same RNG key on all hosts+devices to ensure everyone
# computes the same initial state and parameters.
init_rng = jax.random.PRNGKey(self._random_seed)
init_rng = helpers.bcast_local_devices(init_rng)
self._byol_state = init_byol(rng=init_rng, dummy_input=inputs)
def _build_train_input(self) -> Generator[dataset.Batch, None, None]:
"""Loads the (infinitely looping) dataset iterator."""
num_devices = jax.device_count()
global_batch_size = self._batch_size
per_device_batch_size, ragged = divmod(global_batch_size, num_devices)
if ragged:
raise ValueError(
f'Global batch size {global_batch_size} must be divisible by '
f'num devices {num_devices}')
return dataset.load(
dataset.Split.TRAIN_AND_VALID,
preprocess_mode=dataset.PreprocessMode.PRETRAIN,
transpose=self._should_transpose_images(),
batch_dims=[jax.local_device_count(), per_device_batch_size])
def _eval_batch(
self,
params: hk.Params,
state: hk.State,
batch: dataset.Batch,
) -> Mapping[Text, jnp.ndarray]:
"""Evaluates a batch.
Args:
params: Parameters of the model to evaluate. Typically Byol's online
parameters.
state: State of the model to evaluate. Typically Byol's online state.
batch: Batch of data to evaluate (must contain keys images and labels).
Returns:
Unreduced evaluation loss and top1 accuracy on the batch.
"""
if self._should_transpose_images():
batch = dataset.transpose_images(batch)
outputs, _ = self.forward.apply(params, state, batch, is_training=False)
logits = outputs['logits']
labels = hk.one_hot(batch['labels'], self._num_classes)
loss = helpers.softmax_cross_entropy(logits, labels, reduction=None)
top1_correct = helpers.topk_accuracy(logits, batch['labels'], topk=1)
top5_correct = helpers.topk_accuracy(logits, batch['labels'], topk=5)
# NOTE: Returned values will be summed and finally divided by num_samples.
return {
'eval_loss': loss,
'top1_accuracy': top1_correct,
'top5_accuracy': top5_correct,
}
def _eval_epoch(self, subset: Text, batch_size: int):
"""Evaluates an epoch."""
num_samples = 0.
summed_scalars = None
params = helpers.get_first(self._byol_state.online_params)
state = helpers.get_first(self._byol_state.online_state)
split = dataset.Split.from_string(subset)
dataset_iterator = dataset.load(
split,
preprocess_mode=dataset.PreprocessMode.EVAL,
transpose=self._should_transpose_images(),
batch_dims=[batch_size])
for inputs in dataset_iterator:
num_samples += inputs['labels'].shape[0]
scalars = self.eval_batch_jit(params, state, inputs)
# Accumulate the sum of scalars for each step.
scalars = jax.tree_map(lambda x: jnp.sum(x, axis=0), scalars)
if summed_scalars is None:
summed_scalars = scalars
else:
summed_scalars = jax.tree_map(jnp.add, summed_scalars, scalars)
mean_scalars = jax.tree_map(lambda x: x / num_samples, summed_scalars)
return mean_scalars
def evaluate(self, global_step, **unused_args):
"""Thin wrapper around _eval_epoch."""
global_step = np.array(helpers.get_first(global_step))
scalars = jax.device_get(self._eval_epoch(**self._evaluation_config))
logging.info('[Step %d] Eval scalars: %s', global_step, scalars)
return scalars
================================================
FILE: byol/configs/__init__.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
================================================
FILE: byol/configs/byol.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Config file for BYOL experiment."""
from byol.utils import dataset
# Preset values for certain number of training epochs.
_LR_PRESETS = {40: 0.45, 100: 0.45, 300: 0.3, 1000: 0.2}
_WD_PRESETS = {40: 1e-6, 100: 1e-6, 300: 1e-6, 1000: 1.5e-6}
_EMA_PRESETS = {40: 0.97, 100: 0.99, 300: 0.99, 1000: 0.996}
def get_config(num_epochs: int, batch_size: int):
"""Return config object, containing all hyperparameters for training."""
train_images_per_epoch = dataset.Split.TRAIN_AND_VALID.num_examples
assert num_epochs in [40, 100, 300, 1000]
config = dict(
random_seed=0,
num_classes=1000,
batch_size=batch_size,
max_steps=num_epochs * train_images_per_epoch // batch_size,
enable_double_transpose=True,
base_target_ema=_EMA_PRESETS[num_epochs],
network_config=dict(
projector_hidden_size=4096,
projector_output_size=256,
predictor_hidden_size=4096,
encoder_class='ResNet50', # Should match a class in utils/networks.
encoder_config=dict(
resnet_v2=False,
width_multiplier=1),
bn_config={
'decay_rate': .9,
'eps': 1e-5,
# Accumulate batchnorm statistics across devices.
# This should be equal to the `axis_name` argument passed
# to jax.pmap.
'cross_replica_axis': 'i',
'create_scale': True,
'create_offset': True,
}),
optimizer_config=dict(
weight_decay=_WD_PRESETS[num_epochs],
eta=1e-3,
momentum=.9,
),
lr_schedule_config=dict(
base_learning_rate=_LR_PRESETS[num_epochs],
warmup_steps=10 * train_images_per_epoch // batch_size,
),
evaluation_config=dict(
subset='test',
batch_size=100,
),
checkpointing_config=dict(
use_checkpointing=True,
checkpoint_dir='/tmp/byol',
save_checkpoint_interval=300,
filename='pretrain.pkl'
),
)
return config
================================================
FILE: byol/configs/eval.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Config file for evaluation experiment."""
from typing import Text
from byol.utils import dataset
def get_config(checkpoint_to_evaluate: Text, batch_size: int):
"""Return config object for training."""
train_images_per_epoch = dataset.Split.TRAIN_AND_VALID.num_examples
config = dict(
random_seed=0,
enable_double_transpose=True,
max_steps=80 * train_images_per_epoch // batch_size,
num_classes=1000,
batch_size=batch_size,
checkpoint_to_evaluate=checkpoint_to_evaluate,
# If True, allows training without loading a checkpoint.
allow_train_from_scratch=False,
# Whether the backbone should be frozen (linear evaluation) or
# trainable (fine-tuning).
freeze_backbone=True,
optimizer_config=dict(
momentum=0.9,
nesterov=True,
),
lr_schedule_config=dict(
base_learning_rate=0.2,
warmup_steps=0,
),
network_config=dict( # Should match the evaluated checkpoint
encoder_class='ResNet50', # Should match a class in utils/networks.
encoder_config=dict(
resnet_v2=False,
width_multiplier=1),
bn_decay_rate=0.9,
),
evaluation_config=dict(
subset='test',
batch_size=100,
),
checkpointing_config=dict(
use_checkpointing=True,
checkpoint_dir='/tmp/byol',
save_checkpoint_interval=300,
filename='linear-eval.pkl'
),
)
return config
================================================
FILE: byol/eval_experiment.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Linear evaluation or fine-tuning pipeline.
Use this experiment to evaluate a checkpoint from byol_experiment.
"""
import functools
from typing import Any, Generator, Mapping, NamedTuple, Optional, Text, Tuple, Union
from absl import logging
from acme.jax import utils as acme_utils
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import optax
from byol.utils import checkpointing
from byol.utils import dataset
from byol.utils import helpers
from byol.utils import networks
from byol.utils import schedules
# Type declarations.
OptState = Tuple[optax.TraceState, optax.ScaleByScheduleState, optax.ScaleState]
LogsDict = Mapping[Text, jnp.ndarray]
class _EvalExperimentState(NamedTuple):
backbone_params: hk.Params
classif_params: hk.Params
backbone_state: hk.State
backbone_opt_state: Union[None, OptState]
classif_opt_state: OptState
class EvalExperiment:
"""Linear evaluation experiment."""
def __init__(
self,
random_seed: int,
num_classes: int,
batch_size: int,
max_steps: int,
enable_double_transpose: bool,
checkpoint_to_evaluate: Optional[Text],
allow_train_from_scratch: bool,
freeze_backbone: bool,
network_config: Mapping[Text, Any],
optimizer_config: Mapping[Text, Any],
lr_schedule_config: Mapping[Text, Any],
evaluation_config: Mapping[Text, Any],
checkpointing_config: Mapping[Text, Any]):
"""Constructs the experiment.
Args:
random_seed: the random seed to use when initializing network weights.
num_classes: the number of classes; used for the online evaluation.
batch_size: the total batch size; should be a multiple of the number of
available accelerators.
max_steps: the number of training steps; used for the lr/target network
ema schedules.
enable_double_transpose: see dataset.py; only has effect on TPU.
checkpoint_to_evaluate: the path to the checkpoint to evaluate.
allow_train_from_scratch: whether to allow training without specifying a
checkpoint to evaluate (training from scratch).
freeze_backbone: whether the backbone resnet should remain frozen (linear
evaluation) or be trainable (fine-tuning).
network_config: the configuration for the network.
optimizer_config: the configuration for the optimizer.
lr_schedule_config: the configuration for the learning rate schedule.
evaluation_config: the evaluation configuration.
checkpointing_config: the configuration for checkpointing.
"""
self._random_seed = random_seed
self._enable_double_transpose = enable_double_transpose
self._num_classes = num_classes
self._lr_schedule_config = lr_schedule_config
self._batch_size = batch_size
self._max_steps = max_steps
self._checkpoint_to_evaluate = checkpoint_to_evaluate
self._allow_train_from_scratch = allow_train_from_scratch
self._freeze_backbone = freeze_backbone
self._optimizer_config = optimizer_config
self._evaluation_config = evaluation_config
# Checkpointed experiment state.
self._experiment_state = None
# Input pipelines.
self._train_input = None
self._eval_input = None
backbone_fn = functools.partial(self._backbone_fn, **network_config)
self.forward_backbone = hk.without_apply_rng(
hk.transform_with_state(backbone_fn))
self.forward_classif = hk.without_apply_rng(hk.transform(self._classif_fn))
self.update_pmap = jax.pmap(self._update_func, axis_name='i')
self.eval_batch_jit = jax.jit(self._eval_batch)
self._is_backbone_training = not self._freeze_backbone
self._checkpointer = checkpointing.Checkpointer(**checkpointing_config)
def _should_transpose_images(self):
"""Should we transpose images (saves host-to-device time on TPUs)."""
return (self._enable_double_transpose and
jax.local_devices()[0].platform == 'tpu')
def _backbone_fn(
self,
inputs: dataset.Batch,
encoder_class: Text,
encoder_config: Mapping[Text, Any],
bn_decay_rate: float,
is_training: bool,
) -> jnp.ndarray:
"""Forward of the encoder (backbone)."""
bn_config = {'decay_rate': bn_decay_rate}
encoder = getattr(networks, encoder_class)
model = encoder(
None,
bn_config=bn_config,
**encoder_config)
if self._should_transpose_images():
inputs = dataset.transpose_images(inputs)
images = dataset.normalize_images(inputs['images'])
return model(images, is_training=is_training)
def _classif_fn(
self,
embeddings: jnp.ndarray,
) -> jnp.ndarray:
classifier = hk.Linear(output_size=self._num_classes)
return classifier(embeddings)
# _ _
# | |_ _ __ __ _(_)_ __
# | __| '__/ _` | | '_ \
# | |_| | | (_| | | | | |
# \__|_| \__,_|_|_| |_|
#
def step(self, *,
global_step: jnp.ndarray,
rng: jnp.ndarray) -> Mapping[Text, np.ndarray]:
"""Performs a single training step."""
if self._train_input is None:
self._initialize_train(rng)
inputs = next(self._train_input)
self._experiment_state, scalars = self.update_pmap(
self._experiment_state, global_step, inputs)
scalars = helpers.get_first(scalars)
return scalars
def save_checkpoint(self, step: int, rng: jnp.ndarray):
self._checkpointer.maybe_save_checkpoint(
self._experiment_state, step=step, rng=rng,
is_final=step >= self._max_steps)
def load_checkpoint(self) -> Union[Tuple[int, jnp.ndarray], None]:
checkpoint_data = self._checkpointer.maybe_load_checkpoint()
if checkpoint_data is None:
return None
self._experiment_state, step, rng = checkpoint_data
return step, rng
def _initialize_train(self, rng):
"""BYOL's _ExperimentState initialization.
Args:
rng: random number generator used to initialize parameters. If working in
a multi device setup, this need to be a ShardedArray.
dummy_input: a dummy image, used to compute intermediate outputs shapes.
Returns:
Initial EvalExperiment state.
Raises:
RuntimeError: invalid or empty checkpoint.
"""
self._train_input = acme_utils.prefetch(self._build_train_input())
# Check we haven't already restored params
if self._experiment_state is None:
inputs = next(self._train_input)
if self._checkpoint_to_evaluate is not None:
# Load params from checkpoint
checkpoint_data = checkpointing.load_checkpoint(
self._checkpoint_to_evaluate)
if checkpoint_data is None:
raise RuntimeError('Invalid checkpoint.')
backbone_params = checkpoint_data['experiment_state'].online_params
backbone_state = checkpoint_data['experiment_state'].online_state
backbone_params = helpers.bcast_local_devices(backbone_params)
backbone_state = helpers.bcast_local_devices(backbone_state)
else:
if not self._allow_train_from_scratch:
raise ValueError(
'No checkpoint specified, but `allow_train_from_scratch` '
'set to False')
# Initialize with random parameters
logging.info(
'No checkpoint specified, initializing the networks from scratch '
'(dry run mode)')
backbone_params, backbone_state = jax.pmap(
functools.partial(self.forward_backbone.init, is_training=True),
axis_name='i')(rng=rng, inputs=inputs)
init_experiment = jax.pmap(self._make_initial_state, axis_name='i')
# Init uses the same RNG key on all hosts+devices to ensure everyone
# computes the same initial state and parameters.
init_rng = jax.random.PRNGKey(self._random_seed)
init_rng = helpers.bcast_local_devices(init_rng)
self._experiment_state = init_experiment(
rng=init_rng,
dummy_input=inputs,
backbone_params=backbone_params,
backbone_state=backbone_state)
# Clear the backbone optimizer's state when the backbone is frozen.
if self._freeze_backbone:
self._experiment_state = _EvalExperimentState(
backbone_params=self._experiment_state.backbone_params,
classif_params=self._experiment_state.classif_params,
backbone_state=self._experiment_state.backbone_state,
backbone_opt_state=None,
classif_opt_state=self._experiment_state.classif_opt_state,
)
def _make_initial_state(
self,
rng: jnp.ndarray,
dummy_input: dataset.Batch,
backbone_params: hk.Params,
backbone_state: hk.Params,
) -> _EvalExperimentState:
"""_EvalExperimentState initialization."""
# Initialize the backbone params
# Always create the batchnorm weights (is_training=True), they will be
# overwritten when loading the checkpoint.
embeddings, _ = self.forward_backbone.apply(
backbone_params, backbone_state, dummy_input, is_training=True)
backbone_opt_state = self._optimizer(0.).init(backbone_params)
# Initialize the classifier params and optimizer_state
classif_params = self.forward_classif.init(rng, embeddings)
classif_opt_state = self._optimizer(0.).init(classif_params)
return _EvalExperimentState( # pytype: disable=wrong-arg-types # numpy-scalars
backbone_params=backbone_params,
classif_params=classif_params,
backbone_state=backbone_state,
backbone_opt_state=backbone_opt_state,
classif_opt_state=classif_opt_state,
)
def _build_train_input(self) -> Generator[dataset.Batch, None, None]:
"""See base class."""
num_devices = jax.device_count()
global_batch_size = self._batch_size
per_device_batch_size, ragged = divmod(global_batch_size, num_devices)
if ragged:
raise ValueError(
f'Global batch size {global_batch_size} must be divisible by '
f'num devices {num_devices}')
return dataset.load(
dataset.Split.TRAIN_AND_VALID,
preprocess_mode=dataset.PreprocessMode.LINEAR_TRAIN,
transpose=self._should_transpose_images(),
batch_dims=[jax.local_device_count(), per_device_batch_size])
def _optimizer(self, learning_rate: float):
"""Build optimizer from config."""
return optax.sgd(learning_rate, **self._optimizer_config)
def _loss_fn(
self,
backbone_params: hk.Params,
classif_params: hk.Params,
backbone_state: hk.State,
inputs: dataset.Batch,
) -> Tuple[jnp.ndarray, Tuple[jnp.ndarray, hk.State]]:
"""Compute the classification loss function.
Args:
backbone_params: parameters of the encoder network.
classif_params: parameters of the linear classifier.
backbone_state: internal state of encoder network.
inputs: inputs, containing `images` and `labels`.
Returns:
The classification loss and various logs.
"""
embeddings, backbone_state = self.forward_backbone.apply(
backbone_params,
backbone_state,
inputs,
is_training=not self._freeze_backbone)
logits = self.forward_classif.apply(classif_params, embeddings)
labels = hk.one_hot(inputs['labels'], self._num_classes)
loss = helpers.softmax_cross_entropy(logits, labels, reduction='mean')
scaled_loss = loss / jax.device_count()
return scaled_loss, (loss, backbone_state)
def _update_func(
self,
experiment_state: _EvalExperimentState,
global_step: jnp.ndarray,
inputs: dataset.Batch,
) -> Tuple[_EvalExperimentState, LogsDict]:
"""Applies an update to parameters and returns new state."""
# This function computes the gradient of the first output of loss_fn and
# passes through the other arguments unchanged.
# Gradient of the first output of _loss_fn wrt the backbone (arg 0) and the
# classifier parameters (arg 1). The auxiliary outputs are returned as-is.
grad_loss_fn = jax.grad(self._loss_fn, has_aux=True, argnums=(0, 1))
grads, aux_outputs = grad_loss_fn(
experiment_state.backbone_params,
experiment_state.classif_params,
experiment_state.backbone_state,
inputs,
)
backbone_grads, classifier_grads = grads
train_loss, new_backbone_state = aux_outputs
classifier_grads = jax.lax.psum(classifier_grads, axis_name='i')
# Compute the decayed learning rate
learning_rate = schedules.learning_schedule(
global_step,
batch_size=self._batch_size,
total_steps=self._max_steps,
**self._lr_schedule_config)
# Compute and apply updates via our optimizer.
classif_updates, new_classif_opt_state = \
self._optimizer(learning_rate).update(
classifier_grads,
experiment_state.classif_opt_state)
new_classif_params = optax.apply_updates(experiment_state.classif_params,
classif_updates)
if self._freeze_backbone:
del backbone_grads, new_backbone_state # Unused
# The backbone is not updated.
new_backbone_params = experiment_state.backbone_params
new_backbone_opt_state = None
new_backbone_state = experiment_state.backbone_state
else:
backbone_grads = jax.lax.psum(backbone_grads, axis_name='i')
# Compute and apply updates via our optimizer.
backbone_updates, new_backbone_opt_state = \
self._optimizer(learning_rate).update(
backbone_grads,
experiment_state.backbone_opt_state)
new_backbone_params = optax.apply_updates(
experiment_state.backbone_params, backbone_updates)
experiment_state = _EvalExperimentState( # pytype: disable=wrong-arg-types # numpy-scalars
new_backbone_params,
new_classif_params,
new_backbone_state,
new_backbone_opt_state,
new_classif_opt_state,
)
# Scalars to log (note: we log the mean across all hosts/devices).
scalars = {'train_loss': train_loss}
scalars = jax.lax.pmean(scalars, axis_name='i')
return experiment_state, scalars
# _
# _____ ____ _| |
# / _ \ \ / / _` | |
# | __/\ V / (_| | |
# \___| \_/ \__,_|_|
#
def evaluate(self, global_step, **unused_args):
"""See base class."""
global_step = np.array(helpers.get_first(global_step))
scalars = jax.device_get(self._eval_epoch(**self._evaluation_config))
logging.info('[Step %d] Eval scalars: %s', global_step, scalars)
return scalars
def _eval_batch(
self,
backbone_params: hk.Params,
classif_params: hk.Params,
backbone_state: hk.State,
inputs: dataset.Batch,
) -> LogsDict:
"""Evaluates a batch."""
embeddings, backbone_state = self.forward_backbone.apply(
backbone_params, backbone_state, inputs, is_training=False)
logits = self.forward_classif.apply(classif_params, embeddings)
labels = hk.one_hot(inputs['labels'], self._num_classes)
loss = helpers.softmax_cross_entropy(logits, labels, reduction=None)
top1_correct = helpers.topk_accuracy(logits, inputs['labels'], topk=1) # pytype: disable=wrong-arg-types # jax-ndarray
top5_correct = helpers.topk_accuracy(logits, inputs['labels'], topk=5) # pytype: disable=wrong-arg-types # jax-ndarray
# NOTE: Returned values will be summed and finally divided by num_samples.
return {
'eval_loss': loss,
'top1_accuracy': top1_correct,
'top5_accuracy': top5_correct
}
def _eval_epoch(self, subset: Text, batch_size: int):
"""Evaluates an epoch."""
num_samples = 0.
summed_scalars = None
backbone_params = helpers.get_first(self._experiment_state.backbone_params)
classif_params = helpers.get_first(self._experiment_state.classif_params)
backbone_state = helpers.get_first(self._experiment_state.backbone_state)
split = dataset.Split.from_string(subset)
dataset_iterator = dataset.load(
split,
preprocess_mode=dataset.PreprocessMode.EVAL,
transpose=self._should_transpose_images(),
batch_dims=[batch_size])
for inputs in dataset_iterator:
num_samples += inputs['labels'].shape[0]
scalars = self.eval_batch_jit(
backbone_params,
classif_params,
backbone_state,
inputs,
)
# Accumulate the sum of scalars for each step.
scalars = jax.tree_map(lambda x: jnp.sum(x, axis=0), scalars)
if summed_scalars is None:
summed_scalars = scalars
else:
summed_scalars = jax.tree_map(jnp.add, summed_scalars, scalars)
mean_scalars = jax.tree_map(lambda x: x / num_samples, summed_scalars)
return mean_scalars
================================================
FILE: byol/main_loop.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Training and evaluation loops for an experiment."""
import time
from typing import Any, Mapping, Text, Type, Union
from absl import app
from absl import flags
from absl import logging
import jax
import numpy as np
from byol import byol_experiment
from byol import eval_experiment
from byol.configs import byol as byol_config
from byol.configs import eval as eval_config
flags.DEFINE_string('experiment_mode',
'pretrain', 'The experiment, pretrain or linear-eval')
flags.DEFINE_string('worker_mode', 'train', 'The mode, train or eval')
flags.DEFINE_string('worker_tpu_driver', '', 'The tpu driver to use')
flags.DEFINE_integer('pretrain_epochs', 1000, 'Number of pre-training epochs')
flags.DEFINE_integer('batch_size', 4096, 'Total batch size')
flags.DEFINE_string('checkpoint_root', '/tmp/byol',
'The directory to save checkpoints to.')
flags.DEFINE_integer('log_tensors_interval', 60, 'Log tensors every n seconds.')
FLAGS = flags.FLAGS
Experiment = Union[
Type[byol_experiment.ByolExperiment],
Type[eval_experiment.EvalExperiment]]
def train_loop(experiment_class: Experiment, config: Mapping[Text, Any]):
"""The main training loop.
This loop periodically saves a checkpoint to be evaluated in the eval_loop.
Args:
experiment_class: the constructor for the experiment (either byol_experiment
or eval_experiment).
config: the experiment config.
"""
experiment = experiment_class(**config)
rng = jax.random.PRNGKey(0)
step = 0
host_id = jax.host_id()
last_logging = time.time()
if config['checkpointing_config']['use_checkpointing']:
checkpoint_data = experiment.load_checkpoint()
if checkpoint_data is None:
step = 0
else:
step, rng = checkpoint_data
local_device_count = jax.local_device_count()
while step < config['max_steps']:
step_rng, rng = tuple(jax.random.split(rng))
# Broadcast the random seeds across the devices
step_rng_device = jax.random.split(step_rng, num=jax.device_count())
step_rng_device = step_rng_device[
host_id * local_device_count:(host_id + 1) * local_device_count]
step_device = np.broadcast_to(step, [local_device_count])
# Perform a training step and get scalars to log.
scalars = experiment.step(global_step=step_device, rng=step_rng_device)
# Checkpointing and logging.
if config['checkpointing_config']['use_checkpointing']:
experiment.save_checkpoint(step, rng)
current_time = time.time()
if current_time - last_logging > FLAGS.log_tensors_interval:
logging.info('Step %d: %s', step, scalars)
last_logging = current_time
step += 1
logging.info('Saving final checkpoint')
logging.info('Step %d: %s', step, scalars)
experiment.save_checkpoint(step, rng)
def eval_loop(experiment_class: Experiment, config: Mapping[Text, Any]):
"""The main evaluation loop.
This loop periodically loads a checkpoint and evaluates its performance on the
test set, by calling experiment.evaluate.
Args:
experiment_class: the constructor for the experiment (either byol_experiment
or eval_experiment).
config: the experiment config.
"""
experiment = experiment_class(**config)
last_evaluated_step = -1
while True:
checkpoint_data = experiment.load_checkpoint()
if checkpoint_data is None:
logging.info('No checkpoint found. Waiting for 10s.')
time.sleep(10)
continue
step, _ = checkpoint_data
if step <= last_evaluated_step:
logging.info('Checkpoint at step %d already evaluated, waiting.', step)
time.sleep(10)
continue
host_id = jax.host_id()
local_device_count = jax.local_device_count()
step_device = np.broadcast_to(step, [local_device_count])
scalars = experiment.evaluate(global_step=step_device)
if host_id == 0: # Only perform logging in one host.
logging.info('Evaluation at step %d: %s', step, scalars)
last_evaluated_step = step
if last_evaluated_step >= config['max_steps']:
return
def main(_):
if FLAGS.worker_tpu_driver:
jax.config.update('jax_xla_backend', 'tpu_driver')
jax.config.update('jax_backend_target', FLAGS.worker_tpu_driver)
logging.info('Backend: %s %r', FLAGS.worker_tpu_driver, jax.devices())
if FLAGS.experiment_mode == 'pretrain':
experiment_class = byol_experiment.ByolExperiment
config = byol_config.get_config(FLAGS.pretrain_epochs, FLAGS.batch_size)
elif FLAGS.experiment_mode == 'linear-eval':
experiment_class = eval_experiment.EvalExperiment
config = eval_config.get_config(f'{FLAGS.checkpoint_root}/pretrain.pkl',
FLAGS.batch_size)
else:
raise ValueError(f'Unknown experiment mode: {FLAGS.experiment_mode}')
config['checkpointing_config']['checkpoint_dir'] = FLAGS.checkpoint_root # pytype: disable=unsupported-operands # dict-kwargs
if FLAGS.worker_mode == 'train':
train_loop(experiment_class, config)
elif FLAGS.worker_mode == 'eval':
eval_loop(experiment_class, config)
if __name__ == '__main__':
app.run(main)
================================================
FILE: byol/main_loop_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for BYOL's main training loop."""
from absl import flags
from absl.testing import absltest
import tensorflow_datasets as tfds
from byol import byol_experiment
from byol import eval_experiment
from byol import main_loop
from byol.configs import byol as byol_config
from byol.configs import eval as eval_config
FLAGS = flags.FLAGS
class MainLoopTest(absltest.TestCase):
def test_pretrain(self):
config = byol_config.get_config(num_epochs=40, batch_size=4)
temp_dir = self.create_tempdir().full_path
# Override some config fields to make test lighter.
config['network_config']['encoder_class'] = 'TinyResNet'
config['network_config']['projector_hidden_size'] = 256
config['network_config']['predictor_hidden_size'] = 256
config['checkpointing_config']['checkpoint_dir'] = temp_dir
config['evaluation_config']['batch_size'] = 16
config['max_steps'] = 16
with tfds.testing.mock_data(num_examples=64):
experiment_class = byol_experiment.ByolExperiment
main_loop.train_loop(experiment_class, config)
main_loop.eval_loop(experiment_class, config)
def test_linear_eval(self):
config = eval_config.get_config(checkpoint_to_evaluate=None, batch_size=4)
temp_dir = self.create_tempdir().full_path
# Override some config fields to make test lighter.
config['network_config']['encoder_class'] = 'TinyResNet'
config['allow_train_from_scratch'] = True
config['checkpointing_config']['checkpoint_dir'] = temp_dir
config['evaluation_config']['batch_size'] = 16
config['max_steps'] = 16
with tfds.testing.mock_data(num_examples=64):
experiment_class = eval_experiment.EvalExperiment
main_loop.train_loop(experiment_class, config)
main_loop.eval_loop(experiment_class, config)
def test_pipeline(self):
b_config = byol_config.get_config(num_epochs=40, batch_size=4)
temp_dir = self.create_tempdir().full_path
# Override some config fields to make test lighter.
b_config['network_config']['encoder_class'] = 'TinyResNet'
b_config['network_config']['projector_hidden_size'] = 256
b_config['network_config']['predictor_hidden_size'] = 256
b_config['checkpointing_config']['checkpoint_dir'] = temp_dir
b_config['evaluation_config']['batch_size'] = 16
b_config['max_steps'] = 16
with tfds.testing.mock_data(num_examples=64):
main_loop.train_loop(byol_experiment.ByolExperiment, b_config)
e_config = eval_config.get_config(
checkpoint_to_evaluate=f'{temp_dir}/pretrain.pkl',
batch_size=4)
# Override some config fields to make test lighter.
e_config['network_config']['encoder_class'] = 'TinyResNet'
e_config['allow_train_from_scratch'] = True
e_config['checkpointing_config']['checkpoint_dir'] = temp_dir
e_config['evaluation_config']['batch_size'] = 16
e_config['max_steps'] = 16
with tfds.testing.mock_data(num_examples=64):
main_loop.train_loop(eval_experiment.EvalExperiment, e_config)
if __name__ == '__main__':
absltest.main()
================================================
FILE: byol/requirements.txt
================================================
dm-acme
dm-haiku
dm-tree
jax
jaxlib
numpy>=1.16
optax
tensorflow
tensorflow_datasets
================================================
FILE: byol/setup.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Setup BYOL for pip package."""
import setuptools
setuptools.setup(
name='byol',
description='Bootstrap Your Own Latents',
long_description=open('README.md').read(),
author='DeepMind',
author_email='no-reply@google.com',
url='https://github.com/deepmind/deepmind-research/byol',
install_requires=[
'chex',
'dm-acme',
'dm-haiku',
'dm-tree',
'jax',
'jaxlib',
'numpy>=1.16',
'optax',
'tensorflow',
'tensorflow-datasets',
],
package_dir={'byol': ''},
py_modules=[
'byol.byol_experiment', 'byol.eval_experiment', 'byol.main_loop'
],
packages=['byol.configs', 'byol.utils'])
================================================
FILE: byol/utils/__init__.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
================================================
FILE: byol/utils/augmentations.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data preprocessing and augmentation."""
import functools
from typing import Any, Mapping, Text
import jax
import jax.numpy as jnp
# typing
JaxBatch = Mapping[Text, jnp.ndarray]
ConfigDict = Mapping[Text, Any]
augment_config = dict(
view1=dict(
random_flip=True, # Random left/right flip
color_transform=dict(
apply_prob=1.0,
# Range of jittering
brightness=0.4,
contrast=0.4,
saturation=0.2,
hue=0.1,
# Probability of applying color jittering
color_jitter_prob=0.8,
# Probability of converting to grayscale
to_grayscale_prob=0.2,
# Shuffle the order of color transforms
shuffle=True),
gaussian_blur=dict(
apply_prob=1.0,
# Kernel size ~ image_size / blur_divider
blur_divider=10.,
# Kernel distribution
sigma_min=0.1,
sigma_max=2.0),
solarize=dict(apply_prob=0.0, threshold=0.5),
),
view2=dict(
random_flip=True,
color_transform=dict(
apply_prob=1.0,
brightness=0.4,
contrast=0.4,
saturation=0.2,
hue=0.1,
color_jitter_prob=0.8,
to_grayscale_prob=0.2,
shuffle=True),
gaussian_blur=dict(
apply_prob=0.1, blur_divider=10., sigma_min=0.1, sigma_max=2.0),
solarize=dict(apply_prob=0.2, threshold=0.5),
))
def postprocess(inputs: JaxBatch, rng: jnp.ndarray):
"""Apply the image augmentations to crops in inputs (view1 and view2)."""
def _postprocess_image(
images: jnp.ndarray,
rng: jnp.ndarray,
presets: ConfigDict,
) -> JaxBatch:
"""Applies augmentations in post-processing.
Args:
images: an NHWC tensor (with C=3), with float values in [0, 1].
rng: a single PRNGKey.
presets: a dict of presets for the augmentations.
Returns:
A batch of augmented images with shape NHWC, with keys view1, view2
and labels.
"""
flip_rng, color_rng, blur_rng, solarize_rng = jax.random.split(rng, 4)
out = images
if presets['random_flip']:
out = random_flip(out, flip_rng)
if presets['color_transform']['apply_prob'] > 0:
out = color_transform(out, color_rng, **presets['color_transform'])
if presets['gaussian_blur']['apply_prob'] > 0:
out = gaussian_blur(out, blur_rng, **presets['gaussian_blur'])
if presets['solarize']['apply_prob'] > 0:
out = solarize(out, solarize_rng, **presets['solarize'])
out = jnp.clip(out, 0., 1.)
return jax.lax.stop_gradient(out)
rng1, rng2 = jax.random.split(rng, num=2)
view1 = _postprocess_image(inputs['view1'], rng1, augment_config['view1'])
view2 = _postprocess_image(inputs['view2'], rng2, augment_config['view2'])
return dict(view1=view1, view2=view2, labels=inputs['labels'])
def _maybe_apply(apply_fn, inputs, rng, apply_prob):
should_apply = jax.random.uniform(rng, shape=()) <= apply_prob
return jax.lax.cond(should_apply, inputs, apply_fn, inputs, lambda x: x)
def _depthwise_conv2d(inputs, kernel, strides, padding):
"""Computes a depthwise conv2d in Jax.
Args:
inputs: an NHWC tensor with N=1.
kernel: a [H", W", 1, C] tensor.
strides: a 2d tensor.
padding: "SAME" or "VALID".
Returns:
The depthwise convolution of inputs with kernel, as [H, W, C].
"""
return jax.lax.conv_general_dilated(
inputs,
kernel,
strides,
padding,
feature_group_count=inputs.shape[-1],
dimension_numbers=('NHWC', 'HWIO', 'NHWC'))
def _gaussian_blur_single_image(image, kernel_size, padding, sigma):
"""Applies gaussian blur to a single image, given as NHWC with N=1."""
radius = int(kernel_size / 2)
kernel_size_ = 2 * radius + 1
x = jnp.arange(-radius, radius + 1).astype(jnp.float32)
blur_filter = jnp.exp(-x**2 / (2. * sigma**2))
blur_filter = blur_filter / jnp.sum(blur_filter)
blur_v = jnp.reshape(blur_filter, [kernel_size_, 1, 1, 1])
blur_h = jnp.reshape(blur_filter, [1, kernel_size_, 1, 1])
num_channels = image.shape[-1]
blur_h = jnp.tile(blur_h, [1, 1, 1, num_channels])
blur_v = jnp.tile(blur_v, [1, 1, 1, num_channels])
expand_batch_dim = len(image.shape) == 3
if expand_batch_dim:
image = image[jnp.newaxis, ...]
blurred = _depthwise_conv2d(image, blur_h, strides=[1, 1], padding=padding)
blurred = _depthwise_conv2d(blurred, blur_v, strides=[1, 1], padding=padding)
blurred = jnp.squeeze(blurred, axis=0)
return blurred
def _random_gaussian_blur(image, rng, kernel_size, padding, sigma_min,
sigma_max, apply_prob):
"""Applies a random gaussian blur."""
apply_rng, transform_rng = jax.random.split(rng)
def _apply(image):
sigma_rng, = jax.random.split(transform_rng, 1)
sigma = jax.random.uniform(
sigma_rng,
shape=(),
minval=sigma_min,
maxval=sigma_max,
dtype=jnp.float32)
return _gaussian_blur_single_image(image, kernel_size, padding, sigma)
return _maybe_apply(_apply, image, apply_rng, apply_prob)
def rgb_to_hsv(r, g, b):
"""Converts R, G, B values to H, S, V values.
Reference TF implementation:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/adjust_saturation_op.cc
Only input values between 0 and 1 are guaranteed to work properly, but this
function complies with the TF implementation outside of this range.
Args:
r: A tensor representing the red color component as floats.
g: A tensor representing the green color component as floats.
b: A tensor representing the blue color component as floats.
Returns:
H, S, V values, each as tensors of shape [...] (same as the input without
the last dimension).
"""
vv = jnp.maximum(jnp.maximum(r, g), b)
range_ = vv - jnp.minimum(jnp.minimum(r, g), b)
sat = jnp.where(vv > 0, range_ / vv, 0.)
norm = jnp.where(range_ != 0, 1. / (6. * range_), 1e9)
hr = norm * (g - b)
hg = norm * (b - r) + 2. / 6.
hb = norm * (r - g) + 4. / 6.
hue = jnp.where(r == vv, hr, jnp.where(g == vv, hg, hb))
hue = hue * (range_ > 0)
hue = hue + (hue < 0)
return hue, sat, vv
def hsv_to_rgb(h, s, v):
"""Converts H, S, V values to an R, G, B tuple.
Reference TF implementation:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/adjust_saturation_op.cc
Only input values between 0 and 1 are guaranteed to work properly, but this
function complies with the TF implementation outside of this range.
Args:
h: A float tensor of arbitrary shape for the hue (0-1 values).
s: A float tensor of the same shape for the saturation (0-1 values).
v: A float tensor of the same shape for the value channel (0-1 values).
Returns:
An (r, g, b) tuple, each with the same dimension as the inputs.
"""
c = s * v
m = v - c
dh = (h % 1.) * 6.
fmodu = dh % 2.
x = c * (1 - jnp.abs(fmodu - 1))
hcat = jnp.floor(dh).astype(jnp.int32)
rr = jnp.where(
(hcat == 0) | (hcat == 5), c, jnp.where(
(hcat == 1) | (hcat == 4), x, 0)) + m
gg = jnp.where(
(hcat == 1) | (hcat == 2), c, jnp.where(
(hcat == 0) | (hcat == 3), x, 0)) + m
bb = jnp.where(
(hcat == 3) | (hcat == 4), c, jnp.where(
(hcat == 2) | (hcat == 5), x, 0)) + m
return rr, gg, bb
def adjust_brightness(rgb_tuple, delta):
return jax.tree_map(lambda x: x + delta, rgb_tuple)
def adjust_contrast(image, factor):
def _adjust_contrast_channel(channel):
mean = jnp.mean(channel, axis=(-2, -1), keepdims=True)
return factor * (channel - mean) + mean
return jax.tree_map(_adjust_contrast_channel, image)
def adjust_saturation(h, s, v, factor):
return h, jnp.clip(s * factor, 0., 1.), v
def adjust_hue(h, s, v, delta):
# Note: this method exactly matches TF"s adjust_hue (combined with the hsv/rgb
# conversions) when running on GPU. When running on CPU, the results will be
# different if all RGB values for a pixel are outside of the [0, 1] range.
return (h + delta) % 1.0, s, v
def _random_brightness(rgb_tuple, rng, max_delta):
delta = jax.random.uniform(rng, shape=(), minval=-max_delta, maxval=max_delta)
return adjust_brightness(rgb_tuple, delta)
def _random_contrast(rgb_tuple, rng, max_delta):
factor = jax.random.uniform(
rng, shape=(), minval=1 - max_delta, maxval=1 + max_delta)
return adjust_contrast(rgb_tuple, factor)
def _random_saturation(rgb_tuple, rng, max_delta):
h, s, v = rgb_to_hsv(*rgb_tuple)
factor = jax.random.uniform(
rng, shape=(), minval=1 - max_delta, maxval=1 + max_delta)
return hsv_to_rgb(*adjust_saturation(h, s, v, factor))
def _random_hue(rgb_tuple, rng, max_delta):
h, s, v = rgb_to_hsv(*rgb_tuple)
delta = jax.random.uniform(rng, shape=(), minval=-max_delta, maxval=max_delta)
return hsv_to_rgb(*adjust_hue(h, s, v, delta))
def _to_grayscale(image):
rgb_weights = jnp.array([0.2989, 0.5870, 0.1140])
grayscale = jnp.tensordot(image, rgb_weights, axes=(-1, -1))[..., jnp.newaxis]
return jnp.tile(grayscale, (1, 1, 3)) # Back to 3 channels.
def _color_transform_single_image(image, rng, brightness, contrast, saturation,
hue, to_grayscale_prob, color_jitter_prob,
apply_prob, shuffle):
"""Applies color jittering to a single image."""
apply_rng, transform_rng = jax.random.split(rng)
perm_rng, b_rng, c_rng, s_rng, h_rng, cj_rng, gs_rng = jax.random.split(
transform_rng, 7)
# Whether the transform should be applied at all.
should_apply = jax.random.uniform(apply_rng, shape=()) <= apply_prob
# Whether to apply grayscale transform.
should_apply_gs = jax.random.uniform(gs_rng, shape=()) <= to_grayscale_prob
# Whether to apply color jittering.
should_apply_color = jax.random.uniform(cj_rng, shape=()) <= color_jitter_prob
# Decorator to conditionally apply fn based on an index.
def _make_cond(fn, idx):
def identity_fn(x, unused_rng, unused_param):
return x
def cond_fn(args, i):
def clip(args):
return jax.tree_map(lambda arg: jnp.clip(arg, 0., 1.), args)
out = jax.lax.cond(should_apply & should_apply_color & (i == idx), args,
lambda a: clip(fn(*a)), args,
lambda a: identity_fn(*a))
return jax.lax.stop_gradient(out)
return cond_fn
random_brightness_cond = _make_cond(_random_brightness, idx=0)
random_contrast_cond = _make_cond(_random_contrast, idx=1)
random_saturation_cond = _make_cond(_random_saturation, idx=2)
random_hue_cond = _make_cond(_random_hue, idx=3)
def _color_jitter(x):
rgb_tuple = tuple(jax.tree_map(jnp.squeeze, jnp.split(x, 3, axis=-1)))
if shuffle:
order = jax.random.permutation(perm_rng, jnp.arange(4, dtype=jnp.int32))
else:
order = range(4)
for idx in order:
if brightness > 0:
rgb_tuple = random_brightness_cond((rgb_tuple, b_rng, brightness), idx)
if contrast > 0:
rgb_tuple = random_contrast_cond((rgb_tuple, c_rng, contrast), idx)
if saturation > 0:
rgb_tuple = random_saturation_cond((rgb_tuple, s_rng, saturation), idx)
if hue > 0:
rgb_tuple = random_hue_cond((rgb_tuple, h_rng, hue), idx)
return jnp.stack(rgb_tuple, axis=-1)
out_apply = _color_jitter(image)
out_apply = jax.lax.cond(should_apply & should_apply_gs, out_apply,
_to_grayscale, out_apply, lambda x: x)
return jnp.clip(out_apply, 0., 1.)
def _random_flip_single_image(image, rng):
_, flip_rng = jax.random.split(rng)
should_flip_lr = jax.random.uniform(flip_rng, shape=()) <= 0.5
image = jax.lax.cond(should_flip_lr, image, jnp.fliplr, image, lambda x: x)
return image
def random_flip(images, rng):
rngs = jax.random.split(rng, images.shape[0])
return jax.vmap(_random_flip_single_image)(images, rngs)
def color_transform(images,
rng,
brightness=0.8,
contrast=0.8,
saturation=0.8,
hue=0.2,
color_jitter_prob=0.8,
to_grayscale_prob=0.2,
apply_prob=1.0,
shuffle=True):
"""Applies color jittering and/or grayscaling to a batch of images.
Args:
images: an NHWC tensor, with C=3.
rng: a single PRNGKey.
brightness: the range of jitter on brightness.
contrast: the range of jitter on contrast.
saturation: the range of jitter on saturation.
hue: the range of jitter on hue.
color_jitter_prob: the probability of applying color jittering.
to_grayscale_prob: the probability of converting the image to grayscale.
apply_prob: the probability of applying the transform to a batch element.
shuffle: whether to apply the transforms in a random order.
Returns:
A NHWC tensor of the transformed images.
"""
rngs = jax.random.split(rng, images.shape[0])
jitter_fn = functools.partial(
_color_transform_single_image,
brightness=brightness,
contrast=contrast,
saturation=saturation,
hue=hue,
color_jitter_prob=color_jitter_prob,
to_grayscale_prob=to_grayscale_prob,
apply_prob=apply_prob,
shuffle=shuffle)
return jax.vmap(jitter_fn)(images, rngs)
def gaussian_blur(images,
rng,
blur_divider=10.,
sigma_min=0.1,
sigma_max=2.0,
apply_prob=1.0):
"""Applies gaussian blur to a batch of images.
Args:
images: an NHWC tensor, with C=3.
rng: a single PRNGKey.
blur_divider: the blurring kernel will have size H / blur_divider.
sigma_min: the minimum value for sigma in the blurring kernel.
sigma_max: the maximum value for sigma in the blurring kernel.
apply_prob: the probability of applying the transform to a batch element.
Returns:
A NHWC tensor of the blurred images.
"""
rngs = jax.random.split(rng, images.shape[0])
kernel_size = images.shape[1] / blur_divider
blur_fn = functools.partial(
_random_gaussian_blur,
kernel_size=kernel_size,
padding='SAME',
sigma_min=sigma_min,
sigma_max=sigma_max,
apply_prob=apply_prob)
return jax.vmap(blur_fn)(images, rngs)
def _solarize_single_image(image, rng, threshold, apply_prob):
def _apply(image):
return jnp.where(image < threshold, image, 1. - image)
return _maybe_apply(_apply, image, rng, apply_prob)
def solarize(images, rng, threshold=0.5, apply_prob=1.0):
"""Applies solarization.
Args:
images: an NHWC tensor (with C=3).
rng: a single PRNGKey.
threshold: the solarization threshold.
apply_prob: the probability of applying the transform to a batch element.
Returns:
A NHWC tensor of the transformed images.
"""
rngs = jax.random.split(rng, images.shape[0])
solarize_fn = functools.partial(
_solarize_single_image, threshold=threshold, apply_prob=apply_prob)
return jax.vmap(solarize_fn)(images, rngs)
================================================
FILE: byol/utils/checkpointing.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Checkpoint saving and restoring utilities."""
import os
import time
from typing import Mapping, Text, Tuple, Union
from absl import logging
import dill
import jax
import jax.numpy as jnp
from byol.utils import helpers
class Checkpointer:
"""A checkpoint saving and loading class."""
def __init__(
self,
use_checkpointing: bool,
checkpoint_dir: Text,
save_checkpoint_interval: int,
filename: Text):
if (not use_checkpointing or
checkpoint_dir is None or
save_checkpoint_interval <= 0):
self._checkpoint_enabled = False
return
self._checkpoint_enabled = True
self._checkpoint_dir = checkpoint_dir
os.makedirs(self._checkpoint_dir, exist_ok=True)
self._filename = filename
self._checkpoint_path = os.path.join(self._checkpoint_dir, filename)
self._last_checkpoint_time = 0
self._checkpoint_every = save_checkpoint_interval
def maybe_save_checkpoint(
self,
experiment_state: Mapping[Text, jnp.ndarray],
step: int,
rng: jnp.ndarray,
is_final: bool):
"""Saves a checkpoint if enough time has passed since the previous one."""
current_time = time.time()
if (not self._checkpoint_enabled or
jax.host_id() != 0 or # Only checkpoint the first worker.
(not is_final and
current_time - self._last_checkpoint_time < self._checkpoint_every)):
return
checkpoint_data = dict(
experiment_state=jax.tree_map(
lambda x: jax.device_get(x[0]), experiment_state),
step=step,
rng=rng)
with open(self._checkpoint_path + '_tmp', 'wb') as checkpoint_file:
dill.dump(checkpoint_data, checkpoint_file, protocol=2)
try:
os.rename(self._checkpoint_path, self._checkpoint_path + '_old')
remove_old = True
except FileNotFoundError:
remove_old = False # No previous checkpoint to remove
os.rename(self._checkpoint_path + '_tmp', self._checkpoint_path)
if remove_old:
os.remove(self._checkpoint_path + '_old')
self._last_checkpoint_time = current_time
def maybe_load_checkpoint(
self) -> Union[Tuple[Mapping[Text, jnp.ndarray], int, jnp.ndarray], None]:
"""Loads a checkpoint if any is found."""
checkpoint_data = load_checkpoint(self._checkpoint_path)
if checkpoint_data is None:
logging.info('No existing checkpoint found at %s', self._checkpoint_path)
return None
step = checkpoint_data['step']
rng = checkpoint_data['rng']
experiment_state = jax.tree_map(
helpers.bcast_local_devices, checkpoint_data['experiment_state'])
del checkpoint_data
return experiment_state, step, rng
def load_checkpoint(checkpoint_path):
try:
with open(checkpoint_path, 'rb') as checkpoint_file:
checkpoint_data = dill.load(checkpoint_file)
logging.info('Loading checkpoint from %s, saved at step %d',
checkpoint_path, checkpoint_data['step'])
return checkpoint_data
except FileNotFoundError:
return None
================================================
FILE: byol/utils/dataset.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""ImageNet dataset with typical pre-processing."""
import enum
from typing import Generator, Mapping, Optional, Sequence, Text, Tuple
import jax
import jax.numpy as jnp
import numpy as np
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
Batch = Mapping[Text, np.ndarray]
class Split(enum.Enum):
"""Imagenet dataset split."""
TRAIN = 1
TRAIN_AND_VALID = 2
VALID = 3
TEST = 4
@classmethod
def from_string(cls, name: Text) -> 'Split':
return {
'TRAIN': Split.TRAIN,
'TRAIN_AND_VALID': Split.TRAIN_AND_VALID,
'VALID': Split.VALID,
'VALIDATION': Split.VALID,
'TEST': Split.TEST
}[name.upper()]
@property
def num_examples(self):
return {
Split.TRAIN_AND_VALID: 1281167,
Split.TRAIN: 1271167,
Split.VALID: 10000,
Split.TEST: 50000
}[self]
class PreprocessMode(enum.Enum):
"""Preprocessing modes for the dataset."""
PRETRAIN = 1 # Generates two augmented views (random crop + augmentations).
LINEAR_TRAIN = 2 # Generates a single random crop.
EVAL = 3 # Generates a single center crop.
def normalize_images(images: jnp.ndarray) -> jnp.ndarray:
"""Normalize the image using ImageNet statistics."""
mean_rgb = (0.485, 0.456, 0.406)
stddev_rgb = (0.229, 0.224, 0.225)
normed_images = images - jnp.array(mean_rgb).reshape((1, 1, 1, 3))
normed_images = normed_images / jnp.array(stddev_rgb).reshape((1, 1, 1, 3))
return normed_images
def load(split: Split,
*,
preprocess_mode: PreprocessMode,
batch_dims: Sequence[int],
transpose: bool = False,
allow_caching: bool = False) -> Generator[Batch, None, None]:
"""Loads the given split of the dataset."""
start, end = _shard(split, jax.host_id(), jax.host_count())
total_batch_size = np.prod(batch_dims)
tfds_split = tfds.core.ReadInstruction(
_to_tfds_split(split), from_=start, to=end, unit='abs')
ds = tfds.load(
'imagenet2012:5.*.*',
split=tfds_split,
decoders={'image': tfds.decode.SkipDecoding()})
options = tf.data.Options()
options.experimental_threading.private_threadpool_size = 48
options.experimental_threading.max_intra_op_parallelism = 1
if preprocess_mode is not PreprocessMode.EVAL:
options.experimental_deterministic = False
if jax.host_count() > 1 and allow_caching:
# Only cache if we are reading a subset of the dataset.
ds = ds.cache()
ds = ds.repeat()
ds = ds.shuffle(buffer_size=10 * total_batch_size, seed=0)
else:
if split.num_examples % total_batch_size != 0:
raise ValueError(f'Test/valid must be divisible by {total_batch_size}')
ds = ds.with_options(options)
def preprocess_pretrain(example):
view1 = _preprocess_image(example['image'], mode=preprocess_mode)
view2 = _preprocess_image(example['image'], mode=preprocess_mode)
label = tf.cast(example['label'], tf.int32)
return {'view1': view1, 'view2': view2, 'labels': label}
def preprocess_linear_train(example):
image = _preprocess_image(example['image'], mode=preprocess_mode)
label = tf.cast(example['label'], tf.int32)
return {'images': image, 'labels': label}
def preprocess_eval(example):
image = _preprocess_image(example['image'], mode=preprocess_mode)
label = tf.cast(example['label'], tf.int32)
return {'images': image, 'labels': label}
if preprocess_mode is PreprocessMode.PRETRAIN:
ds = ds.map(
preprocess_pretrain, num_parallel_calls=tf.data.experimental.AUTOTUNE)
elif preprocess_mode is PreprocessMode.LINEAR_TRAIN:
ds = ds.map(
preprocess_linear_train,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
else:
ds = ds.map(
preprocess_eval, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def transpose_fn(batch):
# We use the double-transpose-trick to improve performance for TPUs. Note
# that this (typically) requires a matching HWCN->NHWC transpose in your
# model code. The compiler cannot make this optimization for us since our
# data pipeline and model are compiled separately.
batch = dict(**batch)
if preprocess_mode is PreprocessMode.PRETRAIN:
batch['view1'] = tf.transpose(batch['view1'], (1, 2, 3, 0))
batch['view2'] = tf.transpose(batch['view2'], (1, 2, 3, 0))
else:
batch['images'] = tf.transpose(batch['images'], (1, 2, 3, 0))
return batch
for i, batch_size in enumerate(reversed(batch_dims)):
ds = ds.batch(batch_size)
if i == 0 and transpose:
ds = ds.map(transpose_fn) # NHWC -> HWCN
ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
yield from tfds.as_numpy(ds)
def _to_tfds_split(split: Split) -> tfds.Split:
"""Returns the TFDS split appropriately sharded."""
# NOTE: Imagenet did not release labels for the test split used in the
# competition, we consider the VALID split the TEST split and reserve
# 10k images from TRAIN for VALID.
if split in (Split.TRAIN, Split.TRAIN_AND_VALID, Split.VALID):
return tfds.Split.TRAIN
else:
assert split == Split.TEST
return tfds.Split.VALIDATION
def _shard(split: Split, shard_index: int, num_shards: int) -> Tuple[int, int]:
"""Returns [start, end) for the given shard index."""
assert shard_index < num_shards
arange = np.arange(split.num_examples)
shard_range = np.array_split(arange, num_shards)[shard_index]
start, end = shard_range[0], (shard_range[-1] + 1)
if split == Split.TRAIN:
# Note that our TRAIN=TFDS_TRAIN[10000:] and VALID=TFDS_TRAIN[:10000].
offset = Split.VALID.num_examples
start += offset
end += offset
return start, end
def _preprocess_image(
image_bytes: tf.Tensor,
mode: PreprocessMode,
) -> tf.Tensor:
"""Returns processed and resized images."""
if mode is PreprocessMode.PRETRAIN:
image = _decode_and_random_crop(image_bytes)
# Random horizontal flipping is optionally done in augmentations.preprocess.
elif mode is PreprocessMode.LINEAR_TRAIN:
image = _decode_and_random_crop(image_bytes)
image = tf.image.random_flip_left_right(image)
else:
image = _decode_and_center_crop(image_bytes)
# NOTE: Bicubic resize (1) casts uint8 to float32 and (2) resizes without
# clamping overshoots. This means values returned will be outside the range
# [0.0, 255.0] (e.g. we have observed outputs in the range [-51.1, 336.6]).
assert image.dtype == tf.uint8
image = tf.image.resize(image, [224, 224], tf.image.ResizeMethod.BICUBIC)
image = tf.clip_by_value(image / 255., 0., 1.)
return image
def _decode_and_random_crop(image_bytes: tf.Tensor) -> tf.Tensor:
"""Make a random crop of 224."""
img_size = tf.image.extract_jpeg_shape(image_bytes)
area = tf.cast(img_size[1] * img_size[0], tf.float32)
target_area = tf.random.uniform([], 0.08, 1.0, dtype=tf.float32) * area
log_ratio = (tf.math.log(3 / 4), tf.math.log(4 / 3))
aspect_ratio = tf.math.exp(
tf.random.uniform([], *log_ratio, dtype=tf.float32))
w = tf.cast(tf.round(tf.sqrt(target_area * aspect_ratio)), tf.int32)
h = tf.cast(tf.round(tf.sqrt(target_area / aspect_ratio)), tf.int32)
w = tf.minimum(w, img_size[1])
h = tf.minimum(h, img_size[0])
offset_w = tf.random.uniform((),
minval=0,
maxval=img_size[1] - w + 1,
dtype=tf.int32)
offset_h = tf.random.uniform((),
minval=0,
maxval=img_size[0] - h + 1,
dtype=tf.int32)
crop_window = tf.stack([offset_h, offset_w, h, w])
image = tf.io.decode_and_crop_jpeg(image_bytes, crop_window, channels=3)
return image
def transpose_images(batch: Batch):
"""Transpose images for TPU training.."""
new_batch = dict(batch) # Avoid mutating in place.
if 'images' in batch:
new_batch['images'] = jnp.transpose(batch['images'], (3, 0, 1, 2))
else:
new_batch['view1'] = jnp.transpose(batch['view1'], (3, 0, 1, 2))
new_batch['view2'] = jnp.transpose(batch['view2'], (3, 0, 1, 2))
return new_batch
def _decode_and_center_crop(
image_bytes: tf.Tensor,
jpeg_shape: Optional[tf.Tensor] = None,
) -> tf.Tensor:
"""Crops to center of image with padding then scales."""
if jpeg_shape is None:
jpeg_shape = tf.image.extract_jpeg_shape(image_bytes)
image_height = jpeg_shape[0]
image_width = jpeg_shape[1]
padded_center_crop_size = tf.cast(
((224 / (224 + 32)) *
tf.cast(tf.minimum(image_height, image_width), tf.float32)), tf.int32)
offset_height = ((image_height - padded_center_crop_size) + 1) // 2
offset_width = ((image_width - padded_center_crop_size) + 1) // 2
crop_window = tf.stack([
offset_height, offset_width, padded_center_crop_size,
padded_center_crop_size
])
image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3)
return image
================================================
FILE: byol/utils/helpers.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utility functions."""
from typing import Optional, Text
from absl import logging
import jax
import jax.numpy as jnp
def topk_accuracy(
logits: jnp.ndarray,
labels: jnp.ndarray,
topk: int,
ignore_label_above: Optional[int] = None,
) -> jnp.ndarray:
"""Top-num_codes accuracy."""
assert len(labels.shape) == 1, 'topk expects 1d int labels.'
assert len(logits.shape) == 2, 'topk expects 2d logits.'
if ignore_label_above is not None:
logits = logits[labels < ignore_label_above, :]
labels = labels[labels < ignore_label_above]
prds = jnp.argsort(logits, axis=1)[:, ::-1]
prds = prds[:, :topk]
total = jnp.any(prds == jnp.tile(labels[:, jnp.newaxis], [1, topk]), axis=1)
return total
def softmax_cross_entropy(
logits: jnp.ndarray,
labels: jnp.ndarray,
reduction: Optional[Text] = 'mean',
) -> jnp.ndarray:
"""Computes softmax cross entropy given logits and one-hot class labels.
Args:
logits: Logit output values.
labels: Ground truth one-hot-encoded labels.
reduction: Type of reduction to apply to loss.
Returns:
Loss value. If `reduction` is `none`, this has the same shape as `labels`;
otherwise, it is scalar.
Raises:
ValueError: If the type of `reduction` is unsupported.
"""
loss = -jnp.sum(labels * jax.nn.log_softmax(logits), axis=-1)
if reduction == 'sum':
return jnp.sum(loss)
elif reduction == 'mean':
return jnp.mean(loss)
elif reduction == 'none' or reduction is None:
return loss
else:
raise ValueError(f'Incorrect reduction mode {reduction}')
def l2_normalize(
x: jnp.ndarray,
axis: Optional[int] = None,
epsilon: float = 1e-12,
) -> jnp.ndarray:
"""l2 normalize a tensor on an axis with numerical stability."""
square_sum = jnp.sum(jnp.square(x), axis=axis, keepdims=True)
x_inv_norm = jax.lax.rsqrt(jnp.maximum(square_sum, epsilon))
return x * x_inv_norm
def l2_weight_regularizer(params):
"""Helper to do lasso on weights.
Args:
params: the entire param set.
Returns:
Scalar of the l2 norm of the weights.
"""
l2_norm = 0.
for mod_name, mod_params in params.items():
if 'norm' not in mod_name:
for param_k, param_v in mod_params.items():
if param_k != 'b' not in param_k: # Filter out biases
l2_norm += jnp.sum(jnp.square(param_v))
else:
logging.warning('Excluding %s/%s from optimizer weight decay!',
mod_name, param_k)
else:
logging.warning('Excluding %s from optimizer weight decay!', mod_name)
return 0.5 * l2_norm
def regression_loss(x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
"""Byol's regression loss. This is a simple cosine similarity."""
normed_x, normed_y = l2_normalize(x, axis=-1), l2_normalize(y, axis=-1)
return jnp.sum((normed_x - normed_y)**2, axis=-1)
def bcast_local_devices(value):
"""Broadcasts an object to all local devices."""
devices = jax.local_devices()
def _replicate(x):
"""Replicate an object on each device."""
x = jnp.array(x)
return jax.device_put_sharded(len(devices) * [x], devices)
return jax.tree_util.tree_map(_replicate, value)
def get_first(xs):
"""Gets values from the first device."""
return jax.tree_map(lambda x: x[0], xs)
================================================
FILE: byol/utils/networks.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Networks used in BYOL."""
from typing import Any, Mapping, Optional, Sequence, Text
import haiku as hk
import jax
import jax.numpy as jnp
class MLP(hk.Module):
"""One hidden layer perceptron, with normalization."""
def __init__(
self,
name: Text,
hidden_size: int,
output_size: int,
bn_config: Mapping[Text, Any],
):
super().__init__(name=name)
self._hidden_size = hidden_size
self._output_size = output_size
self._bn_config = bn_config
def __call__(self, inputs: jnp.ndarray, is_training: bool) -> jnp.ndarray:
out = hk.Linear(output_size=self._hidden_size, with_bias=True)(inputs)
out = hk.BatchNorm(**self._bn_config)(out, is_training=is_training)
out = jax.nn.relu(out)
out = hk.Linear(output_size=self._output_size, with_bias=False)(out)
return out
def check_length(length, value, name):
if len(value) != length:
raise ValueError(f'`{name}` must be of length 4 not {len(value)}')
class ResNetTorso(hk.Module):
"""ResNet model."""
def __init__(
self,
blocks_per_group: Sequence[int],
num_classes: Optional[int] = None,
bn_config: Optional[Mapping[str, float]] = None,
resnet_v2: bool = False,
bottleneck: bool = True,
channels_per_group: Sequence[int] = (256, 512, 1024, 2048),
use_projection: Sequence[bool] = (True, True, True, True),
width_multiplier: int = 1,
name: Optional[str] = None,
):
"""Constructs a ResNet model.
Args:
blocks_per_group: A sequence of length 4 that indicates the number of
blocks created in each group.
num_classes: The number of classes to classify the inputs into.
bn_config: A dictionary of three elements, `decay_rate`, `eps`, and
`cross_replica_axis`, to be passed on to the `BatchNorm` layers. By
default the `decay_rate` is `0.9` and `eps` is `1e-5`, and the axis is
`None`.
resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults to
False.
bottleneck: Whether the block should bottleneck or not. Defaults to True.
channels_per_group: A sequence of length 4 that indicates the number
of channels used for each block in each group.
use_projection: A sequence of length 4 that indicates whether each
residual block should use projection.
width_multiplier: An integer multiplying the number of channels per group.
name: Name of the module.
"""
super().__init__(name=name)
self.resnet_v2 = resnet_v2
bn_config = dict(bn_config or {})
bn_config.setdefault('decay_rate', 0.9)
bn_config.setdefault('eps', 1e-5)
bn_config.setdefault('create_scale', True)
bn_config.setdefault('create_offset', True)
# Number of blocks in each group for ResNet.
check_length(4, blocks_per_group, 'blocks_per_group')
check_length(4, channels_per_group, 'channels_per_group')
self.initial_conv = hk.Conv2D(
output_channels=64 * width_multiplier,
kernel_shape=7,
stride=2,
with_bias=False,
padding='SAME',
name='initial_conv')
if not self.resnet_v2:
self.initial_batchnorm = hk.BatchNorm(name='initial_batchnorm',
**bn_config)
self.block_groups = []
strides = (1, 2, 2, 2)
for i in range(4):
self.block_groups.append(
hk.nets.ResNet.BlockGroup(
channels=width_multiplier * channels_per_group[i],
num_blocks=blocks_per_group[i],
stride=strides[i],
bn_config=bn_config,
resnet_v2=resnet_v2,
bottleneck=bottleneck,
use_projection=use_projection[i],
name='block_group_%d' % (i)))
if self.resnet_v2:
self.final_batchnorm = hk.BatchNorm(name='final_batchnorm', **bn_config)
self.logits = hk.Linear(num_classes, w_init=jnp.zeros, name='logits')
def __call__(self, inputs, is_training, test_local_stats=False):
out = inputs
out = self.initial_conv(out)
if not self.resnet_v2:
out = self.initial_batchnorm(out, is_training, test_local_stats)
out = jax.nn.relu(out)
out = hk.max_pool(out,
window_shape=(1, 3, 3, 1),
strides=(1, 2, 2, 1),
padding='SAME')
for block_group in self.block_groups:
out = block_group(out, is_training, test_local_stats)
if self.resnet_v2:
out = self.final_batchnorm(out, is_training, test_local_stats)
out = jax.nn.relu(out)
out = jnp.mean(out, axis=[1, 2])
return out
class TinyResNet(ResNetTorso):
"""Tiny resnet for local runs and tests."""
def __init__(self,
num_classes: Optional[int] = None,
bn_config: Optional[Mapping[str, float]] = None,
resnet_v2: bool = False,
width_multiplier: int = 1,
name: Optional[str] = None):
"""Constructs a ResNet model.
Args:
num_classes: The number of classes to classify the inputs into.
bn_config: A dictionary of two elements, `decay_rate` and `eps` to be
passed on to the `BatchNorm` layers.
resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults
to False.
width_multiplier: An integer multiplying the number of channels per group.
name: Name of the module.
"""
super().__init__(blocks_per_group=(1, 1, 1, 1),
channels_per_group=(8, 8, 8, 8),
num_classes=num_classes,
bn_config=bn_config,
resnet_v2=resnet_v2,
bottleneck=False,
width_multiplier=width_multiplier,
name=name)
class ResNet18(ResNetTorso):
"""ResNet18."""
def __init__(self,
num_classes: Optional[int] = None,
bn_config: Optional[Mapping[str, float]] = None,
resnet_v2: bool = False,
width_multiplier: int = 1,
name: Optional[str] = None):
"""Constructs a ResNet model.
Args:
num_classes: The number of classes to classify the inputs into.
bn_config: A dictionary of two elements, `decay_rate` and `eps` to be
passed on to the `BatchNorm` layers.
resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults
to False.
width_multiplier: An integer multiplying the number of channels per group.
name: Name of the module.
"""
super().__init__(blocks_per_group=(2, 2, 2, 2),
num_classes=num_classes,
bn_config=bn_config,
resnet_v2=resnet_v2,
bottleneck=False,
channels_per_group=(64, 128, 256, 512),
width_multiplier=width_multiplier,
name=name)
class ResNet34(ResNetTorso):
"""ResNet34."""
def __init__(self,
num_classes: Optional[int],
bn_config: Optional[Mapping[str, float]] = None,
resnet_v2: bool = False,
width_multiplier: int = 1,
name: Optional[str] = None):
"""Constructs a ResNet model.
Args:
num_classes: The number of classes to classify the inputs into.
bn_config: A dictionary of two elements, `decay_rate` and `eps` to be
passed on to the `BatchNorm` layers.
resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults
to False.
width_multiplier: An integer multiplying the number of channels per group.
name: Name of the module.
"""
super().__init__(blocks_per_group=(3, 4, 6, 3),
num_classes=num_classes,
bn_config=bn_config,
resnet_v2=resnet_v2,
bottleneck=False,
channels_per_group=(64, 128, 256, 512),
width_multiplier=width_multiplier,
name=name)
class ResNet50(ResNetTorso):
"""ResNet50."""
def __init__(self,
num_classes: Optional[int] = None,
bn_config: Optional[Mapping[str, float]] = None,
resnet_v2: bool = False,
width_multiplier: int = 1,
name: Optional[str] = None):
"""Constructs a ResNet model.
Args:
num_classes: The number of classes to classify the inputs into.
bn_config: A dictionary of two elements, `decay_rate` and `eps` to be
passed on to the `BatchNorm` layers.
resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults
to False.
width_multiplier: An integer multiplying the number of channels per group.
name: Name of the module.
"""
super().__init__(blocks_per_group=(3, 4, 6, 3),
num_classes=num_classes,
bn_config=bn_config,
resnet_v2=resnet_v2,
bottleneck=True,
width_multiplier=width_multiplier,
name=name)
class ResNet101(ResNetTorso):
"""ResNet101."""
def __init__(self,
num_classes: Optional[int],
bn_config: Optional[Mapping[str, float]] = None,
resnet_v2: bool = False,
width_multiplier: int = 1,
name: Optional[str] = None):
"""Constructs a ResNet model.
Args:
num_classes: The number of classes to classify the inputs into.
bn_config: A dictionary of two elements, `decay_rate` and `eps` to be
passed on to the `BatchNorm` layers.
resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults
to False.
width_multiplier: An integer multiplying the number of channels per group.
name: Name of the module.
"""
super().__init__(blocks_per_group=(3, 4, 23, 3),
num_classes=num_classes,
bn_config=bn_config,
resnet_v2=resnet_v2,
bottleneck=True,
width_multiplier=width_multiplier,
name=name)
class ResNet152(ResNetTorso):
"""ResNet152."""
def __init__(self,
num_classes: Optional[int],
bn_config: Optional[Mapping[str, float]] = None,
resnet_v2: bool = False,
width_multiplier: int = 1,
name: Optional[str] = None):
"""Constructs a ResNet model.
Args:
num_classes: The number of classes to classify the inputs into.
bn_config: A dictionary of two elements, `decay_rate` and `eps` to be
passed on to the `BatchNorm` layers.
resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults
to False.
width_multiplier: An integer multiplying the number of channels per group.
name: Name of the module.
"""
super().__init__(blocks_per_group=(3, 8, 36, 3),
num_classes=num_classes,
bn_config=bn_config,
resnet_v2=resnet_v2,
bottleneck=True,
width_multiplier=width_multiplier,
name=name)
class ResNet200(ResNetTorso):
"""ResNet200."""
def __init__(self,
num_classes: Optional[int],
bn_config: Optional[Mapping[str, float]] = None,
resnet_v2: bool = False,
width_multiplier: int = 1,
name: Optional[str] = None):
"""Constructs a ResNet model.
Args:
num_classes: The number of classes to classify the inputs into.
bn_config: A dictionary of two elements, `decay_rate` and `eps` to be
passed on to the `BatchNorm` layers.
resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults
to False.
width_multiplier: An integer multiplying the number of channels per group.
name: Name of the module.
"""
super().__init__(blocks_per_group=(3, 24, 36, 3),
num_classes=num_classes,
bn_config=bn_config,
resnet_v2=resnet_v2,
bottleneck=True,
width_multiplier=width_multiplier,
name=name)
================================================
FILE: byol/utils/optimizers.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Implementation of LARS Optimizer with optax."""
from typing import Any, Callable, List, NamedTuple, Optional, Tuple
import jax
import jax.numpy as jnp
import optax
import tree as nest
# A filter function takes a path and a value as input and outputs True for
# variable to apply update and False not to apply the update
FilterFn = Callable[[Tuple[Any], jnp.ndarray], jnp.ndarray]
def exclude_bias_and_norm(path: Tuple[Any], val: jnp.ndarray) -> jnp.ndarray:
"""Filter to exclude biaises and normalizations weights."""
del val
if path[-1] == "b" or "norm" in path[-2]:
return False # pytype: disable=bad-return-type # jax-ndarray
return True # pytype: disable=bad-return-type # jax-ndarray
def _partial_update(updates: optax.Updates,
new_updates: optax.Updates,
params: optax.Params,
filter_fn: Optional[FilterFn] = None) -> optax.Updates:
"""Returns new_update for params which filter_fn is True else updates."""
if filter_fn is None:
return new_updates
wrapped_filter_fn = lambda x, y: jnp.array(filter_fn(x, y))
params_to_filter = nest.map_structure_with_path(wrapped_filter_fn, params)
def _update_fn(g: jnp.ndarray, t: jnp.ndarray, m: jnp.ndarray) -> jnp.ndarray:
m = m.astype(g.dtype)
return g * (1. - m) + t * m
return jax.tree_map(_update_fn, updates, new_updates, params_to_filter)
class ScaleByLarsState(NamedTuple):
mu: jnp.ndarray
def scale_by_lars(
momentum: float = 0.9,
eta: float = 0.001,
filter_fn: Optional[FilterFn] = None) -> optax.GradientTransformation:
"""Rescales updates according to the LARS algorithm.
Does not include weight decay.
References:
[You et al, 2017](https://arxiv.org/abs/1708.03888)
Args:
momentum: momentum coeficient.
eta: LARS coefficient.
filter_fn: an optional filter function.
Returns:
An (init_fn, update_fn) tuple.
"""
def init_fn(params: optax.Params) -> ScaleByLarsState:
mu = jax.tree_map(jnp.zeros_like, params) # momentum
return ScaleByLarsState(mu=mu)
def update_fn(updates: optax.Updates, state: ScaleByLarsState,
params: optax.Params) -> Tuple[optax.Updates, ScaleByLarsState]:
def lars_adaptation(
update: jnp.ndarray,
param: jnp.ndarray,
) -> jnp.ndarray:
param_norm = jnp.linalg.norm(param)
update_norm = jnp.linalg.norm(update)
return update * jnp.where(
param_norm > 0.,
jnp.where(update_norm > 0,
(eta * param_norm / update_norm), 1.0), 1.0)
adapted_updates = jax.tree_map(lars_adaptation, updates, params)
adapted_updates = _partial_update(updates, adapted_updates, params,
filter_fn)
mu = jax.tree_map(lambda g, t: momentum * g + t,
state.mu, adapted_updates)
return mu, ScaleByLarsState(mu=mu)
return optax.GradientTransformation(init_fn, update_fn)
class AddWeightDecayState(NamedTuple):
"""Stateless transformation."""
def add_weight_decay(
weight_decay: float,
filter_fn: Optional[FilterFn] = None) -> optax.GradientTransformation:
"""Adds a weight decay to the update.
Args:
weight_decay: weight_decay coeficient.
filter_fn: an optional filter function.
Returns:
An (init_fn, update_fn) tuple.
"""
def init_fn(_) -> AddWeightDecayState:
return AddWeightDecayState()
def update_fn(
updates: optax.Updates,
state: AddWeightDecayState,
params: optax.Params,
) -> Tuple[optax.Updates, AddWeightDecayState]:
new_updates = jax.tree_map(lambda g, p: g + weight_decay * p, updates,
params)
new_updates = _partial_update(updates, new_updates, params, filter_fn)
return new_updates, state
return optax.GradientTransformation(init_fn, update_fn) # pytype: disable=wrong-arg-types # numpy-scalars
LarsState = List # Type for the lars optimizer
def lars(
learning_rate: float,
weight_decay: float = 0.,
momentum: float = 0.9,
eta: float = 0.001,
weight_decay_filter: Optional[FilterFn] = None,
lars_adaptation_filter: Optional[FilterFn] = None,
) -> optax.GradientTransformation:
"""Creates lars optimizer with weight decay.
References:
[You et al, 2017](https://arxiv.org/abs/1708.03888)
Args:
learning_rate: learning rate coefficient.
weight_decay: weight decay coefficient.
momentum: momentum coefficient.
eta: LARS coefficient.
weight_decay_filter: optional filter function to only apply the weight
decay on a subset of parameters. The filter function takes as input the
parameter path (as a tuple) and its associated update, and return a True
for params to apply the weight decay and False for params to not apply
the weight decay. When weight_decay_filter is set to None, the weight
decay is not applied to the bias, i.e. when the variable name is 'b', and
the weight decay is not applied to nornalization params, i.e. the
panultimate path contains 'norm'.
lars_adaptation_filter: similar to weight decay filter but for lars
adaptation
Returns:
An optax.GradientTransformation, i.e. a (init_fn, update_fn) tuple.
"""
if weight_decay_filter is None:
weight_decay_filter = lambda *_: True
if lars_adaptation_filter is None:
lars_adaptation_filter = lambda *_: True
return optax.chain(
add_weight_decay(
weight_decay=weight_decay, filter_fn=weight_decay_filter),
scale_by_lars(
momentum=momentum, eta=eta, filter_fn=lars_adaptation_filter),
optax.scale(-learning_rate),
)
================================================
FILE: byol/utils/schedules.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Learning rate schedules."""
import jax.numpy as jnp
def target_ema(global_step: jnp.ndarray,
base_ema: float,
max_steps: int) -> jnp.ndarray:
decay = _cosine_decay(global_step, max_steps, 1.)
return 1. - (1. - base_ema) * decay
def learning_schedule(global_step: jnp.ndarray,
batch_size: int,
base_learning_rate: float,
total_steps: int,
warmup_steps: int) -> float:
"""Cosine learning rate scheduler."""
# Compute LR & Scaled LR
scaled_lr = base_learning_rate * batch_size / 256.
learning_rate = (
global_step.astype(jnp.float32) / int(warmup_steps) *
scaled_lr if warmup_steps > 0 else scaled_lr)
# Cosine schedule after warmup.
return jnp.where(
global_step < warmup_steps, learning_rate,
_cosine_decay(global_step - warmup_steps, total_steps - warmup_steps,
scaled_lr))
def _cosine_decay(global_step: jnp.ndarray,
max_steps: int,
initial_value: float) -> jnp.ndarray:
"""Simple implementation of cosine decay from TF1."""
global_step = jnp.minimum(global_step, max_steps)
cosine_decay_value = 0.5 * (1 + jnp.cos(jnp.pi * global_step / max_steps))
decayed_learning_rate = initial_value * cosine_decay_value
return decayed_learning_rate
================================================
FILE: cadl/README.md
================================================
# A dataset of CAD sketches
when cross-compiling.
build:android --noenable_platform_specific_config
build:ios --noenable_platform_specific_config
# Suppress all C++ compiler warnings, otherwise build logs become 10s of MBs.
build:android --copt=-w
build:ios --copt=-w
build:linux --host_copt=-w
build:macos --copt=-w
build:windows --copt=/W0
build:windows --host_copt=/W0
# Suppress most C++ complier warnings to reduce log size but allow
# for specific warnings to still be present.
build:linux --copt="-Wno-all"
build:linux --copt="-Wno-extra"
build:linux --copt="-Wno-deprecated"
build:linux --copt="-Wno-deprecated-declarations"
build:linux --copt="-Wno-ignored-attributes"
build:linux --copt="-Wno-unknown-warning"
build:linux --copt="-Wno-array-parameter"
build:linux --copt="-Wno-stringop-overflow"
build:linux --copt="-Wno-array-bounds"
# Add unused-result as an error on Linux.
build:linux --copt="-Wunused-result"
build:linux --copt="-Werror=unused-result"
# On Windows, `__cplusplus` is wrongly defined without this switch
# See https://devblogs.microsoft.com/cppblog/msvc-now-correctly-reports-__cplusplus/
build:windows --copt=/Zc:__cplusplus
build:windows --host_copt=/Zc:__cplusplus
# Tensorflow uses M_* math constants that only get defined by MSVC headers if
# _USE_MATH_DEFINES is defined.
build:windows --copt=/D_USE_MATH_DEFINES
build:windows --host_copt=/D_USE_MATH_DEFINES
# Windows has a relatively short command line limit, which TF has begun to hit.
# See https://docs.bazel.build/versions/main/windows.html
build:windows --features=compiler_param_file
# Speed Windows compile times. Available in VS 16.4 (we are on 16.11). See
# https://groups.google.com/a/tensorflow.org/d/topic/build/SsW98Eo7l3o/discussion
build:windows --copt=/d2ReducedOptimizeHugeFunctions
build:windows --host_copt=/d2ReducedOptimizeHugeFunctions
# Default paths for TF_SYSTEM_LIBS
build:linux --define=PREFIX=/usr
build:linux --define=LIBDIR=$(PREFIX)/lib
build:linux --define=INCLUDEDIR=$(PREFIX)/include
build:linux --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include
build:macos --define=PREFIX=/usr
build:macos --define=LIBDIR=$(PREFIX)/lib
build:macos --define=INCLUDEDIR=$(PREFIX)/include
build:macos --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include
# TF_SYSTEM_LIBS do not work on windows.
# By default, build TF in C++ 17 mode.
build:android --cxxopt=-std=c++17
build:android --host_cxxopt=-std=c++17
build:ios --cxxopt=-std=c++17
build:ios --host_cxxopt=-std=c++17
build:linux --cxxopt=-std=c++17
build:linux --host_cxxopt=-std=c++17
build:macos --cxxopt=-std=c++17
build:macos --host_cxxopt=-std=c++17
build:windows --cxxopt=/std:c++17
build:windows --host_cxxopt=/std:c++17
# On windows, we still link everything into a single DLL.
build:windows --config=monolithic
# On linux, we dynamically link small amount of kernels
build:linux --config=dynamic_kernels
# Make sure to include as little of windows.h as possible
build:windows --copt=-DWIN32_LEAN_AND_MEAN
build:windows --host_copt=-DWIN32_LEAN_AND_MEAN
build:windows --copt=-DNOGDI
build:windows --host_copt=-DNOGDI
# MSVC (Windows): Standards-conformant preprocessor mode
# See https://docs.microsoft.com/en-us/cpp/preprocessor/preprocessor-experimental-overview
build:windows --copt=/experimental:preprocessor
build:windows --host_copt=/experimental:preprocessor
# Misc build options we need for windows.
build:windows --linkopt=/DEBUG
build:windows --host_linkopt=/DEBUG
build:windows --linkopt=/OPT:REF
build:windows --host_linkopt=/OPT:REF
build:windows --linkopt=/OPT:ICF
build:windows --host_linkopt=/OPT:ICF
# Verbose failure logs when something goes wrong
build:windows --verbose_failures
# Work around potential issues with large command lines on windows.
# See: https://github.com/bazelbuild/bazel/issues/5163
build:windows --features=compiler_param_file
# Do not risk cache corruption. See:
# https://github.com/bazelbuild/bazel/issues/3360
build:linux --experimental_guard_against_concurrent_changes
# Configure short or long logs
build:short_logs --output_filter=DONT_MATCH_ANYTHING
build:verbose_logs --output_filter=
# Instruction set optimizations
# TODO(gunan): Create a feature in toolchains for avx/avx2 to
# avoid having to define linux/win separately.
build:avx_linux --copt=-mavx
build:avx_linux --host_copt=-mavx
build:avx2_linux --copt=-mavx2
build:native_arch_linux --copt=-march=native
build:avx_win --copt=/arch=AVX
build:avx2_win --copt=/arch=AVX2
# Options to build TensorFlow 1.x or 2.x.
build:v1 --define=tf_api_version=1 --action_env=TF2_BEHAVIOR=0
build:v2 --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
# Disable XLA on mobile.
build:xla --define=with_xla_support=true # TODO: remove, it's on by default.
build:android --define=with_xla_support=false
build:ios --define=with_xla_support=false
# BEGIN TF REMOTE BUILD EXECUTION OPTIONS
# Options when using remote execution
# WARNING: THESE OPTIONS WONT WORK IF YOU DO NOT HAVE PROPER AUTHENTICATION AND PERMISSIONS
# Flag to enable remote config
common --experimental_repo_remote_exec
build:rbe --repo_env=BAZEL_DO_NOT_DETECT_CPP_TOOLCHAIN=1
build:rbe --google_default_credentials
build:rbe --bes_backend=buildeventservice.googleapis.com
build:rbe --bes_results_url="https://source.cloud.google.com/results/invocations"
build:rbe --bes_timeout=600s
build:rbe --define=EXECUTOR=remote
build:rbe --flaky_test_attempts=3
build:rbe --jobs=800
build:rbe --remote_executor=grpcs://remotebuildexecution.googleapis.com
build:rbe --remote_timeout=3600
build:rbe --spawn_strategy=remote,worker,standalone,local
test:rbe --test_env=USER=anon
# Attempt to minimize the amount of data transfer between bazel and the remote
# workers:
build:rbe --remote_download_toplevel
build:rbe_linux_base --config=rbe
build:rbe_linux_base --action_env=PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/go/bin"
build:rbe_linux --config=rbe_linux_base
# Non-rbe settings we should include because we do not run configure
build:rbe_linux --config=avx_linux
# TODO(gunan): Check why we need this specified in rbe, but not in other builds.
build:rbe_linux --linkopt=-lrt
build:rbe_linux --host_linkopt=-lrt
build:rbe_linux --linkopt=-lm
build:rbe_linux --host_linkopt=-lm
# Use the GPU toolchain until the CPU one is ready.
# https://github.com/bazelbuild/bazel/issues/13623
build:rbe_cpu_linux_base --host_crosstool_top="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain"
build:rbe_cpu_linux_base --crosstool_top="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain"
build:rbe_cpu_linux_base --extra_toolchains="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain-linux-x86_64"
build:rbe_cpu_linux_base --extra_execution_platforms="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_cpu_linux_base --host_platform="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_cpu_linux_base --platforms="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_cpu_linux --config=rbe_linux
build:rbe_cpu_linux --config=rbe_cpu_linux_base
build:rbe_lite_linux --config=rbe_linux_base
build:rbe_lite_linux --config=rbe_cpu_linux_base
build:rbe_lite_linux --config=rbe_linux_py3_base
build:rbe_lite_linux --noexperimental_check_desugar_deps
build:rbe_linux_cuda_base --config=rbe_linux
build:rbe_linux_cuda_base --config=cuda
build:rbe_linux_cuda_base --config=tensorrt
build:rbe_linux_cuda_base --action_env=TF_CUDA_VERSION=11
build:rbe_linux_cuda_base --action_env=TF_CUDNN_VERSION=8
build:rbe_linux_cuda_base --repo_env=REMOTE_GPU_TESTING=1
# TensorRT 7 for CUDA 11.1 is compatible with CUDA 11.2, but requires
# libnvrtc.so.11.1. See https://github.com/NVIDIA/TensorRT/issues/1064.
# TODO(b/187962120): Remove when upgrading to TensorRT 8.
test:rbe_linux_cuda_base --test_env=LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda-11.1/lib64"
build:rbe_linux_cuda11.2_nvcc_base --config=rbe_linux_cuda_base
build:rbe_linux_cuda11.2_nvcc_base --host_crosstool_top="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain"
build:rbe_linux_cuda11.2_nvcc_base --crosstool_top="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain"
build:rbe_linux_cuda11.2_nvcc_base --extra_toolchains="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain-linux-x86_64"
build:rbe_linux_cuda11.2_nvcc_base --extra_execution_platforms="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_linux_cuda11.2_nvcc_base --host_platform="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_linux_cuda11.2_nvcc_base --platforms="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_linux_cuda11.2_nvcc_base --repo_env=TF_CUDA_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda"
build:rbe_linux_cuda11.2_nvcc_base --repo_env=TF_TENSORRT_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_tensorrt"
build:rbe_linux_cuda11.2_nvcc_base --repo_env=TF_NCCL_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_nccl"
build:rbe_linux_cuda11.2_nvcc_py3.7 --config=rbe_linux_cuda11.2_nvcc_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_python3.7"
build:rbe_linux_cuda11.2_nvcc_py3.8 --config=rbe_linux_cuda11.2_nvcc_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_python3.8"
build:rbe_linux_cuda11.2_nvcc_py3.9 --config=rbe_linux_cuda11.2_nvcc_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_python3.9"
build:rbe_linux_cuda11.2_nvcc_py3.10 --config=rbe_linux_cuda11.2_nvcc_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_python3.10"
# Map default to CUDA 11.2.
build:rbe_linux_cuda_nvcc_py37 --config=rbe_linux_cuda11.2_nvcc_py3.7
build:rbe_linux_cuda_nvcc_py38 --config=rbe_linux_cuda11.2_nvcc_py3.8
build:rbe_linux_cuda_nvcc_py39 --config=rbe_linux_cuda11.2_nvcc_py3.9
build:rbe_linux_cuda_nvcc_py310 --config=rbe_linux_cuda11.2_nvcc_py3.10
# Deprecated configs that people might still use.
build:rbe_linux_cuda_nvcc --config=rbe_linux_cuda_nvcc_py39
build:rbe_gpu_linux --config=rbe_linux_cuda_nvcc
build:rbe_linux_cuda_clang_base --config=rbe_linux_cuda_base
build:rbe_linux_cuda_clang_base --repo_env TF_CUDA_CLANG=1
build:rbe_linux_cuda_clang_base --@local_config_cuda//:cuda_compiler=clang
build:rbe_linux_cuda_clang_base --crosstool_top="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain"
build:rbe_linux_cuda_clang_base --extra_toolchains="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain-linux-x86_64"
build:rbe_linux_cuda_clang_base --extra_execution_platforms="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_linux_cuda_clang_base --host_platform="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_linux_cuda_clang_base --platforms="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_linux_cuda_clang_base --repo_env=TF_CUDA_CONFIG_REPO="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda"
build:rbe_linux_cuda_clang_base --repo_env=TF_TENSORRT_CONFIG_REPO="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_tensorrt"
build:rbe_linux_cuda_clang_base --repo_env=TF_NCCL_CONFIG_REPO="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_nccl"
build:rbe_linux_cuda_clang_py37 --config=rbe_linux_cuda_clang_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_python3.7"
build:rbe_linux_cuda_clang_py38 --config=rbe_linux_cuda_clang_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_python3.8"
build:rbe_linux_cuda_clang_py39 --config=rbe_linux_cuda_clang_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_python3.9"
build:rbe_linux_cuda_clang_py310 --config=rbe_linux_cuda_clang_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-clang_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_python3.10"
# ROCm
build:rbe_linux_rocm_base --config=rocm
build:rbe_linux_rocm_base --config=rbe_linux
build:rbe_linux_rocm_base --crosstool_top="@ubuntu20.04-gcc9_manylinux2014-rocm_config_rocm//crosstool:toolchain"
build:rbe_linux_rocm_base --extra_toolchains="@ubuntu20.04-gcc9_manylinux2014-rocm_config_rocm//crosstool:toolchain-linux-x86_64"
build:rbe_linux_rocm_base --extra_execution_platforms="@ubuntu20.04-gcc9_manylinux2014-rocm_config_platform//:platform"
build:rbe_linux_rocm_base --host_platform="@ubuntu20.04-gcc9_manylinux2014-rocm_config_platform//:platform"
build:rbe_linux_rocm_base --platforms="@ubuntu20.04-gcc9_manylinux2014-rocm_config_platform//:platform"
build:rbe_linux_rocm_base --action_env=TF_ROCM_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-rocm_config_rocm"
build:rbe_linux_rocm_py3.7 --config=rbe_linux_rocm_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-rocm_config_python3.7"
build:rbe_linux_rocm_py3.8 --config=rbe_linux_rocm_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-rocm_config_python3.8"
build:rbe_linux_rocm_py3.9 --config=rbe_linux_rocm_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-rocm_config_python3.9"
build:rbe_linux_rocm_py3.10 --config=rbe_linux_rocm_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-rocm_config_python3.10"
# Linux CPU
build:rbe_linux_py3 --config=rbe_linux
build:rbe_linux_py3 --config=rbe_linux_py3_base
build:rbe_linux_py3_base --python_path="/usr/local/bin/python3.9"
build:rbe_linux_py3_base --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_python3.9"
build:rbe_win --config=rbe
build:rbe_win --crosstool_top="//tensorflow/tools/toolchains/win/tf_win_06152022:toolchain"
build:rbe_win --extra_toolchains="//tensorflow/tools/toolchains/win/tf_win_06152022:cc-toolchain-x64_windows"
build:rbe_win --extra_execution_platforms="//tensorflow/tools/toolchains/win:rbe_windows_ltsc2019"
build:rbe_win --host_platform="//tensorflow/tools/toolchains/win:rbe_windows_ltsc2019"
build:rbe_win --platforms="//tensorflow/tools/toolchains/win:rbe_windows_ltsc2019"
build:rbe_win --shell_executable=C:\\tools\\msys64\\usr\\bin\\bash.exe
build:rbe_win --experimental_strict_action_env=true
# TODO(gunan): Remove once we use MSVC 2019 with latest patches.
build:rbe_win --define=override_eigen_strong_inline=true
# Don't build the python zip archive in the RBE build.
build:rbe_win --remote_download_minimal
build:rbe_win --enable_runfiles
build:rbe_win --nobuild_python_zip
build:rbe_win_py37 --config=rbe
build:rbe_win_py37 --repo_env=TF_PYTHON_CONFIG_REPO="@windows_py37_config_python"
build:rbe_win_py37 --python_path=C:\\Python37\\python.exe
build:rbe_win_py38 --config=rbe
build:rbe_win_py38 --repo_env=PYTHON_BIN_PATH=C:\\Python38\\python.exe
build:rbe_win_py38 --repo_env=PYTHON_LIB_PATH=C:\\Python38\\lib\\site-packages
build:rbe_win_py38 --repo_env=TF_PYTHON_CONFIG_REPO=//tensorflow/tools/toolchains/win_1803/py38
build:rbe_win_py38 --python_path=C:\\Python38\\python.exe
build:rbe_win_py39 --config=rbe
build:rbe_win_py39 --repo_env=PYTHON_BIN_PATH=C:\\Python39\\python.exe
build:rbe_win_py39 --repo_env=PYTHON_LIB_PATH=C:\\Python39\\lib\\site-packages
build:rbe_win_py39 --repo_env=TF_PYTHON_CONFIG_REPO=//tensorflow/tools/toolchains/win_1803/py39
build:rbe_win_py39 --python_path=C:\\Python39\\python.exe
build:rbe_win_py310 --config=rbe
build:rbe_win_py310 --repo_env=PYTHON_BIN_PATH=C:\\Python310\\python.exe
build:rbe_win_py310 --repo_env=PYTHON_LIB_PATH=C:\\Python310\\lib\\site-packages
build:rbe_win_py310 --repo_env=TF_PYTHON_CONFIG_REPO=//tensorflow/tools/toolchains/win_1803/py310
build:rbe_win_py310 --python_path=C:\\Python310\\python.exe
# These you may need to change for your own GCP project.
build:tensorflow_testing_rbe --project_id=tensorflow-testing
common:tensorflow_testing_rbe_linux --remote_instance_name=projects/tensorflow-testing/instances/default_instance
build:tensorflow_testing_rbe_linux --config=tensorflow_testing_rbe
# Build GPU binaries for the RBE test machines (Tesla T4s).
build:tensorflow_testing_rbe_linux --repo_env=TF_CUDA_COMPUTE_CAPABILITIES=sm_75
common:tensorflow_testing_rbe_win --remote_instance_name=projects/tensorflow-testing/instances/windows
build:tensorflow_testing_rbe_win --config=tensorflow_testing_rbe
# TFLite build configs for generic embedded Linux
build:elinux --crosstool_top=@local_config_embedded_arm//:toolchain
build:elinux --host_crosstool_top=@bazel_tools//tools/cpp:toolchain
build:elinux_aarch64 --config=elinux
build:elinux_aarch64 --cpu=aarch64
build:elinux_armhf --config=elinux
build:elinux_armhf --cpu=armhf
build:elinux_armhf --copt -mfp16-format=ieee
# END TF REMOTE BUILD EXECUTION OPTIONS
# Config-specific options should come above this line.
# Load rc file written by ./configure.
try-import %workspace%/.tf_configure.bazelrc
# Load rc file with user-specific options.
try-import %workspace%/.bazelrc.user
# Here are bazelrc configs for release builds
build:release_base --config=v2
test:release_base --flaky_test_attempts=3
test:release_base --test_size_filters=small,medium
build:release_cpu_linux --config=release_base
build:release_cpu_linux --config=avx_linux
build:release_cpu_linux --crosstool_top="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain"
test:release_cpu_linux --test_env=LD_LIBRARY_PATH
build:release_cpu_macos --config=release_base
build:release_cpu_macos --config=avx_linux
build:release_gpu_base --config=cuda
build:release_gpu_base --action_env=TF_CUDA_VERSION="11"
build:release_gpu_base --action_env=TF_CUDNN_VERSION="8"
build:release_gpu_base --repo_env=TF_CUDA_COMPUTE_CAPABILITIES="sm_35,sm_50,sm_60,sm_70,sm_75,compute_80"
build:release_gpu_linux --config=release_cpu_linux
build:release_gpu_linux --config=release_gpu_base
build:release_gpu_linux --config=tensorrt
build:release_gpu_linux --action_env=CUDA_TOOLKIT_PATH="/usr/local/cuda-11.2"
build:release_gpu_linux --action_env=LD_LIBRARY_PATH="/usr/local/cuda:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda-11.1/lib64:/usr/local/tensorrt/lib"
build:release_gpu_linux --action_env=GCC_HOST_COMPILER_PATH="/dt9/usr/bin/gcc"
build:release_gpu_linux --crosstool_top=@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain
build:release_cpu_windows --config=release_base
build:release_cpu_windows --config=avx_win
build:release_cpu_windows --define=no_tensorflow_py_deps=true
build:release_gpu_windows --config=release_cpu_windows
build:release_gpu_windows --config=release_gpu_base
# Address sanitizer
# CC=clang bazel build --config asan
build:asan --strip=never
build:asan --copt -fsanitize=address
build:asan --copt -DADDRESS_SANITIZER
build:asan --copt -g
build:asan --copt -O3
build:asan --copt -fno-omit-frame-pointer
build:asan --linkopt -fsanitize=address
# Memory sanitizer
# CC=clang bazel build --config msan
build:msan --strip=never
build:msan --copt -fsanitize=memory
build:msan --copt -DMEMORY_SANITIZER
build:msan --copt -g
build:msan --copt -O3
build:msan --copt -fno-omit-frame-pointer
build:msan --linkopt -fsanitize=memory
# Undefined Behavior Sanitizer
# CC=clang bazel build --config ubsan
build:ubsan --strip=never
build:ubsan --copt -fsanitize=undefined
build:ubsan --copt -DUNDEFINED_BEHAVIOR_SANITIZER
build:ubsan --copt -g
build:ubsan --copt -O3
build:ubsan --copt -fno-omit-frame-pointer
build:ubsan --linkopt -fsanitize=undefined
build:ubsan --linkopt -lubsan
# Disable TFRT integration for now unless --config=tfrt is specified.
build --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils
# TODO(b/240450920): We are in the process of migrating JitRt backend to XLA
# and while we are doing this we can't keep it buildable/testable in OSS.
build:tfrt --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils
================================================
FILE: density_functional_approximation_dm21/.bazelversion
================================================
5.3.0
================================================
FILE: density_functional_approximation_dm21/BUILD.bazel
================================================
"""DM21 functionals."""
load("@org_tensorflow//tensorflow/python/tools:tools.bzl", "saved_model_compile_aot")
load("@rules_python//python:defs.bzl", "py_binary", "py_library")
load("@external_py_deps//:requirements.bzl", "requirement")
licenses(["notice"])
py_library(
name = "compute_hfx_density",
srcs = ["density_functional_approximation_dm21/compute_hfx_density.py"],
srcs_version = "PY3",
deps = [
requirement("numpy"),
requirement("pyscf"),
],
)
py_test(
name = "compute_hfx_density_test",
srcs = ["density_functional_approximation_dm21/compute_hfx_density_test.py"],
python_version = "PY3",
srcs_version = "PY3",
deps = [
":compute_hfx_density",
requirement("attrs"),
requirement("numpy"),
requirement("scipy"),
requirement("pyscf"),
"@io_abseil_py//absl/testing:absltest",
"@io_abseil_py//absl/testing:parameterized",
],
)
filegroup(
name = "dm21_checkpoints",
srcs = glob(["density_functional_approximation_dm21/checkpoints/**"]),
)
py_library(
name = "neural_numint",
srcs = ["density_functional_approximation_dm21/neural_numint.py"],
data = [":dm21_checkpoints"],
srcs_version = "PY3",
deps = [
":compute_hfx_density",
requirement("attrs"),
requirement("keras"),
requirement("numpy"),
requirement("pyscf"),
"@org_tensorflow//tensorflow:tensorflow_py",
requirement("tensorflow-hub"),
],
)
py_test(
name = "neural_numint_test",
srcs = ["density_functional_approximation_dm21/neural_numint_test.py"],
python_version = "PY3",
srcs_version = "PY3",
deps = [
":neural_numint",
requirement("attrs"),
requirement("pyscf"),
"@io_abseil_py//absl/testing:parameterized",
"@org_tensorflow//tensorflow:tensorflow_py",
],
)
py_binary(
name = "export_saved_model",
srcs = ["density_functional_approximation_dm21/export_saved_model.py"],
data = [":dm21_checkpoints"],
python_version = "PY3",
srcs_version = "PY3",
deps = [
":neural_numint",
],
)
EXPORTED_SAVED_MODEL_OBJECTS = [
"DM21/saved_model.pb",
"DM21/variables/variables.index",
"DM21/variables/variables.data-00000-of-00001",
]
genrule(
name = "create_model_for_aot_compile",
outs = EXPORTED_SAVED_MODEL_OBJECTS,
# The functional used in dm21_aot_compiled_example can be changed by
# setting the --functional flag to the desired functional.
cmd = "$(location :export_saved_model) --functional DM21 --batch_size 1000 --out_dir $(@D)/DM21",
tools = [":export_saved_model"],
)
filegroup(
name = "dm21_exported_model",
srcs = EXPORTED_SAVED_MODEL_OBJECTS,
)
saved_model_compile_aot(
name = "aot_compiled_dm21",
cpp_class = "dm21::functional",
directory = ":DM21",
filegroups = [":dm21_exported_model"],
force_without_xla_support_flag = False,
multithreading = False,
signature_def = "default",
tag_set = "''",
target_triple = "x86_64-pc-linux",
)
cc_library(
name = "dm21_aot_compiled_example",
srcs = ["cc/dm21_aot_compiled_example.cc"],
hdrs = ["cc/dm21_aot_compiled_example.h"],
deps = [":aot_compiled_dm21"],
)
cc_binary(
name = "run_dm21_aot_compiled_example",
srcs = ["cc/run_dm21_aot_compiled_example.cc"],
copts = ["-DXLA_AVAILABLE"],
deps = [":dm21_aot_compiled_example"],
)
================================================
FILE: density_functional_approximation_dm21/README.md
================================================
# Pushing the Frontiers of Density Functionals by Solving the Fractional Electron Problem
This package provides a PySCF interface to the DM21 (DeepMind 21) family of
exchange-correlation functionals described in the paper ["Pushing the Frontiers
of Density Functionals by Solving the Fractional Electron
Problem"](https://doi.org/10.1126/science.abj6511).
## Installation
`pip install .` installs the interfaces to the DM21 functionals and required
dependencies. This is best done inside a
[virtual environment](https://docs.python-guide.org/dev/virtualenvs/).
Note: using PySCF 2.0 (or later) enables substantially more efficient
calculation of the local Hartree-Fock features, resulting in a large speed
increase.
### Installing directly
To install without cloning or downloading the deepmind_research repository,
execute:
```shell
python3 -m venv ~/venv/DM21
source ~/venv/DM21/bin/activate
pip install git+git://github.com/deepmind/deepmind-research.git#subdirectory=density_functional_approximation_dm21
```
### Downloading and installing from a local git repository
Alternatively, clone the deepmind_research repository and install from a local
repository:
```shell
git clone https://github.com/deepmind/deepmind-research.git
cd deepmind-research/density_functional_approximation_dm21
python3 -m venv ~/venv/DM21
source ~/venv/DM21/bin/activate
pip install .
```
The tests can be run either by running the test files directly or using
`py.test`, again from the `density_functional_approximation_dm21` subdirectory:
```shell
pip install '.[testing]'
py.test
```
## PySCF interface
A typical DFT calculation with PySCF is set up and run using:
```python
from pyscf import gto
from pyscf import dft
# Create the molecule of interest and select the basis set.
mol = gto.Mole()
mol.atom = 'Ne 0.0 0.0 0.0'
mol.basis = 'cc-pVDZ'
mol.build()
# Create a DFT solver and select the exchange-correlation functional.
mf = dft.RKS(mol)
mf.xc = 'b3lyp'
# Run the DFT calculation.
mf.kernel()
```
The DM21 functionals can be used in a very similar way, except we need to
compute local Hartree-Fock features, which does not fit in with the interface
used by conventional functionals. Instead, this package supplies a lightweight
wrapper around PySCF's numerical integration class which evaluates the
exchange-correlation energy and potential over a real-space grid. To use the
DM21 functional with PySCF, the above code needs to be changed to:
```python
import density_functional_approximation_dm21 as dm21
from pyscf import gto
from pyscf import dft
# Create the molecule of interest and select the basis set.
mol = gto.Mole()
mol.atom = 'Ne 0.0 0.0 0.0'
mol.basis = 'cc-pVDZ'
mol.build()
# Create a DFT solver and insert the DM21 functional into the solver.
mf = dft.RKS(mol)
mf._numint = dm21.NeuralNumInt(dm21.Functional.DM21)
# Run the DFT calculation.
mf.kernel()
```
i.e. instead of specifying the functional with `mf.xc = `, the
functional is specified using `mf._numint = dm21.NeuralNumInt()`, where `` is the corresponding member of the
`Functional` enum.
Available functionals are:
* `DM21` - trained on molecules dataset, and fractional charge, and fractional
spin constraints.
* `DM21m` - trained on molecules dataset.
* `DM21mc` - trained on molecules dataset, and fractional charge constraints.
* `DM21mu` - trained on molecules dataset, and electron gas constraints.
Full details of the network architecture, training method and datasets used can
be found in the paper (reference below). Note that the results in our paper also
include D3 corrections, which must be
[included separately](https://pyscf.org/user/dft.html#dispersion-corrections).
### Best practices for using the neural functionals.
In this section, we suggest some tips for using the neural functionals in a way
similar to how they were used for benchmarking in the paper. The tensorflow
network that we used is running at single precision, and as such it is very hard
to converge calculations to the high convergence thresholds which are default in
pyscf. For example, the following script should allow users to run an
atomization energy calculation for methane.
```python
import density_functional_approximation_dm21 as dm21
from pyscf import gto
from pyscf import dft
# Create the molecule of interest and select the basis set.
methane = gto.Mole()
methane.atom = """H 0.000000000000 0.000000000000 0.000000000000
C 0.000000000000 0.000000000000 1.087900000000
H 1.025681956337 0.000000000000 1.450533333333
H -0.512840978169 0.888266630391 1.450533333333
H -0.512840978169 -0.888266630391 1.450533333333"""
methane.basis = 'def2-qzvp'
methane.verbose = 4
methane.build()
carbon = gto.Mole()
carbon.atom = 'C 0.0 0.0 0.0'
carbon.basis = 'def2-qzvp'
carbon.spin = 2
carbon.verbose = 4
carbon.build()
hydrogen = gto.Mole()
hydrogen.atom = 'H 0.0 0.0 0.0'
hydrogen.basis = 'def2-qzvp'
hydrogen.spin = 1
hydrogen.verbose = 4
hydrogen.build()
energies = []
for mol in [methane, carbon, hydrogen]:
# Create a DFT solver and insert the DM21 functional into the solver.
if mol.spin == 0:
mf = dft.RKS(mol)
else:
mf = dft.UKS(mol)
# It will make SCF faster to start close to the solution with a cheaper
# functional.
mf.xc = 'B3LYP'
mf.run()
dm0 = mf.make_rdm1()
mf._numint = dm21.NeuralNumInt(dm21.Functional.DM21)
# It's wise to relax convergence tolerances.
mf.conv_tol = 1E-6
mf.conv_tol_grad = 1E-3
# Run the DFT calculation.
energy = mf.kernel(dm0=dm0)
energies.append(energy)
print({'CH4': energies[0], 'C': energies[1], 'H': energies[2]})
```
This script should produce three energies (in Hartrees) for the water molecule
and the two atoms of `{'CH4': -40.51785372584538, 'C': -37.84542045526023, 'H':
-0.5011533955627797}` , this leads to an atomization energy of 419.06 kcal/mol,
which is then corrected with the D3(BJ) correction for methane (1.20 kcal/mol)
to yield a predicted atomization energy of 420.26 kcal/mol. Comparing this to
the literature value of 420.42, leads us to deduce an error of around 0.2
kcal/mol.
It should also be noted that if a closed shell system is run unrestricted it can
give a small difference between spin densities and eigenvalues with a negligible
effect on the energy.
## Using DM21 from C++
There are two options for using the DM21 from C++.
1. Load the SavedModel using
[TensorFlow's C++ API](https://www.tensorflow.org/guide/saved_model#load_a_savedmodel_in_c).
This requires a run-time dependency on the TensorFlow library.
2. Compile the model ahead-of-time into a standalone library. This requires all
array dimensions to be fixed at compile time, which imposes a minor
limitation on the interface for using the functional. As the DM21
functionals act on grid points independently, this does not restrict the
system size.
The first option is more flexible but trickier to setup. Consequently we
demonstrate the second option: compiling the functional into a standalone
library. An example of running the DM21 functional using a standalone compiled
library is provided in `cc/dm21_aot_compiled_example.cc`. This requires a
link-time dependency on parts of the `xla_compiled_cpu_runtime_standalone`
library, which are not included in the compiled functional library. The easiest
way to build this is to use [Bazel](https://bazel.build). The first step is to
[install Bazel](https://docs.bazel.build/versions/5.3.0/install.html).
[Bazelisk](https://docs.bazel.build/versions/main/install-bazelisk.html) is
another way to install Bazel if a native installer is not available. The
following has been tested with Bazel 5.3.0. It is best to continue working
inside a virtual environment.
Assuming the above installation steps using `git clone` have been followed, and
`Bazel` has been installed, the example can be built and run using:
```
pip install -r requirements_aot_compilation.txt
bazel run --experimental_cc_shared_library --experimental_repo_remote_exec :run_dm21_aot_compiled_example
```
where the `pip install` command is only required if a fresh virtual environment
is used, and installs required prerequisites for TensorFlow. See the
[TensorFlow documentation](https://www.tensorflow.org/install/source) for more
details.
A static library, using the `cc_library` rule, can similarly be built and then
linked against from a separate program with no additional dependencies required,
shown in `cc/dm21_aot_compiled_example.cc` and the `dm21_aot_compiled_example`
build rule.
For calling from C, we recommend wrapping a C++ interface in `extern C { ... }`
to create a C API. For calling from Fortran, the C API can be used via the
Fortran 2003 ISO_C_BINDING feature.
### Detailed explanation
The supplied functionals in the `checkpoints` subdirectory are not easy to use
from C++, as they only contain operations for the forward pass through the
functional. This means they can only be used to evaluate the energy on a fixed
density. Self-consistent calculations require various gradients of the
functional, and it is easiest to create these using TensorFlow's Python API.
`NeuralNumInt` contains a method for exporting the functional and functional
derivatives. Assuming the above installation steps have been followed, a
functional and its derivatives can be exported by:
```shell
export_saved_model.py --functional=DM21 \
--out_dir=/path/to/export/DM21 \
--batch_size=1000
```
where `--out_dir` specifies the directory to save the model containing the
functional and functional derivatives to, and `--batch_size` the number of grid
points the functional will be evaluated on at a time. The functional must be the
name of a functional in the `neural_numint.Functional` enum. Note that the batch
size needs only be fixed for exporting models to be used with ahead-of-time
compilation.
The functional can now be compiled using the `saved_model_cli` tool supplied
with TensorFlow:
```shell
$ saved_model_cli aot_compile_cpu \
--dir /path/to/export/DM21 \
--output_prefix /path/to/compiled/DM21/dm21 \
--cpp_class dm21::functional \
--tag_set '' \
--signature_def_key default
```
The output prefix and C++ class can be arbitrarily chosen. The `tag_set` and
`signature_def_key` arguments must be as given above. This creates the following
files in the output directory:
- dm21.h: header file defining the C++ interface to the DM21 functional.
- dm21_makefile.inc: a snippet to be included in a Makefile for setting
include, library and compilation flags.
- dm21_metadata.o, dm21.o: object files for running the DM21 functional.
## Reference
If this repository is helpful for your research please cite the following
publication:
Pushing the Frontiers of Density Functionals by Solving the Fractional Electron
Problem, James Kirkpatrick, Brendan McMorrow, David H. P. Turban, Alexander L.
Gaunt, James S. Spencer, Alexander G. D. G. Matthews, Annette Obika, Louis
Thiry, Meire Fortunato, David Pfau, Lara Román Castellanos, Stig Petersen,
Alexander W. R. Nelson, Pushmeet Kohli, Paula Mori-Sánchez, Demis Hassabis, Aron
J. Cohen, Science, DOI: https://doi.org/10.1126/science.abj6511
## License
All code is made available under the Apache 2.0 License. Model parameters
(contained in the `density_functional_approximation_dm21/checkpoints/`
subdirectory) are made available under the Creative Commons Attribution 4.0
International (CC BY 4.0) License. See
https://creativecommons.org/licenses/by/4.0/legalcode for more details.
## Disclaimer
This is not an official Google product.
================================================
FILE: density_functional_approximation_dm21/WORKSPACE.bazel
================================================
workspace(name = "org_density_functional_approximation_dm21")
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
http_archive(
name = "rules_python",
sha256 = "934c9ceb552e84577b0faf1e5a2f0450314985b4d8712b2b70717dc679fdc01b",
url = "https://github.com/bazelbuild/rules_python/releases/download/0.3.0/rules_python-0.3.0.tar.gz",
)
load("@rules_python//python:pip.bzl", "pip_install")
# Create a central external repo, @external_py_deps, that contains Bazel
# targets for all the third-party packages specified in the requirements.txt
# file.
pip_install(
name = "external_py_deps",
requirements = "//:requirements.txt",
)
http_archive(
name = "io_abseil_py",
strip_prefix = "abseil-py-1.4.0",
urls = ["https://github.com/abseil/abseil-py/archive/refs/tags/v1.4.0.tar.gz"],
)
http_archive(
name = "org_tensorflow",
patch_args = ["-p1"],
patches = ["tf_bazel.patch"],
strip_prefix = "tensorflow-2.11.0",
urls = ["https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.11.0.tar.gz"],
)
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@org_tensorflow//tensorflow:workspace3.bzl", "tf_workspace3")
tf_workspace3()
load("@org_tensorflow//tensorflow:workspace2.bzl", "tf_workspace2")
tf_workspace2()
load("@org_tensorflow//tensorflow:workspace1.bzl", "tf_workspace1")
tf_workspace1()
load("@org_tensorflow//tensorflow:workspace0.bzl", "tf_workspace0")
tf_workspace0()
================================================
FILE: density_functional_approximation_dm21/cc/dm21_aot_compiled_example.cc
================================================
// Copyright 2021 DeepMind Technologies Limited.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include
// Users should adjust header path to match generated location. The generated
// location is determined by the -output_prefix flags passed to saved_model_cli
// aot_compile_cpu.
#include "aot_compiled_dm21.h" // generated
void run_dm21_compiled_functional() {
// The functional class name is set by a flag passed to saved_model_cli
// aot_compile_cpu.
dm21::functional dm21_xc;
// This assumes the functional was compiled using instructions given in the
// README.md, in particular that the model was exported with a batch size of
// 1000. We will compute the functional just at a single point and pad the
// rest of the batch using grid_weight = 0.
constexpr int batch_dim = 1000;
// See docstring for neural_numint.FunctionalInputs for descriptions on each
// input feature. Note that, due how the model is compiled, we don't need to
// pass any values for the grid coordinates.
float rho_a[6][batch_dim] = {
{1.238148615359934e-11}, {-5.4047671667795604e-11},
{-5.4047671667795604e-11}, {-7.2613887530595865e-12},
{4.436179569857956e-10}, {5.972594378309204e-11},
};
float hfx_a[batch_dim][2] = {{-4.33218591e-13, -4.32842821e-13}};
float grid_weights[batch_dim] = {1.7594189642339968};
// Use same values for both alpha and beta electrons (restricted calculation)
dm21_xc.set_arg_feed_rho_a_data(rho_a);
dm21_xc.set_arg_feed_rho_b_data(rho_a);
dm21_xc.set_arg_feed_hfx_a_data(hfx_a);
dm21_xc.set_arg_feed_hfx_b_data(hfx_a);
dm21_xc.set_arg_feed_grid_weights_data(grid_weights);
std::puts("Running functional...");
bool status = dm21_xc.Run();
if (status) {
std::puts("Successfully ran functional.");
// Fetch results.
// Other methods for fetching results exist which may be more convenient.
// Please see the generated header.
// See neural_numint.NeuralNumint._build_graph and
// See neural_numint.NeuralNumint.eval_xc for more details.
// XC potential at each grid point, shape (batch_dim).
const float* vxc = dm21_xc.result_fetch_vxc_data();
// Derivative of the energy wtih respect to the density.
// In python, this has shape (2, batch_dim), where the zeroth component is
// with respect to the alpha density and the first component with respect to
// the beta density. In C++, a flat 1D array is returned.
const float* vrho = dm21_xc.result_fetch_vrho_data();
// Derivative of the energy wtih respect to sigma.
// In python, this has shape (3, batch_dim), where the zeroth component is
// with respect to the alpha spin channel, the first component with respect
// to the spin channel and the third component with respect to the total. In
// C++, a flat 1D array is returned.
const float* vsigma = dm21_xc.result_fetch_vsigma_data();
// Derivative of the energy wtih respect to tau, the kinetic energy density.
// In python, this has shape (2, batch_dim), where the zeroth component is
// with respect to the alpha spin channel, and the first component with
// respect to the spin channel. In C++, a flat 1D array is returned.
const float* vtau = dm21_xc.result_fetch_vtau_data();
// Intermediates required for evaluating the contribution of local
// Hartree-Fock features to the derivative of the Fock matrix. See
// docstrings and comments in compute_hfx_density.py and neural_numint.py.
// In python, this has shape (2, batch_dim, nomega), where nomega is the
// number of omega values used for the Hartree-Fock kernels. The zeroth
// component is with respect to the Hartree-Fock energy density at each grid
// point for the alpha-spin density and the first component with respect to
// the beta-spin density. In C++, a flat 1D array is returned.
const float* vhf = dm21_xc.result_fetch_vhf_data();
std::printf("vxc[0] = %.6g\n", vxc[0]);
std::printf("vrho[0] = %.6g, %.6g\n", vrho[0], vrho[batch_dim]);
std::printf("vsigma[0] = %.6g, %.6g %.6g\n", vsigma[0], vsigma[batch_dim],
vsigma[2 * batch_dim]);
std::printf("vtau[0] = %.6g, %.6g\n", vtau[0], vtau[batch_dim]);
std::printf("vhf[0] = %.6g, %.6g\n", vhf[0], vhf[1]);
} else {
std::puts("Failed to run functional.");
}
}
================================================
FILE: density_functional_approximation_dm21/cc/dm21_aot_compiled_example.h
================================================
// Copyright 2021 DeepMind Technologies Limited.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef THIRD_PARTY_DEEPMIND_DEEPMIND_RESEARCH_DENSITY_FUNCTIONAL_APPROXIMATION_DM21_CC_DM21_AOT_COMPILED_EXAMPLE_H_
#define THIRD_PARTY_DEEPMIND_DEEPMIND_RESEARCH_DENSITY_FUNCTIONAL_APPROXIMATION_DM21_CC_DM21_AOT_COMPILED_EXAMPLE_H_
void run_dm21_compiled_functional();
#endif // THIRD_PARTY_DEEPMIND_DEEPMIND_RESEARCH_DENSITY_FUNCTIONAL_APPROXIMATION_DM21_CC_DM21_AOT_COMPILED_EXAMPLE_H_
================================================
FILE: density_functional_approximation_dm21/cc/run_dm21_aot_compiled_example.cc
================================================
// Copyright 2021 DeepMind Technologies Limited.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include
#ifdef XLA_AVAILABLE
#include "cc/dm21_aot_compiled_example.h"
#endif
int main(int argc, char** argv) {
#ifdef XLA_AVAILABLE
run_dm21_compiled_functional();
return 0;
#else
std::puts("Built without XLA support. Cannot run functional!");
return 1;
#endif
}
================================================
FILE: density_functional_approximation_dm21/density_functional_approximation_dm21/__init__.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""An interface to DM21 family of exchange-correlation functionals for PySCF."""
from density_functional_approximation_dm21.neural_numint import Functional
from density_functional_approximation_dm21.neural_numint import NeuralNumInt
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FILE: density_functional_approximation_dm21/density_functional_approximation_dm21/checkpoints/DM21/tfhub_module.pb
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FILE: density_functional_approximation_dm21/density_functional_approximation_dm21/checkpoints/DM21m/tfhub_module.pb
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FILE: density_functional_approximation_dm21/density_functional_approximation_dm21/checkpoints/DM21mc/tfhub_module.pb
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FILE: density_functional_approximation_dm21/density_functional_approximation_dm21/checkpoints/DM21mu/tfhub_module.pb
================================================
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FILE: density_functional_approximation_dm21/density_functional_approximation_dm21/compute_hfx_density.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""Computation of the Hartree-Fock exchange density.
We consider two types of potential:
1. Coulomb potential v(r,r') = 1/|r-r'|, which results in the full HF exchange
density and energy.
2. Screened (long-range) Coulomb potential v(r,r') = erf(\omega|r-r'|)/|r-r'|,
which results in the screened HF exchange density energy.
Note that PySCF and libcint treat a value of omega=0 to correspond to the
Coulomb potential. In the following, HF refers to full HF exchange if the
Coulomb potential is used and to screened HF exchange if the screened Coulomb
potential is used.
The Hartree-Fock (HF) exchange energy can be written as:
-2 HF_x = \sum_{a,b,c,d} D_{ab} D_{cd} \int dr \int dr'
[ \chi_a(r) \chi_c(r) v(r, r') \chi_b(r') \chi_d(r') ]
where D is the density matrix, \chi_a the atomic basis functions and r, r' are
coordinates. For clarity we have dropped the spin-channel label of the density
matrix.
Defining the following intermediates:
\nu_{bd}(r) = \int dr' (\chi_b(r') v(r, r') \chi_d(r'))
E_b(r) = \sum_a D_{ab} \chi_a(r)
we get the following expression for HF:
-2 HF_x = \int dr \sum_{bd} E_b(r) E_d(r) \nu_{bd}(r)
Therefore the quantity
exx(r) = -0.5 sum_{bd} E_b(r) E_d(r) \nu_{bd}(r)
represents an energy density at location r which integrates to the HF exchange
energy.
The Fock matrix, F, is the derivative of the energy with respect to the density
matrix. If the energy depends upon the set of features {x}, then the Fock matrix
can be evaluated as \sum_x dE/dx dx/dD_{ab}. The derivatives with respect to the
features can be easily evaluated using automatic differentiation. We hence
require the derivative of exx with respect to the density matrix:
dexx(r)/dD_{ab} = -D_{cd} \chi_a(r) \chi_c(r) \nu_{bd}(r)
This is too large to store, so we instead compute the following intermediate,
and evaluate the derivative as required on the fly:
fxx_a(r) = D_{bd} \chi_a(r) \nu_{bd}(r)
Note: we compute exx and fxx for each spin channel for both restricted and
unrestricted calculations.
"""
from typing import Generator, Optional, Tuple, Union
import attr
import numpy as np
from pyscf.dft import numint
from pyscf.gto import mole
from pyscf.lib import logger
from pyscf.lib import numpy_helper
def _evaluate_nu_slow(mol: mole.Mole,
coords: np.ndarray,
omega: float,
hermi: int = 1) -> np.ndarray:
"""Computes nu integrals for given coordinates using a slow loop."""
nu = []
# Use the Gaussian nuclear model in int1e_rinv_sph to evaluate the screened
# integrals.
with mol.with_rinv_zeta(zeta=omega * omega):
# This is going to be slow...
for coord in coords:
with mol.with_rinv_origin(coord):
nu.append(mol.intor('int1e_rinv_sph', hermi=hermi))
return np.asarray(nu)
def _evaluate_nu(mol: mole.Mole,
coords: np.ndarray,
omega: float,
hermi: int = 1) -> np.ndarray:
"""Computes nu integrals for given coordinates."""
try:
with mol.with_range_coulomb(omega=omega):
# grids keyword argument supported in pyscf 2.0.0-alpha.
nu = mol.intor('int1e_grids_sph', hermi=hermi, grids=coords) # pytype: disable=wrong-keyword-args
except TypeError:
logger.info(
mol, 'Support for int1e_grids not found (requires libcint 4.4.1 and '
'pyscf 2.0.0a or later. Falling back to slow loop over individual grid '
'points.')
nu = _evaluate_nu_slow(mol, coords, omega)
return nu
def _nu_chunk(mol: mole.Mole,
coords: np.ndarray,
omega: float,
chunk_size: int = 1000
) -> Generator[Tuple[int, int, np.ndarray], None, None]:
r"""Yields chunks of nu integrals over the grid.
Args:
mol: pyscf Mole object.
coords: coordinates, r', at which to evaluate the nu integrals, shape (N,3).
omega: range separation parameter. A value of 0 disables range-separation
(i.e. uses the kernel v(r,r') = 1/|r-r'| instead of
v(r,r') = erf(\omega |r-r'|) / |r-r'|)
chunk_size: number of coordinates to evaluate the integrals at a time.
Yields:
start_index, end_index, nu_{ab}(r) where
start_index, end_index are indices into coords,
nu is an array of shape (end_index-start_index, nao, nao), where nao is
the number of atomic orbitals and contains
nu_{ab}(r) = , where a,b are atomic
orbitals and r' are the grid coordinates in coords[start_index:end_index].
Raises:
ValueError: if omega is negative.
"""
if omega < 0:
raise ValueError('Range-separated parameter omega must be non-negative!')
ncoords = len(coords)
for chunk_index in range(0, ncoords, chunk_size):
end_index = min(chunk_index + chunk_size, ncoords)
coords_chunk = coords[chunk_index:end_index]
nu_chunk = _evaluate_nu(mol, coords_chunk, omega=omega)
yield chunk_index, end_index, nu_chunk
def _compute_exx_block(nu: np.ndarray,
e: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
r"""Computes exx and fxx.
Args:
nu: batch of integrals, in format (k,i,j) where r_k is the
position of the k-th grid point, i and j label atomic orbitals.
e: density matrix in the AO basis at each grid point.
Returns:
exx and fxx, where
fxx_{gb} =\sum_c nu_{gbc} e_{gc} and
exx_{g} = -0.5 \sum_b e_{gb} fxx_{gb}.
"""
fxx = np.einsum('gbc,gc->gb', nu, e)
exx = -0.5 * np.einsum('gb,gb->g', e, fxx)
return exx, fxx
def _compute_jk_block(nu: np.ndarray, fxx: np.ndarray, dm: np.ndarray,
ao_value: np.ndarray,
weights: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
"""Computes J and K contributions from the given block of nu integrals."""
batch_size = nu.shape[0]
vj = numpy_helper.dot(nu.reshape(batch_size, -1), dm.reshape(-1, 1))
vj = np.squeeze(vj)
vj_ao = np.einsum('g,gb->gb', vj * weights, ao_value)
j = numpy_helper.dot(ao_value.T, vj_ao)
w_ao = np.einsum('g,gb->gb', weights, ao_value)
k = numpy_helper.dot(fxx.T, w_ao)
return j, k
@attr.s(auto_attribs=True)
class HFDensityResult:
r"""Container for results returned by get_hf_density.
Note that the kernel used in all integrals is defined by the omega input
argument.
Attributes:
exx: exchange energy density at position r on the grid for the alpha, beta
spin channels. Each array is shape (N), where N is the number of grid
points.
fxx: intermediate for evaluating dexx/dD^{\sigma}_{ab}, where D is the
density matrix and \sigma is the spin coordinate. See top-level docstring
for details. Each array is shape (N, nao), where nao is the number of
atomic orbitals.
coulomb: coulomb matrix (restricted calculations) or matrices (unrestricted
calculations). Each array is shape (nao, nao).
Restricted calculations: \sum_{} D_{cd} (ab|cd)
Unrestricted calculations: \sum_{} D^{\sigma}_{cd} (ab|cd)
exchange: exchange matrix (restricted calculations) or matrices
(unrestricted calculations). Each array is shape (nao, nao).
Restricted calculations: \sum_{} D_{cd} (ab|cd)
Unrestricted calculations: \sum_{} D^{\sigma}_{cd} (ac|bd).
"""
exx: Tuple[np.ndarray, np.ndarray]
fxx: Optional[Tuple[np.ndarray, np.ndarray]] = None
coulomb: Optional[Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]] = None
exchange: Optional[Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]] = None
def get_hf_density(
mol: mole.Mole,
dm: Union[Tuple[np.ndarray, np.ndarray], np.ndarray],
coords: np.ndarray,
omega: float = 0.,
deriv: int = 0,
ao: Optional[np.ndarray] = None,
chunk_size: int = 1000,
weights: Optional[np.ndarray] = None,
) -> HFDensityResult:
r"""Computes the (range-separated) HF energy density.
Args:
mol: PySCF molecule.
dm: The density matrix. For restricted calculations, an array of shape
(M, M), where M is the number of atomic orbitals. For unrestricted
calculations, either an array of shape (2, M, M) or a tuple of arrays,
each of shape (M, M), where dm[0] is the density matrix for the alpha
electrons and dm[1] the density matrix for the beta electrons.
coords: The coordinates to compute the HF density at, shape (N, 3), where N
is the number of grid points.
omega: The inverse width of the error function. An omega of 0. means range
separation and a 1/|r-R| kernel is used in the nu integrals. Otherwise,
the kernel erf(\omega|r-R|)/|r-R|) is used. Must be non-negative.
deriv: The derivative order. Only first derivatives (deriv=1) are currently
implemented. deriv=0 indicates no derivatives are required.
ao: The atomic orbitals evaluated on the grid, shape (N, M). These are
computed if not supplied.
chunk_size: The number of coordinates to compute the HF density for at once.
Reducing this saves memory since we don't have to keep as many Nus (nbasis
x nbasis) in memory at once.
weights: weight of each grid point, shape (N). If present, the Coulomb and
exchange matrices are also computed semi-numerically, otherwise only the
HF density and (if deriv=1) its first derivative are computed.
Returns:
HFDensityResult object with the HF density (exx), the derivative of the HF
density with respect to the density (fxx) if deriv is 1, and the Coulomb and
exchange matrices if the weights argument is provided.
Raises:
NotImplementedError: if a Cartesian basis set is used or if deriv is greater
than 1.
ValueError: if omega or deriv are negative.
"""
if mol.cart:
raise NotImplementedError('Local HF exchange is not implmented for basis '
'sets with Cartesian functions!')
if deriv < 0:
raise ValueError(f'`deriv` must be non-negative, got {deriv}')
if omega < 0:
raise ValueError(f'`omega` must be non-negative, got {omega}')
if deriv > 1:
raise NotImplementedError('Higher order derivatives are not implemented.')
if isinstance(dm, tuple) or dm.ndim == 3:
dma, dmb = dm
restricted = False
else:
dma = dm / 2
dmb = dm / 2
restricted = True
logger.info(mol, 'Computing contracted density matrix ...')
if ao is None:
ao = numint.eval_ao(mol, coords, deriv=0)
e_a = np.dot(ao, dma)
e_b = np.dot(ao, dmb)
exxa = []
exxb = []
fxxa = []
fxxb = []
ja = np.zeros_like(dma)
jb = np.zeros_like(dmb)
ka = np.zeros_like(dma)
kb = np.zeros_like(dmb)
for start, end, nu in _nu_chunk(mol, coords, omega, chunk_size=chunk_size):
logger.info(mol, 'Computing exx %s / %s ...', end, len(e_a))
exxa_block, fxxa_block = _compute_exx_block(nu, e_a[start:end])
exxa.extend(exxa_block)
if not restricted:
exxb_block, fxxb_block = _compute_exx_block(nu, e_b[start:end])
exxb.extend(exxb_block)
if deriv == 1:
fxxa.extend(fxxa_block)
if not restricted:
fxxb.extend(fxxb_block)
if weights is not None:
ja_block, ka_block = _compute_jk_block(nu, fxxa_block, dma, ao[start:end],
weights[start:end])
ja += ja_block
ka += ka_block
if not restricted:
jb_block, kb_block = _compute_jk_block(nu, fxxb_block, dmb,
ao[start:end],
weights[start:end])
jb += jb_block
kb += kb_block
exxa = np.asarray(exxa)
fxxa = np.asarray(fxxa)
if restricted:
exxb = exxa
fxxb = fxxa
else:
exxb = np.asarray(exxb)
fxxb = np.asarray(fxxb)
result = HFDensityResult(exx=(exxa, exxb))
if deriv == 1:
result.fxx = (fxxa, fxxb)
if weights is not None:
if restricted:
result.coulomb = 2 * ja
result.exchange = 2 * ka
else:
result.coulomb = (ja, jb)
result.exchange = (ka, kb)
return result
================================================
FILE: density_functional_approximation_dm21/density_functional_approximation_dm21/compute_hfx_density_test.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for compute_hfx_density."""
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
from pyscf import dft
from pyscf import gto
from pyscf import lib
from pyscf import scf
import scipy
from density_functional_approximation_dm21 import compute_hfx_density
class ComputeHfxDensityTest(parameterized.TestCase):
def setUp(self):
super().setUp()
lib.param.TMPDIR = None
lib.num_threads(1)
@parameterized.named_parameters(
{'testcase_name': 'local_hf', 'omega': 0.},
{'testcase_name': 'range_separated_local_hf_0.5', 'omega': 0.5},
{'testcase_name': 'range_separated_local_hf_1.0', 'omega': 1.0},
{'testcase_name': 'range_separated_local_hf_2.0', 'omega': 2.0},
)
def test_closed_shell(self, omega):
mol = gto.M(atom='He 0. 0. 0.', basis='3-21g')
solver = dft.RKS(mol)
solver.grids.level = 2
solver.grids.build()
solver.kernel()
dm = solver.make_rdm1()
with mol.with_range_coulomb(omega=omega):
target_j, target_k = scf.hf.get_jk(mol, dm)
target_hf = -0.25 * np.einsum('ij,ji', dm, target_k)
target_coulomb = np.einsum('ij,ji', dm, target_j)
coords = solver.grids.coords
weights = solver.grids.weights
results = compute_hfx_density.get_hf_density(
mol, dm, coords, omega=omega, weights=weights)
coulomb = np.einsum('ij,ji', dm, results.coulomb)
hf = -0.25 * np.einsum('ij,ji', dm, results.exchange)
predicted_hf = np.sum((results.exx[0] + results.exx[1]) * weights)
with self.subTest('test_hf_density'):
self.assertAlmostEqual(target_hf, predicted_hf)
with self.subTest('test_get_jk'):
np.testing.assert_allclose(results.coulomb, target_j)
np.testing.assert_allclose(results.exchange, target_k)
self.assertAlmostEqual(coulomb, target_coulomb)
self.assertAlmostEqual(hf, target_hf)
@parameterized.named_parameters(
{'testcase_name': 'local_hf', 'omega': 0.},
{'testcase_name': 'range_separated_local_hf_0.5', 'omega': 0.5},
{'testcase_name': 'range_separated_local_hf_1.0', 'omega': 1.0},
{'testcase_name': 'range_separated_local_hf_2.0', 'omega': 2.0},
)
def test_hf_density_on_open_shell(self, omega):
mol = gto.M(atom='He 0. 0. 0.', basis='3-21g', charge=1, spin=1)
solver = dft.UKS(mol)
solver.grids.level = 2
solver.grids.build()
solver.kernel()
dm = solver.make_rdm1()
with mol.with_range_coulomb(omega=omega):
target_j, target_k = scf.hf.get_jk(mol, dm)
target_hf = -0.5 * (
np.einsum('ij,ji', dm[0], target_k[0]) +
np.einsum('ij,ji', dm[1], target_k[1]))
target_coulomb = np.einsum('ij,ji', dm[0], target_j[0]) + np.einsum(
'ij,ji', dm[1], target_j[1])
coords = solver.grids.coords
weights = solver.grids.weights
results = compute_hfx_density.get_hf_density(
mol, dm, coords, omega=omega, weights=weights)
predicted_hf = np.sum((results.exx[0] + results.exx[1]) * weights)
coulomb = (
np.einsum('ij,ji', dm[0], results.coulomb[0]) +
np.einsum('ij,ji', dm[1], results.coulomb[1]))
hf = -0.5 * (
np.einsum('ij,ji', dm[0], results.exchange[0]) +
np.einsum('ij,ji', dm[1], results.exchange[1]))
with self.subTest('test_hf_density'):
self.assertAlmostEqual(target_hf, predicted_hf, places=3)
with self.subTest('test_get_jk'):
np.testing.assert_allclose(results.coulomb[0], target_j[0])
np.testing.assert_allclose(results.coulomb[1], target_j[1])
np.testing.assert_allclose(results.exchange[0], target_k[0])
np.testing.assert_allclose(results.exchange[1], target_k[1])
self.assertAlmostEqual(coulomb, target_coulomb)
self.assertAlmostEqual(hf, target_hf)
def _nu_test_systems():
systems = [
{
'atom': 'N 0 0 0; N 0 0 2.4',
'charge': 0,
'spin': 0,
'basis': 'cc-pVDZ',
'num_grids': -1
},
{
'atom': 'N 0 0 0; N 0 0 2.4',
'charge': 0,
'spin': 0,
'basis': 'cc-pVDZ',
'num_grids': 1
},
{
'atom': 'N 0 0 0; N 0 0 2.4',
'charge': 0,
'spin': 0,
'basis': 'cc-pVDZ',
'num_grids': 2
},
{
'atom': 'N 0 0 0; N 0 0 2.4',
'charge': 0,
'spin': 0,
'basis': 'cc-pVDZ',
'num_grids': 10
},
{
'atom': 'N 0 0 0; N 0 0 2.4',
'charge': 0,
'spin': 0,
'basis': 'cc-pVDZ',
'num_grids': 32
},
{
'atom': 'N 0 0 0; N 0 0 2.4',
'charge': 0,
'spin': 0,
'basis': 'cc-pVDZ',
'num_grids': 33
},
{
'atom': 'Li 0 0 0',
'charge': 0,
'spin': 1,
'basis': 'cc-pVTZ',
'num_grids': -1
},
{
'atom': 'H 0 0 0',
'charge': 0,
'spin': 1,
'basis': 'cc-pVQZ',
'num_grids': -1
},
]
system_names = ['N2', 'N2_1', 'N2_2', 'N2_10', 'N2_32', 'N2_33', 'Li', 'H']
for name, system in zip(system_names, systems):
yield {'testcase_name': f'{name}_hermitian', 'hermi': 0, **system}
yield {'testcase_name': f'{name}_non_hermitian', 'hermi': 1, **system}
class NuTest(parameterized.TestCase):
def setUp(self):
super(NuTest, self).setUp()
lib.param.TMPDIR = None
lib.num_threads(1)
@parameterized.named_parameters(_nu_test_systems())
def test_nu_integrals(self, atom, charge, spin, basis, num_grids, hermi):
mol = gto.M(atom=atom, charge=charge, spin=spin, basis=basis)
mf = dft.UKS(mol)
mf.grids.build()
if num_grids == -1:
test_coords = mf.grids.coords
else:
test_coords = mf.grids.coords[0:num_grids]
nu_slow = compute_hfx_density._evaluate_nu_slow(
mol, test_coords, omega=0.0, hermi=hermi)
nu_fast = compute_hfx_density._evaluate_nu(
mol, test_coords, omega=0.0, hermi=hermi)
np.testing.assert_allclose(nu_slow, nu_fast, atol=1E-13)
def test_range_separated_nu(self):
mol = gto.M(atom='He 0 0 0', basis='cc-pVDZ')
r0 = np.array([[0.1, 0.2, 1.]])
omega = 1.
result = np.squeeze(compute_hfx_density._evaluate_nu(mol, r0, omega=omega))
solver = dft.RKS(mol)
solver.grids.level = 2
solver.grids.build()
coords = solver.grids.coords
weights = solver.grids.weights
ao_value = dft.numint.eval_ao(mol, coords, deriv=0)
dist = np.linalg.norm(coords - r0, axis=1)
erf = scipy.special.erf(omega * dist) / dist
expected_result = np.squeeze(
np.einsum('g,ga,gb->ab', weights * erf, ao_value, ao_value))
np.testing.assert_allclose(result, expected_result)
if __name__ == '__main__':
absltest.main()
================================================
FILE: density_functional_approximation_dm21/density_functional_approximation_dm21/export_saved_model.py
================================================
#!/usr/bin/env python3
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Helper for exporting a functional and its derivatives to a saved_model."""
from typing import Sequence
from absl import app
from absl import flags
from density_functional_approximation_dm21 import neural_numint
_OUT_DIR = flags.DEFINE_string(
'out_dir', None, 'Output directory.', required=True)
_BATCH_SIZE = flags.DEFINE_integer(
'batch_size',
1000,
'Number of grid points exported functional will process in a single call.',
lower_bound=0)
_FUNCTIONAL = flags.DEFINE_enum_class('functional',
neural_numint.Functional.DM21,
neural_numint.Functional,
'Functional to export.')
def export(
functional: neural_numint.Functional,
export_path: str,
batch_dim: int,
) -> None:
"""Export a functional and its derivatives to a single saved_model.
Args:
functional: functional to export.
export_path: path to saved the model to.
batch_dim: number of grid points to process in a single call.
"""
ni = neural_numint.NeuralNumInt(functional)
ni.export_functional_and_derivatives(
export_path=export_path, batch_dim=batch_dim)
def main(argv: Sequence[str]) -> None:
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
export(_FUNCTIONAL.value, _OUT_DIR.value, _BATCH_SIZE.value)
if __name__ == '__main__':
app.run(main)
================================================
FILE: density_functional_approximation_dm21/density_functional_approximation_dm21/neural_numint.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""An interface to DM21 family of exchange-correlation functionals for PySCF."""
import enum
import os
from typing import Generator, Optional, Sequence, Tuple, Union
import attr
import numpy as np
from pyscf import dft
from pyscf import gto
from pyscf.dft import numint
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
from density_functional_approximation_dm21 import compute_hfx_density
tf.disable_v2_behavior()
# TODO(b/196260242): avoid depending upon private function
_dot_ao_ao = numint._dot_ao_ao # pylint: disable=protected-access
@enum.unique
class Functional(enum.Enum):
"""Enum for exchange-correlation functionals in the DM21 family.
Attributes:
DM21: trained on molecules dataset, and fractional charge, and fractional
spin constraints.
DM21m: trained on molecules dataset.
DM21mc: trained on molecules dataset, and fractional charge constraints.
DM21mu: trained on molecules dataset, and electron gas constraints.
"""
# Break pylint's preferred naming pattern to match the functional names used
# in the paper.
# pylint: disable=invalid-name
DM21 = enum.auto()
DM21m = enum.auto()
DM21mc = enum.auto()
DM21mu = enum.auto()
# pylint: enable=invalid-name
# We use attr.s instead of here instead of dataclasses.dataclass as
# dataclasses.asdict returns a deepcopy of the attributes. This is wasteful in
# memory if they are large and breaks (as in the case of tf.Tensors) if they are
# not serializable. attr.asdict does not perform this copy and so works with
# both np.ndarrays and tf.Tensors.
@attr.s(auto_attribs=True)
class FunctionalInputs:
r""""Inputs required for DM21 functionals.
Depending upon the context, this is either a set of numpy arrays (feature
construction) or TF tensors (constructing placeholders/running functionals).
Attributes:
rho_a: Density information for the alpha electrons.
PySCF for meta-GGAs supplies a single array for the total density
(restricted calculations) and a pair of arrays, one for each spin channel
(unrestricted calculations).
Each array/tensor is of shape (6, N) and contains the density and density
derivatives, where:
rho(0, :) - density at each grid point
rho(1, :) - norm of the derivative of the density at each grid point
along x
rho(2, :) - norm of the derivative of the density at each grid point
along y
rho(3, :) - norm of the derivative of the density at each grid point
along z
rho(4, :) - \nabla^2 \rho [not used]
rho(5, :) - tau (1/2 (\nabla \rho)^2) at each grid point.
See pyscf.dft.numint.eval_rho for more details.
We require separate inputs for both alpha- and beta-spin densities, even
in restricted calculations (where rho_a = rho_b = rho/2, where rho is the
total density).
rho_b: as for rho_a for the beta electrons.
hfx_a: local Hartree-Fock energy density at each grid point for the alpha-
spin density for each value of omega. Shape [N, len(omega_values)].
See compute_hfx_density for more details.
hfx_b: as for hfx_a for the beta-spin density.
grid_coords: grid coordinates at which to evaluate the density. Shape
(N, 3), where N is the number of grid points. Note that this is currently
unused by the functional, but is still a required input.
grid_weights: weight of each grid point. Shape (N).
"""
rho_a: Union[tf.Tensor, np.ndarray]
rho_b: Union[tf.Tensor, np.ndarray]
hfx_a: Union[tf.Tensor, np.ndarray]
hfx_b: Union[tf.Tensor, np.ndarray]
grid_coords: Union[tf.Tensor, np.ndarray]
grid_weights: Union[tf.Tensor, np.ndarray]
@attr.s(auto_attribs=True)
class _GridState:
"""Internal state required for the numerical grid.
Attributes:
coords: coordinates of the grid. Shape (N, 3), where N is the number of grid
points.
weight: weight associated with each grid point. Shape (N).
mask: mask indicating whether a shell is zero at a grid point. Shape
(N, nbas) where nbas is the number of shells in the basis set. See
pyscf.dft.gen_grids.make_mask.
ao: atomic orbitals evaluated on the grid. Shape (N, nao), where nao is the
number of atomic orbitals, or shape (:, N, nao), where the 0-th element
contains the ao values, the next three elements contain the first
derivatives, and so on.
"""
coords: np.ndarray
weight: np.ndarray
mask: np.ndarray
ao: np.ndarray
@attr.s(auto_attribs=True)
class _SystemState:
"""Internal state required for system of interest.
Attributes:
mol: PySCF molecule
dms: density matrix or matrices (unrestricted calculations only).
Restricted calculations: shape (nao, nao), where nao is the number of
atomic orbitals.
Unrestricted calculations: shape (2, nao, nao) or a sequence (length 2) of
arrays of shape (nao, nao), and dms[0] and dms[1] are the density matrices
of the alpha and beta electrons respectively.
"""
mol: gto.Mole
dms: Union[np.ndarray, Sequence[np.ndarray]]
def _get_number_of_density_matrices(dms):
"""Returns the number of density matrices in dms."""
# See pyscf.numint.NumInt._gen_rho_evaluator
if isinstance(dms, np.ndarray) and dms.ndim == 2:
return 1
return len(dms)
class NeuralNumInt(numint.NumInt):
"""A wrapper around pyscf.dft.numint.NumInt for the DM21 functionals.
In order to supply the local Hartree-Fock features required for the DM21
functionals, we lightly wrap the NumInt class. The actual evaluation of the
exchange-correlation functional is performed in NeuralNumInt.eval_xc.
Usage:
mf = dft.RKS(...) # dft.ROKS and dft.UKS are also supported.
# Specify the functional by monkey-patching mf._numint rather than using
# mf._xc or mf._define_xc_.
mf._numint = NeuralNumInt(Functional.DM21)
mf.kernel()
"""
def __init__(self,
functional: Functional,
*,
checkpoint_path: Optional[str] = None):
"""Constructs a NeuralNumInt object.
Args:
functional: member of Functional enum giving the name of the
functional.
checkpoint_path: Optional path to specify the directory containing the
checkpoints of the DM21 family of functionals. If not specified, attempt
to find the checkpoints using a path relative to the source code.
"""
self._functional_name = functional.name
if checkpoint_path:
self._model_path = os.path.join(checkpoint_path, self._functional_name)
else:
self._model_path = os.path.join(
os.path.dirname(__file__), 'checkpoints', self._functional_name)
# All DM21 functionals use local Hartree-Fock features with a non-range
# separated 1/r kernel and a range-seperated kernel with \omega = 0.4.
# Note an omega of 0.0 is interpreted by PySCF and libcint to indicate no
# range-separation.
self._omega_values = [0.0, 0.4]
self._graph = tf.Graph()
with self._graph.as_default():
self._build_graph()
self._session = tf.Session()
self._session.run(tf.global_variables_initializer())
self._grid_state = None
self._system_state = None
self._vmat_hf = None
super().__init__()
def _build_graph(self, batch_dim: Optional[int] = None):
"""Builds the TensorFlow graph for evaluating the functional.
Args:
batch_dim: the batch dimension of the grid to use in the model. Default:
None (determine at runtime). This should only be set if building a model
in order to export and ahead-of-time compile it into a standalone
library.
"""
self._functional = hub.Module(spec=self._model_path)
grid_coords = tf.placeholder(
tf.float32, shape=[batch_dim, 3], name='grid_coords')
grid_weights = tf.placeholder(
tf.float32, shape=[batch_dim], name='grid_weights')
# Density information.
rho_a = tf.placeholder(tf.float32, shape=[6, batch_dim], name='rho_a')
rho_b = tf.placeholder(tf.float32, shape=[6, batch_dim], name='rho_b')
# Split into corresponding terms.
rho_only_a, grad_a_x, grad_a_y, grad_a_z, _, tau_a = tf.unstack(
rho_a, axis=0)
rho_only_b, grad_b_x, grad_b_y, grad_b_z, _, tau_b = tf.unstack(
rho_b, axis=0)
# Evaluate |\del \rho|^2 for each spin density and for the total density.
norm_grad_a = (grad_a_x**2 + grad_a_y**2 + grad_a_z**2)
norm_grad_b = (grad_b_x**2 + grad_b_y**2 + grad_b_z**2)
grad_x = grad_a_x + grad_b_x
grad_y = grad_a_y + grad_b_y
grad_z = grad_a_z + grad_b_z
norm_grad = (grad_x**2 + grad_y**2 + grad_z**2)
# The local Hartree-Fock energy densities at each grid point for the alpha-
# and beta-spin densities for each value of omega.
# Note an omega of 0 indicates no screening of the Coulomb potential.
hfxa = tf.placeholder(
tf.float32, shape=[batch_dim, len(self._omega_values)], name='hfxa')
hfxb = tf.placeholder(
tf.float32, shape=[batch_dim, len(self._omega_values)], name='hfxb')
# Make all features 2D arrays on input for ease of handling inside the
# functional.
features = {
'grid_coords': grid_coords,
'grid_weights': tf.expand_dims(grid_weights, 1),
'rho_a': tf.expand_dims(rho_only_a, 1),
'rho_b': tf.expand_dims(rho_only_b, 1),
'tau_a': tf.expand_dims(tau_a, 1),
'tau_b': tf.expand_dims(tau_b, 1),
'norm_grad_rho_a': tf.expand_dims(norm_grad_a, 1),
'norm_grad_rho_b': tf.expand_dims(norm_grad_b, 1),
'norm_grad_rho': tf.expand_dims(norm_grad, 1),
'hfxa': hfxa,
'hfxb': hfxb,
}
tensor_dict = {f'tensor_dict${k}': v for k, v in features.items()}
predictions = self._functional(tensor_dict, as_dict=True)
local_xc = predictions['grid_contribution']
weighted_local_xc = local_xc * grid_weights
unweighted_xc = tf.reduce_sum(local_xc, axis=0)
xc = tf.reduce_sum(weighted_local_xc, axis=0)
# The potential is the local exchange correlation divided by the
# total density. Add a small constant to deal with zero density.
self._vxc = local_xc / (rho_only_a + rho_only_b + 1E-12)
# The derivatives of the exchange-correlation (XC) energy with respect to
# input features. PySCF weights the (standard) derivatives by the grid
# weights, so we need to compute this with respect to the unweighted sum
# over grid points.
self._vrho = tf.gradients(
unweighted_xc, [features['rho_a'], features['rho_b']],
name='GRAD_RHO',
unconnected_gradients=tf.UnconnectedGradients.ZERO)
self._vsigma = tf.gradients(
unweighted_xc, [
features['norm_grad_rho_a'], features['norm_grad_rho_b'],
features['norm_grad_rho']
],
name='GRAD_SIGMA',
unconnected_gradients=tf.UnconnectedGradients.ZERO)
self._vtau = tf.gradients(
unweighted_xc, [features['tau_a'], features['tau_b']],
name='GRAD_TAU',
unconnected_gradients=tf.UnconnectedGradients.ZERO)
# Standard meta-GGAs do not have a dependency on local HF, so we need to
# compute the contribution to the Fock matrix ourselves. Just use the
# weighted XC energy to avoid having to weight this later.
self._vhf = tf.gradients(
xc, [features['hfxa'], features['hfxb']],
name='GRAD_HFX',
unconnected_gradients=tf.UnconnectedGradients.ZERO)
self._placeholders = FunctionalInputs(
rho_a=rho_a,
rho_b=rho_b,
hfx_a=hfxa,
hfx_b=hfxb,
grid_coords=grid_coords,
grid_weights=grid_weights)
outputs = {
'vxc': self._vxc,
'vrho': tf.stack(self._vrho),
'vsigma': tf.stack(self._vsigma),
'vtau': tf.stack(self._vtau),
'vhf': tf.stack(self._vhf),
}
# Create the signature for TF-Hub, including both the energy and functional
# derivatives.
# This is a no-op if _build_graph is called outside of
# hub.create_module_spec.
hub.add_signature(
inputs=attr.asdict(self._placeholders), outputs=outputs)
def export_functional_and_derivatives(
self,
export_path: str,
batch_dim: Optional[int] = None,
):
"""Exports the TensorFlow graph containing the functional and derivatives.
The hub modules supplied contain the TensorFlow operations for the
evaluation of the exchange-correlation energy. Evaluation of the functional
derivatives, required for a self-consistent calculation, are added in
_build_graph. The module created by export_functional_and_derivatives
contains the evaluation of the functional and the functional derivatives.
This is much simpler to use from languages other than Python, e.g. using the
C or C++ TensorFlow API, or using tfcompile to create a standalone C++
library.
Args:
export_path: path to write the Hub model to. The exported model can be
loaded using either TF-Hub or SavedModel APIs.
batch_dim: the batch dimension of the grid to use in the model. Default:
None (determine at runtime). This should only be set if the exported
model is to be ahead-of-time compiled into a standalone library.
"""
with tf.Graph().as_default():
spec = hub.create_module_spec(
self._build_graph, tags_and_args=[(set(), {'batch_dim': batch_dim})])
functional_and_derivatives = hub.Module(spec=spec)
with tf.Session() as session:
session.run(tf.global_variables_initializer())
functional_and_derivatives.export(export_path, session)
# DM21* functionals include the hybrid term directly, so set the
# range-separated and hybrid parameters expected by PySCF to 0 so PySCF
# doesn't also add these contributions in separately.
def rsh_coeff(self, *args):
"""Returns the range separated parameters, omega, alpha, beta."""
return [0.0, 0.0, 0.0]
def hybrid_coeff(self, *args, **kwargs):
"""Returns the fraction of Hartree-Fock exchange to include."""
return 0.0
def _xc_type(self, *args, **kwargs):
return 'MGGA'
def nr_rks(self,
mol: gto.Mole,
grids: dft.Grids,
xc_code: str,
dms: Union[np.ndarray, Sequence[np.ndarray]],
relativity: int = 0,
hermi: int = 0,
max_memory: float = 20000,
verbose=None) -> Tuple[float, float, np.ndarray]:
"""Calculates RKS XC functional and potential matrix on a given grid.
Args:
mol: PySCF molecule.
grids: grid on which to evaluate the functional.
xc_code: XC code. Unused. NeuralNumInt hard codes the XC functional
based upon the functional argument given to the constructor.
dms: the density matrix or sequence of density matrices. Multiple density
matrices are not currently supported. Shape (nao, nao), where nao is the
number of atomic orbitals.
relativity: Unused. (pyscf.numint.NumInt.nr_rks does not currently use
this argument.)
hermi: 0 if the density matrix is Hermitian, 1 if the density matrix is
non-Hermitian.
max_memory: the maximum cache to use, in MB.
verbose: verbosity level. Unused. (PySCF currently does not handle the
verbosity level passed in here.)
Returns:
nelec, excsum, vmat, where
nelec is the number of electrons obtained by numerical integration of
the density matrix.
excsum is the functional's XC energy.
vmat is the functional's XC potential matrix, shape (nao, nao).
Raises:
NotImplementedError: if multiple density matrices are supplied.
"""
# Wrap nr_rks so we can store internal variables required to evaluate the
# contribution to the XC potential from local Hartree-Fock features.
# See pyscf.dft.numint.nr_rks for more details.
ndms = _get_number_of_density_matrices(dms)
if ndms > 1:
raise NotImplementedError(
'NeuralNumInt does not support multiple density matrices. '
'Only ground state DFT calculations are currently implemented.')
nao = mol.nao_nr()
self._vmat_hf = np.zeros((nao, nao))
self._system_state = _SystemState(mol=mol, dms=dms)
nelec, excsum, vmat = super().nr_rks(
mol=mol,
grids=grids,
xc_code=xc_code,
dms=dms,
relativity=relativity,
hermi=hermi,
max_memory=max_memory,
verbose=verbose)
vmat += self._vmat_hf + self._vmat_hf.T
# Clear internal state to prevent accidental re-use.
self._system_state = None
self._grid_state = None
return nelec, excsum, vmat
def nr_uks(self,
mol: gto.Mole,
grids: dft.Grids,
xc_code: str,
dms: Union[Sequence[np.ndarray], Sequence[Sequence[np.ndarray]]],
relativity: int = 0,
hermi: int = 0,
max_memory: float = 20000,
verbose=None) -> Tuple[np.ndarray, float, np.ndarray]:
"""Calculates UKS XC functional and potential matrix on a given grid.
Args:
mol: PySCF molecule.
grids: grid on which to evaluate the functional.
xc_code: XC code. Unused. NeuralNumInt hard codes the XC functional
based upon the functional argument given to the constructor.
dms: the density matrix or sequence of density matrices for each spin
channel. Multiple density matrices for each spin channel are not
currently supported. Each density matrix is shape (nao, nao), where nao
is the number of atomic orbitals.
relativity: Unused. (pyscf.dft.numint.NumInt.nr_rks does not currently use
this argument.)
hermi: 0 if the density matrix is Hermitian, 1 if the density matrix is
non-Hermitian.
max_memory: the maximum cache to use, in MB.
verbose: verbosity level. Unused. (PySCF currently does not handle the
verbosity level passed in here.)
Returns:
nelec, excsum, vmat, where
nelec is the number of alpha, beta electrons obtained by numerical
integration of the density matrix as an array of size 2.
excsum is the functional's XC energy.
vmat is the functional's XC potential matrix, shape (2, nao, nao), where
vmat[0] and vmat[1] are the potential matrices for the alpha and beta
spin channels respectively.
Raises:
NotImplementedError: if multiple density matrices for each spin channel
are supplied.
"""
# Wrap nr_uks so we can store internal variables required to evaluate the
# contribution to the XC potential from local Hartree-Fock features.
# See pyscf.dft.numint.nr_uks for more details.
if isinstance(dms, np.ndarray) and dms.ndim == 2: # RHF DM
ndms = _get_number_of_density_matrices(dms)
else:
ndms = _get_number_of_density_matrices(dms[0])
if ndms > 1:
raise NotImplementedError(
'NeuralNumInt does not support multiple density matrices. '
'Only ground state DFT calculations are currently implemented.')
nao = mol.nao_nr()
self._vmat_hf = np.zeros((2, nao, nao))
self._system_state = _SystemState(mol=mol, dms=dms)
nelec, excsum, vmat = super().nr_uks(
mol=mol,
grids=grids,
xc_code=xc_code,
dms=dms,
relativity=relativity,
hermi=hermi,
max_memory=max_memory,
verbose=verbose)
vmat[0] += self._vmat_hf[0] + self._vmat_hf[0].T
vmat[1] += self._vmat_hf[1] + self._vmat_hf[1].T
# Clear internal state to prevent accidental re-use.
self._system_state = None
self._grid_state = None
self._vmat_hf = None
return nelec, excsum, vmat
def block_loop(
self,
mol: gto.Mole,
grids: dft.Grids,
nao: Optional[int] = None,
deriv: int = 0,
max_memory: float = 2000,
non0tab: Optional[np.ndarray] = None,
blksize: Optional[int] = None,
buf: Optional[np.ndarray] = None
) -> Generator[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray], None,
None]:
"""Loops over the grid by blocks. See pyscf.dft.numint.NumInt.block_loop.
Args:
mol: PySCF molecule.
grids: grid on which to evaluate the functional.
nao: number of basis functions. If None, obtained from mol.
deriv: unused. The first functional derivatives are always computed.
max_memory: the maximum cache to use for the information on the grid, in
MB. Determines the size of each block if blksize is None.
non0tab: mask determining if a shell in the basis set is zero at a grid
point. Shape (N, nbas), where N is the number of grid points and nbas
the number of shells in the basis set. Obtained from grids if not
supplied.
blksize: size of each block. Calculated from max_memory if None.
buf: buffer to use for storing ao. If None, a new array for ao is created
for each block.
Yields:
ao, mask, weight, coords: information on a block of the grid containing N'
points, where
ao: atomic orbitals evaluated on the grid. Shape (N', nao), where nao is
the number of atomic orbitals.
mask: mask indicating whether a shell in the basis set is zero at a grid
point. Shape (N', nbas).
weight: weight associated with each grid point. Shape (N').
coords: coordinates of the grid. Shape (N', 3).
"""
# Wrap block_loop so we can store internal variables required to evaluate
# the contribution to the XC potential from local Hartree-Fock features.
for ao, mask, weight, coords in super().block_loop(
mol=mol,
grids=grids,
nao=nao,
deriv=deriv,
max_memory=max_memory,
non0tab=non0tab,
blksize=blksize,
buf=buf):
# Cache the curent block so we can access it in eval_xc.
self._grid_state = _GridState(
ao=ao, mask=mask, weight=weight, coords=coords)
yield ao, mask, weight, coords
def construct_functional_inputs(
self,
mol: gto.Mole,
dms: Union[np.ndarray, Sequence[np.ndarray]],
spin: int,
coords: np.ndarray,
weights: np.ndarray,
rho: Union[np.ndarray, Tuple[np.ndarray, np.ndarray]],
ao: Optional[np.ndarray] = None,
) -> Tuple[FunctionalInputs, Tuple[np.ndarray, np.ndarray]]:
"""Constructs the input features required for the functional.
Args:
mol: PySCF molecule.
dms: density matrix of shape (nao, nao) (restricted calculations) or of
shape (2, nao, nao) (unrestricted calculations) or tuple of density
matrices for each spin channel, each of shape (nao, nao) (unrestricted
calculations).
spin: 0 for a spin-unpolarized (restricted Kohn-Sham) calculation, and
spin-polarized (unrestricted) otherwise.
coords: coordinates of the grid. Shape (N, 3), where N is the number of
grid points.
weights: weight associated with each grid point. Shape (N).
rho: density and density derivatives at each grid point. Single array
containing the total density for restricted calculations, tuple of
arrays for each spin channel for unrestricted calculations. Each array
has shape (6, N). See pyscf.dft.numint.eval_rho and comments in
FunctionalInputs for more details.
ao: The atomic orbitals evaluated on the grid, shape (N, nao). Computed if
not supplied.
Returns:
inputs, fxx, where
inputs: FunctionalInputs object containing the inputs (as np.ndarrays)
for the functional.
fxx: intermediates, shape (N, nao) for the alpha- and beta-spin
channels, required for computing the first derivative of the local
Hartree-Fock density with respect to the density matrices. See
compute_hfx_density for more details.
"""
if spin == 0:
# RKS
rhoa = rho / 2
rhob = rho / 2
else:
# UKS
rhoa, rhob = rho
# Local HF features.
exxa, exxb = [], []
fxxa, fxxb = [], []
for omega in sorted(self._omega_values):
hfx_results = compute_hfx_density.get_hf_density(
mol,
dms,
coords=coords,
omega=omega,
deriv=1,
ao=ao)
exxa.append(hfx_results.exx[0])
exxb.append(hfx_results.exx[1])
fxxa.append(hfx_results.fxx[0])
fxxb.append(hfx_results.fxx[1])
exxa = np.stack(exxa, axis=-1)
fxxa = np.stack(fxxa, axis=-1)
if spin == 0:
exx = (exxa, exxa)
fxx = (fxxa, fxxa)
else:
exxb = np.stack(exxb, axis=-1)
fxxb = np.stack(fxxb, axis=-1)
exx = (exxa, exxb)
fxx = (fxxa, fxxb)
return FunctionalInputs(
rho_a=rhoa,
rho_b=rhob,
hfx_a=exx[0],
hfx_b=exx[1],
grid_coords=coords,
grid_weights=weights), fxx
def eval_xc(
self,
xc_code: str,
rho: Union[np.ndarray, Tuple[np.ndarray, np.ndarray]],
spin: int = 0,
relativity: int = 0,
deriv: int = 1,
omega: Optional[float] = None,
verbose=None
) -> Tuple[np.ndarray, Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray],
None, None]:
"""Evaluates the XC energy and functional derivatives.
See pyscf.dft.libxc.eval_xc for more details on the interface.
Note: this also sets self._vmat_extra, which contains the contribution the
the potential matrix from the local Hartree-Fock terms in the functional.
Args:
xc_code: unused.
rho: density and density derivatives at each grid point. Single array
containing the total density for restricted calculations, tuple of
arrays for each spin channel for unrestricted calculations. Each array
has shape (6, N), where N is the number of grid points. See
pyscf.dft.numint.eval_rho and comments in FunctionalInputs for more
details.
spin: 0 for a spin-unpolarized (restricted Kohn-Sham) calculation, and
spin-polarized (unrestricted) otherwise.
relativity: Not supported.
deriv: unused. The first functional derivatives are always computed.
omega: RSH parameter. Not supported.
verbose: unused.
Returns:
exc, vxc, fxc, kxc, where:
exc is the exchange-correlation potential matrix evaluated at each grid
point, shape (N).
vxc is (vrho, vgamma, vlapl, vtau), the first-order functional
derivatives evaluated at each grid point, each shape (N).
fxc is set to None. (The second-order functional derivatives are not
computed.)
kxc is set to None. (The third-order functional derivatives are not
computed.)
"""
del xc_code, verbose, deriv # unused
if relativity != 0:
raise NotImplementedError('Relatistic calculations are not implemented '
'for DM21 functionals.')
if omega is not None:
raise NotImplementedError('User-specifed range seperation parameters are '
'not implemented for DM21 functionals.')
# Retrieve cached state.
ao = self._grid_state.ao
if ao.ndim == 3:
# Just need the AO values, not the gradients.
ao = ao[0]
if self._grid_state.weight is None:
weights = np.array([1.])
else:
weights = self._grid_state.weight
mask = self._grid_state.mask
inputs, (fxxa, fxxb) = self.construct_functional_inputs(
mol=self._system_state.mol,
dms=self._system_state.dms,
spin=spin,
rho=rho,
weights=weights,
coords=self._grid_state.coords,
ao=ao)
with self._graph.as_default():
feed_dict = dict(
zip(
attr.asdict(self._placeholders).values(),
attr.asdict(inputs).values(),
))
tensor_list = [
self._vxc,
self._vrho,
self._vsigma,
self._vtau,
self._vhf,
]
exc, vrho, vsigma, vtau, vhf = (
self._session.run(tensor_list, feed_dict=feed_dict))
mol = self._system_state.mol
shls_slice = (0, mol.nbas)
ao_loc_nr = mol.ao_loc_nr()
# Note: tf.gradients returns a list of gradients.
# vrho, vsigma, vtau are derivatives of objects that had
# tf.expand_dims(..., 1) applied. The [:, 0] indexing undoes this by
# selecting the 0-th (and only) element from the second dimension.
if spin == 0:
vxc_0 = (vrho[0][:, 0] + vrho[1][:, 0]) / 2.
# pyscf expects derivatives with respect to:
# grad_rho . grad_rho.
# The functional uses the first and last as inputs, but then has
# grad_(rho_a + rho_b) . grad_(rho_a + rho_b)
# as input. The following computes the correct total derivatives.
vxc_1 = (vsigma[0][:, 0] / 4. + vsigma[1][:, 0] / 4. + vsigma[2][:, 0])
vxc_3 = (vtau[0][:, 0] + vtau[1][:, 0]) / 2.
vxc_2 = np.zeros_like(vxc_3)
vhfs = (vhf[0] + vhf[1]) / 2.
# Local Hartree-Fock terms
for i in range(len(self._omega_values)):
# Factor of 1/2 is to account for adding vmat_hf + vmat_hf.T to vmat,
# which we do to match existing PySCF style. Unlike other terms, vmat_hf
# is already symmetric though.
aow = np.einsum('pi,p->pi', fxxa[:, :, i], -0.5 * vhfs[:, i])
self._vmat_hf += _dot_ao_ao(mol, ao, aow, mask, shls_slice,
ao_loc_nr)
else:
vxc_0 = np.stack([vrho[0][:, 0], vrho[1][:, 0]], axis=1)
# pyscf expects derivatives with respect to:
# grad_rho_a . grad_rho_a
# grad_rho_a . grad_rho_b
# grad_rho_b . grad_rho_b
# The functional uses the first and last as inputs, but then has
# grad_(rho_a + rho_b) . grad_(rho_a + rho_b)
# as input. The following computes the correct total derivatives.
vxc_1 = np.stack([
vsigma[0][:, 0] + vsigma[2][:, 0], 2. * vsigma[2][:, 0],
vsigma[1][:, 0] + vsigma[2][:, 0]
],
axis=1)
vxc_3 = np.stack([vtau[0][:, 0], vtau[1][:, 0]], axis=1)
vxc_2 = np.zeros_like(vxc_3)
vhfs = np.stack([vhf[0], vhf[1]], axis=2)
for i in range(len(self._omega_values)):
# Factors of 1/2 are due to the same reason as in the spin=0 case.
aow = np.einsum('pi,p->pi', fxxa[:, :, i], -0.5 * vhfs[:, i, 0])
self._vmat_hf[0] += _dot_ao_ao(mol, ao, aow, mask, shls_slice,
ao_loc_nr)
aow = np.einsum('pi,p->pi', fxxb[:, :, i], -0.5 * vhfs[:, i, 1])
self._vmat_hf[1] += _dot_ao_ao(mol, ao, aow, mask, shls_slice,
ao_loc_nr)
fxc = None # Second derivative not implemented
kxc = None # Second derivative not implemented
# PySCF C routines expect float64.
exc = exc.astype(np.float64)
vxc = tuple(v.astype(np.float64) for v in (vxc_0, vxc_1, vxc_2, vxc_3))
return exc, vxc, fxc, kxc
================================================
FILE: density_functional_approximation_dm21/density_functional_approximation_dm21/neural_numint_test.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for neural_numint."""
import os
from absl.testing import parameterized
import attr
from pyscf import dft
from pyscf import gto
from pyscf import lib
import tensorflow.compat.v1 as tf
from density_functional_approximation_dm21 import neural_numint
class NeuralNumintTest(tf.test.TestCase, parameterized.TestCase):
def setUp(self):
super().setUp()
lib.param.TMPDIR = None
lib.num_threads(1)
# Golden values were obtained using the version of PySCF (including integral
# generation) reported in the DM21 paper.
@parameterized.parameters(
{
'functional': neural_numint.Functional.DM21,
'expected_energy': -126.898521
},
{
'functional': neural_numint.Functional.DM21m,
'expected_energy': -126.907332
},
{
'functional': neural_numint.Functional.DM21mc,
'expected_energy': -126.922127
},
{
'functional': neural_numint.Functional.DM21mu,
'expected_energy': -126.898178
},
)
def test_rks(self, functional, expected_energy):
ni = neural_numint.NeuralNumInt(functional)
mol = gto.Mole()
mol.atom = [['Ne', 0., 0., 0.]]
mol.basis = 'sto-3g'
mol.build()
mf = dft.RKS(mol)
mf.small_rho_cutoff = 1.e-20
mf._numint = ni
mf.run()
self.assertAlmostEqual(mf.e_tot, expected_energy, delta=2.e-4)
@parameterized.parameters(
{
'functional': neural_numint.Functional.DM21,
'expected_energy': -37.34184876
},
{
'functional': neural_numint.Functional.DM21m,
'expected_energy': -37.3377766
},
{
'functional': neural_numint.Functional.DM21mc,
'expected_energy': -37.33489173
},
{
'functional': neural_numint.Functional.DM21mu,
'expected_energy': -37.34015315
},
)
def test_uks(self, functional, expected_energy):
ni = neural_numint.NeuralNumInt(functional)
mol = gto.Mole()
mol.atom = [['C', 0., 0., 0.]]
mol.spin = 2
mol.basis = 'sto-3g'
mol.build()
mf = dft.UKS(mol)
mf.small_rho_cutoff = 1.e-20
mf._numint = ni
mf.run()
self.assertAlmostEqual(mf.e_tot, expected_energy, delta=2.e-4)
def test_exported_model(self):
mol = gto.Mole()
mol.atom = [['C', 0., 0., 0.]]
mol.spin = 2
mol.basis = 'sto-3g'
mol.build()
ni = neural_numint.NeuralNumInt(neural_numint.Functional.DM21)
mf = dft.UKS(mol)
mf.small_rho_cutoff = 1.e-20
mf._numint = ni
mf.run()
dms = mf.make_rdm1()
ao = ni.eval_ao(mol, mf.grids.coords, deriv=2)
rho_a = ni.eval_rho(mol, ao, dms[0], xctype='MGGA')
rho_b = ni.eval_rho(mol, ao, dms[1], xctype='MGGA')
inputs, _ = ni.construct_functional_inputs(
mol=mol,
dms=dms,
spin=1,
coords=mf.grids.coords,
weights=mf.grids.weights,
rho=(rho_a, rho_b),
ao=ao[0])
feed_dict = dict(
zip(
attr.asdict(ni._placeholders).values(),
attr.asdict(inputs).values(),
))
with ni._graph.as_default():
outputs = ni._session.run(
{
'vxc': ni._vxc,
'vrho': ni._vrho,
'vsigma': ni._vsigma,
'vtau': ni._vtau,
'vhf': ni._vhf
},
feed_dict=feed_dict)
export_path = os.path.join(self.get_temp_dir(), 'export')
ni.export_functional_and_derivatives(export_path)
model = tf.saved_model.load_v2(export_path)
tensor_inputs = {
k: tf.constant(v, dtype=tf.float32)
for k, v in attr.asdict(inputs).items()
}
exported_output_tensors = model.signatures['default'](**tensor_inputs)
with tf.Session() as session:
session.run(tf.global_variables_initializer())
exported_outputs = session.run(exported_output_tensors)
self.assertAllClose(outputs, exported_outputs, atol=5.e-5, rtol=1.e-5)
if __name__ == '__main__':
tf.test.main()
================================================
FILE: density_functional_approximation_dm21/external/tf_bazel.patch
================================================
From 76edcc2da8a754cb2b283d7de9a7b37f51f7f380 Mon Sep 17 00:00:00 2001
From: James Spencer
Date: Thu, 26 Aug 2021 10:00:53 +0100
Subject: [PATCH] Set dependencies for saved_model_compile_aot rule,
visibility for xla_compiled_cpu_runtime_standalone to work outside of
tensorflow, and absl version.
---
tensorflow/compiler/tf2xla/BUILD | 2 +-
tensorflow/compiler/xla/mlir/ir/runtime/BUILD | 1 +
tensorflow/python/tools/tools.bzl | 4 ++--
3 files changed, 4 insertions(+), 3 deletions(-)
diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD
index cfe63b16675..cd273aaf29e 100644
--- a/tensorflow/compiler/tf2xla/BUILD
+++ b/tensorflow/compiler/tf2xla/BUILD
@@ -235,7 +235,7 @@ cc_library(
"-parse_headers",
],
linkstatic = 1,
- visibility = [":friends"],
+ visibility = ["//visibility:public"],
# Note, we specifically removed MKL and multithreaded dependencies so the
# standalone does not require the MKL binary blob or threading libraries.
#
diff --git a/tensorflow/compiler/xla/mlir/ir/runtime/BUILD b/tensorflow/compiler/xla/mlir/ir/runtime/BUILD
index 8c693fa5d49..bf95a3de622 100644
--- a/tensorflow/compiler/xla/mlir/ir/runtime/BUILD
+++ b/tensorflow/compiler/xla/mlir/ir/runtime/BUILD
@@ -90,6 +90,7 @@ cc_library(
"rt_ops.h",
],
compatible_with = get_compatible_with_cloud(),
+ visibility = ["//visibility:public"],
deps = [
":rt_inc_gen",
"//tensorflow/compiler/xla/runtime:constraints",
diff --git a/tensorflow/python/tools/tools.bzl b/tensorflow/python/tools/tools.bzl
index db886746006..bc597e29de9 100644
--- a/tensorflow/python/tools/tools.bzl
+++ b/tensorflow/python/tools/tools.bzl
@@ -148,7 +148,7 @@ def saved_model_compile_aot(
),
tags = tags,
tools = [
- "//tensorflow/python/tools:saved_model_cli",
+ "@org_tensorflow//tensorflow/python/tools:saved_model_cli",
],
)
@@ -170,7 +170,7 @@ def saved_model_compile_aot(
tags = tags,
deps = _maybe_force_compile(
[
- "//tensorflow/compiler/tf2xla:xla_compiled_cpu_runtime_standalone",
+ "@org_tensorflow//tensorflow/compiler/tf2xla:xla_compiled_cpu_runtime_standalone",
],
force_compile = force_without_xla_support_flag,
),
--
2.33.0.259.gc128427fd7-goog
================================================
FILE: density_functional_approximation_dm21/requirements.txt
================================================
absl-py==1.4.0
attrs==22.2.0
h5py==3.8.0
numpy==1.24.1
pyscf==2.1.1
pytest==6.2.4
scipy==1.9.3
tensorflow==2.11.0
tensorflow-hub==0.12.0
================================================
FILE: density_functional_approximation_dm21/requirements_aot_compilation.txt
================================================
Keras-Preprocessing==1.1.2
numpy==1.24.1
tensorflow-estimator==2.11.0
================================================
FILE: density_functional_approximation_dm21/run.sh
================================================
#!/bin/bash
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Install density_functional_approximation_dm21 in a clean virtualenv and run
# the tests. This assumes the working directory is the top-level directory of
# the deepmind-research repository, i.e.:
# git clone git@github.com:deepmind/deepmind-research.git
# cd deepmind_research
# density_functional_approximation_dm21/run.sh
python3 -m venv /tmp/DM21
source /tmp/DM21/bin/activate
pip3 install -r density_functional_approximation_dm21/requirements.txt
pip install 'density_functional_approximation_dm21/[testing]'
py.test density_functional_approximation_dm21/
================================================
FILE: density_functional_approximation_dm21/setup.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Setup for DM21 functionals interface to PySCF."""
from setuptools import setup
REQUIRED_PACKAGES = [
'absl-py',
'attrs',
# Note PySCF 1.7.6 and older do not support h5py 3.3.0:
# https://github.com/pyscf/pyscf/issues/1016
'h5py',
'numpy',
# Note DM21 functionals are compatible with PySCF 1.7.6 if an older version
# of h5py is used.
'pyscf>=2.0',
'tensorflow',
'tensorflow_hub',
]
CHECKPOINT_DATA = ['checkpoints/DM21*/*.pb', 'checkpoints/DM21*/variables/*']
setup(
name='density_functional_approximation_dm21',
version='0.1',
description='An interface to PySCF for the DM21 functionals.',
url='https://github.com/deepmind/deepmind-research/density_functional_approximation_dm21',
author='DeepMind',
author_email='no-reply@google.com',
# Contained modules and scripts.
packages=['density_functional_approximation_dm21'],
package_data={
'density_functional_approximation_dm21': CHECKPOINT_DATA,
},
scripts=['density_functional_approximation_dm21/export_saved_model.py'],
install_requires=REQUIRED_PACKAGES,
platforms=['any'],
license='Apache 2.0',
extras_require={'testing': ['pytest', 'scipy']},
)
================================================
FILE: enformer/README.md
================================================
# Enformer
This package provides an implementation of the Enformer model and examples on
running the model.
If this source code or accompanying files are helpful for your research please
cite the following publication:
"Effective gene expression prediction from sequence by integrating long-range
interactions"
Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam,
Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper,
Pushmeet Kohli, David R. Kelley
## Setup
Requirements:
* dm-sonnet (2.0.0)
* kipoiseq (0.5.2)
* numpy (1.19.5)
* pandas (1.2.3)
* tensoflow (2.4.1)
* tensorflow-hub (0.11.0)
See `requirements.txt`.
To run the unit test:
```shell
python3.8 -m venv enformer_venv
source enformer_venv/bin/activate
pip install -r requirements.txt
python -m enformer_test
```
## Pre-computed variant effect predictions
We precomputed variant effect scores for all frequent variants (MAF>0.5%, in any
population) present in the 1000 genomes project. Variant scores in HDF5 file
format per chromosome for HG19 reference genome can be found
[here](https://console.cloud.google.com/storage/browser/dm-enformer/variant-scores/1000-genomes/enformer).
The HDF5 file has the same format as the output of
[this](https://github.com/calico/basenji/blob/738321c85f8925ae6ac318a6cd4901a42ea6bc3f/bin/basenji_sad.py#L264)
script and contains the following arrays:
* snp \[num_snps](string) - snp id
* chr \[num_snps](string) - chromosome name
* pos \[num_snps](uint32) - position (1-based)
* ref \[num_snps](string) - reference allele
* alt \[num_snps](string) - alternative allele
* target_ids \[num_targets](string) - target ids
* target_labels \[num_targets](string) - target names
* SAD \[num_snps, num_targets](float16) - SNP Activity Difference (SAD)
scores - main variant effect score computed as `model(alternate_sequence) -
model(reference_sequence)`.
* SAR \[num_snps, num_targets](float16) - Same as SAD, by computing
`np.log2(1 + model(alternate_sequence)) - np.log2(1 +
model(reference_sequence))`
Furthermore, we provide the top 20 principal components of variant-effect scores
in the [PC20 folder](https://console.cloud.google.com/storage/browser/dm-enformer/variant-scores/1000-genomes/enformer/PC20)
stored as a tabix-indexed TSV file per chromosome (HG19 reference
genome). The format of these files has the following columns:
* #CHROM - chromosome (chr1)
* POS - variant position (1-based)
* ID - dbSNP identifier
* REF - reference allele (e.g. A)
* ALT - alternate allele (e.g. T)
* PC{i} - i-th principal component of the variant effect prediction.
All model predictions are licensed under
[CC-BY 4.0 license](https://creativecommons.org/licenses/by/4.0/).
## Running Inference
The simplest way to perform inference is to load the model via tfhub.dev (TODO:
LINK). The input sequence length is 393,216 with the prediction corresponding to
128 base pair windows of the center 114,688 base pairs. The input sequence is
one hot encoded using the order of indices being 'ACGT' with N values being all
zeros. Note that only the central 196,608 bp of the input sequence will be used
by the Enformer model. The rest will be cropped within the model.
```python
import tensorflow as tf
import tensorflow_hub as hub
enformer = hub.Module('https://tfhub.dev/deepmind/enformer/1')
SEQ_LENGTH = 393_216
# Numpy array [batch_size, SEQ_LENGTH, 4] one hot encoded in order 'ACGT'. The
# `one_hot_encode` function is available in `enformer.py` and outputs can be
# stacked to form a batch.
inputs = tf.zeros((1, SEQ_LENGTH, 4), dtype=tf.float32)
predictions = enformer.predict_on_batch(inputs)
predictions['human'].shape # [batch_size, 896, 5313]
predictions[mouse].shape # [batch_size, 896, 1643]
```
## Outputs
For each 128 bp window, predictions are made for every track. The mapping from
track idx to track name is found in the corresponding file in the basenji
[dataset](https://github.com/calico/basenji/tree/master/manuscripts/cross2020)
folder (targets_{organism}.txt file).
As an example, to load track annotations for the human targets:
```python
import pandas as pd
targets_txt = 'https://raw.githubusercontent.com/calico/basenji/0.5/manuscripts/cross2020/targets_human.txt'
df_targets = pd.read_csv(targets_txt, sep='\t')
df_targets.shape # (5313, 8) With rows match output shape above.
```
## Training Code
The model is implemented using [Sonnet](https://github.com/deepmind/sonnet). The
full sonnet module is defined in `enformer.py` called Enformer. See
[enformer-training.ipynb](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/enformer/enformer-training.ipynb)
on how to train the model on Basenji2 data and how to load the pre-trained
weights into the Enformer module for fine-tuning.
## Colab
Further usage and training examples are given in the following colab notebooks:
### `enformer-usage.ipynb` [](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/enformer/enformer-usage.ipynb).
This shows how to:
* **Make predictions** with pre-trained Enformer and reproduce Fig. 1d
* **Compute contribution scores** and reproduce parts of Fig. 2a
* **Predict the effect of genetic variants** and reproduce parts of Fig. 3g
* Score multiple variants in a VCF
### `enformer-training.ipynb` [](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/enformer/enformer-training.ipynb).
This colab shows how to:
* Setup training data by directly accessing the Basenji2 data on GCS
* Train the model for a few steps on both human and mouse genomes
* Evaluate the model on human and mouse genomes
* Restore the model from a checkpoint for fine-tuning
## Disclaimer
This is not an official Google product.
================================================
FILE: enformer/attention_module.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TransformerBlock and MultiheadAttention modules used in the paper.
"Effective gene expression prediction from sequence by integrating long-range
interactions"
Žiga Avsec1, Vikram Agarwal2,4, Daniel Visentin1,4, Joseph R. Ledsam1,3,
Agnieszka Grabska-Barwinska1, Kyle R. Taylor1, Yannis Assael1, John Jumper1,
Pushmeet Kohli1, David R. Kelley2*
1 DeepMind, London, UK
2 Calico Life Sciences, South San Francisco, CA, USA
3 Google, Tokyo, Japan
4 These authors contributed equally.
* correspondence: avsec@google.com, pushmeet@google.com, drk@calicolabs.com
Example:
```
mha = MultiheadAttention(
value_size=96,
key_size=64,
num_heads=8,
relative_position_functions=['positional_features_sin_cos'])
mha(tf.ones((2, 1024, 96*8)), is_training=True)
# Transformer block as used in the paper
transformer_block = TransformerBlock(
channels=96 * 8,
dropout_rate=0.4,
attention_kwargs=dict(
value_size=96,
key_size=64,
num_heads=8,
relative_positions=True,
relative_position_symmetric=False,
num_relative_position_features=None,
relative_position_functions=['positional_features_exponential',
'positional_features_central_mask',
'positional_features_gamma'],
positional_dropout_rate=0.01,
attention_dropout_rate=0.05,
)
)
transformer_block(tf.ones((2, 1024, 96*8)), is_training=True)
```
"""
from typing import Any, Dict, List, Optional
import numpy as np
import sonnet as snt
import tensorflow as tf
class TransformerBlock(snt.Module):
"""Full transformer module block."""
def __init__(
self,
channels: int,
dropout_rate: float,
attention_kwargs: Dict[str, Any],
name: str = 'transformer_block',
):
super().__init__(name=name)
self.mha_ln = snt.LayerNorm(axis=-1, create_scale=True, create_offset=True)
self.mha = MultiheadAttention(**attention_kwargs)
self.mha_dropout = snt.Dropout(dropout_rate)
self.mlp_ln = snt.LayerNorm(axis=-1, create_scale=True, create_offset=True)
self.mlp_linear1 = snt.Linear(channels * 2)
self.mlp_dropout1 = snt.Dropout(dropout_rate)
self.mlp_linear2 = snt.Linear(channels)
self.mlp_dropout2 = snt.Dropout(dropout_rate)
def __call__(self, inputs: tf.Tensor, is_training: bool) -> tf.Tensor:
x = self.mha_ln(inputs)
x = self.mha(x, is_training=is_training)
x = self.mha_dropout(x, is_training=is_training)
x += inputs # Residual
mha_output = x
# MLP.
x = self.mlp_ln(mha_output)
x = self.mlp_linear1(x)
x = self.mlp_dropout1(x, is_training=is_training)
x = tf.nn.relu(x)
x = self.mlp_linear2(x)
x = self.mlp_dropout2(x, is_training=is_training)
return x + mha_output
class MultiheadAttention(snt.Module):
"""Multi-head attention."""
def __init__(self,
value_size: int,
key_size: int,
num_heads: int,
scaling: bool = True,
attention_dropout_rate: float = 0.1,
relative_positions: bool = False,
relative_position_symmetric: bool = False,
relative_position_functions: Optional[List[str]] = None,
num_relative_position_features: Optional[int] = None,
positional_dropout_rate: float = 0.1,
zero_initialize: bool = True,
initializer: Optional[snt.initializers.Initializer] = None,
name: str = None):
"""Creates a MultiheadAttention module.
Args:
value_size: The size of each value embedding per head.
key_size: The size of each key and query embedding per head.
num_heads: The number of independent queries per timestep.
scaling: Whether to scale the attention logits.
attention_dropout_rate: Dropout rate for attention logits.
relative_positions: Whether to use TransformerXL style relative attention.
relative_position_symmetric: If True, the symmetric version of basis
functions will be used. If False, a symmetric and asymmetric versions
will be use.
relative_position_functions: List of function names used for relative
positional biases.
num_relative_position_features: Number of relative positional features
to compute. If None, `value_size * num_heads` is used.
positional_dropout_rate: Dropout rate for the positional encodings if
relative positions are used.
zero_initialize: if True, the final linear layer will be 0 initialized.
initializer: Initializer for the projection layers. If unspecified,
VarianceScaling is used with scale = 2.0.
name: Name of module.
"""
super().__init__(name=name)
self._value_size = value_size
self._key_size = key_size
self._num_heads = num_heads
self._attention_dropout_rate = attention_dropout_rate
self._scaling = scaling
self._relative_positions = relative_positions
self._relative_position_symmetric = relative_position_symmetric
self._relative_position_functions = relative_position_functions
if num_relative_position_features is None:
# num_relative_position_features needs to be divisible by the number of
# relative positional functions *2 (for symmetric & asymmetric version).
divisible_by = 2 * len(self._relative_position_functions)
self._num_relative_position_features = (
(self._value_size // divisible_by) * divisible_by)
else:
self._num_relative_position_features = num_relative_position_features
self._positional_dropout_rate = positional_dropout_rate
self._initializer = initializer
if self._initializer is None:
self._initializer = snt.initializers.VarianceScaling(scale=2.0)
key_proj_size = self._key_size * self._num_heads
embedding_size = self._value_size * self._num_heads
self._q_layer = snt.Linear(
key_proj_size,
name='q_layer',
with_bias=False,
w_init=self._initializer)
self._k_layer = snt.Linear(
key_proj_size,
name='k_layer',
with_bias=False,
w_init=self._initializer)
self._v_layer = snt.Linear(
embedding_size,
name='v_layer',
with_bias=False,
w_init=self._initializer)
w_init = snt.initializers.Zeros() if zero_initialize else self._initializer
self._embedding_layer = snt.Linear(
embedding_size,
name='embedding_layer',
w_init=w_init)
# Create additional layers if using relative positions.
if self._relative_positions:
self._r_k_layer = snt.Linear(
key_proj_size,
name='r_k_layer',
with_bias=False,
w_init=self._initializer)
self._r_w_bias = tf.Variable(
self._initializer([1, self._num_heads, 1, self._key_size],
dtype=tf.float32),
name='r_w_bias')
self._r_r_bias = tf.Variable(
self._initializer([1, self._num_heads, 1, self._key_size],
dtype=tf.float32),
name='r_r_bias')
def _multihead_output(self, linear, inputs):
"""Applies a standard linear to inputs and returns multihead output."""
output = snt.BatchApply(linear)(inputs) # [B, T, H * KV]
num_kv_channels = output.shape[-1] // self._num_heads
# Split H * Channels into separate axes.
output = snt.reshape(output,
output_shape=[-1, self._num_heads, num_kv_channels])
# [B, T, H, KV] -> [B, H, T, KV]
return tf.transpose(output, [0, 2, 1, 3])
def __call__(self,
inputs,
is_training=False):
# Initialise the projection layers.
embedding_size = self._value_size * self._num_heads
seq_len = inputs.shape[1]
# Compute q, k and v as multi-headed projections of the inputs.
q = self._multihead_output(self._q_layer, inputs) # [B, H, T, K]
k = self._multihead_output(self._k_layer, inputs) # [B, H, T, K]
v = self._multihead_output(self._v_layer, inputs) # [B, H, T, V]
# Scale the query by the square-root of key size.
if self._scaling:
q *= self._key_size**-0.5
if self._relative_positions:
# For relative positions, we project positions to form relative keys.
distances = tf.range(-seq_len + 1, seq_len, dtype=tf.float32)[tf.newaxis]
positional_encodings = positional_features_all(
positions=distances,
feature_size=self._num_relative_position_features,
seq_length=seq_len,
feature_functions=self._relative_position_functions,
symmetric=self._relative_position_symmetric)
# [1, 2T-1, Cr]
if is_training:
positional_encodings = tf.nn.dropout(
positional_encodings, rate=self._positional_dropout_rate)
# [1, H, 2T-1, K]
r_k = self._multihead_output(self._r_k_layer, positional_encodings)
# Add shifted relative logits to content logits.
# [B, H, T', T]
content_logits = tf.matmul(q + self._r_w_bias, k, transpose_b=True)
# [B, H, T', 2T-1]
relative_logits = tf.matmul(
q + self._r_r_bias, r_k, transpose_b=True)
# [B, H, T', T]
relative_logits = relative_shift(relative_logits)
logits = content_logits + relative_logits
else:
# [B, H, T', T]
logits = tf.matmul(q, k, transpose_b=True)
weights = tf.nn.softmax(logits)
# Dropout on the attention weights.
if is_training:
weights = tf.nn.dropout(weights, rate=self._attention_dropout_rate)
# Transpose and reshape the output.
output = tf.matmul(weights, v) # [B, H, T', V]
output_transpose = tf.transpose(output, [0, 2, 1, 3]) # [B, T', H, V]
# Final linear layer.
attended_inputs = snt.reshape(
output_transpose, output_shape=[embedding_size], preserve_dims=2)
output = self._embedding_layer(attended_inputs)
return output
def relative_shift(x):
"""Shift the relative logits like in TransformerXL."""
# We prepend zeros on the final timescale dimension.
to_pad = tf.zeros_like(x[..., :1])
x = tf.concat([to_pad, x], -1)
_, num_heads, t1, t2 = x.shape
x = tf.reshape(x, [-1, num_heads, t2, t1])
x = tf.slice(x, [0, 0, 1, 0], [-1, -1, -1, -1])
x = tf.reshape(x, [-1, num_heads, t1, t2 - 1])
x = tf.slice(x, [0, 0, 0, 0], [-1, -1, -1, (t2 + 1) // 2])
return x
# Available feature functions:
def get_positional_feature_function(name):
"""Returns positional feature functions."""
available = {
'positional_features_exponential': positional_features_exponential,
'positional_features_central_mask': positional_features_central_mask,
'positional_features_gamma': positional_features_gamma,
'positional_features_cosine': positional_features_cosine,
'positional_features_linear_masks': positional_features_linear_masks,
'positional_features_sin_cos': positional_features_sin_cos,
}
if name not in available:
raise ValueError(f'Function {name} not available in {available.keys()}')
return available[name]
def positional_features_all(positions: tf.Tensor,
feature_size: int,
seq_length: Optional[int] = None,
bin_size: Optional[int] = None,
feature_functions: Optional[List[str]] = None,
symmetric=False):
"""Compute relative positional encodings/features.
Each positional feature function will compute/provide the same fraction of
features, making up the total of feature_size.
Args:
positions: Tensor of relative positions of arbitrary shape.
feature_size: Total number of basis functions.
seq_length: Sequence length denoting the characteristic length that
the individual positional features can use. This is required since the
parametrization of the input features should be independent of `positions`
while it could still require to use the total number of features.
bin_size: Bin sized used to partition the sequence. This can be used to
compute features on the absolute scale relative to the genome.
feature_functions: List of different feature functions to use. Each function
will take as argument: positions, sequence length and number of features
to compute.
symmetric: If True, the resulting features will be symmetric across the
relative position of 0 (i.e. only absolute value of positions will
matter). If false, then both the symmetric and asymmetric version
(symmetric multiplied by sign(positions)) of the features will be used.
Returns:
Tensor of shape: `positions.shape + (feature_size,)`.
"""
if feature_functions is None:
feature_functions = ['positional_features_exponential',
'positional_features_central_mask',
'positional_features_gamma']
num_components = len(feature_functions) # 1 per each basis function
if not symmetric:
num_components = 2 * num_components
# For now, we do not allow odd sized embeddings.
if feature_size % num_components != 0:
raise ValueError(
f'feature_size has to be divisible by {num_components}')
feature_functions = [get_positional_feature_function(f)
for f in feature_functions]
num_basis_per_class = feature_size // num_components
embeddings = tf.concat([f(tf.abs(positions), num_basis_per_class,
seq_length, bin_size)
for f in feature_functions],
axis=-1)
if not symmetric:
embeddings = tf.concat([embeddings,
tf.sign(positions)[..., tf.newaxis] * embeddings],
axis=-1)
tf.TensorShape(embeddings.shape).assert_is_compatible_with(
positions.shape + [feature_size])
return embeddings
def _prepend_dims(x, num_dims):
return tf.reshape(x, shape=[1] * num_dims + x.shape)
def positional_features_exponential(positions: tf.Tensor,
feature_size: int,
seq_length: Optional[int] = None,
bin_size: Optional[int] = None,
min_half_life: Optional[float] = 3.0):
"""Create exponentially decaying positional weights.
Args:
positions: Position tensor (arbitrary shape).
feature_size: Number of basis functions to use.
seq_length: Sequence length.
bin_size: (unused). See `positional_features_all`.
min_half_life: Smallest exponential half life in the grid of half lives.
Returns:
A Tensor with shape [2 * seq_length - 1, feature_size].
"""
del bin_size # Unused.
if seq_length is None:
seq_length = tf.reduce_max(tf.abs(positions)) + 1
# Grid of half lifes from [3, seq_length / 2] with feature_size
# distributed on the log scale.
seq_length = tf.cast(seq_length, dtype=tf.float32)
max_range = tf.math.log(seq_length) / tf.math.log(2.0)
half_life = tf.pow(2.0, tf.linspace(min_half_life, max_range, feature_size))
half_life = _prepend_dims(half_life, positions.shape.rank)
positions = tf.abs(positions)
outputs = tf.exp(-tf.math.log(2.0) / half_life * positions[..., tf.newaxis])
tf.TensorShape(outputs.shape).assert_is_compatible_with(
positions.shape + [feature_size])
return outputs
def positional_features_central_mask(positions: tf.Tensor,
feature_size: int,
seq_length: Optional[int] = None,
bin_size: Optional[int] = None):
"""Positional features using a central mask (allow only central features)."""
del seq_length # Unused.
del bin_size # Unused.
center_widths = tf.pow(2.0, tf.range(1, feature_size + 1, dtype=tf.float32))
center_widths = center_widths - 1
center_widths = _prepend_dims(center_widths, positions.shape.rank)
outputs = tf.cast(center_widths > tf.abs(positions)[..., tf.newaxis],
tf.float32)
tf.TensorShape(outputs.shape).assert_is_compatible_with(
positions.shape + [feature_size])
return outputs
def gamma_pdf(x, concentration, rate):
"""Gamma probability distribution function: p(x|concentration, rate)."""
log_unnormalized_prob = tf.math.xlogy(concentration - 1., x) - rate * x
log_normalization = (tf.math.lgamma(concentration) -
concentration * tf.math.log(rate))
return tf.exp(log_unnormalized_prob - log_normalization)
def positional_features_gamma(positions: tf.Tensor,
feature_size: int,
seq_length: Optional[int] = None,
bin_size: Optional[int] = None,
stddev=None,
start_mean=None):
"""Positional features computed using the gamma distributions."""
del bin_size # Unused.
if seq_length is None:
seq_length = tf.reduce_max(tf.abs(positions)) + 1
if stddev is None:
stddev = seq_length / (2 * feature_size)
if start_mean is None:
start_mean = seq_length / feature_size
mean = tf.linspace(start_mean, seq_length, num=feature_size)
mean = _prepend_dims(mean, positions.shape.rank)
concentration = (mean / stddev)**2
rate = mean / stddev**2
probabilities = gamma_pdf(
tf.abs(tf.cast(positions, dtype=tf.float32))[..., tf.newaxis],
concentration, rate)
probabilities += 1e-8 # To ensure numerical stability.
outputs = probabilities / tf.reduce_max(probabilities,
axis=1, keepdims=True)
tf.TensorShape(outputs.shape).assert_is_compatible_with(
positions.shape + [feature_size])
return outputs
def positional_features_cosine(positions: tf.Tensor,
feature_size: int,
seq_length: Optional[int] = None,
bin_size: Optional[int] = None):
"""Cosine positional features."""
del bin_size # Unused.
del seq_length # Unused.
periodicity = 1.25 * tf.pow(2.0, tf.range(0, feature_size, dtype=tf.float32))
periodicity = _prepend_dims(periodicity, positions.shape.rank)
outputs = tf.math.cos(2 * np.pi * positions[..., tf.newaxis] / periodicity)
tf.TensorShape(outputs.shape).assert_is_compatible_with(
positions.shape + [feature_size])
return outputs
def positional_features_linear_masks(positions: tf.Tensor,
feature_size: int,
seq_length: Optional[int] = None,
bin_size: Optional[int] = None):
"""Exponentially increasing point focuses."""
del bin_size # Unused.
del seq_length # Unused.
distances = tf.range(0, feature_size, dtype=tf.float32)
distances = _prepend_dims(distances, positions.shape.rank)
outputs = tf.cast(distances == tf.abs(positions[..., tf.newaxis]),
dtype=tf.float32)
tf.TensorShape(outputs.shape).assert_is_compatible_with(
positions.shape + [feature_size])
return outputs
def positional_features_sin_cos(positions: tf.Tensor,
feature_size: int,
seq_length: Optional[int] = None,
bin_size: Optional[int] = None,
max_time=10000.0):
"""Sine/cosine positional encodings."""
del bin_size # Unused.
del seq_length # Unused.
if feature_size % 2 != 0:
raise ValueError('feature_size needs to be divisible by 2.')
i = tf.range(0, feature_size, 2, dtype=tf.float32)
i = _prepend_dims(i, positions.shape.rank)
# Concat sines and cosines and return.
outputs = tf.concat([
tf.sin(positions[..., tf.newaxis] / max_time**(i / feature_size)),
tf.cos(positions[..., tf.newaxis] / max_time**(i / feature_size))], -1)
tf.TensorShape(outputs.shape).assert_is_compatible_with(
positions.shape + [feature_size])
return outputs
================================================
FILE: enformer/enformer-training.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "rb_ShvB9E8yM"
},
"source": [
"Copyright 2021 DeepMind Technologies Limited\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
" https://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kXQjDxgdwUmW"
},
"source": [
"This colab showcases training of the Enformer model published in\n",
"\n",
"**\"Effective gene expression prediction from sequence by integrating long-range interactions\"**\n",
"\n",
"Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, David R. Kelley\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2AVkKjy3bh_A"
},
"source": [
"## Steps\n",
"\n",
"- Setup tf.data.Dataset by directly accessing the Basenji2 data on GCS: `gs://basenji_barnyard/data`\n",
"- Train the model for a few steps, alternating training on human and mouse data batches\n",
"- Evaluate the model on human and mouse genomes"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sM_PMOT-2Xhi"
},
"source": [
"## Setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NqR7ol3rxrtM"
},
"source": [
"**Start the colab kernel with GPU**: Runtime -\u003e Change runtime type -\u003e GPU"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vhjR7StI1tZn"
},
"source": [
"### Install dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WiDFm-a41tKW",
"outputId": "8b889c6e-f113-4664-f2c9-91110808ad92"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting dm-sonnet\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/13/28/9185afffefb655ef1a29f4b84aa9f656826408ca2d1b9ffeba81fbfd40ec/dm_sonnet-2.0.0-py3-none-any.whl (254kB)\n",
"\r\u001b[K |█▎ | 10kB 13.3MB/s eta 0:00:01\r\u001b[K |██▋ | 20kB 11.7MB/s eta 0:00:01\r\u001b[K |███▉ | 30kB 8.7MB/s eta 0:00:01\r\u001b[K |█████▏ | 40kB 7.7MB/s eta 0:00:01\r\u001b[K |██████▍ | 51kB 4.5MB/s eta 0:00:01\r\u001b[K |███████▊ | 61kB 5.1MB/s eta 0:00:01\r\u001b[K |█████████ | 71kB 5.1MB/s eta 0:00:01\r\u001b[K |██████████▎ | 81kB 5.5MB/s eta 0:00:01\r\u001b[K |███████████▋ | 92kB 5.5MB/s eta 0:00:01\r\u001b[K |████████████▉ | 102kB 5.7MB/s eta 0:00:01\r\u001b[K |██████████████▏ | 112kB 5.7MB/s eta 0:00:01\r\u001b[K |███████████████▌ | 122kB 5.7MB/s eta 0:00:01\r\u001b[K |████████████████▊ | 133kB 5.7MB/s eta 0:00:01\r\u001b[K |██████████████████ | 143kB 5.7MB/s eta 0:00:01\r\u001b[K |███████████████████▎ | 153kB 5.7MB/s eta 0:00:01\r\u001b[K |████████████████████▋ | 163kB 5.7MB/s eta 0:00:01\r\u001b[K |██████████████████████ | 174kB 5.7MB/s eta 0:00:01\r\u001b[K |███████████████████████▏ | 184kB 5.7MB/s eta 0:00:01\r\u001b[K |████████████████████████▌ | 194kB 5.7MB/s eta 0:00:01\r\u001b[K |█████████████████████████▊ | 204kB 5.7MB/s eta 0:00:01\r\u001b[K |███████████████████████████ | 215kB 5.7MB/s eta 0:00:01\r\u001b[K |████████████████████████████▎ | 225kB 5.7MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▋ | 235kB 5.7MB/s eta 0:00:01\r\u001b[K |███████████████████████████████ | 245kB 5.7MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 256kB 5.7MB/s \n",
"\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.41.1)\n",
"Requirement already satisfied: six\u003e=1.12.0 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (1.15.0)\n",
"Requirement already satisfied: absl-py\u003e=0.7.1 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (0.12.0)\n",
"Requirement already satisfied: numpy\u003e=1.16.3 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (1.19.5)\n",
"Requirement already satisfied: wrapt\u003e=1.11.1 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (1.12.1)\n",
"Requirement already satisfied: tabulate\u003e=0.7.5 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (0.8.9)\n",
"Requirement already satisfied: dm-tree\u003e=0.1.1 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (0.1.5)\n",
"Installing collected packages: dm-sonnet\n",
"Successfully installed dm-sonnet-2.0.0\n"
]
}
],
"source": [
"!pip install dm-sonnet tqdm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CokqDsb-fxme"
},
"outputs": [],
"source": [
"# Get enformer source code\n",
"!wget -q https://raw.githubusercontent.com/deepmind/deepmind-research/master/enformer/attention_module.py\n",
"!wget -q https://raw.githubusercontent.com/deepmind/deepmind-research/master/enformer/enformer.py"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xmffZS_306eb"
},
"source": [
"### Import"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hTGOLrbZxNHK",
"outputId": "f58b5c21-0764-4003-c794-aa89e5d336cc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: TF_ENABLE_GPU_GARBAGE_COLLECTION=false\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"# Make sure the GPU is enabled \n",
"assert tf.config.list_physical_devices('GPU'), 'Start the colab kernel with GPU: Runtime -\u003e Change runtime type -\u003e GPU'\n",
"\n",
"# Easier debugging of OOM\n",
"%env TF_ENABLE_GPU_GARBAGE_COLLECTION=false"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "S9ywsUmT05C1"
},
"outputs": [],
"source": [
"import sonnet as snt\n",
"from tqdm import tqdm\n",
"from IPython.display import clear_output\n",
"import numpy as np\n",
"import pandas as pd\n",
"import time\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YUIbu0Xu1BnA"
},
"outputs": [],
"source": [
"assert snt.__version__.startswith('2.0')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "PWzsyJddILcx",
"outputId": "3f1cac0f-6bce-430e-b3c0-9848d43e654c"
},
"outputs": [
{
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'2.4.1'"
]
},
"execution_count": 7,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"tf.__version__"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xOhdaXG95eOl",
"outputId": "1e57ef49-254a-4050-89af-61bc0f8ea577"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fri Mar 26 12:28:00 2021 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 460.67 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 42C P8 10W / 70W | 3MiB / 15109MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
],
"source": [
"# GPU colab has T4 with 16 GiB of memory\n",
"!nvidia-smi"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0Xx--Nco09fN"
},
"source": [
"### Code"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BbXyDdoShFzX"
},
"outputs": [],
"source": [
"import enformer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MEb8OZli2Nbu"
},
"outputs": [],
"source": [
"# @title `get_targets(organism)`\n",
"def get_targets(organism):\n",
" targets_txt = f'https://raw.githubusercontent.com/calico/basenji/master/manuscripts/cross2020/targets_{organism}.txt'\n",
" return pd.read_csv(targets_txt, sep='\\t')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2BuZ2gmUbpXZ"
},
"outputs": [],
"source": [
"# @title `get_dataset(organism, subset, num_threads=8)`\n",
"import glob\n",
"import json\n",
"import functools\n",
"\n",
"\n",
"def organism_path(organism):\n",
" return os.path.join('gs://basenji_barnyard/data', organism)\n",
"\n",
"\n",
"def get_dataset(organism, subset, num_threads=8):\n",
" metadata = get_metadata(organism)\n",
" dataset = tf.data.TFRecordDataset(tfrecord_files(organism, subset),\n",
" compression_type='ZLIB',\n",
" num_parallel_reads=num_threads)\n",
" dataset = dataset.map(functools.partial(deserialize, metadata=metadata),\n",
" num_parallel_calls=num_threads)\n",
" return dataset\n",
"\n",
"\n",
"def get_metadata(organism):\n",
" # Keys:\n",
" # num_targets, train_seqs, valid_seqs, test_seqs, seq_length,\n",
" # pool_width, crop_bp, target_length\n",
" path = os.path.join(organism_path(organism), 'statistics.json')\n",
" with tf.io.gfile.GFile(path, 'r') as f:\n",
" return json.load(f)\n",
"\n",
"\n",
"def tfrecord_files(organism, subset):\n",
" # Sort the values by int(*).\n",
" return sorted(tf.io.gfile.glob(os.path.join(\n",
" organism_path(organism), 'tfrecords', f'{subset}-*.tfr'\n",
" )), key=lambda x: int(x.split('-')[-1].split('.')[0]))\n",
"\n",
"\n",
"def deserialize(serialized_example, metadata):\n",
" \"\"\"Deserialize bytes stored in TFRecordFile.\"\"\"\n",
" feature_map = {\n",
" 'sequence': tf.io.FixedLenFeature([], tf.string),\n",
" 'target': tf.io.FixedLenFeature([], tf.string),\n",
" }\n",
" example = tf.io.parse_example(serialized_example, feature_map)\n",
" sequence = tf.io.decode_raw(example['sequence'], tf.bool)\n",
" sequence = tf.reshape(sequence, (metadata['seq_length'], 4))\n",
" sequence = tf.cast(sequence, tf.float32)\n",
"\n",
" target = tf.io.decode_raw(example['target'], tf.float16)\n",
" target = tf.reshape(target,\n",
" (metadata['target_length'], metadata['num_targets']))\n",
" target = tf.cast(target, tf.float32)\n",
"\n",
" return {'sequence': sequence,\n",
" 'target': target}\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VzGRXfwV4tYH"
},
"source": [
"## Load dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 203
},
"id": "M_vr1mbl3jbD",
"outputId": "2de351ed-f43e-4469-a681-2a437d97c946"
},
"outputs": [
{
"data": {
"text/html": [
"\u003cdiv\u003e\n",
"\u003cstyle scoped\u003e\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"\u003c/style\u003e\n",
"\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
" \u003cthead\u003e\n",
" \u003ctr style=\"text-align: right;\"\u003e\n",
" \u003cth\u003e\u003c/th\u003e\n",
" \u003cth\u003eindex\u003c/th\u003e\n",
" \u003cth\u003egenome\u003c/th\u003e\n",
" \u003cth\u003eidentifier\u003c/th\u003e\n",
" \u003cth\u003efile\u003c/th\u003e\n",
" \u003cth\u003eclip\u003c/th\u003e\n",
" \u003cth\u003escale\u003c/th\u003e\n",
" \u003cth\u003esum_stat\u003c/th\u003e\n",
" \u003cth\u003edescription\u003c/th\u003e\n",
" \u003c/tr\u003e\n",
" \u003c/thead\u003e\n",
" \u003ctbody\u003e\n",
" \u003ctr\u003e\n",
" \u003cth\u003e0\u003c/th\u003e\n",
" \u003ctd\u003e0\u003c/td\u003e\n",
" \u003ctd\u003e0\u003c/td\u003e\n",
" \u003ctd\u003eENCFF833POA\u003c/td\u003e\n",
" \u003ctd\u003e/home/drk/tillage/datasets/human/dnase/encode/...\u003c/td\u003e\n",
" \u003ctd\u003e32\u003c/td\u003e\n",
" \u003ctd\u003e2\u003c/td\u003e\n",
" \u003ctd\u003emean\u003c/td\u003e\n",
" \u003ctd\u003eDNASE:cerebellum male adult (27 years) and mal...\u003c/td\u003e\n",
" \u003c/tr\u003e\n",
" \u003ctr\u003e\n",
" \u003cth\u003e1\u003c/th\u003e\n",
" \u003ctd\u003e1\u003c/td\u003e\n",
" \u003ctd\u003e0\u003c/td\u003e\n",
" \u003ctd\u003eENCFF110QGM\u003c/td\u003e\n",
" \u003ctd\u003e/home/drk/tillage/datasets/human/dnase/encode/...\u003c/td\u003e\n",
" \u003ctd\u003e32\u003c/td\u003e\n",
" \u003ctd\u003e2\u003c/td\u003e\n",
" \u003ctd\u003emean\u003c/td\u003e\n",
" \u003ctd\u003eDNASE:frontal cortex male adult (27 years) and...\u003c/td\u003e\n",
" \u003c/tr\u003e\n",
" \u003ctr\u003e\n",
" \u003cth\u003e2\u003c/th\u003e\n",
" \u003ctd\u003e2\u003c/td\u003e\n",
" \u003ctd\u003e0\u003c/td\u003e\n",
" \u003ctd\u003eENCFF880MKD\u003c/td\u003e\n",
" \u003ctd\u003e/home/drk/tillage/datasets/human/dnase/encode/...\u003c/td\u003e\n",
" \u003ctd\u003e32\u003c/td\u003e\n",
" \u003ctd\u003e2\u003c/td\u003e\n",
" \u003ctd\u003emean\u003c/td\u003e\n",
" \u003ctd\u003eDNASE:chorion\u003c/td\u003e\n",
" \u003c/tr\u003e\n",
" \u003ctr\u003e\n",
" \u003cth\u003e3\u003c/th\u003e\n",
" \u003ctd\u003e3\u003c/td\u003e\n",
" \u003ctd\u003e0\u003c/td\u003e\n",
" \u003ctd\u003eENCFF463ZLQ\u003c/td\u003e\n",
" \u003ctd\u003e/home/drk/tillage/datasets/human/dnase/encode/...\u003c/td\u003e\n",
" \u003ctd\u003e32\u003c/td\u003e\n",
" \u003ctd\u003e2\u003c/td\u003e\n",
" \u003ctd\u003emean\u003c/td\u003e\n",
" \u003ctd\u003eDNASE:Ishikawa treated with 0.02% dimethyl sul...\u003c/td\u003e\n",
" \u003c/tr\u003e\n",
" \u003ctr\u003e\n",
" \u003cth\u003e4\u003c/th\u003e\n",
" \u003ctd\u003e4\u003c/td\u003e\n",
" \u003ctd\u003e0\u003c/td\u003e\n",
" \u003ctd\u003eENCFF890OGQ\u003c/td\u003e\n",
" \u003ctd\u003e/home/drk/tillage/datasets/human/dnase/encode/...\u003c/td\u003e\n",
" \u003ctd\u003e32\u003c/td\u003e\n",
" \u003ctd\u003e2\u003c/td\u003e\n",
" \u003ctd\u003emean\u003c/td\u003e\n",
" \u003ctd\u003eDNASE:GM03348\u003c/td\u003e\n",
" \u003c/tr\u003e\n",
" \u003c/tbody\u003e\n",
"\u003c/table\u003e\n",
"\u003c/div\u003e"
],
"text/plain": [
" index genome ... sum_stat description\n",
"0 0 0 ... mean DNASE:cerebellum male adult (27 years) and mal...\n",
"1 1 0 ... mean DNASE:frontal cortex male adult (27 years) and...\n",
"2 2 0 ... mean DNASE:chorion\n",
"3 3 0 ... mean DNASE:Ishikawa treated with 0.02% dimethyl sul...\n",
"4 4 0 ... mean DNASE:GM03348\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 43,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"df_targets_human = get_targets('human')\n",
"df_targets_human.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YDSKttXI4hMT"
},
"outputs": [],
"source": [
"human_dataset = get_dataset('human', 'train').batch(1).repeat()\n",
"mouse_dataset = get_dataset('mouse', 'train').batch(1).repeat()\n",
"human_mouse_dataset = tf.data.Dataset.zip((human_dataset, mouse_dataset)).prefetch(2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2vx3116C7oFW"
},
"outputs": [],
"source": [
"it = iter(mouse_dataset)\n",
"example = next(it)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XeztqJZ74ixT",
"outputId": "39dc4051-5a19-4443-b6b0-bf6869faf5ec"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"human\n",
"{'sequence': (TensorShape([1, 131072, 4]), tf.float32), 'target': (TensorShape([1, 896, 5313]), tf.float32)}\n",
"mouse\n",
"{'sequence': (TensorShape([1, 131072, 4]), tf.float32), 'target': (TensorShape([1, 896, 1643]), tf.float32)}\n"
]
}
],
"source": [
"# Example input\n",
"it = iter(human_mouse_dataset)\n",
"example = next(it)\n",
"for i in range(len(example)):\n",
" print(['human', 'mouse'][i])\n",
" print({k: (v.shape, v.dtype) for k,v in example[i].items()})"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SHHNHzFVbvTk"
},
"source": [
"## Model training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0U3hLJaUdZkG"
},
"outputs": [],
"source": [
"def create_step_function(model, optimizer):\n",
"\n",
" @tf.function\n",
" def train_step(batch, head, optimizer_clip_norm_global=0.2):\n",
" with tf.GradientTape() as tape:\n",
" outputs = model(batch['sequence'], is_training=True)[head]\n",
" loss = tf.reduce_mean(\n",
" tf.keras.losses.poisson(batch['target'], outputs))\n",
"\n",
" gradients = tape.gradient(loss, model.trainable_variables)\n",
" optimizer.apply(gradients, model.trainable_variables)\n",
"\n",
" return loss\n",
" return train_step"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZXv5HU_242Ut"
},
"outputs": [],
"source": [
"learning_rate = tf.Variable(0., trainable=False, name='learning_rate')\n",
"optimizer = snt.optimizers.Adam(learning_rate=learning_rate)\n",
"num_warmup_steps = 5000\n",
"target_learning_rate = 0.0005\n",
"\n",
"model = enformer.Enformer(channels=1536 // 4, # Use 4x fewer channels to train faster.\n",
" num_heads=8,\n",
" num_transformer_layers=11,\n",
" pooling_type='max')\n",
"\n",
"train_step = create_step_function(model, optimizer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "code",
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FrbDaOMWcFUl",
"outputId": "6a42f69c-3003-47f2-a8d2-1b94c52eb57e"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20/20 [00:24\u003c00:00, 1.25s/it]\n",
" 0%| | 0/20 [00:00\u003c?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"loss_human 1.774059 loss_mouse 0.94303024 learning_rate 2.0000002e-06\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20/20 [00:17\u003c00:00, 1.13it/s]\n",
" 0%| | 0/20 [00:00\u003c?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"loss_human 1.0067647 loss_mouse 0.8752468 learning_rate 4.0000004e-06\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20/20 [00:17\u003c00:00, 1.13it/s]\n",
" 0%| | 0/20 [00:00\u003c?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"loss_human 1.0471998 loss_mouse 0.89318746 learning_rate 6e-06\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20/20 [00:17\u003c00:00, 1.14it/s]\n",
" 0%| | 0/20 [00:00\u003c?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"loss_human 1.010262 loss_mouse 1.02991 learning_rate 8.000001e-06\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20/20 [00:17\u003c00:00, 1.14it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"loss_human 1.111991 loss_mouse 0.84773445 learning_rate 1.0000001e-05\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# Train the model\n",
"steps_per_epoch = 20\n",
"num_epochs = 5\n",
"\n",
"data_it = iter(human_mouse_dataset)\n",
"global_step = 0\n",
"for epoch_i in range(num_epochs):\n",
" for i in tqdm(range(steps_per_epoch)):\n",
" global_step += 1\n",
"\n",
" if global_step \u003e 1:\n",
" learning_rate_frac = tf.math.minimum(\n",
" 1.0, global_step / tf.math.maximum(1.0, num_warmup_steps)) \n",
" learning_rate.assign(target_learning_rate * learning_rate_frac)\n",
"\n",
" batch_human, batch_mouse = next(data_it)\n",
"\n",
" loss_human = train_step(batch=batch_human, head='human')\n",
" loss_mouse = train_step(batch=batch_mouse, head='mouse')\n",
"\n",
" # End of epoch.\n",
" print('')\n",
" print('loss_human', loss_human.numpy(),\n",
" 'loss_mouse', loss_mouse.numpy(),\n",
" 'learning_rate', optimizer.learning_rate.numpy()\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Cs0f0z0RcCfz"
},
"source": [
"## Evaluate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "8c4lNQrHkXSC"
},
"outputs": [],
"source": [
"# @title `PearsonR` and `R2` metrics\n",
"\n",
"def _reduced_shape(shape, axis):\n",
" if axis is None:\n",
" return tf.TensorShape([])\n",
" return tf.TensorShape([d for i, d in enumerate(shape) if i not in axis])\n",
"\n",
"\n",
"class CorrelationStats(tf.keras.metrics.Metric):\n",
" \"\"\"Contains shared code for PearsonR and R2.\"\"\"\n",
"\n",
" def __init__(self, reduce_axis=None, name='pearsonr'):\n",
" \"\"\"Pearson correlation coefficient.\n",
"\n",
" Args:\n",
" reduce_axis: Specifies over which axis to compute the correlation (say\n",
" (0, 1). If not specified, it will compute the correlation across the\n",
" whole tensor.\n",
" name: Metric name.\n",
" \"\"\"\n",
" super(CorrelationStats, self).__init__(name=name)\n",
" self._reduce_axis = reduce_axis\n",
" self._shape = None # Specified in _initialize.\n",
"\n",
" def _initialize(self, input_shape):\n",
" # Remaining dimensions after reducing over self._reduce_axis.\n",
" self._shape = _reduced_shape(input_shape, self._reduce_axis)\n",
"\n",
" weight_kwargs = dict(shape=self._shape, initializer='zeros')\n",
" self._count = self.add_weight(name='count', **weight_kwargs)\n",
" self._product_sum = self.add_weight(name='product_sum', **weight_kwargs)\n",
" self._true_sum = self.add_weight(name='true_sum', **weight_kwargs)\n",
" self._true_squared_sum = self.add_weight(name='true_squared_sum',\n",
" **weight_kwargs)\n",
" self._pred_sum = self.add_weight(name='pred_sum', **weight_kwargs)\n",
" self._pred_squared_sum = self.add_weight(name='pred_squared_sum',\n",
" **weight_kwargs)\n",
"\n",
" def update_state(self, y_true, y_pred, sample_weight=None):\n",
" \"\"\"Update the metric state.\n",
"\n",
" Args:\n",
" y_true: Multi-dimensional float tensor [batch, ...] containing the ground\n",
" truth values.\n",
" y_pred: float tensor with the same shape as y_true containing predicted\n",
" values.\n",
" sample_weight: 1D tensor aligned with y_true batch dimension specifying\n",
" the weight of individual observations.\n",
" \"\"\"\n",
" if self._shape is None:\n",
" # Explicit initialization check.\n",
" self._initialize(y_true.shape)\n",
" y_true.shape.assert_is_compatible_with(y_pred.shape)\n",
" y_true = tf.cast(y_true, 'float32')\n",
" y_pred = tf.cast(y_pred, 'float32')\n",
"\n",
" self._product_sum.assign_add(\n",
" tf.reduce_sum(y_true * y_pred, axis=self._reduce_axis))\n",
"\n",
" self._true_sum.assign_add(\n",
" tf.reduce_sum(y_true, axis=self._reduce_axis))\n",
"\n",
" self._true_squared_sum.assign_add(\n",
" tf.reduce_sum(tf.math.square(y_true), axis=self._reduce_axis))\n",
"\n",
" self._pred_sum.assign_add(\n",
" tf.reduce_sum(y_pred, axis=self._reduce_axis))\n",
"\n",
" self._pred_squared_sum.assign_add(\n",
" tf.reduce_sum(tf.math.square(y_pred), axis=self._reduce_axis))\n",
"\n",
" self._count.assign_add(\n",
" tf.reduce_sum(tf.ones_like(y_true), axis=self._reduce_axis))\n",
"\n",
" def result(self):\n",
" raise NotImplementedError('Must be implemented in subclasses.')\n",
"\n",
" def reset_states(self):\n",
" if self._shape is not None:\n",
" tf.keras.backend.batch_set_value([(v, np.zeros(self._shape))\n",
" for v in self.variables])\n",
"\n",
"\n",
"class PearsonR(CorrelationStats):\n",
" \"\"\"Pearson correlation coefficient.\n",
"\n",
" Computed as:\n",
" ((x - x_avg) * (y - y_avg) / sqrt(Var[x] * Var[y])\n",
" \"\"\"\n",
"\n",
" def __init__(self, reduce_axis=(0,), name='pearsonr'):\n",
" \"\"\"Pearson correlation coefficient.\n",
"\n",
" Args:\n",
" reduce_axis: Specifies over which axis to compute the correlation.\n",
" name: Metric name.\n",
" \"\"\"\n",
" super(PearsonR, self).__init__(reduce_axis=reduce_axis,\n",
" name=name)\n",
"\n",
" def result(self):\n",
" true_mean = self._true_sum / self._count\n",
" pred_mean = self._pred_sum / self._count\n",
"\n",
" covariance = (self._product_sum\n",
" - true_mean * self._pred_sum\n",
" - pred_mean * self._true_sum\n",
" + self._count * true_mean * pred_mean)\n",
"\n",
" true_var = self._true_squared_sum - self._count * tf.math.square(true_mean)\n",
" pred_var = self._pred_squared_sum - self._count * tf.math.square(pred_mean)\n",
" tp_var = tf.math.sqrt(true_var) * tf.math.sqrt(pred_var)\n",
" correlation = covariance / tp_var\n",
"\n",
" return correlation\n",
"\n",
"\n",
"class R2(CorrelationStats):\n",
" \"\"\"R-squared (fraction of explained variance).\"\"\"\n",
"\n",
" def __init__(self, reduce_axis=None, name='R2'):\n",
" \"\"\"R-squared metric.\n",
"\n",
" Args:\n",
" reduce_axis: Specifies over which axis to compute the correlation.\n",
" name: Metric name.\n",
" \"\"\"\n",
" super(R2, self).__init__(reduce_axis=reduce_axis,\n",
" name=name)\n",
"\n",
" def result(self):\n",
" true_mean = self._true_sum / self._count\n",
" total = self._true_squared_sum - self._count * tf.math.square(true_mean)\n",
" residuals = (self._pred_squared_sum - 2 * self._product_sum\n",
" + self._true_squared_sum)\n",
"\n",
" return tf.ones_like(residuals) - residuals / total\n",
"\n",
"\n",
"class MetricDict:\n",
" def __init__(self, metrics):\n",
" self._metrics = metrics\n",
"\n",
" def update_state(self, y_true, y_pred):\n",
" for k, metric in self._metrics.items():\n",
" metric.update_state(y_true, y_pred)\n",
"\n",
" def result(self):\n",
" return {k: metric.result() for k, metric in self._metrics.items()}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "x80gX9LrhBU-"
},
"outputs": [],
"source": [
"def evaluate_model(model, dataset, head, max_steps=None):\n",
" metric = MetricDict({'PearsonR': PearsonR(reduce_axis=(0,1))})\n",
" @tf.function\n",
" def predict(x):\n",
" return model(x, is_training=False)[head]\n",
"\n",
" for i, batch in tqdm(enumerate(dataset)):\n",
" if max_steps is not None and i \u003e max_steps:\n",
" break\n",
" metric.update_state(batch['target'], predict(batch['sequence']))\n",
"\n",
" return metric.result()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "57fNitK9hzwd",
"outputId": "947aaadb-dad2-4a00-ddac-d765f65d782f"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"101it [00:23, 6.27it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"{'PearsonR': 0.0028573992}\n"
]
}
],
"source": [
"metrics_human = evaluate_model(model,\n",
" dataset=get_dataset('human', 'valid').batch(1).prefetch(2),\n",
" head='human',\n",
" max_steps=100)\n",
"print('')\n",
"print({k: v.numpy().mean() for k, v in metrics_human.items()})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HY_wj95xiDtE",
"outputId": "fea839f7-b6c9-46ed-aece-c56b02e9ea16"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"101it [00:21, 6.54it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"{'PearsonR': 0.005183698}\n"
]
}
],
"source": [
"metrics_mouse = evaluate_model(model,\n",
" dataset=get_dataset('mouse', 'valid').batch(1).prefetch(2),\n",
" head='mouse',\n",
" max_steps=100)\n",
"print('')\n",
"print({k: v.numpy().mean() for k, v in metrics_mouse.items()})"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5k1yaJrNCgvw"
},
"source": [
"# Restore Checkpoint\n",
"\n",
"Note: For the TF-Hub Enformer model, the required input sequence length is 393,216 which actually gets cropped within the model to 196,608. The open source module does not internally crop the sequence. Therefore, the code below crops the central `196,608 bp` of the longer sequence to reproduce the output of the TF hub from the reloaded checkpoint."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DB2cGdH8EGfn"
},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"EXTENDED_SEQ_LENGTH = 393_216\n",
"SEQ_LENGTH = 196_608\n",
"inputs = np.array(np.random.random((1, EXTENDED_SEQ_LENGTH, 4)), dtype=np.float32)\n",
"inputs_cropped = enformer.TargetLengthCrop1D(SEQ_LENGTH)(inputs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mdf35itsCjEY"
},
"outputs": [],
"source": [
"checkpoint_gs_path = 'gs://dm-enformer/models/enformer/sonnet_weights/*'\n",
"checkpoint_path = '/tmp/enformer_checkpoint'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 280,
"status": "ok",
"timestamp": 1653476327690,
"user": {
"displayName": "Kyle Taylor",
"userId": "14169907681771397124"
},
"user_tz": -60
},
"id": "G2P4IHqswLul",
"outputId": "180abe21-ba00-4031-d9d7-2326f1f742f2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mkdir: cannot create directory ‘/tmp/enformer_checkpoint’: File exists\n"
]
}
],
"source": [
"!mkdir /tmp/enformer_checkpoint"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 19279,
"status": "ok",
"timestamp": 1653476347208,
"user": {
"displayName": "Kyle Taylor",
"userId": "14169907681771397124"
},
"user_tz": -60
},
"id": "LTL8EISGCujC",
"outputId": "2b743b9b-480d-44dc-b08e-82d2bc089a47"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gs://dm-enformer/models/enformer/sonnet_weights/checkpoint\n",
"gs://dm-enformer/models/enformer/sonnet_weights/enformer-fine-tuned-human-1.data-00000-of-00001\n",
"gs://dm-enformer/models/enformer/sonnet_weights/enformer-fine-tuned-human-1.index\n"
]
}
],
"source": [
"# Copy checkpoints from GCS to temporary directory.\n",
"# This will take a while as the checkpoint is ~ 1GB.\n",
"for file_path in tf.io.gfile.glob(checkpoint_gs_path):\n",
" print(file_path)\n",
" file_name = os.path.basename(file_path)\n",
" tf.io.gfile.copy(file_path, f'{checkpoint_path}/{file_name}', overwrite=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 322,
"status": "ok",
"timestamp": 1653476347527,
"user": {
"displayName": "Kyle Taylor",
"userId": "14169907681771397124"
},
"user_tz": -60
},
"id": "9VSeTx0sCvcw",
"outputId": "b52d7570-c355-4068-b932-3796b56e5586"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 959M\n",
"-rw-r--r-- 1 root root 111 May 25 10:58 checkpoint\n",
"-rw-r--r-- 1 root root 959M May 25 10:59 enformer-fine-tuned-human-1.data-00000-of-00001\n",
"-rw-r--r-- 1 root root 5.7K May 25 10:59 enformer-fine-tuned-human-1.index\n"
]
}
],
"source": [
"!ls -lh /tmp/enformer_checkpoint"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "00Y2GgRED3aI"
},
"outputs": [],
"source": [
"enformer_model = enformer.Enformer()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mFyIiGyiD5yh"
},
"outputs": [],
"source": [
"checkpoint = tf.train.Checkpoint(module=enformer_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 6,
"status": "ok",
"timestamp": 1653476347529,
"user": {
"displayName": "Kyle Taylor",
"userId": "14169907681771397124"
},
"user_tz": -60
},
"id": "VuyspnpOD9kA",
"outputId": "3b495138-be27-4459-af82-c5da2af5bd2d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/tmp/enformer_checkpoint/enformer-fine-tuned-human-1\n"
]
}
],
"source": [
"latest = tf.train.latest_checkpoint(checkpoint_path)\n",
"print(latest)\n",
"status = checkpoint.restore(latest)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MKkVOTyKEABJ"
},
"outputs": [],
"source": [
"# Using `is_training=False` to match TF-hub predict_on_batch function.\n",
"restored_predictions = enformer_model(inputs_cropped, is_training=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OX650jqCEQdv"
},
"outputs": [],
"source": [
"import tensorflow_hub as hub\n",
"enformer_tf_hub_model = hub.load(\"https://tfhub.dev/deepmind/enformer/1\").model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yiOTFTSdE5H1"
},
"outputs": [],
"source": [
"hub_predictions = enformer_tf_hub_model.predict_on_batch(inputs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 15,
"status": "ok",
"timestamp": 1653476357260,
"user": {
"displayName": "Kyle Taylor",
"userId": "14169907681771397124"
},
"user_tz": -60
},
"id": "uYrWgfaGFbpL",
"outputId": "cb9d3ad0-ee14-46ac-b188-a4b6a697a159"
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.allclose(hub_predictions['human'], restored_predictions['human'], atol=1e-5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4wEOUMeNzK8q"
},
"outputs": [],
"source": [
"# Can run with 'is_training=True' but note that this will\n",
"# change the predictions as the batch statistics will be updated\n",
"# and the outputs will likley not match the TF-hub model.\n",
"# enformer(inputs_cropped, is_training=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jyVHRPAN5w6J"
},
"outputs": [],
"source": [
""
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "//learning/deepmind/research/genomics/dna_to_rna:colab",
"kind": "private"
},
"name": "enformer-training.ipynb",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: enformer/enformer-usage.ipynb
================================================
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "enformer-usage.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "rb_ShvB9E8yM"
},
"source": [
"Copyright 2021 DeepMind Technologies Limited\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
" https://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kXQjDxgdwUmW"
},
"source": [
"This colab showcases the usage of the Enformer model published in\n",
"\n",
"**\"Effective gene expression prediction from sequence by integrating long-range interactions\"**\n",
"\n",
"Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, David R. Kelley"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tFnAHhx-ze9X"
},
"source": [
"**Note:** This colab will not yet work since the model isn't yet publicly available. We are working on enabling this and will update the colab accordingly."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "si-w2NPretDg"
},
"source": [
"### Steps\n",
"\n",
"This colab demonstrates how to\n",
"- Make predictions with Enformer and reproduce Fig. 1d\n",
"- Compute contribution scores and reproduce parts of Fig. 2a\n",
"- Predict the effect of a genetic variant and reproduce parts of Fig. 3g\n",
"- Score multiple variants in a VCF "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wCCJsjaHwTYC"
},
"source": [
"## Setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NqR7ol3rxrtM"
},
"source": [
"**Start the colab kernel with GPU**: Runtime -> Change runtime type -> GPU"
]
},
{
"cell_type": "code",
"metadata": {
"id": "hTGOLrbZxNHK"
},
"source": [
"import tensorflow as tf\n",
"# Make sure the GPU is enabled \n",
"assert tf.config.list_physical_devices('GPU'), 'Start the colab kernel with GPU: Runtime -> Change runtime type -> GPU'"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Eg8hcb45wqMM"
},
"source": [
"!pip install kipoiseq==0.5.2 --quiet > /dev/null\n",
"# You can ignore the pyYAML error"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "MCDk7UQPG0Lr"
},
"source": [
"### Imports"
]
},
{
"cell_type": "code",
"metadata": {
"id": "NRI9KisU11bM"
},
"source": [
"import tensorflow_hub as hub\n",
"import joblib\n",
"import gzip\n",
"import kipoiseq\n",
"from kipoiseq import Interval\n",
"import pyfaidx\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"import seaborn as sns\n",
"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "g0F1A9AaCrkQ"
},
"source": [
"transform_path = 'gs://dm-enformer/models/enformer.finetuned.SAD.robustscaler-PCA500-robustscaler.transform.pkl'\n",
"model_path = 'https://tfhub.dev/deepmind/enformer/1'\n",
"fasta_file = '/root/data/genome.fa'\n",
"clinvar_vcf = '/root/data/clinvar.vcf.gz'"
],
"execution_count": 47,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 141
},
"id": "OlE6JAVfI08a",
"outputId": "25e8b4c7-b2ef-4e8a-a81f-a032a3490bd9"
},
"source": [
"# Download targets from Basenji2 dataset \n",
"# Cite: Kelley et al Cross-species regulatory sequence activity prediction. PLoS Comput. Biol. 16, e1008050 (2020).\n",
"targets_txt = 'https://raw.githubusercontent.com/calico/basenji/master/manuscripts/cross2020/targets_human.txt'\n",
"df_targets = pd.read_csv(targets_txt, sep='\\t')\n",
"df_targets.head(3)"
],
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"
\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" index genome ... sum_stat description\n",
"0 0 0 ... mean DNASE:cerebellum male adult (27 years) and mal...\n",
"1 1 0 ... mean DNASE:frontal cortex male adult (27 years) and...\n",
"2 2 0 ... mean DNASE:chorion\n",
"\n",
"[3 rows x 8 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q8ZhswycGux3"
},
"source": [
"### Download files"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dowTJknFJOHu"
},
"source": [
"Download and index the reference genome fasta file\n",
"\n",
"Credit to Genome Reference Consortium: https://www.ncbi.nlm.nih.gov/grc\n",
"\n",
"Schneider et al 2017 http://dx.doi.org/10.1101/gr.213611.116: Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "flOUYxP7Fjvh",
"outputId": "bc48adb1-393c-408e-d699-5e70b183e2e9"
},
"source": [
"!mkdir -p /root/data\n",
"!wget -O - http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz | gunzip -c > {fasta_file}\n",
"pyfaidx.Faidx(fasta_file)\n",
"!ls /root/data"
],
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": [
"--2021-03-26 11:38:38-- http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz\n",
"Resolving hgdownload.cse.ucsc.edu (hgdownload.cse.ucsc.edu)... 128.114.119.163\n",
"Connecting to hgdownload.cse.ucsc.edu (hgdownload.cse.ucsc.edu)|128.114.119.163|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 983659424 (938M) [application/x-gzip]\n",
"Saving to: ‘STDOUT’\n",
"\n",
"- 100%[===================>] 938.09M 17.4MB/s in 54s \n",
"\n",
"2021-03-26 11:39:33 (17.3 MB/s) - written to stdout [983659424/983659424]\n",
"\n",
"genome.fa genome.fa.fai\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VwMZhF42JH18"
},
"source": [
"Download the clinvar file. Reference:\n",
"\n",
"Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu C, Maddipatla Z, Malheiro A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott DR. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res . 2018 Jan 4. PubMed PMID: 29165669 .\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4PjXFN5OcGbH",
"outputId": "ca4e8ca5-d388-4ba9-afb9-414f30e83f4a"
},
"source": [
"!wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz -O /root/data/clinvar.vcf.gz"
],
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": [
"--2021-03-26 11:40:32-- https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz\n",
"Resolving ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)... 130.14.250.7, 165.112.9.229, 2607:f220:41e:250::7, ...\n",
"Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|130.14.250.7|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 36564426 (35M) [application/x-gzip]\n",
"Saving to: ‘/root/data/clinvar.vcf.gz’\n",
"\n",
"/root/data/clinvar. 100%[===================>] 34.87M 22.0MB/s in 1.6s \n",
"\n",
"2021-03-26 11:40:34 (22.0 MB/s) - ‘/root/data/clinvar.vcf.gz’ saved [36564426/36564426]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Omj-KERcwSdB"
},
"source": [
"### Code (double click on the title to show the code)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "47E4AEgLx1VT",
"cellView": "form"
},
"source": [
"# @title `Enformer`, `EnformerScoreVariantsNormalized`, `EnformerScoreVariantsPCANormalized`,\n",
"SEQUENCE_LENGTH = 393216\n",
"\n",
"class Enformer:\n",
"\n",
" def __init__(self, tfhub_url):\n",
" self._model = hub.load(tfhub_url).model\n",
"\n",
" def predict_on_batch(self, inputs):\n",
" predictions = self._model.predict_on_batch(inputs)\n",
" return {k: v.numpy() for k, v in predictions.items()}\n",
"\n",
" @tf.function\n",
" def contribution_input_grad(self, input_sequence,\n",
" target_mask, output_head='human'):\n",
" input_sequence = input_sequence[tf.newaxis]\n",
"\n",
" target_mask_mass = tf.reduce_sum(target_mask)\n",
" with tf.GradientTape() as tape:\n",
" tape.watch(input_sequence)\n",
" prediction = tf.reduce_sum(\n",
" target_mask[tf.newaxis] *\n",
" self._model.predict_on_batch(input_sequence)[output_head]) / target_mask_mass\n",
"\n",
" input_grad = tape.gradient(prediction, input_sequence) * input_sequence\n",
" input_grad = tf.squeeze(input_grad, axis=0)\n",
" return tf.reduce_sum(input_grad, axis=-1)\n",
"\n",
"\n",
"class EnformerScoreVariantsRaw:\n",
"\n",
" def __init__(self, tfhub_url, organism='human'):\n",
" self._model = Enformer(tfhub_url)\n",
" self._organism = organism\n",
" \n",
" def predict_on_batch(self, inputs):\n",
" ref_prediction = self._model.predict_on_batch(inputs['ref'])[self._organism]\n",
" alt_prediction = self._model.predict_on_batch(inputs['alt'])[self._organism]\n",
"\n",
" return alt_prediction.mean(axis=1) - ref_prediction.mean(axis=1)\n",
"\n",
"\n",
"class EnformerScoreVariantsNormalized:\n",
"\n",
" def __init__(self, tfhub_url, transform_pkl_path,\n",
" organism='human'):\n",
" assert organism == 'human', 'Transforms only compatible with organism=human'\n",
" self._model = EnformerScoreVariantsRaw(tfhub_url, organism)\n",
" with tf.io.gfile.GFile(transform_pkl_path, 'rb') as f:\n",
" transform_pipeline = joblib.load(f)\n",
" self._transform = transform_pipeline.steps[0][1] # StandardScaler.\n",
" \n",
" def predict_on_batch(self, inputs):\n",
" scores = self._model.predict_on_batch(inputs)\n",
" return self._transform.transform(scores)\n",
"\n",
"\n",
"class EnformerScoreVariantsPCANormalized:\n",
"\n",
" def __init__(self, tfhub_url, transform_pkl_path,\n",
" organism='human', num_top_features=500):\n",
" self._model = EnformerScoreVariantsRaw(tfhub_url, organism)\n",
" with tf.io.gfile.GFile(transform_pkl_path, 'rb') as f:\n",
" self._transform = joblib.load(f)\n",
" self._num_top_features = num_top_features\n",
" \n",
" def predict_on_batch(self, inputs):\n",
" scores = self._model.predict_on_batch(inputs)\n",
" return self._transform.transform(scores)[:, :self._num_top_features]\n",
"\n",
"\n",
"# TODO(avsec): Add feature description: Either PCX, or full names."
],
"execution_count": 35,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "uLYRbOXDyA36",
"cellView": "form"
},
"source": [
"# @title `variant_centered_sequences`\n",
"\n",
"class FastaStringExtractor:\n",
" \n",
" def __init__(self, fasta_file):\n",
" self.fasta = pyfaidx.Fasta(fasta_file)\n",
" self._chromosome_sizes = {k: len(v) for k, v in self.fasta.items()}\n",
"\n",
" def extract(self, interval: Interval, **kwargs) -> str:\n",
" # Truncate interval if it extends beyond the chromosome lengths.\n",
" chromosome_length = self._chromosome_sizes[interval.chrom]\n",
" trimmed_interval = Interval(interval.chrom,\n",
" max(interval.start, 0),\n",
" min(interval.end, chromosome_length),\n",
" )\n",
" # pyfaidx wants a 1-based interval\n",
" sequence = str(self.fasta.get_seq(trimmed_interval.chrom,\n",
" trimmed_interval.start + 1,\n",
" trimmed_interval.stop).seq).upper()\n",
" # Fill truncated values with N's.\n",
" pad_upstream = 'N' * max(-interval.start, 0)\n",
" pad_downstream = 'N' * max(interval.end - chromosome_length, 0)\n",
" return pad_upstream + sequence + pad_downstream\n",
"\n",
" def close(self):\n",
" return self.fasta.close()\n",
"\n",
"\n",
"def variant_generator(vcf_file, gzipped=False):\n",
" \"\"\"Yields a kipoiseq.dataclasses.Variant for each row in VCF file.\"\"\"\n",
" def _open(file):\n",
" return gzip.open(vcf_file, 'rt') if gzipped else open(vcf_file)\n",
" \n",
" with _open(vcf_file) as f:\n",
" for line in f:\n",
" if line.startswith('#'):\n",
" continue\n",
" chrom, pos, id, ref, alt_list = line.split('\\t')[:5]\n",
" # Split ALT alleles and return individual variants as output.\n",
" for alt in alt_list.split(','):\n",
" yield kipoiseq.dataclasses.Variant(chrom=chrom, pos=pos,\n",
" ref=ref, alt=alt, id=id)\n",
"\n",
"\n",
"def one_hot_encode(sequence):\n",
" return kipoiseq.transforms.functional.one_hot_dna(sequence).astype(np.float32)\n",
"\n",
"\n",
"def variant_centered_sequences(vcf_file, sequence_length, gzipped=False,\n",
" chr_prefix=''):\n",
" seq_extractor = kipoiseq.extractors.VariantSeqExtractor(\n",
" reference_sequence=FastaStringExtractor(fasta_file))\n",
"\n",
" for variant in variant_generator(vcf_file, gzipped=gzipped):\n",
" interval = Interval(chr_prefix + variant.chrom,\n",
" variant.pos, variant.pos)\n",
" interval = interval.resize(sequence_length)\n",
" center = interval.center() - interval.start\n",
"\n",
" reference = seq_extractor.extract(interval, [], anchor=center)\n",
" alternate = seq_extractor.extract(interval, [variant], anchor=center)\n",
"\n",
" yield {'inputs': {'ref': one_hot_encode(reference),\n",
" 'alt': one_hot_encode(alternate)},\n",
" 'metadata': {'chrom': chr_prefix + variant.chrom,\n",
" 'pos': variant.pos,\n",
" 'id': variant.id,\n",
" 'ref': variant.ref,\n",
" 'alt': variant.alt}}"
],
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Up1oCMFiPucp",
"cellView": "form"
},
"source": [
"# @title `plot_tracks`\n",
"\n",
"def plot_tracks(tracks, interval, height=1.5):\n",
" fig, axes = plt.subplots(len(tracks), 1, figsize=(20, height * len(tracks)), sharex=True)\n",
" for ax, (title, y) in zip(axes, tracks.items()):\n",
" ax.fill_between(np.linspace(interval.start, interval.end, num=len(y)), y)\n",
" ax.set_title(title)\n",
" sns.despine(top=True, right=True, bottom=True)\n",
" ax.set_xlabel(str(interval))\n",
" plt.tight_layout()"
],
"execution_count": 23,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zEwfoz3cwOzt"
},
"source": [
"## Make predictions for a genetic sequenece"
]
},
{
"cell_type": "code",
"metadata": {
"id": "WC-pgC35DgnL"
},
"source": [
"model = Enformer(model_path)\n",
"\n",
"fasta_extractor = FastaStringExtractor(fasta_file)"
],
"execution_count": 36,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8u8Gt8WWyG53"
},
"source": [
"# @title Make predictions for an genomic example interval\n",
"target_interval = kipoiseq.Interval('chr11', 35_082_742, 35_197_430) # @param\n",
"\n",
"sequence_one_hot = one_hot_encode(fasta_extractor.extract(target_interval.resize(SEQUENCE_LENGTH)))\n",
"predictions = model.predict_on_batch(sequence_one_hot[np.newaxis])['human'][0]"
],
"execution_count": 37,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 440
},
"id": "_wL4c66ZXK0k",
"outputId": "3538b7f3-ebb0-48c8-f23a-1e8734253e81"
},
"source": [
"# @title Plot tracks\n",
"tracks = {'DNASE:CD14-positive monocyte female': predictions[:, 41],\n",
" 'DNASE:keratinocyte female': predictions[:, 42],\n",
" 'CHIP:H3K27ac:keratinocyte female': predictions[:, 706],\n",
" 'CAGE:Keratinocyte - epidermal': np.log10(1 + predictions[:, 4799])}\n",
"plot_tracks(tracks, target_interval)"
],
"execution_count": 38,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"| \n", " | index | \n", "genome | \n", "identifier | \n", "file | \n", "clip | \n", "scale | \n", "sum_stat | \n", "description | \n", "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", "0 | \n", "0 | \n", "ENCFF833POA | \n", "/home/drk/tillage/datasets/human/dnase/encode/... | \n", "32 | \n", "2 | \n", "mean | \n", "DNASE:cerebellum male adult (27 years) and mal... | \n", "
| 1 | \n", "1 | \n", "0 | \n", "ENCFF110QGM | \n", "/home/drk/tillage/datasets/human/dnase/encode/... | \n", "32 | \n", "2 | \n", "mean | \n", "DNASE:frontal cortex male adult (27 years) and... | \n", "
| 2 | \n", "2 | \n", "0 | \n", "ENCFF880MKD | \n", "/home/drk/tillage/datasets/human/dnase/encode/... | \n", "32 | \n", "2 | \n", "mean | \n", "DNASE:chorion | \n", "
"
]
},
"metadata": {
"tags": [],
"image/png": {
"width": 1432,
"height": 423
},
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gM2KwV8gwMNj"
},
"source": [
"## Contribution scores example"
]
},
{
"cell_type": "code",
"metadata": {
"id": "o4c2W_MjNBzp"
},
"source": [
"# @title Compute contribution scores\n",
"target_interval = kipoiseq.Interval('chr12', 54_223_589, 54_338_277) # @param\n",
"\n",
"sequence_one_hot = one_hot_encode(fasta_extractor.extract(target_interval.resize(SEQUENCE_LENGTH)))\n",
"predictions = model.predict_on_batch(sequence_one_hot[np.newaxis])['human'][0]\n",
"\n",
"target_mask = np.zeros_like(predictions)\n",
"for idx in [447, 448, 449]:\n",
" target_mask[idx, 4828] = 1\n",
" target_mask[idx, 5111] = 1\n",
"# This will take some time since tf.function needs to get compiled.\n",
"contribution_scores = model.contribution_input_grad(sequence_one_hot.astype(np.float32), target_mask).numpy()\n",
"pooled_contribution_scores = tf.nn.avg_pool1d(np.abs(contribution_scores)[np.newaxis, :, np.newaxis], 128, 128, 'VALID')[0, :, 0].numpy()[1088:-1088]"
],
"execution_count": 39,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 224
},
"id": "y-s0yQMOXO7x",
"outputId": "a5cc620e-2680-4780-d546-845e9ca02740"
},
"source": [
"tracks = {'CAGE predictions': predictions[:, 4828],\n",
" 'Enformer gradient*input': np.minimum(pooled_contribution_scores, 0.03)}\n",
"plot_tracks(tracks, target_interval);"
],
"execution_count": 40,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"tags": [],
"image/png": {
"width": 1433,
"height": 207
},
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iiDN_ScSv3sI"
},
"source": [
"## Variant scoring example"
]
},
{
"cell_type": "code",
"metadata": {
"id": "hIaN9GKDVwLK"
},
"source": [
"# @title Score the variant\n",
"variant = kipoiseq.Variant('chr16', 57025062, 'C', 'T', id='rs11644125') # @param\n",
"\n",
"# Center the interval at the variant\n",
"interval = kipoiseq.Interval(variant.chrom, variant.start, variant.start).resize(SEQUENCE_LENGTH)\n",
"seq_extractor = kipoiseq.extractors.VariantSeqExtractor(reference_sequence=fasta_extractor)\n",
"center = interval.center() - interval.start\n",
"\n",
"reference = seq_extractor.extract(interval, [], anchor=center)\n",
"alternate = seq_extractor.extract(interval, [variant], anchor=center)\n",
"\n",
"# Make predictions for the refernece and alternate allele\n",
"reference_prediction = model.predict_on_batch(one_hot_encode(reference)[np.newaxis])['human'][0]\n",
"alternate_prediction = model.predict_on_batch(one_hot_encode(alternate)[np.newaxis])['human'][0]"
],
"execution_count": 41,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 368
},
"id": "in1PyiWoXXjc",
"outputId": "664617ba-97c1-4ed3-8d4d-e5e8879ce474"
},
"source": [
"# @title Visualize some tracks\n",
"variant_track = np.zeros_like(reference_prediction[:, 0], dtype=bool)\n",
"variant_track[variant_track.shape[0] // 2] = True\n",
"tracks = {'variant': variant_track,\n",
" 'CAGE/neutrofils ref': reference_prediction[:, 4767],\n",
" 'CAGE/neutrofils alt-ref': alternate_prediction[:, 4767] - reference_prediction[:, 4767],\n",
" 'CHIP:H3K27ac:neutrophil ref': reference_prediction[:, 2280],\n",
" 'CHIP:H3K27ac:neutrophil alt-ref': alternate_prediction[:, 2280] - reference_prediction[:, 2280],\n",
" }\n",
"\n",
"plot_tracks(tracks, interval.resize(reference_prediction.shape[0] * 128), height=1)"
],
"execution_count": 42,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"tags": [],
"image/png": {
"width": 1436,
"height": 351
},
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZMCU9woQv6Ea"
},
"source": [
"## Score variants in a VCF file"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c1nJwFS-4P8g"
},
"source": [
"### Report top 20 PCs"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-vmj1MA3chRQ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0e004c93-a817-4e87-842d-1ea04a26b649"
},
"source": [
"enformer_score_variants = EnformerScoreVariantsPCANormalized(model_path, transform_path, num_top_features=20)"
],
"execution_count": 48,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator RobustScaler from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
" UserWarning)\n",
"/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator TruncatedSVD from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
" UserWarning)\n",
"/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator RobustScaler from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
" UserWarning)\n",
"/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator Pipeline from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
" UserWarning)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 275
},
"id": "1oGEYix3dRex",
"outputId": "c7e89a9e-faa6-4f8d-f018-db6bfa303283"
},
"source": [
"# Score the first 5 variants from ClinVar\n",
"# Lower-dimensional scores (20 PCs)\n",
"it = variant_centered_sequences(clinvar_vcf, sequence_length=SEQUENCE_LENGTH,\n",
" gzipped=True, chr_prefix='chr')\n",
"example_list = []\n",
"for i, example in enumerate(it):\n",
" if i >= 5:\n",
" break\n",
" variant_scores = enformer_score_variants.predict_on_batch(\n",
" {k: v[tf.newaxis] for k,v in example['inputs'].items()})[0]\n",
" variant_scores = {f'PC{i}': score for i, score in enumerate(variant_scores)}\n",
" example_list.append({**example['metadata'],\n",
" **variant_scores})\n",
" if i % 2 == 0:\n",
" print(f'Done {i}')\n",
"df = pd.DataFrame(example_list)\n",
"df"
],
"execution_count": 49,
"outputs": [
{
"output_type": "stream",
"text": [
"Done 0\n",
"Done 2\n",
"Done 4\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"_
coil_type, _ = name.split('_')
coil_max.append(spec_mapping[coil_type])
coil_min.append(-spec_mapping[coil_type])
return specs.BoundedArray(
shape=(len(coil_names),), dtype=dtype, minimum=coil_min, maximum=coil_max)
INNER_LIMITER_R = 0.62400001
OUTER_LIMITER_R = 1.14179182
LIMITER_WIDTH = OUTER_LIMITER_R - INNER_LIMITER_R
LIMITER_RADIUS = LIMITER_WIDTH / 2
VESSEL_CENTER_R = INNER_LIMITER_R + LIMITER_RADIUS
================================================
FILE: fusion_tcv/terminations.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Terminations for the fusion environment."""
import abc
from typing import List, Optional
import numpy as np
from fusion_tcv import fge_state
from fusion_tcv import tcv_common
class Abstract(abc.ABC):
"""Abstract reward class."""
@abc.abstractmethod
def terminate(self, state: fge_state.FGEState) -> Optional[str]:
"""Returns a reason if the situation should be considered a termination."""
class CoilCurrentSaturation(Abstract):
"""Terminates if the coils have saturated their current."""
def terminate(self, state: fge_state.FGEState) -> Optional[str]:
# Coil currents are checked by type, independent of the order.
for coil_type, max_current in tcv_common.ENV_COIL_MAX_CURRENTS.items():
if coil_type == "DUMMY":
continue
currents = state.get_coil_currents_by_type(coil_type)
if (np.abs(currents) > max_current).any():
return (f"CoilCurrentSaturation: {coil_type}, max: {max_current}, "
"real: " + ", ".join(f"{c:.1f}" for c in currents))
return None
class OHTooDifferent(Abstract):
"""Terminates if the coil currents are too far apart from one another."""
def __init__(self, max_diff: float):
self._max_diff = max_diff
def terminate(self, state: fge_state.FGEState) -> Optional[str]:
oh_coil_currents = state.get_coil_currents_by_type("OH")
assert len(oh_coil_currents) == 2
oh_current_abs = abs(oh_coil_currents[0] - oh_coil_currents[1])
if oh_current_abs > self._max_diff:
return ("OHTooDifferent: currents: "
f"({oh_coil_currents[0]:.0f}, {oh_coil_currents[1]:.0f}), "
f"diff: {oh_current_abs:.0f}, max: {self._max_diff}")
return None
class IPTooLow(Abstract):
"""Terminates if the magnitude of Ip in any component is too low."""
def __init__(self, singlet_threshold: float, droplet_threshold: float):
self._singlet_threshold = singlet_threshold
self._droplet_threshold = droplet_threshold
def terminate(self, state: fge_state.FGEState) -> Optional[str]:
_, _, ip_d = state.rzip_d
if len(ip_d) == 1:
if ip_d[0] > self._singlet_threshold: # Sign due to negative Ip.
return f"IPTooLow: Singlet, {ip_d[0]:.0f}"
return None
else:
if max(ip_d) > self._droplet_threshold: # Sign due to negative Ip.
return f"IPTooLow: Components: {ip_d[0]:.0f}, {ip_d[1]:.0f}"
return None
class AnyTermination(Abstract):
"""Terminates if any of conditions are met."""
def __init__(self, terminators: List[Abstract]):
self._terminators = terminators
def terminate(self, state: fge_state.FGEState) -> Optional[str]:
for terminator in self._terminators:
term = terminator.terminate(state)
if term:
return term
return None
CURRENT_OH_IP = AnyTermination([
CoilCurrentSaturation(),
OHTooDifferent(max_diff=4000),
IPTooLow(singlet_threshold=-60000, droplet_threshold=-25000),
])
================================================
FILE: fusion_tcv/trajectory.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A trajectory for an episode."""
from typing import List
import dataclasses
import numpy as np
@dataclasses.dataclass
class Trajectory:
"""A trajectory of actions/obs for an episode."""
measurements: np.ndarray
references: np.ndarray
reward: np.ndarray
actions: np.ndarray
@classmethod
def stack(cls, series: List["Trajectory"]) -> "Trajectory":
"""Stack a series of trajectories, adding a trailing time dimension."""
values = {k: np.empty(v.shape + (len(series),))
for k, v in dataclasses.asdict(series[0]).items()
if v is not None}
for i, ts in enumerate(series):
for k, v in values.items():
v[..., i] = getattr(ts, k)
out = cls(**values)
return out
================================================
FILE: fusion_tcv/transforms.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Transforms from actual/target to rewards."""
import abc
import math
from typing import List, Optional
import dataclasses
# Comparison of some of the transforms:
# Order is NegExp, SoftPlus, Sigmoid.
# Over range of good to bad:
# https://www.wolframalpha.com/input/?i=plot+e%5E%28x*ln%280.1%29%29%2C2%2F%281%2Be%5E%28x*ln%2819%29%29%29%2C1%2F%281%2Be%5E%28-+%28-ln%2819%29+-+%28x-1%29*%282*ln%2819%29%29%29%29%29+from+x%3D0+to+2
# When close to good:
# https://www.wolframalpha.com/input/?i=plot+e%5E%28x*ln%280.1%29%29%2C2%2F%281%2Be%5E%28x*ln%2819%29%29%29%2C1%2F%281%2Be%5E%28-+%28-ln%2819%29+-+%28x-1%29*%282*ln%2819%29%29%29%29%29+from+x%3D0+to+0.2
class AbstractTransform(abc.ABC):
@abc.abstractmethod
def __call__(self, errors: List[float]) -> List[float]:
"""Transforms target errors into rewards."""
@property
def outputs(self) -> Optional[int]:
return None
def clip(value: float, low: float, high: float) -> float:
"""Clip a value to the range of low - high."""
if math.isnan(value):
return value
assert low <= high
return max(low, min(high, value))
def scale(v: float, a: float, b: float, c: float, d: float) -> float:
"""Scale a value, v on a line with anchor points a,b to new anchors c,d."""
v01 = (v - a) / (b - a)
return c - v01 * (c - d)
def logistic(v: float) -> float:
"""Standard logistic, asymptoting to 0 and 1."""
v = clip(v, -50, 50) # Improve numerical stability.
return 1 / (1 + math.exp(-v))
@dataclasses.dataclass(frozen=True)
class Equal(AbstractTransform):
"""Returns 1 if the error is 0 and not_equal_val otherwise."""
not_equal_val: float = 0
def __call__(self, errors: List[float]) -> List[float]:
out = []
for err in errors:
if math.isnan(err):
out.append(err)
elif err == 0:
out.append(1)
else:
out.append(self.not_equal_val)
return out
class Abs(AbstractTransform):
"""Take the absolue value of the error. Does not guarantee 0-1."""
@staticmethod
def __call__(errors: List[float]) -> List[float]:
return [abs(err) for err in errors]
class Neg(AbstractTransform):
"""Negate the error. Does not guarantee 0-1."""
@staticmethod
def __call__(errors: List[float]) -> List[float]:
return [-err for err in errors]
@dataclasses.dataclass(frozen=True)
class Pow(AbstractTransform):
"""Return a power of the error. Does not guarantee 0-1."""
pow: float
def __call__(self, errors: List[float]) -> List[float]:
return [err**self.pow for err in errors]
@dataclasses.dataclass(frozen=True)
class Log(AbstractTransform):
"""Return a log of the error. Does not guarantee 0-1."""
eps: float = 1e-4
def __call__(self, errors: List[float]) -> List[float]:
return [math.log(err + self.eps) for err in errors]
@dataclasses.dataclass(frozen=True)
class ClippedLinear(AbstractTransform):
"""Scales and clips errors, bad to 0, good to 1. If good=0, this is a relu."""
bad: float
good: float = 0
def __call__(self, errors: List[float]) -> List[float]:
return [clip(scale(err, self.bad, self.good, 0, 1), 0, 1)
for err in errors]
@dataclasses.dataclass(frozen=True)
class SoftPlus(AbstractTransform):
"""Scales and clips errors, bad to 0.1, good to 1, asymptoting to 0.
Based on the lower half of the logistic instead of the standard softplus as
we want it to be bounded from 0 to 1, with the good value being exactly 1.
Various constants can be chosen to get the softplus to give the desired
properties, but this is much simpler.
"""
bad: float
good: float = 0
# Constant to set the sharpness/slope of the softplus.
# Default was chosen such that the good/bad have 1 and 0.1 reward:
# https://www.wolframalpha.com/input/?i=plot+2%2F%281%2Be%5E%28x*ln%2819%29%29%29+from+x%3D0+to+2
low: float = -math.log(19) # -2.9444389791664403
def __call__(self, errors: List[float]) -> List[float]:
return [clip(2 * logistic(scale(e, self.bad, self.good, self.low, 0)), 0, 1)
for e in errors]
@dataclasses.dataclass(frozen=True)
class NegExp(AbstractTransform):
"""Scales and clips errors, bad to 0.1, good to 1, asymptoting to 0.
This scales the reward in an exponential space. This means there is a sharp
gradient toward reaching the value of good, flattening out at the value of
bad. This can be useful for a reward that gives meaningful signal far away,
but still have a sharp gradient near the true target.
"""
bad: float
good: float = 0
# Constant to set the sharpness/slope of the exponential.
# Default was chosen such that the good/bad have 1 and 0.1 reward:
# https://www.wolframalpha.com/input/?i=plot+e%5E%28x*ln%280.1%29%29+from+x%3D0+to+2
low: float = -math.log(0.1)
def __call__(self, errors: List[float]) -> List[float]:
return [clip(math.exp(-scale(e, self.bad, self.good, self.low, 0)), 0, 1)
for e in errors]
@dataclasses.dataclass(frozen=True)
class Sigmoid(AbstractTransform):
"""Scales and clips errors, bad to 0.05, good to 0.95, asymptoting to 0-1."""
good: float
bad: float
# Constants to set the sharpness/slope of the sigmoid.
# Defaults were chosen such that the good/bad have 0.95 and 0.05 reward:
# https://www.wolframalpha.com/input/?i=plot+1%2F%281%2Be%5E%28-+%28-ln%2819%29+-+%28x-1%29*%282*ln%2819%29%29%29%29%29+from+x%3D0+to+2
high: float = math.log(19) # +2.9444389791664403
low: float = -math.log(19) # -2.9444389791664403
def __call__(self, errors: List[float]) -> List[float]:
return [logistic(scale(err, self.bad, self.good, self.low, self.high))
for err in errors]
================================================
FILE: fusion_tcv/transforms_test.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for transforms."""
import math
from absl.testing import absltest
from fusion_tcv import transforms
NAN = float("nan")
class TransformsTest(absltest.TestCase):
def assertNan(self, value: float):
self.assertTrue(math.isnan(value))
def test_clip(self):
self.assertEqual(transforms.clip(-1, 0, 1), 0)
self.assertEqual(transforms.clip(5, 0, 1), 1)
self.assertEqual(transforms.clip(0.5, 0, 1), 0.5)
self.assertNan(transforms.clip(NAN, 0, 1))
def test_scale(self):
self.assertEqual(transforms.scale(0, 0, 0.5, 0, 1), 0)
self.assertEqual(transforms.scale(0.125, 0, 0.5, 0, 1), 0.25)
self.assertEqual(transforms.scale(0.25, 0, 0.5, 0, 1), 0.5)
self.assertEqual(transforms.scale(0.5, 0, 0.5, 0, 1), 1)
self.assertEqual(transforms.scale(1, 0, 0.5, 0, 1), 2)
self.assertEqual(transforms.scale(-1, 0, 0.5, 0, 1), -2)
self.assertEqual(transforms.scale(0.5, 1, 0, 0, 1), 0.5)
self.assertEqual(transforms.scale(0.25, 1, 0, 0, 1), 0.75)
self.assertEqual(transforms.scale(0, 0, 1, -4, 4), -4)
self.assertEqual(transforms.scale(0.25, 0, 1, -4, 4), -2)
self.assertEqual(transforms.scale(0.5, 0, 1, -4, 4), 0)
self.assertEqual(transforms.scale(0.75, 0, 1, -4, 4), 2)
self.assertEqual(transforms.scale(1, 0, 1, -4, 4), 4)
self.assertNan(transforms.scale(NAN, 0, 1, -4, 4))
def test_logistic(self):
self.assertLess(transforms.logistic(-50), 0.000001)
self.assertLess(transforms.logistic(-5), 0.01)
self.assertEqual(transforms.logistic(0), 0.5)
self.assertGreater(transforms.logistic(5), 0.99)
self.assertGreater(transforms.logistic(50), 0.999999)
self.assertAlmostEqual(transforms.logistic(0.8), math.tanh(0.4) / 2 + 0.5)
self.assertNan(transforms.logistic(NAN))
def test_exp_scaled(self):
t = transforms.NegExp(good=0, bad=1)
self.assertNan(t([NAN])[0])
self.assertAlmostEqual(t([0])[0], 1)
self.assertAlmostEqual(t([1])[0], 0.1)
self.assertLess(t([50])[0], 0.000001)
t = transforms.NegExp(good=10, bad=30)
self.assertAlmostEqual(t([0])[0], 1)
self.assertAlmostEqual(t([10])[0], 1)
self.assertLess(t([3000])[0], 0.000001)
t = transforms.NegExp(good=30, bad=10)
self.assertAlmostEqual(t([50])[0], 1)
self.assertAlmostEqual(t([30])[0], 1)
self.assertAlmostEqual(t([10])[0], 0.1)
self.assertLess(t([-90])[0], 0.00001)
def test_neg(self):
t = transforms.Neg()
self.assertEqual(t([-5, -3, 0, 1, 4]), [5, 3, 0, -1, -4])
self.assertNan(t([NAN])[0])
def test_abs(self):
t = transforms.Abs()
self.assertEqual(t([-5, -3, 0, 1, 4]), [5, 3, 0, 1, 4])
self.assertNan(t([NAN])[0])
def test_pow(self):
t = transforms.Pow(2)
self.assertEqual(t([-5, -3, 0, 1, 4]), [25, 9, 0, 1, 16])
self.assertNan(t([NAN])[0])
def test_log(self):
t = transforms.Log()
self.assertAlmostEqual(t([math.exp(2)])[0], 2, 4) # Low precision from eps.
self.assertNan(t([NAN])[0])
def test_clipped_linear(self):
t = transforms.ClippedLinear(good=0.1, bad=0.3)
self.assertAlmostEqual(t([0])[0], 1)
self.assertAlmostEqual(t([0.05])[0], 1)
self.assertAlmostEqual(t([0.1])[0], 1)
self.assertAlmostEqual(t([0.15])[0], 0.75)
self.assertAlmostEqual(t([0.2])[0], 0.5)
self.assertAlmostEqual(t([0.25])[0], 0.25)
self.assertAlmostEqual(t([0.3])[0], 0)
self.assertAlmostEqual(t([0.4])[0], 0)
self.assertNan(t([NAN])[0])
t = transforms.ClippedLinear(good=1, bad=0.5)
self.assertAlmostEqual(t([1.5])[0], 1)
self.assertAlmostEqual(t([1])[0], 1)
self.assertAlmostEqual(t([0.75])[0], 0.5)
self.assertAlmostEqual(t([0.5])[0], 0)
self.assertAlmostEqual(t([0.25])[0], 0)
def test_softplus(self):
t = transforms.SoftPlus(good=0.1, bad=0.3)
self.assertEqual(t([0])[0], 1)
self.assertEqual(t([0.1])[0], 1)
self.assertAlmostEqual(t([0.3])[0], 0.1)
self.assertLess(t([0.5])[0], 0.01)
self.assertNan(t([NAN])[0])
t = transforms.SoftPlus(good=1, bad=0.5)
self.assertEqual(t([1.5])[0], 1)
self.assertEqual(t([1])[0], 1)
self.assertAlmostEqual(t([0.5])[0], 0.1)
self.assertLess(t([0.1])[0], 0.01)
def test_sigmoid(self):
t = transforms.Sigmoid(good=0.1, bad=0.3)
self.assertGreater(t([0])[0], 0.99)
self.assertAlmostEqual(t([0.1])[0], 0.95)
self.assertAlmostEqual(t([0.2])[0], 0.5)
self.assertAlmostEqual(t([0.3])[0], 0.05)
self.assertLess(t([0.4])[0], 0.01)
self.assertNan(t([NAN])[0])
t = transforms.Sigmoid(good=1, bad=0.5)
self.assertGreater(t([1.5])[0], 0.99)
self.assertAlmostEqual(t([1])[0], 0.95)
self.assertAlmostEqual(t([0.75])[0], 0.5)
self.assertAlmostEqual(t([0.5])[0], 0.05)
self.assertLess(t([0.25])[0], 0.01)
def test_equal(self):
t = transforms.Equal()
self.assertEqual(t([0])[0], 1)
self.assertEqual(t([0.001])[0], 0)
self.assertNan(t([NAN])[0])
t = transforms.Equal(not_equal_val=0.5)
self.assertEqual(t([0])[0], 1)
self.assertEqual(t([0.001])[0], 0.5)
if __name__ == "__main__":
absltest.main()
================================================
FILE: galaxy_mergers/README.md
================================================
# A Deep Learning Approach for Characterizing Major Galaxy Mergers
This repository contains evaluation code and checkpoints to reproduce
figures in https://arxiv.org/abs/2102.05182.
The main evaluation module is `main.py`. It uses the provided checkpoint path
and dataset path to run evaluation.
## Setup
To set up a Python virtual environment with the required dependencies, run:
```shell
python3 -m venv galaxy_mergers_env
source galaxy_mergers_env/bin/activate
pip install --upgrade pip setuptools wheel
pip install -r requirements.txt
```
### License
While the code is licensed under the Apache 2.0 License, the checkpoints weights
are made available for non-commercial use only under the terms of the
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
license. You can find details at:
https://creativecommons.org/licenses/by-nc/4.0/legalcode.
### Citing our work
If you use this work, consider citing our paper:
```bibtex
@article{koppula2021deep,
title={A Deep Learning Approach for Characterizing Major Galaxy Mergers},
author={Koppula, Skanda and Bapst, Victor and Huertas-Company, Marc and Blackwell, Sam and Grabska-Barwinska, Agnieszka and Dieleman, Sander and Huber, Andrea and Antropova, Natasha and Binkowski, Mikolaj and Openshaw, Hannah and others},
journal={Workshop for Machine Learning and the Physical Sciences @ NeurIPS 2020},
year={2021}
}
```
================================================
FILE: galaxy_mergers/antennae_helpers.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Helpers to pre-process Antennae galaxy images."""
import collections
import os
from astropy.io import fits
import numpy as np
from scipy import ndimage
import tensorflow.compat.v2 as tf
def norm_antennae_images(images, scale=1000):
return tf.math.asinh(images/scale)
def renorm_antennae(images):
median = np.percentile(images.numpy().flatten(), 50)
img_range = np.ptp(images.numpy().flatten())
return (images - median) / (img_range / 2)
def get_antennae_images(antennae_fits_dir):
"""Load the raw Antennae galaxy images."""
all_fits_files = [
os.path.join(antennae_fits_dir, f)
for f in os.listdir(antennae_fits_dir)
]
freq_mapping = {'red': 160, 'blue': 850}
paired_fits_files = collections.defaultdict(list)
for f in all_fits_files:
redshift = float(f[-8:-5])
paired_fits_files[redshift].append(f)
for redshift, files in paired_fits_files.items():
paired_fits_files[redshift] = sorted(
files, key=lambda f: freq_mapping[f.split('/')[-1].split('_')[0]])
print('Reading files:', paired_fits_files)
print('Redshifts:', sorted(paired_fits_files.keys()))
galaxy_views = collections.defaultdict(list)
for redshift in paired_fits_files:
for view_path in paired_fits_files[redshift]:
with open(view_path, 'rb') as f:
fits_data = fits.open(f)
galaxy_views[redshift].append(np.array(fits_data[0].data))
batched_images = []
for redshift in paired_fits_files:
img = tf.constant(np.array(galaxy_views[redshift]))
img = tf.transpose(img, (1, 2, 0))
img = tf.image.resize(img, size=(60, 60))
batched_images.append(img)
return tf.stack(batched_images)
def preprocess_antennae_images(antennae_images):
"""Pre-process the Antennae galaxy images into a reasonable range."""
rotated_antennae_images = [
ndimage.rotate(img, 10, reshape=True, cval=-1)[10:-10, 10:-10]
for img in antennae_images
]
rotated_antennae_images = [
np.clip(img, 0, 1e9) for img in rotated_antennae_images
]
rotated_antennae_images = tf.stack(rotated_antennae_images)
normed_antennae_images = norm_antennae_images(rotated_antennae_images)
normed_antennae_images = tf.clip_by_value(normed_antennae_images, 1, 4.5)
renormed_antennae_images = renorm_antennae(normed_antennae_images)
return renormed_antennae_images
================================================
FILE: galaxy_mergers/config.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Default config, focused on model evaluation."""
from ml_collections import config_dict
def get_config(filter_time_intervals=None):
"""Return config object for training."""
config = config_dict.ConfigDict()
config.eval_strategy = config_dict.ConfigDict()
config.eval_strategy.class_name = 'OneDeviceConfig'
config.eval_strategy.kwargs = config_dict.ConfigDict(
dict(device_type='v100'))
## Experiment config.
config.experiment_kwargs = config_dict.ConfigDict(dict(
resnet_kwargs=dict(
blocks_per_group_list=[3, 4, 6, 3], # This choice is ResNet50.
bn_config=dict(
decay_rate=0.9,
eps=1e-5),
resnet_v2=False,
additional_features_mode='mlp',
),
optimizer_config=dict(
class_name='Momentum',
kwargs={'momentum': 0.9},
# Set up the learning rate schedule.
lr_init=0.025,
lr_factor=0.1,
lr_schedule=(50e3, 100e3, 150e3),
gradient_clip=5.,
),
l2_regularization=1e-4,
total_train_batch_size=128,
train_net_args={'is_training': True},
eval_batch_size=128,
eval_net_args={'is_training': True},
data_config=dict(
# dataset loading
dataset_path=None,
num_val_splits=10,
val_split=0,
# image cropping
image_size=(80, 80, 7),
train_crop_type='crop_fixed',
test_crop_type='crop_fixed',
n_crop_repeat=1,
train_augmentations=dict(
rotation_and_flip=True,
rescaling=True,
translation=True,
),
test_augmentations=dict(
rotation_and_flip=False,
rescaling=False,
translation=False,
),
test_time_ensembling='sum',
num_eval_buckets=5,
eval_confidence_interval=95,
task='grounded_unnormalized_regression',
loss_config=dict(
loss='mse',
mse_normalize=False,
),
model_uncertainty=True,
additional_features='',
time_filter_intervals=filter_time_intervals,
class_boundaries={
'0': [[-1., 0]],
'1': [[0, 1.]]
},
frequencies_to_use='all',
),
n_train_epochs=100
))
return config
================================================
FILE: galaxy_mergers/evaluator.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Evaluation runner."""
import collections
from absl import logging
import tensorflow.compat.v2 as tf
from galaxy_mergers import config as tp_config
from galaxy_mergers import helpers
from galaxy_mergers import losses
from galaxy_mergers import model
from galaxy_mergers import preprocessing
class GalaxyMergeClassifierEvaluator():
"""Galaxy Merge Rate Prediction Evaluation Runner."""
def __init__(self, strategy, optimizer_config, total_train_batch_size,
train_net_args, eval_batch_size, eval_net_args,
l2_regularization, data_config, resnet_kwargs, n_train_epochs):
"""Initializes evaluator/experiment."""
logging.info('Initializing evaluator...')
self._strategy = strategy
self._data_config = data_config
self._use_additional_features = bool(data_config['additional_features'])
self._eval_batch_size = eval_batch_size
self._eval_net_args = eval_net_args
self._num_buckets = data_config['num_eval_buckets']
self._n_repeats = data_config['n_crop_repeat']
self._image_size = data_config['image_size']
self._task_type = data_config['task']
self._loss_config = data_config['loss_config']
self._model_uncertainty = data_config['model_uncertainty']
del l2_regularization, optimizer_config, train_net_args
del total_train_batch_size, n_train_epochs
logging.info('Creating model...')
num_classes = 2 if self._model_uncertainty else 1
if self._task_type == losses.TASK_CLASSIFICATION:
num_classes = len(self._data_config['class_boundaries'])
self.model = model.ResNet(
n_repeats=self._data_config['n_crop_repeat'], num_classes=num_classes,
use_additional_features=self._use_additional_features, **resnet_kwargs)
self._eval_input = None
def build_eval_input(self, additional_lambdas=None):
"""Create the galaxy merger evaluation dataset."""
def decode_fn(record_bytes):
parsed_example = tf.io.parse_single_example(
record_bytes,
{
'image':
tf.io.VarLenFeature(tf.float32),
'image_shape':
tf.io.FixedLenFeature([3], dtype=tf.int64),
'axis':
tf.io.FixedLenFeature([], dtype=tf.int64),
'proposed_crop':
tf.io.FixedLenFeature([2, 2], dtype=tf.int64),
'normalized_time':
tf.io.FixedLenFeature([], dtype=tf.float32),
'unnormalized_time':
tf.io.FixedLenFeature([], dtype=tf.float32),
'grounded_normalized_time':
tf.io.FixedLenFeature([], dtype=tf.float32),
'redshift':
tf.io.FixedLenFeature([], dtype=tf.float32),
'sequence_average_redshift':
tf.io.FixedLenFeature([], dtype=tf.float32),
'mass':
tf.io.FixedLenFeature([], dtype=tf.float32),
'time_index':
tf.io.FixedLenFeature([], dtype=tf.int64),
'sequence_id':
tf.io.FixedLenFeature([], dtype=tf.string),
})
parsed_example['image'] = tf.sparse.to_dense(
parsed_example['image'], default_value=0)
dataset_row = parsed_example
return dataset_row
def build_eval_pipeline(_):
"""Generate the processed input evaluation data."""
logging.info('Building evaluation input pipeline...')
ds_path = self._data_config['dataset_path']
ds = tf.data.TFRecordDataset([ds_path]).map(decode_fn)
augmentations = dict(
rotation_and_flip=False,
rescaling=False,
translation=False
)
ds = preprocessing.prepare_dataset(
ds=ds, target_size=self._image_size,
crop_type=self._data_config['test_crop_type'],
n_repeats=self._n_repeats,
augmentations=augmentations,
task_type=self._task_type,
additional_features=self._data_config['additional_features'],
class_boundaries=self._data_config['class_boundaries'],
time_intervals=self._data_config['time_filter_intervals'],
frequencies_to_use=self._data_config['frequencies_to_use'],
additional_lambdas=additional_lambdas)
batched_ds = ds.cache().batch(self._eval_batch_size).prefetch(128)
logging.info('Finished building input pipeline...')
return batched_ds
return self._strategy.experimental_distribute_datasets_from_function(
build_eval_pipeline)
def run_test_model_ensemble(self, images, physical_features, augmentations):
"""Run evaluation on input images."""
image_variations = [images]
image_shape = images.shape.as_list()
if augmentations['rotation_and_flip']:
image_variations = preprocessing.get_all_rotations_and_flips(
image_variations)
if augmentations['rescaling']:
image_variations = preprocessing.get_all_rescalings(
image_variations, image_shape[1], augmentations['translation'])
# Put all augmented images into the batch: batch * num_augmented
augmented_images = tf.stack(image_variations, axis=0)
augmented_images = tf.reshape(augmented_images, [-1] + image_shape[1:])
if self._use_additional_features:
physical_features = tf.concat(
[physical_features] * len(image_variations), axis=0)
n_reps = self._data_config['n_crop_repeat']
augmented_images = preprocessing.move_repeats_to_batch(augmented_images,
n_reps)
logits_or_times = self.model(augmented_images, physical_features,
**self._eval_net_args)
if self._task_type == losses.TASK_CLASSIFICATION:
mu, log_sigma_sq = helpers.aggregate_classification_ensemble(
logits_or_times, len(image_variations),
self._data_config['test_time_ensembling'])
else:
assert self._task_type in losses.REGRESSION_TASKS
mu, log_sigma_sq = helpers.aggregate_regression_ensemble(
logits_or_times, len(image_variations),
self._model_uncertainty,
self._data_config['test_time_ensembling'])
return mu, log_sigma_sq
@property
def checkpoint_items(self):
return {'model': self.model}
def run_model_on_dataset(evaluator, dataset, config, n_batches=16):
"""Runs the model against a dataset, aggregates model output."""
scalar_metrics_to_log = collections.defaultdict(list)
model_outputs_to_log = collections.defaultdict(list)
dataset_features_to_log = collections.defaultdict(list)
batch_count = 1
for all_inputs in dataset:
if config.experiment_kwargs.data_config['additional_features']:
images = all_inputs[0]
physical_features = all_inputs[1]
labels, regression_targets, _ = all_inputs[2:5]
other_dataset_features = all_inputs[5:]
else:
images, physical_features = all_inputs[0], None
labels, regression_targets, _ = all_inputs[1:4]
other_dataset_features = all_inputs[4:]
mu, log_sigma_sq = evaluator.run_test_model_ensemble(
images, physical_features,
config.experiment_kwargs.data_config['test_augmentations'])
loss_config = config.experiment_kwargs.data_config['loss_config']
task_type = config.experiment_kwargs.data_config['task']
uncertainty = config.experiment_kwargs.data_config['model_uncertainty']
conf = config.experiment_kwargs.data_config['eval_confidence_interval']
scalar_metrics, vector_metrics = losses.compute_loss_and_metrics(
mu, log_sigma_sq, regression_targets, labels,
task_type, uncertainty, loss_config, 0, conf, mode='eval')
for i, dataset_feature in enumerate(other_dataset_features):
dataset_features_to_log[i].append(dataset_feature.numpy())
for scalar_metric in scalar_metrics:
v = scalar_metrics[scalar_metric]
val = v if isinstance(v, int) or isinstance(v, float) else v.numpy()
scalar_metrics_to_log[scalar_metric].append(val)
for vector_metric in vector_metrics:
val = vector_metrics[vector_metric].numpy()
model_outputs_to_log[vector_metric].append(val)
regression_targets_np = regression_targets.numpy()
labels_np = labels.numpy()
model_outputs_to_log['regression_targets'].append(regression_targets_np)
model_outputs_to_log['labels'].append(labels_np)
model_outputs_to_log['model_input_images'].append(images.numpy())
if n_batches and batch_count >= n_batches:
break
batch_count += 1
return scalar_metrics_to_log, model_outputs_to_log, dataset_features_to_log
def get_config_dataset_evaluator(filter_time_intervals,
ckpt_path,
config_override=None,
setup_dataset=True):
"""Set-up a default config, evaluation dataset, and evaluator."""
config = tp_config.get_config(filter_time_intervals=filter_time_intervals)
if config_override:
with config.ignore_type():
config.update_from_flattened_dict(config_override)
strategy = tf.distribute.OneDeviceStrategy(device='/gpu:0')
experiment = GalaxyMergeClassifierEvaluator(
strategy=strategy, **config.experiment_kwargs)
helpers.restore_checkpoint(ckpt_path, experiment)
if setup_dataset:
additional_lambdas = [
lambda ds: ds['sequence_id'],
lambda ds: ds['time_index'],
lambda ds: ds['axis'],
lambda ds: ds['normalized_time'],
lambda ds: ds['grounded_normalized_time'],
lambda ds: ds['unnormalized_time'],
lambda ds: ds['redshift'],
lambda ds: ds['mass']
]
ds = experiment.build_eval_input(additional_lambdas=additional_lambdas)
else:
ds = None
return config, ds, experiment
================================================
FILE: galaxy_mergers/helpers.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Helpers for a galaxy merger model evaluation."""
import glob
import os
from astropy import cosmology
from astropy.io import fits
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import tensorflow.compat.v2 as tf
def restore_checkpoint(checkpoint_dir, experiment):
checkpoint_path = tf.train.latest_checkpoint(checkpoint_dir)
global_step = tf.Variable(
0, dtype=tf.int32, trainable=False, name='global_step')
checkpoint = tf.train.Checkpoint(
_global_step_=global_step, **experiment.checkpoint_items)
checkpoint.restore(checkpoint_path)
def sum_average_transformed_mu_and_sigma(mu, log_sigma_sq):
"""Computes , var(mu) + in transformed representation.
This corresponds to assuming that the output distribution is a sum of
Gaussian and computing the mean and variance of the resulting (non-Gaussian)
distribution.
Args:
mu: Tensor of shape [B, ...] representing the means of the input
distributions.
log_sigma_sq: Tensor of shape [B, ...] representing log(sigma**2) of the
input distributions. Can be None, in which case the variance is assumed
to be zero.
Returns:
mu: Tensor of shape [...] representing the means of the output
distributions.
log_sigma_sq: Tensor of shape [...] representing log(sigma**2) of the
output distributions.
"""
av_mu = tf.reduce_mean(mu, axis=0)
var_mu = tf.math.reduce_std(mu, axis=0)**2
if log_sigma_sq is None:
return av_mu, tf.math.log(var_mu)
max_log_sigma_sq = tf.reduce_max(log_sigma_sq, axis=0)
log_sigma_sq -= max_log_sigma_sq
# (sigma/sigma_0)**2
sigma_sq = tf.math.exp(log_sigma_sq)
# ()/sigma_0**2 (<1)
av_sigma_sq = tf.reduce_mean(sigma_sq, axis=0)
# ( + var(mu))/sigma_0**2
av_sigma_sq += var_mu * tf.math.exp(-max_log_sigma_sq)
# log( + var(mu))
log_av_sigma_sq = tf.math.log(av_sigma_sq) + max_log_sigma_sq
return av_mu, log_av_sigma_sq
def aggregate_regression_ensemble(logits_or_times, ensemble_size,
use_uncertainty, test_time_ensembling):
"""Aggregate output of model ensemble."""
out_shape = logits_or_times.shape.as_list()[1:]
logits_or_times = tf.reshape(logits_or_times, [ensemble_size, -1] + out_shape)
mus = logits_or_times[..., 0]
log_sigma_sqs = logits_or_times[..., -1] if use_uncertainty else None
if test_time_ensembling == 'sum':
mu, log_sigma_sq = sum_average_transformed_mu_and_sigma(mus, log_sigma_sqs)
elif test_time_ensembling == 'none':
mu = mus[0]
log_sigma_sq = log_sigma_sqs[0] if use_uncertainty else None
else:
raise ValueError('Unexpected test_time_ensembling')
return mu, log_sigma_sq
def aggregate_classification_ensemble(logits_or_times, ensemble_size,
test_time_ensembling):
"""Averages the output logits across models in the ensemble."""
out_shape = logits_or_times.shape.as_list()[1:]
logits = tf.reshape(logits_or_times, [ensemble_size, -1] + out_shape)
if test_time_ensembling == 'sum':
logits = tf.reduce_mean(logits, axis=0)
return logits, None
elif test_time_ensembling == 'none':
return logits, None
else:
raise ValueError('Unexpected test_time_ensembling')
def unpack_evaluator_output(data, return_seq_info=False, return_redshift=False):
"""Unpack evaluator.run_model_on_dataset output."""
mus = np.array(data[1]['mu']).flatten()
sigmas = np.array(data[1]['sigma']).flatten()
regression_targets = np.array(data[1]['regression_targets']).flatten()
outputs = [mus, sigmas, regression_targets]
if return_seq_info:
seq_ids = np.array(data[2][0]).flatten()
seq_ids = np.array([seq_id.decode('UTF-8') for seq_id in seq_ids])
time_idxs = np.array(data[2][1]).flatten()
axes = np.array(data[2][2]).flatten()
outputs += [seq_ids, axes, time_idxs]
if return_redshift:
redshifts = np.array(data[2][6]).flatten()
outputs += [redshifts]
return outputs
def process_data_into_myrs(redshifts, *data_lists):
"""Converts normalized time to virial time using Planck cosmology."""
# small hack to avoid build tools not recognizing non-standard trickery
# done in the astropy library:
# https://github.com/astropy/astropy/blob/master/astropy/cosmology/core.py#L3290
# that dynamically generates and imports new classes.
planck13 = getattr(cosmology, 'Plank13')
hubble_constants = planck13.H(redshifts) # (km/s)/megaparsec
inv_hubble_constants = 1/hubble_constants # (megaparsec*s) / km
megaparsec_to_km = 1e19*3.1
seconds_to_gigayears = 1e-15/31.556
conversion_factor = megaparsec_to_km * seconds_to_gigayears
hubble_time_gigayears = conversion_factor * inv_hubble_constants
hubble_to_virial_time = 0.14 # approximate simulation-based conversion factor
virial_dyn_time = hubble_to_virial_time*hubble_time_gigayears.value
return [data_list*virial_dyn_time for data_list in data_lists]
def print_rmse_and_class_accuracy(mus, regression_targets, redshifts):
"""Convert to virial dynamical time and print stats."""
time_pred, time_gt = process_data_into_myrs(
redshifts, mus, regression_targets)
time_sq_errors = (time_pred-time_gt)**2
rmse = np.sqrt(np.mean(time_sq_errors))
labels = regression_targets > 0
class_preds = mus > 0
accuracy = sum((labels == class_preds).astype(np.int8)) / len(class_preds)
print(f'95% Error: {np.percentile(np.sqrt(time_sq_errors), 95)}')
print(f'RMSE: {rmse}')
print(f'Classification Accuracy: {accuracy}')
def print_stats(vec, do_print=True):
fvec = vec.flatten()
if do_print:
print(len(fvec), min(fvec), np.mean(fvec), np.median(fvec), max(fvec))
return (len(fvec), min(fvec), np.mean(fvec), np.median(fvec), max(fvec))
def get_image_from_fits(base_dir, seq='475_31271', time='497', axis=2):
"""Read *.fits galaxy image from directory."""
axis_map = {0: 'x', 1: 'y', 2: 'z'}
fits_glob = f'{base_dir}/{seq}/fits_of_flux_psf/{time}/*_{axis_map[axis]}_*.fits'
def get_freq_from_path(p):
return int(p.split('/')[-1].split('_')[2][1:])
fits_image_paths = sorted(glob.glob(fits_glob), key=get_freq_from_path)
assert len(fits_image_paths) == 7
combined_frequencies = []
for fit_path in fits_image_paths:
with open(fit_path, 'rb') as f:
fits_data = np.array(fits.open(f)[0].data.astype(np.float32))
combined_frequencies.append(fits_data)
fits_image = np.transpose(np.array(combined_frequencies), (1, 2, 0))
return fits_image
def stack_desired_galaxy_images(base_dir, seq, n_time_slices):
"""Searth through galaxy image directory gathering images."""
fits_sequence_dir = os.path.join(base_dir, seq, 'fits_of_flux_psf')
all_times_for_seq = os.listdir(fits_sequence_dir)
hop = (len(all_times_for_seq)-1)//(n_time_slices-1)
desired_time_idxs = [k*hop for k in range(n_time_slices)]
all_imgs = []
for j in desired_time_idxs:
time = all_times_for_seq[j]
img = get_image_from_fits(base_dir=base_dir, seq=seq, time=time, axis=2)
all_imgs.append(img)
min_img_size = min([img.shape[0] for img in all_imgs])
return all_imgs, min_img_size
def draw_galaxy_image(image, target_size=None, color_map='viridis'):
normalized_image = image / max(image.flatten())
color_map = plt.get_cmap(color_map)
colored_image = color_map(normalized_image)[:, :, :3]
colored_image = (colored_image * 255).astype(np.uint8)
colored_image = Image.fromarray(colored_image, mode='RGB')
if target_size:
colored_image = colored_image.resize(target_size, Image.ANTIALIAS)
return colored_image
def collect_merger_sequence(ds, seq=b'370_11071', n_examples_to_sift=5000):
images, targets, redshifts = [], [], []
for i, all_inputs in enumerate(ds):
if all_inputs[4][0].numpy() == seq:
images.append(all_inputs[0][0].numpy())
targets.append(all_inputs[2][0].numpy())
redshifts.append(all_inputs[10][0].numpy())
if i > n_examples_to_sift: break
return np.squeeze(images), np.squeeze(targets), np.squeeze(redshifts)
def take_samples(sample_idxs, *data_lists):
return [np.take(l, sample_idxs, axis=0) for l in data_lists]
================================================
FILE: galaxy_mergers/interpretability_helpers.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Helpers to visualize gradients and other interpretability analysis."""
import numpy as np
import tensorflow.compat.v2 as tf
def rotate_by_right_angle_multiple(image, rot=90):
"""Rotate an image by right angles."""
if rot not in [0, 90, 180, 270]:
raise ValueError(f"Cannot rotate by non-90 degree angle {rot}")
if rot in [90, -270]:
image = np.transpose(image, (1, 0, 2))
image = image[::-1]
elif rot in [180, -180]:
image = image[::-1, ::-1]
elif rot in [270, -90]:
image = np.transpose(image, (1, 0, 2))
image = image[:, ::-1]
return image
def compute_gradient(images, evaluator, is_training=False):
inputs = tf.Variable(images[None], dtype=tf.float32)
with tf.GradientTape() as tape:
tape.watch(inputs)
time_sigma = evaluator.model(inputs, None, is_training)
grad_time = tape.gradient(time_sigma[:, 0], inputs)
return grad_time, time_sigma
def compute_grads_for_rotations(images, evaluator, is_training=False):
test_gradients, test_outputs = [], []
for rotation in np.arange(0, 360, 90):
images_rot = rotate_by_right_angle_multiple(images, rotation)
grads, time_sigma = compute_gradient(images_rot, evaluator, is_training)
grads = np.squeeze(grads.numpy())
inv_grads = rotate_by_right_angle_multiple(grads, -rotation)
test_gradients.append(inv_grads)
test_outputs.append(time_sigma.numpy())
return np.squeeze(test_gradients), np.squeeze(test_outputs)
def compute_grads_for_rotations_and_flips(images, evaluator):
grads, time_sigma = compute_grads_for_rotations(images, evaluator)
grads_f, time_sigma_f = compute_grads_for_rotations(images[::-1], evaluator)
grads_f = grads_f[:, ::-1]
all_grads = np.concatenate([grads, grads_f], 0)
model_outputs = np.concatenate((time_sigma, time_sigma_f), 0)
return all_grads, model_outputs
================================================
FILE: galaxy_mergers/losses.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Helpers to compute loss metrics."""
import scipy.stats
import tensorflow.compat.v2 as tf
import tensorflow_probability as tfp
TASK_CLASSIFICATION = 'classification'
TASK_NORMALIZED_REGRESSION = 'normalized_regression'
TASK_UNNORMALIZED_REGRESSION = 'unnormalized_regression'
TASK_GROUNDED_UNNORMALIZED_REGRESSION = 'grounded_unnormalized_regression'
REGRESSION_TASKS = [TASK_NORMALIZED_REGRESSION, TASK_UNNORMALIZED_REGRESSION,
TASK_GROUNDED_UNNORMALIZED_REGRESSION]
ALL_TASKS = [TASK_CLASSIFICATION] + REGRESSION_TASKS
LOSS_MSE = 'mse'
LOSS_SOFTMAX_CROSS_ENTROPY = 'softmax_cross_entropy'
ALL_LOSSES = [LOSS_SOFTMAX_CROSS_ENTROPY, LOSS_MSE]
def normalize_regression_loss(regression_loss, predictions):
# Normalize loss such that:
# 1) E_{x uniform}[loss(x, prediction)] does not depend on prediction
# 2) E_{x uniform, prediction uniform}[loss(x, prediction)] is as before.
# Divides MSE regression loss by E[(prediction-x)^2]; assumes x=[-1,1]
normalization = 2./3.
normalized_loss = regression_loss / ((1./3 + predictions**2) / normalization)
return normalized_loss
def equal32(x, y):
return tf.cast(tf.equal(x, y), tf.float32)
def mse_loss(predicted, targets):
return (predicted - targets) ** 2
def get_std_factor_from_confidence_percent(percent):
dec = percent/100.
inv_dec = 1 - dec
return scipy.stats.norm.ppf(dec+inv_dec/2)
def get_all_metric_names(task_type, model_uncertainty, loss_config, # pylint: disable=unused-argument
mode='eval', return_dict=True):
"""Get all the scalar fields produced by compute_loss_and_metrics."""
names = ['regularization_loss', 'prediction_accuracy', str(mode)+'_loss']
if task_type == TASK_CLASSIFICATION:
names += ['classification_loss']
else:
names += ['regression_loss', 'avg_mu', 'var_mu']
if model_uncertainty:
names += ['uncertainty_loss', 'scaled_regression_loss',
'uncertainty_plus_scaled_regression',
'avg_sigma', 'var_sigma',
'percent_in_conf_interval', 'error_sigma_correlation',
'avg_prob']
if return_dict:
return {name: 0. for name in names}
else:
return names
def compute_loss_and_metrics(mu, log_sigma_sq,
regression_targets, labels,
task_type, model_uncertainty, loss_config,
regularization_loss=0., confidence_interval=95,
mode='train'):
"""Computes loss statistics and other metrics."""
scalars_to_log = dict()
vectors_to_log = dict()
scalars_to_log['regularization_loss'] = regularization_loss
vectors_to_log['mu'] = mu
if task_type == TASK_CLASSIFICATION:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=mu, labels=labels, name='cross_entropy')
classification_loss = tf.reduce_mean(cross_entropy, name='class_loss')
total_loss = classification_loss
sigma = None
scalars_to_log['classification_loss'] = classification_loss
predicted_labels = tf.argmax(mu, axis=1)
correct_predictions = equal32(predicted_labels, labels)
else:
regression_loss = mse_loss(mu, regression_targets)
if 'mse_normalize' in loss_config and loss_config['mse_normalize']:
assert task_type in [TASK_GROUNDED_UNNORMALIZED_REGRESSION,
TASK_NORMALIZED_REGRESSION]
regression_loss = normalize_regression_loss(regression_loss, mu)
avg_regression_loss = tf.reduce_mean(regression_loss)
vectors_to_log['regression_loss'] = regression_loss
scalars_to_log['regression_loss'] = avg_regression_loss
scalars_to_log['avg_mu'] = tf.reduce_mean(mu)
scalars_to_log['var_mu'] = tf.reduce_mean(mse_loss(mu, tf.reduce_mean(mu)))
predicted_labels = tf.cast(mu > 0, tf.int64)
correct_predictions = equal32(predicted_labels, labels)
if model_uncertainty:
# This implements Eq. (1) in https://arxiv.org/pdf/1612.01474.pdf
inv_sigma_sq = tf.math.exp(-log_sigma_sq)
scaled_regression_loss = regression_loss * inv_sigma_sq
scaled_regression_loss = tf.reduce_mean(scaled_regression_loss)
uncertainty_loss = tf.reduce_mean(log_sigma_sq)
total_loss = uncertainty_loss + scaled_regression_loss
scalars_to_log['uncertainty_loss'] = uncertainty_loss
scalars_to_log['scaled_regression_loss'] = scaled_regression_loss
scalars_to_log['uncertainty_plus_scaled_regression'] = total_loss
sigma = tf.math.exp(log_sigma_sq / 2.)
vectors_to_log['sigma'] = sigma
scalars_to_log['avg_sigma'] = tf.reduce_mean(sigma)
var_sigma = tf.reduce_mean(mse_loss(sigma, tf.reduce_mean(sigma)))
scalars_to_log['var_sigma'] = var_sigma
# Compute # of labels that fall into the confidence interval.
std_factor = get_std_factor_from_confidence_percent(confidence_interval)
lower_bound = mu - std_factor * sigma
upper_bound = mu + std_factor * sigma
preds = tf.logical_and(tf.greater(regression_targets, lower_bound),
tf.less(regression_targets, upper_bound))
percent_in_conf_interval = tf.reduce_mean(tf.cast(preds, tf.float32))
scalars_to_log['percent_in_conf_interval'] = percent_in_conf_interval*100
error_sigma_corr = tfp.stats.correlation(x=regression_loss,
y=sigma, event_axis=None)
scalars_to_log['error_sigma_correlation'] = error_sigma_corr
dists = tfp.distributions.Normal(mu, sigma)
probs = dists.prob(regression_targets)
scalars_to_log['avg_prob'] = tf.reduce_mean(probs)
else:
total_loss = avg_regression_loss
loss_name = str(mode)+'_loss'
total_loss = tf.add(total_loss, regularization_loss, name=loss_name)
scalars_to_log[loss_name] = total_loss
vectors_to_log['correct_predictions'] = correct_predictions
scalars_to_log['prediction_accuracy'] = tf.reduce_mean(correct_predictions)
# Validate that metrics outputted are exactly what is expected
expected = get_all_metric_names(task_type, model_uncertainty,
loss_config, mode, False)
assert set(expected) == set(scalars_to_log.keys())
return scalars_to_log, vectors_to_log
================================================
FILE: galaxy_mergers/main.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Simple script to model evaluation on a checkpoint and dataset."""
import ast
from absl import app
from absl import flags
from absl import logging
from galaxy_mergers import evaluator
flags.DEFINE_string('checkpoint_path', '', 'Path to TF2 checkpoint to eval.')
flags.DEFINE_string('data_path', '', 'Path to TFRecord(s) with data.')
flags.DEFINE_string('filter_time_intervals', None,
'Merger time intervals on which to perform regression.'
'Specify None for the default time interval [-1,1], or'
' a custom list of intervals, e.g. [[-0.2,0], [0.5,1]].')
FLAGS = flags.FLAGS
def main(_) -> None:
if FLAGS.filter_time_intervals is not None:
filter_time_intervals = ast.literal_eval(FLAGS.filter_time_intervals)
else:
filter_time_intervals = None
config, ds, experiment = evaluator.get_config_dataset_evaluator(
filter_time_intervals,
FLAGS.checkpoint_path,
config_override={
'experiment_kwargs.data_config.dataset_path': FLAGS.data_path,
})
metrics, _, _ = evaluator.run_model_on_dataset(experiment, ds, config)
logging.info('Evaluation complete. Metrics: %s', metrics)
if __name__ == '__main__':
app.run(main)
================================================
FILE: galaxy_mergers/model.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fork of a generic ResNet to incorporate additional cosmological features."""
from typing import Mapping, Optional, Sequence, Text
import sonnet.v2 as snt
import tensorflow.compat.v2 as tf
class ResNet(snt.Module):
"""ResNet model."""
def __init__(self,
n_repeats: int,
blocks_per_group_list: Sequence[int],
num_classes: int,
bn_config: Optional[Mapping[Text, float]] = None,
resnet_v2: bool = False,
channels_per_group_list: Sequence[int] = (256, 512, 1024, 2048),
use_additional_features: bool = False,
additional_features_mode: Optional[Text] = "per_block",
name: Optional[Text] = None):
"""Constructs a ResNet model.
Args:
n_repeats: The batch dimension for the input is expected to have the form
`B = b * n_repeats`. After the conv stack, the logits for the
`n_repeats` replicas are reduced, leading to an output batch dimension
of `b`.
blocks_per_group_list: A sequence of length 4 that indicates the number of
blocks created in each group.
num_classes: The number of classes to classify the inputs into.
bn_config: A dictionary of two elements, `decay_rate` and `eps` to be
passed on to the `BatchNorm` layers. By default the `decay_rate` is
`0.9` and `eps` is `1e-5`.
resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults to
False.
channels_per_group_list: A sequence of length 4 that indicates the number
of channels used for each block in each group.
use_additional_features: If true, additional vector features will be
concatenated to the residual stack before logits are computed.
additional_features_mode: Mode for processing additional features.
Supported modes: 'mlp' and 'per_block'.
name: Name of the module.
"""
super(ResNet, self).__init__(name=name)
self._n_repeats = n_repeats
if bn_config is None:
bn_config = {"decay_rate": 0.9, "eps": 1e-5}
self._bn_config = bn_config
self._resnet_v2 = resnet_v2
# Number of blocks in each group for ResNet.
if len(blocks_per_group_list) != 4:
raise ValueError(
"`blocks_per_group_list` must be of length 4 not {}".format(
len(blocks_per_group_list)))
self._blocks_per_group_list = blocks_per_group_list
# Number of channels in each group for ResNet.
if len(channels_per_group_list) != 4:
raise ValueError(
"`channels_per_group_list` must be of length 4 not {}".format(
len(channels_per_group_list)))
self._channels_per_group_list = channels_per_group_list
self._use_additional_features = use_additional_features
self._additional_features_mode = additional_features_mode
self._initial_conv = snt.Conv2D(
output_channels=64,
kernel_shape=7,
stride=2,
with_bias=False,
padding="SAME",
name="initial_conv")
if not self._resnet_v2:
self._initial_batchnorm = snt.BatchNorm(
create_scale=True,
create_offset=True,
name="initial_batchnorm",
**bn_config)
self._block_groups = []
strides = [1, 2, 2, 2]
for i in range(4):
self._block_groups.append(
snt.nets.resnet.BlockGroup(
channels=self._channels_per_group_list[i],
num_blocks=self._blocks_per_group_list[i],
stride=strides[i],
bn_config=bn_config,
resnet_v2=resnet_v2,
name="block_group_%d" % (i)))
if self._resnet_v2:
self._final_batchnorm = snt.BatchNorm(
create_scale=True,
create_offset=True,
name="final_batchnorm",
**bn_config)
self._logits = snt.Linear(
output_size=num_classes,
w_init=snt.initializers.VarianceScaling(scale=2.0), name="logits")
if self._use_additional_features:
self._embedding = LinearBNReLU(output_size=16, name="embedding",
**bn_config)
if self._additional_features_mode == "mlp":
self._feature_repr = LinearBNReLU(
output_size=self._channels_per_group_list[-1], name="features_repr",
**bn_config)
elif self._additional_features_mode == "per_block":
self._feature_repr = []
for i, ch in enumerate(self._channels_per_group_list):
self._feature_repr.append(
LinearBNReLU(output_size=ch, name=f"features_{i}", **bn_config))
else:
raise ValueError(f"Unsupported addiitonal features mode: "
f"{additional_features_mode}")
def __call__(self, inputs, features, is_training):
net = inputs
net = self._initial_conv(net)
if not self._resnet_v2:
net = self._initial_batchnorm(net, is_training=is_training)
net = tf.nn.relu(net)
net = tf.nn.max_pool2d(
net, ksize=3, strides=2, padding="SAME", name="initial_max_pool")
if self._use_additional_features:
assert features is not None
features = self._embedding(features, is_training=is_training)
for i, block_group in enumerate(self._block_groups):
net = block_group(net, is_training)
if (self._use_additional_features and
self._additional_features_mode == "per_block"):
features_i = self._feature_repr[i](features, is_training=is_training)
# support for n_repeats > 1
features_i = tf.repeat(features_i, self._n_repeats, axis=0)
net += features_i[:, None, None, :] # expand to spacial resolution
if self._resnet_v2:
net = self._final_batchnorm(net, is_training=is_training)
net = tf.nn.relu(net)
net = tf.reduce_mean(net, axis=[1, 2], name="final_avg_pool")
# Re-split the batch dimension
net = tf.reshape(net, [-1, self._n_repeats] + net.shape.as_list()[1:])
# Average over the various repeats of the input (e.g. those could have
# corresponded to different crops).
net = tf.reduce_mean(net, axis=1)
if (self._use_additional_features and
self._additional_features_mode == "mlp"):
net += self._feature_repr(features, is_training=is_training)
return self._logits(net)
class LinearBNReLU(snt.Module):
"""Wrapper class for Linear layer with Batch Norm and ReLU activation."""
def __init__(self, output_size=64,
w_init=snt.initializers.VarianceScaling(scale=2.0),
name="linear", **bn_config):
"""Constructs a LinearBNReLU module.
Args:
output_size: Output dimension.
w_init: weight Initializer for snt.Linear.
name: Name of the module.
**bn_config: Optional parameters to be passed to snt.BatchNorm.
"""
super(LinearBNReLU, self).__init__(name=name)
self._linear = snt.Linear(output_size=output_size, w_init=w_init,
name=f"{name}_linear")
self._bn = snt.BatchNorm(create_scale=True, create_offset=True,
name=f"{name}_bn", **bn_config)
def __call__(self, x, is_training):
x = self._linear(x)
x = self._bn(x, is_training=is_training)
return tf.nn.relu(x)
================================================
FILE: galaxy_mergers/preprocessing.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pre-processing functions for input data."""
import functools
from absl import logging
import tensorflow.compat.v2 as tf
from galaxy_mergers import losses
CROP_TYPE_NONE = 'crop_none'
CROP_TYPE_FIXED = 'crop_fixed'
CROP_TYPE_RANDOM = 'crop_random'
DATASET_FREQUENCY_MEAN = 4.0
DATASET_FREQUENCY_RANGE = 8.0
PHYSICAL_FEATURES_MIN_MAX = {
'redshift': (0.572788, 2.112304),
'mass': (9.823963, 10.951282)
}
ALL_FREQUENCIES = [105, 125, 160, 435, 606, 775, 850]
VALID_ADDITIONAL_FEATURES = ['redshift', 'sequence_average_redshift', 'mass']
def _make_padding_sizes(pad_size, random_centering):
if random_centering:
pad_size_left = tf.random.uniform(
shape=[], minval=0, maxval=pad_size+1, dtype=tf.int32)
else:
pad_size_left = pad_size // 2
pad_size_right = pad_size - pad_size_left
return pad_size_left, pad_size_right
def resize_and_pad(image, target_size, random_centering):
"""Resize image to target_size (<= image.size) and pad to original size."""
original_shape = image.shape
size = tf.reshape(target_size, [1])
size = tf.concat([size, size], axis=0)
image = tf.image.resize(image, size=size)
pad_size = original_shape[1] - target_size
pad_size_left, pad_size_right = _make_padding_sizes(
pad_size, random_centering)
padding = [[pad_size_left, pad_size_right],
[pad_size_left, pad_size_right], [0, 0]]
if len(original_shape) == 4:
padding = [[0, 0]] + padding
image = tf.pad(image, padding)
image.set_shape(original_shape)
return image
def resize_and_extract(image, target_size, random_centering):
"""Upscale image to target_size (>image.size), extract original size crop."""
original_shape = image.shape
size = tf.reshape(target_size, [1])
size = tf.concat([size, size], axis=0)
image = tf.image.resize(image, size=size)
pad_size = target_size - original_shape[1]
pad_size_left, pad_size_right = _make_padding_sizes(
pad_size, random_centering)
if len(original_shape) == 3:
image = tf.expand_dims(image, 0)
image = tf.cond(pad_size_right > 0,
lambda: image[:, pad_size_left:-pad_size_right, :, :],
lambda: image[:, pad_size_left:, :, :])
image = tf.cond(pad_size_right > 0,
lambda: image[:, :, pad_size_left:-pad_size_right, :],
lambda: image[:, :, pad_size_left:, :])
if len(original_shape) == 3:
image = tf.squeeze(image, 0)
image.set_shape(original_shape)
return image
def resize_and_center(image, target_size, random_centering):
return tf.cond(
tf.math.less_equal(target_size, image.shape[1]),
lambda: resize_and_pad(image, target_size, random_centering),
lambda: resize_and_extract(image, target_size, random_centering))
def random_rotation_and_flip(image):
angle = tf.random.uniform(shape=[], minval=0, maxval=4, dtype=tf.int32)
return tf.image.random_flip_left_right(tf.image.rot90(image, angle))
def get_all_rotations_and_flips(images):
assert isinstance(images, list)
new_images = []
for image in images:
for rotation in range(4):
new_images.append(tf.image.rot90(image, rotation))
flipped_image = tf.image.flip_left_right(image)
new_images.append(tf.image.rot90(flipped_image, rotation))
return new_images
def random_rescaling(image, random_centering):
assert image.shape.as_list()[0] == image.shape.as_list()[1]
original_size = image.shape.as_list()[1]
min_size = 2 * (original_size // 4)
max_size = original_size * 2
target_size = tf.random.uniform(
shape=[], minval=min_size, maxval=max_size // 2,
dtype=tf.int32) * 2
return resize_and_center(image, target_size, random_centering)
def get_all_rescalings(images, image_width, random_centering):
"""Get a uniform sample of rescalings of all images in input."""
assert isinstance(images, list)
min_size = 2 * (image_width // 4)
max_size = image_width * 2
delta_size = (max_size + 2 - min_size) // 5
sizes = range(min_size, max_size + 2, delta_size)
new_images = []
for image in images:
for size in sizes:
new_images.append(resize_and_center(image, size, random_centering))
return new_images
def move_repeats_to_batch(image, n_repeats):
width, height, n_channels = image.shape.as_list()[1:]
image = tf.reshape(image, [-1, width, height, n_channels, n_repeats])
image = tf.transpose(image, [0, 4, 1, 2, 3]) # [B, repeats, x, y, c]
return tf.reshape(image, [-1, width, height, n_channels])
def get_classification_label(dataset_row, class_boundaries):
merge_time = dataset_row['grounded_normalized_time']
label = tf.dtypes.cast(0, tf.int64)
for category, intervals in class_boundaries.items():
for interval in intervals:
if merge_time > interval[0] and merge_time < interval[1]:
label = tf.dtypes.cast(int(category), tf.int64)
return label
def get_regression_label(dataset_row, task_type):
"""Returns time-until-merger regression target given desired modeling task."""
if task_type == losses.TASK_NORMALIZED_REGRESSION:
return tf.dtypes.cast(dataset_row['normalized_time'], tf.float32)
elif task_type == losses.TASK_GROUNDED_UNNORMALIZED_REGRESSION:
return tf.dtypes.cast(dataset_row['grounded_normalized_time'], tf.float32)
elif task_type == losses.TASK_UNNORMALIZED_REGRESSION:
return tf.dtypes.cast(dataset_row['unnormalized_time'], tf.float32)
elif task_type == losses.TASK_CLASSIFICATION:
return tf.dtypes.cast(dataset_row['grounded_normalized_time'], tf.float32)
else:
raise ValueError
def get_normalized_time_target(dataset_row):
return tf.dtypes.cast(dataset_row['normalized_time'], tf.float32)
def apply_time_filter(dataset_row, time_interval):
"""Returns True if data is within the given time intervals."""
merge_time = dataset_row['grounded_normalized_time']
lower_time, upper_time = time_interval
return merge_time > lower_time and merge_time < upper_time
def normalize_physical_feature(name, dataset_row):
min_feat, max_feat = PHYSICAL_FEATURES_MIN_MAX[name]
value = getattr(dataset_row, name)
return 2 * (value - min_feat) / (max_feat - min_feat) - 1
def prepare_dataset(ds, target_size, crop_type, n_repeats, augmentations,
task_type, additional_features, class_boundaries,
time_intervals=None, frequencies_to_use='all',
additional_lambdas=None):
"""Prepare a zipped dataset of image, classification/regression labels."""
def _prepare_image(dataset_row):
"""Transpose, crop and cast an image."""
image = tf.dtypes.cast(dataset_row['image'], tf.float32)
image = tf.reshape(image, tf.cast(dataset_row['image_shape'], tf.int32))
image = tf.transpose(image, perm=[1, 2, 0]) # Convert to NHWC
freqs = ALL_FREQUENCIES if frequencies_to_use == 'all' else frequencies_to_use
idxs_to_keep = [ALL_FREQUENCIES.index(f) for f in freqs]
image = tf.gather(params=image, indices=idxs_to_keep, axis=-1)
# Based on offline computation on the empirical frequency range:
# Converts [0, 8.] ~~> [-1, 1]
image = (image - DATASET_FREQUENCY_MEAN)/(DATASET_FREQUENCY_RANGE/2.0)
def crop(image):
if crop_type == CROP_TYPE_FIXED:
crop_loc = tf.cast(dataset_row['proposed_crop'][0], tf.int32)
crop_size = tf.cast(dataset_row['proposed_crop'][1], tf.int32)
image = image[
crop_loc[0]:crop_loc[0] + crop_size[0],
crop_loc[1]:crop_loc[1] + crop_size[1], :]
image = tf.image.resize(image, target_size[0:2])
image.set_shape([target_size[0], target_size[1], target_size[2]])
elif crop_type == CROP_TYPE_RANDOM:
image = tf.image.random_crop(image, target_size)
image.set_shape([target_size[0], target_size[1], target_size[2]])
elif crop_type != CROP_TYPE_NONE:
raise NotImplementedError
return image
repeated_images = []
for _ in range(n_repeats):
repeated_images.append(crop(image))
image = tf.concat(repeated_images, axis=-1)
if augmentations['rotation_and_flip']:
image = random_rotation_and_flip(image)
if augmentations['rescaling']:
image = random_rescaling(image, augmentations['translation'])
return image
def get_regression_label_wrapper(dataset_row):
return get_regression_label(dataset_row, task_type=task_type)
def get_classification_label_wrapper(dataset_row):
return get_classification_label(dataset_row,
class_boundaries=class_boundaries)
if time_intervals:
for time_interval in time_intervals:
filter_fn = functools.partial(apply_time_filter,
time_interval=time_interval)
ds = ds.filter(filter_fn)
datasets = [ds.map(_prepare_image)]
if additional_features:
additional_features = additional_features.split(',')
assert all([f in VALID_ADDITIONAL_FEATURES for f in additional_features])
logging.info('Running with additional features: %s.',
', '.join(additional_features))
def _prepare_additional_features(dataset_row):
features = []
for f in additional_features:
features.append(normalize_physical_feature(f, dataset_row))
features = tf.convert_to_tensor(features, dtype=tf.float32)
features.set_shape([len(additional_features)])
return features
datasets += [ds.map(_prepare_additional_features)]
datasets += [
ds.map(get_classification_label_wrapper),
ds.map(get_regression_label_wrapper),
ds.map(get_normalized_time_target)]
if additional_lambdas:
for process_fn in additional_lambdas:
datasets += [ds.map(process_fn)]
return tf.data.Dataset.zip(tuple(datasets))
================================================
FILE: galaxy_mergers/requirements.txt
================================================
absl-py==0.11.0
astropy==4.2
astunparse==1.6.3
cachetools==4.2.1
certifi==2020.12.5
chardet==4.0.0
cloudpickle==1.6.0
contextlib2==0.6.0.post1
cycler==0.10.0
decorator==4.4.2
dm-sonnet==2.0.0
dm-tree==0.1.5
flatbuffers==1.12
gast==0.3.3
google-auth==1.27.0
google-auth-oauthlib==0.4.2
google-pasta==0.2.0
grpcio==1.32.0
h5py==2.10.0
idna==2.10
Keras-Preprocessing==1.1.2
kiwisolver==1.3.1
Markdown==3.3.4
matplotlib==3.3.4
ml-collections==0.1.0
numpy==1.19.5
oauthlib==3.1.0
opt-einsum==3.3.0
Pillow==8.1.0
pkg-resources==0.0.0
protobuf==3.15.3
pyasn1==0.4.8
pyasn1-modules==0.2.8
pyerfa==1.7.2
pyparsing==2.4.7
python-dateutil==2.8.1
PyYAML==5.4.1
requests==2.25.1
requests-oauthlib==1.3.0
rsa==4.7.2
scipy==1.6.1
six==1.15.0
tabulate==0.8.9
tensorboard==2.4.1
tensorboard-plugin-wit==1.8.0
tensorflow==2.4.1
tensorflow-estimator==2.4.0
tensorflow-probability==0.12.1
termcolor==1.1.0
typing-extensions==3.7.4.3
urllib3==1.26.3
Werkzeug==1.0.1
wrapt==1.12.1
================================================
FILE: gated_linear_networks/README.md
================================================
# Gated Linear Networks
Gated Linear Networks (GLNs) are a family of backpropation-free neural networks.
Each neuron in a GLN predicts the target density (or probability mass) based on
the outputs of the previous layer and is trained under a logarthmic loss.
## GLN variants
Neurons have probabilistic "activation functions". Implementations are provided
for the following distributions:
- Gaussian, for regression.
- Bernoulli, for binary classification and multi-class classification using a
one-vs-all scheme.
## Examples
Usage examples are provided in [`examples`](examples).
## Implementation details
### Constraint satisfaction
Because each neuron implements a probability density/mass function we need to
ensure that they are well defined. For example, the scale parameter for a
Gaussian density needs to be positive. We implement these constraints using
linear projections and clipping.
### Aggregation
Because each neuron predicts the target, we can use any neuron output as the
"network output", and are not bound to the last layer. Typically last layer
neuron(s) are the best predictors, but they might take longer to converge in
theory. In this implementation, we use a single neuron at the last layer, which
then forms the network output.
There are alternative ways of aggregating, e.g. see Switching Aggregation in
Appendix D of *Gaussian Gated Linear Networks* (link:
https://arxiv.org/pdf/2006.05964.pdf).
## References
Coming soon.
================================================
FILE: gated_linear_networks/base.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Base classes for Gated Linear Networks."""
import abc
import collections
import functools
import inspect
from typing import Any, Callable, Optional, Sequence, Tuple
import chex
import haiku as hk
import jax
import jax.numpy as jnp
Array = chex.Array
DType = Any
Initializer = hk.initializers.Initializer
Shape = Sequence[int]
EPS = 1e-12
MIN_ALPHA = 1e-5
def _l2_normalize(x: Array, axis: int) -> Array:
return x / jnp.sqrt(jnp.maximum(jnp.sum(x**2, axis, keepdims=True), EPS))
def _wrapped_fn_argnames(fun):
"""Returns list of argnames of a (possibly wrapped) function."""
return tuple(inspect.signature(fun).parameters)
def _vmap(fun, in_axes=0, out_axes=0, parameters=None):
"""JAX vmap with human-friendly axes."""
def _axes(fun, d):
"""Maps dict {kwarg_i, : val_i} to [None, ..., val_i, ..., None]."""
argnames = _wrapped_fn_argnames(fun) if not parameters else parameters
for key in d:
if key not in argnames:
raise ValueError(f"{key} is not a valid axis.")
return tuple(d.get(key, None) for key in argnames)
in_axes = _axes(fun, in_axes) if isinstance(in_axes, dict) else in_axes
return jax.vmap(fun, in_axes, out_axes)
# Map a neuron-level function across a layer.
_layer_vmap = functools.partial(
_vmap,
in_axes=({
"weights": 0,
"hyperplanes": 0,
"hyperplane_bias": 0,
}))
class NormalizedRandomNormal(hk.initializers.RandomNormal):
"""Random normal initializer with l2-normalization."""
def __init__(self,
stddev: float = 1.,
mean: float = 0.,
normalize_axis: int = 0):
super(NormalizedRandomNormal, self).__init__(stddev, mean)
self._normalize_axis = normalize_axis
def __call__(self, shape: Shape, dtype: DType) -> Array:
if self._normalize_axis >= len(shape):
raise ValueError("Cannot normalize axis {} for ndim = {}.".format(
self._normalize_axis, len(shape)))
weights = super(NormalizedRandomNormal, self).__call__(shape, dtype)
return _l2_normalize(weights, axis=self._normalize_axis)
class ShapeScaledConstant(hk.initializers.Initializer):
"""Initializes with a constant dependent on last dimension of input shape."""
def __call__(self, shape: Shape, dtype: DType) -> jnp.ndarray:
constant = 1. / shape[-1]
return jnp.broadcast_to(constant, shape).astype(dtype)
class LocalUpdateModule(hk.Module):
"""Abstract base class for GLN variants and utils."""
def __init__(self, name: Optional[str] = None):
if hasattr(self, "__call__"):
raise ValueError("Do not implement `__call__` for a LocalUpdateModule." +
" Implement `inference` and `update` instead.")
super(LocalUpdateModule, self).__init__(name)
@abc.abstractmethod
def inference(self, *args, **kwargs):
"""Module inference step."""
@abc.abstractmethod
def update(self, *args, **kwargs):
"""Module update step."""
@property
@abc.abstractmethod
def output_sizes(self) -> Shape:
"""Returns network output sizes."""
class GatedLinearNetwork(LocalUpdateModule):
"""Abstract base class for a multi-layer Gated Linear Network."""
def __init__(self,
output_sizes: Shape,
context_dim: int,
inference_fn: Callable[..., Array],
update_fn: Callable[..., Array],
init: Initializer,
hyp_w_init: Optional[Initializer] = None,
hyp_b_init: Optional[Initializer] = None,
dtype: DType = jnp.float32,
name: str = "gated_linear_network"):
"""Initialize a GatedLinearNetwork as a sequence of GatedLinearLayers."""
super(GatedLinearNetwork, self).__init__(name=name)
self._layers = []
self._output_sizes = output_sizes
for i, output_size in enumerate(self._output_sizes):
layer = _GatedLinearLayer(
output_size=output_size,
context_dim=context_dim,
update_fn=update_fn,
inference_fn=inference_fn,
init=init,
hyp_w_init=hyp_w_init,
hyp_b_init=hyp_b_init,
dtype=dtype,
name=name + "_layer_{}".format(i))
self._layers.append(layer)
self._name = name
@abc.abstractmethod
def _add_bias(self, inputs):
pass
def inference(self, inputs: Array, side_info: Array, *args,
**kwargs) -> Array:
"""GatedLinearNetwork inference."""
predictions_per_layer = []
predictions = inputs
for layer in self._layers:
predictions = self._add_bias(predictions)
predictions = layer.inference(predictions, side_info, *args, **kwargs)
predictions_per_layer.append(predictions)
return jnp.concatenate(predictions_per_layer, axis=0)
def update(self, inputs, side_info, target, learning_rate, *args, **kwargs):
"""GatedLinearNetwork update."""
all_params = []
all_predictions = []
all_losses = []
predictions = inputs
for layer in self._layers:
predictions = self._add_bias(predictions)
# Note: This is correct because returned predictions are pre-update.
params, predictions, log_loss = layer.update(predictions, side_info,
target, learning_rate, *args,
**kwargs)
all_params.append(params)
all_predictions.append(predictions)
all_losses.append(log_loss)
new_params = dict(collections.ChainMap(*all_params))
predictions = jnp.concatenate(all_predictions, axis=0)
log_loss = jnp.concatenate(all_losses, axis=0)
return new_params, predictions, log_loss
@property
def output_sizes(self):
return self._output_sizes
@staticmethod
def _compute_context(
side_info: Array, # [side_info_size]
hyperplanes: Array, # [context_dim, side_info_size]
hyperplane_bias: Array, # [context_dim]
) -> Array:
# Index weights by side information.
context_dim = hyperplane_bias.shape[0]
proj = jnp.dot(hyperplanes, side_info)
bits = (proj > hyperplane_bias).astype(jnp.int32)
weight_index = jnp.sum(
bits *
jnp.array([2**i for i in range(context_dim)])) if context_dim else 0
return weight_index
class _GatedLinearLayer(LocalUpdateModule):
"""A single layer of a Gated Linear Network."""
def __init__(self,
output_size: int,
context_dim: int,
inference_fn: Callable[..., Array],
update_fn: Callable[..., Array],
init: Initializer,
hyp_w_init: Optional[Initializer] = None,
hyp_b_init: Optional[Initializer] = None,
dtype: DType = jnp.float32,
name: str = "gated_linear_layer"):
"""Initialize a GatedLinearLayer."""
super(_GatedLinearLayer, self).__init__(name=name)
self._output_size = output_size
self._context_dim = context_dim
self._inference_fn = inference_fn
self._update_fn = update_fn
self._init = init
self._hyp_w_init = hyp_w_init
self._hyp_b_init = hyp_b_init
self._dtype = dtype
self._name = name
def _get_weights(self, input_size):
"""Get (or initialize) weight parameters."""
weights = hk.get_parameter(
"weights",
shape=(self._output_size, 2**self._context_dim, input_size),
dtype=self._dtype,
init=self._init,
)
return weights
def _get_hyperplanes(self, side_info_size):
"""Get (or initialize) hyperplane weights and bias."""
hyp_w_init = self._hyp_w_init or NormalizedRandomNormal(
stddev=1., normalize_axis=1)
hyperplanes = hk.get_state(
"hyperplanes",
shape=(self._output_size, self._context_dim, side_info_size),
init=hyp_w_init)
hyp_b_init = self._hyp_b_init or hk.initializers.RandomNormal(stddev=0.05)
hyperplane_bias = hk.get_state(
"hyperplane_bias",
shape=(self._output_size, self._context_dim),
init=hyp_b_init)
return hyperplanes, hyperplane_bias
def inference(self, inputs: Array, side_info: Array, *args,
**kwargs) -> Array:
"""GatedLinearLayer inference."""
# Initialize layer weights.
weights = self._get_weights(inputs.shape[0])
# Initialize fixed random hyperplanes.
side_info_size = side_info.shape[0]
hyperplanes, hyperplane_bias = self._get_hyperplanes(side_info_size)
# Perform layer-wise inference by mapping along output_size (num_neurons).
layer_inference = _layer_vmap(self._inference_fn)
predictions = layer_inference(inputs, side_info, weights, hyperplanes,
hyperplane_bias, *args, **kwargs)
return predictions
def update(self, inputs: Array, side_info: Array, target: Array,
learning_rate: float, *args,
**kwargs) -> Tuple[Array, Array, Array]:
"""GatedLinearLayer update."""
# Fetch layer weights.
weights = self._get_weights(inputs.shape[0])
# Fetch fixed random hyperplanes.
side_info_size = side_info.shape[0]
hyperplanes, hyperplane_bias = self._get_hyperplanes(side_info_size)
# Perform layer-wise update by mapping along output_size (num_neurons).
layer_update = _layer_vmap(self._update_fn)
new_weights, predictions, log_loss = layer_update(inputs, side_info,
weights, hyperplanes,
hyperplane_bias, target,
learning_rate, *args,
**kwargs)
assert new_weights.shape == weights.shape
params = {self.module_name: {"weights": new_weights}}
return params, predictions, log_loss
@property
def output_sizes(self):
return self._output_size
class Mutator(LocalUpdateModule):
"""Abstract base class for GLN Mutators."""
def __init__(
self,
network_factory: Callable[..., LocalUpdateModule],
name: str,
):
super(Mutator, self).__init__(name=name)
self._network = network_factory()
self._name = name
@property
def output_sizes(self):
return self._network.output_sizes
class LastNeuronAggregator(Mutator):
"""Last neuron aggregator: network output is read from the last neuron."""
def __init__(
self,
network_factory: Callable[..., LocalUpdateModule],
name: str = "last_neuron",
):
super(LastNeuronAggregator, self).__init__(network_factory, name)
if self._network.output_sizes[-1] != 1:
raise ValueError(
"LastNeuronAggregator requires the last GLN layer to have"
" output_size = 1.")
def inference(self, *args, **kwargs) -> Array:
predictions = self._network.inference(*args, **kwargs)
return predictions[-1]
def update(self, *args, **kwargs) -> Tuple[Array, Array, Array]:
params_t, predictions_tm1, loss_tm1 = self._network.update(*args, **kwargs)
return params_t, predictions_tm1[-1], loss_tm1[-1]
================================================
FILE: gated_linear_networks/bernoulli.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Bernoulli Gated Linear Network."""
from typing import List, Text, Tuple
import chex
import jax
import jax.numpy as jnp
import rlax
import tensorflow_probability as tfp
from gated_linear_networks import base
tfp = tfp.experimental.substrates.jax
tfd = tfp.distributions
Array = chex.Array
GLN_EPS = 0.01
MAX_WEIGHT = 200.
class GatedLinearNetwork(base.GatedLinearNetwork):
"""Bernoulli Gated Linear Network."""
def __init__(self,
output_sizes: List[int],
context_dim: int,
name: Text = "bernoulli_gln"):
"""Initialize a Bernoulli GLN."""
super(GatedLinearNetwork, self).__init__(
output_sizes,
context_dim,
inference_fn=GatedLinearNetwork._inference_fn,
update_fn=GatedLinearNetwork._update_fn,
init=jnp.zeros,
dtype=jnp.float32,
name=name)
def _add_bias(self, inputs):
return jnp.append(inputs, rlax.sigmoid(1.))
@staticmethod
def _inference_fn(
inputs: Array, # [input_size]
side_info: Array, # [side_info_size]
weights: Array, # [2**context_dim, input_size]
hyperplanes: Array, # [context_dim, side_info_size]
hyperplane_bias: Array, # [context_dim]
) -> Array:
"""Inference step for a single Beurnolli neuron."""
weight_index = GatedLinearNetwork._compute_context(side_info, hyperplanes,
hyperplane_bias)
used_weights = weights[weight_index]
inputs = rlax.logit(jnp.clip(inputs, GLN_EPS, 1. - GLN_EPS))
prediction = rlax.sigmoid(jnp.dot(used_weights, inputs))
return prediction
@staticmethod
def _update_fn(
inputs: Array, # [input_size]
side_info: Array, # [side_info_size]
weights: Array, # [2**context_dim, num_features]
hyperplanes: Array, # [context_dim, side_info_size]
hyperplane_bias: Array, # [context_dim]
target: Array, # []
learning_rate: float,
) -> Tuple[Array, Array, Array]:
"""Update step for a single Bernoulli neuron."""
def log_loss_fn(inputs, side_info, weights, hyperplanes, hyperplane_bias,
target):
"""Log loss for a single Bernoulli neuron."""
prediction = GatedLinearNetwork._inference_fn(inputs, side_info, weights,
hyperplanes,
hyperplane_bias)
prediction = jnp.clip(prediction, GLN_EPS, 1. - GLN_EPS)
return rlax.log_loss(prediction, target), prediction
grad_log_loss = jax.value_and_grad(log_loss_fn, argnums=2, has_aux=True)
((log_loss, prediction),
dloss_dweights) = grad_log_loss(inputs, side_info, weights, hyperplanes,
hyperplane_bias, target)
delta_weights = learning_rate * dloss_dweights
new_weights = jnp.clip(weights - delta_weights, -MAX_WEIGHT, MAX_WEIGHT)
return new_weights, prediction, log_loss
class LastNeuronAggregator(base.LastNeuronAggregator):
"""Bernoulli last neuron aggregator, implemented by the super class."""
pass
================================================
FILE: gated_linear_networks/bernoulli_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for `bernoulli.py`."""
from absl.testing import absltest
from absl.testing import parameterized
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import tree
from gated_linear_networks import bernoulli
def _get_dataset(input_size, batch_size=None):
"""Get mock dataset."""
if batch_size:
inputs = jnp.ones([batch_size, input_size])
side_info = jnp.ones([batch_size, input_size])
targets = jnp.ones([batch_size])
else:
inputs = jnp.ones([input_size])
side_info = jnp.ones([input_size])
targets = jnp.ones([])
return inputs, side_info, targets
class GatedLinearNetworkTest(parameterized.TestCase):
# TODO(b/170843789): Factor out common test utilities.
def setUp(self):
super(GatedLinearNetworkTest, self).setUp()
self._name = "test_network"
self._rng = hk.PRNGSequence(jax.random.PRNGKey(42))
self._output_sizes = (4, 5, 6)
self._context_dim = 2
def gln_factory():
return bernoulli.GatedLinearNetwork(
output_sizes=self._output_sizes,
context_dim=self._context_dim,
name=self._name)
def inference_fn(inputs, side_info):
return gln_factory().inference(inputs, side_info)
def batch_inference_fn(inputs, side_info):
return jax.vmap(inference_fn, in_axes=(0, 0))(inputs, side_info)
def update_fn(inputs, side_info, label, learning_rate):
params, predictions, unused_loss = gln_factory().update(
inputs, side_info, label, learning_rate)
return predictions, params
def batch_update_fn(inputs, side_info, label, learning_rate):
predictions, params = jax.vmap(
update_fn, in_axes=(0, 0, 0, None))(inputs, side_info, label,
learning_rate)
avg_params = tree.map_structure(lambda x: jnp.mean(x, axis=0), params)
return predictions, avg_params
# Haiku transform functions.
self._init_fn, inference_fn_ = hk.without_apply_rng(
hk.transform_with_state(inference_fn))
self._batch_init_fn, batch_inference_fn_ = hk.without_apply_rng(
hk.transform_with_state(batch_inference_fn))
_, update_fn_ = hk.without_apply_rng(hk.transform_with_state(update_fn))
_, batch_update_fn_ = hk.without_apply_rng(
hk.transform_with_state(batch_update_fn))
self._inference_fn = jax.jit(inference_fn_)
self._batch_inference_fn = jax.jit(batch_inference_fn_)
self._update_fn = jax.jit(update_fn_)
self._batch_update_fn = jax.jit(batch_update_fn_)
@parameterized.named_parameters(("Online mode", None), ("Batch mode", 3))
def test_shapes(self, batch_size):
"""Test shapes in online and batch regimes."""
if batch_size is None:
init_fn = self._init_fn
inference_fn = self._inference_fn
else:
init_fn = self._batch_init_fn
inference_fn = self._batch_inference_fn
input_size = 10
inputs, side_info, _ = _get_dataset(input_size, batch_size)
input_size = inputs.shape[-1]
# Initialize network.
gln_params, gln_state = init_fn(next(self._rng), inputs, side_info)
# Test shapes of parameters layer-wise.
layer_input_size = input_size
for layer_idx, output_size in enumerate(self._output_sizes):
name = "{}/~/{}_layer_{}".format(self._name, self._name, layer_idx)
weights = gln_params[name]["weights"]
expected_shape = (output_size, 2**self._context_dim, layer_input_size + 1)
self.assertEqual(weights.shape, expected_shape)
layer_input_size = output_size
# Test shape of output.
output_size = sum(self._output_sizes)
predictions, _ = inference_fn(gln_params, gln_state, inputs, side_info)
expected_shape = (batch_size, output_size) if batch_size else (output_size,)
self.assertEqual(predictions.shape, expected_shape)
@parameterized.named_parameters(("Online mode", None), ("Batch mode", 3))
def test_update(self, batch_size):
"""Test network updates in online and batch regimes."""
if batch_size is None:
init_fn = self._init_fn
inference_fn = self._inference_fn
update_fn = self._update_fn
else:
init_fn = self._batch_init_fn
inference_fn = self._batch_inference_fn
update_fn = self._batch_update_fn
input_size = 10
inputs, side_info, targets = _get_dataset(input_size, batch_size)
# Initialize network.
initial_params, gln_state = init_fn(next(self._rng), inputs, side_info)
# Initial predictions.
initial_predictions, _ = inference_fn(initial_params, gln_state, inputs,
side_info)
# Test that params remain valid after consecutive updates.
gln_params = initial_params
for _ in range(3):
(_, gln_params), gln_state = update_fn(
gln_params, gln_state, inputs, side_info, targets, learning_rate=1e-4)
# Check updated weights layer-wise.
for layer_idx in range(len(self._output_sizes)):
name = "{}/~/{}_layer_{}".format(self._name, self._name, layer_idx)
initial_weights = initial_params[name]["weights"]
new_weights = gln_params[name]["weights"]
# Shape consistency.
self.assertEqual(new_weights.shape, initial_weights.shape)
# Check that different weights yield different predictions.
new_predictions, _ = inference_fn(gln_params, gln_state, inputs,
side_info)
self.assertFalse(np.array_equal(new_predictions, initial_predictions))
def test_batch_consistency(self):
"""Test consistency between online and batch updates."""
input_size = 10
batch_size = 3
inputs, side_info, targets = _get_dataset(input_size, batch_size)
# Initialize network.
gln_params, gln_state = self._batch_init_fn(
next(self._rng), inputs, side_info)
test_layer = "{}/~/{}_layer_0".format(self._name, self._name)
for _ in range(10):
# Update on full batch.
(expected_predictions, expected_params), _ = self._batch_update_fn(
gln_params, gln_state, inputs, side_info, targets, learning_rate=1e-3)
# Average updates across batch and check equivalence.
accum_predictions = []
accum_weights = []
for inputs_, side_info_, targets_ in zip(inputs, side_info, targets):
(predictions, params), _ = self._update_fn(
gln_params,
gln_state,
inputs_,
side_info_,
targets_,
learning_rate=1e-3)
accum_predictions.append(predictions)
accum_weights.append(params[test_layer]["weights"])
# Check prediction equivalence.
actual_predictions = np.stack(accum_predictions, axis=0)
np.testing.assert_array_almost_equal(actual_predictions,
expected_predictions)
# Check weight equivalence.
actual_weights = np.mean(np.stack(accum_weights, axis=0), axis=0)
expected_weights = expected_params[test_layer]["weights"]
np.testing.assert_array_almost_equal(actual_weights, expected_weights)
gln_params = expected_params
if __name__ == "__main__":
absltest.main()
================================================
FILE: gated_linear_networks/colabs/README.md
================================================
# Colabs
## Dendritic Gated Networks
`dendritic_gated_network.ipynb` implements a Dendritic Gated Network (DGN) solving a regression (using quadratic loss) or a binary classification problem (using Bernoulli log loss).
See our paper titled ["A rapid and efficient learning rule for biological neural circuits"](https://www.biorxiv.org/content/10.1101/2021.03.10.434756v1) for details of the DGN model.
### Instructions for running the `dendritic_gated_network.ipynb` colab/notebook.
We suggest running the [dendritic_gated_network.ipynb](https://github.com/deepmind/deepmind-research/blob/master/gated_linear_networks/colabs/dendritic_gated_network.ipynb) notebook using Google Colaboratory (or Colab). All the dependencies are included by default in Colab cloud runtimes (last tested on the 8th of March, 2021). See https://research.google.com/colaboratory/faq.html for web browser requirements. The notebook runs for about a minute using the free tier runtimes.
The code also runs in JupyterLab/JupyterNotebook (tested on Version 1.02).
1. Visit https://colab.research.google.com/
2. Sign in with your Google account.
3. Click on "File" and select "Open notebook".
4. Then you can
* either open the notebook directly from GitHub:
* Click on the GitHub tab
* Paste https://github.com/deepmind/deepmind-research/blob/master/gated_linear_networks/colabs/dendritic_gated_network.ipynb into the URL section and click the search button. If the notebook does not open automatically, then select the correct notebook from the list provided.
* or upload the provided notebook manually:
* Click on the Upload tab
* Choose or drag dendritic_gated_network.ipynb
5. Click Connect (top right corner) to connect to a run time
6. Click on the "Runtime" tab and select "Run all" to run all the cells.
### Expected outputs
We provide the expected outputs below.
Classification (do_classification = True):
```
epoch: 0, test loss: 0.693 (train: 0.693), test accuracy: 0.412 (train: 0.363)
epoch: 1, test loss: 0.099 (train: 0.196), test accuracy: 0.974 (train: 0.963)
epoch: 2, test loss: 0.095 (train: 0.079), test accuracy: 0.974 (train: 0.978)
epoch: 3, test loss: 0.099 (train: 0.070), test accuracy: 0.974 (train: 0.982)
```
Regression (do_classification = False):
```
epoch: 0, test loss: 0.419 (train: 0.500)
epoch: 1, test loss: 0.388 (train: 0.486)
epoch: 2, test loss: 0.354 (train: 0.439)
epoch: 3, test loss: 0.328 (train: 0.400)
epoch: 4, test loss: 0.310 (train: 0.369)
epoch: 5, test loss: 0.297 (train: 0.344)
epoch: 6, test loss: 0.287 (train: 0.324)
epoch: 7, test loss: 0.281 (train: 0.308)
epoch: 8, test loss: 0.277 (train: 0.296)
epoch: 9, test loss: 0.275 (train: 0.285)
epoch: 10, test loss: 0.273 (train: 0.277)
```
================================================
FILE: gated_linear_networks/colabs/dendritic_gated_network.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "s54x-Gq4AiYb"
},
"source": [
"## Simple Dendritic Gated Networks in numpy\n",
"\n",
"This colab implements a Dendritic Gated Network (DGN) solving a regression (using quadratic loss) or a binary classification problem (using Bernoulli log loss).\n",
"\n",
"See our paper titled [\"A rapid and efficient learning rule for biological neural circuits\"](https://www.biorxiv.org/content/10.1101/2021.03.10.434756v1) for details of the DGN model.\n",
"\n",
"\n",
"Some implementation details:\n",
"- We utilize `sklearn.datasets.load_breast_cancer` for binary classification and `sklearn.datasets.load_diabetes` for regression.\n",
"- This code is meant for educational purposes only. It is not optimized for high-performance, both in terms of computational efficiency and quality of fit.\n",
"- Network is trained on 80% of the dataset and tested on the rest. For classification, we report log loss (negative log likelihood) and accuracy (percentage of correctly identified labels). For regression, we report MSE expressed in units of target variance."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jhiajfn0EAxE"
},
"outputs": [],
"source": [
"# Copyright 2021 DeepMind Technologies Limited. All rights reserved.\n",
"#\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nm-F_uZA0_T2"
},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn import datasets\n",
"from sklearn import preprocessing\n",
"from sklearn import model_selection\n",
"from typing import List, Optional"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LOoiBATk1AgQ"
},
"source": [
"## Choose classification or regression"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FCjzwzwh0ycl"
},
"outputs": [],
"source": [
"do_classification = True # if False, does regression"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TA5VmSeV-GTc"
},
"source": [
"### Load dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qnzNZrzNk3Pl"
},
"outputs": [],
"source": [
"if do_classification:\n",
" features, targets = datasets.load_breast_cancer(return_X_y=True)\n",
"else:\n",
" features, targets = datasets.load_diabetes(return_X_y=True)\n",
"\n",
"x_train, x_test, y_train, y_test = model_selection.train_test_split(\n",
" features, targets, test_size=0.2, random_state=0)\n",
"n_features = x_train.shape[-1]\n",
"\n",
"# Input features are centered and scaled to unit variance:\n",
"feature_encoder = preprocessing.StandardScaler()\n",
"x_train = feature_encoder.fit_transform(x_train)\n",
"x_test = feature_encoder.transform(x_test)\n",
"\n",
"if not do_classification:\n",
" # Continuous targets are centered and scaled to unit variance:\n",
" target_encoder = preprocessing.StandardScaler()\n",
" y_train = np.squeeze(target_encoder.fit_transform(y_train[:, np.newaxis]))\n",
" y_test = np.squeeze(target_encoder.transform(y_test[:, np.newaxis]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LTQxvDcok86S"
},
"source": [
"## DGN inference/update"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "F6Yt_tw0lmf1"
},
"outputs": [],
"source": [
"def step_square_loss(inputs: np.ndarray,\n",
" weights: List[np.ndarray],\n",
" hyperplanes: List[np.ndarray],\n",
" hyperplane_bias_magnitude: Optional[float] = 1.,\n",
" learning_rate: Optional[float] = 1e-5,\n",
" target: Optional[float] = None,\n",
" update: bool = False,\n",
" ):\n",
" \"\"\"Implements a DGN inference/update using square loss.\"\"\"\n",
" r_in = inputs\n",
" side_info = np.hstack([hyperplane_bias_magnitude, inputs])\n",
"\n",
" for w, h in zip(weights, hyperplanes): # loop over layers\n",
" r_in = np.hstack([1., r_in]) # add biases\n",
" gate_values = np.heaviside(h.dot(side_info), 0).astype(bool)\n",
" effective_weights = gate_values.dot(w).sum(axis=1)\n",
" r_out = effective_weights.dot(r_in)\n",
"\n",
" if update:\n",
" grad = (r_out[:, None] - target) * r_in[None]\n",
" w -= learning_rate * gate_values[:, :, None] * grad[:, None]\n",
"\n",
" r_in = r_out\n",
" loss = (target - r_out)**2 / 2\n",
" return r_out, loss\n",
"\n",
"def sigmoid(x): # numerically stable sigmoid\n",
" return np.exp(-np.logaddexp(0, -x))\n",
"\n",
"def inverse_sigmoid(x):\n",
" return np.log(x/(1-x))\n",
"\n",
"def step_bernoulli(inputs: np.ndarray,\n",
" weights: List[np.ndarray],\n",
" hyperplanes: List[np.ndarray],\n",
" hyperplane_bias_magnitude: Optional[float] = 1.,\n",
" learning_rate: Optional[float] = 1e-5,\n",
" epsilon: float = 0.01,\n",
" target: Optional[float] = None,\n",
" update: bool = False,\n",
" ):\n",
" \"\"\"Implements a DGN inference/update using Bernoulli log loss.\"\"\"\n",
" r_in = np.clip(sigmoid(inputs), epsilon, 1-epsilon)\n",
" side_info = np.hstack([hyperplane_bias_magnitude, inputs])\n",
"\n",
" for w, h in zip(weights, hyperplanes): # loop over layers\n",
" r_in = np.hstack([sigmoid(1.), r_in]) # add biases\n",
" h_in = inverse_sigmoid(r_in)\n",
" gate_values = np.heaviside(h.dot(side_info), 0).astype(bool)\n",
" effective_weights = gate_values.dot(w).sum(axis=1)\n",
" h_out = effective_weights.dot(h_in)\n",
" r_out_unclipped = sigmoid(h_out)\n",
" r_out = np.clip(r_out_unclipped, epsilon, 1 - epsilon)\n",
" if update:\n",
" update_indicator = np.abs(target - r_out_unclipped) \u003e epsilon\n",
" grad = (r_out[:, None] - target) * h_in[None] * update_indicator[:, None]\n",
" w -= learning_rate * gate_values[:, :, None] * grad[:, None]\n",
" r_in = r_out\n",
" loss = - (target * np.log(r_out) + (1 - target) * np.log(1 - r_out))\n",
" return r_out, loss"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0B7wSn3Azcfb"
},
"outputs": [],
"source": [
"def forward_pass(step_fn, x, y, weights, hyperplanes, learning_rate, update):\n",
" losses, outputs = np.zeros(len(y)), np.zeros(len(y))\n",
" for i, (x_i, y_i) in enumerate(zip(x, y)):\n",
" outputs[i], losses[i] = step_fn(x_i, weights, hyperplanes, target=y_i,\n",
" learning_rate=learning_rate, update=update)\n",
" return np.mean(losses), outputs"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "41aHT8G0lsuu"
},
"source": [
"## Define architecture\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WSbPuwzFvV2N"
},
"outputs": [],
"source": [
"# number of neurons per layer, the last element must be 1\n",
"n_neurons = np.array([100, 10, 1])\n",
"n_branches = 20 # number of dendritic brancher per neuron"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gTk1YDXV-xoD"
},
"source": [
"## Initialise weights and gating parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Uek-2I5IlyN3"
},
"outputs": [],
"source": [
"n_inputs = np.hstack([n_features + 1, n_neurons[:-1] + 1]) # 1 for the bias\n",
"dgn_weights = [np.zeros((n_neuron, n_branches, n_input))\n",
" for n_neuron, n_input in zip(n_neurons, n_inputs)]\n",
"\n",
"# Fixing random seed for reproducibility:\n",
"np.random.seed(12345)\n",
"dgn_hyperplanes = [\n",
" np.random.normal(0, 1, size=(n_neuron, n_branches, n_features + 1))\n",
" for n_neuron in n_neurons]\n",
"# By default, the weight parameters are drawn from a normalised Gaussian:\n",
"dgn_hyperplanes = [\n",
" h_ / np.linalg.norm(h_[:, :, :-1], axis=(1, 2))[:, None, None]\n",
" for h_ in dgn_hyperplanes]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Dy1XUdaSm0ID"
},
"source": [
"## Train"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wublBSqiucQ-"
},
"outputs": [],
"source": [
"if do_classification:\n",
" eta = 1e-4\n",
" n_epochs = 3\n",
" step = step_bernoulli\n",
"else:\n",
" eta = 1e-5\n",
" n_epochs = 10\n",
" step = step_square_loss\n",
"\n",
"if do_classification:\n",
" step = step_bernoulli\n",
"else:\n",
" step = step_square_loss\n",
"\n",
"print('Training on {} problem for {} epochs with learning rate {}.'.format(\n",
" ['regression', 'classification'][do_classification], n_epochs, eta))\n",
"print('This may take a minute. Please be patient...')\n",
"\n",
"for epoch in range(0, n_epochs + 1):\n",
" train_loss, train_pred = forward_pass(\n",
" step, x_train, y_train, dgn_weights,\n",
" dgn_hyperplanes, eta, update=(epoch \u003e 0))\n",
"\n",
" test_loss, test_pred = forward_pass(\n",
" step, x_test, y_test, dgn_weights,\n",
" dgn_hyperplanes, eta, update=False)\n",
" to_print = 'epoch: {}, test loss: {:.3f} (train: {:.3f})'.format(\n",
" epoch, test_loss, train_loss)\n",
"\n",
" if do_classification:\n",
" accuracy_train = np.mean(np.round(train_pred) == y_train)\n",
" accuracy = np.mean(np.round(test_pred) == y_test)\n",
" to_print += ', test accuracy: {:.3f} (train: {:.3f})'.format(\n",
" accuracy, accuracy_train)\n",
" print(to_print)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "//learning/deepmind/dm_python:dm_notebook3",
"kind": "private"
},
"name": "dendritic_gated_network.ipynb",
"private_outputs": true,
"provenance": [
{
"file_id": "1lzQUssVJpeziFs1fdBHueD7DqNp6lkVK",
"timestamp": 1614705435731
}
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: gated_linear_networks/examples/bernoulli_mnist.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Online MNIST classification example with Bernoulli GLN."""
from absl import app
from absl import flags
import haiku as hk
import jax
import jax.numpy as jnp
import rlax
from gated_linear_networks import bernoulli
from gated_linear_networks.examples import utils
MAX_TRAIN_STEPS = flags.DEFINE_integer(
name='max_train_steps',
default=None,
help='Maximum number of training steps to perform (None=no limit)',
)
# Small example network, achieves ~95% test set accuracy =======================
# Network parameters.
NUM_LAYERS = flags.DEFINE_integer(
name='num_layers',
default=2,
help='Number of network layers',
)
NEURONS_PER_LAYER = flags.DEFINE_integer(
name='neurons_per_layer',
default=100,
help='Number of neurons per layer',
)
CONTEXT_DIM = flags.DEFINE_integer(
name='context_dim',
default=1,
help='Context vector size',
)
# Learning rate schedule.
MAX_LR = flags.DEFINE_float(
name='max_lr',
default=0.003,
help='Maximum learning rate',
)
LR_CONSTANT = flags.DEFINE_float(
name='lr_constant',
default=1.0,
help='Learning rate constant parameter',
)
LR_DECAY = flags.DEFINE_float(
name='lr_decay',
default=0.1,
help='Learning rate decay parameter',
)
# Logging parameters.
EVALUATE_EVERY = flags.DEFINE_integer(
name='evaluate_every',
default=1000,
help='Number of training steps per evaluation epoch',
)
def main(unused_argv):
# Load MNIST dataset =========================================================
mnist_data, info = utils.load_deskewed_mnist(
name='mnist', batch_size=-1, with_info=True)
num_classes = info.features['label'].num_classes
(train_images, train_labels) = (mnist_data['train']['image'],
mnist_data['train']['label'])
(test_images, test_labels) = (mnist_data['test']['image'],
mnist_data['test']['label'])
# Build a (binary) GLN classifier ============================================
def network_factory():
def gln_factory():
output_sizes = [NEURONS_PER_LAYER.value] * NUM_LAYERS.value + [1]
return bernoulli.GatedLinearNetwork(
output_sizes=output_sizes, context_dim=CONTEXT_DIM.value)
return bernoulli.LastNeuronAggregator(gln_factory)
def extract_features(image):
mean, stddev = utils.MeanStdEstimator()(image)
standardized_img = (image - mean) / (stddev + 1.)
inputs = rlax.sigmoid(standardized_img)
side_info = standardized_img
return inputs, side_info
def inference_fn(image, *args, **kwargs):
inputs, side_info = extract_features(image)
return network_factory().inference(inputs, side_info, *args, **kwargs)
def update_fn(image, *args, **kwargs):
inputs, side_info = extract_features(image)
return network_factory().update(inputs, side_info, *args, **kwargs)
init_, inference_ = hk.without_apply_rng(
hk.transform_with_state(inference_fn))
_, update_ = hk.without_apply_rng(hk.transform_with_state(update_fn))
# Map along class dimension to create a one-vs-all classifier ================
@jax.jit
def init(dummy_image, key):
"""One-vs-all classifier init fn."""
dummy_images = jnp.stack([dummy_image] * num_classes, axis=0)
keys = jax.random.split(key, num_classes)
return jax.vmap(init_, in_axes=(0, 0))(keys, dummy_images)
@jax.jit
def accuracy(params, state, image, label):
"""One-vs-all classifier inference fn."""
fn = jax.vmap(inference_, in_axes=(0, 0, None))
predictions, unused_state = fn(params, state, image)
return (jnp.argmax(predictions) == label).astype(jnp.float32)
@jax.jit
def update(params, state, step, image, label):
"""One-vs-all classifier update fn."""
# Learning rate schedules.
learning_rate = jnp.minimum(
MAX_LR.value, LR_CONSTANT.value / (1. + LR_DECAY.value * step))
# Update weights and report log-loss.
targets = hk.one_hot(jnp.asarray(label), num_classes)
fn = jax.vmap(update_, in_axes=(0, 0, None, 0, None))
out = fn(params, state, image, targets, learning_rate)
(params, unused_predictions, log_loss), state = out
return (jnp.mean(log_loss), params), state
# Train on train split =======================================================
dummy_image = train_images[0]
params, state = init(dummy_image, jax.random.PRNGKey(42))
for step, (image, label) in enumerate(zip(train_images, train_labels), 1):
(unused_loss, params), state = update(
params,
state,
step,
image,
label,
)
# Evaluate on test split ===================================================
if not step % EVALUATE_EVERY.value:
batch_accuracy = jax.vmap(accuracy, in_axes=(None, None, 0, 0))
accuracies = batch_accuracy(params, state, test_images, test_labels)
total_accuracy = float(jnp.mean(accuracies))
# Report statistics.
print({
'step': step,
'accuracy': float(total_accuracy),
})
if MAX_TRAIN_STEPS.value is not None and step >= MAX_TRAIN_STEPS.value:
return
if __name__ == '__main__':
app.run(main)
================================================
FILE: gated_linear_networks/examples/utils.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Haiku modules for feature processing."""
import copy
from typing import Tuple
import chex
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
from scipy.ndimage import interpolation
import tensorflow_datasets as tfds
Array = chex.Array
def _moments(image):
"""Compute the first and second moments of a given image."""
c0, c1 = np.mgrid[:image.shape[0], :image.shape[1]]
total_image = np.sum(image)
m0 = np.sum(c0 * image) / total_image
m1 = np.sum(c1 * image) / total_image
m00 = np.sum((c0 - m0)**2 * image) / total_image
m11 = np.sum((c1 - m1)**2 * image) / total_image
m01 = np.sum((c0 - m0) * (c1 - m1) * image) / total_image
mu_vector = np.array([m0, m1])
covariance_matrix = np.array([[m00, m01], [m01, m11]])
return mu_vector, covariance_matrix
def _deskew(image):
"""Image deskew."""
c, v = _moments(image)
alpha = v[0, 1] / v[0, 0]
affine = np.array([[1, 0], [alpha, 1]])
ocenter = np.array(image.shape) / 2.0
offset = c - np.dot(affine, ocenter)
return interpolation.affine_transform(image, affine, offset=offset)
def _deskew_dataset(dataset):
"""Dataset deskew."""
deskewed = copy.deepcopy(dataset)
for k, before in dataset.items():
images = before["image"]
num_images = images.shape[0]
after = np.stack([_deskew(i) for i in np.squeeze(images, axis=-1)], axis=0)
deskewed[k]["image"] = np.reshape(after, (num_images, -1))
return deskewed
def load_deskewed_mnist(*a, **k):
"""Returns deskewed MNIST numpy dataset."""
mnist_data, info = tfds.load(*a, **k)
mnist_data = tfds.as_numpy(mnist_data)
deskewed_data = _deskew_dataset(mnist_data)
return deskewed_data, info
class MeanStdEstimator(hk.Module):
"""Online mean and standard deviation estimator using Welford's algorithm."""
def __call__(self, sample: jax.Array) -> Tuple[Array, Array]:
if len(sample.shape) > 1:
raise ValueError("sample must be a rank 0 or 1 DeviceArray.")
count = hk.get_state("count", shape=(), dtype=jnp.int32, init=jnp.zeros)
mean = hk.get_state(
"mean", shape=sample.shape, dtype=jnp.float32, init=jnp.zeros)
m2 = hk.get_state(
"m2", shape=sample.shape, dtype=jnp.float32, init=jnp.zeros)
count += 1
delta = sample - mean
mean += delta / count
delta_2 = sample - mean
m2 += delta * delta_2
hk.set_state("count", count)
hk.set_state("mean", mean)
hk.set_state("m2", m2)
stddev = jnp.sqrt(m2 / count)
return mean, stddev
================================================
FILE: gated_linear_networks/examples/utils_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for `utils.py`."""
from absl.testing import absltest
import haiku as hk
import jax
import numpy as np
from gated_linear_networks.examples import utils
class MeanStdEstimator(absltest.TestCase):
def test_statistics(self):
num_features = 100
feature_size = 3
samples = np.random.normal(
loc=5., scale=2., size=(num_features, feature_size))
true_mean = np.mean(samples, axis=0)
true_std = np.std(samples, axis=0)
def tick_(sample):
return utils.MeanStdEstimator()(sample)
init_fn, apply_fn = hk.without_apply_rng(hk.transform_with_state(tick_))
tick = jax.jit(apply_fn)
params, state = init_fn(rng=None, sample=samples[0])
for sample in samples:
(mean, std), state = tick(params, state, sample)
np.testing.assert_array_almost_equal(mean, true_mean, decimal=5)
np.testing.assert_array_almost_equal(std, true_std, decimal=5)
if __name__ == '__main__':
absltest.main()
================================================
FILE: gated_linear_networks/gaussian.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Gaussian Gated Linear Network."""
from typing import Callable, List, Text, Tuple
import chex
import jax
import jax.numpy as jnp
import tensorflow_probability as tfp
from gated_linear_networks import base
tfp = tfp.experimental.substrates.jax
tfd = tfp.distributions
Array = chex.Array
MIN_SIGMA_SQ_AGGREGATOR = 0.5
MAX_SIGMA_SQ = 1e5
MAX_WEIGHT = 1e3
MIN_WEIGHT = -1e3
def _unpack_inputs(inputs: Array) -> Tuple[Array, Array]:
inputs = jnp.atleast_2d(inputs)
chex.assert_rank(inputs, 2)
(mu, sigma_sq) = [jnp.squeeze(x, 1) for x in jnp.hsplit(inputs, 2)]
return mu, sigma_sq
def _pack_inputs(mu: Array, sigma_sq: Array) -> Array:
mu = jnp.atleast_1d(mu)
sigma_sq = jnp.atleast_1d(sigma_sq)
chex.assert_rank([mu, sigma_sq], 1)
return jnp.vstack([mu, sigma_sq]).T
class GatedLinearNetwork(base.GatedLinearNetwork):
"""Gaussian Gated Linear Network."""
def __init__(
self,
output_sizes: List[int],
context_dim: int,
bias_len: int = 3,
bias_max_mu: float = 1.,
bias_sigma_sq: float = 1.,
name: Text = "gaussian_gln"):
"""Initialize a Gaussian GLN."""
super(GatedLinearNetwork, self).__init__(
output_sizes,
context_dim,
inference_fn=GatedLinearNetwork._inference_fn,
update_fn=GatedLinearNetwork._update_fn,
init=base.ShapeScaledConstant(),
dtype=jnp.float64,
name=name)
self._bias_len = bias_len
self._bias_max_mu = bias_max_mu
self._bias_sigma_sq = bias_sigma_sq
def _add_bias(self, inputs):
mu = jnp.linspace(-1. * self._bias_max_mu, self._bias_max_mu,
self._bias_len)
sigma_sq = self._bias_sigma_sq * jnp.ones_like(mu)
bias = _pack_inputs(mu, sigma_sq)
return jnp.concatenate([inputs, bias], axis=0)
@staticmethod
def _inference_fn(
inputs: Array, # [input_size, 2]
side_info: Array, # [side_info_size]
weights: Array, # [2**context_dim, input_size]
hyperplanes: Array, # [context_dim, side_info_size]
hyperplane_bias: Array, # [context_dim]
min_sigma_sq: float,
) -> Array:
"""Inference step for a single Gaussian neuron."""
mu_in, sigma_sq_in = _unpack_inputs(inputs)
weight_index = GatedLinearNetwork._compute_context(side_info, hyperplanes,
hyperplane_bias)
used_weights = weights[weight_index]
# This projection operation is differentiable and affects the gradients.
used_weights = GatedLinearNetwork._project_weights(inputs, used_weights,
min_sigma_sq)
sigma_sq_out = 1. / jnp.sum(used_weights / sigma_sq_in)
mu_out = sigma_sq_out * jnp.sum((used_weights * mu_in) / sigma_sq_in)
prediction = jnp.hstack((mu_out, sigma_sq_out))
return prediction
@staticmethod
def _project_weights(inputs: Array, # [input_size]
weights: Array, # [2**context_dim, num_features]
min_sigma_sq: float) -> Array:
"""Implements hard projection."""
# This projection should be performed before the sigma related ones.
weights = jnp.minimum(jnp.maximum(MIN_WEIGHT, weights), MAX_WEIGHT)
_, sigma_sq_in = _unpack_inputs(inputs)
lambda_in = 1. / sigma_sq_in
sigma_sq_out = 1. / weights.dot(lambda_in)
# If w.dot(x) < U, linearly project w such that w.dot(x) = U.
weights = jnp.where(
sigma_sq_out < min_sigma_sq, weights - lambda_in *
(1. / sigma_sq_out - 1. / min_sigma_sq) / jnp.sum(lambda_in**2),
weights)
# If w.dot(x) > U, linearly project w such that w.dot(x) = U.
weights = jnp.where(
sigma_sq_out > MAX_SIGMA_SQ, weights - lambda_in *
(1. / sigma_sq_out - 1. / MAX_SIGMA_SQ) / jnp.sum(lambda_in**2),
weights)
return weights
@staticmethod
def _update_fn(
inputs: Array, # [input_size]
side_info: Array, # [side_info_size]
weights: Array, # [2**context_dim, num_features]
hyperplanes: Array, # [context_dim, side_info_size]
hyperplane_bias: Array, # [context_dim]
target: Array, # []
learning_rate: float,
min_sigma_sq: float, # needed for inference (weight projection)
) -> Tuple[Array, Array, Array]:
"""Update step for a single Gaussian neuron."""
def log_loss_fn(inputs, side_info, weights, hyperplanes, hyperplane_bias,
target):
"""Log loss for a single Gaussian neuron."""
prediction = GatedLinearNetwork._inference_fn(inputs, side_info, weights,
hyperplanes,
hyperplane_bias,
min_sigma_sq)
mu, sigma_sq = prediction.T
loss = -tfd.Normal(mu, jnp.sqrt(sigma_sq)).log_prob(target)
return loss, prediction
grad_log_loss = jax.value_and_grad(log_loss_fn, argnums=2, has_aux=True)
(log_loss,
prediction), dloss_dweights = grad_log_loss(inputs, side_info, weights,
hyperplanes, hyperplane_bias,
target)
delta_weights = learning_rate * dloss_dweights
return weights - delta_weights, prediction, log_loss
class ConstantInputSigma(base.Mutator):
"""Input pre-processing by concatenating a constant sigma^2."""
def __init__(
self,
network_factory: Callable[..., GatedLinearNetwork],
input_sigma_sq: float,
name: Text = "constant_input_sigma",
):
super(ConstantInputSigma, self).__init__(network_factory, name)
self._input_sigma_sq = input_sigma_sq
def inference(self, inputs, *args, **kwargs):
"""ConstantInputSigma inference."""
chex.assert_rank(inputs, 1)
sigma_sq = self._input_sigma_sq * jnp.ones_like(inputs)
return self._network.inference(_pack_inputs(inputs, sigma_sq), *args,
**kwargs)
def update(self, inputs, *args, **kwargs):
"""ConstantInputSigma update."""
chex.assert_rank(inputs, 1)
sigma_sq = self._input_sigma_sq * jnp.ones_like(inputs)
return self._network.update(_pack_inputs(inputs, sigma_sq), *args, **kwargs)
class LastNeuronAggregator(base.LastNeuronAggregator):
"""Gaussian last neuron aggregator, implemented by the super class."""
pass
================================================
FILE: gated_linear_networks/gaussian_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for `gaussian.py`."""
from absl.testing import absltest
from absl.testing import parameterized
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import tree
from gated_linear_networks import gaussian
def _get_dataset(input_size, batch_size=None):
"""Get mock dataset."""
if batch_size:
inputs = jnp.ones([batch_size, input_size, 2])
side_info = jnp.ones([batch_size, input_size])
targets = 0.8 * jnp.ones([batch_size])
else:
inputs = jnp.ones([input_size, 2])
side_info = jnp.ones([input_size])
targets = jnp.ones([])
return inputs, side_info, targets
class UtilsTest(absltest.TestCase):
def test_packing_identity(self):
mu = jnp.array([1., 2., 3., 4., 5.])
sigma_sq = jnp.array([6., 7., 8., 9., 10.])
mu_2, sigma_sq_2 = gaussian._unpack_inputs(
gaussian._pack_inputs(mu, sigma_sq))
np.testing.assert_array_equal(mu, mu_2)
np.testing.assert_array_equal(sigma_sq, sigma_sq_2)
class GatedLinearNetworkTest(parameterized.TestCase):
# TODO(b/170843789): Factor out common test utilities.
def setUp(self):
super(GatedLinearNetworkTest, self).setUp()
self._name = "test_network"
self._rng = hk.PRNGSequence(jax.random.PRNGKey(42))
self._output_sizes = (4, 5, 6)
self._context_dim = 2
self._bias_len = 3
def gln_factory():
return gaussian.GatedLinearNetwork(
output_sizes=self._output_sizes,
context_dim=self._context_dim,
bias_len=self._bias_len,
name=self._name,
)
def inference_fn(inputs, side_info):
return gln_factory().inference(inputs, side_info, 0.5)
def batch_inference_fn(inputs, side_info):
return jax.vmap(inference_fn, in_axes=(0, 0))(inputs, side_info)
def update_fn(inputs, side_info, label, learning_rate):
params, predictions, unused_loss = gln_factory().update(
inputs, side_info, label, learning_rate, 0.5)
return predictions, params
def batch_update_fn(inputs, side_info, label, learning_rate):
predictions, params = jax.vmap(
update_fn, in_axes=(0, 0, 0, None))(
inputs,
side_info,
label,
learning_rate)
avg_params = tree.map_structure(lambda x: jnp.mean(x, axis=0), params)
return predictions, avg_params
# Haiku transform functions.
self._init_fn, inference_fn_ = hk.without_apply_rng(
hk.transform_with_state(inference_fn))
self._batch_init_fn, batch_inference_fn_ = hk.without_apply_rng(
hk.transform_with_state(batch_inference_fn))
_, update_fn_ = hk.without_apply_rng(hk.transform_with_state(update_fn))
_, batch_update_fn_ = hk.without_apply_rng(
hk.transform_with_state(batch_update_fn))
self._inference_fn = jax.jit(inference_fn_)
self._batch_inference_fn = jax.jit(batch_inference_fn_)
self._update_fn = jax.jit(update_fn_)
self._batch_update_fn = jax.jit(batch_update_fn_)
@parameterized.named_parameters(("Online mode", None), ("Batch mode", 3))
def test_shapes(self, batch_size):
"""Test shapes in online and batch regimes."""
if batch_size is None:
init_fn = self._init_fn
inference_fn = self._inference_fn
else:
init_fn = self._batch_init_fn
inference_fn = self._batch_inference_fn
input_size = 10
inputs, side_info, _ = _get_dataset(input_size, batch_size)
# Initialize network.
gln_params, gln_state = init_fn(next(self._rng), inputs, side_info)
# Test shapes of parameters layer-wise.
layer_input_size = input_size
for layer_idx, output_size in enumerate(self._output_sizes):
name = "{}/~/{}_layer_{}".format(self._name, self._name, layer_idx)
weights = gln_params[name]["weights"]
expected_shape = (output_size, 2**self._context_dim,
layer_input_size + self._bias_len)
self.assertEqual(weights.shape, expected_shape)
layer_input_size = output_size
# Test shape of output.
output_size = sum(self._output_sizes)
predictions, _ = inference_fn(gln_params, gln_state, inputs, side_info)
expected_shape = (batch_size, output_size,
2) if batch_size else (output_size, 2)
self.assertEqual(predictions.shape, expected_shape)
@parameterized.named_parameters(("Online mode", None), ("Batch mode", 3))
def test_update(self, batch_size):
"""Test network updates in online and batch regimes."""
if batch_size is None:
init_fn = self._init_fn
inference_fn = self._inference_fn
update_fn = self._update_fn
else:
init_fn = self._batch_init_fn
inference_fn = self._batch_inference_fn
update_fn = self._batch_update_fn
inputs, side_info, targets = _get_dataset(10, batch_size)
# Initialize network.
initial_params, gln_state = init_fn(next(self._rng), inputs, side_info)
# Initial predictions.
initial_predictions, _ = inference_fn(initial_params, gln_state, inputs,
side_info)
# Test that params remain valid after consecutive updates.
gln_params = initial_params
for _ in range(3):
(_, gln_params), _ = update_fn(
gln_params, gln_state, inputs, side_info, targets, learning_rate=1e-4)
# Check updated weights layer-wise.
for layer_idx in range(len(self._output_sizes)):
name = "{}/~/{}_layer_{}".format(self._name, self._name, layer_idx)
initial_weights = initial_params[name]["weights"]
new_weights = gln_params[name]["weights"]
# Shape consistency.
self.assertEqual(new_weights.shape, initial_weights.shape)
# Check that different weights yield different predictions.
new_predictions, _ = inference_fn(gln_params, gln_state, inputs,
side_info)
self.assertFalse(np.array_equal(new_predictions, initial_predictions))
def test_batch_consistency(self):
"""Test consistency between online and batch updates."""
batch_size = 3
inputs, side_info, targets = _get_dataset(10, batch_size)
# Initialize network.
gln_params, gln_state = self._batch_init_fn(
next(self._rng), inputs, side_info)
test_layer = "{}/~/{}_layer_0".format(self._name, self._name)
for _ in range(10):
# Update on full batch.
(expected_predictions, expected_params), _ = self._batch_update_fn(
gln_params, gln_state, inputs, side_info, targets, learning_rate=1e-3)
# Average updates across batch and check equivalence.
accum_predictions = []
accum_weights = []
for inputs_, side_info_, targets_ in zip(inputs, side_info, targets):
(predictions, params), _ = self._update_fn(
gln_params,
gln_state,
inputs_,
side_info_,
targets_,
learning_rate=1e-3)
accum_predictions.append(predictions)
accum_weights.append(params[test_layer]["weights"])
# Check prediction equivalence.
actual_predictions = np.stack(accum_predictions, axis=0)
np.testing.assert_array_almost_equal(actual_predictions,
expected_predictions)
# Check weight equivalence.
actual_weights = np.mean(np.stack(accum_weights, axis=0), axis=0)
expected_weights = expected_params[test_layer]["weights"]
np.testing.assert_array_almost_equal(actual_weights, expected_weights)
gln_params = expected_params
if __name__ == "__main__":
absltest.main()
================================================
FILE: gated_linear_networks/requirements.txt
================================================
absl-py==0.10.0
aiohttp==3.6.2
astunparse==1.6.3
async-timeout==3.0.1
attrs==20.2.0
cachetools==4.1.1
certifi==2020.6.20
chardet==3.0.4
chex==0.0.2
cloudpickle==1.6.0
decorator==4.4.2
dill==0.3.2
dm-env==1.2
dm-haiku==0.0.2
dm-tree==0.1.5
future==0.18.2
gast==0.3.3
google-auth==1.22.0
google-auth-oauthlib==0.4.1
google-pasta==0.2.0
googleapis-common-protos==1.52.0
grpcio==1.32.0
h5py==2.10.0
idna==2.10
jax==0.2.0
jaxlib==0.1.55
Keras-Preprocessing==1.1.2
Markdown==3.2.2
multidict==4.7.6
numpy==1.18.5
oauthlib==3.1.0
opt-einsum==3.3.0
promise==2.3
protobuf==3.13.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
requests==2.24.0
requests-oauthlib==1.3.0
rlax==0.0.2
rsa==4.6
scipy==1.5.2
six==1.15.0
tensorboard==2.3.0
tensorboard-plugin-wit==1.7.0
tensorflow==2.3.1
tensorflow-datasets==3.2.1
tensorflow-estimator==2.3.0
tensorflow-metadata==0.24.0
tensorflow-probability==0.11.1
termcolor==1.1.0
toolz==0.11.1
tqdm==4.50.0
================================================
FILE: gated_linear_networks/run.sh
================================================
#!/bin/sh
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
python3 -m venv /tmp/gln_venv
source /tmp/gln_venv/bin/activate
pip3 install --upgrade pip setuptools wheel
pip3 install -r gated_linear_networks/requirements.txt
# Run MNIST example with Bernoulli GLN
python3 -m gated_linear_networks.examples.bernoulli_mnist \
--num_layers=2 \
--neurons_per_layer=100 \
--context_dim=1 \
--max_train_steps=2000
================================================
FILE: geomancer/README.md
================================================
# Geometric Manifold Component Estimator (GEOMANCER)
This package provides an implementation of the Geometric Manifold Component
Estimator, or GEOMANCER, as described in [Disentangling by Subspace Diffusion
(2020)](https://arxiv.org/abs/2006.12982), as well as information about the
[Stanford 3D Objects for Disentangling (S3O4D) dataset](https://console.cloud.google.com/storage/browser/dm_s3o4d).
GEOMANCER is a nonparametric algorithm for disentangling, somewhat similar in
spirit to Laplacian Eigenmaps or Vector Diffusion Maps, except instead of
producing an embedding for the data, it produces a set of subspaces around each
data point, one subspace for each disentangled factor of variation in the data.
This differs from more common algorithms for disentangling that originated in
the deep learning community, such as the beta-VAE, TCVAE or FactorVAE, which
learn a nonlinear embedding and probabilistic generative model of the data.
GEOMANCER is intended for data where the individual factors of variation might
be more than one dimensional, for instance 3D rotations. At the moment,
GEOMANCER works best when some ground truth information about the metric in the
data space is available, for instance knowledge of the "true" nearest neighbors
around each point, and we do not recommend running GEOMANCER directly on
unstructured data from high-dimensional spaces. We are providing the code here
to enable the interested researcher to get some hands-on experience with the
ideas around differential geometry, holonomy and higher-order graph connection
Laplacians we explore in the paper.
## Installation
To install the package locally in a new virtual environment run:
```bash
python3 -m venv geomancer
source geomancer/bin/activate
git clone https://github.com/deepmind/deepmind-research.git .
cd deepmind-research/geomancer
pip install -e .
```
## Example
To run, simply load or generate an array of data, and call the `fit` function:
```
import numpy as np
import geomancer
# Generate data from a product of two spheres
data = []
for i in range(2):
foo = np.random.randn(1000, 3)
data.append(foo / np.linalg.norm(foo, axis=1, keepdims=True))
data = np.concatenate(data, axis=1)
# Run GEOMANCER. The underlying manifold is 4-dimensional.
components, spectrum = geomancer.fit(data, 4)
```
If ground truth information about the tangent spaces is available in a space
that is aligned with the data, then the performance can be evaluated using the
`eval_aligned` function. If ground truth data is only available in an unaligned
space, for instance if the embedding used to generate the data is not the same
as the space in which the data is observed, then the `eval_unaligned` function
can be used, which requires both the data and disentangled tangent vectors in
the ground truth space. Examples of both evaluation metrics are given in the
demo in `train.py`.
## Demo on Synthetic Manifolds
The file `train.py` runs GEOMANCER on a product of manifolds that can be
specified by the user. The number of data points to train on is given by the
`--npts` flag, while the specification of the manifold is given by the
`--specification` flag. The `--rotate` flag specifies whether a random rotation
should be applied to the data. If false, `eval_aligned` will be used to evaluate
the result. If true, `eval_unaligned` will be used to evaluate the result.
For instance, to run on the product of the sphere in 2 and 4 dimensions and the
special orthogonal group in 3 dimensions, run:
```
python3 train.py --specification='S^2','S^4','SO(3)' --npts=100000
```
This passes a list of strings as the manifold specification flag. Note that a
manifold this large will require a large amount of data to work and may require
hours or days to run. The default example should run in just a few minutes.
The demo plots 3 different outputs:
1. The eigenvalue spectrum of the 2nd-order graph Laplacian. This should have
a large gap in the spectrum at the eigenvalue equal to the number of
submanifolds.
2. The basis vectors for each disentangled subspace around one point.
3. The ground truth basis vectors for the disentangled subspaces at the same
point. If `--rotate=False`, and GEOMANCER has sufficient data, each basis matrix
should span the same subspace as the results in the second plot.
## Stanford 3D Objects for Disentangling (S3O4D)
The data used in the "Stanford 3D Objects" section of the experimental results
is available in [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/s3o4d).
The data consists of 100,000 renderings each of the Bunny and Dragon objects
from the [Stanford 3D Scanning Repository](http://graphics.stanford.edu/data/3Dscanrep/).
More objects may be added in the future, but only the Bunny and Dragon are used
in the paper. Each object is rendered with a uniformly sampled illumination from
a point on the 2-sphere, and a uniformly sampled 3D rotation. The true latent
states are provided as NumPy arrays along with the images. The lighting is given
as a 3-vector with unit norm, while the rotation is provided both as a
quaternion and a 3x3 orthogonal matrix.
### Why another dataset?
There are many similarities between S3O4D and existing ML benchmark datasets
like [NORB](https://cs.nyu.edu/~ylclab/data/norb-v1.0/),
[3D Chairs](https://github.com/mathieuaubry/seeing3Dchairs),
[3D Shapes](https://github.com/deepmind/3d-shapes) and many others, which also
include renderings of a set of objects under different pose and illumination
conditions. However, none of these existing datasets include the *full manifold*
of rotations in 3D - most include only a subset of changes to elevation and
azimuth. S3O4D images are sampled uniformly and independently from the full
space of rotations and illuminations, meaning the dataset contains objects that
are upside down and illuminated from behind or underneath. We believe that this
makes S3O4D uniquely suited for research on generative models where the latent
space has non-trivial topology, as well as for general manifold learning
methods where the curvature of the manifold is important.
### Usage
To load from TensorFlow Datasets, simply run:
```
import tensorflow_datasets as tfds
ds = tfds.load('s3o4d', split='bunny_train', shuffle_files=True)
for example in ds.take(1):
image, label, illumination, pose_mat, pose_quat = (
example['image'], example['label'], example['illumination'],
example['pose_mat'], example['pose_quat'])
```
where the split can be any of `bunny_train`, `dragon_train`, `bunny_test` or
`dragon_test`.
If you prefer to not have TensorFlow as a dependency for your project, and want
to download the data manually, you can find the raw data (as zipped JPEGs and
NumPy arrays) on [Google Cloud](https://console.cloud.google.com/storage/browser/dm_s3o4d).
To load the data for a given object, unzip `images.zip` into a folder called
`images` in the same directory as `latents.npz`, and from inside that
directory run:
```
import numpy as np
from PIL import Image
with open('latents.npz', 'r') as f:
data = np.load(f)
illumination = data['illumination'] # lighting source position, a 3-vector
pose_quat = data['pose_quat'] # object pose (3D rotation as a quaternion)
pose_mat = data['pose_mat'] # object pose (3D rotation as a matrix)
def get_data(i):
"""Return data and latent given an index up to 100,000."""
img = np.array(Image.open(f'images/{i:05}.jpg'))
# Uses the matrix, not quaternion, representation,
# similarly to the experiments in the paper
latent = np.concatenate((illumination[i],
pose_mat[i].reshape(-1)))
return img, latent
img, latent = get_data(0)
```
To do the same train/test split as in TensorFlow Datasets, simply use the first
80,000 images for each object as training data and the last 20,000 as test.
## Giving Credit
If you use this code or the Stanford 3D Objects for Disentangling data in your
work, we ask you to cite this paper:
```
@article{pfau2020disentangling,
title={Disentangling by Subspace Diffusion},
author={Pfau, David and Higgins, Irina and Botev, Aleksandar and Racani\`ere,
S{\'e}bastian},
journal={Advances in Neural Information Processing Systems (NeurIPS)},
year={2020}
}
```
## Disclaimer
This is not an official Google product.
================================================
FILE: geomancer/data_writer.py
================================================
# Copyright 2023 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data writer for Stanford Bunny experiments and other objects."""
# pylint: disable=unused-import
import copy
import io
import os
import time
from absl import app
from absl import flags
from absl import logging
from dm_control import mujoco
import numpy as np
_SHARD = flags.DEFINE_integer('shard', 0, 'Shard index')
_SIZE = flags.DEFINE_integer('size', 1000,
'Number of images to save to a shard')
_OBJECT = flags.DEFINE_string('object', 'dragon', 'Which object to render')
_PATH = flags.DEFINE_string('path', '', 'Path to folder with .stl files')
render_height = 1024
render_width = 1024
height = 256
width = 256
def get_normal(x):
"""Get vectors normal to a unit vector."""
_, _, v = np.linalg.svd(x[None, :])
return v[:, 1:]
def render(quat, light, mesh='bunny', meshdir='data'):
"""Script to render an image."""
scale, pos = None, None
if mesh == 'bunny':
scale = 0.03
pos = -1.0
elif mesh == 'dragon':
scale = 0.06
pos = -0.3
simple_world_mjcf_template = """
""" % (render_width, render_height, meshdir, mesh, mesh,
scale, scale, scale, mesh, mesh, pos)
light /= np.linalg.norm(light)
quat /= np.linalg.norm(quat)
simple_world_mjcf = simple_world_mjcf_template.format(
*(np.concatenate((quat,
5*light, -5*light,
-5*light, 5*light)).tolist()))
physics = mujoco.Physics.from_xml_string(simple_world_mjcf)
data = physics.render(camera_id='main',
height=render_height,
width=render_width).astype(np.float32)
data = data.reshape((width, int(render_width/width),
height, int(render_height/height), 3))
return np.mean(np.mean(data, axis=1), axis=2)
def get_tangent(quat, light, mesh='bunny', meshdir='data',
eps=0.03, use_light=True, use_quat=True):
"""Render image along with its tangent vectors by finite differences."""
assert use_light or use_quat
n = 0
light_tangent = None
quat_tangent = None
if use_light:
light_tangent = get_normal(light)
n += 2
if use_quat:
quat_tangent = get_normal(quat)
n += 3
# a triple-wide pixel buffer
try:
data = render(quat, light, mesh=mesh, meshdir=meshdir)
image_tangent = np.zeros((n, height, width, 3), dtype=np.float32)
if use_quat:
for i in range(3):
perturbed = render(quat + eps * quat_tangent[:, i],
light, mesh=mesh, meshdir=meshdir)
image_tangent[i] = (perturbed - data).astype(np.float32) / eps
if use_light:
j = 3 if use_quat else 0
for i in range(2):
perturbed = render(quat, light + eps * light_tangent[:, i],
mesh=mesh, meshdir=meshdir)
image_tangent[i+j] = (perturbed - data) / eps
image_tangent -= np.mean(image_tangent, axis=0)[None, ...]
latent_tangent = np.block(
[[quat_tangent, np.zeros((4, 2), dtype=np.float32)],
[np.zeros((3, 3), dtype=np.float32), light_tangent]])
return (np.mean(data, axis=-1),
np.mean(image_tangent, axis=-1),
latent_tangent)
except: # pylint: disable=bare-except
logging.info('Failed with latents (quat: %s, light: %s)', quat, light)
def main(_):
images = np.zeros((_SIZE.value, height, width), dtype=np.float32)
latents = np.zeros((_SIZE.value, 7), dtype=np.float32)
image_tangents = np.zeros((_SIZE.value, 5, height, width), dtype=np.float32)
latent_tangents = np.zeros((_SIZE.value, 7, 5), dtype=np.float32)
for i in range(_SIZE.value):
light = np.random.randn(3)
light /= np.linalg.norm(light)
quat = np.random.randn(4) # rotation represented as quaternion
quat /= np.linalg.norm(quat)
latents[i] = np.concatenate((light, quat))
images[i], image_tangents[i], latent_tangents[i] = (
get_tangent(quat, light, mesh=_OBJECT.value, meshdir=_PATH.value))
logging.info('Rendered image %d of %d', i, _SIZE.value)
os.makedirs(os.path.join(_PATH.value, _OBJECT.value), exist_ok=True)
with open(os.path.join(
_PATH.value, _OBJECT.value, 'shard_%03d.npz' % _SHARD.value), 'wb') as f:
io_buffer = io.BytesIO()
np.savez(io_buffer, images, latents, image_tangents, latent_tangents)
f.write(io_buffer.getvalue())
if __name__ == '__main__':
app.run(main)
================================================
FILE: geomancer/geomancer.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Code for the Geometric Manifold Component Estimator (GEOMANCER)."""
import itertools
from absl import logging
import numpy as np
import scipy
import scipy.sparse
import scipy.sparse.linalg
from tqdm import tqdm
def sym_op(x, zero_trace=False):
"""Given X, makes L(A) = X @ A @ X' for symmetric matrices A.
If A is not symmetric, L(A) will return X @ (A_L + A_L') @ X' where A_L is
the lower triangular of A (with the diagonal divided by 2).
Args:
x: The matrix from which to construct the operator
zero_trace (optional): If true, restrict the operator to only act on
matrices with zero trace, effectively reducing the dimensionality by one.
Returns:
A matrix Y such that vec(L(A)) = Y @ vec(A).
"""
n = x.shape[0]
# Remember to subtract off the diagonal once
xx = (np.einsum('ik,jl->ijkl', x, x) +
np.einsum('il,jk->ijkl', x, x) -
np.einsum('ik,jl,kl->ijkl', x, x, np.eye(n)))
xx = xx[np.tril_indices(n)]
xx = xx.transpose(1, 2, 0)
xx = xx[np.tril_indices(n)]
xx = xx.T
if zero_trace:
diag_idx = np.cumsum([0]+list(range(2, n)))
proj_op = np.eye(n*(n+1)//2)[:, :-1]
proj_op[-1, diag_idx] = -1
# multiply by operator that completes last element of diagonal
# for a zero-trace matrix
xx = xx @ proj_op
xx = xx[:-1]
return xx
def vec_to_sym(x, n, zero_trace=False):
y = np.zeros((n, n))
if zero_trace:
x = np.append(x, 0.0)
y[np.tril_indices(n)] = x
y += y.T
y[np.diag_indices(n)] /= 2.0
if zero_trace:
y[-1, -1] = -np.trace(y)
return y
def ffdiag(data, lr=1.0, tol=1e-10, verbose=False, eig_init=False):
"""Orthogonal FFDiag algorithm of Ziehe et al 2004."""
n = data.shape[1]
k = data.shape[0]
c = data.copy()
if eig_init:
_, v = np.linalg.eig(data[0])
v = v.T
for i in range(k):
c[i] = v @ c[i] @ v.T
else:
v = np.eye(n)
err_ = np.inf
for t in range(10000):
w = np.zeros((n, n))
for i in range(n):
for j in range(i+1, n):
diag = c[:, i, i] - c[:, j, j]
w[i, j] = np.sum(c[:, i, j] * diag) / np.sum(diag ** 2)
w -= w.T
norm = np.linalg.svd(w, compute_uv=False).max()
if norm > lr:
w *= lr / norm
ew = scipy.linalg.expm(w)
v = ew @ v
for i in range(k):
c[i] = ew @ c[i] @ ew.T
cdiag = c.copy()
for i in range(n):
for j in range(k):
cdiag[j, i, i] = 0
err = np.linalg.norm(cdiag)
if verbose:
logging.info('Iter %d: %f', t, err)
if err_ - err < tol and err_ - err >= 0:
return v
err_ = err
return v
def avg_angle_between_subspaces(xs, ys):
"""Compute the error between two sets of subspaces."""
if len(xs) != len(ys):
return np.pi / 2 # largest possible angle
angles = []
for ys_perm in itertools.permutations(ys):
angles.append([])
for i in range(len(xs)):
if xs[i].shape[1] == ys_perm[i].shape[1]:
sigma = np.linalg.svd(xs[i].T @ ys_perm[i], compute_uv=False)
angles[-1].append(np.arccos(np.min(sigma)))
else:
angles[-1].append(np.pi / 2)
angles = np.array(angles)
return np.min(np.mean(angles, axis=1))
def make_nearest_neighbors_graph(data, k, n=1000):
"""Build exact k-nearest neighbors graph from numpy data.
Args:
data: Data to compute nearest neighbors of, each column is one point
k: number of nearest neighbors to compute
n (optional): number of neighbors to compute simultaneously
Returns:
A scipy sparse matrix in LIL format giving the symmetric nn graph.
"""
shape = data.shape
assert shape[0] % n == 0
nbr_graph = scipy.sparse.lil_matrix((shape[0], shape[0]))
norm = np.sum(data**2, axis=1)
cols = np.meshgrid(np.arange(n), np.ones(k+1))[0]
for i in tqdm(range(0, shape[0], n)):
dot = data @ data[i:i+n].T
dists = np.sqrt(np.abs(norm[:, None] - 2*dot + norm[i:i+n][None, :]))
idx = np.argpartition(dists, k, axis=0)[:k+1]
nbrs = idx[np.argsort(dists[idx, cols], axis=0), cols][1:]
for j in range(n):
nbr_graph[i+j, nbrs[:, j]] = 1
# Symmetrize graph
for i in tqdm(range(shape[0])):
for j in nbr_graph.rows[i]:
if nbr_graph[j, i] == 0:
nbr_graph[j, i] = nbr_graph[i, j]
logging.info('Symmetrized neighbor graph')
return nbr_graph
def make_tangents(data, neighbor_graph, k):
"""Construct all tangent vectors for the dataset."""
tangents = np.zeros((data.shape[0], k, data.shape[1]), dtype=np.float32)
for i in tqdm(range(data.shape[0])):
diff = data[neighbor_graph.rows[i]] - data[i]
_, _, u = np.linalg.svd(diff, full_matrices=False)
tangents[i] = u[:k]
logging.info('Computed all tangents')
return tangents
def make_connection(tangents, neighbor_graph):
"""Make connection matrices for all edges of the neighbor graph."""
connection = {}
for i in tqdm(range(tangents.shape[0])):
for j in neighbor_graph.rows[i]:
if j > i:
uy, _, ux = np.linalg.svd(tangents[j] @ tangents[i].T,
full_matrices=False)
conn = uy @ ux
connection[(i, j)] = conn
connection[(j, i)] = conn.T
logging.info('Constructed all connection matrices')
return connection
def make_laplacian(connection, neighbor_graph, sym=True, zero_trace=True):
"""Make symmetric zero-trace second-order graph connection Laplacian."""
n = neighbor_graph.shape[0]
k = list(connection.values())[0].shape[0]
bsz = (k*(k+1)//2 - 1 if zero_trace else k*(k+1)//2) if sym else k**2
data = np.zeros((neighbor_graph.nnz + n, bsz, bsz), dtype=np.float32)
indptr = []
indices = np.zeros(neighbor_graph.nnz + n)
index = 0
for i in tqdm(range(n)):
indptr.append(index)
data[index] = len(neighbor_graph.rows[i]) * np.eye(bsz)
indices[index] = i
index += 1
for j in neighbor_graph.rows[i]:
if sym:
kron = sym_op(connection[(j, i)], zero_trace=zero_trace)
else:
kron = np.kron(connection[(j, i)], connection[(j, i)])
data[index] = -kron
indices[index] = j
index += 1
indptr.append(index)
indptr = np.array(indptr)
laplacian = scipy.sparse.bsr_matrix((data, indices, indptr),
shape=(n*bsz, n*bsz))
logging.info('Built 2nd-order graph connection Laplacian.')
return laplacian
def cluster_subspaces(omega):
"""Cluster different dimensions from the eigenvectors of the Laplacian."""
w = ffdiag(omega) # simultaneous diagonalization
psi = np.zeros(omega.shape[:2])
for i in range(omega.shape[0]):
psi[i] = np.diag(w @ omega[i] @ w.T) # compute diagonals
# Compute cosine similarity of diagonal vectors
psi_outer = psi.T @ psi
psi_diag = np.diag(psi_outer)
cos_similarity = psi_outer / np.sqrt(np.outer(psi_diag, psi_diag))
adj = cos_similarity > 0.5 # adjacency matrix for graph of clusters
# Use graph Laplacian to find cliques
# (though a greedy algorithm could work too)
lapl = np.diag(np.sum(adj, axis=0)) - adj # graph Laplacian
d, v = np.linalg.eig(lapl)
# connected components of graph
cliques = np.abs(v[:, np.abs(d) < 1e-6]) > 1e-6
tangents = [w[cliques[:, i]] for i in range(sum(np.abs(d) < 1e-6))]
return tangents
def fit(data, k, gamma=None, nnbrs=None, neig=10, shard_size=1000):
"""The Geometric Manifold Component Estimator.
Args:
data: the dataset, a set of points sample from a product manifold.
k: the dimensionality of the manifold.
gamma (optional): the threshold in the spectrum at which to cut off the
number of submanifolds.
nnbrs (optional): number of neighbors to use for each point.
neig (optional): the total number of eigenvectors to compute.
shard_size (optional): the size of shard to use in knn computation.
Returns:
A list of lists of subspace bases, one list for each element of the dataset,
and the spectrum of the 2nd-order graph Laplacian.
"""
if not nnbrs:
nnbrs = 2*k
neighbor_graph = make_nearest_neighbors_graph(data, nnbrs, n=shard_size)
tangents = make_tangents(data, neighbor_graph, k)
connection = make_connection(tangents, neighbor_graph)
laplacian = make_laplacian(connection, neighbor_graph)
eigvals, eigvecs = scipy.sparse.linalg.eigsh(laplacian, k=neig, which='SM')
logging.info('Computed bottom eigenvectors of 2nd-order Laplacian')
bsz = k*(k+1)//2 - 1 # Block size for the projected 2nd-order Laplacian
if gamma:
nm = np.argwhere(eigvals < gamma)[-1, 0] + 1
else: # If no threshold is provided, just use the largest gap in the spectrum
nm = np.argmax(eigvals[1:] - eigvals[:-1]) + 1
eigvecs = eigvecs.reshape(data.shape[0], bsz, neig)
omega = np.zeros((nm, k, k), dtype=np.float32)
components = []
for i in tqdm(range(data.shape[0])):
for j in range(nm):
omega[j] = vec_to_sym(eigvecs[i, :, j], k, zero_trace=True)
components.append([tangents[i].T @ x.T for x in cluster_subspaces(omega)])
logging.info('GEOMANCER completed')
return components, eigvals
def eval_aligned(tangents, true_tangents):
"""Evaluation for aligned data."""
errors = np.zeros(len(tangents))
for i in tqdm(range(len(tangents))):
errors[i] = avg_angle_between_subspaces([gt[i] for gt in true_tangents],
tangents[i])
logging.info('Computed angles between ground truth and GEOMANCER results')
return errors
def eval_unaligned(data, tangents, true_data, true_tangents, k=10, n=1000):
"""Evaluation for unaligned data."""
logging.info('Evaluating unaligned data')
errors = np.zeros(data.shape[0])
nbrs = make_nearest_neighbors_graph(true_data, k=k, n=n)
for i in tqdm(range(data.shape[0])):
tangent = np.concatenate(tangents[i], axis=1)
true_tangent = np.concatenate([t[i] for t in true_tangents], axis=1)
dx_true = (true_data[nbrs.rows[i]] - true_data[i]) @ true_tangent
dx_result = (data[nbrs.rows[i]] - data[i]) @ tangent
# compute canonical correlations between the two dxs
xx = dx_true.T @ dx_true
yy = dx_result.T @ dx_result
xy = dx_true.T @ dx_result
xx_ = np.linalg.inv(xx)
yy_ = np.linalg.inv(yy)
foo = scipy.linalg.sqrtm(xx_) @ xy @ scipy.linalg.sqrtm(yy_)
u, _, v = np.linalg.svd(foo)
# project subspaces for results and ground truth into aligned space
proj = [v @ tangent.T @ s for s in tangents[i]]
true_proj = [u.T @ true_tangent.T @ s[i] for s in true_tangents]
errors[i] = avg_angle_between_subspaces(proj, true_proj)
return errors
================================================
FILE: geomancer/geomancer_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for the Geometric Manifold Component Estimator (GEOMANCER)."""
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
from geomancer import geomancer
class GeomancerTest(parameterized.TestCase):
@parameterized.parameters(
{'zero_trace': False},
{'zero_trace': True})
def test_sym_op(self, zero_trace):
"""sym_op on tril(X) gives same result as QXQ' for symmetric X?"""
n = 5
x = np.random.randn(n, n)
x += x.T
if zero_trace:
np.fill_diagonal(x, np.diag(x)-np.trace(x)/n)
q, _ = np.linalg.qr(np.random.randn(n, n))
sym_q = geomancer.sym_op(q, zero_trace=zero_trace)
tril_x = x[np.tril_indices(n)]
if zero_trace:
tril_x = tril_x[:-1]
vec_y = sym_q @ tril_x
y = q @ x @ q.T
y_ = geomancer.vec_to_sym(vec_y, n, zero_trace=zero_trace)
np.testing.assert_allclose(y_, y)
def test_ffdiag(self):
k = 2
n = 5
w, _ = np.linalg.qr(np.random.randn(n, n))
psi = np.random.randn(k, n)
a = np.zeros((k, n, n))
for i in range(k):
a[i] = w @ np.diag(psi[i]) @ w.T
w_ = geomancer.ffdiag(a)
for i in range(k):
x = w_ @ a[i] @ w_.T
diag = np.diag(x).copy()
np.fill_diagonal(x, 1.0)
# check that x is diagonal
np.testing.assert_allclose(x, np.eye(n), rtol=1e-10, atol=1e-10)
self.assertTrue(np.all(np.min(
np.abs(diag[None, :] - psi[i][:, None]), axis=0) < 1e-10))
def test_make_nearest_neighbor_graph(self):
n = 100
# make points on a circle
data = np.zeros((n, 2))
for i in range(n):
data[i, 0] = np.sin(i*2*np.pi/n)
data[i, 1] = np.cos(i*2*np.pi/n)
graph = geomancer.make_nearest_neighbors_graph(data, 4, n=10)
for i in range(n):
self.assertLen(graph.rows[i], 4)
self.assertIn((i+1) % n, graph.rows[i])
self.assertIn((i+2) % n, graph.rows[i])
self.assertIn((i-1) % n, graph.rows[i])
self.assertIn((i-2) % n, graph.rows[i])
if __name__ == '__main__':
absltest.main()
================================================
FILE: geomancer/run.sh
================================================
#!/bin/sh
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
python3 -m venv /tmp/geomancer-venv
source /tmp/geomancer-venv/bin/activate
pip3 install -U pip
pip3 install geomancer/
python3 -m geomancer.geomancer_test
python3 geomancer/train.py --plot=False
================================================
FILE: geomancer/setup.py
================================================
# Copyright 2020 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Setup for pip package."""
from setuptools import find_packages
from setuptools import setup
REQUIRED_PACKAGES = ['numpy', 'scipy', 'matplotlib', 'absl-py', 'tqdm']
setup(
name='geomancer',
version='0.1',
description='A library for the Geometric Manifold Component Estimator.',
url='https://github.com/deepmind/deepmind-research/geomancer',
author='DeepMind',
author_email='pfau@google.com',
# Contained modules and scripts.
packages=find_packages(),
install_requires=REQUIRED_PACKAGES,
platforms=['any'],
license='Apache 2.0',
)
================================================
FILE: geomancer/train.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Run GEOMANCER on products of synthetic manifolds."""
import re
from absl import app
from absl import flags
from absl import logging
import geomancer
from matplotlib import gridspec
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import special_ortho_group
from tqdm import tqdm
SPECIFICATION = flags.DEFINE_list(
name='specification', default=['S^2', 'S^2'], help='List of submanifolds')
NPTS = flags.DEFINE_integer(
name='npts', default=1000, help='Number of data points')
ROTATE = flags.DEFINE_boolean(
name='rotate', default=False, help='Apply random rotation to the data')
PLOT = flags.DEFINE_boolean(
name='plot', default=True, help='Whether to enable plotting')
def make_so_tangent(q):
"""Given an n x n orthonormal matrix, return a basis for its tangent space."""
n = q.shape[0]
assert np.allclose(q.T @ q, np.eye(n), atol=1e-4, rtol=1e-4)
a = np.zeros((n, n))
ii = 0
dq = np.zeros((n, n, n*(n-1)//2))
for i in range(n):
for j in range(i+1, n):
a[i, j] = 1
a[j, i] = -1
dq[..., ii] = a @ q # tangent vectors are skew-symmetric matrix times Q
a[i, j] = 0
a[j, i] = 0
ii += 1
# reshape and orthonormalize the result
return np.linalg.qr(np.reshape(dq, (n**2, n*(n-1)//2)))[0]
def make_sphere_tangent(x):
_, _, v = np.linalg.svd(x[None, :])
return v[:, 1:]
def make_true_tangents(spec, data):
"""Return a set of orthonormal bases, one for each submanifold."""
for i in range(spec.shape[1]):
assert spec[0, i] == 0 or spec[1, i] == 0
so_dim = sum(dim ** 2 for dim in spec[0])
sphere_dim = sum(dim+1 if dim > 0 else 0 for dim in spec[1])
assert so_dim + sphere_dim == data.shape[0]
ii = 0
tangents = []
for i in range(spec.shape[1]):
if spec[0, i] != 0:
dim = spec[0, i]
tangents.append(make_so_tangent(np.reshape(data[ii:ii+dim**2],
(dim, dim))))
ii += dim ** 2
else:
dim = spec[1, i]
tangents.append(make_sphere_tangent(data[ii:ii+dim+1]))
ii += dim + 1
tangents2 = []
for i in range(len(tangents)):
size1 = sum(x.shape[0] for x in tangents[:i])
size2 = sum(x.shape[0] for x in tangents[i+1:])
tangents2.append(np.concatenate(
(np.zeros((size1, tangents[i].shape[1])),
tangents[i],
np.zeros((size2, tangents[i].shape[1]))), axis=0))
return tangents2
def make_product_manifold(specification, npts):
"""Generate data from a product of manifolds with the given specification."""
data = []
tangents = []
latent_dim = 0
spec_array = np.zeros((2, len(specification)), dtype=np.int32)
for i, spec in enumerate(specification):
so_spec = re.search(r'SO\(([0-9]+)\)', spec) # matches "SO()"
sphere_spec = re.search(r'S\^([0-9]+)', spec) # matches "S^"
if sphere_spec is not None:
dim = int(sphere_spec.group(1))
spec_array[1, i] = dim
latent_dim += dim
dat = np.random.randn(npts, dim+1)
dat /= np.tile(np.sqrt(np.sum(dat**2, axis=1)[..., None]),
[1, dim+1])
elif so_spec is not None:
dim = int(so_spec.group(1))
spec_array[0, i] = dim
latent_dim += dim * (dim - 1) // 2
dat = [np.ndarray.flatten(special_ortho_group.rvs(dim), order='C')
for _ in range(npts)]
dat = np.stack(dat)
else:
raise ValueError(f'Unrecognized manifold: {spec}')
data.append(dat)
data = np.concatenate(data, axis=1)
for i in range(spec_array.shape[1]):
if spec_array[0, i] != 0:
dim = spec_array[0, i]
tangents.append(np.zeros((npts, data.shape[1], dim * (dim - 1) // 2)))
elif spec_array[1, i] != 0:
dim = spec_array[1, i]
tangents.append(np.zeros((npts, data.shape[1], dim)))
for i in tqdm(range(npts)):
true_tangent = make_true_tangents(spec_array, data[i])
for j in range(len(specification)):
tangents[j][i] = true_tangent[j]
logging.info('Constructed data and true tangents for %s',
' x '.join(specification))
return data, latent_dim, tangents
def main(_):
# Generate data and run GEOMANCER
data, dim, tangents = make_product_manifold(SPECIFICATION.value, NPTS.value)
if ROTATE.value:
rot, _ = np.linalg.qr(np.random.randn(data.shape[1], data.shape[1]))
data_rot = data @ rot.T
components, spectrum = geomancer.fit(data_rot, dim)
errors = geomancer.eval_unaligned(data_rot, components, data, tangents)
else:
components, spectrum = geomancer.fit(data, dim)
errors = geomancer.eval_aligned(components, tangents)
logging.info('Error between subspaces: %.2f +/- %.2f radians',
np.mean(errors),
np.std(errors))
if PLOT.value:
# Plot spectrum
plt.figure(figsize=(8, 6))
plt.scatter(np.arange(len(spectrum)), spectrum, s=100)
largest_gap = np.argmax(spectrum[1:]-spectrum[:-1]) + 1
plt.axvline(largest_gap, linewidth=2, c='r')
plt.xticks([])
plt.yticks(fontsize=18)
plt.xlabel('Index', fontsize=24)
plt.ylabel('Eigenvalue', fontsize=24)
plt.title('GeoManCEr Eigenvalue Spectrum', fontsize=24)
# Plot subspace bases
fig = plt.figure(figsize=(8, 6))
bases = components[0]
gs = gridspec.GridSpec(1, len(bases),
width_ratios=[b.shape[1] for b in bases])
for i in range(len(bases)):
ax = plt.subplot(gs[i])
ax.imshow(bases[i])
ax.set_xticks([])
ax.set_yticks([])
ax.set_title(r'$T_{\mathbf{x}_1}\mathcal{M}_%d$' % (i+1), fontsize=18)
fig.canvas.set_window_title('GeoManCEr Results')
# Plot ground truth
fig = plt.figure(figsize=(8, 6))
gs = gridspec.GridSpec(1, len(tangents),
width_ratios=[b.shape[2] for b in tangents])
for i, spec in enumerate(SPECIFICATION.value):
ax = plt.subplot(gs[i])
ax.imshow(tangents[i][0])
ax.set_xticks([])
ax.set_yticks([])
ax.set_title(r'$T_{\mathbf{x}_1}%s$' % spec, fontsize=18)
fig.canvas.set_window_title('Ground Truth')
plt.show()
if __name__ == '__main__':
app.run(main)
================================================
FILE: glassy_dynamics/README.md
================================================
# Unveiling the predictive power of static structure in glassy systems
This repository contains an open source implementation of the graph neural
network model described in our
[paper](http://dx.doi.org/10.1038/s41567-020-0842-8).
The model can be trained using the training binary included in this repository,
and the dataset published with our paper.
Pretrained model checkpoints and the dataset are available via the
[google cloud platform](https://console.cloud.google.com/storage/browser/deepmind-research-glassy-dynamics).
## Abstract
Despite decades of theoretical studies, the nature of the glass transition
remains elusive and debated, while the existence of structural predictors of its
dynamics is a major open question. Recent approaches propose inferring
predictors from a variety of human-defined features using machine learning.
Here we determine the long time evolution of a glassy system solely from the
initial particle positions and without any hand-crafted features, using graph
neural networks as a powerful model. We show that this method outperforms
current state-of-the-art methods, generalizing over a wide range of
temperatures, pressures, and densities. In shear experiments, it predicts the
locations of rearranging particles. The structural predictors learned by our
network exhibit a correlation length which increases with larger timescales to
reach the size of our system. Beyond glasses, our method could apply to many
other physical systems that map to a graph of local interaction.
## Dataset
The dataset was generated with the LAMMPS molecular dynamics package.
The simulated system has periodic boundaries and is a binary mixture of 4096
large (A) and small (B) particles that interact via a 6-12 Lennard-Jones
potential.
The interaction coefficients are set for a typical Kob-Andersen configuration.
### Download
The dataset (and model checkpoints) can be downloaded using [gsutil](https://cloud.google.com/storage/docs/downloading-objects).
To download the entire GCP bucket (~100GB) use:
> gsutil -m cp -R gs://deepmind-research-glassy-dynamics .
### Data format
The data is stored in Python's pickle format protocol version 3.
Each file contains the data for one of the equilibrated systems in a Python
dictionary. The dictionary contains the following entries:
- `positions` the particle positions of the equilibrated system.
- `types` the particle types (0 == type A and 1 == type B) of the equilibrated
system.
- `box` the dimensions of the periodic cubic simulation box.
- `time` the logarithmically sampled time points.
- `time_indices` the indices of the time points for which the sampled
trajectories on average reach a certain value of the intermediate
scattering function.
- `is_values` the values of the intermediate scattering function associated
with each time index.
- `trajectory_start_velocities` the velocities drawn from a Boltzmann
distribution at the start of each trajectory.
- `trajectory_target_positions` the positions of the particles for each of
the trajectories at selected time points (as defined by the `time_indices`
array and the corresponding values of the intermediate scattering function
stored in `is_values`).
- `metadata` a dictionary containing additional metadata:
- `temperature` the temperature at which the system was equilibrated.
- `pressure` the pressure at which the system was equilibrated.
- `fluid` the type of fluid which was simulated (Kob-Andersen).
All units are in Lennard-Jones units. The positions are stored in the absolute
coordinate system i.e. they are outside of the simulation box if the particle
crossed a periodic boundary during the simulation.
## Reference
If this repository is helpful for your research please cite the following
publication:
[Unveiling the predictive power of static structure in glassy systems](http://dx.doi.org/10.1038/s41567-020-0842-8)
V. Bapst, T. Keck, A. Grabska-Barwińska, C. Donner, E. D. Cubuk,
S. S. Schoenholz, A. Obika, A. W. R. Nelson, T. Back, D. Hassabis and P. Kohli
## Disclaimer
This is not an official Google product.
================================================
FILE: glassy_dynamics/apply_binary.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Applies a graph-based network to predict particle mobilities in glasses."""
import os
from absl import app
from absl import flags
from glassy_dynamics import train
FLAGS = flags.FLAGS
flags.DEFINE_string(
'data_directory',
'',
'Directory which contains the train or test datasets.')
flags.DEFINE_integer(
'time_index',
9,
'The time index of the target mobilities.')
flags.DEFINE_integer(
'max_files_to_load',
None,
'The maximum number of files to load.')
flags.DEFINE_string(
'checkpoint_path',
'checkpoints/t044_s09.ckpt',
'Path used to load the model.')
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
file_pattern = os.path.join(FLAGS.data_directory, 'aggregated*')
train.apply_model(
checkpoint_path=FLAGS.checkpoint_path,
file_pattern=file_pattern,
max_files_to_load=FLAGS.max_files_to_load,
time_index=FLAGS.time_index)
if __name__ == '__main__':
app.run(main)
================================================
FILE: glassy_dynamics/graph_model.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A graph neural network based model to predict particle mobilities.
The architecture and performance of this model is described in our publication:
"Unveiling the predictive power of static structure in glassy systems".
"""
import functools
from typing import Any, Dict, Text, Tuple, Optional
from graph_nets import graphs
from graph_nets import modules as gn_modules
from graph_nets import utils_tf
import sonnet as snt
import tensorflow.compat.v1 as tf
def make_graph_from_static_structure(
positions: tf.Tensor,
types: tf.Tensor,
box: tf.Tensor,
edge_threshold: float) -> graphs.GraphsTuple:
"""Returns graph representing the static structure of the glass.
Each particle is represented by a node in the graph. The particle type is
stored as a node feature.
Two particles at a distance less than the threshold are connected by an edge.
The relative distance vector is stored as an edge feature.
Args:
positions: particle positions with shape [n_particles, 3].
types: particle types with shape [n_particles].
box: dimensions of the cubic box that contains the particles with shape [3].
edge_threshold: particles at distance less than threshold are connected by
an edge.
"""
# Calculate pairwise relative distances between particles: shape [n, n, 3].
cross_positions = positions[tf.newaxis, :, :] - positions[:, tf.newaxis, :]
# Enforces periodic boundary conditions.
box_ = box[tf.newaxis, tf.newaxis, :]
cross_positions += tf.cast(cross_positions < -box_ / 2., tf.float32) * box_
cross_positions -= tf.cast(cross_positions > box_ / 2., tf.float32) * box_
# Calculates adjacency matrix in a sparse format (indices), based on the given
# distances and threshold.
distances = tf.norm(cross_positions, axis=-1)
indices = tf.where(distances < edge_threshold)
# Defines graph.
nodes = types[:, tf.newaxis]
senders = indices[:, 0]
receivers = indices[:, 1]
edges = tf.gather_nd(cross_positions, indices)
return graphs.GraphsTuple(
nodes=tf.cast(nodes, tf.float32),
n_node=tf.reshape(tf.shape(nodes)[0], [1]),
edges=tf.cast(edges, tf.float32),
n_edge=tf.reshape(tf.shape(edges)[0], [1]),
globals=tf.zeros((1, 1), dtype=tf.float32),
receivers=tf.cast(receivers, tf.int32),
senders=tf.cast(senders, tf.int32)
)
def apply_random_rotation(graph: graphs.GraphsTuple) -> graphs.GraphsTuple:
"""Returns randomly rotated graph representation.
The rotation is an element of O(3) with rotation angles multiple of pi/2.
This function assumes that the relative particle distances are stored in
the edge features.
Args:
graph: The graphs tuple as defined in `graph_nets.graphs`.
"""
# Transposes edge features, so that the axes are in the first dimension.
# Outputs a tensor of shape [3, n_particles].
xyz = tf.transpose(graph.edges)
# Random pi/2 rotation(s)
permutation = tf.random.shuffle(tf.constant([0, 1, 2], dtype=tf.int32))
xyz = tf.gather(xyz, permutation)
# Random reflections.
symmetry = tf.random_uniform([3], minval=0, maxval=2, dtype=tf.int32)
symmetry = 1 - 2 * tf.cast(tf.reshape(symmetry, [3, 1]), tf.float32)
xyz = xyz * symmetry
edges = tf.transpose(xyz)
return graph.replace(edges=edges)
class GraphBasedModel(snt.AbstractModule):
"""Graph based model which predicts particle mobilities from their positions.
This network encodes the nodes and edges of the input graph independently, and
then performs message-passing on this graph, updating its edges based on their
associated nodes, then updating the nodes based on the input nodes' features
and their associated updated edge features.
This update is repeated several times.
Afterwards the resulting node embeddings are decoded to predict the particle
mobility.
"""
def __init__(self,
n_recurrences: int,
mlp_sizes: Tuple[int],
mlp_kwargs: Optional[Dict[Text, Any]] = None,
name='Graph'):
"""Creates a new GraphBasedModel object.
Args:
n_recurrences: the number of message passing steps in the graph network.
mlp_sizes: the number of neurons in each layer of the MLP.
mlp_kwargs: additional keyword aguments passed to the MLP.
name: the name of the Sonnet module.
"""
super(GraphBasedModel, self).__init__(name=name)
self._n_recurrences = n_recurrences
if mlp_kwargs is None:
mlp_kwargs = {}
model_fn = functools.partial(
snt.nets.MLP,
output_sizes=mlp_sizes,
activate_final=True,
**mlp_kwargs)
final_model_fn = functools.partial(
snt.nets.MLP,
output_sizes=mlp_sizes + (1,),
activate_final=False,
**mlp_kwargs)
with self._enter_variable_scope():
self._encoder = gn_modules.GraphIndependent(
node_model_fn=model_fn,
edge_model_fn=model_fn)
if self._n_recurrences > 0:
self._propagation_network = gn_modules.GraphNetwork(
node_model_fn=model_fn,
edge_model_fn=model_fn,
# We do not use globals, hence we just pass the identity function.
global_model_fn=lambda: lambda x: x,
reducer=tf.unsorted_segment_sum,
edge_block_opt=dict(use_globals=False),
node_block_opt=dict(use_globals=False),
global_block_opt=dict(use_globals=False))
self._decoder = gn_modules.GraphIndependent(
node_model_fn=final_model_fn,
edge_model_fn=model_fn)
def _build(self, graphs_tuple: graphs.GraphsTuple) -> tf.Tensor:
"""Connects the model into the tensorflow graph.
Args:
graphs_tuple: input graph tensor as defined in `graphs_tuple.graphs`.
Returns:
tensor with shape [n_particles] containing the predicted particle
mobilities.
"""
encoded = self._encoder(graphs_tuple)
outputs = encoded
for _ in range(self._n_recurrences):
# Adds skip connections.
inputs = utils_tf.concat([outputs, encoded], axis=-1)
outputs = self._propagation_network(inputs)
decoded = self._decoder(outputs)
return tf.squeeze(decoded.nodes, axis=-1)
================================================
FILE: glassy_dynamics/graph_model_test.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for graph_model."""
import itertools
from absl.testing import parameterized
from graph_nets import graphs
import numpy as np
import tensorflow.compat.v1 as tf
from glassy_dynamics import graph_model
class GraphModelTest(tf.test.TestCase, parameterized.TestCase):
def setUp(self):
"""Initializes a small tractable test (particle) system."""
super(GraphModelTest, self).setUp()
# Fixes random seed to ensure deterministic outputs.
tf.random.set_random_seed(1234)
# In this test we use a small tractable set of particles covering all corner
# cases:
# a) eight particles with different types,
# b) periodic box is not cubic,
# c) three disjoint cluster of particles separated by a threshold > 2,
# d) first two clusters overlap with the periodic boundary,
# e) first cluster is not fully connected,
# f) second cluster is fully connected,
# g) and third cluster is a single isolated particle.
#
# The formatting of the code below separates the three clusters by
# adding linebreaks after each cluster.
self._positions = np.array(
[[0.0, 0.0, 0.0], [2.5, 0.0, 0.0], [0.0, 1.5, 0.0], [0.0, 0.0, 9.0],
[0.0, 5.0, 0.0], [0.0, 5.0, 1.0], [3.0, 5.0, 0.0],
[2.0, 3.0, 3.0]])
self._types = np.array([0.0, 0.0, 1.0, 0.0,
0.0, 1.0, 0.0,
0.0])
self._box = np.array([4.0, 10.0, 10.0])
# Creates the corresponding graph elements, assuming a threshold of 2 and
# the conventions described in `graph_nets.graphs`.
self._edge_threshold = 2
self._nodes = np.array(
[[0.0], [0.0], [1.0], [0.0],
[0.0], [1.0], [0.0],
[0.0]])
self._edges = np.array(
[[0.0, 0.0, 0.0], [-1.5, 0.0, 0.0], [0.0, 1.5, 0.0], [0.0, 0.0, -1.0],
[1.5, 0.0, 0.0], [0.0, 0.0, 0.0], [1.5, 0.0, -1.0],
[0.0, -1.5, 0.0], [0.0, 0.0, 0.0], [0.0, -1.5, -1.0],
[0.0, 0.0, 1.0], [-1.5, 0.0, 1.0], [0.0, 1.5, 1.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [-1.0, 0.0, 0.0],
[0.0, 0.0, -1.0], [0.0, 0.0, 0.0], [-1.0, 0.0, -1.0],
[1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]])
self._receivers = np.array(
[0, 1, 2, 3, 0, 1, 3, 0, 2, 3, 0, 1, 2, 3,
4, 5, 6, 4, 5, 6, 4, 5, 6,
7])
self._senders = np.array(
[0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3,
4, 4, 4, 5, 5, 5, 6, 6, 6,
7])
def _get_graphs_tuple(self):
"""Returns a GraphsTuple containing a graph based on the test system."""
return graphs.GraphsTuple(
nodes=tf.constant(self._nodes, dtype=tf.float32),
edges=tf.constant(self._edges, dtype=tf.float32),
globals=tf.constant(np.array([[0.0]]), dtype=tf.float32),
receivers=tf.constant(self._receivers, dtype=tf.int32),
senders=tf.constant(self._senders, dtype=tf.int32),
n_node=tf.constant([len(self._nodes)], dtype=tf.int32),
n_edge=tf.constant([len(self._edges)], dtype=tf.int32))
def test_make_graph_from_static_structure(self):
graphs_tuple_op = graph_model.make_graph_from_static_structure(
tf.constant(self._positions, dtype=tf.float32),
tf.constant(self._types, dtype=tf.int32),
tf.constant(self._box, dtype=tf.float32),
self._edge_threshold)
graphs_tuple = self.evaluate(graphs_tuple_op)
self.assertLen(self._nodes, graphs_tuple.n_node)
self.assertLen(self._edges, graphs_tuple.n_edge)
np.testing.assert_almost_equal(graphs_tuple.nodes, self._nodes)
np.testing.assert_equal(graphs_tuple.senders, self._senders)
np.testing.assert_equal(graphs_tuple.receivers, self._receivers)
np.testing.assert_almost_equal(graphs_tuple.globals, np.array([[0.0]]))
np.testing.assert_almost_equal(graphs_tuple.edges, self._edges)
def _is_equal_up_to_rotation(self, x, y):
for axes in itertools.permutations([0, 1, 2]):
for mirrors in itertools.product([1, -1], repeat=3):
if np.allclose(x, y[:, axes] * mirrors):
return True
return False
def test_apply_random_rotation(self):
graphs_tuple = self._get_graphs_tuple()
rotated_graphs_tuple_op = graph_model.apply_random_rotation(graphs_tuple)
rotated_graphs_tuple = self.evaluate(rotated_graphs_tuple_op)
np.testing.assert_almost_equal(rotated_graphs_tuple.nodes, self._nodes)
np.testing.assert_almost_equal(rotated_graphs_tuple.senders, self._senders)
np.testing.assert_almost_equal(
rotated_graphs_tuple.receivers, self._receivers)
np.testing.assert_almost_equal(
rotated_graphs_tuple.globals, np.array([[0.0]]))
self.assertTrue(self._is_equal_up_to_rotation(rotated_graphs_tuple.edges,
self._edges))
@parameterized.named_parameters(('no_propagation', 0, (30,)),
('multi_propagation', 5, (15,)),
('multi_layer', 1, (20, 30)))
def test_GraphModel(self, n_recurrences, mlp_sizes):
graphs_tuple = self._get_graphs_tuple()
output_op = graph_model.GraphBasedModel(n_recurrences=n_recurrences,
mlp_sizes=mlp_sizes)(graphs_tuple)
self.assertListEqual(output_op.shape.as_list(), [len(self._types)])
# Tests if the model runs without crashing.
with self.session():
tf.global_variables_initializer().run()
output_op.eval()
if __name__ == '__main__':
tf.test.main()
================================================
FILE: glassy_dynamics/requirements.txt
================================================
# GlassyDynamics
absl-py==0.8.1
astor==0.8.0
cloudpickle==1.1.1
contextlib2==0.6.0.post1
decorator==4.4.0
dm-sonnet==1.35
future==0.18.0
gast==0.2.2
google-pasta==0.1.7
graph-nets==1.0.4
grpcio==1.24.1
h5py==2.10.0
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
Markdown==3.1.1
mock==3.0.5
networkx==2.3
numpy==1.17.2
protobuf==3.10.0
semantic-version==2.8.2
six==1.12.0
tensorboard==1.15.0
tensorflow==1.15.0
tensorflow-estimator==1.15.1
tensorflow-probability==0.7.0
termcolor==1.1.0
Werkzeug==0.16.0
wrapt==1.11.2
================================================
FILE: glassy_dynamics/train.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Training pipeline for the prediction of particle mobilities in glasses."""
import collections
import enum
import pickle
from typing import Any, Dict, List, Optional, Text, Tuple, Sequence
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
import tensorflow_probability as tfp
from glassy_dynamics import graph_model
tf.enable_resource_variables()
LossCollection = collections.namedtuple('LossCollection',
'l1_loss, l2_loss, correlation')
GlassSimulationData = collections.namedtuple('GlassSimulationData',
'positions, targets, types, box')
class ParticleType(enum.IntEnum):
"""The simulation contains two particle types, identified as type A and B.
The dataset encodes the particle type in an integer.
- 0 corresponds to particle type A.
- 1 corresponds to particle type B.
"""
A = 0
B = 1
def get_targets(
initial_positions: np.ndarray,
trajectory_target_positions: Sequence[np.ndarray]) -> np.ndarray:
"""Returns the averaged particle mobilities from the sampled trajectories.
Args:
initial_positions: the initial positions of the particles with shape
[n_particles, 3].
trajectory_target_positions: the absolute positions of the particles at the
target time for all sampled trajectories, each with shape
[n_particles, 3].
"""
targets = np.mean([np.linalg.norm(t - initial_positions, axis=-1)
for t in trajectory_target_positions], axis=0)
return targets.astype(np.float32)
def load_data(
file_pattern: Text,
time_index: int,
max_files_to_load: Optional[int] = None) -> List[GlassSimulationData]:
"""Returns a dictionary containing the training or test dataset.
The dictionary contains:
`positions`: `np.ndarray` containing the particle positions with shape
[n_particles, 3].
`targets`: `np.ndarray` containing particle mobilities with shape
[n_particles].
`types`: `np.ndarray` containing the particle types with shape with shape
[n_particles].
`box`: `np.ndarray` containing the dimensions of the periodic box with shape
[3].
Args:
file_pattern: pattern matching the files with the simulation data.
time_index: the time index of the targets.
max_files_to_load: the maximum number of files to load.
"""
filenames = tf.io.gfile.glob(file_pattern)
if max_files_to_load:
filenames = filenames[:max_files_to_load]
static_structures = []
for filename in filenames:
with tf.io.gfile.GFile(filename, 'rb') as f:
data = pickle.load(f)
static_structures.append(GlassSimulationData(
positions=data['positions'].astype(np.float32),
targets=get_targets(
data['positions'], data['trajectory_target_positions'][time_index]),
types=data['types'].astype(np.int32),
box=data['box'].astype(np.float32)))
return static_structures
def get_loss_ops(
prediction: tf.Tensor,
target: tf.Tensor,
types: tf.Tensor) -> LossCollection:
"""Returns L1/L2 loss and correlation for type A particles.
Args:
prediction: tensor with shape [n_particles] containing the predicted
particle mobilities.
target: tensor with shape [n_particles] containing the true particle
mobilities.
types: tensor with shape [n_particles] containing the particle types.
"""
# Considers only type A particles.
mask = tf.equal(types, ParticleType.A)
prediction = tf.boolean_mask(prediction, mask)
target = tf.boolean_mask(target, mask)
return LossCollection(
l1_loss=tf.reduce_mean(tf.abs(prediction - target)),
l2_loss=tf.reduce_mean((prediction - target)**2),
correlation=tf.squeeze(tfp.stats.correlation(
prediction[:, tf.newaxis], target[:, tf.newaxis])))
def get_minimize_op(
loss: tf.Tensor,
learning_rate: float,
grad_clip: Optional[float] = None) -> tf.Tensor:
"""Returns minimization operation.
Args:
loss: the loss tensor which is minimized.
learning_rate: the learning rate used by the optimizer.
grad_clip: all gradients are clipped to the given value if not None or 0.
"""
optimizer = tf.train.AdamOptimizer(learning_rate)
grads_and_vars = optimizer.compute_gradients(loss)
if grad_clip:
grads, _ = tf.clip_by_global_norm([g for g, _ in grads_and_vars], grad_clip)
grads_and_vars = [(g, pair[1]) for g, pair in zip(grads, grads_and_vars)]
minimize = optimizer.apply_gradients(grads_and_vars)
return minimize
def _log_stats_and_return_mean_correlation(
label: Text,
stats: Sequence[LossCollection]) -> float:
"""Logs performance statistics and returns mean correlation.
Args:
label: label printed before the combined statistics e.g. train or test.
stats: statistics calculated for each batch in a dataset.
Returns:
mean correlation
"""
for key in LossCollection._fields:
values = [getattr(s, key) for s in stats]
mean = np.mean(values)
std = np.std(values)
logging.info('%s: %s: %.4f +/- %.4f', label, key, mean, std)
return np.mean([s.correlation for s in stats])
def train_model(train_file_pattern: Text,
test_file_pattern: Text,
max_files_to_load: Optional[int] = None,
n_epochs: int = 1000,
time_index: int = 9,
augment_data_using_rotations: bool = True,
learning_rate: float = 1e-4,
grad_clip: Optional[float] = 1.0,
n_recurrences: int = 7,
mlp_sizes: Tuple[int] = (64, 64),
mlp_kwargs: Optional[Dict[Text, Any]] = None,
edge_threshold: float = 2.0,
measurement_store_interval: int = 1000,
checkpoint_path: Optional[Text] = None) -> float: # pytype: disable=annotation-type-mismatch
"""Trains GraphModel using tensorflow.
Args:
train_file_pattern: pattern matching the files with the training data.
test_file_pattern: pattern matching the files with the test data.
max_files_to_load: the maximum number of train and test files to load.
If None, all files will be loaded.
n_epochs: the number of passes through the training dataset (epochs).
time_index: the time index (0-9) of the target mobilities.
augment_data_using_rotations: data is augemented by using random rotations.
learning_rate: the learning rate used by the optimizer.
grad_clip: all gradients are clipped to the given value.
n_recurrences: the number of message passing steps in the graphnet.
mlp_sizes: the number of neurons in each layer of the MLP.
mlp_kwargs: additional keyword aguments passed to the MLP.
edge_threshold: particles at distance less than threshold are connected by
an edge.
measurement_store_interval: number of steps between storing objective values
(loss and correlation).
checkpoint_path: path used to store the checkpoint with the highest
correlation on the test set.
Returns:
Correlation on the test dataset of best model encountered during training.
"""
if mlp_kwargs is None:
mlp_kwargs = dict(initializers=dict(w=tf.variance_scaling_initializer(1.0),
b=tf.variance_scaling_initializer(0.1)))
# Loads train and test dataset.
dataset_kwargs = dict(
time_index=time_index,
max_files_to_load=max_files_to_load)
training_data = load_data(train_file_pattern, **dataset_kwargs)
test_data = load_data(test_file_pattern, **dataset_kwargs)
# Defines wrapper functions, which can directly be passed to the
# tf.data.Dataset.map function.
def _make_graph_from_static_structure(static_structure):
"""Converts static structure to graph, targets and types."""
return (graph_model.make_graph_from_static_structure(
static_structure.positions,
static_structure.types,
static_structure.box,
edge_threshold),
static_structure.targets,
static_structure.types)
def _apply_random_rotation(graph, targets, types):
"""Applies random rotations to the graph and forwards targets and types."""
return graph_model.apply_random_rotation(graph), targets, types
# Defines data-pipeline based on tf.data.Dataset following the official
# guideline: https://www.tensorflow.org/guide/datasets#consuming_numpy_arrays.
# We use initializable iterators to avoid embedding the training and test data
# directly into the graph.
# Instead we feed the data to the iterators during the initalization of the
# iterators before the main training loop.
placeholders = GlassSimulationData._make(
tf.placeholder(s.dtype, (None,) + s.shape) for s in training_data[0])
dataset = tf.data.Dataset.from_tensor_slices(placeholders)
dataset = dataset.map(_make_graph_from_static_structure)
dataset = dataset.cache()
dataset = dataset.shuffle(400)
# Augments data. This has to be done after calling dataset.cache!
if augment_data_using_rotations:
dataset = dataset.map(_apply_random_rotation)
dataset = dataset.repeat()
train_iterator = dataset.make_initializable_iterator()
dataset = tf.data.Dataset.from_tensor_slices(placeholders)
dataset = dataset.map(_make_graph_from_static_structure)
dataset = dataset.cache()
dataset = dataset.repeat()
test_iterator = dataset.make_initializable_iterator()
# Creates tensorflow graph.
# Note: We decouple the training and test datasets from the input pipeline
# by creating a new iterator from a string-handle placeholder with the same
# output types and shapes as the training dataset.
dataset_handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(
dataset_handle, train_iterator.output_types, train_iterator.output_shapes)
graph, targets, types = iterator.get_next()
model = graph_model.GraphBasedModel(
n_recurrences, mlp_sizes, mlp_kwargs)
prediction = model(graph)
# Defines loss and minimization operations.
loss_ops = get_loss_ops(prediction, targets, types)
minimize_op = get_minimize_op(loss_ops.l2_loss, learning_rate, grad_clip)
best_so_far = -1
train_stats = []
test_stats = []
saver = tf.train.Saver()
with tf.train.SingularMonitoredSession() as session:
# Initializes train and test iterators with the training and test datasets.
# The obtained training and test string-handles can be passed to the
# dataset_handle placeholder to select the dataset.
train_handle = session.run(train_iterator.string_handle())
test_handle = session.run(test_iterator.string_handle())
feed_dict = {p: [x[i] for x in training_data]
for i, p in enumerate(placeholders)}
session.run(train_iterator.initializer, feed_dict=feed_dict)
feed_dict = {p: [x[i] for x in test_data]
for i, p in enumerate(placeholders)}
session.run(test_iterator.initializer, feed_dict=feed_dict)
# Trains model using stochatic gradient descent on the training dataset.
n_training_steps = len(training_data) * n_epochs
for i in range(n_training_steps):
feed_dict = {dataset_handle: train_handle}
train_loss, _ = session.run((loss_ops, minimize_op), feed_dict=feed_dict)
train_stats.append(train_loss)
if (i+1) % measurement_store_interval == 0:
# Evaluates model on test dataset.
for _ in range(len(test_data)):
feed_dict = {dataset_handle: test_handle}
test_stats.append(session.run(loss_ops, feed_dict=feed_dict))
# Outputs performance statistics on training and test dataset.
_log_stats_and_return_mean_correlation('Train', train_stats)
correlation = _log_stats_and_return_mean_correlation('Test', test_stats)
train_stats = []
test_stats = []
# Updates best model based on the observed correlation on the test
# dataset.
if correlation > best_so_far:
best_so_far = correlation
if checkpoint_path:
saver.save(session.raw_session(), checkpoint_path)
return best_so_far
def apply_model(checkpoint_path: Text,
file_pattern: Text,
max_files_to_load: Optional[int] = None,
time_index: int = 9) -> List[np.ndarray]:
"""Applies trained GraphModel using tensorflow.
Args:
checkpoint_path: path from which the model is loaded.
file_pattern: pattern matching the files with the data.
max_files_to_load: the maximum number of files to load.
If None, all files will be loaded.
time_index: the time index (0-9) of the target mobilities.
Returns:
Predictions of the model for all files.
"""
dataset_kwargs = dict(
time_index=time_index,
max_files_to_load=max_files_to_load)
data = load_data(file_pattern, **dataset_kwargs)
tf.reset_default_graph()
saver = tf.train.import_meta_graph(checkpoint_path + '.meta')
graph = tf.get_default_graph()
placeholders = GlassSimulationData(
positions=graph.get_tensor_by_name('Placeholder:0'),
targets=graph.get_tensor_by_name('Placeholder_1:0'),
types=graph.get_tensor_by_name('Placeholder_2:0'),
box=graph.get_tensor_by_name('Placeholder_3:0'))
prediction_tensor = graph.get_tensor_by_name('Graph_1/Squeeze:0')
correlation_tensor = graph.get_tensor_by_name('Squeeze:0')
dataset_handle = graph.get_tensor_by_name('Placeholder_4:0')
test_initalizer = graph.get_operation_by_name('MakeIterator_1')
test_string_handle = graph.get_tensor_by_name('IteratorToStringHandle_1:0')
with tf.Session() as session:
saver.restore(session, checkpoint_path)
handle = session.run(test_string_handle)
feed_dict = {p: [x[i] for x in data] for i, p in enumerate(placeholders)}
session.run(test_initalizer, feed_dict=feed_dict)
predictions = []
correlations = []
for _ in range(len(data)):
p, c = session.run((prediction_tensor, correlation_tensor),
feed_dict={dataset_handle: handle})
predictions.append(p)
correlations.append(c)
logging.info('Correlation: %.4f +/- %.4f',
np.mean(correlations),
np.std(correlations))
return predictions
================================================
FILE: glassy_dynamics/train_binary.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Trains a graph-based network to predict particle mobilities in glasses."""
import os
from absl import app
from absl import flags
from glassy_dynamics import train as train_using_tf
from glassy_dynamics import train_using_jax
FLAGS = flags.FLAGS
flags.DEFINE_string(
'data_directory',
'',
'Directory which contains the train and test datasets.')
flags.DEFINE_integer(
'time_index',
9,
'The time index of the target mobilities.')
flags.DEFINE_integer(
'max_files_to_load',
None,
'The maximum number of files to load from the train and test datasets.')
flags.DEFINE_string(
'checkpoint_path',
None,
'Path used to store a checkpoint of the best model.')
flags.DEFINE_boolean(
'use_jax',
False,
'Uses jax to train model.')
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
train_file_pattern = os.path.join(FLAGS.data_directory, 'train/aggregated*')
test_file_pattern = os.path.join(FLAGS.data_directory, 'test/aggregated*')
train = train_using_jax if FLAGS.use_jax else train_using_tf
train.train_model(
train_file_pattern=train_file_pattern,
test_file_pattern=test_file_pattern,
max_files_to_load=FLAGS.max_files_to_load,
time_index=FLAGS.time_index,
checkpoint_path=FLAGS.checkpoint_path)
if __name__ == '__main__':
app.run(main)
================================================
FILE: glassy_dynamics/train_test.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for train."""
import os
import numpy as np
import tensorflow.compat.v1 as tf
from glassy_dynamics import train
class TrainTest(tf.test.TestCase):
def test_get_targets(self):
initial_positions = np.array([[0, 0, 0], [1, 2, 3]])
trajectory_target_positions = [
np.array([[1, 0, 0], [1, 2, 4]]),
np.array([[0, 1, 0], [1, 0, 3]]),
np.array([[0, 0, 5], [1, 2, 3]]),
]
expected_targets = np.array([7.0 / 3.0, 1.0])
targets = train.get_targets(initial_positions, trajectory_target_positions)
np.testing.assert_almost_equal(expected_targets, targets)
def test_load_data(self):
file_pattern = os.path.join(os.path.dirname(__file__), 'testdata',
'test_small.pickle')
with self.subTest('ContentAndShapesAreAsExpected'):
data = train.load_data(file_pattern, 0)
self.assertEqual(len(data), 1)
element = data[0]
self.assertTupleEqual(element.positions.shape, (20, 3))
self.assertTupleEqual(element.box.shape, (3,))
self.assertTupleEqual(element.targets.shape, (20,))
self.assertTupleEqual(element.types.shape, (20,))
with self.subTest('TargetsGrowAsAFunctionOfTime'):
previous_mean_target = 0.0
# Time index 9 refers to 1/e = 0.36 in the IS, and therefore it is between
# Time index 5 (0.4) and time index 6 (0.3).
for time_index in [0, 1, 2, 3, 4, 5, 9, 6, 7, 8]:
data = train.load_data(file_pattern, time_index)[0]
current_mean_target = data.targets.mean()
self.assertGreater(current_mean_target, previous_mean_target)
previous_mean_target = current_mean_target
class TensorflowTrainTest(tf.test.TestCase):
def test_get_loss_op(self):
"""Tests the correct calculation of the loss operations."""
prediction = tf.constant([0.0, 1.0, 2.0, 1.0, 2.0], dtype=tf.float32)
target = tf.constant([1.0, 25.0, 0.0, 4.0, 2.0], dtype=tf.float32)
types = tf.constant([0, 1, 0, 0, 0], dtype=tf.int32)
loss_ops = train.get_loss_ops(prediction, target, types)
loss = self.evaluate(loss_ops)
self.assertAlmostEqual(loss.l1_loss, 1.5)
self.assertAlmostEqual(loss.l2_loss, 14.0 / 4.0)
self.assertAlmostEqual(loss.correlation, -0.15289416)
def test_get_minimize_op(self):
"""Tests the minimize operation by minimizing a single variable."""
var = tf.Variable([1.0], name='test')
loss = var**2
minimize = train.get_minimize_op(loss, 1e-1)
with self.session():
tf.global_variables_initializer().run()
for _ in range(100):
minimize.run()
value = var.eval()
self.assertLess(abs(value[0]), 0.01)
def test_train_model(self):
"""Tests if we can overfit to a small test dataset."""
file_pattern = os.path.join(os.path.dirname(__file__), 'testdata',
'test_small.pickle')
best_correlation_value = train.train_model(
train_file_pattern=file_pattern,
test_file_pattern=file_pattern,
n_epochs=1000,
augment_data_using_rotations=False,
learning_rate=1e-4,
n_recurrences=2,
edge_threshold=5,
mlp_sizes=(32, 32),
measurement_store_interval=1000)
# The test dataset contains only a single sample with 20 particles.
# Therefore we expect the model to be able to memorize the targets perfectly
# if the model works correctly.
self.assertGreater(best_correlation_value, 0.99)
def test_apply_model(self):
"""Tests if we can apply a model to a small test dataset."""
checkpoint_path = os.path.join(os.path.dirname(__file__), 'checkpoints',
't044_s09.ckpt')
file_pattern = os.path.join(os.path.dirname(__file__), 'testdata',
'test_large.pickle')
predictions = train.apply_model(checkpoint_path=checkpoint_path,
file_pattern=file_pattern,
time_index=0)
data = train.load_data(file_pattern, 0)
targets = data[0].targets
correlation_value = np.corrcoef(predictions[0], targets)[0, 1]
self.assertGreater(correlation_value, 0.5)
if __name__ == '__main__':
tf.test.main()
================================================
FILE: glassy_dynamics/train_using_jax.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Training pipeline for the prediction of particle mobilities in glasses."""
import enum
import functools
import logging
import pickle
import random
import haiku as hk
import jax
import jax.numpy as jnp
import jraph
import numpy as np
import optax
# Only used for file operations.
# You can use glob.glob and python's open function to replace the tf usage below
# on most platforms.
import tensorflow.compat.v1 as tf
class ParticleType(enum.IntEnum):
"""The simulation contains two particle types, identified as type A and B.
The dataset encodes the particle type in an integer.
- 0 corresponds to particle type A.
- 1 corresponds to particle type B.
"""
A = 0
B = 1
def make_graph_from_static_structure(positions, types, box, edge_threshold):
"""Returns graph representing the static structure of the glass.
Each particle is represented by a node in the graph. The particle type is
stored as a node feature.
Two particles at a distance less than the threshold are connected by an edge.
The relative distance vector is stored as an edge feature.
Args:
positions: particle positions with shape [n_particles, 3].
types: particle types with shape [n_particles].
box: dimensions of the cubic box that contains the particles with shape [3].
edge_threshold: particles at distance less than threshold are connected by
an edge.
"""
# Calculate pairwise relative distances between particles: shape [n, n, 3].
cross_positions = positions[None, :, :] - positions[:, None, :]
# Enforces periodic boundary conditions.
box_ = box[None, None, :]
cross_positions += (cross_positions < -box_ / 2.).astype(np.float32) * box_
cross_positions -= (cross_positions > box_ / 2.).astype(np.float32) * box_
# Calculates adjacency matrix in a sparse format (indices), based on the given
# distances and threshold.
distances = np.linalg.norm(cross_positions, axis=-1)
indices = np.where(distances < edge_threshold)
# Defines graph.
nodes = types[:, None]
senders = indices[0]
receivers = indices[1]
edges = cross_positions[indices]
return jraph.pad_with_graphs(jraph.GraphsTuple(
nodes=nodes.astype(np.float32),
n_node=np.reshape(nodes.shape[0], [1]),
edges=edges.astype(np.float32),
n_edge=np.reshape(edges.shape[0], [1]),
globals=np.zeros((1, 1), dtype=np.float32),
receivers=receivers.astype(np.int32),
senders=senders.astype(np.int32)
), n_node=4097, n_edge=200000)
def get_targets(initial_positions, trajectory_target_positions):
"""Returns the averaged particle mobilities from the sampled trajectories.
Args:
initial_positions: the initial positions of the particles with shape
[n_particles, 3].
trajectory_target_positions: the absolute positions of the particles at the
target time for all sampled trajectories, each with shape
[n_particles, 3].
"""
targets = np.mean([np.linalg.norm(t - initial_positions, axis=-1)
for t in trajectory_target_positions], axis=0)
return targets.astype(np.float32)
def load_data(file_pattern, time_index, max_files_to_load=None):
"""Returns a graphs and targets of the training or test dataset.
Args:
file_pattern: pattern matching the files with the simulation data.
time_index: the time index of the targets.
max_files_to_load: the maximum number of files to load.
"""
filenames = tf.io.gfile.glob(file_pattern)
if max_files_to_load:
filenames = filenames[:max_files_to_load]
graphs_and_targets = []
for filename in filenames:
with tf.io.gfile.GFile(filename, 'rb') as f:
data = pickle.load(f)
mask = (data['types'] == ParticleType.A).astype(np.int32)
# Mask dummy node due to padding
mask = np.concatenate([mask, np.zeros((1,), dtype=np.int32)], axis=-1)
targets = get_targets(
data['positions'], data['trajectory_target_positions'][time_index])
targets = np.concatenate(
[targets, np.zeros((1,), dtype=np.float32)], axis=-1)
graphs_and_targets.append(
(make_graph_from_static_structure(
data['positions'].astype(np.float32),
data['types'].astype(np.int32),
data['box'].astype(np.float32),
edge_threshold=2.0),
targets,
mask))
return graphs_and_targets
def apply_random_rotation(graph):
"""Returns randomly rotated graph representation.
The rotation is an element of O(3) with rotation angles multiple of pi/2.
This function assumes that the relative particle distances are stored in
the edge features.
Args:
graph: The graphs tuple as defined in `graph_nets.graphs`.
"""
# Transposes edge features, so that the axes are in the first dimension.
# Outputs a tensor of shape [3, n_particles].
xyz = np.transpose(graph.edges)
# Random pi/2 rotation(s)
permutation = np.array([0, 1, 2], dtype=np.int32)
np.random.shuffle(permutation)
xyz = xyz[permutation]
# Random reflections.
symmetry = np.random.randint(0, 2, [3])
symmetry = 1 - 2 * np.reshape(symmetry, [3, 1]).astype(np.float32)
xyz = xyz * symmetry
edges = np.transpose(xyz)
return graph._replace(edges=edges)
def network_definition(graph):
"""Defines a graph neural network.
Args:
graph: Graphstuple the network processes.
Returns:
Decoded nodes.
"""
model_fn = functools.partial(
hk.nets.MLP,
w_init=hk.initializers.VarianceScaling(1.0),
b_init=hk.initializers.VarianceScaling(1.0))
mlp_sizes = (64, 64)
num_message_passing_steps = 7
node_encoder = model_fn(output_sizes=mlp_sizes, activate_final=True)
edge_encoder = model_fn(output_sizes=mlp_sizes, activate_final=True)
node_decoder = model_fn(output_sizes=mlp_sizes + (1,), activate_final=False)
node_encoding = node_encoder(graph.nodes)
edge_encoding = edge_encoder(graph.edges)
graph = graph._replace(nodes=node_encoding, edges=edge_encoding)
update_edge_fn = jraph.concatenated_args(
model_fn(output_sizes=mlp_sizes, activate_final=True))
update_node_fn = jraph.concatenated_args(
model_fn(output_sizes=mlp_sizes, activate_final=True))
gn = jraph.InteractionNetwork(
update_edge_fn=update_edge_fn,
update_node_fn=update_node_fn,
include_sent_messages_in_node_update=True)
for _ in range(num_message_passing_steps):
graph = graph._replace(
nodes=jnp.concatenate([graph.nodes, node_encoding], axis=-1),
edges=jnp.concatenate([graph.edges, edge_encoding], axis=-1))
graph = gn(graph)
return jnp.squeeze(node_decoder(graph.nodes), axis=-1)
def train_model(train_file_pattern,
test_file_pattern,
max_files_to_load=None,
n_epochs=1000,
time_index=9,
learning_rate=1e-4,
grad_clip=1.0,
measurement_store_interval=1000,
checkpoint_path=None):
"""Trains GraphModel using tensorflow.
Args:
train_file_pattern: pattern matching the files with the training data.
test_file_pattern: pattern matching the files with the test data.
max_files_to_load: the maximum number of train and test files to load.
If None, all files will be loaded.
n_epochs: the number of passes through the training dataset (epochs).
time_index: the time index (0-9) of the target mobilities.
learning_rate: the learning rate used by the optimizer.
grad_clip: all gradients are clipped to the given value.
measurement_store_interval: number of steps between storing objective values
(loss and correlation).
checkpoint_path: ignored by this implementation.
"""
if checkpoint_path:
logging.warning('The checkpoint_path argument is ignored.')
random.seed(42)
np.random.seed(42)
# Loads train and test dataset.
dataset_kwargs = dict(
time_index=time_index,
max_files_to_load=max_files_to_load)
logging.info('Load training data')
training_data = load_data(train_file_pattern, **dataset_kwargs)
logging.info('Load test data')
test_data = load_data(test_file_pattern, **dataset_kwargs)
logging.info('Finished loading data')
network = hk.without_apply_rng(hk.transform(network_definition))
params = network.init(jax.random.PRNGKey(42), training_data[0][0])
opt_init, opt_update = optax.chain(
optax.clip_by_global_norm(grad_clip),
optax.scale_by_adam(0.9, 0.999, 1e-8),
optax.scale(-learning_rate))
opt_state = opt_init(params)
network_apply = jax.jit(network.apply)
@jax.jit
def loss_fn(params, graph, targets, mask):
decoded_nodes = network_apply(params, graph) * mask
return (jnp.sum((decoded_nodes - targets)**2 * mask) /
jnp.sum(mask))
@jax.jit
def update(params, opt_state, graph, targets, mask):
loss, grads = jax.value_and_grad(loss_fn)(params, graph, targets, mask)
updates, opt_state = opt_update(grads, opt_state)
return optax.apply_updates(params, updates), opt_state, loss
train_stats = []
i = 0
logging.info('Start training')
for epoch in range(n_epochs):
logging.info('Start epoch %r', epoch)
random.shuffle(training_data)
for graph, targets, mask in training_data:
graph = apply_random_rotation(graph)
params, opt_state, loss = update(params, opt_state, graph, targets, mask)
train_stats.append(loss)
if (i+1) % measurement_store_interval == 0:
logging.info('Start evaluation run')
test_stats = []
for test_graph, test_targets, test_mask in test_data:
predictions = network_apply(params, test_graph)
test_stats.append(np.corrcoef(
predictions[test_mask == 1], test_targets[test_mask == 1])[0, 1])
logging.info('Train loss %r', np.mean(train_stats))
logging.info('Test correlation %r', np.mean(test_stats))
train_stats = []
i += 1
================================================
FILE: graph_matching_networks/README.md
================================================
# Graph Matching Networks for Learning the Similarity of Graph Structured Objects
This is the example code for the following ICML 2019 paper. If you use the code
here please cite this paper.
> Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli. *Graph Matching Networks for Learning the Similarity of Graph Structured Objects*. ICML 2019. [\[arXiv\]](https://arxiv.org/abs/1904.12787).
## Running the code
The code is in the format of a colab notebook, which includes:
* an example implementation of the model,
* an example graph similarity learning task,
* an example training loop, and
* some attention visualization tools.
To launch the notebook in Google colab, [click here](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/graph_matching_networks/graph_matching_networks.ipynb).
You can also download the notebook and run it locally with jupyter. The
notebook assumes you are on python 3 and have the latest (as of July 24,
2019) tensorflow, sonnet, numpy etc. installed. You can install the dependencies by running `pip3 install --user -r requirements.txt`.
================================================
FILE: graph_matching_networks/graph_matching_networks.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "HrOZEU_OrYTR"
},
"source": [
"##### Copyright 2019 Google LLC.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "G9suJlqLrfmP"
},
"outputs": [],
"source": [
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "F50gn0G_yZ3s"
},
"source": [
"# Graph Matching Networks for Learning the Similarity of Graph Structured Objects\n",
"\n",
"This is the example code for our ICML 2019 paper. Please refer to the paper for more details:\n",
"\n",
"\u003e Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli. *Graph Matching Networks for Learning the Similarity of Graph Structured Objects*. ICML 2019. [\\[arXiv\\]](https://arxiv.org/abs/1904.12787)\n",
"\n",
"\n",
"## Graph similarity learning\n",
"\n",
"Our goal is to learn a similarity function between graphs. Given two graphs $G_1, G_2$, a graph similarity model can be written as a function $f(G_1, G_2)$ that computes a scalar similarity value.\n",
"\n",
"In this project we build models to learn such a similarity function based on examples of similar / dissimilar pairs or triplets. Because of learning, our model can adapt to different notions of similarity and to different types of graph structure, as long as training data is available.\n",
"\n",
"In the following we will sometimes use the term \"distance\" and say the model learns a \"distance function\" $d(G_1, G_2)$ between graphs when convenient. But this is just the opposite of a similarity function, and you may simply say $f(G_1, G_2) = - d(G_1, G_2)$."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4QqVQJxLoYKx"
},
"source": [
"## Some dependencies and imports\n",
"\n",
"If you want to run the notebook locally, make sure you have all the dependencies first. You can use the following command\n",
"```\n",
"pip3 install --user -r requirements.txt\n",
"```\n",
"\n",
"Note the code should work for both python 3 and 2, but python 3 is recommended."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "-eanBQF6boRu"
},
"outputs": [],
"source": [
"# Let's disable all the warnings first\n",
"import warnings\n",
"warnings.simplefilter(\"ignore\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "dyu1M3fufCaD"
},
"source": [
"These are all the dependencies that will be used in this notebook."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "0Ep7LRdJoZ6O"
},
"outputs": [],
"source": [
"import abc\n",
"import collections\n",
"import contextlib\n",
"import copy\n",
"import random\n",
"import time\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import networkx as nx\n",
"import numpy as np\n",
"import six\n",
"import sonnet as snt\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fzeL1Fk8zD3S"
},
"source": [
"## The models"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gSYjjfgvffJO"
},
"source": [
"### The graph embedding model\n",
"\n",
"The simpler variant of our model is based on embedding each graph **independently** into a vector and then use an existing distance (or similarity) metric in the vector space to compute the distance between graphs. More concretely, we define\n",
"\n",
"$$d(G_1, G_2) = d_H(embed(G_1), embed(G_2)),$$\n",
"\n",
"where $embed$ is a model that maps any graph $G$ into an $H$-dimensional vector, and $d_H$ is a distance metric in that vector space. Typical examples are Euclidean distance in $\\mathbb{R}^H$, i.e. $d_H(x, y) = \\sqrt{\\sum_{i=1}^H (x_i - y_i)^2}$, or Hamming distance in $H$-dimensional space of binary vectors, i.e. $d_H(x, y)=\\sum_{i=1}^H \\mathbb{I}[x_i \\ne y_i]$.\n",
"\n",
"Each graph input contains a set of nodes $V$ and edges $E$. Each node $i\\in V$ may have a feature vector $x_i$ associated with it, and each edge $(i, j)\\in E$ may also have a feature vector $x_{ij}$ encoding e.g. edge type or attributes. The embedding model will therefore jointly reason about the graph structure as well as the graph features to come up with an embedding that reflects the notion of similarity described by the training examples.\n",
"\n",
"The embedding model is composed of 3 parts:\n",
"1. An encoder that maps $x_i$ and $x_{ij}$ into a nice hidden representation space. Here we use separate MLPs (fully connected neural nets) for node and edge representations:\n",
"$$\\begin{array}{rcl}\n",
"h_i^{(0)} \u0026=\u0026 \\mathrm{MLP_{node}}(x_i) \\\\\n",
"e_{ij} \u0026=\u0026 \\mathrm{MLP_{edge}}(x_{ij})\n",
"\\end{array}\n",
"$$\n",
"\n",
"2. A graph neural network (GNN) that communicates information across the graph and computes node representations that encode local neighborhood structure and semantics. More concretely, the GNN computes node representations through an iterative message passing process. In the $t$-th round of message passing, we compute a message vector on each edge, and then each node aggregates all the incoming messages and updates its own representation:\n",
"$$\\begin{array}{rcl}\n",
"m_{i\\rightarrow j} \u0026=\u0026 f_\\mathrm{message}(h_i^{(t)}, h_j^{(t)}, e_{ij}) \\\\\n",
"h_i^{(t+1)} \u0026=\u0026 f_\\mathrm{node}(h_i^{(t)}, \\sum_{j:(j,i)\\in E} m_{j\\rightarrow i})\n",
"\\end{array}\n",
"$$\n",
"Here both $f_\\mathrm{message}$ and $f_\\mathrm{node}$ are neural modules. We use MLPs for $f_\\mathrm{message}$, while $f_\\mathrm{node}$ can also be MLPs or even recurrent neural network cores like LSTMs or GRUs. The GNNs have the nice property of being equivariant to node permutations, and nodes on isomorphic graphs (with the same node and edge features) will have the same representations regardless of the ordering.\n",
"\n",
"3. After we obtained the final node representations after $T$ rounds of message passing, we aggregate across them to get graph representations $h_G=f_G(\\{h_i^{(T)}\\}_{i\\in V})$. This could be implemented by a simple sum that reduces the node representations into a single vector and then transform it:\n",
"$$h_G = \\mathrm{MLP_G}\\left(\\sum_{i\\in V} h_i^{(T)}\\right).$$\n",
"We used the following gated aggregation module proposed in [Li et al., 2015](https://arxiv.org/abs/1511.05493) which we found to work consistently better:\n",
"$$h_G = \\mathrm{MLP_G}\\left(\\sum_{i\\in V} \\sigma(\\mathrm{MLP_{gate}}(h_i^{(T)})) \\odot \\mathrm{MLP}(h_i^{(T)})\\right).$$\n",
"The key to this function is to make sure it is invariant to node orderings, both the above forms satisfy this condition. The gated variant gives the model the capacity to explicitly modulate each node's contribution to the graph representation."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "u8eVpPWEjZ_O"
},
"source": [
"#### The graph encoder"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "b8dFQUFwjcCi"
},
"outputs": [],
"source": [
"class GraphEncoder(snt.AbstractModule):\n",
" \"\"\"Encoder module that projects node and edge features to some embeddings.\"\"\"\n",
"\n",
" def __init__(self,\n",
" node_hidden_sizes=None,\n",
" edge_hidden_sizes=None,\n",
" name='graph-encoder'):\n",
" \"\"\"Constructor.\n",
"\n",
" Args:\n",
" node_hidden_sizes: if provided should be a list of ints, hidden sizes of\n",
" node encoder network, the last element is the size of the node outputs.\n",
" If not provided, node features will pass through as is.\n",
" edge_hidden_sizes: if provided should be a list of ints, hidden sizes of\n",
" edge encoder network, the last element is the size of the edge outptus.\n",
" If not provided, edge features will pass through as is.\n",
" name: name of this module.\n",
" \"\"\"\n",
" super(GraphEncoder, self).__init__(name=name)\n",
"\n",
" # this also handles the case of an empty list\n",
" self._node_hidden_sizes = node_hidden_sizes if node_hidden_sizes else None\n",
" self._edge_hidden_sizes = edge_hidden_sizes\n",
"\n",
" def _build(self, node_features, edge_features=None):\n",
" \"\"\"Encode node and edge features.\n",
"\n",
" Args:\n",
" node_features: [n_nodes, node_feat_dim] float tensor.\n",
" edge_features: if provided, should be [n_edges, edge_feat_dim] float\n",
" tensor.\n",
"\n",
" Returns:\n",
" node_outputs: [n_nodes, node_embedding_dim] float tensor, node embeddings.\n",
" edge_outputs: if edge_features is not None and edge_hidden_sizes is not\n",
" None, this is [n_edges, edge_embedding_dim] float tensor, edge\n",
" embeddings; otherwise just the input edge_features.\n",
" \"\"\"\n",
" if self._node_hidden_sizes is None:\n",
" node_outputs = node_features\n",
" else:\n",
" node_outputs = snt.nets.MLP(\n",
" self._node_hidden_sizes, name='node-feature-mlp')(node_features)\n",
"\n",
" if edge_features is None or self._edge_hidden_sizes is None:\n",
" edge_outputs = edge_features\n",
" else:\n",
" edge_outputs = snt.nets.MLP(\n",
" self._edge_hidden_sizes, name='edge-feature-mlp')(edge_features)\n",
"\n",
" return node_outputs, edge_outputs"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "goVx4QGClJCW"
},
"source": [
"#### The message passing layers"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "5LJIBWdvlIWl"
},
"outputs": [],
"source": [
"def graph_prop_once(node_states,\n",
" from_idx,\n",
" to_idx,\n",
" message_net,\n",
" aggregation_module=tf.unsorted_segment_sum,\n",
" edge_features=None):\n",
" \"\"\"One round of propagation (message passing) in a graph.\n",
"\n",
" Args:\n",
" node_states: [n_nodes, node_state_dim] float tensor, node state vectors, one\n",
" row for each node.\n",
" from_idx: [n_edges] int tensor, index of the from nodes.\n",
" to_idx: [n_edges] int tensor, index of the to nodes.\n",
" message_net: a network that maps concatenated edge inputs to message\n",
" vectors.\n",
" aggregation_module: a module that aggregates messages on edges to aggregated\n",
" messages for each node. Should be a callable and can be called like the\n",
" following,\n",
" `aggregated_messages = aggregation_module(messages, to_idx, n_nodes)`,\n",
" where messages is [n_edges, edge_message_dim] tensor, to_idx is the index\n",
" of the to nodes, i.e. where each message should go to, and n_nodes is an\n",
" int which is the number of nodes to aggregate into.\n",
" edge_features: if provided, should be a [n_edges, edge_feature_dim] float\n",
" tensor, extra features for each edge.\n",
"\n",
" Returns:\n",
" aggregated_messages: an [n_nodes, edge_message_dim] float tensor, the\n",
" aggregated messages, one row for each node.\n",
" \"\"\"\n",
" from_states = tf.gather(node_states, from_idx)\n",
" to_states = tf.gather(node_states, to_idx)\n",
"\n",
" edge_inputs = [from_states, to_states]\n",
" if edge_features is not None:\n",
" edge_inputs.append(edge_features)\n",
"\n",
" edge_inputs = tf.concat(edge_inputs, axis=-1)\n",
" messages = message_net(edge_inputs)\n",
"\n",
" return aggregation_module(messages, to_idx, tf.shape(node_states)[0])\n",
"\n",
"\n",
"class GraphPropLayer(snt.AbstractModule):\n",
" \"\"\"Implementation of a graph propagation (message passing) layer.\"\"\"\n",
"\n",
" def __init__(self,\n",
" node_state_dim,\n",
" edge_hidden_sizes,\n",
" node_hidden_sizes,\n",
" edge_net_init_scale=0.1,\n",
" node_update_type='residual',\n",
" use_reverse_direction=True,\n",
" reverse_dir_param_different=True,\n",
" layer_norm=False,\n",
" name='graph-net'):\n",
" \"\"\"Constructor.\n",
"\n",
" Args:\n",
" node_state_dim: int, dimensionality of node states.\n",
" edge_hidden_sizes: list of ints, hidden sizes for the edge message\n",
" net, the last element in the list is the size of the message vectors.\n",
" node_hidden_sizes: list of ints, hidden sizes for the node update\n",
" net.\n",
" edge_net_init_scale: initialization scale for the edge networks. This\n",
" is typically set to a small value such that the gradient does not blow\n",
" up.\n",
" node_update_type: type of node updates, one of {mlp, gru, residual}.\n",
" use_reverse_direction: set to True to also propagate messages in the\n",
" reverse direction.\n",
" reverse_dir_param_different: set to True to have the messages computed\n",
" using a different set of parameters than for the forward direction.\n",
" layer_norm: set to True to use layer normalization in a few places.\n",
" name: name of this module.\n",
" \"\"\"\n",
" super(GraphPropLayer, self).__init__(name=name)\n",
"\n",
" self._node_state_dim = node_state_dim\n",
" self._edge_hidden_sizes = edge_hidden_sizes[:]\n",
"\n",
" # output size is node_state_dim\n",
" self._node_hidden_sizes = node_hidden_sizes[:] + [node_state_dim]\n",
" self._edge_net_init_scale = edge_net_init_scale\n",
" self._node_update_type = node_update_type\n",
"\n",
" self._use_reverse_direction = use_reverse_direction\n",
" self._reverse_dir_param_different = reverse_dir_param_different\n",
"\n",
" self._layer_norm = layer_norm\n",
"\n",
" def _compute_aggregated_messages(\n",
" self, node_states, from_idx, to_idx, edge_features=None):\n",
" \"\"\"Compute aggregated messages for each node.\n",
"\n",
" Args:\n",
" node_states: [n_nodes, input_node_state_dim] float tensor, node states.\n",
" from_idx: [n_edges] int tensor, from node indices for each edge.\n",
" to_idx: [n_edges] int tensor, to node indices for each edge.\n",
" edge_features: if not None, should be [n_edges, edge_embedding_dim]\n",
" tensor, edge features.\n",
"\n",
" Returns:\n",
" aggregated_messages: [n_nodes, aggregated_message_dim] float tensor, the\n",
" aggregated messages for each node.\n",
" \"\"\"\n",
" self._message_net = snt.nets.MLP(\n",
" self._edge_hidden_sizes,\n",
" initializers={\n",
" 'w': tf.variance_scaling_initializer(\n",
" scale=self._edge_net_init_scale),\n",
" 'b': tf.zeros_initializer()},\n",
" name='message-mlp')\n",
"\n",
" aggregated_messages = graph_prop_once(\n",
" node_states,\n",
" from_idx,\n",
" to_idx,\n",
" self._message_net,\n",
" aggregation_module=tf.unsorted_segment_sum,\n",
" edge_features=edge_features)\n",
"\n",
" # optionally compute message vectors in the reverse direction\n",
" if self._use_reverse_direction:\n",
" if self._reverse_dir_param_different:\n",
" self._reverse_message_net = snt.nets.MLP(\n",
" self._edge_hidden_sizes,\n",
" initializers={\n",
" 'w': tf.variance_scaling_initializer(\n",
" scale=self._edge_net_init_scale),\n",
" 'b': tf.zeros_initializer()},\n",
" name='reverse-message-mlp')\n",
" else:\n",
" self._reverse_message_net = self._message_net\n",
"\n",
" reverse_aggregated_messages = graph_prop_once(\n",
" node_states,\n",
" to_idx,\n",
" from_idx,\n",
" self._reverse_message_net,\n",
" aggregation_module=tf.unsorted_segment_sum,\n",
" edge_features=edge_features)\n",
"\n",
" aggregated_messages += reverse_aggregated_messages\n",
"\n",
" if self._layer_norm:\n",
" aggregated_messages = snt.LayerNorm()(aggregated_messages)\n",
"\n",
" return aggregated_messages\n",
"\n",
" def _compute_node_update(self,\n",
" node_states,\n",
" node_state_inputs,\n",
" node_features=None):\n",
" \"\"\"Compute node updates.\n",
"\n",
" Args:\n",
" node_states: [n_nodes, node_state_dim] float tensor, the input node\n",
" states.\n",
" node_state_inputs: a list of tensors used to compute node updates. Each\n",
" element tensor should have shape [n_nodes, feat_dim], where feat_dim can\n",
" be different. These tensors will be concatenated along the feature\n",
" dimension.\n",
" node_features: extra node features if provided, should be of size\n",
" [n_nodes, extra_node_feat_dim] float tensor, can be used to implement\n",
" different types of skip connections.\n",
"\n",
" Returns:\n",
" new_node_states: [n_nodes, node_state_dim] float tensor, the new node\n",
" state tensor.\n",
"\n",
" Raises:\n",
" ValueError: if node update type is not supported.\n",
" \"\"\"\n",
" if self._node_update_type in ('mlp', 'residual'):\n",
" node_state_inputs.append(node_states)\n",
" if node_features is not None:\n",
" node_state_inputs.append(node_features)\n",
"\n",
" if len(node_state_inputs) == 1:\n",
" node_state_inputs = node_state_inputs[0]\n",
" else:\n",
" node_state_inputs = tf.concat(node_state_inputs, axis=-1)\n",
"\n",
" if self._node_update_type == 'gru':\n",
" _, new_node_states = snt.GRU(self._node_state_dim)(\n",
" node_state_inputs, node_states)\n",
" return new_node_states\n",
" else:\n",
" mlp_output = snt.nets.MLP(\n",
" self._node_hidden_sizes, name='node-mlp')(node_state_inputs)\n",
" if self._layer_norm:\n",
" mlp_output = snt.LayerNorm()(mlp_output)\n",
" if self._node_update_type == 'mlp':\n",
" return mlp_output\n",
" elif self._node_update_type == 'residual':\n",
" return node_states + mlp_output\n",
" else:\n",
" raise ValueError('Unknown node update type %s' % self._node_update_type)\n",
"\n",
" def _build(self,\n",
" node_states,\n",
" from_idx,\n",
" to_idx,\n",
" edge_features=None,\n",
" node_features=None):\n",
" \"\"\"Run one propagation step.\n",
"\n",
" Args:\n",
" node_states: [n_nodes, input_node_state_dim] float tensor, node states.\n",
" from_idx: [n_edges] int tensor, from node indices for each edge.\n",
" to_idx: [n_edges] int tensor, to node indices for each edge.\n",
" edge_features: if not None, should be [n_edges, edge_embedding_dim]\n",
" tensor, edge features.\n",
" node_features: extra node features if provided, should be of size\n",
" [n_nodes, extra_node_feat_dim] float tensor, can be used to implement\n",
" different types of skip connections.\n",
"\n",
" Returns:\n",
" node_states: [n_nodes, node_state_dim] float tensor, new node states.\n",
" \"\"\"\n",
" aggregated_messages = self._compute_aggregated_messages(\n",
" node_states, from_idx, to_idx, edge_features=edge_features)\n",
"\n",
" return self._compute_node_update(node_states,\n",
" [aggregated_messages],\n",
" node_features=node_features)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ubKr-G_Sq95j"
},
"source": [
"#### Graph aggregator"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "NmE-8Xpnq9Zi"
},
"outputs": [],
"source": [
"AGGREGATION_TYPE = {\n",
" 'sum': tf.unsorted_segment_sum,\n",
" 'mean': tf.unsorted_segment_mean,\n",
" 'sqrt_n': tf.unsorted_segment_sqrt_n,\n",
" 'max': tf.unsorted_segment_max,\n",
"}\n",
"\n",
"\n",
"class GraphAggregator(snt.AbstractModule):\n",
" \"\"\"This module computes graph representations by aggregating from parts.\"\"\"\n",
"\n",
" def __init__(self,\n",
" node_hidden_sizes,\n",
" graph_transform_sizes=None,\n",
" gated=True,\n",
" aggregation_type='sum',\n",
" name='graph-aggregator'):\n",
" \"\"\"Constructor.\n",
"\n",
" Args:\n",
" node_hidden_sizes: the hidden layer sizes of the node transformation nets.\n",
" The last element is the size of the aggregated graph representation.\n",
" graph_transform_sizes: sizes of the transformation layers on top of the\n",
" graph representations. The last element of this list is the final\n",
" dimensionality of the output graph representations.\n",
" gated: set to True to do gated aggregation, False not to.\n",
" aggregation_type: one of {sum, max, mean, sqrt_n}.\n",
" name: name of this module.\n",
" \"\"\"\n",
" super(GraphAggregator, self).__init__(name=name)\n",
"\n",
" self._node_hidden_sizes = node_hidden_sizes\n",
" self._graph_transform_sizes = graph_transform_sizes\n",
" self._graph_state_dim = node_hidden_sizes[-1]\n",
" self._gated = gated\n",
" self._aggregation_type = aggregation_type\n",
" self._aggregation_op = AGGREGATION_TYPE[aggregation_type]\n",
"\n",
" def _build(self, node_states, graph_idx, n_graphs):\n",
" \"\"\"Compute aggregated graph representations.\n",
"\n",
" Args:\n",
" node_states: [n_nodes, node_state_dim] float tensor, node states of a\n",
" batch of graphs concatenated together along the first dimension.\n",
" graph_idx: [n_nodes] int tensor, graph ID for each node.\n",
" n_graphs: integer, number of graphs in this batch.\n",
"\n",
" Returns:\n",
" graph_states: [n_graphs, graph_state_dim] float tensor, graph\n",
" representations, one row for each graph.\n",
" \"\"\"\n",
" node_hidden_sizes = self._node_hidden_sizes\n",
" if self._gated:\n",
" node_hidden_sizes[-1] = self._graph_state_dim * 2\n",
"\n",
" node_states_g = snt.nets.MLP(\n",
" node_hidden_sizes, name='node-state-g-mlp')(node_states)\n",
"\n",
" if self._gated:\n",
" gates = tf.nn.sigmoid(node_states_g[:, :self._graph_state_dim])\n",
" node_states_g = node_states_g[:, self._graph_state_dim:] * gates\n",
"\n",
" graph_states = self._aggregation_op(node_states_g, graph_idx, n_graphs)\n",
"\n",
" # unsorted_segment_max does not handle empty graphs in the way we want\n",
" # it assigns the lowest possible float to empty segments, we want to reset\n",
" # them to zero.\n",
" if self._aggregation_type == 'max':\n",
" # reset everything that's smaller than -1e5 to 0.\n",
" graph_states *= tf.cast(graph_states \u003e -1e5, tf.float32)\n",
"\n",
" # transform the reduced graph states further\n",
"\n",
" # pylint: disable=g-explicit-length-test\n",
" if (self._graph_transform_sizes is not None and\n",
" len(self._graph_transform_sizes) \u003e 0):\n",
" graph_states = snt.nets.MLP(\n",
" self._graph_transform_sizes, name='graph-transform-mlp')(graph_states)\n",
"\n",
" return graph_states"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "vYMDsa4nmg-C"
},
"source": [
"#### Putting them together"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "M7MvynyjmkVq"
},
"outputs": [],
"source": [
"class GraphEmbeddingNet(snt.AbstractModule):\n",
" \"\"\"A graph to embedding mapping network.\"\"\"\n",
"\n",
" def __init__(self,\n",
" encoder,\n",
" aggregator,\n",
" node_state_dim,\n",
" edge_hidden_sizes,\n",
" node_hidden_sizes,\n",
" n_prop_layers,\n",
" share_prop_params=False,\n",
" edge_net_init_scale=0.1,\n",
" node_update_type='residual',\n",
" use_reverse_direction=True,\n",
" reverse_dir_param_different=True,\n",
" layer_norm=False,\n",
" name='graph-embedding-net'):\n",
" \"\"\"Constructor.\n",
"\n",
" Args:\n",
" encoder: GraphEncoder, encoder that maps features to embeddings.\n",
" aggregator: GraphAggregator, aggregator that produces graph\n",
" representations.\n",
" node_state_dim: dimensionality of node states.\n",
" edge_hidden_sizes: sizes of the hidden layers of the edge message nets.\n",
" node_hidden_sizes: sizes of the hidden layers of the node update nets.\n",
" n_prop_layers: number of graph propagation layers.\n",
" share_prop_params: set to True to share propagation parameters across all\n",
" graph propagation layers, False not to.\n",
" edge_net_init_scale: scale of initialization for the edge message nets.\n",
" node_update_type: type of node updates, one of {mlp, gru, residual}.\n",
" use_reverse_direction: set to True to also propagate messages in the\n",
" reverse direction.\n",
" reverse_dir_param_different: set to True to have the messages computed\n",
" using a different set of parameters than for the forward direction.\n",
" layer_norm: set to True to use layer normalization in a few places.\n",
" name: name of this module.\n",
" \"\"\"\n",
" super(GraphEmbeddingNet, self).__init__(name=name)\n",
"\n",
" self._encoder = encoder\n",
" self._aggregator = aggregator\n",
" self._node_state_dim = node_state_dim\n",
" self._edge_hidden_sizes = edge_hidden_sizes\n",
" self._node_hidden_sizes = node_hidden_sizes\n",
" self._n_prop_layers = n_prop_layers\n",
" self._share_prop_params = share_prop_params\n",
" self._edge_net_init_scale = edge_net_init_scale\n",
" self._node_update_type = node_update_type\n",
" self._use_reverse_direction = use_reverse_direction\n",
" self._reverse_dir_param_different = reverse_dir_param_different\n",
" self._layer_norm = layer_norm\n",
"\n",
" self._prop_layers = []\n",
" self._layer_class = GraphPropLayer\n",
"\n",
" def _build_layer(self, layer_id):\n",
" \"\"\"Build one layer in the network.\"\"\"\n",
" return self._layer_class(\n",
" self._node_state_dim,\n",
" self._edge_hidden_sizes,\n",
" self._node_hidden_sizes,\n",
" edge_net_init_scale=self._edge_net_init_scale,\n",
" node_update_type=self._node_update_type,\n",
" use_reverse_direction=self._use_reverse_direction,\n",
" reverse_dir_param_different=self._reverse_dir_param_different,\n",
" layer_norm=self._layer_norm,\n",
" name='graph-prop-%d' % layer_id)\n",
"\n",
" def _apply_layer(self,\n",
" layer,\n",
" node_states,\n",
" from_idx,\n",
" to_idx,\n",
" graph_idx,\n",
" n_graphs,\n",
" edge_features):\n",
" \"\"\"Apply one layer on the given inputs.\"\"\"\n",
" del graph_idx, n_graphs\n",
" return layer(node_states, from_idx, to_idx, edge_features=edge_features)\n",
"\n",
" def _build(self,\n",
" node_features,\n",
" edge_features,\n",
" from_idx,\n",
" to_idx,\n",
" graph_idx,\n",
" n_graphs):\n",
" \"\"\"Compute graph representations.\n",
"\n",
" Args:\n",
" node_features: [n_nodes, node_feat_dim] float tensor.\n",
" edge_features: [n_edges, edge_feat_dim] float tensor.\n",
" from_idx: [n_edges] int tensor, index of the from node for each edge.\n",
" to_idx: [n_edges] int tensor, index of the to node for each edge.\n",
" graph_idx: [n_nodes] int tensor, graph id for each node.\n",
" n_graphs: int, number of graphs in the batch.\n",
"\n",
" Returns:\n",
" graph_representations: [n_graphs, graph_representation_dim] float tensor,\n",
" graph representations.\n",
" \"\"\"\n",
" if len(self._prop_layers) \u003c self._n_prop_layers:\n",
" # build the layers\n",
" for i in range(self._n_prop_layers):\n",
" if i == 0 or not self._share_prop_params:\n",
" layer = self._build_layer(i)\n",
" else:\n",
" layer = self._prop_layers[0]\n",
" self._prop_layers.append(layer)\n",
"\n",
" node_features, edge_features = self._encoder(node_features, edge_features)\n",
" node_states = node_features\n",
"\n",
" layer_outputs = [node_states]\n",
"\n",
" for layer in self._prop_layers:\n",
" # node_features could be wired in here as well, leaving it out for now as\n",
" # it is already in the inputs\n",
" node_states = self._apply_layer(\n",
" layer,\n",
" node_states,\n",
" from_idx,\n",
" to_idx,\n",
" graph_idx,\n",
" n_graphs,\n",
" edge_features)\n",
" layer_outputs.append(node_states)\n",
"\n",
" # these tensors may be used e.g. for visualization\n",
" self._layer_outputs = layer_outputs\n",
" return self._aggregator(node_states, graph_idx, n_graphs)\n",
"\n",
" def reset_n_prop_layers(self, n_prop_layers):\n",
" \"\"\"Set n_prop_layers to the provided new value.\n",
"\n",
" This allows us to train with certain number of propagation layers and\n",
" evaluate with a different number of propagation layers.\n",
"\n",
" This only works if n_prop_layers is smaller than the number used for\n",
" training, or when share_prop_params is set to True, in which case this can\n",
" be arbitrarily large.\n",
"\n",
" Args:\n",
" n_prop_layers: the new number of propagation layers to set.\n",
" \"\"\"\n",
" self._n_prop_layers = n_prop_layers\n",
"\n",
" @property\n",
" def n_prop_layers(self):\n",
" return self._n_prop_layers\n",
"\n",
" def get_layer_outputs(self):\n",
" \"\"\"Get the outputs at each layer.\"\"\"\n",
" if hasattr(self, '_layer_outputs'):\n",
" return self._layer_outputs\n",
" else:\n",
" raise ValueError('No layer outputs available.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "rVUVjA1TfaDN"
},
"source": [
"### The graph matching networks\n",
"\n",
"The graph matching networks (GMNs) compute the similarity score for a pair of graphs jointly on the pair. In our current formulation, it still computes a representation for each graph, but the representations for a pair of graphs are computed jointly on the pair, through a cross-graph attention-based matching mechanism.\n",
"\n",
"More concretely, the graph matching model can be formulated as\n",
"\n",
"$$d(G_1, G_2) = d_H(embed\\_and\\_match(G_1, G_2))$$\n",
"\n",
"where $embed\\_and\\_match(G_1, G_2)$ returns a pair of graph representations.\n",
"\n",
"Similar to the embedding model, our GMNs computes graph representations through 3 steps. The difference to the embedding model is in the message passing step, where each node not only gets messages from within the same graph, but also gets cross-graph messages by attending to all the nodes in the other graph. This can be formulated as follows.\n",
"\n",
"We first have within-graph messages as before:\n",
"$$\n",
"m_{i\\rightarrow j} = f_\\mathrm{message}(h_i^{(t)}, h_j^{(t)}, e_{ij}).\n",
"$$\n",
"\n",
"In addition, we also allow each node in one graph to attend to all the other nodes in the other graph. The cross graph attention weight (node $i$ in one graph attending to node $j$ in the other graph, and vice versa) is computed as\n",
"$$\\begin{array}{rcl}\n",
"a_{i\\rightarrow j} \u0026=\u0026 \\frac{\\exp(s(h_i^{(t)}, h_j^{(t)}))}{\\sum_j \\exp(s(h_i^{(t)}, h_j^{(t)}))} \\\\\n",
"a_{j\\rightarrow i} \u0026=\u0026 \\frac{\\exp(s(h_i^{(t)}, h_j^{(t)}))}{\\sum_i \\exp(s(h_i^{(t)}, h_j^{(t)}))},\n",
"\\end{array}\n",
"$$\n",
"where $s(., .)$ is again a vector space similarity function, like Euclidean, dot-product or cosine. Also note the different indices being summed over in the normalizers.\n",
"\n",
"The cross-graph message is then computed as\n",
"$$\\begin{array}{rcl}\n",
"\\mu_i \u0026=\u0026 \\sum_j a_{i\\rightarrow j} (h_i^{(t)} - h_j^{(t)}) = h_i^{(t)} - \\sum_j a_{i\\rightarrow j} h_j^{(t)}, \\\\\n",
"\\mu_j \u0026=\u0026 \\sum_i a_{j\\rightarrow i} (h_j^{(t)} - h_i^{(t)}) = h_j^{(t)} - \\sum_i a_{j\\rightarrow i} h_i^{(t)}.\n",
"\\end{array}\n",
"$$\n",
"Here we are computing an attention-weighted sum of all the node representations from the other graph, and then take the difference. This is essentially **matching** one node in one graph to nodes most similar to it in the other graph, and then compute the difference.\n",
"\n",
"The node updates are then computed as\n",
"$$\n",
"h_i^{(t+1)} = f_\\mathrm{node}\\left(h_i^{(t)}, \\sum_{j:(j,i)\\in E} m_{j\\rightarrow i}, \\mu_i\\right).\n",
"$$\n",
"\n",
"The graph encoder and the graph aggregators are the same as in the embedding model."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "pAcqRtr4mvbK"
},
"source": [
"#### A few similarity functions\n",
"\n",
"These are the functions $s(., .)$ that will be used in the cross-graph attention."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "vhZWiK6vzDGW"
},
"outputs": [],
"source": [
"def pairwise_euclidean_similarity(x, y):\n",
" \"\"\"Compute the pairwise Euclidean similarity between x and y.\n",
"\n",
" This function computes the following similarity value between each pair of x_i\n",
" and y_j: s(x_i, y_j) = -|x_i - y_j|^2.\n",
"\n",
" Args:\n",
" x: NxD float tensor.\n",
" y: MxD float tensor.\n",
"\n",
" Returns:\n",
" s: NxM float tensor, the pairwise euclidean similarity.\n",
" \"\"\"\n",
" s = 2 * tf.matmul(x, y, transpose_b=True)\n",
" diag_x = tf.reduce_sum(x * x, axis=-1, keepdims=True)\n",
" diag_y = tf.reshape(tf.reduce_sum(y * y, axis=-1), (1, -1))\n",
" return s - diag_x - diag_y\n",
"\n",
"\n",
"def pairwise_dot_product_similarity(x, y):\n",
" \"\"\"Compute the dot product similarity between x and y.\n",
"\n",
" This function computes the following similarity value between each pair of x_i\n",
" and y_j: s(x_i, y_j) = x_i^T y_j.\n",
"\n",
" Args:\n",
" x: NxD float tensor.\n",
" y: MxD float tensor.\n",
"\n",
" Returns:\n",
" s: NxM float tensor, the pairwise dot product similarity.\n",
" \"\"\"\n",
" return tf.matmul(x, y, transpose_b=True)\n",
"\n",
"\n",
"def pairwise_cosine_similarity(x, y):\n",
" \"\"\"Compute the cosine similarity between x and y.\n",
"\n",
" This function computes the following similarity value between each pair of x_i\n",
" and y_j: s(x_i, y_j) = x_i^T y_j / (|x_i||y_j|).\n",
"\n",
" Args:\n",
" x: NxD float tensor.\n",
" y: MxD float tensor.\n",
"\n",
" Returns:\n",
" s: NxM float tensor, the pairwise cosine similarity.\n",
" \"\"\"\n",
" x = tf.nn.l2_normalize(x, axis=-1)\n",
" y = tf.nn.l2_normalize(y, axis=-1)\n",
" return tf.matmul(x, y, transpose_b=True)\n",
"\n",
"\n",
"PAIRWISE_SIMILARITY_FUNCTION = {\n",
" 'euclidean': pairwise_euclidean_similarity,\n",
" 'dotproduct': pairwise_dot_product_similarity,\n",
" 'cosine': pairwise_cosine_similarity,\n",
"}\n",
"\n",
"\n",
"def get_pairwise_similarity(name):\n",
" \"\"\"Get pairwise similarity metric by name.\n",
"\n",
" Args:\n",
" name: string, name of the similarity metric, one of {dot-product, cosine,\n",
" euclidean}.\n",
"\n",
" Returns:\n",
" similarity: a (x, y) -\u003e sim function.\n",
"\n",
" Raises:\n",
" ValueError: if name is not supported.\n",
" \"\"\"\n",
" if name not in PAIRWISE_SIMILARITY_FUNCTION:\n",
" raise ValueError('Similarity metric name \"%s\" not supported.' % name)\n",
" else:\n",
" return PAIRWISE_SIMILARITY_FUNCTION[name]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "wpqaeKLJm_x4"
},
"source": [
"#### The cross-graph attention\n",
"\n",
"We implement this cross-graph attention in batches of pairs."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "CfO-r0bdm_DB"
},
"outputs": [],
"source": [
"def compute_cross_attention(x, y, sim):\n",
" \"\"\"Compute cross attention.\n",
"\n",
" x_i attend to y_j:\n",
" a_{i-\u003ej} = exp(sim(x_i, y_j)) / sum_j exp(sim(x_i, y_j))\n",
" y_j attend to x_i:\n",
" a_{j-\u003ei} = exp(sim(x_i, y_j)) / sum_i exp(sim(x_i, y_j))\n",
" attention_x = sum_j a_{i-\u003ej} y_j\n",
" attention_y = sum_i a_{j-\u003ei} x_i\n",
"\n",
" Args:\n",
" x: NxD float tensor.\n",
" y: MxD float tensor.\n",
" sim: a (x, y) -\u003e similarity function.\n",
"\n",
" Returns:\n",
" attention_x: NxD float tensor.\n",
" attention_y: NxD float tensor.\n",
" \"\"\"\n",
" a = sim(x, y)\n",
" a_x = tf.nn.softmax(a, axis=1) # i-\u003ej\n",
" a_y = tf.nn.softmax(a, axis=0) # j-\u003ei\n",
" attention_x = tf.matmul(a_x, y)\n",
" attention_y = tf.matmul(a_y, x, transpose_a=True)\n",
" return attention_x, attention_y\n",
"\n",
"\n",
"def batch_block_pair_attention(data,\n",
" block_idx,\n",
" n_blocks,\n",
" similarity='dotproduct'):\n",
" \"\"\"Compute batched attention between pairs of blocks.\n",
"\n",
" This function partitions the batch data into blocks according to block_idx.\n",
" For each pair of blocks, x = data[block_idx == 2i], and\n",
" y = data[block_idx == 2i+1], we compute\n",
"\n",
" x_i attend to y_j:\n",
" a_{i-\u003ej} = exp(sim(x_i, y_j)) / sum_j exp(sim(x_i, y_j))\n",
" y_j attend to x_i:\n",
" a_{j-\u003ei} = exp(sim(x_i, y_j)) / sum_i exp(sim(x_i, y_j))\n",
"\n",
" and\n",
"\n",
" attention_x = sum_j a_{i-\u003ej} y_j\n",
" attention_y = sum_i a_{j-\u003ei} x_i.\n",
"\n",
" Args:\n",
" data: NxD float tensor.\n",
" block_idx: N-dim int tensor.\n",
" n_blocks: integer.\n",
" similarity: a string, the similarity metric.\n",
"\n",
" Returns:\n",
" attention_output: NxD float tensor, each x_i replaced by attention_x_i.\n",
"\n",
" Raises:\n",
" ValueError: if n_blocks is not an integer or not a multiple of 2.\n",
" \"\"\"\n",
" if not isinstance(n_blocks, int):\n",
" raise ValueError('n_blocks (%s) has to be an integer.' % str(n_blocks))\n",
"\n",
" if n_blocks % 2 != 0:\n",
" raise ValueError('n_blocks (%d) must be a multiple of 2.' % n_blocks)\n",
"\n",
" sim = get_pairwise_similarity(similarity)\n",
"\n",
" results = []\n",
"\n",
" # This is probably better than doing boolean_mask for each i\n",
" partitions = tf.dynamic_partition(data, block_idx, n_blocks)\n",
"\n",
" # It is rather complicated to allow n_blocks be a tf tensor and do this in a\n",
" # dynamic loop, and probably unnecessary to do so. Therefore we are\n",
" # restricting n_blocks to be a integer constant here and using the plain for\n",
" # loop.\n",
" for i in range(0, n_blocks, 2):\n",
" x = partitions[i]\n",
" y = partitions[i + 1]\n",
" attention_x, attention_y = compute_cross_attention(x, y, sim)\n",
" results.append(attention_x)\n",
" results.append(attention_y)\n",
"\n",
" results = tf.concat(results, axis=0)\n",
" # the shape of the first dimension is lost after concat, reset it back\n",
" results.set_shape(data.shape)\n",
" return results"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "AAj9kMygnPch"
},
"source": [
"#### Graph matching layer and graph matching networks\n",
"\n",
"This only involves a small set of changes from the graph embedding model."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "gzfRNzF6nOyZ"
},
"outputs": [],
"source": [
"class GraphPropMatchingLayer(GraphPropLayer):\n",
" \"\"\"A graph propagation layer that also does cross graph matching.\n",
"\n",
" It assumes the incoming graph data is batched and paired, i.e. graph 0 and 1\n",
" forms the first pair and graph 2 and 3 are the second pair etc., and computes\n",
" cross-graph attention-based matching for each pair.\n",
" \"\"\"\n",
"\n",
" def _build(self,\n",
" node_states,\n",
" from_idx,\n",
" to_idx,\n",
" graph_idx,\n",
" n_graphs,\n",
" similarity='dotproduct',\n",
" edge_features=None,\n",
" node_features=None):\n",
" \"\"\"Run one propagation step with cross-graph matching.\n",
"\n",
" Args:\n",
" node_states: [n_nodes, node_state_dim] float tensor, node states.\n",
" from_idx: [n_edges] int tensor, from node indices for each edge.\n",
" to_idx: [n_edges] int tensor, to node indices for each edge.\n",
" graph_idx: [n_onodes] int tensor, graph id for each node.\n",
" n_graphs: integer, number of graphs in the batch.\n",
" similarity: type of similarity to use for the cross graph attention.\n",
" edge_features: if not None, should be [n_edges, edge_feat_dim] tensor,\n",
" extra edge features.\n",
" node_features: if not None, should be [n_nodes, node_feat_dim] tensor,\n",
" extra node features.\n",
"\n",
" Returns:\n",
" node_states: [n_nodes, node_state_dim] float tensor, new node states.\n",
"\n",
" Raises:\n",
" ValueError: if some options are not provided correctly.\n",
" \"\"\"\n",
" aggregated_messages = self._compute_aggregated_messages(\n",
" node_states, from_idx, to_idx, edge_features=edge_features)\n",
"\n",
" # new stuff here\n",
" cross_graph_attention = batch_block_pair_attention(\n",
" node_states, graph_idx, n_graphs, similarity=similarity)\n",
" attention_input = node_states - cross_graph_attention\n",
"\n",
" return self._compute_node_update(node_states,\n",
" [aggregated_messages, attention_input],\n",
" node_features=node_features)\n",
"\n",
"\n",
"class GraphMatchingNet(GraphEmbeddingNet):\n",
" \"\"\"Graph matching net.\n",
"\n",
" This class uses graph matching layers instead of the simple graph prop layers.\n",
"\n",
" It assumes the incoming graph data is batched and paired, i.e. graph 0 and 1\n",
" forms the first pair and graph 2 and 3 are the second pair etc., and computes\n",
" cross-graph attention-based matching for each pair.\n",
" \"\"\"\n",
"\n",
" def __init__(self,\n",
" encoder,\n",
" aggregator,\n",
" node_state_dim,\n",
" edge_hidden_sizes,\n",
" node_hidden_sizes,\n",
" n_prop_layers,\n",
" share_prop_params=False,\n",
" edge_net_init_scale=0.1,\n",
" node_update_type='residual',\n",
" use_reverse_direction=True,\n",
" reverse_dir_param_different=True,\n",
" layer_norm=False,\n",
" similarity='dotproduct',\n",
" name='graph-matching-net'):\n",
" super(GraphMatchingNet, self).__init__(\n",
" encoder,\n",
" aggregator,\n",
" node_state_dim,\n",
" edge_hidden_sizes,\n",
" node_hidden_sizes,\n",
" n_prop_layers,\n",
" share_prop_params=share_prop_params,\n",
" edge_net_init_scale=edge_net_init_scale,\n",
" node_update_type=node_update_type,\n",
" use_reverse_direction=use_reverse_direction,\n",
" reverse_dir_param_different=reverse_dir_param_different,\n",
" layer_norm=layer_norm,\n",
" name=name)\n",
" self._similarity = similarity\n",
" self._layer_class = GraphPropMatchingLayer\n",
"\n",
" def _apply_layer(self,\n",
" layer,\n",
" node_states,\n",
" from_idx,\n",
" to_idx,\n",
" graph_idx,\n",
" n_graphs,\n",
" edge_features):\n",
" \"\"\"Apply one layer on the given inputs.\"\"\"\n",
" return layer(node_states, from_idx, to_idx, graph_idx, n_graphs,\n",
" similarity=self._similarity, edge_features=edge_features)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "tBXC2yUgzlyO"
},
"source": [
"## Training"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "EVVXhPc6zqAy"
},
"source": [
"### Labeled data examples\n",
"\n",
"We train on either pairs of graphs or triplets of graphs. For pairs of graphs, we assume each pair $(G_1, G_2)$ comes with a label $t\\in\\{-1, 1\\}$. $t=1$ if $G_1$ and $G_2$ are similar, and $t=-1$ otherwise.\n",
"\n",
"For triplets of graphs, we assume within each triplet $(G_1, G_2, G_3)$, $G_1$ is similar to $G_2$ but not similar to $G_3$.\n",
"\n",
"The goal of training is to learn the parameters of the function $f(G_1, G_2)$ such that similar graphs have high similarity (or small distance) and dissimilar graphs have low similarity (or high distance)."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8jSKnn5G0KgL"
},
"source": [
"### Training on pairs\n",
"\n",
"Given a dataset of pairs $(G_1, G_2)$ and labels $t\\in\\{-1, 1\\}$, we can use the following margin-based loss if using Euclidean distance:\n",
"\n",
"$$\n",
"L_\\mathrm{pair} = \\mathbb{E}_{(G_1, G_2, t)}[\\max\\{0, \\gamma - t(1 - d(G_1, G_2))\\}]\n",
"$$\n",
"\n",
"This loss encourages similar graphs to have distance smaller than $1-\\gamma$, and dissimilar graphs to have distance greater than $1 + \\gamma$, where $\\gamma$ is a margin parameter.\n",
"\n",
"Alternatively, for many applications it is beneficial to have the representation of graphs be binary which allows efficient indexing and hashing. In this case, Hamming distance (similarity) is more appropriate. On the other hand, the Hamming distance is not differentiable, so we use a smooth approximation\n",
"$$\n",
"s(G_1, G_2) = \\frac{1}{H}\\sum_{i=1}^H \\tanh(h_{G_1, i}) \\cdot \\tanh(h_{G_2, i}),\n",
"$$\n",
"where $s$ is now a similarity (rather than distance) function, $h_{G, i}$ is the i-th dimension of the smooth representation vector for G. We get binary codes by thresholding $h_{G,i}$ at 0, i.e. $\\hat{h}_{G,i}=1$ if $h_{G,i}\\ge 0$ and $-1$ otherwise.\n",
"\n",
"The loss we use with these binary representations is defined as\n",
"\n",
"$$\n",
"L_\\mathrm{pair} = \\mathbb{E}_{(G_1, G_2, t)}[(t - s(G_1, G_2))^2] / 4.\n",
"$$\n",
"\n",
"The factor of $1/4$ is used to normalize the loss to between 0 and 1.\n",
"\n",
"These are just two possible losses, many other types of losses could also be used."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "vwqL12jS2t2V"
},
"outputs": [],
"source": [
"def euclidean_distance(x, y):\n",
" \"\"\"This is the squared Euclidean distance.\"\"\"\n",
" return tf.reduce_sum((x - y)**2, axis=-1)\n",
"\n",
"\n",
"def approximate_hamming_similarity(x, y):\n",
" \"\"\"Approximate Hamming similarity.\"\"\"\n",
" return tf.reduce_mean(tf.tanh(x) * tf.tanh(y), axis=1)\n",
"\n",
"\n",
"def pairwise_loss(x, y, labels, loss_type='margin', margin=1.0):\n",
" \"\"\"Compute pairwise loss.\n",
"\n",
" Args:\n",
" x: [N, D] float tensor, representations for N examples.\n",
" y: [N, D] float tensor, representations for another N examples.\n",
" labels: [N] int tensor, with values in -1 or +1. labels[i] = +1 if x[i]\n",
" and y[i] are similar, and -1 otherwise.\n",
" loss_type: margin or hamming.\n",
" margin: float scalar, margin for the margin loss.\n",
"\n",
" Returns:\n",
" loss: [N] float tensor. Loss for each pair of representations.\n",
" \"\"\"\n",
" labels = tf.cast(labels, x.dtype)\n",
" if loss_type == 'margin':\n",
" return tf.nn.relu(margin - labels * (1 - euclidean_distance(x, y)))\n",
" elif loss_type == 'hamming':\n",
" return 0.25 * (labels - approximate_hamming_similarity(x, y))**2\n",
" else:\n",
" raise ValueError('Unknown loss_type %s' % loss_type)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "E1-k-A4e2uV8"
},
"source": [
"### Training on triplets\n",
"\n",
"Given a dataset of triplets $(G_1, G_2, G_3)$ where $G_1$ is similar to $G_2$ but not similar to $G_3$, we can again use a margin loss or Hamming similarity-based loss.\n",
"\n",
"The following margin loss can be used with Euclidean distance:\n",
"\n",
"$$\n",
"L_\\mathrm{triplet} = \\mathbb{E}_{(G_1, G_2, G_3)}[\\max\\{0, d(G_1, G_2) - d(G_1, G_3) + \\gamma\\}],\n",
"$$\n",
"which encourages $d(G_1, G_2)$ to be smaller by $d(G_1, G_3)$ by at least a margin of $\\gamma$.\n",
"\n",
"If using Hamming distance (similarity) we could use the following loss:\n",
"\n",
"$$\n",
"L_\\mathrm{triplet} = \\mathbb{E}_{(G_1, G_2, G_3)}[(s(G_1, G_2) - 1)^2 + (s(G_1, G_3) + 1)^2] / 8\n",
"$$\n",
"\n",
"The factor of $1/8$ is again used to normalize the loss to within 0 and 1."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "7ibuK0-y4WcI"
},
"outputs": [],
"source": [
"def triplet_loss(x_1, y, x_2, z, loss_type='margin', margin=1.0):\n",
" \"\"\"Compute triplet loss.\n",
"\n",
" This function computes loss on a triplet of inputs (x, y, z). A similarity or\n",
" distance value is computed for each pair of (x, y) and (x, z). Since the\n",
" representations for x can be different in the two pairs (like our matching\n",
" model) we distinguish the two x representations by x_1 and x_2.\n",
"\n",
" Args:\n",
" x_1: [N, D] float tensor.\n",
" y: [N, D] float tensor.\n",
" x_2: [N, D] float tensor.\n",
" z: [N, D] float tensor.\n",
" loss_type: margin or hamming.\n",
" margin: float scalar, margin for the margin loss.\n",
"\n",
" Returns:\n",
" loss: [N] float tensor. Loss for each pair of representations.\n",
" \"\"\"\n",
" if loss_type == 'margin':\n",
" return tf.nn.relu(margin +\n",
" euclidean_distance(x_1, y) -\n",
" euclidean_distance(x_2, z))\n",
" elif loss_type == 'hamming':\n",
" return 0.125 * ((approximate_hamming_similarity(x_1, y) - 1)**2 +\n",
" (approximate_hamming_similarity(x_2, z) + 1)**2)\n",
" else:\n",
" raise ValueError('Unknown loss_type %s' % loss_type)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zQkcwBrdn4YZ"
},
"source": [
"## Datasets\n",
"\n",
"We use an abstract `GraphSimilarityDataset` class to define the general interface for the training and evaluatin data used in graph similarity learning.\n",
"\n",
"We also assume a certain format for packing the graphs into tensors as described below."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "6lAkeOZnn32w"
},
"outputs": [],
"source": [
"GraphData = collections.namedtuple('GraphData', [\n",
" 'from_idx',\n",
" 'to_idx',\n",
" 'node_features',\n",
" 'edge_features',\n",
" 'graph_idx',\n",
" 'n_graphs'])\n",
"\n",
"\n",
"@six.add_metaclass(abc.ABCMeta)\n",
"class GraphSimilarityDataset(object):\n",
" \"\"\"Base class for all the graph similarity learning datasets.\n",
"\n",
" This class defines some common interfaces a graph similarity dataset can have,\n",
" in particular the functions that creates iterators over pairs and triplets.\n",
" \"\"\"\n",
"\n",
" @abc.abstractmethod\n",
" def triplets(self, batch_size):\n",
" \"\"\"Create an iterator over triplets.\n",
"\n",
" Args:\n",
" batch_size: int, number of triplets in a batch.\n",
"\n",
" Yields:\n",
" graphs: a `GraphData` instance. The batch of triplets put together. Each\n",
" triplet has 3 graphs (x, y, z). Here the first graph is duplicated once\n",
" so the graphs for each triplet are ordered as (x, y, x, z) in the batch.\n",
" The batch contains `batch_size` number of triplets, hence `4*batch_size`\n",
" many graphs.\n",
" \"\"\"\n",
" pass\n",
"\n",
" @abc.abstractmethod\n",
" def pairs(self, batch_size):\n",
" \"\"\"Create an iterator over pairs.\n",
"\n",
" Args:\n",
" batch_size: int, number of pairs in a batch.\n",
"\n",
" Yields:\n",
" graphs: a `GraphData` instance. The batch of pairs put together. Each\n",
" pair has 2 graphs (x, y). The batch contains `batch_size` number of\n",
" pairs, hence `2*batch_size` many graphs.\n",
" labels: [batch_size] int labels for each pair, +1 for similar, -1 for not.\n",
" \"\"\"\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PPKfGwXPzFMW"
},
"source": [
"## The graph edit distance task"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "328H0viCAwyG"
},
"source": [
"Here we use the synthetic task of learning approximate graph edit distances (GEDs) to test the effectiveness of the graph similarity learning models.\n",
"\n",
"Given two graphs $G_1$ and $G_2$, we say the edit distance between them is the number of actions needed to transform one into another. Since the difference in graph size (number of nodes, number of edges) is trivial to identify, we only consider the case of substituting one edge with another edge, and define the edit distance between two equal-size graphs to be the number of edge changes needed to make them identical (or isomorphic).\n",
"\n",
"We train a graph similarity or distance model that aligns with the GED, by giving graphs with small edit distance a high similarity score or low learned distance, and otherwise low similarity score or high learned distance. Note that we don't try to match the learned graph distance with the actual GED, as we don't assume the exact scale of the GED is available during training.\n",
"\n",
"To get training data, we sample graph $G$ from the Erdos-Renyi model (other random graph models could be used as well), and then:\n",
"* apply a node permutation to $G$ to get $G_1$,\n",
"* apply $k_1$ edge changes to $G$ to get $G_2$,\n",
"* apply $k_2$ edge changes to $G$ to get $G_3$.\n",
"\n",
"Each edge change action substitutes one edge $(i,j)$ by another edge $(i', j')$ where $i'$ and $j'$ are picked randomly. In this case, we have roughly $GED(G_1, G_2)\\approx k_1$ and $GED(G_1, G_3)\\approx k_2$. We make $k_1 \u003c k_2$ and say $(G_1, G_2)$ is a positive pair that are similar and $(G_1, G_3)$ is a negative pair hence not similar.\n",
"\n",
"Note that the above GED for $G_1, G_2$ and $G_3$ only holds approximately, and $GED(G_1, G_2)\\le k_1$ and $GED(G_1, G_3)\\le k_2$, because of the potential symmetry in $G$, i.e. it is possible that changing one edge may only change the graph into another isomorphic graph. However the probability of this happening is relatively small and decreases with increasing graph size. So we ignore this possibility in this task."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "U_68U4wIo7lT"
},
"source": [
"### A few graph manipulation primitives\n",
"\n",
"These primitives assume the incoming graphs are instances of `networkx.Graph`."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ZyJh0DGNyWox"
},
"outputs": [],
"source": [
"def permute_graph_nodes(g):\n",
" \"\"\"Permute node ordering of a graph, returns a new graph.\"\"\"\n",
" n = g.number_of_nodes()\n",
" new_g = nx.Graph()\n",
" new_g.add_nodes_from(range(n))\n",
" perm = np.random.permutation(n)\n",
" edges = g.edges()\n",
" new_edges = []\n",
" for x, y in edges:\n",
" new_edges.append((perm[x], perm[y]))\n",
" new_g.add_edges_from(new_edges)\n",
" return new_g\n",
"\n",
"\n",
"def substitute_random_edges(g, n):\n",
" \"\"\"Substitutes n edges from graph g with another n randomly picked edges.\"\"\"\n",
" g = copy.deepcopy(g)\n",
" n_nodes = g.number_of_nodes()\n",
" edges = list(g.edges())\n",
" # sample n edges without replacement\n",
" e_remove = [edges[i] for i in np.random.choice(\n",
" np.arange(len(edges)), n, replace=False)]\n",
" edge_set = set(edges)\n",
" e_add = set()\n",
" while len(e_add) \u003c n:\n",
" e = np.random.choice(n_nodes, 2, replace=False)\n",
" # make sure e does not exist and is not already chosen to be added\n",
" if ((e[0], e[1]) not in edge_set and (e[1], e[0]) not in edge_set and\n",
" (e[0], e[1]) not in e_add and (e[1], e[0]) not in e_add):\n",
" e_add.add((e[0], e[1]))\n",
"\n",
" for i, j in e_remove:\n",
" g.remove_edge(i, j)\n",
" for i, j in e_add:\n",
" g.add_edge(i, j)\n",
" return g"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ECXrG7VJpIDl"
},
"source": [
"### Dataset for training, fixed dataset for evaluation\n",
"\n",
"For training we use a procedure to generate graphs in pairs or triplets on the fly, and wrap this process into a `Dataset` instance. For evaluation we need to\n",
"use a fixed set to make sure the evaluation results are consistent and comparable, and we do that by controlling random seeds."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ZW5KEb-gpHlq"
},
"outputs": [],
"source": [
"class GraphEditDistanceDataset(GraphSimilarityDataset):\n",
" \"\"\"Graph edit distance dataset.\"\"\"\n",
"\n",
" def __init__(self,\n",
" n_nodes_range,\n",
" p_edge_range,\n",
" n_changes_positive,\n",
" n_changes_negative,\n",
" permute=True):\n",
" \"\"\"Constructor.\n",
"\n",
" Args:\n",
" n_nodes_range: a tuple (n_min, n_max). The minimum and maximum number of\n",
" nodes in a graph to generate.\n",
" p_edge_range: a tuple (p_min, p_max). The minimum and maximum edge\n",
" probability.\n",
" n_changes_positive: the number of edge substitutions for a pair to be\n",
" considered positive (similar).\n",
" n_changes_negative: the number of edge substitutions for a pair to be\n",
" considered negative (not similar).\n",
" permute: if True (default), permute node orderings in addition to\n",
" changing edges; if False, the node orderings across a pair or triplet of\n",
" graphs will be the same, useful for visualization.\n",
" \"\"\"\n",
" self._n_min, self._n_max = n_nodes_range\n",
" self._p_min, self._p_max = p_edge_range\n",
" self._k_pos = n_changes_positive\n",
" self._k_neg = n_changes_negative\n",
" self._permute = permute\n",
"\n",
" def _get_graph(self):\n",
" \"\"\"Generate one graph.\"\"\"\n",
" n_nodes = np.random.randint(self._n_min, self._n_max + 1)\n",
" p_edge = np.random.uniform(self._p_min, self._p_max)\n",
"\n",
" # do a little bit of filtering\n",
" n_trials = 100\n",
" for _ in range(n_trials):\n",
" g = nx.erdos_renyi_graph(n_nodes, p_edge)\n",
" if nx.is_connected(g):\n",
" return g\n",
"\n",
" raise ValueError('Failed to generate a connected graph.')\n",
"\n",
" def _get_pair(self, positive):\n",
" \"\"\"Generate one pair of graphs.\"\"\"\n",
" g = self._get_graph()\n",
" if self._permute:\n",
" permuted_g = permute_graph_nodes(g)\n",
" else:\n",
" permuted_g = g\n",
" n_changes = self._k_pos if positive else self._k_neg\n",
" changed_g = substitute_random_edges(g, n_changes)\n",
" return permuted_g, changed_g\n",
"\n",
" def _get_triplet(self):\n",
" \"\"\"Generate one triplet of graphs.\"\"\"\n",
" g = self._get_graph()\n",
" if self._permute:\n",
" permuted_g = permute_graph_nodes(g)\n",
" else:\n",
" permuted_g = g\n",
" pos_g = substitute_random_edges(g, self._k_pos)\n",
" neg_g = substitute_random_edges(g, self._k_neg)\n",
" return permuted_g, pos_g, neg_g\n",
"\n",
" def triplets(self, batch_size):\n",
" \"\"\"Yields batches of triplet data.\"\"\"\n",
" while True:\n",
" batch_graphs = []\n",
" for _ in range(batch_size):\n",
" g1, g2, g3 = self._get_triplet()\n",
" batch_graphs.append((g1, g2, g1, g3))\n",
" yield self._pack_batch(batch_graphs)\n",
"\n",
" def pairs(self, batch_size):\n",
" \"\"\"Yields batches of pair data.\"\"\"\n",
" while True:\n",
" batch_graphs = []\n",
" batch_labels = []\n",
" positive = True\n",
" for _ in range(batch_size):\n",
" g1, g2 = self._get_pair(positive)\n",
" batch_graphs.append((g1, g2))\n",
" batch_labels.append(1 if positive else -1)\n",
" positive = not positive\n",
"\n",
" packed_graphs = self._pack_batch(batch_graphs)\n",
" labels = np.array(batch_labels, dtype=np.int32)\n",
" yield packed_graphs, labels\n",
"\n",
" def _pack_batch(self, graphs):\n",
" \"\"\"Pack a batch of graphs into a single `GraphData` instance.\n",
"\n",
" Args:\n",
" graphs: a list of generated networkx graphs.\n",
"\n",
" Returns:\n",
" graph_data: a `GraphData` instance, with node and edge indices properly\n",
" shifted.\n",
" \"\"\"\n",
" graphs = tf.nest.flatten(graphs)\n",
" from_idx = []\n",
" to_idx = []\n",
" graph_idx = []\n",
"\n",
" n_total_nodes = 0\n",
" n_total_edges = 0\n",
" for i, g in enumerate(graphs):\n",
" n_nodes = g.number_of_nodes()\n",
" n_edges = g.number_of_edges()\n",
" edges = np.array(g.edges(), dtype=np.int32)\n",
" # shift the node indices for the edges\n",
" from_idx.append(edges[:, 0] + n_total_nodes)\n",
" to_idx.append(edges[:, 1] + n_total_nodes)\n",
" graph_idx.append(np.ones(n_nodes, dtype=np.int32) * i)\n",
"\n",
" n_total_nodes += n_nodes\n",
" n_total_edges += n_edges\n",
"\n",
" return GraphData(\n",
" from_idx=np.concatenate(from_idx, axis=0),\n",
" to_idx=np.concatenate(to_idx, axis=0),\n",
" # this task only cares about the structures, the graphs have no features\n",
" node_features=np.ones((n_total_nodes, 1), dtype=np.float32),\n",
" edge_features=np.ones((n_total_edges, 1), dtype=np.float32),\n",
" graph_idx=np.concatenate(graph_idx, axis=0),\n",
" n_graphs=len(graphs))\n",
"\n",
"\n",
"@contextlib.contextmanager\n",
"def reset_random_state(seed):\n",
" \"\"\"This function creates a context that uses the given seed.\"\"\"\n",
" np_rnd_state = np.random.get_state()\n",
" rnd_state = random.getstate()\n",
" np.random.seed(seed)\n",
" random.seed(seed + 1)\n",
" try:\n",
" yield\n",
" finally:\n",
" random.setstate(rnd_state)\n",
" np.random.set_state(np_rnd_state)\n",
"\n",
"\n",
"class FixedGraphEditDistanceDataset(GraphEditDistanceDataset):\n",
" \"\"\"A fixed dataset of pairs or triplets for the graph edit distance task.\n",
"\n",
" This dataset can be used for evaluation.\n",
" \"\"\"\n",
"\n",
" def __init__(self,\n",
" n_nodes_range,\n",
" p_edge_range,\n",
" n_changes_positive,\n",
" n_changes_negative,\n",
" dataset_size,\n",
" permute=True,\n",
" seed=1234):\n",
" super(FixedGraphEditDistanceDataset, self).__init__(\n",
" n_nodes_range, p_edge_range, n_changes_positive, n_changes_negative,\n",
" permute=permute)\n",
" self._dataset_size = dataset_size\n",
" self._seed = seed\n",
"\n",
" def triplets(self, batch_size):\n",
" \"\"\"Yield triplets.\"\"\"\n",
"\n",
" if hasattr(self, '_triplets'):\n",
" triplets = self._triplets\n",
" else:\n",
" # get a fixed set of triplets\n",
" with reset_random_state(self._seed):\n",
" triplets = []\n",
" for _ in range(self._dataset_size):\n",
" g1, g2, g3 = self._get_triplet()\n",
" triplets.append((g1, g2, g1, g3))\n",
" self._triplets = triplets\n",
"\n",
" ptr = 0\n",
" while ptr + batch_size \u003c= len(triplets):\n",
" batch_graphs = triplets[ptr:ptr + batch_size]\n",
" yield self._pack_batch(batch_graphs)\n",
" ptr += batch_size\n",
"\n",
" def pairs(self, batch_size):\n",
" \"\"\"Yield pairs and labels.\"\"\"\n",
"\n",
" if hasattr(self, '_pairs') and hasattr(self, '_labels'):\n",
" pairs = self._pairs\n",
" labels = self._labels\n",
" else:\n",
" # get a fixed set of pairs first\n",
" with reset_random_state(self._seed):\n",
" pairs = []\n",
" labels = []\n",
" positive = True\n",
" for _ in range(self._dataset_size):\n",
" pairs.append(self._get_pair(positive))\n",
" labels.append(1 if positive else -1)\n",
" positive = not positive\n",
" labels = np.array(labels, dtype=np.int32)\n",
"\n",
" self._pairs = pairs\n",
" self._labels = labels\n",
"\n",
" ptr = 0\n",
" while ptr + batch_size \u003c= len(pairs):\n",
" batch_graphs = pairs[ptr:ptr + batch_size]\n",
" packed_batch = self._pack_batch(batch_graphs)\n",
" yield packed_batch, labels[ptr:ptr + batch_size]\n",
" ptr += batch_size"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PKY5FeT9pu0s"
},
"source": [
"## Building the model, and the training and evaluation pipelines"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "y5vCmOYjpxFa"
},
"source": [
"### Configs\n",
"\n",
"We put all the configs for model building and training into a single `dict`, but any part of our code can also be used separately with separate configs if you want."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "whHJHVMupwjT"
},
"outputs": [],
"source": [
"def get_default_config():\n",
" \"\"\"The default configs.\"\"\"\n",
" node_state_dim = 32\n",
" graph_rep_dim = 128\n",
" graph_embedding_net_config = dict(\n",
" node_state_dim=node_state_dim,\n",
" edge_hidden_sizes=[node_state_dim * 2, node_state_dim * 2],\n",
" node_hidden_sizes=[node_state_dim * 2],\n",
" n_prop_layers=5,\n",
" # set to False to not share parameters across message passing layers\n",
" share_prop_params=True,\n",
" # initialize message MLP with small parameter weights to prevent\n",
" # aggregated message vectors blowing up, alternatively we could also use\n",
" # e.g. layer normalization to keep the scale of these under control.\n",
" edge_net_init_scale=0.1,\n",
" # other types of update like `mlp` and `residual` can also be used here.\n",
" node_update_type='gru',\n",
" # set to False if your graph already contains edges in both directions.\n",
" use_reverse_direction=True,\n",
" # set to True if your graph is directed\n",
" reverse_dir_param_different=False,\n",
" # we didn't use layer norm in our experiments but sometimes this can help.\n",
" layer_norm=False)\n",
" graph_matching_net_config = graph_embedding_net_config.copy()\n",
" graph_matching_net_config['similarity'] = 'dotproduct'\n",
"\n",
" return dict(\n",
" encoder=dict(\n",
" node_hidden_sizes=[node_state_dim],\n",
" edge_hidden_sizes=None),\n",
" aggregator=dict(\n",
" node_hidden_sizes=[graph_rep_dim],\n",
" graph_transform_sizes=[graph_rep_dim],\n",
" gated=True,\n",
" aggregation_type='sum'),\n",
" graph_embedding_net=graph_embedding_net_config,\n",
" graph_matching_net=graph_matching_net_config,\n",
" # Set to `embedding` to use the graph embedding net.\n",
" model_type='matching',\n",
" data=dict(\n",
" problem='graph_edit_distance',\n",
" dataset_params=dict(\n",
" # always generate graphs with 20 nodes and p_edge=0.2.\n",
" n_nodes_range=[20, 20],\n",
" p_edge_range=[0.2, 0.2],\n",
" n_changes_positive=1,\n",
" n_changes_negative=2,\n",
" validation_dataset_size=1000)),\n",
" training=dict(\n",
" batch_size=20,\n",
" learning_rate=1e-3,\n",
" mode='pair',\n",
" loss='margin',\n",
" margin=1.0,\n",
" # A small regularizer on the graph vector scales to avoid the graph\n",
" # vectors blowing up. If numerical issues is particularly bad in the\n",
" # model we can add `snt.LayerNorm` to the outputs of each layer, the\n",
" # aggregated messages and aggregated node representations to\n",
" # keep the network activation scale in a reasonable range.\n",
" graph_vec_regularizer_weight=1e-6,\n",
" # Add gradient clipping to avoid large gradients.\n",
" clip_value=10.0,\n",
" # Increase this to train longer.\n",
" n_training_steps=10000,\n",
" # Print training information every this many training steps.\n",
" print_after=100,\n",
" # Evaluate on validation set every `eval_after * print_after` steps.\n",
" eval_after=10),\n",
" evaluation=dict(\n",
" batch_size=20),\n",
" seed=8,\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "pIxpwPRFRYE1"
},
"source": [
"### Evaluation\n",
"\n",
"We evaluate the performance of the models by measuring how well they do on held-out data. We look at two metrics, triplet accuracy and pair AUC.\n",
"\n",
"For each triplet $(G_1, G_2, G_3)$, a model is said to make a correct prediction if it predicts $d(G_1, G_2) \u003c d(G_1, G_3)$, i.e. similar graphs have a smaller distance (or larger similarity) than dissimilar graphs. We average the accuracy across a dataset to get the **triplet accuracy** metric.\n",
"\n",
"For each pair $(G_1, G_2)$ a model computes a distance $d(G_1, G_2)$. To compute the **pair AUC** metric, we rank all the pairs by the distance from low to high (similarity from high to low), and then compute the area under the ROC curve for positive pairs."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "PQkjMzZxpmml"
},
"outputs": [],
"source": [
"def exact_hamming_similarity(x, y):\n",
" \"\"\"Compute the binary Hamming similarity.\"\"\"\n",
" match = tf.cast(tf.equal(x \u003e 0, y \u003e 0), dtype=tf.float32)\n",
" return tf.reduce_mean(match, axis=1)\n",
"\n",
"\n",
"def compute_similarity(config, x, y):\n",
" \"\"\"Compute the distance between x and y vectors.\n",
"\n",
" The distance will be computed based on the training loss type.\n",
"\n",
" Args:\n",
" config: a config dict.\n",
" x: [n_examples, feature_dim] float tensor.\n",
" y: [n_examples, feature_dim] float tensor.\n",
"\n",
" Returns:\n",
" dist: [n_examples] float tensor.\n",
"\n",
" Raises:\n",
" ValueError: if loss type is not supported.\n",
" \"\"\"\n",
" if config['training']['loss'] == 'margin':\n",
" # similarity is negative distance\n",
" return -euclidean_distance(x, y)\n",
" elif config['training']['loss'] == 'hamming':\n",
" return exact_hamming_similarity(x, y)\n",
" else:\n",
" raise ValueError('Unknown loss type %s' % config['training']['loss'])\n",
"\n",
"\n",
"def auc(scores, labels, **auc_args):\n",
" \"\"\"Compute the AUC for pair classification.\n",
"\n",
" See `tf.metrics.auc` for more details about this metric.\n",
"\n",
" Args:\n",
" scores: [n_examples] float. Higher scores mean higher preference of being\n",
" assigned the label of +1.\n",
" labels: [n_examples] int. Labels are either +1 or -1.\n",
" **auc_args: other arguments that can be used by `tf.metrics.auc`.\n",
"\n",
" Returns:\n",
" auc: the area under the ROC curve.\n",
" \"\"\"\n",
" scores_max = tf.reduce_max(scores)\n",
" scores_min = tf.reduce_min(scores)\n",
" # normalize scores to [0, 1] and add a small epislon for safety\n",
" scores = (scores - scores_min) / (scores_max - scores_min + 1e-8)\n",
"\n",
" labels = (labels + 1) / 2\n",
" # The following code should be used according to the tensorflow official\n",
" # documentation:\n",
" # value, _ = tf.metrics.auc(labels, scores, **auc_args)\n",
"\n",
" # However `tf.metrics.auc` is currently (as of July 23, 2019) buggy so we have\n",
" # to use the following:\n",
" _, value = tf.metrics.auc(labels, scores, **auc_args)\n",
" return value"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "nB_sczkuzHUD"
},
"source": [
"### Build the model\n",
"\n",
"We need to:\n",
"* set up the placeholders\n",
"* build the model\n",
"* build the computation graphs for training and evaluation\n",
"* build the metrics and statistics to monitor\n",
"\n",
"We assume the graphs are batched, if a batch contains pairs $(G_1^1, G_2^1), (G_1^2, G_2^2), ...$, then the graphs will be packed in a batch as a sequence of graphs $(G_1^1, G_2^1, G_1^2, G_2^2, ...)$. If a batch contains triplets $(G_1^1, G_2^1, G_3^1), (G_1^2, G_2^2, G_3^2)$ then the graphs will be packed in a batch as a sequence of $(G_1^1, G_2^1, G_1^1, G_3^1, G_1^2, G_2^2, G_1^2, G_3^2), ...$. Note that the first graph in each triplet is duplicated once to make the cross-graph attention more easily computable as it requires the graphs to appear in pairs."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "uNBL6Om3zJJb"
},
"outputs": [],
"source": [
"def reshape_and_split_tensor(tensor, n_splits):\n",
" \"\"\"Reshape and split a 2D tensor along the last dimension.\n",
"\n",
" Args:\n",
" tensor: a [num_examples, feature_dim] tensor. num_examples must be a\n",
" multiple of `n_splits`.\n",
" n_splits: int, number of splits to split the tensor into.\n",
"\n",
" Returns:\n",
" splits: a list of `n_splits` tensors. The first split is [tensor[0],\n",
" tensor[n_splits], tensor[n_splits * 2], ...], the second split is\n",
" [tensor[1], tensor[n_splits + 1], tensor[n_splits * 2 + 1], ...], etc..\n",
" \"\"\"\n",
" feature_dim = tensor.shape.as_list()[-1]\n",
" # feature dim must be known, otherwise you can provide that as an input\n",
" assert isinstance(feature_dim, int)\n",
" tensor = tf.reshape(tensor, [-1, feature_dim * n_splits])\n",
" return tf.split(tensor, n_splits, axis=-1)\n",
"\n",
"\n",
"def build_placeholders(node_feature_dim, edge_feature_dim):\n",
" \"\"\"Build the placeholders needed for the model.\n",
"\n",
" Args:\n",
" node_feature_dim: int.\n",
" edge_feature_dim: int.\n",
"\n",
" Returns:\n",
" placeholders: a placeholder name -\u003e placeholder tensor dict.\n",
" \"\"\"\n",
" # `n_graphs` must be specified as an integer, as `tf.dynamic_partition`\n",
" # requires so.\n",
" return {\n",
" 'node_features': tf.placeholder(tf.float32, [None, node_feature_dim]),\n",
" 'edge_features': tf.placeholder(tf.float32, [None, edge_feature_dim]),\n",
" 'from_idx': tf.placeholder(tf.int32, [None]),\n",
" 'to_idx': tf.placeholder(tf.int32, [None]),\n",
" 'graph_idx': tf.placeholder(tf.int32, [None]),\n",
" # only used for pairwise training and evaluation\n",
" 'labels': tf.placeholder(tf.int32, [None]),\n",
" }\n",
"\n",
"\n",
"def build_model(config, node_feature_dim, edge_feature_dim):\n",
" \"\"\"Create model for training and evaluation.\n",
"\n",
" Args:\n",
" config: a dictionary of configs, like the one created by the\n",
" `get_default_config` function.\n",
" node_feature_dim: int, dimensionality of node features.\n",
" edge_feature_dim: int, dimensionality of edge features.\n",
"\n",
" Returns:\n",
" tensors: a (potentially nested) name =\u003e tensor dict.\n",
" placeholders: a (potentially nested) name =\u003e tensor dict.\n",
" model: a GraphEmbeddingNet or GraphMatchingNet instance.\n",
"\n",
" Raises:\n",
" ValueError: if the specified model or training settings are not supported.\n",
" \"\"\"\n",
" encoder = GraphEncoder(**config['encoder'])\n",
" aggregator = GraphAggregator(**config['aggregator'])\n",
" if config['model_type'] == 'embedding':\n",
" model = GraphEmbeddingNet(\n",
" encoder, aggregator, **config['graph_embedding_net'])\n",
" elif config['model_type'] == 'matching':\n",
" model = GraphMatchingNet(\n",
" encoder, aggregator, **config['graph_matching_net'])\n",
" else:\n",
" raise ValueError('Unknown model type: %s' % config['model_type'])\n",
"\n",
" training_n_graphs_in_batch = config['training']['batch_size']\n",
" if config['training']['mode'] == 'pair':\n",
" training_n_graphs_in_batch *= 2\n",
" elif config['training']['mode'] == 'triplet':\n",
" training_n_graphs_in_batch *= 4\n",
" else:\n",
" raise ValueError('Unknown training mode: %s' % config['training']['mode'])\n",
"\n",
" placeholders = build_placeholders(node_feature_dim, edge_feature_dim)\n",
"\n",
" # training\n",
" model_inputs = placeholders.copy()\n",
" del model_inputs['labels']\n",
" model_inputs['n_graphs'] = training_n_graphs_in_batch\n",
" graph_vectors = model(**model_inputs)\n",
"\n",
" if config['training']['mode'] == 'pair':\n",
" x, y = reshape_and_split_tensor(graph_vectors, 2)\n",
" loss = pairwise_loss(x, y, placeholders['labels'],\n",
" loss_type=config['training']['loss'],\n",
" margin=config['training']['margin'])\n",
"\n",
" # optionally monitor the similarity between positive and negative pairs\n",
" is_pos = tf.cast(tf.equal(placeholders['labels'], 1), tf.float32)\n",
" is_neg = 1 - is_pos\n",
" n_pos = tf.reduce_sum(is_pos)\n",
" n_neg = tf.reduce_sum(is_neg)\n",
" sim = compute_similarity(config, x, y)\n",
" sim_pos = tf.reduce_sum(sim * is_pos) / (n_pos + 1e-8)\n",
" sim_neg = tf.reduce_sum(sim * is_neg) / (n_neg + 1e-8)\n",
" else:\n",
" x_1, y, x_2, z = reshape_and_split_tensor(graph_vectors, 4)\n",
" loss = triplet_loss(x_1, y, x_2, z,\n",
" loss_type=config['training']['loss'],\n",
" margin=config['training']['margin'])\n",
"\n",
" sim_pos = tf.reduce_mean(compute_similarity(config, x_1, y))\n",
" sim_neg = tf.reduce_mean(compute_similarity(config, x_2, z))\n",
"\n",
" graph_vec_scale = tf.reduce_mean(graph_vectors**2)\n",
" if config['training']['graph_vec_regularizer_weight'] \u003e 0:\n",
" loss += (config['training']['graph_vec_regularizer_weight'] *\n",
" 0.5 * graph_vec_scale)\n",
"\n",
" # monitor scale of the parameters and gradients, these are typically helpful\n",
" optimizer = tf.train.AdamOptimizer(\n",
" learning_rate=config['training']['learning_rate'])\n",
" grads_and_params = optimizer.compute_gradients(loss)\n",
" grads, params = zip(*grads_and_params)\n",
" grads, _ = tf.clip_by_global_norm(grads, config['training']['clip_value'])\n",
" train_step = optimizer.apply_gradients(zip(grads, params))\n",
"\n",
" grad_scale = tf.global_norm(grads)\n",
" param_scale = tf.global_norm(params)\n",
"\n",
" # evaluation\n",
" model_inputs['n_graphs'] = config['evaluation']['batch_size'] * 2\n",
" eval_pairs = model(**model_inputs)\n",
" x, y = reshape_and_split_tensor(eval_pairs, 2)\n",
" similarity = compute_similarity(config, x, y)\n",
" pair_auc = auc(similarity, placeholders['labels'])\n",
"\n",
" model_inputs['n_graphs'] = config['evaluation']['batch_size'] * 4\n",
" eval_triplets = model(**model_inputs)\n",
" x_1, y, x_2, z = reshape_and_split_tensor(eval_triplets, 4)\n",
" sim_1 = compute_similarity(config, x_1, y)\n",
" sim_2 = compute_similarity(config, x_2, z)\n",
" triplet_acc = tf.reduce_mean(tf.cast(sim_1 \u003e sim_2, dtype=tf.float32))\n",
"\n",
" return {\n",
" 'train_step': train_step,\n",
" 'metrics': {\n",
" 'training': {\n",
" 'loss': loss,\n",
" 'grad_scale': grad_scale,\n",
" 'param_scale': param_scale,\n",
" 'graph_vec_scale': graph_vec_scale,\n",
" 'sim_pos': sim_pos,\n",
" 'sim_neg': sim_neg,\n",
" 'sim_diff': sim_pos - sim_neg,\n",
" },\n",
" 'validation': {\n",
" 'pair_auc': pair_auc,\n",
" 'triplet_acc': triplet_acc,\n",
" },\n",
" },\n",
" }, placeholders, model"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "09KlUpfZt-L9"
},
"source": [
"### The training pipeline\n",
"\n",
"For this we need to build the datasets, handle `feed_dict`s and run some evaluation during training.\n",
"\n",
"Note that this training pipeline is only supposed to be used as an example and you may want to add your own checkpointing or experiment monitoring tools (e.g. tensorboard)."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "HRGXuCBsucKH"
},
"outputs": [],
"source": [
"def build_datasets(config):\n",
" \"\"\"Build the training and evaluation datasets.\"\"\"\n",
" config = copy.deepcopy(config)\n",
"\n",
" if config['data']['problem'] == 'graph_edit_distance':\n",
" dataset_params = config['data']['dataset_params']\n",
" validation_dataset_size = dataset_params['validation_dataset_size']\n",
" del dataset_params['validation_dataset_size']\n",
" training_set = GraphEditDistanceDataset(**dataset_params)\n",
" dataset_params['dataset_size'] = validation_dataset_size\n",
" validation_set = FixedGraphEditDistanceDataset(**dataset_params)\n",
" else:\n",
" raise ValueError('Unknown problem type: %s' % config['data']['problem'])\n",
" return training_set, validation_set\n",
"\n",
"\n",
"def fill_feed_dict(placeholders, batch):\n",
" \"\"\"Create a feed dict for the given batch of data.\n",
"\n",
" Args:\n",
" placeholders: a dict of placeholders.\n",
" batch: a batch of data, should be either a single `GraphData` instance for\n",
" triplet training, or a tuple of (graphs, labels) for pairwise training.\n",
"\n",
" Returns:\n",
" feed_dict: a feed_dict that can be used in a session run call.\n",
" \"\"\"\n",
" if isinstance(batch, GraphData):\n",
" graphs = batch\n",
" labels = None\n",
" else:\n",
" graphs, labels = batch\n",
"\n",
" feed_dict = {\n",
" placeholders['node_features']: graphs.node_features,\n",
" placeholders['edge_features']: graphs.edge_features,\n",
" placeholders['from_idx']: graphs.from_idx,\n",
" placeholders['to_idx']: graphs.to_idx,\n",
" placeholders['graph_idx']: graphs.graph_idx,\n",
" }\n",
" if labels is not None:\n",
" feed_dict[placeholders['labels']] = labels\n",
" return feed_dict\n",
"\n",
"\n",
"def evaluate(sess, eval_metrics, placeholders, validation_set, batch_size):\n",
" \"\"\"Evaluate model performance on the given validation set.\n",
"\n",
" Args:\n",
" sess: a `tf.Session` instance used to run the computation.\n",
" eval_metrics: a dict containing two tensors 'pair_auc' and 'triplet_acc'.\n",
" placeholders: a placeholder dict.\n",
" validation_set: a `GraphSimilarityDataset` instance, calling `pairs` and\n",
" `triplets` functions with `batch_size` creates iterators over a finite\n",
" sequence of batches to evaluate on.\n",
" batch_size: number of batches to use for each session run call.\n",
"\n",
" Returns:\n",
" metrics: a dict of metric name =\u003e value mapping.\n",
" \"\"\"\n",
" accumulated_pair_auc = []\n",
" for batch in validation_set.pairs(batch_size):\n",
" feed_dict = fill_feed_dict(placeholders, batch)\n",
" pair_auc = sess.run(eval_metrics['pair_auc'], feed_dict=feed_dict)\n",
" accumulated_pair_auc.append(pair_auc)\n",
"\n",
" accumulated_triplet_acc = []\n",
" for batch in validation_set.triplets(batch_size):\n",
" feed_dict = fill_feed_dict(placeholders, batch)\n",
" triplet_acc = sess.run(eval_metrics['triplet_acc'], feed_dict=feed_dict)\n",
" accumulated_triplet_acc.append(triplet_acc)\n",
"\n",
" return {\n",
" 'pair_auc': np.mean(accumulated_pair_auc),\n",
" 'triplet_acc': np.mean(accumulated_triplet_acc),\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "I-u2iAaBujIg"
},
"source": [
"### Let's run it!"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "65drio8Qu9qG"
},
"outputs": [],
"source": [
"config = get_default_config()\n",
"\n",
"# Let's just run for a small number of training steps. This may take you a few\n",
"# minutes.\n",
"config['training']['n_training_steps'] = 5000"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"height": 887
},
"colab_type": "code",
"executionInfo": {
"elapsed": 590938,
"status": "ok",
"timestamp": 1563980116419,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "v7Dz1haMuh8O",
"outputId": "6722538e-db5a-4538-dc2c-f08108a1b983"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"iter 100, loss 0.9024, grad_scale 9.9039, param_scale 24.2281, graph_vec_scale 9.9295, sim_pos -0.5319, sim_neg -0.9309, sim_diff 0.3990, time 16.35s\n",
"iter 200, loss 0.8899, grad_scale 10.0000, param_scale 24.2434, graph_vec_scale 15.8820, sim_pos -0.5871, sim_neg -1.1029, sim_diff 0.5158, time 8.78s\n",
"iter 300, loss 0.9187, grad_scale 9.9720, param_scale 24.2605, graph_vec_scale 9.2189, sim_pos -0.5186, sim_neg -0.9006, sim_diff 0.3819, time 10.61s\n",
"iter 400, loss 0.8426, grad_scale 9.9700, param_scale 24.3224, graph_vec_scale 20.1333, sim_pos -0.5781, sim_neg -1.2868, sim_diff 0.7086, time 9.40s\n",
"iter 500, loss 0.8767, grad_scale 9.9918, param_scale 24.3770, graph_vec_scale 16.1800, sim_pos -0.6053, sim_neg -1.1185, sim_diff 0.5132, time 8.59s\n",
"iter 600, loss 0.8756, grad_scale 10.0000, param_scale 24.4452, graph_vec_scale 10.7137, sim_pos -0.5587, sim_neg -1.0466, sim_diff 0.4879, time 8.54s\n",
"iter 700, loss 0.8455, grad_scale 10.0000, param_scale 24.5081, graph_vec_scale 6.1303, sim_pos -0.6762, sim_neg -1.2543, sim_diff 0.5781, time 9.26s\n",
"iter 800, loss 0.8545, grad_scale 10.0000, param_scale 24.5715, graph_vec_scale 7.7584, sim_pos -0.6751, sim_neg -1.2628, sim_diff 0.5877, time 9.35s\n",
"iter 900, loss 0.8324, grad_scale 9.9734, param_scale 24.6531, graph_vec_scale 11.4416, sim_pos -0.6053, sim_neg -1.2179, sim_diff 0.6126, time 10.77s\n",
"iter 1000, loss 0.8502, grad_scale 10.0000, param_scale 24.7729, graph_vec_scale 21.2052, sim_pos -0.6276, sim_neg -1.1885, sim_diff 0.5610, val/pair_auc 0.6311, val/triplet_acc 0.6560, time 23.40s\n",
"iter 1100, loss 0.8568, grad_scale 10.0000, param_scale 24.9137, graph_vec_scale 9.9548, sim_pos -0.6038, sim_neg -1.1830, sim_diff 0.5791, time 9.38s\n",
"iter 1200, loss 0.8258, grad_scale 10.0000, param_scale 25.0311, graph_vec_scale 9.8263, sim_pos -0.6863, sim_neg -1.2977, sim_diff 0.6114, time 10.48s\n",
"iter 1300, loss 0.8167, grad_scale 10.0000, param_scale 25.1117, graph_vec_scale 7.9998, sim_pos -0.6577, sim_neg -1.2564, sim_diff 0.5987, time 9.64s\n",
"iter 1400, loss 0.8393, grad_scale 10.0000, param_scale 25.2091, graph_vec_scale 9.4814, sim_pos -0.6692, sim_neg -1.2338, sim_diff 0.5645, time 9.88s\n",
"iter 1500, loss 0.8124, grad_scale 10.0000, param_scale 25.3498, graph_vec_scale 10.8970, sim_pos -0.6629, sim_neg -1.2851, sim_diff 0.6222, time 10.24s\n",
"iter 1600, loss 0.7997, grad_scale 10.0000, param_scale 25.5068, graph_vec_scale 8.8850, sim_pos -0.6958, sim_neg -1.3773, sim_diff 0.6816, time 10.23s\n",
"iter 1700, loss 0.8036, grad_scale 10.0000, param_scale 25.6802, graph_vec_scale 9.1888, sim_pos -0.7241, sim_neg -1.3902, sim_diff 0.6660, time 10.28s\n",
"iter 1800, loss 0.8282, grad_scale 9.9726, param_scale 25.8778, graph_vec_scale 16.0597, sim_pos -0.7033, sim_neg -1.3089, sim_diff 0.6056, time 10.52s\n",
"iter 1900, loss 0.8561, grad_scale 9.9528, param_scale 26.1749, graph_vec_scale 16.8173, sim_pos -0.5827, sim_neg -1.1226, sim_diff 0.5399, time 11.16s\n",
"iter 2000, loss 0.8543, grad_scale 10.0000, param_scale 26.4489, graph_vec_scale 9.4414, sim_pos -0.6694, sim_neg -1.1820, sim_diff 0.5126, val/pair_auc 0.6423, val/triplet_acc 0.7120, time 15.30s\n",
"iter 2100, loss 0.8282, grad_scale 10.0000, param_scale 26.5936, graph_vec_scale 8.2912, sim_pos -0.6784, sim_neg -1.2598, sim_diff 0.5814, time 12.57s\n",
"iter 2200, loss 0.8040, grad_scale 10.0000, param_scale 26.8179, graph_vec_scale 8.7340, sim_pos -0.7254, sim_neg -1.3695, sim_diff 0.6441, time 10.05s\n",
"iter 2300, loss 0.8089, grad_scale 10.0000, param_scale 27.0014, graph_vec_scale 4.7856, sim_pos -0.7325, sim_neg -1.3514, sim_diff 0.6189, time 10.36s\n",
"iter 2400, loss 0.7894, grad_scale 10.0000, param_scale 27.1475, graph_vec_scale 5.3897, sim_pos -0.7178, sim_neg -1.3501, sim_diff 0.6323, time 10.36s\n",
"iter 2500, loss 0.8112, grad_scale 10.0000, param_scale 27.3124, graph_vec_scale 5.0839, sim_pos -0.7920, sim_neg -1.4146, sim_diff 0.6226, time 10.70s\n",
"iter 2600, loss 0.7852, grad_scale 10.0000, param_scale 27.5082, graph_vec_scale 3.7201, sim_pos -0.7722, sim_neg -1.5174, sim_diff 0.7452, time 10.81s\n",
"iter 2700, loss 0.7873, grad_scale 10.0000, param_scale 27.6644, graph_vec_scale 3.7121, sim_pos -0.7612, sim_neg -1.4150, sim_diff 0.6538, time 10.88s\n",
"iter 2800, loss 0.7846, grad_scale 10.0000, param_scale 27.8754, graph_vec_scale 4.0826, sim_pos -0.7242, sim_neg -1.3944, sim_diff 0.6703, time 9.64s\n",
"iter 2900, loss 0.7984, grad_scale 10.0000, param_scale 28.0754, graph_vec_scale 3.3628, sim_pos -0.7275, sim_neg -1.3811, sim_diff 0.6536, time 10.04s\n",
"iter 3000, loss 0.8040, grad_scale 10.0000, param_scale 28.2285, graph_vec_scale 4.7665, sim_pos -0.8204, sim_neg -1.4319, sim_diff 0.6115, val/pair_auc 0.6521, val/triplet_acc 0.7020, time 14.91s\n",
"iter 3100, loss 0.7661, grad_scale 10.0000, param_scale 28.4321, graph_vec_scale 4.5367, sim_pos -0.7861, sim_neg -1.5052, sim_diff 0.7192, time 12.70s\n",
"iter 3200, loss 0.7918, grad_scale 10.0000, param_scale 28.6223, graph_vec_scale 2.7951, sim_pos -0.7894, sim_neg -1.4368, sim_diff 0.6474, time 10.14s\n",
"iter 3300, loss 0.7888, grad_scale 10.0000, param_scale 28.7832, graph_vec_scale 2.5365, sim_pos -0.7737, sim_neg -1.4076, sim_diff 0.6339, time 11.14s\n",
"iter 3400, loss 0.7880, grad_scale 10.0000, param_scale 28.9723, graph_vec_scale 2.5840, sim_pos -0.8405, sim_neg -1.4963, sim_diff 0.6558, time 10.99s\n",
"iter 3500, loss 0.8063, grad_scale 10.0000, param_scale 29.1109, graph_vec_scale 1.5241, sim_pos -0.7049, sim_neg -1.3258, sim_diff 0.6209, time 10.09s\n",
"iter 3600, loss 0.7781, grad_scale 10.0000, param_scale 29.2500, graph_vec_scale 1.6339, sim_pos -0.8011, sim_neg -1.5460, sim_diff 0.7448, time 10.25s\n",
"iter 3700, loss 0.7626, grad_scale 10.0000, param_scale 29.3812, graph_vec_scale 2.0334, sim_pos -0.7765, sim_neg -1.5622, sim_diff 0.7857, time 11.35s\n",
"iter 3800, loss 0.7545, grad_scale 10.0000, param_scale 29.5007, graph_vec_scale 1.9816, sim_pos -0.7494, sim_neg -1.5253, sim_diff 0.7759, time 10.50s\n",
"iter 3900, loss 0.7786, grad_scale 10.0000, param_scale 29.6472, graph_vec_scale 2.2622, sim_pos -0.8190, sim_neg -1.5184, sim_diff 0.6994, time 10.94s\n",
"iter 4000, loss 0.7696, grad_scale 10.0000, param_scale 29.7945, graph_vec_scale 1.9066, sim_pos -0.7080, sim_neg -1.4729, sim_diff 0.7649, val/pair_auc 0.6588, val/triplet_acc 0.7110, time 15.44s\n",
"iter 4100, loss 0.7702, grad_scale 10.0000, param_scale 29.9186, graph_vec_scale 1.6492, sim_pos -0.7923, sim_neg -1.5920, sim_diff 0.7998, time 11.56s\n",
"iter 4200, loss 0.7618, grad_scale 10.0000, param_scale 30.0597, graph_vec_scale 1.3295, sim_pos -0.8183, sim_neg -1.5480, sim_diff 0.7297, time 12.56s\n",
"iter 4300, loss 0.7595, grad_scale 10.0000, param_scale 30.2331, graph_vec_scale 2.0479, sim_pos -0.8513, sim_neg -1.5582, sim_diff 0.7069, time 9.82s\n",
"iter 4400, loss 0.7722, grad_scale 10.0000, param_scale 30.3986, graph_vec_scale 1.1744, sim_pos -0.7996, sim_neg -1.4669, sim_diff 0.6674, time 10.90s\n",
"iter 4500, loss 0.7850, grad_scale 10.0000, param_scale 30.5059, graph_vec_scale 1.7279, sim_pos -0.8477, sim_neg -1.5368, sim_diff 0.6891, time 11.18s\n",
"iter 4600, loss 0.7562, grad_scale 10.0000, param_scale 30.6486, graph_vec_scale 1.4266, sim_pos -0.7782, sim_neg -1.5346, sim_diff 0.7564, time 10.92s\n",
"iter 4700, loss 0.7843, grad_scale 10.0000, param_scale 30.8270, graph_vec_scale 2.1109, sim_pos -0.7923, sim_neg -1.4582, sim_diff 0.6660, time 11.00s\n",
"iter 4800, loss 0.7712, grad_scale 10.0000, param_scale 30.9745, graph_vec_scale 1.9060, sim_pos -0.8056, sim_neg -1.4885, sim_diff 0.6830, time 11.01s\n",
"iter 4900, loss 0.7894, grad_scale 10.0000, param_scale 31.1943, graph_vec_scale 1.5744, sim_pos -0.7436, sim_neg -1.3567, sim_diff 0.6131, time 10.39s\n",
"iter 5000, loss 0.7866, grad_scale 10.0000, param_scale 31.4067, graph_vec_scale 1.4950, sim_pos -0.7812, sim_neg -1.4002, sim_diff 0.6190, val/pair_auc 0.6675, val/triplet_acc 0.7490, time 15.01s\n"
]
}
],
"source": [
"# Run this if you want to run the code again, otherwise tensorflow would\n",
"# complain that you already created the same graph and the same variables.\n",
"tf.reset_default_graph()\n",
"\n",
"# Set random seeds\n",
"seed = config['seed']\n",
"random.seed(seed)\n",
"np.random.seed(seed + 1)\n",
"tf.set_random_seed(seed + 2)\n",
"\n",
"training_set, validation_set = build_datasets(config)\n",
"\n",
"if config['training']['mode'] == 'pair':\n",
" training_data_iter = training_set.pairs(config['training']['batch_size'])\n",
" first_batch_graphs, _ = next(training_data_iter)\n",
"else:\n",
" training_data_iter = training_set.triplets(config['training']['batch_size'])\n",
" first_batch_graphs = next(training_data_iter)\n",
"\n",
"node_feature_dim = first_batch_graphs.node_features.shape[-1]\n",
"edge_feature_dim = first_batch_graphs.edge_features.shape[-1]\n",
"\n",
"tensors, placeholders, model = build_model(\n",
" config, node_feature_dim, edge_feature_dim)\n",
"\n",
"accumulated_metrics = collections.defaultdict(list)\n",
"\n",
"t_start = time.time()\n",
"\n",
"init_ops = (tf.global_variables_initializer(),\n",
" tf.local_variables_initializer())\n",
"\n",
"# If we already have a session instance, close it and start a new one\n",
"if 'sess' in globals():\n",
" sess.close()\n",
"\n",
"# We will need to keep this session instance around for e.g. visualization.\n",
"# But you should probably wrap it in a `with tf.Session() sess:` context if you\n",
"# want to use the code elsewhere.\n",
"sess = tf.Session()\n",
"sess.run(init_ops)\n",
"\n",
"# use xrange here if you are still on python 2\n",
"for i_iter in range(config['training']['n_training_steps']):\n",
" batch = next(training_data_iter)\n",
" _, train_metrics = sess.run(\n",
" [tensors['train_step'], tensors['metrics']['training']],\n",
" feed_dict=fill_feed_dict(placeholders, batch))\n",
"\n",
" # accumulate over minibatches to reduce variance in the training metrics\n",
" for k, v in train_metrics.items():\n",
" accumulated_metrics[k].append(v)\n",
"\n",
" if (i_iter + 1) % config['training']['print_after'] == 0:\n",
" metrics_to_print = {\n",
" k: np.mean(v) for k, v in accumulated_metrics.items()}\n",
" info_str = ', '.join(\n",
" ['%s %.4f' % (k, v) for k, v in metrics_to_print.items()])\n",
" # reset the metrics\n",
" accumulated_metrics = collections.defaultdict(list)\n",
"\n",
" if ((i_iter + 1) // config['training']['print_after'] %\n",
" config['training']['eval_after'] == 0):\n",
" eval_metrics = evaluate(\n",
" sess, tensors['metrics']['validation'], placeholders,\n",
" validation_set, config['evaluation']['batch_size'])\n",
" info_str += ', ' + ', '.join(\n",
" ['%s %.4f' % ('val/' + k, v) for k, v in eval_metrics.items()])\n",
"\n",
" print('iter %d, %s, time %.2fs' % (\n",
" i_iter + 1, info_str, time.time() - t_start))\n",
" t_start = time.time()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "EDHrQOOYzH2t"
},
"source": [
"Note that albeit a bit noisy, the loss is going down, the similarity gap \n",
"between positive and negative pairs are growing and the evaluation results, i.e. pair AUC and triplet accuracies are going up as well. Overall training seems to be working!\n",
"\n",
"You can train this much longer. We observed improvement in performance even after training for 500,000 steps, but didn't push this much further as it is a synthetic task after all."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "NqaY9In2zKUT"
},
"source": [
"## Test the model and create some visualizations\n",
"\n",
"Once the model is trained, we can test in on unseen data. Our graph matching networks use cross-graph matching-based attention to compute graph similarity, we can visualize these attention weights to see where the model is attending to."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "E5XrWBLbzJxh"
},
"outputs": [],
"source": [
"# visualize on graphs of 10 nodes, bigger graphs become more difficult to\n",
"# visualize\n",
"vis_dataset = GraphEditDistanceDataset(\n",
" [10, 10], [0.2, 0.2], 1, 2, permute=False)\n",
"\n",
"pair_iter = vis_dataset.pairs(2)\n",
"graphs, labels = next(pair_iter)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Wew5G3SZ1XbB"
},
"source": [
"Let's split the batched graphs into individual graphs and visualize them first."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "LR2enkt21Git"
},
"outputs": [],
"source": [
"def split_graphs(graphs):\n",
" \"\"\"Split a batch of graphs into individual `nx.Graph` instances.\"\"\"\n",
" g = [nx.Graph() for _ in range(graphs.n_graphs)]\n",
" node_ids = np.arange(graphs.graph_idx.size, dtype=np.int32)\n",
" for i in range(graphs.n_graphs):\n",
" nodes_in_graph = node_ids[graphs.graph_idx == i]\n",
" n_nodes = len(nodes_in_graph)\n",
" g[i].add_nodes_from(range(n_nodes))\n",
" node_id_min = nodes_in_graph.min()\n",
" node_id_max = nodes_in_graph.max()\n",
"\n",
" edges = []\n",
" for u, v in zip(graphs.from_idx, graphs.to_idx):\n",
" if node_id_min \u003c= u \u003c= node_id_max and node_id_min \u003c= v \u003c= node_id_max:\n",
" edges.append((u - node_id_min, v - node_id_min))\n",
" g[i].add_edges_from(edges)\n",
" \n",
" return g"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"height": 655
},
"colab_type": "code",
"executionInfo": {
"elapsed": 1098,
"status": "ok",
"timestamp": 1563980161995,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "DVY20guU09L2",
"outputId": "eb1488b2-0d57-49d6-a22b-8304a49ccf1b"
},
"outputs": [
{
"data": {
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KkNTUVGbMmMGMGTMYPHgwEyZMwMvLq2AXJyaanjratw9SUsDLy/Ry2tBQvetMRAotKSkJ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\n",
"text/plain": [
"\u003cFigure size 1000x500 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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EZJRr166hYcOG+OOPP+Dm5qZ2HKIih82WgR48eAAvLy9ER0ejWrVqaschIjLKwIED4ePj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\nAAAASUVORK5CYII=\n",
"text/plain": [
"\u003cFigure size 1000x500 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"nx_graphs = split_graphs(graphs)\n",
"\n",
"for i in range(0, len(nx_graphs), 2):\n",
" label = labels[i // 2]\n",
" g1 = nx_graphs[i]\n",
" g2 = nx_graphs[i + 1]\n",
" plt.figure(figsize=(10, 5))\n",
" ax = plt.subplot(121)\n",
" # Compute the positions of graphs first to make sure the visualizations are\n",
" # consistent.\n",
" pos = nx.drawing.spring_layout(g1)\n",
" nx.draw_networkx(g1, pos=pos, ax=ax)\n",
" ax.set_title('Graph 1')\n",
" ax.axis('off')\n",
" ax = plt.subplot(122)\n",
" nx.draw_networkx(g2, pos=pos, ax=ax)\n",
" ax.set_title('Graph 2')\n",
" ax.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "HBkh_OpCHkm8"
},
"source": [
"Build the computation graph for visualization."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "0YerKIp32Vro"
},
"outputs": [],
"source": [
"n_graphs = graphs.n_graphs\n",
"\n",
"model_inputs = placeholders.copy()\n",
"del model_inputs['labels']\n",
"graph_vectors = model(n_graphs=n_graphs, **model_inputs)\n",
"x, y = reshape_and_split_tensor(graph_vectors, 2)\n",
"similarity = compute_similarity(config, x, y)\n",
"\n",
"layer_outputs = model.get_layer_outputs()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Z7dHo4PPF5JH"
},
"outputs": [],
"source": [
"def build_matchings(layer_outputs, graph_idx, n_graphs, sim):\n",
" \"\"\"Build the matching attention matrices from layer outputs.\"\"\"\n",
" assert n_graphs % 2 == 0\n",
" attention = []\n",
" for h in layer_outputs:\n",
" partitions = tf.dynamic_partition(h, graph_idx, n_graphs)\n",
" attention_in_layer = []\n",
" for i in range(0, n_graphs, 2):\n",
" x = partitions[i]\n",
" y = partitions[i + 1]\n",
" a = sim(x, y)\n",
" a_x = tf.nn.softmax(a, axis=1) # i-\u003ej\n",
" a_y = tf.nn.softmax(a, axis=0) # j-\u003ei\n",
" attention_in_layer.append((a_x, a_y))\n",
" attention.append(attention_in_layer)\n",
" return attention"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "34lrRLif9ZOB"
},
"outputs": [],
"source": [
"attentions = build_matchings(\n",
" layer_outputs, placeholders['graph_idx'], n_graphs,\n",
" get_pairwise_similarity(config['graph_matching_net']['similarity']))"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "sMi3nrclIHVp"
},
"outputs": [],
"source": [
"sim, a = sess.run([similarity, attentions],\n",
" feed_dict=fill_feed_dict(placeholders, (graphs, labels)))"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"height": 51
},
"colab_type": "code",
"executionInfo": {
"elapsed": 294,
"status": "ok",
"timestamp": 1563980171214,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "xfFEeS2PIUKx",
"outputId": "a5bdec40-62e6-441e-f33e-110cc3d3996c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1 -1]\n",
"[-0.19060427 -2.23995686]\n"
]
}
],
"source": [
"print(labels)\n",
"print(sim)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "giI_5IW9IgOG"
},
"source": [
"Similarity for positive pair is much higher than the similarity for the negative pair.\n",
"\n",
"Remember that with a margin loss and Euclidean distance, which is how this model is trained, the similarity value is the negative distance. In this case the distance for positive pair is quite small, while the distance between two graphs in the negative pair is large."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "w-Vq-vIDI6P7"
},
"source": [
"### Some tools for visualizing attention"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "VzgCXZlxIqRp"
},
"outputs": [],
"source": [
"def _plot_graph_matching_on_axis(ax, g_base, pos, att, title=None):\n",
" \"\"\"Plot graph matching on an axis.\"\"\"\n",
" original_edges = g_base.edges()\n",
" g = g_base.copy()\n",
" n1, n2 = att.shape\n",
" alpha = []\n",
" att_edges = []\n",
" for i in range(n1):\n",
" for j in range(n2):\n",
" g.add_edge(i, j + n1)\n",
" att_edges.append((i, j + n1))\n",
" alpha.append(att[i][j])\n",
"\n",
" nx.draw_networkx_nodes(\n",
" g, pos, nodelist=range(n1), node_color='lavender', linewidths=3, ax=ax)\n",
" nx.draw_networkx_nodes(\n",
" g, pos, nodelist=range(n1, n1 + n2), node_color='wheat', linewidths=3,\n",
" ax=ax)\n",
" nx.draw_networkx_edges(\n",
" g, pos, edgelist=original_edges, edge_color='k', width=3, ax=ax)\n",
" for i in range(len(att_edges)):\n",
" nx.draw_networkx_edges(\n",
" g, pos, edgelist=att_edges[i:i+1], edge_color='g', alpha=alpha[i],\n",
" ax=ax, width=3)\n",
" ax.axis('off')\n",
" if title:\n",
" ax.set_title(title)\n",
"\n",
" # set xlim and ylim\n",
" coords = np.array(list(pos.values()), dtype=np.float32)\n",
" x_min, y_min = coords.min(axis=0)\n",
" x_max, y_max = coords.max(axis=0)\n",
" x_len = x_max - x_min\n",
" y_len = y_max - y_min\n",
" ax.set_xlim([x_min - x_len * 0.05, x_max + x_len * 0.05])\n",
" ax.set_ylim([y_min - y_len * 0.05, y_max + y_len * 0.05])\n",
"\n",
"\n",
"def plot_graph_matching_pair(g1, g2, matchings, pos=None, seed=0):\n",
" \"\"\"Plot a pair of graphs and the matchings between them.\n",
"\n",
" Args:\n",
" g1: a networkx graph.\n",
" g2: a networkx graph.\n",
" matchings: a pair of n1 x n2 matrices.\n",
" pos: a position dictionary, if provided.\n",
"\n",
" Returns:\n",
" pos: position dictionary, used for other plots between these two graphs.\n",
" \"\"\"\n",
" n1 = g1.number_of_nodes()\n",
" n2 = g2.number_of_nodes()\n",
" assert n1 \u003e= n2\n",
"\n",
" if pos is None:\n",
" with reset_random_state(seed=seed):\n",
" pos1 = nx.spring_layout(g1)\n",
" pos1_values = np.array(list(pos1.values()), dtype=np.float32)\n",
" pos1_values -= pos1_values.mean(axis=0, keepdims=True)\n",
" pos2_values = pos1_values[n1-n2:] + np.array([\n",
" pos1_values[n1-n2:, 0].max(axis=0) * 2 -\n",
" pos1_values[n1-n2:, 0].min(axis=0), 0])\n",
"\n",
" pos = {k: pos1_values[k] for k in pos1.keys()}\n",
" pos.update({(k + n2): pos2_values[k-(n1-n2)]\n",
" for k in list(pos1.keys())[n1-n2:]})\n",
"\n",
" g_base = nx.Graph()\n",
" g_base.add_nodes_from(range(n1))\n",
" g_base.add_nodes_from(range(n1, n1 + n2))\n",
" g_base.add_edges_from(g1.edges())\n",
" g_base.add_edges_from([(i + n1, j + n1) for i, j in g2.edges()])\n",
"\n",
" fig, ax = plt.subplots(1, 2, figsize=(11.2, 4.8))\n",
" _plot_graph_matching_on_axis(\n",
" ax[0], g_base, pos, matchings[0], title='left attend to right')\n",
" _plot_graph_matching_on_axis(\n",
" ax[1], g_base, pos, matchings[1], title='right attend to left')\n",
" fig.tight_layout(pad=0, h_pad=0, w_pad=0)\n",
" return pos"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fZqWAwX_O3_z"
},
"source": [
"### Visualize how the attention pattern changes across layers"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"height": 1000
},
"colab_type": "code",
"executionInfo": {
"elapsed": 9203,
"status": "ok",
"timestamp": 1563980184546,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "jrbvym39JcTw",
"outputId": "ad8d30a2-1efd-4986-9681-bb84f6b2f758"
},
"outputs": [
{
"data": {
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zqtqWFznyMoelWyhZiX7cR1qksDUblm5hlIz4MljTRZInyIoMHacDABinY7iG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1hMV6hQVQ9Ho2j8rPrBeFv4GBgsbmm9SM2N5VDxi3PtX09vHWPMaCkEFyhk0fhei1sIrMDwlTf5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\nnJqA8YY0v6qo6IU97O3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XW5aTeqVruNA1mqSUrBIqVkV8e/pRH77ri7S/F3uUS8I2Wn6L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xW2ttsAyfMjc/7r4/8beZMgcAOMgpE47/IDbJs2QjuU6WKpSriWi41wg7Yz82TE5qYMlUrK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Nil0ZG7OApKC7SfoTNKapQll+TQWGozfTDOUoSptREqmOFdBR3PRjX9LviNYsoIpgYJ3i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\nbQyJAAAAAElFTkSuQmCC\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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HkXz0w32VBsyiCNeRj354SHdEEEcD2RKCODhkSy4tyKE5AobD4lDOMxodznkI4jiR9e9b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nOPrLwk/zeWgedcKUIvc5J/VlITw4MiYOH/U1albAW44FVQAhfmv5eVpPBVb+B468KeNJWp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aHUki4PsZskxW3Dhn4FxycEmB5jRC51UWOzzH8dcZNrMzDW4nOoe+gPixjwt7FxTnebO+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\n/wdLGuwKm3ZUhAAAAABJRU5ErkJggg==\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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ZKcyAgeFC5wKCOAADw6XuJRwqH0In6oDHXDg2hTmhBJLE4BAlQb2oh/Ot86g7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ORurF429V9WZIwjRgmGc1m6hVutW6hbdcRSSV3SVI607VtOC02nyTBAIiAOooHCNEuVwLqu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\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y9mV5DZZh4cLihbFmce/nWDJLaGYYiH5GopMGJ6C/mSiAkegcn/3sf8Pb3vYkTp9ePbTW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\nDQAAAIBgEWgAAAAABOs/B5dbC9OgTdsAAAAASUVORK5CYII=\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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xeWkRxiHqXjL8sp/csq3ZyOrZnhKZnXRmZYAkKlZpVQCarNcMm2h4DeSNPADA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nJJmIRCAGC4sVSYgjln7lCwGuL3TfrKVM3qyGxzAlaTL+7p/aMs2k+SxlA6GClU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teMf+d/TcfHqBh217WyxkeTM/UhChzXfc5Cyn50ZKhMYBY7ySQ1U/27cRsEC8HgUfS7VE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\ngg==\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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WjQni3qZMWgZQ1IswFAMA1nVkgN6sDAA2pZkQAlEQ0faT+umSUYIgCHACB8Pm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hLQz4/Q98gJHFQFD3X5+vbUV92fi1jy48Wt/DQkScC13YkDIEJETbfiAIqsUId1RYcmaamqPu9+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djmstACESCKlJvUtJsIwgBLxhL8yEfP7XGZ54JaQBiHXO6Ude/iJEZGps+bUTLIqTkU9SK2\nBlv82DtOB1WjiiRJ4IUeV0UJooATRJkEKOtIJaDj7BCTkfZBEL50l2n2ujNNE4MB3ajLPRmPXXgM\n26NtNKwGdywfeAMMWgMsZZZwvnz+wMnQote9b/k+CETA9pBuFPdGe0iSBPev3H+iRU0QBXMFSxiH\nh3JqGImTcWWmoGZEhCqqvCiPBPlECxoxu3Ziz/VG3DxerxMaFkEUYHu4PZlKE4LV7CpXNgMmDYs0\nvCxNWGZTGTuY3kyV9TIqRgVMLfNm8C1dF2CdYC75budJiAPKcawYFZS0Et0UphRCAWq6mZ7gcETA\nLOwnhXBgMtSsQbVIPZQ9huWdJEngYQK1W8osQZd1jPwRBs4AIPRYAMAMTJSSEjerBCh3goBwU96m\n3UQQBbwBxVQq767cjSVjCW9eeTP++tpfozFqYOgN6WcK2ghzhlS05tt73wYhBBv5DZwunsZmYRMF\ntYALlQt4pfsKvJD6pTApZ2A8gRpPn3puD6Ig8un+rIcM30ck81D32ThsarO9vQ2EADrAe970HpTy\nJewOd9H3+nzjf6V3BS2nhfuW7uON3qMEm9Q816Q+aX23j29tfwuP1B85VHzguDHracaKl8O8zdiU\nJoxCPvkH6PWniBNPGlmUkfg2YF09seO9U3LJHV/QHCUJ7ezs8O/X19f5QvOPHaZv4sbgxtTkYrOw\nyTs8i3DSLAghECBg6A0xcAdTGH9DMejUIOWEPMu9mR3ZR0kECAlkBQhOQGCj5e7Aj+mI+2r/Ki73\nLkMWZJT1MlZzqygoBWTVsUdOQua6GOniJr0RZ0WMG7oIomBhUk17AAGYW2jY62iyhguZC1AllTtD\n990+nMDB1nALI59KLG8WNjkHRSAC2nYbPbeHlcwKVwNay60hiAP0nT6X9N0b7eFs6SyWjCVUjSqi\nJMLQG3KDzKbdhC5Sl2g2/XEjCpPLKBkq5zwm2facHmSBJnA3cHkCBihXpWyUubw3g6TNnhPmicPO\n50EJaTYZzd4/OS2Hu5W7UVAL2B5uo2k3KSxB1uFHPp5vPQ9DNrBZ2MRKZuVInJh7l+4FIQQ3BrQp\nsW9ShZ77V+4/lrcAM9VjX6yIYefjsO4aezwruP3Y55sOSaCSqOmpIgDuFRFEASS9gshvQ/Rvv6gR\n9KU7Qmbz9RRHaY4lSfKaLGj8yMfOcGeumGFTk9mGBTApblizZuSOeJMGAL+n13JrfP1Lc2/SHM70\nPRonMQQxgaIA/gnkkmHYQQAXXhDD9m3sjnZBCEFWyfJJN1Mhk4k8BaNi74/lvPTmmBlHsiJm0Sad\nN8ES+t7jOOaCPQAQhAESUBnjer6OolqEH1MD46bVRIIEbasNJ6BFx5nSGf6ZiAIVNPIiD3klj0AM\n4IQONEmDSESESQjLob5qDauBc8VzeOfGO7kf2tXeVQpvC210nA6fol3rXcOr/VehiAo2C5s4WzqL\nlcwKh/aavomG1UBGznCOVdtuc/GZnJrD2dJZMDWzOV7SuDhkEL7DitvZXDJ1/xDg7KmzKBQLUEUV\n4kDEvklNpg3ZgCZquNy9jOuD66jn61O+aIfF6eJpCETAs41nAVDZ6G9tfwtvr7/9WEUNa2bNfgVR\ncOjkj5+nMT8mDVNWRIVaZoxV+JiPGXssU65NZA2+lIUS3r4NwJ2US+74guYoSei1mICG3hA7w52p\nYuZU4dQUeTsds8VNEAVoO22+qSeg5k9FrQhDNuYWIRZp3Gw6sbHkpOknU9B0o11ahMTgRFUQurgM\n3AFUWUVWznIfg4ySQUbO8EkFAL4IhHHIjaqYdDHn4zCFsLHZYfq9AJMNLgvmHcO+KkYFAhGwkl1B\nUSti39zHzmiHnt+xf0DP7fHNedtuY9/cp109xCjrZZwunsbuaBftURsAJfOPvBG8yIMf0+7NfSt0\nCtG0mtjqb6Hv9kFA4IQOdkY7qBgV1HN1iEScUp8beAP+WfbcHuo5ev2avok4ianfjprj8qsCESCI\nAmTIE67NzLUQJRGiiG5IDoOksetqZ3tS0NTrdUQxLcxUScW58jnqLeAP0LE7/LXswMZL7Zfwav9V\nbOQ3UMvVbtrRu3fpXhAQrqDWMBt4JnlmzjCNFeCLYGIHXffp10knojiO6bWTSlCiQE3OFhJuQXgy\nIiBc2pvdt6G+eiIFjZQ7ddvP8UYcL47SHBsMBrAsiss1DAPFYnHh3/1Dhhd61Dk+njStatnaQkEO\n9ntgUtyEcYjd0S5fc1iBUjWqqOiVyb0xyzFLbWxTP+QbXF0nJ+JF45IeV8RMHwOTmJZECaqoUnsA\nUYEmazAkg0NfJUHiOY9xFYf+kHMi2fGydWW2GZRee6Z+l1AuDYPb5dU8fa0kQUkvTUk8m74JN6Ce\nYuv5ddTzdcRJDMu3IBIRESLqC2dUEMQT9TCmXNmyWtBlHUEU4MHVB5FTc3hg9QHcGNzA1nALO8Md\n7Ax3MPAG0CQNJa3Ep95s8i0SCg1cyixh6A1xqngKuqRj5I3ghi40UUNGzUyt1bwQTCFCZq8BZux6\nGCSN5ZJevwfbsgEC6LqOfCHPPd3q+Tpq2Rrs0MbQG3IURxAFuNa7hhsDavhcz9dvCiHbLGyCgOA7\nje8AoPutb974Jt5ef/vcHis9aUlPXA5DfbD3NFvUsCkbO28ZJQNFmEiIzwYzZAboZ+2EDr//XK0C\nxbz9guZOyiV3fEFzlCT0WitoBu4AO6PJJlEWZJwunj4yznPkjzBw6aaJFTmqRFVA2CSDFRELVTaS\nad8CFoQQyLIIRSG31VlTFOCdSw/jreF92BvtYcfcQdtuw/ZtuiASeuP3oh5kkToUB1HAPVjiOOae\nNKIgQhEU7lANgJNUWUeJmccxXHB6QfVjH7Ig88Ip/Z5zao4/pyRIWM+vo6AVUNAK2BnSY3ZDF9vD\nbYy8ES4Jl6BJGidH5pQc6vk6n6LUsjV0nS4EIiCMqES1ADpObpgNZJQMZEHGRmEDuqyj7/Y5tMCP\nfeyOdvlkRhQoPluAgOuD64jiCJIowfZtrGRWIAkSKkaFS1IvWixFQeQ47yiJEMXRVEJaBEn7/9l7\n8yDJqvNO9Hf3e/PmnpW1ZS29AE0DTWPobtACwrYUj5GRx++9sKRnm7HHI4dByNYfssPh57BHihlv\ncsSEFbJlJIVGMk8a22HZMZbDjrAgkEAgUAMC0+puaLq7uvYt95t59+X9ceqcvDcrq7qqF0DQX0QH\ndHVWZt7tfOf7vt8yqNu2uLjIRCnGJ8bRcloJ6F9ZL2M8Ow4v8LBoLDLnZnqtXq+/vuMu28HyQfAc\njwvNC0R21liC5Vm4efjmxPTlUoJKhdNOaxiFiLgIEiexY+kPDhzrpFFIAO2ixeWpAZLQ1cwEOLeO\n0Fq/pO8IkI6akBq55N+/FpcWo6OjkCQJnuehVqvBNE2kUkkeY38uuZKQyEsJ27ex2F5MQCHHM+M7\n5rAZroGVzgqCMGC5RBKkge/Rr9xEo1+pkk5vRFm47CmNLHM4VDgAL9yHhtVA3aqjYTeYQSRAJi1m\nSOSJqSF1V+iy7ypwQiLfqaLKzJkpDMgNXFa0UTNnCl9jxxUGzNQ4LaUTEHAKPQXA4N4pKYWMnMFy\nZxnLnWX4kY+6XYflE75KOVVGXs2D4ziS40QiACTyIvJqHqZLRBZoflEFFaIgwo98JgIznh1HSk5h\nND2K28duh+VbWOmsYKG9gLbTTvjE2L6N883zcH0XmqRhKjeF20dvR0pMEeNOgXCFFGFzU5VO8eLX\ne5BKGrA9JI1N+yOgMlnBurmeWEdLqRL2KHsQRcS3Zr49z1T0/NDHXGsOC+0FjGXGMJmd3LIBDBDu\nJ8/xeHnlZQRRgJpVw+PnH2cNMlq8bAc53ipEXmRGrpRnRqf59Hxt9Xu0iKEG3i2ntUn+mgMHNT0B\nPjCv5ZJYvO0Lmv4JTVzFiMZbqaBpWA2mOgKQEeR0bnpHxYwf+qiZtaQm/MZUhpLLgSSxm012uJ4C\nThBu9suIexKks0C7CfiXsG8URSCb4yAIPDJCBlk1iwPlA/ACDzWzhoX2AuoWWdQtj8C1WkELbbvN\nxrGSIBFFlpBMF+iUQRVUSKIEVSDTlYycQcRFCEAIlvB6vjMpOQVd0hNdMzumxU4/qz+oV0BOIUXN\nkrEEy7cw05iB6ZvE6Cwzjr2FvZjMTRIJaN9l55oWRMudZSiiQqAf+ijqVh3LBvlZTslhND2K8cw4\nm8bo0AEV8EIPLafFFM2iKOpJTfsuqkEVlm+hqBXhBA5KqRJLQIPuffq9aJKmXcr+bls84fdD0tjz\nwwPDE8O9JM8BGTnD7l1JIIX5ZHaSmXRSI0o3cHG+cR5zrTlUMhVUshU2/u+fspRSJVTNKmZbswBI\nd63ttHHL8C0XlfGOQ8ToH2CD+B/5DC5AHgkusWGh50/iJTaFiRvShVHIiML951cVVYaRj4bvgLP8\nLMJLmNTwcg7K8B1v+kb5nRg8z6NSqeDChQsASD45cOBA4jVvpVxieRaWjKVEMVPJVgaua/0RhAFW\nOisJHxKO41DUihjWh9nPtsolwMZ0M9zMGei9LkI6C7SaQODv/n4WRSCfJzlJERWMZkYxmhlFFEVM\nnKXpNFkesX0id0y73FSlkInBxNY8SZCIBL8gQREU4tslSmyj7oUeEJI1IiWnkJWz0BWdnVvHdxKS\n14POuSzIGM2MIq2kkVNzWGgtoO22mcnmencdY+kxwnVJ5XrQ741T7oc+MkoGr9dfJ+uRKGE0PcqO\nXeAFaKLG1ChNz4ThGhjWh3HryK0wXROLxiLWumtYM9cQhAFySo7kEM/CmeoZnK2dxVBqCJVMBfuK\n+3Bs/Bi57tuoi9JCbqsCdztIWjyXjOwdYYV0hAhFrcimihxHfGxG06NY665hvj3PuE1hFLLcPKKP\nMM4xPWfxSQt93xNrJ9gx1awabhu97aLPCZ3Gx+FhdGofhAG80OvtubZQ0xR4oceNieWSKIpYXusv\nqHRJR17Nk6amci2XxONtX9AUCgWkUimYpsnI/4VC0tDrrZKE6lYdK50V9ndFUDCdn94RLtRwjERn\nHCALJu3SA9vjpOkmFuiRFSNEAzspPA9k84DR3h38TFaAfE6AIAwerdKE1HW7qFk1JmfcclrMRKvp\nNOGHPlJiihmA+pGPMAxhBzYZx0edRLeNuThreeTkHFJyihH84ZGuSIiQbXYFXkho6/cHz/Eo62Vk\nlSxySg4vr76Mjkc+s2bV2EbeCzzCAdrgM+kyMT89Wz+LlEy6c5qgwYsIERAgibAaVDGkD2FEH0Ep\nVULX7WK1u8oW7CAK4PleQoSAcjocn8DZaIfQcAyYnskEBWhHbqtFjELS4vdEP8SiH5I2vzAPCABk\nYHx8fOMe4ZFVskQmtW9BFniBwQdWO6skGW0UqLZv49T6KZxcP4lyqowRfQSyuBnXvLewFwBYUdOw\nGzixdgKHhg8xmFx/4UKvLU1qVJp8qw4c7dDSbhudwvRz7Kiser9zOtCTp44/QxwvQhl7F5y1F3fV\nXeO1YSjDt79tJDZ/HGNqaurHoqAxPcIlicMkK5nKth1rGh23g2VjOTHppFOZfmXNQbkkDjNjuSQa\nbFzL8UAuDxjtCJ67842VonDI5wXw/OAGjS7r0GUdowHZ4FNie9NuompV0bbbBPLruwSStiF97wSE\nGE/VyzifY9+XGmAqAmk6lfUyckoOkiAROGtMXIaZXHMcywFbRVpOQxM15JQcLjQvYKYxQ9TRHIOJ\njfihjyF9iLwnT5pZpm+ibtVBVTF1WYcmaqyhEoQBum6XQdxyag6loMSg0qqkMjGeGyMimVw36zjD\nnUHdqsMPyKSn7bThR8TL7JXVVzCeGcf+wn7sK+xDRslclB8TL24G8W3ikLT5+Xkiba8Do2Oj7H2G\nUkNsQtX/GSPpEQzrw6iZNcy358l52zhnr9mv4eTaSWSVLFNF649SqoSbyjfh1PopJmlNfWooX4mu\n//E/VHSCXiMv8OBF3qb3p98TIMdICxiaj/qj43bQslub8q4qqsir+aRH3bVckoi319EMCI7jMDU1\nhVdffRUASUJvxYKGkgRpaCIZ+V6s4+yHPupWPdEV5jgOWSXLFLgGRbwTQDfT/f9O1cRoxBMXz0fI\n5ghcwDYjeN7WC/Z3vvNvuOOOm3HDDXt21A2g8DhVVFEzawyiQIsMP/BhOAYMx0CEiEkn61ISE07H\n8xzHwQ1drHZWsdZdgyZqDAKgSzokQYIbuCz5ZpUsum53k6paf8iCDFEQSccyAla6hDsjQsQra68g\nq2QxkZnAeHYcwzqZXDTsBlY7qxA5oua2N7cXeTWPtttGx+2w46DGdykphYJWwJ78HoJ3bi2wqUYQ\nEbx3SSvhrom7cKZ+BiudFdi+jbpdx/Wp68HzhOga591okoa0nIYu6QOLBRoUkiZGYmISFj+/fuhj\nZmkG0ACEwNgYwVdnlSy7d+ObHqqjT6ctsihjMjeJ9e46m3bRmG/PY6G9gLJeRiVT2QRx2VfYB0mQ\nMNeaIyTZ0Md8ax5HK0cTE00qlUkNYy/Go4knMIptHuQvAICp6PW/J4VKbtWM4HgRysgxBOYqfGN2\n22T02JMv4tCd/wF79xx9W3XTfhzjYhDmt0Iu6brErDdezExkJy5KeA6jkCksxiOv5jGSHtlWJAQA\nURMbMJGh36E/l7DOPR8hkwM8N4JtYdvC5jvf+TcUChruu++ndqySSNeprteFHxHJfkXf4LQFPlGE\n3EAEUBWpfn4cBw6qRKasmqghiAIsd5ZRt+oENcBL0CSSVxzfIXA0joMiKIyHtB2HROBJc2o8Mw5F\nUAgkzG1D4AgR3nANjJgj2FvYSxpYPlFUXO2uMiXQvEKQGG7gkikUeNa8M1wDlm8hJaUY9Jl6EQE9\nT5nxzDg+sO8D+PfVf8fxheNY7i7DcA0UtAJEgcBxKe/mydknMZQawr7CPiIskN4aupQQXtiCbxNG\nIS4sXgDSADhS0HAch5JWYmt/giccbua25NU8/NBHq9NiojgAYPomVrorDEHR37Ac0UegCApOV0+z\nnL/YXsR7p96beC0tYJzAYTzV7YLneDZ9uVgusTwLTbvJ8jsNiScWGltNjHabS1JDN+D9P/Mzb8tc\n8rYvaACShGhBMzc3h1tvvTXx7292Ep9yAlkAACAASURBVFrrrqFqVtnfqQLUxdSbum4XDbuReEAk\nQUJJK10UokahRUBycgNs8GsG3Ov9r4u4CJLsQ5QiBAHgOhzCEIgigOOAf/iHv8ef/MlnMDNzFg/+\n+oP4wl9+Ycvv069ORoM5CTsG1s11NC3ywGeVbMI7gS4McY+TrVyh/cDHmr3GNuhu6CIlbkDQ1Cx0\nSd+kqtYvHS1wAuZac2g7bWSVLLlm+SnUzToutAi/o2k3CbGU45CVswBHrrUf+eAikuyoz09BK2Ai\nOwHLt2A4vYW463bZvZFTcthX2IeG3UDNrEEWZKSkFIKQKMX5oc+kmTlwxERTkBJqb7QDZXkW1rEO\nWZCRUTLQZX3rBXMDHz2Ib2P7NhZWF8j9IgDDY8NQBIURKZmaWOix3xmEry7rROmtYTWwaCyi6xFi\ndQSisNOwiGrc3gIpAGl3aywzhnKqjDO1MwAI5v+FpRdwaOQQSz7bSWUCPVlM+mfQc9dP8HR8h02V\n4iHwAiuSLxYcx0HURyHqowi9DoLOEqLARhT64HgRf/fNf8Jn/vSvcO7CMh56aBVf+MKxi77ntbi6\ncTGRmTc7lxiOkZjyi7y4I5I0nejE116RFzGWGUv4xwyK+ESmHwUQ55HEI8FljEgu4ZUQkhwi8CO4\nDhCGHMslz3z/e/jkJx/EzMxZHLnjCO677/iW34cS+ztuJyEvrYgKFFFhMvddt4sgDKArZN3vHRCR\neaZFylZrAj3upt2EH/lMMl8RFaiiCl3SMaJvwKY4jklH9yuJ8hzPYHGqqGI8M468mkfLbmHeIByR\nrtvFrD+LttPG3sJe8OCJJLNH4HNe6GE4PYwwCiELMvJqHhHIWk/XWj/0Sf4JfSiCgkq2go7bQc2s\nEZEGUYPAC1jprkDiJRwcPohb+FtYs5T6psVzSdWsompWcXzxOINj7y/uRyVT2dYCYBDfxvEdnF0+\ny8yHR0ZHUFBIfqRTl3jxshVXh8K6O04HS52lBGzScA2cb5xHUStib34vRtIjCbjXdH4aLyy9wBp4\nzy08hyPjRyALMsth2wWFJNP3HHQOBuWSltNKGNQCZB+WU3MXff7oe26XS773/efx65/8fZy7sIyj\nR4/iA/f/Pxd9zx/HeEcUNG/lJLTSWWGkPoDgIylZbasIwgANu7FJD59OZbarvPsN02gMktccxLnp\nFxGgi7LAR5DEpIrY/v1jmJk5CwB49OuP4r/99/+GfD7P/j2MwoFFDP0cJ3DgBA4TAZjMTmIyO4mu\n20XTaaLttAkZnhehSApUnpAiZUFGRs6wgodCDii/IYxChsWlKjJ+SEbqTuigalYhcAJSElGooXAt\nVVThgPz+QnsBlmexQqecIj4DKSkFURCxZCwRqJecYnLPeZV4x9i+zQQDALCignbpckoOTbvZS7ob\nG/Ku1yVJMj2C/YX9qNt1NsHquB1GRjc9E9P5afAcj2F9GCIvouuS4++HRbmBi5pZQ82skemQ3FOS\nuxjfpm0TSfCl9SVAAsABxeEiVrpkQ0WTdr8EdL9UJ32tJEioZCuYzk+j7bSxYqyg43V605DAxunq\naRS1IqZz0+z6Tuen4QYuTq2fgh/6aNpNNOwGDo8cHriRo2N/WsBcbAoa/97U7I7er3EoQUpK7QjS\nMyh4KQ2+cEPiZ1MHGzh34Q8AAI8++ij++I//GLlcbtCvX4s3KN7KE5q208ZqZ5X9nUoFbwdZjqII\na921RA4CSC4ZTY9u+2wMEpShQaFR8dcCm41547/LvLCkELK00ZDaeIvb77gRi0tzAAe88MMX8P0f\nfB9Hjx5lOSsMQ7bG93vkAGTT2PW68EMixkK5QF7gsRzkhz5kQYYu60RhUyLmlYqgEN+3jQaO6Zms\nAEEEiIIIESJc3yWQ1sBHOyAqXG2n3Wu0iRqDCCuCgpDrKT66gcsmOLIgYywzhlKqhJyWw3xrHuvm\nOhARaK2xYrBixQkcdiy00UZzkMARDo3pmTAcgx0/PR+SICGrZDGeHidwM6tO1k+rCTsgCm8Nq4Hr\nitdBFQk8LSWlMNOYYZLQcVhix+3gldVX8MrqK1BEBXvye7CvsG9bdVZa8HacDpaMJcytzQEqAB4o\njBQw255lrx0kAT0olwAkp46kRzCZm4QbuFjrrqHltHqKqiAIAKpQSs07y6kybhu5Dc8tPkeuX9TG\nEzNP4Mj4kYGFBUWyxGFkOwmaS1p2izXv4sezk73cVjEol9z6rjEsLD8MAHj++edx/PhxHDv29muQ\nvSMKmu2SkG3bqFZJB1wQBIyMvHGKD0vGUmK8Tze5293ElPCXcI3dULTaDlKwm0Im/m8AWUjCMByI\ngQV6XBsqR0h/ds899+Cmm27CqVOnYHZNfP3rX8eDDz3I+AZ0ckAXcgCwfItp38uCjJS4eWOdUTMY\n0ofI9MH30HSaifGvG7ioWTXUrTo0SUNBLWAyS5TDHJ8ULPQ8eiHp4EuQkBJTkAWZTSCcwIHZNRnk\nTeAFqKLKFORo906XdBiuQYzHOAGj+iiGUkOwfRvLnWV4gYe17hpOrp9E3axjMjeJolrEeHYcutSb\njNDpRxRFrNPvhz5L0LSQoNCxcopguGcaM6wLmFWysH0bs81ZFLUieI7HnvweFLQCClqBdS+7Xpfh\nq2l4AfHJadkttkHXJR2yIDPYBv1DYRqO46BpNElXLQBKQ6XEfdHPt+nvXkk8gXf081IySgaVbIV0\nKVvzqNu9DVfNrGG1s4qUlGLGgAWtgOn8NF6vvQ6AdPReXn0Zt43cliBtxnX9dxPUB8jxnU3PkCZq\nTNnuSsY999yDm2++GSdPnkS328Wjjz6K3/iN37iin3Etdhdv1eZY025ivduDmsiCjEq2su29TkUD\n+ifRo+nRbeHKFy1ktmiEAL3JzaDOOtDLJRzHsYlwhAgjwyP4+Z//eXzjG98AAPzVX/0Vbr/jdrTc\nFiti6IaX5jLLt0hDCAFkXh4IAZUECVk1i7SUhizK8EM/ASENo5AJCYi8CE3UMJoeZZ5fbafNGmb1\noA5REIEIrGGycaYYNLVm1dj3TEkpRCAqa6qoEllpkRDLbd9m0thpKY2h7hDmDSLx3LAbqJpVLLYX\nMZYdQ0krYSI7AU0k+YhOAKgfThiF4Hme8QfpdaJiMXZgI6NkkFWymGvNwQpIozQlE7TDancVaSmN\nMApx49CNODRyCIdGDsENXMy15nC+cR7n6+fhBD3Yu+M7eK36Gs7UzhAxikwFe/NEKEcRlcSkpWW3\nWEG93FgGZAA2MDYytuneiPNtBI7I5yd4LfyGzHHfLVjJVmB5FuZb88wXDSBF2Im1E2QamR5DQS1A\nERXcUr4FL628RI4lcPDC0gu4Y/wOJnKz2wImHvS+MRxj034qTvi/klEul/GRj3wEjz76KADgL//y\nL9+WBQ0XDVqV3mbx1a9+Fb/6q78KAPjFX/xFfP3rX2f/du7cOVx33XUASOGznQP0lYooirBkLKHl\n9JQpskoWlUxly01RGIVoWI1N3ae0nGbSjlt9Ft0k98d25onxzx2k0AGgpwS1FSkQHL74xS/ioY8/\nBMjAvgP78MR3n0i8nhLB7YBAs1SB4JT7N7hUKUaTNKiiummSZPs2GlaDTFmCzZtOKl9MoQAiL6Lt\ntBl/xo98NsanZEz2/huFnBM4WGoTngddiKj8piIo0EQyoSmkCiioBbiBi3VzHWfrZzFTn8GCsQDH\nd5DX8jg8chgfvP6DzKU77oFg+3ZCjIGSDvsjiiIoIsFou4GLul0nkw5ewlJnCWEYQuAFTGYncbB8\ncNN1opv0jtNB22kziFgQBggQJAw46XlLSSl4ocfG4/Nz8/jgfR8EXCIIcPz48Z4aGr+ZmE83HHEz\n1P5jGtTFbdpNzDRmiLxpnyyzLuuYyE6gpJWwZCzhTO0M+7yCWsBdk7szTOv/bIqv7/85vR+ZJ8NV\nwCQ/8sgjeOihhwAABw4cwOnTp9+W2Ocfl3jllVdw+PBhAOR6UCgzjXw+j1aLrOtra2sol8tX/Ts1\nrEYCsqyIyraQnzhUKB5pOY2xzNiWm7TtCpmtZHgHvcdW77vdfc2Bw/PPP48777oTUAApI+GJ7z2R\n4MOGUQjLs1hzi3IF+599juOgSzoySgZpOb3peIMwgOVbbHKyqQkIDrJINs/UTLfrdpm6lRM4EDgB\nlmeh63U3qR7SIsfyrJ6qIsDEBmiOS0kpJiFNoW0zjRm8tPISVruraNtt5NQc9uT34P4b7kdZL7M8\nQpXeqKJb3EQ7jqKIBw+ewOdCn/mb5dU8ZluzJCeBQzlVxpHKkU3njEron6ufw9n6WTSsBjMs9kMf\nIXoQ96JWJGiL3GSiQQgAH/wPH4RZNYEIePbZZ5Ev5CFyYqIhFZ+sx81bt7qv+qPrdnGhcQGLncXE\n+afXoJKpYCQ9gqbdxA+XfwgArJh9z9R7ElD33UQUES5r3DuO/lwTNeTVPEMUXI01/vnnn2dFjKIo\nWFhYwNDQ0BX/nDcz3hEFzeOPP44PfOADAIC7774bTz31FPu3J598Evfeey8A4N3vfjeeeeaZq/pd\nooiQ6uKENUpU2+omtn0bdauewG8KvMCI81t9zqUWMtslLqA3Fo1jQfsXDpoUqs0qbv2JW9E1yFj1\nb/72b3Dnu+5kUxoArDvVf/yiILJNtCqqO3rIgzCA6ZmoWlV0nE6i++gHPvzIZ/KKeTUPXSbFTUEr\nJM6JG7gwHOIS3XW7aDktzLXmmARzBOKSTTk5lORIO11pKU1gClGEmlnDufo5vLjyIsIoRFbJ4vDo\nYYzoIzhQOoDJ3CQzV0tAMHiekfc5jmNFG4WNUXhVhAh5JY9yugyJl7DaXUXDaiQ6tuOZcewv7ocq\nqgMNJ2lhSbuatIDqP+e0E6pJGnRJx6svv4pf+L9+AQiBu47dhe89+T2W7Ghy3Y7D0i8Bzc6/78IN\nk87L9POXjeWEgAZAEk5WyWJPfg8A4NVqb6OZltM4Wjm6q6KGQh5Nz9z0HFDu0nZTzSsVnU4HlUoF\n7TbBgT/22GN4//vff0U/41rsPBqNBopFYlKbSqXQ6fQgkYZhIJslkw1FUWBZ1lUvPmtmLQEXU0UV\nlWxly/Xd8R0sGUsJvH7cOHirGNTYAnZWyFxsi7FJLGBALqGcmA/+xw/iRyd+BAD41Kc+hV/+1V9m\nUDMqEJOSUpuKObo+5JQc0spgX65B39sLPDahSTS5YkR/gEx6aHOjv0iiMFX6p+t20XbbTIyHFhyy\nIPfI8lGPj0HFCHjwaNgNzDRm8Ozis3B8BwW1gOuL12MkM4Lp7DT2FPbAD30YrpHYL3Ach5SYYsdO\n8ySdqkRRhNXOKpzAgSIoyCgZohy2oTY605hh16WklXBz+Wak5NRA00kqVLTQXsB8a54of2Lz2uj4\nDvNFqmQrGBKH8JNHfhLwAVmU0aq1mLXBVipp8RgESQNI3ndDlzUG6f3oBR6WjCWsdlcTOYpOzKbz\n08jIGby08hJrokm8hLsm7tp1UWN6JisW4yELMpsKDULPXOk4duwYnn/+eQDAn/zJn+B3fud3rvhn\nvJnxjoOc9cME3kiIQBRFmG/PJ0ySCmoBY5mxLV/ftJubTJV0mYwlt+pKbFXIxDs1W33edgvGoMUi\nDiXwA3/T4q9oCj788x/GV//6q4AIPPL/PYKb7rgJqqiipJWYpCP9XEmQGIQnvgENogBcNHjBigeV\nXM4oGbiBi7bdRt2uo+t2GXGTTkGadhOqqGIiO7GpaKKS16VUCa5PPFIkXmJJico+CyCJiEdP2SsI\niUlXt0X4L4ZrYKW7wrptVGZ5ubOMZWMZGTmDfcV97JrG+Tvx60VxzJZnoWpW0bJJp4fjOHS8DmRb\nJtCrHFmIDcdAzaoxztVadw05lciN9hfCPM8ziEFRKxJVMLcL0zeJYRwnwA1cdgwUi7yyukIkmwNg\ncnwykcipBLQEiRU2/fcXhaQxiWiq34+eBGx8g5OSUthf3I89+T1YM9dQ69Z6JniIMNOcgSIoKKVK\nWOusQeAFdNwOji8ex9HxozviuNBj7/+uIi8iJaUSvJyt/AWuVKTTafzyL/8yPv/5zwMA/uIv/uJa\nQfMmRj6fRzqdRqfTgWmaqNfrKJUIzJKZAuKNMdWkYhk0NEnDeGZ8y816zaxh3VxP3LMpKYXxzPiW\nogFvVCET/xkHMukwHAOGayRQCb/4S7+I3/1/fxeQgEe/9Sju+/B9SCtppsIWJ4pLgoSMTHIB9YAC\nNuTouZ7p8nbfTxZlyKKMTJSB4zusCHC8npmj6ZkQBQIFzik5Qq6PqaRJgoScQIjq9LgoBM3yLTbJ\nCKIAIrdhsRDjmXacDtY6awijEFWrimqnCk3QkJZI4SSLMgzHwIm1EzhTO4Pp/DSG9CF2DSRB2lRk\n0TyphRoTnqHFjRu6EHkRQRRgND0KTdTQsluYaczAizyiFtpZQ07LoagVN0t5cxxKqRKKWhG3jtyK\nrtslxU17HmtdsiZ7oceEXWpWDYZrwGt5wAiABjBRnIAqqYn3vJhKGoWkUf8j2qyj63j//SgJEqbz\n08wvjjbJ6D2x2F4kEEx9FAsG2Sd6oYfnFp7DnRN3btsAoOH4Dpp2MwHHA8DMUePnrl8s4ErnEgB4\n+OGH8Su/8isACGzzt37rtyAIVxbe9mbGO66gWVhYQBAE7CK+UQVNGIWYb80nCGAlrbSl1KHjO4yk\nR4PneBS14kCXZ/qQDypI4qaYg35vp9OYQUEnMZuI/RFYx/+DH/4gvvo3XwUC4Ml/exKdWge5Culw\nUDlMWlDwHD/w+/R3aOjxbPfdZEHGkD6EUqpE/AfMKpp2E0bX6GnIRx7WzXW0nBZSUoqZd9FiyvEd\nzDRn4IUeG3XH3ewdnwgXqKJKPHDcDgzXILAGWWdFj+3byKk5iBxZyBZaC7B8IiqgCAqWjCXsL+7H\nvuI+ZOUsg4NR5+C4Zr0iKCioBUJMNW04gYOUlELdqmPdXGcS19cVr4NX9VA1qwijEHWrDpEXYXom\n0+WnGxlqOBpXcaP/9UOfmOy5baKas8Fv0mUdS8tLgAJAATKTGSwby2SqJOuJzQKVgKYFt+u7cAKH\nKdfE79s4JI0lMvA9M8sN+cuxzBi8wMNCewFLxhLrslExCS/0ULNqKGnEy+f44nEcrRzdcqpJO6n9\nXTRahA6a8MQVf+L36pVMRB//+MdZQfPP//zPmJubS3A5rsUbFxzHYXJyEqdPnwZAGmS0oHkjm2Nr\n3bUEVIcWJoPuO9qJjhcGHMdhWB9GUSsOfP/tCpnt1txLKWJobKVOxoEjUDKvg1vefQsyYxkYNQPr\nM+s4/eJp/NRP/hR7XVypMjHxiH9HRIkmGuVwbtcs4zkemkQabXR6b/kWmlazt8lGCDdwUTWrkAWZ\nyEOLCltjKQwsQsS4fKVUiTVZ6DTIDVwmVND1unBCB4IgwPeJ103LbUGRSA7IyBk0rSbqdh0CJ0AS\nyIR+IjuB60vXo6AWIAnEEJQ2jeK5RORFkrsCD57vwQ6I0lrVrDJTVkUkDaKG1WDr7Jq5BlmUsRqs\nsiYYXR/jhpOyIGMsPYabyjcxv54Tqydwev005o15eAEpbHRZx+uN14ESgBLA7+fxv1/939hX2Ie9\n+b0J6eR+lTTaBKMTmMRkKgZJo9c27lFGc0lZL+PA0AGsdFYw35pnyI4gDJiYwEpnhU1mnlt4DndW\n7mQqpf1B0RP9FAGe48mUUE5vWdBfzVzykY98BJ/61KdQq9UwOzuLf/mXf8HP/uzPXrH3f7PjHVHQ\npFIplEol1Go1+L6P1dVVZgAYn9hcrSQUhAEh28U8NsqpMsr6Znx1FEVoOa2EdC8ARm7vn7BczUJm\nq+7VVkUMVS2zfAuO70AWZKiiimM3H8N7jr4HzzxN4Hzf+MY38F9/778mihigJwfdP2Ie9B3DKGSO\nyf0ymJuOdSOBjGfGmYFZy2nB8i1oIikO6bi87bTZyF0VVax2VnvTAkSsQ0+/E5148BzPVG0oSdT0\nTKx0VtCwG8RFWlAZV2qtu4blzjKq3Sq6HpHfXu4s41zjHG4cuhFj6TFoIsFSU0I+x3FA1Fss2VQF\nGpNGptG0mzA4gzmDN+wG63iW9TL80EfVrGJYHyYk1wEdWkrepcacKSnFNggRyM+XV5bZ60dGR9iG\nhMqA6rKOtExgDhQ+5gYum9bEDV0T1xaAAIGdg/6pCL0mkiBhb4GQTZeMJSy2F9l5yKk5BFGA07XT\nKKklDKWG2KQm3hSg8Is4PJHeV5QfdbGkcjUT0Y033oj3v//9ePzxxxGGIR555BH80R/90RV572ux\n+5iammIFzdzcHG677TYAb0wuoapkcSnatJzGaHp04P3WsMhkNv6MUWngQdPKrab0V6uQ2aqIoQ2d\nrkvWUerrNF2axod/5sP4ype/AgD4m//1N/jQ//EhZGTCh2EcBGpBwPc2vlvlSfozKv6ynVAOjYyS\nYYImTuAQueaNdSKu3inyIhSByDjHu/R0faRFFwCWL+l7UO6eHZBpzrKxTEQEeIFB66bz0zAcgxUy\nlmOh7bRRNauYb83j+tL1mMpOQZVU9lmUM4qIQHuXjCVmCEk3+/G9iu3bcHwH45lxRIjQslvoeB3U\nrTqm89OQeAm2bzOPm60argAh4Q/rwxjWhxkvqGk3MdOcQaPZmzbm8jnMteYw15rDd/FdDOvDzO+m\npJU2FTBbQdJonqDS/IqgEOGG2PeKv2Y8M46x9BjWumuYbxERBpoHhvVhvFp9FSk5hXKqjB8s/gDH\nKscSTYEwCtGykx44AFiTMatkLzrZHJRL6M8vN1RVxcc+9jH86Z/+KQAy8b9W0PwYxtTUFGo1guWc\nm5tjBc3V7qoFYcBIdTSoA3x/uIGLulVPFAk8xzOux6D33m0hs1WBAGyftLYqYuL+MRR/q4oqMnIm\n8eD+l//8X/DME88APvC3X/5b/Nnv/1lipAwknaY3vgmbhMTHzDud3tDPj+voRyCuyXk1z9RWGlaD\nQatoImnaTabHn5EzxOxTjXWhOA5pOY2CSjo0HbfDiOOqqEIVVZT1MsYz4/BDH7qoI0SIIX0ImqBh\nWB9GQS2glq6h2iWqa37kY9FYxLKxjFKKbMA5jiNu8yIpKGSeqLCFCCFAgCIpGNKGIPAksRquQcQA\nNhJTFEVIy2mk5TQUQUHNqkHmZZZ02k4bXa+LcqqMolZM4tkRwXCNxMQiLacTCas11wJsADIwOjra\nu89Ckqyo7wGdcuiSzjZScUhaFEVsStQvpRxEAUI/ZFC3QVKdIi9iKjeFiewElo1lzLeJfwNNNufq\n51A1qyhqRTi+g/dOvReKqPTkV/tCk7QETGUncTWLmk984hN4/PHHAQBf/vKX8Qd/8AdQ1cGTpmtx\ndWMrCPPVziVRFGGls5KAIGeUDEb0kc28gdDHsrG8Ca48lBpi60r/e1/pQma3RQyFb1GoqyIoZJOs\nJoUKfuGjv4D/+YX/iciO8Ow/PwtrxcLUgeTEcie55FKmN3QtpIUBndpQHxPaGKFTAj/0iX+LuQoe\nfCI/0nVQ4ASi3MWTSY/jO4QnCWIMnREyyMk5YtoZOKxBl5WzrOGlSRqKqSLWjDW0vTb80MequYqV\n7grSUhrldJlNJeh3UISe2hhAPn8oNQRZIGpvTuAgCIPeZJyXsK+wj6iHihoM14DES2wv44c+FtoL\nyKt5FLXipuZr3SLQbxq6rGMqNwWO43Avdy9e++fXgAUABQLtjN9jy8YyFtoL+M7Md1ghtye3B2OZ\nsQRag+Z8mksGKZH1m5xushDgOIykRzCSHkHVrGKuNYeO00FaTuNg+SBOrZ9C024ip+TQdbu4d8+9\nKGpFGK6BttPe9BylpBTzTttp9OcSeh6uRD558MEH8Wd/9mcIwxCPPfYYXnvtNRw4cOCy3/etEO+Y\ngmZychIvvURk+Obn53HXXXcBuLpJyA99zDZnE52Z0fTowDF/22knIAQA2RgPWhi2KmToKHZQsroU\nWFmcKB4vYuIjcS/02ALZ332I82H+0//5n/Dp4U9jbm4O9Wod//DNf8ADDzywrXwn/e793badTm8C\nEFyyF3jgwMFwDJZEVEll0qRFrQjLt9CwyBSjYTWw2FlkpNCW3UJezSMIA2SVLDRJw0h6BFklSwiY\nfapoHMchI2dg+zaWuktEHpmLkJEzuHnoZiYk4IUe9oZ7sW6u4/X661hok3sxRIg1cw0dt4NRfRQC\nBDT8BupmHVZAIAmKoCCv5DGWGYPt20jLaWTlLEpqCW7ossRB1d1WOiswPZMtrLIgJ8bq1A+JHheV\nlqRy0FEUDTTfXJ5fBkwAJnDz9M1QRGWTtj4ABEEAN3DRtJsQOCJJndfyyCk5phREr23cuDN+P/ih\nDx8+g6TRwr2/y1bJVjCeGcdqdxVzrTlGnD1bP0sw6GYVs81ZvG/P+zY9i4qobEn430lcre7a/fff\nj6mpKczNzaFareLv//7v8cADD1zWe16LS4s43C+uink1c0kURVjuLCc2hFklOxCy3HbaWOmsJKA3\niqhgPDO+6fndjje53XR/u9iqITaoiKF8ta7Xhe3bUEQFuqQTZ/rYBlDkRaZKduPQjfjZe38W//RP\n/wQAeOSvHsHnPve5XeeSuCz0TqY3iDamOBuFC1XiFDiBNRw1UWNNPtoApHy8AARq64c+mfZHBMaq\niAp4nmcqbW7oJo5DERTYvo2O20HdrBMYmKzguuJ14Hky+XZ8h0CZpDQW24uYa8+xRo3t2Wg6TQyn\nSRPNCzzGI23bbcaxqWQqkHlSKFEOKz3WuDJahAh1q07EFzjSZKJw3wgRGnYDbaeNolZkPJOqWWXq\nnQAYvJtdoyhCY7EBLAJYBD7wax/AXRN34Wz9LObb8wjD3rXpuB2cXDuJk2snIQsypnJT2F8gcG2q\nYEo/ZyvzTWYEG8UgadRyIvZ62gBoWA3MteYQRRFuLt+MU+un0HJaaDnEVuBY5RgmcslnnhpnX6rC\n5tUqavbs2YP7778f3/rWtwAAR+mafwAAIABJREFUX/jCF/C5z33ust7zrRLvmIJmK/+Aq5WEvMDD\nbGs2AWOhDsD9r6tb9cTrOI5DXs1vMnKiEJ3+2K6Q2S3Jf1ARQxWfaBETRAHrnvW7KCuiwoqY/o7E\nQw89hN/93d8FQEadDzzwACum6OfsNCHFIQHxRat/M0l9Q2jnJIoigEPi3Aq8wKYYdauOlt1CWk6T\nJLLRmQE2YFyugbH0GNo2gZYhAuN6AEQFJSWl4AYuVjoreHHpRTSdJjRRQ0kroWE3WJGhCyQJDulD\nODB0AKudVZyqnsKqscp4IAvGAob1YVTSFeLl4gmAuGHoJYhMpCAO8UpJKaTEFIIoSBi7rXXXem7U\n2XFM6BNY7a6y6+wGLuZb80zFK775ySiZhBILVfmZW9kwQhOA4ghR3VPTKuu2UhM6AAzjLQkSRI5I\nnVqexcw8Ke+G+u3EIWnx+yK++ehXSaP3AcdxGE2PYkQfQc2qYbZJDNpOV0/DDVwstBfwdyf/DndV\n7sJ1pesYufVSfAX642okIkEQBj4/1+KNjzd6QhNGIZaN5UQhkFfzmyDLtDERh6MBYMaL/WpiV7uQ\noUa/hpMk9tNNftfrkubMRhEzlBpKPH+KqDAoWT+M6eGHH2YFzde+9jX84R/+IdLp9GXnku2mN1Ss\nxPHI2kw3qmmpl0s4jmNrnCqqaFgNBFIAx3fgBR4UUYHACwzGJQtkWk65ijzPM7NJKsHvhz7mmnM4\nsXYCy51lIjrAC4QvuaFepogKFFFBWkmjkq3goHMQM80ZLLYXYQc2mRIZq7A8C9O5aeiSjjAKUdbL\nTOxEl3V0vA46XoeZiVJDUJ7jYfs2QoQoqkV2TQVegORKuK54HWpWjRVRQRRg3VxH3aoztTYaGSWD\nvJpP3E9+6OPC4gXiQSMAY+Nj2F/Yj/2F/czvZrY1i7nWHLzASxg2LxlLWO4s4/sL38dkdpLwbgp7\nGdSZCg/R/UF/o4xe8/gkrn9qQ33c2k4bcy3SxHhl9RXSiA5b+NaZb+HwyGHcVL4Jw/ow8mp+IPRu\nt3G1ippPfOITrKCJPz8/7vGOkG0GgM9+9rNMou6Tn/wk/vzP/xyu60JRNuAvPA/btiFJg9VedhNu\n4GK2OZvgNExkJzaZlRmOgZbTStywVF0rvrDvxhTzUqYxg4qYOA6Yjr/pJCZexHAcR/w4NrgO26mo\nra+vY2JiAq5Lirfjx4/j6NGjA1+7VUJKHEuMc9P/e2EUsiTi+ASKRSFEOTXHuk/x0TNdrCiBsmE3\n2CSDLtRFlZhVdrwOgjBgBYwq9WAElES/1F7C6/XXGenx9tHbkdfyAyWoKUTMD30stBawYCywIpdC\n5qh7tRu4rCiiinZU5z9+3XmOJxOpjc5iGIUwHIMlzcncJPbm9zIj0iAkk5GOR+SjM3KGdKj0HgyB\nyXP6LhzX6TUKOOJJI8u9blTcfMwLPGbmudVGqp93E38GtlJJi98LcUha//3jBR4WjUW8svIK/n3l\n3xEiZOTVW0ZuwZ78HkzlpnakXLPT2IpUfamxm+fnWly9eOKJJ/DTP/3TAID3vOc9ePrppwEAhw8f\nxiuvvAKAeD4cOXLksj+L+nvEfZAKWoE5m9PouB0sG0mPJkmQMJ4ZTygpbZcfBhUyl1PEWL6V4OFR\nXqEf+qyI6W8ipKQUm8RsaxQdhjh48CDOnDkDgCg2PfjggwNfu5NcAmz2VIvnkrhiluEYbApA4cXx\nzTCVRe43exY5EX7Ug3JRYjtVOwujkJkMy7zMpjbVbhUL7QWcrZ8FQK7/9cXrUUqVSDHUl3MFTmA5\numW3MN+eh+VZRKkyJL46VIzGj3wInMBgiDx4ZkrZf+3joj0cOCwZS/TEYTg1jOuL18MPfdSsGrzQ\nI0I0Zp2hOEqpEob1Yab2Rnkw1OvnnnvvwemThJv27ce+jdt/4vbeMW2YMPMcj/XuOmaaMzjfOL+J\nqxKPYX0Y+wv7sbewN/G8bMW3iUd8XzAolyy0F3Bq/RSeW3wOvk/4rTzP49DwIdxQugFTuamB0M7L\niUH7v0uN3Tw/P07xjp7QLC0tsZ+Njo5ekWLG8R3MtmZZYuHAYSI7kVDpoDrtccMtjiMu7/Gi50oX\nMvHXDypimMnlhtElQMamWSWbKGKY4stGEbPTB6tcLuOjH/1owq32a1/72pbf91K6bfHfo383PZMV\nMLQgo79HR+VNu9lboEE2CJUMcdqm0DoePGp2DVWzmpiUiRzxsem4HZacu24X6xbxgZEECRPZCUa+\nj6uJ0b/Hz+GwPoy91l68Wn0Vq91VcCI5hrpdR1ko40DpAEqpEvOM6bjEb4fjOAbXolKgdCMUIkTX\n6WLVXEUQBNAkDQ2rgZbVQlkvI6fk0PE6WOuugQfpfrWcFmzfhuEaTJK6d2KBlZWV3nceHoamaMxs\njp5vGqqoMhNRCp/oet1N3g50qrPeXWebnrScJp3NDYhIGIWbiputIGl+6LPPycgZvGfqPZjMTuKp\nuafQ9boQeRHn6ucgciLqVh05JYep3NRAjttu40p313bz/FyLqxdv1IQmCINNnjFUDpdGGIVY7ayi\naTcTv5tX80zKGLi6hcygIoYKbdAihqplZZVsooihXEQ6idmpQzrP83j44YfxyU9+EgB5Fn791399\nS+Woi+USAInzE+dXUFgreNKEpE0T6ldCf5fC0uh0mnJ3wIHBsR3fIeqavoeG00DNqiXQGTzHJ/Iq\nNbmkJtyqqBLfuiyBD0q8xGBriqCwCRCNSraCvYW9ON84j4X2AriQg+maaFkEhbAntwfT+WkAYGuv\nF3oJKC+dVtH7kCI2Om4HTbsJWZDRtggCYlQfZaqS55rnGNzb8i3UTeKXRMUS+q9RfD82UZlgKmlU\niYzGVH4KU/kpvG/P+7DeXcf5xnmcb5zHurmeeM+17hrWumt4duFZZJUsExWg0ubbSUD3Q9KolHjL\nIYR/gRdwaOQQJrITeOL8E2i7bciCjNnWLBRRQcftQJM0TGYnE8/h5cSV5Gju5vn5cYp3zITmmWee\nwXvf+14AwJEjR/D888/j6aefxt133w2AGA794Ac/uKzPsH0bs81ZtknmwGEyN5mAN3VdomgVP+2S\nIKGklRLyjjs1xdwNyX9QEeMFHiti3JAsrIMmMSIvsiJmJ14eW8Xx48dx5513ks+5BLfanU5ubN9m\nPBA/9FkXraAVNiX2mlnDbGuWTaXadhtD+gb8IQJ0RScbfqfDsN6GY8AKLEL0lBQGQ6ALpeu7WDAW\nkFNyqGQruHvq7kRRu+k7D1hIvMDDbGMWJ9dPssWaA4diqoiDQwext7CXJYY4xIsazVG4HStyIh+u\n72KmMQPDM4CITFGGUkPEb4GXkVEI98f0TQZloN2xsk6EA+g98fzx53H/ffcDIXDk9iM4/tzxix5T\nfzi+wyY3g8j5NBhMT9bZpG0rvg3QU0nyAo/B0ijcUBVV2J6NZ+afwUpnBS27BYEXcHDoIFIy6Wan\n5TSmclMop8pXZJG/Ut21y31+rsXlh2VZSKXIfSIIAhzHgeM40HUCIRVFEY7jMI7FpUQQBlg0FhNN\nr7JeTkwQTc8kClWxpoDIixjLjLGcs10h09/koq/fKi5WxMSnsFQla9AkRuAFVsD0y7vvJlqtFiqV\nCrpdwiv67ne/i/e97307/v2dTm7CMESIkJH1qcqlLulsikLDCzw07AZDB9DX06D5wfEd5kHjBE7P\nt01Q4EUeHM9BCMIFNT0T8+15qIKKvcW9ODxyGOOZ8YHfdSs+bBQR3suPVn+Es42zRCGUAzJyBgdK\nB7C/tB+KQPI6zYNu4PaU1mLFDC0AwijEheYFZpzJczwms5PQRA2u7zLRnY7bIZ43EjEHpSiJolZk\n94Rt25gYmwBC8j6u5Saen52sl4ZjsOJm0VjccgKjCAr2FvZiX2EfpnJTrMG5Hd/GcAyiPMshUeyl\n5TRkXsbT809jvjXPTG5p0xEgz8BEdgJj6bEdF+zbxZXKJZf7/LwV4x09obmSHTXLs9imGCA3/VRu\nio37KYQpDh0AwKYy8Q3axQqZ3cDKgjCA7fWKGNpdoYuzH5FJEp3EKILCPofiezVR29J4bbdx7Ngx\nHDlyBC+88AIcx8FXvvKVXbnV7qTbRuEBju/A9myGZdVlnf0bhU+tdFbYZMYNXFieRSRQNzoyGTkD\nRVDYQkWxyjk1B1VUYXkWamYNkUCUZ3ieR9fpEuzwRncvDgsY1LWnx0L/nYYkSJguTIPneZypnsFK\ndwUCLyCMQpyqnsJKdwU3Dd3EIIr0XqIdV7rB6LidxCRHkzQsthfRdJpo220sd5YZzhgg90JOzcF0\nTXbMmkgM2NY6a2xxbq20gI3m4tTEZl+UnRDi6fksakUyTdn4nnSTFL+mVOKT5/gE70YUe3wbP/QZ\nGZf+PoWMaJKGjJJhxqV3T9+NF5deJNM0cx2na6dxoHQAaTmNjtvBqfVT0EQNU7mpy+6yXanu2uU+\nP9fi8kPTNJTLZayvryMIAiwvL8Oyeut6pVK5rGLGD30sthcTnXsK1QF60s10TaKRVbIYTY8mpIp3\nUsjsdBozqIhhDQmvy4ovRSRkaErQBkguoVCyfiPGS41cLocHHngAjzzyCADSZd7Nhqw/lwDYlE+i\nKIIf+ezYKRdEFojapOX2fGO6bhdNp4kw7G2MdUln0HP6WVQkiEKCFUFhZpwdvwM/9JGVs5B4CYZr\nwPIspKQUm25RKBMHbiBvNEJEVNNikzeO41BQC5jOTyOMQiy0FxAiREpKYd6YR82uYX9hP8bSRDWM\nyvSX+BIzLLU8q8eL3LBm2F/YDw7E2Nl0TVxoXoDMy2xfIUBAQSvA9Ey0nTZrlFLPtmF9mPAcF2os\nl0xOT256fnaSSzJKBodHD+Pw6GGGlDnfOI8LzQuJZ8kJHLxafRWvVl+FwAmYyE6w6Y0u64nJVMft\noGW3WJMaEXmuKCdWEiREiHDvnnvx7PyzaDkt1MwaXq+/jiAKUE6V4fgOztXPYa41h0qmgkq2cllc\nzSslPJPL5fBLv/RL+OIXvwhg98/PWzHeMRMa3/ehKApTy7BtG5///Ofx27/92wCA3/zN37xkpYeu\n2yVKHDGi8lRuim2kTc9Ew2okOgbUVEsW5G0LGUp+A3ZO8u+fxERRxDZ5jr/R+QHYiJoWMRzHMTIg\nJR5ejfjrv/5r5lY7PT2Nc+fOXbZbbbzIazttOL6DqllFhJ4cMO1a0ge/alax3iWTD9M1YQUWxvQx\nRvLPK6Qb6oc+MyMTeRFZNYusnGWkVloIURhV02riTP0MgjCALuu4uXwzcVze2FBTwv3FuqFBGKBp\nN5nxZMMiXjVx0q8kSJjKTeGG4g2JydlWMBIKE5hrzWG+NY+G3UDVrEIRFDbFkAWZTTJkQU6YadJ7\nI6tk8dg3H8NnP/NZIAA+/uDH8ef/488HilPEz/lOg05YaDG2E96NyItMRSgOSaMiB6yo3ODb8ODR\nclp4YekF4uOz0V0tp8pQJTXR6VQEBZO5ycvusl0JXs3VeH6uxe7iyJEjePHFFwGQ6b/jOPipnyIG\nj3FezW6Dcr3iU5fR9Cib7lqehSVjKbFBE3gBo+lRBle+koUM3dTRIiYMQwZ1pfAkoKcOGC9iNElj\nk5jLmepvFz/60Y9w6NAhAGRaNjs7i0qlclnvGW+E0I173aozLmkQEshufH2gprwAmegEUUAKt41H\nm+d4NumhMDGRF5GWiPS/E5IJfxiFhPcZOHA8ksPONs7C8izktTz25/cT7xehB+0VOTHRkOoPes1p\nQwgAul4XbaeNhtVgRRwPHsVUkTV14r8/yP/O8R203TaW28t4tfYqDMfAancVukhgwhEipBUywRAF\nERIvsamUxEuMf6TLOi6cuICPfuijQAS8+93vxvee+l4COh6P3a6XdNpJpzf9UubxGNFHsK+wj63z\nNPfScyQLMvJqPiGYw3M8EAF2YOO5hefQcQm/tmE1mEVE/F4ReAHjmXFMZCcuWQENuDK55MSJE7j1\n1lvJ97pCz8+bGe+YCY0oiqhUKonpzJWY0HTcDuZb8+yGFzgB0/lpqKLKNqH9brFpOc2gA9t5ydAN\n5k5gZXQDSIuYOB+GatcDm4sYnuMJqV/SEiaXVzP63Wr/9V//FR/60Id29R606+4FXmLqYvs2XN9F\n223DCzxSOPARslI2wblZ666hbhIFFsM1wIPHdYXrGB65qBXhBV7i2vAcj4ycgRM4WOuuAegpzOXU\nHMYyY+i4HZxeP02u/0YHLC2nWReTdqnoz3VZZ+TQeARB0uxREiTcMHQDri9djzO1M5hpzhBjscDD\nufo5rHZWcXDoIHMLZ9LVUcAMLeObj+tL10MSJNStOiayE7A8i5FCaXeNA4cQITJyhvjKOE0mvbpu\nruP52vPAPgAGoIwrmGvNQZd0qJKacGOOH99OF1ye45nyHC3I6fQmTnyOooh4Bm3I1CqiQpTexJ7n\nDSWyst/Z4NsAZGp3x9gdeGmFSLoP68MAiDIUhSty4OAEDs7Wz2K2OYuJ7MQld9muBK/mSjw/1+Ly\nYnJykhU08/PzTKgBuPRc4gYuFtuLPf7lhlIffQaqJpEcj0daTmMsM8ZUAS9WyOymiKESy5QPQ722\naLdaERUUlEJCKpdy3jJK5oooBl4sbrnlFrzvfe/Dk08+iSAI8KUvfQmf+cxndvUe1FOm/4/jO+z/\nKe+DHiPQ49y4gQvbs9m5EwURZa3MGkISL7F1nK5DHDhokoYgDFCza72iguMhCRJyao5MfTbk76nw\nTlkvEzJ9uAFn2xBeocUN4/v0HZ/t2ahbddbomswSKPyysYwz9TOE54QQVbOKlt3CdG4ae/J7GCIg\nCsmxUgNnP/SJuAAnYCI3gTAKca5xDmk5Dcd3kFWypAjfWNYECKzACcKATZ4sj9glPH3haWAvgA5Q\nmC5gvbtOTI03RG3iggu7nUgIPGkwT+WmcO+ee7HWXWPFTf/ztGgs4vX662xKRn9vLDOGvJpHSkpt\nybdRBAXvmngXfrDwA7RdAlsPoxC6pCOIyH6MIj/mW/NYbC9iND2KydzkJjn1ncSVyCWHDh3CPffc\ng6eeeuqSn5+3UrxjChqAJCFa0MzNzV12QWM4BhbaC+wGF3kR07lpKCLRja9b9QR5XOAFFLUiFEHZ\nUvd+kBzxoKA3M1VIoZvbfj4MsLmIoU7DmqTtyAX9Sscgt9pBGzI6uYoXLHG1mUGv9wIPbujCD3y2\nmU3LabYYiryI9e46HN9BWk7DcA0UlAImc5OMK6LLOtzATXgVUJJ7y2klyehRBFVSE5j1rtdFSStB\nERVM5aaQU3IwfZMpulBccctuQZM0Bp+KF5R+6KPjdtiEibpKA8ChkUOoZCs4uXaSFVYdt4MXl1/E\nbGsW1xdJseIFHvv9/mtMYQxdt8uMPiuZCsp6GQ27gabVZPAC0zNh+ibGlXG4vouW04If+Gg2mkSy\nWQXsvI3T66eZ0IEu60hLaTY9UUU1IYYQd2u+WHAcxyAQZb3MYC5Nu4mm1UwUOEzZTiIdT1o00meO\nQQc2IoxCpOQUbh25FS+vvAwvJCTWte4aDo8chu3bmG/PE9EFcPBCDzPNGQIfyFYuqct2uYlop8/P\ntbh60Q9h9ryYouUl5BLHd7BoLLJ8wXEcxtJj0GUdju9sEgfgOR4j6RHk1fy2cv5xpabtIkKUKGK8\nwGNTGNM3e3BUUUFWyrIiJi55z2Ry3+B4+OGH8eSTTwIAvvSlL+H3fu/3EmqLNOLrb/xP/5pAg27a\nqaokz/Fskk0VEummPitn2dqXV/NsouEHhCMT58fSHN91u5s+m3raiLyIareKmlWDJmpIK2lMZCaQ\nU3NwA5flQCrvT4sbWZRZEUUjCImEMs2nKTnFYO4TuQmUUqWe30sUwgs9nG2cxbKxjH2FfcgoGSJZ\nvaGO1n+Nbd9mxsle6CGrZDGRncDB8kEmHkD3KfS/Q9oQ3KDXeKzVamQ3mgfEiojX66+Tc80JTD5a\nFUgjUBblhOHpbuGdw/owhvVh3DVxF9pOGzONGZypncHZ+tmEZ2DbbeNU9RTON88jp+R6vJvsFERB\n3LQHiRBBEiQcmziGHyz8gCiKcjxWuiu4pXwLNEnDXGuOCUYEUcAkp4f14QRFYadxJYqaT3ziE3jq\nqacAbP/8/DjEO6qgmZqawve//30AJAldTkHTsltYNBbZ3yVeIqNgXkLDamwaa+qyzrTX4xswGkx1\nYxs5QY7jEIZkEaNFDOXCxPkwwOYiJm5yeTljzisVDz74ID772c8iiiJ8+7Fv40enf4R91+3bVLjs\nJuh42PYJb4ZuZplJW0Q6MH7oQxZk1K06VFFFJVMBx3EsITm+wxYEnuMhi0S62Q98VhzQxT0jZ1iy\nMj0TVbOKrteFIAjIKlncUr4FIQh0oeMSnDGV6LR8srgbjkHG75JKpgqC0jMz44ivgS7pic5UUSvi\nXZPvwrn6OZxcP9kbc9sNXGhewHWF6zCVn2L4Z0rGpNLQsiBjVB9FVs6ialaRltMMljiaHkVezaNh\nNRKbKEpkDaMQTbuJdrVNcM88kMlnsNJZgS7ryCpZeIGHpkXgDVEUQRZlxnmhXVxVShY5O90MiTyB\nL+SUHHRJJ0pKrgk7sMl9v1GkD+LdpOU0FEHZ9JxllAx+Yuwn8OLSi2wy9/LKy7h97HbcNXEXVjor\nmGvNsS6bH/mYa81hob2A0fQopnJTu+qyXW4iSjw/3/42zpw5gxtuuGHHn38tLi/iSmdzc3MIgt7G\ndLe5xPZtLLZ7JGaO45jccs2ssc0ojZSUwnhmHCIv7qiQ2aqY6S9iHN9h/x9/7vvhZJIgMShZSkq9\n6cpIP/dzP4fx8XEsLS1hZXUFf/vNv8XP/d8/xybTtHDZiQAADSo04oUe2VCLKWiSxmDiAke8ZCj6\nIggD8HyveRaEAZNnjp8fKn/vBA4pjCKOrc8pOcVyM1WXrFt1gCOGnXsLe5GSUmzqTidIcVlpJ3DY\n+iiLBI7WtHtNH4EXUNJKzHeFTopuGb4F5VQZJ9ZPoGWThpXhGFjprmA8M459+X2QBImcF47kPoEX\nGDyOTi9mW7PIqTlEiLDaXcVEdgKaqJH7K1YseIEHy7eYcli71ga9PMViEaudVWiihoJaQBAFif0U\nLXI0UWONLkVUWNNykCjCVkHFX4paEbeN3oaF9gJb1wVeYE3Gft7NZI743ezJ7YEmaYl7SxZk3Dlx\nJ44vHmdm6SfWTuDQyCEcrRxF1axirjUHwzHImh9xWO2sYrWziqHUEKZyU9sKCPUHPdZL5Wgmnp+V\nFfzjP/4jPvrRj+74899KIXz605/+9Jv9Jd6oOH78OCto7rjjDjzxxBMwDKJj/vu///soFAo7ep9+\niV9ZkLEnvwdRFGHdXE8kA7qAUCOr/kWVQpa266LRTXrH7RAjJ7uFlt1C026i63fhhi5ChFAEhW0o\n6YY+q2RR0ArIKtmL+sRczaDdITq+l3UZL518CecWzgEK4EYu3nX3uxLdp+2Cmo5RQiVVX+PAMVK7\nriTN2haMBbTsFrzQQ82sIS2nGURLEiRIvNSDpW0UExzHMWwz0DO0zKk55NV8D1bBAW27jbONs7B9\nGykxhen8NEqpEuvspaU0NFmDJmgQBZFBnyjkwfIsmC4pigyXLHYSL6GYKrLrRqGEXbcL0zehiiqK\napFAsrwu26hTeEpRKyKn5AjETSFTpDAKGU9Il3VoksYgEaZvMsx7RslAFVV4IYEwirzI4ImlVAlf\n+O9fQHetCwTAr/3nX0MxV2SJSuRFKJLCIAeUVNp22qiZNSwby1jrrqFhN1gHj3Jf4rCM+KIcRRGD\nvtDOJoVMlvUyJrOTRDWJ55kbN/tdRL3JmNOCG7jgwUMURIbVlgUZ5VQZa+Ya+y5LxhJyCoETVjIV\npKQUgQJuuHlTXtJie5FNvXbaMKD316CfXyzy+Tx++MMf4rXXXgNA8M/33Xffjj73Wlx+LCws4Jvf\n/CYAYHx8HK1Wi12Lj33sY7jpppt29D6UExMXk6Fy8fPtebYhAsh9MZIewWh6FPj/2XvTYEnOs0z0\nyT2z9vWsvatbaln70lK3sCVhM+ELxuCx8TKGYCIMZjGjuTbDHyLuYDvuDWJiuGaJgAkCuP5xb2DA\nMBNhMMvgC7auMZJttaRuyVp6UW9nr3Nqz8o98/74zvdVZlVWnao61epF51Gc0KnTtWZlfu/3vu/z\nPg/6B9jDG7pB8AMfbaeNqlHFhr6Bil5BpVPBZmcTVaPKZhyoxHIxUURWIWtdQStgNjWLmeQM84m5\nUckMpRfrjg7d1dGwGnjuzHNAAri2cQ3v/+D7YbjG0A4MBY0ZlAacUTLIqBkmYpBTc0grxJOLdv1p\nV4QmL7S4xXM8Y0rQ7zS8ntm+HUtjzmk5SLzEvruqUcW1xjW0nTY0UcNcag6LGTLbwHEk9lGJfNrt\n9uGzOEs9XupmHVWzSjpDPI+ZxAwrvFCvNsMlcYfneZQ0QpNq2S32PptWExv6Bok1CWKenJATbO2j\n+xdVVDGXnmNJnuEYXSbINiWOdskFXiBiM5KGnJrD//jy/8CFs0R97cMf+jCO3nEUjk+6hAEClrDQ\n4xnu0lc6FWx1tsic13Yc6b0E4uZJW3YLm51NlmiJvIiCVsA9M/fghw/9MA7nDrPiYnhmLQChOV+q\nX8JLay/hWvMaLM9i5xBA9n3zqXlsGptMLGND34DIi0RUJz2PrJJl5xE9RwzXYHOyiqiMXSQb5W+9\nEAQBuq7jW9/6FgBgfX0dn/rUp0Z+3ZsJ7xhRAIBQM5555hkAJOh8+ctfjogEUJPNYagaVay1u/4b\nikBoRVT9JQx6sQ7kNYPvC0oUnr9dhfG6HRg6oBh+TLgTQysKdBG5Ee3/cLVop27Ls88+i09+8pMA\ngHQ6jdOnTzPpU4rwLEb4pzcxowos1PSMKpHRRZRWRDpOBw2rgaySxWxylrSJt4c0I8nM9lxSGJS+\nFleVtFwLy81lvLD6AjiZ0L6MAAAgAElEQVQQT6En9j8xcEFyfIfIYW4r0HXcDmyXVBKbVpNUoUQN\neS2PlJJinzsONBlea6/h3NY5WK4FURAhciIEQcAduTtwV+kuJgpBu0y00goAVxtX2aKtiAr2Z/ZH\nPiMVtqD3cT0Xd911FwIvADzgtfOvwedJQKWVS8d32ALv+i6TjnY9d6D0JB0STUpJJOQEskqWuWVT\nf4Qwegf+KYbN3fSCVqBVQYUoiOg4HZxeOc0KEzzH4/7Z+zGbnAXPkw3jlrGFq42rTKAhnHyVEiUc\nzB4cq8o2iRTnP/7jP+L9738/AKJYs7S0dFu4Pd8KeO655/DEE08AAB5+mBgAvvjii+zfTp48ueNz\nUNll+t0LvIDF9CI6Tgcb+kZk/VFFFfOp+YFqk8PWejoTE1Y87DidPsoTvQ4SUoJRb6lHzLRULscB\npXvF/fTGzUqlgqeffhquS67zr33tazh+/HjkPtRwsven97PR2cyW3SKFs212AxU2oDLGNHkIC87Q\nGaPwvCYTrAm9Z9oZSUiJvu/O9V1cqV/BS2svwfEcFLUi7p+7P+I/1Hucwuur5Vok5nrE4DkIAiZm\nU9AKsRV9ChobmmYT56rnSCziBYgcKfzMpGfwrtK7WLJEKWiSIKGcKEPgBVyuX2aJuMiLuLN4Z+QY\nUzXLsPH4+973Ppw9cxbwga/9/ddw/H7y3dECqO3ajLYcNpMe1IHkOEIJpPOq7LwWJFZY6010FUFB\nTs3FClhs6Bu4WL2It2pvMZnqOGSVLA7nDuNw/jBmU7PwAx/fXf4uYysAwPHScaIOx0UNvbc65HnD\nsSSjZMb2RZsklqyuruLAgQPs+nn55ZfxwAMPjPyaNwveUQnN1772NXzoQx8CADz99NMsI52ZmcH6\n+vqOj9/sbLKZBaAbZBpWI6JKw3M8smqW6br3IiwXGQZtU9OqSXioP4xwEiMJEktixjG53C0GJS07\ndVbC8H0fTz75JC69dQnwgS/9n1/Cpz/16UjiMsrnoRWxqkEqUQkpgYRMqm1UK1+3dTTtJtpWGwWt\ngJnkDGzfRkKMVtRp5b7366EdLxp8ersHdbOOVzdexVp7Daqo4mD2IO6duZd15XbqvlmuhZbVwmp7\nlXWRqOSzLMiR75yqpNGKLOVMK6ICL/DweuV1XK5fZpt43yeDqscKx9jQuyqqyKhdE1fLtXCteY29\nzzg3cgBdlbTlq/ihJ34IAFAsFvHS6ZfAgYsoH1GoooqMkolsrJpmkwVhqkgW1yanMy00+FMncU3U\nmHT2KIk7pdOE5WXjIAkSVEFFgACvrL/Crj2O43D/7P3k+AXEmEzgBKYYVzNr5H7ol0kNe4cMw7iB\nyPd9HD9+HOfPnwcA/OEf/iF+8Rd/caTX2sPusLy8zKhl5TLxKtrYILHh2rVrO9LOdFvHans1kszM\npeYIZdXW2f04jkNRKw7czA6i14SvtZbVYlQy3dEj51k4iVEEJTIP83Z186mEfu/PuJTjz33uc/i7\nv/07wAc++fFP4vd++/ciicso60QQEJpZy2qxZEUWZJawUGoeTWYoHZh+hl5H+SAI+mKJxEuR49v7\nHdaMGs5XzxMJZEHGYnoRjy482jXaHkKdo2uq4zlEmn9bJZPSlCMUX56HzJPYQTfXVGRAEiQInICl\n5hLOb51nRtt+4MN2bTZzGe7M0OPr+R7e3HqT7YnSShpH8kf63itVhnN8B3fffTc2N8mA/tkzZ7Gw\nsEC81Hr2PhJPinACLzARpI7dYXuPiDlqz3VBkyNKWaOMFioClJSSI+03GmYDl+qXiN9Nc3ng96GK\nKg5lD2FfZh/W9fWIOumdRSLwQ2MejZ1XG1cje0x6biTl5Fi+aJMooH3iE5/AX/zFXwAAPv3pT+OP\n/uiPdnydmw3vqITmxRdfxCOPPAIAOHLkCN566y0ApMJGFWsGYUPfiChi0Op5b1eGyvrFLZ5xiQyl\nEFmexbjL1Jk+jPCGlsrnjkNvmQS08kN/qLpJnJHhKIjrtvy33/9v+NXP/SoAorhx5syZsZIyWomv\nG2ThlgUZqqQir+YjyUzNJPMgRa2IYqIIy7OQVbKs60E7X+HjScUAaIdkEHzfx5q+hu8tfw9+4COj\nZPDY4mORjWxY276X6+r4DkzHxGprlbxPz4TruVAlNSLnzXM8VEFFSkkhKSWRVbOkgxFTPa0aVZxd\nP4tqh9BI6Pm0mF7Eg3MPopgo9sm41s06k7EGQPjP29Ljvcf8n7/zz/iRD/wIwAP33HsP/uav/wYA\nmNoS5UeHkVbSmE3OQhEVpspHN1s04fR8YpRp+4SC1/sclGKWUTKReZyMkoEkSCMlwlRwgRoADloC\nLdfCG1tvsBkggRNw38x9mE3NsuNAKaNtu41rzWuRNYIGI1Zl04o7ntvjBqLf+73fw2c/+1kAk10/\ne5gMnudBVVVW0aTgeR6WZUEUB68XbbvNKudA1zS2ZtYi57ssyJhPz8cWxuISmbA6WcNqoG212aYv\njHASo4kaUyUbdUM3CcLdFsePDuePUwSjiOu2fP+738cPP/nDAIhX0PLy8sg0cgpKmTVdk3QeeAlp\nJc2G+On79QOfJTOmaxI1xO1jRzs84XWIigeE52TiDxSw1FzCmfUzMFwDeTWPe2buiRhphmNJ77Gj\napYVvYL11jrcgHTDKT06HLdFXoQqqVCFrnxyb5zjOGJS/UblDay2V9k55Qc+MmoG98/cj0N5ooYW\nluxv221crF5kz7OQXkA5WY79yK1OC5liBuDJ662srEAURaYI1rbbkflggCQL5USZzK8ExFtPt/WI\nVw7dp7i+i6bVZEkZBQ/CuMireSTk7vWQlJOs87TTPI7pmrhcv4y3am/hSv1K376NHcfthCUhJTCb\nmoUiEKXRO4t3su+UHm9aXAyvEfR9qKI6si/auLEkbDQ/6fVzo/GOSmg2NzdRLpOLStM0Zob2Ez/x\nE/ja17428HHr7fVIm1HmZWbSSBEgIJssKUqZ8gM/olwGdJMYOhdDaWVxLVCaxNDZhWmaXIbfT1y3\nZSfucRw4cLEUsUGbzHq9jsXFRXQ6hHf77LPP4sknnxz59WjlnSqKUJofx3G4VLsE3SYml47voJwo\nIyWn4AUec7ynXGzqC0BBOdG0/Uwvk7hErm238cbmG7hcvwyFV7CQWcCJxROx75cmMJZLOnG2b8Pz\nPCIOYLfYMaSy35QD7vkeObbbfOmwjj8NrL3UK9dzcXbjLF7ffL07FMoJyCpZ3DNzDw5mD3Z599vf\nzXJzmXGgRV7EgeyB2ErtX/3VX+GjH/0ooAA/8uM/gj/642g1R+AEuIEbMbgEup2LcrIcOd5Unrxp\nNlHpVMh82LbruOd78OAx3ngc6HdPhQfSSppRZSReGlht9gMfuq2z1+rdIFiuhbMbZ2E4ZC5IEzU8\nsvAI47PT907fg+maWGouYUPf6JsFSkqkyjaTnBkaWMYJRLu9fvYwOQ4dOoQrV65E/ra4uBgRm+lF\n02pivd1lA9DYEJ67DIIAeS0/sBobjiXhJKZm1tC22swfK4xwEkM7MGk5HVuw2A0oVbr3Z9BGbyfQ\n7nNv8hJ3PQdBgIceeghnzpwBAPz2b/82Pve5z438WnSmhK7DEi+xgfO23WZ2CLTwpYgKM8rmOI7N\nCVIKFgUHotJIj/WwWKLbOi5UL+DNrTchciLmUnM4tf/UwPWLGkiHk0PLtbDR7q4/SZmsiY7ndNf7\ngMSSsPqnyImMqkVnhcI4v3WeJFrbn1ngSSw5kj+Co4WjTCiAPo7OSdLjc6xwLPZ8u3TpEo4cOQII\nwPyBeZx99Wzk3zlwsD2brM+Irs9JKYlystxXiAyCALqtY01fw1ZnK2K2HAQBS2AGJQXh/Ra1AaBz\np4Me4/kelppLuFi7SPYejh75d0p9b1tt5LQcZpOzOLnvJB7f93jkfvT8cH0Xy61lrLRWuuqH27FE\nFuSRfNHGiSVBEODBBx/E2bPk+I97/dwMeEclNEEQIJFIwDSjmfpnPvMZ/MEf/EHsY2jVvPskiHDj\ngyCAKIjIq3m2QaMVlHAiQ5MYOtRsuiZZgHouUJrEUO4npZLttv3f220J/0zSbaHVq51mW0bBL/3S\nLzG32o997GOs7bkT6GaUUs1UkXQvFEHBpfoltO02towt+L6PmcQM0+unUqcdp8MoavQi5zgOKZnI\nDQ+78IGuD8GGvoHvLX+PyQQ/OPcg5lJzbPGhVTOqutN7vC3HQtWswgs8cAHHBlAlQQIPHqZnMt62\n6ZowPRO+77OOHz1fJF5im3pZkNnsCPXGqVt1NlMUIMBschYPzj3IqHRUGexq4ypbQKnPRS9+93d/\nly12v/yZX8Zvfuk30TAbfQkBz/Es2Pb+vZQoMdEEP/CJUpkbvTb9gJjRhV3KdUdnVDXPjzekpa+R\nkBJsFoB6IwxSVRs0d2O5Fl7ZeIUF8iAIcHf5bhzKH2LD0b3nhu3ZWGmtYF1f7wpKbJ8Pmqhhf3Y/\n5lJzA4PjOIFo0utnD7vDe97znj4DzccffxzPP/987P0bZiNCJ7E9u+/7p8PEYflW2gkMy/nrNvG0\n2uxsMipZL0UrnMRklAzSchppJb3rrj4tysQlLpN0Wyhttm+2ZZsKNQ7+5E/+BJ/+9KcBAHfccQfO\nnTs3sqyv7dmMaiZwZGidqiiGkxkmDOBazCvOdE04HlFEC79nqsQ1SiwByH7j7PpZNKwGUnIKx0vH\ncSR/hDE7qKwyjSm964TneVjVVxktTpVU7M/sZ4pnVDAg7OFGi65Al0VBOy70e6iZxE+PzvdUOhVC\n6QbpEqXkFO4p34NSohTx0DtfPc/WTUVUcGfxzr5z/tvf/jYrwjz++ON49l+eJR2WmIKq6ZJY2BtD\ns0oWxUSRdKGCgIknRYRhtpVJZUFmCRJV7RxGCwe6RcZIkrOdEA0ysQ773dCCOEtqqGpbABzKH8IT\n+59ghp7hrh41cl1trTKFVvp+AEKR3skXbZxY8sd//Mf4hV/4BQDjXz83A95RCQ0A3HXXXTh37lzk\nb7/5m7+JX//1X4/8LQgCrLRW0LDIcBu98AtqgQ1h+/CRkTMswaFVZnpBhxWpmlaTDPn7/fx9msTQ\nC2U38zDT7rYIfPxQ/jQFB8JutaIo4sqVK1hYWNjhUWCmXNQzhlKQLtcvo2ERFbggCFDUyEKX1bJI\niklWrUlKyUglrXdOZhR07A4u1i7i9c3XIXACyskyHlt8jAUMyqseyHX3fba40U5LXiNt3rhuARWG\noNxh6jfEczzjBoucCNM12YaGDs5X9Ap+sPkDEhC2z1+RE3FX8S7cWbqTVdd0W4+o+M2mZpkLOcWv\n/dqv4Utf+hKA7vXjBz4aZgNNq9mv0b+9CerddAkckbcOD2LSJYm+fza3FKJ00KSDKt1QioEXeEzo\nIw7ULyEpkS5ORskgJadiu4jhuZuW1cIrG6+gY3dNco8VjqGcLIPneUbdodctDUaO52C1tYo1fS1y\nTGgiuj+zHwvphYEV517EnUOTXj972B1++qd/Gl/5ylcif/vIRz7C1M/CqBk1Rkf0Ax9NqwlN1Nj3\n7gc+ESoJUUnCiQxNYupmnc3ZdNxO33XG5NGlJFPnouf3uIjrttBEZhLEdVoGdVsmRafTweLiIup1\nMoT993//9yOp/9GCCZVlp3Mz1JOHxk+qhEkLltQ2QRKkSKIoC+R7GOezWa6Fi9WLOLtxFhw4FksE\nTmBJDJVbBuLXgobZIN1kgcz+UHnv3tkbP/AjptSWZzETYervInCk40KpkLTbnZST4Dker1VeQ8Nq\nsH0PQCjNd5fuhiIRDxnbs3Fu6xz792KiiH2Z6HzZn/3ZnzFxoPD1QwvAcbGkVwYa6BZZw0bKdA1V\nRRU5Nce+I3rsaFfNcLepaiFbhVGSHJrg0L0DLT73xvuG2WDJzVJzCVebVyPjCkWtiLnUHJm7yR3C\n4fxhHMgeYAlagIBR25eaS5GZbcrcWEgvDPVFG2VGU9d1LC4uotEg+95Rr5+bBe+4hOZnfubfo1ze\nh4WFfUgmU9D1Nk6dehwnTz4EUexmxsutZTbERT1BZhOEO09lb+lGmZ74NBCZromW1ULTasLyLDLY\n3HPy0CSGUmTCKio74Xp0WwbRxN4uPPXUU8zc6fOf/zx2UhN3fRcNs8EWBdqZWGotoWpU2d9zClEt\nKSaK4MCh7bTJohzii8uCPLGzddWo4rtL30XNqEHkRRzJH8G+7PCBYJEjKmwiJ8L2bUbx4jkeBa0Q\nSbLiZm+okACVkzQdk6ikeTZb7GmAVXgFOY1IriakBDzfw6uVV3G1cTUyZJpRMnh47mHWMal0Kkyp\nhuM4HMgeIN0dpw2vvYL/9x//FutrK2jrBh5/4ik88kM/Cl4ig7PMW8Bq9lcQAw+2S5I8Jlm5PatE\n5WAlgVA9BK5/MzAoyXc8h6k40WvPcAy4gTu0iwOQJIdehxklw+ZzwteA4zmomTU8d+05pkYDAMeK\nxzCXmmNDwkA3MU5ICXDgWGKz1l7DWnstUlygm6fFzCIW04uxdNJRAtG4188edo//8l9+C+vr9Ugs\nOXhwPz784Q+wWAKQNYKeMx2ng6bVRF7NMxd2gRMwn55HSk71DTTTDnSlU4Fu6zA9s+98oElMWk4j\nr+WRltNs07kTaKEhznRykiJYb7clnMS8XbNdv/qrv4rf+Z3fAQB84AMfwNe//vWh96cdFko1E3kR\nCTHBjgs9Dhw4gEMkwQwQRIou1Bhzki7YZmcTZ9fPYrW1ClEQcSBzAEcLR4c+RuAEtta7vhvpAJYS\npT42CV3vw0kCLb55gcd8ZVyPFJ7qZh1e4JEEBwIyagYzyRn2fV6uXcbF2sXuOosAiqDg7tLdWMws\nQuCJcMq1xjX2eodyh5BVsyyWvPzid/GDV8+irRvYf+gYPvjRn2OxhH43NMkIgyahlHbXsBpwfRci\nL5JkXk6zmeZBaqODBDXCszgdp8O6QjslOdQEm84kUZYNz/OR/eHF6kV84+I3cG7rHJsPKmgF1qGh\n68L+zH5m6EnnhbzAw0Z7A9da12C73eICBw48z2M+NY/92f2xn3mUWDLu9XMz4R2R0ARBAMsKoOs+\nbHvwx5VlDokEh4q1jLZDjAqpHvhMYoZtAGn7HlyXr0nldunwmR/4A5MYGnB2mofpraLsptsCYGDS\nciPknXvx1a9+FR//+McBAHNzc7hy5cpAt9ogIPr3lGpGqWQb7Q1sGpswHINVPKmPAK1AJaUkSxx7\n52RGBZ1t0W0db1XfIhU1jkNWyeLxfY8zB2kArJIXp7ZjuRbqRh0BAua4nZSTw16aJTZ0cadDrJSS\nVtEr0F2ddSnoBp1KXtKFVrd0nFk/g6bdZEGOA4fDucO4d+ZeyIKMa81rpBobBEgHFnKuDt/cHPje\neK0MMX0QQmKW+TPUzTrz1KGgim6mZ0bOPUpDCy/Gg5anUTZH9Dtq22207BYaZoPNqu2U5IiCGBEc\nyCgZJKQE/MDH6dXTqOgVRgE8kj8SGdqlzu0Bgshcj8AJcH0ipUpd4RnVcXv2jFbZes/JnQLRONfP\nHibHOLEkmeTR8qqomzX4gY+qUYXpmkxpMAhIISFMPQwQEC8qg/hIte022bT0nO40ickqWRS0AlP+\nG3RdeL4XSxOLo8COAqqG1dtteTuLYINw4cIFHDt2DAC5Ti5cuEBmNAbAdm207BbbuNNkhHZE6Poo\n8mRQnXbJVVFl1ykHokQ1jm8IEJ2pPF89jzNrZxgT5MH5B1lnnBa2IkpkodlAz/ew0lwhtGWOY8Pn\nw8A+W6hoFqairbRWoDs6ozdnFCL7LHIiY27IggzXc/Ha5mvEADaULJUTZdw7cy9ScqrrqRQEyAQ2\n5jgPgTlY/rg3llA6cC/dzPZsVHRSfAvT9wVOQEEr4FDuEIupu4klnu91uzjbkueWa7HXHCXJ0SSN\nsUhUUQUHDi+svoBX1l/Bmk6KXQkpgYXUQh/tLAgCzKfmWXJDafOVTgVLjSV03E63c7ed2JQTZRzI\nHujbU+wUS86fP89Mmke5fm4m3PYJje8HqNc9WNboH9PhdGz6l9Cw6sRcbNtZV+AEFBIFSDxxzLVc\nC02riZbTinD/wycITWKySpYlMeE2NM24e5OWSQPNoG5LWIHkZoTjODh48CBWV1cBAH/+53/ONmi9\nsD0bm/omk6PkOR41o4YtY4t59xTVIpH1lVRYrsUoBJTvutOcTBg0seytXLasFlESM6qQRRmHc4cZ\nRzhcmQxvVig8z0PVrDIKliIqKGpEa37U7ylccbNdGxv6BjOTo/LTLlzmwCzzMltYqWfRcnMZl+qX\n4HoufI4szpqo4f6Z+zGTmsFK8yrynRWorj7knUTBa2UoM4+A297cuL6LullHzah1jc/Q5YN7vtdX\n1cypOcwkZ1jCv5tgFAY112zbbTTNJhpWA5ZrsSRnGCglUBEVXK5fZp0lL/CwL7MPBa0QFQrZ/n6o\nUp0iKiygCbyA9fY6lppLbJaCzkTxHI/59Dz2Z/ZHhmiHBaJxrp89TIZJYonLddDAEtY7a4SSmiiz\nAsZcag5pOc2q/ZVOBVvGFtpWO9YckCYxRa3IOjG9iW9cp2XSbgvtHsb93MyxBAB+9Ed/FP/wD/8A\ngNBjf+u3fiv2fn7go2W1uuaKHJEzdj3CdrA9mx0HaqlAKaY0ju80JxMGZVfQ7g9dL1pWC29svoHl\n1jJEXsT+7H48MPsAOI4UOmgSE6YghZ+zolfYELrIi1hML7Ih9lEQjiW0m9xxCY3X931oEim+BgEp\nfNFZG3ouSLyEqlHF+er57vA9R5LAo/mjOJQ7hLeq51E215H0zZ3f0DZ6YwlNbNpWG3WrztgNAKGg\nW67FaJb0syekBEqJ0lSKZGG4vhvp5FBLgJ26OABJuFRJhSZouFS/hJpJGB4Nq8H2mpHZbUSTm7ya\nx+H8YRzOEb+bqlHFteY1Rl8PJzflZBn7s/sjtPGd6MyjXj83G27rhMb3A1SrHhxn/I9o+C3UcBl5\njRhjppQUUlIKuq2jYTfI/AKdjwgNlAPbG8dtta1w1WZUw8lRcDN3WybFF7/4RUaV+djH/h3+5E/+\nH3hegCAAOA4QBA6KCjTtGrtw6Wa5btXhuA5Mz8RMYobMjohyX+WMDokPO069Q69x35Ef+FhpruDl\ntZcBADkth3cfeDfT+h/23L7vo2k32WIscAKKiWIk0R3Gk+4FNYGjXQfadqfnl+EYjJJGn5v62Sii\nAtM1caF2AW27DS7gEHDkellMzuFe3oHkGcNePha8nIUyfwrcNn+743S6QWh7DoXKUAqcwBy0wzQz\n6sFRSpTYsRlnwHFU0Dm3ltVC0ybDpJZnDe3ieL6Hc9VzsDyLCXjcN3MfjhWPsdm5iLv0duGCVkQp\nrU4TNbTsFpaaSzBcAzx4NoTJg8dsajZSZRv2+cPXzyd+6ifwf//Rb8H3TMB3AV4EL6gQUguMzrGH\n0bGbWNLxGlhyXkdByzG1u9nULAzHwLq+jq3OFpmHobNfodNZFmWkpBRKiRLrxISFNmiXZZDh5Cig\nG9ObsdsyKf72b/8WP/7jPw4AePDBR/BP//Qv4HkxEks0jYcTmBF1SUodp9LzkiABAWB5FvzAj7i3\njzonQwuUNImJw3JzGWfWz8D2bOTVPB6efxj7svt2/A7oADydz+I4DjPJmYioxDixxPVdVPRKV57a\n94nQzrbUfvhcI2+AdLNpAc/xHVypX8G6vt5dqzggr2TwoOBD8kZPZih6Ywkd+DccA5bXNRqntEvT\nM5nsdhhpuSu2Q49dHHYTT1zfZRQ1yg5wfGdokkPFEzY7m8wM9GD2IO4u3Y3l9jIu1y9jtbXaNwNF\nH6uJGpu7SStprLZWUTfrEZsQDhzyWh4HsgfYjO6wWPL1r38dH/zgBwEADz9wN779j38Jgfdu+lhy\n2yY0QRCgVhuvmtYLlzNgiRvMXbZltyJUMo7jIkkMNf1TRDIQF+68TKL+EpZADnts3OzdlkmxurqK\nn/u5z+Bnf/bTeOqpfzPwfj5vwhN06D4ZtDU8g6nMlLQS6YRJWsTAbNicDE0CRpUYpTKS57bOYUPf\ngCIqOJA9gIfnHx7pcxqOgbpZZ8+VVobLpw4LSLTjEObeZ5QMBE6IzNpQrwRaUQrzwunGeq21hkuN\nS0yW9HFVxYww+cAur5XhF+6F6VmRxdP2bOb/Eg461PiMilFQCDypbFOX6+sRiHpBJdUpjZSanYaT\nHM/3cL56PmKEuJBZwKHcISY8QNWC6Nwd0KWk0TWBUhIs32I0IzqUS4NSOUnoAxklMzAQra6s4Jc+\n9VH8/Cf/F7zvPQ8N/Gy9dI49DMc0YomNNppYgiqpMF2TrFuuwa5tACyRoUlMUSsipaTYfMSkhpPk\nqaPdljBd7FYugg2C67r41Kd+GT/2Y/92aCzhRAecZMHnLXiBC1VU2fokCRJ832dUNE3SWPyls36x\nrx1KYFzf3bFa73gOLtYu4krjCiSeqFY9vu/xkb4Xx3Ow0lphhZK0kh7qKj8sljieg0qnEulUFxNF\naKLG6Gj0/KO0a3qbfkY6H9qyWniz+iZJLMDjUUVCWZj8POO1Mpzc3WjYUcEZ2lVSBTXyfVD/GioY\nFP78OZXMlFIlttjjNMV1kRqIGq5BDG5DYj6Mmh4EuFC9gDV9jT2unCjjnvI9SMhknnSzs4m19hqR\ncd6O3+GuDUCKE/sz+1FOlok/mtNmAgX0v4xKfNFKidLAWOK6Ln7h338Y//b9J26pWHLbJjSm6aNW\nm2zWJIx17zw6fr27udjeUIUXOSrdOmlgiDOcnFQC+VbFJHSOpruJy8YraLuE/5xX8yioBaiSyuRK\nqWldLzUjksCMQO+jFSjKXd7QN/Dc0nNsLufE4gmUEqUd37PjOagaVbYoJ6QEsmoWQHdxGoZwQKIy\noxQ8x5Nkpue8oedrx+5Ehig7TgeGZwABuQ9ViLnSuALRquG92XgjtHFgZe+Cr3ZdzqnimsALsFwL\nNbPGZD0p6BAofYy/tq4AACAASURBVD/hx84kZyLHKw7XY2Glw6ltu82U3JpWE6Zr4s2tN9G22uy+\n8+n5yEwNAMZ3FwVybVNndvrdRBI+30bVqMJ2baY8R38KWgEHsgcipq0AEPgu7MqL8I0KRkUvnWMP\n8ZhWLLnUOYOqsxaReQ0QwA2IulRCJGsBnbfqpZyNAkpP6pNAnrJ32c2MSWKJxxsQtA7swCIzSwGY\nAIAkSIxilpASfXMydD5pkJRyLwReiMzBbOgbOL16Gm27jaSUxL0z9+Jg7uCO7zkIAqy11xjdnRqx\n8hw/USyp6JWuxPx2dzyu0EaTmTBVl1Ebt2cH+e3/VlorsPQlnEwMnw0dBc3UQdhylt0OD/x7vher\nfGZ7NtpWm0hxh4tk2zM21LPu7Ywl9H3RLo7u6Ewa/EL1Albbq+x+Ra2I46XjkTjIcRwaZoOYp3bW\n4XndzxZOboKAGKom5K5hKH0eHsTS4GDuYJ/f1a0cS27bhGZryx06tDkqdL+GJfs1sunYHpxOy2lk\n1AzbMI+CcQ0n30nYDZ2j5dZwuvbPKGh5InsoqSzBpCojQLdqNio1g9Iw6OagV873jc03cL56HjzH\nYzY5iyf2P7Hj9+j7PupmnS26kiAxg89e7BSQTNeMULcGJTNxsF0buqMzqWM67EhV0jiOw357Czlu\n/K5iLzw5Czv/LraJj9tYma6JmlHr86Bp221YrtVn8qZJGuZSc+y7vR40tFFAFeUaZgPPLT2HldYK\n637NJmYj5puRxyFgnShaFKEmh5RyR4UvNo1NmK4JTdJI0WSbkpaRMziYO0jmrgIP1trzCOzG2J8h\nTOfYQzymFUvqzgZebf4r3MBlzvJJKYlSooScmhtZnIQDFzuQf7t2W8bBbmKJx1mo4wp4nmNy0tS4\nODwnQ6lXYSnlYaBJEU1iwt+R67t4Y/MNvFZ5DQAwk5jBEweeGEklrW7U2ZwFx3GYT83HnkM7xRLL\nJZ1huvZzHIdyojzS+UiTGTogH46xtGtU0K8iMcbczCDYUgrN9BFiwbA9kxz3fuISG8Mh3ZFeY0yJ\nlyJqcG93YhMGtWN4ceVFNovkBz4KWqEvqaEIggCbnU1s6BuodCowHIMoqG4XxMOiVUFADEXnUnMo\nakXwPE+kp0UNB3IHMJucBRf4t3QsuS0TGtcNUKlMNpsSh/Pm96DJMrJqdqi7LLDXbRkX06BztLwq\nLGEDSZU4+tKZGTp8OYrhGzURo9XMYd9xzajhuaXn0HE6UEUVD8w+gP3Z/Tu+T+pHBJAgl9fyIwWu\n3oBEaWPsvW87NscZfO30vHS+xnRNuB7h/7p2E0edwWpm44KfewKqVtjxfoZjkM5Ez+xJ02rC8Z0+\nEYe0ksZschaKqNzQQASQQPry2svY0Ddge0SKO6fmUEwU0bJaQ6knlGpnuiaTkA47VDs+GdJtWA3G\n4afJT1pK4S7OguQ0J37vvFaGMvvYO76wEodpx5J/2fwbyBKhUGbV7NDOSXhNCq9NkxhOvhMwHWqg\nDkfahCRKbMZQEzU2LE9pp8MwLIHpRc2o4cW1F7HV2YIiKDheOo67Snft+D5Nx8Rae43FhbyW7+va\nxqE3llD6I12beI6PyDKPA9dzYfvdTo3t2fDsFgrNczs/eNTXmDmBdGJmx/dGZ1l6PZPadhu6rbPO\nN4UqqignykwaOQ5v5zX3WuU1XKxeJPsX10ZSTuJo4WjXI2fAe2xZLSw3l7HUWkLdqkPkRMjidmGW\nJ6batKg6m5zFfHoes8lZiIIIlVfwLt6F7LZin3sU3OhYclsmNK2Wh3Z799VlCl9qA3J3A3mzdltu\nxSA3LTqHK1chKUQDnypLDUNYSjksf7nj6/guLlQv4NWNVwEAeTWPpw49tWOnznYJjYgGk5ScingE\njArd1iPJjMiLyCiZyHfPxXBVeo3GelVyqKZ/x+lA6awgZY7ebt4Jz7+6gVfeakFVVaiqCkVRhv7u\nCz4MzgAv8FAUBYIgMG8bL/CQELuKQhzHIa/mUU6WmQJQHN6Oa8MPfLy89jIqevfYHcwdxJ3FO1kn\nh9LV6Dxe7/t1fZclN2wQWVCYCWzbIRxsWj0+ICfwkLz7Yok88yjE5Nyun+d2w7RjicFtwRGj1c9B\nvi03ugh2q8WTacUSX6lDUgLIvAxw2DGBGSSlvBOCIMDF2kW8tPoSAhDlqlP7TyElDx+09n0fK60V\nNuupiirmUnNjf1+6rWPL2GJrEJ1V7E2y6QxG3Noa1/nx/e68TdC8BEm/1nefSfHcK+ssluwUR1RV\nhSAJ8AUfvERiiSiSON2yWtAdvS9xS0pJlJPliPVCL96u6+KNzTdwoXqB3S4nynhk/hG4gcv2AW27\nHWs+CpBkdaW1guXWMtZb63ADl9EdBU6A4RosuZtNzuLRwiE8mdo5Kd4JNzKW3JYJTb3uwjCm97E2\nnSVsupchizJzz93DdFDijkDlMjvfcQc03E1ctV8Z+O8ctoMOL0EUxFjTxlGg2zpeq7yGpt2EyIs4\nlDuEg9l+vnM4APi+j7pVZ8anVKCALYwB/V80yej9G1XWoxA4oetMH35s6PdxFG4o9nk65rjdbwwo\n/q+v/AP+4//2B+M/UAagAoIsQJZlKIoCWZUhpkTIMlGDUSQFiqJAkRWk+BQyUgaaqkFW5GiAU1Ro\nmjZSEIz7XZJGq4r7gY8za2ciBncHsgdwvHQ88nhKjaibdWZC2rbbXaUrEBNSSkMwHRMeiJCA67nM\nd+ijs8dwQN290gyvlaHOPb7r57ndMO1Ysm5dwZpzAaqkkrVoj3I8NZS4O6BNKZYs2T8YfAeODL/v\nln3RcTp4c+tNbHY2mYniseKx/pcLeZIApKvDzJh54t0VLqj1bunikg7DNhhbgL5GVs0OfR6gm9yM\nikWvhTKGi+yMg4ljCQ9ABjiBg6qqJJ6oCqS0BEmTSCxRFBZnkmISaSGNhJqAoirRmCCT3yeNJ4qi\njHwMz22dw7mtboerqBXx2OJjfdYfVKmTzuNQLz4Kz/ew2l7FcmsZK60VRu/2PKLIaXomnjnyHrwr\ns/tE5EbGktuSOD3tFM3zfax31okrb6gSM2pbeQ/xEKFMJZkBgKxYguSosAMyYE618ul3RKVw6dpO\nZ6KAwV2LXvi+j40OMe8ESEKRUTLouJ1o0Oh5aNtqswWE53ioIvHGGQcdpxOR/BR5oiYDrjsIGBe4\ngm62NHJyI015g5VODVZwGwqb/HiyB0MzYHRC4gECAA2AD0TipQ9AB7B7ynYEHMeNHrBUBUbGgKd6\nkGQJsiyjKBaxT9k3NAiKsohADGALNmzYsHkbLu8S/xElCZ/3YXlEzEEWZcwG6akkMwDgGxX4Tvum\nk+G80Zh2LOG3lYd0R+9Tr6QJzl7BbHyIUKaSzAAklqyHYgkAVsjsVRkNEPSzAYLemzHrchCgalSx\npW/B8z0osoKsmo3SbeMSkZBCJgDk5Bwb0h8VHacTEZMReRFZNUtmLobEkt7PN0pyI3DoOx67wcSx\nxAdgAoEQwPAMGIYBNACsg4hvaCBJT/irDAAY5HHT/AwAWGIzSjyx0zbMpAlZliFJEjJSBoflw0io\nieFJk8zB4z0EYgBXcKHwCo6nj+P+wv1oek2stlex1FxCy25hn1CcSjID3NhYclsmNNMuePmBBw5k\nGNDyrL6BM6DLd45LeN6uCtwkHgQ3Ehp2394MI82VsOVfY07GtEtie3Z0oZoQlmthvb3OhicLWmFo\na5o+JpzMUOnPUUGNxMLJDPUwCSOsOc8CUkQRluv7P5Nz7Dk/A5+f6gJ++I5j+A/P/AeYpgnTNGFZ\nVuzvg/4NDsjPdscGPLqJiwQSjByQ71gAkAGQANAGplUcDBDAtEyYlkkC4SiYARBmFTYBjMvk49D9\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B586dAwAUi0U8/fTTEz3XtLCX0OzhlgLHcdjY2MB3vvMdAIDnefjExz/BFLzS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\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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hMA4TXvPyUK3DYBwUzJYwGLuH2ZIbC+bQXGc8j2ypQLMTbJvA81hkjfHCIXBb1xxRo2uZ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\ncBtouk30/T50ReecxLu9u7jRvsH5DgvFBSwWF0EEgpJywJnRZR1lrYyG10D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AhWQ8OU2K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\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2sY9RmNVMdBwEXsDGtQ28593vAQJAgIBL5y/h4PLBG6Lqud5bx+NrjzNhmbcefutQtdwN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\nQRAEQRAzy/8HEA5vI9sQFiIAAAAASUVORK5CYII=\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"# visualize the attention across layers for the positive pair\n",
"plt.close('all')\n",
"for i in range(len(a)):\n",
" _ = plot_graph_matching_pair(nx_graphs[0], nx_graphs[1], a[i][0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "65g2TlJHPFb1"
},
"source": [
"Note that the left-to-right and right-to-left attention patterns are slightly different. Also the attention pattern starts almost uniform, and then gets more concentrated after a few layers.\n",
"\n",
"If we train the model for longer, we can see a clearer matching pattern."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"height": 1000
},
"colab_type": "code",
"executionInfo": {
"elapsed": 6908,
"status": "ok",
"timestamp": 1563980193552,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "yFI9m9TqL4Qd",
"outputId": "a1ee65df-ede9-4dc7-c1b9-8b51a51da200"
},
"outputs": [
{
"data": {
"image/png": 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FgQiAVM8mMG0OwOMLpcw1rRbURFlqN6WYqzeO4Qj1ppMJiWT5iZMYXsTmxcw2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Hz/Jg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c/90Yo//MM/xC/+4i8CANrtNh588EG0Wo9cMv0ojuJKcfr0aZw5cwYA0Ov1cP7CJQynAYpC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\nSROUjTJc08WcM4eVysrEdXNTcHu0jdXeKk3xcyowjtSOYN6dlykJ8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/9Sld9QjZGD6ysp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BvrLwOwME/VpVVFTtKtpem1+LKqvYkdvBiWbam4bOGSQQxAJLR4/yfS677DLcf//9J2U6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\nOBwOh8PhcDgczsLy/wOooBbS8f66EQAAAABJRU5ErkJggg==\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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PszmY/UgkK4Gwtn1F7UciWenIWCKRdCJjiaQdmdAsAWHI5nSgWQiexxCGcmVNsvqJg/p+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\n45pP0VKwVJoD3eu9KGTB9DTdboL+6Ang5OwNnc9k0jMP/Na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Ba7lo+a3SOW1Gm7BMCytY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MCZwRkIKfDP3/lnvPjqF2PYUZTlSy+9FDd94ia0Gi0IKbARbrBpJBVJTtRPzCyQ6aplvaSHtEi3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U/J9EaBgrPds3im/Uxoqoa8yIUmvVhdpAlSPICjO/BjH5ujTTyw+gAMzcCcNzdVLCpQoEKF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\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abpMPdgKTtMqyXUYURSjoBfSb/Yn3fbw1zu+NKqnoN/pxzTXX4NOf/jR/Tjqdxj333IO3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\ngg==\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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HJaiSGYS2Dig7dtJpqVtnE3B2aslE0wyCiBZBVI2yHjZU0CaIRzxYroW8nfdr\n9By/+SedE7cl8wetizFds25dDH22mKkuph6e52EoP8QiMJ7nQRRFpIwUMmYG/el+CIIA0zERUSMg\n8CM3tFH4mviahq8Vi8Wwd+9e7Nu3DwDgui4uv/zyhs/nLG24Q7OEEEURpVIJTz31FAAgfTKNS6++\nFKIgomgXIQgCWowWltYF+JEXURBZfU2whoH2qKG1LQSENeOkAgMe8fzeNZMhYdpUjare0AJPKj4A\nTHXtDakhtIRa0BnpREuoBbqsswfpYO2NS/wxJ0oTOJk7ibHiGPJW3i8InTRufMdt7sy3E0OxXRuZ\nUnm9jCZriGpR9Gf6cTx3HID//UsZKXjwkLWy0BUdYTWMra1bZ/UQIggCPM/D448/DgA4duwYbrrp\nJv6d4XCmobW1Ff/1X/8FADi47yCuu/E6iLLIivNbQi11JZwrHZVgClqQGR0b1HdsgtejNaJRNYqw\nGoYm+e0CKp0bAv8Bu+SUkLfyKDgFlOwSuw+entYcHM+B6ZgwXbNucb8iKtAkjaW2n8r7bjomBrOD\nLMrveR5UWUV7uB1jxTEcnTgKCL5TK4sy4locGSsDQ/a/K5taNs26drezsxP33XcfAGD//v24+eab\nYRiN90PjLF14ytkSY3h4GKtXr4Zp+jvfD/3qIcS6Y2yBOavzLISUEAghZQV2dHfE8RxfijkQEhYF\nESElBEEQfC34io/c9uyyh1Mq0el6LttJC8o104fiemFlqmQzXhpH1sz6ebZ1et9okgZd0ZHQEohp\nMdaQjdMYp9r08lSplGQG4D+AyBoGM4M4MnGEOVYhJYS2UBv2j+yHIioIqSFsSW1BKpSa9XUnJiaw\ncuVKFAoFAMDTTz+Niy66qCn3xOGcjnieh23btvlRGgD/99v/Fxf9hf+b8YiHdYl1SBpJgKCsNia4\nXtRKLZvOaTgVAYHpcD2XiQuU7BJrTVAL6hjR9DSakcBpDPpe05rbWtDN0Ga8r1SSmX6enuchpscQ\n1+IYLgzj0NghVkdFQLAythLHJo5BgIBUKIWuaBdWxVbN+rqEEJx11ll49dVXAQDf+MY38Dd/8zdz\nvh/O4sMjNEuMcDiMgwcP4pVXXgEASK6Ei99/MXNEMmYGHZEO3ygIU7nPrueyaIwqqfAwpYJGQFi6\nlyZrVbtpNO+VpqcBU1EYwN+RN12TRVPomJVRG4os+v0GWowWdEQ6kDSSvkMFgTlLFJe4MB0TaTON\nkfwIc4LouJIg8ZSCCuaruH+ma1ZKMguCgLgehyqpGM4P49D4IbZTqss61sbX4uD4QTjEQVgNI2kk\n0Z3oPqXr67qO/v5+vPDCCwDakHeCAAAgAElEQVSAXC6Hq6++es73xeGcrtDf/aOPPgoA6Dvahxs/\nfSOKjv8bzlpZP/VMFKucGnp+TUndOtGa4DmzERCYDmqHDMVARI2w6A1TZ6uIIrmeb09o9KZoF9ma\nNJeo9OmKRzy/nsmp7yxKgsSK+xVJmbM9JoRgpDCCkcLIVJo6IWgLtyGmxzBaHMWR8SPsmUYSJayM\nrcRocRRFp4ikkYShGNjQMrOoTC0EQYCiKL60OXwlzVtuuaVmuiVnecEjNEuQF154Aeeddx4AQFVV\nvHH4DfQ7/SAgKNgFrE2sRVfEl7ulNTEUGqkBfOnmSsEAahxoelktqUXqdARfo3/ziFclIADMHLWh\nWK7FxAUyZoYtpLWg0ZuY6u/a6Irfa+etapSaLbXcKI7rIGtly6J4iqQgqkUhQMB4cRz7hvexFEJN\n0tAd78ZIcQS96V6ElTBUWcVZnWdBl/VTnkdQ2lySJBw9ehSrVs1+h47DeauQyWSwcuVK5HI5AMAT\nTz2Bjh0dsD0bhBC0GC1sk6Fyoyu4ltRqtglMH62hzEVAYDpoRKFoF1G0i3CIU7ZGVc6TRm90SWfR\nm7eiLaF1MTNFu2gkppkbirZrYyg3xJxqmmnSGemEJEoYL47j4NhBWK5fz6tJGlqMFggQcHjiMEtJ\n3NSyCXE9fsrzKJM2B/Dwww/jz/7sz5pyj5zFg0doliArVqzAY489hoGBAbiui7aWNrz7Pe9G2kxD\nEiWMFcbQFmrzF2QIrJgSQNmuhiRKUCSlTDCA7siIgghZmhQFEKqLOKkBqGzUSWU3aaPOsn4F00Rt\nKJIowVAMJI0k2sPtaDFaEFWjECGy3TtqVGn0JmNmMFLwozcZM8McLXrvpzMLVRdTD9MxkbWyZYaP\n7pYKEJA1s9g3vI91EdckDWvia0BAcHDsIFRZhSqrWB1fjRajZU5zKZM2JwShUAiXXHLJXG+Rwzlt\n0TQNAwMD+MMf/gAAyGVzuP4vr8d4aRyCIKBgFxBRI9Bkv2alstkmMBV1qbWe1xIMqHX+qQoITEdV\n9EYJs+bS00Zv7LyfwuaU4Hkeu+/TOROAbkg2Uhejy/opFffPRMEu4Hj2OCzXYnMKqSF0RbogCAIy\nZgaHxw77ks0gCMkhJEN+dkdvpheiICKux5HUkzP2L5uJMmlzAKOjo/joRz8653vkLC48QrNE+cEP\nfsB+YCtXrsThI4fx6vCrKNgFOJ4DQzawvW07AF++mS7YlGCkBgBKTqlMkYqep8s6BEFguzb1Qs4u\ncauao9FFkso+VzoXjUZtKJZroWAXkC6lkTbTfvTGtWsaQVVUYSgGwmoYCS0BQzHKGsYtZxa6LqYe\ntSSZ6cMPIX608LWh12C5Vpkzo0gKesZ6kCllENEiMGQDZ3We1ZT5BqXN29ra0NfXB03T5jwuh3O6\nsn//fmzf7tsKURRx5MgROFGHqUopooJtbduY0wFUFPNXrDW1ojWzibQ0u86mFnTTreSUWIuBepEi\nQZjsXi9prPaG9lJb7tAoVr2ml0Bz62JqQQjBRGmCSTJTEnoCSSMJ13ORNbM4PH4YOSsHDx4M2UDK\nSEGTNYwUR3AydxKtoVYokoKd7TubUmdbJm0O4MCBA9i8efOcx+UsHjxCs0TZsmUL/uM//gP5fB7Z\nbBZn7joT5515HobyQxAFESWnBE3SEFJCcInrq8IEIi1U/Yw6GXTBChb8ucRf7GRRhiiKZYt45e4W\nfWCljT0rVWUcbzLcL5RHZhqJ2lBoI7aEnkBbuA1JPelHb+juXkDSkzpYWSuL0cIoJswJpEtpP5Vi\ncr7LKXqzGHUx9aglySyJEmJ6zHeeCYHpmHh9+HXmzKiSirWJtVAkBROlCQxmBxFSQhAFEVtbt84p\n1SxIUNq8UChg8+bNOPPMM5syNodzOtLW1obf/va3OHLEF+zQdR1//oE/9/tETW6EEUIQ02IAptaZ\nSoJNM2tGaxqsjWl2nU29a9CWBlEtipASgi7rZfYtuNbSPm00emM6ZtkG4UJsIDUL6syZjrlgdTH1\ncD23TJIZ8J8B2iPtiGkxuJ6LvJXH0fRRP60ZLgzZQGfYT0FziYveTC+r1VwVWzWnVLMgZdLm8J39\nD3zgA00Zm7M4cIdmiSLLMrLZLOtqe/LkSdz0yZtgezayVpblm7aH2/16GOIvBB6mdqEczylzamgk\npZZggACBRVKoY1Orm7MgCNBkDYqklO3UUSPleR6riQk6FFQimoobzPRQTh+Sw2oYraFWpEIpxPU4\nDNnwzyNg/XRo35uiU8R4aRzjxXGkzbS/2zPZH2epigs0WhczXyllldSSZFZlFXEtzlIbbdfG68Ov\nszxoWZSxoWUD+84cGD0AVVKhSAraw+1zTg8IUiltPjAwgBtvvLFp43M4pyPxeBx79uwB4EdsPv+5\nz8NQDaTNNARBQN7KI67HmQTuTE5N5X9TphMMqDXWfDs2FEn0H+BDaggRNVKlphlcg2ladtEpIm/n\nUXJKfk0Hmbq3pebc0DnTfjH16p1oHRGNQs3nfZScEo5njzM7Afh1sZ2RTr93nueiaBdxLHMM6VKa\nPcOsjKxkAkd96T7WjDmkhLA2sbapcw5Km+/fvx+33HILj/gvY3jK2RJmYGAAa9euheP44eJXXnkF\nO3buwAuDL7D0r4gawcaWjQD8RTskh1ByS2UPpHQBC1JPMIDKO1Nczy2rwaFQR4kQAtM1YbvVhf0E\nBLIg1104RUGEJEizjqR4xEPRLqJgFzBRmkDezsNyLFieVX0NiNAVX8YzqkURUSPQ5cUVF1is4v6Z\nqCfJTHsc0bTE/cP7kTbTAPzPcHNqM3MW+9J9GCmOIKSEIIsyzu48e9Z9AmaiUtr8+eefx/nnn9/U\na3A4pxOu62LDhg04duwYAOC+++7Dxz/+cfSM9iBrZQEAuqRjS+sWtu6Igli1VtVal+YiGFDJfAkI\nTIft2n5EwzVRckpT2QY1kEWZieLoss4yHxbDltD12PGcMuXQIHSjcj6jMLXmlbNyZZLMgiAgqkb9\nAv/J3nYFq4DB7CCG8kNwiQtd1rEisoI9D2TMDAYyA0iFUhAEAdtatyGshps610pp83/5l3/BzTff\n3NRrcBYOHqFZwsRiMbz++ut4/fXXAfg/viuuuAKGbGC4MAxBEFBySjAUgzW1BABDNsqckMpIDYC6\nggGWZ1VFdSRRggChzGjRiAvg77qossoWKgotAqXXqDRws43asHED0ZtUKIWUkUJMj7EUJzrXyujN\nRGkCE0VfXS1rZeG4DhM2mO/0tEZTyhYqEhNkOklmWixMH1p6xnowXhpn6jQbkhugy7pvoOwCjk4c\nRVgJQxAErE+uZ2kszaRS2tw0TVx55ZVNvw6Hc7ogiiIsy8KTTz4JAOjr68OnPvUpRNSIX9sAAof4\ndiKiRgD4a1W9h+DKSM2pCgbUGnc+BASmg0ZvaE2mLutTKbJC7ehNySkhb+enIiKTmQALIS7geA5z\nwOoV98uizJpeLqTD5REPo4XRMklmURDRarQiYSTKnJnhwjBO5E/AIx40SUNHuAMRNcJS1I9OHEVC\nT0ASJbSH29EWbmv6fCulzY8cOYKbb755yUXgOI3BIzRLnN/85je48MILAQChUAj9/f1IJpPYP7wf\nw4VhAH4+7Pa27WwhNRQDiqig6BRnjNQAMwsGUIINNyuRRMlXTIMf/akX9pZECbIg10wxAE49akPx\niIeSU2LiAnlryuhULvzB6A3tcUCNWTOMUqNODNA84zxbppNkZvnmk87MkfEjOJ47zpyZNfE1SBkp\nllKwf3g/PPgdnWNaDDvbd87bvCulzfv6+tDe3j5v1+Nwljujo6NYtWoVSqUSAODZZ5/FBRdcgKHc\nEAayAwD89XdLakuZ6hkVjZkpUgPMXTCg1njzLSAwHcHojemYLBpSaz7B6A11JJole0xbLDieU9ee\nSIK/SSkJ85tKVg/LtXAyd7Iqxaw11ApV9tP7aHbFWHEMx9LH4HouVEn1G2WGu5C20hAFEQOZAdie\njZASgiL6QgDztelYKW3+1FNPcfXMZQqP0Cxx1qxZg5/97GcYGhqCbdtob2/HO97xDsT1OE7kTrBI\nhOu5bDfc8RzWYDO4ANaK1AANCAZMLsjUMEmCVC2LOens0NxXTdYgCVJZTU/wOJDahu5UozYUen3a\n2LPFaEFMiyGshMvmTt83Gr1Jm2lMlCaQs3LImlnYrs26FM/WQMyUUlZZ3L9YzkwtSWZd0ZkQAzD1\ngNKf6cdAdqCsb8DK2ErkbT9FbTg/jLSZ9vsTQcC2tm1NTzULsmLFCjz++OPo7++H67pIJBLM8edw\nONWEQiEcOXIEL730EgCgWCziqquuQlgJI2NmmKBKyS6hJVQusV5vjar8+1wFA2qNv1B1NrWoFb2h\njT0Foby5ZzB6U7ALzAmitgaYXSpxsC6mXnE/rYsNFvcvtD2hipcncieYJDNVxGwPt0OWZHZcySkh\nY2ZwdOIoPM/vaZfQE1gbX8vquYp2EcOFYfY8szaxtumpZkE0TcPg4CCTNs9kMrj22mvn7Xqc+YNH\naJYB3/3ud1nh8/r169HT0wNRFDGUG8KB0QMA/MViS+sWGLJf7xBMHSjYhfKH1jqRGkIIik6xqh5G\nl3W2YxfEIx5cz625IxeMsjiew3a3KqGFilQauhZzjdpQ6P0V7AKyZhY5K8fUYKh6GrsH+N3uDdlA\nSAkhokVY9KaWvOVSrYupx3SSzBTqzJzMn8TBsYPMmUkZKWxq2YSMlfGNrmtj3/A+6IqfprEyupI1\n65tPKqXNjx49ClmeH+lRDud04I9//CPOPfdcAH4vjt7eXnR2dqJgF3Bg5ABbw9bE1pQ5NcENjkYi\nNUDtepi51sFMF7Gh81yoNdZ2bd/hcPzaG9qMerrojSIp0KSp6E2lTWu0LoY2Bl1soRuPeEiX0mWS\nzKIgIqknEdfj7LtCbW/OyuHg2EE4ngNZlBFVo9iS2oIJc4K9d0fGjyCk+unjMS2Gzan5l1KulDY/\nfPgwurvn34ZxmguP0CwDtm7diu985zsolUoYHx/H+eefj82bNyOiRli/FtokLRVKAZiMdMDfAVFE\npSpSU0tnn0Y3qGoahRZJKqJSlTs9nSIaVUWju1yKqJTV3tB5up4Lhzh1nZa5Rm0q7y+khJA0kn5T\nz0mhgOD7Qe/d8RwUnSIyVgbpkq+aRp0gOpcZJUqXQCQmCNX8ryfJTKHOzFhxDD2jPey4uBbHltYt\nKDgF5qD2ZnoRlPYOFhbPJ5XS5rt27WJGicPhVNPV1YUnnngCfX198DwPsVgMF110ERRJgUc8FnHN\nWTm0GC1sPaa/70YjNfRvtWph5hJRoXOo9yAfdHbmew2idaiV0Zta9olFWxwTBafA0rLphiB1YmZq\neknrYuZboawRHNfByfzJMklmRVLQEe4oi6hQZ6bklHBw7CDL/AgrYWxObfbFGFw/DXK0OAqPeFAk\nBQIEbE5tnrf+OEEqpc01TcN73/veeb8up7lwh2YZQLvaPvPMMwCAsbExtjMd02I4kTvhF3V6DhTR\nf2AHJmWWIUCW5IadGmB6wYB6jhAVDqjl2NAHfzquKlULCAC+I0ELU+mCVmUM0Xhfm5mgfW+iWhQp\nw5eFpipokiBBhMh633jEg+mayFk5jBfHkTEzyFk55O08200Lvg8L0S9mttiujYxZX5I5iEc8ZM0s\n3hh5gxW7RtQItrdth+M5KDm+AcqaWZwsnGRGZ3NqM/v+zTe1pM0//vGPL8i1OZzlSjgcxk9+8hMA\nfjPBW2+9FZIkIaJGMF4cZw/UtmcjoSfKzj0Vp6ZZggH1xl5IAYHp5iKLMjRZY8qQtL1B8D1jKd2T\nksVpM42MmfHlod0S2wgEwJpSBxXVFjsiQynaRZzInWD1MoIgIKSE0BnuLMv+oD3LTNdEz1gPLNeC\nIvrS0ZtTmyGKIjJmBoBvn0aLo0xVc2V0JRJGovri80RQ2vyNN97AZz7zGSjK/KVNc5oPTzlbJkzX\n1bY/04/D44cB+IvgjrYdZQtfWA1DFmWW6xp0JAzFmHYHpFHBgCC0gLESukCzxZ2QaQUEFEmBKqqA\ngLL6nkpoZKeZi73t2ijYBT89zcqiaBVRcks1xQWoE2nIxpQxkzTfMVoizT1rSTKH1FBN58P1XBTs\nAvae3AvHcyBAgKEYOKP9DIiCiIyV8VM/iN9zhn6eKSOFLa1bFuR+KLWkzXft2rWgc+BwlhOWZaG7\nuxsnTpwAAOzZswfXXHMNAH+Domeshx27LrGurJFh0AmZTfoZPb6ZggG1xl9MAYF60LRcy7VQtIss\nWlErPU0Q/D5vITmEsBKGIitNFReYK4QQZK0shvPDZY5XTIshZaTKUrepM2N5FnpGe1B0iky4YHOL\nv/E1VhyDQ/y1+3j2ONsE1GUdO9p2LOjnVSlt/r3vfQ833HDDgl2fM3d4hGaZkEwm8eKLLzK9dEmS\n8P73vx8AEFWjGCuOsYdt0zGRCqXKZJtpVGQ2kRqgvmAATRGrdR6teYFQvktGoyvAlPGju1q1BARo\nmN4jHhRRYTs/8xm1oUiiBE3yd9uSehJR3U9NM2TfAWTCCJNG1HIt5O08S03LW37HaZpWAGBR1Gc8\nz6uSZBZFEVEtOiVNGsD1XJiOideHX4ft2RDgS2TvbN8JTdb8ZqWT7/NQfoilO0qChG1t2xbcgasl\nbX755Zcv6Bw4nOWEJEnI5/N4+umnAQAnTpzAJz7xCQCAJmv+g/fkznveyqPVaK1KNQ7+u5Lp/t5M\nwYBa4y+mgMB0UPEAWZpSQgsW8QdTs2k9KXV8LNeC7dnMxs0l7XouuJ6L0eIoRgujzAZTSeW4Hq/p\nzDjEwaHxQyjYBfYssbFlI6JaFDk7B9P1N0uzZhamazL7sSG5oWbd7nxSKW3e29uLT33qU0sqy4Iz\nPTxCs4z4xS9+gfe9730A/Ae5gYEBRCJ+4X/OyuGl4y+xhWZjy0ZE1AhzEGRRZnmtpxKpqScYUNlx\nudZ5jufUjMDUKoqcSUBAkzXIggwP3rxEbRop7ndcByW3xMQFaEi9lriAIvo51obs/6MrOsu1nk8V\nMDrPmSSZg9DI2mtDr6HoFFk/gx3tOxBRIyg6ReYYlZwSjkwcYeOsS6xDV7RrXu+nHvWkzTkcTm2O\nHz+ONWvWsMjmSy+9hLPOOguAvw7sG94H2/PX+tZQK1bFVpWdH1w/ZhupAeZHMKCSxRQQoGqeVC2z\nFjQtmR5LHRcmz1zj/VFEhTlDiqjUFRdoNqZjYjg/XCbJrMs62sPtrP4qeF+02fbhicNIl9LMHq+N\nr0UqlILlWhgvjQPwvwtDuSF2DykjhXXJdfN6P/WolDZ/5pln8I53vGNR5sKZPTxCs4xYt24dfvjD\nH2J0dBSmaWL16tVTvTgkFR7xWD5qxsxgRXQFW2jov2nK12wjNbMVDAieR3vUVEo9V+YL039TAQEA\nZdeidUK2Z7PjgkWrQWYTtZlt00tJlKDJGiJqZEoWWg0zzXw6J494cIkL0zX96I05Fb0Jph0AaHqR\nZyOSzEFoceq+4X3I23l2v9vatiGmxeB4TlnK2kBmgH02YSWMDS0bFm0nq560OYfDqU00GsUbb7yB\nvXv3AgAcx8EVV1wBYGrjiD5wFuwCYlqsagPmVCM19LV6tS8zndsoiyEg4HouS6Ou1zMmWBejSApr\ncxBSQtBlnTkslfY46PiUnBJKbqnMAaJrfTMFaAghyNt5DOWGyiSZo2oUnZFOSKJU05lxPAe96V6M\nl8YhC77DtSq6ijXHHCuNsfcmY2bYRqAkSNiU2rRo6XWV0uaFQgFXXXXVosyFM3t4hGaZ8a1vfQu3\n3norAGD79u3Yu3cvW7w84uHFwRfZLkprqBUbkhtYATcA/6Gbpm6dQqSGXqdgF8p2/mlRYCPn1tt9\nqpUn7BGPGYhaX1XaxEyAwJp+NhK1ma+ml47nsNqbglXwa5BoUzZSHnWSBZlFb2jkhv4zXdRrOuhn\nWpliFlbCdUP4HvHgeR7eGHkD46Vx9hlsTm1Ga6gVADBRmmDfk6yVxWB2kJ2/q2MXkwhfLOpJm3M4\nnNo888wzeNe73gUA0HUdAwMDaGmZkmo+NHYIaTMNwG+QuLV1a3nqWUUk5lQiNUDtaM18RU9q1fFQ\nTrXOhtbIzNT0kkZTGh0/WGNqeza7Rq2Mh8roDb0W/edUcD0X6VLadz7IVIpZi9GCuBYv668D+J83\nnetgZhAn8ieYTe8Id2BVbBVEQcR4aZw9k9iujbHiGHtPuuPdzOlZLF566SWcc845AHzhmd7eXnR1\nLU72AWd28AjNMmPbtm349re/DcuyMDw8jAsvvBDr1vnhWUEQEFbDGMoPAfB31uJ6HJqksQXQIU6Z\n8spsIzX0OvSBO+jU2K4NQsi0qVSNKqIFd//ojhZVRgueR3esaId6GkUCakdtgvr+dXcW5yC1THc3\nI2oECT2BqBb1BQMmU/MU0Vdv84gHFy6rvaFKNwW74Kd2OUXmxNWrVarE9VxkrRqSzFqsroNE3/ND\n44cwWhwtSyHriHQA8NMZaSTJIx4GsgPs+9QZ6WTHLSb1pM05HE5tVq1ahYceegjHjx+H4zhobW1l\nDg7g12bSegmq4hhVo1XjzCVSQ1+vtAesEWWTnZqgMlq9OpvgsfUghLCml/VEbahaJ43EzDYKH6wx\nNRSDbXTRzISgnauM3tBmnDSjgSrXNWrTbNfGcGGYObSAnz7dEelAVItO68wM54cxmBuEIvq2uEVv\nQXeiG4IgoOSUkLNy7LyclWP2OKJGFqR/2Ux0dXXhySefZNLm0WgUF1988WJPi9MA3KFZZlR2tc1m\ns0yhBvDzWqm8MACkzTRWx1aXadu7ZCpFjC6awchGI04NMCUYEKx3oc3FZnoIFwWROTaVfWlqFT42\nIiBAw/zBZp2CIMDzPP94kLJrBHvJiILY9H4xwb43cT2OhJ4oU0GjRg5k6n0rOSVkrSyyZpbVrFAH\nh6Wn1RAXmI0kc/A9I4Tg2MQxnMidYJ/XqtgqljMfLBAG/D4BtFeFIirY1rptSajvTCdtzuFwqhEE\nAaqq4sEHHwQA9PT04DOf+QyLbEqiH1WgD7V5K4+EnmDpwMFxav13vWPqvT6fggG1rjdbAQFaD2q5\n1oz9Yqjz0cxU4qCDFFJCU7U0FU02qQIlrUct2SVmGx3Pge3aZanYlVG2klOqkmQOK2F0Rjuhy3pV\nFoQoiMyZGSuOoTfdy5yZuBbH+uR6iKKfFTFaHGXn0Q04+r5tatk073WljRIKhZi0+RtvvMGkzTlL\nG55ytgyZqaut4zl4YfAFlvPaFenC+uT6sp0RVVKZ3jvgP9wW7WKZcxFSQg0VG56qYEDwfJouVgmt\nv6llFKYTEBDgy1/SEL9HfBGBYE1O0FhRZbaFUumihoOmp1F57KCxrLwXQzaYM0RzsHVZr6pvEQRf\nZnm6fjDUmTmePV5W3N8ebsfGlo3smLSZLlPLOzR+iI0RTElbCkwnbc7hcKopFotYvXo1Rkf9B80H\nH3yQ1dJQ3hx9k9mOkBLCltSWqgf5WqnCQWbjlCyEYEAl0wkI0Kh+vVQ1wN/cC9ZPLjTU0aLy0HSD\nrFZ6d7DRNd34ow5RwS5gpDBS5vDE9ThajBaW1h2EOjOWayFjZnBo7BATKTBkw+81M9muYSg/VLYp\nN1wYZv/fGemsEp5YTCqlzf/7v/8b11577SLPijMTPEKzDGlra8Pvfvc7HD58GIQQ6Lpe1tVWFETo\nso7hwjCAqa7PhmKwBcQlLouSAKgZqaEdfWcyJKcqGBA8nzoUlcIB1NmhaWBBKgUE6L3RMWzPhuX5\nUtZ0kZUFuebOXK2ozXwWuVdGb+J6HCE1VKaCRiNYldGbnJnznSGrgNHiKMaKY366HwgUWUFMi9WU\nZKbQB4aRwggOjR9i72tST2JzajO776CogCiIGMgOMOWjhJ5YEukBQaaTNudwONUoioKxsTH87ne/\nA+CrPF133XVlx4TVMEYKIwCmbEJEjVRHJyoiNbXqSRpZUxdCMKDmNQNOk+v5CmV0wyzoZNHrS4Iv\nDkOj7YsZqa4VvVEldUoFTZgSz6F2znT96I3t2Sg6RYzkR5jTQeCnjreH25E0fMXISmdGEiTmzBTs\nAg6NHYIoTjUD3ZzazJppT5QmmESzKIhsMw/wN1cXU1SmFtNJm3OWLjxCs0x58MEH8ed//ucAgFQq\nhb6+PhiGUXbMvuF9zBAZsoFzV5yLklMqi6RE1EjZrhIt+A9+LRqN1ATPPxXBAEo9qWfa76RyLsHd\nNZrTXNYletJz0WQNmqwxw1MrahNkoaM2lMroTTByw3oSuH7zSw8ea+IZ1aJI6Imyxp609ohCDfNE\naQL7h/ez16JqFDvad7AUhEphgYJdQF+mj70vZ3WeNa3TtFhMJ23O4XCqOXbsGNavXw/P89fb/fv3\nY+vWrWXHnMidYEIgoiBie9t2KKIyowjAXCI19PyFEgygD/pBB6Zy/qIgssjGYvSCORVomhmzHd5U\n9Iaqso0Vx1BySix6E1bC6Ip2wVAMliERzJSgzgwVvDkwegAAWJRqS2oLc6hM12TPIYAvMEFtCeC3\nmEjoiYV9UxqgUtr8j3/8I84+++xFnhVnOniEZpmyadMmfP/730c6nUaxWMTGjRtZHwFKXI/jePY4\nK4YnhKAt1AaHOGVpRMGHXhqpCaZxTddEs5K5CAYEx2BqZDWEAyprboLn0d0hmrPLcqUFYUpAgHhT\nctKTKW1B5bPg2AsZtQneRzB6E9NjZZKeoiCynjce8WATmxXuBtPXTNdE3sqX5UwLEJCzctg/vB8Q\nppzNHe07WL637dplKWyKpODI+BH2Xq+Or0aL0VJz7ovNdNLmHA6nmkQigZdffhlvvPEGAH9NuPTS\nS8uOCSthpEtptntfckpIhVJVY1Wuj3OJ1NDj5lMwIFgXQ1O1guOLgggRIlRZZam+tVoFLGXHJhi9\nMWSD1d2IggjbtTFaHOOSROIAACAASURBVGXp6QQEmqQhpsWYs0NToKm4gADfRlDltZ6xHhAQZvc3\nt2yGLuvsfRouDLP3KiSHylLNEnoCK6IrFuFdmZlKaXPbtvHBD35wkWfFmQ4eoVnG3HXXXfjiF78I\nADjnnHPwwgsvVC2sx7PH0TPWA8B/mD2n6xwYioGclWMP8PThOUhlpEYQBBiyMatoBZUwDn7FaG7t\nbMahURTPK1dWoSlzQUercgfQdn0lmnr1OZUNLpdq1AaY7Alg5TFRmmBiAZZrQZb8yJflWLA8ix1P\na29oCpsqqXA9l6UGUAfpjPYzmKQzAUG6lGYOkCIpOJ49zoo5DdnAmZ1nLgkhgHpMJ23O4XCqeeqp\np1jaciQSwcDAAGKxWNkxeSvPduIBYG1iLVqMlppRjErmGqmhYzQrWkMjFLXqL+n8KptWLmajzmbj\neA4ypQxOFk76zslkynJUjSKshllT0ODnJosyNEljm4Oe5+Fo+ig84iEkh+DBw6aWTYioEeZEDeWG\nWKoZzdAIRvp2tu885RYFC0GltHl/fz9SqWpHnrM04A7NMqbRrravnHiFKdVE1SjO6jyLORsUXdar\n+pQ0w6lphmAAMNkoczJUXgkrapym78h0AgJUajlY60PTDWbqLdCIGlwz8DwPWStb9j5KooSQEmI5\nzAW7MKXCMxmhcYnLVG8IIejL9kGALxqgiArO6DgDLXoLS8fLmlm2WycKIjzilT3E7Gjbgbgen/f7\nnQuZTAYrV65ELucXMj/11FO45JJLFnlWHM7ShRCCHTt2YP/+/QD8TYFbbrml6rj+TD9O5k8C8B9Q\nt7dthyzKVc0Vaz3Yz5dT06hgQDDVqt6a3mjvllrzmO18FgNCCIvKUKEH2r6hNdQKRVKY9HPQ6fOI\nx6SZaVuAgewAbNdGRI3AIx7Wt6xHq9EKWZJhyAYyZqZM9jmpJ9Ez1sO+B6tjq5eE5P90EELwtre9\nDX/84x8BAHfeeSf+7u/+bpFnxakHTzlbxlR2tS0WizW72sa1OE7kToDAb9Qli/JUod9k5MLxnCoB\ngLmmn9ExaHi7bJwZBAMqm7QBU9ERCADIVL8YAGUpVbXGowICLB0uEIGhKXlUQIDmClN1lun62gTV\nb+bLiE0nySxLclnfm7Aa9iMvssoiMyJ8x6RnrAd5K+87QFYBK6MrIYmThZ2ehZHCCHJmDgS+ZHZU\ni6JnrId9bm2hNqyILc30gCAzSZtzOJxyaDrtz3/+cwC+YuBf/dVfVa2lETWCseIYW/cs12K2pNaY\nlf8/l/QzNs9ZCAZ4xIPt2X7tqGdXOVWAvzFFVSMbLe6fyWmpFBBYCnjEg+mYGMoPMUlmURARkkPo\njHRCV3SWgk1thyIqfsNOWWWpZoIgoDfdi6yVhSRIKLgFtIfaEVEjcIkLSZSQs3Is1UwQBCSNJIZy\nQ6yhZkgJYW1i7ZJ6f2oxk7Q5Z2nBIzTLnGBXW0VR0Nvbi87Ozqrj+tJ9ODJxBIC/iL1txdugy7rf\n2MqbajQZVaM1jcJcIzXBceoJBtRyYiqhDks94QAAZWkC9agnIEBRJbVMQICes5BRGyoOMFtJZgqt\nhclZObw69KrvFE2mEqyIrIChGmxMwHduNclv4mbIBrJWFqPFUV/iWzZwTtc5S6ZPwEzMJG3O4XDK\nyWazWLlyJbJZvzfIL37xC/zJn/xJ1XEZM4ODYwfZ/29IbkBcj1et3/XWwGZEaug4tVLQALCGkvWk\nlulmVSMqno1QS0CAQp2wxXx4dz0XOStXJslMmy63GC1l6m6VQg+SKMFyLGSsDCzXwtHxoxgtjkIR\nFViehZSeYvVUiuQ3jh4rjkEUfQEFqrrWm+5lDtP2tu0Iq+GFfyNOgUpp85/97Ge8lmaJwiM0y5zK\nrraxWAwXXXRR1XExLYbRwihsz8+VLdgFdEQ6IIsyk+IF/MhFZSpYMyI1dJxKwQDqWFBp5ZrnTUZi\ngkX5rKi/htRzUDig3vwqBQQqOx9TAQHXc8uabjYatXG92lLTjeJ5HvJ2vkxpTBT9qEkj6mI0RUCV\nVAxmB+HB89PMJAUbWzaiM9KJkBxin0fGzKDklFB0iihYBViehYPjB9mDQavRCk3W4BGPvR9LmZmk\nzTkcTjmapuHEiRN4/vnnAQDpdBq7d++uPk7WYLom2+XPWTm0hlp9IZaK4v1aD/HNiNTQ44PRGipt\nb7pm1XoOTDW91CQ/vbaZTS+pbZiuUWfw2IXEdm2Ml8YxWhxln4ki+SlmSSNZtqFVq0bVdm2UXF8B\nbawwhqyVZRufbaE2rIqvYhuAkihhtDCKolv0RQM8G2E5jMMTh9kGZFyLI2kkWePSpW5LKqXNR0ZG\n8LGPfWyRZ8WpBY/QnAbs2bOHGZ6uri4cPXoUqlpdn5I1s3jpxEvs/7ektqAj0lHVmJEWkldSK1IT\nUkKzXpBodCVv56sEA+h4pyLvWauJGHVcGpnjbAUE6L00O2rjuA5yVq7MgVQkpUpiux7B3cLD44cx\nlB9iKm7d8W6sjK2E67m+82IXMFoYLau/USUVPWM9LMc6okZwRscZMGSDqdfQtARae7UUjVIj0uYc\nDmeKN998E1u2bAHgr52HDh3CunXrqo5zPAf7hveVpaOujq8GUB6BmW4db0akhipx1YrW002oRuti\nmslSEBCgm4Uj+ZGyFDNN0tAWbiurma3lzNA0cVprO1oYRV+mD4ZsgIAgokawPrEeAJji5kRxAiPF\nESauQzdSaU88WZSxs30nq6Gltj74GS3FNLRKafN9+/Zh27ZtizwrTiU8QnMasHnzZtx7773I5XLI\n5XLYuXMndu7cWXWcJmtwiYuMmQEApM00OiIdLEzMmm56bk0lr5qRmsnam5kWoWBxPzBV01LWgZkA\nlmv5DTBnaXxoxKZWfjVNTZhJdplq8NNrl8lDE8JkKtluXKAZ22yiNvUMNyF+jVOwoaUgCNAVHVE1\n2lDebtCZ6c/0YzA3yObVFenCmsQaAFNCCKqkMpU7VVYR02IouSVkzSxkyW/I1p3oZoYtY2ZQtP3d\nN1p3lLWyKDklX9Jz8nNYCjQibc7hcKZIpVJ47rnncOjQIQCAqqo1087o+j1RmgDg96mKabG6Qi/N\njNTQ2h3TMVnGQeU5kiD50ZjJNW6hN1wqG3VWEnR25uMB3vV8Cf+h/BATeZFECRE1wmw+MNW4Okgt\nZ2aiNIHedC9zZgzZwMbkRrbe67IOURCRtbLMhtLvw4n8CVaXuirmR3Ns12btBegzgEtcWJ5Vliq4\nVDbKKqXNAVRJm3MWH+7QnAZIkoRcLtdQV9uYFmM68LRIsC3cBlmU4f7/7L1plGRXdSW83xwv5sjI\nsQZVqUpVmkVJgMxggcGAABkaYwbbmM/GshnWAjduwBMG2xibHjDu5WG5vwYPzecGLNENWDIz2Ahb\nGAzWjFSjaq6szIx5ePO934+b5+Z7MeQYJZVKsdeqVZmRES9evIi45+5z9tmHrZCLkIeC6Axo7NQU\nDSFfH6lZrS+GJGiaosnNsKIo8tw2k62hMrkCpW9eDRGbtbJjZDm5EQMBeW1WmWsDQC7cvQSLcyED\n7O2XyZgZpI30uq5DnMycb5/HicYJGRDKdhlXTFyRODbjTJJbCkDldBlnmmegazoM1cC2/DZsz22H\nrdvQFV063bihi7bflq5o5CDkhA7aflv2Jm1UljhKqKoK3/fx9a9/HQBw6tQpvPWtb70oM4BjjHGx\noFgs4tOf/jQA0Yv2rne9C4bR3ztnG7YY/Ltsy9sJOphMTw79fm2F1FBCifoeBzb3Lyek6F88sfJk\nfuefaAMBMpFZdBYT9vvFVBHldFkmxgaRGVqviRABQlJ4vH4clm5JGfO+iX1QVVXGcM45FruLiLiQ\naNu6je257ULuzBlUVUXBKuCy4mUJgskg1A1e5CWMGyhOSqdOPHFz4IZhamoKn/zkJwGI78U73/lO\nWJa1xqPGeCIxlpxdIpifn8dll12GIBD9MPfdd9/QbHTNqeGhhYfk79dOXYtyugzOOdp+O9E0mDEy\nAxcRkiwNkp9tpLm/93jDDAM2A1qwh0nI4mRkreNs1ECAsNZcGwUKVKjoBJ1E5UtTNeSs3Lpfe5zM\nVJ0qDlYOyvPJm3lcM3VNgngBom+GbKBVRUUxVcTR2lFpy2ppVsLim4Z2cs7hMzGIrnf2jaVZSBkp\nKU8DIDOl5JzzRAal9VqbjzHGGAJRFGH//v04duwYAOATn/gEbr/99oH39SMfP1z8oVx75rJzmMvN\nAehPZq22qR8kPwNW5sWstn7qqt7nTjbKmTWjxoUyEKAKf82poeW35PFM1cRkZjJhJDOMzCiKIskM\nB4cTODhSPSKvrwoV+8v7ZbKTXErrbl0mxxQomMnOyKoO3Xbt9LXCSCDypdqBxjFEPJLOp4S4Mykl\nN59MeVqvtfmf/Mmf4F3vetcT9vxjrI1xheYSQTabxaOPPiqn2oZhiFe/+tUD72sbtnDPCkQ1oOk1\nMZedg6oK6ZScGrwckAY5W5GFMlVqKIO2WqPloOb++PF6DQMAyA33ZkgNZcYGGQfQgr6epsTNGAjE\nHzusakPXrObWhDEC51BVIQUrWIV1S7fiAbLpNXGwclBe34yRwTVT18isHN3uBI6cJwAAOSuHTtCR\nTngAsL+8H2lTEErbsJG38ihYBaQMYW9KUoOMkYGhLk+eZoGs0lD1hjYkVNXxQk+aC1xoedp6rc3H\nGGMMAVVVEYYhvvrVrwIATp48ibe97W0D13Va22gz2/E7KNkludlcj0kAkKzURExk5kmONCg5RkMe\nLd0auLEdZO9MPz/ZpOZCGAgwzuAGLhY7i+iGorJCPakz2ZlET2zcNIcwiMz4kY+j1aPQNV3Grv3l\n/TB1EaeJ5Lihi6pTlccqpUowVANHq0fla9mW24aSXZLqB0u3YOvCpIbk2hRjaQ9Bvanx6g1J9Sjm\nUt/sE2EuQPsWsjY/evToQGvzMZ48jCs0lxC+853v4HnPex4AMdX21KmzsKw8ooiDc0BRAE1TYNsq\nuBLi+2e/Lx3OtuW2SUmSH/kJd61hQzA5F9mVbjjcKIAybRv50lNFYJhhwGYxzOqZZHQb2VxvxkCA\nwDhDGAlThEGWzBkjIxf59fQm0evpBl08vPCwDCKWZuH66evledCxQhai4a4MPCOb5gfOPyBlBmW7\njCsnr1zzOnihl6jexK+NH4kqTvx8eqs3mqLJ6g3psEeNXmvz08cPoWT7YJELsBBQdahaClp2G1Qj\nO/LnH2OMpxpqtRq2b98OxxFx4F//9d9wzTU3DowlmgYcqhySCbKMkcH+8n653qx36GYQBYn1ovf+\nNEB5I5n5Yc35F0u1Bti6gQCZ+sQtmXVVR87KJSyZgX4yE08yMs7Q8TtSVn24cliSL8YZ9k3sk1Ue\nGo3AOMO51jlZ7UnpKUxnpnGsdkySnJSewrVT1655vcngIV69IVJDVRy6RpQANTRDVonIYnqUdty9\naLfb2L59O5pNQeC/9c0v47kHLh/HkosEY0JzCYFzjmc/+9mYnNyGX/iFt+OFL+xv5iSYpoJQa+Fg\nY0V6dmD2APJWHoDYHMen0scdtnqlBIPkZxkjs6XsO/WUxGVYiqKIoZFDGk/Xi1E4ohEomxi/VoR4\nyTy+mJMlsxd6svdEURVkzWzfa1vNIS1OZrzQw0MLD8nAois6rp+5XhKHeK9Ow2tIIqarOgqpAs40\nz+BE44R8zhvnbtzwdSYXPPoXsUjKIIjgEIFWoUoiZRsrM41owJ2lWX3XbSu45ZYfRSEV4m1vvg0/\nfsuNQ++n2lPQc7ugpWcumg3PGGM8GXjb296GEyfOrSuWGKkQR5uPyriwM78TU5kpAP3xgkgKrcO9\nSab4z6oiZplsdS0YJEFbq7flycCg8yT0ni9V+BtuQ5ozkAysZJdkLI8fu5fM0LobJzOMMxypHJFD\nMhljuLx0uTxe3LRnsbOYcFCby86hE3RwqHJIPs/+8v6+c1kLcRMeP/JlXKN4Gf/MKFBkz2e8dyo+\na2i98vL14N3/8T/iyA+/O44lFyHGhOYSAmMcDz98AlNT29f9GB9tnPQfAQdD2kjjprmbZHm5E3RW\nhm5CNKgP+2JGLJIZemDzls5959dTLQJEqdvW7S0vEr1ZHwJJoTZy7mSwQHK9OKhx0tRMMMb6LJl1\nTUfGEEPG1tKKUzYtTmaCKMDDCw/LXhZN0XDt1LVycFn8OpHki24vWAUELMD98/fL4+0u7sa23LZ1\nv/ZhGFS9ictJ/MgHg3jOePWG+mwUKNIWOqWnNt9LxUIcf+BuzBTX/3jVnoI1/UwoT6DV6xhjXCxg\njOPkyTosawNZZs3HfHQYHEJOes3UNTIpEq9C0GZ01b7CZWlSvNK/1fV+UO/KxUhqgLWJDSWKqk5V\nVtVVRUVKT2EyPZmwZAaGD8yk5yIywznHsdox6TbKOMPO/E5M2BPyOaji3/bbCanZVFpYQT+y8Ig0\niijbZVxe6rf93igiFiFggtxQ9YY+SyRpjldvyNSHqjcAJLFZz+DtYeAsRPXxe2Cr3XU/ZhxLnjiM\nCc0lAsY4qtUIQbDxt9NlbZwOBanZVdiFXUUxUZ2mC8edUnon1McDzWpGAVvBhTAM6D1+nGAQKMOz\nkUA6zECAApAf+TBUQ+qELd3qM16g8xlmrKBiZchnxCI8sviIzJIpUHD15NUopAry/nRsP/LR8lry\n9qyZhaVbeHTxUdTcGgAhF7lh5oaRZ5Ti1RsncOT1ltWbmLnAsOqNruqyepPSU+szdGAhvHPfAfMb\na963F6pZgDX33HEgGuNpha3EkhAulnAUHGKA4t6JveL25RlX8XU20Wu4vMGmtREYzZyaQbiYDQN6\nMYiEhSyEG7hYcpbEKIBlZUHaSGMyPdm3WV+LzHSDrnyOE/UT6AZdaKoGzjlms7OYzkyLxykrg7GD\nKMB8e14eN2fmULJLONM8g3PtcwBEYu266euGSq+3ck3o8xSv3sQrfvFE7KDqzWbMBcax5OLHmNBc\nAuCco1aL4Hmbfys7rIaz4WMAB26auwlpUxCXIApkBgiA7HUABvfFXChSwzmHG7p9FZBhQ0A3c/xR\nOKLFjxewQMrK4lUKQGzOJ9ITyJrDM6CDHNISAY4DR6pH0A7a8tz2T+xHOV2W94/r2OtuXb4vlm4h\na2ZR6VZwsHJQ3v+GmRtWPadRwY/8Pue0+HwJsusEhldvSJ6W0lMDgybnHN7574E5i5s+T9WegjVz\n80W52RljjFFjFLHERQs1HEfEIuzM70TOysmNb6/kiRzKhq2vF4rUPJWqNcDK+QZRgLbfRsWpSOt/\nUzNRsAoo2aW+azNsYCYdsxN05HU40zyDhteArurgnGMyPSkr9XFHM8455tvzUj5sqAZms7PwIg+P\nLDwin29XYZeUHV5IDKreACsOeXF5+bDqTbxyMyhJOo4lTw2MCc0lANdlqNUGl+83gtPBo+iwKnJm\nDgdmD8gvnhu68ENBJGg2ymqVkbiHPT1mFKQGuHCGAYRhxgEANlWqZoyh6lT77LCzZlYebzUDAUJc\nO0w4UjmCqluVx9xb2puQisUXzkEWzYwz3Dd/nySJs9lZ7Cnt2dDrGwUYZ3ACRxIceo0hCyW5oWZh\nFaokN7Zhy88hyS2ogqOpGsLOPPyF72/5/MzpZ0HPzG75OGOMcbFjVLFkPjqEDqtBV3VcNXmV/J7S\nJtxQjYSEdjVcSFJzsRsGEIjMVLtVNP2mnKdmaibK6bJUTvRKktdLZhbaC1hyluT7VLAKuKxwmXxs\nvIcpYQsNBbPZWRiagYNLB+XtGSODq6euvsBXpR/DqjfD5GnkrNZbvek1FxjHkqcGxvWvSwCdzmBP\n+42ipM2iw6po+S2cbZ3F9vx2KBBkhKoFgDAMyJm5oYs+EQwiNZwLP3vbsLdMOnRVR87MJTa+JI0b\nhWGAoigwNGMgsaHFcD2OaPR4knhlzaxoygSDpa3MrIlYhC7rQg0HGwgQSP5GMrPHa4+j4lTkfWcy\nM5iwJ+CFXh/xcgInYVqQs8R7d7J+UpIZQzUSAeyJhKqoyJgZ2fMTr94YmoEMz6zMvgmFRK0bdAEH\nMFUTtiFc0+JE2lANZBpHMIp8a9g6MQ5CYzwtMKpYUlBn0GE1hCzEmeYZ7C7ulha9cTtlDi6dMIeB\nXLYI63nMekCVXoakBI1x9oTYAK8XEYvghR6WukvwIk/EAk1UtibtyUQyjHEGzjjYch8ToZfMxGVm\nVaeaIDMZI4Od+Z3ysfEB207gSNICACW7BEMzUOlWEiSHZOtPNCh+G5qBDDKy6u9HPtRopf8nniCk\nWBPvdTW4IfcXqqJCbR4byfmNY8mFxZjQPMURhhy+P5oiW0YtwVBSCLmHk42TmMpMSTlX2kij7bdF\nVmt5QaQN6CBoqgbbsGVDP2XhR0FqqEoUNwwg0hSycCSGAbQw9jqicc4RckFshjmicc7hhR46QSch\nvcuaWUkOvdATvvp8RYrhhi68yJOLaq/dJiAW1/nuPKpuVZ7fpD0pyQiHOL8wCuX8nXi1jPqOOn5H\nap0B4PLS5SPpRxoF6PVTFSlevbE0CznkpMWnF3loeS00vEaiepNVVeT8+kjOhzmLYEF7bMM5xiWN\nUcYSWynAQApcFRvGiEfIaCvxopdArBUTeknNeh6zXqiKKt294ufXSwqeaFBSrBt0UelWxNy05X6Z\nrJmVlsxx+Vx8YKa8v6L3kRm6T9Nr4nz7vFz7bd3G7uLuRCN9PPlWcSry/GzdRtbMImQhTjVPydun\nM9N9vbZPFuKVewDSNY1MDyxY8ppRZYdMc6h6Y/EIpltd7WnWjXEsubAYS86e4mi1IrTbo8mqAUCT\nn8P54DgAYMKewHXT18m/kd89YT39KyELEy5lqqIibaRHVtIfZBigKmpCjjSq51mPI1rcklneR1UH\nWjLHLY0HfQ1pYx/HYmcRR2tH5fWbsCdwRekK6RoUv66cc9k3Q9K2ol0E5xwPLTyEtt8GABRTRVwz\ndc0Wrs4Th0G9N3R73B56lrnYxt01jrZ+GMX9MEr7R3a8Mca42DDqWBLoDSyFYlK8qZliwO+AJA2w\nfhlZr/xs1ITjYjEMoERa3JKZqvSDLJkBEXuor4VAvUmKogAcIlYuk5mO38HJ5kloiqjmW5qFPaU9\nMm729pMsdBZkH6imaJjLzUFVVJyon8BiV/SWmJqJ66avu2iqW6shXr2J99702omnnfPIupvvnenF\nOJZcOFwcKdkxNo0oGi0fzRoFHG2LJvOu3xWThjMzK88Xs2fu+B2kjfSa/R+UZSfQ40YWJPhK5oXQ\n8lojMwyIY5gjGkkU4pbMiiK0uDkthyAKBs6qAYSrV8CFgUA8YHcgbLN1VYelW2j7bRyuHJbTpXNW\nDtuy2xJmA0RsGBf20HRNFEWBaqmoO3UsOUtY7IgFWlVUzGRmEkT1YoehGihYBeTMnKzeAMtEdtk4\nIOV5GGIStynwaHTkaIwxLkaMOpYEYYTjteOimqKqqDk1bMttS8zEGjZEczVcaFLzZBsGRCxCEAWo\nu3XpXqmpGkzVRMkuIeIRlrpLicfEZ8zQdSU3TLrNjVwpp/MiD2ebZ2UsMVQDOws7cbIhCGjcnhkQ\nFs1Nryl/L9tleJGHjt/B0dpRefvu4m4crx8f+TV5IkAxOuTJXtXZmMRuFBjHkguHMaF5imPU9TXG\nOAqpgiwtH1w8CFuzYeqiUqBgZSCaAgUt3kLOzMksFmmb44FJV/U++Vk36I6M1CiKAtuwYWhGwjDA\nCz2ELByZYQAAWY2JO6JRpaUdtKVWWVXUgZbMw86fqjFEzOLWkyEL0eg2cLRyFLqmQ1eERef+if1J\n61NFEf090OAEyapVxshAVVQ4oYOjlaNSTrG7uHvkpO+JAvXe2IYtpRktv4WW1wJng8njZsEHkNgx\nxriUMOpYEoQhDM3AQncBAFBza3ADF2kzLddIIjHxSremaIl1tLdCciHlZ8AyeYHaJ0GLeHTBqzW0\n/ledqtxUG5qBlJZCyS4N7N3sHZhJ1zJueuBGrowHfuQnyIwKFTvyO2CogsCQrI3gR36CzJDdP+cc\nZ1pn5O15K7/hAZoXE6jHK2QhgiiAG7lwQxeMBRhBy5bEOJZcOIwJzVMco15bq04Fda+OE7UTUBQx\n2JBxhmumr0FaT0vpUsACUQ2IItTd+sCNO/1OQUtTNbiBK4lPi7WQMTMrw9O2+GIutGEAgXTJmqJJ\nW2vKpEU8AlMY8lZ+1R6jYaCGRmp+D6IAbujiaOUoIkSIogjgwO7J3VIqQOdEIBmerurQocv5LYwz\nnGyclJIDUzMxYU8giIINDxJ9MhC34ZRNnTzZ36QpGvJWHibrAuHoMmvj+QFjXOoYdSzphsJFizHR\nH6gpGk62TuLywuXQtWRfR68bV9+5LVfAe0kO/a9BWyFEI1jLKDnUK0G7UIYBZPPvBA5qbi3hBpc1\ns8hb+aG21r3SPenWtRxnndCR94lYhHPtc2KgMRdkZlthWyI2xu2MOeeoO/XE33JmDgCw1F2S6gBV\nUUcyjPmJAsUPxpnoiaXZb8vvdcACEYOZBxcBoI2QMI9jyQXD+Mo+xaFpCkaprVlw5rHgLiCfymO+\nPS/KykEHDGKTbmom0kYalmaBgyOlrdjk2oa95vFTeiohP/MjP1Gp6SVB8YpP78/DCFDWysILvYQU\nix43CsMAANIcocWFtE1TRQO+qqjIWTk5rGs9jmi9YJzJqokburjv3H0wdRMcHLqq4+ry1bANGwwM\npmrC0q1EgG24DdmUqakaClYBiqKg6lThRZ58n66ZugY5KycfR8FwozN3Rone4WhxEhMHZRE1aH0S\nEc45FN0eLaHRnppVrDHGWC9GHUu4EsHSLVyWvwzn2uegqqIHhC0P3ZTrI1VkVFWuPeslDKvJz4j4\n0PGJ6Kx2+8DXcYElaHGJmaYIS38aMjphTwxNjK02MJPgBI7sq2Gc4WT9JGazs/L+uwq7ZAwgpUD8\nGFWnKqVnChTM3THvBAAAIABJREFU5eagqzr8yEfdrWNHfgcAYEd+hzzuxQIyVaCG/3hc6R1aTSMC\nnNCBF3pizplpwmY2WNgAMLqqyjiWXDiMCc1THLatjrSR84xzDDW3BgaGjt9BxCPYho2j1aO4dupa\nqIqKjt9BG205OBIcSBkpFFNF5K080kYatm4P7K1ZS34ms+3g646tg8gOuZs4gchOKYoCP/QRsWjL\nhgFxS2Y6tqmLQJDSU/Jc1uOI1ot4RjBkIQ5VDkFVhZFCxCPsKe6RhISkbn7kw9AMWJoFL/ISm/+s\nmYWiCJng8fpx6a0/kZpAyS712aGGLESIUBKxC1W1GURYhs3/kecXk1AkspLLjnR0zpqiIVJUwF0Y\n2fl2jTzSUTDyqddjjHGxYNSxJFTaSOvC1XHCnkDNEXGl5tQwnZlGzhCJH3IYC1koNt+KqByo6nLl\nfsBGnbCa/IyqF+EGNqO9krc42VlOoyVuX++U+aHXiIXwQ19aMgOQgx+n0lND15uNkhnOOc62zsJn\nvpSjbcttSyS0qOJPVSgndKRxDCAMaChunqifkNfd1u1En+0TDTLs6SUt8YHUgx4jCUzkIYyWPyPK\n8nVQADdwxR4n4rhshDvlwCpDYeFF4yp6KWF8RZ/i0HUFpqmMxG6z6s/DMFTsNHdCUzV0gg4OLh1E\n3a2j6TURshB7Sntg6iZMddmvPTazZam7hE7QkQujpoh5NLZhy0GIRCY0VfR50CZ1Kz01CclCz2Uw\nNVPYIcdcxzp+R9r7AqtXf3orQcMsmW3DllWRXkc0zjmCKFhTDhEnM4wzHKocklI2VVFx9eTVKNkl\naS0ZD2hBFKDrL1sb6xZ0VZcWzQBwunlaVqx0Vcfeib3S9nnQ4h9xIW/bStWGjt1LWgaZKgxCnMBQ\n9YsIma7qcrpznAgzxlB1q2j7HZQUAzm+9V4aV8+g5ncAvwNDM5A20pK0X2wD+MYYY7MYZSzpsjq6\nrA1TM5HVhF19N+wKa30/xMHFg8JRS9OleUvGyEDXVrYk8bWXZs/EbyO75bh8iAxRNoteCVcvBlVr\n4gMY4wRoGDEiq+WABXADF0vdJelQaagGMmZGWjIPQi+ZGVRdipMZAJhvzyfs+7fnt6NslxNrKxGi\neDwn6VvaSMtKUd2to+E15LF2FXdd8HUwbq3cW8HvrbYMAo1FcANXJv3oPbM0C2ld9HXRjLOIi+qi\n67hohi7mOTBrbD2ZFZkFeODounU5GNVQjaHz58bYGMa2zZcARjXd2dUXUAnOyUnAhmqg6TWl8wnn\nHDsKO5Azc7A0C6ZmilkwoSNdWFJ6ak1iQvehL3FaT0vZlK3bsgEvTlQG/byRj24QBXBCJ/EYGgC6\n3ioEefh7oSclB2TJbGlWHwka5og2KLMXJzOccxyuHkbNrcm/75vYh8n0ZOI4EY+k8QHnHA2vIYOt\npVuYTE/C1Ew4gYP75++X129vaS9msv0ZNSI2wwL6sKrNMNKymY0FB195HiQJTC+xlJucZULT8luo\nOTV4gYeaV0OWOdgPb5VnWx+q6R3wjFzf7SRhJIIzrt6M8VTHqGJJYFTgKk0p/SXDk8PVw/I+U+kp\nlOySHLaJZcWbpVuC5GipNb9TtJZqqgYFSt9Q4YhFCaJD/4bdvh4MIjV0LuvZlHLO5RDgtt+WFWZT\nM1FKlVBMFfuIEa1xvYmnQWTGDd2E42elW0nMj5lKTyXkYXQN4zH1fPu8rBjpqo5tuW3QVA0Ri/DI\n4iPy+FPpqZEO0YxLjuOkZbVqy7DjUGz0Ix+MM5kE641htIeJWCQJRstrYdFZhBu4aPpNzGk6bskW\nt/z6vMJ+RKmJgX8zVEN+DsbVm81hTGguAXDOUatF8LzNv5U+2uho52QJmjSy7aCNw0uH0Qk6CFgA\nTdGwp7RHVjQyZkYOiyQdKhTILyU5pwxDyEJ4kQdN0WDrNjJmBmW7LI+7nv6T9RIfxoWMLk4yyPhg\nNcMACmAtr9VvyWzm1jzHeLCMZxhpo07yOrr98drj0hkIEFaYc9m5xDF7TQCWuksyA6coCgpWQQa7\nw9XDcAIHiqIgZ+Zw/cz1a55vb9WGsnZxDTINn9vMEkJVHwrY9Pt6+43iRKYbdFFxKvBDH27oou7V\nwTiDoejYyzsobEH/rKQm4RevQjd0+ghxL8bVmzGe6hhFLAmUDho4hZyVk99Rn/lgjKHu1sV3NfIR\n8Qi78rskaVFVFaa6sg5HPIKmaGK4oW4lkkbDQOssERtDNeTPayWuaJ2XpCdGdogAxW+PO1ES1uqt\nCZkYetxryWyoBkp2abjjJF9O9sSIjqmafUYIfiRk1USu6k49EUtKqRJ2FnYm1AW9sa/u1lFzVpJp\nM5kZWLoFQFT6aeaMruq4fvr6DfeI9lZb4gmx9VRbeqEqKlSo8hgBC+RnZzVIt1ZFgaWJ19fwGqJ3\nOBSz4VpeC1CAjJ7Bs1MmSsrmq3+qPQWUnyEMByJ/1deqKqqs3IyrN+vHmNBcImCMo1qNEAQbfzuP\nnjyIub1pKJpYbKjhn9DyW/i3M/+GgAUIWYjJ9CQm7ImVgVTLm11N1VBMFVGwCshaWYADXuTJIDCs\nYkGkhqBC9L/QYkubRPpHf9ssvNATcrcY8dE1HSlNHLeXBPmhj5bfSgSB9VoyE+ga9Wb2SPNNGZmz\nrbM40zojpW9zuTnsLuxOVH9UVU1I5NzQRcfvyKxUykhJIrnYXcSx2jEoEHKGZ21/FrLm8CnFvdUW\nL/TgM3/oDJ3Vem2o6bdXJqYqKqBgXUQoLgfshR/6qDgVQeQ40PAbaPttGKqBvJWHF3rIWxnMOfPQ\no+7AY6wG1SzAmnuudKXhnMMJHTnYc9g1ofMeV2/GeCpiK7Hk0SMPobxTR7FYQMhCGKqRiCURi3Co\nckjKq9JmGrPZWXihl5jDRX+nxBj12WiKBlMXWfTVpLtxxF0041LVURigMMb65G7xPr947PMiD27g\nouJU5EZaV4TkbsKeGEoMBlVmBp23F3qJykw36GKpuyRJVsEqYFdhlyRA5FhK0itVURGwAPPtefm8\nOTOHQqogj/fo4qOiMq4o2FPcg8lMUjWQuDZDDF42Wm2Jv+Z4HKF5MV7oDR1OHYelWzBUYyB56gZd\nzLfm4YQOUnoKbuCi6lZhaRamM9MwNRPlVBFz3jzUoL3KswxGbywBIKtHNKZhNeiqLsnNuHozHGNC\ncwmBMY56fWPZta9/64t4+/vfhJ/6qdfiQ7/1IQDLjfu6nchIz7fncaJxYvl5GK4oXyHJjM/El7Ll\nt+AGok8jbYhAVbbLMrsDQOqHafF3Q1dOfY+TGk3RhmbkejeLm9k0Usm/178/bhjAOU8MbuScAwok\nqYoTn2E/9yJObOIyM0VRUHEqON08Le87mZ7E3tLevtfe+zqaflNonbHSyxNEAbphFw/OP4iABVAU\nBduy23BZ4TLZQE8mAPFs2bDzHtZrQ9fNUA2ZRSU/f2o+jQf5tRA3dRh2LWmCNs1GCKMQVbeKgAWC\nTJtZtPwWClYBlm5B4Qyl7hlwd6nv+YZBtadhTd+0qsUmWXaTbfe4ejPGpYJNxZJ7voi3/8abcN0z\nrsXffvxvYZmWTE7F56B0gg4erz0uewj2TexD1srK/kTqY6C+R5Izm3pyMxevwBiaAQVKYm2NY7Wq\nCRGbOMnZjDNl7/efqiSMM7lWVJwKGBPnpqoqMkYGeSs/tDIURqLiEN98r4fMuKGL+fa8fFxKT+Gy\n/GWJxxlakhQyzkT1ha8oF+ayczKhdKR6BE7oQIFQAFw9dfVKH1MsYUkxZTOSY5INSuKiapLA+JGo\nwDuhAzd014wnpmbCNmxZ9er4HVkVi183UjfQfuN8+zwCFqBslzGZnkTEIsxkZ5BP5QEWwqgfGnks\noc+IH/lyJMbQ48WqN73v4dMdY0JziYFzDs/j6HTYqs2dqhri0//nr/Gf3v9OsTBYwG9/4Ldx+8/d\nDkAsmpPpSUlUGGN4ZPERdAIxUX4iNYFnb382nNBB1ami6YlNddtvSw1vEAWyklFIFVBMFftK6qYm\n+m40RZgE1L262CAGDnzmw1TXX26lRvj4P9uwV7Xj7NUb0zlZmoW235Z/oywXWTKvB6uRHantZWKz\nX3WqOFo7KpvwS3YJV05cmXjtvdeBcy7NGoCkRTMAHK4cxsnGSXiRsKG8cvJK2URLgYMWRWBtq2zZ\nyzJgGF78nCiQD6usxBEnMHTegzYHdHvH76Du1uVr7gZdNLwGUppw2dNVHd2wi7yVl8cs22WYmomo\nex5h6wSYszj0fEKtgHR5H7T0zIZIB32WiOD0fqZ6XzP1kWWMzLh6M8ZFifXGkk6nhocPfR+v/fmf\nkEmfn3z9T+KP//CP5fe7lCqBQ/T5RSzCmdYZKWuyNAvP2vYs2IYtN3TUZ+KGLtxImLpELJKyNEqa\nxGFoBlJ6CiktJfo9YpvsiEfrWo8I8epynPCstiYM6q2JeCSldq3lifPSaXIVS2YgKVWmJKACRa7h\n9HcndKSbZ8Qj+KGPU81TK/PGVBO7S7sTr7+31wgAak5NJu9URcVUZkpe48XOIk7UT8hrennxcmiq\nJl0+40Sw1xSBrmXv8NRe0tLrBCoJTCAIzFoEydAM2LogMKTi6PgdtPxWXwUkZCGabhOdoCPPq+t3\nsdBZQNbIYiozBcuwoCkaZrOzco1O6UL9sJ5YUuuqmNl904ZjCZ3fuHqzcYwJzSWMMORwHAbGRMbt\nc5/7v3jooQfwhS/cgT/4gw/hda9/HV71hlfhK9/8CgBAsRT8z//3f+IlP/YS8TsUlNNl2LqNlt/C\nQnsBDy48KI+/u7gbO/M7UUgVoKs6ak4NS90lzLfnEwuxqqoy+5DSxcZzELlRFDGhGRALh67qMrjF\npT6rbRYHgTaP8X/xqlHcMIDIRjfoIqWn5IJsaGLA2SiyIfENe8Qi1NwaHlt6TJKdrJnFNZPXyOyL\nlMb19AZ1/A66vhgiysCQMTKy6lJ363hs6TH5nLuKu5AxMgMXR03RxEDPZRlHPEMWDzh9zm9cXLsI\nkbx2cSIjpQ2xjGLcRa6XCA2r4nAuJl033Ia0++aco+4JYlNMFWHrttxMxTXh5XS573PGgjai9lnw\nyAVnIf7+rn/AD+5/BJ+9+x781gc+jF/8xV/c4DvaD/oMETlfLRjHifhqBHyMMZ4s9MaSb3/7Hvzj\nP34DX/jCHXjta/8Dfv8jv48P/MEH8Ed/9kfSafI9v/4e/MrbfkUeI2/lUbAK0rzjsaXH5HpUTBWx\nu7gbhVQBGSMj+wzimXiSbXmRJ3scKCEzaCNHFeOUnoKlW7L3L05yentg1kLchCBOeAiyIr0cR4Io\nwFJ3CQEL5ByetSyZgdUHZsZB14gQsQgnGydlll9TNFnll8dUVuyuiRi1/bZ0W4tYhLyVh6EZ0t74\ncPWwJEiT6UlMpaeGVkgUZSUx1lv16m3MjzvBMc7kJj5gARhjqzqD0giIlC4cS+n6+JGPtt9Gx+/0\n9anQc7S9NkIeyves6lThRz6mM9PS1Chv5lGySzJ2WZqV2DcA/bHk3n/9Hr7ytX/CZ+++B7e+8ifx\n53/+50Pf4/WCx8YzjKs3q2NMaJ5G+P3f/3188IMfBAC84Q1vwGc+8xm0O2087yXPw8OPPQwAyBQy\nuPOOO3H1lVfLx+WtPEopMbPkkcVHcKhyCIDodblh9gb5RSeJjxu6ONMUGbim1xTWnJolrSrpy6mr\n+kByE0SBNAqwdAsZM4OiVZSWniELpRQsLvkZFJx0WEgrJegwoUAFB0MIH67SgKlrib4ccFGq74Zd\nKeFKGSkUUgVpybxV9FYfukEXP1z8oWyStXUb105dKwO0qqjCGhsrTfn0+mtOTdpDE0kjYvHI4iPy\ndRRTRewt7ZXBPO5eE8+WUYZrtSbEOPGIL6zDHNJUVZVuZaZuykZNCni9bmW9zxXxCA2vgbbXlscO\noxBNv4mUnkLOFI3HuqbLyd6EYqq4agaU8Ed/9Ed473vfCwD4iZ/4Cdx1111rPmYj2Gz1hpwALwQo\nELPIBVgIqDpULQUtuw2qMbzHaowxAOCLX/wibrvtNgDAnj17cOjwITiBgzf/8pvx+S9+HmAAOPBn\nf/FneNUrXiUfZ+s2yukyFCg41TyFhxceluvh7uJuZM2sjAsZMyOqRJEnY0bcPMWPfHiR6IckG95B\nWWrqU6TMfcbMJGTFvZWcYdJbHqlggQ7OVJAlm6IyqEYIw1hJ3qiKKhwoA0+SBFrHs2YW5XR51Y3m\nembMACuJOHl+nMuKPCASVfvK+xLGPEQQ4vIwL/LEjJplN7CUlkLRLsrzOF47jqpbFb2mqo49pT3i\nPJevWy9xkT+vYcQQslDaKLuhu2ZvjaEayBgZKasmZ1QyBXBDF52gI693/Lkp1tBsHXouP/TR8Bpy\nj6GqghBMp6dh6CvXjZQka+Gf/umf8KIXvQgAsG3bNpw6dUrOUxoVLrbqzcUUS8aE5mmEhx9+GNdf\nLxyustksFhcXYVkWTp0+hZtffDPOL54HAGzfsR2f//znMTm50vBn6zamMlMAgO+d/h4qTgVO6KCY\nKuLK8pXyfkRsAEEOIhZJR5fenpV4RkZVVOTMnMi2G7YkNQRyFKOMzKD+Gi8U2uuu34Xnc2hhBiaG\nf6Ec3kSbLaLL6+BYlp+FYvAYLZplu4xyurzpGTlx9JIZL/LwyMIjcl6AruhCFsaT5IUWaKp0MM7Q\n9JryWpqamWj0P9c6h9Ot09IJ5xmzz5BBnDJkdO29yJOyNwCJKgcFQjrv9WqiIxaBgQ0kKSQvWy3Q\nqaqKKBI9Tg2/gSgSwUdRFPm5yFt5GKohZyTomi6rN4Ag4fGhcavh6NGjuOKKKwAAlmVhaWkJ2eyF\nW4ifrOoN51xIJZrHwVbRgKv2FPTcrk1JJcZ4esDzPExOTqLdFg3SDz74IK6+9mo02g3c+pO34gcP\n/AAIxffps5/9LG644Qb5WEM1pJzpaPUoTrdOo+N3YKgGrpi4Qn7GDc1AwRLJJEqiBCxAEAWyck8g\n4hOXEJMNbtz5kkAbVIonvd8rql4EUQjP5/AdFSwc3lej6CE00wdTPUQ8RNtvox6bNWJqJsrp8qrN\n/8DmyYwCBacap9ANu/L3PcU90FRtpRq1HEd615vFzmIiyTWdmZbXo+W1ElLo/eX9csAmkRdKlAVR\nMNAKm+JHwAKZfHQDV1ZIhoESmilNVNgGVbRCFqITCKXCoHWUJGJu6KLti2HgFH+oP2nCnoCpm1AV\nFXkrj7nsXGKGj6Eacpj1WgjDELOzs6hUhE32d7/7Xdx8883reuxmsJHqjQJFfha3Wr25WGPJmNA8\njcA5x759+3D06FEAwN13343bbrsNjDP84L4f4AWvfAFcV5SvbzxwIz57x2ehmisfekM1MJ2ZRjfo\n4oHzD8jG+h35HZiwk97q1BdDizE18je9JipOBQ230fflC7mYmqwqqmyeji/mmqpJJzJyQKOsG2Wy\nN9PM2mV1nHAfRsBFIKQGvZSekvMN0kYaJbuEvJXflAlBnBCQGcKDCw+i63dllWX/5H7Ymp0wDuhd\nBDRVSwRtVVExYU9I2UXEIjy88LCUdO0u7sa23LZVz4uIDVV34sMsdVVU16jBv1f2RtIzMUZiZQge\naeDjVZteWVmvQxpZvDqhg4bXkAYTyweFz3yZaSWUUkIS0PJb8vmzVlbeDiStsgeRLAC44YYb8NBD\nDwEA7rzzTrzuda9b6y0dCZ6o6g1nIbyFH6yq+e6Fak/Bmn7mqs2sYzx98cY3vhF33HEHAOD3fu/3\n8MEPflA0oy/M4wWveAFOnTkFhMD01DS+dPeXMDm7kiBTFRWT6UloioYfLv4QAQvQCTpI62lMZ6YT\nz0PEJr6pJAMAqt70bmOo/4YsfBWuJIZ20jkQ4g3klCzbTCzhqocqP4Fu2JF9h6qiopRasWQmMwND\nMxJyrPUMzARWEiLAclxhDCebJ1F365JQ7MjtQNpMJ0xnKAEUR9Ntouk35e9z2TlpjKMqKg4tHRLj\nGlQNE/aErM4Mff09FXzqf3FCUUWj9TcudyNCRO8DudhRj84gkwc3ENWYuNyOQOY4hmqgE3QSFtl0\nXcNoxa6Z1ALldBmlVEnGGZJiZ83s0CGpgwwa3vKWt+Bv/uZvAAC/8Ru/gY985COrXrNRgqo3RPpX\ng+yhXf4srhcXcywZE5qnGd73vvfhox/9KADg9ttvxyc+8QkAYpP9+bs+j5/6f35K3vdVr3wV/vrj\nf41W0JK3qYqKqfQUTjdP41z7HADIhnNypSFQOT9rZmFqwjOfrI6pclN1qnIh7gXZROuKLjPUcVIT\nh5j4m0LUyYJFG888uKyNo86/gyGSzaWDSrqGZkjP+rVMCKQv/nL/UNy+8nD1sDRYUBUV+8v7kR1Q\nno0TG13V5YJFMr9SqgRTX9nYPrr4qBzImTEyuGHmhnXNbqAMJzXf9kJXhb0oBbp4L8xqiGvJ41aZ\ncXKkQoWiKgjCAO2gLS2ogZXmXD/yEwFZ0zRM2VOiX8iry+ezdbuPXA9D/DV86EMfwh98+A8AAD/9\n0z+Nv/3//nZNEnQhcCGqN5yF8M59B8xvDPz7ahhkNzrGGADwmc98Bj/zMz8DADhw4ADuu+8+mZD4\n4WM/xItf9WK0Wi0gAq666ip8/e6vgxkssWkvpUrwIi8xvHkuNyc3u3EYqoF8Ki/lv/HPO62xw+x7\nqbJLTfqUKBtmN29pKaBb2FQs8XkXFRyDqigwdROT9uTAyki8jzBu30+EJy79pbjhhq5scqd1eqmz\nhJq3MjdmR34HClYh8XzxY1G1JWIRqk5VkqrJ9KS0aAbECIGzrbMARPLpuunr1uz7cUNXJGj87qpW\nyqoihlJT/wv1OQ0DxZGm10TTa0p5XPwfJbtMzUTDawj3Mr8LhpVYYqomwkiMmKDrYagGtuW2wdIt\nSRTpNdvG2o6UpDogovPVr3wVb/65NwMM2HfFPtx/3/0DjRIuNChZSTL/9VRvaLDnUzWWjAnN0wz3\n3nsvnv/85wMApqamcO7cOWjaSvb9o//9o/j13/t1ef93v/Pd+PAHP4yl7lIiEOWtPA5XDstS9Wx2\nFntKe9D0muj4HXm/iEXoBB2k9BTyVh5ZM9tXvmWcSXJTc2p9X7yABfBDX1gm62lkzEzCzQsAwAHV\nK0FlwxfFtaDoAcyMBw4mN5UNT8w2iX9NaOGUjZXLWmL6nzIfcqHuaSY8WjuKhreyIOwt7UUpVQIA\n2ThJFRciEGTh2fAa8lxsw0YhVZCLT6VbwcHKQXnc66evHyi7IgIzrAmfvP2JzMUJjKYKGcBa1YFh\nx6frRH0zgNDCd4IOWl5LNoJSA60CBX7kJ6o/aSONcroMLxIadYKlWSjb5U0FiwcefADPfc5zAQD5\nfB4nT56Eafa/xl4it56fN4tRVG845/DOf29D2bReqPYUrJmbx/KzMRJoNpuYmpqC74vP5bFjx3D5\n5ZfL6sg3v/VNvObnXgPmM4ADL/6xF+MLn/mCkJHG+iWyZhZLnSW0l+d7pI009k3sQ8tvSfdMAuMM\npmZKafKgjReRm0GbOKocUOVm4PaHA7o/AZWt3TMxDEx1YWRcFFL5xDyW3tlV8Uo3bdpp5g5Vt+ND\nhIMoSJCxuluXgy4BMQRzKjMlh2tS47ypmYn+FsYZzrXOyfchpacSlTE3dPHIwiMy5u8q7JKS8/i5\nE4FxQqcvoRl/fWQYY2lWohIWJxbD4Ec+Wl4L3aDbJx2kc8+ZOaT0FBpeA9WucF2lGXgcHClNmAy1\ng7Y8J8YZbMOWQ6tbXkt+Xkglspk1z3VdHDhwAI4jqkJf//rXsW/fvsR9eklQr0HCMLe4rSCeXN1M\n9eapEEvGhOZpBsYYtm/fjvl5MTzrnnvuwS233AJgZbF/x6+8Ax//24/Lx/zZx/4Mv/zzv4yFzkKi\nYuGHPs61z8kP5w0zN6CYKspMChGbuBOLrduYzk4PHe7IOEPDbQhy49ZkFoqyDARTNTGZnhSlZc0A\n83VofmnL16dU0pBKJYOkH/lYaC+g7tbR9ttoB210fZHJWU0PTRIuQPSF2LqNJWcJbuiKjaeexlWT\nV2EuN5ewBR30ZY87sVBzZt7KJ7J8D5x/QF4jIpj02I3OglEVFYyxoUM1KTj1Ggis1WtDCzPnXFYh\n6l4dXrASDHVVF4RRXZEG0HOWUiXkU2JoZsWpyNejqzom0yuSlo3OCOKc4+prrsbJEyJT/IW//wJe\n+pKXrnmt1sJ6iU/cHnsYes0w1lW9CTtQqg9v+XWY08+Cnpnd8nHGuLTwyle+El/60pcAAB/72Mfw\nq7/6qwAgZS9/9cm/wq/8+q8Ay/zl9p+/Hf/jY/8DS85SkqBzMQSYPv/bc9sxk52R/YJxYhPvHSyl\nSkibww1bBpGb3p4a+lnO2gp0GP76qryrYVAsIRJAw51ppgqtz8M2royxxKwrUzPRDbtY6ixJidZs\ndha7CrsAQMaSQfbMAOTsFUCsyXPZucT9Di4dlDbTGSODq6eullV8kpGtd5glJfZWS4INcr3sBl20\n/NbARI6qqMiaWWki0fbbgsj4zUSVhVxV/dCX/UWAWHeJ/JFjqJRac46UIchsXBrX2x8Ul8z14u1v\nf7v8Xrzvfe/DO9/5zlWv03pACcX1EqDVSNBmqje6XwNfenDo/daLCxlLtN/93d/93Qty5DEuSiiK\ngiNHjuAHP/gBAKBYLOLWW2+VfwOAl7/s5fjOv34Hj598HADw1W98FT/yrB/BgSsPCEnSclZHUzU0\n3aZsNmx4DbE5X26qz5iZlZIwmMxUNVxRZaAMTe/52YaQDc1mZ5E1s1AURWau6LkjHsmBnG7oIs2m\noWLr8zwixqEYvsyMk/1jXFNbsAqYTE/KfhpaVONaYMpI6ZrIdFi6hcXuopTXEakxNVMMkYtERYSy\nar0LUTcQ9sy0cGXNrHRP4eA4VjuGuleHAqGV3lfet/x6hi+68WveO0dAURTh+KKtSB/6ep5YCJ/5\nwulGUfqnmohXAAAgAElEQVQ2CvHjU9WF3ktq0mz5LURRJIN5ykwha2bl5HDKohq6gbncHNKmGBra\nS2amMlOJRV1aRqsrdqH0z9CMxPBPIpMnT5zEd//1uwAHctkcbnvlbaI3aATZpHiw7B2iF3ccijst\n0XsnM7XLfWNpIy2d9zRVS3zuCIwzeJEHs30C+hrZuHWdPwugZ3ds+ThjXFrodrvSFdBxHLzlLW8B\nAClpOvCMA2i32/juv4vv1X0P3Id0Jo1bb7lVNJLTZ1OBJB6aqqETdFBKlaT8lxJgPvOlLXTEI9lH\nMcy6WVM12R9jaqac4xLfiC/XXFcGdHrZZSezrSFiHKoZyFhCM1HcyJWbSTINICkzDT2Or7eMie8y\nVcepF6fqVGUze8EqYHt+u3Ro4+BSuta7frX9thxKDAhr+7iCoNKt4HznvKwYzWRn0Ak6qDgVtLwW\n3NAd6q5laqbsYSyny8hbeZl0lJvs5fegF3FZWcWpoBt0+5zPqDpXtstybtFCZwGL3UXUnJr4fEDE\nhLJdRskqiYGakZM4xuXFy1G0iwCQqPxQbCVXMEMz5DUno6CMmUHWzAob8lRBKk8yZkY6pkZ+hLs+\ndxfAgG67i7f8/Fvk52wroHgQslCSES/ypOlSJ+jIuNr0mmj5LbGHCTorRDT05LBWIdW3RM9Sz+cu\njohHUBtHoEb9VbiN4kLGknGF5mmIL3/5y3jFK14BANi9ezeOHTsmFz1aDJvNJp7748/FY0fELJNC\noYBvfelbuOHqG1BxKmj7Qh7gRz5+uPhD5MwcDM3AjvyOvsbBIArQcBtYcpbkxktVxJTkjJmRnver\ngXMxlG2+PZ+oFGmKhqxewDbt2tFdoHQVUAdnLCIWwQlXehtok5k1s7A0YSPphysTjcnVZb49j/n2\nvDxOOV3G9tz2vgwXVWhs3ZbyIWr2p2uUMTOwNEtuhtteGw8sPCDOiQNXTl6JmcxwVxGqCGy0jB13\nVBkkW6OgHDeCiGfdIhbBj3x0gy6aXlN+FugaZoyMrETFF9W0KSRmZPkc/7umaphKT214svcg3HPP\nPXjhC18IAJibm8Pp06cTlpurVXwG9QY9kUsr40xWvCiTqzMf27onR/YcqR0/NrZ0HiOB8+fPY25u\nDpyLeWPz8/OYmhLSJGoKZ4zhZ9/ys7j7K3cDTHzfP/M3n8Eb/sMb0PSaqLuiB45zjqPVo7AMkdXP\nmTmZmCFELJIVm/gaoSoqLN0aON9sEOKWwYl5L0xH0B7hZzxTg9ITS+JN7rSO0mYybj2tqzoYY+gE\nHTAwKdcNogDH68flOpMxM9hb3JsgGWQTDSCRtGGcYbGzKNe1rJlN9Bw6gYPvn/2+lLhO2BOrmsqs\n5Ra3GuLxwwkcQfaWlRxxaS+Z8uSsnKzyBFGAmltDw21I9zJ63cVUEUWrCA6Ohc5CghTlrTx25ndK\nk5te0kTxdquo1WqYnp5GGIr35NSpU9ixY2UTn0hqrVEBotufKChQ5HnQZ8pkIabbx0b2HBcqlowJ\nzdMQvu9jamoKzabI0tx33304cOCA/DstNCdOnMDNL74ZS1XRp7Br1y7c+9V7sW1mG5peE1WnCkDY\nPj5ef1w60dw0d9NASZkXCr97KgnT3BNALCTrITZ0nMXOImpuDTW3hpK6HZP6ZVu7KDG0sYBIb8nB\nbPEKBtlVUrN+fCNN908ESEXBUncJjy09JqtJtm5jJjOTkBDQfXvBOBOlfy4CE1WHKICYqokHFx5E\nyxPygHwqj2smrwGw0o+zWQIzDIwzeKFwF+qtDJCbTtxmU1pEh57QNi9rrem+tiE2L92wi6YrPpNk\n/VywCsin8vI4i51FUalSNOiajpnMzIYcWlZDFEWYm5vD4qLQCN9777147nOfu6VjPhkkiKQhrHEM\ntnNuy8cjGMX9MEr7R3a8MS4N/OiP/ij+5V/+BQDwiU98Arfffrv8G0m+HMfBS1/9Utz/wP0AgHQ6\njW/c9Q0855nPgRM4qDgVSYCO1I4ga2aRM3PYVdiFcrrc95w0kDje30ib6ZQuZoeth9iQnTCtT6Fr\ngHnrs+hdD1o4L2NJ/Hwojhjq8kDj5Z5BAq3T8eGQlN0/VBHOYxGLYGom9hT3IOBiECU9dlBlhnOO\nhe4CgiiQkuHp9DQCvmyFHQU43TyNiiMshw3NwP6J/YkYRxUzkpFtJZHEuBjo2fJafTbcdI2oEkLP\nQ/22RGTIvUyBgpwlxj7kzBxqjtgbxCsvs5lZMQNp+bqQ6oFg6/bIYgkAvOxlL8PXvvY1AMCf/umf\nbll2NogE9Vpk994+CkQsQqo7j4JfGcnxgAsXS8aE5mmKn/3Zn8WnP/1pAMDv/M7voFd5SF+G737v\nu3jRq14Ezxeb0JuffTO++fffRMbOwAkcLHYXwTjDo4uPouk1kTWzmMvN4cbZGwdunqla0/SacEJH\nkgACVWzWypLQhppzjlaTQ41GM/gSABb9UzgbHIKpCqlY1sgiZaSkh3v8tbihm9iMkmWk9PH3Wzha\nPSrvkzWz2F/eLwd9uZELN3DhRq7Mrsc1w92gizBacQfLGBnEq9Z1t45qtwpTNWHoBq6bvg7FVLGP\niG21gjGsyZ9c1yIe9TXCy9850A1F34es5iw3G+asHHRFR82tJV63rumYSE3A1ExRYo9C0UO0LClQ\nIPppLN2Sr3EUZO2973svPvPpzwAA3v6Ot+O33//bWz7mRrFe4tP7997HF93zyAYbd6MZBj13GczJ\nG9a+4xhPK8QH09522224++67E3+neWTnF87jlltvwdkzwjlrdnYW//LVf8Gey/YgiAIsdkWyYr41\nj0VnESk9hXKqjGunrx260fQjH02vKRQDPJkUov6J1dyzgOT3ql4P4bmja1iuBGcwHx6BrujSuMM2\nbGEUo1nSSrq395AqrrJKsZyUOl4/vpI110zsndgrH08VHF3VZfU+fsym10TDbQh5cxTCNm1oWIkL\nTujgdPO0HFWwM78Tk+lJpM20dCEbRfWC5sU5gZPsZVp+HyzNks6N8b91g67skYk7QFL1JmfmoCka\nznfOJ+ycTc3Etuw22ObK8WgYK2E9RjcbxSc/+Um8//3vBwA873nPw9/93d+N9PjrQYLogMnPiWwD\nYEyOqQhZiJCHfffhnGOvEmDbCOeDXqhYMiY0T1PceeedeMMb3gBAzOB44IEH+u5DC8ad/+dO/PTt\nPy030q99zWtxx1/dAU3VEEQBFjoLaHktPLTwEBhnSOkpHJg9gF3FXQOfm3Sc5F5CpfY41kNs6Bhu\n20Dob11yRKj4Z3HSe0T+TtbBqqpCV4RrjG3YyOgZ6JouG2ClDA0KLF1Iwg5WDiZcya4qX9VXlu/d\niNPQtJpTQ7VblQ2YtHknRCzCsdoxSSamMlOYSk/J3ymTRudLssCNYL1N/iQno9I/LZBuIKR3lDUk\nXXLaTCNrZOFHfp/EjHqo6DpxzlFza7IRnnE2UFrSO9dmM/j6N76OX/j5XwAg5Jjf/va3n3B3rzih\n6b2t9/b43+Nkh3GGQvc0MmF7ZOelZbbBmr5pZMcb49LAsWPHsHev2FibpomlpSXkcivuinzZyplz\njoceeQgvedVL0GkLw5jrr7se93zxHhTzRTDOZLP6kcoReMyDoRq4vHh5n/QsfmwOLnszO36n7/tq\nGzYKVmFNW2AOjkadwXVHtyVqRUtYZMcSw4sByB4/cvtK6SmYuik3n/ENO02vP14/LpM+uqpj78Re\nOSSZ0DswMYjEMMuaU8PZ9llZVc+aWaT1lSQgB8eJxglBiBQd5XQZe0t7+2RrFAM3us4SIekEnYEm\nM+QqRgOgSYoHCPJRd+ui7zJoSwc3UzNRSBWQMTPImTm0/BbOt88n1sqcmZN9vQQ3dBPnQD2uo8b8\n/Dye/exni9enqrj//vtRKm3duGi96JWtxYlNb0Wn93GMC7ITRAE87mEf9zGrjS4OXqhYMiY0T1O0\nWi1MTU3B80Tl5ejRo9izJ9n7Et8k/eF/+UN84CMfkKTm1/7Tr+E/f+A/yyayxc4ijtWOyXkClm7h\nxy//8aHT2tt+O+FgRo2PvViL2Hihh2aTIfRG521+1jmGI8790KBJ7/phFQ5N1UQ1ZDmTRpv2iEc4\n2TgpG89NzcRVk1fBUJOEIt5MHycPIQ/Rclfm/6SMFFSoQra2XNU5UjuCSrcinlczsKe4RwZzWpR6\nv96WZiFjit4lW7eRMlJ9c32GVWPi5zzMltiPfHR9UY1pB20pL1MVEZSzltBsW5qFuldH22snjluw\nCn2fmZpbS2Tc8mZeEMaeQXTyOFA2XbVxPRc33HADuh0hi/zGN7+BK/dfuaFjDMIgQrIWSdnoscmO\n1o+Eo8+sX8cUtm4IQBhXaMYYhmc84xl48EHhgHTHHXfg9a9/feLvZOUMAF/52lfwuje/DpyJz+2t\nL70Vf/+pv4dpiAx5zanhXOscHm8IUxpN0XDT7E2YziYHbhLicSpkIVpeS/Z4xmEbNoqp4tBMPOcc\njUYExxndlsjhNSxExySRiCfHekGmM4wxGTdURYWpmjjeOJ7oE9lb2gtTNxOSX4qRXujBjZabv6MA\nDAyVbkX2YZiaiaJVlDHC0AzRiN+tyOe8YuKKtSsWCiS5oWRS75obslBIw4bM1DI0A1kjO3Deix/5\nqHariYZ2YEV6nbWELNHUTJxvn0+MQlAU4WJWTBUTxyVpYfz51yNN3Cxe/epX47777gMA/PEf//GW\nBzZzzqWcrLfHpvdvGwVjDAEPpFmHG7rCHS7o4jpTx/7U6HpeLlQsGU9Le5oil8vhJS95Cf7hH/4B\nAPC5z30O73nPexL3oc02APzmr/0mjhw7gv91x/8CAPzXj/1X7N2zF2/9ubdCVVRMZ6ahqzoqTgUd\nvwMv9PCd09/BC3a9YOCCkTbSUv9sqAZ0XUdBLaDpNROb147fQTfoImNkElpagujV8DDYc2Vz4EqE\nsl2Gqa644ki3rWXXLzqPuDUzLTAhD3GyflKWdLNaFtdOXSsmDqsaVAjHLwpsvc4nZIBAgzZ1VUfJ\nLiWIRN2tw9AN2c+yu7gbuqpLS18qp5P0oBcUYDtBR5oQUDWHzAh6qzm9Tf5x0HmELERbb8PyLaTN\ntBiMutzoSiYQQRSg4TVgazayWfEaDc3AZHqyL1PWcJOSqbyVTxAeklkMqyJtpmpz2y234c477wQA\n3Pvle/HiZ7144P2GEZJBFZZRofc5IxYJR7hlpxuVC9caUzVh8AgIqiN7bkW7cIF/jKc2XvOa10hC\n87nPfa6P0Giq2KwHUYBbX3or/tuH/xve+1tCpvaVr30F737/u/Hn/+XPhTW7LdzNGl4DVbeKiEd4\n4PwDuFm/OTH4kRCPU7RWFlIF1N16YiaaEzhwAmcosVEUBZqmACP83qYtC/vt/SLxx1YSDj4Txioq\n1EQluht2wdUVq/+CVcC51jk5kFpTNOwr70POyklHMzd0pezXj3zp4pgzxTpZ6VZkH5KhGtiZ3ykS\nWsvOb37k4+GFh+X8mtnsrPyZekU3Ak3V4Ic+nFDIuih2ExQo0ilsEGmi/qggCpAxM+DgYg+hiGRW\nzsxJy2YncHCmdQaWbmHGmAEgpIY78zv75t2R8oFA/ZsXEm98xRtx37cEofnWXd/Cu3/x3X33oURm\nL1GJ/04/kzMZAGALohTqHSO3NEoOapGGMBLvua7qiLQIGSUDZYTVGeDCxZJxheZpjL/8y7/EL/3S\nLwEAnv/85+Of//mfB96PFrQgCPCyV70M93z3HgCAYRi467N34dYX3irvO9+ex7dPfFs+Zs/EHlxV\nvmpgpSZkYSLgUOOkF3poeI2+QV2KogwkNmHIsbg4Okrzz4t3weFt5MyctGSkyc2yIX7ZrhhcbMbj\nTYuPLj4qiYKlWbh66mqhRdbTicpHfL4KNYlqqiazWUQESnYp8XoZZ7h//n5J/KbSU32SDJIaxO1C\n4w2mhEGEiqCruiAhy0Rk0ET6uJ+9G7poeS2ZiYwP5zJ1UwRsvysbgOk5CqkC5nJzfaSj5bUS9qIZ\nM4NiqjjwXCk7NYqqzac+9Sm86U1vAhTgxhtvxPe///2RVVPWg9WsPTnn8JnIMtJGhjYdkkhxLuZc\nuBVcj87QY20UY5ezMYbh/vvvx4033ghADKZdXFwcOJg2nql/32++D3/xl38h//bRj3wU73nHSlKt\n43dw76l7Zd/cRGoCV01ehZI9WLaTMGNZTvxQ8iQeZwhpI41iqphI3PhBhMrSaJqpAcAz58FVkdSh\nRnrqK6TRA14kpNMdv5OofFu6hdPN0zKpY2om9pb2opwuy9luXuRBhTow+QRA9mXSQMvtue19c3uO\nVI9Ipzlbt3HN1DV9FXuykyeSM2goacQidMMu2l67z5WLnCyLVhH5VF4qGXqfp+E1UHfr8EMfbb+N\nkIu4njWzKFjC6CFn5aBCRdWp4lz7XOJ9L9tlbM9v70t8hixMzKfRFA0ZM7PW27clRCzCY4cew3XX\nXwcoQCqdwrHHj8FKWX0VlguB+PgCkpBFfIUcqVDlqACf+dIC3Q1ittwcQjkTdfHy7ODYuxmMXc7G\nGDkWFxcxOzsLxsSMi3PnzmFmZqbvfsmmyTp+5Md+BEdOHAEAlEolfOvL38L1V14v7//Y4mN4ePFh\nREw0KF4/cz0m05MopUp9m0namBHilYHViA254KiKinvuuQfVaojnPOcFW74mVf88Hmx+WzZxEnHJ\nmBkph5IOL9AQsCDRyH+ifkJodJdv35HbIbJMnMNUTeRTeSn1im/gqfoRRAE6wUrwzRgikxYv659r\nncO59jnpIX/T3E3r6o1hTLjKkC89kZ0gCkTFKBYEyDN/0OafJtLrqi6b+73Igx+u6LtNzYSpmyhY\nBRiaAc45FruLWOosycVSURQUU0Xkrby0XSV3no7fkUEWWOmrWQ8GVW3iyxwt8nHiECcpjUYDs7Oz\n0nLzyJEj2LVrcD/YehF/rl7jBLptWGUniAJ40QqBiWdOyYiCXiMDk1akjz30GKads7jlwNYtzVV7\nCqnZH9nycca4NME5x549e3D8+HEAwJe+9CW8/OUv77sfOZkBwlXwjW96I778zS8DEMmVO//3nXjt\nK14r77/YWcS/z/+7XDP2FPegnC5jMj3ZlwDpNciIr1/rITaKouDg4YM4cayGGw9s/bPOVBeBVYEK\nVc4piyeqTE3MkAGHtPj3IlFt1RUdC+0FVNyKNJ8pWkVkrax0KSODgZyZk3IzSqKl9BR0VUfNqUmZ\neM7MyUo/oeE2cKR6RD72qsmrhg697gWts12/K+XDg+yFaY7QIKUGzV1zQkfOp3NCR8rPbd2W/ZI5\nK4eUnkLIQpxpnEHdq6/Y9ysa5rJz8vXFHT1JiUDQFA1pI72p3sh478lqFZV47HnRi1+Ew4cOAwA+\n/omP4xUvf8WGn5dAr42GZ8repthwTQWipzXkoezvpe8FVfTd0BWJseW4QiRG13Toig4VKnzmy31N\nu9LGTP0cnnP11p3JLmQsGQ/WfBojk8ngm9/8Jk6cOAEA2L9/P575zGf23S/hHJNK4ZUvfSU+9Xef\nguM5cF0XX/3Hr+L1r309chlRhZlIT6DpNdENu9IFK2tm4UVe30JCM1bivSOGZsgMf8bMwNItmRUi\n+JGPSruCD334Q3jHW9+Beq2G17zmjVu+Jm2cR6SI8r2marLXhGO5utCtoOk1BRFggci86CnkzTzO\nds6i5bcQcGFBube0F9OZaaT0lCBAmjBRiHgkB2kyMNlfAgDtYEX7bekiq0aBkIaOPbb0mFyIaAYL\nGS3QdYobFMQrQTSYMWfm5PDSmeyMsNzWbaS0lNBxY/gwTjdyUXfqqLk1nGqewrHqMcy35sWQzGVD\nAuqVIZnJQmcBbrAyAM/QDZTtssyS/f/snXmcZGV19793r7337tk3FtkHFMFBAgi4AMqmKBATo6JG\njSvEJegLovE1MVFUYoIEo4CAqCzKMrIEkFVQAwwoMwPMwMz09F5d+93v+8dTz9NVPd0zDUwMvunD\nZz7DdFfduvdW1XOec85vkcRePxadx5JbautUdqe7Z5QhVf/f/E8RaVsM3JTCS8tjpHFlQrJD4ZZK\npXjooYd49tlnASFXfvjhMy/AsvCTCVSZiM7g4DzdvVkVIrRvxOIkVsajJa+kMOitUtlJkijzU0lq\nDRPRbdMTncu+dxnnf+V8itUKZxz78gt9q3s/9DludObjf19omsYLL7zAww8/DIjc8ra3vW3Gx0mf\nC13XOemEk1h721pGxkdIkoRbf3krbzzujSxesFgcx85CArWgprrseSdPI2zsIBu8M98tQxeb2KyV\nFV4uLWazUpjkh1f9kLPPPJuhwe2cesrLzyVGygWt6bWFpqBgGlPmwn4kJhExzUJHt8nbecYb47xQ\nfkFM1oMaWStLd7pboAKal6mmJUQKvdCT7qEz3UnaTAuxFcR6aOkC0tt6j+IkZsP4BmVa3ZvupTfT\nC+y4Jk2/vxIeN+lOKtEXibCQsOyCU6An06OI/jOF9GgrNoqU/TKj9VFVfEh4WcEpKIhgPaizeXIz\ntbCm1tq0mWZFxwryqfyUn14LJ1UW0DDlfzf9WqRZpfRZkz5yNb+mEA5lr0zFqygYfKu4kYRtSVhY\nawwPD/PIrx8BwLZs5QHY9llpWizIQjdliWJVoiPyTl5YGEwz8ZTedzGx8nirBTVl3hrGIW7kUvNr\nymizElQo+2IP40WeMLRuFp3y3gRxIHydnE4euushzvvb8xgaHeOM41/ZuWR+QvO/PC6++GI+9alP\nAXDCCSdw6623zvrY1q7DAw88wPGnHK8q+CPWHMHt199ONi02qJPuJI8PPU7JK9EIGuzVsxfd6W5M\nfUfvkCRJxKIuuy26scOiA6iNrh/5bHxmIx//+Md56kmhRqb5Glf/x80cffQbX/rNMHxiZ5KYWCmr\nSKKhVHSTG+EgCgjiQBUIsoCzDAtbt1lSWEJ3qhsv8qj4FbzQU47VjuFgm7Yi48uuj1TokeP4nJ1T\nsoky1o+tp+wLGFbOyrFv3767vCwNTXF2ppM35cZ4euFj6mYbbK0e1Kl4FSp+RcDuIo+6L1THpHCC\nrutqeiO7p1EsijfHcBRHpyvdRV9WmO/JBCKv0Qs94YOQCOnmrJ1lIDs1NXypcK9dcW1kp1AWQpdd\ndhkf/vCHATj66KO5+z/vnrqfL6GztytejSSryk0OoGACYRK2TXhMw5ySfdVNkaS8Crqms/mFzZz7\nmXN5cuOTEIHmadz4rS9x/JpDXvQ5y9DTfTgDh/3R1d7m408r7rvvPo46Smx4BgYGGBwcnJH8DlNS\nzgDbt2/nyGOPZHh8GIAlS5Zw3y/vY8XiFYCYrvx+9PdiCuBX6c/0M5AbQNd0ejO9O3T+p09qZuLP\n+ZGvvExGx0c5/wvnc+cddwIil1x56U0c+4YdJ0xzDdOKSeUDDE0nIVHf74RETf/jJKYeiim5G4iO\nuamZ1IIaw1VxLzRNyNMvzC+k6leph3XiKMYxHAqpAhk7o9at1utvBA2CJFDrxML8wh2Kii2lLQzX\nxOuYuskB/QfsUpZZIghqQU2s/dMmz47hCAW1aY1LObmQDTc3cBmtj9IIGgLu5NdULulwOpR5dN7O\nKxPMicYEE+6EOl9d0+nL9LEov0g9Rq7vEiZX8StTPKAEJd3cOlX579oCy4bWuifWcfyxx0MCnR2d\nbN60mZSdamt4zTUkYV/6O8litDXk72QukU082UxNkkTB/QzNUGbYsqCTKq3lcpmLvnwRt991O/ig\nuRo/+5f/w5v/7NCXfk/+m3PJfEHzvzw2b97MypUrASG5OTo6SqFQmPXxrRvCq6++mr/48F9A8/t4\n5jvP5Mp/vVJp628Y38BQdYiKV8GPfA4cOLBtIWol5EVx1KZMIzf/O7x+HPPtf/02n7vgc3jBFBTt\n0EMPZdu2QS6/+KccvPrFf+ESPSBJTdKRKqgFVoYbupS9spIhdiOh/hHGIZqmMVofZaQ2IjrlhsPi\n/GL26N6DlCEmM7quE0YhlaAiOu2Rp2SVO1JiMuJFXlsnqTPdSdbKio6XboEGQ5UhNkxsUFCjvXv2\n3qlCy1zUyloXUznKlt0iyX0B1OLphR4jtRFlkCmJhZLoKRNikiSqCyR+AGgC9pBzckqEIGNlSJsC\nUudHvvI1ApG0pFCAXHRfakyHZc1kCCofZ+gGo8OjLF68WDmgDw8P09vbO6fXmgvfRhZ6sssnNwcK\nzkCkuMmt3U/HcFQxKjlG8vPws+t/xkX/eJHwjPKAEFYfvJri1jGu+vq5vOagmaVvdxa63YGzcA3a\nbvCfmI//v2O6Me0DDzzAEUccMeNjW6WcAZ584kmOPfFY6r7gORxyyCHc/Yu76cgJEYDR2ihbylto\nhA3Kbpk9uvYgZYm1ryvVtQNHc3rjYqZNYxRH/Hztz/nQxz/EaHG0+URYtXIV6XSWr/+fSzn4oBef\nS3QjJtsR4pi24vG0npeE+07UJ8RGvjk9dgyHRtDghdILarNZSBXYp2cfxeGUU6koaYqBNCcErYpj\nXuQpc0yA7nQ33eluBZWW4jF/GP2DmuCs6Fgxo4GpjHpQp+pX2+DhMqaT/GeD1MrrLzaKYpofR5Tc\nkhBCSBLF0TQ0g7ydVwIxQRSIfURLcSL3EDk7p46t3uNErLuySJKvnzbTmIY5K5R6LiHfF1mITId7\nyX+3TuKTJGH58uVs2bIFgDvvvJPjjjtul68lifuyeepH/owNOcnrleIwEtYIKI6MRLdoaGqiL/3/\nokQ0aEkg0cT38dFHH+Xzn/s8w4PD4AOxsDHozndw8eff84rNJfOQs//l0dnZyU033cTQ0BBRFLF6\n9WoOOOCAOT33wAMPhBDuffBe0ODJp54kMRLe8Po3iE5LqoPh2rAqYmp+jc50p1hsgpra1MMUga1V\nnUsuFDKGhoY486wzueRblxA1IojASll85nOf4fEnHmfz5s3ceNuPOeSgQ1m+dNWM5zxTeFSo6oM4\nlj4OmkIAACAASURBVI0f+6pDJMmaGpqCY+VTeTX+NnWTSXeSkfqIgjdJqJUf+0pBJIgDNXVyTEdt\nThMSKn6FoltksjGpxtU5OyegWk0ejkyAG8c3qhH7yq6VrOxaSd7Jk7EypCyx2ZU+BLKYmb4AKkiU\nru+wqEsYhBeK4qriVRitjVJsFKn5NdEha0xAAnknT2+6l4X5hezdszcru1a2ecOMNcaoeBW12dZ1\nXRhhWo7oWMZCWrjklRhrjDFcHWZzaTMVr6JMz7rSXTiGo4QHZOEkPxNay38zQb5mgntJrpDya5gh\nr8VJTDqb5s677mTr1q0kScK+++6rSM/To80Ic5YCRl6DHP3LglB22KI4UnythER59RRSAm6RsTIK\nzuGGLqO1UWFUSkJxssh5nzmPf//hvxO5EXiiGPzkJz7J808+z/p1G/jJL37Fa1bvzcplC+b8vdDT\n/TgLDpsvZuZjTqHrOuvXr1cytd3d3bzpTW+a8bHyeyrX+/6Bflbvt5rrrr9ONG+Ghnhy/ZO845R3\nKOhvxa+QJGLjP+lOKt6LGwr8f9qckv7VNG1WyFSSJFRqFc77zHmc9+nzqE/WIQQSePdfvpt0Js1D\nDz7Ijbf+mAP2W83K5XvO/R6YIXbOxWk2ggxNrFW1oEbRFe71Nb9GxRObc8nlk9DcF0ovYBgCHteT\n6eHggYPJWBm1CW31r5HTcCldDKL5NlwdVut+2kxTcAqEcUgjbCgI24bxDfiRj6EZdKY6Wd65fGoj\n3tzwx0lMxa8w3hgXBP24XXjH1E06nA56Mj1krMyMDScF841jJt1JhqpDuKGAQJW8EnESk7Ny9Of6\nyVk5OlOdSvgliAPGG+NsnNjISG1EQG+bKmUS7SEn/HICJqfcknsbJRFxHDfVUK2pc0qmoMYSkij5\noDI/S1+cnJ2j4BTUHzmBSltpZTgqodSKx6K1T642bdrEI48I2Fk+n+fEE0/c4V7JhqF8nyp+RfFz\np09iJLys4guJ8iiJ2iCNXiSkuyV0PmWmyDk5tbdJm0LcJyFR0t5o4Hke37n4O1z4hQupFWsQiO/F\n2WefzaLeRdx521385Be/4qD9VrLnikVz/178kXLJfEEzH4yMjHDPPfcAYJrmTrXSpyeKo486mo3r\nN/Lk+icB+NX9v2LZqmUccsAhCps5Wh/F1E3COFQcDWCHRKTUOCSfJg6xdLHx/MUvfsFbTngLjz82\nZQB6wH4HsPbna/npj3/KAw890NycJrzrL05l4eI+NHS0ZPYvUKy7VBjCSgWU/NIUj8WvTqmpaLqS\nxJShazoLcgvIWBnGGmMKx9qZ6mRxfrHgg0QigVQDsdhIB15ZbGhoCjoURIFKJhERQRKoxVhu4DdP\nblZ+LGEcsqSwRPA/NE1tzuVkxTEdtRjLgidti8XXNmxMw1Sb+R3I8ppBglDJ8mJxDtWgymhtVJma\noQnlk0RLMDRDkQc1TVOdw4JTYCAzQHemm75sH4sKi3BMR435WyNJEibdSeVUHMaCRzXpTrK9sl1t\nAuQIPU5ibFMUlLqutxUzrX92Fa2kylZSvoxischdd98FGoRRyFlnnjV1zi3/zRZhHKrCsOSVqAd1\nRdCcXsRILlbeydOZ7lQkWsd0MDVhRtoIGmo6Jr8j9z9wP+9///t58qknVSdt1R6ruOoHV/HA2ge4\nY+0dAERxwvs/+nes2ns1SRyQhPVZz1tP92F174fVtTfay5iKzcf/vjAMg6uvvhoQ3IGPfexjs34X\n5VooP8t77rUnXdkubr/7dtBgw8YNlOtl3vyGNwuOiJ1lrD4mzB11k5h4qosfizVTbtRmi4SE/3r8\nvzjprSdx2623yR/S193HT370E7yqx79f/u+gQxD4HLrmQPZf/Sq0xNhpLom0BnV9lHzOJCEWm1Kv\nwmh9lLJXVpNsaZoZJZFap/KpPJ1OJ+P1cQqpglL02qd3HwV1lrwfud7LJk6SJGI6o5ukzBSNsCE2\ntk1xlY5Uh8o98vVGa6MM14ZxQ5d6UGdhfiEwNX3wI59Jb5IJd0LBX1ubQpLP053uVkXWDve5hZdS\nbBTZVt7GRGOCsl9WnBkJ2QbRwLR0S/FLgygQyIeq4FY5poNt2PRl+lhSWKKQAzOtv27oEiahygsp\nM4VpCFEG6evTWgx2pITYj+SlyHVXNi1nKlJebKRSKX74Q2F5MTg4yMc/8XGlDFr1qwKy3hSFCOJg\nh0ak9HGqBYLTEyVCcCljZZQnW9WvUg2qBHGApVtKyChnC5+fgl0QhWczZ9d98XryHm7ZtIUPv/fD\n3H7r7RABiRB9+t6/fY+8neeb//RNAPwgZM0bTmPNn73pFZdL5iFn88G6des46CBhcpTP5xkdHcVx\ndu6c2/qF8z2f4088ngd+9wAAjuNw8/U3c/zrjwfgqZGn1Ajc1m2WdS5TCyUIs8f+bL/CwdaCmoIE\nNRoNLjz/Qi7910vbXv9Tn/oUX/3qV/mHf/gHVE1uwTf+5Ru8450tBVlsEAcWluZgahaaDqahE+l1\nYl2Q1eWovupX6UoJSVBd0+nL9qlCbNKdpO7X1aJfDao8V3yOnJ1T49uB7ACT3iQlt6TGv1LCGMRE\nIWfn6Eh14Bhic1/xKiJRNXkufZk+HNNRHSc/8il5JTZPblYF0Kt6XqW6WPLrK7tKElIgYwcloJYE\nID10ZOKRxUgjbBCEgeoqBnGgnJnlhKgz1dn2OkmSUHJLVPwpM1CJh+5Kd7XJUssOUhAFykdAdpnk\nc1oTneQutYahGcIBPNVBp9OpfBV2hQHfVbRybZ599lk1rbRsi61bt1LIF3bArLfeA6VG1gJJlKFw\n5E1OjDR1k5M1aJeX1jVdCQSM18fb4B6+7/ONf/oG//H9/1CQT4A/f/ef87ULvsbVl1/NZz/7WfXz\nSy65hI9+9KNT5xJUiaqDJJFLEodouolmpDByi+almefjJYfruvT19VGtCvjwE088ISb5O4lWKWcN\njXM/fS7/duW/qenpt/75W3zsfaIwGqoOMVgZVI9dkFvQRvA3NIO+bJ9aP1r5NEEY8K1vf4svfPEL\nBH7zOQm89cS3cvnll/Pwww9zyimniJ+b8Fcf+isu/MqFUycaG+hRGlt3sDQHTQPNSKhG40wGwn9N\nygGHUdgmSyybTlJq2dREASJVIDeMb1DCAIZmsKpLIAxqfk1dgxR1kSFJ5KZuiqZQY5KiV1S/H8gO\nkLbSqrEiN88bxjeozf2C3AIW5BYI8nyTBC8bkXJTL+9r1hIbfrR2F/rp6l7SH8cLPYpeUTTnooha\nWMOPfDXZkblquh+NhJhV/ap67yzdoj/bL2S2TWsK3sXUlD1JEtE0ahZ+IPYWrceeyWxa3kvTMBV/\n9cVyW3YWSZLg+i5Lli9hojQBOty69tZZJ/7yOVIAJkpEw0ueG0w1y+T1Wrql3i9bt1XxmjJTZK2s\nQMg0DUolb0mGYzj85Kqf8KULv4Tv+ep+HnPMMVz6L5eyaf0mTjzxROJYfEff+973cvnll0/BCF9B\nuWS+oJkPkiRhr732UqpOt9xyy4wj0enPad0cT4xPcNhRh7FpULg79/b2cs/ae9h/z/3xQo/HB58k\no3Viag45K49lmPiJS2zUSfQIQzPoz/arLn7Vr/K73/2Ocz54Ds+ufxZpeL5o0SJ+8IMf8MY3vpHr\nr7+et7/97eocPvGJT3DxxRdTD+qU3FLbJlhONnKWUAiRXZAgCph0J8nZObGxj0LF3ZFmj7KDL4mk\nJa/E+rH1atLQ4XRwxLIjBIysuZBLeFbJL7WpU7WGxLBKcnfOyamJlPQrSJKEp0afEl2+KCBtpVla\nWKq6cNO5JQnCqFRKQ8/WQZseEocrk5OEfpGgMN6appExhax2lERoiaYUUYqNooLngRBRmMkoc/pn\nqOgWiWKheBTFkRIU8KIWfHjz/GSXc7aEJDHm0hhU/lHeD7uINuhYs9A75DWH8Punfg/AVT+6itNO\nOw2YKjykWZ6U6p5+bq1FjKEZbVwYTdMgqENjBC3y0JIYzbDQjBRaZgGhbjHRmGjjc2maxsYNG/n0\nRz7N+mfXK2vk7p5uvv4PX+fMt53JPXfcw1vf+lZ1Lh/4wAe49NJL50n98/FHiXe9611cd911AFx0\n0UV88Ytf3OnjpytRaYnGqW8/lTvuF9NF0zT56TU/5ZQ3nkKSJGwYfRYtSmFgYesOKcvBixvERgN0\nMdWWMCh5/E0vbOKcc87hnrvvUa+TslNc/M2L+eAHP8jvf/97Xve616lC7JhjjuH2228n0iIm3ck2\n6wApA2xoArrqBi6T7iS6LmT3+7P9YlrdtC2Q67mEBNmG3SbGIlUrQWxaD+g/gIyVETkqjpURp0y3\nM00loiRisjGpJjeycdZmxAhsntzM9up2JcSyOL9YSEb7dWKaPD5ihZSwdZuOVAcFu4Bjzo3HGMYh\nJbdELRDFWCNq4PquMMZ0CmRMscZn7IwSD5AGo/WgzkhtRKg1NidCeTvP0o6lu5RalhN8CZezNEvx\njWShJaN1Mjg95PsiJ4GGZqgiZy4+ZrMR9z/xyU9w3Y/F9+JjH/sYf/d3f9f2PCkCI4vE6T49soiR\nhqWO4ahmqq7pGJGHE5SwkxhT0zANh1i3qFt5GmiKvysjZaaoFWuc+/FzuevOu1QusW2bCy64gE9+\n5JNse34bhx12GJOTwkJhzZo13H333btseP9PxXxBMx8AnHfeefzzP/8zAOeccw6XXXbZLp8zvah5\nZuMzHP6Gw5msiQ//q171Ku65+UEsvYDvz/4xi3WXyKyR6B692V5SRoqvfu2rXPh/LyQOm4tOCG8/\n9e1ceuml9PT0sG7dOtasWUOtJjZ7xx9/PLfddhumOdWhr3gVSm5JbTS9yCOMQnRdjM2TJCFlpSh7\nZSWdLEffEv4mielycfUij0e2PkLRLYpF07DYp3cfgVG1BNa2FcctJUEnGhMUG0XlMxCGIbVQLPhR\nHJG20wxkBhRfQtd00laaF0ov8HxJyGpraOzbu68i0MtNP6A28ykrham1TynU75qQs+mSlVJCGkS3\ntOqJzpht2EpCO2PuWBjIjljRLSp34TAOSVkp5Qy9M9OwolvEDaamDh2pDrUJUa9Bgh/6ykzSCz1q\nQQ0v9HYQNYCpTlvrPZCE0NYiJ22m2zDVs8UFF1zAV/7+K6DBu856F5dffnmbjn8UR20KafKapVS0\npVtT0DE5PUoScMfRattI3PFZX9s1MlStTlwzi9Y0QL36P67myxd8mdAK1WTmDce+gW/+4zfZf8X+\nPLPhGQ4//HDKZaGEd+SRR3LXXXfNaHI4H/Px3xHXXHMNZ599NiDI/b/73e92+Ry5+ZPh132OPPZI\nfv+caCZ0dnZy/y8fYUHvyl3kEo/YrJEYntiIOwWu/cm1fPijH6ZULKnHvfrgV3P1j67mVa96FRMT\nExx22GGqobdixQoeffRRJQISJ4L/MVwdpuyVcQNX8R01TSNv58U6rInNbM7J0ZXqImWkyNhCWlfy\nMeX6Kw2H/zD6B0peCSkfv1f3XuSdPHESt22e5ZShlTPSKvoyXBtWa7ht2CzKLRLrW4KyB5hwJ9g4\ntlGpq/Wme9F0Tflcyc23qZmkrBR5K6+gXTIkpFk2ZVrX3ziJlbSxhOlKzouUXrYMi4yZoTPdqYoF\nWYCM1kYZq4+1FR69mV76s/27bMpJVS8ZMudNDznxkNwaCeebKaarf8qQhY1c82XxJ+WbZ8t5a9eu\n5b3vfS8Ae+6xJw898JCAHu/EELq1iCFBFTBKJTZJcMIKGa+IGZR3eL6MupGibBaoG2nSTTj6PXfc\nw0f/+qNMlCdULtlvv/247HuXcdjqw6hVarzuda/j6aefBmDx4sX85je/YcGCufMw/9gxX9DMByAU\naY488kgA+vv7GRwcxDB23Y2Z7s5877338sbT3oiTTvFv3/wRxx01dxOpSHd5fnQd5338XB6++2HR\nMTCEp8E/fv0f+cB7PoBlWIyNjfHa175WmbjtsccePPLII3R2de6g7CU33UqlrEUu1A1dQeJPdVMJ\nKmpiIhcpeQzbsOnP9hMlEetG1uGGroICLetYJnxqmo81dIOuVFebG7P8nSxuxhvjbCltUR2ThISM\nmVEiAtLoLWtlGawMqsVrICsKHqlMI+95GIVqw+9HPoZuKD17x2yqYjUnJ7qut0n+KjhGHIh71IRK\nyMTrmA4Fp6A24yohxKIjWPbK6p6ZhklPpkcVM/LapztMR3EkfAdauCB5Jz9nQzcQGyCpq98IG2pD\nJCcnElPeCj2B9uJFYpDbCh2rHYP/2H89xmsOew2YkOvK8bsnfodhGDskH9lds0yLlCE8BKYXjwAG\nkIytI3ZH53ytrplli9vBpz5yHnfffzc0xf+clMMXzv8Cf/1Xf01PpodSqcThhx/Ohg0bAFi6dCm/\n+c1v6O/vn/Nrzcd8vNwolUr09fURBGKsvmnTJlasWLHL57WuzZqmMTo4ymFHH0bNr7/oXBLrLkXv\nBb504YX89IqfTv0igc9/7vNc8H8uwHEcwjDkhBNO4M47hWRzNpvlgQceYO/99lZeJLJpJHNJySsJ\nLlxzyiIlb23DpjfbS9pIK1l6mOL3yOvKmGKd2TixUa2fYRyyrLCMrnSX4tqAQAnINUmupXESE0ah\nOr+R2gglt6R+15vpRUfkMEl2N3WTZyeeFTYKUYOslWVBbkH7eqiZKl/IfCJ5n0rdyxAwLwmJTVui\nURTHMW7ktt0nP/bJWBkFqzN1k4JTaIOAgciL28rbdoCYLcovamsQqrdw2lou7QXUdTTX9blGkghi\nfJiEhFGoCp7WJd7QReEVxqFSHZOIAaAt70g4XOuExdAMQj9k1apVuK4LOtxz3z3sseceO5xPFEcK\nSiaLGGXC2hJpwyZb2YTmTcz5WkO7g2pqBRf83UWCK2ajIGYf+chHuPCLF9Kb7yWOY0455RRuueUW\nQHCA7rvvPg499KVLNv8xYl6+Zj4AMUocGBhgeHiYkZERHnroIVXg7CykuheIBeboo4/m+9/5D3r7\n9+DgA1/ch9+IU1SHDdatfxKyQB1ee/hr+d5l32PlypVC5jNKOOOMM1Qxk81n+dkNPyPfkd8B0gUo\nMmnGylAPhfRk3RdO6ilSFN2iMGuz88RaLCSWm0IFsuMTxiFj9TEGK4PqZ4Zm8NpFryVlphivjytM\nahRGjNRGcDxHcUfU9ekC3521s3Q6nUy6k4qjIjXg9VCnEQgs87bKNoI4oOAUWJJfwuL8YpXc/NgX\nk4umUk1KS4EDJLRNb6p+FUM31HjaxqYW1ZiMJ1VX0I99iCFtpdXxDd2gw+nYATJm6AZxFDNeH2+D\nQhm6QU+6RxV3bZ42mik6e4hjVbwKjunQZ/YpwmfeybcVS5LXM1u/xTIsejI99GR61L2TncZ6UMcN\nXMVPkvyfVmlNQzdI4kQpjsGUxKiEhuiaTmpJikX7LGLwhUGqtSqPPvwoRx99tIIVRHGEaZikzXQb\n/E+KZ2hoU908ErztDxH7pRmvabZIhTVq69fx6BMPq2LmgAMO4OJvXsyag9aQsTJEUcSZZ56pipl0\nOs1NN900X8zMxx89Ojo6OO6441i7di0AN954I5/85Cd3+TzHcGgkDcXtW7B4ATf/5BYmJiMOOuDV\nL+oc9DjF8HMut92+FnJADZYuXsqVV1zJn/3Zn6nHnXfeeaKYMQETvnX5t+hY3sFQdajteLKYcEyH\nfrNfCb/ItSVBTEkmahPknJxytZcwXhmmJibIzxWfo+yVFfRpaWEpXekuan5NiQZIKXdDN4Qq5QyS\njFIFyzRMkigRzSetSYBHV/A46TkjfUcWdS9SuUnCdNNWWkG/5FoZxZGCvIVRSKIlU4/RhET01vJW\nJU0tJfHzTl6ZSksI3ExFRtkrs72yvQ0KlbfzLMovmhWu3MpJDeMQL/LUumtohnqd6XzR2ULTNGzT\nxsYGa4pno6YjgVAdk41M6a3U9n5oUxLJkh8ZJ7GCkPuxT0DA0W8+ml/e9ksAblt7G3/zN38DTJH+\nJe/SMZwZ75csINOGTTD06xedS0y/xLZ1v+Da635EMx2zYMECvvvd7/KWY9+i4PZf+MIXVDEDcPnl\nl7/iixmYVzmbj2ZomsbGjRv57W9/Cwh1ize/+c1zel5rJEnCsqUH0Nczd0m/1hjoW8j++xzETbdf\nx6c+8ymuuOwKOro61LE/ce4nuOFnN4iugg7XXnstR75+5sJLds8MzcA0RMc+b+dJWSnc0FVwpjAJ\nlURyFAs+T0RE3s4rhbPfj/2eklvCNEziJGbvnr3pSHUgNfgNzRDFTvN2hHGoMMSKL0HTmMyvKSnS\npR1LWd6xnIIjMMoSuzvpTjJWH1PdGsdwqPgV5TmQsYSkb3emm4ydUdMASSxNmSmytnAZtnSLMA6p\nelXG6mNKCaXuC7NMafjmxWLCkzJTdKY6xfnMADEbrY8qfLimaaQt0ZG0DEuN0IEZx/WS3yQjY2fo\nzfSqqZBjOmrRlo7IaTM9pTpjTJlfykmchAuahlA9sw1bwD7SXfRkekRh1hzTG5qh3oN6UBdTrdhX\nSasclJlsTApxh0aJoltkqDLE+q3rIQWREXH46w7HNm1x/9PdSoyg9Vpbu3SGLrqa/shviF9EN601\nFvR3cdA+K7nu9l/x4b/+MJd86xIOWnmQSvqf+cxnuPLKK9Xjf/SjH3H88ce/pNeaj/l4uVGv17n5\n5psBIewioTY7C0lklo2pKI4pZBfT27PwJZ1DX+8A++9zEDfe9mNOe8dp/PxnP2ffffZVhOt//f6/\ncuHXLoQMYMMnzvsEZ5191g7HSZIEQzfIOUJWuDfTS1+mj/5cPxk7o7xRpGqhH/lirW5CZaUyox+K\npteG8Q08X3peqVZJKWBJ2pbyvQlCway1udOqthXFEWONMQDVgOrL9AkfuKYSpXS5317dLuC4TWPr\n7nQ3A9kBVnauFCbQ6W46UlOKWFKKWBpH5+08BacgYMcIqeyh6pBoyEUBfuwrvznp9+KHPpZhCa81\nK922PsZJzFB1iOHqsBJ1kEI8i/KL2oy3ZwuZG2XIYmYmxcvpktSapoE29ZmTBYxsiNX8GtWgqowp\nDd1QcK9WgRt53hLiJgUOJIKj6lepBVMKnX7kc9e9d4EOk9VJ3nba2/ACjxChADvTNEaK3/RmeoWl\ng27hj/z2JeeS/t4ODtp3Jdfd+itOPvlkfnztjzls9WEql1xzzTXKbB3gs5/9bNu/X8kxDzmbDxVr\n167lhBPEWH/lypU8++yzcyYSS+iQ6yaUJmfnTcw11m99lOV7LkTTNDpTnYRRyBVXXMG5554rPAMi\nQTj93Oc+p56jyHzTJBZnMjqM45ix+pgy7AKxIErTrpydYyA3gGVYPD70uPBfQSwuqwdWM5Ab2OHY\nEkPcahAKYgTemeokbaaV9j4IKJuEZ8nNeRiHjNXGeGDLA2yvbKce1Mk5OUE0bXbrZPemM9VJT6ZH\nHLsJcZJmXNJPRiqtSVEC6Z5cbAgJaEM3BJ9Et8in8vSkepQxqsR6S+hW3a+raQaIhbwz3UnezqvJ\nyvSQY3hAqXXJcEyHnnTPiyKrT4cbKCPKFlhbGIdiStPsqEG7wZvkSFWDqlBmawogSAWi1tA1nfVP\nr+fvL/x7iKCro4trr7lWFE/o2KY9BVcz0kptbYdCvzFCMvbEnK9ztvj1tgYHvfpYutPd6jWuuOIK\n3vOe96jHnH/++XzlK1952a81H/PxUmNoaIhFixYpD6qhoSH6+vp2/URQ/kyBr9Go7Hpju6t48LG1\nrD58X9zQJWOKDe+jv32Us886mygUa9Zb3vIWLrnkEtV9twxLGVHKaa2yFGgaN7aqe1W8CsO1YbZX\ntqufpayU4mt2OV10pjsZrAyq6Y+maQxkB4QEf7PIkiFzWKuCm8xtcj0er48rjzNLt1hcWKyaTvWg\nroRUNo5vpNgoEsYhBafAQQMHCXlrfUqKWW6kpXWBUrBK4rY/YRRSdItClbI5/Sk2itSCmip8JOR5\nOrxMmmVraIzVxoShJlOw7kW5ReSd/JzyQZzEQgGu+XwpJz3XXCIn7HKyL4n7M+0VJM9Vvo40OW0V\neHAj4a1TC2qKlzNTPiyVShxz3DFI1Phtt93GQP/AlB9Os1DKWlkKqYK6n60R1rbjj/x2Tte5s7hz\n3QhHv+ntbbC+3/72txx55JECFgecdNJJ3HTTTXOiH7wSYr6gmQ8VnufR19dHpSI2+I899hirV6+e\n8/PjJKY4EeH7u37sriLUGrjWMCAW8nW/W8c73v4OJRJw+smnc81V17R5kMDMXImdRc2rsbm0mbJX\nFknISIkkoqE6JSO1EQU1W1xYzD69+wi+R4uTu/xbQ5Asi26xLUEBCsomifYdTseM57R5cjODlUFB\n3vTr9GR6KLrFNtUvx3BU8pUyyt3pbroz3SrhScU12SFqBA0m3Anc0FW4aonXlt4GUiXNMR1s08bR\nHcI4ZMKdIIymlFWytsBgt8ICZEE2k5lnFEcU3aJ6fyzDoi/Tt9MENFNymUvI4iVJkim9/dCjEQmT\n0oYvODeaPgWHk9FK7gzigCAMcAOX97z3Pep78dX/+1X23ntvYEpdTXkBtWLLzSnztXz5WXR/cs7X\nMOs9cbrJLppyX3/kkUc46qij8DxRiJ188snccMMN6rMxH/PxPxWvf/3refDBBwEBWXnf+943p+cl\nSYIbulRKOlHw8j/HjaTEYPi0+ndlvMK73/luJsZEk2rvfffmp9f9lM58p5rkapqmNrqycEmSZKcq\nV0mS8Pzk84zWhelt1spimkKgRMKHZGMMBFF+RccKUlZKGEk3oVxSnt/UTaU6Jqcz6hr8CvWgrrg1\nBaegoMatMelOsqW0hZQhcsJevXuJxl0ctxUD8prktMExnbbiJkkSSl5JFUZREqlGkPQ7kQWUqZlT\nkNsWBTNd06n4FaViZps2juHQl+ljWceynSpitkacxKpgAxRKYjbhANnkkzzLnRH3W0NDa1vf+1no\nOAAAIABJREFU5XVJ5blG2FDqlvLx0z8bURypvCjP4UMf/hCPPvIoICbr73znOwHR4Ms5OeWlJPOY\naTQnQk2Inz7+BFZQ4WWH002mJZcMDQ1x6KGHsm3bNgD22WcfHn74YTo6Zt6nvBJjnkMzHyocx+Gk\nk07i2muvBQT2+cUUNFHIbilmAMwkjZ5YhHhs3baV9330fcRGDJHgD3zrX74l4F0aO3RWXkxknayQ\nFzYc1V2RC/xQdYix+hi9mV7lebC0sJSaX8PSrTaMa+tCZps2A7kB5YYcxRFu5NLwG5TdMlk7y6L8\nzJC8elBnuDosoHIYHLr4UHozvdT8GiO1EYpukfH6OI2wQRwLeFkjbNCoNthe3a7gYhLGIIsbqc3f\nl+4jIVHdKduwlbGpVDyTKl4SjlYP68LcURe4YE3TyCQZJhoTSj1NykNL7HAr/8UPfcbqYyQkKkHM\nNJl5scWouvct05fWkATOmFgp3JmaSd7Jq8fILppjOAo/LDujrR22Nx78Rq6/6Xow4YmHn+DQAw9V\nJplS8ro1lOBBAmkNjrB3j2Sy5k0QB1V0K8fg4CCnnnqqKmb2228/rrzyyvliZj5eEXHaaaepguaG\nG26Yc0GjaRoGNlHw8if9AGmtA2KDalCi4Tb42/P/lolwAmwo5Ap879++R1e+S33v5YZ3uuDNrjyu\npJGlG7lYhkUQBeRM4VPWCBsMlgdxLIeslaU/088+fftg6qZwc9emOuAziYnIRotsUkkjYjdwZ5Wl\n19Gp+lX6sn3oms5AdkB4zsSxUjXzI18VN62+V27oquLGj3xqfk3xO8I4pOpXBQ+y6dUWxZGCXMki\nq3XT3wgajNXHqPiVKcnqSKNgFzA0g9H6KCkzpaZis8lDywlUazEjVUFlyHVfFi/TPcxmC0MzVPEi\nG37yvkpBITd0hcodU6JBra+dJKIhKK0NYl3kBsnLxIKTjj2JR+9/FDS47877+Mh7P7KDYlyrgqwX\niBxc82uYsc+y3VHMALTkEs/zOP3001Ux09HRwU033fQnVczA/IRmPqbFddddx7ve9S4AVq9ezWOP\nPTbn51YqEdXq7klCAHY6ZKj2HO945zt4+g+iw9bV28WdP7+T5cuWq9HsrsbMreS91sdqaAoeMFgZ\nVNwPQzN4cuRJtle3A4LnsSi7iL5sn9rAA/RkenbpcRInsXJKll0+SYKXEs3yvDQ0nhx5UkHgOpwO\n9u/fXx0riATPJ4xCSl6JicYEk+6kKiDktUiZasd0yJgZbNOmw+lQPB0Q0Lm8Lcb7Eprmhu4UuTOJ\nKdaLjDfG23wQ8k6e7nS36q5NhxS0ykPLJDxSG2kb2Q/kBtT066UWL9PfS3nOEmrXCjdrDZnswkRM\nm2zdVgo2gOpMtuLUoyRi7dq1nHXmWaDBqj1W8eDDDyoiZyMUsppSXtMLPTW9C5OQJQTsn5q76s6u\nwurcmyi9jKOPPppHHnkEEJy3Rx99lD322FE1Zz7m438inn32Wfbcc09ANMtGR0fJ5/O7eJaI3Z1L\nJqNBhoLn+PKXv8zdd9wNMWi6xpWXXslRrz8KmNqYzlTMyDVCTmBbBUbkv6NYGBMPVYaIEQpj3alu\nhmvDPFN8RsGZC06BlZ0rBSfFSKlGj6EbatI7W/iRzzMTzyjzZsuwVFEhCy4J/RqqDjFaF2qKtmGz\nf9/+qlBo9WFplW0mQfFA3dBVHjyyySeNMTtTwsxYHlsqYbYabMrjS/PkSXdSwXql71zOzimYVet1\nt0L+Wj3Z6kG9TWRBer+pqXqzybSrkMR9CbObft9bTZIlWX+mkE0reRw5pXJDd0ZbgSRJKI4VefUB\nr4ZQfOaGhobo7u5WeVxCp4M4oOJVVAGXJAl5f5wFcX3Gc3kpYXXujdm5F+eccw7f//73AaGEeuut\nt86JQ/1Ki/kJzXy0xQknnIDjOHiex+OPP86mTZtYuXLlnJ4bRbu3Nt4yuY2PXPQBnq48DStAMzTe\nd+77eLD0IE9teEot4ilrSm9+tm79TNEqzVwP6gLT25Rw3FreSj2oq41vo6fBpslNQmMfAfNyLEdJ\nUs4WURIJyFMkoF9xHE9NAhAclbyTx9RNio3ilLqOBqs6V7F5cvMO59xq0inH7/WgTtkt40WemC4k\nEX7oExFhIAiNGStDwSkwkBsgY2Vm7DhKk7uJxoRS2wHBhUmZKajShjmWnBRTN9VYXt5/UzdpBEK1\nSENTxmrydeUGYWfv1a7eT5nE5DntLCS2W6oAqUJthtc0dZOUkVKJ1tnLIfWqFG7D5Tn/OX728M9Y\nvmx5m1u2hGKEcUjNF9jyilehs3c57MaCJg5dPvjBD6pixjAMfvKTn8wXM/Pxioo99tiDAw88kHXr\n1uF5HmvXruWMM86Y03N3dy5xA5/v/+j73P3ru6FH/Ozsvzwbb4HwFZPqjtNJ5ErYYxfrlCTyg1Bx\ndENXTFp0jYn6hFLJ0jWdFZ0rqHpVvNgjiZMdhE9mijiJaYQNhqpD1LwaUSJ8SzpTnWwrbxMcEjtL\n3slj6RaNoNGWOxYXFreZl7Yet7WnLa+j6BbF2t2cQLuR2NRnLaEYKicOnU5nm+hNa0iY2lh9TJHr\nAbKWUPkcqY2wtbxViSYAU+T9JlQNmrxHw1JO9RqaMCqeVgTtLKQJqhQImu7Vpq69WdjJxt6sx2vm\nEnmO9bCOF3gzPlbTNFJWipQhijMjbbD/Mfvz1FNPERNzyfWXKCNzWWjKJlkUC7NvNxKNs9ekcixI\nz93eYFeRRC7f+c53VDED8PWvf/1PspiB+YJmPqZFPp/n+OOPV5J9N9xwA5/+9Kfn9NzdPet7Zsuz\nPLTxIaFCA5x+xumsWrVKTA/cIh1Oh+qMzUUVpf1c22FqtmETE1NyS2wrb8MxHYI4IKtnGcgN4EYu\nWVOohlX9KtWgSiMSijR9mb62EbnywiFRsp2mbtLpdGJoBo2oQRInanxfbBSxDKtNKrQ33TsjpliS\nQluN6KQkcUeqgyAMmHAnGKuPCextIjxTwiRUU4uqXyVrZyk4BUXcNHVTYY3rYV352EjpYcd0VPdN\nSjO34oLrUR2CFplmzaQRNdrOu9PpVEaUEpYQJlOO0DI57WzjECXRlA9A0wtitlAGds1EBu3dV+mN\nIGEJCQkGIknq6GLSRSI6eZbNoa85lPvvvx+Ahx96WE0JtUQjjmKlHDfeGKfoFql5NephHa17yewf\nxJcQT//hyTZFs2984xscd9xxu/U15mM+dkecdtpprFu3DhAQ5rkWNLs7l2wbG+SqO66CAhDD6454\nHcccfQy2YePFHlktS84RG0XFJ0Gf04a5tZgBlAdZEAdsKW5RymEZM8NAbkDBYHV0QkLBRQkbIp+1\nqGfJY0gZ+kbYoO7X1Rqbs3Nk7IxSx4ImZyNO2F7Zro4h1/qZQk7J5bpYC2pKhTNjZ4SMdNjA1m0K\n6YJ6vK7p2JqtphfTBQWiJGK8Pq68ZSzDQkenI9UhiP9NdIREKLROiVqhvLLgks012UTLmMIcUua+\n1lBNNm3KZHmmnCKbgwp6twtejcyJpmEq01CpXAeiyGmdfNmmPVXENCd8skF39DFH89RTTwFw76/u\n5cQTT1SFTNkrC5U1v0rZKzPpTirI3z6LDoLdWNAMbd/Wtr/7y7/8yz8ZRbOZYr6gmY8d4tRTT31J\nBc2LEKuaUzy35Vnhu5GGgw85mMX7Lebx4ccVXMnSLDrTU0pfcmys/qDP2jlqgxQ0DSQrfoXR+qha\nTAupAh1OBykjpWAFWqKRs3NU/ApRElHySriBS3+2n7SVbjO9lOpiMlKWcFfOJBnqQb2tY7altIVa\nUCNtCqWsnkzPTu+NxC17oadeU0LHpLSoG7qqMyjPB0QCLrklSm5JyJHaOaGmohmC1NkyQcnb+Tau\nkIS0yYLKjIUHy3QyfdErEiYCc21pFgWnIKZVYZ0kaCZEfUopRl7D9OQkCyclfxrvPOnISZGcFikj\nulmmN5qmCQ5RM8GGSbhDxzKMRTfwNYe/hvsfuR8suPuxuznouIPU++hHvlJWC+OQOBbHCqKA6gyd\n0ZcT993/oPr/973vfXzsYx/brcefj/nYXXHqqady0UUXAXDLLbfg+z62PfMUojV2dy55euMfhBt6\nCpYuW8pRbzmKp0aeQtd1TM1EN3RyVo6+dB/5VF5JF9u6rXh/MxU304sZTdPIW3m8yGNLeQtREuFF\nHhkrw6LcIgWRqod13MRV67iGpjayeSePqZm4kavyR5RESj1TKn8uyC1Qwi6t69tYQ3BV5DRiQW7X\nzu5u6Cq+p7yuRtDAMiyWZZaJpliTF5kyU6pBJBENQRSoqXwUR5TcEn48NZVJGSl6Mj2KrymfGydN\ncYJkamJvaib1sK78xRpBg4gpKwBbtwmTUEGzpbS0/CMLq5lCckclp3JnId9327AVIb8RNvDd2YnC\njukITmlTtKdN2KbZ6NTQeP2Rr+e7//5dsOGB9Q/wn8/8J7W4Rs2riXOTnjbNe2roQoHVi3c+OXqx\nsfaXdxBF4t4efvjhXHrppS9KdfSVFvMFzXzsECeffDIf+tCHiOOYBx54gJGRkTkZ9BmGRpu97suM\n7Vu3wYhQ2/jkuz6JYQpHZulo78e+glzZunBpztk55V+Ss3PkrNyUSkjTD6QV6iThBV7o8ejgo/Rl\n+5SizKGLDhUml4EQAUhbafqz/dT8GhPuBOONcZI4UR2rhbmFdKW7SJkp4kRMe1onHK1kdECpoY3W\nRtkwvkH9/ID+A1jRuWLWqZMsJqQYQsWvMNGYIIojlXgdQyimyClWyS0xXh9npDZCPawrgimI5FXx\nBFmzO91NV6qLgfwAiwuLhZpbS0wn7kuyZCtvpeSWlGeOH/k4hqN4R4ZhkDJSSjpZdq3kY6Sni3TX\n9iJvp1MYCYWT6mvSbE0WMrM+r/l5mA4lieKIYqPIRGOCklcS07jmn70P3Fs4K0ew9ZmtxLWYBV0L\nVFcza2XxQ5Es3chVamfMUb1nrrF1u/CeOOKII/jud7/7J52A5uP/7zj44INZvnw5zz//PKVSibvv\nvntOcJbdnUu2DW+BOnTluzjv/edRyBeUzL4f+2qzWQ7KaHpTsjiOScwEzCn3eSmAIkncURIpZTQp\nHZyQ8MTwlES7js6apWvI2mJ9kMWHbdhi6tLSCKkFNep+Hcd0WOAsUM2k0fooSzuWkrNyFJwCC/ML\nd+B8BHFAI2jw1OhT9GWFRPaSwhKWFpbOSrKvB3UmGhMKYhXFkTD8JKEr1aW8yCzdUmahsoiT0OdW\neeNJT/inSR8bSxdqlosKi9rgdK1myGpCnkxNZaIkmkIMNCHbsnBMW2nFe5HT99Z1XBYVKTOFqZmK\nC+NF3k5zgsybrbwdCeeeCa4nwzEdBcVrvc9xElN2y0x6k8LbzJ2k5JUoe2UqqQr9+/UzMjZCaIQ8\ntf4p9ttvP1JGSinVyUImSiK0RMDudHP3QZcBnnt+EICFCxdy/fXXk0qldvGMV3bMFzTzsUP09/fz\n+te/nvvuu48kSfj5z3/OOeecs8vnpdP6biVy3nTtdSw0F/K1z38NO2Mr0rpjOEy6kxAI8zAv8hh3\nx4WUpN2uAGJohipuslZWKbGkzbQiYSZJwtNjT+OFHkEUYOkW+/buqzDN0o0ZBD66O9OtjrGltEW5\nSA/XhsX5maI7Y+mW8g6QBMrWsAyL3kwvGyc2ous6cRzTnRayy0PVIfKOMDOTiSuKo7ZFWRYzbiDg\ncBIiJX0F5DXqmk5XuouudBeruldR9sqM18eVMdqkO6k6cxPuhDADC2tMNibpzfTSm+1Vhcb0MHVT\nFZEAY7UxXMMFG/zYVwRVmQClcZkMOU2T19TaBZTY8tYk0eqNkzJTCioXJdEuYQOtRW09FIm86lep\n+BUqXkVJocoIo1B9vsIoJNIi9j5obzY8vQESuP/R+zno8IOUGZ5pCN5Nj9NDb6YXS7co+2Ve8GfG\nVr/U+OnNv2LJkiX87Gc/w3F2b7E0H/OxO0PTNE477TQuvvhiQEz851LQ7PZc8tPrsMs2X7zgiyzp\nXSI2vwnk7JzwVfGqBElAHItNaJIkeIbX5rvVuvZIGJPs4EsDyZSRYnt1uxIH0dDYo3sPxTNsJA2h\nFImAZXWnu8lYGQbLg4w1xlS3XBY4Vb9K2kwLc+BmY0SqlrWGPJdW3kzaTNOf6cePfIzEaBM78SNf\n8SRlyHPuSHWoBpSGRtbKkrWyah02dRPTNsmSVcVNPagzVB0SU55EGqOK63NMR5kpy+ueibivfFgw\nsLAIwoBES+hMC6i2zN1SHEc20sIkJCQURpmIIkSafUZx1DY1md4knEnIph4I2PBcixgpnrClvEUU\nLW5JFS6t+ah1+uVFHvsevC8jd48AsO6pdazYcwUxMY7uKJPTjpQwS81aWdzIhaAO7L4pzU9v/hWO\n43DjjTeyaNFLM0N/JcV8QTMfM8Zpp53GfffdB4gkNJeCxjQ1bFvD919+Z+3uB3/J8NZBbrnlFvbb\ncz/1cwnjCqKAodoQ28rbGK+PC1feoKoWqJipTk/ZK6tpiQxDM8g7Ak41XBV+N2kzTRiHLOxcSCNs\nMFGfUB2galhVhHvZpevP9pMxM7xQfoG6X1fTo0ySoRE0FOG/P9s/KxZ7W3kbGhq96V4aQYOlhaXq\nd1LhJG/nlbynDOlsLydHAHZiKwiVrultCVEu5LqmK1nnTqeTbZVtFKwCo/VRGmFDdajCOGSkPsJI\nfQRrwlILa1+2b4fiRuKCa0GNIA7IOTlyiClZxsqo6Y2ctkhTtLInulde6ClyvSwypUyloYuCVJ5z\nh9OhRvlREs2qZibvUT2oK+x5NahS8SpCnGHac+TxpMqMVDeTP5dJ+JADD2HDUxvAgceffpwjjjyC\ngewAWUcUywW7gB/7DFeHqQZVtRkYjSL6doM52R33/pZtQ0Xuv/9+FizYNZRkPubjfzpaC5qbbrqJ\n7373u7uUFt+tueSRX7Jp0zN87Wtf46iDhaJZkiRi82iIxk8jbDBSHaHsl6kFNaJIbIRN3cSPfNVo\n8UKPRtAQXfQWHzKZS6SSl23YhFFI1smytbyVYqNIT6ZHQbVMw1TqaaZuYpkWS/NLKbpCSETKEaeM\nFEWviBmaFJwCi/Ltk47WmHQnKXtl8dpxyPLO5e1qjU3+omzgyJBw2aydpSvXpZ5j6VabsWOcxDvA\nuE3dxMOj5IpCxtItolBA6PJOnjiOGW9MmSm3qovZut32OVBTl2bh1QrXNjSDjJUREte6gW3aFJIC\nSZJQC2qU3BKT3mSb2aZsJkr4nJw0qfzXhKnLIka+dzMhAtzIxQ991ZSr+TU1cZkJzjyTBUGcxGpa\npKFxyAGHcO/N90IMGx7ewOF/fTiLOhYJ/yLDVFzZkbr4XJq6iWFlqSQ18snLL2ruuPe3PLt5O1dc\ncQWHHXbYyz7eKyHmZZvnY8bYtGkTq1atAsC2bUZHRykUZiYWtobrxhSLOzrkvtj4q/NP551vPpXT\nTz69zS25NUzdJIgCJhoTauMqMcgamiJbyo1q60gbxAIzWBls68ovyC2g4BRU92VBbgGmZopJSCS4\nKFkry5LCEjUS9kKxoEv4QJREyjQzZQlfmNYOm4xG0OCxocfUoreqaxU96Z62hVUmU8d06Ex1AqLQ\nSUh2kEwuOAUsw1IFRGvIQk8mivGaKALla0vuSc2vMVwfpubVFBSjNWRC6M/105eZKm7qQZ1io6ge\nl7bSdKe71b+lelrVr1JyS6rAgaki1YvEZkFJKhu2MqdMW2nSRppES5TCjOwiVryKmLQ0i5VaUKMR\nNJRnzvSYrXiBKU5N1sqSsTNkjIzwOkoSan6Np7c9zbmfOVc4PSfw7W9/m+5CN2krrTYGclIj3584\nidkr08UeSW3Gz/GLiTM+8GXe/f5PctZZZ73sY83HfPwxIooiFixYwNiYgEo++OCDrFmzZpfP2225\n5ILTWTLQz+c/9XlloNzKdQTB8cjaWZI4Qdd1Jddr6Ra2aauGiBuIxplymI9CAVFLUNNuGXknT9pI\nY5uCuN6f6RcT/ybnTsJtU9YUjC1rZ8mZOQzDoBE0KDaKyqXdMRwW5hfS4XTsMPGPk5gnR55URUBv\nppflHcuVn5ZUHCu5JdBQE6aqXxXTpSY0GUTxICX+p/NNAQXTTpKEkdoI2yvbqYd1oigiTEI6nA46\n0yJX+aE/40RGwrvSZpqckyNrZtV1RnFEPai3GX+22jNI41WZb+VkvtUTTEHM4lgZRStuS7O5KLkq\ngFIsk02vsldWE3sv9IRgzE68cWBm/7SsJYSEbF3YHGStLGk7jW3Y1Lwabz3lrYxvG4cIfvCDH7Bm\nzRohVBF5Cgoo9xkyFhgmfe72HV7rxcYZH/gy+x78Z/zTP/3Tyz7WKyXmJzTzMWOsXLmS1atX8/jj\nj+P7PmvXrlWOtjsLx9FwHA3Pe+l18l2P3MYdD9/Ckj37eQfvoCfdoxJM65RCkuaydpayV8YxHAq5\nApqmsTi/mO5Mtyo2Kn5FcSFqQQ0/8tlW3kZCohbu3kwvHU4HFb+iRuTPl57H0i0hnRi7YrNrZnFD\nlyWFJWTsjCCWGiaEottV9apUvSqd6U5M3VSds6yVpSvVpWSmnys+pxbBvJ1X5M2+bB+TrsAiywW3\nETQYa4xh68KUUi5w0tdATmkA1Vls1a/3Qg8/FLybVvKn3MD3ZKaOubJrpeDl1CcYqg1R9aqKLB8l\nEeONccYb42zUN9LhdCgSa9oS8DDHdOh0OvEjX71vrTKYWVss8rJgkzA6aeSWJMlUsgp9AYMrifdN\nFiBhEgo8ehKqhNXq8i2vTRaw04sXCV+QCUfC5tKmKEzKXlkUxFFD+R/4kc+KBSvYa/FebHxmI+iw\n4YkNHHfscZS9srh2w1HcHEMzWJhfyKL8Imp+lXp9C5nopQsE/PKe33Dbfz7Ka49cTxzH8waa8/En\nEYZhcPLJJytp2BtuuGFOBc1uySW/uY07HrmFI9YcQcNtsG/fvkw0BP+xETZEFx9dCaVIs8qsI6bC\npm6StbLs3bM3URwxVh9Tk18v8hRZfqw2hhcJX5ggDsgYGboyXcorzA1ctpa2YpuC42ggpgy2YZMN\ns/Rl+7BsAX8u+eI8JhoTTDYmCZOQtJVmYXYh2yrbGKuNkbEy9Of6lXrZYGVQFTOmbrKksETB0Mph\nmeHqsMolSZxQCkpomkZvphfbFI0XDSF4k7EybWuooQlCehzHilRf9+tsr25X4jggiqSB3EBbcZSx\nMopnE8dijZ++QZdwZAnLU0UiU8VMq7FlqwQ00NY4ipNYIBJ0S4kXyOc1wobKq0W3SM2vKf6jfD9N\nXUxGpMCAoRtt6+xMxUvKTCmfN9m87Eh1kDEzNEKBpGj1h/MjX0D70h2ccNQJXHXlVQDceeedvO51\nr2N7ZbtqxlmGgK1rmkZPuoeB3ABxHBGNNzD8qeL5xYbMJZmuZdTrdTKZ3cvN+Z+K+QnNfMwaX/rS\nl7jwwgsBOPPMM7nmmmvm9Lw4TpiYiAiCF//Reuz3v+GMi95EbawKMbx5zZu57OuXsbh7Mbqmq02y\n7JrI8COf5yefJ4zFBjdn5+jP9tOT6WmDe8mFdMPYBp4tPqtIf3JTK4nodX/KjThjZ4iiSCyCTS8Z\nTRcLjOLn2FlqvoAqyKlFwSnM2NVJW2ll5qlrOrquc8jAIRRSBdVhkoT/sldmtDaqOv9Sva0j1UFf\ntk+ZY7aG/EpLGcggEl26il+h7JaVKaauieMUnEKbpGTr8ZIkoepXGa+Ps726napfVQszCNWYsldG\nR/CEulJdLO9cjqmbO5U8lfLTsnMGUPWqjDXGGK+NM1ofZbwxzmR9kmpYVRLRrQ7RpiamStIMU9d0\n0MSxpRCDLFqUUIQl/m6FzCVJIqY7oSDkhkkoOphoVANRSKl7g8atN97KZZdcBhoc+oZDOe+z57Up\n5timrci4OjrlQGCpjQQG3EHsqH16Npf47RMbOfHPz6daEwXR6aefzg9/+ENyud0n4Tkf8/HfFTff\nfDNve9vbANhzzz3ZsGHD3LzCXk4uWf8bzvjam6iNViGEPZftySV/fwlrXrWGjCVgwZPuJBONiTbY\nkDRD9mOfvJ2nK91Fd6qbrD3FI5GQMEM3GK2N8oexP6i8ZBmCCC830FtKWwijkDAJyTk5dHTcwMWP\nfQGx1S2hpmaYCvIrRVGkwpZsgk3frtmmTc7OMVofxdSEKtdePXuxMLeQOImZaEyoQieMQ8WTydk5\nte6aukneybfBy+TjW4n7QRQouPBIbUSgAJrrbcbM0JvtJWtlFWys9W8ZkgcqYcbTIVmNsKFEeZIk\nUTCz1kbm9JBrveTCGJpB1a+qwnV7ZTsj1REmXFFcShGC1vfc0MTU3zEcDMNQ/jJSFjptpulJ91BI\ntRQtzcJF3kcZjaDRtleQISHXaSutirBfP/xr/vxdfw4W9C3v48abbmw7lq7p9Gf7WZhfKK4rqIoc\nGEc4xd9jhC9+6j89lxxyyCHceOONLFu27EUf65UW8wXNfMwaTzzxBKtXrwaEP83o6OicSchxnFAs\nhvizKxzuEHfedytf+975RNmQ9dvWQx2IYf++/fnh5T/kgFUHKNhWq5OvXLDl1EUuirYhphlLC0tJ\nWSm1WG+vbGfT5Cb1uv3Zfvbo2oOEBDdwKbkltpa3UgkqVL2qck7W0BiuDisolqEZdGe60dHxY5+a\nX6PiVZRMZ2dKSEqnjJSaJDmGQ5iErB9dLxKBlWFJxxIW5xerBU9KRMexWOBJBGEziIU0pmM4qpPW\nmepUfjEzhZwuDJYH22BoGSvDosKiGcUKdhYVr6KKm8nGJNur29sMKvN2Xini9GZ66c30TgkwtDgp\ne5GniPgVryKKwWSKt8L/Y++9wyyrqvzvz4k3p8pdTVNdTTdJQUGG4IwkZVBkVIIOKhJMOIOiGFBA\nGBWEB0TEUST+BFFxUHI7oCAiCCIIMkOS1DlUV7753pPfP3btXfdW6G6Uh1HfWs/TT1ehta62AAAg\nAElEQVTduumcc+9ee631DaAmK64vnl8WUhIu5vkeaKJDZps2cX2qUzaVdJJ2Uoz6p6Y3tmGrTZSE\nwNW8moIctkIaqo7wGgKR1DJ2RkH6nln1DMedeBzoYNomP7zhh1iWJXDw2SX0pnrR0AQe3q0ItTNT\nwOa6EwXs4kuEzdHtPue/uP8PfPWyW+jo7OY3v/mNun3PPffkjjvuYOnSpa/oGi7EQrzW0Ww26e7u\nploV36mnn36a17/+9dv12DCMKBaDVzSp+dUjd/G5b32cwRVLeezFx6AGRFCwClzy9Ut4xwHvYFFm\nEYBqHI03xik2i2r9KTVLim8XhAG5eI5lhWV0JbtUMVN2yrw0/lKbaeSOuR2pe3UqToWSU2KoIhpB\nTuCQsBJKCl/6cElneE3TlL/JaH1UTS7iZpzORCcxIwYaSs1Sboo3ljeKqdBUPtghuwOlZklNjCzD\nwsQkJCRjZ8jEMmqKLBtvUhxHrs/SC6btOkQh4/VxxuvjCgZuaAZdyS6liCnV3rYnJNS46TfxA59J\nR5h5SqK/NDaWhZGUQgZULpGCBGVX8GSLzSKTzUl17lqbbyEhRCh4mjRQltOjCGFymrEzar2XRYs8\nNlk0xc24Ok55HSVEUEKpZUhIXMIU8ENpCK6hMVmf5ICDD6BcEeIT37/u++y+2+4YukFvqpeeVI8y\nqK55oniRkL+EbmEUnydsvLJccupZV3DoWw/jxhtvVLf39PRw66238o//+I/b/Vx/jbFQ0CzEvBFF\nEcuXL2f16tUA3HXXXbzjHe/Y7scLAui1nHTSJzj44H+e93733/9LfvCDq/jDcw9zw+03UOgscM5l\n53D/b+4HB2hCt9HNtddey8EHHDzLJCwIAzVWrnt1hqvDlBwBGZPqJYsziykkClTdKi9NvKQem4vl\n2KVzl7bNLKASkFQMixnCp2asNsaLEy9SdarU3BpxS2CvW6cqDa+hlLtChHmYhiZI84E3ndzMBEkr\nyZv63kQhWWgbnUsjrdbulix44kYcTdeUalfWztKR7JilQiY37RONCZEovQaRJrpehXgB0zDbBAO2\nJ+Tov+bWWDW5ivH6uIBhuA0SthAj8AJPFJpTstoxPaZww61+DjKBthYxc4WmaaTMlIBroAsejSYK\nJNMwVcKYmYRl5y5pJUkYwiMoiAKCQHQGZ0K2ZPFX9aqkzJTqWtqGTcWtUGwWFUfqk6d9kg3rNgDw\n1bO+yknvOomeVA8RohjaWBEO2DJp2qZNX7qPtJ3mgV//hv+8+D845UPv5G0H7j3vcd/7wBNc/aO7\neOSPL/Hoo48yODjI5z//eb797W+r+3R1dXHLLbdw4IEHbvc1XIiF+L+I973vffzsZz8D4LzzzuPL\nX/7ydj923bp1fPzjn+Kkk07Zdi754VXc+8h/87Wrv8br93g9P7nzJ/z01p+KXBKCUTU49+xz+cBR\nH2AwP9g2lQjCQHX2q46AKI/WR5VXjKZp7JjbkaWFpcTNOKsnVyuoUxiF9GeEUpSUHJZrSs2rCW5J\nPE9vulfB1iYbk6qpEhFhaiaTzclp3mgkILpe4Ck5ei/0KDcFx8PxRWPIMsRauDi9mEwso3KJNHeW\nRpm6pkOE4mYkrISCgdmGTcIS/MHW5g+ITflIbYSm10TXdTVhX5xZrCBmuja379t8IfN2w2sw3hhX\nEOMgmhZkcHyHqieug+STer7IL9KbBlDr/8wiZmaYukkhUaAn2UM+kSdtpVWRlzAT0xLPU4bYrSHR\nFIZmQISAmk+pbMrXbQ35PlJWirg1bVXQ8IR5pvQV+tp5X+Ou/74LgI+c9BEuOPMCupJdYg8RhpSc\nUptAghRciJkxhoe2cMrJx/LRD7x9u3LJL+5/nJUrV3LEEUdw9dVXc+qpp+L7YlJlWRZXXnklH/7w\nh7f7Gv61xUJBsxBbjc9//vN885vfBOBjH/sYV1999XY/9p3vfCd33SW+qBdeeDGnnvpZwlB03NAi\nbr3lZr7+9XNZs+ZlDNPgvl/dx4q9VrCpsom6V+fqW67mxutvBB+YAMu3uPjii/ngcR+kI9ExJ5zL\n9V1Ga6OM1keZaEwAgpwpscBDlSFFgC/EC7yu53VtG2yY4l6EISW3pJ63I9Gh7jfZmGSkNtK2iZaL\noOzoTTYnFU8FUF2wilNR8AQpaykxy7lYTkEBsnZWOSGjoZyRAcUHav3q6pquphOmJgqbulen6lbF\nxGfKZE2O1VsTT6tgwMyQXTC50Es1si3VLZSdsoLsBZHAl4/VxmgEYqokFX1kSL5O2k6rpNvazZPJ\nNGEmSNpJRc6f+d4kD8YPfJzQUeoz0gBTKhg1vekiVyYPee1bTdMkrMPQDcWpkQXpeGOcycakKrzC\nSHT5bvzJjfzsup/BJLz/qPdzww9uIIxCKk5FbYDk+0zbaZWgAE488UTuuPUOCGCngUUce+SBHP/+\nY1mxfBloJrevvJuzz/s2q9YOoes6d999N//8z9ObuO9///t84hOfwPNEAjVNk8svv5yPf/zjs67f\nQizEX0v85Cc/4QMf+AAAe++9N0888cR2P/bcc8/lvPPOA+D440/ke9/7fwRBhB8GQMTvH/0dp512\nCmtefhk0+Na3v8U7P/BOnhl5hjAMue/x+7juqutolppC9bYOH/rQh/jMpz7DLl27zDmpbngNNpQ2\nsLmymZH6CGEYEjfjav0Yq4+ppkrSTLK8c7kqJCQ8S65hNW9afas71a1gStIcWXJxJhoTjNfHlVx8\n0koKo94WYr5cqxzf4aktT4nNsVdFRydpJwmjUAmTpKyUyj1S8cvQRHGjaVqbmmNrmLqp4LlhFCpl\nLqlQJlUvW40yYXp6MFdIVIUk7csCoOKIZlHFqVDxKmrDX26WldHzXLleR58uJgnbjkFOwtJ2ms5k\nJz3JHnrTvW3805nvrbUwlUbR6v16TbzIU1wYQHFhgihQkDU5uUlaSZXD5PmpOBUBZZwShJDx4IMP\ncsZnzoAq7Lp0V/703J8AlGqq5MGGUagmavIcX3TRRZx99tkQwU5LRS45/LCDefP++6LpJo898RQn\nfeIMVq0VIgIXX3wxX/jCF9pe+5hjjlGCHQCf/vSnueSSSzDNvz2K/UJBsxBbjYceeoi3vOUtgBhL\nbt68WamRbC3WrVvH4OCgWmRWrVqlVNOiKOKCCy+Y7tBFcMUVV3DKKacQRREbShsYqY+wsbyRh558\niKsuvYpaqQajQAgf+/jHOOfsc+jOdLfhV5Xz8BQnotQsMVQfwg8EoX3VxCosQ0hRJu0ku3fvrgoI\nQ2vvMGmaRqlZUouXJAnK19lc2UzVrVL36mho5BN55YYs1cLWFteqTkzdq6NrOmuKa6i7gmCu6zqd\niU4AZRRKhEoauqYTs2J0JboUxCxlT3sCFJtFXL+lczMlS5m0k9TduoKYyds7kh3K+M313Wnd/ynp\nUAmjkAt1za0Jjwa3qgiUVafKltoWpdGv6zqFWIGknRRjdE2j6TdVkpIcHnldTF1MhRJ2gq5EF32p\nPvoz/cJroEVlZ3s6fZIrIyEJURQxVh9jojnBZGNS+RFIPLrXInWpo6uunG0KCVHJ6TE0g8nGJEW3\niIGhJMClm/XizGLK68u89S1vhRBy+RwbNm6g7JfVZNDQBFSiM9FJwkrg+EL4YHh4mN1ftzuBP1Xs\nhkAALz7/IitWrOCyyy7j9NNPV+/z0ksvbftdxsMPP8zRRx/NyMiIuu3UU0/lW9/6Fpa1/RO3hViI\n1ypKpRLd3d2qEF+7di0DAwPbfJzv+wwMDLB5szABvPnmmznmmGMAURDcededHH3M0UJGOYCPfvij\nXH7F5QRRwAtjLyiu5HPrnuOG/7yBzes3i2lNIMxpzz/vfHZbvJsSZQGUHLBctyYbk2yqbBIcjMBj\n9eRqGn5DCaEM5AboTnZTSBTERHiqcSMbKFJkBARqQBpfAopP0vAbypRSQ6Mj0UEmlhEb26bIIw2/\nodbUDeUNjFRHxKQmcOhMdqrpixM4SpjE0A2lxJa0k8R0wZ+0TJFnJHxLTpNlAaGjKxEdmQc1NHLx\nHF1JkZOkEIuEhEnvNplL5US/7tUZr49TckoKildxKow3xtV5kdDcuBVXHEwJO/aDabNk6eMCqKKl\nEC+Qj+fpSHSQS+RImSmVL+fyT5srWnmkgDKGlqI2sqlXcSpMNidncXDkuZOFTMyMYetC1a7slYnC\naFZey8fz5M08g/2DNBoipz7//PPsuGxHBTGTIREdEgkShiG77LoLa1ZPw+eJROPguOOO4/777+ew\nww5T3kbHH388N9xww6z3sHbtWt797nfz1FPTZrBve9vbuOmmm+jo6OBvKRYKmoXYagRBQH9/v9o4\nPfTQQ9uFszznnHM4//zzATjssMO455571N9WrlzJu979LvX7KaecwpVXXAlMFyVrJtcw0ZjgubHn\nGBoe4spvXMn6Z9cLLDRw0MEH8b3vfY/FXYsVqb11IiKlH4MwYF1pHU+PPK26UUkryf6L91ddKllE\nKFLh1Mba8R0q7vRi2ypD3PAagmxYGyGKIjqTneTiOTqTnWoB9UOf0dqo6q4NVYfYUt1C02vSCBos\nySyh4lXYVNokiOctC43OlF9MPDuLwC47NQkrgaEZSgghbsWV07OtCw8cqdpSSBTaOlNSScwNXGpO\njUlnkkqzIqBikUvTExMnWTRomiaSvF9T/gm2bpONZ2f5IkhRBglnkLAI2ZWTimMqiRomOTtHV6qL\nnlSPmpDI0DSRaFuJprJ4cnxHTVFmGpmFhKJQnCK2SgiaVFJrleIG4Qwu8dcpK4VlitexNOH1s2Nu\nRwbyA+K40Fm+03LWbVgHBlx/8/X8wwH/oDYFMTNGb6q3TarbCzwuuOgCvvK1r7Sdr47ODtauXstD\nv3mII484UhFJTzzxRK677rp5i7v169fznve8hyeffFLddsghh/DTn/6Urq6uOR+zEAvxfxlvf/vb\n+eUvfwnAZZddxqc//eltPub222/nqKOOAqC3t5cNGzaoov2FF15g3wP2pVwVHIT999+fX9/za0zL\nVJCfp4afYrg6zHBtmMnqJLdcfQuP//ZxCAAdBgYGuOSSS9hz5z1ZVliGoRlq6iwjZojv8aqJVTy4\n/kEmG5M0/SaGbrBzx85irW2ZinQmO5VojKGLNXqoMi21uyizSK3riudY2UwQBYKkbwv/spmwL7ld\nG6uN8cSWJ4RdgNcgH8+LhkltWIgJmNNrslQv60h0qLXa0AVsKkR4y9imDdG0elcQBm0qj3Ki3ZGY\nEkeYMX2XKmjSkLnqiSaYVBdzfGfa2LjFKDoiEhMvTayZM+HPUkI6YSUwNRNdF3LPUhZfbu4BBZnL\nxrKKrzqf1PJcIjgSJuiH/iz4spTtrzgVnFDIQUsIoJygyeOT56PYFL44Mh9IdETMjNGV7KI/06/U\nSY8++mhuu+02AL5ywVc49dOntl2/VhihfD+/vOeXc1IAVq1aBRHsu+++jI8LD6B99tmHBx98kEQi\nMev+ANVqlRNPPJFbb71V3bbTTjtx5513svvuu8/5mL/GWChoFmKb8fGPf5xrrrkGgM997nPb1C33\nPI+BgQGGhsQC3tpRe+6559h///2pVCqgwYEHHsgvf/lLYnZMbZpBFAMvjb/EUHWIVZOr8ByPH33v\nR/z+9t8Lkr4Gg8sGuf6669lt593Ixto3/hoabuhSapR4dvRZBREzdZOl+aXYpq3c3FtDku4l92ay\nOak2yRk70yZKsHpyNVVHdM0sw2JFxwoBq2oxnvQCT8Gznh5+WkDL4jmW5pZSSBRUJ63slBmpjqjX\nC6JAOdDHrXibU/PMkBOpicYETuAQN0RhlrIFQXUgN4AXCe5O3a1TccXou+SUlFv0TNPRVpUyyQHx\nA7FwS+zwovQietO9ZGNZ1SlLWgKnLYsXOSGReOnJxiSj9VFqbm1OnxgJvVucWUx/tp9CvDCLzC87\nfNLPRp4DBQmb+h1NXLOORAedCaF2V3WrDNeGKTaLijhadspsLG+k7tXVxE8m+qSdZGluKcsKy8jG\nswqSZmgGp51+Gt+96ruQguPefxz/ce5/CKhGLD2nmWoYhixfvpw1a9cIwfypPx/+9sO58IILOfTQ\nQylOFiGAfffZlwd+8wDxeLuCzsyo1+ucfPLJ/PSnP1W3DQ4Ocuedd2436XohFuK1iquuuopPfOIT\nABx00EFtIhfzxRFHHMHdd98NwJlnnskFF1wAQLFYZL/99uPFF18EExYvWcyDDzxId4/wx5Ld/CiK\neHLoSYZrw2ysbIQQHln5CDddfhPEABvS6TQXXHAB/3TAP7EovYik3SJjGwljxZpbY/Xkakbro8pI\nORfLYRnT0+/Wrr2u6+RjQhimEBcSznK9ysQyylcMhFBNxa0QRaKLvzS3VJlvtppjlpwSw5Vhntzy\nJDVPbJbz8TzdqW41/XZ8Ryh6NYqKKxgGIWgCipWP52cpc7VGqVFitDGqzCgNzSBtp+lOdpOL5WgE\nQopYcoyKzaIi47ciBmS0HoOmi/xqaRa6rqsGVVeyi0WZRco6IRfLqSJLivy0Fi8S7h1EgcrXc3FB\nTd0kbsWVwMDMfCPVzuRzzRXSPw1Q4jaS3xJEgYLySaGbLdUtlJolpUoqz4FpmHQnu+lL9yl1NLnP\n+OEPf8gJJ50ANuz1pr1UA1gWt3Pl/mOOOUYUIC2H1Leoj+eefY63vOUtPPvss+K23j4ef/xxFi9e\nPO81B5GfzjvvPKVsC0IM6sYbb+TII4/c6mP/WmKhoFmIbcbdd9/NEUccAcCyZct4+eWXtwoJau2o\n9fX1sX79eizLYmJigv3224+XX34ZgB0HduSxxx6ju1uM31snASAmJC+Ov8iL4y8y0ZzA1EweuO0B\nbrjgBpGITPGF+87l3+HQQw6lEC+ocb2c2Dy++XEFA2r6TZbll5FLCNNLL/CEK++U0gm0Sxbrmq4m\nCXLhzcXFY2VxMFwbVh2sbCyrBAvUdGLKVfjxzY8r48mEleBNfW+aBd1LmAniZpy6X1eQrqbfVP45\nkhvS8Bpq8ZUKK1WnSrlZFnKRfg3fF1r+QRjgBZ4g/5tiIdY1XUmFysXYDVxlNif9C5JWko5EB1k7\nKxLjFDY4ZafoSnSRjWcVBlwWLRKiIVV0JPFe+Q9MwcmktOV4Y1wUN4SYmjmrKM3EMsq7xw99VQjN\nF7ZuK/x0JpZRCUUWQrLjCDBaG2X15GoqTgXTMBUnx/M9LMOiP9tPT7JHJXXZZZOTot/+/rec/OGT\nIYDurm5+99Dv6E51q8/IzLjnnns4/PDDxfu0bVzPBQPOOPMMbr/jdl584UX1nXnggQfYcfGOs3hG\nc0UURVxwwQVtJOt0Os2PfvQj3v3ud2/1sQuxEK9lbNmyhf7+fqHmqOsMDw9vdZq4du1ali1bpjb6\nq1atYnBwkCAIOPLII/nFL34BQDwR597772WPN+wBTMN25UbY8R0e2/QYmyubGW+MY+kWQ08N8Y3P\nfAPXdCENaPDJT3+So44+ShgsT61vMiesK65jsinWcD/w6Un1sENuB3RNp+bUaAZNtTGW650MTdOI\nm3GiKFIb1P5MP5qmKQiT5NJ0JbpIWIJ7IcUA3MBlsjlJuVlmqCqQASDWyBUdK8glcm0NqKSZJBvL\n4oWeKqK8wKMZNJVypmVYbfLFQRgILotboeGKTbycyMOUx5pbbTOmDqIpsQQ0IiLFHVT5FA3DMLA0\ni0w8Qy6WU9DetJVWfEqp+ibNMAMCNbWxdRvbtFUhYRmWMr62DEvxXeQkvvWct4ac8hu6QUQ0p5Kb\njFYVVTl1V883JQjQCo1ueA02VzZTapaEQmkU4gTOtNFpQih+qsJEEygMCU0ulUq8Ya83EDoC2vzU\nU08xuGSwzVC0NYaGhthxxx0VoV/Ge456D1EUcccdd4jvgW1x//33c8D+B7QVlluLW265hRNOOIF6\nfVr988ILL+SMM854RaIP/xfxt8f6WYjXPA499FAymQyVSoXVq1fzzDPPsMcee8x7/6uuukr9/OEP\nfxjLsvB9n3/9139VxUwymeTOO+6kp7tHLSpSSlEWNTEzxrLCMtzQpbyljB/5vO3Yt/HGnd/I2aec\nTcNpUNEqnHTySZz5pTP56Ec/qiR7wyjk+bHnFVGcCPZetDcJMyE6PogixAkc1SlxAqdNqUTKOxab\nReUFIAnqUjkmY2fwQg9Lt6h6VeJmXJlpSm6KNISUC+/izGLKbplCvKCmHdKZGQTO2Q1cZe4ZN+N0\nJUXS90OfMAxpBk3G6mNsLG9kXWkd4/Vx1clCAyLaTCiLzaIoYmZskOVrd6ZEF7Ej3kFvulfwS6bG\n/G7gUnErquNp6ZaSyG5NIDNDyk/LAkBCx+JmnHw8z2BhUHFuRmujSgraCRwlU7lmcg1u6Crfh0Ki\noGRTZXGWjWXJxDJk7elz2GqmWXYEtyUSmAqKzSIbyxtxQ1clVFn4peyU6NBaSQUtkP43fuirqdlY\nY4zkDknSi9NUJ6uMlkZ56dmXWPLmJWrztbXvRSaTEXAAHx649wFefFZ0me2YzY033khvb68QY5jy\ntdgWfOLss8/m9a9/PccffzzVapVqtcp73vMezj//fM4666y/+kS0EP//iL6+Pg444AB+97vfEYYh\nK1eu5OSTT573/tdee61qQPzzP/8zg4ODAHzpS19SxQwIoYx93rQPji+4FV7gCW8XXUDPYmaMvRft\njR/4VL0qju+wwxt34Pt3fJ8zTjiDzeOboRu+e8V3eW7Nc5z00ZPozfSyU8dOWIbFpvImSk6JuBEn\nYSWUfPN4Yxwv8AQ8dUo10tRNql6VslOeNmKcUr8cq4+haRr5eJ6YESMXz1FsFhUcWkOj7tXFem2I\nzXfVrVJqivXLCRxRVEXiOXfI70BHskNt7mNGbJbRsjQNrbpVxSEFFMHdCzxGaiO8MP4Cw9VhYWTs\nVBWnxTRMJakvc4qGNkvVTNeEF1lnXOSSzmQnXckuBZcKCZWqmTwnOsJvZrwxTtNvblWdLG7GycQy\npK00XujR8BtqwiERCYrM7zuqYJFFm4SIyeeTvB/TmN4GG9o0hxSNNpjXrMIG0dgcrg5TcSskzIRq\nZoVRSIchcmk+nldCCE2/KQrIKbhfEAp/O0/32Pste/P4Y4+DDyvvWsnpnzhd7YlmxnXXXaeKmZ6e\nHkUJqFaq/OpXv1JTm6uuvIr999tfFfay+NxaPjjmmGNYvnw573rXu1i/fj1RFPGlL32Jp556imuv\nvXZe2NpfQyxMaBZiu+K4447jpptuAoTh5rnnnjvn/ebrqJ1++ulcdtll6n6tMLSZHXdpqiWj2Czy\n+ObHlXdM2kqzQ3MHPvDeD7Bp8yZIADocdfRRXHThRaQSKcbqY1TcinKLX5RepIoCgLJTboMHSCUq\nqejS9Jtq8au6IgFKbxs5wYgZMeJWnJpbU/hmJ3DI2BlVpNmGzXOjz1Fza5TdMkkzyQ7ZHQAUL6PV\nmVmej6pbVaTJ4eowE40JJfcM01waeb4A5WXgBZ54PadM2SkrWJttioU/H8uTT+TJ2qIQaFURk8Zn\npiZG9RoabuCSNJJYpuAZpayUSm6y6Gu9dqZhiq6aIcziZpqrtYbk48iNxnhtnM3VzYzXx6l61TZe\nlAxbt+lOdbOsYxnL8ssUh6o1JEdGdj6lMtuG0gaCKJgFH+xN9zKYH6SQKKjjkJ8FJ3CoOlUh3dwo\nMlwbVpuPK6+8kgfueQAacOKHTuQLp3+BmBlTnUgJhRgaGmLJkiWKoCmL9taJJMD1P7yeo9979JxT\nKInF3hrJ9ZlnnuFd73oXa9ZME0Xf9773cd111/3duEEvxN92fOMb3+CMM84A4F/+5V+4884757yf\n53nsuOOObNmyBRCd46OPPlrAc044Qd3vzLPO5GvnfQ0QE23pSK/rYkMuN7UAE/UJHtn4COtL64kQ\n05K9M3vzkQ9/hCeffxI6ABN2XrEzZ33xLLq7uklZYqMsocRdyS52zAkTQrmuyE06oEydIyKlWia9\nruS67oc+aKh1VvmKxfIq70h5fD8S711DY6g6RN2tY5s2WTvLzp07Cz7IVJGUttNtDSuZS0rNEmP1\nMTZVNjFSG6HiVBSMSnJclDkxiHVGM/EiT23G5SReFjrSVDoXz1GIFygkCrOg0ZJb0sp5MRCcokbQ\nUJMVOV2RxwrTRUzSSirfFpg9eZEhLRZs3VbIAFk8SNPlOR9jxEiawlrBCeeWazY1UcjIXFdsFhmu\nDlP36iSsRBt0PW2n6c/0q1wiz4P8J89nza0xWh9Vn81bbr6Fb17yTXDggAMO4IbrbxDn2hJiAHKy\nFYYhO+20E2vXrgVEk0B+R1rjM6d/hm9+85tzFomSQ7S1wmZkZIRjjz2W3/72t+q2ffbZh9tvv32b\n8LX/q1goaBZiu+Kmm27iuOOOA+CNb3xjGxG5Nc4++2yFcT788MP5xS9+wfXXX9/WhTv33HP56le/\n2va41g3cTOgZCHjQ/WvvV52q3nQve6X24v3vfz+PPPKIgKBZ4r195dKvQFrwJ+JmnIHcAD2pHpVU\nZGFScgQHQ36pbcNWRlaAWngkZwNEQkpbaQFD0g11/9HaKG7gEoQBCSuhioTVE6sZqY2IsbtusTS/\nlIbXUBtTubBIKeaKU1GeBK3hBZ7Q6Z96DelpIyERcTNONpZVSUUmm5gRUw7RrY7PsvgxdGNaBnlq\noiEVXaQ5XISAuiXNJD3JHtKxdNtr5OI5kqboEEqYmXzO+eBhMtHK6clc2Gs5GSo3Bdk/bsVJW0KV\nrrVrlYkJAu2i9CIysYzwd5hSofECj02VTWwobcALPbKxLLZuq4JkcXYxyzuWk4mJrqWEys2UiXZ9\nl6HqEKP1UZXcQ0L++OgfOfuMswFYMrCEn970U7XBkBOxpJXkmu9ew4XnXQg+7PH6PXj66adnHe8X\nvvAFLr74YnW9pRT1zDB0o81Yb2aMj4/z3ve+l/vvv1/dttdee3HHHXewZMmSOcL874UAACAASURB\nVB+zEAvxWsXLL7/MihUrAIjFYoyNjZFOp2fd77bbbuPoo48GpqHLTz75JAceeCCOIxo7R/7Lkdxy\n6y3TnlIRyvgYxHclbsbbpI+HykM8uP5BRqujNIMmcTPO/v37c9H5F4niKg4koLOjk7O+fBYd/R10\np7rpSfXQmexkMD+o1mcv8AjDkKJTVBMQKZPflewiZsbwAo+yU1bCBJsqmwCoOTVltKhrOjvmdhSw\nJM2g6Ag1tZgeI27HMTWTilthS2ULlik2z8s7lqs1xtAMGr6YapeaJUqOMJmcKZQCKEW1ht8QULmp\nqUEQBcrXLGWnKCQKZGICJibNJpNWErT2DXoYiqmIXJLVVEqqcUVCnKXslFWDCFAqk1LZTEpcxyzB\nsQmZhpJJ+NZ821WZu2ZKXMtQyAWEaI2c6CTMBH7kq+mQrgmBhJgeE/9PiUHIXDbRmGC4OqyQHfLv\nIBTL+jP9KpdsLWRulSpwju8wPDzMoQcfKuBohs4jjzxCLismPpqmoSPQHL9/6Pe8/9j3gw/5bJ5i\nsTjr+Q877DDuuusuTNOcxT+aGVvzD3Jdl0996lNtdh19fX3cdttt7L///ts8ztc6FgqahdiuKJfL\ndHd347pi47l69Wo1/pcxs6N266230tfXx8EHH6wed9RRR3HzzTfPaWo4s9MvbwdBWFtdXM39a+5X\nHZQ39LyBPbr24LRPn8b1379eACi7oLC0wFlnnsWKFSsYyA3whr43KGhT05tWtUpayVnQAF3T6Un1\ntKlTRVHEluoWJhoTlJtlklaSmBkjG8+qEXXFqeAGrhhh6yYpK8Vkc5L/2fI/NPwGQRjQnRYy0xW3\n0vaa0kV+WyF5IMWmWMAs3SJpJskn8vSl+5SUpuyQSQhC3Iwr1RUpX+wH/nRHLJri6nhVMYlwhPrX\nRGNCKAVNGb6lrTQxM0bCmjbJlBOruBlXvJW0PW1WZmiGSgZ1t64klUtOSchpz4NhjpkxxUlK22n8\n0Ge8Ps6W6hbGG+NtsAddFx00L/QgEomlkChQbBYZqgphirSdJmEmlNnlktwSBnID6jpL6eaZn0s/\n9EWyqQ4rRTRTE1Ks+Xgex3FYtvMy4b1jwE233sTA0nYp2jAMOeqooxjeNAwBvONt7+Du2+8W/kpT\n9co73vEOVq5cOYtXJSdmc0H7dE1vuwat4Xkep59+Opdffrm67e/FDXoh/vZjjz324JlnngHgZz/7\nGccee+ys+7Qqop199tmceuqp7LPPPkq+ebfX7cZDDz1ENit4i3LaKwVIZNjGNPQMxGby2ZFnuX/t\n/dS9OmEUMpgf5IAdDuAnP/wJF33lIrEudYPVY/GRj3yE/ffbn+5kN29d9lZiZkwpYskJsoaGEzhq\nzZQywyk7peT+QeSSDeUNrCuuY3NlM7ZhtzWF5OY2l8i1yeEbmsFzo88p7y/LsAQsdoqILhsfEua7\nrXADl/H6FMyLSEHVFmcW053qFgIA8ZyarBua0TZxbuVLAoqTIjfoDb9Bza2JQs+pKlgdTAnvmLE2\nPmfcFFA+6fEjJYrldEJC+WRx0/Sa1DxhJVB36ziBM+c0X76ekqOeUmdzAkdIaU9BqRWvdAq+JvNF\nwkqQMBIU3SIjtRH8wFdG27Jx1ZXsYlF60Xbl8CiK2pAW8rMjp1AH/NMBPPE/T4AJl1x2CUe+ezYZ\n/5Of/CS/uvdXEMFhbz2Me//73ulcEsHy5ct57LHHKBQKsx7bes3mOk9z8TWjKOKKK67gtNNOUwgD\n27a5+uqrOfHEE7d5zK9lLHBoFmK7IpvN8ra3vU0ZZd5+++2z/DFWrlypiplFixax1157ccABB6hi\nZo899uCGG26YtWkElHyy3ODKRab1CziQG2DXrl15dlSodzwz+gyLs4v5/jXfZ+837M1nvvwZQitk\ncmySM888ky99+kvs86/7MFIboSPRQcwQ3TI/EIt/w28Il2PNYKIxIUiNUcCW6hY6k52k7bR6b5lY\nhopToZAoEIQCshRFEc2gqbTqN5Y3UnbL1FzBmdlc2aymQgkroZR3pMu9JAw2/IYqCmSkrJTqYMXM\nGDq6GvmjielR3asTM2KkbTExqvt1AgI6E510JQQB0Qun3ZMlfKziVtoKCS3SKCQK9Gf6lWHlUG2I\nhtugGQhRAlu3FRxgLuiThOmN1kfVba3XLoxC5ZEjSfvydglbS1li4pOyUpiG2dZpM3WTnlQPPake\nlYylWplUofECT8k4l5wScStOX7KP/my/6hSu6FzB0txSNUVSExk0vNDDwFDuz14ooHsjtRE10bF0\ni5SdoifVIzYOCXjnQe/k5ltuBgOefuBp3rznm6k6VdVVfOTRRxjeMgwGZDuzosjqQMjG+rBsyTKu\nvO5KAgIM2jcjhm6Q1JOEZqiuf+s5lR1PWdjIhGRZFt/97nfZc889lRv0yMgIhxxyyN+8G/RC/O3H\nUUcdpQqa22+/fVZBs2bNGqX0pGkaH/rQhzjqqKNUMZPvyHPrrbfOKmZgSonKsFQTQK6zNbcmNtZR\nQE+qh507duaPW/4IwLrSOpYXlvP1z3+dg/Y4iH/90L9S9st4VY8rr7iSoQ1DnPGxM3h29FmWFZaR\nsTMEQYAbCqhUSKiQBa3EeachmhGSW6FpGt3JbkrNErlYjmbQJGfnQBPGi14kuCFbxrYIuFnoYxmW\nkKV3hTS1rukM5AYUVLl1LQ+jEELaipqklSQfzyvEghd41Lwau3TuokRhiESOiplio+4EDsVmkUKi\nQE+yh6SdbCtelOBMNL0eyYJBmkxauuDrlOwS+cQ0jyQMxXoviwf5mjL80KfiVpRlQmvIhqGEe/em\nerGylnqc5M60yilLUQV5fuR0StM0xYGVaAcP8XOxWcQLPWUHYGqCa5pP5EmFKQIrYHFmMUtyS9oK\n1q2F5HO2QtqkLLW8Xse++1ie+P0ToMFDdz3Ev33o36h7dVUkDg0Pcd+v71NKr/muPCQRHkwRJGNJ\nfnTzj0hkEnNyOSX0rhWyLkMqhc6Eo2maxr//+7+z66678t73vpeJiQlc1+Wkk07i6aef5qKLLtou\nb8LXIhYmNAux3XHttdfysY99DBByyw888EDb3w8//HCVhM444wx+/etf8/jjjwPQ2dnJH/7wh1lT\nnZkhN8GSwN0KPQujEM/3uHvV3YzWR9VicMTOR+CHPj+8+4ecc845QhI6Akrwb//+b3zxjC9imkL+\nMGEllGKY7Nyk7JQiprcuNrmYwAZrmsZobZQNpQ1C3cyvYxu2UqepeTXFn5FSmVW3StNrKsjDLl27\nKAKjdLP3Qk916NJWmv5sP93JbizdEmaQgYPri2Kk2CjihNNdHUuzyCfy2IaNEzhqpO4H0wTIhJlo\nU/qS4YdCJKDVOM7UhK9NIV6gGTQVpMvQDWU4J4soOclpBk1c36Xu19Xi6AYuNbdG3avT8BvzQs5i\nRox8PE9vupf+dD99mb5Z0tvKF2Cq2ylhbFJFxw1cgQsvb2JtcS2rJ1dTdspKelt6McT0GAP5AVZ0\nrKAv00fGzggVJN0GbXozpEzoNI0gCCi7ZaVqpCPUdTqTnW1yqwA33ngjH/zgBwGBMf7DH/6gJitN\nv8n7T3w/99x/Dxjw3g+8l5tvvpkoEJ/peDLOD67/AYNLB6d9FFq4TTO7rVEUqS7lXEv3XAICf29u\n0Avxtx9PPvkke++9NwC5XI6RkRFsexpCedZZZ3HhhRcCYlLT29vLD37wAwB0U+euu+7irW99q/h9\nqtM/Mxpeg4YnJIbdwCVmxtQmV0q2P7H5CYaqQ8SNOMlYkiNXHElPqodfPPELTv/s6axbtw5swId/\n/Kd/5MvnfBkrbpG1s3SnupX6o5xkyAJhvD6uJkJS9rgv3UfMjDFeH+eFsReYbE4y0ZhQwjHDlWHG\nm+NiUxlNFSoaaoMdM2KkrBSDHYPkYrm2TaepmQIeFheSxx2JDjqTnSTMBEEklChrXk1wS2dMdDoT\nncrvzA1cgijA1mxVLEVRRMpOkY/n5+Tvub6r8qK0ANA0DUuzhGrZlHqm5B9lbOGP5viOKC48UWBI\nMRiZg8MoVDCyIArmnSzIRlnaSiuBmJmGmkEYMNEQ6AAncFSzyQ1cNXUJo1BBwcbqYwpFYRqmahYZ\nukF3UsAPpamooRvKX6bVXDRmxNT03PEdse9oKT4lH7W16HjhhRfYddddASGcNDY2RiKRUBOxr339\na5x/0flgwj4H7MP69evbzJWvuPIKDjn4ELW3kYbgCTMxJ491rsJGxlw8m9WrV/Pud79bNSNAfD9/\n8pOfkM/n53qa1zQWCpqF2O4YHh5m0aJFRFHETjvtzMMP/4FYLEkUQbVa4dJLL+GOO37K2rWreNe7\n3qWkAw3D4N577+WQQw7Z6vNHUdQm+QjTfBoJMdI0jVKzxM9f/Lna4Pel+8jaWWzTZsOGDXzpc19i\n7WNrwQDicMihh/Cf3/lPshmh/pK2022QhJgZU3CktZNrKTpis19zhUOy67vqdydw1MIlR8xhGCpi\nZbFZpOk32VLdgq7rdMW76M/2M5AfoJAo0JHooD/dTz6Rx9AMRutC719uUlNWSnEjwkhINBedolp0\nTM1UAgaWYRFEgRIBqLpV3LCdiyJN1dJ2WklramhqQlFzp92IwyhkvDFO3BAqZHErTk+qRy2EUuVr\n5gLoBz7D9WGGKkMUm0XqXl39mwkjlAtsykrNucBKh205qZEQNpmc5GdhsjlJsSFgAGuLayk1S8oL\nwAkcpUjUk+4hY2emu01TUtA9yR560j3kYjlFGo4i8b/ruwrD3mq42p/pV9Ow1igWi3R3d+P7PssH\n+3nk/jvIpGwIfeoNh29c+h1+uvI3rNo4xEFvO4gHfv+AmI0bcOm3L+XNb37zrPOg6zoxPUbKT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JdgmZmPBDaRQbnPqxUznt41/mrYe+488+jkZUYoP7LGEU0p8WJlpxK06pWeLp4adVcbEovQhD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T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\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9gvMGzyOUAMcicp76FGSR3L+YHEPNrLHvVt2sI6WlpqkaHTAUH4LlWphoEjqX6ZrgbMJP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\nALYCgFh05Rd/jm0AAAAASUVORK5CYII=\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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DsVRhtoTBmAqzJYxaMIdmHigUWnsS1urxGIzFwMiOLOnxGIylBrMlDMZUmC1h1ILJO8wD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/7oj7p63iuxlNqezW1tC2YBTuDwYLYBHja3aUheLfo9NH10wqc+9Sl85jOfAUAH037ta19reQzb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NmL4GL33NJzOF88j0KrgKgSRTqUhu0Rx+mwHMaRzBFMxCbYQkcV1QDS5SgbZVieRdrLIN2J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\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QSXdaHKNrM91k1xfEqGdIJ/0wymoZ47lxdjwSHGnLnDKv5G2FLI/kYR5eEU8EEU8EiqYw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\nKCAhJXBs4hjO5M4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+NiJmNFHD8YnjSMmpvk7kjutAt3Q0rAZd8Qe25kyC12IQR5wg/0UW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\nrkJggg==\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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sVJgtYTCmw2wJox7MoVkEFKW1O2GtHo/BWA6s4siKHo/BWGkwW8JgTIfZEkY9mLzDImBZ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\nmzbxnvcIp3K/30+1WgUDLrn0Eu6++262bNkCiO/MY489xsJ5CwXlbT9VR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IfJ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JBfOomZtJiZukIgZINtg6KIyThIGmpBSyShavPvDq0IbTbqLttrHaXEVaTqMo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\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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vBIHXsyzii1REaQDPFlC1s0aRf3qcLMpsA6x6s6S8ZrPe2kLPD6gBdOgd6NQ7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BMAxM08TyLB7f+LiozpeSzIzNZFHLInR0OmOdGPh3akC9a+OvWD28iqgviuVZHDTrIHZJ7EJr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\nYkZSSSKjZUaqekm8hKL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0t5LIra6u4o//+I/Z7d/4jd8YJzNjvC7wfR+f//zn2e1cLnfb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agEIfESor4o9if2Y6m2hLpVZ8PdVOJ5K0NO13OxXF/GTHmGDbYX9AJT50poCbxp5E1MTa3b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\nPIZWy2GxhOOAYrGNsbFjmJubxuTkpCeZ6VYA6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78Eza0jqSRxNHEUp9OncTx5vKfS2HZ/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\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jkWS5YBQQV+NIV9LEDqAOZEGe51WTqWaIGMUclopt29h17S589etfBdoAcIAW0HD9rdfj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\n",
"text/plain": [
"\u003cFigure size 1120x480 with 2 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"# negative pair\n",
"plt.close('all')\n",
"for i in range(len(a)):\n",
" _ = plot_graph_matching_pair(nx_graphs[2], nx_graphs[3], a[i][1])"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "0MEhPuw9Y1tP"
},
"outputs": [],
"source": [
""
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"HrOZEU_OrYTR"
],
"last_runtime": {
"build_target": "//learning/deepmind/dm_python:dm_notebook3",
"kind": "private"
},
"name": "graph_matching_networks.ipynb",
"provenance": [
{
"file_id": "1Ys7zuxnbz-5AbNTMmC5Rf4rEOXXvsVEV",
"timestamp": 1563976318651
}
],
"toc_visible": true,
"version": "0.3.2"
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: graph_matching_networks/requirements.txt
================================================
matplotlib==3.1.1
networkx==2.3
dm-sonnet==1.34
numpy==1.16.4
tensorflow==1.14
six==1.12
================================================
FILE: hierarchical_probabilistic_unet/HPU_Net.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "auxiImC5FRng"
},
"source": [
"# Hierarchical Probabilistic U-Net"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9l84MVIwFQ25"
},
"source": [
"Copyright 2019 DeepMind Technologies Limited\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
" https://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ntSZbVLBTzIC"
},
"source": [
"#Colab: [](https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/hierarchical_probabilistic_unet/HPU_Net.ipynb)\n",
"If you haven't already opened this notebook in colab, you can click the button above to open it there."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "BZ37gJ-m8cLJ"
},
"outputs": [],
"source": [
"!pip install tensorflow==1.14.0\n",
"!pip install tensorflow-probability==0.7.0\n",
"!pip install dm-sonnet==1.35\n",
"\n",
"from glob import glob\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.gridspec as gridspec\n",
"import numpy as np\n",
"import os\n",
"import tensorflow as tf\n",
"import sonnet as snt"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fmnE1HsERYzQ"
},
"source": [
"# Clone github repo"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "0QkKLmQTRY8A"
},
"outputs": [],
"source": [
"!git clone https://github.com/deepmind/deepmind-research.git deepmind_research\n",
"%cd deepmind_research/hierarchical_probabilistic_unet\n",
"from model import HierarchicalProbUNet\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1i5Lbz9ClJjr"
},
"source": [
"# Load LIDC test data from Google Cloud Storage"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "SY_lyR2BHRu9"
},
"outputs": [],
"source": [
"!gsutil cp gs://hpunet-data/lidc_crops/test.tar.gz /tmp/\n",
"!tar xvfz /tmp/test.tar.gz -C /tmp/"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "3vudq22sH3Hk"
},
"source": [
"# Load pretrained weights from Google Cloud Storage"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "KbIWpjl4IDCO"
},
"outputs": [],
"source": [
"!gsutil cp gs://hpunet-data/model_checkpoint/checkpoint.tar.gz /tmp/\n",
"!tar xvfz /tmp/checkpoint.tar.gz -C /tmp/"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "fh4tb7_vxrKs"
},
"outputs": [],
"source": [
"#@title Utility to function to batch-up the data.\n",
"\n",
"test_img_dir = '/tmp/test'\n",
"\n",
"def make_batch(patient_ixs):\n",
" \"\"\"Assembles a batch of the first image for each specified patient.\n",
" Args:\n",
" patient_ixs: A list of integers specifying the patients to choose.\n",
" Returns:\n",
" A tuple holding the batched image of shape (b, 128, 128, 1) and the batched\n",
" segmentations of shape (b, 128, 128, 4).\n",
" \"\"\"\n",
" img, seg = [], []\n",
" patient_dirs = glob(os.path.join(test_img_dir, 'images', '*'))\n",
" for i in patient_ixs:\n",
" # get the first image for the patient\n",
" img_path = glob(os.path.join(patient_dirs[i], '*'))[0]\n",
" image = matplotlib.image.imread(img_path)\n",
" image = image[np.newaxis, ..., np.newaxis]\n",
" img.append(image)\n",
" # get the corresponding ground truth labels\n",
" gt_base_path = img_path.replace('images', 'gt')\n",
" labels = []\n",
" for l in range(4):\n",
" gt_path = gt_base_path.replace('.png', '_l{}.png'.format(l))\n",
" label = matplotlib.image.imread(gt_path)\n",
" labels.append(label[np.newaxis, ..., np.newaxis])\n",
" labels = np.concatenate(labels, axis=-1)\n",
" seg.append(labels)\n",
" img_batch = np.concatenate(img, axis=0)\n",
" seg_batch = np.concatenate(seg, axis=0)\n",
"\n",
" # The images and segmentations have a spatial shape of 180 x 180. Crop to\n",
" # spatial shape 128 x 128.\n",
" return img_batch[:, 26:-26, 26:-26], seg_batch[:, 26:-26, 26:-26]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "nYp0LoeRu9Kb"
},
"source": [
"# Run a pre-trained model on the LIDC Test Set.\n",
"First construct a graph for the model. Here we set-up different sampling configurations to examine the sampling behavior when different latent scales are fixed to their means.\n",
"\n",
"---\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ucwHxM8yl1Ut"
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"hpu_net = HierarchicalProbUNet(name='model/HPUNet')\n",
"\n",
"_GRADER_TO_RECONSTRUCT = 3\n",
"_NUM_SAMPLES = 16\n",
"_INSTANCE_INDICES = range(10)\n",
"_NUM_CLASSES = 2\n",
"\n",
"img, seg = make_batch(patient_ixs=_INSTANCE_INDICES)\n",
"img_placeholder = tf.placeholder(shape=img.shape, dtype=tf.float32, name='img')\n",
"seg_placeholder = tf.placeholder(shape=seg.shape, dtype=tf.float32, name='seg')\n",
"seg_one_hot = tf.one_hot(tf.cast(seg[..., _GRADER_TO_RECONSTRUCT], tf.uint8),\n",
" depth=_NUM_CLASSES)\n",
"reconstruction = hpu_net.reconstruct(seg_one_hot, img_placeholder)\n",
"sample_full = hpu_net.sample(img_placeholder)\n",
"sample_local = hpu_net.sample(img_placeholder, mean=[1, 1, 1, 0])\n",
"sample_global = hpu_net.sample(img_placeholder, mean=[0, 1, 1, 1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "U4aZDVeDtcmX"
},
"source": [
"Restore the model from a checkpoint, run a reconstruction and the different sampling configurations."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "MrwcFOS5nw9S"
},
"outputs": [],
"source": [
"saver = tf.train.Saver()\n",
"with tf.train.MonitoredTrainingSession() as sess:\n",
" # restore checkpoint\n",
" saver.restore(sess, '/tmp/checkpoint/tf_graph_data.ckpt')\n",
"\n",
" # sample from the full hierarchy\n",
" rec, s = sess.run([reconstruction, sample_full],\n",
" feed_dict={img_placeholder: img, seg_placeholder: seg})\n",
"\n",
" samples = [s[..., np.newaxis]]\n",
" if _NUM_SAMPLES > 1:\n",
" for _ in range(_NUM_SAMPLES - 1):\n",
" s = sess.run(sample_full, feed_dict={img_placeholder: img})\n",
" samples.append(s[..., np.newaxis])\n",
" samples = np.concatenate(samples, axis=-1)\n",
"\n",
" # sample from global latent only\n",
" samples_global = []\n",
" for _ in range(_NUM_SAMPLES):\n",
" s = sess.run(sample_global, feed_dict={img_placeholder: img})\n",
" samples_global.append(s[..., np.newaxis])\n",
" samples_global = np.concatenate(samples_global, axis=-1)\n",
"\n",
" # sample from most local latents only\n",
" samples_local = []\n",
" for _ in range(_NUM_SAMPLES):\n",
" s = sess.run(sample_local, feed_dict={img_placeholder: img})\n",
" samples_local.append(s[..., np.newaxis])\n",
" samples_local = np.concatenate(samples_local, axis=-1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "_q9Ks_YTu4gR"
},
"source": [
"# Plot Examples.\n",
"Plot batches of images, the corresponding 4 segmentation masks, a posterior reconstruction of one of the masks as well as a number of samples."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "pnwcHuzeunaI"
},
"outputs": [],
"source": [
"#@title Utility functions for plotting.\n",
"\n",
"def to_rgb(arr, cmap={0: (0, 0, 0), 1: (255, 255, 255)}):\n",
" \"\"\"\n",
" Transform an integer-labeled segmentation map using an rgb color-map.\n",
" :param arr: img_arr w/o a color-channel\n",
" :param cmap: dictionary mapping from integer class labels to rgb values\n",
" :return:\n",
" \"\"\"\n",
" new_arr = np.zeros(shape=(arr.shape)+(3, ))\n",
" for c in cmap.keys():\n",
" ixs = np.where(arr == c)\n",
" new_arr[ixs] = [cmap[c][i] / 255. for i in range(3)]\n",
" return new_arr\n",
"\n",
"def make_plot(img, seg, rec, samples, fs=6):\n",
" \"\"\"\n",
" Make a grid plot of a batch of images, set of ground truth segmentations and\n",
" corresponding reconstructions and samples. Each example is displayed in a \n",
" column of the plot.\n",
" :img: Batch of images, array of shape (b, h, w, 1).\n",
" :seg: Batch of segmentations, array of shape (b, h, w, num_graders).\n",
" :samples: Batch of samples, array of shape (b, h, w, num_classes,\n",
" num_samples).\n",
" :fs: Font size, integer.\n",
" \"\"\"\n",
" num_samples = samples.shape[-1]\n",
" num_graders = seg.shape[-1]\n",
" bs = img.shape[0]\n",
" num_rows = 1 + num_graders + 1 + num_samples\n",
"\n",
" f = plt.figure(figsize=(bs * fs, num_rows * fs))\n",
" outer = gridspec.GridSpec(6, 1, wspace=0.1, hspace=0.1,\n",
" height_ratios=[1, num_graders, 1, num_samples, 1, 1])\n",
"\n",
" img_spec = gridspec.GridSpecFromSubplotSpec(\n",
" 1, bs, subplot_spec=outer[0], wspace=0.0, hspace=0.0)\n",
" grader_spec = gridspec.GridSpecFromSubplotSpec(\n",
" num_graders, bs, subplot_spec=outer[1], wspace=0.0, hspace=0.0)\n",
" rec_spec = gridspec.GridSpecFromSubplotSpec(\n",
" 1, bs, subplot_spec=outer[2], wspace=0.0, hspace=0.0)\n",
" sample_spec = gridspec.GridSpecFromSubplotSpec(\n",
" num_samples, bs, subplot_spec=outer[3], wspace=0.0, hspace=0.0)\n",
" grader_std_spec = gridspec.GridSpecFromSubplotSpec(\n",
" 1, bs, subplot_spec=outer[4], wspace=0.0, hspace=0.0)\n",
" sample_std_spec = gridspec.GridSpecFromSubplotSpec(\n",
" 1, bs, subplot_spec=outer[5], wspace=0.0, hspace=0.0)\n",
" for j in range(bs):\n",
" # image\n",
" ax = plt.subplot(img_spec[0, j])\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)\n",
" plt.imshow(np.concatenate([img[j] for _ in range(3)], axis=-1))\n",
" if j == 0:\n",
" ax.annotate('CT scan', (-0.2, 0.5), xycoords='axes fraction',\n",
" va='center', rotation=90)\n",
" # ground-truth\n",
" for i in range(num_graders):\n",
" ax = plt.subplot(grader_spec[i, j])\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)\n",
" plt.imshow(to_rgb(seg[j, ..., i]))\n",
" if j == 0 and i == 1:\n",
" ax.annotate('Graders', (-0.2, 0.0), xycoords='axes fraction',\n",
" va='center', rotation=90)\n",
" # reconstruction\n",
" ax = plt.subplot(rec_spec[0, j])\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)\n",
" plt.imshow(to_rgb(np.argmax(rec[j], axis=-1)))\n",
" if j == 0:\n",
" ax.annotate('Reconstruction', (-0.2, 0.5), xycoords='axes fraction',\n",
" va='center', rotation=90)\n",
" # samples\n",
" if num_samples % 2 != 0:\n",
" xy = (-0.2, 0.5)\n",
" else:\n",
" xy = (-0.2, 1.0)\n",
" for i in range(num_samples):\n",
" ax = plt.subplot(sample_spec[i, j])\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)\n",
" plt.imshow(to_rgb(np.argmax(samples[j, ..., i], axis=-1)))\n",
" if j == 0 and i == num_samples // 2:\n",
" ax.annotate('Samples', xy, xycoords='axes fraction',\n",
" va='center', rotation=90)\n",
"\n",
" # grader standard deviation\n",
" ax = plt.subplot(grader_std_spec[0, j])\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)\n",
" std = np.std(seg[j], axis=-1)\n",
" plt.imshow(std, cmap=plt.get_cmap('jet'))\n",
" if j == 0:\n",
" ax.annotate('Grader', (-0.4, 0.5), xycoords='axes fraction',\n",
" va='center', rotation=90)\n",
" ax.annotate('Std. Dev.', (-0.2, 0.5), xycoords='axes fraction',\n",
" va='center', rotation=90)\n",
"\n",
" # sample standard deviation\n",
" ax = plt.subplot(sample_std_spec[0, j])\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)\n",
" samples_argmax = np.argmax(samples[j], axis=-2)\n",
" std = np.std(samples_argmax, axis=-1)\n",
" plt.imshow(std, cmap=plt.get_cmap('jet'))\n",
" if j == 0:\n",
" ax.annotate('Samples', (-0.4, 0.5), xycoords='axes fraction',\n",
" va='center', rotation=90)\n",
" ax.annotate('Std. Dev.', (-0.2, 0.5), xycoords='axes fraction',\n",
" va='center', rotation=90)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "GK3e3emEdURX"
},
"source": [
"# Plot samples from the full hierarchy.\n",
"When sampling from the full hierarchy of the prior, none of the latent scales is constrained to their respective mean(s)."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"height": 1000
},
"colab_type": "code",
"executionInfo": {
"elapsed": 29434,
"status": "ok",
"timestamp": 1568193363976,
"user": {
"displayName": "Simon Kohl",
"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAV5aCJQ_5RldkCyse6s0nQKEvpBRdNbA80iGeZEw=s64",
"userId": "12229574298416150258"
},
"user_tz": -60
},
"id": "Zjb865P3yJwT",
"outputId": "b51443c1-e6c1-46ec-a7f5-16fb5f16424e"
},
"outputs": [
{
"data": {
"image/png": 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iZrNZnJ2d3Qja3GjRSLL9up880e/+jDtN6pHfr2va+qXVKwqg+Jzqc/CtZdls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fu9X1ldGb+6INcH3HGSLfqJJKMRzXG7vLyMly9fRr/fj93d3RgOhwUbiZ+S7eFweGu+SN+yHrZV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\nV5Mz0BNxm5er5KQGjrztrg8zx9T1wZvKwZuQtisfHh6WBZDNz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or+z7+eVmR+55X/v0jR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6pkyZEh/72MfKDgcq7+Mf/3g89thjnkQDLfLVr341HnvssZg0aVJERJx88snx2GOPxWOPPRZX\nX3218UegJMcff3zZIQBtTvIHgF5p+vTpcdFFF5UdBvQpp556asP3DjroIFecQEm++c1vxoknnlh2\nGEAbk/wB+oz8k6FmzpxZdkh9XkdHR8ycObPwyV3f+c53YtCgQWWH2Od0dnbG8ccfH6tWrSo7FEqw\n4447xsMPPxwPP/xw4fvjx4/vsceNA40NHDgw5syZE3vssUfZoQBtSvIHNsPixYvjvvvuKzsMaHsX\nXHBBzJo1q+wwyPnWt74VTz75ZNlhUIIlS5bE2972tnjb294WF1xwQf31008/vf7zz//8zyVGCH3L\nbbfdFqeffnr9/9NOO63EaIB25mlfsBn+x//4H7FkyZKyw4C219UtJpTr3e9+d/z3f/932WFQos99\n7nP1Wy8XL15ccjT0tFtuuSVuueWWssPok97znvfEHXfcERERCxcujPe85z1x5513Rq1Wi2nTpsXL\nL78cv/rVr+KOO+6I6dOnS8ACm0XyB96gv/qrvyo7BDZTR0dH2SGQmDhxYtkh0IU//vGPsdtuu8U9\n99wTu+yyS9nhFHrmmWfiwAMPLDuMynrttdckffqIBx98MN7//veXHUaf9eSTT8YDDzwQERH77bdf\nRGzc37zzzjv1Y4At4rYvAEpx3nnnlR0Cm/DUU0/F9773vbLDaOjyyy+Pp59+uuwwoK19//vfj/33\n37/sMPq0p556Kvbbb7964gegJ7jyBwBo6Ktf/WocddRRve6qx+233z5effXVssOAtnfnnXfG+vXr\nyw4DgB4m+QMANPTqq6/GxIkT49Zbb62/9r73va+UWFasWBHz58+P4447Ll544YVSYoDeYPXq1Zu8\nBeiBBx6Ifffdt/C9BQsWxN577x0rVqzoifAA6IXc9gVAKS6++OI49thj47vf/W7ZodANBx10UP3n\nrLPOKiWGj370o3HQQQfFH/7wh1KmD+1k+vTpha8vXLgwDjvsMIkfgD5G8geAUtx1111xxRVXxCc+\n8YkYNGhQDBo0KD71qU+VHRbdcN5558WXvvSlHp/OyDJKJwAAIABJREFUW9/61vq6MWjQoLj55pt7\nfJpQdVOnTo2FCxeWHQYALea2LwBKtW7duli3bl1ERFx00UUxevTo2GOPPSIi4l3veleMHDmyzPAo\nUKvV4pFHHombbropIl5/cltWZ83w8ssvx3HHHRe//vWvm1Ym9GU33XRTXHHFFfGjH/2o7FAAKInk\nDwC9yplnnln/+73vfW/cdtttJUZDI9dcc01cc801ERExefLk+PnPfx5jx45tStnTpk1T77CFnn32\n2TjyyCMjIuK6664rORoAyua2LwB6rdtvvz06Ojq6/bN27dqyQ+6TOjs7Y+edd44pU6Zs9lODarVa\nzJw5Mzo6OiR+oAmWL18e1113ncQPABEh+QMANElnZ2dMmzYtfvCDH7zh7371q1+Nc889tweiAgDA\nbV8AQNPceOONceONN8bVV1+90XuXXHJJ7LLLLvX/3//+99f/vuWWW1oSHwBAXyT5AwA0XVEyZ9dd\nd42XXnopIiL+9m//1hOHAABaRPIHAGiZbbbZpuwQAAD6HGP+AAAAAFSY5A8AAABAhUn+AAAAAFSY\n5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAA\nAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+\nAAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABA\nhUn+AAAAAFSY5A8AAABAhUn+AAAAAFTY1mUHsLn23nvvGDt2bNlh9Bk77bRTU8tTf63VzPpTd62n\n/tqXumtv6q996be0N9tee1N/7UvdtbdN1V9HrVartSgWAAAAAFqsba/8eec73xmLFy8uO4w+Y+zY\nsfHAAw80rTz111rNrD9113rqr32pu/am/tqXfkt7s+21N/XXvtRde9tU/bVt8mfx4sXxzDPPlB0G\nm0n9tS91197UX/tSd+1N/bU39de+1F17U3/tS931PgZ8BgAAAKgwyR8AAACACpP8AQAAAKgwyR8A\nAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgw\nyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAA\nAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8\nAQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACtu67AAAgPYyatSoOOGEE97Qd+6555647777eigi\n6LsGDRoUp59++iY/d/vtt8dDDz3UgogA6I0kfwCATRo/fnzcdNNNERExcODAmDBhwhv6/pIlS2Kf\nffaJJ598sifCgz7pW9/6Vuy3336xxx57bPKzJ5xwQrzlLW+JpUuXtiAyAHobyR8AoKFRo0bFDjvs\nEAsWLNiicsaMGROTJk2S/IEm6OjoiM997nPx8Y9/vNvf2XHHHWPSpEmSPwB9lDF/AICGTjrppC1O\n/GR+/OMfx/HHH9+UsqAvGzBgQHzpS196w9+7++674+ijj+6BiADo7SR/AICWufDCC+ORRx6JRx55\npOxQoE+aM2dOfRu85JJLyg4HgBZx2xcA0NBzzz0Xq1evjoEDBzalvEGDBsWUKVMiIuKxxx6Lgw8+\nOCIiVq9e7ZYw6KbujPHTyNChQ+vb4JQpU+Khhx6K73//+80KDYBeypU/AEBDc+bMiUWLFvVI2RMm\nTIgFCxbEggULYv78+bH//vv3yHSgSo488sh4+OGHm1beIYccEv36OSQAqDotPQBQujFjxsS1114b\nu+22W9mhQK921FFHNbW8Qw89NH772982tUwAeh/JHwCgVxgzZkwsWrRIAgi68J//+Z9NL3Py5Mlx\n/fXXN71cAHoPyR8AoEtz585t6fTmzZvX0ulBOznllFM260lfm7LHHnts0VhCAPRukj8AQJfmzJkT\nhx56aMum18ppQTs699xzm17mpEmTYtKkSU0vF6AdjB8/vv4kxBtvvLHscHqEp30BAJv0+OOPt2Q6\nkyZNiieeeKIl06LY9ttvH6NHj97o9SVLlsQLL7xQQkTkrV69Oo499tj43ve+19Ryr7/++ujo6Ghq\nmQDtYODAgRs8CXH27Nlx2mmnxZo1a0qOrHkkfwCAXuPEE0+MT37yk2WH0Scdc8wxscsuu8S+++4b\n73vf+zZ6/9Zbb4358+fHOeecU0J0ANA6M2bMiIioVJ/EbV8AQK+RdbYy6WXY6U9VL8ku0/Tp02PW\nrFmFiZ+IiIMOOihmzZoVjzzySBx55JEtjo68m2++OebPn9/UMk899dSmlgfQzvJ9knYn+QNUVmdn\nZ9RqtYY/s2fPjgEDBpQdJpCTPe1rt912i8cffzymTJmy0c+hhx5qGy7JlClTYrvttis7jD7vxRdf\njH333bepZf7hD39oankA7Wrp0qUxbty4ssNoKskfoM+aMWNGfP3rXy87DCBn/vz5MXPmzHjooYe6\n/NyUKVNim222aVFUAEBfMWfOnHjyySfLDqOpJH+APq1ql3NCFYwZMyZmzZpVOOhw6u67747nnnuu\nRVFB7/Otb32r7BAAKufxxx+PSy65pOwwms6Az0BlTZ48OZ544omGl2wuXbo03vGOd7Q4KmhPq1ev\njueeey5GjRpVdigREbFq1SqJnybbb7/9Nvi/s7MzRo0aVa/zJUuWxPPPPx+//OUv49JLLy0jRHKa\n+WSuiy66KG644YamlQfQTjo7Oyv/tEPJH6DS9tlnn7j22mvjgAMO2Oi9Kl7OCT3liSeeiLlz58bZ\nZ59ddigR8Xo8VTwr15tMnjw5DjzwwDjwwAMjIuK2225r+gDDbJmPf/zjsWzZsjj99NO3qJwvfvGL\n8fnPf75JUQHQG0n+AJW2dOnSOPLIIwuvVli6dGkJEUH7mjt3bnzkIx+p3ACINHbPPffEPffcU3YY\ndOErX/nKFiV/zjzzzPjyl7/cxIgA6I0kf4DKW7p0qUQPNMHSpUtjwYIFvSL5s2jRorJDgF5h2bJl\n8aEPfSjOOuusmDRp0hv67s9//vM477zzeigyAHoTAz4DAN32wQ9+sPTbrebOnRuHHHJIqTFAb3L1\n1VfH5MmT48ILL3xD3/u3f/u3HooIgN5G8gcAeEM+/elPx5QpU0q5+mbOnDlx6qmntny60A5OP/30\n+MY3vtGtz+6zzz7xgx/8oIcjAqC3kPwBAN6Q1atXR2dnZ4wfPz4WLFjQsqduzZs3L2bMmBGrV69u\nyfSg3bz22mtx3nnnxaOPPtrwM6+88kocddRR8dBDD7UwMgDKZswfAGCzpU+EGj16dJxwwglbVN7c\nuXMbJpO+8IUvbFHZ0Bd87GMfiz322KPh+5/85CfjmmuuaWFEAPQGkj8AwBbJngg1cODAmDNnzgbv\nzZs3r1sDRE+ZMiUiXn+Euyt7oGe8+93vjrvuuqvsMAAogeQPANAU2e1gqfHjx2/0WkTEuHHjolar\nxZNPPhn/+I//GE8++WSrwoRK+9rXvhZ77733Rlf/fO1rX5P4AejDJH8AgB41efLkjV476aSTolar\nxdy5c0uICKrtiCOOKDsEAHoZyR8AoOXyt4cBANBzPO0LAAAAoMIkfwAAAAAqTPIHAAAAoMIkfwAA\nAAAqTPIHAAAAoMIkfwAAAAAqTPLn/7F331FS1vf+wD9DaJbYkJKVouwSBFaNYrkqCgZbjoqgyDUa\n9ZpEpay9xCsRgURNxK6AojEaawALlhg7UYTEhhFZlKbIoghYQCPuKs7vDy/7E+kwO8/Os6/XOXvO\nzjPPfL/vw/cMDO95CgAAAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFFP+AAAAAKSY8gcAAAAgxZQ/\nAAAAACmm/AEAAABIMeUPAAAAQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjyBwAAACDF\nlD8AAAAAKab8AQAAAEgx5Q8AAABAiil/AAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAA\nIMWUPwAAAAAppvwBAAAASDHlDwAAAECKKX8AAAAAUqx+0gE2VMuWLZOOUKfk+s/b+uVXLv+8rV3+\nWb/CZe0Km/UrXD63FDbvvcJm/QqXtStsa/szz2Sz2WyesgAAAACQZ077AgAAAEgx5Q8AAABAiil/\nAAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAAIMWUPwAAAAAppvwBAAAASDHlDwAAAECK\nKX8AAAAAUkz5AwAAAJBiyh8AAACAFFP+AAAAAKSY8gcAAAAgxZQ/AAAAACmm/AEAAABIMeUPAAAA\nQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjyBwAAACDFlD8AAAAAKab8AQAAAEgx5Q8A\nAABAiil/AAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAAIMWUPwAAAAAppvwBAAAASDHl\nDwAAAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFKufdIAN1aVLl6ioqEg6Rp3RsmXLmDBhQs7Gs375\nlcv1s3b5Z/0Kl7UrbNavcPncUti89wqb9Stc1q6wrW39Crb8qaioiDlz5iQdgw1k/QqXtSts1q9w\nWbvCZv0Km/UrXNausFm/wmXtah+nfQEAAACkmPIHAAAAIMWUPwAAAAApltg1fz777LN455134j//\n+c8K2/fee++EEgEAAACkTyLlzwMPPBBDhw6NTTfdNBo3bly9PZPJxDPPPJNEJAAAAIBUSqT8ueaa\na+K6666Lrl27JjE9AAAAQJ2RyDV/li1bFl26dEliagAAAIA6JZHy55RTTomRI0fGN998k8T0AAAA\nAHVGIqd93X777bFo0aK49dZbY6uttlrhufHjxycRCQAAACCVEil/hg0blsS0AAAAAHVOIuXPnnvu\nmcS0AAAAAHVOItf8+eqrr+L666+P7t27x0477RTdu3eP66+/PqqqqpKIAwAAAJBaiZ329cYbb8SQ\nIUOiqKgo3n///RgxYkR8/vnncdFFFyURCQAAACCVEil//v73v8e4ceNi6623joiItm3bRseOHePI\nI49U/gAAAADkUCKnfWWz2fXaDgAAAMCGSaT8OfTQQ6Nfv37xwgsvxKxZs+L555+PAQMGxM9+9rMk\n4gAAAACkViKnfZ1//vkxcuTIGDp0aCxYsCCaNWsWhx12WPTv3z+JOAAAAACplUj507BhwzjzzDPj\nzDPPTGJ6AAAAgDojb+XPyy+/HHvssUdEREyaNGm1++299975igQAAACQenkrf4YMGRKPPvpoREQM\nHDhwlftkMpl45pln8hUJAAAAIPXyVv4sL34iIp599tl8TQsAAABQpyVyt69+/fqtcntZWVmekwAA\nAACkWyLlz7/+9a9Vbn/ppZfynAQAAAAg3fJ6t6/rrrsuIiK++uqr6t+Xmzt3bhQVFeUzDgAAAEDq\n5bX8mT9/fkREZLPZ6t+X+9GPfhSnn356PuMAAAAApF5ey5/LL788IiJ23XXX6NOnTz6nBgAAAKiT\nErnmT8OGDeOtt95aYdtbb70VDz30UBJxAAAAAFIrkfLnuuuuix/96EcrbGvRosVK1wECAAAAYOMk\nUv58/vnnsfnmm6+w7Yc//GEsWbIkiTgAAAAAqZVI+VNcXBxPPPHECtueeuqpKC4uTiIOAAAAQGrl\n9YLPy5133nlx6qmnxuOPPx6tWrWK9957LyZNmhSjRo1KIg4AsB6aNm0a/fr1W6/XjB8/Pp5//vka\nSgR1V+PGjeOCCy5Y4z4ffPBB3HLLLXlKBEBtlEj5s/vuu8ejjz4ajz76aHzwwQex8847x8CBA1e6\nDhAAUDuUlJTEgw8+GBERjRo1inbt2q3X6z/88MPYZ599Yvbs2TURD+qkm2++Ofbbb7/o0KHDGvdb\nunRpnHHGGdG9e/dYsGBBntIBUJskUv5ERBQVFcWpp56a1PQAwDpo2rRpTJo0aaNPzW7evHnMmjUr\niouLFUCwkTKZTPzv//7vOn+W3mSTTaK0tDQ6duyo/AGooxIpf84///zIZDKrfO6KK67IcxoAYHUG\nDBiQ02vyTZw4Mfr06eMUMNgIDRs2jEsvvXS9X/fcc8/FJZdcEo899li8+uqrNZAMgNoqkQs+t2nT\nJlq3bl39s8kmm8Tzzz8fW265ZRJxAIA8ad68eYwePTratm2bdBSok4YMGRIPP/xwTJkyJUaOHJl0\nHADyJJEjf8rKylba1rt37xg+fHgCaQCA1Rk8eHAcc8wx0bFjx5yNufwUsBkzZsSRRx4ZERGVlZVO\nB4N1tLZr/KxNUVFRFBUVRWlpaUyaNCn+8pe/5CgZALVVIkf+rEqHDh3ipZdeSjoGAJAn7dq1i/Ly\n8igvL4+JEyfG/vvvn3QkqPX69OkTkydPztl4PXv2jHr1as1/CQCoIYkc+TNp0qQVHn/55Zfx2GOP\nRUlJSRJxAICELT8dzB3BYM2OPfbYnI7Xq1eveOONN6K0tDSn4wJQuyRS/gwcOHCFx5tuumnsuOOO\ncdVVVyURBwBYg1mzZuX0tK/VWX462OpuCgFEzJ07N+djdurUKcaOHRu9e/fO+dgA1A55L3+y2Wzc\nfvvtUVRUFPXrJ3aneQBgHfXo0SOy2WzSMYCIOPPMM+OMM87I+bgdOnSIDh06xLRp03I+NgDJy/sJ\nvplMJnr06OHcYgAoIL169Uo6AvB/OnfunPMxO3bsmJcj/ABqo5KSkpgyZUpMmTIl6Sg1JpEGpkOH\nDvHOO+8kMTUAsAFmzJiRl3lmzZqVl3lYvSZNmlQfBfLdn2222SbpaPyf1157LU4++eScjzt27Nic\njwlQCBo1ahSlpaVRWloa2Ww2brjhhmjYsGHSsXIqkfOu9txzzzjllFOiV69e0aJFixXO7XeuMQDU\nXY8//njSEeqs//mf/4nWrVvHvvvuGwcffPBKzz/xxBMxceLEGDp0aALp+L7XX389Zs2aFcXFxUlH\nAUidsrKyiIg4/fTTE06SO4mUP6+99lpst912K93aPZPJKH8AoA4rKytb6YNW//79o1+/fitsmzFj\nRhx11FH5jJZqxx57bNx4442x2WabrXafQw45JA455JA45phj4ne/+12MHj06jwn5vpoof84555yc\njQVQ6Fb1maSQJVL+3HnnnUlMC9QxU6dOXe31C4488sg46KCD4txzz42qqqo8JwPWZPr06XHkkUdG\nRER5efkq91l+WHbHjh1doDYH7rvvvhgwYEB06dJlrfuWlpY6BayWmDNnTk7He++993I6HkChWrBg\nQey9995Jx8ipRK7507Nnz1Vu9w0ekC/jxo2LsrKyuOqqq5KOAnxPu3btory8fLXFz3Ljx4+PRYsW\n5SkV1D6nnnpq0hEAUmn48OExe/bspGPkVCLlz6q+pchms1FRUZFAGiCtlh85sCbLz+cFCs9zzz0X\nCxcuTDoGAJAiM2bMiJEjRyYdI+fyetrXBRdcEBERX331VfXvy82bNy9KSkryGQdIuZkzZ671eghu\nawvrprKyMhYuXBhNmzZNOkpERHz55ZeKnxzbb7/9Vng8derUaNq0afWaf/jhh/HRRx/FK6+8Ejfd\ndFMSEfmeJ554ImdjXXfddXH//ffnbDyAQjJ16tQVbkSVRnktf1q3br3K3yMidttttzj00EPzGQeo\nA/bZZ58YPXp0dO3adaXnnDIC627mzJkxYsSIuOSSS5KOEhHf5knjt3K1SadOnaJbt27RrVu3iIh4\n8sknY+LEicmGYgV33XXXKu/Mtr5+//vfx8UXX5yDRADUVnktf5afXrHLLrus9O0SQE1YsGBB9OnT\nZ5VHKyxYsMCRAwBrMH78+Bg/fnzSMViNO++8M2bNmhUvvvjiBo8xcODAuPzyy3OYCoDaKJG7fTVo\n0CDmzp0brVq1ioULF8aVV14Z9erVi3POOafWHE4OpMeCBQtiwYIFSceAgjd48ODYbbfd4ogjjkg6\nSsyaNSvpCFArTJw4MY477rj47W9/u96nMv/973+Pyy67rIaSAVCbJHLB5yFDhsQPfvCDiIj4wx/+\nEF9//XVkMhmHmwJALdejR4/ET7caMWLEau8cCnXRvffeG506dYohQ4bEU089tc6vu/vuu2swFQC1\nSSJH/nz44YdRVFQUX3/9dUyYMCGeffbZaNCggVPBAKAAnH322RER0a9fv0Tmv/HGGxOZF2q7wYMH\nx7bbbhvPP/98dOjQYY377rPPPjFp0qQ8JQMgaYkc+bP55pvHokWL4uWXX47i4uLYbLPNIiLi66+/\nTiIOALAeKisro3///pHJZKK8vDxv184qLy+P/v37x7Rp0/IyHxSibDa7xuf/85//xLHHHqv4Aahj\nEjny5xe/+EX07t07vvrqq7jooosiIuK1116Ltm3bJhEHANhA370jVLNmzTb6aKARI0astkwaPHjw\nRo0NdcEvf/nLNR71c/rpp8df//rXPCYCoDZIpPw59dRT46CDDoof/OAH1bd8b968efz+979PIg4A\nsBGW3xGqUaNGMXz48BWeGzduXBQXF691jNLS0oj49hbulZWVNZIT6rru3bvHs88+m3QMABKQSPkT\nEbHDDjtERMQ333wTERFt2rRJKgoAkAOVlZUxderUFbaVlJSstG25tm3bxuzZs+OII46I2bNn5yMi\npN6wYcNir732Wunon2HDhil+AOqwRMqfqVOnxtChQ+Ptt9+u/nYvm81GJpNxHj8ApEynTp1Wub1/\n//4xYsSIPKeB9Ovdu3fSEQCoZRIpfy688MI44IAD4rLLLovGjRsnEQEASJjiBwAgPxIpf+bNmxdn\nn312ZDKZJKYHAAAAqDMSudX7QQcdFBMmTEhiagAAAIA6JZEjfyorK6OsrCw6d+4c22677QrPXXHF\nFUlEAgAAAEilRMqfkpKSKCkpSWJqAAAAgDolkfKnrKwsiWkBAAAA6pxEyp+IiH/+858xbty4WLBg\nQTRr1ix69OgRe++9d1JxAAAAAFIpkQs+jxkzJs4+++xo2rRpHHTQQdGsWbM477zzYvTo0UnEAQAA\nAEitRI78ufXWW+PPf/5z7LjjjtXbfvazn8UZZ5wRffr0SSISAAAAQColcuTPp59+GsXFxStsa9u2\nbSxevDiJOAAAAACplUj5s9tuu8Uf/vCHWLp0aUREfPHFF3HFFVfErrvumkQcAAAAgNRK5LSvIUOG\nxDnnnBO77757bLnllrF48eLYdddd46qrrkoiDgAAAEBqJVL+NGvWLO66666YP39+9d2+WrRokUQU\nAAAAgFRLpPyZMGFCbLfddrHDDjtUlz6zZ8+ODz74IPbdd98kIgEAAACkUiLX/Bk6dGhsttlmK2zb\nbLPNYujQoUnEAQAAAEitRMqfjz76KJo1a7bCtmbNmsXChQuTiAMAAACQWomUP61atYpJkyatsO1f\n//pXtGzZMok4AAAAAKmVyDV/ysrK4vTTT4/evXtHq1atYu7cufHAAw/EZZddlkQcAAAAgNRK5Mif\nAw88MG677bb44osv4h//+Ed88cUXceutt8aBBx6YRBwAAACA1ErkyJ+IiJ133jl23nnnpKYHAAAA\nqBMSOfKnqqoqrrnmmujevXt07tw5Ir69/ftdd92VRBwAAACA1Eqk/Lnsssti+vTpceWVV0Ymk4mI\niHbt2sW9996bRBwAAACA1ErktK+nn346nnzyydh0002jXr1v+6fmzZvHhx9+mEQcAAAAgNRK5Mif\nBg0axLJly1bY9vHHH8dWW22VRBwAAACA1Eqk/Dn00EPjN7/5TcydOzciIhYsWBBDhw6Nww47LIk4\nAAAAAKmVSPlz9tlnx3bbbRc9evSIJUuWxCGHHBLNmjWLAQMGJBEHAAAAILUSKX8aNmwYAwcOjMmT\nJ8fEiRPjtddei6OOOirOO++8JOIAAAAApFZeL/i8dOnSuPnmm+Ott96KNm3axOmnnx7/+c9/YtCg\nQfHiiy9Gz5498xkHAAAAIPXyWv4MHTo0ysvLo0uXLvH888/H9OnTY/bs2dGzZ88YOnRobLPNNvmM\nAwAAAJB6eS1/XnjhhRg3blw0adIkTjjhhOjWrVvceeedsccee+QzBgAAAECdkddr/nzxxRfRpEmT\niIho0aJFbLrppoofAAAAgBqU1yN/li1bFv/85z8jm81Wb/v+47333jufkQAAAABSLa/lT5MmTeKi\niy6qfrzVVlut8DiTycQzzzyTz0gAAAAAqZbX8ufZZ5/N2VgtW7bM2VisXa7/vK1ffuXyz9va5Z/1\nK1zWrrBZv8Llc0th894rbNavcFm7wra2P/NM9rvnXAEAAACQKnm94DMAAAAA+aX8AQAAAEgx5Q8A\nAABAiil/AAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAAIMWUPwAAAAAppvwBAAAASDHl\nDwAAAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFFP+AAAAAKSY8gcAAAAgxZQ/AAAAACmm/AEAAABI\nMeUPAAAAQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjyBwAAACDFlD8AAAAAKab8AQAA\nAEgx5Q8AAABAiil/AAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAAIMWUPwAAAAAppvwB\nAAAASDHlDwAAAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFKufdIAN1aVLl6ioqEg6Rp3RsmXLmDBh\nQs7Gs375lcv1s3b5Z/0Kl7UrbNavcPncUti89wqb9Stc1q6wrW39Crb8qaioiDlz5iQdgw1k/QqX\ntSts1q9wWbvCZv0Km/UrXNausFm/wmXtah9K9yA7AAAgAElEQVSnfQEAAACkmPIHAAAAIMWUPwAA\nAAAppvwBAAAASDHlDwAAAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFFP+AAAAAKSY8gcAAAAgxZQ/\nAAAAACmm/AEAAABIMeUPAAAAQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjyBwAAACDF\nlD8AAAAAKab8AQAAAEgx5Q8AAABAiil/AAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAA\nIMWUPwAAAAApVj/pAABAYbjwwgvjN7/5zTrtu/XWW9dwGgAA1pXyBwBYpc033zxuuummeP7556Ne\nvXpx+eWXr/Nrs9lsREScdtppUV5eHhMmTKipmAAArIXyBwBYpbvvvjt69OgRxx9//AaPcfPNN8cn\nn3wSjzzySEREVFRUxMCBA3MVEQCAdaD8AQBWqUePHjkZZ+utt44TTzwxIr49Iuj888+PXXbZJaZN\nm5aT8YGI5557Lvbdd99VPtewYcM8pwGgtnHBZwAgbzKZTDRo0CDKy8tjzpw50aFDh6QjQcGpV69e\nHH300XH00UfHSSedFNlsNrp16xYNGjRY5U82m41sNhunnHJK0tEBSIgjfwCARLRu3TrGjh0bhxxy\nSFRUVCQdBwrG2LFjo1evXuv9ulGjRkWrVq1i0KBBNZAKgNrMkT8AwErq18/P90MdO3aMuXPnxpQp\nU/IyHxS6Bx54YIOKn+UuvvjiKCsry2EiAAqB8gcAWMEuu+wS7777btIxgO9p06ZNtG7deqPHueGG\nG2KrrbbKQSIACoXyBwBYQa9evWK77bbL65xt2rSJ4447Lq9zQqHZc889o3PnzjkZa+zYsTkZB4DC\noPwBAKp16tQpLrnkkrzP+8Mf/jBKSkryPi/UVd27d4+nn37ancAA6gjlDwBQK+yxxx6x9dZbR9++\nfaNv376x9dZbJx0JapVZs2bF7NmzczZe9+7dY9SoUVGvnv8SAKSdv+kBgGrvvPNO3H777YnMffjh\nh8eTTz4ZI0eOjJEjR8aTTz6ZSA6orV577bU44ogjcjrmSSedFOPHj8/pmADUPsofoM6rX79+FBUV\n5e3uRlCbffHFF3HyySdHJpOJDz/8MO/z77777iv83qxZs7xngNqsvLw8GjduHJ988knOxtxvv/0i\nm81GNpvN2ZgA1C7KH6DOu/zyy2PevHnRrl27pKNArdK9e/ekI8S///3v2HvvvZOOAbVKZWVlHHzw\nwe7KB8A6U/4AdVJxcXG88sor8corr8R5552XdByolWbNmhWjRo1KNEOLFi1i7NixscMOOySaA2qb\nV155JQ466KCkYwBQIJQ/QJ204447RufOnVe4Ze7222+fXCCohb788sv44IMPko4RRUVFseOOOyYd\nA2qdmTNnxjbbbJOTsd5///2cjANA7aT8AVKva9euMWjQoOrH/fr1i0cffXSl/f72t7/lMxYUhPHj\nx8fChQuTjhGHH3540hEglR566KEYMmRIHHjggTFkyJCk4wBQQ1zdFEi9Aw44IC655JL4+c9/HhHh\n9BFYD8vLn6ZNmyaa48Ybb0x0fkijF154IU466aRYsmRJREQMHjw42UAA1BjlD5BqvXv3jksuuSQi\nYq2njXTs2DEfkaDgTJs2LdH3xwcffBBffvllYvNDbbZs2bKYO3dutGrVKqqqqmLq1KkREdGoUaO1\nvm/333//fEQEoBZQ/gCpNmbMmHXa78knn4wFCxbUcBooTL179070FtA33XRTvPPOO4nND7XZkiVL\n4sgjj4wDDzwwPvvss7jpppsiImKbbbaJX/3qV3H++eev8si95V+MAFA3KH8AIuLFF1+Mjz76KOkY\nUGsdeOCB8fTTT+d93rfffjuuvfbavM8LhWTy5MkxefLkFbZ9/PHHMWzYsLjzzjtXeeH266+/Pl/x\nAKgFXPAZSLVMJuMClpADs2fPjvfeey/v83711VfV1yMB1t/8+fOTjgBALaD8AQDW6p133onevXvX\nilu/AwCwfpQ/QJ03bdq0GDZsWNIxoNZ7+eWXo0OHDknHANbTT3/606QjAJAw5Q+QerNmzYrPP/98\ntc+/8MILsXTp0jwmgsK1ePHiKC0trdE5Pv/88/jFL34RmUwmdtpppxqdC+qCxYsXJx0BgIQpf4DU\nu/POO9d4rZLTTjstj2mg8M2bNy9OPvnkOPnkk2tk/HfffTfuvvvuGhkbAKAuUv4AdcKuu+4anTt3\nXuV2YP18+umncfvtt8ftt98ejRs3jsaNG8e0adOSjgUAwGoof4A6oaqqKl577bXIZDJx/fXXx3XX\nXReZTCZef/31pKNBQausrIzKysro2LFjPPPMMzkZ06lekFuvvvrqStvuvPPOBJIAkBTlD1DnnHnm\nmXHWWWclHQNS56ijjoo+ffpERUXFer3unHPOWeXvwMa79tprV7n966+/znMSAJKk/AEAcmLJkiUx\nZsyYaNWqVSxbtmyt+y9btiwGDRoU11xzTZSWlsayZcvi73//ex6SQt1x1llnrfL92LNnzwTSAJCU\n+kkHAADSZ/vtt4/LL798jfuccMIJ1b9PnTo16tf3sQRqQklJSTz22GPRsWPHpKMAkBCfsgCAnKuo\nqFih3AGS8+6778bf/vY35Q9AHea0LwAASLnzzz8/5s2bl3QMABKi/AEAgDrgrrvuSjoCAAlR/gAA\nQB1w4YUXJh0BgIQofwAAoI746KOPko4AQAKUPwAAUEf06tUr6QgAJED5AwAAdcTrr78e999/f9Ix\nAMgz5Q8AANQRn332WUydOjWWLVuWdBQA8qh+0gEAAID8ueSSS6KysjLpGADkkSN/AACgjrnsssuS\njgBAHil/AAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAAIMWUPwAAAAAppvwBAAAASDHl\nDwAAAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFFP+AAAAAKSY8gcAAAAgxZQ/AAAAACmm/AEAAABI\nMeUPAAAAQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjyBwAAACDFlD8AAAAAKVY/6QAb\naq+99oqWLVsmHaPOKCoqyul41i+/crl+1i7/rF/hsnaFzfoVLp9bCpv3XmGzfoXL2hW2ta1fJpvN\nZvOUBQAAAIA8K9gjf7p06RIVFRVJx6gzWrZsGRMmTMjZeNYvv3K5ftYu/6xf4bJ2hc36FS6fWwqb\n915hs36Fy9oVtrWtX8GWPxUVFTFnzpykY7CBrF/hsnaFzfoVLmtX2KxfYbN+hcvaFTbrV7isXe3j\ngs8AAAAAKab8AQAAAEgx5Q8AAABAiil/AAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAA\nIMWUPwAAAAAppvwBAAAASDHlDwAAAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFFP+AAAAAKSY8gcA\nAAAgxZQ/AAAAACmm/AEAAABIMeUPAAAAQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjy\nBwAAACDFlD8AAAAAKab8AQAAAEgx5Q8AAABAiil/AAAAAFJM+QMA5ESXLl2SjgAAwCrUTzoAAFD4\nLrvssujbt2888sgjERFRUVERAwcOTDgVAAARyh8AYCNdfPHFceGFF0Ymk4kTTzwxIiKy2Wycf/75\nscsuu8S0adMSTgh1Q1VV1UrbOnXqFDNmzEggDQC1idO+AID11rFjx3jvvfcim83G0KFDI5PJrPB8\nJpOJBg0aRHl5ecyZMyc6dOiQUFJIry5dusRJJ50U2Ww2stlsNGjQYKWf6dOnx8yZM6Ndu3ZJxwUg\nQcofAGC99enTJ1q1arVO+7Zu3TrGjh0bLVu2rOFUUDc0aNAgxo4dG2PHjo3bb799rfsXFxfH2LFj\nY5tttqn5cADUSsofAGC9DR48ODKZTFRVVcWyZcvWun/Hjh1j7ty51UcjlJeXx5tvvhn16vkoAuur\nqqoqjj766GjevPk6v2bnnXeOjz76KOrXd9UHgLrIJy4AYIM1atQoevfuHZ9++uk67V9VVRVVVVXR\noUOH6NSpU4wdO7b6OkHA2nXr1m2jXv/mm2/Gj3/849yEAaBgKH8AgI3y0EMPxeGHH75Br+3Vq1fc\ncccdMXLkyByngnRavHjxRr2+ffv28cADD8Tmm2+eo0QAFALlDwCpstVWW0WDBg2SjlHnvPjii9Gh\nQ4dYunTpBr2+b9++kc1m47/+679ynAzSZfLkyRs9RqdOneKzzz6LrbbaKho2bJiDVADUdsofAFLh\ngAMOiL59+8Ynn3wSPXr0SDpOnfTWW2/FMcccEx9//PEGjzFp0qTo06dPDlMBq/PJJ5/Esccem3QM\nKHh9+/Zd4adz585JR4KVuOIbAAVvzz33jHvvvTeaN28e//jHP+If//hH0pHqrMceeywOPvjgeOWV\nVzZ4jNtuuy022WSTuOOOO3KYDFiViy66KO655574+uuvk44CBemGG26IsrKyFba9++67sd9++0VF\nRUVCqWBljvwBoCAVFRVFUVFRfPLJJ/Gvf/2r+q43ixYtikWLFiWcrm579dVXN+r1m222Wdx+++0b\nfWFbSKNbbrklp+O1b98+vvrqK7eBh/WUyWTioosuWqn4iYjYfvvtY/vtt89/KFgD5U+e7bPPPnHu\nuefGL37xi6SjABSkrl27xrnnnhvz5s2LefPmxVZbbZV0JGqIu4Al54QTTkg6Aqtxyimn1Mi4zz33\nXJx77rk1MjakxQ9+8IM499xz49xzz43f/va3cemll6523xdeeCHOPffc2H333fOYkA3VpEmT6rU9\n6aSTko5TI5z2lSd33313tG/fPn70ox9FUVFR/Oc//4mIiLvuuivhZACF45VXXontttsuWrRokXQU\n1mD06NE5GWfYsGE5GYd117x583jssceiQ4cOceaZZ8bNN9+c8yNNqJ123nnnuPLKK+OYY45x4XVY\njfr168eVV165zvtfeeWVUVFRER9++KESqJZr0aJF9douXbo0Tj/99DjmmGPinXfeSThZ7jjyp4bV\nr18//vjHP8Zxxx0XnTt3jqKiooj49pD2q6++Opo1a5ZwQtZV+/btI5vNRjabjdatWycdh3UwderU\n6jX7/g+139Zbbx3t2rWLdu3axQ033BDZbDY6d+6s+KnFlq/VMcccs9FjXX/99TFr1qxo2LBhtGvX\nLmbMmBHt2rVzakoNatmyZcyfPz86d+4cm266aXTu3DlGjRoV2Ww2pkyZUv1+bNeuXWy55ZZJx63T\nGjVqFPPnz6+RsRU/sHrZbHa933stW7aMzp07x0svveTvzgKxySabROfOnWP27Nnx9ttvp+azh/Kn\nhrVo0SIuuOCClba///770bNnz1iwYEECqdgQH330UfVFZCdNmhR77713wonYGH379o2IiH79+sWg\nQYNi0KBBse222yaciuW22GKLuO+++2L69Okxffr0VZ5PT+3RtWvXGDRoUE7X6owzzoirrroqrr76\n6pg+fXqUlJTE9OnTY+zYsd6rNWD33XePCRMmrPb50tLS6vfj9OnT4+c//3ke0/F9VVVV8b//+79J\nx4A6Z2Pee82aNYt27drlOBE17cc//nFqPoc67Ssh++yzT8yZMyfpGKyHRYsWxfjx46Nr165RVFQU\nDz30UOy0004KvAJ17bXXxplnnhk77LBDNGrUKCIixowZ40LBtcBdd90Ve+21V5SUlCQdhdUoKSmJ\nRx55pPrxtttuWyOFzKo+bL355puxZMmSnM9V182cOTMWLVoUbdq0SToK6+jBBx+MX//617Hvvvvm\ndNw33ngjdt5555yOCUTsv//+8d577yUdg9WYMWNGDB8+PAYMGLDK7WngyJ8aVlFREZlMJu69996Y\nPHlynHbaaZHJZBQ/BWrw4MGRyWQi4tv2/plnnkk4Eatz2mmnRdu2bVf7fKNGjWLHHXesLn5Izjbb\nbBM/+clPqk/JO/744xU/tVDjxo3jpptuimw2GzNmzIgdd9yx+idfR+KMGTMmzjjjjKiqqsrLfHXJ\np59+Gv/+97/Xef+RI0e62HrCFi9eXH0NyVxS/MCaffzxx+v1JcS8efOiWbNmip9arqqqKsrKyqK8\nvHyF7a+//np89NFHCaXKLeVPnhx33HGx2267xahRo5KOQo48+uijTv2qxW6++eaYPXt20jFYiz/+\n8Y9x1113xeTJk3M2Zmlpaey00045G49vFRcXx2mnnZbY/FdddVX06dMnsfnrgl/96lfrtN/s2bPj\nggsuiKVLl9ZwIvLtzjvvTDoC1HoPP/xw/PrXv17n/a+55ppYuHBhDSaiJqXps4fTvmADtGrVKhYv\nXhyff/550lGgYI0aNapGblncvn37ePbZZ6Np06Y5H5vk/OlPf0o6Av+noqLCndhSaty4cUlHgIIw\nZsyY2GmnnWLKlClr3O+NN97w5X+B6d69e3zwwQfVv6eJI39gA1RUVMRnn32WdAzWolOnTuu875w5\nc3yLnUcXX3xxjRQ/y2277baxzz771Nj4ddGbb76Z2Nzen/nTvHnzVd7WdsKECVFRUREvvfRSvPHG\nGwkko6ZMnz49JkyYEJlMJu6///6k40DBmDNnTsyaNWuN+7Rt2zYOO+ywPCUiF+bPnx+ZTCYymUw8\n++yzScfJKeUPkGqXXnrpWvcZMGBA9O7dO959992aD0Rcf/31MXTo0BqfZ/z48TU+B/nx4Ycfxpdf\nfpl0jDphwYIFcfTRR8eAAQOq36dDhgyJ/fbbL/77v/87fvrTn8bpp5+ecEpypby8PHr06BH77bdf\n0lGg4Hz22Wdx9dVXr3GfX/7yl3HfffflKRGsmdO+gFS75JJL4uqrr46JEydG+/btV7nPiBEj8pyq\nbuvfv3/SESgwe+65Z2y99dYxf/78pKPUCZMnT47JkydHvXr14rrrrotPP/00IiImTpyYcDJyqbi4\nOBYuXOhIZsiBJk2aRETEc889V33R9D333DNefvnlJGPBCpQ/QKotW7YsPv7449hxxx2jTZs2zruu\nBZ566qk49NBDa3SO3r17O30hx7bffvsNev/ss88+sfnmm6/Xa5588sn4y1/+El27do02bdrEX/7y\nl5g2bdp6z83G+eabb+Ljjz9OOgarUK9evfjjH/8YBx988Arbn3/++dh///3X+NpPPvkkTjjhBDdF\ngBwYMWLECl8i7rLLLvHEE09ERMT777+fVCxYJeUPUGfMmTMnDjnkkKRj1Hk/+9nPIpvNJh2D9bSh\n75+ePXuucEvwP//5z2vcf+DAgXHZZZdFRMTdd9+93vNBXdCgQYM477zzqh8/88wzcdddd8Xo0aPj\nz3/+8xrvTnP88cfH448/no+YUCf5rEltpfwBIO+OP/74Gv2P/T333BONGjWqsfFZdw899NAKj++9\n996I+PbUog4dOqy0/4MPPpiXXFDIKisr49RTT41Ro0bFxx9/HIceemh8/fXXERFx3HHHxYknnrjG\n1wJQ9yh/AMi7e+65J+6555544IEHVvl8r169NmjccePGxTfffLMx0ahhy//j2bFjx3j66aerb6P6\n4IMPxn333ef0LlhHt9xyS9xyyy0rbV+2bFksW7YsgUQA1GbKHwASc9RRR61ye9++fWPkyJHrPM7r\nr78el112Wdx///3KnwJy1FFHVR8eP2bMmITTAACkl/IHgFrnpptuiptuumml7bvttlu89NJLEfHt\nhU1/+tOf5jsaObRkyRKlDwBAHih/ACgYr732WtSv758uAABYH/WSDgAAAABAzVH+AAAAAKSY8gcA\nAAAgxZQ/AAAAACmm/AEAAABIMeUPAAAAQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjy\nBwAAACDFlD8AAAAAKab8AQAAAEgx5Q8AAABAiil/AAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACk\nmPIHAAAAIMWUPwAAAAAppvwBAAAASDHlDwAAAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFFP+AAAA\nAKSY8gcAAAAgxZQ/AAAAACmm/AEAAABIMeUPAAAAQIopfwAAAABSTPkDAAAAkGL1kw6wofbaa69o\n2bJl0jHqjKKiopyOZ/3yK5frZ+3yz/oVLmtX2Kxf4fK5pbB57xU261e4rF1hW9v6ZbLZbDZPWQAA\nAADIs4I98qdLly5RUVGRdIw6o2XLljFhwoScjWf98iuX62ft8s/6FS5rV9isX+HyuaWwee8VNutX\nuKxdYVvb+hVs+VNRURFz5sxJOgYbyPoVLmtX2Kxf4bJ2hc36FTbrV7isXWGzfoXL2tU+LvgMAAAA\nkGLKHwAAAIAUU/4AAAAApJjyBwAAACDFlD8AAAAAKab8AQAAAEgx5Q8AAABAiil/AAAAAFJM+QMA\nAACQYsofAAAAgBRT/gAAAACkmPIHAAAAIMWUPwAAAAAppvwBAAAASDHlDwAAAECKKX8AAAAAUkz5\nAwAAAJBiyh8AAACAFFP+AAAAAKSY8gcAAAAgxZQ/AAAAACmm/AEAAABIMeUPAAAAQIopfwAAAABS\nTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjyBwAAACDFlD8AwEZ76KGHko4AAMBq1E86AABQOzVq1Cja\ntm1b/XjhwoWxaNGi6sdt27aNWbNmVT/OZrOrHevGG2+MESNGxLRp02omLAAAq6X8AQBWqaSkJN58\n883qx88991yMHz+++vGAAQPWeayysrIoKyuLfv36xU033ZTLmMD/2WKLLeKss86KUaNGxfz585OO\nA0AtovwBAFbpwQcfXOHxAQccEAcccMBGjXnNNddERCiAIMfuuOOO2GuvvaJ9+/ZxwgknxJdffhkR\nEYcffnjMmTMn4XQAJM01fwCAFQwePDiy2Wy0a9cu52M3btw4Ro4cGYcffnjOx4a6qF69ejF06NA4\n8cQTo3379hHx7VF7paWlUVpaGu+++25ks9m44oorokOHDgmnBSApyh8AoFrTpk2ja9euNT7PI488\nEn379q3xeSDtLr300rj44ovXut/5558f5eXlMWjQoOjcuXMekgFQmyh/AEiVKVOmxEEHHZR0jILV\nrFmz6NatW17muuaaaxRAsIF69uwZU6ZMiQsvvHC9XjdkyJB4+OGHY4sttqihZADURsofAAreZptt\nFg8++GBks9l4++2346mnnko6EuugcePG0alTp2jYsGHSUaCgNGrUKB588MEoLS3doNcXFRXF4sWL\n4/HHH89xMgBqK+UPAAWvS5cu0bNnz6RjsAHKysriqquuSjoG1EnFxcWx8847Jx0DgDxQ/gBQ8L57\n56izzz47wSSFb+bMmVFaWhqzZs3K25xlZWXxwAMP5G0+KHSvvvpqTsZp165dPPbYY1G/vhsAA6Sd\n8geAgtWiRYv49NNPY/vtt6/eNnfu3OQCpUBlZWVMnTo1SkpK8jpvr1694oYbbnAKGKyDTp065Wys\nli1bxldffZWz8QConZQ/ABSk1q1bx8MPPxxbbrll0lFSa+TIkXmdr6ysLIqLi/M6JyvaYost4sAD\nD0w6BgCQY8ofAArSc889F3vssccK23bdddeE0qTT8OHD8z7nuHHj8j4n37rjjjvipZdecre8AuBW\n7QA1p6SkJJV3I1X+AFAQiouLo0OHDnHbbbdFNpuNtm3brrTP1KlTE0iWXlOnTo0hQ4bkdc527drl\ndT4i6tWrF0OHDo399tsv2rdvHxdccEEcddRR0aFDh+ofapc333wz3n///ZyO+cQTT8QPf/jDnI4J\nUCgaNWoUw4cPj2w2GzNmzIiRI0dGNpuN6dOnx7bbbpt0vJxQ/uRRkyZNYtCgQSv8+EcWYM2W/935\nz3/+M8rLy+Pkk09OOhKkym9/+9v45ptvolu3bnHNNddERMT9998f5eXl1T+nnHJKwin5rqqqqhg4\ncGBOxzz44IPj4IMPzumYUBc1adIk9t9//4gIf3cWkJKSkujfv/9K29u1axejR49ORQHk0v55cPLJ\nJ8ekSZPi4YcfXukbzVtvvTU+++yzhJJB3VJSUhIPPvhg9ePhw4evcJcoap/Ro0fHT37yE0eD1DEP\nPPBAHHXUUUnHqDNGjx4db731VkREXH311au8Y951110XZ5xxRnTv3j0WLFiQ74jkydVXXx33339/\n0jGgYC3/3LLFFlvEwoULo7i4OKqqquKOO+5IOhob4YADDoimTZvGokWLko6yUZQ/Naxly5Zx2223\nrfK5YcOG+QAFNaxRo0Zx9dVXr7LJHzlyZIwcOTJmzJgR++yzT8H/hZ4W22+/fbRt2zaeeeaZpKMQ\nEQsXLozKyspo1KhR3uZU9uXX8uJn++23j3feeWeV+2yyySZRWlrqluC1yKeffhqff/55bL755jkb\ns3Xr1jkbC+qaJk2aRGlpafW/Yc2bN4+pU6fGI488knAycuGRRx7J+51Qc81pXwk6//zz4w9/+EPS\nMSDVVncI53el6XDOQrf77rvH+PHjFT+1yPDhw+Pcc8/N+52/yL8777xzrfucc845eUjCunjooYdi\n4sSJSccA/s8vf/nLla6R1qlTJ6erp8QRRxyRdISNpvxJ2N/+9rekIwDx/w/nJDlTpkyJcePGRZs2\nbZKOwvcMHz48r3f+6tWrV97m4lvHHnvsOt0tz+eW2uXUU09NOgIABUL5U8MqKioik8msdOHEMWPG\nRCaTiWeffTbpiJBqb7755jrvW1xcXINJWJ0tt9wyJkyYEKWlpVFUVLRRY1VVVeUoFd9XWVkZCxcu\nrPF5OnbsGDNnzqzxefj/6tWrFx07dozNNttshe3f/dyydOnSOPnkk31uqWXmzJkTW265ZXzwwQdJ\nR4E677bbbotp06attL158+ZOmS1wCxYsiMrKyqRjbDTlT5707t07OnXqVP3Tp0+fpCMB3+Oc7GT0\n6tUr9t1336RjsBYzZ86MESNGJB2DGrUQzvoAACAASURBVNCoUaP45ptvYsiQIdV3+7r55ptX+Nxy\n1llnxe23355sUFZpyZIlceSRR8aQIUM2eqwxY8bkIBHUTas67Svi20t9NGvWLIFE5MKCBQuiT58+\nMXv27KSjbDQVJJBaDz30UNIRWIuGDRvG7373u6RjQJ22dOnSGDx4cERE1K9fP/70pz/FrFmzVthn\n1KhRCSRjXb388svx8ssvxwsvvBBPP/30Bo/z17/+NYepgNLS0ogIN/kpADNnzqxer++qrKxMzRHJ\nyh8gtdb1jkFffvllnHPOOS5om4BMJhMtW7bM6Zhjx46N3r1753RMvjV48OA45phjomPHjklHoYZ8\n/fXXMXXq1KRjsIHefvvt9X7NvHnzYvHixfHUU0+5zTtshGHDhsVee+21wtE//j4tHJWVlalfL+UP\nUCd99/D4BQsWKH5gHY0YMSI6deoU/fr1SzoK8D1LliyJp556Kg466KB12n/mzJnRu3fv+Pe//13D\nyaBu8OUTtZnyB0i94cOHr1TupL3Zr8sOPPDAmDJlSuy0005JR0ml4cOHR6NGjSIicl4ADR8+PBXn\n1ENSNqT8UfwA1A3KHyC1OnXqlHQE1iKbzcb777+/0Xf5+q4tt9wylixZkrPxWFllZWX0798/+vfv\nv9p9SkpKYty4cVFcXBzZbLb61MrvF69NmzaNpk2bRnl5eUydOjUVd9OAJH331JPl76+lS5fGO++8\ns9K+7733XgIJAUiC8geAxFRVVcVhhx0WY8eOjeLi4pyNe/bZZ+dsLDbMzJkzo1OnTjFgwIDIZrPV\nR999v5Tt1q1bdOvWrfqCw8DGW37qyfL31/vvv++i3QB1nPIHgES9/vrr0b1799huu+3ixRdfTDoO\nOTZ8+PA1Pj9+/PgYP358fsJAHeP9BcByyh8AEjdnzpyYM2dOZDKZiFjxmkwNGzaMkpKS9Rov13cQ\nAwCAQqb8AaDW+e6pQdtuu22UlZVFRMQll1yyTq+/9tpr47rrrquRbAAAUGiUPwDUaosWLaq+HsyY\nMWNWev7FF1+MLbfcsvrxU0895Zo/AADwHcofAArG9+8UFRGx1VZbJZAEAAAKR72kAwAAAABQc5Q/\nAAAAACmm/AEAAABIMeUPAAAAQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjyBwAAACDF\nlD/w/9i79yir63p//K8pBOxGqTCCqAmMOcxkKZqJClYnL4UgYhy1vJDpwoEuitlNkcFjNxFb2oBa\nejRvJxgRgyQ1C4yoc0KtYAa5h423gUKxElDcvz/6MV9HhtuwZ3/257Mfj7X2WjOf/dnv/aTX2rB7\n+rkAAABAhil/AAAAADJM+QMAAACQYcofAAAAgAxT/gAAAABkmPIHAAAAIMOUPwAAAAAZpvwBAAAA\nyDDlDwAAAECGKX8AAAAAMkz5AwAAAJBhyh8AAACADFP+AAAAAGSY8gcAAAAgw5Q/AAAAABmm/AEA\nAADIMOUPAAAAQIYpfwAAAAAyTPkDAAAAkGHKHwAAAIAMU/4AAAAAZJjyBwAAACDDOiUdoL2OOeaY\n6N27d9IxSkavXr3yup75FVY+52d2hWd+6WV26WZ+6eV7S7r57KWb+aWX2aXbzuZXlsvlcgXKAgAA\nAECBpfbIn+OPPz6ampqSjlEyevfuHfPnz8/beuZXWPmcn9kVnvmll9mlm/mll+8t6eazl27ml15m\nl247m19qy5+mpqZYs2ZN0jFoJ/NLL7NLN/NLL7NLN/NLN/NLL7NLN/NLL7MrPi74DAAAAJBhyh8A\nAACADFP+AAAAAGSY8gcAAAAgw5Q/AAAAABmm/AEAAADIMOUPAAAAQIYpfwAAAAAyTPkDAAAAkGHK\nHwAAAIAMU/4AAAAAZJjyBwAAACDDlD8AAAAAGab8AQAAAMgw5Q8AAABAhil/AAAAADJM+QMAAACQ\nYcofAAAAgAxT/gAAAABkmPIHAAAAIMOUPwAAAAAZpvwBAAAAyDDlDwAAAECGKX8AAAAAMkz5AwAA\nAJBhyh8AAACADFP+AAAAAGSY8gcAAAAgw5Q/AED069cvTjnllMjlcjt9nHLKKXHKKafEO9/5zqRj\nAwCwCzolHQAASNYBBxwQ9fX18aEPfWiX9p8zZ05ERDz44IPx8ssvt7nP+eefn7d8AADsGeUPAJS4\n1atXx1577bXbrxs2bNh2nzv77LNj4sSJ8V//9V97Eg3YBaNHj44bb7xxm+0+gwBs5bQvAChR/fv3\njxEjRrSr+NmZvfbaK6655prI5XLxyU9+Mu/r037HHntsDB8+POkYtNOMGTO2ORVz6tSpsddee23z\n2PoZ3Pq46KKLYsSIEdGnT5+k/xgAFJjyBwBK1MiRI6O+vr7D32f69Onxmc98psPfh12Ty+Xi3nvv\nTToG7VBfXx8f+chH2v36W2+9Nerr66O+vt41uwBKjPIHgEzZ+l+82bkJEybEn/70pw5/n27dusW0\nadMil8uZTxH4/e9/H3vvvXfSMWiHYcOGxQEHHLDH6xxxxBHxj3/8Izp1cgUIgFKh/AEgUzZv3hyP\nPPJI0jFS48Mf/nBB32/z5s2xefPm+PrXvx7nnXdeHH744QV9f0izc845J6/rPfHEE9G3b9+8rglA\ncVL+AJAZP/vZzyIi4oc//GHCSdJl9OjRBX/P73znO3HnnXdGfX19HHTQQQV/f0ijb37zm3ld7/DD\nD4/jjjuu5fdvfetbMWvWrLy+BwDFQfkDQKp169YtRowYEblcLk477bTYvHlzvPbaa0nHSpV58+bF\nq6++msh7V1RUxJo1a6KxsTG6du2aSAZIi29/+9t5X/POO++MHj16xHvf+944+uij47TTTsv7ewCQ\nPOUPAKnUs2fPGD16dDz33HOtLlo8a9asliOA2DVPP/10fP/73080Q2VlZUyfPj2Ro5AgLVauXBmr\nVq3K+7ovvvhirF+/PoYNG5b3tQEoDq7yBkDq1NfXR79+/eJDH/pQ0lHIoyFDhsSQIUPiox/9aFxw\nwQVJx4Gi8+STT8Zpp50WDQ0NHfYe8+bNa/l58ODBHfY+ABSW8qeAOnXqFD169Gi17YUXXog33ngj\noUQA6dGjR4848sgjY86cOUlHyawXX3wxysvLk44RxxxzTLz73e+OV155JekoUHS2niK5cePGDll/\n0KBBERGxbt26DlkfgGQ47atAzj333Lj22mvj2WefbfWYMGFC0tEAito+++wT48aNi8WLFyt+OtCC\nBQvi8MMPjzvuuCPpKHHYYYfFpZdemnSMkrf1s/e+970v6Si8xZYtW+IXv/hFh77Hueee26HrQ9Z9\n/vOfTzoC7bTvvvvGsccem3SMvFP+FMB5550XN998c1xxxRXbPHfVVVclkAggHe655554/PHHY9Kk\nSdG9e/ek42TaI488Es3NzTFp0qSko1AE3vzZe/zxx1u2P/TQQwmmYqvXX389Pve5z8Xvf//7pKMA\nbbj44oujrq4u6RjsposvvjgWLlwY8+bNi/r6+li4cGHSkfLKaV8dbNCgQXHnnXcmHQN4i4MPPjjW\nrFmTdAwi4j3veU+Ul5fHsmXLko5CRDQ0NERtbW1cffXVieaora2N6dOnx5IlSxLNUSq6desWjz32\nWAwYMGCb56qrqyOXy7X83rt372hqaipkPNrwt7/9LY499thYvnx59OvXL+/rz5kzJ8rKyvK+LmRR\np06d4pBDDomIiD59+sQtt9wSERELFy6Mo446Kslo7KJPf/rTLXPbqlevXrF06dI49thj4+9//3tC\nyfLHkT8d7M0XzWvL5MmTC5QE2OqSSy6J//3f/225rgHJOuOMMxQ/kKCuXbvGPffc02bx05Z77rmn\ngxOxOw4//HBHZEHCvve978WyZcti2bJlHX5KJh3j05/+dJvbDz300Bg7dmyB03QM5U/Crr/++qQj\nQMno169fLFmyJG644YYoLy+PE088MelIdIAvfelLSUeAVNm8eXM88cQTu7z/UUcdFWeddVYHJmJ3\nvPrqq/HZz342DjvssKSjQMm67LLL2txeVVUVo0aNKnAa2mNHp+mNHTs2+vbtW8A0HUP508Geeuqp\neOqpp+Jf//pX0lGgpB122GGxfPnyOOyww6JLly4xffr0mDhxYtKxSl7nzp3zeuH7P/7xj/Hcc8/l\nbb1S9fzzz3fYnYQoPm+88Ua8+OKLu7z/O97xjrjvvvscPVlEXnrppVi6dGksXrx4j9fK5XLx1FNP\nxXHHHZeHZJB9v/nNb7b7XENDQ/z3f/93AdPQXhs3boznn3++zee6d+8enTt3LnCi/FP+dLAjjzwy\njjzyyKipqYkrrrgirrjiili7dm3Lzxs2bEg6IpSE+++/v+Xn66+/PkaOHJlgGrbavHlzXsufj3zk\nI3lbq5TdcsstsWrVqqRjUEC/+93vYvXq1RERMXfu3Ljiiit2ePHvGTNmxDHHHFOoeOyiU089dY/X\neP311+PII4+MBQsW5CERlLYbb7wx6QjsopUrV8att96adIwO5YLPBfLmiz7fdddd8cILLySYBkrL\npZdeGhUVFS2/33bbbQmm4a2mTZsWo0aNystRBCtXroyDDjooD6n4xCc+sd3/Akb2PPXUU/Hss89G\nly5d4vTTT4+XX345PvCBD8Tll1++zb4HHnhgrF+/Pv75z38mkJS36tatW1x++eXRv3//+OhHP5p0\nHOD/N3jw4FZ3S6T4TZ48Oc4+++w49NBDt9m+fPnyhFLljyN/EqD4gcK64YYbonPnztGnT58YNWqU\nuwcVmXe9613RtWvXvKx14IEH5mUd/v1v1ZNPPplohrvvvjvR9y81J5xwQhxwwAHx8ssvR0TE0qVL\no6ysbJtHU1OT4qeIvPHGG3H22WfHGWecEb169Wr3OuvWrYv58+dn4tQGKKQ///nPMX/+/KipqWm5\n3MeoUaMUPym0YcOG+MAHPtDy/efJJ5+M+fPnx6JFi+L1119PON2ec+QPUDJWr17dcloDxeNTn/qU\n07WK1HnnnZeXa4i01+c+97nE3hvS4pVXXonJkyfv8GKlO/PlL3/ZXYqgncaMGdPy89/+9rd4+9vf\nHvfdd1+CidhTn/zkJ+Oss86K++67L9avX590nLxx5A8AqfCRj3wk9t1335bHaaedts0+PXv2TCBZ\ndj399NPx/e9/P5H33nrbXGDnbrvttvj973+/26977LHHYt99940bb7xR8QN5MG3aNMVPBvz973+P\nKVOmZKr4iXDkDwAJu+OOO+LQQw+NAQMGtPn8ySef3Ob22bNnR1lZWUREPPzww3Hdddc5rTbPtmzZ\nEl/72tdiyJAh0b9//4K97z/+8Y9YtGhRbNmypWDvCWm2adOmWLBgwXav+fPII4+0+v3GG2+Mn//8\n54WIBkCRUP4AkLhvfvObe/T67RVE5Me3vvWteOCBBwr2fn/5y1/innvuKdj7QRaMGzcuLrvssjaf\n83ckAE77AgB2aObMmds9MqsjFPK9AABKgfIHANipJ598Ms4666zYsGFDh75P//79Y/PmzR36HgAA\npcZpXwDALvnpT38aL730UodcGPaZZ56Jyy+/PJ599tm8rw2lYuTIkdts+8EPfpBAEgCKjfIHANhl\nDz/8cFRXV8ef/vSniIh4+9vf3u61crlcvPHGGxERcfDBB+clH5Sy6dOn79I2AEqP8gcA2C0NDQ3R\nqdO/v0Lcddddcfjhh8fhhx++3f3vvvvuNrc3NTXFN77xjQ7JCADA/6P8AQDa7dxzz40JEybssPw5\n99xzC5gIAIC3csFnAGCPTJgwIRobG7fZvnHjxjjssMMSSAQAwJs58gcA2GPTpk2Lnj17xlFHHRUD\nBgyIW265JWbNmhVLly5NOhoAQMlT/gAAe6y2tjYiIt7//vfHwQcfHPPmzUs4EQAAWyl/AIC8+ctf\n/hJ/+ctfko4BAMCbuOYPAAAAQIYpfwAAAAAyTPkDAAAAkGHKHwAAAIAMU/4AAAAAZJjyBwAAACDD\nlD8AAAAAGab8AQAAAMgw5Q8AAABAhil/AAAAADJM+QMAAACQYcofAAAAgAxT/gAAAABkmPIHAAAA\nIMOUPwAAAAAZpvwBAAAAyDDlDwAAAECGKX8AAAAAMkz5AwAAAJBhyh8AAACADFP+AAAAAGSY8gcA\nAAAgw5Q/AAAAABmm/AEAAADIMOUPAAAAQIYpfwAAAAAyTPkDAAAAkGGdkg7QXsccc0z07t076Rgl\no1evXnldz/wKK5/zM7vCM7/0Mrt0M7/08r0l3Xz20s380svs0m1n8yvL5XK5AmUBAAAAoMBSe+TP\n8ccfH01NTUnHKBm9e/eO+fPn52098yusfM7P7ArP/NLL7NLN/NLL95Z089lLN/NLL7NLt53NL7Xl\nT1NTU6xZsybpGLST+aWX2aWb+aWX2aWb+aWb+aWX2aWb+aWX2RUfF3wGAAAAyDDlDwAAAECGKX8A\nAAAAMkz5AwAAAJBhyh8AAACADFP+AAAAAGSY8gcAAAAgw5Q/AAAAABmm/AEAAADIMOUPAAAAQIYp\nfwAAAAAyTPkDAAAAkGHKHwAAAIAMU/4AAAAAZJjyBwAAACDDlD8AAAAAGab8AQAAAMgw5Q8AAABA\nhil/AAAAADJM+QMAAACQYcofAAAAgAxT/gAAAABkmPIHAAAAIMM6JR0AAMieLl26RJ8+fSIiYsmS\nJQmnAQAobY78AQDy6pJLLonrr78+Ghsbo7GxMUaPHt3y3Jt/BgCgMBz5AwDkRb9+/eKBBx6Ifv36\nRdeuXVu233DDDTFmzJiWfSIibr755kQyAgCUIkf+AAB7rE+fPrF8+fKorq5uVfxERHTt2jWqq6tb\nnps6dWrkcrmW08IAAOhYyh8AYI8MHjw4fve73+326xYsWBCDBg3qgEQAALyZ8gcAaLd+/frFtGnT\nokePHrv92vLy8pg2bZojgAAAOphr/gAALaqqqmLx4sUREdG/f//t7jd27NioqanZ4/crLy+PlStX\nRt++fWPVqlV7vB4AANtS/gAAbWpsbCzYez344IPxwQ9+sGDvBwBQSpQ/AEDi+vXrF6NHj3YXMNgN\nt9xySwwcOLDN51avXh1Dhw4tcCIAipXyBwBIXNeuXXd43aA+ffo4LYyS9573vCcOOOCAlt+rqqqi\nurq6zX2rq6sjl8vFpz71qXj66adj9erVhYoJQBFS/gAARW/WrFlRVVWVdAxI1MknnxzTpk3brdc8\n9NBD8dxzz8Utt9wSERFr1qyJO++8syPiAVDElD8AQFGrqalxRzDYA7169Yra2tqIiHj88ceVPwAl\nyK3eAYCiNXTo0Kirq4uuXbsmHQUSt2HDhmhsbIwlS5a0e41BgwZFbW1tVFZW5jEZAMVO+QMAtLjk\nkksSed/m5uaYN2/eNttPOeWUlp9Hjx5dyEhQdB5++OGoqqqKI444Yo/WGT9+fDQ2NsY555yTp2QA\nFDvlDwCpdeWVV8aiRYtaHp06OZt5T40ZMyaR991e+VNXV9fyc1LZIKtuvvlmf3cClAh/0wOQOhUV\nFbFs2bJttr/22mtRVlaWQCKA9Hn3u98d1dXV8dprr0X//v1btu/JaWUAFCdH/gCQKp/85Cdj4cKF\nScfIrKlTpybyvt27d49BgwYl8t6QNlu2bIkHH3wwr2s2NjZGY2NjnHnmmXldF4DioPzpYI8++mjS\nEQAyY9GiRXHvvffGe97znu3uM3ny5AImyp5LL700kQKovLw8TjzxxG22r1ixoiVPv379XPenA915\n550tp1AedthhScdhB15//fWYOXNm3te97LLL4pprrsn7ugAkT/nTwf7jP/4jcrlcnHHGGUlHAUiV\nfffdNyorK6OysjLq6uoil8tFdXV17Lfffjt8XVVVVbz73e8uUMrs2bRpU9TU1MSsWbNajgTYtGlT\nQd67trY2hgwZ0uq27ieffHLLRai7du0aU6dOjVwuF7lcLm666SZ3LMqjPn36RHV1dVRXV8eSJUta\n/nfO5XItn0XXhiked9xxR/zoRz/K23r3339/3HDDDXlbD4DiovwpkPvvvz9qamqSjgGQCuPHj497\n7723pXzYnb8/TzrppDjppJM6MF1pGDp0aFRVVUVVVVWMGzeuYO87a9asWLBgQYwfPz7Gjx+/w1Nb\nxo4dG42NjQXLVsq2fha/+93vJh2FN7n44ouTjgBASih/Cmjy5MmxaNGiOPjgg5OOAlCUzjrrrFi0\naFHU1tbuUYHj1K/8evMdtwqhvLw8amtro7a2dpf2nzFjRgcnKg3nnXfeTvcZN25c3HHHHR0fhl12\n3HHHJR0BgBRQ/nSwN9+NpkuXLlFdXR1/+ctfkgsEJapPnz4tpy8MGTIkhg4d2vL70KFD46abborO\nnTsnHbOkffSjH4377rsvqqur93itgw46KA+J6NKlS1RWVsaKFSuSjrJDFRUVSUfIhNWrV0fPnj3j\nxRdf3OF+559/ftTW1sbb3uZrZDG4/fbb87JOU1NTXtYBoDj5V7uDHX300S76DAkbPHhw/O53v2v5\nfdasWa1OJXnwwQdj7Nix0bdv3yTiERGf/exnW82I4nD99ddHY2Ojz0YJeeGFF+L666/f6X7jx4/f\n4YXXKZwjjjgiamtr45lnntmjdb7yla/kKREAxUj508E2bNgQ55xzTixZsiSqq6tj6tSpMWTIkKRj\nQUlZsmRJvPLKK0nHYAd25XST3fXUU0/lfc1SM2bMmKQjUMQWLFiQdAQi4tVXX40JEya0ebe8XXXE\nEUfkLxAARUn5UwDr1q2L/v37R0NDQ9TU1MTPf/7zpCNBScnlcru0nwvHFl5ZWVl885vf7JALNFdV\nVeV9TYrTypUrk46QKdddd12UlZXt9HS/srKyAiViV6xevTrKysriwgsvjI0bN+7y684///z44x//\n2IHJACgGyh8g88aMGbPLp62MHj26g9PwZp07d45rr7026Rik3Omnn550hEw69thjY968eTt8nuJz\n++23x1e+8pVdulD7ihUr4k9/+lMBUgGQNOUPwJs4zQX+bebMmUlH2CXDhg1LOkJmrVu3LkaOHNnm\nRdivueaa2LBhQwKp2BW33HJLXHbZZTu90YjyB6B0dEo6AEBHW7t2bTQ2NkaXLl12eATQypUr44Mf\n/GABk5HL5eK5556LXr16dcj6vXr1iueee65D1s66tNxBa/ny5UlHyLTm5uZobm5uOcXrV7/6VXz8\n4x9POBW7YvPmzdHQ0BCHHHJINDQ0RHl5eey7776t9jn11FMTSgdAoTnyB8i8urq6qKqqioEDB7Z5\nCkNzc3PU1tY6hSEBmzdvjm9961sdsvZee+0V3/nOdzpkbShVip90qqqqinPOOSdqa2vjjjvuiIiI\n733ve8mGAqCgHPkDlIzm5uaYO3duTJ48udUFYjdt2rTTC5vScWbOnBkXXXRRDBw4MOkovMmwYcOi\nuro6HnjggaSjAHnwyCOPxCOPPBLveMc7YtKkSbFkyZKkIwFQQMofoKRMmDAh6Qi8xUsvvRQNDQ07\nLX9WrFgRmzdv3uE+lZWV8corr8SZZ54Zjz76aD5jlpwVK1ZEly5dEnnvjRs3xqpVqyIitnu65tZ9\nNm3aVOh4kGr/+te/oqGhIekYABSY8geAxF188cVx0UUXtdr229/+Nn75y1+2/H7TTTfF3/72tx2u\nc9VVV8XSpUsVPyk2ZcqUaGhoiClTpkRERI8ePaKmpiZqamqie/fube4DAMCOKX8AKApvvaPQunXr\n4sUXX9ytNa655pp8RqLA6urqYty4ca2O5mlubo4JEybE3Xff3XIk0ooVKxzxAwCwG5Q/ABQFpyGU\ntsWLF8fYsWO3+7zrcgEAtJ+7fQEAbVq7dm2bd8jrCN27d49BgwYV5L0AAEqN8gcAaNPWO+TlQ11d\nXVRXV7d6DB8+vOX58vLymDZtWvTp0ycv7wcAwP+j/AEAtmvChAlRVlYWjY2NLY+VK1dGc3Nzy+81\nNTVRVla23TU2btwYDQ0N2zxmzpwZtbW1LfuVl5fHypUrY9myZVFZWRmdO3cuxB8RACDzXPMHANip\nqqqqlp979OgRlZWV25wSNnXq1Ljkkku2ee2KFSti6tSpu/xeFRUV0djYGP37948lS5a0PzQAABHh\nyB8AYDc1Nze3eS2gurq6BNIAALAzyh8AoEOtXLlyu8+tXbt2m9u2b9y4MWpqahz1AwCQJ077AgDy\nYu3ata2u4bPVhAkTtvuarUcL/fCHP2zZNm7cuN06TQwAgB1T/gAAedHc3LzDomd76urq4tlnn40H\nHngghg0bFj/72c/yHw4AoIQpfwCAxM2cOXOHdwwDAKD9XPMHAAAAIMOUPwAAAAAZpvwBAAAAyDDl\nDwAAAECGKX8AAAAAMkz5AwAAAJBhyh8AAACADFP+AAAAAGSY8gcAAAAgw5Q/AAAAABmm/AEAAADI\nMOUPAAAAQIYpfwAAAAAyTPkDAAAAkGHKHwAAAIAMU/4AAAAAZJjyBwAAACDDlD8AAAAAGab8AQAA\nAMgw5Q8AAABAhil/AAAAADJM+QMAAACQYcofAAAAgAxT/gAAAABkmPIHAAAAIMM6JR2gvY455pjo\n3bt30jFKRq9evfK6nvkVVj7nZ3aFZ37pZXbpZn7p5XtLuvnspZv5pZfZpdvO5leWy+VyBcoCAAAA\nQIGl9sif448/PpqampKOUTJ69+4d8+fPz9t65ldY+Zyf2RWe+aWX2aWb+aWX7y3p5rOXbuaXXmaX\nbjubX2rLn6amplizZk3SMWgn80svs0s380svs0s380s380svs0s380svsys+LvgMAAAAkGHKHwAA\nAIAMU/4AAAAAZJjyBwAAACDDlD8AAAAAGab8AQAAAMgw5Q8AAABAhil/AAAAADJM+QMAAACQYcof\nAAAAgAxT/gAAAABkmPIHAAAAIMOUPwAAAAAZpvwBAAAAyDDlDwAAAECGKX8AAAAAMkz5AwAAAJBh\nyh8AAACADFP+AAAAAGSY8gcAm/iGuwAAIABJREFUAAAgw5Q/AAAAABmm/AEAAADIMOUPAAAAQIYp\nfwAAAAAyTPkDAAAAkGHKHwAAAIAMU/4AAAAAZJjyBwAAACDDlD8AAAAAGab8AQAAAMgw5Q8AAABA\nhil/AAAAADKsU9IBAACA3fOxj30sxowZs0v7nnnmmR2cBoBip/wBAICU6NSpU+y9997xq1/9apdf\nk8vlWn5+/PHHY/DgwR0RDYAi5rQvAABIiWnTpsWGDRva/fry8vI49NBD85gIgDRQ/gAAQIn4wAc+\nEB//+MeTjgFAgSl/AEitvffeO9avXx+5XK7lUV9fn3QsgA7xmc98JoYPH77H60ydOjXe//7373kg\nAFJD+QNAKvXs2TN+8YtfxHvf+96kowAUxMqVK2PVqlV5WWv16tV5WQeAdFD+AJA69fX1MWfOnBg0\naFDSUQAK5sknn4w//elPeVtv3rx5cfLJJ+dtPQCKl7t9FVCnTp2iR48e22x/7rnnEkgDkD6nnHJK\nzJkzZ7vPP/vss25pDGTaGWecEU1NTXHAAQfs8VqDBg2KQYMGRefOneO1117LQzoAipXypwCOPvro\nGDRoUPTo0SOuuOKKbZ4vKytLIBVAegwcODCOPfbYmDRp0g73O+SQQwqUCLKja9euMWbMmIiIeOWV\nV+LWW2+NiIh99tknRo0aFbfffnusX78+yYi8Rd++fWPjxo15W+/SSy+N73//+3lbD4Di47SvDlZe\nXh4zZsyISZMmtVn8RETLlyygcBYuXBgXXXRR0jHYBQsXLozp06fvtPgB2mfvvfeOSZMmxaRJk+LK\nK69s2f7444/HpEmT4vHHH4+FCxfG5z73uQRT8mabN2+O2travK33ve99L29rAVCclD8d7IUXXoje\nvXvvcJ8Pf/jD0a1btwIlgtJVUVERFRUVsXz58hgwYEDceuutkcvl4uCDD046Gm8xaNCglrt3DRgw\nIHr16rXT17zwwgsFSAbZs379+pYjfw488MCWz15VVVVERFRXV8eAAQPirrvuilwuF//3f//ne0vC\ncrlc/PnPf45//vOfeVuzZ8+eeVsLgOKj/CkCRx99dJx99tlJx4DMGjx4cIwfPz6WLVsWy5Yti379\n+rV6fvbs2Qkloy0XXHBBzJs3b7df97Wvfc01K6Cd/vCHP8QzzzyzS/v63lIcZsyYEb/97W/ztt5N\nN92Ut7UAKD7KHyDT+vXrF/X19Ts8PL6ioiJqamoKmIrtOfvss6Ouri7pGFBydqf8AQDSR/nTwRYv\nXrzT52+77ba4+eabC5QISsuHP/zh2G+//Xa4z89+9rOYMmVKgRLRlr59+8bgwYPj3nvvjXe84x1J\nx4GStGzZsqQjsJvyeZv21atX520tAIqP8qeDDRw4MK644oo2L/Y8e/bsOPbYY+MLX/hCAsmgNEyf\nPn2n+4wcObIASdieo446Kh599NGYO3fuHq3zn//5n/H2t789P6GgBF144YVJR6AdJkyYkJd1vvrV\nr+ZlHQCKk/Kng73yyitx3XXXxXXXXRcHHnhgHHjggbFq1ar485//HOecc0784x//SDoiZNZjjz22\nS/s9+uijHZyE7enSpUs8/PDDeblF+6c+9al429v8swaFcO2117roc5G45pprWt2lrT369u2bpzQA\nFKtOSQcoJU1NTRHhH1golE984hORy+V2ut+xxx4bF1xwQdxxxx0dH4pWBg4cGPvss0/e1tu8eXNU\nVlbG008/nbc1oZSUlZW1+v3zn/98jBo1qtW2P/7xj/HFL36xkLHYgTfeeCMaGxvj5Zdfblcht2zZ\nMtd7AigB/hMpkGnXXnvtTvdZvXq14icBF110UfzqV7/K+7qzZ8+OD3/4w3lfF0rR7bffHieccEKr\nh+KneIwZMyYiIh544IFYtGjRbr++sbExhg4dGq+//nq+owFQZJQ/QKZdffXVse+++8bSpUvbfH7g\nwIFx/PHHFzgVERGnnnpqh6zbt29fR1gCJWHo0KGxbt26WLduXRxzzDG7/fpnnnlmu/8+ApAtTvsC\nMm3Lli3x97//PQ477LCko/AWZ5xxRuy1114xe/bsVtuPPvroeN/73pdQKoD0eOKJJ+Kkk05q12vf\n9773xUsvvZTnRAAUK+UPAIl57bXXtrlV8amnnhrl5eUtv/fs2TO+/e1vFzoaQNH75je/Gd/4xjd2\n+3U/+clP4l//+lcHJAKgWCl/ACgqc+bMafV7WVlZTJ48ORYtWhQVFRW7tMa9994bXbp06Yh4AKk2\nf/78+PznPx9btmxJOgoABeSaPwAUtVwuF5s2bYpDDz00+vbtGw888EC8+uqrO3zNU089VaB0AMna\n3Tsmzps3T/EDUIIc+QNAaqxatSrOOOOMGDJkSOy9997bPH/vvffGOeecE7/73e8SSAeQjIcffjhu\nu+22bbaPHDkyzjzzzFbbrrzyykLFAqCIKH8ASJ23XiR6q+nTpxc4CUCy1q9fH6ecckqbz239O3Hr\nrdwPOuigguUCoLgofwAAIMM6dfKVH6DUueYPAAAAQIYpfwAAAAAyTPkDAAAAkGHKHwAAAIAMU/4A\nAAAAZJjyBwAAACDDlD8AAAAAGab8AQAAAMgw5Q8AAABAhil/AAAAADJM+QMAAACQYcofAAAAgAxT\n/gAAAABkmPIHAAAAIMOUPwAAAAAZpvwBAAAAyDDlDwAAAECGKX8AAAAAMkz5AwAAAJBhyh8AAACA\nDFP+AAAAAGSY8gcAAAAgw5Q/AAAAABmm/AEAAADIMOUPAAAAQIYpfwAAAAAyTPkDAAAAkGHKHwAA\nAIAMU/4AAAAAZJjyBwAAACDDlD8AAAAAGab8AQAAAMiwTkkHaK9jjjkmevfunXSMktGrV6+8rmd+\nhZXP+Zld4Zlfeplduplfevnekm4+e+lmfulldum2s/mV5XK5XIGyAAAAAFBgqT3y5/jjj4+mpqak\nY5SM3r17x/z58/O2nvkVVj7nZ3aFZ37pZXbpZn7p5XtLuvnspZv5pZfZpdvO5pfa8qepqSnWrFmT\ndAzayfzSy+zSzfzSy+zSzfzSzfzSy+zSzfzSy+yKjws+AwAAAGSY8gcAAAAgw5Q/AAAAABmm/AEA\nAADIMOUPAAAAQIYpfwAAAAAyTPkDAAAAkGHKHwAAAIAMU/4AAAAAZJjyBwAAACDDlD8AAAAAGab8\nAQAAAMgw5Q8AAABAhil/AAAAADJM+QMAAACQYcofAAAAgAxT/gAAAABkmPIHAAAAIMOUPwAAAAAZ\npvwBAAAAyDDlDwAAAECGKX8AAAAAMkz5AwAAAJBhyh8AAACADFP+AAAAAGRYp6QDAADptWjRola/\n19XVxc0335xQGgAA2qL8AQDapaGhIfr3799q29SpU2Pq1KlRVlYWERF9+vSJLl26tDy/ZMmSgmYE\nAED5AwC0w+DBg2O//fbb7vPjx4+PiIgxY8ZEjx49WrZfcskljgwCACgw5Q8AsNs+9rGPtSp13qq2\ntrbN7TfccENEhAIIAKCAXPAZAGjRpUuXqKuriyFDhkSfPn3a3Gfo0KFx9dVXt2v9rl27RlVVVVRW\nVu5JTAAAdoPyBwBocf3110dNTU3MmjUrFixYEOPHj4/99tsvunfvHuPHj4/x48fHgw8+uEfvMXbs\n2GhsbMxTYgAAdsZpXwBARETMnDkzhg0b1vJ7eXl51NbWxuc+97mIiKioqMjr+82YMSPOOOOMvK4J\nAMC2HPkDAMSECRNaFT9vVlFRkffiJyKiurq61UWju3fvHitWrIhcLtdywWigbd26dYvKysqorKyM\niRMnRi6X2+Hj+9//vtMtAUqY8gcASlz37t1j8ODBBX/fioqKmDZtWksBNGbMmOjbt2/Bc0AaDR8+\nPBobG6OxsTGuuuqqne7/1a9+NRobG2P8+PExYMCAAiQEoJgofwCgxPXo0SNOPPHERN77Yx/7WCxY\nsKDVtuXLl8fUqVMTyQNZV1tbG0cffXTSMQAoMNf8AQASVVFREblcruX3TZs2xdq1axNMBNk2derU\n+J//+Z946aWXko4CQIE48gcAStzatWtj3rx5Scdo0b179xg0aFDSMSDTvva1ryUdAYACUv4AQIlr\nbm6OkSNHRnV1dQwfPjzpOFFeXh7Tpk2LPn36JB0FMuvrX/+60ysBSojyB8i0Ll26RF1dXQwZMiTp\nKORZZWVl3Hfffa3uZrPXXnslHSu1mpubo6GhIWbOnBljx46NTZs2JZqnvLw8Vq5cqQCCDjR69Oj4\n+te/nnQM3qRLly5RWVkZnTt3TjoKkDHKHyDT+vXrFzU1NTFr1qyko5BnjY2NcdZZZ7XaduWVVyaU\nJlvq6upi5cqVSceIiIgFCxY4BQza8Mc//jEvn9PvfOc7UVtbm4dE5MP1118fjY2N7nwI5J3yBygZ\nixYtitGjRycdg3aaNWtWLFq0qOXRlvHjxxc4VXYNGzYs6QgR4RQw2J58lT8R//67c/LkyXlZi/ap\nqamJRYsWxZgxY5KOAmSU8qdADjnkkKisrNzm0amTG65BR1q8eHHLz9XV1TF16tSWU4RuuummqKys\nTDAd21NZWRmTJk1qdUrXkCFDorq6uuVBx1qxYkXSEVpsPQVs2bJlsd9++yUdp+Q4AqE4devWLd75\nznfmbb2qqqp497vfnbf12HVDhw6Nuro6/7ZBwrp3757pUy41DwUwcODAqK+vj549e27z3PXXXx+X\nX355Aqkg+y655JIdPj927Nh429ve5r+yFZGjjjoqPvWpT+3RKQif+cxnYvr06XlMVbqmTp26089R\nIVVUVMS0adNi5MiRsW7duqTjZNJ73vOe+MpXvtJq2xe/+MW46aabWn6fOHFioWPRhuHDh8dxxx2X\nt/VOOumkOOmkk+L+++/P25rsmlNOOWWbbTU1NdHQ0BA333xzAomgNI0ZMyb23XffWLt2bURETJky\nJVvfN3IpdfDBB+ciougf5eXlueeff36Hf5Y77rgj8Zw7exx88MElOb+sPPI5v7TMrqamJvfqq6/u\n9M9TWVmZeFbz+/dj0aJFuWeffXaP/4wvvfRS7pOf/GTif54szK6qqipv+fNp2bJluRk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lW8/vrrDZCU7Vm9enXU1tbG2rVrk44CTU63bt1iwYIF9d6H9unTJ3r16pWZn0nlD+yE0aNH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\nnnoq6QgVRvkDQOJ2deHYTz/9ND799NMdlrnLHgAA7D3X/AEAAABIM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Fz3HxER8YMD/xb/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\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Okeqa9Bdgy37j+nMfjAPh/RgHxUUHxu0TTzIiaBcf0KkGWypl6fPFVnavhm35cMYCN0VF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\nyR9JkiRJkqQEZvJHkiRJkiQpgf0/0N0491MSX4IAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"make_plot(img, seg, rec, samples, fs=2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "TcHaNVj0dGmC"
},
"source": [
"# Plot samples from the global latent only\n",
"When sampling from the global latent, all but the most global latent scale are constrained to their means."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"height": 1000
},
"colab_type": "code",
"executionInfo": {
"elapsed": 16249,
"status": "ok",
"timestamp": 1568134290653,
"user": {
"displayName": "Simon Kohl",
"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAV5aCJQ_5RldkCyse6s0nQKEvpBRdNbA80iGeZEw=s64",
"userId": "12229574298416150258"
},
"user_tz": -60
},
"id": "K_L4aKY4dKqj",
"outputId": "672fc01e-bf6e-4873-90e8-31c434785e03"
},
"outputs": [
{
"data": {
"image/png": 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iZrNZnJ2d3Qja3GjRSLL9up880e/+jDtN6pHfr2va+qXVKwqg+Jzqc/CtZdls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fu9X1ldGb+6INcH3HGSLfqJJKMRzXG7vLyMly9fRr/fj93d3RgOhwUbiZ+S7eFweGu+SN+yHrZV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\nV5Mz0BNxm5er5KQGjrztrg8zx9T1wZvKwZuQtisfHh6WBZDNz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or+z7+eVmR+55X/v0jR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6pkyZEh/72MfKDgcq7+Mf/3g89thjnkQDLfLVr341HnvssZg0aVJERJx88snx2GOPxWOPPRZX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\nMil/Ro0aFYMHD46Pf/z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AAAlT/gAAAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAA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\n3LgxrrjiirjiiivipZdeigMPPPBTvf/VV1+N7t27K/WAVm/dunW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BBx0UBx10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FGjMp05YcKEGD16dKYz+eS6du0au+66a94xoFVL9VBY5Q9spz322CPGjx8f48ePzzsKtFqV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\nAJAw5Q8AAABAw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vdBs26A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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"make_plot(img, seg, rec, samples_global, fs=2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "whYBzCFlWhPO"
},
"source": [
"# Plot samples from the local latents only\n",
"When sampling from the local latents, all but the most local latent scales are constrained to their means."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"height": 1000
},
"colab_type": "code",
"executionInfo": {
"elapsed": 14843,
"status": "ok",
"timestamp": 1568134333457,
"user": {
"displayName": "Simon Kohl",
"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAV5aCJQ_5RldkCyse6s0nQKEvpBRdNbA80iGeZEw=s64",
"userId": "12229574298416150258"
},
"user_tz": -60
},
"id": "MIbB8BFPWmir",
"outputId": "043f11ce-1c0d-4073-967d-d53cbf665bb9"
},
"outputs": [
{
"data": {
"image/png": 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iZrNZnJ2d3Qja3GjRSLL9up880e/+jDtN6pHfr2va+qXVKwqg+Jzqc/CtZdls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fu9X1ldGb+6INcH3HGSLfqJJKMRzXG7vLyMly9fRr/fj93d3RgOhwUbiZ+S7eFweGu+SN+yHrZV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\nV5Mz0BNxm5er5KQGjrztrg8zx9T1wZvKwZuQtisfHh6WBZDNz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or+z7+eVmR+55X/v0jR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6pkyZEh/72MfKDgcq7+Mf/3g89thjnkQDLfLVr341HnvssZg0aVJERJx88snx2GOPxWOPPRZX\nX3218UegJMcff3zZIQBtTvIHgF5p+vTpcdFFF5UdBvQpp556asP3DjroIFecQEm++c1vxoknnlh2\nGEAbk/wB+oz8k6FmzpxZdkh9XkdHR8ycObPwyV3f+c53YtCgQWWH2Od0dnbG8ccfH6tWrSo7FEqw\n4447xsMPPxwPP/xw4fvjx4/vsceNA40NHDgw5syZE3vssUfZoQBtSvIHNsPixYvjvvvuKzsMaHsX\nXHBBzJo1q+wwyPnWt74VTz75ZNlhUIIlS5bE2972tnjb294WF1xwQf31008/vf7zz//8zyVGCH3L\nbbfdFqeffnr9/9NOO63EaIB25mlfsBn+x//4H7FkyZKyw4C219UtJpTr3e9+d/z3f/932WFQos99\n7nP1Wy8XL15ccjT0tFtuuSVuueWWssPok97znvfEHXfcERERCxcujPe85z1x5513Rq1Wi2nTpsXL\nL78cv/rVr+KOO+6I6dOnS8ACm0XyB96gv/qrvyo7BDZTR0dH2SGQmDhxYtkh0IU//vGPsdtuu8U9\n99wTu+yyS9nhFHrmmWfiwAMPLDuMynrttdckffqIBx98MN7//veXHUaf9eSTT8YDDzwQERH77bdf\nRGzc37zzzjv1Y4At4rYvAEpx3nnnlR0Cm/DUU0/F9773vbLDaOjyyy+Pp59+uuwwoK19//vfj/33\n37/sMPq0p556Kvbbb7964gegJ7jyBwBo6Ktf/WocddRRve6qx+233z5effXVssOAtnfnnXfG+vXr\nyw4DgB4m+QMANPTqq6/GxIkT49Zbb62/9r73va+UWFasWBHz58+P4447Ll544YVSYoDeYPXq1Zu8\nBeiBBx6Ifffdt/C9BQsWxN577x0rVqzoifAA6IXc9gVAKS6++OI49thj47vf/W7ZodANBx10UP3n\nrLPOKiWGj370o3HQQQfFH/7wh1KmD+1k+vTpha8vXLgwDjvsMIkfgD5G8geAUtx1111xxRVXxCc+\n8YkYNGhQDBo0KD71qU+VHRbdcN5558WXvvSlHp/OyDJKJwAAIABJREFUW9/61vq6MWjQoLj55pt7\nfJpQdVOnTo2FCxeWHQYALea2LwBKtW7duli3bl1ERFx00UUxevTo2GOPPSIi4l3veleMHDmyzPAo\nUKvV4pFHHombbropIl5/cltWZ83w8ssvx3HHHRe//vWvm1Ym9GU33XRTXHHFFfGjH/2o7FAAKInk\nDwC9yplnnln/+73vfW/cdtttJUZDI9dcc01cc801ERExefLk+PnPfx5jx45tStnTpk1T77CFnn32\n2TjyyCMjIuK6664rORoAyua2LwB6rdtvvz06Ojq6/bN27dqyQ+6TOjs7Y+edd44pU6Zs9lODarVa\nzJw5Mzo6OiR+oAmWL18e1113ncQPABEh+QMANElnZ2dMmzYtfvCDH7zh7371q1+Nc889tweiAgDA\nbV8AQNPceOONceONN8bVV1+90XuXXHJJ7LLLLvX/3//+99f/vuWWW1oSHwBAXyT5AwA0XVEyZ9dd\nd42XXnopIiL+9m//1hOHAABaRPIHAGiZbbbZpuwQAAD6HGP+AAAAAFSY5A8AAABAhUn+AAAAAFSY\n5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAA\nAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+\nAAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABAhUn+AAAAAFSY5A8AAABA\nhUn+AAAAAFSY5A8AAABAhUn+AAAAAFTY1mUHsLn23nvvGDt2bNlh9Bk77bRTU8tTf63VzPpTd62n\n/tqXumtv6q996be0N9tee1N/7UvdtbdN1V9HrVartSgWAAAAAFqsba/8eec73xmLFy8uO4w+Y+zY\nsfHAAw80rTz111rNrD9113rqr32pu/am/tqXfkt7s+21N/XXvtRde9tU/bVt8mfx4sXxzDPPlB0G\nm0n9tS91197UX/tSd+1N/bU39de+1F17U3/tS931PgZ8BgAAAKgwyR8AAACACpP8AQAAAKgwyR8A\nAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgw\nyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAA\nAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACpP8\nAQAAAKgwyR8AAACACpP8AQAAAKgwyR8AAACACtu67AAAgPYyatSoOOGEE97Qd+6555647777eigi\n6LsGDRoUp59++iY/d/vtt8dDDz3UgogA6I0kfwCATRo/fnzcdNNNERExcODAmDBhwhv6/pIlS2Kf\nffaJJ598sifCgz7pW9/6Vuy3336xxx57bPKzJ5xwQrzlLW+JpUuXtiAyAHobyR8AoKFRo0bFDjvs\nEAsWLNiicsaMGROTJk2S/IEm6OjoiM997nPx8Y9/vNvf2XHHHWPSpEmSPwB9lDF/AICGTjrppC1O\n/GR+/OMfx/HHH9+UsqAvGzBgQHzpS196w9+7++674+ijj+6BiADo7SR/AICWufDCC+ORRx6JRx55\npOxQoE+aM2dOfRu85JJLyg4HgBZx2xcA0NBzzz0Xq1evjoEDBzalvEGDBsWUKVMiIuKxxx6Lgw8+\nOCIiVq9e7ZYw6KbujPHTyNChQ+vb4JQpU+Khhx6K73//+80KDYBeypU/AEBDc+bMiUWLFvVI2RMm\nTIgFCxbEggULYv78+bH//vv3yHSgSo488sh4+OGHm1beIYccEv36OSQAqDotPQBQujFjxsS1114b\nu+22W9mhQK921FFHNbW8Qw89NH772982tUwAeh/JHwCgVxgzZkwsWrRIAgi68J//+Z9NL3Py5Mlx\n/fXXN71cAHoPyR8AoEtz585t6fTmzZvX0ulBOznllFM260lfm7LHHnts0VhCAPRukj8AQJfmzJkT\nhx56aMum18ppQTs699xzm17mpEmTYtKkSU0vF6AdjB8/vv4kxBtvvLHscHqEp30BAJv0+OOPt2Q6\nkyZNiieeeKIl06LY9ttvH6NHj97o9SVLlsQLL7xQQkTkrV69Oo499tj43ve+19Ryr7/++ujo6Ghq\nmQDtYODAgRs8CXH27Nlx2mmnxZo1a0qOrHkkfwCAXuPEE0+MT37yk2WH0Scdc8wxscsuu8S+++4b\n73vf+zZ6/9Zbb4358+fHOeecU0J0ANA6M2bMiIioVJ/EbV8AQK+RdbYy6WXY6U9VL8ku0/Tp02PW\nrFmFiZ+IiIMOOihmzZoVjzzySBx55JEtjo68m2++OebPn9/UMk899dSmlgfQzvJ9knYn+QNUVmdn\nZ9RqtYY/s2fPjgEDBpQdJpCTPe1rt912i8cffzymTJmy0c+hhx5qGy7JlClTYrvttis7jD7vxRdf\njH333bepZf7hD39oankA7Wrp0qUxbty4ssNoKskfoM+aMWNGfP3rXy87DCBn/vz5MXPmzHjooYe6\n/NyUKVNim222aVFUAEBfMWfOnHjyySfLDqOpJH+APq1ql3NCFYwZMyZmzZpVOOhw6u67747nnnuu\nRVFB7/Otb32r7BAAKufxxx+PSy65pOwwms6Az0BlTZ48OZ544omGl2wuXbo03vGOd7Q4KmhPq1ev\njueeey5GjRpVdigREbFq1SqJnybbb7/9Nvi/s7MzRo0aVa/zJUuWxPPPPx+//OUv49JLLy0jRHKa\n+WSuiy66KG644YamlQfQTjo7Oyv/tEPJH6DS9tlnn7j22mvjgAMO2Oi9Kl7OCT3liSeeiLlz58bZ\nZ59ddigR8Xo8VTwr15tMnjw5DjzwwDjwwAMjIuK2225r+gDDbJmPf/zjsWzZsjj99NO3qJwvfvGL\n8fnPf75JUQHQG0n+AJW2dOnSOPLIIwuvVli6dGkJEUH7mjt3bnzkIx+p3ACINHbPPffEPffcU3YY\ndOErX/nKFiV/zjzzzPjyl7/cxIgA6I0kf4DKW7p0qUQPNMHSpUtjwYIFvSL5s2jRorJDgF5h2bJl\n8aEPfSjOOuusmDRp0hv67s9//vM477zzeigyAHoTAz4DAN32wQ9+sPTbrebOnRuHHHJIqTFAb3L1\n1VfH5MmT48ILL3xD3/u3f/u3HooIgN5G8gcAeEM+/elPx5QpU0q5+mbOnDlx6qmntny60A5OP/30\n+MY3vtGtz+6zzz7xgx/8oIcjAqC3kPwBAN6Q1atXR2dnZ4wfPz4WLFjQsqduzZs3L2bMmBGrV69u\nyfSg3bz22mtx3nnnxaOPPtrwM6+88kocddRR8dBDD7UwMgDKZswfAGCzpU+EGj16dJxwwglbVN7c\nuXMbJpO+8IUvbFHZ0Bd87GMfiz322KPh+5/85CfjmmuuaWFEAPQGkj8AwBbJngg1cODAmDNnzgbv\nzZs3r1sDRE+ZMiUiXn+Euyt7oGe8+93vjrvuuqvsMAAogeQPANAU2e1gqfHjx2/0WkTEuHHjolar\nxZNPPhn/+I//GE8++WSrwoRK+9rXvhZ77733Rlf/fO1rX5P4AejDJH8AgB41efLkjV476aSTolar\nxdy5c0uICKrtiCOOKDsEAHoZyR8AoOXyt4cBANBzPO0LAAAAoMIkfwAAAAAqTPIHAAAAoMIkfwAA\nAAAqTPIHAAAAoMIkfwAAAAAqTPLn/7F359FR1/f++F9REKv1iwubyKIsPQJpvGptK6LiFcTWU1xY\ntFptvVYqGrWKeltBWaxLFbUo4Fpc2uoVqRW1toJKUARrXS7FRIssUlAhIC51Y7Hz+8MfuUVAFCfz\nybzn8Thnzsl8Mnl/nvI6ScZnPgsAAABAwpQ/AAAAAAlT/gAAAAAkTPkDAAAAkDDlDwAAAEDClD8A\nAAAACVP+AAAAACRM+QMAAACQMOUPAAAAQMKUPwAAAAAJU/4AAAAAJEz5AwAAAJAw5Q8AAABAwpQ/\nAAAAAAlT/gAAAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+AAAAACRM+QMAAACQMOUPAAAAQMKU\nPwAAAAAJU/4AAAAAJEz5AwAAAJAw5Q8AAABAwpQ/AAAAAAlrlHWALdWmTZusI5SUfP97m19h5fPf\n2+wKz/yKl9kVN/MrXt63FDffe8XN/IqX2RW3zf2bl+VyuVyBsgAAAABQYE77AgAAAEiY8gcAAAAg\nYcofAAAAgIQpfwAAAAASpvwBAAAASJjyBwAAACBhyh8AAACAhCl/AAAAABKm/AEAAABImPIHAAAA\nIGHKHwAAAICEKX8AAAAAEqb8AQAAAEiY8gcAAAAgYcofAAAAgIQpfwAAAAASpvwBAAAASJjyBwAA\nACBhyh8AAACAhCl/AAAAABKm/AEAAABImPIHAAAAIGHKHwAAAICEKX8AAAAAEqb8AQAAAEiY8gcA\nAAAgYcofAAAAgIQpfwAAAAASpvwBAAAASJjyBwAAACBhyh8AAACAhCl/AAAAABKm/AEAAABImPIH\nAAAAIGHKHwAAAICEKX8AAAAAEqb8AQAAAEhYo6wDbKkePXrEkiVLso5RMtq0aRMzZszI23rmV1j5\nnJ/ZFZ75FS+zK27mV7y8byluvveKm/kVL7MrbpubX9GWP0uWLIlFixZlHYMtZH7Fy+yKm/kVL7Mr\nbuZX3MyveJldcTO/4mV2DY/TvgAAAAASpvwBAAAASJjyBwAAACBhmV3z55///GcsXLgw3n///fW2\n77///hklAgAAAEhPJuXPfffdF6NGjYrtttsutt1227rtZWVl8dhjj2URCQAAACBJmZQ/1157bYwZ\nMyYOPvjgLHYPAAAAUDIyuebPxx9/HD169Mhi1wAAAAAlJZPy59RTT40bbrgh/vWvf2WxewAAAICS\nkclpX7fffnusWLEibr311thxxx3X+1xVVVUWkQAAAACSlEn5c9VVV2WxWwAAAICSk0n5881vfjOL\n3QIAAACUnEyu+bNmzZq47rrr4tBDD42vf/3rceihh8Z1110Xq1evziIOAAAAQLIyO+3rb3/7W4wc\nOTJat24dr7/+eowfPz7ee++9uPDCC7OIBAAAAJCkTMqfP//5zzF58uTYaaedIiKiQ4cO0bVr1zjy\nyCOVPwAAAAB5lMlpX7lc7gttBwAAAGDLZFL+HH744TF48OB48sknY/78+fHEE0/EGWecEd/5zney\niAMAAACQrExO+zr//PPjhhtuiFGjRkVtbW20aNEijjjiiDj99NOziAMAAACQrEzKn2222SbOPvvs\nOPvss7PYPQAAAEDJKFj589e//jX222+/iIiYNWvWJl+3//77FyoSAAAAQPIKVv6MHDkyHnrooYiI\nGDp06EZfU1ZWFo899lihIgEAAAAkr2Dlz7riJyLi8ccfL9RuAQAAAEpaJnf7Gjx48Ea3V1ZWFjgJ\nAAAAQNoyKX/+8pe/bHT7M888U+AkAAAAAGkr6N2+xowZExERa9asqft4ncWLF0fr1q0LGQcAAAAg\neQUtf5YuXRoREblcru7jdXbdddc488wzCxkHAAAAIHkFLX8uv/zyiIjYe++9Y+DAgYXcNQAAAEBJ\nyuSaP9tss028/PLL6217+eWX4/77788iDgAAAECyMil/xowZE7vuuut621q1arXBdYAAAAAA+HIy\nKX/ee++9+OpXv7reth122CHefffdLOIAAAAAJCuT8qdjx47xyCOPrLdt6tSp0bFjxyziAAAAACSr\noBd8Xue8886LQYMGxZ/+9Kdo27Zt/OMf/4hZs2bFzTffnEUcAOALaN68eQwePPgLfU1VVVU88cQT\n9ZQISte2224bF1xwwWZfN3Xq1Jg1a1YBEgHQEGVS/nzjG9+Ihx56KB566KF44403oqKiIoYOHbrB\ndYAAgIahU6dO8Yc//CEiIpo0aRKdO3f+Ql8/fPhw5Q/k2U033RQHHnhgdOnSZbOvff/995U/ACUs\nk/InIqJ169YxaNCgrHYPAHwOzZs3j1mzZn3pU7NHjhwZzz//fDz00EN5Sgalq6ysLH7+859/offS\no0ePjueeey6qqqrqLxgADVYm5c/5558fZWVlG/3clVdeWeA0AMCmnHHGGXm7Jt+DDz4YgwcPjhtv\nvDEv60Gp2mabbeLSSy/9wl83bdq0+NGPfhR33HFHPaQCoCHL5ILP7du3j3bt2tU9vvKVr8QTTzwR\nTZs2zSIOAFAg1157bcyZMyc6dOiQdRQoSePGjYs5c+bEnDlz4oYbbsg6DgAFksmRP5WVlRts69+/\nf4wbNy6DNADApowYMSIGDBgQXbt2zct62267bZSXl8f8+fMjImLs2LExfvz4WLVqVSxYsCAv+4DU\nfZ5r/GzK9ttvH+Xl5RERUV5eHosWLYorrrgiX9EAaKAyOfJnY7p06RLPPPNM1jEAgAKqrKyMmpqa\nmDlzZhx00EFZx4EGb+DAgfHCCy/kbb3LL788GjdunLf1AGiYMjny59N3Gvjoo4/ij3/8Y3Tq1CmL\nOABAxlq2bBkTJ06M7t27OwIIPsNxxx2X9zVfeOGFuqOBAEhTJuXP0KFD13u+3XbbxZ577hlXX311\nFnEAgAagZcuWMX/+/OjYsaMCCDZh8eLFeV+zW7duMWnSpOjfv3/e1wagYSh4+ZPL5eL222+P1q1b\nR6NGmd1pHgBooCZPnhxf//rXs44BDdLZZ58dZ511Vt7X7dKlS3Tp0iVeeumlvK8NQPYKfs2fsrKy\n6Nu3b2y1VYO53BAA8BmOPPLIgu7v6KOPLuj+oNjsu+++eV+za9euebuwO0Cx6dSpU92dEO+7776s\n49SLTA696dKlSyxcuDA6duyYxe4BgC9g3rx5UV5eHi+++GLB9kd2dtlll2jRosUG25ctWxYrV67M\nIBGf9uKLL8brr78erVu3zuu6kyZNirKysryuCVAMmjRpst6dEK+//voYMmRIrF69OuNk+ZNJ+fPN\nb34zTj311Dj66KOjVatW6/2Sca4xAEDh/ehHP4p27drFAQccEIcddtgGn3/kkUdi5syZMWrUqAzS\n8e9Wr14dQ4cOjdtuuy3rKABJqqysjIiIM888M+Mk+ZNJ+fP888/HbrvttsGt3cvKypQ/AMB6Tj/9\n9Bg8ePB621555ZU45phjMkqUnuOOOy7Gjh0b22+//SZf06dPn+jTp08MGDAgLrnkkpg4cWIBE/Jp\n999/f5x66qnRvXv3rKMAJKmyslL582X95je/yWK3QImprq7+zOsXjB07NrnDOSEFc+fOrbvOUE1N\nzUZfk+oh2VkpKyv73Kf7lJeXx84771zPidict99+O6qrq/Na/vgjLMAnamtrY//99886Rl5lctXl\no446aqPb/QUPKKTKysq4+uqrs44BfErnzp2jpqZmk8XPOuXl5dG0adMCpUrb3XffHc8//3zWMfiC\nBg0alHUEgCSNGzcuFixYkHWMvMqk/Fm0aNEG23K5XCxZsiSDNEApW3c+L1B8pk2bFsuXL886BmTm\npptuyjoCQHJeeeWVuOGGG7KOkXcFPe3rggsuiIiINWvW1H28zmuvvRadOnUqZBwgcd26dYt58+Zt\n8s6CKR7OCfVl1apVsXz58mjevHnWUSLik7sdufBwfh144IHrPa+uro7mzZvXzXzZsmXx5ptvxrPP\nPhs33nhjFhH5lHzemWvMmDHx+9//Pm/rARST6urq5O92WNDyp127dhv9OCJin332icMPP7yQcYAS\n0L1795g4cWIcfPDB622vra2NgQMHJnc4J9SXefPmxfjx42P48OFZR6FAunXrFj179oyePXtGRMSU\nKVNi5syZ2YZiPYMGDYq33nprgz+qflG/+MUv4qKLLspTKgAaooKWP+tOr9hrr702+OsSQH1YV/J8\n+miFVatWxbx58zJKBcVp/Pjx8YMf/GCTR9ORnqqqqqiqqso6Bp/hiiuu+FLlz9ChQ+Pyyy/PYyIA\nGqJM7vbVuHHjWLx4cbRt2zaWL18eo0ePjq222irOPffcBnM4OZCO2traqK2tzToGFL3a2tqoqalp\nEOXP/Pnzs44ADcJbb70Vxx9/fNx6662x3XbbfaGvfe+996K6ujpyuVw9pQOgocjkgs8jR46Mrbfe\nOiI++WvF2rVro6yszOGmANDA9e3bN0aOHBnTp0/PLMP48eM3eedQKEV33313nHnmmXHttdd+oa8b\nPHhwTJ48uZ5SAdCQZHLkz7Jly6J169axdu3amDFjRjz++OPRuHFjp4IBQBEYMWJEtGjRImbOnFnw\no4DGjRsXQ4YMKeg+oRhMmDAhGjVqFLlcLs4999zNvr579+4xa9asAiQDoCHI5Mifr371q7FixYr4\n61//Gh07doztt98+IiLWrl2bRRwA4Auqra2NTp06RU1NTcFutz558uSorKyMVatWFWR/UGzWrl0b\n1dXV8dFHH23yNe+//34cd9xxih+AEpPJkT8/+MEPon///rFmzZq48MILIyLi+eefjw4dOmQRBwDY\nQv9+R6gWLVrE4MGDt3itqqqqzzydbMSIEVu8NpSKCRMmRGVlZey9994b/fyZZ54Z99xzT4FTAZC1\nTMqfQYMGRe/evWPrrbeuu+V7y5Yt4xe/+EUWcQCAL2HdHaGaNGkS48aNW+9zkydP/lynhpWXl0dt\nbW3BjiKCUnTooYfG448/nnUMADKQSfkTEbHHHntERMS//vWviIho3759VlEAgDxYtWpVVFdXr7et\nU6dOG2yLiOjYsWPMnz8/xo4dGzfccEOhIkJJWLBgQTRp0mS9bVdddZXiB6CEZVL+VFdXx6hRo+Lv\nf/973Xn7uVwuysrK4qWXXsoiEgBQT7p167bBtjPOOGODo4SA/Ojfv3/WEQBoYDIpf372s5/FIYcc\nEpdddllsu+22WUQAADKk+AEAKJxMyp/XXnstzjnnnCgrK8ti9wAAAAAlI5Nbvffu3TtmzJiRxa4B\nAAAASkomR/6sWrUqKisrY999941mzZqt97krr7wyi0gAAAAAScqk/OnUqVN06tQpi10DAAAAlJRM\nyp/KysosdgsAAABQcjIpfyIinn766Zg8eXLU1tZGixYtom/fvrH//vtnFQcAAAAgSZlc8Pnee++N\nc845J5o3bx69e/eOFi1axHnnnRcTJ07MIg4AAABAsjI58ufWW2+N2267Lfbcc8+6bd/5znfirLPO\nioEDB2YRCQAAACBJmRz58/bbb0fHjh3X29ahQ4d45513sogDAAAAkKxMyp999tknrrjiivjwww8j\nIuKDDz6IK6+8Mvbee+8s4gAAAAAkK5PTvkaOHBnnnntufOMb34imTZvGO++8E3vvvXdcffXVWcQB\nAAAASFYm5U+LFi3it7/9bSxdurTubl+tWrXKIgoAAABA0jIpf2bMmBG77bZb7LHHHnWlz4IFC+KN\nN96IAw44IItIAAAAAEnK5Jo/o0aNiu233369bdtvv32MGjUqizgAAAAAycqk/HnzzTejRYsW621r\n0aJFLF++PIs4AAAAAMnKpPxp27ZtzJo1a71tf/nLX6JNmzZZxAEAAABIVibX/KmsrIwzzzwz+vfv\nH23bto3FixfHfffdF5dddlkWcQAAAACSlcmRP7169YoJEybEBx98ENOnT48PPvggbr311ujVq1cW\ncQAAAACSlcmRPxERFRUVUVFRkdXuAQAAAEpCJkf+rF69Oq699to49NBDY999942IT27//tvf/jaL\nOAAAAADJyqT8ueyyy2Lu3LkxevToKCsri4iIzp07x913351FHAAAAIBkZXLa16OPPhpTpkyJ7bbb\nLrba6pP+qWXLlrFs2bIs4gAAAAAkK5Mjfxo3bhwff/zxettWrlwZO+64YxZxAAAAAJKVSflz+OGH\nx3//93/H4sWLIyKitrY2Ro0aFUcccUQWcQAAAACSlUn5c84558Ruu+0Wffv2jXfffTf69OkTLVq0\niDPOOCOLOAAAAADJyqT82WabbWLo0KHxwgsvxMyZM+P555+PY445Js4777ws4gAAAAAkq6AXfP7w\nww/jpptuipdffjnat28fZ555Zrz//vtx8cUXx1NPPRVHHXVUIeMAAAAAJK+g5c+oUaOipqYmevTo\nEU888UTMnTs3FixYEEcddVSMGjUqdt5550LGAQAAAEheQcufJ598MiZPnhy77LJLnHjiidGzZ8/4\nzW9+E/vtt18hYwAAAACUjIJe8+eDDz6IXXbZJSIiWrVqFdttt53iBwAAAKAeFfTIn48//jiefvrp\nyOVydds+/Xz//fcvZCQAAACApBW0/Nlll13iwgsvrHu+4447rve8rKwsHnvssUJGAgAAAEhaQcuf\nxx9/PG9rtWnTJm9rsXn5/vc2v8LK57+32RWe+RUvsytu5le8vG8pbr73ipv5FS+zK26b+zcvy/37\nOVcAAAAAJKWgF3wGAAAAoLCUPwAAAAAJU/4AAAAAJEz5AwAAAJAw5Q8AAABAwpQ/AAAAAAlT/gAA\nAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+AAAAACRM+QMAAACQMOUPAAAAQMKUPwAAAAAJU/4A\nAAAAJEz5AwAAAJAw5Q8AAABAwpQ/AAAAAAlT/gAAAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+\nAAAAACRM+QMAAACQMOUPAAAAQMKUPwAAAAAJU/4AAAAAJEz5AwAAAJAw5Q8AAABAwpQ/AAAAAAlT\n/gAAAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+AAAAACRM+QMAAACQMOUPAAAAQMKUPwAAAAAJ\na5R1gC3Vo0ePWLJkSdYxSkabNm1ixowZeVvP/Aorn/Mzu8Izv+JldsXN/IqX9y3FzfdecTO/4mV2\nxW1z8yva8mfJkiWxaNGirGOwhcyveJldcTORlV0JAAAgAElEQVS/4mV2xc38ipv5FS+zK27mV7zM\nruFx2hcAAABAwpQ/AAAAAAlT/gAAAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+AAAAACRM+QMA\nAACQMOUPAAAAQMKUPwAAAAAJU/4AAAAAJEz5AwAAAJAw5Q8AAABAwpQ/AAAAAAlT/gAAAAAkTPkD\nAAAAkDDlDwAAAEDClD8AAAAACVP+AAAAACRM+QMAAACQMOUPAAAAQMKUPwAAAAAJU/4AAAAAJEz5\nAwAAAJAw5Q8AAABAwpQ/AAAAAAlT/gAAAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+AACfafbs\n2bF69erP/ejSpUvWkaHkTJs2bYPvxWHDhmUdC4AGQvkDAGzSiBEjoqKiIho3bvy5HzU1NZHL5eJ/\n/ud/ol+/ftG7d++s/zMgWT/84Q8jl8tFz549N/hevOSSSyKXy8W8efOiX79+0a9fvzj44IOzjgxA\nBhplHQAAaJjatm0bAwYM2OKvP/bYY+PYY4+Nd955Jx599NGIiOjfv3++4kHJu+iii2LUqFGbfV3H\njh1j0qRJERGxYsWKOOGEE2LKlCn1HQ+ABkT5AwBs1C677BJdu3b90us0bdo0+vXrFxERuVwuIiL2\n2muvqKmpibVr137p9aGUTJw48UuVss2aNYtHHnkkWrZsGStXrvQ9CFAinPYFAGzUCy+8UG9rz549\nOxYuXBgnnXRSnHTSSfW2H0jNwIED87LOsmXL4rnnnouOHTvmZT0AGjblDwCwUaeddlq9rt+mTZu4\n44474o477og//elPcfzxx9fr/iAVhx9+eFxzzTVfep2Kioq47777olEjJwMApM5PegBgo2bMmFGw\nfR1++OFx+OGHx4IFC+Lpp58u2H6hGD3yyCOx66675mWtioqKWLNmTZSVleVlPQAaJkf+AAANxqxZ\ns/J2Wguk7OWXX47FixdnHQOAIqH8AQAalAkTJsQPf/jDrGNAg/b000/HSy+9lLf1Ps9dwwAoXsof\nAIrSMcccE7lcLrp27RqtW7eue+y8885ZR0tGdXV1jBw5suD73X777eP222+PLl26FHzfUKouuuii\nOPXUU7OOAZCJRo0a1b2XTJXyB4Ci0bJlyxgyZEgMGTIkfv/730fEJwXFa6+9VveYNm1adO7cOeOk\n5MPJJ5+cdQQoKTfffHPWEQAycfnll9e9l1z3XjO1Pyi64HMBPfvss3Uff+Mb38gwCUBxmjZt2maP\nBqmoqIiKiop45ZVXCpSK+nL++edH+/bt49hjj806CpSECy64IOsIAJk477zz6j4ePXp0RHzyR6jq\n6upk3ocofwpgp512ihkzZkTXrl0jIqJPnz4ZJwJo2D595M5ZZ50VlZWVGaUhSwMHDozq6mrXIymQ\nRo0axaWXXhozZ86MmpqaiIh49dVXY82aNRknoxAWLFiQdYSSl8vl4rrrrovzzz8/Vq9enXUcKGnd\nunWLxo0bx8477xwrV67MOs6XpvwpgB//+Md1xU/EJ7fnHD58eEydOjVmzZqVYTKAhuPiiy+u+ziL\n68wAEa1atdrg6I/rrrsuzj777IwSUSg1NTV5vYA0X9zgwYMj4pM/eNx4443mAQ3A1772taisrEzi\nj1DKn4yMHDkyzjjjjFi5cqULWgIl7eijj47LLrss9txzz7ytOWbMmLprAvHljB07Nk444YTo1KlT\n1lHIyFlnnRWHHXZY3XPvW9LUtm3baNOmTd0RXxSeI1yB+uSCzxlas2ZNHHTQQVnHAMhU06ZN81r8\nRETstttusddee+V1zVK1YsWKTC+gXVZWlsRf24rFkiVLNnrdlz333LPuoQhsGPr27Rvf/va387be\nDjvsEDvssEPe1uOL69evX9YRgE/58MMPY+nSpVnHyAtH/hTA9OnT44ILLogWLVpERUVFPProoxER\nUVVVFcuXL884HUBa5syZE7/5zW+iV69eMXv27Kzj8CVcffXVWUdgI/bbb7+YN29e1jFK3gMPPBBP\nP/30ekdlUdy6d+8eERFTpkyJ2trajNNAabnmmmvi3HPP3WD7/Pnzk7kTovKnAJ555pl45plnolGj\nRrHddtvFu+++m3UkgGTNnTs3rrrqqqxjJKdXr151f7wolF//+tcF3R+fuPHGG+Okk06K8vLyjX7+\n7rvvLnAiKA0TJkyIKVOmxLvvvuv/F6DAJkyYsNHyJyXKnwJau3atH+QA/6ZJkyZx2223ZR2DzyGV\nQ57ZvH/+85/x9a9/PSIi/uu//itOPvnkjBNB6ViyZEnWEaAkVVdXxymnnLLe77wTTzwxXn311exC\n5ZnyBwDYrDfeeCMefvjh+O53vxuXXnppDB06tN73OWzYsDjhhBPqfT9s2oQJE2LChAlZx2ATxo4d\nG5MnT95g+89+9rNo27ZtBokAilfqv/OUPwDAZq1cuTL69+8fX/nKV+Kdd96Jxo0bb/TCwPl0/PHH\nK3/gMzz44IMb3f673/0u3n777c+9zq677hqrV6+O9957L1/RAGhglD8AZKZXr155X9NFnuvPhx9+\nGB9++GFERPz3f/937LTTTtGtW7e6i5Tm07x582LBggV5XxdKwTvvvBNt27aNqqqq6Nix4yZft2LF\nivj+97/vtE6AEuBW7wBkpnXr1nlf85JLLsn7mmzcoEGD4ogjjoiTTz45Xn/99byte/LJJ0f//v2j\nT58+eVsTSs2SJUuif//+n1nsHH/88QW/kDsA2VD+AJCZW265Jbbddlv/81HE3n777bj99ttjjz32\niH333fdLr7f33nvH7bff7gguyIP//d//jfbt22/y81OnTi1gGgCy5LQvADK1atWq6N27d93zSy+9\nNLp06RIREYccckjsuOOOm11j2bJlcdxxx0VVVVV9xWQzVq9eHc8//3yUlZVFRETbtm1jzJgxdZ/f\nc8896+b6aY8++mh885vfjMceeyxWrFhRkLxQKlavXh3Tpk2LZs2axbx58+q2n3jiiRmmAqDQlD8A\nNCj/fhep3r17b7L8mThxYt3HAwYMiCeffLLes/H5LV68OI455pi65926dYuuXbtG27Zt4+qrr46I\niH/84x9x3nnnxSOPPBLf/va3Y8qUKVnFhaT169cvmjdvHnPnzs06CgAZUf4A0GB91ikJ644woThU\nV1dHdXV1RERcc8018be//S0qKirqPq/4gfrz1ltvxVtvvZV1DAAy5Jo/AEDB/XvxAwBA/VL+AAAA\nACRM+QMAAACQMOUPAAAAQMKUPwAAAAAJU/4AAAAAJEz5AwAAAJAw5Q8AAABAwpQ/AAAAAAlT/gAA\nAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+AAAAACRM+QMAAACQMOUPAAAAQMKUPwAAAAAJU/4A\nAAAAJEz5AwAAAJAw5Q8AAABAwpQ/AAAAAAlT/gAAAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+\nAAAAACRM+QMAAACQMOUPAAAAQMKUPwAAAAAJU/4AAAAAJEz5AwAAAJAw5Q8AAABAwpQ/AAAAAAlr\nlHWALfWtb30r2rRpk3WMktG6deu8rmd+hZXP+Zld4Zlf8TK74mZ+xcv7luLme6+4mV/xMrvitrn5\nleVyuVyBsgAAAABQYEV75E+PHj1iyZIlWccoGW3atIkZM2bkbT3zK6x8zs/sCs/8ipfZFTfzK17e\ntxQ333vFzfyKl9kVt83Nr2jLnyVLlsSiRYuyjsEWMr/iZXbFzfyKl9kVN/MrbuZXvMyuuJlf8TK7\nhscFnwEAAAASpvwBAAAASJjyBwAAACBhyh8AAACAhCl/AAAAABKm/AEAAABImPIHAAAAIGHKHwAA\nAICEKX8AAAAAEqb8AQAAAEiY8gcAAAAgYcofAAAAgIQpfwAAAAASpvwBAAAASJjyBwAAACBhyh8A\nAACAhCl/AAAAABKm/AEAAABImPIHAAAAIGHKHwAAAICEKX8AAAAAEqb8AQAAAEiY8gcAAAAgYcof\nAAAAgIQpfwAAAAASpvwBAAAASJjyBwAAACBhyh8AAACAhDXKOgAA0LD16NEjvvrVr27x1//5z3/O\nYxoAAL4o5Q8AsElHH310TJgwIXbcccctXuOyyy6LoUOH5jEVAABfhNO+AICN6tq1a9x7771fqviJ\niPj5z38eq1evrnsMGzYsTwkBAPg8HPkDAGxUWVlZbL311nlZp3HjxnXPL7nkkvjnP/8ZY8aM+dJr\nA5/o2bNn7LLLLhv93KRJk+LOO++Mn/zkJ/HRRx8VOBkADYHyBwAouF/96ldx4IEHxj333BP33ntv\n1nGgaE2aNCkiIg4++OBo1qzZJl930kknRbNmzeK5556Liy++uFDxAGggnPYFAGSiX79+MXHixKio\nqMg6ChSdrbbaKqZMmRL9+vWLfv36fWbxs853v/vduOiiiyKXy0WLFi2iUSN/BwYoFcofACBTs2fP\njr59+2YdA4rKSSedFL17997ir1+2bFk899xz0bFjxzymAqChUv4AAJm79NJLs44AJaeioiLuu+8+\nRwABlADlDwCQufLy8rj88stjq628NYHPY/Xq1bFmzZovvU5FRUVe1gGgYfMOCwDYqJUrV8azzz5b\nsP397Gc/i+HDhxdsf1DM7rrrrpg2bVrWMQAoEsofAIpSRUVFTJ8+PaZPn551lGS98cYb8cc//rGg\n+3QXIsjGqFGjso4AQD1S/gBQNPbZZ5/I5XKRy+Vi9uzZcdBBB8VBBx0Ujz/+eHzlK1/JOh55sGzZ\nsqwjQEnad999o3Xr1lnHAMjMDjvsEK1bt47//M//jNGjRyf3M1H5A0DROPHEEze6/ZBDDokJEya4\nXkw9mDlzZrz55psF298hhxxSsH1BsZs0aVLe1vrud78br732Wt7W44vp3r17DBkyJIYMGRL7779/\n1nGg5Oyyyy4xadKkeO211+Kxxx6LIUOGJPcz0bvkAsrnL2iAUvHss8/WPX76059u8nXHHXdc/PWv\nfy1gstIwZcoUR+NAA3XLLbdkHYE8Oeyww2L06NExevTomDRpUuyxxx5ZR4KSMn369DjssMM22H7P\nPfdkkKZ+KH/qWaNGjeKXv/xl5HK56NevX93pCp07d47OnTvH1ltvnXVEgAajadOm0blz57qflblc\nLvbdd9+6x+bss88+seuuuxYgaWl59dVXC7av3XfffaPb27dvX/e7c5tttilYnlLTpk2b9b7/xowZ\nE507d4527dqt9xqg/rRu3Tr23HPPrGNASenWrdtGt//Hf/xH7LzzzgVOUz+UP/WsVatWccEFF2yw\nfe7cuTF37tz45S9/mUEqgIZn2223jd/97ncxd+7cL7XO9ddfn6dErHPEEUcUbF8PP/xwnHbaaRvd\nvu5359VXX12wPKXmnHPOWe/5WWedFXPnzo1Zs2bFxRdfHBdffHE8+eSTn6uMpTCuvPLKvK43bNiw\nvK7Hlnn44Yfj4osvjoMOOijrKFDSvva1r0VlZWXWMfJC+VPPNncLziFDhhQoCUDDNnv27IKWDDRc\nv/rVr+L000+ve15ZWRkdO3ase/7nP/85i1gl4cEHH9zo9tatW8fIkSNj5MiRsfvuu8d+++1X4GRs\nyhVXXJHX9dxxr+EYOXJkTJo0ab2ffwBbqlHWAVL261//Ojp37lz3vLq6Orp27ZphIoCGpaysLPba\na68YP358fO1rX8s6Dp+hvLw8XnzxxYLsq0mTJjFu3LhYunRpbLXVVusdzTV8+PCC336+lMybN2+z\nr3nvvfdixYoVBUhDoS1cuDA6dOiQdYyS9uGHH8bLL78cEZ98Pw4cODDjREAqlD/16JRTTlnv+YEH\nHhinnHJKnH/++dG8efN46KGH4oknnsgoHUD2Ro8eHeeee27WMWigfv/732cdgY0YNGiQm1g0ICNG\njMjLOnPmzIn+/fvnZS2+uKlTp8Zpp50WP//5z+O2227LOg7w/1u6dGk8+uijWcfIC6d9FdDKlSvj\nqquuioqKimjbtm0cf/zxcdVVV2UdCyATN998s+IHGpg33ngjLrvsss98zd13312gNHweZ511Vl7W\nWXdNLbLx1FNPxZtvvqn4gYz06tVro9tXrFgRM2fOLHCa+uHInwwsXbo06wgAmRowYECceuqpWceg\nSAwfPjxGjRqVdYyS8PHHH8eLL74YM2bMyDoKBTJ37tzo1q1brF27NusoJW9TdxsC6t+CBQs2+N13\n4oknFvSOp/VN+QNAwT3//PMxe/bs2GuvvbKOQgP3+uuvx8MPP5x1jJJy9913O7qniFxwwQVf6o5f\nl156qeIHKHkLFy6MAw88MOsY9cppXwAU3Pz586NHjx5Zx6AIrFy5Mp599tmsY0CDdeutt673/Prr\nr4+//e1vn+trH3vssbjnnnvqIxYADYzyB4BMvPfee/WyrguW1o/33nsvpkyZUm9zA7bMW2+9FX37\n9o233norfve738VZZ531uY6qfPzxx6NXr16xatWqAqQEIGtO+wIgM4MGDYqbb745b+u5jlD9WbRo\nUfTp0yeOOuqo2HHHHTf6mvq4UOlFF12U9zUhNQ8++GCccMIJ8ac//Wmzr/373/8eV1xxRdx1110F\nSAZAQ6H8ASAzt9xyS9x5550REfHRRx/FjTfeGD/96U/jo48+2qL17rjjjnzGYyPuv//+TX7u7rvv\njosuuiiGDh1akP0B/+ffi59Zs2Zt8nUVFRWxevXqQkQCoAFR/gCQqXWnHJSVldVtW/fx6NGjY8iQ\nIZtdY9myZXHcccfFmjVr6ickn8uqVati2LBhMWzYsLjvvvs2+/qjjz56o9vffffdeOyxx/IdD0rG\n/vvvX/fx2LFjo3Xr1nH77bfHAw88kGEqALKk/AGgwTrvvPPiL3/5SwwcOPAzr+XzwgsvRFVVVeGC\nsVnHHHPMZl8zYMCAjW5/5513YsqUKfmOBCWpsrIy6wgANADKHwAatHvvvTfuvffeiIiN3o64WbNm\n8fbbbxc6Fnmwbq4AANQv5Q8ARaNRI7+2AADgi3KrdwAAAICEKX8AAAAAEqb8AQAAAEiY8gcAAAAg\nYcofAAAAgIQpfwAAAAASpvwBAAAASJjyBwAAACBhyh8AAACAhCl/AAAAABKm/AEAAABImPIHAAAA\nIGHKHwAAAICEKX8AAAAAEqb8AQAAAEiY8gcAAAAgYcofAAAAgIQpfwAAAAASpvwBAAAASJjyBwAA\nACBhyh8AAACAhCl/AAAAABKm/AEAAABImPIHAAAAIGHKHwAAAICEKX8AAAAAEqb8AQAAAEiY8gcA\nAAAgYcofAAAAgIQpfwAAAAASpvwBAAAASJjyBwAAACBhjbIOsKW+9a1vRZs2bbKOUTJat26d1/XM\nr7DyOT+zKzzzK15mV9zMr3h531LcfO8VN/MrXmZX3DY3v7JcLpcrUBYAAAAACqxoj/zp0aNHLFmy\nJOsYJaNNmzYxY8aMvK1nfoWVz/mZXeGZX/Eyu+JmfsXL+5bi5nuvuJlf8TK74ra5+RVt+bNkyZJY\ntGhR1jHYQuZXvMyuuJlf8TK74mZ+xc38ipfZFTfzK15m1/C44DMAAABAwpQ/AAAAAAlT/gAAAAAk\nTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+AAAAACRM+QMAAACQMOUPAAAAQMKUPwAAAAAJU/4AAAAA\nJEz5AwAAAJAw5Q8AAABAwpQ/AAAAAAlT/gAAAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+AAAA\nACRM+QMAAACQMOUPAAAAQMKUPwAAAAAJU/4AAAAAJEz5AwAAAJAw5Q8AAABAwpQ/AAAAAAlT/gAA\nAAAkTPkDAAAAkDDlDwAAAEDCGmUdAABo+Lp06VL3cU1NzQaf/973vhfz58+ve75q1apYsGBBQbIB\nAPDZlD8AwGcaPHhwjB8//jNf8+CDD673fNmyZTF+/PgYP358rFixoj7jARHxox/9KNq1axczZ86M\nRx99NOs4ADQwyh8AYKM6deoUf/jDH6JTp05f+GtbtmwZI0eOjHvvvVf5A/XsuOOOi7Fjx8b2228f\ny5cvj2XLlsXChQujb9++WUcDoIFQ/gAAG9W1a9coLy//UmvU1NRE165dY/ny5UogyLN27drFokWL\n1tvWvHnzaN68eZSXl0cul4uIiOrq6jjooINi5cqVWcQEoAFQ/gAAGzV58uS8rFNTUxPTpk2Lqqqq\n9baPGjUqL+tDqbn44osjIuLEE0/8XK/v1q1b3HXXXTFz5kzfdwAlSvkDANS7Qw45JA455JD1tg0Y\nMCDGjRsXN954Y0apoDiNHDnyC39Nnz59ok+fPjFgwIC45JJLYuLEifWQDICGyq3eAYA6c+fOjWbN\nmkV1dXW976u8vDxuuOGGyOVy0aFDh3rfHxS7Ll26xNSpU7/UGuXl5XHPPfdELperezRu3DhPCQFo\nqJQ/AECdu+66K7p167bZu3vlW75OMYOU1dTURK9evfK+7rBhw/K+JgANi/IHAKgzYsSImD59evTu\n3bug++3UqVOcdtppBd0nFJshQ4bUy7rrriEEQLqUPwAUrVatWsXbb78d7dq1yzpKUs4444zo3Llz\nQfe57bbbxg033BBdunQp6H6hmFRXV8e7775bL2s/8sgjscMOO9TL2gBkT/kDQFFq165dPPDAA9G0\nadO46aabso6TlHHjxkW3bt0y2ffpp5+eyX6hGDzyyCPx2GOP1cvahx12WNxyyy1OAQNIlPIHgKLR\ntWvXmDNnTsyZMyemTZsW++23X0REHHDAAdG3b9+M06Xn6KOPLvg+Kysr47777iv4foGIY489NkaN\nGhWXXnpp1lEAMtOpU6eYM2dOcjejUP4UWPPmzaNLly6xxx57ZB0FoGg0b948Fi5cGNXV1VFeXh7l\n5eXr/UIuKyuLyy+/PBo1apRhyvTcf//9UVlZGatWrSrofsvLy6NZs2YF3SfwibKysrjwwgvj8MMP\nzzpKyenQoUPkcrm4/vrrY5tttsk6DpSUJk2axLhx4yKXy8Urr7wS5eXlMX/+/JgzZ07W0fLGu+QC\n2mWXXWLixInRs2fPeP3116N///4xa9asrGMBNGjrfnbuvvvum3zNjBkz4jvf+U7hQpWQcePGxemn\nnx5du3Yt2D47d+4cEydOjKqqqrpt48ePjxUrVhQsQynr3r179OrVK1asWFHwu76xeffcc0/U1NTE\n0KFD620fs2fPjnnz5tXb+mzo4IMPjokTJ0bEJ0dARkQsX748qqqq4oknnsgyGpSEq6++Ov1Tz3NF\nqn379rmIKIrHySefnJszZ05u7ty56/03vPHGG7kWLVpknu/zPNq3b1+y80vhkc/5mZ35Ffrx6Z+d\nG/Puu+/m+vbtm3nWVGfXqVOnvP13bKm5c+fm5syZkzvttNPMr54f559/fi6Xy+U++uij3Jw5c4oq\ne33MriHOr6ysLNetW7fcU089ldf/znXuuOOOZObX0Ga3qceIESM2yD537txc8+bNM89mfqXzKOXZ\nbcqcOXMyz5av+Tnyp561atUqLr/88mjZsuUGnysrK8sgEUDD8e93dqqpqdnidXbYYYeYPHlyRES0\nbds2lixZ8qWz8X/mzZsXnTt3jpkzZ0bz5s0zybDu7mMtWrTIZP+lolGjRnXvWZo0aRLl5eXx6quv\nrvea3XffPRYtWpRBOtbJ5XJRXV0d3//+980iEcuXL49Vq1ZFkyZN1tuey+UySgSlo7q6OusIBeGa\nP/Vs6dKl0a9fv3jjjTfW2/7666/HMcccE7W1tRklA8heTU1N3SNffvWrX+VtLf7PvHnzMj8FqLa2\nNqZPn55phtS1atUqhgwZ8pmvufPOOwuUhs255ZZbso5AnowbNy7mz58fERFVVVUxcuTIePTRR9M/\nDQUoGEf+FMBTTz0VBx544HrnTu+00051f0UFIH969eoVvXv3jqlTp2YdhTw74IADXIekni1dujSu\nueaaOPfcc7OOQkbWrFkTF154YdYxStYrr7wSAwcOjOXLl0eTJk3i//2//5d1JCARyp8CmT9/fnTo\n0CEeeuihiIjo1q1bxokAslVft/Nu2rSpN8v1oEmTJpmd8rWO4qf+rV27NoYMGRKHHHJI7L333lnH\nIQMvvfRSvPbaa1nHKEmf/v+DVatWxfLlyzNKA6WjW7duGz3FsmvXrvHSSy9lkKh+OO2rgBYuXBjd\nunVT/ABQdDp16uT0AyIi4vbbb886AvXk3nvvjb322ivrGAAFN3LkyORPLXfkDwAFd9hhh8Whhx6a\ndQyKyJFHHpl1hJIyYMCAmDp1anzve9/b4HOlcmHMUjN16tT48Y9/nHUMgEyMGDEiWrRoUXeU8xln\nnJFxovxT/gBQcE7NKj7rTj8o1KlftbW10bRp02jSpEkMHz48HnjggYLsl0+sO12d9L322mux++67\nx9q1a7OOApCp2trauhsypXi0s9O+ACi4mpqaejuHuj7XLmWFvNtXbW1tDBw4MIYMGeIOX1BPfvnL\nX8bIkSPjiCOOUPwAlABH/gBQcNXV1dGnT5/4xz/+kfe1X3rppbzeOp7/M378+PjBD34QHTt2rNf9\nrLur1/Tp0+ORRx5xoWfYhEGDBsVuu+0WTz311Of+mvLy8oj45Gflv/71r/qKBkAD48gfADKxePHi\nejlCZ8mSJXlfk0/U1tZGp06dYv78+Xlfu6amJk4//fQoKytbr+xR/MCmLVq0KGbOnBllZWV1j1NO\nOSU++uij9V734YcfxsknnxxlZWVRXV0d1dXVih+AEuPIHwAyU1FREcOGDYuIiOHDh+dlzZ/+9Kd5\nWYdN6969e9258C1atIjBgwdv8VpVVVUxffr0GDFiRJ7SQWmbMGFCNG7cOG688caIiBg7dmzMmTPH\nXdoASpzyB4DMrF27tu5/+u+9996IiLjzzjtjn3322aL19t5773xF4zPU1tbWza1JkyYxbty4DV7z\n4osvfuYaRx55ZMyfPz9qa2tj+fLl9W3NIXEAACAASURBVBETStZNN90UM2bMiIiIV155JVavXp1x\nIgCypvwBoEFYd/vofffdt27bpEmTokuXLtG8efNN3mXq5ZdfjieeeCJ+8pOfFCQn61u1atVGb/1d\nVlaWQRpgnY19XwJQupQ/ADRY/fv3j4iInj17Rs+ePTf6miuuuGKD61sAAAD/R/kDQINXVVUVVVVV\nWccAAICi5G5fAAAAAAlT/gAAAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+AAAAACRM+QMAAACQ\nMOUPAAAAQMKUPwAAAAAJU/4AAAAAJEz5AwAAAJAw5Q8AAABAwpQ/AAAAAAlT/gAAAAAkTPkDAAAA\nkDDlDwAAAEDClD8AAAAACVP+AAAAACRM+QMAAACQMOUPAAAAQMKUPwAAAAAJU/4AAAAAJEz5AwAA\nAJAw5Q8AAABAwpQ/AAAAAAlT/gAAAAAkTPkDAAAAkDDlDwAAAEDClD8AAAAACVP+AAAAACRM+QMA\nAACQMOUPAAAAQMIaZR1gS33rW9+KNm3aZB2jZLRu3Tqv65lfYeVzfmZXeOZXvMyuuJlf8fK+pbj5\n3itu5le8zK64bW5+ZblcLlegLAAAAAAUWNEe+dOjR49YsmRJ1jFKRps2bWLGjBl5W8/8Ciuf8zO7\nwjO/4mV2xc38ipf3LcXN915xM7/iZXbFbXPzK9ryZ8mSJbFo0aKsY7CFzK94mV1xM7/iZXbFzfyK\nm/kVL7MrbuZXvMyu4XHBZwAAAICEKX8AgP+PvXuPjrK+8wf+CRKNq4IKSOUHVkEUQtBSbVUKXrYq\neFmRq1VbXLaVVcNxV1hd92AjaNVtVVZbQattxWprDd4oW9vG3VUsslbRI5cEjQFFg0pEUOoNROf3\nhyUrEu4z82Seeb3OyWnyzMzneZ/97sTJm+cCAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFFP+AAAA\nAKSY8gcAAAAgxZQ/AAAAACmm/AEAAABIMeUPAAAAQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4A\nAAAApJjyBwAAACDFlD8AAAAAKab8AQAAAEgx5Q8AAABAiil/AAAAAFJM+QMAAACQYsofAAAAgBRT\n/gAAAACkmPIHAAAAIMWUPwAAAAAppvwBAAAASDHlDwAAAECKKX8AAAAAUkz5AwAAAJBiyh8AYIft\nueeecc8998TYsWNjwIABzdsHDx4cgwcPjj322CPBdAAARES0TToAAFCYrr322ujTp0+cccYZce65\n58bq1atj1qxZERExevToiIiYOXNm1NbWxsSJE5OMCgBQ1JQ/AMB2KS8vj+effz7atm0bJSUlzdv3\n2Wef5tJngyFDhsQZZ5wRH374YfzgBz/Id1QAAMJpXwDAZlxyySWRyWTin/7pn2L48OFRXV0dmUwm\namtro7S0dKPiZ0tKSkri6quvjt69e+c4MRSHs88+OzKZzDZ/DR8+PIYPHx5lZWVJRwcgIY78AQBa\nNGXKlIiIuOmmmxJOAnxeJpPZrufff//9ERHxyCOPxLPPPhtVVVW5iAVAK+bIHwCg2YajdLb3j8tt\nUVpa2vxVV1cXmUwmJk+eHKWlpVnfF6TZQw89tEOvO/XUU+P73/9+ZDKZ2HvvvbOcCoDWTPkDADTr\n0qVL/N3f/V1OZs+fPz/WrVsX69ataz4FrKqqKtatWxdnnHFGTvYJtOzss89OOgIAeaT8AQCaLV++\nPB5++OG87/eee+6Jc845J+/7hWI1bdq0uPrqq5OOAUCeKH8AgI1MmjQpnnrqqbzuc6+99oq+fftG\nmzY+mkC+XHHFFXHhhRcmHQOAPPAJCwDYxDHHHJP3fV5++eVx6KGH5n2/UGg++eSTeOyxx7Iya9q0\naTFu3LiszAKg9VL+AFDwZs+eHbNnz462bd3EEki/9evXxy9/+cuszfvJT34SV111VdbmAdD6KH8A\nKEjDhg2LTCYTmUwmjj322Dj22GPj448/TjpWqvTq1SvWrFmTdAygBR988EF89NFHWZv3/e9/P84/\n//yszQMoRPvtt1906dIl9tprr6SjZJ3yJ4/23XffmDBhQkyYMCHpKAAFpX///rHPPvtstO2BBx5o\n8bnuGpU9L774YowcOTLefvvtvOzvySefzNu+oNBVV1fHE088kdWZt99+e1bnARSS/v37x4IFC2L5\n8uUxY8aMmDBhQpx33nlJx8oa5U+e/OpXv4onnngibrjhhrjhhhti3rx5MW/evOjUqVPS0QBatS9/\n+csxY8aMeOKJJ+Luu++OiIhHHnlks88fPXp0vqIVhZqamlixYkVe9nXQQQfFHnvskZd9se0eeOCB\nmDdvXnz7299OOgpfUFlZmfWZ+b7YO/+nurq6+W+Egw46KOk4UFR69OgRM2bMiM6dO0dExKBBg+KG\nG26If/mXf0k4Wfa4OEKOHXvssTF79uxNth9xxBEREVFaWprvSACtUps2baJHjx4REbH//vtv8ruz\nS5cuUVFRsdU/QPfcc8/Yc88947333stZVnKjS5cusXTp0jjwwANj2bJlSccpas8991z069dvo213\n3313vPLKKzFnzpyEUvFFDQ0NWZ/51a9+NTp37py30pfPTJo0KUaOHNn889KlS6O+vj6OOeaYWLVq\nVYLJoDhs7vdpRUVFVFVVpeK6aI78ybEzzzxzi4+PHz8+T0kAWq9zzz03rrnmmqivr4/6+voWS/Nt\n1a1btzjggAOymI58+/Of/xzHHnts0jGKUrt27aKqqiq+9KUvtfj4n/70pzwnIt9KS0vjRz/6UdIx\niIhDDjnEndiArHHkT45ddtll8cknn2z2cLEJEyak6lAygO2x6667xvz586Nbt25ZO91n8eLFUVdX\nl5VZJKNz585x//33x9tvvx29e/dOOk5RefbZZ+Pggw9OOgYA5M2sWbOSjpAXjvzJsfXr10dTU9MW\nn/Ozn/0sT2kAWpeSkpLo1auX67ywiU6dOkWvXr2a7+iWyWSiV69eScdKvQEDBsTy5cu3+JydOTIP\n2HYffvhhvPnmm0nHgNTr3r37Fh+/4IILUnEdLkf+5MHs2bNj+fLl8f/+3/9r3tbY2Bg//vGPIyLi\n+uuvTyoaAGzVjTfeGD//+c+TjhEzZsyIvn37Jh0j1VasWBHDhg2L4447brOn/hx00EFx9NFHuzBw\nK/Dd73436QhkyaOPPhrvv//+RttWrlwZd955Z0KJgIiIN998M0aMGBEvv/xy0lF2mvInD55++uk4\n4ogjorS0NMaPHx+XXHJJrFixQukDFL1cXKz0lFNOiVNPPXWLdwRj+/ziF7+IZcuWxX/9138lmuPE\nE09MdP/F4umnn46nn346nnnmmXjsscc2ebxbt27xla98RfnTCowaNSrrMz/++OO45JJLsj6XLXvy\nySfjySefTDoGFKVvfvOb8cYbb7T42Fe+8pXUXABf+ZMnG/4fZvz48S7yDPBXXbt2zfrMv/mbv4nd\nd98963OL3dKlS+PVV19N5GLay5Yti+OPPz41H74KxRtvvBHLli2L1157LV588cX43ve+l3QkvmDR\nokVx8sknZ2XWypUr44UXXoiBAwdmZR5AoXjzzTeje/fu8ctf/nKj7d/5zndS9dlD+QNAIs4///yk\nI7AdXn755RgxYkTMnDkz9t9//7zt9/XXX48RI0bEK6+8krd98pkVK1bEiBEjYt68eUlHYTMmTJgQ\nS5Ysaf556tSpOzzrO9/5TvzhD3/IRiyAgvPyyy+nvvxW/gCQiFNOOSXpCGynZ555Jnr37h0VFRUx\nZ86cvOxz1apVyoeEvPPOO/5vXwCmTZvW/P2OlD+VlZXxm9/8JlatWpXNWAC0Mu72BUAihg0bFrvu\numssWrQo67MPP/zwrM/kM++++248+eSTMXr06KipqYm5c+cmHQn4qyeeeGKrz6mpqWn+KikpiWnT\npil+AIqA8geAxHz88ccxdOjQrBdA++67b1bnsam77747Bg0aFKeddlqMGTMmXn/99azvY+LEifH9\n738/63MhrU455ZQYM2ZMLFu2bLPPGTRoUPMXAMVD+QNAohoaGuKrX/1qdOnSJSvzysrK4uKLL87K\nLLbunXfeienTp8dBBx0URxxxxE7P69evX5SVlUVZWVlcd9118fDDD2chJRSHDz74IKZPnx6HHHJI\nlJWVxTXXXJN0JABaCdf8ASBxH3/8cbzxxhtRUlIS11xzTfTu3Xuzz62oqIiePXu2+NjMmTNj7dq1\nuYrJFqxbty6ee+65KCkpiYjPbgd+8803R0TE0KFDt/r6m2++Of75n/85pxmhWKxbty4iIq644oq4\n4oorEk4DQGug/AGgVZk4ceIWHz/ssMPi0EMPbfGxBx54IBeR2AGvvfZaDBs2LCIixo8fHzfeeGOL\nz6uuro77778/ZsyYkc94AABFRfkDQEFZsGBBLFiwIOkYbIcpU6bElClTYsGCBVFeXh4REX379o3F\nixcnnAwAoDgofwCAvDjssMOSjgAAUJRc8BkAAAAgxZQ/AAAAACmm/AEAAABIMeUPAAAAQIopfwAA\nAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjyBwAAACDFlD8AAAAAKab8AQAAAEgx5Q8AAABAiil/\nAAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAAIMWUPwAAAAAppvwBAAAASDHlDwAAAECK\nKX8AAAAAUkz5AwAAAJBiyh8AAACAFFP+AAAAAKSY8gcAAAAgxZQ/AAAAACmm/AEAAABIMeUPAAAA\nQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApFjbpAPsqKOOOiq6du2adIyi0aVLl6zOs375\nlc31s3b5Z/0Kl7UrbNavcPncUti89wqb9Stc1q6wbW39SjKZTCZPWQAAAADIs4I98mfAgAHR2NiY\ndIyi0bVr15gzZ07W5lm//Mrm+lm7/LN+hcvaFTbrV7h8bils3nuFzfoVLmtX2La2fgVb/jQ2Nsay\nZcuSjsEOsn6Fy9oVNutXuKxdYbN+hc36FS5rV9isX+Gydq2PCz4DAAAApJjyBwAAACDFlD8AAAAA\nKab8AQAAAEgx5Q8AAABAiil/AAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAAIMWUPwAA\nAAAppvwBAAAASDHlDwAAAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFFP+AAAAAKSY8gcAAAAgxZQ/\nAAAAACmm/AEAAABIMeUPAAAAQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjyBwAAACDF\nlD8AAAAAKab8AQAAAEgx5Q8AAABAiil/AAAAAFKsbdIBAIB0qKqqanF7U1NT3HbbbXlOAwDABsof\nAGCHPPzww9GjR4/mnysqKlp83kcffRSVlZUxZMiQWLp0ab7iQVEaM2ZMjB8/Pvr27Zt0FABaEeUP\nANCi3XbbLbp37x6zZs2Kv/u7v9vosXHjxsWQIUO2aU5ZWVlUVFTEkiVLNtreo0cPZRDspKOPPjr+\n93//d5PtmUym+fva2toYOXJkRER88MEHsWzZsrzlA6B1UP4AAC06+OCDY9GiRRERUVdXl/X5c+fO\njVGjRsUTTzyR9dlQDM4999y45557tvq8Pn36NL+HX3nllbjzzjsjIuKqq67KaT4AWg/lDwDQooce\neiin8zt37hzV1dXRv39/RwDBdjrzzDN36FpaBx54YEyePDkiovlooH79+sX69euzmg+A1sXdvgCA\nFvXs2TPn++jcuXOUl5fnfD+QJrvttls89NBDseeee+7UnIqKiqioqPAeBCgCyh8AIFGzZs1KOgIU\ntfnz5ycdAYAcU/4AAC0aOnRo3vb14IMP5m1fAADFRvkDALTopZdeytu+hg4dGplMJk4//fS87RP4\nP127dk06AgA55ILPABScSy65JPbaa6/mnxsaGuLXv/51gonS6a233orZs2fHcccdl7d9zpo1Ky68\n8MIdupAtsONuuummGDFiRNIxAMgR5Q8ABWXChAlx3XXXRWlpafO2P/zhD8qfHGhqaorHH388r+VP\nRERlZaXyB/LsxBNPjJNOOikeffTRpKMAkANO+0pAjx49ko4AUHB69OgRmUwmbrjhho2Kn4iIwYMH\nx+jRoxNKlm6TJk2Kurq6vO6zoqIiqqqq8rpPPtO2bdvo3bv3Rl8vv/xyHHTQQUlHI8fat28fNTU1\n0bt376SjFLUN77tdd9016ShQVHbbbbeYOnVqZDKZyGQy8ZOf/CR69+4d3bt3Tzpa1ih/8qB///5R\nVVXV/PXUU0/F+eefn3QsgIJx3HHHxVNPPbXF55x55pnRpo3/rOXCtGnTko5Anvzwhz+Murq6jb4O\nPPDAmDNnThxzzDFJx+OvPvnkk5g5c2ZOZrvzV3IuvPDC5vfdjTfemHQcKBoXXnhh3HjjjXHRRRc1\nbxs3blzU1dXF3Llz49hjj00wXfb4lJxjnTt3jgceeCAmT57c/NWxY8e4+eabY+HChbHffvslHRGg\nVVu4cGFUV1dHx44dt/i8oUOHxoIFC/KUqrhMnTo16Qjkyfjx41vc3qVLl3jwwQd9bmkl1q9fH3//\n938fc+fOTToKWTRu3LiNvl+4cGFccMEFCSaC4jBu3LiorKxs8bHOnTvH8ccfn99AOaL8ybEVK1bE\nlClTNtm+++67R6dOnRJIBNB67bPPPhudblJXVxcVFRXb/Adnnz59cpyweFVUVOR1f5MnT47TTz89\nVYdbt3Z/+tOftvj4l770pSgvL48vf/nLeUrElrzzzjvx3nvvZX3u4sWLsz6THbM9//0D2BrlTx7M\nnTs33njjjY22TZ48OYYNGxZNTU0JpQJoXcrKyuLuu+/e6HQT154obrNmzUrV4dZp8Nhjj8Xjjz8e\nRxxxRNJRit7hhx+ek3J0xowZWZ/Jtpk2bVrceuutzT83NTXF7NmzE0wExaFYTm9X/uTBk08+GQMH\nDtxo26RJkxyqC/A5zz33XJx22mlJx6CV6dy5c1RXVzsCqBU58MAD42tf+1rSMYpev3794uCDD876\nXBdbT87UqVPjxBNPjKlTp0ZFRUV84xvfUP5AHhTL6e1u9Z4nS5YsiZKSkqisrNzoQlIAxa59+/bx\nu9/9zlE+rVj37t1j0aJFie2/c+fOUV5eHkuXLk0sQzEYOHBgZDKZrT6vrq4uVq9enYdEUHwOOeSQ\npCMAnzNz5sy46qqrko6RFY78ybOpU6e6JgXAX+2yyy4xffr0+MY3vpF0FLZg1qxZSUdoFRmKwedv\nUNGSKVOmRJ8+feK+++7LczI+b5dddokhQ4bkZPbVV1+dk7kArdnnT7ncYNq0aXHmmWcmkCY3HPkD\nQGLmz5+f9UJ8ypQpm71jETtmyJAh8dJLLyUdgzyYNGlS8/efv/ZLeXl5fP3rX49/+7d/SyAVX/Tp\np5/GvHnzcvJHifIHKEaXXHJJTJ06NWbOnNlcrjc0NCScKruUPwAkJhdHQh5wwAFZn1nsGhoaomfP\nnjF37ty83amyqakpVq5cGRERt9xyS4v/Ikdu1dbWbvS9CwG3HplMJmpra6Ouri522WWXOPTQQ3d6\n5po1a2LEiBFZSAdQeNauXRu1tbU5uZZaa+G0LwBSZcNt4smuhoaGGDVqVLz11lt52d+G06T79Omj\n+IEWPPzww9GnT5845phjsjLv/PPPj0cffTQrswBofZQ/ACQmF7eLLi8vj/Ly8qzPJeLxxx+P/v37\nx9ChQ3O+r4suusgdviAPJk6cGBUVFVFdXZ10FAByyGlfACTmueeei5KSkoiIaNeuXbzwwgux//77\n7/Tcrl277vQMWtbQ0BANDQ1RUlISv/3tb6O8vDx69OiRldkbTvV66aWXUnWBRcil1atXxznnnBNX\nXHFFRER06NAhOnfuvNXX7bPPPvHOO+/kOh4ArYTyB4BWYc2aNTFkyJA47bTTol27dnHJJZfs8Kxv\nfOMbcfPNN2cxHS0544wzYr/99ovq6uo47rjjdmjG448/HrNnz46IiMcee6z5e2Db3XvvvXHvvfdG\nxGe//0466aRNnnPSSSdF//79Y9myZTF9+vT46KOP8h0TgAQpfwBoNZ555pl45plnom3btvHzn/88\nIiIWLVq0Ta+dOHFizJw5MyIiXnvttZxlZGNNTU0xatSo6NSpU/Ts2TMeeuihbXrdkCFDYsmSJdHU\n1JS36whBMXjyySfjySef3GT7rbfeGh07dowPPvggXn755QSSAZAk5Q8Arc769eub7zS04bSwiM+u\n5/P5Ow5dddVVcd999+U9HxtramqKpqamqK2tbV6vDevXo0eP2G233ZpP6crFHd6ArVuxYkWsWLEi\n6RgAJET5A0DBqKurUx4UiA3rVFlZGZ06dXJKFwBAgpQ/AEDOTJ06NekIAABFz63eAQAAAFJM+QMA\nAACQYsofAAAAgBRT/gAAAACkmPIHAAAAIMWUPwAAAAAppvwBAAAASDHlDwAAAECKKX8AAAAAUkz5\nAwAAAJBiyh8AAACAFFP+AAAAAKSY8gcAAAAgxZQ/AAAAACmm/AEAAABIMeUPAAAAQIopfwAAAABS\nTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjyBwAAACDFlD8AAAAAKab8AQAAAEgx5Q8AAABAiil/AAAA\nAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAAIMWUPwAAAAAppvwBAAAASDHlDwAAAECKtU06\nwI466qijomvXrknHKBpdunTJ6jzrl1/ZXD9rl3/Wr3BZu8Jm/QqXzy2FzXuvsFm/wmXtCtvW1q8k\nk8lk8pQFAAAAgDwr2CN/BgwYEI2NjUnHKBpdu3aNOXPmZG2e9cuvbK6ftcs/61e4rF1hs36Fy+eW\nwua9V9isX+GydoVta+tXsOVPY2NjLFu2LOkY7CDrV7isXWGzfoXL2hU261fYrF/hsnaFzfoVLmvX\n+rjgMwAAAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFFP+AAAAAKSY8gcAAAAgxZQ/AAAAACmm/AEA\nAABIMeUPAAAAQIopfwAAAABSTPkDAAAAkGLKHwAAAIAUU/4AAAAApJjyBwAAACDFlD8AAAAAKab8\nAQAAAEgx5Q8AAABAiil/AAAAAFJM+QMAAACQYsofAAAAgBRT/gAAAACkmPIHAAAAIMWUPwAAAAAp\npvwBAAAASDHlDwAAAECKKX8AAAAAUkz5AwAAAJBiyh8AAACAFGubdAAAoPU6+OCD4+CDD97s4++9\n917MmTMnj4kAANheyh8AYLO+/e1vx5VXXrnZx1evXh2zZs2KiIjGxsaYOHFivqIBALCNlD8AwA7b\nZ599YvTo0RERkclk4tJLL93o8cMPPzwWL16cRDQAAP7KNX8AgKwoKSmJ0tLSjb7q6uripJNOSjoa\nFIXhw4c3f2UymfjNb34Tw4cPTzoWAK2AI38AgJyaMWNGnH/++TFjxoyko0AqDR48OL73ve9tUvSc\nddZZcdZZZ8UDDzywyWvOO++8eP/99/MVEYCEKX8AgJxq3759VFdXx+GHHx4LFixIOg6kQps2bWKX\nXXaJdevWbfW5LR39M3z48CgpKclFNABaIad9AQB5MX/+/Bg9enSMHj062rdvn3QcKFht2rSJn/70\np9tU/GzJiSeemKVEALR2jvwBAFr05S9/Oc4999yszrzrrrsiImLOnDnx3nvvRUTEP/7jP8arr76a\n1f1AmpWWlsb3vve9nZ7z61//Os4555z4r//6ryykAqA1c+QPANCiZcuWxa9+9auczB4wYEAMHjw4\nBg8eHMuWLYtMJhPXXXdd7L333rH33ntHWVlZTvYLhW6PPfaIjz76KCuzOnXqFI8++mjst99+scce\ne2RlJgCtk/IHANisZ555JlatWpWXfV1++eWxevXqWL16dcyYMSMuuOCCvOwXCkm2j8aLiFixYkX8\n6U9/iu7du2d9NgCtg/IHANis3/3ud/Hmm2/mfb+nn3563HrrrTF9+vS87xtas9tvvz0nc/v16xez\nZs3KyWwAkqf8AaBglJaWRpcuXaJLly7RsWPHiIjYf//9o0OHDrH33nsnnC69+vTpEytWrEhk30cc\ncUQi+4ViVF5evtMXkQYoVHvttVe0bZveyyIrfwAoGD/60Y9i+fLlsXz58vjzn/8cEyZMiIaGhpgz\nZ048+uijcdlllyUdMbW++c1vJh0ByIM2bdrEKaecknQMgLzq0KFD3H///XHttdfGhAkT4phjjkk6\nUtalt9ZqZX71q1/FoYceutG2m266Ke65556EEgEUhnnz5jV///mjQLp37x433HBDRET06tUrIiLG\njRuX33AACejfv3/MnTs3J7N32WWX+Na3vhW///3vczKfzauuro7u3bvHT3/607jjjjuSjgNFZfbs\n2dGnT584+eSTIyLi9ddfjwEDBsTLL7+ccLLsUf7kSe/evaNfv34bbbv77rvjf/7nf+L1119PKBVA\n69K+ffvYb7/9Yv/994/Zs2dv9+ufeuqpKCkpyUEyKD6lpaVx4IEHxiuvvBJt2rSJAw44ICIiXnrp\npYST8dxzz+VsdiaTSeQ6X8Vu0qRJMXLkyIj47LpOt99+exx44IGxbNmyhJNBcejTp89GP3fp0iWW\nLl0ap59+epx88slx6aWXFvxpsU77AqBVKCsri1/96ldRX1+/Q8XPBhs+PJNdb7311k6ty47q1KlT\nHHvssXnfb7EbO3Zs3HjjjVFfXx833HBD3HzzzVFfXx/19fVx3nnnJR2v6E2cODFns6+88sr413/9\n15zNZ9uddtppSUeAovef//mfcfHFF0ePHj2SjrLTHPmTB9/97nejd+/eSccAaNXmz58fhxxyyE7P\n+fnPfx7vvvtu1NTUZCEVGzQ1NcXjjz8exx13XF7327lz5zj++OPjiSeeyOt+i9XixYsjIuLAAw+M\nsrKyiIi4+OKLN3rOP/zDP8Rd5CCJ8AAAIABJREFUd92V92z8n+9///s5m3311VfnbDbbZ9q0aUlH\nAFLEkT95sO+++zZ/gPqi5cuX5zkNQOvzxz/+MSvFT8Rnd2o47LDDsjKL1mHy5MkxbNiwpGMUhV69\nekWvXr02+7mF1uH555/Pydw0XduiUG0oYDf8L5CsDz/8MP7xH/8xFe9J5U8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0rZjt+zW7ZsSTgJAElxtS8AErVly5ZdbplMJs4999yYOnVq/e3tt9/e4xrr1q2Ls88+W/GT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\nmPIHAAAAIMWUP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ToqKirqrxL1yU9+Mqqrq2PSpEn1j+24OXQhd6qrqxPbdk1NTXz2s59N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\nSyfUZFt70mGaPofu+VPNKioqYsGCB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wBQrWbOnJm1ewUBALApj3oHAAAASDHl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"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"make_plot(img, seg, rec, samples_local, fs=2)\n"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "",
"kind": "private"
},
"name": "HPU-Net",
"provenance": []
},
"kernelspec": {
"name": "pycharm-7906112d",
"language": "python",
"display_name": "PyCharm (segmentation)"
},
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================================================
FILE: hierarchical_probabilistic_unet/README.md
================================================
# Hierarchical Probabilistic U-Net
This package provides an implementation of the Hierarchical Probabilistic U-Net
(HPU-Net) as published in [A Hierarchical Probabilistic U-Net for Modeling
Multi-Scale Ambiguities (2019)](https://arxiv.org/abs/1905.13077).
The HPU-Net combines a hierarchical VAE with a U-Net and learns an
image-conditional distribution over plausible outputs, here segmentation maps.
The hierarchical latent space decomposition allows to model independent
variations across locations and scales and increases the granularity of
predicted segmentations as compared to prior work ([the Probabilisitc
U-Net](https://arxiv.org/abs/1806.05034)).
The architecture, depicted above, interleaves the U-Net's decoder with a
prior that is used when sampling at test time (see Subfigure a) above). Training
proceeds as is typical for VAEs, i.e. a separate posterior network is employed
whose latents are injected into the decoder (see Subfigure b) above).
The animated gif below shows 16 segmentation samples when sampling from either
a) the full hierarchy or fixing some of the latents to the prior's mean: b)
fixing all but the most local latents and c) fixing all but the most global
latent. The first row depicts CT scans showing potential lung abnormalities and
the rows below show individual samples and the standard deviations cross them.
In addition to the model code we provide the preprocessed version of the
[LIDC-IDRI
dataset](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)
that we employ as well as pretrained model weights. Both can be loaded in a
public colab, see below.
## Colab [](https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/hierarchical_probabilistic_unet/HPU_Net.ipynb)
To quickly tinker with the pretrained model and the dataset without the need of
installing anything locally click the `Open in Colab`-button above to follow the
link to the colab.
## Installation
To install the package locally run:
```bash
git clone https://github.com/deepmind/deepmind-research.git .
cd deepmind-research/hierarchical_probabilistic_unet
pip install -e .
```
## LIDC 2D crops
We provide a preprocessed version of the Lung Image Database Consortium image
collection dataset
([LIDC-IDRI](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI))
as used and described in [A Hierarchical Probabilistic U-Net for Modeling
Multi-Scale Ambiguities (2019)](https://arxiv.org/abs/1905.13077).
The original dataset consists of 3D lung CT scans with semantic segmentations of
possible lung abnormalities, as graded by four expert readers. We have cleaned
up the data resulting in a slightly changed number of images for
each data set split, see below (which leaves the results the same). The data is
hosted in Google Cloud Storage ([this bucket](https://console.cloud.google.com/storage/browser/hpunet-data/lidc_crops/)).
#####Preprocessing:
We resampled the CT scans to 0.5mm x 0.5mm in-plane resolution and then cropped
2D images of size 180 x 180 pixels, centered on the abnormality position. The
abnormality positions are those where at least one of the experts segmented an
abnormality and we assumed that two masks from different graders correspond to
the same abnormality if their tightest bounding boxes overlap. We only used
those abnormaliies that were specified as a polygon (outline) in the XML files
of the LIDC dataset, disregarding the ones that only have center of shape. That
is, according to the LIDC paper we use the ones that are larger than 3mm, and
filter out the others, that are clinically less relevant ([2], see below).
We also filterd out each Dicom file whose absolute value in the XML element of
`SliceLocation` differs from the absolute value of the last element in
`ImagePositionPatient`.
This preprocessing results in 8843 images for training, 1993 for validation and
1980 for testing (corresponding to 530, 111 and 103 patients respectively).
#####Directory Structure
The GCS bucket contains tar.gz-files for the training, validation and test data.
Each tar.gz-file contains a zipped directory with two subdirectories, one named
`images` and `gt`. Their subdirectories comprise a directory for each patient
that is part of that data split. Each patient's directory holds the
corresponding cropped 2D images in .png-format. The naming scheme follows
`z-_c.png` for CT scans and
`z-_c_l.png` for
the binary segmentation maps, allowing to match the images and their
corresponding four annotations.
#####Citations & Data Usage Policy:
The [LIDC-IDRI
dataset](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)
was published in [1, 2, 3] and is made available under the [CC BY 3.0
license](https://creativecommons.org/licenses/by/3.0/).
[1] Armato III, Samuel G., McLennan, Geoffrey, Bidaut, Luc, McNitt-Gray, Michael
F., Meyer, Charles R., Reeves, Anthony P., … Clarke, Laurence P. (2015). Data
From LIDC-IDRI. The Cancer Imaging Archive.
([Link](http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX))
[2] Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP,
Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek
EJR, Yankelevitz D, et al.: The Lung Image Database Consortium (LIDC) and Image
Database Resource Initiative (IDRI): A completed reference database of lung
nodules on CT scans. Medical Physics, 38: 915--931, 2011.
([Paper](https://www.ncbi.nlm.nih.gov/pubmed/21452728))
[3] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips
S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA):
Maintaining and Operating a Public Information Repository, Journal of Digital
Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057.
([Paper](https://link.springer.com/article/10.1007%2Fs10278-013-9622-7))
We make the `LIDC 2D crops` data, accessible from above GCS bucket, available
under the [CC BY 3.0 license](https://creativecommons.org/licenses/by/3.0/).
## Pretrained Model
We provide a pretrained model checkpoint ([Google Cloud Storage
bucket](https://console.cloud.google.com/storage/browser/hpunet-data/model_checkpoint/))
that can be loaded as exemplified in our colab.
## Giving Credit
If you use this code in your work, we ask you to cite this paper:
```
@article{kohl2019hierarchical,
title={A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities},
author={Kohl, Simon AA and Romera-Paredes, Bernardino and Maier-Hein, Klaus H and Rezende, Danilo Jimenez and Eslami, SM and Kohli, Pushmeet and Zisserman, Andrew and Ronneberger, Olaf},
journal={arXiv preprint arXiv:1905.13077},
year={2019}
}
```
## Disclaimer
This is not an official Google product.
================================================
FILE: hierarchical_probabilistic_unet/geco_utils.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utility Functions for the GECO-objective.
(GECO is described in `Taming VAEs`, see https://arxiv.org/abs/1810.00597).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import sonnet as snt
import tensorflow.compat.v1 as tf
class MovingAverage(snt.AbstractModule):
"""A thin wrapper around snt.MovingAverage.
The module adds the option not to differentiate through the last element that
is added to the moving average, specified by means of the kwarg
`differentiable`.
"""
def __init__(self, decay, local=True, differentiable=False,
name='snt_moving_average'):
super(MovingAverage, self).__init__(name=name)
self._differentiable = differentiable
self._moving_average = snt.MovingAverage(
decay=decay, local=local, name=name)
def _build(self, inputs):
if not self._differentiable:
inputs = tf.stop_gradient(inputs)
return self._moving_average(inputs)
class LagrangeMultiplier(snt.AbstractModule):
"""A lagrange multiplier sonnet module."""
def __init__(self,
rate=1e-2,
name='snt_lagrange_multiplier'):
"""Initializer for the sonnet module.
Args:
rate: Scalar used to scale the magnitude of gradients of the Lagrange
multipliers, defaulting to 1e-2.
name: Name of the Lagrange multiplier sonnet module.
"""
super(LagrangeMultiplier, self).__init__(name=name)
self._rate = rate
def _build(self, ma_constraint):
"""Connects the module to the graph.
Args:
ma_constraint: A loss minus a target value, denoting a constraint that
shall be less or equal than zero.
Returns:
An op, which when added to a loss and calling minimize on the loss
results in the optimizer minimizing w.r.t. to the model's parameters and
maximizing w.r.t. the Lagrande multipliers, hence enforcing the
constraints.
"""
lagmul = snt.get_lagrange_multiplier(
shape=ma_constraint.shape, rate=self._rate,
initializer=np.ones(ma_constraint.shape))
return lagmul
def _sample_gumbel(shape, eps=1e-20):
"""Transforms a uniform random variable to be standard Gumbel distributed."""
return -tf.log(
-tf.log(tf.random_uniform(shape, minval=0, maxval=1) + eps) + eps)
def _topk_mask(score, k):
"""Returns a mask for the top-k elements in score."""
_, indices = tf.nn.top_k(score, k=k)
return tf.scatter_nd(tf.expand_dims(indices, -1), tf.ones(k),
tf.squeeze(score).shape.as_list())
def ce_loss(logits, labels, mask=None, top_k_percentage=None,
deterministic=False):
"""Computes the cross-entropy loss.
Optionally a mask and a top-k percentage for the used pixels can be specified.
The top-k mask can be produced deterministically or sampled.
Args:
logits: A tensor of shape (b,h,w,num_classes)
labels: A tensor of shape (b,h,w,num_classes)
mask: None or a tensor of shape (b,h,w).
top_k_percentage: None or a float in (0.,1.]. If None, a standard
cross-entropy loss is calculated.
deterministic: A Boolean indicating whether or not to produce the
prospective top-k mask deterministically.
Returns:
A dictionary holding the mean and the pixelwise sum of the loss for the
batch as well as the employed loss mask.
"""
num_classes = logits.shape.as_list()[-1]
y_flat = tf.reshape(logits, (-1, num_classes), name='reshape_y')
t_flat = tf.reshape(labels, (-1, num_classes), name='reshape_t')
if mask is None:
mask = tf.ones(shape=(t_flat.shape.as_list()[0],))
else:
assert mask.shape.as_list()[:3] == labels.shape.as_list()[:3],\
'The loss mask shape differs from the target shape: {} vs. {}.'.format(
mask.shape.as_list(), labels.shape.as_list()[:3])
mask = tf.reshape(mask, (-1,), name='reshape_mask')
n_pixels_in_batch = y_flat.shape.as_list()[0]
xe = tf.nn.softmax_cross_entropy_with_logits_v2(labels=t_flat, logits=y_flat)
if top_k_percentage is not None:
assert 0.0 < top_k_percentage <= 1.0
k_pixels = tf.cast(tf.floor(n_pixels_in_batch * top_k_percentage), tf.int32)
stopgrad_xe = tf.stop_gradient(xe)
norm_xe = stopgrad_xe / tf.reduce_sum(stopgrad_xe)
if deterministic:
score = tf.log(norm_xe)
else:
# Use the Gumbel trick to sample the top-k pixels, equivalent to sampling
# from a categorical distribution over pixels whose probabilities are
# given by the normalized cross-entropy loss values. This is done by
# adding Gumbel noise to the logarithmic normalized cross-entropy loss
# (followed by choosing the top-k pixels).
score = tf.log(norm_xe) + _sample_gumbel(norm_xe.shape.as_list())
score = score + tf.log(mask)
top_k_mask = _topk_mask(score, k_pixels)
mask = mask * top_k_mask
# Calculate batch-averages for the sum and mean of the loss
batch_size = labels.shape.as_list()[0]
xe = tf.reshape(xe, shape=(batch_size, -1))
mask = tf.reshape(mask, shape=(batch_size, -1))
ce_sum_per_instance = tf.reduce_sum(mask * xe, axis=1)
ce_sum = tf.reduce_mean(ce_sum_per_instance, axis=0)
ce_mean = tf.reduce_sum(mask * xe) / tf.reduce_sum(mask)
return {'mean': ce_mean, 'sum': ce_sum, 'mask': mask}
================================================
FILE: hierarchical_probabilistic_unet/model.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Open Source Version of the Hierarchical Probabilistic U-Net."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import geco_utils
import sonnet as snt
import tensorflow as tf
from tensorflow_probability import distributions as tfd
import unet_utils
class _HierarchicalCore(snt.AbstractModule):
"""A U-Net encoder-decoder with a full encoder and a truncated decoder.
The truncated decoder is interleaved with the hierarchical latent space and
has as many levels as there are levels in the hierarchy plus one additional
level.
"""
def __init__(self, latent_dims, channels_per_block,
down_channels_per_block=None, activation_fn=tf.nn.relu,
initializers=None, regularizers=None, convs_per_block=3,
blocks_per_level=3, name='HierarchicalDecoderDist'):
"""Initializes a HierarchicalCore.
Args:
latent_dims: List of integers specifying the dimensions of the latents at
each scale. The length of the list indicates the number of U-Net decoder
scales that have latents.
channels_per_block: A list of integers specifying the number of output
channels for each encoder block.
down_channels_per_block: A list of integers specifying the number of
intermediate channels for each encoder block or None. If None, the
intermediate channels are chosen equal to channels_per_block.
activation_fn: A callable activation function.
initializers: Optional dict containing ops to initialize the filters (with
key 'w') or biases (with key 'b'). The default initializer for the
weights is a truncated normal initializer, which is commonly used when
the inputs are zero centered (see
https://arxiv.org/pdf/1502.03167v3.pdf). The default initializer for the
bias is a zero initializer.
regularizers: Optional dict containing regularizers for the filters
(with key 'w') and the biases (with key 'b'). As a default, no
regularizers are used. A regularizer should be a function that takes a
single `Tensor` as an input and returns a scalar `Tensor` output, e.g.
the L1 and L2 regularizers in `tf.contrib.layers`.
convs_per_block: An integer specifying the number of convolutional layers.
blocks_per_level: An integer specifying the number of residual blocks per
level.
name: A string specifying the name of the module.
"""
super(_HierarchicalCore, self).__init__(name=name)
self._latent_dims = latent_dims
self._channels_per_block = channels_per_block
self._activation_fn = activation_fn
self._initializers = initializers
self._regularizers = regularizers
self._convs_per_block = convs_per_block
self._blocks_per_level = blocks_per_level
if down_channels_per_block is None:
self._down_channels_per_block = channels_per_block
else:
self._down_channels_per_block = down_channels_per_block
self._name = name
def _build(self, inputs, mean=False, z_q=None):
"""A build-method allowing to sample from the module as specified.
Args:
inputs: A tensor of shape (b,h,w,c). When using the module as a prior the
`inputs` tensor should be a batch of images. When using it as a posterior
the tensor should be a (batched) concatentation of images and
segmentations.
mean: A boolean or a list of booleans. If a boolean, it specifies whether
or not to use the distributions' means in ALL latent scales. If a list,
each bool therein specifies whether or not to use the scale's mean. If
False, the latents of the scale are sampled.
z_q: None or a list of tensors. If not None, z_q provides external latents
to be used instead of sampling them. This is used to employ posterior
latents in the prior during training. Therefore, if z_q is not None, the
value of `mean` is ignored. If z_q is None, either the distributions
mean is used (in case `mean` for the respective scale is True) or else
a sample from the distribution is drawn.
Returns:
A Dictionary holding the output feature map of the truncated U-Net
decoder under key 'decoder_features', a list of the U-Net encoder features
produced at the end of each encoder scale under key 'encoder_outputs', a
list of the predicted distributions at each scale under key
'distributions', a list of the used latents at each scale under the key
'used_latents'.
"""
encoder_features = inputs
encoder_outputs = []
num_levels = len(self._channels_per_block)
num_latent_levels = len(self._latent_dims)
if isinstance(mean, bool):
mean = [mean] * num_latent_levels
distributions = []
used_latents = []
# Iterate the descending levels in the U-Net encoder.
for level in range(num_levels):
# Iterate the residual blocks in each level.
for _ in range(self._blocks_per_level):
encoder_features = unet_utils.res_block(
input_features=encoder_features,
n_channels=self._channels_per_block[level],
n_down_channels=self._down_channels_per_block[level],
activation_fn=self._activation_fn,
initializers=self._initializers,
regularizers=self._regularizers,
convs_per_block=self._convs_per_block)
encoder_outputs.append(encoder_features)
if level != num_levels - 1:
encoder_features = unet_utils.resize_down(encoder_features, scale=2)
# Iterate the ascending levels in the (truncated) U-Net decoder.
decoder_features = encoder_outputs[-1]
for level in range(num_latent_levels):
# Predict a Gaussian distribution for each pixel in the feature map.
latent_dim = self._latent_dims[level]
mu_logsigma = snt.Conv2D(
2 * latent_dim,
(1, 1),
padding='SAME',
initializers=self._initializers,
regularizers=self._regularizers,
)(decoder_features)
mu = mu_logsigma[..., :latent_dim]
logsigma = mu_logsigma[..., latent_dim:]
dist = tfd.MultivariateNormalDiag(loc=mu, scale_diag=tf.exp(logsigma))
distributions.append(dist)
# Get the latents to condition on.
if z_q is not None:
z = z_q[level]
elif mean[level]:
z = dist.loc
else:
z = dist.sample()
used_latents.append(z)
# Concat and upsample the latents with the previous features.
decoder_output_lo = tf.concat([z, decoder_features], axis=-1)
decoder_output_hi = unet_utils.resize_up(decoder_output_lo, scale=2)
decoder_features = tf.concat(
[decoder_output_hi, encoder_outputs[::-1][level + 1]], axis=-1)
# Iterate the residual blocks in each level.
for _ in range(self._blocks_per_level):
decoder_features = unet_utils.res_block(
input_features=decoder_features,
n_channels=self._channels_per_block[::-1][level + 1],
n_down_channels=self._down_channels_per_block[::-1][level + 1],
activation_fn=self._activation_fn,
initializers=self._initializers,
regularizers=self._regularizers,
convs_per_block=self._convs_per_block)
return {'decoder_features': decoder_features,
'encoder_features': encoder_outputs,
'distributions': distributions,
'used_latents': used_latents}
class _StitchingDecoder(snt.AbstractModule):
"""A module that completes the truncated U-Net decoder.
Using the output of the HierarchicalCore this module fills in the missing
decoder levels such that together the two form a symmetric U-Net.
"""
def __init__(self, latent_dims, channels_per_block, num_classes,
down_channels_per_block=None, activation_fn=tf.nn.relu,
initializers=None, regularizers=None, convs_per_block=3,
blocks_per_level=3, name='StitchingDecoder'):
"""Initializes a StichtingDecoder.
Args:
latent_dims: List of integers specifying the dimensions of the latents at
each scale. The length of the list indicates the number of U-Net
decoder scales that have latents.
channels_per_block: A list of integers specifying the number of output
channels for each encoder block.
num_classes: An integer specifying the number of segmentation classes.
down_channels_per_block: A list of integers specifying the number of
intermediate channels for each encoder block. If None, the
intermediate channels are chosen equal to channels_per_block.
activation_fn: A callable activation function.
initializers: Optional dict containing ops to initialize the filters (with
key 'w') or biases (with key 'b'). The default initializer for the
weights is a truncated normal initializer, which is commonly used when
the inputs are zero centered (see
https://arxiv.org/pdf/1502.03167v3.pdf). The default initializer for the
bias is a zero initializer.
regularizers: Optional dict containing regularizers for the filters
(with key 'w') and the biases (with key 'b'). As a default, no
regularizers are used. A regularizer should be a function that takes a
single `Tensor` as an input and returns a scalar `Tensor` output, e.g.
the L1 and L2 regularizers in `tf.contrib.layers`.
convs_per_block: An integer specifying the number of convolutional layers.
blocks_per_level: An integer specifying the number of residual blocks per
level.
name: A string specifying the name of the module.
"""
super(_StitchingDecoder, self).__init__(name=name)
self._latent_dims = latent_dims
self._channels_per_block = channels_per_block
self._num_classes = num_classes
self._activation_fn = activation_fn
self._initializers = initializers
self._regularizers = regularizers
self._convs_per_block = convs_per_block
self._blocks_per_level = blocks_per_level
if down_channels_per_block is None:
down_channels_per_block = channels_per_block
self._down_channels_per_block = down_channels_per_block
def _build(self, encoder_features, decoder_features):
"""Build-method that returns the segmentation logits.
Args:
encoder_features: A list of tensors of shape (b,h_i,w_i,c_i).
decoder_features: A tensor of shape (b,h,w,c).
Returns:
Logits, i.e. a tensor of shape (b,h,w,num_classes).
"""
num_latents = len(self._latent_dims)
start_level = num_latents + 1
num_levels = len(self._channels_per_block)
for level in range(start_level, num_levels, 1):
decoder_features = unet_utils.resize_up(decoder_features, scale=2)
decoder_features = tf.concat([decoder_features,
encoder_features[::-1][level]], axis=-1)
for _ in range(self._blocks_per_level):
decoder_features = unet_utils.res_block(
input_features=decoder_features,
n_channels=self._channels_per_block[::-1][level],
n_down_channels=self._down_channels_per_block[::-1][level],
activation_fn=self._activation_fn,
initializers=self._initializers,
regularizers=self._regularizers,
convs_per_block=self._convs_per_block)
return snt.Conv2D(output_channels=self._num_classes,
kernel_shape=(1, 1),
padding='SAME',
initializers=self._initializers,
regularizers=self._regularizers,
name='logits')(decoder_features)
class HierarchicalProbUNet(snt.AbstractModule):
"""A Hierarchical Probabilistic U-Net."""
def __init__(self,
latent_dims=(1, 1, 1, 1),
channels_per_block=None,
num_classes=2,
down_channels_per_block=None,
activation_fn=tf.nn.relu,
initializers=None,
regularizers=None,
convs_per_block=3,
blocks_per_level=3,
loss_kwargs=None,
name='HPUNet'):
"""Initializes a HierarchicalProbUNet.
The default values are set as for the LIDC-IDRI experiments in
`A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities',
see https://arxiv.org/abs/1905.13077.
Args:
latent_dims: List of integers specifying the dimensions of the latents at
each scales. The length of the list indicates the number of U-Net
decoder scales that have latents.
channels_per_block: A list of integers specifying the number of output
channels for each encoder block.
num_classes: An integer specifying the number of segmentation classes.
down_channels_per_block: A list of integers specifying the number of
intermediate channels for each encoder block. If None, the
intermediate channels are chosen equal to channels_per_block.
activation_fn: A callable activation function.
initializers: Optional dict containing ops to initialize the filters (with
key 'w') or biases (with key 'b').
regularizers: Optional dict containing regularizers for the filters
(with key 'w') and the biases (with key 'b').
convs_per_block: An integer specifying the number of convolutional layers.
blocks_per_level: An integer specifying the number of residual blocks per
level.
loss_kwargs: None or dictionary specifying the loss setup.
name: A string specifying the name of the module.
"""
super(HierarchicalProbUNet, self).__init__(name=name)
base_channels = 24
default_channels_per_block = (
base_channels, 2 * base_channels, 4 * base_channels, 8 * base_channels,
8 * base_channels, 8 * base_channels, 8 * base_channels,
8 * base_channels
)
if channels_per_block is None:
channels_per_block = default_channels_per_block
if down_channels_per_block is None:
down_channels_per_block =\
tuple([i / 2 for i in default_channels_per_block])
if initializers is None:
initializers = {
'w': tf.orthogonal_initializer(gain=1.0, seed=None),
'b': tf.truncated_normal_initializer(stddev=0.001)
}
if regularizers is None:
regularizers = {
'w': tf.keras.regularizers.l2(1e-5),
'b': tf.keras.regularizers.l2(1e-5)
}
if loss_kwargs is None:
self._loss_kwargs = {
'type': 'geco',
'top_k_percentage': 0.02,
'deterministic_top_k': False,
'kappa': 0.05,
'decay': 0.99,
'rate': 1e-2,
'beta': None
}
else:
self._loss_kwargs = loss_kwargs
if down_channels_per_block is None:
down_channels_per_block = channels_per_block
with self._enter_variable_scope():
self._prior = _HierarchicalCore(
latent_dims=latent_dims,
channels_per_block=channels_per_block,
down_channels_per_block=down_channels_per_block,
activation_fn=activation_fn,
initializers=initializers,
regularizers=regularizers,
convs_per_block=convs_per_block,
blocks_per_level=blocks_per_level,
name='prior')
self._posterior = _HierarchicalCore(
latent_dims=latent_dims,
channels_per_block=channels_per_block,
down_channels_per_block=down_channels_per_block,
activation_fn=activation_fn,
initializers=initializers,
regularizers=regularizers,
convs_per_block=convs_per_block,
blocks_per_level=blocks_per_level,
name='posterior')
self._f_comb = _StitchingDecoder(
latent_dims=latent_dims,
channels_per_block=channels_per_block,
num_classes=num_classes,
down_channels_per_block=down_channels_per_block,
activation_fn=activation_fn,
initializers=initializers,
regularizers=regularizers,
convs_per_block=convs_per_block,
blocks_per_level=blocks_per_level,
name='f_comb')
if self._loss_kwargs['type'] == 'geco':
self._moving_average = geco_utils.MovingAverage(
decay=self._loss_kwargs['decay'], differentiable=True,
name='ma_test')
self._lagmul = geco_utils.LagrangeMultiplier(
rate=self._loss_kwargs['rate'])
self._cache = ()
def _build(self, seg, img):
"""Inserts all ops used during training into the graph exactly once.
The first time this method is called given the input pair (seg, img) all
ops relevant for training are inserted into the graph. Calling this method
more than once does not re-insert the modules into the graph (memoization),
thus preventing multiple forward passes of submodules for the same inputs.
The method is private and called when setting up the loss.
Args:
seg: A tensor of shape (b, h, w, num_classes).
img: A tensor of shape (b, h, w, c)
Returns: None
"""
inputs = (seg, img)
if self._cache == inputs:
return
else:
self._q_sample = self._posterior(
tf.concat([seg, img], axis=-1), mean=False)
self._q_sample_mean = self._posterior(
tf.concat([seg, img], axis=-1), mean=True)
self._p_sample = self._prior(
img, mean=False, z_q=None)
self._p_sample_z_q = self._prior(
img, z_q=self._q_sample['used_latents'])
self._p_sample_z_q_mean = self._prior(
img, z_q=self._q_sample_mean['used_latents'])
self._cache = inputs
return
def sample(self, img, mean=False, z_q=None):
"""Sample a segmentation from the prior, given an input image.
Args:
img: A tensor of shape (b, h, w, c).
mean: A boolean or a list of booleans. If a boolean, it specifies whether
or not to use the distributions' means in ALL latent scales. If a list,
each bool therein specifies whether or not to use the scale's mean. If
False, the latents of the scale are sampled.
z_q: None or a list of tensors. If not None, z_q provides external latents
to be used instead of sampling them. This is used to employ posterior
latents in the prior during training. Therefore, if z_q is not None, the
value of `mean` is ignored. If z_q is None, either the distributions
mean is used (in case `mean` for the respective scale is True) or else
a sample from the distribution is drawn
Returns:
A segmentation tensor of shape (b, h, w, num_classes).
"""
prior_out = self._prior(img, mean, z_q)
encoder_features = prior_out['encoder_features']
decoder_features = prior_out['decoder_features']
return self._f_comb(encoder_features=encoder_features,
decoder_features=decoder_features)
def reconstruct(self, seg, img, mean=False):
"""Reconstruct a segmentation using the posterior.
Args:
seg: A tensor of shape (b, h, w, num_classes).
img: A tensor of shape (b, h, w, c).
mean: A boolean, specifying whether to sample from the full hierarchy of
the posterior or use the posterior means at each scale of the hierarchy.
Returns:
A segmentation tensor of shape (b,h,w,num_classes).
"""
self._build(seg, img)
if mean:
prior_out = self._p_sample_z_q_mean
else:
prior_out = self._p_sample_z_q
encoder_features = prior_out['encoder_features']
decoder_features = prior_out['decoder_features']
return self._f_comb(encoder_features=encoder_features,
decoder_features=decoder_features)
def rec_loss(self, seg, img, mask=None, top_k_percentage=None,
deterministic=True):
"""Cross-entropy reconstruction loss employed in the ELBO-/ GECO-objective.
Args:
seg: A tensor of shape (b, h, w, num_classes).
img: A tensor of shape (b, h, w, c).
mask: A mask of shape (b, h, w) or None. If None no pixels are masked in
the loss.
top_k_percentage: None or a float in (0.,1.]. If None, a standard
cross-entropy loss is calculated.
deterministic: A Boolean indicating whether or not to produce the
prospective top-k mask deterministically.
Returns:
A dictionary holding the mean and the pixelwise sum of the loss for the
batch as well as the employed loss mask.
"""
reconstruction = self.reconstruct(seg, img, mean=False)
return geco_utils.ce_loss(
reconstruction, seg, mask, top_k_percentage, deterministic)
def kl(self, seg, img):
"""Kullback-Leibler divergence between the posterior and the prior.
Args:
seg: A tensor of shape (b, h, w, num_classes).
img: A tensor of shape (b, h, w, c).
Returns:
A dictionary with keys indexing the hierarchy's levels and corresponding
values holding the KL-term for each level (per batch).
"""
self._build(seg, img)
posterior_out = self._q_sample
prior_out = self._p_sample_z_q
q_dists = posterior_out['distributions']
p_dists = prior_out['distributions']
kl = {}
for level, (q, p) in enumerate(zip(q_dists, p_dists)):
# Shape (b, h, w).
kl_per_pixel = tfd.kl_divergence(q, p)
# Shape (b,).
kl_per_instance = tf.reduce_sum(kl_per_pixel, axis=[1, 2])
# Shape (1,).
kl[level] = tf.reduce_mean(kl_per_instance)
return kl
def loss(self, seg, img, mask):
"""The full training objective, either ELBO or GECO.
Args:
seg: A tensor of shape (b, h, w, num_classes).
img: A tensor of shape (b, h, w, c).
mask: A mask of shape (b, h, w) or None. If None no pixels are masked in
the loss.
Returns:
A dictionary holding the loss (with key 'loss') and the tensorboard
summaries (with key 'summaries').
"""
summaries = {}
top_k_percentage = self._loss_kwargs['top_k_percentage']
deterministic = self._loss_kwargs['deterministic_top_k']
rec_loss = self.rec_loss(seg, img, mask, top_k_percentage, deterministic)
kl_dict = self.kl(seg, img)
kl_sum = tf.reduce_sum(
tf.stack([kl for _, kl in kl_dict.iteritems()], axis=-1))
summaries['rec_loss_mean'] = rec_loss['mean']
summaries['rec_loss_sum'] = rec_loss['sum']
summaries['kl_sum'] = kl_sum
for level, kl in kl_dict.iteritems():
summaries['kl_{}'.format(level)] = kl
# Set up a regular ELBO objective.
if self._loss_kwargs['type'] == 'elbo':
loss = rec_loss['sum'] + self._loss_kwargs['beta'] * kl_sum
summaries['elbo_loss'] = loss
# Set up a GECO objective (ELBO with a reconstruction constraint).
elif self._loss_kwargs['type'] == 'geco':
ma_rec_loss = self._moving_average(rec_loss['sum'])
mask_sum_per_instance = tf.reduce_sum(rec_loss['mask'], axis=-1)
num_valid_pixels = tf.reduce_mean(mask_sum_per_instance)
reconstruction_threshold = self._loss_kwargs['kappa'] * num_valid_pixels
rec_constraint = ma_rec_loss - reconstruction_threshold
lagmul = self._lagmul(rec_constraint)
loss = lagmul * rec_constraint + kl_sum
summaries['geco_loss'] = loss
summaries['ma_rec_loss_mean'] = ma_rec_loss / num_valid_pixels
summaries['num_valid_pixels'] = num_valid_pixels
summaries['lagmul'] = lagmul
else:
raise NotImplementedError('Loss type {} not implemeted!'.format(
self._loss_kwargs['type']))
return dict(supervised_loss=loss, summaries=summaries)
if __name__ == '__main__':
hpu_net = HierarchicalProbUNet()
================================================
FILE: hierarchical_probabilistic_unet/model_test.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for the Hierarchical Probabilistic U-Net open-source version."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from model import HierarchicalProbUNet
import tensorflow.compat.v1 as tf
_NUM_CLASSES = 2
_BATCH_SIZE = 2
_SPATIAL_SHAPE = [32, 32]
_CHANNELS_PER_BLOCK = [5, 7, 9, 11, 13]
_IMAGE_SHAPE = [_BATCH_SIZE] + _SPATIAL_SHAPE + [1]
_BOTTLENECK_SIZE = _SPATIAL_SHAPE[0] // 2 ** (len(_CHANNELS_PER_BLOCK) - 1)
_SEGMENTATION_SHAPE = [_BATCH_SIZE] + _SPATIAL_SHAPE + [_NUM_CLASSES]
_LATENT_DIMS = [3, 2, 1]
_INITIALIZERS = {'w': tf.orthogonal_initializer(gain=1.0, seed=None),
'b': tf.truncated_normal_initializer(stddev=0.001)}
def _get_placeholders():
"""Returns placeholders for the image and segmentation."""
img = tf.placeholder(dtype=tf.float32, shape=_IMAGE_SHAPE)
seg = tf.placeholder(dtype=tf.float32, shape=_SEGMENTATION_SHAPE)
return img, seg
class HierarchicalProbUNetTest(tf.test.TestCase):
def test_shape_of_sample(self):
hpu_net = HierarchicalProbUNet(latent_dims=_LATENT_DIMS,
channels_per_block=_CHANNELS_PER_BLOCK,
num_classes=_NUM_CLASSES,
initializers=_INITIALIZERS)
img, _ = _get_placeholders()
sample = hpu_net.sample(img)
self.assertEqual(sample.shape.as_list(), _SEGMENTATION_SHAPE)
def test_shape_of_reconstruction(self):
hpu_net = HierarchicalProbUNet(latent_dims=_LATENT_DIMS,
channels_per_block=_CHANNELS_PER_BLOCK,
num_classes=_NUM_CLASSES,
initializers=_INITIALIZERS)
img, seg = _get_placeholders()
reconstruction = hpu_net.reconstruct(img, seg)
self.assertEqual(reconstruction.shape.as_list(), _SEGMENTATION_SHAPE)
def test_shapes_in_prior(self):
hpu_net = HierarchicalProbUNet(latent_dims=_LATENT_DIMS,
channels_per_block=_CHANNELS_PER_BLOCK,
num_classes=_NUM_CLASSES,
initializers=_INITIALIZERS)
img, _ = _get_placeholders()
prior_out = hpu_net._prior(img)
distributions = prior_out['distributions']
latents = prior_out['used_latents']
encoder_features = prior_out['encoder_features']
decoder_features = prior_out['decoder_features']
# Test number of latent disctributions.
self.assertEqual(len(distributions), len(_LATENT_DIMS))
# Test shapes of latent scales.
for level in range(len(_LATENT_DIMS)):
latent_spatial_shape = _BOTTLENECK_SIZE * 2 ** level
latent_shape = [_BATCH_SIZE, latent_spatial_shape, latent_spatial_shape,
_LATENT_DIMS[level]]
self.assertEqual(latents[level].shape.as_list(), latent_shape)
# Test encoder shapes.
for level in range(len(_CHANNELS_PER_BLOCK)):
spatial_shape = _SPATIAL_SHAPE[0] // 2 ** level
feature_shape = [_BATCH_SIZE, spatial_shape, spatial_shape,
_CHANNELS_PER_BLOCK[level]]
self.assertEqual(encoder_features[level].shape.as_list(), feature_shape)
# Test decoder shape.
start_level = len(_LATENT_DIMS)
latent_spatial_shape = _BOTTLENECK_SIZE * 2 ** start_level
latent_shape = [_BATCH_SIZE, latent_spatial_shape, latent_spatial_shape,
_CHANNELS_PER_BLOCK[::-1][start_level]]
self.assertEqual(decoder_features.shape.as_list(), latent_shape)
def test_shape_of_kl(self):
hpu_net = HierarchicalProbUNet(latent_dims=_LATENT_DIMS,
channels_per_block=_CHANNELS_PER_BLOCK,
num_classes=_NUM_CLASSES,
initializers=_INITIALIZERS)
img, seg = _get_placeholders()
kl_dict = hpu_net.kl(img, seg)
self.assertEqual(len(kl_dict), len(_LATENT_DIMS))
if __name__ == '__main__':
tf.test.main()
================================================
FILE: hierarchical_probabilistic_unet/run.sh
================================================
#!/bin/sh
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
python3 -m venv /tmp/hpu-net-venv
source /tmp/hpu-net-venv/bin/activate
pip3 install -U pip
pip3 install hierarchical_probabilistic_unet/
python3 hierarchical_probabilistic_unet/model_test.py
================================================
FILE: hierarchical_probabilistic_unet/setup.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Setup for pip package."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from setuptools import find_packages
from setuptools import setup
REQUIRED_PACKAGES = ['numpy', 'dm-sonnet==1.35', 'tensorflow==1.14',
'tensorflow-probability==0.7.0']
setup(
name='hpu_net',
version='0.1',
description='A library for the Hierarchical Probabilistic U-Net model.',
url='https://github.com/deepmind/deepmind-research/hierarchical_probabilistic_unet',
author='DeepMind',
author_email='no-reply@google.com',
# Contained modules and scripts.
packages=find_packages(),
install_requires=REQUIRED_PACKAGES,
platforms=['any'],
license='Apache 2.0',
)
================================================
FILE: hierarchical_probabilistic_unet/unet_utils.py
================================================
# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Architectural blocks and utility functions of the U-Net."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sonnet as snt
import tensorflow.compat.v1 as tf
def res_block(input_features, n_channels, n_down_channels=None,
activation_fn=tf.nn.relu, initializers=None, regularizers=None,
convs_per_block=3):
"""A pre-activated residual block.
Args:
input_features: A tensor of shape (b, h, w, c).
n_channels: An integer specifying the number of output channels.
n_down_channels: An integer specifying the number of intermediate channels.
activation_fn: A callable activation function.
initializers: Initializers for the weights and biases.
regularizers: Regularizers for the weights and biases.
convs_per_block: An Integer specifying the number of convolutional layers.
Returns:
A tensor of shape (b, h, w, c).
"""
# Pre-activate the inputs.
skip = input_features
residual = activation_fn(input_features)
# Set the number of intermediate channels that we compress to.
if n_down_channels is None:
n_down_channels = n_channels
for c in range(convs_per_block):
residual = snt.Conv2D(n_down_channels,
(3, 3),
padding='SAME',
initializers=initializers,
regularizers=regularizers)(residual)
if c < convs_per_block - 1:
residual = activation_fn(residual)
incoming_channels = input_features.shape[-1]
if incoming_channels != n_channels:
skip = snt.Conv2D(n_channels,
(1, 1),
padding='SAME',
initializers=initializers,
regularizers=regularizers)(skip)
if n_down_channels != n_channels:
residual = snt.Conv2D(n_channels,
(1, 1),
padding='SAME',
initializers=initializers,
regularizers=regularizers)(residual)
return skip + residual
def resize_up(input_features, scale=2):
"""Nearest neighbor rescaling-operation for the input features.
Args:
input_features: A tensor of shape (b, h, w, c).
scale: An integer specifying the scaling factor.
Returns: A tensor of shape (b, scale * h, scale * w, c).
"""
assert scale >= 1
_, size_x, size_y, _ = input_features.shape.as_list()
new_size_x = int(round(size_x * scale))
new_size_y = int(round(size_y * scale))
return tf.image.resize(
input_features,
[new_size_x, new_size_y],
align_corners=True,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
def resize_down(input_features, scale=2):
"""Average pooling rescaling-operation for the input features.
Args:
input_features: A tensor of shape (b, h, w, c).
scale: An integer specifying the scaling factor.
Returns: A tensor of shape (b, h / scale, w / scale, c).
"""
assert scale >= 1
return tf.nn.avg_pool2d(
input_features,
ksize=(1, scale, scale, 1),
strides=(1, scale, scale, 1),
padding='VALID')
================================================
FILE: hierarchical_transformer_memory/README.md
================================================
# Towards mental time travel: A hierarchical memory for RL agents
This provides an implementation of two components of the paper "Towards mental
time travel: A hierarchical memory for reinforcement learning agents." The
article can be found on arXiv at [https://arxiv.org/abs/2105.14039](https://arxiv.org/abs/2105.14039)
Specifically, this repository contains:
1) A JAX/Haiku implementation of hierarchical transformer attention over memory.
2) An implementation of the Ballet environment used in the paper.
We have also released the Rapid Word Learning tasks from the paper, but to
simplify dependencies they are located in the `dm_fast_mapping` repository:
[deepmind/dm_fast_mapping](https://github.com/deepmind/dm_fast_mapping) see the
[documentation](https://github.com/deepmind/dm_fast_mapping/blob/master/docs/index.md)
for that repository for further details about using those tasks.
## Setup
For easy installation, run:
```shell
python3 -m venv htm_env
source htm_env/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
```
Note that this installs the components needed for both the attention module and
the environment. If you only wish to use the environment, you do not need to
install JAX, Haiku, or Chex.
## Using the hierarchical attention module:
Please see `hierarchical_attention/htm_attention_test.py` for some examples of
the expected inputs for this module.
## Running the ballet environment
The ballet environment is contained in the `pycolab_ballet/` subfolder. To load
a simple ballet environment with 2 dancers and short delays, and watch a few
steps of the dances, you can do:
```
from pycolab_ballet import ballet_environment
env = ballet_environment.simple_builder(level_name='2_delay16')
timestep = env.reset()
for _ in range(5):
action = 0
timestep = env.step(action)
```
## Citing this work
If you use this code, please cite the associated paper:
```
@article{lampinen2021towards,
title={Towards mental time travel:
a hierarchical memory for reinforcement learning agents},
author={Lampinen, Andrew Kyle and Chan, Stephanie CY and Banino, Andrea and
Hill, Felix},
journal={arXiv preprint arXiv:2105.14039},
year={2021}
}
```
================================================
FILE: hierarchical_transformer_memory/hierarchical_attention/htm_attention.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Haiku module implementing hierarchical attention over memory."""
import functools
import inspect
from typing import Optional, NamedTuple
import chex
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
_EPSILON = 1e-3
class HierarchicalMemory(NamedTuple):
"""Structure of the hierarchical memory.
Where 'B' is batch size, 'M' is number of memories, 'C' is chunk size, and 'D'
is memory dimension.
"""
keys: jnp.ndarray # [B, M, D]
contents: jnp.ndarray # [B, M, C, D]
steps_since_last_write: jnp.ndarray # [B], steps since last memory write
accumulator: jnp.ndarray # [B, C, D], accumulates experiences before write
def sinusoid_position_encoding(
sequence_length: int,
hidden_size: int,
min_timescale: float = 2.,
max_timescale: float = 1e4,
) -> jnp.ndarray:
"""Creates sinusoidal encodings.
Args:
sequence_length: length [L] of sequence to be position encoded.
hidden_size: dimension [D] of the positional encoding vectors.
min_timescale: minimum timescale for the frequency.
max_timescale: maximum timescale for the frequency.
Returns:
An array of shape [L, D]
"""
freqs = np.arange(0, hidden_size, min_timescale)
inv_freq = max_timescale**(-freqs / hidden_size)
pos_seq = np.arange(sequence_length - 1, -1, -1.0)
sinusoid_inp = np.einsum("i,j->ij", pos_seq, inv_freq)
pos_emb = np.concatenate(
[np.sin(sinusoid_inp), np.cos(sinusoid_inp)], axis=-1)
return pos_emb
class HierarchicalMemoryAttention(hk.Module):
"""Multi-head attention over hierarchical memory."""
def __init__(self,
feature_size: int,
k: int,
num_heads: int = 1,
memory_position_encoding: bool = True,
init_scale: float = 2.,
name: Optional[str] = None) -> None:
"""Constructor.
Args:
feature_size: size of feature dimension of attention-over-memories
embedding.
k: number of memories to sample.
num_heads: number of attention heads.
memory_position_encoding: whether to add positional encodings to memories
during within memory attention.
init_scale: scale factor for Variance weight initializers.
name: module name.
"""
super().__init__(name=name)
self._size = feature_size
self._k = k
self._num_heads = num_heads
self._weights = None
self._memory_position_encoding = memory_position_encoding
self._init_scale = init_scale
@property
def num_heads(self):
return self._num_heads
@hk.transparent
def _singlehead_linear(self,
inputs: jnp.ndarray,
hidden_size: int,
name: str):
linear = hk.Linear(
hidden_size,
with_bias=False,
w_init=hk.initializers.VarianceScaling(scale=self._init_scale),
name=name)
out = linear(inputs)
return out
def __call__(
self,
queries: jnp.ndarray,
hm_memory: HierarchicalMemory,
hm_mask: Optional[jnp.ndarray] = None) -> jnp.ndarray:
"""Do hierarchical attention over the stored memories.
Args:
queries: Tensor [B, Q, E] Query(ies) in, for batch size B, query length
Q, and embedding dimension E.
hm_memory: Hierarchical Memory.
hm_mask: Optional boolean mask tensor of shape [B, Q, M]. Where false,
the corresponding query timepoints cannot attend to the corresponding
memory chunks. This can be used for enforcing causal attention on the
learner, not attending to memories from prior episodes, etc.
Returns:
Value updates for each query slot: [B, Q, D]
"""
# some shape checks
batch_size, query_length, _ = queries.shape
(memory_batch_size, num_memories,
memory_chunk_size, mem_embbedding_size) = hm_memory.contents.shape
assert batch_size == memory_batch_size
chex.assert_shape(hm_memory.keys,
(batch_size, num_memories, mem_embbedding_size))
chex.assert_shape(hm_memory.accumulator,
(memory_batch_size, memory_chunk_size,
mem_embbedding_size))
chex.assert_shape(hm_memory.steps_since_last_write,
(memory_batch_size,))
if hm_mask is not None:
chex.assert_type(hm_mask, bool)
chex.assert_shape(hm_mask,
(batch_size, query_length, num_memories))
query_head = self._singlehead_linear(queries, self._size, "query")
key_head = self._singlehead_linear(
jax.lax.stop_gradient(hm_memory.keys), self._size, "key")
# What times in the input [t] attend to what times in the memories [T].
logits = jnp.einsum("btd,bTd->btT", query_head, key_head)
scaled_logits = logits / np.sqrt(self._size)
# Mask last dimension, replacing invalid logits with large negative values.
# This allows e.g. enforcing causal attention on learner, or blocking
# attention across episodes
if hm_mask is not None:
masked_logits = jnp.where(hm_mask, scaled_logits, -1e6)
else:
masked_logits = scaled_logits
# identify the top-k memories and their relevance weights
top_k_logits, top_k_indices = jax.lax.top_k(masked_logits, self._k)
weights = jax.nn.softmax(top_k_logits)
# set up the within-memory attention
assert self._size % self._num_heads == 0
mha_key_size = self._size // self._num_heads
attention_layer = hk.MultiHeadAttention(
key_size=mha_key_size,
model_size=self._size,
num_heads=self._num_heads,
w_init_scale=self._init_scale,
name="within_mem_attn")
# position encodings
augmented_contents = hm_memory.contents
if self._memory_position_encoding:
position_embs = sinusoid_position_encoding(
memory_chunk_size, mem_embbedding_size)
augmented_contents += position_embs[None, None, :, :]
def _within_memory_attention(sub_inputs, sub_memory_contents, sub_weights,
sub_top_k_indices):
top_k_contents = sub_memory_contents[sub_top_k_indices, :, :]
# Now we go deeper, with another vmap over **tokens**, because each token
# can each attend to different memories.
def do_attention(sub_sub_inputs, sub_sub_top_k_contents):
tiled_inputs = jnp.tile(sub_sub_inputs[None, None, :],
reps=(self._k, 1, 1))
sub_attention_results = attention_layer(
query=tiled_inputs,
key=sub_sub_top_k_contents,
value=sub_sub_top_k_contents)
return sub_attention_results
do_attention = hk_vmap(do_attention, in_axes=0, split_rng=False)
attention_results = do_attention(sub_inputs, top_k_contents)
attention_results = jnp.squeeze(attention_results, axis=2)
# Now collapse results across k memories
attention_results = sub_weights[:, :, None] * attention_results
attention_results = jnp.sum(attention_results, axis=1)
return attention_results
# vmap across batch
batch_within_memory_attention = hk_vmap(_within_memory_attention,
in_axes=0, split_rng=False)
outputs = batch_within_memory_attention(
queries,
jax.lax.stop_gradient(augmented_contents),
weights,
top_k_indices)
return outputs
@functools.wraps(hk.vmap)
def hk_vmap(*args, **kwargs):
"""Helper function to support older versions of Haiku."""
# Older versions of Haiku did not have split_rng, but the behavior has always
# been equivalent to split_rng=False.
if "split_rng" not in inspect.signature(hk.vmap).parameters:
kwargs.setdefault("split_rng", False)
if kwargs.get["split_rng"]:
raise ValueError("The installed version of Haiku only supports "
"`split_rng=False`, please upgrade Haiku.")
del kwargs["split_rng"]
return hk.vmap(*args, **kwargs)
================================================
FILE: hierarchical_transformer_memory/hierarchical_attention/htm_attention_test.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for hierarchical_attention.htm_attention."""
from absl.testing import absltest
from absl.testing import parameterized
import haiku as hk
import numpy as np
from hierarchical_transformer_memory.hierarchical_attention import htm_attention
def _build_queries_and_memory(query_length, num_memories, mem_chunk_size,
batch_size=2, embedding_size=12):
"""Builds dummy queries + memory contents for tests."""
queries = np.random.random([batch_size, query_length, embedding_size])
memory_contents = np.random.random(
[batch_size, num_memories, mem_chunk_size, embedding_size])
# summary key = average across chunk
memory_keys = np.mean(memory_contents, axis=2)
# to accumulate newest memories before writing
memory_accumulator = np.zeros_like(memory_contents[:, -1, :, :])
memory = htm_attention.HierarchicalMemory(
keys=memory_keys,
contents=memory_contents,
accumulator=memory_accumulator,
steps_since_last_write=np.zeros([batch_size,], dtype=np.int32))
return queries, memory
class HierarchicalAttentionTest(parameterized.TestCase):
@parameterized.parameters([
{
'query_length': 1,
'num_memories': 7,
'mem_chunk_size': 5,
'mem_k': 4,
},
{
'query_length': 9,
'num_memories': 7,
'mem_chunk_size': 5,
'mem_k': 4,
},
])
@hk.testing.transform_and_run
def test_output_shapes(self, query_length, num_memories, mem_chunk_size,
mem_k):
np.random.seed(0)
batch_size = 2
embedding_size = 12
num_heads = 3
queries, memory = _build_queries_and_memory(
query_length=query_length, num_memories=num_memories,
mem_chunk_size=mem_chunk_size, embedding_size=embedding_size)
hm_att = htm_attention.HierarchicalMemoryAttention(
feature_size=embedding_size,
k=mem_k,
num_heads=num_heads)
results = hm_att(queries, memory)
self.assertEqual(results.shape,
(batch_size, query_length, embedding_size))
self.assertTrue(np.all(np.isfinite(results)))
@hk.testing.transform_and_run
def test_masking(self):
np.random.seed(0)
batch_size = 2
embedding_size = 12
num_heads = 3
query_length = 5
num_memories = 7
mem_chunk_size = 6
mem_k = 4
queries, memory = _build_queries_and_memory(
query_length=query_length, num_memories=num_memories,
mem_chunk_size=mem_chunk_size, embedding_size=embedding_size)
hm_att = htm_attention.HierarchicalMemoryAttention(
feature_size=embedding_size,
k=mem_k,
num_heads=num_heads)
# get a random boolean mask
mask = np.random.binomial(
1, 0.5, [batch_size, query_length, num_memories]).astype(bool)
results = hm_att(queries, memory, hm_mask=mask)
self.assertEqual(results.shape,
(batch_size, query_length, embedding_size))
self.assertTrue(np.all(np.isfinite(results)))
if __name__ == '__main__':
absltest.main()
================================================
FILE: hierarchical_transformer_memory/pycolab_ballet/ballet_environment.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A pycolab environment for going to the ballet.
A pycolab-based environment for testing memory for sequences of events. The
environment contains some number of "dancer" characters in (implicit) 3 x 3
squares within a larger 9 x 9 room. The agent starts in the center of the room.
At the beginning of an episode, the dancers each do a dance solo of a fixed
length, separated by empty time of a fixed length. The agent's actions do
nothing during the dances. After the last dance ends, the agent must go up to a
dancer, identified using language describing the dance. The agent is rewarded +1
for approaching the correct dancer, 0 otherwise.
The room is upsampled at a size of 9 pixels per square to render a view for the
agent, which is cropped in egocentric perspective, i.e. the agent is always in
the center of its view (see https://arxiv.org/abs/1910.00571).
"""
from absl import app
from absl import flags
from absl import logging
import dm_env
import numpy as np
from pycolab import cropping
from hierarchical_transformer_memory.pycolab_ballet import ballet_environment_core as ballet_core
FLAGS = flags.FLAGS
UPSAMPLE_SIZE = 9 # pixels per game square
SCROLL_CROP_SIZE = 11 # in game squares
DANCER_SHAPES = [
"triangle", "empty_square", "plus", "inverse_plus", "ex", "inverse_ex",
"circle", "empty_circle", "tee", "upside_down_tee",
"h", "u", "upside_down_u", "vertical_stripes", "horizontal_stripes"
]
COLORS = {
"red": np.array([255, 0, 0]),
"green": np.array([0, 255, 0]),
"blue": np.array([0, 0, 255]),
"purple": np.array([128, 0, 128]),
"orange": np.array([255, 165, 0]),
"yellow": np.array([255, 255, 0]),
"brown": np.array([128, 64, 0]),
"pink": np.array([255, 64, 255]),
"cyan": np.array([0, 255, 255]),
"dark_green": np.array([0, 100, 0]),
"dark_red": np.array([100, 0, 0]),
"dark_blue": np.array([0, 0, 100]),
"olive": np.array([100, 100, 0]),
"teal": np.array([0, 100, 100]),
"lavender": np.array([215, 200, 255]),
"peach": np.array([255, 210, 170]),
"rose": np.array([255, 205, 230]),
"light_green": np.array([200, 255, 200]),
"light_yellow": np.array([255, 255, 200]),
}
def _generate_template(object_name):
"""Generates a template object image, given a name with color and shape."""
object_color, object_type = object_name.split()
template = np.zeros((UPSAMPLE_SIZE, UPSAMPLE_SIZE))
half = UPSAMPLE_SIZE // 2
if object_type == "triangle":
for i in range(UPSAMPLE_SIZE):
for j in range(UPSAMPLE_SIZE):
if (j <= half and i >= 2 * (half - j)) or (j > half and i >= 2 *
(j - half)):
template[i, j] = 1.
elif object_type == "square":
template[:, :] = 1.
elif object_type == "empty_square":
template[:2, :] = 1.
template[-2:, :] = 1.
template[:, :2] = 1.
template[:, -2:] = 1.
elif object_type == "plus":
template[:, half - 1:half + 2] = 1.
template[half - 1:half + 2, :] = 1.
elif object_type == "inverse_plus":
template[:, :] = 1.
template[:, half - 1:half + 2] = 0.
template[half - 1:half + 2, :] = 0.
elif object_type == "ex":
for i in range(UPSAMPLE_SIZE):
for j in range(UPSAMPLE_SIZE):
if abs(i - j) <= 1 or abs(UPSAMPLE_SIZE - 1 - j - i) <= 1:
template[i, j] = 1.
elif object_type == "inverse_ex":
for i in range(UPSAMPLE_SIZE):
for j in range(UPSAMPLE_SIZE):
if not (abs(i - j) <= 1 or abs(UPSAMPLE_SIZE - 1 - j - i) <= 1):
template[i, j] = 1.
elif object_type == "circle":
for i in range(UPSAMPLE_SIZE):
for j in range(UPSAMPLE_SIZE):
if (i - half)**2 + (j - half)**2 <= half**2:
template[i, j] = 1.
elif object_type == "empty_circle":
for i in range(UPSAMPLE_SIZE):
for j in range(UPSAMPLE_SIZE):
if abs((i - half)**2 + (j - half)**2 - half**2) < 6:
template[i, j] = 1.
elif object_type == "tee":
template[:, half - 1:half + 2] = 1.
template[:3, :] = 1.
elif object_type == "upside_down_tee":
template[:, half - 1:half + 2] = 1.
template[-3:, :] = 1.
elif object_type == "h":
template[:, :3] = 1.
template[:, -3:] = 1.
template[half - 1:half + 2, :] = 1.
elif object_type == "u":
template[:, :3] = 1.
template[:, -3:] = 1.
template[-3:, :] = 1.
elif object_type == "upside_down_u":
template[:, :3] = 1.
template[:, -3:] = 1.
template[:3, :] = 1.
elif object_type == "vertical_stripes":
for j in range(half + UPSAMPLE_SIZE % 2):
template[:, 2*j] = 1.
elif object_type == "horizontal_stripes":
for i in range(half + UPSAMPLE_SIZE % 2):
template[2*i, :] = 1.
else:
raise ValueError("Unknown object: {}".format(object_type))
if object_color not in COLORS:
raise ValueError("Unknown color: {}".format(object_color))
template = np.tensordot(template, COLORS[object_color], axes=0)
return template
# Agent and wall templates
_CHAR_TO_TEMPLATE_BASE = {
ballet_core.AGENT_CHAR:
np.tensordot(
np.ones([UPSAMPLE_SIZE, UPSAMPLE_SIZE]),
np.array([255, 255, 255]),
axes=0),
ballet_core.WALL_CHAR:
np.tensordot(
np.ones([UPSAMPLE_SIZE, UPSAMPLE_SIZE]),
np.array([40, 40, 40]),
axes=0),
}
def get_scrolling_cropper(rows=9, cols=9, crop_pad_char=" "):
return cropping.ScrollingCropper(rows=rows, cols=cols,
to_track=[ballet_core.AGENT_CHAR],
pad_char=crop_pad_char,
scroll_margins=(None, None))
class BalletEnvironment(dm_env.Environment):
"""A Python environment API for pycolab ballet tasks."""
def __init__(self, num_dancers, dance_delay, max_steps, rng=None):
"""Construct a BalletEnvironment that wraps pycolab games for agent use.
This class inherits from dm_env and has all the expected methods and specs.
Args:
num_dancers: The number of dancers to use, between 1 and 8 (inclusive).
dance_delay: How long to delay between the dances.
max_steps: The maximum number of steps to allow in an episode, after which
it will terminate.
rng: An optional numpy Random Generator, to set a fixed seed use e.g.
`rng=np.random.default_rng(seed=...)`
"""
self._num_dancers = num_dancers
self._dance_delay = dance_delay
self._max_steps = max_steps
# internal state
if rng is None:
rng = np.random.default_rng()
self._rng = rng
self._current_game = None # Current pycolab game instance.
self._state = None # Current game step state.
self._game_over = None # Whether the game has ended.
self._char_to_template = None # Mapping of chars to sprite images.
# rendering tools
self._cropper = get_scrolling_cropper(SCROLL_CROP_SIZE, SCROLL_CROP_SIZE,
" ")
def _game_factory(self):
"""Samples dancers and positions, returns a pycolab core game engine."""
target_dancer_index = self._rng.integers(self._num_dancers)
motions = list(ballet_core.DANCE_SEQUENCES.keys())
positions = ballet_core.DANCER_POSITIONS.copy()
colors = list(COLORS.keys())
shapes = DANCER_SHAPES.copy()
self._rng.shuffle(positions)
self._rng.shuffle(motions)
self._rng.shuffle(colors)
self._rng.shuffle(shapes)
dancers_and_properties = []
for dancer_i in range(self._num_dancers):
if dancer_i == target_dancer_index:
value = 1.
else:
value = 0.
dancers_and_properties.append(
(ballet_core.POSSIBLE_DANCER_CHARS[dancer_i],
positions[dancer_i],
motions[dancer_i],
shapes[dancer_i],
colors[dancer_i],
value))
logging.info("Making level with dancers_and_properties: %s",
dancers_and_properties)
return ballet_core.make_game(
dancers_and_properties=dancers_and_properties,
dance_delay=self._dance_delay)
def _render_observation(self, observation):
"""Renders from raw pycolab image observation to agent-usable ones."""
observation = self._cropper.crop(observation)
obs_rows, obs_cols = observation.board.shape
image = np.zeros([obs_rows * UPSAMPLE_SIZE, obs_cols * UPSAMPLE_SIZE, 3],
dtype=np.float32)
for i in range(obs_rows):
for j in range(obs_cols):
this_char = chr(observation.board[i, j])
if this_char != ballet_core.FLOOR_CHAR:
image[
i * UPSAMPLE_SIZE:(i + 1) * UPSAMPLE_SIZE, j *
UPSAMPLE_SIZE:(j + 1) * UPSAMPLE_SIZE] = self._char_to_template[
this_char]
image /= 255.
language = np.array(self._current_game.the_plot["instruction_string"])
full_observation = (image, language)
return full_observation
def reset(self):
"""Start a new episode."""
# Build a new game and retrieve its first set of state/reward/discount.
self._current_game = self._game_factory()
# set up rendering, cropping, and state for current game
self._char_to_template = {
k: _generate_template(v) for k, v in self._current_game.the_plot[
"char_to_color_shape"]}
self._char_to_template.update(_CHAR_TO_TEMPLATE_BASE)
self._cropper.set_engine(self._current_game)
self._state = dm_env.StepType.FIRST
# let's go!
observation, _, _ = self._current_game.its_showtime()
observation = self._render_observation(observation)
return dm_env.TimeStep(
step_type=self._state,
reward=None,
discount=None,
observation=observation)
def step(self, action):
"""Apply action, step the world forward, and return observations."""
# If needed, reset and start new episode.
if self._state == dm_env.StepType.LAST:
self._clear_state()
if self._current_game is None:
return self.reset()
# Execute the action in pycolab.
observation, reward, discount = self._current_game.play(action)
self._game_over = self._is_game_over()
reward = reward if reward is not None else 0.
observation = self._render_observation(observation)
# Check the current status of the game.
if self._game_over:
self._state = dm_env.StepType.LAST
else:
self._state = dm_env.StepType.MID
return dm_env.TimeStep(
step_type=self._state,
reward=reward,
discount=discount,
observation=observation)
@property
def observation_spec(self):
image_shape = (SCROLL_CROP_SIZE * UPSAMPLE_SIZE,
SCROLL_CROP_SIZE * UPSAMPLE_SIZE,
3)
return (
# vision
dm_env.specs.Array(
shape=image_shape, dtype=np.float32, name="image"),
# language
dm_env.specs.Array(
shape=[], dtype=str, name="language"),
)
@property
def action_spec(self):
return dm_env.specs.BoundedArray(
shape=[], dtype="int32",
minimum=0, maximum=7,
name="grid_actions")
def _is_game_over(self):
"""Returns whether it is game over, either from the engine or timeout."""
return (self._current_game.game_over or
(self._current_game.the_plot.frame >= self._max_steps))
def _clear_state(self):
"""Clear all the internal information about the game."""
self._state = None
self._current_game = None
self._char_to_template = None
self._game_over = None
def simple_builder(level_name):
"""Simplifies building from fixed defs.
Args:
level_name: '{num_dancers}_delay{delay_length}', where each variable is an
integer. The levels used in the paper were:
['2_delay16', '4_delay16', '8_delay16',
'2_delay48', '4_delay48', '8_delay48']
Returns:
A BalletEnvironment with the requested settings.
"""
num_dancers, dance_delay = level_name.split("_")
num_dancers = int(num_dancers)
dance_delay = int(dance_delay[5:])
max_steps = 320 if dance_delay == 16 else 1024
level_args = dict(
num_dancers=num_dancers,
dance_delay=dance_delay,
max_steps=max_steps)
return BalletEnvironment(**level_args)
def main(argv):
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
env = simple_builder("4_delay16")
for _ in range(3):
obs = env.reset().observation
for _ in range(300):
obs = env.step(0).observation
print(obs)
if __name__ == "__main__":
app.run(main)
================================================
FILE: hierarchical_transformer_memory/pycolab_ballet/ballet_environment_core.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""The pycolab core of the environment for going to the ballet.
This builds the text-based (non-graphical) engine of the environment, and offers
a UI which a human can play (for a fixed level). However, the logic of level
creation, the graphics, and anything that is external to the pycolab engine
itself is contained in ballet_environment.py.
"""
import curses
import enum
from absl import app
from absl import flags
from pycolab import ascii_art
from pycolab import human_ui
from pycolab.prefab_parts import sprites as prefab_sprites
FLAGS = flags.FLAGS
ROOM_SIZE = (11, 11) # one square around edge will be wall.
DANCER_POSITIONS = [(2, 2), (2, 5), (2, 8),
(5, 2), (5, 8), # space in center for agent
(8, 2), (8, 5), (8, 8)]
AGENT_START = (5, 5)
AGENT_CHAR = "A"
WALL_CHAR = "#"
FLOOR_CHAR = " "
RESERVED_CHARS = [AGENT_CHAR, WALL_CHAR, FLOOR_CHAR]
POSSIBLE_DANCER_CHARS = [
chr(i) for i in range(65, 91) if chr(i) not in RESERVED_CHARS
]
DANCE_SEQUENCE_LENGTHS = 16
# movement directions for dancers / actions for agent
class DIRECTIONS(enum.IntEnum):
N = 0
NE = 1
E = 2
SE = 3
S = 4
SW = 5
W = 6
NW = 7
DANCE_SEQUENCES = {
"circle_cw": [
DIRECTIONS.N, DIRECTIONS.E, DIRECTIONS.S, DIRECTIONS.S, DIRECTIONS.W,
DIRECTIONS.W, DIRECTIONS.N, DIRECTIONS.N, DIRECTIONS.E, DIRECTIONS.E,
DIRECTIONS.S, DIRECTIONS.S, DIRECTIONS.W, DIRECTIONS.W, DIRECTIONS.N,
DIRECTIONS.E
],
"circle_ccw": [
DIRECTIONS.N, DIRECTIONS.W, DIRECTIONS.S, DIRECTIONS.S, DIRECTIONS.E,
DIRECTIONS.E, DIRECTIONS.N, DIRECTIONS.N, DIRECTIONS.W, DIRECTIONS.W,
DIRECTIONS.S, DIRECTIONS.S, DIRECTIONS.E, DIRECTIONS.E, DIRECTIONS.N,
DIRECTIONS.W
],
"up_and_down": [
DIRECTIONS.N, DIRECTIONS.S, DIRECTIONS.S, DIRECTIONS.N, DIRECTIONS.N,
DIRECTIONS.S, DIRECTIONS.S, DIRECTIONS.N, DIRECTIONS.N, DIRECTIONS.S,
DIRECTIONS.S, DIRECTIONS.N, DIRECTIONS.N, DIRECTIONS.S, DIRECTIONS.S,
DIRECTIONS.N
],
"left_and_right": [
DIRECTIONS.E, DIRECTIONS.W, DIRECTIONS.W, DIRECTIONS.E, DIRECTIONS.E,
DIRECTIONS.W, DIRECTIONS.W, DIRECTIONS.E, DIRECTIONS.E, DIRECTIONS.W,
DIRECTIONS.W, DIRECTIONS.E, DIRECTIONS.E, DIRECTIONS.W, DIRECTIONS.W,
DIRECTIONS.E
],
"diagonal_uldr": [
DIRECTIONS.NW, DIRECTIONS.SE, DIRECTIONS.SE, DIRECTIONS.NW,
DIRECTIONS.NW, DIRECTIONS.SE, DIRECTIONS.SE, DIRECTIONS.NW,
DIRECTIONS.NW, DIRECTIONS.SE, DIRECTIONS.SE, DIRECTIONS.NW,
DIRECTIONS.NW, DIRECTIONS.SE, DIRECTIONS.SE, DIRECTIONS.NW
],
"diagonal_urdl": [
DIRECTIONS.NE, DIRECTIONS.SW, DIRECTIONS.SW, DIRECTIONS.NE,
DIRECTIONS.NE, DIRECTIONS.SW, DIRECTIONS.SW, DIRECTIONS.NE,
DIRECTIONS.NE, DIRECTIONS.SW, DIRECTIONS.SW, DIRECTIONS.NE,
DIRECTIONS.NE, DIRECTIONS.SW, DIRECTIONS.SW, DIRECTIONS.NE
],
"plus_cw": [
DIRECTIONS.N, DIRECTIONS.S, DIRECTIONS.E, DIRECTIONS.W, DIRECTIONS.S,
DIRECTIONS.N, DIRECTIONS.W, DIRECTIONS.E, DIRECTIONS.N, DIRECTIONS.S,
DIRECTIONS.E, DIRECTIONS.W, DIRECTIONS.S, DIRECTIONS.N, DIRECTIONS.W,
DIRECTIONS.E
],
"plus_ccw": [
DIRECTIONS.N, DIRECTIONS.S, DIRECTIONS.W, DIRECTIONS.E, DIRECTIONS.S,
DIRECTIONS.N, DIRECTIONS.E, DIRECTIONS.W, DIRECTIONS.N, DIRECTIONS.S,
DIRECTIONS.W, DIRECTIONS.E, DIRECTIONS.S, DIRECTIONS.N, DIRECTIONS.E,
DIRECTIONS.W
],
"times_cw": [
DIRECTIONS.NE, DIRECTIONS.SW, DIRECTIONS.SE, DIRECTIONS.NW,
DIRECTIONS.SW, DIRECTIONS.NE, DIRECTIONS.NW, DIRECTIONS.SE,
DIRECTIONS.NE, DIRECTIONS.SW, DIRECTIONS.SE, DIRECTIONS.NW,
DIRECTIONS.SW, DIRECTIONS.NE, DIRECTIONS.NW, DIRECTIONS.SE
],
"times_ccw": [
DIRECTIONS.NW, DIRECTIONS.SE, DIRECTIONS.SW, DIRECTIONS.NE,
DIRECTIONS.SE, DIRECTIONS.NW, DIRECTIONS.NE, DIRECTIONS.SW,
DIRECTIONS.NW, DIRECTIONS.SE, DIRECTIONS.SW, DIRECTIONS.NE,
DIRECTIONS.SE, DIRECTIONS.NW, DIRECTIONS.NE, DIRECTIONS.SW
],
"zee": [
DIRECTIONS.NE, DIRECTIONS.W, DIRECTIONS.W, DIRECTIONS.E, DIRECTIONS.E,
DIRECTIONS.SW, DIRECTIONS.NE, DIRECTIONS.SW, DIRECTIONS.SW,
DIRECTIONS.E, DIRECTIONS.E, DIRECTIONS.W, DIRECTIONS.W, DIRECTIONS.NE,
DIRECTIONS.SW, DIRECTIONS.NE
],
"chevron_down": [
DIRECTIONS.NW, DIRECTIONS.S, DIRECTIONS.SE, DIRECTIONS.NE, DIRECTIONS.N,
DIRECTIONS.SW, DIRECTIONS.NE, DIRECTIONS.SW, DIRECTIONS.NE,
DIRECTIONS.S, DIRECTIONS.SW, DIRECTIONS.NW, DIRECTIONS.N, DIRECTIONS.SE,
DIRECTIONS.NW, DIRECTIONS.SE
],
"chevron_up": [
DIRECTIONS.SE, DIRECTIONS.N, DIRECTIONS.NW, DIRECTIONS.SW, DIRECTIONS.S,
DIRECTIONS.NE, DIRECTIONS.SW, DIRECTIONS.NE, DIRECTIONS.SW,
DIRECTIONS.N, DIRECTIONS.NE, DIRECTIONS.SE, DIRECTIONS.S, DIRECTIONS.NW,
DIRECTIONS.SE, DIRECTIONS.NW
],
}
class DancerSprite(prefab_sprites.MazeWalker):
"""A `Sprite` for dancers."""
def __init__(self, corner, position, character, motion, color, shape,
value=0.):
super(DancerSprite, self).__init__(
corner, position, character, impassable="#")
self.motion = motion
self.dance_sequence = DANCE_SEQUENCES[motion].copy()
self.color = color
self.shape = shape
self.value = value
self.is_dancing = False
def update(self, actions, board, layers, backdrop, things, the_plot):
if the_plot["task_phase"] == "dance" and self.is_dancing:
if not self.dance_sequence:
raise ValueError(
"Dance sequence is empty! Was this dancer repeated in the order?")
dance_move = self.dance_sequence.pop(0)
if dance_move == DIRECTIONS.N:
self._north(board, the_plot)
elif dance_move == DIRECTIONS.NE:
self._northeast(board, the_plot)
elif dance_move == DIRECTIONS.E:
self._east(board, the_plot)
elif dance_move == DIRECTIONS.SE:
self._southeast(board, the_plot)
elif dance_move == DIRECTIONS.S:
self._south(board, the_plot)
elif dance_move == DIRECTIONS.SW:
self._southwest(board, the_plot)
elif dance_move == DIRECTIONS.W:
self._west(board, the_plot)
elif dance_move == DIRECTIONS.NW:
self._northwest(board, the_plot)
if not self.dance_sequence: # done!
self.is_dancing = False
the_plot["time_until_next_dance"] = the_plot["dance_delay"]
else:
if self.position == things[AGENT_CHAR].position:
# Award the player the appropriate amount of reward, and end episode.
the_plot.add_reward(self.value)
the_plot.terminate_episode()
class PlayerSprite(prefab_sprites.MazeWalker):
"""The player / agent character.
MazeWalker class methods handle basic movement and collision detection.
"""
def __init__(self, corner, position, character):
super(PlayerSprite, self).__init__(
corner, position, character, impassable="#")
def update(self, actions, board, layers, backdrop, things, the_plot):
if the_plot["task_phase"] == "dance":
# agent's actions are ignored, this logic updates the dance phases.
if the_plot["time_until_next_dance"] > 0:
the_plot["time_until_next_dance"] -= 1
if the_plot["time_until_next_dance"] == 0: # next phase time!
if the_plot["dance_order"]: # start the next dance!
next_dancer = the_plot["dance_order"].pop(0)
things[next_dancer].is_dancing = True
else: # choice time!
the_plot["task_phase"] = "choice"
the_plot["instruction_string"] = the_plot[
"choice_instruction_string"]
elif the_plot["task_phase"] == "choice":
# agent can now move and make its choice
if actions == DIRECTIONS.N:
self._north(board, the_plot)
elif actions == DIRECTIONS.NE:
self._northeast(board, the_plot)
elif actions == DIRECTIONS.E:
self._east(board, the_plot)
elif actions == DIRECTIONS.SE:
self._southeast(board, the_plot)
elif actions == DIRECTIONS.S:
self._south(board, the_plot)
elif actions == DIRECTIONS.SW:
self._southwest(board, the_plot)
elif actions == DIRECTIONS.W:
self._west(board, the_plot)
elif actions == DIRECTIONS.NW:
self._northwest(board, the_plot)
def make_game(dancers_and_properties, dance_delay=16):
"""Constructs an ascii map, then uses pycolab to make it a game.
Args:
dancers_and_properties: list of (character, (row, column), motion, shape,
color, value), for placing objects in the world.
dance_delay: how long to wait between dances.
Returns:
this_game: Pycolab engine running the specified game.
"""
num_rows, num_cols = ROOM_SIZE
level_layout = []
# upper wall
level_layout.append("".join([WALL_CHAR] * num_cols))
# room
middle_string = "".join([WALL_CHAR] + [" "] * (num_cols - 2) + [WALL_CHAR])
level_layout.extend([middle_string for _ in range(num_rows - 2)])
# lower wall
level_layout.append("".join([WALL_CHAR] * num_cols))
def _add_to_map(obj, loc):
"""Adds an ascii character to the level at the requested position."""
obj_row = level_layout[loc[0]]
pre_string = obj_row[:loc[1]]
post_string = obj_row[loc[1] + 1:]
level_layout[loc[0]] = pre_string + obj + post_string
_add_to_map(AGENT_CHAR, AGENT_START)
sprites = {AGENT_CHAR: PlayerSprite}
dance_order = []
char_to_color_shape = []
# add dancers to level
for obj, loc, motion, shape, color, value in dancers_and_properties:
_add_to_map(obj, loc)
sprites[obj] = ascii_art.Partial(
DancerSprite, motion=motion, color=color, shape=shape, value=value)
char_to_color_shape.append((obj, color + " " + shape))
dance_order += obj
if value > 0.:
choice_instruction_string = motion
this_game = ascii_art.ascii_art_to_game(
art=level_layout,
what_lies_beneath=" ",
sprites=sprites,
update_schedule=[[AGENT_CHAR],
dance_order])
this_game.the_plot["task_phase"] = "dance"
this_game.the_plot["instruction_string"] = "watch"
this_game.the_plot["choice_instruction_string"] = choice_instruction_string
this_game.the_plot["dance_order"] = dance_order
this_game.the_plot["dance_delay"] = dance_delay
this_game.the_plot["time_until_next_dance"] = 1
this_game.the_plot["char_to_color_shape"] = char_to_color_shape
return this_game
def main(argv):
del argv # unused
these_dancers_and_properties = [
(POSSIBLE_DANCER_CHARS[1], (2, 2), "chevron_up", "triangle", "red", 1),
(POSSIBLE_DANCER_CHARS[2], (2, 5), "circle_ccw", "triangle", "red", 0),
(POSSIBLE_DANCER_CHARS[3], (2, 8), "plus_cw", "triangle", "red", 0),
(POSSIBLE_DANCER_CHARS[4], (5, 2), "plus_ccw", "triangle", "red", 0),
(POSSIBLE_DANCER_CHARS[5], (5, 8), "times_cw", "triangle", "red", 0),
(POSSIBLE_DANCER_CHARS[6], (8, 2), "up_and_down", "plus", "blue", 0),
(POSSIBLE_DANCER_CHARS[7], (8, 5), "left_and_right", "plus", "blue", 0),
(POSSIBLE_DANCER_CHARS[8], (8, 8), "zee", "plus", "blue", 0),
]
game = make_game(dancers_and_properties=these_dancers_and_properties)
# Note that these colors are only for human UI
fg_colours = {
AGENT_CHAR: (999, 999, 999), # Agent is white
WALL_CHAR: (300, 300, 300), # Wall, dark grey
FLOOR_CHAR: (0, 0, 0), # Floor
}
for (c, _, _, _, col, _) in these_dancers_and_properties:
fg_colours[c] = (999, 0, 0) if col == "red" else (0, 0, 999)
bg_colours = {
c: (0, 0, 0) for c in RESERVED_CHARS + POSSIBLE_DANCER_CHARS[1:8]
}
ui = human_ui.CursesUi(
keys_to_actions={
# Basic movement.
curses.KEY_UP: DIRECTIONS.N,
curses.KEY_DOWN: DIRECTIONS.S,
curses.KEY_LEFT: DIRECTIONS.W,
curses.KEY_RIGHT: DIRECTIONS.E,
-1: 8, # Do nothing.
},
delay=500,
colour_fg=fg_colours,
colour_bg=bg_colours)
ui.play(game)
if __name__ == "__main__":
app.run(main)
================================================
FILE: hierarchical_transformer_memory/pycolab_ballet/ballet_environment_test.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for pycolab_ballet.ballet_environment_wrapper."""
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
from hierarchical_transformer_memory.pycolab_ballet import ballet_environment
from hierarchical_transformer_memory.pycolab_ballet import ballet_environment_core
class BalletEnvironmentTest(parameterized.TestCase):
def test_full_wrapper(self):
env = ballet_environment.BalletEnvironment(
num_dancers=1, dance_delay=16, max_steps=200,
rng=np.random.default_rng(seed=0))
result = env.reset()
self.assertIsNone(result.reward)
level_size = ballet_environment_core.ROOM_SIZE
upsample_size = ballet_environment.UPSAMPLE_SIZE
# wait for dance to complete
for i in range(30):
result = env.step(0).observation
self.assertEqual(result[0].shape,
(level_size[0] * upsample_size,
level_size[1] * upsample_size,
3))
self.assertEqual(str(result[1])[:5],
np.array("watch"))
for i in [1, 1, 1, 1]: # first gets eaten before agent can move
result = env.step(i)
self.assertEqual(result.observation[0].shape,
(level_size[0] * upsample_size,
level_size[1] * upsample_size,
3))
self.assertEqual(str(result.observation[1])[:11],
np.array("up_and_down"))
self.assertEqual(result.reward, 1.)
# check egocentric scrolling is working, by checking object is in center
np.testing.assert_array_almost_equal(
result.observation[0][45:54, 45:54],
ballet_environment._generate_template("orange plus") / 255.)
@parameterized.parameters(
"2_delay16",
"4_delay16",
"8_delay48",
)
def test_simple_builder(self, level_name):
dance_delay = int(level_name[-2:])
np.random.seed(0)
env = ballet_environment.simple_builder(level_name)
# check max steps are set to match paper settings
self.assertEqual(env._max_steps,
320 if dance_delay == 16 else 1024)
# test running a few steps of each
env.reset()
level_size = ballet_environment_core.ROOM_SIZE
upsample_size = ballet_environment.UPSAMPLE_SIZE
for i in range(8):
result = env.step(i) # check all 8 movements work
self.assertEqual(result.observation[0].shape,
(level_size[0] * upsample_size,
level_size[1] * upsample_size,
3))
self.assertEqual(str(result.observation[1])[:5],
np.array("watch"))
self.assertEqual(result.reward, 0.)
if __name__ == "__main__":
absltest.main()
================================================
FILE: hierarchical_transformer_memory/requirements.txt
================================================
absl-py
chex>=0.0.8
dm-env
dm-haiku>=0.0.5.dev
jax>=0.2.17
numpy
pycolab
================================================
FILE: himo/README.md
================================================
# Accompanying code for Value-driven Hindsight Modelling
`himo_example.ipynb` contains code for the illustrative task of Section 3.2 in the [paper](https://arxiv.org/abs/2002.08329).
## Requirements and Installation
This is a Colaboratory notebook: [](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/himo/himo_example.ipynb)
If your runtime doesn't include [Haiku](https://github.com/deepmind/dm-haiku) and [Tree](https://github.com/deepmind/tree) please run the cell labeled "Install extra dependencies" which contains the following pip installation commands:
```!pip install dm-haiku```
```!pip install dm-tree```
## Reference
**Value-driven Hindsight Modelling** Arthur Guez, Fabio Viola, Théophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, and Nicolas Heess
Neural Information Processing Systems 2020, URL: https://arxiv.org/abs/2002.08329
================================================
FILE: himo/himo_example.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7lyGjcyb5S8B"
},
"source": [
"Copyright 2020 The \"Value-driven Hindsight Modelling\" Authors. All rights reserved.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
" https://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BVWs0Qh1HL00"
},
"source": [
"# Hindsight Modelling: Illustrative task of section 3.2.\n",
"This implements the task and experiments defined in Section 3.2 and Appendix Section A3 of the \"Value-driven Hindsight Modelling paper\".\n",
"\n",
"#Task description:\n",
" This is a value estimation problem in a 1-step Markov Reward Process (no actions), namely \n",
"each episode consists of a single transition from initial state $x$ to terminal state $x'$, with a reward $r(x,x')$ on the way. The agent is trained on multiple episodes of each MRP instance. This learning process is repeated independently and averaged for multiple instances of the environment. \n",
"Each instance of the task is parametrized by a square matrix $W$ and a vector $b$ sampled from a unit normal distribution, as well as randomly initialized MLP (see below), which together determine the uncontrolled MDP.\n",
"Initial states $x$ are of dimension $D$ and sampled from a multivariate unit normal distribution ($x_i \\sim N(0, 1)$ for all state dimension~$i$).\n",
"Given $x=\\left(\\begin{smallmatrix}x_1\\\\x_2\\end{smallmatrix}\\right)$, where $x_1$ and $x_2$ are of dimension $D_1$ and $D_2$ ($D=D_1+D_2$),\n",
"the next state ${x' = \\left(\\begin{smallmatrix}x_1'\\\\x_2'\\end{smallmatrix}\\right)}$ is determined according to the transition function: $x_1'=\\text{MLP}(x) + \\epsilon$ and $x_2' = \\sigma(W x_2 + b)$ where $\\sigma$ is the Heaviside function, and MLP is a randomly sampled Multi-Layer Perceptron. $x_1'$ acts as a distractor here, with additive noise $\\epsilon \\sim N(0,1)$. The reward obtained is $r(x,x') = \\sum_i x^{(i)}_1 \\sum_i x'^{(i)}_2 / \\sqrt{D}$. The true value in the start state is also $v(x) = r(x,x')$.\n"
]
},
{
"cell_type": "code",
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "_7-a7M9ANbS5"
},
"source": [
"#@title Install extra dependencies\n",
"!pip install dm-haiku\n",
"!pip install dm-tree"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "grsC1l3BF3eM"
},
"outputs": [],
"source": [
"#@title Imports\n",
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"import functools\n",
"from functools import reduce\n",
"\n",
"import haiku as hk\n",
"import IPython\n",
"import jax\n",
"from jax import numpy as jnp\n",
"from jax import random\n",
"from jax.experimental import optix\n",
"import matplotlib\n",
"from matplotlib import pyplot as plt\n",
"import matplotlib.cm as cm\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import tree\n",
"\n",
"sns.set(rc={\"lines.linewidth\": 2.0})\n",
"sns.set_style(\"whitegrid\")\n",
"\n",
"ipython = IPython.get_ipython()\n",
"ipython.magic(\"%config InlineBackend.figure_format = 'retina'\")"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "eRFwE0SdIZ7W"
},
"outputs": [],
"source": [
"#@title Functions to generate dynamics and trajectories\n",
"\n",
"def gen_dynamics(rng_key, x_dims):\n",
" \"\"\"Generate dynamics (transition+reward functions) for one problem instance. \"\"\"\n",
" x1_dim, x2_dim = x_dims\n",
" x_dim = x1_dim + x2_dim\n",
"\n",
" # Sample dynamics parameters to compute x2' from x2\n",
" rng_key, w_rng_key, b_rng_key = random.split(rng_key, 3)\n",
" w_dy = random.normal(w_rng_key, shape=(x2_dim, x2_dim))\n",
" b_dy = random.normal(b_rng_key, shape=(x2_dim,))\n",
"\n",
" # Sample MLP parameters that controls the x-\u003ex1' transition\n",
" rng_key, w_rng_key, b_rng_key = random.split(rng_key, 3)\n",
" w2_dy = random.normal(w_rng_key, shape=(x_dim, x_dim))\n",
" b2_dy = random.normal(b_rng_key, shape=(x_dim,))\n",
" rng_key, w_rng_key, b_rng_key = random.split(rng_key, 3)\n",
" w3_dy = random.normal(w_rng_key, shape=(x_dim, x1_dim))\n",
" b3_dy = random.normal(b_rng_key, shape=(x1_dim,))\n",
"\n",
" @jax.vmap\n",
" def transition_fn(rng_key, x):\n",
" \"\"\"Sample x' from x.\"\"\"\n",
" x2 = x[-x2_dim:]\n",
" x2p = jnp.heaviside(x2.dot(w_dy) + b_dy, 0.0)\n",
" x1p = jax.nn.sigmoid(x.dot(w2_dy) + b2_dy)\n",
" x1p = jax.nn.sigmoid(x1p.dot(w3_dy) + b3_dy)\n",
" x1p += random.normal(rng_key, shape=(x1_dim,))\n",
" xp = jnp.concatenate([x1p, x2p])\n",
" return xp\n",
"\n",
" @jax.vmap\n",
" def reward_fn(x, xp):\n",
" \"\"\"Compute reward from x and x'.\n",
"\n",
" Note: this is also a deterministic function of x by construction.\n",
" \"\"\"\n",
" x2p = xp[-x2_dim:]\n",
" x1 = x[:x1_dim]\n",
" r = jnp.sum(x2p) * jnp.sum(x1) / jnp.sqrt(x_dim)\n",
" return r\n",
"\n",
" def data_generator(rng_key, batch_size):\n",
" def body(rng_key):\n",
" rng_key, x_rng_key, xp_rng_key = random.split(rng_key, 3)\n",
" x = random.normal(x_rng_key, shape=(batch_size, x_dim))\n",
" xp = transition_fn(random.split(xp_rng_key, batch_size), x)\n",
" r = reward_fn(x, xp)\n",
" return rng_key, (x, xp, r)\n",
" body = jax.jit(body)\n",
"\n",
" while True:\n",
" rng_key, data = body(rng_key)\n",
" yield data\n",
"\n",
" return data_generator"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {
"height": 860
},
"colab_type": "code",
"executionInfo": {
"elapsed": 6090,
"status": "ok",
"timestamp": 1584470940976,
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"userId": ""
},
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},
"id": "IIr1786RQQ9z",
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"outputs": [
{
"data": {
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BgfDx8Sn39dWrVw+TJk3C77//Lrjf9fPzq/RWxkRERES16cGDB6JJVV27doWV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\nurq6gpvy1NRUeHl54eLFi2ptEb1kyRI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YLoaCc93efwDfcTHGzH311DwH2aYG8tircTm0lnVnoK9913Lw888MCEw30g7zchrneyjxBCCCGE\nEEKITwbZ3wkhJkJRFJYtW8bx48fp7e3F7XZjMBiwWq0XfI4bCoVwu92cPXsWRVGYPXs23/72tyMa\nd3qtNTQ08Morr2C1Wnnqqacu6F52MU6nk8WLF7N//36amppYunTpBd3+xPWttraWDz/8kOjoaGbM\niKwBxKjOzk58Ph8rVqyIeJz19WT27Nnk5eVhMpno6Oigp6eH9vZ2Wlpa6OzsJBAIkJyczN13382X\nv/zlK7pWXdfZvHkz/f39PPjgg2HfW5szZw4+n4+amhp6e3tZvXr1hGsQQlzeVOwjZESvEEJMEYPB\nwBNPPIGiKJSVHcTTUoq33YY5LhOjIwXFYELXAgQH2/C7z2AIeYiyGMnLG+nsZTBcOOLzeqVpGiUl\nJXh8Qaxp+ai22LDWqbZYrKl5eFr2UlJSQmFh4Q113QK8Xi/oOoo6sZGvisEEuo7H45nkyq4ug8FA\ncXExhYWFVFVVUVpaisvlwuPxYLPZSExMJD8/n+zs7Ct+jcv7TQghhBBCCCGEEEIIIa5vTqeT73//\n+zz77LNUV1fT1tZGa2sr06dPHwvv+f1+enp6UBQFk8lEVlYW3/jGN7DbJ/Bl+mto586dAKxduzas\ncN+olJQU8vPz2bZtGzt37uTxxx+fqhLFFBhtYqBp2oTPMbr2ShoiXC+SkpJ46KGHuO+++zh+/Djd\n3d14vV6ioqJISUkhKytrUu7J1NXV0dzcjMPhoKioKKK1d911FyUlJWPdBq9kRLAQ4uq78f9PKYQQ\n1zGTycSTTz7JggULKCkpoa29A5/7OB7XMdB1UBRUVSHKpJIyM20sIHSjhW4qKytp7+hEU+2YpkXW\nhts0bRbe9iO0tXdQVVXF4sWLp6hKMRWsVisoCnooMKH1uhYARbloq/4bkcFgYPHixVP6Opb3mxBC\nCCGEEEIIIYQQQlz/oqOjeeqppzh06BA7d+6kpqaGgYEBdF0HRjr9Wa1WMjMzKSwsZMWKFTdc0Glo\naIiysjJgJOAXqYKCArZt20Z5eTkPPfTQuGNGxfVn2rRpKIoy9pqOdCqZpmkMDAygKAoxMTFTVOXk\n0HWduro6Kioqxq7X4XCwaNEisrKyzrt2s9nMypUrp6yWXbt2AXDbbbdhMkXWfMPhcHDLLbewb98+\ndu3axUMPPTQVJQohpsiN9S8EIYS4AV3Nzl7XSmlpKb5ACHNcJooS2TUoigFzXCY+93FKS0slcHSD\nSUxMRK2uIzjYjtEeeXvh4GAbqqqQmJg4BdVNHk3TqKysHHv/er1erFbr2Ps3Jyfnqr1/5f0mhBBC\nCCGEEEIIIYQQNwaTycSaNWtYs2YNjY2NVFVVMTg4iKIoOBwOcnJymDkzsi9yX08aGhoIBoPMnTt3\nQmNaZ8yYwdy5c6mrq6OhoYGcnJwpqFJMhaysLGJiYnC5XPT09BAbG960oVEul4tAIEBGRgbp6elT\nVOWVCQQCHDhwgB07dtDY2EgwGDwvoPvmm2+SmppKYWEht912GxaLZcpramxsBGDVqlUTWr9q1Sr2\n7ds3dh4hxI1DAn5CCHGVXI3OXteKy+UiFNIxO5IntN7oSMHjOobL5ZrkysRUy8/Pp/zgIYbcZ7Ak\nZEcUONN1Db/7DFEmlfz8/CmscuI0TWP79u2UlJTQ3tGJLxAiFNL/2oGzuo7yg4dISZ5BUVERRUVF\nUx70k/ebEEIIIYQQQgghhBBC3HgyMjLIyMi41mVMqsHBQYCIw10fN336dGCkG6CYGm63m66urrHm\nIwkJCcTFxV3ROY1GI2vXruVPf/oTjY2NEb8GGhsbMRqNFBQURNz9Lxy6rlNfX8+pU6cYGhpCURSc\nTidLly4Na5R0f3//2IjtQCCAwWAgKSlpZLIV4PP5cLlc1NfX09zczK5du/jWt751xb/Xyxl9n0RH\nR09ovdPpPO88QogbhwT8hBBCXDGv1wu6jqJG1gp6lGIwga7j8XgmuTIx1Z3ncnJySJ6RRH1jK4G+\nJswxGWGvDfSdxRDykDIzjezs7AnXMJ4rvfZAIMCWLVsoLz+ExxdEU+2Y4zIxO5JRVBN6KEBwsJ0h\n9xnqGlpoe/Elampq2LRpU8Rt0SMh7zchhBBCCCGEEEIIIYQQ14PRz9g1TZvwOUbXRnqvIhQK4XK5\nxjoiOp1OEhISrqtpWbquU1NTw/79++nu7sbn82Gz2UhJSeH222+fUNfDcIVCIY4dO8bOnTupqKhA\n07SxUbqKorBkyRIKCgpYtmwZqqpO6DHWrl3L22+/TUdHB62traSmpoa1rrGxke7ubqKjo7n11lsn\n9Njj8fv9lJWVsXPnThobG9F1/byue7///e9ZtGgRBQUFLF++/KLXPjg4yH/8x3/Q1NSEwWBg/vz5\nF31tZWRk4Ha7aWxspL6+nn//93/nBz/4wVhodSqM3n8KBAITWh8MBoGRUcJCiBuLBPyEEEJcMavV\nCoqCHprYPyZ1LQCKgs1mC3vN9TQy9Xqs8+Od55pbWvH5g2h/bTyHQTnNvv0HSE9Lpbi4eMKd50ZH\nUL/44ksMtR5AtUSj2i7/La2QpxtvaxlRFiPFxcWT+juYjK57mqaxZcsWDpQdxBNQsKblY5o284IO\nhUZ7PJaEbAJ9TQy1HmD/gXIAnnzyySl7/V2L95sQQgghhBBCCCGEEEII8bccDgcAnZ2dY+GxSOi6\nTmdn53nnupze3l727NnD7t276enpOe9niYmJFBQUcNtttxEVFRVRLZNJ0zRKS0vZvn07zc3NF/z8\n2LFjvPvuuyxevJj169dPehOE5uZmfvazn9HW1kYoFMJoNJKYmIjZbMbv99PV1cUHH3zA0aNHSUlJ\n4etf//qExuTGxcXx4IMP8vLLL3PixAlCoRDp6enjvg5Gu+qdOXMGi8XCI488gt1uv9LLHdPd3c1P\nfvITmpqa0HUdq9VKdnY20dHR6LpOd3c3J0+epLKykpMnT5KTk8NXv/rV82rQdZ3nn3+epqYmTCYT\nS5YsGTcMZzAYSEhIYPr06VRUVNDZ2clPf/pT/t//+38TDk1ezrRp0+jq6qKpqWlCAdGmpiZg4h0A\nhRDXjgT8hBBCXLHExETU6jqCg+0Y7fERrw8OtqGqComJiZc99nocmXq91RkIBNi8eTN79+3H59fQ\njXb06EywJILBiK4F0XwuQoMNVNee5WzT/1FdXc0TTzwxoc5zRUVFVFdXc6DsIEP1W7Gmrr5oGA5G\nxvIG+s7ibS3DZtLJy7uFwsLCybhsYPK67m3fvn3kHAGFqDmfuWRoUVEMmGMyUC3RDNVvpazsIAsW\nLKC4uHjSruvjrub7TQghhBBCCCGEEEIIIYQYz/z584mKiqKlpYX6+nrmzp0b0fq6ujpaW1txOBzM\nmzfvksdqmsYf//hHtm7dSigUAkbCTqMjUbu6unC5XPz+97/nj3/8I/fffz933nnnlIx/vRS/388v\nf/lLDh8+DIyMRL311luZN28eZrOZ4eFhjh8/zuHDh6moqKCiooLPf/7z3HXXXZNSa21tLT/60Y/o\n6+tj+vTp5OTkkJWVRVRU1Nj5h4eHOXXqFBUVFTQ3N/P000/z1FNPXfY5uJg777yToaEh3nzzTaqq\nqmhsbGTWrFmkpqZiNI7EUQKBAC0tLZw9exaPx4PFYmHjxo2sWbPmiq93VE9PDz/84Q9xuVzExMSQ\nn59Pdnb2WA0fr/fEiROUlpZSUVHBM888w7/8y79gsViAkddkVVUVuq6zePHisDrdGY1GcnJy+OCD\nD2hoaODEiRPk5uZO2rV93M0330xtbS27du1i1apVEa3VdZ1du3aNnUcIcWNRn3766aevdRHXK7fb\njd/vx2w2Ex8f+Q10IYT4tLBarRwsL8c72Is5PiuiDYiua3ia92Iz6fz9xo0kJSWNe+xocO2dd96j\ny92HX7dijMvBkrQcc0I2xpi56EYH3sFeervdnKyqpKOjndzc3Cn7psz1Vqemafz85z9nd+le/JoJ\nPW4FxOai2JNRzE4UUxSK2YFiTUB3zEY3RRMa6uTs2Xrc57pYuXJlxBtIRVHIzc2ly9VJZ3srvp4G\nfN016HoIFBU9FEDzD+LvqcXTvBetrw67xcDq1bewadOmCzZXV3Ltmzdv5kDZQYYDCpa0W7Gl3YLJ\nMQODyY7BaMVgsmOMShp5nVpi8PQ2097WQperkxUrVqAoylj3vi53H5a0WzE5k8N6fIPJhmJ24Otp\npKf7HIWFhVPywcHVer8JISZO9hFCCCGEEEII8ckg+zshhLg0VVUZHByktraWUCjETTfdFNH6119/\nnebmZoqKiliyZMm4x4VCIV544YWxcFJubi7/8A//wBe/+EXWrl3L7bffzp133smsWbMYGhqio6OD\nyspKvF4vOTk5Vy3kFwqFeO655/jwww+x2Wx86Utf4uGHHyYnJ4ekpCTi4+NJTk4mNzeXtWvXYjKZ\nqKmpoaqqCrPZzIIFC67o8bu6uvjhD39Ib28v8+fP5+/+7u+YM2cO0dHRWK1WzGYzZrMZu91OcnIy\nWVlZ9PX10d7ezuHDh7n55pvD7qQ4SlEUFi1aRGxsLGfPnmVoaAiXy0Vtbe3Y6Nqamhq6u7uBkSYG\njz/+OLfffvsVXevHhUIh/vu//5uWlhZSUlL40pe+RHp6+kUbaxiNRlJTU8nKyuLMmTN0dHTQ2dk5\nFpZ77bXXqK+vJzk5OaK/+1VVRdd1ent78fl8rF69etKu7+OSk5MpKSkZq9npdIa9trq6mnfffRen\n08ljjz12XUxCE+KTair2ERLwuwTZuAkhRHgSEhIoLy+jt6cbxRKDao0Je22grxGtr45Z6Sls3Lhx\n3E3WZIW3ptq1rnPbtm386Y0/E9TN6En5KFFpKEYLKIaR2bxj/xlQVBOKOQbdGo8+2Erz2Xpip0+P\n+Bt2MLJxWbFiBdHRTnq6z+Eb7ic42I6/u4aA+zTB3loUbyc2k86s9BTuu+9eHnjggUkL9wGUlJTw\nzjvvMfxR1z2TM3nc36WiKKjWGIzOVIbddXS2txId7WTu3LlUVFTw/rYS/LoVW9otET0fBss0fN01\n+Ib7WbBg/pQE6K7G+00IcWVkHyGEEEIIIYQQnwyyvxNCiMtLSkqipKSE1tZW5syZE/bn4hUVFbz+\n+uvous6mTZsuOVL397//PXv27MFqtfLNb36Tu+++m8TExPM+4zYYDKSkpLBmzRqSk5M5evQoNTU1\nWCwW5s+ff8XXGY633nqL3bt3Y7fb+ed//meys7PHbexgNpvJzMwkKSmJo0ePUlVVRWZmJgkJCRN+\n/N/85jecPn2a2bNnc/fddxMTE4PJZLrgXoCiKBiNRmw2GxkZGXR2dtLe3o7b7Z5wMC0jI4Pi4mLS\n0tIYHBykv78fg8GAwWDAaDSyZMkSNm7cyMMPPzyhccCXcvToUd5//30cDkfYY39tNhtz587l6NGj\ntLa2snLlSgwGA7/61a/wer1kZWVFfA/LbrfT3NxMd3c3t95665SMiTaZTJw7d46zZ89SU1PDLbfc\nEtZ0roGBAZ599lmGhoa48847ycnJmfTahBB/JQG/q0w2bkIIEZ7RjcHJqko8vc0YnakYTLbLrgt5\nuvE07cZuMXDfffdesvX3ZIW3ptq1rFPTNH70ox/RP+RFj1uB4kgHw2U6AioGFGMUusGCNtRK09mG\nCbeBVxSFuXPnUlhYyIIF81HQcNitRDvtpKYkkb0oi7/fuJGNGzcyb968SQ2XTWbXvddff526xiaM\ncTmYHDMiqkNRFHQ9RHCwHQUt4vbo4T4GTO37TQhxZWQfIYQQQgghhBCfDLK/E0KIy4uKiiIQCFBd\nXc0HH3zAzJkzmTHj0p+tHz9+nOeee45gMMiGDRtYuXLluMe2tLTwwgsvoKoq3/rWt1i0aNFla0pL\nSyM5OZnDhw9z+vRp1q1bNzaCdaoEAgF+8Ytf4Pf7+ad/+qew7/WkpqYSCoWoqalhcHCQvLy8CT3+\nwMAAzz//PAaDgXvuuYeEhISw7sOYzWYSEhI4fvw4zc3NfOYzn0HTNHp7exkYGEDXdcxmc1jnUlWV\ntLQ08vPz2bBhAwUFBaxfv57Pfe5z5Ofnk5KSMiVd41566SU6OztZu3Yts2fPDnud3W5nYGCA9vZ2\nFLMaMW8AACAASURBVEXB4XCwZ88ebDbbhEKIqqoyMDCAz+cjKyuL1NTUiM8Rjnnz5vHBBx/Q1tbG\nqVOnWLp0KVarddzju7q6eOaZZ+jo6GDmzJk89thjYYUChRATNxX7iMlrmyOEEOJTraioiOrqag6U\nHWSofivW1NWYps1EUS78h7quawT6zuJtLcNm0snLu4XCwsJxz61pGiUlJXh8Qaxp+ai22LBqUm2x\nWFPz8LTspaSkhMLCwiltN32t66yoqODcOTe6Go3iyBjp2hcOxYDiyEDvqaSr6xxvvfUWLS0tuFwu\nvF4vVquVxMRE8vPzycnJuWxtBoOBxYsXs3jx4oivYaIqKytp7+hEU+2Yps2MaK1p2iy87Udoa++g\nqqoKl8tFKKRjdoQXEvxbRkcKHtcxXC7XhNaHYyrfb0IIIYQQQgghhBBCCCFEJO6//376+vrYt28f\nzz77LMuWLaOwsJBFixaN3VPQNI2TJ0+yY8cOjh07hq7r3Hrrrdx3332XPPfoWN78/HwWLlwYdk2r\nVq3iwIEDHDt2jNLSUjZs2DDxCwzD4cOHGRgYID09PaI6AQoLC3n//fc5ceIELpeLxMTEiB9/9+7d\nBAIB5s+fT3JyZPc3ZsyYQUZGBtXV1Xzve99jYGDggp8XFBRE1JXOZDIxffr0iOqYiI6OkXs7qqqy\ndOnSiNffdNNNHDlyhP3797NgwQJ0Xb+i8JvJZELXdYaHhyd8jstxOp18+9vf5plnnqG+vp7vfOc7\n3HLLyL2f0YCjruvU1NSwfft2PvjgA0KhEKmpqXzzm9/EZrt80wghxPVHAn5CCCEmhcFg4IknnkBR\nFMrKDuJpKcXbbsMcl4nRkYJiMKFrAYKDbfjdZzCEPERZjOTl3cKmTZsuGRqbzPDWVIbOrnWdb775\nJiHdAM45oEa4+VBN4JhNqPcEL774IqrZRiikg66DoqBW11F+8BApyTMoKiqiqKhoSsOSkSotLcUX\nCGGOy7xoyO1SFMWAOS4Tn/s4paWleL1e0HWUSH+Ho+czmEDX8Xg8E1ofjql8vwkhhBBCCCGEEEII\nIYQQkTAYDDz++OPExcXx9ttvc/ToUY4ePUpsbCyxsSPNENxuNz09PcBIp7MNGzZwzz33XLIznMfj\nYf/+/QAUFBREXFdhYSHHjh1j586drF+/ftxxuZNh9+7dAKxbty7iCUZOp5OVK1dy4MABXnvtNRYs\nWIDH48FisRAfH8+SJUsuGzrbs2cPqqpecizweEKhEIsWLaKuro62tjamTZuG0+lEVVUGBwdpb2/n\n5Zdf5vXXX2f9+vXcc8891819hvr6enRdZ/78+ZfsYjeepKQkEhIS6O7upre3FxgJo06UpmkoijLl\nHfKSkpL4t3/7N/7v//6P48ePs2/fPvbt24fFYsFqteLxePD7/cDIZKhVq1aFPb5YCHF9koCfEEKI\nSWMymXjyySdZsGABJSUltLV34HMfx+M69tegmKoQZVJJmZlGcXFxWN3qJjO8NZUBvyut0xS7AE/X\nh/ziF78gPj4+4u55DQ0NgIJij7zznB7ygzUBHQNBTcMUtwyzIxlFNaGHAgQH2xlyn6GuoYW2F1+i\npqaGTZs2XTctvCez657VagVFQQ8FJnQuXQuAokz5N6Cm6v0mhBBCCCGEEEIIIYQQQkRKURTuvfde\n1q1bR2lpKbt376a7u5vu7u6xY2JjY1m3bh35+flMmzbtsuc8ffo0Xq+XuXPnTmhkanZ2NnFxcbjd\nbpqamiIa3xqp9vZ2gAndhxocHCQQCDAwMMDu3bspLy9H13UURUFRFKKjo8nPz2fdunUkJCRc9Bxd\nXV0oijLuz8fj9/sZHh4mOjoag8HA9OnTefrpp8eCYKFQiJMnT7Jv3z6qq6t588036ejo4IknnpjS\nwGS4RjvlORyOCZ8jKioKt9uN0WjEYDAwPDyMpmkR30/RdZ2hoSEURQnr9X2lpk2bxje/+U06OjrY\ntWsX+/btY2hoCJ/PB0B0dDS33347a9euJS4ubsrrEUJMLQn4CSGEmFQGg4Hi4mLWrVvH22+/zbZt\n2+jr6yMYDGE0Gpk2bRp33HEHGzZswGgM76+hG2FkKky8Tl3X8btP4XefRvP7aOvooqOrN+LueV6v\nFxTAYI7s8UN+0IJgMI2M9TU5sSaevwE12uOxJGQT6GtiqPUA+w+UA/Dkk09eF4Gxyey6N3PmTNTq\nOoKD7Rjt8RGfKzjYhqoqE2qhH6nR91thYSFVVVWUlpbicrnweDzYbLaxcGh2dvZ18TwJIYQQQggh\nhBBCCCGE+GSLiYnhrrvuIjU1lffff5+uri58Ph82m42UlBRSUlLCDmONjoqdMWPGhGoxGAwkJSXh\ndrsZHByc0DnCNTrVJ9Iv/58+fZqf//zn9Pf3YzQaiY6OZubMmZjNZoLBIB0dHXR3d/PWW2+xdetW\nHn30UW677bYLzhMKhQAiuhcQCATGAnIWiwVVVXE6ned1eVNVlcWLF7N48WLOnDnDr3/9aw4ePIjD\n4eBLX/pSRNc6FUZDhsFgcMLnGO26N/r6bGhooLu7m/j4yO4R9fX14fF4SE5OJjMzM+I6mpqa2Lt3\nLy6XC5/Ph9VqZcaMGeTn55OSkjLuuhkzZvCFL3yBBx98EK/XO7bWarVG3E1SCHH9koCfEEKISaVp\nGtu3b6ekpIT2jk58gdBHo14NBDQdf3cfr//xTxw4cCDsUa83wshUmFiduhZiuHkvgd5GdF1HN8Wg\nT5uLLX52xN3zdF0HHdAj2MRowZH/UGD0H/mGi9evKAbMMRmolmiG6rdSVnaQBQsWUFxcHP7jTZHJ\n7LqXn59P+cFDDLnPYEnIjqgbo65r+N1niDKp5OfnR1SDpmlUVlaOhfQi6eBoMBjGNthCCCGEEEII\nIYQQQgghxLUweo9o27ZtnDt37ryfDQ8P43a7qaioICYmhqKiosuOzR0dlXolX2AfPf9oAG6qmM1m\nAoEAfr8fi8US1prKykqee+45vF4viYmJY50KnU7n2DG6ruNyuaisrKSxsZFf/epX+Hw+ioqKzjuX\nqqpjXQCTk8NrRDF638xisYw9X5cac5uZmcmmTZv4+c9/zo4dO8jLy2P+/PlhPdZUiY6OBkY6GE5E\nKBTC7XYDIx3xCgoKePHFF2lra4s44NfW1obJZOL2228Pu8mJruscOnSIkpISamtrL3rM1q1bycrK\n4s477yQ3N3fccxkMBux2u4zhFeITSgJ+QgghJk0gEGDLli2Ulx/C4wuiqXbMcZlXPOr1RhmZGmmd\nuq7/NdynmCBhBVgSUM0mjPa/bt7C6Z6naRqqqqITgOEOsPy11baCMtLZ72I1aB+FAQ0m8I50OFRM\nl/7mnGqLxZqah6dlLyUlJdfF2NfExMRJ67qXk5ND8owk6htbCfQ1YY7JCPs8gb6zGEIeUmamkZ2d\nHdaa8UOxkXVwFEIIIYQQQgghhBBCCCGulUAgwC9/+UsOHz4MQHx8PAUFBeTk5GC1WvH5fJw6dYod\nO3bQ2dnJ66+/TnV1NV/96lfHDcRFRUUB0NvbO+G6enp6zjvXVImLi2NoaIja2tpLhrBGtbe3s3nz\nZrxeL1lZWSxbtoxAIHBBMExRFJKSkkhKSqKqqory8nJ+97vfkZCQwNKlS9F1ndraWjRNIxgMUlFR\ncV7obrwOboFAYGwMrcViobKyEoA5c+Zcsu45c+awdu1aduzYwY4dO8YN+LW0tPDBBx/Q39+PpmnY\nbDbmz5/PkiVLJnW0b3Z2Nna7ndbWVjo6OiLu9nj69GmGh4dJT08nJSWF6dOn8/rrr9Pb20trayup\nqalhnaezsxO3201UVBRr164Na00oFOKll15i165dwEj3xzVr1rBw4UKsVisej4fKykoOHDjA6dOn\nOX36NOvXr+fzn/+83CcS4lNIAn5CCCEmhaZpbNmyhQNlB/EEFKxp+Zimzbyg+9lERr1OZnhrKkVa\np9996q/hvpRCMDlBC2AwXLixGa97XmFh4Vg4bHjYA5oGA7UwbeHIuF1ARx85x98G/fQQ6BpgGOne\n1z/yzSA1+tKbNwDTtFl424/Q1t5BVVXVBZ3jrqQb3URMZte90bG3L774EkOtB1At0ai22MueJ+Tp\nxttaRpTFSHFxcVjXN1WhWCGEEEIIIYQQQgghhBDiatE0jRdeeIHDhw9js9l47LHHuOmmmy74nDw9\nPZ3i4mKOHz/OCy+8wIkTJ3j++ef5+te/ftHQ17x58zAYDFRVVdHT08P06dMjqqupqYmWlhZsNhsz\nZ868omu8nDVr1tDU1MTu3bvDCvi99957DA8Pk5GRQV5e3tg4YrPZPO6a7OxsfD4fx44d44033kBV\nVf7whz/Q3NyM3+8nGAxSXV3N4ODg2BhkXf/oHtHfBP18Pt/Y4w0NDXHmzBkMBgN5eXmXrT0vL48d\nO3Zw+PBhNm7cONZFLxQKceTIEXbs2MGZM2fQNO28x1cUhYSEBNatW8fatWvDHtV8KVarlVtvvZVt\n27Zx+PBh7r777ojWHzlyBEVRKCgoQFEU7HY7jzzyCJs3b6a+vh5N00hLSxs3KKnrOh0dHdTW1mK1\nWvnCF75AbOzl7ynpus6LL77Inj17MBqNPPjgg9x2220XdFBcsWIFDzzwALt37+b111/nvffeQ9d1\nHnrooYiuUwhx41Offvrpp691Edcrt9uN3+/HbDZH3H5VCCE+bUpKSnjnnfcYDihEzfkMJmfyuP/Y\nVRQF1RqD0ZnKsLuOzvZWoqOdzJ0796LHW61WDpaX4x3sxRyfNe55L0bXNTzNe7GZdP5+40aSkpIm\ndH3hiKTO0e59WsgPiXlgn4Ee8qMw8g0dVR1nFKvJhmJ24OtppNvdRXV1Ne++u5Uudx8hxQIh30hw\nzzIdLNMueo7RunQtOBLwMxhhuBUG6lCMUdhSb7nst6cURUHXQwQH21HQWLVqFTCyiS8pKWHLli28\nv62EusYmOl3ddPf04TrXQ1NLCwfLyzl4sBxd15k9e3ZEz+d4EhISKC8vo7enG8USg2qNCXttoK8R\nra+OWekpbNy4EUVRmD17Nu3tbXS0tTDsrkMxOzFYoi9aq65rBPoa8TTtxmbSWb36Fh544IHLXpem\naWzevJkDZQcZDihY0m7FlnYLJscMDCY7BqMVg8mOMSpp5PVkicHT20x7Wwtdrk5WrFgxKb87IcTk\nk32EEEIIIYQQQnwyyP5OCCHCs3PnTt577z2sVitPPfUUixYtuuQ9ohkzZrB8+XLKy8tpaWnBbDaz\nYMGCC4612Ww0NTXR1taGzWYjKysrorreeOMNGhsbWbt2LcuXL5/QtYUrOTmZkpISOjo6WLly5Xlj\ndv/WwMAAv/3tbwkEAmNTewKBAKqqXnJELozcDzl58iSdnZ2Ul5czMDCA0+mksLCQmpoaQqEQXq+X\nzMzMC9aO3R/S9bHxvDabjZKSEjo7O8nMzBxrhHApdrudpqYmXC4XKSkpzJo1C6/Xy3PPPcdf/vIX\nXC4XqqqycOFCMjMzycjIICEhgf7+ftxuNydPnqS8vJzs7GymTbv4vaxIxMfHs2PHDjo6OkhOTiYu\nLu7yi4BDhw7xwQcfYLfb+cd//Mexxgrp6elERUVx8uRJ3G43nZ2d6Lr+0f27kftnwWCQjo4Oqqur\ncblcWK1W7r33XjZs2BDWY5eWlvLnP/8Zo9HId77zHW6++eZxx/qaTCbmz59PRkYGhw4doqamhuTk\nZNLS0sJ6LCHE1TcV+wgJ+F2CbNyEECI8o937utx9WNJuxeRMDmvdx8NqPd3nKCwsvOiGb7LDW1Ml\nkjqDg234z50C1Q4JK0eCdloIo1HFZreNN1EXAINlGr7uGnq7O2luacUbUkfCYelr8HdXo4e84O0C\nWwoYbSPd+f6GoiigBQAdAkPg2ge6hmF6NvbY1Es+/l9PouLvrsFht7Ju3ToCgQCbN2/mnXfeo8vd\nh1+3YozLwZK0HHNCNsaYuehGB97BXnq73ZysqqSjo53c3Nwrbsc++ryerKrE09uM0ZmKwXT5kcwh\nTzeept3YLQbuu+9e5s2bN3a+3NxculyddLa34utpwNddg66HQFHRQwE0/yD+nlo8zXvR+uqwWwys\nXn0LmzZtGncT9nFTGYoVQlxbso8QQgghhBBCiE8G2d8JIcTl6brOL3/5SwYHB/nyl79MTk5OWOuc\nTidpaWmUl5fT1tY27mQcp9PJ/v37aWlpYdWqVdjt9rDO39TUxEsvvYSmaXz5y1++ZOBuMpjNZtxu\nN42NjZw6dYpVq1aN241v165dnDhxgpSUFLKyshgeHgY4L0A2HoPBwPDwMK2trYRCIe655x6+8Y1v\nsGzZMtra2qivr6e7uxuv18ucOXMu+J0qioKmafj9fhRFYd++fRw7dgyTycTnP//5sKdhtba20tjY\nyLx585g9ezY//vGPqaiowGQyceutt3LHHXcwb948kpOTmTFjBunp6SxdupTk5GR6enro6uri0KFD\nLFu2bKwD4EQ5nU40TaO6upqTJ08SHx9PQkLCuMfrus7BgwfZtm0bqqry6KOPXnC/Ze7cuaSlpXH2\n7FmGh4fp7u6mqamJ1tZWmpubaWpqoq+vD4PBQEJCAl/84he58847w6pX13V+8YtfMDQ0xGOPPRZ2\n+HTGjBnYbDYqKipwu91hjwIWQlx9EvC7ymTjJoQQ4amoqOD9bSX4dSu2tFsiCtGNhtV8w/0sWDD/\noh32Jju8NVUiqdPb8SEhby/ELAJrPLrmR1FGNm9G4+W75wUCPgID7YQ0Dcf8uz8KhxnAoBLsbx4J\n7w23gNE5MvpXMZw3nldBAc0PQy1wrhy0AIo9FVvKSkxhhNMA9FCAgPs00U47hYWF17wb3WR33VNV\nlRUrVhAd7aSn+xy+4X6Cg+34u2sIuE8T7K1F8XZiM+nMSk/hvvvu5YEHHggr3DfVoVghxLUl+wgh\nhBBCCCGE+GSQ/Z0QQlzeqVOneP/994mJieHRRx+9bPe3j0tKSuLgwYOcO3eOmTNnkpKScsEx8fHx\n1NTU0NrayokTJ8jNzb1syK+lpYX/+Z//YXh4mNWrV1+1IFRmZibHjx+nra2N48ePk5mZedHw2nvv\nvUd7eztLlizBYrEAIwHBy3Xvg5FwmKZp1NfXM23aNJ566qmxUODs2bPZv38/AwMDuFwuzp49i9ls\nJjY29rznJRgMcurUKfbs2UN1dfXYiNhFixaF3ZChrq6O+vp6FixYQFlZGR988AE2m43Pfe5zZGRk\nXPQ8iqIQExNDVlYW586dw+Vycfz4cfLz88e6503UwoUL6e/vp6GhgcrKShoaGjAajcTFxY1d++h4\n47/85S+cOHECVVV58MEHKSwsvOg5U1NTKSoqYvbs2Xi9Xnp7e1FVFVVVMZlMZGdn89BDD/Hoo48y\ne/bssGstLy9n27ZtmEwmZs+eTVtbG3a7PayRxenp6ezYsYOuri6WLFkS8dhqIcTVIQG/q0w2bkII\nEZ7XXnuNusYmjHE5mBwzIlo73qjXv3U1RqZOhnDr9LlOoAU9MH0xuqKi8NHmzWa9bPc8HfB6fej9\nDaiWaGwpK8Z+ptoTCPn70Tw9IwG+4VYYbBgZ26uoEApAcHDkz84dgsFG0EMoUWmYkvOwRdnD694H\naP5Bgr21pKYkEQwGr3k3uqnouqcoCnPnzqWwsJAFC+ajoOGwW4l22klNSSJ7URZ/v3EjGzduZN68\neWG9xjRN46233mLPnlICwSCarxd/d+3Ie0A1YTA7L3mecEKxQohrS/YRQgghhBBCCPHJIPs78Wk1\nPDxMZWUlNTU1NDY24na7cTgcY0EkIT7uj3/8Iy0tLaxfv56FCxdGtHa0m1xFRQU+n4+8vLyLHpOb\nmzsWnCsvL0fXdZKTky/okNfT08PWrVv5zW9+w+DgIJmZmXz1q1+94ilC4TKZTCxfvpyqqira2trY\nvXs3NTU1WCwW7HY7uq7T39/PO++8Q19fH7NnzyYqKgqTyYTdbg/rHoPf7ycYDHLmzBmcTid33XXX\n2M8cDgdZWVkcOnSI4eFh+vv7qa6u5sSJE3R0dNDQ0EBVVRU7duygsrKS7u5uHA4HX/jCF1iyZMm4\nHQcv5sMPP6S1tZXMzExKSkrQNI3Pfe5zYY3HNRgMzJ07l8bGRrq7u4mNjWXOnDlhP/bFKIrC0qVL\nsVgsNDQ04Ha7OXXqFOXl5Rw9epTy8vKx58Pj8RATE8Njjz3GunXrLltrcnIya9asYcOGDRQXF7N+\n/Xruv/9+1q5dS1paWlihVl3Xqaqq4pVXXuHVV19F0zQAzpw5w/Hjx9m+fTu1tbXYbDYSExPHPaeq\nqgwMDFBbW4uu6+Tm5kb+yxJCTLmp2EeE16JHCPGpomkalZWVlJaW4nK58Hq9WK1WEhMTyc/PJycn\nJ6Jv34hPPpfLRSikY3aE14XsbxkdKXhcx3C5XOMeYzAYeOKJJ1AUhbKyg3haSvG22zDHZWJ0pKAY\nTOhaYGT0rfsMhpCHKIuRvLyR8NbVes2GW6fm60PXNdD1sXCf3R5euC4YCKLpKrqigOH8TamiKETN\nvJ0hFAI9dRDyoWt+cB8d6eI3StdGuvahQ9RMTClriIqKCjvcByNjhlVVISEhgZKSEjy+INa0fFRb\nbFjrVVss1tQ8PC17KSkpobCw8IqfJ5PJxJNPPsmCBQsoKSmhrb0Dn/s4Htcx0HVQFFRVIcqkkjIz\njeLi4rAe12AwsHjxYhYvXjzh2jRNY/v27ZSUlFDf0EggBCgqId/AyAGebgK9jRgsTizxCzHHLbzo\nZl5RDJjjMvG5j1NaWnpFNQkhhBBCCCGEEEIIIcSopqYmduzYQVlZGX6//7yfqarKypUrKSwsDPvL\nzmLq9ff3U1paSmVlJQMDI581OxwOsrOzyc/PJyYmZspr6OrqAog43DdqdN2l7hHZ7Xaeeuop/vd/\n/5fTp0/z6quv8qc//Ylly5YRGxs7tv7EiRNjwakVK1awadOmK+4MF6np06fzve99j1dffZUDBw5w\n+vRpTp8+fd4xg4ODY/clbDZbROFZn8+HpmkjzRQuElzMysriBz/4AT/96U/HRvWOdp8babqhEwwG\nCQQCWCwW7r//fhYvXhxRuC8QCHDixAkAzp07RygUYt68eRGFWIxGIzfddBNbt25l586dkzKxSFEU\nPvvZz7Ju3TrKysrYuXMnzc3N9Pf3jx2TlZVFYWEhK1asCGsi09/WPJFxwj6fj82bN3P06FEAoqKi\nyM7OJiUlBVVVcbvdfPjhh1RWVlJZWUlWVhZf+9rXiIqKuuj5li1bxtatW2lra4u4FiHEjUsCfkKI\nMR8Pn7R3dOILhAiF9L+GYqrrKD94iJTkGRQVFVFUVCRBv+vA9RDI9Hq9I0E1dWKbJMVgAl3H4/Fc\n8ripCm9NtnDq1IMeFMWIwaBhtdsxW8xhh+v8fj+6FkQBFPXCDZdiUImatRa/Ywberio0b99IBz89\nMNL+7yMGgwIYQBvGHmUjkn2Trmv43WeIMqmkp6dTfvAQmmrHNG1m+CcBTNNm4W0/Qlt7B1VVVZMS\nVjMYDGPPfVVV1dh7w+PxjH3zKT8/n+zs7Kv22ggEAmzZsoXy8kN4fEECigN9egZKVBoYzCPPzXAn\nen8NId8AntZDBIdc2NNvQzFcuEEPJxQrhBBCCCGEEEIIIYQQ4QgGg/zud79jz549Y382f/58EhMT\nURQFt9vN6dOnKS8vp7y8nNzcXL7yla9IR79rqLOzkzfeeIPDhw+PBb50feQGgKIoVFVV8ec//5kV\nK1Zwzz33XHT07WTxer0AYY2XvZjRdT6f75LHRUVF8d3vfpcTJ06wc+dOKioqOHLkyHnHGAwGVqxY\nQUFBAQsXXvxL9FeD3W7nkUce4YEHHmDfvn3s37+f7u5ufD4fNpsNs9nM4ODgWMguXMFgEE3TxgKC\nTqfzosfNmzePZ555hrKyMrZv305zczOapqFp2tg5nE4nuq5z6tQpbr755oiu7/jx4wwPDzNr1iwq\nKirQNG1C93fmzp2LzWajtbWVM2fOkJWVFfE5LsZms1FQUMC6desYGhoa+305HI7LjneebH6/n2ee\neWasO9/69evJzc3F4XAQHR09FtL8/+zdeXxU9b3/8dc5Z7bMZN9JAkjYFwVkCwJhSUKx4nrrba/a\nW71ypeitu7e9Ptr7a++ttrXqtbZuFZeqj6JW616RJQlhC0EIBMISSCD7AtlnMus55/fHMCMhgaxA\nwO/z8eiDdmbOzDlnzkwzc97z/tjtdrZs2cI//vEPDh06xG9+8xsef/zxbtc3cFlP51UFQbi8iICf\nIAhA1/CJplgxxYzHFDoMSTGiq1589locjYcpPVZFzVvvcOTIkYvyyxfBbygFMi0Wiz+0pnr7tbyu\neUGSCAkJ6fG2QzG81Z/1PHHiBPUnmzHobZjNI/t036qmgrMBkJBN3X94kyTpVAvcBH+rYdMRNE/7\nqedIR3M2MmrUSAwGA2XHq/G1VmCKvKLX6+BtLUdWnSSNSKGiogK3V8UUMx5J6ts+P59tdIPRujcY\nNE3jlVdeYdv2HTi9EpaUdFQpyv96NfhfOwCYYyByAjiq0E8U4G05TgdgHbGwy5cQvQ3FCoIgCIIg\nCIIgCIIgCIIgnIuqqvzxj39k7969GAwGFi1axJIlS7oEwk6ePElubi4bN26ksLCQp556iv/8z/8U\nIb+LoKSkhOeee47W1lZUVSUpKYnU1FRCQ0MBcDgclJWVUVNTw9atWykqKuL+++9n0qRJ52V9AgG9\n/n5f3ZeAoKIoTJ8+nenTp1NfX8+hQ4dwOBxIkkRoaChTpkwhKiqqX+vRG263m8OHD9PW1oamaVit\nVkaPHn3Wx7RarSxdupSlS5d2unzbtm28/PLLHDp0iEmTJvU6iBgIcR47dgxFUbj66qvPelur1UpG\nRgZLlizh6NGj1NTU4HQ6ycnJoaqqirvvvpu3336bffv2UVlZyfDhw3u1Di6Xi40bNyJJErNnDLY5\nwgAAIABJREFUz+aDDz7AarX2K0SqKApjxoyhuLiY48ePD1rALyBwXAReG06nk8rKSlwuF2azmaio\nqLOGJAfLa6+9xtGjR4mKiuKxxx4jKSmJtrY2VFXtdLvQ0FCWLVvGrFmz+P3vf09VVRV/+tOfeOyx\nx7ocH4EwrHj/FYRvFxHwEwSh2/CJMWJEl6COwRqLOW4y3tYKHNXb2LotH4BVq1aJJr8LbKgFMuPj\n41FKSvHZazFY+z5DPjDqNT4+vtfLXIzwVn/aEs+2nkVFRTz1+6dxNB7GHDe5T8E4XdPAfgxJAlP0\n2HPeVpIkjGHJGMOSg5eprlY6jnyM0WgkMzOTt956B0f1NhRzeK/G66rOJlzV27GZDWRlZbF58+bz\nPqJ5oC5m0+WGDRv8r1WvhC11GUpINK72dlBV/JWKp30wk2QIHQHGMPSajXhbjuOxJWCO7TzaoC+h\nWEEQBEEQBEEQBEEQBEEQhLP561//yt69e7HZbDz88MOMHj2629vFxsbyve99j2uuuYann36a0tJS\nXn75Ze6//34xrvcCOn78OE8//TR2u53ExERmzJgRDC8FREVFkZKSgsPhYPfu3dTU1PDss8/ys5/9\njDFjxgz6OiUmJlJaWkpxcTHjx4/v8/L79+8H6NM5IoCEhAQSEhL6/Hj9UVdXR05ODps3b6ajo6PT\ndbIsM23aNDIyMnoM69XW1tLY2IjBYMBsNtPc3ExDQ0NwO3Rdp76+npKSkmDDn9FoJDIykvHjxxMW\nFobL5aKiogKz2cyiRYt6XHdJkhg7dixjx/rPJ5WVlVFfX4/VamXhwoXk5OTw6quvsmrVKoYNO/d5\nHrfbzeuvv05dXR2JiYlMnDgRXdcJCQnp9/tA4DzHmft1MJWVlZGdnU1+fj4+ny94uSRJTJ06lYyM\njPNSHFJZWcmOHTswmUw88sgjwRCkLMuoqorP5+syZjkmJoZHH32UX/7ylxw4cICDBw92CeceOXIE\nIDieWhCEbwcR8BMEodvwydlIkowp8goUcziOsrVs376DcePGkZWVdQHX+NttKAYy09PTyd9R0L+w\n2mmjXtPT0wd1vQbLYLQlnhkwczqd+LweNI8bV9MxLDGjkfB/eAs27rnb/KN4ZQOyORxT9FgMoUnQ\nUY3kcyCZQ/3/u49OD4dlZmZSUlLCtu07cJStxZJ8TbfHE/ifK29rOa7q7YQYdebOTSMjI4P169df\nkBHN/XGxmy41TWP9+vU43T4sKenB91dFVvBJKuiaP9R3JnMUxM1Cb9iG++QBTDETOn047k8oVhAE\nQRAEQRAEQRAEQRAE4XT19fVs3LgRRVF46KGHzhruO11SUhKPPvoo//u//0thYSFHjx4NBocEv8rK\nSnbu3Bls2LNaraSmpjJjxowBlTD4fD6ef/557HY7w4cPJy0t7ZzfZ9tsNubPn8+OHTsoLy/n+eef\n5/e///2gt34tWLCArVu3smnTJm644QYMht5HEDRNIycnB4CFCxcO6noNBl3X+fvf/85nn30WvGzk\nyJEMGzYMRVFobW3lwIED7N69m927dzN+/Hh+8pOfdApdejweCgoKyM7OpqysLHi50+nE6/WSl5fH\ntddeS1VVFcXFxTQ1NaHrerCtDwheFxcXh9vtRlVVpk2bRmxs30s3HA4H4A/W3XHHHdTX13PgwAGe\nf/55srKymDNnDjabrdMyPp+P/fv3s27dOmpra4mIiOCRRx5BVVUkSeoUmuurwLLno6CkubmZl156\niZKSkuBliYmJ2Gw23G43tbW17Nmzhz179pCYmMi9997LiBEjBu3xs7OzAZg/fz4pKSnBy00mE16v\nF7fbjclk6hKOjI2NJSMjg08++YTs7OxOAb/TXzPz5s0btHUVBGHoEwE/QfiWO1v4pCdKSDSW5Lk4\nqzazfv16MjIyRIvfKee7KWwoBjKnTJnCsMQEyo5X4x3AqNfJkycP6noNhoG2JZ4rYKZpKrrqw1W9\nDbdqxKi2oLYeQXO3o5+5Is4mvC3HkY0haB4HaF4US3S/fhF1ejhMlmVWrlyJJEls374DZ1UertoQ\nTDHjMYQmIclGdM3rDx02HkZWndjMBubOTeOee+5BluULOqK5L4ZC0+X+/fupratHU6wYI775UGgy\nmfB4PMEAZ7dsw0Gxornb8dlrgi2Ml0IoVhAEQRAEQRAEQRAEQRCEoS83NxeAtLS0PjW7JSUlkZGR\nweeff87GjRtFwA//qOOdO3eSnZ3dKUx0urCwMNLT08nMzOzXGNmvv/6aEydOEBoaypw5c3p1rikw\nRrW5uZnm5mYKCgpYsGBBnx/7XMaPH09ycjLV1dXk5+czf/78Xi9bWFhIQ0MD0dHRXHXVVYO6XgOl\n6zpvvvkmmzZtQpIk5s2bR0ZGBqNGjep0u+bmZvLy8tiwYQOHDx/miSee4PHHHycsLIyDBw/y4osv\n0t7eDvhDdVdccQUmk4m2tjb27t3LiRMnePfdd4MjW41GI8nJyURGRqIoCj6fj8bGRmpra6mtrcXr\n9Z+LWb58eZ+3yW63c/jwYcD/OjYYDDz00EO88sorfP3113z22Wf84x//YOrUqSQkJGAwGGhtbWXP\nnj20tbUhSRKJiYk8/PDDJCQkYLfbAWhvbw+eD+2rkydPIkkS4eHhfV72XE6cOMFvf/tbGhsbsVqt\npKens3jx4k6tj21tbeTl5ZGdnU1dXR1PPvkkDz30UL+aKM/kdDrZtm0bAEuWLOl0ndFo7NTi1905\nqUWLFvHZZ5+xe/dumpubg+8ZgWMmJiZmyL1mBEE4v0TATxC+5c4WPukNY8RIXLVfU1NbR3Fx8QUd\nlToUXYimsKEayJRlmaysrAGPeh1qIdGBtiWuWLGCV1999awBM83nwVm5GdVxAq3qK9yShCQpYLBB\n+FiwJoBkBN0LHfXobUdQ3e2g+5Bl8Dmb0XVtwI2JRqORVatWMW7cONavX09NbR3uxr04G/Z8c/wq\nEjajQtKIFLKysjodQxdjRHNPhkrTZV5eHm6viilmfKfHNhgNKIqMz6eBpoKsdF1YkiF8DHpzEZ6m\nI8GA31APxQqCIAiCIAiCIAiCIAiCMPQFmsOga/ikNxYtWsQXX3zBzp07ue222wY9nHMpcbvdvPzy\nyxQWFgJgsViYO3cuKSkpGAwG2tvbyc/Pp6qqii+++ILc3FwefPDBPgcjs7OzUVWVcePGdRnreS6y\nLDN+/Hh27tzJxo0bBz3gJ0kSS5cu5Y033uCtt94iMTGxV4HRyspKVq9eDcDSpUv7tE0XwhdffMGm\nTZswGo38x3/8B1OnTu32dlFRUdx4440sWLCAZ555hurqav7whz+wbNkyXnrpJVRVZfjw4cF2vNMb\nFI8ePcrjjz+O0+nEbDYzduxYkpKSOu0LXdeJjIwkOTmZuro6jh8/jtfrZc2aNfziF7/o0zZt3rwZ\nn8/HlVdeSVxcHOAvJLjvvvvYu3cv2dnZ7Nu3L3gs67oeLJpITk4mIyODefPmBQsbQkNDmTJlCnv3\n7uXgwYNMnz69T+vT2tpKRUUFFouFq6+++qy30zQNl8uF2Wzu1XHicDh45plnaGxsJDU1lQcffLDb\n96jw8HCWL1/Od77zHV599VUKCgp47rnn+MUvfhEcp9tflZWVuN1uRowY0am9D/yvGZPJhMvloqOj\ng7CwsC7npKKiopg4cSLFxcWUlpYyc+ZM6uvref311wHIyMgYcq8ZQRDOLxHwE4TLWG+a5M4WPukN\nSZIxxYzH3biXvLy8b3XA70I1hQ3lQOZgjHodagbSlrht+w6qqqoor6g6a8BMMYMy9gbaD76P6mkH\n2YoeMx1sI06Nu5UAHXQNPcwGtpFIzhqk5j2gOpE8LXhayjFHjTrrep3pbOGwQEgzIyOD4uLiTqOE\nQ0JCgu8bkydP7vIh43yNaB5IG+ZQabpsaGhAVXVMocM6PyZgNptRVSe65kGSzN2P6g1JhOYiNI//\n131DPRQrCIIgCIIgCIIgCIIgCMLQUVZWRk5ODseOHcPpdGIwGIiIiGDOnDkMGzYMh8PBsGHDSE1N\n7fN9x8bGMnHiRA4cOEBJSQkzZ848D1sw9Pl8Pp577jkOHjxISEgI3/ve97jmmmu6TKy57rrrOHr0\nKB999BEHDhzgd7/7HT/96U97HfKrq6vj8OHDyLLMFVdc0ef1HDFiBIWFhZSVlVFZWcnw4cP7fB/n\nkp6eTklJCVu3buWpp57iX//1X0lLS+t2XK+qqnz99de8+eabOJ1OZsyYwdKlSwd1fQaqo6ODTz75\nBIAf//jHZw33nS46OppHH32UX/3qVxw+fJjDhw+jKAqZmZn8y7/8S5cwlqqqFBQUoCgKFouFyZMn\nExoaitvt7vTdf2BUr6IoDB8+nKioKAoLCyksLGTz5s29Dmx2dHSwceNGoGuoV5Zlpk+fzvTp06mv\nr6ewsJC2tjY0TcNmszF27FjGjx/f7VSpJUuWsG/fPoqKipg2bVqfJk/t27cPSZJIS0sjLCysy/pu\n2bKF3NxcampqgvshMjKSefPmsXjx4mBI8UxffvkldXV1pKSk8Oijj2K1Ws+5HkajkZUrV+L1eiks\nLOS9997joYce6vV2dCcwCjkyMrLb6y0WCz6fD5/PR3t7OzabrcvrJdDa53A4OHz4MC+88ALt7e1M\nnjx5yL1mBEE4/0TATxAuQ31pknM4HPh8GtYzwie9ZQhNwtmwh4aGhkHeikvHhWwKG8qBzMEY9TqU\nDLQtsaNiE4cOHUI3hBI6umvATNd1fPYanDUFaB47GKwQvwBMEaCr6D4VfwzM/48EKEYj5tBxKDFJ\n2I9+CV47zopcDJaIQWtMlGWZK6+8sk/Hx2CPaB5oG+ZQarp0uVyg66cCm52ZzGZ8PtU/qld1I8mm\nrk1+sn85XfXgaTk25EOxgiAIgiAIgiAIgiAIgiBcfLt37+bTTz+ltLQUp9OJ1+tF0zQkSUJRFPbt\n24fZbA42R/UlkHO66Gj/d6+BIMu30Zo1azh48CDh4eH87Gc/O2vrlyRJjB07lkceeYQ33niDLVu2\n8Nxzz/Hkk08SERHR4+PU19ejaRoxMTF9Ko4IMBgMxMXF0dDQQH19/aAH/CRJ4q677kKSJLZs2cLq\n1av529/+xqJFi5gyZQoWiwW3282BAwfIzc2lqakJgJkzZ7Jy5cohd45oy5YteL1eJk2axIwZM3q9\nXFRUFMuXL+eNN97A5XLx3e9+l9tvv73Ta6ylpYW8vDxycnI4fvw4siwzadIkwsLC0HUdXdeDr9cA\no9GIyWTCaDQSHh5Oa2srR48e5bXXXmP27NmdWgG743a7+eMf/8jJkydJTk4+52jXhIQEli1b1utt\nnjZtGrGxsdTX17N58+YuZQ5nU1lZSWFhIYqidAocer1e3n//fXJzc+no6MDj8QT3h67rOBwOPv74\nY7744gumTZvGnXfe2WnktdfrZdOmTQDccccdlJeX09bWhs/nw2q1csUVV3Q7IltRFO666y6KioqC\nY3DPFiDsjcAxHRi9fCZJkrDZbKfO1ftDfgaDAbPZjMFgQJIkfD4fXq+Xjz76iJaWFgAmTpzIfffd\n1214VhCEy5t41QvCZaavTXKaz42m6903R/WCJBtB13E6nYO8JZeOC9kUdrY2sN4634HMgY56HUoG\n0pZoiBiJKlvA58ISM6HTMaHrOp7Gg7hPHkR1tYHqRpeNED3dH+7z3wqQkGXJ/4EFHelUm5/b40aR\nLZiHzcFdvRlJ82Av/ZKQlHkXrTFxMEc0D0YbZuC5U+UQkBUc5blo7jZ0zYckG5DN4Ziix/qDp2d8\ncTXYTZcWiwUkCV31drlOguCvxvwhPw9oEpJsOPWeLIHmBl1DdTbhqdo8pEOxgiAIgiAIgiAIgiAI\ngiBcXLqu89lnn/G3v/0Nu90eHP/odDqDARmj0YjVasVisSBJEgcOHBhwkOXbqq2tjdzcXCRJ4qGH\nHurVSM9AiOjkyZMcOnSI3Nxcbrzxxh6Xc7vdAP0K9wUElnW5XP2+j3MxGAzcfffdjB07lq+++oqa\nmho++eSTYBPe6eLj44fsOSJd14NNd/05l5KWlsa7775LR0cHCxcuDJ6H0HWdTz/9lE8//RRV9f/4\nPxDyio+PDwb7AgwGA7IsB0NeXq//PIMsy4wYMYLy8nJaWlr4f//v/3HnnXd227Cn6zqHDx/m3Xff\n5fjx44SFhfHAAw8M6mhXRVFYsWIFTz/9NHv27EHXdRYsWHDO57WsrIy1a9ciyzLLli1j9OjRADid\nTv7whz+wb98+3G43VquV5ORkwsPDgwE/p9NJU1MTra2tFBQUUFlZyWOPPcawYf5zp7t27aK1tZWQ\nkBBeeuklWltbOz22JElMmzaNzMxMJk6c2Gk9w8PDmTVrFvn5+eTm5nLrrbf2e78ERgIHwrlnK90I\nDQ3F6XTi8XiCjX4BlZWVuFwuTpw4QUREBIsWLeKWW24R4T5B+JYSr3xBuIz0q0muYjP4OnDVFGBL\n/U6ff6Wla16QpC5V498WF7op7FxtYL1xIQKZAxn1OpQMpC1R9WkQOgqa96F52oKX65pKR+VmvC3H\n0QFkI7qugjHMf3tJBl0FzQec9kFOkvwByVN8kookR4MSgowPk+zDc5EbEwdjRPNgtWFu2rSJDqcT\nHZWOYxvRz1wBZxPeluPI5jDMsRMxxUwMvvcNdtNlfHw8SkkpPnstBmtsl+slCaw2KwaDAbfbjaqq\n/vfVwErbq5F0DaNBYvSooR2KFQRBEARBEARBEARBEATh4lq7di3vv/8+LS0ttLa2Yrfbu9zG5/Ph\ndDqRJCk4OvKpp57iF7/4RTCQ0luBMoHQ0NCBr/wlKC8vD1VVmTp1KqNGjer1coqicMMNN3Do0CFy\ncnJYvnx5j4GrQEPb6eGfvgoExCwWS7/voyeSJLFw4ULS09M5fPgwmzZtor6+HpfLhcViITY2lvT0\ndCZNmkRrayvZ2dm0trbi8/kICQkhNTWVSZMmXdTvwJuamqivr8dmszFt2rQ+Ly/LMlOnTmXbtm0c\nO3aMMWPGoOs6b775ZrBZbvr06TQ2NnLkyBFGjRoVHE/r8/lwuVx4vd5gAPCb8xdSMASoaRrx8fFU\nVVVRVlbGb3/7W5KTk1mwYAGxsf5zESdPnmTz5s1UV1cD/rHaDz/8MPHx8YOxmzqZNGkSP/7xj3n5\n5ZcpKiriyJEjXHnllcGxw4FtO3r0KHv37qW+vh5FUUhPT+f73/9+8PoXX3yRoqIiVFVl1KhRXUbr\nSpKE1WrFarWSkJBARUUF1dXVPP300/ziF78gMjKSTz75hLa2NlRVxe12k5iYSEpKCgaDgba2Ng4d\nOhQccTxmzBjuv//+Tu99ixYtIj8/n927dw8o4DdixAiio6M5efIkBw4cYMqUKd3eLrBNISEheDye\nYGNhRUUF5eXlKIrC3Xffzbx583psahQE4fImAn6CcBnpT5OcWzPircrG216Fp/EQ5tiJfXpMn70G\nRZHOyx+Dl4KBtLz1pynsXG1gvXEhA5k9jXrVNI2ioqJgADDw4S4QAJwyZcpF/QA3kLZEj8eDbolH\nkmT/+F38v5IKhvskI8TNBns5OKogfCzIgf9LlkDS/EG/4P82ICmK/7+jg66haz6wjUJt2YfFYiIu\nLo66+oaL1pg4GCOa161bN+A2zEWLFrF79258Xh/IMhhs/v1rTQDJCLoXOurR246guttxVhfgczRg\nHb4A6dR43MFsukxPTyd/RwGOxsOY4yZ3G3iUALPZhMlswuf1nfrwpvo/pDuOYzRI/OsP7+D6668X\nwT5BEARBEARBEARBEARBELpVVlbGX//6V1pbW2lqaurxh/6BAMnw4cMpKyvj9ddf58EHH+z149XW\n1lJSUoLRaGTixL6dW7oc6LpOTk4O0L+Wt4kTJ5KYmEhdXR179uzpcQxsfHw8sizT2NiIz+frc4OX\nqqqcPHkSSZIuSFujJElMmDCBCRMmdLpc13UOHTrEiy++yO7duzs11gXEx8ezePFi0tPTsdls531d\nzxQYOR0ZGdmvpjuv10tUVBRGozEYsv3kk0/YtGkTRqORe++9l+nTp/PYY4+haVqn50OW5WDbZqBx\nMzCa9/T7d7vdREVFUV1djSRJhIaGUl1dzbvvvttlfSIiIli4cCFZWVnBIOH5MHv2bMLDw3nzzTep\nra2loKCAHTt2YDKZUBQFl8uFrusoikJ4eDjXXXcd1113XTDAuGnTJgoLC/H5fKSmpvYYZjMajYwa\nNYpjx45RU1PDmjVriI+P5+jRo4SGhjJ79myysrKYMGFClxHJmzZtIjs7m6NHj/LrX/+a//qv/wqO\n7Q00Aba3tw9ofyiKwuLFi/nwww/ZuHHjWQN+AZIkYTabg9u9c+dODAYD3/3udzuNMBYE4dtLBPwE\n4TLR3yY5S1gCvujpcGIH7pMHMMVM6HWLn65reBoPYzP6f2HxbTSQlrf+NIX11AbWk6EQyNQ0jQ0b\nNrB+/Xpq6+pxe1VUVf8mkFZSSv6OApKGJZKZmUlmZuZFCTUNpC1R1VSQDMA3YUxP48Fvwn1JGWCO\ngpZi/wIhCaeW1EHznAr3SSAbvxnViuT/Bwkk2T/G1ZYELftpa2tn1qxZ/PCHP2TLli0XrTFxICOa\nB6MNc926dRw6dIjmljZ0gxUpdg6EDu86gtwcA5ETwFGFfqIAb8txOgDrCH9V/mA2XU6ZMoVhiQmU\nHa/G21qBKfKKs95WAoxGA0aj/88zT8sxPLgZnTpKhPsEQRAEQRAEQRAEQRAEQTinDRs24HQ6aWtr\n69V3m4HwUFVVFRaLhcLCQurr60lISOhxWSAYbps7d26Xlq1vA4fDQWNjIxaLpcfgTnckSWLmzJl8\n/vnnVFRU9BjwGzZsGGPHjuXAgQMcO3aM1NRUwB8I6815vYqKCrxeL2PGjGHEiL4VVgwWVVV56623\ngi12sixz9dVXk5ycjMFgwG63s2vXLhoaGnjvvfdYu3YtjzzyyAVf375OOzuTfmoiUyCk19zczKef\nfookScFwH3wzKjkQ1tR1HbvdjqZpKIqCzWbrdl2MRiNGoxG3240kSfh8PpKSkli4cCGHDh0KhgpD\nQ0OZMmUKM2bMuGAjXSdMmMBvfvMbDh48SHZ2Nrt370ZV1eA2jRw5koyMDNLS0joF+AJjkT0eD0lJ\nSb1uqpNlmeHDh3PkyBG2bNmCoihYrVbuuOMOFi1a1O1I68jISG688UYWLlzIs88+S0VFBc899xw/\n//nPMRqNwVCnqqpdlu2r9PR0Pv74YwoLC9mxYwdz5szp1XL79+9ny5YtACxevHjA6yEIwuVBBPwE\n4TLR3yY5g9GAHJaC2rwf1dWCz16DMSy5V8t6W8uRVSdJI1KYPHlyf1f9kjaQljfoW1OYpmmkpKSg\n+Ty4Gw7gNo9AkmUUWcFkMmEwGjjXR46hEMj0er288sor/pY2tw9VDkEKH49uiQWMgBfddRJvWxml\nx6qoeesdjhw5wj333NPtH+Hn04DaEnVOjdkFSTGi6zrukwf9U1fjZvvDfXxzG6RT26ap34T7FPOp\n0byBu9SRznyGFQtIMqrmIz+/gPHjx3Pffff1fX0HUX9HNA9GG2Z5RSWVlVVosgXi08Ea3zXcFyDJ\nEDoCjGHoNRvxthzHY0vAHDtxUJsuA/vjrbfewVG9DcUc3qvwoupswlW9HZvZQFZWlgj3CYIgCIIg\nCIIgCIIgCIJwVu3t7ezYsQOXyxVsH+sNk8mEy+WitbUVq9VKbm5ucFzmuZSVlQUDft/WZqnAfg4N\nDe3397eB0aW9ec7q6+sxm824XC4OHjxITExM8HENBgNms/ms51E0TaOkpARFUcjIyBhwgK0/NE3j\nlVdeoaCgAIPBwLJly0hLS+vS0Lds2TIOHz7MunXrOHbsGE8++SSPP/74BQ35BZ6XpqYmvF5vv85P\nnThxAoCwsDByc3PRNI1Zs2YFw33wzdjlQJDM6XQGg3ChoaHBkbzgD8Cd+bzpuh4M7pWUlDBt2jTu\nvvvuPq/rYJMkiUmTJjFp0iRUVaWjowOfz0doaOhZ9+Xhw4eprKxEkqQ+jwo3mUyEhobS2tqK2Wzm\n3/7t35g5c2Zw351NZGQkjz76KP/zP/9DeXk527ZtY+HChTQ3NwMMSntkREQE3/ve93jvvff485//\njK7rpKWlnXOZvXv38sILL6BpGtdee22wUVAQBEEE/AThMtHfJjkJsFhC6AgbBc37cDfs71XAT4RP\n/AbS8gb0qins9Ma7mto6fD4vYEdtrwRrCj5JxePxoCjKqTGf5m6Dfhc7kBn48LZt+w4cbpBiZqJZ\nEtElORhiA8AWgWQdBa462hsL2botH4BVq1Zd0ONsQG2JEuDyhzZlUxg+ew2aux0UK9hSvrldYCyv\n7sU/evdU4E82nroTToX89G/20elP7qkgmmKJwul2sX79+vM2grevehrRfKbBaMN01exE0lUMwxbi\nNUeArp094BdgjoK4WegN24ItpoPddJmZmUlJSYn/2C9biyX5GowRI7rdTl3X8LaW46reTohRZ+7c\ntH6NdxAEQRAEQRAEQRAEQRAE4dsjPz8fh8OB0+nsU+uULMtYLBaam5uJjIxk3bp1/PM///M5A2Cl\npaX83//9H16vlwULFnDFFVcMwhZcekwmE+AvNugvn89/TuBcATK3280bb7xBfn4+uq6j6zptbW3s\n37+fadOmoes6Pp8Pn8+HLMtYrdZObW26rrNr1y5aWlqIjo7uMVx0vnzyyScUFBRgsVj48Y9/zMiR\nIwF/GExRlGCYTVVVJk6cyNixY3n77bfZs2cPzzzzDE888QShoaHouk5ZWRm5ubnU1tbicrkwm83E\nxMQwf/58pkyZMuBzJJGRkaSkpFBVVcWuXbv6vM86OjooKipC0zQmTpzI7373O6BrGDYmJobq6mqa\nm5uxWq3BY+n05r7TQ35nampqQpZlpkyZQklJCdnZ2SxbtmxInCMKUBSlV2OB8/Pzg6OnJ8suAAAg\nAElEQVSN+xNADQ8Pp7W1FUmSWLBgAS6XC4/HE3ydnmu5m266idWrV7Nx40bS09PZtm0bAOPHj+/z\nenRn2bJltLa2snbtWl5++WVyc3PJyMhg+vTpwdeqqqrs27eP7OxsioqKAJg3bx633nrroKyDIAiX\nBxHwE4TLxECa5ExmM56wJHwt+/G2leNpOS7CJ700oJY36LEp7MzGO02xYoiIx9teidS0B8zRYIpA\n13z4fCqq6sTnU7FarZz+9+9QCGRu2LCB7fk7cLh19PgFaMYIkCT/qNngGFoddA1d86FakpDirTga\ntrBtez7jxo0jKysLTdPYv39/sBnO5XJhsViCzXCD8eEN/LXZ+TsKcDQexhw3uU+hM1mSwHEMUDFF\nj8XTdMSfzwsf2zlwZgwDdws468EU6Q+kIYOkdHu/XVr8nHUAKLY41I6T1NTWUVxc3OtQ3VAy0DZM\nLPHogI5MSPQofI4OdM3nP756YhsOihXN3Y63vWrQmy5lWWblypVIksT27TtwVuXhqg3BFDMeQ2gS\nkmxE17z47DV4Gg8jq05sZgNz56Zxzz33DKkP44IgCIIgCIIgCIIgCIIgDD0nTpzA5/Phdrv7vKzF\nYqGjowNVVTl58iTPP/88S5cuZcKECZ2CNseOHWPjxo3k5+fj8/mYOnUqP/rRjwZzMy4pgea+1tZW\nGhsbiYmJ6fN9lJeXA/5AWXecTidPP/00paWlGAwG0tLSGDduHG+//Ta1tbVIksTVV18dHNeqqip2\nux2r1YrJZMLpdLJ7924qKyuxWq3cf//9vR59Opg6Ojr48ssv0XWdO++8k5EjR54qrTBjMpk6HWea\npuHxeHC73fzwhz+kpaWFY8eOkZOTQ2xsLGvXrg3ut9OVlpZSUFBAfHw8GRkZZGZmBket9pUkSWRk\nZPCXv/yFjRs39jngV1BQgNPpJC4uDqfTSWtrK/Hx8UyYMKHT7ebPn8+BAwcoLy8nLi4u2MjXm3MC\nmqZRWVmJwWDgxhtv5I033uDkyZPs27ePqVOn9ml9h4Lm5mY0Tev3ZKXAMWQymTCbzbjdbrxeL6qq\n9ngczJkzhzVr1lBRUcGhQ4fIy8sDBq+dVJIkvv/97xMdHc0HH3zAoUOHOHToEDabjZiYmOAY57a2\nNsDfyHnddddx0003XZS2TUEQhi4R8BOEy8RAmuQkIMQajl2SkfHhEeGTXhtQyxucsyns9MY7p1fC\nkpJ+anSpREfFJrwtx9FrcyBuNpItBXQdXfPg8XgAsNqsMEQCmZqmsX79euwdHvSYWeimCCTZBPKZ\nf1RLIMn+UJamokuREDUNR+NOvvrqq2CbYW1dPW6viqrq/nY7SUIpKSV/RwFJwxLJzMwkMzNzQMfm\nlClTGJaYQNnxarytFZgir+j1soqrDsnnAF1DsSWi1e7yX2FN6HzDsFSwV0LbEQgd5b+s0z6Rvvnn\nzB9o6Rq0HUUCTNHjUENicDfuJS8v75IM+A20DdN36kepssGC0WRCcbnw+TT/2OMux9kZJBnCx6A3\nF+Gu33Nemi6NRiOrVq1i3LhxwTZOd+NenA17vjmGFQmbUSFpREpwzPG3+f1VEARBEARBEARBEARB\nEITecblcwXa3/rBarcH/vmvXLgoLC4mPjycuLg5ZlmlqaqK6ujp4m8WLF3P77bd3aorriaqqVFdX\n097ejqZp2Gw2UlJSemzYGqqMRiMzZ86koKCATZs2ccstt/Rp+ebmZnbt2oUsy8yaNavL9aqq8sIL\nL1BaWkp0dDSPPPIIycn+CVyxsbE8//zz1NbW8tlnn5GcnMzo0aMxGAx4PB5OnDhBXV0dNTU1wZGn\n9913H+PGjRuUbe+rbdu24XQ6GTduHOPHj8dsNhMSEtJteCnQKmk2m3E4HCxbtow//elPvP/++8iy\njCRJ2Gw20tPTmTp1KiEhIbjd7mCDXUNDA2vWrOHAgQPce++9/Q40zp07l/fee48jR46wZcsW5s+f\n36vl6uvrWb9+PR6PB7vdHhz3Gh8f32V7A8GyxsZGTpw4QWhoaK/Xt7a2Fq/XS2pqKpMmTWLRokV8\n8MEHbN++/ZIM+AXObfYn0KbrOpqmAf7GQFmWMRqNeDweXC7XqVKUs9+v0WgkLS2NjRs38uGHH+Jw\nOBg5ciSpqan925huSJLE0qVLmT9/Plu3bmXjxo3U1dV1Gs8dGxvLkiVLWLBgQa9aDwVB+PYRAT9B\nuEwMtEkO3YuiKCQlJGO1Wgc9fHKhWtcutIG0vOm6ds6msA0bNvib+7wSttRlKCHRweuswxfQAf6Q\nX8NW/+jX8DFIIfHouo7H7UNvbUFrPTokApn79++nvKISXQlBtyYhKeaeR6fKCpJkRrcmoTVbKC0t\npaq6Bq8PNMWKKWY8ptBhSIoRXfXis9fiaDxM6bEqat56hyNHjnDPPfecs9r+nA8vy2RlZfHWW+/g\nqN6GYg7v9BycjepswltfgIIGsoKvrRJdOzV6VzpjXUKGIRlt6F4HdFSDNemb/dLTZxhHJagdyOYw\nfxBXMeNs2ENDQ0PfN3YIGOh7mOpzgw6SMQQJMJvNqKoTXfMgSb043kISoWkvPnst4aGh56XpMnBM\nZWRkUFxcHHw/dDqdhISEBN8PJ0+efEm+HwqCIAiCIAiCIAiCIAiCcHFYLBYkSRrQ94qKohAeHs7y\n5cvZunUrDQ0Nnb5vtlqtpKens3jxYhISEs5xT521tbWRl5dHTk4OjY2Nna7r730OFUuWLAkG/K6/\n/vo+nY/YtGkTmqYxY8YMoqO7nnvYuXMn+/fvJywsjJ/+9Ked9s+kSZP47//+bz766CN27dpFXV1d\nMICpaRqqqqLrOmFhYVx99dXcfPPNDB8+fOAb3E85OTnous68efMwmUxnDfedLhDkmzx5Mkajkfb2\ndiIjI7n99ttJT0/vsq/HjRvHtddeS2FhIW+88QZ79+7lhRde4IEHHuhXk5/FYuHWW2/l7bff5vXX\nX0eWZa655ppzLlNbW8szzzyDw+EgPDwcTdPYvHkzQLevTZPJxKJFi/j00085ePAgV111FeHh4T2u\nW1tbG8XFxRiNRjIzM5EkiVGj/CUSLS0tfd7WoSAwlrgvI8YDTg/42Ww2wP/8eb1ePB4P0qlJauc6\n5mJjY/H5fBQVFWG1WrnlllvOS3ue1WolKyuLzMxMTp48id1uR9d1bDZbMFAtCIJwNiLgJwiXicFq\nkps4cSKrVq0atPBJoHFt/fr1F6R17UIbSMubt7X8rE1hgcY7p9uHJSW9S7BMkhWsIxbisSXgPnkA\nzd2O3lwEzSDp/jG3PgnCQkOGRBvYpk2bcHtU9MhRSIql57BVgCQjyWZ0SUHVwOmVsQ6f3+0IaYM1\nFnPcZLytFTiqt7F1Wz4Aq1at6vd2Z2ZmUlJSwrbtO3CUrcWSfE2vx1ePHD2OiooqOqq3gXLqF1f6\nGeE1SYKI8XByNzTtBoMNQgIf0s/xwcHdDCd2+oNssZOQJAlJNoKu43Q6+7WtF9tA38N0ZwOgIZv8\nv2oymc34fCoejwdddZ+lMfJ0MjoaCjpz5845r02Xsixz5ZVXXpJNi4IgCIIgCIIgCIIgCIIgDD0J\nCQkYDAbMZjN2u73Py5tMJgwGA1FRUdx6663ccsstlJeXB8MnoaGhjBgxok9te7qu8+WXX/Lhhx8G\nQzvR0dHEx8cHWwHr6upYu3Yta9euZeHChfzwhz/sUyvgxTZ+/HiSk5Oprq7mzTffZMWKFb0KBR05\ncoTPP/8c4KzfRWdnZwNw8803dxt+TEpK4r777qO5uZnc3Fz2798ffL4qKirQdZ0VK1b0unnufHE6\nnVRUVGAymbjqqqt6Fe4LkCSJgwcP4nK5CA8P5yc/+QnTp08/6+0VRWHmzJkkJSXxxBNPUFRUxOef\nf86NN97Yr3XPyMigqamJL774gj//+c/s2LGDJUuWcOWVV3Y671RTU0N2djZbtmzB5XKRmprK9ddf\nzx//+Ed27dqFqqo0NTV1+xg333wzJSUl7N69m6KiorMGPsH/mmpoaKCoqAhZlpkzZw6LFi0CCL42\nvd5+FsFcZMOHD0dRFNra2s46svpcAufGRowYAfiPBZvNhsPhCI6vtlgsGAyGTsefruuoqhoc6Qtw\n++2397oFUVVVSkpKaGpqwuPxEBISQnJyco+BWkmSiIuLIy4urs/bKgjCt9el8xeSIAjnNJhNcoMV\nPvF6vbzyyiv+Fjq374K0rl1oA2l5c1Vvx2Y2dNsUtn//fmrr6tEU66mxvF1JkoQ5diKmmAn+8clN\nR9A87eiqB9XZhEGRuPV7/8T1119/0UOTZWVl/gmzloSex6WeyV4K3nZ0xYqUmI4psvv9ASBJMqbI\nK1DM4TjK1rJ9+w7GjRtHVlZWv9ZblmVWrlyJJEls374DZx/GV69YsYJXX33VHw7saAZk6KgHc0zn\nBwkfh+Q6gd5WCvWbIW422IYDp+2nwFQFXQNHlT/cp3sxRl6BKWbCqau8cOpXSJeigb6H6W1loKsY\nwlIAfzwyMFbCH/LzgCb5xz9LMsG5x7rmb1j0OZGAqKjIXjddXq7NpIIgCIIgCIIgCIIgCIIgXFrS\n0tJYs2YNdrsdRVH63IJls9mwWCwsXLgQSZIwGAyMHj263+uj6zpr1qxh3bp1AEydOpWMjIwu35mW\nlZWRnZ1Nfn4+mzZtorGxkQceeOCSOUckSRIrVqzgN7/5DVu3bkXTNO66665zBiH37t3Liy++iM/n\nY/HixUycOLHLbSorKykpKcFisTB37txzrkNUVBQ333wzN998c/Cyzz//nA8++ICdO3de9IBfR0cH\nXq+XiIgIQkJC+vyd+fr16zGZTNx4442MGTOmV8skJSWxatUqnn76aTZs2MB1113X7+DorbfeSkRE\nBO+99x579+5l7969xMTEkJiYiMFgoKWlhfLy8uDtZ86cyb//+79jNpu56667WL16NXa7nfLyckpL\nS0lNTe0UMDMYDNx7773ce++9dHR0kJ+fT0REBCNHjiQ6OhpFUfB6vZw4cYKKigqcTidGo5FZs2ax\ncuXK4P4MBHtPH7d9KUlPT+fjjz+mvb0dr9fb5/eA9vZ2dF3vdLwbjcZgyM/n82G324PjeyVJQtd1\nfD4fqqrS0NCAruvMnTu3V+cUA82k2dnZ3YY3R48ezZIlS5g9e/Yl834mCMLQJwJ+gnCZOF9Ncv2l\naRqvvPIK27bvwOmVsKSkX7DWtQttIC1vc+emdfvrrLy8PNxeFVPM+B6DTpIkYQxLxhiWHLzM1VCE\n1riXqqqqIbEfA7X7ksHctwV1HVoP+wNZMdPRDT1XkwMoIdFYkufirNrM+vXrB9ReaDQaWbVqFePG\njWP9+vV9Gl8dCAdu3ryFDqcTva0EKXwMyAY6Bcyip0NHLfjs0LAVDKEQPsY/NlYyguYBZz1S+1FQ\nO5AAY+QVWIcvCH4QDLRwxsfH92s7L7aBv4d1oEt8Mw4Zf0Gi1WbFYDAEf6Gla95vApMAkv+ZkLwn\nURSFGTNm9Phh63JvJhUEQRAEQRAEQRAEQRAE4dJis9mYO3cuX375JaGhobS2tvZ6WUVRCAkJISQk\nJNgGNlBr165l3bp1KIrCypUrmT17dre3S01NJTU1lYyMDJ599ln279/Pa6+9Fvxu/VIwatQo7r//\nfp5//nm2b99OUVERCxYs6DR22Ov1UlBQQHZ2NqWlpYA/lHnHHXd0u527d+8O3qY/P+pPT0/nww8/\npKioCJ/Pd1FbEQ0GA16vF1VV+9QACQRDcTabjbS0tOAY1t6YPHkyKSkpVFVV8fXXX5OWltbXVQ9a\nunQpaWlpnUZNnz5u2mQyMXfuXDIyMoINcgALFizAarXy9NNP43a7WbduHbfddlvw+QgEzCRJ4uqr\nr+brr7/GbDbjcrkoLi5G0zR0XQ+O3zYYDCQkJLB06VKWLVvWafTw119/DXBRRzEPRFRUFDNmzGDL\nli2cOHGCpKSkXi/b3t4e3I8dHR2drjMajYSHh+PxeHC73Wiahtvt7nQbn8/H7t27sVgs3HTTTT0+\nXn5+PqtXr8bn85+PiouLY/To0ZhMJjo6OiguLqa0tJTS0lL+/ve/89BDD5GSktLr7REEQTgbEfAT\nhMvE+WqS668NGzb4m/u8ErbUZedcl8FuXbvQBtLydramsIaGBlRVxxQ6rF/rZAhNwtmwh4aGhoFu\n3qBQVdUfqtJ7/+ELAGctutdxanRt3/aFMWIkrtqvqamto7i4eECNlIHXV0ZGRp/GVwfCgWPHjmX1\n6tW4vHZ0ezlYT/tDXvK/BqS42eh1eaD50H0OaCoCigik0ST8YU7ZHIY5dhKmmAnBD/5ntnBeigb6\nHmYxK2iaGW9TCZb4KcFgrASYzSZMZhM+rw+Px4OmqYEsHrKsYDQacNUdxxpiYuHChed8vG9DM6kg\nCIIgCIIgCIIgCIIgCJeerKwsf3nAqVGTZwZduiNJEtHR0dhsNmbPnk1MTEyPy/TEbrfz4YcfApwz\n3He6UaNG8eijj/Lkk0+Sn59PRkYGY8eOHfC6XChTpkzh8ccf54033qC8vDw4djgw+vj05yIkJIRl\ny5adc/pSe3s7AImJif1an/DwcGw2G3a7HYfDQURERL/uZzDYbDZ0XcfhcNDe3n7W8bPdycvLA/xB\nR7O5bwUSkiSxZMkS3nrrLTZt2jSggB/49+ny5cu59tprqaiooK2tDU3TsFqtDB8+/KzNeTNmzODX\nv/41P//5z9m5cyfXXHNNpxAg+EOQWVlZFBcXBxsZt27dSkNDA83NzaiqitlsJjExkZEjR3YJv9nt\ndnbs2AHQ4zmOocDn89HR0YHRaMRisQTPdV177bV8/fXXNDc3YzKZiI2N7fG+HA4H1dXVmM1mJEki\nJyeHJUuWdArOyrKMxWLBbDbj8/mCwcnAdfn5+bhcLkaPHs2oUaPO+Xg5OTn85S9/AeCqq64iMzOz\nSzOp2+0mPz+fdevWUV1dzRNPPMHPfvYzRo4c2ed9JQiCcDoR8BOEy8j5aJLrD03TWL9+PU63D0tK\neq9COjC4rWsX2kBa3rrjcrlA15GU/gWCJNkIuo7T6RzIZg0a/3Zq4KwHS89/kAe1l/n/DUs9NVa1\n9yRJxhQzHnfjXvLy8gY8chro1/hqWZZZunQpuq7zxpt/wdm8B9kSAabIYMDMZDKhREZgb92H6mpD\nipwEXju4T6J7HUhoIMtIRhuyJRrZ3LnJ8Hy0cA623oyzHch72DVzr+H48eMcK6/ptgFQAoxGA0Zj\n1z99PC3HerX/vk3NpIIgCIIgCIIgCIIgCIIgXFpGjBjBnXfeyWuvvQb4m/nsdnswyHImo9FIVFQU\n4eHhjBkzhjvvvHNQ1iMvLw+fz8eVV17Zq3BfwMiRI8nMzOSLL75g48aNl1TAD+CKK67gl7/8ZXDs\n8M6dO/F4PHg8HsC/fUuWLOlVWC3QVHd6Q1tfBZbtS+vd+WAwGLBYLHg8HvLy8nrVkBZQV1eHruvB\nY6GvrY5TpkwBoL6+vk/LnYuiKD2GwM40evRoMjMzycvLY/Xq1Tz44IOkpKQgSRKKoqAoClOnTiUh\nIYGGhgbsdjvJyckcP34cXddRFAWfz0d1dTXV1dVs27aN6OhoFi9eTGZmJv/4xz+Cr7lAa+RQ09HR\nwbZt28jJyaGqqip4ucViIS0tjenTp1NeXs6oUaPYt28ftbW1OBwOEhISsFgsXe7P5/PR3NxMQ0MD\nFouF9PR0ioqKqKqqIjs7u9tz3pIkdSliaG5u5uOPPwZgyZIl59yGoqIi3nrrLQC+//3vs2zZsm6P\nSbPZzMKFC5k7dy4vvfQShYWFPPvss/zyl78kKiqq550lCIJwFiLgJwiXkfPRJNcf+/fvp7auHk2x\nYowY0fMCpxnM1rULrb8tb92xWCwgSeiqt1/romtekKR+VbefD7GxsbS2HYO2oxA5sfdhPW87oIPJ\nHwrs6zE6lJoMs7KyOHLkiD8cVr+52/CaOXYiHVU7oOUAKBZ01eWvmUMBJDRvB1prOb7W8lNNfhNR\nrAnnpYVzsPR2nG1EeBhhYWEoioJBkcDpwFmeg9MYijm2d+9hOTk557XF9NvUTCoIgiAIgiAIgiAI\ngiAIwqVn8eLF+Hw+3n77bYxGI6GhoXR0dOByuVBVNRhwsdlsWCwWrFYrY8eO5eGHH8Zmsw348TVN\nIycnB+g5LNOdRYsW8cUXX7Bz505uu+02wsPDe15oCJEkidGjRzN69GhWrFgRbFO0Wq19CusFnouW\nlpZ+rYfX68VutwOctVnuQoqLi6OyspKcnByWL1/e65HBLpcLTdOCgci+nv8IbPtQKMP40Y9+RFNT\nE8XFxTz11FMsWbKEJUuWEBcXB/i37brrruOVV15h9erV2Gw2FEVh3LhxXHPNNURFRaFpGnV1dWza\ntImGhgY+/PBD1q5dS3t7O7Is893vfvcib2VXqqry4YcfsmHDBtxud3DksMViCTb5ffXVV3z66afB\npj2DwYDT6aSlpYXW1lasVisREREYjUY0TaOjo4PW1tbgePGlS5dy2223sX37dlavXs0777yD0Wjs\nceJVU1MTzzzzDE1NTaSmpp6z5VHXdd5//310XeeGG27g2muv7XHbTSYT9913H7///e85fPgwX331\nFT/4wQ/6vA8FQRACRMBPEC4zg90k1x95eXm4vSqmmPHdNm+dy/loXbvQ+tPydqb4+HiUklJ89loM\n1j403p3is9egKBLx8fH9XofBlJqaStmxY+g+OziqILSXwU/N5//31PHZ2w99AUOpybA3AVzJHI0k\ngeZz+8cZG0MhfCxYE0Eygu6Fjnr0tiOo7nY6qrYjoWMNsTB37txBa+EcLL0ZZ+tqqaStqYQ2ewNo\nNUiS5H/ekNE1N7g8uGp2IskKsiSd8z3sfLaYfhubSQVBEARBEARBEARBEARBuPRkZWWRlJTE559/\nTnFxMU6nE6/Xi6ZpwcYwi8VCdHQ06enpLF++fNBCYLW1tZw4cYKIiAiuuuqqPi8fFxfH5MmTKS4u\n5sCBAwMeq3oxBUJM3bWP9STQWJefn88tt9zS5ya/goICVFVl5MiRfR5tez7MnDmTyspKGhsbWbNm\nDXfccUev2vhUVUVVVdxuN5Ik9fkcUeDcUH+eg8FmMBh48MEHee2118jPz+fLL7/kyy+/ZNKkScTF\nxSFJEsePH8fhcGCz2ZgyZQr/9E//xOjRo7ucU7j22mvZt28f77zzDhUVFXi9Xu666y4mTpx4kbau\ne16vlz/96U/s2bMHXdcZPXo08+bNY/LkycEQX3V1NTt37mTv3r10dHSgKArf+c530DSN7du3U19f\nj8PhwOFwYDAYMBgMyLKMzWbjqquuIiMjg6lTpyJJEvPnz6exsZGPPvqI119/nV27drFkyRKuvPLK\nTvuwsbGR3NxccnJysNvtJCYm8uCDD3Zp9zvd0aNHqaqqIiwsjOuvv77X+8BgMPCDH/yAX/3qV+Tl\n5XHLLbdgMpkGtF8FQfj2EgE/QbgMDWaTXH80NDSgqjqm0GH9Wn4ota5dLOnp6eTvKMDReBhz3OQ+\nBSV1XcPTeBibUenx1ykXysKFC9m8ZQsulwdOFIAxDMy9rKHWNX/QT6LPf/QOtSbDcwVwdU1DUz3+\ncQlGG0RPh5BhIBv8/5Fk/DshCsLHoNsroGk3qE6iIsNZsWLFkAqO9TTOVtf9lezekFHow0aCsxap\nqRBd82IIS8acMB2fvRbPif3gtSNpKqFhoYwcOZLExMRu38POZ4vpt7WZVBAEQRAEQRAEQRAEQRCE\nS8/kyZOZPHkyNTU15ObmcuzYMTo6OjAajURERDBnzhxmzZp1zkBLfwRa4+Li4vo9XjYhIYHi4mLa\n29sHc9UuKVOmTCEuLo4TJ05QVFTE9OnT+7R8dnY20L8WxfMhIyODr776CofDwYYNGwC47bbbznmM\nlJaWcvToUVRV5ciRI0yePLnP54gOHToEEGzJu9iMRiMrV64kMzOT7OxsduzYwYEDBwB/Q1xbWxs2\nm4158+bxgx/8AFmWaWtrw2g0YjAYkCQJXdfxer2MGDGCBx54gBdeeIHKykoOHTrUp+DZ+aZpGq++\n+ip79uzBYrFw1113MXr06OD1HR0deDweYmNjuemmm7jhhht47/+zd97hcZQH/v/MzHZVSy5qlivu\nxoANLrjKhRITervkcpe7hJwTEhJIeciR++VCCjwBAoGAuTRCCeEICeEgMZYl23KRJdvYsuUiFxVb\nvZct2p2deX9/rHawrG5L4JD38zw8tjXtnbKLZuez3+8bb1BcXMzOnTv5+te/zqc//WnOnDnDm2++\nyYkTJxBCMGPGDObNm8eSJUt6rSO++eabiYuL47XXXqOoqIiioiJGjx5Neno6drudtrY2Tp06ZVWX\nz5gxg/vvv5/Y2Nh+9ycnJweIPL8d6vvmpEmTmDhxIuXl5RQUFLBs2bIhLS+RSCRRpOAnkXyCGY4k\nuQuhs7MThEDRLuzG8FJKXfu4mDNnDqkp4ygtr0JvO4MjceKgl9XbKlCNAGmZGcyePXvkBjkE5syZ\nQ+b48Zw8VRpJZavOgTHXQExG73W9wgTfWQi2gAIEG7DFjMZmH9r/ti61JEPoW8CtqamhobEJU/Xg\nmXgdOOIJBkMYhhERFcU5K1FAiUlHdcYjavNoa/eSl5d3SdW/9ldnK/jw5k0Ais0FCVPBlQzVOYQ7\nqrHHj8c97nJcY+egt52hs2o3YUOQkpLChg0b+pTxRirF9B89mVQikUgkEolEIpFIJBKJRCKR/P2R\nlpbGP/3TP31k24tKM4NJZ+uL6LKmaQ7LmP4eUVWVVatW8b//+7+8/fbbzJo1a9BJfPv37+f06dO4\n3e5LJgExLS2NuXPnUlRURDAYZMuWLRQVFbFq1SqWLVtmVTGbpsnRo0et6VGZraCggFtuuWXIIQdR\nKetSEqoURWHq1KlMnTqVe+65h2PHjuH1eikuLmbv3r1MmzaNL33pSwghrIrnUHbWYTgAACAASURB\nVChEKBTqsZ5Ro0bx0EMP8b3vfY8jR45QUVHBhAkTPqY9687+/fspKCjA4XDw5S9/mbS0NGtaMBi0\n9sfj8VjJjJ/73Od4+eWXKS4u5o033uCBBx4gMzOThx56iB07dvDWW29RUVHBZz7zmV7lvihZWVks\nWLCAvLw8tm7dSmNjI42NjdZ0TdNYsGABq1ev5rLLLhvU+9WxY8eAC7+Wli5dSnl5OUePHr2krkeJ\nRPL3hRT8JBLJsONyuUBREIZ+QctfaqlrHwdRCezll1/FV7UbzRk/qEpQI9BMZ1U+MU4ba9euvWQS\n3VRVZd26ddTUvILXHwARQtTvAs0D8VPBnQKqHUwdArXQfgoMPygKihkGbxmOlHkM5SOBSzHJ8FzO\nFXBN0+Q73/kOTS1eXBlLccRFvk3mcDoJ62FCoRCmaUT9NFRVw+FwYLMnotuX0HmJ1b8OVGcb6rp5\nE4CiOT+UPJ2jYMzViPrdBBuP4kieEZHjEieiOePxlW4iP7+AadOm9SszjkSKqUwmlUgkEolEIpFI\nJBKJRCKRSCSS/omJiQGgpaUFIcQFiX7Nzc0AAyZqfdJZuXIlubm5VFRU8Nxzz3H//fcPKPkVFxfz\nwgsvAHDTTTcNaz2vEIJTp05x4MABOjo6EEIQExPDjBkzuPzyywdMbLz99ts5efIknZ2daJpGQ0MD\nb775Jm+99RaJiYnYbDa8Xi9+vx+IfM4/ZswYzpw5Q1tbGwcPHmTlypWDHu/JkycpLy/H4/GwcOHC\ni9n1ESM+Pp6FCxcihGDz5s3YbDY+9alPWcKb3W7HMAx0XccwDCAi9qmqisPhsKpqly5dSnZ2Nrm5\nuXz+85//OHfJIicnByEEN9xwQze5DyKCnxCim9wHkXN+zz338Oijj1JWVkZ1dbW17LJly6xq3b/+\n9a888MAD/W4/Pj6e9evXc8MNN1BeXk57ezuhUIiYmBgyMzMtqXSwRNNJk5IGflbbG8nJyQD4fL4L\nWl4ikUhACn4SiWQEGDt2LNqJ04S9Ndg8o4e8/KWYuvZxsGbNGk6cOMHu/AJ8pZtwpS/pVnF6LkKY\n6G0VdFbl47YLFi9exOrVq4d1PKZpUlxcbMlSnZ2duFwuS5aaM2dOv7JUdH927d6Dv1NH1RwIw49o\nOQQth7rNqwCK5sAUJqqqgBFA7awF18RBj/dSTDLsi77qXxXAbrdh7ye58FKsf+2vzlYQvXmLnOMe\nCY4x40HzYAY7CHurscelA6C5k3ClLyYwBJlxOFNMZTKpRCKRSCQSiUQikUgkEolEIpH0T2pqKgkJ\nCTQ2NlJSUsKMGTOGtHx7ezuHDh1CURSmT58+QqMcHqKy0cmTJ/H7/WiaRnx8PFdddRWLFi26aLnO\n4/Hw4IMP8qMf/YjDhw/zgx/8gBtvvJFrrrmmR0VoTU0Nubm55OTkYJomK1as4IYbbrio7UfRdZ2d\nO3eSk5NDZWVlj+nvv/8+SUlJZGVlsXLlyj7FzKlTp3LfffexceNGDMMgJSUFu91OZWWlJXUCJCYm\nMnHiROrq6qipqSEmJgYhBL///e8ZP358t5rXvmhsbOQXv/gFEBElh1rt+1FTUlJCXV0diYmJXHHF\nFd2maZo2oDyZlZVFdnY2+fn53HvvvZEglmHk7NmzHDt2DJ/Ph6qqxMbGcvnll/dZfVxVVcWxY8dw\nOBwsWLCg2zRd1zFNE1VVe626dblcLFiwgF27drFz507uuuuubvu5Y8cODh48SFNTkyXN9YemaYO6\nZgYiKitHU0ovdj0SiURyIUjBTyKRDDvLly9nT0EhvqYSnGNmD6nO8lJPXYtysbLbYFBVlS996Uso\nikJ+fgGByjw6a9w4kqdji01DUe0IUyfsrSbUVIJqBIhx2li8eBH33XffsCW5mabJli1byM7Opqa2\njqBuYBjiw7rTE6fZU1BIWmoKa9asYc2aNb1u+/z98XfqmJod1eYGBJgGqBqgIMIBVKETG+Ni/Pgp\nnDlbif8TkGTYF5dy/euFXOv97U9YD2MYZlcUYS83pYoK8VMRLYcINZ+0BD/4eGVGmUwqkUgkEolE\nIpFIJBKJRCKRSCT9Y7PZWLFiBe+88w65ublDFvzy8vIwDIN58+YxevTQAyQ+CsrKynjnnXc4ePBg\nr7JPUVERf/jDH1i2bBk33XQTcXFxF7yttLQ0vve97/Hkk09SVVXFL3/5S37/+99z1VVXERcXRzgc\n5syZMxw/ftxa5sYbb+SOO+4YFpGovb2dZ555htOnTwMQFxfHtddey7hx41BVlebmZvLz86mvr+eP\nf/wj27Zt48EHH+yR2Bblmmuuwel0snHjRurq6qx1TpgwAZvNhmEYnDp1ioMHDwKQkJDAAw88QE5O\nDjt37uSxxx7js5/9LNdee2235LcoQgiOHz/Oxo0baWtrY9q0adx6660XfRxGmqg4OW/evAFlvt5I\nTU0lJSWF2tpaGhsbycjIuOgxhcNh9u3bR25uLidOnOh1nnnz5pGVlcXcuXO7PScqLCwE4Kqrrurx\nTCSa3tefALt48WJ27drF/v37ufnmm3E4HCiKQnx8PPPmzePAgQNs376d22677aL3c7DExsbS2tpK\nfX0948ePH/Ly0ev9Hz2ZVCKRXBxS8JNIJMPOnDlzSE0ZR2l5FXrbGRyJEwe97KWeujZcsttgsdvt\nbNiwgWnTppGdnU11TS3BpiIC9Qc/3KamEGPXSMvMsGpJh0to03WdF198kT17CgkEw5iaB0fydByx\nqSiaHWHohL01+JpKOF1WSfXLr3Ly5Enuu+++Xr950+v+6L4Pj6EZ2R+nSyMtdTxr165lxYoVPPbY\nYxwsKqL9+B9RHXGo9hhUZzyOpMsisqP1zZmRTzIcCS7F+teLudb72x+rmlft5VcQAQIB7nEozYKQ\nvwV8/q46YtuIy4z9IZNJJRKJRCKRSCQSiUQikUgkEolkYFauXMm7777Lvn37OH78+KAlv4aGBt5/\n/32AS/Zz/YKCAv7nf/4HwzDQNI0FCxawaNEiEhMTMQyDmpoatm7dSmlpKZs3b+bgwYM89NBDjBs3\n7oK3mZKSwg9/+EP27NlDTk4OZ86cYceOHd3mcTgcLFoUeR4yYcKEi91NIFIl+vjjj1NVVUVSUhJ3\n3XUX8+fP7/Hs5+abb+bIkSP88Y9/pKKigh//+Mc88sgjpKSk9LreefPm8eSTT7Jz505yc3Opra2l\nuLi42zwTJ05k9erVXHPNNZSWltLZ2UlnZydtbW387Gc/45lnniEzM5PrrruOq666inA4zIkTJ7ql\nDE6fPp0HHnig12dVHzdCCE6cOMGOHTuoq6ujsrKSjo4Ojh49yrFjx5gxY8aQBc1oPfZwtAi1tbXx\n9NNPU1ZWBnyYqjdq1CiEENTX17N//36KioooKipi3rx5fPnLX7akvdbWVoQQpKen91h3tGr4/PMS\nlWWFEIwZMwZN0wgEArS3t2O327Hb7TidTi6//HI++OADKioqLno/h8K8efPYvn07eXl5fOYznxnS\nskII8vLyrPVIJBLJhSIFP4lEMuyoqsratWt5+eVX8X2CUteGW3YbLNHjuXr1ao4cOWIlqQUCAdxu\nt5WkNnv27GE9ZqZp8uKLL7I7v4CAruDKWN5rRbDNMxrnmNnobWfwVe1m1+49AGzYsKHPJL/B7M/M\nmTPJzc3lP//zP6mpqUOggSIwdT9myA/+RvTWUlR7LLbEiSiaE735xIglGQ4n56filZSUYISCdNZ+\ngHPM7G7S4mAY7vrXi73W+6uzNUyDLsPvwx9GxT7ry472yF8NnWAoRCgUQtM0nE4HWmwa+jDLjIPh\nHyGZVCKRSCQSiUQikUgkEolEIpFILpakpCTWrFnD5s2beeaZZ3jwwQe57LLL+l2moaGBJ554go6O\nDmbOnMmcOXM+otEOng8++ICNGzcihGDFihXcdtttJCQkdJtn6tSpLFu2jLKyMn77299y5swZHn/8\ncb73ve8xatSoC9620+lkxYoVLF++nNLSUsrLy/H5fNjtdhISErjiiivweDwXu4vd2LhxI1VVVaSl\npfGtb32rz/GrqsrcuXOZNm0azz77LMXFxTz11FP86Ec/6vPZmMfjYd26daxdu5bTp0/T1NREZ2cn\nbreblJQUMjMzOXr0KP/93/9NdXU1EJHMYmNjURSFYDBIXV0dv/rVrzBNE5fLZcllcXFxrFq1iptu\nuukjl/sMw+Ds2bO0t7djGAYxMTFkZGRY50YIwY4dO3j//feprKzEMAyEEJimiaZpnDlzhscee4z0\n9HTWrl3LypUrB/2sKBgMAlxUNXR7ezs1NTU8//zztLS0kJyczKc//WkWLVrUI4mvvb2dnTt38t57\n71FUVMRPf/pTvv3tb+NwOAiFQkBPiS96DKB7Va0QotvPFUXBbrdjGIY1n67r6LpuJTcO1/OwwbJ6\n9Wq2b9/Ozp07ueOOO4Z0nEtKSqiuriYhIYGrrrpqBEcpkUg+6UjBTyKRjAhr1qzhxIkT7M4vwFe6\nCVf6kl7lMPj7SF0bKdltKERvkj6q1LItW7ZEBC9dIWby9f1Kmoqi4kiciOaMx1e6ifz8AqZNm8ba\ntWv7XKa//dF1nY0bN3YXzFLmIhyjCYUFZjiI6KxHdJQhgh0YtQdQNZUYj4f0EUgyHAyDqbIFek3F\nM8ICodjR26sId1SjOuNwjp6JI3nmoG7e+qp/HWy97rnz1dXVUVFRQUeHFxMV57gr8Yy7AvW8Ot3+\nrvV+62wtiS+auniu2BclspyiOUC1I8ww4bCBYQSwE0YMo8w4WD7JyaQSiUQikUgkEolEIpFIJBKJ\nRDKc3H333TQ1NbF//34ef/xxVq5cSVZWVo/q1paWFvLy8sjOzsbr9ZKRkcFXv/rVS+6L+83Nz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FUWhra+Pw4cOUlJRgs9mw2+1MmTKFzMxMNE2jo6OD5uZmKioqKCkp4dSpUxw5coTPf/7zg2rh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\ny1H6QHQi+AvBW4DRcQBdDdHa2oouefEV3ozkTiV4boc5vX/qoNR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+nDFjxjB27FhUVU2p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\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YdKyzUvM8Hk9SWqAQghtuuIGWlhbKy8vRNI3MzEx8Ph8ejwefz0d6ejpVVVWEw2FefPFF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Ge7oCluTEcGZnwAMxZAb9uPrFgxtSow5OTVO0b8LzYMZ/6MAMxUUFzgyBXQlxaE8Bnh1geQNiUZ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\nr6OH21AUi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NwmVpFtB7nUJsa4yM0EaSSMZaUcDAMCzkv2QnAGVl8ldLyZciEcNSu0QdEO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wJ+kL1xYMeOHVy4cEHF1sfjcfx+v4KA29raCmBfQQUVVFBBBRVUUEEFFVRQQQUVVNAviZ58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REWvXrmV4eBiXy8V73/tevvOd7/Bv//ZvhEIhPB4Pu3fvZmRkhC9/+csOxNnV1UV/fz+z\nZs1i3rx5hEIhUqkU8XicgYEBbty4wcDAAJqmsXHjRv7kT/6EgYEB6uvreeWVV4jFYixdupSPfvSj\nDrQpne9efPFFUqkUhYWFPPDAA+zcuRNN07Asi7feeot/+7d/c5w1R0dHGRoacvZLjjPp4llZWcni\nxYsdR0gZX5utbCc+mYyV3U7ApNCM7FsZJSzbX8ZTjweMLcti3rx5/PCHP6Srq4tvfvObOeNscHCQ\n+vp6Tp8+TUlJCTU1NSxZsoRYLMa1a9doaGggFos549rlclFQUOA4UpqmyejoKP39/YTDYTo6OnLG\nwlRw3/h28Pv9hEIhhoaGOH36tHNMkPss4brxPIh0ApSAtKZpzlhZsWKFA+9OBvbFYjEAB9QcHBzk\n9u3b6LrOxo0bHSdKVVWJx+MYhuGsoyiKE+Uq26ekpITPf/7zLFu2zBkPwWCQ2tpampqaKCoq4vnn\nn6erq4u//Mu/5Pjx41RUVDjHH13XnSjm8ft+8+ZNvvOd75BOpykuLqanp4evf/3rPP300xQUFJDJ\nZJz1ZOql1OnTp3nppZcA4ZZ35MgRzp8/z+joqNP2S5Ysobq6muvXr3P48GG8Xi+zZ89m165dbN68\nmeeff57Ozk5SqZTjmrlhwwY2b95McXExf/3Xfw3gwJnJZJJ0Ok0wGHSOm3KMyz7J1po1azh16hSG\nYXD//fdPOV7GKx6PO86V0jVz8eLFGIbBmTNncLvdbN++/Z6c43Rd5+GHH6a5uZkDBw7wsY997Fdu\nCFZVVcWTTz7Jhz70Id555x3HTdXr9TJjxgzWrVvnHHfLy8tZvXo1ra2tHDhwgFu3bhGPx9E0jcLC\nQjZu3IhlWbz88svMmTPnrvvW3NxMe3s7oVDIcY2UxwJ57ppM8v0HH3yQCxcucOjQIX7nd37njnHX\n2co+vp05c4bh4eEJ8cR3Um9vLxcuXEBV8/eIpqP/MsAP3j2YL6/fXKXTab797W9z8mQ98WQGU/Xj\nLl2KO1iFoupYRprMaBfR/uvcvN2B161SVBhCGYmQuFsknu1utHnzJkFzX785aZScoij452wnBgLy\n6zkmbpYXLBKQkEsHMwmjHZjR26RIOuV+6lOf+pX/eBvvanevEu1m0tbWJsBKLSBAHP9sAbRky1Mq\nYCPp6DXaJjCSiq3gLoL0ECR60PT5WMgvKozFwSLc+izTyLWpU1wo6UFwqWgFs9EL5xJvP8q+ffuo\nra112riiogK18YbjGKTpGqrqIpORkbB2fXNc/OwXigLxsHhfDwroKRMFzS9Ams46FCuNXjQPT9ky\n0oNNWCkFKzvycvx50KWJtsjEULQAluOONd6ZzMqNqJXyV4n2Tg1B9yEB+ekhARtmYvaaWcdR6aCo\nuMDMCCdFu1oZVwCrdB2Ej2KlRzFG2hy3rgwZ2dBTglhKTOwHehC85SI+0QI8ZaD6MNND+HWT2XNm\noes6rW0dJJIZDK0Q1a1iJIccsMkKzQd3megPMwOJXojeFuVbloDV9EII74e0Dd0F50H5JgHdSQjJ\ntKC/XkS7unQxLgNzbLDKhpZ8FSgjzVipYbFMJipc9wYv4sCSEsrSQ8J1MrgQyIyVg8t2G9TG4C8Y\nc4VzxoCsW9Zn2ZIxuklhK4wWwIFbZbtb4ETzYtlgZ6WAKyONY5G/qlvUPdkD3WFQvcJRMLgISAvH\nw5EmMeewxHpaQDgAemaA5hmD4iI3BOw2cHbMDdDlEc59nnFOqBPSik0xbyI3xD6bKbFPWkDAnFi5\nLnrOcFXG1pf77redKzOjokz/zDHIDGzg0obn5FzV7Kf7jOSYI19wgfjrcos5ZdrQpbwglBqG3tNj\nfWtlULSAOE5luwkrLuHSpwXAiKH5S9GCM7FSUVIDTVjdRwXEGJg7MaZb7l+iU7gnGjFRb9Unths+\nCCVrRBtZGcAj+lOOEcsSzn19op6KuxCGG0Qcst0JAk1WUFJ2lDImLkVh1X3LeOKJJ7jvvvtwuVwc\nPnzYhqctLEUTYGHIhrNdGoSPQCYixgVy7qg2DMo4aHWSce2rFOcYxZr2F+07OfIODAwQTyTF9saD\nadOQEqzGGrpEV1g8WVdTU3NP6/8y5HK5qKmpuWtdpgsDfvGLXwRr4K7n9dRAkxgtBYsnfShhymdh\nLMbgzcGLpAaa7gnwU1w6CuKCjGHESA+34i6aN+3108MtuIw4M6tnO/BjXnnllVdeeeWVV1555ZVX\nXnnl9dunTCbD1772NS5duoSqqmzYsIHa2loWLVrk3F80DIPz589TV1fH1atXSafTFBYWEolEaG9v\nR1VViouLHbgkk8kwPDxMNBrF7XZTUlLCf/tv/43GxkYuXLjgQBPj9cADDxAKhfjnf/5nBgcH6ejo\noL29Hbfb7ZSbSCSwLIvCwkJ27NjBU089NSUs9Z9Rf38/ly9fZnR0FMuyCAQCrFixghkzJnmAnDEw\n4xe9JytBgEwmQyqV4vbt23R1dU2492UYBt3d3XR3dzNjxgzmz59PY2Mjuq4zZ84cZsyY4QB+EuCT\ncI8EAWQkpYzllfVWVZVEIoHb7WbNmjVs376db3zjGxw4cIDa2loHblu8eDHnzp1zwCwZSynjTrPh\nm2w3tuy4UFVV8Xq9NDY2oqoq5eXlfOUrX3GiYv/wD/+Qmpoarl69iqIoDA4OOmWOd/HSdd1xLqyo\nqJgA1MnlEomEuJeXtZ4EGhcsWMDAwACGYdDW1sb3vvc9p4xEIuHASy6XC5/Px6FDhzhy5AiWZTF7\ntri2apomsViM973vfRw+fJihoSHi8Ther9cBKxVFwe12T+ouaBgGPT09eL1etm3b5kBJpmmycuVK\niouL6e7uZvfu3ezevZsXX3yR27dvY5omCxcuZOHChTQ0NNDa2kpHRwdtbW3OvmaPBb/fz+zZs0km\nk/z1X/81bW1tDvC5adMmPvGJTxCLxZy26+rq4lvf+hapVIqNGzfyiU98wnFOlHHCtbW17Nu3D9M0\n+eQnP8nVq1c5d+4ckUiEeDyOoigEg0FmzpzJzJkzHddH2UeJRAKXy+UAgPJ1NsQm6yPBQzmWxks6\nvUmnuZkzZzoxrtnOaKZpOtGzkUiEaDTqvFdQUMDs2bMdeDUSidDV1cXAwAAHDhzg4MGDzvFOwp+6\nrjNz5kwKCgomgLMlJSUUFRUxPDxMd3e34xAp3fkmkxwj6XSawcFBIpGIs9/Z7ZK939IpTAJmqqo6\nUcTjAdtMJkNbWxuLFy+esF35t71dmH5UVlZiWRZXrlxxALOioiIHupVx3/L4Jdv029/+NvF4nKKi\nIjKZDJs2bXLgPrmupmnU1NRQVlZGf38/TU1NrFixgo9+9KN84xvf4I033qC3t5cnnnhiUggtnU5z\n4sQJXn/9dec4UV5eTiwWo6mpia985Svs3LmTnTt3UlZWlhPL3NLSwuuvv86JEycwDIPS0lLeeOMN\np25S8nwk1wsEAnzxi1/MOU9Kt89AIMDmzZt5//vf74w9CZ26XC5KS0ud6OVMJsPo6KgT0W5ZFqqq\nTgquSshSwrl3U3t7OwcOHODYsWOOM6CUBEuTySShUOgXMlFav349JSUldHZ2cvHixV+5i66U1+tl\n69at01q2urp6SvfBgwcPAkwLtpNugevXr59w/ruT5OdLly4lFAoxODjI0NAQpaWl06m+A4pqmoZp\nmrz22mt8+tOfntb3EMMwePXVVwHhBPtuuUL+JuuXDvhVVFTkwb68piXTNPn2t7/N8ROniKcVvLN3\nTOrIp/nL8JTfR3q4lVjHccyhEebNnUMmkxGOf3dwwZE301988UWuN9+aMkpOcan4q3eSCswg2XcV\nMxnBciAhhEubZaJrCgsXzHfKfbfhvrvFFK9cuXLCNse72t2rLDONZRmkMgaW5h+LmBWfMsGKbpyj\nlzXahuK5LqCuvjMwehusDWQyJoZhCqAmGxRUVJTJnMhGbqAA7pLFaMEqEl1n6OwK5wAcO3bs4OSp\neqJZjkEejwfDiGOZKRRlLApTUZTcCFDTENGgWOAph956AbZkEtB7SvyoKpqHf852Qbd7CiA+gBLv\nAU9ZliNg1q4oKla0RfynYDG4siNOs6I4DdtpDHCcwGx3PQoWw4A9flODkBoQrmx68A4OihEUK4OV\niWEaBvGEHb/rnSngHiOOMnBeAJmeIiwjJdrYpdnVG/cDLjko3AsV8M5YiWo/WWWaBpalYhUuRBm6\nxIrlyykuLubEyfqcOQsKqf4GZ+4wdMXuVwvLMpFAm8NdxtrGHPmwBDwVWmzDWlk/PkZvjMF9zrg0\nHchOcdlAXtEy6Dsr2rB4FST7BOiX6BftHpwDBSsEWGgZYI09beX0lYXdR1nOfBn7i6eMkc5xPxt3\nOpUxrpYJo7fEe6FFIoY1mzhVFLENVROgVd8JO97ZPQbo+arE/40kJMIQuSX2Z+gSDF3Gia+1bHdM\n3YZypWthNkQaWiDgyXgX9J8TcJ2EHQNzmFz2vIk0Cve7TNZFL+lcGJqfBRRZY+CfSxtrQ8uw43Vt\nt0tFEe6KMvY4YANF2bHAIOofuSFeB+aKv7EOETmrerNis+2+UFUb1kyL5QbOghEHLQSF98HAORE3\nne1YZyHqpLqFs+HQZQdyUgPlMNAooL2+0zB0VQDf3nFzMdKc5Qo5R4w3Iylc/5J90HvK7tMFNsSm\nQCZuA6+3xLEHC0V1YyWHRf00v5j33grb+TLjzHsyo5hmmnA4zLJly5wLb/39/eJHn+a3XTRninaS\nsKr8q/tt58M0jvOhS7sztApIp0W3rt8VxpqOI28mlcDIpMXcmACmTUMuDQLzMUbEufLXAfC7F00H\nBpzued1MjogX/vEXd+XFwylWlO/bLqVmKjLFgpPLMtOgKJSXlzM8HCHacRzVU4DqK7nrukZ8gETH\nCQIejT179vzKH5LIK6+88sorr7zyyiuvvPLKK6+8fjWyLIt/+qd/4tKlSwSDQT772c+ycOHCCcup\nqsq6detYt24db7/9Nt/73veIx+OsW7eOjo4Oenp6HAc7wHGJKikpYfPmzTzyyCNUVVU5kMGZM2f4\nwAc+MOl9zHXr1rFq1SpOnz7NgQMHnMhFWS6IONu/+7u/o7y8/F1vjytXrlBXV8f58+cndd1ZuXIl\ntbW1rFq1KufGv9/vR1GUScGb6SiZTGIYBul0msbGRgYGBpw6TXW/t7u7G9M0WbRoEQ0NDZSUiOtC\n0jkoHo87133Gx4rCRPczCaj4fD527drFypUrKSwspLOzk4aGBpYvXw7Arl27uHr1Kslk0ukbv9/P\n6Oios/9TQX4SPtJ1nevXrzvAzP79+/F4PPh8Pp5++mne8573AMK16mc/+xkNDQ1s2bIlx81Rtoum\naYyMjGBZFmVlZTmfZzv1JRIJFEVxIkSzJe+rSwgsmUw6UciRSCTHPU6O7aKiIvx+P2fPnqWxsZHK\nykoMw8Dr9bJr1y7eeustLMtiZGSE0tJSJ7JR9kU2fJhMJunt7UVVVRYuXMgf/MEf4PP5HNDIsiy2\nbNnCvn378Hg8fPOb3+T27dsUFhbyxBNPOEDImjVr6O/v5/z58zQ0NDgOeRJCk/WRYKWsh3Tqa21t\npb+/n9LSUqeffvzjH5NIJFi/fj2f/vSnnbEl57PP56OgoIBt27bx9ttv8/rrr/PJT36S3/3d3+Xi\nxYu89NJLeL1eNmzY4IwxCTPJ/c+uh4TR5BiSdVdV1ZknwIR4UxiD+0zTpKurC0VRWLduHaFQKGcc\nSvhN13XC4bCzj2VlZTlRvBIAlRBld3c3N27cIBaLYRgG1dXVtLe3O/MwFAoBOLCuHH8grkkXFRWh\n6zotLS0O4DeZZD3j8ThdXV1O/8hY1/GOhlISUMyO5JbvZ0tVVQzDoKurywH8xh/vhoaGGBkZwefz\nMXfuXPr6+rh8+bJzXIlGozmxtxKa1XWdpqYmXnrpJXp6eli0aBHr169n7969zv5mA3TSBXDFihUc\nOnSIzs5OVqxY4URVx+NxTp48SX19PStXruQ973kPfr+fdDrNrVu3OH78uONSun37dlpaWujo6GDu\n3Lm0trbS1dXFD3/4Q9566y1qamrwer2k02k6Ozvp6OiwH6AXY2pwcNAB3Xfs2EFpaSmKojAwMMCx\nY8c4deoUpmkyMDDA9evXnbYbGRnh1q1b+P1+3vve9/LYY485LqzS4VDeS5IQpoxdljCkPGZlt2l2\n/42MiPsQs2bNmvR4LmUYBt///vcdQA1gwYIFTjLUwMAAjY2NNDc3oygKlZWVU4Lrd5Lb7ea+++6j\nu7ubmzdv/toAfu+W5FiV7X4nSdfK8ZDc+O8u4yWPXzJ2ORKJEIvFpg34ye9HFRUVZDIZTp48ic/n\n48knn7wjmJjJZPje977HuXPn8Pl89xQL/NusXzrgJ/OS88rrbtq/f79w7ksrBBY8fMcbw9kxpNGb\nP6etrYOnnnqSqqqqO7rgyAPUZGDYxG0oeMqW4y5dJlwAB5owUxEsI4URH0DXFJ766JO8//3v/6WA\nfdOJKZ5ZVek8ITOVq929Kh3pEDfrXTqUrEXxFDumdznud+PlKYayDdBzDGv4OkrZ/YCFkomSHm4l\nrVfYJkHTOOxE28CI4fKEhBujouAuXUqy/0IOwLFy5UqqKmdw83aH4xjk9njIZOwfCUYSxeV2gELn\nyzmW7bQXBUUXcZNGXNTXTKP6SvFU1OAuXeas4y5ZLBwdR5qgaNnk8YMWYw50/hm50cDSqcsBrhTb\n5WxcOb4Z9mcu4V6mugWk458NmpccwNJTKqCxaAvWwDmMaC+R2wcxi9dhKQqK5oWildB/RgBOXQfs\n+Et7XGS5yFlYKFii7XtPj7kX2m2g61k209p84iPX6OnpobGxedI5O37uGIkRjEzSBhelA5llg3Gu\nrDFluy92/AwKFkLxOtDcoi0iNihXtlE4RGZDdi7NhsmAgiUo8R6syE0BY5VtELBb3ykBz3mrwFua\n5cSXDaIxzlXPHAPkEj3iQ803Fp2b7XyWPRAkJBUPi3GmB22Hu2w7SekMqIh26au34T4dStcJqCt7\nfKgeAaaFFopyB84JQExxifjY9LBYJgfKRdTfysgdy3Wq69wvqhGcP3Feyx9RlgG9J8XYABtSWyTG\nau8JAdo5+5b9wyurHbLfs0wxJy1FlDGIcKmU88Ipw65PrMN21wyKeif7BQRrpkU/tL855rDqAHfh\n3Bjm0ELhCDncIIrVg1k1yqqzogrwUxGQk2VZpPqviz4OzIHkgChz4AKTSg9BwVIBqkVbRUx0Ji72\nxTKEO+fgRQG9OuClCbiEa5/iEnCf6rYdKu14ePmkmgKKt1RArKOtWH31dIV7+Md//EeeeeYZ9u/f\nz+DQsAADZ+wUjpASuJSuii5N1MdM20CkS4CIlgHyop+iokxwKQQsE8uIoyjiybM7nfum68hr9FyF\nzIDYRU/J5Gnmd5QC3gqsYYWenp57Xvv/B033vG7J84yij//APq1M/kNKdalkFAOwL6Td4wMCmdFO\nVFVxLqweP3GK6M2f4521ZdIHNQAsyyQ93EKi4wQ+3WLz5k2/0NN5eeWVV1555ZVXXnnllVdeeeWV\n12+GmpubOXbsGG63m8997nPMnz//ruts27YNgJdeeomGhga++tWv0tzczJkzZ4hEImQyGQKBAIsW\nLWLbtm05sMKaNWsoLCykq6srBxgbL13X2bJlC1u2bHGcq9LpNP/yL/9CQ0MDH/zgB991uC+ZTPLN\nb36T8+fPAwKAWbNmDeXl5SiKQn9/P2fPnuXy5ctcvnyZZcuW8ad/+qcOBDB79mxUVSUSidxTRJ5U\nb28vhmHQ19dHOBx2IhrHQ1CTrSdhpNu3bzvwmnQWkyCTBEIkeDQexnS5XEQiEVwuF1VVVdTU1KCq\nKlu3buVnP/sZFy9edPprw4YNlJSUOJCTdJUKBAI5kauqqjqQQXbcKuA46blcLsdk4w//8A/ZtGlT\nTjzlkiVLmD17Nrdv3+bGjRssXbp00vaQIFU2WGhZlvO+aZooioLP55sUfJCOcdFo1ImXvXz5Mul0\nGp/Pl1OnTCZDS0sLzc3NLF++HF3X+epXv8pzzz1HZWUlgUCAPXv2cPDgQcepTT4g7vV68fv9jjtd\nKpVyxrfH42Hx4sU8++yz4hp5KuW4VwIO9HH27FknjljXdd544w00TaOgoICVK1cyf/58tm3bxsaN\nGx1Xt3g8zt69ex0QV4KE8+bNwzRNGhoa6Ovro7Ozky984Qs8+uij/O7v/i69vb00Njbi8Xj4oz/6\noxznMQksRiIRRkZG2L59O9euXaOlpYUXX3yRRx55hJUrV+L3+xkZGWFkZMSB5QBnfIwH/GSkpYwd\nHhkZwTRN22hEXEv3eDwT+lFGtUrnOdM0qa6udhwWs+eS3GYqleL1118nkUhQWVnJqlWrnPjfTCbj\nvJZjpKqqisrKSurr6x34zjRN5syZ48B9clm3250DYknnwfFOheMl4bdEIkFnZ6ez76FQyAFTJdCW\nvS/jYaJsYE/OBV3XHZBMjrFkMpnjbChB5WvXruFyuVixYgVDQ0McPHjQATMPHTrEuXPn2LJlCxs2\nbKCgoIBkMklrayuHDh1y3CPnzp3LZz/7Wb7+9a8DMGfO5IYXmqY5QKscXwcPHsTv9/P7v//73Lp1\ni3PnznHp0iUuXbo0Yf0lS5ZQW1vLxo0baW5u5n/9r//FlStXCAQCrFy5ktOnTzM6OsqJEydy2jgU\nCrFp0ybq6+sxTZP169fz5JNPOk6BcrmKigpWrFjBH/zBH/D973+fo0eP8sorr9Dd3U1VVZUDWq1Y\nsYL3ve99TtsbhsHo6Ci6rlNSUkJfXx9NTU3OsTQQCDjjOzvuOFsy4vnmzZtYluXAz5PJMAxeeOEF\nzp07h6Zp7Nixg127djlzQCocDvN//+//5ciRI/h8PsdF8F7Nw2Rk+1SuvP8/S34funz5MpFIJGd+\nj5c872QD/tmw8lT39GTEfPaxZrrxvCAYH4Da2loWL17MP/zDP3Dw4EFu3rzJnj17uP/++3Ng9mQy\nycmTJ9m3b5/jjvzss89SWVk57W3+Nuu/NKI3r7ymkmma7Nu3j3gyg3f2jmm5vgCoJyQj8QAAIABJ\nREFUvhK8szYTbz/K/v37+fKXvzwt957JwLCppCgKemiWE1eXGrpFqv0oC+fP/qXAffcSU3zjVjud\nL3+fpqYmPvWpT6Hr+rTgxalkWSap3suCItGDwvkJC0Vx5brfyS9j2edXi6zIzSiuzABobhQMEh3H\nsWZsB1coCyqbKpp3RABmgKdsxdiTT8GZxHvO5wAcLpeLPXv28PLL389xDJI/1AXklwJTsd3dRDSr\nkhzA6qtHMVOgpFEVhaISYeE8FInhm/sgeiD3R7kWnInLE8JIRgQcGKyerAVtkAoBWCiItpRubihZ\ncJ97zEkrW4rbdpUzRVtW7hKwjoz1zQYkLVMs65+F4i7CCh/GjLSBVoxSvMIGuapg0I3icoGVtiOn\nvRCYLxzTpIOXdCAzYmCZKLoXIzFM5PqPUVwaLk+B7aYo469NYd9tKijecmJtb2OZmZxl1eBMTHcZ\nRnEIwzDHvsTLSGfb5U1xByEwF8tbKSJopUNd5AaMtkFxjR23GxGQl69SuCBKV0TLjnCNtgrYzMzY\nbWiIcnqOi/HsKbHd4JogONeGnbIhSwncWcA4JzjLEE58RkqAldJ5cbzLWTZ0mBoWEB4I6GuyL6Ty\nvZFG0S7SnVAvFFHgDvxoA77Spc4/S0CO4UOiToYN+pVtzIX7wHbZy4IQZTS0u0i0SyYm9kkyh9lP\nR1nmGNwnY5H9syeCqapXwIV2eykKzo9C4fw49qMYM43icom4aVkvM5MV7esa64vkoAAfQUBz8U4n\nxlZEPpsilnjgAigXJ7ZvNnCX7QQYsp/+dcBlxkBLlyZwV0NEvJvJiJiLJfaPlUQvxNoEzGulxVzX\ngyIq2Fc51qeB2WLupgYEyFqwHEauYdODjMUju1Eqd0FmBKv/nID7qmoFNO30YVY9ZZ8G54LLg9Fz\nlP11B7hw4QIjIyPEUxZK+UYsPZS1knRVdIEaAAbFnPeU4sDGOdDqJHCfaQhn1EQPqktx7Osn0704\n8ia91Ri3fyr6HwXLSIkY92nLco6L8mLCb5qme153AHorF9CzbMfUqZ6mk7EclpFAgbtGAeeUbZmk\n+q8T0FV27tzJsmUCCj9x4hTx9iMkuny4S5dmnTvEvEr1X8dlxAl4NDZv3sSnPvWpvHtfXnnllVde\neeWVV1555ZVXXnn9Fquurg6Ahx56aFpwn9S2bds4duwY165d48SJE+zZs4c1a9bcdT1N03jggQfY\nu3cvP/nJT1iyZMldb2YXFxdTXFxMS0sLjY2NDrTwbiqVSvH888/T3NyM3+/nkUceYceOHRNAvdHR\nUd5++23HUe5v//Zv+e///b/j9/vZsWMHe/fudRyZJotYnErSnckwDMLhsPOevG4jIZNsAEfKsizC\n4TClpaX09PRQUlLiOCtdu3bNcbaTkFc2sJRdhoSqSktLeeaZZ5x+kSBlNsCh6zof+MAH+N73vodp\nmo5rmq7rBINBYrFYjoOZhJBCoZDjZBUKhVi7di1btmzhhRdeIBAIsH379gnXqhRFYdeuXfyf//N/\nOHbsGLNnz57geiYBQ8DZngTEsiEn6ciWXXZ2H0jns0gkwo0bN9A0jXQ6TTKZzAH8EokEyWQSRVFo\nbW11yn3zzTd59tlnUVWVGTNmOI6AH/nIR3jrrbe4desWsVjMgdAk+KHrOmVlZcybN494PM5zzz3n\n1Nvn8zmx2aZpkkwmuX79ugMLyvECAtppampy4qRXrVqFpml4vV5CoRDvfe97+fGPf+y4DIbDYZLJ\nJM8++yy/93u/x5UrVzh48CCNjY28+eabHD58mJCdNLVt2zYnfhUEvHLhwgWOHj3qxCqDcLLKZDIM\nDAzwr//6r7z55pv4/X6Gh4dpaWmhurra2efxynbtkzGqPT09zthsa2tj6dKlDvSY7W4oARnTNGlv\nb6e1tRVd13nggQcmbCO73y9dusTw8DChUIh169Y541lCsdJVM3sc6brO/fffz4EDBxzTG+meOZWy\nYdPx+5pOp532kPsj3fVM08Tn81FYWDgpfJX9nozjlXWUMK2MjpZzMnsOyPekq5yu66TTac6ePcvw\n8DAej4eOjg4uXLiAqqrcd999dHR0MDo6SiQS4fDhwxw+fHhCvQoKCti5cyfve9/7CIfD3LhxA6/X\n60TLjocts6OOPR6PA4xXV1dz//33s2nTJp544gkuXLhAR0cHsVgMj8dDUVERmzdvzgEHFy5cyKxZ\ns7h+/TplZWVUVVU5EaaqqhIKhRgaGsLn8/H3f//3/M3f/A2GYbBt2zY+8YlPTHm9XEbzPvnkk+i6\nTl1dHW+++SahUIhIJILf72fXrl0ATnuqquqAquvWrePf//3fOXDgQM59Hgl5jY/mlcBqOp0mFotx\n+vRp3G73hDGdrVdffZVz587h9/t57rnnWLRo0aTLVVZW8sgjj3D69GnS6TSZTIZYLOY40U5HEhCV\noORvmsrLy1m1ahUXL17k6NGjPProo1MuK537Ojo6nPekw6nb7Z5yTMlj4fDwMP39/Tll3U39/f28\n8847KIrCAw88QGlpKX/+53/O17/+dVpaWnjppZd47bXXWLRoET6fj3g8TmNjo3M/r7CwkM985jP3\n9N3vt115wC+vXwtdvnyZrnA3puq3Iz6nL71w7qTxrXfSVGDY3fTLjpL7RWKKox3HOXb8JADPPPPM\nPcGL45UebsFKR8GloRYtJONy2c4/rlz3u3EJmo4UFxQsQhm8iDJ6C1XXqSgtYmg4wmjnIShZixWo\nZszFLkuWCbFOrMHzKJk4aqAcvWRpVtE62E9MZGv37t00NjZOcAzyB/xomubYZFtmWsBAsU4YPIeS\niaHrKgvmz+ehhx5yopsPv30CIxqeAPhJR8d4Rz1Wb70AhzzjQCo79la8lj9OxyJkHU3m3CdlpgSA\n5bIdvDwlOPGZZmYs3tgc517nK8eyHRSV0ZtQfF/WthQUPYi3fAXJvqsYsX6s9EUYvkoOOZQFIprp\nJGSybLXjA6SHbuPyhFBDszGNFMm0IfYj3p+7D/ay6AGswAKs4ALhSChd8kLzxbbtiFUrk4Dh6yiZ\nKFbJGhHPGw/DwDsCPhuw43ZdqoCanHhjRUB3I42QickQyrF2d2liPSsjYLtUBKwkpBERtcEF46C7\ncc5xTuSrAiM37WhaU7ivqX7h6KZI8E46/lnOWBZRy2kRKxtaPG47Wdu1LBi5Ll47gJ45tqzqHXtt\nx7gCAnYs2wDdRwWY6S4W8N3dZI79eBMOiraDoZnJAkjtbUSaxuC+8c6AzvrYY1Gx4TAFy0yRyWTw\n+/14PH6nxFhU2LVbGRvmk7HCLp0cCE1RbJjvjHhPLxDxwIawlyYwGwqX2fueBHcRiku1o0JVAdaF\nFoN/5lgfS9dOLWDHF49z78tqHwULRdVJDTSJJQoWg6qLNvJVCOdB6Yg3lSxL9H16GFSfWB/s+S1j\nnjNQuALLVwkdZ8TnZRsnHlsmlC3AKtyFWMVrMPrqCYe7xUeeYvDa8cwSJpbbtUwxbmPtAugtXIoT\ns5xzLLCwMFBsKNkyM2BZKJgo0dsE/F527tw5ZfXuxZFXU3UMzW87MtptorhgOm6vgONQaT/1+puo\n6Z7XZZQ8MQlvItrUslA1FU2fvE01XUNVXWTiPWCZuNzT/xGcHm7BZcSZWT3bcUp+5plnWLJkCfv2\n7aOzK0yy/wLxHjt6XlFQVYWArjKzejZ79uyhtrY2D/fllVdeeeWVV1555ZVXXnnllddvsUZGRjh9\n+jTAHYGBqbRr1y6uXbtGXV0du3fvnjYU8OCDD7Jv3z6uXbvGyy+/zFNPPXVXyK+rq4uvfvWrWJbF\njh07pn0DfLp66aWXaG5upqSkhM9//vNUVVVNulwwGOThhx9mw4YNPP/887S3t/PCCy/w+c9/ntLS\nUtauXcuJEyfo6+u7J0ec/v5+B8CLxWKOg5bL5WI8zJf9V2poaIhYLIbP56Onpwefz8fHP/5xjh8/\nzve//30HuJKOaRI8yYbaQNyPKSoqoqysDMuyaGpq4p133iEWi3Ht2jXeeOMNVq9ezfz589m1axc9\nPT28+eabjnOjaZqOk5yEUzKZjBO9q2kaHo+HD33oQ/zO7/wOwWAQ0zQpKytjcHCQa9eucd99901o\nnx07dnDs2DGampr40Y9+xOOPP+6MAQnyyQjW/v5+qqurHWhFSsaHytfjJR0UDcOgra3NiYSVsZrS\nDVHuE+C4bbW2tlJQUOA8DF5cXJzjolZbW8u2bdv45Cc/SSqVorq62oEGQ6EQPp+PGzdu0NDQ4ABe\nHo/HifiUEFU6nSYej6PrOn6/n+XLl1NdXY3b7SaTyRAOhx2os76+nu7ubh5//HHnAeSFCxdSUlJC\nJBLhAx/4AJcuXeLGjRt8+ctf5tlnn+U973kPW7du5ZVXXqGurs5x5gsGg2zevNkBpBobG3n55ZcZ\nGhrCNE3HnUw6WA4NDTmRyDLqOJVK0dfXR0VFxaQOl+Od6GQcr+wXgPb2dqLRKHPnznWcNWWfmabJ\n8PAwHR0d9PX14Xa7efjhh5k3b96kUduyb86ePYtpmixevBhVVZ1lTdN0ALl0Ok00GnXgOXkPxuv1\nkkqlKCoquuN11mxnQhhz+ZL7nEwmJxwDJfCr6/oEuG98bHc27CidIdPpdA4gLGFVOU/lsSUbtIzH\n49y4ccMB6OQxaHBwkGAwyIMPPshDDz1Ec3Mz3/zmNzEMgy1bttDX1+cct0KhENu3b2fDhg2OW+Bb\nb70FwP33358DymbLNE1u3bqFZVmUlJRw9OhRQJwv3G436XSaVCrljP9gMIjP56OysjLHzTWVSpFI\nJNi6dSu3b9+mqKiIkydPkkwmKSkpYcuWLZw5cwZFUXjve99LY2Mjg4ODzJo1i49//ON3PI9lj6PH\nHnuM1tZWJ5o7kUhQUVHBkiVLnGXkMU+CcFu3buU//uM/OHv2rBODDQIAk8cV6XKZHWEMcPr0aTKZ\nDPfddx8zZ86ctH7d3d3U1dWhqiqf/exnp4T7pEpKSnC5XPT29joRsx6PZwKcPh5ylXCqaZqOq+BU\ndfr/Xbt27eLixYv8x3/8B1u3bp3SnXfNmjW88sorXLhwgYGBAQoLCx0Y+k5xyiDa/PTp08RiMaqr\nqwkGg3dcHgRI/b//9//GMAzWr1/vjKUlS5bwla98hVOnTlFXV0dLSwsXLuQmoy1cuJDa2lpnjuY1\nfeUBv7x+LXTkyBGSaQN36dJ7cpwDRFzvJPGtd9NUYNivMkruPxNTfOLEKZYsWcKePXv+U/CiSwHL\npeIumINh4LiyiY0igA8mB2MUFAHODF7EiPcTCAV4+umnOX78OPvrDpDpPQWDl0UcqLcCNNvxK95t\nx2mOOqBQJj5EvO0IvtnbMGLdJHsuYaQTtLW18Vd/9VdO9PLKlSv59Kc/PaVjkN+tk0klSA13YAw3\no2SiuBSDqllVPP3006xevXra0c3u0uVkoj0iqrezTsTdBsbczCwzg6IFRNxorBv0IttpzrFGsxvx\nDmN8pEksowfHYC0HvjHBSNtwpA2AuTThdpXloGiloyjxsHDvs+FCl+Z2YnNHm39GJtIhnMJUDyga\nVmYUyzBQ0EAPCKDJPwMn8jTWjTViR+3GzglOw+UW9SxYMmFZc6QJUqOQuoiSGoTyzVjOfrhE+QMX\nxXgJzBWOftE2AT2VbxXxvJ5i6KqDTBxFsbBwgXfGGFTVVy+gLayx2Fi/HdOaiUKiG0ZbxGvLFKBT\nJiPaZOA8uEvvDlIBJIdh8PwYGJeJQe9xGAzYY7l8DJBL9MDobXssZ8T4KN9k83xTfCGPdwk3OM0v\nls+RlfvSpWUBo5btsmmPKelKZ6Zy10MRIJ7iyoI45VhU7aGVEX1nZcUlWxYMN4rXkzkDgoAMU9lu\ncIj1baeuRCJOJpPGME2nSopLwTJMUc9oh6iTotnui4bdhraLowQAUxFRT80em4G5tmPhBuHQiIk1\n8xGxbSMlyrThVtGHWU6AoUUT3RQdptMQDn0WuNwhzOSIeN8/QwCERlrUL9piO/jZsJ4esh38Zoi2\nNQ3AtON+VQFpRm7aEKUHpCOj4hL/j4chPSr2LzBrYjtnjwjLhqwlJB2YDcPXhJMhLpTQIixFG/vc\nMsTYd9mOlL4qx2mVWDsEbKheji3pfpk1ihRFQdVU1GQvlpVkZtXsSS9wwb078rrdblJaACs1DKl+\n8BTlnnecHZ/c9RXTQEn0oGkuKioq7rit6cg0TS5fvsyRI0fo6elxYjmyzzn/1TDadB9KGB8lDwjX\nRUX8OJvqkoBw99MxorfATKMFZkyrXlM99CDrW1tby5UrV5y2lE+RyraUQGBeeeWVV1555ZVXXnnl\nlVdeeeX1263Lly9jGAY1NTWUlZXd8/pr164lFAoRDofp7u6eNtBWVFTEs88+y/PPP8/hw4cZGBjg\n8ccfZ8GCBRPgimQyyYkTJ3j99deJRqMsW7aMj3zkI/dc1zupra2N+vp6J6Z4KrgvW6WlpXzuc5/j\nf/yP/8HVq1eduOFHH32Uc+fOMTQ0hNvtvqurF4gb9b29vU4kp8fjIZFIOK5l2eCTBD/GS1EU4vE4\nhmGgqirLly9n1qxZPPbYY7z22mtOTCDkxo7KGFGfz0dpaSmpVIqenh7+/M//nKKiIsLhsAMKdXV1\n8cYbb/DGG284gN8HP/hBgsGgE3GaSCScyF5VVfF4PI5DkcfjYc6cOfzZn/1ZzvVEl8vFAw88wI9/\n/GPq6uomvf6p6zqf+cxn+PKXv0xbWxs/+MEPWL58OatWrSIUCmEYBkuWLOH8+fOEw2GGhoYcuEjT\nNMdNa7x7W7YaGhqcOM14PO4sp+s6iUSCaDSa0w+BQCDHNXBwcJBQKMShQ4d44oknGBoaAnDieH0+\nHzU1NTQ0NPDwww+zZ88eDMPgO9/5DsePH8eyLObMmcOWLVtYvXo1brcby7Lo7e3lxIkTTjyqz+dj\n/fr1LF++3AHQpMrKyrjvvvtob2/nyJEjtLa28sYbb/DBD37QSf5Zu3Ythw4dor29nS9+8Yu88MIL\nnD9/nm9/+9t86UtfQtM0PvaxjxGPxzl58iTRaBRVVVmwYAG6rnPq1Cm++93vkslkqKioYP369dTU\n1IjroIrCyMgInZ2dXLt2jStXrpDJZPD5fE4MZXNzMytWrMiJ7p6sTxKJBA0NDaTT6RxHxv7+fgYH\nB/H5fBQXF6OqKoZhMDw87MRDW5bFrl27WLt27YT5kr2tjo4Ouru70XXdmffZEK2MTZVjJ3v9VCrl\nxAvL/cuO55TR2HJd6dooxzyMgX7Zc1+WPzIi7pGMj02VZei67pQ5mdOnLHOytjVN04ENk8kkV69e\nxTRN+vv7HTc/l8tFMBhk/vz5bN26lTVr1hAMBkmn0yxbtoxNmzZx4sQJ3G43v/d7v+fUpbi4mLKy\nMmf+/ehHP+L06dN3dV5tbGyktbUVVVVZvXo1e/fuBWDBggW0tLTw7//+71y6dMnpk2y9+uqrbNq0\niW3btjnnsvnz56PrOt3d3QwPD1NVVcXjjz/O3r176evrA+DRRx/lG9/4BiAiTuV8Hg9QZks6Maqq\nysMPP0xLSwuRSASfz8eqVavw+/0kk0lM0ySRSDiOeB6Ph1mzZrF69WrOnz/P17/+db7whS84kLCq\nqjnR6VIej4e2tjZ++tOfoigKu3fvnrINDx48CMCmTZtYvHjxlMtJlZaWsnz5choaGnjnnXfYvn07\nyWTSgRIlVDmZ6yvA7du3aW1txefzsXbt2rtu727q6OjgwIEDXLx4kdHRUccxcfny5dTW1v5KnOZq\nampYsGABN2/e5Ktf/Sqf+9znJn3AoLi4mPXr11NfX8+BAwd46KGHHFfbu7n5qqrK8ePHSaVSdHR0\n8POf/5za2top4bu2tja+9a1v0dHRQUVFBR/72MdyPvd4POzYsYPt27fT3t7u3G+T94jGxzXnNX3l\nAb+8fi3U09ODYVi4g3f/wTKZJotvvZtcLtcdwbD/6ii5dyOmeN++fdTW1v6n4EXNGyQSS6N5vKhJ\nhUzGtKNhs57ayAL9Jsh22lMsg5lVlaxevZqamhpOnTrF0NAQVjoNw1ey3OOyitULbKcsH/SdJjV4\ng/TQLTsx1AJFI5Y0uXytGbXxBidP1TOzqpLdu3fz6U9/+o6OQZqqEPCqzKyaN6Vj0N1ckhRFwT9n\nOzEQEEXPMeHkVrBIgDwW4C2FaBvW8HUBzjhtbn95d2kT9nusM0wYvSnaOrQoa10lC76REcAuAcDY\nEIyFDQuFFsHgRQFM+qsEOASOI5OiKHgr1xCN9WK5dJj9KPSeEJCW6sUq24gSnDMRQvSUCrevzn1Y\n8TBofqzStSih+ROdttylIhY1ZkfxRtuBk1C+WYBVCraD2kUBGQWrBSjYVSec70Zvim15SgRY1nPc\nhtYUAcEpOvQemyQ2Nivy2OUGdyEUrYRYh4C7jMTYGDXTED4gALHJImdlf8Ta7UjYtL1eUpRrxIUz\n29Clcf1pjsFdiiqc1O4ILVsC/AIx9hVXLrwFjEXmWsC4sixDQJpGUgB4ZnKKzRjkOLRJ1zw9KMCw\nZC94iuzoW9thMt4tQEUtMIUzoAWheSKuNtIsHPUkxKnIH4YmhpEa10Y2YCjHu5kW47SzFyeO2BSO\nbKgBAc/pIeE85ykDzTsWw+wuE/VN9kPbT2xg0XbwC8wT9Y53jPWhfyaEFoz9mHb+2ts10gIuVBTc\nJYuJd5yy66zBSDMMXROA53ilhsR41AIQWmC7Q6qirihivBhJsU9mRjSRvwor3gejNyA9KMoJLbL7\neLzL47i/so0ARfOIY1D/Wduhb6YYE9LlUzqAutyibNVjHycuiHbRCgTomjNOczOBNU3Dp6aItZ2+\nq4PtvTryarqGq2A+RqxTzIXgQsCOH1dUMDO2g6A5cWXLtMfRLVSXxbZt2+66valkmib79+9n3759\ndIW7SaYNDEOCmMqEc87u3bv/S+G06ZzXc6LkR1uxfDOcaF63585PZ7kSYVxGHBRIhM+iaL7/9EMP\nLpeLmpqaaT94kVdeeeWVV1555ZVXXnnllVdeef12KhKJADBjxvQeOhwvTdMoLy8nEonkxLdOR0uX\nLuXP/uzP+NrXvsalS5e4dOkSc+fO5f7773fc39rb2zl27JiTLrR69Wr+5E/+5F13nDlw4AAA27dv\nZ9asOz8EnK3y8nJ2797N3r17OXDgAMuXL2fx4sU89dRT/PM//zM9PT2kUiknMne8DMNgaGiI3t5e\ndF3PcdVyu90OWKHr+gS3rskkncwkxPalL32JmzdvOk5kO3fuJJlM0tTURDwedwCedevW8eCDDzJ7\n9myuXr3Kiy++6ECbZWVlbN++3dmHgYEBTpw4wa1bt/jud7/LoUOHePbZZ1m/fj2HDh3iyJEjDA8P\n5ziUaZrG8uXL2bVrF+vXr58Udti5cyd79+7l3LlztLa2Ul098fpmQUEBf/EXf8G3vvUtLl68yKVL\nlzh//jxFRUWO210qlcI0Tdra2hwYREb2ZkNa4xWNRhkYGMA0TQYGBnI+kxCX7B+v1+tExGarv7+f\n8vJyzp49yxNPPMHbb78NkBPFWVtbS0NDA3V1ddTW1vLqq69y4sQJdF3nIx/5CCtXrswpU1EUKioq\nWLBgAYcPH8bn87Fz504qKysnzINsd63q6moee+wxfvKTn9Da2sqxY8ccl04Jsg0ODqLrOs888wx/\n8zd/Q1tbG++88w6bN2/m9OnTeL1eEomEE1EcjUbp6uriu9/9Lul0mu3bt7Nr164c6FSCUZWVlSxe\nvJitW7fygx/8gHA4jKqq+P1+EokEly5dYubMmVRWVk6YG6lUiu7ubjo7O8lkMoRCIaLRKJqm8cwz\nz3DhwgUuXLjA0ND/Y++8o+S86vP/ed93etleJe2qS6suGXXJsmXJ2AhjwDGxQ4lDMyEBHAcf0k44\nPsEcThID4QQMpjrBgDHY5shykyxZXau1Ja162dWutvc2Mzt93vf3x517Z3ZXsiUZjPNjnnP2SLvz\nlnu/99532ud9nmFGR0fVfnJsJKAm3bPG1zNb3d3dWJZFRUWFgvAkWJS9JuU8yF5f0pFP/p7tFilr\nIeN+s8cHUG6T8XhcwWKmaRKJRLDZbGrt67quHO+y95fxzNJV8HIQoxwLt9ut4o6zAUSpSCRCf3+/\nctdcsWIFN954I3PnzsXn82Gz2QiFQupaJN3oiouLCYVCbN26ld27d48599KlS1m+fDknT57k8OHD\naJrGfffdR1lZ2QSATUKJr776KvF4nDVr1qiYYMuy2Llzp3Lz03WdFStWMGvWLBwOB+FwmPr6ehob\nG9m9ezf79u3jvvvuY/Xq1cpprb+/H8MwiEajPPHEE6RSKfLz80kkErz88ss0NDTgdrtZs2bNhNqM\nrzuI64kEh1esWMFTTz1Fb2+vmt/SAU9GVUciETwej3LjvPvuu2lvb6elpYV///d/52/+5m9UnLcE\nhy3LUo5+dXV1/PKXvySZTHLzzTdzww03XLad8Xhc1elqjZJk/PnZs2fZt28fa9asUXM7O85dOj1K\nl0npaLp3715isRh5eXkEAgHlInetam5u5te//jXnzp2b8Fg0GmX//v3s37+f6dOn85GPfIT58+df\n13muR4Zh8MADD/DII4/Q0tLC1772Ne68805WrVo14dq1du1a9u/fz4svvsjMmTOZOXPmhDj5y+m5\n556ju7sbv9+PZVk89dRTbNu2jQ0bNrBixQoF1ra2trJr1y4uXBAGMZWVlXz5y1++YjyypmlUVVWN\nia/O6e0pB/jl9K5QNBoVUJhxfW+IrhTf+laSLxrfDVFyv++Y4uuFF7u7uzl97iKkEjidflKpSNoB\nyPkWoFJaZgzLMtHTd4U8/PDDdHd3EwgEARv4Z6NhZtz6JGTknync2aLdEGwS8FkyiGWZaJoNyz8D\nzTcDt78AQzdJhroYHTjPxeZ2Ov/3SRoaGrj//vvflmPQ1bgkabqBp/om4t536rlcAAAgAElEQVRy\nYv1nMGNBrMHjIi4TxLyxEgJci3SBR74Rl8DOm9Qw3J6piXv8XYZZoKDhEHP+cvKUwxDCDcwyIdAo\nAI+izF0SYyCQvjqsUFYEq7NworuZVLARKzYoIKay9SIeVNMn4IoKNvRWi2jVrp0w2iogyLw56VKk\n2y/d6ByFGTe2kfNg8wv4KxEEM44l40XDnaKOl42Nlc50ZBkmaqIdNj+0bxNRwYWLxHFHW6H3QMb9\nz53lQhjpEdBaMv3GzDNJnGf4tAAPi1cKKK7/dXEsEDCSs1BEww7WC6gr1AQF8zPg2uWUDGbc3Hr2\ni7Ez48LVzu4Vx3NXZOBIKS0doS3nhoxA1o0s18c0SCfjg8cMVEqsu3A7BJvF/zUtE5MbbBTbjYFN\nGXscR7GA6ca7wcn2mAkBoxqOtPtaMr27BuEuSI6iYYr1YzgEyKfcGCePnYupWNpdzhTjFGgUEcKp\niGhfIpRpZ3xIjK+VhiI1A3xToGhZBqIcW4wM0JkcxXAXpK+V6ZjngdexIn2McYuUMb3muPkydFIA\nf6WrIZm+LiRHyQCaKTRbHlRuRmvfJmLRLVPUxFOeKfGEZZj1R8sUv2vpvrkr0o+ZmbWlGWDoWXVL\npoG5BPimifkb7oTunWlIdnJmXhnO9LXMBDNGcuQSoaF6vE7tLR1sr9WRVwOcBdVE+o9hJUfFc4C7\nQswXUlmR0nJuS9g83b7gJUiMktCS7Nu3j/nz51/zh6uJRILHH39cOOjGkpiGB0fxXBy+SjTDjpVK\nXPE5552yDr/amxJ0h59UuB+rvw6t/EYcvjJxd96bHFs58XkcVFfNoK2t449+00NOOeWUU0455ZRT\nTjnllFNOOeX0pyMJe7ydzxeyIzWvVTU1NTzyyCO8+uqr7N27l5aWFlpaWiZsJ+PkVq1a9ZZRvteq\nSCTCgQMHABHFd626+eabef755zly5AhDQ0MUFhYq6Ol//ud/CIVCKt7S5/MpkCccDhMIBDAMQzm7\nHTt2TPVPQjkyGnE85DdeMtIUxLjW19eTTCYV3GZZFgcOHKC4uJjNmzezZcsWBaklEgneeOMNnnji\nCc6fP6/gIsuyGBoa4sCBA1RXVzN//nxmzJjBBz/4QY4fP85zzz2n4l3/5V/+hXvvvZe77rqL06dP\nMzw8TCKRwOPxMG3atLd0DCooKGDDhg3s3r2bb37zm/zTP/3TZR0hvV4vX/7yl2lpacncNNzVNcZx\ny7Is2traKCwspKioSM3N7MezFY/HVVQ1CEczqfGOf9KVMFvyeNK1KxgMkkqlFPSU/ZnusmXLyM/P\np7u7m5/97Gfs3bsXXdf59Kc//abuVHv27MGyLFatWkVlZeUVI2ez25yXl8emTZvYtm0bJ06cYN26\nddjtdvW5quyn0+nk9ttv54c//CG/+MUveOqpp1Q8rJw/kUiEhx56CNM0SSaTbNiwQfVrfFtkRKx0\n+Lv33nv55je/icPhYMOGDZw5c4aOjg7a2tpoa2ujqKhIOdfFYjEGBwfVMYuLi7nhhhuor68nEAio\n6NXW1lZGRkZwOp1UV1ezYMEC8vLy6Ojo4NixY/T19bF//36CwaCKlL7cdU46ZQ4PD/PKK6+Mcdkz\nDIPq6mpmzpypHLs0TVNujKOjo2Oc8wzDGLNOpQOadBQEAUVLl7a8vDwFKebl5aHrunKHC4fDCuK7\nXI3l+dxuN5FIZMIcz56z2VHV0k1TtsmyLEpKSrj33nvxeDxUVVUxZcqUCdcaGZEbi8Xo7+/nu9/9\nLv39/TidTlwuF5MnT8blchGJRGhvb+fw4cPs2bMHGfn96U9/+k2/P9i5cyd1dXXE43F1HZZ92717\nNw6Hgy1btrBx48Yx8dfJZJLbbruN1tZWXnrpJd544w1+9rOf4XQ6KSwsVKCnruvouq5idD/zmc/w\nne98h5deeoloNKqcN+fMmaOcZK/klJq9bmw2G3PmzKG/v1859sm54/P5CAaDJBIJdc2X8cNf+cpX\nePTRR2lpaeEf//EfWbx4MatXr2bKlCnYbDai0ShHjx5V17d4PM6tt97KJz7xiSs+D7S2tjI6Osqk\nSZOYMWPGZbe5nG644QaKi4vp7e3lt7/9Lffccw+hUEjBqxJozl4/lmVRV1fHkSNHFOz3yCOP8K//\n+q9X5VqbrWPHjvHYY4+RSCRwOBysW7eODRs2UFZWhoxc379/P/v27aO5uZlHH32UT37yk9x4443X\ndJ63o/z8fP75n/+Zb3/727S2tvKTn/yEX/3qV6xatYri4mIsy6K3t5fDhw+rmOUf/OAHPPDAA28K\nI5qmyXPPPcfzzz+Pruv83d/9HbFYjOeff562tjZefPFFXnzxxQn7uVwu1q1bx1133XVVAGFOvz/l\nAL+c3hVyuVygaSL+8DpkmQlI3wFwrXq3RMn9vmOKrxdefOyxxzjX0EQy1IWzrIRkUrzwsFIxEcmq\nv8kbVzOFFeoQAB8Wjc2tpFJW+kWbDQwNRi8JoC9/roC9NE20J3AB+l8XsItyuxMRvhamcKQLNhEd\n8GP4KnAWz8E398MkA22MdhzkwMFaAD7/+c+/Lcegq3FJ0jQNZ8k87EVziHUfI9pzDE0CVLodze4X\nUM1gPTiLsOx5GZBNveYZ96IsO0JU0wU4lK3sF0tv8qZpDDg32gapMLrTj803acxmNl8lqfAAVuCc\nqHPJSnAUXAEsSp9z5Lz4f8lK4fZmSXer7O2yuqZpAngrWSFAusAFAatpmoDoYGw/3ZMFvBMbEO56\nEuTR7WAZgAkDb6BZpojrLV03Du4jq/FZIJSmi3bY/QK0cpZBwSIBHAYuCEBv6LgAI8dLOrLl1aQd\n+06LaNZINwy+AcmIgPCKbhBxufFB4QBnJUR7Y4PQuQMKF6chvcvUNR4UznsDRyYWPzEiICy7Xzj8\n+WdlHtPsYMUFSJdCOLYZY9/UKzc9Ix3PaiYytbJMUT/dmQZSu0X0rYyAluCijJ3NPiakHdbIuEb2\n1wmQ0lmQOa/sZirtJCcGVECNQ8fFdd/mE/BZ2YbMvoZrImiq2yCVEhG8g0fF/Aaw54NvKjizopJj\nfQJaTKZjdB1FUHhDBogb3xdMEc07WK+gRCwTzeGHUBdWuEs4i5asSIOHemb8rGQaUp4uxmrwKIRa\nM22TUKLdD3nzYOg4lp6+S9NdDrGzkAqLuvTWCodI38x03LR+mfVuquuJirGVccQmmbUF4m+6XUCb\nsv5WShy3bB30HRZ17D2Ydj2cCu4yMe/NBER70AKNYi5YSabOrnlLmOt6HHmdLhfxwrkk+46JdVC2\nXlyP5AVJt6efe7Lio9Egno7P1sDUHBw8JO7C+/znP3/Vz9emafL4449z8NBhIgkN15QNl3Wus3lK\ncJYuIDHSOuE5552C267meV3XwW4zSJlRrN4D2J3rgGouB5ePd+Jbu2Y1n/vc53jttdf+6Dc95JRT\nTjnllFNOOeWUU0455ZRTTn868vl8gHDyuh5JAAwYE7d5LSouLuaee+7hwx/+MHV1dTQ3NzM6Oord\nbicvL4+VK1cyderU6zo2QCgUoqGhQcWr+nw+Zs+erdrb2tpKPB5n6tSp1+TeJ1VYWMi8efM4ffo0\nTU1NvOc97wFg48aNTJ06le3bt1NXV0c0GlWxkCC+H/N4PCxYsIDNmzczY8YMHnzwQfx+v4KAfD4f\noVBoDKQi3eSkTNNU8bMFBQUKZCksLMTtdivwL5lM0t/fT1dXF8888wynT5/mS1/6EoFAgO985zt0\ndnYSiUSIxWLE43Hx3VQaMIpEIvT19XHs2DE8Hg/5+fmsW7eOz372s/z85z+ns7OTxx57jIceegiH\nw3HdUY0f+9jH6Orq4vz583zta1/jIx/5CGvWrLksUJdIJOjr61POeosWLWLz5s0sWLCAAwcO8Oyz\nz3L69GnmzZs3BggaD+0MDw9TX1+v4lEl2HgljXdpG98uOSavvPIKQ0NDlJeXM2/evDHbLV++nK1b\nt/Liiy8quO7N4L6Ojg6ampqw2WzU1NSMgcay41gv1+6KigpKSkoYGBjg3LlzLFq0SBm2ZK9Zl8tF\nKBRS0KnD4cDj8YyJ1g0EAgr6kdDS5ZwlZR1jsZiCoOTxHA4Ha9eupbe3l8bGRjo7Oy97/SktLWXa\ntGkUFxfj9XopKSlhcHCQZ555hpGRERKJBHl5eXziE58gHA6zZ88euru7FRQrQbKTJ09y7tw5FZ/5\nnve8R4GwsViMU6dOqf7puo7NZlORzolEgkuXLnHp0iVKSkqYN2+eAvgkUOdyuRgeHiYSiVBQUDCh\nHjJmWY6ZdADUdZ2ioiKGhoYIh8MKAJaAX7YbY7bGj7XH41FArnxMuoDK32XfJKglneHk9/qzZ89W\n1y3ZhvHnttvt6LrOuXPn+PGPf0wsFqO8vJz169dzww034PV61fZDQ0McPHiQQ4cOMTAwgMPhoLi4\nWNUj+7jJZJIXX3yR3/zmN4RCIWw2G48//jgPPvigarvX6+Xv/u7vlLulBMrkHATxPPKxj32M0tJS\nXnjhBR577DGWL19OIpHAZrOxdu1aFixYwM9//nMsy2L37t2Ew2FVq/7+fp5++mkAqqur2bRpE6tX\nr54AJMoIaAlKyvku58TZs2fVmpTx57FYTEGY0uXP6/Xy1a9+laeeeora2lrq6+s5cuSIun5nS0bm\nrlu37k0Bd+nIe60uena7nb/+67/mP/7jPzhw4ADxeJy77757DJQuJcfklVde4be//S0An/70pzly\n5AgXL17kW9/6Fg8//PBbRtJKnTt3ju9+97ukUinWr1/PRz/60QmvJTweD3/xF3/BXXfdxe9+9zte\neuklfvKTn+ByuVixYsU19fXtqLCwkH/913+ltraWnTt3cunSJRWJnK0lS5YQi8VoaWnhP/7jP1i0\naBG33HILixcvVrUcHR1l//797Nq1i56eHnRd55Of/CRLliwBYMWKFVy8eJHXXnuNlpYWwuGweo5f\nvXo1a9asuS4uJ6e3rxzgl9O7QmVlZRgXLpIMdWHzlFzz/slQJ4YhLKKvV3+oKDnTNDl16pQCB6PR\nKC6XS4GDCxcuRNf1P0hM8fXAixs2bKD2cB2jA+dxli5QT2IC8ouDqQmoZJxDmGUmBVgTahagkTMP\nvXgRuquMZDSOZabQ4oMQaMRKhKD/CFq0T8Bi/WkXORCQiX+mgK/QBZiShnWs5Cip+AjmYJDE8CUM\nVx7Oknl4pt9GuPkVDh06zJw5c7j11luve7yuNbrZigunPM2w4566EUfBdEAj3LpHxPh2vYZWuhLL\nUZRxDtPGOXGlo2wxExlAyxwPu2a9KbpcVKV6LGu/vteFO1bJfPUiOj5wllj/WVLRQOY4hhvcct5Z\naWZvnFvcaIcYN5sXvFMyjmCWKeA7Te09sU3eKWK/RDDtzlUJYREdjM2X3jEF/bWZiFi7TwCg7nIB\nZCVGx8wDrCSEW8E7OWsuZkmziWOaKTAkBGWkgScdMMXx/bMF2BZqSrseJsS+No9wo3OVifPrhvg7\nQKwfevcDKREhW7oe4gPQtT0T36rpApwD0e6e14SbYd7sNOSYHse+Q8KBTreLWihnuDSoJp3hEkER\nwRrtTbvQpYFHLDF+iRFxHs/EuwnTDRrr6KYgWkMAZaNBGDwmHPmcBelxTQNhun1ifbEyc9Q3XUCZ\n4Y60G9wK8FRlAD01X9NtiHRl5rtnioC0kiHh4AdpJ0Ir48CoupAe54E6AdKpeOZJ49aLJqKy/bMh\n0in6FR+CgdehZPU4wA+xb6hFbJcMo6GRGu1l5NSTIN+kGk6o2CjcG7HSc+syroieSWKu9+wTUDKG\nmFOeKVBxE0T7RT1SUWh7XqwpzZaZW4mA+BltSwOBczJQLKTPm4Y0syK6xwDE4R4BNErpBpjS7VGO\nu13sW7YOAg0QOC9cB4dPwEgGpNNI32lpd6OlIsqm/810PY68GuCrXEIw0k8q1CrqV7xUQL+2LNhT\nRSpnrpu6lcReOBNn6aLreh549dVXhXNfQsM74/YJrq1j2qnpOAqmYTjzGG16+ffynHOtuprn9bVr\n13LgwAHRr2t04rPZbO+Kmx5yyimnnHLKKaeccsopp5xyyimnPx3NnTsXEA46oVBIAX9Xq/PnzzM4\nOEh+fv5l3dauRQ6Hg/Xr17N+/fq3dRyppqYmdu3aRW1t7RhXLk3T8Hg8rF27lltuuUVFfBYUFFz3\nueS+2XGh8lwOh4PJkycr+MhmszF58mRqamq48cYbmTQpYxCwfPlydu3aRXFxMT09PQpIjEajCriT\nkYnjVVlZic1mw+fzqc+PAoEAlmUpSKO0tJRQKMSlS5c4e/YsX//61xkZGaGvr49wOEw4HCYUCjE6\nOqrqJWN88/Ly8Hg8RCIR4vE427Zt46WXXuL2229neHhYAY4zZ8687jra7XYefPBBvv/973P8+HGe\neOIJfv3rX7Nu3TomT56M3W4nEAhQW1vLpUuXVHzsJz/5ST70oQ+p43zwgx8kmUyydetWFflYWVlJ\nZWUlXq+XVCpFIBCgpaWFkZERDMNgypQpOJ1OTpw4gc1mG+MImO3ABoyJbZXjLCNdJfwmYaG7775b\nOdq99NJL7Nixg5GREXRdJx6P4/F4WLRokXJpzI58NU2TWCzG/v37sSyLOXPm4HQ6FdQpQaPLQWDZ\nmj9/Pnv37uXkyZMsWrSIhoYGNE1Tror19fV873vfA8Q6LCwsVNcCCaRJqCwajRKLxTh06BCJRIIP\nfOADE1wObTYbuq6reFoJWUnQ1DRNysvLKS8vJxwO09PTo1zRpPOZz+dTdZZ1icfjDA8PK+jrvvvu\n49y5cxw8eFC5f02fPp3CwkLlWjcyMkJ3dzf9/f3s2LGDS5cusWXLFmKxGL/97W/p7u7G6XRSWlrK\n1KlTycvLU30ZHR2lo6ODzs5O+vv7qa2tZenSpRQUFBCJRLAsi0mTJtHZ2cnw8LCKPh4/Fpqm4XQ6\nVRyvZVk4nU4FhqVSKQYGBhSA53A4cLlcCtjKrq2M/ZV/s9ls+P1+hoeHFewmH8++5snYVxnp6/V6\nWbduHbW1tQrGMgxDfQcxHvIzTZPe3l4F961Zs4ZPfepTCuTM3qe4uJgPfehD3HLLLfz4xz/mzJkz\nfP3rX+cLX/iCujYNDw9z8OBBdu/ezeDgIJFIhOnTp+Pz+Th79ixf+cpXsCwLj8fDPffcw6xZs9R8\nyr6eS+hZnv+9730vg4OD7Nmzh9deew3DMPjqV7/KnDlzOHnyJJFIhEgkQjQapaCggEWLFlFaWord\nbicSiVBXV0dLSws//elPeeWVV3jwwQcpLi4mlUopEFRel2VtotEohmHgcrno7u7m3LlzCup1Op3E\nYjEFYMoxBxHT/NnPfpZ7772X7373u7z++us4nU68Xq9yRXzve99LS0sLW7du5bXXXmPBggUT5tiV\n1v21aO7cuXzpS1/iG9/4BocOHeLkyZOsXbuWjRs3UlFRgWEYDA4OcvDgQfbs2cPw8DCWZXHPPfew\nZcsWNmzYwMMPP0x7eztHjhxh1apVb3nORCLBY489RiqVYtOmTXz84x9/0z44nU7uuecePB4Pzzzz\nDD/84Q+ZO3euctd8J2S327nxxhu58cYbaWpq4uzZs4RCITUnFi9ezJQpU0ilUjzzzDNs376dkydP\ncvLkSdxuN36/H9M0GRoaUg7KhYWF/NVf/ZWC+0CM5axZs5g1a9aVmpLTH0k5wC+nd4XGQ2XX4mBn\nWSbxgfN47QYbNmz4A7by2mSaZsaau7uHWCJFKiUdzzSMCxepPVzHpMoKNm/eLO5W+T3HFF8JLqyu\nrh4DF2Zr4cKFVFaU03Spg8RIK46CaXi8Hmw2G7FYTLyJMBMTDb0sS0A3yVF0Zx7++feg6wajo2Es\ne1y0z1sJ+TUwKsAMK9iCFu7GMuOgOzKwjpXKcvvyCUep/AUQ6RAuf8kwWipGarSXcLgP3ZGHvWgW\n4YGz7Nix4207Cl2t+6HdSpGw4miGA9/sO7F5MnckeKpuJAwC8us9ALpLgFCeSvH/y8XAeqsz4Eqk\nJw0TjYekeHMHv3APYEJ8GE23YS+YhqO4BstMEW7bJ9oDAkKy+QQY5p8ugLlUSkA/lo6VZjc1LAFv\nyrjWvFloupHmOgXUaek2AQRma3yUrH+WcGwLNoko5mCjOIF/puhP3yEBNRkuKFoqXNIMSf5bYn44\nC8VxIl3CaU06uJWuG3c+GYWcBvmkY5lyN0xm/Q0BxWWDcZaZjqlNSzqHpSJp8C3tCqg7oGQ5hC5m\n2mLzpgHVMtHubDc5CelFeqD4PcKpLNwBNreA9rzV4pjZi8tZLNaMhEBH28X5i1dkokttXgEyhpqh\nYOEY57yJynJ0k4oHxTlT8ay41ikZd0UrmXHahLGQmax38Q3i93Bn2g0uXQdHYRowSwl3w2BTVuzx\nZLFfz770uEhILRselv9NxwwHL2bgvspNadfJ8eshvZPhSMcz+8Q5RlvEcYoWC6DOjAvQVMGdSdDt\nWJomoOVkAs1KiDEpXibGKRUT54t2i+MlRoGUmFt2n1jjrjLh6NhfK5wZsQk40LAJQNNKQDIpYqcl\n1CnrBAICVFDnEbFPyYp0PyWgZ0OTQCQISFVGgAcahEMqEijW0mNgoiKL5bk0DfLniLFqfVYAjnaf\ncFbUDHRnHp6yeRjeCkLnn6Wru0fFwF9J1+vIq+sa/hkbCV18hWSgVbgL2v3piOqJ8dlaMoymgb1g\nGp6qG9F0A9fkNUTa913184BpmuzYsYNILIlryoY3hfuyZbiLrvlcv2+91U0JS5YsYe7cudftxPeH\nuukhp5xyyimnnHLKKaeccsopp5xyymm8ysvLWbBgAadPn2b//v3cfvvt17T/rl27ABFTe7WOPX9o\nxeNxfvzjH1NbW0symSSVSlFYWKiiLsPhMMPDw2zfvp1du3axYMECBc1cr+QX9RI0OXLkCC+88AJN\nTU2XbV9zczN9fX3ous6dd96pHOpuueUWDh06RHFxMQMDAwomcbvduFwuEokE8Xgc0zQVgCadoiZN\nmoTD4WDatGkYhqGgNOlKBuJzJ7/fz9y5czl37hwXLlzANE0ikQgjIyPE43EFLkknM13XSSaT9Pb2\n4vV6KS4uxmazYZompmnyzDPPKKfAl19+mb/927+97jqC+IzzS1/6EocPH2bnzp1cvHiRV199dcw2\nlmURCoWU89Rtt91GT08PQ0NDyplsy5YtVFdX89vf/pbz58/T3NxMa2urGiNN0zAMg/z8fFauXMnm\nzZs5cOAA586do6ioiOHhYbWdhColNJVIJC7rKigdFCWsdfvtt1NeXk5HRwdPP/00x48fB2Dy5MmU\nlpZSV1dHTU2NAiej0agC9mR9AQYHBwEUkGe32xU4Z7fb39RxUJ4PhFthPB7n9OnT6LrOzTffTGdn\nJ9/73vcIhULKYU8CjjA2elvCsXl5efT393PkyBHKyspYs2bNhHM6nU4ikYhyntI0TbkfJpNJNSc9\nHo9yL0ylUgoulYCgYRgEg0EFkc2ePZv29nZmzJhBQ0MDBw8eJJVKsXTpUiZNmqRiboPBIKZpkp+f\nT1VVFUNDQ5w7d05FUEciEXp7e/F4PMyfPx+3260ATTnOPp+PmpoaZs6cyfHjxxkcHOT48eMsX74c\nt9ut1gGIdS2jdrPrlT0/HA6HWpcyFjgbpOvo6KCwsJC8vDxVn3g8832SnBNyzdtsNuLxOA6Hg4KC\nAkZGRtS1YTyQGgwGVdyqx+Phox/9KAUFBdTW1hIIBFQbssFA6dYmAbVnn32WWCzG8uXL+cxnPqMg\nVQmuyVhm2YaioiLuv/9+/vu//5sLFy7wzW9+U7mOWZZFPB4nFouhaRqVlZWkUilaW1vVseLxOOXl\n5axYsYJYLKbg0jfeeIMTJ04QCoUU7Dl9+nQ2btzI5MmTufPOO6mtrVXfy09PxzP/7Gc/Q9M0ioqK\neP/738/NN9+My+VSUct+v597772X119/neeff56Ojg4eeeQR/v7v/16Nq67reL1edR1JJBKcO3cO\nTdNYs2YN+/btY/v27dTU1KhrjARbJfA9Xg6Hg56eHvLy8vjKV77C1KlTicfjuFwu3G43M2bMYNu2\nbRw9epSRkRHy8/Mvu84llNvX1zcGUL5aySjrM2fOkEwm2bNnD3v27JmwnWVZVFZWcvfddysHPZ/P\nx+23386TTz7Jrl27rgrwe/311wkEAlRVVfHRj370qtt7xx13cP78eU6dOsX+/fvZsmXLNfXz96UZ\nM2ZcMQrZMAz+/M//nC1btrB37152796tzBykFixYwC233MLSpUvf0twjp3eP/qivdEOhECdPnqSh\noYFAIKBy69/qRcCVpGka//Zv//Z7bmVO74QuB5VdrRIjLeipCJOqp7wpNf5OKpFI8Pjjjwv3nFgS\n0/DgKJ6Lw1eJZtixUgmSoS5GB85zsbmdzv99ErvdEOmmv4eY4muFCzdv3qy+2JfuQP/7v08y2nEQ\nw5mH4S7C6XTgcDpIJpLpN28peTgsC1KRAayhemGxXLEMPQ2QpMxUGjIRx9d0A8tXLaI8O1/CSgYF\n5FJ5i4BcVJRkGkTSDMQBUiLitGw99OzDSo0CwkkwlRjF7D4KlkVTUxPHjx+/bvt1qbdySSotLeXs\n2bN09QzinrIBKxVltGU3ZiyAZSbRdBu6w4+jeA7xoYtY8SEYDsLwqYkAVrZTV6RbwFzBRgF2aYha\nSJhLjDZWMjrGSVGAeAkROZuKg2agGQ7MWIDg+ecw40GsZFTAhWVrwFcFHa9A0hDRshKGG+ccKPgg\nLR0hqmVAOM3IDH62S96V5C4XEbjJkOhfclT021Uu2jzaloG2DFe6PRIoy3pO0HQBhjkKofs1sZ+r\nQdQvXSoVmyr3N9OQlt0nnNxifcLh7XKudFaWM52EzMwUkAaLMMVxUnHh3jfaAeF020tWZcW3Whmn\nPFdxFph4DEZbheNdMiIgtPINohbKYU3L+jfdZ296zXTvFICbsykNR/ryhsoAACAASURBVCKiZbGE\ne2C4XcTVjteYGkqHNktEyabCaIYby1OZBvQOCEBPRnqGu9IgnZmuhTn2OFZSwFclayDYICC85KiA\nMMcoDUcaHhFZ7J8h9rV5hHtctFe4CWaPi5UU57TScJoETUtWpCNcGdseTc/a3hR9cOQLaLS/TkB5\n4c4sZ8B0nzRDwK2anolltiys4ZPi+uStTkOUsn/hiTWOD2Wc9/yzxHHi4rpM/2HhHjh8WpzD8Aho\n0F2Z5SiYHnNXqQD0wu3Q/7o4ppUSUKeuC1haz1pvlgmBRvHG1+nHTIbRRjuwvFXjxlzGYmdF3UqF\n28FMojmLoOoOEcmOhcfnw24X5xofA38lvR1HXl030L2VaME2sQ6T4Ynx2ZrAiXWXH2fJfBzFNepN\nlz1/KtGuN+js6n5LEBHg1KlTdHX3YBoe7PnV19TWaz3XO63rcfDNKaeccsopp5xyyimnnHLKKaec\ncvpjSX6G8dJLL7Fq1SoKCwuvar8LFy7wxhtvoOs6N9100x+4lVeneDzOo48+ypkzZzBNk6qqKqqr\nq/F6vWO2CwaDtLa20t7eTn19PfF4nM7OzgnObFerrq4uALxeL88++yxbt24FBLx04403snz5crxe\nr4L7du3aRVtbGy+88AKnTp3i7//+78nPz2fevHnMmzePWCzG9OnTaWpqUvCghEPGAyKxWIy5c+fi\n9XrJz8/H7/cDGTet8U5zIIASv9+v3PpkDHA23CejNCVUKOsro0Xtdjter5dwOKy+X37llVew2+18\n+MMfprS09JrrKGUYBmvXrmXt2rW0tLRQV1dHIBBQME8sFmPv3r1UV1fjdrt5+OGHaWpqUrCVBJlW\nrVpFKpVSrnemaVJQUEBBQQF+v58bbriBJUuWqJquXLmS7du3U1hYqCA66ZSXHYUr3Q6zAUtN09Ta\nSSaTJBIJXnnlFV5++WUikYhq++c+9znWr1/Pr3/9a2w2GzNmzMDlcilwc3z0rnSvA1QkqAQvJVA1\nPkZ0vOTjsViMY8eOkUgkqKmpYcqUKTzxxBOEQiFcLhc+n28MUAYoWCt7DrlcLoqLixkcHOTAgQOs\nXLlyDKAiYSwJoxmGgdPpJBQKEQwGx8CREsiT0F12pLT8u2VZ9Pb2YpqmgmbLysrYt28fqVSKNWvW\nUFQ09gZyh8NBNBpVY1ZYWMiSJUuor69XQJbX62XRokUKksyO9pV9lfVbtmwZR48eZWhoiIaGBhYt\nWoRhGLS0tCgwsLW1VbksXkkSyotEIrS3t2NZFrNmzcLpdNLc3MzQ0BADAwNjEt5isdgEMNVmsxEK\nhQiHw2quyPPGYrEJ4ya3cTgcfOYzn2HlypX09fVhs9m4dOkSfX19VFZWjnGtDIfD6vozMDDAmTNn\nsNlsfOITn1DjJiOtdV1X35FHIhESiQThcBiHw8Hdd9/NN7/5TQKBgAL6NE1TbqZlZWU0NorvWcrK\nyti4cSNHjhyhsbGRzZs34/f7GRoaYuvWrdTV1aljZEteV+fMmcMdd9zB4sWLOXz4MJFIhC9+8Yt4\nvV56e3upqKjgb/7mb6ioqFBAqZzn8Xgcp9PJ2rVrWbJkCf/1X/9FY2Ojih+XznrZ19S6ujpCoRDV\n1dV8+MMfVnG727ZtU+6W2WM/vt3JZJLvf//7DA8PM3nyZObOnatgTDmehYWFTJ8+nYsXL9LT03NF\nwG/q1Kn4/X66u7tpbGxk9uzZV5yHl1NnZyft7e1omsYDDzxAU1OTcvc1TRO3282cOXO46aabqKmp\nUXNA9mnt2rU8/fTTnD9/no6OjreMvN+5cycAmzdvvibATdM0br31Vk6dOsWuXbu47bbb3rWAnM/n\nY8uWLbzvfe8jEAgwOjqqnHbHRxHn9H9D7zjgl0wm2bZtG7/4xS84ffr0dcN84yUXbw7w+7+pK0Fl\nb6VUZJBoxyG8ThEp9274kto0TR5//HEOHjpMJKHhmrIBe371BFdCm6cEZ+kCEiOtjHYcRIuGsEyT\nRLDzbcUUl5SU8Nhjj10TXNjQ0MD999+vXjRu3ryZCxcucPDQYUabXsY1ea3qg91uU8AHgGmZBLvO\nYvUfRTMT2Atn4CiuyTRMLfHMCwZN08BZiKU7BbRTtEzAOmYiy7kPAfdYKdJ2ceJvjkLhmtZXKxy1\nStZBtBMr2AzJUWLxBF//+tf59Kc/zW233fa258SVXIxOnDjBodrDpNCJ9RzFjAUnhtNGBtPmY3E0\nxIsMrIQAgGy+tOPXDAEuyhdUnkrx98SogG68VZkaZBfVMkVkcrZG2yEZQsMEzYaZimNGBgWoZJlp\nyCeFNnQCzCikEginM6cAzSwTBWFJ2E1KxZFmvUnTbKI/ZkLE3r6Zc5x0z0tGBLQEaSjPgpHz4veS\nlQLGSsUyYCMAKSbIWSi27z0gAEEZY2qRBYlm1cuMg2+agOuCzRkgMFuWBBytzH6i8+IxGT9thtJ9\n8mXgvspNYm5mOpyO2U1kHP88k8W4d+8VMJjuFOCZ3Z85l9xWHSYrmtRZKMC23oPCCc83A6I9Ar60\n+cR66X9dRAE73+IDMN0mnOIG68FMYBXMRit+j3CzGzmPlQiiYmgDDaJ2ql3psY72ie3jgTTkZwO7\nFwqWiKHr3S/6b/OI7VMx0R8zKWKO82YBBninCugu1CygRakx1wMd4n0C+LJ5RQSwAjAl/GqJNhiy\n7knU3JHRufEEmpUF9DlKAQ0r1p8V+TtZPN6zR5zDP0u0f7BuolOjuyLtiJgYG6c8eEysY80Q7Qg0\npOdPUuxbvj4LUJy4tsdCnbsU1KkVLsyCE9MabYNUGN3px1FcQ7Tzdaz+OnF+R+FEh8PxEcWxocya\nzJ+rYErDZmDLutZfLgb+cnq7jrzJoQuAhb1kAc6imcQHGzDjQcxkHEszwOZF803D8pSTNGzoyRQ2\nuy0dJ6xfNYgIsHfvXmKJFI7iudfUTq7jXH8s5Zz4csopp5xyyimnnHLKKaeccsopp/8LWrJkCbNm\nzaKxsZFHH32Uhx566C0hv4sXL/Kd73wH0zS59dZbJ8A1fwxZlqXiIHVdZ9WqVQp2Gy+/38+CBQuY\nNGkSb7zxBqZp0tbWxtmzZ6/ZzKK5uZmWlhbcbjdNTU1s3boVTdP4yEc+wqZNmybAPtOmTePmm2+m\noaGBH/7wh7S0tPDoo4/yz//8z7jdbr70pS/xyCOPYFkWhmHQ3d3NyMjIhO90NU3D7/dTVlaGz+fD\n7/cza9ast3RBku5w0u2rv79fgWLy+95kMqnc1rL3k7G/+fn5KmZV0zSGhoYIBAIkEglefPFFDh48\nyGc/+1nWrVt3TbW8nKZOncrUqWNvrP+3f/s3kskkbW1tPP744woyklBiMplkcHCQ7du3E4vFcLlc\n3HnnnezYsQNN0/jYxz522ThVv9/P4sWL2b9/P5MnT6a5uVlBjxIUk/WV/0qXs2nTpilQUEJAHo9H\nAVkSxPrJT37C+fPnlUObhCkdDodydjNNUwFyiURCfdeWHc/sdDoJh8MKuswGXMa7d8n9LMti3759\nGIbB+973PsLhMIcOHSIej1NSUqLgPsMw1P7jgT95HJfLhWEYjIyMcPbsWRYuXAiIWFs5HtnwY01N\nDW+88Qatra0UFRUpAE06xGUDaTIW2DAMdF2nv7+fcDg8BmrcvXs3mqaxdOnSCdef7EjhbKdLn8/H\nnDlzqK+vx2azMWvWLDVnZN0vF3ssj7FgwQIOHDhAX18f8XicS5cuMTo6Sn5+PkVFRbS2tnLhwgWm\nTZs2JsI1W4ZhEIlE6OzsVO538+fPZ/ny5TQ3N3P06FEuXryoHjNNk3A4rFz9pBufjNod7zwqI43l\nmBcXF+NwOFSdY7EYP/7xj3E6nSxatIgbbriB2tpa9u/fz5/92Z+pOSiPr+s6DoeDI0eOALBixQoF\nmGXPDblO7Ha7Ar+kpk2bxtSpUzl//jxFRUVUVVVRXFzMhg0bOH/+PFu3bsVut3Pfffexdu1adF3n\nhRdeAAR0Ozg4yLe//W26u7sBmDNnDhs3bmTKlCnYbDaCwSB1dXUcPHiQCxcu8K1vfYulS5dis9lU\ntO7w8DCFhYXcf//9VFRUqDkh54oE/OR88Hq9PPDAA3zta1+jq6uLM2fOTADZk8kk27dvBwQoLx0L\nv/e97/Hss88SCoX40Ic+NCHuWKqvr48nnniC06dP4/F4+MIXvqCAVllHKQmDhcOXMcBIy263s2HD\nBl544QV27tx5zYDfzp07icfjFBQUqBjav/zLvyQej6t46ezxln1yOBw4nU48Hg9Lly6lrq6Opqam\nCYCfZVlcunSJ4eFhenp6OHfuHF6vl9WrV19TO0GYV5WUlNDf309DQwM1NTVvvdMfUZqmkZ+ff0U4\nM6f/OzIefvjhh9+pk9XX1/Pxj3+c3/3ud/T29v7e4D4pTdP4whe+8Hs73sDAgLqQlpRcO3CV07Vp\n+vTpdHV10t3ZTnjgIprDj+7Mu+yLD8sySYxcItK6G7fdYu3a1fz5n//5NVu9/iG0Y8cOXnjhJcIJ\nDe+M27H7K6/YLk3TMFwF2PyTiQ1dwkrGSEUHcZYtuqa+WJZJpG0fLpuJ1+vhWP0JwgkN55T1uKes\nxu6rQLd70G0udLsHm7ccR0kNmrOAyHAbXZ3t9PX2sHz5cvViYtmyZfT19tDT1UFsqJnYYANWGoyx\nUgnMeIj4UCPh1n2YgYtoqRiOoll4qjagZT3hx+JxTNMSsa7ZfYp0CScsm1dEZkq4bGzPsn404cKm\n28GeD6FLAvbxVouIWd9UAYfFBzCTcY7X19Pf38eyZcv+INT8r3/9ay5caMSyUlippHDkKpgPxUuE\n855/ejoCNyic9TDBUSSAJd2AshtFNKbdL+oi6JRMjSJd4sddMRHImeA8B8RHhENZKia2d6RdAQsX\nQt5MUSdHvnBWS45CuEvEj1pmuq0eNN1A0wzRPi3tZiYBq1CLiBv1TQObGw0xTxRcZaXQ0MeOsfq/\nJpz7ghdFzC0amq9awG2RbuH6ZvNC8XIUTKfpmX4r6C772DZRu9AlcWxnkah3uA2GT8LIBXG+UItw\n7NNs4CgWv6fCohbOQnFcGTlrjf2wIOMiaYdolwC0DGc6EjcpzqsbULpGjNMESVhSHssh2mhGBRxn\nzxMubpqR7qvsu1wrWsbMTxQ5q8+joi1DJ8Q+RUsFsBYfEhCjzZeZW+PRUyslYNC+Q5AaFZuUrERz\n+oXbYN4ccJSI4ybS8J6jQPxodhhtEvGpwQbxuBkT9TNjAm4Ltwnw0IyLGld/UEBy+XPBWSKc+uJD\nYlvvNAEAyrhbR774UXBv1rofOiHOlz8fPOXpvphZEKAlamgmyVxLZB1F7C6xPuF8WboCKm4CUljB\nJnGOyk1jYdvhM6JPhYthqD7jMlm6RsxVd5lou+EQsLGrDPJmizpFusX1Sc4pGS8rY51dZZn5pRz5\ntIlzwOZBc/jQRtsgOYqWN3ssABobgp79aJi4KpbhLJmHGRvBjA4Kd0a7nAdk6qTbMvUYbVUgpuar\ngsLFAkjWSNv7Z30gk0qQGDhHnt/Drbfeepn5LlRaWkpt7SGGhwbRnAUYroIrbjteiZFLJIYugJXC\nU70Bm68Cy11J0jUF0zsdyzcNyz0Zy/BimRYpM0UiHlcfFBg2G5pmEB9swOdxsXHjRnVs0zQ5efIk\nv/nNb9i2bRsvvfQSZ86cIRKJYMufhuEpufbXEFc4V045Qe59RE455ZRTTjnllFNOOeWU0/8vyr2/\ne2ek6zrLli3j2LFjdHV1UVtbqyL4xrvF9fT0sG3bNn7+858TjUZZtmwZn/rUp94VBhBNTU384he/\nIJVKvSncly23201RURFdXV3KVWv9+vXXdN5nn32W1tZWFi9ezO7duwH427/92zeNLdY0jeLiYlat\nWsXRo0fp6upiZGSEG264AafTyfLly7l48SKxWAy/309BQQE2m01BFAUFBVRVVVFWVobT6cTtdrNw\n4cIx45XtfibjiaUGBwfp6+sjHA4zMDAwJvZTQlXZMZ+6ro+BnnRdVxBbYWEhhmHg9XpVPGcsFuPk\nyZPk5eVdMcLwehWLxfjJT36i3MV0Xae4uJiKigoKCwvJz89XMafxrM8vw+EwU6ZMoaenh/r6esrL\nyykpmfi5ZGlpKcePHx8DCsp6Stcxl8uFy+VScbYVFRUK8CsoKGDx4sWsW7eORYsWMXPmTGpqapgx\nYwamaTIwMEBLSwsDAwPEYjGmTJmiwEz5Ix3aZNuls5bL5VIxvYZhqNhaCcSN/5Fzrbm5mebmZhVZ\n+qEPfYiNGzeyd+9e6urqlKOUdLyTMJGEo3Rdx+l0KsAne55FIhEikQiTJk3itddeY/v27Rw8eJC6\nujpOnjzJ0NAQ+fn5VFRUcOTIEUZHR1Ucq4SGxoOTdrsdp9Op4Nj6+npisRi33norCxcupL+/n5GR\nEZxOJ0uWLJnwPaR0Wcx2QXS73USjUXp7exkaGlLPK/n5+WP6m70WskFBQLnmjY6OMjQ0RG9vLzab\njS1btrB06VJ6e3sJBAL09/czPDysxhIEDDY0NERbWxv9/f0KgrTb7cybN49JkyZRVFTE/PnzmTNn\nDsXFxRQVFdHd3U0ikcDj8WCaJr29vUSjUdVW6TCZHc0rXfdsNhsLFy7E5XLh9/upqKjAsiyGhoY4\nceIEixcvZubMmezdu5eenh6WLFmCz+dTTopyXthsNn75y18SDAb5+Mc/Tmlp6Zgau1wuUqmUcvuU\n8zH7umGaJqdPnyYcDjNp0iT+8i//klQqxY9+9CN0XeeLX/wiq1atQtM0otEoW7duxbIsbr31Vv7z\nP/+T3t5eqqqqeOihh7jjjjuoqqpSjqXFxcUsXryYTZs2YbPZOH/+PO3t7aRSKWbPns3MmTMZHBxk\n5cqV6vqu6zo+nw+3243T6SSRSChgUjrtyfV94sQJAoHAGMDPNE2eeOIJTpw4QV5eHp/61KeUI2FJ\nSQn19fU0NjayY8cO+vv7VTRxNBqloaGBp556ip///Of09fXh9/t56KGHKCsrU+Cc1+sd87y6e/du\nBgYGuOmmm97UHbW8vJwdO3bQ2dnJ7Nmzr8pJtauri9/85je8/PLLRKNRVdPS0lIFEct1IWsiayTn\noHRVvHjxIk1NTcydO5dZs2YB4tq7e/dufvSjH/HCCy9w+PBh6uvriUajRKNRenp68Hg8lJWVXfX3\nRLqu09TURHt7O/Pnz6eqquqtd8rpT05/iPcR7xjg99xzz/HFL36RkZERgMs+yb+dH3nMHOD3f1fX\nApVF2vZhjlzE49RZu3Y1999//xXfpLyTku59fQMjOKesx+6fePfN5aTb3egOP4mRVkhF0d0l2NxX\nZ0EPAoowRy6Sn+elq7uXyDXCheGBi/R0dZCX52fmTOGgZRgGy5cvJy/Pz9BgP7FwgGSoi/hgA4mB\ncySHG9GiPWipMJaZwF4wE++0W8bAfQDJZIqUmRLuSNlgyuBx4fxVUAPOYtmqLODFnoZSTBTkk46j\nFQBOCiK9gCngPisNP3kmQ7gTMxmjvaODgYF+BS7+viTHORyOCLCvbI0AhhTw4xT/ukuxfNNEu2KD\nkIoKGCgVh3A2fJMF9oGoR2JYxLiGWwVQZvNmoK+sOmm6DcIdwtEwFRZgUOkaAXy5SkRb9DSA5C4X\nkJUEkMyYgH5seeApE+fOZsE0Xbn5afEBAVcZXuFiN1QvAKhAA9poK1q0FwtdxOuOKXU6LjfQJCA5\nLDT/dBHnqukiflNCW+6yDMynO9IQUirtDqeLvihgKg0AWinRl9ggBM4LZ7tEMA2dxbOgs3bxAwKO\niw2IOuu2jGNhpuPp89uEK9vA6zB0UoBjhhtcRQLOiw+Lf4uWjI1MVRMlnjmeZhfQ2/AJAehZiTSQ\nFxQ1s+ehADUFOMrxzo7XTYNZ0W7h6oYl4M2iZeCdIvoaHxQwWqg5DXXpGSAxdEnExYaawIygWSZo\ndrT8WcJ9T57D5hGud4ZTAHnRXhEnO1QvHBfNuJiT+fNF//NrwJeGWpOhdIRtUvyeNysDMDrywDNF\ngGXxIdF3T5lYv5EuAT66SsU6ke6S8roxfEasoeKlon3SmRHEWCkYGNFn3ZaB5zSbgDGDzWKORfvQ\n4sMCdDQTYs14KjJzV8ZdmwnRxuDFjFNjNgRI1prEEm115IlahdvAiotrRNEyUUO7DwqXjt1fwrS6\nnnbbGzcH7HkK6tScxeL4Es7r2Y9mJbAXTMNVKa5z9rwqzHhAQH6jrZn5JuOUU1Hxt75asV4sU8B9\nJSuxrCQa4sMRl9s1Zimb8RDJ4UYmTyp/U5hNXmvPnD5FZLgNm38yut19xe0tyyIZ6iTSfpBoz7G0\nsyhYqRjxBMRSNkxTzEtNt6MZdvGvbqBpurqzTX5wZ9MhOZgBEU3TZMeOHTz++OO8sn0HFy+10tM7\nyODQCLF4EguLVKiLxLCIVTDcVw/6XS30mNOfpnLvI3LKKaeccsopp5xyyimnnP7/UO793Tsnp9PJ\n6tWraWhooLOzk9OnT7N9+3ZaWlpoaGigvr6el19+mV/96lc0NjZimiYbNmzgs5/97Lvi+yGAZ555\nhqamJqqrq98yGjBbbrebcDhMIBCgt7eXqVOnXvX+p06d4umnn8ayLAoKCujv7+eOO+5g8+bNtLe3\n097eTldXF8FgELfbPSFK1eVysXDhQnbu3ElHRwcbN25UwN66desoKysjHA6TTCbx+/0UFRUp8Keg\noIC8vDxSqZSCXbKl67oC/AzDGANByTYNDg4qqEsCa3a7HZvNpsArCfxJaZpGKpXC7/ejaSLZSoJF\nfr9fASqpVIoTJ06oKF+5/dvV66+/rtzbiouLqaysxOVyTTi2YRhYloXb7SaRSKgxqK6upru7m2PH\njnH8+HFAuGNZlkUoFFLRzYFAAK/XS15eHslkUoEv0rlO13Xy8vKorKxk8uTJOBwOpk2bxvvf/35K\nS0uV25yEwwoKCpgzZw7Tp0+nubmZkZERotEo8XictWvXjqmvBMMkBFZSUsLrr7/OwMAAqVSKuro6\njh49ytmzZ2lubmZ4eBi3241hGBNAr0QiwYEDBxgZGcHhcHDPPfdw5513ArBr1y4uXryIz+dT2zud\nTjVPw+EwpmmqOSnnkwS3pIPf8PAwR44coampiYGBAeUwFwgE6O7u5tSpU/T09DAyMkIikVBxpHKu\nSac1ENcih8NBf3+/SPM6dIiRkRFSqRSdnZ0EAgHsdjvBYJApU6bg9/uV65pULBYjkUgoyGxwcJBT\np05x/vx5BgcH1efa/f39tLW1EYlE8Hg8KspZAnPZ45ENt3Z1dTE6OorT6eTmm29m5syZ2Gw2Zs+e\njaZpDA8PE4/HGRkZoa+vj56eHvr6+ggGgxOcBQ3DoKSkhGnTpilYUv6tqqoKp9OpAGAZlToe6pOS\nYyhhLE3TiEQilJWVqc/wy8vLVdu6urq46667aGxspKWlhXPnzjF//nycTieGYeDz+dSxt27dSiwW\n4+6771ZxyxKGkxHocr5LENntdit3t0QiwcGDBzFNk0AgwOHDh+nq6qK3t5fbb79dfcafSCSIRqPs\n2rVLjfmlS5eYPn06//AP/zABys3+v81mo6amhsLCQo4fP04kEqGqqoq2tjZSqRSf+tSnKCsrG/O9\nht1uxzAM7Ha7ghNTqZQC2CorK9m1axc9PT0sW7ZMXed/+tOfUltbi81m48tf/jJlZWWqHdXV1cyb\nN4+enh46Ojpoamqirq6O1157jZ07d3L48GF6enowDINVq1bxuc99juLiYnXtlHXLns9PP/00yWSS\nO++8c8y4jJfH4yEWi9HQ0MCRI0eYMWPGZSE/y7I4evQoTz75JE8++SQXLlxQMGwikaC+vp6dO3cS\nCoWoqKigqKgIr9erniMkhCvjqaWz5qlTp2htbWXp0qXMmDGDN954g2984xsq6jc/P5+amhqqqqoo\nLCwkHA7T3d1NbW0tR48eZcmSJVcdXXvy5ElaWlpYsGAB06dPv6p9cvrT0h/ifcQ78or32LFjfPWr\nXyWZTI65yF2rg9/b2Ten/xuy2+18/vOfZ86cOYLu7uomNnCcSG+9AB80DcPQ8NoNJlVP4dZbb2XT\npk3vijuzQLyR6uruwTQ82POrr2lfe/5UNIcPKzpEtG0vNlf+NcUUexzijVE0lsQ5+UbMZIRoy27M\nWADLTKLpNnRnHo6i2dh8k9R6MtxFuCavIdK+jx07doypp4xO3rRpE6dPn2bv3r309vYSiURwu92U\nlZXR2NjIpbZuXBXLLvvGyOFwiBfc6TYoJYKABS7pfJaGebJdy6T7mW7PggPTTm6utINXMiQgGN0A\n0xQwTDq6NRaLsWPHTs6cOcO8efPYsGEDCxcufNvz5dVXX2V4OIBl86BVbhLA15vJM1m493XvEkCS\nI0/0v/eAgKTyZqWhobTLV6QbogMCzkqOCrc0m1cAVJ5JAj5LjkKkGyvYCIlQOibXgEnvBWdpFhcm\nHc0ssY1miNhfmw+6d4r6jZyGwnnAZaJh03W1vFMh2AKDR8R4jJEGiQBauBNsXiz/DBEfK49hmQIo\ns0zwz4HSVViaLtqXSMfdusvTMJoEm/RMmyENiGmZOFQzCYYhYDArkY68taejU2eJ4+k2sV2kOxOd\nKgHCZBC6X4PiFQKMk+CchPGCjcIFMBFARb/qmgAGw53p42gC0JQuctlOiypa2hLudIEGUWtZD90h\n2hZuEz92n3DO805Lj1k83V+bGBfpxGeZmdhVKwHOcgGPkXZ9K10rahK4kI6KPX7ZKamlYUlD10hZ\nQLQXy1Uq2mSlMmvPN13E6Y52QMdLGdCyZKUA9eQ4SRTMUSBcHiOdIqY2ERAgWela0Q/LFA59Mmo4\ncF643uXNFTDlaCv07BPOht6pmeNKUBTEWJjJzFhqhhh7S8/UTQKCqsMW6C7RXru4a9YKtYi+OgrE\nHJBzWboHajZR55AAv0SEdGF6rsg+ZztLZsGYziLh1NdzQEB+zkbRPgAAIABJREFU0T5xbv8sAScq\n18iUaLNsr+7IRExnzwH/TBg6gTV8Bi0xAoFGSIXRAHvBNDxVN2ZudtANPNU3EfeWE+s/QyoawBo8\nnmmnAkdBs/sgbzaWdxqYKTRNXLM9Hs8En1AZA5/9JvFKerOYdynLsogPnCXWf5ZUdDj9HG+IiGXL\nIhHowAp0iEje/DkCIh3zHKOBpovnFTOFZcbTd3EFQdPUh2aPP/74FSPrQ8FhzFA3hFpIxYJEOupI\njvaKeupv7fxqmQl1rpxyyimnnHLKKaeccsopp5xyyimnnN6+fD4f//iP/8iJEyfYtWsXJ0+eVLGM\nUjabjdWrV7Nx40ZlWPBuUDAYpLa2llQqRXX1tX0/BCIGtr29nVgsxg9+8ANM03zL2MDjx4/zve99\nD9M0ufnmm9mzZw8gwLJ/+qd/oqura8z2TqeTNWvWsGnTpjFtrKysZMmSJRw/fpy9e/dyxx13AOKz\nwo0bN3LzzTfT2NjIyZMnCYVCWJaFz+dj4cKFbN++nf37908AB7PPGYlEiMViY7aRoFb2d8fZ3zFl\nu6qNj9uU+413CpNuc+Xl5bS3t+P1eolEIjzxxBM888wzTJkyhVtuuYU1a9Zc92d6lmXxzDPPAGK+\nvtkX9hKistlsVFVV0dLSQltbG+95z3uYMWMG+/bto6+vj9/97ncT9tU0jaKiIoLBIIZhUFRURCwW\nU4CWjPD0eDwKdJs+fTpbtmxRIKWsjwSrIpEIyWSSwsJCPvCBD/Dcc88Ri8Xo6Ojg4sWLzJw5cwxI\nBpkYXl3XsdlsDA0NcejQIQUoybb29fVx4cIFKioq+H/svXmQXNV9/v259/Y23T37Jo1G+4rQDmgx\nQkISwhiBY7AxDi4XSVyGsoND4jhxCqeSVH527F9eY1dix8Y4xDbeTQJlgzEgCSEJ7Qta0L4Mml0z\no9l6777L+8e558ztnhmQsJ0473ufKtWoe+5ylu89093308+zYMEC9Tmypmn09fXR19dHMBjkkUce\nYenSper4MlZVuj3KczqOQyqVwjRN5dYIAjySEbxyzmWErGVZ9Pf309fXp0ClUChEZWUl9fX1DA4O\nKmjqypUrbN26lRtvvJEJEyYo969QKMTg4CBvvPGGipCW8KkEHjs6OjBNU8GL0j1MttHrXJnL5Th5\n8iSJREIdSzqjaZqmYLK33nqL1tZWZsyYwYwZM5RjmdzHKwmTaprG7bffTkNDA4lEgmAwSCgUYvny\n5Sxbtozz589z8uRJBTUGAgHKy8uZPXs2J0+epK+vD8MwyOfzHDt2TEXKAgqg0nWd1atXk0ql2Lt3\nr4LSSiOYvfUu/y9BwMHBQTo7O6mrq1ORz9OmTePKlStcuHCBlpYWHnzwQXp7e3nrrbd4/PHHee97\n38uaNWuUgyGg6s00TTWeuq4Ti8UU1CfHLRAIjLoXLEHO2bNnEw6HOXnyJG1tbVRUVLBhwwa1XTab\nBaCyspKuri6OHTtGWVkZjz76aBH45XVX9LZT0zS1Zm7ZsoWenh7S6TQNDQ3MmDFDOffJOk6lUpSX\nlyugUdZ9IpFQ8dmLFi1i9+7dvPzyy6TTaY4ePYrjOESjUT796U8rpzqv5s6dy2OPPcYXv/hFDh48\nqJwwm5qaaGxsZMmSJdx8883qXorst3QU9GrPnj1kMhlmzZpFY2PjqHOV6r777mNgYIC9e/fyla98\nhRUrVrBhwwa1xpimyb//+7+za9cubNsmEomwatUqpk6diq7rpNNpjh8/zltvvcW2bdvYu3cvn/70\np8eMr5fzGggESKfTtLe3UygUKC8vZ/v27Xz3u99V43HbbbexdOlStXYkk0kymQxvvPEGW7Zsoa2t\njX/8x3/kscceu6p+plIpgKsGAn35+m3odw74WZbFX/3VXylKHcSiPnv2bP74j/+YFStWUFdXx+LF\ni9XiV1FRoRaKwcFBWlpaOHjwIM8//zydnZ3qOA0NDXzpS196V7nYvn5/dTVQ2Zo1a7j++ut/b8A+\nqR07dpArWIRq5xaBDFcjTdMJ180j330QjcK4UISUiCm+RLZjD2VBh7lzZ3Pm7DlMR8O+fBg7lygN\n5oRMP4XBt9DD5YTrriNUe52g4Sunku06SGdXNydOnGDhwoVFu+m6zsKFC0c9D/DXf/3Xwr/KGPvN\nWyAYwDB0TNMWzm+64XJKLrhlyGXI80JQwn2OjYCbvKCFdMpyz1cEgLnAT3QSTiAGBQvbcWhr7+By\n3wB79+2naeIEbrvtNm677bZ3VT/SDcrWDAFWha8yfjJcrcBDHAuqFgqHNTPpQljHSnZwIFgFgYhw\n6MvnYSgNQyc9m9giWhMbRwtCwxo3KtZxuSMHMAQI526rwKFwlWjP5Z1oZhon2SacEDU5xoyMq5MV\nbcWFngJRKJ89AtE5loCzhs+BmUYbOI6TuwK1NwjwKtOFZmXQwjU4tUtwrPwItCmhLT1QPJcSgJIA\nl+bWiRZAzbNtwcAJ8XygzIXOJo/AoI4ptimf4UJnXXDlDeF06DjCyazndbFv+VzhIGiExHxkusQ4\nBGIiwjgyQcyFbYq+Js4JOFE6/NXdJJza5NxJSOzKIddpjxH4MFwzAjFmelz4MAlXDkO2T4Bh0lVQ\njolXXvAoMkEcx84jXOsMAYyWzxJ9SEpHQ1OMcSCGXjEdw0piDJ3AsXKYeROGL0B8JsUOeIY4dsPN\n0PGyiDrWI6LOIjVumdjuLpYYa2wBYcWmiEjk7q0C2ovUi3mwbTE2scmijgpJAWBG6kS/c/0uFLhP\nOPZJWFPCdjhQSI9AphLuk+55cn2QDnh46lm62AXjULcC2p4XbS2fUQwQyyjbYLlwQzRTAhyONo/J\n8o2Wu1FZk5jzwrAAUGEkylkzwNBFpLbjjp2MztVDI/Ujr9tQtfh/ut2NnNYxwuWE6+YTqp036o20\npmnuGj+PbPchct2HwRHfXHO0gIAn49NwIg2ga2iID/3C4TChcGgU3Oc4NvkrZ4gFDdasWTNex5V0\nXefhhx9G0zT27NlHpn0H2a4yQrVzCcSbAJ1s134KiXZwNJxADK18OoHySaAbFLIZMfaJi2CmcK4c\nQctdEU6LY4F3uoGmhUU9JzoJODb19fV8+9vfZveefWQKGpHmNaP+ngbMIJZRKeLVM904vfspDL5F\nGohOWfuO3+a9FujRly9fvnz58uXLly9fvnz58uXL19XJMAyWLl3K0qVL6enp4ezZs6RSKQU/LFiw\n4G3dg/6ndO7cOfL5PNXV1e/qZr+Mekwmk+TzeZ544gm2b9/O+vXrFZAA4r7r8ePHefXVVzl2TNxX\nWL16NdXV1WSzWWzb5rnnnlNudvX19RiGQTKZpKuri23btvHaa6+xbNkyHnroIQW6bdiwYRTgJ6Vp\nGrNnz2b27Nmj2r1jxw7lnjSWQqGQAsuki42EouSxveeRj2UUq7yP4wVoxpL3HnRFRYVyD5TRneFw\nmPb2dp5++mmeeeYZPv7xj3PjjTe+w6yM1qlTp5TzlXSdG+9zRC/wEwgEqK+vp6enhwMHDvDpT3+a\n1atXc/bsWfbs2aNiT8PhMNXV1axcuVI5K/7iF7/AMAyqq6uVW6EcI6873913310E90mnu2AwqCA0\nCUFWVVWxfPlytm/fTj6fZ+vWrQo+8ioQCNDe3s5Pf/pThoeH1XG8/dN1XYF4ra2t9PT0sGLFCqZP\nn046nWbXrl0Eg0E2bdrEggULVCxuKBTCMAwVwxsOh4lEIuTz+aJITnk9SVhS1lsmk1HuSL29vXR0\ndKhoVgnZDQ8P09vby4ULF2hubmb69OnKbS+Xy7Fz506CwSAzZ86kpqaGVCrFqVOnsG2bUChEfX29\nitGV7oR9fX20traSSCTo7OzEsizlOifbKdt39OhRstksgUCAmpoaysvL1bxJCK1QKDA0NMTw8DAX\nL16kUCgwb9485WxZaoDknaNp06Yp1zcZTyvV1NREU1NTUR3Kfu/Zs4dAIEA+nyccDpNKpTh37hyL\nFi2irKxsVB1IlzkJNr6dZL0DCgi8fPkyDQ0N2LZNPp8nHo9TX19PX18fL730Evfddx9/9md/xhe+\n8AU6Ozv5yU9+wgsvvKA4Ek3TFMTZ1dXF5MmTVSy3VxIozufzBIPBojVjYEDcp6moqOCRRx7hs5/9\nLJlMhuuvv57a2loFH9q2jWEYrFy5kp/97GcYhsGqVauoqnr7+8GlwN+aNWvYvn07g4OD6Lo+ykE0\nFoupa8rr5BeLxdS1WigUME1TXRdbtmxRrnU33HADd999N1OnTh23TQcPHuT8+fPEYjHKysoYGhpi\nypQpPPTQQ6otsma8jnheWZbF1q1bAVi/fv3bjoGUrus89NBDVFdX8+tf/5o9e/awZ88empqaqKur\n4/Tp0wwODlJeXs6mTZtYuXIlVVVVlJWVkUgksCyLu+++m7a2Np5//nmOHj3KV7/6VT7zmc+MCfmB\n+FvT0dHBuXPnFOD7/e9/H4B7772Xu+++u2j85VoZjUZ53/vex6233sq//Mu/cPbsWR5//HH+/u//\nflR9eSXhXRDXmi9f/136nQN+L774Iu3t7WpR0zSNu+66iy9/+ctva5ktXxjF43Gam5u55ZZbePTR\nR/nFL37Bl770JRKJBL29vTz88MN84Qtf4AMf+MDvuiu+/pv1dlDZ76t6enqwLIdQ/OqieUsViDdR\n0ANUxsMUCtYoKELTgzh2ATPZSf7KGXQrQywcYNWqlS7ln8ZBw7ItARtVzIZo44gzXPoyzvC5MZ2K\nQrVzyV0Rb96uZcwjkQhoGo41BoiEQF3C4TCWlcGx84B01nIhDdsEPcxookSCXx6YwwvVODLKtQQs\ndNx9ymeJ+Fcc4cQUn04qdZkLLe10Pv1Dzp07x0MPPTTut8rGk3RpJBAXbnpFINE7KDZZAD9mSkBG\nEzcIZ690KxRSos9aQESlRqcIKCo/KCAwPSicz/IDaI4Jji3iq9EFsBOsgPgUxqWOSoEiDBHL2R/H\nyQ1A7x5xjFI3QscZgdQCMRH9W9bkRvG6b5g1DS1SK+KWU+3Qu1+4+aHhVMxGu3JYfMOpYQFWOCze\n/El3MglvFtIQdmOYpeOgbLcEuLx9cSzh/pbtErXeuFaMlywk6YIGgA5GULgKhqqFc5+Vdd0ATTAz\nInp2wADNda4zosJFLjrJhRE9f6/CNcLZLtMB/UfE2PQdgPqb3XrVBLAlx00PFjve2TnXxS8M4Trh\nTJZuh779AoYDsb03jtg7H7Y58jh5ESpnu/22BUDnbWdYzKemgWEEhEV+QCd5+r+wrTxmwUTDwZEx\nxtFmt78lUK10HaxdBqHyscFD2VYtIPYPh0ag1uGzwoVOdEL0Iz4NBo4JZ8tglah7IyKgVz0grpOB\no+Cycdh5cfzcZYhUu+BnSTslKFrkqOjWTqZb/D8QF8BtsFycI1Qj6kGOr3TlK58lxldz1xPpOqk5\n7wD3ufs7jqiToRNgpdz2edYbTXdrMO/OtXetc6FWCeS666CGg+NYaNWLcapmQyRc4mpXLDs7QKH/\nLOXxOB/72Ef55S9/SUdXH1RMRos1CZ5QNwiFQgSCgVHLsFRh6BK6laFpSvO4b55KNZ4jb/ryG9hm\nTrzRDUTRapZilE8mEikjFA5hFkwKTkLUbuV8ATj37cdJton2Ndw8dp81HU0L4CRacKwC0WiUV199\nTUXWj+WIqxxmHRstPgWC5TidWykMvkU+1ki47rpx+3et0KMvX758+fLly5cvX758+fLly5eva1dD\nQ8P/mi9XSme7d+sMp3lSKdauXcv+/fs5deoUp06dIh6PU1NTg6ZpDAwMMDw8DAj4atOmTXzgAx/g\nb//2b8lms0SjUaZPn86qVauYN29eUSxuT08P+/bt4/Dhwxw6dIgvf/nLfO5znyMajTJ//nwAent7\nlTPW1UhGwSYSiTFjGGW/MpkM6XRaue8ZhlEEApVCTOMBgBLYkaCXt39yG03TqK6uZmhoiKamJjo6\nOlixYgXz589ny5YtnD9/nm984xv80R/9EbfeeutV9VPq1VdfVe5QmqYpQGoseaFDgPLycnp6eujq\n6qKlpYXp06czc+ZM5s2bp/ojx0dCkBL8Wb16NYODg1y6dIlcLqcgO13XCYVCLFy4cNRYeCXd2DKZ\njAKH5syZw969e8nlcpw5c4Znn32WD37wg0Vzf/nyZX784x8zMDBAoVAgl8sVAUEyhli6zcl/e/fu\nJZFIcPHiRRKJBI2Njaxbt454PE4ymSSZTHLw4EEOHTqEaZoKYpIuYnKOo9HoqBqQEFd/fz+5XI7u\n7m7OnTuntnUch0wmo2pIxvl2d3cDMHPmTAVSDgwMYFkWp0+fVrChYRg0NjYyZcoUQqEQoVCoKPms\noaGBeDxOf38/LS0t9PT0EAgEWLx4cVFc6JtvvqnAxYkTJ6r5KYVVJUgYi8Xo6uqira2NaDSqHMQq\nKyuxLEuBkfI6chyHkydPMmfOHBVBK90avceXkKdhiHsBBw4cwHEcVWPZbBbHcdizZw9z5swZBTXl\ncjnefPNNLMtSkcbyXJL9kP/kWEvJdpqmycDAAOXl5di2TTabJRaL0dHRwcsvv8yePXuoqamhpqaG\ngYEBKisrSaVSypUUhNujpmkcPHiQuXPnjrq/Kx0YJVBcql27duE4jtp38eLFdHd3M3nyZIaGhlR7\n5XHWrFnDD37wA+Lx+DWvE4VCgYaGBubNm8eZM2cUsFraXq/DaSAQUABnWVkZkUhEwYqylvP5PLqu\ns2bNGq677joymQypVIpYLDYKNN6/fz9PPvkkAH/4h3/I6tWrefTRRzlx4gTbt2/ntttuU2uohG1L\n5TgOP/nJT5TT4U033XTVY6DrOvfffz/r169n27Zt7Nixg87OTi5cuIBpmtTV1fHII4+o61GeXwKn\nuVyOmTNn8uijj/KjH/2IrVu38vWvf50vfOEL4zqn7ty5U/19+eEPf4ht29x1110qEry0fTIOOJ/P\nE4vF+Iu/+Av+6Z/+iba2NrZs2cIf/MEfjBqPlpYWBgYGOHr0KENDQ0ybNu1dufb68vVu9TsH/H72\ns58VPZ47dy7//M///K6cs3Rd55577mH58uX80R/9Ee3t7Zimyec//3lqamr8G6y+/seVzWbBccZ1\ns3snaXoQHIf6+no2btx41THF69at4xOf+IR4cRCIQf0KN3a05DoLj0BYpU5FgXgTmZ4j9PT0XFOb\nGxoaMM5ewEx2EYiO/Qc1FA5jmia5XB6svIBXgjEoDAnopnwmYxB+clC8I4SCZzLihTiBuPucM7KJ\npkF0goCHjBCOI4Cf8nn3UhhqJdWxm1279wLwyU9+8prWI+nSGKyZT17TR0cPl0pzY0kdS8A8FbOE\nQ1yqVTiTlTWIf+Mp0wNY4GjKBczRAi5kFBD9tk0BjQ2fE8566kVcic2YFyiyTeFIVzFbuNrZOQFa\n1S0vrp3EuRFIbeIGN17UjZBVLnye47uQDJ1bIdWGlm4XwJURxLHzhLQ0eSuLY7qxpbl+wIZcj3AV\nlNGwXlBs1Ji6Ea2J8+Jx7VIRsyql4D6tBFZzxDUgoTMjCjXzBKRnpUU7QPSxca2A78aENzXQdTd6\n2QUG050CYqsSH4KQvFg8bqHq4v1FQxEOlbpwvAuUexzv6sRzgKYHcGxzZGyyvYAtZtdMCQAqOml0\nK90X5mVlZUXwVn6wBSefxLJNHK9bYv8bAnaL1BYfKN0loLtAXEB5KsbWdPviRghrgZHxkjG20WbX\nxS4h3A7DrhOdkxPjiybWBIbEemDlxJg1rBE1mbokIDzbBCcqtkleEg6YpXGtYrBG5tr7O8eGxAXx\nMD7Vjat2Hf/0wMjYSukBcX3ibhPxfDA1/pdEPdu4boZlDcJ103GdDZVLpWyvgYoutiUsXAKz6kER\nn+s+1mwT58oBrOEzpMM15KtmE6ubga57PwArdnhdtWolGzduRNM0nn76h6T6jxCrmnBNMfCxcICN\nGzde03o5liPvqVOn6OjowjLKCDVvIFzeMA5c6K7rck3p2iogv8g5qJwz9gkznWhmCgeLQ4cOkcmZ\nRJrXjNvPUQ6z4WqovwmnZze5vpNF7oiO4wi4vv8cdm4Yu5DEySXQgnH1odXvm6uwL1++fPny5cuX\nL1++fPny5cuXr/8+vVMaxLXoxhtv5IEHHmDXrl1s3bqV7u5uksmk+n1dXR3r16/nlltuoby8nN27\nd3P27FnKysq4++67ufnmm8c8bkNDA3fffTerVq3ie9/7Hi0tLXz961/ns5/9rIo4lHDZeNBaqVav\nXs1zzz2nALCxTBUkfJTP50mn04BwrxoaGiIejzMwMKBAv1KXvvHG1RtpWRoXq2macu9qaGigo6OD\n8+fP8yd/8iesWLGCX/3qV/znf/4n3//+96murmbx4sVX1VfHcThy5AiFQoGJEycq2O3tAD9d15WL\nWSAQoLKykkQiwZkzZ5gxY4ZymvM6r0nZts3BgwcpFAoqyrJQKBT1F8S9yY6ODqZNm0ZtbW3RuHkj\nU2WKiwQEvXGttm2zf/9+hoaGuP3225k8eTKO4/CDH/xAza2Ej+Qx8/m8gjQloCgd3mzb5sCBAyqK\nNpFI8Dd/8zdqu2w26943zKkxkJ+xSiex8YDFSCRCIpHANE1SqRRnz54tMhXKZrPKyc8LTmqaRk9P\nDxUVFYRCISKRCEuXLuXs2bMqjjcQCDBx4kRmzpypakiCcF4nPBDg3Zw5czh9+jQdHR1MmjSJCRMm\nYJom3d3dpFIpdTxvX3Rdx7IsLMsq+kw5Go0yYcIEurq6uHDhAnV1dcp90+ua19vbq9py8uRJJk+e\nrCKGS2G7UCik6tM0TYaGhjh27BjpdJpIJKJqKRKJMDw8zL/9278xYcIEKisraW5uZvHixUWOaPK4\nEqCT96He7rNx+fl5b28v5eXlCir0rhUSXu7o6MAwDO6++26mTJnCqVOnSKVSqu42b97MgQMH+MM/\n/MMxzyX7XjpX3d3dHD9+HMMwWLJkiQKyZb155wVQccfBYJBYLDYmvDze+aV7I8D8+fM5e/Ystm1z\n+fJlBeyCuCZDoRDZbFbVmHccZfywrCcQ0KtlWbzyyits3boVwzCYOXMma9euZc2aNRiGwZEjR3j1\n1Vc5cUIkoG3cuJFNmzahaRqf/OQneeKJJ/jFL36Bbdu8//3vH9cIJ5PJ8JOf/IQdO3ZgGAaf+tSn\nrtk0BwQE/uEPf5h77rmHU6dO8dWvfhXHcXjkkUdYsGDBmAlVQFH09Uc/+lH6+vo4evQoW7Zs4SMf\n+cio85w4cYLXXntNXffd3d1MnDhxlEmYBMi993Sy2SyhUIiysjLuv/9+vvKVr/Daa69x1113YRgG\nqVSK119/nVdffZXLly8XQcQtLS189atfZf369e8IWvvy9dvQ7xTwk5nt3j+cf/mXf/kb3wCdNGkS\nTz31FPfdd5+yLn3sscd46aWXfi9tuX39/0fv5Gb3TnLsArjfZrqWmOJXXnmFwcFh4YY0ccNoFzav\nPBCW16koEG8C9w/StWjNmjXs3bef1JUzhOuvHzeaeOQ9mRtfGpvqQlEXhMuVHhqvwSUPXXc0CeuU\nz0RF+nq3lxG+mgFOATufQNN0QlXTMMIVpC6+xJ49+5gzZw4bN2686v5Kl8ayymYssyR6eKzW64aA\ns2QEZ9kE4KgAptBcEMoLF7nPaYaAnYZOiMHT9ZGI17KGESAo1yNiLK2McIzL9kL9ShWFXCTHKQaK\nHAunrNF1vgqL+uvZJQC3ilkQaRQxqSDido2op63OiAufHsBBR0PASU4gBtWLoG8/mpUVL+41C6v3\nMIWeI2BZbkF4gKdEC1TMGXEu0/SxXbocR0xxtkeAX0YUyiaOQJSOjFjVRNwuXmDKPZ6EzsykgKca\nb4HuHcJlzQiL+NaIC106bqRsKTAmFaocAQYT511Y1Rmpz7rlJXAfHmc20xNRTXGM85VDMHQesFyH\nxhjEpon6SV0S4xSfAuk2AeYF4hCqRNc1941HuAiacoBCwSSbuIzZ/roL0oXQ65ZhX94t+lRIQfsL\nYmwCMeEyGZ/pudaki52Mz3aPboQYdZ3KC17TR9w0kxchfIO7gS4AU00X/W5Y7cYdH3R/bUC4UTiQ\nqukLQceLYt7SbWINUb0bY268SreLegnEBMRoF9w6Q8yDAunyjESDa6KNlvxgxRn/+KV9VxHkkeJ2\nZS4LyNQLJOvGSHSxXLfUOuYeL9uLg42GJmrUccDM4JjtFFJtDPXsJ1w7h2DFFKxU1yiH14ceeghd\n17nttts4e/Ysu/fsu+YY+FWrVrJhw4Z37v8Yko68119/PZ/73OfQA2EizbcQqhptW170QZYEMcPV\nIga7ZzcMnRFgcun6kBuA3gMCftYMui/3YAcqCVaOfHPKAcyCiAKxbMs1kxQgoWPl0bSwcFo1oti5\nBGayk0C8ifyVU+T6TmHnEmLmHEfMhx4glSnw/3zl8d84/t2XL1++fPny5cuXL1++fPny5cvX/27F\n43EVI/lu5DgO6XQaTdOIx+NEo1E2btzIbbfdRl9fnwJSJGwiP4MqFAr85Cc/IRgMsnHjxquKna2r\nq+PjH/843/rWtzh58iQHDx7khhtuwDRNBcddrerq6liyZAn79u2jp6eHSZOKv4wu+yWjYSV4U1FR\nQW9vr4plvVpAUm4Xj8dVbC2MRPqWtj0SEZ/RplIptf9dd91FPp/nl7/8JT/60Y9YuHDhVX2ml81m\nlcNcMBhUzlq5XG5cyM8bGRoIBBQ0JtsjJZ3PJDBlGAbPPPMMnZ2d6tzBYJCysjIsy1K1IseztbWV\nvr4+br/9dpqbm8ftQyAQIJfLYZom2WyWyspKNE1j+fLlnDt3jjNnznD69Gmam5uZMGECra2t5PN5\nBeTJ+/3SuU9Gw8r5BVSdSte4cDhc5Ponx6O5uZkPfvCDvPjii1y6dAnDMEbFmI4n6WbX2dlZFK0q\nIUPZV9keb9Rtd3c39fX19Pb2MmPGDObNm8fx48dJp9OzW7w4AAAgAElEQVSUlZUxb948bNtWAM94\n0dCyn9Il8tKlS0SjURzHobOzE8dxqKmpUXCaPL+stbGOG4vFlHPbnj17VLtDoRANDQ1MnTqVnp4e\notEogUCAvr4+9u3bx8qVK9WxA4GAgtMcx1FAZz6f59VXX2VoaAhN01i1ahW7du0qGjvbtuns7KSr\nq4uzZ8+ya9cuotGoml857l6VQnVjxVbL8ZTnkQCdjK598sknOX36NN/61rfo7+8nmUxSX1/P9OnT\nyeVyartLly5x4cIFfvnLX3LfffcVtWE82bbNM888A8CyZctU3G0kEsEwDDKZjIKF5dhICFU66Zmm\nSTKZHNMpzyvLskilUgoWlfUcj8dJp9Ps37+/CMCW15J0XvRGksta7u7uVi6AjY2NCj7N5XJcvHiR\nCxcucObMGb7zne9QWVmprrVAIMAHP/hB7rjjDtXmlStXkslkePrpp3n++efZtm0ba9asYfXq1dTU\n1CgQc/v27ezevVvFS3/qU59i3rx54/b7ahQMBunt7QWEIdh4aYLemG3JLITDYd7//vdz9OhRdu7c\nyb333ksoNMIVHDlyhG9+85vYts2mTZtUrPnatWuL4qKlC6kXmJUuk0NDQ0SjUa677joaGxu5fPky\nR44cwbIs/v3f/12Na1VVFZMmTSIQCJBOp2lpaeHYsWMcO3aM5uZm/vzP/3xch0Ffvn4b+p0Cfm++\n+aai9wFqa2tZvXr1b+XYU6ZM4ZOf/CRf/vKX0TSNK1eu8PTTT/OpT33qt3J8X77eja7Gze7tZCY7\nMQxNWc5fTUyxbdts3rwZWzOgZqlwQbsalTgV6WV1Ci68Fi1YsICJExq5+FYHhaFWQlXTin7vOJBO\np0agDekYFmkQQI2ZdF38ZoxzhjGgGi+sExEW1djuH2Pppue4byb0INjZIujSKKshMmkVmfadbN68\nmQ0bNrzjmzfbtnnzzTd56623sEyTVDoPQREdq8CQseBGzRiBomwLFfUqY2gl6Cad5uQLQ8eBy5sF\n9BOMCegs2jziAiYdASPVAirL9UHfQUi1if0bxv6GnhgTARQ5Zn6EjQrG0CrnwdAZnEIS+o+Jc9um\ncMeLNADCYUw6FipXORdmKnorEm1yndsKOHoQDANsk0hQJ2OKb2I5sSlQMR/6D4n5zPa5McNXodQl\n8bN8ujtmHqgK3ChUj+Nj0Zx4oLNUq3C8LJ8OgydEm6PNIwAeJc5upbJNDzCYgkyXeN5Mu8eaNLoN\nXpc5b5SsY0G4HowyEc3sDIz8rjDkOgKGwMyghSpw6m5CuwJkOqHndZyaJTjRJkBTcJ8D5HM5stkM\ndqIdp/8NMNPCALLmRqxM30ibZG1aWfEvd0WMj22K6ypSJ8DA0eTo6DH2qqxRxOwWEiPn0sMu5Ir4\nv5WHfL/or10QkdGhKoyKGWhR8Y03NAcq5wr4se8ABCrEOjaqLSXKDYj4YxDrjLzGgnExrrle4Vro\nvX5ldwLlYPeLNkl3wbFcJdXpXTdEXJhWArGBqIBwE+dFJDPec+kj+1ouwIrriGib4njJFnGsirlQ\nNQcNDcfMQfYyJFpwCilyXQfIdx8iGI4QCwWUw6t3fdN1nYcffhhN09izZ981xcBLSPA3kYw4t41o\nEXjnlWW7gK6mu2PijnusGQJRnEISLdMN0YnuuNli3es9gOYUCJQ3Yw63YgHh2rlomq6uA/HGzXYh\nvVGTh2NmQQ+gVczCGThG/spZ8v3nKAy+JTY3olA+Eydcg6YHCBgaQWuA1JUzv3H8uy9fvnz58uXL\nly9fvnz58uXLl6//3Zo7dy6RSITBwUGSyeQ1G5IMDAyQTqcVxCOlaRr19fXjOkgdPHiQ4eFhJk6c\nqCC9q3Hfq66uZt26dQrykPeGvPDg1eq9730vb7zxBt3d3cRiMaqqRu5TpVIpBYYBRdCGdKyqr6+n\nv79/TOBpLFiopqZGxbdKFzIJJ8q+S0czCfiVuir9wR/8ATt37qSnp4eTJ0+yYMGCcfuXyWTYvXs3\nmzdvJpFIKFe10v6MNe6lDl3efkkHMQkrerVv3z42b96snPe8sanyfKFQSEX12rZNMpnk5Zdf5q67\n7lLxrt7zyZ8yYlWCdvl8nt7eXv7yL/+SQ4cOsX37djo7Ozl37hy5XE61H1DwmIRrstnsKOMQCSZJ\nwK+6upr+/n4VyywhuCVLlrBu3ToCgQDf+c53GBwcpKys7B0/W7Vtm1wuR6FQoKurS/XLO77eOFzv\nTxBx2olEgnA4TCKRIBKJqGjXuro6BQHJ2pOxtBI6kqCQhP/q6+vp6Oigt7eXmTNnks1mSaVSo+ZN\nHs87F5ZlFQFIpmkSjUZJp9Nks1kFfeZyOVKpFBcvXsS2bSZPnsyDDz7IN7/5TVpaWigUCixbtky5\nEwaDwSLYrLe3l/3799Pb24tlWTQ1NXH27FnlullRUUFzc7Ny8czlcvT19dHf38/w8HARRCnrQNM0\n5ezoHWNZ2/I573Vt2zZlZWXEYjF1jYZCIX75y1+yY8cO5Rb5X//1X/z6179m2bJlrF27lubmZkzT\n5N577+UrX/kKL774IvF4vAhe855bAnKWZfGDH/yAQ4cOEYlE1Bf0Hcdh4sSJ6LrOm2++yV133TWq\nziR4LGNxTdNkeHhYuSJ610k5Zl6ny1gspq6NGTNmcPbsWbZt2zbKYVWOUSqVKnJflDDu7t27MU2T\nxYsX89hjjxEOh7Ftm3w+TyKR4ODBg2zfvp1Lly7R09NDc3Mzd999N6tXrx4Vtwywbt06amtreeaZ\nZ2hra+PFF1/kxRdfLNpGztnMmTN54IEHmDVr1qjjvBtt27YN4G3NJGSNhUKhonVq0qRJTJ06lUuX\nLrF//35WrlzJG2+8wdatWzl9+jQgHGUfeOABnn32WSKRiOKSCoUCqVRK9UuOr+yr/Bsl3SJvvvlm\nnn32WX71q19x8eJFAObNm8f69euZO3euAlXj8TiZTIadO3eyZcsW2tvb+T//5//w2GOPjVqDffn6\nbel3CvjJP6ogFqfFixdfFXXvpfzfTg888AD/+q//SiaTwXEcnn32WR/w8/U/qqt1sxtLjmOTv3KG\nWNC4prhpCUwQiAuoyrEZO1Z0DHmcivJXThXBhVcrGcH49NM/JNWxGyNcoeIQHSCdTpPLF5QLFek2\nKAwLaKOQADToPyxctcJVjMBCEnTz9scpgXVmuQ54bhympo9sqyJ8y8BMjIpNDlZOJdt1kM6ubk6c\nODEuRGnbNlu2bGHz5s10dV8mkUjgEMAu5ECPqnY5Zg6M4JhxvZoewLHyAtSxcyPPif8JaEtBPu4L\n3StvCJc6IwoT1rlxplIedzTHQkW8BiuhS0TjMnxOuFyV7iNmbeQ5KyfGOD8kHjdvQsteFq5tibdw\ndEPAb0YITTeK4CZNFwCSY5VYx7tRwFrlbBg4hhGbSKB8EvkrZ8ibaRw0HHQBZ8aawErClcNiXoPl\nY0BbJX2Q7XUcAcSNgss0T82M8zdHQmem+y05MyUgtvg0AYcqp0JdAJEyzhXHhRqlq5/7r3ymiIRO\nt42cs3wWRfHAsj3OyJtMEV+suRG0ltg1PgMGjwu3vrobxe+yPWJOCwnAxNF0NM0hOPE9aP2HKAy+\nBX0HcAJl5OPT0QtTCIajZNLDmIlOnEQLmplCk8BisAotdxkn8ZYA7IJx0d5IveirlRfgW6IFrAFw\nSvqhBz2uifLag7H4OuVKp+Jp3ceZy2KHXD9cfs39nQu7mikw01jpTvRgHC0+A6d8JlrFTJxMl4Ad\nu7cKx8PoJA8k6XHydGwBA/ftF+eOTYH4rJFGxqaI30v3SG/jHbctwbiIxs5dEXVp50dqArfPqibc\ndUhKD7l9RLg4FjTRr3S7il8eLXukGbJO0l0iQjpUDQ0rFaCohYFoA07FHBHT3P8GupOjobaKhx9+\neNxvnQaDQT75yU8yZ86cUTHwjm3jIPqiaQ6BYJDKymps2+bUqVMsWLDgN4L8ZMR5yAXvxpRc3owg\nju1eG3Lcy6fDwJsweNod324YPg9WGg0IVk0jXL+IxNBbOI5BwagmP5wo/palhJRLrukRYNnECVah\nOTaFIRfs04JQdyNOpBHQ0DTx5jIajaJpzYTrr/+N4999+fLly5cvX758+fLly5cvX758/e9UOp0m\nkRCfQS1btoydO3fS2trK/Pnzr+k4ra2tBAIB1q5de01fHt26dSu2bbNq1SoF3HidoN5OS5cu5aWX\nXuLUqVMKjrqW+1NS8+fP5wMf+ADPPfccFy5coLm5mfr6euUUl8/nGRoaUqlsMrITUBCUpmn09/cX\nPS8/1/PeW66urqayspJgMKigRwm5BYNB5UI2ODhIOBxW25SXlxe12TAMbr31Vp577jm2bt06LuC3\nd+9evve975HNZosiaiV4JduYyWRUtHFp9Kh020qlUsoYR3y2qKm+eyGowcFBvvOd7yjoJBaLqfN6\n43klxOY4DsFgkEKhQDqdVhGWpTUgzyEBP13XFch24cIFvvjFL3LPPffwta99jZ07d/LEE08o+CUS\niShgzyvvOUrjemV7Fy1aRCwW4+zZs5w/f17VxRtvvIFpmqxevZqDBw9y6NAhuru7aWhoeFtINZ1O\nKxdDL9TnbYd0GJSPS8chkUhQU1NDLpcjGAySTCYxDIPa2lrl0ihBudL9DcMgl8uRSCSUu1wsFiOZ\nTDI4OKggr/Ly8lFufRKU88aQyjbK+S4rK1PzPXXqVAzDoFAoMDg4KO5ROg6Dg4NomsZnP/tZ/vVf\n/5WOjg5aW1uZMGECM2fOpL6+nnQ6zdDQEOfPn1eApZyfTCZDOp1WMcP19fVYlqWc5CzLora2llwu\nx/Hjx4siwmX9mqZZ5Dw3FgPirWt53ci57enpUX154YUXAGEWFYlEKBQKCl47cOAAzc3NPPjgg8ya\nNYsPf/jD/PSnP+XnP/85Z8+eZePGjcyfP1/Vs9TBgwd56aWXOH/+PMFgkE9/+tNMmDBBQbMLFy4k\nFovR3t5OS0sLS5YsUe20bZtYLEYsFiOVStHb20tjY6MC+byRyF6gUSoUCqFpGidOnMBxHBYuXEhb\nWxvnz5/npZde4o477igCPb1j5IVIL1y4wM6dOwmFQnzsYx9TY6frOpFIhHA4zO23387y5cv52c9+\nxp49ezBNkwULFowJ90ktWrSIhQsXcuHCBbZu3cqpU6dIJpNqTiWge/HiRb70pS8xf/581q9fz6JF\ni951BK1pmrS3t6NpGjfccIN6vhTKlHUkXR6lk2Ymk2HhwoWcOXOGp556iqeffpqhoSHl+Ddp0iQc\nx+H48eMKwAuFQgwODqrHgUBArdGlYGg+n1fuftK18ujRo8Tjce655x7Wr19fFO0bi8WUS+Odd97J\nrbfeyr/8y79w5swZHn/8cf7+7//+befAl693q98p4Dc4OFj0eObMmeNu672IiuLZ3kahUIhVq1ax\ndetWAPXHa8qUq3SA8uXrN5B0dJPxudlslnA4jIYDZnJMN7u3U2HoErqVoWlKM9dff/1V7yeBiWDN\nfPKajmObY0JmY0rTwXUqKgycpzwWeVdv3saLfcznC+RzORcWuyBczaQcW0ATjime794CtTcK8EXG\ntDqWgGaMgOvS5IF1ok2u65/HuU0LoMCe4fPiOUN860wPFb950zSdUO1ccleOsmPHjjEBv0KhwLe/\n/W327t1PJmdiG1H0snqs7BBaYQBiE3AcCyzXscvK49gFND1YBI84I8QOpDpFjQTdb+/pwREHOjku\nqTYYPCb6U7sUwjXF8JR6recFIa3iGMvhMy5gVtTroh+gQf6KaKNjQd9htGwv1K8S0aLDF0Az0KJN\nbgzrGCpyPdPQ9NBIXHHZRBg4jmMXiDQsVBBMoXUnOGkYOA6V1wk4VUJdXZuhZpmAtiT4qFwNEfXQ\nf0QAV3poxLHRC9DpAU8nx3GXk5CZjPM10+Jc4XpU/OxYbm0OkOmF5AXID4v61QJifBwL8smR9pZ5\nv53hAVZVvbrOjlZuZBs9CLFJIprZLghoE4SLYmyqcOvrfwMKCfSBN4hO34AWX0s+1kiu7yRWdhAG\njpMbPEFO03AcG6RlflkVaDpWegCMAE6qQ0TQ1iwV8b8KlnUg4Ih6Kp8FnS+DmXGhRk9/Rl2jYxUI\njLhpBlDRt44NibMCJNQDwo2vfKbrFOmIyNp8PyTOYxeSAp7M9eHU3STiojUdUh1upLSMrm4U0Ked\nF1Be4sIIwBmbIqKr5Xqh6a6LaMSF7jrdeGVZK25Mb3ymuB6TLa7znhtPbI/n7OheqNK9L+GuQ/Gp\nYNaKmu/bL2pegqyqdPWRqHLp0pkfgoEj4rhjxdJqBlowihObDME4Tu8uhoeT9PT0vO0HeBLMljHw\n27dv5/Tp0/T09FAwLQW15i2Nrp4Beq7sZd/+A79xDK2MOA/FJ1IoicpFA0M3it7MaoZYAxR8F6oV\nP5NvCahRxmdrBloojuM4ZC8fFsCeAwXLAU3WrbymdRxNE3HHyi1RQH+ObYr1XA+IdlhZnGA5NNwC\n4Uo0UJESoXBIVctvI/7dly9fvnz58uXLly9fvnz58uXL1/8eWZbFkSNH2Lp1KydPnlTPy9jT1tZW\nmpqaipzs3k59fX10d3cTiUS49dZbr7od6XSac+fOEQgEWLJkCaZpKvexq0lrikQiLFq0iEOHDnH8\n+HFisdi7ukcEcM8991AoFPjVr35FZ2cnnZ2dxONxBepIiKM0LlX+jEQi1NfXk0qlSKfTBAIBBVNI\nCLC8vFw5vFVXV5NOpxW8YRgGVVVVlJWVqfvTM2bM4Ny5cwBFQInU2rVree655zh27NiYToGvvPIK\nP/7xjwGYM2cOGzZsYNu2bQpMk5GWMoK4UChgmmZR3K6EpUDUzdDQELquM3ny5CIXMDk2ly5d4j/+\n4z8UUOiV1yFNPtZ1XZ1DxgYPDw9z6dIlpk+fPmp/CSTJfVtbWwmHw8yZM4eWlhaeffZZhoeHmTZt\nmnKpkyDRWJIwnHRP9H5uLEGlZDLJpEmTWL58OVOnTuXVV18llUpx5coV9u3bR21trQJuCoUCHR0d\nhEIhKioqCIfDamyy2SyJREJxBBJ0LIWspFNgqZOcV945kduEw2ECAfHZsAQavZJOeJ2dnQwPD49y\nprNtW7lYwkhEsDy/BOgkYCbdHb2Rt7J2ZGyrhBVBOFdWVlYyODhINpvl29/+Np///Of5h3/4B154\n4QX27t1Le3u7gqjkeUOhEFVVVaxcuZLNmzerqNyKigpmz55NOBwmlUqNCUuGw2EaGxtJp9Oq1gDl\n/CdrfLwYY2//JWAo3fC6urowTZPy8nJWrFjBhg0bmDVrFslkUkX67ty5k507d9LR0cFXv/pVHn74\nYVatWkVVVRX/8R//wZEjRzhy5AgTJ05kzpw5wMi6KNeBeDzOo48+yuzZs0mn0+TzedWWBQsWsH37\ndl555RUWLlyo+lMoFIhEItxyyy289NJLvPbaa3z84x/HsizleumtP13Xixw1Q6EQHR0dnD59mlAo\nxPLly6mqquKpp57ipz/9KbZtc8cdd6galG5wcq6TySRvvvkm3/3ud7EsizVr1jBjxugkPDmu8Xic\n+++/n3w+z5EjR3juued45JFH3nFOZs2axaxZs3jzzTf54Q9/SHd3t4JAZf2apsnx48c5fvw4tbW1\n3HfffSoS+lok3QwlwCqd+WzP/TZZ/3JcotGogpdzuRzRqDDeGRgYAER9yuMNDg6yY8cOXn75ZXVM\nCfbJ60D+XSxdF+TvZYS4F96+/fbbWbt2rZqnUChEJBIpWuvk7x566CEef/xxWlpa+OY3v8mf/umf\nqjb78vXb0u8U8EskRByffKHwdnbUZWVl6sKW1rpXYyFd+uLkzJkzPuDn63cqr6NbW3sHubyJ7Sat\nCk7LxLEdUq07sbQIkcoJbxdeCYCV6SfbsYdYOMDGjRuvCZyQwERZZTOWqWOarpOUfpUEfdkE6D8K\ndoGmidOuCS6UGi/20YpOxkn3CicwNBFTGZ8u3Oh0F9rL9gl4x0wJMC14FMpnu7AOLqzTD4mLI7BO\ndJKIRe3bD4WkgDz0oHB/K58poCEzjRaMQaYHDQjVzB7V7kC8iUzPEXp6ekb9zrZtvv3tb7N7zz4y\nBY1I8xqClVMwk12kLm7GGT4PVdcJmE8Pug59JjiO62jnjWR1XyQ4Nk6qRfQpc1k4skWbQAsJCCpz\nWQBBhWFRUMGYgOQcFzbyHE9lTHohKy0gXBkDUTEumW6ITvDsJOUhBRMtAkaqWgCJCzjJVrRCEvIJ\ncVzdwEEDM+seRi9y8huJPtbQjJKYYj1YtI2EYDKmgdXxmpjPS8+ItskX/2YKevcKaCs+XTjK6UEB\n3QyfEu5n3jcKtgfeUd1zeMeLzvFAoVrABYW0EWDQCI8+ZuIcDJ0ZiZb1quC2JdcrXCM9/R85hje+\n1Y2ptXMeoFACjRI+NMU/6QynaQJUC9Widb+Kk2yn0H+acN11hOuuI1Q7j0KinXTLZjRbxC876AQr\nphJuWEAg3kTy3POAjVMYFudvuAVC5e65xngDpukQrgWrQ/RNQml2wR0jXbTNGzVcKuliF4iNrEvJ\nFiikRJ8aVou6lftbOXHMsnoB1aXbRCRvulP8rL0J6m+GyHkYOi1qZuCocGSUDn6yDoPlwp2vXMJx\nHjBPMyBUCelh10W0WmyPO5dGQKyPgbgbJd4lxt8xPXHQmntcDRUvDKKmvFHiZRPFc7kB8Xz3qwLG\njU72tKkE2E21i35JoDk+Xow5Ak4LVaHX3UCmb/9VR4/rus68efPYvn07g4PDWE4AQhVuZO9E4aJn\nFcS691uIoc1ms9i2TSqdwzaSo6JyTc1SdehYBbRAWETm6mKdc6wwI66J7prnwtS2mcMeuCgga3lA\n2zO23mvaQYHX3g92pLOfk7PUfkbdDeixGnRdfOAi46+lHMBUsGIQp3oJid69PP3009TX17No0SLf\nyc+XL1++fPny5cuXL1++fPny5ev/Qzp69Cjf+9736Ovrw3EcQqEQlZWVGIZBIpEgm82Sy+XYt28f\nS5cufcfUpL6+Pg4fPkwwGOTOO++krq7ubbf3St6HjcfjhMNh5fSVy+UUQPROqq6uVsDUqlWrqKio\nuOrze6VpGvfffz9Tp07lhRde4Ny5c2qM5O9Lwb6ysjIcx1EucuFwmHA4TEVFBfl8XjmdScclwzAU\nmNPf318U/atpGgMDA4TDYeU4d9NNN/Gf//mfBAKBMcHFqqoqQqEQ+XyebDZbBEUeOHBAwX0f+chH\nFIwjIz0HBweprq5WEbuWZSkwSYKJ3rEJhUKk02kFcz333HN0dnYyadIkAoEAyWSSw4cP09raqtzS\nJFCVz+dV1LA8nuy3FxILBAIKDDt58iTTpk0rGnspuW9XVxfDw8M0Njby+c9/nqNHj/Ktb32LF198\nsQjaymQy6h6+BJnkOEuwpRTu857HC7A1NjayYsUKXn/9dXK5HN/4xjdUFKqM/5VOWr29vUVQo3ds\nSyE4bx8ty3pHyE+6DMoxk32T25V+9p1MJjlx4oQCGg3DoKKiAsMwsG1bufnJ+43SgU+Cf3I8S6OS\nw+GwOuZYLnjeGgeIRqPE43E6Ozu5cuUKX/ziF/m7v/s7Pv7xj/ORj3yEH//4x7z88svkcjni8TjL\nly/nxhtvZPny5fT19fHKK68oIHbhwoVF15nst9cZ0jRN6uvraWtrUxHCErb0ulPKqOFSCFWOp67r\nVFVVqblubW0ln88TCoX40pe+RFNTk9o3HA6TyWSIx+N86EMf4v3vfz9PPvkkhw8f5sknn+Qv/uIv\nWLVqFfPmzWPr1q3s2LGDrq4u2traVEpkWVkZEydOZMOGDbznPe9RkFUgEFDrnYTY8vk8x48f5+c/\n/7m6zuU1t27dOl566SX27t3LBz/4QaqqqgiHw2rMgsEgZWVlCk6V8Kau67z88ssALF++nIaGBiZM\nmEAul+NHP/oRP//5z9m9ezdr1qxh0aJFxONxBTUfOXKEl19+mVOnTqFpGjfddBP33HMPmUxGOX+W\nKhgMous69957L8eOHePw4cMMDAxQXT1eYtqIdu7cyXe/+11s26ayspJbb72VW2+9Ve2bSCR4/fXX\n2bZtG5cvX+ab3/wmFy9e5K677iIWi121o18wGFTr2dDQUFF9yJrxXiu5XE6BdDIW3OvuV1FRwQ03\n3MBNN92k4pDPnTvH7t27aW1tVe6QNTU1KpGp1C3Re355DUSjUYaGhpSb5p133inuDbnrk7e93mhm\nENfw+9//fr7+9a+ze/duzp49y3ve8x42btxIc3PzVY2TL1/vpN8p4Ff6wtFLqpfKm0MOAlqaPHny\nO56jdGHyxgL78vXbVqFQ4IknnmDn67vI5W2cQBSnYi6EG4Tjj21C9jLa4Akw02QvbcZsWE6sYfaY\nN/gdx6YwdIlsxx7Kgg6rVq1829z5sZTNZoVDlxEkbISxrAyOnUfTSmCr8aQZONgYOtcMF3pVGvt4\nqbWNTN8RAb8YUeFEVzYRr7MdaAKgqZovYL3hM5AbhMIRdzuXnJRAVKBcADn5AeHeVKr8EKRaXcjG\nEGBSqhU9XE4g3jS663oQXOv0Um3ZskU49xU0YjPuULHDgXgTergcK5cQAE58ijuMwk3OMXOAg2Ho\n7osBFBjiJNvI2lnQNaxCEm3wTRg8UWJ8pqFpOo4RgopZot/SIc2zDeC6ILquaBKy0gNiv/6jkLzo\nAn4uhCRfuEgIKt0pAKRgHKoWQqwZurfhZLrQ1LYIYDJgq30d23QBNRcuBOFgV1pvLuxUGo9slNVg\n1SwRc25lRTxz5RzhqJZud2HOpIiplfCf474B1IMCfNTcuNP8AEQnuvCc6+blmGA7rhuap9a8ktBZ\nMO5Cde742i7op9y9EOPfu0c4uUGxY5weFP3MdAtg00wJuFIPu/2Xjm8l8a2lboxeJ0fHdbHV9GK3\nP82NCg7FcWqWQO9e0m27KCS7CdfOIRBvIlQxGd6EHUkAACAASURBVHvijWQ7D6A5FrHptxc5idpW\nwYXXwgIwU86WDuPGEcemiVpJvuW6QrrXppVDOdXZBTDkfh4VudhNFzWT7RORzI4J1YtELDJyVxey\ntN1IayMkoLpAhYjkTXdC+CJUXS/AvfgMUTPD5yHTIWolVCHin+MzxPpS9GbHC95qrgujcN+k+1Xh\nHlnWyEj8tQGVc+HKIdd5LyqAPxC/k457cqzkOOYHi6PEpQNmw3tGaqlnt6il2FQBsgaiQALSnlrS\ndAE/1iwV9WObHtdKb7d00YToRByj7B2jx1U9jAMyl8bnGtE69Kq55PpbGO7Zz+Ytr3Lw4EGWLVvG\n2rVrryq6t1Ao0Nvbi21bOIU8GPG3j8p1LBwrL9ZWOd62iYpPDlUJV8NoI2hBHDMtrsNEC05hSIxX\ntlvUg3JVldM1sr45lHwzV9Mh3yf210OUT5yHPsbfUgfIu1b8lmWPwIrhCaBHGRoe5Mv/9/8yZfLk\n38j10JcvX758+fLly5cvX758+fLly9fvj3bu3MmTTz6J4zg0NjayevVqVqxYocAwmfz0/e9/n4GB\nAQ4cOMDEiROZNWtWETgnwYNLly7R3d1NMBhk9erVfOhDH3pX7ZKfbwUCASKRCNlslnQ6jW3bInlq\nDCAEUPG5tm1TXV3NAw888K7O79XKlStZsWIFH/vYx5QTkhcekgCXhMMkOCEhOwlzxGIxLMtS4IQE\nGLPZLNlslnw+T09Pj7q/IyN7y8rKFAS4b98+HMfhhhtueEc3RS/4YZomP/zhDwH48Ic/rKAfELHG\ntbW1dHR0KMgPBPBVXl6uIL9IJKKAJwl79fT0EAgEME2T4eFhdu3aNcr5LBgMKhc5GU+Zz+eL5lGC\nfzJG0+sUKONjOzo6SCaTxOPxMd3rLMvi6NGjGIbBmjVrsG2b+fPn89GPfpSnnnpKQYZe+EUCOHIO\npCTQVCovjOPVpEmTmDBhAm1tbViWRUVFBevWreOGG24gHA7T1dXF1q1bOXjwIJlMpiiy9PLly3R3\nd7Nw4UKi0agCHOX5pAOYhMfGi1Ctrq5WzpDScEjCa6XjlUgkOHbsGKZpUlZWxoQJE6ipqVEQIaAi\ndHt7exkYGMCyLLLZrKpf2T4JcUk4yHs+L6Doje71uvjl83kcx6Gqqop0Ok13dzd/9Vd/xeLFi9mw\nYQMPPvggJ06coLW1lfe+9708+OCD6vwSnJRxphJClXGvXsc2CauBYDhqamro6elRQCMI8FOCZd76\n8zpFeuelsbFROdt1dnbiOA6f+MQnFNznBfzy+TyWZZFKpYhGo3zqU5/iG9/4BkeOHOHZZ5/lc5/7\nHFVVVdxzzz2sW7eOn//85+zYsQNd17njjjtYuXIlc+bMGTWXXjhL0zR1DQD8+te/5vLly9x55500\nNDSQzWZpbGxkyZIlHDlyhK997Wt85jOfUdebYRgK7pPXBQg+Ztu2bezYsQNN07jjjjvUeNx2221U\nVFTwox/9iPb2dr7//e8TiUSoqakhEAgwNDREMpkkk8lgGAb3338/mzZtUutAMBgcdT15+1hRUcGy\nZcs4ePAgr732Gvfccw9vp8OHD/PUU08BcNddd/GBD3xgFM9TXl7Oxo0bueWWW9i8eTPPPvsszzzz\nDM8//zx1dXXccsstrF+//h3h9GQySTqdVmthc3OzWtfl+HiBORmlLaOuc7kcLS0t2LbNddddx2c+\n8xm1rufzeVKpFDNmzGDTpk08/vjjnD59mr1793LnnXeqeSpdD8Zz+NyzZw+FQoGbbrqJysrKMeHl\nTCajHBvl3zfpptvU1MSlS5dIpVJs376d119/nU984hPvyvnQl69S/U4BP+nYJy8K+U2SsVRRUUFf\nX5963NnZeVWAnzdLHRgT1PHl67ch27b51re+xWs7dmI6IZy6GyE6SUUIKkAh2oBTMQsu74BsL2bX\n6wxfOUKk/joC8SY0PYhjFzCTneSvnEG3MsTCAVatWslDDz10zQBAJBIBTcOxCoSilZimsAd2rFxx\nXOqYnbJwzDQa4ptK1woXlsob+/joo49yqbVNQCsNtwgwTzNGYjqlc5mE0hpWQcVc6HwF7CwYnthW\nOw8Y4rEE+8aErLoE6OOkxHGHL4AeIFw3f8w3sI5dAPdbYkXDYtvCpjpnEmleo+A+EOtZuO46Mh37\ncXr3C8cv6Wqm6WhGSEApmkZ5RbnCiaxMP6nOPcTKQlRU1NB5uQ8j1oRjFbDMHA46TiAO8Wk4gycE\nIFQ2EVFXJQ5pmkFRNKoXiNJ0ATVxFAoJikA1CfnZbvRn/xvi+fh0AZUFYlDtgndOHjQXkJMQHYyc\n17EFFCUOLFz9SpXpFnXhiUd2AMd2hCNZIAam40JeU0Tbg+XCbS3bA8lLIoq3MOQCnlGouUH0L9sj\nQKnEBRHzqwXA8MBmjiXGpAjyE20ths6miZ/BOBQGhUtdpHYECrPtESBLD0LdcuEeKV3bpEKV4liZ\nLujZA1gCPAt4rZddgM423Tl1f6KPzC1OseMdCBczxxHgkwT+yibgBGKQH6QwcBFz6BJ6uJxw3XU4\n4XoxzugEK0scbR1LjEQgNhKHWxpH7Niuw6E7ZmWN7lylRP+ik1DxwhK8lMBfaaxxus3jYtcEyVbo\n2wdWWhy3erGnce546gY4bo1beXHMcJUY+55dAgCtuA50N+7XtkSNagEonwb17xmZPwkNqjfvnjfx\nmU6w0miaAfHJAh7s3SPGNT5djE8gKkDhYDnk+qFrm4CVo82uQ6MExdzoX8eFyq4cEo9jk6HqOlQN\narpoX+S8AJrzQyKOG30EQJM1HIhCzRKxziGihx3bBAw1VEUxs26NvVP0uFfjgcyqXCiB2MKN0HAL\nVs9OrvQPsm37zquK7pUg4dDwMDgaWr4fRkHXGioq13Jr3TZFhbrrKulu0aqyiTDp9pExs92o7GAl\nVF4Pg2+KKO9Ei+veWLI+KZiWoqVBjX/ioqiriulYpo0eLH3zhrLyd1zgsAhWrJwN/UfJ5Kzf2PXQ\nly9fvnz58uXLly9fvnz58uXL1++Hjhw5whNPPIGmabzvfe/jfe97H5FIhFAopD4Ts22bG2+8keuv\nv57vfOc77Nu3j7a2Nrq6uqioqFD3ItLpNKlUSjl4bdq0iQ996EPXfH9IRoEmk0nl4Pb/svee0XFc\nd5r+U1Wd0ciBCCTBJAZJlEhKFIMokmJQsjUKPvKZHe3u+KzklWV712HPzvF4P+w5M+P1jNdy3PGM\nrRl77NV4RrIlJ8mimMUMkmACSIIZgQCIDHTurq6q/4db93Y3AFK2Rtr07/ccEkCjwr2/e291N+rp\n9w0EAsr9SLoJyuhBeZ9Egn2WZRGJRNA0ja1bt35gUYKXL19W94TLy8uJx+M4jkNJSYmCLCREJCM7\nJYyVSqXwer08+uijAHR2dtLW1kYmk1FOct3d3QwODk6JSL127RqVlZXKua6trY36+nqeeeaZadsZ\nj8fJZDIKcJI6efIkExMTNDQ0qHZIeTwennnmGX7wgx8wNDSknNwgB0pJpz55TNu26e3txbIsKisr\nSafT3HvvvTQ1NTExMUE6ncbr9TJ79mxSqRQ//elPFeAnxymRSKjx8fl8qn4Smky7f8eVIKCMxi0t\nLS1ov3RfO3DgAMPDw1RVVXHvvfcSj4sErbvvvptFixZx6tQpVd/y8vKCuSMBHBl/eTODHwl2TW6D\nZVk0NzfT399PaWkpX/7yl9Vcbm1tpb29nWw2y7JlyxgcHOTy5cuYpsmFCxeYmJhQcOeMGTNoampS\nMczy2BI6k/WRgKXsT2VlJcFgkGAwSFlZmXJYzGQyxGKxAhg3nU6r9lRVVTF//nx1vMkqLy+nqqqK\nrq4uent7SSQSCgSUjoOyLqZpKlhPur5JxWIxFRMs4UMJCkqQSDrijYyMYJom7e3tnDt3joULF9LY\n2Mj169e5du0a3/nOd/B4PJSWljJz5ky15mpra7EsC13XVcSpjJ6drm8zZsxgdHRUObB5PB7VBzk3\npPOahCzzvy8tLcUwDC5cuOAaAdjqfrKcl/nzJhwOE41GFeTn8/l47rnn+OIXv8j58+cZGBigpqaG\n06dPs337ds6ePYvjODz//PNs2bKl4Fg3c23bvXs34+PjCiD867/+a44cOcKJEydYtGgRK1asoLq6\nmhUrVtDe3s7Fixf56le/yrPPPsvcuXOVm55t2yriOJ1Os2vXLn7zm98A8Md//MfMmjWroA333Xcf\nK1as4Fvf+hZHjhwhHo8TjUbV+pZuiP/hP/wH1qxZo/ZLJBKkUqlbGmppmsYDDzzA8ePHaW9vvyXg\nl06nefnllwF48sknefLJJ6dsI6890oVz48aNBINBXnnlFeLxOBMTE/z2t7/lt7/9Lffddx+f+MQn\npn0eSSQSfOMb31Dr8ujRoyxatGhK2yXAmM1mVU1jsRihUIjR0VFaW1sxDIMXX3xRwX0yJh1Qa+aZ\nZ57hz/7szzh8+DAf+chH1Njng6Q3g/zOnz9PX18ftm3z+OOPT3FQdRxH3R/SNG3K6wAQ8e8///nP\nWblyJT6fj4MHD/K3f/u3AEXIr6h/sT5UwG/GjBkFP0cikZtuO3fuXK5cuaJ+bmtrY9WqVe95jq6u\nroKfizdOi/qwtGPHDt6VcN+M9WiB2mngORdQ8JVC48M4oyeEQ1t6DHPgBObgKWSWr2FolHgNGmfP\nVC9i3o+7T11dHcbFK2Rj/XhCNeqJU0B+GbC1W7gkOWipQQxdY8WKFR+ou9DQ0BBoXrSa+3AC1S6A\nkQcyaR4BkUkoTdMhUCVAv8FDwmGs6TEgCwOHId4JqQEBvVTfmxfp6eQc0krnCzAneUNEbmYT6F4/\n3qpF07YxG+vDMLQpFvnt7e303xjANkJTASnAV72EbHwQc7wTp28X1N4n3O80CXBpWJZF1szi8ehT\nXBo7OzvRtTGCM9dg+MtdiCajbKyd0VPiRLrXhffMnEOapiMc+yaBfxL6s9IoUkXBV3kgmm0Kl8Ox\nU2Bn0cMzccoW5F5cuuCdkwWt6i4YOysguorbc+Oke0S9bRfwm85RzLEhcnlKPHImnRYvBDVDuG+N\nnYFEt3AadBwB7sW7IJtw3QEdsa0RdEHRCvGYhK+ycQGRlbjjpOnCLc3KiP0dF/zJ0TuF0JmvWjwW\nniPOG70mQFNZj+ilHNzXsFnEuMrj5Mu2XLe1ZqjOwMjRHFyke1Due2iu4aKZixfW8+A+xxb1BnEs\nTRewk3RKVC57efXzluI4NlY6SrL3KATqcGwHDYvohV+i6R50fxm+qtvEutcNsU4U3Odz4U+3XtLh\nMV+l82CsTUBTnrCogwRrc4PuzgnXhS8zISJ1HVsAcr2/dYHdtIBH6zflQKv8kmpum+xMDtSUbp8y\nfjpyQewQvSLcHu2MW5NFYv8C6I0c4yn7lpmA4ePgmGjeMGULtmKOdJDoOSDgWukeKeeUdBK1Eq6T\n3znhlBlw3f6yKRdKvQaW+0GDktlQuyYP3HP/03ThPuivEUCzlRBt91WLmmbGxHb1mwRsmgfvKSga\n1yI/HzB0S3er6PF83QpkloecFmILzYCaVTB4EBsPsYz3PSE2CRLa+NB0Gyd6BSpvz9VmkjTdg6Np\nYh3bWdFPyxT11XSourtgX0etJdepr/IuGD8r5ltqwIWYnUnzIjf3HBwBSwLEe0RMOQ5OeJ77qbjc\nNc4hry7gurdOei0QbACtDSNYha/uLuK9hzh46AgAL774YtHJr6iiiiqqqKKKKqqooooqqqiiiirq\n/zJls1m+973vAfDoo4/y1FNPqejAfOm6jsfjIRgM8h//43/k8OHDvPzyy6TTaeX2IyGC2tpa1q9f\nz4MPPkhtbe37aldJSQmzZ8+ms7OT9vZ2li1bBqBc7OQ9Bwl/Tdev8+fPEwgEWLFixftqw3TasWMH\nkKuHjFI1TVPBhtLJLpVKqVp6vV5M0ySbzTI+Ps7KlStZuHAhIyMjXLlyhXg8zqlTpwrqCIWARiQS\n4cyZMyxevFjcPzMMZUgzWfv37wdgyZIlBWO5e/duADZt2jStecS6desYHBzkF7/4Bf39/cRiMSor\nKxXokUwmyWazZLNZYrEYo6OjOI5DTU0NDz74IG+++Sbl5eV87GMfK+hDNpvl7bffLuhPKBQiFosp\n8CoUCikoUrqmyehLGc8s4SoZXytlWRZXrlzh9OnTDA8PU1FRwWc/+1nKysqUYxzA+vXruXxZmCQk\nEomCuSNjTVOplHIZyx8DKQm0lZeXU19frx6XTo11dXWEw2FM0+TGjRucP3+eQ4cOqehOCWbJvl+6\ndIkbN26o4/T29irAr6urS8WuAgrsk33Od9oDaGxsxOv1UldXRzabxefzUV9fT2dnJ0NDQ1RUVKj+\n9PT0kMlkKCsrU3DfZEmwTcKO8+fPZ2xsjFQqRSwWo7a2dgrAJtuW/7OUhGNLS0tVJLGEEPMjQmtr\naxkbG8Pn87FlyxZaWlro6OhQLnvnzp2b4iIpY0flMeT30vFTtkdChHIcampqaGhooKenR803OQcn\nA3TSgVAeQ9d1LMvi3Llz6vuamhpeeOGFAgAv/zgSCpTwsowEXrFiBfv37+crX/kKHo+HsbExBXgF\nAgHl+Jg/TvnnkKDasWPH+PnPf04ymWTJkiWsWbOG7u5uXn/9dXVdPHv2bMHYpNNpOjs7+frXv86C\nBQvYsGED9fX12LZNJBLh5MmTynXS4/Hw7LPP8uCDDxbUBoRZ1WuvvcbZs2cpLy/HNE10Xec//+f/\nTGNjI1/72tfo7+9Xka5y7JPJpKptPhDqOE5BRKx8PrmV8RbAoUOHSCaTzJ8/nyeeeALTNOns7FSA\naTAYpKamRs0hGc39yCOPcPnyZY4dO8b999+PbdscO3aMo0eP0tfXx5/8yZ9MiXp//fXX6evrY+bM\nmYyMjHDo0CGeeeaZAnAuX/K6Lcc/Ho9z5MgRUqkUy5Yto6GhQW0rr40SmAdYvHgxhmEwOjrKzp07\nefrppwvGYLooaTnGr776qnJNrampwTTNgnam02kF98lo5cnKdzN8/vnnqa+v5/XXX+fll1+msbGR\n2bOnsgdFFfW76kMF/ObNm1fw87Vr12667cKFC9m5c6daUIcOHeL555+/5fEty+LgwYMFF+b3slgu\nqqj3I9u2+fnPf45pgVOzHC1Yd1MwQcnwoNXci+OtQBs+gtdwWLRoAalUimAwSF1dHevXr+eOO+74\nF93sX79+PUdajhIfuYC/9g40TSdUIl7gK2DMNgt5JJd/0Q0NEl2UhAJs3Ljxfbdhstrb20mnTRxP\nKVrJLMEO2VkBQekubFXg0pXn/FUyy4V4osLJLNQA/ioBpRmBnBugbQLmpDNr4hgSUhvcj5PNYI5e\nwF+zpGBLx7HJjFygxCvsx/O1b98+0qaFr3rRlKhKcN9UzXqABAjIb/CgiCEuWwDBejQ0HCtFMnYF\nYlenuDT++Z//OdJ10W01fr8Pn99H1swS0z1iuGxTwGmaPalO+lTwLx+IyiZdh700ZEYETOWYkOjP\nRX/ioJc2Uz5vE+gG0Uh0CnjnpCdEHK4ZL4gjdqkWVDQw03xaKdaDlk2gB3LxyA6QSqfFtrpPOLiN\nIWC+6BUX1krkD5KAwXSfcE3zlbtH0YVbX/kiGDkpIDJPmeukmOdIZ2dcl8M8+CY9JrYHAa1piFr5\na8TXbFwAouFmcaiJC2Lbmvvy4L5JcmT8ri7OW74EJs7lHO9K5+dv7NYNdx/N/dltd6LXhQ/DAmJ0\nnNx2utcFidw3FKEGAd3pXuFmFruOPXQE4r2uAx9Y8UGxeayfzOiVHPDorxbHkC50VobCi0QeSIgj\nwLn0qIAdB/aL8SiZ7gWohBR7BAxoJVxSrA/l+ochnPtUvG1+bWQpNAFqKhDSEXUumSMc74aPCggY\nRHS3pgtob2CvgM9KpMviJFmmcFYcOw12Bg0d3V+G7jpzpkcuYiUGcQJ1or9WKlen8DxxzkSvuD6N\nnJzafgmalS2AmtXTO6g6tmjD8FGxj+6+MahcJsbqxrviOEMHRdSzNwzh+RCoyQF+upEbLgcFw+q6\nfsvo8Xzlg8yeslmY0V4yo5ew0xEcO4vt6DhGECc8By00szAmPDwLRktwrASh5tVgWzeF2ApAwlnr\nSQ+cmBJxPkUaqPUk13GiD6wkmrfMdSl1JR1FXcBe7K+DJ+COUyt4K8T1QUGW7kmkq6msZWYMho7l\n1onuxbZzf9gCASnn4D7/9K8FdPHHI8fO4quYg+EvI351G4cPt7Bw4UK2bt16y7EpqqiiiiqqqKKK\nKqqooooqqqiiiirq/ywdPnyYkZER5s2bx1NPPfWeTne6rhMKhVi7di0DAwP8+te/5vbbb+exxx5T\nUMCcOXP+xaYlmqaxadMm/uEf/oGWlhYF+AEKBsp365N/x5eOeW1tbaTTaebPn8/8+fNvdprfS/F4\nnNbW1gJXMtkO6SYIAk6MxWJYlqWiPqUbXSqVoq2tjXvuuYfe3l4FWcn25ruxTY43lZDSpUuXKCkp\nYXh4mD179vDggw/i9XoV7GXbdgHIJ5VOpzl//jyGYXD//ffftJ9PP/00oVCIf/7nfyadTivwKRAI\nqGjhgYEBDMPA5/PR2NjIf/pP/0ndK89PtZPt93q9yhVMPubxeApAl0gkoqBJGQsaDAZVXaWzXjqd\n5vjx4/T39ysg7+rVq+o4VVVVfOlLX2Lu3LkK1gMB1qxevZpXX32ViYkJ5Q6YHyUray/HK/8ep4QO\nJdw5f/78grGJx+PK9aqkpITx8XF+/OMfKwiytraW++67j9raWrq7u3nrrbdIJBL09fWpczuOQyQS\nYXx8nIqKCpYtW8aJEycUvAWoNsivss3z5s1TMGZNTY2KPa2rq+PKlSuMjY0pJ0TLshgYEIlLs2fP\nnnIvdzKol+/Q19zcTEdHBxMTE5SWlipAaLoI4PxjTUxMYJqmin+VIF0oFJpyfo/Hg8/nw3Ecbrvt\nNu644w6+//3vK4e8/DhdGQcMqHpIV0/psJkv6dIn14xhGNx+++2Mj48r109ZXxkpnQ865gNock7U\n1tZSVlZGb28vH/3oR6eAl5NBP+kuGIvF1HZr1qzh4MGD9PT0UFJSgq7rVFVVUVFRQX9/Pz/72c/o\n7u7mIx/5CHPnzi3ok+M4XL9+nX379nHgwAEFcUnHz6amJkKhEEuWLGHRokWcPHmSs2fPks1mVQS3\nz+cjEonQ1tbG+fPn1ZhIVkXO74qKCoaGhujq6qK2thbbtrlx4wYHDhzg8OHDyqn0hRde4OjRoxw9\nepTjx48TCATo6uoilUrx3//7f6eqqorFixfz4IMPUl5ertwzvV6vgimvXLnCwMAAmUyG0tLS34mV\ncRyHXbt2AXDvvfeqiGPp5JkPg65du5aHHnqo4LibN2/m+PHjnD17lpdeeomnn36ab3zjG1y/fp1v\nfvObfOlLX1KwXTKZ5ODBgwB8/vOf58c//jFXrlzh5Zdf5tOf/nTBXJGS8ysYDBKPx+nq6uKtt95C\n1/UCh8bJ0chS0uGyv7+f3/zmNwQCAfX8K/uX/72M3P3ud79LZ2cn2WxWOY9OBlgl8FxSUnJLN8X8\nvnz0ox9lcHCQ/fv388477/DJT37yPfcrqqib6UMF/GbOnElJSYnK0863yJ2se+65R33vOA5Hjhzh\n8uXLLFiw4Kb7vPbaawwPDxdc/G+1fVFFvV+1tbUxPDyCY5Shhee8N9wnpelopXNxxs+SyUT52Mc+\nxt133/3e+/0euvPOO2mon8HVzl7MiW58FXOmAGOZTAbbtqR5ILou3lA48R4ydorGhpnccccdH1ib\n9u3bh6PpUDrXddPSc3BPAeQ32aXLhYpK5wtnsolzkB4WbnyaB616OXqwSvVlquSDuoBhalbhDB4k\nMdCOGZiN3+/H4/WgAeZEF7qVpHH21L4PDg5iWQ6+cMOUM0hpukFo9gYyJTNID5/DTkdxxs6IdjsO\nmmPj6BAuCU5xaZzsuqiOKb/xlAhYKTUo+jGdm5nmKXTt073in6NDahiwhbNZ745cXRwL7KyIu6y8\nG0/1EtDFp/iyVp4DlgLvosKBbvSkiFX1hHNxxI4jokhjnS5QKCMyw+Cvg7HTODjYJcIBy+f3kzWz\nWFkLBeJo7out9JhwTZN9l9HLmTEYOurGu7pxxRJwRIPSRaKv8R64sSsvPlfPbYOdg38kVCWjU8sW\niXbbpoCH0MTPI8dF1KeVFDX0lIjjTisn52InYS5Ng4o7Rc1GTggw0S/HWQNt0uSVsFF6zIW+cOFD\n6TCmuTDbpFhgzf3jj22K9WAlxdqyLNC9OAWxr45wq3Nk/V1QUMYFT5Et4ok11yFS90Ld/TDwrojZ\nHToCY+3CGS04Q8C3TlZAudHLwmXPNnPH9oaF8+boGRGFHKrP1UrW0cn/Xv5OuugZ4udgHYzraJjg\nCeLoATQFvek42SQMHhBj5gK3aF5wMi7gelkAlJouQDEziuHPfZLICJRjJYYEXFkyW6znbMJ1vuxE\nuOi5bn75YKfXBTJTwyJSOtYtAMGyBRCoFdvYGUgOQfyq61DpCJDUUyaAw4F33fFy3HjyBJAQ6yB+\nXcC24bluZK87N5L9AozNRMDJYhpebF8Q27Fu+sknqX379pHKZNEDtcQu/lJcw+QvHekOOCbWhrcU\nyhcK50F5TS9bgDN2BnPsCiXNG28KseWDhL6KZrCS00ecT5KmaTiuIyqZceE6CgJABhd2tIVTLeSe\nV1Q8sxvPbWemXh8c3HWoqWM58etow8fQHBM8ARxLXC/zn2sccP+Y5Tr33ey1gHtN0AyxRo1gFYGm\nNSSv72fHjh3v27G3qKKKKqqooooqqqiiiiqqqKKKKqqoD1eO49DT08P4+DimaRIKhWhqauKNN97A\n5/OxYcMGFbP7XtI0jWAwyLp169i+fTsdHR185jOfobLyJh8kf59as2YNr776Kl1dXbS1tbF06dKC\n30vHtcmKx+Ps2bMHXddVTOYHoY6ODuW2FMw5sQAAIABJREFUJeNgTdNUzlMSGvP7/YTDYeLxuHL3\nk05Wtm0zMjLCP//zP9PX10c6nXbv21gq8lcCS5Od0SR0ZhgGAwMDlJeXs3fvXlavXk0qlcIwDPx+\nP/v27WNwcJDKysoCMDIWiwEQDoffE+R85JFHuOeee9izZw/vvvuuituVffF6vTQ1NdHc3MyMGTM4\nduwYlZWV6LrO2bNnGR0dpaoql6ziOA7V1dUYhkEqlSpwhSstLSWVSqkYVVlfCaZIoFM6N2qaxvDw\nMCMjI4CA0KTL3dKlS3nxxRepqakhEokoCEwCUNFoFE0TSVUzZswgk8koyE9KujBOlmEY6nHbtunr\n6yMajbJw4UIFJem6jt/vV85rg4ODNDQ08MQTTzBv3jwFoB08eJBMJkN/f786Xj7Yee7cOVasWEF5\neTkrV66ko6ODsbExta2E46Tz4ezZs6mtraWkpIS77rqroA2yz9lslo6ODpYuXcrExATZbJZwOKwi\nhPMlAavpoLaaGpG4JuHExsZGFZ8s2z85MnRiYoLR0VFAxAjL30mXvengQLleenp62LNnD7FYTAG8\n5eXleL1eFcUtnQAjkQjxeJzy8nI136XyoSfpQinHS9d1qqurVXQqoCBECfTmb+v1eonH45imybx5\n8/iv//W/8jd/8zf09/fT1NQ05ZzyvPlfpTOdz+cjEAiwYMGCAjdCXdcZHR1VcGckEmH37t0cPnyY\nBQsWsGzZMoLBIKlUinPnznH+/Hksy8Ln81FRUUEymVTXzMWLFwPiGrZp0ybeffddSkpK1PVLrm0Z\nMxuPx5XLYW1tLYsWLeL48eMKaHznnXd45513VH8cxyGTyWCaJiUlJTzyyCPccccdRKNR9uzZw5tv\nvqlAzpKSEiKRCNFolK6uLrZv387ixYt56KGHWLRoEePj47z77rvs3btXwbFSEvC92ZwB6Ovro6en\nB9u2efXVV9XjjY2NygFweHiY69evs3v3bvbs2cPmzZv5oz/6IwzDYPHixTQ0NNDf309HRwd33nkn\nf/qnf8qf//mfc+3aNbZv387jjz8OoIDG2267jdmzZ/Pv/t2/4y/+4i9obW3le9/7Hp/85CenvZ8l\nr+eXL1/m7/7u74hGo6xdu5YFCxYQjUYL1p9hGApYlf0Nh8MEAgFs2+ZnP/sZ586dY+vWrSxduhTD\nMFTN4vE4Bw4cYNeuXQwPD1NWVqYiu8fHxwscdiVAnX+dm04S4JbusRLy279/Py0tLfzhH/7hlOjy\noor6XfWhAn75VqkgJnNnZ6ciofO1evVqKisrGR8fV5+c+MIXvsAPf/jDaa2pDx06xNe+9rWCi1I4\nHObOO+/80PpT1P/bsm2b9vZ29aJe2oLX1dXR2dmJ5egCujF+z09UGV4onYc1doZf//rXHzjgp+s6\nW7du5Sc/eYV47yEMf5mKWtQAr9dTEC8oZSVHifcepsTvYevWrR8obDA4OCjgIn+tcBOLd6FlIsJJ\nUPOAr0xAfMEG16UrLxrUtoVTmmMLcCvRi+Y44AsTqJ6PbdtkMjaFbmN5YJ+MQwUINeF4SsCMkYlc\nxwzOEE/yThzzFn1PpVIC0nuPsdZc1y9f9WKysT7hfpWJ4phJ7NQYoUCQhbctwDRNduzYwf79+6mr\nq2PmzJn4vUaB66JUJpPBCc8RUE3kEpQvRsXO5tfJuUk0qqZD/Jr43l8jHMiycbGPpqOVNOHUrkXT\nxYuXifFxbNtxgb0hEV+bGRfQYGpYROP6KgVkdGOXgLTslGibGWOKMqMC8HJs0DzY41dIJAfJVCwg\n65Wuce58dDK5tnvDULPSBXDc8Zs4LyZx6RzxmOEj53bn0qq1a0Rf490weLAQENQ0EZ2aHnZBRPHJ\nE0pmiXPJ2NvMuIAYHQt8VQKUu7FLAI0gjjcdyONYLsjjoGKSpcoWiRplRuHGnjy4KP+TKDm4aAp8\nWBCh683b1skBTO4ccDSP6Hu8RzzuqxSRw/46AYVJ8C5yCceMinOMnYa6tTnQb5oYb+WM5tig2QI0\nrbhLrGk7I+bEeEyAuJP75C0TLoHx62AnhQtksAFwXe90bwGriLQVdZy8NiDaAGiGu669YdV/JzMO\nmleAaI6VA+8sU8z7kQlARiM7OcDVW+JGuHYIW/G8CGlf1W1khi+IeFdZD1+lgPeshDiubebAWjlG\ngVrhHNi7TVzjDB+YEQF4FnTUVn1CMyA1CoyI42m6mL/huaJ2nqDYPjmYW29jbZAaEfM7eknUNe9a\naGfBSY+BnaGzs5Pt27ezZcuWaa/vN27cwEyncMwehCtmSMBzoRk4uA6hLrDpmDEYPoGWGhJrTjcE\nPDl2BjsjbN5vBrFNdkS9ZcT5JGk4OIleUUc7DSWzcUKzxboWsyA3BpqeB4winns1nxib1FDu+qCi\nlb1AVjh2Ri6jZcUn67wVc3AcG3O8C9KDaOEchJ01s1iW7ZLy07gzSiVFVITuy71B85Y3k+o/Tl//\nDc6ePTvlD61FFVVUUUUVVVRRRRVVVFFFFVVUUUX971MikeDQoUPs2rVLwURSmqYxNjZGVVUV69at\nmxaWuJk0TaO+vp477riD1tZWTp48WeAW90EoEAjw5JNP8k//9E+89tpreDwelixZcst9YrEYP/7x\njxkbG6O5uZnbbruNy5cvk8lkCIVCzJgx43cGGScrGo1iWZYCxCRYlA+4pNNpEokEfr9fgR0SnpGw\nmWVZ9Pf3K7hmbGyMQCCgXP8ABXPkQ0ESSNJ1nUgkQiKRoLe3l+vXrzN79mwsy2LPnj380z/9EwDP\nPPPMlLhLYFpXqelUW1vLxz/+cZ566ina29uZmJigtbWVlpYWAMbGxhR0JiVhpL1796roSPl4c3Mz\ns2bN4tKlS2SzWeXyaBgGJSUlqob5oJsEejKZjHLOmzNnDjNnzqS/v5/r168ryG3Tpk08+eSTGIah\nIjxjsRhHjhzh8OHDChZKJpPYts3g4KCK25WRv3I8M5lMAWxpGAahUEhFLluWRW9vL4ZhcPnyZSor\nK5k7dy5z5swhlUoxMjJCNpvFtm28Xi/79+9nYGCA5cuXEwwGuXbtGul0moGBgSkOc/L8p06d4q67\n7iIcDnPvvfcSi8Xo7e1VTmqhUIj6+noqKyvxer0Eg0EWL16Mz+dT/RgfH+fixYvYtq2c+06fPq1q\nPx2nMBnuy3cPA3HvduHChZw6dQrTNLl+/TrhcJjy8nICgYDaz7ZtkskksVhMOYNVVVUpuFQ66Mlz\nTga2ZITvm2++SSaTIRgMMnPmTEpLSwkGg+o42WyWgYEBurq6SCQStLe3c/vtt1NaWqrGN/8+guyf\nhNWSySR+v5+JiQkVXXrjxg0aGhrIZrPKhbOiooLVq1fzwAMP4PV6efvtt3nrrbc4f/48/f39au1O\nB0dNdoiUYww5dza5dqQTqs/nY2JiosAB1DRNYrEYp0+fpqOjQ4GcqVSKbDbLunXrWLVqFX//939P\naWkp9957LyCgzLvvvpuWlha+/e1vYxgGs2fP5iMf+Qj33HMPY2NjjI+Pk81mldPmoUOH2LFjB6lU\nimvXruH1ennsscfwer384he/UO6IEtQNBoOUlJRg2zbbtm1jx44dZDIZDMNQUelr1qyhoqICr9dL\nOp3m4MGDHDlyhPPnz3PhwgXuueceTp48qeZfXV0dTU1NeDweIpEIFy9eJJvN0tXVxV/91V/x2c9+\ndkpM+ejoKIlEQtVxzZo1bNq0SaVzyjHo7Oxkz5496vlxZGSEz372s+oa09/fz/i4MHApLy/nj//4\nj3nppZfYs2cPjz32GIZhcOSISH6SccVNTU184Qtf4Jvf/Catra2cO3eOdevWsWnTJhW9m81maW1t\nZffu3Zw7d45EIsGcOXN49tlnFbyb307pBjmZG5KRwjt27KC9vZ2zZ89SXV1Nc3MzPp+PeDxOR0eH\nAklnzpzJ5z//eV577TWOHDlCS0uLAhUBtUbzYd3Jsm2bffv2ARQYDM2YMYOlS5fS1tbGgQMHePTR\nR6fdv6ii3ksfKuAHsGrVKgX4gXix8olPfGLKdoZh8NRTT/HDH/5QLcBLly7x+OOP8/TTT7NixQrK\nysoYHBxk165dvPPOO4o0lxfGp556quiIUtTvLdu22blzJzt27KD/xgBp08KyHAXSGBevkEnGcDSf\ngALehzQ3SvPq1asfcOuFtmzZwsWLFzl0uIX41W0EmtbiLZ89bbys49iYE12keg8T8NgsXLiQjo4O\n9u/fXwA1rl+/njvvvPN9ralkMgmOhTbcgmOlyAEu7gtwc0LASN5S4QhVttCNnLQFoWKIF2oaNhoO\nmseHv/Z2LNsmnc5Mc8a849smYOXiRaUbYLwHJ1BHdqILa+wkHi3DqlXrp/1kWiAQID9C972kaRre\n0ia8pU0ihna8j3TnNpKpFG3nLkyZTz6PjplJg5VRrotSlm2Bvw7NG8aZHI2raRRYbKm+S6c3B2Jd\nOfe0zISAXnQfmjcM5YtwwnNdxzdHvGBxHIhdnRqP60ZMkuxHWVhlk8JlTP7eE3ZBzRmi1tkUpAch\nek3AdLYJ5ghONoKZ6BPRy6Xzocz948LEBReSQkBR4+fFvAjPE/CQGRPn9te67ZFzMa+/mgG1a4XT\nYeSiC0GdFg6E4Lr3udt5SgSkG56XA9eS/QLus+LiuCWzwYqJuidviPMGasjF6Tq5eSrHQDPcuNm8\nN5iaJsCl3m2iboOH3PPPdx3dEKBXalA4w8nal8yCquV5oJJ028uT40aMJm+Ic2bjArbTvS5I2JQD\nDw2/2N9fLWDR6DURWZoaFNBU7drC2F91Tl1EskqIUbpHpgYFDOipEvAiugvkZnNzIjxPzAnHFPUe\n3C/aOHw0B3faN1lbMjIVcg55mq5q4EjnOzTQg6LZtlUIWzk2EqTTsEQNrAxo4ATroWELpPrATqL7\ncxHSAEZJgzvMWTBKBLQXanLbIOde3nniPaJfiT4Y2CP66SsTrnzJXrcPumiDJyjqE2wUj0cugelO\nVE+JiD0Ou5bx0tHU8AtQt3yxONdQi5gv8W53HPKAVt0r1nbiOlrsGtFEip/85BUuXbrEv//3/74g\n7sO2bbq6usSbH80HtatykJ1DbgwCNVCxRKyH4aM4sR5Rgbr7czG0edfJ6SC2yY6o7xVxLvuhwDsz\nimObaCUz8TasyTVPAyub7+Y66c2UpxS0cQjUi+NGXefE0TMUAuKy8WGCM+5UwLY5fhVi19Bqcm/A\nVDSvfouXz44t2g0F8KiAGxeRHjnNvn37ioBfUUUVVVRRRRVVVFFFFVVUUUUVVdT/IWppaeGHP/yh\nunFfXl7OzJkz8Xg8xGIxLl68qCIXZeTl73PfxOfz0dDQgOM49Pb2fih9ePjhhxkaGmLnzp288sor\nLF++nNWrVzNzZmEyTSKR4Pjx4xw8eJBIJKLcov7kT/6kAJqTsYybN29m9uzZv1dbJiYmClz7otEo\nN27cIJFIYNs2Pp+Puro6ampqVHywdEizbZtsNquS4aSka100GlVgh7yXLF2bJIgkYbd0Ok0wGGRi\nYoLKykr6+vrwer3s3LmTlpYW4vE4jY2NCu6RkkBUJBJRwOGxY8fo7e1VTnlVVVWsWrWKyspKhoeH\n2bdvHz09PaRSKXRdp7+/X7k8LV26lDlz5iiY5MSJE+pY27ZtY+XKlcyaNQtA3e9ev3493d3dpFIp\nPB5PAQgmgad8eb1eBb3IGvT09Kh4Xp/Px5w5c9i8ebMyypHuZ62trfzjP/4jpinuG1VUVNDY2KjO\n19vbq4BC2Q7pbCXXjJSmacolsby8nFAohOM4lJWVEYvFGBkZYWRkhI6ODpLJpHKCi8VinD17Fl3X\nOX36NG+//TbLli1TkOFkoDEfAEsmk7S2tjJr1iwaGxsJh8MsWrRIbSvhtWAwSHV1NQ0NDcrVy7Zt\nJiYmuHLliqqrpmk0NTXR29tLJBJR+0toNT+KVm7v8/mmdUsLBoMKZDMMg3g8TjQaVWMKAmSSMcKG\nYVBZWVngHDldjLc8t1wXcsxnzJihorYTiYQC/qS7YG1tLaFQiM7OTsbGxujo6GD58uXTuhNKIA1y\nrmWDg4PK+W/t2rX85je/AeDb3/626lO+c2cqleKjH/0ow8PDHDx4kB/+8IcK4JIxv9OdV/ZRukbK\nSOpYLKZgMo/Ho6DW0tJS7r77bkKhELZtMz4+zunTp0kmk8rJzrZtPB4PTz31FI899hjf+c53ANiw\nYUNBjdeuXcu7775LMBhkxYoVfOYzn1FwYW1tLbW1tQVtnDVrFnfeeSff/va36evrU5HVv/rVr9B1\nnQULFrBhwwbWrl2rIl0ty+LEiRO8+eabnD17lmAwyLJly/ijP/ojZsyYwejoKMPDw9i2TVNTE889\n9xx/+Id/yOuvv85bb73F7t27CYfDrFixgs2bN7NkyRLlJGjbNt3d3Rw6dIjDhw/T0dHBX/zFX/Dl\nL3+ZsrJcmtWOHTuwbZvq6mq+8IUvFEDh+dfeefPmMW/ePNavX8+3vvUtTp06xSuvvMInPvEJVbf8\na9Idd9xBXV0dg4ODnDlzhuXLl6sxyzcAW7hwIf/lv/wXvve973HlyhV++ctf8stf/lKBjvmS15mH\nH36Y6upqtXZN01TxvLL/+Vq6dClXr15leHiYP/uzP6OlpYXDhw8zODionE1lfxcvXszWrVtZsWIF\nhmEoB8dDhw7xB3/wB2pb+Tw53bqUOn/+vHKHXb58ecHvVq5cSVtbG93d3Tfdv6ii3ksfOuC3efNm\nvv71r6tFtW3btmkBP4BPf/rT/OpXv1L2swDj4+P86Ec/4kc/+lHBtpOfJEtLS3nuuec++A4U9f+0\nTNPk+9//PkeOHCWZzooIwepF+MINaIYXxzLJxvpxrh8WO2gajpUR0Xy/j3SxvXyi+aCl6zovvPAC\nmqZx+HALyev7SPUH8VUvwhNuRNO92FaGzMh5zNHLLjwC6azO6dNnQDewHd3lVwSEdqTlKI0N9WzZ\nsuWmDlDTyTRNhoeHcWwLh1QOQAm5UZl2RoAb0StgRnOwUe0q1wVNFw5cAGhUVVUxEUtj+6snwX16\nDuIDl/iQ4JWMF7WF8xagJW9A3w4XPMuA5+afvpIRuma0D8dKC2e+dATHzqLpHnR/Gb6q2zBKGrDi\n/QW/tx1dREbaWSzdwFe9bMp8SoxcgGwK20qT6DmA7ivDE3Jt2GU0adkiAWANtwgwyFvqgktS+S9U\nHFEPMyJiLB1T/Nbwi7qXzodgPY5jC9BJ7q9pMHxMuPZBbqwCNbmaZkZdJ7qYgJ401+mrerlwZMuH\nSI0A+CtE21MD4th2Rri52VYOrol1CcApM5ZzXLSS4l9mLAd/SodA3TcVcsvvu6YJSLR0oZhbsati\nXyuRi/4tmwMl8wS0lhkXjl6xa2hW0o0C9eJYaYh3CQDMV+m6r7my0tOeXTjR3eSpVA+IOa97xRia\nUTfCOX8cXdDQVyXc00qa3aGVENtNjm0LiEg4ySVF7Rs2i3bjiP1s0wVmZTx2FkKNArQc3A+JXohd\nEeN1M2mG2N9Ki+NFL4nHK24XIKWVgsq7IdSAihR2cB31XDfO6nsF4Bi5kIs6Tg5AoPom5wTsXPSx\nhKkcKwMJF2o0AqJ2tiOAYD3g1syNzAWwUmJMLfEGziifh1W5AiczjDZ0VESZ19xe8FrCHO1wodgA\n1D0gIoHVWpv8ZlMTQKYnDDd2iz7hwojJPrH+qlYIoE933QCl06N0gBs+Ltpfu0441A0ddgHdjJhX\n3lIXfGuA0CzwX4JEQvS/5l53vriQr+ZGLntCaBWLCDBOvPcQBw+JT0e9+OKL6jq+c+dOYvEEjieE\n1rAZAnkxEJPhN00XkLG3FPp3CcgvcElAo1DgdDodxDadI6qmGwRnrUczfGRGLuBkRkUct4QoNeEo\nqWkaDhaapqNVLyecZ1lumtkp8QEFKp0nriWxq2JtlC4Qaz96RURv2yZoXjRvCU5oFkZpA373Da5R\nUg+OjZaNY6QHwP10m2Vb7tK8xfNhvAesxBR4FMATbiQ5eIrBwcGb719UUUUVVVRRRRVVVFFFFVVU\nUUUVVdT/Mu3atYv/+T//JwCLFi1iy5YtLF++vMBd6sSJE3zta1/D6/WSzYq/SYXD4d/5nkk+hHbL\nv2f9C6RpGv/6X/9rysrK+MUvfsHJkyc5ceIEjY2NzJghko3Gx8c5f/486XRaAUoSvoNCqCMajfL2\n22+ze/duli1bxqc+9alpISCpsbExzp49y8DAAL/4xS/IZDJMTExw6dIl5RKXD8UMDw/j9/uZM2cO\n9fX12LZNSUkJhmGQyWSUS5eM8pWRotL4RcbsTnZNkw5+0g1QAlWJRIK///u/V7Cbx+NRUaM/+clP\neP7559UxQqEQzc3NXL16lZdeeomuri4ikYhyqZPQ2CuvvEI4HCYajarYzXz3QV3XmTNnDvfccw8P\nPPCAcmH7+Mc/TltbG9/5zncYGxvj61//Ol/84hdpbm5WbVi1ahXbtm2jr69PQW7SYUpCVPkRvfmu\nZhKQkRG/GzZsYOvWrdTX1ytQLj+m+B//8R+xLIsFCxZw3333sWDBAizLUvGjXq+XU6dO8eabbxKP\nx0mn0+Ie4E2iP6V7YiwWw3EcVaPbb7+d4eFhenp6GBoaIpvNMj4+ztDQkDqOz+ejurqasrIy9u7d\nqxwDJ7v3TX4sm81y7do1Ojs7qa2tpaamBp/PRzAYpKysTIFz1dXVpFIpIpEIsViMsbExDMPA5/Mp\ncM+2bUpLS9m8eTN79uwhlUopcHI6SeewyW3Mb6vf72fjxo1cu3aNrq4uNZflNUTWqaKiQsXrptPp\ngmNPV+uxsTEFatbU1LBgwQK1nYQSM5nMFEiyubkZ27aJRCL09PSwaNGiWzqTer1eFZVs2zZ33nkn\nDQ0NVFdXMzExwalTp1i5cmXBnJTQVyAQ4A/+4A84evQoZ86coa5O3LNtaWlhw4YNBXXKVz68FQgE\nSCaTZLNZTp48qeaFjMXt7e3l8OHDBfs3NDRQW1vL+fPnGR8fx7Ztnn32WdasWcP/+B//g0uXLlFf\nX8/DDz9csN/g4CCBQID58+fzb/7Nv5niNJh/HZP1XLRoEf/23/5bfvCDH2CaJm+88QYej4ePfOQj\nPPTQQ2qcpWOez+djxYoVvPHGG4TDYZYvX86WLVvYvn07x48fV/2WY3vvvfeyadMmmpublePes88+\ny5YtW1R75NdkMkl5eTmPPfYYjzzyCC+99BLXrl3jU5/6FLNmzaK0tJTZs2fT2tpKaWkpL7zwwhTH\n13x4Veq2227jC1/4An/5l3/J3r172bJlC/G4SE3LB1J1XWfjxo289tprtLS0sHz5cnXtklCcZVm0\ntLSwa9cu+vr6lGumfF6KxWJks1mqq6v56Ec/ytWrVzl27JiKJpduk8lkcorrY742btzIb37zG1pb\nW3n88cd58MEHeeKJJ7h27RpjY2Ok02lCoRAVFRVUVlYSDofVdbqyspJsNsvo6Cjbt2/n6aefLhjz\nm62XVCqlIo83btw4xQlWOikmEokp+xZV1O+qDx3wmzt3LosXL6ajowOA06dP09bWNq1zSTgc5qtf\n/Sqf+cxn1MUZpl+U+b8zDIP/9t/+GzNmvD93taL+/ynbtvn+97/PocMtJE2NwMz107reeUI1JIcv\nCTDAFhGPDvx+kJ+T/WAbP428Xi8vvvgiCxcuZMeOHfT13yA9cprEwEkcy8S23TZoHuW8lLVBuN5Z\n6L4Q3soFeMpmY8VvEB+5wJVr1+m7iQOUVH608cDAAJ2dnUSicRxPSIA9Jc2F7leOk3MTS1wXEFi8\nRwBB1SsFpJIaBGwaGxsoKytjPNpDOmO5Vyw3sjTfdUzFlupgGG5OpftPAlRmBHQ/eqAcX/kcMqMX\naGk5xuLFu9i6dWtBn9atW8e+/ftJ9R1B031TvZ4SI2RGL4va5bmL4bhojAs3Gd5SNN3ACFara5Yn\nVIO/9g4y410ku3bhZOPErvyW0Kx1eMtn5xiisgVoqUGc2DXo3+MCdY0CBpocpWplINENo6dEfG6g\nHtKjok51a0HTcaxsXpysLsCzoUMC7tO9bjzuLBewTIu66T7hDFa+GAaPQPSCgPtmPAC+creheU6C\nmkfE+sauQjYqfrYTkB6BssXCuW7wIKSHxPm95VA6V0BfyrVLRIJiRgU8pvtEW6ZIyzu3k3so1CD+\ngZhHfTsEOBXrhFi36xAnHN4020IPVhKouwu8YRKX38TJxsV+pfNdN8SIO7/0wnNLZ8ACd8pJL+gc\nU5zPWwqNDwn4cLwNEv0oR0BNF//KFwsHNRDzSR3/Jn8kSlzPi172COc+CffJ44I7P9KFUKGvHCqX\nwchx4XpYuhBu8eZNrbloj4DPvGViXphzRTxv7Kqoef5ztYw/1g0INwsYMD2uokuJXhZ9Vn8Ey1vL\nBdHHLuRoZ8U8j11z3Rcd0A00w49jZ27uCOgJuP230A0PpAexho5DNolR2oC3amGuyY5Devi86Gv1\ncuHEZ6VdiHMaF0U572Q9h1vENSc1JKC/urx1IusvHf00AyYuiu9DM2HkaKGDppQ5IcbaWyrqnhoU\nIG7dOjHe+XCfY6s66LqBt2wOthEi3rmdnbt2c/r0aerr66mtraWjowMbj3CL9JVNPW/ekCj5K8V1\nYvCQcN903QbzY2hhKsQ22RHVcRwyI+dJD5/HTkdx0ED3o1w53Tmg6Tq+6oVkIr046Qi6Vvg8Kt30\nhOugjYo5lgo2iIhqMyacOktmFV4f1Kc8xXOFYeReEmcj3Riuq6x5owVfqBIjWJVn/HeT9ZIeg6Fj\n08KjoqlecD9VWlRRRRVVVFFFFVVUUUUVVVRRRRVV1P9enTx5UsF9H//4x3n00UenvWlfX1+v3Ndk\n7GwsFqO0tPR3juuVsNOHmUKmaRpPPPEEq1atYu/evezbt48bN27Q19enHPUkQCG/5sMh+fddZSRn\nKpXi6NGjDA8P86UvfYny8nJ1PscJ3C/2AAAgAElEQVRx6OjoYPfu3bS2tmLbNtFolFQqxdjYGGfO\nnFHAm9w+v17pdJoLFy5gWRYzZ85U9c3vz5YtW9i1axdAASRyM9jQsizS6XTBPWZZcwnJGYbBhg0b\n2LBhA3/5l3/JoUOHePrpp6mqyn0Ieu7cuZw+fZojR44ooM/j8ahjyajP0dFR9VVG1cqY1/r6erq6\nuvjBD37AmTNneOGFF/D7/ei6zt133813v/tdPve5z9HX18dXvvIVHnzwQdavX08oFMLn8/G5z32O\nr3zlK4yMjCiwUUaQynt1MqkpH4rUNI3nnnuOw4cPc+nSJRobG6murlaAl8fjIRwOc+rUKX76059i\nWRYPP/wwq1evVjWT8IkELCsqKpTT3MjIyLT37SUMGAwGCQQCBbGyqVSKy5cvK/ByeHiYy5cvk0gk\nKC0tVdvG43EVxdzU1ERVVRWapinA673kOA6Dg4Pqb9Pz5s1TMO7o6CgTExNomqagN03TWLlyJVu2\nbOH111+nq6uLbDbLxYsXmTFjBvPmzVNzVNZczgc5DyTMOd21QDqPeb1ewuEwS5cuxe/3097ersZL\nrotMJqOgRAnI3YqTyGazjI2NqbbNnTu3YD3ruq6ic+Ux8q8/TU1NTExMMDg4yLx58xSEfDMNDAyo\n+S55jFmzZtHe3q4g4fzY1Pw5O2fOHJYsWcLJkyfZv38/uq5z7tw5+vv7laNfvitjKpVS7n0yolhC\nkQcOHCCdTlNTU0M8HufgwYOAmKsVFRWAcAfs7+9XDpbhcJh4PE5raysHDhygv7+fdDpNU1MT4+Pj\nlLrGApZlsXfvXvx+v4qXjUaj+P1+tXZlG+V1Uroo3nnnncydO5dz585hGAaPP/44jzzyiKqr3F4C\nyCdOnGBgYICamhpM0ywwy6qpqSEYDJLJZBgaGuLYsWMcPXqUZDJJKBTiX/2rf8V9991HJpMpcLtL\nJpPqOinH+4UXXuAb3/gG169fp7u7G7/fT1tbG47jsHr1aubNm4dlWb9TJPmCBQt44IEH2LNnD++8\n8w7t7e0AU1xepSNpJBIBBAA4NjZGLBajrKyMv/3bv+XkyZOAcLm8//77mT17Nh6Ph2g0yrFjx7h0\n6RKWZbFt2zYFpOanJFqWVeCqKR1I8+dwZWUlK1as4OjRo/zsZz/jueeewzAMFixYoMbQtm31/Cxr\nkM1meeWVVwgGg2SzWX79618TCAR45JFHbgnzJhIJvvOd79Dd3U1dXd20CYKTI6eLKur96EMH/AC+\n+tWvFlhN3upTHuvXr+eb3/wmX/7yl5U9682eUBzHIRgM8rWvfY0tW7Z84O0u6v9t7dy5Uzj3mRol\n8x4RN+9vIs0XxslMCDczf6WATTT95u5ak+VGaU62lf2gpes6W7duZfPmzZw9e5a9e/dy4sQJxsdd\n10FPGK10HravGkf3oKEJGCVyCdtMkB5sw85ECc16AH/tHZgT3Td1gJou2tjMZLAtE8dT4sItFXmu\nXnmxqqK1oPmEc1miR0SHpoZEHGVqCGyTiYkJIpEIlmUJOMSjuZGj+W+CJ0FFTjYPZtIElAk4aCLu\nV9PQfSECTWtI9h5gx44dbN68WfXLNE3279+PmckCBo4REs5qoRmuA2EKBltw0iMCrDJKoGw+WqgR\nR/MKICjZB7FO7GySZO9RsvFBQrMeQHNjRDVNx185F8P3BLFLvwIzTqp7LylviYg39VYJ4CWbEu13\nEiIG1BMudEN0TOFoFr2cc6SS2ZWeIGTjONFOAboUxMl6BdgVd+G+hs1irNAojEV1wZnIJeHcpknw\nKfcmXkGbKubXjbpVk9IrxiTSIX6P4zoArhDt0r3K4RIQ8GfFYoj3wo29oi3JAeXE+N7Kmw+pIRQc\n6NiiWZpfgGHZCGgO3qrF+GuWCCt3XymOGYPRVvA8AN6wAPwyo6Lm+bKzwmXNNkHXpgfkkgPiq7dU\n1NIICHDR8AuoK94tjuE4As70lYn5rxkCSpRRvAVxzAiIaPioO8810c5QE4WQYR546LigoKa70Ksm\nnPzGXVfBZL8LPd3ij1GZnDskwTrR30AtTOA6LeaDnnltlq6cpQvBPJkD98yocEsMN7vndUFc26IQ\n6HTfwNpZEYNrJVyQy3X1s00BvJUtEBHqk0HRrPgEkaNpmCMXMDxX8eqAR8dJjRK78IZyOrWSI9jp\nCbGmg9J5zcnF5U7rGOq2N9Qg2pEZE22rXiGcMBUY6BT2L9EvaoAj4FOYNm5XuZ1mImI+G/48mFOC\njq5zn6wt4Dg20UgEy/LhVC7DGWrhxsAgQ6NRdO0C2UwGx1cBoQblTPo7qWQWeEI4ZhRt4vyUGFqY\nCrFJR9RsrB8jUEmiZ7+I5gU3mjfv+uqYkBiAyCUcK0Fm5GKudqlhIPcpVummpxke1zXVcp+T3Xks\nnVCHj8LISRdMzXsO1txxtLNoWu4NlZUcJdV7mFAwwOzZM+nqvk786jYCTWtx9MrceE4X1zx0DM0x\n8VbMwVe9eEr5HFtAv8Fg8Herd1FFFVVUUUUVVVRRRRVVVFFFFVVUUR+KbNvmpz/9KYCKbLyZKisr\nKS8vV25Xzc3NCuzw+/3veS7TNDl58iSmaRZEFH5Yqq+v5/HHH2fdunWcP3+et99+m56eHhV/G4vF\nCqJVPR4PgUCAQCAwxRnOcRwSiQRXrlzhW9/6Fn/6p3+Kz+cjk8nw8ssvc+zYMUDcm5o/fz6nT58m\nnU5z7ty5gshfqXwwoqysjMbGRmpqahQ4ORnK27ZtG9lsVkE+mqbd9B5zJpMpcEXyer14vV7Ky8sJ\nBALcd999RCIRuru72b9/P5cuXeKOO+6gra2Nd999l6eeegqAY8eOsXv3bgUrSWBNKp1OY1kWpmnS\n3d3N9evXyWaz+Hw+AoEAsVgM27YZGhqirq6OpqYmDh06RDab5XOf+5yCSEKhEN/+9rf5/Oc/z9DQ\nEG+++Sbbtm1j7ty5ClSSDln58E2+Y1++M5v8m6Nt2/z1X/81gUCAdDrNtm3bWLJkCdXV1QqOdByH\nV199Fcuy2LRpE6tXr1bHN01TAZIyavTIkSOk02kVnTx5TDVNo7y8XMFpEqbMj54dGxtD13XS6TSX\nLl0ilUoVwHL5SqVSXL16VdW0sbGRK1euTDuPbiZN02hoaCi4pwkigruuro7r16+j6zoPPfQQd999\nN+Pj4/z4xz+moqKCgYEB3n33XZqbmzEMg5GREWbNmjWFV5AueaZp4vV6p/RFgob19fVomsbly5c5\nd+4cXq+XjRs3cuDAAUzTxO/3k8lkiEQihMNhNb75yncOsyxLrWnp3ievRbKN+bCdYRjKiU6ufb/f\nLwxWxse5fv06c+fOndadLJvNqnlu2zZer5fLly8r8NmyrCmR2vljIK8nK1euVA6iEs567bXX+NSn\nPqXOZ5qmAtTyayzhrdbWVgYGBhQEqGkac+bMYdOmTaxatUrVwDRNWltb2b17NxcvXlTz8OzZs5SV\nlVFbW0s0GuX06dOcOnWK2267jbvuuovh4WH6+vpoaGhQMc+2bau44XxYbzpXx/vvv5/Lly+jaRpP\nPvmkim7OHz8JIR84cEDBZefOnSMUCrF27Vo2btyoADkQLo3vvvsub731looV37BhA6lUSj0Hmaap\n6jpZ1dXVPPnkk/zoRz+iqamJj33sY7z00ksYhsG6desUTPq73rPYtGkTe/bsYe/evei6zuLFi2ls\nnJRk5M41OY+bm5vp7e3l6NGjdHd3q1jij3/846xdu3bK8+jDDz9Md3c3v/zlLzlx4gSnTp0inU5z\n8uRJIpEIZWVlan57vV41n0zTnALOPf300xw8eJATJ05QUlLCJz/5SRUnnQ8GyvUrkx/b29uprKzk\noYce4o033uC1116jra2NtWvXsmTJEizLUv2MxWLs27ePnTt3Mjo6Snl5OV/84heVW1++Ojs7AdQ1\nvqii3o/+lwB+S5YsmWLveStJC+zvfve7bN++vSCyV6q0tJRHH32UT3/609TX109zlKKKurls22bH\njh0k01kCM9ffEu4DMMrmY8d6BWhRthCc7O8ORTgySjPLvHnzPqAeTK/p3PRi8SS2EcTfuAZKGkkl\n064DoQvKBWtdoOo6ztBRzPFOEkBo9gZ8FXMw/GXEr27j8OEWFi5cyNatW6eNNvZWLYSR8+Ak0Gru\nE/CIBDM0HQX8OAhYbOKCgNJAAF7SaSzZp2Jk4/EE4ODoXuH6FpzBVJc02flMzjVMwTi6C9xo4K/G\nwcFKR0n2HsVb0YylB+jrv8HZs2dZunSpcnU8fOQothGCyrtxQo1onqALRzkweEZAYJ4S4VAYELCT\noxmuO6IPvAvRKm6H5I0pNc1/YewpqSUw+0HSXbsF0JXJYA8dQ3PyQjKNkIiaTPYLWGnsjPg3uQie\nsADmotcg1Y9yuBs+nnPck+6NjuO6hzHJ+S3nAIbmEWMydFgASI4tHPdKZovfKV7ThOEjAr4SnZoa\nyZzohehVF7ZyHRyD9WJcpo231SE8S0BSw8fEuitfrJwnpwBv0z3m2GI/2xJxq96w63iXFWCapqGZ\nEczhszgzlqJpOoH65SR7DuBkEzCwX7gLggAcyxcXgqUSXHNs0cfJLm/y/ADhOQLmGz4q6htqFM5p\n6RHImGAExeMD+8V4lORevOf65DrbSddL2xSwl5URboPK1dGVcrTTcm1zo7nRPeDowoFtvE38C9aj\nXDbz5djuOY+6zo6aAE/lcXDEHChorTbJ9dIRa1fT3ahpS4B6w8dF3wuAUVCumLIPjiWgxrFTuYhp\nTRcQbM19wvlQjY0bEVzgEirbbuEx4LnnnqOvr489e/YQi4+T6juWc5jTdJzyOWh5n8RyCypikZnG\nKVCeOzwXxqOI+TvXdRnNly4ioh3LXVNmDrituU9An8qRj8J+jJ0RLohGSFxzdEO0xzEh67YPxPEc\nC8vKg5xLZsP4OTQrjq9+JZmRDhxzFCc8R7RJgodT2juNNF3AlKOnIJtAD1VPiaGdDLGtX7+eIy1H\niQ13YCWGMSe6BAxdO3nsXCnIVzwnkY2BY2NPXMFpWp5z2VVLPn8tSsjPVekCsV6sBAzsg5pVhed0\nnTINw4Ph0cmMXyPVe5ig12Ht2tU8//zz/N3f/R2HD7eQvL4PR/NDyRyxho1ADsKMXAYrgQZ4K+YI\noHsa6Dcb68MwNBVDUFRRRRVVVFFFFVVUUUUVVVRRRRVV1P8etbe3MzQ0pCIAbyWv18v999/Pb3/7\nW/bs2fP/sXfmUXJd9Z3/vFev9qX3Vbtakq3N1mLtsmTJQsaAQ3wwBEJMOJMZAgzxwSGZzElmErKd\nHDgeCFkIJiQxxMEzECcYgzEIydbWWmxrX1pLa+ldvXfXXvWW+eO+e+tVd0syJGQmM/U9x8et7rfc\n+7tLdb/61PfLxz/+cRVV6gU4bqc33niDsbExNE3jgQce+InaOTk5SWdnp4pLTSQSLFmyZEb3n1wu\nx5EjR9i3bx9dXV0KzpPubtlsdkYIREJ/6XSaRCKh+uR11Mpms1y6dIkDBw6wfft2vvCFL9DR0UE4\nHGb37t089NBD/NM//ROnTp1ibGxMQSczOfj5fD6WLVtWBjgMDw9jmia6rhOJRIhEIhiGocALwzBo\nbm5mYmJixrhSL9wnXedkFGZ1dTXRaJR3vOMdVFdXMzY2xre+9S36+/uVK9jrr7/Oz/3cz9HX18dX\nvvIVUqkUhmFgmia5XA6fz6cimmVNL126xK1bt1TfZEyvBKr8fj9jY2NkMhkWLVrEG2+8wXe+8x3e\n9773qXZHIhH+y3/5L/y3//bfVNzkqVOnymq1YMEClixZwsmTJ0mn02UQlK7rhMNhqqurefDBB1m3\nbh1f/epXuXbtGrquK9Dwr/7qr/jkJz/JwoUL0TSNCxcuMDAwQCwWY8uWLao9xWJR1dEwDF544QWu\nXLmiIEJZCy/MpWkatbW1+Hw+TNNkeHiY3t5exsbG1DE1NTUK5jQMg5UrV3LmzBnl6DfTfHYch4sX\nL7J27VpaWlq4fv26cp68mxzHUdCbpmnk83kKhUJZ3GooFCKTyfDDH/6Q+++/n02bNvHiiy8yMTGh\nAN5r164p4HFiYoJEQqTheAFGeT/pDilBVNM0VfxwJBLhwIEDjIyMKAfJbdu20dXVRX9/P48++ih7\n9uxhfHwc0zRpaGhQ95kaCZvJZBgYGFDrRdd1mpqa1JyZGttsGMY04EpGnNbX1zMxMcH169fJ5XI0\nNzcTDocViDk4OMitW7cUfLtkyRKuX79OZ2cnDz30kKrNnUBnOV4ygnvlypX4/X6OHz/OG2+8QSKR\n4L3vfa+CI72R5hJGzOfznDt3jm9+85uk02kCgQA+n48nn3yShx56qAwkBLFnb9y4kY0bN3L8+HEV\nnWsYBmvXruWxxx7j5Zdf5vjx42QyGU6dOsXZs2cxTRPDMFizZo2CIuWaBsrc8QAVp+z3+0mlUqxe\nvZr/9b/+l4IPZ3ptkDHkFy5cIJPJoOs6c+bM4dd//denQV+aplFTU8N73/teTp8+TTabZevWreTz\neXRdx7IsJicny8BMOQ8Mw1BjuXHjRr797W/T19fHxMQEgUCAWbNmsWjRIgUHypreTXPmzGHBggWc\nO3eOQCDAzp07px3jde4D2LFjB+3t7Xzve99Tr2P/9b/+V2bNmnXb+8ydO5dPfepTvPDCC7zyyitq\nr3jttdd473vfW9bnYDBIJpMpgzClwuEwfr+fZDLJ0aNH6erqYteuXWzevFnB6iDAyx/96Ef8+Mc/\nZnBwkHA4zKc//WkWLVpEc3MzX/va17h48SLnzp2jurqaefPmEYlESKfTXLp0Sa2t2bNn8+lPf5r6\n+vppfSoUChw8eBCgDKquqKKfVP8mgN9Po7q6Oj772c/ye7/3e+qXpLGxMWKxGE1NTSxbtuxtbTQV\nVTSTzp07R//ALQGmVc296/HB6rmYgxER35ntF05RKhLwLvMwLWItfbrGz/3cz72t9nlBvcHBQXK5\nHKFQiMbGRrZt28aKFSvKPglyVze9hq1ktSrIuGDOVBc8TYfYXPDHcfr2Uhy/QSHaRLB+Kb5wrXC7\n6znInj172LFjx4zRxmaqn/xAVgBesbloju26KhUF9CYjJIeOiJpAuWOV4zpl5YcEpGZlcOwimmML\nd6rkdahegQJ4pDMgeOA+D8ykue59yU7x/Zr7xH3ywy50dxM9mCBXyHHgwAFWrlxZ5uoYW/RO8nZI\n/OFi5dH0ACSv4qQ8rnfBmhKY6FglwFA3RJTubWoKqJjKwvBFbMcGfOi6AT7HBYlK0I0WiEP9A5C7\nJfpTdN36dL8A1+JtEG4WLmeBarTh4/g0U0A+lgur1a0WoKCmubCgCynKWFgV7+m4c1qHocNufLIj\n+pNY7HHbc90Yh98QcJ+ClGZNXxOBKtHGbL8Ag3KDMHJCAJLTA5BLl4+3wfgFAealu8S/Z4rCnel7\nMsJW3n/WoyXYK9sP/hiO5kMrpihOdBGonk+wfhm54Ys4mUFRt/EOwALTEfePunuFY01xmpvq8qaL\nyGQzLWox/Ka4HggwqG6tANsSSwS0FWkV56S7RfypERFQYLBeRMw6zjRHOiKtUHDnQbi51A7ZPrke\nfAFKMb/SVU8XYGW4CcbPCTe5nu+5a7ERtIAAx6beM9wM2UGwXJdG2xRt0/xl9S//A9t179RcOFP3\nQ8u70AYP4uRuweBBqF3j1sCFZDW9VFvHFi6UI2+JyGYZMe0LQstOsQa9ffNK08WYGXEY2IvjmOQL\nBV544QUs2yFXsASYa5tgO8oZUAs1EgwGCYfD6mFNqU+efUe2V7oPhhpErX2hmWE5earmE3Anjji2\n5WEXsp3SD3W8LsZAD7rQqXRlBOVWKqFIBTnLvVCAn058EYydJj/Zh+O4QHK4BOY5VgFNm/K6MNNS\nAwEYug6GM8XQToXYVqxYQUtzE1c7b1AYnxTAbevDnrGbQfI1yRfBGdiHZqbRzaRaq6o+bkM13Si9\n3kj3URBrHh1N9wvwcPCwWF+JRaV+2EX03ASp3qvoVpZo0GDTpo0qlv4Tn/gES5YsYc+ePXR195AZ\nOwtj58rGWAP0YJxg/TICdffO+Ie749gURi4R9fvYtm3b7fteUUUVVVRRRRVVVFFFFVVUUUUVVVTR\nz1wy9nXHjh13fa/R5/OxZcsWXn31VU6ePMnNmzdpbGxUcYsyunMm5XI5Xn75ZQqFAosWLaKuru6u\nbXMchytXrrB3717eeOMNFVUJKDjiwQcfZOfOnQrsefXVV3nppZeUk5aE4ySAMz4+Xgb2SXBoqhPb\nxMQEVVVVM0J+uVyOvXv3cu3aNTo6OqiqquI3f/M3mT17toILs9ksIyMjGIaB3+9X8ASgoJP777+f\nSCRCsVikp6eHnp4ecrmcuqemiWeLLS0tVFdXq/60trYqRzsv5GfbtoLSQqFQGSxWX19PKBRixYoV\nhEIhbNumtbWVj3/84/z1X/81PT09FAoFlSb1yiuvkEql8Pl8hMNhBUVKqEi6IHZ3dyu4z1tTEECV\n12Uwn89z8+ZNAoEAe/fu5d3vfjd+v59isciePXvYt28flmURDAbVXJJzS0KSS5Ys4SMf+Qjt7e10\ndHQo4DMWi7Fq1SrWr1+PpmmkUimefPJJPv/5z6s4zFwuR3d3N8888ww7d+7koYceUg6FDzzwgALz\nJEADApJ85ZVXuHr1qgLJvLG2XrhGwn2pVEpBSFOfj05MTDAxMYFhGNx3330qrlY6W94urjKZTJLJ\nZAgGgyxatIhLly7dYeWU3psIBAIsXrwYn8+nXA5lnKl0+5KRy2fOnOHQoUNs3bqVT3ziE/z+7/8+\njuPQ3Nys4pFN06Szs5MlS5aUvTcrIUzvmEn3Qxn3C3D+/Hl0XScUCvHwww+zcuVKAoEA69at46WX\nXqK3t5enn36av/iLv1BQbyAQIBqNKpe+fD7P2NgY+XwewzBobGykWCwyOjqq3DdlDXI58V6wjJae\nWh/pnBiJRNR5w8PDDA0NzVjXRCLBggULqKur4/r16+TzeXw+H/39/di2TUNDw4zRpd4ocLkX1NXV\n8fGPf5wDBw7w1a9+lX379jE0NMQ73/lOlixZoqBMuaaHh4fZt28f+/btI5lMKrfKj370o2XP2W8H\nfq5fvx7DMPizP/szJiYmeO211zh+/DiBQEA5RFqWRS6XU2s4Ho8TDAbVzyQI7L2PfA8JUGlGfr+f\nSCSCbdtqz/DOFXnu8PCwGseWlhZ+67d+i1gsNq0P8t/ZbJbr168TDofZuHFj2XESKA4EAmWxxuFw\nWI1tMBhkw4YN7Nu3j9OnT+M4Dg0NDUSjUSYnJ1V7o9HoXWPkHcdR+3JVVRVr166ddsyxY8cAEXUO\nItq3tbWVCxcukEgk+PSnP31HuE9K13V+/ud/nt7eXt566y2KxSIvvfQSbW1tLFmyRLUnEAiouOxM\nJqPmdT6f58///M/RdZ01a9YwNjZGf38/f//3f8+3vvUt5syZo16nuru71RxtaGjgqaeeUk6K69ev\nZ/ny5Rw8eJAf/ehH3Lhxg/7+fiKRiKrXypUr2blzJ/fdd99tf6c4duwY6XSaefPm/cwNoSr6f1v/\n1wJ+Upqmce+993LvvdOjziqq6KfVgQMHyBctAnX3lNyA7iB/wI9WtRhn5LQAhJofAiOKY1tod/rj\nLz8GQ8fBMWlobGDlypV3vM9MoJ5luTCHpuG73MnRY8dpbWlm165d7Nq1C8uyprnpaYmF2KMdOI4O\ndetFVKRtUiI2XIpfxWm6CtZAwzqcwXbywxcUqOCvmkeu/036+gf4xje+MWO0cWH0ikBTEotLgIWE\n8+yCAGGGj5ZiYaXzloSPHDdiMlgjYhVzgzB8XECVjiUAl0yvC1k5pa6UwUxTABUJeRlR12mvHGS0\n8uM4OAwODk5zdTTCtfjc35EKhQKOmXeBLwfq10GwulRP3V/mjqb5vJGz5TXVEoso5HOYt47gpHpF\n1wPVEJuPHp9FJBYnm05RmOxxQccszsgptPwINGxyo1RnkGODY6JFZ6FNxrDz42j+KLqmYxXTYt6O\nXxBgS6ZfnBNv84B9HlBID7gRvj0uRBgXcaLhpvJ7Jq9Mifm9TXylvG5kNgTqYGCvGMtgrYDcbgfJ\narpwLxs9IYDAQJWA3so7Pv1eKsLWBXD9MQGseWGvwiQ6DpoGud529EAcx8rh5MbE9ZSDnAOWDYPH\noDk4xW1OL0E+juXW0obCBAy9IcBPCV0ZUQFYxhaWYKxwE4wh4L/WR4RT4OQlAXCOn0PFLXv/GDai\nYtwSS6Dvh24zvICd4651+f2Z9jf3WF+k1BYzLWDDsRkOl20PNkB2r2if5nMjkB1RX9nGMhAOlIOn\ndPnTDTQjAK27BGyVdqFLL+wro7Az/ZC+DsW0OF8PAWZp/wjU3JYP9TaBYI1Ys4OHcaw8Y2PjaKE6\nQvUrCcZb0Hx+HKtIuvNVLEvsVfIPIflJUfnJVvWHk+534T4HLG+9ccdcQoDalAa5+7mZFv2sX+dZ\nNx45U74oum6nEspzPJ+y9QXFteUclGCn1+ku3IQzBlZ+sjQ/fEEBDdp5wBF7nM+Pphk4MxbWcSPD\n3YcAvuC0GNqZIDZd19m1axdXr34FHEPAyneC+9TFbBx/FK12NdrIG+AUyPYcxhdM4AvX4tN9mJrb\nZ90ovd5YeTEWxUkBIGsa1K0Vf8xOXMIpJgVorB5mgB4MEPb7aJ07W8Xcyz/MdF1X3zt79izPPPMM\nk5MpNCOCHoihB+IEahdjxFrv+Gnt4sRNdCtL69zZLF++/O79r6iiiiqqqKKKKqqooooqqqiiiiqq\n6GciabAAvK0PYkrgbOvWrRw4cIAvfelLPPXUUzQ0NNwR8MvlcvzZn/0ZN27cwLZtnnjiibveK5PJ\n8Fd/9VecOnVKQUkSRAJUTOpLL73ED37wA97znveQzWYVJDZ37lzWrFnDnDlz+OY3v6kcwfx+v4Jt\nvNCehG+8z7WSySS1tbXTnmGRlY8AACAASURBVHUVi0Vu3rxJV1cXkUiEz3zmM8yeLQwERkdHVYyl\nhCQk6GKaprrP8uXLlevRiRMnFIgkx0U6oMn429HRUebPn6/iRFtbW+nr61Ptl/GZEgL0jkU8Hqep\nqYlwOMzatWvL+h4KhfjoRz/K5z73OVKpFH6/n6GhIY4dO0Y+nycejyvnNV3XFegnndF6enrK6uid\nK6FQaBokk0qlSCaTjI6Osn//fu6//36ee+45Lly4gKZpzJs3j82bNytnw+HhYc6fP097ezvj4+M8\n//zzXL16lf/4H/8ju3fvnjZvLMsilUrhOA4LFixg4cKFnDt3jpaWFhUNm0qleOWVV3jllVfIZDIY\nhsE999xDMpkse/bt9/sZHh7mzJkzmKZJa2sr3d3d0/rpOI5yZZPj6XU4m1oD6T735ptvsm7dOqLR\nKPPnz+fatWszLQVAwJIdHR2sXbuW2bNnY9s2V65cmfFYL3i1atUq5R4GYt3kcjmCwaByhZNOX7lc\njq9+9ascPHiQrq4u8vk8tm0zPDwMiOfDjuOQTCbp7Oxk4cKFylVSuvVpmqZc/YrFIt3d3QwNDWGa\nJpFIhJaWFlavXk1bWxvBYFCBX/JD8pOTkyxatIh3v/vdHDp0iK6uLnK5HJlMRrn+gYCNa2trWbt2\nLdu2bePZZ59V4+EFyLxOfd76SPhPwprSnS4ej9Pc3Mzw8LAyPPD5fCQSCZqbmxWwKterbduMjIwo\nwG/t2rUzuuhJd7hQKMT169cBAfhJF8NwOMzf/M3fcP78eS5cuMCsWbNYvXo1kUiEyclJbty4QUdH\nB7lcTrnWhcNhVq5c+RN9iH7NmjVs2bKFvXv3qnmwY8cOdu/eTVNTk4LB/sf/+B+cPXtW7Su2bROJ\nRIjH4xQKBfU+kYQ1ZV3lvJfxyd55I7/vlTSWiEajPPnkk9OiXKeCfhJsTiQS1NTUKChPuteFQiEF\nA0s4WY6HHBMJl8sxlG2Kx+NMTEwoJ9dQKKT20anOgNIJVu7n73nPe8oAUhCvBSdPnkTXdbZv367O\nb2xs5PLlyyrS1zRNtX5uJ+m8uXv3bs6fP6/g3D/90z/lySefZMWKFWquR6NRUqmUmr/JZJJnn32W\nGzduUFdXx6/92q8Ri8V46623ePnll+no6FBrPBwOYxgGS5cu5eGHH2b16tXTIL1oNMo73/lOdu/e\nzR//8R9z8eJFGhsb+eAHP8i8efOU6+bt1NfXxwsvvACIJNO7Of9WVNGd9H894FdRRT8LDQ4OYlkO\ngdhtYKkp0oBQwzKy2SGcTB8MvA61q4RbmW+GT5Y4tgCfho6DmcFv+HjiiSfuSL7PFHsbqLuHQKwE\noJipftIjl+i83kPfN57n8uXL2LbN0WNvCDe9Wdso+OspTvbiWHnXoW0O6C5s53jiCz0xsGUvJNE5\n4Itg55OYqT788Vlomk6g7h7yI6d57bXXyOatadHGdl5Y7hIpQWCaLyB+AXAsmOy4DQwm5amNLwSx\neQIs6/uxG61quSBQXMB10kXNC7rYxZIDWGFcwB1Qcq6TcqE7bh3GtoV1+0yujpoGkagAfHJjN7Ek\nLBiZXQ4WqXhUB20m5y63plZukvRQJ076ZrnrXbgVnCKmpmESQgsakIhA1VIR/zh8HCfVLXilxi1T\noCHAtnDsAhoQCIYoxhfgFM5ghGtxbBvNzIMREdGzo2eEE5ruF2PgjXKV8JFjC9AMRPtkJLDueVAx\nY8yv/Jk715SjnAceDNaI4wcPi9jeWNvMDBqIsYy3Qeq6iFoeeE1EbEZmiPWU9/FG2KKLbkXnKQCy\nBHu141gFbA18ZoZkxz+CbogW6y4c5dPdeWaClRJgYp0b8SmPkYChZojj0jcFjGhlxFqLzhdQZbjJ\njZe1S2tQutrZReEqmBsUcJgeEMCo7T7cCFQJGDI6X+w56nyjdL6muWPneNrkdWNzxFxK3QAzK2oh\nXTP91VC3DtLXBBRlmyWwM7bQBTsdyLpAn5kRsG52WNQnNt9zT3ceSbDRtkQsbdb9NKMRw3Fcx7va\ntWLeyPhmL2DouC6WjiPaqhlufzURRx2ZfZtJUzaB5MXE8UYUrII7K33kk7fQI7X4w3XiE2vBBHZ2\nFDQRMaweREUjBIIBzKJJOp3Ctl0XQQ1PvXXRTihFF6N5gEePsgPi50ZUjOc0lW0u7rdMFMSMx+FQ\n091/u/AdlDn3qeupuWKW1rFTFK8NuPMNR9RHc/dRPNfwulbaRfHJRLduXk2F2OTD0qNHjwqTRCMq\nIF0zh+abEmvtlWdP8yXmYk+cxy4UoJhi8sr3MRrXY1TNEUNgm2guWO2AWGfJbjfSOY8WaRXuk44J\nsQVoExfFHHbEJykXLljIokWL2LZtG8uXL7/t67Su69x///08+eSTfOMbz5MuOERmbyl7HbydrOwo\nud4jRIMG73jHO+76KbiKKqqooooqqqiiiiqqqKKKKqqooop+dpJOPxIaezsKBAI8/vjjDA0N0dHR\nwTPPPMPDDz/Mtm3blIuTlGmaCiK4du0auVyOhQsXzuh05FUmk+Fzn/scly9fVqlO1dXV00CDYrFI\nLpcjmUzy7W9/GxAw3Qc+8AEWL15MJpOht7eXwcFBBT8Eg0EFPtzOJcrriCehnKnHZbNZDMNgxYoV\nXL9+nZs3bxKLxQgExHtBXpdATdOIxWIkk0kVO5pIJMjn87z11lsKtJqp1lLJZFJBIcFgkJYW8Z7e\n9evXFaQknbVM0ySZTOL3+2lsbKStrY2qqip27txJa2trWdwpCLBl9erVwhAkn+fUqVOqf97ndxJ4\nyWQy5PN5hoaGbtt2QAGH0rFQgj7Dw8NUV1dz8OBBTp06xcWLF4nH4/zKr/wKS5cuVeeFQiGi0Siz\nZs3iscce4/Tp0zz33HMcOXIETdP42Mc+pvoho2QlfGgYBsFgkM2bN3P58mWqqqoYHBwkGo3y1FNP\n8eabbyqI0e/3EwwGFbAkwUrTNDl27BimaVJTU6PmwUxAioQ4Ozo6ymKDZzpW1kM+M96wYQNNTU1c\nv369zJFx6lyYmJhgbGyMmpoa5syZQ01NDb29vQwMDJRF5MqatbS0KKBV1l7WVsJdMkJVRrCOj49z\n+PBhbNsmm81SLBbx+/0K5JPXmZyc5OLFi7S2tiowTMpxHMbHx+nt7VXQ5KpVq3j00UeVw50EsyQU\nJWNWL1y4wEc+8hHlCCjjYWU7GxsbaWpqYvny5dx///0KwJIgVz6fL3Mt846DXCPj4+MMDAwoFzzZ\nZuneZts2K1asUFHUMuJaHmdZloLIZFSqaZrE43EFfxmGoWoSCAQoFosqIvzIkSM4jsPGjRuZnJzE\nNE2WLFnCb//2b3P48GGOHDlCT08Pvb29ygnRtm3ljBgIBLAsC7/fz8MPP3zb9TfTvHMchx07dnDw\n4EGy2SzZbNZNdDvK2rVrefjhh5k/fz7Lli3j8uXLKlK9UCgoiA4EMCnXi4TnvLHco6OjCp6NRCIz\nQsAAN27cQNd1WlpaWLp06dvuhxxX6TrnBWnl+1leJ1RvZLM8X86ZkZER5eQYjUZJp9PKAU/eQ/ZT\nRpDL9TY2NkYoFKKqqorh4WEikYh6HfzOd76D4zisW7eORCLB2bNn+fGPf8zBgwcJhUJs3bpVjYHP\n51PAq3fPtSyLfD6v5nJbWxtz5szh0qVLGIZBOp3ma1/7GnV1dWzZsoVNmzZRW1uLaZpcunSJgwcP\ncv78eYrFIlVVVTz++OP09fUxODjIgQMH6Onpwe/3k81maWtr4z/8h/9AY2PjXSE9Wf9PfvKT/MEf\n/AHDw8P8+Mc/5j//5/98x3OuXr3Kl770JTKZDKtXr2bz5s1va8wrquh2qgB+Ff1/qVwuB44jAIO3\nqWAohNmymWL/EZxMDwwdA39UOGmFm10Xt6KANybdWEu7gGH4eOih7ezateu217Zte8bY26nugkak\nnmDDcooTXaR729l/4BC2ZWLpYaJtIlK2WCjgpG6IExKLPTGCnnhOlf0o4jMdPL88azokFuGMnSE3\nfImCXoNlWzhaFXa+SD6bB83AvnWa3MAJNN0QYEwx4zItU2qqaSL+Mtkp/l2/DgLVMNUhSv4erOkl\ngC1YC7WrYVjY+WIVBGRVu1rUvKw+Lkgox2D0pPg6OkdASqrvrqJzBGBTEJ+4uJ2rowYEgwHMfC+2\npuEkFrlQjPfWLkzlWDhmodR+TUfTfTjowp1s7LRwZismp8T8OmAV3U8wZd3TNRcQ87jOpbrQgvVQ\ndY/bX1vE8jrCiU5YSvspBBpQcJGEOpt3olkZSHbiJK+LNvoClMWiyqhjN1ZawYzjF9yJWiz1OTtQ\nivmVsJWE4VR07RQ5lnALC7eI88y0iB2OzfNMAO+ccJ0ga9eIcTezbsRm9A5xspnS3MmNQCAhIrVB\ntM3nc2GvCI4l9gHTMkHz4/iiEJ8PwUaxtm1TgJjJTtFWqyhii8erxdoKN4naOaZ7/yuibk5RQGhz\nHy/BeHIyCYLQbU9egGy5Iej7kTsWHqhKQXKmcLWULpRS/pgAH7O3IFjnrjUvZIa4fvKKgDHN1NQB\nEdcrTsDYW+Ie9RtcoM7niWN2VRhz7c58kOoqAbWBGjDzbjyxu99omtsP1yEyeVV8Pzq31EZNE+MY\nXyzmU/qGqLOZEWsEB83dTxzN54KpPgF9vg3n1TK4TnPXYPEE2EWc4iRWcZJ0ugdfME6wfil6IA7Z\nUcgNoYXqcKy8+gPOcRyxD3ocCjU0HBXLLR0NbRG/7I3ynapUpwuvLrhLBzzujWo8TdAjboy3e4zq\nq+ve551DEkBU8GFQzO3CuIhbDtaLttue9joOynFxhjZpqRtgF7FyY0x2/JN6DTCiTeQGThALGuza\ntavMjTaVSuPgE3123V0dM4/c5zQJRXr2NDQBSVqWjROdj1Y448ZCZzD7D2EOR3FiC4SzpBEBbLTs\ngHDClOsQXayP3le8VUULxNCsLIva5vO5z33uJwLudu3axeXLl2k/coz0tVcJzdo84+u1KKVNceIm\nud4jhP0OmzZt/IkePlRUUUUVVVRRRRVVVFFFFVVUUUUVVfSz00/ioKNpGlVVVfyn//Sf+Id/+AeO\nHj3KP//zP/Pqq6+ybt06WlpaMAyDiYkJ3njjDcbHx8nlchSLRRobG/nN3/zNO0YB27bNX/7lX3L5\n8mXy+fyMYJ+UBI9yuRyTk5NomsaaNWu45557SKXEM+CrV69SLBbLAKipbkzer6cCVhKEAuE+Z9u2\nip+0LIvTp0/T0dGhzvH5fOTz+Wk19UIpMpLx2rVr0wA5edxUN0FN0xgeHmZycpKmpiYCgQCNjY3E\n43EGBgbo6+tTYIumaTQ0NNDa2kpVVRXhcJgNGzbw6KOPMjk5qdrp1caNG2lvbyebzdLf339bR0YJ\nXTmOo+Afr7wgj2maqu4SIpOgmnRBlPG6v/M7v0NLS0tZlLG3Nn6/nw0bNtDQ0MDnP/952tvbmTdv\nHg8++KByR5QKBAJEIhGy2SyzZs1SsblybOfPn8/atWv5wAc+wKc+9SnlUChhMjnuo6Ojyi2rpqYG\nn8+nAEBd1xVQKWNhU6kU4+PjZWMn55Q8VoJEsh65XE7FQTc2NiqHsqmS52UyGaqqqlREdTwep62t\njWw2q6Av6baoaZqChyTE440hDgaDyk0wHo8rYG18fFwBWhJOldGnkUiERCKh+iSdLGOxmIIWU6lU\n2ZpbtWoVH/7wh2dcxxJgunXrlgLYYrEYixYtYsmSJYRCIfL5PO3t7XR3dzMyMsL8+fMJhUKkUilq\naoThxuLFi7l58yYDAwPU1NRgmqaaS/KZdy6X4/Lly8p5DQTwK2E12eYbN27Q1dXF4sWLqa+vn+bY\naBiGivDVNI3OTvGe74IFCxSMJecIlOJ5Lcti//79ZLNZ6uvrlWNjIpHA5/MxZ84cPvCBD/Ce97yH\nkydP0tHRwf79+1WErWEYCvYDqK2tvWtanldyLi5cuJA5c+aQTqfVXE2n0xw4cICDBw/S1tam3sc/\nfvw4TzzxhIruvd1+Jdc7iLV65MgRisUiDzzwgIpJn0k3b97E7/ezadOmu7Zf0zTi8TggnB7z+TyB\nQKBsnGU7NE0r27+8+750q5s3bx6XLl2iq6uLrq4u5s2bRyAQIJvNKghaOuV5+yr15ptvcv78eUzT\n5Nlnn1Xj3draSnV1NefPn8cwDLZu3crv//7vc/PmTbVugsEg999/v9pHZLx6NptVc1K6YMr2a5qG\naZrcd999XLlyRa33VCpFOp1mYGCA7373u9NgxFwuh2EYZLNZvva1r5X1Qe5dfr+fX/qlX/qJE5fq\n6ur4jd/4DZ555hk6Ojp4+umn2bhxIzt37mTBggWqzSdOnGDfvn10dHQAsHz5cj7+8Y/f8XeBiip6\nO6oAfhX9f6lQKCRcmqzbQQzTpQHRWJx06xaKt96C8fM4+aKI+iuDkxzhcIRDMBhk27YH+dVf/dU7\nAgTikwLTY29nbIemE6iejx5MkLzyfTDzBOrnYulRCrmsYOysLA7a9EhV1ZMpX09J1STcDKOnMbPj\nOIWC+LktgTHAsbBS/QI60XwCirFyAu6zcoDnU2+OLZzJzIwLg82Z0gZP3WZSdBaMuzCY5hOQ0tBR\nca1EG4SaBYjkuC5oky6M5RQFGFf/gAJ9ytz1NF04+41Oksvl7urqKB0KtUhzCboBsE0XSJHAjaPu\np2AVdBGTDGiFMRzNL0CqYG3pHLdNDg6OLf+4FqCOEyq53jkTlyAyV7TBTVj2Ge6nHIIBMukMjm6g\nabpythIyRbxvpAWtmMTJu1G0vmCpL9I5Ln1D/Du+SNRpKkgGkLpWfoyEA8WFXNjLBY0kfGTLCFFb\nuL6NnxeOd9E5twe2dMN1yfSJr/0xAX/dLk7WHxeuaJNXRY0SS8DnL8XtOraYR7EFkB8V4JgvAnWr\n0SKtYt1AySktWCdgvkwPjJwUznyFMbHu1TxwryvBtVAdND1UDvd55ViiXpk+FJTlrxLwU7BBnGcV\nRY2TnWAmofdV18mvQQBu4WbhfpjuFvBc1b0loFOOgWOJtZLuFt+S8b4SSC5mIH8LUjcFEDXyllir\ntaumj4dju5CeJmK/x8+ImoZcQEzGEzPDnpq6IcbMsWDsPKS7BOgXagLDjZeNzRPOiPkR4ZAqXRHl\n2pAR2FrABSu9tXe/lO6Bt4vtDTWKuugBaN0N2Vs4yatY+STZ3uP4Ig0Cy5u8AtX3gubDsU0BhGta\n+XUdR8TYOh7QLnVNzHEjOv14r4opUd9gQ8nh8LZynQCNOOTHxRoMVJd+LIHKMvfAKXAfmuugqIn1\nE1so5sTkFTFvNF2cZxddqNQGF/ouAxrTN2DyCk5+1F3ztoj8BcgMUxi5hK7BrIVLuHTpEseOvaHc\naDEAM48WnY3jC3lgYPF66dhF5IYm+WZHdkHThAvf+AV80Tr8idkURi5hFybQxs7goIteunC4Bujh\naoxYC7aZxymmcKwims+PHogrEDEaCbN79+6f2E1P13V+9Vd/FU3TOHLkGNmeA+T6wwTq7hExvbof\nxy5ipvpETaws0aDBpk0b+djHPlZx76uooooqqqiiiiqqqKKKKqqooooq+jeU4zh0dHRw+vRpBTtF\nIhEVNZhOp1X85N1kGAbV1dV85CMfwbIsDh48SKFQoL29vQz4kDGOwWCQ2bNn8xu/8Rs0Nzff8dpn\nz57l9OnT5PN5qqqq3hYAIB3bLMvi4sWLjI6OEggECAaDZDIZBdjcTTNBfhKwAFTErmyTdK0KBALM\nnj2bYDBIZ2en+p508pKSMZdVVVUUCgUGBgamQXxQApKmPj/TdZ2+vj6qq8VzUb/fj8/nY+HChcyb\nN085aEkYSEJG8rxsNqvuI6FFqbq6OmKxGMVisQyGu129vSCN93u3k4THJFzmOA6pVIpEIsHHPvYx\nWltb1XEgwCsJCHqhuIULF/KRj3yEv/7rv2bfvn2sX79eQUrSiU+OT6FQUO5bxWJRRXCOjIxQV1dH\nTU0N8XicVCpFNpslEokwODioXP1u3LiBaZpEo1EFC8XjcYrFIuFwWEGkoVAI27bp6+srcwuzLOu2\nc0+Cgpqm0dfXpwC/ixcvTnOMlJJ9k+BeLpcjGo2WRd3KsUgmkySTSerq6ggGgypqVQJ+EiqS61Ou\n1xs3bijnMu/YynpKIFFGUsv+TkxMqGOls1swGGTr1q1s3LhxRuB1aGiI4eFh8vm8chlbuHAhn/rU\np5g1axaWZZFMJjl+/LgCMCWIdujQIYLBIMuWLWPjxo2sXbuW119/neHhYcbHx6ftG7lcjgsXLlAo\nFBQcGQ6H1RqTawdQ80HCgHPmzFFtlpJj3dTUxPDwMKZp8sgjjyiHUC8YJuvS1dXFd77zHeUQ9/Wv\nf12l5TzyyCPcc889Kg64paWFb33rW2qNFYtF5WpoWRY+n4/GxsbbrtGpYzdVzc3NXL58mQ9/+MM8\n8sgj3Lp1i/3793Po0CE6Ozvp7u6mqamJgYEBDh06pAA8uc6kQ6L3+rZdMjxpb28nn8+zbNmyabXz\n1kTCkE1NTRQKhWn70tTjQ6EQbW1tdHZ2cvToUR588EH1c/kaAJRFRnvf3ykUChw7dgxN01i9ejWZ\nTIY9e/awb98+PvrRj6r+FQoFBXHLfVNe4/r16/zP//k/6e7uxjAMotGoeq1KpVJ0dXVx+fJlTNNk\n06ZN/O3f/i3j4+NUVVWxefNmvv/97xOLxaiqqlL7ptfhUbpVzjSefr+fmpoagsEg69ev553vfCft\n7e289tprTExMkMlkCAaDNDc3U1VVxaZNm3jggQc4ffo0Fy9eVFBnLBZj5cqVnD59mpMnT9Lc3Mx9\n991329rfSXPmzOF3f/d3ee655zh79iyHDh3i0KFDZbWUCgQC7Ny5kyeeeGJapHFFFf00qsyiiv6f\nlrR6PnDgAIODg8rWfGhoCA2LYrIPI1L/tq+naRCNRUlPxilO+jF0GwcLx7KFCx4amk8nFAkxZ/Zs\ndu/ezcMPP3zHN/Rt22bPnj1k8+a02Ns7yTESwslu6BhWZhAzlxOmR76AxyHrNg6FGsJ1TsIfoNov\nXqxLcY6a7hfQk+k6Q/njoPtF3CsOWrhJgDqjp3CwBbQUqBbtkErfFP9PuDCYh81RhZ3qSKXa6sJY\n42cFPOgLCBcqKwtj54BzU85zRMSvrQvHv+E3RBSopjE1DtIJiojJUCh0V1dHZ4aaOlahVGsJteFx\nIPTGWsoq2xYEqwTMVLqQe20dTTdwTBHN6jgOmi6cwojOwTGiaGYavTiMHm1G18Wnhwy/oXpv2RZY\npls6f8mVLOOB8/xxyI0KV7eJiwKatE0B+Rgx4e4FpWhWCQRJkEzTBaQkj1Fwn+bGg055+KAB+ATI\nJF0Wg/Xi2sW0+LceoOT65j3Xjb7VNOF4V71JtDPZKdrt2GJM/DEBDdomjLwhgKfoXOEOp3l+mXcs\nFxTNiXsZEWjcBoGECwm5a8CxS06NvpCYg/44DBxw3Tld9zGvM6Y/IeoTmz8DIOeJyC26AGphQrQz\nvhgaNqIc+yYvl9wIfcESRGZmwLwhAEw9KGpmFcTPU9emxL06JbhPRkGraGPZPz+EaqB6pRttfFwc\n71gCQPUq0yP6bcREXTVdHO+Onaa5c1u2VcJlhQkBQ0p4zJwU/2V6xbWq7hEQJjake4RDouXWVkO4\nvsn5JCE/x3HnhBfYfRuftpWAr+YCt8E6MV6ZXpzh45jpQXEZKyNcLsMS9hWwoebzCSjckWtaL+1h\nmR7hMAkCpnPs0hyY+sekhPF8AXEdOSdLnQGPwyo4AkaWUF5iiedQF+T07CHTaqHgTEqApxET6yfd\nA7G57v0FBKv5AjhWAd2nY/gMCvkszvCbIp4eXBh1oYCvNV048eVuoaWu45gZrl69ypXOa9h6mLDr\nRpu89B0xV/UAmu7GCut+sa+68K2u40YnyE+9aeJ1RPeJsZcW8q3rCbWso5jsIX/rFGayT+0/Rnw2\nweZV+OOzZ/gUbclNLxKATZs2/NRuen6/n0984hMsWbKEPXv20Nc/QH7kNNnBU2K8NQ2fTyPq99E6\ndzbveMc77vq7QEUVVVRRRRVVVFFFFVVUUUUVVVRRRf96yufzHDx4kL1796rIR69rnYRRvvzlL/OJ\nT3yCWCz2tq4rwYauri4FjEmgSV47GAwSi8XYunUr7373u5Xb1p20b98+CoUCoVDobcN9Mn3EMAwV\nMbthwwZCoVBZf6Vu59wnHaAk3DbV7c/rvObz+aiurlbOVl1dXWzevJlf/uVf5gtf+AJXrlyhtraW\n/v7+MnCypqYGx3EYHBycFpUr7yPvNdVBzzAMBgcHWbRokXKbk3Gd0nHPC7XI9pqmyc2bN3nuuef4\nhV/4BXWcV4VCwU1GCtz22Z0cXznG8ripcN/UqGPv1964WMdxaGlpUWCJHEsQMFswGFSOg7ZtK2hn\nw4YNfPvb3+bWrVv09PRw7733TosTltdPpVLKoe/ee++lp6eHgwcPsmSJeLZ83333cejQIb75zW8y\nPj5eBqNI4CYcDqs+1dTUMDExQTgcVnGu0tFQ3kvCj17gaapkHaUDl1wvEvAJBoNlbo4gnsUmEgk0\nTSMajaqIzpGREdU+OUaxWIzm5mblnjY5OanmtbeNjuMoKO3GjRtqTsr4XOls543SdRyHW7du0dLS\ngq7rKp5Y1i6RSNDc3EwgEKC/v5+XX36Z+vp67rvvPpqbm7lw4QLt7e309fWpcZIx0/l8ntOnT5NI\nJOjt7eUrX/kK4+PjZLNZ5Y5nmia6rhMKhXjzzTc5efKkarPP51NubF5Ht0uXLqk5XldXp34mawYo\nMDYUCpFOpxkdHaWrq4tQKMTs2bPV8SMjIySTSQzDYOnSpZw9e5axsTFSqRThcJhwOKxqK9vb0dHB\nc889RyqVYvbs2SxbtoyJiQk6Ojo4efIkp06dYvXq1WzatIn9+/dz6tQpUqkUuq4TCAQIh8PU1dUR\nCAQYHh5mdHQUn8/HQxubMQAAIABJREFU5OSkcm30rrc7wbayfj6fTzkvtra28qEPfYj3ve99PP/8\n8xw6dIje3l4KhQIvv/wyixcvZvbs2Wp/8I6ZnKPy/i+++CJjY8KV5MaNG3eEDSWsqOu6cuS7k6Os\njBi+evUqe/fuZcuWLer+st4zwcny3sePHyeTybBgwQIWLFhAMBhkz549HD58mG3bttHW1lZ2jhxL\nub8eO3aM559/HsuyaGpqYvPmzWzfvp2qqiq1ds+ePcvhw4e5fPkyx44dIxQKsXTpUp5++ml0XecH\nP/iBAsdl3eTem8vlVIy2hMQltC0hV7mHVldXs2jRIhYtWsT73/9+XnrpJb73ve+RTqepr6/nqaee\nUq+5u3fvZvfu3apvg4ODfOMb3+DcuXOEw2Geeuqpf5GbXl1dHZ/5zGcYGBjgtddeo729nWQyqfaE\nWbNmsWPHDjZv3kwkEvmp71NRRVP1fxzwu3jxIm+++Sbd3d2Mj48ri+JoNMqf//mf/59uXkX/TmXb\ndlk8YL5oYVmOeuNdw8EsFCj2HUXTfQTrl93xxbNMjo09cZVEPML7n3gfPT09DA4Oks1mCYfDNDY2\nsm3bNpYvX/623sw/d+4c/QO3sH0R/FVz33YfC4UCTqQVzR/DyichPeA6c/k8cY5eNy0PdKb5mWYv\n5eA6UpXO03xB4XDmAFlhvUy4GRo3Q7oXho/jZAfRfEFofkg4byWvQ3yRgKUk5Fd07bVnchTUcIEd\nGXU5ZVvSdAi5kbMaMOtR4WCVGRDRo1MhLwmwFMah/8cCYAnUuLG2HtmW6xCmqV9eva6ODmAWhZ21\nZVtYjnCic+wCmgOOLeE+L9TmApNyLmmuC5kE/ST0JF3v5J3cvmvS9c6NsBTQinucrqMlFsHYGYx8\nL9HmxdNrKRueGxTNCsQJ1C6mOH6j5Eom22sXIHnZE8Pqtjk/JgArX6DURi8QlOkRgJeElCRgpNwA\n77KONB/4BNyD47ixsY5oz0xwIEBuGAVJDrULh7TofFFHI4qKyB15S0BoINrYsHEG8MuFw7I94n61\na0TUr5WnbE1I4FHOO80Q87dhHQy2i/HxV4l55tjCTQ8EwJe8Kq7tTwg4sjgpal2cEpErI3kzPdD3\nquhPbgQyMzjuoQnANT8k1piZdvvq1m7kpBijQEIcO3mlBPe1PCzWgCqBU4pblm3wREGLWOJrUC0+\nZUR+TMB84EasApFWEfVqpgS4GG1Fc38RFuCr61A4dkrAkJFWqLlf1KowIq5vpmDkTQE0WkURFQxi\nThpRj6OhX4zH0CEBidlFF/jyxAjf8ZOo7s9M10bd8Dys03Sc2By0QBz69ipwmZG3oPFBsfdIaE7T\n0HwGjumuZ8MQtchPCJAY0IwIjpUtgXMzSZP7s3QnLIJPwq3aFADb3VNCzW6stQvlhRvFuVPnbRk9\n7Z6b6RZwqD/mRptrYj8ceUvEn/vjwh1Syo3stS0bf8iPdesIVqYPR/cLd8dwi6ibdNzzhfFF6gg0\n3481+AaF4QtghPC3PIS/ulX0aqbXJA00nwG6jmPlhQGl434iGPH6o/YgW74miYd7mqYRSMwhkJiD\nmRkl1fkKFNM42UFyvUex/w3c9HRdV+De+fPn1QcJftrfBSqqqKKKKqqooooqqqiiiiqqqKKKKvrX\n0fj4OF/84hfp7OzENE18Ph+tra2Ew2E0TSOXy9Hf3086nebIkSPcunWLz3zmM7S0zJzuM1Xnz59n\nfHycBQsWsHv3bi5cuKAiRuPxOPfddx+bNm26rSPZVA0NDXHq1CmKxeLbBg29rnoyhvTMmTNs2bJF\nQVNTQTMJSZQnd5R+7o1U9UJs8utAIIDP52PWrFnU19czMjLC1atXOXz4MIZh8LGPfYw//MM/pKWl\nhZ6eHvWcDFDQnnSFmioJ5c3kZCUhpkKhQDgcLnPok5GkwWBwGsAn3bauXr1KR0cHa9euFe+ruX2S\noI4EuWQErGVZGIZBoVBQbozeayYSCQYHB9/WOHnPi8fjqs47duwoc0uUkJaE+WQsqXTjkzXcvn07\n3/3udzl27NiMzlNyXE+cOAHAggUL2LFjBz/84Q85cuQIH/zgBwEBumQyGbq7uwkEAtTU1CgwdGBg\nQLncyehMCXBZlkVVVVVZTLGcM3eD+7zyRtlKJz3pdOj3+8uAp6qqKoLBoII//X4/oVBIuX9J5zwZ\nuykjRpPJ5IztkevMcRxGRkbI5XJUVYnn497xLhaL6ppShUJBuR6uWrWKhQsX8qMf/Ug58vX09JRF\nm16+fJkjR45gWZaad36/n6amJnw+H4VCgYmJCUZHR3nxxRf5x3/8R2zbJpvNquhSCT45jkM2m8U0\nTRKJBDU1NcIAxAUSJfw2e/Zs5RSYzWYxDEPBfbJd3rHyAk7RaBTbthkfH6erq0vBjBMTE1y4cAGA\nuXPnKjhufHycF154QYFjPp+PkZER3nrrLQ4dOsTNm8L8ZcOGDXz0ox9VkNzY2BhHjx5VroT79+9X\ncygajdLQ0MCWLVvYtGkT8XicSCTCxYsXeeaZZ5Qbm3TdjMVid4Xj5NilUikFaXsVDAb5lV/5FXw+\nHz/4wQ8oFAqMjIzw5S9/maefflo5N8rxKxQKCsh2HIeXXnqJvXv3YhgG4XCYY8eO8a53veu2rykS\nTJSwbCaTUQCYFwzMZDIcPnyYc+fOMTk5STab5dq1a+zbt49169YpiNXb16nufRMTE7z00ksA7Ny5\nExBxutu3b2f//v188Ytf5Nd//ddpamoqu78Eby9evMg//MM/YFkWO3bs4D3veY/ap3I5YVYj44a3\nb9/O3/3d3/HDH/6Q2bNnK3jetm0F8vX396u6yHvJ1wkZay+vGY1G1TE3btwAUE6u8rwPfvCDJBIJ\nvvnNb3Lq1Ck+/elPs2HDBrZs2UJdXZ0Cy/fv38+ZM2cAiMViZeP6L1VzczMf+tCH+NCHPqT27VAo\nVHlfqKKfmf6PAH7Dw8N8/etf51vf+lbZxiPlOA6JRGKGM0v6/Oc/r0hogMcee4zNmzf/q7e1on9/\nKhaLPPvssyLy1o0HDNTdQyDWgubz41hFzFQ/xcELUEyR7T2KlRkiMufB8gjX211/4ia6laV17mwe\ne+yxf/EGfeDAAfJFi0DdPWi3iymdQZbtwl+JRTB6Bid1E026wvk9cY7StU03wLI8YNtUyWhLDXJu\nnKMRczkRjwNUrE1AF7G54j79e3FS3WjBBrRAAqeYElG5kVkeWO0ujoLSEUseP1VyXIxYCVSKtECo\nVrRNxqnKbEccAQG6sbYkr0FiiXLGE/e00RzxiY1wOExDQwO+y52YqX4sX9z9Rd4WJXEQMa6MQ+YW\njr+qBPdJCGUmwEh+SzegMIqCbux86Rjb23fX4t7nF65YtulCb+6x4WYYO4NdSM5cRxAuiqnraDgE\nahdjxFrRg3EBgaa6INODk+oS1zWiYv6EW1zgzhTg5MhbLmmTFcfofqhaAiMnBOhlxEpzyMwLhzvd\nz13hPiXPcVbeBW9xHdo0N/pZxvtapejT6FwBOJlpERGLNn2++OPC4Sy+2AP3eR38HMj2lvoWaRXX\ncKHH0oMNF7wsA041iM4D44xoQ90a4SSYvAapTg8s6aowLn4uY4H9CRH3G2p0r18U8yJ5DQpJUVtN\nB194uuMebn2CNQIEzPYLZzwrJ+aJmYZbB6FutYDBJi+Lc+rXT4H7rFJc8dSxCNZ41sxVUcdsr4DX\n7KKoVWyhe5oOUddZc+QtsQ6ks2dmAFLXhcsmlGBL6RwYrBaOiNl+ASZKgNMTl0y4tRT9q7v7RqhR\nQIOFURFvLaOOp9Z9JsmocJxSNLbHgJFgjYA3+/eL65oZGDzkGQcJ7fpKAK5VFLDdyAmxpmMLhDPl\n6Ekxlv646Gt5Q9zI63HIDQmwzrFcdzsJuLoNU66P1wQcamXFOIwcF/BhoEocp1wZnbLbAC6cKeBD\nEveU+pFYLOqR7ob+vSLKPFiv1pOmGzh2kdzQeeykC4o2PgiBBLpe+hSs10UUxyGZGQQ9gFO7mqIW\noZAvEAwG0IOJ6U6iUpqOpgvXwEKxAI7r3Odd29kBQEDLU2VEaonM2UKuaz8Bw8EwChT+Dd30dF1n\n5cqVrFy58l/lehVVVFFFFVVUUUUVVVRRRRVVVFFFFf30SqVS/Mmf/ImKEVy6dCn19fXTngXNnz+f\nnp4euru76erq4o//+I/57//9vyvI4XbKZDK8+OKLAOzatYtHHnmERx555F/U5osXL1IsFlWs6tuR\nF8STbmXj4+Mqcri1tVU57slj/X7/jM5+MzlMSTjR6/Ll8/kIhULU1taiaRr19fUEg0H14dd7772X\ntrY2stks9957LxcvXiyLjr2dJKwnHZ2myusuJ9s61S3PNM1pcKB08ysUCrz11lusWLGi7DqO4zA0\nNEQ6nVaOi+3t7Sqq1Ns+6VbnOA5NTU1cu3btbcNsUrNmzVLX8cJx8l5eKDMcDpNMJhX8J+fFsmXL\n+O53v3tbwFBCjcePH8dxHHbu3ElzczPLly/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1a5w/fx6AtWvX3hHuk/L5fITDYRYtWsTF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scdd3y0nbxKzZw5k9/7vd/js5/9LOfPnyeTyWAYBrFYjDlz5kz5WaimaSxYsEBFZAKMjIww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\nTt26u7s5duwYBw8e5NixY3R3d1MsFmlra+PRRx/lox/9qDPO+Xyen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OI73/kOP/3pT1m0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\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N3J49e3j++RfIljTCyx7CG22/4Rt0TdPwBOowop1kEgNcGxzg2tAIBcuDv2sbwa478Ubb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\nTxn7LYHgY8eO8fTTT7Nhwwbuvfde/H6/Whfy99xaS8BPXkfk4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/kXwuEwO3bs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\n1fHEE08wf/58Wltbef3111mwYIGMbQakK+DixYuHtMUeJxCQqu1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ThXOfogrIyHbgU71i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JRCIKihkYGFDOf1LS6auuro6Ojg4SiQQbN27E5/MxZ84czpw5QzqdZs6cOXR2dk4bzyvP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\nH//4x0fM7+rVqyQSCQoLC/nGN77B4cOHOXToEAMDA4RCoRF1rK2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cNnbt2vWeVZCnq7Lt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113ceutt150+pQkSXzhC18gNzeXDRs2cODAAQ4cOEBGRgZZWVnouk5nZyfRaBQwxah77rmH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\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2LYcOHaKwsJDa2loaGxtJTk6mtbV10GNYrVZsNhtNTU2cPXvWSCC02+3U1dUBkJuby9q1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7W3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\nhBARtGnTpmB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trYD3994r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\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uXO3lo/qa0dHR0dHRGU20tbXxySefsHfvXpx2K2guoOfmuMDR/AP868MP6ezqwiSpRIZo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\nAlG9AEeOHBmQemhoDBWuVRwjSnokUSQ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nz+vUKWzPnj24PDKGyAk9XktBENAHx6EP/lJE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DsLXa/BwCFb0BeYDRlLJ10dLzNJEiXw5WJh34iCfZOPZSFIF283XdA5gSWnQMYKKPwYFN0B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\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wsjbpaqujsfYM3W1XnMnAaCSAIIikZU0ctdhj0PFRwMKOCobBNmtW/kzqLu5noLeFQF87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fWPsu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\n8JSb3cf9PHz35IxY4XQGTEssDF06edrRJ9EzqBLIL2TTp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w6yW8XWTZL9w0Rbu+noPU06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bQcJNWH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\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brRzdSq0cbdmWJdmeAcOQYWDWGJjBgAj2DD8GD7uMwbs8wBqGYcH+zeAZwnrGYWwsbGws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UIBQSKlYFpSIc7vlWp6igJrl6Q52+HH7xVsvz2BYebULDxTfab8Te6lRRYP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UxPYMyE/zfjxVSU1X29iZmfePfx4RVdChYyJKFlxd7XG8RYGxmp+H5QpiBA+WCzJBpTXRoKDa\nvh1T1PcIFwF7E+3e9XNlCBWpQFgjoa4lPwBhBcZK1wIhZl3ZXwySC6FUMjEBKTzFRn/isDifvTRY\naVWfInO8rzzodHqe++UBngKi6oC7JfXYqVM4+RRvFD7QG46EsS0lhW8OXVAp8xUE9YgH9k0/t5y0\nS7bTwVWgK2UVc/QS1thltEiSSPVKwlUrfy0w1v9rdbp3Mm6//XYOHDxEZvg8kTmrr6oqrGs6tvAA\nWh/uHm9DAOHKN17IYk10IR1TKUuK0Jt6r7e0tHDLLbewf//+N8zLf0m74ezZs/T29ePqcUJlDW9w\nJVMjVLaQfO/r9PT2cfr06d8oBeRrcS1+k8PvD0Xf3lwW0QiAZGRkhP/5P/4G2xxhy3UF2roEfUNK\n5c9/teoCYhG1T6EgyZkEg/eR8NTBYF/h5jdl0G3Xrl088cQ/Iu0J4hGT2zc63LreoaJUIl3oTwle\nPWzSfDLL6RMH+bO2Vr70pS8HtsWpVIqjRw6jiQLLFjr87ZMhXj+reXCeJBKSuFJw9HyUQ6dj1FTY\nbFyRZ8VCk7G0zo7XY1zsCVOedPgPHxylplIpLpYlwLIgk1fglq4pcAsU8PYfPjjCyLhBa2eYpfNN\nhsd0xtIamZxGwRKEw1E+9alPce+996rVseNDLF3g8uWPpdGFSzjk8l9+b4Bf7Cll34k4bV0h2rqm\nWlwULI18QZVTIuYwb45DNOJyqTdMe3eYm9fmVRtIwHVNefYcS9DZH+I//o9aAKrKHB757SGWzrcQ\ngkAh0LSUQmBdlc3vvneMG1ZnefypKvpTBl39Bl9+tJZFdRaJuEtJzGX1ogKrFueDppSUcPpShO/9\nshzTEmzfnOH37hsjVxBk8wqia+0M8T//bwXP7E1i6HD7+gyuo0C1Ygvpy72CSz0a8ZjD6sV5XCkC\nq67pVtOapwRh2xamJdBd2LI+yzN7kxw+G2UiK0jGJ9t+roR8QZWfQCMS0ZESyhIuH7pznI+/d4Jj\nLRH6UxquK6gsc6irsphT4WBoBHCfyn+X1ISCFwdHdX6yo5RtmzJUlzsIIG8p8PHcpQgHTsU50R5B\nuoL3b0mzeZVS0V7e6NB/zCWfHsHxlIV9YCygBL2Qrott5YPvdD2EmU+rZ0q8lM13/wcmRnrp7zjB\ncG8Lw32tpMf7yWfG0PUQK67/AKWV8ybzTjdwXAfXtTwVQIljF6hbtB6QdJ7fj2tb9F48QmXNEnRP\nKVDXDFxMpbTmtVU1zVCgnaYzOniZ0cHL2HaBVH8bIwPtk+fUDKrqlzN34XVEYklcR/WFXMeaCXxK\n9blKa0jZ6E4DIaV0sUzVHw5HkyQr6hkb7sCxLYRwSPW3MzJwYfKQUqIbYSLxMszcBGYhQyxezorr\nH5jSTrVtE8fKI4RGf9dpshNDRBMVrLrhgxhGJDinNm1sy7+GhmW3IF2HnguH6Ti1m5WbP4TQBHXL\nrmeg4yRm/wj2yARGZam60NmevR58F1lUjxY9hZOaoDAwQtYw0DSNwtAoAhFMxvsxNDSEKyXRumpl\na+yrwmgCEQkF6ZwtXE/pTy+NK3U8xwXbmWHjKi1nsmpKNaGWiMexbBu9rASzc3CGauBsEY5ECHvE\nUez9AAAgAElEQVQTw2ahwOjRNnBcRDhEbHWjsv6dljXScsi3dpE+cFrd0EIQbqgl0liLCIdwswWs\ngVHMS71kO/vJdvZTtmYJc7ZtIjp/Dpm+Ufbv389dd931hul7O9HV1cVzzz1Hc3Mz2Ww2mPSsr6/n\nIx/5CJs2bfqN6BNt2LCBqopKuocGyHcPEps/uwI9oJT0DQPLtnFtBy2kkzt1ESGgdO3SN2w7jBw+\nR2FoFC0cwgxrJFcuYu66JYQrVN2VjkO6rYuxE20M9Qzx1E+f5uzZs1RVVXHo0CHylonrw5KAoems\nWrmS7du3s2HDBoaHh/nzb/45gyMptJpy5t2+gcjcqhnpKlu7lPxAiqHdx+jrHeRb3/oWv//7v8+5\n8+dxDY3kisa3lIdCCMrWNTG69zivvPLKNcDvWlyLNxm+es2/5Fno73vkyBEuXryIrut88IMf5Cc/\n+Qm2bU8BsAzDCBaJZDKZwA7Rt5ScLX4T+kS2bfOd73yH/fv3B/acVVVVAZzoq8WNjIwEKlmtra38\n0R/9UaBQ2tzcTC6XY8mSJQwODvKjH/2Itra2KXDC6Ogou3bt4tVXX6WhoYG1a9dSWVlJX18fe/fu\nZWJiIlDq838nEglyuZyC9D3702IFOt9mVtd16urqGBkZCUAYKSVVVVX88R//MU1NTXzta18LVLH8\nvqpv2esDg7FYbMYYsK+e5UOd1dXVjI6Oous6nZ2drFq1CiEEixYtIplMMjo6yvHjxzl69Ci6rlNf\nX8/9998fHNd13QAClVLS0NBAU1MTR44cCcrg4sWL9PT0BMp20WiUefPmkUgkyOfzgRVtW1tbAAg9\n/PDDrF+/PrBOjcfjPPnkk+zcuZPdu3dz2223UV5ePkUBzo+WlhZs2yYSiQRgUiwWmwF2AVPmBX21\nvHg8TjqdZufOnTMAPz+PHccJys91XWpqapg3T/UXurq6AtvbRCJBMpkMwNzi8vZtJl3X5fz588yb\nN4+VK1cGaSoUCoFl68mTJxkdHSUejwdKcbquU1lZycjICJZlBSplV7o/bdsO6rBfB3zlx5qaGpqa\nmhgeHqa3t5eJiQmOHDnChQsXGBkZIZFIsHnz5iBtPqTpA4O+EqPruqxevZqTJ0+SSqWwbZvu7u7A\nthemqlb6afWV7AqFAu3t7QEM295e3B9SlsarV68O6uls4FCxBXGhUAiAykKhQD6fR9f1AGrzIWYh\nBIsXLyaTyTA6Ohrs29HRMSMffcjNt4ndtGkTN954Y/C9v69pmoRCIXbu3IlpmixYsIA1a9YEYJ2u\n61cEpq+//noOHDjA6OgoR44cYc2aNUSjUTZv3syvfvUr9u7dy+233x4oSU6PYvjuzjvv5LXXXqO5\nuZn77rsvACl9lcTpfaLh4WEAli1bFhy7GO4rPv70SKfTQX0CtWjf8Ppg0/PH/9uHA5XIkBJqGRoa\nCqDlK4WvFBiNRoNn0PPPPx88P9etW0c0Gp2SP756349//GNef/314LpuvvlmFi9ejKZppFIpTp06\nRUtLC3v27GH//v189rOf5cEHH2T37t0cP36cwcFB5syZc8W0vd1wHIeTJ0/y85//nNbW1qBuRqNR\n1q5dy8c+9jHmz5//jp/37cS2bdvYt28fu3fv5sEHH7yqVW84HA7eZb6l94EDBwDYunXrG57rf/2v\n/0Uul6OkpCRQ0Lv11lsDi/fx8XF2797Njh07GBgY4LHHHmPLli0MDAzQ0tIy43g+TLp9+3YWLVrE\n/v37+e53vxtc1/333z9DKXnDhg088MADHDlyhB/84AecPXuWv/zLv2T16tU4jqP6iG9SydCP+vp6\nVq5cydmzZ9m3b981p6c3iGuA37X4VxlvyRJvlpCuhSslExNpCJUEVnKFgRPeBrMct1iZzjS9RlZ4\nxnGvBIv8OmO6rW50/u2EyhpmgAfF1ouZ7v3s269eIp///OfRNC2wqHS0GK6VYeL807iFicmpAT2i\nIC9cGDuPGDvvQT+qISacHGgRjNobiJbXe6uuXIShGiLKUtJW1rNaSAFFgdrWfjBiiKpNMNGOzA8i\nrDE0Iaivr+Nzn/scq1ev5qtf/Sr5gkN8we1YoSoKpqUAKU1TsFGySYFV6YsKYHGtSXgsP6R+lzQq\n5bnC8KRNbMJbxauHAB2kqeAgO6tUBeduVdasvhKcdCfV4EABMiUNYCSQfa+qvJAT0PELBd0YcaSR\nmLQnLQa8Mp0BxCZKGqH21qkD9VIiBvYh0x1KyaxyPZY+H0lsypi1mWpVZZVcouA+VOMhPIvtSTgc\nIZfLK6ldx1TnK2lU+TFyUgFrxWnwgFf/mK4exkUqWFEIBQSGK1S+6FECO2LXVCCa9GxiBQp68hXs\nrAno+qUqj6ivuJjzFBcvqb+REK/37H29PItUKEtc3wrX9erUbLBNAPeJyW0mBr2MKFeKkr4SX7ZT\nqcgVUuq8VbdOhftgKuAZgKNSqaZVbVC2vq6lVBjD5UqNzy/rQMGxKG+FAHMCRo6p/cIVKg+lpUDG\nUImC2mK1Xl56HTbhvdJdm+mTeOBOgpOBYqRdtI+pFPuEAKNcWStnOtVnelid37ef9cE2p+ABep7i\nRtVGAuXFXK8CFK0JlR7NUHkoJVTd4pWRZ0WNb+nkSykUJTtQk5xU30PXFSRaDOwl5jOpWDg9PGAw\nucRT9ryME69lfGICRi+jIWftgBSD5BIwCwWQAqGHQehK6eQKE1pTYUuvLKQ9FVz1QVMvDEPZwGua\nTi6vIwE3P8ZEyy/QIqWEK5swSuqvODgogFDIwDA0zPQFde7kIkQoNvM+8Ovp8BGlnIrwlBo9tVSh\ngZ1DmsMwcRGnMEGu+xB2ZoDY/FvJ67H/JzBWsXpcT28f+YKJbVnee0Z1nnfu3MXcubU88MAD3H33\n3e/6pNaaNWuom1vLhUvdWGMdhMsbr7htOBxWdsqurRYOZDrByaJFkhgl9VfcD5SyTaH3iBowEFFi\n82+96nvdHL3MRMcuXn75FXbuehXNCF9Vie/OO+/8F7UbXNcNFC+vBjnOFkJohKuWUxg+zu7du68B\nftfiWrzJiMViIARvgnuYNXJ5kAh6e3uJhky2bcrziftsvvm9MH1DMJAyAkU2PwQK8hNCki1ALptT\nA+ZF9/1gSi2Smr76892IgwcP8r1//N/gjPP+20x+6w6b8LQxtmRCsnSBxcfutfjBsw4HTjn89V8/\nxn/6T19lyZIl7N27F9fJM7/a5W9+FMZ1JUJINq7Is3FZnnhM2au2doZpPhljPKPxq+YSXjooiUcl\nmbwgZEjWLyuwfLFSCLEdF9eVhNXaMLIFyBYUwBUJS3RNNXfev2WCv/nnStJZjU/fP8oze0o4fTFK\nWbycRx75/9iwYQOWZbF7925wc3zqfoto2MW2IRISGDHJv3vPOB++c4Lmk1FeOxtjIqtj2RCLSOZW\nWew5Hke6gi9/NMX1K/K0dYX58yeqaekIY1pQsLxXhwa6ruxnXVtQmXT4zAMpaiqcQHHOF2QCtdYm\nHpW4EkrjLssXFugbNnClUiBs7w4FIscHT8WIhF3WLClQVWpztDXG8JgOEm5ak+NT7x/F0BVcl4yr\nCavrVxb4xPvGeeLZMn66K8nRlihbrstx87oCiUQkgPdOtmkULLiuqYCuC3TdUIoD00gfiQwm4UCQ\n97pD4ZBkfo3F+Y4IvzqQ4IHb0uganqKluoeEUJNR5UmB4wi6BkMsa7AwdMnmVXnGMyo/ykpcXM8a\n2XYhkxMkYirHNA1SY6qdKCX8cl8Jz+0vYcl8k0TUpWAJeodCjIzrCE1i6PCx94yyaUUe2xKERIS7\nbzTZc9RmuK+FeUs2eypZShlO04ygvymlDOA+oekYhgKffKvbyrlNCCEorZxHaeU85i+9gbOv/Zxc\negjHsZm3ZNMUuE+Vt47jHdt1bBzHVO1oBHMb15Pqa2c81Ul6tJ/W48+zYuMDCF31B9WCNycA9EC1\n+fKZFG3HX8CxCiRKaymrWoDrWOihCIlkNdXzVmKEwtimUtjW9RDhSALLzJIe7SNZUafGHvDgOQGu\nY+M6Fppm4Er1t4IgBbaVJz2qlJXrF22kYcVtXD67m56LryNdiWNbhCIJQqEImm4gkeQzY2TG+lXa\nhWDhii2EwjFs22So+yy9l4+TnRgKrssqqMnmqqXLMEIRHNuHDo3JySJEkVWxigXLb2Ww6yzZiRTp\nsT5Kq+oxQmGq5i2nv+ME+dYuSm5cqZT4jJmTYn53RBg6oXnVFNp6sMfS5KuSuNkC6fYuwracMWhv\nmqYCJwxldWxbSk3aiEYUMzjbRJa/5mk2xTnbUQSzv6lpIb1JaAGUJBIYoRCWZWKZpvrQdRnae5x8\n3zBla5cSra++KixhGAa6rtN9sh00DaOmnEjtTGVp6bjkWjpJ7zsFAmKrGolvWIpRmpjkDQsW0ab5\niC1rybd0kj50lrFT7bi2Q3LNEoa7D7Jjxw62b9/+jgIcHR0dPP7447S2tmLZlvIv9yyQC/kc59ta\n+e9/9meUl5Xx4Q9/mPvvv/8dO/fbCcMw2Lp1K//36adIHTpNfX014ip9tHAkgmXbSNvGHB6j0DGA\nbuiUrrq60kV+cIShnYfRwiHiTQuof8/NaNNe6kLXSS5fSHL5QjKXeuh+aicHDx3CiITB0IguqCVa\nkQRNw8nkyF7s4cipE5w6c5olixaTz+cZHBnBmD+Huvu3oBlXnjaJ1lQy78Gt9D23n9SlXh5//HFM\nx6Zk1cIZ6XozkVzVyPDeY5w+fZpCoTArdHEtrsW1mBo+qDAb0PRmwwdwzp49SywW4+GHH2b58uU8\n9dRTwcT7dODEBy4ymQyWZZHJZEgmp7oy+BDN9M9/3SGl5Lvf/S779u3DNE3mzZunFroUvbd0XScU\nClFaWhoo5bW3t/Poo4/yta99jXg8zu7du1Ub0rZ5/PHHA6Wp+vp6ysqU8EAul2NgYICRkRE6Ojq4\ndOkS0WiUUCgUWF5GIhHi8XjgkhKJRIjFYriui2VZFAqFAKT0gSlfDbGsrIz58+dz+vRp0uk0Cxcu\n5E/+5E+oqKjgzJkzdHV1YZrmFGXCYoAxl8sFylnF6mRSSqLRKKWlpdxwww0kEgnOnTtHd3c3586d\nY9GiRVMU/4QQAWRSXV3NnXfeieM4gVpjsY2hYRjE43Hy+TyVlZVUVFTQ19cXAD39/f1BWbS3txOL\nxaitrcV1Xfr6+oK0/d7v/R7XX389QogAugT4xCc+wfj4OIcPH2bHjh3MmzePpUuX0tjYGABbUko6\nOzuxLCtQdYtEIrO+Z3xlNz9/stlscE2WZXHy5EnOnTtHU1NToGhZDGdVVVUF9sHpdJrS0lLKyspY\ntGhRYAcdi8WC+8Y/l58uXdcD4ZR8Ps/LL7/M7t27qaurQwgR5Jnruui6Tm1tLZ/5zGcCS9mSkhK2\nbNnCL37xCyYmJgLQyQfHiseQfRDPP6+v2Dc+Ph6UraZp1NTUUFNTw8DAAK2trfT396NpGqtWrZqh\n9OYDgsWQp18Pli9fzoEDBwLALx6PB/bdvppa8bPMB08HBgbo6urCcRxWrlwZlFs0GmXBggU0NjZO\nAcRKS0sRQjA4OBjkUzE05sNsvtV48X3nQ8upVArDMNi2bRvl5eX89Kc/pbe3N8izRCIR5JkPeGaz\n2QDyvOuuuxBCBDDsyZMng8UqvtKhYRiBc8KVlPP8Oul/t3r1apqbm2lpaWHVqlWEQiEaGhpIJBIM\nDg7S2trKihUrAsWx4ghU2qVk+fLlGIbB+Pg4+byaDz1w4ADj4+OBfXJx+Hnrq1v66nq+Wtps4SvN\nFdv4+mGaZgDqua4bKPb52yWTSXxrXf8dlMvlePTRR9mwYQPbtm27KkznA5yDg4P09vYSiUS4+eab\nZ2UWcrkc3//+9zlx4gSRSISPfOQj3HrrrcG2UkrGxsa47bbbyOVyPPvss+zbt4/HH3+cz3/+89xw\nww00Nzezc+dOHnrooSum6e3Eq6++yg9/+EOGhoZmqDvm83mam5vZt28fDQ0NfPGLX2TZsqsIj/wa\nYsmSJSxYsIDOzk5eeeUV3vOe91xxWx8o94Ha559/HsuyWLVqFXV1V3et/OUvf8mZM2dIJpN8/OMf\n5+67757RFy0tLeW+++7jve99Lz/+8Y956qmneP7554nH48TjcTZs2EBpaSmu69Ld3c3Zs2fZu3cv\ne/fuZfPmzRw+fBiAhx56iPe9731XTIuu62zevJnGxka+8Y1v0NbWFkDAt99++5vNuilxxx13cPbs\nWY4dO3YN8HuDuAb4XYt/lfGWLPFmCTvdg3TVSuHYvFsCKzktUgq5FGT7FeAzPYSG0MJIx6RQKBCO\nhKdMDdjpHnRdBET+uxXTbXWvZpUnhDbFerG5+SDLli3j7rvvZvfu3eRNG6FFyfe85ik7eZa08UlL\nWpHtRY63eQCdg9BDaLFKnHQfMqTU44yQ4dnAeiCcpiP0sDqmD365moL84vWeetw4In0JCsMIO4uu\n6Wzfvo3Pfe5zhEIhTpw4MUUxKJ/OqIHrIskRqaGOl6hnypJt6UDnLxUglFyiADY9pAAgawJp5z1o\nzwPWtCRCOKreVG5QMIw7y4yqp/gohQ5WDgpDatLCwYN7NAVFOXkFPGU6lc1vbJ4C3XxbXoBEA3LO\nTTMHi4VQ0JNrK7viwYM4I6cYL1tCpHwBRjgGroU90aNAvHBlAOLF41OVJ31wyV/1GHwqZQCiyUw3\novOXqtyTS5Ce8pevNhaPx8mGk5AZ8AC7yiAvAphMeGUrPRU5p6DgOryyKqTUb91TjEsd95JSZDsn\nNEQoiSxtgkQj4E7CoaVLYeDApBWu0Dz7NeFBVpo3QeArpolJpU7pKiU4UOBc0fmUilwCencq22hn\nNnVOf7bMVSAoTMJ78XowkmCNqP37dkLlJgVAejZUkyXhHSPjQYVOXqUvuRQqVk87Z7EKoQcqhkvB\nGlPqg5GqybwTnkqiv71/XukQqAVKB/KD6nyRKpUP5hiYKaVGZ6XVd6I4LwvgTZop2NdRls5j55FW\nZmZ6fRB29Di4eUh4cvSeZazKSX9Cq6hzlFgwFeYraVDbJ5cqG+jMZWXdq79BkyY2V93ftrJxs00T\nRlvRrDxnz57lxRdfnGJnWgyS25aN43jKpZqSmy/yFpt6nfj5xOTzQ3r1LFBuKwJBIejwqc4uYE4+\nW+zsMORSWKOXpijpXWkSyRy95EGVEUR83qyQq3Q8Vchsj7oPqj2r9WI7Zz0K0SqoWAOZLuTgIazR\nSwCEKpdRSJ14R2EsXz2uufkg2Vwex/XqqghN3mJILMels6ubv/u7v2PXrl386Z/+6bs6+aFpGnff\nfTff//4/kenejx4pveI7d8p7MDcEg68pBcbqVW84KZjr2qcWERhxEkvei3E1C1wpsccvA+Bi4IoE\nobLlxMoXXFGJb+fOnXR0dL3tdkNZWTJQvHw7YZTUkxs4xsDAwNva/1pci3+LUVtbS3fHeU61aTTM\nfeuTWifbdFxX4DgWdXUFPv4+pSR7XZPD+UsGrx6NsWllftZ9I2GwHYlpq4HkWHRyoHDXYR1EJLB/\neLdidHSUv//774A7wUN3F3jflqvnUSIGn/2gRSgk2XM0xbe//W2+9a1v0d/fT6FQ4EK3Rjjksn1z\nhvfdkqaqbKo18q3X5XnornH2HY/xkx1ljGc0SksEhg4FS3Lb+hyapuyfCoU8uYI6Z9hT7csWBKat\nfnQNDF2ybEGB8hKXC90hvvn9KmxbAy3C1/7jV4L376FDh8ikR1hYZ7NkvmQi7bctVLsKoaDBOzZm\nuWNjluJxbgnkChpHz0fpHzaUglLCQUoYz2ikc4oKEgLGJnSiYcl1TXlOtUe5aW2WxfMs380yiJCh\n6kfIAOlK2jrCfPsnlUxkdJJxl8Z6kxtXZ6ksVeUxMq5z4FScCz1h9p+IY1qCeNSlrtrmnhvT3LU5\ng9BmviNdCTeuUZME//xyKa2dYVo7wjy902VulUM4rJHJ6bR36tiugi1DoTDxeAJt2jvXdhwymbQ3\n+C7RhPQULFV7Lx6VuC48vTPJq4fjfPqBURbVW4DAMELEYlEM3WDJfJdXDunsO5Fg66YsBRPiUa/Z\nLMFxFLhZEnOZyGlYjmAiK4iEJOGQZNeRBLomuW9LhoIpaD4V42RbFMdBKQ8akgW1Fls3ZrhtfZbS\nhMtoWqn+WWaB+XNsErEEmUKW1MAF5sxbgZQujm3iYAYAviu969QMjFAEELiOxUDnaQBqF66bkj+x\nkkpW3fghTu7/Efn0CLoeZrIhPA2UdF0cWfDqoIYRimKZOWoWrCEz3odjF0j1tXOy+UfULdpEeVWD\nskKVUvUxBVhmlv6OE3S1HgzU7ZIVdTStnzpArkBFtfhLaDpGKMqc+avoufA6/R3HSVbUBdCg5tn2\nCs3AsfK4QRtdetsIrEKWVP8FhKYTT1bTeux5Un1t3rkcpGtj5sawTcMD8tSiJyldpHTRtDBmIUtX\n2yG62g/h2KpfqGkGRjiqLElRkGF320GGes6xcPmtlFbOVwCmH/6CqaL+kKbp1DSsobvtEIOdZyit\nUoBl1bxlDHScxB4aU+m0ZirkTR5XHVKEFOwppUSEQuTbLiBNm4Lj8thf/zWf+N3fDRTpYrGYmmw0\nLQoFUy3NMvTJSl187KBg1I/mLSp0c3mlrCk0pOsiPBJYWraXJwI09bxIF01uIQRuroAEnGyeifOX\nmTh/mUhVGRXXr7yqQtr46Qs42TwIDT0x++Jbsy9Fuvm06lbeuob4dUuLrsFPg5f/YYPE+qWE5lYy\n8sx+Js5dIlxZiqtrdHd3k8vl3jH7oBMnTvDNv/gLBUKEdEQiSnRxPeH6akRIRxYs8pf7MTv6Gc1M\n8N3//b9paWnhj/7oj96R87/d2Lp1Ky+99BKDXUMMvPI6NduvvyLkFw6FyGsahaExxl56Tb171i1F\nj129T9fz9C6kgOjieurff6tSlbxCSNdl/PRFlYZEhPDKRqo2LCdePXWxplMwmTh3mdHD5zjb2oKV\ny2NUlTH3fbdeFe7zQ+gate+9ic4fvcj44BgS3pI1b3HokTBaLII0ZaA2dS2uxbW4epSXlxONRqfA\nYm8lfAtdH0q599572bBhAwBNTU2B4taVQIp4PM74+HgAzPhQiOM4jIyMEI1GWbdu3az7/rpi3759\nAdzX0NDwhqIU0WiUxsZGLl26xIULF3jyySf51Kc+RW9vL5lMhra2NlzXZfny5dTV1c0AYRYuXMjE\nxASXL1+mp6cHx3FYunQpLS0tAUQUDofJ5XIBlBSJREgkEmSzWUzTDFTVfBtWTdPI5/OcPHkSwzBw\nHIfa2toA7gPYsWNHYGt7JVt737bQhy+Lw4fSfDgwEongui6pVCoAznygs7S0lJqaGnp7e7nxxhsD\naMoPH64Jh8MBXHXy5Emam5sDi9Tq6mrmzJkTXE86nQ7yuLW1FYBkMsnKlSv5wAc+wIoVK2YtLyEE\nn/zkJ0kkEuzZs4eLFy9y6dIlkskkJSUlgUX98PBwABxdSbnPNM3AJhWmqqn5+ZrNZvnGN77B0qVL\n+fSnPx1YtfrqiD4Q50Oeq1evxjTNKc4hsVgs2N5Xb/TvQcdx6OjoIBaLce+993LmzBkuXrxIS0tL\nkIZ4PM7atWvZtm0bGzZsQAgRWKZOTExw55138vOf/5xsNhvYHPsgb3G98P/3QTUhBNlsNkjj9IUv\nc+bMQUrJ+fPnA6tcv14Vl4d/nf7foVAIwzCQUgkM9PX1YVkWra2tjI2NUVdXRyKRCI5j23ZwTd3d\n3fT29gZw08qVK1m+fPmUdPmAHqh5ugULFlBZWUkqlaKjo4OGhoYplrGggDXfZtw/pw/9dXZ2UigU\nqKmpIZVKBe46PhDr11d/UY1f/n5+hsNhBgYGeP311zl//jyWZU2xfPa3cxyHPXv2MGfOHJYtWxbY\nxhbn5XR4rqKigmQySSaToaOjg5UrV6JpGkuXLuXkyZN0dnayYsUKNUZ1hWedXy6+VbV/v+7du5dC\nocDw8DBf//rXeeSRRwKbVb+d779rYKatcfGxi9UC/X3HxsaC703TJBKJYJpm8NzybZpd12V8fDw4\nRjE83NnZSW9vL8899xzXXXcdH/rQhwJIcnq4rssPfvCDQOW2unp2huLZZ5/l+PHjJBIJvvKVrwTX\nXPwc9cti7ty5AVD79NNP853vfIff+Z3fobm5mba2tlmP/3ZCSsk//dM/8bOf/QxQZVVeXs5tt90W\nwL4jIyPs27eP7u5uenp6+OpXv8of/MEfvG2o7J0IIQQPPPAA3/72t/nnf/5nKisrueGGG664vV8H\nXnnlFX7+85+jaRr33XffVc8xMDDAE088QTwe5+Mf/zj33HPPVbcfGxvj8OHDxGIxampquPPOO9m+\nffuMvmtfXx87d+7k5ZdfZs+ePTiOw913331VuK845syZwyOPPMLXv/51hoeHSSQSbwgqXilqa5Vj\niG8dfS2uHNcAv2vxrzLeiiXe9JDSpTB0XgEQ4eQUK7lwZRPW6CXkeCuUr5gVjFB2nGpFhW3ZgdWp\nlC7m8HkSIf1dfZFMt9W92iR9ceixyhnWi/39/ViFPFI4CgKZ40Eg0/MlUoUoXwWZLmVDKy1kYUJB\nLiWLVCMSPBnwHNI1gcgkMKSJSfU7f5VTYiGMnlCqXahFy1u3buULX/hC0CnYvXv3VMWgWYEbL+S0\nj4WuoLDUcaXYF68DPJsZx/TgKKEAJ6EhImXIQkrZopY0et/LqYPvQkcYXuPOLsDwIcj2qG/DFZBs\nVGp9mqHqX2FQAWl2BsbOqP20sDqHrz44fVDUtT3lSglVmyFyAUZPgzmKO3yMXOoEoOS4pZMHEUIP\nRYjG4zOAVCkJOs9KrKIYhhMKPBQChIa0JpTaV64fUb1J2RFFIsExtUhSKZSlL6p0u06R/bAzqWSm\nhT0VP88m1j/XhOq8UrFRKfGlL3oApUToUQXalS5FRmu9gnQVJIhUxwlVqb99K9ySxqI6NTqH2k4A\nACAASURBVJsiZ5G6X7Zb7RcqURDY9AiVQuV6GH5N2faWLgsA0iAjg3qlqTotdHVuoSnL55HjCGkr\n8G2wWQFrJYuUJbBfH/KDkLnoqRQKiM5Vn6XboXzl1PtOFoGKupqYo2SxggMn2pT6oOsNLghD7atr\nKs/8uu0DZ65nyZu+qPYpDEF/z+S+ns2W2l5ZgBGugGg9cvS0ghpdFwaakZbqcGB4IHDMA4HtLOT7\nlNWxlfHqUp+n+lfUIfKztbj/JrSpMF+J98yO1SrbYDur8tq3wb1SFNs4axFl++tkkQi6e4d4YppN\neTFI7iTi6hGiGVeB+/z/papLhWF1j0er1X3suiAtT+TThy8VBKsmBsXkMcwhBRcmFkDZaoSVQo63\nT1HSiy+4bYbtrpNLke/c4x3GrxuzxHirB/eFlF1ykW249zCYmv8lDcqevOcVrNFLRObEcRz5jsFY\nvursvv0HyObySP9ZMQtQTrYfxltxrAlOnznHI488wl/91V+9qxMgd911Fy0tLexvPkjmwq+Izrtl\nVvU7BVqHcMbbkamjCGkTKl9EuGr2gTo/7Oww5tBZ0AzCdTddFe6TUpLt3KPaMiIMtdcjI3NwQyH0\neDIo2elKfGfPtYCURBvvflvtBmtoCOkKhP7W1SoAhKZkrPyB02txLa7FG8cdd9zBkcMH2fF6nntv\ncdDfgnim7cCrR3TyBYiEXLZvdvDHUm/b4PD0DoMTbVEGUjo1lbODceEwmPbk6mOBYHBEcLzVQA/H\n39X+EKjVvraZ4bomk/fe+uYASE2DT95n09pRoG+4n6NHjzI4OEi+4JBMuPy794xx9w0KxrdsBbH5\nIYBEFO65MceKhRZ//kQVYxM6pi2oKrNZVG8C8WAQz7JdsjlJNCIJGZDU5RR7W8dVT+w1S/J0D5WQ\nzevEYjG+9KUvT4HrX3/9dXALbN3kIGbpAqnEiaC96gm5BmnetinD0ZYor5+Lcd+WNK53Xk14Ezsa\njKZ1LvWGiIYlF7rDaJrk3psySpHOU+TLFUTA+4Q8/qdjwOCxH1eRLwiWLyzwsbvHqCh1gjT4dfbW\n63J09hs8tbOMc5fDhEOSz30gRWO95aV1sm0iUba4+YL6e+PyHGsW5zl0JsYTz5bTO2QwMOIrKAuM\nUBTDsECLUJKYOfxlO3agMq9rkkgIQsZk/87PukhYUlthkxo3+Pb/reQLH8mwfoWO7rXFCiYcOKnj\nSo3BkRCtHVGWL8yjW0qpsWApK9+4ruDBZMwlndNwXAV3dg0YHG2JYZqCbF7QMxTy8kliORoVSYc/\n+fQwjXWmAg+LckXBUxLLllSVWoz3mnS3HaSyZjHhaIlnWavam34lUDZzqg8hpeTC6Z3YZp5EWQ0l\nZTP7Q+FIgoZlt9B+4kWG+9qY33RzUdkwRXFOaDqaHkLz8kZoGpV1S+lqbcaxR3HsAuOpbtJjA0Ri\npVTWLsEIRZHSJZceYWTgglLeE4KqumWMDFxkpL8dM58mHPXBGaksd6UMQEKA2oa19Fw4zFBPC/OX\n3oxuGAhNC9pkmqZDKIJjFYJ7wnEs9fzqOoPrWAhNo+3EC/7VEIkmEbqBbeaV8qFUk4tllfUsXXcv\nl8/vpb/zFK5j0nF+X9CMLq2cx9yF66mcuxRN07GtAoXcOKn+dgY6T1HIjtFy9HkaV93B3IXXeWfz\ntCWnT2hJqFmgAL+x4ctBvoeiyoJHmj6w6CItW0F8s4VQECCAFg3jpMbJnbwAukZk0VzGOwf4xye+\nh+u63HPPPZSVlYHjkuvsJ5bJInUNzVCLnaR3PMFk10ig1jUhQS9LICIh7LEs9uAY4foqpOXg5s2i\n9Ai0SAhpqYWUSqla1RnXcbG6BtFCBsltG3BG0+Rbu8gPjdH3wgEKAyNU3bZ+xgR9ur2bgZcO+Yef\ndWgIIHuiHaQktmbRVLiPomuaJsgRnltJ2faNjP7qNcaOtaJFw8iCUi15JwC/9vZ2vvHnf04un0PE\nwpRcv4LYmkb0+FQrrvi6JdhjaTJHWsmdvsTuPXsIhUJ8+ctf/hen4e1GRUUFjzzyCN/8i79g/Owl\netJZKm9YPaviomNa2G09jDafws3kMJIJKjatvOKxXctm4JVDmKkx9NIEc++58apwH8Dw/hOk2zoR\n0RDl996AUZHEncUmS4+EKb+uiWTTAjqe3onbW0A6jgJZ32RohkH5+mUMvvyauhdngdLfdAhV+4rV\nn67FtbgWV45wOMzNN9/Miy++SCqVesuTyf6YuOM4hEIhtm/fHny3fft2zp07x8DAANXVs6vH+sCM\nD43574Lh4WGEEKxcufJdtw58+eWXA1jnzTpOGYbB/PnzuXjxIvv27eOhhx5iZGQksNDdvHkzyWQy\ngFGKlamEEJSWlrJ27VrKyspoaWmhvb19xnaxWIxsNhuMAfnAl69o5NuRFj8P/f+rq6v5xje+EcB9\njuNw9OjRAJS5UswGDPnhq9v19/eTTCanQG4+wLR//36EEDQ0NNDR0REAeP6ibdM0lRuMdy4f6jp2\n7Bj79+/HdV0aGxtZsGBBcF5N09B11bZqbGwMVMgKhQLz58/n4YcfnrWN4at++eDUhz/8YbZv387P\nfvYzdu/ezfDwMKlUKjiHrzwWj8dnHT8tFApTyqIYtvKV5XRdJx6PU1paSnt7O4899hhf+cpXAqU7\nUABSf38/juPQ1dXFihUryGazAWzoKwH65/ChOh/qvHTpEqZpUlpaSl9fH6lUKji267ps3ryZz3zm\nMwF0K4SYAtMVq3y5rsvw8DCLFy8OIDMfEixWSytWzxscHEQIwdy5cwNFP/+4oKBiH57r7u6mqalp\nSj76x/brjG8P7O9fV1fH0NBQANf19fUxODhIMpmkvLw8UMQbGxtjYmIisOnduHEjJ0+e5Pjx44FN\nrH8u0zSDcvMtQdesWcPu3bs5e/Ysc+fODb73IxKJBIqcfl750F9LS0sAKb/wwgvYto2UMrDxLc5r\nXddZu3Ytt9xyC//wD//AyMgIhUKBH//4x4H9qK7rRKPRAKLMZDKBoqBpmvT19TE+Ps6GDRuCZ9QV\n3YuEYMGCBZw9e5bu7u4AfPX38/PCtwKeTRHQP44vdpJMJnnppZfo6uqivLyc+fPn09bWxje/+U3+\n83/+zzQ0NFBSUhI8Z9atW4emacH9PVt6i581TU1N2LbN0aNHefDBBwOlxLGxsWB7X+mzGOzz69Dx\n48cxTZNFixbx0EMPcfToUQ4fPszx48c5e/Ysn//85wMw3Q/HcfjhD3/I0aNHA0h6tmefaZrs3LkT\nIQRf+MIXArhv+jUUfyaE4P7772dgYIC9e/dy4oRyRPRVWt+JePrpp/npT3+KYRjU1dXx0Y9+dIod\nth8PPPAAZ86c4cknn6SlpYXHHnuMeDzO9ddf/46l5a3G5s2beeCBB/jFL37B3/7t33LhwgXuvvvu\nWa1qh4aGePbZZ9m5cyfZbJatW7fOAHiLY3h4mP/6X/8rAJs2bQoW5V0pLMviL//yL0mlUqxYsYJ/\n/+///QyQ1o+5c+fy27/922zYsIFHH32UVCp1VYvh2aKhoYG1a9dy8ODBQJ317UTxM/9aXD3eVcDP\ndV1Onz7N4cOH6ezsZHR0lNHRUSzLIpFI8Pjjj7+bybsWv8HxVizxpoc1dtlTo5KE56yZMgFvlNSj\nRZI4hQkFq/kgybQQmoF0La+xYATH1Zwc9Q3zWb16utrWry98W11f1e6tRKhsIfne1wPrxf7+fjUR\noBlQuU4BMaOnJy03Q0kFL8XqZkAg0hxXgEakJuCffCsOs2BOKrj5QJRuBAARSIjWoJZy22iGwbat\nd/Dwww9P6QwODAxMVQwK3hmTk0CzWdwEEZsLnEDYac8h1QPBPBhN6CGYuODZz3hAX2mTpzxXFNKD\nzaQzaY862DwJ0FTf6NmpepOLWlhtE61WsFi2W9m3Onmlfldzm6dq5ioOSPPUAQLw0LtYPYSoWK2u\no/tX+FY4QloIlMyvLXXi8bCyJClOMkVwHyjbUU33mEU/vzyALFoD5atg6BAi240xZpBo3BaoXzi5\nFObweXRN4Do5ZK4HmZiP0CMBwBRAfr5Kn+tZxEoX0l3qnnQtSB0JBjVBgFECCz+sZgBVQUzmuZQK\noEpfVlCgr2o3fFQBaJFKAgXBKTCmmATBCiPKDhk85b/iKYqi3IrXw1hCWc7m+j0g1A8fpFOqKpPh\nVcjoHBA6Utqgx9THTlZBnWMUpc2D7bSwUoksXQ7dzyllzGyXghz9tLm+BWxo8jyxuSq/7LS6V32r\n1eAZJ9T2btGkirTUeTPdSv0P1PdGQtX1aI23j6Wue6INaWfBnECEFBQpzRGELCgA1oir+j4dBNbD\nSmGwfI26L4YOetawEmq2eJNDYho455e1LIL5Ml4Riklgz7fDdT37oivN4sii+9scgeHXAYGs3Ihr\nREmPHGPf/gNIKdmyZQupVArbzOMMnIH6eWpbvFne4npVXN4+TFmySKlCTlyEslUE9sGyCOwT3v3g\nFCb398tz4qL6nVwKkXJkpAJRuhxyvYGSXhaIN9zhdbhcrLHL5Lub0TBxhaZyYTa7eSknFSurb5gK\n901uNPOjSAXM2Ywc2K+U/DyLi3ciXn755UC5TwodxNWBcspXQLoDOXSIrp4+/vRP/5Q/+7M/e9fs\nejVN43Of+xxCCJqbD5Lr2k2+N0a4armyVdZCSNfCTvdgDp9HM9O4jqnUZkoXMJNAV+GXa65jNwAi\nlCRWveSqaTGHz3pwXwjqPXjTzs9YlAAESnyuHid38QWws0j7rZWp324wzVGk0D0A/a2HdC3wVhJf\ni2txLWZGJpNR1q62raxAy8tZs2YN1XPqGOrPcOSsxebVb37wY/9xnbF0CMeFWNTmlusmAbhkAm5c\n47LvuMb3ny/jDz+WYrZ5bkMHXZM4rqsGhkWIf3rOABFh8+YbKC0tfScu/W2F4zjs2rULZJ57broK\n+DZLGDpsv8Hhh8/n2bFjh7I0CbncuDrLyLjOI39V46mmCQxd0jDXYtumLDeuzgX2vw1zbb744RH+\n4vtVWLYgEXODdrOu6cRjcdKZNKYFjqsRCUvChlKZi0Ukli0CxbeSuBu8Id7//vu49dZbp6RXrWp1\nqa+ZtH5xHG/NlFduCrpRbZjpb5x5c2wEkM7qCE2jb0S1RUriEl2TlMThF3vigGDpgjznLkVpmGvR\nWOdZMQlVDwxdKutZB/Km6r78tQf3rV+W54sfVvUonVPbxKOSSGiyvREJW3zhw8M88WwFJ9si/N1P\nK/nqJweJhJWaoeYJUBcsdW0CTy0wJCkvhftvy3LwdJxLfWFuX+/Q2qnRO6TjCoF0dS526Ug5tS44\nrhPAfSr/PUaKySZbviDoGggRNuArH0/x/IESdh+N8/c/S/Jfft9kfq0km4e/+VGYiz1RKirKsK0s\nT77i8AcftQCHsKGu07TEFAvm0oSLaUM6o/HEc+XkPUjytTNxEJLyEhfHFUxkJL99zxiL55mqX+sB\nkqYlSOcFr5+J03wyTt+wwXhGIKVDPjvGmUM/YcXmB4mXVE4qANgWuLZnWSuQrsuF0zsY7DqN0HQW\nrrzdG4id2R8qn9NIOJoklx5mYqSb0srJCWvp9U00XQ9gu+LQNYNwLEkhP0F59UJiJZVMjPRQyI3T\n33ECpMSVLtJxFHCn6TSuuI35TTcpJb3+Ns4f+SWrbvwgureYwPXOqRuT/dxovIyKmsXe9s+wdN29\nTIwqmNCx8ghNJxSOUVa9kJKy2mDiYmy4g672Q9i2SSgcIxSJU7tgLbUN6wKoUEqX8VQX/ZdPkupv\nY3ykh9YTLzB/yQ0M9ZzHdSwsK0cklmTZhvdTWTu9zSYJhWPUL97E/KU30HF+///P3ptH13XVd9+f\nM91zR02WZMuyJMvzIM+xYztx4tgx4YEMTKEkPEBpCy28LSUF2vJ2vWv1KU8pb3lXYTVtCEkLgZSQ\nEOIQyBxPceJYdjxIsi0PsiVbsyVdjXe+55z9/rHvObqSZcfOkzZt8W8tcCSds88+v73PPsP+7O+X\nrnMHOd+8BzMQkdsr8tvFpfMvAjNQgKKo2FYW27LQfeNKGWgqqs/ASWcRWZmXqSA/4Thke6PytTme\nYuiNJoRlE1xcTWjDUlKnO4i9dZzHfvITDh06xNlzZ6XCjqaSPNuJf2GVB/DhOAhLWpB5r9g5clfR\nNRRdQy+OkBnrI3W6A9+sMkC6aCiqKtXp3JuL+z6nKKh+H4qqkGw8h7AdjMpSzJrpKLNnEFw5j3Rr\nN7G3jjN09DSKpjHtJqmMlBkaY6SxhdGmFgyhoBg+HOFgDcVwbJtMRz/pc904yTROJkum/SKK6SO4\nct4lefLI3jw1QTfM2gr0kgjW0Jh0jlCNKSdJrjVs2+bv//7vSSaTKEGT4g+tx6yZftnt9cIwhbet\nQi8pYOyNJnbu2sUNN9zAxo0b/4/r8m5j/vz5/MWf/znf//73GeqK0vPMbvRpBYQXVKMFTITtkBkY\nJnb6Aqot8AtwfCbCcrjw2POEF1RTWDcHvVBaR1qxJKPNbYydbMMeS6DqOgVLajFDV4YprXiS4aNn\nEEDxB2/EN7MUK5HCymZxbHtKOFDx+yjcuprB5/djx1PEznYQWVhz1eceWVhD/94GnFSGdN/QtaYO\nAMeycZJpFNV4zxQhr8f1+O8UQgiGh4eJx+OoqkpBQQHhcJitW7eye/duhoeHPUW0qy0vGo16gM/K\nlSsnKPXdcMMNnp1qf3//Zd2aTNMknU6TyWQIBoOk02l6enrw+Xxs2bLlPTn3dxutra20trbi2tte\nS/j9fs9Kd8+ePZ4K2+zZs2lra2NgYMBT/9J1nWnTplFVVUVxcbE3qV9dXe0pbdm27cEhIOFM15ow\nkUjg8/k81Tufz+eBYC6E5P63oih87Wtfm6Culkwmvb/DOBxwOZhvqnDBLxcWi8fjKIpCKBTCMAz8\nfj979uzxgCDHcViyZIl3LE2Ti7F0XScWi5FKpTAMg97eXvbu3ettX1NT41kGA569sRu6rhMOh2lq\naqK9vZ2nnnqKT3ziExNsix3H8UBC1+bRNE2Ki4v53Oc+R2NjI36/n1WrVtHQ0OCpVGqaRiaTIRQK\nTTj3bDbr1ScYDHoqb24OXeVDVVWprq7m61//Ov/wD/9AZ2cnDz/8MN/85jdRVZXW1la+973vYds2\nJSUlpFIpmpqaWLlyJfF43FPSc61h3X6g6zqZTIb+/n6am5vJZDLEYjGOHTvmAZWdnZ1omsbv/d7v\nUV5ePqFt0+k0vb29HDhwgAMHDnhqfoDX/6qrqz1VRvf3rn2toihYlkVnZ6f3vcVVkJocQghPobCz\ns5N58+ZNgKfcfm6a5gQ4xm3jYDCIaz29atUqHMfhwoULpFIpenp6PIjVBVz9fj+f/vSnmTVrFi0t\nLfT29lJfX8/69es9Rb185Ty3jgsXLuTAgQP09PTQ0NBAbW0t3d3d3jclXdeJRCKeZbfbDxoaGjxA\n04UlS0tLiUQi3nm6lrwjIyOkUikaGxsJBAIsXbqU+vp6stkso6OjqKrqqTpOziHgWW+7ds0NDQ3c\ndNNNnnVtfhu7136+Il46ncayLA9WdMt0x+R4PE4oFJryntDa2uqN2S+//DLPPfcciqLwhS98gXnz\n5vGTn/yEI0eO8K1vfYvKykpaW1sZGxvj6NGj3H333d69wh2fMpmMN4YoiuJBtYZheMDW4OAgzc3N\nntOFm1/TNCfAgm5bun3l9ddfB/Dgr0WLFvGxj32M5557jv379/NP//RPfOMb3/CsiZubm3nhhRc4\nefIkjuN4kGlPTw+JRII333yTM2fOEI/HiUajDA0NsWjRIurq6qbs8/nnld+f77zzTt58800aGxu9\na+m9iPb2dn7+85+jaRqzZ8/mm9/85gRb8vxQFIWlS5fyV3/1Vzz44IMcOnSI7373u/zkJz/x+tH7\nER/96Efx+Xz88pe/5OWXX+aVV15hxYoVLF68GL/fTzKZ5NixY5w4ccLrQ6ZpcuTIEb72ta9x2223\nceONNxKJRMhms3R0dLBr1y6OHj3KyMgIoVCIO+644x3nwQ4cOEBnZyfl5eU88MAD+Hw+4vG4dKb0\n+aYE8Gpra/n93/99HnroIfbt28e9997rjRFXE1u2bOHo0aMkEgkGBwcvO5ZeKVw4ffK96npcGu8L\n4Hfx4kUee+wxfvnLX3ryovkhhHjHCYHvfOc7XkMD3HXXXWzatOk9r+v1eP/CcRyOHz/uyQCnUin8\nfj/l5eXccsst3H777Tz++M/e0RIvP+zkIKmu/ShYoBoYkZkT/q4oCmbpYpJdBxH9ByXANhX8kFOL\nc3IKWW65IVNn27Zt7xtkAFOo2l1DKIqKb9pC0tFGXn/99dxKAqmwJgYOX7pDehhiHShGWCrOFSzw\nIBC6d8lpAVW/zKSaGFdwU3MWoi60I+w8iE6g5r0A5kcqlZJAW+4jv6ZqWErOBtQ993xFsHzJChgH\nhJzcSp/kRbmpHpbAW6IT7ASqLyxVDyAHHk5OnOqBU8LOSngmXx3LVzwOBClqDiAEb9YtVCXBrL43\nJXgUvwCRWk95TkyYr5UQkKKO2xCjmaDqaGYBkYUfITvSTqrrLWw7DihTWllnch8fJNxnTp2vhMyH\n4otApEYCWt07sUfbyUZP4Zu20IOKgj6onruQ8xfaSQweBT2M8BWOF+hk82AzZdyuNx2FoQb5UV8P\nymtOMyHZJ1c+l6zwJiS9EEJawY6dzam3KUi1PB+IzLgVbulaCFaNQ1OucqCag0kTnRLuc7I5G+fx\nVSpTtnFknrR49RQfc+Ep5U1e1ZCrs9veRgFU/g9Zl1Q/JDok2CisHHAoID0i65juB2e27AdDx2Q9\n9fA4tCgNksZBRTlTAoULJbg2cBCKlkrrYyueO4YuleWCVTnoUKo8kB6WwB1IsC8f0PPyLnJg1WIJ\nPw8cRMQ7ZXku+KYFYMZtEly9Uh7Duf7eu1NeJ6NnoXChN2F5aQ7zYD7HIke9jsNrik/mId/6eSol\nv9z1jQB6d0mVyXA1FC1GiCwYYWIX32DHzt3sfeMNHDT5QcCJIRLdEKzMs3i+zEci9xp3lRJdRclQ\n3opdNe96Ix/Yzf0+dgGsBIoRRgTch2AhJ0795SgzbkX07iE73EbKCKLofgmN2UlCpo5hFDA4NCLb\nJN4r6zLWKuFUx5J1zIyArxCCU6wkVhhXUp18DwlVgRbEycZQFN4TGMtVnfWU+xTfOJh2uVBUiMxG\n0cOI3l2cOnWGHTt2vKMs+b9nGIbBl770JRYsWMBrr71Gd08v6Wgjyb4G796jaQohQ6Oiqhpd12lv\n7yTVtY9075HLw4B2Eo0sim7gK12MeoX7uhCC9MBJ2TvLxuHNqRYl5IethhElq1AGDpKJnsQsvbwF\n9ORwnxsyvYdA2FPeb64mrFg3mqZc9qP59bgev41h2zYNDQ3s3LmT5hMnkPcMef8vmVbK5s2b2bRp\nE89u7+Ffn7MpLU5TO/OdJzFa2hV++oKBjR/TtKmanmaSOBAfuc2iscXH8XMmD28v4g8/Oszk4UNB\nQkq2A+mMw0+fN2hs8RMqKOejH/3oe5SFdxcnT55kaLCP8uIsi2uvfdXnTStsnt6R4cSJY6RSGWwb\n6o8Hc4pz7sSRwLKhtcugtauIn79awAdujHP3phiqCnVzM8wqtzh1wYdlyY+8CgqOkBMy7uOt7UAi\npZBSFHRdeI/ili2V/LKW/I2hw1tv7WPr1q3MnDn+DutZC+VuD4ZhSJuenHKcG97jtBhfKwGgaSJ3\nPNmmrx+WH8yXz0sR9EPHRZ03GkKAwoLqLCfP+5lWaF3yWqWqEPJL69l0Bhpa/AyM6MwosfjSx4fQ\nc4LQqipQHOUSVXHLkufwuQ8N8f8+XkbnRYOX9kf40E1jWLaSW7Qlt02mQaBIK2BN5P4i+6OuwtYb\nbb74cYu9RzR+/GsYTqp0D2gcPydYNDsHP3mr9h0MTRAMjL8GKXmWofXHAyRSCrUzM5RPs/ndD4+Q\nSiscbA7wxMs6NRWCvUc04ukAhSUVfPWrD/DQQw/R2dHCg78o4QsfGaSsyJZwpQKxhEok6Hi5S6cV\nfvirEs53+ygI2dy+NkZhxGF6sUX98SAHmgMUFzjctDwx4aN/1oJ/e7mQfU1BbFtBUaSKX3mxQ3RE\nI2tbJGNDNL3xb0yrmM/06hVEiisQuQVntpWh90IjF9ubSCdHUVSNuctuJxguyT23TLwmpLWPRlnl\nYrrbDhPtOTMB8LNdK1xt4qIyr4GR6nm6blI1fwOhQjkxNzbcRXy4H9tKo6gamu4jPnKRob5Wus8f\nITJtFmVVSxnsO8dI/wUO7XiEGdXLKCitIRQplep8at77EDBt5gIudhxnZKCdI3v+FU2/9ON17/lG\n/KEiSisXoao6F07uxcqkMHwBSmcuYv6KO1A116lhXEWhcFoVRaU1xEf7Ofn2rxgb6qbn/FFU3QeZ\nJIYvwPwVH6S4fM4Uecj/QaF64U2gKHSdPUDrsZ0Ulc2+RLz/kv3d83AchO2QHIvK3AZ8EvIzxyE/\nYTkeaOe2Z+Z8L04siXAEY/uOoagqZm0FBZtXoWgqgUXV2KMJ4g1nefvQIdSAiTatQCr9NV/AnDsT\nYTtSBVA4l9RPCCHrlpWUsT0WR1g26fO9WP0jaAVyfFH8EjB1WUGRk/9UfDqKpmLHUyQa5YKkwOIa\n2X65iSX/nJmopo+RnYeJ7m8iMzyGk8mSar+IoWqEdJP5i+Zx/PRJMraNNTDCwGOv4KTHF7mJrAUC\nfLPKUDQNJ51BMfSJlrKOkBbCioKSJ5GrKArBpbMZfaMJJ55ELfC9J5MPDQ0N9PX1oZg6BZtXXhHu\ny4/QirnYo3HiR1t48skn31fAD2DBggX87d/+LTt37uT1119naHiYWH0zLl6uKgpBVWdJ3WK2bt1K\nWVkZTz/9NMeOHydzuoPuk+c9hUhVUdBVlYCikVJUbEOleNXlVS3cGD3RinAczNkzidtC7wAAIABJ\nREFU8FXK9xJV1xCWTSabxT/FBKRj2yh+H8Hlc4jXNzPSdPaaAD/VZxCYVU7s1HliZzsRtjOh31xN\nxFo6UB2oqa25Dvhdj+uRF7FYjDfffJNdu3Zd4iIxb948tmzZwuLFiz0gqqam5h1BAyEE/f39HnRk\nGAbz5k0Evg3D4L777uPhhx+ms7MTRVGmtOp1ValEzpHg7NmzqKrK0qVL31cVIcCz5nWVwa41iouL\n6enp4aWXXvIAj1OnTnlqaPnPSK7FbCQSYcGCBV6uamtruXDhggeYufVwFdvyLU1dtS8XunLhn3zF\nNdM0eeaZZ/iLv/gLD9xxQcP8Z2Vd18lmsxOsk9265qusueHu64KFvb296LrO8uXLCYVCvPTSS/T3\n91NaWkowGPRAv8nluDBgKpUikUhw+PBhHMehqqqKmpqaS7bPDxfW8vv9LFu2jLfffptdu3axdu1a\nqqurPfjQhaySyaQHFrqqfG5/9Pv9/NEf/RHZbJZ//dd/5c033/QsiCORyIR8XA7uy8/X2NgYhmGw\nbt06iouL+frXv863vvUtzp07xzPPPEM0GvXOdenSpdx11118+9vfpqOjA8dxWL58uQfdZbNZEonE\nBLW20dFR6uvlovtFixaxfv16wuEwZWVlPP7442iaxrp165g+ffqEdu7t7eVf/uVfaGlp8QBSn8/H\n9OnTPVvZeDzOqVOnKCkpoaSkxIOpXAU5Vy3PhcdcVb7J1rvuz6FQiGAwSDKZZGhoiGnTpnngpatM\nNxkqm2x3Wlxc7CmGZjIZ2traGB0d9axbVVXl5MmTDA4O8sorr/DJT36SNWvWsHv3bvbt28e5c+dY\nsGAB1dXVHoSaH7quU11dTWNjI0ePHqWpqclTisw/p4aGBioqKqitraW3t5dTp05510xZWRkFBQWX\n9FcXsC4oKGB0dJT+/n7q6+tZvXr1BMVDF3S7UrgqdS7kd/r0aVasWDGlctzk/3bBKCEEg4ODKIpC\nJBIhEAh4yoaulfBk4HLnzp1YlkU6nWb79u1omsbnPvc51q9fj2VZfPaznyUajXL69GlisRhFRUUe\nHLV7924++clPkkqlcq5oE+vqgpfu+NbV1SUX3VsWL774oqf6qarqhHcI9/pwwWK3fU6fPo3f72fd\nunVePzRNk3vvvRfDMNi9ezff/va3+cAHPkBTU5N3nwwEAhQXF9Pf34/jODz//PPs2rVrgl3y2NgY\npmmyYcMGRkdHPeAwv93yFSLz22HGjBksXbqUhoaGS8aV/5N49tlnsW2b0tJSvv71r18W7ssPn8/H\nH//xH/PXf/3XtLS0sH37du6///73pD7vJlwActGiRbz22mscOnSIhoYGGhoaJmyn6zobNmxg27Zt\ndHZ28txzz3Hx4kWeffZZnn322UvKda3Mq6qqWLz48urnbuzatQuQ7FQ4HMa1a7Zt+7JQpm3bLFy4\nkLq6Opqbm9m7d+9V2/QCLF++3IO533zzzauq5+TYt28fwGWt6a/HePyHA36PPPIIDz74oDf4vtsI\nh8M89thj3qBy8eLF64Dff5NwHIcdO3bw2muv0dN7kXTWxrbF+AT5mXPUHzhIxYzpzJo1k/aOrita\n4sG4Ck6qaz8BQ6D7Q4zFM1NayfmmLcaK90kFnO6dl1ERUry6ZobbvHI3bFg/Qc79/YhLVO2uMfTw\nTJJ9DZw7d45UOi2BCyc7rugVyLNKTEqrRJGNwcARlFQ/lG2QEIiiyTZzsp49TyadJpvJIgEvXYIm\nwrXlnaT642Sk9auvAEdROHDgbRYt2sm2bdu8Tfx+KbPgKga5q7+EY6GoeSpFE1T88nQrPEUvQ9Zj\n9KzcKjwbsqPQ/zYK4C+rI9V/IgcsGnkrufPqq2jyF8JBGcuVU7pOwn2Tt/XCVQJzJGxTvFKCVkMN\nEgbzl+a2EZDqg/gFFDt5qYKi4stVwfAUmTSzgFjLb3CsJMm+ZtL+ahRUyRGpam4MRoKMk20kFan8\nwNhZWctgFYpjyTqWrkX07SPVc4hU3zE0J0XQp1FdPUvajWUycoaw7w2pQueqOyKkQqGij+fbA+ws\nqXhWslr2qaGjsiLhGmn36zYbyD7T95bcF3L9ch4YJaCqKOkBCedacbmdHoLI3JxaowA7IyHKsXNy\nG5DKeCWrQBHjgNZUESiXKnLZPDBd5GyAJyjluX/LTXo4lvybHpTnj5BlBaYAWTIjMnexDnmduWp7\n2Rh0/kba9hYukoDe5IcwkYMc7SyQkpbCrlqkV/6QtPF1LYJVU24nbDBCObvWPFh6soBHnlInPTtl\nfXFkOdNWyXLzAdtLQoBQJHhUug769kkb7oL5eCp33nHzVfzy1PccSyp+usCeEZ7a+lnVxoFSYUsb\naCcjoVIESsFcOV6pGopQEUYRFK/EGThIKp3FP3MNpp0i038CkYNW8bkrXieV717Lji1VIaOHc+cj\nYOBtmRc97yO9kvu/fKlpRc0pSr4tfy5chKL7ETkratX9CGWEvbEi1XsEnz9IyKczs3oW27Zt47XX\nXmNwOAeORg8h8lUeXbBaUXM21jm7byUPGBZ40OpkC2AUVVplDzagYL8nMNbx48fp7unFdvKs4K8E\n9+VHoBRKVmMP1PPrX/+a22+//X0F7FVVZdu2bWzdupUTJ054CxSSySSBQMBboOCq/O7cufMdYcCZ\n1bOIx+P09A2hR658X7di3TjpMWltnA+VTlqUMDlsx4bATNDDOOkxrFg3RqTyqs9bD88ko6gowiIT\nPY1ZtvSaFhgI4ZCJniZkaO+7pef1uB7/WeLYsWPS4mSwD5wUupqhpNBB02AsrjDYP8r2X3aj6gGK\nissYGnT4zo/hc3dmWFfnTKm4l7XgrSaNx18wsESEFStWcfzY0QkQmBtlxYIH7s/w/z3u49DJAO29\nBlvWJti0IkEoMP5gkEwpvNEQ4K1jYfqHA/hDpXz1q199V6s038uIRqMgbBZUiysDM5eJoB+qyh2O\ntyXJZCwCplTYW7c0yda1MWZXZNFUSKYVDp8KsPtQiNZug1+9HqGzT+ePPjYslQDXxmk+72NwVCNt\nyUmXdDqN7dhSHS8g2yWdlaBfJjvp47kCPQM6fhMqy2x6olGeeuopHnjgAW8bOTGiMBKTzyM+w0dS\nSUpA0Bbe46KCknu0GlcHUxQYHtNy5yzoG9RoOutHUwUbl8Xp6tf5hydKyGRVbl7pUDlddhZX2Crv\nrUrWV5OQnmXDGw1BFOD2dTFMQ0z5+uQ4kMpIW2L377oGm1bGefLVQp7bG6Gxxc+GZUluWJSkocXP\nm41B+ocmLiBbUpvmtjUJhkZlY4dzj1y3rLZRlBQPPmWSTMHL+2BW6fhzvJx4EAT8AmXSy5qCggPs\nPBRGoLBxeYLRuIKmCu7aNMrBZj9vNWkcbzVRtQBz5y9g/fr1PPLII1zs7SKdFlzo1fm7x0pZMT/F\nphUJaislLDoSU4mOaLzRGKT+WJBURqUgbPONTw9QWmTT0uHjpf0RTl/wYRqCB+6L4jcFqYzMUyqt\n8ND2Elo6TBQEy+al2HpDnLmz0tiOQku7yfefLCNtKwjHZqDrFAPdpzHMEJpm4Dg22XTCg/38wSLm\nrfgggXAxwpEKepo+cWBwv7sFwvI9IZsZV/x1HHeBneK994/n2Mn9T0h7W0VBM3wSmlIUCkqqKCip\ncjf22qWteTf9XSc5eXC7WxIoCtl0nI6WejhTjxksoGL2aqbXrEABBi+epfdCI2PDPYCDqhqomk6k\naAYlFQsw/RFAkBiL0td5gmR8kPPNexCOrKNu+CmtXMSCVR+6wkIH+ftQQRlzlm7h5KFfMdBzBiEc\nNN2gvKqOcFEFViaJ7vPnylHcJE44T4Cq+RsYuniWxOgAg73nKJ25YOK2uVMHQTYdRzg2qqajqjpY\nNv0XjuMoguCcmfJYuoaqgJPOIu16pWWvK0KeOHoWJ2PlZDAVwhuWEr5xsQe2KaZBoK6WZPN5nHSW\nyo9tJlAxjQs/fZF0dISxvU1Ebl0hwSVFyQMIx6UvRdbGyWQZ3XUUeySBEvCB4zDyykEKtq5GLylA\nWDaqIV0ThGV5OVEUBTueYviF/TjxJMb0YhRDZ/T1Bux4CsV2UAwdrSiMf/4sks3niZ1oJRgKURKK\nsH69/B7X1NTEiVMnZds6Dk4yjVYcJrBkNnpxmPiRFrI9Ucxa+WwtLBthOaim4akKihwsMJVVqzln\nJrzRBI5g2bJlU1rdXS56eno8+7JAIMDMmTMpKCjgV7/6FQ4CLRIisLDqnQvKi9DqBSSOt9He0UFn\nZ+f7bgdZXFzMJz7xCe655x4OHz5MW1sbiUQCXdcpKCjgxhtvnACqf+Mb36C3t5fdu3d7SkOO4xAO\nh1m0aBEbN27kO9/5DjHFxld05clD4TiMHjuHEBCsy1vAqaqALaHNqfZzwZG5lcQPnyHZ3U96YBiz\ntOiqz1sP5CZE01li5zqJLLg2R5WRprP4NO19/6Z8Pa7Hf5ZwHIfnnnuOF154wQO4AoEAhYWFOI7D\n8PAwZ8+e5ezZs55t6MjICG1tbVRWVl52QaplWfT19TEyMkIwGKSuro4TJ05cAjYBbNy4kcHBQX7x\ni1/Q0dHB0NAQZWVlFBUVTXhWyGQyDA8PE4vFMAyDuXPn8pWvfOU9UzR6tzEwMIDjOO8aGg4GgziO\nQ3d3tweopFIp+vr66O/vJ52WjiQ+n4+ysjKmT59OKpUiFouxZMkSqqqq8Pv9FBQUEI1GPcUqkK5C\nIOd28tX8bNv2QBc3XGgtEolQXFxMS0sL+/bt49ZbbwXwYJj8NjFNE8uyLgH88iMftnOPYRgG3d3d\ngITWa2pq2LVrF7/4xS9QFIVPfepTHD582Nt/qvltwzC8PLS1tSGEmGB9OTncvLrz5YqieKr9AwMD\nfOc732HZsmXcfPPNzJgxg3379nHgwAESiYS3vd/vZ8OGDSxYsMDLiVuXL37xi2SzWXbt2oVlWUSj\n0QnPLkIIdF2f8hrIr19BQQGrV69mZGQEVVW54447eOyxx3j66ac9hbdbbrmFYDDIP/7jP3rKZh0d\nHfT391NdXU1NTQ2BQIBUKkU6nWZkZIRz587R09ODrussW7aMP/uzPyMWi9HY2MiPfvQjRkZGqKmp\n4fOf/7wHhgohaG9v58EHHyQej+P3+9m4cSO33XYbhYWFCCHYvn07O3bswO/3k8lkGBoaYmBgwOsP\njuN4inuqqlJaWsrixYs9EHKyrTSMA3qBQIBkMuldA26egAk2tm64UJVrieuqi7lqf5NBFiEEixcv\n5umnn6ajo4OHH37YAzxt26a7u5vu7m4URaGyspLVq1czd+5cYrEYJ0+e5MSJE54FbL56YEFBAYYh\nVcDHxsYYHh7mwoULtLa2epCipmlMnz79igJMbpkFBQWk02mGhobYv3+/lwP3XKdSCpsM2bpAZDab\npbu7m8WLF3t9cSoI1wVSfT4fQgivD+m6zg033OBdP6qqeip/LmynKAojIyPs37+feDxOMpkkHA7z\nla98hdWrVwNSjbOgoICtW7dy/vx5ioqKPMXK//W//he7d++moqKCDRs2eNu7Knz5wHImk6Gnp4eH\nHnoIx3GIRCJ0dXXxgx/8gM9//vMEg8EJ45NrGexGc3MzDz30EIqiePDe0aNHPcEsF0Z11R1feOEF\nT8lz8+bNbN68me9///v09fWRTqfRNI10Os3SpUvZsGEDxcXFfPe730VRFE9N0rVVDoVCXr1cwG+q\n950bbriBo0ePYts2a9asuWx/mRyWZdHR0eGBteFw2IOY6+vr8fl8bNy48ZrmnUzT5I477uDChQu8\n9tpr3HfffVctXvDvFfPmzWPevHlenxsYGCCVShEIBJg+fboHMoNUvN2wYQMnT55k165dtLa2kkwm\n0XWdwsJC1q1bh9/v54knnmD27NnveG5tbW20trYSDAZZt24dgDfWuTDyVPdGtw9u3ryZ5uZmdu/e\nzQc/+MGrnm9zofPh4WH279/P/ffff02L4YaGhjhy5AiqqrJ58+ar3u+3Nf7DAL9UKsWf/umfsnfv\n3gnE+lRxNeDf/fffz6OPPupR0m+//Ta9vb2ep/z1+K8Z2WyWH/7wh9TXHySZtnC0IL5pC/GFK1A0\nA2FnsWI9xKOnaT3fhd+nUVQYQRkdI/VOlng5daMNG9bT09ND8+nWKa3kFEUhWLWJBEjIr2+fnCwv\nmCchIdUAJw2xLpz4eTKkvXK/+MUvvq9wAVyqanetIfPm0NHRgRBKTtFrXQ7am8IqsXDRuKJXrEN+\nvi6/SQIamSFI9+PzVSNwH1QYt4NFqvUJZ9IKcEVFyQ6BqqEXzMIorCHZ+QavvfYaW7du9XJcXl6O\nduacpxikGzqapmJZriVsni8V5D465/5DUSDZK39vhCX0ZMVBD6IIC7p3oogsRtFszNJFZIdaEBlF\nKn2RV15+qLrMhZVA0UMITx1rsjKZmGhR60awQuY7MwwX90jIz4hI2NBK5PbMGzddBUVFBceSSoq5\nallqCDFtDfS+gcjGsEc7PLUuC/dhW7ksiKUk5HlghMFfJu0TBWCWghbAyQ4TNBxmVVViGAbtHV2k\n0ha2Xojm07DTwzDwNkIPSgjRVyrbw7Gkel38vCxfCAky+oqhdwdkc9BdeDaUrZfQnQshOQKiB6W1\nq2qM90vhjENLgXKU0bOIzIjcxopL1b2hJjxY0oWyjIhUnQzPBazxcnKqlZ6Fqjcp41oIuX3ArVve\n3/LDtdHNSGUF9BDjim2XCaNQQnxjZ2HszLjlr+aTdU/3wcVe0PxSUTA8D8hKxcPRFsgOy3qpZg5u\nrAVzOuimhNySF3NwY0zaIbtqgKoplfvMSUqok/u4cOR1M3ZOnrOTkfnRQxLmRExU0fO6qzK+v9vn\ngrPkflZMlhmcOXEySxHjkJ97reqh8bznAFTCOYUM1SevKScHXeZ/vI/nbKCFAzgoeliOU+6qL9ce\nNzgToYdQnRR6cBp6eCYiEycz2IK4+IaEGEM1l9p0u+eX6pbQqp2Q9dQCMke9u6FkpcyRsAAzB0Dn\n+ogQUrlv4G2pKBquku2b7EUZPYfIjCKw0XQfQgtBuApHD6E7Y6yoW8xHPvIRli5diqqqUrpduEox\nugQLIzk4W9Whdy9YY7Jf4F47Wg4GZRK0OkW/DsyQ9xhFXDWMdSVF3sHBQZKptDzeZDDtKkIJVyOG\nj9HTe5ETJ06wbNmya9r/3yNUVWXZsmXvWJerhQH/8i//EsTgO97XM4Mt8pItmD/looTLPl4LxuHN\noSYygy3XBPgpqoGC/CBj2wmyI+34imZf9f7ZkQuodpKZ1bM8+PF6XI/f5njrrbf4l0cfwbFGmFGS\nYcs6m5tX2p7KnuNAc1uWXW9nOHoqwVA0RSBUSDKh8sizCZ56LcvmNTaLZjsSCkorHD+nsveIxljC\nB2qQW27dwqZNmzhxvJH4ZZy551YJ/u/fz/DgkwZ9gwZPvlrAL3dGqJ2ZJWA6pDIq57oMEkkVRQ8y\nc2Y1X/nKV6iuvrYJ7asJ27Y5ffo0Q0NDZDIZAoEAs2bNuizEID+0C0zfu19QKBCkU/Jc6+ak+b8+\nMUhh2JnwqBL0CzatTLBpZYLj50z++ZclHDoZ4Ee/EXzhnhHWLU2iby8ilVY50BzgwzfbZNIZQNrB\nqiqYPvD5pB2v7eCJp6kqDI1pNLf58enwJ7+T4f95WKWxsYH+/n5PFWP+/PmcOHaY/U0p1ix2PNWC\ndDpFIg3hwPjjlaIonmKyC+jtPybtd+fOyvCj5wsRAhbOTvPqwQhvNQXJZFWWznX4/D1ZznbIj9YD\nI7qbpEseFX26oDdq0NJh4jcEG5clJtx/HCe3UE7AaEKdAPYZukBR4NZVcZ5/M0J0RKd3UOPpnQU8\n+VoBAVOgKgK/6RAJOjgOjMQ0mtt8NJ01iSdVyksEpYWy0HQmzbI5SermFHL4VIDGFpM3G/3ctjqB\nJZ1CkeJmAkfkJmzc9hfw1GuFdPQZFIZhXZ0AR8V2pG3xivlp9h8LUVE5ly984Qs0Njbys3/7KTgx\nisNZPrzRpqNX4dg5laNnghw9E8A05DWZzigkUipZSx5v+rQs86vTPLOngAs9BhcHdRxHoaTA5k8/\nFaW00GYsIe/rtgOPPldMS4dJccTmT38nSu3MLLYj1QEBVi1MU1mepr3fT82CNSSSNv1dp8ikYlg5\nMM8wQxSXz2F6zQqKSmsQwsGx5Lc128rg2BaKqkqYTNE8cN9tLyc38SqEg52VE1vaFM8pjm0hECRG\n+8mkYhi+AGagAFXVUDXdAyslG2bh2BaOYyNs+Y3AQUE3TKZVzCcQniYXbqbiDF48Rzo5RlvzLlqP\n70A3/CiqiuPY2Flps1syfR6Vc9fiD060pCucVsX06mUM9bfRcWY/ydggCAXDF2De8g9c5jtlDj50\nHPp7TtF7oYnYcE8uIdLqWNVNymctzfVzm2w6KfOn6bkJJdXLiaoZgOzv06uX03Z8N70XGiitXDjF\nYeWx+7tOgoCCYglGJcYGGe3vAL+CuaBq/DuqoqL6fWBLlT9hS8gtcfgMmZ6orLOmooWDRDaMP3eJ\n3Llo4QDmnArSZzpJnO8mOKucirs2cf5ffk26rQeRtQjfVIdRPmlBjiKPbY2OEnvzGJmuAUTWQvUH\nMUoLyXT0MfLSQfxLaggsrkEtlZOvWLIvOZksqbNdJJrO4cSSKLqGHUsy8spBJh9IuTgk97UdNE3n\nU/d+ki1btniTB2fOnCGdTCI0VVrErl+CWVsh4T0BiWNtEkgNB1ADvpzioY2TznpLzoQl3yWVKWy9\nVL/PI52vxn4xk8lw4MABdu7cSWtbm7SjzqXM0A2WL1tGc3Mz6BrBpbMnKgleRWghP+acClIn23n6\n6acnQODvZxiGwfr161m/fv07bjtjxgzuu+8+7rvvvkv+Njw8LBX9rkIRLzsaJxtLoAbNnC20DI9B\nvdyOLuDh0/HXVpA600Gyq/+aAD8na0l1E2DwwHGCNTPQzKlhickxdqadzMUo5QXFV5Wv63E9/ruH\nbds8+uij1NfXA1BXV8fWrVtZvnz5BBhi//797Ny5k46ODmzbpqCgwIOqTNOkpKTEgxJs22Z4eJix\nsTHPnvKLX/wiXV1dnDhxwgPOJsedd95JQUEBjz/+OIlEgvPnz3vWk4qieMpTLqSwZs0a/vAP//A9\ncbyYHK7CVSwW8443f/78y4I4LpjxbiEHFwRwLXI7ciD55PIymQxdXV10d3czc+ZMqquraW5uxjAM\nZsyYwYwZM4hGo57Vo2s/6oJpLjDl9/u9v7nwj2sraxiGB7k9+uij7Ny5k1tuucWD8mbPnu3Zjwoh\nPFtKF0zMV1TLV2Nzj+NuHwgEaGlpQdM0ampq+MEPfkBTUxMAv/M7v8O6des4d+6cpzrnljE5b7qu\n09raiuM4lJWVeap/+du7iuIusOTWw1UwrK2tZXh4mGw2y8mTJzly5IhXdxeQcyE0n8/HCy+8wIsv\nvogQwntPdpUlP/7xj3PgwAFGR0dJJpP4/X40TfOgMReWmty2juPQ39+PaZrceOONFBcXe+DkihUr\niEQipNNpbr75Zj70oQ/x2GOPeSpwrjLcqVOniEajtLa2cu7cOS/XrmojSFirpqaGkpISvve973H2\n7FnPRnfZsmV8+ctflir1OaAuGo16cN/SpUv50pe+RDgc9vKpaRrbtm1j79696LrO/fffT2NjI8eO\nHfO2cftfRUUFlZWVBINB75pxc+yqTrrw1lT5ATmn7cJtk9X0gEuU5mbMmOHZK7vKaC705oKumUzG\nU6GMx+MEg0Gqq6vx+XxYlkUsFuPixYt0dXXR3t6OqqqYpukpE9q2ja7rzJgxY0olvkAgQElJCUND\nQ0SjUQ+CC4VClx1T3D5iWRYjIyOMjIx4550P97mRXw/3GnTHlXy4zc1tNpuls7OTuXPnXnJcNzo7\npeiHq+bo9rfVq1dTVlY2vmgjZ/ntwnau6uUjjzzi2fcKIbj55ps9uM9tA8MwWLlyJUVFRYyMjNDR\n0cH8+fP5xCc+wY9//GOeeOIJBgYGuPPOO6dUrlNVlRMnTvDTn/6UgYEBbNumqqqKaDTK8ePH+d73\nvsfWrVvZuHEjhYWF2LbtXQv9/f289NJLntretGnTeOWVVyaoprpx+vRpD4gOhUJ8+ctfZsWKFRPy\nmkgkCIVCLF++nHvuuYd58+Z5begCX2VlZV5+bNsmFosRiURIpVIeCDYVDOaqJWqa5gGPV4qhoSH2\n7NnDnj17PADVjVAoRGVlJclkkkgkwu233/6O5U2ODRs28NRTT3Hx4kVOnTr1rtTj/j2isLCQD37w\ng++4naIoLFmyhCVLlkz59927dwNTjzGT4/Tp04CEMPPhzHd6HnD/XldXRygUor+/n+HhYUpK3tk9\nE/DGMDlHZPPMM8/wmc985qqeQ4QQPP300ziOww033HBV6o2/7fEfAvgJIfja177m+YXny6jmx7U8\nbJaUlLBp0yZ27NjhlbVz504+/elPv0e1vh7/0eE4Dj/84Q95a/8BklkF/6xbplTk04OlmGVLyY60\nk+h6C2d4lNk1VViWJRX/rqCC406mP/TQQ5w+23ZZKzlF1QhW30omNJ30QDNOegzhQUKAcFCEg6Er\nzJ1T65X7XsN972RTXFdXd8kxJ6vaXWsIJ4sQNhnLlnBWxRbwXWEAn6ToJWIdKP4WqZrWfwAl1oam\nr8XKWti2XClO/sp+RUOZSols9BwK4CuZjx6uINVziO6e3gkAxy233EL9gYPE8xSDTNPEtpMIJ4Oi\njFth5k9oyeTa0hoUAWYZ9B+UYIuVkvVWFIyi2QSrNsmHHbMAkoMoyT4wS/MUAfNORdEQ8Qvyh4L5\ncmbOG+fyrDjtnNIY4CmB5dT1KJgPg7n+mxmCzCAoPgnaXVZBcQxFWAgrgWPbJFM5+13/TAn32EmU\nwQYJZJpFCDsjc6zquepNeoFLD0n1QgX80+vQIhEymYycbBEaonAuyvAxliz6ZYJjAAAgAElEQVRe\nTHFxMfvrD064ZkEhEz01fu0Mn5Cwkdu2OBJIdJs70SmhPfmThKci83OwVh6kFTs3Dve51sc4HmSn\nqDkgr2gRDByROSxeDukBCfqlpJoL4SooWCJV8IQNYtyux2srQa6N8pT5rNyqLNdGeoL62aTbqWvj\nKhwYa5O/C9XI9lf18fYWueM5tmzT6OGcvbNvHNALVMif7TSkemV5VhyGj8HwcTz7WpFTxzRyUK6r\nWpgPkUbmSHgy2QPRoxKuc2HH0OWUAnLXzdgZaY1s5SkYusqFkdo8oEiMg3/euaq5nFk55UFFtlVk\n7rjtcSgHFHnXTO5f4Uig0MshEDsv66FHcufJeFtoGp4lNkICs0MN8nfhubIP+QryVDQYB5wUHcK1\niJFmD3LSQmUweEZCewNvw3CzBL79k67FsbN5qpBVsr/Zaan6lx6A/gO5Np2Tg9gUsJI54LVNjj2A\nEqqS12rn8wgXeM2NOHZWAWUIJd4pFVJUleLiYm9MdBzHWxUr9GBORXOmBEJdWNX91wjmlA+zeMqH\nqn5laBVwlRZ9hvGOMNbVKPJamRS2lZUWxZeAaVcRqg6hWuxRea/8zwD4XUtcDQx4tfd1Jy0/LhKc\nrJzlfjy8zI7u73MqpU5m7J0rnl+6kwVF2tWMjIwR73oLzSxAC7zzy5+dHCTVtZ+QqbNt27b3fZHE\n9bge73ccP348B/cN8+Gb03x8i3WJAp20f3Wom+twrsPi+z93GI0LqmrmSYWFrnaeez3Nc69b4CEE\nOqh+qmbP5o477uCmm24iHo+j6T46Lmr0DymUFV/6bFs1XfB3f5yhsUVl50GNE+dUWjrkxyGBVBNU\nVR+f+8xn+NCHPvSeq1QMDw/z+uuvs2fPHqlm6C5MQC7EmDd/IVu3buWGG26Y8JHLVbVLpN79it1z\nnSqmz2FxbZqvfipKwJyofDc56uam+fPPDPB3PynlraYgK+anWVidwqcLUlmN3W/rbF2TwhEOmjqu\nrAe5FtK4RHlxd84u98Y6h5nlsHapTf3xFHv27OHee+8F4NZbb+XXz/2KI6cSDI1lKY7I+0Y2m8Wy\nbWJJQSgg1QClgLCaeyeSoNmbjUEcBzovapztNLFshcYWP3qrgqrCTSscfvfuLLoGtTMdQn7o6jM4\n3+ujtiLrYk/emeg6XByUz8ULajIE/eP9yhFgObIeqbQ01jU0gd8UqHk5NXRYWJ2h6azC9BKLth4f\nfp9gYXWaD9wYY/WilJerREqew6sHwnQP6IzGVd5o0Fi/NE4ylQQEH1gXp7nNTzKt8stdhQihcsvK\nGIoCPsN93hQyL4pKJqPw5GsRdh8OoWsKX/pElqICOQHg5NTott6o0NAiJwFPnTrFb379LKoY5b7/\nkeG2G2yvftFh2HNY5/UjGiMxjVQGEilpwSwEqIrgfI+Pzj5DAihCQpCOIxUiEynFy5nPELzZEOTU\neZOCoM1ffraf6SU2lg3xlIoAfIbM392bYjz6nI+200eYu+wDzJp3I+lUjGNv/Rxh26y45bMEwyU4\ndhYrmyS/c8tXFLkgzXZsbEVBzSnFW9mEVLvz+XFsCzv3fqSoeg5aywshcGxphdrffRKEoKxyMT4z\nlFOJHu8b0hZZQ1E1WhpeZnjgAmaggIrZq5hWsRAzEEHVNE+Zf9a89QwPnKe77QixkV6sbIpwUQXp\n5AiqGWB69XJql27JvePKeriwIQJUTadk+lzCBdNp2vcEjpWlZMaCS88hL6xsmjNHX2RkQL7va7pJ\nWeUi0skY0Z7TFJZWEwgVS8VChIQf7QyOnZXftLzvEQ6OY3vPPGWVi7lw6k1GB7vIpOL4/KFLVmUI\n4XCxvQmBoGzWEmzH4vyJPdhWBsUISkJY5NddAU2q64l0hkT9aZLNF1B0jchtq4jtbZSQ2nhTuawi\niqFh1swgdaaTdN/QeJG6ishYZLoHGHpmL0bFNPwLq9AiQRACeyxB6mQ72dw+WkEQLRIk2zuIncog\nHAc7liBx5AzJY634amaghQOyfeIp0h19kmDNhZO1UCwbrTAklfemF6OouTqc7yF5ugOEQzadpbGx\nkTvvvNPb9/jx4whFQZ9WQPGHN4CuITI5e+5cXgCEZcs+75OWwSJr5ax8Zb+Rtr2XDvh2QvZ717rv\nSnH8+HEefvhhhkZHyDo2jqFhlhVJgDGVJdk3yP5DB+UEe8CHf87MK5Z3ufDPqSB1ptOb9PzvFKFQ\nCAWw01mE41wRgHTS8n1JC5pTqr5cbt5BVVXZ6o6DGvLnypr8nejyIRyHdG8Un2EwrbiE3ugAPb9+\ng4q7NqH5rwz5xdu66Xv1AEHNxz333HNNipDX43r8d40nn3yS+vp6TNPkT/7kT6irq7tkG9M02bx5\nM7feeiuvvPIKTz75JKlUilWrVnl2l319fR6A46pEhcNhVq1axV133cWcOXM8iPDo0aN85CMfmXKc\nuOWWW1izZo1nFdzT0+PBGK4STiQS4W/+5m+uqNT2buP8+fPs3LmT+vr6CfaKINVy1q5dy9atWz1w\nww1XJczJ/7Z+DeFCMdlsltbWVi5evHjF8VQIQVdXF47jUFtby/HjxyksLPTgHdcO1n0GcmEkN1zA\nbXKZ7kLYLVu2sHLlSp544gna29tpbW31QKAtW7bQ0tJCJpOZoPgYj8c9AOZykJ8Lr/h8Ps6cOePZ\npL788sv4fD50Xedzn/uc5x63bNkyXnrpJU6dOsX69etRVXXCPLfb14aHhxFCeAuzXDgq34rThftc\nGCk/KioqaGhowO/3Y5qmt9gtGo0yMjLi7evuX1JSQlFREaZpcuzYMTo6OigqKsKyLILBILfccguv\nvvoqAKOjo5SWlnqQj5v3fMgvm83S398PQFVVFZ/5zGcIh8MegCmEYOPGjezatYuSkhIee+wxTp48\nSTAY5O6776ayUn7nv+mmm+ju7ubo0aMekOQqNrqgZzab5cSJEx6U4toQg7TpTqVSBINBNE3DMAy2\nb99OPB5nyZIlfPWrX0XX9QmwpN/vp7q6mtWrV3PkyBFefPFF/uAP/oD77ruPI0eO8LOf/Qy/38/a\ntWsBKZgyOjp6Sf92r29Xec/tQ67okK7rE/qc3++/5LtmNpv1ynFV91atWkU4HJ7Qd9xrxOfzMTAw\nwPbt20kkEhQVFTF79mzKy8sxTdMDQF0Qq6enh7a2Nk99rbKykt5eKZQwZ84ciorkYgUXds0H8QzD\noLS0FNM0aW9v9wC/qcKtZzqdpru72ytH0zT8fj/JZPKS8ck9rtvm+bmZrK6paZqnTuhe15P5kZGR\nEYaHhzFN0wNgjx075kFhLgzshptP0zTp6urihz/8Ie3t7cyaNYuNGzfym9/8ZsL55h8vFApRV1fH\n3r17aW9vZ/78+d5YkUgk2LFjB6+//jpr165lzZo1hEIhz255z549DA8PA7BixQp6e3u5cOECc+fO\npaenhwsXLvDTn/6U5557jtWrV3vfb7q7uzlz5gyapnl9zoVCFy9ezKZNmygtLUVRFAYHB9m3bx9N\nTU3ouk40GmV4eNjLaTwe5/z58wQCAW6++Wbuu+8+r8+4Cn35sKV7f4zH41iWNeF6cIH2yf3BVROs\nqKi4ItQuhODZZ5/l+eef9+5H5eXl3tgYjUbp7e31YOry8vJ3pUhumiZ1dXUMDAxw+vTp/zSA33sV\nrhrv2Ng7z9u4ixYmQ3JX807kbldYWEg8Hicej1814Hf+/Hls26akpATbttm1axehUIiPfexjV+S/\nHMfhySef5K233sLn83H33Xdf1fF+2+M/BPD753/+Z3bu3HnJy/WGDRu45557WLNmDaWlpaxateqa\nIL9t27axY8cOb5/6+vrrgN9/4dixY4dU7ssqhOZ88IoTw/k2pPHWl+no6OKzn/2fVFRUXFEFxx2g\npgLDLj2Gglm6GN+0RVIFcLAFJzOGsDPYyUEMXeGzn/mf3HXXXf8uYN/V2BTPrJjB7bffPsGWcLKq\n3bVGdqxLTtarhoRDzOK8b+/uJOEUYRZD6VppizpyCjFtHQoCrDjWSDtZozzH0FzFsBPvADuBakak\nGqOi4Ju2kHS0cQLAUVdXR8WM6bSe7/IUg3ymiWXJh0Zhp1FUnwcUenAxIqe0FwfFkHaTtpwAUpws\nWmAaZvkyfNMWefv4SuZLRcfRFihaNGWfQTCuQBecPtEa2FXq8oCrnGLY5HIC03N/U0EoUrmtZFVO\n7cw/Mf/mNKnyFb+AGDyKHe9n7PxunOI1CEVB0f1QVAfRQ2AnoWdXzv4y1y/cYwuZEwUhc9//9rh6\nYS4HhjHebpZeS3L0JH19fZw5c3bKa3bytWOnRrGtnNR0xl2hIXJgnJo3U5qD3bpehIK5ULwGdJ/M\nhQvKla4DX9FEyE7VczAZULAAJdmHGGuVMFbpWgmBDRyQ8Jy/AvzTGFfiuxx0Z8l/XUAu1Sf/qAfG\nrXPzlc/yO4ILSSV7c8qQIdmGag5u8yanc2WgQd/BHNxnwLQ14C+f2D80U4Jpkbmy3MGjEhBTVGkf\nmx2R23jwo1sdR8KSbl9UVAhWyjp175B1CNdeOlvtvtQIG/rrZd+AHKQ2T/bV/v2QHcup9ynkDRYT\n85D/O+HIPAhFljGEVKl0r4u8SWJAAnlWXJ5jsFJa7A42SFBWy8p2VPJtaHPniDLRBjoyRyokJrsk\nMOvVKO94qibBzxEhx3shyERPyzYOVUF6UNZlsJEpw4hAwUIJqsXb5XhoJWXZws7Bhk0wfCIPvHQA\nFcVXKPdL9SOiDblcB+Xv/OWgGgiRlX17tAVhxXEci8OHD5PNZjEMgx07djA0PCL3m36rVAF1gUtX\nVVGVdlg42RwQqUoQUdhychAkeH2JSiEgHISdRFGgrKzsive+q1XktfuawRqU3c0sudwd5gqhgL8c\nMaLQ19d3zXv/V4irva8L9z6jTJ5gd3K3lanBG03VsBQbyE12XuMCASvWjaYp3ovzW/sPEG99GX/l\nxikXaoCcqM6OXCDVtZ+AIdiwYf11O6rr8Vsftm3zox/9CMca4cM3p7n39ktXQE+OuVWCb3w2w7d/\nFKez4zxffeDr+P1+3njjDW9xkPs+dOutt06Y9AmHw6xdu476fTvZczh72eNpGqxe5LB6kUN0GPqH\nVZJpONuh8Ju9AaprF3PnnXe+a2WIy8W+ffv48Y9+hJWNg0hRXpxlTqXAZwjiSYXmVpWzp49wtqWZ\n7eWVPPDAA57lX0VFBSg6x8+pWPal4Nw7xenzCqMxhaKIzefvHMbQlRx8NQ75uY9x+VE7M8u9W0f5\nt5cKefVAkNkVaakQZ+lER3R+9nKaj20WmMb4E49ty/Us7luWpsqcn2j18Up9CFDYslaOy5vXWNQf\nS3PkyBEP8CsuLmb1mhs4dGAP23da/N49WVRFJRwOE4vFsGybsbjAZ0j4SzprSrvel96KMBpXyVgK\np9v9pDMa0l1QsOUGi3s2W9RUjD/bmT7YtMrmlf0auw+FmHP3SC4Hed95FGm7CxA0nQmTZ64FsQsS\n+XRBwJxaTynod0hmVLr6DaYV2PzuncMsmp3G0MSE9gz6BbetiXPD4iSv7A/zcn2EH/9aRxEOy+cJ\ngn7B2qVpZpZa9AxIZbztuyO8diDI+roEt62JU1ZsY9vQN6SztyHEvsYgybQq4b57syyZMz4xrOUW\nQJSXyDe9aDTK0794inR6lAVVNoeaNY6cVIkEYeVCm7VLHe7ZnOYDN6aJxbMkUgJFcXj8pWKa2/yk\nMgqqAjOmWdRWZFBV6Oo36I3qxFMq//vH5axfmuAP7hnCNASvN4RAgfvvGKG8xPYsjp1cGnVVWgAv\nqM5w68oxfrNPoaXxFSrnrGF69XIiRTMZG+xipP88PjMolfQBFBVN01FcBe4czOjYWQlvWRkURSHa\nexbbymL6IxPgPt2YDKRIS14hHBJjUQZ7z4KiUFG7Kgfn5Lf7uBpG+6k3GIm2Y5hBFq/9KKY/LK87\nx8J2rPG9FCgqm01R+Wxam3Yw1N9GJjUGAgpLq6hdelvemCQBxYnwniCbTuDzh9F0Xw4+XIQQjsyB\nu5WQK4FsO8vJt39FbLgHwxegauFNlFYsQtMNOlrqGbx4lnDhdHSfX4JsdtbLrVQOkxe5grweHCuD\nZgZAgKb78AcLiI8OkIoPYvgCE+qOAj1tjaSTo5ihQsKlMzl7+CXGhrtl+wyO0v/TV/HPnUlgcQ1a\ngZyksmNJUqfbSZ/tQlg2iqYS2boGJyYnPvWi8IR85I4GqoqSUx1zQbaRphZUXceYV4nq95Fu7Sbb\nEyXbE5186Uqb30XVBFbMQ/UZRP/tVey+YdA0zJoy0hcuIpJp0ue6cDuuO0b4ayuwY0mskRhaKEDB\n5pX4aqbLfOTGX0UBf3U54fVLSDSeI3bwJEcbGnjwwQdZsWIFmUyGI0ePogRMCreuQTENRE4eVWSy\noDmogZyaU/8wzJX3DcXQwRES+uP/Z++9o+y4CnTfX1WdOvl0DlK3WqmVs2wrS7bUkgMOYIPx2I8L\nvMsbDDbBGMOaBYswAZjBfvjZnjue8RAGHITBBgyWbMvKOadWVmd1q/uoczq5qvb7Y5+qPt3qlmRj\nuHdA31pa6u5Tp8KuXWHv/dvfJ1BcGuow7ghW0iB1qROEYPTo0VcE2/fv389/vPAC/YkYrsIc8uZO\nIjhlLGoGYGD0R+mqrCa+4+hA/5F9oO9Bqscj4el4/D1977+DdF1n9OjRRBsbiNQ2E5w08oDfSIYC\nIu0UOdL50jQNzeWSDjsZ4MW1KlLXgojEKSkt44knnuAHP/gBl8LtNL76LjnzpxKaNh7NM7g+Jdq6\n6amsou9UHT5N59Y1a7j99tuveZvXdV1/qaqrq2Pjxo1omsbjjz9+WXTlUCmK4sTHrV27ltraWp58\n8kmOHTvGoUOH6OvrwzRN/H4/kyZNYuXKleTn5zvft8GMCxcuUFNTw6RJk4bdTiAQ4Pbbb+e2226j\nqamJnp4eUqkU77zzDmfPnuUjH/nIBw73GYbBz3/+c3bt2uX8bcqUKU5sYUdHB2fOnGHfvn3s27eP\nG2+8kYcfftgBhUtKStA0jf7+/ivGbY6ktrY2LMuiq6uLxsZGZ71XAwRaWlrIzs7G7XZTX1/vQHK2\nY53tzGcDbbarnw2NqarqQEyRSARFUZyxY5fLxfLly9mwYQOHDx92QKDFixfz6quvOkCf7cLl9/uJ\nRqNO5GqmE5W9/zZoaD9Dbdcr2/ltxYoVZGcPOELPmDGDUaNGcfHiRerr6ykvL7+sLGxoDS53W8qE\nQ0HCM67hHINVFVVViUajRKNR+vr6qKysJJlM4vf7BwGBiUSCuro6UqkUM2fOxO1289RTT/H444+T\nl5dHMBjk9ttvZ/fu3aRSKVRVdeA9r9dLMBjEjmhNJBL09/cTi8XweDyUlZXx9a9/3YmSzOwLLioq\nQlEUjh49Sm1tLUIIcnNzHee8rKwsZs2aRUlJCUVFRaxYsYJoNIphGMRiMdavX09vby+WZaHrOhMm\nTKCkpIRkMkl1dTV9fX1UV1fz+OOP88ADD3DPPffQ2dnJqVOn0HWdRx55xDlftsOZpmkIIdtp9957\nL3V1dTQ2NvL888/z4Q9/mBkzZuD1eolGo477pn0uMuueLRtQsyzLgfns+4rtpmfXo6Hn0TRNJ3q1\npaUFwzAoKSlh/Pjxg9rJme6AqVSK119/nf7+fvLy8pg3b94gp0DbZdCuW2PHjqWkpIQDBw6QTCbp\n7OzEsixKS0sduA8GQNbMiQSpVIp4PI7L5XI+Hym2UwjhwH2maaLrOqFQyIngtd0VM48lE7DO/N1e\npw2W2WCpvY2hoJ69/NmzZ1FVlWnTphGLxdiyZYsTVb1x40YqKyu5+eabWbBggQP8XbhwgW3btkm3\nbKTz31e/+lV+/vOfA+m+Iwa/O9rQq+28aTvbbd68mUAgwF133UV9fT1nzpxh79697N2797IyGz16\nNKtXr2bVqlWcPXuWp556ihMnThAIBJg8eTLHjx+ns7OTzZs3O+UrhMDj8XDTTTdx4sQJDMNg+vTp\nfPKTn3T6uuwyLC8vZ9GiRbS2tvLiiy9y5MgRfvKTn9DZ2UlxcTFnz57FMAymTJnC/fffP6hO9vf3\n43a7yc7Opru7m/r6eqev0O/309fX58C3fr//snptWRaRSMSJIJ8/f/5lx5953n7605+ya9cuFEVx\ngPSpU6cOem+vra3l17/+NYcOHSIQCDgui++1nzEUCqEoigMf/iVp3DhpfGK7DtvA33Aa7pqzIV/b\nuXE46bruwLo2ZP1e2JfNmzcDErofP348L7zwAm+++Sa1tbXcdtttzJ49+zLQ9/jx42zYsIFz586h\nqiqPPPLInyQZ5i9Rf3LAr729nZ/+9KeDLtZgMMiTTz55TVEGV1JmBrMQggMHhkY3XNd/F1mWxcaN\nG4klDLxjbr4m1xcAzZeHt3QJsaadbNq0iX/5l3+5Jvee4cCwkaQoCnqo1ImrS3bXkWzaSfmEMX8S\nuO+9xBTX1DXR/OLLVFVV8fDDD6Pr+jXBiyNJCItk20k5MqMHwVci+/ftEai029uInY2BMnD5Eal+\nlEQbqC40xSJ+cQ+ieAWooQyobKRo3l4JmAGeghnOvcMVLCHWemwQwKGqKrfeeisvvvjyIMcg++Em\nIb8kWEra3U1CP0qiE9F+AMVKgpJCUxRy8nIQQtDdF8U3bhV6YCDOw96+6glhJvokHBgc7iEj0iAV\nErCwC892EkPJgPvcOE5amVLccnlhSZBqVIWEddIuCmQCkrZLmb8UxZ2DCG/H6msEVy5K7ow0yDUa\nutxyIEWk0pHTXghMkI5ptoOX7UBmRkFYKLoXM95D37nfSUcGT1baTdGOv7a4dOkShqWgeAuJNu5C\nWMagZbVgCZa7ADM3hGlaAy/KdqRz2uVNcQchMA7hHSUjaG2Hur4a6G+E3NnpuN0+CXn5Rkm4y3ZF\nFEICb5ELEjazjHQZmnI9rXtkffbkyTLrq4LguDTslAlZps+FIoAMJzjb4ay/Tm7Xk4/jvDjU5SwT\nOkz2SAgPIHtqBtyXeb7Tv/dVDXYn1LNlFLgDP9q2CmmXOn+phBzD2+Q+mWnQr2DhYLgP5N+VDAjR\njoZ258hyMaLymOyR5czOcGENwH12LLJ/zOVgquaVcGG6vBRFzoxU01FZ0hUiPfvOSkn3BZSB/bKM\njGhfdaCcEl0S0AMJusWa0zG26VjZZC/Uvw65c8E/igFHvXSMsBGR6wqMlft+8W25rlDa6t25r9nn\nQnOuMXmvbcZK9MlrMU/atRNvg2ijhHltuFAPyqhg36iBcxoYI6/dZKcEWbOmQ+8ZuT9W2kUTAaob\nZVSF3P+2PYhIU0YEtV3WYuD8eAukS2V/I6J9P51dPTz88MPk5+fT1NRELClQChci9FDGwYmB+Gk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8egF+WmY0QVgotn0rvtKFY0Qc4dCxGGKV0OXRqaz4vqlZN9Um3d9G4+AoBv+njH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ADvnx1V37vJckYeEsqQqOZZHvG6XQN8rKZctpbm7m85//PJ293RRUmci6JdTefwcVt62i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\nLjn0RBHuE2pRec4x3WNFse0Eq2R7GXxFWrNWLCuuCKfa5T0SC+U1LLd+fGt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GCr9CKAQq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VOokSDhZXOxDdMHI715oMDsGRS6LpA9dZ5C3zCR5XOpeu9mRn+xDccwUStiRNYsIji/ASWg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\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qdZLvu+w9L7Atg4mBKzi2BaqCHFKRkzHCCxuF1axlYU9k0LuHwHGQo2GSuzcR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t8hXRW0WJjYljVE1nfjFkryRIFADZnoDcn5/ZUqlC9NSCA2bJC8ZwuqoSNcgWE5FE5fYmJ8mmd/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RJSCwWhvMShb0iQdUTe1UpeEwZ1+aD8dHcAF0Ek5LDyxgQrF6WwHYGb98CeO0Nedw2MyKha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\nzVA6L+dKOIIujKkNVUu4DGR7mAkZWxuolPVInJXf8RSC7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sOpsSVg3zGV3z7vJZEJMXP2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2+PxHBviXn1ZxqC3Ci7vVvgJJJOwpiFBCRcIlGpb0Dev0DBo8/0oZ2bzAkwKhqQlPTXgk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O0c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NY14asy1lVbrF6YZdfxKAoKsbDbzS1a6LlwNcCxcyHCIcGD9/QypdqivUf2O8pLxqTljokM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eTlXjaufFYeG2bFTNibpKTREurnTayLkntS1MI49JnvkkyQqmmfeqbdsWkiRTs2g9kyOv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8Obl/CiGoqKjg8ccfZ/78+Tz//PM888wzeHHVN+pgeLU8UO5Wz4kWLVrEokWLGBkZYe/evfT0\n9JBKpQgEApSUlLBx40aqq6uvefyrr74KwIYNG276+/f+++9n+/btHDp0iA996EMzPidvhVRV5cMf\n/jCbN29m165d7N27l/b2dv87HuT31/r169myZQvz589/09fynvfOzk4uX77MokUzrw9fS7t27QJg\n8+bNb7oMs3rn65YCflcDfZ6l5LV0/Phx/99CCFasWPGGOxivJoGvHizM6vdfb+fi/KzeWs0If/hj\nmakDLem+pchFBdeRQJoaws2MILpeAkWVDg1GRL5gNk3+5m/+hnQ67S9oebDTW9X2coeGixGd6kol\nAF3X0HX5FZod6yRhjsvfjDbKuFehSXe08Fzp5gcSJNMjMgo0UCzhu96dE5CVUCfWUzy5joSxBg+D\n6yIwYfgErlEEgaKJF+xCgdK7JMzlOf0NnwI7I13XPChqosInRZ06Odc+uXgiBP7kw8nIGNTJg8iK\nigrOX2rByQzgGoUTIJ4QMi44Ok9G4CbaJPTk2hIg06Lyd6kBiJ0FzZDP8NgFeWIfjpS17C9ouc4k\niM5hfGwchI5bfDtu/+sI14Si23NuiH7FTW0x4U78KFAoXf7698u6MnJQoHKVrbjXXmZctkG4Oldn\nk2JhvfqbLNfGHWtDmOMIPYAame5qlh1qwoy1gxpGVN2Hq0XleXJt5PrPR67Q4TmgRXH69xIbG+fM\nufOcv9TC64cOU11Vyf33388nP/lJFi1axA9+8ANsR5PQoFEo78uD9oQrHRIR8p7690vILW+hdDn0\n7s0cgVQv2FnpRJi3cMLpT1zr77Ir43V9gPBVWYbIvGt83OvbRyTwGaqScF2iA+Jt8jp6gXReDFfi\nRzUneyWA50Gk2RFE+caZzy/kIlw2kyGbyUDfPunMp0fk8xKpkX3QyQCKhDeNYtlPk10wfFKWaeAg\nlG9gOkU40RdEsATK7sTtPyBdDF0Fl2vHhVxPQtGnPXe3Sm8mvtzTTBD0jUoIBaNkCZmhU+zZs+eW\nAX7w1sCMN6IVK1ZQVVlBy5UuzFj7NOfO68mMtaHYKapr57J8+fI3XYZZzWpW71yFQiGeeuopWlpa\n2LVrF6+//jrNPancOERGxBeXlbN582Y2bdrkx5XM6rer48ePExsdpLrUZNl8D7TxIjOnflZXoSAC\nWVPubaitNKmrzHL+SoCDZ8IEdJhT7jKvyualgxrpdIrv/cO3ELhYjoOZdSkoLKSqqoq6ujoWLFjA\n+vXrrxtRdT3JcYhLNOzO7KyHXJQRis721w0SKcGVbp0fvVAopzS5+1NVWNeQ4sG74iyYY/Ku1Qle\nej1K0HBJOoL9p8L0DWn8wYY4ty1Ko131p7izX2Pn0Qg7j0RxXDAthf84kEfQcHj3PXG5NybnVJfO\nCNavSLGuIcW5liC7jkU4cUHFRcYgK/oEEKgIOdtwXMCW7oLZrAT7ABShEIlGffhv8kLB0qVLEYpB\n94DGxQ6DtUvS0mlQgXtXJ7l9cZorPTqHz4UZHlcxLY1gAMqLbe5ZlaWp2eVCWwChGowNd+LYJgUl\nNRSU1OTqTRbS25Piug5WNo2LAy7+4lFl3WqG+6+AbRItqLwqEnaig0lnOmdi/5QQhCJFVC+4k/YL\n+xjobMTNQYKTFc4rJa9oDuMjXbSd30vd0nsBFzOTmFikEKAIFUXV/IYws2k6Lh7AtrKkrAxNJ7ZT\nu3AtRiBCICRf+A/3txOPDRAI5bN07XtRVBXbNnEdG9u1ZX27EwsnQghUI8Ci1Q+TScVIjPVTXLGQ\n+Fg/HV29/PgnP+XkyZN86lOfora2lq9+7WsEQ/nMX75ZRtpOXkhWNZas3Ubr2V30dZyh58oJeq6c\nIL94DkZQLiiYmSSJsQEc26K/4ywllRJuc2wLV5s5Ps11HYRQWLDyAZoO/Zx4rJfGw89RUrWIitpV\n5BdX+2NmM5umv/MsfW2nSSdHEYrKolUPMz7SQ+flQ8RHe9H1IHlF1VTOW0VReT2aHsB1bMZGuum9\ncpLhvstYIwlGfrGP8vdvAWNq+3nt7jmHZAZGiO89jTA0onc1ELljYoFdaCquZeGaNhiyHYP11RjV\npYy+eIBs1wDxQ01EN0wap3r905L90agsIX/TSmI7T2AnUri4OJZ9NQ94Q3JMC4F4099fNyvDMNi4\ncSMbN84wr7yO9u7dS9axyb9tsdzkdxMSQlCwchEju4+zb9++Wwr4gYQmHnzwQR544AFaWloYGRkh\nk8kQDoepqqp6ywDxLVu2sGffXsbOtlCwchF6/o258NmpDLGTFzEU7Za5Ns5qVrP6/VdtbS1/93d/\nx/Hjx9m1axfnz5+nsbFxymcWLVrE1q1bfQemWf32tXPnTkzTpKqq6g3XeyfHz2azWYqLi+nt7aWj\no4NYLIaiKDQ0NGBZlu/o+M1vftOHoBzHoaysjLlz5zJnzhxWrVr1G73v897NXgta8ZJjBgcHOXbs\nGNlslj179rBnz54pnysoKGDLli1s3ryZFStWUFFRQV9fH5WVlfT29nL27Fmqq6uZO3euD6d542DX\ndRkaGqKlpcWPn83Ly0MIwcDAgB8ZCxMQ4Pj4OIODg+Tn5/sg3kzjV0VRcJyJtQ3LkqYW3nxD0zQi\nkYgP5U2uxzVr1vD888+TTCanwG2qqlJWVkY4HCadTvt16DkHhkIhwuEwvb29uK5LXl4ely9fxrZt\nCgsLr9lHPKdEr068a4ZCIcbHx/3o1JkAIdd1sSzLv1fvXubPn++PizwI1btfz41v06ZNPP/88/59\nCCH82OXJ4JZ3f5PbTtM0LMvi0KFDLFy4kBUrVlBYWOjHbr7yyiuYpklDQwMbN270I529mN/J5feu\nUVZWxqOPPsq//Mu/ABKqOn78OOfPn+f8+fNs27aN//bf/htf/vKXOXnyJKtXr2bNmjXT4LeysjI+\n9alP8aMf/Yiuri7OnTtHU1MTZWVlqKqKbdukUim/LD09PSxYsMBvh2ttCHEch2AwyJo1a9i/f7/v\n2ug5H05uXw8kTaVSaJpGaWkpH/jAB3juuefo7e1FVVUKCwu588472bx5M/X19WiaRiqV4ujRo+zY\nsYMrV67w2muvkU6nefLJJ6eVxwNQdV0nk8mwfft2Tp06RXFxMU899RTLli3z61hRFB8gFkIQDod5\n73vfy7x58/jhD3/Iz372M+bNm+cfM1PffM973kNHRwdHjx71+8mblRdh/XbNiYqKinjkkUdu6hjb\ntjlw4ADAmxrDl5eXs3LlSk6fPs2hQ4e4//77b/ocN6PKyko+8pGP8Oijj3L58mXi8TiO4xCJRKiv\nr39LHMR1Xedd73oXL774Is899xyf/exnb9itvbGxkYsXLxIIBG5JLPOs3jm6pYCfN+jx/mj09fVd\n9/NHjx4FJnYlrFmz5g2vcbVL4CzU9c7U27U4P6u3VjPCH/4YcoZYWkVDKJoEpBwb8hYgRk4jVA29\noA6h6mRjHbiuQzyZ4WzTZVzHkQsdZ86xc9cuDF2ntLSUpUuX8q53ves3Av7SaenQJtSZXwa4rkt2\nqIlU1yEJxyn6hGMeTMTVahEZYZu/WMbZDh2X0a+hckj1T4WswhWADm5W/s5zw3OyCBTUcDlOJobr\ng4Fzc85ijgSX8uolZJXqg1gT2L2yLD7c59vMge1M4S0FoGoqgUAAI2DIZSXHlAtAk2DqDRs28Oru\n13DGWiBaDziAMuHkp6jy3kLl+NCUyF3EsWEoB3Sl+iWgZcbl/YevioBxXRmlaZt+uW07twIHEKwC\nLYprxiRQ6UUCT7pNCc1Nci30bjZULa9pJWTsK0gAUQDuJBuQ/CUSQBs6Lj8fKJbXmSyhyut7cGZ2\nGEZO4jomTiZL7OT/RgkWoYXLMEoWo4QrSfedwrUzchGm/yBC0XDVMERqEeEKVFXxY8/kNRREqEy2\nef8B0MOI4mUkhi/S3NpJ99PPcOnSJbkoIBTQg/Iec+DslDoRGpTfA2OXJFxpxmU75Orcv3dXOnS4\nVgJSPbJ9HDt3r9dwrRBCAoTeMQOHJPSat1D2B8WQ/TTVm3PklOC/iNbgFjRA169lLLAelXBgeK48\nxruea0NeBKJ18vzDJ2S87+BBeU+TJ76O5TtRplJJ3JGzkBmQDqEV90mg0bd88WKKJ0X0hmvAKJEQ\nbqIdxsrkMzxZjo2M4VZkP4jUyPPb0gFDYPJmNNNz97uoa0HQN6q3w6lwsn4TmPFGz//AAw/w9NPP\nkOg6gBrIRw0Vv+FxdmqYdNdBIgGNBx54YHYcM6tZzeq6WrBgAQsWLOBDH/oQ7e3tJJNJVFX1Y2Zu\nVcTfrN6cDh48CE6aLXfa/jBFfs+L3Lh2qoSAgAGGAbYNW+9M0tZrUFUK2zZZ/Hynzp4TKobuEgma\nFBfGGBwRZFICRXEZG00yHuvm4vkTaHqIn/70J9xzz0YefPBBKipuLj7eWxA1zWu7lY2Owzd+bHCl\nR8HQHCIhh9sXpynKc7Bs6BnSaGwJcPBMiNfPhth8R4I/vG+MprYAbT06+WGHjCW41GHwzZ8VU1po\nc/viNJGgQ9YStHQZXGgLkM4KsqZgfrXDtk02T7+o8fNXCzjTHGTrHQnWLkthWtLZEBfGkwrdAxpD\no6oP/43GNSpLLB88zJrCh/kmtYAEFw3pZKEqKn3DAlD891uu6/LjH/+YdDqJYwn2HI+wcmGGoDEB\nvOm6S22lSW3lGJpuoE6aTzoO/OC5CBlLATtN67lXEQgq6lYyiZnz3fBs2/LjYUGgGwF/bl1QWkck\nr5SxkW7C0WIc2/Qd5lzXxbEtbNuc0crPJkNJ5SI6Lh4gNtSBEArRgsrcsfglmLdsEyf3Pk1/5zkU\nRWPu4vXy+kL4s3nXdbCtLIoq3T0un3qJxNggqqrjuA5Xzr9O28VDqKpBYelcqutuo+3yEbKZJI5t\nceSV76FqBsFwAaVVSyifuxRdD5DNZiXYp+momuG7Gc5ZsJZLp14mkx5nw0P/L0O9rTQdf5lzjU18\n9atfpaurC6FolM1dTiAU9Y9zXG9BD4RQqV/5ABV1K+ltO8Vg13liQ52yzi05/1S1IMJ1iI/2MtLf\nSnFlPa7j4NhmLqL4GnJdhCLdNh3bZKjnIkM9F9EDYTQ9hOvYZNLj8p0HEAjls3DlgxSU1jA+2oPr\nOqh6gCVrt1FQWoOiaPJcjoXrOISjJSxYsYU59XfSfGYH46M9DDz/KhUffBA1PLH4Y9tyLuItko+9\nJjfJhhbXEV451c1I6DnAz7LxCF2hKGgl+RQ8tI6R5/aSarxC+I7FKB4sIcA1LVxb1quiqQQXzmX8\nYCP2aBwXF6t/GLGo7tp1dQ2lugZQhKCkpOSmj307NTQ0hOM6hOaUvfGHZ1BoTilDrsvg4OBbXLJr\nSwhBfX39LTv/ggULWHXbSo6dOkn3L15jzvs3o0WvP6+101m6f7kXJZmlvn4hK1euvGXlm9WsZvX7\nL03TWLduHevWraOvr8+PqwwEAm+5o/WsfnN5IJ5lWVRVTbw/nAzSXG0U40FgwWAQy7IoKSkhFoux\nceNGysvLeeGFF0ilUkQiEUpKSlBVlf7+fh+k6e3tpbe3l+PHj7N9+3YqKyvZsmUL9913302DMhNz\nomu/421sbOSrX/0qpmn67mh1dXUYhoFpmnR0dDAyMsJzzz3Hr371Kz75yU/yxBNP8JWvfIW+vj6q\nqqro6emhr6+P7u5uCgoKKCwsRFEUTNNkcHDQB61c1+XRRx9lbGyMgwcPoigK8XicRCJBNpslmUz6\nTmeqKtd5PLgvFAr5bnxe1Gw6nb4m+GcYhl9fhmGgKBNzong8zk9+8hPfFc1zWfPeaXrxw8Fg0Af6\nJr/v9GJSAdra2nzwbjJIOTkSdzJA5fUPr9xeHbiuS0VFhd8OgB+veq32E0Iwb948hoeHuXTpEoZh\nsGDBAv/aIN1At23bxtNPP00gEPDd466WV69e7LGu62iahqZpxGIxvvOd76DrOuFwmPXr17Ns2TJ+\n9atfkUqlOH/+PBcuXPC/xxoaGqivr8e2bT96OBwO+zG3oVCIhQsX0tzcTGVlJV/72td49dVX+fnP\nf84LL7xAMpnk2LFjBINBNm7c6AOFXjm9uissLOTJJ5+ksbGR/fv3c+nSJQYGBrAsy4/XLSoqIhaL\n0dHRwZw5c9B13XdSvN47bNd1UVUVx3F8V8CrnQk9h8hgMEhdXR0PPfSQH7Hrui7z58/niSee8KFD\nL5radV1WrlzJbbfdRlNTE08//TRHjhzhH/7hH3jyyScnbap0/bYyDIOenh6ef/55DMPgYx/7GAsX\nLvR5FCEEgUCAVCrll9M77t5776Wvr48XX3yRl19+eQrg57quTI5yXb/NH3roIY4ePYplWcRiMXp7\ne2/IVfJqXbx4EeB3ek7k1VcoFKKmpuZNnWPx4sWcPn2aoaGht7h011YgELilJgtbt27l1VdfpbGx\nkX/6p3/i8ccff8P3tc3NzXz7298G4KGHHvqdXxuc1W9XtxTwKyub+oLD+zKaSbZtc/DgwSl/OG8k\nb3tsbGzKf0/+QzWrd55u9eL8rN5azQR/qIqKJeycu9Y1BoBClREukbkQa0QNSle37NAl6TxgFELB\nQlTVwRq9gmvGcVHBcUllHTq6++nuHeDQoSNUV0t3s/vvv/+moYlgMCijhuzpEwDXsUl27MUcvSJj\nb4xC6TQWqpyIO031TQB6Q8elq13Z+py7XQekhyfc7qz4zJG2ro1wbRAqenE94Zp7SXXuIzt6BdcH\nAxdIEElRJcyWHoB4K9hJIBf1mh2FUMUU2E1RQCgKihAoiophGGi6NgW7tOLdqKrwLeIdx2H//v25\neCgP/KoBTS46SCc/TQKKIMtE7pLClTCWlZLgmevIexa5+GLhfdCdGcxTdYSQCyWQi8aNzofRs5Bs\nl3GpipY7hRdHfJW7ni9b1v3IGYhfkWWILmDychouYJRKIM02oW8vlKyWTnOT+64r44MRQtaH70hX\njpuNg5PCTg3jpEbJDl+SZXddWVYrxQToOgLJLpRAPuTX44bq5MKZYkzUY7QWhk/hmkm0cAnB8g9g\nxtpJdB1g/4HXOXv2LJmsjVs4H6EFJ46TV5XwogdbFiyWfTbVC+PNEjo1Y2AlEXo+VNwD2THpHjl0\nQgKQRkEOpLzO8CEbk8ehymdhWlzzJPcMPSpdH/MXIdIyIgihQPm9uWhhz13vKgk11++i0L8Xd/wK\nIlAKhUvl7/1IZ1UuOMbaZDSv0GUbBnKTIq/dvD7rmBNtq6jSJbN0nYRwxy5C3qKJ4k9yXBSeG6ZQ\nZJzz0PHcszgI3PyC1tXP3e+q3giCfiO9nU6Fb5fuv/9+Ll68yIGDh0i0vERwzgb0gtoZHQ5d18GM\ntZHuOsTZHDYAACAASURBVEhId7n77vVs3br1t1DqWc1qVr+PikajNDQ0/LaLMas30MjICOAwv3pi\nTGoYBulUCtOSsNdMUxSBjHxdVGOiKpAxBf/yHzpZ02VOmcn6FUkutEdoatUBl9JCm3tWpqguk2DS\nWELl5MU0rT0pdu54gb179/Cf//N/YdWqVTdc9vz8fBRFYzQuGBmDoqveR4+Mw9/+MMDgCMwtN9ly\nR5z770wQDrr+eMl1oG9YZfexCDuPRnn1aITxhMqnHxvi6z8tob1Xx3GhKM8mnVHoH9F45XAE1xXY\nDpiWBPuEgKpShy88nqW8BPIjLv/7OZ0LbQHOtwUoiNpUlVjomksyo3ClW8d2JNZVWmhhmgpHG0N8\n+KEYWVOQtQSuIwCBqkgXdS8myjAMP+rUdeG1YyooAT8u6aWXXuLVXTsIB9KkEZy8FGTHoQjv2Rj3\nIc6Q4ZLJyvP748Tc+f6/V4J0DhgEQgU4DqQSMYxQlLyiahxbOgW45KA524vcclGEim6EpizACSEo\nqqgnHuuXG4Y8pz4XLDM9AfYJISGxHO3oug6uY6NqOoVl8xjoasJxLCrrVl0FA7oM9zVjm7nF0raT\nxGO9VNatoqR6Caqq47oOjm2RTY0z0H2egc5Gspk4eiDCkjXvYaj3Mj0tx7CsLELRGOprZbC3GUWo\nqKqRA+HAtrIkxgZIxPrpajlMafUyqhfcgREITwEkAUqqltDa+BpjI72Mj/ZTWlXPnZs/xrHXfsz5\nCxdJJOIYgQjV81cjmL5gOXk+Ei2oYOHKB5m3bBPDfS1k03F6206RToxQt/ReFAHNZ3fScnYniqpR\nUFKDbWURioqiXP2CXmCZGS6f3k4qPkS0sIKlax6hv+sc/Z2NZNMJsumELJOAorJ5VNatorB8Pq7j\nkM0kscwMAkFl7apcn7BxPMfwyfMooRDOK2HZne+j6cjzjI100fuTlyi67w5C86pxkO3iZi2Sza2M\nnLiANTCKEgoQWjF/YtpNbkFLURC6JoG9rDXxJQTolcXo1aWY3YOkL3QQXiHhQNe0cLK5BeSALp8j\nVSG0rI7EkfO46QypCx2Ie1bP0AbXluu6xE5fwlBU7r333ps69u1WJpORrzr0N/d6Xeg6Lu60zeu/\nzxJC8Gd/9mf87d/+LS3tbXT+bAfFd68gurgW5SqnF9d2iDd3Mvz6WUQsSVVZOZ/+9KdnNyvMalaz\numFVVFTc9CaWWb29Gh8fxzRNPwrWkweAZTKZaYCfJ298XlBQQDwep6uriwMHDuC6LrfffjsNDQ3s\n3LnTB+UrKipYunQpoVAI0zQZGxvj0qVLdHV18eMf/5h9+/bxmc98huLiN96M66moSK5LXblyZcbf\nnzp1im9961ukUinq6upYvXo1DQ0NU9ahbNumo6OD48eP09LSwne/+13++I//mE9/+tN861vfoq+v\nD1VVKSkpYXR0lGQy6QNrHgTpQUj33nsvH/3oR+U8oKiIl19+mVAoRDQaJZVK+RCRYRg+JOkBUyDd\nq2KxmB/BOhlu8lztDMOY4n4ZCATIy8sjGo2ybNkybNvm29/+Nt3d3VRWVtLf308sFiMSiUxJEvBg\nqavdA23bpqenB8MwqKyspKenB9M0CYVCU5wAQa5FeRGrk+9r8vkMw/BhSu++POc8D+7yYLPJcJl3\n3tLSUhRFIZVKUVxc7HMJk+NxDx065INxgUDAP/9kl0WvLj2oz+u78+bNo6WlhfHxcT+6d/v27Wzf\nvt0/xksmTCQSjI6O0traSlFREXfccQcLFy4kLy9v2vho9erVXL58md27d7Nt2zbe/e53U11dzbe/\n/W1eeOEFhBDU1ta+YUy5qqr+mvvAwAAXL15kdHSUV155BYCvfOUrfP/73+fEiRMcO3aMNWvWyHca\n6fS0tvA0NjbGqVOnUBSFO+64g8WLF3Pq1ClaWlr8NhZCYBgGDQ0NrFy5kpKSEjKZDOPj42SzWVRV\n5eMf/zj5+fkzuuB53w9r166loKCAb3zjG+zZs4d4PM5HP/pR5s2b54N3AwMD/OIXv2DXrl1YlsW8\nefNoaGjwoUzvfIFAwHew9Fz8vO+nBx98kJdeeolTp07R39/v8y/JZNJ30fSArEWLFlFTU8OFCxfI\nZDLs37+fxYsXT7uH66m3t5dz586h6zrr1q27qWPfTnnPWCAQeNPn8P42vJPmREVFRTz55JP8/d//\nPXv37mVwcJBHHnlEpkFc9cyMjY2xZ88efvGLX2CaJnfccQfve9/7fksln9Xvi24p4LdkyRL/367r\ncubMGT/X/Grt27ePWCw2hbq/EQe/q10Bb2ZwNqtZzerWaib4w9ut4TrWlMWOGZWLTLVTQ9ipYVyh\n45bcjgiVo4ydITvcLlEsNZqL8awAFFwrhZ3uZzzRNsXd7E//9E9vypq/vLwc9WIzVrwHLVzq/9x1\n3Qm4T+i4ZXdKsE8Lk1sdkgsjgRIJLyW7JPSV6JAnKF0v/z/RIeEyNSRhJTsFdhrhmjKg1k6TyyVC\nqApqqAgnM0qg/HaEHiYzcB43OyJdzJi0MijkYhJaFCJlkLgiAa6CZdJNz3XANXFd0FWNcCQ847KH\n6zpkhy4Q0VU2bdoESNvwQ4eO4Co5h7/hHPillE1E/irqJK5uUrnSIxIWAyhcJYG/oROyrEZxzvVs\nWilA0WWE8xTlShwsk1CVlZCfdewccOdNBhVZHj9W1oMHbQjk2tTJQqBMtqG3oOXFx/btxY++s5Oy\n/J7bYqhcntfKQLpfQpVWUhYtPBfK7pLueL2vgpXGVQ3ZxkKTEbF5C6Qr3lVAqJMZxx04AeF+RPmG\nKZCeB5C5I6fJDl9Cz5uDUTgPNZBPouUlkj29uGgQrJh63LUkhAQjw1Xy3pM90LtLPnvhKghVItL9\nuPHWSYBjdc6x7qrVaNeFRKeE+VxL3l/pXZDqkv3cSkjoTdERep78fahKOj+4FiS6AQeCZYhAIXix\nZL7bZy5/Tcj2FAJcpUTGMw8elm6BjgXhCjk5diyU9CjJjouQGcsBmxF5TXJtSu5ZzZ1zSt/x7i88\nV7a5OS5hyHBlDiDNOUsq2kTfBwhV5sKlHdyxFlx39U3F18703P2u6noQ9I3o98Wp8GakKAqf/OQn\nEUJw8OAhUp17SPeEMEqWoEWrEYqO65hY8W6yQxdQ7BSRgMbdd6/nT//0T2fd+2Y1q1nN6h0m6RTg\nMnnNShEKum6QNTNkTJfQdd6DGpocD3X2CfLCDnffluS9m2L84JclXOrQCQdsHntgjLtXpAkYnucb\nJFPwrjVJugcNdh4p5PgFk29+4+s89ZnP3vBmuUAgwJq1azl6aDe7j1m8f7Pl/86ypXPf4AjUVWX5\nk0eGiYYdQoFJG2VyqiiWZVyzLM03flLC0aYg5UVR/vITg/xsZz77T4UZimnkTMBwHCW3B0NF0XQ0\nQFUEibTDF76rUD/XIRx0qa10uNimMJYQjCcUugd0FMUloLsYussdS1PcsypJKODwd0+Xsf90mEe3\njBEMuAQNQTzlYjsgFEE0Gp0RBGtqVegb1iksKeP2228nnU7zy1/8Auxx/uyDWVJpwTd/qvOzVwrI\nmIL3vWtcMlGTpoTkYoAzGfi3XUH2nQySdSIsWfMQrtA5d+BZtJw7nW1NnQ/50U56AFXTp5XRBXRd\nLtjZVpqulmOMD3dhmanccSEKy+qoqF2JlnvhPrEIJQGwQEguAiqKSl5RtRx5C0E2naDt/F66Wo6i\n6QGiRVWkE6MkxgZoObuT9gv7COeXo6o6lpkmPtqLbWWxbROBYPGaR8grrCKvaA5mOs5gzwXpZqEZ\nCKFQUFJD2ZwGCsvmYQRCWFaG0f4r9LafZnykm54rJ0mOD7LszvdPaxdV1SgsrWWo9xLJ8WHyiyoJ\nhvNYteEDHNz+AwCMYB6B4PR4romam1qXmh6ktHoJVjYlIb8rJ3Edi+pF60mMD9Jz5QQXT/yK8rnL\nKZ/bQChagqYHEDnHRNvKMtDVRFfzEZLjQwRCUZbe8T50I0T1/LXMmb8Wy0yRzSQRQkUzQmi6bBMz\nI13NHddhbLAdoaiUVs33HS/kRi0J4ymKiqoZEi4UAk0zWLz6Dzjx2j9jxuIM/Wq/nIKrqpzt5FxE\nrGQaVBWjtgK1IIJrO4jcBjA398AKXZMAqJlbdJs0Lg2tmCcBv6Y2QsvqwLJxndzGQUNDaBNzT2NO\nKYkjsprdeJpEaw/R+qvc+q+jVEcf9kicytJyH6z9XVUwGEQATubNzYecTBbBO2s+BHLz/ec//3m+\n8Y1vcOHSRYZ3HmNwz0nyltShF8jkBGs8yfiFNkhlMVSNeXXz+MxnPuODFLOa1axmNat3hia7rk2W\nB/h5IM214j69Yy3LoqmpiWg0yic+8Qmqqqr4+te/zvj4OGVlZdxzzz3U1NRMiapNJBKsWrWKnp4e\njhw5QltbG1/+8pf50pe+NAVEu55WrlxJKBSiubmZtrY26uomNnJ3dnby3e9+l0wmw+rVq1m7di2a\npk25Vw/gqq2tpaamhiNHjrBv3z7+8R//kb/4i7/gr/7qr/jXf/1XGhsb6evr8x3xJrvBGYaBZVkE\nAgHOnDnDU089RU1NDbquM2/ePC5evIimaYTDYcLhMKqqEg6HKSoqYsOGDWzYsIHDhw/z85//nNLS\nUhKJBJqmYds2iUQC13UJBALXdDcsKSkhFAqxYcMGwuEwJ0+e5MKFC+Tn5/OlL32JH/zgBxw6dIju\n7u4p8cEeTDc57tY0TXp7e3Ech/LycrZt28aRI0c4fPiw7+p4NeQH+HGpM63tefUkhCAWi9HV1eWD\npZ7bX11dHTU1NdMgOc/hLxgMkkgkWLBgAZqm+XOmjo4Ovv71r9Pa2ooQgmAw6MNnk90QvTJ6be+B\nfvX19QSDQZYtW8bp06d9h3LvOM/xzXM/tG2bTCZDOp2mr6+PHTt2kEqlZtz0UldXh6IoDA8Pk81m\nCQTkprRHH32Uf/7nf0YIMaW/3ojKysrIy8sjlUpx9uxZhoaGyGQyPPXUU3zxi1+ko6ODw4cPU1NT\nQ3V1NcAUyC+ZTNLS0kJrayumabJ48WI2bNiAZVls2rSJjRs3kkwmyWazvpuh9+zHYjEAhoeHicfj\nFBYWUl1dPaX/eHXruUt69b1s2TLe//7388wzz3Ds2DHOnDnjuyhOdoGMx+NEo1G2bNmCoih+OSZH\nLUejUcbHx33nRO/e8vPzufPOOzl48CCvvfYa73nPe/xYcCHkOwWvfwkhWLp0KRcvXiSbzXLo0CEe\ne+yxmzKo2rFjBwDr169/S2Jjb5Umw3ne993NKpmU8+F32pxo8eLFfO5zn+Mb3/gGTU1NNDU1UVVV\nxbp164hGo5imSXt7O0eOHPG/9zZu3Mjjjz8+u0Y0qzfULQX85syZQ2lpqW+rads23/ve9/jiF784\n7bM//OEP/X8LIVixYsUUO95rqampaYrrX21t7VtU+lnNala/qWaCPzRdQ1UVLCsHWV0PQnJknJDr\nZEDLg6rNoIQQw0ewk924QoeyddLpbxJAI4wC3ECRdAWzh0j0H2H/gdcBeOKJJ274j+OmTZt4/dBh\nEkMXCJQt9yGd7FCTD/dRvVUCeq6Dv1DguhNGcEKBSK0sf+9OCY0Fy6+KSB2DxBig5OKCFAxDIxAK\nEQ7JAVIimSI7fIbUwGnfdczQwEbJRQiBKxREoASMIhnVG6oA18HNDEi4KtkJ0dpcDLKCa2fIZrNo\nmkYgMH2nnBlrQ7FTVNfOZfny5TiOIycVGYtQ7X1YY21kR5px+/fKONXo/BzoJPAjTwUShkp2SQjL\nMSFcDXnz5WfGW6V7oRq4ChgTeHGp0+G+SfLAKistz28mJFwmNNDzcrHHVUyNlFVA1UCbPGB0cq5z\nlgTtxi5J10OBBBiLb5fRsfHWnCPd6avcFl0J6blI6LBsI6gKqEEouRP6dsvjtQiU3CH7gAdiCiEh\nv/wo5C3ATfXC0NFcnR2aFj1LqBJGTuNkx/0fqaFignPuJtH8Mq4CaG9ix4yYdJwVz/1MQPkG2Yzj\nrRKi0yKyrcPVoATANXNw4iUJNHpR0aV3AhYiVIFRUIOmqWQycgeUz+15UKdry7p1HQIVqwkXFmJZ\nVm7XXRbH8aKDczHeueoQqgHROtyRkxLAywGk0qRQAcNAWCauayHUAOQvwhWK/O5RNSZWn8UEaDkt\nglmR/WjkFMQvS1c/7zhFm6F/KnIxTQhUJ4UZa8conHfDzXD1c/e7rGtB0Deq3xenwpuVrus88cQT\nLF68mB07dtDd00tm6BSp/pP+97eqCiK6SnXtXB544AG2bt06O3Gb1axmNat3oOQLSkE8OcklGjAC\nAbJmlkwWNNXlWuZPibSC48g/H6uXpPnQAzH+bVc+l9oNCqIWn//4MNVl1pRjBBAOgZtyqS7N8p8e\niVG6F7a/Dt/5zrf5H//jb6elLVxLW7Zs4eiR19l9LM22TZZn6MXRRoW2bkFpgcVnPzKMwMFyJPh3\nrXtZNDfLf/m/h/if/1rKS69HefCuOB97OMb73zXG/3ymlNOXgziuXBQpKipi/vz53Hnnndi2zYUL\nF2hubmZwcJDTzS6GpqGoAqGpLF5cxeXLlzG0DJvXJrl9cZrKYpOCPMcH7ObPyXKl2+Cn2wt4fNsY\nigLREIwnXRk/lDWnOYck0/Cvv9ZABLnvvvtQVZU9e/aQSsZYWGtx53JJJF7uFLywR+PfX81n97EI\n961NcM+qJIri4jqC/lGF/acMDp3ViacNLDfC/OX3Ey6Yg21lUTVDOkpoAVx3wp1COu3Z0n1P1a7h\nRAeOI4/p72ySEOCUeU+MeKyXruYjFFcuZO6i9YSj0slaCAnbKaqeW2SyaD33KnlFVYwOXGGw+wLZ\ndBxND1BWs5x5SzfhODajA60MdDURH+1lbKhjSs8rqVqMmUkwPtpLx4V9lFTUoyiCynm309t+GkWx\niBZUsGTtNoxgHraVBVyy2RQCKCitpbB8PomxAS6d+BVjw11cOP4iy+547zS3PFUPgAuWOQFFRgvK\niBZWMdLfiqLO1BFzTvDuVdPDnBRFkc58qobjWMQG21EUlWhBJaXVSxnoaqK37ST9HWeJFlURjpag\nKKqEEweuSMDRyuLYFvkldWiagevYfpyZqhZh2zbZbIZs1szFLstyKYpCrL+DdHKUcF4hVTVL5KKT\naeLYNqZp5VxCZByyq2qomo7juCTHBn3gTzggXIFHzLqOjR4MYAuBoyoE51chVAXXdnAtR4J5k6ZE\nwtBlTK+n3O8CdTLu0Boax0lnEYqMs1YC+hS4D0Boqu+SEtR0Bl45jFG8FeNqG9AZZMbi9L38OgFV\nY8uWLddd7P9dUGVlJWpTI8m2HsI1N+8glbzSgyrElMjCd4ry8vL4whe+wOuvv86uXbu43HyZ9Lkr\npPzFWQgqKrXzFrBlyxY2btz4Gzl/zGpWs5rVrH43FQ6HEUJMcaGDCbesTCZDMpkkGo1e872YaZpY\nloWu63z84x9n+fLlfOUrX2F8fJy5c+eybdu2aeCXN/4aHx9nzpw51NbW8uKLL9LX18d3vvMd/vIv\n//KGQJRAIMDGjRvZsWMHr7zyCn/8x3/s/+6Xv/wlqVSKJUuWsHnzZh8Kmgly8f573bp1JJNJTpw4\nwb//+7/z+c9/nj//8z+nra2N//7f/zujo6PynbKqUlFRQUNDAytWrKC/v5/z58/T1tZGX18fQ0ND\nflxrJBKhurqaK1euUFBQwKOPPkpVVRW1tbUYhpxrrF+/nl//+tcUFxczOjrK2NiYDwUmEgk/5vrq\ncofDYUpLSwmHw37qyK5duwB4+OGHKSsr47Of/Syf/exn6ejooLOzk2AwSHFxsX9tz7VubGyMZDKJ\npmmUlJTw8MMPYxgGZWVlGIaBqqpTjgH8+lQU5brjQs+R79y5czO6Y/X393P69Gnmz5/P0qVL/f7i\nufp5INfp06f9+zt48CCNjY0kEgmEEBQXFxMMBrFtm3Q6zf/P3psHx3Eddv6fvubGfQMESZAASIqg\nKIriTUHiJVO2btmyZUeK15YlOdnNL15XqjabVJJ1UnHlcm3FiWOpXE4q9sbxFVkbybJEkaJISRRJ\nQSTBEwdBgMR9zX319PH74003BrxE2Za9UeZbhQI5mOl+/fp1z3v9Pu/7TSaTbhxyYbsLBAKk02ks\ny2JiYoLFixejaRrl5eWMjIy4gJrf78cwDCzLmpdu48CMzn7efPNNAoHAFYmHhY5z6XTa7Ud1dnby\nL//yLy689n7luPOpqoqu67z22mtUV1eze/duXn75ZYaGhujt7eX8+fNUV1e7DpGZTIbp6WkkSSKb\nzSLLMsuXL3chX03TCAaDVFZWuu6KhS6IDgza09ODbdvcfvvtVFRUuPHMlmWh67r727Zt120ymUyS\nzWZdV0rDMPLjLt0F/BwQUVEU1q1b596XnKjhQsjP7/eTSCTmRf0C3HLLLRw6dIiLFy+6znWKohAM\nBq+AR53r0+v1kslk+Nu//Vu+/OUv39A5efvtt9m7dy8Au3btet/n8Fcpv99PaWmp65j6fp0KAbq7\nuwE+lGOitrY2/uzP/oz9+/ezf/9+xsbGeP7556943+rVq9mxYwerVq36uSDJov7z6QN/UnL33Xfz\nne98x4Xwvvvd71JSUsLnP/95gsEgqVSKr33taxw5csR9jyRJfPSjH33PbSeTSYaGhua99n6J9KKK\nKuqD09XgDwkxMDLNNLalI0mXg10FSo/nYRsZqm/DVgNIsfPYqVFw4DrvVVb3SjKS7ME2dUxvPYEl\nu0kN/IxDhw7T3t5+w52ijo4OGurrGBgccSEd27bJTp8Vz7lr1ov9m3reFa8gdtglmPK/veUFUZ/n\nBABV2ipiYqcOQ7wfCYPy8jIWLVpEXV0dzc3NXLx4kcnJScLhsGvVHQgEqKqqoq6ujs2bN/ONb3yD\nyekoVK/HDi6Y262pi4f0JUsFkDZ9FDyloswFdZTNZvF4PfOmjMz0LJmRQwS9Krt27UKWZbq7uxkb\nn8BSAnjKF+EpE0C1PtuHPXUYwqfEMTlucKYOeli4Bxqi7AQaBeDmAH1yQYdW8RbUnQX5VQu2qV8b\n8rMMUe+52JVRrrmogBq1Eihtz8erFhylc74kBbJhGP6/c8Cg486mBKGuE3xV4rWSNnFMiYE8zJbL\nw4Qh8NYKCCwXEyBYabvYhpW3llb8UL9dnAM3ftgucA3Ml8tfJyJqJw9iJy4i+WpFnK6jfJ1d7pqm\nlS1ynQola/7gcp6uNKzISxLgGwjwbvilvOOeCmoIqawdOzEowMdIt4hGdmKZQRyHbYjXfPVgW0iS\nGBSKhzliMtvIGfnBmOlOrhnxETCSyFqAQOViJAk0TUXTVGw74K7ssq08rOeApLF+AcmaWWxZw3GM\nlCSQJXGOVUVGNyWw826FDnyaj76eV/mOi59bT/nX/bUC6MzFxR8kWZThchdS28I200gSVJRXYBgW\nyZG3ULylKP73dhi+2nX3/7KuBUHfiP4jORX+PJJl2QX3Tp8+zYEDB5icnCSdTuP3+6mtraWzs5OV\nK1f+P3+eiyqqqKKK+vnV3NxMf283XWdlVrTMxfRqqorP5xMTA2nw+2w82pVdtHfO+sgZEqpi8+Ad\nUeIpmTe7AyDB//ep8BVwnyMJ8HshZ4CRy/HILp2psMSx3ih79+7lU5/61A2Vf8WKFTQ0NjM2nOYf\nn7f4/AM5ZBn2HhELJT62NUFp0CKrC1PrbE5GU/Ng0Vw6rPv/FYt1bm7NcKzXzyuHg+zakCSjS4xO\ni/5te3s7TzzxBJqmsWfPHn70ox9i5lL5/plwO7QsGVtSWbb8Jnbs2EEmk+Hb3/omLQ0ZHvuYjq4b\noltqi+9XRVH41F05/ub/eDlwPIhHg0c/EkORweeFVMYmq8+PBosnhUPhyJSf+qbF3HXXXUB+MsvO\nsGPdXL1/7n4DTYXnXlMZnZb58b4y/u/B0oKkWxnT1siZHoJlDSxp20iorA5Z9aBpfmRFxTR09EwC\nX7Dc3a5p6O5x5/Q0Ho9/Xl/LxsY0dFLxaWzbQpJlqurbqFlwE76AcCTJpmNMXjpNeHKAmbFeIlOD\nLLv1XsqqF7pbyelJ4UJn2wz3HULNxwCbOV3AbeX1tKy4EyRQJIm65lXUL7qFdGKWbCaOZeZQVC/+\nYDkeXwjLNDlx8DtkUhHCUxeorFvKzFgfsqwQKKlmxfqHUFQPlimcnC9v87ZlEghVsmztfZx75ydE\npgYZHzpBY8v8hA/LyIkyqXPjSdu2qW5azuzkBQz9SgeBOVfFq8uJQzZzWSzLJDx1gVh4xP27L1CO\nZYk44sjUINHpIVxo0LLE8UgCbotMXcDQNxAIleL3B9z+nqIo+P0BhDmBAxtKpFJJxoe6sW2bBUvW\nuECjR/OABj6fmNxOp1MiqszQyabjnD+5h9isKKPPX0ZlfSv+YDma5sHIpZkcPoueSQAy6AaWaSJr\nKqapY+cMJEUSOeE2YNkC7nPHi3MDR0lRkBQF2zDAtFD8/ivAPnH+bMyEADZrampYsWIFR9/tYuSH\ne6n7yCb8C+uuOmFh2zaZkSnGXzqEljW5uWMVH/vYx659svKKRqNcuHCBVCqFoiiUlJTQ1tb2c01m\n/jy68847ef3A68ROD1C5seOKCNrrybYsoifP45VV7rjjjg+wlL8+qarK1q1b2bp1KxcuXODEiRMk\nEmIxYTAYZMWKFSxbtqw4iVVUUUUV9SGW4yQ3OjpKJBKZ59Tq8/lcECeRSMxz83LkxGtalkVDQwO3\n3HILXV1dTE5OUlpayj333HPN733HcS2VSiFJEvfddx/f/e536e3tfV8gyvbt29mzZw8HDx6ko6OD\nDRs2EIlE6OrqwrZtOjs7XUc3B1zy+XxXuI452rRpEydPnuTs2bOcO3eOhoYGhoeH3UjQ3bt388lP\nfpKenh727t3Lt7/9bRdsKgTfgsEgt956Kx/72Md47rnnmJ2d5f7772fbtm0uOOX0h6urq3nggQf4\ngFM6KwAAIABJREFU0Y9+xOLFixkcHCQWi6FpGpqmufG2hbB9MBikpaWFiooKduzYwcKFC11QTlVV\n11XO4/HwN3/zN/zBH/wB/f39pNNpxsbGAFx3M0VRXGfB1tZWNm/e7EYIV1ZWCgfxPJBWWFdOxKpl\nWaRSKRcYdeQ43jlglyzLNDY2UlVVhcfjwbIs4vE4o6OjJJNJzp07x8TEBFu2bHGdx5xtyLJMKpXi\nmWeecZ3EHMe9kpISSktLyeVyKIpCRUUF5eXlbpwriGfBmqa5znDj4+NMTk6yYMECFEVxEwwVRcHr\n9V4BvRa2eWebDmy3d+9elixZcoXTsQPPFY5lvV4vLS0t9PT0uP2uQhUaJl2tD2ZZYjyfTqfJZDLs\n2bPHhdds22bBggVEIhHi8bh7ngs/W+hieerUKRobG92oaEdOjPHlGh8fp7+/H1VV2bZtm1uvTrt0\nnqE4cKBhGFy6dIlnn32WaDSKpmlUVlbS0tLiulmGw2H6+vrcGN1MJoPH43HbnAMYO7o82rlQjpNe\nOp3Oj+v8qKp6RT3quu7GbG/ZsoWBgQHOnTvHX/7lX/LUU09RXX11o4RcLsfevXv5/ve/D8AnP/nJ\nGzK1Gh4eZmJignQ6jc/no7a2dp6j6QcpWZbp7OzkhRdeYO/eve8b8Lt48SL9/f34/X7WrVv3AZXy\n16uKigoefPBB7r33Xo4dO8bQ0BCpVMqN8V63bt0NL8ItqihHHzjg9+ijj/K9730P0zTdL45/+Id/\n4Nlnn3VXCzhfQo6CweAN5UsfPHjQ7SCAgPtuxPWvqKKK+tXoWvCHx+vFMPIrKMwskuy50snPtoSL\nmmWAFsL21SDZNlJiYD5cdy3JCliSWOXjL8XXtIn08EH27Nlzw05JDqDxz//8XRfSsYw0VjYOSkA4\nBwKSrGBbRoErWKEKOlGBpnzUZwKSF12HPbLTyJLE/fffx+OPP86+ffvYs2cPbx8+QjZnYpr2PNen\nnCEGcG1tbUiSRDqTRfaW4qttJ5cz5oFTsqygNdxMjrhwHRzdO+d6WFBHRs7Iw1QWuegQmZFD+DWb\nTZs2uquzDhw4QDZn4qlaJs6lBIGFd6AEasiMHsHWIyKyd/bE/NkTSc7H2i6B0NI83JevFy0kgLn0\nhIg0Ljz/jiwjDwOq81+zLchMAZZwX6u4WbjbyZqA8fKRt+TiMNMl3luzMQ912QIgBRHPq4eRbBPb\nUyGillPDovxVawTcZeW/pxR1LtJWFJQCu0ZQfXmIs1fAgADRXlGmqjVi2862kPLHJBfMfJqiHXlK\noWI1zByF6DkRQe3UaWoUzCxWJkzs3L8hySqytxRPZRt4yiAXxU6NI/nnu6JJzMUvXal8vHFKxOTa\nSAJ6dNpvNgLYSEoApaoFOzmGlQ3jxBfbiMksty7Cx5B9ZfhK6+bBoxJz4J4jMz1LbPpdsA08VVeu\nUJEkCATFA55sNiuuaTMD0+/kywtopRBahOSrxRcoQVPBSIyhz/Rg6TFRPjsHCDDPNnVxHpR83Vv5\neqeg3WEB+fuSpM21u6u69gGWKaDlzCSKLLF27VokSeKtQ4dJDvwMX9NmtLKFV4XgrnfdXU2WZXHq\n1CkXGstkMu7grbOzk46Ojl8JNHY1CPpG9R/JqfAXkSzLrFq16objEIsqqqiiivpwadu2bby271Xe\nPJ7iEzsNCk2znUmfbDZLKgOZrI1XA00T3ZNcDva/6yebk1i1NMPErMYrh0OkszK3tGdoqslhWqI7\nUyirYP2IptjkTAvD0LnndoVj5zIcPHCAhx566ArHuqtJkiSefPJJvvrnf86b3dOYFuzaYNB3UcLn\nMdnUIZwGPBqks8LBzzQFL+TI6S3LsijXjnVJjvf6eLM7yPbbkhw6GSCZkWlZ0spf/dVf8dJLL/GD\n7/8rWGkgy+o2g5uWWPi9Yh+nz8t092foOfsuPT1nCQZL8tCdRcAfcCdjnMkSCYnSEvitT+T4+x9o\nvHo0yLkhDzvWpdjYkUaSxAp/wzSIxlX2d6ns71KIpwNU1izgy1/+sutsceniEB41x203WfPq6bGP\nGXQstXjm3zRmIhIZXcobqEl4fSFqFiynpvEmfKEqMamjepHzkyRVDe1MjZxh4tJJFi2fi16yLAMJ\nCUmSRV8xl8XjcY7NQtfT5LJpZifOA9Cx6VOUVzc7Zw6AQEk1FbVLyKbjDJ59ndnxPs698zw3bXiY\nkooGTENndqwfSZLxBcrIpKKUVDRSWtXMSP/b2JZF/aLVaF6/GyMr5cft/lAl/lBFwf7EmZYVlbqF\nNzN07gDjQycoq2pmauQMsqKxdNVOZFnBzi8qUhQNWVbyUbD5ODXTwDJz+AKlLFp+O/0nXmZs8Dj1\ni29BkoRTdiI6ydToOXJ6mt7u17hw7m08vgA1jW2UVTYjQd5JcIzSisbCFo27yKqgngBMMyfAPtsm\nPDUI2FTUthIqr8M0dCJTQ2TTIjbKFyynvKqJVGKWTCqKnkkLNz5bIlQSwLY95IwsA6f2cOvtn7xO\nv1zKD/FsBs4cJDpzEUXVaFx881XfLSZgS8nlDNKpOGeOvUgqNoXHF6S5dSNl1YtQNQ/l5WXusbWt\nuoOJ4V76Tx0gGZsk8fYZ/C0NyF4PlmFiZXLIXg3bsvLRvHOLz2zTFsMgW8Botpl3lPR5rg73GSaW\nnkMfmUZGYt26dTz++OP83d/9HcdOHGf8+QOolSWU3dyKf2E9ikfDyhmkLk0Q7e4nNx3Bp2jc3LGK\n//bf/ts1XVps26anp4d9+/bxzjvvoBs5cc+TxJi3oqyMzs5Otm3bds3Js1+WlixZQsviFs709xE7\nfYHy1W03/NlE/zBWPEVdQ9N/irFCS0sLLS0tv+5iFFVUUUUV9SuWLMvceeed/PCHP2RsbGweoOS4\nzyWTSRfyc+AnB5qJRCJufOP27dt55513eO6558jlcnR0dFwTYHHgNkVRXKeu0tJSVq1axTvvvPO+\nQJSGhgYeeeQRfvCDH/DMM8+QTqeJxWLous7SpUspLRUuxQ60pev6FWOtQpDL5/Nx00030d3dzRtv\nvMHDDz/Ma6+9Ri6X49FHH+WBBx7gG9/4Bl1dXWQyGRdeK4SNHJe0119/ne7ubhKJBB6Ph+3btxMK\nha6ACyVJ4oEHHiAWi/HKK68gyzKRSITp6WnhaJ7Lkc1m8Xq9rmufA7Ft3LiRxx57DIDe3l5AuE2V\nlJS45fF4PPzFX/wFP/jBD3jhhRfc+GVn/r62tpaVK1fS3t6O3+93o3NlWaa5uZlQKEQ4HCabzeL3\n+12oz4H2ALduHdjLgbMctzyfz8fGjRvdPqRT36FQiPr6emKxGD09PYTDYd544w3uuOMOVFVlYmIC\nXdcpLS11YdANGzZQVVXF9773Pde9T1VVN/bX2b7X670CAnPagtfrJZfLMT097bpVOu59DsDnxPoW\nbtM5diciVpZlMpkMx44dc6E327Z59913icViyLLMH/3RH+Hz+aipqWHz5s10dHRw5swZenp63jMC\nu1COycL4+DgzMzOoqsrmzZvx+XzEYjFOnTrluky2trZSXV3NpUuXiEQibtyzaZpUVFSQSCQYGRnh\n5MmTbN269T33nclkeOWVV0in09x22200Nzdf8R5JklwwVNd1+vr6+PrXv046naa+vp5bb73VhTuD\nwaDbBu644w6OHz/O0aNHSaVSfOMb3+BLX/qSGwntAMbpdHqeK6MT7eucHwf8cyKCL4eLLcsim82S\nTqc5d+4ciiKMDR5++GH++q//mr6+Pn7v936Pm2++me3bt7NgwQI0TSMajXL48GFef/114nGR2vXA\nAw+we/fua9aXruscOXKEffv2MTAwcMXfm5ub2bFjB5s2bfrAXbK3bdvGiy++yDvvvMPk5OQNpzXZ\nts1Pf/pTgHnQ7YdVqqqybt26Dy3IWNSvVsqf/Mmf/MkHuYOKigri8TjHjh1zOxLOF5TTcYO5Ly5J\nkvjiF794Qzf8f/iHf6Cvr8/9/LZt29i5c+cvrewzMzNuZ+yDfihUVFEfRtXU1PD224eIhGeRvOUo\nPuFIICAfTXSQTFM84LfN+av3ExeFO5dtYgcXIqVGIHIa9DC2VgKVq0UHzXXMM/OQXU5AOI6DWf6+\n4iupJjvbRzYVo729jbq6G4tPaWlpYWxslPHRYVIz5zFTs1hGGspvEuAX5B3B8nCQJOfLdNmkgQSQ\nf19mQrw30ASpEeTUEG2tLfzu7/4uzz77LC+++BJTM1F024da1YG37lY8NStRy5diqyEyiQiR2RnO\nnD7FqVMniSZSaNUdeEsb8Xg0dwDh9XrxeDQURUErbcbSY1iZWRETHL8g6lxWsS0dO5fATgyRvnQQ\nK3qegFdm8+aNPPnkk+4A4IUXXmBichZv3a3IWkAcliShBmvx1K4GO4eVCYOlCwc5Jyq3ZpOA77yV\noHjy9ePE02qQHBSRsGVtc/XlOOjJGk78sZQH/Fw4y7YEuGfloP5OARCqAQEQqgERlVvaBp5yAfPp\nYeGuF1ggtj19WHy2cg3os4CJVLtJRPImBoTzX/W6vKubMVeeOcs65sF9ID6TyB+Pr0b8jvWKbVbc\nnAf58nGwcr4uRJ7rXNuR1DwUGRAgqB4FbzVSagym3sKOD7jvt00dy0hjZiLkwgOifZlZMNNIZcvm\nOxZe/ryj0E7F0sXPTJeIEi5thcpboXw5lC4R5TcSSGYKshG00gUE2+5DUlTM1BSKDGWlIZYta0NT\nFXJ6BisxjOYvR/GWXsOlwSIXHSR9cT+SKSaH/Qs7kdUrO/ISoKoKHq8XRVEwx9+G5Ig4HzWboGot\neMpRvSFCpRXIWgA1WIenejkGQaz0FNg5JCMl3DNhzsXP+cEW16ikiOvTZi5y2kgKJ0o1AKGWAkjP\nFvceUwfLRMJCmn2XkF/hNz7zGXbv3s3U5AQTYyNkwxfIzvblo9YUbDOHpSfQw/3Xve4K5cRkP/PM\nM7z8yh7OD15kYnKW2XCUyekwF4eHOfz22xw+/Da2bdPS0vKBrtRytn3m9CnSkUuoJU3Imv89PiWg\nzvTF/QS8Mg899CCtra0fWBmLKurXpQ/TOOLDdCxFFfWrVllZGSdPnWZycgq/16R9YcGkDJK7wt8y\nbSxbAHLZnERWl9jXFeSdM350Q0KR4Y0TAQbHNTyqzYN3xqgsNcnm8kmckvhsKiMguGxO/FgW2EhY\nlkVdtYfjvTJTYWhoXHDVB+ZXU0VFBS1LltB17BRDIzn2dwlnwI2r0mzoyLhdTMMUZTFMCU21ubwL\n4sx7VJeZvH4sSCSuUFNh8aN9ZUhKGU984UneffddfvTDfwUzxl2b0jz9cI4d601am20WNdi0Ntts\nutliy2ox9jt/yWBmNoosG/z2IzkURdSrAOOkebG2DdU2yxbZnOxXmAornOjzsvdogNMDPt4552Pv\nUS8/2uul52IA3SxhadtN/N7v/R5VVWIRUjgc5tU9r1ARynD3FpPL1VBts3uzyZImi5whEYmDYSr4\nghW0r7kbzRsUC7AULe86J8YFHn+IyUunSCdmaWhZgyTJWJaBbRpIkozq8WGZYnGTiJ0Vkba2ZTE1\nepbI1CDlNYtoXLK2IJZ2fuWrmpeq+jaymTjJ6ASRqUHqF61mauQMs+PnCZXXU924jPjsCL5AGVUN\nrUwMdeMLlNG6ejeSLLuOe4pyuUOKMx6a268/WMn44HEyyTCyohGZGqS0oon6xavFkEpR0TwBAfe5\n/er8OZNkZEXDtkyis8OEpy6QSUWYHu1h4mI3F3veYvj8EWzLRFE0LMskp6fJpGLMTlxgcvg0pikW\nvUmSRFV927ySFpbb6cuaRg7TEJM18fAoE5dO4guUc9P6ByiraqaybikNLWsIldeTy6ZIJ8Nk03EW\nt69H1XzomTihUICnn36KL3/5y+zatYsTJ44Tnp1kYrSfkrIavP6Sq/bL08koPcdfZWzwBEYuS9uq\nbdQ0LL3ifYXlVhSFnuOvEJ2+iC9YzqpNn6S8ZjF2/ph9vrn+uCTJlJTVEKpcSDI2RTYeJTs2RWDV\nEnHmLEu49pl5B0JFEfCeuHkgaYq4v0zMkjl3Ednvxb9iEbaZX2xt2timiZXNYRsCAky8eZKg4uGz\nn/0stbW1rF+/HlVRmRqfQI8mSA6OETnRS6Srh+jxPjKDY2gZk+qyCj569938l//yX645CZVMJvnf\n//t/86N/+zEDF4dIGznUxiqUugrk8iC6ZRILR+jt7WXfq68iyzLt7e0f2JjImWQ89m4XsaFRfA3V\naGXvvfA8MzHLxItv4pNUHnrwQdrabhwMLKqo/0j6MI0jPkzHUlRRv2rV1dWxd+9eotEo1dXV8+A3\nB3iCuThWBzbLZDIMDAy4blkjIyMcPXqUmZkZ/H4/W7ZscYEaZ27Zgb4cly8notNRdXU1x44dY3Jy\nkm3btt0w+NLa2ophGPT29nL8+HFOnjyJaZps3brVvSc40J0DBV0O/xSWw+v1curUKUzTJBwOc/To\nUUpLS3n66af5+te/zrFjx0gkEvMiTC+XE12aTCbRdZ2VK1e6zuPOXLzz47y2evVqNE1zo3TLysqo\nrKyksrKSmpoaGhoaXAe88vJydu/ezec//3nXwe3MmTOcOnWKlStXcvPN8xelSJJER0cHO3fupLq6\nmmg0SjKZRJZlVqxYIfqEeXDT7/e78KXjeOe4GIZCIfdcWpaFx+PB4/G4caoejyfvbJ3Gtm1isRiW\nZbF48WLKysrcuN3Ly+YAcDMzMyQSCTKZDI2NjXR3d5NMJlm1apXr+Ldjxw7Onj3LyMgIwWCQ6upq\nTNN0AT8H+LqaE15hfadSKbesjmOXUz7H9bFwMZBzvmRZRlEULMua51Q3MDDA4cOHOXjwIKdPn3bb\nfSwWY2ZmhtHRUY4ePcrY2JgbGd3U1ER9ff28fVwOi4LoZztmTG+++SYjIyNs3LiR3bt3097ezurV\nq9myZQvl5eVuTLRt29xzzz1Eo1FyuRxVVVX8+Z//OY8//rhw8j56lOHhYaLRKLW1tVe93mzbZnR0\nlBdeeIHx8XEsy+IrX/nKdWEvx4nvr//6r4nH47S2tnLvvfdSVlbmuiwW7kvTNJqamlwgcXR0FNM0\nWbNmjesiWngf8fl84jlNHu5znCXfeOMN+vr6aGtro62tzYVQDcMgk8mQSqUwDIP+/n727dtHdXU1\njz32GOXl5WzYsIFoNMrY2BhjY2O8/fbbvPLKK/z0pz9l37599Pb2ks1maW5u5rHHHmPHjh3XHMMM\nDAzwp3/6pxw6dIhwOIzf72flypW0tLRQU1NDIpFgenqa48ePs3//fpYsWfKB9l0CgQCjo6MMDw9z\n8uRJ1q9ff0P31pdeeomXX34ZRVH4whe+UDTwKupDqw9iHPGBA34gLIfPnDnD4ODgFR2Lwg4GCEr3\nK1/5yns+fEkkEvzxH/+xS1NLkuR+afyyVBy4FVXUL6brwR+SBJpHdGht23J/sEzITMPUITAzwhvM\nTArAxkhiSzKULROwmGXOwXyOo5u7In9uZb7TEVNk4eolYbF+/fobPoY1a9a4kE4mMQNIULkKVOfB\nuT23b9sS9hRSgSvbvA3KED+PJMko/hqk6SOE/BoPP/Qge/bs4a1Dh0nlJLwLtuJfsBEtVI+sBZBV\n3zxoSfKWk45cIhGLYBo5vA3rUDzB6xyHjFa2CFn1YekxyCUFaBgfQIr1Y8cHkPUp/JrNouZGHnro\nQR555JF5kNFLL73EbDiKp2blFRCWJElopc14625BLWkUE4l6TEyqBZuEIx3SfEBOkoSDX3IQjJQA\n8TxleeDKce3z5OtWxE7ZhfBmahSSQwKqq1xTAA7OK5jYpr9BvFcPCwDQTOeBraAA72wLMhNIuZhw\n+jPTULZCRApLzAFg8wA/Cv4tzUF6Vh7itK05qLBsuWizAIpvDkwt3I4k5ZutLdwHo2cE3GdlIT4g\nXAUtHZQQlC9HqlojyljSIgC8XFy81zbFteOtFNHQ8/ZyFeDPysd/pUbFudBKoe524UYoqyB7wV8j\nHAm9ApY001PoUychPU0o4OOOO27nK1/5Cjt37uSuu+5ienrqfUFtwaCfrAFaRasLj161HQNGuIfc\nzFmQPUiNO5EC9eJSs0wURZ43eJAkCdlbji5XQGoM9AiS6s+7GxZcty50qeWBTtHmXOgyMSjOqbda\ntAn3viPAXgdA1HJTyIlBFjU38ulPfxpVVbntttsoLS0hPDtNNhXDSIyRnekhO30WfbYHIz6CZKUJ\nBrzs2LGdz33uc1eNlsjlcnzzm998XwDw+PgYa9ascR/CfBC6HIKWPCXINwB1+jWbzZs38sgjj3yg\nEGJRRf269GEaR3yYjqWoon4dKi0t5fDhLs6eN2iqsWiqnT9BoyoqHq8HVVVdCO7UeS/f/VkZibQY\nw8gSlJWYpDMyAZ/NE/dGUGSwLAnTBD0noDtn2Ykiic84smwbXddJZ1XODXmorW2io6Pjho+htraW\n1atXc3F4gvHxMKZp0LE0w6J6nYwOGR0sWwBrti1hmBKqIiG6IFe6Mx865WcmqnJ6wA9KGXd95KPU\n19fzj9/+Flgxnno4y0e3mASvsW4g6Ieb2yyqynN098lksjIrl1rUV10nfxWoLrfZucGkqdYmmpCZ\nnJWZmFUZmdSIJP14/RWs37CV3/zNz/Lggw+6boAgYnj2vPIyPi3DRzZfCfiBGAY21dpsXWOyY73J\nu2dl4kkTr0clUNYASCiaVzjR5Z+FebwhIpMXyKSiZJNRKuuXinhebBTVk3fMs8VYGRvTEA4eqcQ0\nF06/hgQ0t23EFyhDkuW8q/aVkiSJ8prFhCcGyKQiaF4/YwNdmKZOc9tGyqsWMXHpJMnYFInYJGYu\nQ0XdUmoX3AQgAD+kgkjcy13w5p7vyYpKdHqIbDqOnklg5DI0t2/CFyxHkmU0z7UXhOjZJEPn3uB8\n9x4i04NIeVdAy9DJZVMYuTS2aeANlLFoeSdLOrZTv/BmyioXkMtlyaQi2JYAQLPpGDWNy+fvTyoE\nEsG2TBfuk2WVwbMH0NMxGltupaS8EWwLyxLAoC9QTnXjMkxDJzY7wsTwOXLZGMGAj6efforbb7/d\nnRBas2YNZ8+cJjIzyciFbiZH+4SjhaGTScWJzo7Sf/oAvSdeJZucAVsAi40tqykpu77jQSI2Tc/x\nV5EVjVWbPok/WO62DVmWrzoZJssKwbImItOD6PEYSmUJntoKbNNy0wJkr4akqkiy5L4uyeIGlDx8\nFmM2hr+jBa2+ai7O17LAsvPjIRVzaJLcwBhtS5by0EMPuROUy5cv56677mJB0wIiM7OkYgmyqTR2\nTkT+ypKIjlu+fDlNTU1XdRhNJBJ89atf5WxfL1lVonRNO7Uf2Uj5Le2E2poJtS3MuwPWkcvlSEzN\n0Heuh1QqxapVVzrF/7LU1NREKpliaGCA8LlBZI+Gp6oM6XJ7VcA2TeLnBpl46S28tsztm7fwiU98\nojgeKupDqw/TOOLDdCxFFfWrlt/vZ3p6mkuXLjExMUFVVdW8Z5+SJBY+eb3e/FyRcJ8bGRkhHA5j\nmqYbqVldXU0mk6GlpcVNBHEgKCcu1YF0Lge9TNN0QcFEIsGaNWtu+HqWJImVK1dSVVXFhQsXmJmZ\nQZIkVqxYgaqqLlDolL0Q8rt8DhzmHNji8ThDQ0Nomsbv/M7vsG/fPg4dOuTGe96InOjRZDLJXXfd\ndV2ndkmSWLZsGXfeeSfl5eWuA5zH43HjlJuamvjoRz/Kk08+yaZNm+YBaAMDA5w8eZKWlhZWr159\n1X14vV7a2trYuXMny5cv5/Dhw8zOztLa2uqCU16vdx4fUFFRwbFjx0in02iaMKzQdR3AhQEdwM+B\nu2zbdh38PB4PS5YsAXDBwavJidcdHR0lHo8TCoXo7e1FVVW2bNmCJElcunSJnp4eRkdHMQyDyspK\n/H6/286A6ybnOPtWVZVoNIplWa7LoDNv4cB9l3/G+bdhGG5ErtOWLMtyo3EzmQwg3CBbWlpobGyk\nsrISTdNIJpPE43HXBTAej7Nu3brrljmTybhxxLqu8/zzz2OaJg8//DB+v9+NrZUkiaamJlavXs3A\nwABDQ0McOXKEbDZLbW0tv//7v09TUxMgwN6FCxdy4sQJxsfHOXbsGBMTE8iyTDqdJhKJcOHCBV59\n9VW6urrQdZ1cLkcwGOT++++/ZvS2oz179tDV1UVdXR333nuvMIXIQ5iqql5xHThQZXV1Nf39/Vy6\ndImdO3cSCARcZz5VVQkGg+51m8vlME3Tded89tln0XWdT33qU5SWlrrwZWHb1DSN5557jqmpKXbv\n3u3ep/x+P7fddhvbtm3D7/czNjbmQrDOvUuWZRYuXEhrayu1tbVXPWfnzp3jr/7qr0ilUjQ3N/Px\nj3+cJ554gq1bt3LbbbexceNGdu3aRUNDA7Ozs0xOTnLo0CEWL148D/T8ZWvlypWcOHHChUybmpqo\nqam56rUYiUT44Q9/yAsvvADAU089xfLlyz+wshVV1K9b/2EBP1mW2b17N7Isc+LEiXkWp45UVeXR\nRx/lq1/96g3Zxf7Lv/wLr732mntzkGWZ//W//heBwLXBgPer4sCtqKJ+cV0P/ih05VJVVUA1qWGY\nehvZTINtYUseJK1UOOaZaQHVVOajTgtjXB14TPbkoSQ1D0wJ0M8wBDBmxy4QCvhcO+sbkaIoLqTT\n3X0Cw7KxS5YCyhzkIxUMuGxLTNrIsgCq8qZtSIBlIMX6hHtD6iIBD2zZvJHKykp++tOfkcpJBJfs\nRitpuOZARJIkFF85akkTmdkBsHRkbwlaqOGq7y/8nBqowVO1AjVY69afnYsR9HvovH0Ln/n0p/n0\npz9Na2vrFfs/ePAgk9Nh1PKl14SwJElC8ZbiKW9BVn0YiTHszAT46gTM50w4Odt2fqfHhMuev37u\nPbI253Bn5yce7Pxkmh6FmSPi9bLlAg6UlKtDlSBgTC0k3Av1iHDGw4aKDvBUuBCXpEeRjISjep1b\nAAAgAElEQVRw0atcDVoemnQBPyeG1j3iud/uMQmIU0QgCzc9Km4W7oVI+d9X1Jw4vnifgFvjvQIM\ndCEz59gkUL3C0S+0WEB4akAAeGXtAs7LTIFtICUvQaC5AES9rOggQErbuKI+pWCDmBwsBFexBEjo\nq4XUCJKZpbamgk9/+tF5MGjh9VIItemzfeRmzmFE+pEyE/NgUk3TuDQygq2GUIPXdte0bZvUpYNY\npg61myBQX3B+LDRVw+OZG/zZ5FczWrKI1U6NghGfi0+2TVy4rxC6dNuccLIQbo+GgCoV0fZlWRZQ\nn6rh9/vxSGn04QNXuNJJksTSpUvZtm0bsViU4YtD6HomD6vm61dWsWyJSxcvcuTI4Svc9yzL4pvf\n/Ob7BoDHRoeZmpzgtttu+0BdKwoh6F+GU2FRRX0Y9GEaR3yYjqWoon4damhowEbiXM8AR0+bGAY0\n1lj4ChY0S0goskIm5+G1rhDfeyVEPKUgS9C+MMtjd8d46M44r3UFCQUs7u9M4NFEHK9p5sdVCvg9\nNkG/jc9r4/WIHzlvLm7ZMDop093npXnhEtasWfO+jqOsrIw77riDZCrFhYFeWhdkaV+Uy5deRlVV\nF4gzLRvdENCh46YnursSuiGz750gE7MqqifEzp0f4TOf+Qzf+ta3CM9c4uPbM+xYb12/MHktqDPJ\n6VnOD2tE4jKdt14dvCuUIsOCOpvOW0223GJhGHBp0sftnTv4H//jf7Blyxaqqqqu6DvJssyLL7yI\nnsuwa4OB9h5dGJ8Xaittus7C1MQkkuKhpKIRRfHkhw1zzg6B0lomL50kNjvC6IUuZsZ6mR3vIxmb\nwuMN4vWXYpk5F/KLh0fpP/YzDCNLTdNNNC1dh2ULZ/z5rnjzJUnCbSI8McDs5ACWmSNUVs/im+5E\nkmRGL3RhGjq5bBJJVlz3OnAAP+G+N79u5gN+jmbH+8kkw8IhzjZpbtuIomioqqfAaXC+0okwZw7/\niOjMRWzbpLRyAQvbN1G36BZqm1dSWbcERfWgZ5NYRo7ozEVC5XWUVy8kUFJN7YKbqKxvJRmdIKen\nMXNZItNDVDe0o2reudIWQH5mLotwi1cYOvcGsxP9KJqX1lV3IStqPnoXpDxkadsWJZVN6Jk46eQs\nixY285WvfOWKiLdgMMjmzZtRFJnpqUlSiQgz4wOMXzzN+MVTTI/2kstECfg9bN60kZtuuonBoSEs\nS6JuwfUnNwbOvklsdoza5g5qm/MT23nXwqtNZpmmKSadbNGOo1NDmNks/vZmrJzpwn1zed/582hZ\n2KaFlcmROHACgPJdt6H4vcLBD/B4vXg0DX/Ajz2TYHrPYfyyxicfeYRFixbNK8f4+Dg//vGPOT8w\ngG4ZWIqMFPAiBbwYlkU0FuPUyVPsffVVUqkUy5cvdye1TNPka1/7Gj3n+7HK/Cz4+A5CrQtQvPMn\n/iRJQisJEmptxlNdTqTvIhcHLhAIBD4w13Bnwj+TTnPxwgWSg2OET/RhJMXEq5nKkpuNET19nsmX\n3ybdO4xfUuncevs8R5yiivow6sM0jvgwHUtRRf061NHRQU9PD9PT04yPj7tA2eVgkxODeunSJWZn\nZzEMA7/fz+bNm3nqqaeoq6uju7ubxsZG2tvbXTc3B65RVRWfzyee1ead3xxYxwHvBgcHiUajrF+/\nnoaG68+nXK5Fixaxa9cujh49SjweZ9myZfNcp5xUJSee1nH0K4T8HJc4B2gLhUI8/fTTLF68mGef\nfZZYLHbDcB/ggnCSJFFVVXVDzsA+n49ly5axc+dO1q9fT39/P7qu88UvfpEnnniCVatWEQxeaSYx\nMTFBV1eX29e9niRJoq6ujnA4zODgID09PdTX17txt4Xn3oE7L1y4QDQaJRaLkUqlyGazLuApy7Ib\nqWrbNolEgng8jqqqdHR0EAgEXEe160F+mqYRj8dJJpOMjIygKApr165lyZIlxGIxBgcHXehKkiTK\ny8vxeDzzAL/r9d8K9xuNRl3g1GmfThmuVT7HldGBxnRdJ5vNks1myeVyGIbhbs80TdLptAshhkIh\nampqUBSFRCLhQoGRSISVK1fOiwJ25ICSIPrc//iP/0g0GmXZsmV0dna69V0IrzoLeHp6xGKa7du3\n86UvfYny8vlmEw0NDaxZs4ZsNsvk5CSzs7P09/dz5swZzp07x8WLF9F1ncrKSnbv3o1lWcRiMRob\nG6+bOGCaJt/61reIx+Ns376dykphrOEAkR6PZ96cg+OyZxgGgUCAqakpIpEIpaWlLFq0yI0xdtwj\nQYybnHNgmibvvPMOhw8fZtGiRXziE59wywHkk9Q8+P1+XnrpJQ4cOIDH4+ELX/jCvEWDAMeOHePf\n//3fiUQi7v2qoqLCbb9TU1McPnyYN954wwVuHY2Pj/MXf/EXZLNZtmzZwu/+7u/S0tJyxfyKoig0\nNzdz++23k0wmOX/+PO+++y6rV6+mrKzsmvX6i0jTNNauXcu5c+cYHR3lrbfe4vDhwy58HYlEGBwc\n5Cc/+Qn/9E//xPnz55Flmc9//vPveS8pqqj/6PogxhG/sllVVVX5r//1v/LZz36W/fv309fXRzgc\nJhAIsGTJEu64444bjswE6Orqmkf0Llq0yI1OKaqoov7fkSzLPPXUU0iSxKFDh0kPHyAz5sdTtQw1\n1Igka9hWDjMxijXTg2KmUaQctiJjyD6oulU4lMmqgKYckM+F+6Q5EKwQsoK8o5qAlGw7h2GClO/0\n/jzHsWvXLg4cOMCps31IigSa4ix2R5YVPB4Puq4L63dTB0tCktX8BEt+EsHK5p3d4gRLSti0aSNP\nPPEEf/iHf0g6a+Bb0Inir7yhMin+SuSatVjjh9Bn+/DVrbkhiEc89G5CK2nCSE2THvgp7e1t/PZv\n//Z1P1dbW4vSex4jMYYaeO8vIU/VCozkJPpsL/bEQaheD8FmYW1RqJI24ZCWGoXhfxeuekpAuP6F\nls5FITvAVWoUwscFXBlcmIe1LOFE5ziwXU2BBQKG08MgaRBaDKXLBOQlO1+HBYNn2VPwX2muDLIy\nlzF2Ncn5SQYrV/CaAzZeY6WUbQqwL3lJ/F8NinhhX634m2WIcsfPCzfLcDe2HhGRwoXbDi4QjnsT\nB7HNJNLIz6Buq3hdmhvA2djiGEwdUiMQPiHKG2iEkqVzbpr590uyWPUoYpqqoHYd9tRhysrK2LFj\nxxWriZzrZceOHZw+fZoDBw4wOTlJOp3G7/dTW1tLZ2enO7Csq6vj8JGjJGd68NasvOakpJEYxcrG\nRfsILpirPssQE0z5ySwb0PMDX9MUA3sCDaJe9Zhw83McFWVNtMnCUyqpIOUjfOMXwEgiaSEINCLZ\nFrapI8syoZISsC1y0SHSI4fwazabNm1kx44d88qdy+V45plnePvtI6SzJngq8VUtQw01ICkatpnD\nSIyRnOnh/IVhRv/5u/T19fHkk0+iaRqvvvqq+GweAL7ePUKSZDzli1G8pSQHfsahQ4dpb29n165d\n1/zMLypN0/jiF79Ie3s7e/bsYXRsnOzMCdKTx11HEEWRCGoKjQsXuG3jeisHiyqqqKKKKurDpAce\neABVVfnxj37AC2+k+embWW5bYdLRauH32mR0iTMDMkdOKxiWh0TKQlVgdWuSz903S1WpiH0FyOXE\n76wukc1JSBL4vTaaKjozjoufI0210TQbw5DJGWAaJkNDQ+7fL1y4wJkzZ0gkEm60T3l5OcFgkFAo\nxLJly1ixYoU7zli0aBEej5dwwk95+Vy/23GK9ng8wgEgq2NaFqnsfKe3nAHhmIIkq3zmM4+ze/du\nBgcHGTjfh9+rs2vje0N67j4liTvXptj3TpDeIZlLExLNdTc+GVZTYSPJoGkqixcvvm4kjN/vZ9ny\n5fSceYdD3SI6+L20doXFYx/VefY5uNjzFtHpYRpa1lBZ14osS1imwcj5o4wNHsM0dCRZwbJMMskI\nSJCITjI5fIpgWR21C1YiITE1cobY7AimoVPdtJzW1R9BdmJ9LQsjl0FWtDyId3lfy6aibgmKqpFL\npfCW1rB83f0oqoZlXb4Q185DfaI+ZUUVEJlpoGre95x0FC6E5KFE3IVv14L7sukYZw7/CD2bJFRW\nz9KbdxEoqRYO0HoabBtV81LduIycnma4723Gh07Qd/xnKLJKVYOYyPT6S2hfey99x35KbHaYVGya\n7je/x+Kb7qCqvlUAkACSjGWZ2LZFMjbN+NAxwpMXkCSZpas/ItzjbRNZkvH6PKiq5k4YSpJE283b\nCE8OMDsbvuYEXyAQ4OMf/zj3338/XV1ddHV1EY/HMQyDYDBIW1sbnZ2dlJaWEg6H2b9/P9OjfWTS\ncXz+kmvW69jQaWygftGca4pp5oR5fQHc50z4GUZODF8lqKxrZbjvMPrwFPpkGKU0ICJ5FRmXO5Uk\nJE3FsmysrE705aPYhol3SQNKSX6xk6pgG8JlwuvxEj83yPT+LnwobN+2jS1btswrd39/P3/zta8x\nG4tiajIlK5dQv6oVT2WpaCeWRXJglGh3H7OXJvn3n77I8PAwv/M7v4PH46Grq4sz585h+FSaH7gT\nteS9F3eHli7AvmsDkz87xL/927/R2dn5S10UXihFUfiN3/gN2traeOWVV+jr7yd98jyJE/0415Ai\nyfgUhcVLWrnrrrvYunVr0bmvqKKKKqqo/zTyeDz89//+3/n7v/97uru76evr4/z589TX1xMMBt2Y\n1qmpKeLxOLIsu25eDz30EDt37qSsrIzR0VEAF7RKJpOu89XlwKAjJwbY6/WSyWTcOM2ZmRl3WydO\nnGB4eJhkMsnMzAzpdJqqqir8fj9lZWWsXbvWdb9SFIUFCxYwMTGBYRguMFO4b03TSKfT5HI596dQ\nU1NT2LZNaWkp//N//k+WLFnCc8895zrTvR/Jskwmk8Hj8bBv3z7XbOdGpCgKTU1N6LqOpmm0tbVd\n1zmto6MDRVE4deoUU1NT1NTUvOc+fvM3f5NoNMqbb77J888/z8qVK1m7dq3LA0QiEd58801Onz7t\nOpkVJvel02lmZ2cJBoN4vV6y2SypVArTNF1Hwrq6Omzbdp3rstksqqpeFfSzbZva2lqmp6cxDIOV\nK1eybt26K5wWnf87YJuzrcIxwfXknEfHldLR1SKEHeVyOVKp1Dywz9lWISjoxE/7fD5SqRS9vb0s\nX77cdbr0+Xw0NjYyNjaGYRgcPXqUZDLJ3XffzcKFC+e5J6bTaUzT5Pz587z66qvMzMxQXV3Nfffd\n586jOi7hzvGDGB9v376dn/zkJ0xMTFwzkrW5uZmnn36aRx99lIMHD9LX10cymURVVUpLS7n11lu5\n7bbbUFWV6upqvv3tb7Nv377rQl9O+3MAPZiLrHaud0e6rpPJZFwYD2D58uUMDw/z6quvuuMW5/gK\nFQwGSSQSXLx4ke985ztYlsXOnTuRZRm/3+/CxR6Ph1Qqxfe//33XmOqLX/wiFRUV87b34osv8sMf\n/hAQDoc7duxg69at7hglkUjwxhtvsG/fPiYnJ/nGN77BzMwMd999NwDPP/886XSaW265hc997nPv\nuVDIGaOkUikOHTrEj3/8Y770pS9d9zO/iMrKyvj93/99fvazn/Haa68xPj7Ov/7rv17xPscw4p57\n7mHp0qUfWHmKKurDrF+5bUooFOKee+75hbfz9a9//ZdQmqKKKupXofcDfwRKQ0QiUXTbg9a4DV0q\neADrQFhmdi4qVfEyN4Vl52e0JPe/+VkEJGTsbBhsy+0knzp1igMHDjAxMcHs7Ky7UiUYDFJRUUFd\nXR2dnZ10dHS4g6K6ujrO9Q0gm2F8JXOAkSNVU1FV1V1dZFu5+eBQahQJm7KyEh577DF27NjBqVOn\nGBufwFICaGUL31fdKiULMWe6sfQ4RmIUraTpvT9UICMxiqJI1NZePwYIoLOzk7cPH3lPCMuRJEkE\nmm9HD+ehtKm3IByE0lbh1IcKiX6I9QuXO1kBZHF+zawA2pIXBczlbxTAXXJQbAsbQi1Qs0mAa2Za\nvGbpYhuyUgD65R3oLBOCi4UznuKDmo35gsouzFZaWko2myWTQ8BdTtOSVTBN8T75Pb467fyAXdZw\nT76RyUfz5j9bmAxl23Nwn6wJEDKQB/JsS0ChSAJQK1sugLzpI6JuJoG6LW6bty0DvGVIVbdgTx0R\n9TjxRh4YzNe7rCFZOezUGMT6RH1KMgSaBFALwq0OUFRFrEDyeuZNFNuhduLhk4yNT3D69GlWrVp1\n1aqQZZlVq1Zd8++OOjo6aKivY2BwhFz0Ip7yxVd9nz7bJ6qttG0OlrRMsG0UVUHVRLRdKpUSoK1z\nP1A0AUmGWiByUsQf124R9SpdDvflz42sQSYCs8fEOTAUiJwRdWhbmHqOVPI8ZqQP2UwT9Kps2iRc\n6QofoliWxTPPPMNbhw6Tzkn4FnSilS284vpRA9V4a1aSi14kOfIWb771NiAsyvfs2fNzAcC+pk2k\nhw+yZ8+eDxyoe79QZ1FFFVVUUUX9Z5IkSdx77720tbXx8ssvc/z4MY6cTXPkTN4lmvzCJNlHXUMd\nxvAgLQ1JfusTKWzLRs8JiM+jWWR0iZEplaBPPFgP+Cw8Wt7sWpiXu0OiObROwqPB2LSGadv09Jzj\npZde4ujRo5zv7yWnZ8jqYnW6ptpiAZMioygqiuqnoWEB27dvZ+vWrdx8883Iio8TvUkicYmKyzgk\nCQm/z4/P5yOXy6Hr+rxJkGO9ftK6l5UrV7F7924kSWL//v1gZ7j9FhPvtROlrpAsyYT8CutvSvP6\nsSD7jir85j1XJkZcS5ksHDmlgOS5ocji7du303PuFHuPZNi+zrymeXihdm4w+fcDJhdGM0Snh4iH\nR/B4Q/iC5cTDoxi5DJKsECytpWbBTYTK6pBlBcPIEpkaYnr0HPHZESJ5+MzOO8mFSmtZse4Bt08p\nyyqmrWPnwTzLzCHJzoRWfmIqD/GFyuvRM0kWLduK5glg2xbZdMxdxFReXk40FiM+O4JtW8iygqJo\nLuBnq9c/SUYuSyI6AYCq+chm4uSMLB5/yVXHkLZt03vsJfRsktLKJpavexBF0dy/gbiGHDhQ8wRY\n0rEdzRPgUt8heo+/xK3l9Xj8IQEgKiptqz9C95vfQ8/EyWZi9B57Ec0bpHbBTQRKqpElmXQqysxY\nL4noRD6ySWPpzXdRWtGAIiuuE8PVJuC8Xi/VDa1EpwY4cOAA99133zXrQ9M0Nm7cyMaNG6/5noqK\nCtauXcubbx1mqOcwy27ZedX3pRJhTEPHH6okWComVM083Cni1US9GYZBMpnAshxAU0NRVQxZpax6\nIVMjZ0idukBo003IiuLeN/KVDuDCfbmxWWzDIDcZIfFuL57GalBkzHia1Og02f5RpIyOX9HYdued\nPP744/PqbHR0lL/52teYjkbwtdRT95GNyB4NyzCInb1AdnwWU88hyTK++ipCyxcz+8ZxjnWf4Nln\nn+W3fuu32LdvH7plULHulhuC+xyVtC8k2t1PciLCW2+9xc6dV6/XX5Y2bNjAhg0bGBwc5PXXX3fH\nQ16vl+rqajo7O6+allBUUUUVVVRR/xkUCAT40pe+xBtvvMHevXsZGhpiYmLCBYacCE3H4S0ajbJ7\n927uuusuN3qzoqICSZKYmJhwHc0kSXIhwUKnMef7trA/qWka4XAYy7J46aWXiMVivP7664TDYReo\nclziQAAyHo+H73//+6xatYodO3awZs0a1qxZw7Fjx+ju7r7qOEKWZYLBoAtq5XK5eeXo6+tD0zQe\nfPBBlixZgmma7N+//+cC/FRVJZX6/9l77+A6rsPe/7PtdnQQJECwgJ1iEwWSYhM7LclqeYqbpDx7\nxpZbrNh+9sRy3kwyKY7jPFnKL47iPMtPTiRZsmTLRbJVLFKkCJEAewHEBoJoRO+4F7duOb8/9u4C\nFwCrS2Llfmc4xOBuObvn7MU5ez7n+41hGAYdHR1XfFc/merq6hgeHqa4uPiqc0S5ubmsXr2agwcP\n8s4777hOZlcr3xe/+EUOHDhANBrlvffe4/Tp0xQXF6OqKq2trS6AlUgkGBkZcV3jFEUhFArh9/tJ\npVIuHOfU7cyZM12nNyeW2WlPjmPbWKDOcaDLyclxHfWcpC/HFVAIgaZproNbLBYjN9delOLxeNw2\ncrVUmkQi4cJmjpOjc02TybJG5y0dgG+8xgOITiw02Av3Fi5cmJ4TEfh8PkpKSujq6nIBvn/5l39h\n+vTpVFZWkpubi2VZtLa2cvLkSSKRCABlZWV87GMfIxAIoKoqXq/3stDn6tWreeONN6ivr+fSpUtX\ndN3Ly8u7Khuydu1aXnzxRS5evMiZM2e46aabJt2uqakJIQTz589374kTW+zUnRCCRCLh/t6JhlZV\nlRkzZuDxeGhpaaGxsZG5c+eiquqkz15TUxP/9m//xuDgIJIkUVVVhaIolJWVoes6vb29HD9+nJMn\nT7ox4J/+9KcnJCbs27fPhfseeOABdu7cabvrDw6yd+9eenp6SCaT+Hx2+lw8HufVV1/lpZdeIhQK\nsWLFCg4fPgzAQw89dM0u4JIk8cADD3D48GFOnTp1zWDujcrr9XLfffdx9913c/LkSQ4dOsTw8LDr\nnlhRUcHmzZuzhl1ZZfUbKpuLllVWWf1edC3wx8aNG3nuuefoHxzBX74BKViCPjJiu3PJKmg5kBy0\n41y1+WOc+7iio5qUdvaTkgMgBL29vXzta1+js6ubeCKB4aygklRAon9ohEvtPShnL3Dw0GHKSqex\nY8cOduzYcVXITQK8Xg8erwdDN9ITWma6eBZWtJmA38NXv/pVVqywV9xXVVWR1E08RQuvCs2Nl9fr\nQw9VwGAdqYH66wL8hLBI9Z8nqCls2rTpqttfK4Q1VpID2kmmDbgZURioBXEKRIq0JRxoeZAzx47y\nlWTbTS7Z67qnMXzGKTW29cAKO6rZjcVVRuNWsdLRp/rEAvlK7G3V4BgAUIJUHxIWubm5xONxkoMR\nSHSBv9g9vrAc4M68vEsgQNyezEJNO4AkByDZA96CMe11zPaRC6NwX+l2OzLYleXcyNHrDM4ENQe6\n3kZEW5HCJXY8bxp0U1UFOX82+tBpSA4hyTKYMcRgLQzWjtaNlQZQ1QDkL4FQBZIso6gKStqRUtXU\nCam+kHaJK1pIsv8UVVVV1/XSYDI53w/PPvtDou3VKN7cSUE2Kxm2fwikHX+FhbBSSBLuKjEX7gMk\nxTPqcCirCF8JINmQZKzdvpf2gUZPIhiNC+87giRSSIrXjpxN30NJ2NCoqUgEA74rutJN5r4nhECP\ntJMauICVDLvfcbI3F0/hfAIVtxNr+jU1NYcIBAI3DABrebNIdB6lo7Prul/u3KiuFerMKqusssoq\nq/+OWrRoEYsWLaK/v5/9+/dnjIeKi4vZsGED//zP/4ym6Nx9m0FujpfwcALdEPi9sHZpgqoTAd4+\nHOSeTRE0RZDmeUaBvnS3xoH97A8hHJU5ds6HhIRlxvmP/3gan2aSSBpIksDvFQR9FpWLExTnGUgy\nDEcUjp3z09kW5YUftrB7926++tWvsvKWSo4d3kfVMZ37tkzuZGdDhR48WiYMduCUB1kJsH37dvdl\nfHNzMwid1Uuu3b3Pkcfr4eYFCfadCNDaeX1jqZo6hXjKw7yFC5k58+r9rMrKSvLyi+noi/JGtckH\nN1xbeXXDhtPyimdhpGLERgaIRnqRJBl/sICKJVvJK56JotowmbAsTNMgJ7+U6XNW0dt+htb6GoxU\nHEWznQVyCqdnTPDIsoJl2jCf47YnLJPxo2RJVlA1H4rmyYiq7bn0HkJYeDzetOOBRCoZZbC7keLS\nBTYsKCsIy8TQE27k7ahEeu2eRF/7WSxTJ7ewHMvUiUcHGe5tITd/8gi0keFuRoY6UTUfCyvvdeE+\nSC9gQtjvA+wrcK97+rxbGRnuZrCnka7WWkor7MVKmmZHMhWXLqCz+STBnCJkRSEa7qf94hH32A6w\nqHkDTCm/iWkzl5FfOBWv177+q6l05hIGuho4f/78Vbe9Ft15550cO3aM9sbjBHIKmTH3lgnb6Kn0\nxJXHhtwsYWLoyTHjITuqayQ6grAEsqKial7XYVPTfGgePyARf68Jo2cQ/7I5eOeUIns9CMPE6Bki\nfrqZZEuXPb70e5F9ORjRONFDZxlxGpVlIkxB0OujYvYc7rjjjkld6Z599lkGwsN4Z09l2l0bMEbi\nDBw6Tfh0I2Zy4sQlgLcoj3h/mJpDh6ioqODM2bNYikTOolmTbn8l5S2fR9+bB9mzZ0/G987vUrNn\nz2b27Nm/8/NklVVWWWWV1R+aVFVly5YtbN68mYsXL3L48GHC4TC6rrtpb3PnzuWv/uqvXFBE0zQ3\nrnXhwoUUFBTQ19dHS0sLJSUlbrwrkOG45vzs/B5sV2Fd11FVlcbGRrq77ff4yWQSwzBcBzUH+JMk\nCb/fT1FREdXV1Zw8eZINGzbw8Y9/nBdffJGOjg56enouC8Y5zmc+n8/9XSQSoampCY/Hw+bNmwEI\nh8MMDg5muIxdq2RZRtM0dF0nkUjQ0tJyXe9l9+zZA9iLma4FGtq+fTsHDx5k165drF69+pr6PE5k\nsdfrZcWKFTQ0NNDT0+M68cViMYaGhtxrcaKadV2nv78fRVHIy8sjGAyiKArFxcVEIpEJDmmapmWA\ngM55x0vTNBRFcZ0inQje+vp6TNPE6/WiKAq6rhOJROxkqzTs6cB6TvTy5RSJRJAk21ijtbXVbZeT\nLXwXQrhgnrNIbvznMOoqOPa8zvVGIhHXwRBw25yqqpimyZIlS2htbaWzs5Nf/vKX7nGdtj9jxgxW\nrVrFqlWryM/PvyrA6Jxj6dKlHD9+nPr6+isCftcir9fLzp07eeWVV/jud7/Lo48+OukxY7EYQgg3\n/tYBIh33Qud3DmTpRHY79y0UCuH1eolEIjz++OOsWLGCHTt2MG/ePLxeL9FolFOnTk0+Ds4AACAA\nSURBVLFnzx637kpLS0mlUjQ0NNDQ0ODePyc2ORAIsGLFCu69994JrnThcJjnnnsOsB0tt27dyrlz\n59i9ezfHjx+ftI06TqHNzc0888wz3HnnnZimyYoVK64b0BsL5u7du5ePfOQj17X/jciJvq6srPyd\nnyurrP47Kgv4ZZVVVr9XXQn+qK2tpau7ZxRkkWQURcYw0lBVzhyINEOk0Y5lHQtZSYAYY4s2NglK\nwgZ2IhdBmMTjBg2NLQiRjiqVvWmHs7ngK0EgIUwdkewhEm2hYUxk5sMPP3xNkJuEHfWkaaNfs6mh\nJlIiyYwZ5RnX39PTg2kKPKHJJzyuJFVTkYNTsQbfw4z2Xte++nALshmnbGY5S5Ysuer21wphjZeE\nQEgS5MxH8hVDpAGibQgh2XBZYSWEZqYBS6f+LPAVQ+4CG8TqPwFmDEi7/OXMzZxzkdQ04Iddn8KA\n8bZsDtiHZH/usIFC2CChMPH5fEybNo3+geO2s2D+TaPRtrKajl7WQXEa1hg5s6hhu4NPzhzQR2yA\nL9IEeYtHgcSx+wzX2z8XrxkH92G7zkG6rUu4uUm+Qnv7ngMwfN6O1XVBNx8ej4dI3nzMvhNgJpG0\nILLqt8tnmSArmPFBJElBTNmI5C/E4/GkowyuWqUAqKEy4j0n6enpmfTzsS6ZPT09JBIJd+XYeGdM\ngB07dlBfX091zSGijW/im75+gtOdcO+HZk8oWSk3mtfj9ZBKD+ZsuM87Cka6hU5PMAsTeg/B0Gn7\nu8Q/1T6m0G1AM9Jgx/ICnoK5+Ms3Ysa6bSAvFcHSY4jEEPm5+XzlK1+5rCudZVkZ7nuyr4Bk3xmS\nfWexkpEJE67EB9CHmpG9OSg55cSGm9izZw9J3bohAPi3DWJmlVVWWWWVVVa/mXRd59ChQ9TW1hKJ\nRBBCEAqFqKioYP369bS1tdHe1kJOQGfVTRayJKNpHlJ6knhSsHVVlH3HAxyoDbBz7QihvMwXwc5o\nyInjtH8pISFRdcKPYUksnJWkqUMjnkySXyAYEpAbMrh/ywhrl8bxe21QK6VDIgV33xbhbFOANw/l\n0t7ZyN///d/zsY99jGNHD/FGdZJbFlvXHIv79mGFi+0a/lBehptZPG5HsF6HMZcrj8dDKGD3/zr7\nrh3cGYzAq/tUkO1ooWuRqqo89NBDfPdf/4UfvyVQ5CS3r7vyJFxnn0TPoGQvDjFT3LzpE9SfeI2+\njvP4AnnctPbD+AK5mROQsowqewAPCJg+dw0efy4Np36NaaSQFRUzHbsqYTvi2wur7AVJkqygqR4b\n8HMWwkl239COVbL71IrqQULCNFP0tp9Nx+BqFBYWMjQcwbJMOptOUDRtftrxxEsqFU9Dfsk0kJh5\nvZZl0tV6CoBAThGdzSexTJ3e9jPMXGjHLI13RuhusRchlcxYiqr5xnwisEwDhF3WsXAf2OPTsjmV\nLuBXMmMpmqa5EUels5bRfamO4f428opmMGPuLSTiEQw9iWno9Hc3IkkyN936IQKhAvx+/2WjpSaT\n1xdEgJsEMF7RaJT9+/dTXV3NwMAAyWQSv99PeXk5W7ZsYeXKlRkTZ3PnzuXjH/84//7v/8GFU7uJ\nRvqZveBWfIHcMddsvwMRlolp6hh6CiSBpnncyaxoNOrCfdqY+ymEQE/FsdLu9RgmRt8wI/vrGNlf\nN/ECJAnv7GlM2XQL/oJcYq1dRE43oUeiWCkDfSiCZOp85MMf5r777pt0grOtrY0zZ89iKhLlt68l\n3t5L16/2Y6bs9quV5OObW4Yc8CJMC6M/TPz8JZL9wwjDJJZM8eabb2JYJsEF5SjXY/GZVmhuOb0e\nlbb2doaGhiZMBGf1/lUkEqG6upqurq4MJ8X169dnXUOyyiqrrP4TNTg4SFVVFc3NzcRiMVRVJT8/\nn8rKSpYtW8bLL78M2G5eoVAIIQTxeBzDsB3Ht2zZwssvv8x7773H9u3bJ7iLjYX8xkoIQW1trRvr\neuHCBbxeL5ZlMTIywvDwcEbKk+Ok1t3dzaVLl5g2bRrTpk2jqqqKeDzO+vXr2bVrF7t27eIjH/nI\nFaNtHZmmya5du5AkiVtuucXtl0Sj0Ukhn2uVx+NxHescaPFa5LgQqqrKbbfddk37zJs3j3Xr1lFT\nU8Pjjz/Ol7/85avGbL777rvu9U2dOpX777+fb3/728TjcRKJBLFYzIW1nHrTNA1N01xYc2RkxHXf\nC4fDyLI8AYh0AD/LstzjOc55kBm7a1kWiqK49Xbp0iWi0SiapmVEOsuyzPDwMIWFhS4s5rRHBxIc\nLycW1kn6cspgGEYG7OloLOB3NbhvMjll6ezspKSkBE3T8Pv9aJpGXl4efX19HD16lLVr11JcXMzA\nwADxeJzBwUEaGhooKyvjC1/4ArIsEwqFrtkdDnAjqqPR6KSfd3R0sHfvXk6dOuW6BAaDQRYvXsy2\nbduoqKjI2P6+++6jtbWVEydO8A//8A98+MMfZv369RnjNGcMZRiGbdaRjjIOBAIoioJpmm68cCAQ\nyIjsHRuD7Lg2njp1itOnT08ouxCCYDDI5s2buf/++zFNk5qaGo4dO0YkEsE0Tc6fP4+iKHzrW99y\nY7zHq6qqCsMwWLFiBVu2bOHnP/85r7zyCmCPaVevXs2iRYvcNldXV+fGhjuOke+++y4AGzduvEqN\nTK5NmzZx8ODBSa8zq/e32traOHToEENDQ+i6TjAYZPbs2axZs+a63n9k9V9LWcAvq6yy+r3oWmCf\nyZzsvF4vphm3QR7fVLsDbkRtd7WcOWPOMAncB+5KdaKXbCc4xQdGEuHAZJIXpqyBYPkoyAU2POQr\nsKNAU71E+4+5kZk7duzgueeevy7IzYwPkGivIehVXftlR4lEAoRAUq4+CBwvCfB4gyQkMBODmPGB\n37g8V9K1QFiOhLDQh1sQht3BZqQRCpeAEUZE22zYrnS77WznWow41J08CnAGZthueN3vpiE/bMhO\nAJITaSYzZkozHd2cURj790IHJCRZG4Xtoq3p2F97Zcm9997LiRMnMPURiLbZ8CHY0bzCsqE7MzXG\nQdI5rzTaztSQvc3AKXsfI2o7T4ZmZ7pNxjvBGLHvRXBc5LMYG9nm3N8xg7hAOahBhB6B6CUk/1QX\ndJMAf9EsYoN1+D0SiiqR1KOYZjq7zZKQMBCyhuLx4QsEJsTwXk2SbGfBOYMlR5ZlsXv3bnbt2kVn\nVzdJ3Rw9rySh1F+c4IwpyzKyLPPZz34WSZKoqTlEvK2KRKcfT9FC1FCZfb503LLQoyBpSBIumAik\nbenTzn3j2qQwU5BuixKy7V5oRGHwFAxmXJl9HjVog5NFi5AUFS1nuuuQaSaGiV34BVOmTLkiNDc2\nflvNmU6sdR/6ULP9FaUE7O+XwBi4MNaNCF/ATEawkhHAIjKSQFK8BG4AAIarg5hZvT91vYBtVlll\nlVVWv1uFw2HeeOMNqqqqiI4MgpXE7bcic/TwAX7yk58wdepULCvF6iUmaror7PP70A2dlGExtcCg\ntNiguVPjudfz+coD/Zc/qcN1SXChVeMX+3IQAu5YG+HVd3O42O5lIAxTCwwe/Xg/UwpGJ0ckCbwe\n0FQYiQuWzo2zuMLg/71ayLnmDl599VVWrV7L0cMHeOxZwf96KEVFmX3CwTC8e0Khs08mnrSPU5Q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SzZ5xGWfa5YBwycHOMwaCB5ciFvoe3U1rkbjBh0vm0fK1Bul0sfsbf3pwf0QtjHtfTR68KZ\n0JpkcO8vsd3nDHs14FjekVQKybLo6Ozk+eefZ8eOHXz+85936/vJJ59k3/6aG253xkgHiiJRUmJP\nTFqWxfe+9z2qaw4R1yV85ZsmBU3VQDHeKUvQh1uJtle7zphO2Zwo6O3bt3P69GnXgcyxvm9pvYQZ\nayU0azXymGO70bzyxK6NsAy7rqLNgGSDdIl++3dT07bm6UGOkEYHOhL2859MJjMcDsdf++XU3d2N\nYZiIeARrqAkha1C6DbwFo+6i4yXJtmuklgMdu8HUQRg3DABfK4yY1R++flPANqusssoqq9+++vr6\n+Md//BbDAx3MLo3zPz+oM6dcTIg1vW2lSVuPxLef1ejok4jFUhimhJp2YNNUjVBOiOhIlKAf/D4L\ny4KTF3x8+Z+msX5ZnK2rRiifYiBJkEhJHDnrZ8/REC2dGiCxanGSD20P4/NCV7+GpgpuXRqntHgi\nUBdLSFTX+jnb7CWakLAs8HsFKxaY7Fxr8sPXE+zbt4+vf/3rPPfcc/z617/mYF0Mr8fCqwluXhBn\nyZwkXk2Q1CUutnk5es5HyjA5fKiaUCjEQw89hGmajIyMsGTJEk7XnWDv0SQbV14f4CcE7DmsoGpB\ntm3cyLFjRzlRH+PE+QQlBTplUwSqAuGoxIVWBYEXZC+LlyzjkUceIRi8MVewdevWcfPNN1NTU8Pb\nb79Ne1srkXZ77AcyvlCQDRs2snXrVs6fP8/TP/h34rE44cEOVM3LtFnLJznq+Nglgakn06gkaB4f\nBVPn0td+FtPQ6Ww+QfH0RW5sKxIomtfeR1joqRiyoiIrGpIk25G7zSdACDRvkOPv/DvJeBhF1Vh0\nywc4ffiXIAxeeuklEskUwhJYwsA0U3Q1n6C37QxFZQspKb8Jry8HIQSxSB89bacZ6m1OuwemUFQP\n02bfzOxFt6FpPkwjRePpPeipGGcO/5SZCzdSUn4TqubFNOw+rh3BawONpmlgGSn3ukcfmDGu/WPk\n7GsaOrKkICmj4yfL1DFNk6NHj1NSUsxXvvIVZs60Hdp/8Ytf8JOXf0p70ymKplZMOO7V1N50CkWx\nJ6jBdkP49re/TUtrGx5/AZVr/4hgbqaTg6p5mTGvkvK5t9Db2cCZI6+x/0A1qqryqU99yq3/iooK\nPve5z/HAAw9w4MAB1zHG6/UyPDzMqdo6+jrOMntB5WhrEQJdd+KXM/t2pqEjgN72MwhZEFg4i1jD\nJfTuAYyhCGr+6AT6GD4SWVMxdduZRDcMt8+Y6h8m1T1AUSh3QqyWo4GBAfr6+zFlwXBjG1ZKx1Ne\nQmjDUpAkLMv+LrzcJJNakEP+HWsY+FkVVjxFaiB8TfUyXsI0MUfiSCg3/Lxn9Yeh3bt388xzzxIz\nUuQum0fpLQvR8jIB1Lzl8zGicYbrGhg6cpZfvPoqqVSKBx988D+p1FlllVVW738dOnSIp556CrAX\nMtx3330THKtWrFjBnXfeSW1tLd/61rcQQhAOh914VsfFT5Zl4vE4JSUleDwe17GutraWpUuXsnjx\nYgKBgAvlnDlzhtOnT9uLFTSNu+66iwULFuDxeFw4a2BgYNJya5pGbm4uHo8HWZZJpVIuEBiJRAiF\nQly4cIG//Mu/5Dvf+Q5nz56lt7d3AnzlwFbOdRiGwQ9+8AM8Hg+rVq0iHo8TjUZZvHgxhw8fzoDe\nrkeyLFNQUMDSpUupq6vjrbfe4q233mLu3LkUFBRgmibd3d10dHS45bnvvvv4oz/6oxuCflRV5ROf\n+AR33nkne/fudd/hj02RmTJlClu3bmXTpk1873vf4+jRoy6YZRjGhDhap1xj5YB6zmexWAxd193F\nMZcuXcqICJZlGZ/P5zrsxeNxVFV1t4/FYvT396OqKgMDA7z44osIIZg5cyZr167lhz/8IbIs873v\nfQ9dt8cSDiTX1dVFb28v+fn5rpufJElEIhFGRkZcEBHsGNqFCxcya9YsJEmiubmZ3t5eNxLa5/Oh\nquploc7JgMexkN/Yz8ZCaUIIt8069w9sB7qDBw+ybNkyHn30Ufx+P0II/vqv/5qmpiZOnTrFqlWr\nrl7xYzQ0NMTZs2dRFIXly+3xbX19Pd/5zndIpVLucz0+ltjv93PHHXewbds23nnnHQ4cOMDTTz+N\nz+dj9erVbj2uW7eOdevW0draSnV1NYODg6RSKQKBAOfPn6ezs5OmpqYMVzsHrFVVNcNV0DRN9zMn\nVnfz5s388pe/ZP/+/dx1110T2p7z/eP3+4lEIqRSKfx+v7vdgQMHAFi8ONOlfqzq6+tJJBKUlJTw\n+uuvA/DAAw+wcuXKCfU+2XO4adMmOjs7+fnPf04ymaSvr++y468rqa+vDyA7V24s3QAAIABJREFU\nHnqfyzRNvvOd71BXV4ff7+dDH/oQ69evnzA3eOedd9LU1MQrr7zCyZMneeKJJ/jKV76Shfz+gJQF\n/LLKKqvfiW4I9ml6C7CIRyOEfHku/iJJEAgGUFWVmOpBGBr4pkKiB3oOpKM059mQlKSCMJAS3RBu\nGAWuAtOheBVEL9kvrLUQ0mSAmRB2bGqkEfSI7folyZhqELN4Iabso6Ozi/r6ej7/+c8zd+5cnn/+\necLhEYSkItQg5FSAtwQUFckykFJ9SCPNRONxfvjDFzhy5Aj5+fn09fWRSCTsjr+eRO+pw1N80+gE\nzTVICItU/3mCHoXPfvaz9PT0XBN05wBUN+rcdCUIy+/3uzGQS5Ys4b333uMf/89jjESTCCthQ3c5\nc9ITNWPc+1y2bzLIDRt8Cs6268WIQuwSBNPxuZJk7ydrYJHePwXIaTc32d7eiCJJShr6jCMBat5M\njHAbWAnOnD2HKSRMUyBkL5KZsuNgu/fb7Sx3LvhLQfKASNlOguGGtAOcAVhIyKB6bIjQWwBCBiUE\n5giYicw2aybS5VfS7oO2W1v6l+nrt7jsn2vJnmARlhM9rOLGBo8M2BMnqJPCPJs2beLgocNE+8/j\nnbJkUsfHy8ltd5rCpk2bAPtl+sGDh4nrEsE5d1wxKlqSZDz5s1G8uUQb36Sm5hALFixg586d7jay\nLLNs2bKMCFzLsnj00UdpbG7HGG7Fkz/b/cy0zHRTGncdDogZ6wIjhqTlQP4S6KqC8AXIW5QGN5k4\nXygrYEn2QF430DR10msfL8dF7fz585iGhYi02h8Ur7EdHAXuhKU0tu2PlbcAitcgut9FslLokY7f\nCoiZ1ftTvy3ANqussspqvEzT5OTJkzQ0NBCLxZBlmdzcXCorK11IJqvJZVkW3/nOdxge6GTRrDj/\n66EUVzJNLi8R3LXR5OlXVDr6FKLRCLm5ue6iAFVRyc3LpSDPju8tL9XJDVicbfaw91iAvccDSICi\ngGEAkoSERNAPd6zX2bBsEFmy+x/tvSoeTbBtVWY8S++gwmsHQlTX+Ujpowtp7EUkEifqIRSwo1Pr\nz5+lo6OD0tJSPJqCRxFsq4yypTJKXmjUMUBRFDavUvi4maLquMVPdlu8+eZr6fjSOBL2ZER4JM7J\neoWf7lG45zYTz2XYc4HtfiEsgUDQ2K7S3KkSys3n05/+NA888ABVVVW888479Az00jOUhu4kCcXj\nY9Wq1Wzfvp358+df80SWruucO3eO4eFhDMMgEAgwe/ZsSkpK2LZtG1u3bqWzs5NwOIxhGASDQUpL\nS92JjKKiIn7yk5+QTOmYqQQgEcyfihAWk3VChRB2BK6RAmEhSQqqx4NppMgtnE5fxzlkWSEW6aPx\nvT3MXbYjY9JH1XwYRgphGVim/U8AzWf2MTLUjWkk6e+6gCzLBEIFLFp5O2eOvkYqFUfTvMQSBnlF\nM8hXfRiGzshQD/FoP4aRorv5JL1tZzLunWWZGHpidMGMZdHVfJL+9vMUlFQgqfYiPSEEppGi5cw+\nLtVXM2X6YryBPHt/I4mhJ7DMUdcKWVbTznMm4EmfMzOqC+xjIoHHF0TzjL64joZ70VNxPL4gvtAU\nunt6+fu//yb/+3//BbNmzWLz5s28+uqr9HU0kIhH8PmvLZYMIDzYSWSok+LCPDfi+Uc/+hGtl9rw\nBApZtflBNI/vsvtLkkRJ2Xy8t32E41Uvsa/qXZYuXepG0TnKy8vjgx/8YMbvkskkX/7ylxkY7KKl\n/hCzF65N10N6caEkZywmsiwTISyG+1robbMBv9Dy+fY9OtdM/HQzORuWpcs1icG+piBSBqlk0gX8\nhmsb0BSFDRs2ZLhYODp37hz/9E//RCIeRwr5SF5oA8B/yzySjR0kmzqxYkmwBJJXwzOtEP9Ns1AC\nmfdMm5KPp7yEZGMHkfMtlOxYjXKd7vMjDW1IusnMitnk5+dfdfus/jB19uxZnvvhc8SMFMVbK8lb\nNu+y26pBP0Vrl+GbWkTXawd449dvMnPmTDZu3Ph7LHFWWWX1h6RoNEpNTQ3d3d1urGNJSQlr164l\nNzf3P7t4/6XV1dXlwn3333//FZ3iFEVh5cqV3HzzzdTV1dHe3s6cOXNcIMWJEvV4POTk5OD1elm2\nbBnt7e309/dTU1NDdXW1+37LsiwXqistLXVja1VV5fz5864jVywWyyiH3++noKDABQUdCMdxAysu\nLnbBsT179vCBD3yAUCiEoihuvDCMwkGSJKGqqhsXm0wmGRgY4PHHH6ewsJCRkRE3ptWJTL2ai5/j\nCOhco6IoeL1e1qxZw+c//3mamprYs2cPNTU1XLx4MWPfnJwcNm/ezJYtW64r7nN4eJgLFy647m+h\nUIiFCxdSUlLCRz/6Ue6//353UYokSeTk5FBWVubWx7Zt26itrXVhuUQiQSqVuuKiY13X3ahZr9eL\npmluvKoDWrW2tpKTk5Px7ltRFPx+v+tmp+s6uq5jGAanT592I3EbGxvddnf77bfzN3/zN260ryzL\nzJo1C7CNBbq7u0kmk+i6Tl9fH4ODg+75ZFl268NRMpnkvffeo6mpyXWYc+6FJEn2WFiS8Hg8rvue\nU7dO2xl77PGR047GA6S5ubkZMM/g4CBer5fZs2e7scqPPfYYjz76KF6vl23btvGDH/yA6urq6wb8\nDh48iBCCVatWUVBQgK7r/Ou//iupVIrKykruvvvuK465PR4PH/jAB/D5fOzZs4ennnqKhQsXTvhe\nnTlz5oT3T7W1tTz++OMcPnyYmTNnuo6Yzv0Y79CeTNppV0eOHCEWizF9+nTuueceqqqq3Ejtm266\nadLnzwFEHSjV6/VimiZ79+4FcCN7x0oIwSuvvMKPf/xjDMN2RO/o6CAvL4/Kykpee+0193lSFIWc\nnBwqKytZvXr1hGdi27Zt7Nq1i76+Pt58800XgrweVVVVAWTMtWX1/tNPfvIT6urqCIVCPProo8yY\nMeOy21ZUVPDFL36RF154gd27d/Pkk0/yjW9844ZjoLP6/eqGAL+/+Iu/+G2X47ciSZL45je/+Z9d\njKzeJ7Isi/fee88FlxKJBD6fzwWXli5dmp0Mv4JuBPZJ5c5ED7dhRNpJhUoyIjElwOv1YAQKSKWG\nEf5ptqNauN4GvgZP2U5m6W3tCS2QvTmQMw/DPxNJ8dhAj+wAZmPqTwj7WMPnbaArQwKRGibR2oGs\nqMTiKfbt28eSJUtoaGjAtEDyhFCKVyEC0wDZTgmVFTweD6pWgbBuId5WTaTvDEePnbAjc2TVhe9M\nS0IYYSJnXsI37WY8RYszOr8CMHS7AzkKMoEUbQcjSumMGSxbtgxZlq8JurvWtnstz8F4CGu8li5d\nSlnpNC42tWHYZBN4CsFIjsJ3LutnZEJukmJDew7w55ti15tl2JG3ai548zOja2WPfRwH7LIsSA2n\nob4kSCoSFrI3B0/RIlKDFxFGHEvWSIq041WwlHhKR4/HIHwOYu2QGrJd+qgdbTvCsoFSYa+EkmUF\ngUAYIzBwHAbsa5OsdFlkDRSfDZ4OnrKje2UN9KhdbvsooxGzlm5fq5IZh+S2WWE7Xkiyx46ddT+z\nIHwRSZIJVGwHy5wA8yxdupTSaVNpbG5HHwfLXU36cAuyGadsZjlLlizBsix27dpFPGngK990xed9\nrBR/oeuMuWvXrqtCpw5Y+uyzPyTaXo3izR09l9sExq20sky7/gdP2J/lLYRAGZIWtJ/1aJvtmHcZ\nSbKKsPT0Cwd1wrVPuDdjXNSSSR2Q7chmJWi35579tnOjMEBSEVoIcuYiBUon1nFwug2DpnRSve/h\nK1n6G4OYWb0/9dsGbLPKKqusIpEIe/bsYe/evQwN9toLG5z+lqTwyi9+xrz5C9m+fTtr1qzJWBmd\nla2zZ8/S2tJIXijOFx+4MtznaNUSkxfeVHnvopeBcASfL4XXMxorIyExb4aKR5Xo6NX40iPd6IbE\n3mMBDp/2E44q6LoN+c2ZDltX66xZYgEpolFQFbjY5kE3JMpLdCpKR2GqC60a/9+LhUQTNti3aHaS\nDctjFOaaCAFd/Sr7TgRo7fZgCoiORHnjjTeoqT6ALEZ4+H/obFipYJkhLDHqzCXLNmzkBYrzBR7N\nYngkxrAZxeeBnKAFAhQhEU9JvPhrlV0HVT603WDHraNufpYQ6KkUyVTSdSGIxiWe+mkhsYRgxuxC\nBgYGmDp1Kvfddx933303jY2NhMNhdF0nGAwya9as65qE7e3tdd0ohofDdvcbu8umyBLLly9n27Zt\nLF++nLKyMsrKyiY9jt/vZ/369bz2+hsYOE7RMnoyhiQryOkYWfs6LRtqE85EjYyiee0+rQBF0UAI\nPP4cTD1Fb9tpTCPJ7MWb8TqAmmQ7xQmhYZkGieggrfXV6fjcJKrHT8n0+ZTPWUluwVSO7H2eaKQf\nXyCf8jk3Uz53JYrqJx6Pu5NJ0eEuOptPMtB9ESMVR1E9KJoXy9SxTBNJUhCY5ObkpF02UiTjQ3Rf\nqsVGTS0sIVAUjUBOMbFwDz2ttVhCYOgJEtEh4iODeP059j1RNNuFG7tPKyxzdIzkyp78Gu6/BIAv\nkJfxaVdrLRJQPncl85dt5fSRX9HfdYEnnniCv/u7v6OgoIDKykoOVB/k7LE3WbH+/mtabGcYKc4d\nfwtNldm4cSNer5dIJEJ1TQ26IVi19o+uCPeNVV5hGfOXbeFC7W527do1AfCbTF6vl09+8pM8+eS/\ncvF0FUIIZi+8lVFn+8ztLctgoPsijaf3YkomOZWL0QpyCd40xwb8zrbgWzQTrShvwrkAZFXFSBno\n6ciwZO8g4TNNBGV10smsM2fO8O1vf5twIgaKjEgZmKaFrKlEdh3Dik90pUm1dhM9eh7vnDKCt8xH\nK7bLIoTAt2gmqUs9iJRO+HQTBbcsvOo9Gqvh2gY0WWHu3Lm88MILNDQ00NPTg2maBINBioqKmDdv\nHlu2bMkuivoD1uuvv07C0MmrXHRFuG+sghVlFG+5hYE9x/jlL3/Jhg0bsrFlWWWVVYZaW1vZtWsX\nBw8enDQa8aWXXmLNmjXs2LEjw0Esq1Ht2rUL0zRZu3Yt99xzzzXts379es6ePUt1dTVr167FsqyM\n98VOtOrevXvp7e3lz//8z7lw4QI1NTU0NDS4kJ3f72fx4sWsW7eOiooK4vG4C5QdPnwYy7LcCFlH\nBQUFFBUVuVBYd3c3fX19GIaBLMvk5eVRVlbmAj4tLS0888wznDp1imQySSgUmgBkOYk1MAr9maZJ\nJBJxIz4dsE9VVWRZvmxcr2mapFKpDJc/VVUJhUIYhkFubi6xWIyKigo+9alP8bGPfYympiYXysvJ\nyWHu3LnXnOQhhKC+vp63336bY8eOueMwR16vl/Xr17Nt2zZmzJjB7NmzL3us5cuXU1hYSGdnp3tt\nTkyv47bmRKKOdVsDGwTzeDzuc+hsM2/ePNra2lyXxvLycvfdhCzLBINB1wFwaGiIixcvugBiUVGR\nG2MciUT4xje+gWmaFBcXs3z5cmbPno1lWS5gaJombW1tnDt3jsHBQUzTJBQKoaqqC+s59V5YWEg0\nGiWVSjE8PEwkEsmof5/P50b8Oi6BgFt2Zxwty7Ib2zsWABxbP+O3HxtXLISgr68PTdP4kz/5E6ZM\nmcJjjz1GY2MjTz31FH/2Z3/G2rVrefHFF2lpaWH//v3XvOChra2Nffv2IUkSW7duBeDo0aMMDQ0x\nZcoUPvjBD15zv+q2226jpaWFxsZG3n33Xe66666r7rNs2TI2btzIu+++y89+9jPuueceysvLLwvH\nJpNJDh48SH19PX6/n4cffhiPx8OWLVt45ZVXePnll/n617+ecf/GyuPxuBCu1+t1gbvi4uJJ50Zf\neukl3nzzTSRJQtM02tvb7bGNz8fXvvY1TNOcUNajR4/yox/9iNtuu4277rrLhZt9Ph+rVq1iz549\n1NXV0d3dnRHxfTUNDQ1x7NgxhBBomsb3v/99GhoaCIdth/T8/HwKCwuprKxk3bp12SSoP1DFYjHe\nfvttAL70pS9dEe5zJMsyDz74IL29vZw6dYq3336bj370o7/romb1W9ANAX4///nP/8sNeJ0/jFnA\nL6vfVI7z0q5du+js6iapm5imGHVBq7/IwUOHKSudxo4dO9ixY8cNgX7vZ4DwRmEfb8ky9Eg7ItJE\nomBRRiSmI0/hfPShZohchJn3QN4C20UtchGRCiNZOormQ/Hl4imcjxoqIzIyAnp68JEctKN3A6Vj\nCmxCbw1ixJ4YQA1A7vy0I6BmT2jGuhGRBiwjimUZHDt2zI6iSgMVobmXByqEZRK/9K5dbtmDUP2Y\noQq03HKQFEwziYh2I8KNmHqUWNtBjGgPgRm3gayQSibTlvHWuAjWIeg5gmwkGR4eZvfu3W57vBp0\ndzX9tp+D6dOn09DQCKYJiicNs6Xhu8kkyTYMBXYErnPRDhQpqzb81vU2FK9OR96OOb+k2v+slB3F\nm47PlRQfSqgcX8li1GApsaa3MEe6QA2gla7FXzwX07AYiUaxhAI+H/g22JBd+JwdDWwl0+1STpdT\ntifZvB6WL1tKQUEBTU1N9PX1YZomiqJQVFREIpFgYHCYpGnhKVmGkRjGGG5BCBOSfeArTkcKy2kw\ncYyDoTAZjep1JCDebf/oGec2Eb0EZgzZm4OWU26v5JsE5rksLHcFmfEBEu01BL0qO3fuRJZlamtr\n6ezqxlICaHnX5+aj5c0i0XmUjs4uTp8+fdV2u2PHDurr66muOUS08U1809fb53S/LMbYTQjLjuke\nOA6WjhSaZT/bUhr06zsOfYftOFzvZdwcJBmE7Xwx2bWP1XgXNSVUhhFpB0uAlITud8cdXEBq0HYW\n1XKQ8hZA7oJM0C+nAgbrQB+5IRBTMmLkFRWyd+9efvCDH9DX14dlWW67nDNnDps3b/6D/pvz312/\nL8A2q6yy+u+jlpYWnnjiCYYHu0HYMafrllvkhQSWBe29EgdrFRrOH6fhwlmqq2/hT//0T6/7JaDj\nDlhVVeU6W/t8PqZOncrmzZvdxSt/qNqzZw9YCbatMghcG+tDQQ5ULrY4VCdRU+vn7tsSeDyeDDeu\nUABuXWpx4JTEO8eDfHRHhP95Z4Q/uTNCW7dEdV2QnkE/SV3hyGmFC60yKxcazCixuzUHav1IEuQG\nLZzb29Sh8djzRaR0WDYvwcd2DjN9ipFRtpvmJNlaGaWpw8Mzr+dz4RLs3rULv9fivs2pdLSu7R4x\nGSL11kGFF95QAcHccp31y2KsvimevjeClA7Hz/vZfzJAS5fGM79S6B4QPHiHRUpPEo/HXcc2WRIM\nhBX+788KaO/VME241FLPo1/7c27btJlPfOITqKrK/Pnzr7/isN8vvfHGG/z4xz/m/2fvvcPjqg68\n/8+9d3pTGXVZsmVbbnLvNsYYF8AbCAYSk2RT383rLJuypGye3bzPb9sTkuzmYf+AvBtSN4TABpKQ\n7Aa/FOOCe7clF9lWr6MZSTPS9Du3/f64mrGE5EYIS0Df5xFCnrntnNvOOZ/z/SqqhqrquHzFePKK\nEQWJTCZJJNTOyVNnOFtfz/SaGh599NFxUWNZybJMX18fGTmdA/eMEWc+XdfQdW28obMg5MA/NZMm\n2y7QNXOgKx2PUDZtCeFgM+FAM+FgC4Ul0ymeUofD5QME0skooa5zREKtaGoGUbQwc9FWCktrcHvy\nsNttnNjzDLGhIG5fMXOX34fbW2AOGmYS5hYFEzL05JdTu7icSKiN1vO7UeSEGX1ktWFzu/CX1jAY\nbANBZcOG29m6dSvt7e0Eg8HcoMb+/fsJ9IVQ5BjT561DUVLEo0MMDXSgZtIM9F5m6tzbgZFIWf3q\nYJamqVgmuB/EhwIkYwNYbU4KS68OrGfkJP09jSAITKlZjCRZmL/iXk4feJ6BwT727NnDtm3b+NCH\nPkRjYyMD/e2cO/bfzF95rwlRXkNKJkX9kd+SjPUzpbKMe++9F4ADBw6QkRX8pTW4vDf3LpZV+dQ6\nmi+8QVNzMx0dHTmnkOtpxYoVfOpTn+Tpp39OW+NBAh3nqahZiKdgKhareS/WNZWBwBV6288QjwTQ\nBA3Polq8y8wIKVtxAa6ZVSSbuxjaeYT8D6zB6p8AgB3ltJ/ujxD4r/04ELlt7dpxgwbBYJAnnniC\nmJzCs3AGcl+YRFcfKKoZyatqWPw+nPOmYSnwgiiiJ1Okm3qQ2/tIN3cjtwfI27IcR005hqpjqyhC\ntFnQ5AyRExfxzJyC1Xdz0VLDDU0kOwKImsHO//f/UJQMiCJIotmvHRmgtbuTs+ca2LlzJwsXLuT+\n++9n5sybA8Qm9e5QMBikvqEBVYCCpXNuaVnfvBrCR8/T2xfIObdMalKTmhSY7/LPPPNMDsCoq6tj\n/vz5OVewxsZG6uvrOXLkCEeOHGH79u1s3br1lsdNE4kEBw4c4NSpU0SjUXRdx+VyMXPmTDZu3Ehl\nZeUf4/DeEaXTaQ4fPgxwS7BPFjhqa2ujs7OT2tracW3NZcuW4fP56Ovro6urizlz5jBnzhx0Xefy\n5cucP3+eZDJJMpnk4MGDXLp0ifnz55sO6YJAa2vrOLgmC/dpmkZbWxtdXV05uCqrgYEBWltbKSsr\nY/bs2QiCwKuvvkomk8HtducArYkmwBmGkYMMM5kM4XCY3t5ewuFwrmysVitlZWU5N3CLxZLbz2w0\nbVaiKGK1WnE6nUiShNVqZdeuXezfv59PfOITrFu3Drfb/ZYjF1OpFE899RT19fWACZHV1dVRUFCA\nYRgEg0Gam5vZu3cve/fuZdOmTXzsYx+75uS/rANmOp3GMAyz3ShJOWgxu43RslgsWK1WNE0jHo/n\n/j0LCPb19bFgwYKcU15HRwfl5eUUFxdjtVrRdZ1oNEpXV1fOKbGqqopPfepTVFdXk5+fTzgc5tvf\n/jbpdJqamhrWrl2bO/7R54gkSUydOpWqqioaGhpobm4mHo/jdrtz/RhTpkzJgZCPPPIIU6ZM4cqV\nK8TjcSwWC7Iss2vXLpLJJC6Xi6qqKhKJBIlEIucImI391XV9HFCZdWx8s6xWK6IoUlhYOKb8syBi\nVVUV8+bNQxRFvvrVr/JP//RPnDp1is7OTqqrq/n4xz/OD3/4Q377298C3BDya29v5yc/+QmKorB2\n7VrmzDHfv3bv3o2u67c8CVQQBFatWkVLSwt79+7lnnvuueHygiDwmc98hmQyyalTp/j1r39NdXU1\nc+bMobS0NFd38Xic8+fPU19fn4vW/vznP5/rK9i8eTMHDhygtbWVJ598ks9//vMTQn6jnUH37dvH\nL3/5SwA+/OEPj+u3euONN3jllVeQJIlPf/rTPP3004RCoRzHIkkSixcvZs2aNeTn56NpGoFAgL17\n99LV1cXOnTs5ffo0X/3qV3P3pMWLF3Pw4EFSqRS/+MUvePTRR2+qjHVd54c//CGxWAzDMPj5z3+O\nqqqmMc1IRHQ0GqWzs5OGhgZeeOEF1q5dy/33309e3sQTwCb17tTBgwdRFIV58+bdUl+YKIp88IMf\npL6+nv379/PAAw9cE3Sd1LtH0j/+4z/+460u9L3vfe+mbILfaQmCwBe+8IW3bX2Dg4NkMhlsNtuk\nJeX7RIqi8NRTT7Fz58v0Dw6TMRxY/POxly7FVlyHJX8GhsVDOj7EUHiQixfO09cXYMmSJTf9wpId\niP/BD37Aq6/toqW9k2AoTDgyTGggQmd3N8eOHuXYMdPeuKam5l0H1N5I586d49XXdpExHDinrL7p\n/RdtXpThNoxMDMPixeIsRJLE8d8ZajWdt2z5JpRj9YKzAkQ7oiEjoqIrCbREEDXRh66LGKILIdVn\nRu+KFoT8eSDZTVis/7AJ94lWKF5jAmPOEhP0k+xgcZrOcXm1CLZ8jHQIOZXgyuVLJNMqjim3Y/WW\nT3hMhmGQ7NqPMtSOIVihZA34l4KtAA0rGhYMwQ52v+kqaMuDdAg9OYCSiqDaSpDlDLpugnWCaEUQ\nJYRUH0L/EQQ9gyBKpNMKFy9cuOXzcSK9nddBdl3Hjp1AzpgRU2ZE7wyz3oxsI0W4CsuJthHXPtX8\nMWvehPrUBMTbzOXsRebfiS6It5v/JogmGKfGId4K/cfMz3QF3FOhbAOGawpILtS+YyiRZhPum7IJ\ni7ecVDJFWk6Pf74JIjhKzKjndB+CoSM6fFjzp+GsXI29ZBGZoQ5iQ2E2b97EX/3VX3H//fcza9as\nMfb6qpJBkZNoyX7QVQxHqekup6VGwC5MmDB33KNAtWyMbO7k0mHgmAmuFSwC28hgjByB4EEEdBxl\nS7C4i80StDoRbB7kSDuR8ACbNm1i+vTpBAK99PV2kxxsQbB5Ee2+Ca9Zw9BRhttJde7DaTVYu3Y1\n27dvRxAEfvWrX9HS3onFPx+rp+yWzjfzfUJDjQcQ0HMRV9f7/pIlS+gPBQkGepAjbcjhJtB1dAQE\nXQU1CbE2CB1FiLWCnkZwVUHpOnIj2XY/gjJslleiAywe85x8k809AFoGIdmLEjg47thHa9euXezc\n+TLJERc10VmIEr48suOi6caXPw8KF5nRwN4asHpMRz81Dsk+c59clSCIGFoGQRARk50I6KiJIBZv\nJaL1xgCFmhwk0foqaGmSyRQtrS2EI0OkMxqyopOWVSJDUVrb2jl48AAnT54A+JN85rzf9VafuQCi\nPQ853IScjDJrVu0tzf6b1PX1XmpHvJeOZVI3VmdnJ9/+9rdIRPuYOSXJX9yf4aN3q8ybrlNTaTB9\nisHiWTqbV2r48zRauhS6ewa4fKWF1atX39Q7qKZpvPzyyzz11FO8se91gr2txIaDpOJhYtF+Ar1d\nHD16nEOHjwB/ms+m4eFhfvYfP0UwYux4SMFpv/EyWXlccLhBoj1gZVZVmuJCaVyHcb7HYP9pCz0h\nC6vq0kRiEs+94uXZV/K50mmnd0CkPywQHBToCAgcabBx6pKdRErkYIOJNt4gAAAgAElEQVSLZFqk\npFDlzmUp0rLAN//DTzItsGZBir96KDwmYjerkXk+FObpLJmVpq3XxsAQZDI6X/1EBvt1TCCONIj8\n7L+tgMGHN0X5ywfCVBarSJKBJBrYbQYOm8HUMoVVdSmKC1QutDo432xhIKIwuyoOmN/tDtr4/UEv\nv9yVRzxpoarM4PPbM1hFld5+mfb2bpqa21i1atVbbhO98MIL/Pa3vyMlqxRXzGXOkruYUXc7pZWz\nKa6opaxqHlNqlmCxOYlGBhgYCHH61EmWL18+Lq5UURQef/xxzp+/iCDZ0TQFQRBx55Xg9hWbkai6\nbsaqihKiICFarFgsNjN6Sx0Z7BIlJIuNYNcFEtEQAOlEBIvVic3hQlXSJONhBgNXCHY00NdRT3/P\nRRLDIVRVJs9fRd3qD5NfXA0IxIf7uXT6FcLBNpzufOat2IbV7kJVTcdGBAlJtCCKlqtRUoDDk4/T\nlcfwYBeCAHOW3sX8FR+grHoehSXT6G49R1+glw984APU1taSTCZpaGjg0qVLyLKMnE4hp5MMD/YQ\nGx7Ek1+GJ6+MWKQXOTWMv2KOOQEv52BoXvvGSMyy6bBnRvUahkF7435SsUFKquaTXzTVdLlQZC6e\n+C2Z5DCFpdOYOmvlSBmKOFx59LafZ6A/yJYtW/D5fMyaNYszZ04xNNhHT/t5dFXB5S3AYrnaqZ1K\nDNF++SgXT72CkhqitMTP17/+dfx+PwA/+9nPGAgPUbtg4y0DfqIoIafiRCMBnA77TU/Uq6mpYerU\natpam0nEhwgH2+lpqyfUfYFA2xm6mg4z2NeEnBxCVWVcc6ZRsGHZmPupo7qMTHAQNRwl3dSNLmeQ\nfG4k59gOfSUcJXWulaED9dhVWDCvjs9//vPjrrHf/OY3XLjciG1aGaV3rUZXNBJXOhGsFixFeeRv\nXYVn1TxspYXmdrwuLIU+HDMrccypxpAzqP1DpFt7sRTlI7rsSKKI3NSDIKtYEIi19eCaVo7kuP6N\nNXyykdCrRzEMsx9OF0GwWbFVFuFeORfXkpk4Zldjqy5FEyAZHibQ18exw0coLi6+KceDSb07tHPn\nTi5evoRzdhXe2TcGZEdLEAQMRSXV3Y+uqDfsj5jUrem91I54Lx3LpG6s1157jV/84hcA3Hnnnfzl\nX/4ld999N7W1tUybNo2ZM2eyZs2anPNnS0sLFy5cyDnL3YwikQjPPfccP/7xj2loaCAcDpNIJEgm\nkwwPD+ciVi9dukRhYSHFxcV/zEP+o+jw4cOcOHGCmTNn3rR7H5hQVzQapXlk8sOSJUtwu91j3mEk\nSSKRSHDlyhUikQiLFi3iwoUL/OpXv2L37t20tLTQ2dlJb28v3d3dtLS0cPLkyVyc78WLF3Oxmdm4\n19LSUjRN48KFC3R3d4+JW32zskBW9n4gSRIOx7VndY12q0smk5w7d47Ozk5SqRRw1aFOFEVisRhd\nXV1YrdYcNDga7rPZbLjdbpxOJw6HA5/Px+bNm9m2bRuxWIxQKMTp06ex2WxvecJTKpXiX/7lX7h8\n+TIul4t77rmHHTt2sHnzZpYuXcqyZctYv349y5cvRxAEurq6aGlpobe3l2XLlo1rv/b29vLYY4+R\nTCbJz8/POdolk8kxEbWSJOWgPqfTic1my7ncgQmyZY85W16Dg4P4/f7cd6PRKIFAgO7ubnp6enLm\nKoIgsHXrVh599FGKiorQdZ36+nqeeOIJIpEIU6ZMyTlGZuvearXmfrLOjKIoUlpaSjKZJJFIUF5e\nzhe/+EU++tGP5vpELl26RDgc5sEHH6SsrIxAIMDZs2dpa2vLgZ6yLBOJRFAUhfz8fCRJysGPsizn\nYL7R7o9gwjjZON7sNZGFtYqKinC5XAiCQCKRoK2tDbvdzkMPPZRzGfV6vQwNDdHa2oqu6yxZsoTq\n6mrsdjsXLlygsbGRy5cvI0kSJSUlY7bf0tLCSy+9xEsvvYSiKCxcuJBHHnkESZIIBoM8//zzWK1W\nHnjggXERuTdSQUEB9fX1RCIR5s6de1P3PFEUWbFiBZqm0dXVRSQSobm5mfPnz3Px4kVOnTrF8ePH\n6e3tJRaLoes6X//611myZEluHXa7nfnz53Ps2DG6u7s5ceIEkiRRVlaWgy3B7Me6ePEiv/vd73j1\n1VcB2L59e869MCtN03jyySdJpVJ8+tOfZsOGDZw5c4ZAIIDH42HdunU8+uijbNy4kSlTplBUVERx\ncTE1NTXceeedzJ8/n/b2dnp7ezl9+jSLFy9GkiRUVeXw4cOoqkokEqGvry/32bWkaRqPPfYYJ06c\nyDmHOhwOvF4va9as4UMf+hBbtmxh9erVzJw5k0QiQSgUoq2tjRMnTrBgwQK8Xu811z+pd5d+9KMf\nEY/H+chHPnLNdIlrqaCggLNnz9Lf3095eflkW/ht1h+jHfEHAX7vph+YBPwm9YdJ13WeeuopDh85\nRlIRsE9Zh3PKaqyeMkSrC9HiQLS6sLhLsRXNQbDnkxrqItDbTX8omHuZvZ7eCYDw3aC3Cvtky0+J\ndWGk+zEcJdidvgm/o8Z6INkHzjKId2D0HzaBLjWOrqYwNBldTaGlhzDinZBoQ4i3mnGqohV8NSbA\nF72CMXTJBMrKN5mufVmKSoAxRJUgINh8YPNDspdMKoEh2XBV337Nus8MNiL3XzDhvopNGM5SQLwK\ntgnSCNBmMd0CLR4TJEv2oKdCaKkIWLzmvS7rRtd/DOKtCOjYCqbjmXkvoqPwls/HifR2Xgej15VS\nBBxVt6MlBzD0jFn2tlEDH5LdPH5BGoHclJEyEszykUY+i3eajo3ooGVGym0kAjUdNAHO2BWINkMq\nYIJeYEJVefPMiN5YO3roGFqsCyQrUulqcBQjp2UTpMxWfjYeOLsNY2Q/JRdCug/R5sYzYyuS3Ydo\ndeXgufBgP4Zh8MMf/nAMxBsZipLOqICBoWUwlJTpwpiNE7Z6TAAMYwTmy+YWjygH+Ynm/iW7TSdL\nixvy68DQEZI9JtxnKFjzp+EoH3sevBnmKSsrmxCWMwwNBAlDU9AzcTKRZlJdB9CHW3DZRdauXc2O\nHTtyDbWXXnqJYCiMvXQponXsoOZNSZDIhJvwuBzjGkUTSZIkli9fjs/nJRIeQE5G0RJ96NEWjFiz\nea2ngwiGYpaLIELpbWYZ57YpgKsSQY2OQH6dEG8345YFyQQtlRFQdOAEQqINj8My7tizyrr39Q8O\nY5+yDounDLnvDGo6YtZRyWooWvkmeNhlXu/eWhNYTgVAHjL3yVkBhoYk6AiJNrweJ5Jg3BSImRlq\nIdn6CoamYCCg6waGaANrnnmuFCwaiSquAqsHTU4wONhPQ0M9oVDwT+6Z837XOwnYTurm9V5qR7yX\njmVS11cymeSb3/wm8eE+ls9N8dcfUygrMpjoldJigZpKg2XzdE5f1OgJDBMZirF06dLrbkOWZZ58\n8kn27nmVdKKfkvwkH7xD5uG7Mmxdq3L7YoXiAoXgQIaBwSHOnb9IoC90w47Ld5s6OzvZt+913A6Z\nvkGRN05JnLwo0dwl4nEZFFwnJbYo32BwSORyu0hDi53plRql/rEDJAU+aO0R6QlJHG5w8toxN11B\nE8hZVCvzwXVx7lhqOuRVFKmEwhKDwxbqmx0kUlKuTu9aleDwORdHzzuZWq7w1w8PIpnm1Lk4WsB8\nP8+mf4oCqgZ1NTKnLztIZSSWzNYpLph4AmgyDf/yMxuaZvDRu4a5e3U8t31NF5AkcNoNJAtYLWC3\nGZT6VUoKNM41O2hss7H3tIuDZ93sPOTlUIOL3n4bILCyTudLH1GoLoOlc3UW1mqcuaTR3RshGBp4\nS22iPXv28Ktf/RpZMZi/4l6mz7sNh2v8e5dksZLvr6Ssuo5wqJNIOMSlxousW7duzHvif/zHf3Di\n5CmQHKzY8HHsDg/hUBuaquAvm2EO1Agm5GWx2hElCUEQ0TUtB/dJFjsWqx1NzdDS8BoANfM2kk5E\nyMhx1IyMgW46TqsKupZB05QRtz8dUbJhtTlJRIOE+5oIdp6ju+kYseE+JIuV6tlr8BZU5vZFGKlw\nw9DNfZMsWCx2BNE8OZyeAmLhXhQ5QXHFTPL9UwCwOz0kYoMkhvtpa2vld7/7HW+8sZ+e3gBDQ1Hi\nyZTpCg9k5DSqkiYxHCIRDaGpGXRNJRnrp7BkBpLFNiamGDDBP7LuCQK9rafo6ziLIErUzNuAKEok\nov1cPv17EtEQDpePJes+NAbUc7jy6OtqJBkfYtq0qVRUVFBYWMiSJUtoarpCbDhMuL+TzqaTBLsv\nEei8QGfTCVobD5EYDmARDWbPruVv/uZvxgw6vfDCC6RSGWYv3oIk3XpYiqbI9PdeobjIf0vvY+Xl\n5WzZsoWamhrSqSSB3i7SyTiaKiOKEnkFZXjyiklnEjhnVuKoHBtBK4girhlT0KJxlIEISl+Y1Pk2\nMoFBMj2DyG19JBtaSRy7iNI7iNtm57Y1a/nCF74wLtotmUzy4x//mFg6RekHbsPichA+fA5lOI61\nrJD8e9di9U/chjH7WizYppWBpqMEBpHbAjhmVuL0uImdbcIlWZlaVUV0IEzkYitaWsaa50FyjI0h\nS3WHCL5+nMiJiwgWM95OtFmxlvnJ/8Aa3EtnYy3OR/K6kHwupHwPtqpiHLVT0FSVZGiQc2fPMqVy\nyp+0a9L7SS+//DJdgQAFq+uwXe8Bew2JTjvDDc2IBmzZsuWPsIfvX72X2hHvpWOZ1PV18eJFnnrq\nKQA+9alPsW3bNjwez4TfdbvdLFiwgLKyMk6dOkVjYyNVVVU3HFjv7u7mO9/5DpcvX0bXderq6ti+\nfTsPPPAAW7ZsYcWKFVitVnp7ewkGgxw5cgSfz0dNTc3bfrx/TB0+fJjLly/j8/loaGjgwIED1NfX\nEwgEKCkpua4DfE1NDWfOnKGjo4OWlhaWLl06DqCrqKjg8OHDBAIBTp48yaFDh+jp6SEWi9HX10cg\nECAUChEOh5FlGcMw6O/v58qVK7k400wmQzwep6SkBEmScs59b9ab31+ysMzoZW022zXbHqNhwqyT\n2Oh12e32HKSVBcpCoRBWqxWPx4PD4cBut+NyubDZbNjtdtxuN0VFRTz00EN85CMfoaqqittvvx2v\n10tDQwMXLlygpKTklkENwzB48sknuXz5MkVFRXzjG99gxYoV4yYyAfh8PhYtWsTcuXM5efIknZ2d\npNPpMRNWkskk3/rWtxgeHmbBggX8wz/8A+fOnWNwcBBFUUgmkzl4LQstZp3FUqlUzjjB7XaPOfYZ\nM2awdOlSgsEg8XicTCaTg/NUVUXTNDRNy4Fy2bK7cOECJ06cYOfOnezdu5dwOIzNZuP2228f044b\n7aKXBTidTmcO9PP7/bS0tKAoCg899FAuTnX69Ons2bOHUChES0sLzz77LOfPn2doaCh3PJIk5eJ/\nFUUhkUigKAqKoowxV7JYLGiaNoaDyLrAZf92OBxYrVZcLhcFBaYbeywWo729HYvFwtq1a8cZFRQV\nFbF7924CgQCbNm3CarVSW1tLcXExly5dYmBggHPnznHo0CHOnDnD8ePH2bVrFwcPHiQUCiGKIps2\nbeKzn/1srsw6Ozs5dOgQFRUVrFix4pbOuWx5Z4HMefPmUV19cylRoihSV1fHpk2bco6MfX19uShm\nm83GypUrc7HWW7duJT9/bJKTz+dj2bJlNDQ0mM7Q9fXs2bOHjo4OLly4wPHjx3nxxRfZtWsXPT09\n2O12PvWpT0343nj27Fn27dtHSUkJf/EXf0E0GuX5559HFEU2b97MZz7zmQmfKdn68fv9rF69msbG\nRnp6emhvb2flypXE43EOHTpEVVUVqqrS2dnJyZMnEQSB8vLyMW0zWZY5dOgQjz/+OFeuXMFuNydG\nud1uNm/ezF//9V+zbt06Kioq8Pv9FBcXU1lZyfLly1m4cCE9PT309fVx8uRJVq5cOeG1P6l3lxRF\n4T//8z8RRZEdO3bcchpKFgq+ePHiNWOnJ/XW9a4C/P5QjX6Y3KwT4I2WmQT8JvWH6M3OS1Zv+TVf\nyAVBQHLkY/FWkhxsIRjowefz5mZBTKR3AiB8t+j3v/89gWAIDJAHLiL3XyATbjIBAsmKaPNe81gk\nZxFash89NYAe70ay540DWSRnEbo8jJYahOFLGKleE5CyuE1nPv8odyyLB0OJmqCXoZhwnyCCxQu2\nfIzQERMmK149AvddRwIIhg6SDcHmgUQvoihhL1l0DdDGINl1AF3LQMkaE+7L5RxxFdgSLSZsJAhm\nrKzFAZLThH0yYUh0mi5k8ZYctCTZvTjKluAoX44oSrd8Pl5Lb+d1MH5dlYiOApShVrM+PNOuRpHm\ngDbMujRUs5wkO2RdGgwN+o+an/lmmb+19Ii7gzECBY7ActkfACRQhiDWbIKRcr/p/Adgy8PIX4g2\n4Yw8Y2QdxlXYTxBNKCvRiZEZRnKXItnNDuQsPDcUDtLQ0MBgJDYO4rXmz0Cw+VASA6ApIIoIktOM\nEpYHwVkOVt/ItnVygCMA+shx6ebxhA6Zf3tqTFBw4AREmxF0GUGyINp9iBb7mOttIphnIlhOjQfI\nhJtQBi+hDjUjpIM4rQZTqyp48MEH2L59+5gG78svv0w4MoytuA7RcpMZdKNLWlNQBi/h87puukNd\nEARmzJjBpk2bmDWrFgGdQG83miIj2X1YfZU4K1aYzieZmHnNO98080sQwV1l3heUKIKagHTIBCej\nTebvVB+CliDf5+aTn/zEuGPP6s0uapnBS8j9FzEkO5TebgJ7o8/zN++HNc+s/0QnyGEQJARHIXZJ\nw4i1Ujuzhnlz59wQxEx27kcZbDQHREXJ/NziNN1J/cvN+5zVZTqTWr2mO6lnGtjyUBNBOjtaGRzo\nZ8WKFX8yz5z3u95pwHZSN6f3UjvivXQsk7q+9u7dy4ljB5lWnuDLf65gvQlGxeOEuhk6b5zS6ewe\n4Pbbb79m5192JnPD2RO4HTE+/2GZP9+qUlttkOcBtxPyvFBbbbBplUZVica5JpWOziDB0ADLli37\nk3g2NTU18dxzz9HR2YWiGoTC0B8R6BsQaO0xYb8zVySsEkwpHQ9QCgIsnKVz4KyFUNjCyUYrrT0C\nTjuUFBqII82G2iqd149ZCEUkJBHWLEzyhQ9HuGtViuoylTK/RkWRxuypGdYvSVDmV+kIWElnRGxW\nAU0Hv0/jtWNuYkmRhzdHmVqmTnxQY+aciKQyAhYLyIpIW68VRRVYOX9ih4s3TkmcuSQye6rMJ/9s\nKHeMogCyMjIoYbt6XIIAditUlykEwxY6g1YSKRFNFxBEgXyvwN1rNHY8qHDHMh3bKMYo3wvzZ+oc\nrdfp6Opn9uybm/mfVSaT4d/+7d+IxVPMXbaVsuq6Gy4jWayUVM6ir/sKkXA/RX5/bvA1GAzyk5/8\nlIxisGz9R/HkFePNL6WntZ50cojCsplYrY6R8jUQJcvVCW2KPLJ+G9IIoNbbeprhgQ7yi6Yyff5G\nyqYtxuUrRskkUeSE2STSFAwMCktmUDVrLbGhAJoqoyopMukYqXhk5LsGuq5itbuZOucORNEEoQTE\nsZONDAND19B1FVG0jIB3BqIgMBhsIZ0YZsqMpbn9ttrddLWcpaeni1Rawe72M23OWmoX3snU2aup\nnLYIm9PLYF+bWXZV8/D4inC4C0gnh8ikYiSiIdx5JVisdgRRwmpzIogihq6Zk1nScbqbjtDTfAwD\ng6raNahKmo5L++luOko6OYSmKRiaxnC4B1GUcHkLc24XipJmaKCL4uKiXGSZz+fjzjvvZPbs2SiK\nzEB/CF1NoWbioMs4HDbW3baWT3/602zbtm3cfe7FF18ko2jMqFt3FUi8BaWTUYLdjZSUFOViwW5W\noihSXl7ObbfdxvLlyzl0+DCINtZs/gw1c9dis7sJdjWiJOK462aMH6AWRew1lVjKCjA0DX04gR5N\nog4Oow4Oo0ViGGmFirIyvvF332DLli0TAtf79+/n2MkTSOV+CpfPRR4YYuBQPYLdSv7W1Ugu+9Vo\n3GtIEARslUUooSG0SAzRasFdXMjwqct4HA6++c1v0hcIMNAXRO4dJHL2MsmOPhJtvcQutRM5doHo\nmStkgmEEqwXRbgVRxFruJ/++tUhe09FEEEdNGhcFBEkCUcBWUQSCQKp3gPP1Ddy2du3kgNafgHbv\n3k1osB/folos3rdQXwYMnb6EXbLwgQ984O3fwfex3kvtiPfSsUzq+vrpT3/KwMAA9913H3/2Z392\nU8tMmTIlBw6FQiE2bNhwze/29/fz7W9/m+HhYWbOnMnXv/517r77biorK/F6vXg8HoqKili8eDGb\nNm1C13Wampqor69/S7DW/4Q0TePAgQP89re/JRqN5lyh+vv76enpobGxkV27dtHV1YXf76ewcLz7\nsc1mY9GiRbz00kuEw2EOHjxIOBymqKgIn8/si3c6neTn57N//34GBweJx+O0t7fT0dFBNBolnU6T\nyWRIp9NEIhF6enpIpVJ4vd4cRCcIAkNDQxQXF6NpGufPn7+uc19WkmS6rKfTaQoLC3E6nVgslmtO\nSpNlmUwmQ0tLCwMDAwBjIK3RgGDWtc1utxONRiksLMRqtSJJEnl5ebjdbmbPns327dv57Gc/y9y5\nc8e8X02fPh2Px5Nz0b7rrrtuabJcY2MjL774Im63m2984xs3lfbh9/uZOXMmhw8fpqWlZUy/wN69\nezl27BjV1dV87Wtfw2634/P5OHv2LLquk0wmc2VuGEYuGlLTNNLpNMCY+GOXy4Xb7eauu+5i8+bN\nrF69GqfTydDQUM75TlEUJEli+/bt1NXV0dzcTDqdzjkcBoPBnIseQGVl5ZjkgNHmQoZhoOs6mUwG\nVVVzsKVhGAwPDzM4OIjH42HevHm5uozFYjknSIvFwqJFi/jIRz7CQw89xD333MOaNWtyIFpFRQVr\n1qyhsrISj8fD0NBQzskwe/xZuDO7L1nHwyzc53A4qKioYHh4mL6+PsLhcC7ed3BwkN7eXnw+H36/\nH0EQ8Hq9nDlzhnA4zIIFC3LPterqarZs2UJRURHhcDh3XUWjUWRZpqCggLvvvpvPfe5zrF69egxE\n1NPTw9GjR/H7/SxatOimz7fRam5upq+vj/nz5zN9+vRbWtZmszFz5kw2b96MzWajtbWVWbNm8Z3v\nfIe1a9fS39+fgx4XLlw4bnmPx8P69espKCggGo3S399PIBCgo6OD7u5u+vv7kWWZlStX8rd/+7fM\nnTt3wv149tlnCYVC3HfffdTW1vLaa69x8eJF5s6dy8c//vHctTxRmyjXprZaWbJkCW+88QZ9fX0s\nWrSInp4ezp07x6xZs9ixYwf19fX09/fT0NDA66+/zuXLlzl9+jQHDhzgueee48iRIwwODuagVLvd\nzrZt29i+fTtOpzN3n8me51mXSqfTydKlS2lrayMQCNDS0sL69ev/JPri3s+Kx+O88soruN1u7r33\n3re0jt7eXs6ePUtFRcUNJ29P6tb0x2hH3Pq0UuDSpUtvaWMvv/wy//zP/8zQkNmpaxgGhYWFbN26\nlcWLFzNnzhz8fn+OXo7H4wwODnLp0iXOnj1rAgThcO5GUlBQwN///d+zdevWt7Q/k5pUVtnY3JSs\n4piyHsl5c5EqkrMQR+UaUt0H2LVrF5s2bbomGf36669z9OhxUiOw0/W2IQgitvxpSHYfidZXOHLk\nGLNmzXrXzyTVdT33MqGrGfR4gDEOeKkwylA7ot2LvWguNv/c8Z3LgoCjfCVqtBNRT5Pp3k864MTm\nn43FU4EgWjF0BcGeB3rGHAsRXeBfguCZasJyWYsJQ8fwusFdbcJy4TMmBKYrGMOXQbSboJfFbTpZ\nMdFLytXRLAERQ1fNQCBXJbrFjaEnUeO9WL3jZ3ar8V50OQaSC1xTxgyMkev0N8YCPwKmk513Ogxf\nMJ3F9AxYPVic+Yg2L7bCWrMs3uwicQvn40R6O68DYMJ1Wb2VSI58NDkG8gCGs9yE1HQVpBGQb8SZ\nIQfUZZXoNuvL6gX/yCygRBck2kBNmZ8pMZBsUHqnWb+BXaDLCJIdRBuGIJkgqBJBz0RNsGkk4sk8\nd0bF4Br61XgoI3PVbdHiBE8NxlADcuAkNt+UkboTwTMdZeAMqqbgmrYZa171uAEeyeknE76CpmsY\nRSsQXJUIgycwEj0Q3A/+ZaarmyCNOBsKYEhmOWkZSHZC+CxoI+6EQ40gGCPlZoBgwdB1lOFO1OHO\ncdebxVNBKnSWUCiU2ydRFNmyZQubNm3iwoUL7N+/n1AoRCqVwul0UlJSwvr166mrq5vwnHI4HCAI\nGJpyU+fMm2XoCgjCdWduXkuiKLJgwQIWLFjArFmz+PnPf0Eio+IoWZg779ThDoxoE+TPGXtOmVvH\n8ExFcFdjN4Yx4u3omRiGpiCIFrTUAE6Xk69+9avXbZzu378fWdGw+WcDAvJAo3nJ+5eZ0dvomO6d\nxnh30KzsBWZEeOgQQqwFq38OQrITSRIoKyvjkUceYdasWezatYveQB/yYD2p0NlcZp4kCdhQUQQB\nTTBj8AzRZrqT2grGw4UCJlQsiOY9yuJBCR1g3xv7mT17Nnfdddct18ek3nml02kwDATpOtmI15Eg\nWmEkHmJSk5rU+1eGYbBnzx4w0tx7u3pTcF9WU0oNls/TOHYhxb59+3jooYcm/N7LL79MQ/1J3I4Y\n/+d/ZagovvZEP0mEFXU6xQUy3/kZHD92iDlz5rBx48ZbPLJ3Vrt27eLZZ59BV+Lke1QWz0qxqi6N\ny2GQUQRauq0cOOuio1fkx7+z0NAk8r8fHA9TWiSom64TitjRcHKuReVcs4zTruB1Gei6wMCwwFBM\nwOXUuHt1nHvXxREFg0QapJFHvm5ARgEDgQUzZCpLBvm/vyokGLEST4m8dMhDf8SCz6OxfF5q3KuC\nkfvPiAQB2TSVQxRF1i5Msuu4m9OXRKJx8L1pIrphwJ4TEmBw53bCCaUAACAASURBVPKRCTZCblW5\n70zUVywKcNeqOEfPu1A0gdJChf/vL6IUFrhHHOYmVlWpwd1rVX63L83u3btzAy03o+PHjxONxfHk\nld4U3JeV1eZk+rx1NJ58iT179nDnnXciCAL79u1DVTVKq+bhzS8Z+a6Dimnz6W45S3P9K8xfsx1R\nsoxAdBoWixVNM12/BUFEHHm+R0KtdDeZsdVl08xIIUEQyfNX4SuoQJRsCKLpatd15TBWu4uK6UvJ\nL5lG/YFfoKky0+s2IlntiKJEMhai7eIBCkpqsNocZjywZBmJwL0qXdNyToCakgaLDYvVTkHZTKyX\nDhAbCpJJJ7A7PYCBxZ6HzeFBUdLMXnI3lTVvmgznBKFXwGpzUFAynZq6jYCBzWojkxqm/shvGB7o\n5NzB58grqqJs6hKcngIEUSKTihHquchg7xVUNY2uKoiSlY7LB01gUVMxDB2LzYnd4UVV0gwN9jIc\nDnClYQ+1CzZQMW0hdocbwzCj1UYr6/5QV1dHPB7Pub04HA78fv91QS+Hw0EimUFVZGz2WweMVEU2\nDcb/QJispqaGZUuXcvLUac4d+2+W3v4w/tJpuNz5xGJh5K4gjqnlY5YxAE3NYCnykb9+Ee6NK0j3\nDaKlM2AYDBysR0CguLiYF198EZfLlXOIKSgoyK2npaUFVdfIn226bQyfawbAMaca0WXH0HQE3cAY\naY6N6wsiOx9SwLVoBpnOIHJzD1GLFZsosXLFSnw+H1/+8pdpbW1lz549HD16FLk/ik4UABsCHqeb\nqG6Az4USjSPYbeRtXYV0rRxxQUCQBESHDT2VwTG/Bi0cI9kTZu/evXz4wx/+g+pkUn98ZSfg6ap2\ng29OLENTQSAHFExqUpN6/6q3t5fGxkZsNtstjzlu3ryZnTt30t7eTmtr64RwimEYPPXUU0SjUebO\nnctXvvKVcY64o+VyuXj44YfJy8vjl7/8JT/5yU9yLlvvVsmyzFNPPcWZM2dQVZWysjLWrVvH7Nmz\nsVqtJBIJTp8+zenTpzl58iSnT5/mE5/4xIQTTktKSigoKCAcDpNKpdi7dy979+4lLy8Pp9NJKpUi\nFAqRyWSQZZlz584RjUYRRTHnAAcmKKaq5kSmzs5O4vE4CxcuxOl0IkkSxcXFGIZBMBjMOaiNVtZs\nZqKIVMMw6O3tJS8vj0wmM2F9ZoEzRVHo6+sDGLP86HW/WZIkMTQ0lHOvu+OOO/jYxz6Wc4u7ljZt\n2sSBAwfo6Ojg2LFjrFu37rrfH609e/YApqvtzcB9Wc2ZM4fly5dz/PjxXL+AYRjs3r0bgPvvvz/n\nJLZs2TLKyspIp9OUlJQQj8eJxWI5xz1JkshkzIan3W7PwX0OhwOHw0FeXl4OQHE4HCxZsoTFixfj\n9XpRFIWf/OQn9PT0UFFRwYYNGygtLeXnP/85Pp+PT37ykyiKgtvt5tlnn6WtrY2pU6ciimIOrBtr\nlmCgqmrOFTAWi+HxePB4PNTU1NDZ2cnx48dz/SCaprFw4UJeeuklRFHku9/97rhr1u/3I8syHo+H\nBx98MHcsLpeL/fv388wzzzA0NITH40HX9dy7TtbdEMhBYuYEJiUX/5t1+HO5XFitVlKpFIcOHeLo\n0aNUV1ezY8cOqquryc/Pp6Ojg2QyOWbf7HY7GzZs4I477mBgYIB4PI6mabhcLkpKSq4ZvZsd18lC\niW9F2WX/kDaRIAhs3ryZ119/ndbWVn75y1/yiU98go0bN7J3714OHTrEhz70oXGOoLquI8syS5cu\nZdWqVQwNDdHd3U0ymSQajfLCCy8giiKGYfDTn/4Uj8fD/PnzWbly5ZjrvqWlBYDVq1ejaRr79u1D\nEMx4aEEQxt0nrgXOeb1e1q1bx2uvvcbRo0dpbjbbVmvWrKG6upp//dd/5dSpU+zevZsrV65w/vz5\nMctn3T+nTp1KZ2cnixcvZtu2bROO7WX3wWaz5e5Fn/nMZ/jWt75FS0sLra2tb8lMZlLvnLLnVDbO\n/K0oe8+dbBP9aegtAX5vRU888QTf//73AfNlxe/387WvfY0PfvCD15w9UFhYSGFhIbW1tdx33318\n4xvf4L/+6794/PHHGRwcZGhoiK985Ss0NTXxpS996Z06lEm9B3X+/HkCfUF0yYU17+asf7Oy5k0l\nHThJb6CPCxcuTGhd+k4AhP/TUhSFH/zgBxw9ehxZBcOaj5BXC65yE1gzFEgGMaJNaHKMVM9x1EQI\nV9XtCOKb7wE6osVOZZnZiT4RyIJhPqgEiwvK7gCrz3SzGt2hN2KUJ1mt6FIVusUDoYMmMKdEIToC\nK/tmjrjEvVkGGDnaC0NXwTCQLOYgle6pwYheJBNumhDwy4SbzA5qXy2GIJj7nhuMGvXilgXcxo6e\ngXcGqA0mlCjasVbdhcNhv2493Mz5eC29ndeBYRgTrksQBOxFc0n1HMcYOIlQsQlDco5AdPrI6N4I\nCCWMqhM5DAPHzf/3zRo5BzTTkc1ZaoJwGND1e9MdUBDA6YeSNRAyHQyouhfB0DBUGTp/Y27DXmJ+\nNpHLgmAByTJSPyORwXrGBAhdFTB0HjU1kGs0ZWQZzVoICIiOAmz50yYsqxz4aXEjeGtMYNW/DEkU\n0WNdGKHDJnTqm2k6umWvn1TQdCFUE6ArCJhufCbUZwPfDHBPxcjGZ13zers2zDMalrsVlZSUIF1p\nQY0HsLhufQaCGu9FkgRKSkpu/OXraPPmzVy5coXDR46RaH0FR+VaLL4qRLvXhEoT3eAZdW7rGoae\nQQBsdjtO9zQE/7Tcx5mhNtJdb2C1WPj1r3/Ns88+i8PhyAGP8+fPz92TQ6EQmmZg85SPgXsFT7UJ\nPuYgVkbuK9eAGlyVZv2rCWxamPTgZdxWifXr198QxCwuLqaxsZFAMIyAxXQPLVppgoPXGQg3nUSt\nJohYsBhl4Di//vWv2bx587v2mTOpq/qfBGwnNalJvXd06dIl+gLd5HkUlsy+sWPBm7Vphcqx82n2\n7dvH/fffP67TV1VVdu3aBXqSHQ8o14X7RmtahcEn783wwxeTvPbaazlw6t2o119/nWd/8XPQotyz\nNs1tCyLkuVXyPFcBthXz0jx4Z4wj553856s+jl+QUDQrX3hYQRr1yNV1aOk2Z09/4YuPEgqF2Lt3\nL/2hIKlhs+xSsozTnmFlXYaH74ZMxoqqqmSUN5etkIt6KhRS/O9tEf7l50XEVYnOPgtWixm3OxH7\nkn1jyYJ+qgqyYn4iiiJ5HpXqMoWOgJ1QRMDnGbvtnn6B3n4Bn1tl6axUbp1jtiGYTThZEVBVgezY\nliBAmV9lanmGxnY7wbCF1m7I82lYbhCBescylf9+Q+bM6VOEw+EJXUEm0p49e1BVnRkzltzyeVZS\nOYsr9U46O7u4cuUK06dPZ//+/aiazpTpi8d8t3bBBqLhANFIH+cOP8/MhXfhcBegqwq6KKGPPNNF\nyYqua/R3X6Dtwj4MQ6ds6hJUJUVPywk0NYMoiri8RRSUzkQURcqqF9LdfIxIqI10chiXp5CSKfMI\ndjaQSoSZNu8O5GSMy6dfAsDu8I1Af6PL9Go9ipIJ/umagqZm0NQMCAJWqwOr3YWciiKn49idHtJp\nGU0zXQGt6TievOIJy7G3rQEDKJ+2BKvNiZJJoSgZXF4/Kzd8nIMvfx9FSTE82EU03DNueUPXsdk9\naBbFdCPUTciypHoBlTNWmPtmc2IYOgO9lwm0nyURDXHx1CukkzGsdvOd53pOJtlBu5tVRUUFkaEY\nA30tVEy99Tib/r4WRNGMV/pDtWPHDkLf/CYdnd2c2PcssxdtonLaIq5c3Mfw8QvYyosQR6wvdV1H\nU2V03Yz+cns8WCwWPDOcyJkMkVONaMk0ZFQaW5oAAUEAy1GR3/7udyxftox77rmHGTNmkEgkMADJ\n5URXNWKN7RgGuOfXIFgkDFXD0DQEwTzXjInaQyP/ZK0owlLgRYvEidZfwSVZcxMJs07uM2bM4KMf\n/SgdHR0kEgkkScLr9fL0008TbW9FcNogJuCYU43kvH4/CphOhoJFBMXAMbeaaEeQ/fv3s23btuvC\nF5P6n1d+fj6iICCHIriqbh5EyEoORRARxsW1TWpSk3r/ad++fYAJUNwqYJKN+HzllVfYs2fPhIBf\na2srLS0tuN1uvvjFL9708+Wee+6htbWV48ePs2fPHh5++OFb2rd3Spqm8b3vfY9z587hdDrZvn07\n8+fPx+l0jnmvWrt2LZFIhJ07d/L666/z9NNPI4oid9xxx5j1dXV1kclkKC4u5u/+7u/Yt28fhw4d\nYnh4mOHhYQzDyMWydnd3oyhKLj41CyqMVtYhLxKJ0NLSQm1tLZIk4fP50DSNwcHB67pqgTm2nQX8\nstsKh8M5V7WJpCgKmqYRCoVyoOFE28lkMmQyGTRNGxPDahgGZWVlKIrC6dOnefDBB3G5XDd0Rd60\naRM//elP2bNnz00DfpFIhNOnT09YHzejTZs2cfz4cd544w3uv/9+rly5QjAYJD8/n8WLr7aJLBYL\nX/rSl3jsscdyx5qNMU6lUrhcrhyokr1OXC4XTqcTn8/Hfffdx5kzZ0gmkzlXpFmzZpGfn48kSaxd\nu5bnn3+e3bt3c8cdd3DnnXeyc+dOIpEIbreb6dOn09TURFNTUw6Estvt4/rBR0flWq3WXP3E43G8\nXi/5+fkIgpADNw3DIJlM4vF4EATz3WIiILerq4u2tjbcbneubpLJJKlUio0bN6KqKj/+8Y/RNA2r\n1WznZ/cl6z5psVhwuVzE4/EcGOf1epk9ezbl5eVYLBZ8Ph+pVIrm5uYcqPXYY4/x5S9/Oefwd602\nkSCYE3xuFiguLS1FFEUCgQCxWAyv13tTy2WlKAqtra1kI2f/EPl8Pr70pS/x3e9+l7179zI0NMRD\nDz1EbW0tTU1N/OY3v+HP//zPAXIAZ9ZJUpIkXC4XHo+HiooKEokETzzxRO7avXjxYm47R44c4bnn\nnmP9+vXcc889eDye3Jibz+fj3LlzhMNhSkpKWLRoEbFYLAeKXqvcR8O+GzZs4LXXXuPAgQMYhkF+\nfn4OBrVYLKxatYpVq1bR19dHMBgknU7ngNjHH38cq9WaM826++67bzjOk40Ll2UZl8vFypUr2bt3\nL3v27JkE/N7lcjqd2Gw2ZFkmEAi8pWuoo6MDgLy8vLd79yb1R9A7Avg988wz/Pu//3vu74ULF/Kj\nH/3olk8SSZJ48MEH2bhxIzt27KChoQGA73//++Tn5/PJT37ybd3vSb1/NNp56VbjVARBxOafjTxY\nz/79+ycEY/7YAOH/tHRd5wc/+AGHjxwjpQhIpbeh24pH4lVH3WbsftNBK9GN0X8cZaidJOCqvmNM\ng0SN92KxiMydO5dHHnlkHMjicDhoa2sjjoKj6nas+dWoijrSoNNyDKAomtbmFquFjCyTNPIxChaZ\noJguQypk7qOz7MYHaYxEwAqG+ZKTkU2XteGLZvznROUim7PIGR3Ne3WFI7900OSJt2krJDvCZShx\n5KFO7GW118N0bup8vJbezuvAMIxrrsvmn4uaCKEMtWP07kYoWoZhLx4p35HvZoFLQzOhrIHj5uf2\nohHQrdWE7QSL6eiXheF8MyHcYEarusrBXQUWF4YSR0j1mcsHdptAi2g33fiEaw3sZEcXJdNGRpNH\nID9tBCgUQFPM89VbiSzLGILlhmU3GvxEtJig3giIZbO5yIQaMJRhiJwzf66WsvnL4gEBjIwJCVK0\nEtxTRkZHZUAHi/2a15utqO5th3nWr1/P0WPHSQxexl5cd0vnj2HoZEZBbH+IRFHkc5/7HIIgcOTI\nMVLd+0lLTkR7Hpo8DP3HzDKz5eWAXWFkpr7L5bpq3gikh/tIdx5EUGUS6Fy41HLVKe9KC0ePHaei\nvIzNmzezefPmMS5qcv/5UXVsHeUEqY+cb9eBGgQRwTcTIg3IobOIWoqK6inU1V11kbkWiNnQ0MDR\nY8fRBasJslrcpjPfde8a2ZVKoIvgqsCwuOnvH+TcuXNv2VJ/Uu+c3i2A7aQmNak/bbW3t4OhsHyu\nxi2k9+RkxuxqDEeHiEQi4zqBz549y/DQAGWFCgtrbw0gXFmn8/xrCn2BbhobG2/Jke2dUlNTE8/+\n4hnQonz6gzIblunE4iKqKpBRDOyjJsDarHDHkhQ15QrfftrPmUsSv99vYduGq/G451tE+ocs+EtK\nWbJkCZIksXXrVoaHh3Mdz4899hipWC8fvEPHbrNht9nQdA1FUXKdwoIgYLFYsYyq1DJ/igUz0xw9\n50LVzBlRNqsxzklvtHufAGQ0gWRawDCE/5+9N4+uqzzMvX97Oufo6GiWLFnyIM8jHjB4wNh4wJA4\nJAR6aRqalKZNaFN6m5LctXKbtZp126a99yb5CLfJx/3IJbmEECATlEAx2Hi25dmWZVu2ZEmWrHme\nzryH9/tjn711JJ0jSwQaaPWsxQKOzt773Xu/e593+L3PQ0aGzwaDTIMMj/2lcHT8AoahoL3DmYUG\nasKs25GzLsuyYDg8vu0oBFimRHG+QU2TF9OEYxf9LF8QQfVPPJSVlwVrlpicr7GdPCYzORWLxaiv\nr0cgUTwrddzORJJlhZLZy2hvvEB1dTWZmZkMDwfx+nPJzhs9uKqoHtbc/QiVx3/NUH87V079Cn+g\ngKKyZWTmlCArCoYeY7i/la7my+jxCEJY+DLz6Gy+REdToq64N0kiIyufkjmrKZq1nPzihfS219Df\ndYOZ5WsombuazptVdLVcZfbCTVSfeY14NISiagnXPjWpPy4QYmy/VbhOgqYRx9TjaF4/ICEQ9HXd\nJDuv2O4POXVGllP2CSzLJBIetN0HC+fYdVTzYsSjxGIxwoPtyIpKTtZsiufcRl9nnX3+loWqecnK\nK6WobDl9nXW0XD+Fx5vJglW7mDFrxUj5jDimqaNpPopnr2TGrBV0Nl+m4fJ+Gq4eJzt/JpLEpCed\n2traOHLkiDtp4vF4KCoqYuvWrcyZY4/vbNu2jZra67TUX5gy4BeLhuhurSXDq7Jly5YpbZtKmZmZ\nfP3rX+d73/se9Q03qDr5Kh5PAFX2EO8ZpGfPcfJ23YlQ7dhjAFmSyMzMRFVVe2IyFGLwcgPBU9VY\nukH28nlkr5iPpCpY0RjB+laGGlo5cqKCM2fP8oU//uORSSrLwgiGsXQDJSsDNT8bYVqYpoXQTXsx\nmixP2CVSFBnvnGKC3QPIpsz8ZYtTTioFAoFR/aS6ujputjRjeVTMnkEA/CvnTfraSaqKMEyUolyU\nvAD9gwNUVlZy5513Tnof0/q316ZNm6g4eZKhS3Xk3r5kyoD2YFUdHlll48aNH1AJpzWtaX1U1NjY\nCPCe3/vr16/n7bffdvczVo4z2tatW6cMEN5///2cPn2aI0eO8PDDD38o4fNXX32VS5cuEQgE+K//\n9b9SWlrK0NAQuq5jWdYouCQvL4/Pfe5zlJSU8OKLL/KTn/yEOXPmMG/eyO+2c73uuusu5syZwx/9\n0R/x2c9+1o3fvXnzJj/4wQ8YGBhgcHAQr9eL1+t1AZpkxz0njhdsELGtrY3Zs2eTmZmJpmmJvs3E\nTrCSJLnRvI5DWigUcsGfdC58jqtaMBh09zNWDtyXvA/nv4eGhlyIbWhoiAsXLrB169Zbuixt2LCB\nF198kYaGBoaGhtxo44lUU1ODZVmsXr16lFPzZLV48WKKioro7u6mra3NdR1bv379OKCpvLycr33t\nazz99NP09va6Do8OsKaqKh6Ph0AggMfjwev14vP5KCoq4uc//7l7XR3t3buXJUuWsHHjRlasWIHP\n5+PmzZv09fVRUFDAtm3beO2119i/fz9+v59vf/vbLlzo8XgmXPzjHMsBaCzLIhKJuPcgGAwSDofx\neDwYhuHW93TPaXt7O2C7HjquhoZhuG6UDvy3fv16VFWlqamJSCSCLMuuc9yiRYv41a9+RSgUIjc3\nly1btrjMRSgUwjTtsQG/38+qVatYsWIFp06dorm5maefftot22TqhWVZXLlyhVOnTjE4OIhhGPj9\nfubNm8fWrVvJzs52IU7HmXOqgOjly5eJRqPMnz+fuXPnTmnbVFq6dClf+9rX+P73v8+FCxe4cOEC\npaWlGIbBO++8g8fj4YEHHnAhYbChuczMTGRZxjRN+vv7eeGFF1x3vM9+9rOUl5cDduTmkSNHuHnz\nJnv27OHkyZM8+eSTLvhrWZZ7n1etWuXGcMdiMSKRCJmZmaNcPFOptLSUGTNm0NDQgN/vZ9u2bSkd\nFEtKSigpGZnj/sUvfgHYz1hdXR1lZWVpI4XHynkeotEod911lxux/bnPfW7aHOBDLEmS2LBhA0eP\nHuXgwYM8+uijU9o+GAxy6tQpwP7tmNaHXx844NfU1MR3vvMdt9FSXl7OT37yk9/qRZCbm8vzzz/P\nQw89xM2bNxFC8N3vfpctW7aMagBOa1qTVbLz0ntRqsjLZH3QAOHvWmPjhy01GyMYtONs5TGvGUm2\nHbS0LETbfvSBRuKZxXgL7QbGWNgnFchSVVXFt7/zXVADdgQqoGkq2gRZYp5E5y7mL7WhF90YAW6Q\nxmdDJTesHAc3BBKS21A3Es4R6VyThJXo3EkpyiWSJzZlG65xQTM7XhjZk/RdHWuwAaNg3oTnCbeu\nj+n0fj4HQoi0+5IkCf/sLYTBhvy6T4KSAZnl4CuyoVBJgkj3iGOdMAEZot3jM7z0QQi3ghawnfWE\nACOYOJg8Av0N1YG4DrEebHtHObFfkvYpkiYaknEvKQFqxUEYtqNeItk33ncdfMWYpmX/TZImjMp0\nwU9/YmW5rIAl2Q1/IwqKFykwDzBBDyYcHDX7/LIWIGL9dkSvkohe9SZ1tmUFLAthmUiKkvJ5E2Z8\n0jCPZVlcvnzZBWyj0WhKB7uVK1cys6SYhsZW9MGbad0LU0kfbEoJsb1XaZo2Lso2Gu1AsgyE0BHt\n+yF/LZK/FEXV8Hq9eLwe925blkWoqxaj64ztBqloqDPuQMsqRVI0hKljBNsJ9dZQf6OFthde5Pr1\n63aHPuGiNvYeS7JqQ5yWbsOikpRmQivxHsoogb6LGMF2sgMBdu3aNSknPee3BsVvA35Z6dxJU0ly\n6w+BeZgDl/jNb34zDfh9BPRhAWynNa1pfbRlx7IIAu8xCUWSIMsvGAyndgk+evQoWFF2rDdTxrFO\nJFWBbetMXj8c5ciRIx9KwO/tt99GmCE+dlecbevs9qUzuRTTBR7PeNx+TonBE/+pn+/+rIB9JxU+\ncbcdjSwE7DupgORj+/bt7kSD4wCQm5tLRUUFkfAg5aUG88tGGhWKrKB4FQTCHVQWwsIwBbKs4PV6\n0eM6d68Kc6EmA01APC4RiUkMh2Q8HoFHFaPuUdyAeFzGsEa6SE50UTweJxKTEIDXM75xoyea2qqS\nAA6T/hbTZXd/sgRezT62w/1YFsR1CU2xjxs3ZK41etDjQ1gZFvItfu+K8wVgMTycejHWWAWDQQS2\nE5yivrcJU5/fnhwJhUK2m5kQeL2p3TU8Xj9L1u7iRvVxejsbCA11ERzsGokwBtfpXlE09HiEWNgG\nlrILZhPInoGsqOjxCP2d9USCfdyoPkhr/RlyZ5QDYMTtZzEzu4iMQAHh4R5qLrxJcKAdSVaQJJl4\nNDi6YEm30TahH/FxtB0FDYRlYegx9HgYBISGe9B1Ox5XWJZbzlRRtaYed8/JuS6KrGJKEpZl0tPe\nYLuUzLmN0vnrKJu/btw+wsF+WupOgQSL1nycwtIl7t9kWcXEXvg3ch4SJXNuQ5Jk6qreoaetjqys\nLNatG7/vZFVWVvLOO+9QXV2NblhYYqSvKMsSe/fuY/HiRezatYs777yTl19+mZ6+Dvq6msifkXpS\nyj7HegZ6mtHjUSRJZnigE2EZrF69lsLCqS/WSKXc3Fy+8Y1vsH//fg4cOEBnVzdYBlYsRqy5k+5X\nD5KxbC7+RbPwZgXweb3IioKwLPquNzF8uYF4UycIwYwdd5C/fnQ/LWtpOUYwTP+ZqwxfqudHP/4R\nixctRkYi1juIlmu79Egee+xCUmRkTcHSTayYjuzVGGVb6ih5WEa2x2l8Hg9/+qd/Oiloq7GxEdOy\n8JQWELnRjpLtR82dvBOjpMj2givJQi0tQL/aMuVxlWn922vVqlXMKCzkZmc74aZ2MstLJ71tvG+I\naHMX+ZlZ7wtgO61pTeujLSeqcjLASyo5iwfGRl6CvZjEmTzftm3blPftQC9NTU2cP3/+QzcBH4vF\nXCDvL//yL5k1axaA63oWi8VSzgnfe++9tLW1ceDAAd555x3+/M//HLDb5hUVFQCj4ns1TaOgoACA\nf/mXfyESidDX1zdqn6qquhCMA/qZpukCepmZmQwODtLZ2cmCBQvw+/1ubKaiKIk+1Oi+TXIsL4zA\nfs48kfPZREpehDX2M+dzVVXdYzlQmRNbK4QgFApRU1PDhg0bbgn4eb1e8vLy6OzsJBgMTqpeOxDi\nZB3Qx0qSJPLz8+nu7nahNyCtS+6iRYv4/Oc/z969e93xdb/fPwrO9Pl8eL1eVFV13alkWWbevHmu\nM+Tw8DBNTU1cvXqVmpoaFi9eTHZ2Nj09PQwPD1NQUMDGjRt57bXXqK2t5emnnyYYDLrXOtUz65yP\no2TILxqNous6oVDI/XtjY6MLpjlukOkWFDmOe8nPhNfrdUHP6upqhBDcc889afsHhw8f5saNG2ia\nxvbt20fFNmua5gJ+Tj1RFIVNmzaNcrwsLy93Fyylkq7rbn+itbWVSCTiPlOyLOPxeHj11VdZv349\nu3fvZufOnZw/f54zZ86wYcOGcTG4jsLhMFVVVfT29rowp5MKtnPnzvcttWH58uX8t//233j77bep\nqKigra3NnvsJhfiXf/kXrl69ytatW1m1ahV+vx+v1+vWh0OHDnHo0CFaWlrw+/184xvfGLfgaOfO\nnTQ0NPDyyy9TV1fH//yf/xOfz0c4HKatrc0dG3Pus8/nePdIhgAAIABJREFUcwHkUCjkwoTJ74bk\nd4JlWW5sbllZGbt3757UeTc1NSGEcJ/jFStWTOmaOnW8sLDQBXb7+/unAb8PuZxo9qNHj/Lwww+n\nff5S6ciRIxiGwW233TalaPZp/e70gQN+zz77rGuHLEkSf/d3f/e+vAT8fj9///d/z2OPPeZmzP/w\nhz/kv//3//5b73ta//GU7Lz0XjRR5CV88ADh71Kp4odl7BXXhmHZzlWpABNvHhTdieiqINZTjadg\nqf0sTwL2mQiYFOC6+ZmW6bJZiqwgyw7wNA8Gq21YC8CIgOJPQHYybhiVSJSf0S4jhmHg8XiID9uQ\n4DiIMSH3c6HjFsS9cAnXBVmzXejGwoVSAgKSZNfVTkTaiMVitwT8blUf0+l9fw7S7EsIgRHqsFdI\nqV5EPIhkxkG/hHBntJLvq0hcE1zIjYySBHCXFF2rB2Hwmg3ZmUk2/BklQBVEuxBOfK+UKFek03a6\nc4+TfEIkOS+KBIAp2/Ui3JH4XMaKD9sr7AAp1guA7EnvBuGAn24ZSABglo4ZHbLLl7s4qVyjLh5S\nzxm7WIV3job7IFFGfQxAyqjnTR9sJCuQOSHMY1kW7777Lvv27aO9o5OYbmKazn1I7WC3a9cuXnjh\nRUKtFSje7HFR5EIIjGAb8b7rWLGhxHUQWNF+Mrza+xoHmyrKtqOjg6amJoaDIUTvGRgK4ClcgqKW\nYkU1hGW7MUa7ryLiQRAGalYpmfM/5jqWOFL9hXiLVqAP3iTUWsHxipPk5mQhy2AE28ffY1lNvE8M\n2wlS1hLvxbFQsTubhcBCQbBp0wY3jupWcn5rhJS4/xlTbIw79SdjBgxINDQ0TG37af1O9GECbKc1\nrWl9dOVMgBgTmxZMqHgiujXVimK7H2OyfN7U438Bls83ef2wSU9Pz3sv4Aekvr4+Lpw/hyzFuP+u\nERc+TdPsld+WIBIVZPjGQ34r5seZU6Jzs8PD6csym9dY/OsxhUv1HjRfIG17raWlBYTB2iWjb5gl\nBHo8TiweG+dAIUkSmubBsiwWzTHx+yyCERlNEzS0etAtMGM27CcnCprEMyFJoKn2OqlYLEYgECCu\nq7T2aBiGRH6K+aJMX8J1IjS6jWdadtwvgM8jyPCOhgoRNvuT4RVEdQlVAUmy6B5QqbmpcftyE1m9\nVbyMvaN0UVnjv//bTyKMxAvbE372OrLRx7dMg87WGlrqLzDY12ZvZwkbyjQTkJywUFQvRTOXYFkG\nfZ0NyIpGydxVlMxdTUbAaWfbZbaWb2Ogq4HW+rMEBzvobKpCIEYdW/P6EUMW/V03Em54doXs7bhO\n+fJtqNr4CNORuYXRkJ9pxehtr8XQY5AA8+Jx271voKcJw4iRnVfiAo/JkhPwpGkZrmMG2HHEphEn\nFrUn1zwZ6ftTXS2XEUJQOHPxKLgvcfHHFt5V8ewV9LTX0NNWQ25ujuu+MP68Bb/61a9444030Q0T\ngULJ7JXkF89D1byYpk5/903amy5z+co1amuvs337NrZv385vfvMGl079hnX3fJZA9shkXDwWpqX+\nAq03KolFQ+7EjRCWfd8tk5aWFg4fPszmzZtTvkfHyrIsGhoa6O/vR9d1MjIymDVrluug6vV62b17\nN/fffz+XL1/m3LlzdHd3c+XKFcJDEWJna4lW1pMxpxjF50EYFpHOXvT+IYRhIXs9zNh5BzkrU8cx\nqQE/RdvXoeVm0Xe0kms1NcgChq40EFg82z4/feQ9JHs0EGAZNuQnyRKSqiDZD7h9TYSFpVtgmlhx\nAwmJHTt2TDjxmKxwOGw/S4mXmAMYTkXOWixJUzFNc8rjKtP6t5eiKOzYsYOXf/Fzut49w+zP7ELN\nuvWKBTOm07GnAo+ictddd03ZTWta05rWvz85jlap4l0no7GRosnq7+/HMAwKCwvf0+S5JEksW7aM\npqamD2Wf6MSJE0QiERYuXMjSpUvdzx1oyYF4Ul2b3bt3c+DAAc6cOcOjjz5KRkYGP/jBD4jFYixb\ntsyFBceqtbWVeDzO4ODgqM8tyyIWi7mOeMnwTLKrWl9fnwuJhUIhcnJyRsX0Jm/n/OMsvjJNk3g8\njtfrdZ24btWfGHvuTj/F7qdp48bFk4/pOBBKksSpU6d48MEHJ+VG7exzqn2iiVzFbqXkbZ3jOxCk\no4GBAQ4fPsyhQ4dcQNNZRCaEcIHGgoICVq9eTX19PT09PWRkZLB69WoWLVrkOt851yUajVJTU8PF\nixddJ0LHnRFGwN2+vj6EEG7EbTwep76+fsL2ZvJ1ce6JaZpcvXoVsMdTgsGg+w44fvw4AHfccUfK\n/Tllj8VGkr0cMFXXdRe0TOeiKITg9OnTWJbFqlWrRsF9Y8s79vMNGzbQ1taGaZpuWkAqBYNB/tf/\n+l9cvnzZhTUdIM+yLFRVJTc3l5ycHPbu3cvp06d5/PHHmTNnDo2Njbzyyis8+uijo0DU9vZ2Tp48\nyaVLl0bBgg7Eqqoq586do6ioaNKOc9FolLq6OveaBQIB5s+f77briouLeeyxx3jkkUc4ceIE169f\np6Wlhbq6Oq5evUpdXR35+fksXLgQr9dLOBymurqaoaEhLMuitLSUr371q8yePXvcsSVJYsGCBXz9\n61/nn//5n7l06ZIL6R08eNDtlzn32XFgDAaDmKbJ8PAwmqbZKXRJ/T/HzVHXdWKxGLIs84UvfMGt\nN7dSJBIZ9f6aKpOT/C7zer2uY+W0PtwqLy9nwYIF1NfX88Mf/pAnnnhiQmdSR/X19bz22msAk56H\nnNbvXh8o4BePx9mzZ4/7Qps7dy7r169/3/a/YcMGysvLXRr57bff5u/+7u9uuXJhWtMaK5/P5zov\nvRcJS2eiyMsPGiD8XSpV/LCE/cNvmhGEFUeSvGOArYQyZ4Pix4oNYwTbkNUMoq0nyPSqEzpWpQIm\nBRCPxYjFYnb0i/NhQoZkjgzyZxTDYDWS7LEnPWI9NgCVrqMjyTaAhUBYduPKn+lHincjRi0xHy3Z\nmw2RPhsi8yTBTsLEBf7Gwn3JinTa3/HPhlgvmGHMgVoIrE39fWf3t6iP6fR+PgciAYIl70sIQbz3\nKrGeq1ix4SSjPBWwQJhIwrAhP28+aLlgRiDWZ0fpFt5px42OjdT1FkDuskSU7ykb+jNDI66Mkv38\noAcBYTsjBuZBsNEGA3OWAqndA8ZJVsA0YPg6DvQnTN2GSS0LhhuQAE/+orTXaTT46X6YYEoTn0lp\n3hWRdoQeHIleHes8OZEyZ9tOiWaU3JzstDCPrus8++yztitnzMBS/HgKluAJzJzQwe6LX/witbW1\nVJw4RajhbXxldyXeCVLq+56YwAHQDYN9+/YBTAr0m6yz4FgHUMuy2L9/v+vsF+u9SKSrcuQ6Cgt0\nu8PlK1mHb+a6tIMzkiTjyS1H8WYTangbq38AGZN4bw2S4rhvjtxjSfHY524ZNlxsjXHuFLbLDJaJ\nMCJIQF5eLo8//vikwUfnt0aQmEiT32NcR6KOOqsJp/XhlgO0TgTYppIZ6ZvUb+60pjWt/xiyV9Qr\n3Gx/b5BTMAJ9gzIgp3QGiMVithOT971NFGR4AMSH8rfp6NGjWGaE9ctM8pLmWCTsyMvgcJCYbiEQ\nZHhx4Tmwmx877wjxf9/U2H9GpbXb4l+Paggli9///c/YCyQMYxzsY//mW/iTmvvxeJxwJJwYyBXI\nku2GJwGWANOSiMejiXUzAr9PEItLBPwmvYMqTe0eFs2OY5j29x0pMng94HAyg0Hhxg6dr80kFLGd\n+KwUt7a0SKCpcLPDQ3uvSmmhgRAQjsoIbHdGvy99nQhFJC7X+/BoFuuWRjh1xc9P/jWHFQvD3GLN\nEwPD9mKcsZMd6ZSZmWlHEcci6PEommfyq50dRUIDSNiTCtnZ2fbq/2A/pmmgKCrDg11crHiVaHgI\nIQSK6iWnYBaqakNjw/3txKJDGPEoHm8m/uwimmsrUBSVxbc/QH7x/MSRRj+nsqxQMHMxecULuF65\nh57Wa5h6bNT3hGVimQaSJKOoHnKL5iKAod5muluvMrN8zS3Ozob8ZFnBRKKr+RLCMpEVFUVW3Imz\nzmZ7UmPW/LUp29GKouLx+olFQwQH2snKK3XPweklTyTLMulqvoIkSRTPXT0KEryVhLAoKltGf2e9\nvSDQNFMOeP/yl7/kjTf/lbhuMW/ZFmbNXzOuPswoXcTCFVtoa7pM3aVDHDhwkK1bt7BmzWouXLjI\n2UM/Y8maeymetZTQUC+Vx39JLBKy3wOBfIpKl6J6MjCNONHwAH0d12lt6+S5537E8ePH+au/+ivX\nkWSsgsGgG73T0dE54iwogSJLrFq1ih07drhRUIqisHr1ateZ2zRNqqqqePfdd7l0+TJmcw96YqxE\nD4WwLIu8O5dTsOk21Mxbjynkrl1MvH+Q6JUmvKqKHooS6xpAkmXM4TDmcBglAVvJXg1kCaEbNtga\nN2yb0JG7hISEBJhdA2T4fFNahKKqKsmDQcmA4VQldHvCcSrOB9P63en+++/n4sWLXL52lZZf7Wfm\nJ7fgLUztFgRgDIdpf/Mooi/InNIyHnnkkX/D0k5rWtP6sMrpx9y8eZOFCxdOefubN28CuDGZyXIA\nj9/md8XZ9sPYJzp06BAAO3bsGPW5qqr4fD6i0SihUIiMjAw8Hs+o9lthYSGrV6/m4sWLvPPOO9TW\n1lJbW0tWVhYPPfQQwWAQv98/btzKgViSFzaFw+FRYN9YwM+yLBc2cyCyQCBAOBymtLSUzs5OF2BK\nlqZprouc49zmAH5lZWUoiuIea2zb1GmPFRUV0dTUBIwGrxzXvnTKy8tD0zRycnIQiTnCX//61/zV\nX/3VhO1g0zRd+HGyfSKn/flejUYsy3IB1KysLPeZam1tdb9z+PBhXnjhBQzDXvBTWFjI7NmzUVWV\nUChEbW0t4XAYwzBYuHAh3d3d9PT0kJWVxe7duwkEAqOcFB35fD5Wr17NvHnzeOutt+jp6UHXdffc\nHZgsGo267nNr1qzh3LlztLa2EgwG07a/nWM599gB+pqbm932ovP3oaEhKisrkWU5bUyt48p3/fr1\nUf19p37dqn/T1NREZ2cnHo+HsrKyKfaJBKWlpdy4cSPtfHM0GuU73/kO1dXV9Pf309LSwtDQ0Ljv\nDQwMoGkaxcXFxONxnnnmGR577DFef/11Ghsbef755/n4xz/OrFmzOHfuHG+88QbRaNTtixmG4UZ4\na5qGz+fj+PHjnD9/nk9/+tM8/PDDac+rtbWVAwcOcPz48XHvRI/Hw+bNm9mxY4cL5vn9fnbu3OkC\nTOFwmGPHjrF//346Ozs5e/YsYMN1oVAIn8/Hl7/8ZTZv3nzLSHRN03jiiSf45je/SXt7O6Zpcvz4\ncT7/+c8DUFtb637XgfwikQi6rruujTDewW9wcNCt+6kAw3Ry6pPT153qb0byO9N5H073iT4a+pM/\n+RO+9a1vcf78eX7wgx/w+OOPT8gJXLp0iWeeeQZd19myZct0mtdHSB8o4OdQ2A5Bf9ddd73vx9i0\naRONjY2A/ZKqqqpKS8VPa1rpNGPGDJTaeoxgO6p/6pEoRrBtwsjLDxog/F0qnZueHYlrr2QSZgxJ\n9ox38kvEp4r+KiLt5xDRfmTiaFo2+/bt4+jRo+OAHRgPTAqR3HkDJMmGqMa48dnX33bHkxBIqg+M\nGCLYCDnLcFwJkssnJQE4krAQwrDjfoSFFGwEYWJF+jAjfeOACk/+IjsSdagOspck9mMl4n5JuPel\naXgLy4bPwAbQjBD0nMYarEOUrZmwwX6r+phO7+dzIIQYtS9hmYSbj9rXA2zHxOxFdoSppNkgVLgT\nes8DFgTK7evVf2kkitaTesUSgBtFq2ZA+0EwozB0HXIWJyAre1LVBvkkyF4M0R47yjfcApm3cgRw\nXPxkCLeBHkJSM8GM2NCbwP7cCCH7slED6SNhXPAznOwemLifqeC/ZA0nHNWyFiRBs2PcISeQCJQj\n9Q+TnZ2dcuDAsiyeffZZKk6cIqJL+GZttWOwxwC6qRzsAL70pS8hSRInTpwi0nKESJsPSVYwY4Mg\nJBtMzJqP8BaCpCAJA8Xowxq+QUNjKy8kYMHHH388ZcfpvTgLJp9nKme/rq4uIpEIPp+PGzduEBQW\n3tnb8ebNm9Q1VTLy8ZVtIt5yFEUSSGYIlMRqqlH32Ib8kGTb4U9YCah49L2WJAlJ70FRFNatW3fL\nDmSynN8at26kq0e3kmXc+jvT+lDp3nvvTQnYporrFcJCH2wi2nqCDE2wadPG6dVZ05rWtLj99tv5\n6Qt+LtWH6OozmJE/NRDv6HkFw/KwcvVtKQfFvV4vSLY73K0RnvGKxAGk32pQMRKJUFFRQU1NjRvH\nEwgEWLNmDXfcccekHLNSqbW1FYTBmqXjQRJVUckMZNoTQLqFrgs8Gng0cJooqxbGsCy4cE2mukEj\nrkv4fDo/e/EFfvbiT1E1jTvvXM/OnTtZsGABkiTZixolKeGaCNFYNDE4L1AVgVcDTRvdQjQtQSxu\nOy0KAdG4Ha27bZ3gzWMSh85lsmZxzG71CmfCJMkQjRHXPSEEw8NhDp3LwzBtp4F/+pHgP/9BnIWz\nR+6v3webVpkcPidz6Fwmn9k1SDgqY5j2fgMZ6RZY2Qc8XuUnbsCyeTH+8OODdPRptHRpnL2qsm2C\ndNVwFM5dVUDysGTJkvRfTJLX62XZsmVcrLpMR3M1sxfcPqntHJlGnI7majRFZvXq1RQVFTF3zhyu\n192gq7UGfyCPC0d/ga7H8AcKmDnvdopKl6CoIwtULcukt6OOa2d/QzweprXuFJIksXD1fS7cJ0ny\nqEF/V8KG7xau/hix8BADPU3EokPufsPBXizLQFE8SLJCRqCA7Pwyhnqbaa0/TX7xArwTuOYlq6+z\njuH+djuaSVHRPBkIAR2NlQz1taIotgNJX1cj2Xkzx7kDzpy7kqaaU7Q3VbmAn1PRVM1+xp2Y37Ea\n6m8lHgvhD+STnV8GiERXYIxz35j+siUsjHiE7IJZ+DJziUQj3LhxY9zk/dmzZ3kzAffdtvHTFM1M\nP7mvqB5mL7idrJwZXDj2C44cPcbnP/eHqKrK2bPnuHbuLa5d2JeISpbIyitl1uKNBHJmuo7fkmRH\nNqnr7qOrtYa6S4e4Un2Vb3/72/zN3/zNuDGgU6dO8dxzzxGJxNBNC483QHZeCbKiYsQjDPQ0c/bc\nBSovXmTO7Nn89V//tesc4ZZbUVi7di1r166ls7OT5uZmIpEIV69e5cChg0ilBRTfO7XF2bm3L6X5\ncgOyAZok01dRhX/uTEI3WglXN5G1YcSBQ9ZU0FSEYWIlQD/nvkmA1+eFwRBm7yAF2XmsXTvxAsdR\n5cjNRZIkzOEIKBLmcBhjMISaM7lJbSGEDR4Kgd7eiyTEuOs3rQ+nNE3jK1/5Ck899RTXrtfS8tI7\nZJTPJOe2hfjnliAl4s+ibT0MVtURqm/Bg8zc0jL+y3/5LxNO6E9rWtP6j6ONGzdy8eJFDhw4wPbt\n26fs8OxE1KaKz30/4Dxn29+mT9TV1cWRI0dobW0lGo3i8XgoKChg8+bNzJ8//z27Wre0tACk/N1O\nPvdwOEw0GsXr9bqudACrV6/m7Nmz/PznP0eWZQzDwDAM/uEf/gFJksjLy+Oee+5h27ZtbuykAwo6\nY78OdOc4wKWK2nW+78RiOjGWubm5BINBSkpKXIc3B5oaG9Pq7NdZ9OQAeLIsu2PMyePRqqqiKAqB\nQICcnByGhoZGRdDeymGpuLgYTdMoKSkhEAhw6dIlLly4wODgYNroW4CLFy8SDoeZMWPGhN9L1vLl\ny1FVlerqarq7u6fcDrpy5Qp9fX3k5+dTWlqKz+fjlVde4ezZswwNDXHs2DF+/vOfI4Rg5cqVbN26\nlcWLF4+6XqFQiL179/LGG29QXV3txpR+/OMfd+G+ieppdnY2u3bt4vXXXycUCjE8PExxcbEbS6so\niutKVlRUxIIFC6itreXMmTPcc889E8KWyQBWVVUVuq678GkgEMAwDJ5//nlisRgLFiygoaGBnJwc\n5s2bN+o+z58/n5KSEjo6Orhw4QJ33nmnu39JktwY6d7e3pRzfBcuXMCyLObPn+/Cpc72wLj/dxSL\nxYhEIsyePZvm5maqqqpSnudPf/pTrl27Rm9vL/X19a4zYSrpuu5eW0mSeOmll/jKV77CCy+8QHt7\nO8899xx+v5/+/n7XWdP5xzAM95w1TXOfKV3XefXVV9F1nT/4gz8YdTzTNHnxxRc5ePCg+1l5eTlF\nRUVIkkRPTw8NDQ0cPHiQgwcPcvfdd/PHf/zH48Z7/H4/9913H7t27aK+vp7e3l5isRhvvfUWra2t\n/OEf/uGU4tR9Ph/33nsvL730EqqqEo/HqaysxO/309jYSENDA/Pn2316WbYXIia7jTrvLOfvPp/P\nPcd169ZNyWnagYEdmPnq1atTgkAdaLqvr4/u7m5kWU7rJjmtD5fKysr46le/ylNPPcWFCxd48skn\n2bx5M9u3b6esrMxNRD19+jQHDhygvr4egDvvvNNNTJ3WR0MfKOA3Ntrtg8htHrvPGzduTAN+05qy\ntm7dyslTpwn11uAtWpFyQjydhLCI99aQqSlpI5Q+aIDwVpqs29V7Ubr4YQncRocN+cXBSgHeeQvB\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0ahOZty+244En3hkiAfhFrjQiDJM1d66ZkqP6R02GYRAOh93JxVs5CH0U5PV6+cIXvsDv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XbVrir0rgomZKZx++23X9IEp67rFBYW8ve//50tW7bw3nvvsXfvXoqLi7FarSQmJg65f0EQ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\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dnc8OIwKwsdzcPokkSVx99dW88sorVFVVMWfOnDHjYM+mvb2dlpYWbDYb11zjXyjlcDhw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\nR8Y0f/ToUUUiEaWmpsbkEd0A/oSCHwBAkrR8+XLlzcmVGR1U+Hz7qGbD5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"text/plain": [
"\u003cFigure size 1800x1200 with 6 Axes\u003e"
]
},
"metadata": {
"image/png": {
"height": 843,
"width": 1276
},
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#@title Visualization of example trajectories\n",
"\n",
"FONT_SIZE = 26\n",
"LINE_WIDTH = 1.1\n",
"MARKER_SIZE = 70\n",
"ALPHA = 0.9\n",
"GRAY = (0.3, 0.3, 0.3)\n",
"SZ = 6\n",
"N = 2000\n",
"scatter_plot_settings = dict(s=MARKER_SIZE, alpha=ALPHA, edgecolor=GRAY)\n",
"\n",
"x1_dim = 2\n",
"x2_dim = 2\n",
"\n",
"env_key, episode_key = random.split(random.PRNGKey(14))\n",
"generator_fn = gen_dynamics(env_key, x_dims=(x1_dim, x2_dim))\n",
"gen = generator_fn(episode_key, batch_size=N)\n",
"(x, xp, r) = next(gen)\n",
"ind = jnp.argsort(r)\n",
"(x, xp, r) = (jax.device_get(d[ind]) for d in (x, xp, r))\n",
"\n",
"plt.figure(figsize=(3*SZ, 2*SZ))\n",
"x2 = x[:, x1_dim:]\n",
"x2p = xp[:, x1_dim:]\n",
"plt.subplot(2, 3, 1)\n",
"plt.scatter(x2[:, 0], x2[:, 1], c='b', **scatter_plot_settings)\n",
"plt.title('Start state', fontdict=dict(size=FONT_SIZE))\n",
"plt.ylabel('Direct Learning', fontdict=dict(size=FONT_SIZE))\n",
"\n",
"plt.subplot(2, 3, 4)\n",
"plt.scatter(x2[:, 0], x2[:, 1], c='b', **scatter_plot_settings)\n",
"plt.ylabel('Hindsight Learning', fontdict=dict(size=FONT_SIZE))\n",
"\n",
"plt.subplot(2, 3, 2)\n",
"plt.title('Terminal state', fontdict=dict(size=FONT_SIZE))\n",
"\n",
"sums = np.sum(x2p, axis=1)\n",
"sums[(sums!=1) * (sums!=0)] = 2\n",
"plt.subplot(2, 3, 5)\n",
"plt.scatter(x2[:, 0], x2[:, 1], c=cm.Set2(sums/3), **scatter_plot_settings)\n",
"\n",
"# Visualize rewards with colors\n",
"plt.subplot(2, 3, 3)\n",
"plt.scatter(x2[:, 0], x2[:, 1], c=cm.binary(r/2.0), **scatter_plot_settings)\n",
"plt.title('Rewards', fontdict=dict(size=FONT_SIZE))\n",
"plt.subplot(2, 3, 6)\n",
"plt.scatter(x2[:, 0], x2[:, 1], c=cm.binary(r/2.0), **scatter_plot_settings)\n",
"\n",
"for i in range(6):\n",
" plt.subplot(2, 3, i+1)\n",
" plt.xlim([-3.5, 3.5])\n",
" plt.ylim([-3.5, 3.5])\n",
" plt.grid('off')\n",
" plt.tick_params(\n",
" axis='both', which='both', bottom=False, top=False, left=False,\n",
" right=False, labelbottom=False, labelleft=False) \n",
"\n",
"plt.subplot(2, 3, 2)\n",
"plt.axis('off')\n",
"plt.tight_layout(pad=1.4)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "WT-tzJHQ2Svn"
},
"outputs": [],
"source": [
"#@title Network and loss definitions\n",
"\n",
"class HindsightValueNet(hk.Module):\n",
" \"\"\"HiMo value estimation architecture.\n",
"\n",
" This defines the network for the value function and hindsight value functions. It can\n",
" also be used as a model-free baseline with the baseline_mode flag to control for capacity.\n",
" \"\"\"\n",
"\n",
" def __init__(self,\n",
" hidden_size=8,\n",
" phi_size=4,\n",
" baseline_mode=False,\n",
" name=\"HindsightValueNet\"):\n",
" \"\"\"Init.\n",
"\n",
" Args:\n",
" hidden_size: Dimension of the hidden layers throughout the network.\n",
" phi_size: Dimension of the hindsight bottleneck phi vector, and its approximation phi_hat.\n",
" baseline_mode: boolean, whether to use the network in model-free mode as a baseline.\n",
" \"\"\"\n",
" super(HindsightValueNet, self).__init__(name=name)\n",
" self._phi_size = phi_size\n",
" self._hidden_size = hidden_size\n",
" self._baseline_mode = baseline_mode\n",
"\n",
" def __call__(self, inputs):\n",
" \"\"\"Network definition based on inputs.\n",
"\n",
" Args:\n",
" inputs: Network inputs, tuple composed of initial observation x and next obs x'.\n",
"\n",
" Returns:\n",
" The predictions v_pred (only uses x), v_hind_pred (uses both x and x'), and the phi/phi_hat\n",
" vectors.\n",
" \"\"\"\n",
" x, xp = inputs\n",
"\n",
" phi_net = hk.nets.MLP(\n",
" output_sizes=[self._hidden_size, self._phi_size], activate_final=False)\n",
" phi = phi_net(xp)\n",
"\n",
" h_net = hk.nets.MLP(\n",
" output_sizes=[self._hidden_size, self._hidden_size],\n",
" activate_final=True)\n",
" h = h_net(x)\n",
"\n",
" phi_hat_net = hk.nets.MLP(\n",
" output_sizes=[self._phi_size], activate_final=False)\n",
" phi_hat = phi_hat_net(h)\n",
"\n",
" v_hind_pred_net = hk.nets.MLP(\n",
" output_sizes=[self._hidden_size, 1])\n",
" v_hind_pred = v_hind_pred_net(jnp.concatenate([h, phi], axis=-1))\n",
"\n",
" v_pred_net = hk.nets.MLP(\n",
" output_sizes=[self._hidden_size, 1])\n",
" if not self._baseline_mode:\n",
" v_pred_inputs = jnp.concatenate(\n",
" [h, jax.lax.stop_gradient(phi_hat)], axis=-1)\n",
" else:\n",
" v_pred_inputs = jnp.concatenate([h, phi_hat], axis=-1)\n",
" v_pred = v_pred_net(v_pred_inputs)\n",
"\n",
" return v_pred, v_hind_pred, phi, phi_hat\n",
"\n",
"\n",
"class ModelValueNet(hk.Module):\n",
" \"\"\"Model-based value architecture.\n",
"\n",
" This trains a value network that depends on x and x' (in this case can be interpreted as a\n",
" reward function r(x,x') that depends on that single transition), and a model network to predict x'\n",
" from x. The value is estimated at test time by passing the predicted x' to the value network.\n",
" \"\"\"\n",
" def __init__(self,\n",
" hidden_size=8,\n",
" name=\"ModelValueNet\"):\n",
" \"\"\"Init.\n",
"\n",
" Args:\n",
" hidden_size: Dimension of the hidden layers throughout the network.\n",
" \"\"\"\n",
" super(ModelValueNet, self).__init__(name=name)\n",
" self._hidden_size = hidden_size\n",
"\n",
" def __call__(self, inputs):\n",
" \"\"\"Network definition based on inputs.\n",
"\n",
" Args:\n",
" inputs: Network inputs, tuple composed of initial observation x and next obs x'.\n",
"\n",
" Returns:\n",
" The predictions v_pred (uses x and x'), v_pred_test (uses x and x' prediction), and the x'\n",
" prediction (xp_pred).\n",
" \"\"\"\n",
" x, xp = inputs\n",
"\n",
" v_pred_net = hk.nets.MLP(\n",
" output_sizes=[self._hidden_size, self._hidden_size,\n",
" self._hidden_size, 1])\n",
" v_pred = v_pred_net(jnp.concatenate([x, xp], axis=-1))\n",
"\n",
" xp_pred_net = hk.nets.MLP(\n",
" output_sizes=[self._hidden_size, self._hidden_size, x.shape[1]])\n",
" xp_pred = xp_pred_net(x)\n",
" v_pred_test = v_pred_net(jnp.concatenate([x, xp_pred], axis=-1))\n",
"\n",
" return v_pred, v_pred_test, xp_pred\n",
"\n",
"\n",
"def vnet_loss(hidden_size, phi_size):\n",
" \"\"\"Computes the loss for the model-free value network baseline.\"\"\"\n",
" def loss_fn(inputs):\n",
" x, xp, r = inputs\n",
"\n",
" hind_v_net = HindsightValueNet(\n",
" hidden_size=hidden_size, phi_size=phi_size, baseline_mode=True)\n",
" v_pred, _, _, _ = hind_v_net([x, xp])\n",
"\n",
" loss = jnp.mean((v_pred - r)**2)\n",
" return loss, (loss,)\n",
" return hk.transform(loss_fn)\n",
"\n",
"\n",
"def vnet_himo_loss(hidden_size, phi_size, model_loss_weight=3.0):\n",
" \"\"\"Computes the loss for the hindsight modelling network.\"\"\"\n",
" def loss_fn(inputs):\n",
" x, xp, r = inputs\n",
"\n",
" hind_v_net = HindsightValueNet(hidden_size=hidden_size, phi_size=phi_size)\n",
" v_pred, v_hind_pred, phi, phi_hat = hind_v_net([x, xp])\n",
"\n",
" v_loss = jnp.mean((v_pred - r)**2)\n",
" v_hind_loss = jnp.mean((v_hind_pred - r)**2)\n",
" model_loss = jnp.mean((phi_hat - jax.lax.stop_gradient(phi))**2)\n",
"\n",
" loss = v_loss + v_hind_loss + model_loss_weight * model_loss\n",
" return loss, (v_loss, v_hind_loss, model_loss)\n",
"\n",
" return hk.transform(loss_fn)\n",
"\n",
"\n",
"def vnet_model_loss(hidden_size, model_loss_weight=3.0):\n",
" \"\"\"Computes the loss for the model-based baseline.\"\"\"\n",
" def loss_fn(inputs):\n",
" x, xp, r = inputs\n",
" model_v_net = ModelValueNet(hidden_size=hidden_size)\n",
"\n",
" v_pred, v_pred_test, xp_pred = model_v_net([x, xp])\n",
"\n",
" train_v_loss = jnp.mean((v_pred - r)**2)\n",
" test_v_loss = jnp.mean((v_pred_test - r)**2)\n",
" model_loss = jnp.mean((xp_pred - jax.lax.stop_gradient(xp))**2)\n",
"\n",
" loss = train_v_loss + model_loss_weight * model_loss\n",
"\n",
" return loss, (test_v_loss, train_v_loss, model_loss)\n",
"\n",
" return hk.transform(loss_fn)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "uE4XpUWCbkwa"
},
"outputs": [],
"source": [
"#@title Training loops\n",
"def get_update_fn(loss_fn, opt):\n",
" \"\"\"Builds and returns a pmapped update function that runs on all devices.\n",
"\n",
" Args:\n",
" loss_fn: the loss function to be optimized.\n",
" opt: the optix optimizer to be used for loss minimization.\n",
"\n",
" Returns:\n",
" pmapped update function.\n",
" \"\"\"\n",
"\n",
" def update_fn(params, opt_state, *inputs):\n",
" rng = None\n",
" grads, loss = jax.grad(loss_fn, has_aux=True)(params, rng, *inputs)\n",
" grads = jax.lax.pmean(grads, axis_name='i')\n",
" updates, opt_state = opt.update(grads, opt_state)\n",
" params = optix.apply_updates(params, updates)\n",
" return params, opt_state, loss\n",
"\n",
" update_fn = jax.pmap(update_fn, axis_name='i')\n",
" return update_fn\n",
"\n",
"\n",
"def train(rng_key,\n",
" loss_fn,\n",
" data_generator,\n",
" batch_size=16,\n",
" learning_rate=1e-3,\n",
" num_training_iterations=5000,\n",
" report_interval=100,\n",
" mv_avg_decay=0.99,\n",
" logging_prefix=''):\n",
" \"\"\"Traininig script.\"\"\"\n",
"\n",
" num_devices = jax.local_device_count()\n",
"\n",
" opt = optix.adam(learning_rate=learning_rate, eps=1e-3)\n",
" update_fn = get_update_fn(loss_fn.apply, opt)\n",
" reshape = lambda x: jnp.reshape(\n",
" x, [num_devices, batch_size//num_devices, -1])\n",
" gen = map(lambda x: tree.map_structure(reshape, x),\n",
" data_generator(rng_key, batch_size=batch_size))\n",
"\n",
" # NOTE: use the same key on all devices to initialise parameters to the same\n",
" # value on all devices.\n",
" rng_key, _ = random.split(rng_key)\n",
" params = jax.pmap(loss_fn.init)(\n",
" jnp.stack([rng_key] * num_devices), next(gen))\n",
" opt_state = jax.pmap(opt.init)(params)\n",
"\n",
" for train_iteration in range(num_training_iterations):\n",
" params, opt_state, aux = update_fn(params, opt_state, next(gen))\n",
" # The loss returned by `update_fn` is an array of shape [n_devices].\n",
" # Take the mean to get a single value.\n",
" aux = tree.map_structure(jnp.mean, aux)\n",
" if train_iteration == 0:\n",
" aux_smoothed = aux\n",
" aux_data = tree.map_structure(lambda x: [], aux) + ([],)\n",
" else:\n",
" mv_avg = lambda x, y: x * mv_avg_decay + y * (1 - mv_avg_decay)\n",
" aux_smoothed = tree.map_structure(mv_avg, aux_smoothed, aux)\n",
"\n",
" if (train_iteration + 1) % report_interval == 0:\n",
" message = logging_prefix + ' - progress: {0:3.0f}%'.format(\n",
" train_iteration/(num_training_iterations-1)*100)\n",
" print(message, end='\\r')\n",
" to_add = aux_smoothed + (train_iteration,)\n",
" tree.map_structure_up_to(\n",
" to_add, lambda x, y: x.append(y), aux_data, to_add)\n",
" return aux_data"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {
"height": 712
},
"colab_type": "code",
"executionInfo": {
"elapsed": 2867818,
"status": "ok",
"timestamp": 1584473852216,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 240
},
"id": "ejSJXzTCgiAo",
"outputId": "005d2791-1581-4403-b772-2de3dbb616c9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on 1 devices of type cpu:0\n",
"Num runs: 10 -- Batch size: 64\n",
"Training vnet...\n",
"Training himo...\n",
"Training model-based vnet...\n",
"Done!\n"
]
},
{
"data": {
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P1HvLWVdGhsojRURERKTBRo8eDcCcOXOinp8zZw4ul4sRI0YcyWUdM3bu3AlA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DXpIQQrRNgjDRtZRSaG5eRVgcp0zN15krbzM8HnBtA7tL2iNPbjMwf0tgpxQUS3kY5m1XFXac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urvyF1xOUt+/dewerpbtFpFURaLC/+ZuKI0SQgBormpkdAIhJ2s3kTDfIAgbE2z/ONsU8My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\n0fOPcWr1JdZvIgS7bK1V5gunvtr1OR5kSimGnDzj/gj1oM5SfYUL1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h+9///jnjR44c2dY4hDgfTVVQNYiTdHRvoC3W7Aw5utxmpuhiGpOZmJkUuqaSz9i0ewGt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\nkbuUDlyzKhjDmy7NQo15b5DJMqgcuzK+khuVPM9Z8BZZ9Dq0vDZpnmK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p+R06QQ/yHFsr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vYj1TEkKIB3iuheeYKKXY2I1YanlnIkT62stP02q4/INv/Jiy0vPLf/CTXf7W3/8Of+1XPnuK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\n",
"text/plain": [
"\u003cFigure size 1000x1000 with 1 Axes\u003e"
]
},
"metadata": {
"image/png": {
"height": 593,
"width": 609
},
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#@title Training example\n",
"\n",
"# Shared hyper-parameters\n",
"hidden_size = 16\n",
"phi_size = 3\n",
"x1_dim, x2_dim = 28, 4\n",
"batch_size = 8 * 8\n",
"learning_rate = 4e-3\n",
"num_training_iterations = int(8e3)\n",
"rng_key = random.PRNGKey(14)\n",
"num_runs = 10\n",
"\n",
"\n",
"def execute(rng_keys, loss):\n",
" stats = []\n",
" for run_index, rng_key in enumerate(rng_keys):\n",
" logging_prefix = 'Executing run {}/{}'.format(run_index+1, len(rng_keys))\n",
" generator = gen_dynamics(rng_key, x_dims=(x1_dim, x2_dim))\n",
" stats.append(train(\n",
" rng_key,\n",
" loss,\n",
" data_generator=generator,\n",
" batch_size=batch_size,\n",
" learning_rate=learning_rate,\n",
" num_training_iterations=num_training_iterations,\n",
" report_interval=100,\n",
" mv_avg_decay=0.99,\n",
" logging_prefix=logging_prefix))\n",
" return stats\n",
"\n",
"\n",
"def process(stats, index, *args, **kwargs):\n",
" it = stats[0][-1]\n",
" stats = [stat[index] for stat in stats]\n",
" stats = np.stack(stats)\n",
" mean = np.mean(stats, axis=0)\n",
" std = np.std(stats, axis=0)\n",
" plt.plot(it, mean, **kwargs)\n",
" plt.fill_between(it, mean-std, mean+std, alpha=0.2, *args, **kwargs)\n",
"\n",
"\n",
"print('Running on {} devices of type {}'.format(\n",
" jax.local_device_count(), jax.devices()[0]))\n",
"print('Num runs: {} -- Batch size: {}'.format(num_runs, batch_size))\n",
"\n",
"rng_keys = random.split(rng_key, num_runs)\n",
"\n",
"print('Training vnet...')\n",
"vnet_loss_fn = vnet_loss(hidden_size=hidden_size, phi_size=phi_size)\n",
"vnet_stats = execute(rng_keys, vnet_loss_fn)\n",
"\n",
"print('Training himo...')\n",
"himo_loss_fn = vnet_himo_loss(hidden_size=hidden_size, phi_size=phi_size)\n",
"himo_stats = execute(rng_keys, himo_loss_fn)\n",
"\n",
"print('Training model-based vnet...')\n",
"vnet_model_loss_fn = vnet_model_loss(hidden_size=hidden_size)\n",
"vnet_model_stats = execute(rng_keys, vnet_model_loss_fn)\n",
"\n",
"print('Done!')\n",
"\n",
"\n",
"plt.figure(figsize=(10, 10))\n",
"process(vnet_stats, 0, color='g')\n",
"process(himo_stats, 0, color='b')\n",
"process(himo_stats, 1, color='b', linestyle='--')\n",
"process(vnet_model_stats, 0, color='r')\n",
"plt.ylim([0, .3])\n",
"plt.legend([\n",
" 'Model-free value',\n",
" 'HiMo value $v^m$',\n",
" 'HiMo hindsight value $v^+$',\n",
" 'Model-based value',\n",
"])\n",
"plt.title('Value Loss');"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: iodine/Eval.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Copyright 2019 Deepmind Technologies Limited. All Rights Reserved.**\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
" http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License.\n",
"\n",
"# IODINE Interactive Evaluation\n",
"This notebook can load checkpoints and run the model.\n",
"You need to download the data before you can run this (see README.md).\n",
"It includes several plots to inspect the decomposition, iterations, and inputs to the refinement network, as well as prior samples, latent traversals and code to run on a custom example.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pathlib\n",
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n",
"import warnings\n",
"from absl import logging\n",
"\n",
"# Ignore all tensorflow deprecation warnings \n",
"logging._warn_preinit_stderr = 0\n",
"warnings.filterwarnings('ignore', module='.*tensorflow.*')\n",
"import tensorflow.compat.v1 as tf\n",
"tf.logging.set_verbosity(tf.logging.ERROR)\n",
"\n",
"import sonnet as snt\n",
"from shapeguard import ShapeGuard\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from main import ex, load_checkpoint, build, get_train_step\n",
"from iodine.modules import plotting, utils\n",
"\n",
"sns.set_style('whitegrid')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Adjust this to load checkpoints for different datasets\n",
"configuration = 'clevr6'\n",
"#configuration = 'multi_dsprites'\n",
"#configuration = 'tetrominoes'\n",
"\n",
"# Configuration values to adjust\n",
"config_updates = {\n",
" 'batch_size':1, # to speed up interactive evaluation\n",
" 'data.shuffle_buffer': None, # to speed up interactive evaluation\n",
" # 'num_components':4, # uncomment to change the number of components\n",
" # 'num_iters': 5, # uncomment to change the number of iterations\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully restored Checkpoint \"checkpoints/clevr6/model.ckpt\"\n",
"Variable Shape Type Collections Device\n",
"encoder_net/cnn/conv_net_2d/conv_2d_0/b 64 float32 global_variables, trainable_variables (legacy)\n",
"encoder_net/cnn/conv_net_2d/conv_2d_0/w 3x3x18x64 float32 global_variables, trainable_variables (legacy)\n",
"encoder_net/cnn/conv_net_2d/conv_2d_1/b 64 float32 global_variables, trainable_variables (legacy)\n",
"encoder_net/cnn/conv_net_2d/conv_2d_1/w 3x3x64x64 float32 global_variables, trainable_variables (legacy)\n",
"encoder_net/cnn/conv_net_2d/conv_2d_2/b 64 float32 global_variables, trainable_variables (legacy)\n",
"encoder_net/cnn/conv_net_2d/conv_2d_2/w 3x3x64x64 float32 global_variables, trainable_variables (legacy)\n",
"encoder_net/cnn/conv_net_2d/conv_2d_3/b 64 float32 global_variables, trainable_variables (legacy)\n",
"encoder_net/cnn/conv_net_2d/conv_2d_3/w 3x3x64x64 float32 global_variables, trainable_variables (legacy)\n",
"encoder_net/cnn/mlp/linear_0/b 256 float32 global_variables, trainable_variables (legacy)\n",
"encoder_net/cnn/mlp/linear_0/w 64x256 float32 global_variables, trainable_variables (legacy)\n",
"encoder_net/cnn/mlp/linear_1/b 256 float32 global_variables, trainable_variables (legacy)\n",
"encoder_net/cnn/mlp/linear_1/w 256x256 float32 global_variables, trainable_variables (legacy)\n",
"factor_evaluator/repres_content/position_mean_var/M2 3 float32 global_variables (legacy)\n",
"factor_evaluator/repres_content/position_mean_var/mean 3 float32 global_variables (legacy)\n",
"factor_evaluator/repres_content/position_mean_var/total 3 float32 global_variables (legacy)\n",
"factor_evaluator/repres_content/predict_latents/b 19 float32 global_variables, trainable_variables (legacy)\n",
"factor_evaluator/repres_content/predict_latents/w 64x19 float32 global_variables, trainable_variables (legacy)\n",
"global_step int64 global_step, global_variables (legacy)\n",
"model/iodine/initial_sample_distribution 128 float32 global_variables, trainable_variables (legacy)\n",
"model/iodine/preprocess/layer_norm_counterfactual/beta 1 float32 global_variables, trainable_variables (legacy)\n",
"model/iodine/preprocess/layer_norm_counterfactual/gamma 1 float32 global_variables, trainable_variables (legacy)\n",
"model/iodine/preprocess/layer_norm_dcomponents/beta 3 float32 global_variables, trainable_variables (legacy)\n",
"model/iodine/preprocess/layer_norm_dcomponents/gamma 3 float32 global_variables, trainable_variables (legacy)\n",
"model/iodine/preprocess/layer_norm_dmask/beta 1 float32 global_variables, trainable_variables (legacy)\n",
"model/iodine/preprocess/layer_norm_dmask/gamma 1 float32 global_variables, trainable_variables (legacy)\n",
"model/iodine/preprocess/layer_norm_dzp/beta 128 float32 global_variables, trainable_variables (legacy)\n",
"model/iodine/preprocess/layer_norm_dzp/gamma 128 float32 global_variables, trainable_variables (legacy)\n",
"model/iodine/preprocess/layer_norm_log_prob/beta 1 float32 global_variables, trainable_variables (legacy)\n",
"model/iodine/preprocess/layer_norm_log_prob/gamma 1 float32 global_variables, trainable_variables (legacy)\n",
"pixel_decoder/broadcast_conv/conv_net_2d/conv_2d_0/b 64 float32 global_variables, trainable_variables (legacy)\n",
"pixel_decoder/broadcast_conv/conv_net_2d/conv_2d_0/w 3x3x67x64 float32 global_variables, trainable_variables (legacy)\n",
"pixel_decoder/broadcast_conv/conv_net_2d/conv_2d_1/b 64 float32 global_variables, trainable_variables (legacy)\n",
"pixel_decoder/broadcast_conv/conv_net_2d/conv_2d_1/w 3x3x64x64 float32 global_variables, trainable_variables (legacy)\n",
"pixel_decoder/broadcast_conv/conv_net_2d/conv_2d_2/b 64 float32 global_variables, trainable_variables (legacy)\n",
"pixel_decoder/broadcast_conv/conv_net_2d/conv_2d_2/w 3x3x64x64 float32 global_variables, trainable_variables (legacy)\n",
"pixel_decoder/broadcast_conv/conv_net_2d/conv_2d_3/b 64 float32 global_variables, trainable_variables (legacy)\n",
"pixel_decoder/broadcast_conv/conv_net_2d/conv_2d_3/w 3x3x64x64 float32 global_variables, trainable_variables (legacy)\n",
"pixel_decoder/broadcast_conv/conv_net_2d/conv_2d_4/b 4 float32 global_variables, trainable_variables (legacy)\n",
"pixel_decoder/broadcast_conv/conv_net_2d/conv_2d_4/w 3x3x64x4 float32 global_variables, trainable_variables (legacy)\n",
"recurrent_net/lstm/lstm/b_gates 1024 float32 global_variables, trainable_variables (legacy)\n",
"recurrent_net/lstm/lstm/w_gates 769x1024 float32 global_variables, trainable_variables (legacy)\n",
"refinement_head/residual_head/linear/b 128 float32 global_variables, trainable_variables (legacy)\n",
"refinement_head/residual_head/linear/w 256x128 float32 global_variables, trainable_variables (legacy)\n"
]
}
],
"source": [
"sess = tf.InteractiveSession()\n",
"\n",
"# create a sacred run\n",
"r = ex._create_run(named_configs=[configuration], config_updates=config_updates, options={'--force': True, '--unobserved': True})\n",
"\n",
"# restore the checkpoint and get the model, data, etc.\n",
"restored = load_checkpoint(session=sess)\n",
"model = restored[\"model\"]\n",
"info = restored[\"info\"]\n",
"dataset = restored[\"dataset\"]\n",
"inputs = restored[\"inputs\"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# run evaluation and get the info dict\n",
"rinfo = sess.run(info)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Decomposition\n",
"Let's plot the decomposition at the last timestep.\n",
"Set `mask_components=False` to see the full reconstruction for each component"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" chrom pos id ref ... PC16 PC17 PC18 PC19\n",
"0 chr1 925952 1019397 G ... -2.159512 -9.733231 7.727090 -0.669298\n",
"1 chr1 930188 846933 G ... 2.896945 1.757883 3.686084 -6.673547\n",
"2 chr1 930200 1043045 G ... 5.153117 4.041459 0.512155 -2.865725\n",
"3 chr1 930203 972363 C ... 0.138479 12.087408 -5.559704 9.171222\n",
"4 chr1 930222 998906 GAACTC ... -26.604349 -68.656372 -36.595196 38.175354\n",
"\n",
"[5 rows x 25 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 49
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_G5cANX34SLw"
},
"source": [
"### Report all 5,313 features (z-score normalized)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "get8hogCySnt",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "efe3d5fd-faf5-4589-de79-b022a5c28ab2"
},
"source": [
"enformer_score_variants_all = EnformerScoreVariantsNormalized(model_path, transform_path)"
],
"execution_count": 50,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator RobustScaler from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
" UserWarning)\n",
"/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator TruncatedSVD from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
" UserWarning)\n",
"/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator Pipeline from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
" UserWarning)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 339
},
"id": "Q48earqRyFa6",
"outputId": "fb26f901-7d3e-4305-9fb8-141046e56210"
},
"source": [
"# Score the first 5 variants from ClinVar\n",
"# All Scores\n",
"it = variant_centered_sequences(clinvar_vcf, sequence_length=SEQUENCE_LENGTH,\n",
" gzipped=True, chr_prefix='chr')\n",
"example_list = []\n",
"for i, example in enumerate(it):\n",
" if i >= 5:\n",
" break\n",
" variant_scores = enformer_score_variants_all.predict_on_batch(\n",
" {k: v[tf.newaxis] for k,v in example['inputs'].items()})[0]\n",
" variant_scores = {f'{i}_{name[:20]}': score for i, (name, score) in enumerate(zip(df_targets.description, variant_scores))}\n",
" example_list.append({**example['metadata'],\n",
" **variant_scores})\n",
" if i % 2 == 0:\n",
" print(f'Done {i}')\n",
"df = pd.DataFrame(example_list)\n",
"df"
],
"execution_count": 51,
"outputs": [
{
"output_type": "stream",
"text": [
"Done 0\n",
"Done 2\n",
"Done 4\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"| \n", " | chrom | \n", "pos | \n", "id | \n", "ref | \n", "alt | \n", "PC0 | \n", "PC1 | \n", "PC2 | \n", "PC3 | \n", "PC4 | \n", "PC5 | \n", "PC6 | \n", "PC7 | \n", "PC8 | \n", "PC9 | \n", "PC10 | \n", "PC11 | \n", "PC12 | \n", "PC13 | \n", "PC14 | \n", "PC15 | \n", "PC16 | \n", "PC17 | \n", "PC18 | \n", "PC19 | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", "chr1 | \n", "925952 | \n", "1019397 | \n", "G | \n", "A | \n", "13.865371 | \n", "9.379375 | \n", "1.322473 | \n", "7.198019 | \n", "11.926774 | \n", "-4.407538 | \n", "-5.878580 | \n", "-10.701156 | \n", "-3.140507 | \n", "2.994015 | \n", "4.716916 | \n", "1.098637 | \n", "2.569388 | \n", "13.693736 | \n", "8.564518 | \n", "9.383035 | \n", "-2.159512 | \n", "-9.733231 | \n", "7.727090 | \n", "-0.669298 | \n", "
| 1 | \n", "chr1 | \n", "930188 | \n", "846933 | \n", "G | \n", "A | \n", "-61.468933 | \n", "-5.653942 | \n", "-2.758731 | \n", "6.289482 | \n", "1.844845 | \n", "3.446712 | \n", "6.052454 | \n", "0.632046 | \n", "2.584915 | \n", "1.117951 | \n", "1.942497 | \n", "-6.513691 | \n", "-4.948788 | \n", "-1.172066 | \n", "-2.903700 | \n", "0.482835 | \n", "2.896945 | \n", "1.757883 | \n", "3.686084 | \n", "-6.673547 | \n", "
| 2 | \n", "chr1 | \n", "930200 | \n", "1043045 | \n", "G | \n", "A | \n", "-61.995975 | \n", "-10.007704 | \n", "-0.312641 | \n", "10.605079 | \n", "-5.349404 | \n", "-2.555728 | \n", "7.008485 | \n", "9.793589 | \n", "-14.216670 | \n", "4.411201 | \n", "0.295830 | \n", "-4.968991 | \n", "-10.770261 | \n", "-1.512434 | \n", "0.186349 | \n", "7.461446 | \n", "5.153117 | \n", "4.041459 | \n", "0.512155 | \n", "-2.865725 | \n", "
| 3 | \n", "chr1 | \n", "930203 | \n", "972363 | \n", "C | \n", "T | \n", "21.486368 | \n", "-9.248886 | \n", "3.684767 | \n", "9.606424 | \n", "0.538447 | \n", "5.264496 | \n", "1.418155 | \n", "-14.005326 | \n", "12.898662 | \n", "9.391730 | \n", "-5.201692 | \n", "-3.091272 | \n", "1.975370 | \n", "-5.240757 | \n", "-14.367105 | \n", "-7.802942 | \n", "0.138479 | \n", "12.087408 | \n", "-5.559704 | \n", "9.171222 | \n", "
| 4 | \n", "chr1 | \n", "930222 | \n", "998906 | \n", "GAACTC | \n", "TTCTTCTG | \n", "13.672197 | \n", "181.645172 | \n", "-302.586548 | \n", "184.414001 | \n", "146.373199 | \n", "284.204163 | \n", "-108.902489 | \n", "100.794731 | \n", "-205.568008 | \n", "302.769409 | \n", "203.416458 | \n", "111.947685 | \n", "-61.380695 | \n", "222.271515 | \n", "152.539993 | \n", "114.129166 | \n", "-26.604349 | \n", "-68.656372 | \n", "-36.595196 | \n", "38.175354 | \n", "
\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" chrom pos ... CAGE:Smooth muscle c CAGE:parietal cortex\n",
"0 chr1 925952 ... 23.365688 2.915666\n",
"1 chr1 930188 ... -46.487968 -11.499000\n",
"2 chr1 930200 ... -38.066742 -8.078856\n",
"3 chr1 930203 ... -4.402274 3.053887\n",
"4 chr1 930222 ... 26.421061 27.204330\n",
"\n",
"[5 rows x 3378 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 51
}
]
}
]
}
================================================
FILE: enformer/enformer.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tensorflow implementation of Enformer model.
"Effective gene expression prediction from sequence by integrating long-range
interactions"
Žiga Avsec1, Vikram Agarwal2,4, Daniel Visentin1,4, Joseph R. Ledsam1,3,
Agnieszka Grabska-Barwinska1, Kyle R. Taylor1, Yannis Assael1, John Jumper1,
Pushmeet Kohli1, David R. Kelley2*
1 DeepMind, London, UK
2 Calico Life Sciences, South San Francisco, CA, USA
3 Google, Tokyo, Japan
4 These authors contributed equally.
* correspondence: avsec@google.com, pushmeet@google.com, drk@calicolabs.com
"""
import inspect
from typing import Any, Callable, Dict, Optional, Text, Union, Iterable
import attention_module
import numpy as np
import sonnet as snt
import tensorflow as tf
SEQUENCE_LENGTH = 196_608
BIN_SIZE = 128
TARGET_LENGTH = 896
class Enformer(snt.Module):
"""Main model."""
def __init__(self,
channels: int = 1536,
num_transformer_layers: int = 11,
num_heads: int = 8,
pooling_type: str = 'attention',
name: str = 'enformer'):
"""Enformer model.
Args:
channels: Number of convolutional filters and the overall 'width' of the
model.
num_transformer_layers: Number of transformer layers.
num_heads: Number of attention heads.
pooling_type: Which pooling function to use. Options: 'attention' or max'.
name: Name of sonnet module.
"""
super().__init__(name=name)
# pylint: disable=g-complex-comprehension,g-long-lambda,cell-var-from-loop
heads_channels = {'human': 5313, 'mouse': 1643}
dropout_rate = 0.4
assert channels % num_heads == 0, ('channels needs to be divisible '
f'by {num_heads}')
whole_attention_kwargs = {
'attention_dropout_rate': 0.05,
'initializer': None,
'key_size': 64,
'num_heads': num_heads,
'num_relative_position_features': channels // num_heads,
'positional_dropout_rate': 0.01,
'relative_position_functions': [
'positional_features_exponential',
'positional_features_central_mask',
'positional_features_gamma'
],
'relative_positions': True,
'scaling': True,
'value_size': channels // num_heads,
'zero_initialize': True
}
trunk_name_scope = tf.name_scope('trunk')
trunk_name_scope.__enter__()
# lambda is used in Sequential to construct the module under tf.name_scope.
def conv_block(filters, width=1, w_init=None, name='conv_block', **kwargs):
return Sequential(lambda: [
snt.distribute.CrossReplicaBatchNorm(
create_scale=True,
create_offset=True,
scale_init=snt.initializers.Ones(),
moving_mean=snt.ExponentialMovingAverage(0.9),
moving_variance=snt.ExponentialMovingAverage(0.9)),
gelu,
snt.Conv1D(filters, width, w_init=w_init, **kwargs)
], name=name)
stem = Sequential(lambda: [
snt.Conv1D(channels // 2, 15),
Residual(conv_block(channels // 2, 1, name='pointwise_conv_block')),
pooling_module(pooling_type, pool_size=2),
], name='stem')
filter_list = exponential_linspace_int(start=channels // 2, end=channels,
num=6, divisible_by=128)
conv_tower = Sequential(lambda: [
Sequential(lambda: [
conv_block(num_filters, 5),
Residual(conv_block(num_filters, 1, name='pointwise_conv_block')),
pooling_module(pooling_type, pool_size=2),
],
name=f'conv_tower_block_{i}')
for i, num_filters in enumerate(filter_list)], name='conv_tower')
# Transformer.
def transformer_mlp():
return Sequential(lambda: [
snt.LayerNorm(axis=-1, create_scale=True, create_offset=True),
snt.Linear(channels * 2),
snt.Dropout(dropout_rate),
tf.nn.relu,
snt.Linear(channels),
snt.Dropout(dropout_rate)], name='mlp')
transformer = Sequential(lambda: [
Sequential(lambda: [
Residual(Sequential(lambda: [
snt.LayerNorm(axis=-1,
create_scale=True, create_offset=True,
scale_init=snt.initializers.Ones()),
attention_module.MultiheadAttention(**whole_attention_kwargs,
name=f'attention_{i}'),
snt.Dropout(dropout_rate)], name='mha')),
Residual(transformer_mlp())], name=f'transformer_block_{i}')
for i in range(num_transformer_layers)], name='transformer')
crop_final = TargetLengthCrop1D(TARGET_LENGTH, name='target_input')
final_pointwise = Sequential(lambda: [
conv_block(channels * 2, 1),
snt.Dropout(dropout_rate / 8),
gelu], name='final_pointwise')
self._trunk = Sequential([stem,
conv_tower,
transformer,
crop_final,
final_pointwise],
name='trunk')
trunk_name_scope.__exit__(None, None, None)
with tf.name_scope('heads'):
self._heads = {
head: Sequential(
lambda: [snt.Linear(num_channels), tf.nn.softplus],
name=f'head_{head}')
for head, num_channels in heads_channels.items()
}
# pylint: enable=g-complex-comprehension,g-long-lambda,cell-var-from-loop
@property
def trunk(self):
return self._trunk
@property
def heads(self):
return self._heads
def __call__(self, inputs: tf.Tensor,
is_training: bool) -> Dict[str, tf.Tensor]:
trunk_embedding = self.trunk(inputs, is_training=is_training)
return {
head: head_module(trunk_embedding, is_training=is_training)
for head, head_module in self.heads.items()
}
@tf.function(input_signature=[
tf.TensorSpec([None, SEQUENCE_LENGTH, 4], tf.float32)])
def predict_on_batch(self, x):
"""Method for SavedModel."""
return self(x, is_training=False)
class TargetLengthCrop1D(snt.Module):
"""Crop sequence to match the desired target length."""
def __init__(self,
target_length: Optional[int],
name: str = 'target_length_crop'):
super().__init__(name=name)
self._target_length = target_length
def __call__(self, inputs):
if self._target_length is None:
return inputs
trim = (inputs.shape[-2] - self._target_length) // 2
if trim < 0:
raise ValueError('inputs longer than target length')
elif trim == 0:
return inputs
else:
return inputs[..., trim:-trim, :]
class Sequential(snt.Module):
"""snt.Sequential automatically passing is_training where it exists."""
def __init__(self,
layers: Optional[Union[Callable[[], Iterable[snt.Module]],
Iterable[Callable[..., Any]]]] = None,
name: Optional[Text] = None):
super().__init__(name=name)
if layers is None:
self._layers = []
else:
# layers wrapped in a lambda function to have a common namespace.
if hasattr(layers, '__call__'):
layers = layers()
self._layers = [layer for layer in layers if layer is not None]
def __call__(self, inputs: tf.Tensor, is_training: bool, **kwargs):
outputs = inputs
for _, mod in enumerate(self._layers):
if accepts_is_training(mod):
outputs = mod(outputs, is_training=is_training, **kwargs)
else:
outputs = mod(outputs, **kwargs)
return outputs
def pooling_module(kind, pool_size):
"""Pooling module wrapper."""
if kind == 'attention':
return SoftmaxPooling1D(pool_size=pool_size, per_channel=True,
w_init_scale=2.0)
elif kind == 'max':
return tf.keras.layers.MaxPool1D(pool_size=pool_size, padding='same')
else:
raise ValueError(f'Invalid pooling kind: {kind}.')
class SoftmaxPooling1D(snt.Module):
"""Pooling operation with optional weights."""
def __init__(self,
pool_size: int = 2,
per_channel: bool = False,
w_init_scale: float = 0.0,
name: str = 'softmax_pooling'):
"""Softmax pooling.
Args:
pool_size: Pooling size, same as in Max/AvgPooling.
per_channel: If True, the logits/softmax weights will be computed for
each channel separately. If False, same weights will be used across all
channels.
w_init_scale: When 0.0 is equivalent to avg pooling, and when
~2.0 and `per_channel=False` it's equivalent to max pooling.
name: Module name.
"""
super().__init__(name=name)
self._pool_size = pool_size
self._per_channel = per_channel
self._w_init_scale = w_init_scale
self._logit_linear = None
@snt.once
def _initialize(self, num_features):
self._logit_linear = snt.Linear(
output_size=num_features if self._per_channel else 1,
with_bias=False, # Softmax is agnostic to shifts.
w_init=snt.initializers.Identity(self._w_init_scale))
def __call__(self, inputs):
_, length, num_features = inputs.shape
self._initialize(num_features)
inputs = tf.reshape(
inputs,
(-1, length // self._pool_size, self._pool_size, num_features))
return tf.reduce_sum(
inputs * tf.nn.softmax(self._logit_linear(inputs), axis=-2),
axis=-2)
class Residual(snt.Module):
"""Residual block."""
def __init__(self, module: snt.Module, name='residual'):
super().__init__(name=name)
self._module = module
def __call__(self, inputs: tf.Tensor, is_training: bool, *args,
**kwargs) -> tf.Tensor:
return inputs + self._module(inputs, is_training, *args, **kwargs)
def gelu(x: tf.Tensor) -> tf.Tensor:
"""Applies the Gaussian error linear unit (GELU) activation function.
Using approximiation in section 2 of the original paper:
https://arxiv.org/abs/1606.08415
Args:
x: Input tensor to apply gelu activation.
Returns:
Tensor with gelu activation applied to it.
"""
return tf.nn.sigmoid(1.702 * x) * x
def one_hot_encode(sequence: str,
alphabet: str = 'ACGT',
neutral_alphabet: str = 'N',
neutral_value: Any = 0,
dtype=np.float32) -> np.ndarray:
"""One-hot encode sequence."""
def to_uint8(string):
return np.frombuffer(string.encode('ascii'), dtype=np.uint8)
hash_table = np.zeros((np.iinfo(np.uint8).max, len(alphabet)), dtype=dtype)
hash_table[to_uint8(alphabet)] = np.eye(len(alphabet), dtype=dtype)
hash_table[to_uint8(neutral_alphabet)] = neutral_value
hash_table = hash_table.astype(dtype)
return hash_table[to_uint8(sequence)]
def exponential_linspace_int(start, end, num, divisible_by=1):
"""Exponentially increasing values of integers."""
def _round(x):
return int(np.round(x / divisible_by) * divisible_by)
base = np.exp(np.log(end / start) / (num - 1))
return [_round(start * base**i) for i in range(num)]
def accepts_is_training(module):
return 'is_training' in list(inspect.signature(module.__call__).parameters)
================================================
FILE: enformer/enformer_test.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Test enformer model by applying random sequence as input.
Test:
$ python enformer_test.py
"""
import random
import unittest
import enformer
import numpy as np
class TestEnformer(unittest.TestCase):
def test_enformer(self):
model = enformer.Enformer(channels=1536, num_transformer_layers=11)
inputs = _get_random_input()
outputs = model(inputs, is_training=True)
self.assertEqual(outputs['human'].shape, (1, enformer.TARGET_LENGTH, 5313))
self.assertEqual(outputs['mouse'].shape, (1, enformer.TARGET_LENGTH, 1643))
def _get_random_input():
seq = ''.join(
[random.choice('ACGT') for _ in range(enformer.SEQUENCE_LENGTH)])
return np.expand_dims(enformer.one_hot_encode(seq), 0).astype(np.float32)
if __name__ == '__main__':
unittest.main()
================================================
FILE: enformer/requirements.txt
================================================
dm-sonnet==2.0.0
kipoiseq==0.5.2
numpy==1.19.5
pandas==1.2.3
tensorflow==2.5.0
tensorflow-hub==0.11.0
================================================
FILE: ensemble_loss_landscape/README.md
================================================
# Accompanying code for Deep Ensemble: A Loss Landscape Perspective
[](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/ensemble_loss_landscape/cifar10_medium_cnn_experiments.ipynb)
The Colab notebook `cifar10_medium_cnn_experiments.ipynb` illustrates the CIFAR-10
experiments in the paper:
[Deep Ensembles: A Loss Landscape Perspective](https://arxiv.org/abs/1912.02757)
by Stanislav Fort, Huiyi Hu and Balaji Lakshminarayanan
These experiments investigate the effects of ensembling and variational Bayesian
methods, please see the paper for more details.
================================================
FILE: ensemble_loss_landscape/cifar10_medium_cnn_experiments.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "WU--cjOphQKk"
},
"outputs": [],
"source": [
" # Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.\n",
"\n",
" # Licensed under the Apache License, Version 2.0 (the \"License\");\n",
" # you may not use this file except in compliance with the License.\n",
" # You may obtain a copy of the License at\n",
"\n",
" # http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
" # Unless required by applicable law or agreed to in writing, software\n",
" # distributed under the License is distributed on an \"AS IS\" BASIS,\n",
" # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
" # See the License for the specific language governing permissions and\n",
" # limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "MGjUZ2Q55OWM"
},
"source": [
"#Ensemble and Subspace Sampling Experiments on CIFAR10\n",
"\n",
" \u003c!-- Copyright 2020 Stanislav Fořt, Huiyi Hu, Balaji Lakshminarayanan\n",
"\n",
" Licensed under the Apache License, Version 2.0 (the \"License\");\n",
" you may not use this file except in compliance with the License.\n",
" You may obtain a copy of the License at\n",
"\n",
" http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
" Unless required by applicable law or agreed to in writing, software\n",
" distributed under the License is distributed on an \"AS IS\" BASIS,\n",
" WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
" See the License for the specific language governing permissions and\n",
" limitations under the License. --\u003e\n",
"\n",
"\n",
"\n",
"This notebook illustrates the CIFAR-10 experiments in the paper: \n",
"\n",
"[Deep Ensembles: A Loss Landscape Perspective](https://arxiv.org/abs/1912.02757) by Stanislav Fort, Huiyi Hu and Balaji Lakshminarayanan\n",
"\n",
"\n",
"These experiments investigate the effects of ensembling and variational Bayesian methods, please see the paper for more details."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "U5sMeD_4pTe2"
},
"source": [
"# Setting up"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "code",
"colab": {},
"colab_type": "code",
"id": "bUXPnWQIEKJh"
},
"outputs": [],
"source": [
"%tensorflow_version 1.x\n",
"\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"from tensorflow.contrib import layers\n",
"import tensorflow_datasets as tfds\n",
"\n",
"from scipy.special import softmax\n",
"from matplotlib import patches as mpatch\n",
"\n",
"# Plot Style\n",
"mpl.style.use('seaborn-colorblind')\n",
"mpl.rcParams.update({\n",
" 'font.size': 14,\n",
" 'lines.linewidth': 2,\n",
" 'figure.figsize': (6, 6 / 1.61)\n",
"})\n",
"mpl.rcParams['grid.color'] = 'k'\n",
"mpl.rcParams['grid.linestyle'] = ':'\n",
"mpl.rcParams['grid.linewidth'] = 0.5\n",
"mpl.rcParams['lines.markersize'] = 6\n",
"mpl.rcParams['lines.marker'] = None\n",
"mpl.rcParams['axes.grid'] = True\n",
"\n",
"DEFAULT_FONTSIZE = 13\n",
"mpl.rcParams.update({\n",
" 'font.size': DEFAULT_FONTSIZE,\n",
" 'lines.linewidth': 2,\n",
" 'legend.fontsize': DEFAULT_FONTSIZE,\n",
" 'axes.labelsize': DEFAULT_FONTSIZE,\n",
" 'xtick.labelsize': DEFAULT_FONTSIZE,\n",
" 'ytick.labelsize': DEFAULT_FONTSIZE,\n",
" 'figure.figsize': (7, 7.0 / 1.4)\n",
"})\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8_a0OnCQrkFu"
},
"source": [
"#Getting the datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "AlG96jyJr8cW"
},
"outputs": [],
"source": [
"def give_me_data():\n",
" print(\"Reading CIFAR10\")\n",
" cifar_data = {}\n",
" N_val = 500\n",
"\n",
" # Construct a tf.data.Dataset\n",
" ds_train, ds_test = tfds.load(\n",
" name=\"cifar10\", split=[\"train\", \"test\"], batch_size=-1)\n",
"\n",
" numpy_train = tfds.as_numpy(ds_train)\n",
" x_train_raw, y_train_raw = numpy_train[\"image\"], numpy_train[\"label\"]\n",
"\n",
" numpy_test = tfds.as_numpy(ds_test)\n",
" x_test_raw, y_test_raw = numpy_test[\"image\"], numpy_test[\"label\"]\n",
"\n",
" N_train = x_train_raw.shape[0] - N_val\n",
"\n",
" X_train = x_train_raw[:N_train]\n",
" y_train = y_train_raw[:N_train]\n",
" X_val = x_train_raw[N_train:N_train + N_val]\n",
" y_val = y_train_raw[N_train:N_train + N_val]\n",
" X_test = x_test_raw\n",
" y_test = y_test_raw\n",
"\n",
" Hn = 32\n",
" Wn = 32\n",
" Cn = 3\n",
" cifar_data['Hn'] = Hn\n",
" cifar_data['Wn'] = Wn\n",
" cifar_data['Cn'] = Cn\n",
" cifar_data['classes'] = 10\n",
"\n",
" cifar_data['X_train'] = X_train.reshape([-1, Hn, Wn, Cn])\n",
" cifar_data['X_val'] = X_val.reshape([-1, Hn, Wn, Cn])\n",
" cifar_data['X_test'] = X_test.reshape([-1, Hn, Wn, Cn])\n",
"\n",
" cifar_data['y_train'] = y_train.reshape([-1])\n",
" cifar_data['y_val'] = y_val.reshape([-1])\n",
" cifar_data['y_test'] = y_test.reshape([-1])\n",
" return cifar_data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 35
},
"colab_type": "code",
"executionInfo": {
"elapsed": 16490,
"status": "ok",
"timestamp": 1596259193156,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 420
},
"id": "P9ius35wN76P",
"outputId": "2f9f551f-b17c-4e0d-8442-b4749f18f33c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading CIFAR10\n"
]
}
],
"source": [
"cifar_ds = give_me_data()\n",
"X_train = cifar_ds['X_train']\n",
"y_train = cifar_ds['y_train']\n",
"X_val = cifar_ds['X_val']\n",
"y_val = cifar_ds['y_val']\n",
"X_test = cifar_ds['X_test']\n",
"y_test = cifar_ds['y_test']\n",
"Hn = cifar_ds['Hn']\n",
"Wn = cifar_ds['Wn']\n",
"Cn = cifar_ds['Cn']\n",
"classes = cifar_ds['classes']\n",
"\n",
"N_train = X_train.shape[0]\n",
"N_val = X_val.shape[0]\n",
"N_test = X_test.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "DsEjVGc_pqAL"
},
"source": [
"# Defining a CNN"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "fWU-qKjRvvKV"
},
"outputs": [],
"source": [
"# Builds a CNN network and returns various graph nodes for training and\n",
"# evaluation.\n",
"# In the 'eval' part of the graph, placeholders for network weights (Ws \u0026 bs)\n",
"# are created to facilitate inferencing at given weights (e.g. from subspace\n",
"# sampling).\n",
"def multilayer_CNN(X_ph_in,\n",
" y_ph_in,\n",
" dropout_rate_ph_in=None,\n",
" filter_sizes=(3, 3, 3, 3),\n",
" pools=(2, 2, 2, 2),\n",
" channels=(32, 64, 128, 256),\n",
" classes=10):\n",
" net_hooks = {\n",
" 'weights': {},\n",
" 'placeholder': {},\n",
" 'train_hook': {},\n",
" 'train_output': {},\n",
" 'eval_output': {}\n",
" }\n",
" f_nonlin = tf.nn.relu\n",
" with tf.variable_scope('train'):\n",
" a = X_ph_in\n",
" Ws = []\n",
" bs = []\n",
" for i in range(len(filter_sizes)):\n",
" _, _, _, Cnow = a.get_shape().as_list()\n",
" W = tf.get_variable(\n",
" 'Wconv' + str(i),\n",
" shape=[filter_sizes[i], filter_sizes[i], Cnow, channels[i]],\n",
" initializer=layers.xavier_initializer(),\n",
" trainable=True)\n",
" b = tf.get_variable(\n",
" 'bconv' + str(i),\n",
" shape=[1, 1, channels[i]],\n",
" initializer=layers.xavier_initializer(),\n",
" trainable=True)\n",
" Ws.append(W)\n",
" bs.append(b)\n",
"\n",
" h = tf.nn.conv2d(a, W, strides=[1, 1, 1, 1], padding='SAME') + b\n",
" h = tf.nn.dropout(h, rate=dropout_rate_ph_in)\n",
" h = tf.nn.max_pool(\n",
" h,\n",
" ksize=[1, pools[i], pools[i], 1],\n",
" strides=[1, pools[i], pools[i], 1],\n",
" padding='SAME')\n",
" if i \u003c len(filter_sizes) - 1:\n",
" a = f_nonlin(h)\n",
" else:\n",
" a = h\n",
" _, H_out, W_out, C_out = a.get_shape().as_list()\n",
" _, height_final, width_final, channels_final = a.get_shape().as_list()\n",
"\n",
" # Final fully connected layer.\n",
" W_final = tf.get_variable(\n",
" 'Wfinal',\n",
" shape=[height_final * width_final * channels_final, classes],\n",
" initializer=layers.xavier_initializer(),\n",
" trainable=True)\n",
" b_final = tf.get_variable(\n",
" 'bfinal',\n",
" shape=[1, classes],\n",
" initializer=layers.xavier_initializer(),\n",
" trainable=True)\n",
"\n",
" Ws.append(W_final)\n",
" bs.append(b_final)\n",
" net_hooks['weights']['Ws'] = Ws\n",
" net_hooks['weights']['bs'] = bs\n",
"\n",
" a = tf.matmul(\n",
" tf.reshape(a, [-1, height_final * width_final * channels_final]),\n",
" W_final) + b_final\n",
" y = a\n",
" net_hooks['train_output']['y'] = y\n",
" y_ph_onehot = tf.one_hot(y_ph_in, classes, dtype=tf.int32)\n",
"\n",
" # Weights and biases for regularization.\n",
" net_hooks['train_hook']['L2_reg_Ws'] = tf.reduce_sum(\n",
" [tf.reduce_sum(W_now**2.0) for W_now in Ws])\n",
" net_hooks['train_hook']['L2_reg_bs'] = tf.reduce_sum(\n",
" [tf.reduce_sum(b_now**2.0) for b_now in bs])\n",
"\n",
" loss = tf.nn.softmax_cross_entropy_with_logits(labels=y_ph_onehot, logits=y)\n",
" net_hooks['train_hook']['loss'] = tf.reduce_mean(loss)\n",
" \n",
" # Defining all weights and biases as placeholders so that inference can be\n",
" # performed at given weight values.\n",
" with tf.variable_scope('eval'):\n",
" \n",
" Ws_ph = []\n",
" for W in Ws:\n",
" W_ph_now = tf.placeholder(tf.float32, W.get_shape())\n",
" Ws_ph.append(W_ph_now)\n",
" bs_ph = []\n",
" for b in bs:\n",
" b_ph_now = tf.placeholder(tf.float32, b.get_shape())\n",
" bs_ph.append(b_ph_now)\n",
"\n",
" a = X_ph_in\n",
"\n",
" for i in range(len(filter_sizes)):\n",
" _, _, _, Cnow = a.get_shape().as_list()\n",
"\n",
" h = tf.nn.conv2d(\n",
" a, Ws_ph[i], strides=[1, 1, 1, 1], padding='SAME') + bs_ph[i]\n",
" h = tf.nn.dropout(h, rate=dropout_rate_ph_in)\n",
"\n",
" h = tf.nn.max_pool(\n",
" h,\n",
" ksize=[1, pools[i], pools[i], 1],\n",
" strides=[1, pools[i], pools[i], 1],\n",
" padding='SAME')\n",
"\n",
" if i \u003c len(filter_sizes) - 1:\n",
" a = f_nonlin(h)\n",
" else:\n",
" a = h\n",
" _, H_out, W_out, C_out = a.get_shape().as_list()\n",
" _, height_final, width_final, channels_final = a.get_shape().as_list()\n",
"\n",
" last_layer = tf.reshape(a,\n",
" [-1, height_final * width_final * channels_final])\n",
" net_hooks['train_hook']['last_layer'] = last_layer\n",
"\n",
" a = tf.matmul(last_layer, Ws_ph[-1]) + bs_ph[-1]\n",
" y_eval = a\n",
" net_hooks['eval_output']['pred_eval'] = tf.nn.softmax(y_eval, axis=-1)\n",
" y_ph_onehot = tf.one_hot(y_ph_in, classes, dtype=tf.int32)\n",
" loss_eval = tf.nn.softmax_cross_entropy_with_logits(\n",
" labels=y_ph_onehot, logits=y_eval)\n",
" net_hooks['eval_output']['loss_eval'] = tf.reduce_mean(loss_eval)\n",
" net_hooks['eval_output']['y_eval'] = y_eval\n",
" net_hooks['placeholder']['Ws_ph'] = Ws_ph\n",
" net_hooks['placeholder']['bs_ph'] = bs_ph\n",
" return net_hooks"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "xebqAIwmq9ia"
},
"source": [
"# Build model graph"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 35
},
"colab_type": "code",
"executionInfo": {
"elapsed": 971,
"status": "ok",
"timestamp": 1596259216214,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 420
},
"id": "-WWIxPS1q5B6",
"outputId": "7098c352-b897-434b-fc79-1972dbf29243"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of free parameters=971146\n"
]
}
],
"source": [
"tf.reset_default_graph()\n",
"\n",
"X_ph = tf.placeholder(tf.float32, [None, Hn, Wn, Cn])\n",
"y_ph = tf.placeholder(tf.int32, [None])\n",
"lr_ph = tf.placeholder(tf.float32)\n",
"dropout_rate_ph = tf.placeholder(tf.float32, [])\n",
"L2_reg_constant_ph = tf.placeholder(tf.float32, [])\n",
"\n",
"architecture = 'CNN'\n",
"\n",
"if architecture == 'CNN':\n",
" with tf.variable_scope('to_get_shape'):\n",
" # Medium CNN\n",
" filter_sizes = (3, 3, 3, 3)\n",
" pools = (2, 2, 2, 2)\n",
" channels = (64, 128, 256, 256)\n",
"\n",
" dummy_cnn_hooks = multilayer_CNN(\n",
" X_ph,\n",
" y_ph,\n",
" dropout_rate_ph_in=dropout_rate_ph,\n",
" filter_sizes=filter_sizes,\n",
" pools=pools,\n",
" channels=channels,\n",
" classes=classes)\n",
" # Number of parameters.\n",
" params = dummy_cnn_hooks['weights']['Ws'] + dummy_cnn_hooks['weights']['bs']\n",
" flat_params = tf.concat([tf.reshape(v, [-1]) for v in params], axis=0)\n",
" number_of_params = flat_params.get_shape().as_list()[0]\n",
" print('Number of free parameters=' + str(number_of_params))\n",
"\n",
" cnn_hooks = multilayer_CNN(\n",
" X_ph,\n",
" y_ph,\n",
" dropout_rate_ph_in=dropout_rate_ph,\n",
" filter_sizes=filter_sizes,\n",
" pools=pools,\n",
" channels=channels,\n",
" classes=classes)\n",
" y = cnn_hooks['train_output']['y']\n",
" loss = cnn_hooks['train_hook']['loss']\n",
" y_eval = cnn_hooks['eval_output']['y_eval']\n",
" pred_eval = cnn_hooks['eval_output']['pred_eval']\n",
" loss_eval = cnn_hooks['eval_output']['loss_eval']\n",
" Ws_ph = cnn_hooks['placeholder']['Ws_ph']\n",
" bs_ph = cnn_hooks['placeholder']['bs_ph']\n",
" Ws = cnn_hooks['weights']['Ws']\n",
" bs = cnn_hooks['weights']['bs']\n",
" L2_reg_Ws = cnn_hooks['train_hook']['L2_reg_Ws']\n",
" L2_reg_bs = cnn_hooks['train_hook']['L2_reg_bs']\n",
"\n",
"loss_to_be_optimized = loss + L2_reg_constant_ph * (L2_reg_Ws + L2_reg_bs)\n",
"\n",
"train_step = tf.train.AdamOptimizer(lr_ph).minimize(loss_to_be_optimized)\n",
"\n",
"correct_prediction = tf.equal(tf.cast(tf.argmax(y, 1), dtype=tf.int32), y_ph)\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
"\n",
"correct_prediction_eval = tf.equal(\n",
" tf.cast(tf.argmax(y_eval, 1), dtype=tf.int32), y_ph)\n",
"accuracy_eval = tf.reduce_mean(tf.cast(correct_prediction_eval, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8PHJJ59SuMAo"
},
"source": [
"## Helper Functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "eL52WPi6uPDD"
},
"outputs": [],
"source": [
"# Helper functions to flatten weights to a vector, and reform a flattened vector\n",
"# to weights.\n",
"def flatten(Ws1, bs1):\n",
" lists_now = []\n",
" for W_now in Ws1:\n",
" lists_now.append(W_now.reshape([-1]))\n",
" for b_now in bs1:\n",
" lists_now.append(b_now.reshape([-1]))\n",
" return np.concatenate(lists_now, axis=0)\n",
"\n",
"\n",
"def get_flat_name(Ws_tf, bs_tf):\n",
" names = []\n",
" for W in Ws_tf:\n",
" names = names + [W.name] * np.prod(W.get_shape().as_list())\n",
" for b in bs_tf:\n",
" names = names + [b.name] * np.prod(b.get_shape().as_list())\n",
" return names\n",
"\n",
"\n",
"def reform(flat1):\n",
" sofar = 0\n",
" Ws_out_now = []\n",
" bs_out_now = []\n",
" for W in Ws:\n",
" shape_now = W.get_shape().as_list()\n",
" size_now = np.prod(shape_now)\n",
" elements = flat1[sofar:sofar + size_now]\n",
" sofar = sofar + size_now\n",
" Ws_out_now.append(np.array(elements).reshape(shape_now))\n",
" for b in bs:\n",
" shape_now = b.get_shape().as_list()\n",
" size_now = np.prod(shape_now)\n",
" elements = flat1[sofar:sofar + size_now]\n",
" sofar = sofar + size_now\n",
" bs_out_now.append(np.array(elements).reshape(shape_now))\n",
" return Ws_out_now, bs_out_now\n",
"\n",
"\n",
"# Brier score for evaluating uncertainty performance.\n",
"def brier_scores(labels, probs=None, logits=None):\n",
" \"\"\"Compute elementwise Brier score.\n",
"\n",
" Args:\n",
" labels: Tensor of integer labels shape [N1, N2, ...]\n",
" probs: Tensor of categorical probabilities of shape [N1, N2, ..., M].\n",
" logits: If `probs` is None, class probabilities are computed as a softmax\n",
" over these logits, otherwise, this argument is ignored.\n",
"\n",
" Returns:\n",
" Tensor of shape [N1, N2, ...] consisting of Brier score contribution from\n",
" each element. The full-dataset Brier score is an average of these values.\n",
" \"\"\"\n",
" assert (probs is None) != (\n",
" logits is None), \"Exactly one of probs and logits should be None.\"\n",
" if probs is None:\n",
" probs = softmax(logits, axis=-1)\n",
" nlabels = probs.shape[-1]\n",
" flat_probs = probs.reshape([-1, nlabels])\n",
" flat_labels = labels.reshape([len(flat_probs)])\n",
"\n",
" plabel = flat_probs[np.arange(len(flat_labels)), flat_labels]\n",
" out = np.square(flat_probs).sum(axis=-1) - 2 * plabel\n",
" return out.reshape(labels.shape) + 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "W2OrJv2tvdLv"
},
"source": [
"# The root training loop (Independent solutions)\n",
"This section could be time-consuming depending on the size of 'points_to_collect' ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "T6T6KJVWvNVP"
},
"outputs": [],
"source": [
"# Choosing a subset of the train data for faster eval.\n",
"N_train_subset = N_val\n",
"train_chosen_indices = np.random.choice(\n",
" range(N_train), N_train_subset, replace=False)\n",
"X_train_subset = X_train[train_chosen_indices]\n",
"y_train_subset = y_train[train_chosen_indices]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Ls0ziO7ivQo4"
},
"outputs": [],
"source": [
"# Number of independent solutions to collect.\n",
"points_to_collect = 5\n",
"# Number of ensemble models to use.\n",
"max_ens_size = 4\n",
"# Number of full training trajectory to collect for analysis such as T-SNE.\n",
"num_trajectory_record = 3\n",
"# Collect checkpoints along trajectory for subspace sampling or SWA.\n",
"collect_solution_after_epoch = 30\n",
"\n",
"# Training hyperparams for each of the runs.\n",
"epochs = 40\n",
"batch_size = 128\n",
"dropout = 0.1\n",
"L2_constant = 0.0\n",
"\n",
"learning_rate = 1.6e-3\n",
"# Defining the LR schedule\n",
"lr_halving_epoch = 10\n",
"lr_halving_maxcount = 1000\n",
"lr_halving_power = 2.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Fw6WhPz-slnf"
},
"outputs": [],
"source": [
"print(\"train_step\", train_step)\n",
"\n",
"Ws_many = []\n",
"bs_many = []\n",
"\n",
"losses_many = []\n",
"accs_many = []\n",
"\n",
"# Collecting last epochs from each trajectory.\n",
"Ws_by_epochs_many = [[] for _ in range(points_to_collect)]\n",
"bs_by_epochs_many = [[] for _ in range(points_to_collect)]\n",
"\n",
"# Collecting whole trajectory.\n",
"Ws_trajectory = [[] for _ in range(num_trajectory_record)]\n",
"bs_trajectory = [[] for _ in range(num_trajectory_record)]\n",
"\n",
"global_init_op = tf.global_variables_initializer()\n",
"with tf.Session() as sess:\n",
" for point_id in range(points_to_collect):\n",
" print(\"Optimization \" + str(point_id) + \" with starting lr=\" +\n",
" str(learning_rate) + \" dropout rate=\" + str(dropout) + \" batch=\" +\n",
" str(batch_size) + \" L2const=\" + str(L2_constant))\n",
" sess.run(global_init_op)\n",
" for e in range(epochs):\n",
" iterations = int(np.floor(float(N_train) / float(batch_size)))\n",
"\n",
" losses_train_list = []\n",
" accs_train_list = []\n",
"\n",
" halvings_count_now = np.floor(float(e) / lr_halving_epoch)\n",
" halvings_to_be_used = np.min([halvings_count_now, lr_halving_maxcount])\n",
" learning_rate_now = learning_rate / (\n",
" (lr_halving_power)**halvings_to_be_used)\n",
"\n",
" for it in range(iterations):\n",
" indices = np.random.choice(range(N_train), batch_size)\n",
" X_batch = X_train[indices]\n",
" y_batch = y_train[indices]\n",
"\n",
" feed_dict = {\n",
" X_ph: X_batch,\n",
" y_ph: y_batch,\n",
" lr_ph: learning_rate_now,\n",
" dropout_rate_ph: dropout,\n",
" L2_reg_constant_ph: L2_constant,\n",
" }\n",
"\n",
" variables = [train_step, loss, accuracy]\n",
"\n",
" _, loss_out, acc_out = sess.run(variables, feed_dict=feed_dict)\n",
"\n",
" losses_train_list.append(loss_out)\n",
" accs_train_list.append(acc_out)\n",
"\n",
" # Evaluating current epoch.\n",
" feed_dict = {\n",
" X_ph: X_val,\n",
" y_ph: y_val,\n",
" dropout_rate_ph: 0.0,\n",
" L2_reg_constant_ph: 0.0,\n",
" }\n",
"\n",
" loss_val_out, acc_val_out, Ws_opt_out_now, bs_opt_out_now = sess.run(\n",
" [loss, accuracy, Ws, bs], feed_dict=feed_dict)\n",
"\n",
" feed_dict[X_ph] = X_train_subset\n",
" feed_dict[y_ph] = y_train_subset\n",
"\n",
" loss_train_out, acc_train_out = sess.run([loss, accuracy],\n",
" feed_dict=feed_dict)\n",
"\n",
" feed_dict[X_ph] = X_test\n",
" feed_dict[y_ph] = y_test\n",
"\n",
" loss_test_out, acc_test_out = sess.run([loss, accuracy],\n",
" feed_dict=feed_dict)\n",
"\n",
" print(\"e=\" + str(e) + \" train loss=\" + f\"{loss_train_out:.4f}\" +\n",
" \" train acc=\" + f\"{acc_train_out:.4f}\" + \" val loss=\" +\n",
" f\"{loss_val_out:.4f}\" + \" val acc=\" + f\"{acc_val_out:.4f}\" +\n",
" \" test loss=\" + f\"{loss_test_out:.4f}\" + \" test acc=\" +\n",
" f\"{acc_test_out:.4f}\")\n",
" if e \u003e= collect_solution_after_epoch:\n",
" Ws_by_epochs_many[point_id].append(Ws_opt_out_now)\n",
" bs_by_epochs_many[point_id].append(bs_opt_out_now)\n",
" if point_id \u003c num_trajectory_record:\n",
" Ws_trajectory[point_id].append(Ws_opt_out_now)\n",
" bs_trajectory[point_id].append(bs_opt_out_now)\n",
"\n",
" # Saving model weights of the last checkpoint.\n",
" Ws_opt_out_now, bs_opt_out_now = sess.run([Ws, bs])\n",
"\n",
" Ws_many.append(Ws_opt_out_now)\n",
" bs_many.append(bs_opt_out_now)\n",
" losses_many.append(loss_val_out)\n",
" accs_many.append(acc_val_out)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KapGgJupd9SZ"
},
"source": [
"###Collecting Validation Predictions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "8SJntXp5U2Ir"
},
"outputs": [],
"source": [
"trajectory_preds_test = np.zeros(\n",
" (num_trajectory_record, epochs, N_val, classes))\n",
"independent_preds_test = np.zeros((points_to_collect, N_val, classes))\n",
"\n",
"with tf.Session() as sess:\n",
" for id_now in range(points_to_collect):\n",
" for j, W in enumerate(Ws_many[id_now]):\n",
" feed_dict[Ws_ph[j]] = W\n",
" for j, b in enumerate(bs_many[id_now]):\n",
" feed_dict[bs_ph[j]] = b\n",
"\n",
" feed_dict[dropout_rate_ph] = 0.0\n",
"\n",
" feed_dict[X_ph] = X_val\n",
" feed_dict[y_ph] = y_val\n",
" pred_eval_out = sess.run(pred_eval, feed_dict=feed_dict)\n",
" independent_preds_test[id_now] = pred_eval_out\n",
"\n",
" for id_now in range(num_trajectory_record):\n",
" for e in range(epochs):\n",
" for j, W in enumerate(Ws_trajectory[id_now][e]):\n",
" feed_dict[Ws_ph[j]] = W\n",
" for j, b in enumerate(bs_trajectory[id_now][e]):\n",
" feed_dict[bs_ph[j]] = b\n",
" pred_eval_out = sess.run(pred_eval, feed_dict=feed_dict)\n",
" trajectory_preds_test[id_now][e] = pred_eval_out"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "MSyiWg_W9b4F"
},
"source": [
"#Cosine and Fraction of Disagreement"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "wVnTmgAD9l9Y"
},
"outputs": [],
"source": [
"def cos_between(v1, v2):\n",
" \"\"\" Returns the angle in radians between vectors 'v1' and 'v2'\"\"\"\n",
" v1_u = v1 / np.linalg.norm(v1)\n",
" v2_u = v2 / np.linalg.norm(v2)\n",
" return np.dot(v1_u, v2_u)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "FV_a7LZ6BROc"
},
"source": [
"### Within Trajectory"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "HLjL_tcXGAZp"
},
"outputs": [],
"source": [
"trajectory_id = 0\n",
"flat_p_list = []\n",
"num_epochs = len(Ws_trajectory[trajectory_id])\n",
"cos_matrix = np.zeros((num_epochs, num_epochs))\n",
"\n",
"for e in range(num_epochs):\n",
" flat_p_list.append(\n",
" flatten(Ws_trajectory[trajectory_id][e], bs_trajectory[trajectory_id][e]))\n",
"for i in range(num_epochs):\n",
" for j in range(i, num_epochs):\n",
" cos_matrix[i][j] = cos_between(flat_p_list[i], flat_p_list[j])\n",
" cos_matrix[j][i] = cos_matrix[i][j]\n",
"\n",
"plt.imshow(cos_matrix, interpolation=\"nearest\", cmap=\"bwr\", origin=\"lower\")\n",
"plt.colorbar()\n",
"plt.grid(\"off\")\n",
"\n",
"title = \"Cosine Along Train Trajectory\"\n",
"plt.title(title)\n",
"\n",
"plt.xlabel(\"Checkpoint id\")\n",
"plt.ylabel(\"Checkpoint id\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "gprmfsa2n8a_"
},
"outputs": [],
"source": [
"preds_now = trajectory_preds_test[trajectory_id]\n",
"targets_now = y_val\n",
"\n",
"classes_predicted = np.argmax(preds_now, axis=-1)\n",
"fractional_differences = np.mean(\n",
" classes_predicted.reshape([1, epochs, len(targets_now)]) !=\n",
" classes_predicted.reshape([epochs, 1, len(targets_now)]),\n",
" axis=-1)\n",
"\n",
"plt.imshow(\n",
" fractional_differences, interpolation=\"nearest\", cmap=\"bwr\", origin=\"lower\")\n",
"plt.colorbar()\n",
"plt.grid(\"off\")\n",
"\n",
"title = \"Disagreement Fraction Along Train Trajectory\"\n",
"plt.title(title)\n",
"\n",
"plt.xlabel(\"Checkpoint id\")\n",
"plt.ylabel(\"Checkpoint id\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "-FEPT3_OBT-Y"
},
"source": [
"###Between Independent Runs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Wta3MB2tH4jB"
},
"outputs": [],
"source": [
"flat_p_list = []\n",
"cos_matrix = np.zeros((points_to_collect, points_to_collect))\n",
"for i in range(points_to_collect):\n",
" flat_p_list.append(flatten(Ws_many[i], bs_many[i]))\n",
"\n",
"for i in range(points_to_collect):\n",
" for j in range(i, points_to_collect):\n",
" cos_matrix[i][j] = cos_between(flat_p_list[i], flat_p_list[j])\n",
" cos_matrix[j][i] = cos_matrix[i][j]\n",
"\n",
"plt.imshow(cos_matrix, cmap=\"bwr\", origin=\"lower\")\n",
"plt.colorbar()\n",
"plt.grid(\"off\")\n",
"\n",
"title = \"Cosine Between Independent Solutions\"\n",
"plt.title(title)\n",
"\n",
"plt.xlabel(\"Independent Solution\")\n",
"plt.ylabel(\"Independent Solution\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Cglsoi44tCnL"
},
"outputs": [],
"source": [
"preds_now = independent_preds_test\n",
"targets_now = y_val\n",
"\n",
"classes_predicted = np.argmax(preds_now, axis=-1)\n",
"fractional_differences = np.mean(\n",
" classes_predicted.reshape(\n",
" [1, points_to_collect, len(targets_now)]) != classes_predicted.reshape(\n",
" [points_to_collect, 1, len(targets_now)]),\n",
" axis=-1)\n",
"\n",
"plt.imshow(\n",
" fractional_differences, interpolation=\"nearest\", cmap=\"bwr\", origin=\"lower\")\n",
"plt.colorbar()\n",
"plt.grid(\"off\")\n",
"\n",
"title = \"Disagreement Fraction Btw Independent Solutions\"\n",
"plt.title(title)\n",
"\n",
"plt.xlabel(\"Independent Solution\")\n",
"plt.ylabel(\"Independent Solution\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8hgROgxil29I"
},
"source": [
"#T-SNE Visualization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "W6aVhQ5Yl7A6"
},
"outputs": [],
"source": [
"from sklearn.manifold import TSNE\n",
"\n",
"reshaped_prediction = trajectory_preds_test.reshape([-1, N_val * classes])\n",
"\n",
"prediction_embed = TSNE(n_components=2).fit_transform(reshaped_prediction)\n",
"traj_embed = prediction_embed.reshape([num_trajectory_record, epochs, 2])\n",
"\n",
"colors_list = [\"r\", \"b\", \"g\"]\n",
"labels_list = [\"traj_{}\".format(i) for i in range(num_trajectory_record)]\n",
"for i in range(num_trajectory_record):\n",
" plt.plot(\n",
" traj_embed[i, :, 0],\n",
" traj_embed[i, :, 1],\n",
" color=colors_list[i],\n",
" alpha=0.8,\n",
" linestyle=\"\",\n",
" marker=\"o\",\n",
" label=labels_list[i])\n",
" plt.plot(\n",
" traj_embed[i, :, 0],\n",
" traj_embed[i, :, 1],\n",
" color=colors_list[i],\n",
" alpha=0.3,\n",
" linestyle=\"-\",\n",
" marker=\"\")\n",
" plt.plot(\n",
" traj_embed[i, 0, 0],\n",
" traj_embed[i, 0, 1],\n",
" color=colors_list[i],\n",
" alpha=1.0,\n",
" linestyle=\"\",\n",
" marker=\"*\",\n",
" markersize=20)\n",
"plt.legend(loc=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ewL6qSnHlNEy"
},
"source": [
"#Effects of Ensemble + Subspace Sampling"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "FN3eJMBc55hG"
},
"source": [
"## Gaussian Sampling\n",
"Sample from model weight space according to a Gaussian distribution formed by last epoch checkpoints along a training trajectory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "d5TWmKrc53kK"
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"\n",
"\n",
"def get_gaussian_sample(var_mean, var_std, scale=1.0):\n",
" var_sample = np.random.normal(var_mean, scale * var_std)\n",
" return var_sample\n",
"\n",
"\n",
"def get_pca_gaussian_flat_sampling(pca, means, rank, scale=1.0):\n",
" standard_normals = np.random.normal(loc=0.0, scale=scale, size=(rank))\n",
" shifts = pca.inverse_transform(standard_normals)\n",
" return shifts + means\n",
"\n",
"\n",
"def get_rand_norm_direction(shape):\n",
" random_direction = np.random.normal(loc=0.0, scale=1.0, size=shape)\n",
" random_direction_normed = random_direction / np.linalg.norm(random_direction)\n",
" return random_direction_normed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "mtGq9Qkj7iEG"
},
"outputs": [],
"source": [
"# PCA rank.\n",
"rank = 5\n",
"num_sample = 30\n",
"\n",
"dial_gaussian_whole_Ws = [[] for _ in range(points_to_collect)]\n",
"dial_gaussian_whole_bs = [[] for _ in range(points_to_collect)]\n",
"\n",
"pca_gaussian_whole_Ws = [[] for _ in range(points_to_collect)]\n",
"pca_gaussian_whole_bs = [[] for _ in range(points_to_collect)]\n",
"\n",
"for mid in range(points_to_collect):\n",
" Ws_traj = Ws_by_epochs_many[mid]\n",
" bs_traj = bs_by_epochs_many[mid]\n",
"\n",
" vs_list = []\n",
" for i in range(len(Ws_traj)):\n",
" vs_list.append(flatten(Ws_traj[i], bs_traj[i]))\n",
"\n",
" vs_np = np.stack(vs_list, axis=0)\n",
"\n",
" means = np.mean(vs_np, axis=0)\n",
" stds = np.std(vs_np, axis=0)\n",
" vs_np_centered = vs_np - means.reshape([1, -1])\n",
"\n",
" pca = PCA(n_components=rank)\n",
" pca.fit(vs_np_centered)\n",
" for i in range(num_sample):\n",
" v_sample = get_gaussian_sample(means, stds, scale=1.0)\n",
" w_sample, b_sample = reform(v_sample)\n",
" dial_gaussian_whole_Ws[mid].append(w_sample)\n",
" dial_gaussian_whole_bs[mid].append(b_sample)\n",
"\n",
" v_sample = get_pca_gaussian_flat_sampling(pca, means, rank, scale=1.0)\n",
" w_sample, b_sample = reform(v_sample)\n",
"\n",
" pca_gaussian_whole_Ws[mid].append(w_sample)\n",
" pca_gaussian_whole_bs[mid].append(b_sample)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "U_kvSzLKzBSW"
},
"source": [
"## Random Sampling\n",
"Randomly sample from model weight space around the final checkpoint."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "PGILtMX7zUHp"
},
"outputs": [],
"source": [
"# Random samples need to meet this accuracy threshold to be included.\n",
"acc_threshold = 0.70\n",
"\n",
"rand_Ws = [[] for _ in range(points_to_collect)]\n",
"rand_bs = [[] for _ in range(points_to_collect)]\n",
"\n",
"with tf.Session() as sess:\n",
" feed_dict = {\n",
" X_ph: X_val,\n",
" y_ph: y_val,\n",
" lr_ph: 0.0,\n",
" dropout_rate_ph: 0.0,\n",
" L2_reg_constant_ph: 0.0,\n",
" }\n",
" for mid in range(points_to_collect):\n",
" for i in range(num_sample):\n",
" vs = flatten(Ws_many[mid], bs_many[mid]) \n",
" for trial in range(5):\n",
" scale = 10*np.random.uniform() \n",
" v_sample = vs + scale * get_rand_norm_direction(vs.shape)\n",
" w_sample, b_sample = reform(v_sample)\n",
"\n",
" for j,W in enumerate(w_sample):\n",
" feed_dict[Ws_ph[j]] = w_sample[j]\n",
" for j,b in enumerate(b_sample):\n",
" feed_dict[bs_ph[j]] = b_sample[j] \n",
" \n",
" val_acc = sess.run(accuracy_eval,feed_dict = feed_dict)\n",
" if val_acc \u003e= acc_threshold:\n",
" rand_Ws[mid].append(w_sample)\n",
" rand_bs[mid].append(b_sample)\n",
" print('Obtaining 1 rand sample at scale {} with validation acc {} at {}th try'.format(scale, val_acc,trial))\n",
" break\n",
" if trial ==4:\n",
" print('No luck -------------------')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "NJ5_RCriP-p5"
},
"outputs": [],
"source": [
"# PCA low-rank approximation of the random samplings\n",
"pca_gaussian_rand_Ws = [[] for _ in range(points_to_collect)]\n",
"pca_gaussian_rand_bs = [[] for _ in range(points_to_collect)]\n",
"\n",
"for mid in range(points_to_collect):\n",
" Ws_traj = rand_Ws[mid]\n",
" bs_traj = rand_bs[mid]\n",
"\n",
" vs_list = []\n",
" for i in range(len(Ws_traj)):\n",
" vs_list.append(flatten(Ws_traj[i], bs_traj[i]))\n",
"\n",
" vs_np = np.stack(vs_list, axis=0)\n",
"\n",
" means = np.mean(vs_np, axis=0)\n",
" stds = np.std(vs_np, axis=0)\n",
" vs_np_centered = vs_np - means.reshape([1, -1])\n",
"\n",
" pca = PCA(n_components=rank)\n",
" pca.fit(vs_np_centered)\n",
" for i in range(num_sample):\n",
" v_sample = get_pca_gaussian_flat_sampling(pca, means, rank, scale=1.0)\n",
" w_sample, b_sample = reform(v_sample)\n",
"\n",
" pca_gaussian_rand_Ws[mid].append(w_sample)\n",
" pca_gaussian_rand_bs[mid].append(b_sample)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "nEQ96SeTdnkZ"
},
"source": [
"## Collecting predictions from Original and Subspace"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "gKnPAilsoSNB"
},
"outputs": [],
"source": [
"# Average a list of weights.\n",
"def average_var(w_list):\n",
" avg = [[] for _ in w_list[0]]\n",
" for w_now in w_list:\n",
" for i, w in enumerate(w_now):\n",
" avg[i].append(w)\n",
"\n",
" for i, v in enumerate(avg):\n",
" avg[i] = np.mean(np.stack(v, axis=0), axis=0)\n",
"\n",
" return avg\n",
"\n",
"\n",
"# Given a list of model weights, feed_dict and a session, returns the model\n",
"# predictions as a list.\n",
"def get_pred_list(Ws_list, bs_list, feed_dict, sess):\n",
" pred_list = []\n",
" for id in range(len(Ws_list)):\n",
" Ws_now = Ws_list[id]\n",
" bs_now = bs_list[id]\n",
" for j, W in enumerate(Ws):\n",
" feed_dict[Ws_ph[j]] = Ws_now[j]\n",
" for j, b in enumerate(bs):\n",
" feed_dict[bs_ph[j]] = bs_now[j]\n",
"\n",
" pred_eval_out = sess.run(pred_eval, feed_dict=feed_dict)\n",
" pred_list.append(pred_eval_out)\n",
" return pred_list\n",
"\n",
"\n",
"# Consider a list of subspaces, each has a list of sampled weights. This\n",
"# function computes model predictions, ensembles the predictions within each\n",
"# subspace, and returns the list of ensembled predictions.\n",
"def get_subspace_pred_list(Ws_subspace_list, bs_subspace_list, feed_dict, sess):\n",
" subspace_pred = []\n",
" num_subspace = len(Ws_subspace_list)\n",
" for mid in range(num_subspace):\n",
" pred_list_now = get_pred_list(Ws_subspace_list[mid], bs_subspace_list[mid],\n",
" feed_dict, sess)\n",
" subspace_pred.append(np.mean(np.stack(pred_list_now, axis=0), axis=0))\n",
" return subspace_pred"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "xeAuHy2xQ39g"
},
"outputs": [],
"source": [
"# Returns a list of all possible k-subset from [1, ..., n]\n",
"# Don't scale well for large n. Use with caution.\n",
"def choose_k_from_n(n, k):\n",
" if k\u003en or k \u003c1 or n \u003c 1:\n",
" return []\n",
" if k == n :\n",
" return [list(range(1, n+1))]\n",
" if k == 1:\n",
" return [[i] for i in range(1, n+1)]\n",
" a = choose_k_from_n(n-1,k)\n",
" b = choose_k_from_n(n-1, k-1)\n",
" b_new = []\n",
" for g in b:\n",
" b_new.append(g+[n])\n",
" return a+b_new\n",
"\n",
"\n",
"def get_acc_brier(y, pred, is_logits=False):\n",
" acc = np.mean(np.argmax(pred,axis=-1)==y)\n",
" if is_logits:\n",
" brier = brier_scores(y,logits=pred)\n",
" else:\n",
" brier = brier_scores(y,probs=pred)\n",
" return acc, np.mean(brier)\n",
"\n",
"\n",
"# Given a list of model predictions, compute the accuracy and brier score for \n",
"# each individual model as well as the ensemble of them.\n",
"def get_all_models_metrics(pred_list,y_test,max_ens_size=5, ens_size_list=None):\n",
" acc_list = []\n",
" acc_list_ensemble = []\n",
" b_list = []\n",
" b_list_ensemble = []\n",
" num_models = len(pred_list)\n",
" for i in range(num_models):\n",
" acc,brier = get_acc_brier(y_test, pred_list[i])\n",
" acc_list.append(acc)\n",
" b_list.append(brier)\n",
"\n",
" max_ens_size = np.min([max_ens_size, num_models])\n",
" if ens_size_list is None:\n",
" ens_size_list = range(1, max_ens_size+1)\n",
" for ens_size in ens_size_list:\n",
" # Pick all possible subset with size of ens_size from available models.\n",
" # Compute ensemble for each such subset.\n",
" ens_index_list = choose_k_from_n(num_models,ens_size)\n",
" ens_acc = []\n",
" ens_brier=[]\n",
" for ens_ind in ens_index_list:\n",
" ens_pred_list = []\n",
" for ind in ens_ind:\n",
" ens_pred_list.append(pred_list[ind-1])\n",
" ens_np = np.mean(np.stack(ens_pred_list,axis=0), axis=0)\n",
" acc,brier = get_acc_brier(y_test, ens_np)\n",
" ens_acc.append(acc)\n",
" ens_brier.append(brier)\n",
" acc_list_ensemble.append(np.mean(ens_acc))\n",
" b_list_ensemble.append(np.mean(ens_brier))\n",
" metrics = {'accuracy': {},\n",
" 'brier':{},}\n",
" metrics['accuracy']['individual'] = acc_list\n",
" metrics['accuracy']['ensemble'] = acc_list_ensemble\n",
" metrics['brier']['individual'] = b_list\n",
" metrics['brier']['ensemble'] = b_list_ensemble\n",
" return metrics\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "RR2UKvMcdpJm"
},
"outputs": [],
"source": [
"# Get predictions on test data.\n",
"orig_pred = []\n",
"wa_pred = []\n",
"\n",
"# Compute averaged weights.\n",
"wa_Ws = [[] for _ in range(points_to_collect)]\n",
"wa_bs = [[] for _ in range(points_to_collect)]\n",
"for i in range(points_to_collect):\n",
" wa_Ws[i] = average_var(Ws_by_epochs_many[i])\n",
" wa_bs[i] = average_var(bs_by_epochs_many[i])\n",
"\n",
"with tf.Session() as sess:\n",
" feed_dict[X_ph] = X_test\n",
" feed_dict[y_ph] = y_test\n",
" orig_pred = get_pred_list(Ws_many, bs_many, feed_dict, sess)\n",
" wa_pred = get_pred_list(wa_Ws, wa_bs, feed_dict, sess)\n",
" diag_gaus_pred = get_subspace_pred_list(dial_gaussian_whole_Ws,\n",
" dial_gaussian_whole_bs, feed_dict,\n",
" sess)\n",
" pca_gaus_pred = get_subspace_pred_list(pca_gaussian_whole_Ws,\n",
" pca_gaussian_whole_bs, feed_dict, sess)\n",
" pca_rand_pred = get_subspace_pred_list(pca_gaussian_rand_Ws,\n",
" pca_gaussian_rand_bs, feed_dict, sess)\n",
" rand_pred = get_subspace_pred_list(rand_Ws, rand_bs, feed_dict, sess)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "sCSsNdA06lm1"
},
"outputs": [],
"source": [
"max_ens_size = points_to_collect - 1\n",
"\n",
"orig_metrics = get_all_models_metrics(\n",
" orig_pred, y_test, max_ens_size=max_ens_size)\n",
"wa_metrics = get_all_models_metrics(wa_pred, y_test, max_ens_size=max_ens_size)\n",
"\n",
"diag_metrics = get_all_models_metrics(\n",
" diag_gaus_pred, y_test, max_ens_size=max_ens_size)\n",
"pca_metrics = get_all_models_metrics(\n",
" pca_gaus_pred, y_test, max_ens_size=max_ens_size)\n",
"\n",
"rand_metrics = get_all_models_metrics(\n",
" rand_pred, y_test, max_ens_size=max_ens_size)\n",
"pca_rand_metrics = get_all_models_metrics(\n",
" pca_rand_pred, y_test, max_ens_size=max_ens_size)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "yQtoJGx817vV"
},
"source": [
"##Plot Metrics for Ensembles + Subspace"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "utAi4c29CvgO"
},
"outputs": [],
"source": [
"title = \"Ensemble ACC test\"\n",
"\n",
"plt.xlabel(\"Ensemble size\")\n",
"plt.ylabel(\"Test Acc\")\n",
"\n",
"ensemble_sizes = np.asarray(range(max_ens_size)) + 1\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" orig_metrics[\"accuracy\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"probs ensembling\",\n",
" color=\"navy\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" [np.mean(orig_metrics[\"accuracy\"][\"individual\"])] * len(ensemble_sizes),\n",
" label=\"original\",\n",
" color=\"blue\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" [np.mean(diag_metrics[\"accuracy\"][\"individual\"])] * len(ensemble_sizes),\n",
" label=\"Diag\",\n",
" color=\"pink\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" [np.mean(pca_metrics[\"accuracy\"][\"individual\"])] * len(ensemble_sizes),\n",
" label=\"PCA\",\n",
" color=\"green\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" diag_metrics[\"accuracy\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"Diag+Ensemble\",\n",
" color=\"red\")\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" wa_metrics[\"accuracy\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"WA+ensembling\",\n",
" color=\"grey\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" pca_metrics[\"accuracy\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"PCA+Ensemble\",\n",
" color=\"green\")\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" rand_metrics[\"accuracy\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"Rand+Ensemble\",\n",
" color=\"yellow\")\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" pca_rand_metrics[\"accuracy\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"PCA Rand+Ensemble\",\n",
" color=\"m\")\n",
"\n",
"plt.legend()\n",
"plt.xlim(1, max_ens_size)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "8vLQoHba6eXt"
},
"outputs": [],
"source": [
"title = \"Ensemble Brier test\"\n",
"\n",
"plt.xlabel(\"Ensemble size\")\n",
"plt.ylabel(\"Test Brier\")\n",
"\n",
"ensemble_sizes = np.asarray(range(max_ens_size)) + 1\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" orig_metrics[\"brier\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"probs ensembling\",\n",
" color=\"navy\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" [np.mean(orig_metrics[\"brier\"][\"individual\"])] * len(ensemble_sizes),\n",
" label=\"original\",\n",
" color=\"blue\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" [np.mean(diag_metrics[\"brier\"][\"individual\"])] * len(ensemble_sizes),\n",
" label=\"Diag\",\n",
" color=\"pink\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" [np.mean(pca_metrics[\"brier\"][\"individual\"])] * len(ensemble_sizes),\n",
" label=\"PCA\",\n",
" color=\"green\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" diag_metrics[\"brier\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"Diag Ensemble\",\n",
" color=\"red\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" pca_metrics[\"brier\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"PCA Ensemble\",\n",
" color=\"green\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" wa_metrics[\"brier\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"WA ensembling\",\n",
" color=\"grey\")\n",
"\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" rand_metrics[\"brier\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"Rand Ensemble\",\n",
" color=\"yellow\")\n",
"plt.plot(\n",
" ensemble_sizes,\n",
" pca_rand_metrics[\"brier\"][\"ensemble\"],\n",
" marker=\"s\",\n",
" label=\"PCA Rand Ensemble\",\n",
" color=\"m\")\n",
"\n",
"plt.xlim(1, max_ens_size)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "bGelr5_KBL-O"
},
"source": [
"## Evaluating on Corrupted CIFAR10\n",
"See [paper](https://arxiv.org/pdf/1906.02530.pdf) for data description.\n",
"\n",
"CAUTION: This section will be very time-consuming. If one wants to test the code quickly, consider only run a small portion of CIFAR10-C -- reducing 'ALL_CORRUPTIONS' to containing only one type, and 'intensity_range' to [0, 1]."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "K2jeOP8AERPn"
},
"outputs": [],
"source": [
"ALL_CORRUPTIONS = [\n",
" 'gaussian_noise',\n",
" 'shot_noise',\n",
" # 'impulse_noise',\n",
" # 'defocus_blur',\n",
" # 'frosted_glass_blur',\n",
" # 'motion_blur',\n",
" # 'zoom_blur',\n",
" # 'snow',\n",
" # 'frost',\n",
" # 'fog',\n",
" # 'brightness',\n",
" # 'contrast',\n",
" # 'elastic',\n",
" # 'pixelate',\n",
" # 'jpeg_compression',\n",
" # 'gaussian_blur',\n",
" # 'saturate',\n",
" # 'spatter',\n",
" # 'speckle_noise',\n",
"]\n",
"\n",
"num_models_to_use = 5\n",
"ens_size_list = [2, 4]\n",
"\n",
"model_names = ['original', 'wa', 'diag', 'pca', 'rand', 'pca_rand']\n",
"model_names_plus_ens = []\n",
"for i in ens_size_list:\n",
" model_names_plus_ens = model_names_plus_ens + [\n",
" name + '_ens_{}'.format(i) for name in model_names\n",
" ]\n",
"\n",
"all_model_names = model_names + model_names_plus_ens\n",
"\n",
"model_to_acc_every_level = {name: [] for name in all_model_names}\n",
"model_to_brier_every_level = {name: [] for name in all_model_names}\n",
"\n",
"intensity_range = range(6)\n",
"for level in intensity_range:\n",
" print('========= Level {} ======='.format(level))\n",
" model_to_acc_this_level = {name: [] for name in all_model_names}\n",
" model_to_brier_this_level = {name: [] for name in all_model_names}\n",
" if level == 0:\n",
" corruptions = ['no_corruption']\n",
" else:\n",
" corruptions = ALL_CORRUPTIONS\n",
" for corruption_type in corruptions:\n",
" if corruption_type is 'no_corruption':\n",
" ds_test = tfds.load(name='cifar10', split=['test'], batch_size=-1)\n",
" else:\n",
" # Load corrupted data.\n",
" corruption_config_name = corruption_type + '_{}'.format(level)\n",
" ds_test = tfds.load(\n",
" name='cifar10_corrupted',\n",
" split=['test'],\n",
" builder_kwargs={'config': corruption_config_name},\n",
" batch_size=-1)\n",
" numpy_ds = tfds.as_numpy(ds_test)\n",
" x_test, y_test = numpy_ds[0]['image'], numpy_ds[0]['label']\n",
" x_test = x_test.reshape([-1, Hn, Wn, Cn])\n",
" y_test = y_test.reshape([-1])\n",
" N_test = len(y_test)\n",
" # Run inference\n",
" with tf.Session() as sess:\n",
" feed_dict[X_ph] = x_test\n",
" feed_dict[y_ph] = y_test\n",
" #Get predictions\n",
" for name in model_names:\n",
" if name is 'original':\n",
" pred_list = get_pred_list(Ws_many[0:num_models_to_use],\n",
" bs_many[0:num_models_to_use], feed_dict,\n",
" sess)\n",
" elif name is 'wa':\n",
" # Weight Averaged\n",
" pred_list = get_pred_list(wa_Ws[0:num_models_to_use],\n",
" wa_bs[0:num_models_to_use], feed_dict, sess)\n",
" elif name is 'diag':\n",
" # Subspacesampling\n",
" pred_list = get_subspace_pred_list(\n",
" dial_gaussian_whole_Ws[0:num_models_to_use],\n",
" dial_gaussian_whole_bs[0:num_models_to_use], feed_dict, sess)\n",
" elif name is 'pca':\n",
" pred_list = get_subspace_pred_list(\n",
" pca_gaussian_whole_Ws[0:num_models_to_use],\n",
" pca_gaussian_whole_bs[0:num_models_to_use], feed_dict, sess)\n",
" elif name is 'pca_rand':\n",
" pred_list = get_subspace_pred_list(\n",
" pca_gaussian_rand_Ws[0:num_models_to_use],\n",
" pca_gaussian_rand_bs[0:num_models_to_use], feed_dict, sess)\n",
" elif name is 'rand':\n",
" pred_list = get_subspace_pred_list(rand_Ws[0:num_models_to_use],\n",
" rand_bs[0:num_models_to_use],\n",
" feed_dict, sess)\n",
" corrupt_metrics = get_all_models_metrics(\n",
" pred_list, y_test, ens_size_list=ens_size_list)\n",
" model_to_acc_this_level[name].append(\n",
" np.mean(corrupt_metrics['accuracy']['individual']))\n",
" model_to_brier_this_level[name].append(\n",
" np.mean(corrupt_metrics['brier']['individual']))\n",
" for i, ens_size in enumerate(ens_size_list):\n",
" model_to_acc_this_level[name + '_ens_{}'.format(ens_size)].append(\n",
" corrupt_metrics['accuracy']['ensemble'][i])\n",
" model_to_brier_this_level[name + '_ens_{}'.format(ens_size)].append(\n",
" corrupt_metrics['brier']['ensemble'][i])\n",
"\n",
" for name in all_model_names:\n",
" model_to_acc_every_level[name].append(model_to_acc_this_level[name])\n",
" model_to_brier_every_level[name].append(model_to_brier_this_level[name])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "rGS9GPU2mo5j"
},
"source": [
"## Plot the metrics across corruption intensity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "b5cAs29LkPYu"
},
"outputs": [],
"source": [
"ens_size_list = [2, 4]\n",
"model_names = ['original', 'wa', 'diag', 'pca', 'rand', 'pca_rand']\n",
"model_names_plus_ens = []\n",
"for i in ens_size_list:\n",
" model_names_plus_ens = model_names_plus_ens + [\n",
" name + '_ens_{}'.format(i) for name in model_names\n",
" ]\n",
"\n",
"all_model_names = model_names + model_names_plus_ens\n",
"\n",
"# Model names used by Jensen Shannon plots.\n",
"js_model_names = ['inde', 'traj', 'diag', 'pca', 'rand', 'pca_rand']\n",
"\n",
"model_name_to_color = {\n",
" 'original': 'indianred',\n",
" 'wa': 'dimgray',\n",
" 'diag': 'gold',\n",
" 'pca': 'blue',\n",
" 'rand': 'mediumseagreen',\n",
" 'pca_rand': 'fuchsia',\n",
" 'inde': 'red',\n",
" 'traj': 'grey'\n",
"}\n",
"\n",
"for name in model_names:\n",
" for i in ens_size_list:\n",
" model_name_to_color[name + '_ens_{}'.format(i)] = model_name_to_color[name]\n",
"\n",
"model_name_to_label = {\n",
" 'original': 'Original',\n",
" 'wa': 'Weight Avg',\n",
" 'diag': 'Diag Gaus',\n",
" 'pca': 'PCA Gaus',\n",
" 'rand': 'Random',\n",
" 'pca_rand': 'PCA Random',\n",
" 'inde': 'Independent',\n",
" 'traj': 'Train Trajectory'\n",
"}\n",
"\n",
"for name in model_names:\n",
" model_name_to_label[name + '_ens'] = model_name_to_label[name] + '+Ens'\n",
" for i in ens_size_list:\n",
" model_name_to_label[\n",
" name +\n",
" '_ens_{}'.format(i)] = model_name_to_label[name] + '+Ens {}'.format(i)\n",
"\n",
"# Line style, currently can only support 2 ensemble sizes. Beyond that, solid\n",
"# line will be used for all.\n",
"model_name_to_ls = {}\n",
"for name in model_names:\n",
" model_name_to_ls[name] = 'dashed'\n",
" model_name_to_ls[name + '_ens_{}'.format(ens_size_list[0])] = 'dotted'\n",
" for i in range(1, len(ens_size_list)):\n",
" model_name_to_ls[name + '_ens_{}'.format(ens_size_list[i])] = 'solid'\n",
"\n",
"\n",
"def plot_metric_over_corrupted_data(metric_name, intensity_range,\n",
" all_model_names, base_model_names,\n",
" color_map, ls_map):\n",
" plt.figure(figsize=(10, 9))\n",
"\n",
" ylabel = {\n",
" 'acc': 'Accuracy',\n",
" 'brier': 'Brier',\n",
" }\n",
" plt.xlabel('Corruption Intensity', fontsize=16)\n",
" plt.ylabel(ylabel[metric_name], fontsize=16)\n",
" label_map = {'original': 'Single'}\n",
" for ens_size in ens_size_list:\n",
" label_map['original_ens_{}'.format(ens_size)] = '{}-Ensemble'.format(\n",
" ens_size)\n",
" model_to_mean = {name: [] for name in all_model_names}\n",
" for name in all_model_names:\n",
" if metric_name is 'acc':\n",
" model_to_metric_every_level = model_to_acc_every_level[name]\n",
" elif metric_name is 'brier':\n",
" model_to_metric_every_level = model_to_brier_every_level[name]\n",
" for t in intensity_range:\n",
" model_to_mean[name].append(np.mean(model_to_metric_every_level[t]))\n",
"\n",
" if name in label_map:\n",
" label = label_map[name]\n",
" else:\n",
" label = None\n",
" plt.plot(\n",
" intensity_range,\n",
" model_to_mean[name],\n",
" marker='s',\n",
" label=label,\n",
" color=color_map[name],\n",
" ls=ls_map[name])\n",
"\n",
" legend0 = plt.legend(loc=3, fontsize=12)\n",
"\n",
" patch_list = []\n",
" for name in base_model_names:\n",
" patch_list.append(\n",
" mpatch.Patch(\n",
" color=model_name_to_color[name],\n",
" label=model_name_to_label[name]))\n",
" legend1 = plt.legend(handles=patch_list)\n",
" ax = plt.gca().add_artist(legend0)\n",
"\n",
" plt.xlim(np.min(intensity_range), np.max(intensity_range))\n",
" plt.tight_layout()\n",
"\n",
"\n",
"def plot_js_over_corrupted_data(intensity_range, all_model_names, color_map,\n",
" label_map):\n",
" plt.figure(figsize=(10, 9))\n",
" plt.xlabel('Corruption Intensity', fontsize=16)\n",
" plt.ylabel('Jensen Shannon', fontsize=16)\n",
"\n",
" model_to_mean = {name: [] for name in all_model_names}\n",
"\n",
" for name in all_model_names:\n",
" model_to_metric_every_level = model_to_js_every_level[name]\n",
" for t in intensity_range:\n",
" model_to_mean[name].append(np.mean(model_to_metric_every_level[t]))\n",
" plt.plot(\n",
" intensity_range,\n",
" model_to_mean[name],\n",
" marker='s',\n",
" label=label_map[name],\n",
" color=color_map[name])\n",
"\n",
" plt.xlim(np.min(intensity_range), np.max(intensity_range))\n",
" plt.tight_layout()\n",
" plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "cszvIYjZkHLi"
},
"outputs": [],
"source": [
"intensity_range = range(6)\n",
"plot_metric_over_corrupted_data('acc', intensity_range, all_model_names,\n",
" model_names, model_name_to_color,\n",
" model_name_to_ls)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "aLsVQu1SYzaX"
},
"outputs": [],
"source": [
"intensity_range = range(6)\n",
"plot_metric_over_corrupted_data('brier', intensity_range, all_model_names,\n",
" model_names, model_name_to_color,\n",
" model_name_to_ls)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ZwOzmfhn3Z4l"
},
"source": [
"##Jensen Shannon"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "tqiAL-z73cPe"
},
"outputs": [],
"source": [
"def get_entropy(p):\n",
" return np.sum(-p * np.log(p + 1e-5), axis=1)\n",
"\n",
"\n",
"# Compute the Jensen Shannon score given a list of model predictions.\n",
"def get_jensen_shannon(pred_list, is_logit=False):\n",
" n = len(pred_list)\n",
" if is_logit:\n",
" p_list = []\n",
" for i in range(n):\n",
" p_list.append(softmax(pred_list[i], axis=1))\n",
" else:\n",
" p_list = pred_list\n",
" p_np = np.stack(p_list, axis=0)\n",
" ensemble_pred = np.mean(p_np, axis=0)\n",
" ensemble_entropy = np.mean(get_entropy(ensemble_pred))\n",
"\n",
" mean_entropy = np.mean(np.sum(-p_np * np.log(p_np + 1e-5), axis=2))\n",
" return ensemble_entropy - mean_entropy"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "mCgvOt_gbRYG"
},
"source": [
"### Eval on SVHN\n",
"JS on OOD data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4WralaRlbVOW"
},
"outputs": [],
"source": [
"_, ds_test = tfds.load(\n",
" name=\"svhn_cropped\", split=[\"train\", \"test\"], batch_size=-1)\n",
"\n",
"numpy_ds = tfds.as_numpy(ds_test)\n",
"X_test_raw, y_test_fine_raw = numpy_ds[\"image\"], numpy_ds[\"label\"]\n",
"\n",
"X_test_svhn = X_test_raw\n",
"y_test_svhn = y_test_fine_raw\n",
"\n",
"subsample = np.random.choice(len(y_test_svhn), 10000, replace=False)\n",
"X_svhn = X_test_svhn[subsample, :, :, :]\n",
"y_svhn = y_test_svhn[subsample]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ICYLKUto1mrv"
},
"outputs": [],
"source": [
"traj_svhn_js = []\n",
"pca_svhn_js = []\n",
"diag_svhn_js = []\n",
"rand_svhn_js = []\n",
"pca_rand_svhn_js = []\n",
"\n",
"with tf.Session() as sess:\n",
" feed_dict[X_ph] = X_svhn\n",
" feed_dict[y_ph] = y_svhn\n",
" feed_dict[lr_ph] = 0.0\n",
" feed_dict[dropout_rate_ph] = 0.0\n",
" indi_svhn_pred = get_pred_list(Ws_many[0:10], bs_many[0:10], feed_dict, sess)\n",
" for traj_id in range(points_to_collect):\n",
" trajectory_svhn_pred = get_pred_list(Ws_by_epochs_many[traj_id],\n",
" bs_by_epochs_many[traj_id], feed_dict,\n",
" sess)\n",
" traj_svhn_js.append(get_jensen_shannon(trajectory_svhn_pred))\n",
"\n",
" diag_gaus_whole_svhn_pred = get_pred_list(dial_gaussian_whole_Ws[traj_id],\n",
" dial_gaussian_whole_bs[traj_id],\n",
" feed_dict, sess)\n",
" diag_svhn_js.append(get_jensen_shannon(diag_gaus_whole_svhn_pred))\n",
"\n",
" pca_gaus_whole_svhn_pred = get_pred_list(pca_gaussian_whole_Ws[traj_id],\n",
" pca_gaussian_whole_bs[traj_id],\n",
" feed_dict, sess)\n",
" pca_svhn_js.append(get_jensen_shannon(pca_gaus_whole_svhn_pred))\n",
"\n",
" pca_rand_svhn_pred = get_pred_list(pca_gaussian_rand_Ws[traj_id],\n",
" pca_gaussian_rand_bs[traj_id], feed_dict,\n",
" sess)\n",
" pca_rand_svhn_js.append(get_jensen_shannon(pca_rand_svhn_pred))\n",
"\n",
" rand_svhn_pred = get_pred_list(rand_Ws[traj_id], rand_bs[traj_id],\n",
" feed_dict, sess)\n",
" rand_svhn_js.append(get_jensen_shannon(rand_svhn_pred))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "-1j-4_KhAZEg"
},
"outputs": [],
"source": [
"print(\"Jensen-Shannon for Independent run is {:.4f}\".format(\n",
" get_jensen_shannon(indi_svhn_pred)))\n",
"print(\"Jensen-Shannon for within-trajectory is {:.4f}\".format(\n",
" np.mean(traj_svhn_js)))\n",
"\n",
"print(\"Jensen-Shannon for Diag Gaussian is {:.4f}\".format(\n",
" np.mean(diag_svhn_js)))\n",
"print(\"Jensen-Shannon for PCA Gaussian is {:.4f}\".format(np.mean(pca_svhn_js)))\n",
"\n",
"print(\"Jensen-Shannon for Rand is {:.4f}\".format(np.mean(rand_svhn_js)))\n",
"print(\"Jensen-Shannon for PCA Rand is {:.4f}\".format(np.mean(pca_rand_svhn_js)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "FLCIw3GwQeaa"
},
"source": [
"###Eval on Cifar-10-C\n",
"\n",
"CAUTION: This section will be very time-consuming. If one wants to test the code quickly, consider only run a small portion of CIFAR10-C -- reducing 'ALL_CORRUPTIONS' to containing only one type, and 'intensity_range' to [0, 1]."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "UNM4VieiQjAp"
},
"outputs": [],
"source": [
"# Choose any training trajectory to study the JS of subspace samplings.\n",
"traj_id = 3\n",
"\n",
"model_to_js_every_level = {name: [] for name in js_model_names}\n",
"\n",
"intensity_range = range(6)\n",
"for level in intensity_range:\n",
" print(\"========= Level {} =======\".format(level))\n",
" model_to_js_this_level = {name: [] for name in js_model_names}\n",
" if level == 0:\n",
" corruptions = [\"no_corruption\"]\n",
" else:\n",
" corruptions = ALL_CORRUPTIONS\n",
" for corruption_type in corruptions:\n",
" if corruption_type is \"no_corruption\":\n",
" ds_test = tfds.load(name=\"cifar10\", split=[\"test\"], batch_size=-1)\n",
" else:\n",
" # Load corrupted data.\n",
" corruption_config_name = corruption_type + \"_{}\".format(level)\n",
" ds_test = tfds.load(\n",
" name=\"cifar10_corrupted\",\n",
" split=[\"test\"],\n",
" builder_kwargs={\"config\": corruption_config_name},\n",
" batch_size=-1)\n",
" numpy_ds = tfds.as_numpy(ds_test)\n",
" x_test, y_test = numpy_ds[0][\"image\"], numpy_ds[0][\"label\"]\n",
" x_test = x_test.reshape([-1, Hn, Wn, Cn])\n",
" y_test = y_test.reshape([-1])\n",
" N_test = len(y_test)\n",
" # Run inference\n",
" with tf.Session() as sess:\n",
" feed_dict[X_ph] = x_test\n",
" feed_dict[y_ph] = y_test\n",
" #Get predictions\n",
" for name in js_model_names:\n",
" if name is \"inde\":\n",
" pred_list = get_pred_list(Ws_many, bs_many, feed_dict,\n",
" sess)\n",
" elif name is \"traj\":\n",
" # Weight Averaged\n",
" pred_list = get_pred_list(Ws_by_epochs_many[traj_id],\n",
" bs_by_epochs_many[traj_id], feed_dict, sess)\n",
" elif name is \"diag\":\n",
" pred_list = get_pred_list(dial_gaussian_whole_Ws[traj_id],\n",
" dial_gaussian_whole_bs[traj_id], feed_dict,\n",
" sess)\n",
" elif name is \"pca\":\n",
" pred_list = get_pred_list(pca_gaussian_whole_Ws[traj_id],\n",
" pca_gaussian_whole_bs[traj_id], feed_dict,\n",
" sess)\n",
" elif name is \"pca_rand\":\n",
" pred_list = get_pred_list(pca_gaussian_rand_Ws[traj_id],\n",
" pca_gaussian_rand_bs[traj_id], feed_dict,\n",
" sess)\n",
" elif name is \"rand\":\n",
" pred_list = get_pred_list(rand_Ws[traj_id], rand_bs[traj_id],\n",
" feed_dict, sess)\n",
"\n",
" model_to_js_this_level[name].append(get_jensen_shannon(pred_list))\n",
"\n",
" for name in js_model_names:\n",
" model_to_js_every_level[name].append(model_to_js_this_level[name])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "2mcbo-46d4s2"
},
"outputs": [],
"source": [
"plot_js_over_corrupted_data(intensity_range, js_model_names,\n",
" model_name_to_color, model_name_to_label)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zCZUtnjKljz-"
},
"source": [
"#Diversity vs Accuracy\n",
"This section plots the Diversiy vs Accuracy plane for different subspace sampling methods, compared to independent solutions:\n",
"\n",
"\n",
"* Random\n",
"* Gaussian Diag\n",
"* Gaussian Low rank (PCA)\n",
"* Dropout\n",
"\n",
"Note that unlike the previous section \"Effects of Ensemble + Subspace Sampling\" where we only keep samples with good accuracy performance, in this section we include samples that sacrifices accuracy to explore the full spectrum of function diversity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "D3eKK8UPz-UB"
},
"outputs": [],
"source": [
"base_id = 0\n",
"base_v = flatten(Ws_many[base_id], bs_many[base_id])\n",
"\n",
"\n",
"def get_acc_and_diff(pred_class, base_pred, y_label):\n",
" diff = np.mean(pred_class != base_pred)\n",
" acc = np.mean(pred_class == y_label)\n",
" return acc, diff\n",
"\n",
"\n",
"# Given a list of weights, compute their accuracy and their difference to the\n",
"# base prediction.\n",
"def get_acc_and_diff_from_weights(Ws_list, bs_list, feed_dict, base_pred):\n",
" assert len(Ws_list) == len(bs_list)\n",
" with tf.Session() as sess:\n",
" pred_list = get_pred_list(Ws_list, bs_list, feed_dict, sess)\n",
"\n",
" acc_list = []\n",
" diff_list = []\n",
" for i in range(len(Ws_list)):\n",
" acc, diff = get_acc_and_diff(\n",
" np.argmax(pred_list[i], axis=-1), base_pred, feed_dict[y_ph])\n",
" acc_list.append(acc)\n",
" diff_list.append(diff)\n",
" return acc_list, diff_list"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "0oScJ4X09-F9"
},
"source": [
"##Independent Solutions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "TkwIdoOJvgz2"
},
"outputs": [],
"source": [
"feed_dict = {\n",
" X_ph: X_test,\n",
" y_ph: y_test,\n",
" lr_ph: 0.0,\n",
" dropout_rate_ph: 0.0,\n",
" L2_reg_constant_ph: 0.0,\n",
"}\n",
"\n",
"with tf.Session() as sess:\n",
" orig_pred_list = get_pred_list(Ws_many, bs_many, feed_dict, sess)\n",
"\n",
"base_pred = np.argmax(orig_pred_list[base_id], axis=-1)\n",
"\n",
"independent_acc = []\n",
"independent_diff = []\n",
"for i in range(points_to_collect):\n",
" class_pred = np.argmax(orig_pred_list[i], axis=-1)\n",
" acc, diff = get_acc_and_diff(class_pred, base_pred, y_test)\n",
" independent_acc.append(acc)\n",
" independent_diff.append(diff)\n",
"\n",
"data_to_show = [\n",
" (\"independent optima\", \"red\", 300, \"*\", 1.0, independent_acc,\n",
" independent_diff),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "cotIEVchK3Vh"
},
"outputs": [],
"source": [
"data_to_show = [\n",
" (\"independent optima\", \"red\", 300, \"*\", 1.0, independent_acc,\n",
" independent_diff),\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "hT1pKG9W-BTZ"
},
"source": [
"##Gaussian Sampling"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "czoWhgyGAFoC"
},
"outputs": [],
"source": [
"num_sample = 50\n",
"rank = 5 # for PCA\n",
"\n",
"Ws_traj = Ws_by_epochs_many[base_id]\n",
"bs_traj = bs_by_epochs_many[base_id]\n",
"\n",
"dial_gaussian_whole_Ws = []\n",
"dial_gaussian_whole_bs = []\n",
"\n",
"pca_gaussian_whole_Ws = []\n",
"pca_gaussian_whole_bs = []\n",
"\n",
"vs_list = []\n",
"for i in range(len(Ws_traj)):\n",
" vs_list.append(flatten(Ws_traj[i], bs_traj[i]))\n",
"\n",
"vs_np = np.stack(vs_list, axis=0)\n",
"\n",
"means = np.mean(vs_np, axis=0)\n",
"stds = np.std(vs_np, axis=0)\n",
"vs_np_centered = vs_np - means.reshape([1, -1])\n",
"\n",
"pca = PCA(n_components=rank)\n",
"pca.fit(vs_np_centered)\n",
"for i in range(num_sample):\n",
" scale = np.random.uniform()\n",
" # One can adjust the constant in front of scale to get a fuller range in\n",
" # diversity-acc plane.\n",
" v_sample = get_gaussian_sample(means, stds, scale=10.0 * scale)\n",
" w_sample, b_sample = reform(v_sample)\n",
" dial_gaussian_whole_Ws.append(w_sample)\n",
" dial_gaussian_whole_bs.append(b_sample)\n",
" # One can adjust the scale value to get a fuller range in diversity-acc plane.\n",
" v_sample = get_pca_gaussian_flat_sampling(pca, means, rank, scale=6.0)\n",
" w_sample, b_sample = reform(v_sample)\n",
"\n",
" pca_gaussian_whole_Ws.append(w_sample)\n",
" pca_gaussian_whole_bs.append(b_sample)\n",
"\n",
"pca_acc, pca_diff = get_acc_and_diff_from_weights(pca_gaussian_whole_Ws,\n",
" pca_gaussian_whole_bs,\n",
" feed_dict, base_pred)\n",
"diag_acc, diag_diff = get_acc_and_diff_from_weights(dial_gaussian_whole_Ws,\n",
" dial_gaussian_whole_bs,\n",
" feed_dict, base_pred)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "mfnrhHSa5Xiw"
},
"outputs": [],
"source": [
"data_to_show = data_to_show + [\n",
" (\"diag gaussian\", \"purple\", 10, \"o\", 0.6, diag_acc, diag_diff),\n",
" (\"pca gaussian\", \"fuchsia\", 10, \"o\", 0.3, pca_acc, pca_diff),\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Jn-g6-GZZ5tc"
},
"source": [
"##Random Sampling"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "uJpRJaDLaFRa"
},
"outputs": [],
"source": [
"rand_Ws = []\n",
"rand_bs = []\n",
"for i in range(num_sample):\n",
" # One can adjust the constant in front of scale to get a fuller range in\n",
" # diversity-acc plane. \n",
" scale = 70.0 * np.random.uniform()\n",
" v_sample = base_v + scale * get_rand_norm_direction(base_v.shape)\n",
" w_sample, b_sample = reform(v_sample)\n",
" rand_Ws.append(w_sample)\n",
" rand_bs.append(b_sample)\n",
"\n",
"rand_acc, rand_diff = get_acc_and_diff_from_weights(rand_Ws, rand_bs, feed_dict,\n",
" base_pred)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "h1my3OECbzEi"
},
"outputs": [],
"source": [
"data_to_show = data_to_show + [\n",
" (\"random subspace\", \"blue\", 3, \"o\", 0.3, rand_acc, rand_diff),\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "JJWIEJZBZ9Jf"
},
"source": [
"##Dropout Sampling"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "MBiCleCgb8r1"
},
"outputs": [],
"source": [
"def apply_dropout_to_array(anchor_array, dropout_to_use):\n",
" shape_now = anchor_array.shape\n",
" random_mask = np.random.rand(*shape_now)\n",
" mask = (random_mask \u003c (1.0 - dropout_to_use))\n",
" array_dropped = (anchor_array * mask) / (1.0 - dropout_to_use)\n",
" return array_dropped\n",
"\n",
"\n",
"dropout_Ws = []\n",
"dropout_bs = []\n",
"for i in range(num_sample):\n",
" # One can adjust the constant to get a fuller range in diversity-acc plane.\n",
" dropout = 0.1 * np.random.uniform()\n",
" v_sample = apply_dropout_to_array(base_v, dropout)\n",
" w_sample, b_sample = reform(v_sample)\n",
" dropout_Ws.append(w_sample)\n",
" dropout_bs.append(b_sample)\n",
"\n",
"dropout_acc, dropout_diff = get_acc_and_diff_from_weights(\n",
" dropout_Ws, dropout_bs, feed_dict, base_pred)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "E5qdldWHb9c4"
},
"outputs": [],
"source": [
"data_to_show = data_to_show + [\n",
" (\"dropout subspace\", \"orange\", 10, \"o\", 1.0, dropout_acc, dropout_diff),\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "VwNL8PPM7DrM"
},
"source": [
"##Plotting Figure"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "AqhcI78L52hM"
},
"outputs": [],
"source": [
"# Functions for computing the analytic limit curves (see paper).\n",
"\n",
"def perturbed_reference_analytic(desired_accuracy, reference_accuracy, classes):\n",
" return (reference_accuracy - desired_accuracy) / (\n",
" reference_accuracy + (reference_accuracy - 1.0) / (classes - 1.0))\n",
"\n",
"\n",
"def random_average_case(desired_accuracy, reference_accuracy, classes):\n",
" part1 = reference_accuracy * (1.0 - desired_accuracy)\n",
" part2 = desired_accuracy * (1.0 - reference_accuracy)\n",
" part3 = (1.0 - reference_accuracy) * (1.0 -\n",
" desired_accuracy) * (classes - 2.0) / (\n",
" classes - 1.0)\n",
" return part1 + part2 + part3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "DjOAuW3F56qu"
},
"outputs": [],
"source": [
"plt.figure(figsize=(7, 5))\n",
"accs_fit_random = np.linspace(0.1, 0.71, 100)\n",
"diffs_fit_random = random_average_case(accs_fit_random, 0.71, 10)\n",
"plt.plot(\n",
" accs_fit_random,\n",
" diffs_fit_random / (1.0 - accs_fit_random),\n",
" color=\"black\",\n",
" linestyle=\"-.\",\n",
" label=\"Upper limit\")\n",
"\n",
"# Analytic limits curve.\n",
"accs_fit_perturbed = np.linspace(0.1, 0.71, 100)\n",
"diffs_fit_perturbed = perturbed_reference_analytic(accs_fit_perturbed, 0.71, 10)\n",
"plt.plot(\n",
" accs_fit_perturbed,\n",
" diffs_fit_perturbed / (1.0 - accs_fit_perturbed),\n",
" color=\"black\",\n",
" linestyle=\"--\",\n",
" label=\"Lower limit\")\n",
"\n",
"for (name_now, color_now, size_now, marker_now, alpha_now, accs_now,\n",
" diffs_now) in data_to_show:\n",
"\n",
" metric_now = np.asarray(diffs_now) / (1.0 - np.asarray(accs_now))\n",
"\n",
" if name_now != \"independent optima\":\n",
" plt.scatter(\n",
" accs_now,\n",
" metric_now,\n",
" color=color_now,\n",
" s=size_now,\n",
" marker=marker_now,\n",
" alpha=alpha_now)\n",
" plt.scatter([], [],\n",
" color=color_now,\n",
" label=name_now,\n",
" s=10,\n",
" marker=marker_now,\n",
" alpha=1.0)\n",
" else:\n",
" plt.scatter(\n",
" accs_now,\n",
" metric_now,\n",
" color=color_now,\n",
" s=size_now,\n",
" marker=marker_now,\n",
" alpha=alpha_now,\n",
" label=name_now)\n",
"\n",
" if name_now == \"independent optima\":\n",
" base_star_color = \"green\"\n",
" plt.scatter([accs_now[base_id]], [metric_now[base_id]],\n",
" color=base_star_color,\n",
" label=\"baseline optimum\",\n",
" s=size_now,\n",
" marker=marker_now,\n",
" alpha=alpha_now)\n",
"\n",
"plt.xlabel(\"Validation accuracy\", fontsize=14)\n",
"plt.ylabel(\"Fraction of labels changes / (1.0-accuracy)\", fontsize=14)\n",
"\n",
"plt.title(\"MediumCNN on Cifar10\", fontsize=18)\n",
"plt.legend(loc=3, fancybox=True, framealpha=0.5, fontsize=16)\n",
"plt.ylim([-0.1, 1.2])\n",
"plt.xlim([0, 0.78])\n",
"\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"8PHJJ59SuMAo"
],
"name": "Release cifar10 medium cnn experiments.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: functional_regularisation_for_continual_learning/README.md
================================================
# Functional Regularisation for Continual Learning (FRCL) with Gaussian Processes
This notebook contains code to accompany the ICLR 2020 paper on
Functional Regularisation for Continual Learning (FRCL). [](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/functional_regularisation_for_continual_learning/frcl.ipynb)
The experiments in the paper can be reproduced by choosing the respective
experimental protocol detailed in the publication. Default values are set to
replicate results on Permuted MNIST.
If you find this code useful, please consider citing this paper:
```
@article{titsias2019functional,
title={Functional regularisation for continual learning using gaussian processes},
author={Titsias, Michalis K and Schwarz, Jonathan and Matthews, AG de G and Pascanu, Razvan and Teh, Yee Whye},
journal={ICLR 2020},
year={2020}
}
```
================================================
FILE: functional_regularisation_for_continual_learning/frcl.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ksftVXR8lUeP"
},
"source": [
"Copyright 2020 DeepMind Technologies Limited.\n",
"\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
"https://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TEf5_rHhlXmd"
},
"source": [
"# [Functional Regluarisation for Continual Learning with Gaussian Processes](https://arxiv.org/abs/1901.11356) (Published at ICLR 2020)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pqifXRtRPcGw"
},
"source": [
"**Functional Regluarisation for Continual Learning (FRCL)** is a method designed to tackle the Continual Learning (CL) problem, combining recent ideas and advances in Bayesian methods, Functional Regularisation and Experience Replay. FRCL is one of the few CL algorithms that provide a principled criterion to choose representative training points for replay. In addition, the Bayesian formulation provides uncertainty estimates that can be used in the task-agonstic setting.\n",
"\n",
"\n",
"This code implements training and replicates published results presented for the Permuted MNIST and Sequential Omniglot datasets. If you find this code useful, please cite the paper:\n",
"\n",
"```\n",
"@article{titsias2019functional,\n",
" title={Functional regularisation for continual learning using gaussian processes},\n",
" author={Titsias, Michalis K and Schwarz, Jonathan and Matthews, AG de G and Pascanu, Razvan and Teh, Yee Whye},\n",
" journal={ICLR 2020},\n",
" year={2020}\n",
"}\n",
"```\n",
"If you have any questions, please do not hestiate to reach out to the\n",
"code authors:\n",
"\n",
"* Jonathan Schwarz (schwarzjn@google.com)\n",
"* Michalis Titsias (mtitsias@google.com)\n",
"* Alex Matthews (alexmatthews@google.com)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kDn_lVxg3Z2G"
},
"source": [
"# Installation and Setup\n",
"\n",
"We begin by installing and importing all necessary dependencies.\n",
"\n",
"In order to allow efficient training, please ensure to use a colab kernel with a GPU by doing the following:\n",
"\n",
" \u003e `Edit` -\u003e `Notebook settings` -\u003e select GPU under `Hardware accelerator` -\u003e `Save`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "GQ18Kd5F3uKe"
},
"outputs": [],
"source": [
"#@title Installing and Importing Dependencies\n",
"\n",
"print('Installing necessary libraries...')\n",
"\n",
"def install_libraries():\n",
" !pip install gpflow\n",
" !pip install dm-sonnet\n",
" !pip install tfa-nightly\n",
"\n",
"import IPython\n",
" \n",
"with IPython.utils.io.capture_output() as captured:\n",
" install_libraries()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "__3eqm3q3sr-"
},
"outputs": [],
"source": [
"#@title Imports\n",
"\n",
"import collections\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"\n",
"import tensorflow as tf\n",
"import tensorflow_addons as tfa\n",
"import tensorflow_datasets as tfds\n",
"\n",
"# Python 3 type annotations\n",
"from typing import Optional, Sequence, Text, Tuple"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZhCA3yCJVWxA"
},
"source": [
"Let's define a few useful contants and set some global properties related to plotting and logging."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "WY9arMfM1as2"
},
"outputs": [],
"source": [
"#@title Setup\n",
"\n",
"# TF logging level (disables some gpflow internal warnings we can safely ignore)\n",
"tf.get_logger().setLevel('ERROR')\n",
"\n",
"# Plotting options\n",
"sns.set_style(\"white\")\n",
"matplotlib.use('Agg') \n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4LAxYQ5gVtIX"
},
"source": [
"Run the code below to check that you chose the correct colab runtime."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "H-ogeIN5QV8X"
},
"outputs": [],
"source": [
"#@title Test for GPU\n",
"\n",
"device_name = tf.test.gpu_device_name()\n",
"if device_name != '/device:GPU:0':\n",
" print('NOT using a GPU. Please follow the instructions above.')\n",
"else:\n",
" print(\"Using a GPU. You're good to go.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CdtWhHZvjz7S"
},
"source": [
"# Setting up Data\n",
"\n",
"As a first step, we will setup the CL problems to be used in the code below. Specifically, the code below implements the following experiments:\n",
"\n",
"* Permuted MNIST (Proposed as a CL benchmark in [3])\n",
"* Sequential Omniglot (Proposed as a CL benchmark in [2])\n",
"\n",
"We start by defining a few useful objects that hold data at various stages throughout the training pipeline:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "QYOn2oR-k0Of"
},
"outputs": [],
"source": [
"#@title Data utility objects\n",
"\n",
"# Holds underlying data source for all tasks. `task_metadata` contains all\n",
"# metadata necessary to split this data into a sequence of tasks.\n",
"RawData = collections.namedtuple(\n",
" 'RawData', ['inputs', 'outputs', 'task_metadata'])\n",
"\n",
"# `RawData` split into train/valid \u0026 test sets for a specific task.\n",
"DatasetSplit = collections.namedtuple(\n",
" 'DatasetSplit', ['train', 'valid', 'test',\n",
" 'num_train', 'num_valid', 'num_test'])\n",
"\n",
"# TF iterators used to fetch data in the training pipeline.\n",
"# `train`: Training dataset. Possibly augmented.\n",
"# `discr_search`: Dataset to choose replay/inducing points from. Not augmented.\n",
"# `discr_search_eval`: Dataset to evaluate inducing point approximation on.\n",
"# Either training or validation set. Not augmented.\n",
"# `test`: Test dataset. Not augmented.\n",
"Iterators = collections.namedtuple(\n",
" 'Iterators', ['train', 'discr_search', 'discr_search_eval', 'test'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AaTKGUDXWr3T"
},
"source": [
"And next define a few functions to operate on those objects, performing converstions along with the necessary processing of those objects."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "OQS5wddJWr-T"
},
"outputs": [],
"source": [
"#@title Data utility functions\n",
"\n",
"def make_tf_dataset(inputs: tf.Tensor,\n",
" outputs: tf.Tensor,\n",
" num_datapoints: int) -\u003e tf.data.Dataset:\n",
" \"\"\"Create a dataset for supervised learning.\n",
"\n",
" Args:\n",
" inputs: A tensor of shape `[num_datapoitns, ...]`.\n",
" outputs: Associated outputs. A tensor of shape `[num_datapoitns, ...]`.\n",
" num_datapoints: Number of expected datapoints.\n",
" Returns:\n",
" dataset: A tf.data.Dataset instance. Implements shuffling and repetition.\n",
" \"\"\"\n",
" dataset = tf.data.Dataset.from_tensor_slices((inputs, outputs))\n",
" return dataset.shuffle(num_datapoints).repeat()\n",
"\n",
"def create_iterators(data: RawData) -\u003e Iterators:\n",
" \"\"\"Create dataset tensorflow dataset iterators.\n",
"\n",
" Args:\n",
" data: A RawData object.\n",
"\n",
" Returns:\n",
" tensorflow Dataset iterators.\n",
" \"\"\"\n",
" # Setup dataset iterators\n",
" train_iterator = iter(data.train.batch(train_batch_size))\n",
" test_iterator = iter(data.test.batch(data.num_test))\n",
"\n",
" # Dataset to choose inducing points from\n",
" discr_search_iterator = iter(data.train.batch(data.num_train))\n",
" if data.valid is not None:\n",
" # Dataset to evaluate the quality of the inducing approximation on\n",
" discr_search_eval_iterator = iter(data.valid.batch(data.num_valid))\n",
" else:\n",
" # Use only a quarter fo the train dataset to avoid long training times\n",
" discr_search_eval_iterator = iter(data.train.batch(data.num_train // 4))\n",
"\n",
" return Iterators(\n",
" train_iterator,\n",
" discr_search_iterator,\n",
" discr_search_eval_iterator,\n",
" test_iterator)\n",
"\n",
"def concat(x: tf.Tensor, y: tf.Tensor) -\u003e tf.Tensor:\n",
" \"\"\"Concatenates two tensors along first dimension.\n",
"\n",
" Args:\n",
" x: A tensor of shape `[N, ...]`.\n",
" y: A tensor of shape `[M, ...]`.\n",
" Returns:\n",
" A tensor of shape `[N+M, ...]`.\n",
" \"\"\"\n",
"\n",
" return tf.concat([x, y], axis=0)\n",
"\n",
"def select_datapoints(\n",
" data: tf.Tensor,\n",
" select_idx: np.array) -\u003e tf.Tensor:\n",
" \"\"\"Select subset of datapoints provided by `select_idx` from `data`.\n",
"\n",
" Args:\n",
" data: A tensor of shape `[num_datapoints, ...]`.\n",
" select_idx: Indices of data to be returned. Shape (N \u003c= num_datapoints, ...)\n",
" Returns:\n",
" A tensor of shape `[N, ...]`.\n",
" \"\"\"\n",
"\n",
" return tf.gather(data, select_idx, axis=0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6hgLRArrYpdU"
},
"source": [
"Let's define all functions necessary to generate the Permuted MNIST benchmark. This includes a function to fetch the data and apply some elimentary preprocessing (returning a `RawData` object) as well as one to split the data into tasks based on different random permuations (returning a `DatasetSplit` object)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "2upl5WdRpP5c"
},
"outputs": [],
"source": [
"#@title Permuted MNIST\n",
"\n",
"def generate_permutation(num_idx: int) -\u003e Tuple[np.array, np.array]:\n",
" \"\"\"Create a permuation of `num_idx` indices.\n",
"\n",
" Args:\n",
" num_idx: Number of indices to be permuted\n",
"\n",
" Returns:\n",
" perm: Permuted indices.\n",
" inv_perm: Inverse of `perm`. Needed to invert permuation.\n",
" \"\"\"\n",
" perm = np.random.permutation(range(num_idx))\n",
" inv_perm = np.argsort(perm)\n",
" return perm, inv_perm\n",
"\n",
"def prepare_mnist(num_mnist_tasks: int) -\u003e RawData:\n",
" \"\"\"Download MNIST data and create permutations.\n",
"\n",
" Args:\n",
" num_mnist_tasks: How many tasks to use.\n",
"\n",
" Returns:\n",
" Data re-organised with permutation masks to distinguish between tasks.\n",
" \"\"\"\n",
"\n",
" mnist = tf.keras.datasets.mnist.load_data()\n",
"\n",
" x_train, y_train = mnist[0]\n",
" x_test, y_test = mnist[1]\n",
"\n",
" all_images = concat(x_train, x_test)\n",
" all_labels = concat(y_train, y_test)\n",
"\n",
" # Safe scalling and casting\n",
" all_images = tf.image.convert_image_dtype(all_images, tf.float64)\n",
" # Reshape\n",
" all_images = snt.Flatten()(all_images)\n",
" num_indicies = all_images.shape[1]\n",
"\n",
" # Define permutations\n",
" task_perms, task_inv_perms = zip(*[generate_permutation(num_indicies)\n",
" for _ in range(num_mnist_tasks)])\n",
"\n",
" return RawData(all_images.numpy(), all_labels.numpy(), (task_perms, task_inv_perms))\n",
"\n",
"\n",
"def load_mnist_permutation(\n",
" full_mnist: RawData,\n",
" task_id: int,\n",
" construct_valid: bool,\n",
" use_data_augmentationt: bool) -\u003e DatasetSplit:\n",
" \"\"\"Extract data for a single task by premuting and create a train/valid/test split.\n",
"\n",
" Args:\n",
" full_mnist: Contains original MNIST data. Unpermuted.\n",
" task_id: Id of task requested.\n",
" construct_valid: Whether to construct a validation dataset.\n",
" use_data_augmentationt: Unused.\n",
"\n",
" Returns:\n",
" Data re-organised with alphabet ids to distinguish between tasks.\n",
" \"\"\"\n",
" del use_data_augmentationt # Unused\n",
" # Apply permutation for task given by `task_id`\n",
" permuted_inputs = full_mnist.inputs[:, full_mnist.task_metadata[0][task_id]]\n",
"\n",
" num_train = 60000\n",
" num_test = 10000\n",
"\n",
" # Choose a random permutation\n",
" x_test = permuted_inputs[-num_test:]\n",
" y_test = full_mnist.outputs[-num_test:]\n",
" x_train = permuted_inputs[:-num_test]\n",
" y_train = full_mnist.outputs[:-num_test]\n",
"\n",
" # Construct an optional validation set\n",
" if construct_valid:\n",
" perm = np.random.permutation(num_train)\n",
"\n",
" num_valid = 10000\n",
" num_train -= num_valid\n",
"\n",
" x_valid = x_train[perm[:num_valid]]\n",
" y_valid = y_train[perm[:num_valid]]\n",
" x_train = x_train[perm[num_valid:]]\n",
" y_train = y_train[perm[num_valid:]]\n",
" valid_dataset = make_tf_dataset(x_valid, y_valid, num_valid)\n",
" else:\n",
" num_valid = 0\n",
" valid_dataset = None\n",
"\n",
" train_dataset = make_tf_dataset(x_train, y_train, num_train)\n",
" test_dataset = make_tf_dataset(x_test, y_test, num_test)\n",
"\n",
" return DatasetSplit(train_dataset, valid_dataset, test_dataset,\n",
" num_train, num_valid, num_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "__Bhwg5qWDH4"
},
"source": [
"The Omiglot setup is fairly similar with the exception of the additional data augmentation (growing the size of the datset for each task 20-fold) proposed in [2]. This augmentation consists of random rotations and shifts of images which we implement below:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "KGlT7BF9XgXa"
},
"outputs": [],
"source": [
"#@title Data augmentation functions\n",
"\n",
"def random_img_shift_and_rotation(\n",
" imgs: tf.Tensor,\n",
" degree_range: Sequence[float] = None,\n",
" shift_range: Sequence[float] = None) -\u003e tf.Tensor:\n",
" \"\"\"Augments image tensor by random shifts and rotations.\n",
"\n",
" Args:\n",
" imgs: A tensor of shape (num_images, num_rows, num_columns, num_channels).\n",
" degree_range: Min./Max. degrees by which each image is rotated.\n",
" shift_range: Min./Max. diff (in both x/y) by which each image is shifted.\n",
"\n",
" Returns:\n",
" Augmented Image(s) with the same type and shape as images.\n",
" \"\"\"\n",
"\n",
" if degree_range is None:\n",
" degree_range = [-30.0, 30.0]\n",
"\n",
" if shift_range is None:\n",
" shift_range = [-5.0, 5.0]\n",
"\n",
" num_datapoints = imgs.shape[0]\n",
"\n",
" # Random rotation\n",
" angles = np.random.uniform(\n",
" degree_range[0], degree_range[1], size=num_datapoints)\n",
" imgs = tfa.image.rotate(imgs, np.radians(angles))\n",
"\n",
" # Random shift (https://stackoverflow.com/questions/42252040/how-to-translateor-shift-images-in-tensorflow/45662771)\n",
" diff = np.random.uniform(shift_range[0], shift_range[1],\n",
" size=num_datapoints*2).reshape(num_datapoints, 2)\n",
" transforms = np.tile([[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]],\n",
" [num_datapoints, 1]).astype(np.float32)\n",
" transforms[:, 2] = -diff[:, 0]\n",
" transforms[:, 5] = -diff[:, 1]\n",
"\n",
" return tfa.image.transform(imgs, transforms)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qQG-BsEHaowB"
},
"source": [
"Setting up the rest of the functions for Seq. Omniglot:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "SEh1pp6LhnBX"
},
"outputs": [],
"source": [
"#@title Sequential Omniglot\n",
"\n",
"def prepare_omniglot(num_alphabets: int) -\u003e RawData:\n",
" \"\"\"Download Omniglot data and merge original train/test sets.\n",
"\n",
" Args:\n",
" num_alphabets: How many alphabets to use. Must be \u003c= 50.\n",
"\n",
" Returns:\n",
" Data re-organised with alphabet ids to distinguish between tasks.\n",
" \"\"\"\n",
" # The original train/test split is designed for few-shot learning. As this\n",
" # split no longer applies to us, we will merge this data and perform a manual\n",
" # train/test split later on.\n",
" omniglot_train_ds = tfds.load(\n",
" 'omniglot', batch_size=-1, split='train',\n",
" as_supervised=False, shuffle_files=False)\n",
" omniglot_test_ds = tfds.load(\n",
" 'omniglot', batch_size=-1, split='test',\n",
" as_supervised=False, shuffle_files=False)\n",
"\n",
" # The provided train/test split does not apply, so we'll merge and re-organise\n",
" all_images = concat(\n",
" omniglot_train_ds['image'], omniglot_test_ds['image'])\n",
" all_labels = concat(\n",
" omniglot_train_ds['alphabet_char_id'],\n",
" omniglot_test_ds['alphabet_char_id'])\n",
"\n",
" # Allows us to distinguish between tasks (i.e. alphabets)\n",
" all_alphabet_ids = concat(\n",
" omniglot_train_ds['alphabet'],\n",
" omniglot_test_ds['alphabet']).numpy()\n",
"\n",
" # To determine the size of a prediction vector.\n",
" num_chars_per_alphabet = [tf.reduce_max(select_datapoints(all_labels, np.argwhere(all_alphabet_ids == t))).numpy()\n",
" for t in range(num_alphabets)]\n",
"\n",
" # Discard all data not needed\n",
" idx = np.argwhere(all_alphabet_ids \u003c num_alphabets).flatten()\n",
" all_alphabet_ids = all_alphabet_ids[idx]\n",
" all_images = select_datapoints(all_images, idx)\n",
" all_labels = select_datapoints(all_labels, idx)\n",
"\n",
" return RawData(all_images, all_labels, (all_alphabet_ids, num_chars_per_alphabet))\n",
"\n",
"\n",
"def load_omniglot_alphabet(\n",
" full_omniglot: RawData,\n",
" alphabet_id: int,\n",
" construct_valid: bool,\n",
" use_data_augmentationt: bool) -\u003e DatasetSplit:\n",
" \"\"\"Extract a single alphabet, preprocess and create a train/valid/test split.\n",
"\n",
" Args:\n",
" full_omniglot: Contains Omniglot data of all alphabets.\n",
" alphabet_id: Id of alphabet requested. Must be \u003c= 50.\n",
" construct_valid: Whether to construct a validation dataset.\n",
" use_data_augmentationt: Whether to augment the dataset.\n",
"\n",
" Returns:\n",
" Data re-organised with alphabet ids to distinguish between tasks.\n",
" \"\"\"\n",
" # Selecting an alphabet\n",
" idx = np.argwhere(full_omniglot.task_metadata[0] == alphabet_id).flatten()\n",
" alphabet_images = select_datapoints(full_omniglot.inputs, idx)\n",
" alphabet_labels = select_datapoints(full_omniglot.outputs, idx)\n",
"\n",
" # Pre-processing\n",
" alphabet_images = tf.image.convert_image_dtype(alphabet_images, tf.float32)\n",
" alphabet_images = tf.image.rgb_to_grayscale(alphabet_images)\n",
" # Resize and invert colour (from black on white background to white on black).\n",
" alphabet_images = 1.0 - tf.image.resize(alphabet_images, [28, 28])\n",
" alphabet_images = tf.cast(alphabet_images, tf.float64)\n",
"\n",
" num_datapoints = alphabet_labels.shape[0]\n",
"\n",
" # Let's shuffle the datapoints randomnly.\n",
" perm = np.random.permutation(num_datapoints)\n",
" alphabet_images = select_datapoints(alphabet_images, perm)\n",
" alphabet_labels = select_datapoints(alphabet_labels, perm)\n",
"\n",
" # Number of datapoints in alphabet\n",
" num_datapoints = alphabet_images.shape[0]\n",
" num_test = int(np.ceil(0.2 * num_datapoints))\n",
"\n",
" # Choose a test set (20% of all data).\n",
" x_test = alphabet_images[:num_test]\n",
" y_test = alphabet_labels[:num_test]\n",
"\n",
" # Construct an optional validation set (20% of all data).\n",
" if construct_valid:\n",
" num_valid = num_test\n",
"\n",
" x_valid = alphabet_images[num_test:num_test+num_valid]\n",
" y_valid = alphabet_labels[num_test:num_test+num_valid]\n",
" valid_dataset = make_tf_dataset(x_valid, y_valid, num_valid)\n",
" else:\n",
" num_valid = 0\n",
" valid_dataset = None\n",
"\n",
" # Use the remaining data for the training set.\n",
" num_train = num_datapoints - num_valid - num_test\n",
" x_train = alphabet_images[-num_train:]\n",
" y_train = alphabet_labels[-num_train:]\n",
"\n",
" # Dataset Augmentation proposed in [2].\n",
" if use_data_augmentationt:\n",
" num_train *= 20\n",
" x_train = tf.concat([x_train] + [random_img_shift_and_rotation(x_train)\n",
" for _ in range(19)], axis=0)\n",
" y_train = tf.tile(y_train, [20])\n",
"\n",
" train_dataset = make_tf_dataset(x_train, y_train, num_train)\n",
" test_dataset = make_tf_dataset(x_test, y_test, num_test)\n",
"\n",
" return DatasetSplit(train_dataset, valid_dataset, test_dataset,\n",
" num_train, num_valid, num_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ViGySN-Yq5Td"
},
"source": [
"# Implementing FRCL"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lp-jvHiTElFs"
},
"source": [
"There are two key architectural components: \n",
"- (i) A neural network defining the feature mapping $\\phi(x)$.\n",
"- (ii) A Sparse Variational Gaussian Process operating on the feature mapping. \n",
"\n",
"We will use standard networks for (i) and focus our attention primarily on the implementation of (ii). The code below uses Sonnet to build Neural Network Architectures and GPFlow to implement sparse GPs:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "J9e-hWJ_r5IH"
},
"outputs": [],
"source": [
"#@title Import Sonnet \u0026 GPFlow\n",
"\n",
"import gpflow\n",
"import sonnet as snt"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I-OFqE1gbxws"
},
"source": [
"For Permuted MNIST, we use a standard Sonnet Multi-Layer Perceptron (MLP) which we augment by concatenating a bias term for the GP:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "03HfC6x0SaIO"
},
"outputs": [],
"source": [
"#@title Define an MLP Network to provide the feature mapping for MNIST\n",
"\n",
"class MLPNetworkWithBias(snt.nets.MLP):\n",
" \"\"\"Fully connected MLP base network using Sonnet.\"\"\"\n",
"\n",
" def __call__(self, inputs: tf.Tensor, *args, **kwargs) -\u003e tf.Tensor:\n",
" \"\"\"Applies MLP to `inputs` and adds a column of ones to the feature matrix.\n",
"\n",
" Args:\n",
" inputs: A Tensor of shape `[batch_size, num_input_dimensions]`.\n",
" *args: Arguments to snt.nets.MLP. See Sonnet documentation.\n",
" **kwargs: Named arguments to snt.nets.MLP. See Sonnet documentation.\n",
"\n",
" Returns:\n",
" outputs: Model output and bias term of shape `[batch_size, output_size+1]`.\n",
" \"\"\"\n",
" outputs = super(MLPNetworkWithBias, self).__call__(inputs, *args, **kwargs)\n",
"\n",
" # Add a column of ones to the feature vector to account for the bias\n",
" outputs = tf.concat(\n",
" [outputs, tf.ones((outputs.shape[0], 1), dtype=outputs.dtype)], axis=1)\n",
"\n",
" return outputs"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h5j5lOSib7cE"
},
"source": [
"For Sequential Omniglot, we use a good old-fashioned ConvNet with MaxPooling which we define below:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "9_QDJa5kj5_o"
},
"outputs": [],
"source": [
"#@title Define a Conv. Network to provide the feature mapping for Omniglot\n",
"\n",
"class ConvNetworkWithBias(snt.Module):\n",
" \"\"\"Builds a module out of a sequence of callables.\"\"\"\n",
"\n",
" def __init__(self,\n",
" output_sizes: Sequence[int],\n",
" data_format: Text = 'NHWC',\n",
" conv_padding: Text = 'SAME',\n",
" conv_kernel_shape: Sequence[int] = None,\n",
" conv_kernel_stride: Sequence[int] = None,\n",
" maxpool_padding: Text = 'VALID',\n",
" maxpool_kernel_shape: Sequence[int] = None,\n",
" maxpool_kernel_stride: Sequence[int] = None,\n",
" name: Optional[Text] = None):\n",
" \"\"\"Constructor for ConvNetworkWithBias.\n",
"\n",
" Args:\n",
" output_sizes: Defines the number of output channels for each ConvLayer.\n",
" data_format: Specifies semantics for each input batch dimension.\n",
" conv_padding: Either `SAME` or `VALID`.\n",
" conv_kernel_shape: Size of the convolutional kernel.\n",
" conv_kernel_stride: Convolution stride.\n",
" maxpool_padding: Either `SAME` or `VALID`.\n",
" maxpool_kernel_shape: Size of the maxpool kernel.\n",
" maxpool_kernel_stride: MaxPool stride.\n",
" name: Optional model name.\n",
" \"\"\"\n",
" super(ConvNetworkWithBias, self).__init__(name=name)\n",
"\n",
" if conv_kernel_shape is None:\n",
" conv_kernel_shape = [3, 3]\n",
" if conv_kernel_stride is None:\n",
" conv_kernel_stride = [1, 1]\n",
" if maxpool_padding is None:\n",
" maxpool_padding = 'VALID'\n",
" if maxpool_kernel_shape is None:\n",
" maxpool_kernel_shape = [1, 2, 2, 1]\n",
" if maxpool_kernel_stride is None:\n",
" maxpool_kernel_stride = [1, 2, 2, 1]\n",
"\n",
" self._output_sizes = output_sizes\n",
" self._num_layers = len(self._output_sizes)\n",
" self._conv_kernel_shapes = [conv_kernel_shape] * self._num_layers\n",
" self._conv_strides = [conv_kernel_stride] * self._num_layers\n",
" self._maxpool_kernel_shapes = [maxpool_kernel_shape] * self._num_layers\n",
" self._maxpool_kernel_strides = [maxpool_kernel_stride] * self._num_layers\n",
"\n",
" # Instantiate modules\n",
" self._conv_modules = list(\n",
" snt.Conv2D( # pylint: disable=g-complex-comprehension\n",
" output_channels=self._output_sizes[i],\n",
" kernel_shape=self._conv_kernel_shapes[i],\n",
" stride=self._conv_strides[i],\n",
" padding=conv_padding,\n",
" data_format=data_format,\n",
" name='conv_2d_{}'.format(i))\n",
" for i in range(self._num_layers))\n",
" \n",
" self._maxpool_modules = list(\n",
" lambda x: tf.nn.max_pool(x, \n",
" ksize=self._maxpool_kernel_shapes[i],\n",
" strides=self._maxpool_kernel_strides[i],\n",
" padding=maxpool_padding,\n",
" data_format=data_format,\n",
" name='maxpool_2d_{}'.format(i)) \n",
" for i in range(self._num_layers))\n",
" self._flatten = snt.Flatten(name='Flatten')\n",
"\n",
" def __call__(self, inputs: tf.Tensor) -\u003e tf.Tensor:\n",
" \"\"\"Applies ConvNet to `inputs` and adds a column of ones to the feature matrix.\n",
"\n",
" Args:\n",
" inputs: A Tensor of shape `[batch_size, height, width, num_channels]`.\n",
"\n",
" Returns:\n",
" outputs: Model output and bias term `[batch_size, output_size+1]`.\n",
" \"\"\"\n",
"\n",
" # Ensure correct data type\n",
" original_dtype = inputs.dtype\n",
" outputs = tf.cast(inputs, tf.float32)\n",
"\n",
" for conv_layer, maxpool_layer in zip(\n",
" self._conv_modules, self._maxpool_modules):\n",
" outputs = conv_layer(outputs)\n",
" outputs = maxpool_layer(outputs)\n",
" outputs = tf.nn.relu(outputs)\n",
"\n",
" outputs = self._flatten(outputs)\n",
" # Add a column of ones to the feature vector to account for the bias\n",
" outputs = tf.concat(\n",
" [outputs, tf.ones((outputs.shape[0], 1), dtype=outputs.dtype)], axis=1)\n",
"\n",
" return tf.cast(outputs, original_dtype)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h5UQZKq-HGV2"
},
"source": [
"Let's turn our attention to (ii) Implementing the GP to work on the feature embedding. This is the heart of FRCL:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "wYuUL3P-3el8"
},
"outputs": [],
"source": [
"#@title Implementing FRCL\n",
"\n",
"class BaseInducingApproximation(object):\n",
" \"\"\"Holds the parameters of a posterior approximation.\"\"\"\n",
" def __init__(self,\n",
" q_z: tf.Tensor,\n",
" q_mean: tf.Variable,\n",
" q_sqrt: tf.Variable,\n",
" q_z_init: tf.Tensor):\n",
" \"\"\"Constructor for BaseInducingApproximation.\n",
"\n",
" Args:\n",
" q_z: Inducing points. A Tensor of shape `[num_inducing_points, num_input_features]`.\n",
" q_mean: Mean of the variational distribution of shape `[num_inducing_points, num_classes]`.\n",
" q_sqrt: Cholesky (sqrt) matrices of the variational distribution `[]`.\n",
" q_z_init: Initial inducing points. A Tensor of shape `[num_inducing_points, num_input_features]`.\n",
" \"\"\"\n",
" self.q_z = q_z\n",
" self.q_mean = q_mean\n",
" self.q_sqrt = q_sqrt\n",
" self.q_z_init = q_z_init\n",
"\n",
"\n",
"class InducingApproximation(BaseInducingApproximation):\n",
" \"\"\"Creates a variational approximation in function/GP space.\"\"\"\n",
"\n",
" def __init__(self,\n",
" num_inducing_points: int,\n",
" num_classes: int,\n",
" inducing_init_value: tf.Tensor):\n",
" \"\"\"Constructor for InducingApproximation.\n",
"\n",
" Args:\n",
" num_inducing_points: Number of inducing points to use.\n",
" num_classes: Number of classes in classification problem.\n",
" inducing_init_value: Initial value. A Tensor of shape `[num_inducing_points, num_input_features]`.\n",
" \"\"\"\n",
" q_mean = tf.Variable(np.zeros(\n",
" (num_inducing_points, num_classes)).astype(np.float64))\n",
" q_sqrt = tf.Variable(np.array(\n",
" [np.eye(num_inducing_points).astype(np.float64)\n",
" for _ in range(num_classes)]).T)\n",
"\n",
" q_z = tf.constant(inducing_init_value)\n",
" q_z_init = tf.constant(inducing_init_value)\n",
" super(InducingApproximation, self).__init__(q_z, q_mean, q_sqrt, q_z_init)\n",
"\n",
"\n",
"class WeightSpaceApproximation(object):\n",
" \"\"\"Creates a variational approximation in weight space.\"\"\"\n",
"\n",
" def __init__(self,\n",
" num_features: int,\n",
" num_classes: int,\n",
" rng: np.random.RandomState):\n",
" \"\"\"Constructor for WeightSpaceApproximation.\n",
"\n",
" Args:\n",
" num_features: Number of inducing points to use.\n",
" num_classes: Number of classes in classification problem.\n",
" rng: Random number generator.\n",
" \"\"\"\n",
" scalar = 1.0/np.sqrt(num_features)\n",
" self.q_w_mean = tf.Variable(scalar*rng.randn(\n",
" num_features, num_classes).astype(np.float64))\n",
" self.q_w_sqrt = tf.Variable(np.array(\n",
" [np.eye(num_features).astype(np.float64)\n",
" for _ in range(num_classes)]).T)\n",
"\n",
"class ContinualGPmodel(object):\n",
" \"\"\"Continual learning GP-based model tha uses variational inducing points.\"\"\"\n",
"\n",
" def __init__(self,\n",
" num_input_dimensions: int,\n",
" num_features: int,\n",
" num_classes: int,\n",
" base_network: snt.Module,\n",
" likelihood: gpflow.likelihoods,\n",
" noise_variance: float = 1e-3):\n",
" \"\"\"Constructor for continual GP model.\n",
"\n",
" Args:\n",
" num_input_dimensions: Dimensionality of the input space.\n",
" num_features: Size of the feature including the bias term.\n",
" num_classes: Number of classes in classification problem.\n",
" base_network: Provides the feature mappping.\n",
" likelihood: Likelihood for the datatype at hand.\n",
" noise_variance: Added to diagonal of covariance matrix.\n",
" \"\"\"\n",
"\n",
" self.num_input_dimensions = num_input_dimensions\n",
" self.num_features = num_features\n",
" self.num_classes = num_classes\n",
" self.past_inducing_approxs = []\n",
" self.current_inducing_approx = None\n",
" self.current_weight_space_approx = None\n",
" self.base_network = base_network\n",
" self.likelihood = likelihood\n",
" # Constant noise/jitter to be added only to the inducing point covariances\n",
" self.noise_variance = tf.constant(noise_variance, dtype=tf.float64)\n",
"\n",
" def get_weight_space_approx(self, rng):\n",
" \"\"\"Initialise a new weight space approximation.\n",
"\n",
" Args:\n",
" rng: Random number generator.\n",
" \"\"\"\n",
" self.current_weight_space_approx = WeightSpaceApproximation(\n",
" self.num_features, self.num_classes, rng)\n",
"\n",
" def covariance_self(self, matrix_a: tf.Tensor) -\u003e tf.Tensor:\n",
" \"\"\"Compute linear kernel plus noise for features with themself.\n",
"\n",
" Args:\n",
" matrix_a: Matrix of shape `[batch, num_features]`.\n",
"\n",
" Returns:\n",
" Covariance matrix of shape `[batch, batch]`.\n",
"\n",
" \"\"\"\n",
" return tf.matmul(\n",
" matrix_a, matrix_a, transpose_b=True) + self.noise_variance * tf.eye(\n",
" tf.cast(matrix_a.shape[0], tf.int32), dtype=tf.float64)\n",
"\n",
" def covariance_cross(self,\n",
" matrix_a: tf.Tensor,\n",
" matrix_b: tf.Tensor) -\u003e tf.Tensor:\n",
" \"\"\"Compute linear kernel for features with other features.\n",
"\n",
" No noise in these case because features are assumed distinct.\n",
"\n",
" Args:\n",
" matrix_a: Tensor of shape `[batch_a, num_features]`.\n",
" matrix_b: Tensor of shape `[batch_b, num_features]`.\n",
"\n",
" Returns:\n",
" Covariance matrix of shape `[batch_a, batch_b]`.\n",
"\n",
" \"\"\"\n",
" return tf.matmul(matrix_a, matrix_b, transpose_b=True)\n",
"\n",
" def covariance_diag(self, matrix: tf.Tensor) -\u003e tf.Tensor:\n",
" \"\"\"Compute diagonal of linear kernel matrix for features with themself.\n",
"\n",
" Args:\n",
" matrix: Tensor of shape `[batch, num_features]`.\n",
"\n",
" Returns:\n",
" Diagonal covariance vector of shape `[batch]`.\n",
" \"\"\"\n",
"\n",
" # We do not add noise_variance/jitter to this diagonal, but only to the\n",
" # inducing matrix, so inducing variables are noisy function values and\n",
" # through them we approximate the exact noise-free GP model.\n",
" return tf.reduce_sum(matrix * matrix, axis=1)\n",
"\n",
" def objective_weight_space(self,\n",
" inputs: tf.Tensor,\n",
" outputs: tf.Tensor,\n",
" num_task_points: int):\n",
" \"\"\"Compute the variational objective for the model.\n",
"\n",
" Args:\n",
" inputs: A Tensor of shape `[batch_size, num_input_dimensions]`.\n",
" outputs: Class labels. Shape (batch, num_classes).\n",
" num_task_points: Total number of data points in task.\n",
"\n",
" Returns:\n",
" variational objective for the bound.\n",
" \"\"\"\n",
" batch_size = tf.cast(inputs.shape[0], tf.int32)\n",
"\n",
" # Add KL divergences for the all previous tasks.\n",
" # Rightmost term of Eq. (4) in the paper.\n",
" kl_historical = 0\n",
" for inducing_approx in self.past_inducing_approxs:\n",
" # z_features shape (num_inducing_points, num_features)\n",
" z_features = self.base_network(inducing_approx.q_z)\n",
" # p_cov shape (num_inducing_points, num_inducing_points)\n",
" p_cov = self.covariance_self(z_features)\n",
" kl_historical += gpflow.kullback_leiblers.gauss_kl(\n",
" inducing_approx.q_mean,\n",
" tf.transpose(inducing_approx.q_sqrt, (2, 0, 1)),\n",
" K=p_cov)\n",
"\n",
" # For the current task we do inference in the weight space\n",
" data_features = self.base_network(inputs)\n",
" data_mean = tf.matmul(data_features,\n",
" self.current_weight_space_approx.q_w_mean)\n",
"\n",
" tr_q_w_sqrt = tf.compat.v1.matrix_band_part(tf.transpose(\n",
" self.current_weight_space_approx.q_w_sqrt, (2, 0, 1)), -1, 0)\n",
" expand_data_features = tf.tile(\n",
" tf.expand_dims(data_features, 0), [self.num_classes, 1, 1])\n",
" feat_w_sqrt = tf.matmul(expand_data_features, tr_q_w_sqrt)\n",
" data_var = tf.transpose(tf.reduce_sum(tf.square(feat_w_sqrt), axis=2))\n",
"\n",
" # Middle term of Eq. (4) in the paper.\n",
" kl_current = gpflow.kullback_leiblers.gauss_kl(\n",
" self.current_weight_space_approx.q_w_mean,\n",
" tf.transpose(self.current_weight_space_approx.q_w_sqrt, (2, 0, 1)),\n",
" K=None)\n",
"\n",
" # Left term of Eq. (4) in the paper.\n",
" exp_likes = self.likelihood.variational_expectations(\n",
" data_mean, data_var, outputs)\n",
" # Scale this correclty by the total number of datapoints in the task.\n",
" ratio = tf.cast(\n",
" num_task_points, tf.float64) / tf.cast(batch_size, tf.float64)\n",
" reconstruction_cost = tf.reduce_sum(exp_likes) * ratio\n",
"\n",
" # Full objective. Eq. (4) in the paper.\n",
" objective = kl_historical + kl_current - reconstruction_cost\n",
" return objective, reconstruction_cost, kl_current\n",
"\n",
" def trace_term(self,\n",
" inputs: tf.Tensor,\n",
" q_z: tf.Tensor) -\u003e tf.Tensor:\n",
" \"\"\"Compute the trace term. Useful for search over inducing points.\n",
"\n",
" Args:\n",
" inputs: A Tensor of shape `[batch_size, num_input_dimensions]`.\n",
" q_z: Inducing points of shape `[num_inducing_points, num_input_dimensions]`.\n",
"\n",
" Returns:\n",
" variational objective for the bound.\n",
" \"\"\"\n",
"\n",
" # Shape [num_inducing_points, num_features]\n",
" inducing_features = self.base_network(q_z)\n",
" # Shape [batch_size, num_features]\n",
" data_features = self.base_network(inputs)\n",
"\n",
" # Compute covariance\n",
" kmn = self.covariance_cross(inducing_features, data_features)\n",
" kmm = self.covariance_self(inducing_features)\n",
" knn = self.covariance_diag(data_features)\n",
"\n",
" # Implements Equation (6) in the paper and speeds up computations by\n",
" # applying the matrix inversion formula if needed.\n",
" if q_z.shape[0] \u003c= self.num_features:\n",
" lm = tf.compat.v1.cholesky(kmm)\n",
" v = tf.compat.v1.matrix_triangular_solve(lm, kmn, lower=True)\n",
" fvar = knn - tf.reduce_sum(tf.square(v), 0)\n",
" else:\n",
" phimphim = tf.matmul(inducing_features, inducing_features,\n",
" transpose_a=True)\n",
" phimphim_noise = phimphim + self.noise_variance * tf.eye(\n",
" tf.cast(self.num_features, tf.int32), dtype=tf.float64)\n",
"\n",
" sqrt_phimphin_noise = tf.compat.v1.cholesky(phimphim_noise)\n",
"\n",
" inv_sqrt_phimphin_noise_phim = tf.compat.v1.matrix_triangular_solve(\n",
" sqrt_phimphin_noise, tf.transpose(inducing_features),\n",
" lower=True)\n",
" v = tf.matmul(inv_sqrt_phimphin_noise_phim, kmn)\n",
"\n",
" fvar = knn - (1.0/self.noise_variance)*(\n",
" tf.reduce_sum(tf.square(kmn), 0) - tf.reduce_sum(tf.square(v), 0))\n",
"\n",
" return tf.reduce_sum(fvar)\n",
"\n",
" def function_space_prediction(self,\n",
" inputs: tf.Tensor,\n",
" q_z: tf.Tensor,\n",
" q_mean: tf.Variable,\n",
" q_sqrt: tf.Variable,\n",
" outputs: tf.Tensor = None):\n",
" \"\"\"Compute predictions from the model in function/GP space.\n",
"\n",
" Args:\n",
" inputs: Tensor of shape `[batch, num_input_dimensions]`.\n",
" q_z: Inducing points of shape `[num_inducing_points, input_dimensions]`.\n",
" q_mean: Mean of the variational distribution of shape `[num_inducing_points, num_classes]`.\n",
" q_sqrt: Cholesky (sqrt) matrices of the variational distribution `[]`.\n",
" outputs: Class labels. Optional tensor of Shape `[batch, num_clsses]`.\n",
"\n",
" Returns:\n",
" When outputs=None, it returns predictive mean and variance from model\n",
" otherwise it returns the log predictive density for the outputs.\n",
" \"\"\"\n",
"\n",
" inducing_features = self.base_network(q_z)\n",
" data_features = self.base_network(inputs)\n",
"\n",
" # Compute covariance\n",
" kmn = self.covariance_cross(inducing_features, data_features)\n",
" kmm = self.covariance_self(inducing_features)\n",
" knn = self.covariance_diag(data_features)\n",
"\n",
" func_mean, func_var = gpflow.conditionals.base_conditional(\n",
" kmn, kmm, knn, f=q_mean, q_sqrt=tf.transpose(q_sqrt, (2, 0, 1)),\n",
" full_cov=False, white=False)\n",
"\n",
" if outputs is None:\n",
" return self.likelihood.predict_mean_and_var(func_mean, func_var)\n",
" else:\n",
" return self.likelihood.predict_density(func_mean, func_var, outputs)\n",
"\n",
" def complete_task_weight_space(self,\n",
" z: tf.Tensor,\n",
" z_init: tf.Tensor):\n",
" \"\"\"Completes training of current task in weight space.\n",
"\n",
" Args:\n",
" z: Final inducing points for current task of shape `[num_inducing_points, num_features]`.\n",
" z_init: Initial inducing points for current task of shape `[num_inducing_points, num_features]`.\n",
" \"\"\"\n",
"\n",
" current_inducing_features = self.base_network(z)\n",
" current_q_mean = tf.matmul(\n",
" current_inducing_features, self.current_weight_space_approx.q_w_mean)\n",
" noise_matrix = self.noise_variance * tf.eye(\n",
" tf.cast(tf.shape(z)[0], tf.int32), dtype=tf.float64)\n",
" noise_matrix = tf.tile(\n",
" tf.expand_dims(noise_matrix, 0), [self.num_classes, 1, 1])\n",
" tr_q_w_sqrt = tf.linalg.band_part(tf.transpose(\n",
" self.current_weight_space_approx.q_w_sqrt, (2, 0, 1)), -1, 0)\n",
" expand_current_inducing_features = tf.tile(\n",
" tf.expand_dims(current_inducing_features, 0), [self.num_classes, 1, 1])\n",
" feat_w_sqrt = tf.matmul(expand_current_inducing_features, tr_q_w_sqrt)\n",
" q_cov = tf.matmul(feat_w_sqrt, feat_w_sqrt, transpose_b=True)\n",
" q_cov = q_cov + noise_matrix\n",
" current_q_sqrt = tf.transpose(tf.compat.v1.cholesky(q_cov), (1, 2, 0))\n",
"\n",
" self.past_inducing_approxs.append(\n",
" BaseInducingApproximation(z, tf.identity(current_q_mean),\n",
" tf.identity(current_q_sqrt), z_init))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QGRQ3-vmpVOu"
},
"source": [
"The only remaning bit is to define the evaluation functions that allow us to measure model performance:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "iDfyq2E8pUha"
},
"outputs": [],
"source": [
"#@title Evaluation utility functions\n",
"\n",
"def get_accuracy_rate(\n",
" predictions: np.array,\n",
" labels: np.array) -\u003e float:\n",
" \"\"\"Compute mean classification accuracy.\n",
"\n",
" Args:\n",
" predictions: A numpy array of shape `[batch_size, input_size]`.\n",
" labels: Ground truth labels of shape `[batch_size, input_size]`.\n",
"\n",
" Returns:\n",
" outputs: Model output and bias term `[batch_size, output_size+1]`.\n",
" \"\"\"\n",
" return np.mean(np.equal(np.argmax(predictions, axis=1), labels))\n",
"\n",
"def evaluate_on_all_tasks(\n",
" eval_iterators: Sequence[Iterators],\n",
" model: ContinualGPmodel) -\u003e Sequence[float]:\n",
" \"\"\"Evaluates models on all tasks. Assumes iterators provide entire dataset.\n",
"\n",
" Args:\n",
" eval_iterators: Provides entire data to evaluate model on. One per task.\n",
" model: A ContinualGPmodel to be evaluated.\n",
"\n",
" Returns:\n",
" List of accuracies achieved on each task.\n",
" \"\"\"\n",
"\n",
" # Evaluate test accuracies for all tasks\n",
" eval_accuracies = []\n",
" for task, eval_iterator in enumerate(eval_iterators):\n",
" # Fetch the whole eval dataset\n",
" eval_input, eval_output = next(eval_iterator)\n",
"\n",
" # Model prediction\n",
" pred_prob = model.function_space_prediction(\n",
" eval_input,\n",
" model.past_inducing_approxs[task].q_z,\n",
" model.past_inducing_approxs[task].q_mean,\n",
" model.past_inducing_approxs[task].q_sqrt)[0]\n",
"\n",
" test_accuracy_rate = get_accuracy_rate(pred_prob, eval_output)\n",
" eval_accuracies.append(test_accuracy_rate)\n",
"\n",
" return eval_accuracies"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nnl_sw4cqrfN"
},
"source": [
"# Training\n",
"\n",
"Now that we've set up the model, let's define a basic Continual Learning Setup. The next code block allows you to choose between the two data sources and vary some of the fundamental CL experimental parameters. The values suggested below are those used in the paper to report result."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "DfBnBfSYDhqK"
},
"outputs": [],
"source": [
"#@title Defining the training protocol\n",
"\n",
"exp_name = 'Permuted MNIST' #@param [\"Permuted MNIST\", \"Sequential Omniglot\"] {allow-input: false}\n",
"\n",
"# Random number generator\n",
"rng_seed = 42 #@param\n",
"rng_state = np.random.RandomState(rng_seed)\n",
"\n",
"#@markdown P-MNIST: 10 Omniglot: 50\n",
"num_tasks = 11 #@param {type:\"slider\", min:1, max:1000, step:1}\n",
"\n",
"# General training settings (Default: Permuted MNIST)\n",
"learning_rate = 1e-3 #@param\n",
"#@markdown P-MNIST: 128 Omniglot: 32\n",
"train_batch_size = 128 #@param {type:\"slider\", min:1, max:1000, step:1}\n",
"#@markdown P-MNIST: 2000 Omniglot: 2500\n",
"num_train_iters = 2000 #@param {type:\"slider\", min:1, max:5000, step:1}\n",
"use_validation_dataset = False #@param {type:\"boolean\"}\n",
"\n",
"\n",
"# FRCL settings\n",
"which_discrete_loss = 'trace_term' #@param [\"trace_term\", \"random\"] {allow-input: false}\n",
"num_optim_steps_discrete_search = 1000 #@param {type:\"slider\", min:1, max:5000, step:1}\n",
"\n",
"#@markdown Only used for Omniglot\n",
"use_data_augmentation = False #@param {type:\"boolean\"}\n",
"\n",
"\n",
"if exp_name == 'Permuted MNIST':\n",
" tasks = list(range(num_tasks))\n",
" download_data = prepare_mnist\n",
" load_task_data = load_mnist_permutation\n",
"\n",
" # MLP Size. Based on the experimental protocol in [1].\n",
" hidden_units = [100, 100]\n",
"\n",
" num_output_dim = 10\n",
"\n",
" # Instantiate network that defines the feature mapping\n",
" BaseNetwork = MLPNetworkWithBias\n",
"\n",
" if use_data_augmentation:\n",
" print('Data augmentation is non-standard with Permuted MNIST. Ignoring option.')\n",
" use_data_augmentation = False\n",
"elif exp_name == 'Sequential Omniglot':\n",
" tasks = list(range(num_tasks))\n",
" assert num_tasks \u003c= 50, 'Only 50 tasks are available'\n",
"\n",
" download_data = prepare_omniglot\n",
" load_task_data = load_omniglot_alphabet\n",
"\n",
" # ConvNet output channels. Based on the experimental protocol in [2].\n",
" hidden_units = [64, 64, 64, 64]\n",
"\n",
" # This is the maximum number of characters in any Omniglot alphabet\n",
" num_output_dim = 54\n",
"\n",
" # Instantiate network that defines the feature mapping\n",
" BaseNetwork = ConvNetworkWithBias\n",
"else:\n",
" print('unknown dataset')\n",
"\n",
"print('\\nDownloading dataset...')\n",
"with IPython.utils.io.capture_output() as captured:\n",
" raw_data = download_data(num_tasks)\n",
"\n",
"#@markdown P-MNIST: 200 Omniglot: 2 (to be multiplied by the #characters/alphabet)\n",
"num_inducing_points_per_task = 200 #@param {type:\"slider\", min:1, max:1000, step:1}\n",
"\n",
"# The specification of inducing points is absolute for P-MNIST and relative to the number of characters/alphabet for Omniglot\n",
"if exp_name == 'Permuted MNIST':\n",
" num_inducing_points_per_task = [num_inducing_points_per_task] * num_tasks\n",
"else:\n",
" num_inducing_points_per_task = [num_inducing_points_per_task * t for t in\n",
" raw_data.task_metadata[1]]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7Sz-EzPNDxum"
},
"source": [
"Let's setup data iterators and instatiate an FRCL model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "V-6cBCCfDu8u"
},
"outputs": [],
"source": [
"#@title Instantiate model\n",
"\n",
"num_pixels = 784\n",
"num_gp_features = hidden_units[-1] + 1 # Add one to account for the bias\n",
"\n",
"# Instantiate Continal Learning Gaussian Process Model\n",
"model_likelihood = gpflow.likelihoods.MultiClass(num_output_dim)\n",
"network = BaseNetwork(output_sizes=hidden_units)\n",
"FRCL = ContinualGPmodel(num_pixels, num_gp_features,\n",
" num_output_dim, network, model_likelihood)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GOIN529ADhLP"
},
"source": [
"Let's start training. The code below runs the main training loop:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "eQiqr9a10skX"
},
"outputs": [],
"source": [
"#@title Main training loop\n",
"\n",
"discr_search_losses = []\n",
"test_accuracies = []\n",
"test_iterators = []\n",
"\n",
"for t in range(num_tasks):\n",
" print('Learning task {}'.format(t+1))\n",
" discr_search_loss = []\n",
"\n",
" task_data = load_task_data(raw_data, t,\n",
" use_validation_dataset,\n",
" use_data_augmentation)\n",
" iterators = create_iterators(task_data)\n",
"\n",
" # Get a weight space approximation for the current task\n",
" FRCL.get_weight_space_approx(rng_state)\n",
" # Set up a task-specific optimiser\n",
" optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n",
"\n",
" # --- Task Training ---\n",
" print('\\tStarting optimisation')\n",
" for training_index in range(num_train_iters):\n",
" train_inputs, train_outputs = next(iterators.train)\n",
"\n",
" def loss_fn():\n",
" train_loss, _, _ = FRCL.objective_weight_space(\n",
" train_inputs, train_outputs, task_data.num_train)\n",
"\n",
" return train_loss\n",
"\n",
" optimizer.minimize(loss_fn)\n",
"\n",
" print('\\tFinished training')\n",
"\n",
" # --- Discrete inducing point Optimisation ---\n",
" num_inducing_points_for_task = num_inducing_points_per_task[t]\n",
"\n",
" # Select a random set of inducing inputs from the discrete search set\n",
" x_discr = next(iterators.discr_search)[0]\n",
" perm_train = np.random.permutation(task_data.num_train)\n",
"\n",
" z_idx = perm_train[-num_inducing_points_for_task:]\n",
" z_ = select_datapoints(x_discr, z_idx)\n",
"\n",
" if 'random' == which_discrete_loss:\n",
" print('\\tUsing random inducing points')\n",
" elif 'trace_term' == which_discrete_loss:\n",
" # Indicates a candidate training point to replace the inducing point with\n",
" train_set_id = 0\n",
"\n",
" # Data to evaluate inducing set on\n",
" discr_search_input, _ = next(iterators.discr_search_eval)\n",
" current_loss = -FRCL.trace_term(discr_search_input, z_)\n",
" discr_search_loss.append((0, current_loss, z_))\n",
"\n",
" accepted_moves = 0\n",
" print('\\tOptimising inducing points using discrete search.')\n",
" for disr_search_iter in range(num_optim_steps_discrete_search):\n",
" inducing_set_id = disr_search_iter % num_inducing_points_for_task\n",
" z_idx_proposed = z_idx.copy()\n",
"\n",
" # Replace inducing point and re-evaluate\n",
" z_idx_proposed[inducing_set_id] = perm_train[train_set_id]\n",
" proposed_loss = -FRCL.trace_term(\n",
" discr_search_input, select_datapoints(x_discr, z_idx_proposed))\n",
"\n",
" if proposed_loss \u003e current_loss:\n",
" z_idx = z_idx_proposed.copy()\n",
" current_loss = proposed_loss\n",
" accepted_moves += 1\n",
"\n",
" discr_search_loss.append(\n",
" (disr_search_iter, current_loss, z_))\n",
"\n",
" if train_set_id == task_data.num_train-1:\n",
" perm_train = np.random.permutation(task_data.num_train)\n",
" train_set_id = 0\n",
" else:\n",
" train_set_id += 1\n",
"\n",
" print('\\tAccepted moves: {}'.format(accepted_moves))\n",
" discr_search_losses.append(discr_search_loss)\n",
"\n",
" # --- Complete the task ---\n",
" FRCL.complete_task_weight_space(z_, discr_search_loss[0][2])\n",
"\n",
" # --- Evaluation on all tasks thus far ---\n",
" test_iterators.append(iterators.test)\n",
" test_accuracies.append(\n",
" evaluate_on_all_tasks(test_iterators, FRCL))\n",
"\n",
"print('Done.')\n",
"\n",
"mean_test_accuracy = list(map(np.mean, test_accuracies))\n",
"\n",
"print('\\n---\\nFinal accuracy: {}\\n---'.format(mean_test_accuracy[-1]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1UmeQb-3bOW7"
},
"source": [
"Let's visualise overall (first column) along with task-specific performance that allows us to visualise forward/backward transfer:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "BmZwTixhRv67"
},
"outputs": [],
"source": [
"#@title Visualising overall and per-task accuracies\n",
"\n",
"from matplotlib.ticker import MaxNLocator\n",
"\n",
"\n",
"def _format_axis(axis: matplotlib.pyplot.axis,\n",
" y_limit: Sequence[float],\n",
" title_str: Text = None):\n",
" \"\"\"Format axis.\n",
"\n",
" Args:\n",
" axis: Axis to format.\n",
" y_limit: Limit to apply on the y-axis.\n",
" title_str: String to use for axis title.\n",
" \"\"\"\n",
" axis.set_xlim((0.5, num_tasks + 0.5))\n",
" axis.set_ylim(y_limit)\n",
" # Force only integer tirkcs\n",
" axis.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
"\n",
" if title_str is not None:\n",
" axis.set_title(title_str)\n",
"\n",
"\n",
"colours = sns.color_palette()\n",
"\n",
"fig = plt.figure(figsize=(10, 10))\n",
"layout = (int(np.ceil(num_tasks / 2) + 1), 2)\n",
"ylim = (np.min([np.min(t) for t in test_accuracies]) * 0.975, 1.0)\n",
"\n",
"# Plot the overall mean accuracy\n",
"ax = plt.subplot2grid(layout, (0, 0), colspan=2)\n",
"ax.plot(range(1, num_tasks + 1), mean_test_accuracy, marker='o', c='k')\n",
"ax.set_xlabel('Evaluated after learning task')\n",
"ax.set_ylabel('Test accuracy (%)')\n",
"_format_axis(\n",
" ax, ylim,\n",
" 'Overall mean test accuracy: FRCL ({})'.format(which_discrete_loss))\n",
"\n",
"# Plot results for each task\n",
"for t in range(num_tasks):\n",
" ax = plt.subplot2grid(layout, (t // 2 + 1, t % 2))\n",
" ax.plot(\n",
" range(t + 1, num_tasks + 1),\n",
" test_accuracies[-t - 1],\n",
" marker='o',\n",
" c=colours[t])\n",
"\n",
" _format_axis(ax, ylim, 'Test accuracy for task: {}'.format(t + 1))\n",
"\n",
"sns.despine()\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bR0yr75Bbfgh"
},
"source": [
"The code below allows us to look at some of the inducing points we optimised for. Each row shows inducing points for a different task:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "61mZYuu9MTH6"
},
"outputs": [],
"source": [
"#@title Visualising inducing points\n",
"\n",
"num_inducing_points_per_task_to_show = 20 #@param\n",
"max_number_of_tasks = 5 #@param\n",
"\n",
"max_number_of_tasks = min(max_number_of_tasks, num_tasks)\n",
"num_inducing_points_per_task_to_show = min(\n",
" num_inducing_points_per_task_to_show, max(num_inducing_points_per_task))\n",
"\n",
"fig, axarr = plt.subplots(\n",
" max_number_of_tasks,\n",
" num_inducing_points_per_task_to_show,\n",
" figsize=(10, 10))\n",
"\n",
"for i in range(max_number_of_tasks):\n",
" task_inducing_points = FRCL.past_inducing_approxs[i].q_z.numpy()\n",
"\n",
" if 'Permuted MNIST' == exp_name:\n",
" task_inv_perm = raw_data.task_metadata[1][i]\n",
" # Apply inverse permutation to allow visualisation\n",
" task_inducing_points = task_inducing_points[:, task_inv_perm]\n",
" task_inducing_points = task_inducing_points.reshape([-1, 28, 28])\n",
" for j in range(num_inducing_points_per_task_to_show):\n",
" axarr[i, j].imshow(task_inducing_points[j], cmap='gray')\n",
" axarr[i, j].axis('off')\n",
"\n",
"plt.subplots_adjust(wspace=0, hspace=0)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VhaEvkSdhYWQ"
},
"source": [
"# References\n",
"\n",
"[1] Zenke, Friedemann, Ben Poole, and Surya Ganguli. \"Continual learning through synaptic intelligence.\" Proceedings of machine learning research 70 (2017): 3987.\n",
"\n",
"[2] Schwarz, J., Luketina, J., Czarnecki, W. M., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., \u0026 Hadsell, R. (2018). Progress \u0026 compress: A scalable framework for continual learning. arXiv preprint arXiv:1805.06370.\n",
"\n",
"[3] Goodfellow, Ian J., et al. \"An empirical investigation of catastrophic forgetting in gradient-based neural networks.\" arXiv preprint arXiv:1312.6211 (2013)."
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"last_runtime": {},
"name": "Functional Regularisation for Continual Learning",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: fusion_tcv/README.md
================================================
# TCV Fusion Control Objectives
This code release contains the rewards, control targets, noise model and
parameter variation for the paper *Magnetic control of tokamak plasmas through
deep reinforcement learning*, published in Nature in ... 2021.
## Disclaimer
This release is useful for understanding the details of specific elements of the
learning architecture, however it does not contain the simulator
([FGE](https://infoscience.epfl.ch/record/283775), part of
[LIUQE](https://www.epfl.ch/research/domains/swiss-plasma-center/liuqe-suite/)),
the trained/exported control policies, nor the agent training infrastructure.
This release is useful for replicating our results which can be done by
assembling all the components, many of which are open source elsewhere. Please
see the "Code Availability" statement in the paper for more information.
The learning algorithm we used is
[MPO](https://arxiv.org/abs/1812.02256), which has an open source
[reference implementation](https://github.com/deepmind/acme) in
[Acme](https://arxiv.org/abs/2006.00979). Additionally, the open source software
libraries [launchpad](https://arxiv.org/abs/2106.04516)
([code](https://github.com/deepmind/launchpad)),
[dm_env](https://github.com/deepmind/dm_env),
[sonnet](https://github.com/deepmind/sonnet),
[tensorflow](https://arxiv.org/abs/1603.04467)
([code](https://www.tensorflow.org/)) and
[reverb](https://arxiv.org/pdf/2102.04736.pdf)
([code](https://github.com/deepmind/reverb)) were used.
FGE and LIUQE are available on request from the
[Swiss Plasma Center](https://www.epfl.ch/research/domains/swiss-plasma-center/)
at [EPFL](https://www.epfl.ch/en/)
(email [Federico Felici](mailto:federico.felici@epfl.ch)), subject to agreement.
## Objectives used in published TCV experiments
Take a look at `rewards_used.py` and `references.py` for the rewards and control
targets used in the paper.
To print the actual control targets, run:
```sh
$ python3 -m fusion_tcv.references_main --refs=snowflake
```
Make sure to install the dependencies:
`pip install -r fusion_tcv/requirements.txt`.
## Overview of files
* `agent.py`: An interface for what a real agent might look like. This mainly
exists so the run loop builds.
* `combiners.py`: Defined combiners that combine multiple values into one.
Useful for creating a scalar reward from a set of reward components.
* `environment.py`: Augments the FGE simulator to make it an RL environment
(parameter variation, reward computation, etc.).
* `experiments.py`: The environment definitions published in the paper.
* `fge_octave.py`: An interface for the simulator. Given that FGE isn't open
source, this is a just a sketch of how you might use it.
* `fge_state.py`: A python interface to the simulator state. Given that FGE
isn't open source, this is a sketch and returns fake data.
* `named_array.py`: Makes it easier to interact with numpy arrays by name.
Useful for referring to parts of the control targets/references.
* `noise.py`: Adds action and observation noise.
* `param_variation.py`: Defines the physics parameters used by the simulation.
* `ref_gen.py`: The tools to generate control targets per time step.
* `references.py`: The control targets used in the experiments.
* `references_main.py`: Runnable script to output the references to the command
line.
* `rewards.py`: Computes all of the reward components and combines them together
to create a single scalar reward for the environment.
* `rewards_used.py`: The actual reward definitions used in the experiments.
* `run_loop.py`: An example of interacting with the environment to generate a
trajectory to send to the replay buffer. This code is notional as a complete
version would require an Agent and a Simulator implementation.
* `targets.py`: The reward components that pull data from the control targets
and the simulator state for generating rewards. This depends on FGE, so
cannot be run.
* `tcv_common.py`: Defines the physical attributes of the TCV fusion reactor,
and how to interact with it.
* `terminations.py`: Defines when the simulation should stop.
* `trajectory.py`: Stores the history of the episode.
* `transforms.py`: Turns error values into normalized values.
================================================
FILE: fusion_tcv/agent.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""An agent interface for interacting with the environment."""
import abc
import dm_env
import numpy as np
from fusion_tcv import tcv_common
class AbstractAgent(abc.ABC):
"""Agent base class."""
def reset(self):
"""Reset to the initial state."""
@abc.abstractmethod
def step(self, timestep: dm_env.TimeStep) -> np.ndarray:
"""Return the action given the current observations."""
class ZeroAgent(AbstractAgent):
"""An agent that always returns "zero" actions."""
def step(self, timestep: dm_env.TimeStep) -> np.ndarray:
del timestep
return np.zeros(tcv_common.action_spec().shape,
tcv_common.action_spec().dtype)
================================================
FILE: fusion_tcv/combiners.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Reward combiners."""
import abc
import math
from typing import List, Optional, Tuple
import dataclasses
import numpy as np
from scipy import special
from fusion_tcv import targets
class AbstractCombiner(targets.AbstractTarget):
"""Combines a set of rewards, possibly weighted."""
@abc.abstractmethod
def __call__(self, values: List[float], # pytype: disable=signature-mismatch # overriding-return-type-checks
weights: Optional[List[float]] = None) -> List[float]:
"""Combines a set of rewards, possibly weighted."""
@property
def outputs(self) -> int:
"""All combiners return exactly one value, even if it's NaN."""
return 1
@staticmethod
def _clean_values_weights(
values: List[float],
weights: Optional[List[float]] = None) -> Tuple[List[float], List[float]]:
"""Validate the values and weights, and if no weights, return equal."""
if weights is None:
weights = [1] * len(values)
else:
if len(values) != len(weights):
raise ValueError("Number of weights don't match values. "
f"values: {len(values)}, weights: {len(weights)}")
for w in weights:
if w < 0:
raise ValueError(f"Weights must be >=0: {w}")
new_values_weights = [(v, w) for v, w in zip(values, weights)
if not np.isnan(v) and w > 0]
return tuple(zip(*new_values_weights)) if new_values_weights else ([], [])
class Mean(AbstractCombiner):
"""Take the weighted mean of the values.
Ignores NaNs and values with weight 0.
"""
def __call__(self, values: List[float],
weights: Optional[List[float]] = None) -> List[float]:
values, weights = self._clean_values_weights(values, weights)
if not values:
return [float("nan")]
return [sum(r * w for r, w in zip(values, weights)) / sum(weights)]
def _multiply(values, weights, mean):
"""Multiplies the values taking care to validate the weights.
Defines 0^0 = 1 so a reward with no weight is "off" even if the value is 0.
Args:
values: The reward values.
weights: The reward weights.
mean: If true, divides by the sum of the weights (computes the geometric
mean).
Returns:
Product of v^w across the components.
"""
# If weight and value are both zero, set the value to 1 so that 0^0 = 1.
values = [1 if (v == 0 and w == 0) else v for (v, w) in zip(values, weights)]
if any(v == 0 for v in values):
return [0]
den = sum(weights) if mean else 1
return [math.exp(sum(np.log(values) * weights) / den)]
class Multiply(AbstractCombiner):
"""Combine by multiplying the (weighted) values together.
This is the same as Geometric mean, but without the n^th root taken at the
end. This means doing poorly on several rewards compounds, rather than
averages. As such it likely only makes sense after the non-linearities, ie
where the values are in the 0-1 range, otherwise it'll cause them to increase.
This is even harsher than Min or SmoothMax(-inf).
Ignores NaNs and values with weight 0.
"""
def __call__(self, values: List[float],
weights: Optional[List[float]] = None) -> List[float]:
values, weights = self._clean_values_weights(values, weights)
if not values:
return [float("nan")]
return _multiply(values, weights, mean=False)
class GeometricMean(AbstractCombiner):
"""Take the weighted geometric mean of the values.
Pushes values towards 0, so likely only makes sense after the non-linear
transforms.
Ignores NaNs and values with weight 0.
"""
def __call__(self, values: List[float],
weights: Optional[List[float]] = None) -> List[float]:
values, weights = self._clean_values_weights(values, weights)
if not values:
return [float("nan")]
return _multiply(values, weights, mean=True)
class Min(AbstractCombiner):
"""Take the min of the values. Ignores NaNs and values with weight 0."""
def __call__(self, values: List[float],
weights: Optional[List[float]] = None) -> List[float]:
values, _ = self._clean_values_weights(values, weights)
if not values:
return [float("nan")]
return [min(values)]
class Max(AbstractCombiner):
"""Take the max of the values. Ignores NaNs and values with weight 0."""
def __call__(self, values: List[float],
weights: Optional[List[float]] = None) -> List[float]:
values, _ = self._clean_values_weights(values, weights)
if not values:
return [float("nan")]
return [max(values)]
@dataclasses.dataclass(frozen=True)
class LNorm(AbstractCombiner):
"""Take the l-norm of the values.
Reasonable norm values (assuming normalized):
- 1: avg of the values
- 2: euclidean distance metric
- inf: max value
Values in between go between the average and max. As the l-norm goes up, the
result gets closer to the max.
Normalized means dividing by the max possible distance, such that the units
still make sense.
This likely only makes sense before the non-linear transforms. SmoothMax is
similar but more flexible and understandable.
Ignores NaNs and values with weight 0.
"""
norm: float
normalized: bool = True
def __call__(self, values: List[float],
weights: Optional[List[float]] = None) -> List[float]:
values, _ = self._clean_values_weights(values, weights)
if not values:
return [float("nan")]
lnorm = np.linalg.norm(values, ord=self.norm)
if self.normalized:
lnorm /= np.linalg.norm(np.ones(len(values)), ord=self.norm)
return [float(lnorm)]
@dataclasses.dataclass(frozen=True)
class SmoothMax(AbstractCombiner):
"""Combines component rewards using a smooth maximum.
https://en.wikipedia.org/wiki/Smooth_maximum
alpha is the exponent for the smooth max.
- alpha -> inf: returns the maximum
- alpha == 0: returns the weighted average
- alpha -> -inf: returns the minimum
alpha in between returns values in between.
Since this varies between min, mean and max, it keeps the existing scale.
Alpha >= 0 make sense before converting to 0-1, alpha <= 0 make sense after.
Ignores NaNs and values with weight 0.
"""
alpha: float
def __call__(self, values: List[float],
weights: Optional[List[float]] = None) -> List[float]:
values, weights = self._clean_values_weights(values, weights)
if not values:
return [float("nan")]
if math.isinf(self.alpha):
return [max(values) if self.alpha > 0 else min(values)]
# Compute weights in a numerically-friendly way.
log_soft_weights = [np.log(w) + c * self.alpha
for w, c in zip(weights, values)]
log_soft_weights -= special.logsumexp(log_soft_weights)
soft_weights = np.exp(log_soft_weights)
return Mean()(values, soft_weights) # weighted mean
================================================
FILE: fusion_tcv/combiners_test.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for combiners."""
import math
from absl.testing import absltest
from fusion_tcv import combiners
NAN = float("nan")
class CombinersTest(absltest.TestCase):
def assertNan(self, value):
self.assertLen(value, 1)
self.assertTrue(math.isnan(value[0]))
def test_errors(self):
c = combiners.Mean()
with self.assertRaises(ValueError):
c([0, 1], [1])
with self.assertRaises(ValueError):
c([0, 1], [1, 2, 3])
with self.assertRaises(ValueError):
c([0, 1], [-1, 2])
def test_mean(self):
c = combiners.Mean()
self.assertEqual(c([0, 2, 4]), [2])
self.assertEqual(c([0, 0.5, 1]), [0.5])
self.assertEqual(c([0, 0.5, 1], [0, 0, 1]), [1])
self.assertEqual(c([0, 1], [1, 3]), [0.75])
self.assertEqual(c([0, NAN], [1, 3]), [0])
self.assertNan(c([NAN, NAN], [1, 3]))
def test_geometric_mean(self):
c = combiners.GeometricMean()
self.assertEqual(c([0.5, 0]), [0])
self.assertEqual(c([0.3]), [0.3])
self.assertEqual(c([4, 4]), [4])
self.assertEqual(c([0.5, 0.5]), [0.5])
self.assertEqual(c([0.5, 0.5], [1, 3]), [0.5])
self.assertEqual(c([0.5, 1], [1, 2]), [0.5**(1/3)])
self.assertEqual(c([0.5, 1], [2, 1]), [0.5**(2/3)])
self.assertEqual(c([0.5, 0], [2, 0]), [0.5])
self.assertEqual(c([0.5, 0, 0], [2, 1, 0]), [0])
self.assertEqual(c([0.5, NAN, 0], [2, 1, 0]), [0.5])
self.assertNan(c([NAN, NAN], [1, 3]))
def test_multiply(self):
c = combiners.Multiply()
self.assertEqual(c([0.5, 0]), [0])
self.assertEqual(c([0.3]), [0.3])
self.assertEqual(c([0.5, 0.5]), [0.25])
self.assertEqual(c([0.5, 0.5], [1, 3]), [0.0625])
self.assertEqual(c([0.5, 1], [1, 2]), [0.5])
self.assertEqual(c([0.5, 1], [2, 1]), [0.25])
self.assertEqual(c([0.5, 0], [2, 0]), [0.25])
self.assertEqual(c([0.5, 0, 0], [2, 1, 0]), [0])
self.assertEqual(c([0.5, NAN], [1, 1]), [0.5])
self.assertNan(c([NAN, NAN], [1, 3]))
def test_min(self):
c = combiners.Min()
self.assertEqual(c([0, 1]), [0])
self.assertEqual(c([0.5, 1]), [0.5])
self.assertEqual(c([1, 0.75]), [0.75])
self.assertEqual(c([1, 3]), [1])
self.assertEqual(c([1, 1, 3], [0, 1, 1]), [1])
self.assertEqual(c([NAN, 3]), [3])
self.assertNan(c([NAN, NAN], [1, 3]))
def test_max(self):
c = combiners.Max()
self.assertEqual(c([0, 1]), [1])
self.assertEqual(c([0.5, 1]), [1])
self.assertEqual(c([1, 0.75]), [1])
self.assertEqual(c([1, 3]), [3])
self.assertEqual(c([1, 1, 3], [0, 1, 1]), [3])
self.assertEqual(c([NAN, 3]), [3])
self.assertNan(c([NAN, NAN], [1, 3]))
def test_lnorm(self):
c = combiners.LNorm(1)
self.assertEqual(c([0, 2, 4]), [2])
self.assertEqual(c([0, 0.5, 1]), [0.5])
self.assertEqual(c([3, 4]), [7 / 2])
self.assertEqual(c([0, 2, 4], [1, 1, 0]), [1])
self.assertEqual(c([0, 2, NAN]), [1])
self.assertNan(c([NAN, NAN], [1, 3]))
c = combiners.LNorm(1, normalized=False)
self.assertEqual(c([0, 2, 4]), [6])
self.assertEqual(c([0, 0.5, 1]), [1.5])
self.assertEqual(c([3, 4]), [7])
c = combiners.LNorm(2)
self.assertEqual(c([3, 4]), [5 / 2**0.5])
c = combiners.LNorm(2, normalized=False)
self.assertEqual(c([3, 4]), [5])
c = combiners.LNorm(math.inf)
self.assertAlmostEqual(c([3, 4])[0], 4)
c = combiners.LNorm(math.inf, normalized=False)
self.assertAlmostEqual(c([3, 4])[0], 4)
def test_smoothmax(self):
# Max
c = combiners.SmoothMax(math.inf)
self.assertEqual(c([0, 1]), [1])
self.assertEqual(c([0.5, 1]), [1])
self.assertEqual(c([1, 0.75]), [1])
self.assertEqual(c([1, 3]), [3])
# Smooth Max
c = combiners.SmoothMax(1)
self.assertAlmostEqual(c([0, 1])[0], 0.7310585786300049)
# Mean
c = combiners.SmoothMax(0)
self.assertEqual(c([0, 2, 4]), [2])
self.assertEqual(c([0, 0.5, 1]), [0.5])
self.assertEqual(c([0, 0.5, 1], [0, 0, 1]), [1])
self.assertEqual(c([0, 2, NAN]), [1])
self.assertEqual(c([0, 2, NAN], [0, 1, 1]), [2])
self.assertAlmostEqual(c([0, 1], [1, 3])[0], 0.75)
self.assertNan(c([NAN, NAN], [1, 3]))
# Smooth Min
c = combiners.SmoothMax(-1)
self.assertEqual(c([0, 1])[0], 0.2689414213699951)
# Min
c = combiners.SmoothMax(-math.inf)
self.assertEqual(c([0, 1]), [0])
self.assertEqual(c([0.5, 1]), [0.5])
self.assertEqual(c([1, 0.75]), [0.75])
self.assertEqual(c([1, 3]), [1])
if __name__ == "__main__":
absltest.main()
================================================
FILE: fusion_tcv/environment.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Environment API for FGE simulator."""
from typing import Dict, List, Optional
import dm_env
from dm_env import auto_reset_environment
from dm_env import specs
import numpy as np
from fusion_tcv import fge_octave
from fusion_tcv import fge_state
from fusion_tcv import named_array
from fusion_tcv import noise
from fusion_tcv import param_variation
from fusion_tcv import ref_gen
from fusion_tcv import rewards
from fusion_tcv import tcv_common
from fusion_tcv import terminations
# Re-export as fge_octave should be an implementation detail.
ShotCondition = fge_octave.ShotCondition
class Environment(auto_reset_environment.AutoResetEnvironment):
"""An environment using the FGE Solver.
The simulator will return a flux map, which is the environment's hidden state,
and some flux measurements, which will be used as observations. The actions
represent current levels that are passed to the simulator for the next
flux calculation.
"""
def __init__(
self,
shot_condition: ShotCondition,
reward: rewards.AbstractReward,
reference_generator: ref_gen.AbstractReferenceGenerator,
max_episode_length: int = 10000,
termination: Optional[terminations.Abstract] = None,
obs_act_noise: Optional[noise.Noise] = None,
power_supply_delays: Optional[Dict[str, List[float]]] = None,
param_generator: Optional[param_variation.ParamGenerator] = None):
"""Initializes an Environment instance.
Args:
shot_condition: A ShotCondition, specifying shot number and time. This
specifies the machine geometry (eg with or without the baffles), and the
initial measurements, voltages, current and plasma state.
reward: Function to generate a reward term.
reference_generator: Generator for the signal to send to references.
max_episode_length: Maximum number of steps before episode is truncated
and restarted.
termination: Decide if the state should be considered a termination.
obs_act_noise: Type for setting the observation and action noise. If noise
is set to None then the default noise level is used.
power_supply_delays: A dict with power supply delays (in seconds), keys
are coil type labels ('E', 'F', 'G', 'OH'). `None` means default delays.
param_generator: Generator for Liuqe parameter settings. If None then
the default settings are used.
"""
super().__init__()
if power_supply_delays is None:
power_supply_delays = tcv_common.TCV_ACTION_DELAYS
self._simulator = fge_octave.FGESimulatorOctave(
shot_condition=shot_condition,
power_supply_delays=power_supply_delays)
self._reward = reward
self._reference_generator = reference_generator
self._max_episode_length = max_episode_length
self._termination = (termination if termination is not None else
terminations.CURRENT_OH_IP)
self._noise = (obs_act_noise if obs_act_noise is not None else
noise.Noise.use_default_noise())
self._param_generator = (param_generator if param_generator is not None else
param_variation.ParamGenerator())
self._params = None
self._step_counter = 0
self._last_observation = None
def observation_spec(self):
"""Defines the observations provided by the environment."""
return tcv_common.observation_spec()
def action_spec(self) -> specs.BoundedArray:
"""Defines the actions that should be provided to `step`."""
return tcv_common.action_spec()
def _reset(self) -> dm_env.TimeStep:
"""Starts a new episode."""
self._step_counter = 0
self._params = self._param_generator.generate()
state = self._simulator.reset(self._params)
references = self._reference_generator.reset()
zero_act = np.zeros(self.action_spec().shape,
dtype=self.action_spec().dtype)
self._last_observation = self._extract_observation(
state, references, zero_act)
return dm_env.restart(self._last_observation)
def _simulator_voltages_from_voltages(self, voltages):
voltage_simulator = np.copy(voltages)
if self._params.psu_voltage_offset is not None:
for coil, offset in self._params.psu_voltage_offset.items():
voltage_simulator[tcv_common.TCV_ACTION_INDICES[coil]] += offset
voltage_simulator = np.clip(
voltage_simulator,
self.action_spec().minimum,
self.action_spec().maximum)
g_coil = tcv_common.TCV_ACTION_RANGES.index("G")
if abs(voltage_simulator[g_coil]) < tcv_common.ENV_G_COIL_DEADBAND:
voltage_simulator[g_coil] = 0
return voltage_simulator
def _step(self, action: np.ndarray) -> dm_env.TimeStep:
"""Does one step within TCV."""
voltages = self._noise.add_action_noise(action)
voltage_simulator = self._simulator_voltages_from_voltages(voltages)
try:
state = self._simulator.step(voltage_simulator)
except (fge_state.InvalidSolutionError,
fge_state.StopSignalException):
return dm_env.termination(
self._reward.terminal_reward(), self._last_observation)
references = self._reference_generator.step()
self._last_observation = self._extract_observation(
state, references, action)
term = self._termination.terminate(state)
if term:
return dm_env.termination(
self._reward.terminal_reward(), self._last_observation)
reward, _ = self._reward.reward(voltages, state, references)
self._step_counter += 1
if self._step_counter >= self._max_episode_length:
return dm_env.truncation(reward, self._last_observation)
return dm_env.transition(reward, self._last_observation)
def _extract_observation(
self, state: fge_state.FGEState,
references: named_array.NamedArray,
action: np.ndarray) -> Dict[str, np.ndarray]:
return {
"references": references.array,
"measurements": self._noise.add_measurement_noise(
state.get_observation_vector()),
"last_action": action,
}
================================================
FILE: fusion_tcv/experiments.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""The environment definitions used for our experiments."""
from fusion_tcv import environment
from fusion_tcv import references
from fusion_tcv import rewards_used
# Used in TCV#70915
def fundamental_capability() -> environment.Environment:
return environment.Environment(
shot_condition=environment.ShotCondition(70166, 0.0872),
reward=rewards_used.FUNDAMENTAL_CAPABILITY,
reference_generator=references.fundamental_capability(),
max_episode_length=10000)
# Used in TCV#70920
def elongation() -> environment.Environment:
return environment.Environment(
shot_condition=environment.ShotCondition(70166, 0.45),
reward=rewards_used.ELONGATION,
reference_generator=references.elongation(),
max_episode_length=5000)
# Used in TCV#70600
def iter() -> environment.Environment: # pylint: disable=redefined-builtin
return environment.Environment(
shot_condition=environment.ShotCondition(70392, 0.0872),
reward=rewards_used.ITER,
reference_generator=references.iter(),
max_episode_length=1000)
# Used in TCV#70457
def negative_triangularity() -> environment.Environment:
return environment.Environment(
shot_condition=environment.ShotCondition(70166, 0.45),
reward=rewards_used.NEGATIVE_TRIANGULARITY,
reference_generator=references.negative_triangularity(),
max_episode_length=5000)
# Used in TCV#70755
def snowflake() -> environment.Environment:
return environment.Environment(
shot_condition=environment.ShotCondition(70166, 0.0872),
reward=rewards_used.SNOWFLAKE,
reference_generator=references.snowflake(),
max_episode_length=10000)
# Used in TCV#69545
def droplet() -> environment.Environment:
return environment.Environment(
shot_condition=environment.ShotCondition(69198, 0.418),
reward=rewards_used.DROPLETS,
reference_generator=references.droplet(),
max_episode_length=2000)
================================================
FILE: fusion_tcv/experiments_test.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for experiments."""
from absl.testing import absltest
from absl.testing import parameterized
from dm_env import test_utils
from fusion_tcv import agent
from fusion_tcv import experiments
from fusion_tcv import run_loop
class FundamentalCapabilityTest(test_utils.EnvironmentTestMixin,
absltest.TestCase):
def make_object_under_test(self):
return experiments.fundamental_capability()
class ElongationTest(test_utils.EnvironmentTestMixin, absltest.TestCase):
def make_object_under_test(self):
return experiments.elongation()
class IterTest(test_utils.EnvironmentTestMixin, absltest.TestCase):
def make_object_under_test(self):
return experiments.iter()
class NegativeTriangularityTest(test_utils.EnvironmentTestMixin,
absltest.TestCase):
def make_object_under_test(self):
return experiments.negative_triangularity()
class SnowflakeTest(test_utils.EnvironmentTestMixin, absltest.TestCase):
def make_object_under_test(self):
return experiments.snowflake()
class DropletTest(test_utils.EnvironmentTestMixin, absltest.TestCase):
def make_object_under_test(self):
return experiments.droplet()
class ExperimentsTest(parameterized.TestCase):
@parameterized.named_parameters(
("fundamental_capability", experiments.fundamental_capability),
("elongation", experiments.elongation),
("iter", experiments.iter),
("negative_triangularity", experiments.negative_triangularity),
("snowflake", experiments.snowflake),
("droplet", experiments.droplet),
)
def test_env(self, env_fn):
traj = run_loop.run_loop(env_fn(), agent.ZeroAgent(), max_steps=10)
self.assertGreaterEqual(len(traj.reward), 1)
if __name__ == "__main__":
absltest.main()
================================================
FILE: fusion_tcv/fge_octave.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Actually interact with FGE via octave."""
from typing import Dict, List
import dataclasses
import numpy as np
from fusion_tcv import fge_state
from fusion_tcv import param_variation
SUBSTEPS = 5
@dataclasses.dataclass
class ShotCondition:
"""Represents a shot and time from a real shot."""
shot: int
time: float
class FGESimulatorOctave:
"""Would interact with the FGE solver via Octave.
Given that FGE isn't open source, this is just a sketch.
"""
def __init__(
self,
shot_condition: ShotCondition,
power_supply_delays: Dict[str, List[float]]):
"""Initialize the simulator.
Args:
shot_condition: A ShotCondition, specifying shot number and time. This
specifies the machine geometry (eg with or without the baffles), and the
initial measurements, voltages, current and plasma shape.
power_supply_delays: A dict with power supply delays (in seconds), keys
are coil type labels ('E', 'F', 'G', 'OH'). `None` means default delays.
"""
del power_supply_delays
# Initialize the simulator:
# - Use oct2py to load FGE through Octave.
# - Load the data for the shot_condition.
# - Set up the reactor geometry from the shot_condition.
# - Set the timestep to `tcv_common.DT / SUBSTEPS`.
# - Set up the solver for singlets or droplets based on the shot_condition.
self._num_plasmas = 2 if shot_condition.shot == 69198 else 1
# - Set up the power supply, including the limits, initial data, and delays.
def reset(self, variation: param_variation.Settings) -> fge_state.FGEState:
"""Restarts the simulator with parameters."""
del variation
# Update the simulator with the current physics parameters.
# Reset to the initial state from the shot_condition.
return fge_state.FGEState(self._num_plasmas) # Filled with the real state.
def step(self, voltages: np.ndarray) -> fge_state.FGEState:
"""Run the simulator with `voltages`, returns the state."""
del voltages
# for _ in range(SUBSTEPS):
# Step the simulator with `voltages`.
# raise fge_state.InvalidSolutionError if the solver doesn't converge.
# raise fge_state.StopSignalException if an internal termination triggered
return fge_state.FGEState(self._num_plasmas) # Filled with the real state.
================================================
FILE: fusion_tcv/fge_state.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A nice python representation of the underlying FGE state."""
from typing import List, Tuple
import numpy as np
from fusion_tcv import shape
from fusion_tcv import shapes_known
from fusion_tcv import tcv_common
class StopSignalException(Exception): # pylint: disable=g-bad-exception-name
"""This is raised if the FGE environment raises the Stop/Alarm signal."""
pass
class InvalidSolutionError(RuntimeError):
"""This is raised if returned solution is invalid."""
pass
class UnhandledOctaveError(Exception):
"""This is raised if some Octave code raises an unhandled error."""
pass
class FGEState:
"""A nice python representation of the underlying FGE State.
Given that FGE isn't open source, all of these numbers are made up, and only
a sketch of what it could look like.
"""
def __init__(self, num_plasmas):
self._num_plasmas = num_plasmas
@property
def num_plasmas(self) -> int:
return self._num_plasmas # Return 1 for singlet, 2 for droplets.
@property
def rzip_d(self) -> Tuple[List[float], List[float], List[float]]:
"""Returns the R, Z, and Ip for each plasma domain."""
if self.num_plasmas == 1:
return [0.9], [0], [-120000]
else:
return [0.9, 0.88], [0.4, -0.4], [-60000, -65000]
def get_coil_currents_by_type(self, coil_type) -> np.ndarray:
currents = tcv_common.TCV_ACTION_RANGES.new_random_named_array()
return currents[coil_type] * tcv_common.ENV_COIL_MAX_CURRENTS[coil_type] / 5
def get_lcfs_points(self, domain: int) -> shape.ShapePoints:
del domain # Should be plasma domain specific
return shapes_known.SHAPE_70166_0872.canonical().points
def get_observation_vector(self) -> np.ndarray:
return tcv_common.TCV_MEASUREMENT_RANGES.new_random_named_array().array
@property
def elongation(self) -> List[float]:
return [1.4] * self.num_plasmas
@property
def triangularity(self) -> List[float]:
return [0.25] * self.num_plasmas
@property
def radius(self) -> List[float]:
return [0.23] * self.num_plasmas
@property
def limit_point_d(self) -> List[shape.Point]:
return [shape.Point(tcv_common.INNER_LIMITER_R, 0.2)] * self.num_plasmas
@property
def is_diverted_d(self) -> List[bool]:
return [False] * self.num_plasmas
@property
def x_points(self) -> shape.ShapePoints:
return []
@property
def flux(self) -> np.ndarray:
"""Return the flux at the grid coordinates."""
return np.random.random((len(self.z_coordinates), len(self.r_coordinates)))
@property
def magnetic_axis_flux_strength(self) -> float:
"""The magnetic flux at the center of the plasma."""
return 2
@property
def lcfs_flux_strength(self) -> float:
"""The flux at the LCFS."""
return 1
@property
def r_coordinates(self) -> np.ndarray:
"""The radial coordinates of the simulation."""
return np.arange(tcv_common.INNER_LIMITER_R, tcv_common.OUTER_LIMITER_R,
tcv_common.LIMITER_WIDTH / 10) # Made up grid resolution.
@property
def z_coordinates(self):
"""The vertical coordinates of the simulation."""
return np.arange(-0.75, 0.75, 1.5 / 30) # Made up numbers.
================================================
FILE: fusion_tcv/named_array.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Give names to parts of a numpy array."""
from typing import Iterable, List, Mapping, MutableMapping, Tuple, Union
import numpy as np
def lengths_to_ranges(
lengths: Mapping[str, int]) -> MutableMapping[str, List[int]]:
"""Eg: {a: 2, b: 3} -> {a: [0, 1], b: [2, 3, 4]} ."""
ranges = {}
start = 0
for key, length in lengths.items():
ranges[key] = list(range(start, start + length))
start += length
return ranges
class NamedRanges:
"""Given a map of {key: count}, give various views into it."""
def __init__(self, counts: Mapping[str, int]):
self._ranges = lengths_to_ranges(counts)
self._size = sum(counts.values())
def __getitem__(self, name) -> List[int]:
return self._ranges[name]
def __contains__(self, name) -> bool:
return name in self._ranges
def set_range(self, name: str, value: List[int]):
"""Overwrite or create a custom range, which may intersect with others."""
self._ranges[name] = value
def range(self, name: str) -> List[int]:
return self[name]
def index(self, name: str) -> int:
rng = self[name]
if len(rng) != 1:
raise ValueError(f"{name} has multiple values")
return rng[0]
def count(self, name: str) -> int:
return len(self[name])
def names(self) -> Iterable[str]:
return self._ranges.keys()
def ranges(self) -> Iterable[Tuple[str, List[int]]]:
return self._ranges.items()
def counts(self) -> Mapping[str, int]:
return {k: len(v) for k, v in self._ranges.items()}
@property
def size(self) -> int:
return self._size
def named_array(self, array: np.ndarray) -> "NamedArray":
return NamedArray(array, self)
def new_named_array(self) -> "NamedArray":
return NamedArray(np.zeros((self.size,)), self)
def new_random_named_array(self) -> "NamedArray":
return NamedArray(np.random.uniform(size=(self.size,)), self)
class NamedArray:
"""Given a numpy array and a NamedRange, access slices by name."""
def __init__(self, array: np.ndarray, names: NamedRanges):
if array.shape != (names.size,):
raise ValueError(f"Wrong sizes: {array.shape} != ({names.size},)")
self._array = array
self._names = names
def __getitem__(
self, name: Union[str, Tuple[str, Union[int, List[int],
slice]]]) -> np.ndarray:
"""Return a read-only view into the array by name."""
if isinstance(name, str):
arr = self._array[self._names[name]]
else:
name, i = name
arr = self._array[np.array(self._names[name])[i]]
if not np.isscalar(arr):
# Read-only because it's indexed by an array of potentially non-contiguous
# indices, which isn't representable as a normal tensor, which forces a
# copy and therefore writes don't modify the underlying array as expected.
arr.flags.writeable = False
return arr
def __setitem__(
self, name: Union[str, Tuple[str, Union[int, List[int], slice]]], value):
"""Set one or more values of a range to a value."""
if isinstance(name, str):
self._array[self._names[name]] = value
else:
name, i = name
self._array[np.array(self._names[name])[i]] = value
@property
def array(self) -> np.ndarray:
return self._array
@property
def names(self) -> NamedRanges:
return self._names
def to_dict(self) -> Mapping[str, np.ndarray]:
return {k: self[k] for k in self._names.names()}
================================================
FILE: fusion_tcv/named_array_test.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for named_array."""
from absl.testing import absltest
import numpy as np
from fusion_tcv import named_array
class NamedRangesTest(absltest.TestCase):
def test_lengths_to_ranges(self):
self.assertEqual(named_array.lengths_to_ranges({"a": 2, "b": 3}),
{"a": [0, 1], "b": [2, 3, 4]})
def test_named_ranges(self):
action_counts = {"E": 8, "F": 8, "OH": 2, "DUMMY": 1, "G": 1}
actions = named_array.NamedRanges(action_counts)
self.assertEqual(actions.range("E"), list(range(8)))
self.assertEqual(actions["F"], list(range(8, 16)))
self.assertEqual(actions.range("G"), [19])
self.assertEqual(actions.index("G"), 19)
with self.assertRaises(ValueError):
actions.index("F")
for k, v in action_counts.items():
self.assertEqual(actions.count(k), v)
self.assertEqual(actions.counts(), action_counts)
self.assertEqual(list(actions.names()), list(action_counts.keys()))
self.assertEqual(actions.size, sum(action_counts.values()))
refs = actions.new_named_array()
self.assertEqual(refs.array.shape, (actions.size,))
np.testing.assert_array_equal(refs.array, np.zeros((actions.size,)))
refs = actions.new_random_named_array()
self.assertEqual(refs.array.shape, (actions.size,))
self.assertFalse(np.array_equal(refs.array, np.zeros((actions.size,))))
class NamedArrayTest(absltest.TestCase):
def test_name_array(self):
action_counts = {"E": 8, "F": 8, "OH": 2, "DUMMY": 1, "G": 1}
actions_ranges = named_array.NamedRanges(action_counts)
actions_array = np.arange(actions_ranges.size) + 100
actions = named_array.NamedArray(actions_array, actions_ranges)
for k in action_counts:
self.assertEqual(list(actions[k]), [v + 100 for v in actions_ranges[k]])
actions["G"] = -5
self.assertEqual(list(actions["G"]), [-5])
self.assertEqual(actions_array[19], -5)
for i in range(action_counts["E"]):
actions.names.set_range(f"E_{i}", [i])
actions["E_3"] = 53
self.assertEqual(list(actions["E_1"]), [101])
self.assertEqual(list(actions["E_3"]), [53])
self.assertEqual(actions_array[3], 53)
actions["F", 2] = 72
self.assertEqual(actions_array[10], 72)
actions["F", [4, 5]] = 74
self.assertEqual(actions_array[12], 74)
self.assertEqual(actions_array[13], 74)
actions["F", 0:2] = 78
self.assertEqual(actions_array[8], 78)
self.assertEqual(actions_array[9], 78)
self.assertEqual(list(actions["F"]), [78, 78, 72, 111, 74, 74, 114, 115])
with self.assertRaises(ValueError):
actions["F"][5] = 85
if __name__ == "__main__":
absltest.main()
================================================
FILE: fusion_tcv/noise.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Settings for adding noise to the action and measurements."""
import numpy as np
from numpy import random
from fusion_tcv import tcv_common
class Noise:
"""Class for adding noise to the action and measurements."""
def __init__(self,
action_mean=None,
action_std=None,
measurements_mean=None,
measurements_std=None,
seed=None):
"""Initializes the class.
Args:
action_mean: mean of the Gaussian noise (action bias).
action_std: std of the Gaussian action noise.
measurements_mean: mean of the Gaussian noise (measurement bias).
measurements_std: Dictionary mapping the tcv measurement names to noise.
seed: seed for the random number generator. If none seed is unset.
"""
# Check all of the shapes are present and correct.
assert action_std.shape == (tcv_common.NUM_ACTIONS,)
assert action_mean.shape == (tcv_common.NUM_ACTIONS,)
for name, num in tcv_common.TCV_MEASUREMENTS.items():
assert name in measurements_std
assert measurements_mean[name].shape == (num,)
assert measurements_std[name].shape == (num,)
self._action_mean = action_mean
self._action_std = action_std
self._meas_mean = measurements_mean
self._meas_std = measurements_std
self._meas_mean_vec = tcv_common.dict_to_measurement(self._meas_mean)
self._meas_std_vec = tcv_common.dict_to_measurement(self._meas_std)
self._gen = random.RandomState(seed)
@classmethod
def use_zero_noise(cls):
no_noise_mean = dict()
no_noise_std = dict()
for name, num in tcv_common.TCV_MEASUREMENTS.items():
no_noise_mean[name] = np.zeros((num,))
no_noise_std[name] = np.zeros((num,))
return cls(
action_mean=np.zeros((tcv_common.NUM_ACTIONS)),
action_std=np.zeros((tcv_common.NUM_ACTIONS)),
measurements_mean=no_noise_mean,
measurements_std=no_noise_std)
@classmethod
def use_default_noise(cls, scale=1):
"""Returns the default observation noise parameters."""
# There is no noise added to the actions, because the noise should be added
# to the action after/as part of the power supply model as opposed to the
# input to the power supply model.
action_noise_mean = np.zeros((tcv_common.NUM_ACTIONS))
action_noise_std = np.zeros((tcv_common.NUM_ACTIONS))
meas_noise_mean = dict()
for key, l in tcv_common.TCV_MEASUREMENTS.items():
meas_noise_mean[key] = np.zeros((l,))
meas_noise_std = dict(
clint_vloop=np.array([0]),
clint_rvloop=np.array([scale * 1e-4] * 37),
bm=np.array([scale * 1e-4] * 38),
IE=np.array([scale * 20] * 8),
IF=np.array([scale * 5] * 8),
IOH=np.array([scale * 20] *2),
Bdot=np.array([scale * 0.05] * 20),
DIOH=np.array([scale * 30]),
FIR_FRINGE=np.array([0]),
IG=np.array([scale * 2.5]),
ONEMM=np.array([0]),
vloop=np.array([scale * 0.3]),
IPHI=np.array([0]),
)
return cls(
action_mean=action_noise_mean,
action_std=action_noise_std,
measurements_mean=meas_noise_mean,
measurements_std=meas_noise_std)
def add_action_noise(self, action):
errs = self._gen.normal(size=action.shape,
loc=self._action_mean,
scale=self._action_std)
return action + errs
def add_measurement_noise(self, measurement_vec):
errs = self._gen.normal(size=measurement_vec.shape,
loc=self._meas_mean_vec,
scale=self._meas_std_vec)
# Make the IOH measurements consistent. The "real" measurements are IOH
# and DIOH, so use those.
errs = tcv_common.measurements_to_dict(errs)
errs["IOH"][1] = errs["IOH"][0] + errs["DIOH"][0]
errs = tcv_common.dict_to_measurement(errs)
return measurement_vec + errs
================================================
FILE: fusion_tcv/param_variation.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tools for varying parameters from simulation to simulation."""
from typing import Dict, Optional, Tuple
import dataclasses
import numpy as np
from fusion_tcv import tcv_common
# Pylint does not like variable names like `qA`.
# pylint: disable=invalid-name
RP_DEFAULT = 5e-6
LP_DEFAULT = 2.05e-6
BP_DEFAULT = 0.25
QA_DEFAULT = 1.3
@dataclasses.dataclass
class Settings:
"""Settings to modify solver/plasma model."""
# Inverse of the resistivity.
# Plasma circuit equation is roughly
# k * dIoh/dt = L * dIp/dt + R * I = Vloop
# where R is roughly (1 / signeo) or rp.
# Value is multiplier on the default value.
# This parameter does not apply to the OhmTor diffusion.
signeo: Tuple[float, float] = (1, 1)
# Rp Plasma resistivity. The value is an absolute value.
rp: float = RP_DEFAULT
# Plasma self-inductance. The value is an absolute value.
lp: float = LP_DEFAULT
# Proportional to the plasma pressure. The value is an absolute value.
bp: float = BP_DEFAULT
# Plasma current profile. Value is absolute.
qA: float = QA_DEFAULT
# Initial OH coil current. Applied to both coils.
ioh: Optional[float] = None
# The voltage offsets for the various coils.
psu_voltage_offset: Optional[Dict[str, float]] = None
def _psu_voltage_offset_string(self) -> str:
"""Return a short-ish, readable string of the psu voltage offsets."""
if not self.psu_voltage_offset:
return "None"
if len(self.psu_voltage_offset) < 8: # Only a few, output individually.
return ", ".join(
f"{coil.replace('_00', '')}: {offset:.0f}"
for coil, offset in self.psu_voltage_offset.items())
# Otherwise, too long, so output in groups.
groups = []
for coil, action_range in tcv_common.TCV_ACTION_RANGES.ranges():
offsets = [self.psu_voltage_offset.get(tcv_common.TCV_ACTIONS[i], 0)
for i in action_range]
if any(offsets):
groups.append(f"{coil}: " + ",".join(f"{offset:.0f}"
for offset in offsets))
return ", ".join(groups)
class ParamGenerator:
"""Varies parameters using uniform/loguniform distributions.
Absolute parameters are varied using uniform distributions while scaling
parameters use a loguniform distribution.
"""
def __init__(self,
rp_bounds: Optional[Tuple[float, float]] = None,
lp_bounds: Optional[Tuple[float, float]] = None,
qA_bounds: Optional[Tuple[float, float]] = None,
bp_bounds: Optional[Tuple[float, float]] = None,
rp_mean: float = RP_DEFAULT,
lp_mean: float = LP_DEFAULT,
bp_mean: float = BP_DEFAULT,
qA_mean: float = QA_DEFAULT,
ioh_bounds: Optional[Tuple[float, float]] = None,
psu_voltage_offset_bounds: Optional[
Dict[str, Tuple[float, float]]] = None):
# Do not allow Signeo variation as this does not work with OhmTor current
# diffusion.
no_scaling = (1, 1)
self._rp_bounds = rp_bounds if rp_bounds else no_scaling
self._lp_bounds = lp_bounds if lp_bounds else no_scaling
self._bp_bounds = bp_bounds if bp_bounds else no_scaling
self._qA_bounds = qA_bounds if qA_bounds else no_scaling
self._rp_mean = rp_mean
self._lp_mean = lp_mean
self._bp_mean = bp_mean
self._qA_mean = qA_mean
self._ioh_bounds = ioh_bounds
self._psu_voltage_offset_bounds = psu_voltage_offset_bounds
def generate(self) -> Settings:
return Settings(
signeo=(1, 1),
rp=loguniform_rv(*self._rp_bounds) * self._rp_mean,
lp=loguniform_rv(*self._lp_bounds) * self._lp_mean,
bp=loguniform_rv(*self._bp_bounds) * self._bp_mean,
qA=loguniform_rv(*self._qA_bounds) * self._qA_mean,
ioh=np.random.uniform(*self._ioh_bounds) if self._ioh_bounds else None,
psu_voltage_offset=(
{coil: np.random.uniform(*bounds)
for coil, bounds in self._psu_voltage_offset_bounds.items()}
if self._psu_voltage_offset_bounds else None))
def loguniform_rv(lower, upper):
"""Generate loguniform random variable between min and max."""
if lower == upper:
return lower
assert lower < upper
return np.exp(np.random.uniform(np.log(lower), np.log(upper)))
================================================
FILE: fusion_tcv/ref_gen.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Generators for References vector."""
import abc
import copy
from typing import List, Optional
import dataclasses
from fusion_tcv import named_array
from fusion_tcv import shape as shape_lib
from fusion_tcv import tcv_common
class AbstractReferenceGenerator(abc.ABC):
"""Abstract class for generating the reference signal."""
@abc.abstractmethod
def reset(self) -> named_array.NamedArray:
"""Resets the class for a new episode and returns the first reference."""
@abc.abstractmethod
def step(self) -> named_array.NamedArray:
"""Returns the reference signal."""
@dataclasses.dataclass
class LinearTransition:
reference: named_array.NamedArray # Reference at which to end the transition.
transition_steps: int # Number of intermediate steps between the shapes.
steady_steps: int # Number of steps in the steady state.
class LinearTransitionReferenceGenerator(AbstractReferenceGenerator):
"""A base class for generating references that are a series of transitions."""
def __init__(self, start_offset: int = 0):
self._last_ref = None
self._reset_counters()
self._start_offset = start_offset
@abc.abstractmethod
def _next_transition(self) -> LinearTransition:
"""Override this in the subclass."""
def reset(self) -> named_array.NamedArray:
self._last_ref = None
self._reset_counters()
for _ in range(self._start_offset):
self.step()
return self.step()
def _reset_counters(self):
self._steady_step = 0
self._transition_step = 0
self._transition = None
def step(self) -> named_array.NamedArray:
if (self._transition is None or
self._steady_step == self._transition.steady_steps):
if self._transition is not None:
self._last_ref = self._transition.reference
self._reset_counters()
self._transition = self._next_transition()
# Ensure at least one steady step in middle transitions.
# If we would like this to not have to be true, we need to change the
# logic below which assumes there is at least one step in the steady
# phase.
assert self._transition.steady_steps > 0
assert self._transition is not None # to make pytype happy
transition_steps = self._transition.transition_steps
if self._last_ref is None: # No transition at beginning of episode.
transition_steps = 0
if self._transition_step < transition_steps: # In transition phase.
self._transition_step += 1
a = self._transition_step / (self._transition.transition_steps + 1) # pytype: disable=attribute-error
return self._last_ref.names.named_array(
self._last_ref.array * (1 - a) + self._transition.reference.array * a) # pytype: disable=attribute-error
else: # In steady phase.
self._steady_step += 1
return copy.deepcopy(self._transition.reference)
class FixedReferenceGenerator(LinearTransitionReferenceGenerator):
"""Generates linear transitions from a fixed set of references."""
def __init__(self, transitions: List[LinearTransition],
start_offset: int = 0):
self._transitions = transitions
self._current_transition = 0
super().__init__(start_offset=start_offset)
def reset(self) -> named_array.NamedArray:
self._current_transition = 0
return super().reset()
def _next_transition(self) -> LinearTransition:
if self._current_transition == len(self._transitions):
# Have gone through all of the transitions. Return the final reference
# for a very long time.
return LinearTransition(steady_steps=50000, transition_steps=0,
reference=self._transitions[-1].reference)
self._current_transition += 1
return copy.deepcopy(self._transitions[self._current_transition - 1])
@dataclasses.dataclass
class TimedTransition:
steady_steps: int # Number of steps to hold the shape.
transition_steps: int # Number of steps to transition.
@dataclasses.dataclass
class ParametrizedShapeTimedTarget:
"""RZIP condition with a timestep attached."""
shape: shape_lib.Shape
timing: TimedTransition
class PresetShapePointsReferenceGenerator(FixedReferenceGenerator):
"""Generates a fixed set of shape points."""
def __init__(
self, targets: List[ParametrizedShapeTimedTarget], start_offset: int = 0):
if targets[0].timing.transition_steps != 0:
raise ValueError("Invalid first timing, transition must be 0, not "
f"{targets[0].timing.transition_steps}")
transitions = []
for target in targets:
transitions.append(LinearTransition(
steady_steps=target.timing.steady_steps,
transition_steps=target.timing.transition_steps,
reference=target.shape.canonical().gen_references()))
super().__init__(transitions, start_offset=start_offset)
class ShapeFromShot(PresetShapePointsReferenceGenerator):
"""Generate shapes from EPFL references."""
def __init__(
self, time_slices: List[shape_lib.ReferenceTimeSlice],
start: Optional[float] = None):
"""Given a series of time slices, start from time_slice.time==start."""
if start is None:
start = time_slices[0].time
dt = 1e-4
targets = []
time_slices = shape_lib.canonicalize_reference_series(time_slices)
prev = None
for i, ref in enumerate(time_slices):
assert prev is None or prev.hold < ref.time
if ref.time < start:
continue
if prev is None and start != ref.time:
raise ValueError("start must be one of the time slice times.")
steady = (max(1, int((ref.hold - ref.time) / dt))
if i < len(time_slices) - 1 else 100000)
transition = (0 if prev is None else
(int((ref.time - prev.time) / dt) -
max(1, int((prev.hold - prev.time) / dt))))
targets.append(ParametrizedShapeTimedTarget(
shape=ref.shape,
timing=TimedTransition(
steady_steps=steady, transition_steps=transition)))
prev = ref
assert targets
super().__init__(targets)
@dataclasses.dataclass
class RZIpTarget:
r: float
z: float
ip: float
def make_symmetric_multidomain_rzip_reference(
target: RZIpTarget) -> named_array.NamedArray:
"""Generate multi-domain rzip references."""
refs = tcv_common.REF_RANGES.new_named_array()
refs["R"] = (target.r, target.r)
refs["Z"] = (target.z, -target.z)
refs["Ip"] = (target.ip, target.ip)
return refs
================================================
FILE: fusion_tcv/references.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""References used in the experiments."""
from fusion_tcv import ref_gen
from fusion_tcv import shape
from fusion_tcv import shapes_known
from fusion_tcv import tcv_common
# pylint: disable=bad-whitespace
# Used in TCV#70915
def fundamental_capability() -> ref_gen.AbstractReferenceGenerator:
return ref_gen.ShapeFromShot([
# Start at the handover state and hold for ~50ms.
shape.ReferenceTimeSlice(
time=0.0872,
hold=0.15,
shape=shape.Shape(
ip=-110000,
params=shapes_known.SHAPE_70166_0872.params,
points=shapes_known.SHAPE_70166_0872.points,
limit_point=shapes_known.SHAPE_70166_0872.limit_point,
diverted=shape.Diverted.LIMITED,
),
),
# Ramp the Ip over 50ms then hold for 50ms.
shape.ReferenceTimeSlice(
time=0.2,
hold=0.25,
shape=shape.Shape(
ip=-150000,
params=shape.ParametrizedShape(
r0=0.8796,
z0=0.2339,
kappa=1.2441,
delta=0.2567,
radius=0.2390,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6299, 0.1413),
shape.Point( 0.6481, 0.0577),
shape.Point( 0.6804, -0.0087),
shape.Point( 0.7286, -0.0513),
shape.Point( 0.7931, -0.0660),
shape.Point( 0.8709, -0.0513),
shape.Point( 0.9543, -0.0087),
shape.Point( 1.0304, 0.0577),
shape.Point( 1.0844, 0.1413),
shape.Point( 1.1040, 0.2340),
shape.Point( 1.0844, 0.3267),
shape.Point( 1.0304, 0.4103),
shape.Point( 0.9543, 0.4767),
shape.Point( 0.8709, 0.5193),
shape.Point( 0.7931, 0.5340),
shape.Point( 0.7286, 0.5193),
shape.Point( 0.6804, 0.4767),
shape.Point( 0.6481, 0.4103),
shape.Point( 0.6299, 0.3267),
shape.Point( 0.6240, 0.2340),
],
limit_point=shape.Point( 0.6240, 0.2340),
diverted=shape.Diverted.LIMITED,
),
),
# Transform the shape to the DM handover shape and hold for 50ms.
shape.ReferenceTimeSlice(
time=0.3,
hold=0.35,
shape=shape.Shape(
ip=-150000,
params=shape.ParametrizedShape(
r0=0.8822,
z0=0.2340,
kappa=1.4396,
delta=0.2760,
radius=0.2388,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6299, 0.1265),
shape.Point( 0.6481, 0.0295),
shape.Point( 0.6804, -0.0475),
shape.Point( 0.7286, -0.0970),
shape.Point( 0.7931, -0.1140),
shape.Point( 0.8709, -0.0970),
shape.Point( 0.9543, -0.0475),
shape.Point( 1.0304, 0.0295),
shape.Point( 1.0844, 0.1265),
shape.Point( 1.1040, 0.2340),
shape.Point( 1.0844, 0.3415),
shape.Point( 1.0304, 0.4385),
shape.Point( 0.9543, 0.5155),
shape.Point( 0.8709, 0.5650),
shape.Point( 0.7931, 0.5820),
shape.Point( 0.7286, 0.5650),
shape.Point( 0.6804, 0.5155),
shape.Point( 0.6481, 0.4385),
shape.Point( 0.6299, 0.3415),
shape.Point( 0.6240, 0.2340),
],
limit_point=shape.Point( 0.6240, 0.2340),
diverted=shape.Diverted.LIMITED,
),
),
# Shift down by 20cm and hold for 50ms.
shape.ReferenceTimeSlice(
time=0.4,
hold=0.45,
shape=shape.Shape(
ip=-150000,
params=shape.ParametrizedShape(
r0=0.8822,
z0=0.0340,
kappa=1.4396,
delta=0.2760,
radius=0.2388,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6299, -0.0735),
shape.Point( 0.6481, -0.1705),
shape.Point( 0.6804, -0.2475),
shape.Point( 0.7286, -0.2970),
shape.Point( 0.7931, -0.3140),
shape.Point( 0.8709, -0.2970),
shape.Point( 0.9543, -0.2475),
shape.Point( 1.0304, -0.1705),
shape.Point( 1.0844, -0.0735),
shape.Point( 1.1040, 0.0340),
shape.Point( 1.0844, 0.1415),
shape.Point( 1.0304, 0.2385),
shape.Point( 0.9543, 0.3155),
shape.Point( 0.8709, 0.3650),
shape.Point( 0.7931, 0.3820),
shape.Point( 0.7286, 0.3650),
shape.Point( 0.6804, 0.3155),
shape.Point( 0.6481, 0.2385),
shape.Point( 0.6299, 0.1415),
shape.Point( 0.6240, 0.0340),
],
limit_point=shape.Point( 0.6240, 0.0340),
diverted=shape.Diverted.LIMITED,
),
),
# Add an X-point and allow to be ANY.
shape.ReferenceTimeSlice(
time=0.451,
shape=shape.Shape(
ip=-150000,
params=shape.ParametrizedShape(
r0=0.8822,
z0=0.0340,
kappa=1.4396,
delta=0.2760,
radius=0.2388,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6299, -0.0735),
shape.Point( 0.6481, -0.1705),
shape.Point( 0.6804, -0.2475),
shape.Point( 0.7286, -0.2970),
shape.Point( 0.7931, -0.3140),
shape.Point( 0.8709, -0.2970),
shape.Point( 0.9543, -0.2475),
shape.Point( 1.0304, -0.1705),
shape.Point( 1.0844, -0.0735),
shape.Point( 1.1040, 0.0340),
shape.Point( 1.0844, 0.1415),
shape.Point( 1.0304, 0.2385),
shape.Point( 0.9543, 0.3155),
shape.Point( 0.8709, 0.3650),
shape.Point( 0.7931, 0.3820),
shape.Point( 0.7286, 0.3650),
shape.Point( 0.6804, 0.3155),
shape.Point( 0.6481, 0.2385),
shape.Point( 0.6299, 0.1415),
shape.Point( 0.6240, 0.0340),
],
x_points=[shape.Point( 0.6240, -0.7)],
diverted=shape.Diverted.ANY,
),
),
# Make diverted and hold for 350ms.
shape.ReferenceTimeSlice(
time=0.50,
hold=0.85,
shape=shape.Shape( # based on 70519 @ 0.840
ip=-150000,
params=shape.ParametrizedShape(
r0=0.8618,
z0=0.0130,
kappa=1.5585,
delta=0.3175,
radius=0.232,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[
shape.Point(0.8286, -0.3612),
shape.Point(0.7722, -0.3807),
shape.Point(0.7512, -0.3375),
shape.Point(0.7246, -0.2900),
shape.Point(0.6995, -0.2425),
shape.Point(0.6783, -0.1950),
shape.Point(0.6627, -0.1475),
shape.Point(0.6531, -0.1000),
shape.Point(0.6469, -0.0288),
shape.Point(0.6461, 0.0425),
shape.Point(0.6509, 0.1137),
shape.Point(0.6594, 0.1612),
shape.Point(0.6738, 0.2087),
shape.Point(0.6970, 0.2562),
shape.Point(0.7327, 0.2991),
shape.Point(0.7722, 0.3243),
shape.Point(0.8117, 0.3348),
shape.Point(0.8709, 0.3290),
shape.Point(0.9104, 0.3143),
shape.Point(0.9499, 0.2912),
shape.Point(0.9893, 0.2597),
shape.Point(1.0164, 0.2325),
shape.Point(1.0486, 0.1932),
shape.Point(1.0696, 0.1612),
shape.Point(1.0938, 0.1137),
shape.Point(1.1084, 0.0662),
shape.Point(1.1078, -0.0050),
shape.Point(1.0937, -0.0525),
shape.Point(1.0721, -0.1000),
shape.Point(1.0486, -0.1416),
shape.Point(1.0288, -0.1720),
shape.Point(0.9935, -0.2187),
shape.Point(0.9696, -0.2464),
shape.Point(0.9301, -0.2856),
shape.Point(0.8961, -0.3137),
shape.Point(0.8641, -0.3375),
],
x_points=[shape.Point(0.7722, -0.3807)],
limit_point=shape.Point(0.7722, -0.3807),
diverted=shape.Diverted.DIVERTED,
),
),
# Remove the X-point and let be ANY.
shape.ReferenceTimeSlice( # based on 70519 @ 0.840
time=0.851,
shape=shape.Shape(
ip=-150000,
params=shape.ParametrizedShape(
r0=0.8618,
z0=0.0130,
kappa=1.5585,
delta=0.3175,
radius=0.232,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[
shape.Point(0.8286, -0.3612),
shape.Point(0.7722, -0.3807),
shape.Point(0.7512, -0.3375),
shape.Point(0.7246, -0.2900),
shape.Point(0.6995, -0.2425),
shape.Point(0.6783, -0.1950),
shape.Point(0.6627, -0.1475),
shape.Point(0.6531, -0.1000),
shape.Point(0.6469, -0.0288),
shape.Point(0.6461, 0.0425),
shape.Point(0.6509, 0.1137),
shape.Point(0.6594, 0.1612),
shape.Point(0.6738, 0.2087),
shape.Point(0.6970, 0.2562),
shape.Point(0.7327, 0.2991),
shape.Point(0.7722, 0.3243),
shape.Point(0.8117, 0.3348),
shape.Point(0.8709, 0.3290),
shape.Point(0.9104, 0.3143),
shape.Point(0.9499, 0.2912),
shape.Point(0.9893, 0.2597),
shape.Point(1.0164, 0.2325),
shape.Point(1.0486, 0.1932),
shape.Point(1.0696, 0.1612),
shape.Point(1.0938, 0.1137),
shape.Point(1.1084, 0.0662),
shape.Point(1.1078, -0.0050),
shape.Point(1.0937, -0.0525),
shape.Point(1.0721, -0.1000),
shape.Point(1.0486, -0.1416),
shape.Point(1.0288, -0.1720),
shape.Point(0.9935, -0.2187),
shape.Point(0.9696, -0.2464),
shape.Point(0.9301, -0.2856),
shape.Point(0.8961, -0.3137),
shape.Point(0.8641, -0.3375),
],
diverted=shape.Diverted.ANY,
),
),
# Shift back to round shape.
shape.ReferenceTimeSlice(
time=0.90,
shape=shape.Shape(
ip=-150000,
params=shapes_known.SHAPE_70166_0872.params,
points=shapes_known.SHAPE_70166_0872.points,
limit_point=shapes_known.SHAPE_70166_0872.limit_point,
diverted=shape.Diverted.LIMITED,
),
),
# Ramp the Ip down.
shape.ReferenceTimeSlice(
time=1.00,
shape=shape.Shape(
ip=-70000,
params=shapes_known.SHAPE_70166_0872.params,
points=shapes_known.SHAPE_70166_0872.points,
limit_point=shapes_known.SHAPE_70166_0872.limit_point,
diverted=shape.Diverted.LIMITED,
),
),
])
# Used in TCV#70920
def elongation() -> ref_gen.AbstractReferenceGenerator:
return ref_gen.ShapeFromShot([
shape.ReferenceTimeSlice(
time=0.45,
hold=0.475,
shape=shape.Shape(
params=shapes_known.SHAPE_70166_0450.params,
diverted=shapes_known.SHAPE_70166_0450.diverted,
limit_point=shape.Point(tcv_common.INNER_LIMITER_R, 0.25),
ip=shapes_known.SHAPE_70166_0450.ip)
),
shape.ReferenceTimeSlice(
time=0.55,
shape=shape.Shape(
params=shape.ParametrizedShape(
r0=0.875, z0=0.2, kappa=1.9, delta=0.3, radius=0.235,
lambda_=0, side=shape.ShapeSide.LEFT),
ip=-190000,
limit_point=shape.Point(tcv_common.INNER_LIMITER_R, 0.2),
diverted=shape.Diverted.LIMITED)
),
])
# Used in TCV#70457
def negative_triangularity() -> ref_gen.AbstractReferenceGenerator:
return ref_gen.ShapeFromShot([
shape.ReferenceTimeSlice(
time=0.45,
shape=shape.Shape(
ip=-120000,
params=shape.ParametrizedShape(
r0=0.89, z0=0.15, kappa=1.4, delta=-0.8, radius=0.25,
lambda_=0, side=shape.ShapeSide.LEFT),
x_points=[
shape.Point(tcv_common.OUTER_LIMITER_R - 0.03, 0.50),
shape.Point(tcv_common.OUTER_LIMITER_R - 0.03, -0.2),
],
diverted=shape.Diverted.DIVERTED,
),
),
])
# Used in TCV#70755
def snowflake() -> ref_gen.AbstractReferenceGenerator:
return ref_gen.ShapeFromShot([
shape.ReferenceTimeSlice(
time=0.0872,
shape=shape.Shape(
ip=-110000,
params=shapes_known.SHAPE_70166_0872.params,
points=shapes_known.SHAPE_70166_0872.points,
limit_point=shapes_known.SHAPE_70166_0872.limit_point,
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=0.15, # 0.2680,
shape=shape.Shape(
ip=-120000,
params=shape.ParametrizedShape(
r0=0.8713,
z0=0.0662,
kappa=1.6059,
delta=0.3814,
radius=0.2378,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6270, 0.0660),
shape.Point( 0.6290, -0.0840),
shape.Point( 0.6530, -0.2170),
shape.Point( 0.7500, -0.3114),
shape.Point( 0.9260, -0.2270),
shape.Point( 1.0530, -0.0930),
shape.Point( 1.1020, 0.0660),
shape.Point( 1.0530, 0.2250),
shape.Point( 0.9460, 0.3590),
shape.Point( 0.8100, 0.4340),
shape.Point( 0.7000, 0.4240),
shape.Point( 0.6500, 0.3490),
shape.Point( 0.6290, 0.2160),
],
x_points=[
shape.Point( 0.7000, -0.4700),
],
diverted=shape.Diverted.ANY,
),
),
shape.ReferenceTimeSlice(
time=0.2, # 0.4280,
shape=shape.Shape(
ip=-120000,
params=shape.ParametrizedShape(
r0=0.8884,
z0=0.0319,
kappa=1.6229,
delta=0.3875,
radius=0.2261,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6550, 0.0080),
shape.Point( 0.6650, -0.1260),
shape.Point( 0.6840, -0.2600),
shape.Point( 0.7600, -0.3750),
shape.Point( 0.9400, -0.2790),
shape.Point( 1.0500, -0.1450),
shape.Point( 1.1060, 0.0080),
shape.Point( 1.0760, 0.1620),
shape.Point( 0.9780, 0.2960),
shape.Point( 0.8410, 0.3530),
shape.Point( 0.7300, 0.3380),
shape.Point( 0.6750, 0.2670),
shape.Point( 0.6550, 0.1430),
],
x_points=[
shape.Point( 0.7600, -0.3750),
],
limit_point=shape.Point( 0.7600, -0.3750),
diverted=shape.Diverted.DIVERTED,
),
),
shape.ReferenceTimeSlice(
time=0.25, # 0.5000,
shape=shape.Shape(
ip=-120000,
params=shape.ParametrizedShape(
r0=0.8886,
z0=0.0044,
kappa=1.5419,
delta=0.2430,
radius=0.2137,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 14 points
shape.Point( 0.6780, -0.1200),
shape.Point( 0.7200, -0.2360),
shape.Point( 0.7840, -0.3754),
shape.Point( 0.9090, -0.3040),
shape.Point( 1.0040, -0.2110),
shape.Point( 1.0660, -0.1110),
shape.Point( 1.0950, -0.0030),
shape.Point( 1.0800, 0.1070),
shape.Point( 1.0210, 0.2070),
shape.Point( 0.9250, 0.2740),
shape.Point( 0.8090, 0.2730),
shape.Point( 0.7170, 0.2040),
shape.Point( 0.6660, 0.1010),
shape.Point( 0.6570, -0.0100),
],
x_points=[
shape.Point( 0.7840, -0.3754),
shape.Point( 0.8600, -0.7500),
],
legs=[
shape.Point( 0.8220, -0.5627),
],
limit_point=shape.Point( 0.7840, -0.3754),
diverted=shape.Diverted.DIVERTED,
),
),
shape.ReferenceTimeSlice(
time=0.4, # 0.6000,
hold=0.8,
shape=shape.Shape(
ip=-120000,
params=shape.ParametrizedShape(
r0=0.8886,
z0=0.0044,
kappa=1.5419,
delta=0.2430,
radius=0.2137,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 14 points
shape.Point( 0.6780, -0.1200),
shape.Point( 0.7200, -0.2360),
shape.Point( 0.7840, -0.3754),
shape.Point( 0.9090, -0.3040),
shape.Point( 1.0040, -0.2110),
shape.Point( 1.0660, -0.1110),
shape.Point( 1.0950, -0.0030),
shape.Point( 1.0800, 0.1070),
shape.Point( 1.0210, 0.2070),
shape.Point( 0.9250, 0.2740),
shape.Point( 0.8090, 0.2730),
shape.Point( 0.7170, 0.2040),
shape.Point( 0.6660, 0.1010),
shape.Point( 0.6570, -0.0100),
],
x_points=[
shape.Point( 0.7840, -0.3754),
shape.Point( 0.8028, -0.4153),
],
legs=[
shape.Point( 0.7934, -0.3953),
],
limit_point=shape.Point( 0.7840, -0.3754),
diverted=shape.Diverted.DIVERTED,
),
),
shape.ReferenceTimeSlice(
time=0.9, # 0.5000,
shape=shape.Shape(
ip=-120000,
params=shape.ParametrizedShape(
r0=0.8886,
z0=0.0044,
kappa=1.5419,
delta=0.2430,
radius=0.2137,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 14 points
shape.Point( 0.6780, -0.1200),
shape.Point( 0.7200, -0.2360),
shape.Point( 0.7840, -0.3754),
shape.Point( 0.9090, -0.3040),
shape.Point( 1.0040, -0.2110),
shape.Point( 1.0660, -0.1110),
shape.Point( 1.0950, -0.0030),
shape.Point( 1.0800, 0.1070),
shape.Point( 1.0210, 0.2070),
shape.Point( 0.9250, 0.2740),
shape.Point( 0.8090, 0.2730),
shape.Point( 0.7170, 0.2040),
shape.Point( 0.6660, 0.1010),
shape.Point( 0.6570, -0.0100),
],
x_points=[
shape.Point( 0.7840, -0.3754),
shape.Point( 0.8600, -0.7500),
],
legs=[
shape.Point( 0.8220, -0.5627),
],
limit_point=shape.Point( 0.7840, -0.3754),
diverted=shape.Diverted.DIVERTED,
),
),
shape.ReferenceTimeSlice(
time=0.95, # 0.4280,
shape=shape.Shape(
ip=-120000,
params=shape.ParametrizedShape(
r0=0.8884,
z0=0.0319,
kappa=1.6229,
delta=0.3875,
radius=0.2261,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6550, 0.0080),
shape.Point( 0.6650, -0.1260),
shape.Point( 0.6840, -0.2600),
shape.Point( 0.7600, -0.3750),
shape.Point( 0.9400, -0.2790),
shape.Point( 1.0500, -0.1450),
shape.Point( 1.1060, 0.0080),
shape.Point( 1.0760, 0.1620),
shape.Point( 0.9780, 0.2960),
shape.Point( 0.8410, 0.3530),
shape.Point( 0.7300, 0.3380),
shape.Point( 0.6750, 0.2670),
shape.Point( 0.6550, 0.1430),
],
x_points=[
shape.Point( 0.7600, -0.3750),
],
diverted=shape.Diverted.ANY,
),
),
shape.ReferenceTimeSlice(
time=1.0, # 0.2680,
shape=shape.Shape(
ip=-120000,
params=shape.ParametrizedShape(
r0=0.8713,
z0=0.0662,
kappa=1.6059,
delta=0.3814,
radius=0.2378,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6270, 0.0660),
shape.Point( 0.6290, -0.0840),
shape.Point( 0.6530, -0.2170),
shape.Point( 0.7500, -0.3114),
shape.Point( 0.9260, -0.2270),
shape.Point( 1.0530, -0.0930),
shape.Point( 1.1020, 0.0660),
shape.Point( 1.0530, 0.2250),
shape.Point( 0.9460, 0.3590),
shape.Point( 0.8100, 0.4340),
shape.Point( 0.7000, 0.4240),
shape.Point( 0.6500, 0.3490),
shape.Point( 0.6290, 0.2160),
],
x_points=[
shape.Point( 0.7000, -0.4700),
],
limit_point=shape.Point( 0.6240, 0.0660),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=1.05, # 0.0872,
shape=shape.Shape(
ip=-70000,
params=shape.ParametrizedShape(
r0=0.8703,
z0=0.0547,
kappa=1.2459,
delta=0.2431,
radius=0.2395,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6299, -0.0377),
shape.Point( 0.6481, -0.1213),
shape.Point( 0.6804, -0.1877),
shape.Point( 0.7286, -0.2303),
shape.Point( 0.7931, -0.2450),
shape.Point( 0.8709, -0.2303),
shape.Point( 0.9543, -0.1877),
shape.Point( 1.0304, -0.1213),
shape.Point( 1.0844, -0.0377),
shape.Point( 1.1040, 0.0550),
shape.Point( 1.0844, 0.1477),
shape.Point( 1.0304, 0.2313),
shape.Point( 0.9543, 0.2977),
shape.Point( 0.8709, 0.3403),
shape.Point( 0.7931, 0.3550),
shape.Point( 0.7286, 0.3403),
shape.Point( 0.6804, 0.2977),
shape.Point( 0.6481, 0.2313),
shape.Point( 0.6299, 0.1477),
shape.Point( 0.6240, 0.0550),
],
limit_point=shape.Point( 0.6240, 0.0550),
diverted=shape.Diverted.LIMITED,
),
),
])
# Used in TCV#70600
def iter() -> ref_gen.AbstractReferenceGenerator: # pylint: disable=redefined-builtin
return ref_gen.ShapeFromShot([
# Taken from TCV#70392.
shape.ReferenceTimeSlice(
time=0.0872,
shape=shape.Shape(
ip=-135000,
params=shape.ParametrizedShape(
r0=0.8831,
z0=0.0501,
kappa=1.2500,
delta=0.1083,
radius=0.2400,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6330, -0.0427),
shape.Point( 0.6596, -0.1263),
shape.Point( 0.7033, -0.1927),
shape.Point( 0.7623, -0.2353),
shape.Point( 0.8329, -0.2500),
shape.Point( 0.9094, -0.2353),
shape.Point( 0.9839, -0.1927),
shape.Point( 1.0468, -0.1263),
shape.Point( 1.0891, -0.0427),
shape.Point( 1.1040, 0.0500),
shape.Point( 1.0891, 0.1427),
shape.Point( 1.0468, 0.2263),
shape.Point( 0.9839, 0.2927),
shape.Point( 0.9094, 0.3353),
shape.Point( 0.8329, 0.3500),
shape.Point( 0.7623, 0.3353),
shape.Point( 0.7033, 0.2927),
shape.Point( 0.6596, 0.2263),
shape.Point( 0.6330, 0.1427),
shape.Point( 0.6240, 0.0500),
],
limit_point=shape.Point( 0.6240, 0.0500),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=0.1328,
shape=shape.Shape(
ip=-140000,
params=shape.ParametrizedShape(
r0=0.8885,
z0=0.0503,
kappa=1.4887,
delta=0.2421,
radius=0.2448,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6305, -0.0636),
shape.Point( 0.6505, -0.1660),
shape.Point( 0.6855, -0.2473),
shape.Point( 0.7366, -0.2995),
shape.Point( 0.8037, -0.3175),
shape.Point( 0.8830, -0.2995),
shape.Point( 0.9666, -0.2473),
shape.Point( 1.0420, -0.1660),
shape.Point( 1.0949, -0.0636),
shape.Point( 1.1140, 0.0500),
shape.Point( 1.0949, 0.1636),
shape.Point( 1.0420, 0.2660),
shape.Point( 0.9666, 0.3473),
shape.Point( 0.8830, 0.3995),
shape.Point( 0.8037, 0.4175),
shape.Point( 0.7366, 0.3995),
shape.Point( 0.6855, 0.3473),
shape.Point( 0.6505, 0.2660),
shape.Point( 0.6305, 0.1636),
shape.Point( 0.6240, 0.0500),
],
limit_point=shape.Point( 0.6240, 0.0500),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=0.1880,
shape=shape.Shape(
ip=-140000,
params=shape.ParametrizedShape(
r0=0.8858,
z0=0.0502,
kappa=1.4899,
delta=0.3813,
radius=0.2443,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6284, -0.0636),
shape.Point( 0.6427, -0.1660),
shape.Point( 0.6694, -0.2473),
shape.Point( 0.7122, -0.2995),
shape.Point( 0.7736, -0.3175),
shape.Point( 0.8528, -0.2995),
shape.Point( 0.9425, -0.2473),
shape.Point( 1.0282, -0.1660),
shape.Point( 1.0909, -0.0636),
shape.Point( 1.1140, 0.0500),
shape.Point( 1.0909, 0.1636),
shape.Point( 1.0282, 0.2660),
shape.Point( 0.9425, 0.3473),
shape.Point( 0.8528, 0.3995),
shape.Point( 0.7736, 0.4175),
shape.Point( 0.7122, 0.3995),
shape.Point( 0.6694, 0.3473),
shape.Point( 0.6427, 0.2660),
shape.Point( 0.6284, 0.1636),
shape.Point( 0.6240, 0.0500),
],
limit_point=shape.Point( 0.6240, 0.0500),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=0.2280,
shape=shape.Shape(
ip=-140000,
params=shape.ParametrizedShape(
r0=0.8820,
z0=0.0501,
kappa=1.4817,
delta=0.4028,
radius=0.2386,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 14 points
shape.Point( 0.6317, -0.1062),
shape.Point( 0.6589, -0.2315),
shape.Point( 0.7163, -0.3010),
shape.Point( 0.8130, -0.3010),
shape.Point( 0.9398, -0.2315),
shape.Point( 1.0559, -0.1062),
shape.Point( 1.1040, 0.0500),
shape.Point( 1.0559, 0.2062),
shape.Point( 0.9398, 0.3315),
shape.Point( 0.8130, 0.4010),
shape.Point( 0.7163, 0.4010),
shape.Point( 0.6589, 0.3315),
shape.Point( 0.6317, 0.2062),
shape.Point( 0.6240, 0.0500),
],
limit_point=shape.Point( 0.6240, 0.0500),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=0.2680,
shape=shape.Shape(
ip=-140000,
params=shape.ParametrizedShape(
r0=0.8780,
z0=0.0664,
kappa=1.6140,
delta=0.3943,
radius=0.2376,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6270, 0.0660),
shape.Point( 0.6290, -0.0840),
shape.Point( 0.6530, -0.2170),
shape.Point( 0.7500, -0.3114),
shape.Point( 0.9260, -0.2270),
shape.Point( 1.0530, -0.0930),
shape.Point( 1.1020, 0.0660),
shape.Point( 1.0530, 0.2250),
shape.Point( 0.9460, 0.3590),
shape.Point( 0.8100, 0.4340),
shape.Point( 0.7000, 0.4240),
shape.Point( 0.6500, 0.3490),
shape.Point( 0.6290, 0.2160),
],
x_points=[
shape.Point( 0.7000, -0.4700),
],
limit_point=shape.Point( 0.6240, 0.0660),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=0.3080,
shape=shape.Shape(
ip=-140000,
params=shape.ParametrizedShape(
r0=0.8670,
z0=0.0394,
kappa=1.5594,
delta=0.2588,
radius=0.2252,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6250, 0.0404),
shape.Point( 0.6250, -0.0868),
shape.Point( 0.6550, -0.2151),
shape.Point( 0.7500, -0.3244),
shape.Point( 0.9290, -0.2331),
shape.Point( 1.0460, -0.1058),
shape.Point( 1.0760, 0.0404),
shape.Point( 1.0460, 0.1867),
shape.Point( 0.9480, 0.3141),
shape.Point( 0.8110, 0.3777),
shape.Point( 0.7000, 0.3692),
shape.Point( 0.6450, 0.2960),
shape.Point( 0.6250, 0.1677),
],
x_points=[
shape.Point( 0.7200, -0.4355),
],
limit_point=shape.Point( 0.6240, 0.0404),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=0.3480,
shape=shape.Shape(
ip=-140000,
params=shape.ParametrizedShape(
r0=0.8771,
z0=0.0286,
kappa=1.6824,
delta=0.4348,
radius=0.2310,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6350, 0.0263),
shape.Point( 0.6350, -0.1045),
shape.Point( 0.6550, -0.2344),
shape.Point( 0.7400, -0.3462),
shape.Point( 0.9390, -0.2528),
shape.Point( 1.0560, -0.1229),
shape.Point( 1.0860, 0.0263),
shape.Point( 1.0560, 0.1745),
shape.Point( 0.9580, 0.3054),
shape.Point( 0.8210, 0.3703),
shape.Point( 0.7100, 0.3606),
shape.Point( 0.6550, 0.2860),
shape.Point( 0.6350, 0.1561),
],
x_points=[
shape.Point( 0.7300, -0.3943),
],
limit_point=shape.Point( 0.6240, 0.0260),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=0.3880,
shape=shape.Shape(
ip=-140000,
params=shape.ParametrizedShape(
r0=0.8878,
z0=0.0130,
kappa=1.6511,
delta=0.4385,
radius=0.2291,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6450, 0.0060),
shape.Point( 0.6500, -0.1230),
shape.Point( 0.6650, -0.2540),
shape.Point( 0.7500, -0.3660),
shape.Point( 0.9490, -0.2730),
shape.Point( 1.0660, -0.1420),
shape.Point( 1.0960, 0.0060),
shape.Point( 1.0660, 0.1560),
shape.Point( 0.9680, 0.2860),
shape.Point( 0.8310, 0.3510),
shape.Point( 0.7200, 0.3430),
shape.Point( 0.6650, 0.2680),
shape.Point( 0.6450, 0.1370),
],
x_points=[
shape.Point( 0.7400, -0.3960),
],
diverted=shape.Diverted.ANY,
),
),
shape.ReferenceTimeSlice(
time=0.5000,
shape=shape.Shape(
ip=-175000,
params=shape.ParametrizedShape(
r0=0.9069,
z0=0.0099,
kappa=1.6399,
delta=0.4400,
radius=0.2255,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6690, -0.1340),
shape.Point( 0.6880, -0.2690),
shape.Point( 0.7377, -0.3840),
shape.Point( 0.8150, -0.3650),
shape.Point( 0.9600, -0.2880),
shape.Point( 1.0760, -0.1540),
shape.Point( 1.1060, 0.0000),
shape.Point( 1.0760, 0.1540),
shape.Point( 0.9790, 0.2880),
shape.Point( 0.8440, 0.3550),
shape.Point( 0.6890, 0.2690),
shape.Point( 0.6600, 0.1340),
shape.Point( 0.6600, 0.0000),
],
x_points=[
shape.Point( 0.7377, -0.3840),
],
legs=[
shape.Point( 0.7930, -0.5700),
],
limit_point=shape.Point( 0.7377, -0.3840),
diverted=shape.Diverted.DIVERTED,
),
),
shape.ReferenceTimeSlice(
time=0.6200,
shape=shape.Shape(
ip=-200000,
params=shape.ParametrizedShape(
r0=0.9044,
z0=0.0142,
kappa=1.7484,
delta=0.4440,
radius=0.2257,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6690, -0.1490),
shape.Point( 0.7080, -0.2840), # concave: 181.6 degrees
shape.Point( 0.7377, -0.3990),
shape.Point( 0.8150, -0.3800),
shape.Point( 0.9600, -0.3030),
shape.Point( 1.0760, -0.1690),
shape.Point( 1.1060, -0.0066),
shape.Point( 1.0760, 0.1659),
shape.Point( 0.9790, 0.3160),
shape.Point( 0.8440, 0.3910),
shape.Point( 0.6890, 0.2947),
shape.Point( 0.6600, 0.1435),
shape.Point( 0.6600, -0.0066),
],
x_points=[
shape.Point( 0.7377, -0.3990),
],
legs=[
shape.Point( 0.7930, -0.5850),
],
limit_point=shape.Point( 0.7377, -0.3990),
diverted=shape.Diverted.DIVERTED,
),
),
shape.ReferenceTimeSlice(
time=0.9000,
shape=shape.Shape(
ip=-240000,
params=shape.ParametrizedShape(
r0=0.9010,
z0=0.0152,
kappa=1.7482,
delta=0.4427,
radius=0.2257,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6690, -0.1490),
shape.Point( 0.7080, -0.2840), # concave: 181.6 degrees
shape.Point( 0.7377, -0.3990),
shape.Point( 0.8150, -0.3800),
shape.Point( 0.9600, -0.3030),
shape.Point( 1.0760, -0.1690),
shape.Point( 1.1060, -0.0066),
shape.Point( 1.0760, 0.1659),
shape.Point( 0.9790, 0.3160),
shape.Point( 0.8440, 0.3910),
shape.Point( 0.6890, 0.2947),
shape.Point( 0.6600, 0.1435),
shape.Point( 0.6600, -0.0066),
],
x_points=[
shape.Point( 0.7377, -0.3990),
],
legs=[
shape.Point( 0.7930, -0.5850),
],
limit_point=shape.Point( 0.7377, -0.3990),
diverted=shape.Diverted.DIVERTED,
),
),
shape.ReferenceTimeSlice(
time=0.9500,
shape=shape.Shape(
ip=-245000,
params=shape.ParametrizedShape(
r0=0.8985,
z0=0.0173,
kappa=1.7213,
delta=0.4905,
radius=0.2261,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 14 points
shape.Point( 0.6690, -0.1490),
shape.Point( 0.7200, -0.2840),
shape.Point( 0.7600, -0.3890),
shape.Point( 0.8500, -0.3700),
shape.Point( 0.9600, -0.2850),
shape.Point( 1.0760, -0.1590),
shape.Point( 1.1060, 0.0030),
shape.Point( 1.0660, 0.1760),
shape.Point( 1.0000, 0.2650),
shape.Point( 0.9200, 0.3360),
shape.Point( 0.7500, 0.3850),
shape.Point( 0.6650, 0.2850),
shape.Point( 0.6600, 0.1430),
shape.Point( 0.6600, -0.0070),
],
x_points=[
shape.Point( 0.7600, -0.3890),
],
legs=[
shape.Point( 0.8430, -0.5850),
],
limit_point=shape.Point( 0.7600, -0.3890),
diverted=shape.Diverted.DIVERTED,
),
),
shape.ReferenceTimeSlice(
time=1.1500,
shape=shape.Shape(
ip=-260000,
params=shape.ParametrizedShape(
r0=0.8970,
z0=0.0180,
kappa=1.7142,
delta=0.4877,
radius=0.2262,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 14 points
shape.Point( 0.6690, -0.1490),
shape.Point( 0.7200, -0.2840),
shape.Point( 0.7600, -0.3890),
shape.Point( 0.8500, -0.3700),
shape.Point( 0.9600, -0.2850),
shape.Point( 1.0760, -0.1590),
shape.Point( 1.1060, 0.0030),
shape.Point( 1.0660, 0.1760),
shape.Point( 1.0000, 0.2650),
shape.Point( 0.9200, 0.3360),
shape.Point( 0.7500, 0.3850),
shape.Point( 0.6650, 0.2850),
shape.Point( 0.6600, 0.1430),
shape.Point( 0.6600, -0.0070),
],
x_points=[
shape.Point( 0.7600, -0.3890),
],
legs=[
shape.Point( 0.8430, -0.5850),
],
limit_point=shape.Point( 0.7600, -0.3890),
diverted=shape.Diverted.DIVERTED,
),
),
shape.ReferenceTimeSlice(
time=1.4500,
shape=shape.Shape(
ip=-280000,
params=shape.ParametrizedShape(
r0=0.8955,
z0=0.0186,
kappa=1.7143,
delta=0.4841,
radius=0.2262,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 14 points
shape.Point( 0.6690, -0.1490),
shape.Point( 0.7200, -0.2840),
shape.Point( 0.7600, -0.3890),
shape.Point( 0.8500, -0.3700),
shape.Point( 0.9600, -0.2850),
shape.Point( 1.0760, -0.1590),
shape.Point( 1.1060, 0.0030),
shape.Point( 1.0660, 0.1760),
shape.Point( 1.0000, 0.2650),
shape.Point( 0.9200, 0.3360),
shape.Point( 0.7500, 0.3850),
shape.Point( 0.6650, 0.2850),
shape.Point( 0.6600, 0.1430),
shape.Point( 0.6600, -0.0070),
],
x_points=[
shape.Point( 0.7600, -0.3890),
],
legs=[
shape.Point( 0.8430, -0.5850),
],
limit_point=shape.Point( 0.7600, -0.3890),
diverted=shape.Diverted.DIVERTED,
),
),
shape.ReferenceTimeSlice(
time=1.7000,
shape=shape.Shape(
ip=-280000,
params=shape.ParametrizedShape(
r0=0.8955,
z0=0.0186,
kappa=1.7143,
delta=0.4841,
radius=0.2262,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 14 points
shape.Point( 0.6690, -0.1490),
shape.Point( 0.7200, -0.2840),
shape.Point( 0.7600, -0.3890),
shape.Point( 0.8500, -0.3700),
shape.Point( 0.9600, -0.2850),
shape.Point( 1.0760, -0.1590),
shape.Point( 1.1060, 0.0030),
shape.Point( 1.0660, 0.1760),
shape.Point( 1.0000, 0.2650),
shape.Point( 0.9200, 0.3360),
shape.Point( 0.7500, 0.3850),
shape.Point( 0.6650, 0.2850),
shape.Point( 0.6600, 0.1430),
shape.Point( 0.6600, -0.0070),
],
x_points=[
shape.Point( 0.7600, -0.3890),
],
legs=[
shape.Point( 0.8430, -0.5850),
],
limit_point=shape.Point( 0.7600, -0.3890),
diverted=shape.Diverted.DIVERTED,
),
),
shape.ReferenceTimeSlice(
time=1.7600,
shape=shape.Shape(
ip=-140000,
params=shape.ParametrizedShape(
r0=0.8873,
z0=0.0281,
kappa=1.6534,
delta=0.4388,
radius=0.2288,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6450, 0.0210),
shape.Point( 0.6500, -0.1080),
shape.Point( 0.6650, -0.2390),
shape.Point( 0.7500, -0.3510),
shape.Point( 0.9490, -0.2580),
shape.Point( 1.0660, -0.1270),
shape.Point( 1.0960, 0.0210),
shape.Point( 1.0660, 0.1710),
shape.Point( 0.9680, 0.3010),
shape.Point( 0.8310, 0.3660),
shape.Point( 0.7200, 0.3580),
shape.Point( 0.6650, 0.2830),
shape.Point( 0.6450, 0.1520),
],
x_points=[
shape.Point( 0.7400, -0.3810),
],
diverted=shape.Diverted.ANY,
),
),
shape.ReferenceTimeSlice(
time=1.7800,
shape=shape.Shape(
ip=-140000,
params=shape.ParametrizedShape(
r0=0.8770,
z0=0.0436,
kappa=1.6840,
delta=0.4370,
radius=0.2306,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6350, 0.0413),
shape.Point( 0.6350, -0.0895),
shape.Point( 0.6550, -0.2194),
shape.Point( 0.7400, -0.3312),
shape.Point( 0.9390, -0.2378),
shape.Point( 1.0560, -0.1079),
shape.Point( 1.0860, 0.0413),
shape.Point( 1.0560, 0.1895),
shape.Point( 0.9580, 0.3204),
shape.Point( 0.8210, 0.3853),
shape.Point( 0.7100, 0.3756),
shape.Point( 0.6550, 0.3010),
shape.Point( 0.6350, 0.1711),
],
x_points=[
shape.Point( 0.7300, -0.3793),
],
limit_point=shape.Point( 0.6240, 0.0410),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=1.8000,
shape=shape.Shape(
ip=-140000,
params=shape.ParametrizedShape(
r0=0.8784,
z0=0.0865,
kappa=1.6166,
delta=0.3958,
radius=0.2378,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 13 points
shape.Point( 0.6270, 0.0860),
shape.Point( 0.6290, -0.0640),
shape.Point( 0.6530, -0.1970),
shape.Point( 0.7500, -0.2914),
shape.Point( 0.9260, -0.2070),
shape.Point( 1.0530, -0.0730),
shape.Point( 1.1020, 0.0860),
shape.Point( 1.0530, 0.2450),
shape.Point( 0.9460, 0.3790),
shape.Point( 0.8100, 0.4540),
shape.Point( 0.7000, 0.4440),
shape.Point( 0.6500, 0.3690),
shape.Point( 0.6290, 0.2360),
],
x_points=[
shape.Point( 0.7000, -0.4500),
],
limit_point=shape.Point( 0.6240, 0.0860),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=1.8100,
shape=shape.Shape(
ip=-120000,
params=shape.ParametrizedShape(
r0=0.8865,
z0=0.0501,
kappa=1.3991,
delta=0.1188,
radius=0.2402,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6330, -0.0538),
shape.Point( 0.6596, -0.1475),
shape.Point( 0.7033, -0.2218),
shape.Point( 0.7623, -0.2696),
shape.Point( 0.8329, -0.2860),
shape.Point( 0.9094, -0.2696),
shape.Point( 0.9839, -0.2218),
shape.Point( 1.0468, -0.1475),
shape.Point( 1.0891, -0.0538),
shape.Point( 1.1040, 0.0500),
shape.Point( 1.0891, 0.1538),
shape.Point( 1.0468, 0.2475),
shape.Point( 0.9839, 0.3218),
shape.Point( 0.9094, 0.3696),
shape.Point( 0.8329, 0.3860),
shape.Point( 0.7623, 0.3696),
shape.Point( 0.7033, 0.3218),
shape.Point( 0.6596, 0.2475),
shape.Point( 0.6330, 0.1538),
shape.Point( 0.6240, 0.0500),
],
limit_point=shape.Point( 0.6240, 0.0500),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=1.8200,
shape=shape.Shape(
ip=-94000,
params=shape.ParametrizedShape(
r0=0.8523,
z0=0.0500,
kappa=1.0904,
delta=0.0128,
radius=0.2106,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6339, -0.0207),
shape.Point( 0.6627, -0.0845),
shape.Point( 0.7078, -0.1352),
shape.Point( 0.7653, -0.1677),
shape.Point( 0.8298, -0.1789),
shape.Point( 0.8951, -0.1677),
shape.Point( 0.9547, -0.1352),
shape.Point( 1.0024, -0.0845),
shape.Point( 1.0333, -0.0207),
shape.Point( 1.0440, 0.0500),
shape.Point( 1.0333, 0.1207),
shape.Point( 1.0024, 0.1845),
shape.Point( 0.9547, 0.2352),
shape.Point( 0.8951, 0.2677),
shape.Point( 0.8298, 0.2789),
shape.Point( 0.7653, 0.2677),
shape.Point( 0.7078, 0.2352),
shape.Point( 0.6627, 0.1845),
shape.Point( 0.6339, 0.1207),
shape.Point( 0.6240, 0.0500),
],
limit_point=shape.Point( 0.6240, 0.0500),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=1.8300,
shape=shape.Shape(
ip=-53000,
params=shape.ParametrizedShape(
r0=0.8048,
z0=0.0501,
kappa=1.0631,
delta=0.0036,
radius=0.1680,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6322, -0.0050),
shape.Point( 0.6559, -0.0545),
shape.Point( 0.6928, -0.0939),
shape.Point( 0.7394, -0.1192),
shape.Point( 0.7910, -0.1279),
shape.Point( 0.8426, -0.1192),
shape.Point( 0.8892, -0.0939),
shape.Point( 0.9261, -0.0545),
shape.Point( 0.9498, -0.0050),
shape.Point( 0.9580, 0.0500),
shape.Point( 0.9498, 0.1050),
shape.Point( 0.9261, 0.1545),
shape.Point( 0.8892, 0.1939),
shape.Point( 0.8426, 0.2192),
shape.Point( 0.7910, 0.2279),
shape.Point( 0.7394, 0.2192),
shape.Point( 0.6928, 0.1939),
shape.Point( 0.6559, 0.1545),
shape.Point( 0.6322, 0.1050),
shape.Point( 0.6240, 0.0500),
],
limit_point=shape.Point( 0.6240, 0.0500),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=1.8400,
shape=shape.Shape(
ip=-22000,
params=shape.ParametrizedShape(
r0=0.7484,
z0=0.0489,
kappa=1.0666,
delta=0.0258,
radius=0.1176,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6297, 0.0109),
shape.Point( 0.6462, -0.0243),
shape.Point( 0.6718, -0.0523),
shape.Point( 0.7042, -0.0703),
shape.Point( 0.7400, -0.0764),
shape.Point( 0.7758, -0.0703),
shape.Point( 0.8082, -0.0523),
shape.Point( 0.8338, -0.0243),
shape.Point( 0.8503, 0.0109),
shape.Point( 0.8560, 0.0500),
shape.Point( 0.8503, 0.0891),
shape.Point( 0.8338, 0.1243),
shape.Point( 0.8082, 0.1523),
shape.Point( 0.7758, 0.1703),
shape.Point( 0.7400, 0.1764),
shape.Point( 0.7042, 0.1703),
shape.Point( 0.6718, 0.1523),
shape.Point( 0.6462, 0.1243),
shape.Point( 0.6297, 0.0891),
shape.Point( 0.6240, 0.0500),
],
limit_point=shape.Point( 0.6240, 0.0500),
diverted=shape.Diverted.LIMITED,
),
),
shape.ReferenceTimeSlice(
time=1.8500,
shape=shape.Shape(
ip=-12000,
params=shape.ParametrizedShape(
r0=0.7149,
z0=0.0494,
kappa=1.0830,
delta=0.0382,
radius=0.0871,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point( 0.6282, 0.0198),
shape.Point( 0.6402, -0.0075),
shape.Point( 0.6590, -0.0291),
shape.Point( 0.6827, -0.0430),
shape.Point( 0.7090, -0.0477),
shape.Point( 0.7353, -0.0430),
shape.Point( 0.7590, -0.0291),
shape.Point( 0.7778, -0.0075),
shape.Point( 0.7898, 0.0198),
shape.Point( 0.7940, 0.0500),
shape.Point( 0.7898, 0.0802),
shape.Point( 0.7778, 0.1075),
shape.Point( 0.7590, 0.1291),
shape.Point( 0.7353, 0.1430),
shape.Point( 0.7090, 0.1478),
shape.Point( 0.6827, 0.1430),
shape.Point( 0.6590, 0.1291),
shape.Point( 0.6402, 0.1075),
shape.Point( 0.6282, 0.0802),
shape.Point( 0.6240, 0.0500),
],
limit_point=shape.Point( 0.6240, 0.0500),
diverted=shape.Diverted.LIMITED,
),
),
])
# Used in TCV#69545
def droplet() -> ref_gen.AbstractReferenceGenerator:
"""Hold the droplet at handover for TCV#69198 then ramp the Ip."""
init = ref_gen.RZIpTarget(r=0.875, z=0.55, ip=-72500)
final = ref_gen.RZIpTarget(r=0.875, z=0.47, ip=-110000)
return ref_gen.FixedReferenceGenerator([
ref_gen.LinearTransition(
transition_steps=0,
steady_steps=350, # Intentionally short. Mostly want to ramp.
reference=ref_gen.make_symmetric_multidomain_rzip_reference(init)),
ref_gen.LinearTransition(
transition_steps=400,
steady_steps=100000, # Hold forever.
reference=ref_gen.make_symmetric_multidomain_rzip_reference(final)),
])
_REFERENCES = [
fundamental_capability,
elongation,
iter,
negative_triangularity,
snowflake,
droplet,
]
REFERENCES = {f.__name__: f for f in _REFERENCES}
================================================
FILE: fusion_tcv/references_main.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Print the values of references."""
from typing import Sequence
from absl import app
from absl import flags
from fusion_tcv import named_array
from fusion_tcv import references
from fusion_tcv import tcv_common
_refs = flags.DEFINE_enum("refs", None, references.REFERENCES.keys(),
"Which references to print")
_count = flags.DEFINE_integer("count", 100, "How many timesteps to print.")
_freq = flags.DEFINE_integer("freq", 1, "Print only every so often.")
_fields = flags.DEFINE_multi_enum(
"field", None, tcv_common.REF_RANGES.names(),
"Which reference fields to print, default of all.")
flags.mark_flag_as_required("refs")
def print_ref(step: int, ref: named_array.NamedArray):
print(f"Step: {step}")
for k in (_fields.value or ref.names.names()):
print(f" {k}: [{', '.join(f'{v:.3f}' for v in ref[k])}]")
def main(argv: Sequence[str]) -> None:
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
if _freq.value <= 0:
raise app.UsageError("`freq` must be >0.")
ref = references.REFERENCES[_refs.value]()
print_ref(0, ref.reset())
for i in range(1, _count.value + 1):
for _ in range(_freq.value - 1):
ref.step()
print_ref(i * _freq.value, ref.step())
print(f"Stopped after {_count.value * _freq.value} steps.")
if __name__ == "__main__":
app.run(main)
================================================
FILE: fusion_tcv/requirements.txt
================================================
absl-py>=0.13.0
dm-env>=1.5
numpy>=1.21.0
scipy>=1.7.0
================================================
FILE: fusion_tcv/rewards.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Reward function for the fusion environment."""
import abc
import collections
import functools
from typing import Callable, Dict, List, Optional, Text, Tuple, Union
from absl import logging
import dataclasses
import numpy as np
from fusion_tcv import combiners
from fusion_tcv import fge_state
from fusion_tcv import named_array
from fusion_tcv import targets as targets_lib
from fusion_tcv import transforms
class AbstractMeasure(abc.ABC):
@abc.abstractmethod
def __call__(self, targets: List[targets_lib.Target]) -> List[float]:
"""Returns a list of error measures."""
class AbsDist(AbstractMeasure):
"""Return the absolute distance between the actual and target."""
@staticmethod
def __call__(targets: List[targets_lib.Target]) -> List[float]:
return [abs(t.actual - t.target) for t in targets]
@dataclasses.dataclass(frozen=True)
class MeasureDetails:
min: float
mean: float
max: float
@dataclasses.dataclass
class RewardDetails:
reward: float # 0-1 reward value.
weighted: float # Should sum to < 0-1.
weight: float
measure: Optional[MeasureDetails] = None
class AbstractReward(abc.ABC):
"""Abstract reward class."""
@abc.abstractmethod
def reward(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray,
) -> Tuple[float, Dict[Text, List[RewardDetails]]]:
"""Returns the reward and log dict as a function of the penalty term."""
@abc.abstractmethod
def terminal_reward(self) -> float:
"""Returns the reward if the simulator crashed."""
WeightFn = Callable[[named_array.NamedArray], float]
WeightOrFn = Union[float, WeightFn]
@dataclasses.dataclass
class Component:
target: targets_lib.AbstractTarget
transforms: List[transforms.AbstractTransform]
measure: AbstractMeasure = dataclasses.field(default_factory=AbsDist)
weight: Union[WeightOrFn, List[WeightOrFn]] = 1
name: Optional[str] = None
class Reward(AbstractReward):
"""Combines a bunch of reward components into a single reward.
The component parts are applied in the order: target, measure, transform.
- Targets represent some error value as one or more pair of values
(target, actual), usually with some meaningful physical unit (eg distance,
volts, etc).
- Measures combine the (target, actual) into a single float, for example
absolute distance, for each error value.
- Transforms can make arbitrary conversions, but one of them must change from
the arbitrary (often meaningful) scale to a reward in the 0-1 range.
- Combiners are a special type of transform that reduces a vector of values
down to a single value. The combiner can be skipped if the target only
outputs a single value, or if you want a vector of outputs for the final
combiner.
- The component weights are passed to the final combiner, and must match the
number of outputs for that component.
"""
def __init__(self,
components: List[Component],
combiner: combiners.AbstractCombiner,
terminal_reward: float = -5,
reward_scale: float = 0.01):
self._components = components
self._combiner = combiner
self._terminal_reward = terminal_reward
self._reward_scale = reward_scale
self._weights = []
component_count = collections.Counter()
for component in self._components:
num_outputs = component.target.outputs
for transform in component.transforms:
if transform.outputs is not None:
num_outputs = transform.outputs
if not isinstance(component.weight, list):
component.weight = [component.weight]
if len(component.weight) != num_outputs:
name = component.name or component.target.name
raise ValueError(f"Wrong number of weights for '{name}': got:"
f" {len(component.weight)}, expected: {num_outputs}")
self._weights.extend(component.weight)
def terminal_reward(self) -> float:
return self._terminal_reward * self._reward_scale
def reward(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray,
) -> Tuple[float, Dict[Text, List[RewardDetails]]]:
values = []
weights = [weight(references) if callable(weight) else weight
for weight in self._weights]
reward_dict = collections.defaultdict(list)
for component in self._components:
name = component.name or component.target.name
num_outputs = len(component.weight)
component_weights = weights[len(values):(len(values) + num_outputs)]
try:
target = component.target(voltages, state, references)
except targets_lib.TargetError:
logging.exception("Target failed.")
# Failed turns into minimum reward.
measure = [987654321] * num_outputs
transformed = [0] * num_outputs
else:
measure = component.measure(target)
transformed = functools.reduce(
(lambda e, fn: fn(e)), component.transforms, measure)
assert len(transformed) == num_outputs
for v in transformed:
if not np.isnan(v) and not 0 <= v <= 1:
raise ValueError(f"The transformed value in {name} is invalid: {v}")
values.extend(transformed)
for weight, value in zip(component_weights, transformed):
measure = [m for m in measure if not np.isnan(m)] or [float("nan")]
reward_dict[name].append(RewardDetails(
value, weight * value * self._reward_scale,
weight if not np.isnan(value) else 0,
MeasureDetails(
min(measure), sum(measure) / len(measure), max(measure))))
sum_weights = sum(sum(d.weight for d in detail)
for detail in reward_dict.values())
for reward_details in reward_dict.values():
for detail in reward_details:
detail.weighted /= sum_weights
final_combined = self._combiner(values, weights)
assert len(final_combined) == 1
return final_combined[0] * self._reward_scale, reward_dict
================================================
FILE: fusion_tcv/rewards_used.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""The rewards used in our experiments."""
from fusion_tcv import combiners
from fusion_tcv import rewards
from fusion_tcv import targets
from fusion_tcv import transforms
# Used in TCV#70915
FUNDAMENTAL_CAPABILITY = rewards.Reward([
rewards.Component(
target=targets.ShapeLCFSDistance(),
transforms=[transforms.SoftPlus(good=0.005, bad=0.05),
combiners.SmoothMax(-1)]),
rewards.Component(
target=targets.XPointFar(),
transforms=[transforms.Sigmoid(good=0.3, bad=0.1),
combiners.SmoothMax(-5)]),
rewards.Component(
target=targets.LimitPoint(),
transforms=[transforms.Sigmoid(bad=0.2, good=0.1)]),
rewards.Component(
target=targets.XPointNormalizedFlux(num_points=1),
transforms=[transforms.SoftPlus(bad=0.08)]),
rewards.Component(
target=targets.XPointDistance(num_points=1),
transforms=[transforms.Sigmoid(good=0.01, bad=0.15)]),
rewards.Component(
target=targets.XPointFluxGradient(num_points=1),
transforms=[transforms.SoftPlus(bad=3)],
weight=0.5),
rewards.Component(
target=targets.Ip(),
transforms=[transforms.SoftPlus(good=500, bad=20000)]),
rewards.Component(
target=targets.OHCurrentsClose(),
transforms=[transforms.SoftPlus(good=50, bad=1050)]),
], combiners.SmoothMax(-0.5))
# Used in TCV#70920
ELONGATION = rewards.Reward([
rewards.Component(
target=targets.ShapeLCFSDistance(),
transforms=[transforms.SoftPlus(good=0.003, bad=0.03),
combiners.SmoothMax(-1)],
weight=3),
rewards.Component(
target=targets.ShapeRadius(),
transforms=[transforms.SoftPlus(good=0.002, bad=0.02)]),
rewards.Component(
target=targets.ShapeElongation(),
transforms=[transforms.SoftPlus(good=0.005, bad=0.2)]),
rewards.Component(
target=targets.ShapeTriangularity(),
transforms=[transforms.SoftPlus(good=0.005, bad=0.2)]),
rewards.Component(
target=targets.XPointCount(),
transforms=[transforms.Equal()]),
rewards.Component(
target=targets.LimitPoint(), # Stay away from the top/baffles.
transforms=[transforms.Sigmoid(bad=0.3, good=0.2)]),
rewards.Component(
target=targets.Ip(),
transforms=[transforms.SoftPlus(good=500, bad=30000)]),
rewards.Component(
target=targets.VoltageOOB(),
transforms=[combiners.Mean(), transforms.SoftPlus(bad=1)]),
rewards.Component(
target=targets.OHCurrentsClose(),
transforms=[transforms.ClippedLinear(good=50, bad=1050)]),
rewards.Component(
name="CurrentsFarFromZero",
target=targets.EFCurrents(),
transforms=[transforms.Abs(),
transforms.SoftPlus(good=100, bad=50),
combiners.GeometricMean()]),
], combiner=combiners.SmoothMax(-5))
# Used in TCV#70600
ITER = rewards.Reward([
rewards.Component(
target=targets.ShapeLCFSDistance(),
transforms=[transforms.SoftPlus(good=0.005, bad=0.05),
combiners.SmoothMax(-1)],
weight=3),
rewards.Component(
target=targets.Diverted(),
transforms=[transforms.Equal()]),
rewards.Component(
target=targets.XPointNormalizedFlux(num_points=2),
transforms=[transforms.SoftPlus(bad=0.08)],
weight=[1] * 2),
rewards.Component(
target=targets.XPointDistance(num_points=2),
transforms=[transforms.Sigmoid(good=0.01, bad=0.15)],
weight=[0.5] * 2),
rewards.Component(
target=targets.XPointFluxGradient(num_points=2),
transforms=[transforms.SoftPlus(bad=3)],
weight=[0.5] * 2),
rewards.Component(
target=targets.LegsNormalizedFlux(),
transforms=[transforms.Sigmoid(good=0.1, bad=0.3),
combiners.SmoothMax(-5)],
weight=2),
rewards.Component(
target=targets.Ip(),
transforms=[transforms.SoftPlus(good=500, bad=20000)],
weight=2),
rewards.Component(
target=targets.VoltageOOB(),
transforms=[combiners.Mean(), transforms.SoftPlus(bad=1)]),
rewards.Component(
target=targets.OHCurrentsClose(),
transforms=[transforms.ClippedLinear(good=50, bad=1050)]),
rewards.Component(
name="CurrentsFarFromZero",
target=targets.EFCurrents(),
transforms=[transforms.Abs(),
transforms.SoftPlus(good=100, bad=50),
combiners.GeometricMean()]),
], combiner=combiners.SmoothMax(-5))
# Used in TCV#70755
SNOWFLAKE = rewards.Reward([
rewards.Component(
target=targets.ShapeLCFSDistance(),
transforms=[transforms.SoftPlus(good=0.005, bad=0.05),
combiners.SmoothMax(-1)],
weight=3),
rewards.Component(
target=targets.LimitPoint(),
transforms=[transforms.Sigmoid(bad=0.2, good=0.1)]),
rewards.Component(
target=targets.XPointNormalizedFlux(num_points=2),
transforms=[transforms.SoftPlus(bad=0.08)],
weight=[1] * 2),
rewards.Component(
target=targets.XPointDistance(num_points=2),
transforms=[transforms.Sigmoid(good=0.01, bad=0.15)],
weight=[0.5] * 2),
rewards.Component(
target=targets.XPointFluxGradient(num_points=2),
transforms=[transforms.SoftPlus(bad=3)],
weight=[0.5] * 2),
rewards.Component(
target=targets.LegsNormalizedFlux(),
transforms=[transforms.Sigmoid(good=0.1, bad=0.3),
combiners.SmoothMax(-5)],
weight=2),
rewards.Component(
target=targets.Ip(),
transforms=[transforms.SoftPlus(good=500, bad=20000)],
weight=2),
rewards.Component(
target=targets.VoltageOOB(),
transforms=[combiners.Mean(), transforms.SoftPlus(bad=1)]),
rewards.Component(
target=targets.OHCurrentsClose(),
transforms=[transforms.ClippedLinear(good=50, bad=1050)]),
rewards.Component(
name="CurrentsFarFromZero",
target=targets.EFCurrents(),
transforms=[transforms.Abs(),
transforms.SoftPlus(good=100, bad=50),
combiners.GeometricMean()]),
], combiner=combiners.SmoothMax(-5))
# Used in TCV#70457
NEGATIVE_TRIANGULARITY = rewards.Reward([
rewards.Component(
target=targets.ShapeLCFSDistance(),
transforms=[transforms.SoftPlus(good=0.005, bad=0.05),
combiners.SmoothMax(-1)],
weight=3),
rewards.Component(
target=targets.ShapeRadius(),
transforms=[transforms.SoftPlus(bad=0.04)]),
rewards.Component(
target=targets.ShapeElongation(),
transforms=[transforms.SoftPlus(bad=0.5)]),
rewards.Component(
target=targets.ShapeTriangularity(),
transforms=[transforms.SoftPlus(bad=0.5)]),
rewards.Component(
target=targets.Diverted(),
transforms=[transforms.Equal()]),
rewards.Component(
target=targets.XPointNormalizedFlux(num_points=2),
transforms=[transforms.SoftPlus(bad=0.08)],
weight=[1] * 2),
rewards.Component(
target=targets.XPointDistance(num_points=2),
transforms=[transforms.Sigmoid(good=0.02, bad=0.15)],
weight=[0.5] * 2),
rewards.Component(
target=targets.XPointFluxGradient(num_points=2),
transforms=[transforms.SoftPlus(bad=3)],
weight=[0.5] * 2),
rewards.Component(
target=targets.Ip(),
transforms=[transforms.SoftPlus(good=500, bad=20000)],
weight=2),
rewards.Component(
target=targets.VoltageOOB(),
transforms=[combiners.Mean(), transforms.SoftPlus(bad=1)]),
rewards.Component(
target=targets.OHCurrentsClose(),
transforms=[transforms.ClippedLinear(good=50, bad=1050)]),
rewards.Component(
name="CurrentsFarFromZero",
target=targets.EFCurrents(),
transforms=[transforms.Abs(),
transforms.SoftPlus(good=100, bad=50),
combiners.GeometricMean()]),
], combiner=combiners.SmoothMax(-0.5))
# Used in TCV#69545
DROPLETS = rewards.Reward([
rewards.Component(
target=targets.R(indices=[0, 1]),
transforms=[transforms.Sigmoid(good=0.02, bad=0.5)],
weight=[1, 1]),
rewards.Component(
target=targets.Z(indices=[0, 1]),
transforms=[transforms.Sigmoid(good=0.02, bad=0.2)],
weight=[1, 1]),
rewards.Component(
target=targets.Ip(indices=[0, 1]),
transforms=[transforms.Sigmoid(good=2000, bad=20000)],
weight=[1, 1]),
rewards.Component(
target=targets.OHCurrentsClose(),
transforms=[transforms.ClippedLinear(good=50, bad=1050)]),
], combiner=combiners.GeometricMean())
================================================
FILE: fusion_tcv/run_loop.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Run an agent on the environment."""
import numpy as np
from fusion_tcv import environment
from fusion_tcv import trajectory
def run_loop(env: environment.Environment, agent,
max_steps: int = 100000) -> trajectory.Trajectory:
"""Run an agent."""
results = []
agent.reset()
ts = env.reset()
for _ in range(max_steps):
obs = ts.observation
action = agent.step(ts)
ts = env.step(action)
results.append(trajectory.Trajectory(
measurements=obs["measurements"],
references=obs["references"],
actions=action,
reward=np.array(ts.reward)))
if ts.last():
break
return trajectory.Trajectory.stack(results)
================================================
FILE: fusion_tcv/shape.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities for time varying shape control."""
import copy
import enum
import random
from typing import List, Optional, NamedTuple, Tuple, Union
import dataclasses
import numpy as np
from scipy import interpolate
from fusion_tcv import named_array
from fusion_tcv import tcv_common
class Point(NamedTuple):
"""A point in r,z coordinates."""
r: float
z: float
def to_polar(self) -> "PolarPoint":
return PolarPoint(np.arctan2(self.z, self.r),
np.sqrt(self.r**2 + self.z**2))
def __neg__(self):
return Point(-self.r, -self.z)
def __add__(self, pt_or_val: Union["Point", float]):
if isinstance(pt_or_val, Point):
return Point(self.r + pt_or_val.r, self.z + pt_or_val.z)
else:
return Point(self.r + pt_or_val, self.z + pt_or_val)
def __sub__(self, pt_or_val: Union["Point", float]):
if isinstance(pt_or_val, Point):
return Point(self.r - pt_or_val.r, self.z - pt_or_val.z)
else:
return Point(self.r - pt_or_val, self.z - pt_or_val)
def __mul__(self, pt_or_val: Union["Point", float]):
if isinstance(pt_or_val, Point):
return Point(self.r * pt_or_val.r, self.z * pt_or_val.z)
else:
return Point(self.r * pt_or_val, self.z * pt_or_val)
def __truediv__(self, pt_or_val: Union["Point", float]):
if isinstance(pt_or_val, Point):
return Point(self.r / pt_or_val.r, self.z / pt_or_val.z)
else:
return Point(self.r / pt_or_val, self.z / pt_or_val)
__div__ = __truediv__
def dist(p1: Union[Point, np.ndarray], p2: Union[Point, np.ndarray]) -> float:
return np.hypot(*(p1 - p2))
ShapePoints = List[Point]
def to_shape_points(array: np.ndarray) -> ShapePoints:
return [Point(r, z) for r, z in array]
def center_point(points: ShapePoints) -> Point:
return sum(points, Point(0, 0)) / len(points)
class ShapeSide(enum.Enum):
LEFT = 0
RIGHT = 1
NOSHIFT = 2
class PolarPoint(NamedTuple):
angle: float
dist: float
def to_point(self) -> Point:
return Point(np.cos(self.angle) * self.dist, np.sin(self.angle) * self.dist)
def evenly_spaced_angles(num: int):
return np.arange(num) * 2 * np.pi / num
def angle_aligned_dists(points: np.ndarray, angles: np.ndarray) -> np.ndarray:
"""Return a new set of points along angles that intersect with the shape."""
# TODO(tewalds): Walk the two arrays together for an O(n+m) algorithm instead
# of the current O(n*m). This would work as long as they are both sorted
# around the radial direction, so the next intersection will be near the last.
return np.array([dist_angle_to_surface(points, a) for a in angles])
def angle_aligned_points(points: np.ndarray, num_points: int,
origin: Point) -> np.ndarray:
"""Given a set of points, return a new space centered at origin."""
angles = evenly_spaced_angles(num_points)
dists = angle_aligned_dists(points - origin, angles)
return np.stack((np.cos(angles) * dists,
np.sin(angles) * dists), axis=-1) + origin
def dist_angle_to_surface(points: np.ndarray, angle: float) -> float:
"""Distance along a ray to the surface defined by a list of points."""
for p1, p2 in zip(points, np.roll(points, 1, axis=0)):
d = dist_angle_to_segment(p1, p2, angle)
if d is not None:
return d
raise ValueError(f"Intersecting edge not found for angle: {angle}")
def dist_angle_to_segment(p1, p2, angle: float) -> Optional[float]:
"""Distance along a ray from the origin to a segment defined by two points."""
x0, y0 = p1[0], p1[1]
x1, y1 = p2[0], p2[1]
a0, b0 = np.cos(angle), np.sin(angle)
a1, b1 = 0, 0
# Segment/segment algorithm inspired by https://stackoverflow.com/q/563198
denom = (b0 - b1) * (x0 - x1) - (y0 - y1) * (a0 - a1)
if denom == 0:
return None # Angle parallel to the segment, so can't intersect.
xy = (a0 * (y1 - b1) + a1 * (b0 - y1) + x1 * (b1 - b0)) / denom
eps = 0.00001 # Allow intersecting slightly beyond the endpoints.
if -eps <= xy <= 1 + eps: # Check it hit the segment, not just the line.
ab = (y1 * (x0 - a1) + b1 * (x1 - x0) + y0 * (a1 - x1)) / denom
if ab > 0: # Otherwise it hit in the reverse direction.
# If ab <= 1 then it's within the segment defined above, but given it's
# a unit vector with one end at the origin this tells us the distance to
# the intersection of an infinite ray out from the origin.
return ab
return None
def dist_point_to_surface(points: np.ndarray, point: np.ndarray) -> float:
"""Distance from a point to the surface defined by a list of points."""
return min(dist_point_to_segment(p1, p2, point)
for p1, p2 in zip(points, np.roll(points, 1, axis=0)))
def dist_point_to_segment(v: np.ndarray, w: np.ndarray, p: np.ndarray) -> float:
"""Return minimum distance between line segment vw and point p."""
# Inspired by: https://stackoverflow.com/a/1501725
l2 = dist(v, w)**2
if l2 == 0.0:
return dist(p, v) # v == w case
# Consider the line extending the segment, parameterized as v + t (w - v).
# We find projection of point p onto the line.
# It falls where t = [(p-v) . (w-v)] / |w-v|^2
# We clamp t from [0,1] to handle points outside the segment vw.
t = max(0, min(1, np.dot(p - v, w - v) / l2))
projection = v + t * (w - v) # Projection falls on the segment
return dist(p, projection)
def sort_by_angle(points: ShapePoints) -> ShapePoints:
center = sum(points, Point(0, 0)) / len(points)
return sorted(points, key=lambda p: (p - center).to_polar().angle)
def spline_interpolate_points(
points: ShapePoints, num_points: int,
x_points: Optional[ShapePoints] = None) -> ShapePoints:
"""Interpolate along a spline to give a smooth evenly spaced shape."""
ends = []
if x_points:
# Find the shape points that must allow sharp corners.
for xp in x_points:
for i, p in enumerate(points):
if np.hypot(*(p - xp)) < 0.01:
ends.append(i)
if not ends:
# No x-points forcing sharp corners, so use a periodic spline.
tck, _ = interpolate.splprep(np.array(points + [points[0]]).T, s=0, per=1)
unew = np.arange(num_points) / num_points
out = interpolate.splev(unew, tck)
assert len(out[0]) == num_points
return sort_by_angle(to_shape_points(np.array(out).T))
# Generate a spline with an shape==x-point at each end.
new_pts = []
for i, j in zip(ends, ends[1:] + [ends[0]]):
pts = points[i:j+1] if i < j else points[i:] + points[:j+1]
num_segment_points = np.round((len(pts) - 1) / len(points) * num_points)
unew = np.arange(num_segment_points + 1) / num_segment_points
tck, _ = interpolate.splprep(np.array(pts).T, s=0)
out = interpolate.splev(unew, tck)
new_pts += to_shape_points(np.array(out).T)[:-1]
if len(new_pts) != num_points:
raise AssertionError(
f"Generated the wrong number of points: {len(new_pts)} != {num_points}")
return sort_by_angle(new_pts)
@dataclasses.dataclass
class ParametrizedShape:
"""Describes a target shape from the parameter set."""
r0: float # Where to put the center along the radial axis.
z0: float # Where to put the center along the vertical axis.
kappa: float # Elongation of the shape. (0.8, 3)
delta: float # Triangulation of the shape. (-1, 1)
radius: float # Radius of the shape (0.22, 2.58)
lambda_: float # Squareness of the shape. Recommend (0, 0)
side: ShapeSide # Whether and which side to shift the shape to.
@classmethod
def uniform_random_shape(
cls,
r_bounds=(0.8, 0.9),
z_bounds=(0, 0.2),
kappa_bounds=(1.0, 1.8), # elongation
delta_bounds=(-0.5, 0.6), # triangulation
radius_bounds=(tcv_common.LIMITER_WIDTH / 2 - 0.04,
tcv_common.LIMITER_WIDTH / 2),
lambda_bounds=(0, 0), # squareness
side=(ShapeSide.LEFT, ShapeSide.RIGHT)):
"""Return a random shape."""
return cls(
r0=np.random.uniform(*r_bounds),
z0=np.random.uniform(*z_bounds),
kappa=np.random.uniform(*kappa_bounds),
delta=np.random.uniform(*delta_bounds),
radius=np.random.uniform(*radius_bounds),
lambda_=np.random.uniform(*lambda_bounds),
side=side if isinstance(side, ShapeSide) else random.choice(side))
def gen_points(self, num_points: int) -> Tuple[ShapePoints, Point]:
"""Generates a set of shape points, return (points, modified (r0, z0))."""
r0 = self.r0
z0 = self.z0
num_warped_points = 32
points = np.zeros((num_warped_points, 2))
theta = evenly_spaced_angles(num_warped_points)
points[:, 0] = r0 + self.radius * np.cos(theta + self.delta * np.sin(theta)
- self.lambda_ * np.sin(2 * theta))
points[:, 1] = z0 + self.radius * self.kappa * np.sin(theta)
if self.side == ShapeSide.LEFT:
wall_shift = np.min(points[:, 0]) - tcv_common.INNER_LIMITER_R
points[:, 0] -= wall_shift
r0 -= wall_shift
elif self.side == ShapeSide.RIGHT:
wall_shift = np.max(points[:, 0]) - tcv_common.OUTER_LIMITER_R
points[:, 0] -= wall_shift
r0 -= wall_shift
return (spline_interpolate_points(to_shape_points(points), num_points),
Point(r0, z0))
def trim_zero_points(points: ShapePoints) -> Optional[ShapePoints]:
trimmed = [p for p in points if p.r != 0]
return trimmed if trimmed else None
class Diverted(enum.Enum):
"""Whether a shape is diverted or not."""
ANY = 0
LIMITED = 1
DIVERTED = 2
@classmethod
def from_refs(cls, references: named_array.NamedArray) -> "Diverted":
diverted = (references["diverted", 0] == 1)
limited = (references["limited", 0] == 1)
if diverted and limited:
raise ValueError("Diverted and limited doesn't make sense.")
if diverted:
return cls.DIVERTED
if limited:
return cls.LIMITED
return cls.ANY
@dataclasses.dataclass
class Shape:
"""Full specification of a shape."""
params: Optional[ParametrizedShape] = None
points: Optional[ShapePoints] = None
x_points: Optional[ShapePoints] = None
legs: Optional[ShapePoints] = None
diverted: Diverted = Diverted.ANY
ip: Optional[float] = None
limit_point: Optional[Point] = None
@classmethod
def from_references(cls, references: named_array.NamedArray) -> "Shape":
"""Extract a Shape from the references."""
if any(np.any(references[name] != 0)
for name in ("R", "Z", "kappa", "delta", "radius", "lambda")):
params = ParametrizedShape(
r0=references["R"][0],
z0=references["Z"][0],
kappa=references["kappa"][0],
delta=references["delta"][0],
radius=references["radius"][0],
lambda_=references["lambda"][0],
side=ShapeSide.NOSHIFT)
else:
params = None
ip = references["Ip", 0]
return cls(
params,
points=trim_zero_points(points_from_references(references, "shape1")),
x_points=trim_zero_points(points_from_references(references,
"x_points")),
legs=trim_zero_points(points_from_references(references, "legs")),
limit_point=trim_zero_points(points_from_references(
references, "limit_point")[0:1]),
diverted=Diverted.from_refs(references),
ip=float(ip) if ip != 0 else None)
def gen_references(self) -> named_array.NamedArray:
"""Return the references for the parametrized shape."""
refs = tcv_common.REF_RANGES.new_named_array()
if self.ip is not None:
refs["Ip", 0] = self.ip
refs["diverted", 0] = 1 if self.diverted == Diverted.DIVERTED else 0
refs["limited", 0] = 1 if self.diverted == Diverted.LIMITED else 0
if self.params is not None:
refs["R", 0] = self.params.r0
refs["Z", 0] = self.params.z0
refs["kappa", 0] = self.params.kappa
refs["delta", 0] = self.params.delta
refs["radius", 0] = self.params.radius
refs["lambda", 0] = self.params.lambda_
if self.points is not None:
points = np.array(self.points)
assert refs.names.count("shape_r") >= points.shape[0]
refs["shape_r", :points.shape[0]] = points[:, 0]
refs["shape_z", :points.shape[0]] = points[:, 1]
if self.x_points is not None:
x_points = np.array(self.x_points)
assert refs.names.count("x_points_r") >= x_points.shape[0]
refs["x_points_r", :x_points.shape[0]] = x_points[:, 0]
refs["x_points_z", :x_points.shape[0]] = x_points[:, 1]
if self.legs is not None:
legs = np.array(self.legs)
assert refs.names.count("legs_r") >= legs.shape[0]
refs["legs_r", :legs.shape[0]] = legs[:, 0]
refs["legs_z", :legs.shape[0]] = legs[:, 1]
if self.limit_point is not None:
refs["limit_point_r", 0] = self.limit_point.r
refs["limit_point_z", 0] = self.limit_point.z
return refs
def canonical(self) -> "Shape":
"""Return a canonical shape with a fixed number of points and params."""
num_points = tcv_common.REF_RANGES.count("shape_r")
out = copy.deepcopy(self)
if out.points is None:
if out.params is None:
raise ValueError("Can't canonicalize with no params or points.")
out.points, center = out.params.gen_points(num_points)
out.params.r0 = center.r
out.params.z0 = center.z
out.params.side = ShapeSide.NOSHIFT
else:
out.points = spline_interpolate_points(
out.points, num_points, out.x_points or [])
if out.params:
out.params.side = ShapeSide.NOSHIFT
else:
# Copied from FGE. Details: https://doi.org/10.1017/S0022377815001270
top = max(out.points, key=lambda p: p.z)
left = min(out.points, key=lambda p: p.r)
right = max(out.points, key=lambda p: p.r)
bottom = min(out.points, key=lambda p: p.z)
center = Point((left.r + right.r) / 2,
(top.z + bottom.z) / 2)
radius = (right.r - left.r) / 2
kappa = (top.z - bottom.z) / (right.r - left.r)
delta_lower = (center.r - bottom.r) / radius # upper triangularitiy
delta_upper = (center.r - top.r) / radius # lower triangularity
delta = (delta_lower + delta_upper) / 2
out.params = ParametrizedShape(
r0=center.r, z0=center.z, radius=radius, kappa=kappa, delta=delta,
lambda_=0, side=ShapeSide.NOSHIFT)
return out
def points_from_references(
references: named_array.NamedArray, key: str = "shape1",
num: Optional[int] = None) -> ShapePoints:
points = np.array([references[f"{key}_r"], references[f"{key}_z"]]).T
if num is not None:
points = points[:num]
return to_shape_points(points)
@dataclasses.dataclass
class ReferenceTimeSlice:
shape: Shape
time: float
hold: Optional[float] = None # Absolute time.
def __post_init__(self):
if self.hold is None:
self.hold = self.time
def canonicalize_reference_series(
time_slices: List[ReferenceTimeSlice]) -> List[ReferenceTimeSlice]:
"""Canonicalize a full sequence of time slices."""
outputs = []
for ref in time_slices:
ref_shape = ref.shape.canonical()
prev = outputs[-1] if outputs else None
if prev is not None and prev.hold + tcv_common.DT < ref.time:
leg_diff = len(ref_shape.legs or []) != len(prev.shape.legs or [])
xp_diff = len(ref_shape.x_points or []) != len(prev.shape.x_points or [])
div_diff = ref_shape.diverted != prev.shape.diverted
limit_diff = (
bool(ref_shape.limit_point and ref_shape.limit_point.r > 0) !=
bool(prev.shape.limit_point and prev.shape.limit_point.r > 0))
if leg_diff or xp_diff or div_diff or limit_diff:
# Try not to interpolate between a real x-point and a non-existent
# x-point. Non-existent x-points are represented as being at the
# origin, i.e. out to the left of the vessel, and could be interpolated
# into place, but that's weird, so better to pop it into existence by
# adding an extra frame one before with the new shape targets.
# This doesn't handle the case of multiple points appearing/disappearing
# out of order, or of one moving while the other disappears.
outputs.append(
ReferenceTimeSlice(
time=ref.time - tcv_common.DT,
shape=Shape(
ip=ref_shape.ip,
params=ref_shape.params,
points=ref_shape.points,
x_points=(prev.shape.x_points
if xp_diff else ref_shape.x_points),
legs=(prev.shape.legs if leg_diff else ref_shape.legs),
limit_point=(prev.shape.limit_point
if limit_diff else ref_shape.limit_point),
diverted=(prev.shape.diverted
if div_diff else ref_shape.diverted))))
outputs.append(ReferenceTimeSlice(ref_shape, ref.time, ref.hold))
return outputs
================================================
FILE: fusion_tcv/shapes_known.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A set of known shapes."""
from fusion_tcv import shape
from fusion_tcv import tcv_common
SHAPE_70166_0450 = shape.Shape(
ip=-120000,
params=shape.ParametrizedShape(
r0=0.89,
z0=0.25,
kappa=1.4,
delta=0.25,
radius=0.25,
lambda_=0,
side=shape.ShapeSide.NOSHIFT),
limit_point=shape.Point(tcv_common.INNER_LIMITER_R, 0.25),
diverted=shape.Diverted.LIMITED)
SHAPE_70166_0872 = shape.Shape(
ip=-110000,
params=shape.ParametrizedShape(
r0=0.8796,
z0=0.2339,
kappa=1.2441,
delta=0.2567,
radius=0.2390,
lambda_=0,
side=shape.ShapeSide.NOSHIFT,
),
points=[ # 20 points
shape.Point(0.6299, 0.1413),
shape.Point(0.6481, 0.0577),
shape.Point(0.6804, -0.0087),
shape.Point(0.7286, -0.0513),
shape.Point(0.7931, -0.0660),
shape.Point(0.8709, -0.0513),
shape.Point(0.9543, -0.0087),
shape.Point(1.0304, 0.0577),
shape.Point(1.0844, 0.1413),
shape.Point(1.1040, 0.2340),
shape.Point(1.0844, 0.3267),
shape.Point(1.0304, 0.4103),
shape.Point(0.9543, 0.4767),
shape.Point(0.8709, 0.5193),
shape.Point(0.7931, 0.5340),
shape.Point(0.7286, 0.5193),
shape.Point(0.6804, 0.4767),
shape.Point(0.6481, 0.4103),
shape.Point(0.6299, 0.3267),
shape.Point(0.6240, 0.2340),
],
limit_point=shape.Point(tcv_common.INNER_LIMITER_R, 0.2339),
diverted=shape.Diverted.LIMITED)
================================================
FILE: fusion_tcv/targets.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Reward targets that return target+actual."""
import abc
import math
from typing import List, Optional, Sequence, Tuple
import dataclasses
import numpy as np
import scipy
from fusion_tcv import fge_state
from fusion_tcv import named_array
from fusion_tcv import shape
from fusion_tcv import tcv_common
class TargetError(Exception):
"""For when a target can't be computed."""
@dataclasses.dataclass(frozen=True)
class Target:
actual: float
target: float
@classmethod
def invalid(cls):
"""This target is invalid and should be ignored. Equivalent to weight=0."""
return cls(float("nan"), float("nan"))
class AbstractTarget(abc.ABC):
"""Measure something about the simulation, with a target and actual value."""
@property
def name(self) -> str:
"""Returns a name for the target."""
return self.__class__.__name__
@abc.abstractproperty
def outputs(self) -> int:
"""Return the number of outputs this produces."""
@abc.abstractmethod
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
"""Returns a list of targets."""
@dataclasses.dataclass(frozen=True)
class AbstractSingleValuePerDomainTarget(AbstractTarget):
"""Base class for single value per plasma domain targets."""
target: Optional[Sequence[float]] = None
indices: List[int] = dataclasses.field(default_factory=lambda: [0])
def __post_init__(self):
if self.indices not in ([0], [1], [0, 1]):
raise ValueError(
f"Invalid indices: {self.indices}, must be [0], [1] or [0, 1].")
if self.target and len(self.target) != len(self.indices):
raise ValueError("Wrong number of targets.")
@property
def outputs(self) -> int:
return len(self.indices)
@property
def name(self) -> str:
return f"{super().name}: " + ",".join(str(i) for i in self.indices)
@dataclasses.dataclass(frozen=True)
class R(AbstractSingleValuePerDomainTarget):
"""Target for R."""
def __call__(self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
r_d, _, _ = state.rzip_d
if self.target is None:
return [Target(r_d[idx], references["R"][idx]) for idx in self.indices]
else:
return [Target(r_d[idx], target)
for idx, target in zip(self.indices, self.target)]
@dataclasses.dataclass(frozen=True)
class Z(AbstractSingleValuePerDomainTarget):
"""Target for Z."""
def __call__(self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
_, z_d, _ = state.rzip_d
if self.target is None:
return [Target(z_d[idx], references["Z"][idx]) for idx in self.indices]
else:
return [Target(z_d[idx], target)
for idx, target in zip(self.indices, self.target)]
@dataclasses.dataclass(frozen=True)
class Ip(AbstractSingleValuePerDomainTarget):
"""Target for Ip."""
def __call__(self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
_, _, ip_d = state.rzip_d
if self.target is None:
return [Target(ip_d[idx], references["Ip"][idx]) for idx in self.indices]
else:
return [Target(ip_d[idx], target)
for idx, target in zip(self.indices, self.target)]
class OHCurrentsClose(AbstractTarget):
"""Target for keeping OH currents close."""
@property
def outputs(self) -> int:
return 1
def __call__(self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
oh_coil_currents = state.get_coil_currents_by_type("OH")
diff = abs(oh_coil_currents[0] - oh_coil_currents[1])
return [Target(diff, 0)]
class EFCurrents(AbstractTarget):
"""EFCurrents, useful for avoiding stuck coils."""
@property
def outputs(self) -> int:
return 16
def __call__(self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
currents = np.concatenate([state.get_coil_currents_by_type("E"),
state.get_coil_currents_by_type("F")])
return [Target(c, 0) for c in currents]
@dataclasses.dataclass(frozen=True)
class VoltageOOB(AbstractTarget):
"""Target for how much the voltages exceed the bounds."""
relative: bool = True
@property
def outputs(self) -> int:
return tcv_common.NUM_ACTIONS
def __call__(self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
bounds = tcv_common.action_spec()
excess = (np.maximum(bounds.minimum - voltages, 0) +
np.maximum(voltages - bounds.maximum, 0))
if self.relative:
excess /= (bounds.maximum - bounds.minimum)
return [Target(v, 0) for v in excess]
@dataclasses.dataclass(frozen=True)
class ShapeElongation(AbstractSingleValuePerDomainTarget):
"""Try to keep the elongation close to the references."""
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
if self.target is not None:
targets = self.target
else:
targets = references["kappa"][self.indices]
return [Target(state.elongation[i], target)
for i, target in zip(self.indices, targets)]
@dataclasses.dataclass(frozen=True)
class ShapeTriangularity(AbstractSingleValuePerDomainTarget):
"""Try to keep the triangularity close to the references."""
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
if self.target is not None:
targets = self.target
else:
targets = references["delta"][self.indices]
return [Target(state.triangularity[i], target)
for i, target in zip(self.indices, targets)]
@dataclasses.dataclass(frozen=True)
class ShapeRadius(AbstractSingleValuePerDomainTarget):
"""Try to keep the shape radius close to the references."""
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
if self.target is not None:
targets = self.target
else:
targets = references["radius"][self.indices]
return [Target(state.radius[i], target)
for i, target in zip(self.indices, targets)]
@dataclasses.dataclass(frozen=True)
class AbstractPointsTarget(AbstractTarget):
"""Base class for shape point targets."""
points: Optional[shape.ShapePoints] = None
ref_name: Optional[str] = None
num_points: Optional[int] = None
def __post_init__(self):
if self.points is not None:
return
elif self.ref_name is None:
raise ValueError("Must specify points or ref_name")
else:
ref_name = f"{self.ref_name}_r"
if ref_name not in tcv_common.REF_RANGES:
raise ValueError(f"{self.ref_name} is invalid.")
elif (self.num_points is not None and
self.num_points > tcv_common.REF_RANGES.count(ref_name)):
raise ValueError(
(f"Requesting more points ({self.num_points}) than {self.ref_name} "
"provides."))
@property
def outputs(self) -> int:
return len(self.points) if self.points is not None else self.num_points
def _target_points(
self, references: named_array.NamedArray) -> shape.ShapePoints:
if self.points is not None:
return self.points
else:
return shape.points_from_references(
references, self.ref_name, self.num_points)
def splined_lcfs_points(
state: fge_state.FGEState,
num_points: int,
domain: int = 0) -> shape.ShapePoints:
"""Return a smooth lcfs, cleaning FGE x-point artifacts."""
points = state.get_lcfs_points(domain)
x_point = (shape.Point(*state.limit_point_d[domain])
if state.is_diverted_d[domain] else None)
if x_point is not None:
x_points = [x_point]
# Drop points near the x-point due to noise in the shape projection near
# the x-point.
points = [p for p in points if shape.dist(p, x_point) > 0.1]
points.append(x_point)
points = shape.sort_by_angle(points)
else:
x_points = []
return shape.spline_interpolate_points(points, num_points, x_points)
@dataclasses.dataclass(frozen=True)
class ShapeLCFSDistance(AbstractPointsTarget):
"""Try to keep the shape close to the references.
Check the distance from the target shape points to the smooth LCFS.
"""
ref_name: str = dataclasses.field(default="shape", init=False)
domain: int = dataclasses.field(default=0, init=False)
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
lcfs = splined_lcfs_points(state, 90, self.domain)
outputs = []
for p in self._target_points(references):
if p.r == 0: # For invalid/changing number of points.
outputs.append(Target.invalid())
continue
dist = shape.dist_point_to_surface(np.array(lcfs), np.array(p))
outputs.append(Target(dist, 0))
return outputs
def flux_at_points(state: fge_state.FGEState, points: np.ndarray) -> np.ndarray:
"""Return the normalized interpolated flux values at a set of points."""
# Normalized flux such that the LCFS has a value of 1, 0 in the middle,
# and bigger than 1 farther out.
normalized_flux = ( # (LY.Fx - LY.FA) / (LY.FB - LY.FA)
(state.flux - state.magnetic_axis_flux_strength) /
(state.lcfs_flux_strength - state.magnetic_axis_flux_strength)).T
smooth_flux = scipy.interpolate.RectBivariateSpline(
np.squeeze(state.r_coordinates),
np.squeeze(state.z_coordinates),
normalized_flux)
return smooth_flux(points[:, 0], points[:, 1], grid=False)
@dataclasses.dataclass(frozen=True)
class ShapeNormalizedLCFSFlux(AbstractPointsTarget):
"""Try to keep the shape close to the references using flux.
Check the normalized flux values at points along the target shape. This works
in flux space, not linear distance, so may encourage smaller plasmas than the
distance based shape rewards.
"""
ref_name: str = dataclasses.field(default="shape1", init=False)
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
outputs = []
for p in self._target_points(references):
if p.r == 0: # For invalid/changing number of points.
outputs.append(Target.invalid())
else:
outputs.append(Target(
flux_at_points(state, np.array([p]))[0], 1))
return outputs
@dataclasses.dataclass(frozen=True)
class LegsNormalizedFlux(ShapeNormalizedLCFSFlux):
"""Try to keep the legs references close to the LCFS."""
ref_name: str = dataclasses.field(default="legs", init=False)
@dataclasses.dataclass(frozen=True)
class AbstractXPointTarget(AbstractPointsTarget):
"""Base class for x-point targets."""
ref_name: str = dataclasses.field(default="x_points", init=False)
@dataclasses.dataclass(frozen=True)
class XPointFluxGradient(AbstractXPointTarget):
"""Keep target points as an X point by attempting 0 flux gradient."""
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
eps = 0.01
targets = []
for point in self._target_points(references):
if point.r == 0: # For invalid/changing number of points.
targets.append(Target.invalid())
continue
diff_points = np.array([
[point.r - eps, point.z],
[point.r + eps, point.z],
[point.r, point.z - eps],
[point.r, point.z + eps],
])
flux_values = flux_at_points(state, diff_points)
diff = ((np.abs(flux_values[0] - flux_values[1]) / (2 * eps)) +
(np.abs(flux_values[2] - flux_values[3]) / (2 * eps)))
targets.append(Target(diff, 0))
return targets
def _dist(p1: shape.Point, p2: shape.Point):
return math.hypot(p1.r - p2.r, p1.z - p2.z)
def _min_dist(pt: shape.Point, points: shape.ShapePoints,
min_dist: float) -> Tuple[Optional[int], float]:
index = None
for i, point in enumerate(points):
dist = _dist(pt, point)
if dist < min_dist:
index = i
min_dist = dist
return index, min_dist
@dataclasses.dataclass(frozen=True)
class XPointDistance(AbstractXPointTarget):
"""Keep target points as an X point by attempting to minimize distance.
This assigns the x-points to targets without replacement. The first target
will get the distance to the nearest x-point. The second target will get the
closest, but ignoring the one assigned to the first target point. If none are
within `max_dist`, then no x-point is assigned and that distance will be
returned.
It may be worth switching to a fancier algorithm that tries to minimize the
total distance between targets and x-points, but that's slower, and we may
actually care about some x-points more (eg a diverted point is more
important than one farther away).
"""
max_dist: float = 0.2
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
x_points = state.x_points
targets = []
for target_point in self._target_points(references):
if target_point.r == 0: # For invalid/changing number of points.
targets.append(Target.invalid())
continue
index, min_dist = _min_dist(target_point, x_points, self.max_dist)
if index is not None:
x_points.pop(index)
targets.append(Target(min_dist, 0))
return targets
@dataclasses.dataclass(frozen=True)
class XPointFar(AbstractXPointTarget):
"""Keep extraneous x-points far away from the LCFS.
Returns the distance from the LCFS to any true x-point that is far from a
target x-point.
This assigns the x-points to targets without replacement. The first target
will get the distance to the nearest x-point. The second target will get the
closest, but ignoring the one assigned to the first target point. If none are
within `max_dist`, then no x-point is assigned and that distance will be
returned.
It may be worth switching to a fancier algorithm that tries to minimize the
total distance between targets and x-points, but that's slower, and we may
actually care about some x-points more (eg a diverted point is more
important than one farther away).
"""
max_dist: float = 0.2
domain: int = 0
diverted: Optional[shape.Diverted] = None
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
if self.diverted is not None:
target = self.diverted
else:
target = shape.Diverted.from_refs(references)
if target == shape.Diverted.ANY:
return [] # Don't care.
x_points = state.x_points
# Filter out x-points that are near target x-points.
for target_point in self._target_points(references):
if target_point.r == 0: # For invalid/changing number of points.
continue
index, _ = _min_dist(target_point, x_points, self.max_dist)
if index is not None:
x_points.pop(index)
if not x_points:
return [Target(100, 0)] # No x-point gives full reward, not weight=0.
lcfs = state.get_lcfs_points(self.domain)
return [Target(shape.dist_point_to_surface(np.array(lcfs), np.array(p)), 0)
for p in x_points]
@dataclasses.dataclass(frozen=True)
class XPointNormalizedFlux(AbstractXPointTarget):
"""Keep the actual X points close to the LCFS.
Choose the x-points based on their distance to the target x-points.
"""
max_dist: float = 0.2
diverted: Optional[shape.Diverted] = None
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
if self.diverted is not None:
diverted = self.diverted
else:
diverted = shape.Diverted.from_refs(references)
x_points = state.x_points
fluxes = list(flux_at_points(state, np.array(x_points).reshape((-1, 2))))
targets = []
# We should probably minimize the overall distance between targets and
# x-points, but the algorithm is complicated, so instead be greedy and
# assume they're given in priority order, or farther apart than max_dist.
for target_point in self._target_points(references):
if target_point.r == 0 or diverted != shape.Diverted.DIVERTED:
# For invalid/changing number of points.
targets.append(Target.invalid())
continue
index, _ = _min_dist(target_point, x_points, self.max_dist)
if index is not None:
targets.append(Target(fluxes[index], 1))
x_points.pop(index)
fluxes.pop(index)
else:
targets.append(Target(0, 1))
return targets
@dataclasses.dataclass(frozen=True)
class XPointCount(AbstractTarget):
"""Target for number of x-points. Useful to avoid more than you want."""
target: Optional[int] = None
@property
def outputs(self) -> int:
return 1
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
if self.target is not None:
target = self.target
else:
target_points = shape.points_from_references(
references, "x_points", tcv_common.REF_RANGES.count("x_points_r"))
target = sum(1 for p in target_points if p.r != 0)
return [Target(len(state.x_points), target)]
@dataclasses.dataclass(frozen=True)
class Diverted(AbstractTarget):
"""Target for whether the plasma is diverted by an x-point."""
diverted: Optional[shape.Diverted] = None
@property
def outputs(self) -> int:
return 1
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
if self.diverted is not None:
target = self.diverted
else:
target = shape.Diverted.from_refs(references)
actual = 1 if state.is_diverted_d[0] else 0
if target == shape.Diverted.ANY:
return [Target.invalid()] # Don't care.
elif target == shape.Diverted.DIVERTED:
return [Target(actual, 1)]
return [Target(actual, 0)]
@dataclasses.dataclass(frozen=True)
class LimitPoint(AbstractPointsTarget):
"""Target for where the plasma is limited, either on the wall or x-point."""
ref_name: str = dataclasses.field(default="limit_point", init=False)
num_points: int = dataclasses.field(default=1, init=False)
diverted: Optional[shape.Diverted] = None
max_dist: float = 1
def __call__(
self,
voltages: np.ndarray,
state: fge_state.FGEState,
references: named_array.NamedArray) -> List[Target]:
if self.diverted is not None:
diverted_target = self.diverted
else:
diverted_target = shape.Diverted.from_refs(references)
if diverted_target == shape.Diverted.ANY:
return [Target.invalid()]
target_point = self._target_points(references)[0]
if target_point.r == 0:
return [Target.invalid()]
limit_point = shape.Point(*state.limit_point_d[0])
dist = np.hypot(*(target_point - limit_point))
is_diverted = state.is_diverted_d[0]
if diverted_target == shape.Diverted.DIVERTED:
return [Target((dist if is_diverted else self.max_dist), 0)]
return [Target((dist if not is_diverted else self.max_dist), 0)]
================================================
FILE: fusion_tcv/tcv_common.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Constants and general tooling for TCV plant."""
import collections
from typing import Sequence, Text
from dm_env import specs
import numpy as np
from fusion_tcv import named_array
DT = 1e-4 # ie 10kHz
# Below are general input/output specifications used for controllers that are
# run on hardware. This interface corresponds to the so called "KH hybrid"
# controller specification that is used in various experiments by EPFL. Hence,
# this interface definition contains measurements and actions not used in our
# tasks.
# Number of actions the environment is exposing. Includes dummy (FAST) action.
NUM_ACTIONS = 20
# Number of actuated coils in sim (without the dummy action coil).
NUM_COILS_ACTUATED = 19
# Current and voltage limits by coil type
# Note this are the limits used in the environments and are different
# from the 'machine engineering limits' (as used/exposed by FGE).
# We apply a safety factor (=< 1.0) to the engineering limits.
CURRENT_SAFETY_FACTOR = 0.8
ENV_COIL_MAX_CURRENTS = collections.OrderedDict(
E=7500*CURRENT_SAFETY_FACTOR,
F=7500*CURRENT_SAFETY_FACTOR,
OH=26000*CURRENT_SAFETY_FACTOR,
DUMMY=2000*CURRENT_SAFETY_FACTOR,
G=2000*CURRENT_SAFETY_FACTOR)
# The g-coil has a saturation voltage that is tunable on a shot-by-shot basis.
# There is a deadband, where an action with absolute value of less than 8% of
# the saturation voltage is treated as zero.
ENV_G_COIL_SATURATION_VOLTAGE = 300
ENV_G_COIL_DEADBAND = ENV_G_COIL_SATURATION_VOLTAGE * 0.08
VOLTAGE_SAFETY_FACTOR = 1.0
ENV_COIL_MAX_VOLTAGE = collections.OrderedDict(
E=1400*VOLTAGE_SAFETY_FACTOR,
F=2200*VOLTAGE_SAFETY_FACTOR,
OH=1400*VOLTAGE_SAFETY_FACTOR,
DUMMY=400*VOLTAGE_SAFETY_FACTOR,
# This value is also used to clip values for the internal controller,
# and also to set the deadband voltage.
G=ENV_G_COIL_SATURATION_VOLTAGE)
# Ordered actions send by a controller to the TCV.
TCV_ACTIONS = (
'E_001', 'E_002', 'E_003', 'E_004', 'E_005', 'E_006', 'E_007', 'E_008',
'F_001', 'F_002', 'F_003', 'F_004', 'F_005', 'F_006', 'F_007', 'F_008',
'OH_001', 'OH_002',
'DUMMY_001', # GAS, ignored by TCV.
'G_001' # FAST
)
TCV_ACTION_INDICES = {n: i for i, n in enumerate(TCV_ACTIONS)}
TCV_ACTION_TYPES = collections.OrderedDict(
E=8,
F=8,
OH=2,
DUMMY=1,
G=1,
)
# Map the TCV actions to ranges of indices in the array.
TCV_ACTION_RANGES = named_array.NamedRanges(TCV_ACTION_TYPES)
# The voltages seem not to be centered at 0, but instead near these values:
TCV_ACTION_OFFSETS = {
'E_001': 6.79,
'E_002': -10.40,
'E_003': -1.45,
'E_004': 0.18,
'E_005': 11.36,
'E_006': -0.95,
'E_007': -4.28,
'E_008': 44.22,
'F_001': 38.49,
'F_002': -2.94,
'F_003': 5.58,
'F_004': 1.09,
'F_005': -36.63,
'F_006': -9.18,
'F_007': 5.34,
'F_008': 10.53,
'OH_001': -53.63,
'OH_002': -14.76,
}
TCV_ACTION_DELAYS = {
'E': [0.0005] * 8,
'F': [0.0005] * 8,
'OH': [0.0005] * 2,
'G': [0.0001],
}
# Ordered measurements and their dimensions from to the TCV controller specs.
TCV_MEASUREMENTS = collections.OrderedDict(
clint_vloop=1, # Flux loop 1
clint_rvloop=37, # Difference of flux between loops 2-38 and flux loop 1
bm=38, # Magnetic field probes
IE=8, # E-coil currents
IF=8, # F-coil currents
IOH=2, # OH-coil currents
Bdot=20, # Selection of 20 time-derivatives of magnetic field probes (bm).
DIOH=1, # OH-coil currents difference: OH(0) - OH(1).
FIR_FRINGE=1, # Not used, ignore.
IG=1, # G-coil current
ONEMM=1, # Not used, ignore
vloop=1, # Flux loop 1 derivative
IPHI=1, # Current through the Toroidal Field coils. Constant. Ignore.
)
NUM_MEASUREMENTS = sum(TCV_MEASUREMENTS.values())
# map the TCV measurements to ranges of indices in the array
TCV_MEASUREMENT_RANGES = named_array.NamedRanges(TCV_MEASUREMENTS)
# Several of the measurement probes for the rvloops are broken. Add an extra key
# that allows us to only grab the usable ones
BROKEN_RVLOOP_IDXS = [9, 10, 11]
TCV_MEASUREMENT_RANGES.set_range('clint_rvloop_usable', [
idx for i, idx in enumerate(TCV_MEASUREMENT_RANGES['clint_rvloop'])
if i not in BROKEN_RVLOOP_IDXS])
TCV_COIL_CURRENTS_INDEX = [
*TCV_MEASUREMENT_RANGES['IE'],
*TCV_MEASUREMENT_RANGES['IF'],
*TCV_MEASUREMENT_RANGES['IOH'],
*TCV_MEASUREMENT_RANGES['IPHI'], # In place of DUMMY.
*TCV_MEASUREMENT_RANGES['IG'],
]
# References for what we want the agent to accomplish.
REF_RANGES = named_array.NamedRanges({
'R': 2,
'Z': 2,
'Ip': 2,
'kappa': 2,
'delta': 2,
'radius': 2,
'lambda': 2,
'diverted': 2, # bool, must be diverted
'limited': 2, # bool, must be limited
'shape_r': 32,
'shape_z': 32,
'x_points_r': 8,
'x_points_z': 8,
'legs_r': 16, # Use for diverted/snowflake
'legs_z': 16,
'limit_point_r': 2,
'limit_point_z': 2,
})
# Environments should use a consistent datatype for interacting with agents.
ENVIRONMENT_DATA_TYPE = np.float64
def observation_spec():
"""Observation spec for all TCV environments."""
return {
'references':
specs.Array(
shape=(REF_RANGES.size,),
dtype=ENVIRONMENT_DATA_TYPE,
name='references'),
'measurements':
specs.Array(
shape=(TCV_MEASUREMENT_RANGES.size,),
dtype=ENVIRONMENT_DATA_TYPE,
name='measurements'),
'last_action':
specs.Array(
shape=(TCV_ACTION_RANGES.size,),
dtype=ENVIRONMENT_DATA_TYPE,
name='last_action'),
}
def measurements_to_dict(measurements):
"""Converts a single measurement vector or a time series to a dict.
Args:
measurements: A single measurement of size `NUM_MEASUREMENTS` or a time
series, where the batch dimension is last, shape: (NUM_MEASUREMENTS, t).
Returns:
A dict mapping keys `TCV_MEASUREMENTS` to the corresponding measurements.
"""
assert measurements.shape[0] == NUM_MEASUREMENTS
measurements_dict = collections.OrderedDict()
index = 0
for key, dim in TCV_MEASUREMENTS.items():
measurements_dict[key] = measurements[index:index + dim, ...]
index += dim
return measurements_dict
def dict_to_measurement(measurement_dict):
"""Converts a single measurement dict to a vector or time series.
Args:
measurement_dict: A dict with the measurement keys containing np arrays of
size (meas_size, ...). The inner sizes all have to be the same.
Returns:
An array of size (num_measurements, ...)
"""
assert len(measurement_dict) == len(TCV_MEASUREMENTS)
# Grab the shape of the first array.
shape = measurement_dict['clint_vloop'].shape
out_shape = list(shape)
out_shape[0] = NUM_MEASUREMENTS
out_shape = tuple(out_shape)
measurements = np.zeros((out_shape))
index = 0
for key, dim in TCV_MEASUREMENTS.items():
dim = TCV_MEASUREMENTS[key]
measurements[index:index + dim, ...] = measurement_dict[key]
index += dim
return measurements
def action_spec():
return get_coil_spec(TCV_ACTIONS, ENV_COIL_MAX_VOLTAGE, ENVIRONMENT_DATA_TYPE)
def get_coil_spec(coil_names: Sequence[Text],
spec_mapping,
dtype=ENVIRONMENT_DATA_TYPE) -> specs.BoundedArray:
"""Maps specs indexed by coil type to coils given their type."""
coil_max, coil_min = [], []
for name in coil_names:
# Coils names are | \n", " | chrom | \n", "pos | \n", "id | \n", "ref | \n", "alt | \n", "DNASE:cerebellum mal | \n", "DNASE:frontal cortex | \n", "DNASE:chorion | \n", "DNASE:Ishikawa treat | \n", "DNASE:GM03348 | \n", "DNASE:GM03348 geneti | \n", "DNASE:AG08395 | \n", "DNASE:AG08396 | \n", "DNASE:AG20443 | \n", "DNASE:H54 | \n", "DNASE:GM10248 | \n", "DNASE:GM12878 | \n", "DNASE:GM12891 | \n", "DNASE:GM12892 | \n", "DNASE:GM18507 | \n", "DNASE:GM19238 | \n", "DNASE:GM19239 | \n", "DNASE:GM19240 | \n", "DNASE:H1-hESC | \n", "DNASE:H7-hESC | \n", "DNASE:H9 | \n", "DNASE:heart male adu | \n", "DNASE:HEK293T | \n", "DNASE:HeLa-S3 treate | \n", "DNASE:HeLa-S3 | \n", "DNASE:hepatocyte | \n", "DNASE:HepG2 | \n", "DNASE:HTR-8/SVneo | \n", "DNASE:endothelial ce | \n", "DNASE:CWRU1 male | \n", "DNASE:iPS-NIHi11 mal | \n", "DNASE:iPS-NIHi7 fema | \n", "DNASE:K562 treated w | \n", "DNASE:K562 G2 phase | \n", "DNASE:K562 G1 phase | \n", "... | \n", "CAGE:CD14+CD16- Mono | \n", "CAGE:achilles tendon | \n", "CAGE:cerebrospinal f | \n", "CAGE:cruciate ligame | \n", "CAGE:eye - vitreous | \n", "CAGE:eye - muscle su | \n", "CAGE:eye - muscle la | \n", "CAGE:eye - muscle me | \n", "CAGE:eye - muscle in | \n", "CAGE:Fingernail (inc | \n", "CAGE:optic nerve, | \n", "CAGE:Skin - palm, | \n", "CAGE:tongue epidermi | \n", "CAGE:Urethra, | \n", "CAGE:CD14+ monocytes | \n", "CAGE:Hep-2 cells tre | \n", "CAGE:Hep-2 cells moc | \n", "CAGE:immature langer | \n", "CAGE:migratory lange | \n", "CAGE:CD34 cells diff | \n", "CAGE:amygdala - adul | \n", "CAGE:thalamus - adul | \n", "CAGE:hippocampus - a | \n", "CAGE:parietal lobe - | \n", "CAGE:cerebellum - ad | \n", "CAGE:pineal gland - | \n", "CAGE:spinal cord - a | \n", "CAGE:Olfactory epith | \n", "CAGE:gamma delta pos | \n", "CAGE:Mast cell, expa | \n", "CAGE:adipose, | \n", "CAGE:cerebellum, new | \n", "CAGE:spinal cord, ne | \n", "CAGE:amygdala, newbo | \n", "CAGE:hippocampus, ne | \n", "CAGE:putamen, newbor | \n", "CAGE:thalamus, newbo | \n", "CAGE:thymic carcinom | \n", "CAGE:Smooth muscle c | \n", "CAGE:parietal cortex | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", "chr1 | \n", "925952 | \n", "1019397 | \n", "G | \n", "A | \n", "-3.953313 | \n", "-2.132398 | \n", "-1.133056 | \n", "-0.018265 | \n", "-7.324341 | \n", "-5.928950 | \n", "-2.696301 | \n", "-2.234811 | \n", "-1.869620 | \n", "16.743450 | \n", "2.218565 | \n", "3.167206 | \n", "1.084149 | \n", "0.671318 | \n", "6.513158 | \n", "0.971147 | \n", "6.255165 | \n", "5.352125 | \n", "1.729454 | \n", "-3.350782 | \n", "1.896389 | \n", "-1.563089 | \n", "-2.268677 | \n", "11.647644 | \n", "14.272476 | \n", "-7.074303 | \n", "1.850793 | \n", "5.803924 | \n", "0.381484 | \n", "-0.443644 | \n", "-0.299062 | \n", "-0.139204 | \n", "-8.861604 | \n", "-8.072408 | \n", "-6.212577 | \n", "... | \n", "-0.046439 | \n", "4.083508 | \n", "3.535412 | \n", "11.717014 | \n", "-5.239547 | \n", "1.081644 | \n", "2.213174 | \n", "0.853902 | \n", "-0.216257 | \n", "2.654844 | \n", "0.402727 | \n", "1.567667 | \n", "3.049335 | \n", "1.010953 | \n", "-0.800231 | \n", "157.583069 | \n", "172.648773 | \n", "0.576450 | \n", "5.026437 | \n", "0.464598 | \n", "2.133233 | \n", "1.689052 | \n", "2.324542 | \n", "1.849092 | \n", "-1.319847 | \n", "-25.130344 | \n", "2.456997 | \n", "11.093707 | \n", "-4.529293 | \n", "5.072914 | \n", "-6.317924 | \n", "-2.482907 | \n", "1.591787 | \n", "-5.362659 | \n", "0.643638 | \n", "-1.936050 | \n", "2.499822 | \n", "12.372701 | \n", "23.365688 | \n", "2.915666 | \n", "
| 1 | \n", "chr1 | \n", "930188 | \n", "846933 | \n", "G | \n", "A | \n", "-1.791559 | \n", "-15.679683 | \n", "-22.533566 | \n", "0.523771 | \n", "-3.826311 | \n", "-3.219894 | \n", "-2.668196 | \n", "-9.468966 | \n", "-3.158970 | \n", "-7.704255 | \n", "-3.150757 | \n", "-0.523286 | \n", "-11.126574 | \n", "-11.116987 | \n", "-3.674609 | \n", "-8.464477 | \n", "-11.757355 | \n", "-8.595486 | \n", "-0.536293 | \n", "0.227047 | \n", "-0.779173 | \n", "-15.736004 | \n", "0.879350 | \n", "-6.572251 | \n", "-4.200795 | \n", "-8.630462 | \n", "-0.465244 | \n", "-3.442806 | \n", "-0.311721 | \n", "-4.801476 | \n", "-5.145035 | \n", "-1.415882 | \n", "0.362791 | \n", "-1.997213 | \n", "-2.307346 | \n", "... | \n", "-33.052082 | \n", "-2.994223 | \n", "-8.416894 | \n", "-5.190420 | \n", "-4.534584 | \n", "-5.453042 | \n", "-3.034095 | \n", "-7.163062 | \n", "-2.291493 | \n", "-8.985706 | \n", "-4.789318 | \n", "-4.794244 | \n", "-1.449592 | \n", "-1.989321 | \n", "-22.916094 | \n", "-35.752350 | \n", "-35.275730 | \n", "-13.375345 | \n", "-28.358622 | \n", "-31.596754 | \n", "-11.371334 | \n", "-9.254264 | \n", "-9.006978 | \n", "-9.230368 | \n", "-3.783889 | \n", "-0.283448 | \n", "-12.094806 | \n", "-59.170837 | \n", "-66.303123 | \n", "-34.475914 | \n", "-21.272675 | \n", "-4.328580 | \n", "-16.075752 | \n", "-6.281566 | \n", "-10.389462 | \n", "-6.422119 | \n", "-9.181828 | \n", "-14.224935 | \n", "-46.487968 | \n", "-11.499000 | \n", "
| 2 | \n", "chr1 | \n", "930200 | \n", "1043045 | \n", "G | \n", "A | \n", "-0.526763 | \n", "-14.688834 | \n", "-17.484657 | \n", "2.908407 | \n", "2.734465 | \n", "3.514852 | \n", "0.294236 | \n", "-4.154023 | \n", "1.062205 | \n", "-7.452735 | \n", "-2.387097 | \n", "-0.988034 | \n", "-7.148329 | \n", "-7.479169 | \n", "-3.070057 | \n", "-9.537191 | \n", "-12.243765 | \n", "-10.340226 | \n", "0.070186 | \n", "0.338647 | \n", "-0.706960 | \n", "-13.103579 | \n", "3.004640 | \n", "-8.224607 | \n", "-7.412826 | \n", "-9.011602 | \n", "0.814177 | \n", "-2.572933 | \n", "0.154302 | \n", "-4.661432 | \n", "-3.152854 | \n", "-1.105172 | \n", "-2.336678 | \n", "-4.403462 | \n", "-4.830977 | \n", "... | \n", "-26.200890 | \n", "-4.082983 | \n", "-11.688838 | \n", "-5.358507 | \n", "-5.728800 | \n", "-6.592316 | \n", "-5.169149 | \n", "-8.596480 | \n", "-3.361437 | \n", "-6.800953 | \n", "-4.874091 | \n", "-4.676669 | \n", "-2.054912 | \n", "-2.659639 | \n", "-19.059345 | \n", "-86.237938 | \n", "-90.280151 | \n", "-13.633442 | \n", "-32.443680 | \n", "-19.412699 | \n", "-7.266642 | \n", "-7.957299 | \n", "-6.725488 | \n", "-6.803071 | \n", "-6.814243 | \n", "1.440345 | \n", "-8.739142 | \n", "-70.331505 | \n", "-39.735146 | \n", "-31.107134 | \n", "-18.125492 | \n", "-3.016214 | \n", "-8.161813 | \n", "-12.665734 | \n", "-9.362435 | \n", "-5.167178 | \n", "-3.976753 | \n", "-12.334543 | \n", "-38.066742 | \n", "-8.078856 | \n", "
| 3 | \n", "chr1 | \n", "930203 | \n", "972363 | \n", "C | \n", "T | \n", "3.887291 | \n", "-0.802319 | \n", "-1.669034 | \n", "4.747784 | \n", "1.695362 | \n", "2.217486 | \n", "-0.282765 | \n", "-1.139245 | \n", "0.368520 | \n", "-3.772724 | \n", "3.466754 | \n", "1.096701 | \n", "2.536208 | \n", "5.108255 | \n", "1.874467 | \n", "3.965143 | \n", "1.232345 | \n", "3.818713 | \n", "0.651470 | \n", "2.554193 | \n", "0.991104 | \n", "1.639103 | \n", "5.514153 | \n", "-6.208551 | \n", "-13.487192 | \n", "6.362380 | \n", "0.164872 | \n", "-4.553425 | \n", "-0.160736 | \n", "-1.997855 | \n", "-2.654027 | \n", "0.416074 | \n", "7.220230 | \n", "4.069194 | \n", "2.957357 | \n", "... | \n", "33.843147 | \n", "-0.583508 | \n", "0.264617 | \n", "-3.204525 | \n", "5.382181 | \n", "1.291355 | \n", "0.997021 | \n", "2.093935 | \n", "-0.294896 | \n", "4.008999 | \n", "-0.024318 | \n", "-0.550827 | \n", "-1.001716 | \n", "0.569551 | \n", "27.447952 | \n", "-22.858391 | \n", "-32.015335 | \n", "12.032137 | \n", "15.755173 | \n", "19.405922 | \n", "2.799939 | \n", "3.406132 | \n", "1.644471 | \n", "2.383641 | \n", "-1.271341 | \n", "35.166897 | \n", "-0.529095 | \n", "-15.446460 | \n", "42.712193 | \n", "22.990261 | \n", "-2.007191 | \n", "1.718304 | \n", "1.981185 | \n", "5.209938 | \n", "6.387944 | \n", "3.160304 | \n", "0.638121 | \n", "-0.786704 | \n", "-4.402274 | \n", "3.053887 | \n", "
| 4 | \n", "chr1 | \n", "930222 | \n", "998906 | \n", "GAACTC | \n", "TTCTTCTG | \n", "-6.190521 | \n", "-137.378281 | \n", "34.811737 | \n", "-1.299490 | \n", "-44.415581 | \n", "-15.935372 | \n", "-6.835675 | \n", "-44.718884 | \n", "12.754320 | \n", "71.546776 | \n", "-100.758392 | \n", "-128.016174 | \n", "-168.724716 | \n", "-330.777496 | \n", "-142.454132 | \n", "-350.230988 | \n", "-131.790482 | \n", "-207.531921 | \n", "-7.376725 | \n", "-45.294678 | \n", "-16.531658 | \n", "-183.512756 | \n", "10.864501 | \n", "18.745724 | \n", "9.173240 | \n", "15.418909 | \n", "13.582534 | \n", "24.021170 | \n", "-13.055187 | \n", "41.811131 | \n", "57.707191 | \n", "46.170353 | \n", "-125.677353 | \n", "-66.172997 | \n", "-53.709393 | \n", "... | \n", "45.992233 | \n", "1.093487 | \n", "-150.802185 | \n", "16.499266 | \n", "24.087925 | \n", "-66.276947 | \n", "-92.977158 | \n", "-139.676208 | \n", "-51.160301 | \n", "-36.617786 | \n", "-4.993864 | \n", "5.801898 | \n", "-10.315315 | \n", "-5.569825 | \n", "353.592102 | \n", "544.979797 | \n", "542.144470 | \n", "44.670349 | \n", "-91.620117 | \n", "-66.408241 | \n", "65.320213 | \n", "27.775331 | \n", "36.321232 | \n", "27.018156 | \n", "-37.838741 | \n", "-20.264828 | \n", "-15.695430 | \n", "-72.608513 | \n", "-119.713341 | \n", "-22.978678 | \n", "20.147364 | \n", "3.596015 | \n", "20.581316 | \n", "-82.511292 | \n", "10.583510 | \n", "99.163254 | \n", "87.440636 | \n", "28.396255 | \n", "26.421061 | \n", "27.204330 | \n", "
5 rows × 3378 columns
\n", "
The data used in the "Stanford 3D Objects" section of the experimental results
is available in [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/s3o4d).
The data consists of 100,000 renderings each of the Bunny and Dragon objects
from the [Stanford 3D Scanning Repository](http://graphics.stanford.edu/data/3Dscanrep/).
More objects may be added in the future, but only the Bunny and Dragon are used
in the paper. Each object is rendered with a uniformly sampled illumination from
a point on the 2-sphere, and a uniformly sampled 3D rotation. The true latent
states are provided as NumPy arrays along with the images. The lighting is given
as a 3-vector with unit norm, while the rotation is provided both as a
quaternion and a 3x3 orthogonal matrix.
### Why another dataset?
There are many similarities between S3O4D and existing ML benchmark datasets
like [NORB](https://cs.nyu.edu/~ylclab/data/norb-v1.0/),
[3D Chairs](https://github.com/mathieuaubry/seeing3Dchairs),
[3D Shapes](https://github.com/deepmind/3d-shapes) and many others, which also
include renderings of a set of objects under different pose and illumination
conditions. However, none of these existing datasets include the *full manifold*
of rotations in 3D - most include only a subset of changes to elevation and
azimuth. S3O4D images are sampled uniformly and independently from the full
space of rotations and illuminations, meaning the dataset contains objects that
are upside down and illuminated from behind or underneath. We believe that this
makes S3O4D uniquely suited for research on generative models where the latent
space has non-trivial topology, as well as for general manifold learning
methods where the curvature of the manifold is important.
### Usage
To load from TensorFlow Datasets, simply run:
```
import tensorflow_datasets as tfds
ds = tfds.load('s3o4d', split='bunny_train', shuffle_files=True)
for example in ds.take(1):
image, label, illumination, pose_mat, pose_quat = (
example['image'], example['label'], example['illumination'],
example['pose_mat'], example['pose_quat'])
```
where the split can be any of `bunny_train`, `dragon_train`, `bunny_test` or
`dragon_test`.
If you prefer to not have TensorFlow as a dependency for your project, and want
to download the data manually, you can find the raw data (as zipped JPEGs and
NumPy arrays) on [Google Cloud](https://console.cloud.google.com/storage/browser/dm_s3o4d).
To load the data for a given object, unzip `images.zip` into a folder called
`images` in the same directory as `latents.npz`, and from inside that
directory run:
```
import numpy as np
from PIL import Image
with open('latents.npz', 'r') as f:
data = np.load(f)
illumination = data['illumination'] # lighting source position, a 3-vector
pose_quat = data['pose_quat'] # object pose (3D rotation as a quaternion)
pose_mat = data['pose_mat'] # object pose (3D rotation as a matrix)
def get_data(i):
"""Return data and latent given an index up to 100,000."""
img = np.array(Image.open(f'images/{i:05}.jpg'))
# Uses the matrix, not quaternion, representation,
# similarly to the experiments in the paper
latent = np.concatenate((illumination[i],
pose_mat[i].reshape(-1)))
return img, latent
img, latent = get_data(0)
```
To do the same train/test split as in TensorFlow Datasets, simply use the first
80,000 images for each object as training data and the last 20,000 as test.
## Giving Credit
If you use this code or the Stanford 3D Objects for Disentangling data in your
work, we ask you to cite this paper:
```
@article{pfau2020disentangling,
title={Disentangling by Subspace Diffusion},
author={Pfau, David and Higgins, Irina and Botev, Aleksandar and Racani\`ere,
S{\'e}bastian},
journal={Advances in Neural Information Processing Systems (NeurIPS)},
year={2020}
}
```
## Disclaimer
This is not an official Google product.
================================================
FILE: geomancer/data_writer.py
================================================
# Copyright 2023 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data writer for Stanford Bunny experiments and other objects."""
# pylint: disable=unused-import
import copy
import io
import os
import time
from absl import app
from absl import flags
from absl import logging
from dm_control import mujoco
import numpy as np
_SHARD = flags.DEFINE_integer('shard', 0, 'Shard index')
_SIZE = flags.DEFINE_integer('size', 1000,
'Number of images to save to a shard')
_OBJECT = flags.DEFINE_string('object', 'dragon', 'Which object to render')
_PATH = flags.DEFINE_string('path', '', 'Path to folder with .stl files')
render_height = 1024
render_width = 1024
height = 256
width = 256
def get_normal(x):
"""Get vectors normal to a unit vector."""
_, _, v = np.linalg.svd(x[None, :])
return v[:, 1:]
def render(quat, light, mesh='bunny', meshdir='data'):
"""Script to render an image."""
scale, pos = None, None
if mesh == 'bunny':
scale = 0.03
pos = -1.0
elif mesh == 'dragon':
scale = 0.06
pos = -0.3
simple_world_mjcf_template = """
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plotting.example_plot(rinfo, t=-1, mask_components=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Iterations\n",
"This plots the decompositions for all timesteps and an additional row with the ground truth segmentation, as well as the predicted mask-logits."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plotting.iterations_plot(rinfo, mask_components=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also plot error and metrics over iterations to see how they improve over the refinement steps:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Errors and metrics over iterations\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"b = 0 # sample index inside batch\n",
"\n",
"fig, axes = plt.subplots(nrows=2, ncols=3, sharex=True, figsize=(12, 6))\n",
"\n",
"axes[0, 0].plot(rinfo[\"losses\"][\"recons\"][b], color='r')\n",
"axes[0, 0].set_title(\"Reconstruction Loss\")\n",
"\n",
"axes[0, 1].plot(rinfo[\"metrics\"][\"mse\"][b], color='orange')\n",
"axes[0, 1].set_title(\"MSE\")\n",
"\n",
"# Plot total KL and faintly also the individual object KLs\n",
"axes[0, 2].plot(rinfo[\"losses\"][\"kl\"][b].sum(axis=1), color='purple')\n",
"axes[0, 2].plot(rinfo[\"losses\"][\"kl\"][b], color='purple', alpha=0.2)\n",
"axes[0, 2].set_title(\"KL\")\n",
"\n",
"axes[1, 0].plot(rinfo[\"metrics\"][\"ari\"][b])\n",
"axes[1, 0].set_title(\"ARI\")\n",
"axes[1, 0].set_ylim((0, 1))\n",
"axes[1, 0].set_xlabel('Iteration')\n",
"\n",
"axes[1, 1].plot(rinfo[\"metrics\"][\"ari_nobg\"][b])\n",
"axes[1, 1].set_title(\"ARI (ignoring background)\")\n",
"axes[1, 1].set_ylim((0, 1))\n",
"axes[1, 1].set_xlabel('Iteration')\n",
"\n",
"axes[1, 2].plot(rinfo[\"losses\"][\"factor\"], color='g')\n",
"axes[1, 2].set_title(\"Factor Regression Loss\")\n",
"axes[1, 2].set_xlabel('Iteration')\n",
"\n",
"print(\"Errors and metrics over iterations\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inputs to the Refinement Network\n",
"This plots all the spatial inputs that are being fed to the refinement network for a given timestep."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plotting.inputs_plot(rinfo, t=-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Latent Traversal"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"sg = ShapeGuard()\n",
"_ = sg.guard(rinfo['latent']['z'], \"B, T, K, Z\")\n",
"z_placeholder = tf.placeholder(tf.float32, shape=sg[\"B, K, Z\"])\n",
"params, out_dist = model.decode(z_placeholder)\n",
"img = out_dist.mean()\n",
"highest_kl_obj_idx = rinfo['losses']['kl'][b, -1].argmax()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"b = 0\n",
"obj_idx = highest_kl_obj_idx\n",
"interesting_latents = [3, 4, 26, 43, 56, 57, 61, 28]\n",
"\n",
"\n",
"fig, axes = plt.subplots(nrows=len(interesting_latents), ncols=9, sharex=True, sharey=True, figsize=(18, len(interesting_latents)*2))\n",
"for row, lat in enumerate(interesting_latents):\n",
" z_adjusted = rinfo['latent']['z'][b:b+1, -1].copy()\n",
" for col, val in enumerate(np.linspace(-1.25, 1.25, 9)):\n",
" z_adjusted[b, obj_idx, lat] = val\n",
" rimg = sess.run(img, feed_dict={z_placeholder:z_adjusted})\n",
" plotting.show_img(rimg[0, 0], ax=axes[row, col])\n",
" axes[row, 0].set_ylabel(lat)\n",
"plt.subplots_adjust(wspace=0.01, hspace=0.01)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prior-samples\n",
"We can also sample a few `z` from the prior and decode them."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"samples = model.get_sample_images()\n",
"rsamples = sess.run(samples)\n",
"\n",
"fig, ax = plt.subplots(figsize=(12, 12))\n",
"ax.imshow(rsamples[0])\n",
"_ = ax.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Running on a custom image\n",
"To run IODINE on a custom image we need a set of placeholders. Then we can just evaluate it as usual:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Get a set of placeholders for the dataset and evaluate the model using that\n",
"input_ph = dataset.get_placeholders()\n",
"custom_info = model.eval(input_ph)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Construct zero-valued fillers for all placeholders\n",
"sg = ShapeGuard()\n",
"sg.guard(input_ph['image'], \"B, 1, H, W, C\")\n",
"sg.guard(input_ph['mask'], \"B, 1, L, H, W, 1\")\n",
" \n",
"fillers = {\n",
" input_ph['image']: np.zeros(sg['1, 1, H, W, C']),\n",
" input_ph['mask']: np.zeros(sg['1, 1, L, H, W, 1']),\n",
" input_ph['latent']['color']: np.zeros(sg[\"1, L, 1\"]),\n",
" input_ph['latent']['shape']: np.zeros(sg[\"1, L, 1\"]),\n",
" input_ph['latent']['size']: np.zeros(sg[\"1, L, 1\"]),\n",
" input_ph['latent']['position']: np.zeros(sg[\"1, L, 3\"]),\n",
" input_ph['latent']['rotation']: np.zeros(sg[\"1, L, 1\"]),\n",
" input_ph['visibility']: np.zeros(sg[\"1, L\"]),\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can evaluate the model on any custom image like this real-world replica of the CLEVR dataset\n",
"\n",
"\n",
"\n",
"(From [\"Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding\"](https://www.semanticscholar.org/paper/Neural-Symbolic-VQA%3A-Disentangling-Reasoning-from-Yi-Wu/9d15ebe3f5aaf32a9f835f88703241461324c35b/figure/16) Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, Joshua B. Tenenbaum)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = np.array(Image.open('images/realworld_clevr.png')) / 255.\n",
"fillers[input_ph['image']] = img.reshape(sg['1, 1, H, W, C'])\n",
"custom_rinfo = sess.run(custom_info, feed_dict=fillers)\n",
"fig = plotting.iterations_plot(custom_rinfo)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Getting detailed information\n",
"The info returned by `model.eval()` contains many variables from all components and iterations.\n",
"For a rough overview see the following sketch:\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Dimensions:\n",
"\n",
"* `B`: batch-size\n",
"* `T`: number of iterations + 1\n",
"* `K`: number of components\n",
"* `L`: maximum number of ground-truth objects\n",
"* `H`: image height\n",
"* `W`: image width\n",
"* `C`: image channels (usually 3 for RGB)\n",
"* `Z`: size of latent representation\n",
"\n",
"Content of `info`:\n",
" \n",
"* data: Inputs from the dataset (for the CLEVR dataset)\n",
" * image: `(B, 1, H, W, C)` Input Image (For sequential data this has dimension `(B, T, H, W, C)`)\n",
" * true_mask: `(B, 1, L, H, W, 1)` ground truth mask (used for ARI and factor regressor only)\n",
" * visibility: `(B, L)` How many object are in this scene\n",
" * factors: Information about true factors (used to train the factor regressor)\n",
" * color: `(B, L, 1)` The color of each object (categorical 9 choices)\n",
" * position: `(B, L, 3)` The position of each object (x, y, z)\n",
" * shape: `(B, L, 1)` The shape of each object (categorical 4 choices)\n",
" * size: `(B, L, 1)` The size of each object (categorical 3 choices)\n",
"* inputs: Inputs to the refinement network\n",
" * flat:\n",
" * dzp: `(B, T, K, 2*Z)`\n",
" * flat_capacity: `(B, T, K, 1)`\n",
" * zp: `(B, T, K, 2*Z)`\n",
" * spatial:\n",
" * capacity: `(B, T, K, H, W, 1)`\n",
" * components: `(B, T, K, H, W, C)`\n",
" * coordinates: `(B, T, 1, H, W, 2)`\n",
" * counterfactual: `(B, T, K, H, W, 1)`\n",
" * dcomponents: `(B, T, K, H, W, C)`\n",
" * dmask: `(B, T, K, H, W, 1)`\n",
" * image: `(B, T, 1, H, W, C)`\n",
" * log_prob: `(B, T, 1, H, W, 1)`\n",
" * posterior: `(B, T, K, H, W, 1)`\n",
" * pred_mask: `(B, T, K, H, W, 1)`\n",
" * pred_mask_logits: `(B, T, K, H, W, 1)`\n",
"* latent: \n",
" * z: `(B, T, K, Z)`\n",
" * z_mean: `(B, T, K, Z)`\n",
" * z_stddev: `(B, T, K, Z)`\n",
"* outputs: Output of the decoder network\n",
" * components: `(B, T, K, H, W, C)` Predicted means for each component.\n",
" * pred_mask: `(B, T, K, H, W, 1)` Predicted mask\n",
" * pred_mask_logits: `(B, T, K, H, W, 1)` Predicted mask logits (pre-softmax) for each component\n",
" * recons: `(B, T, 1, H, W, C)` image reconstruction (mean of the spatial mixture)\n",
"* losses:\n",
" * total: float\n",
" * recons: `(B, T)`\n",
" * kl: `(B, T, K)`\n",
" * factor: `(T,)`\n",
"* metrics\n",
" * ari: `(B, T)`\n",
" * kl: `(B, T, K)`\n",
" * mse: `(B, T)`\n",
"* factor_regressor\n",
" * assignment: `(B, T, L, K)`\n",
" * predictions: \n",
" * color: `(B, T, L, 9)`\n",
" * position: `(B, T, L, 3)`\n",
" * shape: `(B, T, L, 4)`\n",
" * size: `(B, T, L, 3)`\n",
" * metrics:\n",
" * color: `(T,)` Color prediction accuracy\n",
" * shape: `(T,)` Shape prediction accuracy\n",
" * size: `(T,)` Size prediction accuracy\n",
" * position: `(T,)` R^2 metric for position prediction\n",
""
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"out_info:\n",
" data:\n",
" image: [1, 1, 128, 128, 3] @ \n",
" true_mask: [1, 1, 11, 128, 128, 1] @ \n",
" visibility: [1, 11] @ \n",
" factors:\n",
" color: [1, 11, 1] @ \n",
" position: [1, 11, 3] @ \n",
" rotation: [1, 11, 1] @ \n",
" shape: [1, 11, 1] @ \n",
" size: [1, 11, 1] @ \n",
" factor_regressor:\n",
" assignment: [1, 6, 11, 7] @ \n",
" metrics:\n",
" color: [6] @ \n",
" position: [6] @ \n",
" shape: [6] @ \n",
" size: [6] @ \n",
" predictions:\n",
" color: [1, 6, 11, 9] @ \n",
" position: [1, 6, 11, 3] @ \n",
" shape: [1, 6, 11, 4] @ \n",
" size: [1, 6, 11, 3] @ \n",
" inputs:\n",
" flat:\n",
" dzp: [1, 6, 7, 128] @ \n",
" flat_capacity: [1, 6, 7, 1] @ \n",
" zp: [1, 6, 7, 128] @ \n",
" spatial:\n",
" capacity: [1, 6, 7, 128, 128, 1] @ \n",
" components: [1, 6, 7, 128, 128, 3] @ \n",
" coordinates: [1, 6, 1, 128, 128, 2] @ \n",
" counterfactual: [1, 6, 7, 128, 128, 1] @ \n",
" dcomponents: [1, 6, 7, 128, 128, 3] @ \n",
" dmask: [1, 6, 7, 128, 128, 1] @ \n",
" image: [1, 6, 1, 128, 128, 3] @ \n",
" log_prob: [1, 6, 1, 128, 128, 1] @ \n",
" mask: [1, 6, 7, 128, 128, 1] @ \n",
" posterior: [1, 6, 7, 128, 128, 1] @ \n",
" pred_mask: [1, 6, 7, 128, 128, 1] @ \n",
" latent:\n",
" z: [1, 6, 7, 64] @ \n",
" z_mean: [1, 6, 7, 64] @ \n",
" z_std: [1, 6, 7, 64] @ \n",
" losses:\n",
" factor: [6] @ \n",
" kl: [1, 6, 1, 7] @ \n",
" recons: [6, 1, 1] @ \n",
" total: [] @ \n",
" metrics:\n",
" ari: [1, 6] @ \n",
" ari_nobg: [1, 6] @ \n",
" mse: [1, 6] @ \n",
" outputs:\n",
" components: [1, 6, 7, 128, 128, 3] @ \n",
" pred_mask: [1, 6, 7, 128, 128, 1] @ \n",
" pred_mask_logits: [1, 6, 7, 128, 128, 1] @ \n",
" recons: [1, 6, 1, 128, 128, 3] @ \n"
]
}
],
"source": [
"utils.print_shapes('out_info', info)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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}
================================================
FILE: iodine/README.md
================================================
# IODINE
Reference implementation for the paper ["Multi-Object Representation Learning with Iterative Variational Inference"](https://arxiv.org/abs/1903.00450).
This repository contains:
* An IODINE implementation in Tensorflow v1.
* Configurations used in the paper (checkpoints available in Cloud Storage) for:
* CLEVR
* Multi-dSprites
* Tetrominoes
* A notebook for running and inspecting the model and plotting the results
## Installation
1. Clone the DeepMind research repository:
``` bash
git clone https://github.com/deepmind/deepmind-research.git
cd deepmind-research
```
2. Download the checkpoints from GCP. A shell script is provided:
```bash
./iodine/download_checkpoints.sh
```
On platforms without wget, the files can be downloaded from [this webpage](https://console.cloud.google.com/storage/browser/deepmind-research-iodine?pli=1)
and the unzipped `checkpoints/` folder should be placed in
`deepmind-research/iodine/checkpoints`.
3. Prepare a Python 3 environment - virtualenv is recommended.
```bash
python3 -m venv iodine_venv
source iodine_venv/bin/activate
```
4. Install dependencies:
```bash
pip3 install -r iodine/requirements.txt
```
5. The `multi_object_datasets` package installed via requirements.txt provides python code to open the data files, but not the data files themselves.
Download the desired datasets either manually from the [Google Cloud Storage](https://console.cloud.google.com/storage/browser/multi-object-datasets) or using the commands below:
```bash
pushd iodine/multi_object_datasets
# CLEVR
wget https://storage.googleapis.com/multi-object-datasets/clevr_with_masks/clevr_with_masks_train.tfrecords
# Multi-dSprites
wget https://storage.googleapis.com/multi-object-datasets/multi_dsprites/multi_dsprites_colored_on_grayscale.tfrecords
# Tetrominoes
wget https://storage.googleapis.com/multi-object-datasets/tetrominoes/tetrominoes_train.tfrecords
# Get back to location containing 'iodine' directory
popd
```
See [multi_object_datasets repository](https://github.com/deepmind/multi_object_datasets)
for further details.
6. Make sure that you have CUDA 10 and CuDNN 7 installed
## Interact with a Model
Use the jupyter notebook `Eval.ipynb` to load and run one of the checkpoints.
It also contains code to plot the outputs and latent traversals.
## Train a Model
To train your own model use the [Sacred](https://github.com/IDSIA/sacred) experiment defined in `main.py`.
The configurations used in the paper for the different datasets are available as [named configs](https://sacred.readthedocs.io/en/latest/configuration.html#named-configurations) inside of `configuration.py`.
### Train a new model
* CLEVR6
```bash
python3 -m iodine.main -f with clevr6
```
* Multi-dSprites
```bash
python3 -m iodine.main -f with multi_dsprites
```
* Tetrominoes
```bash
python3 -m iodine.main -f with tetrominoes
```
It is recommended to add an observer to your run to let Sacred record the details of run.
To add a [FileStorageObserver](https://sacred.readthedocs.io/en/latest/command_line.html#filestorage-observer) add `-F my_storage_dir`, and add `-m my_db_name` for a [MongoObserver](https://sacred.readthedocs.io/en/latest/command_line.html#mongodb-observer).
### Adjusting Config Values
The experiment has a configuration that can be printed and adjusted from the commandline. E.g.:
``` bash
# print configuration
python3 -m iodine.main -f print_config with clevr6
# run experiment after adjusting batch_size and the size of the shuffle buffer
python3 -m iodine.main -f with clevr6 batch_size=2 data.shuffle_buffer=100
```
### Tensorboard
Each run stores checkpoints and summaries in the directory specified by `checkpoint_dir`, to which a suffix based on the run_id is appended.
If an observer is added the `run_id` is set automatically. Otherwise it should be set manually using e.g. `run_id=5`.
Summaries can be viewed using tensorboard. E.g. like this for clevr6 (assuming `run_id=1`):
```bash
tensorboard --log-dir iodine/checkpoints/clevr6_1
```
### Continue Previous Run
To continue a previous run pass `continue_run=True` and the path of the checkpoints:
```bash
python3 -m iodine.main -f with clevr6 checkpoint_dir=iodine/checkpoints/clevr6_1
```
## Code Structure
The main experiment defined in `main.py` uses `sacred` and the configurations for the different datasets are added as named configs and can be found in `configuration.py`.
The model implementation can be found in the `modules` directory and is based on `tensorflow` and `sonnet`:
* `iodine.py` The main IODINE module that assembles the decoder, refinement network, distributions and factor regressor.
* `decoder.py` The ComponentDecoder which is a wrapper around networks that takes care of splitting the output channels into means and masks.
* `refinement.py` The refinement components assembles the encoder network, LSTM and refinement head.
* `networks.py` Different standard networks such as CNN, BroadcastCNN, and LSTM.
* `distribution.py` Definition of the latent and pixel distributions.
* `factor_eval.py` Contains the factor regressor which predicts the true factors from the inferred object latents.
* `data.py` Dataset wrappers around `multi_object_datasets` that take care of shuffling, batching and preprocessing.
* `plotting.py` Helper functions for plotting results.
* `utils.py` General helper functions.
---
**DISCLAIMER**
This is not an officially supported Google product.
---
================================================
FILE: iodine/configurations.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Configurations for IODINE."""
# pylint: disable=missing-docstring, unused-variable
import math
def clevr6():
n_z = 64 # number of latent dimensions
num_components = 7 # number of components (K)
num_iters = 5
checkpoint_dir = "iodine/checkpoints/clevr6"
# For the paper we used 8 GPUs with a batch size of 4 each.
# This means a total batch size of 32, which is too large for a single GPU.
# When reducing the batch size, the learning rate should also be lowered.
batch_size = 4
learn_rate = 0.001 * math.sqrt(batch_size / 32)
data = {
"constructor": "iodine.modules.data.CLEVR",
"batch_size": batch_size,
"path": "multi_object_datasets/clevr_with_masks_train.tfrecords",
"max_num_objects": 6,
}
model = {
"constructor": "iodine.modules.iodine.IODINE",
"n_z": n_z,
"num_components": num_components,
"num_iters": num_iters,
"iter_loss_weight": "linspace",
"coord_type": "linear",
"decoder": {
"constructor": "iodine.modules.decoder.ComponentDecoder",
"pixel_decoder": {
"constructor": "iodine.modules.networks.BroadcastConv",
"cnn_opt": {
# Final channels is irrelevant with target_output_shape
"output_channels": [64, 64, 64, 64, None],
"kernel_shapes": [3],
"strides": [1],
"activation": "elu",
},
"coord_type": "linear",
},
},
"refinement_core": {
"constructor": "iodine.modules.refinement.RefinementCore",
"encoder_net": {
"constructor": "iodine.modules.networks.CNN",
"mode": "avg_pool",
"cnn_opt": {
"output_channels": [64, 64, 64, 64],
"strides": [2],
"kernel_shapes": [3],
"activation": "elu",
},
"mlp_opt": {
"output_sizes": [256, 256],
"activation": "elu"
},
},
"recurrent_net": {
"constructor": "iodine.modules.networks.LSTM",
"hidden_sizes": [256],
},
"refinement_head": {
"constructor": "iodine.modules.refinement.ResHead"
},
},
"latent_dist": {
"constructor": "iodine.modules.distributions.LocScaleDistribution",
"dist": "normal",
"scale_act": "softplus",
"scale": "var",
"name": "latent_dist",
},
"output_dist": {
"constructor": "iodine.modules.distributions.MaskedMixture",
"num_components": num_components,
"component_dist": {
"constructor":
"iodine.modules.distributions.LocScaleDistribution",
"dist":
"logistic",
"scale":
"fixed",
"scale_val":
0.03,
"name":
"pixel_distribution",
},
},
"factor_evaluator": {
"constructor":
"iodine.modules.factor_eval.FactorRegressor",
"mapping": [
("color", 9, "categorical"),
("shape", 4, "categorical"),
("size", 3, "categorical"),
("position", 3, "scalar"),
],
},
}
optimizer = {
"constructor": "tensorflow.train.AdamOptimizer",
"learning_rate": {
"constructor": "tensorflow.train.exponential_decay",
"learning_rate": learn_rate,
"global_step": {
"constructor": "tensorflow.train.get_or_create_global_step"
},
"decay_steps": 1000000,
"decay_rate": 0.1,
},
"beta1": 0.95,
}
def multi_dsprites():
n_z = 16 # number of latent dimensions
num_components = 6 # number of components (K)
num_iters = 5
checkpoint_dir = "iodine/checkpoints/multi_dsprites"
# For the paper we used 8 GPUs with a batch size of 16 each.
# This means a total batch size of 128, which is too large for a single GPU.
# When reducing the batch size, the learning rate should also be lowered.
batch_size = 16
learn_rate = 0.0003 * math.sqrt(batch_size / 128)
data = {
"constructor":
"iodine.modules.data.MultiDSprites",
"batch_size":
batch_size,
"path":
"multi_object_datasets/multi_dsprites_colored_on_grayscale.tfrecords",
"dataset_variant":
"colored_on_grayscale",
"min_num_objs":
3,
"max_num_objs":
3,
}
model = {
"constructor": "iodine.modules.iodine.IODINE",
"n_z": n_z,
"num_components": num_components,
"num_iters": num_iters,
"iter_loss_weight": "linspace",
"coord_type": "cos",
"coord_freqs": 3,
"decoder": {
"constructor": "iodine.modules.decoder.ComponentDecoder",
"pixel_decoder": {
"constructor": "iodine.modules.networks.BroadcastConv",
"cnn_opt": {
# Final channels is irrelevant with target_output_shape
"output_channels": [32, 32, 32, 32, None],
"kernel_shapes": [5],
"strides": [1],
"activation": "elu",
},
"coord_type": "linear",
},
},
"refinement_core": {
"constructor": "iodine.modules.refinement.RefinementCore",
"encoder_net": {
"constructor": "iodine.modules.networks.CNN",
"mode": "avg_pool",
"cnn_opt": {
"output_channels": [32, 32, 32],
"strides": [2],
"kernel_shapes": [5],
"activation": "elu",
},
"mlp_opt": {
"output_sizes": [128],
"activation": "elu"
},
},
"recurrent_net": {
"constructor": "iodine.modules.networks.LSTM",
"hidden_sizes": [128],
},
"refinement_head": {
"constructor": "iodine.modules.refinement.ResHead"
},
},
"latent_dist": {
"constructor": "iodine.modules.distributions.LocScaleDistribution",
"dist": "normal",
"scale_act": "softplus",
"scale": "var",
"name": "latent_dist",
},
"output_dist": {
"constructor": "iodine.modules.distributions.MaskedMixture",
"num_components": num_components,
"component_dist": {
"constructor":
"iodine.modules.distributions.LocScaleDistribution",
"dist":
"logistic",
"scale":
"fixed",
"scale_val":
0.03,
"name":
"pixel_distribution",
},
},
"factor_evaluator": {
"constructor":
"iodine.modules.factor_eval.FactorRegressor",
"mapping": [
("color", 3, "scalar"),
("shape", 4, "categorical"),
("scale", 1, "scalar"),
("x", 1, "scalar"),
("y", 1, "scalar"),
("orientation", 2, "angle"),
],
},
}
optimizer = {
"constructor": "tensorflow.train.AdamOptimizer",
"learning_rate": {
"constructor": "tensorflow.train.exponential_decay",
"learning_rate": learn_rate,
"global_step": {
"constructor": "tensorflow.train.get_or_create_global_step"
},
"decay_steps": 1000000,
"decay_rate": 0.1,
},
"beta1": 0.95,
}
def tetrominoes():
n_z = 32 # number of latent dimensions
num_components = 4 # number of components (K)
num_iters = 5
checkpoint_dir = "iodine/checkpoints/tetrominoes"
# For the paper we used 8 GPUs with a batch size of 32 each.
# This means a total batch size of 256, which is too large for a single GPU.
# When reducing the batch size, the learning rate should also be lowered.
batch_size = 128
learn_rate = 0.0003 * math.sqrt(batch_size / 256)
data = {
"constructor": "iodine.modules.data.Tetrominoes",
"batch_size": batch_size,
"path": "iodine/multi_object_datasets/tetrominoes_train.tfrecords",
}
model = {
"constructor": "iodine.modules.iodine.IODINE",
"n_z": n_z,
"num_components": num_components,
"num_iters": num_iters,
"iter_loss_weight": "linspace",
"coord_type": "cos",
"coord_freqs": 3,
"decoder": {
"constructor": "iodine.modules.decoder.ComponentDecoder",
"pixel_decoder": {
"constructor": "iodine.modules.networks.BroadcastConv",
"cnn_opt": {
# Final channels is irrelevant with target_output_shape
"output_channels": [32, 32, 32, 32, None],
"kernel_shapes": [5],
"strides": [1],
"activation": "elu",
},
"coord_type": "linear",
"coord_freqs": 3,
},
},
"refinement_core": {
"constructor": "iodine.modules.refinement.RefinementCore",
"encoder_net": {
"constructor": "iodine.modules.networks.CNN",
"mode": "avg_pool",
"cnn_opt": {
"output_channels": [32, 32, 32],
"strides": [2],
"kernel_shapes": [5],
"activation": "elu",
},
"mlp_opt": {
"output_sizes": [128],
"activation": "elu"
},
},
"recurrent_net": {
"constructor": "iodine.modules.networks.LSTM",
"hidden_sizes": [], # No recurrent layer used for this dataset
},
"refinement_head": {
"constructor": "iodine.modules.refinement.ResHead"
},
},
"latent_dist": {
"constructor": "iodine.modules.distributions.LocScaleDistribution",
"dist": "normal",
"scale_act": "softplus",
"scale": "var",
"name": "latent_dist",
},
"output_dist": {
"constructor": "iodine.modules.distributions.MaskedMixture",
"num_components": num_components,
"component_dist": {
"constructor":
"iodine.modules.distributions.LocScaleDistribution",
"dist":
"logistic",
"scale":
"fixed",
"scale_val":
0.03,
"name":
"pixel_distribution",
},
},
"factor_evaluator": {
"constructor":
"iodine.modules.factor_eval.FactorRegressor",
"mapping": [
("position", 2, "scalar"),
("color", 3, "scalar"),
("shape", 20, "categorical"),
],
},
}
optimizer = {
"constructor": "tensorflow.train.AdamOptimizer",
"learning_rate": {
"constructor": "tensorflow.train.exponential_decay",
"learning_rate": learn_rate,
"global_step": {
"constructor": "tensorflow.train.get_or_create_global_step"
},
"decay_steps": 1000000,
"decay_rate": 0.1,
},
"beta1": 0.95,
}
================================================
FILE: iodine/download_checkpoints.sh
================================================
#!/bin/bash
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
pushd iodine
wget http://storage.googleapis.com/deepmind-research-iodine/iodine_checkpoints.zip
unzip iodine_checkpoints.zip
popd
================================================
FILE: iodine/main.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: disable=g-importing-member, g-multiple-import, g-import-not-at-top
# pylint: disable=protected-access, g-bad-import-order, missing-docstring
# pylint: disable=unused-variable, invalid-name, no-value-for-parameter
from copy import deepcopy
import os.path
import warnings
from absl import logging
import numpy as np
from sacred import Experiment, SETTINGS
# Ignore all tensorflow deprecation warnings
logging._warn_preinit_stderr = 0
warnings.filterwarnings("ignore", module=".*tensorflow.*")
import tensorflow.compat.v1 as tf
tf.logging.set_verbosity(tf.logging.ERROR)
import sonnet as snt
from sacred.stflow import LogFileWriter
from iodine.modules import utils
from iodine import configurations
SETTINGS.CONFIG.READ_ONLY_CONFIG = False
ex = Experiment("iodine")
@ex.config
def default_config():
continue_run = False # set to continue experiment from an existing checkpoint
checkpoint_dir = ("checkpoints/iodine"
) # if continue_run is False, "_{run_id}" will be appended
save_summaries_steps = 10
save_checkpoint_steps = 1000
n_z = 64 # number of latent dimensions
num_components = 7 # number of components (K)
num_iters = 5
learn_rate = 0.001
batch_size = 4
stop_after_steps = int(1e6)
# Details for the dataset, model and optimizer are left empty here.
# They can be found in the configurations for individual datasets,
# which are provided in configurations.py and added as named configs.
data = {} # Dataset details will go here
model = {} # Model details will go here
optimizer = {} # Optimizer details will go here
ex.named_config(configurations.clevr6)
ex.named_config(configurations.multi_dsprites)
ex.named_config(configurations.tetrominoes)
@ex.capture
def build(identifier, _config):
config_copy = deepcopy(_config[identifier])
return utils.build(config_copy, identifier=identifier)
def get_train_step(model, dataset, optimizer):
loss, scalars, _ = model(dataset("train"))
global_step = tf.train.get_or_create_global_step()
grads = optimizer.compute_gradients(loss)
gradients, variables = zip(*grads)
global_norm = tf.global_norm(gradients)
gradients, global_norm = tf.clip_by_global_norm(
gradients, 5.0, use_norm=global_norm)
grads = zip(gradients, variables)
train_op = optimizer.apply_gradients(grads, global_step=global_step)
with tf.control_dependencies([train_op]):
overview = model.get_overview_images(dataset("summary"))
scalars["debug/global_grad_norm"] = global_norm
summaries = {
k: tf.summary.scalar(k, v) for k, v in scalars.items()
}
summaries.update(
{k: tf.summary.image(k, v) for k, v in overview.items()})
return tf.identity(global_step), scalars, train_op
@ex.capture
def get_checkpoint_dir(continue_run, checkpoint_dir, _run, _log):
if continue_run:
assert os.path.exists(checkpoint_dir)
_log.info("Continuing run from checkpoint at {}".format(checkpoint_dir))
return checkpoint_dir
run_id = _run._id
if run_id is None: # then no observer was added that provided an _id
if not _run.unobserved:
_log.warning(
"No run_id given or provided by an Observer. (Re-)using run_id=1.")
run_id = 1
checkpoint_dir = checkpoint_dir + "_{run_id}".format(run_id=run_id)
_log.info(
"Starting a new run using checkpoint dir: '{}'".format(checkpoint_dir))
return checkpoint_dir
@ex.capture
def get_session(chkp_dir, loss, stop_after_steps, save_summaries_steps,
save_checkpoint_steps):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
hooks = [
tf.train.StopAtStepHook(last_step=stop_after_steps),
tf.train.NanTensorHook(loss),
]
return tf.train.MonitoredTrainingSession(
hooks=hooks,
config=config,
checkpoint_dir=chkp_dir,
save_summaries_steps=save_summaries_steps,
save_checkpoint_steps=save_checkpoint_steps,
)
@ex.command(unobserved=True)
def load_checkpoint(use_placeholder=False, session=None):
dataset = build("data")
model = build("model")
if use_placeholder:
inputs = dataset.get_placeholders()
else:
inputs = dataset()
info = model.eval(inputs)
if session is None:
session = tf.Session()
saver = tf.train.Saver()
checkpoint_dir = get_checkpoint_dir()
checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
saver.restore(session, checkpoint_file)
print('Successfully restored Checkpoint "{}"'.format(checkpoint_file))
# print variables
variables = tf.global_variables() + tf.local_variables()
for row in snt.format_variables(variables, join_lines=False):
print(row)
return {
"session": session,
"model": model,
"info": info,
"inputs": inputs,
"dataset": dataset,
}
@ex.automain
@LogFileWriter(ex)
def main(save_summaries_steps):
checkpoint_dir = get_checkpoint_dir()
dataset = build("data")
model = build("model")
optimizer = build("optimizer")
gstep, train_step_exports, train_op = get_train_step(model, dataset,
optimizer)
loss, ari = [], []
with get_session(checkpoint_dir, train_step_exports["loss/total"]) as sess:
while not sess.should_stop():
out = sess.run({
"step": gstep,
"loss": train_step_exports["loss/total"],
"ari": train_step_exports["loss/ari_nobg"],
"train": train_op,
})
loss.append(out["loss"])
ari.append(out["ari"])
step = out["step"]
if step % save_summaries_steps == 0:
mean_loss = np.mean(loss)
mean_ari = np.mean(ari)
ex.log_scalar("loss", mean_loss, step)
ex.log_scalar("ari", mean_ari, step)
print("{step:>6d} Loss: {loss: >12.2f}\t\tARI-nobg:{ari: >6.2f}".format(
step=step, loss=mean_loss, ari=mean_ari))
loss, ari = [], []
================================================
FILE: iodine/modules/__init__.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
================================================
FILE: iodine/modules/data.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data loading functionality for IODINE."""
# pylint: disable=g-multiple-import, missing-docstring, unused-import
import os.path
from iodine.modules.utils import flatten_all_but_last, ensure_3d
from multi_object_datasets import (
clevr_with_masks,
multi_dsprites,
tetrominoes,
objects_room,
)
from shapeguard import ShapeGuard
import sonnet as snt
import tensorflow.compat.v1 as tf
class IODINEDataset(snt.AbstractModule):
num_true_objects = 1
num_channels = 3
factors = {}
def __init__(
self,
path,
batch_size,
image_dim,
crop_region=None,
shuffle_buffer=1000,
max_num_objects=None,
min_num_objects=None,
grayscale=False,
name="dataset",
**kwargs,
):
super().__init__(name=name)
self.path = os.path.abspath(os.path.expanduser(path))
self.batch_size = batch_size
self.crop_region = crop_region
self.image_dim = image_dim
self.shuffle_buffer = shuffle_buffer
self.max_num_objects = max_num_objects
self.min_num_objects = min_num_objects
self.grayscale = grayscale
self.dataset = None
def _build(self, subset="train"):
dataset = self.dataset
# filter by number of objects
if self.max_num_objects is not None or self.min_num_objects is not None:
dataset = self.dataset.filter(self.filter_by_num_objects)
if subset == "train":
# normal mode returns a shuffled dataset iterator
if self.shuffle_buffer is not None:
dataset = dataset.shuffle(self.shuffle_buffer)
elif subset == "summary":
# for generating summaries and overview images
# returns a single fixed batch
dataset = dataset.take(self.batch_size)
# repeat and batch
dataset = dataset.repeat().batch(self.batch_size, drop_remainder=True)
iterator = dataset.make_one_shot_iterator()
data = iterator.get_next()
# preprocess the data to ensure correct format, scale images etc.
data = self.preprocess(data)
return data
def filter_by_num_objects(self, d):
if "visibility" not in d:
return tf.constant(True)
min_num_objects = self.max_num_objects or 0
max_num_objects = self.max_num_objects or 6
min_predicate = tf.greater_equal(
tf.reduce_sum(d["visibility"]),
tf.constant(min_num_objects - 1e-5, dtype=tf.float32),
)
max_predicate = tf.less_equal(
tf.reduce_sum(d["visibility"]),
tf.constant(max_num_objects + 1e-5, dtype=tf.float32),
)
return tf.logical_and(min_predicate, max_predicate)
def preprocess(self, data):
sg = ShapeGuard(dims={
"B": self.batch_size,
"H": self.image_dim[0],
"W": self.image_dim[1]
})
image = sg.guard(data["image"], "B, h, w, C")
mask = sg.guard(data["mask"], "B, L, h, w, 1")
# to float
image = tf.cast(image, tf.float32) / 255.0
mask = tf.cast(mask, tf.float32) / 255.0
# crop
if self.crop_region is not None:
height_slice = slice(self.crop_region[0][0], self.crop_region[0][1])
width_slice = slice(self.crop_region[1][0], self.crop_region[1][1])
image = image[:, height_slice, width_slice, :]
mask = mask[:, :, height_slice, width_slice, :]
flat_mask, unflatten = flatten_all_but_last(mask, n_dims=3)
# rescale
size = tf.constant(
self.image_dim, dtype=tf.int32, shape=[2], verify_shape=True)
image = tf.image.resize_images(
image, size, method=tf.image.ResizeMethod.BILINEAR)
mask = tf.image.resize_images(
flat_mask, size, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
if self.grayscale:
image = tf.reduce_mean(image, axis=-1, keepdims=True)
output = {
"image": sg.guard(image[:, None], "B, T, H, W, C"),
"mask": sg.guard(unflatten(mask)[:, None], "B, T, L, H, W, 1"),
"factors": self.preprocess_factors(data, sg),
}
if "visibility" in data:
output["visibility"] = sg.guard(data["visibility"], "B, L")
else:
output["visibility"] = tf.ones(sg["B, L"], dtype=tf.float32)
return output
def preprocess_factors(self, data, sg):
return {
name: sg.guard(ensure_3d(data[name]), "B, L, *")
for name in self.factors
}
def get_placeholders(self, batch_size=None):
batch_size = batch_size or self.batch_size
sg = ShapeGuard(
dims={
"B": batch_size,
"H": self.image_dim[0],
"W": self.image_dim[1],
"L": self.num_true_objects,
"C": 3,
"T": 1,
})
return {
"image": tf.placeholder(dtype=tf.float32, shape=sg["B, T, H, W, C"]),
"mask": tf.placeholder(dtype=tf.float32, shape=sg["B, T, L, H, W, 1"]),
"visibility": tf.placeholder(dtype=tf.float32, shape=sg["B, L"]),
"factors": {
name:
tf.placeholder(dtype=dtype, shape=sg["B, L, {}".format(size)])
for name, (dtype, size) in self.factors
},
}
class CLEVR(IODINEDataset):
num_true_objects = 11
num_channels = 3
factors = {
"color": (tf.uint8, 1),
"shape": (tf.uint8, 1),
"size": (tf.uint8, 1),
"position": (tf.float32, 3),
"rotation": (tf.float32, 1),
}
def __init__(
self,
path,
crop_region=((29, 221), (64, 256)),
image_dim=(128, 128),
name="clevr",
**kwargs,
):
super().__init__(
path=path,
crop_region=crop_region,
image_dim=image_dim,
name=name,
**kwargs)
self.dataset = clevr_with_masks.dataset(self.path)
def preprocess_factors(self, data, sg):
return {
"color": sg.guard(ensure_3d(data["color"]), "B, L, 1"),
"shape": sg.guard(ensure_3d(data["shape"]), "B, L, 1"),
"size": sg.guard(ensure_3d(data["color"]), "B, L, 1"),
"position": sg.guard(ensure_3d(data["pixel_coords"]), "B, L, 3"),
"rotation": sg.guard(ensure_3d(data["rotation"]), "B, L, 1"),
}
class MultiDSprites(IODINEDataset):
num_true_objects = 6
num_channels = 3
factors = {
"color": (tf.float32, 3),
"shape": (tf.uint8, 1),
"scale": (tf.float32, 1),
"x": (tf.float32, 1),
"y": (tf.float32, 1),
"orientation": (tf.float32, 1),
}
def __init__(
self,
path,
# variant from ['binarized', 'colored_on_grayscale', 'colored_on_colored']
dataset_variant="colored_on_grayscale",
image_dim=(64, 64),
name="multi_dsprites",
**kwargs,
):
super().__init__(path=path, name=name, image_dim=image_dim, **kwargs)
self.dataset_variant = dataset_variant
self.dataset = multi_dsprites.dataset(self.path, self.dataset_variant)
class Tetrominoes(IODINEDataset):
num_true_objects = 6
num_channels = 3
factors = {
"color": (tf.uint8, 3),
"shape": (tf.uint8, 1),
"position": (tf.float32, 2),
}
def __init__(self, path, image_dim=(35, 35), name="tetrominoes", **kwargs):
super().__init__(path=path, name=name, image_dim=image_dim, **kwargs)
self.dataset = tetrominoes.dataset(self.path)
def preprocess_factors(self, data, sg):
pos_x = ensure_3d(data["x"])
pos_y = ensure_3d(data["y"])
position = tf.concat([pos_x, pos_y], axis=2)
return {
"color": sg.guard(ensure_3d(data["color"]), "B, L, 3"),
"shape": sg.guard(ensure_3d(data["shape"]), "B, L, 1"),
"position": sg.guard(ensure_3d(position), "B, L, 2"),
}
================================================
FILE: iodine/modules/decoder.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Decoders for rendering images."""
# pylint: disable=missing-docstring
from iodine.modules.distributions import MixtureParameters
import shapeguard
import sonnet as snt
class ComponentDecoder(snt.AbstractModule):
def __init__(self, pixel_decoder, name="component_decoder"):
super().__init__(name=name)
self._pixel_decoder = pixel_decoder
self._sg = shapeguard.ShapeGuard()
def set_output_shapes(self, pixel, mask):
self._sg.guard(pixel, "K, H, W, Cp")
self._sg.guard(mask, "K, H, W, 1")
self._pixel_decoder.set_output_shapes(self._sg["H, W, 1 + Cp"])
def _build(self, z):
self._sg.guard(z, "B, K, Z")
z_flat = self._sg.reshape(z, "B*K, Z")
pixel_params = self._pixel_decoder(z_flat).params
self._sg.guard(pixel_params, "B*K, H, W, 1 + Cp")
mask_params = pixel_params[..., 0:1]
pixel_params = pixel_params[..., 1:]
output = MixtureParameters(
pixel=self._sg.reshape(pixel_params, "B, K, H, W, Cp"),
mask=self._sg.reshape(mask_params, "B, K, H, W, 1"),
)
del self._sg.B
return output
================================================
FILE: iodine/modules/distributions.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Collection of sonnet modules that wrap useful distributions."""
# pylint: disable=missing-docstring, g-doc-args, g-short-docstring-punctuation
# pylint: disable=g-space-before-docstring-summary
# pylint: disable=g-no-space-after-docstring-summary
import collections
from iodine.modules.utils import get_act_func
from iodine.modules.utils import get_distribution
import shapeguard
import sonnet as snt
import tensorflow.compat.v1 as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
FlatParameters = collections.namedtuple("ParameterOut", ["params"])
MixtureParameters = collections.namedtuple("MixtureOut", ["pixel", "mask"])
class DistributionModule(snt.AbstractModule):
"""Distribution Base class supporting shape inference & default priors."""
def __init__(self, name="distribution"):
super().__init__(name=name)
self._output_shape = None
def set_output_shape(self, shape):
self._output_shape = shape
@property
def output_shape(self):
return self._output_shape
@property
def input_shapes(self):
raise NotImplementedError()
def get_default_prior(self, batch_dim=(1,)):
return self(
tf.zeros(list(batch_dim) + self.input_shapes.params, dtype=tf.float32))
class BernoulliOutput(DistributionModule):
def __init__(self, name="bernoulli_output"):
super().__init__(name=name)
@property
def input_shapes(self):
return FlatParameters(self.output_shape)
def _build(self, params):
return tfd.Independent(
tfd.Bernoulli(logits=params, dtype=tf.float32),
reinterpreted_batch_ndims=1)
class LocScaleDistribution(DistributionModule):
"""Generic IID location / scale distribution.
Input parameters are concatenation of location and scale (2*Z,)
Args:
dist: Distribution or str Kind of distribution used. Supports Normal,
Logistic, Laplace, and StudentT distributions.
dist_kwargs: dict custom keyword arguments for the distribution
scale_act: function or str or None activation function to be applied to
the scale input
scale: str
different modes for computing the scale:
* stddev: scale is computed as scale_act(s)
* var: scale is computed as sqrt(scale_act(s))
* prec: scale is computed as 1./scale_act(s)
* fixed: scale is a global variable (same for all pixels) if
scale_val==-1. then it is a trainable variable initialized to 0.1
else it is fixed to scale_val (input shape is only (Z,) in this
case)
scale_val: float determines the scale value (only used if scale=='fixed').
loc_act: function or str or None activation function to be applied to the
location input. Supports optional activation functions for scale and
location.
Supports different "modes" for scaling:
* stddev:
"""
def __init__(
self,
dist=tfd.Normal,
dist_kwargs=None,
scale_act=tf.exp,
scale="stddev",
scale_val=1.0,
loc_act=None,
name="loc_scale_dist",
):
super().__init__(name=name)
self._scale_act = get_act_func(scale_act)
self._loc_act = get_act_func(loc_act)
# supports Normal, Logstic, Laplace, StudentT
self._dist = get_distribution(dist)
self._dist_kwargs = dist_kwargs or {}
assert scale in ["stddev", "var", "prec", "fixed"], scale
self._scale = scale
self._scale_val = scale_val
@property
def input_shapes(self):
if self._scale == "fixed":
param_shape = self.output_shape
else:
param_shape = self.output_shape[:-1] + [self.output_shape[-1] * 2]
return FlatParameters(param_shape)
def _build(self, params):
if self._scale == "fixed":
loc = params
scale = None # set later
else:
n_channels = params.get_shape().as_list()[-1]
assert n_channels % 2 == 0
assert n_channels // 2 == self.output_shape[-1]
loc = params[..., :n_channels // 2]
scale = params[..., n_channels // 2:]
# apply activation functions
if self._scale != "fixed":
scale = self._scale_act(scale)
loc = self._loc_act(loc)
# apply the correct parametrization
if self._scale == "var":
scale = tf.sqrt(scale)
elif self._scale == "prec":
scale = tf.reciprocal(scale)
elif self._scale == "fixed":
if self._scale_val == -1.0:
scale_val = tf.get_variable(
"scale", initializer=tf.constant(0.1, dtype=tf.float32))
else:
scale_val = self._scale_val
scale = tf.ones_like(loc) * scale_val
# else 'stddev'
dist = self._dist(loc=loc, scale=scale, **self._dist_kwargs)
return tfd.Independent(dist, reinterpreted_batch_ndims=1)
class MaskedMixture(DistributionModule):
def __init__(
self,
num_components,
component_dist,
mask_activation=None,
name="masked_mixture",
):
"""
Spatial Mixture Model composed of a categorical masking distribution and
a custom pixel-wise component distribution (usually logistic or
gaussian).
Args:
num_components: int Number of mixture components >= 2
component_dist: the distribution to use for the individual components
mask_activation: str or function or None activation function that
should be applied to the mask before the softmax.
name: str
"""
super().__init__(name=name)
self._num_components = num_components
self._dist = component_dist
self._mask_activation = get_act_func(mask_activation)
def set_output_shape(self, shape):
super().set_output_shape(shape)
self._dist.set_output_shape(shape)
def _build(self, pixel, mask):
sg = shapeguard.ShapeGuard()
# MASKING
sg.guard(mask, "B, K, H, W, 1")
mask = tf.transpose(mask, perm=[0, 2, 3, 4, 1])
mask = sg.reshape(mask, "B, H, W, K")
mask = self._mask_activation(mask)
mask = mask[:, tf.newaxis] # add K=1 axis since K is removed by mixture
mix_dist = tfd.Categorical(logits=mask)
# COMPONENTS
sg.guard(pixel, "B, K, H, W, Cp")
params = tf.transpose(pixel, perm=[0, 2, 3, 1, 4])
params = params[:, tf.newaxis] # add K=1 axis since K is removed by mixture
dist = self._dist(params)
return tfd.MixtureSameFamily(
mixture_distribution=mix_dist, components_distribution=dist)
@property
def input_shapes(self):
pixel = [self._num_components] + self._dist.input_shapes.params
mask = pixel[:-1] + [1]
return MixtureParameters(pixel, mask)
def get_default_prior(self, batch_dim=(1,)):
pixel = tf.zeros(
list(batch_dim) + self.input_shapes.pixel, dtype=tf.float32)
mask = tf.zeros(list(batch_dim) + self.input_shapes.mask, dtype=tf.float32)
return self(pixel, mask)
================================================
FILE: iodine/modules/factor_eval.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Factor Evaluation Module."""
# pylint: disable=unused-variable
import collections
import functools
from iodine.modules import utils
import shapeguard
import sonnet as snt
import tensorflow.compat.v1 as tf
Factor = collections.namedtuple("Factor", ["name", "size", "type"])
class FactorRegressor(snt.AbstractModule):
"""Assess representations by learning a linear mapping to latents."""
def __init__(self, mapping=None, name="repres_content"):
super().__init__(name=name)
if mapping is None:
self._mapping = [
Factor("color", 3, "scalar"),
Factor("shape", 4, "categorical"),
Factor("scale", 1, "scalar"),
Factor("x", 1, "scalar"),
Factor("y", 1, "scalar"),
Factor("orientation", 2, "angle"),
]
else:
self._mapping = [Factor(*m) for m in mapping]
def _build(self, z, latent, visibility, pred_mask, true_mask):
sg = shapeguard.ShapeGuard()
z = sg.guard(z, "B, K, Z")
pred_mask = sg.guard(pred_mask, "B, K, H, W, 1")
true_mask = sg.guard(true_mask, "B, L, H, W, 1")
visibility = sg.guard(visibility, "B, L")
num_visible_obj = tf.reduce_sum(visibility)
# Map z to predictions for all latents
sg.M = sum([m.size for m in self._mapping])
self.predictor = snt.Linear(sg.M, name="predict_latents")
z_flat = sg.reshape(z, "B*K, Z")
all_preds = sg.guard(self.predictor(z_flat), "B*K, M")
all_preds = sg.reshape(all_preds, "B, 1, K, M")
all_preds = tf.tile(all_preds, sg["1, L, 1, 1"])
# prepare latents
latents = {}
mean_var_tot = {}
for m in self._mapping:
with tf.name_scope(m.name):
# preprocess, reshape, and tile
lat_preprocess = self.get_preprocessing(m)
lat = sg.guard(
lat_preprocess(latent[m.name]), "B, L, {}".format(m.size))
# compute mean over latent by training a variable using mse
if m.type in {"scalar", "angle"}:
mvt = utils.OnlineMeanVarEstimator(
axis=[0, 1], ddof=1, name="{}_mean_var".format(m.name))
mean_var_tot[m.name] = mvt(lat, visibility[:, :, tf.newaxis])
lat = tf.reshape(lat, sg["B, L, 1"] + [-1])
lat = tf.tile(lat, sg["1, 1, K, 1"])
latents[m.name] = lat
# prepare predictions
idx = 0
predictions = {}
for m in self._mapping:
with tf.name_scope(m.name):
assert m.name in latent, "{} not in {}".format(m.name, latent.keys())
pred = all_preds[..., idx:idx + m.size]
predictions[m.name] = sg.guard(pred, "B, L, K, {}".format(m.size))
idx += m.size
# compute error
total_pairwise_errors = None
for m in self._mapping:
with tf.name_scope(m.name):
error_fn = self.get_error_func(m)
sg.guard(latents[m.name], "B, L, K, {}".format(m.size))
sg.guard(predictions[m.name], "B, L, K, {}".format(m.size))
err = error_fn(latents[m.name], predictions[m.name])
sg.guard(err, "B, L, K")
if total_pairwise_errors is None:
total_pairwise_errors = err
else:
total_pairwise_errors += err
# determine best assignment by comparing masks
obj_mask = true_mask[:, :, tf.newaxis]
pred_mask = pred_mask[:, tf.newaxis]
pairwise_overlap = tf.reduce_sum(obj_mask * pred_mask, axis=[3, 4, 5])
best_match = sg.guard(tf.argmax(pairwise_overlap, axis=2), "B, L")
assignment = tf.one_hot(best_match, sg.K)
assignment *= visibility[:, :, tf.newaxis] # Mask non-visible objects
# total error
total_error = (
tf.reduce_sum(assignment * total_pairwise_errors) / num_visible_obj)
# compute scalars
monitored_scalars = {}
for m in self._mapping:
with tf.name_scope(m.name):
metric = self.get_metric(m)
scalar = metric(
latents[m.name],
predictions[m.name],
assignment[:, :, :, tf.newaxis],
mean_var_tot.get(m.name),
num_visible_obj,
)
monitored_scalars[m.name] = scalar
return total_error, monitored_scalars, mean_var_tot, predictions, assignment
@snt.reuse_variables
def predict(self, z):
sg = shapeguard.ShapeGuard()
z = sg.guard(z, "B, Z")
all_preds = sg.guard(self.predictor(z), "B, M")
idx = 0
predictions = {}
for m in self._mapping:
with tf.name_scope(m.name):
pred = all_preds[:, idx:idx + m.size]
predictions[m.name] = sg.guard(pred, "B, {}".format(m.size))
idx += m.size
return predictions
@staticmethod
def get_error_func(factor):
if factor.type in {"scalar", "angle"}:
return sse
elif factor.type == "categorical":
return functools.partial(
tf.losses.softmax_cross_entropy, reduction="none")
else:
raise KeyError(factor.type)
@staticmethod
def get_metric(factor):
if factor.type in {"scalar", "angle"}:
return r2
elif factor.type == "categorical":
return accuracy
else:
raise KeyError(factor.type)
@staticmethod
def one_hot(f, nr_categories):
return tf.one_hot(tf.cast(f[..., 0], tf.int32), depth=nr_categories)
@staticmethod
def angle_to_vector(theta):
return tf.concat([tf.math.cos(theta), tf.math.sin(theta)], axis=-1)
@staticmethod
def get_preprocessing(factor):
if factor.type == "scalar":
return tf.identity
elif factor.type == "categorical":
return functools.partial(
FactorRegressor.one_hot, nr_categories=factor.size)
elif factor.type == "angle":
return FactorRegressor.angle_to_vector
else:
raise KeyError(factor.type)
def sse(true, pred):
# run our own sum squared error because we want to reduce sum over last dim
return tf.reduce_sum(tf.square(true - pred), axis=-1)
def accuracy(labels, logits, assignment, mean_var_tot, num_vis):
del mean_var_tot # unused
pred = tf.argmax(logits, axis=-1, output_type=tf.int32)
labels = tf.argmax(labels, axis=-1, output_type=tf.int32)
correct = tf.cast(tf.equal(labels, pred), tf.float32)
return tf.reduce_sum(correct * assignment[..., 0]) / num_vis
def r2(labels, pred, assignment, mean_var_tot, num_vis):
del num_vis # unused
mean, var, _ = mean_var_tot
# labels, pred: (B, L, K, n)
ss_res = tf.reduce_sum(tf.square(labels - pred) * assignment, axis=2)
ss_tot = var[tf.newaxis, tf.newaxis, :] # (1, 1, n)
return tf.reduce_mean(1.0 - ss_res / ss_tot)
================================================
FILE: iodine/modules/iodine.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Stochastic Variational inference Auto-Encoder."""
# pylint: disable=unused-variable, g-bad-todo
import collections
from iodine.modules import utils
from multi_object_datasets.segmentation_metrics import adjusted_rand_index
import numpy as np
import shapeguard
import sonnet as snt
import tensorflow.compat.v1 as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
logging = tf.logging
DEFAULT_INPUTS = (
"image",
"zp",
"mask",
"components",
"dmask",
"dzp",
"dcomponents",
"posterior",
"log_prob",
"pred_mask",
"capacity",
"flat_capacity",
"coordinates",
"counterfactual",
)
DEFAULT_PREPROCESSING = [
"dcomponents", "dmask", "dzp", "log_prob", "counterfactual"
]
DEFAULT_STOP_GRADIENT = ("dzp", "dmask", "dcomponents", "log_prob",
"counterfactual")
class IODINE(snt.AbstractModule):
"""Iterative Amortized Variational Autoencoder.
Args:
decoder (decoders.ComponentDecoder): The decoder.
refinement_core (refinement.RefinementCore): The recurrent (refinement)
encoder.
latent_dist (distributions.Distribution): The distribution of the latent
z variables.
output_dist (distributions.MaskedMixture): The pixel-wise output
distribution (a spatial mixture).
n_z (int): Dimensionality of the per-object latents z_k.
num_components (int): Number of available object slots (K).
num_iters (int): Number of refinement iterations.
sequential (bool): Whether the input data is sequential.
factor_evaluator (factor_eval.FactorRegressor): The factor evaluation
model that is trained to predict the true factors from the inferred
latents.
stop_gradients (List[str]): For which refinement inputs to stop
gradients from backpropagating through the iterations. (see inputs for
valid values)
Default is: ["dcomponents", "dmask", "dzp", "log_prob",
"counterfactual"]
iter_loss_weight ("linspace" | float | List[float]): How to weigh the
loss terms for each timestep.
Can be:
"linspace": Linearly increasing weights from 0 to 1.0.
float: A fixed value for all steps.
List[float]: Manually specify all weight.
inputs (List[str]): list of inputs to use for the refinement network.
Can include the following (default is to use all): ["image", "zp",
"mask", "components", "dmask", "dzp", "dcomponents", "posterior",
"log_prob", "pred_mask", "capacity", "flat_capacity",
"coordinates", "counterfactual"]
preprocess (List[str]): Specifies the subset of inputs that be
preprocessed with layernorm.
Default is: ["dcomponents", "dmask", "dzp", "log_prob",
"counterfactual"]
coord_type (str): Type of coordinate channels to append to the
refinement inputs. Can be "linear" (default) or "cos".
coord_freqs (int): If using cos based coordinate channels, then this
specifies the number of frequencies used.
name (str): Name of the module (within the tensorflow graph).
"""
def __init__(
self,
decoder,
refinement_core,
latent_dist,
output_dist,
n_z,
num_components,
num_iters,
sequential=False,
factor_evaluator=None,
stop_gradients=DEFAULT_STOP_GRADIENT,
iter_loss_weight="linspace",
inputs=DEFAULT_INPUTS,
preprocess=None,
coord_type="linear",
coord_freqs=3,
name="iodine",
):
super().__init__(name=name)
self._sg = shapeguard.ShapeGuard(dims={"K": num_components})
self.decoder = decoder
self.refinement_core = refinement_core
self.latent_dist = latent_dist
self.output_dist = output_dist
self.n_z = n_z
self.num_components = num_components
self.num_iters = num_iters
self.sequential = sequential
self.iter_loss_weights = self._parse_iter_loss_weights(iter_loss_weight)
self.factor_evaluator = factor_evaluator
self.stop_gradients = stop_gradients
self.inputs = inputs
self.preprocess = DEFAULT_PREPROCESSING if preprocess is None else preprocess
self.coord_type = coord_type
self.coord_freqs = coord_freqs
with self._enter_variable_scope():
self.latent_dist.set_output_shape([self.n_z])
logging.info("VAE: z shape: %s", [self.n_z])
with tf.name_scope("prior"):
self.prior = self.latent_dist.get_default_prior((self.num_components,))
self._sg.guard(self.prior, "K, Z")
with tf.variable_scope("preprocess"):
self._layernorms = {
name: snt.LayerNorm(name="layer_norm_" + name)
for name in self.preprocess
}
def _build(self, data):
data["image"] = data["image"][:, :self.num_iters + 1]
if "mask" in data:
data["mask"] = data["mask"][:, :self.num_iters + 1]
x = self._sg.guard(data["image"], "B, T, H, W, C")
self._propagate_shape_info(x.get_shape().as_list())
# run iterative encoder
iterations = self.encode(x)
z_dist = self._sg.guard(iterations["z_dist"][-1], "B, K, Z")
z = self._sg.guard(iterations["z"][-1], "B, K, Z")
# decode
x_params, x_dist = self.decode(z)
iterations["x_dist"].append(self._sg.guard(x_dist, "B, 1, H, W, C"))
# compute loss
kl = self._sg.guard(self._raw_kl(z_dist), "B, K")
img = self._get_image_for_iter(x, self.num_iters)
re = self._sg.guard(self._reconstruction_error(x_dist, img), "B")
iterations["kl"].append(kl)
iterations["re"].append(re)
iterations["recons_loss"] = [tf.reduce_mean(re) for re in iterations["re"]]
total_rec_loss = sum([
w * re
for w, re in zip(self.iter_loss_weights, iterations["recons_loss"])
])
total_kl_loss = sum([
w * tf.reduce_mean(tf.reduce_sum(kl, axis=1))
for w, kl in zip(self.iter_loss_weights, iterations["kl"])
])
total_loss = total_rec_loss + total_kl_loss
scalars = {
"loss/kl":
sum([
tf.reduce_mean(tf.reduce_sum(kl, axis=1))
for kl in iterations["kl"]
]),
"loss/recons":
total_rec_loss,
}
if self.factor_evaluator:
pred_mask = self._sg.guard(x_dist.mixture_distribution.probs,
"B, 1, H, W, K")
pred_mask = tf.transpose(pred_mask, [0, 4, 2, 3, 1])
mask_true = self._sg.guard(data["mask"], "B, T, L, H, W, 1")
mask_true = self._get_image_for_iter(mask_true, self.num_iters)
mask_true = mask_true[:, 0]
factor_loss, factor_scalars, _, _, _ = self.factor_evaluator(
tf.stop_gradient(z),
data["factors"],
data["visibility"],
tf.stop_gradient(pred_mask),
mask_true,
)
total_loss += factor_loss
scalars["factor/loss"] = factor_loss
scalars.update({"factor/" + k: v for k, v in factor_scalars.items()})
scalars["loss/total"] = total_loss
scalars.update(self._get_monitored_scalars(x_dist, data))
logging.info(self._sg.dims)
return total_loss, scalars, iterations
@snt.reuse_variables
def encode(self, images):
sg = self._sg
sg.guard(images, "B, T, H, W, C")
zp, z_dist, z = self._get_initial_z()
iterations = {
"z": [z],
"zp": [zp],
"z_dist": [z_dist],
"x_dist": [],
"inputs": [],
"kl": [],
"re": [],
}
state = self.refinement_core.initial_state(sg["B*K"][0])
for t in range(self.num_iters):
img = sg.guard(self._get_image_for_iter(images, t), "B, 1, H, W, C")
x_params, x_dist = self.decode(z)
# compute loss
kl = self._sg.guard(self._raw_kl(z_dist), "B, K")
re = self._sg.guard(self._reconstruction_error(x_dist, img), "B")
loss = tf.reduce_mean(re) + tf.reduce_mean(tf.reduce_sum(kl, axis=1))
inputs = self._get_inputs_for(x_params, x_dist, img, z_dist, zp, loss)
zp, state = self.refinement_core(inputs, state)
sg.guard(zp, "B, K, Zp")
z_dist = sg.guard(self.latent_dist(zp), "B, K, Z")
z = z_dist.sample()
# append local variables to iteration collections
for v, name in zip(
[z, zp, z_dist, x_dist, inputs, kl, re],
["z", "zp", "z_dist", "x_dist", "inputs", "kl", "re"],
):
iterations[name].append(v)
return iterations
@snt.reuse_variables
def decode(self, z):
sg = shapeguard.ShapeGuard()
sg.guard(z, "B, K, Z")
# legacy
z = tf.concat([z, 5.0 * tf.ones(sg["B, K, 1"], dtype=tf.float32)], axis=2)
params = self.decoder(z)
out_dist = self.output_dist(*params)
return params, out_dist
@snt.reuse_variables
def eval(self, data):
total_loss, scalars, iterations = self._build(data)
sg = shapeguard.ShapeGuard()
def get_components(dist):
return tf.transpose(dist.components_distribution.mean()[:, 0, :, :, :, :],
[0, 3, 1, 2, 4])
def get_mask(dist):
return tf.transpose(dist.mixture_distribution.probs[:, :, :, :, :],
[0, 4, 2, 3, 1])
def get_mask_logits(dist):
return tf.transpose(dist.mixture_distribution.logits[:, :, :, :, :],
[0, 4, 2, 3, 1])
def stack_iters(list_of_variables, pad_zero=False):
if pad_zero:
list_of_variables.insert(0, tf.zeros_like(list_of_variables[0]))
return tf.stack(list_of_variables, axis=1)
# data
image = sg.guard(data["image"], "B, 1, H, W, C")
true_mask = sg.guard(data["mask"], "B, 1, L, H, W, 1")
visibility = sg.guard(data["visibility"], "B, L")
factors = data["factors"]
# inputs
inputs_flat = {
k: stack_iters([inp["flat"][k] for inp in iterations["inputs"]],
pad_zero=True)
for k in iterations["inputs"][0]["flat"].keys()
}
inputs_spatial = {
k: stack_iters([inp["spatial"][k] for inp in iterations["inputs"]],
pad_zero=True)
for k in iterations["inputs"][0]["spatial"].keys()
}
# latent
z = sg.guard(stack_iters(iterations["z"]), "B, T, K, Z")
z_mean = stack_iters([zd.mean() for zd in iterations["z_dist"]])
z_std = stack_iters([zd.stddev() for zd in iterations["z_dist"]])
# outputs
recons = stack_iters([xd.mean() for xd in iterations["x_dist"]])
pred_mask = stack_iters([get_mask(xd) for xd in iterations["x_dist"]])
pred_mask_logits = stack_iters(
[get_mask_logits(xd) for xd in iterations["x_dist"]])
components = stack_iters(
[get_components(xd) for xd in iterations["x_dist"]])
# metrics
tm = tf.transpose(true_mask[..., 0], [0, 1, 3, 4, 2])
tm = tf.reshape(tf.tile(tm, sg["1, T, 1, 1, 1"]), sg["B * T, H * W, L"])
pm = tf.transpose(pred_mask[..., 0], [0, 1, 3, 4, 2])
pm = tf.reshape(pm, sg["B * T, H * W, K"])
ari = tf.reshape(adjusted_rand_index(tm, pm), sg["B, T"])
ari_nobg = tf.reshape(adjusted_rand_index(tm[..., 1:], pm), sg["B, T"])
mse = tf.reduce_mean(tf.square(recons - image[:, None]), axis=[2, 3, 4, 5])
# losses
loss_recons = stack_iters(iterations["re"])
kl = stack_iters(iterations["kl"])
info = {
"data": {
"image": sg.guard(image, "B, 1, H, W, C"),
"true_mask": sg.guard(true_mask, "B, 1, L, H, W, 1"),
"visibility": sg.guard(visibility, "B, L"),
"factors": factors,
},
"inputs": {
"flat": inputs_flat,
"spatial": inputs_spatial
},
"latent": {
"z": sg.guard(z, "B, T, K, Z"),
"z_mean": sg.guard(z_mean, "B, T, K, Z"),
"z_std": sg.guard(z_std, "B, T, K, Z"),
},
"outputs": {
"recons": sg.guard(recons, "B, T, 1, H, W, C"),
"pred_mask": sg.guard(pred_mask, "B, T, K, H, W, 1"),
"pred_mask_logits": sg.guard(pred_mask_logits, "B, T, K, H, W, 1"),
"components": sg.guard(components, "B, T, K, H, W, C"),
},
"losses": {
"total": total_loss,
"recons": sg.guard(loss_recons, "B, T"),
"kl": sg.guard(kl, "B, T, K"),
},
"metrics": {
"ari": ari,
"ari_nobg": ari_nobg,
"mse": mse
},
}
if self.factor_evaluator:
# factor evaluation information
factor_info = {
"loss": [],
"metrics": collections.defaultdict(list),
"predictions": collections.defaultdict(list),
"assignment": [],
}
for t in range(z.get_shape().as_list()[1]):
floss, fscalars, _, fpred, fass = self.factor_evaluator(
z[:, t], factors, visibility, pred_mask[:, t], true_mask[:, 0])
factor_info["loss"].append(floss)
factor_info["assignment"].append(fass)
for k in fpred:
factor_info["predictions"][k].append(
tf.reduce_sum(fpred[k] * fass[..., None], axis=2))
factor_info["metrics"][k].append(fscalars[k])
info["losses"]["factor"] = sg.guard(tf.stack(factor_info["loss"]), "T")
info["factor_regressor"] = {
"assignment":
sg.guard(stack_iters(factor_info["assignment"]), "B, T, L, K"),
"metrics": {
k: tf.stack(factor_info["metrics"][k], axis=0)
for k in factor_info["metrics"]
},
"predictions": {
k: stack_iters(factor_info["predictions"][k])
for k in factor_info["predictions"]
},
}
return info
@snt.reuse_variables
def get_sample_images(self, nr_samples=16):
with tf.name_scope("prior_samples"):
prior_z = self.prior.sample(nr_samples)
_, prior_out = self.decode(prior_z)
prior_out = tf.clip_by_value(prior_out.mean(), 0.0, 1.0)
return utils.images_to_grid(prior_out[:, 0])[tf.newaxis]
@snt.reuse_variables
def get_overview_images(self, data, nr_images=4, mask_components=False):
x = data["image"][:nr_images, :self.num_iters + 1]
old_b, self._sg.B = self._sg.B, x.get_shape().as_list()[0]
iterations = self.encode(x)
z = iterations["z"][-1]
_, x_dist = self.decode(z)
self._sg.B = old_b
t = min(self.num_iters, x.get_shape().as_list()[1]) - 1
# iterations view
recons = tf.stack([x_dist.mean() for x_dist in iterations["x_dist"]],
axis=1)
masks = tf.stack(
[
tf.transpose(x_dist.mixture_distribution.probs, [0, 4, 2, 3, 1])
for x_dist in iterations["x_dist"]
],
axis=1,
)
return {
"overview":
utils.get_overview_image(
x[:, t:t + 1], x_dist, mask_components=mask_components),
"sequence":
utils.construct_iterations_image(x[:, :t + 1, tf.newaxis], recons,
masks),
"samples":
self.get_sample_images(),
}
def _get_initial_z(self):
# Initial z distribution
zp_init = tf.get_variable(
"initial_sample_distribution",
shape=self.latent_dist.input_shapes.params,
dtype=tf.float32,
)
zp = tf.tile(zp_init[tf.newaxis, tf.newaxis], self._sg["B, K, 1"])
z_dist = self.latent_dist(zp)
z = z_dist.sample()
self._sg.guard(zp, "B, K, Zp")
self._sg.guard(z_dist, "B, K, Z")
self._sg.guard(z, "B, K, Z")
return zp, z_dist, z
def _parse_iter_loss_weights(self, iter_loss_weight):
if iter_loss_weight == "linspace":
iter_weights = np.linspace(0.0, 1.0, self.num_iters + 1).tolist()
elif isinstance(iter_loss_weight, (float, int)):
iter_weights = [float(iter_loss_weight)] * (self.num_iters + 1)
elif isinstance(iter_loss_weight, (tuple, list)):
iter_weights = [float(w) for w in iter_loss_weight]
else:
raise ValueError("Unknown iter_loss_weight type {}.".format(
repr(iter_loss_weight)))
assert len(iter_weights) == (self.num_iters + 1), iter_loss_weight
return iter_weights
def _propagate_shape_info(self, image_shape):
image_shape = image_shape[-3:] # ignore batch dims
logging.info("VAE: image shape: %s", image_shape)
z_param_shape = self._sg.guard(self.latent_dist.input_shapes.params, "Zp")
logging.info("VAE: z parameter shape: %s", z_param_shape)
self.output_dist.set_output_shape(image_shape)
out_param_shapes = self.output_dist.input_shapes
logging.info("VAE: output parameter shapes: %s", out_param_shapes)
self.decoder.set_output_shapes(*out_param_shapes)
def _get_image_for_iter(self, images, t):
"""Return current frame or first image."""
if self.sequential:
return images[:, t:t + 1]
else:
return images[:, :1]
@staticmethod
def _get_mask_posterior(out_dist, img):
p_comp = out_dist.components_distribution.prob(img[..., tf.newaxis, :])
posterior = p_comp / (tf.reduce_sum(p_comp, axis=-1, keepdims=True) + 1e-6)
return tf.transpose(posterior, [0, 4, 2, 3, 1])
def _get_inputs_for(self, out_params, out_dist, img, z_dist, zp, loss):
sg = self._sg
# gradients of loss wrt z, components and mask
dzp, dxp, dmp = tf.gradients(loss, [zp, out_params.pixel, out_params.mask])
log_prob = sg.guard(
out_dist.log_prob(img)[..., tf.newaxis], "B, 1, H, W, 1")
counterfactual_log_probs = []
for k in range(0, self.num_components):
mask = tf.concat([out_params.mask[:, :k], out_params.mask[:, k + 1:]],
axis=1)
pixel = tf.concat([out_params.pixel[:, :k], out_params.pixel[:, k + 1:]],
axis=1)
out_dist_k = self.output_dist(pixel, mask)
log_prob_k = out_dist_k.log_prob(img)[..., tf.newaxis]
counterfactual_log_probs.append(log_prob_k)
counterfactual = log_prob - tf.concat(counterfactual_log_probs, axis=1)
pred_mask = tf.transpose(out_dist.mixture_distribution.probs,
[0, 4, 2, 3, 1])
potential_inputs = {
# spatial
"image":
sg.guard(img, "B, 1, H, W, C"),
"log_prob":
sg.guard(log_prob, "B, 1, H, W, 1"),
"mask":
sg.guard(out_params.mask, "B, K, H, W, 1"),
"pred_mask":
sg.guard(pred_mask, "B, K, H, W, 1"),
"components":
sg.guard(out_params.pixel, "B, K, H, W, Cp"),
"dmask":
sg.guard(dmp, "B, K, H, W, Mp"),
"dcomponents":
sg.guard(dxp, "B, K, H, W, Cp"),
"posterior":
sg.guard(self._get_mask_posterior(out_dist, img), "B, K, H, W, 1"),
"capacity":
0.5 *
tf.ones(sg["B, K, H, W, 1"], dtype=tf.float32), # TODO: legacy
"coordinates":
self._get_coord_channels(),
"counterfactual":
self._sg.guard(counterfactual, "B, K, H, W, 1"),
# flat
"zp":
sg.guard(zp, "B, K, Zp"),
"dzp":
sg.guard(dzp, "B, K, Zp"),
"flat_capacity":
0.5 * tf.ones(sg["B, K, 1"], dtype=tf.float32), # TODO: legacy
}
# collect used inputs, stop gradients and preprocess where needed
final_inputs = {"spatial": {}, "flat": {}}
for k, v in potential_inputs.items():
# skip unused inputs
if k not in self.inputs:
continue
# stop gradients
if k in self.stop_gradients:
v = tf.stop_gradient(v)
# preprocess
v = self._apply_preprocessing(k, v)
# sort into flat / spatial according to their shape
structure = "flat" if len(v.get_shape().as_list()) == 3 else "spatial"
final_inputs[structure][k] = v
return final_inputs
def _apply_preprocessing(self, name, val):
if name in self.preprocess:
if self._sg.matches(val, "B, K, _z"):
flat_val = tf.reshape(val, self._sg["B*K"] + [-1])
elif self._sg.matches(val, "B, 1, _z"):
flat_val = val[:, 0, :]
elif self._sg.matches(val, "B, K, H, W, _c"):
flat_val = tf.reshape(val, self._sg["B*K, H*W"] + [-1])
elif self._sg.matches(val, "B, 1, H, W, _c"):
flat_val = tf.reshape(val, self._sg["B, H*W"] + [-1])
else:
raise ValueError("Cannot handle shape {}".format(
val.get_shape().as_list()))
ln = self._layernorms[name]
norm_val = ln(flat_val)
return tf.reshape(norm_val, val.shape.as_list())
else:
return val
def _get_coord_channels(self):
if self.coord_type == "linear":
x_coords = tf.linspace(-1.0, 1.0, self._sg.W)[None, None, None, :, None]
y_coords = tf.linspace(-1.0, 1.0, self._sg.H)[None, None, :, None, None]
x_coords = tf.tile(x_coords, self._sg["B, 1, H, 1, 1"])
y_coords = tf.tile(y_coords, self._sg["B, 1, 1, W, 1"])
return tf.concat([x_coords, y_coords], axis=-1)
elif self.coord_type == "cos":
freqs = self._sg.guard(tf.range(0.0, self.coord_freqs), "F")
valx = tf.linspace(0.0, np.pi, self._sg.W)[None, None, None, :, None,
None]
valy = tf.linspace(0.0, np.pi, self._sg.H)[None, None, :, None, None,
None]
x_basis = tf.cos(valx * freqs[None, None, None, None, :, None])
y_basis = tf.cos(valy * freqs[None, None, None, None, None, :])
xy_basis = tf.reshape(x_basis * y_basis, self._sg["1, 1, H, W, F*F"])
coords = tf.tile(xy_basis, self._sg["B, 1, 1, 1, 1"])[..., 1:]
return coords
else:
raise KeyError('Unknown coord_type: "{}"'.format(self.coord_type))
def _raw_kl(self, z_dist):
return tfd.kl_divergence(z_dist, self.prior)
def _reconstruction_error(self, x_dist, img):
log_prob = self._sg.guard(x_dist.log_prob(img), "B, 1, H, W")
return -tf.reduce_sum(log_prob, axis=[1, 2, 3])
def _get_monitored_scalars(self, out_dist, data):
self._sg.guard(out_dist, "B, 1, H, W, C")
img = self._get_image_for_iter(data["image"], self.num_iters)
scalars = {}
with tf.name_scope("monitored_scalars"):
# ######### Loss Monitoring #########
scalars["loss/mse"] = tf.losses.mean_squared_error(
img, out_dist.mean())
# ########## Mask Monitoring #######
if "mask" in data:
true_mask = self._sg.guard(data["mask"], "B, T, L, H, W, 1")
true_mask = tf.transpose(true_mask[:, -1, ..., 0], [0, 2, 3, 1])
true_mask = self._sg.reshape(true_mask, "B, H*W, L")
else:
true_mask = None
pred_mask = self._sg.guard(out_dist.mixture_distribution.probs,
"B, 1, H, W, K")
pred_mask = self._sg.reshape(pred_mask, "B, H*W, K")
if pred_mask is not None and true_mask is not None:
self._sg.guard(pred_mask, "B, H*W, K")
self._sg.guard(true_mask, "B, H*W, L")
scalars["loss/ari"] = tf.reduce_mean(
adjusted_rand_index(true_mask, pred_mask))
scalars["loss/ari_nobg"] = tf.reduce_mean(
adjusted_rand_index(true_mask[..., 1:], pred_mask))
return scalars
================================================
FILE: iodine/modules/networks.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Network modules."""
# pylint: disable=g-multiple-import, g-doc-args, g-short-docstring-punctuation
# pylint: disable=g-no-space-after-docstring-summary
from iodine.modules.distributions import FlatParameters
from iodine.modules.utils import flatten_all_but_last, get_act_func
import numpy as np
import shapeguard
import sonnet as snt
import tensorflow.compat.v1 as tf
class CNN(snt.AbstractModule):
"""ConvNet2D followed by an MLP.
This is a typical encoder architecture for VAEs, and has been found to work
well. One small improvement is to append coordinate channels on the input,
though for most datasets the improvement obtained is negligible.
"""
def __init__(self, cnn_opt, mlp_opt, mode="flatten", name="cnn"):
"""Constructor.
Args:
cnn_opt: Dictionary. Kwargs for the cnn. See vae_lib.ConvNet2D for
details.
mlp_opt: Dictionary. Kwargs for the mlp. See vae_lib.MLP for details.
name: String. Optional name.
"""
super().__init__(name=name)
if "activation" in cnn_opt:
cnn_opt["activation"] = get_act_func(cnn_opt["activation"])
self._cnn_opt = cnn_opt
if "activation" in mlp_opt:
mlp_opt["activation"] = get_act_func(mlp_opt["activation"])
self._mlp_opt = mlp_opt
self._mode = mode
def set_output_shapes(self, shape):
# assert self._mlp_opt['output_sizes'][-1] is None, self._mlp_opt
sg = shapeguard.ShapeGuard()
sg.guard(shape, "1, Y")
self._mlp_opt["output_sizes"][-1] = sg.Y
def _build(self, image):
"""Connect model to TensorFlow graph."""
assert self._mlp_opt["output_sizes"][-1] is not None, "set output_shapes"
sg = shapeguard.ShapeGuard()
flat_image, unflatten = flatten_all_but_last(image, n_dims=3)
sg.guard(flat_image, "B, H, W, C")
cnn = snt.nets.ConvNet2D(
activate_final=True,
paddings=("SAME",),
normalize_final=False,
**self._cnn_opt)
mlp = snt.nets.MLP(**self._mlp_opt)
# run CNN
net = cnn(flat_image)
if self._mode == "flatten":
# flatten
net_shape = net.get_shape().as_list()
flat_shape = net_shape[:-3] + [np.prod(net_shape[-3:])]
net = tf.reshape(net, flat_shape)
elif self._mode == "avg_pool":
net = tf.reduce_mean(net, axis=[1, 2])
else:
raise KeyError('Unknown mode "{}"'.format(self._mode))
# run MLP
output = sg.guard(mlp(net), "B, Y")
return FlatParameters(unflatten(output))
class MLP(snt.AbstractModule):
"""MLP."""
def __init__(self, name="mlp", **mlp_opt):
super().__init__(name=name)
if "activation" in mlp_opt:
mlp_opt["activation"] = get_act_func(mlp_opt["activation"])
self._mlp_opt = mlp_opt
assert mlp_opt["output_sizes"][-1] is None, mlp_opt
def set_output_shapes(self, shape):
sg = shapeguard.ShapeGuard()
sg.guard(shape, "1, Y")
self._mlp_opt["output_sizes"][-1] = sg.Y
def _build(self, data):
"""Connect model to TensorFlow graph."""
assert self._mlp_opt["output_sizes"][-1] is not None, "set output_shapes"
sg = shapeguard.ShapeGuard()
flat_data, unflatten = flatten_all_but_last(data)
sg.guard(flat_data, "B, N")
mlp = snt.nets.MLP(**self._mlp_opt)
# run MLP
output = sg.guard(mlp(flat_data), "B, Y")
return FlatParameters(unflatten(output))
class DeConv(snt.AbstractModule):
"""MLP followed by Deconv net.
This decoder is commonly used by vanilla VAE models. However, in practice
BroadcastConv (see below) seems to disentangle slightly better.
"""
def __init__(self, mlp_opt, cnn_opt, name="deconv"):
"""Constructor.
Args:
mlp_opt: Dictionary. Kwargs for vae_lib.MLP.
cnn_opt: Dictionary. Kwargs for vae_lib.ConvNet2D for the CNN.
name: Optional name.
"""
super().__init__(name=name)
assert cnn_opt["output_channels"][-1] is None, cnn_opt
if "activation" in cnn_opt:
cnn_opt["activation"] = get_act_func(cnn_opt["activation"])
self._cnn_opt = cnn_opt
if mlp_opt and "activation" in mlp_opt:
mlp_opt["activation"] = get_act_func(mlp_opt["activation"])
self._mlp_opt = mlp_opt
self._target_out_shape = None
def set_output_shapes(self, shape):
self._target_out_shape = shape
self._cnn_opt["output_channels"][-1] = self._target_out_shape[-1]
def _build(self, z):
"""Connect model to TensorFlow graph."""
sg = shapeguard.ShapeGuard()
flat_z, unflatten = flatten_all_but_last(z)
sg.guard(flat_z, "B, Z")
sg.guard(self._target_out_shape, "H, W, C")
mlp = snt.nets.MLP(**self._mlp_opt)
cnn = snt.nets.ConvNet2DTranspose(
paddings=("SAME",), normalize_final=False, **self._cnn_opt)
net = mlp(flat_z)
output = sg.guard(cnn(net), "B, H, W, C")
return FlatParameters(unflatten(output))
class BroadcastConv(snt.AbstractModule):
"""MLP followed by a broadcast convolution.
This decoder takes a latent vector z, (optionally) applies an MLP to it,
then tiles the resulting vector across space to have dimension [B, H, W, C]
i.e. tiles across H and W. Then coordinate channels are appended and a
convolutional layer is applied.
"""
def __init__(
self,
cnn_opt,
mlp_opt=None,
coord_type="linear",
coord_freqs=3,
name="broadcast_conv",
):
"""Args:
cnn_opt: dict Kwargs for vae_lib.ConvNet2D for the CNN.
mlp_opt: None or dict If dictionary, then kwargs for snt.nets.MLP. If
None, then the model will not process the latent vector by an mlp.
coord_type: ["linear", "cos", None] type of coordinate channels to
add.
None: add no coordinate channels.
linear: two channels with values linearly spaced from -1. to 1. in
the H and W dimension respectively.
cos: coord_freqs^2 many channels containing cosine basis functions.
coord_freqs: int number of frequencies used to construct the cosine
basis functions (only for coord_type=="cos")
name: Optional name.
"""
super().__init__(name=name)
assert cnn_opt["output_channels"][-1] is None, cnn_opt
if "activation" in cnn_opt:
cnn_opt["activation"] = get_act_func(cnn_opt["activation"])
self._cnn_opt = cnn_opt
if mlp_opt and "activation" in mlp_opt:
mlp_opt["activation"] = get_act_func(mlp_opt["activation"])
self._mlp_opt = mlp_opt
self._target_out_shape = None
self._coord_type = coord_type
self._coord_freqs = coord_freqs
def set_output_shapes(self, shape):
self._target_out_shape = shape
self._cnn_opt["output_channels"][-1] = self._target_out_shape[-1]
def _build(self, z):
"""Connect model to TensorFlow graph."""
assert self._target_out_shape is not None, "Call set_output_shape"
# reshape components into batch dimension before processing them
sg = shapeguard.ShapeGuard()
flat_z, unflatten = flatten_all_but_last(z)
sg.guard(flat_z, "B, Z")
sg.guard(self._target_out_shape, "H, W, C")
if self._mlp_opt is None:
mlp = tf.identity
else:
mlp = snt.nets.MLP(activate_final=True, **self._mlp_opt)
mlp_output = sg.guard(mlp(flat_z), "B, hidden")
# tile MLP output spatially and append coordinate channels
broadcast_mlp_output = tf.tile(
mlp_output[:, tf.newaxis, tf.newaxis],
multiples=tf.constant(sg["1, H, W, 1"]),
) # B, H, W, Z
dec_cnn_inputs = self.append_coordinate_channels(broadcast_mlp_output)
cnn = snt.nets.ConvNet2D(
paddings=("SAME",), normalize_final=False, **self._cnn_opt)
cnn_outputs = cnn(dec_cnn_inputs)
sg.guard(cnn_outputs, "B, H, W, C")
return FlatParameters(unflatten(cnn_outputs))
def append_coordinate_channels(self, output):
sg = shapeguard.ShapeGuard()
sg.guard(output, "B, H, W, C")
if self._coord_type is None:
return output
if self._coord_type == "linear":
w_coords = tf.linspace(-1.0, 1.0, sg.W)[None, None, :, None]
h_coords = tf.linspace(-1.0, 1.0, sg.H)[None, :, None, None]
w_coords = tf.tile(w_coords, sg["B, H, 1, 1"])
h_coords = tf.tile(h_coords, sg["B, 1, W, 1"])
return tf.concat([output, h_coords, w_coords], axis=-1)
elif self._coord_type == "cos":
freqs = sg.guard(tf.range(0.0, self._coord_freqs), "F")
valx = tf.linspace(0.0, np.pi, sg.W)[None, None, :, None, None]
valy = tf.linspace(0.0, np.pi, sg.H)[None, :, None, None, None]
x_basis = tf.cos(valx * freqs[None, None, None, :, None])
y_basis = tf.cos(valy * freqs[None, None, None, None, :])
xy_basis = tf.reshape(x_basis * y_basis, sg["1, H, W, F*F"])
coords = tf.tile(xy_basis, sg["B, 1, 1, 1"])[..., 1:]
return tf.concat([output, coords], axis=-1)
else:
raise KeyError('Unknown coord_type: "{}"'.format(self._coord_type))
class LSTM(snt.RNNCore):
"""Wrapper around snt.LSTM that supports multi-layers and runs K components in
parallel.
Expects input data of shape (B, K, H) and outputs data of shape (B, K, Y)
"""
def __init__(self, hidden_sizes, name="lstm"):
super().__init__(name=name)
self._hidden_sizes = hidden_sizes
with self._enter_variable_scope():
self._lstm_layers = [snt.LSTM(hidden_size=h) for h in self._hidden_sizes]
def initial_state(self, batch_size, **kwargs):
return [
lstm.initial_state(batch_size, **kwargs) for lstm in self._lstm_layers
]
def _build(self, data, prev_states):
assert not self._hidden_sizes or self._hidden_sizes[-1] is not None
assert len(prev_states) == len(self._hidden_sizes)
sg = shapeguard.ShapeGuard()
sg.guard(data, "B, K, H")
data = sg.reshape(data, "B*K, H")
out = data
new_states = []
for lstm, pstate in zip(self._lstm_layers, prev_states):
out, nstate = lstm(out, pstate)
new_states.append(nstate)
sg.guard(out, "B*K, Y")
out = sg.reshape(out, "B, K, Y")
return out, new_states
================================================
FILE: iodine/modules/plotting.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Plotting tools for IODINE."""
# pylint: disable=unused-import, missing-docstring, unused-variable
# pylint: disable=invalid-name, unexpected-keyword-arg
import functools
from iodine.modules.utils import get_mask_plot_colors
from matplotlib.colors import hsv_to_rgb
import matplotlib.pyplot as plt
import numpy as np
__all__ = ("get_mask_plot_colors", "example_plot", "iterations_plot",
"inputs_plot")
def clean_ax(ax, color=None, lw=4.0):
ax.set_xticks([])
ax.set_yticks([])
if color is not None:
for spine in ax.spines.values():
spine.set_linewidth(lw)
spine.set_color(color)
def optional_ax(fn):
def _wrapped(*args, **kwargs):
if kwargs.get("ax", None) is None:
figsize = kwargs.pop("figsize", (4, 4))
fig, ax = plt.subplots(figsize=figsize)
kwargs["ax"] = ax
return fn(*args, **kwargs)
return _wrapped
def optional_clean_ax(fn):
def _wrapped(*args, **kwargs):
if kwargs.get("ax", None) is None:
figsize = kwargs.pop("figsize", (4, 4))
fig, ax = plt.subplots(figsize=figsize)
kwargs["ax"] = ax
color = kwargs.pop("color", None)
lw = kwargs.pop("lw", 4.0)
res = fn(*args, **kwargs)
clean_ax(kwargs["ax"], color, lw)
return res
return _wrapped
@optional_clean_ax
def show_img(img, mask=None, ax=None, norm=False):
if norm:
vmin, vmax = np.min(img), np.max(img)
img = (img - vmin) / (vmax - vmin)
if mask is not None:
img = img * mask + np.ones_like(img) * (1.0 - mask)
return ax.imshow(img.clip(0.0, 1.0), interpolation="nearest")
@optional_clean_ax
def show_mask(m, ax):
color_conv = get_mask_plot_colors(m.shape[0])
color_mask = np.dot(np.transpose(m, [1, 2, 0]), color_conv)
return ax.imshow(color_mask.clip(0.0, 1.0), interpolation="nearest")
@optional_clean_ax
def show_mat(m, ax, vmin=None, vmax=None, cmap="viridis"):
return ax.matshow(
m[..., 0], cmap=cmap, vmin=vmin, vmax=vmax, interpolation="nearest")
@optional_clean_ax
def show_coords(m, ax):
vmin, vmax = np.min(m), np.max(m)
m = (m - vmin) / (vmax - vmin)
color_conv = get_mask_plot_colors(m.shape[-1])
color_mask = np.dot(m, color_conv)
return ax.imshow(color_mask, interpolation="nearest")
def example_plot(rinfo,
b=0,
t=-1,
mask_components=False,
size=2,
column_titles=True):
image = rinfo["data"]["image"][b, 0]
recons = rinfo["outputs"]["recons"][b, t, 0]
pred_mask = rinfo["outputs"]["pred_mask"][b, t]
components = rinfo["outputs"]["components"][b, t]
K, H, W, C = components.shape
colors = get_mask_plot_colors(K)
nrows = 1
ncols = 3 + K
fig, axes = plt.subplots(ncols=ncols, figsize=(ncols * size, nrows * size))
show_img(image, ax=axes[0], color="#000000")
show_img(recons, ax=axes[1], color="#000000")
show_mask(pred_mask[..., 0], ax=axes[2], color="#000000")
for k in range(K):
mask = pred_mask[k] if mask_components else None
show_img(components[k], ax=axes[k + 3], color=colors[k], mask=mask)
if column_titles:
labels = ["Image", "Recons.", "Mask"
] + ["Component {}".format(k + 1) for k in range(K)]
for ax, title in zip(axes, labels):
ax.set_title(title)
plt.subplots_adjust(hspace=0.03, wspace=0.035)
return fig
def iterations_plot(rinfo, b=0, mask_components=False, size=2):
image = rinfo["data"]["image"][b]
true_mask = rinfo["data"]["true_mask"][b]
recons = rinfo["outputs"]["recons"][b]
pred_mask = rinfo["outputs"]["pred_mask"][b]
pred_mask_logits = rinfo["outputs"]["pred_mask_logits"][b]
components = rinfo["outputs"]["components"][b]
T, K, H, W, C = components.shape
colors = get_mask_plot_colors(K)
nrows = T + 1
ncols = 2 + K
fig, axes = plt.subplots(
nrows=nrows, ncols=ncols, figsize=(ncols * size, nrows * size))
for t in range(T):
show_img(recons[t, 0], ax=axes[t, 0])
show_mask(pred_mask[t, ..., 0], ax=axes[t, 1])
axes[t, 0].set_ylabel("iter {}".format(t))
for k in range(K):
mask = pred_mask[t, k] if mask_components else None
show_img(components[t, k], ax=axes[t, k + 2], color=colors[k], mask=mask)
axes[0, 0].set_title("Reconstruction")
axes[0, 1].set_title("Mask")
show_img(image[0], ax=axes[T, 0])
show_mask(true_mask[0, ..., 0], ax=axes[T, 1])
vmin = np.min(pred_mask_logits[T - 1])
vmax = np.max(pred_mask_logits[T - 1])
for k in range(K):
axes[0, k + 2].set_title("Component {}".format(k + 1)) # , color=colors[k])
show_mat(
pred_mask_logits[T - 1, k], ax=axes[T, k + 2], vmin=vmin, vmax=vmax)
axes[T, k + 2].set_xlabel(
"Mask Logits for\nComponent {}".format(k + 1)) # , color=colors[k])
axes[T, 0].set_xlabel("Input Image")
axes[T, 1].set_xlabel("Ground Truth Mask")
plt.subplots_adjust(wspace=0.05, hspace=0.05)
return fig
def inputs_plot(rinfo, b=0, t=0, size=2):
B, T, K, H, W, C = rinfo["outputs"]["components"].shape
colors = get_mask_plot_colors(K)
inputs = rinfo["inputs"]["spatial"]
rows = [
("image", show_img, False),
("components", show_img, False),
("dcomponents", functools.partial(show_img, norm=True), False),
("mask", show_mat, True),
("pred_mask", show_mat, True),
("dmask", functools.partial(show_mat, cmap="coolwarm"), True),
("posterior", show_mat, True),
("log_prob", show_mat, True),
("counterfactual", show_mat, True),
("coordinates", show_coords, False),
]
rows = [(n, f, mcb) for n, f, mcb in rows if n in inputs]
nrows = len(rows)
ncols = K + 1
fig, axes = plt.subplots(
nrows=nrows,
ncols=ncols,
figsize=(ncols * size - size * 0.9, nrows * size),
gridspec_kw={"width_ratios": [1] * K + [0.1]},
)
for r, (name, plot_fn, make_cbar) in enumerate(rows):
axes[r, 0].set_ylabel(name)
if make_cbar:
vmin = np.min(inputs[name][b, t])
vmax = np.max(inputs[name][b, t])
if np.abs(vmin - vmax) < 1e-6:
vmin -= 0.1
vmax += 0.1
plot_fn = functools.partial(plot_fn, vmin=vmin, vmax=vmax)
# print("range of {:<16}: [{:0.2f}, {:0.2f}]".format(name, vmin, vmax))
for k in range(K):
if inputs[name].shape[2] == 1:
m = inputs[name][b, t, 0]
color = (0.0, 0.0, 0.0)
else:
m = inputs[name][b, t, k]
color = colors[k]
mappable = plot_fn(m, ax=axes[r, k], color=color)
if make_cbar:
fig.colorbar(mappable, cax=axes[r, K])
else:
axes[r, K].set_visible(False)
for k in range(K):
axes[0, k].set_title("Component {}".format(k + 1)) # , color=colors[k])
plt.subplots_adjust(hspace=0.05, wspace=0.05)
return fig
================================================
FILE: iodine/modules/refinement.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Iterative refinement modules."""
# pylint: disable=g-doc-bad-indent, unused-variable
from iodine.modules import utils
import shapeguard
import sonnet as snt
import tensorflow.compat.v1 as tf
class RefinementCore(snt.RNNCore):
"""Recurrent Refinement Module.
Refinement modules take as inputs:
* previous state (which could be an arbitrary nested structure)
* current inputs which include
* image-space inputs like pixel-based errors, or mask-posteriors
* latent-space inputs like the previous z_dist, or dz
They use these inputs to produce:
* output (usually a new z_dist)
* new_state
"""
def __init__(self,
encoder_net,
recurrent_net,
refinement_head,
name="refinement"):
super().__init__(name=name)
self._encoder_net = encoder_net
self._recurrent_net = recurrent_net
self._refinement_head = refinement_head
self._sg = shapeguard.ShapeGuard()
def initial_state(self, batch_size, **unused_kwargs):
return self._recurrent_net.initial_state(batch_size)
def _build(self, inputs, prev_state):
sg = self._sg
assert "spatial" in inputs, inputs.keys()
assert "flat" in inputs, inputs.keys()
assert "zp" in inputs["flat"], inputs["flat"].keys()
zp = sg.guard(inputs["flat"]["zp"], "B, K, Zp")
x = sg.guard(self.prepare_spatial_inputs(inputs["spatial"]), "B*K, H, W, C")
h1 = sg.guard(self._encoder_net(x).params, "B*K, H1")
h2 = sg.guard(self.prepare_flat_inputs(h1, inputs["flat"]), "B*K, H2")
h2_unflattened = sg.reshape(h2, "B, K, H2")
h3, next_state = self._recurrent_net(h2_unflattened, prev_state)
sg.guard(h3, "B, K, H3")
outputs = sg.guard(self._refinement_head(zp, h3), "B, K, Y")
del self._sg.B
return outputs, next_state
def prepare_spatial_inputs(self, inputs):
values = []
for name, val in sorted(inputs.items(), key=lambda it: it[0]):
if val.shape.as_list()[1] == 1:
self._sg.guard(val, "B, 1, H, W, _C")
val = tf.tile(val, self._sg["1, K, 1, 1, 1"])
else:
self._sg.guard(val, "B, K, H, W, _C")
values.append(val)
concat_inputs = self._sg.guard(tf.concat(values, axis=-1), "B, K, H, W, C")
return self._sg.reshape(concat_inputs, "B*K, H, W, C")
def prepare_flat_inputs(self, hidden, inputs):
values = [self._sg.guard(hidden, "B*K, H1")]
for name, val in sorted(inputs.items(), key=lambda it: it[0]):
self._sg.guard(val, "B, K, _")
val_flat = tf.reshape(val, self._sg["B*K"] + [-1])
values.append(val_flat)
return tf.concat(values, axis=-1)
class ResHead(snt.AbstractModule):
"""Updates Zp using a residual mechanism."""
def __init__(self, name="residual_head"):
super().__init__(name=name)
def _build(self, zp_old, inputs):
sg = shapeguard.ShapeGuard()
sg.guard(zp_old, "B, K, Zp")
sg.guard(inputs, "B, K, H")
update = snt.Linear(sg.Zp)
flat_zp = sg.reshape(zp_old, "B*K, Zp")
flat_inputs = sg.reshape(inputs, "B*K, H")
zp = flat_zp + update(flat_inputs)
return sg.reshape(zp, "B, K, Zp")
class PredictorCorrectorHead(snt.AbstractModule):
"""This refinement head is used for sequential data.
At every step it computes a prediction from the λ of the previous timestep
and an update from the refinement network of the current timestep.
The next step λ' is computed as a gated combination of both:
λ' = g * λ_corr + (1-g) * λ_pred
"""
def __init__(
self,
hidden_sizes=(64,),
pred_gate_bias=0.0,
corrector_gate_bias=0.0,
activation=tf.nn.elu,
name="predcorr_head",
):
super().__init__(name=name)
self._hidden_sizes = hidden_sizes
self._activation = utils.get_act_func(activation)
self._pred_gate_bias = pred_gate_bias
self._corrector_gate_bias = corrector_gate_bias
def _build(self, zp_old, inputs):
sg = shapeguard.ShapeGuard()
sg.guard(zp_old, "B, K, Zp")
sg.guard(inputs, "B, K, H")
update = snt.Linear(sg.Zp)
update_gate = snt.Linear(sg.Zp)
predict = snt.nets.MLP(
output_sizes=list(self._hidden_sizes) + [sg.Zp * 2],
activation=self._activation,
)
flat_zp = sg.reshape(zp_old, "B*K, Zp")
flat_inputs = sg.reshape(inputs, "B*K, H")
g = tf.nn.sigmoid(update_gate(flat_inputs) + self._corrector_gate_bias)
u = update(flat_inputs)
# a slightly more efficient way of computing the gated update
# (1-g) * flat_zp + g * u
zp_corrected = flat_zp + g * (u - flat_zp)
predicted = predict(flat_zp)
pred_up = predicted[:, :sg.Zp]
pred_gate = tf.nn.sigmoid(predicted[:, sg.Zp:] + self._pred_gate_bias)
zp = zp_corrected + pred_gate * (pred_up - zp_corrected)
return sg.reshape(zp, "B, K, Zp")
================================================
FILE: iodine/modules/utils.py
================================================
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities for IODINE."""
# pylint: disable=g-doc-bad-indent, g-doc-return-or-yield, g-doc-args
# pylint: disable=missing-docstring
import importlib
import math
from absl import logging
from matplotlib.colors import hsv_to_rgb
import numpy as np
import shapeguard
import sonnet as snt
import tensorflow.compat.v1 as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
ACT_FUNCS = {
"identity": tf.identity,
"sigmoid": tf.nn.sigmoid,
"tanh": tf.nn.tanh,
"relu": tf.nn.relu,
"elu": tf.nn.elu,
"selu": tf.nn.selu,
"softplus": tf.nn.softplus,
"exp": tf.exp,
"softmax": tf.nn.softmax,
}
def get_act_func(name_or_func):
if name_or_func is None:
return tf.identity
if callable(name_or_func):
return name_or_func
elif isinstance(name_or_func, str):
return ACT_FUNCS[name_or_func.lower()]
else:
raise KeyError(
'Unknown activation function "{}" of type {}"'.format(
name_or_func, type(name_or_func)
)
)
DISTS = {
"normal": tfd.Normal,
"log_normal": tfd.LogNormal,
"laplace": tfd.Laplace,
"logistic": tfd.Logistic,
}
def get_distribution(name_or_dist):
if isinstance(name_or_dist, type(tfd.Normal)):
return name_or_dist
elif isinstance(name_or_dist, str):
return DISTS[name_or_dist.lower()]
raise KeyError(
'Unknown distribution "{}" of type {}"'.format(name_or_dist,
type(name_or_dist)))
def get_mask_plot_colors(nr_colors):
"""Get nr_colors uniformly spaced hues to plot mask values."""
hsv_colors = np.ones((nr_colors, 3), dtype=np.float32)
hsv_colors[:, 0] = np.linspace(0, 1, nr_colors, endpoint=False)
color_conv = hsv_to_rgb(hsv_colors)
return color_conv
def color_transform(masks):
with tf.name_scope("color_transform"):
n_components = masks.shape.as_list()[-1]
colors = tf.constant(get_mask_plot_colors(n_components), name="mask_colors")
return tf.tensordot(masks, colors, axes=1)
def construct_diagnostic_image(
images,
recons,
masks,
components,
border_width=2,
nr_images=6,
clip=True,
mask_components=False,
):
"""Construct a single image containing image, recons., mask, and components.
Args:
images: (B, H, W, C)
recons: (B, H, W, C)
masks: (B, H, W, K)
components: (B, H, W, K, C)
border_width: int. width of the border in pixels. (default=2)
nr_images: int. Number of images to include. (default=6)
clip: bool. Whether to clip the final image to range [0, 1].
Returns:
diag_images: (nr, H+border_width*2, (W+border_width*2) * (K+3), 3)
"""
with tf.name_scope("diagnostic_image"):
# transform the masks into RGB images
recolored_masks = color_transform(masks[:nr_images])
if images.get_shape().as_list()[-1] == 1:
# deal with grayscale images
images = tf.tile(images[:nr_images], [1, 1, 1, 3])
recons = tf.tile(recons[:nr_images], [1, 1, 1, 3])
components = tf.tile(components[:nr_images], [1, 1, 1, 1, 3])
if mask_components:
components *= masks[:nr_images, ..., tf.newaxis]
# Pad everything
no_pad, pad = (0, 0), (border_width, border_width)
paddings = tf.constant([no_pad, pad, pad, no_pad])
paddings_components = tf.constant([no_pad, pad, pad, no_pad, no_pad])
pad_images = tf.pad(images[:nr_images], paddings, constant_values=0.5)
pad_recons = tf.pad(recons[:nr_images], paddings, constant_values=0.5)
pad_masks = tf.pad(recolored_masks, paddings, constant_values=1.0)
pad_components = tf.pad(
components[:nr_images], paddings_components, constant_values=0.5
)
# reshape components into single wide image
pad_components = tf.transpose(pad_components, [0, 1, 3, 2, 4])
pc_shape = pad_components.shape.as_list()
pc_shape[2] = pc_shape[2] * pc_shape.pop(3)
pad_components = tf.reshape(pad_components, pc_shape)
# concatenate all parts along width
diag_imgs = tf.concat(
[pad_images, pad_recons, pad_masks, pad_components], axis=2
)
# concatenate all images along height
diag_shape = diag_imgs.shape.as_list()
final_img = tf.reshape(diag_imgs, [1, -1, diag_shape[2], diag_shape[3]])
if clip:
final_img = tf.clip_by_value(final_img, 0.0, 1.0)
return final_img
def construct_reconstr_image(images, recons, border_width=2,
nr_images=6, clip=True):
"""Construct a single image containing image, and recons.
Args:
images: (B, H, W, C)
recons: (B, H, W, C)
border_width: int. width of the border in pixels. (default=2)
nr_images: int. Number of images to include. (default=6)
clip: bool. Whether to clip the final image to range [0, 1].
Returns:
rec_images: (nr, H+border_width*2, (W+border_width*2) * 2, 3)
"""
with tf.name_scope("diagnostic_image"):
# Pad everything
no_pad, pad = (0, 0), (border_width, border_width)
paddings = tf.constant([no_pad, pad, pad, no_pad])
pad_images = tf.pad(images[:nr_images], paddings, constant_values=0.5)
pad_recons = tf.pad(recons[:nr_images], paddings, constant_values=0.5)
# concatenate all parts along width
diag_imgs = tf.concat([pad_images, pad_recons], axis=2)
# concatenate all images along height
diag_shape = diag_imgs.shape.as_list()
final_img = tf.reshape(diag_imgs, [1, -1, diag_shape[2], diag_shape[3]])
if clip:
final_img = tf.clip_by_value(final_img, 0.0, 1.0)
return final_img
def construct_iterations_image(
images, recons, masks, border_width=2, nr_seqs=2, clip=True
):
"""Construct a single image containing image, and recons.
Args:
images: (B, T, 1, H, W, C)
recons: (B, T, 1, H, W, C)
masks: (B, T, K, H, W, 1)
border_width: int. width of the border in pixels. (default=2)
nr_seqs: int. Number of sequences to include. (default=2)
clip: bool. Whether to clip the final image to range [0, 1].
Returns:
rec_images: (nr, H+border_width*2, (W+border_width*2) * 2, 3)
"""
sg = shapeguard.ShapeGuard()
sg.guard(recons, "B, T, 1, H, W, C")
if images.get_shape().as_list()[1] == 1:
images = tf.tile(images, sg["1, T, 1, 1, 1, 1"])
sg.guard(images, "B, T, 1, H, W, C")
sg.guard(masks, " B, T, K, H, W, 1")
if sg.C == 1: # deal with grayscale
images = tf.tile(images, [1, 1, 1, 1, 1, 3])
recons = tf.tile(recons, [1, 1, 1, 1, 1, 3])
sg.S = min(nr_seqs, sg.B)
with tf.name_scope("diagnostic_image"):
# convert masks to rgb
masks_trans = tf.transpose(masks[:nr_seqs], [0, 1, 5, 3, 4, 2])
recolored_masks = color_transform(masks_trans)
# Pad everything
no_pad, pad = (0, 0), (border_width, border_width)
paddings = tf.constant([no_pad, no_pad, no_pad, pad, pad, no_pad])
pad_images = tf.pad(images[:nr_seqs], paddings, constant_values=0.5)
pad_recons = tf.pad(recons[:nr_seqs], paddings, constant_values=0.5)
pad_masks = tf.pad(recolored_masks, paddings, constant_values=0.5)
# concatenate all parts along width
triples = tf.concat([pad_images, pad_recons, pad_masks], axis=3)
triples = sg.guard(triples[:, :, 0], "S, T, 3*Hp, Wp, 3")
# concatenate iterations along width and sequences along height
final = tf.reshape(
tf.transpose(triples, [0, 2, 1, 3, 4]), sg["1, S*3*Hp, Wp*T, 3"]
)
if clip:
final = tf.clip_by_value(final, 0.0, 1.0)
return final
def get_overview_image(image, output_dist, mask_components=False):
recons = output_dist.mean()[:, 0]
image = image[:, 0]
if hasattr(output_dist, "mixture_distribution") and hasattr(
output_dist, "components_distribution"
):
mask = output_dist.mixture_distribution.probs[:, 0]
components = output_dist.components_distribution.mean()[:, 0]
return construct_diagnostic_image(
image, recons, mask, components, mask_components=mask_components
)
else:
return construct_reconstr_image(image, recons)
class OnlineMeanVarEstimator(snt.AbstractModule):
"""Online estimator for mean and variance using Welford's algorithm."""
def __init__(self, axis=None, ddof=0.0, name="online_mean_var"):
super().__init__(name=name)
self._axis = axis
self._ddof = ddof
def _build(self, x, weights=None):
if weights is None:
weights = tf.ones_like(x)
if weights.get_shape().as_list() != x.get_shape().as_list():
weights = tf.broadcast_to(weights, x.get_shape().as_list())
sum_weights = tf.reduce_sum(weights, axis=self._axis)
shape = sum_weights.get_shape().as_list()
total = tf.get_variable(
"total",
shape=shape,
dtype=weights.dtype,
initializer=tf.zeros_initializer(),
trainable=False,
)
mean = tf.get_variable(
"mean",
shape=shape,
dtype=x.dtype,
initializer=tf.zeros_initializer(),
trainable=False,
)
m2 = tf.get_variable(
"M2",
shape=shape,
dtype=x.dtype,
initializer=tf.zeros_initializer(),
trainable=False,
)
total_update = tf.assign_add(total, sum_weights)
with tf.control_dependencies([total_update]):
delta = (x - mean) * weights
mean_update = tf.assign_add(
mean, tf.reduce_sum(delta, axis=self._axis) / total
)
with tf.control_dependencies([mean_update]):
delta2 = x - mean
m2_update = tf.assign_add(
m2, tf.reduce_sum(delta * delta2, axis=self._axis)
)
with tf.control_dependencies([m2_update]):
return tf.identity(mean), m2 / (total - self._ddof), tf.identity(total)
def print_shapes(name, value, indent=""):
if isinstance(value, dict):
print("{}{}:".format(indent, name))
for k, v in sorted(value.items(),
key=lambda x: (isinstance(x[1], dict), x[0])):
print_shapes(k, v, indent + " ")
elif isinstance(value, list):
print(
"{}{}[{}]: {} @ {}".format(
indent, name, len(value), value[0].shape, value[0].dtype
)
)
elif isinstance(value, np.ndarray):
print("{}{}: {} @ {}".format(indent, name, value.shape, value.dtype))
elif isinstance(value, tf.Tensor):
print(
"{}{}: {} @ {}".format(
indent, name, value.get_shape().as_list(), value.dtype
)
)
elif np.isscalar(value):
print("{}{}: {}".format(indent, name, value))
else:
print("{}{}.type: {}".format(indent, name, type(value)))
def _pad_images(images, image_border_value=0.5, border_width=2):
"""Pad images to create gray borders.
Args:
images: Tensor of shape [B, H], [B, H, W], or [B, H, W, C].
image_border_value: Scalar value of greyscale borderfor images.
border_width: Int. Border width in pixels.
Raises:
ValueError: if the image provided is not {2,3,4} dimensional.
Returns:
Tensor of same shape as images, except H and W being H + border_width and
W + border_width.
"""
image_rank = len(images.get_shape())
border_paddings = (border_width, border_width)
if image_rank == 2: # [B, H]
paddings = [(0, 0), border_paddings]
elif image_rank == 3: # [B, H, W]
paddings = [(0, 0), border_paddings, border_paddings]
elif image_rank == 4: # [B, H, W, C]
paddings = [(0, 0), border_paddings, border_paddings, (0, 0)]
else:
raise ValueError("expected image to be 2D, 3D or 4D, got %d" % image_rank)
paddings = tf.constant(paddings)
return tf.pad(images, paddings, "CONSTANT",
constant_values=image_border_value)
def images_to_grid(
images,
grid_height=None,
grid_width=4,
max_grid_height=4,
max_grid_width=4,
image_border_value=0.5,
):
"""Combine images and arrange them in a grid.
Args:
images: Tensor of shape [B, H], [B, H, W], or [B, H, W, C].
grid_height: Height of the grid of images to output, or None. Either
`grid_width` or `grid_height` must be set to an integer value.
If None, `grid_height` is set to ceil(B/`grid_width`), and capped at
`max_grid_height` when provided.
grid_width: Width of the grid of images to output, or None. Either
`grid_width` or `grid_height` must be set to an integer value.
If None, `grid_width` is set to ceil(B/`grid_height`), and capped at
`max_grid_width` when provided.
max_grid_height: Maximum allowable height of the grid of images to
output or None. Only used when `grid_height` is None.
max_grid_width: Maximum allowable width of the grid of images to output,
or None. Only used when `grid_width` is None.
image_border_value: None or scalar value of greyscale borderfor images.
If None, then no border is rendered.
Raises:
ValueError: if neither of grid_width or grid_height are set to a positive
integer.
Returns:
images: Tensor of shape [height*H, width*W, C].
C will be set to 1 if the input was provided with no channels.
Contains all input images in a grid.
"""
# If only one dimension is set, infer how big the other one should be.
if grid_height is None:
if not isinstance(grid_width, int) or grid_width <= 0:
raise ValueError(
"if `grid_height` is None, `grid_width` must be " "a positive integer"
)
grid_height = int(math.ceil(images.get_shape()[0].value / grid_width))
if max_grid_height is not None:
grid_height = min(max_grid_height, grid_height)
if grid_width is None:
if not isinstance(grid_height, int) or grid_height <= 0:
raise ValueError(
"if `grid_width` is None, `grid_height` must be " "a positive integer"
)
grid_width = int(math.ceil(images.get_shape()[0].value / grid_height))
if max_grid_width is not None:
grid_width = min(max_grid_width, grid_width)
images = images[: grid_height * grid_width, ...]
# Pad with extra blank frames if grid_height x grid_width is less than the
# number of frames provided.
pre_images_shape = images.get_shape().as_list()
if pre_images_shape[0] < grid_height * grid_width:
pre_images_shape[0] = grid_height * grid_width - pre_images_shape[0]
if image_border_value is not None:
dummy_frames = image_border_value * tf.ones(
shape=pre_images_shape, dtype=images.dtype
)
else:
dummy_frames = tf.zeros(shape=pre_images_shape, dtype=images.dtype)
images = tf.concat([images, dummy_frames], axis=0)
if image_border_value:
images = _pad_images(images, image_border_value=image_border_value)
images_shape = images.get_shape().as_list()
images = tf.reshape(images, [grid_height, grid_width] + images_shape[1:])
if len(images_shape) == 2:
images = tf.expand_dims(images, -1)
if len(images_shape) <= 3:
images = tf.expand_dims(images, -1)
image_height, image_width, channels = images.get_shape().as_list()[2:]
images = tf.transpose(images, perm=[0, 2, 1, 3, 4])
images = tf.reshape(
images, [grid_height * image_height, grid_width * image_width, channels]
)
return images
def flatten_all_but_last(tensor, n_dims=1):
shape = tensor.shape.as_list()
batch_dims = shape[:-n_dims]
flat_tensor = tf.reshape(tensor, [np.prod(batch_dims)] + shape[-n_dims:])
def unflatten(other_tensor):
other_shape = other_tensor.shape.as_list()
return tf.reshape(other_tensor, batch_dims + other_shape[1:])
return flat_tensor, unflatten
def ensure_3d(tensor):
if tensor.shape.ndims == 2:
return tensor[..., None]
assert tensor.shape.ndims == 3
return tensor
built_element_cache = {
"none": None,
"global_step": tf.train.get_or_create_global_step(),
}
def build(plan, identifier):
logging.debug("building %s", identifier)
if identifier in built_element_cache:
logging.debug("%s is already built, returning", identifier)
return built_element_cache[identifier]
elif not isinstance(plan, dict):
return plan
elif "constructor" in plan:
ctor = _resolve_constructor(plan)
kwargs = {
k: build(v, identifier=k) for k, v in plan.items() if k != "constructor"
}
with tf.variable_scope(identifier):
built_element_cache[identifier] = ctor(**kwargs)
return built_element_cache[identifier]
else:
return {k: build(v, identifier=k) for k, v in plan.items()}
def _resolve_constructor(plan_subsection):
assert "constructor" in plan_subsection, plan_subsection
if isinstance(plan_subsection["constructor"], str):
module, _, ctor = plan_subsection["constructor"].rpartition(".")
mod = importlib.import_module(module)
return getattr(mod, ctor)
else:
return plan_subsection["constructor"]
================================================
FILE: iodine/requirements.txt
================================================
tensorflow-gpu==1.14.0
tensorflow-probability==0.7.0
dm-sonnet==1.35
sacred>=0.7,<0.8
shapeguard
seaborn
pymongo
jupyterlab
git+git://github.com/deepmind/multi_object_datasets.git
================================================
FILE: iodine/run.sh
================================================
#!/bin/sh
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
echo "downloading checkpoints from GCP"
iodine/download_checkpoints.sh
python3 -m venv iodine_venv
source iodine_venv/bin/activate
pip3 install --upgrade setuptools wheel
pip3 install -r iodine/requirements.txt
# Get some fake data and put it where the real multi_objects_dataset files live.
mkdir -p iodine/multi_object_datasets
cp iodine/test_data/tetrominoes_mini.tfrecords iodine/multi_object_datasets/tetrominoes_train.tfrecords
# Run training with a cut down size.
python3 -m iodine.main \
-f with tetrominoes \
data.shuffle_buffer=2 \
data.batch_size=2 \
n_z=4 \
num_components=3 \
stop_after_steps=11
================================================
FILE: kfac_ferminet_alpha/README.md
================================================
# Accompanying code for Better, Faster Fermionic Neural Networks
All package requirements are listed in `requirements.txt`.
## Contributing
This is purely research code, provided with no further intentions of support or
any guarantees of backward compatibility.
## Installation
```shell
git clone git@github.com:deepmind/deepmind-research.git
pip install deepmind_research/kfac_ferminet_alpha/
```
## Usage
You can find examples of how to use the codebase through the [FermiNet project].
We also provide an [example training script].
## Reference
**Better, Faster Fermionic Neural Networks**
James S. Spencer, David Pfau, Aleksandar Botev, and W. M. C. Foulkes.
URL: https://arxiv.org/abs/2011.07125.
**Optimizing Neural Networks with Kronecker-factored Approximate Curvature**
James Martens, Roger Grosse
URL: https://arxiv.org/abs/1503.05671
[FermiNet Project]: https://github.com/deepmind/ferminet/
[example training script]:
https://github.com/deepmind/deepmind-research/blob/master/kfac_ferminet_alpha/example.py
================================================
FILE: kfac_ferminet_alpha/__init__.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module for anything that an end user would use."""
from kfac_ferminet_alpha.loss_functions import register_normal_predictive_distribution
from kfac_ferminet_alpha.loss_functions import register_squared_error_loss
from kfac_ferminet_alpha.optimizer import Optimizer
================================================
FILE: kfac_ferminet_alpha/curvature_blocks.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module for all of the different curvature blocks."""
import abc
from typing import Any, Callable, Dict, Mapping, MutableMapping, Optional, Sequence, Union
import jax
from jax import core
import jax.numpy as jnp
from kfac_ferminet_alpha import tag_graph_matcher as tgm
from kfac_ferminet_alpha import utils
_Arrays = Sequence[jnp.ndarray]
_BlockInfo = Mapping[str, Any]
class CurvatureBlock(utils.Stateful, abc.ABC):
"""Top level class."""
def __init__(self, layer_tag_eq: tgm.jax_core.JaxprEqn):
super(CurvatureBlock, self).__init__()
self._layer_tag_eq = layer_tag_eq
@property
def layer_tag_primitive(self) -> tgm.tags.LayerTag:
assert isinstance(self._layer_tag_eq.primitive, tgm.tags.LayerTag)
return self._layer_tag_eq.primitive
@property
def outputs_shapes(self) -> Sequence[Sequence[int]]:
output_vars = self.layer_tag_primitive.split_all_inputs(
self._layer_tag_eq.invars)[0]
return jax.tree_map(lambda x: x.aval.shape, output_vars)
@property
def inputs_shapes(self) -> Sequence[Sequence[int]]:
input_vars = self.layer_tag_primitive.split_all_inputs(
self._layer_tag_eq.invars)[1]
return jax.tree_map(lambda x: x.aval.shape, input_vars)
@property
def params_shapes(self) -> Sequence[Sequence[int]]:
params_vars = self.layer_tag_primitive.split_all_inputs(
self._layer_tag_eq.invars)[2]
return jax.tree_map(lambda x: x.aval.shape, params_vars)
@abc.abstractmethod
def init(self, rng: jnp.ndarray) -> MutableMapping[str, Any]:
"""This initializes/creates all of the arrays for the state of the block.
Usually this would include the arrays used for storing the curvature
approximation, as well as the arrays for storing any approximate
inverses/powers of the curvature block.
Args:
rng: The Jax PRNG key to use if any of the state is supposed to be
initialized randomly.
Returns:
A mutable mapping of the state.
"""
@abc.abstractmethod
def update_curvature_matrix_estimate(
self,
info: _BlockInfo,
batch_size: int,
ema_old: Union[float, jnp.ndarray],
ema_new: Union[float, jnp.ndarray],
pmap_axis_name: str
) -> None:
pass
@abc.abstractmethod
def update_curvature_inverse_estimate(
self,
diagonal_weight: Union[float, jnp.ndarray],
pmap_axis_name: str
) -> None:
pass
@abc.abstractmethod
def multiply_matpower(
self,
vec: _Arrays,
exp: Union[float, int],
diagonal_weight: Union[float, jnp.ndarray]
) -> _Arrays:
pass
CurvatureBlockCtor = Callable[[core.JaxprEqn], CurvatureBlock]
@utils.Stateful.infer_class_state
class NaiveDiagonal(CurvatureBlock):
"""The naively estimated diagonal block."""
diagonal_factor: utils.WeightedMovingAverage
def init(self, rng: jnp.ndarray) -> Dict[str, Any]:
del rng
return dict(
diagonal_factor=utils.WeightedMovingAverage.zero(
self.outputs_shapes[0])
)
def update_curvature_matrix_estimate(
self,
info: _BlockInfo,
batch_size: int,
ema_old: Union[float, jnp.ndarray],
ema_new: Union[float, jnp.ndarray],
pmap_axis_name: str
) -> None:
dw, = info["outputs_tangent"]
diagonal_update = dw * dw / batch_size
self.diagonal_factor.update(diagonal_update, ema_old, ema_new)
self.diagonal_factor.sync(pmap_axis_name)
def update_curvature_inverse_estimate(
self,
diagonal_weight: Union[float, jnp.ndarray],
pmap_axis_name: str
) -> None:
pass
def multiply_matpower(
self,
vec: _Arrays,
exp: Union[float, int],
diagonal_weight: Union[float, jnp.ndarray]
) -> _Arrays:
w, = vec
if exp == 1:
return w * (self.diagonal_factor.value + diagonal_weight),
elif exp == -1:
return w / (self.diagonal_factor.value + diagonal_weight),
else:
raise NotImplementedError()
@utils.Stateful.infer_class_state
class TwoKroneckerFactored(CurvatureBlock, abc.ABC):
"""A factor that is the Kronecker product of two matrices."""
inputs_factor: utils.WeightedMovingAverage
inputs_factor_inverse: jnp.ndarray
outputs_factor: utils.WeightedMovingAverage
outputs_factor_inverse: jnp.ndarray
extra_scale: Optional[Union[int, float, jnp.ndarray]]
@property
def has_bias(self) -> bool:
return len(self._layer_tag_eq.invars) == 4
@abc.abstractmethod
def input_size(self) -> int:
pass
@abc.abstractmethod
def output_size(self) -> int:
pass
def compute_extra_scale(self) -> Optional[Union[int, float, jnp.ndarray]]:
return 1
def init(self, rng: jnp.ndarray) -> Dict[str, Any]:
# The extra scale is technically a constant, but in general it could be
# useful for anyone examining the state to know it explicitly,
# hence we actually keep it as part of the state.
d_in = self.input_size()
d_out = self.output_size()
return dict(
inputs_factor=utils.WeightedMovingAverage.zero([d_in, d_in]),
inputs_factor_inverse=jnp.zeros([d_in, d_in]),
outputs_factor=utils.WeightedMovingAverage.zero([d_out, d_out]),
outputs_factor_inverse=jnp.zeros([d_out, d_out]),
extra_scale=self.compute_extra_scale()
)
def update_curvature_inverse_estimate(
self,
diagonal_weight: Union[float, jnp.ndarray],
pmap_axis_name: str
) -> None:
self.inputs_factor.sync(pmap_axis_name)
self.outputs_factor.sync(pmap_axis_name)
# This computes the approximate inverse factor using the pi-adjusted
# inversion from the original KFAC paper.
# Note that the damping is divided by extra_scale since:
# (s * A kron B + lambda I)^-1 = s^-1 (A kron B + s^-1 * lambda I)^-1
# And the extra division by the scale is included in `multiply_matpower`.
(self.inputs_factor_inverse,
self.outputs_factor_inverse) = utils.pi_adjusted_inverse(
factor_0=self.inputs_factor.value,
factor_1=self.outputs_factor.value,
damping=diagonal_weight / self.extra_scale,
pmap_axis_name=pmap_axis_name)
def multiply_matpower(
self,
vec: _Arrays,
exp: Union[float, int],
diagonal_weight: Union[float, jnp.ndarray]
) -> _Arrays:
if self.has_bias:
w, b = vec
vec = jnp.concatenate([w.reshape([-1, w.shape[-1]]), b[None]], axis=0)
else:
w, = vec
vec = w.reshape([-1, w.shape[-1]])
if exp == 1:
inputs_factor, outputs_factor = (self.inputs_factor.value,
self.outputs_factor.value)
scale = self.extra_scale
elif exp == -1:
inputs_factor, outputs_factor = (self.inputs_factor_inverse,
self.outputs_factor_inverse)
scale = 1.0 / self.extra_scale
diagonal_weight = 0
else:
raise NotImplementedError()
result = jnp.matmul(inputs_factor, vec)
result = jnp.matmul(result, outputs_factor)
result = result * scale + diagonal_weight * vec
if self.has_bias:
w_new, b_new = result[:-1], result[-1]
return w_new.reshape(w.shape), b_new
else:
return result.reshape(w.shape),
class DenseTwoKroneckerFactored(TwoKroneckerFactored):
"""Factor for a standard dense layer."""
def input_size(self) -> int:
if self.has_bias:
return self.params_shapes[0][0] + 1
else:
return self.params_shapes[0][0]
def output_size(self) -> int:
return self.params_shapes[0][1]
def update_curvature_matrix_estimate(
self,
info: _BlockInfo,
batch_size: int,
ema_old: Union[float, jnp.ndarray],
ema_new: Union[float, jnp.ndarray],
pmap_axis_name: str
) -> None:
del pmap_axis_name
(x,), (dy,) = info["inputs"], info["outputs_tangent"]
utils.check_first_dim_is_batch_size(batch_size, x, dy)
if self.has_bias:
x_one = jnp.ones_like(x[:, :1])
x = jnp.concatenate([x, x_one], axis=1)
input_stats = jnp.matmul(x.T, x) / batch_size
output_stats = jnp.matmul(dy.T, dy) / batch_size
self.inputs_factor.update(input_stats, ema_old, ema_new)
self.outputs_factor.update(output_stats, ema_old, ema_new)
@utils.Stateful.infer_class_state
class ScaleAndShiftDiagonal(CurvatureBlock):
"""A scale and shift block with a diagonal approximation to the curvature."""
scale_factor: Optional[utils.WeightedMovingAverage]
shift_factor: Optional[utils.WeightedMovingAverage]
@property
def has_scale(self) -> bool:
return self._layer_tag_eq.params["has_scale"]
@property
def has_shift(self) -> bool:
return self._layer_tag_eq.params["has_shift"]
def init(self, rng: jnp.ndarray) -> Dict[str, Any]:
del rng
if self.has_scale and self.has_shift:
return dict(
scale_factor=utils.WeightedMovingAverage.zero(
self.params_shapes[0]
),
shift_factor=utils.WeightedMovingAverage.zero(
self.params_shapes[1]
)
)
elif self.has_scale:
return dict(
scale_factor=utils.WeightedMovingAverage.zero(
self.params_shapes[0]
),
shift_factor=None
)
elif self.has_shift:
return dict(
scale_factor=None,
shift_factor=utils.WeightedMovingAverage.zero(
self.params_shapes[0]
),
)
else:
raise ValueError("Neither `has_scale` nor `has_shift`.")
def update_curvature_matrix_estimate(
self,
info: _BlockInfo,
batch_size: int,
ema_old: Union[float, jnp.ndarray],
ema_new: Union[float, jnp.ndarray],
pmap_axis_name: str
) -> None:
(x,), (dy,) = info["inputs"], info["outputs_tangent"]
utils.check_first_dim_is_batch_size(batch_size, x, dy)
if self.has_scale:
assert self.scale_factor is not None
scale_shape = info["params"][0].shape
full_scale_shape = (1,) * (len(x.shape) - len(scale_shape)) + scale_shape
axis = [i for i, s in enumerate(full_scale_shape) if s == 1 and i != 0]
d_scale = jnp.sum(x * dy, axis=axis)
scale_diag_update = jnp.sum(d_scale * d_scale, axis=0) / batch_size
self.scale_factor.update(scale_diag_update, ema_old, ema_new) # pytype: disable=attribute-error # trace-all-classes
self.scale_factor.sync(pmap_axis_name) # pytype: disable=attribute-error # trace-all-classes
if self.has_shift:
assert self.shift_factor is not None
shift_shape = info["params"][1].shape
full_shift_shape = (1,) * (len(x.shape) - len(shift_shape)) + shift_shape
axis = [i for i, s in enumerate(full_shift_shape) if s == 1 and i != 0]
d_shift = jnp.sum(dy, axis=axis)
shift_diag_update = jnp.sum(d_shift * d_shift, axis=0) / batch_size
self.shift_factor.update(shift_diag_update, ema_old, ema_new) # pytype: disable=attribute-error # trace-all-classes
self.shift_factor.sync(pmap_axis_name) # pytype: disable=attribute-error # trace-all-classes
def update_curvature_inverse_estimate(
self,
diagonal_weight: Union[float, jnp.ndarray],
pmap_axis_name: str
) -> None:
pass
def multiply_matpower(
self,
vec: _Arrays,
exp: Union[float, int],
diagonal_weight: Union[float, jnp.ndarray]
) -> _Arrays:
if self.has_scale and self.has_shift:
factors = (self.scale_factor.value, self.shift_factor.value) # pytype: disable=attribute-error # trace-all-classes
elif self.has_scale:
factors = (self.scale_factor.value,) # pytype: disable=attribute-error # trace-all-classes
elif self.has_shift:
factors = (self.shift_factor.value,) # pytype: disable=attribute-error # trace-all-classes
else:
raise ValueError("Neither `has_scale` nor `has_shift`.")
factors = jax.tree_map(lambda x: x + diagonal_weight, factors)
if exp == 1:
return jax.tree_map(jnp.multiply, vec, factors)
elif exp == -1:
return jax.tree_map(jnp.divide, vec, factors)
else:
raise NotImplementedError()
@utils.Stateful.infer_class_state
class ScaleAndShiftFull(CurvatureBlock):
"""A scale and shift block with full approximation to the curvature."""
factor: utils.WeightedMovingAverage
inverse_factor: jnp.ndarray
@property
def _has_scale(self) -> bool:
return self._layer_tag_eq.params["has_scale"]
@property
def _has_shift(self) -> bool:
return self._layer_tag_eq.params["has_shift"]
def init(self, rng: jnp.ndarray) -> Dict[str, Any]:
del rng
dims = sum(utils.product(shape) for shape in self.params_shapes)
return dict(
factor=utils.WeightedMovingAverage.zero([dims, dims]),
inverse_factor=jnp.zeros([dims, dims])
)
def update_curvature_matrix_estimate(
self,
info: _BlockInfo,
batch_size: int,
ema_old: Union[float, jnp.ndarray],
ema_new: Union[float, jnp.ndarray],
pmap_axis_name: str
) -> None:
del pmap_axis_name
(x,), (dy,) = info["inputs"], info["outputs_tangent"]
utils.check_first_dim_is_batch_size(batch_size, x, dy)
grads = list()
if self._has_scale:
# Scale gradients
scale_shape = info["params"][0].shape
full_scale_shape = (1,) * (len(x.shape) - len(scale_shape)) + scale_shape
axis = [i for i, s in enumerate(full_scale_shape) if s == 1 and i != 0]
d_scale = jnp.sum(x * dy, axis=axis)
d_scale = d_scale.reshape([batch_size, -1])
grads.append(d_scale)
if self._has_shift:
# Shift gradients
shift_shape = info["params"][1].shape
full_shift_shape = (1,) * (len(x.shape) - len(shift_shape)) + shift_shape
axis = [i for i, s in enumerate(full_shift_shape) if s == 1 and i != 0]
d_shift = jnp.sum(dy, axis=axis)
d_shift = d_shift.reshape([batch_size, -1])
grads.append(d_shift)
grads = jnp.concatenate(grads, axis=1)
factor_update = jnp.matmul(grads.T, grads) / batch_size
self.factor.update(factor_update, ema_old, ema_new)
def update_curvature_inverse_estimate(
self,
diagonal_weight: Union[float, jnp.ndarray],
pmap_axis_name: str
) -> None:
self.factor.sync(pmap_axis_name)
self.inverse_factor = utils.psd_inv_cholesky(self.factor.value,
diagonal_weight)
def multiply_matpower(
self,
vec: _Arrays,
exp: Union[float, int],
diagonal_weight: Union[float, jnp.ndarray]
) -> _Arrays:
# Remember the vector is a tuple of all parameters
if self._has_scale and self._has_shift:
flat_vec = jnp.concatenate([v.flatten() for v in vec])
else:
flat_vec = vec[0].flatten()
if exp == 1:
flat_result = (
jnp.matmul(self.factor.value, flat_vec) + diagonal_weight * flat_vec)
elif exp == -1:
flat_result = jnp.matmul(self.inverse_factor, flat_vec)
else:
raise NotImplementedError()
if self._has_scale and self._has_shift:
scale_dims = int(vec[0].size)
scale_result = flat_result[:scale_dims].reshape(vec[0].shape)
shift_result = flat_result[scale_dims:].reshape(vec[1].shape)
return scale_result, shift_result
else:
return flat_vec.reshape(vec[0].shape),
_default_tag_to_block: MutableMapping[str, CurvatureBlockCtor] = dict(
dense_tag=DenseTwoKroneckerFactored,
generic_tag=NaiveDiagonal,
scale_and_shift_tag=ScaleAndShiftDiagonal,
)
def copy_default_tag_to_block() -> MutableMapping[str, CurvatureBlockCtor]:
return dict(_default_tag_to_block)
def get_default_tag_to_block(tag_name: str) -> CurvatureBlockCtor:
return _default_tag_to_block[tag_name]
def set_default_tag_to_block(
tag_name: str,
block_class: CurvatureBlockCtor,
) -> None:
_default_tag_to_block[tag_name] = block_class
================================================
FILE: kfac_ferminet_alpha/distributions.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module for all distribution implementations needed for the loss functions."""
import math
import jax
import jax.numpy as jnp
class MultivariateNormalDiag:
"""Multivariate normal distribution on `R^k`."""
def __init__(
self,
loc: jnp.ndarray,
scale_diag: jnp.ndarray):
"""Initializes a MultivariateNormalDiag distribution.
Args:
loc: Mean vector of the distribution. Can also be a batch of vectors.
scale_diag: Vector of standard deviations.
"""
super().__init__()
self._loc = loc
self._scale_diag = scale_diag
@property
def loc(self) -> jnp.ndarray:
"""Mean of the distribution."""
return self._loc
@property
def scale_diag(self) -> jnp.ndarray:
"""Scale of the distribution."""
return self._scale_diag
def _num_dims(self) -> int:
"""Dimensionality of the events."""
return self._scale_diag.shape[-1]
def _standardize(self, value: jnp.ndarray) -> jnp.ndarray:
return (value - self._loc) / self._scale_diag
def log_prob(self, value: jnp.ndarray) -> jnp.ndarray:
"""See `Distribution.log_prob`."""
log_unnormalized = -0.5 * jnp.square(self._standardize(value))
log_normalization = 0.5 * math.log(2 * math.pi) + jnp.log(self._scale_diag)
return jnp.sum(log_unnormalized - log_normalization, axis=-1)
def mean(self) -> jnp.ndarray:
"""Calculates the mean."""
return self.loc
def sample(self, seed: jnp.ndarray) -> jnp.ndarray:
"""Samples an event.
Args:
seed: PRNG key or integer seed.
Returns:
A sample.
"""
eps = jax.random.normal(seed, self.loc.shape)
return self.loc + eps * self.scale_diag
================================================
FILE: kfac_ferminet_alpha/estimator.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Defines the high-level Fisher estimator class."""
import collections
from typing import Any, Callable, Mapping, Optional, Sequence, Union, TypeVar
import jax
import jax.numpy as jnp
import jax.random as jnr
import numpy as np
from kfac_ferminet_alpha import curvature_blocks
from kfac_ferminet_alpha import tracer
from kfac_ferminet_alpha import utils
_CurvatureBlock = curvature_blocks.CurvatureBlock
TagMapping = Mapping[str, curvature_blocks.CurvatureBlockCtor]
BlockVector = Sequence[jnp.ndarray]
_StructureT = TypeVar("_StructureT")
_OptionalStateT = TypeVar("_OptionalStateT", bound=Optional[Mapping[str, Any]])
@utils.Stateful.infer_class_state
class CurvatureEstimator(utils.Stateful):
"""Curvature estimator class supporting various curvature approximations."""
blocks: "collections.OrderedDict[str, _CurvatureBlock]"
damping: Optional[jnp.ndarray]
def __init__(self,
tagged_func: Callable[[Any], jnp.ndarray],
func_args: Sequence[Any],
l2_reg: Union[float, jnp.ndarray],
estimation_mode: str = "fisher_gradients",
params_index: int = 0,
layer_tag_to_block_cls: Optional[TagMapping] = None):
"""Create a FisherEstimator object.
Args:
tagged_func: The function which evaluates the model, in which layer and
loss tags has already been registered.
func_args: Arguments to trace the function for layer and loss tags.
l2_reg: Scalar. The L2 regularization coefficient, which represents
the following regularization function: `coefficient/2 ||theta||^2`.
estimation_mode: The type of curvature estimator to use. One of: *
'fisher_gradients' - the basic estimation approach from the original
K-FAC paper. (Default) * 'fisher_curvature_prop' - method which
estimates the Fisher using self-products of random 1/-1 vectors times
"half-factors" of the
Fisher, as described here: https://arxiv.org/abs/1206.6464 *
'fisher_exact' - is the obvious generalization of Curvature
Propagation to compute the exact Fisher (modulo any additional
diagonal or Kronecker approximations) by looping over one-hot
vectors for each coordinate of the output instead of using 1/-1
vectors. It is more expensive to compute than the other three
options by a factor equal to the output dimension, roughly
speaking. * 'fisher_empirical' - computes the 'empirical' Fisher
information matrix (which uses the data's distribution for the
targets, as opposed to the true Fisher which uses the model's
distribution) and requires that each registered loss have
specified targets. * 'ggn_curvature_prop' - Analogous to
fisher_curvature_prop, but estimates the Generalized
Gauss-Newton matrix (GGN). * 'ggn_exact'- Analogous to
fisher_exact, but estimates the Generalized Gauss-Newton matrix
(GGN).
params_index: The index of the arguments accepted by `func` which
correspond to parameters.
layer_tag_to_block_cls: An optional dict mapping tags to specific classes
of block approximations, which to override the default ones.
"""
if estimation_mode not in ("fisher_gradients", "fisher_empirical",
"fisher_exact", "fisher_curvature_prop",
"ggn_exact", "ggn_curvature_prop"):
raise ValueError(f"Unrecognised estimation_mode={estimation_mode}.")
super().__init__()
self.tagged_func = tagged_func
self.l2_reg = l2_reg
self.estimation_mode = estimation_mode
self.params_index = params_index
self.vjp = tracer.trace_estimator_vjp(self.tagged_func)
# Figure out the mapping from layer
self.layer_tag_to_block_cls = curvature_blocks.copy_default_tag_to_block()
if layer_tag_to_block_cls is None:
layer_tag_to_block_cls = dict()
layer_tag_to_block_cls = dict(**layer_tag_to_block_cls)
self.layer_tag_to_block_cls.update(layer_tag_to_block_cls)
# Create the blocks
self._in_tree = jax.tree_structure(func_args)
self._jaxpr = jax.make_jaxpr(self.tagged_func)(*func_args).jaxpr
self._layer_tags, self._loss_tags = tracer.extract_tags(self._jaxpr)
self.blocks = collections.OrderedDict()
counters = dict()
for eqn in self._layer_tags:
cls = self.layer_tag_to_block_cls[eqn.primitive.name]
c = counters.get(cls.__name__, 0)
self.blocks[cls.__name__ + "_" + str(c)] = cls(eqn)
counters[cls.__name__] = c + 1
@property
def diagonal_weight(self) -> jnp.ndarray:
return self.l2_reg + self.damping
def vectors_to_blocks(
self,
parameter_structured_vector: Any,
) -> Sequence[BlockVector]:
"""Splits the parameters to values for the corresponding blocks."""
in_vars = jax.tree_unflatten(self._in_tree, self._jaxpr.invars)
params_vars = in_vars[self.params_index]
params_vars_flat = jax.tree_flatten(params_vars)[0]
params_values_flat = jax.tree_flatten(parameter_structured_vector)[0]
assert len(params_vars_flat) == len(params_values_flat)
params_dict = dict(zip(params_vars_flat, params_values_flat))
per_block_vectors = []
for eqn in self._layer_tags:
if eqn.primitive.name == "generic_tag":
block_vars = eqn.invars
else:
block_vars = eqn.primitive.split_all_inputs(eqn.invars)[2] # pytype: disable=attribute-error # trace-all-classes
per_block_vectors.append(tuple(params_dict.pop(v) for v in block_vars))
if params_dict:
raise ValueError(f"From the parameters the following structure is not "
f"assigned to any block: {params_dict}. Most likely "
f"this part of the parameters is not part of the graph "
f"reaching the losses.")
return tuple(per_block_vectors)
def blocks_to_vectors(self, per_block_vectors: Sequence[BlockVector]) -> Any:
"""Reverses the function self.vectors_to_blocks."""
in_vars = jax.tree_unflatten(self._in_tree, self._jaxpr.invars)
params_vars = in_vars[self.params_index]
assigned_dict = dict()
for eqn, block_values in zip(self._layer_tags, per_block_vectors):
if eqn.primitive.name == "generic_tag":
block_params = eqn.invars
else:
block_params = eqn.primitive.split_all_inputs(eqn.invars)[2] # pytype: disable=attribute-error # trace-all-classes
assigned_dict.update(zip(block_params, block_values))
params_vars_flat, params_tree = jax.tree_flatten(params_vars)
params_values_flat = [assigned_dict[v] for v in params_vars_flat]
assert len(params_vars_flat) == len(params_values_flat)
return jax.tree_unflatten(params_tree, params_values_flat)
def init(
self,
rng: jnp.ndarray,
init_damping: Optional[jnp.ndarray],
) -> Mapping[str, Any]:
"""Returns an initialized variables for the curvature approximations and the inverses.."""
return dict(
blocks=collections.OrderedDict(
(name, block.init(block_rng)) #
for (name, block), block_rng #
in zip(self.blocks.items(), jnr.split(rng, len(self.blocks)))),
damping=init_damping)
@property
def mat_type(self) -> str:
return self.estimation_mode.split("_")[0]
def vec_block_apply(
self,
func: Callable[[_CurvatureBlock, BlockVector], BlockVector],
parameter_structured_vector: Any,
) -> Any:
"""Executes func for each approximation block on vectors."""
per_block_vectors = self.vectors_to_blocks(parameter_structured_vector)
assert len(per_block_vectors) == len(self.blocks)
results = jax.tree_map(func, tuple(self.blocks.values()),
per_block_vectors)
parameter_structured_result = self.blocks_to_vectors(results)
utils.check_structure_shapes_and_dtype(parameter_structured_vector,
parameter_structured_result)
return parameter_structured_result
def multiply_inverse(self, parameter_structured_vector: Any) -> Any:
"""Multiplies the vectors by the corresponding (damped) inverses of the blocks.
Args:
parameter_structured_vector: Structure equivalent to the parameters of the
model.
Returns:
A structured identical to `vectors` containing the product.
"""
return self.multiply_matpower(parameter_structured_vector, -1)
def multiply(self, parameter_structured_vector: Any) -> Any:
"""Multiplies the vectors by the corresponding (damped) blocks.
Args:
parameter_structured_vector: A vector in the same structure as the
parameters of the model.
Returns:
A structured identical to `vectors` containing the product.
"""
return self.multiply_matpower(parameter_structured_vector, 1)
def multiply_matpower(
self,
parameter_structured_vector: _StructureT,
exp: int,
) -> _StructureT:
"""Multiplies the vectors by the corresponding matrix powers of the blocks.
Args:
parameter_structured_vector: A vector in the same structure as the
parameters of the model.
exp: A float representing the power to raise the blocks by before
multiplying it by the vector.
Returns:
A structured identical to `vectors` containing the product.
"""
def func(block: _CurvatureBlock, vec: BlockVector) -> BlockVector:
return block.multiply_matpower(vec, exp, self.diagonal_weight)
return self.vec_block_apply(func, parameter_structured_vector)
def update_curvature_matrix_estimate(
self,
ema_old: Union[float, jnp.ndarray],
ema_new: Union[float, jnp.ndarray],
batch_size: int,
rng: jnp.ndarray,
func_args: Sequence[Any],
pmap_axis_name: str,
) -> None:
"""Updates the curvature estimate."""
# Compute the losses and the VJP function from the function inputs
losses, losses_vjp = self.vjp(func_args)
# Helper function that updates the blocks given a vjp vector
def _update_blocks(vjp_vec_, ema_old_, ema_new_):
blocks_info_ = losses_vjp(vjp_vec_)
for block_, block_info_ in zip(self.blocks.values(), blocks_info_):
block_.update_curvature_matrix_estimate(
info=block_info_,
batch_size=batch_size,
ema_old=ema_old_,
ema_new=ema_new_,
pmap_axis_name=pmap_axis_name)
if self.estimation_mode == "fisher_gradients":
keys = jnr.split(rng, len(losses)) if len(losses) > 1 else [rng]
vjp_vec = tuple(
loss.grad_of_evaluate_on_sample(key, coefficient_mode="sqrt")
for loss, key in zip(losses, keys))
_update_blocks(vjp_vec, ema_old, ema_new)
elif self.estimation_mode in ("fisher_curvature_prop",
"ggn_curvature_prop"):
keys = jnr.split(rng, len(losses)) if len(losses) > 1 else [rng]
vjp_vec = []
for loss, key in zip(losses, keys):
if self.estimation_mode == "fisher_curvature_prop":
random_b = jnr.bernoulli(key, shape=loss.fisher_factor_inner_shape())
vjp_vec.append(loss.multiply_fisher_factor(random_b * 2.0 - 1.0))
else:
random_b = jnr.bernoulli(key, shape=loss.ggn_factor_inner_shape())
vjp_vec.append(loss.multiply_ggn_factor(random_b * 2.0 - 1.0))
_update_blocks(tuple(vjp_vec), ema_old, ema_new)
elif self.estimation_mode in ("fisher_exact", "ggn_exact"):
# We use the following trick to simulate summation. The equation is:
# estimate = ema_old * estimate + ema_new * (sum_i estimate_index_i)
# weight = ema_old * weight + ema_new
# Instead we update the estimate n times with the following updates:
# for k = 1
# estimate_k = ema_old * estimate + (ema_new/n) * (n*estimate_index_k)
# weight_k = ema_old * weight + (ema_new/n)
# for k > 1:
# estimate_k = 1.0 * estimate_k-1 + (ema_new/n) * (n*estimate_index_k)
# weight_k = 1.0 * weight_k-1 + (ema_new/n)
# Which is mathematically equivalent to the original version.
zero_tangents = jax.tree_map(jnp.zeros_like,
list(loss.inputs for loss in losses))
if self.estimation_mode == "fisher_exact":
num_indices = [
(l, int(np.prod(l.fisher_factor_inner_shape[1:]))) for l in losses
]
else:
num_indices = [
(l, int(np.prod(l.ggn_factor_inner_shape()))) for l in losses
]
total_num_indices = sum(n for _, n in num_indices)
for i, (loss, loss_num_indices) in enumerate(num_indices):
for index in range(loss_num_indices):
vjp_vec = zero_tangents.copy()
if self.estimation_mode == "fisher_exact":
vjp_vec[i] = loss.multiply_fisher_factor_replicated_one_hot([index])
else:
vjp_vec[i] = loss.multiply_ggn_factor_replicated_one_hot([index])
if isinstance(vjp_vec[i], jnp.ndarray):
# In the special case of only one parameter, it still needs to be a
# tuple for the tangents.
vjp_vec[i] = (vjp_vec[i],)
vjp_vec[i] = jax.tree_map(lambda x: x * total_num_indices, vjp_vec[i])
_update_blocks(tuple(vjp_vec), ema_old, ema_new / total_num_indices)
ema_old = 1.0
elif self.estimation_mode == "fisher_empirical":
raise NotImplementedError()
else:
raise ValueError(f"Unrecognised estimation_mode={self.estimation_mode}")
def update_curvature_estimate_inverse(
self,
pmap_axis_name: str,
state: _OptionalStateT,
) -> _OptionalStateT:
if state is not None:
old_state = self.get_state()
self.set_state(state)
for block in self.blocks.values():
block.update_curvature_inverse_estimate(self.diagonal_weight,
pmap_axis_name)
if state is None:
return None
else:
state = self.pop_state()
self.set_state(old_state)
return state
================================================
FILE: kfac_ferminet_alpha/example.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of running KFAC."""
from absl import app
from absl import flags
import jax
import jax.numpy as jnp
import numpy as np
import kfac_ferminet_alpha as kfac_ferminet_alpha
from kfac_ferminet_alpha import utils
TRAINING_STEPS = flags.DEFINE_integer(
name="training_steps",
default=100,
help="Number of training steps to perform")
BATCH_SIZE = flags.DEFINE_integer(
name="batch_size", default=128, help="Batch size")
LEARNING_RATE = flags.DEFINE_float(
name="learning_rate", default=1e-3, help="Learning rate")
L2_REG = flags.DEFINE_float(
name="l2_reg", default=1e-3, help="L2 regularization coefficient")
MOMENTUM = flags.DEFINE_float(
name="momentum", default=0.8, help="Momentum coefficient")
DAMPING = flags.DEFINE_float(
name="damping", default=1e-2, help="Damping coefficient")
MULTI_DEVICE = flags.DEFINE_bool(
name="multi_device",
default=False,
help="Whether the computation should be replicated across multiple devices")
SEED = flags.DEFINE_integer(name="seed", default=12412321, help="JAX RNG seed")
def glorot_uniform(shape, key):
dim_in = np.prod(shape[:-1])
dim_out = shape[-1]
c = jnp.sqrt(6 / (dim_in + dim_out))
return jax.random.uniform(key, shape=shape, minval=-c, maxval=c)
def fully_connected_layer(params, x):
w, b = params
return jnp.matmul(x, w) + b[None]
def model_init(rng_key, batch, encoder_sizes=(1000, 500, 250, 30)):
"""Initialize the standard autoencoder."""
x_size = batch.shape[-1]
decoder_sizes = encoder_sizes[len(encoder_sizes) - 2::-1]
sizes = (x_size,) + encoder_sizes + decoder_sizes + (x_size,)
keys = jax.random.split(rng_key, len(sizes) - 1)
params = []
for rng_key, dim_in, dim_out in zip(keys, sizes, sizes[1:]):
# Glorot uniform initialization
w = glorot_uniform((dim_in, dim_out), rng_key)
b = jnp.zeros([dim_out])
params.append((w, b))
return params, None
def model_loss(params, inputs, l2_reg):
"""Evaluate the standard autoencoder."""
h = inputs.reshape([inputs.shape[0], -1])
for i, layer_params in enumerate(params):
h = fully_connected_layer(layer_params, h)
# Last layer does not have a nonlinearity
if i % 4 != 3:
h = jnp.tanh(h)
l2_value = 0.5 * sum(jnp.square(p).sum() for p in jax.tree_leaves(params))
error = jax.nn.sigmoid(h) - inputs.reshape([inputs.shape[0], -1])
mean_squared_error = jnp.mean(jnp.sum(error * error, axis=1), axis=0)
regularized_loss = mean_squared_error + l2_reg * l2_value
return regularized_loss, dict(mean_squared_error=mean_squared_error)
def random_data(multi_device, batch_shape, rng):
if multi_device:
shape = (multi_device,) + tuple(batch_shape)
else:
shape = tuple(batch_shape)
while True:
rng, key = jax.random.split(rng)
yield jax.random.normal(key, shape)
def main(argv):
del argv # Unused.
learning_rate = jnp.asarray([LEARNING_RATE.value])
momentum = jnp.asarray([MOMENTUM.value])
damping = jnp.asarray([DAMPING.value])
# RNG keys
global_step = jnp.zeros([])
rng = jax.random.PRNGKey(SEED.value)
params_key, opt_key, step_key, data_key = jax.random.split(rng, 4)
dataset = random_data(MULTI_DEVICE.value, (BATCH_SIZE.value, 20), data_key)
example_batch = next(dataset)
if MULTI_DEVICE.value:
global_step = utils.replicate_all_local_devices(global_step)
learning_rate = utils.replicate_all_local_devices(learning_rate)
momentum = utils.replicate_all_local_devices(momentum)
damping = utils.replicate_all_local_devices(damping)
params_key, opt_key = utils.replicate_all_local_devices(
(params_key, opt_key))
step_key = utils.make_different_rng_key_on_all_devices(step_key)
split_key = jax.pmap(lambda x: tuple(jax.random.split(x)))
jit_init_parameters_func = jax.pmap(model_init)
else:
split_key = jax.random.split
jit_init_parameters_func = jax.jit(model_init)
# Initialize or load parameters
params, func_state = jit_init_parameters_func(params_key, example_batch)
# Make optimizer
optim = kfac_ferminet_alpha.Optimizer(
value_and_grad_func=jax.value_and_grad(
lambda p, x: model_loss(p, x, L2_REG.value), has_aux=True),
l2_reg=L2_REG.value,
value_func_has_aux=True,
value_func_has_state=False,
value_func_has_rng=False,
learning_rate_schedule=None,
momentum_schedule=None,
damping_schedule=None,
norm_constraint=1.0,
num_burnin_steps=10,
)
# Initialize optimizer
opt_state = optim.init(params, opt_key, example_batch, func_state)
for t in range(TRAINING_STEPS.value):
step_key, key_t = split_key(step_key)
params, opt_state, stats = optim.step(
params,
opt_state,
key_t,
dataset,
learning_rate=learning_rate,
momentum=momentum,
damping=damping)
global_step = global_step + 1
# Log any of the statistics
print(f"iteration: {t}")
print(f"mini-batch loss = {stats['loss']}")
if "aux" in stats:
for k, v in stats["aux"].items():
print(f"{k} = {v}")
print("----")
if __name__ == "__main__":
app.run(main)
================================================
FILE: kfac_ferminet_alpha/layers_and_loss_tags.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A module for registering already known functions for tagging patterns."""
import functools
from typing import Sequence, Tuple, TypeVar
import jax
from jax import core as jax_core
from jax import lax
from jax import lib as jax_lib
from jax.interpreters import batching as jax_batching
import jax.numpy as jnp
_T = TypeVar("_T")
class LossTag(jax_core.Primitive):
"""A tagging primitive specifically for losses."""
multiple_results = True
def __init__(self, cls, num_inputs: int, num_targets: int = 1):
super().__init__(cls.__name__ + "_tag")
self._cls = cls
self._num_inputs = num_inputs
self._num_targets = num_targets
jax.xla.translations[self] = self.xla_translation
jax.ad.primitive_jvps[self] = self.jvp
# This line defines how does the tag behave under vmap. It is required for
# any primitive that can be used inside a vmap. The reason why we want to
# allow this is two fold - one to not break user code when the tags are not
# used at all, and two - to be able to define a network with code for a
# single example which is the vmap-ed for a batch.
jax_batching.primitive_batchers[self] = self.batching
@property
def num_inputs(self) -> int:
return self._num_inputs
@property
def num_targets(self) -> int:
return self._num_targets
def loss(self, *args, weight: float = 1.0, **kwargs):
return self._cls(*args, weight=weight, **kwargs)
def loss_evaluate(self, *args, weight: float = 1.0, **kwargs):
return self.loss(*args, weight=weight, **kwargs).evaluate()
def get_outputs(self, *args, weight: float, return_loss: bool, **kwargs):
if len(args) < self.num_inputs:
raise ValueError("Inputs to the tag are not enough.")
if len(args) < self.num_inputs + self.num_targets:
if len(args) != self.num_inputs:
raise ValueError("Inputs to the tag are not quite enough.")
if return_loss:
raise ValueError("Can not have return_loss=True when there are no "
"targets.")
return args
if len(args) > self.num_inputs + self.num_targets:
raise ValueError("Inputs to the tag are too many.")
if return_loss:
return self.loss(*args, weight=weight, **kwargs).evaluate()
else:
return args
def impl(self, *args, weight: float, return_loss: bool, **kwargs):
return self.get_outputs(*args, weight=weight, return_loss=return_loss)
def abstract_eval(self, *args, weight: float, return_loss: bool, **kwargs):
jax_version = (
jax.__version_info__ if hasattr(jax, "__version_info__")
else tuple(map(int, jax.__version__.split("."))))
if jax_version > (0, 3, 4):
return (self.get_outputs(*args, weight=weight, return_loss=return_loss),
jax_core.no_effects)
return self.get_outputs(*args, weight=weight, return_loss=return_loss)
def xla_translation(
self,
c,
*args,
weight: float = 1.0,
return_loss: bool = False,
**kwargs,
):
outputs = self.get_outputs(
*args, weight=weight, return_loss=return_loss, **kwargs)
if isinstance(outputs, tuple):
return jax_lib.xla_client.ops.Tuple(c, outputs)
return outputs
def jvp(
self,
arg_values,
arg_tangents,
weight: float,
return_loss: bool,
**kwargs,
):
if len(arg_values) != len(arg_tangents):
raise ValueError("Values and tangents are not the same length.")
primal_output = self.bind(
*arg_values, weight=weight, return_loss=return_loss, **kwargs)
if len(arg_values) == self.num_inputs:
tangents_out = self.get_outputs(
*arg_tangents, weight=weight, return_loss=return_loss, **kwargs)
elif return_loss:
tangents_out = jax.jvp(
functools.partial(self.loss_evaluate, weight=weight, **kwargs),
arg_tangents, arg_tangents)[1]
else:
tangents_out = arg_tangents
return primal_output, tangents_out
def batching(self, batched_args, batched_dims, **kwargs):
return self.bind(*batched_args, **kwargs), batched_dims[0]
class LayerTag(jax_core.Primitive):
"""A tagging primitive that is used to mark/tag computation."""
def __init__(self, name: str, num_inputs: int, num_outputs: int):
super().__init__(name)
if num_outputs > 1:
raise NotImplementedError(
f"Only single outputs are supported, got: num_outputs={num_outputs}")
self._num_outputs = num_outputs
self._num_inputs = num_inputs
jax.xla.translations[self] = self.xla_translation
jax.ad.deflinear(self, self.transpose)
jax.ad.primitive_transposes[self] = self.transpose
# This line defines how does the tag behave under vmap. It is required for
# any primitive that can be used inside a vmap. The reason why we want to
# allow this is two fold - one to not break user code when the tags are not
# used at all, and two - to be able to define a network with code for a
# single example which is the vmap-ed for a batch.
jax_batching.primitive_batchers[self] = self.batching
@property
def num_outputs(self) -> int:
return self._num_outputs
@property
def num_inputs(self) -> int:
return self._num_inputs
def split_all_inputs(
self,
all_inputs: Sequence[_T],
) -> Tuple[Sequence[_T], Sequence[_T], Sequence[_T]]:
outputs = tuple(all_inputs[:self.num_outputs])
inputs = tuple(all_inputs[self.num_outputs:self.num_outputs +
self.num_inputs])
params = tuple(all_inputs[self.num_outputs + self.num_inputs:])
return outputs, inputs, params
def get_outputs(self, *operands: _T, **kwargs) -> _T:
assert self.num_outputs == 1
return operands[0]
def xla_translation(self, c, *operands: _T, **kwargs) -> _T:
return self.get_outputs(*operands, **kwargs)
@staticmethod
def transpose(cotangent, *operands, **kwargs):
return (cotangent,) + (None,) * (len(operands) - 1)
def impl(self, *operands, **kwargs):
return self.get_outputs(*operands, **kwargs)
def abstract_eval(self, *abstract_operands, **kwargs):
jax_version = (
jax.__version_info__ if hasattr(jax, "__version_info__")
else tuple(map(int, jax.__version__.split("."))))
if jax_version > (0, 3, 4):
return self.get_outputs(*abstract_operands, **kwargs), jax_core.no_effects
return self.get_outputs(*abstract_operands, **kwargs)
def batching(self, batched_operands, batched_dims, **kwargs):
return self.bind(*batched_operands, **kwargs), batched_dims[0]
# _____ _
# / ____| (_)
# | | __ ___ _ __ ___ _ __ _ ___
# | | |_ |/ _ \ '_ \ / _ \ '__| |/ __|
# | |__| | __/ | | | __/ | | | (__
# \_____|\___|_| |_|\___|_| |_|\___|
#
#
generic_tag = LayerTag(name="generic_tag", num_inputs=0, num_outputs=1)
def register_generic(parameter: _T) -> _T:
return generic_tag.bind(parameter)
# _____
# | __ \
# | | | | ___ _ __ ___ ___
# | | | |/ _ \ '_ \/ __|/ _ \
# | |__| | __/ | | \__ \ __/
# |_____/ \___|_| |_|___/\___|
#
dense_tag = LayerTag(name="dense_tag", num_inputs=1, num_outputs=1)
def register_dense(y, x, w, b=None):
if b is None:
return dense_tag.bind(y, x, w)
return dense_tag.bind(y, x, w, b)
def dense_func(x, params):
"""Example of a dense layer function."""
w = params[0]
y = jnp.matmul(x, w)
if len(params) == 1:
# No bias
return y
# Add bias
return y + params[1]
def dense_tagging(jaxpr, inverse_map, values_map):
"""Correctly registers a dense layer pattern."""
del inverse_map
in_values = [values_map[v] for v in jaxpr.invars]
out_values = [values_map[v] for v in jaxpr.outvars]
return register_dense(out_values[0], *in_values)
# ___ _____ _____ _ _ _
# |__ \| __ \ / ____| | | | | (_)
# ) | | | | | | ___ _ ____ _____ | |_ _| |_ _ ___ _ __
# / /| | | | | | / _ \| '_ \ \ / / _ \| | | | | __| |/ _ \| "_ \
# / /_| |__| | | |___| (_) | | | \ V / (_) | | |_| | |_| | (_) | | | |
# |____|_____/ \_____\___/|_| |_|\_/ \___/|_|\__,_|\__|_|\___/|_| |_|
#
conv2d_tag = LayerTag(name="conv2d_tag", num_inputs=1, num_outputs=1)
def register_conv2d(y, x, w, b=None, **kwargs):
if b is None:
return conv2d_tag.bind(y, x, w, **kwargs)
return conv2d_tag.bind(y, x, w, b, **kwargs)
def conv2d_func(x, params):
"""Example of a conv2d layer function."""
w = params[0]
y = lax.conv_general_dilated(
x,
w,
window_strides=(2, 2),
padding="SAME",
dimension_numbers=("NHWC", "HWIO", "NHWC"))
if len(params) == 1:
# No bias
return y
# Add bias
return y + params[1][None, None, None]
def conv2d_tagging(jaxpr, inverse_map, values_map):
"""Correctly registers a conv2d layer pattern."""
in_values = [values_map[v] for v in jaxpr.invars]
out_values = [values_map[v] for v in jaxpr.outvars]
keys = [k for k in inverse_map.keys() if isinstance(k, str)]
keys = [k for k in keys if k.startswith("conv_general_dilated")]
if len(keys) != 1:
raise ValueError("Did not find any conv_general_dilated!")
kwargs = inverse_map[keys[0]].params
return register_conv2d(out_values[0], *in_values, **kwargs)
# _____ _ _ _____ _ _ __ _
# / ____| | | | | / ____| | (_)/ _| |
# | (___ ___ __ _| | ___ __ _ _ __ __| | | (___ | |__ _| |_| |_
# \___ \ / __/ _` | |/ _ \ / _` | '_ \ / _` | \___ \| '_ \| | _| __|
# ____) | (_| (_| | | __/ | (_| | | | | (_| | ____) | | | | | | | |_
# |_____/ \___\__,_|_|\___| \__,_|_| |_|\__,_| |_____/|_| |_|_|_| \__|
#
scale_and_shift_tag = LayerTag(
name="scale_and_shift_tag", num_inputs=1, num_outputs=1)
def register_scale_and_shift(y, args, has_scale: bool, has_shift: bool):
assert has_scale or has_shift
x, args = args[0], args[1:]
return scale_and_shift_tag.bind(
y, x, *args, has_scale=has_scale, has_shift=has_shift)
def scale_and_shift_func(x, params, has_scale: bool, has_shift: bool):
"""Example of a scale and shift function."""
if has_scale and has_shift:
scale, shift = params
return x * scale + shift
elif has_scale:
return x * params[0]
elif has_shift:
return x + params[0]
else:
raise ValueError()
def scale_and_shift_tagging(
jaxpr,
inverse_map,
values_map,
has_scale: bool,
has_shift: bool,
):
"""Correctly registers a scale and shift layer pattern."""
del inverse_map
in_values = [values_map[v] for v in jaxpr.invars]
out_values = [values_map[v] for v in jaxpr.outvars]
return register_scale_and_shift(out_values[0], in_values, has_scale,
has_shift)
def batch_norm_func(
inputs: Tuple[jnp.ndarray, jnp.ndarray],
params: Tuple[jnp.ndarray, jnp.ndarray],
) -> jnp.ndarray:
"""Example of batch norm as is defined in Haiku."""
x, y = inputs
scale, shift = params
inv = scale * y
return x * inv + shift
def batch_norm_tagging_func(
jaxpr,
inverse_map,
values_map,
has_scale: bool,
has_shift: bool,
):
"""Correctly registers a batch norm layer pattern as is defined in Haiku."""
del inverse_map
in_values = [values_map[v] for v in jaxpr.invars]
out_values = [values_map[v] for v in jaxpr.outvars]
# The first two are both multipliers with the scale so we merge them
in_values = [in_values[0] * in_values[1]] + in_values[2:]
return register_scale_and_shift(out_values[0], in_values, has_scale,
has_shift)
================================================
FILE: kfac_ferminet_alpha/loss_functions.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Loss functions to be used by LayerCollection."""
import abc
from typing import Tuple, Optional, Union, Sequence
import jax
import jax.numpy as jnp
from kfac_ferminet_alpha import distributions
from kfac_ferminet_alpha import layers_and_loss_tags as tags
from kfac_ferminet_alpha import utils
ArrayPair = Tuple[jnp.ndarray, jnp.ndarray]
FloatArray = Union[float, jnp.ndarray]
Index = Tuple[int]
class LossFunction(abc.ABC):
"""Abstract base class for loss functions.
Note that unlike typical loss functions used in neural networks these are
neither summed nor averaged over the batch and hence the output of evaluate()
will not be a scalar. It is up to the user to then to correctly manipulate
them as needed.
"""
def __init__(self, weight: FloatArray):
self._weight = weight
@property
def weight(self) -> FloatArray:
return self._weight
@property
@abc.abstractmethod
def targets(self) -> Optional[jnp.ndarray]:
"""The targets being predicted by the model.
Returns:
None or Tensor of appropriate shape for calling self._evaluate() on.
"""
pass
@property
@abc.abstractmethod
def inputs(self) -> Sequence[jnp.ndarray]:
"""The inputs to the loss function (excluding the targets)."""
pass
@abc.abstractmethod
def copy_with_different_inputs(self, inputs: Sequence[jnp.ndarray]):
pass
def evaluate(
self,
targets: Optional[jnp.ndarray] = None,
coefficient_mode: str = "regular",
) -> jnp.ndarray:
"""Evaluate the loss function on the targets."""
if targets is None and self.targets is None:
raise ValueError("Cannot evaluate losses with unspecified targets.")
elif targets is None:
targets = self.targets
if coefficient_mode == "regular":
multiplier = self.weight
elif coefficient_mode == "sqrt":
multiplier = jnp.sqrt(self.weight)
elif coefficient_mode == "off":
multiplier = 1.0
else:
raise ValueError(f"Unrecognized coefficient_mode={coefficient_mode}.")
return self._evaluate(targets) * multiplier
@abc.abstractmethod
def _evaluate(self, targets: jnp.ndarray) -> jnp.ndarray:
"""Evaluates the negative log probability of the targets.
Args:
targets: Tensor that distribution can calculate log_prob() of.
Returns:
negative log probability of each target, summed across all targets.
"""
pass
def grad_of_evaluate(
self,
targets: Optional[jnp.ndarray],
coefficient_mode: str,
) -> Sequence[jnp.ndarray]:
"""Evaluates the gradient of the loss function.
Note that the targets of the loss must not be `None`.
Args:
targets: The potential targets on which to evaluate the gradient.
coefficient_mode: The coefficient mode to use for evaluation.
Returns:
The gradient of the loss evaluation function with respect to the inputs.
"""
def evaluate_sum(inputs: Sequence[jnp.ndarray]) -> jnp.ndarray:
instance = self.copy_with_different_inputs(inputs)
return jnp.sum(instance.evaluate(targets, coefficient_mode))
return jax.grad(evaluate_sum)(self.inputs)
def multiply_ggn(self, vector: jnp.ndarray) -> jnp.ndarray:
"""Right-multiply a vector by the GGN.
Here the 'GGN' is the GGN matrix (whose definition is slightly flexible)
of the loss function with respect to its inputs.
Args:
vector: The vector to multiply. Must be the same shape(s) as the 'inputs'
property.
Returns:
The vector right-multiplied by the GGN. Will be of the same shape(s)
as the 'inputs' property.
"""
return utils.scalar_mul(self.multiply_ggn_unweighted(vector), self.weight)
@abc.abstractmethod
def multiply_ggn_unweighted(self, vector: jnp.ndarray) -> jnp.ndarray:
"""Same as `multiply_ggn`, but without taking into account the weight."""
pass
def multiply_ggn_factor(self, vector: jnp.ndarray) -> jnp.ndarray:
"""Right-multiply a vector by a factor B of the GGN.
Here the 'GGN' is the GGN matrix (whose definition is slightly flexible)
of the loss function with respect to its inputs. Typically this will be
block-diagonal across different cases in the batch, since the loss function
is typically summed across cases.
Note that B can be any matrix satisfying B * B^T = G where G is the GGN,
but will agree with the one used in the other methods of this class.
Args:
vector: The vector to multiply. Must be of the shape given by the
'ggn_factor_inner_shape' property.
Returns:
The vector right-multiplied by B. Will be of the same shape(s) as the
'inputs' property.
"""
return utils.scalar_mul(
self.multiply_ggn_factor_unweighted(vector), jnp.sqrt(self.weight))
@abc.abstractmethod
def multiply_ggn_factor_unweighted(self, vector: jnp.ndarray) -> jnp.ndarray:
"""Same as `multiply_ggn_factor`, but without taking into account the weight."""
pass
def multiply_ggn_factor_transpose(self, vector: jnp.ndarray) -> jnp.ndarray:
"""Right-multiply a vector by the transpose of a factor B of the GGN.
Here the 'GGN' is the GGN matrix (whose definition is slightly flexible)
of the loss function with respect to its inputs. Typically this will be
block-diagonal across different cases in the batch, since the loss function
is typically summed across cases.
Note that B can be any matrix satisfying B * B^T = G where G is the GGN,
but will agree with the one used in the other methods of this class.
Args:
vector: The vector to multiply. Must be the same shape(s) as the 'inputs'
property.
Returns:
The vector right-multiplied by B^T. Will be of the shape given by the
'ggn_factor_inner_shape' property.
"""
return utils.scalar_mul(
self.multiply_ggn_factor_transpose_unweighted(vector),
jnp.sqrt(self.weight))
@abc.abstractmethod
def multiply_ggn_factor_transpose_unweighted(
self,
vector: jnp.ndarray
) -> jnp.ndarray:
"""Same as `multiply_ggn_factor_transpose`, but without taking into account the weight."""
pass
def multiply_ggn_factor_replicated_one_hot(self, index: Index) -> jnp.ndarray:
"""Right-multiply a replicated-one-hot vector by a factor B of the GGN.
Here the 'GGN' is the GGN matrix (whose definition is slightly flexible)
of the loss function with respect to its inputs. Typically this will be
block-diagonal across different cases in the batch, since the loss function
is typically summed across cases.
A 'replicated-one-hot' vector means a tensor which, for each slice along the
batch dimension (assumed to be dimension 0), is 1.0 in the entry
corresponding to the given index and 0 elsewhere.
Note that B can be any matrix satisfying B * B^T = G where G is the GGN,
but will agree with the one used in the other methods of this class.
Args:
index: A tuple representing in the index of the entry in each slice that
is 1.0. Note that len(index) must be equal to the number of elements of
the 'ggn_factor_inner_shape' tensor minus one.
Returns:
The vector right-multiplied by B^T. Will be of the same shape(s) as the
'inputs' property.
"""
return utils.scalar_mul(
self.multiply_ggn_factor_replicated_one_hot_unweighted(index),
jnp.sqrt(self.weight))
@abc.abstractmethod
def multiply_ggn_factor_replicated_one_hot_unweighted(
self,
index: Index
) -> jnp.ndarray:
pass
@property
@abc.abstractmethod
def ggn_factor_inner_shape(self) -> Sequence[int]:
"""The shape of the tensor returned by multiply_ggn_factor."""
pass
class NegativeLogProbLoss(LossFunction):
"""Abstract base class for loss functions that are negative log probs."""
@property
def inputs(self):
return self.params
@property
@abc.abstractmethod
def params(self):
"""Parameters to the underlying distribution."""
pass
def multiply_fisher(self, vector: jnp.ndarray) -> jnp.ndarray:
"""Right-multiply a vector by the Fisher.
Args:
vector: The vector to multiply. Must be the same shape(s) as the 'inputs'
property.
Returns:
The vector right-multiplied by the Fisher. Will be of the same shape(s)
as the 'inputs' property.
"""
return utils.scalar_mul(
self.multiply_fisher_unweighted(vector), self.weight)
@abc.abstractmethod
def multiply_fisher_unweighted(self, vector: jnp.ndarray) -> jnp.ndarray:
pass
def multiply_fisher_factor(self, vector: jnp.ndarray) -> jnp.ndarray:
"""Right-multiply a vector by a factor B of the Fisher.
Here the 'Fisher' is the Fisher information matrix (i.e. expected outer-
product of gradients) with respect to the parameters of the underlying
probability distribution (whose log-prob defines the loss). Typically this
will be block-diagonal across different cases in the batch, since the
distribution is usually (but not always) conditionally iid across different
cases.
Note that B can be any matrix satisfying B * B^T = F where F is the Fisher,
but will agree with the one used in the other methods of this class.
Args:
vector: The vector to multiply. Must be of the shape given by the
'fisher_factor_inner_shape' property.
Returns:
The vector right-multiplied by B. Will be of the same shape(s) as the
'inputs' property.
"""
return utils.scalar_mul(
self.multiply_fisher_factor_unweighted(vector), jnp.sqrt(self.weight))
@abc.abstractmethod
def multiply_fisher_factor_unweighted(
self,
vector: jnp.ndarray
) -> jnp.ndarray:
pass
def multiply_fisher_factor_transpose(
self,
vector: jnp.ndarray
) -> jnp.ndarray:
"""Right-multiply a vector by the transpose of a factor B of the Fisher.
Here the 'Fisher' is the Fisher information matrix (i.e. expected outer-
product of gradients) with respect to the parameters of the underlying
probability distribution (whose log-prob defines the loss). Typically this
will be block-diagonal across different cases in the batch, since the
distribution is usually (but not always) conditionally iid across different
cases.
Note that B can be any matrix satisfying B * B^T = F where F is the Fisher,
but will agree with the one used in the other methods of this class.
Args:
vector: The vector to multiply. Must be the same shape(s) as the 'inputs'
property.
Returns:
The vector right-multiplied by B^T. Will be of the shape given by the
'fisher_factor_inner_shape' property.
"""
return utils.scalar_mul(
self.multiply_fisher_factor_transpose_unweighted(vector),
jnp.sqrt(self.weight))
@abc.abstractmethod
def multiply_fisher_factor_transpose_unweighted(
self,
vector: jnp.ndarray
) -> jnp.ndarray:
pass
def multiply_fisher_factor_replicated_one_hot(
self,
index: Index
) -> jnp.ndarray:
"""Right-multiply a replicated-one-hot vector by a factor B of the Fisher.
Here the 'Fisher' is the Fisher information matrix (i.e. expected outer-
product of gradients) with respect to the parameters of the underlying
probability distribution (whose log-prob defines the loss). Typically this
will be block-diagonal across different cases in the batch, since the
distribution is usually (but not always) conditionally iid across different
cases.
A 'replicated-one-hot' vector means a tensor which, for each slice along the
batch dimension (assumed to be dimension 0), is 1.0 in the entry
corresponding to the given index and 0 elsewhere.
Note that B can be any matrix satisfying B * B^T = H where H is the Fisher,
but will agree with the one used in the other methods of this class.
Args:
index: A tuple representing in the index of the entry in each slice that
is 1.0. Note that len(index) must be equal to the number of elements of
the 'fisher_factor_inner_shape' tensor minus one.
Returns:
The vector right-multiplied by B. Will be of the same shape(s) as the
'inputs' property.
"""
return utils.scalar_mul(
self.multiply_fisher_factor_replicated_one_hot_unweighted(index),
jnp.sqrt(self.weight))
@abc.abstractmethod
def multiply_fisher_factor_replicated_one_hot_unweighted(
self,
index: Index
) -> jnp.ndarray:
pass
@property
@abc.abstractmethod
def fisher_factor_inner_shape(self) -> Sequence[int]:
"""The shape of the tensor returned by multiply_fisher_factor."""
pass
@abc.abstractmethod
def sample(self, rng_key: jnp.ndarray) -> jnp.ndarray:
"""Sample 'targets' from the underlying distribution."""
pass
def grad_of_evaluate_on_sample(
self,
rng_key: jnp.ndarray,
coefficient_mode: str,
) -> Sequence[jnp.ndarray]:
"""Evaluates the gradient of the log probability on a random sample.
Args:
rng_key: Jax PRNG key for sampling.
coefficient_mode: The coefficient mode to use for evaluation.
Returns:
The gradient of the log probability of targets sampled from the
distribution.
"""
return self.grad_of_evaluate(self.sample(rng_key), coefficient_mode)
class NaturalParamsNegativeLogProbLoss(NegativeLogProbLoss, abc.ABC):
"""Base class for neg log prob losses whose inputs are 'natural' parameters.
We will take the GGN of the loss to be the Fisher associated with the
distribution, which also happens to be equal to the Hessian for this class
of loss functions. See here: https://arxiv.org/abs/1412.1193
'Natural parameters' are defined for exponential-family models. See for
example: https://en.wikipedia.org/wiki/Exponential_family
"""
def multiply_ggn_unweighted(self, vector: jnp.ndarray) -> jnp.ndarray:
return self.multiply_fisher_unweighted(vector)
def multiply_ggn_factor_unweighted(self, vector: jnp.ndarray) -> jnp.ndarray:
return self.multiply_fisher_factor_unweighted(vector)
def multiply_ggn_factor_transpose_unweighted(
self,
vector: jnp.ndarray
) -> jnp.ndarray:
return self.multiply_fisher_factor_transpose_unweighted(vector)
def multiply_ggn_factor_replicated_one_hot_unweighted(
self,
index: Index
) -> jnp.ndarray:
return self.multiply_fisher_factor_replicated_one_hot_unweighted(index)
@property
def ggn_factor_inner_shape(self) -> Sequence[int]:
return self.fisher_factor_inner_shape
class DistributionNegativeLogProbLoss(NegativeLogProbLoss):
"""Base class for neg log prob losses that use the distribution classes."""
@property
@abc.abstractmethod
def dist(self):
"""The underlying distribution instance."""
pass
def _evaluate(self, targets: jnp.ndarray):
return -self.dist.log_prob(targets)
def sample(self, rng_key: jnp.ndarray):
return self.dist.sample(seed=rng_key)
@property
def fisher_factor_inner_shape(self) -> Sequence[int]:
return self.dist.mean().shape
class NormalMeanNegativeLogProbLoss(DistributionNegativeLogProbLoss,
NaturalParamsNegativeLogProbLoss):
"""Neg log prob loss for a normal distribution parameterized by a mean vector.
Note that the covariance is treated as the identity divided by 2.
Also note that the Fisher for such a normal distribution with respect the mean
parameter is given by:
F = (1 / variance) * I
See for example https://www.ii.pwr.edu.pl/~tomczak/PDF/[JMT]Fisher_inf.pdf.
"""
def __init__(
self,
mean: jnp.ndarray,
targets: Optional[jnp.ndarray] = None,
variance: float = 0.5,
weight: float = 1.0,
):
super().__init__(weight=weight)
self._mean = mean
self._targets = targets
self._variance = variance
if not isinstance(variance, float):
raise ValueError("The `variance` argument should be python float.")
@property
def targets(self) -> Optional[jnp.ndarray]:
return self._targets
@property
def dist(self):
scale_diag = jnp.full_like(self._mean, jnp.sqrt(self._variance))
return distributions.MultivariateNormalDiag(self._mean, scale_diag)
@property
def params(self):
return self._mean,
def copy_with_different_inputs(self, inputs: Sequence[jnp.ndarray]):
[mean] = inputs
return NormalMeanNegativeLogProbLoss(
mean=mean,
targets=self.targets,
variance=self._variance,
weight=self.weight,
)
def multiply_fisher_unweighted(self, vector: jnp.ndarray) -> jnp.ndarray:
return vector / self._variance
def multiply_fisher_factor_unweighted(
self,
vector: jnp.ndarray,
) -> jnp.ndarray:
return vector / jnp.sqrt(self._variance)
def multiply_fisher_factor_transpose_unweighted(
self,
vector: jnp.ndarray,
) -> jnp.ndarray:
return self.multiply_fisher_factor_unweighted(vector) # it's symmetric
def multiply_fisher_factor_replicated_one_hot_unweighted(
self,
index: Index,
) -> jnp.ndarray:
assert len(index) == 1, f"Length of index was {len(index)}."
index = index[0]
ones_slice = jnp.ones([self._mean.shape[0]])[..., None]
output_slice = ones_slice / jnp.sqrt(self._variance)
return insert_slice_in_zeros(output_slice, 1, self._mean.shape[1], index)
def insert_slice_in_zeros(
slice_to_insert: jnp.ndarray,
dim: int,
dim_size: int,
position: int,
) -> jnp.ndarray:
"""Inserts slice into a larger tensor of zeros.
Forms a new tensor which is the same shape as slice_to_insert, except that
the dimension given by 'dim' is expanded to the size given by 'dim_size'.
'position' determines the position (index) at which to insert the slice within
that dimension.
Assumes slice_to_insert.shape[dim] = 1.
Args:
slice_to_insert: The slice to insert.
dim: The dimension which to expand with zeros.
dim_size: The new size of the 'dim' dimension.
position: The position of 'slice_to_insert' in the new tensor.
Returns:
The new tensor.
Raises:
ValueError: If the slice's shape at the given dim is not 1.
"""
slice_shape = slice_to_insert.shape
if slice_shape[dim] != 1:
raise ValueError(f"Expected slice_to_insert.shape to have {dim} dim of 1,"
f" but was {slice_to_insert.shape[dim]}.")
before = [0] * int(len(slice_shape))
after = before[:]
before[dim] = position
after[dim] = dim_size - position - 1
return jnp.pad(slice_to_insert, list(zip(before, after)))
# _______ _____ _ _ _ _
# |__ __| | __ \ (_) | | | | (_)
# | | __ _ __ _ | |__) |___ __ _ _ ___| |_ _ __ __ _| |_ _ ___ _ __
# | |/ _` |/ _` | | _ // _ \/ _` | / __| __| '__/ _` | __| |/ _ \| '_ \
# | | (_| | (_| | | | \ \ __/ (_| | \__ \ |_| | | (_| | |_| | (_) | | | |
# |_|\__,_|\__, | |_| \_\___|\__, |_|___/\__|_| \__,_|\__|_|\___/|_| |_|
# __/ | __/ |
# |___/ |___/
NormalMeanNegativeLogProbLoss_tag = tags.LossTag(
NormalMeanNegativeLogProbLoss, num_inputs=1)
def register_normal_predictive_distribution(
mean: jnp.ndarray,
targets: Optional[jnp.ndarray] = None,
variance: float = 0.5,
weight: float = 1.0,
):
"""Registers a normal predictive distribution.
This corresponds to a squared error loss of the form
weight/(2*var) * ||target - mean||^2
Args:
mean: A tensor defining the mean vector of the distribution. The first
dimension must be the batch size.
targets: (OPTIONAL) The targets for the loss function. Only required if one
wants to use the "empirical Fisher" instead of the true Fisher (which is
controlled by the 'estimation_mode' to the optimizer).
(Default: None)
variance: float. The variance of the distribution. Note that the default
value of 0.5 corresponds to a standard squared error loss weight *
||target - prediction||^2. If you want your squared error loss to be of
the form 0.5*coeff*||target - prediction||^2 you should use
variance=1.0.
(Default: 0.5)
weight: A scalar coefficient to multiply the log prob loss associated with
this distribution. The Fisher will be multiplied by the corresponding
factor. In general this is NOT equivalent to changing the temperature of
the distribution, but in the ase of normal distributions it may be.
(Default: 1.0)
Returns:
The mean and targets as dependable on the tag.
"""
if targets is None:
targets = jnp.zeros_like(mean)
return NormalMeanNegativeLogProbLoss_tag.bind(
mean, targets, variance=variance, weight=weight, return_loss=False)
def register_squared_error_loss(
prediction: jnp.ndarray,
targets: Optional[jnp.ndarray] = None,
weight: float = 1.0,
):
"""Registers a squared error loss function.
This assumes the squared error loss of the form ||target - prediction||^2,
averaged across the mini-batch. If your loss uses a coefficient of 0.5
you need to set the "weight" argument to reflect this.
Args:
prediction: The prediction made by the network (i.e. its output). The first
dimension must be the batch size.
targets: (OPTIONAL) The targets for the loss function. Only required if one
wants to use the "empirical Fisher" instead of the true Fisher (which is
controlled by the 'estimation_mode' to the optimizer).
(Default: None)
weight: A float coefficient to multiply the loss function by.
(Default: 1.0)
Returns:
The mean and targets as dependable on the tag.
"""
return register_normal_predictive_distribution(
prediction, targets=targets, variance=0.5, weight=weight)
================================================
FILE: kfac_ferminet_alpha/optimizer.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A module for the main curvature optimizer class."""
from typing import Any, Callable, Iterator, Mapping, Optional, Sequence, Tuple, Union
import jax
import jax.lax as lax
import jax.numpy as jnp
import jax.random as jnr
from kfac_ferminet_alpha import estimator
from kfac_ferminet_alpha import tag_graph_matcher as tgm
from kfac_ferminet_alpha import utils
ScheduleType = Callable[[jnp.ndarray], Optional[jnp.ndarray]]
Parameters = Any
Batch = Any
FuncState = Any
State = Mapping[str, Any]
@utils.Stateful.infer_class_state
class Optimizer(utils.Stateful):
"""The default optimizer class."""
velocities: Parameters
estimator: estimator.CurvatureEstimator
step_counter: jnp.ndarray
def __init__(
self,
value_and_grad_func,
l2_reg: Union[float, jnp.ndarray],
value_func_has_aux: bool = False,
value_func_has_state: bool = False,
value_func_has_rng: bool = False,
learning_rate_schedule: Optional[ScheduleType] = None,
momentum_schedule: Optional[ScheduleType] = None,
damping_schedule: Optional[ScheduleType] = None,
min_damping: Union[float, jnp.ndarray] = 1e-8,
max_damping: Union[float, jnp.ndarray] = jnp.inf,
norm_constraint: Optional[Union[float, jnp.ndarray]] = None,
num_burnin_steps: int = 10,
estimation_mode: str = "fisher_gradients",
curvature_ema: Union[float, jnp.ndarray] = 0.95,
inverse_update_period: int = 5,
register_only_generic: bool = False,
layer_tag_to_block_cls: Optional[estimator.TagMapping] = None,
patterns_to_skip: Sequence[str] = (),
donate_parameters: bool = False,
donate_optimizer_state: bool = False,
donate_batch_inputs: bool = False,
donate_func_state: bool = False,
batch_process_func: Optional[Callable[[Any], Any]] = None,
multi_device: bool = False,
use_jax_cond: bool = True,
debug: bool = False,
pmap_axis_name="kfac_axis",
):
"""Initializes the K-FAC optimizer with the given settings.
Args:
value_and_grad_func: Python callable. The function should return the value
of the loss to be optimized and its gradients. If the argument
`value_func_has_aux` is `False` then the interface should be: loss,
loss_grads = value_and_grad_func(params, batch)
If `value_func_has_aux` is `True` then the interface should be: (loss,
aux), loss_grads = value_and_grad_func(params, batch)
l2_reg: Scalar. Set this value to tell the optimizer what L2
regularization coefficient you are using (if any). Note the coefficient
appears in the regularizer as coeff / 2 * sum(param**2). Note that the
user is still responsible for adding regularization to the loss.
value_func_has_aux: Boolean. Specifies whether the provided callable
`value_and_grad_func` returns the loss value only, or also some
auxiliary data. (Default: False)
value_func_has_state: Boolean. Specifies whether the provided callable
`value_and_grad_func` has a persistent state that is inputed and
it also outputs an update version of it. (Default: False)
value_func_has_rng: Boolean. Specifies whether the provided callable
`value_and_grad_func` additionally takes as input an rng key.
(Default: False)
learning_rate_schedule: Callable. A schedule for the learning rate. This
should take as input the current step number and return a single
`jnp.ndarray` that represents the learning rate. (Default: None)
momentum_schedule: Callable. A schedule for the momentum. This should take
as input the current step number and return a single `jnp.ndarray`
that represents the momentum. (Default: None)
damping_schedule: Callable. A schedule for the damping. This should take
as input the current step number and return a single `jnp.ndarray`
that represents the learning rate. (Default: None)
min_damping: Scalar. Minimum value the damping parameter can take. Note
that the default value of 1e-8 is quite arbitrary, and you may have to
adjust this up or down for your particular problem. If you are using a
non-zero value of l2_reg you *may* be able to set this to
zero. (Default: 1e-8)
max_damping: Scalar. Maximum value the damping parameter can take.
(Default: Infinity)
norm_constraint: Scalar. If specified, the update is scaled down so that
its approximate squared Fisher norm `v^T F v` is at most the specified
value.(Note that here `F` is the approximate curvature matrix, not the
exact.) (Default: None)
num_burnin_steps: Int. At the start of optimization, e.g. the first step,
before performing the actual step the optimizer will perform this many
times updates to the curvature approximation without updating the
actual parameters. (Default: 10)
estimation_mode: String. The type of estimator to use for the curvature
matrix. Can be one of: * fisher_empirical * fisher_exact *
fisher_gradients * fisher_curvature_prop * ggn_exact *
ggn_curvature_prop See the doc-string for CurvatureEstimator (in
estimator.py) for a more
detailed description of these options. (Default: 'fisher_gradients').
curvature_ema: The decay factor used when calculating the covariance
estimate moving averages. (Default: 0.95)
inverse_update_period: Int. The number of steps in between updating the
the computation of the inverse curvature approximation. (Default: 5)
register_only_generic: Boolean. Whether when running the auto-tagger to
register only generic parameters, or allow it to use the graph matcher
to automatically pick up any kind of layer tags. (Default: False)
layer_tag_to_block_cls: Dictionary. A mapping from layer tags to block
classes which to override the default choices of block approximation for
that specific tag. See the doc-string for CurvatureEstimator (in
estimator.py) for a more detailed description of this.
patterns_to_skip: Tuple. A list of any patterns that should be skipped by
the graph matcher when auto-tagging.
donate_parameters: Boolean. Whether to use jax's `donate_argnums` to
donate the parameter values of each call to `step`. Note that this
implies that you will not be able to access the old parameter values'
buffers after calling into `step`.
donate_optimizer_state: Boolean. Whether to use jax's `donate_argnums` to
donate the optimizer state of each call to `step`. Note that this
implies that you will not be able to access the old optimizer state
values' buffers after calling into `step`.
donate_batch_inputs: Boolean. Whether to use jax's `donate_argnums` to
donate the batch values of each call to `step`. Note that this implies
that you will not be able to access the old batch values' buffers after
calling into `step`.
donate_func_state: Boolean. Whether to use jax's `donate_argnums` to
donate the persistent function state of each call to `step`. Note that
this implies that you will not be able to access the old function state
values' buffers after calling into `step`.
batch_process_func: Callable. A function which to be called on each batch
before feeding to the KFAC on device. This could be useful for specific
device input optimizations.
multi_device: Boolean. Whether to use `pmap` and run the optimizer on
multiple devices. (Default: False)
use_jax_cond: Not used for the moment.
debug: Boolean. If non of the step or init functions would be jitted. Note
that this also overrides `multi_device` and prevents using `pmap`.
(Default: False)
pmap_axis_name: String. The name of the `pmap` axis to use when
`multi_device` is set to True. (Default: curvature_axis)
"""
super().__init__()
self.value_and_grad_func = value_and_grad_func
self.value_func_has_aux = value_func_has_aux
self.value_func_has_state = value_func_has_state
self.value_func_has_rng = value_func_has_rng
self.value_func = utils.convert_value_and_grad_to_value_func(
value_and_grad_func, has_aux=value_func_has_aux)
self.l2_reg = l2_reg
self.learning_rate_schedule = learning_rate_schedule
if momentum_schedule is not None:
def schedule_with_first_step_zero(global_step: jnp.ndarray):
value = momentum_schedule(global_step)
check = jnp.equal(global_step, 0)
return check * jnp.zeros_like(value) + (1 - check) * value
self.momentum_schedule = schedule_with_first_step_zero
else:
self.momentum_schedule = None
self.damping_schedule = damping_schedule
self.min_damping = min_damping
self.max_damping = max_damping
self.norm_constraint = norm_constraint
self.num_burnin_steps = num_burnin_steps
self.estimation_mode = estimation_mode
self.curvature_ema = curvature_ema
self.inverse_update_period = inverse_update_period
self.register_only_generic = register_only_generic
self.layer_tag_to_block_cls = layer_tag_to_block_cls
self.patterns_to_skip = patterns_to_skip
self.donate_parameters = donate_parameters
self.donate_optimizer_state = donate_optimizer_state
self.donate_batch_inputs = donate_batch_inputs
self.donate_func_state = donate_func_state
self.batch_process_func = batch_process_func or (lambda x: x)
self.multi_device = multi_device
self.use_jax_cond = use_jax_cond
self.debug = debug
self.pmap_axis_name = pmap_axis_name if multi_device else None
self._rng_split = utils.p_split if multi_device else jnr.split
# Attributes filled in during self.init()
self.finalized = False
self.tagged_func = None
self.flat_params_shapes = None
self.params_treedef = None
# Special attributes related to jitting/pmap
self._jit_init = None
self._jit_burnin = None
self._jit_step = None
def finalize(
self,
params: Parameters,
rng: jnp.ndarray,
batch: Batch,
func_state: Optional[FuncState] = None,
) -> None:
"""Finalizes the optimizer by tracing the model function with the params and batch."""
if self.finalized:
raise ValueError("Optimizer has already been finalized.")
if self.multi_device:
# We assume that the parameters and batch are replicated, while tracing
# must happen with parameters for a single device call
params, rng, batch = jax.tree_map(lambda x: x[0], (params, rng, batch))
if func_state is not None:
func_state = jax.tree_map(lambda x: x[0], func_state)
batch = self.batch_process_func(batch)
# These are all tracing operations and we can run them with abstract values
func_args = utils.make_func_args(params, func_state, rng, batch,
self.value_func_has_state,
self.value_func_has_rng)
# Run all tracing with abstract values so no computation is done
flat_params, self.params_treedef = jax.tree_flatten(params)
self.flat_params_shapes = tuple(p.shape for p in flat_params)
self.tagged_func = tgm.auto_register_tags(
func=self.value_func,
func_args=func_args,
params_index=0,
register_only_generic=self.register_only_generic,
patterns_to_skip=self.patterns_to_skip)
self.estimator = estimator.CurvatureEstimator(
self.tagged_func,
func_args,
self.l2_reg,
self.estimation_mode,
layer_tag_to_block_cls=self.layer_tag_to_block_cls)
# Arguments: params, opt_state, rng, batch, func_state
donate_argnums = []
if self.donate_parameters:
donate_argnums.append(0)
if self.donate_optimizer_state:
donate_argnums.append(1)
if self.donate_batch_inputs:
donate_argnums.append(3)
if self.donate_func_state and self.value_func_has_state:
donate_argnums.append(4)
donate_argnums = tuple(donate_argnums)
if self.debug:
self._jit_init = self._init
self._jit_burnin = self._burnin
self._jit_step = self._step
elif self.multi_device:
self._jit_init = jax.pmap(
self._init, axis_name=self.pmap_axis_name, donate_argnums=[0])
# batch size is static argnum and is at index 5
self._jit_burnin = jax.pmap(
self._burnin,
axis_name=self.pmap_axis_name,
static_broadcasted_argnums=[5])
self._jit_step = jax.pmap(
self._step,
axis_name=self.pmap_axis_name,
donate_argnums=donate_argnums,
static_broadcasted_argnums=[5])
else:
self._jit_init = jax.jit(self._init, donate_argnums=[0])
# batch size is static argnum and is at index 5
self._jit_burnin = jax.jit(self._burnin, static_argnums=[5])
self._jit_step = jax.jit(
self._step, donate_argnums=donate_argnums, static_argnums=[5])
self.finalized = True
def _init(self, rng: jnp.ndarray) -> State:
"""This is the non-jitted version of initializing the state."""
flat_velocities = [jnp.zeros(shape) for shape in self.flat_params_shapes]
return dict(
velocities=jax.tree_unflatten(self.params_treedef, flat_velocities),
estimator=self.estimator.init(rng, None),
step_counter=jnp.asarray(0))
def verify_args_and_get_step_counter(
self,
params: Parameters,
state: State,
rng: jnp.ndarray,
data_iterator: Iterator[Batch],
func_state: Optional[FuncState] = None,
learning_rate: Optional[jnp.ndarray] = None,
momentum: Optional[jnp.ndarray] = None,
damping: Optional[jnp.ndarray] = None,
global_step_int: Optional[int] = None,
) -> int:
"""Verifies that the arguments passed to `Optimizer.step` are correct."""
if not self.finalized:
rng, rng_finalize = self._rng_split(rng)
self.finalize(params, rng_finalize, next(data_iterator), func_state)
# Verify correct arguments invocation
if self.learning_rate_schedule is not None and learning_rate is not None:
raise ValueError("When you have passed a `learning_rate_schedule` you "
"should not pass a value to the step function.")
if self.momentum_schedule is not None and momentum is not None:
raise ValueError("When you have passed a `momentum_schedule` you should "
"not pass a value to the step function.")
if self.damping_schedule is not None and damping is not None:
raise ValueError("When you have passed a `damping_schedule` you should "
"not pass a value to the step function.")
# Do a bunrnin on the first iteration
if global_step_int is None:
if self.multi_device:
return int(utils.get_first(state["step_counter"]))
else:
return int(state["step_counter"])
return global_step_int
def _burnin(
self,
params: Parameters,
state: State,
rng: jnp.ndarray,
batch: Batch,
func_state: Optional[FuncState],
batch_size: Optional[int],
) -> Tuple[State, Optional[FuncState]]:
"""This is the non-jitted version of a single burnin step."""
self.set_state(state)
batch = self.batch_process_func(batch)
rng, func_rng = jnr.split(rng) if self.value_func_has_rng else (rng, None)
func_args = utils.make_func_args(params, func_state, func_rng, batch,
self.value_func_has_state,
self.value_func_has_rng)
# Compute batch size
if batch_size is None:
batch_size = jax.tree_flatten(batch)[0][0].shape[0]
# Update curvature estimate
ema_old, ema_new = 1.0, 1.0 / self.num_burnin_steps
self.estimator.update_curvature_matrix_estimate(ema_old, ema_new,
batch_size, rng, func_args,
self.pmap_axis_name)
if func_state is not None:
out, _ = self.value_and_grad_func(*func_args)
_, func_state, _ = utils.extract_func_outputs(out,
self.value_func_has_aux,
self.value_func_has_state)
return self.pop_state(), func_state
def _step(
self,
params: Parameters,
state: State,
rng: jnp.ndarray,
batch: Batch,
func_state: Optional[FuncState],
batch_size: Optional[int],
learning_rate: Optional[jnp.ndarray],
momentum: Optional[jnp.ndarray],
damping: Optional[jnp.ndarray],
) -> Union[Tuple[Parameters, State, FuncState, Mapping[str, jnp.ndarray]],
Tuple[Parameters, State, Mapping[str, jnp.ndarray]]]:
"""This is the non-jitted version of a single step."""
# Unpack and set the state
self.set_state(state)
if damping is not None:
assert self.estimator.damping is None
self.estimator.damping = damping
else:
assert self.estimator.damping is not None
# Preprocess the batch and construct correctly the function arguments
batch = self.batch_process_func(batch)
rng, func_rng = jnr.split(rng) if self.value_func_has_rng else (rng, None)
func_args = utils.make_func_args(params, func_state, func_rng, batch,
self.value_func_has_state,
self.value_func_has_rng)
# Compute the batch size
if batch_size is None:
batch_size = jax.tree_flatten(batch)[0][0].shape[0]
# Compute schedules if applicable
if self.learning_rate_schedule is not None:
assert learning_rate is None
learning_rate = self.learning_rate_schedule(self.step_counter)
else:
assert learning_rate is not None
if self.momentum_schedule is not None:
assert momentum is None
momentum = self.momentum_schedule(self.step_counter)
else:
assert momentum is not None
if self.damping_schedule is not None:
assert damping is None
damping = self.damping_schedule(self.step_counter)
else:
assert damping is not None
# Compute current loss and gradients
out, grads = self.value_and_grad_func(*func_args)
loss, new_func_state, aux = utils.extract_func_outputs(
out, self.value_func_has_aux, self.value_func_has_state)
# Sync loss and grads
loss, grads = utils.pmean_if_pmap((loss, grads), self.pmap_axis_name)
# Update curvature estimate
self.estimator.update_curvature_matrix_estimate(
self.curvature_ema,
1.0,
batch_size,
rng,
func_args,
self.pmap_axis_name,
)
# Optionally update the inverse estimate
self.estimator.set_state(
lax.cond(
self.step_counter % self.inverse_update_period == 0,
lambda s: self.estimator.update_curvature_estimate_inverse( # pylint: disable=g-long-lambda
self.pmap_axis_name, s),
lambda s: s,
self.estimator.pop_state()))
# Compute proposed directions
vectors = self.propose_directions(
grads,
self.velocities,
learning_rate,
momentum,
)
# The learning rate is defined as the negative of the coefficient by which
# we multiply the gradients, while the momentum is the coefficient by
# which we multiply the velocities.
neg_learning_rate = -learning_rate # pytype: disable=unsupported-operands # trace-all-classes
# Compute the coefficients of the update vectors
assert neg_learning_rate is not None and momentum is not None
coefficients = (neg_learning_rate, momentum)
# Update velocities and compute new delta
self.velocities, delta = self.velocities_and_delta(
self.velocities,
vectors,
coefficients,
)
# Update parameters: params = params + delta
params = jax.tree_map(jnp.add, params, delta)
# Optionally compute the reduction ratio and update the damping
self.estimator.damping = None
rho = jnp.nan
# Statistics with useful information
stats = dict()
stats["step"] = self.step_counter
stats["loss"] = loss
stats["learning_rate"] = -coefficients[0]
stats["momentum"] = coefficients[1]
stats["damping"] = damping
stats["rho"] = rho
if self.value_func_has_aux:
stats["aux"] = aux
self.step_counter = self.step_counter + 1
if self.value_func_has_state:
return params, self.pop_state(), new_func_state, stats # pytype: disable=bad-return-type # jax-ndarray
else:
assert new_func_state is None
return params, self.pop_state(), stats # pytype: disable=bad-return-type # jax-ndarray
def init(
self,
params: Parameters,
rng: jnp.ndarray,
batch: Batch,
func_state: Optional[FuncState] = None,
) -> State:
"""Initializes the optimizer and returns the appropriate optimizer state."""
if not self.finalized:
self.finalize(params, rng, batch, func_state)
return self._jit_init(rng)
def step(
self,
params: Parameters,
state: Mapping[str, Any],
rng: jnp.ndarray,
data_iterator: Iterator[Any],
func_state: Any = None,
learning_rate: Optional[jnp.ndarray] = None,
momentum: Optional[jnp.ndarray] = None,
damping: Optional[jnp.ndarray] = None,
batch_size: Optional[int] = None,
global_step_int: Optional[int] = None,
) -> Union[Tuple[Parameters, State, FuncState, Mapping[str, jnp.ndarray]],
Tuple[Parameters, State, Mapping[str, jnp.ndarray]]]:
"""Performs a single update step using the optimizer.
Args:
params: The parameters of the model.
state: The state of the optimizer.
rng: A Jax PRNG key.
data_iterator: An iterator that returns a batch of data.
func_state: Any function state that gets passed in and returned.
learning_rate: This must be provided when
`use_adaptive_learning_rate=False` and `learning_rate_schedule=None`.
momentum: This must be provided when
`use_adaptive_momentum=False` and `momentum_schedule=None`.
damping: This must be provided when
`use_adaptive_damping=False` and `damping_schedule=None`.
batch_size: The batch size to use for KFAC. The default behaviour when it
is None is to use the leading dimension of the first data array.
global_step_int: The global step as a python int. Note that this must
match the step inte rnal to the optimizer that is part of its state.
Returns:
(params, state, stats)
where:
params: The updated model parameters.
state: The updated optimizer state.
stats: A dictionary of key statistics provided to be logged.
"""
step_counter_int = self.verify_args_and_get_step_counter(
params=params,
state=state,
rng=rng,
data_iterator=data_iterator,
func_state=func_state,
learning_rate=learning_rate,
momentum=momentum,
damping=damping,
global_step_int=global_step_int)
if step_counter_int == 0:
for _ in range(self.num_burnin_steps):
rng, rng_burn = self._rng_split(rng)
batch = next(data_iterator)
state, func_state = self._jit_burnin(params, state, rng_burn, batch,
func_state, batch_size)
# On the first step we always treat the momentum as 0.0
if self.momentum_schedule is None:
momentum = jnp.zeros([])
if self.multi_device:
momentum = utils.replicate_all_local_devices(momentum)
batch = next(data_iterator)
return self._jit_step(params, state, rng, batch, func_state, batch_size,
learning_rate, momentum, damping)
def propose_directions(
self,
grads: Parameters,
velocities: Parameters,
learning_rate: Optional[jnp.ndarray],
momentum: Optional[jnp.ndarray],
) -> Tuple[Parameters, Parameters]:
"""Computes the vector proposals for the next step."""
del momentum # not used in this, but could be used in subclasses
preconditioned_grads = self.estimator.multiply_matpower(grads, -1)
if self.norm_constraint is not None:
assert learning_rate is not None
sq_norm_grads = utils.inner_product(preconditioned_grads, grads)
sq_norm_scaled_grads = sq_norm_grads * learning_rate**2
# We need to sync the norms here, because reduction can be
# non-deterministic. They specifically are on GPUs by default for better
# performance. Hence although grads and preconditioned_grads are synced,
# the inner_product operation can still produce different answers on
# different devices.
sq_norm_scaled_grads = utils.pmean_if_pmap(sq_norm_scaled_grads,
self.pmap_axis_name)
max_coefficient = jnp.sqrt(self.norm_constraint / sq_norm_scaled_grads)
coefficient = jnp.minimum(max_coefficient, 1)
preconditioned_grads = utils.scalar_mul(preconditioned_grads, coefficient)
return preconditioned_grads, velocities
def velocities_and_delta(
self,
velocities: Parameters,
vectors: Sequence[Parameters],
coefficients: Sequence[jnp.ndarray],
) -> Sequence[Parameters]:
"""Computes the new velocities and delta (update to parameters)."""
del velocities
assert len(vectors) == len(coefficients)
delta = utils.scalar_mul(vectors[0], coefficients[0])
for vi, wi in zip(vectors[1:], coefficients[1:]):
delta = jax.tree_map(jnp.add, delta, utils.scalar_mul(vi, wi))
return delta, delta
================================================
FILE: kfac_ferminet_alpha/requirements.txt
================================================
jax>=0.2.10
dataclasses>=0.6
networkx>=2.1
numpy>=1.16.4
typing>=3.7.4.3
ordered-set>=4.0.2
absl-py>=0.12.0
================================================
FILE: kfac_ferminet_alpha/run.sh
================================================
#!/bin/bash
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script installs kfac_ferminet_alpha in a clean virtualenv and runs an
# example training loop. It is designed to be run from the parent directory,
# e.g.:
#
# git clone git@github.com:deepmind/deepmind-research.git
# cd deepmind_research
# kfac_ferminet_alpha/run.sh
python3 -m venv /tmp/kfac_ferminet_alpha_example
source /tmp/kfac_ferminet_alpha_example/bin/activate
pip3 install -U pip
pip3 install -r kfac_ferminet_alpha/requirements.txt
# For a GPU you have to do:
# pip3 install --upgrade jax jaxlib==0.1.64+cuda110 -f https://storage.googleapis.com/jax-releases/jax_releases.html
pip3 install jaxlib
pip3 install kfac_ferminet_alpha/
python3 kfac_ferminet_alpha/example.py
================================================
FILE: kfac_ferminet_alpha/setup.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Setup for pip package."""
from setuptools import setup
REQUIRED_PACKAGES = (
"absl-py",
"dataclasses",
"jax",
"networkx",
"numpy",
"ordered-set",
"typing",
)
LONG_DESCRIPTION = "\n".join([
"Kronecker-Factored Approximate Curvature (K-FAC) optimizer implemented in "
"JAX.",
"",
"Accompanying code for 'Better, Faster Fermionic Neural Networks'",
"James S. Spencer, David Pfau, Aleksandar Botev, and W. M. C. Foulkes.",
"https://arxiv.org/abs/2011.07125.",
])
setup(
name="kfac_ferminet_alpha",
version="0.0.1",
description="A K-FAC optimizer implemented in JAX",
long_description=LONG_DESCRIPTION,
url="https://github.com/deepmind/deepmind-research/kfac_ferminet_alpha",
author="DeepMind",
package_dir={"kfac_ferminet_alpha": "."},
packages=["kfac_ferminet_alpha"],
install_requires=REQUIRED_PACKAGES,
platforms=["any"],
license="Apache License, Version 2.0",
)
================================================
FILE: kfac_ferminet_alpha/tag_graph_matcher.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A module for tagging and graph manipulation."""
import collections
import functools
import itertools
from typing import Any, NamedTuple, Sequence
from absl import logging
import jax
from jax import core as jax_core
from jax import lax
from jax import util as jax_util
from jax.interpreters import partial_eval as pe
import jax.numpy as jnp
import networkx as nx
from networkx.algorithms import isomorphism
import numpy as np
import ordered_set
from kfac_ferminet_alpha import layers_and_loss_tags as tags
USE_NETWORKX = False
def match_nodes(g1, g2, mapping, node1, node2):
"""Matching nodes when doing graph search."""
if not kfac_node_match(g1.nodes[node1], g2.nodes[node2]):
return False
# Check predecessors
p1 = set(n for n in g1.predecessors(node1) if n in mapping.keys())
p2 = set(n for n in g2.predecessors(node2) if n in mapping.values())
if len(p1) != len(p2):
return False
for p1_i in p1:
if mapping[p1_i] not in p2:
return False
# Check successors
s1 = set(n for n in g1.successors(node1) if n in mapping.keys())
s2 = set(n for n in g2.successors(node2) if n in mapping.values())
if len(s1) != len(s2):
return False
for s1_i in s1:
if mapping[s1_i] not in s2:
return False
return True
def generate_candidates(g1, g2, mapping, node1, node2):
"""Generates the initial candidates for graph search."""
# Check predecessors
p1 = set(n for n in g1.predecessors(node1) if n not in mapping.keys())
p2 = set(n for n in g2.predecessors(node2) if n not in mapping.values())
candidates = ordered_set.OrderedSet(itertools.product(p1, p2))
s1 = set(n for n in g1.successors(node1) if n not in mapping.keys())
s2 = set(n for n in g2.successors(node2) if n not in mapping.values())
candidates.update(list(itertools.product(s1, s2)))
return candidates
def find_mappings(pattern, graph, mapping, terminals):
"""Finds all mappings from graph search of the pattern."""
if len(mapping) == len(pattern):
for k, v in terminals.items():
v.add(mapping[k])
return [frozenset(mapping.items())]
mappings = set()
nodes_list = list(mapping.keys())
for node1 in reversed(nodes_list):
for s1 in pattern.successors(node1):
if s1 not in mapping.keys():
for s2 in graph.successors(mapping[node1]):
if s2 not in mapping.values():
if s1 not in terminals or s2 not in terminals[s1]:
if match_nodes(pattern, graph, mapping, s1, s2):
mapping[s1] = s2
mappings.update(
find_mappings(pattern, graph, mapping, terminals))
mapping.pop(s1)
for p1 in pattern.predecessors(node1):
if p1 not in mapping.keys():
for p2 in graph.predecessors(mapping[node1]):
if p2 not in mapping.values():
if p1 not in terminals or p2 not in terminals[p1]:
if match_nodes(pattern, graph, mapping, p1, p2):
mapping[p1] = p2
mappings.update(
find_mappings(pattern, graph, mapping, terminals))
mapping.pop(p1)
return mappings
def match_pattern(pattern, graph):
"""Given a pattern returns all matches inside the graph."""
if USE_NETWORKX:
matcher = isomorphism.GraphMatcher(
graph, pattern, node_match=kfac_node_match)
mappings = list(
dict((k, v)
for v, k in mapping.items())
for mapping in matcher.subgraph_isomorphisms_iter())
else:
mapping = collections.OrderedDict()
params1 = [n for n in pattern.nodes if pattern.nodes[n]["op"] == "param"]
params2 = [n for n in graph.nodes if graph.nodes[n]["op"] == "param"]
terminals = {
n: set() for n in pattern.nodes if not list(pattern.successors(n))
}
mappings = set()
for node1, node2 in itertools.product(params1, params2):
mapping[node1] = node2
mappings.update(find_mappings(pattern, graph, mapping, terminals))
mapping.pop(node1)
for v in terminals.values():
v.clear()
mappings = list(dict(mapping) for mapping in mappings)
var_mappings = []
for mapping in mappings:
var_mappings.append(dict())
for k, v in mapping.items():
cond = pattern.nodes[k]["op"] in ("param", "array")
source = pattern.nodes[k]["var"] if cond else k
target = graph.nodes[v]["var"] if cond else graph.nodes[v]["eqn"]
var_mappings[-1][source] = target
return var_mappings
def read_env(env, var):
# Literals are values baked into the Jaxpr
if isinstance(var, jax.core.Literal):
return var.val
return env[var]
def write_env(env, var, val):
env[var] = val
def abstract_single_value(value):
if isinstance(value, jnp.ndarray):
value = jax_core.ShapedArray(np.shape(value), np.result_type(value))
return pe.PartialVal.unknown(value)
else:
return value
def abstract_args(args):
return jax.tree_map(abstract_single_value, args)
def _extract_call_jaxpr(primitive, params):
if not (primitive.call_primitive or primitive.map_primitive):
return None, params
else:
params = dict(params)
return params.pop("call_jaxpr"), params
def evaluate_eqn(eqn, in_values, write_func):
"""Evaluate a single Jax equation and writes the outputs."""
in_values = list(in_values)
# This is logic specifically to handle `xla_call`
call_jaxpr, params = _extract_call_jaxpr(eqn.primitive, eqn.params)
if call_jaxpr:
subfuns = [
jax.core.lu.wrap_init(
functools.partial(jax.core.eval_jaxpr, call_jaxpr, ()))
]
else:
subfuns = []
ans = eqn.primitive.bind(*(subfuns + in_values), **params)
if eqn.primitive.multiple_results:
jax_util.safe_map(write_func, eqn.outvars, ans)
else:
write_func(eqn.outvars[0], ans)
return ans
def clean_jaxpr_eqns(jaxpr, preserve_tags=True):
"""Performs dead code elimination on the jaxpr, preserving loss and layer tags."""
eqns = []
dependants = set(jaxpr.outvars)
for eqn in reversed(jaxpr.eqns):
check = False
for v in eqn.outvars:
if v in dependants:
dependants.remove(v)
check = True
if isinstance(eqn.primitive, (tags.LossTag, tags.LayerTag)):
check = check or preserve_tags
if check:
eqns.append(eqn)
new_dependants = set(
v for v in eqn.invars if not isinstance(v, jax_core.Literal))
dependants = dependants.union(new_dependants)
# Dependants should only be invars
dependants = dependants - set(jaxpr.invars + jaxpr.constvars)
if dependants:
raise ValueError("Something went wrong with the dead code elimination.")
return reversed(eqns)
def broadcast_merger(f):
"""Transforms `f` into a function where all consecutive broadcasts are merged."""
def merged_func(*func_args):
typed_jaxpr, out_avals = jax.make_jaxpr(f, return_shape=True)(*func_args)
out_tree = jax.tree_structure(out_avals)
jaxpr, consts = typed_jaxpr.jaxpr, typed_jaxpr.literals
# Mapping from variable -> value
env = dict()
read = functools.partial(read_env, env)
write = functools.partial(write_env, env)
# Bind args and consts to environment
flat_args = jax.tree_flatten(func_args)[0]
jax_util.safe_map(write, jaxpr.invars, flat_args)
jax_util.safe_map(write, jaxpr.constvars, consts)
# Bind args and consts to environment
jax_util.safe_map(write, jaxpr.invars, flat_args)
jax_util.safe_map(write, jaxpr.constvars, consts)
# Loop through equations and evaluate primitives using `bind`
broadcasts_outputs = dict()
for eqn in clean_jaxpr_eqns(jaxpr):
# We ignore broadcasting of constants
if (eqn.primitive.name == "broadcast_in_dim" and
not all(isinstance(v, jax_core.Literal) for v in eqn.invars)):
if eqn.invars[0] in broadcasts_outputs:
x, dims = broadcasts_outputs[eqn.invars[0]]
kept_dims = eqn.params["broadcast_dimensions"]
kept_dims = [kept_dims[d] for d in dims]
y = lax.broadcast_in_dim(x, eqn.params["shape"], kept_dims)
jax_util.safe_map(write, eqn.outvars, [y])
broadcasts_outputs[eqn.outvars[0]] = (x, kept_dims)
else:
inputs = jax_util.safe_map(read, eqn.invars)
evaluate_eqn(eqn, inputs, write)
broadcasts_outputs[eqn.outvars[0]] = (
inputs[0], eqn.params["broadcast_dimensions"])
else:
evaluate_eqn(eqn, jax_util.safe_map(read, eqn.invars), write)
return jax.tree_unflatten(out_tree, jax_util.safe_map(read, jaxpr.outvars))
return merged_func
class JaxGraph(NamedTuple):
jaxpr: Any
consts: Any
params: Any
params_tree: Any
in_tree: Any
out_tree: Any
digraph: nx.DiGraph
tagging_func: Any
SPECIAL_OP_COMPARE_RULES = dict()
def default_compare(node1, node2):
if node1["op"] != node2["op"]:
return False
params1, params2 = node1["eqn"].params, node2["eqn"].params
if set(params1.keys()) != set(params2.keys()):
return False
for k in params1.keys():
if params1[k] != params2[k]:
return False
return True
def reshape_compare(node1, node2):
"""Compares two reshape nodes."""
assert node1["op"] == node2["op"] == "reshape"
params1, params2 = node1["eqn"].params, node2["eqn"].params
if params1["dimensions"] != params2["dimensions"]:
return False
return True
def broadcast_in_dim_compare(node1, node2):
"""Compares two reshape nodes."""
assert node1["op"] == node2["op"] == "broadcast_in_dim"
return True
def conv_compare(node1, node2):
"""Compares two conv_general_dialted nodes."""
assert node1["op"] == node2["op"] == "conv_general_dilated"
params1, params2 = node1["eqn"].params, node2["eqn"].params
for k in ("window_strides", "padding", "lhs_dilation", "rhs_dilation",
"lhs_shape", "rhs_shape"):
if len(params1[k]) != len(params2[k]):
return False
if (len(params1["dimension_numbers"].lhs_spec) != #
len(params2["dimension_numbers"].lhs_spec)):
return False
if (len(params1["dimension_numbers"].rhs_spec) != #
len(params2["dimension_numbers"].rhs_spec)):
return False
if (len(params1["dimension_numbers"].out_spec) != #
len(params2["dimension_numbers"].out_spec)):
return False
if ((params1["feature_group_count"] > 1) != #
(params2["feature_group_count"] > 1)):
return False
if ((params1["batch_group_count"] > 1) != #
(params2["batch_group_count"] > 1)):
return False
return True
SPECIAL_OP_COMPARE_RULES["reshape"] = reshape_compare
SPECIAL_OP_COMPARE_RULES["broadcast_in_dim"] = broadcast_in_dim_compare
SPECIAL_OP_COMPARE_RULES["conv_general_dilated"] = conv_compare
def kfac_node_match(node1, node2):
"""Checks if two nodes are equivalent."""
# Parameters match with each other and nothing else
if node1["op"] == "param" and node2["op"] == "param":
return True
# return node1["rank"] == node2["rank"]
if node1["op"] == "param" or node2["op"] == "param":
return False
# Arrays always match each other and nothing else
if node1["op"] == "array" and node2["op"] == "array":
return True
if node1["op"] == "array" or node2["op"] == "array":
return False
# Operators match first on name
if node1["op"] != node2["op"]:
return False
compare = SPECIAL_OP_COMPARE_RULES.get(node1["op"], default_compare)
return compare(node1, node2)
def var_to_str(var):
"""Returns a string representation of the variable of a Jax expression."""
if isinstance(var, jax.core.Literal):
return str(var)
elif not isinstance(var, jax.core.Var):
raise ValueError(f"Idk what to do with this {type(var)}?")
c = int(var.count)
if c == -1:
return "_"
str_rep = ""
while c > 25:
str_rep += chr(c % 26 + ord("a"))
c = c // 26
str_rep += chr(c + ord("a"))
return str_rep[::-1]
def extract_param_vars_flat(jaxpr, in_tree, params_index):
if params_index is None:
params_index = []
elif isinstance(params_index, int):
params_index = [params_index]
in_vars = jax.tree_unflatten(in_tree, jaxpr.invars)
return jax.tree_flatten([in_vars[i] for i in params_index])
def fill_jaxpr_to_graph(graph, jaxpr, in_vars=None, out_vars=None):
"""Fills the graph with the jaxpr."""
in_vars = in_vars or [var_to_str(v) for v in jaxpr.invars + jaxpr.constvars]
in_map = dict(zip(jaxpr.invars + jaxpr.constvars, in_vars))
out_vars = out_vars or [var_to_str(v) for v in jaxpr.outvars]
out_map = dict(zip(jaxpr.outvars, out_vars))
for eqn in jaxpr.eqns:
in_vars = []
for v in eqn.invars:
if isinstance(v, jax.core.Literal):
in_vars.append(var_to_str(v))
else:
in_vars.append(in_map.get(v, var_to_str(v)))
out_vars = [out_map.get(v, var_to_str(v)) for v in eqn.outvars]
in_str = ",".join(in_vars)
out_str = ",".join(out_vars)
if isinstance(eqn.primitive, tags.LossTag):
func_name = "__loss_tag"
elif isinstance(eqn.primitive, tags.LayerTag):
func_name = "__layer_tag"
else:
func_name = eqn.primitive.name
node_c = f"{func_name}({in_str})->{out_str}"
graph.add_node(node_c, op=eqn.primitive.name, eqn=eqn)
# Create incoming edges
for v, name in zip(eqn.invars, in_vars):
if not isinstance(v, jax.core.Literal):
graph.add_edge(name, node_c)
# Create output nodes and edges
for v, name in zip(eqn.outvars, out_vars):
graph.add_node(name, op="array", var=v)
graph.add_edge(node_c, name)
def create_digraph(jaxpr, params):
"""Creates a directed graph from the given jaxpr and parameters."""
graph = nx.DiGraph()
# Create input nodes
for v in jaxpr.invars + jaxpr.constvars:
if v in params:
graph.add_node(var_to_str(v), op="param", var=v)
else:
graph.add_node(var_to_str(v), op="array", var=v)
fill_jaxpr_to_graph(graph, jaxpr)
return graph
def function_to_jax_graph(func, args, params_index, tagging_func=None):
"""Creates a `JaxGraph` instance from the provided function."""
in_tree = jax.tree_structure(args)
typed_jaxpr = jax.make_jaxpr(func)(*args)
jaxpr, consts = typed_jaxpr.jaxpr, typed_jaxpr.literals
params, params_tree = extract_param_vars_flat(jaxpr, in_tree, params_index)
digraph = create_digraph(jaxpr, params)
if tagging_func is not None:
tagging_func = functools.partial(tagging_func, jaxpr)
return JaxGraph(
jaxpr=jaxpr,
consts=consts,
params=params,
params_tree=params_tree,
in_tree=in_tree,
out_tree=None,
digraph=digraph,
tagging_func=tagging_func)
def print_nice_jaxpr(jaxpr):
for eqn in jaxpr.eqns:
print(tuple(eqn.invars), "->", eqn.primitive.name, tuple(eqn.outvars))
def auto_register_tags(func,
func_args,
params_index: int = 0,
register_only_generic: bool = False,
compute_only_loss_tags: bool = True,
patterns_to_skip: Sequence[str] = ()):
"""Transform the function to one that is populated with tags."""
func = broadcast_merger(func)
graph = function_to_jax_graph(func, func_args, params_index=params_index)
matches = dict()
# Extract the tagged losses variables and all their ancestors
loss_output_vars = []
num_losses = 0
loss_ancestors = set()
for node in graph.digraph.nodes:
if node.startswith("__loss_tag"):
num_losses += 1
ancestors = nx.ancestors(graph.digraph, node)
ancestors.add(node)
for output_node in node.split("->")[-1].split(","):
ancestors.add(output_node)
loss_output_vars.append(graph.digraph.nodes[output_node]["var"])
loss_ancestors = loss_ancestors.union(ancestors)
loss_output_vars = tuple(loss_output_vars)
# Extract the sub-graph that leads to losses
sub_graph = nx.induced_subgraph(graph.digraph, loss_ancestors)
# First collect all parameters that are already part of a layer tag
tagged_params = dict()
pattern_counters = dict()
for tag_node in (
node for node in sub_graph.nodes if node.startswith("__layer_tag")):
inputs = graph.digraph.nodes[tag_node]["eqn"].invars
tag_instance = graph.digraph.nodes[tag_node]["eqn"].primitive
if tag_instance.name == "generic_tag":
tag_params = tag_instance.split_all_inputs(inputs)[0]
else:
tag_params = tag_instance.split_all_inputs(inputs)[2]
pattern_number = pattern_counters.get(tag_instance.name, 0)
for param in tag_params:
if param not in graph.params:
raise ValueError(f"You have registered a layer tag with parameter "
f"that is not part of the parameters at index "
f"{params_index}.")
if param in tagged_params:
raise ValueError(f"You have registered twice the parameter {param}.")
tagged_params[param] = f"Manual[{tag_instance.name}_{pattern_number}]"
if tag_instance.name not in pattern_counters:
pattern_counters[tag_instance.name] = 1
else:
pattern_counters[tag_instance.name] += 1
if not register_only_generic:
for pattern_name, patterns in get_graph_patterns():
if pattern_name in patterns_to_skip:
logging.info("Skipping graph pattern %s", pattern_name)
continue
logging.info("Matching graph pattern %s", pattern_name)
for pattern in patterns:
for match_map in match_pattern(pattern.digraph, sub_graph):
if len(pattern.jaxpr.outvars) > 1:
raise NotImplementedError()
output = pattern.jaxpr.outvars[0]
if matches.get(match_map[output]) is not None:
raise ValueError(f"Found more than one match for equation "
f"{match_map[output]}. Examine the jaxpr:\n "
f"{graph.jaxpr}")
# Mark the parameters as already tagged
match_params = set()
match_params_already_tagged = False
for param in match_map.values():
if param in graph.params:
match_params.add(param)
if param in tagged_params.keys():
match_params_already_tagged = True
# Register the match only if no parameters are already registered
if not match_params_already_tagged:
matches[match_map[output]] = (match_map, pattern.tagging_func)
pattern_number = pattern_counters.get(pattern_name, 0)
for param in match_params:
tagged_params[param] = f"Auto[{pattern_name}_{pattern_number}]"
if pattern_name not in pattern_counters:
pattern_counters[pattern_name] = 1
else:
pattern_counters[pattern_name] += 1
# Mark remaining parameters as orphans
orphan_params = sorted(
set(graph.params) - set(tagged_params.keys()), key=lambda v: v.count)
params_regs = [tagged_params.get(p, "Orphan") for p in graph.params]
params_regs = jax.tree_unflatten(graph.params_tree, params_regs)
logging.info("=" * 50)
logging.info("Graph parameter registrations:")
logging.info(params_regs)
logging.info("=" * 50)
# Construct a function with all of the extra tag registrations
@functools.wraps(func)
def wrapped_auto_registered(*args):
flat_args, _ = jax.tree_flatten(args)
# Mapping from variable -> value
env = {}
read = functools.partial(read_env, env)
write = functools.partial(write_env, env)
def tag(var):
if matches.get(var) is not None:
inv_map, tagging_func = matches[var]
var_map = {k: v for k, v in inv_map.items() if not isinstance(k, str)}
val_map = jax.tree_map(read, var_map)
val = tagging_func(inv_map, val_map)
env[var] = val
# Bind args and consts to environment
jax_util.safe_map(write, graph.jaxpr.invars, flat_args)
jax_util.safe_map(write, graph.jaxpr.constvars, graph.consts)
# Register any orphan parameters as generic
for param_var in orphan_params:
write(param_var, tags.register_generic(read(param_var)))
# Set the correct output variables
if compute_only_loss_tags:
output_vars = loss_output_vars
out_tree = jax.tree_structure(loss_output_vars)
else:
output_vars = graph.jaxpr.outvars
out_tree = graph.out_tree
# Loop through equations and evaluate primitives using `bind`
losses_evaluated = 0
for eqn in graph.jaxpr.eqns:
evaluate_eqn(eqn, jax_util.safe_map(read, eqn.invars), write)
jax_util.safe_map(tag, eqn.outvars)
# If we want to output only tagged losses
if isinstance(eqn.primitive, tags.LossTag):
losses_evaluated += 1
if compute_only_loss_tags and num_losses == losses_evaluated:
break
outputs = jax_util.safe_map(read, output_vars)
return jax.tree_unflatten(out_tree, outputs)
return wrapped_auto_registered
# Registered graphs
NAME_TO_JAX_GRAPH = dict()
DEFERRED_REGISTRATIONS = []
def register_function(name, func, tagging_func, example_args, params_index,
precedence):
"""Registers a function as a pattern in the graph matcher registry.
The graph matcher needs to trace at least once the full function, which means
you need to provide it with dummy arguments. The shapes of the arguments do
not matter, as the graph matcher ignores their values, however the rank does.
Especially if there is some broadcasting happening you should register with
every possible broadcast pattern. As a general advice avoid using a shape to
be 1, unless you want the pattern to specifically match that, as some
operations, like squeeze for example, can have special behaviour then.
Args:
name: The name of the pattern that is being registered to.
func: The function that performs the computation.
tagging_func: Function that correctly creates the tag.
example_args: Example arguments that can be inputted into `func`.
params_index: Specifies at which index of the `example_args` are considered
a parameter.
precedence: This specifies what precedence the graph matcher is going to
assign to the provided pattern. The graph matcher will go from lowest to
highest precedence, randomly breaking ties, when matching. Note that the
pattern that matches a parameter with the lowest precedence will get
registered and no other will. Specifically useful when there is a pattern
for a layer with and without bias, in which case the with bias
registration always should go with lower precedence.
"""
# This is required because we can not use Jax before InitGoogle() runs
def register():
jnp_args = jax.tree_map(jnp.asarray, example_args)
graph = function_to_jax_graph(
func, jnp_args, params_index=params_index, tagging_func=tagging_func)
if NAME_TO_JAX_GRAPH.get(name) is None:
NAME_TO_JAX_GRAPH[name] = (precedence, [])
assert precedence == NAME_TO_JAX_GRAPH[name][0]
NAME_TO_JAX_GRAPH[name][1].append(graph)
DEFERRED_REGISTRATIONS.append(register)
def get_graph_patterns():
"""Returns all graph patterns sorted by their precedence."""
while DEFERRED_REGISTRATIONS:
DEFERRED_REGISTRATIONS.pop()()
return [(name, pattern) for name, (_, pattern) in sorted(
NAME_TO_JAX_GRAPH.items(), key=lambda pair: pair[1][0])]
# Dense with bias
register_function(
"dense_with_bias",
tags.dense_func,
tags.dense_tagging,
[np.zeros([11, 13]), [np.zeros([13, 7]), np.zeros([7])]],
params_index=1,
precedence=0)
# Dense without bias
register_function(
"dense_no_bias",
tags.dense_func,
tags.dense_tagging, [np.zeros([11, 13]), [np.zeros([13, 7])]],
params_index=1,
precedence=1)
# Conv2d with bias
register_function(
"conv2d_with_bias",
tags.conv2d_func,
tags.conv2d_tagging,
[np.zeros([2, 8, 8, 5]), [np.zeros([3, 3, 5, 4]),
np.zeros([4])]],
params_index=1,
precedence=0)
# Conv2d without bias
register_function(
"conv2d_no_bias",
tags.conv2d_func,
tags.conv2d_tagging, [np.zeros([2, 8, 8, 5]), [np.zeros([3, 3, 5, 4])]],
params_index=1,
precedence=1)
# Standard scale and shift with both scale and shift
register_function(
"scale_and_shift",
functools.partial(
tags.scale_and_shift_func, has_scale=True, has_shift=True),
functools.partial(
tags.scale_and_shift_tagging, has_scale=True, has_shift=True),
[np.zeros([2, 13]), [np.zeros([13]), np.zeros([13])]],
params_index=1,
precedence=0)
# Same but no broadcasting
register_function(
"scale_and_shift",
functools.partial(
tags.scale_and_shift_func, has_scale=True, has_shift=True),
functools.partial(
tags.scale_and_shift_tagging, has_scale=True, has_shift=True),
[np.zeros([13]), [np.zeros([13]), np.zeros([13])]],
params_index=1,
precedence=0)
# Scale and shift as implemented in batch norm layers in Haiku
register_function(
"scale_and_shift",
tags.batch_norm_func,
functools.partial(
tags.batch_norm_tagging_func, has_scale=True, has_shift=True),
[[np.zeros([2, 13]), np.zeros([13])], [np.zeros([13]),
np.zeros([13])]],
params_index=1,
precedence=0)
# Same but no broadcasting
register_function(
"scale_and_shift",
tags.batch_norm_func,
functools.partial(
tags.batch_norm_tagging_func, has_scale=True, has_shift=True),
[[np.zeros([13]), np.zeros([13])], [np.zeros([13]),
np.zeros([13])]],
params_index=1,
precedence=0)
# Only scale
register_function(
"scale_only",
functools.partial(
tags.scale_and_shift_func, has_scale=True, has_shift=False),
functools.partial(
tags.scale_and_shift_tagging, has_scale=True, has_shift=False),
[np.zeros([2, 13]), [np.zeros([13])]],
params_index=1,
precedence=1)
================================================
FILE: kfac_ferminet_alpha/tests/common.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Common functions used across more than one test."""
import jax
import jax.numpy as jnp
import jax.random as jnr
from kfac_ferminet_alpha import loss_functions
def fully_connected_layer(params, x):
w, b = params
return jnp.matmul(x, w) + b[None]
def init_autoencoder(key, data_shape):
"""Initialize the standard autoencoder."""
assert len(data_shape) == 1
x_size = data_shape[0]
sizes = [x_size, 1000, 500, 250, 30, 250, 500, 1000, x_size]
keys = jnr.split(key, len(sizes) - 1)
params = []
for key, dim_in, dim_out in zip(keys, sizes, sizes[1:]):
# Glorot uniform initialization
c = jnp.sqrt(6 / (dim_in + dim_out))
w = jax.random.uniform(key, shape=(dim_in, dim_out), minval=-c, maxval=c)
b = jnp.zeros([dim_out])
params.append((w, b))
return params
def autoencoder(all_params, x_in):
"""Evaluate the standard autoencoder.
Note that the objective of this autoencoder is not standard, bur rather a sum
of the standard sigmoid crossentropy and squared loss. The reason for this is
to test on handling multiple losses.
Args:
all_params: All parameter values.
x_in: Inputs to the network.
Returns:
The value of the two losses and intermediate layer values.
"""
h_in = x_in
layers_values = []
for i, params in enumerate(all_params):
h_out = fully_connected_layer(params, h_in)
layers_values.append((h_out, h_in))
# Last layer does not have a nonlinearity
if i % 4 != 3:
# h_in = nn.leaky_relu(h_out)
h_in = jnp.tanh(h_out)
else:
h_in = h_out
h1, _ = loss_functions.register_normal_predictive_distribution(h_in, x_in)
h2, _ = loss_functions.register_normal_predictive_distribution(
h_in, targets=x_in, weight=0.1)
l1 = (h1 - x_in)**2 + jnp.log(jnp.pi) / 2
l1 = jnp.sum(l1, axis=-1)
l2 = (h2 - x_in)**2 + jnp.log(jnp.pi) / 2
l2 = jnp.sum(l2, axis=-1)
return [l1, l2 * 0.1], layers_values
================================================
FILE: kfac_ferminet_alpha/tests/graph_matcher_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from absl.testing import absltest
import jax
import jax.numpy as jnp
import jax.random as jnr
from kfac_ferminet_alpha import layers_and_loss_tags
from kfac_ferminet_alpha import loss_functions
from kfac_ferminet_alpha import tag_graph_matcher
from kfac_ferminet_alpha.tests import common
def tagged_autoencoder(all_params, x_in):
h_in = x_in
layers_values = []
for i, params in enumerate(all_params):
h_out = common.fully_connected_layer(params, h_in)
h_out = layers_and_loss_tags.register_dense(h_out, h_in, params[0],
params[1],)
layers_values.append((h_out, h_in))
# Last layer does not have a nonlinearity
if i % 4 != 3:
h_in = jnp.tanh(h_out)
else:
h_in = h_out
h1, _ = loss_functions.register_normal_predictive_distribution(
h_in, targets=x_in, weight=1.0)
h2, t2 = loss_functions.register_normal_predictive_distribution(
h_in, targets=x_in, weight=0.1)
return [[h1, t2], [h2, t2]]
class TestGraphMatcher(unittest.TestCase):
"""Class for running all of the tests for integrating the systems."""
def _test_jaxpr(self, init_func, model_func, tagged_model, data_shape):
data_shape = tuple(data_shape)
rng_key = jnr.PRNGKey(12345)
init_key, data_key = jnr.split(rng_key)
params = init_func(init_key, data_shape)
data = jnr.normal(data_key, (11,) + data_shape)
func = tag_graph_matcher.auto_register_tags(model_func, (params, data))
jaxpr = jax.make_jaxpr(func)(params, data).jaxpr
tagged_jaxpr = jax.make_jaxpr(tagged_model)(params, data).jaxpr
self.assertEqual(len(jaxpr.invars), len(tagged_jaxpr.invars))
self.assertEqual(len(jaxpr.constvars), len(tagged_jaxpr.constvars))
self.assertEqual(len(jaxpr.outvars), len(tagged_jaxpr.outvars))
for eq, tagged_eq in zip(jaxpr.eqns, tagged_jaxpr.eqns):
eq_in_vars = [v for v in eq.invars]
tagged_in_vars = [v for v in tagged_eq.invars]
self.assertEqual(len(eq_in_vars), len(tagged_in_vars))
self.assertEqual(len(eq.outvars), len(tagged_eq.outvars))
self.assertEqual(eq.primitive, tagged_eq.primitive)
for variable, t_variable in zip(eq_in_vars + eq.outvars,
tagged_in_vars + tagged_eq.outvars):
if isinstance(variable, jax.core.Literal):
self.assertEqual(variable.aval, t_variable.aval)
else:
if variable.count != t_variable.count:
print("0")
self.assertEqual(variable.count, t_variable.count)
def test_autoencoder(self):
self._test_jaxpr(common.init_autoencoder, common.autoencoder,
tagged_autoencoder, [784])
if __name__ == "__main__":
absltest.main()
================================================
FILE: kfac_ferminet_alpha/tests/tracer_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from absl.testing import absltest
import jax
import jax.numpy as jnp
import jax.random as jnr
import numpy as np
from kfac_ferminet_alpha import loss_functions
from kfac_ferminet_alpha import tag_graph_matcher as tgm
from kfac_ferminet_alpha import tracer
from kfac_ferminet_alpha import utils
from kfac_ferminet_alpha.tests import common
def autoencoder_aux(all_aux, all_params, x_in):
h_in = x_in
layers_values = []
for i, (params, aux) in enumerate(zip(all_params, all_aux)):
h_out = common.fully_connected_layer(params, h_in + aux[1]) + aux[0]
layers_values.append((h_out, h_in))
# Last layer does not have a nonlinearity
if i % 4 != 3:
h_in = jnp.tanh(h_out)
else:
h_in = h_out
h1, _ = loss_functions.register_normal_predictive_distribution(h_in, x_in)
h2, _ = loss_functions.register_normal_predictive_distribution(
h_in, targets=x_in, weight=0.1)
l1 = (h1 - x_in)**2 + jnp.log(jnp.pi) / 2
l2 = (h2 - x_in)**2 + jnp.log(jnp.pi) / 2
return [l1, l2 * 0.1], layers_values
class TestTracer(unittest.TestCase):
"""Class for running all of the tests for integrating the systems."""
@staticmethod
def generate_data(init_func, func, data_shape, rng_key):
n = 3
rng_key, key = jnr.split(rng_key)
params = init_func(key, data_shape)
rng_key, key = jnr.split(rng_key)
p_tangents = init_func(key, data_shape)
rng_key, key = jnr.split(rng_key)
data = jnr.normal(key, [n] + data_shape)
loss_vals, layer_vals = func(params, data)
h = layer_vals[-1][0]
keys = jnr.split(key, len(loss_vals))
h_tangents = tuple(jnr.normal(key, shape=h.shape) for key in keys)
return params, data, p_tangents, h_tangents
def assertStructureAllClose(self, x, y, rtol=1E-5, atol=1E-5, **kwargs):
x_v, x_tree = jax.tree_flatten(x)
y_v, y_tree = jax.tree_flatten(y)
self.assertEqual(x_tree, y_tree)
for xi, yi in zip(x_v, y_v):
self.assertEqual(xi.shape, yi.shape)
self.assertEqual(xi.dtype, yi.dtype)
np.testing.assert_allclose(xi, yi, rtol=rtol, atol=atol, **kwargs)
def test_tacer_jvp(self):
init_func = common.init_autoencoder
func = common.autoencoder
data_shape = [784]
rng_key = jnr.PRNGKey(12345)
params, data, p_tangents, _ = self.generate_data(init_func, func,
data_shape, rng_key)
def no_data_func(args):
outputs = func(args, data)
return outputs[0], outputs[1][-1][0]
# True computation
(primals_out, tangents_out) = jax.jvp(no_data_func, [params], [p_tangents])
loss_vals, _ = primals_out
_, h_tangents = tangents_out
loss_tangents = ((h_tangents,),) * len(loss_vals)
# Tracer computation
tracer_jvp = tracer.trace_losses_matrix_vector_jvp(func)
tracer_losses, tracer_loss_tangents = tracer_jvp((params, data), p_tangents)
tracer_losses = [loss.evaluate(None) for loss in tracer_losses]
self.assertStructureAllClose(loss_vals, tracer_losses)
self.assertStructureAllClose(loss_tangents, tracer_loss_tangents)
def test_tracer_vjp(self):
init_func = common.init_autoencoder
func = common.autoencoder
data_shape = [784]
rng_key = jnr.PRNGKey(12345)
params, data, _, h_tangents = self.generate_data(init_func, func,
data_shape, rng_key)
def no_data_func(args):
outputs = func(args, data)
return outputs[0], outputs[1][-1][0]
# True computation
(loss_vals, _), vjp_func = jax.vjp(no_data_func, params)
loss_tangents = jax.tree_map(jnp.zeros_like, loss_vals)
summed_h_tangents = sum(jax.tree_flatten(h_tangents)[0])
p_tangents = vjp_func((loss_tangents, summed_h_tangents))
# Tracer computation
trace_vjp = tracer.trace_losses_matrix_vector_vjp(func)
tracer_losses, tracer_vjp_func = trace_vjp(params, data)
tracer_losses = [loss.evaluate(None) for loss in tracer_losses]
tracer_p_tangents = tracer_vjp_func(h_tangents)
self.assertStructureAllClose(loss_vals, tracer_losses)
self.assertStructureAllClose(p_tangents, tracer_p_tangents, atol=3e-6)
def test_tracer_hvp(self):
init_func = common.init_autoencoder
func = common.autoencoder
data_shape = [784]
rng_key = jnr.PRNGKey(12345)
params, data, p_tangents, _ = self.generate_data(init_func, func,
data_shape, rng_key)
def no_data_func(args):
outputs = func(args, data)
return sum(jax.tree_map(jnp.sum, outputs[0]))
# True computation
grad_func = jax.grad(no_data_func)
def grad_time_tangents(args):
return utils.inner_product(grad_func(args), p_tangents)
hvp = jax.grad(grad_time_tangents)
hvp_vectors = hvp(params)
# Tracer computation
tracer_hvp = tracer.trace_losses_matrix_vector_hvp(func)
tracer_hvp_vectors = tracer_hvp((params, data), p_tangents)
self.assertStructureAllClose(hvp_vectors, tracer_hvp_vectors, atol=1e-4)
def test_trace_estimator(self):
init_func = common.init_autoencoder
func = common.autoencoder
aux_func = autoencoder_aux
data_shape = [784]
rng_key = jnr.PRNGKey(12345)
params, data, _, h_tangents = self.generate_data(init_func, func,
data_shape, rng_key)
def aux_last_layer(aux, args):
outs = aux_func(aux, args, data)
return outs[1][-1][0]
# True computation
loss_vals, layer_vals = func(params, data)
aux_vals = jax.tree_map(jnp.zeros_like, layer_vals)
_, vjp = jax.vjp(aux_last_layer, aux_vals, params)
summed_h_tangents = sum(jax.tree_flatten(h_tangents)[0])
aux_tangents, p_tangents = vjp(summed_h_tangents)
layers_info = []
for aux_p, p_p in zip(layer_vals, params):
info = dict()
info["outputs"] = (aux_p[0],)
info["inputs"] = (aux_p[1],)
info["params"] = (p_p[0], p_p[1])
layers_info.append(info)
for i, (aux_t, p_t) in enumerate(zip(aux_tangents, p_tangents)):
info = dict()
info["outputs_tangent"] = (aux_t[0],)
info["inputs_tangent"] = (aux_t[1],)
info["params_tangent"] = (p_t[0], p_t[1])
layers_info[i].update(info)
layers_info = tuple(layers_info)
func = tgm.auto_register_tags(func, (params, data))
tracer_vjp = tracer.trace_estimator_vjp(func)
tracer_losses, tracer_vjp_func = tracer_vjp((params, data))
tracer_losses = [loss.evaluate(None) for loss in tracer_losses]
tracer_outputs = tracer_vjp_func((h_tangents[:1], h_tangents[1:]))
self.assertStructureAllClose(loss_vals, tracer_losses)
self.assertStructureAllClose(tracer_outputs, layers_info, atol=3e-6)
if __name__ == "__main__":
absltest.main()
================================================
FILE: kfac_ferminet_alpha/tracer.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module for the Jax tracer functionality for tags."""
import functools
from typing import Any, Callable, Sequence, Tuple
import jax
from jax import core
from jax import util as jax_util
import jax.numpy as jnp
from kfac_ferminet_alpha import layers_and_loss_tags as tags
from kfac_ferminet_alpha import tag_graph_matcher as tgm
from kfac_ferminet_alpha import utils
_Function = Callable[[Any], Any]
_Loss = tags.LossTag
def extract_tags(
jaxpr: core.Jaxpr
) -> Tuple[Sequence[core.JaxprEqn], Sequence[core.JaxprEqn]]:
"""Extracts all of the tag equations."""
# Loop through equations and evaluate primitives using `bind`
layer_tags = []
loss_tags = []
for eqn in jaxpr.eqns:
if isinstance(eqn.primitive, tags.LossTag):
loss_tags.append(eqn)
elif isinstance(eqn.primitive, tags.LayerTag):
layer_tags.append(eqn)
return tuple(layer_tags), tuple(loss_tags)
def construct_compute_losses_inputs(
jaxpr: core.Jaxpr,
consts: Tuple[Any],
num_losses: int,
primals: Any,
params_index: int) -> Callable[[Any], Sequence[Sequence[jnp.ndarray]]]:
"""Constructs a function that computes all of the inputs to all losses."""
primals_ = list(primals)
def forward_compute_losses(
params_primals: Any,
) -> Sequence[Sequence[jnp.ndarray]]:
primals_[params_index] = params_primals
flat_args = jax.tree_flatten(primals_)[0]
# Mapping from variable -> value
env = dict()
read = functools.partial(tgm.read_env, env)
write = functools.partial(tgm.write_env, env)
# Bind args and consts to environment
jax_util.safe_map(write, jaxpr.invars, flat_args)
jax_util.safe_map(write, jaxpr.constvars, consts)
# Loop through equations and evaluate primitives using `bind`
losses_so_far = 0
loss_tags = []
for eqn in jaxpr.eqns:
tgm.evaluate_eqn(eqn, jax_util.safe_map(read, eqn.invars), write)
if isinstance(eqn.primitive, tags.LossTag):
loss_tags.append(eqn)
losses_so_far += 1
if num_losses is not None and losses_so_far == num_losses:
break
return tuple(tuple(read(v) for v in tag.invars) for tag in loss_tags)
# return tuple(jax_util.safe_map(read, tag.invars) for tag in loss_tags)
return forward_compute_losses
# We know when `.primitive` will be either a `LossTag` or a `LayerTag`, however
# pytype cannot infer its subclass, so we need to unbox it.
def _unbox_loss_tag(jaxpr_eqn: core.JaxprEqn) -> tags.LossTag:
assert isinstance(jaxpr_eqn.primitive, tags.LossTag)
return jaxpr_eqn.primitive
def _unbox_layer_tag(jaxpr_eqn: core.JaxprEqn) -> tags.LayerTag:
assert isinstance(jaxpr_eqn.primitive, tags.LayerTag)
return jaxpr_eqn.primitive
def trace_losses_matrix_vector_vjp(tagged_func: _Function,
params_index: int = 0):
"""Returns the Jacobian-transposed vector product (backward mode) function in equivalent form to jax.vjp."""
def vjp(*primals):
typed_jaxpr = jax.make_jaxpr(tagged_func)(*primals)
jaxpr, consts = typed_jaxpr.jaxpr, typed_jaxpr.literals
_, loss_jaxpr_eqns = extract_tags(jaxpr)
n = len(loss_jaxpr_eqns)
losses_func = construct_compute_losses_inputs(
jaxpr, consts, n, primals, params_index)
losses_inputs, full_vjp_func = jax.vjp(losses_func, primals[params_index])
losses = []
for jaxpr_eqn, inputs in zip(loss_jaxpr_eqns, losses_inputs):
loss_tag = _unbox_loss_tag(jaxpr_eqn)
losses.append(loss_tag.loss(*inputs, weight=jaxpr_eqn.params["weight"]))
losses = tuple(losses)
def vjp_func(tangents):
flat_tangents = jax.tree_flatten(tangents)[0]
loss_invars = []
loss_targets = []
for jaxpr_eqn, inputs in zip(loss_jaxpr_eqns, losses_inputs):
num_inputs = _unbox_loss_tag(jaxpr_eqn).num_inputs
loss_invars.append(tuple(jaxpr_eqn.invars[:num_inputs]))
loss_targets.append(inputs[num_inputs:])
treedef = jax.tree_structure(loss_invars)
tangents = jax.tree_unflatten(treedef, flat_tangents)
# Since the losses could also take and targets as inputs and we don't want
# this function to computes vjp w.r.t to those (e.g. the user should not
# be providing tangent vectors for the targets, only for inputs) we have
# to manually fill in these "extra" tangents with zeros.
targets_tangents = jax.tree_map(jnp.zeros_like, loss_targets)
tangents = tuple(ti + tti for ti, tti in zip(tangents, targets_tangents))
input_tangents = full_vjp_func(tangents)[0]
return input_tangents,
return losses, vjp_func
return vjp
def trace_losses_matrix_vector_jvp(
tagged_func: _Function,
params_index: int = 0):
"""Returns the Jacobian vector product (forward mode) function in equivalent form to jax.jvp."""
def jvp(primals, params_tangents):
typed_jaxpr = jax.make_jaxpr(tagged_func)(*primals)
jaxpr, consts = typed_jaxpr.jaxpr, typed_jaxpr.literals
_, loss_tags = extract_tags(jaxpr)
n = len(loss_tags)
losses_func = construct_compute_losses_inputs(jaxpr, consts, n,
primals, params_index)
primals = (primals[params_index],)
tangents = (params_tangents,)
(primals_out, tangents_out) = jax.jvp(losses_func, primals, tangents)
tangents_out = tuple(tuple(t[:tag.primitive.num_inputs])
for t, tag in zip(tangents_out, loss_tags))
losses = tuple(tag.primitive.loss(*inputs, weight=tag.params["weight"])
for tag, inputs in zip(loss_tags, primals_out))
return losses, tangents_out
return jvp
def trace_losses_matrix_vector_hvp(tagged_func, params_index=0):
"""Returns the Hessian vector product function of **the tagged losses**, rather than the output value of `tagged_func`."""
# The function uses backward-over-forward mode.
def hvp(primals, params_tangents):
typed_jaxpr = jax.make_jaxpr(tagged_func)(*primals)
jaxpr, consts = typed_jaxpr.jaxpr, typed_jaxpr.literals
_, loss_tags = extract_tags(jaxpr)
n = len(loss_tags)
losses_func = construct_compute_losses_inputs(
jaxpr, consts, n, primals, params_index)
def losses_sum(param_primals):
loss_inputs = losses_func(param_primals)
losses = [
_unbox_loss_tag(jaxpr_eqn).loss(
*inputs, weight=jaxpr_eqn.params["weight"])
for jaxpr_eqn, inputs in zip(loss_tags, loss_inputs)
]
# This computes the sum of losses evaluated. Makes it easier as we can
# now use jax.grad rather than jax.vjp for taking derivatives.
return sum(jnp.sum(loss.evaluate(None)) for loss in losses)
def grads_times_tangents(params_primals):
grads = jax.grad(losses_sum)(params_primals)
return utils.inner_product(grads, params_tangents)
return jax.grad(grads_times_tangents)(primals[params_index])
return hvp
def trace_estimator_vjp(tagged_func: _Function) -> _Function:
"""Creates the function needed for an estimator of curvature matrices.
Args:
tagged_func: An function that has been annotated with tags both for layers
and losses.
Returns:
A function with the same signatures as `tagged_func`, which when provided
with inputs returns two things:
1. The instances of all losses objected that are tagged.
2. A second function, which when provide with tangent vectors for each
of the loss instances' parameters, returns for every tagged layer a
dictionary containing the following elements:
inputs - The primal values of the inputs to the layer.
outputs - The primal values of the outputs to the layer.
params - The primal values of the layer.
inputs_tangent - The tangent value of layer, given the provided
tangents of the losses.
inputs_tangent - The tangent value of layer, given the provided
tangents of the losses.
inputs_tangent - The tangent value of layer, given the provided
tangents of the losses.
"""
def full_vjp_func(func_args):
# Trace the tagged function
typed_jaxpr = jax.make_jaxpr(tagged_func)(*func_args)
jaxpr, consts = typed_jaxpr.jaxpr, typed_jaxpr.literals
layer_tags, loss_tags = extract_tags(jaxpr)
layer_vars_flat = jax.tree_flatten([tag.invars for tag in layer_tags])[0]
layer_input_vars = tuple(set(layer_vars_flat))
def forward():
own_func_args = func_args
# Mapping from variable -> value
env = dict()
read = functools.partial(tgm.read_env, env)
write = functools.partial(tgm.write_env, env)
# Bind args and consts to environment
jax_util.safe_map(write, jaxpr.invars, jax.tree_flatten(own_func_args)[0])
jax_util.safe_map(write, jaxpr.constvars, consts)
# Loop through equations and evaluate primitives using `bind`
num_losses_passed = 0
for eqn in jaxpr.eqns:
tgm.evaluate_eqn(eqn, jax_util.safe_map(read, eqn.invars), write)
if isinstance(eqn.primitive, tags.LossTag):
num_losses_passed += 1
if num_losses_passed == len(loss_tags):
break
if num_losses_passed != len(loss_tags):
raise ValueError("This should be unreachable.")
return jax_util.safe_map(read, layer_input_vars)
def forward_aux(aux):
own_func_args = func_args
# Mapping from variable -> value
env = dict()
read = functools.partial(tgm.read_env, env)
def write(var, val):
if not isinstance(var, jax.core.Literal):
val = val + aux[var] if var in aux else val
env[var] = val
# Bind args and consts to environment
jax_util.safe_map(write, jaxpr.invars, jax.tree_flatten(own_func_args)[0])
jax_util.safe_map(write, jaxpr.constvars, consts)
# Loop through equations and evaluate primitives using `bind`
num_losses_passed = 0
losses_inputs_values = []
losses_kwargs_values = []
for eqn in jaxpr.eqns:
input_values = jax_util.safe_map(read, eqn.invars)
tgm.evaluate_eqn(eqn, input_values, write)
if isinstance(eqn.primitive, tags.LossTag):
loss = eqn.primitive.loss(*input_values, weight=eqn.params["weight"])
losses_inputs_values.append(loss.inputs)
losses_kwargs_values.append(dict(
targets=loss.targets,
weight=eqn.params["weight"]
))
num_losses_passed += 1
if num_losses_passed == len(loss_tags):
break
if num_losses_passed != len(loss_tags):
raise ValueError("This should be unreachable.")
# Read the inputs to the loss functions, but also return the target values
return tuple(losses_inputs_values), tuple(losses_kwargs_values)
layer_input_values = forward()
primals_dict = dict(zip(layer_input_vars, layer_input_values))
primals_dict.update(zip(jaxpr.invars, jax.tree_flatten(func_args)[0]))
aux_values = jax.tree_map(jnp.zeros_like, layer_input_values)
aux_dict = dict(zip(layer_input_vars, aux_values))
losses_args, aux_vjp, losses_kwargs = jax.vjp(forward_aux, aux_dict,
has_aux=True)
losses = tuple(tag.primitive.loss(*inputs, **kwargs)
for tag, inputs, kwargs in
zip(loss_tags, losses_args, losses_kwargs))
def vjp_func(tangents):
all_tangents = aux_vjp(tangents)
tangents_dict, inputs_tangents = all_tangents[0], all_tangents[1:]
inputs_tangents = jax.tree_flatten(inputs_tangents)[0]
tangents_dict.update(zip(jaxpr.invars, inputs_tangents))
read_primals = functools.partial(tgm.read_env, primals_dict)
read_tangents = functools.partial(tgm.read_env, tangents_dict)
layers_info = []
for jaxpr_eqn in layer_tags:
layer_tag = _unbox_layer_tag(jaxpr_eqn)
info = dict()
primals = jax_util.safe_map(read_primals, tuple(jaxpr_eqn.invars))
(
info["outputs"],
info["inputs"],
info["params"],
) = layer_tag.split_all_inputs(primals)
tangents = jax_util.safe_map(read_tangents, tuple(jaxpr_eqn.invars))
(
info["outputs_tangent"],
info["inputs_tangent"],
info["params_tangent"],
) = layer_tag.split_all_inputs(tangents)
layers_info.append(info)
return tuple(layers_info)
return losses, vjp_func
return full_vjp_func
================================================
FILE: kfac_ferminet_alpha/utils.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities related to multi-device operations."""
import collections
from typing import Any, Mapping, Optional, Sequence, Tuple, TypeVar, Union
import dataclasses
import jax
from jax import core
from jax import lax
import jax.numpy as jnp
from jax.scipy import linalg
import jax.tree_util as tree_util
T = TypeVar("T")
def wrap_if_pmap(p_func):
def p_func_if_pmap(obj, axis_name):
try:
core.axis_frame(axis_name)
return p_func(obj, axis_name)
except NameError:
return obj
return p_func_if_pmap
pmean_if_pmap = wrap_if_pmap(lax.pmean)
psum_if_pmap = wrap_if_pmap(lax.psum)
compute_mean = jax.pmap(lambda x: lax.pmean(x, "i"), axis_name="i")
compute_sum = jax.pmap(lambda x: lax.psum(x, "i"), axis_name="i")
def get_first(obj: T) -> T:
return jax.tree_map(lambda x: x[0], obj)
def get_mean(obj: T) -> T:
return get_first(compute_mean(obj))
def get_sum(obj: T) -> T:
return get_first(compute_sum(obj))
broadcast_all_local_devices = jax.pmap(lambda x: x)
def replicate_all_local_devices(obj: T) -> T:
n = jax.local_device_count()
obj_stacked = jax.tree_map(lambda x: jnp.stack([x] * n, axis=0), obj)
return broadcast_all_local_devices(obj_stacked)
def make_different_rng_key_on_all_devices(rng: jnp.ndarray) -> jnp.ndarray:
rng = jax.random.fold_in(rng, jax.host_id())
rng = jax.random.split(rng, jax.local_device_count())
return broadcast_all_local_devices(rng)
p_split = jax.pmap(lambda key: tuple(jax.random.split(key)))
def scalar_mul(obj: T, scalar: Union[float, jnp.ndarray]) -> T:
return jax.tree_map(lambda x: x * scalar, obj)
def scalar_div(obj: T, scalar: Union[float, jnp.ndarray]) -> T:
return jax.tree_map(lambda x: x / scalar, obj)
def make_func_args(params, func_state, rng, batch, has_state: bool,
has_rng: bool):
"""Correctly puts all arguments to the function together."""
func_args = (params,)
if has_state:
if func_state is None:
raise ValueError("The `func_state` is None, but the argument `has_state` "
"is True.")
func_args += (func_state,)
if has_rng:
if rng is None:
raise ValueError("The `rng` is None, but the argument `has_rng` is True.")
func_args += (rng,)
func_args += (batch,)
return func_args
def extract_func_outputs(
raw_outputs: Any,
has_aux: bool,
has_state: bool,
) -> Tuple[jnp.ndarray, Any, Any]:
"""Given the function output returns separately the loss, func_state, aux."""
if not has_aux and not has_state:
return raw_outputs, None, None
loss, other = raw_outputs
if has_aux and has_state:
func_state, aux = other
elif has_aux:
func_state, aux = None, other
else:
func_state, aux = other, None
return loss, func_state, aux
def inner_product(obj1: T, obj2: T) -> jnp.ndarray:
if jax.tree_structure(obj1) != jax.tree_structure(obj2):
raise ValueError("The two structures are not identical.")
elements_product = jax.tree_map(lambda x, y: jnp.sum(x * y), obj1, obj2)
return sum(jax.tree_flatten(elements_product)[0])
def psd_inv_cholesky(matrix: jnp.ndarray, damping: jnp.ndarray) -> jnp.ndarray:
assert matrix.ndim == 2
identity = jnp.eye(matrix.shape[0])
matrix = matrix + damping * identity
return linalg.solve(matrix, identity, sym_pos=True)
def solve_maybe_small(a: jnp.ndarray, b: jnp.ndarray) -> jnp.ndarray:
"""Computes a^-1 b more efficiently for small matrices."""
assert a.shape[-1] == a.shape[-2] == b.shape[-1]
d = a.shape[-1]
if d == 0:
return a
elif d == 1:
return b / a[..., 0]
elif d == 2:
det = a[..., 0, 0] * a[..., 1, 1] - a[..., 0, 1] * a[..., 1, 0]
b_0 = a[..., 1, 1] * b[..., 0] - a[..., 0, 1] * b[..., 1]
b_1 = a[..., 0, 0] * b[..., 1] - a[..., 1, 0] * b[..., 0]
return jnp.stack([b_0, b_1], axis=-1) / det
elif d == 3:
raise NotImplementedError()
return jnp.linalg.solve(a, b)
def pi_adjusted_inverse(
factor_0: jnp.ndarray,
factor_1: jnp.ndarray,
damping: jnp.ndarray,
pmap_axis_name: str,
) -> Tuple[jnp.ndarray, jnp.ndarray]:
"""Performs inversion with pi-adjusted damping."""
# Compute the norms of each factor
norm_0 = jnp.trace(factor_0)
norm_1 = jnp.trace(factor_1)
# We need to sync the norms here, because reduction can be non-deterministic.
# They specifically are on GPUs by default for better performance.
# Hence although factor_0 and factor_1 are synced, the trace operation above
# can still produce different answers on different devices.
norm_0, norm_1 = pmean_if_pmap((norm_0, norm_1), axis_name=pmap_axis_name)
# Compute the overall scale
scale = norm_0 * norm_1
def regular_inverse(
operand: Sequence[jnp.ndarray]) -> Tuple[jnp.ndarray, jnp.ndarray]:
factor0, factor1, norm0, norm1, s, d = operand
# Special cases with one or two scalar factors
if factor0.size == 1 and factor1.size == 1:
value = jnp.ones_like(factor0) / jnp.sqrt(s)
return value, value
if factor0.size == 1:
factor1_normed = factor1 / norm1
damping1 = d / norm1
factor1_inv = psd_inv_cholesky(factor1_normed, damping1)
return jnp.full((1, 1), s), factor1_inv
if factor1.size == 1:
factor0_normed = factor0 / norm0
damping0 = d / norm0
factor0_inv = psd_inv_cholesky(factor0_normed, damping0)
return factor0_inv, jnp.full((1, 1), s)
# Invert first factor
factor0_normed = factor0 / norm0
damping0 = jnp.sqrt(d * factor1.shape[0] / (s * factor0.shape[0]))
factor0_inv = psd_inv_cholesky(factor0_normed, damping0) / jnp.sqrt(s)
# Invert second factor
factor1_normed = factor1 / norm1
damping1 = jnp.sqrt(d * factor0.shape[0] / (s * factor1.shape[0]))
factor1_inv = psd_inv_cholesky(factor1_normed, damping1) / jnp.sqrt(s)
return factor0_inv, factor1_inv
def zero_inverse(
operand: Sequence[jnp.ndarray]) -> Tuple[jnp.ndarray, jnp.ndarray]:
return (jnp.eye(factor_0.shape[0]) / jnp.sqrt(operand[-1]),
jnp.eye(factor_1.shape[0]) / jnp.sqrt(operand[-1]))
# In the special case where for some reason one of the factors is zero, then
# the correct inverse of `(0 kron A + lambda I)` is
# `(I/sqrt(lambda) kron (I/sqrt(lambda)`. However, because one of the norms is
# zero, then `pi` and `1/pi` would be 0 and infinity leading to NaN values.
# Hence, we need to make this check explicitly.
return lax.cond(
jnp.greater(scale, 0.0),
regular_inverse,
zero_inverse,
operand=(factor_0, factor_1, norm_0, norm_1, scale, damping))
def convert_value_and_grad_to_value_func(
value_and_grad_func,
has_aux: bool = False,
):
"""Converts a value_and_grad function to value_func only."""
def value_func(*args, **kwargs):
out, _ = value_and_grad_func(*args, **kwargs)
if has_aux:
return out[0]
else:
return out
return value_func
def check_structure_shapes_and_dtype(obj1: T, obj2: T) -> None:
"""Verifies that the two objects have the same pytree structure."""
assert jax.tree_structure(obj1) == jax.tree_structure(obj2)
for v1, v2 in zip(jax.tree_flatten(obj1)[0], jax.tree_flatten(obj2)[0]):
assert v1.shape == v2.shape
assert v1.dtype == v2.dtype
def check_first_dim_is_batch_size(batch_size: int, *args: jnp.ndarray) -> None:
for i, arg in enumerate(args):
if arg.shape[0] != batch_size:
raise ValueError(f"Expecting first dimension of arg[{i}] with shape "
f"{arg.shape} to be equal to the batch size "
f"{batch_size}.")
def py_tree_registered_dataclass(cls, *args, **kwargs):
"""Creates a new dataclass type and registers it as a pytree node."""
dcls = dataclasses.dataclass(cls, *args, **kwargs)
tree_util.register_pytree_node(
dcls,
lambda instance: ( # pylint: disable=g-long-lambda
[getattr(instance, f.name)
for f in dataclasses.fields(instance)], None),
lambda _, instance_args: dcls(*instance_args))
return dcls
class WeightedMovingAverage:
"""A wrapped class for a variable for which we keep exponential moving average."""
def __init__(self, weight: jnp.ndarray, array: jnp.ndarray):
self._weight = weight
self._array = array
@staticmethod
def zero(shape: Sequence[int]) -> "WeightedMovingAverage":
return WeightedMovingAverage(weight=jnp.zeros([]), array=jnp.zeros(shape))
@property
def weight(self) -> jnp.ndarray:
return self._weight
@property
def value(self) -> jnp.ndarray:
return self._array / self._weight
@property
def raw_value(self) -> jnp.ndarray:
return self._array
def update(self, value: jnp.ndarray, old_weight_multiplier: float,
new_weight: float) -> None:
self._weight = old_weight_multiplier * self._weight + new_weight
self._array = old_weight_multiplier * self._array + new_weight * value
def sync(self, pmap_axis_name: str) -> None:
self._array = pmean_if_pmap(self._array, pmap_axis_name)
def __str__(self) -> str:
return (f"ExponentialMovingAverage(weight={self._weight}, "
f"array={self._array})")
def __repr__(self) -> str:
return self.__str__()
tree_util.register_pytree_node(
WeightedMovingAverage,
lambda instance: ((instance.weight, instance.raw_value), None),
lambda _, instance_args: WeightedMovingAverage(*instance_args),
)
class Stateful:
"""A class for stateful objects."""
def __init__(self, stateful_fields_names: Optional[Sequence[str]] = ()):
self.__stateful_fields_names = stateful_fields_names
def _add_stateful_fields_names(self, value: Sequence[str]) -> None:
self.__stateful_fields_names += tuple(value)
def get_state(self) -> Mapping[str, Any]:
"""Returns the state of the object."""
state = dict()
for name in self.__stateful_fields_names:
state[name] = Stateful._get_state_from_instance(getattr(self, name))
return state
def set_state(self, value):
"""Sets the state of the object with the provided value and returns the object."""
assert isinstance(value, dict)
for name in self.__stateful_fields_names:
setattr(self, name,
Stateful._set_state_to_instance(getattr(self, name), value[name]))
return self
def clear_state(self) -> None:
"""Clears the state of the object."""
for name in self.__stateful_fields_names:
setattr(self, name,
Stateful._clear_state_from_instance(getattr(self, name)))
def pop_state(self) -> Mapping[str, Any]:
"""Returns the current state of the object, while simultaneously clearing it."""
state = self.get_state()
self.clear_state()
return state
@staticmethod
def _get_state_from_instance(obj):
"""Recursively gets the state of the object and returns it."""
if isinstance(obj, Stateful):
return obj.get_state()
if isinstance(obj, list):
return [Stateful._get_state_from_instance(i) for i in obj]
if isinstance(obj, tuple):
return tuple(Stateful._get_state_from_instance(i) for i in obj)
if isinstance(obj, collections.OrderedDict):
return collections.OrderedDict(
(k, Stateful._get_state_from_instance(v)) for k, v in obj.items())
if isinstance(obj, dict):
return dict(
(k, Stateful._get_state_from_instance(v)) for k, v in obj.items())
return obj
@staticmethod
def _set_state_to_instance(obj, value):
"""Recursively sets the state of the object and returns it."""
if isinstance(obj, Stateful):
obj.set_state(value)
return obj
if isinstance(value, list):
if obj is None:
obj = [None] * len(value)
return [
Stateful._set_state_to_instance(obj_i, value_i)
for obj_i, value_i in zip(obj, value)
]
if isinstance(value, tuple):
if obj is None:
obj = [None] * len(value)
return tuple(
Stateful._set_state_to_instance(obj_i, value_i)
for obj_i, value_i in zip(obj, value))
if isinstance(value, collections.OrderedDict):
if obj is None:
obj = dict((k, None) for k in value)
return collections.OrderedDict(
(k, Stateful._set_state_to_instance(obj[k], value[k])) for k in obj)
if isinstance(value, dict):
obj = dict((k, None) for k in value)
return dict(
(k, Stateful._set_state_to_instance(obj[k], value[k])) for k in obj)
return value
@staticmethod
def _clear_state_from_instance(obj):
"""Recursively clears the state of the object and returns it."""
if isinstance(obj, Stateful):
obj.clear_state()
return obj
if isinstance(obj, list):
return [Stateful._clear_state_from_instance(obj_i) for obj_i in obj]
if isinstance(obj, tuple):
return tuple(Stateful._clear_state_from_instance(obj_i) for obj_i in obj)
if isinstance(obj, collections.OrderedDict):
return collections.OrderedDict(
(k, Stateful._clear_state_from_instance(obj[k])) for k in obj)
if isinstance(obj, dict):
return dict((k, Stateful._clear_state_from_instance(obj[k])) for k in obj)
return None
@staticmethod
def infer_class_state(class_type):
"""Infers a stateful class state attributes from class annotations."""
if not issubclass(class_type, Stateful):
raise ValueError(
f"In order to annotate a class as stateful it must inherit "
f"{Stateful!r}")
class_type = dataclasses.dataclass(
class_type, init=False, repr=False, eq=False) # pytype: disable=wrong-keyword-args
fields_names = tuple(field.name for field in dataclasses.fields(class_type))
original_init = getattr(class_type, "__init__", None)
if original_init is None:
def injected_init(self, *args, **kwargs):
super(self.__class__, self).__init__(*args, **kwargs) # pylint: disable=bad-super-call
Stateful._add_stateful_fields_names(self, fields_names)
for field_name in fields_names:
if getattr(self, field_name, None) is None:
setattr(self, field_name, None)
setattr(class_type, "__init__", injected_init)
else:
def injected_init(self, *args, **kwargs):
original_init(self, *args, **kwargs)
Stateful._add_stateful_fields_names(self, fields_names)
for field_name in fields_names:
if getattr(self, field_name, None) is None:
setattr(self, field_name, None)
setattr(class_type, "__init__", injected_init)
return class_type
def compute_sq_norm_relative_abs_diff(obj, pmap_axis_name):
sq_norm = inner_product(obj, obj)
synced_sq_norm = psum_if_pmap(sq_norm, pmap_axis_name)
synced_sq_norm = (synced_sq_norm - sq_norm) / (jax.device_count() - 1.0)
sq_norm_abs_diff = jnp.abs(sq_norm - synced_sq_norm)
return sq_norm_abs_diff / sq_norm
def product(iterable_object):
x = 1
for element in iterable_object:
x *= element
return x
================================================
FILE: learned_free_energy_estimation/README.md
================================================
# Targeted free energy estimation via learned mappings
This repository contains supporting data for our publication
([journal](https://doi.org/10.1063/5.0018903), [arXiv](https://arxiv.org/abs/2002.04913)).
Here, we provide
- molecular dynamics (MD) datasets underlying the results reported in our paper,
- a LAMMPS input script to generate these datasets, and
- the data plotted in Fig. 5 of our paper to facilitate comparison.
## Abstract
Free energy perturbation (FEP) was proposed by Zwanzig more than six decades ago
as a method to estimate free energy differences, and has since inspired a huge
body of related methods that use it as an integral building block. Being an
importance sampling based estimator, however, FEP suffers from a severe
limitation: the requirement of sufficient overlap between distributions.
One strategy to mitigate this problem, called Targeted Free Energy Perturbation,
uses a high-dimensional mapping in configuration space to increase overlap of
the underlying distributions. Despite its potential, this method has attracted
only limited attention due to the formidable challenge of formulating a
tractable mapping. Here, we cast Targeted FEP as a machine learning problem in
which the mapping is parameterized as a neural network that is optimized so as
to increase overlap. We develop a new model architecture that respects
permutational and periodic symmetries often encountered in atomistic simulations
and test our method on a fully-periodic solvation system. We demonstrate that
our method leads to a substantial variance reduction in free energy estimates
when compared against baselines, without requiring any additional data.
## Dataset
We generated the datasets using the open-source MD package
[LAMMPS](https://lammps.sandia.gov). The prototypical solvation problem of study
consists of a solute particle immersed in a liquid comprising 125 solvent
particles. The solvent-solvent interactions are modelled using a Lennard-Jones
potential and the solute-solvent interactions via a Weeks-Chandler-Andersen
(WCA) potential. Further simulation details can be found in the LAMMPS script
provided (see below) and in our [paper](https://arxiv.org/abs/2002.04913)
(see Sec. 4 and Appendix B).
### Download
You can download the compressed datasets (~3.8GB) using the command:
> wget https://storage.googleapis.com/learned_free_energy_estimation/learned_free_energy_estimation_datasets.tar.bz2
or by copying the above link directly into your browser.
Once the archive `learned_free_energy_estimation_datasets.tar.bz2` is
downloaded, you can extract it with the command:
> tar -xvf learned_free_energy_estimation_datasets.tar.bz2
### Data format
The archive contains a total of 40 files:
- 10 train datasets for ensemble *A* (`ensemble_a_train_<>.dat`),
- 10 train datasets for ensemble *B* (`ensemble_b_train_<>.dat`),
- 10 test datasets for ensemble *A* (`ensemble_a_test_<>.dat`) and
- 10 test datasets for ensemble *B* (`ensemble_b_test_<>.dat`).
Each file is text-based and stored in a LAMMPS compatible format (see [dump command](https://lammps.sandia.gov/doc/dump.html)). Train datasets contain 90k records
each and test datasets contain 10k records, totalling 1M records for each
ensemble.
Each record contains 135 lines and is structured as follows:
- lines 1-9: Header information.
- lines 10-135: A matrix with shape `[126, 5]` containing the
- `id` (column 1),
- `type` (column 2) and
- `x, y, z` coordinates (columns 3-5)
of all particles.
For information on how the data was generated and partitioned into the final
datasets we refer to Sec. 4 and Appendix B of our [paper](https://arxiv.org/abs/2002.04913).
## LAMMPS script
The file `lammps.dat` contains a sample input script to generate data from
ensemble *A*. You can generate data from ensemble *B* by updating the value of
the solute radius, as suggested in the inline comment. For more information on
how the datasets were post-processed and partitioned, we refer to Sec. 4 and
Appendix B of our [paper](https://arxiv.org/abs/2002.04913).
## Figures
The subdirectory `figures` contains 4 files:
- `figure_5a_work_values.dat`: contains data underlying the histogram of work values in Fig. 5a.
- `figure_5b_df_bar.dat`: contains the BAR estimate of dF in Fig. 5b.
- `figure_5b_df_lbar.dat`: contains the LBAR estimate of dF in Fig. 5b.
- `figure_5b_df_mbar.dat`: contains the MBAR estimate of dF in Fig. 5b.
## Reference
If you find this repository helpful for your research, please cite our publication:
```
@article{Wirnsberger2020,
title={Targeted free energy estimation via learned mappings},
author={Wirnsberger, Peter and Ballard, Andrew J and Papamakarios, George and
Abercrombie, Stuart and Racanière, Sébastien and Pritzel, Alexander and
Jimenez Rezende, Danilo and Blundell, Charles},
journal={J. Chem. Phys.},
volume={153},
number={14},
pages={144112},
year={2020},
doi={10.1063/5.0018903}
}
```
## Disclaimer
This is not an official Google product.
================================================
FILE: learning_to_simulate/README.md
================================================
# Learning to Simulate Complex Physics with Graph Networks (ICML 2020)
ICML poster: [icml.cc/virtual/2020/poster/6849](https://icml.cc/virtual/2020/poster/6849)
Video site: [sites.google.com/view/learning-to-simulate](https://sites.google.com/view/learning-to-simulate)
ArXiv: [arxiv.org/abs/2002.09405](https://arxiv.org/abs/2002.09405)
If you use the code here please cite this paper:
@inproceedings{sanchezgonzalez2020learning,
title={Learning to Simulate Complex Physics with Graph Networks},
author={Alvaro Sanchez-Gonzalez and
Jonathan Godwin and
Tobias Pfaff and
Rex Ying and
Jure Leskovec and
Peter W. Battaglia},
booktitle={International Conference on Machine Learning},
year={2020}
}
## Example usage: train a model and display a trajectory
After downloading the repo, and from the parent directory. Install dependencies:
pip install -r learning_to_simulate/requirements.txt
mkdir -p /tmp/rollous
Download dataset (e.g. WaterRamps):
mkdir -p /tmp/datasets
bash ./learning_to_simulate/download_dataset.sh WaterRamps /tmp/datasets
Train a model:
mkdir -p /tmp/models
python -m learning_to_simulate.train \
--data_path=/tmp/datasets/WaterRamps \
--model_path=/tmp/models/WaterRamps
Generate some trajectory rollouts on the test set:
mkdir -p /tmp/rollouts
python -m learning_to_simulate.train \
--mode="eval_rollout" \
--data_path=/tmp/datasets/WaterRamps \
--model_path=/tmp/models/WaterRamps \
--output_path=/tmp/rollouts/WaterRamps
Plot a trajectory:
python -m learning_to_simulate.render_rollout \
--rollout_path=/tmp/rollouts/WaterRamps/rollout_test_0.pkl
## Datasets
Datasets are available to download via:
* Metadata file with dataset information (sequence length, dimensionality, box bounds, default connectivity radius, statistics for normalization, ...):
`https://storage.googleapis.com/learning-to-simulate-complex-physics/Datasets/{DATASET_NAME}/metadata.json`
* TFRecords containing data for all trajectories (particle types, positions, global context, ...):
`https://storage.googleapis.com/learning-to-simulate-complex-physics/Datasets/{DATASET_NAME}/{DATASET_SPLIT}.tfrecord`
Where:
* `{DATASET_SPLIT}` is one of:
* `train`
* `valid`
* `test`
* `{DATASET_NAME}` one of the datasets following the naming used in the paper:
* `WaterDrop`
* `Water`
* `Sand`
* `Goop`
* `MultiMaterial`
* `RandomFloor`
* `WaterRamps`
* `SandRamps`
* `FluidShake`
* `FluidShakeBox`
* `Continuous`
* `WaterDrop-XL`
* `Water-3D`
* `Sand-3D`
* `Goop-3D`
The provided script `./download_dataset.sh` may be used to download all files from each dataset into a folder given its name.
An additional smaller dataset `WaterDropSample`, which includes only the first two trajectories of `WaterDrop` for each split, is provided for debugging purposes.
## Code structure
* `train.py`: Script for training, evaluating and generating rollout trajectories.
* `learned_simulator.py`: Implementation of the learnable one-step model that returns the next position of the particles given inputs. It includes data preprocessing, Euler integration, and a helper method for building normalized training outputs and targets.
* `graph_network.py`: Implementation of the graph network used at the core of the learnable part of the model.
* `render_rollout.py`: Visualization code for displaying rollouts such as the example animation.
* `{noise/connectivity/reading}_utils.py`: Util modules for adding noise to the inputs, computing graph connectivity and reading datasets form TFRecords.
* `model_demo.py`: example connecting the model to input dummy data.
Note this is a reference implementation not designed to scale up to TPUs (unlike the one used for the paper). We have tested that the model can be trained with a batch size of 2 on a single NVIDIA V100 to reach similar qualitative performance (except for the XL and 3D datasets due to OOM).
================================================
FILE: learning_to_simulate/connectivity_utils.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Tools to compute the connectivity of the graph."""
import functools
import numpy as np
from sklearn import neighbors
import tensorflow.compat.v1 as tf
def _compute_connectivity(positions, radius, add_self_edges):
"""Get the indices of connected edges with radius connectivity.
Args:
positions: Positions of nodes in the graph. Shape:
[num_nodes_in_graph, num_dims].
radius: Radius of connectivity.
add_self_edges: Whether to include self edges or not.
Returns:
senders indices [num_edges_in_graph]
receiver indices [num_edges_in_graph]
"""
tree = neighbors.KDTree(positions)
receivers_list = tree.query_radius(positions, r=radius)
num_nodes = len(positions)
senders = np.repeat(range(num_nodes), [len(a) for a in receivers_list])
receivers = np.concatenate(receivers_list, axis=0)
if not add_self_edges:
# Remove self edges.
mask = senders != receivers
senders = senders[mask]
receivers = receivers[mask]
return senders, receivers
def _compute_connectivity_for_batch(
positions, n_node, radius, add_self_edges):
"""`compute_connectivity` for a batch of graphs.
Args:
positions: Positions of nodes in the batch of graphs. Shape:
[num_nodes_in_batch, num_dims].
n_node: Number of nodes for each graph in the batch. Shape:
[num_graphs in batch].
radius: Radius of connectivity.
add_self_edges: Whether to include self edges or not.
Returns:
senders indices [num_edges_in_batch]
receiver indices [num_edges_in_batch]
number of edges per graph [num_graphs_in_batch]
"""
# TODO(alvarosg): Consider if we want to support batches here or not.
# Separate the positions corresponding to particles in different graphs.
positions_per_graph_list = np.split(positions, np.cumsum(n_node[:-1]), axis=0)
receivers_list = []
senders_list = []
n_edge_list = []
num_nodes_in_previous_graphs = 0
# Compute connectivity for each graph in the batch.
for positions_graph_i in positions_per_graph_list:
senders_graph_i, receivers_graph_i = _compute_connectivity(
positions_graph_i, radius, add_self_edges)
num_edges_graph_i = len(senders_graph_i)
n_edge_list.append(num_edges_graph_i)
# Because the inputs will be concatenated, we need to add offsets to the
# sender and receiver indices according to the number of nodes in previous
# graphs in the same batch.
receivers_list.append(receivers_graph_i + num_nodes_in_previous_graphs)
senders_list.append(senders_graph_i + num_nodes_in_previous_graphs)
num_nodes_graph_i = len(positions_graph_i)
num_nodes_in_previous_graphs += num_nodes_graph_i
# Concatenate all of the results.
senders = np.concatenate(senders_list, axis=0).astype(np.int32)
receivers = np.concatenate(receivers_list, axis=0).astype(np.int32)
n_edge = np.stack(n_edge_list).astype(np.int32)
return senders, receivers, n_edge
def compute_connectivity_for_batch_pyfunc(
positions, n_node, radius, add_self_edges=True):
"""`_compute_connectivity_for_batch` wrapped in a pyfunc."""
partial_fn = functools.partial(
_compute_connectivity_for_batch, add_self_edges=add_self_edges)
senders, receivers, n_edge = tf.py_function(
partial_fn,
[positions, n_node, radius],
[tf.int32, tf.int32, tf.int32])
senders.set_shape([None])
receivers.set_shape([None])
n_edge.set_shape(n_node.get_shape())
return senders, receivers, n_edge
================================================
FILE: learning_to_simulate/download_dataset.sh
================================================
#!/bin/bash
# Copyright 2020 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Usage:
# bash download_dataset.sh ${DATASET_NAME} ${OUTPUT_DIR}
# Example:
# bash download_dataset.sh WaterDrop /tmp/
set -e
DATASET_NAME="${1}"
OUTPUT_DIR="${2}/${DATASET_NAME}"
BASE_URL="https://storage.googleapis.com/learning-to-simulate-complex-physics/Datasets/${DATASET_NAME}/"
mkdir -p ${OUTPUT_DIR}
for file in metadata.json train.tfrecord valid.tfrecord test.tfrecord
do
wget -O "${OUTPUT_DIR}/${file}" "${BASE_URL}${file}"
done
================================================
FILE: learning_to_simulate/graph_network.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Graph network implementation accompanying ICML 2020 submission.
"Learning to Simulate Complex Physics with Graph Networks"
Alvaro Sanchez-Gonzalez*, Jonathan Godwin*, Tobias Pfaff*, Rex Ying,
Jure Leskovec, Peter W. Battaglia
https://arxiv.org/abs/2002.09405
The Sonnet `EncodeProcessDecode` module provided here implements the learnable
parts of the model.
It assumes an encoder preprocessor has already built a graph with
connectivity and features as described in the paper, with features normalized
to zero-mean unit-variance.
Dependencies include Tensorflow 1.x, Sonnet 1.x and the Graph Nets 1.1 library.
"""
from typing import Callable
import graph_nets as gn
import sonnet as snt
import tensorflow as tf
Reducer = Callable[[tf.Tensor, tf.Tensor, tf.Tensor], tf.Tensor]
def build_mlp(
hidden_size: int, num_hidden_layers: int, output_size: int) -> snt.Module:
"""Builds an MLP."""
return snt.nets.MLP(
output_sizes=[hidden_size] * num_hidden_layers + [output_size]) # pytype: disable=bad-return-type # gen-stub-imports
class EncodeProcessDecode(snt.AbstractModule):
"""Encode-Process-Decode function approximator for learnable simulator."""
def __init__(
self,
latent_size: int,
mlp_hidden_size: int,
mlp_num_hidden_layers: int,
num_message_passing_steps: int,
output_size: int,
reducer: Reducer = tf.math.unsorted_segment_sum,
name: str = "EncodeProcessDecode"):
"""Inits the model.
Args:
latent_size: Size of the node and edge latent representations.
mlp_hidden_size: Hidden layer size for all MLPs.
mlp_num_hidden_layers: Number of hidden layers in all MLPs.
num_message_passing_steps: Number of message passing steps.
output_size: Output size of the decode node representations as required
by the downstream update function.
reducer: Reduction to be used when aggregating the edges in the nodes in
the interaction network. This should be a callable whose signature
matches tf.math.unsorted_segment_sum.
name: Name of the model.
"""
super().__init__(name=name)
self._latent_size = latent_size
self._mlp_hidden_size = mlp_hidden_size
self._mlp_num_hidden_layers = mlp_num_hidden_layers
self._num_message_passing_steps = num_message_passing_steps
self._output_size = output_size
self._reducer = reducer
with self._enter_variable_scope():
self._networks_builder()
def _build(self, input_graph: gn.graphs.GraphsTuple) -> tf.Tensor:
"""Forward pass of the learnable dynamics model."""
# Encode the input_graph.
latent_graph_0 = self._encode(input_graph)
# Do `m` message passing steps in the latent graphs.
latent_graph_m = self._process(latent_graph_0)
# Decode from the last latent graph.
return self._decode(latent_graph_m)
def _networks_builder(self):
"""Builds the networks."""
def build_mlp_with_layer_norm():
mlp = build_mlp(
hidden_size=self._mlp_hidden_size,
num_hidden_layers=self._mlp_num_hidden_layers,
output_size=self._latent_size)
return snt.Sequential([mlp, snt.LayerNorm()])
# The encoder graph network independently encodes edge and node features.
encoder_kwargs = dict(
edge_model_fn=build_mlp_with_layer_norm,
node_model_fn=build_mlp_with_layer_norm)
self._encoder_network = gn.modules.GraphIndependent(**encoder_kwargs)
# Create `num_message_passing_steps` graph networks with unshared parameters
# that update the node and edge latent features.
# Note that we can use `modules.InteractionNetwork` because
# it also outputs the messages as updated edge latent features.
self._processor_networks = []
for _ in range(self._num_message_passing_steps):
self._processor_networks.append(
gn.modules.InteractionNetwork(
edge_model_fn=build_mlp_with_layer_norm,
node_model_fn=build_mlp_with_layer_norm,
reducer=self._reducer))
# The decoder MLP decodes node latent features into the output size.
self._decoder_network = build_mlp(
hidden_size=self._mlp_hidden_size,
num_hidden_layers=self._mlp_num_hidden_layers,
output_size=self._output_size)
def _encode(
self, input_graph: gn.graphs.GraphsTuple) -> gn.graphs.GraphsTuple:
"""Encodes the input graph features into a latent graph."""
# Copy the globals to all of the nodes, if applicable.
if input_graph.globals is not None:
broadcasted_globals = gn.blocks.broadcast_globals_to_nodes(input_graph)
input_graph = input_graph.replace(
nodes=tf.concat([input_graph.nodes, broadcasted_globals], axis=-1),
globals=None)
# Encode the node and edge features.
latent_graph_0 = self._encoder_network(input_graph)
return latent_graph_0
def _process(
self, latent_graph_0: gn.graphs.GraphsTuple) -> gn.graphs.GraphsTuple:
"""Processes the latent graph with several steps of message passing."""
# Do `m` message passing steps in the latent graphs.
# (In the shared parameters case, just reuse the same `processor_network`)
latent_graph_prev_k = latent_graph_0
latent_graph_k = latent_graph_0
for processor_network_k in self._processor_networks:
latent_graph_k = self._process_step(
processor_network_k, latent_graph_prev_k)
latent_graph_prev_k = latent_graph_k
latent_graph_m = latent_graph_k
return latent_graph_m
def _process_step(
self, processor_network_k: snt.Module,
latent_graph_prev_k: gn.graphs.GraphsTuple) -> gn.graphs.GraphsTuple:
"""Single step of message passing with node/edge residual connections."""
# One step of message passing.
latent_graph_k = processor_network_k(latent_graph_prev_k)
# Add residuals.
latent_graph_k = latent_graph_k.replace(
nodes=latent_graph_k.nodes+latent_graph_prev_k.nodes,
edges=latent_graph_k.edges+latent_graph_prev_k.edges)
return latent_graph_k
def _decode(self, latent_graph: gn.graphs.GraphsTuple) -> tf.Tensor:
"""Decodes from the latent graph."""
return self._decoder_network(latent_graph.nodes)
================================================
FILE: learning_to_simulate/learned_simulator.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Full model implementation accompanying ICML 2020 submission.
"Learning to Simulate Complex Physics with Graph Networks"
Alvaro Sanchez-Gonzalez*, Jonathan Godwin*, Tobias Pfaff*, Rex Ying,
Jure Leskovec, Peter W. Battaglia
https://arxiv.org/abs/2002.09405
"""
import graph_nets as gn
import sonnet as snt
import tensorflow.compat.v1 as tf
from learning_to_simulate import connectivity_utils
from learning_to_simulate import graph_network
STD_EPSILON = 1e-8
class LearnedSimulator(snt.AbstractModule):
"""Learned simulator from https://arxiv.org/pdf/2002.09405.pdf."""
def __init__(
self,
num_dimensions,
connectivity_radius,
graph_network_kwargs,
boundaries,
normalization_stats,
num_particle_types,
particle_type_embedding_size,
name="LearnedSimulator"):
"""Inits the model.
Args:
num_dimensions: Dimensionality of the problem.
connectivity_radius: Scalar with the radius of connectivity.
graph_network_kwargs: Keyword arguments to pass to the learned part
of the graph network `model.EncodeProcessDecode`.
boundaries: List of 2-tuples, containing the lower and upper boundaries of
the cuboid containing the particles along each dimensions, matching
the dimensionality of the problem.
normalization_stats: Dictionary with statistics with keys "acceleration"
and "velocity", containing a named tuple for each with mean and std
fields, matching the dimensionality of the problem.
num_particle_types: Number of different particle types.
particle_type_embedding_size: Embedding size for the particle type.
name: Name of the Sonnet module.
"""
super().__init__(name=name)
self._connectivity_radius = connectivity_radius
self._num_particle_types = num_particle_types
self._boundaries = boundaries
self._normalization_stats = normalization_stats
with self._enter_variable_scope():
self._graph_network = graph_network.EncodeProcessDecode(
output_size=num_dimensions, **graph_network_kwargs)
if self._num_particle_types > 1:
self._particle_type_embedding = tf.get_variable(
"particle_embedding",
[self._num_particle_types, particle_type_embedding_size],
trainable=True, use_resource=True)
def _build(self, position_sequence, n_particles_per_example,
global_context=None, particle_types=None):
"""Produces a model step, outputting the next position for each particle.
Args:
position_sequence: Sequence of positions for each node in the batch,
with shape [num_particles_in_batch, sequence_length, num_dimensions]
n_particles_per_example: Number of particles for each graph in the batch
with shape [batch_size]
global_context: Tensor of shape [batch_size, context_size], with global
context.
particle_types: Integer tensor of shape [num_particles_in_batch] with
the integer types of the particles, from 0 to `num_particle_types - 1`.
If None, we assume all particles are the same type.
Returns:
Next position with shape [num_particles_in_batch, num_dimensions] for one
step into the future from the input sequence.
"""
input_graphs_tuple = self._encoder_preprocessor(
position_sequence, n_particles_per_example, global_context,
particle_types)
normalized_acceleration = self._graph_network(input_graphs_tuple)
next_position = self._decoder_postprocessor(
normalized_acceleration, position_sequence)
return next_position
def _encoder_preprocessor(
self, position_sequence, n_node, global_context, particle_types):
# Extract important features from the position_sequence.
most_recent_position = position_sequence[:, -1]
velocity_sequence = time_diff(position_sequence) # Finite-difference.
# Get connectivity of the graph.
(senders, receivers, n_edge
) = connectivity_utils.compute_connectivity_for_batch_pyfunc(
most_recent_position, n_node, self._connectivity_radius)
# Collect node features.
node_features = []
# Normalized velocity sequence, merging spatial an time axis.
velocity_stats = self._normalization_stats["velocity"]
normalized_velocity_sequence = (
velocity_sequence - velocity_stats.mean) / velocity_stats.std
flat_velocity_sequence = snt.MergeDims(start=1, size=2)(
normalized_velocity_sequence)
node_features.append(flat_velocity_sequence)
# Normalized clipped distances to lower and upper boundaries.
# boundaries are an array of shape [num_dimensions, 2], where the second
# axis, provides the lower/upper boundaries.
boundaries = tf.constant(self._boundaries, dtype=tf.float32)
distance_to_lower_boundary = (
most_recent_position - tf.expand_dims(boundaries[:, 0], 0))
distance_to_upper_boundary = (
tf.expand_dims(boundaries[:, 1], 0) - most_recent_position)
distance_to_boundaries = tf.concat(
[distance_to_lower_boundary, distance_to_upper_boundary], axis=1)
normalized_clipped_distance_to_boundaries = tf.clip_by_value(
distance_to_boundaries / self._connectivity_radius, -1., 1.)
node_features.append(normalized_clipped_distance_to_boundaries)
# Particle type.
if self._num_particle_types > 1:
particle_type_embeddings = tf.nn.embedding_lookup(
self._particle_type_embedding, particle_types)
node_features.append(particle_type_embeddings)
# Collect edge features.
edge_features = []
# Relative displacement and distances normalized to radius
normalized_relative_displacements = (
tf.gather(most_recent_position, senders) -
tf.gather(most_recent_position, receivers)) / self._connectivity_radius
edge_features.append(normalized_relative_displacements)
normalized_relative_distances = tf.norm(
normalized_relative_displacements, axis=-1, keepdims=True)
edge_features.append(normalized_relative_distances)
# Normalize the global context.
if global_context is not None:
context_stats = self._normalization_stats["context"]
# Context in some datasets are all zero, so add an epsilon for numerical
# stability.
global_context = (global_context - context_stats.mean) / tf.math.maximum(
context_stats.std, STD_EPSILON)
return gn.graphs.GraphsTuple(
nodes=tf.concat(node_features, axis=-1),
edges=tf.concat(edge_features, axis=-1),
globals=global_context, # self._graph_net will appending this to nodes.
n_node=n_node,
n_edge=n_edge,
senders=senders,
receivers=receivers,
)
def _decoder_postprocessor(self, normalized_acceleration, position_sequence):
# The model produces the output in normalized space so we apply inverse
# normalization.
acceleration_stats = self._normalization_stats["acceleration"]
acceleration = (
normalized_acceleration * acceleration_stats.std
) + acceleration_stats.mean
# Use an Euler integrator to go from acceleration to position, assuming
# a dt=1 corresponding to the size of the finite difference.
most_recent_position = position_sequence[:, -1]
most_recent_velocity = most_recent_position - position_sequence[:, -2]
new_velocity = most_recent_velocity + acceleration # * dt = 1
new_position = most_recent_position + new_velocity # * dt = 1
return new_position
def get_predicted_and_target_normalized_accelerations(
self, next_position, position_sequence_noise, position_sequence,
n_particles_per_example, global_context=None, particle_types=None): # pylint: disable=g-doc-args
"""Produces normalized and predicted acceleration targets.
Args:
next_position: Tensor of shape [num_particles_in_batch, num_dimensions]
with the positions the model should output given the inputs.
position_sequence_noise: Tensor of the same shape as `position_sequence`
with the noise to apply to each particle.
position_sequence, n_node, global_context, particle_types: Inputs to the
model as defined by `_build`.
Returns:
Tensors of shape [num_particles_in_batch, num_dimensions] with the
predicted and target normalized accelerations.
"""
# Add noise to the input position sequence.
noisy_position_sequence = position_sequence + position_sequence_noise
# Perform the forward pass with the noisy position sequence.
input_graphs_tuple = self._encoder_preprocessor(
noisy_position_sequence, n_particles_per_example, global_context,
particle_types)
predicted_normalized_acceleration = self._graph_network(input_graphs_tuple)
# Calculate the target acceleration, using an `adjusted_next_position `that
# is shifted by the noise in the last input position.
next_position_adjusted = next_position + position_sequence_noise[:, -1]
target_normalized_acceleration = self._inverse_decoder_postprocessor(
next_position_adjusted, noisy_position_sequence)
# As a result the inverted Euler update in the `_inverse_decoder` produces:
# * A target acceleration that does not explicitly correct for the noise in
# the input positions, as the `next_position_adjusted` is different
# from the true `next_position`.
# * A target acceleration that exactly corrects noise in the input velocity
# since the target next velocity calculated by the inverse Euler update
# as `next_position_adjusted - noisy_position_sequence[:,-1]`
# matches the ground truth next velocity (noise cancels out).
return predicted_normalized_acceleration, target_normalized_acceleration
def _inverse_decoder_postprocessor(self, next_position, position_sequence):
"""Inverse of `_decoder_postprocessor`."""
previous_position = position_sequence[:, -1]
previous_velocity = previous_position - position_sequence[:, -2]
next_velocity = next_position - previous_position
acceleration = next_velocity - previous_velocity
acceleration_stats = self._normalization_stats["acceleration"]
normalized_acceleration = (
acceleration - acceleration_stats.mean) / acceleration_stats.std
return normalized_acceleration
def time_diff(input_sequence):
return input_sequence[:, 1:] - input_sequence[:, :-1]
================================================
FILE: learning_to_simulate/model_demo.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Example script accompanying ICML 2020 submission.
"Learning to Simulate Complex Physics with Graph Networks"
Alvaro Sanchez-Gonzalez*, Jonathan Godwin*, Tobias Pfaff*, Rex Ying,
Jure Leskovec, Peter W. Battaglia
https://arxiv.org/abs/2002.09405
Here we provide the utility function `sample_random_position_sequence()` which
returns a sequence of positions for a variable number of particles, similar to
what a real dataset would provide, and connect the model to it, in both,
single step inference and training mode.
Dependencies include Tensorflow 1.x, Sonnet 1.x and the Graph Nets 1.1 library.
"""
import collections
from learning_to_simulate import learned_simulator
from learning_to_simulate import noise_utils
import numpy as np
import tensorflow.compat.v1 as tf
INPUT_SEQUENCE_LENGTH = 6
SEQUENCE_LENGTH = INPUT_SEQUENCE_LENGTH + 1 # add one target position.
NUM_DIMENSIONS = 3
NUM_PARTICLE_TYPES = 6
BATCH_SIZE = 5
GLOBAL_CONTEXT_SIZE = 6
Stats = collections.namedtuple("Stats", ["mean", "std"])
DUMMY_STATS = Stats(
mean=np.zeros([NUM_DIMENSIONS], dtype=np.float32),
std=np.ones([NUM_DIMENSIONS], dtype=np.float32))
DUMMY_CONTEXT_STATS = Stats(
mean=np.zeros([GLOBAL_CONTEXT_SIZE], dtype=np.float32),
std=np.ones([GLOBAL_CONTEXT_SIZE], dtype=np.float32))
DUMMY_BOUNDARIES = [(-1., 1.)] * NUM_DIMENSIONS
def sample_random_position_sequence():
"""Returns mock data mimicking the input features collected by the encoder."""
num_particles = tf.random_uniform(
shape=(), minval=50, maxval=1000, dtype=tf.int32)
position_sequence = tf.random.normal(
shape=[num_particles, SEQUENCE_LENGTH, NUM_DIMENSIONS])
return position_sequence
def main():
# Build the model.
learnable_model = learned_simulator.LearnedSimulator(
num_dimensions=NUM_DIMENSIONS,
connectivity_radius=0.05,
graph_network_kwargs=dict(
latent_size=128,
mlp_hidden_size=128,
mlp_num_hidden_layers=2,
num_message_passing_steps=10,
),
boundaries=DUMMY_BOUNDARIES,
normalization_stats={"acceleration": DUMMY_STATS,
"velocity": DUMMY_STATS,
"context": DUMMY_CONTEXT_STATS,},
num_particle_types=NUM_PARTICLE_TYPES,
particle_type_embedding_size=16,
)
# Sample a batch of particle sequences with shape:
# [TOTAL_NUM_PARTICLES, SEQUENCE_LENGTH, NUM_DIMENSIONS]
sampled_position_sequences = [
sample_random_position_sequence() for _ in range(BATCH_SIZE)]
position_sequence_batch = tf.concat(sampled_position_sequences, axis=0)
# Count how many particles are present in each element in the batch.
# [BATCH_SIZE]
n_particles_per_example = tf.stack(
[tf.shape(seq)[0] for seq in sampled_position_sequences], axis=0)
# Sample particle types.
# [TOTAL_NUM_PARTICLES]
particle_types = tf.random_uniform(
[tf.shape(position_sequence_batch)[0]],
0, NUM_PARTICLE_TYPES, dtype=tf.int32)
# Sample global context.
global_context = tf.random_uniform(
[BATCH_SIZE, GLOBAL_CONTEXT_SIZE], -1., 1., dtype=tf.float32)
# Separate input sequence from target sequence.
# [TOTAL_NUM_PARTICLES, INPUT_SEQUENCE_LENGTH, NUM_DIMENSIONS]
input_position_sequence = position_sequence_batch[:, :-1]
# [TOTAL_NUM_PARTICLES, NUM_DIMENSIONS]
target_next_position = position_sequence_batch[:, -1]
# Single step of inference with the model to predict next position for each
# particle [TOTAL_NUM_PARTICLES, NUM_DIMENSIONS].
predicted_next_position = learnable_model(
input_position_sequence, n_particles_per_example, global_context,
particle_types)
print(f"Per-particle output tensor: {predicted_next_position}")
# Obtaining predicted and target normalized accelerations for training.
position_sequence_noise = (
noise_utils.get_random_walk_noise_for_position_sequence(
input_position_sequence, noise_std_last_step=6.7e-4))
# Both with shape [TOTAL_NUM_PARTICLES, NUM_DIMENSIONS]
predicted_normalized_acceleration, target_normalized_acceleration = (
learnable_model.get_predicted_and_target_normalized_accelerations(
target_next_position, position_sequence_noise,
input_position_sequence, n_particles_per_example, global_context,
particle_types))
print(f"Predicted norm. acceleration: {predicted_normalized_acceleration}")
print(f"Target norm. acceleration: {target_normalized_acceleration}")
with tf.train.SingularMonitoredSession() as sess:
sess.run([predicted_next_position,
predicted_normalized_acceleration,
target_normalized_acceleration])
if __name__ == "__main__":
tf.disable_v2_behavior()
main()
================================================
FILE: learning_to_simulate/noise_utils.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Methods to calculate input noise."""
import tensorflow.compat.v1 as tf
from learning_to_simulate import learned_simulator
def get_random_walk_noise_for_position_sequence(
position_sequence, noise_std_last_step):
"""Returns random-walk noise in the velocity applied to the position."""
velocity_sequence = learned_simulator.time_diff(position_sequence)
# We want the noise scale in the velocity at the last step to be fixed.
# Because we are going to compose noise at each step using a random_walk:
# std_last_step**2 = num_velocities * std_each_step**2
# so to keep `std_last_step` fixed, we apply at each step:
# std_each_step `std_last_step / np.sqrt(num_input_velocities)`
# TODO(alvarosg): Make sure this is consistent with the value and
# description provided in the paper.
num_velocities = velocity_sequence.shape.as_list()[1]
velocity_sequence_noise = tf.random.normal(
tf.shape(velocity_sequence),
stddev=noise_std_last_step / num_velocities ** 0.5,
dtype=position_sequence.dtype)
# Apply the random walk.
velocity_sequence_noise = tf.cumsum(velocity_sequence_noise, axis=1)
# Integrate the noise in the velocity to the positions, assuming
# an Euler intergrator and a dt = 1, and adding no noise to the very first
# position (since that will only be used to calculate the first position
# change).
position_sequence_noise = tf.concat([
tf.zeros_like(velocity_sequence_noise[:, 0:1]),
tf.cumsum(velocity_sequence_noise, axis=1)], axis=1)
return position_sequence_noise
================================================
FILE: learning_to_simulate/reading_utils.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Utilities for reading open sourced Learning Complex Physics data."""
import functools
import numpy as np
import tensorflow.compat.v1 as tf
# Create a description of the features.
_FEATURE_DESCRIPTION = {
'position': tf.io.VarLenFeature(tf.string),
}
_FEATURE_DESCRIPTION_WITH_GLOBAL_CONTEXT = _FEATURE_DESCRIPTION.copy()
_FEATURE_DESCRIPTION_WITH_GLOBAL_CONTEXT['step_context'] = tf.io.VarLenFeature(
tf.string)
_FEATURE_DTYPES = {
'position': {
'in': np.float32,
'out': tf.float32
},
'step_context': {
'in': np.float32,
'out': tf.float32
}
}
_CONTEXT_FEATURES = {
'key': tf.io.FixedLenFeature([], tf.int64, default_value=0),
'particle_type': tf.io.VarLenFeature(tf.string)
}
def convert_to_tensor(x, encoded_dtype):
if len(x) == 1:
out = np.frombuffer(x[0].numpy(), dtype=encoded_dtype)
else:
out = []
for el in x:
out.append(np.frombuffer(el.numpy(), dtype=encoded_dtype))
out = tf.convert_to_tensor(np.array(out))
return out
def parse_serialized_simulation_example(example_proto, metadata):
"""Parses a serialized simulation tf.SequenceExample.
Args:
example_proto: A string encoding of the tf.SequenceExample proto.
metadata: A dict of metadata for the dataset.
Returns:
context: A dict, with features that do not vary over the trajectory.
parsed_features: A dict of tf.Tensors representing the parsed examples
across time, where axis zero is the time axis.
"""
if 'context_mean' in metadata:
feature_description = _FEATURE_DESCRIPTION_WITH_GLOBAL_CONTEXT
else:
feature_description = _FEATURE_DESCRIPTION
context, parsed_features = tf.io.parse_single_sequence_example(
example_proto,
context_features=_CONTEXT_FEATURES,
sequence_features=feature_description)
for feature_key, item in parsed_features.items():
convert_fn = functools.partial(
convert_to_tensor, encoded_dtype=_FEATURE_DTYPES[feature_key]['in'])
parsed_features[feature_key] = tf.py_function(
convert_fn, inp=[item.values], Tout=_FEATURE_DTYPES[feature_key]['out'])
# There is an extra frame at the beginning so we can calculate pos change
# for all frames used in the paper.
position_shape = [metadata['sequence_length'] + 1, -1, metadata['dim']]
# Reshape positions to correct dim:
parsed_features['position'] = tf.reshape(parsed_features['position'],
position_shape)
# Set correct shapes of the remaining tensors.
sequence_length = metadata['sequence_length'] + 1
if 'context_mean' in metadata:
context_feat_len = len(metadata['context_mean'])
parsed_features['step_context'] = tf.reshape(
parsed_features['step_context'],
[sequence_length, context_feat_len])
# Decode particle type explicitly
context['particle_type'] = tf.py_function(
functools.partial(convert_fn, encoded_dtype=np.int64),
inp=[context['particle_type'].values],
Tout=[tf.int64])
context['particle_type'] = tf.reshape(context['particle_type'], [-1])
return context, parsed_features
def split_trajectory(context, features, window_length=7):
"""Splits trajectory into sliding windows."""
# Our strategy is to make sure all the leading dimensions are the same size,
# then we can use from_tensor_slices.
trajectory_length = features['position'].get_shape().as_list()[0]
# We then stack window_length position changes so the final
# trajectory length will be - window_length +1 (the 1 to make sure we get
# the last split).
input_trajectory_length = trajectory_length - window_length + 1
model_input_features = {}
# Prepare the context features per step.
model_input_features['particle_type'] = tf.tile(
tf.expand_dims(context['particle_type'], axis=0),
[input_trajectory_length, 1])
if 'step_context' in features:
global_stack = []
for idx in range(input_trajectory_length):
global_stack.append(features['step_context'][idx:idx + window_length])
model_input_features['step_context'] = tf.stack(global_stack)
pos_stack = []
for idx in range(input_trajectory_length):
pos_stack.append(features['position'][idx:idx + window_length])
# Get the corresponding positions
model_input_features['position'] = tf.stack(pos_stack)
return tf.data.Dataset.from_tensor_slices(model_input_features)
================================================
FILE: learning_to_simulate/render_rollout.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Simple matplotlib rendering of a rollout prediction against ground truth.
Usage (from parent directory):
`python -m learning_to_simulate.render_rollout --rollout_path={OUTPUT_PATH}/rollout_test_1.pkl`
Where {OUTPUT_PATH} is the output path passed to `train.py` in "eval_rollout"
mode.
It may require installing Tkinter with `sudo apt-get install python3.7-tk`.
""" # pylint: disable=line-too-long
import pickle
from absl import app
from absl import flags
from matplotlib import animation
import matplotlib.pyplot as plt
import numpy as np
flags.DEFINE_string("rollout_path", None, help="Path to rollout pickle file")
flags.DEFINE_integer("step_stride", 3, help="Stride of steps to skip.")
flags.DEFINE_boolean("block_on_show", True, help="For test purposes.")
FLAGS = flags.FLAGS
TYPE_TO_COLOR = {
3: "black", # Boundary particles.
0: "green", # Rigid solids.
7: "magenta", # Goop.
6: "gold", # Sand.
5: "blue", # Water.
}
def main(unused_argv):
if not FLAGS.rollout_path:
raise ValueError("A `rollout_path` must be passed.")
with open(FLAGS.rollout_path, "rb") as file:
rollout_data = pickle.load(file)
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
plot_info = []
for ax_i, (label, rollout_field) in enumerate(
[("Ground truth", "ground_truth_rollout"),
("Prediction", "predicted_rollout")]):
# Append the initial positions to get the full trajectory.
trajectory = np.concatenate([
rollout_data["initial_positions"],
rollout_data[rollout_field]], axis=0)
ax = axes[ax_i]
ax.set_title(label)
bounds = rollout_data["metadata"]["bounds"]
ax.set_xlim(bounds[0][0], bounds[0][1])
ax.set_ylim(bounds[1][0], bounds[1][1])
ax.set_xticks([])
ax.set_yticks([])
ax.set_aspect(1.)
points = {
particle_type: ax.plot([], [], "o", ms=2, color=color)[0]
for particle_type, color in TYPE_TO_COLOR.items()}
plot_info.append((ax, trajectory, points))
num_steps = trajectory.shape[0]
def update(step_i):
outputs = []
for _, trajectory, points in plot_info:
for particle_type, line in points.items():
mask = rollout_data["particle_types"] == particle_type
line.set_data(trajectory[step_i, mask, 0],
trajectory[step_i, mask, 1])
outputs.append(line)
return outputs
unused_animation = animation.FuncAnimation(
fig, update,
frames=np.arange(0, num_steps, FLAGS.step_stride), interval=10)
plt.show(block=FLAGS.block_on_show)
if __name__ == "__main__":
app.run(main)
================================================
FILE: learning_to_simulate/requirements.txt
================================================
absl-py
graph-nets>=1.1
tensorflow>=1.15,<2
numpy
dm-sonnet<2
tensorflow_probability<0.9
sklearn
dm-tree
matplotlib
================================================
FILE: learning_to_simulate/run.sh
================================================
#!/bin/bash
# Copyright 2020 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Fail on any error.
set -e
# Display commands being run.
set -x
TMP_DIR=`mktemp -d`
virtualenv --python=python3.6 "${TMP_DIR}/learning_to_simulate"
source "${TMP_DIR}/learning_to_simulate/bin/activate"
# Install dependencies.
pip install --upgrade -r learning_to_simulate/requirements.txt
# Run the simple demo with dummy inputs.
python -m learning_to_simulate.model_demo
# Run some training and evaluation in one of the dataset samples.
# Download a sample of a dataset.
DATASET_NAME="WaterDropSample"
bash ./learning_to_simulate/download_dataset.sh ${DATASET_NAME} "${TMP_DIR}/datasets"
# Train for a few steps.
DATA_PATH="${TMP_DIR}/datasets/${DATASET_NAME}"
MODEL_PATH="${TMP_DIR}/models/${DATASET_NAME}"
python -m learning_to_simulate.train --data_path=${DATA_PATH} --model_path=${MODEL_PATH} --num_steps=10
# Evaluate on validation split.
python -m learning_to_simulate.train --data_path=${DATA_PATH} --model_path=${MODEL_PATH} --mode="eval" --eval_split="valid"
# Generate test rollouts.
ROLLOUT_PATH="${TMP_DIR}/rollouts/${DATASET_NAME}"
mkdir -p ${ROLLOUT_PATH}
python -m learning_to_simulate.train --data_path=${DATA_PATH} --model_path=${MODEL_PATH} --mode="eval_rollout" --output_path=${ROLLOUT_PATH}
# Plot the first rollout.
python -m learning_to_simulate.render_rollout --rollout_path="${ROLLOUT_PATH}/rollout_test_0.pkl" --block_on_show=False
# Clean up.
rm -r ${TMP_DIR}
================================================
FILE: learning_to_simulate/train.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
# pylint: disable=line-too-long
"""Training script for https://arxiv.org/pdf/2002.09405.pdf.
Example usage (from parent directory):
`python -m learning_to_simulate.train --data_path={DATA_PATH} --model_path={MODEL_PATH}`
Evaluate model from checkpoint (from parent directory):
`python -m learning_to_simulate.train --data_path={DATA_PATH} --model_path={MODEL_PATH} --mode=eval`
Produce rollouts (from parent directory):
`python -m learning_to_simulate.train --data_path={DATA_PATH} --model_path={MODEL_PATH} --output_path={OUTPUT_PATH} --mode=eval_rollout`
"""
# pylint: enable=line-too-long
import collections
import functools
import json
import os
import pickle
from absl import app
from absl import flags
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import estimator as tf_estimator
import tree
from learning_to_simulate import learned_simulator
from learning_to_simulate import noise_utils
from learning_to_simulate import reading_utils
flags.DEFINE_enum(
'mode', 'train', ['train', 'eval', 'eval_rollout'],
help='Train model, one step evaluation or rollout evaluation.')
flags.DEFINE_enum('eval_split', 'test', ['train', 'valid', 'test'],
help='Split to use when running evaluation.')
flags.DEFINE_string('data_path', None, help='The dataset directory.')
flags.DEFINE_integer('batch_size', 2, help='The batch size.')
flags.DEFINE_integer('num_steps', int(2e7), help='Number of steps of training.')
flags.DEFINE_float('noise_std', 6.7e-4, help='The std deviation of the noise.')
flags.DEFINE_string('model_path', None,
help=('The path for saving checkpoints of the model. '
'Defaults to a temporary directory.'))
flags.DEFINE_string('output_path', None,
help='The path for saving outputs (e.g. rollouts).')
FLAGS = flags.FLAGS
Stats = collections.namedtuple('Stats', ['mean', 'std'])
INPUT_SEQUENCE_LENGTH = 6 # So we can calculate the last 5 velocities.
NUM_PARTICLE_TYPES = 9
KINEMATIC_PARTICLE_ID = 3
def get_kinematic_mask(particle_types):
"""Returns a boolean mask, set to true for kinematic (obstacle) particles."""
return tf.equal(particle_types, KINEMATIC_PARTICLE_ID)
def prepare_inputs(tensor_dict):
"""Prepares a single stack of inputs by calculating inputs and targets.
Computes n_particles_per_example, which is a tensor that contains information
about how to partition the axis - i.e. which nodes belong to which graph.
Adds a batch axis to `n_particles_per_example` and `step_context` so they can
later be batched using `batch_concat`. This batch will be the same as if the
elements had been batched via stacking.
Note that all other tensors have a variable size particle axis,
and in this case they will simply be concatenated along that
axis.
Args:
tensor_dict: A dict of tensors containing positions, and step context (
if available).
Returns:
A tuple of input features and target positions.
"""
# Position is encoded as [sequence_length, num_particles, dim] but the model
# expects [num_particles, sequence_length, dim].
pos = tensor_dict['position']
pos = tf.transpose(pos, perm=[1, 0, 2])
# The target position is the final step of the stack of positions.
target_position = pos[:, -1]
# Remove the target from the input.
tensor_dict['position'] = pos[:, :-1]
# Compute the number of particles per example.
num_particles = tf.shape(pos)[0]
# Add an extra dimension for stacking via concat.
tensor_dict['n_particles_per_example'] = num_particles[tf.newaxis]
if 'step_context' in tensor_dict:
# Take the input global context. We have a stack of global contexts,
# and we take the penultimate since the final is the target.
tensor_dict['step_context'] = tensor_dict['step_context'][-2]
# Add an extra dimension for stacking via concat.
tensor_dict['step_context'] = tensor_dict['step_context'][tf.newaxis]
return tensor_dict, target_position
def prepare_rollout_inputs(context, features):
"""Prepares an inputs trajectory for rollout."""
out_dict = {**context}
# Position is encoded as [sequence_length, num_particles, dim] but the model
# expects [num_particles, sequence_length, dim].
pos = tf.transpose(features['position'], [1, 0, 2])
# The target position is the final step of the stack of positions.
target_position = pos[:, -1]
# Remove the target from the input.
out_dict['position'] = pos[:, :-1]
# Compute the number of nodes
out_dict['n_particles_per_example'] = [tf.shape(pos)[0]]
if 'step_context' in features:
out_dict['step_context'] = features['step_context']
out_dict['is_trajectory'] = tf.constant([True], tf.bool)
return out_dict, target_position
def batch_concat(dataset, batch_size):
"""We implement batching as concatenating on the leading axis."""
# We create a dataset of datasets of length batch_size.
windowed_ds = dataset.window(batch_size)
# The plan is then to reduce every nested dataset by concatenating. We can
# do this using tf.data.Dataset.reduce. This requires an initial state, and
# then incrementally reduces by running through the dataset
# Get initial state. In this case this will be empty tensors of the
# correct shape.
initial_state = tree.map_structure(
lambda spec: tf.zeros( # pylint: disable=g-long-lambda
shape=[0] + spec.shape.as_list()[1:], dtype=spec.dtype),
dataset.element_spec)
# We run through the nest and concatenate each entry with the previous state.
def reduce_window(initial_state, ds):
return ds.reduce(initial_state, lambda x, y: tf.concat([x, y], axis=0))
return windowed_ds.map(
lambda *x: tree.map_structure(reduce_window, initial_state, x))
def get_input_fn(data_path, batch_size, mode, split):
"""Gets the learning simulation input function for tf.estimator.Estimator.
Args:
data_path: the path to the dataset directory.
batch_size: the number of graphs in a batch.
mode: either 'one_step_train', 'one_step' or 'rollout'
split: either 'train', 'valid' or 'test.
Returns:
The input function for the learning simulation model.
"""
def input_fn():
"""Input function for learning simulation."""
# Loads the metadata of the dataset.
metadata = _read_metadata(data_path)
# Create a tf.data.Dataset from the TFRecord.
ds = tf.data.TFRecordDataset([os.path.join(data_path, f'{split}.tfrecord')])
ds = ds.map(functools.partial(
reading_utils.parse_serialized_simulation_example, metadata=metadata))
if mode.startswith('one_step'):
# Splits an entire trajectory into chunks of 7 steps.
# Previous 5 velocities, current velocity and target.
split_with_window = functools.partial(
reading_utils.split_trajectory,
window_length=INPUT_SEQUENCE_LENGTH + 1)
ds = ds.flat_map(split_with_window)
# Splits a chunk into input steps and target steps
ds = ds.map(prepare_inputs)
# If in train mode, repeat dataset forever and shuffle.
if mode == 'one_step_train':
ds = ds.repeat()
ds = ds.shuffle(512)
# Custom batching on the leading axis.
ds = batch_concat(ds, batch_size)
elif mode == 'rollout':
# Rollout evaluation only available for batch size 1
assert batch_size == 1
ds = ds.map(prepare_rollout_inputs)
else:
raise ValueError(f'mode: {mode} not recognized')
return ds
return input_fn
def rollout(simulator, features, num_steps):
"""Rolls out a trajectory by applying the model in sequence."""
initial_positions = features['position'][:, 0:INPUT_SEQUENCE_LENGTH]
ground_truth_positions = features['position'][:, INPUT_SEQUENCE_LENGTH:]
global_context = features.get('step_context')
def step_fn(step, current_positions, predictions):
if global_context is None:
global_context_step = None
else:
global_context_step = global_context[
step + INPUT_SEQUENCE_LENGTH - 1][tf.newaxis]
next_position = simulator(
current_positions,
n_particles_per_example=features['n_particles_per_example'],
particle_types=features['particle_type'],
global_context=global_context_step)
# Update kinematic particles from prescribed trajectory.
kinematic_mask = get_kinematic_mask(features['particle_type'])
next_position_ground_truth = ground_truth_positions[:, step]
next_position = tf.where(kinematic_mask, next_position_ground_truth,
next_position)
updated_predictions = predictions.write(step, next_position)
# Shift `current_positions`, removing the oldest position in the sequence
# and appending the next position at the end.
next_positions = tf.concat([current_positions[:, 1:],
next_position[:, tf.newaxis]], axis=1)
return (step + 1, next_positions, updated_predictions)
predictions = tf.TensorArray(size=num_steps, dtype=tf.float32)
_, _, predictions = tf.while_loop(
cond=lambda step, state, prediction: tf.less(step, num_steps),
body=step_fn,
loop_vars=(0, initial_positions, predictions),
back_prop=False,
parallel_iterations=1)
output_dict = {
'initial_positions': tf.transpose(initial_positions, [1, 0, 2]),
'predicted_rollout': predictions.stack(),
'ground_truth_rollout': tf.transpose(ground_truth_positions, [1, 0, 2]),
'particle_types': features['particle_type'],
}
if global_context is not None:
output_dict['global_context'] = global_context
return output_dict
def _combine_std(std_x, std_y):
return np.sqrt(std_x**2 + std_y**2)
def _get_simulator(model_kwargs, metadata, acc_noise_std, vel_noise_std):
"""Instantiates the simulator."""
# Cast statistics to numpy so they are arrays when entering the model.
cast = lambda v: np.array(v, dtype=np.float32)
acceleration_stats = Stats(
cast(metadata['acc_mean']),
_combine_std(cast(metadata['acc_std']), acc_noise_std))
velocity_stats = Stats(
cast(metadata['vel_mean']),
_combine_std(cast(metadata['vel_std']), vel_noise_std))
normalization_stats = {'acceleration': acceleration_stats,
'velocity': velocity_stats}
if 'context_mean' in metadata:
context_stats = Stats(
cast(metadata['context_mean']), cast(metadata['context_std']))
normalization_stats['context'] = context_stats
simulator = learned_simulator.LearnedSimulator(
num_dimensions=metadata['dim'],
connectivity_radius=metadata['default_connectivity_radius'],
graph_network_kwargs=model_kwargs,
boundaries=metadata['bounds'],
num_particle_types=NUM_PARTICLE_TYPES,
normalization_stats=normalization_stats,
particle_type_embedding_size=16)
return simulator
def get_one_step_estimator_fn(data_path,
noise_std,
latent_size=128,
hidden_size=128,
hidden_layers=2,
message_passing_steps=10):
"""Gets one step model for training simulation."""
metadata = _read_metadata(data_path)
model_kwargs = dict(
latent_size=latent_size,
mlp_hidden_size=hidden_size,
mlp_num_hidden_layers=hidden_layers,
num_message_passing_steps=message_passing_steps)
def estimator_fn(features, labels, mode):
target_next_position = labels
simulator = _get_simulator(model_kwargs, metadata,
vel_noise_std=noise_std,
acc_noise_std=noise_std)
# Sample the noise to add to the inputs to the model during training.
sampled_noise = noise_utils.get_random_walk_noise_for_position_sequence(
features['position'], noise_std_last_step=noise_std)
non_kinematic_mask = tf.logical_not(
get_kinematic_mask(features['particle_type']))
noise_mask = tf.cast(
non_kinematic_mask, sampled_noise.dtype)[:, tf.newaxis, tf.newaxis]
sampled_noise *= noise_mask
# Get the predictions and target accelerations.
pred_target = simulator.get_predicted_and_target_normalized_accelerations(
next_position=target_next_position,
position_sequence=features['position'],
position_sequence_noise=sampled_noise,
n_particles_per_example=features['n_particles_per_example'],
particle_types=features['particle_type'],
global_context=features.get('step_context'))
pred_acceleration, target_acceleration = pred_target
# Calculate the loss and mask out loss on kinematic particles/
loss = (pred_acceleration - target_acceleration)**2
num_non_kinematic = tf.reduce_sum(
tf.cast(non_kinematic_mask, tf.float32))
loss = tf.where(non_kinematic_mask, loss, tf.zeros_like(loss))
loss = tf.reduce_sum(loss) / tf.reduce_sum(num_non_kinematic)
global_step = tf.train.get_global_step()
# Set learning rate to decay from 1e-4 to 1e-6 exponentially.
min_lr = 1e-6
lr = tf.train.exponential_decay(learning_rate=1e-4 - min_lr,
global_step=global_step,
decay_steps=int(5e6),
decay_rate=0.1) + min_lr
opt = tf.train.AdamOptimizer(learning_rate=lr)
train_op = opt.minimize(loss, global_step)
# Calculate next position and add some additional eval metrics (only eval).
predicted_next_position = simulator(
position_sequence=features['position'],
n_particles_per_example=features['n_particles_per_example'],
particle_types=features['particle_type'],
global_context=features.get('step_context'))
predictions = {'predicted_next_position': predicted_next_position}
eval_metrics_ops = {
'loss_mse': tf.metrics.mean_squared_error(
pred_acceleration, target_acceleration),
'one_step_position_mse': tf.metrics.mean_squared_error(
predicted_next_position, target_next_position)
}
return tf_estimator.EstimatorSpec(
mode=mode,
train_op=train_op,
loss=loss,
predictions=predictions,
eval_metric_ops=eval_metrics_ops)
return estimator_fn
def get_rollout_estimator_fn(data_path,
noise_std,
latent_size=128,
hidden_size=128,
hidden_layers=2,
message_passing_steps=10):
"""Gets the model function for tf.estimator.Estimator."""
metadata = _read_metadata(data_path)
model_kwargs = dict(
latent_size=latent_size,
mlp_hidden_size=hidden_size,
mlp_num_hidden_layers=hidden_layers,
num_message_passing_steps=message_passing_steps)
def estimator_fn(features, labels, mode):
del labels # Labels to conform to estimator spec.
simulator = _get_simulator(model_kwargs, metadata,
acc_noise_std=noise_std,
vel_noise_std=noise_std)
num_steps = metadata['sequence_length'] - INPUT_SEQUENCE_LENGTH
rollout_op = rollout(simulator, features, num_steps=num_steps)
squared_error = (rollout_op['predicted_rollout'] -
rollout_op['ground_truth_rollout']) ** 2
loss = tf.reduce_mean(squared_error)
eval_ops = {'rollout_error_mse': tf.metrics.mean_squared_error(
rollout_op['predicted_rollout'], rollout_op['ground_truth_rollout'])}
# Add a leading axis, since Estimator's predict method insists that all
# tensors have a shared leading batch axis fo the same dims.
rollout_op = tree.map_structure(lambda x: x[tf.newaxis], rollout_op)
return tf_estimator.EstimatorSpec(
mode=mode,
train_op=None,
loss=loss,
predictions=rollout_op,
eval_metric_ops=eval_ops)
return estimator_fn
def _read_metadata(data_path):
with open(os.path.join(data_path, 'metadata.json'), 'rt') as fp:
return json.loads(fp.read())
def main(_):
"""Train or evaluates the model."""
if FLAGS.mode in ['train', 'eval']:
estimator = tf_estimator.Estimator(
get_one_step_estimator_fn(FLAGS.data_path, FLAGS.noise_std),
model_dir=FLAGS.model_path)
if FLAGS.mode == 'train':
# Train all the way through.
estimator.train(
input_fn=get_input_fn(FLAGS.data_path, FLAGS.batch_size,
mode='one_step_train', split='train'),
max_steps=FLAGS.num_steps)
else:
# One-step evaluation from checkpoint.
eval_metrics = estimator.evaluate(input_fn=get_input_fn(
FLAGS.data_path, FLAGS.batch_size,
mode='one_step', split=FLAGS.eval_split))
logging.info('Evaluation metrics:')
logging.info(eval_metrics)
elif FLAGS.mode == 'eval_rollout':
if not FLAGS.output_path:
raise ValueError('A rollout path must be provided.')
rollout_estimator = tf_estimator.Estimator(
get_rollout_estimator_fn(FLAGS.data_path, FLAGS.noise_std),
model_dir=FLAGS.model_path)
# Iterate through rollouts saving them one by one.
metadata = _read_metadata(FLAGS.data_path)
rollout_iterator = rollout_estimator.predict(
input_fn=get_input_fn(FLAGS.data_path, batch_size=1,
mode='rollout', split=FLAGS.eval_split))
for example_index, example_rollout in enumerate(rollout_iterator):
example_rollout['metadata'] = metadata
filename = f'rollout_{FLAGS.eval_split}_{example_index}.pkl'
filename = os.path.join(FLAGS.output_path, filename)
logging.info('Saving: %s.', filename)
if not os.path.exists(FLAGS.output_path):
os.mkdir(FLAGS.output_path)
with open(filename, 'wb') as file:
pickle.dump(example_rollout, file)
if __name__ == '__main__':
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: memo/README.md
================================================
# MEMO: A Deep Network For Flexible Combination Of Episodic Memories.
This package contains a [Colaboratory notebook](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/memo/load_memo_data.ipynb)
that loads a version of the dataset for the Paired associative
inference task (length 3 and 4) presented in the ICLR 2020 submission (also on
[arXiv](https://arxiv.org/abs/2001.10913)).
If you use the dataset, please cite:
```
@inproceedings{
banino2020memo:,
title={MEMO: A Deep Network for Flexible Combination of Episodic Memories},
author={Andrea Banino and Adrià Puigdomènech Badia and
Raphael Köster and Martin J. Chadwick and Vinicius Zambaldi and
Demis Hassabis and Caswell Barry and Matthew Botvinick and
Dharshan Kumaran and Charles Blundell},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=rJxlc0EtDr}
}
```
================================================
FILE: memo/load_memo_data.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "mP9QIqyCf6G4"
},
"source": [
"Copyright 2020 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
"\n",
"https://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ng3jUYyQgSjB"
},
"source": [
"# The dataset used for the Paired associate inference task\n",
"\n",
"This is the dataset used for the paired associated inference task in\n",
"[\"MEMO: A Deep Network for Flexible Combination of Episodic Memories\n",
"\"](https://arxiv.org/abs/2001.10913)."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "2Nd8cdyccWld"
},
"outputs": [],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"import numpy as np\n",
"\n",
"import tensorflow as tf\n",
"import collections\n",
"import os\n",
"\n",
"from google.colab import auth\n",
"auth.authenticate_user()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "GP0u6GCUF_6R"
},
"outputs": [],
"source": [
"#@title Choices about the dataset you want to load.\n",
"# Make choices about the dataset here.\n",
"chain_length = 3 #@param {type:\"slider\", min:3, max:4, step:1}\n",
"mode = 'valid' #@param ['train', 'test', 'valid']"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "JUWNNIwziHyC"
},
"source": [
"**If you choose chain_length 3 the data will look like this:**\n",
"\n",
"* trials shape: (48, 3, 1000); 48 trials x the target picture, left and right option x picture dimensions.\n",
"* correct answer: (48); whether the left or right picture is correct.\n",
"* difficulty (48); How far apart are the target picture and the two options.(e.g. AB are 0 steps apart, AC is 1)\n",
"* trial type (48); See below.\n",
"* memory shape (32, 2, 1000); Content of memory store, 32 pairs of images.\n",
"\n",
"Trial types:\n",
"* 1: AB\n",
"* 2: BC\n",
"* 3: AC\n",
"\n",
"\n",
"**If you choose chain_length 4 the data will look like this:**\n",
"* trials: (96, 3, 1000)\n",
"* correct answer: (96)\n",
"* difficulty: (96)\n",
"* trial type: (96)\n",
"* memory shape: (48, 2, 1000)\n",
"\n",
"Trial types:\n",
"* 1: AB\n",
"* 2: BC\n",
"* 3: AC\n",
"* 4: CD\n",
"* 5: BD\n",
"* 6: AD"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "0QETPFeEgr5d"
},
"outputs": [],
"source": [
"# Train has 500 shards, valid 150, test 100.\n",
"if mode == 'train':\n",
" num_shards = 500\n",
"elif mode == 'test':\n",
" num_shards = 100\n",
"elif mode == 'valid':\n",
" num_shards = 150"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "jE3_9k8DOMyZ"
},
"outputs": [],
"source": [
"DatasetInfo = collections.namedtuple(\n",
" 'DatasetInfo',\n",
" ['basepath', 'size', 'chain_length']\n",
")\n",
"\n",
"_DATASETS = dict(\n",
" memo=DatasetInfo(\n",
" basepath=mode,\n",
" size=num_shards,\n",
" chain_length=chain_length)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "N3D11lxl3kjF"
},
"outputs": [],
"source": [
"def _get_dataset_files(dataset_info, root):\n",
" \"\"\"Generates lists of files for a given dataset version.\"\"\"\n",
" basepath = dataset_info.basepath\n",
" base = os.path.join(root, basepath)\n",
" num_files = dataset_info.size\n",
" length = len(str(num_files))\n",
" template = 'trials-{:0%d}-of-{:0%d}' % (5, 5)\n",
" return [os.path.join(base, template.format(i, num_files))\n",
" for i in range(num_files)]"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "6yqLJYAnsyZF"
},
"outputs": [],
"source": [
"def parser_tf_examples(raw_data, chain_length=chain_length):\n",
" if chain_length == 3:\n",
" feature_map = {\n",
" 'trials' : tf.io.FixedLenFeature(\n",
" shape=[48, 3, 1000],\n",
" dtype=tf.float32),\n",
" 'correct_answer': tf.io.FixedLenFeature(\n",
" shape=[48],\n",
" dtype=tf.int64),\n",
" 'difficulty': tf.io.FixedLenFeature(\n",
" shape=[48],\n",
" dtype=tf.int64),\n",
" 'trial_type': tf.io.FixedLenFeature(\n",
" shape=[48],\n",
" dtype=tf.int64),\n",
" 'memory': tf.io.FixedLenFeature(\n",
" shape=[32, 2, 1000],\n",
" dtype=tf.float32),\n",
" }\n",
" elif chain_length == 4: \n",
" feature_map = {\n",
" 'trials' : tf.io.FixedLenFeature(\n",
" shape=[96, 3, 1000],\n",
" dtype=tf.float32),\n",
" 'correct_answer': tf.io.FixedLenFeature(\n",
" shape=[96],\n",
" dtype=tf.int64),\n",
" 'difficulty': tf.io.FixedLenFeature(\n",
" shape=[96],\n",
" dtype=tf.int64),\n",
" 'trial_type': tf.io.FixedLenFeature(\n",
" shape=[96],\n",
" dtype=tf.int64),\n",
" 'memory': tf.io.FixedLenFeature(\n",
" shape=[48, 2, 1000],\n",
" dtype=tf.float32),\n",
" }\n",
" example = tf.io.parse_example(raw_data, feature_map)\n",
" batch = [example[\"trials\"],\n",
" example[\"correct_answer\"],\n",
" example[\"difficulty\"],\n",
" example[\"trial_type\"],\n",
" example[\"memory\"]]\n",
" return batch"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "nXMhOoHWj0oP"
},
"source": [
"## Load the data."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "rXIOBlWKyMY0"
},
"outputs": [],
"source": [
"dataset_info = 'memo'\n",
"root = 'gs://deepmind-memo/length' + str(chain_length) + '/'\n",
"num_epochs = 100\n",
"shuffle_buffer_size = 150\n",
"num_readers = 4\n",
"dataset_info = _DATASETS['memo']\n",
"filenames = _get_dataset_files(dataset_info, root)\n",
"num_map_threads = 4\n",
"batch_size = 10"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "e2G5MSVf9Hpm"
},
"outputs": [],
"source": [
"data = tf.data.Dataset.from_tensor_slices(filenames)\n",
"data = data.repeat(num_epochs)\n",
"data = data.shuffle(shuffle_buffer_size)\n",
"data = data.interleave(tf.data.TFRecordDataset,\n",
" cycle_length=num_readers, block_length=1)\n",
"data = data.shuffle(shuffle_buffer_size)\n",
"data = data.map(parser_tf_examples, num_parallel_calls=num_map_threads)\n",
"data = data.batch(batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1z3dsDNqkBHD"
},
"source": [
"# Looking at what we loaded."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ibVadDeeAU4Q"
},
"outputs": [],
"source": [
"iterator = data.__iter__()\n",
"element = iterator.get_next()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Lh7-f08nAeGq"
},
"outputs": [],
"source": [
"print(element[0].shape) # trials\n",
"print(element[1].shape) # correct answer\n",
"print(element[2].shape) # difficulty\n",
"print(element[3].shape) # trialtype\n",
"print(element[4].shape) # memory"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "load_memo_data.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: meshgraphnets/README.md
================================================
# Learning Mesh-Based Simulation with Graph Networks (ICLR 2021)
Video site: [sites.google.com/view/meshgraphnets](https://sites.google.com/view/meshgraphnets)
Paper: [arxiv.org/abs/2010.03409](https://arxiv.org/abs/2010.03409)
If you use the code here please cite this paper:
@inproceedings{pfaff2021learning,
title={Learning Mesh-Based Simulation with Graph Networks},
author={Tobias Pfaff and
Meire Fortunato and
Alvaro Sanchez-Gonzalez and
Peter W. Battaglia},
booktitle={International Conference on Learning Representations},
year={2021}
}
## Overview
This release contains the full datasets used in the paper, as well as data
loaders (dataset.py), and the learned model core (core_model.py).
These components are designed to work with all of our domains.
We also include demonstration code for a full training and evaluation pipeline,
for the `cylinder_flow` and `flag_simple` domains only. This
includes graph encoding, evaluation, rollout and plotting trajectory.
Refer to the respective `cfd_*` and `cloth_*` files for details.
## Setup
Prepare environment, install dependencies:
virtualenv --python=python3.6 "${ENV}"
${ENV}/bin/activate
pip install -r meshgraphnets/requirements.txt
Download a dataset:
mkdir -p ${DATA}
bash meshgraphnets/download_dataset.sh flag_simple ${DATA}
## Running the model
Train a model:
python -m meshgraphnets.run_model --mode=train --model=cloth \
--checkpoint_dir=${DATA}/chk --dataset_dir=${DATA}/flag_simple
Generate some trajectory rollouts:
python -m meshgraphnets.run_model --mode=eval --model=cloth \
--checkpoint_dir=${DATA}/chk --dataset_dir=${DATA}/flag_simple \
--rollout_path=${DATA}/rollout_flag.pkl
Plot a trajectory:
python -m meshgraphnets.plot_cloth --rollout_path=${DATA}/rollout_flag.pkl
The instructions above train a model for the `flag_simple` domain; for
the `cylinder_flow` dataset, use `--model=cfd` and the `plot_cfd` script.
## Datasets
Datasets can be downloaded using the script `download_dataset.sh`. They contain
a metadata file describing the available fields and their shape, and tfrecord
datasets for train, valid and test splits.
Dataset names match the naming in the paper.
The following datasets are available:
airfoil
cylinder_flow
deforming_plate
flag_minimal
flag_simple
flag_dynamic
flag_dynamic_sizing
sphere_simple
sphere_dynamic
sphere_dynamic_sizing
`flag_minimal` is a truncated version of flag_simple, and is only used for
integration tests. `flag_dynamic_sizing` and `sphere_dynamic_sizing` can be
used to learn the sizing field. These datasets have the same structure as
the other datasets, but contain the meshes in their state before remeshing,
and define a matching `sizing_field` target for each mesh.
================================================
FILE: meshgraphnets/cfd_eval.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Functions to build evaluation metrics for CFD data."""
import tensorflow.compat.v1 as tf
from meshgraphnets.common import NodeType
def _rollout(model, initial_state, num_steps):
"""Rolls out a model trajectory."""
node_type = initial_state['node_type'][:, 0]
mask = tf.logical_or(tf.equal(node_type, NodeType.NORMAL),
tf.equal(node_type, NodeType.OUTFLOW))
def step_fn(step, velocity, trajectory):
prediction = model({**initial_state,
'velocity': velocity})
# don't update boundary nodes
next_velocity = tf.where(mask, prediction, velocity)
trajectory = trajectory.write(step, velocity)
return step+1, next_velocity, trajectory
_, _, output = tf.while_loop(
cond=lambda step, cur, traj: tf.less(step, num_steps),
body=step_fn,
loop_vars=(0, initial_state['velocity'],
tf.TensorArray(tf.float32, num_steps)),
parallel_iterations=1)
return output.stack()
def evaluate(model, inputs):
"""Performs model rollouts and create stats."""
initial_state = {k: v[0] for k, v in inputs.items()}
num_steps = inputs['cells'].shape[0]
prediction = _rollout(model, initial_state, num_steps)
error = tf.reduce_mean((prediction - inputs['velocity'])**2, axis=-1)
scalars = {'mse_%d_steps' % horizon: tf.reduce_mean(error[1:horizon+1])
for horizon in [1, 10, 20, 50, 100, 200]}
traj_ops = {
'faces': inputs['cells'],
'mesh_pos': inputs['mesh_pos'],
'gt_velocity': inputs['velocity'],
'pred_velocity': prediction
}
return scalars, traj_ops
================================================
FILE: meshgraphnets/cfd_model.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Model for CylinderFlow."""
import sonnet as snt
import tensorflow.compat.v1 as tf
from meshgraphnets import common
from meshgraphnets import core_model
from meshgraphnets import normalization
class Model(snt.AbstractModule):
"""Model for fluid simulation."""
def __init__(self, learned_model, name='Model'):
super(Model, self).__init__(name=name)
with self._enter_variable_scope():
self._learned_model = learned_model
self._output_normalizer = normalization.Normalizer(
size=2, name='output_normalizer')
self._node_normalizer = normalization.Normalizer(
size=2+common.NodeType.SIZE, name='node_normalizer')
self._edge_normalizer = normalization.Normalizer(
size=3, name='edge_normalizer') # 2D coord + length
def _build_graph(self, inputs, is_training):
"""Builds input graph."""
# construct graph nodes
node_type = tf.one_hot(inputs['node_type'][:, 0], common.NodeType.SIZE)
node_features = tf.concat([inputs['velocity'], node_type], axis=-1)
# construct graph edges
senders, receivers = common.triangles_to_edges(inputs['cells'])
relative_mesh_pos = (tf.gather(inputs['mesh_pos'], senders) -
tf.gather(inputs['mesh_pos'], receivers))
edge_features = tf.concat([
relative_mesh_pos,
tf.norm(relative_mesh_pos, axis=-1, keepdims=True)], axis=-1)
mesh_edges = core_model.EdgeSet(
name='mesh_edges',
features=self._edge_normalizer(edge_features, is_training),
receivers=receivers,
senders=senders)
return core_model.MultiGraph(
node_features=self._node_normalizer(node_features, is_training),
edge_sets=[mesh_edges])
def _build(self, inputs):
graph = self._build_graph(inputs, is_training=False)
per_node_network_output = self._learned_model(graph)
return self._update(inputs, per_node_network_output)
@snt.reuse_variables
def loss(self, inputs):
"""L2 loss on velocity."""
graph = self._build_graph(inputs, is_training=True)
network_output = self._learned_model(graph)
# build target velocity change
cur_velocity = inputs['velocity']
target_velocity = inputs['target|velocity']
target_velocity_change = target_velocity - cur_velocity
target_normalized = self._output_normalizer(target_velocity_change)
# build loss
node_type = inputs['node_type'][:, 0]
loss_mask = tf.logical_or(tf.equal(node_type, common.NodeType.NORMAL),
tf.equal(node_type, common.NodeType.OUTFLOW))
error = tf.reduce_sum((target_normalized - network_output)**2, axis=1)
loss = tf.reduce_mean(error[loss_mask])
return loss
def _update(self, inputs, per_node_network_output):
"""Integrate model outputs."""
velocity_update = self._output_normalizer.inverse(per_node_network_output)
# integrate forward
cur_velocity = inputs['velocity']
return cur_velocity + velocity_update
================================================
FILE: meshgraphnets/cloth_eval.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Functions to build evaluation metrics for cloth data."""
import tensorflow.compat.v1 as tf
from meshgraphnets.common import NodeType
def _rollout(model, initial_state, num_steps):
"""Rolls out a model trajectory."""
mask = tf.equal(initial_state['node_type'][:, 0], NodeType.NORMAL)
def step_fn(step, prev_pos, cur_pos, trajectory):
prediction = model({**initial_state,
'prev|world_pos': prev_pos,
'world_pos': cur_pos})
# don't update kinematic nodes
next_pos = tf.where(mask, prediction, cur_pos)
trajectory = trajectory.write(step, cur_pos)
return step+1, cur_pos, next_pos, trajectory
_, _, _, output = tf.while_loop(
cond=lambda step, last, cur, traj: tf.less(step, num_steps),
body=step_fn,
loop_vars=(0, initial_state['prev|world_pos'], initial_state['world_pos'],
tf.TensorArray(tf.float32, num_steps)),
parallel_iterations=1)
return output.stack()
def evaluate(model, inputs):
"""Performs model rollouts and create stats."""
initial_state = {k: v[0] for k, v in inputs.items()}
num_steps = inputs['cells'].shape[0]
prediction = _rollout(model, initial_state, num_steps)
error = tf.reduce_mean((prediction - inputs['world_pos'])**2, axis=-1)
scalars = {'mse_%d_steps' % horizon: tf.reduce_mean(error[1:horizon+1])
for horizon in [1, 10, 20, 50, 100, 200]}
traj_ops = {
'faces': inputs['cells'],
'mesh_pos': inputs['mesh_pos'],
'gt_pos': inputs['world_pos'],
'pred_pos': prediction
}
return scalars, traj_ops
================================================
FILE: meshgraphnets/cloth_model.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Model for FlagSimple."""
import sonnet as snt
import tensorflow.compat.v1 as tf
from meshgraphnets import common
from meshgraphnets import core_model
from meshgraphnets import normalization
class Model(snt.AbstractModule):
"""Model for static cloth simulation."""
def __init__(self, learned_model, name='Model'):
super(Model, self).__init__(name=name)
with self._enter_variable_scope():
self._learned_model = learned_model
self._output_normalizer = normalization.Normalizer(
size=3, name='output_normalizer')
self._node_normalizer = normalization.Normalizer(
size=3+common.NodeType.SIZE, name='node_normalizer')
self._edge_normalizer = normalization.Normalizer(
size=7, name='edge_normalizer') # 2D coord + 3D coord + 2*length = 7
def _build_graph(self, inputs, is_training):
"""Builds input graph."""
# construct graph nodes
velocity = inputs['world_pos'] - inputs['prev|world_pos']
node_type = tf.one_hot(inputs['node_type'][:, 0], common.NodeType.SIZE)
node_features = tf.concat([velocity, node_type], axis=-1)
# construct graph edges
senders, receivers = common.triangles_to_edges(inputs['cells'])
relative_world_pos = (tf.gather(inputs['world_pos'], senders) -
tf.gather(inputs['world_pos'], receivers))
relative_mesh_pos = (tf.gather(inputs['mesh_pos'], senders) -
tf.gather(inputs['mesh_pos'], receivers))
edge_features = tf.concat([
relative_world_pos,
tf.norm(relative_world_pos, axis=-1, keepdims=True),
relative_mesh_pos,
tf.norm(relative_mesh_pos, axis=-1, keepdims=True)], axis=-1)
mesh_edges = core_model.EdgeSet(
name='mesh_edges',
features=self._edge_normalizer(edge_features, is_training),
receivers=receivers,
senders=senders)
return core_model.MultiGraph(
node_features=self._node_normalizer(node_features, is_training),
edge_sets=[mesh_edges])
def _build(self, inputs):
graph = self._build_graph(inputs, is_training=False)
per_node_network_output = self._learned_model(graph)
return self._update(inputs, per_node_network_output)
@snt.reuse_variables
def loss(self, inputs):
"""L2 loss on position."""
graph = self._build_graph(inputs, is_training=True)
network_output = self._learned_model(graph)
# build target acceleration
cur_position = inputs['world_pos']
prev_position = inputs['prev|world_pos']
target_position = inputs['target|world_pos']
target_acceleration = target_position - 2*cur_position + prev_position
target_normalized = self._output_normalizer(target_acceleration)
# build loss
loss_mask = tf.equal(inputs['node_type'][:, 0], common.NodeType.NORMAL)
error = tf.reduce_sum((target_normalized - network_output)**2, axis=1)
loss = tf.reduce_mean(error[loss_mask])
return loss
def _update(self, inputs, per_node_network_output):
"""Integrate model outputs."""
acceleration = self._output_normalizer.inverse(per_node_network_output)
# integrate forward
cur_position = inputs['world_pos']
prev_position = inputs['prev|world_pos']
position = 2*cur_position + acceleration - prev_position
return position
================================================
FILE: meshgraphnets/common.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Commonly used data structures and functions."""
import enum
import tensorflow.compat.v1 as tf
class NodeType(enum.IntEnum):
NORMAL = 0
OBSTACLE = 1
AIRFOIL = 2
HANDLE = 3
INFLOW = 4
OUTFLOW = 5
WALL_BOUNDARY = 6
SIZE = 9
def triangles_to_edges(faces):
"""Computes mesh edges from triangles."""
# collect edges from triangles
edges = tf.concat([faces[:, 0:2],
faces[:, 1:3],
tf.stack([faces[:, 2], faces[:, 0]], axis=1)], axis=0)
# those edges are sometimes duplicated (within the mesh) and sometimes
# single (at the mesh boundary).
# sort & pack edges as single tf.int64
receivers = tf.reduce_min(edges, axis=1)
senders = tf.reduce_max(edges, axis=1)
packed_edges = tf.bitcast(tf.stack([senders, receivers], axis=1), tf.int64)
# remove duplicates and unpack
unique_edges = tf.bitcast(tf.unique(packed_edges)[0], tf.int32)
senders, receivers = tf.unstack(unique_edges, axis=1)
# create two-way connectivity
return (tf.concat([senders, receivers], axis=0),
tf.concat([receivers, senders], axis=0))
================================================
FILE: meshgraphnets/core_model.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Core learned graph net model."""
import collections
import functools
import sonnet as snt
import tensorflow.compat.v1 as tf
EdgeSet = collections.namedtuple('EdgeSet', ['name', 'features', 'senders',
'receivers'])
MultiGraph = collections.namedtuple('Graph', ['node_features', 'edge_sets'])
class GraphNetBlock(snt.AbstractModule):
"""Multi-Edge Interaction Network with residual connections."""
def __init__(self, model_fn, name='GraphNetBlock'):
super(GraphNetBlock, self).__init__(name=name)
self._model_fn = model_fn
def _update_edge_features(self, node_features, edge_set):
"""Aggregrates node features, and applies edge function."""
sender_features = tf.gather(node_features, edge_set.senders)
receiver_features = tf.gather(node_features, edge_set.receivers)
features = [sender_features, receiver_features, edge_set.features]
with tf.variable_scope(edge_set.name+'_edge_fn'):
return self._model_fn()(tf.concat(features, axis=-1))
def _update_node_features(self, node_features, edge_sets):
"""Aggregrates edge features, and applies node function."""
num_nodes = tf.shape(node_features)[0]
features = [node_features]
for edge_set in edge_sets:
features.append(tf.math.unsorted_segment_sum(edge_set.features,
edge_set.receivers,
num_nodes))
with tf.variable_scope('node_fn'):
return self._model_fn()(tf.concat(features, axis=-1))
def _build(self, graph):
"""Applies GraphNetBlock and returns updated MultiGraph."""
# apply edge functions
new_edge_sets = []
for edge_set in graph.edge_sets:
updated_features = self._update_edge_features(graph.node_features,
edge_set)
new_edge_sets.append(edge_set._replace(features=updated_features))
# apply node function
new_node_features = self._update_node_features(graph.node_features,
new_edge_sets)
# add residual connections
new_node_features += graph.node_features
new_edge_sets = [es._replace(features=es.features + old_es.features)
for es, old_es in zip(new_edge_sets, graph.edge_sets)]
return MultiGraph(new_node_features, new_edge_sets)
class EncodeProcessDecode(snt.AbstractModule):
"""Encode-Process-Decode GraphNet model."""
def __init__(self,
output_size,
latent_size,
num_layers,
message_passing_steps,
name='EncodeProcessDecode'):
super(EncodeProcessDecode, self).__init__(name=name)
self._latent_size = latent_size
self._output_size = output_size
self._num_layers = num_layers
self._message_passing_steps = message_passing_steps
def _make_mlp(self, output_size, layer_norm=True):
"""Builds an MLP."""
widths = [self._latent_size] * self._num_layers + [output_size]
network = snt.nets.MLP(widths, activate_final=False)
if layer_norm:
network = snt.Sequential([network, snt.LayerNorm()])
return network
def _encoder(self, graph):
"""Encodes node and edge features into latent features."""
with tf.variable_scope('encoder'):
node_latents = self._make_mlp(self._latent_size)(graph.node_features)
new_edges_sets = []
for edge_set in graph.edge_sets:
latent = self._make_mlp(self._latent_size)(edge_set.features)
new_edges_sets.append(edge_set._replace(features=latent))
return MultiGraph(node_latents, new_edges_sets)
def _decoder(self, graph):
"""Decodes node features from graph."""
with tf.variable_scope('decoder'):
decoder = self._make_mlp(self._output_size, layer_norm=False)
return decoder(graph.node_features)
def _build(self, graph):
"""Encodes and processes a multigraph, and returns node features."""
model_fn = functools.partial(self._make_mlp, output_size=self._latent_size)
latent_graph = self._encoder(graph)
for _ in range(self._message_passing_steps):
latent_graph = GraphNetBlock(model_fn)(latent_graph)
return self._decoder(latent_graph)
================================================
FILE: meshgraphnets/dataset.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Utility functions for reading the datasets."""
import functools
import json
import os
import tensorflow.compat.v1 as tf
from meshgraphnets.common import NodeType
def _parse(proto, meta):
"""Parses a trajectory from tf.Example."""
feature_lists = {k: tf.io.VarLenFeature(tf.string)
for k in meta['field_names']}
features = tf.io.parse_single_example(proto, feature_lists)
out = {}
for key, field in meta['features'].items():
data = tf.io.decode_raw(features[key].values, getattr(tf, field['dtype']))
data = tf.reshape(data, field['shape'])
if field['type'] == 'static':
data = tf.tile(data, [meta['trajectory_length'], 1, 1])
elif field['type'] == 'dynamic_varlen':
length = tf.io.decode_raw(features['length_'+key].values, tf.int32)
length = tf.reshape(length, [-1])
data = tf.RaggedTensor.from_row_lengths(data, row_lengths=length)
elif field['type'] != 'dynamic':
raise ValueError('invalid data format')
out[key] = data
return out
def load_dataset(path, split):
"""Load dataset."""
with open(os.path.join(path, 'meta.json'), 'r') as fp:
meta = json.loads(fp.read())
ds = tf.data.TFRecordDataset(os.path.join(path, split+'.tfrecord'))
ds = ds.map(functools.partial(_parse, meta=meta), num_parallel_calls=8)
ds = ds.prefetch(1)
return ds
def add_targets(ds, fields, add_history):
"""Adds target and optionally history fields to dataframe."""
def fn(trajectory):
out = {}
for key, val in trajectory.items():
out[key] = val[1:-1]
if key in fields:
if add_history:
out['prev|'+key] = val[0:-2]
out['target|'+key] = val[2:]
return out
return ds.map(fn, num_parallel_calls=8)
def split_and_preprocess(ds, noise_field, noise_scale, noise_gamma):
"""Splits trajectories into frames, and adds training noise."""
def add_noise(frame):
noise = tf.random.normal(tf.shape(frame[noise_field]),
stddev=noise_scale, dtype=tf.float32)
# don't apply noise to boundary nodes
mask = tf.equal(frame['node_type'], NodeType.NORMAL)[:, 0]
noise = tf.where(mask, noise, tf.zeros_like(noise))
frame[noise_field] += noise
frame['target|'+noise_field] += (1.0 - noise_gamma) * noise
return frame
ds = ds.flat_map(tf.data.Dataset.from_tensor_slices)
ds = ds.map(add_noise, num_parallel_calls=8)
ds = ds.shuffle(10000)
ds = ds.repeat(None)
return ds.prefetch(10)
def batch_dataset(ds, batch_size):
"""Batches input datasets."""
shapes = ds.output_shapes
types = ds.output_types
def renumber(buffer, frame):
nodes, cells = buffer
new_nodes, new_cells = frame
return nodes + new_nodes, tf.concat([cells, new_cells+nodes], axis=0)
def batch_accumulate(ds_window):
out = {}
for key, ds_val in ds_window.items():
initial = tf.zeros((0, shapes[key][1]), dtype=types[key])
if key == 'cells':
# renumber node indices in cells
num_nodes = ds_window['node_type'].map(lambda x: tf.shape(x)[0])
cells = tf.data.Dataset.zip((num_nodes, ds_val))
initial = (tf.constant(0, tf.int32), initial)
_, out[key] = cells.reduce(initial, renumber)
else:
merge = lambda prev, cur: tf.concat([prev, cur], axis=0)
out[key] = ds_val.reduce(initial, merge)
return out
if batch_size > 1:
ds = ds.window(batch_size, drop_remainder=True)
ds = ds.map(batch_accumulate, num_parallel_calls=8)
return ds
================================================
FILE: meshgraphnets/download_dataset.sh
================================================
#!/bin/bash
# Copyright 2020 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Usage:
# sh download_dataset.sh ${DATASET_NAME} ${OUTPUT_DIR}
# Example:
# sh download_dataset.sh flag_simple /tmp/
set -e
DATASET_NAME="${1}"
OUTPUT_DIR="${2}/${DATASET_NAME}"
BASE_URL="https://storage.googleapis.com/dm-meshgraphnets/${DATASET_NAME}/"
mkdir -p ${OUTPUT_DIR}
for file in meta.json train.tfrecord valid.tfrecord test.tfrecord
do
wget -O "${OUTPUT_DIR}/${file}" "${BASE_URL}${file}"
done
================================================
FILE: meshgraphnets/normalization.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Online data normalization."""
import sonnet as snt
import tensorflow.compat.v1 as tf
class Normalizer(snt.AbstractModule):
"""Feature normalizer that accumulates statistics online."""
def __init__(self, size, max_accumulations=10**6, std_epsilon=1e-8,
name='Normalizer'):
super(Normalizer, self).__init__(name=name)
self._max_accumulations = max_accumulations
self._std_epsilon = std_epsilon
with self._enter_variable_scope():
self._acc_count = tf.Variable(0, dtype=tf.float32, trainable=False)
self._num_accumulations = tf.Variable(0, dtype=tf.float32,
trainable=False)
self._acc_sum = tf.Variable(tf.zeros(size, tf.float32), trainable=False)
self._acc_sum_squared = tf.Variable(tf.zeros(size, tf.float32),
trainable=False)
def _build(self, batched_data, accumulate=True):
"""Normalizes input data and accumulates statistics."""
update_op = tf.no_op()
if accumulate:
# stop accumulating after a million updates, to prevent accuracy issues
update_op = tf.cond(self._num_accumulations < self._max_accumulations,
lambda: self._accumulate(batched_data),
tf.no_op)
with tf.control_dependencies([update_op]):
return (batched_data - self._mean()) / self._std_with_epsilon()
@snt.reuse_variables
def inverse(self, normalized_batch_data):
"""Inverse transformation of the normalizer."""
return normalized_batch_data * self._std_with_epsilon() + self._mean()
def _accumulate(self, batched_data):
"""Function to perform the accumulation of the batch_data statistics."""
count = tf.cast(tf.shape(batched_data)[0], tf.float32)
data_sum = tf.reduce_sum(batched_data, axis=0)
squared_data_sum = tf.reduce_sum(batched_data**2, axis=0)
return tf.group(
tf.assign_add(self._acc_sum, data_sum),
tf.assign_add(self._acc_sum_squared, squared_data_sum),
tf.assign_add(self._acc_count, count),
tf.assign_add(self._num_accumulations, 1.))
def _mean(self):
safe_count = tf.maximum(self._acc_count, 1.)
return self._acc_sum / safe_count
def _std_with_epsilon(self):
safe_count = tf.maximum(self._acc_count, 1.)
std = tf.sqrt(self._acc_sum_squared / safe_count - self._mean()**2)
return tf.math.maximum(std, self._std_epsilon)
================================================
FILE: meshgraphnets/plot_cfd.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Plots a CFD trajectory rollout."""
import pickle
from absl import app
from absl import flags
from matplotlib import animation
from matplotlib import tri as mtri
import matplotlib.pyplot as plt
FLAGS = flags.FLAGS
flags.DEFINE_string('rollout_path', None, 'Path to rollout pickle file')
def main(unused_argv):
with open(FLAGS.rollout_path, 'rb') as fp:
rollout_data = pickle.load(fp)
fig, ax = plt.subplots(1, 1, figsize=(12, 8))
skip = 10
num_steps = rollout_data[0]['gt_velocity'].shape[0]
num_frames = len(rollout_data) * num_steps // skip
# compute bounds
bounds = []
for trajectory in rollout_data:
bb_min = trajectory['gt_velocity'].min(axis=(0, 1))
bb_max = trajectory['gt_velocity'].max(axis=(0, 1))
bounds.append((bb_min, bb_max))
def animate(num):
step = (num*skip) % num_steps
traj = (num*skip) // num_steps
ax.cla()
ax.set_aspect('equal')
ax.set_axis_off()
vmin, vmax = bounds[traj]
pos = rollout_data[traj]['mesh_pos'][step]
faces = rollout_data[traj]['faces'][step]
velocity = rollout_data[traj]['pred_velocity'][step]
triang = mtri.Triangulation(pos[:, 0], pos[:, 1], faces)
ax.tripcolor(triang, velocity[:, 0], vmin=vmin[0], vmax=vmax[0])
ax.triplot(triang, 'ko-', ms=0.5, lw=0.3)
ax.set_title('Trajectory %d Step %d' % (traj, step))
return fig,
_ = animation.FuncAnimation(fig, animate, frames=num_frames, interval=100)
plt.show(block=True)
if __name__ == '__main__':
app.run(main)
================================================
FILE: meshgraphnets/plot_cloth.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Plots a cloth trajectory rollout."""
import pickle
from absl import app
from absl import flags
from matplotlib import animation
import matplotlib.pyplot as plt
FLAGS = flags.FLAGS
flags.DEFINE_string('rollout_path', None, 'Path to rollout pickle file')
def main(unused_argv):
with open(FLAGS.rollout_path, 'rb') as fp:
rollout_data = pickle.load(fp)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection='3d')
skip = 10
num_steps = rollout_data[0]['gt_pos'].shape[0]
num_frames = len(rollout_data) * num_steps // skip
# compute bounds
bounds = []
for trajectory in rollout_data:
bb_min = trajectory['gt_pos'].min(axis=(0, 1))
bb_max = trajectory['gt_pos'].max(axis=(0, 1))
bounds.append((bb_min, bb_max))
def animate(num):
step = (num*skip) % num_steps
traj = (num*skip) // num_steps
ax.cla()
bound = bounds[traj]
ax.set_xlim([bound[0][0], bound[1][0]])
ax.set_ylim([bound[0][1], bound[1][1]])
ax.set_zlim([bound[0][2], bound[1][2]])
pos = rollout_data[traj]['pred_pos'][step]
faces = rollout_data[traj]['faces'][step]
ax.plot_trisurf(pos[:, 0], pos[:, 1], faces, pos[:, 2], shade=True)
ax.set_title('Trajectory %d Step %d' % (traj, step))
return fig,
_ = animation.FuncAnimation(fig, animate, frames=num_frames, interval=100)
plt.show(block=True)
if __name__ == '__main__':
app.run(main)
================================================
FILE: meshgraphnets/requirements.txt
================================================
tensorflow-gpu>=1.15,<2
dm-sonnet<2
matplotlib
absl-py
numpy
================================================
FILE: meshgraphnets/run.sh
================================================
#!/bin/bash
# Copyright 2020 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Fail on any error.
set -e
# Display commands being run.
set -x
TMP_DIR=`mktemp -d`
virtualenv --python=python3.6 "${TMP_DIR}/env"
source "${TMP_DIR}/env/bin/activate"
# Install dependencies.
pip install --upgrade -r meshgraphnets/requirements.txt
# Download minimal dataset
DATA_DIR="${TMP_DIR}/flag_minimal"
bash meshgraphnets/download_dataset.sh flag_minimal ${TMP_DIR}
# Train for a few steps.
CHK_DIR="${TMP_DIR}/checkpoint"
python -m meshgraphnets.run_model --model=cloth --mode=train --checkpoint_dir=${CHK_DIR} --dataset_dir=${DATA_DIR} --num_training_steps=10
# Generate a rollout trajectory
ROLLOUT_PATH="${TMP_DIR}/rollout.pkl"
python -m meshgraphnets.run_model --model=cloth --mode=eval --checkpoint_dir=${CHK_DIR} --dataset_dir=${DATA_DIR} --rollout_path=${ROLLOUT_PATH} --num_rollouts=1
# Plot the rollout trajectory
python -m meshgraphnets.plot_cloth --rollout_path=${ROLLOUT_PATH}
# Clean up.
rm -r ${TMP_DIR}
echo "Test run complete."
================================================
FILE: meshgraphnets/run_model.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Runs the learner/evaluator."""
import pickle
from absl import app
from absl import flags
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
from meshgraphnets import cfd_eval
from meshgraphnets import cfd_model
from meshgraphnets import cloth_eval
from meshgraphnets import cloth_model
from meshgraphnets import core_model
from meshgraphnets import dataset
FLAGS = flags.FLAGS
flags.DEFINE_enum('mode', 'train', ['train', 'eval'],
'Train model, or run evaluation.')
flags.DEFINE_enum('model', None, ['cfd', 'cloth'],
'Select model to run.')
flags.DEFINE_string('checkpoint_dir', None, 'Directory to save checkpoint')
flags.DEFINE_string('dataset_dir', None, 'Directory to load dataset from.')
flags.DEFINE_string('rollout_path', None,
'Pickle file to save eval trajectories')
flags.DEFINE_enum('rollout_split', 'valid', ['train', 'test', 'valid'],
'Dataset split to use for rollouts.')
flags.DEFINE_integer('num_rollouts', 10, 'No. of rollout trajectories')
flags.DEFINE_integer('num_training_steps', int(10e6), 'No. of training steps')
PARAMETERS = {
'cfd': dict(noise=0.02, gamma=1.0, field='velocity', history=False,
size=2, batch=2, model=cfd_model, evaluator=cfd_eval),
'cloth': dict(noise=0.003, gamma=0.1, field='world_pos', history=True,
size=3, batch=1, model=cloth_model, evaluator=cloth_eval)
}
def learner(model, params):
"""Run a learner job."""
ds = dataset.load_dataset(FLAGS.dataset_dir, 'train')
ds = dataset.add_targets(ds, [params['field']], add_history=params['history'])
ds = dataset.split_and_preprocess(ds, noise_field=params['field'],
noise_scale=params['noise'],
noise_gamma=params['gamma'])
inputs = tf.data.make_one_shot_iterator(ds).get_next()
loss_op = model.loss(inputs)
global_step = tf.train.create_global_step()
lr = tf.train.exponential_decay(learning_rate=1e-4,
global_step=global_step,
decay_steps=int(5e6),
decay_rate=0.1) + 1e-6
optimizer = tf.train.AdamOptimizer(learning_rate=lr)
train_op = optimizer.minimize(loss_op, global_step=global_step)
# Don't train for the first few steps, just accumulate normalization stats
train_op = tf.cond(tf.less(global_step, 1000),
lambda: tf.group(tf.assign_add(global_step, 1)),
lambda: tf.group(train_op))
with tf.train.MonitoredTrainingSession(
hooks=[tf.train.StopAtStepHook(last_step=FLAGS.num_training_steps)],
checkpoint_dir=FLAGS.checkpoint_dir,
save_checkpoint_secs=600) as sess:
while not sess.should_stop():
_, step, loss = sess.run([train_op, global_step, loss_op])
if step % 1000 == 0:
logging.info('Step %d: Loss %g', step, loss)
logging.info('Training complete.')
def evaluator(model, params):
"""Run a model rollout trajectory."""
ds = dataset.load_dataset(FLAGS.dataset_dir, FLAGS.rollout_split)
ds = dataset.add_targets(ds, [params['field']], add_history=params['history'])
inputs = tf.data.make_one_shot_iterator(ds).get_next()
scalar_op, traj_ops = params['evaluator'].evaluate(model, inputs)
tf.train.create_global_step()
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.checkpoint_dir,
save_checkpoint_secs=None,
save_checkpoint_steps=None) as sess:
trajectories = []
scalars = []
for traj_idx in range(FLAGS.num_rollouts):
logging.info('Rollout trajectory %d', traj_idx)
scalar_data, traj_data = sess.run([scalar_op, traj_ops])
trajectories.append(traj_data)
scalars.append(scalar_data)
for key in scalars[0]:
logging.info('%s: %g', key, np.mean([x[key] for x in scalars]))
with open(FLAGS.rollout_path, 'wb') as fp:
pickle.dump(trajectories, fp)
def main(argv):
del argv
tf.enable_resource_variables()
tf.disable_eager_execution()
params = PARAMETERS[FLAGS.model]
learned_model = core_model.EncodeProcessDecode(
output_size=params['size'],
latent_size=128,
num_layers=2,
message_passing_steps=15)
model = params['model'].Model(learned_model)
if FLAGS.mode == 'train':
learner(model, params)
elif FLAGS.mode == 'eval':
evaluator(model, params)
if __name__ == '__main__':
app.run(main)
================================================
FILE: mmv/README.md
================================================
# Self-supervised Multimodal Versatile Networks
This is the code for the models in MMV - https://arxiv.org/abs/2006.16228.
We also make available the code for linear evaluation of a pre-trained model
in UCF101 and the JAX checkpoints for our best models.
We use different parameters for video compression in UCF101 than the ones
used in `tensorflow_datasets`. We provide the code to download and
preprocess the dataset. The eval_ucf101.py script reproduces the results we
report in Table 2 of the paper, using the checkpoints provided below.
Visual Backbone | Training Dataset | Results on Linear UCF101
------- | -------- | --------
S3D-G | AudioSet + HowTo | 89.6
Resnet TSM-50 | AudioSet + HowTo | 91.5
Resnet TSM-50 (x2) | AudioSet + HowTo | 91.8
## Setup
To set up a Python virtual environment with the required dependencies, run:
```shell
python3 -m venv mmv_env
source mmv_env/bin/activate
pip install --upgrade pip setuptools wheel
pip install -r mmv/requirements.txt --use-feature=2020-resolver
```
### Linear evaluation
The linear evaluation on UCF101 can be run using:
```shell
python -m mmv.eval_ucf101 \
--checkpoint_path= \
--dataset_folder=
```
## Checkpoints
We provide three checkpoints containing the best pre-trained weights for each
of the visual backbones we use in the paper, i. e., S3D-G, Resnet-50 TSM,
and Resnet-50 TSM x 2.
- [S3D-G](https://storage.googleapis.com/deepmind-research-mmv/mmv_s3d.pkl)
- [Resnet-50 TSM](https://storage.googleapis.com/deepmind-research-mmv/mmv_tsm_resnet_x1.pkl)
- [Resnet-50 TSMx2](https://storage.googleapis.com/deepmind-research-mmv/mmv_tsm_resnet_x2.pkl)
## References
### Citing our work
If you use that code for your research, please consider citing our paper:
```bibtex
@inproceedings{alayrac2020self,
title={{S}elf-{S}upervised {M}ulti{M}odal {V}ersatile {N}etworks},
author={Alayrac, Jean-Baptiste and Recasens, Adri{\`a} and Schneider, Rosalia and Arandjelovi{\'c}, Relja and Ramapuram, Jason and De Fauw, Jeffrey and Smaira, Lucas and Dieleman, Sander and Zisserman, Andrew},
booktitle={NeurIPS},
year={2020}
}
```
### Models in TF
You may also be interested in using our TF-Hub release models available at:
- [S3D-G](https://tfhub.dev/deepmind/mmv/s3d/1)
- [Resnet-50 TSM](https://tfhub.dev/deepmind/mmv/tsm-resnet50/1)
- [Resnet-50 TSMx2](https://tfhub.dev/deepmind/mmv/tsm-resnet50x2/1)
## License
While the code is licensed under the Apache 2.0 License, the checkpoints weights
are made available for non-commercial use only under the terms of the
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
license. You can find details at:
https://creativecommons.org/licenses/by-nc/4.0/legalcode.
================================================
FILE: mmv/config.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Configuration parameters for MMV."""
def get_model_config(ckpt_path):
"""Returns the model configuration to be used with each checkpoint."""
config = {
'audio_backbone': 'resnet50',
'audio_model_kwargs': {
'bn_config': {
'create_offset': True,
'create_scale': True,
'decay_rate': 0.9,
'eps': 1.0e-5
}
},
'bn_config_proj': {
'create_offset': True,
'create_scale': True,
'decay_rate': 0.9,
'eps': 1.0e-5
},
'config_audio_text': {
'embedding_dim': 512,
'toaud_bn_after_proj': False,
'toaud_head_mode': 'linear',
'totxt_bn_after_proj': False,
'totxt_head_mode': 'linear'
},
'config_video_audio': {
'embedding_dim': 512,
'toaud_bn_after_proj': True,
'toaud_head_mode': 'mlp@512',
'tovid_bn_after_proj': False,
'tovid_head_mode': 'linear'
},
'config_video_text': {
'embedding_dim': 256,
'totxt_bn_after_proj': True,
'totxt_head_mode': 'linear',
'tovid_bn_after_proj': False,
'tovid_head_mode': 'linear'
},
'mm_embedding_graph': 'fac_relu',
'name': 'text_audio_video',
'sentence_dim': 2048,
'use_xreplica_bn': True,
'vision_model_kwargs': {
'bn_config': {
'create_offset': True,
'create_scale': True,
'decay_rate': 0.9,
'eps': 1.0e-5
},
'n_frames': 32,
'width_mult': 1,
},
}
if 's3d' in ckpt_path:
config['visual_backbone'] = 's3d'
if 'tsm_resnet_x1' in ckpt_path:
config['visual_backbone'] = 'resnet50tsm'
if 'tsm_resnet_x2' in ckpt_path:
config['visual_backbone'] = 'resnet50tsm'
config['vision_model_kwargs']['width_mult'] = 2
return config
================================================
FILE: mmv/eval_ucf101.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""UCF101 linear evaluation."""
import functools
from typing import Any, Dict, Optional
from absl import app
from absl import flags
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import sklearn
from sklearn import preprocessing
import sklearn.linear_model
import sklearn.svm
import tensorflow as tf
import tensorflow_datasets as tfds
from mmv import config
from mmv.models import mm_embeddings
from mmv.utils import checkpoint
from mmv.utils import ucf101_dataset
flags.DEFINE_string('checkpoint_path', '~/tmp/mmv_s3d.pkl',
'The directory to load pre-trained weights from.')
flags.DEFINE_string('dataset_folder', '/tmp/ucf101',
'The directory with the ucf101 dataset.')
flags.DEFINE_integer('eval_batch_size', 1,
'The batch size for evaluation.')
flags.DEFINE_integer('train_batch_size', 16,
'The batch size for training.')
flags.DEFINE_integer('num_train_epochs', 10,
'How many epochs to collect features during training.')
flags.DEFINE_integer('num_test_windows', 10,
'How many windows to average on during test.')
flags.DEFINE_integer('min_resize', 224,
'Min value to resize images to during preprocessing.')
flags.DEFINE_integer('crop_size', 224,
'Value to resize images to during preprocessing.')
flags.DEFINE_integer('num_frames', 32,
'Number of video frames.')
flags.DEFINE_integer('stride', 2,
'Stride for video frames.')
flags.DEFINE_integer('ucf101_split', 1,
'Which split of ucf101 to use.')
FLAGS = flags.FLAGS
def get_sampling_offset(sequence: tf.Tensor,
num_steps: Optional[int],
is_training: bool,
stride: int = 1,
seed: Optional[int] = None) -> tf.Tensor:
"""Calculates the initial offset for a sequence where all steps will fit.
Args:
sequence: any tensor where the first dimension is timesteps.
num_steps: The number of timesteps we will output. If None,
deterministically start at the first frame.
is_training: A boolean indicates whether the graph is for training or not.
If False, the starting frame always the first frame.
stride: distance to sample between timesteps.
seed: a deterministic seed to use when sampling.
Returns:
The first index to begin sampling from. A best effort is made to provide a
starting index such that all requested steps fit within the sequence (i.e.
offset + 1 + (num_steps - 1) * stride < len(sequence)). If this is not
satisfied, the starting index is chosen randomly from the full sequence.
"""
if num_steps is None or not is_training:
return tf.constant(0)
sequence_length = tf.shape(sequence)[0]
max_offset = tf.cond(
tf.greater(sequence_length, (num_steps - 1) * stride),
lambda: sequence_length - (num_steps - 1) * stride,
lambda: sequence_length)
offset = tf.random.uniform(
(),
maxval=tf.cast(max_offset, tf.int32),
dtype=tf.int32,
seed=seed)
return offset
def sample_or_pad_sequence_indices(sequence: tf.Tensor,
num_steps: Optional[int],
is_training: bool,
repeat_sequence: bool = True,
stride: int = 1,
offset: Optional[int] = None) -> tf.Tensor:
"""Returns indices to take for sampling or padding a sequence to fixed size.
Samples num_steps from the sequence. If the sequence is shorter than
num_steps, the sequence loops. If the sequence is longer than num_steps and
is_training is True, then we seek to a random offset before sampling. If
offset is provided, we use that deterministic offset.
This method is appropriate for sampling from a tensor where you want every
timestep between a start and end time. See sample_stacked_sequence_indices for
more flexibility.
Args:
sequence: any tensor where the first dimension is timesteps.
num_steps: how many steps (e.g. frames) to take. If None, all steps from
start to end are considered and `is_training` has no effect.
is_training: A boolean indicates whether the graph is for training or not.
If False, the starting frame is deterministic.
repeat_sequence: A boolean indicates whether the sequence will repeat to
have enough steps for sampling. If False, a runtime error is thrown if
num_steps * stride is longer than sequence length.
stride: distance to sample between timesteps.
offset: a deterministic offset to use regardless of the is_training value.
Returns:
Indices to gather from the sequence Tensor to get a fixed size sequence.
"""
sequence_length = tf.shape(sequence)[0]
sel_idx = tf.range(sequence_length)
if num_steps:
if offset is None:
offset = get_sampling_offset(sequence, num_steps, is_training, stride)
if repeat_sequence:
# Repeats sequence until num_steps are available in total.
num_repeats = tf.cast(
tf.math.ceil(
tf.math.divide(
tf.cast(num_steps * stride + offset, tf.float32),
tf.cast(sequence_length, tf.float32)
)), tf.int32)
sel_idx = tf.tile(sel_idx, [num_repeats])
steps = tf.range(offset, offset + num_steps * stride, stride)
else:
steps = tf.range(0, sequence_length, stride)
return tf.gather(sel_idx, steps)
def random_sample_sequence(sequence: tf.Tensor,
num_steps: int,
stride: int = 1) -> tf.Tensor:
"""Randomly sample a segment of size num_steps from a given sequence."""
indices = sample_or_pad_sequence_indices(
sequence=sequence,
num_steps=num_steps,
is_training=True, # Random sample.
repeat_sequence=True, # Will repeat the sequence if request more.
stride=stride,
offset=None)
indices.set_shape((num_steps,))
output = tf.gather(sequence, indices)
return output
def sample_linspace_sequence(sequence: tf.Tensor,
num_windows: int,
num_steps: int,
stride: int = 1) -> tf.Tensor:
"""Samples num_windows segments from sequence with linearly spaced offsets.
The samples are concatenated in a single Tensor in order to have the same
format structure per timestep (e.g. a single frame). If num_steps * stride is
bigger than the number of timesteps, the sequence is repeated. This function
can be used in evaluation in order to extract enough segments in order to span
the entire sequence.
Args:
sequence: Any tensor where the first dimension is timesteps.
num_windows: Number of windows retrieved from the sequence.
num_steps: Number of steps (e.g. frames) to take.
stride: Distance to sample between timesteps.
Returns:
A single Tensor with first dimension num_windows * num_steps. The Tensor
contains the concatenated list of num_windows tensors which offsets have
been linearly spaced from input.
"""
sequence_length = tf.shape(sequence)[0]
max_offset = tf.maximum(0, sequence_length - num_steps * stride)
offsets = tf.linspace(0.0, tf.cast(max_offset, tf.float32), num_windows)
offsets = tf.cast(offsets, tf.int32)
all_indices = []
for i in range(num_windows):
all_indices.append(
sample_or_pad_sequence_indices(
sequence=sequence,
num_steps=num_steps,
is_training=False,
repeat_sequence=True, # Will repeat the sequence if request more.
stride=stride,
offset=offsets[i]))
indices = tf.concat(all_indices, axis=0)
indices.set_shape((num_windows * num_steps,))
output = tf.gather(sequence, indices)
return output
def resize_smallest(frames: tf.Tensor, min_resize: int) -> tf.Tensor:
"""Resizes frames so that min(height, width) is equal to min_resize.
This function will not do anything if the min(height, width) is already equal
to min_resize. This allows to save compute time.
Args:
frames: A Tensor of dimension [timesteps, input_h, input_w, channels].
min_resize: Minimum size of the final image dimensions.
Returns:
A Tensor of shape [timesteps, output_h, output_w, channels] of type
frames.dtype where min(output_h, output_w) = min_resize.
"""
shape = tf.shape(frames)
input_h = shape[1]
input_w = shape[2]
output_h = tf.maximum(min_resize, (input_h * min_resize) // input_w)
output_w = tf.maximum(min_resize, (input_w * min_resize) // input_h)
def resize_fn():
frames_resized = tf.image.resize(frames, (output_h, output_w))
return tf.cast(frames_resized, frames.dtype)
should_resize = tf.math.logical_or(tf.not_equal(input_w, output_w),
tf.not_equal(input_h, output_h))
frames = tf.cond(should_resize, resize_fn, lambda: frames)
return frames
def process_samples(features_dict, num_frames=32, stride=1, is_training=True,
min_resize=224, crop_size=224, num_windows=1):
"""Process video frames."""
video = features_dict['video']
if is_training:
assert num_windows == 1
video = random_sample_sequence(video, num_frames, stride)
is_flipped = tf.random.uniform((), minval=0, maxval=2, dtype=tf.int32)
video = tf.cond(tf.equal(is_flipped, 1),
true_fn=lambda: tf.image.flip_left_right(video),
false_fn=lambda: video)
else:
video = sample_linspace_sequence(video, num_windows, num_frames, stride)
# Resize smallest side.
video = resize_smallest(video, min_resize)
if is_training:
# Random crop.
video = tf.image.random_crop(video, [num_frames, crop_size, crop_size, 3])
else:
# Central crop.
video = tf.image.resize_with_crop_or_pad(video, crop_size, crop_size)
video = tf.cast(video, tf.float32)
video /= 255.0 # Set between [0, 1].
features_dict['video'] = video
return features_dict
def space_to_depth_batch(features_dict):
images = features_dict['video']
_, l, h, w, c = images.shape
images = tf.reshape(images, [-1, l // 2, 2, h // 2, 2, w // 2, 2, c])
images = tf.transpose(images, [0, 1, 3, 5, 2, 4, 6, 7])
images = tf.reshape(images, [-1, l // 2, h // 2, w // 2, 8 * c])
features_dict['video'] = images
return features_dict
def reshape_windows(features_dict, num_frames):
x = features_dict['video']
x = tf.reshape(x, (-1, num_frames, x.shape[2], x.shape[3], x.shape[4]))
features_dict['video'] = x
return features_dict
def compute_accuracy_metrics(pred, gt, prefix=''):
order_pred = np.argsort(pred, axis=1)
assert len(gt.shape) == len(order_pred.shape) == 2
top1_pred = order_pred[:, -1:]
top5_pred = order_pred[:, -5:]
top1_acc = np.mean(top1_pred == gt)
top5_acc = np.mean(np.max(top5_pred == gt, 1))
return {prefix + 'top1': top1_acc,
prefix + 'top5': top5_acc}
def forward_fn(images: jnp.ndarray,
audio_spectrogram: jnp.ndarray,
word_ids: jnp.ndarray,
is_training: bool,
model_config: Dict[str, Any]):
"""Forward pass of the model."""
# This should contain the pre-trained weights. We set it to zero because it
# will be loaded from the checkpoint.
language_model_vocab_size = 65536
word_embedding_dim = 300
dummy_embedding_matrix = jnp.zeros(shape=(language_model_vocab_size,
word_embedding_dim))
module = mm_embeddings.AudioTextVideoEmbedding(
**model_config,
word_embedding_matrix=dummy_embedding_matrix)
return module(images=images,
audio_spectrogram=audio_spectrogram,
word_ids=word_ids,
is_training=is_training)['vid_repr']
def main(argv):
del argv
sklearn_reg = 0.001
model_config = config.get_model_config(FLAGS.checkpoint_path)
forward = hk.without_apply_rng(hk.transform_with_state(forward_fn))
forward_apply = jax.jit(functools.partial(forward.apply,
is_training=False,
model_config=model_config))
# Get the UCF101 config.
dset_config = tfds.video.ucf101.Ucf101.BUILDER_CONFIGS[FLAGS.ucf101_split]
builder = ucf101_dataset.ModUcf101(
data_dir=FLAGS.dataset_folder,
config=dset_config)
# Create the tfrecord files (no-op if already exists)
dl_config = tfds.download.DownloadConfig(verify_ssl=False)
builder.download_and_prepare(download_config=dl_config)
# Generate the training dataset.
train_ds = builder.as_dataset(split='train', shuffle_files=False)
train_ds = train_ds.map(lambda x: process_samples( # pylint: disable=g-long-lambda
x, num_frames=FLAGS.num_frames, stride=FLAGS.stride, is_training=True,
min_resize=FLAGS.min_resize, crop_size=FLAGS.crop_size))
train_ds = train_ds.batch(batch_size=FLAGS.train_batch_size)
if model_config['visual_backbone'] == 's3d':
train_ds = train_ds.map(space_to_depth_batch)
train_ds = train_ds.repeat(FLAGS.num_train_epochs)
# Generate the test dataset.
test_ds = builder.as_dataset(split='test', shuffle_files=False)
test_ds = test_ds.map(lambda x: process_samples( # pylint: disable=g-long-lambda
x, num_frames=FLAGS.num_frames, stride=FLAGS.stride, is_training=False,
min_resize=FLAGS.min_resize, crop_size=FLAGS.crop_size,
num_windows=FLAGS.num_test_windows))
test_ds = test_ds.batch(batch_size=FLAGS.eval_batch_size)
test_ds = test_ds.map(lambda x: reshape_windows( # pylint: disable=g-long-lambda
x, num_frames=FLAGS.num_frames))
if model_config['visual_backbone'] == 's3d':
test_ds = test_ds.map(space_to_depth_batch)
test_ds = test_ds.repeat(1)
pretrained_weights = checkpoint.load_checkpoint(FLAGS.checkpoint_path)
params = pretrained_weights['params']
state = pretrained_weights['state']
# Collect training samples.
audio_frames = 96
mel_filters = 40
num_tokens = 16
dummy_audio = jnp.zeros(
shape=(FLAGS.train_batch_size, audio_frames, mel_filters, 1))
dummy_word_ids = jnp.zeros(
shape=(FLAGS.train_batch_size, num_tokens), dtype=jnp.int32)
train_features = []
train_labels = []
print('Computing features on train')
training_examples = iter(tfds.as_numpy(train_ds))
for train_ex in training_examples:
vid_representation, _ = forward_apply(params=params,
state=state,
images=train_ex['video'],
audio_spectrogram=dummy_audio,
word_ids=dummy_word_ids)
train_labels.append(train_ex['label'])
train_features.append(vid_representation)
if len(train_labels) % 50 == 0:
print(f'Processed {len(train_labels)} examples.')
train_labels = np.concatenate(train_labels, axis=0)
train_features = np.concatenate(train_features, axis=0)
print(f'Finish collecting train features of shape {train_features.shape}')
# Collect test samples.
dummy_audio = jnp.zeros(
shape=(FLAGS.eval_batch_size, audio_frames, mel_filters, 1))
dummy_word_ids = jnp.zeros(
shape=(FLAGS.eval_batch_size, num_tokens), dtype=jnp.int32)
test_features = []
test_labels = []
print('Computing features on test')
test_examples = iter(tfds.as_numpy(test_ds))
for test_ex in test_examples:
vid_representation_test, _ = forward_apply(params=params,
state=state,
images=test_ex['video'],
audio_spectrogram=dummy_audio,
word_ids=dummy_word_ids)
test_labels.append(test_ex['label'])
test_features.append(vid_representation_test)
if len(test_labels) % 50 == 0:
print(f'Processed {len(test_labels)} examples.')
test_features = np.concatenate(test_features, axis=0)
test_labels = np.concatenate(test_labels, axis=0)
print(f'Finish collecting test features of shape {test_features.shape}')
# Train classifier
print('Training linear classifier!')
classifier = sklearn.svm.LinearSVC(C=sklearn_reg)
scaler = preprocessing.StandardScaler().fit(train_features)
train_features = scaler.transform(train_features)
classifier.fit(train_features, train_labels.ravel())
print('Training done !')
# Evaluation.
test_features = scaler.transform(test_features)
print('Running inference on train')
pred_train = classifier.decision_function(train_features)
print('Running inference on test')
pred_test = classifier.decision_function(test_features)
if FLAGS.num_test_windows > 1:
pred_test = np.reshape(
pred_test, (test_labels.shape[0], -1, pred_test.shape[1]))
pred_test = pred_test.mean(axis=1)
# Compute accuracies.
metrics = compute_accuracy_metrics(pred_train, train_labels[:, None],
prefix='train_')
metrics.update(
compute_accuracy_metrics(pred_test, test_labels[:, None], prefix='test_'))
print(metrics)
if __name__ == '__main__':
app.run(main)
================================================
FILE: mmv/models/mm_embeddings.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Model for text-video-audio embeddings."""
from typing import Any, Dict, Optional
import haiku as hk
import jax
import jax.numpy as jnp
from mmv.models import normalization
from mmv.models import resnet
from mmv.models import s3d
from mmv.models import tsm_resnet
_DEFAULT_CFG_AUDTXT = {
"totxt_head_mode": "linear",
"toaud_head_mode": "linear",
"toaud_bn_after_proj": False,
"totxt_bn_after_proj": False,
"embedding_dim": 512}
_DEFAULT_CFG_VIDAUD = {
"tovid_head_mode": "linear",
"toaud_head_mode": "mlp@512",
"tovid_bn_after_proj": False,
"toaud_bn_after_proj": True,
"embedding_dim": 512}
_DEFAULT_CFG_VIDTXT = {
"tovid_head_mode": "linear",
"totxt_head_mode": "mlp@512",
"tovid_bn_after_proj": False,
"totxt_bn_after_proj": True,
"embedding_dim": 512}
_DEFAULT_CFG_BN = {"decay_rate": 0.9, "eps": 1e-5,
"create_scale": True, "create_offset": True}
def _setkey_if_not_exists(d, key, value):
if key not in d:
d[key] = value
class AudioTextVideoEmbedding(hk.Module):
"""Module to fuse audio, text and video for joint embedding learning."""
def __init__(
self,
# Language parameters.
word_embedding_matrix,
sentence_dim=2048,
# Audio parameters.
audio_backbone="resnet18",
audio_model_kwargs=None,
# Vision parameters.
visual_backbone="s3d",
vision_model_kwargs=None,
# Common parameters.
mm_embedding_graph="fac_relu",
use_xreplica_bn=True,
bn_config_proj=None,
config_video_text=None,
config_video_audio=None,
config_audio_text=None,
use_audio_text=False,
name="audio_text_video_model"):
"""Initialize the AudioTextVideoEmbedding class.
Args:
word_embedding_matrix: 2d matrix [vocab_size, embed_size] to embed words.
sentence_dim: The dimension of the sentence representation.
audio_backbone: Backbone for audio.
audio_model_kwargs: Other specific parameters to pass to the audio
module.
visual_backbone: The video backbone.
vision_model_kwargs: Other specific parameters to pass to the vision
module.
mm_embedding_graph: Embedding graph merging strategy.
Can be `shared`, `disjoint` or `fac` (fac can be followed by an
activation function name e.g. `fac_relu`).
use_xreplica_bn: Whether or not to use the cross replica batch norm.
bn_config_proj: BN config of the projection heads.
config_video_text: Config for the video and the text branches.
config_video_audio: Config for the video and the audio branches.
config_audio_text: Config for the audio and the text branches.
use_audio_text: Whether or not the audio text branch is used during
training.
name: graph name.
"""
super(AudioTextVideoEmbedding, self).__init__(name=name)
# Audio parameters.
self._audio_backbone = audio_backbone
self._audio_model_kwargs = audio_model_kwargs
# Language parameters.
self._sentence_dim = sentence_dim
self._word_embedding_matrix = word_embedding_matrix
# Vision parameters.
self._visual_backbone = visual_backbone
self._vision_model_kwargs = vision_model_kwargs
# Joint parameters.
self._use_xreplica_bn = use_xreplica_bn
if self._use_xreplica_bn:
self._normalizer_name = "cross_replica_batch_norm"
else:
self._normalizer_name = "batch_norm"
# Projection head parameters.
if config_video_text is None:
config_video_text = _DEFAULT_CFG_VIDTXT
for k, v in _DEFAULT_CFG_VIDTXT.items():
_setkey_if_not_exists(config_video_text, k, v)
self._cfg_vid_txt = config_video_text
if config_video_audio is None:
config_video_audio = _DEFAULT_CFG_VIDAUD
for k, v in _DEFAULT_CFG_VIDAUD.items():
_setkey_if_not_exists(config_video_audio, k, v)
self._cfg_vid_aud = config_video_audio
if config_audio_text is None:
config_audio_text = _DEFAULT_CFG_AUDTXT
for k, v in _DEFAULT_CFG_AUDTXT.items():
_setkey_if_not_exists(config_audio_text, k, v)
self._cfg_aud_txt = config_audio_text
self._use_audio_text = use_audio_text
self._mm_embedding_graph = mm_embedding_graph
self._use_separate_heads = (
mm_embedding_graph == "disjoint" or
mm_embedding_graph.startswith("fac"))
self._bn_config_proj = bn_config_proj or _DEFAULT_CFG_BN
def _get_pair_embedding_heads(self,
embedding_dim_1, embedding_dim_2,
mode1, mode2,
use_bn_out1, use_bn_out2,
name1, name2):
embd1_module = EmbeddingModule(
embedding_dim_1,
mode=mode1,
use_bn_out=use_bn_out1,
bn_config=self._bn_config_proj,
use_xreplica_bn=self._use_xreplica_bn,
name=name1)
if self._use_separate_heads:
embd2_module = EmbeddingModule(
embedding_dim_2,
mode=mode2,
use_bn_out=use_bn_out2,
use_xreplica_bn=self._use_xreplica_bn,
bn_config=self._bn_config_proj,
name=name2)
else:
assert embedding_dim_1 == embedding_dim_2, (
"Using shared heads but inconsistent embedding dims where provided.")
assert mode1 == mode2, (
"Using shared heads but inconsistent modes where provided.")
assert use_bn_out1 == use_bn_out2, (
"Using shared heads but inconsistent bn conf where provided.")
embd2_module = embd1_module
return embd1_module, embd2_module
def _activate_interaction(self, inputs, activation_fn, is_training,
activation_module=None):
"""Activation function for the interaction modules."""
if activation_fn == "relu":
inputs = jax.nn.relu(inputs)
elif activation_fn == "bnrelu":
if activation_module is None:
activation_module = normalization.get_normalize_fn(
normalizer_name=self._normalizer_name,
normalizer_kwargs=self._bn_config_proj)
inputs = activation_module(inputs, is_training=is_training)
inputs = jax.nn.relu(inputs)
else:
raise ValueError(f"{activation_fn} not supported.")
return inputs, activation_module
def __call__(self,
images,
audio_spectrogram,
word_ids,
is_training,
return_intermediate_audio=False):
"""Computes video, text and audio embeddings.
Args:
images: The videos tensor of shape [B1, T, H, W, 3] where B1 is the batch
size, T is the number of frames per clip, H the height, W the width
and 3 the rgb channels.
audio_spectrogram: The audio tensor of shape [B2, T', F] where B2 is the
batch size, T' is the number of temporal frames, F is the number of
frequency frames.
word_ids: If words_embeddings is set to None, it will use the word indices
input instead so that we can compute the word embeddings within the
model graph. The expected shape is [B3, N, D] where B3 is the batch size
and N the maximum number of words per sentence.
is_training: Whether or not to activate the graph in training mode.
return_intermediate_audio: Return audio intermediate representation.
Returns:
if return_intermediate_audio = True
audio_representation: the 4-dim audio representation taken before
averaging over spatial dims in the Resnet.
else
visual_embd: a dict containing the video embeddings in audio and text
of shape [B1, d_embd].
audio_embd: a dict containing the audio embeddings in video and text
of shape [B2, d_embd].
txt_embd: a dict containing the text embeddings in video and audio
of shape[B3, d_embd].
visual_representation: the video rep of shape [B1, d_visual].
audio_representation: the audio rep of shape [B2, d_audio].
"""
# Computes the visual representation.
video_cnn = VisualModule(backbone=self._visual_backbone,
use_xreplica_bn=self._use_xreplica_bn,
model_kwargs=self._vision_model_kwargs)
visual_representation = video_cnn(images, is_training=is_training)
# Projection heads: Video -> Text and Video -> Audio.
vid2txt_embd_module, vid2aud_embd_module = self._get_pair_embedding_heads(
embedding_dim_1=self._cfg_vid_txt["embedding_dim"],
embedding_dim_2=self._cfg_vid_aud["embedding_dim"],
mode1=self._cfg_vid_txt["totxt_head_mode"],
mode2=self._cfg_vid_aud["toaud_head_mode"],
use_bn_out1=self._cfg_vid_txt["totxt_bn_after_proj"],
use_bn_out2=self._cfg_vid_aud["toaud_bn_after_proj"],
name1="vis_embd",
name2="vid2audio_embd")
video_embd = {}
if self._mm_embedding_graph in ["shared", "disjoint"]:
video_embd["toaud"] = vid2aud_embd_module(visual_representation,
is_training=is_training)
video_embd["totxt"] = vid2txt_embd_module(visual_representation,
is_training=is_training)
elif self._mm_embedding_graph.startswith("fac"):
# Activation function if specificed in the name, e.g. fac_relu.
activation_fn = None
if len(self._mm_embedding_graph.split("_")) == 2:
activation_fn = self._mm_embedding_graph.split("_")[1]
video_embd["toaud"] = vid2aud_embd_module(visual_representation,
is_training=is_training)
fine_rep = video_embd["toaud"]
# Eventually activate the fine grained representation.
if activation_fn:
fine_rep, activation_module = self._activate_interaction(
inputs=fine_rep, activation_fn=activation_fn,
is_training=is_training)
video_embd["totxt"] = vid2txt_embd_module(fine_rep,
is_training=is_training)
else:
raise ValueError(
f"{self._mm_embedding_graph} is not a valid MM embedding graph.")
# Computes the audio representation.
audio_cnn = AudioModule(backbone=self._audio_backbone,
use_xreplica_bn=self._use_xreplica_bn,
model_kwargs=self._audio_model_kwargs)
if return_intermediate_audio:
return audio_cnn(audio_spectrogram,
is_training=is_training,
return_intermediate=True)
audio_representation = audio_cnn(audio_spectrogram, is_training=is_training)
# Projection heads: Audio -> Video and Audio -> Text.
aud2vid_embd_module, aud2txt_embd_module = self._get_pair_embedding_heads(
embedding_dim_1=self._cfg_vid_aud["embedding_dim"],
embedding_dim_2=self._cfg_aud_txt["embedding_dim"],
mode1=self._cfg_vid_aud["tovid_head_mode"],
mode2=self._cfg_aud_txt["totxt_head_mode"],
use_bn_out1=self._cfg_vid_aud["tovid_bn_after_proj"],
use_bn_out2=self._cfg_aud_txt["totxt_bn_after_proj"],
name1="audio_embd",
name2="audio2txt_embd")
audio_embd = {}
audio_embd["tovid"] = aud2vid_embd_module(audio_representation,
is_training=is_training)
# Computes the projection to the text domain depending on the MM graph mode.
if (self._mm_embedding_graph.startswith("fac") and
(self._use_audio_text or (not is_training))):
# In case the audio text branch is not used during training, we do that
# only at eval time (is_training=False) in order to not pollute the BN
# stats in vid2txt_embd_module with audio features during training.
fine_rep_audio = audio_embd["tovid"]
if activation_fn:
fine_rep_audio, _ = self._activate_interaction(
inputs=fine_rep_audio, activation_fn=activation_fn,
is_training=is_training, activation_module=activation_module)
audio_embd["totxt"] = vid2txt_embd_module(fine_rep_audio,
is_training=is_training)
else:
audio_embd["totxt"] = aud2txt_embd_module(audio_representation,
is_training=is_training)
# Computes the text representation.
txt_representation = TextModule(
sentence_dim=self._sentence_dim,
word_embedding_matrix=self._word_embedding_matrix)(
word_ids, is_training=is_training)
# Projection heads: Text -> Video and Text -> Audio.
txt2vid_embd_module, txt2aud_embd_module = self._get_pair_embedding_heads(
embedding_dim_1=self._cfg_vid_txt["embedding_dim"],
embedding_dim_2=self._cfg_aud_txt["embedding_dim"],
mode1=self._cfg_vid_txt["tovid_head_mode"],
mode2=self._cfg_aud_txt["toaud_head_mode"],
use_bn_out1=self._cfg_vid_txt["tovid_bn_after_proj"],
use_bn_out2=self._cfg_aud_txt["toaud_bn_after_proj"],
name1="txt_embd",
name2="txt2audio_embd")
txt_embd = {}
txt_embd["tovid"] = txt2vid_embd_module(txt_representation,
is_training=is_training)
txt_embd["toaud"] = txt2aud_embd_module(txt_representation,
is_training=is_training)
return {
"vid_embd": video_embd,
"aud_embd": audio_embd,
"txt_embd": txt_embd,
"vid_repr": visual_representation,
"aud_repr": audio_representation,
}
class EmbeddingModule(hk.Module):
"""Final Embedding module."""
def __init__(self,
embedding_dim: int,
mode: str = "linear",
use_bn_out: bool = False,
bn_config: Optional[Dict[str, Any]] = None,
use_xreplica_bn: bool = True,
name="embedding_module"):
self._embedding_dim = embedding_dim
self._use_bn_out = use_bn_out
self._mode = mode
# Set default BN config.
bn_config = bn_config or _DEFAULT_CFG_BN
if use_xreplica_bn:
normalizer_name = "cross_replica_batch_norm"
else:
normalizer_name = "batch_norm"
self._batch_norm = normalization.get_normalize_fn(
normalizer_name=normalizer_name,
normalizer_kwargs=bn_config)
super(EmbeddingModule, self).__init__(name=name)
def __call__(self, input_feature, is_training):
if self._mode == "linear":
proj = hk.Linear(self._embedding_dim, name="final_projection")
embedding = proj(input_feature)
elif self._mode.startswith("mlp"):
if "@" not in self._mode:
raise ValueError(
("Please specify the inner dimensions of the MLP with `@` symbol"
"e.g. mlp@512 or mlp@512@256 for a 2 layer MLP."))
inner_dims = [int(dim) for dim in self._mode.split("@")[1:]]
embedding = input_feature
for inner_dim in inner_dims:
embedding = hk.Linear(inner_dim, with_bias=True,
name="final_projection_inner")(embedding)
if not self._mode.startswith("mlp_nobn"):
embedding = self._batch_norm(embedding, is_training=is_training)
embedding = jax.nn.relu(embedding)
# Final projection.
embedding = hk.Linear(self._embedding_dim, name="final_projection",
with_bias=not self._use_bn_out)(embedding)
else:
raise NotImplementedError
if self._use_bn_out:
embedding = self._batch_norm(embedding, is_training=is_training)
return embedding
class VisualModule(hk.Module):
"""The visual module selects which CNN backbone to connect to the graph."""
def __init__(self,
use_xreplica_bn=True,
backbone="s3d",
model_kwargs=None,
name="visual_module"):
self._backbone = backbone
super(VisualModule, self).__init__(name=name)
if model_kwargs is None:
model_kwargs = {}
bn_config = model_kwargs.get("bn_config", _DEFAULT_CFG_BN)
if use_xreplica_bn:
normalizer_name = "cross_replica_batch_norm"
else:
normalizer_name = "batch_norm"
normalize_fn = normalization.get_normalize_fn(
normalizer_name=normalizer_name,
normalizer_kwargs=bn_config)
if backbone == "s3d":
self._cnn = s3d.S3D(normalize_fn=normalize_fn)
elif backbone == "resnet50tsm":
width_mult = model_kwargs.get("width_mult", 1)
self._cnn = tsm_resnet.TSMResNetV2(
normalize_fn=normalize_fn,
depth=50,
num_frames=model_kwargs["n_frames"],
width_mult=width_mult)
else:
raise NotImplementedError
def __call__(self, images, is_training):
"""Connects graph to images."""
features = self._cnn(images, is_training=is_training)
return features
class AudioModule(hk.Module):
"""The audio module selects which CNN backbone to connect to the graph."""
def __init__(self,
backbone="resnet18",
use_xreplica_bn=True,
model_kwargs=None,
name="audio_module"):
super(AudioModule, self).__init__(name=name)
model_kwargs = model_kwargs or {}
bn_config = model_kwargs.get("bn_config", _DEFAULT_CFG_BN)
backbone_to_depth = {
"resnet18": 18,
"resnet34": 34,
"resnet50": 50,
"resnet101": 101
}
assert backbone in backbone_to_depth, (
f"backbone should be in {backbone_to_depth.keys()}")
if use_xreplica_bn:
normalizer_name = "cross_replica_batch_norm"
else:
normalizer_name = "batch_norm"
self._cnn = resnet.ResNetV2(
depth=backbone_to_depth[backbone],
normalize_fn=normalization.get_normalize_fn(
normalizer_name=normalizer_name,
normalizer_kwargs=bn_config),
num_classes=None)
def __call__(self,
audio_spectrogram,
is_training,
return_intermediate=False):
"""Connects graph to audio spectrogram."""
final_endpoint = "output"
if return_intermediate:
final_endpoint = "last_conv"
return self._cnn(audio_spectrogram,
is_training=is_training,
final_endpoint=final_endpoint)
class TextModule(hk.Module):
"""Text module computes the sentences representation."""
def __init__(self,
word_embedding_matrix,
sentence_dim=1024,
name="text_module"):
"""Initialize text module.
Args:
word_embedding_matrix: 2d matrix [vocab_size, embed_size] to embed words.
sentence_dim: dimension of sentence representation.
name: module name.
"""
super(TextModule, self).__init__(name=name)
self._word_embedding_module = hk.Embed(
embedding_matrix=word_embedding_matrix)
self._conv1d_module = hk.Conv1D(sentence_dim, 1, name="text_conv1")
def __call__(self, word_ids, is_training):
"""Connects graph to sentence representation."""
word_embeddings = self._word_embedding_module(word_ids)
word_embeddings = jax.lax.stop_gradient(word_embeddings)
output = self._conv1d_module(word_embeddings)
output = jax.nn.relu(output)
output = jnp.amax(output, axis=1)
return output
================================================
FILE: mmv/models/normalization.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Normalize functions constructors."""
from typing import Any, Dict, Optional, Sequence, Union
import haiku as hk
from jax import numpy as jnp
from mmv.models import types
class _BatchNorm(hk.BatchNorm):
"""A `hk.BatchNorm` with adapted default arguments."""
def __init__(self,
create_scale: bool = True,
create_offset: bool = True,
decay_rate: float = 0.9,
eps: float = 1e-5,
test_local_stats: bool = False,
**kwargs):
# Check args.
if kwargs.get('cross_replica_axis', None) is not None:
raise ValueError(
'Attempting to use \'batch_norm\' normalizer, but specifying '
'`cross_replica_axis`. If you want this behavior use '
'`normalizer=\'cross_replica_batch_norm\'` directly.')
self._test_local_stats = test_local_stats
super().__init__(create_scale=create_scale,
create_offset=create_offset,
decay_rate=decay_rate,
eps=eps,
**kwargs)
def __call__(self,
x: types.TensorLike,
is_training: bool) -> jnp.ndarray:
return super().__call__(x, is_training,
test_local_stats=self._test_local_stats)
class _CrossReplicaBatchNorm(hk.BatchNorm):
"""A `hk.BatchNorm` with adapted default arguments for cross replica."""
def __init__(self,
create_scale: bool = True,
create_offset: bool = True,
decay_rate: float = 0.9,
eps: float = 1e-5,
test_local_stats: bool = False,
**kwargs):
# Check args.
if 'cross_replica_axis' in kwargs and kwargs['cross_replica_axis'] is None:
raise ValueError(
'Attempting to use \'cross_replica_batch_norm\' normalizer, but '
'specifying `cross_replica_axis` to be None. If you want this '
'behavior use `normalizer=\'batch_norm\'` directly.')
self._test_local_stats = test_local_stats
kwargs['cross_replica_axis'] = kwargs.get('cross_replica_axis', 'i')
super().__init__(create_scale=create_scale,
create_offset=create_offset,
decay_rate=decay_rate,
eps=eps,
**kwargs)
def __call__(self,
x: types.TensorLike,
is_training: bool) -> jnp.ndarray:
return super().__call__(x, is_training,
test_local_stats=self._test_local_stats)
class _LayerNorm(hk.LayerNorm):
"""A `hk.LayerNorm` accepting (and discarding) an `is_training` argument."""
def __init__(self,
axis: Union[int, Sequence[int]] = (1, 2),
create_scale: bool = True,
create_offset: bool = True,
**kwargs):
super().__init__(axis=axis,
create_scale=create_scale,
create_offset=create_offset,
**kwargs)
def __call__(self, # pytype: disable=signature-mismatch # overriding-parameter-count-checks
x: types.TensorLike,
is_training: bool) -> jnp.ndarray:
del is_training # Unused.
return super().__call__(x)
_NORMALIZER_NAME_TO_CLASS = {
'batch_norm': _BatchNorm,
'cross_replica_batch_norm': _CrossReplicaBatchNorm,
'layer_norm': _LayerNorm,
}
def get_normalize_fn(
normalizer_name: str = 'batch_norm',
normalizer_kwargs: Optional[Dict[str, Any]] = None,
) -> types.NormalizeFn:
"""Handles NormalizeFn creation.
These functions are expected to be used as part of Haiku model. On each
application of the returned normalization_fn, a new Haiku layer will be added
to the model.
Args:
normalizer_name: The name of the normalizer to be constructed.
normalizer_kwargs: The kwargs passed to the normalizer constructor.
Returns:
A `types.NormalizeFn` that when applied will create a new layer.
Raises:
ValueError: If `normalizer_name` is unknown.
"""
# Check args.
if normalizer_name not in _NORMALIZER_NAME_TO_CLASS:
raise ValueError(f'Unrecognized `normalizer_name` {normalizer_name}.')
normalizer_class = _NORMALIZER_NAME_TO_CLASS[normalizer_name]
normalizer_kwargs = normalizer_kwargs or dict()
return lambda *a, **k: normalizer_class(**normalizer_kwargs)(*a, **k) # pylint: disable=unnecessary-lambda
================================================
FILE: mmv/models/resnet.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""ResNet V2 modules.
Equivalent to hk.Resnet except accepting a final_endpoint to return
intermediate activations.
"""
from typing import Optional, Sequence, Text, Type, Union
import haiku as hk
import jax
import jax.numpy as jnp
from mmv.models import types
class BottleneckBlock(hk.Module):
"""Implements a bottleneck residual block (ResNet50 and ResNet101)."""
# pylint:disable=g-bare-generic
def __init__(self,
channels: int,
stride: Union[int, Sequence[int]],
use_projection: bool,
normalize_fn: Optional[types.NormalizeFn] = None,
name: Optional[Text] = None):
super(BottleneckBlock, self).__init__(name=name)
self._channels = channels
self._stride = stride
self._use_projection = use_projection
self._normalize_fn = normalize_fn
if self._use_projection:
self._proj_conv = hk.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=stride,
with_bias=False,
padding='SAME',
name='shortcut_conv')
self._conv_0 = hk.Conv2D(
output_channels=channels // 4,
kernel_shape=1,
stride=1,
with_bias=False,
padding='SAME',
name='conv_0')
self._conv_1 = hk.Conv2D(
output_channels=channels // 4,
kernel_shape=3,
stride=stride,
with_bias=False,
padding='SAME',
name='conv_1')
self._conv_2 = hk.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding='SAME',
name='conv_2')
def __call__(self,
inputs,
is_training):
net = inputs
shortcut = inputs
for i, conv_i in enumerate([self._conv_0, self._conv_1, self._conv_2]):
if self._normalize_fn is not None:
net = self._normalize_fn(net, is_training=is_training)
net = jax.nn.relu(net)
if i == 0 and self._use_projection:
shortcut = self._proj_conv(net)
# Now do the convs.
net = conv_i(net)
return net + shortcut
class BasicBlock(hk.Module):
"""Implements a basic residual block (ResNet18 and ResNet34)."""
# pylint:disable=g-bare-generic
def __init__(self,
channels: int,
stride: Union[int, Sequence[int]],
use_projection: bool,
normalize_fn: Optional[types.NormalizeFn] = None,
name: Optional[Text] = None):
super(BasicBlock, self).__init__(name=name)
self._channels = channels
self._stride = stride
self._use_projection = use_projection
self._normalize_fn = normalize_fn
if self._use_projection:
self._proj_conv = hk.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=stride,
with_bias=False,
padding='SAME',
name='shortcut_conv')
self._conv_0 = hk.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding='SAME',
name='conv_0')
self._conv_1 = hk.Conv2D(
output_channels=channels,
kernel_shape=3,
stride=stride,
with_bias=False,
padding='SAME',
name='conv_1')
def __call__(self,
inputs,
is_training):
net = inputs
shortcut = inputs
for i, conv_i in enumerate([self._conv_0, self._conv_1]):
if self._normalize_fn is not None:
net = self._normalize_fn(net, is_training=is_training)
net = jax.nn.relu(net)
if i == 0 and self._use_projection:
shortcut = self._proj_conv(net)
# Now do the convs.
net = conv_i(net)
return net + shortcut
class ResNetUnit(hk.Module):
"""Unit (group of blocks) for ResNet."""
# pylint:disable=g-bare-generic
def __init__(self,
channels: int,
num_blocks: int,
stride: Union[int, Sequence[int]],
block_module: Type[BottleneckBlock],
normalize_fn: Optional[types.NormalizeFn] = None,
name: Optional[Text] = None,
remat: bool = False):
super(ResNetUnit, self).__init__(name=name)
self._channels = channels
self._num_blocks = num_blocks
self._stride = stride
self._normalize_fn = normalize_fn
self._block_module = block_module
self._remat = remat
def __call__(self,
inputs,
is_training):
input_channels = inputs.shape[-1]
self._blocks = []
for id_block in range(self._num_blocks):
use_projection = id_block == 0 and self._channels != input_channels
self._blocks.append(
self._block_module(
channels=self._channels,
stride=self._stride if id_block == 0 else 1,
use_projection=use_projection,
normalize_fn=self._normalize_fn,
name='block_%d' % id_block))
net = inputs
for block in self._blocks:
if self._remat:
# Note: we can ignore cell-var-from-loop because the lambda is evaluated
# inside every iteration of the loop. This is needed to go around the
# way variables are passed to jax.remat.
net = hk.remat(lambda x: block(x, is_training=is_training))(net) # pylint: disable=cell-var-from-loop
else:
net = block(net, is_training=is_training)
return net
class ResNetV2(hk.Module):
"""ResNetV2 model."""
# Endpoints of the model in order.
VALID_ENDPOINTS = (
'resnet_stem',
'resnet_unit_0',
'resnet_unit_1',
'resnet_unit_2',
'resnet_unit_3',
'last_conv',
'output',
)
# pylint:disable=g-bare-generic
def __init__(self,
depth=50,
num_classes: Optional[int] = 1000,
width_mult: int = 1,
normalize_fn: Optional[types.NormalizeFn] = None,
name: Optional[Text] = None,
remat: bool = False):
"""Creates ResNetV2 Haiku module.
Args:
depth: depth of the desired ResNet (18, 34, 50, 101, 152 or 202).
num_classes: (int) Number of outputs in final layer. If None will not add
a classification head and will return the output embedding.
width_mult: multiplier for channel width.
normalize_fn: normalization function, see helpers/utils.py
name: Name of the module.
remat: Whether to rematerialize intermediate activations (saves memory).
"""
super(ResNetV2, self).__init__(name=name)
self._normalize_fn = normalize_fn
self._num_classes = num_classes
self._width_mult = width_mult
self._strides = [1, 2, 2, 2]
num_blocks = {
18: [2, 2, 2, 2],
34: [3, 4, 6, 3],
50: [3, 4, 6, 3],
101: [3, 4, 23, 3],
152: [3, 8, 36, 3],
200: [3, 24, 36, 3],
}
if depth not in num_blocks:
raise ValueError(
f'`depth` should be in {list(num_blocks.keys())} ({depth} given).')
self._num_blocks = num_blocks[depth]
if depth >= 50:
self._block_module = BottleneckBlock
self._channels = [256, 512, 1024, 2048]
else:
self._block_module = BasicBlock
self._channels = [64, 128, 256, 512]
self._initial_conv = hk.Conv2D(
output_channels=64 * self._width_mult,
kernel_shape=7,
stride=2,
with_bias=False,
padding='SAME',
name='initial_conv')
if remat:
self._initial_conv = hk.remat(self._initial_conv)
self._block_groups = []
for i in range(4):
self._block_groups.append(
ResNetUnit(
channels=self._channels[i] * self._width_mult,
num_blocks=self._num_blocks[i],
block_module=self._block_module,
stride=self._strides[i],
normalize_fn=self._normalize_fn,
name='block_group_%d' % i,
remat=remat))
if num_classes is not None:
self._logits_layer = hk.Linear(
output_size=num_classes, w_init=jnp.zeros, name='logits')
def __call__(self, inputs, is_training, final_endpoint='output'):
self._final_endpoint = final_endpoint
net = self._initial_conv(inputs)
net = hk.max_pool(
net, window_shape=(1, 3, 3, 1),
strides=(1, 2, 2, 1),
padding='SAME')
end_point = 'resnet_stem'
if self._final_endpoint == end_point:
return net
for i_group, block_group in enumerate(self._block_groups):
net = block_group(net, is_training=is_training)
end_point = f'resnet_unit_{i_group}'
if self._final_endpoint == end_point:
return net
end_point = 'last_conv'
if self._final_endpoint == end_point:
return net
if self._normalize_fn is not None:
net = self._normalize_fn(net, is_training=is_training)
net = jax.nn.relu(net)
# The actual representation
net = jnp.mean(net, axis=[1, 2])
assert self._final_endpoint == 'output'
if self._num_classes is None:
# If num_classes was None, we just return the output
# of the last block, without fully connected layer.
return net
return self._logits_layer(net)
================================================
FILE: mmv/models/s3d.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A Haiku S3D model."""
import collections
from typing import Optional, Sequence
import haiku as hk
import jax
from jax import numpy as jnp
from mmv.models import types
class _MaxPool(hk.MaxPool):
"""A `hk.MaxPool` accepting (and discarding) an `is_training` argument."""
def __call__(self,
x: types.TensorLike,
is_training: bool = True) -> jnp.ndarray:
del is_training # Unused.
return super().__call__(x)
def self_gating(inputs: types.TensorLike) -> jnp.ndarray:
"""Feature gating as used in S3D-G.
Transforms the input features by aggregating features from all spatial and
temporal locations, and applying gating conditioned on the aggregated
features. More details can be found at: https://arxiv.org/abs/1712.04851.
Args:
inputs: A 5-D float array of shape `[B, T, H, W, C]`.
Returns:
A tensor with the same shape as input_tensor.
Raises:
ValueError: If `inputs` has the wrong shape.
"""
if inputs.ndim != 5:
raise ValueError(
f'Expected an input of shape `[B, T, H, W, C]` but got {inputs.shape}.')
input_shape = inputs.shape
num_channels = input_shape[4]
spatiotemporal_average = jnp.mean(inputs, axis=(1, 2, 3))
weights = hk.Linear(num_channels, name='self_gating')(spatiotemporal_average)
weights = jax.nn.sigmoid(weights)
return jnp.multiply(weights[:, None, None, None, :], inputs)
class SUnit3D(hk.Module):
"""Base 3d Unit combining Conv3d + Batch Norm + non-linearity."""
def __init__(
self,
output_channels: int,
kernel_shape: Sequence[int] = (1, 1, 1),
stride: Sequence[int] = (1, 1, 1),
with_bias: bool = False,
separable: bool = False,
normalize_fn: Optional[types.NormalizeFn] = None,
activation_fn: Optional[types.ActivationFn] = jax.nn.relu,
self_gating_fn: Optional[types.GatingFn] = None,
name='SUnit3D'):
"""Initializes the SUnit3D module.
Args:
output_channels: Number of output channels.
kernel_shape: The shape of the kernel. A sequence of length 3.
stride: Stride for the kernel. A sequence of length 3.
with_bias: Whether to add a bias to the convolution.
separable: Whether to use separable.
normalize_fn: Function used for normalization.
activation_fn: Function used as non-linearity.
self_gating_fn: Function used for self-gating.
name: The name of the module.
Raises:
ValueError: If `kernel_shape` or `stride` has the wrong shape.
"""
super().__init__(name=name)
# Check args.
if len(kernel_shape) != 3:
raise ValueError(
'Given `kernel_shape` must have length 3 but has length '
f'{len(kernel_shape)}.')
if len(stride) != 3:
raise ValueError(
f'Given `stride` must have length 3 but has length {len(stride)}.')
self._normalize_fn = normalize_fn
self._activation_fn = activation_fn
self._self_gating_fn = self_gating_fn
k0, k1, k2 = kernel_shape
if separable and k1 != 1:
spatial_kernel_shape = [1, k1, k2]
temporal_kernel_shape = [k0, 1, 1]
s0, s1, s2 = stride
spatial_stride = [1, s1, s2]
temporal_stride = [s0, 1, 1]
self._convolutions = [
hk.Conv3D(
output_channels=output_channels,
kernel_shape=spatial_kernel_shape,
stride=spatial_stride,
padding='SAME',
with_bias=with_bias),
hk.Conv3D(
output_channels=output_channels,
kernel_shape=temporal_kernel_shape,
stride=temporal_stride,
padding='SAME',
with_bias=with_bias)
]
else:
self._convolutions = [
hk.Conv3D(
output_channels=output_channels,
kernel_shape=kernel_shape,
stride=stride,
padding='SAME',
with_bias=with_bias)]
def __call__(
self,
inputs: types.TensorLike,
is_training: bool) -> jnp.ndarray:
"""Connects the module to inputs.
Args:
inputs: A 5-D float array of shape `[B, T, H, W, C]`.
is_training: Whether to use training mode.
Returns:
A 5-D float array of shape `[B, new_t, new_h, new_w, output_channels]`.
"""
x = inputs
for conv in self._convolutions:
x = conv(x)
if self._normalize_fn is not None:
x = self._normalize_fn(x, is_training=is_training)
if self._activation_fn is not None:
x = self._activation_fn(x)
if self._self_gating_fn:
x = self._self_gating_fn(x)
return x # pytype: disable=bad-return-type # jax-devicearray
class InceptionBlockV13D(hk.Module):
"""A 3D Inception v1 block.
This allows use of separable 3D convolutions and self-gating, as described in:
Rethinking Spatiotemporal Feature Learning For Video Understanding.
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu and Kevin Murphy.
https://arxiv.org/abs/1712.04851.
"""
def __init__(self,
output_channels: Sequence[int],
normalize_fn: Optional[types.NormalizeFn],
temporal_kernel_size: int = 3,
self_gating_fn: Optional[types.GatingFn] = None,
name: str = 'InceptionBlockV13D'):
"""Initializes the InceptionBlockV13D module.
Args:
output_channels: The size of the output channels of each block, ordered as
[Conv2d_0a_1x1, Conv2d_0a_1x1, Conv2d_0b_3x3, Conv2d_0a_1x1,
Conv2d_0b_3x3, Conv2d_0b_1x1]
normalize_fn: Function used for normalization.
temporal_kernel_size: The size of the temporal convolutional filters in
the conv3d_spatiotemporal blocks.
self_gating_fn: Function which optionally performs self-gating. If `None`,
no self-gating is applied.
name: The name of the module.
Raises:
ValueError: If `output_channels` has the wrong shape.
"""
super().__init__(name=name)
# Check args.
if len(output_channels) != 6:
raise ValueError(
'Given `output_channels` must have length 6 but has length '
f'{len(output_channels)}.')
self._output_channels = output_channels
self._normalize_fn = normalize_fn
self._temporal_kernel_size = temporal_kernel_size
if self_gating_fn is None:
self._self_gating_fn = lambda x: x
else:
self._self_gating_fn = self_gating_fn
def __call__(
self,
inputs: types.TensorLike,
is_training: bool) -> jnp.ndarray:
"""Connects the module to inputs.
Args:
inputs: A 5-D float array of shape `[B, T, H, W, C]`.
is_training: Whether to use training mode.
Returns:
A 5-D float array of shape
`[B, new_t, new_h, new_w, sum(output_channels)]`.
"""
# Branch 0
branch_0 = SUnit3D(
output_channels=self._output_channels[0],
kernel_shape=(1, 1, 1),
separable=False,
normalize_fn=self._normalize_fn,
self_gating_fn=self._self_gating_fn,
name='Branch_0_Conv2d_0a_1x1')(
inputs, is_training=is_training)
# Branch 1
branch_1 = SUnit3D(
output_channels=self._output_channels[1],
kernel_shape=(1, 1, 1),
separable=False,
normalize_fn=self._normalize_fn,
self_gating_fn=None,
name='Branch_1_Conv2d_0a_1x1')(
inputs, is_training=is_training)
branch_1 = SUnit3D(
output_channels=self._output_channels[2],
kernel_shape=(self._temporal_kernel_size, 3, 3),
separable=True,
normalize_fn=self._normalize_fn,
self_gating_fn=self._self_gating_fn,
name='Branch_1_Conv2d_0b_3x3')(
branch_1, is_training=is_training)
# Branch 2
branch_2 = SUnit3D(
output_channels=self._output_channels[3],
kernel_shape=(1, 1, 1),
separable=False,
normalize_fn=self._normalize_fn,
self_gating_fn=None,
name='Branch_2_Conv2d_0a_1x1')(
inputs, is_training=is_training)
branch_2 = SUnit3D(
output_channels=self._output_channels[4],
kernel_shape=(self._temporal_kernel_size, 3, 3),
separable=True,
normalize_fn=self._normalize_fn,
self_gating_fn=self._self_gating_fn,
name='Branch_2_Conv2d_0b_3x3')(
branch_2, is_training=is_training)
# Branch 3
branch_3 = hk.MaxPool(
window_shape=(1, 3, 3, 3, 1),
strides=(1, 1, 1, 1, 1),
padding='SAME',
name='Branch_3_MaxPool_0a_3x3')(
inputs)
branch_3 = SUnit3D(
output_channels=self._output_channels[5],
kernel_shape=(1, 1, 1),
separable=False,
normalize_fn=self._normalize_fn,
self_gating_fn=self._self_gating_fn,
name='Branch_3_Conv2d_0b_1x1')(
branch_3, is_training=is_training)
return jnp.concatenate((branch_0, branch_1, branch_2, branch_3), axis=4)
_Layer = collections.namedtuple('_Layer', ('name', 'module', 'kwargs'))
class S3D(hk.Module):
"""S3D architecture.
Any intermediary representation can be obtained by choosing one of the valid
`final_endpoint`s. The final value returned by this model (when 'Embeddings'
is used as `final_endpoint`) is a single 1-D representation for each video in
the batch. Another layer can be externally added on top of that to obtain
logits.
"""
# Endpoints of the model in order.
VALID_ENDPOINTS = (
'Conv2d_1a_7x7',
'MaxPool_2a_3x3',
'Conv2d_2b_1x1',
'Conv2d_2c_3x3',
'MaxPool_3a_3x3',
'Mixed_3b',
'Mixed_3c',
'MaxPool_4a_3x3',
'Mixed_4b',
'Mixed_4c',
'Mixed_4d',
'Mixed_4e',
'Mixed_4f',
'MaxPool_5a_2x2',
'Mixed_5b',
'Mixed_5c',
'Embeddings',
)
def __init__(self,
normalize_fn: Optional[types.NormalizeFn] = None,
first_temporal_kernel_size: int = 7,
temporal_conv_startat: Optional[str] = 'Conv2d_2c_3x3',
gating_startat: Optional[str] = 'Conv2d_2c_3x3',
name='S3D'):
"""Initializes the S3D module.
Args:
normalize_fn: Function used for normalization.
first_temporal_kernel_size: Specifies the temporal kernel size for the
first conv3d filter. A larger value slows down the model but provides
little accuracy improvement. Must be set to one of 1, 3, 5 or 7.
temporal_conv_startat: Specifies the first conv block to use separable 3D
convs rather than 2D convs (implemented as [1, k, k] 3D conv). This is
used to construct the inverted pyramid models. 'Conv2d_2c_3x3' is the
first valid block to use separable 3D convs. If provided block name is
not present, all valid blocks will use separable 3D convs.
gating_startat: Specifies the first conv block to use self gating.
'Conv2d_2c_3x3' is the first valid block to use self gating. If provided
block name is not present, all valid blocks will use separable 3D convs.
name: The name of the module.
Raises:
ValueError: If `temporal_conv_startat`, `gating_startat` or
`first_temporal_kernel_size` is not recognized.
"""
super().__init__(name=name)
self._first_temporal_kernel_size = first_temporal_kernel_size
self._temporal_conv_startat = temporal_conv_startat
self._gating_startat = gating_startat
self._normalize_fn = normalize_fn
if (temporal_conv_startat not in self.VALID_ENDPOINTS
and temporal_conv_startat is not None):
raise ValueError(
f'Provided `temporal_conv_startat`: {temporal_conv_startat} not '
f'valid. It must be one of: {self.VALID_ENDPOINTS}, or `None`.')
if (gating_startat not in self.VALID_ENDPOINTS
and gating_startat is not None):
raise ValueError(
f'Provided `gating_startat`: {gating_startat} not valid. '
f'It must be one of: {self.VALID_ENDPOINTS}, or `None`.')
if first_temporal_kernel_size not in [1, 3, 5, 7]:
raise ValueError('`first_temporal_kernel_size` can only be 1, 3, 5 or 7.')
def __call__(self,
inputs: types.TensorLike,
is_training: bool,
final_endpoint: str = 'Embeddings') -> jnp.ndarray:
"""Connects the model to inputs.
Args:
inputs: A 5-D float array of shape `[B, T, H, W, C]`.
is_training: Whether to use training mode.
final_endpoint: Up to which endpoint to run / return.
Returns:
A 5-D float array of shape
`[B, new_t, new_h, new_w, sum(output_channels)]`.
Returns:
Network output at location `final_endpoint`. A float array which shape
depends on `final_endpoint`.
Raises:
ValueError: If `final_endpoint` is not recognized.
"""
if final_endpoint not in self.VALID_ENDPOINTS:
raise ValueError(f'Provided final_endpoint: {final_endpoint} not valid.'
f' It must be one of: {self.VALID_ENDPOINTS}')
x = inputs
# We define layers with tuples (name, module, kwargs)
# Not all kwargs are present, as we will need to fill in certain properties
# as we move down the network.
layers = []
# The first layer is conditional on the input data shape: the channel size
# is used to identify whether the `space_to_depth` transformation has been
# applied to the input. This is used to speed up computation on TPUs.
if x.shape[-1] == 3:
layers.append(
_Layer('Conv2d_1a_7x7', SUnit3D,
dict(output_channels=64, stride=(2, 2, 2), separable=False,
kernel_shape=(self._first_temporal_kernel_size, 7, 7),
normalize_fn=self._normalize_fn)))
else:
layers.append(
_Layer('Conv2d_1a_7x7', SUnit3D,
dict(output_channels=64, kernel_shape=(2, 4, 4),
stride=(1, 1, 1), separable=False,
normalize_fn=self._normalize_fn)))
layers.extend([
_Layer('MaxPool_2a_3x3', _MaxPool,
dict(window_shape=(1, 1, 3, 3, 1), strides=(1, 1, 2, 2, 1),
padding='SAME')),
_Layer('Conv2d_2b_1x1', SUnit3D,
dict(output_channels=64, kernel_shape=(1, 1, 1),
normalize_fn=self._normalize_fn)),
_Layer('Conv2d_2c_3x3', SUnit3D,
dict(output_channels=192, separable=True,
normalize_fn=self._normalize_fn)),
_Layer('MaxPool_3a_3x3', _MaxPool,
dict(window_shape=(1, 1, 3, 3, 1), strides=(1, 1, 2, 2, 1),
padding='SAME')),
_Layer('Mixed_3b', InceptionBlockV13D,
dict(output_channels=(64, 96, 128, 16, 32, 32),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_3c', InceptionBlockV13D,
dict(output_channels=(128, 128, 192, 32, 96, 64),
normalize_fn=self._normalize_fn)),
_Layer('MaxPool_4a_3x3', _MaxPool,
dict(window_shape=(1, 3, 3, 3, 1), strides=(1, 2, 2, 2, 1),
padding='SAME')),
_Layer('Mixed_4b', InceptionBlockV13D,
dict(output_channels=(192, 96, 208, 16, 48, 64),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_4c', InceptionBlockV13D,
dict(output_channels=(160, 112, 224, 24, 64, 64),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_4d', InceptionBlockV13D,
dict(output_channels=(128, 128, 256, 24, 64, 64),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_4e', InceptionBlockV13D,
dict(output_channels=(112, 144, 288, 32, 64, 64),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_4f', InceptionBlockV13D,
dict(output_channels=(256, 160, 320, 32, 128, 128),
normalize_fn=self._normalize_fn)),
_Layer('MaxPool_5a_2x2', _MaxPool,
dict(window_shape=(1, 2, 2, 2, 1), strides=(1, 2, 2, 2, 1),
padding='SAME')),
_Layer('Mixed_5b', InceptionBlockV13D,
dict(output_channels=(256, 160, 320, 32, 128, 128),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_5c', InceptionBlockV13D,
dict(output_channels=(384, 192, 384, 48, 128, 128),
normalize_fn=self._normalize_fn)),
])
# These parameters may change thoughout the computation.
self_gating_fn = None
temporal_kernel_size = 1
# Iterate over layers.
for layer in layers:
# Update
if layer.name == self._gating_startat:
self_gating_fn = self_gating
if layer.name == self._temporal_conv_startat:
temporal_kernel_size = 3
kwargs = layer.kwargs
if layer.module is SUnit3D:
kwargs['self_gating_fn'] = self_gating_fn
if 'kernel_shape' not in kwargs:
kwargs['kernel_shape'] = (temporal_kernel_size, 3, 3)
elif layer.module is InceptionBlockV13D:
kwargs['self_gating_fn'] = self_gating_fn
kwargs['temporal_kernel_size'] = temporal_kernel_size
module = layer.module(name=layer.name, **kwargs)
x = module(x, is_training=is_training)
if final_endpoint == layer.name:
return x
assert final_endpoint == 'Embeddings'
return jnp.mean(x, axis=(1, 2, 3))
================================================
FILE: mmv/models/s3d_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for s3d."""
from absl.testing import absltest
from absl.testing import parameterized
import haiku as hk
import jax
import numpy as np
from mmv.models import normalization
from mmv.models import s3d
class _CallableS3D:
"""Wrapper around S3D that take care of parameter book keeping."""
def __init__(self, *args, **kwargs):
self._model = hk.transform_with_state(
lambda *a, **k: # pylint: disable=g-long-lambda,unnecessary-lambda
s3d.S3D(
normalize_fn=normalization.get_normalize_fn(),
*args, **kwargs)(*a, **k))
self._rng = jax.random.PRNGKey(42)
self._params, self._state = None, None
def init(self, inputs, **kwargs):
self._params, self._state = self._model.init(
self._rng, inputs, is_training=True, **kwargs)
def __call__(self, inputs, **kwargs):
if self._params is None:
self.init(inputs)
output, _ = self._model.apply(
self._params, self._state, self._rng, inputs, **kwargs)
return output
class S3DTest(parameterized.TestCase):
# Testing all layers is quite slow, added in comments for completeness.
@parameterized.parameters(
# dict(endpoint='Conv2d_1a_7x7', expected_size=(2, 8, 112, 112, 64)),
# dict(endpoint='MaxPool_2a_3x3', expected_size=(2, 8, 56, 56, 64)),
# dict(endpoint='Conv2d_2b_1x1', expected_size=(2, 8, 56, 56, 64)),
# dict(endpoint='Conv2d_2c_3x3', expected_size=(2, 8, 56, 56, 192)),
# dict(endpoint='MaxPool_3a_3x3', expected_size=(2, 8, 28, 28, 192)),
# dict(endpoint='Mixed_3b', expected_size=(2, 8, 28, 28, 256)),
# dict(endpoint='Mixed_3c', expected_size=(2, 8, 28, 28, 480)),
# dict(endpoint='MaxPool_4a_3x3', expected_size=(2, 4, 14, 14, 480)),
# dict(endpoint='Mixed_4b', expected_size=(2, 4, 14, 14, 512)),
# dict(endpoint='Mixed_4c', expected_size=(2, 4, 14, 14, 512)),
# dict(endpoint='Mixed_4d', expected_size=(2, 4, 14, 14, 512)),
# dict(endpoint='Mixed_4e', expected_size=(2, 4, 14, 14, 528)),
# dict(endpoint='Mixed_4f', expected_size=(2, 4, 14, 14, 832)),
# dict(endpoint='MaxPool_5a_2x2', expected_size=(2, 2, 7, 7, 832)),
# dict(endpoint='Mixed_5b', expected_size=(2, 2, 7, 7, 832)),
# dict(endpoint='Mixed_5c', expected_size=(2, 2, 7, 7, 1024)),
dict(endpoint='Embeddings', expected_size=(2, 1024)),
)
def test_endpoint_expected_output_dimensions(self, endpoint, expected_size):
inputs = np.random.normal(size=(2, 16, 224, 224, 3))
model = _CallableS3D()
output = model(inputs, is_training=False, final_endpoint=endpoint)
self.assertSameElements(output.shape, expected_size)
def test_space_to_depth(self):
inputs = np.random.normal(size=(2, 16//2, 224//2, 224//2, 3*2*2*2))
model = _CallableS3D()
output = model(inputs, is_training=False, final_endpoint='Conv2d_1a_7x7')
self.assertSameElements(output.shape, (2, 8, 112, 112, 64))
if __name__ == '__main__':
absltest.main()
================================================
FILE: mmv/models/tsm_resnet.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Temporal Shift Module w/ ResNet-50 and ResNet-101.
Based on:
TSM: Temporal Shift Module for Efficient Video Understanding
Ji Lin, Chuang Gan, Song Han
https://arxiv.org/pdf/1811.08383.pdf.
"""
from typing import Optional
import haiku as hk
import jax
import jax.numpy as jnp
from mmv.models import tsm_utils as tsmu
from mmv.models import types
class TSMResNetBlock(hk.Module):
"""A ResNet subblock with Temporal Channel Shifting.
Combines a typical ResNetV2 block implementation
(see https://arxiv.org/abs/1512.03385) with a pre-convolution Temporal
Shift Module (see https://arxiv.org/pdf/1811.08383.pdf) in the residual.
"""
def __init__(self,
output_channels: int,
stride: int,
use_projection: bool,
tsm_mode: str,
normalize_fn: Optional[types.NormalizeFn] = None,
channel_shift_fraction: float = 0.125,
num_frames: int = 8,
name: str = 'TSMResNetBlock'):
"""Initializes the TSMResNetBlock module.
Args:
output_channels: Number of output channels.
stride: Stride used in convolutions.
use_projection: Whether to use a projection for the shortcut.
tsm_mode: Mode for TSM ('gpu' or 'tpu').
normalize_fn: Function used for normalization.
channel_shift_fraction: The fraction of temporally shifted channels. If
`channel_shift_fraction` is 0, the block is the same as a normal ResNet
block.
num_frames: Size of frame dimension in a single batch example
name: The name of the module.
"""
super().__init__(name=name)
self._output_channels = output_channels
self._bottleneck_channels = output_channels // 4
self._stride = stride
self._use_projection = use_projection
self._normalize_fn = normalize_fn
self._tsm_mode = tsm_mode
self._channel_shift_fraction = channel_shift_fraction
self._num_frames = num_frames
def __call__(self,
inputs: types.TensorLike,
is_training: bool = True) -> jnp.ndarray:
"""Connects the ResNetBlock module into the graph.
Args:
inputs: A 4-D float array of shape `[B, H, W, C]`.
is_training: Whether to use training mode.
Returns:
A 4-D float array of shape
`[B * num_frames, new_h, new_w, output_channels]`.
"""
# ResNet V2 uses pre-activation, where the batch norm and relu are before
# convolutions, rather than after as in ResNet V1.
preact = inputs
if self._normalize_fn is not None:
preact = self._normalize_fn(preact, is_training=is_training)
preact = jax.nn.relu(preact)
if self._use_projection:
shortcut = hk.Conv2D(
output_channels=self._output_channels,
kernel_shape=1,
stride=self._stride,
with_bias=False,
padding='SAME',
name='shortcut_conv')(
preact)
else:
shortcut = inputs
# Eventually applies Temporal Shift Module.
if self._channel_shift_fraction != 0:
preact = tsmu.apply_temporal_shift(
preact, tsm_mode=self._tsm_mode, num_frames=self._num_frames,
channel_shift_fraction=self._channel_shift_fraction)
# First convolution.
residual = hk.Conv2D(
self._bottleneck_channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding='SAME',
name='conv_0')(
preact)
# Second convolution.
if self._normalize_fn is not None:
residual = self._normalize_fn(residual, is_training=is_training)
residual = jax.nn.relu(residual)
residual = hk.Conv2D(
output_channels=self._bottleneck_channels,
kernel_shape=3,
stride=self._stride,
with_bias=False,
padding='SAME',
name='conv_1')(
residual)
# Third convolution.
if self._normalize_fn is not None:
residual = self._normalize_fn(residual, is_training=is_training)
residual = jax.nn.relu(residual)
residual = hk.Conv2D(
output_channels=self._output_channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding='SAME',
name='conv_2')(
residual)
# NOTE: we do not use block multiplier.
output = shortcut + residual
return output
class TSMResNetUnit(hk.Module):
"""Block group for TSM ResNet."""
def __init__(self,
output_channels: int,
num_blocks: int,
stride: int,
tsm_mode: str,
num_frames: int,
normalize_fn: Optional[types.NormalizeFn] = None,
channel_shift_fraction: float = 0.125,
name: str = 'tsm_resnet_unit'):
"""Creates a TSMResNet Unit.
Args:
output_channels: Number of output channels.
num_blocks: Number of ResNet blocks in the unit.
stride: Stride of the unit.
tsm_mode: Which temporal shift module to use.
num_frames: Size of frame dimension in a single batch example.
normalize_fn: Function used for normalization.
channel_shift_fraction: The fraction of temporally shifted channels. If
`channel_shift_fraction` is 0, the block is the same as a normal ResNet
block.
name: The name of the module.
"""
super().__init__(name=name)
self._output_channels = output_channels
self._num_blocks = num_blocks
self._normalize_fn = normalize_fn
self._stride = stride
self._tsm_mode = tsm_mode
self._channel_shift_fraction = channel_shift_fraction
self._num_frames = num_frames
def __call__(self,
inputs: types.TensorLike,
is_training: bool) -> jnp.ndarray:
"""Connects the module to inputs.
Args:
inputs: A 4-D float array of shape `[B * num_frames, H, W, C]`.
is_training: Whether to use training mode.
Returns:
A 4-D float array of shape
`[B * num_frames, H // stride, W // stride, output_channels]`.
"""
net = inputs
for idx_block in range(self._num_blocks):
net = TSMResNetBlock(
self._output_channels,
stride=self._stride if idx_block == 0 else 1,
use_projection=idx_block == 0,
normalize_fn=self._normalize_fn,
tsm_mode=self._tsm_mode,
channel_shift_fraction=self._channel_shift_fraction,
num_frames=self._num_frames,
name=f'block_{idx_block}')(
net, is_training=is_training)
return net # pytype: disable=bad-return-type # jax-devicearray
class TSMResNetV2(hk.Module):
"""TSM based on ResNet V2 as described in https://arxiv.org/abs/1603.05027."""
# Endpoints of the model in order.
VALID_ENDPOINTS = (
'tsm_resnet_stem',
'tsm_resnet_unit_0',
'tsm_resnet_unit_1',
'tsm_resnet_unit_2',
'tsm_resnet_unit_3',
'last_conv',
'Embeddings',
)
def __init__(self,
normalize_fn: Optional[types.NormalizeFn] = None,
depth: int = 50,
num_frames: int = 16,
channel_shift_fraction: float = 0.125,
width_mult: int = 1,
name: str = 'TSMResNetV2'):
"""Constructs a ResNet model.
Args:
normalize_fn: Function used for normalization.
depth: Depth of the desired ResNet.
num_frames: Number of frames (used in TPU mode).
channel_shift_fraction: Fraction of channels that are temporally shifted,
if `channel_shift_fraction` is 0, a regular ResNet is returned.
width_mult: Whether or not to use a width multiplier.
name: The name of the module.
Raises:
ValueError: If `channel_shift_fraction` or `depth` has invalid value.
"""
super().__init__(name=name)
if not 0. <= channel_shift_fraction <= 1.0:
raise ValueError(
f'channel_shift_fraction ({channel_shift_fraction})'
' has to be in [0, 1].')
self._num_frames = num_frames
self._channels = (256, 512, 1024, 2048)
self._strides = (1, 2, 2, 2)
num_blocks = {
50: (3, 4, 6, 3),
101: (3, 4, 23, 3),
152: (3, 8, 36, 3),
200: (3, 24, 36, 3),
}
if depth not in num_blocks:
raise ValueError(
f'`depth` should be in {list(num_blocks.keys())} ({depth} given).')
self._num_blocks = num_blocks[depth]
self._width_mult = width_mult
self._channel_shift_fraction = channel_shift_fraction
self._normalize_fn = normalize_fn
def __call__(
self,
inputs: types.TensorLike,
is_training: bool = True,
final_endpoint: str = 'Embeddings') -> jnp.ndarray:
"""Connects the TSM ResNetV2 module into the graph.
Args:
inputs: A 4-D float array of shape `[B, H, W, C]`.
is_training: Whether to use training mode.
final_endpoint: Up to which endpoint to run / return.
Returns:
Network output at location `final_endpoint`. A float array which shape
depends on `final_endpoint`.
Raises:
ValueError: If `final_endpoint` is not recognized.
"""
# Prepare inputs for TSM.
inputs, tsm_mode, num_frames = tsmu.prepare_inputs(inputs)
num_frames = num_frames or self._num_frames
self._final_endpoint = final_endpoint
if self._final_endpoint not in self.VALID_ENDPOINTS:
raise ValueError(f'Unknown final endpoint {self._final_endpoint}')
# Stem convolution.
end_point = 'tsm_resnet_stem'
net = hk.Conv2D(
output_channels=64 * self._width_mult,
kernel_shape=7,
stride=2,
with_bias=False,
name=end_point,
padding='SAME')(
inputs)
net = hk.MaxPool(
window_shape=(1, 3, 3, 1),
strides=(1, 2, 2, 1),
padding='SAME')(
net)
if self._final_endpoint == end_point:
return net
# Residual block.
for unit_id, (channels, num_blocks, stride) in enumerate(
zip(self._channels, self._num_blocks, self._strides)):
end_point = f'tsm_resnet_unit_{unit_id}'
net = TSMResNetUnit(
output_channels=channels * self._width_mult,
num_blocks=num_blocks,
stride=stride,
normalize_fn=self._normalize_fn,
channel_shift_fraction=self._channel_shift_fraction,
num_frames=num_frames,
tsm_mode=tsm_mode,
name=end_point)(
net, is_training=is_training)
if self._final_endpoint == end_point:
return net
if self._normalize_fn is not None:
net = self._normalize_fn(net, is_training=is_training)
net = jax.nn.relu(net)
end_point = 'last_conv'
if self._final_endpoint == end_point:
return net
net = jnp.mean(net, axis=(1, 2))
# Prepare embedding outputs for TSM (temporal average of features).
net = tsmu.prepare_outputs(net, tsm_mode, num_frames)
assert self._final_endpoint == 'Embeddings'
return net
================================================
FILE: mmv/models/tsm_resnet_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for TSM ResNet model."""
from absl.testing import absltest
from absl.testing import parameterized
import haiku as hk
import jax
import jax.numpy as jnp
from mmv.models import tsm_resnet
class TSMResNetTest(parameterized.TestCase):
@parameterized.parameters(
('tsm_resnet_stem', (2 * 32, 56, 56, 64)),
('tsm_resnet_unit_0', (2 * 32, 56, 56, 256)),
('tsm_resnet_unit_1', (2 * 32, 28, 28, 512)),
('tsm_resnet_unit_2', (2 * 32, 14, 14, 1024)),
('tsm_resnet_unit_3', (2 * 32, 7, 7, 2048)),
('last_conv', (2 * 32, 7, 7, 2048)),
('Embeddings', (2, 2048)),
)
def test_output_dimension(self, final_endpoint, expected_shape):
input_shape = (2, 32, 224, 224, 3)
def f():
data = jnp.zeros(input_shape)
net = tsm_resnet.TSMResNetV2()
return net(data, final_endpoint=final_endpoint)
init_fn, apply_fn = hk.transform(f)
out = apply_fn(init_fn(jax.random.PRNGKey(42)), None)
self.assertEqual(out.shape, expected_shape)
def test_tpu_mode(self):
input_shape = (32 * 2, 224, 224, 3)
def f():
data = jnp.zeros(input_shape)
net = tsm_resnet.TSMResNetV2(num_frames=32)
return net(data, final_endpoint='Embeddings')
init_fn, apply_fn = hk.transform(f)
out = apply_fn(init_fn(jax.random.PRNGKey(42)), None)
self.assertEqual(out.shape, (2, 2048))
if __name__ == '__main__':
absltest.main()
================================================
FILE: mmv/models/tsm_utils.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utils functions for TSM."""
from typing import Tuple
import jax
import jax.numpy as jnp
from mmv.models import types
def prepare_inputs(
inputs: types.TensorLike) -> Tuple[jnp.ndarray, str, int]:
"""Deduces input mode for TSM."""
# Deduce if we run on TPU based on input shape.
if len(inputs.shape) == 5:
# Input is given in the standard [B, T, H, W, 3] format.
tsm_mode = 'gpu'
num_frames = inputs.shape[1]
inputs = jnp.reshape(inputs, [-1] + list(inputs.shape[2:]))
else:
# Input is given in the [T * B, H, W, 3] format.
tsm_mode = 'tpu'
num_frames = None
return inputs, tsm_mode, num_frames
def prepare_outputs(outputs: types.TensorLike,
tsm_mode: str,
num_frames: int) -> jnp.ndarray:
"""Processes output of TSM by averaging representations over time axis."""
n_channels = outputs.shape[-1]
if tsm_mode == 'tpu':
outputs = jnp.reshape(outputs, [num_frames, -1, n_channels])
outputs = jnp.mean(outputs, axis=0)
elif tsm_mode == 'gpu':
outputs = jnp.reshape(outputs, [-1, num_frames, n_channels])
outputs = jnp.mean(outputs, axis=1)
else:
raise ValueError(
f'`tsm_mode` should be \'tpu\' or \'gpu\' ({tsm_mode} given)')
return outputs
def apply_temporal_shift(
x: types.TensorLike,
tsm_mode: str,
num_frames: int,
channel_shift_fraction: float = 0.125) -> jnp.ndarray:
"""Performs a temporal shift: https://arxiv.org/abs/1811.08383 with mode."""
if tsm_mode == 'tpu':
outputs = temporal_shift_tpu(x, num_frames, channel_shift_fraction)
elif tsm_mode == 'gpu':
outputs = temporal_shift_gpu(x, num_frames, channel_shift_fraction)
else:
raise ValueError(
f'`tsm_mode` should be \'tpu\' or \'gpu\' ({tsm_mode} given)')
return outputs
def temporal_shift_gpu(
x: types.TensorLike,
num_frames: int,
channel_shift_fraction: float = 0.125) -> jnp.ndarray:
"""Performs a temporal shift: https://arxiv.org/abs/1811.08383."""
# B, T, H, W, C = batch_size, num_frames, im_height, im_width, channels
# Input is (B * T, H, W, C)
orig_shp = tuple(x.shape)
reshaped_x = jnp.reshape(x, (-1, num_frames) + orig_shp[1:])
n_channels = orig_shp[-1]
n_shift = int(n_channels * channel_shift_fraction)
new_shp = tuple(reshaped_x.shape)
# shifted_backward = reshaped_x[:, 1:, :, :, -n_shift:]
shifted_backward = jax.lax.slice(
reshaped_x, (0, 1, 0, 0, new_shp[4] - n_shift),
(new_shp[0], new_shp[1], new_shp[2], new_shp[3], new_shp[4]))
shifted_backward_padding = ((0, 0), (0, 1), (0, 0), (0, 0), (0, 0))
shifted_backward = jnp.pad(shifted_backward, shifted_backward_padding)
# shifted_forward = reshaped_x[:, :-1, :, :, :n_shift]
shifted_forward = jax.lax.slice(
reshaped_x, (0, 0, 0, 0, 0),
(new_shp[0], new_shp[1] - 1, new_shp[2], new_shp[3], n_shift))
shifted_forward_padding = ((0, 0), (1, 0), (0, 0), (0, 0), (0, 0))
shifted_forward = jnp.pad(shifted_forward, shifted_forward_padding)
no_shift = reshaped_x[:, :, :, :, n_shift:-n_shift]
shifted_x = jnp.concatenate([shifted_backward, no_shift, shifted_forward],
axis=4)
return jnp.reshape(shifted_x, (-1,) + orig_shp[1:])
def temporal_shift_tpu(
x: types.TensorLike,
num_frames: int,
channel_shift_fraction: float = 0.125) -> jnp.ndarray:
"""Performs a temporal shift: https://arxiv.org/abs/1811.08383.
TPU optimized version of TSM. Reshape is avoided by having the images
reshaped in [T * B, :] so that frames corresponding to same time frame in
videos are contiguous in memory. Thanks to cr/288510308 which allows to fuse
pad->slice into convolution, we reformulate the slice pad into a pad then
slice. Finally, to avoid concatenate that prevent some fusion from happening
we simply sum masked version of the features.
Args:
x: Input expected to be [T * B, H, W, C] (where the batch has been reshaped
from a time major version of the input).
num_frames: number of frames T per video.
channel_shift_fraction: fraction of the channel to shift forward and
backward.
Returns:
The temporal shifted version of x.
"""
# B, T, H, W, C = batch_size, num_frames, im_height, im_width, channels
# Input is (T * B, H, W, C)
original_shape = list(x.shape)
batch_size = int(original_shape[0] / num_frames)
n_channels = int(original_shape[-1])
n_shift = int(n_channels * channel_shift_fraction)
# Cast to bfloat16.
x = x.astype(jnp.bfloat16)
# For the following, assume that x has 3 channels [x1, x2, x3] and n_shift=1.
# Shift backward, we first pad by zeros [x1, x2, x3, 0, 0].
orig_shp = list(x.shape)
shifted_backward_padding = ((0, batch_size, 0), (0, 0, 0), (0, 0, 0),
(0, n_channels - n_shift, 0))
x_backward_padding = jax.lax.pad(
x,
padding_value=jnp.bfloat16(0.),
padding_config=shifted_backward_padding)
# The following shift gets to [x3^+1, 0, 0] (where +1 means from the future).
shifted_backward = jax.lax.slice(x_backward_padding,
(batch_size, 0, 0, n_channels - n_shift),
(orig_shp[0] + batch_size, orig_shp[1],
orig_shp[2], 2 * n_channels - n_shift))
# Shift forward, we first pad by zeros [0, 0, x1, x2, x3].
shifted_forward_padding = ((batch_size, 0, 0), (0, 0, 0), (0, 0, 0),
(n_channels - n_shift, 0, 0))
x_forward_padding = jax.lax.pad(
x,
padding_value=jnp.bfloat16(0.),
padding_config=shifted_forward_padding)
# The following shift gets to [0, 0, x1^-1] (where -1 means from the past).
shifted_forward = jax.lax.slice(
x_forward_padding, (0, 0, 0, 0),
(orig_shp[0], orig_shp[1], orig_shp[2], n_channels))
# No shift is in the middle, this gets [0, x2, 0].
mask_noshift = (jnp.reshape((jnp.arange(n_channels) >= n_shift) &
(jnp.arange(n_channels) < n_channels - n_shift),
(1, 1, 1, -1))).astype(jnp.bfloat16)
no_shift = mask_noshift * x
# By summing everything together, we end up with [x3^+1, x2, x1^-1].
# Note: channels have been reordered but that doesn't matter for the model.
shifted_x = shifted_backward + shifted_forward + no_shift
return shifted_x.astype(jnp.float32)
================================================
FILE: mmv/models/tsm_utils_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for tsm_utils."""
from absl.testing import absltest
from absl.testing import parameterized
import jax.numpy as jnp
import numpy as np
from mmv.models import tsm_utils
class TsmUtilsTest(parameterized.TestCase):
@parameterized.parameters(
((2, 32, 224, 224, 3), 'gpu', (2 * 32, 224, 224, 3), 32),
((32, 224, 224, 3), 'tpu', (32, 224, 224, 3), None),
)
def test_prepare_inputs(self, input_shape, expected_mode, expected_shape,
expected_num_frames):
data = jnp.zeros(input_shape)
out, mode, num_frames = tsm_utils.prepare_inputs(data)
self.assertEqual(out.shape, expected_shape)
self.assertEqual(mode, expected_mode)
self.assertEqual(num_frames, expected_num_frames)
def test_prepare_outputs(self):
data = jnp.concatenate([jnp.zeros(4), jnp.ones(4)]).reshape(4, 2)
out_gpu = tsm_utils.prepare_outputs(data, 'gpu', 2)
out_tpu = tsm_utils.prepare_outputs(data, 'tpu', 2)
expected_gpu = np.concatenate([np.zeros(2), np.ones(2)]).reshape(2, 2)
expected_tpu = 0.5 * jnp.ones((2, 2))
np.testing.assert_allclose(out_gpu, expected_gpu)
np.testing.assert_allclose(out_tpu, expected_tpu)
def test_apply_tsm(self):
shape = (32, 224, 224, 16)
data = jnp.zeros(shape)
out_gpu = tsm_utils.apply_temporal_shift(data, 'gpu', 16)
out_tpu = tsm_utils.apply_temporal_shift(data, 'tpu', 16)
self.assertEqual(out_gpu.shape, shape)
self.assertEqual(out_tpu.shape, shape)
if __name__ == '__main__':
absltest.main()
================================================
FILE: mmv/models/types.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Type Aliases."""
from typing import Callable, Tuple, Union
import jax
import numpy as np
import optax
TensorLike = Union[np.ndarray, jax.Array]
ActivationFn = Callable[[TensorLike], TensorLike]
GatingFn = Callable[[TensorLike], TensorLike]
NetworkFn = Callable[[TensorLike], TensorLike]
# Callable doesn't allow kwargs to be used, and we often want to
# pass in is_training=..., so ignore the arguments for the sake of pytype.
NormalizeFn = Callable[..., TensorLike]
OptState = Tuple[optax.TraceState, optax.ScaleByScheduleState, optax.ScaleState]
================================================
FILE: mmv/requirements.txt
================================================
dm-haiku
dm-tree
jax
jaxlib
numpy>=1.16
optax
sklearn
tensorflow
tensorflow_datasets
================================================
FILE: mmv/utils/checkpoint.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Checkpoint restoring utilities."""
from absl import logging
import dill
def load_checkpoint(checkpoint_path):
try:
with open(checkpoint_path, 'rb') as checkpoint_file:
checkpoint_data = dill.load(checkpoint_file)
logging.info('Loading checkpoint from %s', checkpoint_path)
return checkpoint_data
except FileNotFoundError:
return None
================================================
FILE: mmv/utils/ucf101_dataset.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Ucf101 with custom decoding params."""
import tensorflow as tf
import tensorflow_datasets as tfds
# Utilities functions.
tf.compat.v1.enable_eager_execution()
_CITATION = """\
@article{DBLP:journals/corr/abs-1212-0402,
author = {Khurram Soomro and
Amir Roshan Zamir and
Mubarak Shah},
title = {{UCF101:} {A} Dataset of 101 Human Actions Classes From Videos in
The Wild},
journal = {CoRR},
volume = {abs/1212.0402},
year = {2012},
url = {http://arxiv.org/abs/1212.0402},
archivePrefix = {arXiv},
eprint = {1212.0402},
timestamp = {Mon, 13 Aug 2018 16:47:45 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1212-0402},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_LABELS_FNAME = 'video/ucf101_labels.txt'
class ModUcf101(tfds.video.Ucf101):
"""Ucf101 action recognition dataset with better quality.
"""
def _info(self):
ffmpeg_extra_args = ('-qscale:v', '2', '-r', '25', '-t', '00:00:20')
video_shape = (
None, self.builder_config.height, self.builder_config.width, 3)
labels_names_file = tfds.core.tfds_path(_LABELS_FNAME)
features = tfds.features.FeaturesDict({
'video': tfds.features.Video(video_shape,
ffmpeg_extra_args=ffmpeg_extra_args,
encoding_format='jpeg'), # pytype: disable=wrong-arg-types # gen-stub-imports
'label': tfds.features.ClassLabel(names_file=labels_names_file),
})
return tfds.core.DatasetInfo(
builder=self,
description='A 101-label video classification dataset.',
features=features,
homepage='https://www.crcv.ucf.edu/data-sets/ucf101/',
citation=_CITATION,
)
================================================
FILE: neural_mip_solving/README.md
================================================
# Neural MIP Solving - NN Verification Dataset
This is the “Neural Network Verification” dataset used in the paper
[Solving Mixed Integer Programs Using Neural Networks (Nair et al., 2020)](https://arxiv.org/abs/2012.13349).
It contains a set of mixed integer programs (MIPs) for the problem of verifying
a neural network’s robustness to perturbations to its inputs. The MIP
formulation is described in the paper
[On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models (Gowal et al., 2018)](https://arxiv.org/abs/1810.12715).
This dataset corresponds to MIPs defined for
verifying a neural network with the architecture labelled as “small” in Table 1
of Gowal et al., 2018, and trained on the MNIST image dataset. The code used to
train the neural network to be verified is available at
https://github.com/deepmind/interval-bound-propagation. The MIPs are split into
the same training, validation, and test sets as that in Nair et al., 2020.
## Dataset Location
The dataset is available in the following
[link](https://storage.cloud.google.com/neural-mip-solving/nn_verification.tar.gz)
## Dataset Metadata
The following table is necessary for this dataset to be indexed by search
engines such as Google Dataset Search.
## Citing this work
If you use this dataset in your work, we ask you to cite this paper:
```
@misc{nair2020solving,
title={Solving Mixed Integer Programs Using Neural Networks},
author={Vinod Nair and Sergey Bartunov and Felix Gimeno and Ingrid von Glehn and Pawel Lichocki and Ivan Lobov and Brendan O'Donoghue and Nicolas Sonnerat and Christian Tjandraatmadja and Pengming Wang and Ravichandra Addanki and Tharindi Hapuarachchi and Thomas Keck and James Keeling and Pushmeet Kohli and Ira Ktena and Yujia Li and Oriol Vinyals and Yori Zwols},
year={2020},
eprint={2012.13349},
archivePrefix={arXiv},
primaryClass={math.OC}
}
```
## License
This dataset is made available under the terms of the Creative Commons
Attribution 4.0 International (CC BY 4.0) license.
You can find details at: https://creativecommons.org/licenses/by/4.0/legalcode
## Disclaimer
This is not an officially supported Google product.
================================================
FILE: nfnets/README.md
================================================
# Code for Normalizer-Free Networks
This repository contains code for the ICLR 2021 paper
["Characterizing signal propagation to close the performance gap in unnormalized
ResNets,"](https://arxiv.org/abs/2102.06171) by Andrew Brock, Soham De, and
Samuel L. Smith, and the arXiv preprint ["High-Performance Large-Scale Image
Recognition Without Normalization"](http://dpmd.ai/06171) by
Andrew Brock, Soham De, Samuel L. Smith, and Karen Simonyan.
## Running this code
Using `run.sh` will create and activate a virtualenv, install all necessary
dependencies and run a test program to ensure that you can import all the
modules and take a single experiment step. To train with this code, use this
virtualenv and use one of the experiment.py files in combination with
[JAXline](https://github.com/deepmind/jaxline). The provided
demo Colab can be run online, or by starting a jupyter notebook within this
virtualenv.
Note that you will need a local copy of ImageNet compatible with the TFDS format
used in dataset.py in order to train on ImageNet.
## Pre-Trained Weights
We provide pre-trained weights for NFNet-F0 through F5 (trained without SAM),
and for NFNet-F6 trained with SAM. All models are pre-trained on ImageNet for
360 epochs at batch size 4096, and are provided as numpy files containing
parameter trees compatible with haiku. In utils.py we provide a
`load_haiku_file` function which loads these parameter trees, and
`flatten_haiku_tree` to convert these to flat dictionaries
which may prove easier to port to other frameworks. Note that we do not provide
model `states`, as these models, lacking batchnorm, do not have running stats.
Note also that the conv layer weights are in the format HWIO, so for frameworks
like PyTorch which use OIHW you'll need to swap the axes appropriately to the
layout you use.
| Model | #FLOPs | #Params | Top-1 | Top-5 | TPUv3 Train | GPU Train | link |
|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
F0 | 12.38B | 71.5M | 83.6 | 96.8 | 73.3ms | 56.7ms | [haiku](https://storage.googleapis.com/dm-nfnets/F0_haiku.npz)
F1 | 35.54B | 132.6M | 84.7 | 97.1 | 158.5ms | 133.9ms | [haiku](https://storage.googleapis.com/dm-nfnets/F1_haiku.npz)
F2 | 62.59B | 193.8M | 85.1 | 97.3 | 295.8ms | 226.3ms | [haiku](https://storage.googleapis.com/dm-nfnets/F2_haiku.npz)
F3 | 114.76B | 254.9M | 85.7 | 97.5 | 532.2ms | 524.5ms | [haiku](https://storage.googleapis.com/dm-nfnets/F3_haiku.npz)
F4 | 215.24B | 316.1M | 85.9 | 97.6 | 1033.3ms | 1190.6ms | [haiku](https://storage.googleapis.com/dm-nfnets/F4_haiku.npz)
F5 | 289.76B | 377.2M | 86.0 | 97.6 | 1398.5ms | 2177.1ms | [haiku](https://storage.googleapis.com/dm-nfnets/F5_haiku.npz)
F6+SAM | 377.28B | 438.4M | 86.5 | 97.9 | 2774.1ms | - | [haiku](https://storage.googleapis.com/dm-nfnets/F6_haiku.npz)
## Demo Colab [](https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/nfnets/nfnet_demo_colab.ipynb)
We also include a Colab notebook with a demo showing how to run an NFNet to
classify an image.
## Giving Credit
If you use this code in your work, we ask you to please cite one or both of the
following papers.
The reference for the Normalizer-Free structure and NF-ResNets or NF-Regnets:
```
@inproceedings{brock2021characterizing,
author={Andrew Brock and Soham De and Samuel L. Smith},
title={Characterizing signal propagation to close the performance gap in
unnormalized ResNets},
booktitle={9th International Conference on Learning Representations, {ICLR}},
year={2021}
}
```
The reference for Adaptive Gradient Clipping (AGC) and the NFNets models:
```
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:2102.06171},
year={2021}
}
```
================================================
FILE: nfnets/agc_optax.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Adaptive gradient clipping transform for Optax."""
import jax
import jax.numpy as jnp
import optax
def compute_norm(x, axis, keepdims):
"""Axis-wise euclidean norm."""
return jnp.sum(x ** 2, axis=axis, keepdims=keepdims) ** 0.5
def unitwise_norm(x):
"""Compute norms of each output unit separately, also for linear layers."""
if len(jnp.squeeze(x).shape) <= 1: # Scalars and vectors
axis = None
keepdims = False
elif len(x.shape) in [2, 3]: # Linear layers of shape IO or multihead linear
axis = 0
keepdims = True
elif len(x.shape) == 4: # Conv kernels of shape HWIO
axis = [0, 1, 2,]
keepdims = True
else:
raise ValueError(f'Got a parameter with shape not in [1, 2, 4]! {x}')
return compute_norm(x, axis, keepdims)
def my_clip(g_norm, max_norm, grad):
"""Applies my gradient clipping unit-wise."""
trigger = g_norm < max_norm
# This little max(., 1e-6) is distinct from the normal eps and just prevents
# division by zero. It technically should be impossible to engage.
clipped_grad = grad * (max_norm / jnp.maximum(g_norm, 1e-6))
return jnp.where(trigger, grad, clipped_grad)
def adaptive_grad_clip(clip, eps=1e-3) -> optax.GradientTransformation:
"""Clip updates to be at most clipping * parameter_norm.
References:
[Brock, Smith, De, Simonyan 2021] High-Performance Large-Scale Image
Recognition Without Normalization.
Args:
clip: Maximum allowed ratio of update norm to parameter norm.
eps: epsilon term to prevent clipping of zero-initialized params.
Returns:
An (init_fn, update_fn) tuple.
"""
def init_fn(_):
return optax.ClipByGlobalNormState()
def update_fn(updates, state, params):
g_norm = jax.tree_map(unitwise_norm, updates)
p_norm = jax.tree_map(unitwise_norm, params)
# Maximum allowable norm
max_norm = jax.tree_map(lambda x: clip * jnp.maximum(x, eps), p_norm)
# If grad norm > clipping * param_norm, rescale
updates = jax.tree_map(my_clip, g_norm, max_norm, updates)
return updates, state
return optax.GradientTransformation(init_fn, update_fn)
================================================
FILE: nfnets/autoaugment.py
================================================
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""AutoAugment and RandAugment policies for enhanced image preprocessing.
AutoAugment Reference: https://arxiv.org/abs/1805.09501
RandAugment Reference: https://arxiv.org/abs/1909.13719
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import inspect
import math
from ml_collections import config_dict
import tensorflow.compat.v1 as tf
from tensorflow_addons import image as contrib_image
# pylint: disable=deprecated-method
# This signifies the max integer that the controller RNN could predict for the
# augmentation scheme.
_MAX_LEVEL = 10.
def policy_v0():
"""Autoaugment policy that was used in AutoAugment Paper."""
# Each tuple is an augmentation operation of the form
# (operation, probability, magnitude). Each element in policy is a
# sub-policy that will be applied sequentially on the image.
policy = [
[('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
[('Color', 0.4, 9), ('Equalize', 0.6, 3)],
[('Color', 0.4, 1), ('Rotate', 0.6, 8)],
[('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
[('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
[('Color', 0.2, 0), ('Equalize', 0.8, 8)],
[('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
[('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
[('Color', 0.6, 1), ('Equalize', 1.0, 2)],
[('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
[('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
[('Color', 0.4, 7), ('Equalize', 0.6, 0)],
[('Posterize', 0.4, 6), ('AutoContrast', 0.4, 7)],
[('Solarize', 0.6, 8), ('Color', 0.6, 9)],
[('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
[('Rotate', 1.0, 7), ('TranslateY', 0.8, 9)],
[('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
[('ShearY', 0.8, 0), ('Color', 0.6, 4)],
[('Color', 1.0, 0), ('Rotate', 0.6, 2)],
[('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
[('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
[('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
[('Posterize', 0.8, 2), ('Solarize', 0.6, 10)],
[('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
[('Color', 0.8, 6), ('Rotate', 0.4, 5)],
]
return policy
def policy_vtest():
"""Autoaugment test policy for debugging."""
# Each tuple is an augmentation operation of the form
# (operation, probability, magnitude). Each element in policy is a
# sub-policy that will be applied sequentially on the image.
policy = [
[('TranslateX', 1.0, 4), ('Equalize', 1.0, 10)],
]
return policy
def blend(image1, image2, factor):
"""Blend image1 and image2 using 'factor'.
Factor can be above 0.0. A value of 0.0 means only image1 is used.
A value of 1.0 means only image2 is used. A value between 0.0 and
1.0 means we linearly interpolate the pixel values between the two
images. A value greater than 1.0 "extrapolates" the difference
between the two pixel values, and we clip the results to values
between 0 and 255.
Args:
image1: An image Tensor of type uint8.
image2: An image Tensor of type uint8.
factor: A floating point value above 0.0.
Returns:
A blended image Tensor of type uint8.
"""
if factor == 0.0:
return tf.convert_to_tensor(image1)
if factor == 1.0:
return tf.convert_to_tensor(image2)
image1 = tf.to_float(image1)
image2 = tf.to_float(image2)
difference = image2 - image1
scaled = factor * difference
# Do addition in float.
temp = tf.to_float(image1) + scaled
# Interpolate
if factor > 0.0 and factor < 1.0:
# Interpolation means we always stay within 0 and 255.
return tf.cast(temp, tf.uint8)
# Extrapolate:
#
# We need to clip and then cast.
return tf.cast(tf.clip_by_value(temp, 0.0, 255.0), tf.uint8)
def cutout(image, pad_size, replace=0):
"""Apply cutout (https://arxiv.org/abs/1708.04552) to image.
This operation applies a (2*pad_size x 2*pad_size) mask of zeros to
a random location within `img`. The pixel values filled in will be of the
value `replace`. The located where the mask will be applied is randomly
chosen uniformly over the whole image.
Args:
image: An image Tensor of type uint8.
pad_size: Specifies how big the zero mask that will be generated is that
is applied to the image. The mask will be of size
(2*pad_size x 2*pad_size).
replace: What pixel value to fill in the image in the area that has
the cutout mask applied to it.
Returns:
An image Tensor that is of type uint8.
"""
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
# Sample the center location in the image where the zero mask will be applied.
cutout_center_height = tf.random_uniform(
shape=[], minval=0, maxval=image_height,
dtype=tf.int32)
cutout_center_width = tf.random_uniform(
shape=[], minval=0, maxval=image_width,
dtype=tf.int32)
lower_pad = tf.maximum(0, cutout_center_height - pad_size)
upper_pad = tf.maximum(0, image_height - cutout_center_height - pad_size)
left_pad = tf.maximum(0, cutout_center_width - pad_size)
right_pad = tf.maximum(0, image_width - cutout_center_width - pad_size)
cutout_shape = [image_height - (lower_pad + upper_pad),
image_width - (left_pad + right_pad)]
padding_dims = [[lower_pad, upper_pad], [left_pad, right_pad]]
mask = tf.pad(
tf.zeros(cutout_shape, dtype=image.dtype),
padding_dims, constant_values=1)
mask = tf.expand_dims(mask, -1)
mask = tf.tile(mask, [1, 1, 3])
image = tf.where(
tf.equal(mask, 0),
tf.ones_like(image, dtype=image.dtype) * replace,
image)
return image
def solarize(image, threshold=128):
# For each pixel in the image, select the pixel
# if the value is less than the threshold.
# Otherwise, subtract the pixel from 255.
return tf.where(image < threshold, image, 255 - image)
def solarize_add(image, addition=0, threshold=128):
# For each pixel in the image less than threshold
# we add 'addition' amount to it and then clip the
# pixel value to be between 0 and 255. The value
# of 'addition' is between -128 and 128.
added_image = tf.cast(image, tf.int64) + addition
added_image = tf.cast(tf.clip_by_value(added_image, 0, 255), tf.uint8)
return tf.where(image < threshold, added_image, image)
def color(image, factor):
"""Equivalent of PIL Color."""
degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(image))
return blend(degenerate, image, factor)
def contrast(image, factor):
"""Equivalent of PIL Contrast."""
degenerate = tf.image.rgb_to_grayscale(image)
# Cast before calling tf.histogram.
degenerate = tf.cast(degenerate, tf.int32)
# Compute the grayscale histogram, then compute the mean pixel value,
# and create a constant image size of that value. Use that as the
# blending degenerate target of the original image.
hist = tf.histogram_fixed_width(degenerate, [0, 255], nbins=256)
mean = tf.reduce_sum(tf.cast(hist, tf.float32)) / 256.0
degenerate = tf.ones_like(degenerate, dtype=tf.float32) * mean
degenerate = tf.clip_by_value(degenerate, 0.0, 255.0)
degenerate = tf.image.grayscale_to_rgb(tf.cast(degenerate, tf.uint8))
return blend(degenerate, image, factor)
def brightness(image, factor):
"""Equivalent of PIL Brightness."""
degenerate = tf.zeros_like(image)
return blend(degenerate, image, factor)
def posterize(image, bits):
"""Equivalent of PIL Posterize."""
shift = 8 - bits
return tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift)
def rotate(image, degrees, replace):
"""Rotates the image by degrees either clockwise or counterclockwise.
Args:
image: An image Tensor of type uint8.
degrees: Float, a scalar angle in degrees to rotate all images by. If
degrees is positive the image will be rotated clockwise otherwise it will
be rotated counterclockwise.
replace: A one or three value 1D tensor to fill empty pixels caused by
the rotate operation.
Returns:
The rotated version of image.
"""
# Convert from degrees to radians.
degrees_to_radians = math.pi / 180.0
radians = degrees * degrees_to_radians
# In practice, we should randomize the rotation degrees by flipping
# it negatively half the time, but that's done on 'degrees' outside
# of the function.
image = contrib_image.rotate(wrap(image), radians)
return unwrap(image, replace)
def translate_x(image, pixels, replace):
"""Equivalent of PIL Translate in X dimension."""
image = contrib_image.translate(wrap(image), [-pixels, 0])
return unwrap(image, replace)
def translate_y(image, pixels, replace):
"""Equivalent of PIL Translate in Y dimension."""
image = contrib_image.translate(wrap(image), [0, -pixels])
return unwrap(image, replace)
def shear_x(image, level, replace):
"""Equivalent of PIL Shearing in X dimension."""
# Shear parallel to x axis is a projective transform
# with a matrix form of:
# [1 level
# 0 1].
image = contrib_image.transform(
wrap(image), [1., level, 0., 0., 1., 0., 0., 0.])
return unwrap(image, replace)
def shear_y(image, level, replace):
"""Equivalent of PIL Shearing in Y dimension."""
# Shear parallel to y axis is a projective transform
# with a matrix form of:
# [1 0
# level 1].
image = contrib_image.transform(
wrap(image), [1., 0., 0., level, 1., 0., 0., 0.])
return unwrap(image, replace)
def autocontrast(image):
"""Implements Autocontrast function from PIL using TF ops.
Args:
image: A 3D uint8 tensor.
Returns:
The image after it has had autocontrast applied to it and will be of type
uint8.
"""
def scale_channel(image):
"""Scale the 2D image using the autocontrast rule."""
# A possibly cheaper version can be done using cumsum/unique_with_counts
# over the histogram values, rather than iterating over the entire image.
# to compute mins and maxes.
lo = tf.to_float(tf.reduce_min(image))
hi = tf.to_float(tf.reduce_max(image))
# Scale the image, making the lowest value 0 and the highest value 255.
def scale_values(im):
scale = 255.0 / (hi - lo)
offset = -lo * scale
im = tf.to_float(im) * scale + offset
im = tf.clip_by_value(im, 0.0, 255.0)
return tf.cast(im, tf.uint8)
result = tf.cond(hi > lo, lambda: scale_values(image), lambda: image)
return result
# Assumes RGB for now. Scales each channel independently
# and then stacks the result.
s1 = scale_channel(image[:, :, 0])
s2 = scale_channel(image[:, :, 1])
s3 = scale_channel(image[:, :, 2])
image = tf.stack([s1, s2, s3], 2)
return image
def sharpness(image, factor):
"""Implements Sharpness function from PIL using TF ops."""
orig_image = image
image = tf.cast(image, tf.float32)
# Make image 4D for conv operation.
image = tf.expand_dims(image, 0)
# SMOOTH PIL Kernel.
kernel = tf.constant(
[[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=tf.float32,
shape=[3, 3, 1, 1]) / 13.
# Tile across channel dimension.
kernel = tf.tile(kernel, [1, 1, 3, 1])
strides = [1, 1, 1, 1]
with tf.device('/cpu:0'):
# Some augmentation that uses depth-wise conv will cause crashing when
# training on GPU. See (b/156242594) for details.
degenerate = tf.nn.depthwise_conv2d(
image, kernel, strides, padding='VALID', rate=[1, 1])
degenerate = tf.clip_by_value(degenerate, 0.0, 255.0)
degenerate = tf.squeeze(tf.cast(degenerate, tf.uint8), [0])
# For the borders of the resulting image, fill in the values of the
# original image.
mask = tf.ones_like(degenerate)
padded_mask = tf.pad(mask, [[1, 1], [1, 1], [0, 0]])
padded_degenerate = tf.pad(degenerate, [[1, 1], [1, 1], [0, 0]])
result = tf.where(tf.equal(padded_mask, 1), padded_degenerate, orig_image)
# Blend the final result.
return blend(result, orig_image, factor)
def equalize(image):
"""Implements Equalize function from PIL using TF ops."""
def scale_channel(im, c):
"""Scale the data in the channel to implement equalize."""
im = tf.cast(im[:, :, c], tf.int32)
# Compute the histogram of the image channel.
histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
# For the purposes of computing the step, filter out the nonzeros.
nonzero = tf.where(tf.not_equal(histo, 0))
nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [-1])
step = (tf.reduce_sum(nonzero_histo) - nonzero_histo[-1]) // 255
def build_lut(histo, step):
# Compute the cumulative sum, shifting by step // 2
# and then normalization by step.
lut = (tf.cumsum(histo) + (step // 2)) // step
# Shift lut, prepending with 0.
lut = tf.concat([[0], lut[:-1]], 0)
# Clip the counts to be in range. This is done
# in the C code for image.point.
return tf.clip_by_value(lut, 0, 255)
# If step is zero, return the original image. Otherwise, build
# lut from the full histogram and step and then index from it.
result = tf.cond(tf.equal(step, 0),
lambda: im,
lambda: tf.gather(build_lut(histo, step), im))
return tf.cast(result, tf.uint8)
# Assumes RGB for now. Scales each channel independently
# and then stacks the result.
s1 = scale_channel(image, 0)
s2 = scale_channel(image, 1)
s3 = scale_channel(image, 2)
image = tf.stack([s1, s2, s3], 2)
return image
def invert(image):
"""Inverts the image pixels."""
image = tf.convert_to_tensor(image)
return 255 - image
def wrap(image):
"""Returns 'image' with an extra channel set to all 1s."""
shape = tf.shape(image)
extended_channel = tf.ones([shape[0], shape[1], 1], image.dtype)
extended = tf.concat([image, extended_channel], 2)
return extended
def unwrap(image, replace):
"""Unwraps an image produced by wrap.
Where there is a 0 in the last channel for every spatial position,
the rest of the three channels in that spatial dimension are grayed
(set to 128). Operations like translate and shear on a wrapped
Tensor will leave 0s in empty locations. Some transformations look
at the intensity of values to do preprocessing, and we want these
empty pixels to assume the 'average' value, rather than pure black.
Args:
image: A 3D Image Tensor with 4 channels.
replace: A one or three value 1D tensor to fill empty pixels.
Returns:
image: A 3D image Tensor with 3 channels.
"""
image_shape = tf.shape(image)
# Flatten the spatial dimensions.
flattened_image = tf.reshape(image, [-1, image_shape[2]])
# Find all pixels where the last channel is zero.
alpha_channel = flattened_image[:, 3]
replace = tf.concat([replace, tf.ones([1], image.dtype)], 0)
# Where they are zero, fill them in with 'replace'.
flattened_image = tf.where(
tf.equal(alpha_channel, 0),
tf.ones_like(flattened_image, dtype=image.dtype) * replace,
flattened_image)
image = tf.reshape(flattened_image, image_shape)
image = tf.slice(image, [0, 0, 0], [image_shape[0], image_shape[1], 3])
return image
NAME_TO_FUNC = {
'AutoContrast': autocontrast,
'Equalize': equalize,
'Invert': invert,
'Rotate': rotate,
'Posterize': posterize,
'Solarize': solarize,
'SolarizeAdd': solarize_add,
'Color': color,
'Contrast': contrast,
'Brightness': brightness,
'Sharpness': sharpness,
'ShearX': shear_x,
'ShearY': shear_y,
'TranslateX': translate_x,
'TranslateY': translate_y,
'Cutout': cutout,
}
def _randomly_negate_tensor(tensor):
"""With 50% prob turn the tensor negative."""
should_flip = tf.cast(tf.floor(tf.random_uniform([]) + 0.5), tf.bool)
final_tensor = tf.cond(should_flip, lambda: tensor, lambda: -tensor)
return final_tensor
def _rotate_level_to_arg(level):
level = (level/_MAX_LEVEL) * 30.
level = _randomly_negate_tensor(level)
return (level,)
def _shrink_level_to_arg(level):
"""Converts level to ratio by which we shrink the image content."""
if level == 0:
return (1.0,) # if level is zero, do not shrink the image
# Maximum shrinking ratio is 2.9.
level = 2. / (_MAX_LEVEL / level) + 0.9
return (level,)
def _enhance_level_to_arg(level):
return ((level/_MAX_LEVEL) * 1.8 + 0.1,)
def _shear_level_to_arg(level):
level = (level/_MAX_LEVEL) * 0.3
# Flip level to negative with 50% chance.
level = _randomly_negate_tensor(level)
return (level,)
def _translate_level_to_arg(level, translate_const):
level = (level/_MAX_LEVEL) * float(translate_const)
# Flip level to negative with 50% chance.
level = _randomly_negate_tensor(level)
return (level,)
def level_to_arg(hparams):
return {
'AutoContrast': lambda level: (),
'Equalize': lambda level: (),
'Invert': lambda level: (),
'Rotate': _rotate_level_to_arg,
'Posterize': lambda level: (int((level/_MAX_LEVEL) * 4),),
'Solarize': lambda level: (int((level/_MAX_LEVEL) * 256),),
'SolarizeAdd': lambda level: (int((level/_MAX_LEVEL) * 110),),
'Color': _enhance_level_to_arg,
'Contrast': _enhance_level_to_arg,
'Brightness': _enhance_level_to_arg,
'Sharpness': _enhance_level_to_arg,
'ShearX': _shear_level_to_arg,
'ShearY': _shear_level_to_arg,
'Cutout': lambda level: (int((level/_MAX_LEVEL) * hparams.cutout_const),),
# pylint:disable=g-long-lambda
'TranslateX': lambda level: _translate_level_to_arg(
level, hparams.translate_const),
'TranslateY': lambda level: _translate_level_to_arg(
level, hparams.translate_const),
# pylint:enable=g-long-lambda
}
def _parse_policy_info(name, prob, level, replace_value, augmentation_hparams):
"""Return the function that corresponds to `name` and update `level` param."""
func = NAME_TO_FUNC[name]
args = level_to_arg(augmentation_hparams)[name](level)
# Check to see if prob is passed into function. This is used for operations
# where we alter bboxes independently.
# pytype:disable=wrong-arg-types
if 'prob' in inspect.getargspec(func)[0]:
args = tuple([prob] + list(args))
# pytype:enable=wrong-arg-types
# Add in replace arg if it is required for the function that is being called.
# pytype:disable=wrong-arg-types
if 'replace' in inspect.getargspec(func)[0]:
# Make sure replace is the final argument
assert 'replace' == inspect.getargspec(func)[0][-1]
args = tuple(list(args) + [replace_value])
# pytype:enable=wrong-arg-types
return (func, prob, args)
def _apply_func_with_prob(func, image, args, prob):
"""Apply `func` to image w/ `args` as input with probability `prob`."""
assert isinstance(args, tuple)
# If prob is a function argument, then this randomness is being handled
# inside the function, so make sure it is always called.
# pytype:disable=wrong-arg-types
if 'prob' in inspect.getargspec(func)[0]:
prob = 1.0
# pytype:enable=wrong-arg-types
# Apply the function with probability `prob`.
should_apply_op = tf.cast(
tf.floor(tf.random_uniform([], dtype=tf.float32) + prob), tf.bool)
augmented_image = tf.cond(
should_apply_op,
lambda: func(image, *args),
lambda: image)
return augmented_image
def select_and_apply_random_policy(policies, image):
"""Select a random policy from `policies` and apply it to `image`."""
policy_to_select = tf.random_uniform([], maxval=len(policies), dtype=tf.int32)
# Note that using tf.case instead of tf.conds would result in significantly
# larger graphs and would even break export for some larger policies.
for (i, policy) in enumerate(policies):
image = tf.cond(
tf.equal(i, policy_to_select),
lambda selected_policy=policy: selected_policy(image),
lambda: image)
return image
def build_and_apply_nas_policy(policies, image,
augmentation_hparams):
"""Build a policy from the given policies passed in and apply to image.
Args:
policies: list of lists of tuples in the form `(func, prob, level)`, `func`
is a string name of the augmentation function, `prob` is the probability
of applying the `func` operation, `level` is the input argument for
`func`.
image: tf.Tensor that the resulting policy will be applied to.
augmentation_hparams: Hparams associated with the NAS learned policy.
Returns:
A version of image that now has data augmentation applied to it based on
the `policies` pass into the function.
"""
replace_value = [128, 128, 128]
# func is the string name of the augmentation function, prob is the
# probability of applying the operation and level is the parameter associated
# with the tf op.
# tf_policies are functions that take in an image and return an augmented
# image.
tf_policies = []
for policy in policies:
tf_policy = []
# Link string name to the correct python function and make sure the correct
# argument is passed into that function.
for policy_info in policy:
policy_info = list(policy_info) + [replace_value, augmentation_hparams]
tf_policy.append(_parse_policy_info(*policy_info))
# Now build the tf policy that will apply the augmentation procedue
# on image.
def make_final_policy(tf_policy_):
def final_policy(image_):
for func, prob, args in tf_policy_:
image_ = _apply_func_with_prob(
func, image_, args, prob)
return image_
return final_policy
tf_policies.append(make_final_policy(tf_policy))
augmented_image = select_and_apply_random_policy(
tf_policies, image)
return augmented_image
def distort_image_with_autoaugment(image, augmentation_name):
"""Applies the AutoAugment policy to `image`.
AutoAugment is from the paper: https://arxiv.org/abs/1805.09501.
Args:
image: `Tensor` of shape [height, width, 3] representing an image.
augmentation_name: The name of the AutoAugment policy to use. The available
options are `v0` and `test`. `v0` is the policy used for
all of the results in the paper and was found to achieve the best results
on the COCO dataset. `v1`, `v2` and `v3` are additional good policies
found on the COCO dataset that have slight variation in what operations
were used during the search procedure along with how many operations are
applied in parallel to a single image (2 vs 3).
Returns:
A tuple containing the augmented versions of `image`.
"""
available_policies = {'v0': policy_v0,
'test': policy_vtest}
if augmentation_name not in available_policies:
raise ValueError('Invalid augmentation_name: {}'.format(augmentation_name))
policy = available_policies[augmentation_name]()
# Hparams that will be used for AutoAugment.
augmentation_hparams = config_dict.ConfigDict(dict(
cutout_const=100, translate_const=250))
return build_and_apply_nas_policy(policy, image, augmentation_hparams)
def distort_image_with_randaugment(image, num_layers, magnitude):
"""Applies the RandAugment policy to `image`.
RandAugment is from the paper https://arxiv.org/abs/1909.13719,
Args:
image: `Tensor` of shape [height, width, 3] representing an image.
num_layers: Integer, the number of augmentation transformations to apply
sequentially to an image. Represented as (N) in the paper. Usually best
values will be in the range [1, 3].
magnitude: Integer, shared magnitude across all augmentation operations.
Represented as (M) in the paper. Usually best values are in the range
[5, 30].
Returns:
The augmented version of `image`.
"""
replace_value = [128] * 3
tf.logging.info('Using RandAug.')
augmentation_hparams = config_dict.ConfigDict(dict(
cutout_const=40, translate_const=100))
available_ops = [
'AutoContrast', 'Equalize', 'Invert', 'Rotate', 'Posterize',
'Solarize', 'Color', 'Contrast', 'Brightness', 'Sharpness',
'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Cutout', 'SolarizeAdd']
for layer_num in range(num_layers):
op_to_select = tf.random_uniform(
[], maxval=len(available_ops), dtype=tf.int32)
random_magnitude = float(magnitude)
with tf.name_scope('randaug_layer_{}'.format(layer_num)):
for (i, op_name) in enumerate(available_ops):
prob = tf.random_uniform([], minval=0.2, maxval=0.8, dtype=tf.float32)
func, _, args = _parse_policy_info(op_name, prob, random_magnitude,
replace_value, augmentation_hparams)
image = tf.cond(
tf.equal(i, op_to_select),
# pylint:disable=g-long-lambda
lambda selected_func=func, selected_args=args: selected_func(
image, *selected_args),
# pylint:enable=g-long-lambda
lambda: image)
return image
================================================
FILE: nfnets/base.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Architecture definitions for different models."""
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
# Model settings for NF-RegNets
nf_regnet_params = {
'B0': {'width': [48, 104, 208, 440], 'depth': [1, 3, 6, 6],
'train_imsize': 192, 'test_imsize': 224,
'drop_rate': 0.2},
'B1': {'width': [48, 104, 208, 440], 'depth': [2, 4, 7, 7],
'train_imsize': 224, 'test_imsize': 256,
'drop_rate': 0.2},
'B2': {'width': [56, 112, 232, 488], 'depth': [2, 4, 8, 8],
'train_imsize': 240, 'test_imsize': 272,
'drop_rate': 0.3},
'B3': {'width': [56, 128, 248, 528], 'depth': [2, 5, 9, 9],
'train_imsize': 288, 'test_imsize': 320,
'drop_rate': 0.3},
'B4': {'width': [64, 144, 288, 616], 'depth': [2, 6, 11, 11],
'train_imsize': 320, 'test_imsize': 384,
'drop_rate': 0.4},
'B5': {'width': [80, 168, 336, 704], 'depth': [3, 7, 14, 14],
'train_imsize': 384, 'test_imsize': 456,
'drop_rate': 0.4},
'B6': {'width': [88, 184, 376, 792], 'depth': [3, 8, 16, 16],
'train_imsize': 448, 'test_imsize': 528,
'drop_rate': 0.5},
'B7': {'width': [96, 208, 416, 880], 'depth': [4, 10, 19, 19],
'train_imsize': 512, 'test_imsize': 600,
'drop_rate': 0.5},
'B8': {'width': [104, 232, 456, 968], 'depth': [4, 11, 22, 22],
'train_imsize': 600, 'test_imsize': 672,
'drop_rate': 0.5},
}
nfnet_params = {}
# F-series models
nfnet_params.update(**{
'F0': {
'width': [256, 512, 1536, 1536], 'depth': [1, 2, 6, 3],
'train_imsize': 192, 'test_imsize': 256,
'RA_level': '405', 'drop_rate': 0.2},
'F1': {
'width': [256, 512, 1536, 1536], 'depth': [2, 4, 12, 6],
'train_imsize': 224, 'test_imsize': 320,
'RA_level': '410', 'drop_rate': 0.3},
'F2': {
'width': [256, 512, 1536, 1536], 'depth': [3, 6, 18, 9],
'train_imsize': 256, 'test_imsize': 352,
'RA_level': '410', 'drop_rate': 0.4},
'F3': {
'width': [256, 512, 1536, 1536], 'depth': [4, 8, 24, 12],
'train_imsize': 320, 'test_imsize': 416,
'RA_level': '415', 'drop_rate': 0.4},
'F4': {
'width': [256, 512, 1536, 1536], 'depth': [5, 10, 30, 15],
'train_imsize': 384, 'test_imsize': 512,
'RA_level': '415', 'drop_rate': 0.5},
'F5': {
'width': [256, 512, 1536, 1536], 'depth': [6, 12, 36, 18],
'train_imsize': 416, 'test_imsize': 544,
'RA_level': '415', 'drop_rate': 0.5},
'F6': {
'width': [256, 512, 1536, 1536], 'depth': [7, 14, 42, 21],
'train_imsize': 448, 'test_imsize': 576,
'RA_level': '415', 'drop_rate': 0.5},
'F7': {
'width': [256, 512, 1536, 1536], 'depth': [8, 16, 48, 24],
'train_imsize': 480, 'test_imsize': 608,
'RA_level': '415', 'drop_rate': 0.5},
})
# Minor variants FN+, slightly wider
nfnet_params.update(**{
**{f'{key}+': {**nfnet_params[key], 'width': [384, 768, 2048, 2048],}
for key in nfnet_params}
})
# Nonlinearities with magic constants (gamma) baked in.
# Note that not all nonlinearities will be stable, especially if they are
# not perfectly monotonic. Good choices include relu, silu, and gelu.
nonlinearities = {
'identity': lambda x: x,
'celu': lambda x: jax.nn.celu(x) * 1.270926833152771,
'elu': lambda x: jax.nn.elu(x) * 1.2716004848480225,
'gelu': lambda x: jax.nn.gelu(x) * 1.7015043497085571,
'glu': lambda x: jax.nn.glu(x) * 1.8484294414520264,
'leaky_relu': lambda x: jax.nn.leaky_relu(x) * 1.70590341091156,
'log_sigmoid': lambda x: jax.nn.log_sigmoid(x) * 1.9193484783172607,
'log_softmax': lambda x: jax.nn.log_softmax(x) * 1.0002083778381348,
'relu': lambda x: jax.nn.relu(x) * 1.7139588594436646,
'relu6': lambda x: jax.nn.relu6(x) * 1.7131484746932983,
'selu': lambda x: jax.nn.selu(x) * 1.0008515119552612,
'sigmoid': lambda x: jax.nn.sigmoid(x) * 4.803835391998291,
'silu': lambda x: jax.nn.silu(x) * 1.7881293296813965,
'soft_sign': lambda x: jax.nn.soft_sign(x) * 2.338853120803833,
'softplus': lambda x: jax.nn.softplus(x) * 1.9203323125839233,
'tanh': lambda x: jnp.tanh(x) * 1.5939117670059204,
}
class WSConv2D(hk.Conv2D):
"""2D Convolution with Scaled Weight Standardization and affine gain+bias."""
@hk.transparent
def standardize_weight(self, weight, eps=1e-4):
"""Apply scaled WS with affine gain."""
mean = jnp.mean(weight, axis=(0, 1, 2), keepdims=True)
var = jnp.var(weight, axis=(0, 1, 2), keepdims=True)
fan_in = np.prod(weight.shape[:-1])
# Get gain
gain = hk.get_parameter('gain', shape=(weight.shape[-1],),
dtype=weight.dtype, init=jnp.ones)
# Manually fused normalization, eq. to (w - mean) * gain / sqrt(N * var)
scale = jax.lax.rsqrt(jnp.maximum(var * fan_in, eps)) * gain
shift = mean * scale
return weight * scale - shift
def __call__(self, inputs: jnp.ndarray, eps: float = 1e-4) -> jnp.ndarray:
w_shape = self.kernel_shape + (
inputs.shape[self.channel_index] // self.feature_group_count,
self.output_channels)
# Use fan-in scaled init, but WS is largely insensitive to this choice.
w_init = hk.initializers.VarianceScaling(1.0, 'fan_in', 'normal')
w = hk.get_parameter('w', w_shape, inputs.dtype, init=w_init)
weight = self.standardize_weight(w, eps)
out = jax.lax.conv_general_dilated(
inputs, weight, window_strides=self.stride, padding=self.padding,
lhs_dilation=self.lhs_dilation, rhs_dilation=self.kernel_dilation,
dimension_numbers=self.dimension_numbers,
feature_group_count=self.feature_group_count)
# Always add bias
bias_shape = (self.output_channels,)
bias = hk.get_parameter('bias', bias_shape, inputs.dtype, init=jnp.zeros)
return out + bias
def signal_metrics(x, i):
"""Things to measure about a NCHW tensor activation."""
metrics = {}
# Average channel-wise mean-squared
metrics[f'avg_sq_mean_{i}'] = jnp.mean(jnp.mean(x, axis=[0, 1, 2])**2)
# Average channel variance
metrics[f'avg_var_{i}'] = jnp.mean(jnp.var(x, axis=[0, 1, 2]))
return metrics
def count_conv_flops(in_ch, conv, h, w):
"""For a conv layer with in_ch inputs, count the FLOPS."""
# How many outputs are we producing? Note this is wrong for VALID padding.
output_shape = conv.output_channels * (h * w) / np.prod(conv.stride)
# At each OHW location we do computation equal to (I//G) * kh * kw
flop_per_loc = (in_ch / conv.feature_group_count)
flop_per_loc *= np.prod(conv.kernel_shape)
return output_shape * flop_per_loc
class SqueezeExcite(hk.Module):
"""Simple Squeeze+Excite module."""
def __init__(self, in_ch, out_ch, se_ratio=0.5,
hidden_ch=None, activation=jax.nn.relu,
name=None):
super().__init__(name=name)
self.in_ch, self.out_ch = in_ch, out_ch
if se_ratio is None:
if hidden_ch is None:
raise ValueError('Must provide one of se_ratio or hidden_ch')
self.hidden_ch = hidden_ch
else:
self.hidden_ch = max(1, int(self.in_ch * se_ratio))
self.activation = activation
self.fc0 = hk.Linear(self.hidden_ch, with_bias=True)
self.fc1 = hk.Linear(self.out_ch, with_bias=True)
def __call__(self, x):
h = jnp.mean(x, axis=[1, 2]) # Mean pool over HW extent
h = self.fc1(self.activation(self.fc0(h)))
h = jax.nn.sigmoid(h)[:, None, None] # Broadcast along H, W
return h
class StochDepth(hk.Module):
"""Batchwise Dropout used in EfficientNet, optionally sans rescaling."""
def __init__(self, drop_rate, scale_by_keep=False, name=None):
super().__init__(name=name)
self.drop_rate = drop_rate
self.scale_by_keep = scale_by_keep
def __call__(self, x, is_training) -> jnp.ndarray:
if not is_training:
return x
batch_size = x.shape[0]
r = jax.random.uniform(hk.next_rng_key(), [batch_size, 1, 1, 1],
dtype=x.dtype)
keep_prob = 1. - self.drop_rate
binary_tensor = jnp.floor(keep_prob + r)
if self.scale_by_keep:
x = x / keep_prob
return x * binary_tensor
================================================
FILE: nfnets/dataset.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ImageNet dataset with typical pre-processing and advanced augs."""
# pylint: disable=logging-format-interpolation
import enum
import itertools as it
import logging
import re
from typing import Generator, Mapping, Optional, Sequence, Text, Tuple
import jax
import jax.numpy as jnp
import numpy as np
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
import tensorflow_probability as tfp
from nfnets import autoaugment
Batch = Mapping[Text, np.ndarray]
MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)
STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)
AUTOTUNE = tf.data.experimental.AUTOTUNE
class Split(enum.Enum):
"""Imagenet dataset split."""
TRAIN = 1
TRAIN_AND_VALID = 2
VALID = 3
TEST = 4
@classmethod
def from_string(cls, name: Text) -> 'Split':
return {'TRAIN': Split.TRAIN, 'TRAIN_AND_VALID': Split.TRAIN_AND_VALID,
'VALID': Split.VALID, 'VALIDATION': Split.VALID,
'TEST': Split.TEST}[name.upper()]
@property
def num_examples(self):
return {Split.TRAIN_AND_VALID: 1281167, Split.TRAIN: 1271167,
Split.VALID: 10000, Split.TEST: 50000}[self]
def load(
split: Split,
*,
is_training: bool,
batch_dims: Sequence[int],
name: str = 'imagenet',
dtype: jnp.dtype = jnp.float32,
transpose: bool = False,
fake_data: bool = False,
image_size: Tuple[int, int] = (224, 224),
augment_name: Optional[str] = None,
eval_preproc: str = 'crop_resize',
augment_before_mix: bool = True,
) -> Generator[Batch, None, None]:
"""Loads the given split of the dataset.
Args:
split: Dataset split to use.
is_training: If true, use training preproc and augmentation.
batch_dims: List indicating how to batch the dataset (typically expected to
be of shape (num_devices, bs_per_device)
name: Which dataset to use, (must be 'imagenet')
dtype: One of float32 or bfloat16 (bf16 may not be supported fully)
transpose: If true, employs double transpose trick.
fake_data: Return batches of fake data for debugging purposes.
image_size: Final image size returned by dataset pipeline. Note that the
exact procedure to arrive at this size will depend on the chosen preproc.
augment_name: Optional additional aug strategy (applied atop the default
of distorted bboxes and random L/R flips). Specified with a string
such as 'cutmix_mixup_0.4_randaugment_415'. See README for deets.
eval_preproc: Eval preproc method, either 'crop_resize' (crop on the long
edge then resize) or `resize_crop_{pct}`, which will resize the image to
`image_size / pct` on each side then take a center crop.
augment_before_mix: Apply augs like RA/AA before or after cutmix/mixup.
Yields:
A TFDS numpy iterator.
"""
start, end = _shard(split, jax.host_id(), jax.host_count())
if fake_data:
print('Using fake data!')
images = np.zeros(tuple(batch_dims) + image_size + (3,), dtype=dtype)
labels = np.zeros(tuple(batch_dims), dtype=np.int32)
if transpose:
axes = tuple(range(images.ndim))
axes = axes[:-4] + axes[-3:] + (axes[-4],) # NHWC -> HWCN
images = np.transpose(images, axes)
yield from it.repeat({'images': images, 'labels': labels}, end - start)
return
total_batch_size = np.prod(batch_dims)
if name.lower() == 'imagenet':
tfds_split = tfds.core.ReadInstruction(_to_tfds_split(split),
from_=start, to=end, unit='abs')
ds = tfds.load('imagenet2012:5.*.*', split=tfds_split,
decoders={'image': tfds.decode.SkipDecoding()})
else:
raise ValueError('Only imagenet is presently supported for this dataset.')
options = tf.data.Options()
options.experimental_threading.private_threadpool_size = 48
options.experimental_threading.max_intra_op_parallelism = 1
options.experimental_optimization.map_parallelization = True
options.experimental_optimization.parallel_batch = True
if is_training:
options.experimental_deterministic = False
ds = ds.with_options(options)
if is_training:
if jax.host_count() > 1:
# Only cache if we are reading a subset of the dataset.
ds = ds.cache()
ds = ds.repeat()
ds = ds.shuffle(buffer_size=10 * total_batch_size, seed=None)
else:
if split.num_examples % total_batch_size != 0:
raise ValueError(f'Test/valid must be divisible by {total_batch_size}')
def augment_normalize(batch):
"""Optionally augment, then normalize an image."""
batch = dict(**batch)
image = _augment_image(batch['images'], is_training, augment_name)
batch['images'] = _normalize_image(image)
return batch
def preprocess(example):
image = _preprocess_image(example['image'], is_training, image_size,
eval_preproc)
label = tf.cast(example['label'], tf.int32)
out = {'images': image, 'labels': label}
if augment_name is not None and 'cutmix' in augment_name:
out['mask'] = cutmix_padding(*image_size)
out['cutmix_ratio'] = tf.reduce_mean(out['mask'])
if augment_name is not None and 'mixup' in augment_name:
mixup_alpha = 0.2 # default to alpha=0.2
# If float provided, get it
if 'mixup_' in augment_name:
alpha = augment_name.split('mixup_')[1].split('_')
if any(alpha) and re.match(r'^-?\d+(?:\.\d+)?$', alpha[0]) is not None:
mixup_alpha = float(alpha[0])
beta = tfp.distributions.Beta(mixup_alpha, mixup_alpha)
out['mixup_ratio'] = beta.sample()
# Apply augs before mixing?
if augment_before_mix or augment_name is None:
out = augment_normalize(out)
return out
ds = ds.map(preprocess, num_parallel_calls=AUTOTUNE)
ds = ds.prefetch(AUTOTUNE)
def transpose_fn(batch):
# Applies the double-transpose trick for TPU.
batch = dict(**batch)
batch['images'] = tf.transpose(batch['images'], (1, 2, 3, 0))
return batch
def cast_fn(batch):
batch = dict(**batch)
batch['images'] = tf.cast(batch['images'], tf.dtypes.as_dtype(dtype))
return batch
for i, batch_size in enumerate(reversed(batch_dims)):
if i == 0:
# Deal with vectorized MixUp + CutMix ops
if augment_name is not None:
if 'mixup' in augment_name or 'cutmix' in augment_name:
ds = ds.batch(batch_size * 2)
else:
ds = ds.map(augment_normalize, num_parallel_calls=AUTOTUNE)
ds = ds.batch(batch_size)
# Apply mixup, cutmix, or mixup + cutmix
if 'mixup' in augment_name and 'cutmix' not in augment_name:
logging.info('Applying MixUp!')
ds = ds.map(my_mixup, num_parallel_calls=AUTOTUNE)
elif 'cutmix' in augment_name and 'mixup' not in augment_name:
logging.info('Applying CutMix!')
ds = ds.map(my_cutmix, num_parallel_calls=AUTOTUNE)
elif 'mixup' in augment_name and 'cutmix' in augment_name:
logging.info('Applying MixUp and CutMix!')
ds = ds.map(my_mixup_cutmix, num_parallel_calls=AUTOTUNE)
# If applying augs after mixing, unbatch, map, and rebatch
if (not augment_before_mix and
('mixup' in augment_name or 'cutmix' in augment_name)):
ds = ds.unbatch().map(augment_normalize, num_parallel_calls=AUTOTUNE)
ds = ds.batch(batch_size)
else:
ds = ds.batch(batch_size)
# Transpose and cast as needbe
if transpose:
ds = ds.map(transpose_fn) # NHWC -> HWCN
# NOTE: You may be tempted to move the casting earlier on in the pipeline,
# but for bf16 some operations will end up silently placed on the TPU and
# this causes stalls while TF and JAX battle for the accelerator.
ds = ds.map(cast_fn)
else:
ds = ds.batch(batch_size)
ds = ds.prefetch(AUTOTUNE)
ds = tfds.as_numpy(ds)
yield from ds
def cutmix_padding(h, w):
"""Returns image mask for CutMix.
Taken from (https://github.com/google/edward2/blob/master/experimental
/marginalization_mixup/data_utils.py#L367)
Args:
h: image height.
w: image width.
"""
r_x = tf.random.uniform([], 0, w, tf.int32)
r_y = tf.random.uniform([], 0, h, tf.int32)
# Beta dist in paper, but they used Beta(1,1) which is just uniform.
image1_proportion = tf.random.uniform([])
patch_length_ratio = tf.math.sqrt(1 - image1_proportion)
r_w = tf.cast(patch_length_ratio * tf.cast(w, tf.float32), tf.int32)
r_h = tf.cast(patch_length_ratio * tf.cast(h, tf.float32), tf.int32)
bbx1 = tf.clip_by_value(tf.cast(r_x - r_w // 2, tf.int32), 0, w)
bby1 = tf.clip_by_value(tf.cast(r_y - r_h // 2, tf.int32), 0, h)
bbx2 = tf.clip_by_value(tf.cast(r_x + r_w // 2, tf.int32), 0, w)
bby2 = tf.clip_by_value(tf.cast(r_y + r_h // 2, tf.int32), 0, h)
# Create the binary mask.
pad_left = bbx1
pad_top = bby1
pad_right = tf.maximum(w - bbx2, 0)
pad_bottom = tf.maximum(h - bby2, 0)
r_h = bby2 - bby1
r_w = bbx2 - bbx1
mask = tf.pad(
tf.ones((r_h, r_w)),
paddings=[[pad_top, pad_bottom], [pad_left, pad_right]],
mode='CONSTANT',
constant_values=0)
mask.set_shape((h, w))
return mask[..., None] # Add channel dim.
def my_cutmix(batch):
"""Cutmix."""
batch = dict(**batch)
bs = tf.shape(batch['images'])[0] // 2
mask = batch['mask'][:bs]
images = (mask * batch['images'][:bs] + (1.0 - mask) * batch['images'][bs:])
mix_labels = batch['labels'][bs:]
labels = batch['labels'][:bs]
ratio = batch['cutmix_ratio'][:bs]
return {'images': images, 'labels': labels,
'mix_labels': mix_labels, 'ratio': ratio}
def my_mixup(batch):
"""Mixup."""
batch = dict(**batch)
bs = tf.shape(batch['images'])[0] // 2
ratio = batch['mixup_ratio'][:bs, None, None, None]
images = (ratio * batch['images'][:bs] + (1.0 - ratio) * batch['images'][bs:])
mix_labels = batch['labels'][bs:]
labels = batch['labels'][:bs]
ratio = ratio[..., 0, 0, 0] # Unsqueeze
return {'images': images, 'labels': labels,
'mix_labels': mix_labels, 'ratio': ratio}
def mixup_or_cutmix(batch):
"""Randomly applies one of cutmix or mixup to a batch."""
logging.info('Randomly applying cutmix or mixup with 50% chance!')
return tf.cond(
tf.cast(tf.random.uniform([], maxval=2, dtype=tf.int32), tf.bool),
lambda: my_mixup(batch),
lambda: my_cutmix(batch))
def my_mixup_cutmix(batch):
"""Apply mixup to half the batch, and cutmix to the other."""
batch = dict(**batch)
bs = tf.shape(batch['images'])[0] // 4
mixup_ratio = batch['mixup_ratio'][:bs, None, None, None]
mixup_images = (mixup_ratio * batch['images'][:bs]
+ (1.0 - mixup_ratio) * batch['images'][bs:2*bs])
mixup_labels = batch['labels'][:bs]
mixup_mix_labels = batch['labels'][bs:2*bs]
cutmix_mask = batch['mask'][2*bs:3*bs]
cutmix_images = (cutmix_mask * batch['images'][2*bs:3*bs]
+ (1.0 - cutmix_mask) * batch['images'][-bs:])
cutmix_labels = batch['labels'][2*bs:3*bs]
cutmix_mix_labels = batch['labels'][-bs:]
cutmix_ratio = batch['cutmix_ratio'][2*bs : 3*bs]
return {'images': tf.concat([mixup_images, cutmix_images], axis=0),
'labels': tf.concat([mixup_labels, cutmix_labels], axis=0),
'mix_labels': tf.concat([mixup_mix_labels, cutmix_mix_labels], 0),
'ratio': tf.concat([mixup_ratio[..., 0, 0, 0], cutmix_ratio], axis=0)}
def _to_tfds_split(split: Split) -> tfds.Split:
"""Returns the TFDS split appropriately sharded."""
if split in (Split.TRAIN, Split.TRAIN_AND_VALID, Split.VALID):
return tfds.Split.TRAIN
else:
assert split == Split.TEST
return tfds.Split.VALIDATION
def _shard(split: Split, shard_index: int, num_shards: int) -> Tuple[int, int]:
"""Returns [start, end) for the given shard index."""
assert shard_index < num_shards
arange = np.arange(split.num_examples)
shard_range = np.array_split(arange, num_shards)[shard_index]
start, end = shard_range[0], (shard_range[-1] + 1)
if split == Split.TRAIN:
# Note that our TRAIN=TFDS_TRAIN[10000:] and VALID=TFDS_TRAIN[:10000].
offset = Split.VALID.num_examples
start += offset
end += offset
return start, end
def _preprocess_image(
image_bytes: tf.Tensor,
is_training: bool,
image_size: Sequence[int],
eval_preproc: str = 'crop_resize'
) -> tf.Tensor:
"""Returns processed and resized images."""
# NOTE: Bicubic resize (1) casts uint8 to float32 and (2) resizes without
# clamping overshoots. This means values returned will be outside the range
# [0.0, 255.0] (e.g. we have observed outputs in the range [-51.1, 336.6]).
if is_training:
image = _decode_and_random_crop(image_bytes, image_size)
image = tf.image.random_flip_left_right(image)
assert image.dtype == tf.uint8
image = tf.image.resize(image, image_size, tf.image.ResizeMethod.BICUBIC)
else:
if eval_preproc == 'crop_resize':
image = _decode_and_center_crop(image_bytes, image_size=image_size)
assert image.dtype == tf.uint8
image = tf.image.resize(image, image_size, tf.image.ResizeMethod.BICUBIC)
elif 'resize_crop' in eval_preproc:
# Pass in crop percent
crop_pct = float(eval_preproc.split('_')[-1])
image = _decode_and_resize_then_crop(image_bytes, image_size=image_size,
crop_pct=crop_pct)
else:
raise ValueError(f'Unknown Eval Preproc {eval_preproc} provided!')
return image
def _augment_image(
image: tf.Tensor,
is_training: bool,
augment_name: Optional[str] = None,
) -> tf.Tensor:
"""Applies AA/RA to an image."""
if is_training and augment_name:
if 'autoaugment' in augment_name or 'randaugment' in augment_name:
input_image_type = image.dtype
image = tf.clip_by_value(image, 0.0, 255.0)
# Autoaugment requires a uint8 image; we cast here and then cast back
image = tf.cast(image, dtype=tf.uint8)
if 'autoaugment' in augment_name:
logging.info(f'Applying AutoAugment policy {augment_name}')
image = autoaugment.distort_image_with_autoaugment(image, 'v0')
elif 'randaugment' in augment_name:
magnitude = int(augment_name.split('_')[-1]) # pytype: disable=attribute-error
# Allow passing in num_layers as a magnitude > 100
if magnitude > 100:
num_layers = magnitude // 100
magnitude = magnitude - int(num_layers * 100)
else:
num_layers = 2
logging.info(f'Applying RA {num_layers} x {magnitude}')
image = autoaugment.distort_image_with_randaugment(
image, num_layers=num_layers, magnitude=magnitude)
image = tf.cast(image, dtype=input_image_type)
return image
def _normalize_image(image: tf.Tensor) -> tf.Tensor:
"""Normalize the image to zero mean and unit variance."""
image -= tf.constant(MEAN_RGB, shape=[1, 1, 3], dtype=image.dtype)
image /= tf.constant(STDDEV_RGB, shape=[1, 1, 3], dtype=image.dtype)
return image
def _distorted_bounding_box_crop(
image_bytes: tf.Tensor,
*,
jpeg_shape: tf.Tensor,
bbox: tf.Tensor,
min_object_covered: float,
aspect_ratio_range: Tuple[float, float],
area_range: Tuple[float, float],
max_attempts: int,
) -> tf.Tensor:
"""Generates cropped_image using one of the bboxes randomly distorted."""
bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
jpeg_shape,
bounding_boxes=bbox,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
max_attempts=max_attempts,
use_image_if_no_bounding_boxes=True)
# Crop the image to the specified bounding box.
offset_y, offset_x, _ = tf.unstack(bbox_begin)
target_height, target_width, _ = tf.unstack(bbox_size)
crop_window = [offset_y, offset_x, target_height, target_width]
image = crop(image_bytes, crop_window)
return image
def _decode_and_random_crop(image_bytes: tf.Tensor,
image_size: Sequence[int] = (224, 224),
jpeg_shape: Optional[tf.Tensor] = None
) -> tf.Tensor:
"""Make a random crop of chosen size."""
if jpeg_shape is None:
jpeg_shape = get_shape(image_bytes)
bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
image = _distorted_bounding_box_crop(
image_bytes,
jpeg_shape=jpeg_shape,
bbox=bbox,
min_object_covered=0.1,
aspect_ratio_range=(3 / 4, 4 / 3),
area_range=(0.08, 1.0),
max_attempts=10)
if tf.reduce_all(tf.equal(jpeg_shape, tf.shape(image))):
# If the random crop failed fall back to center crop.
image = _decode_and_center_crop(image_bytes, jpeg_shape, image_size)
return image
def _decode_and_center_crop(
image_bytes: tf.Tensor,
jpeg_shape: Optional[tf.Tensor] = None,
image_size: Sequence[int] = (224, 224),
) -> tf.Tensor:
"""Crops to center of image with padding then scales."""
if jpeg_shape is None:
jpeg_shape = get_shape(image_bytes)
image_height = jpeg_shape[0]
image_width = jpeg_shape[1]
# Pad the image with at least 32px on the short edge and take a
# crop that maintains aspect ratio.
scale = tf.minimum(tf.cast(image_height, tf.float32) / (image_size[0] + 32),
tf.cast(image_width, tf.float32) / (image_size[1] + 32))
padded_center_crop_height = tf.cast(scale * image_size[0], tf.int32)
padded_center_crop_width = tf.cast(scale * image_size[1], tf.int32)
offset_height = ((image_height - padded_center_crop_height) + 1) // 2
offset_width = ((image_width - padded_center_crop_width) + 1) // 2
crop_window = [offset_height, offset_width,
padded_center_crop_height, padded_center_crop_width]
image = crop(image_bytes, crop_window)
return image
def get_shape(image_bytes):
"""Gets the image shape for jpeg bytes or a uint8 decoded image."""
if image_bytes.dtype == tf.dtypes.string:
image_shape = tf.image.extract_jpeg_shape(image_bytes)
else:
image_shape = tf.shape(image_bytes)
return image_shape
def crop(image_bytes, crop_window):
"""Helper function to crop a jpeg or a decoded image."""
if image_bytes.dtype == tf.dtypes.string:
image = tf.image.decode_and_crop_jpeg(image_bytes,
tf.stack(crop_window),
channels=3)
else:
image = tf.image.crop_to_bounding_box(image_bytes, *crop_window)
return image
def _decode_and_resize_then_crop(
image_bytes: tf.Tensor,
image_size: Sequence[int] = (224, 224),
crop_pct: float = 1.0,
) -> tf.Tensor:
"""Rescales an image to image_size / crop_pct, then center crops."""
image = tf.image.decode_jpeg(image_bytes, channels=3)
# Scale image to "scaled size" before taking a center crop
if crop_pct > 1.0: # If crop_pct is >1, treat it as num pad pixels (like VGG)
scale_size = tuple([int(x + crop_pct) for x in image_size])
else:
scale_size = tuple([int(float(x) / crop_pct) for x in image_size])
image = tf.image.resize(image, scale_size, tf.image.ResizeMethod.BICUBIC)
crop_height = tf.cast(image_size[0], tf.int32)
crop_width = tf.cast(image_size[1], tf.int32)
offset_height = ((scale_size[0] - crop_height) + 1) // 2
offset_width = ((scale_size[1] - crop_width) + 1) // 2
crop_window = [offset_height, offset_width, crop_height, crop_width]
image = crop(image, crop_window)
return image
================================================
FILE: nfnets/experiment.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Basic Jaxline ImageNet experiment."""
import importlib
import sys
from absl import flags
from absl import logging
import haiku as hk
import jax
import jax.numpy as jnp
from jaxline import base_config
from jaxline import experiment
from jaxline import platform
from jaxline import utils as jl_utils
from ml_collections import config_dict
import numpy as np
from nfnets import dataset
from nfnets import optim
from nfnets import utils
# pylint: disable=logging-format-interpolation
FLAGS = flags.FLAGS
# We define the experiment launch config in the same file as the experiment to
# keep things self-contained in a single file, but one might consider moving the
# config and/or sweep functions to a separate file, if necessary.
def get_config():
"""Return config object for training."""
config = base_config.get_base_config()
# Experiment config.
train_batch_size = 1024 # Global batch size.
images_per_epoch = 1281167
num_epochs = 90
steps_per_epoch = images_per_epoch / train_batch_size
config.training_steps = ((images_per_epoch * num_epochs) // train_batch_size)
config.random_seed = 0
config.experiment_kwargs = config_dict.ConfigDict(
dict(
config=dict(
lr=0.1,
num_epochs=num_epochs,
label_smoothing=0.1,
model='ResNet',
image_size=224,
use_ema=False,
ema_decay=0.9999, # Quatros nuevos amigos
ema_start=0,
which_ema='tf1_ema',
augment_name=None, # 'mixup_cutmix',
augment_before_mix=True,
eval_preproc='crop_resize',
train_batch_size=train_batch_size,
eval_batch_size=50,
eval_subset='test',
num_classes=1000,
which_dataset='imagenet',
fake_data=False,
which_loss='softmax_cross_entropy', # For now, must be softmax
transpose=True, # Use the double-transpose trick?
bfloat16=False,
lr_schedule=dict(
name='WarmupCosineDecay',
kwargs=dict(
num_steps=config.training_steps,
start_val=0,
min_val=0,
warmup_steps=5 * steps_per_epoch),
),
lr_scale_by_bs=True,
optimizer=dict(
name='SGD',
kwargs={
'momentum': 0.9,
'nesterov': True,
'weight_decay': 1e-4,
},
),
model_kwargs=dict(
width=4,
which_norm='BatchNorm',
norm_kwargs=dict(
create_scale=True,
create_offset=True,
decay_rate=0.9,
), # cross_replica_axis='i'),
variant='ResNet50',
activation='relu',
drop_rate=0.0,
),
),))
# Training loop config: log and checkpoint every minute
config.log_train_data_interval = 60
config.log_tensors_interval = 60
config.save_checkpoint_interval = 60
config.eval_specific_checkpoint_dir = ''
return config
class Experiment(experiment.AbstractExperiment):
"""Imagenet experiment."""
CHECKPOINT_ATTRS = {
'_params': 'params',
'_state': 'state',
'_ema_params': 'ema_params',
'_ema_state': 'ema_state',
'_opt_state': 'opt_state',
}
def __init__(self, mode, config, init_rng):
super().__init__(mode=mode)
self.mode = mode
self.config = config
self.init_rng = init_rng
# Checkpointed experiment state.
self._params = None
self._state = None
self._ema_params = None
self._ema_state = None
self._opt_state = None
# Input pipelines.
self._train_input = None
self._eval_input = None
# Get model, loaded in from the zoo
module_prefix = 'nfnets.'
self.model_module = importlib.import_module(
(module_prefix + self.config.model.lower()))
self.net = hk.transform_with_state(self._forward_fn)
# Assign image sizes
if self.config.get('override_imsize', False):
self.train_imsize = self.config.image_size
self.test_imsize = self.config.get('eval_image_size', self.train_imsize)
else:
variant_dict = getattr(self.model_module, self.config.model).variant_dict
variant_dict = variant_dict[self.config.model_kwargs.variant]
self.train_imsize = variant_dict.get('train_imsize',
self.config.image_size)
# Test imsize defaults to model-specific value, then to config imsize
test_imsize = self.config.get('eval_image_size', self.config.image_size)
self.test_imsize = variant_dict.get('test_imsize', test_imsize)
donate_argnums = (0, 1, 2, 6, 7) if self.config.use_ema else (0, 1, 2)
self.train_fn = jax.pmap(
self._train_fn, axis_name='i', donate_argnums=donate_argnums)
self.eval_fn = jax.pmap(self._eval_fn, axis_name='i')
def _initialize_train(self):
self._train_input = self._build_train_input()
# Initialize net and EMA copy of net if no params available.
if self._params is None:
inputs = next(self._train_input)
init_net = jax.pmap(
lambda *a: self.net.init(*a, is_training=True), axis_name='i')
init_rng = jl_utils.bcast_local_devices(self.init_rng)
self._params, self._state = init_net(init_rng, inputs)
if self.config.use_ema:
self._ema_params, self._ema_state = init_net(init_rng, inputs)
num_params = hk.data_structures.tree_size(self._params)
logging.info(f'Net parameters: {num_params / jax.local_device_count()}')
self._make_opt()
def _make_opt(self):
# Separate conv params and gains/biases
def pred(mod, name, val): # pylint:disable=unused-argument
return (name in ['scale', 'offset', 'b'] or 'gain' in name or
'bias' in name)
gains_biases, weights = hk.data_structures.partition(pred, self._params)
# Lr schedule with batch-based LR scaling
if self.config.lr_scale_by_bs:
max_lr = (self.config.lr * self.config.train_batch_size) / 256
else:
max_lr = self.config.lr
lr_sched_fn = getattr(optim, self.config.lr_schedule.name)
lr_schedule = lr_sched_fn(max_val=max_lr, **self.config.lr_schedule.kwargs)
# Optimizer; no need to broadcast!
opt_kwargs = {key: val for key, val in self.config.optimizer.kwargs.items()}
opt_kwargs['lr'] = lr_schedule
opt_module = getattr(optim, self.config.optimizer.name)
self.opt = opt_module([{
'params': gains_biases,
'weight_decay': None
}, {
'params': weights
}], **opt_kwargs)
if self._opt_state is None:
self._opt_state = self.opt.states()
else:
self.opt.plugin(self._opt_state)
def _forward_fn(self, inputs, is_training):
net_kwargs = {
'num_classes': self.config.num_classes,
**self.config.model_kwargs
}
net = getattr(self.model_module, self.config.model)(**net_kwargs)
if self.config.get('transpose', False):
images = jnp.transpose(inputs['images'], (3, 0, 1, 2)) # HWCN -> NHWC
else:
images = inputs['images']
if self.config.bfloat16 and self.mode == 'train':
images = utils.to_bf16(images)
return net(images, is_training=is_training)['logits']
def _one_hot(self, value):
"""One-hot encoding potentially over a sequence of labels."""
y = jax.nn.one_hot(value, self.config.num_classes)
return y
def _loss_fn(self, params, state, inputs, rng):
logits, state = self.net.apply(params, state, rng, inputs, is_training=True)
y = self._one_hot(inputs['labels'])
if 'mix_labels' in inputs: # Handle cutmix/mixup label mixing
logging.info('Using mixup or cutmix!')
y1 = self._one_hot(inputs['mix_labels'])
y = inputs['ratio'][:, None] * y + (1. - inputs['ratio'][:, None]) * y1
if self.config.label_smoothing > 0: # get smoothy
spositives = 1. - self.config.label_smoothing
snegatives = self.config.label_smoothing / self.config.num_classes
y = spositives * y + snegatives
if self.config.bfloat16: # Cast logits to float32
logits = logits.astype(jnp.float32)
which_loss = getattr(utils, self.config.which_loss)
loss = which_loss(logits, y, reduction='mean')
metrics = utils.topk_correct(logits, inputs['labels'], prefix='train_')
# Average top-1 and top-5 correct labels
metrics = jax.tree_map(jnp.mean, metrics)
metrics['train_loss'] = loss # Metrics will be pmeaned so don't divide here
scaled_loss = loss / jax.device_count() # Grads get psummed so do divide
return scaled_loss, (metrics, state)
def _train_fn(self, params, states, opt_states, inputs, rng, global_step,
ema_params, ema_states):
"""Runs one batch forward + backward and run a single opt step."""
grad_fn = jax.grad(self._loss_fn, argnums=0, has_aux=True)
if self.config.bfloat16:
in_params, states = jax.tree_map(utils.to_bf16, (params, states))
else:
in_params = params
grads, (metrics, states) = grad_fn(in_params, states, inputs, rng)
if self.config.bfloat16:
states, metrics, grads = jax.tree_map(utils.from_bf16,
(states, metrics, grads))
# Sum gradients and average losses for pmap
grads = jax.lax.psum(grads, 'i')
metrics = jax.lax.pmean(metrics, 'i')
# Compute updates and update parameters
metrics['learning_rate'] = self.opt._hyperparameters['lr'](global_step) # pylint: disable=protected-access
params, opt_states = self.opt.step(params, grads, opt_states, global_step)
if ema_params is not None:
ema_fn = getattr(utils, self.config.get('which_ema', 'tf1_ema'))
ema = lambda x, y: ema_fn(x, y, self.config.ema_decay, global_step)
ema_params = jax.tree_map(ema, ema_params, params)
ema_states = jax.tree_map(ema, ema_states, states)
return {
'params': params,
'states': states,
'opt_states': opt_states,
'ema_params': ema_params,
'ema_states': ema_states,
'metrics': metrics
}
# _ _
# | |_ _ __ __ _(_)_ __
# | __| '__/ _` | | '_ \
# | |_| | | (_| | | | | |
# \__|_| \__,_|_|_| |_|
#
def step(self, global_step, rng, *unused_args, **unused_kwargs):
if self._train_input is None:
self._initialize_train()
inputs = next(self._train_input)
out = self.train_fn(
params=self._params,
states=self._state,
opt_states=self._opt_state,
inputs=inputs,
rng=rng,
global_step=global_step,
ema_params=self._ema_params,
ema_states=self._ema_state)
self._params, self._state = out['params'], out['states']
self._opt_state = out['opt_states']
self._ema_params, self._ema_state = out['ema_params'], out['ema_states']
self.opt.plugin(self._opt_state)
return jl_utils.get_first(out['metrics'])
def _build_train_input(self):
num_devices = jax.device_count()
global_batch_size = self.config.train_batch_size
bs_per_device, ragged = divmod(global_batch_size, num_devices)
if ragged:
raise ValueError(
f'Global batch size {global_batch_size} must be divisible by '
f'num devices {num_devices}')
return dataset.load(
dataset.Split.TRAIN_AND_VALID,
is_training=True,
batch_dims=[jax.local_device_count(), bs_per_device],
transpose=self.config.get('transpose', False),
image_size=(self.train_imsize,) * 2,
augment_name=self.config.augment_name,
augment_before_mix=self.config.get('augment_before_mix', True),
name=self.config.which_dataset,
fake_data=self.config.get('fake_data', False))
# _
# _____ ____ _| |
# / _ \ \ / / _` | |
# | __/\ V / (_| | |
# \___| \_/ \__,_|_|
#
def evaluate(self, global_step, **unused_args):
metrics = self._eval_epoch(self._params, self._state)
if self.config.use_ema:
ema_metrics = self._eval_epoch(self._ema_params, self._ema_state)
metrics.update({f'ema_{key}': val for key, val in ema_metrics.items()})
logging.info(f'[Step {global_step}] Eval scalars: {metrics}')
return metrics
def _eval_epoch(self, params, state):
"""Evaluates an epoch."""
num_samples = 0.
summed_metrics = None
for inputs in self._build_eval_input():
num_samples += np.prod(inputs['labels'].shape[:2]) # Account for pmaps
metrics = self.eval_fn(params, state, inputs)
# Accumulate the sum of metrics for each step.
metrics = jax.tree_map(lambda x: jnp.sum(x[0], axis=0), metrics)
if summed_metrics is None:
summed_metrics = metrics
else:
summed_metrics = jax.tree_map(jnp.add, summed_metrics, metrics)
mean_metrics = jax.tree_map(lambda x: x / num_samples, summed_metrics)
return jax.device_get(mean_metrics)
def _eval_fn(self, params, state, inputs):
"""Evaluate a single batch and return loss and top-k acc."""
logits, _ = self.net.apply(params, state, None, inputs, is_training=False)
y = self._one_hot(inputs['labels'])
which_loss = getattr(utils, self.config.which_loss)
loss = which_loss(logits, y, reduction=None)
metrics = utils.topk_correct(logits, inputs['labels'], prefix='eval_')
metrics['eval_loss'] = loss
return jax.lax.psum(metrics, 'i')
def _build_eval_input(self):
"""Builds the evaluation input pipeline."""
bs_per_device = (self.config.eval_batch_size // jax.local_device_count())
split = dataset.Split.from_string(self.config.eval_subset)
eval_preproc = self.config.get('eval_preproc', 'crop_resize')
return dataset.load(
split,
is_training=False,
batch_dims=[jax.local_device_count(), bs_per_device],
transpose=self.config.get('transpose', False),
image_size=(self.test_imsize,) * 2,
name=self.config.which_dataset,
eval_preproc=eval_preproc,
fake_data=self.config.get('fake_data', False))
if __name__ == '__main__':
flags.mark_flag_as_required('config')
platform.main(Experiment, sys.argv[1:])
================================================
FILE: nfnets/experiment_nf_regnets.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""ImageNet experiment with NF-RegNets."""
from ml_collections import config_dict
from nfnets import experiment
def get_config():
"""Return config object for training."""
config = experiment.get_config()
# Experiment config.
train_batch_size = 1024 # Global batch size.
images_per_epoch = 1281167
num_epochs = 360
steps_per_epoch = images_per_epoch / train_batch_size
config.training_steps = ((images_per_epoch * num_epochs) // train_batch_size)
config.random_seed = 0
config.experiment_kwargs = config_dict.ConfigDict(
dict(
config=dict(
lr=0.4,
num_epochs=num_epochs,
label_smoothing=0.1,
model='NF_RegNet',
image_size=224,
use_ema=True,
ema_decay=0.99999, # Cinco nueves amigos
ema_start=0,
augment_name='mixup_cutmix',
train_batch_size=train_batch_size,
eval_batch_size=50,
eval_subset='test',
num_classes=1000,
which_dataset='imagenet',
which_loss='softmax_cross_entropy', # One of softmax or sigmoid
bfloat16=False,
lr_schedule=dict(
name='WarmupCosineDecay',
kwargs=dict(num_steps=config.training_steps,
start_val=0,
min_val=0.001,
warmup_steps=5*steps_per_epoch),
),
lr_scale_by_bs=False,
optimizer=dict(
name='SGD',
kwargs={'momentum': 0.9, 'nesterov': True,
'weight_decay': 5e-5,},
),
model_kwargs=dict(
variant='B0',
width=0.75,
expansion=2.25,
se_ratio=0.5,
alpha=0.2,
stochdepth_rate=0.1,
drop_rate=None,
activation='silu',
),
)))
# Set weight decay based on variant (scaled as 5e-5 + 1e-5 * level)
variant = config.experiment_kwargs.config.model_kwargs.variant
weight_decay = {'B0': 5e-5, 'B1': 6e-5, 'B2': 7e-5,
'B3': 8e-5, 'B4': 9e-5, 'B5': 1e-4}[variant]
config.experiment_kwargs.config.optimizer.kwargs.weight_decay = weight_decay
return config
Experiment = experiment.Experiment
================================================
FILE: nfnets/experiment_nfnets.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""ImageNet experiment with NFNets."""
import sys
from absl import flags
import haiku as hk
from jaxline import platform
from ml_collections import config_dict
from nfnets import experiment
from nfnets import optim
FLAGS = flags.FLAGS
def get_config():
"""Return config object for training."""
config = experiment.get_config()
# Experiment config.
train_batch_size = 4096 # Global batch size.
images_per_epoch = 1281167
num_epochs = 360
steps_per_epoch = images_per_epoch / train_batch_size
config.training_steps = ((images_per_epoch * num_epochs) // train_batch_size)
config.random_seed = 0
config.experiment_kwargs = config_dict.ConfigDict(
dict(
config=dict(
lr=0.1,
num_epochs=num_epochs,
label_smoothing=0.1,
model='NFNet',
image_size=224,
use_ema=True,
ema_decay=0.99999,
ema_start=0,
augment_name=None,
augment_before_mix=False,
eval_preproc='resize_crop_32',
train_batch_size=train_batch_size,
eval_batch_size=50,
eval_subset='test',
num_classes=1000,
which_dataset='imagenet',
which_loss='softmax_cross_entropy', # One of softmax or sigmoid
bfloat16=True,
lr_schedule=dict(
name='WarmupCosineDecay',
kwargs=dict(num_steps=config.training_steps,
start_val=0,
min_val=0.0,
warmup_steps=5*steps_per_epoch),
),
lr_scale_by_bs=True,
optimizer=dict(
name='SGD_AGC',
kwargs={'momentum': 0.9, 'nesterov': True,
'weight_decay': 2e-5,
'clipping': 0.01, 'eps': 1e-3},
),
model_kwargs=dict(
variant='F0',
width=1.0,
se_ratio=0.5,
alpha=0.2,
stochdepth_rate=0.25,
drop_rate=None, # Use native drop-rate
activation='gelu',
final_conv_mult=2,
final_conv_ch=None,
use_two_convs=True,
),
)))
# Unlike NF-RegNets, use the same weight decay for all, but vary RA levels
variant = config.experiment_kwargs.config.model_kwargs.variant
# RandAugment levels (e.g. 405 = 4 layers, magnitude 5, 205 = 2 layers, mag 5)
augment = {'F0': '405', 'F1': '410', 'F2': '410', 'F3': '415',
'F4': '415', 'F5': '415', 'F6': '415', 'F7': '415'}[variant]
aug_base_name = 'cutmix_mixup_randaugment'
config.experiment_kwargs.config.augment_name = f'{aug_base_name}_{augment}'
return config
class Experiment(experiment.Experiment):
"""Experiment with correct parameter filtering for applying AGC."""
def _make_opt(self):
# Separate conv params and gains/biases
def pred_gb(mod, name, val):
del mod, val
return (name in ['scale', 'offset', 'b']
or 'gain' in name or 'bias' in name)
gains_biases, weights = hk.data_structures.partition(pred_gb, self._params)
def pred_fc(mod, name, val):
del name, val
return 'linear' in mod and 'squeeze_excite' not in mod
fc_weights, weights = hk.data_structures.partition(pred_fc, weights)
# Lr schedule with batch-based LR scaling
if self.config.lr_scale_by_bs:
max_lr = (self.config.lr * self.config.train_batch_size) / 256
else:
max_lr = self.config.lr
lr_sched_fn = getattr(optim, self.config.lr_schedule.name)
lr_schedule = lr_sched_fn(max_val=max_lr, **self.config.lr_schedule.kwargs)
# Optimizer; no need to broadcast!
opt_kwargs = {key: val for key, val in self.config.optimizer.kwargs.items()}
opt_kwargs['lr'] = lr_schedule
opt_module = getattr(optim, self.config.optimizer.name)
self.opt = opt_module([{'params': gains_biases, 'weight_decay': None,},
{'params': fc_weights, 'clipping': None},
{'params': weights}], **opt_kwargs)
if self._opt_state is None:
self._opt_state = self.opt.states()
else:
self.opt.plugin(self._opt_state)
if __name__ == '__main__':
flags.mark_flag_as_required('config')
platform.main(Experiment, sys.argv[1:])
================================================
FILE: nfnets/fixup_resnet.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ResNet (post-activation) with FixUp."""
# pylint: disable=invalid-name
import functools
import haiku as hk
import jax
import jax.numpy as jnp
from nfnets import base
nonlinearities = {
'swish': jax.nn.silu,
'relu': jax.nn.relu,
'identity': lambda x: x}
class FixUp_ResNet(hk.Module):
"""Fixup based ResNet."""
variant_dict = {'ResNet50': {'depth': [3, 4, 6, 3]},
'ResNet101': {'depth': [3, 4, 23, 3]},
'ResNet152': {'depth': [3, 8, 36, 3]},
'ResNet200': {'depth': [3, 24, 36, 3]},
'ResNet288': {'depth': [24, 24, 24, 24]},
'ResNet600': {'depth': [50, 50, 50, 50]},
}
def __init__(self, num_classes, variant='ResNet50', width=4,
stochdepth_rate=0.1, drop_rate=None,
activation='relu', fc_init=jnp.zeros,
name='FixUp_ResNet'):
super().__init__(name=name)
self.num_classes = num_classes
self.variant = variant
self.width = width
# Get variant info
block_params = self.variant_dict[self.variant]
self.width_pattern = [item * self.width for item in [64, 128, 256, 512]]
self.depth_pattern = block_params['depth']
self.activation = nonlinearities[activation]
if drop_rate is None:
self.drop_rate = block_params['drop_rate']
else:
self.drop_rate = drop_rate
self.which_conv = functools.partial(hk.Conv2D,
with_bias=False)
# Stem
ch = int(16 * self.width)
self.initial_conv = self.which_conv(ch, kernel_shape=7, stride=2,
padding='SAME',
name='initial_conv')
# Body
self.blocks = []
num_blocks = sum(self.depth_pattern)
index = 0 # Overall block index
block_args = (self.width_pattern, self.depth_pattern, [1, 2, 2, 2])
for block_width, stage_depth, stride in zip(*block_args):
for block_index in range(stage_depth):
# Block stochastic depth drop-rate
block_stochdepth_rate = stochdepth_rate * index / num_blocks
self.blocks += [ResBlock(ch, block_width, num_blocks,
stride=stride if block_index == 0 else 1,
activation=self.activation,
which_conv=self.which_conv,
stochdepth_rate=block_stochdepth_rate,
)]
ch = block_width
index += 1
# Head
self.fc = hk.Linear(self.num_classes, w_init=fc_init, with_bias=True)
def __call__(self, x, is_training=True, return_metrics=False):
"""Return the output of the final layer without any [log-]softmax."""
# Stem
outputs = {}
out = self.initial_conv(x)
bias1 = hk.get_parameter('bias1', (), x.dtype, init=jnp.zeros)
out = self.activation(out + bias1)
out = hk.max_pool(out, window_shape=(1, 3, 3, 1),
strides=(1, 2, 2, 1), padding='SAME')
if return_metrics:
outputs.update(base.signal_metrics(out, 0))
# Blocks
for i, block in enumerate(self.blocks):
out, res_avg_var = block(out, is_training=is_training)
if return_metrics:
outputs.update(base.signal_metrics(out, i + 1))
outputs[f'res_avg_var_{i}'] = res_avg_var
# Final-conv->activation, pool, dropout, classify
pool = jnp.mean(out, [1, 2])
outputs['pool'] = pool
# Optionally apply dropout
if self.drop_rate > 0.0 and is_training:
pool = hk.dropout(hk.next_rng_key(), self.drop_rate, pool)
bias2 = hk.get_parameter('bias2', (), pool.dtype, init=jnp.zeros)
outputs['logits'] = self.fc(pool + bias2)
return outputs
def count_flops(self, h, w):
flops = []
flops += [base.count_conv_flops(3, self.initial_conv, h, w)]
h, w = h / 2, w / 2
# Body FLOPs
for block in self.blocks:
flops += [block.count_flops(h, w)]
if block.stride > 1:
h, w = h / block.stride, w / block.stride
# Count flops for classifier
flops += [self.blocks[-1].out_ch * self.fc.output_size]
return flops, sum(flops)
class ResBlock(hk.Module):
"""Post-activation Fixup Block."""
def __init__(self, in_ch, out_ch, num_blocks, bottleneck_ratio=0.25,
kernel_size=3, stride=1,
which_conv=hk.Conv2D, activation=jax.nn.relu,
stochdepth_rate=None, name=None):
super().__init__(name=name)
self.in_ch, self.out_ch = in_ch, out_ch
self.kernel_size = kernel_size
self.activation = activation
# Bottleneck width
self.width = int(self.out_ch * bottleneck_ratio)
self.stride = stride
# Conv 0 (typically expansion conv)
conv0_init = hk.initializers.RandomNormal(
stddev=((2 / self.width)**0.5) * (num_blocks**(-0.25)))
self.conv0 = which_conv(self.width, kernel_shape=1, padding='SAME',
name='conv0', w_init=conv0_init)
# Grouped NxN conv
conv1_init = hk.initializers.RandomNormal(
stddev=((2 / (self.width * (kernel_size**2)))**0.5)
* (num_blocks**(-0.25)))
self.conv1 = which_conv(self.width, kernel_shape=kernel_size, stride=stride,
padding='SAME', name='conv1', w_init=conv1_init)
# Conv 2, typically projection conv
self.conv2 = which_conv(self.out_ch, kernel_shape=1, padding='SAME',
name='conv2', w_init=hk.initializers.Constant(0))
# Use shortcut conv on channel change or downsample.
self.use_projection = stride > 1 or self.in_ch != self.out_ch
if self.use_projection:
shortcut_init = hk.initializers.RandomNormal(
stddev=(2 / self.out_ch) ** 0.5)
self.conv_shortcut = which_conv(self.out_ch, kernel_shape=1,
stride=stride, padding='SAME',
name='conv_shortcut',
w_init=shortcut_init)
# Are we using stochastic depth?
self._has_stochdepth = (stochdepth_rate is not None and
stochdepth_rate > 0. and stochdepth_rate < 1.0)
if self._has_stochdepth:
self.stoch_depth = base.StochDepth(stochdepth_rate)
def __call__(self, x, is_training):
bias1a = hk.get_parameter('bias1a', (), x.dtype, init=jnp.zeros)
bias1b = hk.get_parameter('bias1b', (), x.dtype, init=jnp.zeros)
bias2a = hk.get_parameter('bias2a', (), x.dtype, init=jnp.zeros)
bias2b = hk.get_parameter('bias2b', (), x.dtype, init=jnp.zeros)
bias3a = hk.get_parameter('bias3a', (), x.dtype, init=jnp.zeros)
bias3b = hk.get_parameter('bias3b', (), x.dtype, init=jnp.zeros)
scale = hk.get_parameter('scale', (), x.dtype, init=jnp.ones)
out = x + bias1a
shortcut = out
if self.use_projection: # Downsample with conv1x1
shortcut = self.conv_shortcut(shortcut)
out = self.conv0(out)
out = self.activation(out + bias1b)
out = self.conv1(out + bias2a)
out = self.activation(out + bias2b)
out = self.conv2(out + bias3a)
out = out * scale + bias3b
# Get average residual variance for reporting metrics.
res_avg_var = jnp.mean(jnp.var(out, axis=[0, 1, 2]))
# Apply stochdepth if applicable.
if self._has_stochdepth:
out = self.stoch_depth(out, is_training)
# SkipInit Gain
out = out + shortcut
return self.activation(out), res_avg_var
def count_flops(self, h, w):
# Count conv FLOPs based on input HW
expand_flops = base.count_conv_flops(self.in_ch, self.conv0, h, w)
# If block is strided we decrease resolution here.
dw_flops = base.count_conv_flops(self.width, self.conv1, h, w)
if self.stride > 1:
h, w = h / self.stride, w / self.stride
if self.use_projection:
sc_flops = base.count_conv_flops(self.in_ch, self.conv_shortcut, h, w)
else:
sc_flops = 0
contract_flops = base.count_conv_flops(self.width, self.conv2, h, w)
return sum([expand_flops, dw_flops, contract_flops, sc_flops])
================================================
FILE: nfnets/nf_regnet.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Normalizer-Free RegNets."""
# pylint: disable=invalid-name
import haiku as hk
import jax
import jax.numpy as jnp
from nfnets import base
class NF_RegNet(hk.Module):
"""Normalizer-Free RegNets."""
variant_dict = base.nf_regnet_params
def __init__(self, num_classes, variant='B0',
width=0.75, expansion=2.25, group_size=8, se_ratio=0.5,
alpha=0.2, stochdepth_rate=0.1, drop_rate=None,
activation='swish', fc_init=jnp.zeros,
name='NF_RegNet'):
super().__init__(name=name)
self.num_classes = num_classes
self.variant = variant
self.width = width
self.expansion = expansion
self.group_size = group_size
self.se_ratio = se_ratio
# Get variant info
block_params = self.variant_dict[self.variant]
self.train_imsize = block_params['train_imsize']
self.test_imsize = block_params['test_imsize']
self.width_pattern = block_params['width']
self.depth_pattern = block_params['depth']
self.activation = base.nonlinearities[activation]
if drop_rate is None:
self.drop_rate = block_params['drop_rate']
else:
self.drop_rate = drop_rate
self.which_conv = base.WSConv2D
# Stem
ch = int(self.width_pattern[0] * self.width)
self.initial_conv = self.which_conv(ch, kernel_shape=3, stride=2,
padding='SAME', name='initial_conv')
# Body
self.blocks = []
expected_std = 1.0
num_blocks = sum(self.depth_pattern)
index = 0 # Overall block index
for block_width, stage_depth in zip(self.width_pattern, self.depth_pattern):
for block_index in range(stage_depth):
# Scalar pre-multiplier so each block sees an N(0,1) input at init
beta = 1./ expected_std
# Block stochastic depth drop-rate
block_stochdepth_rate = stochdepth_rate * index / num_blocks
# Use a bottleneck expansion ratio of 1 for first block following EffNet
expand_ratio = 1 if index == 0 else expansion
out_ch = (int(block_width * self.width))
self.blocks += [NFBlock(ch, out_ch,
expansion=expand_ratio, se_ratio=se_ratio,
group_size=self.group_size,
stride=2 if block_index == 0 else 1,
beta=beta, alpha=alpha,
activation=self.activation,
which_conv=self.which_conv,
stochdepth_rate=block_stochdepth_rate,
)]
ch = out_ch
index += 1
# Reset expected std but still give it 1 block of growth
if block_index == 0:
expected_std = 1.0
expected_std = (expected_std **2 + alpha**2)**0.5
# Head with final conv mimicking EffNets
self.final_conv = self.which_conv(int(1280 * ch // 440), kernel_shape=1,
padding='SAME', name='final_conv')
self.fc = hk.Linear(self.num_classes, w_init=fc_init, with_bias=True)
def __call__(self, x, is_training=True, return_metrics=False):
"""Return the output of the final layer without any [log-]softmax."""
# Stem
outputs = {}
out = self.initial_conv(x)
if return_metrics:
outputs.update(base.signal_metrics(out, 0))
# Blocks
for i, block in enumerate(self.blocks):
out, res_avg_var = block(out, is_training=is_training)
if return_metrics:
outputs.update(base.signal_metrics(out, i + 1))
outputs[f'res_avg_var_{i}'] = res_avg_var
# Final-conv->activation, pool, dropout, classify
out = self.activation(self.final_conv(out))
pool = jnp.mean(out, [1, 2])
outputs['pool'] = pool
# Optionally apply dropout
if self.drop_rate > 0.0 and is_training:
pool = hk.dropout(hk.next_rng_key(), self.drop_rate, pool)
outputs['logits'] = self.fc(pool)
return outputs
def count_flops(self, h, w):
flops = []
flops += [base.count_conv_flops(3, self.initial_conv, h, w)]
h, w = h / 2, w / 2
# Body FLOPs
for block in self.blocks:
flops += [block.count_flops(h, w)]
if block.stride > 1:
h, w = h / block.stride, w / block.stride
# Head module FLOPs
out_ch = self.blocks[-1].out_ch
flops += [base.count_conv_flops(out_ch, self.final_conv, h, w)]
# Count flops for classifier
flops += [self.final_conv.output_channels * self.fc.output_size]
return flops, sum(flops)
class NFBlock(hk.Module):
"""Normalizer-Free RegNet Block."""
def __init__(self, in_ch, out_ch, expansion=2.25, se_ratio=0.5,
kernel_size=3, group_size=8, stride=1,
beta=1.0, alpha=0.2,
which_conv=base.WSConv2D, activation=jax.nn.relu,
stochdepth_rate=None, name=None):
super().__init__(name=name)
self.in_ch, self.out_ch = in_ch, out_ch
self.expansion = expansion
self.se_ratio = se_ratio
self.kernel_size = kernel_size
self.activation = activation
self.beta, self.alpha = beta, alpha
# Round expanded with based on group count
width = int(self.in_ch * expansion)
self.groups = width // group_size
self.width = group_size * self.groups
self.stride = stride
# Conv 0 (typically expansion conv)
self.conv0 = which_conv(self.width, kernel_shape=1, padding='SAME',
name='conv0')
# Grouped NxN conv
self.conv1 = which_conv(self.width, kernel_shape=kernel_size, stride=stride,
padding='SAME', feature_group_count=self.groups,
name='conv1')
# Conv 2, typically projection conv
self.conv2 = which_conv(self.out_ch, kernel_shape=1, padding='SAME',
name='conv2')
# Use shortcut conv on channel change or downsample.
self.use_projection = stride > 1 or self.in_ch != self.out_ch
if self.use_projection:
self.conv_shortcut = which_conv(self.out_ch, kernel_shape=1,
padding='SAME', name='conv_shortcut')
# Squeeze + Excite Module
self.se = base.SqueezeExcite(self.width, self.width, self.se_ratio)
# Are we using stochastic depth?
self._has_stochdepth = (stochdepth_rate is not None and
stochdepth_rate > 0. and stochdepth_rate < 1.0)
if self._has_stochdepth:
self.stoch_depth = base.StochDepth(stochdepth_rate)
def __call__(self, x, is_training):
out = self.activation(x) * self.beta
if self.stride > 1: # Average-pool downsample.
shortcut = hk.avg_pool(out, window_shape=(1, 2, 2, 1),
strides=(1, 2, 2, 1), padding='SAME')
if self.use_projection:
shortcut = self.conv_shortcut(shortcut)
elif self.use_projection:
shortcut = self.conv_shortcut(out)
else:
shortcut = x
out = self.conv0(out)
out = self.conv1(self.activation(out))
out = 2 * self.se(out) * out # Multiply by 2 for rescaling
out = self.conv2(self.activation(out))
# Get average residual standard deviation for reporting metrics.
res_avg_var = jnp.mean(jnp.var(out, axis=[0, 1, 2]))
# Apply stochdepth if applicable.
if self._has_stochdepth:
out = self.stoch_depth(out, is_training)
# SkipInit Gain
out = out * hk.get_parameter('skip_gain', (), out.dtype, init=jnp.zeros)
return out * self.alpha + shortcut, res_avg_var
def count_flops(self, h, w):
# Count conv FLOPs based on input HW
expand_flops = base.count_conv_flops(self.in_ch, self.conv0, h, w)
# If block is strided we decrease resolution here.
dw_flops = base.count_conv_flops(self.width, self.conv1, h, w)
if self.stride > 1:
h, w = h / self.stride, w / self.stride
if self.use_projection:
sc_flops = base.count_conv_flops(self.in_ch, self.conv_shortcut, h, w)
else:
sc_flops = 0
# SE flops happen on avg-pooled activations
se_flops = self.se.fc0.output_size * self.width
se_flops += self.se.fc0.output_size * self.se.fc1.output_size
contract_flops = base.count_conv_flops(self.width, self.conv2, h, w)
return sum([expand_flops, dw_flops, se_flops, contract_flops, sc_flops])
================================================
FILE: nfnets/nf_resnet.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Norm-Free Residual Networks."""
# pylint: disable=invalid-name
import haiku as hk
import jax
import jax.numpy as jnp
from nfnets import base
class NF_ResNet(hk.Module):
"""Norm-Free preactivation ResNet."""
variant_dict = {'ResNet50': {'depth': [3, 4, 6, 3]},
'ResNet101': {'depth': [3, 4, 23, 3]},
'ResNet152': {'depth': [3, 8, 36, 3]},
'ResNet200': {'depth': [3, 24, 36, 3]},
'ResNet288': {'depth': [24, 24, 24, 24]},
'ResNet600': {'depth': [50, 50, 50, 50]},
}
def __init__(self, num_classes, variant='ResNet50', width=4,
alpha=0.2, stochdepth_rate=0.1, drop_rate=None,
activation='relu', fc_init=None, skipinit_gain=jnp.zeros,
use_se=False, se_ratio=0.25,
name='NF_ResNet'):
super().__init__(name=name)
self.num_classes = num_classes
self.variant = variant
self.width = width
# Get variant info
block_params = self.variant_dict[self.variant]
self.width_pattern = [item * self.width for item in [64, 128, 256, 512]]
self.depth_pattern = block_params['depth']
self.activation = base.nonlinearities[activation]
if drop_rate is None:
self.drop_rate = block_params['drop_rate']
else:
self.drop_rate = drop_rate
self.which_conv = base.WSConv2D
# Stem
ch = int(16 * self.width)
self.initial_conv = self.which_conv(ch, kernel_shape=7, stride=2,
padding='SAME', with_bias=False,
name='initial_conv')
# Body
self.blocks = []
expected_std = 1.0
num_blocks = sum(self.depth_pattern)
index = 0 # Overall block index
block_args = (self.width_pattern, self.depth_pattern, [1, 2, 2, 2])
for block_width, stage_depth, stride in zip(*block_args):
for block_index in range(stage_depth):
# Scalar pre-multiplier so each block sees an N(0,1) input at init
beta = 1./ expected_std
# Block stochastic depth drop-rate
block_stochdepth_rate = stochdepth_rate * index / num_blocks
self.blocks += [NFResBlock(ch, block_width,
stride=stride if block_index == 0 else 1,
beta=beta, alpha=alpha,
activation=self.activation,
which_conv=self.which_conv,
stochdepth_rate=block_stochdepth_rate,
skipinit_gain=skipinit_gain,
use_se=use_se,
se_ratio=se_ratio,
)]
ch = block_width
index += 1
# Reset expected std but still give it 1 block of growth
if block_index == 0:
expected_std = 1.0
expected_std = (expected_std **2 + alpha**2)**0.5
# Head. By default, initialize with N(0, 0.01)
if fc_init is None:
fc_init = hk.initializers.RandomNormal(0.01, 0)
self.fc = hk.Linear(self.num_classes, w_init=fc_init, with_bias=True)
def __call__(self, x, is_training=True, return_metrics=False):
"""Return the output of the final layer without any [log-]softmax."""
# Stem
outputs = {}
out = self.initial_conv(x)
out = hk.max_pool(out, window_shape=(1, 3, 3, 1),
strides=(1, 2, 2, 1), padding='SAME')
if return_metrics:
outputs.update(base.signal_metrics(out, 0))
# Blocks
for i, block in enumerate(self.blocks):
out, res_avg_var = block(out, is_training=is_training)
if return_metrics:
outputs.update(base.signal_metrics(out, i + 1))
outputs[f'res_avg_var_{i}'] = res_avg_var
# Final-conv->activation, pool, dropout, classify
pool = jnp.mean(self.activation(out), [1, 2])
outputs['pool'] = pool
# Optionally apply dropout
if self.drop_rate > 0.0 and is_training:
pool = hk.dropout(hk.next_rng_key(), self.drop_rate, pool)
outputs['logits'] = self.fc(pool)
return outputs
def count_flops(self, h, w):
flops = []
flops += [base.count_conv_flops(3, self.initial_conv, h, w)]
h, w = h / 2, w / 2
# Body FLOPs
for block in self.blocks:
flops += [block.count_flops(h, w)]
if block.stride > 1:
h, w = h / block.stride, w / block.stride
# Count flops for classifier
flops += [self.blocks[-1].out_ch * self.fc.output_size]
return flops, sum(flops)
class NFResBlock(hk.Module):
"""Normalizer-Free pre-activation ResNet Block."""
def __init__(self, in_ch, out_ch, bottleneck_ratio=0.25,
kernel_size=3, stride=1,
beta=1.0, alpha=0.2,
which_conv=base.WSConv2D, activation=jax.nn.relu,
skipinit_gain=jnp.zeros,
stochdepth_rate=None,
use_se=False, se_ratio=0.25,
name=None):
super().__init__(name=name)
self.in_ch, self.out_ch = in_ch, out_ch
self.kernel_size = kernel_size
self.activation = activation
self.beta, self.alpha = beta, alpha
self.skipinit_gain = skipinit_gain
self.use_se, self.se_ratio = use_se, se_ratio
# Bottleneck width
self.width = int(self.out_ch * bottleneck_ratio)
self.stride = stride
# Conv 0 (typically expansion conv)
self.conv0 = which_conv(self.width, kernel_shape=1, padding='SAME',
name='conv0')
# Grouped NxN conv
self.conv1 = which_conv(self.width, kernel_shape=kernel_size, stride=stride,
padding='SAME', name='conv1')
# Conv 2, typically projection conv
self.conv2 = which_conv(self.out_ch, kernel_shape=1, padding='SAME',
name='conv2')
# Use shortcut conv on channel change or downsample.
self.use_projection = stride > 1 or self.in_ch != self.out_ch
if self.use_projection:
self.conv_shortcut = which_conv(self.out_ch, kernel_shape=1,
stride=stride, padding='SAME',
name='conv_shortcut')
# Are we using stochastic depth?
self._has_stochdepth = (stochdepth_rate is not None and
stochdepth_rate > 0. and stochdepth_rate < 1.0)
if self._has_stochdepth:
self.stoch_depth = base.StochDepth(stochdepth_rate)
if self.use_se:
self.se = base.SqueezeExcite(self.out_ch, self.out_ch, self.se_ratio)
def __call__(self, x, is_training):
out = self.activation(x) * self.beta
shortcut = x
if self.use_projection: # Downsample with conv1x1
shortcut = self.conv_shortcut(out)
out = self.conv0(out)
out = self.conv1(self.activation(out))
out = self.conv2(self.activation(out))
if self.use_se:
out = 2 * self.se(out) * out
# Get average residual standard deviation for reporting metrics.
res_avg_var = jnp.mean(jnp.var(out, axis=[0, 1, 2]))
# Apply stochdepth if applicable.
if self._has_stochdepth:
out = self.stoch_depth(out, is_training)
# SkipInit Gain
out = out * hk.get_parameter('skip_gain', (), out.dtype,
init=self.skipinit_gain)
return out * self.alpha + shortcut, res_avg_var
def count_flops(self, h, w):
# Count conv FLOPs based on input HW
expand_flops = base.count_conv_flops(self.in_ch, self.conv0, h, w)
# If block is strided we decrease resolution here.
dw_flops = base.count_conv_flops(self.width, self.conv1, h, w)
if self.stride > 1:
h, w = h / self.stride, w / self.stride
if self.use_projection:
sc_flops = base.count_conv_flops(self.in_ch, self.conv_shortcut, h, w)
else:
sc_flops = 0
# SE flops happen on avg-pooled activations
se_flops = self.se.fc0.output_size * self.width
se_flops += self.se.fc0.output_size * self.se.fc1.output_size
contract_flops = base.count_conv_flops(self.width, self.conv2, h, w)
return sum([expand_flops, dw_flops, se_flops, contract_flops, sc_flops])
================================================
FILE: nfnets/nfnet.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Norm-Free Nets."""
# pylint: disable=unused-import
# pylint: disable=invalid-name
import functools
import haiku as hk
import jax
import jax.numpy as jnp
import jax.random as jrandom
import numpy as np
from nfnets import base
class NFNet(hk.Module):
"""Normalizer-Free Networks with an improved architecture.
References:
[Brock, Smith, De, Simonyan 2021] High-Performance Large-Scale Image
Recognition Without Normalization.
"""
variant_dict = base.nfnet_params
def __init__(self, num_classes, variant='F0',
width=1.0, se_ratio=0.5,
alpha=0.2, stochdepth_rate=0.1, drop_rate=None,
activation='gelu', fc_init=None,
final_conv_mult=2, final_conv_ch=None,
use_two_convs=True,
name='NFNet'):
super().__init__(name=name)
self.num_classes = num_classes
self.variant = variant
self.width = width
self.se_ratio = se_ratio
# Get variant info
block_params = self.variant_dict[self.variant]
self.train_imsize = block_params['train_imsize']
self.test_imsize = block_params['test_imsize']
self.width_pattern = block_params['width']
self.depth_pattern = block_params['depth']
self.bneck_pattern = block_params.get('expansion', [0.5] * 4)
self.group_pattern = block_params.get('group_width', [128] * 4)
self.big_pattern = block_params.get('big_width', [True] * 4)
self.activation = base.nonlinearities[activation]
if drop_rate is None:
self.drop_rate = block_params['drop_rate']
else:
self.drop_rate = drop_rate
self.which_conv = base.WSConv2D
# Stem
ch = self.width_pattern[0] // 2
self.stem = hk.Sequential([
self.which_conv(16, kernel_shape=3, stride=2,
padding='SAME', name='stem_conv0'),
self.activation,
self.which_conv(32, kernel_shape=3, stride=1,
padding='SAME', name='stem_conv1'),
self.activation,
self.which_conv(64, kernel_shape=3, stride=1,
padding='SAME', name='stem_conv2'),
self.activation,
self.which_conv(ch, kernel_shape=3, stride=2,
padding='SAME', name='stem_conv3'),
])
# Body
self.blocks = []
expected_std = 1.0
num_blocks = sum(self.depth_pattern)
index = 0 # Overall block index
stride_pattern = [1, 2, 2, 2]
block_args = zip(self.width_pattern, self.depth_pattern, self.bneck_pattern,
self.group_pattern, self.big_pattern, stride_pattern)
for (block_width, stage_depth, expand_ratio,
group_size, big_width, stride) in block_args:
for block_index in range(stage_depth):
# Scalar pre-multiplier so each block sees an N(0,1) input at init
beta = 1./ expected_std
# Block stochastic depth drop-rate
block_stochdepth_rate = stochdepth_rate * index / num_blocks
out_ch = (int(block_width * self.width))
self.blocks += [NFBlock(ch, out_ch,
expansion=expand_ratio, se_ratio=se_ratio,
group_size=group_size,
stride=stride if block_index == 0 else 1,
beta=beta, alpha=alpha,
activation=self.activation,
which_conv=self.which_conv,
stochdepth_rate=block_stochdepth_rate,
big_width=big_width,
use_two_convs=use_two_convs,
)]
ch = out_ch
index += 1
# Reset expected std but still give it 1 block of growth
if block_index == 0:
expected_std = 1.0
expected_std = (expected_std **2 + alpha**2)**0.5
# Head
if final_conv_mult is None:
if final_conv_ch is None:
raise ValueError('Must provide one of final_conv_mult or final_conv_ch')
ch = final_conv_ch
else:
ch = int(final_conv_mult * ch)
self.final_conv = self.which_conv(ch, kernel_shape=1,
padding='SAME', name='final_conv')
# By default, initialize with N(0, 0.01)
if fc_init is None:
fc_init = hk.initializers.RandomNormal(mean=0, stddev=0.01)
self.fc = hk.Linear(self.num_classes, w_init=fc_init, with_bias=True)
def __call__(self, x, is_training=True, return_metrics=False):
"""Return the output of the final layer without any [log-]softmax."""
# Stem
outputs = {}
out = self.stem(x)
if return_metrics:
outputs.update(base.signal_metrics(out, 0))
# Blocks
for i, block in enumerate(self.blocks):
out, res_avg_var = block(out, is_training=is_training)
if return_metrics:
outputs.update(base.signal_metrics(out, i + 1))
outputs[f'res_avg_var_{i}'] = res_avg_var
# Final-conv->activation, pool, dropout, classify
out = self.activation(self.final_conv(out))
pool = jnp.mean(out, [1, 2])
outputs['pool'] = pool
# Optionally apply dropout
if self.drop_rate > 0.0 and is_training:
pool = hk.dropout(hk.next_rng_key(), self.drop_rate, pool)
outputs['logits'] = self.fc(pool)
return outputs
def count_flops(self, h, w):
flops = []
ch = 3
for module in self.stem.layers:
if isinstance(module, hk.Conv2D):
flops += [base.count_conv_flops(ch, module, h, w)]
if any([item > 1 for item in module.stride]):
h, w = h / module.stride[0], w / module.stride[1]
ch = module.output_channels
# Body FLOPs
for block in self.blocks:
flops += [block.count_flops(h, w)]
if block.stride > 1:
h, w = h / block.stride, w / block.stride
# Head module FLOPs
out_ch = self.blocks[-1].out_ch
flops += [base.count_conv_flops(out_ch, self.final_conv, h, w)]
# Count flops for classifier
flops += [self.final_conv.output_channels * self.fc.output_size]
return flops, sum(flops)
class NFBlock(hk.Module):
"""Normalizer-Free Net Block."""
def __init__(self, in_ch, out_ch, expansion=0.5, se_ratio=0.5,
kernel_shape=3, group_size=128, stride=1,
beta=1.0, alpha=0.2,
which_conv=base.WSConv2D, activation=jax.nn.gelu,
big_width=True, use_two_convs=True,
stochdepth_rate=None, name=None):
super().__init__(name=name)
self.in_ch, self.out_ch = in_ch, out_ch
self.expansion = expansion
self.se_ratio = se_ratio
self.kernel_shape = kernel_shape
self.activation = activation
self.beta, self.alpha = beta, alpha
# Mimic resnet style bigwidth scaling?
width = int((self.out_ch if big_width else self.in_ch) * expansion)
# Round expanded with based on group count
self.groups = width // group_size
self.width = group_size * self.groups
self.stride = stride
self.use_two_convs = use_two_convs
# Conv 0 (typically expansion conv)
self.conv0 = which_conv(self.width, kernel_shape=1, padding='SAME',
name='conv0')
# Grouped NxN conv
self.conv1 = which_conv(self.width, kernel_shape=kernel_shape,
stride=stride, padding='SAME',
feature_group_count=self.groups, name='conv1')
if self.use_two_convs:
self.conv1b = which_conv(self.width, kernel_shape=kernel_shape,
stride=1, padding='SAME',
feature_group_count=self.groups, name='conv1b')
# Conv 2, typically projection conv
self.conv2 = which_conv(self.out_ch, kernel_shape=1, padding='SAME',
name='conv2')
# Use shortcut conv on channel change or downsample.
self.use_projection = stride > 1 or self.in_ch != self.out_ch
if self.use_projection:
self.conv_shortcut = which_conv(self.out_ch, kernel_shape=1,
padding='SAME', name='conv_shortcut')
# Squeeze + Excite Module
self.se = base.SqueezeExcite(self.out_ch, self.out_ch, self.se_ratio)
# Are we using stochastic depth?
self._has_stochdepth = (stochdepth_rate is not None and
stochdepth_rate > 0. and stochdepth_rate < 1.0)
if self._has_stochdepth:
self.stoch_depth = base.StochDepth(stochdepth_rate)
def __call__(self, x, is_training):
out = self.activation(x) * self.beta
if self.stride > 1: # Average-pool downsample.
shortcut = hk.avg_pool(out, window_shape=(1, 2, 2, 1),
strides=(1, 2, 2, 1), padding='SAME')
if self.use_projection:
shortcut = self.conv_shortcut(shortcut)
elif self.use_projection:
shortcut = self.conv_shortcut(out)
else:
shortcut = x
out = self.conv0(out)
out = self.conv1(self.activation(out))
if self.use_two_convs:
out = self.conv1b(self.activation(out))
out = self.conv2(self.activation(out))
out = (self.se(out) * 2) * out # Multiply by 2 for rescaling
# Get average residual standard deviation for reporting metrics.
res_avg_var = jnp.mean(jnp.var(out, axis=[0, 1, 2]))
# Apply stochdepth if applicable.
if self._has_stochdepth:
out = self.stoch_depth(out, is_training)
# SkipInit Gain
out = out * hk.get_parameter('skip_gain', (), out.dtype, init=jnp.zeros)
return out * self.alpha + shortcut, res_avg_var
def count_flops(self, h, w):
# Count conv FLOPs based on input HW
expand_flops = base.count_conv_flops(self.in_ch, self.conv0, h, w)
# If block is strided we decrease resolution here.
dw_flops = base.count_conv_flops(self.width, self.conv1, h, w)
if self.stride > 1:
h, w = h / self.stride, w / self.stride
if self.use_two_convs:
dw_flops += base.count_conv_flops(self.width, self.conv1b, h, w)
if self.use_projection:
sc_flops = base.count_conv_flops(self.in_ch, self.conv_shortcut, h, w)
else:
sc_flops = 0
# SE flops happen on avg-pooled activations
se_flops = self.se.fc0.output_size * self.out_ch
se_flops += self.se.fc0.output_size * self.se.fc1.output_size
contract_flops = base.count_conv_flops(self.width, self.conv2, h, w)
return sum([expand_flops, dw_flops, se_flops, contract_flops, sc_flops])
================================================
FILE: nfnets/nfnet_demo_colab.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "jev9nsjEePyx"
},
"source": [
"# Download and run an NFNet-F0 model pre-trained on ImageNet\n",
"This demo shows how to run a pre-trained NFNet classifier, from the paper \n",
"[High-Performance Large-Scale Image Recognition Without Normalization](http://dpmd.ai/06171) (Brock, De, Smith, Simonyan, 2021). It uses code from [the official JAX + Haiku implementation](http://dpmd.ai/nfnets).\n",
"\n",
"\n",
"It's recommended to use `Runtime-\u003eChange Runtime Type` to pick a GPU for speed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 16619,
"status": "ok",
"timestamp": 1613472332810,
"user": {
"displayName": "Andy Brock",
"photoUrl": "",
"userId": "04378600802759613630"
},
"user_tz": 0
},
"id": "bvEKqIQAZjxo",
"outputId": "846ba4c5-08cb-4d14-de38-8426de8d564e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting dm-haiku\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/61/dc/6da61c7f96bffd7ebc2888510e5ec82ee260cbc1d7a3f4f0a862914646f8/dm_haiku-0.0.3-py3-none-any.whl (260kB)\n",
"\r\u001b[K |█▎ | 10kB 18.5MB/s eta 0:00:01\r\u001b[K |██▌ | 20kB 22.7MB/s eta 0:00:01\r\u001b[K |███▊ | 30kB 22.3MB/s eta 0:00:01\r\u001b[K |█████ | 40kB 18.3MB/s eta 0:00:01\r\u001b[K |██████▎ | 51kB 15.6MB/s eta 0:00:01\r\u001b[K |███████▌ | 61kB 12.2MB/s eta 0:00:01\r\u001b[K |████████▉ | 71kB 12.9MB/s eta 0:00:01\r\u001b[K |██████████ | 81kB 12.7MB/s eta 0:00:01\r\u001b[K |███████████▎ | 92kB 12.9MB/s eta 0:00:01\r\u001b[K |████████████▋ | 102kB 12.7MB/s eta 0:00:01\r\u001b[K |█████████████▉ | 112kB 12.7MB/s eta 0:00:01\r\u001b[K |███████████████ | 122kB 12.7MB/s eta 0:00:01\r\u001b[K |████████████████▍ | 133kB 12.7MB/s eta 0:00:01\r\u001b[K |█████████████████▋ | 143kB 12.7MB/s eta 0:00:01\r\u001b[K |██████████████████▉ | 153kB 12.7MB/s eta 0:00:01\r\u001b[K |████████████████████▏ | 163kB 12.7MB/s eta 0:00:01\r\u001b[K |█████████████████████▍ | 174kB 12.7MB/s eta 0:00:01\r\u001b[K |██████████████████████▋ | 184kB 12.7MB/s eta 0:00:01\r\u001b[K |████████████████████████ | 194kB 12.7MB/s eta 0:00:01\r\u001b[K |█████████████████████████▏ | 204kB 12.7MB/s eta 0:00:01\r\u001b[K |██████████████████████████▍ | 215kB 12.7MB/s eta 0:00:01\r\u001b[K |███████████████████████████▊ | 225kB 12.7MB/s eta 0:00:01\r\u001b[K |█████████████████████████████ | 235kB 12.7MB/s eta 0:00:01\r\u001b[K |██████████████████████████████▏ | 245kB 12.7MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▌| 256kB 12.7MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 266kB 12.7MB/s \n",
"\u001b[?25hRequirement already satisfied: absl-py\u003e=0.7.1 in /usr/local/lib/python3.6/dist-packages (from dm-haiku) (0.10.0)\n",
"Requirement already satisfied: numpy\u003e=1.18.0 in /usr/local/lib/python3.6/dist-packages (from dm-haiku) (1.19.5)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from absl-py\u003e=0.7.1-\u003edm-haiku) (1.15.0)\n",
"Installing collected packages: dm-haiku\n",
"Successfully installed dm-haiku-0.0.3\n",
"Requirement already satisfied: dill in /usr/local/lib/python3.6/dist-packages (0.3.3)\n",
"Cloning into 'deepmind-research'...\n",
"remote: Enumerating objects: 10, done.\u001b[K\n",
"remote: Counting objects: 100% (10/10), done.\u001b[K\n",
"remote: Compressing objects: 100% (10/10), done.\u001b[K\n",
"remote: Total 1307 (delta 0), reused 4 (delta 0), pack-reused 1297\u001b[K\n",
"Receiving objects: 100% (1307/1307), 74.41 MiB | 20.20 MiB/s, done.\n",
"Resolving deltas: 100% (656/656), done.\n"
]
}
],
"source": [
"# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"# ==============================================================================\n",
"!pip install dm-haiku\n",
"!pip install dill\n",
"!git clone https://github.com/deepmind/deepmind-research/\n",
"import dill\n",
"import haiku as hk\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import os\n",
"import numpy as np\n",
"from PIL import Image\n",
"os.chdir('deepmind-research')\n",
"from nfnets import nfnet, base"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "d3oIj5O1dyVB"
},
"source": [
"# ImageNet Class List"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "A9pD88rU5wsw"
},
"outputs": [],
"source": [
"# Get ImageNet class list\n",
"imagenet_classlist = {0: 'tench, Tinca tinca',\n",
" 1: 'goldfish, Carassius auratus',\n",
" 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',\n",
" 3: 'tiger shark, Galeocerdo cuvieri',\n",
" 4: 'hammerhead, hammerhead shark',\n",
" 5: 'electric ray, crampfish, numbfish, torpedo',\n",
" 6: 'stingray',\n",
" 7: 'rooster',\n",
" 8: 'hen',\n",
" 9: 'ostrich, Struthio camelus',\n",
" 10: 'brambling, Fringilla montifringilla',\n",
" 11: 'goldfinch, Carduelis carduelis',\n",
" 12: 'house finch, linnet, Carpodacus mexicanus',\n",
" 13: 'junco, snowbird',\n",
" 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea',\n",
" 15: 'robin, American robin, Turdus migratorius',\n",
" 16: 'bulbul',\n",
" 17: 'jay',\n",
" 18: 'magpie',\n",
" 19: 'chickadee',\n",
" 20: 'water ouzel, dipper',\n",
" 21: 'kite',\n",
" 22: 'bald eagle, American eagle, Haliaeetus leucocephalus',\n",
" 23: 'vulture',\n",
" 24: 'great grey owl, great gray owl, Strix nebulosa',\n",
" 25: 'European fire salamander, Salamandra salamandra',\n",
" 26: 'common newt, Triturus vulgaris',\n",
" 27: 'eft',\n",
" 28: 'spotted salamander, Ambystoma maculatum',\n",
" 29: 'axolotl, mud puppy, Ambystoma mexicanum',\n",
" 30: 'bullfrog, Rana catesbeiana',\n",
" 31: 'tree frog, tree-frog',\n",
" 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',\n",
" 33: 'loggerhead, loggerhead turtle, Caretta caretta',\n",
" 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',\n",
" 35: 'mud turtle',\n",
" 36: 'terrapin',\n",
" 37: 'box turtle, box tortoise',\n",
" 38: 'banded gecko',\n",
" 39: 'common iguana, iguana, Iguana iguana',\n",
" 40: 'American chameleon, anole, Anolis carolinensis',\n",
" 41: 'whiptail, whiptail lizard',\n",
" 42: 'agama',\n",
" 43: 'frilled lizard, Chlamydosaurus kingi',\n",
" 44: 'alligator lizard',\n",
" 45: 'Gila monster, Heloderma suspectum',\n",
" 46: 'green lizard, Lacerta viridis',\n",
" 47: 'African chameleon, Chamaeleo chamaeleon',\n",
" 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis',\n",
" 49: 'African crocodile, Nile crocodile, Crocodylus niloticus',\n",
" 50: 'American alligator, Alligator mississipiensis',\n",
" 51: 'triceratops',\n",
" 52: 'thunder snake, worm snake, Carphophis amoenus',\n",
" 53: 'ringneck snake, ring-necked snake, ring snake',\n",
" 54: 'hognose snake, puff adder, sand viper',\n",
" 55: 'green snake, grass snake',\n",
" 56: 'king snake, kingsnake',\n",
" 57: 'garter snake, grass snake',\n",
" 58: 'water snake',\n",
" 59: 'vine snake',\n",
" 60: 'night snake, Hypsiglena torquata',\n",
" 61: 'boa constrictor, Constrictor constrictor',\n",
" 62: 'rock python, rock snake, Python sebae',\n",
" 63: 'Indian cobra, Naja naja',\n",
" 64: 'green mamba',\n",
" 65: 'sea snake',\n",
" 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',\n",
" 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus',\n",
" 68: 'sidewinder, horned rattlesnake, Crotalus cerastes',\n",
" 69: 'trilobite',\n",
" 70: 'harvestman, daddy longlegs, Phalangium opilio',\n",
" 71: 'scorpion',\n",
" 72: 'black and gold garden spider, Argiope aurantia',\n",
" 73: 'barn spider, Araneus cavaticus',\n",
" 74: 'garden spider, Aranea diademata',\n",
" 75: 'black widow, Latrodectus mactans',\n",
" 76: 'tarantula',\n",
" 77: 'wolf spider, hunting spider',\n",
" 78: 'tick',\n",
" 79: 'centipede',\n",
" 80: 'black grouse',\n",
" 81: 'ptarmigan',\n",
" 82: 'ruffed grouse, partridge, Bonasa umbellus',\n",
" 83: 'prairie chicken, prairie grouse, prairie fowl',\n",
" 84: 'peacock',\n",
" 85: 'quail',\n",
" 86: 'partridge',\n",
" 87: 'African grey, African gray, Psittacus erithacus',\n",
" 88: 'macaw',\n",
" 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',\n",
" 90: 'lorikeet',\n",
" 91: 'coucal',\n",
" 92: 'bee eater',\n",
" 93: 'hornbill',\n",
" 94: 'hummingbird',\n",
" 95: 'jacamar',\n",
" 96: 'toucan',\n",
" 97: 'drake',\n",
" 98: 'red-breasted merganser, Mergus serrator',\n",
" 99: 'goose',\n",
" 100: 'black swan, Cygnus atratus',\n",
" 101: 'tusker',\n",
" 102: 'echidna, spiny anteater, anteater',\n",
" 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus',\n",
" 104: 'wallaby, brush kangaroo',\n",
" 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',\n",
" 106: 'wombat',\n",
" 107: 'jellyfish',\n",
" 108: 'sea anemone, anemone',\n",
" 109: 'brain coral',\n",
" 110: 'flatworm, platyhelminth',\n",
" 111: 'nematode, nematode worm, roundworm',\n",
" 112: 'conch',\n",
" 113: 'snail',\n",
" 114: 'slug',\n",
" 115: 'sea slug, nudibranch',\n",
" 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore',\n",
" 117: 'chambered nautilus, pearly nautilus, nautilus',\n",
" 118: 'Dungeness crab, Cancer magister',\n",
" 119: 'rock crab, Cancer irroratus',\n",
" 120: 'fiddler crab',\n",
" 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica',\n",
" 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus',\n",
" 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish',\n",
" 124: 'crayfish, crawfish, crawdad, crawdaddy',\n",
" 125: 'hermit crab',\n",
" 126: 'isopod',\n",
" 127: 'white stork, Ciconia ciconia',\n",
" 128: 'black stork, Ciconia nigra',\n",
" 129: 'spoonbill',\n",
" 130: 'flamingo',\n",
" 131: 'little blue heron, Egretta caerulea',\n",
" 132: 'American egret, great white heron, Egretta albus',\n",
" 133: 'bittern',\n",
" 134: 'crane',\n",
" 135: 'limpkin, Aramus pictus',\n",
" 136: 'European gallinule, Porphyrio porphyrio',\n",
" 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana',\n",
" 138: 'bustard',\n",
" 139: 'ruddy turnstone, Arenaria interpres',\n",
" 140: 'red-backed sandpiper, dunlin, Erolia alpina',\n",
" 141: 'redshank, Tringa totanus',\n",
" 142: 'dowitcher',\n",
" 143: 'oystercatcher, oyster catcher',\n",
" 144: 'pelican',\n",
" 145: 'king penguin, Aptenodytes patagonica',\n",
" 146: 'albatross, mollymawk',\n",
" 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus',\n",
" 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',\n",
" 149: 'dugong, Dugong dugon',\n",
" 150: 'sea lion',\n",
" 151: 'Chihuahua',\n",
" 152: 'Japanese spaniel',\n",
" 153: 'Maltese dog, Maltese terrier, Maltese',\n",
" 154: 'Pekinese, Pekingese, Peke',\n",
" 155: 'Shih-Tzu',\n",
" 156: 'Blenheim spaniel',\n",
" 157: 'papillon',\n",
" 158: 'toy terrier',\n",
" 159: 'Rhodesian ridgeback',\n",
" 160: 'Afghan hound, Afghan',\n",
" 161: 'basset, basset hound',\n",
" 162: 'beagle',\n",
" 163: 'bloodhound, sleuthhound',\n",
" 164: 'bluetick',\n",
" 165: 'black-and-tan coonhound',\n",
" 166: 'Walker hound, Walker foxhound',\n",
" 167: 'English foxhound',\n",
" 168: 'redbone',\n",
" 169: 'borzoi, Russian wolfhound',\n",
" 170: 'Irish wolfhound',\n",
" 171: 'Italian greyhound',\n",
" 172: 'whippet',\n",
" 173: 'Ibizan hound, Ibizan Podenco',\n",
" 174: 'Norwegian elkhound, elkhound',\n",
" 175: 'otterhound, otter hound',\n",
" 176: 'Saluki, gazelle hound',\n",
" 177: 'Scottish deerhound, deerhound',\n",
" 178: 'Weimaraner',\n",
" 179: 'Staffordshire bullterrier, Staffordshire bull terrier',\n",
" 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier',\n",
" 181: 'Bedlington terrier',\n",
" 182: 'Border terrier',\n",
" 183: 'Kerry blue terrier',\n",
" 184: 'Irish terrier',\n",
" 185: 'Norfolk terrier',\n",
" 186: 'Norwich terrier',\n",
" 187: 'Yorkshire terrier',\n",
" 188: 'wire-haired fox terrier',\n",
" 189: 'Lakeland terrier',\n",
" 190: 'Sealyham terrier, Sealyham',\n",
" 191: 'Airedale, Airedale terrier',\n",
" 192: 'cairn, cairn terrier',\n",
" 193: 'Australian terrier',\n",
" 194: 'Dandie Dinmont, Dandie Dinmont terrier',\n",
" 195: 'Boston bull, Boston terrier',\n",
" 196: 'miniature schnauzer',\n",
" 197: 'giant schnauzer',\n",
" 198: 'standard schnauzer',\n",
" 199: 'Scotch terrier, Scottish terrier, Scottie',\n",
" 200: 'Tibetan terrier, chrysanthemum dog',\n",
" 201: 'silky terrier, Sydney silky',\n",
" 202: 'soft-coated wheaten terrier',\n",
" 203: 'West Highland white terrier',\n",
" 204: 'Lhasa, Lhasa apso',\n",
" 205: 'flat-coated retriever',\n",
" 206: 'curly-coated retriever',\n",
" 207: 'golden retriever',\n",
" 208: 'Labrador retriever',\n",
" 209: 'Chesapeake Bay retriever',\n",
" 210: 'German short-haired pointer',\n",
" 211: 'vizsla, Hungarian pointer',\n",
" 212: 'English setter',\n",
" 213: 'Irish setter, red setter',\n",
" 214: 'Gordon setter',\n",
" 215: 'Brittany spaniel',\n",
" 216: 'clumber, clumber spaniel',\n",
" 217: 'English springer, English springer spaniel',\n",
" 218: 'Welsh springer spaniel',\n",
" 219: 'cocker spaniel, English cocker spaniel, cocker',\n",
" 220: 'Sussex spaniel',\n",
" 221: 'Irish water spaniel',\n",
" 222: 'kuvasz',\n",
" 223: 'schipperke',\n",
" 224: 'groenendael',\n",
" 225: 'malinois',\n",
" 226: 'briard',\n",
" 227: 'kelpie',\n",
" 228: 'komondor',\n",
" 229: 'Old English sheepdog, bobtail',\n",
" 230: 'Shetland sheepdog, Shetland sheep dog, Shetland',\n",
" 231: 'collie',\n",
" 232: 'Border collie',\n",
" 233: 'Bouvier des Flandres, Bouviers des Flandres',\n",
" 234: 'Rottweiler',\n",
" 235: 'German shepherd, German shepherd dog, German police dog, alsatian',\n",
" 236: 'Doberman, Doberman pinscher',\n",
" 237: 'miniature pinscher',\n",
" 238: 'Greater Swiss Mountain dog',\n",
" 239: 'Bernese mountain dog',\n",
" 240: 'Appenzeller',\n",
" 241: 'EntleBucher',\n",
" 242: 'boxer',\n",
" 243: 'bull mastiff',\n",
" 244: 'Tibetan mastiff',\n",
" 245: 'French bulldog',\n",
" 246: 'Great Dane',\n",
" 247: 'Saint Bernard, St Bernard',\n",
" 248: 'Inuit dog, husky',\n",
" 249: 'malamute, malemute, Alaskan malamute',\n",
" 250: 'Siberian husky',\n",
" 251: 'dalmatian, coach dog, carriage dog',\n",
" 252: 'affenpinscher, monkey pinscher, monkey dog',\n",
" 253: 'basenji',\n",
" 254: 'pug, pug-dog',\n",
" 255: 'Leonberg',\n",
" 256: 'Newfoundland, Newfoundland dog',\n",
" 257: 'Great Pyrenees',\n",
" 258: 'Samoyed, Samoyede',\n",
" 259: 'Pomeranian',\n",
" 260: 'chow, chow chow',\n",
" 261: 'keeshond',\n",
" 262: 'Brabancon griffon',\n",
" 263: 'Pembroke, Pembroke Welsh corgi',\n",
" 264: 'Cardigan, Cardigan Welsh corgi',\n",
" 265: 'toy poodle',\n",
" 266: 'miniature poodle',\n",
" 267: 'standard poodle',\n",
" 268: 'Mexican hairless',\n",
" 269: 'timber wolf, grey wolf, gray wolf, Canis lupus',\n",
" 270: 'white wolf, Arctic wolf, Canis lupus tundrarum',\n",
" 271: 'red wolf, maned wolf, Canis rufus, Canis niger',\n",
" 272: 'coyote, prairie wolf, brush wolf, Canis latrans',\n",
" 273: 'dingo, warrigal, warragal, Canis dingo',\n",
" 274: 'dhole, Cuon alpinus',\n",
" 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',\n",
" 276: 'hyena, hyaena',\n",
" 277: 'red fox, Vulpes vulpes',\n",
" 278: 'kit fox, Vulpes macrotis',\n",
" 279: 'Arctic fox, white fox, Alopex lagopus',\n",
" 280: 'grey fox, gray fox, Urocyon cinereoargenteus',\n",
" 281: 'tabby, tabby cat',\n",
" 282: 'tiger cat',\n",
" 283: 'Persian cat',\n",
" 284: 'Siamese cat, Siamese',\n",
" 285: 'Egyptian cat',\n",
" 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',\n",
" 287: 'lynx, catamount',\n",
" 288: 'leopard, Panthera pardus',\n",
" 289: 'snow leopard, ounce, Panthera uncia',\n",
" 290: 'jaguar, panther, Panthera onca, Felis onca',\n",
" 291: 'lion, king of beasts, Panthera leo',\n",
" 292: 'tiger, Panthera tigris',\n",
" 293: 'cheetah, chetah, Acinonyx jubatus',\n",
" 294: 'brown bear, bruin, Ursus arctos',\n",
" 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus',\n",
" 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',\n",
" 297: 'sloth bear, Melursus ursinus, Ursus ursinus',\n",
" 298: 'mongoose',\n",
" 299: 'meerkat, mierkat',\n",
" 300: 'tiger beetle',\n",
" 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',\n",
" 302: 'ground beetle, carabid beetle',\n",
" 303: 'long-horned beetle, longicorn, longicorn beetle',\n",
" 304: 'leaf beetle, chrysomelid',\n",
" 305: 'dung beetle',\n",
" 306: 'rhinoceros beetle',\n",
" 307: 'weevil',\n",
" 308: 'fly',\n",
" 309: 'bee',\n",
" 310: 'ant, emmet, pismire',\n",
" 311: 'grasshopper, hopper',\n",
" 312: 'cricket',\n",
" 313: 'walking stick, walkingstick, stick insect',\n",
" 314: 'cockroach, roach',\n",
" 315: 'mantis, mantid',\n",
" 316: 'cicada, cicala',\n",
" 317: 'leafhopper',\n",
" 318: 'lacewing, lacewing fly',\n",
" 319: \"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\",\n",
" 320: 'damselfly',\n",
" 321: 'admiral',\n",
" 322: 'ringlet, ringlet butterfly',\n",
" 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',\n",
" 324: 'cabbage butterfly',\n",
" 325: 'sulphur butterfly, sulfur butterfly',\n",
" 326: 'lycaenid, lycaenid butterfly',\n",
" 327: 'starfish, sea star',\n",
" 328: 'sea urchin',\n",
" 329: 'sea cucumber, holothurian',\n",
" 330: 'wood rabbit, cottontail, cottontail rabbit',\n",
" 331: 'hare',\n",
" 332: 'Angora, Angora rabbit',\n",
" 333: 'hamster',\n",
" 334: 'porcupine, hedgehog',\n",
" 335: 'fox squirrel, eastern fox squirrel, Sciurus niger',\n",
" 336: 'marmot',\n",
" 337: 'beaver',\n",
" 338: 'guinea pig, Cavia cobaya',\n",
" 339: 'sorrel',\n",
" 340: 'zebra',\n",
" 341: 'hog, pig, grunter, squealer, Sus scrofa',\n",
" 342: 'wild boar, boar, Sus scrofa',\n",
" 343: 'warthog',\n",
" 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius',\n",
" 345: 'ox',\n",
" 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',\n",
" 347: 'bison',\n",
" 348: 'ram, tup',\n",
" 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis',\n",
" 350: 'ibex, Capra ibex',\n",
" 351: 'hartebeest',\n",
" 352: 'impala, Aepyceros melampus',\n",
" 353: 'gazelle',\n",
" 354: 'Arabian camel, dromedary, Camelus dromedarius',\n",
" 355: 'llama',\n",
" 356: 'weasel',\n",
" 357: 'mink',\n",
" 358: 'polecat, fitch, foulmart, foumart, Mustela putorius',\n",
" 359: 'black-footed ferret, ferret, Mustela nigripes',\n",
" 360: 'otter',\n",
" 361: 'skunk, polecat, wood pussy',\n",
" 362: 'badger',\n",
" 363: 'armadillo',\n",
" 364: 'three-toed sloth, ai, Bradypus tridactylus',\n",
" 365: 'orangutan, orang, orangutang, Pongo pygmaeus',\n",
" 366: 'gorilla, Gorilla gorilla',\n",
" 367: 'chimpanzee, chimp, Pan troglodytes',\n",
" 368: 'gibbon, Hylobates lar',\n",
" 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus',\n",
" 370: 'guenon, guenon monkey',\n",
" 371: 'patas, hussar monkey, Erythrocebus patas',\n",
" 372: 'baboon',\n",
" 373: 'macaque',\n",
" 374: 'langur',\n",
" 375: 'colobus, colobus monkey',\n",
" 376: 'proboscis monkey, Nasalis larvatus',\n",
" 377: 'marmoset',\n",
" 378: 'capuchin, ringtail, Cebus capucinus',\n",
" 379: 'howler monkey, howler',\n",
" 380: 'titi, titi monkey',\n",
" 381: 'spider monkey, Ateles geoffroyi',\n",
" 382: 'squirrel monkey, Saimiri sciureus',\n",
" 383: 'Madagascar cat, ring-tailed lemur, Lemur catta',\n",
" 384: 'indri, indris, Indri indri, Indri brevicaudatus',\n",
" 385: 'Indian elephant, Elephas maximus',\n",
" 386: 'African elephant, Loxodonta africana',\n",
" 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',\n",
" 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',\n",
" 389: 'barracouta, snoek',\n",
" 390: 'eel',\n",
" 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch',\n",
" 392: 'rock beauty, Holocanthus tricolor',\n",
" 393: 'anemone fish',\n",
" 394: 'sturgeon',\n",
" 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus',\n",
" 396: 'lionfish',\n",
" 397: 'puffer, pufferfish, blowfish, globefish',\n",
" 398: 'abacus',\n",
" 399: 'abaya',\n",
" 400: \"academic gown, academic robe, judge's robe\",\n",
" 401: 'accordion, piano accordion, squeeze box',\n",
" 402: 'acoustic guitar',\n",
" 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier',\n",
" 404: 'airliner',\n",
" 405: 'airship, dirigible',\n",
" 406: 'altar',\n",
" 407: 'ambulance',\n",
" 408: 'amphibian, amphibious vehicle',\n",
" 409: 'analog clock',\n",
" 410: 'apiary, bee house',\n",
" 411: 'apron',\n",
" 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin',\n",
" 413: 'assault rifle, assault gun',\n",
" 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack',\n",
" 415: 'bakery, bakeshop, bakehouse',\n",
" 416: 'balance beam, beam',\n",
" 417: 'balloon',\n",
" 418: 'ballpoint, ballpoint pen, ballpen, Biro',\n",
" 419: 'Band Aid',\n",
" 420: 'banjo',\n",
" 421: 'bannister, banister, balustrade, balusters, handrail',\n",
" 422: 'barbell',\n",
" 423: 'barber chair',\n",
" 424: 'barbershop',\n",
" 425: 'barn',\n",
" 426: 'barometer',\n",
" 427: 'barrel, cask',\n",
" 428: 'barrow, garden cart, lawn cart, wheelbarrow',\n",
" 429: 'baseball',\n",
" 430: 'basketball',\n",
" 431: 'bassinet',\n",
" 432: 'bassoon',\n",
" 433: 'bathing cap, swimming cap',\n",
" 434: 'bath towel',\n",
" 435: 'bathtub, bathing tub, bath, tub',\n",
" 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon',\n",
" 437: 'beacon, lighthouse, beacon light, pharos',\n",
" 438: 'beaker',\n",
" 439: 'bearskin, busby, shako',\n",
" 440: 'beer bottle',\n",
" 441: 'beer glass',\n",
" 442: 'bell cote, bell cot',\n",
" 443: 'bib',\n",
" 444: 'bicycle-built-for-two, tandem bicycle, tandem',\n",
" 445: 'bikini, two-piece',\n",
" 446: 'binder, ring-binder',\n",
" 447: 'binoculars, field glasses, opera glasses',\n",
" 448: 'birdhouse',\n",
" 449: 'boathouse',\n",
" 450: 'bobsled, bobsleigh, bob',\n",
" 451: 'bolo tie, bolo, bola tie, bola',\n",
" 452: 'bonnet, poke bonnet',\n",
" 453: 'bookcase',\n",
" 454: 'bookshop, bookstore, bookstall',\n",
" 455: 'bottlecap',\n",
" 456: 'bow',\n",
" 457: 'bow tie, bow-tie, bowtie',\n",
" 458: 'brass, memorial tablet, plaque',\n",
" 459: 'brassiere, bra, bandeau',\n",
" 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty',\n",
" 461: 'breastplate, aegis, egis',\n",
" 462: 'broom',\n",
" 463: 'bucket, pail',\n",
" 464: 'buckle',\n",
" 465: 'bulletproof vest',\n",
" 466: 'bullet train, bullet',\n",
" 467: 'butcher shop, meat market',\n",
" 468: 'cab, hack, taxi, taxicab',\n",
" 469: 'caldron, cauldron',\n",
" 470: 'candle, taper, wax light',\n",
" 471: 'cannon',\n",
" 472: 'canoe',\n",
" 473: 'can opener, tin opener',\n",
" 474: 'cardigan',\n",
" 475: 'car mirror',\n",
" 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig',\n",
" 477: \"carpenter's kit, tool kit\",\n",
" 478: 'carton',\n",
" 479: 'car wheel',\n",
" 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM',\n",
" 481: 'cassette',\n",
" 482: 'cassette player',\n",
" 483: 'castle',\n",
" 484: 'catamaran',\n",
" 485: 'CD player',\n",
" 486: 'cello, violoncello',\n",
" 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone',\n",
" 488: 'chain',\n",
" 489: 'chainlink fence',\n",
" 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour',\n",
" 491: 'chain saw, chainsaw',\n",
" 492: 'chest',\n",
" 493: 'chiffonier, commode',\n",
" 494: 'chime, bell, gong',\n",
" 495: 'china cabinet, china closet',\n",
" 496: 'Christmas stocking',\n",
" 497: 'church, church building',\n",
" 498: 'cinema, movie theater, movie theatre, movie house, picture palace',\n",
" 499: 'cleaver, meat cleaver, chopper',\n",
" 500: 'cliff dwelling',\n",
" 501: 'cloak',\n",
" 502: 'clog, geta, patten, sabot',\n",
" 503: 'cocktail shaker',\n",
" 504: 'coffee mug',\n",
" 505: 'coffeepot',\n",
" 506: 'coil, spiral, volute, whorl, helix',\n",
" 507: 'combination lock',\n",
" 508: 'computer keyboard, keypad',\n",
" 509: 'confectionery, confectionary, candy store',\n",
" 510: 'container ship, containership, container vessel',\n",
" 511: 'convertible',\n",
" 512: 'corkscrew, bottle screw',\n",
" 513: 'cornet, horn, trumpet, trump',\n",
" 514: 'cowboy boot',\n",
" 515: 'cowboy hat, ten-gallon hat',\n",
" 516: 'cradle',\n",
" 517: 'crane',\n",
" 518: 'crash helmet',\n",
" 519: 'crate',\n",
" 520: 'crib, cot',\n",
" 521: 'Crock Pot',\n",
" 522: 'croquet ball',\n",
" 523: 'crutch',\n",
" 524: 'cuirass',\n",
" 525: 'dam, dike, dyke',\n",
" 526: 'desk',\n",
" 527: 'desktop computer',\n",
" 528: 'dial telephone, dial phone',\n",
" 529: 'diaper, nappy, napkin',\n",
" 530: 'digital clock',\n",
" 531: 'digital watch',\n",
" 532: 'dining table, board',\n",
" 533: 'dishrag, dishcloth',\n",
" 534: 'dishwasher, dish washer, dishwashing machine',\n",
" 535: 'disk brake, disc brake',\n",
" 536: 'dock, dockage, docking facility',\n",
" 537: 'dogsled, dog sled, dog sleigh',\n",
" 538: 'dome',\n",
" 539: 'doormat, welcome mat',\n",
" 540: 'drilling platform, offshore rig',\n",
" 541: 'drum, membranophone, tympan',\n",
" 542: 'drumstick',\n",
" 543: 'dumbbell',\n",
" 544: 'Dutch oven',\n",
" 545: 'electric fan, blower',\n",
" 546: 'electric guitar',\n",
" 547: 'electric locomotive',\n",
" 548: 'entertainment center',\n",
" 549: 'envelope',\n",
" 550: 'espresso maker',\n",
" 551: 'face powder',\n",
" 552: 'feather boa, boa',\n",
" 553: 'file, file cabinet, filing cabinet',\n",
" 554: 'fireboat',\n",
" 555: 'fire engine, fire truck',\n",
" 556: 'fire screen, fireguard',\n",
" 557: 'flagpole, flagstaff',\n",
" 558: 'flute, transverse flute',\n",
" 559: 'folding chair',\n",
" 560: 'football helmet',\n",
" 561: 'forklift',\n",
" 562: 'fountain',\n",
" 563: 'fountain pen',\n",
" 564: 'four-poster',\n",
" 565: 'freight car',\n",
" 566: 'French horn, horn',\n",
" 567: 'frying pan, frypan, skillet',\n",
" 568: 'fur coat',\n",
" 569: 'garbage truck, dustcart',\n",
" 570: 'gasmask, respirator, gas helmet',\n",
" 571: 'gas pump, gasoline pump, petrol pump, island dispenser',\n",
" 572: 'goblet',\n",
" 573: 'go-kart',\n",
" 574: 'golf ball',\n",
" 575: 'golfcart, golf cart',\n",
" 576: 'gondola',\n",
" 577: 'gong, tam-tam',\n",
" 578: 'gown',\n",
" 579: 'grand piano, grand',\n",
" 580: 'greenhouse, nursery, glasshouse',\n",
" 581: 'grille, radiator grille',\n",
" 582: 'grocery store, grocery, food market, market',\n",
" 583: 'guillotine',\n",
" 584: 'hair slide',\n",
" 585: 'hair spray',\n",
" 586: 'half track',\n",
" 587: 'hammer',\n",
" 588: 'hamper',\n",
" 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier',\n",
" 590: 'hand-held computer, hand-held microcomputer',\n",
" 591: 'handkerchief, hankie, hanky, hankey',\n",
" 592: 'hard disc, hard disk, fixed disk',\n",
" 593: 'harmonica, mouth organ, harp, mouth harp',\n",
" 594: 'harp',\n",
" 595: 'harvester, reaper',\n",
" 596: 'hatchet',\n",
" 597: 'holster',\n",
" 598: 'home theater, home theatre',\n",
" 599: 'honeycomb',\n",
" 600: 'hook, claw',\n",
" 601: 'hoopskirt, crinoline',\n",
" 602: 'horizontal bar, high bar',\n",
" 603: 'horse cart, horse-cart',\n",
" 604: 'hourglass',\n",
" 605: 'iPod',\n",
" 606: 'iron, smoothing iron',\n",
" 607: \"jack-o'-lantern\",\n",
" 608: 'jean, blue jean, denim',\n",
" 609: 'jeep, landrover',\n",
" 610: 'jersey, T-shirt, tee shirt',\n",
" 611: 'jigsaw puzzle',\n",
" 612: 'jinrikisha, ricksha, rickshaw',\n",
" 613: 'joystick',\n",
" 614: 'kimono',\n",
" 615: 'knee pad',\n",
" 616: 'knot',\n",
" 617: 'lab coat, laboratory coat',\n",
" 618: 'ladle',\n",
" 619: 'lampshade, lamp shade',\n",
" 620: 'laptop, laptop computer',\n",
" 621: 'lawn mower, mower',\n",
" 622: 'lens cap, lens cover',\n",
" 623: 'letter opener, paper knife, paperknife',\n",
" 624: 'library',\n",
" 625: 'lifeboat',\n",
" 626: 'lighter, light, igniter, ignitor',\n",
" 627: 'limousine, limo',\n",
" 628: 'liner, ocean liner',\n",
" 629: 'lipstick, lip rouge',\n",
" 630: 'Loafer',\n",
" 631: 'lotion',\n",
" 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system',\n",
" 633: \"loupe, jeweler's loupe\",\n",
" 634: 'lumbermill, sawmill',\n",
" 635: 'magnetic compass',\n",
" 636: 'mailbag, postbag',\n",
" 637: 'mailbox, letter box',\n",
" 638: 'maillot',\n",
" 639: 'maillot, tank suit',\n",
" 640: 'manhole cover',\n",
" 641: 'maraca',\n",
" 642: 'marimba, xylophone',\n",
" 643: 'mask',\n",
" 644: 'matchstick',\n",
" 645: 'maypole',\n",
" 646: 'maze, labyrinth',\n",
" 647: 'measuring cup',\n",
" 648: 'medicine chest, medicine cabinet',\n",
" 649: 'megalith, megalithic structure',\n",
" 650: 'microphone, mike',\n",
" 651: 'microwave, microwave oven',\n",
" 652: 'military uniform',\n",
" 653: 'milk can',\n",
" 654: 'minibus',\n",
" 655: 'miniskirt, mini',\n",
" 656: 'minivan',\n",
" 657: 'missile',\n",
" 658: 'mitten',\n",
" 659: 'mixing bowl',\n",
" 660: 'mobile home, manufactured home',\n",
" 661: 'Model T',\n",
" 662: 'modem',\n",
" 663: 'monastery',\n",
" 664: 'monitor',\n",
" 665: 'moped',\n",
" 666: 'mortar',\n",
" 667: 'mortarboard',\n",
" 668: 'mosque',\n",
" 669: 'mosquito net',\n",
" 670: 'motor scooter, scooter',\n",
" 671: 'mountain bike, all-terrain bike, off-roader',\n",
" 672: 'mountain tent',\n",
" 673: 'mouse, computer mouse',\n",
" 674: 'mousetrap',\n",
" 675: 'moving van',\n",
" 676: 'muzzle',\n",
" 677: 'nail',\n",
" 678: 'neck brace',\n",
" 679: 'necklace',\n",
" 680: 'nipple',\n",
" 681: 'notebook, notebook computer',\n",
" 682: 'obelisk',\n",
" 683: 'oboe, hautboy, hautbois',\n",
" 684: 'ocarina, sweet potato',\n",
" 685: 'odometer, hodometer, mileometer, milometer',\n",
" 686: 'oil filter',\n",
" 687: 'organ, pipe organ',\n",
" 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO',\n",
" 689: 'overskirt',\n",
" 690: 'oxcart',\n",
" 691: 'oxygen mask',\n",
" 692: 'packet',\n",
" 693: 'paddle, boat paddle',\n",
" 694: 'paddlewheel, paddle wheel',\n",
" 695: 'padlock',\n",
" 696: 'paintbrush',\n",
" 697: \"pajama, pyjama, pj's, jammies\",\n",
" 698: 'palace',\n",
" 699: 'panpipe, pandean pipe, syrinx',\n",
" 700: 'paper towel',\n",
" 701: 'parachute, chute',\n",
" 702: 'parallel bars, bars',\n",
" 703: 'park bench',\n",
" 704: 'parking meter',\n",
" 705: 'passenger car, coach, carriage',\n",
" 706: 'patio, terrace',\n",
" 707: 'pay-phone, pay-station',\n",
" 708: 'pedestal, plinth, footstall',\n",
" 709: 'pencil box, pencil case',\n",
" 710: 'pencil sharpener',\n",
" 711: 'perfume, essence',\n",
" 712: 'Petri dish',\n",
" 713: 'photocopier',\n",
" 714: 'pick, plectrum, plectron',\n",
" 715: 'pickelhaube',\n",
" 716: 'picket fence, paling',\n",
" 717: 'pickup, pickup truck',\n",
" 718: 'pier',\n",
" 719: 'piggy bank, penny bank',\n",
" 720: 'pill bottle',\n",
" 721: 'pillow',\n",
" 722: 'ping-pong ball',\n",
" 723: 'pinwheel',\n",
" 724: 'pirate, pirate ship',\n",
" 725: 'pitcher, ewer',\n",
" 726: \"plane, carpenter's plane, woodworking plane\",\n",
" 727: 'planetarium',\n",
" 728: 'plastic bag',\n",
" 729: 'plate rack',\n",
" 730: 'plow, plough',\n",
" 731: \"plunger, plumber's helper\",\n",
" 732: 'Polaroid camera, Polaroid Land camera',\n",
" 733: 'pole',\n",
" 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria',\n",
" 735: 'poncho',\n",
" 736: 'pool table, billiard table, snooker table',\n",
" 737: 'pop bottle, soda bottle',\n",
" 738: 'pot, flowerpot',\n",
" 739: \"potter's wheel\",\n",
" 740: 'power drill',\n",
" 741: 'prayer rug, prayer mat',\n",
" 742: 'printer',\n",
" 743: 'prison, prison house',\n",
" 744: 'projectile, missile',\n",
" 745: 'projector',\n",
" 746: 'puck, hockey puck',\n",
" 747: 'punching bag, punch bag, punching ball, punchball',\n",
" 748: 'purse',\n",
" 749: 'quill, quill pen',\n",
" 750: 'quilt, comforter, comfort, puff',\n",
" 751: 'racer, race car, racing car',\n",
" 752: 'racket, racquet',\n",
" 753: 'radiator',\n",
" 754: 'radio, wireless',\n",
" 755: 'radio telescope, radio reflector',\n",
" 756: 'rain barrel',\n",
" 757: 'recreational vehicle, RV, R.V.',\n",
" 758: 'reel',\n",
" 759: 'reflex camera',\n",
" 760: 'refrigerator, icebox',\n",
" 761: 'remote control, remote',\n",
" 762: 'restaurant, eating house, eating place, eatery',\n",
" 763: 'revolver, six-gun, six-shooter',\n",
" 764: 'rifle',\n",
" 765: 'rocking chair, rocker',\n",
" 766: 'rotisserie',\n",
" 767: 'rubber eraser, rubber, pencil eraser',\n",
" 768: 'rugby ball',\n",
" 769: 'rule, ruler',\n",
" 770: 'running shoe',\n",
" 771: 'safe',\n",
" 772: 'safety pin',\n",
" 773: 'saltshaker, salt shaker',\n",
" 774: 'sandal',\n",
" 775: 'sarong',\n",
" 776: 'sax, saxophone',\n",
" 777: 'scabbard',\n",
" 778: 'scale, weighing machine',\n",
" 779: 'school bus',\n",
" 780: 'schooner',\n",
" 781: 'scoreboard',\n",
" 782: 'screen, CRT screen',\n",
" 783: 'screw',\n",
" 784: 'screwdriver',\n",
" 785: 'seat belt, seatbelt',\n",
" 786: 'sewing machine',\n",
" 787: 'shield, buckler',\n",
" 788: 'shoe shop, shoe-shop, shoe store',\n",
" 789: 'shoji',\n",
" 790: 'shopping basket',\n",
" 791: 'shopping cart',\n",
" 792: 'shovel',\n",
" 793: 'shower cap',\n",
" 794: 'shower curtain',\n",
" 795: 'ski',\n",
" 796: 'ski mask',\n",
" 797: 'sleeping bag',\n",
" 798: 'slide rule, slipstick',\n",
" 799: 'sliding door',\n",
" 800: 'slot, one-armed bandit',\n",
" 801: 'snorkel',\n",
" 802: 'snowmobile',\n",
" 803: 'snowplow, snowplough',\n",
" 804: 'soap dispenser',\n",
" 805: 'soccer ball',\n",
" 806: 'sock',\n",
" 807: 'solar dish, solar collector, solar furnace',\n",
" 808: 'sombrero',\n",
" 809: 'soup bowl',\n",
" 810: 'space bar',\n",
" 811: 'space heater',\n",
" 812: 'space shuttle',\n",
" 813: 'spatula',\n",
" 814: 'speedboat',\n",
" 815: \"spider web, spider's web\",\n",
" 816: 'spindle',\n",
" 817: 'sports car, sport car',\n",
" 818: 'spotlight, spot',\n",
" 819: 'stage',\n",
" 820: 'steam locomotive',\n",
" 821: 'steel arch bridge',\n",
" 822: 'steel drum',\n",
" 823: 'stethoscope',\n",
" 824: 'stole',\n",
" 825: 'stone wall',\n",
" 826: 'stopwatch, stop watch',\n",
" 827: 'stove',\n",
" 828: 'strainer',\n",
" 829: 'streetcar, tram, tramcar, trolley, trolley car',\n",
" 830: 'stretcher',\n",
" 831: 'studio couch, day bed',\n",
" 832: 'stupa, tope',\n",
" 833: 'submarine, pigboat, sub, U-boat',\n",
" 834: 'suit, suit of clothes',\n",
" 835: 'sundial',\n",
" 836: 'sunglass',\n",
" 837: 'sunglasses, dark glasses, shades',\n",
" 838: 'sunscreen, sunblock, sun blocker',\n",
" 839: 'suspension bridge',\n",
" 840: 'swab, swob, mop',\n",
" 841: 'sweatshirt',\n",
" 842: 'swimming trunks, bathing trunks',\n",
" 843: 'swing',\n",
" 844: 'switch, electric switch, electrical switch',\n",
" 845: 'syringe',\n",
" 846: 'table lamp',\n",
" 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle',\n",
" 848: 'tape player',\n",
" 849: 'teapot',\n",
" 850: 'teddy, teddy bear',\n",
" 851: 'television, television system',\n",
" 852: 'tennis ball',\n",
" 853: 'thatch, thatched roof',\n",
" 854: 'theater curtain, theatre curtain',\n",
" 855: 'thimble',\n",
" 856: 'thresher, thrasher, threshing machine',\n",
" 857: 'throne',\n",
" 858: 'tile roof',\n",
" 859: 'toaster',\n",
" 860: 'tobacco shop, tobacconist shop, tobacconist',\n",
" 861: 'toilet seat',\n",
" 862: 'torch',\n",
" 863: 'totem pole',\n",
" 864: 'tow truck, tow car, wrecker',\n",
" 865: 'toyshop',\n",
" 866: 'tractor',\n",
" 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi',\n",
" 868: 'tray',\n",
" 869: 'trench coat',\n",
" 870: 'tricycle, trike, velocipede',\n",
" 871: 'trimaran',\n",
" 872: 'tripod',\n",
" 873: 'triumphal arch',\n",
" 874: 'trolleybus, trolley coach, trackless trolley',\n",
" 875: 'trombone',\n",
" 876: 'tub, vat',\n",
" 877: 'turnstile',\n",
" 878: 'typewriter keyboard',\n",
" 879: 'umbrella',\n",
" 880: 'unicycle, monocycle',\n",
" 881: 'upright, upright piano',\n",
" 882: 'vacuum, vacuum cleaner',\n",
" 883: 'vase',\n",
" 884: 'vault',\n",
" 885: 'velvet',\n",
" 886: 'vending machine',\n",
" 887: 'vestment',\n",
" 888: 'viaduct',\n",
" 889: 'violin, fiddle',\n",
" 890: 'volleyball',\n",
" 891: 'waffle iron',\n",
" 892: 'wall clock',\n",
" 893: 'wallet, billfold, notecase, pocketbook',\n",
" 894: 'wardrobe, closet, press',\n",
" 895: 'warplane, military plane',\n",
" 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin',\n",
" 897: 'washer, automatic washer, washing machine',\n",
" 898: 'water bottle',\n",
" 899: 'water jug',\n",
" 900: 'water tower',\n",
" 901: 'whiskey jug',\n",
" 902: 'whistle',\n",
" 903: 'wig',\n",
" 904: 'window screen',\n",
" 905: 'window shade',\n",
" 906: 'Windsor tie',\n",
" 907: 'wine bottle',\n",
" 908: 'wing',\n",
" 909: 'wok',\n",
" 910: 'wooden spoon',\n",
" 911: 'wool, woolen, woollen',\n",
" 912: 'worm fence, snake fence, snake-rail fence, Virginia fence',\n",
" 913: 'wreck',\n",
" 914: 'yawl',\n",
" 915: 'yurt',\n",
" 916: 'web site, website, internet site, site',\n",
" 917: 'comic book',\n",
" 918: 'crossword puzzle, crossword',\n",
" 919: 'street sign',\n",
" 920: 'traffic light, traffic signal, stoplight',\n",
" 921: 'book jacket, dust cover, dust jacket, dust wrapper',\n",
" 922: 'menu',\n",
" 923: 'plate',\n",
" 924: 'guacamole',\n",
" 925: 'consomme',\n",
" 926: 'hot pot, hotpot',\n",
" 927: 'trifle',\n",
" 928: 'ice cream, icecream',\n",
" 929: 'ice lolly, lolly, lollipop, popsicle',\n",
" 930: 'French loaf',\n",
" 931: 'bagel, beigel',\n",
" 932: 'pretzel',\n",
" 933: 'cheeseburger',\n",
" 934: 'hotdog, hot dog, red hot',\n",
" 935: 'mashed potato',\n",
" 936: 'head cabbage',\n",
" 937: 'broccoli',\n",
" 938: 'cauliflower',\n",
" 939: 'zucchini, courgette',\n",
" 940: 'spaghetti squash',\n",
" 941: 'acorn squash',\n",
" 942: 'butternut squash',\n",
" 943: 'cucumber, cuke',\n",
" 944: 'artichoke, globe artichoke',\n",
" 945: 'bell pepper',\n",
" 946: 'cardoon',\n",
" 947: 'mushroom',\n",
" 948: 'Granny Smith',\n",
" 949: 'strawberry',\n",
" 950: 'orange',\n",
" 951: 'lemon',\n",
" 952: 'fig',\n",
" 953: 'pineapple, ananas',\n",
" 954: 'banana',\n",
" 955: 'jackfruit, jak, jack',\n",
" 956: 'custard apple',\n",
" 957: 'pomegranate',\n",
" 958: 'hay',\n",
" 959: 'carbonara',\n",
" 960: 'chocolate sauce, chocolate syrup',\n",
" 961: 'dough',\n",
" 962: 'meat loaf, meatloaf',\n",
" 963: 'pizza, pizza pie',\n",
" 964: 'potpie',\n",
" 965: 'burrito',\n",
" 966: 'red wine',\n",
" 967: 'espresso',\n",
" 968: 'cup',\n",
" 969: 'eggnog',\n",
" 970: 'alp',\n",
" 971: 'bubble',\n",
" 972: 'cliff, drop, drop-off',\n",
" 973: 'coral reef',\n",
" 974: 'geyser',\n",
" 975: 'lakeside, lakeshore',\n",
" 976: 'promontory, headland, head, foreland',\n",
" 977: 'sandbar, sand bar',\n",
" 978: 'seashore, coast, seacoast, sea-coast',\n",
" 979: 'valley, vale',\n",
" 980: 'volcano',\n",
" 981: 'ballplayer, baseball player',\n",
" 982: 'groom, bridegroom',\n",
" 983: 'scuba diver',\n",
" 984: 'rapeseed',\n",
" 985: 'daisy',\n",
" 986: \"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum\",\n",
" 987: 'corn',\n",
" 988: 'acorn',\n",
" 989: 'hip, rose hip, rosehip',\n",
" 990: 'buckeye, horse chestnut, conker',\n",
" 991: 'coral fungus',\n",
" 992: 'agaric',\n",
" 993: 'gyromitra',\n",
" 994: 'stinkhorn, carrion fungus',\n",
" 995: 'earthstar',\n",
" 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa',\n",
" 997: 'bolete',\n",
" 998: 'ear, spike, capitulum',\n",
" 999: 'toilet tissue, toilet paper, bathroom tissue'}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wqHZIbjyZXoY"
},
"source": [
"# Load F0 weights and grab an image\n",
"The chosen image is released to the public domain and was taken in 2019 so we can be sure it isn't a part of the ImageNet training set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 25019,
"status": "ok",
"timestamp": 1613472346442,
"user": {
"displayName": "Andy Brock",
"photoUrl": "",
"userId": "04378600802759613630"
},
"user_tz": 0
},
"id": "KmA2FCPS6VkA",
"outputId": "1cfd1ff2-9adc-47c7-f3e8-af87f8bc2f26"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2021-02-16 10:45:40-- https://storage.googleapis.com/dm-nfnets/F0_haiku.npz\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 108.177.127.128, 173.194.69.128, 173.194.79.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.127.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 285976842 (273M) [application/octet-stream]\n",
"Saving to: ‘F0_haiku.npz’\n",
"\n",
"F0_haiku.npz 100%[===================\u003e] 272.73M 64.7MB/s in 4.2s \n",
"\n",
"2021-02-16 10:45:45 (64.7 MB/s) - ‘F0_haiku.npz’ saved [285976842/285976842]\n",
"\n",
"Model loaded w/ 71.49M Params\n",
"--2021-02-16 10:45:45-- https://live.staticflickr.com/65535/50594927526_f6c3b2a5d4_b.jpg\n",
"Resolving live.staticflickr.com (live.staticflickr.com)... 13.35.250.20, 2600:9000:2057:7200:0:5a51:64c9:c681, 2600:9000:2057:4e00:0:5a51:64c9:c681, ...\n",
"Connecting to live.staticflickr.com (live.staticflickr.com)|13.35.250.20|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: unspecified [image/jpeg]\n",
"Saving to: ‘peppers.jpg’\n",
"\n",
"peppers.jpg [ \u003c=\u003e ] 139.76K --.-KB/s in 0.02s \n",
"\n",
"2021-02-16 10:45:45 (7.95 MB/s) - ‘peppers.jpg’ saved [143110]\n",
"\n"
]
}
],
"source": [
"# Load F0 weights\n",
"variant = 'F0'\n",
"os.environ['VARIANT'] = variant\n",
"!wget https://storage.googleapis.com/dm-nfnets/${VARIANT}_haiku.npz\n",
"with open(f'{variant}_haiku.npz', 'rb') as in_file:\n",
" params = dill.load(in_file)\n",
"print(f'Model loaded w/ {hk.data_structures.tree_size(params)/1e6:.2f}M Params')\n",
"# public domain image from https://www.flickr.com/photos/alabama_extension/50594927526\n",
"!wget https://live.staticflickr.com/65535/50594927526_f6c3b2a5d4_b.jpg -O peppers.jpg\n",
"im = Image.open('peppers.jpg')\n",
"# Resize and crop to variant test size\n",
"imsize = base.nfnet_params[variant]['test_imsize']\n",
"im = im.resize((imsize + 32, imsize + 32))\n",
"im = im.crop((16, 16, 16+imsize, 16+imsize))\n",
"# Convert im to tensor and normalize with channel-wise RGB\n",
"MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)\n",
"STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)\n",
"x = (np.float32(im) - MEAN_RGB) / STDDEV_RGB\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8pxUnQbC4MPj"
},
"source": [
"# Run NFNet-F0 to classify a single image.\n",
"You can either run it in eager mode by not jitting the forward function, which will be relatively slow but not incur any compilation cost, or JIT the forward\n",
"function, which will incur a compilation cost but yield faster inference. If you JIT (the default) then the first time you call the function will trigger the compilation, and all subsequent evaluations will be fast."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Gr8tIBEwYOtb"
},
"outputs": [],
"source": [
"# Prepare the forward fn\n",
"def forward(inputs, is_training): \n",
" model = nfnet.NFNet(num_classes=1000, variant=variant)\n",
" return model(inputs, is_training=is_training)['logits']\n",
"net = hk.without_apply_rng(hk.transform(forward))\n",
"fwd = jax.jit(lambda inputs: net.apply(params, inputs, is_training=False))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 290
},
"executionInfo": {
"elapsed": 27901,
"status": "ok",
"timestamp": 1613472375251,
"user": {
"displayName": "Andy Brock",
"photoUrl": "",
"userId": "04378600802759613630"
},
"user_tz": 0
},
"id": "qeotZfkBYrIg",
"outputId": "84aa4055-e224-4838-9574-f483b6369079"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ImageNet class: bell pepper.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"\u003cPIL.Image.Image image mode=RGB size=256x256 at 0x7F474593F6D8\u003e"
]
},
"execution_count": 5,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"# We split this into two cells so that we don't repeatedly jit the fwd fn.\n",
"logits = fwd(x[None]) # Give X a newaxis to make it batch-size-1\n",
"which_class = imagenet_classlist[int(logits.argmax())]\n",
"print(f'ImageNet class: {which_class}.')\n",
"im"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [
"d3oIj5O1dyVB"
],
"name": "Public: Load and Test NFNet Models.ipynb",
"provenance": [
{
"file_id": "1PvnPRhmYywGYGsEf-UYRSt1y8SpBhiwv",
"timestamp": 1613472601457
}
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: nfnets/optim.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Optimizers and Schedulers, inspired by the PyTorch API."""
from collections import ChainMap # pylint:disable=g-importing-member
from typing import Callable, Mapping
import haiku as hk
import jax
import jax.numpy as jnp
import tree
from nfnets import utils
class Optimizer(object):
"""Optimizer base class."""
def __init__(self, params, defaults):
# Flag indicating if parameters have been broadcasted
self._broadcasted = False
# Optimizer hyperparameters; this is a dict to support using param_groups
self._hyperparameters = {}
# Mapping from model parameters to optimizer hyperparameters
self._params2hyperparams = {}
# Assign defaults
self._hyperparameters = dict(**defaults)
# Prepare parameter groups and mappings
self.create_param_groups(params, defaults)
# Join params at top-level if params is a list of groups
if isinstance(params, list):
if any(_is_non_empty_two_level_mapping(g['params']) for g in params):
params = hk.data_structures.merge(*[g['params'] for g in params])
else:
params = dict(ChainMap(*[g['params'] for g in params]))
# Prepare states
create_buffers = lambda k, v: self.create_buffers('/'.join(k), v)
self._states = tree.map_structure_with_path(create_buffers, params)
def add_hyperparam_group(self, group, suffix, defaults):
"""Adds new hyperparameters to the hyperparams dict."""
# Use default hyperparams unless overridden by group hyperparams
group_dict = {key: key for key in defaults if key not in group}
for key in group:
if key != 'params': # Reserved keyword 'params'
group_dict[key] = '%s_%s' % (key, suffix)
self._hyperparameters[group_dict[key]] = group[key]
# Set up params2hyperparams
def set_p2h(k, _):
self._params2hyperparams['/'.join(k)] = group_dict
tree.map_structure_with_path(set_p2h, group['params'])
def create_param_groups(self, params, defaults):
"""Creates param-hyperparam mappings."""
if isinstance(params, list):
for group_index, group in enumerate(params):
# Add group to hyperparams and get this group's full hyperparameters
self.add_hyperparam_group(group, group_index, defaults)
else:
mapping = {key: key for key in self._hyperparameters}
def set_p2h(k, _):
self._params2hyperparams['/'.join(k)] = mapping
tree.map_structure_with_path(set_p2h, params)
def create_buffers(self, name, params):
"""Method to be overridden by child classes."""
pass
def get_opt_params(self, param_name, itr):
"""Returns hyperparams corresponding to param_name."""
mapping = self._params2hyperparams[param_name]
output = {}
for key in mapping:
hyper = self._hyperparameters[mapping[key]]
# Handle the case where a hyper is a class, for hybrids
if isinstance(hyper, Callable) and not isinstance(hyper, type):
output[key] = hyper(itr)
else:
output[key] = hyper
return output
def get_hyper(self, param_name, hyper_name):
"""Get an individual hyperparam for a given param."""
mapping = self._params2hyperparams[param_name]
return self._hyperparameters[mapping[hyper_name]]
def plugin(self, states):
self._states = states
def states(self):
return self._states
def broadcast(self):
"""Brodcasts all buffers and parameters."""
self._broadcasted = True
for name, state in self._states.items():
self._states[name] = {key: utils.broadcast(state[key]) for key in state}
def gather(self):
"""Gathers state (if broadcasted) for saving."""
states = {}
for name in self._states:
state = self._states[name]
states[name] = {key: state[key] if state[key] is None else state[key][0]
for key in state}
return states
def __setattr__(self, name, value):
"""Overrides the object's set-attribute function to register states, etc."""
if '_hyperparameters' in self.__dict__ and name in self._hyperparameters:
self._hyperparameters[name] = value
elif '_states' in self.__dict__ and name in self._states:
self._states[name] = value
else:
object.__setattr__(self, name, value)
def __getattr__(self, name):
"""Override the object's get-attribute function to return states, etc."""
if '_hyperparameters' in self.__dict__ and name in self._hyperparameters:
return self._hyperparameters[name]
elif '_states' in self.__dict__ and name in self._states:
return self._states[name]
else:
object.__getattribute__(self, name)
def step(self, params, grads, states, itr=None):
"""Takes a single optimizer step.
Args:
params: a dict containing the parameters to be updated.
grads: a dict containing the gradients for each parameter in params.
states: a dict containing any optimizer buffers (momentum, etc) for
each parameter in params.
itr: an optional integer indicating the current step, for scheduling.
Returns:
The updated params and optimizer buffers.
"""
get_hyper = lambda k, v: self.get_opt_params('/'.join(k), itr)
hypers = tree.map_structure_with_path(get_hyper, params)
outs = tree.map_structure_up_to(params, self.update_param,
params, grads, states, hypers)
return utils.split_tree(outs, params, 2)
def _is_non_empty_two_level_mapping(obj):
instof = lambda t: lambda v: isinstance(v, t)
# Basically: isinstance(obj, Mapping[str, Mapping[str, Any]]) ...
return (isinstance(obj, Mapping) and all(map(instof(str), obj.keys())) and
all(map(instof(Mapping), obj.values())) and
all(map(lambda v: all(map(instof(str), v.keys())), obj.values())) and
# ... and has at least one leaf.
bool(obj) and any(map(bool, obj.values())))
class Schedule(object):
"""Hyperparameter scheduling objects."""
class CosineDecay(Schedule):
"""Cosine decay."""
def __init__(self, min_val, max_val, num_steps):
self.min_val = min_val
self.max_val = max_val
self.num_steps = num_steps
def __call__(self, itr):
cos = (1 + jnp.cos(jnp.pi * itr / self.num_steps))
return 0.5 * (self.max_val - self.min_val) * cos + self.min_val
class WarmupCosineDecay(Schedule):
"""Cosine decay with linear warmup."""
def __init__(self, start_val, min_val, max_val, num_steps, warmup_steps):
self.start_val = start_val
self.min_val = min_val
self.max_val = max_val
self.num_steps = num_steps
self.warmup_steps = warmup_steps
def __call__(self, itr):
warmup_val = ((self.max_val - self.start_val) * (itr / self.warmup_steps)
+ self.start_val)
cos_itr = (itr - self.warmup_steps) / (self.num_steps - self.warmup_steps)
cos = 1 + jnp.cos(jnp.pi * cos_itr)
cos_val = 0.5 * (self.max_val - self.min_val) * cos + self.min_val
# Select warmup_val if itr < warmup, else cosine val
values = jnp.array([warmup_val, cos_val])
index = jnp.sum(jnp.array(self.warmup_steps) < itr)
return jnp.take(values, index)
class WarmupExpDecay(Schedule):
"""Exponential step decay with linear warmup."""
def __init__(self, start_val, max_val, warmup_steps,
decay_factor, decay_interval):
self.start_val = start_val
self.max_val = max_val
self.warmup_steps = warmup_steps
self.decay_factor = decay_factor
self.decay_interval = decay_interval
def __call__(self, itr):
warmup_val = ((self.max_val - self.start_val) * (itr / self.warmup_steps)
+ self.start_val)
# How many decay steps have we taken?
num_decays = jnp.floor((itr - self.warmup_steps) / self.decay_interval)
exp_val = self.max_val * (self.decay_factor ** num_decays)
# Select warmup_val if itr < warmup, else exp_val
values = jnp.array([warmup_val, exp_val])
index = jnp.sum(jnp.array(self.warmup_steps) < itr)
return jnp.take(values, index)
class SGD(Optimizer):
"""Standard SGD with (nesterov) momentum and weight decay.
Attributes:
params: Either a dict mapping param names to JAX tensors, or a list where
each member of the list is a dict containing parameters
and hyperparameters, allowing one to specify param-specific hyperparams.
lr: Learning rate.
weight_decay: Weight decay parameter. Note that this is decay, not L2 reg.
momentum: Momentum parameter
dampening: Dampening parameter
nesterov: Bool indicating this optimizer will use the NAG formulation.
"""
defaults = {'weight_decay': None, 'momentum': None, 'dampening': 0,
'nesterov': None}
def __init__(self, params, lr, weight_decay=None,
momentum=None, dampening=0, nesterov=None):
super().__init__(
params, defaults={'lr': lr, 'weight_decay': weight_decay,
'momentum': momentum, 'dampening': dampening,
'nesterov': nesterov})
def create_buffers(self, name, param):
"""Prepares all momentum buffers for each parameter."""
state = {'step': jnp.zeros(jax.local_device_count())}
if self.get_hyper(name, 'momentum') is not None:
state['momentum'] = jnp.zeros_like(param)
return state
def update_param(self, param, grad, state, opt_params):
"""The actual update step for this optimizer."""
if param is None:
return param, state
# Apply weight decay
if opt_params.get('weight_decay') is not None:
grad = grad + param * opt_params['weight_decay']
# Update momentum buffers if needed
if 'momentum' in state:
state['momentum'] = (opt_params['momentum'] * state['momentum']
+ (1 - opt_params['dampening']) * grad)
if opt_params['nesterov'] is not None:
grad = grad + opt_params['momentum'] * state['momentum']
else:
grad = state['momentum']
state['step'] += 1
return param - opt_params['lr'] * grad, state
class Adam(Optimizer):
"""Adam optimizer, Kingma & Ba, arxiv.org/abs/1412.6980.
Args:
params (iterable): nested list of params to optimize
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (default: 0)
use_adamw (bool, optional): If not None, use decoupled weight decay
as in arxiv.org/abs/1711.05101. The paper version adds an additional
"schedule" hyperparameter eta, which we instead just replace with the
learning rate following the PyTorch implementation.
Note that this implementation will not instantiate a buffer if the
beta term for that buffer is passed in as None, thus conserving memory.
"""
defaults = {'beta1': 0.9, 'beta2': 0.999, 'weight_decay': None, 'eps': 1e-8,
'use_adamw': None}
def __init__(self, params, lr, beta1=0.9, beta2=0.999,
eps=1e-8, weight_decay=None, use_adamw=None):
super().__init__(params=params,
defaults={'lr': lr, 'beta1': beta1,
'beta2': beta2, 'eps': eps,
'weight_decay': weight_decay,
'use_adamw': use_adamw})
def create_buffers(self, name, param):
"""Prepare exp_avg and exp_avg_sq buffers."""
state = {'step': jnp.zeros(jax.local_device_count())}
if self.get_hyper(name, 'beta1') is not None:
state['exp_avg'] = jnp.zeros_like(param)
if self.get_hyper(name, 'beta2') is not None:
state['exp_avg_sq'] = jnp.zeros_like(param)
return state
def update_param(self, param, grad, state, opt_params):
"""The actual update step for this optimizer."""
if param is None:
return param, state
state['step'] = state['step'] + 1
# Apply weight decay
if opt_params.get('weight_decay') is not None:
if opt_params.get('use_adamw') is not None:
param = param * (1 - opt_params['lr'] * opt_params['weight_decay'])
else:
grad = grad + param * opt_params['weight_decay']
# First moment
if 'exp_avg' in state:
bias_correction1 = 1 - opt_params['beta1'] ** state['step']
state['exp_avg'] = (state['exp_avg'] * opt_params['beta1']
+ (1 - opt_params['beta1']) * grad)
step_size = opt_params['lr'] * state['exp_avg'] / bias_correction1
else:
step_size = opt_params['lr'] * grad
# Second moment
if 'exp_avg_sq' in state:
bias_correction2 = 1 - opt_params['beta2'] ** state['step']
state['exp_avg_sq'] = (state['exp_avg_sq'] * opt_params['beta2']
+ (1 - opt_params['beta2']) * grad * grad)
denom = jnp.sqrt(state['exp_avg_sq']) * jax.lax.rsqrt(bias_correction2)
denom = denom + opt_params['eps']
else:
denom = jnp.abs(grad) + opt_params['eps'] # Add eps to avoid divide-by-0
return param - step_size / denom, state
class RMSProp(Optimizer):
"""RMSProp optimizer, Tieleman and Hinton, ref: powerpoint slides.
Implements RMSProp as
rms = decay * rms{t-1} + (1-decay) * gradient ** 2
mom = momentum * mom{t-1} + learning_rate * g_t / sqrt(rms + epsilon)
param -= mom
Note that the rms buffer is initialized with ones as in TF, as opposed to
zeros as in all other implementations.
Args:
params (iterable): nested list of params to optimize
lr (float): learning rate (default: 1e-3)
decay (float): EMA decay rate for running estimate of squared gradient.
momentum (float or None): Use heavy ball momentum instead of instant grad.
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (NOT ADAMW (default: 0))
"""
defaults = {'weight_decay': None, 'eps': 1e-8}
def __init__(self, params, lr, decay, momentum, weight_decay=None, eps=1e-8):
super().__init__(params=params,
defaults={'lr': lr, 'decay': decay,
'momentum': momentum, 'eps': eps,
'weight_decay': weight_decay})
def create_buffers(self, name, param):
"""Prepare exp_avg and exp_avg_sq buffers."""
state = {'step': jnp.zeros(jax.local_device_count())}
state['rms'] = jnp.ones_like(param)
if self.get_hyper(name, 'momentum') is not None:
state['momentum'] = jnp.zeros_like(param)
return state
def update_param(self, param, grad, state, opt_params):
"""The actual update step for this optimizer."""
if param is None:
return param, state
state['step'] = state['step'] + 1
# Apply weight decay
if opt_params.get('weight_decay') is not None:
grad = grad + param * opt_params['weight_decay']
# EMA of the squared gradient
state['rms'] = (state['rms'] * opt_params['decay']
+ (1 - opt_params['decay']) * (grad ** 2))
scaled_grad = (opt_params['lr'] * grad
/ (state['rms'] + opt_params['eps']) ** 0.5)
if state['momentum'] is not None:
state['momentum'] = (state['momentum'] * opt_params['momentum']
+ scaled_grad)
step_size = state['momentum']
else:
step_size = scaled_grad
return param - step_size, state
class Fromage(Optimizer):
"""Fromage optimizer, Bernstein et al. arXiv.org/abs/2002.03432.
This version optionally includes weight decay.
Attributes:
params (iterable): nested list of params to optimize
lr (float): learning rate.
eps (float, optional): Minimum allowable norm. This term is required for
in case parameters are zero-initialized (default: 1e-5).
weight_decay (float, optional): weight decay (default: 0).
"""
defaults = {'weight_decay': None, 'eps': 1e-5}
def __init__(self, params, lr, weight_decay=None, eps=1e-5):
super().__init__(
params, defaults={'lr': lr, 'weight_decay': weight_decay, 'eps': eps})
def create_buffers(self, name, param): # pylint: disable=unused-argument
"""Prepares all momentum buffers for each parameter."""
return {'step': jnp.zeros(1)}
def update_param(self, param, grad, state, opt_params):
"""The actual update step for this optimizer."""
if param is None:
return param, state
if opt_params['weight_decay'] is not None:
grad = grad + param * opt_params['weight_decay']
grad_norm = jnp.maximum(jnp.linalg.norm(grad), opt_params['eps'])
param_norm = jnp.maximum(jnp.linalg.norm(param), opt_params['eps'])
mult = jax.lax.rsqrt(1 + opt_params['lr'] ** 2)
out = (param - opt_params['lr'] * grad * (param_norm / grad_norm)) * mult
return out, state
def compute_norm(x, axis, keepdims):
"""Returns norm over arbitrary axis."""
norm = jnp.sum(x ** 2, axis=axis, keepdims=keepdims) ** 0.5
return norm
def unitwise_norm(x):
"""Computes norms of each output unit separately, assuming (HW)IO weights."""
if len(jnp.squeeze(x).shape) <= 1: # Scalars and vectors
axis = None
keepdims = False
elif len(x.shape) in [2, 3]: # Linear layers of shape IO
axis = 0
keepdims = True
elif len(x.shape) == 4: # Conv kernels of shape HWIO
axis = [0, 1, 2,]
keepdims = True
else:
raise ValueError(f'Got a parameter with shape not in [1, 2, 3, 4]! {x}')
return compute_norm(x, axis, keepdims)
class SGD_AGC(Optimizer): # pylint:disable=invalid-name
"""SGD with Unit-Adaptive Gradient-Clipping.
References:
[Brock, Smith, De, Simonyan 2021] High-Performance Large-Scale Image
Recognition Without Normalization.
"""
defaults = {'weight_decay': None, 'momentum': None, 'dampening': 0,
'nesterov': None, 'clipping': 0.01, 'eps': 1e-3}
def __init__(self, params, lr, weight_decay=None,
momentum=None, dampening=0, nesterov=None,
clipping=0.01, eps=1e-3):
super().__init__(
params, defaults={'lr': lr, 'weight_decay': weight_decay,
'momentum': momentum, 'dampening': dampening,
'clipping': clipping, 'nesterov': nesterov,
'eps': eps})
def create_buffers(self, name, param):
return SGD.create_buffers(self, name, param)
def update_param(self, param, grad, state, opt_params):
"""Clips grads if necessary, then applies the optimizer update."""
if param is None:
return param, state
if opt_params['clipping'] is not None:
param_norm = jnp.maximum(unitwise_norm(param), opt_params['eps'])
grad_norm = unitwise_norm(grad)
max_norm = param_norm * opt_params['clipping']
# If grad norm > clipping * param_norm, rescale
trigger = grad_norm > max_norm
# Note the max(||G||, 1e-6) is technically unnecessary here, as
# the clipping shouldn't trigger if the grad norm is zero,
# but we include it in practice as a "just-in-case".
clipped_grad = grad * (max_norm / jnp.maximum(grad_norm, 1e-6))
grad = jnp.where(trigger, clipped_grad, grad)
return SGD.update_param(self, param, grad, state, opt_params)
class Hybrid(Optimizer):
"""Optimizer which permits passing param groups with different base opts.
The API for this class follows the case for any other optimizer where one
specifies a list of dicts with separate hyperparams, but in this case it
requires the user to also specify an 'opt' key for each group, such as e.g.
[{'params': params0, 'opt': optim.Adam, 'lr': 0.1}].
The user must also provide values for any arg in the selected optimizers which
does not have a default value associated
"""
def __init__(self, param_groups):
if any(['opt' not in group for group in param_groups]):
raise ValueError('All parameter groups must have an opt key!')
self.defaults = ChainMap(*[group['opt'].defaults for group in param_groups])
super().__init__(param_groups, defaults=dict(self.defaults))
def create_buffers(self, name, param):
return self.get_hyper(name, 'opt').create_buffers(self, name, param)
def update_param(self, param, grad, state, opt_params):
return opt_params['opt'].update_param(self, param, grad, state, opt_params)
================================================
FILE: nfnets/requirements.txt
================================================
absl-py==0.10.0
chex>=0.0.2
dill>=0.3.3
dm-haiku>=0.0.3
jax>=0.2.8
jaxlib>=0.1.58
jaxline>=0.0.1
ml_collections>=0.1
numpy>=1.18.0
tensorflow>=2.3.1
tensorflow-addons>=0.12.0
tensorflow-datasets>=4.1.0
tensorflow-probability>=0.12.1
typing_extensions>=3.7
wrapt>=1.11.2
================================================
FILE: nfnets/resnet.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ResNet model family."""
import functools
import haiku as hk
import jax
import jax.numpy as jnp
from nfnets import base
class ResNet(hk.Module):
"""ResNetv2 Models."""
variant_dict = {'ResNet50': {'depth': [3, 4, 6, 3]},
'ResNet101': {'depth': [3, 4, 23, 3]},
'ResNet152': {'depth': [3, 8, 36, 3]},
'ResNet200': {'depth': [3, 24, 36, 3]},
'ResNet288': {'depth': [24, 24, 24, 24]},
'ResNet600': {'depth': [50, 50, 50, 50]},
}
def __init__(self, width, num_classes,
variant='ResNet50',
which_norm='BatchNorm', norm_kwargs=None,
activation='relu', drop_rate=0.0,
fc_init=jnp.zeros, conv_kwargs=None,
preactivation=True, use_se=False, se_ratio=0.25,
name='ResNet'):
super().__init__(name=name)
self.width = width
self.num_classes = num_classes
self.variant = variant
self.depth_pattern = self.variant_dict[variant]['depth']
self.activation = getattr(jax.nn, activation)
self.drop_rate = drop_rate
self.which_norm = getattr(hk, which_norm)
if norm_kwargs is not None:
self.which_norm = functools.partial(self.which_norm, **norm_kwargs)
if conv_kwargs is not None:
self.which_conv = functools.partial(hk.Conv2D, **conv_kwargs)
else:
self.which_conv = hk.Conv2D
self.preactivation = preactivation
# Stem
self.initial_conv = self.which_conv(16 * self.width, kernel_shape=7,
stride=2, padding='SAME',
with_bias=False, name='initial_conv')
if not self.preactivation:
self.initial_bn = self.which_norm(name='initial_bn')
which_block = ResBlockV2 if self.preactivation else ResBlockV1
# Body
self.blocks = []
for multiplier, blocks_per_stage, stride in zip([64, 128, 256, 512],
self.depth_pattern,
[1, 2, 2, 2]):
for block_index in range(blocks_per_stage):
self.blocks += [which_block(multiplier * self.width,
use_projection=block_index == 0,
stride=stride if block_index == 0 else 1,
activation=self.activation,
which_norm=self.which_norm,
which_conv=self.which_conv,
use_se=use_se,
se_ratio=se_ratio)]
# Head
self.final_bn = self.which_norm(name='final_bn')
self.fc = hk.Linear(self.num_classes, w_init=fc_init, with_bias=True)
def __call__(self, x, is_training, test_local_stats=False,
return_metrics=False):
"""Return the output of the final layer without any [log-]softmax."""
outputs = {}
# Stem
out = self.initial_conv(x)
if not self.preactivation:
out = self.activation(self.initial_bn(out, is_training, test_local_stats))
out = hk.max_pool(out, window_shape=(1, 3, 3, 1),
strides=(1, 2, 2, 1), padding='SAME')
if return_metrics:
outputs.update(base.signal_metrics(out, 0))
# Blocks
for i, block in enumerate(self.blocks):
out, res_var = block(out, is_training, test_local_stats)
if return_metrics:
outputs.update(base.signal_metrics(out, i + 1))
outputs[f'res_avg_var_{i}'] = res_var
if self.preactivation:
out = self.activation(self.final_bn(out, is_training, test_local_stats))
# Pool, dropout, classify
pool = jnp.mean(out, axis=[1, 2])
# Return pool before dropout in case we want to regularize it separately.
outputs['pool'] = pool
# Optionally apply dropout
if self.drop_rate > 0.0 and is_training:
pool = hk.dropout(hk.next_rng_key(), self.drop_rate, pool)
outputs['logits'] = self.fc(pool)
return outputs
class ResBlockV2(hk.Module):
"""ResNet preac block, 1x1->3x3->1x1 with strides and shortcut downsample."""
def __init__(self, out_ch, stride=1, use_projection=False,
activation=jax.nn.relu, which_norm=hk.BatchNorm,
which_conv=hk.Conv2D, use_se=False, se_ratio=0.25,
name=None):
super().__init__(name=name)
self.out_ch = out_ch
self.stride = stride
self.use_projection = use_projection
self.activation = activation
self.which_norm = which_norm
self.which_conv = which_conv
self.use_se = use_se
self.se_ratio = se_ratio
self.width = self.out_ch // 4
self.bn0 = which_norm(name='bn0')
self.conv0 = which_conv(self.width, kernel_shape=1, with_bias=False,
padding='SAME', name='conv0')
self.bn1 = which_norm(name='bn1')
self.conv1 = which_conv(self.width, stride=self.stride,
kernel_shape=3, with_bias=False,
padding='SAME', name='conv1')
self.bn2 = which_norm(name='bn2')
self.conv2 = which_conv(self.out_ch, kernel_shape=1, with_bias=False,
padding='SAME', name='conv2')
if self.use_projection:
self.conv_shortcut = which_conv(self.out_ch, stride=stride,
kernel_shape=1, with_bias=False,
padding='SAME', name='conv_shortcut')
if self.use_se:
self.se = base.SqueezeExcite(self.out_ch, self.out_ch, self.se_ratio)
def __call__(self, x, is_training, test_local_stats):
bn_args = (is_training, test_local_stats)
out = self.activation(self.bn0(x, *bn_args))
if self.use_projection:
shortcut = self.conv_shortcut(out)
else:
shortcut = x
out = self.conv0(out)
out = self.conv1(self.activation(self.bn1(out, *bn_args)))
out = self.conv2(self.activation(self.bn2(out, *bn_args)))
if self.use_se:
out = self.se(out) * out
# Get average residual standard deviation for reporting metrics.
res_avg_var = jnp.mean(jnp.var(out, axis=[0, 1, 2]))
return out + shortcut, res_avg_var
class ResBlockV1(ResBlockV2):
"""Post-Ac Residual Block."""
def __call__(self, x, is_training, test_local_stats):
bn_args = (is_training, test_local_stats)
if self.use_projection:
shortcut = self.conv_shortcut(x)
shortcut = self.which_norm(name='shortcut_bn')(shortcut, *bn_args)
else:
shortcut = x
out = self.activation(self.bn0(self.conv0(x), *bn_args))
out = self.activation(self.bn1(self.conv1(out), *bn_args))
out = self.bn2(self.conv2(out), *bn_args)
if self.use_se:
out = self.se(out) * out
res_avg_var = jnp.mean(jnp.var(out, axis=[0, 1, 2]))
return self.activation(out + shortcut), res_avg_var
================================================
FILE: nfnets/run.sh
================================================
#!/bin/sh
# Copyright 2021 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
python3 -m venv /tmp/nfnets_venv
source /tmp/nfnets_venv/bin/activate
pip3 install --upgrade pip setuptools wheel
pip3 install -r nfnets/requirements.txt
python3 -m nfnets.test
================================================
FILE: nfnets/skipinit_resnet.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ResNetV2 (Pre-activation) with SkipInit."""
# pylint: disable=invalid-name
import haiku as hk
import jax
import jax.numpy as jnp
from nfnets import base
# Nonlinearities
nonlinearities = {
'swish': jax.nn.silu,
'relu': jax.nn.relu,
'identity': lambda x: x}
class SkipInit_ResNet(hk.Module):
"""Skip-Init based ResNet."""
variant_dict = {'ResNet50': {'depth': [3, 4, 6, 3]},
'ResNet101': {'depth': [3, 4, 23, 3]},
'ResNet152': {'depth': [3, 8, 36, 3]},
'ResNet200': {'depth': [3, 24, 36, 3]},
'ResNet288': {'depth': [24, 24, 24, 24]},
'ResNet600': {'depth': [50, 50, 50, 50]},
}
def __init__(self, num_classes, variant='ResNet50', width=4,
stochdepth_rate=0.1, drop_rate=None,
activation='relu', fc_init=jnp.zeros,
name='SkipInit_ResNet'):
super().__init__(name=name)
self.num_classes = num_classes
self.variant = variant
self.width = width
# Get variant info
block_params = self.variant_dict[self.variant]
self.width_pattern = [item * self.width for item in [64, 128, 256, 512]]
self.depth_pattern = block_params['depth']
self.activation = nonlinearities[activation]
if drop_rate is None:
self.drop_rate = block_params['drop_rate']
else:
self.drop_rate = drop_rate
self.which_conv = hk.Conv2D
# Stem
ch = int(16 * self.width)
self.initial_conv = self.which_conv(ch, kernel_shape=7, stride=2,
padding='SAME', with_bias=False,
name='initial_conv')
# Body
self.blocks = []
num_blocks = sum(self.depth_pattern)
index = 0 # Overall block index
block_args = (self.width_pattern, self.depth_pattern, [1, 2, 2, 2])
for block_width, stage_depth, stride in zip(*block_args):
for block_index in range(stage_depth):
# Block stochastic depth drop-rate
block_stochdepth_rate = stochdepth_rate * index / num_blocks
self.blocks += [NFResBlock(ch, block_width,
stride=stride if block_index == 0 else 1,
activation=self.activation,
which_conv=self.which_conv,
stochdepth_rate=block_stochdepth_rate,
)]
ch = block_width
index += 1
# Head
self.fc = hk.Linear(self.num_classes, w_init=fc_init, with_bias=True)
def __call__(self, x, is_training=True, return_metrics=False):
"""Return the output of the final layer without any [log-]softmax."""
# Stem
outputs = {}
out = self.initial_conv(x)
out = hk.max_pool(out, window_shape=(1, 3, 3, 1),
strides=(1, 2, 2, 1), padding='SAME')
if return_metrics:
outputs.update(base.signal_metrics(out, 0))
# Blocks
for i, block in enumerate(self.blocks):
out, res_avg_var = block(out, is_training=is_training)
if return_metrics:
outputs.update(base.signal_metrics(out, i + 1))
outputs[f'res_avg_var_{i}'] = res_avg_var
# Final-conv->activation, pool, dropout, classify
pool = jnp.mean(self.activation(out), [1, 2])
outputs['pool'] = pool
# Optionally apply dropout
if self.drop_rate > 0.0 and is_training:
pool = hk.dropout(hk.next_rng_key(), self.drop_rate, pool)
outputs['logits'] = self.fc(pool)
return outputs
def count_flops(self, h, w):
flops = []
flops += [base.count_conv_flops(3, self.initial_conv, h, w)]
h, w = h / 2, w / 2
# Body FLOPs
for block in self.blocks:
flops += [block.count_flops(h, w)]
if block.stride > 1:
h, w = h / block.stride, w / block.stride
# Count flops for classifier
flops += [self.blocks[-1].out_ch * self.fc.output_size]
return flops, sum(flops)
class NFResBlock(hk.Module):
"""Normalizer-Free pre-activation ResNet Block."""
def __init__(self, in_ch, out_ch, bottleneck_ratio=0.25,
kernel_size=3, stride=1,
which_conv=hk.Conv2D, activation=jax.nn.relu,
stochdepth_rate=None, name=None):
super().__init__(name=name)
self.in_ch, self.out_ch = in_ch, out_ch
self.kernel_size = kernel_size
self.activation = activation
# Bottleneck width
self.width = int(self.out_ch * bottleneck_ratio)
self.stride = stride
# Conv 0 (typically expansion conv)
self.conv0 = which_conv(self.width, kernel_shape=1, padding='SAME',
name='conv0')
# Grouped NxN conv
self.conv1 = which_conv(self.width, kernel_shape=kernel_size, stride=stride,
padding='SAME', name='conv1')
# Conv 2, typically projection conv
self.conv2 = which_conv(self.out_ch, kernel_shape=1, padding='SAME',
name='conv2')
# Use shortcut conv on channel change or downsample.
self.use_projection = stride > 1 or self.in_ch != self.out_ch
if self.use_projection:
self.conv_shortcut = which_conv(self.out_ch, kernel_shape=1,
stride=stride, padding='SAME',
name='conv_shortcut')
# Are we using stochastic depth?
self._has_stochdepth = (stochdepth_rate is not None and
stochdepth_rate > 0. and stochdepth_rate < 1.0)
if self._has_stochdepth:
self.stoch_depth = base.StochDepth(stochdepth_rate)
def __call__(self, x, is_training):
out = self.activation(x)
shortcut = x
if self.use_projection: # Downsample with conv1x1
shortcut = self.conv_shortcut(out)
out = self.conv0(out)
out = self.conv1(self.activation(out))
out = self.conv2(self.activation(out))
# Get average residual standard deviation for reporting metrics.
res_avg_var = jnp.mean(jnp.var(out, axis=[0, 1, 2]))
# Apply stochdepth if applicable.
if self._has_stochdepth:
out = self.stoch_depth(out, is_training)
# SkipInit Gain
out = out * hk.get_parameter('skip_gain', (), out.dtype, init=jnp.zeros)
return out + shortcut, res_avg_var
def count_flops(self, h, w):
# Count conv FLOPs based on input HW
expand_flops = base.count_conv_flops(self.in_ch, self.conv0, h, w)
# If block is strided we decrease resolution here.
dw_flops = base.count_conv_flops(self.width, self.conv1, h, w)
if self.stride > 1:
h, w = h / self.stride, w / self.stride
if self.use_projection:
sc_flops = base.count_conv_flops(self.in_ch, self.conv_shortcut, h, w)
else:
sc_flops = 0
# SE flops happen on avg-pooled activations
contract_flops = base.count_conv_flops(self.width, self.conv2, h, w)
return sum([expand_flops, dw_flops, contract_flops, sc_flops])
================================================
FILE: nfnets/test.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Quick script to test that experiment can import and run."""
import jax
import jax.numpy as jnp
from nfnets import experiment
from nfnets import experiment_nfnets
def test_experiment():
"""Tests the main experiment."""
config = experiment.get_config()
exp_config = config.experiment_kwargs.config
exp_config.train_batch_size = 2
exp_config.eval_batch_size = 2
exp_config.lr = 0.1
exp_config.fake_data = True
exp_config.model_kwargs.width = 2
print(exp_config.model_kwargs)
xp = experiment.Experiment('train', exp_config, jax.random.PRNGKey(0))
bcast = jax.pmap(lambda x: x)
global_step = bcast(jnp.zeros(jax.local_device_count()))
rng = bcast(jnp.stack([jax.random.PRNGKey(0)] * jax.local_device_count()))
print('Taking a single experiment step for test purposes!')
result = xp.step(global_step, rng)
print(f'Step successfully taken, resulting metrics are {result}')
def test_nfnet_experiment():
"""Tests the NFNet experiment."""
config = experiment_nfnets.get_config()
exp_config = config.experiment_kwargs.config
exp_config.train_batch_size = 2
exp_config.eval_batch_size = 2
exp_config.lr = 0.1
exp_config.fake_data = True
exp_config.model_kwargs.width = 2
print(exp_config.model_kwargs)
xp = experiment_nfnets.Experiment('train', exp_config, jax.random.PRNGKey(0))
bcast = jax.pmap(lambda x: x)
global_step = bcast(jnp.zeros(jax.local_device_count()))
rng = bcast(jnp.stack([jax.random.PRNGKey(0)] * jax.local_device_count()))
print('Taking a single NFNet experiment step for test purposes!')
result = xp.step(global_step, rng)
print(f'NFNet Step successfully taken, resulting metrics are {result}')
test_experiment()
test_nfnet_experiment()
================================================
FILE: nfnets/utils.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utils."""
import dill
import jax
import jax.numpy as jnp
import tree
def reduce_fn(x, mode):
"""Reduce fn for various losses."""
if mode == 'none' or mode is None:
return jnp.asarray(x)
elif mode == 'sum':
return jnp.sum(x)
elif mode == 'mean':
return jnp.mean(x)
else:
raise ValueError('Unsupported reduction option.')
def softmax_cross_entropy(logits, labels, reduction='sum'):
"""Computes softmax cross entropy given logits and one-hot class labels.
Args:
logits: Logit output values.
labels: Ground truth one-hot-encoded labels.
reduction: Type of reduction to apply to loss.
Returns:
Loss value. If `reduction` is `none`, this has the same shape as `labels`;
otherwise, it is scalar.
Raises:
ValueError: If the type of `reduction` is unsupported.
"""
loss = -jnp.sum(labels * jax.nn.log_softmax(logits), axis=-1)
return reduce_fn(loss, reduction)
def topk_correct(logits, labels, mask=None, prefix='', topk=(1, 5)):
"""Calculate top-k error for multiple k values."""
metrics = {}
argsorted_logits = jnp.argsort(logits)
for k in topk:
pred_labels = argsorted_logits[..., -k:]
# Get the number of examples where the label is in the top-k predictions
correct = any_in(pred_labels, labels).any(axis=-1).astype(jnp.float32)
if mask is not None:
correct *= mask
metrics[f'{prefix}top_{k}_acc'] = correct
return metrics
@jax.vmap
def any_in(prediction, target):
"""For each row in a and b, checks if any element of a is in b."""
return jnp.isin(prediction, target)
def tf1_ema(ema_value, current_value, decay, step):
"""Implements EMA with TF1-style decay warmup."""
decay = jnp.minimum(decay, (1.0 + step) / (10.0 + step))
return ema_value * decay + current_value * (1 - decay)
def ema(ema_value, current_value, decay, step):
"""Implements EMA without any warmup."""
del step
return ema_value * decay + current_value * (1 - decay)
to_bf16 = lambda x: x.astype(jnp.bfloat16) if x.dtype == jnp.float32 else x
from_bf16 = lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x
def _replicate(x, devices=None):
"""Replicate an object on each device."""
x = jax.numpy.array(x)
if devices is None:
devices = jax.local_devices()
return jax.device_put_sharded(len(devices) * [x], devices)
def broadcast(obj):
"""Broadcasts an object to all devices."""
if obj is not None and not isinstance(obj, bool):
return _replicate(obj)
else:
return obj
def split_tree(tuple_tree, base_tree, n):
"""Splits tuple_tree with n-tuple leaves into n trees."""
return [tree.map_structure_up_to(base_tree, lambda x: x[i], tuple_tree) # pylint: disable=cell-var-from-loop
for i in range(n)]
def load_haiku_file(filename):
"""Loads a haiku parameter tree, using dill."""
with open(filename, 'rb') as in_file:
output = dill.load(in_file)
return output
def flatten_haiku_tree(haiku_dict):
"""Flattens a haiku parameter tree into a flat dictionary."""
out = {}
for module in haiku_dict.keys():
out_module = module.replace('/~/', '.').replace('/', '.')
for key in haiku_dict[module]:
out_key = f'{out_module}.{key}'
out[out_key] = haiku_dict[module][key]
return out
================================================
FILE: noisy_label/README.md
================================================
# An Instance-Dependent Simulation Framework for Learning with Label Noise
We propose a simulation framework for generating instance-dependent
noisy labels via a pseudo-labeling paradigm. We show that this framework
generates synthetic noisy labels whose distribution is closer to human labels
compared to independent and class-conditional random flipping.
Equipped with controllable label noise, we study the negative impact of
noisy labels across a few practical settings to
understand when label noise is more problematic. Additionally, with the
availability of annotator information from our simulation framework, we propose
a new technique, Label Quality Model (LQM), that leverages annotator features to
predict and correct against noisy labels. We show that by adding LQM as a label
correction step before applying existing noisy label techniques, we can further
improve the models' performance.
[An Instance-Dependent Simulation Framework for Learning with Label Noise](https://arxiv.org/pdf/2107.11413.pdf).
In this repository, we provide the link to the datasets that we used in Sections
4 and 5 of the above paper, along with a colab that demonstrates how to load the
data and rater features.
We consider 4 tasks:
[CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html),
[CIFAR100](https://www.cs.toronto.edu/~kriz/cifar.html),
[Patch Camelyon](https://patchcamelyon.grand-challenge.org/),
and
[Cats vs Dogs](https://www.microsoft.com/en-us/download/details.aspx?id=54765).
For each task, we generate three synthetic noisy label
datasets, named as "low", "medium", and "high" according to the amount of label
noise. The data are stored as TFRecords and the rater features are stored as
json files.
The data is available under
[noisy label synthetic dataset GCP bucket](https://console.cloud.google.com/storage/browser/noisy_label_synthetic_datasets).
The colab that contains details of the datasets and examples for data loading
is at
[this colab example](https://github.com/deepmind/deepmind-research/blob/master/noisy_label/noisy_label_datasets_and_rater_features.ipynb)
## License
The noisy labels and rater features in our datasets are under the
[CC0 License](https://choosealicense.com/licenses/cc0-1.0/).
Other parts of the datasets are under the original license of the datasets.
When using the datasets based on CIFAR10/CIFAR100, users are required to
attribute the following paper:
Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009
When using the datasets based on Patch Camelyon, users are required to
attribute the following paper:
Rotation Equivariant CNNs for Digital Pathology, Bastiaan S. Veeling,
Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling, arXiv:1806.03962.
When using the datasets based on Cats vs Dogs, users are required to
attribute the following paper:
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization,
Jeremy Elson, John R. Douceur, Jon Howell, and Jared Saul, ACM Conference on
Computer and Communications Security, 2007.
The colab example is provided under the Apache License, Version 2.0.
## Citation
Please use the following bibtex for citations to our paper:
```
@article{gu2021instance,
title={An Instance-Dependent Simulation Framework for Learning with Label Noise},
author={Gu, Keren and Masotto, Xander and Bachani, Vandana and Lakshminarayanan, Balaji and Nikodem, Jack and Yin, Dong},
year={2021}
}
```
# Dataset Metadata
The following table is necessary for this dataset to be indexed by search
engines such as Google Dataset Search.
================================================
FILE: noisy_label/noisy_label_datasets_and_rater_features.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "cv38ildJKsei"
},
"source": [
"Copyright 2021 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use\n",
"this file except in compliance with the License. You may obtain a copy of the\n",
"License at\n",
"\n",
"[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed\n",
"under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n",
"CONDITIONS OF ANY KIND, either express or implied. See the License for the\n",
"specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tAJQfAHhAxz9"
},
"source": [
"# An Instance-Dependent Simulation Framework for Learning with Label Noise\n",
"\n",
"In this colab, we provide metadata and examples for data loading for the noisy label datasets generated using the pseudo-labeling paradigm propsed in the paper *An Instance-Dependent Simulation Framework for Learning with Label Noise*.\n",
"We also provide the associated rater features. We consider 4 tasks: CIFAR10 [1], CIFAR100 [1], Patch Camelyon [2,3], and Cats vs Dogs [4]. For each task, we generate three synthetic noisy label datasets, named as \"low\", \"medium\", and \"high\" according to the amount of label noise.\n",
"\n",
"* [1] Krizhevsky, Alex, and Geoffrey Hinton. \"Learning multiple layers of features from tiny images.\", 2009.\n",
"* [2] Veeling, Bastiaan S., Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling. \"Rotation equivariant CNNs for digital pathology.\" In International Conference on Medical image computing and computer-assisted intervention, pp. 210-218. Springer, Cham, 2018.\n",
"* [3] Bejnordi, Babak Ehteshami, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak et al. \"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.\" Jama 318, no. 22 (2017): 2199-2210.\n",
"* [4] Elson, Jeremy, John R. Douceur, Jon Howell, and Jared Saul. \"Asirra: a CAPTCHA that exploits interest-aligned manual image categorization.\" In ACM Conference on Computer and Communications Security, vol. 7, pp. 366-374. 2007."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_9SigscwVH_s"
},
"outputs": [],
"source": [
"# @title Imports and global variable\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"import json\n",
"import tensorflow as tf\n",
"\n",
"root_dir = '/root/directory/to/the/dataset/'"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7Xv86m2rNOFH"
},
"source": [
"**CIFAR10 noisy label datasets**\n",
"\n",
"**Download size**\n",
"* 79MB for each of the low, medium, and high noise datasets.\n",
"\n",
"**Number of examples**\n",
"* train: 19987, valid: 5021, for each of the low, medium, and high noise datasets.\n",
"\n",
"Both the train and valid splits are subsampled from the train split of the original CIFAR10 dataset.\n",
"\n",
"**Data features**\n",
"* \"image/raw\": images in bytes, shape = (32, 32, 3).\n",
"* \"image/class/label\": clean label, tf.int64.\n",
"* \"noisy_labels\": the noisy label given by rater models, a list of 10 tf.int64 integers.\n",
"* \"rater_ids\": the ID of the rater models, a list of 10 tf.string.\n",
"\n",
"**Rater features**\n",
"* model_name: name of the model\n",
"* accuracy: accuracy of the rater model on the rater validation set\n",
"* loss: loss of the rater model on the rater validation set\n",
"* experience: the total number of data that the rater model has seen during training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aPDR8avkiFWA"
},
"outputs": [],
"source": [
"# @title An example for loading CIFAR10 noisy label datasets\n",
"task_name = 'cifar10'\n",
"\n",
"# One of ['low', 'medium', 'high']\n",
"noise_level = 'low'\n",
"\n",
"# One of ['train', 'valid']. The `valid` split should be used for\n",
"# hyperparameter tuning. The model should be tested on the original test\n",
"# slipt for these tasks.\n",
"split = 'train'\n",
"\n",
"# We have 10 rater models for CIFAR10.\n",
"num_raters = 10\n",
"\n",
"directory = os.path.join(root_dir, task_name, noise_level, split) + '*'\n",
"raw_image_dataset = tf.data.TFRecordDataset(tf.io.gfile.glob(directory))\n",
"\n",
"# Create a dictionary describing the features.\n",
"image_feature_description = {\n",
" # the raw image\n",
" 'image/raw': tf.io.FixedLenFeature([], tf.string),\n",
" # the clean label\n",
" 'image/class/label': tf.io.FixedLenFeature([1], tf.int64),\n",
" # noisy labels from all the raters\n",
" 'noisy_labels': tf.io.FixedLenFeature([num_raters], tf.int64),\n",
" # the IDs of rater models\n",
" 'rater_ids': tf.io.FixedLenFeature([num_raters], tf.string),\n",
"}\n",
"\n",
"def _parse_image_function(example_proto):\n",
" # Parse the input tf.train.Example proto using the dictionary above.\n",
" return tf.io.parse_single_example(example_proto, image_feature_description)\n",
"\n",
"parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n",
"\n",
"for features in parsed_image_dataset.take(1):\n",
" # Check the IDs of the rater models. The rater IDs are the same for all the\n",
" # examples in the dataset.\n",
" rater_ids = features['rater_ids']\n",
" rater_id_string = [r.numpy().decode('utf-8') for r in rater_ids]\n",
" print('The IDs of the rater models for this dataset are:')\n",
" print(rater_id_string)\n",
" clean_label = features['image/class/label'].numpy()\n",
" print('The clean label for the following example is %d' % clean_label)\n",
" noisy_labels = features['noisy_labels'].numpy()\n",
" print('The noisy labels from the rater models are:')\n",
" print(noisy_labels)\n",
" image = tf.reshape(tf.io.decode_raw(features['image/raw'], tf.uint8),\n",
" (32, 32, 3))\n",
" plt.imshow(image)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YahpOXMmmhFO"
},
"source": [
"**CIFAR100 noisy label datasets**\n",
"\n",
"**Download size**\n",
"* 82MB for each of the low, medium, and high noise datasets.\n",
"\n",
"**Number of examples**\n",
"* train: 20114, valid: 4978, for each of the low, medium, and high noise datasets.\n",
"\n",
"Both the train and valid splits are subsampled from the train split of the original CIFAR100 dataset.\n",
"\n",
"**Data features**\n",
"* \"image/encoded\": images in bytes, shape=(32, 32, 3).\n",
"* \"image/class/fine_label\": clean fine-grained label, tf.int64.\n",
"* \"image/class/coarse_label\": clean coarse label, tf.int64\n",
"* \"noisy_labels\": the noisy label given by rater models, a list of 11 tf.int64 integers.\n",
"* \"rater_ids\": the ID of the rater models, a list of 11 tf.string.\n",
"\n",
"**Rater features**\n",
"* model_name: name of the model\n",
"* accuracy: accuracy of the rater model on the rater validation set\n",
"* loss: loss of the rater model on the rater validation set\n",
"* mAP: the mean average precision of the rater model on the rater validation set\n",
"* experience: the total number of data that the rater model has seen during training\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "AccCP_BZnz3S"
},
"outputs": [],
"source": [
"# @title An example for loading CIFAR100 noisy label datasets\n",
"task_name = 'cifar100'\n",
"\n",
"# One of ['low', 'medium', 'high']\n",
"noise_level = 'high'\n",
"\n",
"# One of ['train', 'valid']. The `valid` split should be used for\n",
"# hyperparameter tuning. The model should be tested on the original test\n",
"# slipt for these tasks.\n",
"split = 'train'\n",
"\n",
"# We have 11 rater models for CIFAR100.\n",
"num_raters = 11\n",
"\n",
"directory = os.path.join(root_dir, task_name, noise_level, split) + '*'\n",
"raw_image_dataset = tf.data.TFRecordDataset(tf.io.gfile.glob(directory))\n",
"\n",
"# Create a dictionary describing the features.\n",
"image_feature_description = {\n",
" # the raw image\n",
" 'image/encoded': tf.io.FixedLenFeature([], tf.string),\n",
" # the fine-grained clean label, value in [0, 99]\n",
" 'image/class/fine_label': tf.io.FixedLenFeature([1], tf.int64),\n",
" # the coarse clean label, value in [0, 19]\n",
" 'image/class/coarse_label': tf.io.FixedLenFeature([1], tf.int64),\n",
" # noisy labels from all the raters\n",
" 'noisy_labels': tf.io.FixedLenFeature([num_raters], tf.int64),\n",
" # the IDs of rater models\n",
" 'rater_ids': tf.io.FixedLenFeature([num_raters], tf.string),\n",
"}\n",
"\n",
"def _parse_image_function(example_proto):\n",
" # Parse the input tf.train.Example proto using the dictionary above.\n",
" return tf.io.parse_single_example(example_proto, image_feature_description)\n",
"\n",
"parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n",
"\n",
"for features in parsed_image_dataset.take(1):\n",
" # Check the IDs of the rater models. The rater IDs are the same for all the\n",
" # examples in the dataset.\n",
" rater_ids = features['rater_ids']\n",
" rater_id_string = [r.numpy().decode('utf-8') for r in rater_ids]\n",
" print('The IDs of the rater models for this dataset are:')\n",
" print(rater_id_string)\n",
" clean_label = features['image/class/fine_label'].numpy()\n",
" print('The clean label for the following example is %d' % clean_label)\n",
" noisy_labels = features['noisy_labels'].numpy()\n",
" print('The noisy labels from the rater models are:')\n",
" print(noisy_labels)\n",
" image = tf.reshape(tf.io.decode_raw(features['image/encoded'], tf.uint8),\n",
" (32, 32, 3))\n",
" plt.imshow(image)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ASlnEQ0DpPY2"
},
"source": [
"**Patch Camelyon noisy label datasets**\n",
"\n",
"**Download size**\n",
"* 3.27GB for each of the low, medium, and high noise datasets.\n",
"\n",
"**Number of examples**\n",
"* train: 130982, valid: 16394, for each of the low, medium, and high noise datasets.\n",
"\n",
"The train and valid splits are subsampled from the train and valid splits of the original Patch Camelyon dataset, respectively.\n",
"\n",
"**Data features**\n",
"* \"image\": images in png format, shape=(96, 96, 3).\n",
"* \"label\": clean label, tf.int64.\n",
"* \"id\": the ID of this image in the original Patch Camelyon dataset, a tf.string that begins with \"train_\" or \"valid_\".\n",
"* \"noisy_labels\": the noisy label given by rater models, a list of 20 (low and high noise) or 19 (medium noise) tf.int64 integers.\n",
"* \"rater_ids\": the ID of the rater models, a list of 20 (low and high noise) or 19 (medium noise) tf.string.\n",
"\n",
"**Rater features**\n",
"* model_name: name of the model\n",
"* accuracy: accuracy of the rater model on the rater validation set\n",
"* loss: loss of the rater model on the rater validation set\n",
"* experience: the total number of data that the rater model has seen during training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Usw0YmwupYgP"
},
"outputs": [],
"source": [
"# @title An example for loading Patch Camelyon noisy label datasets\n",
"task_name = 'patch_camelyon'\n",
"\n",
"# One of ['low', 'medium', 'high']\n",
"noise_level = 'medium'\n",
"\n",
"# One of ['train', 'valid']. The `valid` split should be used for\n",
"# hyperparameter tuning. The model should be tested on the original test\n",
"# slipt for these tasks.\n",
"split = 'train'\n",
"\n",
"# We have 20 rater models for low and high noise for Patch Camelyon.\n",
"# For medium noise, we have 19 rater models.\n",
"num_raters = 19 if noise_level == 'medium' else 20\n",
"\n",
"directory = os.path.join(root_dir, task_name, noise_level, split) + '*'\n",
"raw_image_dataset = tf.data.TFRecordDataset(tf.io.gfile.glob(directory))\n",
"\n",
"# Create a dictionary describing the features.\n",
"image_feature_description = {\n",
" # the raw image\n",
" 'image': tf.io.FixedLenFeature([], tf.string),\n",
" # the clean label, value in {0, 1}\n",
" 'label': tf.io.FixedLenFeature([1], tf.int64),\n",
" # noisy labels from all the raters\n",
" 'noisy_labels': tf.io.FixedLenFeature([num_raters], tf.int64),\n",
" # the IDs of rater models\n",
" 'rater_ids': tf.io.FixedLenFeature([num_raters], tf.string),\n",
"}\n",
"\n",
"def _parse_image_function(example_proto):\n",
" # Parse the input tf.train.Example proto using the dictionary above.\n",
" return tf.io.parse_single_example(example_proto, image_feature_description)\n",
"\n",
"parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n",
"\n",
"for features in parsed_image_dataset.take(1):\n",
" # Check the IDs of the rater models. The rater IDs are the same for all the\n",
" # examples in the dataset.\n",
" rater_ids = features['rater_ids']\n",
" rater_id_string = [r.numpy().decode('utf-8') for r in rater_ids]\n",
" print('The IDs of the rater models for this dataset are:')\n",
" print(rater_id_string)\n",
" clean_label = features['label'].numpy()\n",
" print('The clean label for the following example is %d' % clean_label)\n",
" noisy_labels = features['noisy_labels'].numpy()\n",
" print('The noisy labels from the rater models are:')\n",
" print(noisy_labels)\n",
" image = tf.io.decode_png(features['image'])\n",
" plt.imshow(image)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CLYyqNIOsV4N"
},
"source": [
"**Cats vs Dogs noisy label datasets**\n",
"\n",
"**Download size**\n",
"* 2.4MB for each of the low, medium, and high noise datasets.\n",
"\n",
"**Number of examples**\n",
"* train: 9302, valid: 1184, for each of the low, medium, and high noise datasets.\n",
"\n",
"Both the train and valid splits are subsampled from the original Cats vs Dogs dataset.\n",
"\n",
"**Data features**\n",
"* \"noisy_labels\": the noisy label given by rater models, a list of 10 tf.int64 integers. Label 0 for cats, 1 for dogs.\n",
"* \"rater_ids\": the ID of the rater models, a list of 10 tf.string.\n",
"* \"image/filename\": the filename of the image, corresponding to the filename in the original Cats vs Dogs dataset, tf.string.\n",
"\n",
"\n",
"**Rater features**\n",
"* model_name: name of the model\n",
"* accuracy: accuracy of the rater model on the rater validation set\n",
"* loss: loss of the rater model on the rater validation set\n",
"* mAP: the mean average precision of the rater model on the rater validation set\n",
"* auc_PR: the area under curve--precision recall of the rater model on the rater validation set\n",
"* auc_ROC: the area under curve--ROC of the rater model on the rater validation set\n",
"* experience: the total number of data that the rater model has seen during training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GmZ44wRJsnhR"
},
"outputs": [],
"source": [
"# @title An example for loading Cats vs Dongs noisy label datasets\n",
"task_name = 'cats_vs_dogs'\n",
"\n",
"# One of ['low', 'medium', 'high']\n",
"noise_level = 'medium'\n",
"\n",
"# One of ['train', 'valid']. The `valid` split should be used for\n",
"# hyperparameter tuning. The model should be tested on the original test\n",
"# slipt for these tasks.\n",
"split = 'train'\n",
"\n",
"# We have 10 rater models for Cats vs Dogs.\n",
"num_raters = 10\n",
"\n",
"directory = os.path.join(root_dir, task_name, noise_level, split) + '*'\n",
"raw_image_dataset = tf.data.TFRecordDataset(tf.io.gfile.glob(directory))\n",
"\n",
"# Create a dictionary describing the features.\n",
"image_feature_description = {\n",
" # noisy labels from all the raters\n",
" 'noisy_labels': tf.io.FixedLenFeature([num_raters], tf.int64),\n",
" # the IDs of rater models\n",
" 'rater_ids': tf.io.FixedLenFeature([num_raters], tf.string),\n",
" # filename of the image\n",
" 'image/filename': tf.io.FixedLenFeature([1], tf.string),\n",
"}\n",
"\n",
"def _parse_image_function(example_proto):\n",
" # Parse the input tf.train.Example proto using the dictionary above.\n",
" return tf.io.parse_single_example(example_proto, image_feature_description)\n",
"\n",
"parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n",
"\n",
"for features in parsed_image_dataset.take(1):\n",
" # Check the IDs of the rater models. The rater IDs are the same for all the\n",
" # examples in the dataset.\n",
" rater_ids = features['rater_ids']\n",
" rater_id_string = [r.numpy().decode('utf-8') for r in rater_ids]\n",
" print('The IDs of the rater models for this dataset are:')\n",
" print(rater_id_string)\n",
" print('Image filename:')\n",
" print(features['image/filename'][0].numpy().decode('utf-8'))\n",
" noisy_labels = features['noisy_labels'].numpy()\n",
" print('The noisy labels from the rater models are:')\n",
" print(noisy_labels)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6AO3XJnrQPeY"
},
"outputs": [],
"source": [
"# @title An example for loading rater features\n",
"for task_name in ['cifar10', 'cifar100', 'patch_camelyon', 'cats_vs_dogs']:\n",
" for noise_level in ['low', 'medium', 'high']:\n",
" dir = os.path.join(root_dir, task_name, noise_level, 'rater_features.json')\n",
" with tf.io.gfile.GFile(dir, 'rb') as fj:\n",
" rater_features_dict = json.load(fj)\n",
" print(rater_features_dict)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "//learning/deepmind/dm_python:dm_notebook3",
"kind": "private"
},
"name": "noisy_label_datasets_and_rater_features.ipynb",
"private_outputs": true,
"provenance": [
{
"file_id": "1zhPgvKIniqkpiY2SEnAdmiatJ__WyBz2",
"timestamp": 1621924313986
}
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: nowcasting/Open_sourced_dataset_and_model_snapshot_for_precipitation_nowcasting.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "wFD0zFFyuHzH"
},
"source": [
"# Open-sourced dataset and model snapshot for precipitation nowcasting, accompanying the paper *Skillful Precipitation Nowcasting using Deep Generative Models of Radar, Ravuri et al. 2021.*\n",
"\n",
"This colab contains:\n",
"* Code to read the dataset using [Tensorflow 2](https://www.tensorflow.org/), with documentation of the available splits, variants and fields\n",
"* Example plots and animations of the data using [matplotlib](https://matplotlib.org/) and [cartopy](https://scitools.org.uk/cartopy/docs/latest/)\n",
"* A [TF-Hub](https://www.tensorflow.org/hub) snapshot of the model from the paper\n",
"* Example code to load this model and use it to make predictions.\n",
"\n",
"It has been tested in a public Google colab kernel."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "d-H23Kuo7YM0"
},
"source": [
"## How to run this notebook\n",
"\n",
"All sections with the exception of 'Making predictions on a row from the full-frame test set (1536x1280)' can be evaluated on a free public Colab kernel. The final section requires more RAM than is available with a free kernel. To evaluate these cells you can either run your own local kernel (with \u003e= 24GB of RAM), or upgrade to Colab Pro.\n",
"\n",
"To launch a local colab kernel, please follow these [instructions](https://research.google.com/colaboratory/local-runtimes.html)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7V0C4sb4MEhx"
},
"source": [
"## License and attribution"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "N2SmJ8joMQ7G"
},
"source": [
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
"\n",
"[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed\n",
"under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n",
"CONDITIONS OF ANY KIND, either express or implied. See the License for the\n",
"specific language governing permissions and limitations under the License.\n",
"\n",
"The datasets and the model snapshots associated with this colab are made available for use under the terms of the\n",
"[Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).\n",
"\n",
"This colab and the associated model snapshots are Copyright 2021 DeepMind Technologies Limited.\n",
"\n",
"The associated datasets contain public sector information licensed by the [Met Office](https://www.metoffice.gov.uk/) under the\n",
"[UK Open Government Licence v3.0](http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EE0_Q3CXv_wH"
},
"source": [
"## Library dependency installs and imports"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2AVIRsf0gcMd"
},
"source": [
"The following libraries are required. You can skip these `pip install` cells if your kernel already has them installed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vfV8LIhQgeFU"
},
"outputs": [],
"source": [
"!pip -q install tensorflow~=2.5.0 numpy~=1.19.5 matplotlib~=3.2.2 tensorflow_hub~=0.12.0 cartopy~=0.19.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qNvCVcQYitBK"
},
"outputs": [],
"source": [
"# Workaround for cartopy crashes due to the shapely installed by default in\n",
"# google colab kernel (https://github.com/anitagraser/movingpandas/issues/81):\n",
"!pip uninstall -y shapely\n",
"!pip install shapely --no-binary shapely"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rxRQBC3vjGNM"
},
"source": [
"## Imports:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "z_mTZ79PIw3j"
},
"outputs": [],
"source": [
"import datetime\n",
"import os\n",
"\n",
"import cartopy\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import animation\n",
"import numpy as np\n",
"import shapely.geometry as sgeom\n",
"import tensorflow as tf\n",
"import tensorflow_hub\n",
"\n",
"from google.colab import auth"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lcndQrFmjPuT"
},
"source": [
"## Dataset location"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "htgPqZQMJ3hZ"
},
"outputs": [],
"source": [
"# This Google Cloud Storage (GCS) bucket is free to access and contains an\n",
"# example subset of the full dataset (just the first shard of each\n",
"# split/variant):\n",
"EXAMPLE_DATASET_BUCKET_PATH = \"gs://dm-nowcasting-example-data/datasets/nowcasting_open_source_osgb/nimrod_osgb_1000m_yearly_splits/radar/20200718\"\n",
"\n",
"# This bucket is requester-pays and will require authentication. It contains the\n",
"# full dataset. We recommend downloading a local copy first and updating\n",
"# ROOT_DATASET_DIR below to the local path. This should save on transfer costs\n",
"# and speed up training.\n",
"FULL_DATASET_BUCKET_PATH = \"gs://dm-nowcasting/datasets/nowcasting_open_source_osgb/nimrod_osgb_1000m_yearly_splits/radar/20200718\"\n",
"\n",
"# Update this as required:\n",
"DATASET_ROOT_DIR = EXAMPLE_DATASET_BUCKET_PATH"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i3JWo990gxN8"
},
"source": [
"Use this to authenticate as required for access to GCS buckets:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-kxuWoPmg_fs"
},
"outputs": [],
"source": [
"auth.authenticate_user()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "t4C0NDbfT0t9"
},
"source": [
"## Dataset reader code\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kKn1BcQuJsKd"
},
"outputs": [],
"source": [
"_FEATURES = {name: tf.io.FixedLenFeature([], dtype)\n",
" for name, dtype in [\n",
" (\"radar\", tf.string), (\"sample_prob\", tf.float32),\n",
" (\"osgb_extent_top\", tf.int64), (\"osgb_extent_left\", tf.int64),\n",
" (\"osgb_extent_bottom\", tf.int64), (\"osgb_extent_right\", tf.int64),\n",
" (\"end_time_timestamp\", tf.int64),\n",
" ]}\n",
"\n",
"_SHAPE_BY_SPLIT_VARIANT = {\n",
" (\"train\", \"random_crops_256\"): (24, 256, 256, 1),\n",
" (\"valid\", \"subsampled_tiles_256_20min_stride\"): (24, 256, 256, 1),\n",
" (\"test\", \"full_frame_20min_stride\"): (24, 1536, 1280, 1),\n",
" (\"test\", \"subsampled_overlapping_padded_tiles_512_20min_stride\"): (24, 512, 512, 1),\n",
"}\n",
"\n",
"_MM_PER_HOUR_INCREMENT = 1/32.\n",
"_MAX_MM_PER_HOUR = 128.\n",
"_INT16_MASK_VALUE = -1\n",
"\n",
"\n",
"def parse_and_preprocess_row(row, split, variant):\n",
" result = tf.io.parse_example(row, _FEATURES)\n",
" shape = _SHAPE_BY_SPLIT_VARIANT[(split, variant)]\n",
" radar_bytes = result.pop(\"radar\")\n",
" radar_int16 = tf.reshape(tf.io.decode_raw(radar_bytes, tf.int16), shape)\n",
" mask = tf.not_equal(radar_int16, _INT16_MASK_VALUE)\n",
" radar = tf.cast(radar_int16, tf.float32) * _MM_PER_HOUR_INCREMENT\n",
" radar = tf.clip_by_value(\n",
" radar, _INT16_MASK_VALUE * _MM_PER_HOUR_INCREMENT, _MAX_MM_PER_HOUR)\n",
" result[\"radar_frames\"] = radar\n",
" result[\"radar_mask\"] = mask\n",
" return result\n",
"\n",
"\n",
"def reader(split=\"train\", variant=\"random_crops_256\", shuffle_files=False):\n",
" \"\"\"Reader for open-source nowcasting datasets.\n",
" \n",
" Args:\n",
" split: Which yearly split of the dataset to use:\n",
" \"train\": Data from 2016 - 2018, excluding the first day of each month.\n",
" \"valid\": Data from 2016 - 2018, only the first day of the month.\n",
" \"test\": Data from 2019.\n",
" variant: Which variant to use. The available variants depend on the split:\n",
" \"random_crops_256\": Available for the training split. 24x256x256 pixel\n",
" crops, sampled with a bias towards crops containing rainfall. Crops at\n",
" all spatial and temporal offsets were able to be sampled, some crops may\n",
" overlap.\n",
" \"subsampled_tiles_256_20min_stride\": Available for the validation set.\n",
" Non-spatially-overlapping 24x256x256 pixel crops, subsampled from a\n",
" regular spatial grid with stride 256x256 pixels, and a temporal stride\n",
" of 20mins (4 timesteps at 5 minute resolution). Sampling favours crops\n",
" containing rainfall.\n",
" \"subsampled_overlapping_padded_tiles_512_20min_stride\": Available for the\n",
" test set. Overlapping 24x512x512 pixel crops, subsampled from a\n",
" regular spatial grid with stride 64x64 pixels, and a temporal stride\n",
" of 20mins (4 timesteps at 5 minute resolution). Subsampling favours\n",
" crops containing rainfall.\n",
" These crops include extra spatial context for a fairer evaluation of\n",
" the PySTEPS baseline, which benefits from this extra context. Our other\n",
" models only use the central 256x256 pixels of these crops.\n",
" \"full_frame_20min_stride\": Available for the test set. Includes full\n",
" frames at 24x1536x1280 pixels, every 20 minutes with no additional\n",
" subsampling.\n",
" shuffle_files: Whether to shuffle the shard files of the dataset\n",
" non-deterministically before interleaving them. Recommended for the\n",
" training set to improve mixing and read performance (since\n",
" non-deterministic parallel interleave is then enabled).\n",
"\n",
" Returns:\n",
" A tf.data.Dataset whose rows are dicts with the following keys:\n",
"\n",
" \"radar_frames\": Shape TxHxWx1, float32. Radar-based estimates of\n",
" ground-level precipitation, in units of mm/hr. Pixels which are masked\n",
" will take on a value of -1/32 and should be excluded from use as\n",
" evaluation targets. The coordinate reference system used is OSGB36, with\n",
" a spatial resolution of 1000 OSGB36 coordinate units (approximately equal\n",
" to 1km). The temporal resolution is 5 minutes.\n",
" \"radar_mask\": Shape TxHxWx1, bool. A binary mask which is False\n",
" for pixels that are unobserved / unable to be inferred from radar\n",
" measurements (e.g. due to being too far from a radar site). This mask\n",
" is usually static over time, but occasionally a whole radar site will\n",
" drop in or out resulting in large changes to the mask, and more localised\n",
" changes can happen too. \n",
" \"sample_prob\": Scalar float. The probability with which the row was\n",
" sampled from the overall pool available for sampling, as described above\n",
" under 'variants'. We use importance weights proportional to 1/sample_prob\n",
" when computing metrics on the validation and test set, to reduce bias due\n",
" to the subsampling.\n",
" \"end_time_timestamp\": Scalar int64. A timestamp for the final frame in\n",
" the example, in seconds since the UNIX epoch (1970-01-01 00:00:00 UTC).\n",
" \"osgb_extent_left\", \"osgb_extent_right\", \"osgb_extent_top\",\n",
" \"osgb_extent_bottom\":\n",
" Scalar int64s. Spatial extent for the crop in the OSGB36 coordinate\n",
" reference system.\n",
" \"\"\"\n",
" shards_glob = os.path.join(DATASET_ROOT_DIR, split, variant, \"*.tfrecord.gz\")\n",
" shard_paths = tf.io.gfile.glob(shards_glob)\n",
" shards_dataset = tf.data.Dataset.from_tensor_slices(shard_paths)\n",
" if shuffle_files:\n",
" shards_dataset = shards_dataset.shuffle(buffer_size=len(shard_paths))\n",
" return (\n",
" shards_dataset\n",
" .interleave(lambda x: tf.data.TFRecordDataset(x, compression_type=\"GZIP\"),\n",
" num_parallel_calls=tf.data.AUTOTUNE,\n",
" deterministic=not shuffle_files)\n",
" .map(lambda row: parse_and_preprocess_row(row, split, variant),\n",
" num_parallel_calls=tf.data.AUTOTUNE)\n",
" # Do your own subsequent repeat, shuffle, batch, prefetch etc as required.\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iiC7oGKRlNaj"
},
"source": [
"## Dataset reader documentation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "amkOoDKqlEll",
"outputId": "3141d2cd-fe88-452a-c9f8-c6b2eb449068"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function reader in module __main__:\n",
"\n",
"reader(split='train', variant='random_crops_256', shuffle_files=False)\n",
" Reader for open-source nowcasting datasets.\n",
" \n",
" Args:\n",
" split: Which yearly split of the dataset to use:\n",
" \"train\": Data from 2016 - 2018, excluding the first day of each month.\n",
" \"valid\": Data from 2016 - 2018, only the first day of the month.\n",
" \"test\": Data from 2019.\n",
" variant: Which variant to use. The available variants depend on the split:\n",
" \"random_crops_256\": Available for the training split. 24x256x256 pixel\n",
" crops, sampled with a bias towards crops containing rainfall. Crops at\n",
" all spatial and temporal offsets were able to be sampled, some crops may\n",
" overlap.\n",
" \"subsampled_tiles_256_20min_stride\": Available for the validation set.\n",
" Non-spatially-overlapping 24x256x256 pixel crops, subsampled from a\n",
" regular spatial grid with stride 256x256 pixels, and a temporal stride\n",
" of 20mins (4 timesteps at 5 minute resolution). Sampling favours crops\n",
" containing rainfall.\n",
" \"subsampled_overlapping_padded_tiles_512_20min_stride\": Available for the\n",
" test set. Overlapping 24x512x512 pixel crops, subsampled from a\n",
" regular spatial grid with stride 64x64 pixels, and a temporal stride\n",
" of 20mins (4 timesteps at 5 minute resolution). Subsampling favours\n",
" crops containing rainfall.\n",
" These crops include extra spatial context for a fairer evaluation of\n",
" the PySTEPS baseline, which benefits from this extra context. Our other\n",
" models only use the central 256x256 pixels of these crops.\n",
" \"full_frame_20min_stride\": Available for the test set. Includes full\n",
" frames at 24x1536x1280 pixels, every 20 minutes with no additional\n",
" subsampling.\n",
" shuffle_files: Whether to shuffle the shard files of the dataset\n",
" non-deterministically before interleaving them. Recommended for the\n",
" training set to improve mixing and read performance (since\n",
" non-deterministic parallel interleave is then enabled).\n",
" \n",
" Returns:\n",
" A tf.data.Dataset whose rows are dicts with the following keys:\n",
" \n",
" \"radar_frames\": Shape TxHxWx1, float32. Radar-based estimates of\n",
" ground-level precipitation, in units of mm/hr. Pixels which are masked\n",
" will take on a value of -1/32 and should be excluded from use as\n",
" evaluation targets. The coordinate reference system used is OSGB36, with\n",
" a spatial resolution of 1000 OSGB36 coordinate units (approximately equal\n",
" to 1km). The temporal resolution is 5 minutes.\n",
" \"radar_mask\": Shape TxHxWx1, bool. A binary mask which is False\n",
" for pixels that are unobserved / unable to be inferred from radar\n",
" measurements (e.g. due to being too far from a radar site). This mask\n",
" is usually static over time, but occasionally a whole radar site will\n",
" drop in or out resulting in large changes to the mask, and more localised\n",
" changes can happen too. \n",
" \"sample_prob\": Scalar float. The probability with which the row was\n",
" sampled from the overall pool available for sampling, as described above\n",
" under 'variants'. We use importance weights proportional to 1/sample_prob\n",
" when computing metrics on the validation and test set, to reduce bias due\n",
" to the subsampling.\n",
" \"end_time_timestamp\": Scalar int64. A timestamp for the final frame in\n",
" the example, in seconds since the UNIX epoch (1970-01-01 00:00:00 UTC).\n",
" \"osgb_extent_left\", \"osgb_extent_right\", \"osgb_extent_top\",\n",
" \"osgb_extent_bottom\":\n",
" Scalar int64s. Spatial extent for the crop in the OSGB36 coordinate\n",
" reference system.\n",
"\n"
]
}
],
"source": [
"help(reader)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QqZfqhvoTImW"
},
"source": [
"## Reading a row from the training set and inspecting types/shapes/values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "r3YPkBVWX6_q"
},
"outputs": [],
"source": [
"row = next(iter(reader(split=\"train\", variant=\"random_crops_256\")))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Z__EMLrRX_Oc"
},
"outputs": [],
"source": [
"{k: (v.dtype, v.shape) for k, v in row.items()}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "byuVVcSnXz4q"
},
"source": [
"Values for scalar features:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2i8X9KYXXtJi",
"outputId": "5b8a8980-f891-467d-994e-ccb28d9f1a16"
},
"outputs": [
{
"data": {
"text/plain": [
"{'end_time_timestamp': 1514725200,\n",
" 'osgb_extent_bottom': 555000,\n",
" 'osgb_extent_left': -9000,\n",
" 'osgb_extent_right': 247000,\n",
" 'osgb_extent_top': 811000,\n",
" 'sample_prob': 9.889281e-06}"
]
},
"execution_count": 10,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"{k: v.numpy() for k, v in row.items() if v.shape.ndims == 0}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "10_lz7FYZ6sl"
},
"source": [
"Decoding the end_time_timestamp:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "WtAV24gyZ5jL",
"outputId": "cf8628ed-b6eb-4754-cce8-f1ff8bad14bf"
},
"outputs": [
{
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'2017-12-31T13:00:00'"
]
},
"execution_count": 11,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"datetime.datetime.utcfromtimestamp(row[\"end_time_timestamp\"]).isoformat()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e4HPIMX1VV5-"
},
"source": [
"## Visualization helpers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ieLdf614RxTu"
},
"outputs": [],
"source": [
"matplotlib.rc('animation', html='jshtml')\n",
"\n",
"\n",
"def plot_animation(field, figsize=None,\n",
" vmin=0, vmax=10, cmap=\"jet\", **imshow_args):\n",
" fig = plt.figure(figsize=figsize)\n",
" ax = plt.axes()\n",
" ax.set_axis_off()\n",
" plt.close() # Prevents extra axes being plotted below animation\n",
" img = ax.imshow(field[0, ..., 0], vmin=vmin, vmax=vmax, cmap=cmap, **imshow_args)\n",
"\n",
" def animate(i):\n",
" img.set_data(field[i, ..., 0])\n",
" return (img,)\n",
"\n",
" return animation.FuncAnimation(\n",
" fig, animate, frames=field.shape[0], interval=24, blit=False)\n",
"\n",
"\n",
"class ExtendedOSGB(cartopy.crs.OSGB):\n",
" \"\"\"MET office radar data uses OSGB36 with an extended bounding box.\"\"\"\n",
"\n",
" def __init__(self):\n",
" super().__init__(approx=False)\n",
"\n",
" @property\n",
" def x_limits(self):\n",
" return (-405000, 1320000)\n",
"\n",
" @property\n",
" def y_limits(self):\n",
" return (-625000, 1550000)\n",
"\n",
" @property\n",
" def boundary(self):\n",
" x0, x1 = self.x_limits\n",
" y0, y1 = self.y_limits\n",
" return sgeom.LinearRing([(x0, y0), (x0, y1), (x1, y1), (x1, y0), (x0, y0)])\n",
"\n",
"\n",
"def plot_rows_on_map(rows, field_name=\"radar_frames\", timestep=0, num_rows=None,\n",
" cbar_label=None, **imshow_kwargs):\n",
" fig = plt.figure(figsize=(10, 10))\n",
" axes = fig.add_subplot(1, 1, 1, projection=ExtendedOSGB())\n",
" if num_rows is None:\n",
" num_rows = next(iter(rows.values())).shape[0]\n",
" for b in range(num_rows):\n",
" extent = (rows[\"osgb_extent_left\"][b].numpy(),\n",
" rows[\"osgb_extent_right\"][b].numpy(),\n",
" rows[\"osgb_extent_bottom\"][b].numpy(),\n",
" rows[\"osgb_extent_top\"][b].numpy())\n",
" im = axes.imshow(rows[field_name][b, timestep, ..., 0].numpy(),\n",
" extent=extent, **imshow_kwargs)\n",
"\n",
" axes.set_xlim(*axes.projection.x_limits)\n",
" axes.set_ylim(*axes.projection.y_limits)\n",
" axes.set_facecolor(\"black\")\n",
" axes.gridlines(alpha=0.5)\n",
" axes.coastlines(resolution=\"50m\", color=\"white\")\n",
" if cbar_label:\n",
" cbar = fig.colorbar(im)\n",
" cbar.set_label(cbar_label)\n",
" return fig\n",
"\n",
"\n",
"def plot_animation_on_map(row):\n",
" fig = plt.figure(figsize=(10, 10))\n",
" axes = fig.add_subplot(1, 1, 1, projection=ExtendedOSGB())\n",
" plt.close() # Prevents extra axes being plotted below animation\n",
"\n",
" axes.gridlines(alpha=0.5)\n",
" axes.coastlines(resolution=\"50m\", color=\"white\")\n",
"\n",
" extent = (row[\"osgb_extent_left\"].numpy(),\n",
" row[\"osgb_extent_right\"].numpy(),\n",
" row[\"osgb_extent_bottom\"].numpy(),\n",
" row[\"osgb_extent_top\"].numpy())\n",
"\n",
" img = axes.imshow(\n",
" row[\"radar_frames\"][0, ..., 0].numpy(),\n",
" extent=extent, vmin=0, vmax=15., cmap=\"jet\")\n",
"\n",
" cbar = fig.colorbar(img)\n",
" cbar.set_label(\"Precipitation, mm/hr\")\n",
"\n",
" def animate(i):\n",
" return img.set_data(row[\"radar_frames\"][i, ..., 0].numpy()),\n",
"\n",
" return animation.FuncAnimation(\n",
" fig, animate, frames=row[\"radar_frames\"].shape[0],\n",
" interval=24, blit=False)\n",
"\n",
"\n",
"def plot_mask_on_map(row):\n",
" fig = plt.figure(figsize=(10, 10))\n",
" axes = fig.add_subplot(1, 1, 1, projection=ExtendedOSGB())\n",
" axes.gridlines(alpha=0.5)\n",
" axes.coastlines(resolution=\"50m\", color=\"black\")\n",
"\n",
" extent = (row[\"osgb_extent_left\"].numpy(),\n",
" row[\"osgb_extent_right\"].numpy(),\n",
" row[\"osgb_extent_bottom\"].numpy(),\n",
" row[\"osgb_extent_top\"].numpy())\n",
"\n",
" img = axes.imshow(\n",
" row[\"radar_mask\"][0, ..., 0].numpy(),\n",
" extent=extent, vmin=0, vmax=1, cmap=\"viridis\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZrfahN2wZS_G"
},
"source": [
"## Visualizing rows"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pn6C9qi4ZcYl"
},
"source": [
"Animation of a single row from the random_crops_256 training set (sequence of 24 frames at 256x256)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "J4_aB6vbXB0A"
},
"outputs": [],
"source": [
"plot_animation(row[\"radar_frames\"].numpy())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fsDrJOCwZtZv"
},
"source": [
"And its mask. This may not always be interesting, sometimes it will be all ones. I only plot the first frame as this is usually static over time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LtqAPI2utIDJ"
},
"outputs": [],
"source": [
"plt.imshow(row[\"radar_mask\"][0, ..., 0].numpy(), vmin=0, vmax=1);"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jKalHlONmmTF"
},
"source": [
"Plotting an animation of a row from the full-frame test set"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gBZWTLFEbbal"
},
"outputs": [],
"source": [
"dataset = reader(split=\"test\", variant=\"full_frame_20min_stride\")\n",
"full_frame_test_set_row = next(iter(dataset))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qUTqwhC0gmIm"
},
"outputs": [],
"source": [
"plot_animation_on_map(full_frame_test_set_row)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6Go5BVATqVwd"
},
"source": [
"And just its mask:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "T7cgRtRilKfv"
},
"outputs": [],
"source": [
"plot_mask_on_map(full_frame_test_set_row)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KtXg3lJxa1_q"
},
"source": [
"Plotting a few different crops from the training set on the same map, using their OSGB extents. Note these will have been sampled at different timestamps so won't be consistent with each other. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "x7HXOz7PfdtH"
},
"outputs": [],
"source": [
"BATCH_SIZE = 60\n",
"dataset = reader(split=\"train\", variant=\"random_crops_256\")\n",
"rows = next(iter(dataset.batch(BATCH_SIZE)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "76plhdrhaz60"
},
"outputs": [],
"source": [
"plot_rows_on_map(rows, field_name=\"radar_frames\", num_rows=10, vmin=0, vmax=15.,\n",
" cmap=\"jet\", cbar_label=\"Precipitation, mm/hr\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YwliM5nOaivq"
},
"source": [
"And plotting their masks, which will be more consistent with each other since they change less frequently."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rDUNaca0yXrC"
},
"outputs": [],
"source": [
"plot_rows_on_map(rows, field_name=\"radar_mask\", vmin=0, vmax=1, alpha=0.5, cmap=\"spring\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-KxxSOvwsUaP"
},
"source": [
"## Making predictions using model loaded from TF-Hub snapshots"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IvWq8_4uvRBb"
},
"source": [
"Location of snapshots:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GZfhJ2orvWCV"
},
"outputs": [],
"source": [
"TFHUB_BASE_PATH = \"gs://dm-nowcasting-example-data/tfhub_snapshots\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aFLv08o5vX7y"
},
"source": [
"### Helper code for loading snapshots and making predictions with them"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zOFI9xQztNIW"
},
"outputs": [],
"source": [
"def load_module(input_height, input_width):\n",
" \"\"\"Load a TF-Hub snapshot of the 'Generative Method' model.\"\"\"\n",
" hub_module = tensorflow_hub.load(\n",
" os.path.join(TFHUB_BASE_PATH, f\"{input_height}x{input_width}\"))\n",
" # Note this has loaded a legacy TF1 model for running under TF2 eager mode.\n",
" # This means we need to access the module via the \"signatures\" attribute. See\n",
" # https://github.com/tensorflow/hub/blob/master/docs/migration_tf2.md#using-lower-level-apis\n",
" # for more information.\n",
" return hub_module.signatures['default']\n",
"\n",
"\n",
"def predict(module, input_frames, num_samples=1,\n",
" include_input_frames_in_result=False):\n",
" \"\"\"Make predictions from a TF-Hub snapshot of the 'Generative Method' model.\n",
"\n",
" Args:\n",
" module: One of the raw TF-Hub modules returned by load_module above.\n",
" input_frames: Shape (T_in,H,W,C), where T_in = 4. Input frames to condition\n",
" the predictions on.\n",
" num_samples: The number of different samples to draw.\n",
" include_input_frames_in_result: If True, will return a total of 22 frames\n",
" along the time axis, the 4 input frames followed by 18 predicted frames.\n",
" Otherwise will only return the 18 predicted frames.\n",
"\n",
" Returns:\n",
" A tensor of shape (num_samples,T_out,H,W,C), where T_out is either 18 or 22\n",
" as described above.\n",
" \"\"\"\n",
" input_frames = tf.math.maximum(input_frames, 0.)\n",
" # Add a batch dimension and tile along it to create a copy of the input for\n",
" # each sample:\n",
" input_frames = tf.expand_dims(input_frames, 0)\n",
" input_frames = tf.tile(input_frames, multiples=[num_samples, 1, 1, 1, 1])\n",
"\n",
" # Sample the latent vector z for each sample:\n",
" _, input_signature = module.structured_input_signature\n",
" z_size = input_signature['z'].shape[1]\n",
" z_samples = tf.random.normal(shape=(num_samples, z_size))\n",
"\n",
" inputs = {\n",
" \"z\": z_samples,\n",
" \"labels$onehot\" : tf.ones(shape=(num_samples, 1)),\n",
" \"labels$cond_frames\" : input_frames\n",
" }\n",
" samples = module(**inputs)['default']\n",
" if not include_input_frames_in_result:\n",
" # The module returns the input frames alongside its sampled predictions, we\n",
" # slice out just the predictions:\n",
" samples = samples[:, NUM_INPUT_FRAMES:, ...]\n",
"\n",
" # Take positive values of rainfall only.\n",
" samples = tf.math.maximum(samples, 0.)\n",
" return samples\n",
"\n",
"\n",
"# Fixed values supported by the snapshotted model.\n",
"NUM_INPUT_FRAMES = 4\n",
"NUM_TARGET_FRAMES = 18\n",
"\n",
"\n",
"def extract_input_and_target_frames(radar_frames):\n",
" \"\"\"Extract input and target frames from a dataset row's radar_frames.\"\"\"\n",
" # We align our targets to the end of the window, and inputs precede targets.\n",
" input_frames = radar_frames[-NUM_TARGET_FRAMES-NUM_INPUT_FRAMES : -NUM_TARGET_FRAMES]\n",
" target_frames = radar_frames[-NUM_TARGET_FRAMES : ]\n",
" return input_frames, target_frames\n",
"\n",
"\n",
"def horizontally_concatenate_batch(samples):\n",
" n, t, h, w, c = samples.shape\n",
" # N,T,H,W,C =\u003e T,H,N,W,C =\u003e T,H,N*W,C\n",
" return tf.reshape(tf.transpose(samples, [1, 2, 0, 3, 4]), [t, h, n*w, c])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YuTsqBqM1PeO"
},
"source": [
"### Making predictions for a row from the validation set (256x256 crops)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kX6wgACVtcHz"
},
"outputs": [],
"source": [
"module = load_module(256, 256)\n",
"row = next(iter(reader(split=\"valid\", variant=\"subsampled_tiles_256_20min_stride\")))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LKBlYh0qx7nL"
},
"outputs": [],
"source": [
"num_samples = 5\n",
"input_frames, target_frames = extract_input_and_target_frames(row[\"radar_frames\"])\n",
"samples = predict(module, input_frames,\n",
" num_samples=num_samples, include_input_frames_in_result=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NDOh7j2g65wP"
},
"source": [
"We will plot an animation of 5 different samples, including the input frames first (so all 5 will start the same). You can see they end up in different places."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_Ymu_GFBzGJd"
},
"outputs": [],
"source": [
"plot_animation(horizontally_concatenate_batch(samples), figsize=(4*num_samples, 4))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "57uh8X2XEqqj"
},
"source": [
"### Making predictions on a row from the full-frame test set (1536x1280)\n",
"\n",
"Warning: this will require more RAM than is available in a free public colab kernel, even if you reduce num_samples to 1."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RdnIsm725Nvk"
},
"outputs": [],
"source": [
"# This is the same model with same parameters as above; we have had to export\n",
"# separate copies of the graph for each input size as the input size is\n",
"# unfortunately hardcoded into the graph as static shapes.\n",
"module = load_module(1536, 1280)\n",
"\n",
"full_frame_test_set_row = next(iter(\n",
" reader(split=\"test\", variant=\"full_frame_20min_stride\")))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sLQdfet59caW"
},
"outputs": [],
"source": [
"num_samples = 2\n",
"input_frames, target_frames = extract_input_and_target_frames(\n",
" full_frame_test_set_row[\"radar_frames\"])\n",
"samples = predict(module, input_frames,\n",
" num_samples=num_samples, include_input_frames_in_result=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K_2UwKa_E3Xo"
},
"source": [
"Plotting two different predicted samples following on from the input frames. The first sample:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MytvlcaA8P4P"
},
"outputs": [],
"source": [
"row_with_predictions = full_frame_test_set_row.copy()\n",
"row_with_predictions[\"radar_frames\"] = samples[0]\n",
"plot_animation_on_map(row_with_predictions)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AznUbE6-E53h"
},
"source": [
"And the second sample:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Er86IKdm-W5l"
},
"outputs": [],
"source": [
"row_with_predictions[\"radar_frames\"] = samples[1]\n",
"plot_animation_on_map(row_with_predictions)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mnNy6Uurp1_k"
},
"source": [
"The ground truth, for comparison, was plotted earlier as an example row from the test set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "b9v4xCWqqdzV"
},
"outputs": [],
"source": [
""
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"EE0_Q3CXv_wH",
"t4C0NDbfT0t9",
"e4HPIMX1VV5-"
],
"name": "Open-sourced dataset and model snapshot for precipitation nowcasting",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: nowcasting/README.md
================================================
# Skillful Precipitation Nowcasting Using Deep Generative Models of Radar
This repository is a supplement to "Skillful Precipitation Nowcasting using Deep
Generative Models of Radar" and provides necessary code for loading data from a
large scale nowcasting dataset and obtaining predictions with the pretrained
model.
Please see the Colab notebook for further details:
[](https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/nowcasting/Open_sourced_dataset_and_model_snapshot_for_precipitation_nowcasting.ipynb)
## License
The Colab notebook is licensed under the Apache License, Version 2.0. The
associated model snapshots are made available for use under the terms of the
[Creative Commons Attribution 4.0 International License][cc-by].
The provided post-processed nowcasting dataset is licensed under a
[Creative Commons Attribution 4.0 International License][cc-by] and it contains
public sector information licensed by the Met Office under the
[Open Government Licence v3.0][open-govt-license].
## Pseudocode
The pseudocode is relased in the same cloud storage bucket as the datasets:
`gs://dm-nowcasting-example-data/pseudocode.zip`.
You can access it with e.g. a [gsutil](https://cloud.google.com/storage/docs/gsutil).
## Citation
If you use this work, consider citing our paper:
```latex
@article{ravuris2021skillful,
author={Suman Ravuri and Karel Lenc and Matthew Willson and Dmitry Kangin and Remi Lam and Piotr Mirowski and Megan Fitzsimons and Maria Athanassiadou and Sheleem Kashem and Sam Madge and Rachel Prudden Amol Mandhane and Aidan Clark and Andrew Brock and Karen Simonyan and Raia Hadsell and Niall Robinson Ellen Clancy and Alberto Arribas† and Shakir Mohamed},
title={Skillful Precipitation Nowcasting using Deep Generative Models of Radar},
journal={Nature},
volume={597},
pages={672--677},
year={2021}
}
```
[cc-by]: http://creativecommons.org/licenses/by/4.0/
[open-govt-license]: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
================================================
FILE: object_attention_for_reasoning/README.md
================================================
Implementation of the ALOE model
["Attention over learned object embeddings enables complex visual reasoning"](https://arxiv.org/abs/2012.08508)
[1].
This package includes source code for the ALOE transformer model,
pre-trained model parameters for the CLEVRER task,
and MONet [2] latent variables for all videos in the training
and validation sets. It does not include the model training code.
See Section 2 of [1] for details.
[1] David Ding, Felix Hill, Adam Santoro, Malcolm Reynolds, Matt Botvinick.
*Attention over learned object embeddings enables complex visual reasoning*.
arXiv preprint arXiv:2012.08508, 2020.
[2] Chris P. Burgess, Loic Matthey, Nick Watters, Rishabh Kabra, Irina Higgins,
Matt Botvinick, and Alexander Lerchner
*MONet: Unsupervised scene decomposition and representation*.
arXiv preprint arXiv:1901.11390, 2019.
# Instructions
Note: This code depends on Tensorflow 1 and Sonnet 1. Tensorflow 1 is only
available on PYPI for Python 3.7 and earlier.
To run this code, execute the following commands from the `deepmind_research/`
directory:
```shell
# Download checkpoints and MONet latents
wget https://storage.googleapis.com/object-attention-for-reasoning/checkpoints_and_latents.zip
unzip checkpoints_and_latents.zip
python3.7 -m venv object_based_attention_venv
source object_based_attention_venv/bin/activate
pip install --upgrade setuptools wheel
pip install -r requirements.txt
python -m object_attention_for_reasoning.run_model
```
If the code runs correctly, you should see the model's predicted answer to two
CLEVRER questions (a descriptive one and a multiple choice one), and both
answers should be correct.
If you find the provided code useful, please cite this paper:
```
@article{aloe2020,
title={Attention over learned object embeddings enables complex visual reasoning},
author={David Ding and Felix Hill and Adam Santoro and Malcolm Reynolds and Matt Botvinick},
journal={arXiv preprint arXiv:2012.08508},
year={2020}
}
```
================================================
FILE: object_attention_for_reasoning/model.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Model code. Provided settings are identical to what was used in the paper."""
import sonnet as snt
import tensorflow.compat.v1 as tf
from object_attention_for_reasoning import transformer
QUESTION_VOCAB_SIZE = 82
ANSWER_VOCAB_SIZE = 22
MAX_QUESTION_LENGTH = 20
MAX_CHOICE_LENGTH = 12
NUM_CHOICES = 4
EMBED_DIM = 16
PRETRAINED_MODEL_CONFIG = dict(
use_relative_positions=True,
shuffle_objects=True,
transformer_layers=28,
head_size=128,
num_heads=10,
embed_dim=EMBED_DIM,
)
def append_ids(tensor, id_vector, axis):
id_vector = tf.constant(id_vector, tf.float32)
for a in range(len(tensor.shape)):
if a != axis:
id_vector = tf.expand_dims(id_vector, axis=a)
tiling_vector = [s if i != axis else 1 for i, s in enumerate(tensor.shape)]
id_tensor = tf.tile(id_vector, tiling_vector)
return tf.concat([tensor, id_tensor], axis=axis)
class ClevrerTransformerModel(object):
"""Model from Ding et al. 2020 (https://arxiv.org/abs/2012.08508)."""
def __init__(self, use_relative_positions, shuffle_objects,
transformer_layers, num_heads, head_size, embed_dim):
"""Instantiate Sonnet modules."""
self._embed_dim = embed_dim
self._embed = snt.Embed(QUESTION_VOCAB_SIZE, embed_dim - 2)
self._shuffle_objects = shuffle_objects
self._memory_transformer = transformer.TransformerTower(
value_size=embed_dim + 2,
num_heads=num_heads,
num_layers=transformer_layers,
use_relative_positions=use_relative_positions,
causal=False)
self._final_layer_mc = snt.Sequential(
[snt.Linear(head_size), tf.nn.relu, snt.Linear(1)])
self._final_layer_descriptive = snt.Sequential(
[snt.Linear(head_size), tf.nn.relu,
snt.Linear(ANSWER_VOCAB_SIZE)])
self._dummy = tf.get_variable("dummy", [embed_dim + 2], tf.float32,
initializer=tf.zeros_initializer)
self._infill_linear = snt.Linear(embed_dim + 2)
self._mask_embedding = tf.get_variable(
"mask", [embed_dim + 2], tf.float32, initializer=tf.zeros_initializer)
def _apply_transformers(self, lang_embedding, vision_embedding):
"""Applies transformer to language and vision input.
Args:
lang_embedding: tensor,
vision_embedding: tensor, "validation", or "test".
Returns:
tuple, output at dummy token, all output embeddings, infill loss
"""
def _unroll(tensor):
"""Unroll the time dimension into the object dimension."""
return tf.reshape(
tensor, [tensor.shape[0], -1, tensor.shape[3]])
words = append_ids(lang_embedding, [1, 0], axis=2)
dummy_word = tf.tile(self._dummy[None, None, :], [tf.shape(words)[0], 1, 1])
vision_embedding = append_ids(vision_embedding, [0, 1], axis=3)
vision_over_time = _unroll(vision_embedding)
transformer_input = tf.concat([dummy_word, words, vision_over_time], axis=1)
output, _ = self._memory_transformer(transformer_input,
is_training=False)
return output[:, 0, :]
def apply_model_descriptive(self, inputs):
"""Applies model to CLEVRER descriptive questions.
Args:
inputs: dict of form: {
"question": tf.int32 tensor of shape [batch, MAX_QUESTION_LENGTH],
"monet_latents": tf.float32 tensor of shape [batch, frames, 8, 16],
}
Returns:
Tensor of shape [batch, ANSWER_VOCAB_SIZE], representing logits for each
possible answer word.
"""
question = inputs["question"]
# Shape: [batch, question_len, embed_dim-2]
question_embedding = self._embed(question)
# Shape: [batch, question_len, embed_dim]
question_embedding = append_ids(question_embedding, [0, 1], 2)
choices_embedding = self._embed(
tf.zeros([question.shape[0], MAX_CHOICE_LENGTH], tf.int64))
choices_embedding = append_ids(choices_embedding, [0, 1], 2)
# Shape: [batch, choices, question_len + choice_len, embed_dim]
lang_embedding = tf.concat([question_embedding, choices_embedding], axis=1)
# Shape: [batch, frames, num_objects, embed_dim]
vision_embedding = inputs["monet_latents"]
if self._shuffle_objects:
vision_embedding = tf.transpose(vision_embedding, [2, 1, 0, 3])
vision_embedding = tf.random.shuffle(vision_embedding)
vision_embedding = tf.transpose(vision_embedding, [2, 1, 0, 3])
output = self._apply_transformers(lang_embedding, vision_embedding)
output = self._final_layer_descriptive(output)
return output
def apply_model_mc(self, inputs):
"""Applies model to CLEVRER multiple-choice questions.
Args:
inputs: dict of form: {
"question": tf.int32 tensor of shape [batch, MAX_QUESTION_LENGTH],
"choices": tf.int32 tensor of shape [batch, 4, MAX_CHOICE_LENGTH],
"monet_latents": tf.float32 tensor of shape [batch, frames, 8, 16],
}
Returns:
Tensor of shape [batch, 4], representing logits for each choice
"""
question = inputs["question"]
choices = inputs["choices"]
# Shape: [batch, question_len, embed_dim-2]
question_embedding = self._embed(question)
# Shape: [batch, question_len, embed_dim]
question_embedding = append_ids(question_embedding, [1, 0], 2)
# Shape: [batch, choices, choice_len, embed_dim-2]
choices_embedding = snt.BatchApply(self._embed)(choices)
# Shape: [batch, choices, choice_len, embed_dim]
choices_embedding = append_ids(choices_embedding, [0, 1], 3)
# Shape: [batch, choices, question_len + choice_len, embed_dim]
lang_embedding = tf.concat([
tf.tile(question_embedding[:, None],
[1, choices_embedding.shape[1], 1, 1]),
choices_embedding], axis=2)
# Shape: [batch, frames, num_objects, embed_dim]
vision_embedding = inputs["monet_latents"]
if self._shuffle_objects:
vision_embedding = tf.transpose(vision_embedding, [2, 1, 0, 3])
vision_embedding = tf.random.shuffle(vision_embedding)
vision_embedding = tf.transpose(vision_embedding, [2, 1, 0, 3])
output_per_choice = []
for c in range(NUM_CHOICES):
output = self._apply_transformers(
lang_embedding[:, c, :, :], vision_embedding)
output_per_choice.append(output)
output = tf.stack(output_per_choice, axis=1)
output = tf.squeeze(snt.BatchApply(self._final_layer_mc)(output), axis=2)
return output
================================================
FILE: object_attention_for_reasoning/requirements.txt
================================================
absl-py==0.11.0
dm-sonnet==1.36
numpy==1.20.1
tensorflow==1.15.0
================================================
FILE: object_attention_for_reasoning/run_model.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example code for running model on CLEVRER."""
import json
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v1 as tf
from object_attention_for_reasoning import model as modellib
BATCH_SIZE = 1
NUM_FRAMES = 25
NUM_OBJECTS = 8
_BASE_DIR = flags.DEFINE_string(
"base_dir", "./clevrer_monet_latents",
"Directory containing checkpoints and MONet latents.")
_SCENE_IDX = flags.DEFINE_string(
"scene_idx", 1000, "Scene index of CLEVRER video.")
def load_monet_latents(base_dir, scene_index):
filename = f"{base_dir}/train/{scene_index}.npz"
with open(filename, "rb") as f:
return np.load(f)
def _split_string(s):
"""Splits string to words and standardize alphabet."""
return s.lower().replace("?", "").split()
def _pad(array, length):
"""Pad an array to desired length."""
return np.pad(array, [(0, length - array.shape[0])], mode="constant")
def encode_sentence(token_map, sentence, pad_length):
"""Encode CLEVRER question/choice sentences as sequence of token ids."""
ret = np.array(
[token_map["question_vocab"][w] for w in _split_string(sentence)],
np.int32)
return _pad(ret, pad_length)
def encode_choices(token_map, choices):
"""Encode CLEVRER choices."""
arrays = [encode_sentence(token_map, choice["choice"],
modellib.MAX_CHOICE_LENGTH)
for choice in choices]
return _pad(np.stack(arrays, axis=0), modellib.NUM_CHOICES)
def main(unused_argv):
base_dir = _BASE_DIR.value
with open(f"{base_dir}/vocab.json", "rb") as f:
token_map = json.load(f)
reverse_answer_lookup = {v: k for k, v in token_map["answer_vocab"].items()}
with open(f"{base_dir}/train.json", "rb") as f:
questions_data = json.load(f)
tf.reset_default_graph()
model = modellib.ClevrerTransformerModel(**modellib.PRETRAINED_MODEL_CONFIG)
inputs_descriptive = {
"monet_latents": tf.placeholder(
tf.float32,
[BATCH_SIZE, NUM_FRAMES, NUM_OBJECTS, modellib.EMBED_DIM]),
"question": tf.placeholder(
tf.int32, [BATCH_SIZE, modellib.MAX_QUESTION_LENGTH]),
}
inputs_mc = {
"monet_latents": tf.placeholder(
tf.float32,
[BATCH_SIZE, NUM_FRAMES, NUM_OBJECTS, modellib.EMBED_DIM]),
"question": tf.placeholder(tf.int32,
[BATCH_SIZE, modellib.MAX_QUESTION_LENGTH]),
"choices": tf.placeholder(
tf.int32, [BATCH_SIZE, modellib.NUM_CHOICES,
modellib.MAX_CHOICE_LENGTH]),
}
output_descriptive = model.apply_model_descriptive(inputs_descriptive)
output_mc = model.apply_model_mc(inputs_mc)
# Restore from checkpoint
saver = tf.train.Saver()
checkpoint_dir = f"{base_dir}/checkpoints/"
sess = tf.train.SingularMonitoredSession(checkpoint_dir=checkpoint_dir)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
def eval_descriptive(monet_latents, question_json):
# CLEVRER provides videos with 128 frames. In our model, we subsample 25
# frames (as was done in Yi et al (2020)).
# For training, we randomize the choice of 25 frames, and for evaluation, we
# sample the 25 frames as evenly as possible.
# We do that by doing strided sampling of the frames.
stride, rem = divmod(monet_latents.shape[0], NUM_FRAMES)
monet_latents = monet_latents[None, :-rem:stride]
assert monet_latents.shape[1] == NUM_FRAMES
question = encode_sentence(token_map, question_json["question"],
modellib.MAX_QUESTION_LENGTH)
batched_question = np.expand_dims(question, axis=0)
logits = sess.run(output_descriptive, feed_dict={
inputs_descriptive["monet_latents"]: monet_latents,
inputs_descriptive["question"]: batched_question,
})
descriptive_answer = np.argmax(logits)
return reverse_answer_lookup[descriptive_answer]
def eval_mc(monet_latents, question_json):
stride, rem = divmod(monet_latents.shape[0], NUM_FRAMES)
monet_latents = monet_latents[None, :-rem:stride]
assert monet_latents.shape[1] == NUM_FRAMES
question = encode_sentence(
token_map, question_json["question"], modellib.MAX_QUESTION_LENGTH)
choices = encode_choices(
token_map, question_json["choices"])
mc_answer = sess.run(output_mc, feed_dict={
inputs_mc["monet_latents"]: monet_latents,
inputs_mc["question"]: np.expand_dims(question, axis=0),
inputs_mc["choices"]: np.expand_dims(choices, axis=0),
})
return mc_answer >= 0
sample_scene_idx = _SCENE_IDX.value
question_json = questions_data[sample_scene_idx]["questions"][0]
print("Descriptive Question: ", question_json["question"])
print("Model Answer: ",
eval_descriptive(load_monet_latents(base_dir, sample_scene_idx),
question_json))
print("True Answer: ", question_json["answer"])
question_json = questions_data[sample_scene_idx]["questions"][-1]
print("Multiple-Choice Question: ", question_json["question"])
for i, choice_json in enumerate(question_json["choices"]):
print(f"{i+1}) {choice_json['choice']}")
print("Model Answer: ",
eval_mc(load_monet_latents(base_dir, sample_scene_idx), question_json))
print("True Answer: ",
[choice_json["answer"] for choice_json in question_json["choices"]])
if __name__ == "__main__":
app.run(main)
================================================
FILE: object_attention_for_reasoning/transformer.py
================================================
# Fork of Sonnet transformer model with small modifications
#
# Copyright 2017 The Sonnet Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Implementation of Transformer networks.
Size glossary:
* Batch size (B).
* Sequence length (N).
* Memory size (M). The size of the optional memory, passed in via `state`.
* Number of heads (H): the number of attention heads.
* Value size (V): the size of each value embedding per head.
* Key size (K): the size of each key embedding per head. Equally, the size
of each query embedding per head. Typically K <= V.
* Embedding size (HV). The size of the activation or embedding relating to
each input between layers. Equal to value_size * num_heads.
* All attention size (F). The size of all attention activations over every
head.
* QKV size (F / H): The size of the query, key and value per head. Equal to
2K + V or equivalently F / H.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
from sonnet.python.modules import base
from sonnet.python.modules import basic
from sonnet.python.modules import layer_norm as snt_ln
from sonnet.python.modules import util
from sonnet.python.modules.nets import mlp as snt_mlp
import tensorflow.compat.v1 as tf
AttentionState = collections.namedtuple('AttentionState',
('queries', 'keys', 'values', 'logits',
'weights', 'embeddings', 'read_words'))
CompressedMemoryState = collections.namedtuple(
'CompressedMemoryState', ('episodic_memory', 'compressed_memory', 'index'))
def rel_shift(position_logits):
"""Shifting of logits for relative attention.
Args:
position_logits: A tensor of shape [B, H, N, N + M].
Returns:
The shifted logits. Example, for input (H=1, B=1):
[5, 4, 3, 2, 1]
[5, 4, 3, 2, 1]
[5, 4, 3, 2, 1]
[5, 4, 3, 2, 1]
[5, 4, 3, 2, 1]
the function outputs:
[1, 0, 5, 4, 3]
[2, 1, 0, 5, 4]
[3, 2, 1, 0, 5]
[4, 3, 2, 1, 0]
[5, 4, 3, 2, 1]
Raises:
ValueError if position_logits is not 4D.
Note: this is not an exact shift as the upper triangle is non-zero. This
works as intended in the causally-masked case. If this is used with un-masked
attention, we'd want these to also be zero.
"""
if position_logits.get_shape().ndims != 4:
raise ValueError('Expected 4D position logits.')
input_shape = position_logits.shape
batch_size = input_shape[0]
num_heads = input_shape[1]
t1 = input_shape[2]
t2 = input_shape[3]
# We prepend zeros on the final timescale dimension.
to_pad = tf.zeros([batch_size, num_heads, t1, 1])
position_logits = tf.concat([to_pad, position_logits], -1)
# Reshape trick to shift input.
position_logits = tf.reshape(position_logits,
[batch_size, num_heads, t2 + 1, t1])
# Remove extra time dimension and re-shape.
position_logits = position_logits[:, :, 1:]
position_logits = tf.reshape(position_logits, input_shape)
return position_logits
def _layer_norm(inputs):
if inputs.get_shape().ndims > 2:
return basic.BatchApply(snt_ln.LayerNorm())(inputs)
else:
return snt_ln.LayerNorm()(inputs)
def _concat_and_slice(prev_memory, new_memory):
original_memory_size = prev_memory.get_shape().as_list()[1]
concat_memory = tf.concat([prev_memory, new_memory], 1)
memory = concat_memory[:, -original_memory_size:]
return memory, concat_memory
def simple_attention(queries, keys, values):
logits = tf.matmul(queries, keys, transpose_b=True)
weights = tf.nn.softmax(logits)
return tf.matmul(weights, values)
class ResidualDropoutWrapper(base.AbstractModule):
"""Wrapper class that applies residual connections, dropout and layer norm.
By default applies a relu to the module output before the other operations.
"""
def __init__(self,
layer,
dropout_rate,
layer_norm='input',
name='residual_dropout_wrapper'):
self._module = layer
self._dropout_rate = dropout_rate
self._layer_norm = layer_norm
super(ResidualDropoutWrapper, self).__init__(name=name)
def _build(self, inputs, *args, **kwargs):
if self._layer_norm in ('both', 'input'):
normed_inputs = _layer_norm(inputs)
else:
normed_inputs = inputs
module_output = self._module(normed_inputs, *args, **kwargs)
module_state = None
# If module outputs multiple items, assumes (output, state) tuple.
if isinstance(module_output, tuple):
module_output, module_state = module_output
if kwargs['is_training']: # kwargs must contain is_training.
module_output = tf.nn.dropout(module_output, rate=self._dropout_rate)
output = inputs + module_output
if self._layer_norm in ('both', 'output'):
output = _layer_norm(output)
if module_state is None:
return output
else:
return output, module_state
def future_mask(chunk_size, dtype):
"""Creates attention mask to ensure an element i cannot attend to j > i."""
square = tf.ones([chunk_size, chunk_size], dtype=dtype)
# Create upper diagonal matrix and remove diagonal entries (allow self-attn).
mask = tf.matrix_band_part(square, 0, -1) - tf.matrix_band_part(square, 0, 0)
# Multiply by -1e6 and expand to broadcast with [B, H, N, N] logits.
mask = -1e6 * tf.reshape(mask, [1, 1, chunk_size, chunk_size])
return mask
def _memory_size(state):
if isinstance(state, CompressedMemoryState):
return (state.episodic_memory.get_shape().as_list()[1] +
state.compressed_memory.get_shape().as_list()[1])
else:
return state.get_shape().as_list()[1]
def create_mask(inputs, state, equal_window):
"""Creates mask for future sequence positions.
Args:
inputs: inputs tensor of shape [B, N, D]
state: optional tensor of shape [B, M, D], CompressedMemoryState or a list
where the ith entry corresponds to the ith layer's state.
equal_window: if True, then each activation has an equally-sized attention
window of length 'M'. This only makes sense if a state is given.
Returns:
Float tensor of shape [1, 1, N, N + M], to be summed with logits.
"""
chunk_size = inputs.get_shape().as_list()[1]
dtype = inputs.dtype
mask = future_mask(chunk_size, dtype)
if state is not None:
if isinstance(state, (tuple, list)):
largest_memory_layer = np.argmax([_memory_size(s) for s in state])
state = state[largest_memory_layer]
mem_size = _memory_size(state)
mask = tf.concat(
[tf.zeros([1, 1, chunk_size, mem_size], dtype=dtype), mask], 3)
if equal_window:
attn_mask = tf.ones([chunk_size, chunk_size], dtype=dtype)
mask_dia = tf.cast(tf.matrix_band_part(attn_mask, 0, 0), dtype=dtype)
mask_l = tf.cast(tf.matrix_band_part(attn_mask, -1, 0), dtype=dtype)
start_mask = tf.reshape(mask_l - mask_dia,
[1, 1, chunk_size, chunk_size]) * -1e6
mask = tf.concat(
[mask[:, :, :, :chunk_size] + start_mask, mask[:, :, :, chunk_size:]],
3)
return mask
def default_mlp(hidden_sizes, activate_final=False, init_std=2., **kwargs):
"""Standard batch-applied MLP for transformer modules."""
init = {'w': tf.variance_scaling_initializer(init_std, distribution='normal')}
mlp = snt_mlp.MLP(
hidden_sizes,
activate_final=activate_final,
use_dropout=True,
initializers=init,
**kwargs)
return basic.BatchApply(mlp)
def get_position_encodings(sequence_length,
hidden_size,
clamp_value,
max_timescale=10000.,
min_timescale=2.0):
"""Creates sinusoidal encodings of shape [1, N + M, D]."""
# NOTE: when not using relative position encodings, min_timescale must be 2.0
# and hidden_size must be an even number. Otherwise, the dimensions do not
# match.
pos_seq = tf.range(sequence_length - 1, -1, -1.0)
if clamp_value > 0:
pos_seq = tf.minimum(pos_seq, clamp_value)
freqs = tf.range(0, hidden_size, min_timescale)
inv_freq = 1 / (max_timescale**(freqs / hidden_size))
sinusoid_inp = tf.einsum('i,j->ij', pos_seq, inv_freq)
pos_emb = tf.concat([tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)], -1)
pos_emb = tf.expand_dims(pos_emb, 0)
output_dim = pos_emb.get_shape().as_list()[-1]
if output_dim != hidden_size:
raise ValueError(
'position embedding dimension ({}) does not match that of the input ({}).'
.format(output_dim, hidden_size))
return pos_emb
class MultiheadAttention(base.AbstractModule):
"""Implements multi-head attention with optional state context."""
def __init__(self,
value_size,
key_size,
num_heads,
mask=None,
scaling=True,
positional_encodings=None,
use_relative_positions=False,
init_std=2.,
name='multihead_attention'):
"""Creates a MultiheadAttention module.
Args:
value_size: V parameter. See size glossary in class docstring.
key_size: K parameter. See size glossary in class docstring.
num_heads: The number of independent queries per timestep.
mask: Optional mask to attention logits. This can prevent attending to
future positions or unused memory slots.
scaling: Whether to scale the attention logits.
positional_encodings: Either None (none given), or an iterable of
`(key_positional_encodings, query_positional_encodings)` tuples, where
the first encodings in the list indicate the oldest entries in memory
and the final encodings indicate the newest entries in memory and the
sequence.
use_relative_positions: If True then relative positions are incorporated,
vs absolute, into the attention logits. This is done exactly as
described in the TransformerXL, Dai et al. 2019.
init_std: scaling of standard deviation for weight matrices init.
name: Name of module.
"""
super(MultiheadAttention, self).__init__(name=name)
self._value_size = value_size
self._key_size = key_size
self._sizes = {
'value': self._value_size,
'key': self._key_size,
'query': self._key_size,
'relative_keys': self._key_size,
'relative_keys_0': self._key_size,
}
self._num_heads = num_heads
self._mask = mask
self._scaling = scaling
self._positional_encodings = positional_encodings
self._use_relative_positions = use_relative_positions
self._init = {'w': tf.variance_scaling_initializer(init_std)}
@util.reuse_variables
def multihead_linear(self, inputs, name):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
hidden_size = self._sizes[name]
input_size = inputs.shape[-1].value
w = tf.get_variable(
'linear/w',
shape=[input_size, self._num_heads * hidden_size],
initializer=self._init['w'])
w = tf.reshape(w, [input_size, self._num_heads, hidden_size])
out = tf.einsum('bij,jhk->bhik', inputs, w)
return out
def _build(self,
inputs,
query_inputs=None,
state=None,
is_training=False,
dropout_keep_prob=0.5,
key_value_inputs=None):
"""Calculates multi-layer self attention.
Args:
inputs: Tensor of shape [batch_size, num_steps, output_dim_size]. Inputs
used as the query, key, and value to the attention layer.
query_inputs: optional Tensor of shape [batch_size, num_steps,
output_dim_size]. Query inputs to the attention layer. Set when
query_inputs is different from the inputs argument.
state: optional CompressedMemoryState or a Tensor of shape [batch_size,
memory_size, dim_size] concatenated to the inputs. Set when attend to
the memory from previous steps.
is_training: if currently training.
dropout_keep_prob: dropout rate applied to attention weights.
key_value_inputs: optional Tensor of shape [batch_size, num_steps,
output_dim_size]. It is used as the key and value of the multihead
attention. Set when the key and value are different from the inputs
argument.
Returns:
output: the result Tensor of shape
[batch_size, num_steps, output_dim_size].
attention_state: named tuple of AttentionState.
"""
if key_value_inputs is not None and state is not None:
raise ValueError('Only one of the key_value_input and state is needed.')
embedding_size = self._value_size * self._num_heads
q_inputs = inputs if query_inputs is None else query_inputs
# Denoted by L. If query_inputs is None, L = N.
_, query_size = q_inputs.get_shape().as_list()[:2]
if key_value_inputs is not None:
k_inputs = key_value_inputs
v_inputs = k_inputs
elif state is not None:
if isinstance(state, CompressedMemoryState):
state_memory_list = [state.compressed_memory, state.episodic_memory]
else:
state_memory_list = [state]
k_inputs = tf.concat(state_memory_list + [inputs], 1)
v_inputs = k_inputs
else:
k_inputs = inputs
v_inputs = inputs
# Batch size denoted by B
batch_size = tf.shape(inputs)[0]
# Chunk_size denoted by N
chunk_size = inputs.get_shape().as_list()[1]
# Denoted by N + M
att_size = k_inputs.get_shape().as_list()[1]
if self._positional_encodings and not self._use_relative_positions:
if len(self._positional_encodings) != 1:
raise ValueError(
'Absolute positional encodings only supported for 1 memory. '
'Found %i.' % len(self._positional_encodings))
key_positions, query_positions = self._positional_encodings[0]
k_inputs += key_positions
q_inputs += query_positions
# [B, H, L, K]
q = self.multihead_linear(q_inputs, 'query')
# [B, H, N + M, K]
k = self.multihead_linear(k_inputs, 'key')
# [B, H, N + M, V]
v = self.multihead_linear(v_inputs, 'value')
# Scaling the dot-product
if self._scaling:
q *= self._key_size**-0.5
# [B, H, L, N + M]
if self._use_relative_positions:
r_w_bias = tf.get_variable(
'r_w_bias', [1, self._num_heads, 1, self._key_size],
dtype=inputs.dtype)
content_logits = tf.matmul(q + r_w_bias, k, transpose_b=True)
all_relative_logits = []
# Loop over multiple positional encodings, for the case of multiple
# memory types.
for i, positional_encodings in enumerate(self._positional_encodings):
key_positions, query_positions = positional_encodings
if key_positions.get_shape().as_list()[-1] != att_size:
key_positions = key_positions[:, -att_size:] # Crop to layer mem size
is_final = i == len(self._positional_encodings) - 1
suffix = '' if is_final else '_%d' % i
relative_keys = self.multihead_linear(
key_positions, name='relative_keys' + suffix)
# [B, H, N, D]
r_r_bias = tf.get_variable(
'r_r_bias' + suffix, [1, self._num_heads, 1, self._key_size],
dtype=inputs.dtype)
relative_keys = tf.tile(relative_keys, [batch_size, 1, 1, 1])
relative_logits = tf.matmul(
q + r_r_bias, relative_keys, transpose_b=True)
relative_logits = rel_shift(relative_logits)
if not is_final: # Include relative positions for input sequence.
relative_logits = relative_logits[:, :, :, :-chunk_size]
all_relative_logits.append(relative_logits)
all_relative_logits = tf.concat(all_relative_logits, 3)
logits = content_logits + all_relative_logits
else:
# [B, H, N, N + M]
logits = tf.matmul(q, k, transpose_b=True)
content_logits = logits
if self._mask is not None:
if self._mask.get_shape().as_list()[-1] != att_size:
mask = self._mask[:, :, :, -att_size:]
else:
mask = self._mask
logits += mask
weights = tf.nn.softmax(logits)
if is_training:
weights = tf.nn.dropout(weights, dropout_keep_prob)
# [B, L, H, V], where V is value_size
output_transpose = tf.einsum('bhij,bhjk->bihk', weights, v)
# [B, L, H, V] -> [B, L, HV]
attended_inputs = basic.BatchReshape([query_size, embedding_size])(
output_transpose)
# Apply final mlp to mix information between heads.
output = basic.BatchApply(basic.Linear(embedding_size))(attended_inputs)
attention_state = AttentionState(
queries=q,
keys=k,
values=v,
weights=weights,
logits=content_logits,
embeddings=inputs,
read_words=output)
return output, attention_state
class TransformerTower(base.AbstractModule):
"""Transformer tower.
Deep residual network using blocks of attention and MLPs, specified in
Vaswani et al. 2017.
"""
def __init__(self,
value_size,
num_heads,
num_layers,
causal=True,
key_size=None,
shared_attention=False,
output_size=None,
mlp_hidden_sizes=tuple([1024]),
dropout_rate=0.1,
use_relative_positions=True,
clamp_time_range=0,
same_attention_length=False,
layer_norm='input',
name='transformer_tower'):
"""Initializes TransformerTower.
Args:
value_size: dimensionality of values per-head.
num_heads: number of attention heads.
num_layers: number of transformer blocks, where each block contains a
multi-head attention layer and an MLP.
causal: if True, applies a causal mask.
key_size: optional dimensionality of key size. If unspecified then it is
set to `value_size`.
shared_attention: if True, attention params are shared across all layers.
output_size: if set, the desired output dimensionality. By default the
output size is `value_size` x `num_heads`.
mlp_hidden_sizes: tuple containing dimensionality of mlp layer(s). If
multiple values are specified, the mlp contains multiple layers for each
transformer block.
dropout_rate: dropout rate applied to hidden activations, attention, and
positional encodings.
use_relative_positions: if False, applies absolute positional encodings.
If true, uses relative positional encodings from Dai et al. 2019.
clamp_time_range: clamps max temporal positional encoding if specified.
same_attention_length: if True, attention is masked to ensure each
position in the sequence contains the same length of attention.
layer_norm: Where to apply layer-norm in Transformer block. Can be one of
'input' (Vaswani et al. 2017), 'output', or 'both'.
name: name of variable scope.
"""
super(TransformerTower, self).__init__(name=name)
self._causal = causal
self._mask = None
if key_size is None:
key_size = value_size
self._key_size = key_size
self._value_size = value_size
self._shared_attention = shared_attention
self._num_heads = num_heads
self._num_layers = num_layers
self._output_size = output_size
self._embedding_size = self._value_size * self._num_heads
self._mlp_hidden_sizes = list(mlp_hidden_sizes) + [self._embedding_size]
self._multihead_attention = None
self._object_embeddings = None
self._dropout_rate = dropout_rate
self._positional_encodings = None
self._use_relative_positions = use_relative_positions
self._clamp_time_range = clamp_time_range
self._same_attention_length = same_attention_length
self._layer_norm = layer_norm
self._attention_modules = []
self._object_mlps = []
def get_sublayers(self, is_training):
if self._multihead_attention is None or not self._shared_attention:
attention_module = MultiheadAttention(
value_size=self._value_size,
key_size=self._key_size,
num_heads=self._num_heads,
mask=self._mask,
positional_encodings=self._positional_encodings,
use_relative_positions=self._use_relative_positions,
init_std=2. / np.sqrt(self._num_layers),
)
self._multihead_attention = ResidualDropoutWrapper(
attention_module, self._dropout_rate, layer_norm=self._layer_norm)
mlp = default_mlp(
self._mlp_hidden_sizes, init_std=2. / np.sqrt(self._num_layers))
object_mlp = ResidualDropoutWrapper(
mlp, self._dropout_rate, layer_norm=self._layer_norm)
self._attention_modules.append(attention_module)
self._object_mlps.append(mlp)
return self._multihead_attention, object_mlp
def _build(self,
inputs,
state=None,
condition=None,
is_training=True,
final_layer_key_value_inputs=None):
"""Calculates multi-layer self attention and mlp transformation.
Args:
inputs: Tensor of shape [batch_size, num_steps, dim_size].
state: optional list of length num_layers of tensors of shape
[batch_size, memory_size, dim_size].
condition: optional tensor to condition on. The shape is shape
[batch_size, dim_size].
is_training: If true, dropout is applied.
final_layer_key_value_inputs: optional Tensor to be used as the key and
value for the final multi-head attention layer of shape
[batch_size, num_steps, dim_size]. Useful when the tower is a Seq2Seq
decoder and it can attend to encoder outputs.
Returns:
output: tensor of shape [batch_size, num_steps, output_dim_size].
state: list of length `num_layers` containing AttentionState tuples.
"""
# inputs: [B, N, F]
if final_layer_key_value_inputs is not None and state is not None and len(
state) == (self._num_layers - 1):
raise ValueError('When the final_layer_key_value_input is set, exclude'
'the state of the last layer.')
if condition is not None:
condition_tile = tf.tile(
tf.expand_dims(condition, 1), [1, tf.shape(inputs)[1], 1])
inputs = tf.concat([inputs, condition_tile], -1)
# Map inputs to be of `embedding_size` dimension.
if inputs.get_shape().as_list()[-1] != self._embedding_size:
inputs = default_mlp([self._embedding_size], activate_final=True)(
inputs,
is_training=is_training,
dropout_keep_prob=1 - self._dropout_rate)
if state is None:
memory_sizes = [0]
elif isinstance(state[0], CompressedMemoryState):
cm_mem_size = max(_memory_size(s.compressed_memory) for s in state)
em_mem_size = max(_memory_size(s.episodic_memory) for s in state)
memory_sizes = [cm_mem_size, em_mem_size]
else:
memory_sizes = [max([_memory_size(s) for s in state])]
chunk_size = inputs.get_shape().as_list()[1]
self._positional_encodings = []
# Creates positional encodings for different memory types.
for i, memory_size in enumerate(memory_sizes):
seq_len = chunk_size + memory_size
key_positions = get_position_encodings(
sequence_length=seq_len,
hidden_size=inputs.get_shape().as_list()[2],
clamp_value=self._clamp_time_range,
)
if is_training:
key_positions = tf.nn.dropout(key_positions, rate=self._dropout_rate)
key_positions = tf.cast(key_positions, dtype=inputs.dtype)
query_positions = key_positions[:, -chunk_size:, :]
self._positional_encodings.append((key_positions, query_positions))
if self._causal:
self._mask = create_mask(inputs, state, self._same_attention_length)
layer_i_inputs = inputs
attention_states = []
key_value_inputs = None
for i in range(self._num_layers):
with tf.variable_scope('layer_%d' % i, reuse=tf.AUTO_REUSE):
multihead_attention, object_mlp = self.get_sublayers(is_training)
# Multihead attention with residuals.
state_i = None if state is None else state[i]
if i == (self._num_layers -
1) and final_layer_key_value_inputs is not None:
# When the final_layer_key_value_inputs is set, the finaly layer
# of attention will use it as the key & value, thus no need for state.
key_value_inputs = final_layer_key_value_inputs
state_i = None
attention_outputs, attention_state = multihead_attention(
layer_i_inputs,
state=state_i,
is_training=is_training,
dropout_keep_prob=1. - self._dropout_rate,
key_value_inputs=key_value_inputs)
attention_states.append(attention_state)
# Feed-forward with residuals.
output = object_mlp(
attention_outputs,
is_training=is_training,
dropout_keep_prob=1 - self._dropout_rate)
layer_i_inputs = output
if self._output_size is not None:
output = basic.BatchApply(
basic.Linear(self._output_size, use_bias=False))(
output)
return output, attention_states
def attention_module(self, i):
"""Returns the i-th layer attention module."""
return self._attention_modules[i]
================================================
FILE: ode_gan/README.md
================================================
# ODE-GAN: Training GANs by Solving Ordinary Differential Equations
Mixture of Gaussian Example (Colab):
[](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/ode_gan/odegan_mog16.ipynb)
Cifar 10 Example (Tensorflow):
Launch Training from
https://github/deepmind/deepmind_research/tree/master/cs_gan/run_ode.sh
This package demos the algorithm [ODE-GAN](https://arxiv.org/abs/2010.15040).
If you make use of any code in your work, please cite:
```
@article{qin2020training,
title={Training Generative Adversarial Networks by Solving Ordinary Differential Equations},
author={Qin, Chongli and Wu, Yan and Springenberg, Jost Tobias and
Brock, Andy and Donahue, Jeff and Lillicrap, Timothy and Kohli, Pushmeet},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
}
```
================================================
FILE: ode_gan/odegan_mog16.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "OYWMcJafmrfI"
},
"source": [
"Copyright 2020 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
"\n",
"https://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yAHjf0hcm8Az"
},
"source": [
"# **This code implements ODE-GAN for Mixture of Gaussians.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "n8p0WAstrhUT"
},
"outputs": [],
"source": [
"#@title Imports\n",
"!pip install dm-haiku\n",
"import jax\n",
"from jax import lax\n",
"import jax.numpy as jnp\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import haiku as hk\n",
"import scipy as sp\n",
"import functools"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "aoIaRyCysZEs"
},
"outputs": [],
"source": [
"#@title An MLP Haiku Module \n",
"\n",
"class MLP(hk.Module):\n",
" def __init__(self, depth, hidden_size, out_dim, name='SimpleNet'):\n",
" super(MLP, self).__init__(name=name)\n",
" self._depth = depth\n",
" self._hidden_size = hidden_size\n",
" self._out_dim = out_dim\n",
" layers = []\n",
" for i in range(self._depth):\n",
" layers.append(hk.Linear(self._hidden_size, name='linear_%d'%(i)))\n",
" self._layers = layers\n",
" self._final_layer = hk.Linear(self._out_dim, name='final_layer')\n",
"\n",
" def __call__(self, input):\n",
" h = input\n",
" for i in range(self._depth):\n",
" h = jax.nn.relu(self._layers[i](h))\n",
" return self._final_layer(h)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "KBgWwKKyv6VI"
},
"outputs": [],
"source": [
"#@title Real Data\n",
"def real_data(batch_size):\n",
" mog_mean = np.array([\n",
" [ 1.50, 1.50],\n",
" [ 1.50, 0.50],\n",
" [ 1.50, -0.50],\n",
" [ 1.50, -1.50],\n",
" [ 0.50, 1.50],\n",
" [ 0.50, 0.50],\n",
" [ 0.50, -0.50],\n",
" [ 0.50, -1.50],\n",
" [-1.50, 1.50],\n",
" [-1.50, 0.50],\n",
" [-1.50, -0.50],\n",
" [-1.50, -1.50],\n",
" [-0.50, 1.50],\n",
" [-0.50, 0.50],\n",
" [-0.50, -0.50],\n",
" [-0.50, -1.50],\n",
" ])\n",
" temp = np.tile(mog_mean, (batch_size // 16 + 1,1))\n",
" mus = temp[0:batch_size,:]\n",
" return mus + 0.02 * np.random.normal(size=(batch_size, 2))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "E6uViIllRDlL"
},
"outputs": [],
"source": [
"#@title ODE-integrators\n",
"def euler_step(func, y0, f0, t0, dt):\n",
" # Euler update\n",
" y1 = jax.tree_multimap(lambda u, v: dt * v + u, y0, f0)\n",
" return y1\n",
"\n",
"def euler_heun_step(func, y0, f0, t0, dt):\n",
" # RK2 Butcher tableaux\n",
" alpha = jnp.array([1. / 2., 0.])\n",
" beta = jnp.array([\n",
" [1 / 2, 0,],\n",
" ])\n",
" c_sol = jnp.array([1 / 2, 1 / 2])\n",
"\n",
" def body_fun(i, k):\n",
" ti = t0 + dt * alpha[i-1]\n",
" yi = jax.tree_multimap(lambda u, v: u + dt * jnp.tensordot(beta[i-1, :], v, axes=1), y0, k)\n",
" ft = func(yi, ti)\n",
" return jax.tree_multimap(lambda x, y: x.at[i, :].set(y), k, ft)\n",
" k = jax.tree_map(lambda f: jnp.zeros((2,) + f.shape,\n",
" f.dtype).at[0, :].set(f), f0)\n",
" k = lax.fori_loop(1, 2, body_fun, k)\n",
"\n",
" y1 = jax.tree_multimap(lambda u, v: dt * jnp.tensordot(c_sol, v, axes=1) + u, y0, k)\n",
" return y1\n",
"\n",
"def runge_kutta_step(func, y0, f0, t0, dt):\n",
" # RK4 Butcher tableaux\n",
" alpha = jnp.array([1. / 2., 1. / 2., 1., 0])\n",
" beta = jnp.array([\n",
" [1. / 2., 0, 0, 0],\n",
" [0, 1. / 2., 0, 0],\n",
" [0, 0, 1., 0],\n",
" ])\n",
" c_sol = jnp.array([1. / 6., 1. / 3., 1. / 3., 1. / 6.])\n",
"\n",
" def body_fun(i, k):\n",
" ti = t0 + dt * alpha[i-1]\n",
" yi = jax.tree_multimap(lambda u, v: u + dt * jnp.tensordot(beta[i-1, :], v, axes=1), y0, k)\n",
" ft = func(yi, ti)\n",
" return jax.tree_multimap(lambda x, y: x.at[i, :].set(y), k, ft)\n",
" k = jax.tree_map(lambda f: jnp.zeros((4,) + f.shape,\n",
" f.dtype).at[0, :].set(f), f0)\n",
" k = lax.fori_loop(1, 4, body_fun, k)\n",
"\n",
" y1 = jax.tree_multimap(lambda u, v: dt * jnp.tensordot(c_sol, v, axes=1) + u, y0, k)\n",
" return y1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "NHCYH1tnwaTL"
},
"outputs": [],
"source": [
"#@title Utility Functions.\n",
"def disc_loss(disc_params, gen_params, real_examples, latents):\n",
" fake_examples = gen_model.apply(gen_params, None, latents)\n",
" real_logits = disc_model.apply(disc_params, None, real_examples)\n",
" fake_logits = disc_model.apply(disc_params, None, fake_examples)\n",
" disc_real = real_logits - jax.nn.log_sigmoid(real_logits)\n",
" disc_fake = - jax.nn.log_sigmoid(fake_logits)\n",
" return - jnp.mean(disc_real + disc_fake)\n",
"\n",
"def gen_loss(disc_params, gen_params, real_examples, latents):\n",
" fake_examples = gen_model.apply(gen_params, None, latents)\n",
" fake_logits = disc_model.apply(disc_params, None, fake_examples)\n",
" disc_fake = fake_logits - jax.nn.log_sigmoid(fake_logits)\n",
" return - jnp.mean(disc_fake)\n",
"\n",
"def gen_norm(disc_params, gen_params, real_examples, latents):\n",
" grad = jax.grad(gen_loss, argnums=1)(\n",
" disc_params, gen_params, real_examples, latents)\n",
" flat, _ = jax.tree_flatten(grad)\n",
" norm = 0.\n",
" for a in flat:\n",
" norm += jnp.sum(a * a)\n",
" return - norm\n",
"\n",
"def get_gen_grad(gen_params, t, disc_params, real_examples, latents):\n",
" return jax.grad(gen_loss, argnums=1)(\n",
" disc_params, gen_params, real_examples, latents)\n",
"\n",
"def get_disc_grad(disc_params, t, gen_params, real_examples, latents):\n",
" return jax.grad(disc_loss, argnums=0)(\n",
" disc_params, gen_params, real_examples, latents)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "xjTBhJuOh_wO"
},
"outputs": [],
"source": [
"#@title Visualising the data.\n",
"\n",
"def kde(mu, tau, contours=None, bbox=None, xlabel=\"\", ylabel=\"\", cmap='Blues', st=0):\n",
" values = np.vstack([mu, tau])\n",
" kernel = sp.stats.gaussian_kde(values)\n",
"\n",
" fig, ax = plt.subplots()\n",
" ax.axis(bbox)\n",
" ax.set_aspect(abs(bbox[1]-bbox[0])/abs(bbox[3]-bbox[2]))\n",
" ax.set_xlabel(xlabel)\n",
" ax.set_ylabel(ylabel)\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
"\n",
" xx, yy = np.mgrid[bbox[0]:bbox[1]:300j, bbox[2]:bbox[3]:300j]\n",
" positions = np.vstack([xx.ravel(), yy.ravel()])\n",
" \n",
" f = np.reshape(kernel(positions).T, xx.shape)\n",
" cfset = ax.contourf(xx, yy, f, cmap=cmap)\n",
" if contours is not None:\n",
" x = np.arange(-2., 2., 0.1)\n",
" y = np.arange(-2., 2., 0.1)\n",
" cx, cy = np.meshgrid(x, y)\n",
" new_set = ax.contour(cx, cy, contours.squeeze().reshape(cx.shape), levels=20, colors='k', linewidths=0.8, alpha=0.5) \n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "G2G32j5N1psa"
},
"outputs": [],
"source": [
"#@title Integration\n",
"n_itrs = 30001 #@param {type : 'integer'}\n",
"n_save = 2000 #@param {type : 'integer'}\n",
"latent_size = 32 #@param {type : 'integer'}\n",
"bs = 512 #@param {type : 'integer'}\n",
"odeint = 'runge_kutta_step' #@param ['euler_step', 'euler_heun_step', 'runge_kutta_step'] {type : 'string'}\n",
"delta_t = 0.10 #@param {type : 'number'}\n",
"reg_param = 0.07 #@param {type : 'number'}\n",
"\n",
"def forward_disc(batch):\n",
" disc_model = MLP(2, 25, 1)\n",
" return disc_model(batch)\n",
"\n",
"def forward_gen(batch):\n",
" gen_model = MLP(2, 25, 2)\n",
" return gen_model(batch)\n",
"\n",
"\n",
"disc_model = hk.transform(forward_disc)\n",
"gen_model = hk.transform(forward_gen)\n",
"real_examples = real_data(bs)\n",
"\n",
"ODEINT = {'runge_kutta_step': runge_kutta_step, \n",
" 'euler_heun_step': euler_heun_step,\n",
" 'euler_step': euler_step}\n",
"@jax.jit\n",
"def ode_update(i, disc_params, gen_params, real_examples, latents):\n",
" dloss, disc_grad = jax.value_and_grad(disc_loss, argnums=0)(\n",
" disc_params, gen_params, real_examples, latents)\n",
" gloss, gen_grad = jax.value_and_grad(gen_loss, argnums=1)(\n",
" disc_params, gen_params, real_examples, latents)\n",
" disc_gen_grad = jax.grad(gen_norm, argnums=0)(\n",
" disc_params, gen_params, real_examples, latents)\n",
" grad_disc_fn = functools.partial(get_disc_grad,\n",
" **{'gen_params' : gen_params,\n",
" 'real_examples' : real_examples,\n",
" 'latents' : latents})\n",
" grad_gen_fn = functools.partial(get_gen_grad, \n",
" **{'disc_params' : disc_params,\n",
" 'real_examples' : real_examples,\n",
" 'latents' : latents}) \n",
" new_gen_params = ODEINT[odeint](\n",
" grad_gen_fn, gen_params, gen_grad, 0., delta_t)\n",
" new_disc_params = ODEINT[odeint](\n",
" grad_disc_fn, disc_params, disc_grad, 0., delta_t)\n",
" new_disc_params = jax.tree_multimap(\n",
" lambda x, y: x + delta_t * reg_param * y, new_disc_params, disc_gen_grad)\n",
" return new_disc_params, new_gen_params, -dloss, -gloss\n",
"\n",
"rng = jax.random.PRNGKey(np.random.randint(low=0, high=int(1e7)))\n",
"test_latents = np.random.normal(size=(bs * 10, latent_size))\n",
"latents = np.random.normal(size=(bs, latent_size))\n",
"disc_params = disc_model.init(rng, real_examples)\n",
"gen_params = gen_model.init(\n",
" jax.random.PRNGKey(np.random.randint(low=0, high=int(1e7))), latents)\n",
"\n",
"x = np.arange(-2., 2., 0.1)\n",
"y = np.arange(-2., 2., 0.1)\n",
"X, Y = np.meshgrid(x, y)\n",
"pairs = np.stack((X, Y), axis=-1)\n",
"pairs = np.reshape(pairs, (-1, 2))\n",
"\n",
"for e in range(n_itrs):\n",
" real_examples = real_data(bs)\n",
" latents = np.random.normal(size=(bs, latent_size))\n",
"\n",
" (disc_params, gen_params,\n",
" dloss, gloss) = ode_update(e, disc_params, gen_params, real_examples, latents)\n",
" \n",
" if e % n_save == 0:\n",
" real_logits = disc_model.apply(disc_params, None, pairs)\n",
" disc_contour = - real_logits + jax.nn.log_sigmoid(real_logits)\n",
" print('i = %d, discriminant loss = %s, generator loss = %s' %\n",
" (e, dloss, gloss))\n",
" bbox = [-2, 2, -2, 2]\n",
" fake_examples = gen_model.apply(gen_params, None, test_latents)\n",
" kde(fake_examples[:, 0], fake_examples[:, 1], contours=disc_contour, bbox=bbox, st=e)\n",
" disc_error = 0\n",
" gen_error = 0"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "odegan_mog16.ipynb"
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: ogb_lsc/README.md
================================================
# DeepMind entry for OGB-LSC
This repository contains DeepMind's entry to the [PCQM4M-LSC](https://ogb.stanford.edu/kddcup2021/pcqm4m/) (quantum chemistry) and
[MAG240M-LSC](https://ogb.stanford.edu/kddcup2021/mag240m/) (academic graph)
tracks of the [OGB Large-Scale Challenge](https://ogb.stanford.edu/kddcup2021/)
(OGB-LSC).
For full details regarding this entry, please see our [technical report](https://arxiv.org/abs/2107.09422).
## Code structure
* `pcq/`: Scripts for training, evaluating on the [PCQ dataset](https://ogb.stanford.edu/docs/graphprop/).
* `mag/`: Scripts for training, evaluating on the [MAG dataset](https://ogb.stanford.edu/docs/nodeprop/).
## Citation
To cite this work:
```latex
@article{deepmind2021ogb,
author = {Ravichandra Addanki and Peter Battaglia and David Budden and Andreea
Deac and Jonathan Godwin and Thomas Keck and Wai Lok Sibon Li and Alvaro
Sanchez-Gonzalez and Jacklynn Stott and Shantanu Thakoor and Petar
Veli\v{c}kovi\'{c}},
title = {Large-scale graph representation learning with very deep GNNs and
self-supervision},
year = {2021},
journal={arXiv preprint arXiv:2107.09422},
}
```
================================================
FILE: ogb_lsc/mag/README.md
================================================
# DeepMind entry for MAG240M-LSC
This repository contains DeepMind's entry to the [MAG240M-LSC](https://ogb.stanford.edu/kddcup2021/mag240m/) (academic graph) track of the
[OGB Large-Scale Challenge](https://ogb.stanford.edu/kddcup2021/) (OGB-LSC).
For full details regarding this entry, please see our [technical report](https://arxiv.org/abs/2107.09422).
## DeepMind MAG Team ("Academic")
(in alphabetical order)
- Ravichandra Addanki
- Peter Battaglia
- David Budden
- Andreea Deac
- Jonathan Godwin
- Thomas Keck
- Alvaro Sanchez-Gonzalez
- Jacklynn Stott
- Shantanu Thakoor
- Petar Veličković
## Performance
Our final test set performance was achieved by pooling an ensemble of 10 folds.
See [technical report](https://arxiv.org/abs/2107.09422) for details.
Each model was trained for < 72 hours using 4x Google Cloud TPUv4 and 1x AMD
EPYC 7B12 64-core CPU @2.25GHz.
Inference takes < 12 hours on 4x NVIDIA V100 16GB GPU and 1x Intel Xeon Gold
6148 20-core CPU @2.40GHz.
# Running our model
## Setup
You can set up Python virtual environment (you might need to install the
`python3-venv` package first) with all needed dependencies inside the forked
`deepmind_research` repository using:
```bash
python3 -m venv /tmp/mag_venv
source /tmp/mag_venv/bin/activate
pip3 install --upgrade pip setuptools wheel
pip3 install -r ogb_lsc/mag/requirements.txt
```
Use the following command to get a jaxlib version built compatible with V100 GPUs.
```bash
pip install --upgrade jax jaxlib==0.1.67+cuda110 -f https://storage.googleapis.com/jax-releases/jax_releases.html
```
See https://github.com/google/jax/issues/5231 for details.
## Download and pre-process data
**1. Download the dataset using the contest toolkit ([here](https://ogb.stanford.edu/kddcup2021/mag240m/#dataset)) to a local directory
`ROOT`.**
**2. Run this script to reorganize the data into a flat directory structure with
transparent names.**
```bash
/bin/bash organize_data.sh -r ROOT
```
Once this completes, a new directory `ROOT/mag240m_kddcup2021/raw` will be
created, with contents:
- `node_feat.npy`
- `node_label.npy`
- `node_year.npy`
- `author_affiliated_with_institution_edges.npy`
- `author_writes_paper_edges.npy`
- `paper_cites_paper_edges.npy`
- `train_idx.npy`
- `valid_idx.npy`
- `test_idx.npy`
We refer to this as the "raw" data.
**3. Run the preprocessing code.**
```bash
/bin/bash run_preprocessing.sh -r ROOT
```
The pre-processing is both time- and memory-consuming, and should only be run
to verify the full pipeline. You can download the pre-processed data using the
following script, for use in training and evaluating models:
```bash
python3 download_mag.py --task_root=${HOME}/mag --payload="data"
```
## Reproducing our final results
We have provided pre-trained weights of our final submission for convenience.
They can be downloaded with:
```bash
python3 download_mag.py --task_root=${HOME}/mag --payload="models"
```
Then to reproduce our final results, please run:
```bash
/bin/bash run_preprocessing.sh -r ${HOME}/mag/
```
## Retraining our model
Disclaimer: This script is provided for illustrative purposes. It is not
practical for actual training since it only uses a single machine, and likely
requires reducing the batch size and/or model size to fit on a single GPU.
To train a model, please run:
```bash
/bin/bash run_training.sh -r ${HOME}/mag/
```
To simply validate that the code is running correctly on your hardware setup,
consider setting `debug=True` in `config.py`, which trains a smaller model.
# Citation
To cite this work (together with our PCQM4M-LSC entry):
```latex
@article{deepmind2021ogb,
author = {Ravichandra Addanki and Peter Battaglia and David Budden and Andreea
Deac and Jonathan Godwin and Thomas Keck and Wai Lok Sibon Li and Alvaro
Sanchez-Gonzalez and Jacklynn Stott and Shantanu Thakoor and Petar
Veli\v{c}kovi\'{c}},
title = {Large-scale graph representation learning with very deep GNNs and
self-supervision},
year = {2021},
journal={arXiv preprint arXiv:2107.09422},
}
```
Our technical report can be found [here](https://arxiv.org/abs/2107.09422).
================================================
FILE: ogb_lsc/mag/batching_utils.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Dynamic batching utilities."""
from typing import Generator, Iterable, Iterator, Sequence, Tuple
import jax.tree_util as tree
import jraph
import numpy as np
_NUMBER_FIELDS = ("n_node", "n_edge", "n_graph")
def dynamically_batch(graphs_tuple_iterator: Iterator[jraph.GraphsTuple],
n_node: int, n_edge: int,
n_graph: int) -> Generator[jraph.GraphsTuple, None, None]:
"""Dynamically batches trees with `jraph.GraphsTuples` to `graph_batch_size`.
Elements of the `graphs_tuple_iterator` will be incrementally added to a batch
until the limits defined by `n_node`, `n_edge` and `n_graph` are reached. This
means each element yielded by this generator
For situations where you have variable sized data, it"s useful to be able to
have variable sized batches. This is especially the case if you have a loss
defined on the variable shaped element (for example, nodes in a graph).
Args:
graphs_tuple_iterator: An iterator of `jraph.GraphsTuples`.
n_node: The maximum number of nodes in a batch.
n_edge: The maximum number of edges in a batch.
n_graph: The maximum number of graphs in a batch.
Yields:
A `jraph.GraphsTuple` batch of graphs.
Raises:
ValueError: if the number of graphs is < 2.
RuntimeError: if the `graphs_tuple_iterator` contains elements which are not
`jraph.GraphsTuple`s.
RuntimeError: if a graph is found which is larger than the batch size.
"""
if n_graph < 2:
raise ValueError("The number of graphs in a batch size must be greater or "
f"equal to `2` for padding with graphs, got {n_graph}.")
valid_batch_size = (n_node - 1, n_edge, n_graph - 1)
accumulated_graphs = []
num_accumulated_nodes = 0
num_accumulated_edges = 0
num_accumulated_graphs = 0
for element in graphs_tuple_iterator:
element_nodes, element_edges, element_graphs = _get_graph_size(element)
if _is_over_batch_size(element, valid_batch_size):
graph_size = element_nodes, element_edges, element_graphs
graph_size = {k: v for k, v in zip(_NUMBER_FIELDS, graph_size)}
batch_size = {k: v for k, v in zip(_NUMBER_FIELDS, valid_batch_size)}
raise RuntimeError("Found graph bigger than batch size. Valid Batch "
f"Size: {batch_size}, Graph Size: {graph_size}")
if not accumulated_graphs:
# If this is the first element of the batch, set it and continue.
accumulated_graphs = [element]
num_accumulated_nodes = element_nodes
num_accumulated_edges = element_edges
num_accumulated_graphs = element_graphs
continue
else:
# Otherwise check if there is space for the graph in the batch:
if ((num_accumulated_graphs + element_graphs > n_graph - 1) or
(num_accumulated_nodes + element_nodes > n_node - 1) or
(num_accumulated_edges + element_edges > n_edge)):
# If there is, add it to the batch
batched_graph = _batch_np(accumulated_graphs)
yield jraph.pad_with_graphs(batched_graph, n_node, n_edge, n_graph)
accumulated_graphs = [element]
num_accumulated_nodes = element_nodes
num_accumulated_edges = element_edges
num_accumulated_graphs = element_graphs
else:
# Otherwise, return the old batch and start a new batch.
accumulated_graphs.append(element)
num_accumulated_nodes += element_nodes
num_accumulated_edges += element_edges
num_accumulated_graphs += element_graphs
# We may still have data in batched graph.
if accumulated_graphs:
batched_graph = _batch_np(accumulated_graphs)
yield jraph.pad_with_graphs(batched_graph, n_node, n_edge, n_graph)
def _batch_np(graphs: Sequence[jraph.GraphsTuple]) -> jraph.GraphsTuple:
# Calculates offsets for sender and receiver arrays, caused by concatenating
# the nodes arrays.
offsets = np.cumsum(np.array([0] + [np.sum(g.n_node) for g in graphs[:-1]]))
def _map_concat(nests):
concat = lambda *args: np.concatenate(args)
return tree.tree_map(concat, *nests)
return jraph.GraphsTuple(
n_node=np.concatenate([g.n_node for g in graphs]),
n_edge=np.concatenate([g.n_edge for g in graphs]),
nodes=_map_concat([g.nodes for g in graphs]),
edges=_map_concat([g.edges for g in graphs]),
globals=_map_concat([g.globals for g in graphs]),
senders=np.concatenate([g.senders + o for g, o in zip(graphs, offsets)]),
receivers=np.concatenate(
[g.receivers + o for g, o in zip(graphs, offsets)]))
def _get_graph_size(graph: jraph.GraphsTuple) -> Tuple[int, int, int]:
n_node = np.sum(graph.n_node)
n_edge = len(graph.senders)
n_graph = len(graph.n_node)
return n_node, n_edge, n_graph
def _is_over_batch_size(
graph: jraph.GraphsTuple,
graph_batch_size: Iterable[int],
) -> bool:
graph_size = _get_graph_size(graph)
return any([x > y for x, y in zip(graph_size, graph_batch_size)])
================================================
FILE: ogb_lsc/mag/config.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Experiment config for MAG240M-LSC entry."""
from jaxline import base_config
from ml_collections import config_dict
def get_config(debug: bool = False) -> config_dict.ConfigDict:
"""Get Jaxline experiment config."""
config = base_config.get_base_config()
config.random_seed = 42
# E.g. '/data/pretrained_models/k0_seed100' (and set k_fold_split_id=0, below)
config.restore_path = config_dict.placeholder(str)
config.experiment_kwargs = config_dict.ConfigDict(
dict(
config=dict(
debug=debug,
predictions_dir=config_dict.placeholder(str),
# 5 for model selection and early stopping, 50 for final eval.
num_eval_iterations_to_ensemble=5,
dataset_kwargs=dict(
data_root='/data/',
online_subsampling_kwargs=dict(
max_nb_neighbours_per_type=[
[[40, 20, 0, 40], [0, 0, 0, 0], [0, 0, 0, 0]],
[[40, 20, 0, 40], [40, 0, 10, 0], [0, 0, 0, 0]],
],
remove_future_nodes=True,
deduplicate_nodes=True,
),
ratio_unlabeled_data_to_labeled_data=10.0,
k_fold_split_id=config_dict.placeholder(int),
use_all_labels_when_not_training=False,
use_dummy_adjacencies=debug,
),
optimizer=dict(
name='adamw',
kwargs=dict(weight_decay=1e-5, b1=0.9, b2=0.999),
learning_rate_schedule=dict(
use_schedule=True,
base_learning_rate=1e-2,
warmup_steps=50000,
total_steps=config.get_ref('training_steps'),
),
),
model_config=dict(
mlp_hidden_sizes=[32] if debug else [512],
latent_size=32 if debug else 256,
num_message_passing_steps=2 if debug else 4,
activation='relu',
dropout_rate=0.3,
dropedge_rate=0.25,
disable_edge_updates=True,
use_sent_edges=True,
normalization_type='layer_norm',
aggregation_function='sum',
),
training=dict(
loss_config=dict(
bgrl_loss_config=dict(
stop_gradient_for_supervised_loss=False,
bgrl_loss_scale=1.0,
symmetrize=True,
first_graph_corruption_config=dict(
feature_drop_prob=0.4,
edge_drop_prob=0.2,
),
second_graph_corruption_config=dict(
feature_drop_prob=0.4,
edge_drop_prob=0.2,
),
),
),
# GPU memory may require reducing the `256`s below to `48`.
dynamic_batch_size_config=dict(
n_node=256 if debug else 340 * 256,
n_edge=512 if debug else 720 * 256,
n_graph=4 if debug else 256,
),
),
eval=dict(
split='valid',
ema_annealing_schedule=dict(
use_schedule=True,
base_rate=0.999,
total_steps=config.get_ref('training_steps')),
dynamic_batch_size_config=dict(
n_node=256 if debug else 340 * 128,
n_edge=512 if debug else 720 * 128,
n_graph=4 if debug else 128,
),
))))
## Training loop config.
config.training_steps = 500000
config.checkpoint_dir = '/tmp/checkpoint/mag/'
config.train_checkpoint_all_hosts = False
config.log_train_data_interval = 10
config.log_tensors_interval = 10
config.save_checkpoint_interval = 30
config.best_model_eval_metric = 'accuracy'
config.best_model_eval_metric_higher_is_better = True
return config
================================================
FILE: ogb_lsc/mag/csr_builder.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Builds CSR matrices which store the MAG graphs."""
import pathlib
from absl import app
from absl import flags
from absl import logging
import numpy as np
import scipy.sparse
# pylint: disable=g-bad-import-order
import data_utils
Path = pathlib.Path
FLAGS = flags.FLAGS
_DATA_FILES_AND_PARAMETERS = {
'author_affiliated_with_institution_edges.npy': {
'content_names': ('author', 'institution'),
'use_boolean': False
},
'author_writes_paper_edges.npy': {
'content_names': ('author', 'paper'),
'use_boolean': False
},
'paper_cites_paper_edges.npy': {
'content_names': ('paper', 'paper'),
'use_boolean': True
},
}
flags.DEFINE_string('data_root', None, 'Data root directory')
flags.DEFINE_boolean('skip_existing', True, 'Skips existing CSR files')
flags.mark_flags_as_required(['data_root'])
def _read_edge_data(path):
try:
return np.load(path, mmap_mode='r')
except FileNotFoundError:
# If the file path can't be found by np.load, use the file handle w/o mmap.
with path.open('rb') as fid:
return np.load(fid)
def _build_coo(edges_data, use_boolean=False):
if use_boolean:
mat_coo = scipy.sparse.coo_matrix(
(np.ones_like(edges_data[1, :],
dtype=bool), (edges_data[0, :], edges_data[1, :])))
else:
mat_coo = scipy.sparse.coo_matrix(
(edges_data[1, :], (edges_data[0, :], edges_data[1, :])))
return mat_coo
def _get_output_paths(directory, content_names, use_boolean):
boolean_str = '_b' if use_boolean else ''
transpose_str = '_t' if len(set(content_names)) == 1 else ''
output_prefix = '_'.join(content_names)
output_prefix_t = '_'.join(content_names[::-1])
output_filename = f'{output_prefix}{boolean_str}.npz'
output_filename_t = f'{output_prefix_t}{boolean_str}{transpose_str}.npz'
output_path = directory / output_filename
output_path_t = directory / output_filename_t
return output_path, output_path_t
def _write_csr(path, csr):
path.parent.mkdir(parents=True, exist_ok=True)
with path.open('wb') as fid:
scipy.sparse.save_npz(fid, csr)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
raw_data_dir = Path(FLAGS.data_root) / data_utils.RAW_DIR
preprocessed_dir = Path(FLAGS.data_root) / data_utils.PREPROCESSED_DIR
for input_filename, parameters in _DATA_FILES_AND_PARAMETERS.items():
input_path = raw_data_dir / input_filename
output_path, output_path_t = _get_output_paths(preprocessed_dir,
**parameters)
if FLAGS.skip_existing and output_path.exists() and output_path_t.exists():
# If both files exist, skip. When only one exists, that's handled below.
logging.info(
'%s and %s exist: skipping. Use flag `--skip_existing=False`'
'to force overwrite existing.', output_path, output_path_t)
continue
logging.info('Reading edge data from: %s', input_path)
edge_data = _read_edge_data(input_path)
logging.info('Building CSR matrices')
mat_coo = _build_coo(edge_data, use_boolean=parameters['use_boolean'])
# Convert matrices to CSR and write to disk.
if not FLAGS.skip_existing or not output_path.exists():
logging.info('Writing CSR matrix to: %s', output_path)
mat_csr = mat_coo.tocsr()
_write_csr(output_path, mat_csr)
del mat_csr # Free up memory asap.
else:
logging.info(
'%s exists: skipping. Use flag `--skip_existing=False`'
'to force overwrite existing.', output_path)
if not FLAGS.skip_existing or not output_path_t.exists():
logging.info('Writing (transposed) CSR matrix to: %s', output_path_t)
mat_csr_t = mat_coo.transpose().tocsr()
_write_csr(output_path_t, mat_csr_t)
del mat_csr_t # Free up memory asap.
else:
logging.info(
'%s exists: skipping. Use flag `--skip_existing=False`'
'to force overwrite existing.', output_path_t)
del mat_coo # Free up memory asap.
if __name__ == '__main__':
app.run(main)
================================================
FILE: ogb_lsc/mag/data_utils.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Dataset utilities."""
import functools
import pathlib
from typing import Dict, Tuple
from absl import logging
from graph_nets import graphs as tf_graphs
from graph_nets import utils_tf
import numpy as np
import scipy.sparse as sp
import tensorflow as tf
import tqdm
# pylint: disable=g-bad-import-order
import sub_sampler
Path = pathlib.Path
NUM_PAPERS = 121751666
NUM_AUTHORS = 122383112
NUM_INSTITUTIONS = 25721
EMBEDDING_SIZE = 768
NUM_CLASSES = 153
NUM_NODES = NUM_PAPERS + NUM_AUTHORS + NUM_INSTITUTIONS
NUM_EDGES = 1_728_364_232
assert NUM_NODES == 244_160_499
NUM_K_FOLD_SPLITS = 10
OFFSETS = {
"paper": 0,
"author": NUM_PAPERS,
"institution": NUM_PAPERS + NUM_AUTHORS,
}
SIZES = {
"paper": NUM_PAPERS,
"author": NUM_AUTHORS,
"institution": NUM_INSTITUTIONS
}
RAW_DIR = Path("raw")
PREPROCESSED_DIR = Path("preprocessed")
RAW_NODE_FEATURES_FILENAME = RAW_DIR / "node_feat.npy"
RAW_NODE_LABELS_FILENAME = RAW_DIR / "node_label.npy"
RAW_NODE_YEAR_FILENAME = RAW_DIR / "node_year.npy"
TRAIN_INDEX_FILENAME = RAW_DIR / "train_idx.npy"
VALID_INDEX_FILENAME = RAW_DIR / "train_idx.npy"
TEST_INDEX_FILENAME = RAW_DIR / "train_idx.npy"
EDGES_PAPER_PAPER_B = PREPROCESSED_DIR / "paper_paper_b.npz"
EDGES_PAPER_PAPER_B_T = PREPROCESSED_DIR / "paper_paper_b_t.npz"
EDGES_AUTHOR_INSTITUTION = PREPROCESSED_DIR / "author_institution.npz"
EDGES_INSTITUTION_AUTHOR = PREPROCESSED_DIR / "institution_author.npz"
EDGES_AUTHOR_PAPER = PREPROCESSED_DIR / "author_paper.npz"
EDGES_PAPER_AUTHOR = PREPROCESSED_DIR / "paper_author.npz"
PCA_PAPER_FEATURES_FILENAME = PREPROCESSED_DIR / "paper_feat_pca_129.npy"
PCA_AUTHOR_FEATURES_FILENAME = (
PREPROCESSED_DIR / "author_feat_from_paper_feat_pca_129.npy")
PCA_INSTITUTION_FEATURES_FILENAME = (
PREPROCESSED_DIR / "institution_feat_from_paper_feat_pca_129.npy")
PCA_MERGED_FEATURES_FILENAME = (
PREPROCESSED_DIR / "merged_feat_from_paper_feat_pca_129.npy")
NEIGHBOR_INDICES_FILENAME = PREPROCESSED_DIR / "neighbor_indices.npy"
NEIGHBOR_DISTANCES_FILENAME = PREPROCESSED_DIR / "neighbor_distances.npy"
FUSED_NODE_LABELS_FILENAME = PREPROCESSED_DIR / "fused_node_labels.npy"
FUSED_PAPER_EDGES_FILENAME = PREPROCESSED_DIR / "fused_paper_edges.npz"
FUSED_PAPER_EDGES_T_FILENAME = PREPROCESSED_DIR / "fused_paper_edges_t.npz"
K_FOLD_SPLITS_DIR = Path("k_fold_splits")
def get_raw_directory(data_root):
return Path(data_root) / "raw"
def get_preprocessed_directory(data_root):
return Path(data_root) / "preprocessed"
def _log_path_decorator(fn):
def _decorated_fn(path, **kwargs):
logging.info("Loading %s", path)
output = fn(path, **kwargs)
logging.info("Finish loading %s", path)
return output
return _decorated_fn
@_log_path_decorator
def load_csr(path, debug=False):
if debug:
# Dummy matrix for debugging.
return sp.csr_matrix(np.zeros([10, 10]))
return sp.load_npz(str(path))
@_log_path_decorator
def load_npy(path):
return np.load(str(path))
@functools.lru_cache()
def get_arrays(data_root="/data/",
use_fused_node_labels=True,
use_fused_node_adjacencies=True,
return_pca_embeddings=True,
k_fold_split_id=None,
return_adjacencies=True,
use_dummy_adjacencies=False):
"""Returns all arrays needed for training."""
logging.info("Starting to get files")
data_root = Path(data_root)
array_dict = {}
array_dict["paper_year"] = load_npy(data_root / RAW_NODE_YEAR_FILENAME)
if k_fold_split_id is None:
train_indices = load_npy(data_root / TRAIN_INDEX_FILENAME)
valid_indices = load_npy(data_root / VALID_INDEX_FILENAME)
else:
train_indices, valid_indices = get_train_and_valid_idx_for_split(
k_fold_split_id, num_splits=NUM_K_FOLD_SPLITS,
root_path=data_root / K_FOLD_SPLITS_DIR)
array_dict["train_indices"] = train_indices
array_dict["valid_indices"] = valid_indices
array_dict["test_indices"] = load_npy(data_root / TEST_INDEX_FILENAME)
if use_fused_node_labels:
array_dict["paper_label"] = load_npy(data_root / FUSED_NODE_LABELS_FILENAME)
else:
array_dict["paper_label"] = load_npy(data_root / RAW_NODE_LABELS_FILENAME)
if return_adjacencies:
logging.info("Starting to get adjacencies.")
if use_fused_node_adjacencies:
paper_paper_index = load_csr(
data_root / FUSED_PAPER_EDGES_FILENAME, debug=use_dummy_adjacencies)
paper_paper_index_t = load_csr(
data_root / FUSED_PAPER_EDGES_T_FILENAME, debug=use_dummy_adjacencies)
else:
paper_paper_index = load_csr(
data_root / EDGES_PAPER_PAPER_B, debug=use_dummy_adjacencies)
paper_paper_index_t = load_csr(
data_root / EDGES_PAPER_PAPER_B_T, debug=use_dummy_adjacencies)
array_dict.update(
dict(
author_institution_index=load_csr(
data_root / EDGES_AUTHOR_INSTITUTION,
debug=use_dummy_adjacencies),
institution_author_index=load_csr(
data_root / EDGES_INSTITUTION_AUTHOR,
debug=use_dummy_adjacencies),
author_paper_index=load_csr(
data_root / EDGES_AUTHOR_PAPER, debug=use_dummy_adjacencies),
paper_author_index=load_csr(
data_root / EDGES_PAPER_AUTHOR, debug=use_dummy_adjacencies),
paper_paper_index=paper_paper_index,
paper_paper_index_t=paper_paper_index_t,
))
if return_pca_embeddings:
array_dict["bert_pca_129"] = np.load(
data_root / PCA_MERGED_FEATURES_FILENAME, mmap_mode="r")
assert array_dict["bert_pca_129"].shape == (NUM_NODES, 129)
logging.info("Finish getting files")
# pytype: disable=attribute-error
assert array_dict["paper_year"].shape[0] == NUM_PAPERS
assert array_dict["paper_label"].shape[0] == NUM_PAPERS
if return_adjacencies and not use_dummy_adjacencies:
array_dict = _fix_adjacency_shapes(array_dict)
assert array_dict["paper_author_index"].shape == (NUM_PAPERS, NUM_AUTHORS)
assert array_dict["author_paper_index"].shape == (NUM_AUTHORS, NUM_PAPERS)
assert array_dict["paper_paper_index"].shape == (NUM_PAPERS, NUM_PAPERS)
assert array_dict["paper_paper_index_t"].shape == (NUM_PAPERS, NUM_PAPERS)
assert array_dict["institution_author_index"].shape == (
NUM_INSTITUTIONS, NUM_AUTHORS)
assert array_dict["author_institution_index"].shape == (
NUM_AUTHORS, NUM_INSTITUTIONS)
# pytype: enable=attribute-error
return array_dict
def add_nodes_year(graph, paper_year):
nodes = graph.nodes.copy()
indices = nodes["index"]
year = paper_year[np.minimum(indices, paper_year.shape[0] - 1)].copy()
year[nodes["type"] != 0] = 1900
nodes["year"] = year
return graph._replace(nodes=nodes)
def add_nodes_label(graph, paper_label):
nodes = graph.nodes.copy()
indices = nodes["index"]
label = paper_label[np.minimum(indices, paper_label.shape[0] - 1)]
label[nodes["type"] != 0] = 0
nodes["label"] = label
return graph._replace(nodes=nodes)
def add_nodes_embedding_from_array(graph, array):
"""Adds embeddings from the sstable_service for the indices."""
nodes = graph.nodes.copy()
indices = nodes["index"]
embedding_indices = indices.copy()
embedding_indices[nodes["type"] == 1] += NUM_PAPERS
embedding_indices[nodes["type"] == 2] += NUM_PAPERS + NUM_AUTHORS
# Gather the embeddings for the indices.
nodes["features"] = array[embedding_indices]
return graph._replace(nodes=nodes)
def get_graph_subsampling_dataset(
prefix, arrays, shuffle_indices, ratio_unlabeled_data_to_labeled_data,
max_nodes, max_edges,
**subsampler_kwargs):
"""Returns tf_dataset for online sampling."""
def generator():
labeled_indices = arrays[f"{prefix}_indices"]
if ratio_unlabeled_data_to_labeled_data > 0:
num_unlabeled_data_to_add = int(ratio_unlabeled_data_to_labeled_data *
labeled_indices.shape[0])
unlabeled_indices = np.random.choice(
NUM_PAPERS, size=num_unlabeled_data_to_add, replace=False)
root_node_indices = np.concatenate([labeled_indices, unlabeled_indices])
else:
root_node_indices = labeled_indices
if shuffle_indices:
root_node_indices = root_node_indices.copy()
np.random.shuffle(root_node_indices)
for index in root_node_indices:
graph = sub_sampler.subsample_graph(
index,
arrays["author_institution_index"],
arrays["institution_author_index"],
arrays["author_paper_index"],
arrays["paper_author_index"],
arrays["paper_paper_index"],
arrays["paper_paper_index_t"],
paper_years=arrays["paper_year"],
max_nodes=max_nodes,
max_edges=max_edges,
**subsampler_kwargs)
graph = add_nodes_label(graph, arrays["paper_label"])
graph = add_nodes_year(graph, arrays["paper_year"])
graph = tf_graphs.GraphsTuple(*graph)
yield graph
sample_graph = next(generator())
return tf.data.Dataset.from_generator(
generator,
output_signature=utils_tf.specs_from_graphs_tuple(sample_graph))
def paper_features_to_author_features(
author_paper_index, paper_features):
"""Averages paper features to authors."""
assert paper_features.shape[0] == NUM_PAPERS
assert author_paper_index.shape[0] == NUM_AUTHORS
author_features = np.zeros(
[NUM_AUTHORS, paper_features.shape[1]], dtype=paper_features.dtype)
for author_i in range(NUM_AUTHORS):
paper_indices = author_paper_index[author_i].indices
author_features[author_i] = paper_features[paper_indices].mean(
axis=0, dtype=np.float32)
if author_i % 10000 == 0:
logging.info("%d/%d", author_i, NUM_AUTHORS)
return author_features
def author_features_to_institution_features(
institution_author_index, author_features):
"""Averages author features to institutions."""
assert author_features.shape[0] == NUM_AUTHORS
assert institution_author_index.shape[0] == NUM_INSTITUTIONS
institution_features = np.zeros(
[NUM_INSTITUTIONS, author_features.shape[1]], dtype=author_features.dtype)
for institution_i in range(NUM_INSTITUTIONS):
author_indices = institution_author_index[institution_i].indices
institution_features[institution_i] = author_features[
author_indices].mean(axis=0, dtype=np.float32)
if institution_i % 10000 == 0:
logging.info("%d/%d", institution_i, NUM_INSTITUTIONS)
return institution_features
def generate_fused_paper_adjacency_matrix(neighbor_indices, neighbor_distances,
paper_paper_csr):
"""Generates fused adjacency matrix for identical nodes."""
# First construct set of identical node indices.
# NOTE: Since we take only top K=26 identical pairs for each node, this is not
# actually exhaustive. Also, if A and B are equal, and B and C are equal,
# this method would not necessarily detect A and C being equal.
# However, this should capture almost all cases.
logging.info("Generating fused paper adjacency matrix")
eps = 0.0
mask = ((neighbor_indices != np.mgrid[:neighbor_indices.shape[0], :1]) &
(neighbor_distances <= eps))
identical_pairs = list(map(tuple, np.nonzero(mask)))
del mask
# Have a csc version for fast column access.
paper_paper_csc = paper_paper_csr.tocsc()
# Construct new matrix as coo, starting off with original rows/cols.
paper_paper_coo = paper_paper_csr.tocoo()
new_rows = [paper_paper_coo.row]
new_cols = [paper_paper_coo.col]
for pair in tqdm.tqdm(identical_pairs):
# STEP ONE: First merge papers being cited by the pair.
# Add edges from second paper, to all papers cited by first paper.
cited_by_first = paper_paper_csr.getrow(pair[0]).nonzero()[1]
if cited_by_first.shape[0] > 0:
new_rows.append(pair[1] * np.ones_like(cited_by_first))
new_cols.append(cited_by_first)
# Add edges from first paper, to all papers cited by second paper.
cited_by_second = paper_paper_csr.getrow(pair[1]).nonzero()[1]
if cited_by_second.shape[0] > 0:
new_rows.append(pair[0] * np.ones_like(cited_by_second))
new_cols.append(cited_by_second)
# STEP TWO: Then merge papers that cite the pair.
# Add edges to second paper, from all papers citing the first paper.
citing_first = paper_paper_csc.getcol(pair[0]).nonzero()[0]
if citing_first.shape[0] > 0:
new_rows.append(citing_first)
new_cols.append(pair[1] * np.ones_like(citing_first))
# Add edges to first paper, from all papers citing the second paper.
citing_second = paper_paper_csc.getcol(pair[1]).nonzero()[0]
if citing_second.shape[0] > 0:
new_rows.append(citing_second)
new_cols.append(pair[0] * np.ones_like(citing_second))
logging.info("Done with adjacency loop")
paper_paper_coo_shape = paper_paper_coo.shape
del paper_paper_csr
del paper_paper_csc
del paper_paper_coo
# All done; now concatenate everything together and form new matrix.
new_rows = np.concatenate(new_rows)
new_cols = np.concatenate(new_cols)
return sp.coo_matrix(
(np.ones_like(new_rows, dtype=bool), (new_rows, new_cols)),
shape=paper_paper_coo_shape).tocsr()
def generate_k_fold_splits(
train_idx, valid_idx, output_path, num_splits=NUM_K_FOLD_SPLITS):
"""Generates splits adding fractions of the validation split to training."""
output_path = Path(output_path)
np.random.seed(42)
valid_idx = np.random.permutation(valid_idx)
# Split into `num_parts` (almost) identically sized arrays.
valid_idx_parts = np.array_split(valid_idx, num_splits)
for i in range(num_splits):
# Add all but the i'th subpart to training set.
new_train_idx = np.concatenate(
[train_idx, *valid_idx_parts[:i], *valid_idx_parts[i+1:]])
# i'th subpart is validation set.
new_valid_idx = valid_idx_parts[i]
train_path = output_path / f"train_idx_{i}_{num_splits}.npy"
valid_path = output_path / f"valid_idx_{i}_{num_splits}.npy"
np.save(train_path, new_train_idx)
np.save(valid_path, new_valid_idx)
logging.info("Saved: %s", train_path)
logging.info("Saved: %s", valid_path)
def get_train_and_valid_idx_for_split(
split_id: int,
num_splits: int,
root_path: str,
) -> Tuple[np.ndarray, np.ndarray]:
"""Returns train and valid indices for given split."""
new_train_idx = load_npy(f"{root_path}/train_idx_{split_id}_{num_splits}.npy")
new_valid_idx = load_npy(f"{root_path}/valid_idx_{split_id}_{num_splits}.npy")
return new_train_idx, new_valid_idx
def generate_fused_node_labels(neighbor_indices, neighbor_distances,
node_labels, train_indices, valid_indices,
test_indices):
"""Generates fused adjacency matrix for identical nodes."""
logging.info("Generating fused node labels")
valid_indices = set(valid_indices.tolist())
test_indices = set(test_indices.tolist())
valid_or_test_indices = valid_indices | test_indices
train_indices = train_indices[train_indices < neighbor_indices.shape[0]]
# Go through list of all pairs where one node is in training set, and
for i in tqdm.tqdm(train_indices):
for j in range(neighbor_indices.shape[1]):
other_index = neighbor_indices[i][j]
# if the other is not a validation or test node,
if other_index in valid_or_test_indices:
continue
# and they are identical,
if neighbor_distances[i][j] == 0:
# assign the label of the training node to the other node
node_labels[other_index] = node_labels[i]
return node_labels
def _pad_to_shape(
sparse_csr_matrix: sp.csr_matrix,
output_shape: Tuple[int, int]) -> sp.csr_matrix:
"""Pads a csr sparse matrix to the given shape."""
# We should not try to expand anything smaller.
assert np.all(sparse_csr_matrix.shape <= output_shape)
# Maybe it already has the right shape.
if sparse_csr_matrix.shape == output_shape:
return sparse_csr_matrix
# Append as many indptr elements as we need to match the leading size,
# This is achieved by just padding with copies of the last indptr element.
required_padding = output_shape[0] - sparse_csr_matrix.shape[0]
updated_indptr = np.concatenate(
[sparse_csr_matrix.indptr] +
[sparse_csr_matrix.indptr[-1:]] * required_padding,
axis=0)
# The change in trailing size does not have structural implications, it just
# determines the highest possible value for the indices, so it is sufficient
# to just pass the new output shape, with the correct trailing size.
return sp.csr.csr_matrix(
(sparse_csr_matrix.data,
sparse_csr_matrix.indices,
updated_indptr),
shape=output_shape)
def _fix_adjacency_shapes(
arrays: Dict[str, sp.csr.csr_matrix],
) -> Dict[str, sp.csr.csr_matrix]:
"""Fixes the shapes of the adjacency matrices."""
arrays = arrays.copy()
for key in ["author_institution_index",
"author_paper_index",
"paper_paper_index",
"institution_author_index",
"paper_author_index",
"paper_paper_index_t"]:
type_sender = key.split("_")[0]
type_receiver = key.split("_")[1]
arrays[key] = _pad_to_shape(
arrays[key], output_shape=(SIZES[type_sender], SIZES[type_receiver]))
return arrays
================================================
FILE: ogb_lsc/mag/datasets.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""MAG240M-LSC datasets."""
import threading
from typing import NamedTuple, Optional
import jax
import jraph
from ml_collections import config_dict
import numpy as np
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
# pylint: disable=g-bad-import-order
# pytype: disable=import-error
import batching_utils
import data_utils
# We only want to load these arrays once for all threads.
# `get_arrays` uses an LRU cache which is not thread safe.
LOADING_RAW_ARRAYS_LOCK = threading.Lock()
NUM_CLASSES = data_utils.NUM_CLASSES
_MAX_DEPTH_IN_SUBGRAPH = 3
class Batch(NamedTuple):
"""NamedTuple to represent batches of data."""
graph: jraph.GraphsTuple
node_labels: np.ndarray
label_mask: np.ndarray
central_node_mask: np.ndarray
node_indices: np.ndarray
absolute_node_indices: np.ndarray
def build_dataset_iterator(
data_root: str,
split: str,
dynamic_batch_size_config: config_dict.ConfigDict,
online_subsampling_kwargs: dict, # pylint: disable=g-bare-generic
debug: bool = False,
is_training: bool = True,
k_fold_split_id: Optional[int] = None,
ratio_unlabeled_data_to_labeled_data: float = 0.0,
use_all_labels_when_not_training: bool = False,
use_dummy_adjacencies: bool = False,
):
"""Returns an iterator over Batches from the dataset."""
if split == 'test':
use_all_labels_when_not_training = True
if not is_training:
ratio_unlabeled_data_to_labeled_data = 0.0
# Load the master data arrays.
with LOADING_RAW_ARRAYS_LOCK:
array_dict = data_utils.get_arrays(
data_root, k_fold_split_id=k_fold_split_id,
use_dummy_adjacencies=use_dummy_adjacencies)
node_labels = array_dict['paper_label'].reshape(-1)
train_indices = array_dict['train_indices'].astype(np.int32)
is_train_index = np.zeros(node_labels.shape[0], dtype=np.int32)
is_train_index[train_indices] = 1
valid_indices = array_dict['valid_indices'].astype(np.int32)
is_valid_index = np.zeros(node_labels.shape[0], dtype=np.int32)
is_valid_index[valid_indices] = 1
is_train_or_valid_index = is_train_index + is_valid_index
def sstable_to_intermediate_graph(graph):
indices = tf.cast(graph.nodes['index'], tf.int32)
first_index = indices[..., 0]
# Add an additional absolute index, but adding offsets to authors, and
# institution indices.
absolute_index = graph.nodes['index']
is_author = graph.nodes['type'] == 1
absolute_index = tf.where(
is_author, absolute_index + data_utils.NUM_PAPERS, absolute_index)
is_institution = graph.nodes['type'] == 2
absolute_index = tf.where(
is_institution,
absolute_index + data_utils.NUM_PAPERS + data_utils.NUM_AUTHORS,
absolute_index)
is_same_as_central_node = tf.math.equal(indices, first_index)
input_nodes = graph.nodes
graph = graph._replace(
nodes={
'one_hot_type':
tf.one_hot(tf.cast(input_nodes['type'], tf.int32), 3),
'one_hot_depth':
tf.one_hot(
tf.cast(input_nodes['depth'], tf.int32),
_MAX_DEPTH_IN_SUBGRAPH),
'year':
tf.expand_dims(input_nodes['year'], axis=-1),
'label':
tf.one_hot(
tf.cast(input_nodes['label'], tf.int32),
NUM_CLASSES),
'is_same_as_central_node':
is_same_as_central_node,
# Only first node in graph has a valid label.
'is_central_node':
tf.one_hot(0,
tf.shape(input_nodes['label'])[0]),
'index':
input_nodes['index'],
'absolute_index': absolute_index,
},
globals=tf.expand_dims(graph.globals, axis=-1),
)
return graph
ds = data_utils.get_graph_subsampling_dataset(
split,
array_dict,
shuffle_indices=is_training,
ratio_unlabeled_data_to_labeled_data=ratio_unlabeled_data_to_labeled_data,
max_nodes=dynamic_batch_size_config.n_node - 1, # Keep space for pads.
max_edges=dynamic_batch_size_config.n_edge,
**online_subsampling_kwargs)
if debug:
ds = ds.take(50)
ds = ds.map(
sstable_to_intermediate_graph,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
if is_training:
ds = ds.shard(jax.process_count(), jax.process_index())
ds = ds.shuffle(buffer_size=1 if debug else 128)
ds = ds.repeat()
ds = ds.prefetch(1 if debug else tf.data.experimental.AUTOTUNE)
np_ds = iter(tfds.as_numpy(ds))
batched_np_ds = batching_utils.dynamically_batch(
np_ds,
**dynamic_batch_size_config,
)
def intermediate_graph_to_batch(graph):
central_node_mask = graph.nodes['is_central_node']
label = graph.nodes['label']
node_indices = graph.nodes['index']
absolute_indices = graph.nodes['absolute_index']
### Construct label as a feature for non-central nodes.
# First do a lookup with node indices, with a np.minimum to ensure we do not
# index out of bounds due to num_authors being larger than num_papers.
is_same_as_central_node = graph.nodes['is_same_as_central_node']
capped_indices = np.minimum(node_indices, node_labels.shape[0] - 1)
label_as_feature = node_labels[capped_indices]
# Nodes which are not in train set should get `num_classes` label.
# Nodes in test set or non-arXiv nodes have -1 or nan labels.
# Mask out invalid labels and non-papers.
use_label_as_feature = np.logical_and(label_as_feature >= 0,
graph.nodes['one_hot_type'][..., 0])
if split == 'train' or not use_all_labels_when_not_training:
# Mask out validation papers and non-arxiv papers who
# got labels from fusing with arxiv papers.
use_label_as_feature = np.logical_and(is_train_index[capped_indices],
use_label_as_feature)
label_as_feature = np.where(use_label_as_feature, label_as_feature,
NUM_CLASSES)
# Mask out central node label in case it appears again.
label_as_feature = np.where(is_same_as_central_node, NUM_CLASSES,
label_as_feature)
# Nodes which are not papers get `NUM_CLASSES+1` label.
label_as_feature = np.where(graph.nodes['one_hot_type'][..., 0],
label_as_feature, NUM_CLASSES+1)
nodes = {
'label_as_feature': label_as_feature,
'year': graph.nodes['year'],
'bitstring_year': _get_bitstring_year_representation(
graph.nodes['year']),
'one_hot_type': graph.nodes['one_hot_type'],
'one_hot_depth': graph.nodes['one_hot_depth'],
}
graph = graph._replace(
nodes=nodes,
globals={},
)
is_train_or_valid_node = np.logical_and(
is_train_or_valid_index[capped_indices],
graph.nodes['one_hot_type'][..., 0])
if is_training:
label_mask = np.logical_and(central_node_mask, is_train_or_valid_node)
else:
# `label_mask` is used to index into valid central nodes by prediction
# calculator. Since that computation is only done when not training, and
# at that time we are guaranteed all central nodes have valid labels,
# we just set label_mask = central_node_mask when not training.
label_mask = central_node_mask
batch = Batch(
graph=graph,
node_labels=label,
central_node_mask=central_node_mask,
label_mask=label_mask,
node_indices=node_indices,
absolute_node_indices=absolute_indices)
# Transform integers into one-hots.
batch = _add_one_hot_features_to_batch(batch)
# Gather PCA features.
return _add_embeddings_to_batch(batch, array_dict['bert_pca_129'])
batch_list = []
for batch in batched_np_ds:
with jax.profiler.StepTraceAnnotation('batch_postprocessing'):
batch = intermediate_graph_to_batch(batch)
if is_training:
batch_list.append(batch)
if len(batch_list) == jax.local_device_count():
yield jax.device_put_sharded(batch_list, jax.local_devices())
batch_list = []
else:
yield batch
def _get_bitstring_year_representation(year: np.ndarray):
"""Return year as bitstring."""
min_year = 1900
max_training_year = 2018
offseted_year = np.minimum(year, max_training_year) - min_year
return np.unpackbits(offseted_year.astype(np.uint8), axis=-1)
def _np_one_hot(targets: np.ndarray, nb_classes: int):
res = np.zeros(targets.shape + (nb_classes,), dtype=np.float16)
np.put_along_axis(res, targets.astype(np.int32)[..., None], 1.0, axis=-1)
return res
def _get_one_hot_year_representation(
year: np.ndarray,
one_hot_type: np.ndarray,
):
"""Returns good representation for year."""
# Bucket edges found based on quantiles to bucket into 20 equal sized buckets.
bucket_edges = np.array([
1964, 1975, 1983, 1989, 1994, 1998, 2001, 2004,
2006, 2008, 2009, 2011, 2012, 2013, 2014, 2016,
2017, # 2018, 2019, 2020 contain last-year-of-train, eval, test nodes
])
year = np.squeeze(year, axis=-1)
year_id = np.searchsorted(bucket_edges, year)
is_paper = one_hot_type[..., 0]
bucket_id_for_non_paper = len(bucket_edges) + 1
bucket_id = np.where(is_paper, year_id, bucket_id_for_non_paper)
one_hot_year = _np_one_hot(bucket_id, len(bucket_edges) + 2)
return one_hot_year
def _add_one_hot_features_to_batch(batch: Batch) -> Batch:
"""Transforms integer features into one-hot features."""
nodes = batch.graph.nodes.copy()
nodes['one_hot_year'] = _get_one_hot_year_representation(
nodes['year'], nodes['one_hot_type'])
del nodes['year']
# NUM_CLASSES plus one category for papers for which a class is not provided
# and another for nodes that are not papers.
nodes['one_hot_label_as_feature'] = _np_one_hot(
nodes['label_as_feature'], NUM_CLASSES + 2)
del nodes['label_as_feature']
return batch._replace(graph=batch.graph._replace(nodes=nodes))
def _add_embeddings_to_batch(batch: Batch, embeddings: np.ndarray) -> Batch:
nodes = batch.graph.nodes.copy()
nodes['features'] = embeddings[batch.absolute_node_indices]
graph = batch.graph._replace(nodes=nodes)
return batch._replace(graph=graph)
================================================
FILE: ogb_lsc/mag/download_mag.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Download data required for training and evaluating models."""
import pathlib
from absl import app
from absl import flags
from absl import logging
from google.cloud import storage
# pylint: disable=g-bad-import-order
import data_utils
Path = pathlib.Path
_BUCKET_NAME = 'deepmind-ogb-lsc'
_MAX_DOWNLOAD_ATTEMPTS = 5
FLAGS = flags.FLAGS
flags.DEFINE_enum('payload', None, ['data', 'models'],
'Download "data" or "models"?')
flags.DEFINE_string('task_root', None, 'Local task root directory')
DATA_RELATIVE_PATHS = (
data_utils.RAW_NODE_YEAR_FILENAME,
data_utils.TRAIN_INDEX_FILENAME,
data_utils.VALID_INDEX_FILENAME,
data_utils.TEST_INDEX_FILENAME,
data_utils.K_FOLD_SPLITS_DIR,
data_utils.FUSED_NODE_LABELS_FILENAME,
data_utils.FUSED_PAPER_EDGES_FILENAME,
data_utils.FUSED_PAPER_EDGES_T_FILENAME,
data_utils.EDGES_AUTHOR_INSTITUTION,
data_utils.EDGES_INSTITUTION_AUTHOR,
data_utils.EDGES_AUTHOR_PAPER,
data_utils.EDGES_PAPER_AUTHOR,
data_utils.PCA_MERGED_FEATURES_FILENAME,
)
class DataCorruptionError(Exception):
pass
def _get_gcs_root():
return Path('mag') / FLAGS.payload
def _get_gcs_bucket():
storage_client = storage.Client.create_anonymous_client()
return storage_client.bucket(_BUCKET_NAME)
def _write_blob_to_destination(blob, task_root, ignore_existing=True):
"""Write the blob."""
logging.info("Copying blob: '%s'", blob.name)
destination_path = Path(task_root) / Path(*Path(blob.name).parts[1:])
logging.info(" ... to: '%s'", str(destination_path))
if ignore_existing and destination_path.exists():
return
destination_path.parent.mkdir(parents=True, exist_ok=True)
checksum = 'crc32c'
for attempt in range(_MAX_DOWNLOAD_ATTEMPTS):
try:
blob.download_to_filename(destination_path.as_posix(), checksum=checksum)
except storage.client.resumable_media.common.DataCorruption:
pass
else:
break
else:
raise DataCorruptionError(f"Checksum ('{checksum}') for {blob.name} failed "
f'after {attempt + 1} attempts')
def main(unused_argv):
bucket = _get_gcs_bucket()
if FLAGS.payload == 'data':
relative_paths = DATA_RELATIVE_PATHS
else:
relative_paths = (None,)
for relative_path in relative_paths:
if relative_path is None:
relative_path = str(_get_gcs_root())
else:
relative_path = str(_get_gcs_root() / relative_path)
logging.info("Copying relative path: '%s'", relative_path)
blobs = bucket.list_blobs(prefix=relative_path)
for blob in blobs:
_write_blob_to_destination(blob, FLAGS.task_root)
if __name__ == '__main__':
flags.mark_flag_as_required('payload')
flags.mark_flag_as_required('task_root')
app.run(main)
================================================
FILE: ogb_lsc/mag/ensemble_predictions.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Ensemble k-fold predictions and generate final submission file."""
import collections
import os
from absl import app
from absl import flags
from absl import logging
import dill
import jax
import numpy as np
from ogb import lsc
# pylint: disable=g-bad-import-order
import data_utils
import losses
_NUM_KFOLD_SPLITS = 10
FLAGS = flags.FLAGS
_DATA_ROOT = flags.DEFINE_string('data_root', None, 'Path to the data root')
_SPLIT = flags.DEFINE_enum('split', None, ['valid', 'test'], 'Data split')
_PREDICTIONS_PATH = flags.DEFINE_string(
'predictions_path', None, 'Path with the output of the k-fold models.')
_OUTPUT_PATH = flags.DEFINE_string('output_path', None, 'Output path.')
def _np_one_hot(targets: np.ndarray, nb_classes: int):
res = np.zeros(targets.shape + (nb_classes,), dtype=np.float32)
np.put_along_axis(res, targets.astype(np.int32)[..., None], 1.0, axis=-1)
return res
def ensemble_predictions(
node_idx_to_logits_list,
all_labels,
node_indices,
use_mode_break_tie_by_mean: bool = True,
):
"""Ensemble together predictions for each node and generate final predictions."""
# First, assert that each node has the same number of predictions to ensemble.
num_predictions_per_node = [
len(x) for x in node_idx_to_logits_list.values()
]
num_models = np.unique(num_predictions_per_node)
assert num_models.shape[0] == 1
num_models = num_models[0]
# Gather all logits, shape should be [num_nodes, num_models, num_classes].
all_logits = np.stack(
[np.stack(node_idx_to_logits_list[idx]) for idx in node_indices])
assert all_logits.shape == (node_indices.shape[0], num_models,
data_utils.NUM_CLASSES)
# Softmax on the final axis.
all_probs = jax.nn.softmax(all_logits, axis=-1)
# Take average across models axis to get probabilities.
mean_probs = np.mean(all_probs, axis=1)
# Assert there are no 2 equal logits for different classes.
max_logit_value = np.max(all_logits, axis=-1)
num_classes_with_max_value = (
all_logits == max_logit_value[..., None]).sum(axis=-1)
num_logit_ties = (num_classes_with_max_value > 1).sum()
if num_logit_ties:
logging.warn(
'Found %d models with the exact same logits for two of the classes. '
'`argmax` will choose the first.', num_logit_ties)
# Each model votes on one class per type.
all_votes = np.argmax(all_logits, axis=-1)
assert all_votes.shape == (node_indices.shape[0], num_models)
all_votes_one_hot = _np_one_hot(all_votes, data_utils.NUM_CLASSES)
assert all_votes_one_hot.shape == (node_indices.shape[0], num_models,
data_utils.NUM_CLASSES)
num_votes_per_class = np.sum(all_votes_one_hot, axis=1)
assert num_votes_per_class.shape == (
node_indices.shape[0], data_utils.NUM_CLASSES)
if use_mode_break_tie_by_mean:
# Slight hack, give high weight to votes (any number > 1 works really)
# and add probabilities between [0, 1] per class to tie-break only within
# classes with equal votes.
total_score = 10 * num_votes_per_class + mean_probs
else:
# Just take mean.
total_score = mean_probs
ensembled_logits = np.log(total_score)
return losses.Predictions(
node_indices=node_indices,
labels=all_labels,
logits=ensembled_logits,
predictions=np.argmax(ensembled_logits, axis=-1),
)
def load_predictions(predictions_path, split):
"""Loads set of predictions made by given XID."""
# Generate list of predictions per node.
# Note for validation each validation index is only present in exactly 1
# model of the k-fold, however for test it is present in all of them.
node_idx_to_logits_list = collections.defaultdict(list)
# For the 10 models in the ensemble.
for i in range(_NUM_KFOLD_SPLITS):
path = os.path.join(predictions_path, str(i))
# Find subdirectories.
# Directories will be something like:
# os.path.join(path, "step_104899_2021-06-14T18:20:05", "(test|valid).dill")
# So we make sure there is only one.
candidates = []
for date_str in os.listdir(path):
candidate_path = os.path.join(path, date_str, f'{split}.dill')
if os.path.exists(candidate_path):
candidates.append(candidate_path)
if not candidates:
raise ValueError(f'No {split} predictions found at {path}')
elif len(candidates) > 1:
raise ValueError(f'Found more than one {split} predictions: {candidates}')
path_for_kth_model_predictions = candidates[0]
with open(path_for_kth_model_predictions, 'rb') as f:
results = dill.load(f)
logging.info('Loaded %s', path_for_kth_model_predictions)
for (node_idx, logits) in zip(results.node_indices,
results.logits):
node_idx_to_logits_list[node_idx].append(logits)
return node_idx_to_logits_list
def generate_ensembled_predictions(
data_root: str, predictions_path: str, split: str) -> losses.Predictions:
"""Ensemble checkpoints from all WIDs in XID and generates submission file."""
array_dict = data_utils.get_arrays(
data_root=data_root,
return_pca_embeddings=False,
return_adjacencies=False)
# Load all valid and test predictions.
node_idx_to_logits_list = load_predictions(predictions_path, split)
# Assert that the indices loaded are as expected.
expected_idx = array_dict[f'{split}_indices']
idx_found = np.array(list(node_idx_to_logits_list.keys()))
assert np.all(np.sort(idx_found) == expected_idx)
if split == 'valid':
true_labels = array_dict['paper_label'][expected_idx.astype(np.int32)]
else:
# Don't know the test labels.
true_labels = np.full(expected_idx.shape, np.nan)
# Ensemble together all predictions.
return ensemble_predictions(
node_idx_to_logits_list, true_labels, expected_idx)
def evaluate_validation(valid_predictions):
evaluator = lsc.MAG240MEvaluator()
evaluator_ouput = evaluator.eval(
dict(y_pred=valid_predictions.predictions.astype(np.float64),
y_true=valid_predictions.labels))
logging.info(
'Validation accuracy as reported by MAG240MEvaluator: %s',
evaluator_ouput)
def save_test_submission_file(test_predictions, output_dir):
evaluator = lsc.MAG240MEvaluator()
evaluator.save_test_submission(
dict(y_pred=test_predictions.predictions.astype(np.float64)), output_dir)
logging.info('Test submission file generated at %s', output_dir)
def main(argv):
del argv
split = _SPLIT.value
ensembled_predictions = generate_ensembled_predictions(
data_root=_DATA_ROOT.value,
predictions_path=_PREDICTIONS_PATH.value,
split=split)
output_dir = _OUTPUT_PATH.value
os.makedirs(output_dir, exist_ok=True)
if split == 'valid':
evaluate_validation(ensembled_predictions)
elif split == 'test':
save_test_submission_file(ensembled_predictions, output_dir)
ensembled_predictions_path = os.path.join(output_dir, f'{split}.dill')
assert not os.path.exists(ensembled_predictions_path)
with open(ensembled_predictions_path, 'wb') as f:
dill.dump(ensembled_predictions, f)
logging.info(
'%s predictions stored at %s', split, ensembled_predictions_path)
if __name__ == '__main__':
app.run(main)
================================================
FILE: ogb_lsc/mag/experiment.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: disable=line-too-long
r"""MAG240M-LSC Jaxline experiment.
Usage:
```
# A path pointing to the data root.
DATA_ROOT=/tmp/mag/data
# A path for checkpoints.
CHECKPOINT_DIR=/tmp/checkpoint/
# A path for output predictions.
OUTPUT_DIR=/tmp/predictions/
# Whether we are training a model of a k_fold of models (None for no k-fold)
K_FOLD_INDEX=0
```
Some reusable arguments:
```
SHARED_ARGUMENTS="--config=ogb_lsc/mag/config.py \
--config.experiment_kwargs.config.dataset_kwargs.data_root=${DATA_ROOT} \
--config.experiment_kwargs.config.dataset_kwargs.k_fold_split_id=${K_FOLD_INDEX} \
--config.checkpoint_dir=${CHECKPOINT_DIR}"
```
Train only:
```
python -m ogb_lsc.mag.experiment \
${SHARED_ARGUMENTS} --jaxline_mode="train"
RESTORE_PATH=${CHECKPOINT_DIR}/models/latest/step_${STEP}_${TIMESTAMP}
```
Train with early stopping on a separate eval thread:
```
python -m ogb_lsc.mag.experiment \
${SHARED_ARGUMENTS} --jaxline_mode="train_eval_multithreaded"
RESTORE_PATH=${CHECKPOINT_DIR}/models/best/step_${STEP}_${TIMESTAMP}
```
Produce predictions with a pretrained model:
```
SPLIT="valid" # Or "test"
EPOCHS_TO_ENSEMBLE=50 # We used this in the submission.
python -m ogb_lsc.mag.experiment \
${SHARED_ARGUMENTS} --jaxline_mode="eval" \
--config.one_off_evaluate=True \
--config.experiment_kwargs.config.num_eval_iterations_to_ensemble=${EPOCHS_TO_ENSEMBLE} \
--config.restore_path=${RESTORE_PATH} \
--config.experiment_kwargs.config.predictions_dir=${OUTPUT_DIR} \
--config.experiment_kwargs.config.eval.split=${SPLIT}
```
Note it is also possible to pass a `restore_path` with `--jaxline_mode="train"`
and training will continue where it left off. In the case of
`--jaxline_mode="train_eval_multithreaded"` this will also work, but early
stopping will not take into account any past best performance up to that
restored model.
Other useful options:
To reduce the training batch size in case of OOM, for example for a batch size
of approximately 48 on average.
```
SHARED_ARGUMENTS="${SHARED_ARGUMENTS} \
--config.experiment_kwargs.config.training.dynamic_batch_size_config.n_node=16320 \
--config.experiment_kwargs.config.training.dynamic_batch_size_config.n_edge=34560 \
--config.experiment_kwargs.config.training.dynamic_batch_size_config.n_graph=48"
```
To reduce lead time by using dummy adjacency matrices, instead of loading the
the full ones into memory.
```
SHARED_ARGUMENTS="${SHARED_ARGUMENTS} \
--config.experiment_kwargs.config.dataset_kwargs.use_dummy_adjacencies=True"
```
"""
# pylint: enable=line-too-long
import datetime
import functools
import os
import signal
import threading
from typing import Tuple
from absl import app
from absl import flags
from absl import logging
import chex
import dill
import haiku as hk
import jax
from jax.config import config as jax_config
import jax.numpy as jnp
from jaxline import experiment
from jaxline import platform
from jaxline import utils
import jraph
from ml_collections import config_dict
import numpy as np
import optax
import tensorflow.compat.v2 as tf
# pylint: disable=g-bad-import-order
import datasets
import losses
import models
import schedules
FLAGS = flags.FLAGS
class Experiment(experiment.AbstractExperiment):
"""MAG240M-LSC Jaxline experiment."""
CHECKPOINT_ATTRS = {
'_params': 'params',
'_opt_state': 'opt_state',
'_network_state': 'network_state',
'_ema_network_state': 'ema_network_state',
'_ema_params': 'ema_params',
}
def __init__(
self,
mode: str,
init_rng: jnp.ndarray,
config: config_dict.ConfigDict,
):
"""Initializes experiment."""
super(Experiment, self).__init__(mode=mode, init_rng=init_rng)
tf.config.experimental.set_visible_devices([], device_type='GPU')
tf.config.experimental.set_visible_devices([], device_type='TPU')
if mode not in ('train', 'eval', 'train_eval_multithreaded'):
raise ValueError(f'Invalid mode {mode}.')
self.mode = mode
self.config = config
self.init_rng = init_rng
self.forward = hk.transform_with_state(self._forward_fn)
self._predictions = None
# Needed for checkpoint restore.
self._params = None
self._ema_params = None
self._network_state = None
self._ema_network_state = None
self._opt_state = None
# Track what has started.
self._training = False
self._evaluating = False
def _train_init(self):
iterator = self._build_numpy_dataset_iterator('train', is_training=True)
self._train_input = utils.py_prefetch(lambda: iterator)
dummy_batch = next(self._train_input)
if self._params is None:
self._initialize_experiment_state(self.init_rng, dummy_batch)
self._update_func = jax.pmap(
self._update_func,
axis_name='i',
donate_argnums=3,
)
self._training = True
def _eval_init(self):
split = self.config.eval.split
# Will build the iterator at each evaluation.
self._make_eval_dataset_iterator = functools.partial(
utils.py_prefetch,
lambda: self._build_numpy_dataset_iterator(split, is_training=False))
self.eval_forward = jax.jit(
functools.partial(self.forward.apply, is_training=False))
self._evaluating = True
# _ _
# | |_ _ __ __ _(_)_ __
# | __| '__/ _` | | '_ \
# | |_| | | (_| | | | | |
# \__|_| \__,_|_|_| |_|
#
def step(
self,
global_step: jnp.ndarray,
rng: jnp.ndarray,
**unused_args,
) -> losses.LogsDict:
"""See Jaxline base class."""
if not self._training:
self._train_init()
with jax.profiler.StepTraceAnnotation('next_train_input'):
batch = next(self._train_input)
with jax.profiler.StepTraceAnnotation('update_step'):
(self._params, self._ema_params, self._network_state,
self._ema_network_state, self._opt_state, stats) = self._update_func(
self._params,
self._ema_params,
self._network_state,
self._ema_network_state,
self._opt_state,
global_step,
rng,
batch,
)
del batch # Buffers donated to _update_func.
with jax.profiler.StepTraceAnnotation('get_stats'):
stats = utils.get_first(stats)
return stats
def _build_numpy_dataset_iterator(self, split: str, is_training: bool):
if is_training:
dynamic_batch_size_config = self.config.training.dynamic_batch_size_config
else:
dynamic_batch_size_config = self.config.eval.dynamic_batch_size_config
return datasets.build_dataset_iterator(
split=split,
dynamic_batch_size_config=dynamic_batch_size_config,
debug=self.config.debug,
is_training=is_training,
**self.config.dataset_kwargs)
def _initialize_experiment_state(
self,
init_rng: jnp.ndarray,
dummy_batch: datasets.Batch,
):
"""Initialize parameters and opt state if not restoring from checkpoint."""
dummy_graph = dummy_batch.graph
# Cast features to float32 so that parameters are as appropriate.
dummy_graph = dummy_graph._replace(
nodes=jax.tree_map(lambda x: x.astype(np.float32), dummy_graph.nodes),
edges=jax.tree_map(lambda x: x.astype(np.float32), dummy_graph.edges),
)
init_key = utils.bcast_local_devices(init_rng)
p_init = jax.pmap(functools.partial(self.forward.init, is_training=True))
params, network_state = p_init(init_key, dummy_graph)
opt_init, _ = self._optimizer(
utils.bcast_local_devices(jnp.zeros([], jnp.int32)))
opt_state = jax.pmap(opt_init)(params)
# For EMA decay to work correctly, params/state must be floats.
chex.assert_type(jax.tree_leaves(params), jnp.floating)
chex.assert_type(jax.tree_leaves(network_state), jnp.floating)
self._params = params
self._ema_params = params
self._network_state = network_state
self._ema_network_state = network_state
self._opt_state = opt_state
def _get_learning_rate(self, global_step: jnp.ndarray) -> jnp.ndarray:
return schedules.learning_schedule(
global_step,
**self.config.optimizer.learning_rate_schedule,
)
def _optimizer(
self,
learning_rate: jnp.ndarray,
) -> optax.GradientTransformation:
optimizer_fn = getattr(optax, self.config.optimizer.name)
return optimizer_fn(
learning_rate=learning_rate,
**self.config.optimizer.kwargs,
)
def _forward_fn(
self,
input_graph: jraph.GraphsTuple,
is_training: bool,
stop_gradient_embedding_to_logits: bool = False,
):
model = models.NodePropertyEncodeProcessDecode(
num_classes=datasets.NUM_CLASSES,
**self.config.model_config,
)
return model(input_graph, is_training, stop_gradient_embedding_to_logits)
def _bgrl_loss(
self,
params: hk.Params,
ema_params: hk.Params,
network_state: hk.State,
ema_network_state: hk.State,
rng: jnp.ndarray,
batch: datasets.Batch,
) -> Tuple[jnp.ndarray, Tuple[losses.LogsDict, hk.State]]:
"""Computes fully supervised loss."""
# First compute 2 graph corrupted views.
first_corruption_key, second_corruption_key, rng = jax.random.split(rng, 3)
(first_model_key, first_model_key_ema, second_model_key,
second_model_key_ema, rng) = jax.random.split(rng, 5)
first_corrupted_graph = losses.get_corrupted_view(
batch.graph,
rng_key=first_corruption_key,
**self.config.training.loss_config.bgrl_loss_config.first_graph_corruption_config, # pylint:disable=line-too-long
)
second_corrupted_graph = losses.get_corrupted_view(
batch.graph,
rng_key=second_corruption_key,
**self.config.training.loss_config.bgrl_loss_config.second_graph_corruption_config, # pylint:disable=line-too-long
)
# Then run the model on both.
first_corrupted_output, _ = self.forward.apply(
params,
network_state,
first_model_key,
first_corrupted_graph,
is_training=True,
stop_gradient_embedding_to_logits=True,
)
second_corrupted_output, _ = self.forward.apply(
params,
network_state,
second_model_key,
second_corrupted_graph,
is_training=True,
stop_gradient_embedding_to_logits=True,
)
first_corrupted_output_ema, _ = self.forward.apply(
ema_params,
ema_network_state,
first_model_key_ema,
first_corrupted_graph,
is_training=True,
stop_gradient_embedding_to_logits=True,
)
second_corrupted_output_ema, _ = self.forward.apply(
ema_params,
ema_network_state,
second_model_key_ema,
second_corrupted_graph,
is_training=True,
stop_gradient_embedding_to_logits=True,
)
# These also contain projections for non-central nodes; remove them.
num_nodes_per_graph = batch.graph.n_node
node_central_indices = jnp.concatenate(
[jnp.array([0]), jnp.cumsum(num_nodes_per_graph[:-1])])
bgrl_loss, bgrl_stats = losses.bgrl_loss(
first_online_predictions=first_corrupted_output
.node_projection_predictions[node_central_indices],
second_target_projections=second_corrupted_output_ema
.node_embedding_projections[node_central_indices],
second_online_predictions=second_corrupted_output
.node_projection_predictions[node_central_indices],
first_target_projections=first_corrupted_output_ema
.node_embedding_projections[node_central_indices],
symmetrize=self.config.training.loss_config.bgrl_loss_config.symmetrize,
valid_mask=batch.central_node_mask[node_central_indices],
)
# Finally train decoder on original graph with optional stop gradient.
stop_gradient = (
self.config.training.loss_config.bgrl_loss_config
.stop_gradient_for_supervised_loss)
model_output, new_network_state = self.forward.apply(
params,
network_state,
rng,
batch.graph,
is_training=True,
stop_gradient_embedding_to_logits=stop_gradient,
)
supervised_loss, supervised_stats = losses.node_classification_loss(
model_output.node_logits,
batch,
)
stats = dict(**supervised_stats, **bgrl_stats)
total_loss = (
supervised_loss +
self.config.training.loss_config.bgrl_loss_config.bgrl_loss_scale *
bgrl_loss)
return total_loss, (stats, new_network_state)
def _loss(
self,
params: hk.Params,
ema_params: hk.Params,
network_state: hk.State,
ema_network_state: hk.State,
rng: jnp.ndarray,
batch: datasets.Batch,
) -> Tuple[jnp.ndarray, Tuple[losses.LogsDict, hk.State]]:
"""Compute loss from params and batch."""
# Cast to float32 since some losses are unstable with float16.
graph = batch.graph._replace(
nodes=jax.tree_map(lambda x: x.astype(jnp.float32), batch.graph.nodes),
edges=jax.tree_map(lambda x: x.astype(jnp.float32), batch.graph.edges),
)
batch = batch._replace(graph=graph)
return self._bgrl_loss(params, ema_params, network_state, ema_network_state,
rng, batch)
def _update_func(
self,
params: hk.Params,
ema_params: hk.Params,
network_state: hk.State,
ema_network_state: hk.State,
opt_state: optax.OptState,
global_step: jnp.ndarray,
rng: jnp.ndarray,
batch: datasets.Batch,
) -> Tuple[hk.Params, hk.Params, hk.State, hk.State, optax.OptState,
losses.LogsDict]:
"""Updates parameters."""
grad_fn = jax.value_and_grad(self._loss, has_aux=True)
(_, (stats, new_network_state)), grads = grad_fn(
params,
ema_params,
network_state,
ema_network_state,
rng,
batch)
learning_rate = self._get_learning_rate(global_step)
_, opt_apply = self._optimizer(learning_rate)
grad = jax.lax.pmean(grads, axis_name='i')
updates, opt_state = opt_apply(grad, opt_state, params)
params = optax.apply_updates(params, updates)
# Stats and logging.
param_norm = optax.global_norm(params)
grad_norm = optax.global_norm(grad)
ema_rate = schedules.ema_decay_schedule(
step=global_step, **self.config.eval.ema_annealing_schedule)
num_non_padded_nodes = (
batch.graph.n_node.sum() -
jraph.get_number_of_padding_with_graphs_nodes(batch.graph))
num_non_padded_edges = (
batch.graph.n_edge.sum() -
jraph.get_number_of_padding_with_graphs_edges(batch.graph))
num_non_padded_graphs = (
batch.graph.n_node.shape[0] -
jraph.get_number_of_padding_with_graphs_graphs(batch.graph))
avg_num_nodes = num_non_padded_nodes / num_non_padded_graphs
avg_num_edges = num_non_padded_edges / num_non_padded_graphs
stats.update(
dict(
global_step=global_step,
grad_norm=grad_norm,
param_norm=param_norm,
learning_rate=learning_rate,
ema_rate=ema_rate,
avg_num_nodes=avg_num_nodes,
avg_num_edges=avg_num_edges,
))
ema_fn = (lambda x, y: # pylint:disable=g-long-lambda
schedules.apply_ema_decay(x, y, ema_rate))
ema_params = jax.tree_map(ema_fn, ema_params, params)
ema_network_state = jax.tree_map(
ema_fn,
ema_network_state,
network_state,
)
return (params, ema_params, new_network_state, ema_network_state, opt_state,
stats)
# _
# _____ ____ _| |
# / _ \ \ / / _` | |
# | __/\ V / (_| | |
# \___| \_/ \__,_|_|
#
def evaluate(self, global_step, rng, **unused_kwargs):
"""See base class."""
if not self._evaluating:
self._eval_init()
global_step = np.array(utils.get_first(global_step))
ema_params = utils.get_first(self._ema_params)
ema_network_state = utils.get_first(self._ema_network_state)
rng = utils.get_first(rng)
# Evaluate using the ema params.
results, predictions = self._evaluate_with_ensemble(ema_params,
ema_network_state, rng)
results['global_step'] = global_step
# Store predictions if we got a path.
self._maybe_save_predictions(predictions, global_step)
return results
def _evaluate_with_ensemble(
self,
params: hk.Params,
state: hk.State,
rng: jnp.ndarray,
):
predictions_for_ensemble = []
num_iterations = self.config.num_eval_iterations_to_ensemble
for iteration in range(num_iterations):
results, predictions = self._evaluate_params(params, state, rng)
self._log_results(f'Eval iteration {iteration}/{num_iterations}', results)
predictions_for_ensemble.append(predictions)
if len(predictions_for_ensemble) > 1:
predictions = losses.ensemble_predictions_by_probability_average(
predictions_for_ensemble)
results = losses.get_accuracy_dict(predictions)
self._log_results(f'Ensembled {num_iterations} iterations', results)
return results, predictions
def _maybe_save_predictions(self, predictions, global_step):
if not self.config.predictions_dir:
return
split = self.config.eval.split
output_dir = os.path.join(
self.config.predictions_dir, _get_step_date_label(global_step))
os.makedirs(output_dir, exist_ok=True)
output_path = os.path.join(output_dir, split + '.dill')
with open(output_path, 'wb') as f:
dill.dump(predictions, f)
logging.info('Saved %s predictions at: %s', split, output_path)
def _evaluate_params(
self,
params: hk.Params,
state: hk.State,
rng: jnp.ndarray,
):
"""Evaluate given set of parameters."""
num_valid = 0
predictions_list = []
labels_list = []
logits_list = []
indices_list = []
for i, batch in enumerate(self._make_eval_dataset_iterator()):
model_output, _ = self.eval_forward(
params,
state,
rng,
batch.graph,
)
(masked_indices,
masked_predictions,
masked_labels,
masked_logits) = losses.get_predictions_labels_and_logits(
model_output.node_logits, batch)
predictions_list.append(masked_predictions)
indices_list.append(masked_indices)
labels_list.append(masked_labels)
logits_list.append(masked_logits)
num_valid += jnp.sum(batch.label_mask)
if i % 10 == 0:
logging.info('Generate predictons for %d batches so far', i + 1)
predictions = losses.Predictions(
np.concatenate(indices_list, axis=0),
np.concatenate(labels_list, axis=0),
np.concatenate(predictions_list, axis=0),
np.concatenate(logits_list, axis=0))
if self.config.eval.split == 'test':
results = dict(num_valid=num_valid, accuracy=np.nan)
else:
results = losses.get_accuracy_dict(predictions)
return results, predictions
def _log_results(self, prefix, results):
logging_str = ', '.join(
['{}={:.4f}'.format(k, float(results[k]))
for k in sorted(results.keys())])
logging.info('%s: %s', prefix, logging_str)
def _restore_state_to_in_memory_checkpointer(restore_path):
"""Initializes experiment state from a checkpoint."""
# Load pretrained experiment state.
python_state_path = os.path.join(restore_path, 'checkpoint.dill')
with open(python_state_path, 'rb') as f:
pretrained_state = dill.load(f)
logging.info('Restored checkpoint from %s', python_state_path)
# Assign state to a dummy experiment instance for the in-memory checkpointer,
# broadcasting to devices.
dummy_experiment = Experiment(
mode='train', init_rng=0, config=FLAGS.config.experiment_kwargs.config)
for attribute, key in Experiment.CHECKPOINT_ATTRS.items():
setattr(dummy_experiment, attribute,
utils.bcast_local_devices(pretrained_state[key]))
jaxline_state = dict(
global_step=pretrained_state['global_step'],
experiment_module=dummy_experiment)
snapshot = utils.SnapshotNT(0, jaxline_state)
# Finally, seed the jaxline `utils.InMemoryCheckpointer` global dict.
utils.GLOBAL_CHECKPOINT_DICT['latest'] = utils.CheckpointNT(
threading.local(), [snapshot])
def _get_step_date_label(global_step):
# Date removing microseconds.
date_str = datetime.datetime.now().isoformat().split('.')[0]
return f'step_{global_step}_{date_str}'
def _save_state_from_in_memory_checkpointer(
save_path, experiment_class: experiment.AbstractExperiment):
"""Saves experiment state to a checkpoint."""
logging.info('Saving model.')
for checkpoint_name, checkpoint in utils.GLOBAL_CHECKPOINT_DICT.items():
if not checkpoint.history:
logging.info('Nothing to save in "%s"', checkpoint_name)
continue
pickle_nest = checkpoint.history[-1].pickle_nest
global_step = pickle_nest['global_step']
state_dict = {'global_step': global_step}
for attribute, key in experiment_class.CHECKPOINT_ATTRS.items():
state_dict[key] = utils.get_first(
getattr(pickle_nest['experiment_module'], attribute))
save_dir = os.path.join(
save_path, checkpoint_name, _get_step_date_label(global_step))
python_state_path = os.path.join(save_dir, 'checkpoint.dill')
os.makedirs(save_dir, exist_ok=True)
with open(python_state_path, 'wb') as f:
dill.dump(state_dict, f)
logging.info(
'Saved "%s" checkpoint to %s', checkpoint_name, python_state_path)
def _setup_signals(save_model_fn):
"""Sets up a signal for model saving."""
# Save a model on Ctrl+C.
def sigint_handler(unused_sig, unused_frame):
# Ideally, rather than saving immediately, we would then "wait" for a good
# time to save. In practice this reads from an in-memory checkpoint that
# only saves every 30 seconds or so, so chances of race conditions are very
# small.
save_model_fn()
logging.info(r'Use `Ctrl+\` to save and exit.')
# Exit on `Ctrl+\`, saving a model.
prev_sigquit_handler = signal.getsignal(signal.SIGQUIT)
def sigquit_handler(unused_sig, unused_frame):
# Restore previous handler early, just in case something goes wrong in the
# next lines, so it is possible to press again and exit.
signal.signal(signal.SIGQUIT, prev_sigquit_handler)
save_model_fn()
logging.info(r'Exiting on `Ctrl+\`')
# Re-raise for clean exit.
os.kill(os.getpid(), signal.SIGQUIT)
signal.signal(signal.SIGINT, sigint_handler)
signal.signal(signal.SIGQUIT, sigquit_handler)
def main(argv, experiment_class: experiment.AbstractExperiment):
# Maybe restore a model.
restore_path = FLAGS.config.restore_path
if restore_path:
_restore_state_to_in_memory_checkpointer(restore_path)
# Maybe save a model.
save_dir = os.path.join(FLAGS.config.checkpoint_dir, 'models')
if FLAGS.config.one_off_evaluate:
save_model_fn = lambda: None # No need to save checkpoint in this case.
else:
save_model_fn = functools.partial(
_save_state_from_in_memory_checkpointer, save_dir, experiment_class)
_setup_signals(save_model_fn) # Save on Ctrl+C (continue) or Ctrl+\ (exit).
try:
platform.main(experiment_class, argv)
finally:
save_model_fn() # Save at the end of training or in case of exception.
if __name__ == '__main__':
jax_config.update('jax_debug_nans', False)
flags.mark_flag_as_required('config')
app.run(lambda argv: main(argv, Experiment))
================================================
FILE: ogb_lsc/mag/generate_validation_splits.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Generates the k-fold validation splits."""
import os
from absl import app
from absl import flags
import data_utils
_DATA_ROOT = flags.DEFINE_string(
'data_root', None, required=True,
help='Path containing the downloaded data.')
_OUTPUT_DIR = flags.DEFINE_string(
'output_dir', None, required=True,
help='Output directory to write the splits to')
def main(argv):
del argv
array_dict = data_utils.get_arrays(
data_root=_DATA_ROOT.value,
return_pca_embeddings=False,
return_adjacencies=False)
os.makedirs(_OUTPUT_DIR.value, exist_ok=True)
data_utils.generate_k_fold_splits(
train_idx=array_dict['train_indices'],
valid_idx=array_dict['valid_indices'],
output_path=_OUTPUT_DIR.value,
num_splits=data_utils.NUM_K_FOLD_SPLITS)
if __name__ == '__main__':
app.run(main)
================================================
FILE: ogb_lsc/mag/losses.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Losses and related utilities."""
from typing import Mapping, Tuple, Sequence, NamedTuple, Dict, Optional
import jax
import jax.numpy as jnp
import jraph
import numpy as np
# pylint: disable=g-bad-import-order
import datasets
LogsDict = Mapping[str, jnp.ndarray]
class Predictions(NamedTuple):
node_indices: np.ndarray
labels: np.ndarray
predictions: np.ndarray
logits: np.ndarray
def node_classification_loss(
logits: jnp.ndarray,
batch: datasets.Batch,
extra_stats: bool = False,
) -> Tuple[jnp.ndarray, LogsDict]:
"""Gets node-wise classification loss and statistics."""
log_probs = jax.nn.log_softmax(logits)
loss = -jnp.sum(log_probs * batch.node_labels, axis=-1)
num_valid = jnp.sum(batch.label_mask)
labels = jnp.argmax(batch.node_labels, axis=-1)
is_correct = (jnp.argmax(log_probs, axis=-1) == labels)
num_correct = jnp.sum(is_correct * batch.label_mask)
loss = jnp.sum(loss * batch.label_mask) / (num_valid + 1e-8)
accuracy = num_correct / (num_valid + 1e-8)
entropy = -jnp.mean(jnp.sum(jax.nn.softmax(logits) * log_probs, axis=-1))
stats = {
'classification_loss': loss,
'prediction_entropy': entropy,
'accuracy': accuracy,
'num_valid': num_valid,
'num_correct': num_correct,
}
if extra_stats:
for k in range(1, 6):
stats[f'top_{k}_correct'] = topk_correct(logits, labels,
batch.label_mask, k)
return loss, stats
def get_predictions_labels_and_logits(
logits: jnp.ndarray,
batch: datasets.Batch,
) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray, jnp.ndarray]:
"""Gets prediction labels and logits."""
mask = batch.label_mask > 0.
indices = batch.node_indices[mask]
logits = logits[mask]
predictions = jnp.argmax(logits, axis=-1)
labels = jnp.argmax(batch.node_labels[mask], axis=-1)
return indices, predictions, labels, logits
def topk_correct(
logits: jnp.ndarray,
labels: jnp.ndarray,
valid_mask: jnp.ndarray,
topk: int,
) -> jnp.ndarray:
"""Calculates top-k accuracy."""
pred_ranking = jnp.argsort(logits, axis=1)[:, ::-1]
pred_ranking = pred_ranking[:, :topk]
is_correct = jnp.any(pred_ranking == labels[:, jnp.newaxis], axis=1)
return (is_correct * valid_mask).sum()
def ensemble_predictions_by_probability_average(
predictions_list: Sequence[Predictions]) -> Predictions:
"""Ensemble predictions by ensembling the probabilities."""
_assert_consistent_predictions(predictions_list)
all_probs = np.stack([
jax.nn.softmax(predictions.logits, axis=-1)
for predictions in predictions_list
],
axis=0)
ensembled_logits = np.log(all_probs.mean(0))
return predictions_list[0]._replace(
logits=ensembled_logits, predictions=np.argmax(ensembled_logits, axis=-1))
def get_accuracy_dict(predictions: Predictions) -> Dict[str, float]:
"""Returns the accuracy dict."""
output_dict = {}
output_dict['num_valid'] = predictions.predictions.shape[0]
matches = (predictions.labels == predictions.predictions)
output_dict['accuracy'] = matches.mean()
pred_ranking = jnp.argsort(predictions.logits, axis=1)[:, ::-1]
for k in range(1, 6):
matches = jnp.any(
pred_ranking[:, :k] == predictions.labels[:, None], axis=1)
output_dict[f'top_{k}_correct'] = matches.mean()
return output_dict
def bgrl_loss(
first_online_predictions: jnp.ndarray,
second_target_projections: jnp.ndarray,
second_online_predictions: jnp.ndarray,
first_target_projections: jnp.ndarray,
symmetrize: bool,
valid_mask: jnp.ndarray,
) -> Tuple[jnp.ndarray, LogsDict]:
"""Implements BGRL loss."""
first_side_node_loss = jnp.sum(
jnp.square(
_l2_normalize(first_online_predictions, axis=-1) -
_l2_normalize(second_target_projections, axis=-1)),
axis=-1)
if symmetrize:
second_side_node_loss = jnp.sum(
jnp.square(
_l2_normalize(second_online_predictions, axis=-1) -
_l2_normalize(first_target_projections, axis=-1)),
axis=-1)
node_loss = first_side_node_loss + second_side_node_loss
else:
node_loss = first_side_node_loss
loss = (node_loss * valid_mask).sum() / (valid_mask.sum() + 1e-6)
return loss, dict(bgrl_loss=loss)
def get_corrupted_view(
graph: jraph.GraphsTuple,
feature_drop_prob: float,
edge_drop_prob: float,
rng_key: jnp.ndarray,
) -> jraph.GraphsTuple:
"""Returns corrupted graph view."""
node_key, edge_key = jax.random.split(rng_key)
def mask_feature(x):
mask = jax.random.bernoulli(node_key, 1 - feature_drop_prob, x.shape)
return x * mask
# Randomly mask features with fixed probability.
nodes = jax.tree_map(mask_feature, graph.nodes)
# Simulate dropping of edges by changing genuine edges to self-loops on
# the padded node.
num_edges = graph.senders.shape[0]
last_node_idx = graph.n_node.sum() - 1
edge_mask = jax.random.bernoulli(edge_key, 1 - edge_drop_prob, [num_edges])
senders = jnp.where(edge_mask, graph.senders, last_node_idx)
receivers = jnp.where(edge_mask, graph.receivers, last_node_idx)
# Note that n_edge will now be invalid since edges in the middle of the list
# will correspond to the final graph. Set n_edge to None to ensure we do not
# accidentally use this.
return graph._replace(
nodes=nodes,
senders=senders,
receivers=receivers,
n_edge=None,
)
def _assert_consistent_predictions(predictions_list: Sequence[Predictions]):
first_predictions = predictions_list[0]
for predictions in predictions_list:
assert np.all(predictions.node_indices == first_predictions.node_indices)
assert np.all(predictions.labels == first_predictions.labels)
assert np.all(
predictions.predictions == np.argmax(predictions.logits, axis=-1))
def _l2_normalize(
x: jnp.ndarray,
axis: Optional[int] = None,
epsilon: float = 1e-6,
) -> jnp.ndarray:
return x * jax.lax.rsqrt(
jnp.sum(jnp.square(x), axis=axis, keepdims=True) + epsilon)
================================================
FILE: ogb_lsc/mag/models.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""MAG240M-LSC models."""
from typing import Callable, NamedTuple, Sequence
import haiku as hk
import jax
import jax.numpy as jnp
import jraph
_REDUCER_NAMES = {
'sum':
jax.ops.segment_sum,
'mean':
jraph.segment_mean,
'softmax':
jraph.segment_softmax,
}
class ModelOutput(NamedTuple):
node_embeddings: jnp.ndarray
node_embedding_projections: jnp.ndarray
node_projection_predictions: jnp.ndarray
node_logits: jnp.ndarray
def build_update_fn(
name: str,
output_sizes: Sequence[int],
activation: Callable[[jnp.ndarray], jnp.ndarray],
normalization_type: str,
is_training: bool,
):
"""Builds update function."""
def single_mlp(inner_name: str):
"""Creates a single MLP performing the update."""
mlp = hk.nets.MLP(
output_sizes=output_sizes,
name=inner_name,
activation=activation)
mlp = jraph.concatenated_args(mlp)
if normalization_type == 'layer_norm':
norm = hk.LayerNorm(
axis=-1,
create_scale=True,
create_offset=True,
name=name + '_layer_norm')
elif normalization_type == 'batch_norm':
batch_norm = hk.BatchNorm(
create_scale=True,
create_offset=True,
decay_rate=0.9,
name=f'{inner_name}_batch_norm',
cross_replica_axis=None if hk.running_init() else 'i',
)
norm = lambda x: batch_norm(x, is_training)
elif normalization_type == 'none':
return mlp
else:
raise ValueError(f'Unknown normalization type {normalization_type}')
return jraph.concatenated_args(hk.Sequential([mlp, norm]))
return single_mlp(f'{name}_homogeneous')
def build_gn(
output_sizes: Sequence[int],
activation: Callable[[jnp.ndarray], jnp.ndarray],
suffix: str,
use_sent_edges: bool,
is_training: bool,
dropedge_rate: float,
normalization_type: str,
aggregation_function: str,
):
"""Builds an InteractionNetwork with MLP update functions."""
node_update_fn = build_update_fn(
f'node_processor_{suffix}',
output_sizes,
activation=activation,
normalization_type=normalization_type,
is_training=is_training,
)
edge_update_fn = build_update_fn(
f'edge_processor_{suffix}',
output_sizes,
activation=activation,
normalization_type=normalization_type,
is_training=is_training,
)
def maybe_dropedge(x):
"""Dropout on edge messages."""
if not is_training:
return x
return x * hk.dropout(
hk.next_rng_key(),
dropedge_rate,
jnp.ones([x.shape[0], 1]),
)
dropped_edge_update_fn = lambda *args: maybe_dropedge(edge_update_fn(*args))
return jraph.InteractionNetwork(
update_edge_fn=dropped_edge_update_fn,
update_node_fn=node_update_fn,
aggregate_edges_for_nodes_fn=_REDUCER_NAMES[aggregation_function],
include_sent_messages_in_node_update=use_sent_edges,
)
def _get_activation_fn(name: str) -> Callable[[jnp.ndarray], jnp.ndarray]:
if name == 'identity':
return lambda x: x
if hasattr(jax.nn, name):
return getattr(jax.nn, name)
raise ValueError('Unknown activation function %s specified. '
'See https://jax.readthedocs.io/en/latest/jax.nn.html'
'for the list of supported function names.')
class NodePropertyEncodeProcessDecode(hk.Module):
"""Node Property Prediction Encode Process Decode Model."""
def __init__(
self,
mlp_hidden_sizes: Sequence[int],
latent_size: int,
num_classes: int,
num_message_passing_steps: int = 2,
activation: str = 'relu',
dropout_rate: float = 0.0,
dropedge_rate: float = 0.0,
use_sent_edges: bool = False,
disable_edge_updates: bool = False,
normalization_type: str = 'layer_norm',
aggregation_function: str = 'sum',
name='NodePropertyEncodeProcessDecode',
):
super().__init__(name=name)
self._num_classes = num_classes
self._latent_size = latent_size
self._output_sizes = list(mlp_hidden_sizes) + [latent_size]
self._num_message_passing_steps = num_message_passing_steps
self._activation = _get_activation_fn(activation)
self._dropout_rate = dropout_rate
self._dropedge_rate = dropedge_rate
self._use_sent_edges = use_sent_edges
self._disable_edge_updates = disable_edge_updates
self._normalization_type = normalization_type
self._aggregation_function = aggregation_function
def _dropout_graph(self, graph: jraph.GraphsTuple) -> jraph.GraphsTuple:
node_key, edge_key = hk.next_rng_keys(2)
nodes = hk.dropout(node_key, self._dropout_rate, graph.nodes)
edges = graph.edges
if not self._disable_edge_updates:
edges = hk.dropout(edge_key, self._dropout_rate, edges)
return graph._replace(nodes=nodes, edges=edges)
def _encode(
self,
graph: jraph.GraphsTuple,
is_training: bool,
) -> jraph.GraphsTuple:
node_embed_fn = build_update_fn(
'node_encoder',
self._output_sizes,
activation=self._activation,
normalization_type=self._normalization_type,
is_training=is_training,
)
edge_embed_fn = build_update_fn(
'edge_encoder',
self._output_sizes,
activation=self._activation,
normalization_type=self._normalization_type,
is_training=is_training,
)
gn = jraph.GraphMapFeatures(edge_embed_fn, node_embed_fn)
graph = gn(graph)
if is_training:
graph = self._dropout_graph(graph)
return graph
def _process(
self,
graph: jraph.GraphsTuple,
is_training: bool,
) -> jraph.GraphsTuple:
for idx in range(self._num_message_passing_steps):
net = build_gn(
output_sizes=self._output_sizes,
activation=self._activation,
suffix=str(idx),
use_sent_edges=self._use_sent_edges,
is_training=is_training,
dropedge_rate=self._dropedge_rate,
normalization_type=self._normalization_type,
aggregation_function=self._aggregation_function)
residual_graph = net(graph)
graph = graph._replace(nodes=graph.nodes + residual_graph.nodes)
if not self._disable_edge_updates:
graph = graph._replace(edges=graph.edges + residual_graph.edges)
if is_training:
graph = self._dropout_graph(graph)
return graph
def _node_mlp(
self,
graph: jraph.GraphsTuple,
is_training: bool,
output_size: int,
name: str,
) -> jnp.ndarray:
decoder_sizes = list(self._output_sizes[:-1]) + [output_size]
net = build_update_fn(
name,
decoder_sizes,
self._activation,
normalization_type=self._normalization_type,
is_training=is_training,
)
return net(graph.nodes)
def __call__(
self,
graph: jraph.GraphsTuple,
is_training: bool,
stop_gradient_embedding_to_logits: bool = False,
) -> ModelOutput:
# Note that these update configs may need to change if
# we switch back to GraphNetwork rather than InteractionNetwork.
graph = self._encode(graph, is_training)
graph = self._process(graph, is_training)
node_embeddings = graph.nodes
node_projections = self._node_mlp(graph, is_training, self._latent_size,
'projector')
node_predictions = self._node_mlp(
graph._replace(nodes=node_projections),
is_training,
self._latent_size,
'predictor',
)
if stop_gradient_embedding_to_logits:
graph = jax.tree_map(jax.lax.stop_gradient, graph)
node_logits = self._node_mlp(graph, is_training, self._num_classes,
'logits_decoder')
return ModelOutput(
node_embeddings=node_embeddings,
node_logits=node_logits,
node_embedding_projections=node_projections,
node_projection_predictions=node_predictions,
)
================================================
FILE: ogb_lsc/mag/neighbor_builder.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Find neighborhoods around paper feature embeddings."""
import pathlib
from absl import app
from absl import flags
from absl import logging
import annoy
import numpy as np
import scipy.sparse as sp
# pylint: disable=g-bad-import-order
import data_utils
Path = pathlib.Path
_PAPER_PAPER_B_PATH = 'ogb_mag_adjacencies/paper_paper_b.npz'
FLAGS = flags.FLAGS
flags.DEFINE_string('data_root', None, 'Data root directory')
def _read_paper_pca_features():
data_root = Path(FLAGS.data_root)
path = data_root / data_utils.PCA_PAPER_FEATURES_FILENAME
with open(path, 'rb') as fid:
return np.load(fid)
def _read_adjacency_indices():
# Get adjacencies.
return data_utils.get_arrays(
data_root=FLAGS.data_root,
use_fused_node_labels=False,
use_fused_node_adjacencies=False,
return_pca_embeddings=False,
)
def build_annoy_index(features):
"""Build the Annoy index."""
logging.info('Building annoy index')
num_vectors, vector_size = features.shape
annoy_index = annoy.AnnoyIndex(vector_size, 'euclidean')
for i, x in enumerate(features):
annoy_index.add_item(i, x)
if i % 1000000 == 0:
logging.info('Adding: %d / %d (%.3g %%)', i, num_vectors,
100 * i / num_vectors)
n_trees = 10
_ = annoy_index.build(n_trees)
return annoy_index
def _get_annoy_index_path():
return Path(FLAGS.data_root) / data_utils.PREPROCESSED_DIR / 'annoy_index.ann'
def save_annoy_index(annoy_index):
logging.info('Saving annoy index')
index_path = _get_annoy_index_path()
index_path.parent.mkdir(parents=True, exist_ok=True)
annoy_index.save(str(index_path))
def read_annoy_index(features):
index_path = _get_annoy_index_path()
vector_size = features.shape[1]
annoy_index = annoy.AnnoyIndex(vector_size, 'euclidean')
annoy_index.load(str(index_path))
return annoy_index
def compute_neighbor_indices_and_distances(features):
"""Use the pre-built Annoy index to compute neighbor indices and distances."""
logging.info('Computing neighbors and distances')
annoy_index = read_annoy_index(features)
num_vectors = features.shape[0]
k = 20
pad_k = 5
search_k = -1
neighbor_indices = np.zeros([num_vectors, k + pad_k + 1], dtype=np.int32)
neighbor_distances = np.zeros([num_vectors, k + pad_k + 1], dtype=np.float32)
for i in range(num_vectors):
neighbor_indices[i], neighbor_distances[i] = annoy_index.get_nns_by_item(
i, k + pad_k + 1, search_k=search_k, include_distances=True)
if i % 10000 == 0:
logging.info('Finding neighbors %d / %d', i, num_vectors)
return neighbor_indices, neighbor_distances
def _write_neighbors(neighbor_indices, neighbor_distances):
"""Write neighbor indices and distances."""
logging.info('Writing neighbors')
indices_path = Path(FLAGS.data_root) / data_utils.NEIGHBOR_INDICES_FILENAME
distances_path = (
Path(FLAGS.data_root) / data_utils.NEIGHBOR_DISTANCES_FILENAME)
indices_path.parent.mkdir(parents=True, exist_ok=True)
distances_path.parent.mkdir(parents=True, exist_ok=True)
with open(indices_path, 'wb') as fid:
np.save(fid, neighbor_indices)
with open(distances_path, 'wb') as fid:
np.save(fid, neighbor_distances)
def _write_fused_edges(fused_paper_adjacency_matrix):
"""Write fused edges."""
data_root = Path(FLAGS.data_root)
edges_path = data_root / data_utils.FUSED_PAPER_EDGES_FILENAME
edges_t_path = data_root / data_utils.FUSED_PAPER_EDGES_T_FILENAME
edges_path.parent.mkdir(parents=True, exist_ok=True)
edges_t_path.parent.mkdir(parents=True, exist_ok=True)
with open(edges_path, 'wb') as fid:
sp.save_npz(fid, fused_paper_adjacency_matrix)
with open(edges_t_path, 'wb') as fid:
sp.save_npz(fid, fused_paper_adjacency_matrix.T)
def _write_fused_nodes(fused_node_labels):
"""Write fused nodes."""
labels_path = Path(FLAGS.data_root) / data_utils.FUSED_NODE_LABELS_FILENAME
labels_path.parent.mkdir(parents=True, exist_ok=True)
with open(labels_path, 'wb') as fid:
np.save(fid, fused_node_labels)
def main(unused_argv):
paper_pca_features = _read_paper_pca_features()
# Find neighbors.
annoy_index = build_annoy_index(paper_pca_features)
save_annoy_index(annoy_index)
neighbor_indices, neighbor_distances = compute_neighbor_indices_and_distances(
paper_pca_features)
del paper_pca_features
_write_neighbors(neighbor_indices, neighbor_distances)
data = _read_adjacency_indices()
paper_paper_csr = data['paper_paper_index']
paper_label = data['paper_label']
train_indices = data['train_indices']
valid_indices = data['valid_indices']
test_indices = data['test_indices']
del data
fused_paper_adjacency_matrix = data_utils.generate_fused_paper_adjacency_matrix(
neighbor_indices, neighbor_distances, paper_paper_csr)
_write_fused_edges(fused_paper_adjacency_matrix)
del fused_paper_adjacency_matrix
del paper_paper_csr
fused_node_labels = data_utils.generate_fused_node_labels(
neighbor_indices, neighbor_distances, paper_label, train_indices,
valid_indices, test_indices)
_write_fused_nodes(fused_node_labels)
if __name__ == '__main__':
flags.mark_flag_as_required('data_root')
app.run(main)
================================================
FILE: ogb_lsc/mag/organize_data.sh
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#!/bin/bash
set -e
while getopts ":i:o:" opt; do
case ${opt} in
i )
INPUT_DIR=$OPTARG
;;
o )
TASK_ROOT=$OPTARG
;;
\? )
echo "Usage: organize_data.sh -i -o "
;;
: )
echo "Invalid option: $OPTARG requires an argument" 1>&2
;;
esac
done
shift $((OPTIND -1))
# Get this script's directory.
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
if [[ -z "${INPUT_DIR}" ]]; then
echo "Need INPUT_DIR argument (-i )"
exit 1
fi
if [[ -z "${TASK_ROOT}" ]]; then
echo "Need TASK_ROOT argument (-o )"
exit 1
fi
DATA_ROOT="${TASK_ROOT}"/data
# Create raw directory to move all files to it.
mkdir "${INPUT_DIR}"/mag240m_kddcup2021/raw
mv "${INPUT_DIR}"/mag240m_kddcup2021/processed/paper/node_feat.npy \
"${INPUT_DIR}"/mag240m_kddcup2021/processed/paper/node_label.npy \
"${INPUT_DIR}"/mag240m_kddcup2021/processed/paper/node_year.npy \
"${DATA_ROOT}"/raw
mv "${INPUT_DIR}"/mag240m_kddcup2021/processed/author___affiliated_with___institution/edge_index.npy \
"${DATA_ROOT}"/raw/author_affiliated_with_institution_edges.npy
mv "${ROOT}"/mag240m_kddcup2021/processed/author___writes___paper/edge_index.npy \
"${DATA_ROOT}"/raw/author_writes_paper_edges.npy
mv "${ROOT}"/mag240m_kddcup2021/processed/paper___cites___paper/edge_index.npy \
"${DATA_ROOT}"/raw/paper_cites_paper_edges.npy
# Split and save the train/valid/test indices to the raw directory, with names
"train_idx.npy", "valid_idx.npy", "test_idx.npy":
python3 "${SCRIPT_DIR}"/split_and_save_indices.py --data_root=${DATA_ROOT}
================================================
FILE: ogb_lsc/mag/pca_builder.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Apply PCA to the papers' BERT features.
Compute papers' PCA features.
Recompute author and institution features from the paper PCA features.
"""
import pathlib
import time
from absl import app
from absl import flags
from absl import logging
import numpy as np
# pylint: disable=g-bad-import-order
import data_utils
Path = pathlib.Path
_NUMBER_OF_PAPERS_TO_ESTIMATE_PCA_ON = 1000000 # None indicates all.
FLAGS = flags.FLAGS
flags.DEFINE_string('data_root', None, 'Data root directory')
def _sample_vectors(vectors, num_samples, seed=0):
"""Randomly sample some vectors."""
rand = np.random.RandomState(seed=seed)
indices = rand.choice(vectors.shape[0], size=num_samples, replace=False)
return vectors[indices]
def _pca(feat):
"""Returns evals (variances), evecs (rows are principal components)."""
cov = np.cov(feat.T)
_, evals, evecs = np.linalg.svd(cov, full_matrices=True)
return evals, evecs
def _read_raw_paper_features():
"""Load raw paper features."""
path = Path(FLAGS.data_root) / data_utils.RAW_NODE_FEATURES_FILENAME
try: # Use mmap if possible.
features = np.load(path, mmap_mode='r')
except FileNotFoundError:
with open(path, 'rb') as fid:
features = np.load(fid)
return features
def _get_principal_components(features,
num_principal_components=129,
num_samples=10000,
seed=2,
dtype='f4'):
"""Estimate PCA features."""
sample = _sample_vectors(
features[:_NUMBER_OF_PAPERS_TO_ESTIMATE_PCA_ON], num_samples, seed=seed)
# Compute PCA basis.
_, evecs = _pca(sample)
return evecs[:num_principal_components].T.astype(dtype)
def _project_features_onto_principal_components(features,
principal_components,
block_size=1000000):
"""Apply PCA iteratively."""
num_principal_components = principal_components.shape[1]
dtype = principal_components.dtype
num_vectors = features.shape[0]
num_features = features.shape[0]
num_blocks = (num_features - 1) // block_size + 1
pca_features = np.empty([num_vectors, num_principal_components], dtype=dtype)
# Loop through in blocks.
start_time = time.time()
for i in range(num_blocks):
i_start = i * block_size
i_end = (i + 1) * block_size
f = np.array(features[i_start:i_end].copy())
pca_features[i_start:i_end] = np.dot(f, principal_components).astype(dtype)
del f
elapsed_time = time.time() - start_time
time_left = elapsed_time / (i + 1) * (num_blocks - i - 1)
logging.info('Features %d / %d. Elapsed time %.1f. Time left: %.1f', i_end,
num_vectors, elapsed_time, time_left)
return pca_features
def _read_adjacency_indices():
# Get adjacencies.
return data_utils.get_arrays(
data_root=FLAGS.data_root,
use_fused_node_labels=False,
use_fused_node_adjacencies=False,
return_pca_embeddings=False,
)
def _compute_author_pca_features(paper_pca_features, index_arrays):
return data_utils.paper_features_to_author_features(
index_arrays['author_paper_index'], paper_pca_features)
def _compute_institution_pca_features(author_pca_features, index_arrays):
return data_utils.author_features_to_institution_features(
index_arrays['institution_author_index'], author_pca_features)
def _write_array(path, array):
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, 'wb') as fid:
np.save(fid, array)
def main(unused_argv):
data_root = Path(FLAGS.data_root)
raw_paper_features = _read_raw_paper_features()
principal_components = _get_principal_components(raw_paper_features)
paper_pca_features = _project_features_onto_principal_components(
raw_paper_features, principal_components)
del raw_paper_features
del principal_components
paper_pca_path = data_root / data_utils.PCA_PAPER_FEATURES_FILENAME
author_pca_path = data_root / data_utils.PCA_AUTHOR_FEATURES_FILENAME
institution_pca_path = (
data_root / data_utils.PCA_INSTITUTION_FEATURES_FILENAME)
merged_pca_path = data_root / data_utils.PCA_MERGED_FEATURES_FILENAME
_write_array(paper_pca_path, paper_pca_features)
# Compute author and institution features from paper PCA features.
index_arrays = _read_adjacency_indices()
author_pca_features = _compute_author_pca_features(paper_pca_features,
index_arrays)
_write_array(author_pca_path, author_pca_features)
institution_pca_features = _compute_institution_pca_features(
author_pca_features, index_arrays)
_write_array(institution_pca_path, institution_pca_features)
merged_pca_features = np.concatenate(
[paper_pca_features, author_pca_features, institution_pca_features],
axis=0)
del author_pca_features
del institution_pca_features
_write_array(merged_pca_path, merged_pca_features)
if __name__ == '__main__':
flags.mark_flag_as_required('data_root')
app.run(main)
================================================
FILE: ogb_lsc/mag/requirements.txt
================================================
wheel
absl-py>=0.12.0
chex>=0.0.7
dill>=0.3.3
dm-haiku>=0.0.4
# jaxline
git+https://github.com/deepmind/jaxline@927695a0b88e480d085590736e2daa36c2f2c68b
optax>=0.0.8
ml_collections
numpy>=1.16.4
tensorflow>=2.5.0
tensorflow-datasets>=4.3.0
dm-tree>=0.1.6
ogb>=1.3.1
# graph_nets
git+git://github.com/deepmind/graph_nets.git@64771dff0d74ca8e77b1f1dcd5a7d26634356d61
scipy>=1.2.1
jaxlib>=0.1.67+cuda110
tqdm>=4.28.1
annoy>=1.0.3
# jraph
git+git://github.com/deepmind/jraph.git@80d2f9e4d82f841a71d56ba44fa9e8781e93ae1f
google-cloud-storage
================================================
FILE: ogb_lsc/mag/run_preprocessing.sh
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#!/bin/bash
set -e
set -x
while getopts ":r:" opt; do
case ${opt} in
r )
TASK_ROOT=$OPTARG
;;
\? )
echo "Usage: preprocess_data.sh -r "
;;
: )
echo "Invalid option: $OPTARG requires an argument" 1>&2
;;
esac
done
shift $((OPTIND -1))
# Get this script's directory.
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
DATA_ROOT="${TASK_ROOT}"/data
PREPROCESSED_DIR="${DATA_ROOT}"/preprocessed
# Create preprocessed directory to move all files to it.
mkdir -p "${PREPROCESSED_DIR}"
# Run the CSR edge builder.
python "${SCRIPT_DIR}"/csr_builder.py --data_root="${DATA_ROOT}"
# Run the PCA feature builder.
python "${SCRIPT_DIR}"/pca_builder.py --data_root="${DATA_ROOT}"
# Run the neighbor-finder/fuser builder.
python "${SCRIPT_DIR}"/neighbor_builder.py --data_root="${DATA_ROOT}"
# Run the validation split generator.
python "${SCRIPT_DIR}"/generate_validation_splits.py \
--data_root="${DATA_ROOT}" \
--output_dir="${DATA_ROOT}/k_fold_splits"
================================================
FILE: ogb_lsc/mag/run_pretrained_eval.sh
================================================
#!/bin/bash
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
set -x
while getopts ":r:" opt; do
case ${opt} in
r )
TASK_ROOT=$OPTARG
;;
\? )
echo "Usage: run_pretrained_eval.sh -r "
;;
: )
echo "Invalid option: $OPTARG requires an argument" 1>&2
;;
esac
done
shift $((OPTIND -1))
# Get this script's directory.
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
echo "
Note this script may take several days to run with default parameters on
a single machine. Reducing EPOCHS_TO_ENSEMBLE from 50 to < 5 should yield
slighly lower validation performance but possibly similar test performance.
"
echo "
Pre-requisites (See README):
* Python dependencies have been installed.
* pre-processed data is available in the task dir.
* pre-trained model weights are available in the task dir.
"
read -p "Press enter to continue"
# Can set this to "valid" or "test".
# On test the results will be ensembled for 10 models, and a submission file
# will be created.
# On validation, the results will be "gathered" for 10 models. This is because
# each model is trained on the train split + 90% of the validation split,
# and only evaluated on the remaining 10%, such that each validation paper
# is left out from training in exactly one of the 10 models.
SPLIT="test"
# We used 50 epochs in the submission to sample different subgraphs around
# each central node.
# For each of the 10 models in the ensemble, a single epoch takes about 1
# 10-25 minutes (depending on the GPU) on the test set, and about 2-3 minutes
# for the corresponding k-fold split of the validation set.
EPOCHS_TO_ENSEMBLE=50
DATA_ROOT=${TASK_ROOT}/data/
MODELS_ROOT=${TASK_ROOT}/models/
CHECKPOINT_DIR=${TASK_ROOT}/checkpoints/
OUTPUT_DIR=${TASK_ROOT}/predictions/
# We run two seeds for each model of the k=10 k-fold
# first seed group: [100, 101, 102, 103, 104, 105, 106, 107, 108, 109]
# second seed group: [110, 111, 112, 113, 114, 115, 116, 117, 118, 119]
# Thes are the seeds that was selected based on cross validation for each fold.
BEST_SEEDS=(100 111 102 113 104 105 106 107 108 109)
for K_FOLD_INDEX in {0..9}; do
SEED=${BEST_SEEDS[${K_FOLD_INDEX}]}
RESTORE_PATH=${MODELS_ROOT}/k${K_FOLD_INDEX}_seed${SEED}
echo "Running k=${K_FOLD_INDEX} on ${SPLIT} split using ${RESTORE_PATH}"
# This saves the predictions for the K_FOLD_INDEXd'th model in the k-fold to
# "config.experiment_kwargs.config.predictions_dir" for subsequent ensembling
python "${SCRIPT_DIR}"/experiment.py \
--jaxline_mode="eval" \
--config="${SCRIPT_DIR}"/config.py \
--config.one_off_evaluate=True \
--config.checkpoint_dir=${CHECKPOINT_DIR}/${K_FOLD_INDEX} \
--config.restore_path=${RESTORE_PATH} \
--config.experiment_kwargs.config.dataset_kwargs.data_root=${DATA_ROOT} \
--config.experiment_kwargs.config.dataset_kwargs.k_fold_split_id=${K_FOLD_INDEX} \
--config.experiment_kwargs.config.num_eval_iterations_to_ensemble=${EPOCHS_TO_ENSEMBLE} \
--config.experiment_kwargs.config.predictions_dir=${OUTPUT_DIR}/${K_FOLD_INDEX} \
--config.experiment_kwargs.config.eval.split=${SPLIT}
done
python "${SCRIPT_DIR}"/ensemble_predictions.py \
--split=${SPLIT} \
--data_root=${DATA_ROOT} \
--predictions_path=${OUTPUT_DIR} \
--output_path=${OUTPUT_DIR}
echo "Done"
================================================
FILE: ogb_lsc/mag/run_training.sh
================================================
#!/bin/bash
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
set -x
while getopts ":r:" opt; do
case ${opt} in
r )
TASK_ROOT=$OPTARG
;;
\? )
echo "Usage: run_training.sh -r "
;;
: )
echo "Invalid option: $OPTARG requires an argument" 1>&2
;;
esac
done
shift $((OPTIND -1))
# Get this script's directory.
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
echo "
These scripts are provided for illustrative purposes. It is not practical for
actual training since it only uses a single machine, and likely requires
reducing the batch size and/or model size to fit on a single GPU.
For the actual submission we used training distributed in different ways:
* We used 4x Cloud TPU v4s to implement batch parallelism, so batch
size is effectively 8 times larger than the values in the config.
* We used separate CPUs to subsample neighborhoods around each central node, to
maximize TPU usage by always having a batch ready for the next iteration.
* We ran the online early-stopping evaluator on a separate machine with an
NVIDIA V100 GPU.
* We run identical replicas of all of the above for each of the 20 models
trained.
Using those mechanisms, training runs at ~2 steps per second, reaching 500k
steps in under 3 days.
"
echo "
Pre-requisites (See README):
* Python dependencies have been installed.
* pre-processed data is available in the task dir.
"
read -p "Press enter to continue"
# During early stopping seed/selection we ensembled 5 iterations.
EPOCHS_TO_ENSEMBLE=5
DATA_ROOT=${TASK_ROOT}/data/
CHECKPOINT_DIR=${TASK_ROOT}/checkpoints/
# We run two seeds for each model of the k=10 k-fold.
BASE_SEED=100
for SEED_OFFSET in 0 10; do
for K_FOLD_INDEX in {0..9}; do
MODEL_SEED=`expr ${BASE_SEED} + ${SEED_OFFSET} + ${K_FOLD_INDEX}`
SUFFIX=k${K_FOLD_INDEX}_seed${MODEL_SEED}
echo "Running k=${K_FOLD_INDEX} with init seed ${MODEL_SEED}"
# This runs training (each model is trained on train split + 90% of the
# validation split) with early stopping, storing both "latest" model and
# "best" early-stopped model at `--config.checkpoint_dir`.
# Models are early stopped based on accuracy of on 10% of the validation data
# (each K_FOLD_INDEX leaves a different 10% of data out from training) left
# out from training.
# Models are stored at the end of training. Intermediate models can also be
# stored while training by sending a SIGINT signal (Ctrl+C) which will not
# interrupt the training.
# It is possible to interrupt training using (Ctrl+\) and then continue
# passing the corresponding `--config.restore_path=${RESTORE_PATH}` which is
# stored when interrupting.
python "${SCRIPT_DIR}"/experiment.py \
--jaxline_mode="train_eval_multithreaded" \
--config="${SCRIPT_DIR}"/config.py \
--config.random_seed=${MODEL_SEED} \
--config.checkpoint_dir=${CHECKPOINT_DIR}/${SUFFIX} \
--config.experiment_kwargs.config.dataset_kwargs.data_root=${DATA_ROOT} \
--config.experiment_kwargs.config.dataset_kwargs.k_fold_split_id=${K_FOLD_INDEX} \
--config.experiment_kwargs.config.num_eval_iterations_to_ensemble=${EPOCHS_TO_ENSEMBLE} \
--config.experiment_kwargs.config.eval.split="valid"
done
done
# Each of the 20 (two for each value of k) jobs generate paths of the form:
# RESTORE_PATH=${--config.checkpoint_dir}/models/best/step_${STEP}_${TIMESTAMP}
# From each pair, the best one is selected (based on the validation accuracy
# as reported on the logs or in tensorboard events also stored at
# `${--config.checkpoint_dir}/eval`). These can then be used as the
# "RESTORE_PATHS" for `./run_pretrained_eval.sh`.
echo "Done"
================================================
FILE: ogb_lsc/mag/schedules.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Scheduling utilities."""
import jax.numpy as jnp
def apply_ema_decay(
ema_value: jnp.ndarray,
current_value: jnp.ndarray,
decay: jnp.ndarray,
) -> jnp.ndarray:
"""Implements EMA."""
return ema_value * decay + current_value * (1 - decay)
def ema_decay_schedule(
base_rate: jnp.ndarray,
step: jnp.ndarray,
total_steps: jnp.ndarray,
use_schedule: bool,
) -> jnp.ndarray:
"""Anneals decay rate to 1 with cosine schedule."""
if not use_schedule:
return base_rate
multiplier = _cosine_decay(step, total_steps, 1.)
return 1. - (1. - base_rate) * multiplier
def _cosine_decay(
global_step: jnp.ndarray,
max_steps: int,
initial_value: float,
) -> jnp.ndarray:
"""Simple implementation of cosine decay from TF1."""
global_step = jnp.minimum(global_step, max_steps).astype(jnp.float32)
cosine_decay_value = 0.5 * (1 + jnp.cos(jnp.pi * global_step / max_steps))
decayed_learning_rate = initial_value * cosine_decay_value
return decayed_learning_rate
def learning_schedule(
global_step: jnp.ndarray,
base_learning_rate: float,
total_steps: int,
warmup_steps: int,
use_schedule: bool,
) -> float:
"""Cosine learning rate scheduler."""
# Compute LR & Scaled LR
if not use_schedule:
return base_learning_rate
warmup_learning_rate = (
global_step.astype(jnp.float32) / int(warmup_steps) *
base_learning_rate if warmup_steps > 0 else base_learning_rate)
# Cosine schedule after warmup.
decay_learning_rate = _cosine_decay(global_step - warmup_steps,
total_steps - warmup_steps,
base_learning_rate)
return jnp.where(global_step < warmup_steps, warmup_learning_rate,
decay_learning_rate)
================================================
FILE: ogb_lsc/mag/split_and_save_indices.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Split and save the train/valid/test indices.
Usage:
python3 split_and_save_indices.py --data_root="mag_data"
"""
import pathlib
from absl import app
from absl import flags
import numpy as np
import torch
Path = pathlib.Path
FLAGS = flags.FLAGS
flags.DEFINE_string('data_root', None, 'Data root directory')
def main(argv) -> None:
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
mag_directory = Path(FLAGS.data_root) / 'mag240m_kddcup2021'
raw_directory = mag_directory / 'raw'
raw_directory.parent.mkdir(parents=True, exist_ok=True)
splits_dict = torch.load(str(mag_directory / 'split_dict.pt'))
for key, indices in splits_dict.items():
np.save(str(raw_directory / f'{key}_idx.npy'), indices)
if __name__ == '__main__':
flags.mark_flag_as_required('root')
app.run(main)
================================================
FILE: ogb_lsc/mag/sub_sampler.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities for subsampling the MAG dataset."""
import collections
import jraph
import numpy as np
def get_or_sample_row(node_id: int,
nb_neighbours: int,
csr_matrix, remove_duplicates: bool):
"""Either obtain entire row or a subsampled set of neighbours."""
if node_id + 1 >= csr_matrix.indptr.shape[0]:
lo = 0
hi = 0
else:
lo = csr_matrix.indptr[node_id]
hi = csr_matrix.indptr[node_id + 1]
if lo == hi: # Skip empty neighbourhoods
neighbours = None
elif hi - lo <= nb_neighbours:
neighbours = csr_matrix.indices[lo:hi]
elif hi - lo < 5 * nb_neighbours: # For small surroundings, sample directly
nb_neighbours = min(nb_neighbours, hi - lo)
inds = lo + np.random.choice(hi - lo, size=(nb_neighbours,), replace=False)
neighbours = csr_matrix.indices[inds]
else: # Otherwise, do not slice -- sample indices instead
# To extend GraphSAGE ("uniform w/ replacement"), modify this call
inds = np.random.randint(lo, hi, size=(nb_neighbours,))
if remove_duplicates:
inds = np.unique(inds)
neighbours = csr_matrix.indices[inds]
return neighbours
def get_neighbours(node_id: int,
node_type: int,
neighbour_type: int,
nb_neighbours: int,
remove_duplicates: bool,
author_institution_csr, institution_author_csr,
author_paper_csr, paper_author_csr,
paper_paper_csr, paper_paper_transpose_csr):
"""Fetch the edge indices from one node to corresponding neighbour type."""
if node_type == 0 and neighbour_type == 0:
csr = paper_paper_transpose_csr # Citing
elif node_type == 0 and neighbour_type == 1:
csr = paper_author_csr
elif node_type == 0 and neighbour_type == 3:
csr = paper_paper_csr # Cited
elif node_type == 1 and neighbour_type == 0:
csr = author_paper_csr
elif node_type == 1 and neighbour_type == 2:
csr = author_institution_csr
elif node_type == 2 and neighbour_type == 1:
csr = institution_author_csr
else:
raise ValueError('Non-existent edge type requested')
return get_or_sample_row(node_id, nb_neighbours, csr, remove_duplicates)
def get_senders(neighbour_type: int,
sender_index,
paper_features):
"""Get the sender features from given neighbours."""
if neighbour_type == 0 or neighbour_type == 3:
sender_features = paper_features[sender_index]
elif neighbour_type == 1 or neighbour_type == 2:
sender_features = np.zeros((sender_index.shape[0],
paper_features.shape[1])) # Consider averages
else:
raise ValueError('Non-existent node type requested')
return sender_features
def make_edge_type_feature(node_type: int, neighbour_type: int):
edge_feats = np.zeros(7)
edge_feats[node_type] = 1.0
edge_feats[neighbour_type + 3] = 1.0
return edge_feats
def subsample_graph(paper_id: int,
author_institution_csr,
institution_author_csr,
author_paper_csr,
paper_author_csr,
paper_paper_csr,
paper_paper_transpose_csr,
max_nb_neighbours_per_type,
max_nodes=None,
max_edges=None,
paper_years=None,
remove_future_nodes=False,
deduplicate_nodes=False) -> jraph.GraphsTuple:
"""Subsample a graph around given paper ID."""
if paper_years is not None:
root_paper_year = paper_years[paper_id]
else:
root_paper_year = None
# Add the center node as "node-zero"
sub_nodes = [paper_id]
num_nodes_in_subgraph = 1
num_edges_in_subgraph = 0
reached_node_budget = False
reached_edge_budget = False
node_and_type_to_index_in_subgraph = dict()
node_and_type_to_index_in_subgraph[(paper_id, 0)] = 0
# Store all (integer) depths as an additional feature
depths = [0]
types = [0]
sub_edges = []
sub_senders = []
sub_receivers = []
# Store all unprocessed neighbours
# Each neighbour is stored as a 4-tuple (node_index in original graph,
# node_index in subsampled graph, type, number of hops away from source).
# TYPES: 0: paper, 1: author, 2: institution, 3: paper (for bidirectional)
neighbour_deque = collections.deque([(paper_id, 0, 0, 0)])
max_depth = len(max_nb_neighbours_per_type)
while neighbour_deque and not reached_edge_budget:
left_entry = neighbour_deque.popleft()
node_index, node_index_in_sampled_graph, node_type, node_depth = left_entry
# Expand from this node, to a node of related type
for neighbour_type in range(4):
if reached_edge_budget:
break # Budget may have been reached in previous type; break here.
nb_neighbours = max_nb_neighbours_per_type[node_depth][node_type][neighbour_type] # pylint:disable=line-too-long
# Only extend if we want to sample further in this edge type
if nb_neighbours > 0:
sampled_neighbors = get_neighbours(
node_index,
node_type,
neighbour_type,
nb_neighbours,
deduplicate_nodes,
author_institution_csr,
institution_author_csr,
author_paper_csr,
paper_author_csr,
paper_paper_csr,
paper_paper_transpose_csr,
)
if sampled_neighbors is not None:
if remove_future_nodes and root_paper_year is not None:
if neighbour_type in [0, 3]:
sampled_neighbors = [
x for x in sampled_neighbors
if paper_years[x] <= root_paper_year
]
if not sampled_neighbors:
continue
nb_neighbours = len(sampled_neighbors)
edge_feature = make_edge_type_feature(node_type, neighbour_type)
for neighbor_original_idx in sampled_neighbors:
# Key into dict of existing nodes using both node id and type.
neighbor_key = (neighbor_original_idx, neighbour_type % 3)
# Get existing idx in subgraph if it exists.
neighbor_subgraph_idx = node_and_type_to_index_in_subgraph.get(
neighbor_key, None)
if (not reached_node_budget and
(not deduplicate_nodes or neighbor_subgraph_idx is None)):
# If it does not exist already, or we are not deduplicating,
# just create a new node and update the dict.
neighbor_subgraph_idx = num_nodes_in_subgraph
node_and_type_to_index_in_subgraph[neighbor_key] = (
neighbor_subgraph_idx)
num_nodes_in_subgraph += 1
sub_nodes.append(neighbor_original_idx)
types.append(neighbour_type % 3)
depths.append(node_depth + 1)
if max_nodes is not None and num_nodes_in_subgraph >= max_nodes:
reached_node_budget = True
continue # Move to next neighbor which might already exist.
if node_depth < max_depth - 1:
# If the neighbours are to be further expanded, enqueue them.
# Expand only if the nodes did not already exist.
neighbour_deque.append(
(neighbor_original_idx, neighbor_subgraph_idx,
neighbour_type % 3, node_depth + 1))
# The neighbor id within graph is now fixed; just add edges.
if neighbor_subgraph_idx is not None:
# Either node existed before or was successfully added.
sub_senders.append(neighbor_subgraph_idx)
sub_receivers.append(node_index_in_sampled_graph)
sub_edges.append(edge_feature)
num_edges_in_subgraph += 1
if max_edges is not None and num_edges_in_subgraph >= max_edges:
reached_edge_budget = True
break # Break out of adding edges for this neighbor type
# Stitch the graph together
sub_nodes = np.array(sub_nodes, dtype=np.int32)
if sub_senders:
sub_senders = np.array(sub_senders, dtype=np.int32)
sub_receivers = np.array(sub_receivers, dtype=np.int32)
sub_edges = np.stack(sub_edges, axis=0)
else:
# Use empty arrays.
sub_senders = np.zeros([0], dtype=np.int32)
sub_receivers = np.zeros([0], dtype=np.int32)
sub_edges = np.zeros([0, 7])
# Finally, derive the sizes
sub_n_node = np.array([sub_nodes.shape[0]])
sub_n_edge = np.array([sub_senders.shape[0]])
assert sub_nodes.shape[0] == num_nodes_in_subgraph
assert sub_edges.shape[0] == num_edges_in_subgraph
if max_nodes is not None:
assert num_nodes_in_subgraph <= max_nodes
if max_edges is not None:
assert num_edges_in_subgraph <= max_edges
types = np.array(types)
depths = np.array(depths)
sub_nodes = {
'index': sub_nodes.astype(np.int32),
'type': types.astype(np.int16),
'depth': depths.astype(np.int16),
}
return jraph.GraphsTuple(nodes=sub_nodes,
edges=sub_edges.astype(np.float16),
senders=sub_senders.astype(np.int32),
receivers=sub_receivers.astype(np.int32),
globals=np.array([0], dtype=np.int16),
n_node=sub_n_node.astype(dtype=np.int32),
n_edge=sub_n_edge.astype(dtype=np.int32))
================================================
FILE: ogb_lsc/pcq/README.md
================================================
# DeepMind entry for PCQM4M-LSC
This repository contains DeepMind's entry to the [PCQM4M-LSC](https://ogb.stanford.edu/kddcup2021/pcqm4m/) (quantum chemistry)
track of the [OGB Large-Scale Challenge](https://ogb.stanford.edu/kddcup2021/)
(OGB-LSC).
For full details regarding this entry, please see our [technical report](https://arxiv.org/abs/2107.09422).
## DeepMind PCQ Team ("Quantum")
(in alphabetical order)
- Ravichandra Addanki
- Peter Battaglia
- David Budden
- Andreea Deac
- Jonathan Godwin
- Alvaro Sanchez-Gonzalez
- Wai Lok Sibon Li
- Jacklynn Stott
- Shantanu Thakoor
- Petar Veličković
## Performance
Our final test set performance was achieved by pooling an ensemble of 20 models
(10 folds x 2 seeds). See [technical report](https://arxiv.org/abs/2107.09422) for details.
Each model was trained for < 48 hours using 4x Google Cloud TPUv4 and 1x AMD
EPYC 7B12 64-core CPU @2.25GHz.
Inference takes < 6 hours on 1x NVIDIA V100 16GB GPU and 1x Intel Xeon Gold 6148
20-core CPU @2.40GHz.
# Running our model
## Setup
You can set up Python virtual environment (you might need to install the
`python3-venv` package first) with all needed dependencies inside the forked
`deepmind_research` repository using:
```bash
python3 -m venv /tmp/pcq_venv
source /tmp/pcq_venv/bin/activate
pip3 install --upgrade pip setuptools wheel
pip3 install -r ogb_lsc/pcq/requirements.txt
```
## Download and pre-process data
All the additional features used in training (k-fold splits and conformer
position features) can be generated by running:
```bash
/bin/bash run_preprocessing.sh -r ${HOME}/pcq/
```
Or downloaded using:
```bash
python download_pcq.py --task_root=${HOME}/pcq/ --payload="data"
```
## Reproducing our final results
We have provided pre-trained weights of our final submission (~150 GB worth of
model checkpoints) for convenience, which can be downloaded with:
```bash
python download_pcq.py --task_root=${HOME}/pcq/ --payload="models"
```
Then to reproduce our final results please run:
```bash
/bin/bash run_pretrained_eval.sh -r ${HOME}/pcq/
```
Note that this script does not use the downloaded conformer position features,
and instead computes them for the test set as part of the script.
## Retraining our model
Disclaimer: This script is provided for illustrative purposes. It is not
practical for actual training since it only uses a single machine, and likely
requires reducing the batch size and/or model size to fit on a single GPU.
To train a model, please run:
```bash
/bin/bash run_training.sh -r ${HOME}/pcq/
```
To simply validate that the code is running correctly on your hardware setup,
consider setting `debug=True` in `config.py`, which trains a smaller model.
# Citation
To cite this work (together with our MAG240M-LSC entry):
```latex
@article{deepmind2021ogb,
author = {Ravichandra Addanki and Peter Battaglia and David Budden and Andreea
Deac and Jonathan Godwin and Thomas Keck and Wai Lok Sibon Li and Alvaro
Sanchez-Gonzalez and Jacklynn Stott and Shantanu Thakoor and Petar
Veli\v{c}kovi\'{c}},
title = {Large-scale graph representation learning with very deep GNNs and
self-supervision},
year = {2021},
journal={arXiv preprint arXiv:2107.09422},
}
```
Our technical report can be found [here](https://arxiv.org/abs/2107.09422).
================================================
FILE: ogb_lsc/pcq/batching_utils.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Dynamic batching utilities."""
from typing import Generator, Iterator, Sequence, Tuple
import jax.tree_util as tree
import jraph
import numpy as np
_NUMBER_FIELDS = ("n_node", "n_edge", "n_graph")
def dynamically_batch(graphs_tuple_iterator: Iterator[jraph.GraphsTuple],
n_node: int, n_edge: int,
n_graph: int) -> Generator[jraph.GraphsTuple, None, None]:
"""Dynamically batches trees with `jraph.GraphsTuples` to `graph_batch_size`.
Elements of the `graphs_tuple_iterator` will be incrementally added to a batch
until the limits defined by `n_node`, `n_edge` and `n_graph` are reached. This
means each element yielded by this generator
For situations where you have variable sized data, it"s useful to be able to
have variable sized batches. This is especially the case if you have a loss
defined on the variable shaped element (for example, nodes in a graph).
Args:
graphs_tuple_iterator: An iterator of `jraph.GraphsTuples`.
n_node: The maximum number of nodes in a batch.
n_edge: The maximum number of edges in a batch.
n_graph: The maximum number of graphs in a batch.
Yields:
A `jraph.GraphsTuple` batch of graphs.
Raises:
ValueError: if the number of graphs is < 2.
RuntimeError: if the `graphs_tuple_iterator` contains elements which are not
`jraph.GraphsTuple`s.
RuntimeError: if a graph is found which is larger than the batch size.
"""
if n_graph < 2:
raise ValueError("The number of graphs in a batch size must be greater or "
f"equal to `2` for padding with graphs, got {n_graph}.")
valid_batch_size = (n_node - 1, n_edge, n_graph - 1)
accumulated_graphs = []
num_accumulated_nodes = 0
num_accumulated_edges = 0
num_accumulated_graphs = 0
for element in graphs_tuple_iterator:
element_nodes, element_edges, element_graphs = _get_graph_size(element)
if _is_over_batch_size(element, valid_batch_size):
graph_size = element_nodes, element_edges, element_graphs
graph_size = {k: v for k, v in zip(_NUMBER_FIELDS, graph_size)}
batch_size = {k: v for k, v in zip(_NUMBER_FIELDS, valid_batch_size)}
raise RuntimeError("Found graph bigger than batch size. Valid Batch "
f"Size: {batch_size}, Graph Size: {graph_size}")
if not accumulated_graphs:
# If this is the first element of the batch, set it and continue.
accumulated_graphs = [element]
num_accumulated_nodes = element_nodes
num_accumulated_edges = element_edges
num_accumulated_graphs = element_graphs
continue
else:
# Otherwise check if there is space for the graph in the batch:
if ((num_accumulated_graphs + element_graphs > n_graph - 1) or
(num_accumulated_nodes + element_nodes > n_node - 1) or
(num_accumulated_edges + element_edges > n_edge)):
# If there is, add it to the batch
batched_graph = _batch_np(accumulated_graphs)
yield jraph.pad_with_graphs(batched_graph, n_node, n_edge, n_graph)
accumulated_graphs = [element]
num_accumulated_nodes = element_nodes
num_accumulated_edges = element_edges
num_accumulated_graphs = element_graphs
else:
# Otherwise, return the old batch and start a new batch.
accumulated_graphs.append(element)
num_accumulated_nodes += element_nodes
num_accumulated_edges += element_edges
num_accumulated_graphs += element_graphs
# We may still have data in batched graph.
if accumulated_graphs:
batched_graph = _batch_np(accumulated_graphs)
yield jraph.pad_with_graphs(batched_graph, n_node, n_edge, n_graph)
def _batch_np(graphs: Sequence[jraph.GraphsTuple]) -> jraph.GraphsTuple:
# Calculates offsets for sender and receiver arrays, caused by concatenating
# the nodes arrays.
offsets = np.cumsum(np.array([0] + [np.sum(g.n_node) for g in graphs[:-1]]))
def _map_concat(nests):
concat = lambda *args: np.concatenate(args)
return tree.tree_map(concat, *nests)
return jraph.GraphsTuple(
n_node=np.concatenate([g.n_node for g in graphs]),
n_edge=np.concatenate([g.n_edge for g in graphs]),
nodes=_map_concat([g.nodes for g in graphs]),
edges=_map_concat([g.edges for g in graphs]),
globals=_map_concat([g.globals for g in graphs]),
senders=np.concatenate([g.senders + o for g, o in zip(graphs, offsets)]),
receivers=np.concatenate(
[g.receivers + o for g, o in zip(graphs, offsets)]))
def _get_graph_size(graph: jraph.GraphsTuple) -> Tuple[int, int, int]:
n_node = np.sum(graph.n_node)
n_edge = len(graph.senders)
n_graph = len(graph.n_node)
return n_node, n_edge, n_graph
def _is_over_batch_size(
graph: jraph.GraphsTuple,
graph_batch_size: Tuple[int, int, int],
) -> bool:
graph_size = _get_graph_size(graph)
return any([x > y for x, y in zip(graph_size, graph_batch_size)])
================================================
FILE: ogb_lsc/pcq/config.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Experiment config for PCQM4M-LSC entry."""
from jaxline import base_config
from ml_collections import config_dict
def get_config(debug: bool = False) -> config_dict.ConfigDict:
"""Get Jaxline experiment config."""
config = base_config.get_base_config()
# E.g. '/data/pretrained_models/k0_seed100' (and set k_fold_split_id=0, below)
config.restore_path = config_dict.placeholder(str)
training_batch_size = 64
eval_batch_size = 64
## Experiment config.
loss_config_name = 'RegressionLossConfig'
loss_kwargs = dict(
exponent=1., # 2 for l2 loss, 1 for l1 loss, etc...
)
dataset_config = dict(
data_root=config_dict.placeholder(str),
augment_with_random_mirror_symmetry=True,
k_fold_split_id=config_dict.placeholder(int),
num_k_fold_splits=config_dict.placeholder(int),
# Options: "in" or "out".
# Filter=in would keep the samples with nans in the conformer features.
# Filter=out would keep the samples with no NaNs anywhere in the conformer
# features.
filter_in_or_out_samples_with_nans_in_conformers=(
config_dict.placeholder(str)),
cached_conformers_file=config_dict.placeholder(str))
model_config = dict(
mlp_hidden_size=512,
mlp_layers=2,
latent_size=512,
use_layer_norm=False,
num_message_passing_steps=32,
shared_message_passing_weights=False,
mask_padding_graph_at_every_step=True,
loss_config_name=loss_config_name,
loss_kwargs=loss_kwargs,
processor_mode='resnet',
global_reducer='sum',
node_reducer='sum',
dropedge_rate=0.1,
dropnode_rate=0.1,
aux_multiplier=0.1,
add_relative_distance=True,
add_relative_displacement=True,
add_absolute_positions=False,
position_normalization=2.,
relative_displacement_normalization=1.,
ignore_globals=False,
ignore_globals_from_final_layer_for_predictions=True,
)
if debug:
# Make network smaller.
model_config.update(dict(
mlp_hidden_size=32,
mlp_layers=1,
latent_size=32,
num_message_passing_steps=1))
config.experiment_kwargs = config_dict.ConfigDict(
dict(
config=dict(
debug=debug,
predictions_dir=config_dict.placeholder(str),
ema=True,
ema_decay=0.9999,
sample_random=0.05,
optimizer=dict(
name='adam',
optimizer_kwargs=dict(b1=.9, b2=.95),
lr_schedule=dict(
warmup_steps=int(5e4),
decay_steps=int(5e5),
init_value=1e-5,
peak_value=1e-4,
end_value=0.,
),
),
model=model_config,
dataset_config=dataset_config,
# As a rule of thumb, use the following statistics:
# Avg. # nodes in graph: 16.
# Avg. # edges in graph: 40.
training=dict(
dynamic_batch_size={
'n_node': 256 if debug else 16 * training_batch_size,
'n_edge': 512 if debug else 40 * training_batch_size,
'n_graph': 2 if debug else training_batch_size,
},),
evaluation=dict(
split='valid',
dynamic_batch_size=dict(
n_node=256 if debug else 16 * eval_batch_size,
n_edge=512 if debug else 40 * eval_batch_size,
n_graph=2 if debug else eval_batch_size,
)))))
## Training loop config.
config.training_steps = int(5e6)
config.checkpoint_dir = '/tmp/checkpoint/pcq/'
config.train_checkpoint_all_hosts = False
config.save_checkpoint_interval = 300
config.log_train_data_interval = 60
config.log_tensors_interval = 60
config.best_model_eval_metric = 'mae'
config.best_model_eval_metric_higher_is_better = False
return config
================================================
FILE: ogb_lsc/pcq/conformer_utils.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Conformer utilities."""
import copy
from typing import List, Optional
from absl import logging
import numpy as np
import rdkit
from rdkit import Chem
from rdkit.Chem import AllChem
import tensorflow.compat.v2 as tf
def generate_conformers(
molecule: Chem.rdchem.Mol,
max_num_conformers: int,
*,
random_seed: int = -1,
prune_rms_thresh: float = -1.0,
max_iter: int = -1,
fallback_to_random: bool = False,
) -> Chem.rdchem.Mol:
"""Generates conformers for a given molecule.
Args:
molecule: molecular representation of the compound.
max_num_conformers: maximum number of conformers to generate. If pruning is
done, the returned number of conformers is not guaranteed to match
max_num_conformers.
random_seed: random seed to use for conformer generation.
prune_rms_thresh: RMSD threshold which allows to prune conformers that are
too similar.
max_iter: Maximum number of iterations to perform when optimising MMFF force
field. If set to <= 0, energy optimisation is not performed.
fallback_to_random: if conformers cannot be obtained, use random coordinates
to initialise.
Returns:
Copy of a `molecule` with added hydrogens. The returned molecule contains
force field-optimised conformers. The number of conformers is guaranteed to
be <= max_num_conformers.
"""
mol = copy.deepcopy(molecule)
mol = Chem.AddHs(mol)
mol = _embed_conformers(
mol,
max_num_conformers,
random_seed,
prune_rms_thresh,
fallback_to_random,
use_random=False)
if max_iter > 0:
mol_with_conformers = _minimize_by_mmff(mol, max_iter)
if mol_with_conformers is None:
mol_with_conformers = _minimize_by_uff(mol, max_iter)
else:
mol_with_conformers = mol
# Aligns conformations in a molecule to each other using the first
# conformation as the reference.
AllChem.AlignMolConformers(mol_with_conformers)
# We remove hydrogens to keep the number of atoms consistent with the graph
# nodes.
mol_with_conformers = Chem.RemoveHs(mol_with_conformers)
return mol_with_conformers
def atom_to_feature_vector(
atom: rdkit.Chem.rdchem.Atom,
conformer: Optional[np.ndarray] = None,
) -> List[float]:
"""Converts rdkit atom object to feature list of indices.
Args:
atom: rdkit atom object.
conformer: Generated conformers. Returns -1 values if set to None.
Returns:
List containing positions (x, y, z) of each atom from the conformer.
"""
if conformer:
pos = conformer.GetAtomPosition(atom.GetIdx())
return [pos.x, pos.y, pos.z]
return [np.nan, np.nan, np.nan]
def compute_conformer(smile: str, max_iter: int = -1) -> np.ndarray:
"""Computes conformer.
Args:
smile: Smile string.
max_iter: Maximum number of iterations to perform when optimising MMFF force
field. If set to <= 0, energy optimisation is not performed.
Returns:
A tuple containing index, fingerprint and conformer.
Raises:
RuntimeError: If unable to convert smile string to RDKit mol.
"""
mol = rdkit.Chem.MolFromSmiles(smile)
if not mol:
raise RuntimeError('Unable to convert smile to molecule: %s' % smile)
conformer_failed = False
try:
mol = generate_conformers(
mol,
max_num_conformers=1,
random_seed=45,
prune_rms_thresh=0.01,
max_iter=max_iter)
except IOError as e:
logging.exception('Failed to generate conformers for %s . IOError %s.',
smile, e)
conformer_failed = True
except ValueError:
logging.error('Failed to generate conformers for %s . ValueError', smile)
conformer_failed = True
except: # pylint: disable=bare-except
logging.error('Failed to generate conformers for %s.', smile)
conformer_failed = True
atom_features_list = []
conformer = None if conformer_failed else list(mol.GetConformers())[0]
for atom in mol.GetAtoms():
atom_features_list.append(atom_to_feature_vector(atom, conformer))
conformer_features = np.array(atom_features_list, dtype=np.float32)
return conformer_features
def get_random_rotation_matrix(include_mirror_symmetry: bool) -> tf.Tensor:
"""Returns a single random rotation matrix."""
rotation_matrix = _get_random_rotation_3d()
if include_mirror_symmetry:
random_mirror_symmetry = _get_random_mirror_symmetry()
rotation_matrix = tf.matmul(rotation_matrix, random_mirror_symmetry)
return rotation_matrix
def rotate(vectors: tf.Tensor, rotation_matrix: tf.Tensor) -> tf.Tensor:
"""Batch of vectors on a single rotation matrix."""
return tf.matmul(vectors, rotation_matrix)
def _embed_conformers(
molecule: Chem.rdchem.Mol,
max_num_conformers: int,
random_seed: int,
prune_rms_thresh: float,
fallback_to_random: bool,
*,
use_random: bool = False,
) -> Chem.rdchem.Mol:
"""Embeds conformers into a copy of a molecule.
If random coordinates allowed, tries not to use random coordinates at first,
and uses random only if fails.
Args:
molecule: molecular representation of the compound.
max_num_conformers: maximum number of conformers to generate. If pruning is
done, the returned number of conformers is not guaranteed to match
max_num_conformers.
random_seed: random seed to use for conformer generation.
prune_rms_thresh: RMSD threshold which allows to prune conformers that are
too similar.
fallback_to_random: if conformers cannot be obtained, use random coordinates
to initialise.
*:
use_random: Use random coordinates. Shouldn't be set by any caller except
this function itself.
Returns:
A copy of a molecule with embedded conformers.
Raises:
ValueError: if conformers cannot be obtained for a given molecule.
"""
mol = copy.deepcopy(molecule)
# Obtains parameters for conformer generation.
# In particular, ETKDG is experimental-torsion basic knowledge distance
# geometry, which allows to randomly generate an initial conformation that
# satisfies various geometric constraints such as lower and upper bounds on
# the distances between atoms.
params = AllChem.ETKDGv3()
params.randomSeed = random_seed
params.pruneRmsThresh = prune_rms_thresh
params.numThreads = -1
params.useRandomCoords = use_random
conf_ids = AllChem.EmbedMultipleConfs(mol, max_num_conformers, params)
if not conf_ids:
if not fallback_to_random or use_random:
raise ValueError('Cant get conformers')
return _embed_conformers(
mol,
max_num_conformers,
random_seed,
prune_rms_thresh,
fallback_to_random,
use_random=True)
return mol
def _minimize_by_mmff(
molecule: Chem.rdchem.Mol,
max_iter: int,
) -> Optional[Chem.rdchem.Mol]:
"""Minimizes forcefield for conformers using MMFF algorithm.
Args:
molecule: a datastructure containing conformers.
max_iter: number of maximum iterations to use when optimising force field.
Returns:
A copy of a `molecule` containing optimised conformers; or None if MMFF
cannot be performed.
"""
molecule_props = AllChem.MMFFGetMoleculeProperties(molecule)
if molecule_props is None:
return None
mol = copy.deepcopy(molecule)
for conf_id in range(mol.GetNumConformers()):
ff = AllChem.MMFFGetMoleculeForceField(
mol, molecule_props, confId=conf_id, ignoreInterfragInteractions=False)
ff.Initialize()
# minimises a conformer within a mol in place.
ff.Minimize(max_iter)
return mol
def _minimize_by_uff(
molecule: Chem.rdchem.Mol,
max_iter: int,
) -> Chem.rdchem.Mol:
"""Minimizes forcefield for conformers using UFF algorithm.
Args:
molecule: a datastructure containing conformers.
max_iter: number of maximum iterations to use when optimising force field.
Returns:
A copy of a `molecule` containing optimised conformers.
"""
mol = copy.deepcopy(molecule)
conf_ids = range(mol.GetNumConformers())
for conf_id in conf_ids:
ff = AllChem.UFFGetMoleculeForceField(mol, confId=conf_id)
ff.Initialize()
# minimises a conformer within a mol in place.
ff.Minimize(max_iter)
return mol
def _get_symmetry_rotation_matrix(sign: tf.Tensor) -> tf.Tensor:
"""Returns the 2d/3d matrix for mirror symmetry."""
zero = tf.zeros_like(sign)
one = tf.ones_like(sign)
# pylint: disable=bad-whitespace,bad-continuation
rot = [sign, zero, zero,
zero, one, zero,
zero, zero, one]
# pylint: enable=bad-whitespace,bad-continuation
shape = (3, 3)
rot = tf.stack(rot, axis=-1)
rot = tf.reshape(rot, shape)
return rot
def _quaternion_to_rotation_matrix(quaternion: tf.Tensor) -> tf.Tensor:
"""Converts a batch of quaternions to a batch of rotation matrices."""
q0 = quaternion[0]
q1 = quaternion[1]
q2 = quaternion[2]
q3 = quaternion[3]
r00 = 2 * (q0 * q0 + q1 * q1) - 1
r01 = 2 * (q1 * q2 - q0 * q3)
r02 = 2 * (q1 * q3 + q0 * q2)
r10 = 2 * (q1 * q2 + q0 * q3)
r11 = 2 * (q0 * q0 + q2 * q2) - 1
r12 = 2 * (q2 * q3 - q0 * q1)
r20 = 2 * (q1 * q3 - q0 * q2)
r21 = 2 * (q2 * q3 + q0 * q1)
r22 = 2 * (q0 * q0 + q3 * q3) - 1
matrix = tf.stack([r00, r01, r02,
r10, r11, r12,
r20, r21, r22], axis=-1)
return tf.reshape(matrix, [3, 3])
def _get_random_rotation_3d() -> tf.Tensor:
random_quaternions = tf.random.normal(
shape=[4], dtype=tf.float32)
random_quaternions /= tf.linalg.norm(
random_quaternions, axis=-1, keepdims=True)
return _quaternion_to_rotation_matrix(random_quaternions)
def _get_random_mirror_symmetry() -> tf.Tensor:
random_0_1 = tf.random.uniform(
shape=(), minval=0, maxval=2, dtype=tf.int32)
random_signs = tf.cast((2 * random_0_1) - 1, tf.float32)
return _get_symmetry_rotation_matrix(random_signs)
================================================
FILE: ogb_lsc/pcq/dataset_utils.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Dataset utilities."""
import functools
from typing import List, Optional
import jax
import jraph
from ml_collections import config_dict
import numpy as np
from ogb import utils
from ogb.utils import features
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
import tree
# pylint: disable=g-bad-import-order
# pytype: disable=import-error
import batching_utils
import conformer_utils
import datasets
curry = lambda f: functools.partial(functools.partial, f)
def build_dataset_iterator(
data_root: str,
split: str,
dynamic_batch_size_config: config_dict.ConfigDict,
sample_random: float,
cached_conformers_file: str,
debug: bool = False,
is_training: bool = True,
augment_with_random_mirror_symmetry: bool = False,
positions_noise_std: Optional[float] = None,
k_fold_split_id: Optional[int] = None,
num_k_fold_splits: Optional[int] = None,
filter_in_or_out_samples_with_nans_in_conformers: Optional[str] = None,
):
"""Returns an iterator over Batches from the dataset."""
if debug:
max_items_to_read_from_dataset = 10
prefetch_buffer_size = 1
shuffle_buffer_size = 1
else:
max_items_to_read_from_dataset = -1 # < 0 means no limit.
prefetch_buffer_size = 64
# It can take a while to fill the shuffle buffer with k fold splits.
shuffle_buffer_size = 128 if k_fold_split_id is None else int(1e6)
num_local_devices = jax.local_device_count()
# Load all smile strings.
indices, smiles, labels = _load_smiles(
data_root,
split,
k_fold_split_id=k_fold_split_id,
num_k_fold_splits=num_k_fold_splits)
if debug:
indices = indices[:100]
smiles = smiles[:100]
labels = labels[:100]
# Generate all conformer features from smile strings ahead of time.
# This gives us a boost from multi-parallelism as opposed to doing it
# online.
conformers = _load_conformers(indices, smiles, cached_conformers_file)
data_generator = (
lambda: _get_pcq_graph_generator(indices, smiles, labels, conformers))
# Create a dataset yielding graphs from smile strings.
example = next(data_generator())
signature_from_example = tree.map_structure(_numpy_to_tensor_spec, example)
ds = tf.data.Dataset.from_generator(
data_generator, output_signature=signature_from_example)
ds = ds.take(max_items_to_read_from_dataset)
ds = ds.cache()
if is_training:
ds = ds.shuffle(shuffle_buffer_size)
# Apply transformations.
def map_fn(graph, conformer_positions):
graph = _maybe_one_hot_atoms_with_noise(
graph, is_training=is_training, sample_random=sample_random)
# Add conformer features.
graph = _add_conformer_features(
graph,
conformer_positions,
augment_with_random_mirror_symmetry=augment_with_random_mirror_symmetry,
noise_std=positions_noise_std,
is_training=is_training,
)
return _downcast_ints(graph)
ds = ds.map(map_fn, num_parallel_calls=tf.data.AUTOTUNE)
if filter_in_or_out_samples_with_nans_in_conformers:
if filter_in_or_out_samples_with_nans_in_conformers not in ("in", "out"):
raise ValueError(
"Unknown value specified for the argument "
"`filter_in_or_out_samples_with_nans_in_conformers`: %s" %
filter_in_or_out_samples_with_nans_in_conformers)
filter_fn = _get_conformer_filter(
with_nans=(filter_in_or_out_samples_with_nans_in_conformers == "in"))
ds = ds.filter(filter_fn)
if is_training:
ds = ds.shard(jax.process_count(), jax.process_index())
ds = ds.repeat()
ds = ds.prefetch(prefetch_buffer_size)
it = tfds.as_numpy(ds)
# Dynamic batching.
batched_gen = batching_utils.dynamically_batch(
it,
n_node=dynamic_batch_size_config.n_node + 1,
n_edge=dynamic_batch_size_config.n_edge,
n_graph=dynamic_batch_size_config.n_graph + 1,
)
if is_training:
# Stack `num_local_devices` of batches together for pmap updates.
batch_size = num_local_devices
def _batch(l):
assert l
return tree.map_structure(lambda *l: np.stack(l, axis=0), *l)
def batcher_fn():
batch = []
for sample in batched_gen:
batch.append(sample)
if len(batch) == batch_size:
yield _batch(batch)
batch = []
if batch:
yield _batch(batch)
for sample in batcher_fn():
yield sample
else:
for sample in batched_gen:
yield sample
def _get_conformer_filter(with_nans: bool):
"""Selects a conformer filter to apply.
Args:
with_nans: Filter only selects samples with NaNs in conformer features.
Else, selects samples without any NaNs in conformer features.
Returns:
A function that can be used with tf.data.Dataset.filter().
Raises:
ValueError:
If the input graph to the filter has no conformer features to filter.
"""
def _filter(graph: jraph.GraphsTuple) -> tf.Tensor:
if ("positions" not in graph.nodes) or (
"positions_targets" not in graph.nodes) or (
"positions_nan_mask" not in graph.globals):
raise ValueError("Conformer features not available to filter.")
any_nan = tf.logical_not(tf.squeeze(graph.globals["positions_nan_mask"]))
return any_nan if with_nans else tf.logical_not(any_nan)
return _filter
def _numpy_to_tensor_spec(arr: np.ndarray) -> tf.TensorSpec:
if not isinstance(arr, np.ndarray):
return tf.TensorSpec([],
dtype=tf.int32 if isinstance(arr, int) else tf.float32)
elif arr.shape:
return tf.TensorSpec((None,) + arr.shape[1:], arr.dtype)
else:
return tf.TensorSpec([], arr.dtype)
def _sample_uniform_categorical(num: int, size: int) -> tf.Tensor:
return tf.random.categorical(tf.math.log([[1 / size] * size]), num)[0]
@curry(jax.tree_map)
def _downcast_ints(x):
if x.dtype == tf.int64:
return tf.cast(x, tf.int32)
return x
def _one_hot_atoms(atoms: tf.Tensor) -> tf.Tensor:
vocab_sizes = features.get_atom_feature_dims()
one_hots = []
for i in range(atoms.shape[1]):
one_hots.append(tf.one_hot(atoms[:, i], vocab_sizes[i], dtype=tf.float32))
return tf.concat(one_hots, axis=-1)
def _sample_one_hot_atoms(atoms: tf.Tensor) -> tf.Tensor:
vocab_sizes = features.get_atom_feature_dims()
one_hots = []
num_atoms = tf.shape(atoms)[0]
for i in range(atoms.shape[1]):
sampled_category = _sample_uniform_categorical(num_atoms, vocab_sizes[i])
one_hots.append(
tf.one_hot(sampled_category, vocab_sizes[i], dtype=tf.float32))
return tf.concat(one_hots, axis=-1)
def _one_hot_bonds(bonds: tf.Tensor) -> tf.Tensor:
vocab_sizes = features.get_bond_feature_dims()
one_hots = []
for i in range(bonds.shape[1]):
one_hots.append(tf.one_hot(bonds[:, i], vocab_sizes[i], dtype=tf.float32))
return tf.concat(one_hots, axis=-1)
def _sample_one_hot_bonds(bonds: tf.Tensor) -> tf.Tensor:
vocab_sizes = features.get_bond_feature_dims()
one_hots = []
num_bonds = tf.shape(bonds)[0]
for i in range(bonds.shape[1]):
sampled_category = _sample_uniform_categorical(num_bonds, vocab_sizes[i])
one_hots.append(
tf.one_hot(sampled_category, vocab_sizes[i], dtype=tf.float32))
return tf.concat(one_hots, axis=-1)
def _maybe_one_hot_atoms_with_noise(
x,
is_training: bool,
sample_random: float,
):
"""One hot atoms with noise."""
gt_nodes = _one_hot_atoms(x.nodes)
gt_edges = _one_hot_bonds(x.edges)
if is_training:
num_nodes = tf.shape(x.nodes)[0]
sample_node_or_not = tf.random.uniform([num_nodes],
maxval=1) < sample_random
nodes = tf.where(
tf.expand_dims(sample_node_or_not, axis=-1),
_sample_one_hot_atoms(x.nodes), gt_nodes)
num_edges = tf.shape(x.edges)[0]
sample_edges_or_not = tf.random.uniform([num_edges],
maxval=1) < sample_random
edges = tf.where(
tf.expand_dims(sample_edges_or_not, axis=-1),
_sample_one_hot_bonds(x.edges), gt_edges)
else:
nodes = gt_nodes
edges = gt_edges
return x._replace(
nodes={
"atom_one_hots_targets": gt_nodes,
"atom_one_hots": nodes,
},
edges={
"bond_one_hots_targets": gt_edges,
"bond_one_hots": edges
})
def _load_smiles(
data_root: str,
split: str,
k_fold_split_id: int,
num_k_fold_splits: int,
):
"""Loads smiles trings for the input split."""
if split == "test" or k_fold_split_id is None:
indices = datasets.load_splits()[split]
elif split == "train":
indices = datasets.load_all_except_kth_fold_indices(
data_root, k_fold_split_id, num_k_fold_splits)
indices += datasets.load_splits()["train"]
else:
assert split == "valid"
indices = datasets.load_kth_fold_indices(data_root, k_fold_split_id)
smiles_and_labels = datasets.load_smile_strings(with_labels=True)
smiles, labels = list(zip(*smiles_and_labels))
return indices, [smiles[i] for i in indices], [labels[i] for i in indices]
def _convert_ogb_graph_to_graphs_tuple(ogb_graph):
"""Converts an OGB Graph to a GraphsTuple."""
senders = ogb_graph["edge_index"][0]
receivers = ogb_graph["edge_index"][1]
edges = ogb_graph["edge_feat"]
nodes = ogb_graph["node_feat"]
n_node = np.array([ogb_graph["num_nodes"]])
n_edge = np.array([len(senders)])
graph = jraph.GraphsTuple(
nodes=nodes,
edges=edges,
senders=senders,
receivers=receivers,
n_node=n_node,
n_edge=n_edge,
globals=None)
return tree.map_structure(lambda x: x if x is not None else np.array(0.),
graph)
def _load_conformers(indices: List[int],
smiles: List[str],
cached_conformers_file: str):
"""Loads conformers."""
smile_to_conformer = datasets.load_cached_conformers(cached_conformers_file)
conformers = []
for graph_idx, smile in zip(indices, smiles):
del graph_idx # Unused.
if smile not in smile_to_conformer:
raise KeyError("Cache did not have conformer entry for the smile %s" %
str(smile))
conformers.append(dict(conformer=smile_to_conformer[smile]))
return conformers
def _add_conformer_features(
graph,
conformer_features,
augment_with_random_mirror_symmetry: bool,
noise_std: float,
is_training: bool,
):
"""Adds conformer features."""
if not isinstance(graph.nodes, dict):
raise ValueError("Expected a dict type for `graph.nodes`.")
# Remove mean position to center around a canonical origin.
positions = conformer_features["conformer"]
# NaN's appear in ~0.13% of training, 0.104% of validation and 0.16% of test
# nodes.
# See this colab: http://shortn/_6UcuosxY7x.
nan_mask = tf.reduce_any(tf.math.is_nan(positions))
positions = tf.where(nan_mask, tf.constant(0., positions.dtype), positions)
positions -= tf.reduce_mean(positions, axis=0, keepdims=True)
# Optionally augment with a random rotation.
if is_training:
rot_mat = conformer_utils.get_random_rotation_matrix(
augment_with_random_mirror_symmetry)
positions = conformer_utils.rotate(positions, rot_mat)
positions_targets = positions
# Optionally add noise to the positions.
if noise_std and is_training:
positions = tf.random.normal(tf.shape(positions), positions, noise_std)
return graph._replace(
nodes=dict(
positions=positions,
positions_targets=positions_targets,
**graph.nodes),
globals={
"positions_nan_mask":
tf.expand_dims(tf.logical_not(nan_mask), axis=0),
**(graph.globals if isinstance(graph.globals, dict) else {})
})
def _get_pcq_graph_generator(indices, smiles, labels, conformers):
"""Returns a generator to yield graph."""
for idx, smile, conformer_positions, label in zip(indices, smiles, conformers,
labels):
graph = utils.smiles2graph(smile)
graph = _convert_ogb_graph_to_graphs_tuple(graph)
graph = graph._replace(
globals={
"target": np.array([label], dtype=np.float32),
"graph_index": np.array([idx], dtype=np.int32),
**(graph.globals if isinstance(graph.globals, dict) else {})
})
yield graph, conformer_positions
================================================
FILE: ogb_lsc/pcq/datasets.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PCQM4M-LSC datasets."""
import functools
import pickle
from typing import Dict, List, Tuple, Union
import numpy as np
from ogb import lsc
NUM_VALID_SAMPLES = 380_670
NUM_TEST_SAMPLES = 377_423
NORMALIZE_TARGET_MEAN = 5.690944545356371
NORMALIZE_TARGET_STD = 1.1561347795107815
def load_splits() -> Dict[str, List[int]]:
"""Loads dataset splits."""
dataset = _get_pcq_dataset(only_smiles=True)
return dataset.get_idx_split()
def load_kth_fold_indices(data_root: str, k_fold_split_id: int) -> List[int]:
"""Loads k-th fold indices."""
fname = f"{data_root}/k_fold_splits/{k_fold_split_id}.pkl"
return list(map(int, _load_pickle(fname)))
def load_all_except_kth_fold_indices(data_root: str, k_fold_split_id: int,
num_k_fold_splits: int) -> List[int]:
"""Loads indices except for the kth fold."""
if k_fold_split_id is None:
raise ValueError("Expected integer value for `k_fold_split_id`.")
indices = []
for index in range(num_k_fold_splits):
if index != k_fold_split_id:
indices += load_kth_fold_indices(data_root, index)
return indices
def load_smile_strings(
with_labels=False) -> List[Union[str, Tuple[str, np.ndarray]]]:
"""Loads the smile strings in the PCQ dataset."""
dataset = _get_pcq_dataset(only_smiles=True)
smiles = []
for i in range(len(dataset)):
smile, label = dataset[i]
if with_labels:
smiles.append((smile, label))
else:
smiles.append(smile)
return smiles
@functools.lru_cache()
def load_cached_conformers(cached_fname: str) -> Dict[str, np.ndarray]:
"""Returns cached dict mapping smile strings to conformer features."""
return _load_pickle(cached_fname)
@functools.lru_cache()
def _get_pcq_dataset(only_smiles: bool):
return lsc.PCQM4MDataset(only_smiles=only_smiles)
def _load_pickle(fname: str):
with open(fname, "rb") as f:
return pickle.load(f)
================================================
FILE: ogb_lsc/pcq/download_pcq.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Download data required for training and evaluating models."""
import pathlib
from absl import app
from absl import flags
from absl import logging
from google.cloud import storage
Path = pathlib.Path
_BUCKET_NAME = 'deepmind-ogb-lsc'
_MAX_DOWNLOAD_ATTEMPTS = 5
FLAGS = flags.FLAGS
flags.DEFINE_enum('payload', None, ['data', 'models'],
'Download "data" or "models"?')
flags.DEFINE_string('task_root', None, 'Local task root directory')
# GCS_DATA_ROOT = Path('pcq/data')
# GCS_MODEL_ROOT = Path('pcq/models')
DATA_RELATIVE_PATHS = [
'raw/data.csv.gz', 'preprocessed/smile_to_conformer.pkl',
'k_fold_splits'
]
class DataCorruptionError(Exception):
pass
def _get_gcs_root():
return Path('pcq') / FLAGS.payload
def _get_gcs_bucket():
storage_client = storage.Client.create_anonymous_client()
return storage_client.bucket(_BUCKET_NAME)
def _write_blob_to_destination(blob, task_root, ignore_existing=True):
"""Write the blob."""
logging.info("Copying blob: '%s'", blob.name)
destination_path = Path(task_root) / Path(*Path(blob.name).parts[1:])
logging.info(" ... to: '%s'", str(destination_path))
if ignore_existing and destination_path.exists():
return
destination_path.parent.mkdir(parents=True, exist_ok=True)
checksum = 'crc32c'
for attempt in range(_MAX_DOWNLOAD_ATTEMPTS):
try:
blob.download_to_filename(destination_path.as_posix(), checksum=checksum)
except storage.client.resumable_media.common.DataCorruption:
pass
else:
break
else:
raise DataCorruptionError(f"Checksum ('{checksum}') for {blob.name} failed "
f'after {attempt + 1} attempts')
def main(unused_argv):
bucket = _get_gcs_bucket()
if FLAGS.payload == 'data':
relative_paths = DATA_RELATIVE_PATHS
else:
relative_paths = (None,)
for relative_path in relative_paths:
if relative_path is None:
relative_path = str(_get_gcs_root())
else:
relative_path = str(_get_gcs_root() / relative_path)
logging.info("Copying relative path: '%s'", relative_path)
blobs = bucket.list_blobs(prefix=relative_path)
for blob in blobs:
_write_blob_to_destination(blob, FLAGS.task_root)
if __name__ == '__main__':
flags.mark_flag_as_required('payload')
flags.mark_flag_as_required('task_root')
app.run(main)
================================================
FILE: ogb_lsc/pcq/ensemble_predictions.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Script to generate ensembled PCQ test predictions."""
import collections
import os
import pathlib
from typing import List, NamedTuple
from absl import app
from absl import flags
from absl import logging
import dill
import numpy as np
from ogb import lsc
# pylint: disable=g-bad-import-order
# pytype: disable=import-error
import datasets
_NUM_SEEDS = 2
_CLIP_VALUE = 20.
_NUM_KFOLD_SPLITS = 10
_SEED_START = flags.DEFINE_integer(
'seed_start', 42, 'Initial seed for the list of ensemble models.')
_CONFORMER_PATH = flags.DEFINE_string(
'conformer_path', None, 'Path to conformer predictions.', required=True)
_NON_CONFORMER_PATH = flags.DEFINE_string(
'non_conformer_path',
None,
'Path to non-conformer predictions.',
required=True)
_OUTPUT_PATH = flags.DEFINE_string('output_path', None, 'Output path.')
_SPLIT = flags.DEFINE_enum('split', 'test', ['test', 'valid'],
'Split: valid or test.')
class _Predictions(NamedTuple):
predictions: np.ndarray
indices: np.ndarray
def _load_dill(fname) -> bytes:
with open(fname, 'rb') as f:
return dill.load(f)
def _sort_by_indices(predictions: _Predictions) -> _Predictions:
order = np.argsort(predictions.indices)
return _Predictions(
predictions=predictions.predictions[order],
indices=predictions.indices[order])
def load_predictions(path: str, split: str) -> _Predictions:
"""Load written prediction file."""
if len(os.listdir(path)) != 1:
raise ValueError('Prediction directory must have exactly '
'one prediction sub-directory: %s' % path)
prediction_subdir = os.listdir(path)[0]
return _Predictions(*_load_dill(f'{path}/{prediction_subdir}/{split}.dill'))
def mean_mae_distance(x, y):
return np.abs(x - y).mean()
def _load_valid_labels() -> np.ndarray:
labels = [label for _, label in datasets.load_smile_strings(with_labels=True)]
return np.array([labels[i] for i in datasets.load_splits()['valid']])
def evaluate_valid_predictions(ensembled_predictions: _Predictions):
"""Evaluates the predictions on the validation set."""
ensembled_predictions = _sort_by_indices(ensembled_predictions)
evaluator = lsc.PCQM4MEvaluator()
results = evaluator.eval(
dict(
y_pred=ensembled_predictions.predictions,
y_true=_load_valid_labels()))
logging.info('MAE on validation dataset: %f', results['mae'])
def clip_predictions(predictions: _Predictions) -> _Predictions:
return predictions._replace(
predictions=np.clip(predictions.predictions, 0., _CLIP_VALUE))
def _generate_test_prediction_file(test_predictions: np.ndarray,
output_path: pathlib.Path) -> pathlib.Path:
"""Generates the final file for submission."""
# Check that predictions are not nuts.
assert test_predictions.dtype in [np.float64, np.float32]
assert not np.any(np.isnan(test_predictions))
assert np.all(np.isfinite(test_predictions))
assert test_predictions.min() >= 0.
assert test_predictions.max() <= 40.
# Too risky to overwrite.
if output_path.exists():
raise ValueError(f'{output_path} already exists')
# Write to a local directory, and copy to final path (possibly cns).
# It is not possible to write directlt on CNS.
evaluator = lsc.PCQM4MEvaluator()
evaluator.save_test_submission(
dict(y_pred=test_predictions), str(output_path))
return output_path
def merge_complementary_results(split: str, results_a: _Predictions,
results_b: _Predictions) -> _Predictions:
"""Merges two prediction results with no overlap."""
indices_a = set(results_a.indices)
indices_b = set(results_b.indices)
assert not indices_a.intersection(indices_b)
if split == 'test':
merged_indices = list(sorted(indices_a | indices_b))
expected_indices = datasets.load_splits()[split]
assert np.all(expected_indices == merged_indices)
predictions = np.concatenate([results_a.predictions, results_b.predictions])
indices = np.concatenate([results_a.indices, results_b.indices])
predictions = _sort_by_indices(
_Predictions(indices=indices, predictions=predictions))
return predictions
def ensemble_valid_predictions(
predictions_list: List[_Predictions]) -> _Predictions:
"""Ensembles a list of predictions."""
index_to_predictions = collections.defaultdict(list)
for predictions in predictions_list:
for idx, pred in zip(predictions.indices, predictions.predictions):
index_to_predictions[idx].append(pred)
for idx, ensemble_list in index_to_predictions.items():
if len(ensemble_list) != _NUM_SEEDS:
raise RuntimeError(
'Graph index in the validation set received wrong number of '
'predictions to ensemble.')
index_to_predictions = {
k: np.median(pred_list, axis=0)
for k, pred_list in index_to_predictions.items()
}
return _sort_by_indices(
_Predictions(
indices=np.array(list(index_to_predictions.keys())),
predictions=np.array(list(index_to_predictions.values()))))
def ensemble_test_predictions(
predictions_list: List[_Predictions]) -> _Predictions:
"""Ensembles a list of predictions."""
predictions = np.median([pred.predictions for pred in predictions_list],
axis=0)
common_indices = predictions_list[0].indices
for preds in predictions_list[1:]:
assert np.all(preds.indices == common_indices)
return _Predictions(predictions=predictions, indices=common_indices)
def create_submission_from_predictions(
output_path: pathlib.Path, test_predictions: _Predictions) -> pathlib.Path:
"""Creates a submission for predictions on a path."""
assert _SPLIT.value == 'test'
output_path = _generate_test_prediction_file(
test_predictions.predictions,
output_path=output_path / 'submission_files')
return output_path / 'y_pred_pcqm4m.npz'
def merge_predictions(split: str) -> List[_Predictions]:
"""Generates features merged from conformer and non-conformer predictions."""
merged_predictions: List[_Predictions] = []
seed = _SEED_START.value
# Load conformer and non-conformer predictions.
for unused_seed_group in (0, 1):
for k in range(_NUM_KFOLD_SPLITS):
conformer_predictions: _Predictions = load_predictions(
f'{_CONFORMER_PATH.value}/k{k}_seed{seed}', split)
non_conformer_predictions: _Predictions = load_predictions(
f'{_NON_CONFORMER_PATH.value}/k{k}_seed{seed}', split)
merged_predictions.append(
merge_complementary_results(_SPLIT.value, conformer_predictions,
non_conformer_predictions))
seed += 1
return merged_predictions
def main(_):
split: str = _SPLIT.value
# Merge conformer and non-conformer predictions.
merged_predictions = merge_predictions(split)
# Clip before ensembling.
clipped_predictions = list(map(clip_predictions, merged_predictions))
# Ensemble predictions.
if split == 'valid':
ensembled_predictions = ensemble_valid_predictions(clipped_predictions)
else:
assert split == 'test'
ensembled_predictions = ensemble_test_predictions(clipped_predictions)
# Clip after ensembling.
ensembled_predictions = clip_predictions(ensembled_predictions)
ensembled_predictions_path = pathlib.Path(_OUTPUT_PATH.value)
ensembled_predictions_path.mkdir(parents=True, exist_ok=True)
with open(ensembled_predictions_path / f'{split}_predictions.dill',
'wb') as f:
dill.dump(ensembled_predictions, f)
if split == 'valid':
evaluate_valid_predictions(ensembled_predictions)
else:
assert split == 'test'
output_path = create_submission_from_predictions(ensembled_predictions_path,
ensembled_predictions)
logging.info('Submission files written to %s', output_path)
if __name__ == '__main__':
app.run(main)
================================================
FILE: ogb_lsc/pcq/experiment.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PCQM4M-LSC Jaxline experiment."""
import datetime
import functools
import os
import signal
import threading
from typing import Iterable, Mapping, NamedTuple, Tuple
from absl import app
from absl import flags
from absl import logging
import chex
import dill
import haiku as hk
import jax
from jax.config import config as jax_config
import jax.numpy as jnp
from jaxline import experiment
from jaxline import platform
from jaxline import utils
import jraph
import numpy as np
import optax
import tensorflow as tf
import tree
# pylint: disable=g-bad-import-order
import dataset_utils
import datasets
import model
FLAGS = flags.FLAGS
def _get_step_date_label(global_step: int):
# Date removing microseconds.
date_str = datetime.datetime.now().isoformat().split('.')[0]
return f'step_{global_step}_{date_str}'
class _Predictions(NamedTuple):
predictions: np.ndarray
indices: np.ndarray
def tf1_ema(ema_value, current_value, decay, step):
"""Implements EMA with TF1-style decay warmup."""
decay = jnp.minimum(decay, (1.0 + step) / (10.0 + step))
return ema_value * decay + current_value * (1 - decay)
def _sort_predictions_by_indices(predictions: _Predictions):
sorted_order = np.argsort(predictions.indices)
return _Predictions(
predictions=predictions.predictions[sorted_order],
indices=predictions.indices[sorted_order])
class Experiment(experiment.AbstractExperiment):
"""OGB Graph Property Prediction GraphNet experiment."""
CHECKPOINT_ATTRS = {
'_params': 'params',
'_opt_state': 'opt_state',
'_network_state': 'network_state',
'_ema_network_state': 'ema_network_state',
'_ema_params': 'ema_params',
}
def __init__(self, mode, init_rng, config):
"""Initializes experiment."""
super(Experiment, self).__init__(mode=mode, init_rng=init_rng)
if mode not in ('train', 'eval', 'train_eval_multithreaded'):
raise ValueError(f'Invalid mode {mode}.')
# Do not use accelerators in data pipeline.
tf.config.experimental.set_visible_devices([], device_type='GPU')
tf.config.experimental.set_visible_devices([], device_type='TPU')
self.mode = mode
self.init_rng = init_rng
self.config = config
self.loss = None
self.forward = None
# Needed for checkpoint restore.
self._params = None
self._network_state = None
self._opt_state = None
self._ema_network_state = None
self._ema_params = None
# _ _
# | |_ _ __ __ _(_)_ __
# | __| "__/ _` | | "_ \
# | |_| | | (_| | | | | |
# \__|_| \__,_|_|_| |_|
#
def step(self, global_step: jnp.ndarray, rng: jnp.ndarray, **unused_args):
"""See Jaxline base class."""
if self.loss is None:
self._train_init()
graph = next(self._train_input)
out = self.update_parameters(
self._params,
self._ema_params,
self._network_state,
self._ema_network_state,
self._opt_state,
global_step,
rng,
graph._asdict())
(self._params, self._ema_params, self._network_state,
self._ema_network_state, self._opt_state, scalars) = out
return utils.get_first(scalars)
def _construct_loss_config(self):
loss_config = getattr(model, self.config.model.loss_config_name)
if self.config.model.loss_config_name == 'RegressionLossConfig':
return loss_config(
mean=datasets.NORMALIZE_TARGET_MEAN,
std=datasets.NORMALIZE_TARGET_STD,
kwargs=self.config.model.loss_kwargs)
else:
raise ValueError('Unknown Loss Config')
def _train_init(self):
self.loss = hk.transform_with_state(self._loss)
self._train_input = utils.py_prefetch(
lambda: self._build_numpy_dataset_iterator('train', is_training=True))
init_stacked_graphs = next(self._train_input)
init_key = utils.bcast_local_devices(self.init_rng)
p_init = jax.pmap(self.loss.init)
self._params, self._network_state = p_init(init_key,
**init_stacked_graphs._asdict())
# Learning rate scheduling.
lr_schedule = optax.warmup_cosine_decay_schedule(
**self.config.optimizer.lr_schedule)
self.optimizer = getattr(optax, self.config.optimizer.name)(
learning_rate=lr_schedule, **self.config.optimizer.optimizer_kwargs)
self._opt_state = jax.pmap(self.optimizer.init)(self._params)
self.update_parameters = jax.pmap(self._update_parameters, axis_name='i')
if self.config.ema:
self._ema_params = self._params
self._ema_network_state = self._network_state
def _loss(
self, **graph: Mapping[str, chex.ArrayTree]) -> chex.ArrayTree:
graph = jraph.GraphsTuple(**graph)
model_instance = model.GraphPropertyEncodeProcessDecode(
loss_config=self._construct_loss_config(), **self.config.model)
loss, scalars = model_instance.get_loss(graph)
return loss, scalars
def _maybe_save_predictions(
self,
predictions: jnp.ndarray,
split: str,
global_step: jnp.ndarray,
):
if not self.config.predictions_dir:
return
output_dir = os.path.join(self.config.predictions_dir,
_get_step_date_label(global_step))
os.makedirs(output_dir, exist_ok=True)
output_path = os.path.join(output_dir, split + '.dill')
with open(output_path, 'wb') as f:
dill.dump(predictions, f)
logging.info('Saved %s predictions at: %s', split, output_path)
def _build_numpy_dataset_iterator(self, split: str, is_training: bool):
dynamic_batch_size_config = (
self.config.training.dynamic_batch_size
if is_training else self.config.evaluation.dynamic_batch_size)
return dataset_utils.build_dataset_iterator(
split=split,
dynamic_batch_size_config=dynamic_batch_size_config,
sample_random=self.config.sample_random,
debug=self.config.debug,
is_training=is_training,
**self.config.dataset_config)
def _update_parameters(
self,
params: hk.Params,
ema_params: hk.Params,
network_state: hk.State,
ema_network_state: hk.State,
opt_state: optax.OptState,
global_step: jnp.ndarray,
rng: jnp.ndarray,
graph: jraph.GraphsTuple,
) -> Tuple[hk.Params, hk.Params, hk.State, hk.State, optax.OptState,
chex.ArrayTree]:
"""Updates parameters."""
def get_loss(*x, **graph):
(loss, scalars), network_state = self.loss.apply(*x, **graph)
return loss, (scalars, network_state)
grad_loss_fn = jax.grad(get_loss, has_aux=True)
scaled_grads, (scalars, network_state) = grad_loss_fn(
params, network_state, rng, **graph)
grads = jax.lax.psum(scaled_grads, axis_name='i')
updates, opt_state = self.optimizer.update(grads, opt_state, params)
params = optax.apply_updates(params, updates)
if ema_params is not None:
ema = lambda x, y: tf1_ema(x, y, self.config.ema_decay, global_step)
ema_params = jax.tree_map(ema, ema_params, params)
ema_network_state = jax.tree_map(ema, ema_network_state,
network_state)
return params, ema_params, network_state, ema_network_state, opt_state, scalars
# _
# _____ ____ _| |
# / _ \ \ / / _` | |
# | __/\ V / (_| | |
# \___| \_/ \__,_|_|
#
def evaluate(self, global_step: jnp.ndarray, rng: jnp.ndarray,
**unused_kwargs) -> chex.ArrayTree:
"""See Jaxline base class."""
if self.forward is None:
self._eval_init()
if self.config.ema:
params = utils.get_first(self._ema_params)
state = utils.get_first(self._ema_network_state)
else:
params = utils.get_first(self._params)
state = utils.get_first(self._network_state)
rng = utils.get_first(rng)
split = self.config.evaluation.split
predictions, scalars = self._get_predictions(
params, state, rng,
utils.py_prefetch(
functools.partial(
self._build_numpy_dataset_iterator, split, is_training=False)))
self._maybe_save_predictions(predictions, split, global_step[0])
return scalars
def _sum_regression_scalars(self, preds: jnp.ndarray,
graph: jraph.GraphsTuple) -> chex.ArrayTree:
"""Creates unnormalised values for accumulation."""
targets = graph.globals['target']
graph_mask = jraph.get_graph_padding_mask(graph)
# Sum for accumulation, normalise later since there are a
# variable number of graphs per batch.
mae = model.sum_with_mask(jnp.abs(targets - preds), graph_mask)
mse = model.sum_with_mask((targets - preds)**2, graph_mask)
count = jnp.sum(graph_mask)
return {'values': {'mae': mae.item(), 'mse': mse.item()},
'counts': {'mae': count.item(), 'mse': count.item()}}
def _get_prediction(
self,
params: hk.Params,
state: hk.State,
rng: jnp.ndarray,
graph: jraph.GraphsTuple,
) -> np.ndarray:
"""Returns predictions for all the graphs in the dataset split."""
model_out, _ = self.eval_apply(params, state, rng, **graph._asdict())
prediction = np.squeeze(model_out['globals'], axis=1)
return prediction
def _get_predictions(
self,
params: hk.Params,
state: hk.State,
rng: jnp.ndarray,
graph_iterator: Iterable[jraph.GraphsTuple],
) -> Tuple[_Predictions, chex.ArrayTree]:
all_scalars = []
predictions = []
graph_indices = []
for i, graph in enumerate(graph_iterator):
prediction = self._get_prediction(params, state, rng, graph)
if 'target' in graph.globals and not jnp.isnan(
graph.globals['target']).any():
scalars = self._sum_regression_scalars(prediction, graph)
all_scalars.append(scalars)
num_padding_graphs = jraph.get_number_of_padding_with_graphs_graphs(graph)
num_valid_graphs = len(graph.n_node) - num_padding_graphs
depadded_prediction = prediction[:num_valid_graphs]
predictions.append(depadded_prediction)
graph_indices.append(graph.globals['graph_index'][:num_valid_graphs])
if i % 1000 == 0:
logging.info('Generated predictions for %d batches so far', i + 1)
predictions = _sort_predictions_by_indices(
_Predictions(
predictions=np.concatenate(predictions),
indices=np.concatenate(graph_indices)))
if all_scalars:
sum_all_args = lambda *l: sum(l)
# Sum over graphs in the dataset.
accum_scalars = tree.map_structure(sum_all_args, *all_scalars)
scalars = tree.map_structure(lambda x, y: x / y, accum_scalars['values'],
accum_scalars['counts'])
else:
scalars = {}
return predictions, scalars
def _eval_init(self):
self.forward = hk.transform_with_state(self._forward)
self.eval_apply = jax.jit(self.forward.apply)
def _forward(self, **graph: Mapping[str, chex.ArrayTree]) -> chex.ArrayTree:
graph = jraph.GraphsTuple(**graph)
model_instance = model.GraphPropertyEncodeProcessDecode(
loss_config=self._construct_loss_config(), **self.config.model)
return model_instance(graph)
def _restore_state_to_in_memory_checkpointer(restore_path):
"""Initializes experiment state from a checkpoint."""
# Load pretrained experiment state.
python_state_path = os.path.join(restore_path, 'checkpoint.dill')
with open(python_state_path, 'rb') as f:
pretrained_state = dill.load(f)
logging.info('Restored checkpoint from %s', python_state_path)
# Assign state to a dummy experiment instance for the in-memory checkpointer,
# broadcasting to devices.
dummy_experiment = Experiment(
mode='train', init_rng=0, config=FLAGS.config.experiment_kwargs.config)
for attribute, key in Experiment.CHECKPOINT_ATTRS.items():
setattr(dummy_experiment, attribute,
utils.bcast_local_devices(pretrained_state[key]))
jaxline_state = dict(
global_step=pretrained_state['global_step'],
experiment_module=dummy_experiment)
snapshot = utils.SnapshotNT(0, jaxline_state)
# Finally, seed the jaxline `utils.InMemoryCheckpointer` global dict.
utils.GLOBAL_CHECKPOINT_DICT['latest'] = utils.CheckpointNT(
threading.local(), [snapshot])
def _save_state_from_in_memory_checkpointer(
save_path, experiment_class: experiment.AbstractExperiment):
"""Saves experiment state to a checkpoint."""
logging.info('Saving model.')
for checkpoint_name, checkpoint in utils.GLOBAL_CHECKPOINT_DICT.items():
if not checkpoint.history:
logging.info('Nothing to save in "%s"', checkpoint_name)
continue
pickle_nest = checkpoint.history[-1].pickle_nest
global_step = pickle_nest['global_step']
state_dict = {'global_step': global_step}
for attribute, key in experiment_class.CHECKPOINT_ATTRS.items():
state_dict[key] = utils.get_first(
getattr(pickle_nest['experiment_module'], attribute))
save_dir = os.path.join(
save_path, checkpoint_name, _get_step_date_label(global_step))
python_state_path = os.path.join(save_dir, 'checkpoint.dill')
os.makedirs(save_dir, exist_ok=True)
with open(python_state_path, 'wb') as f:
dill.dump(state_dict, f)
logging.info(
'Saved "%s" checkpoint to %s', checkpoint_name, python_state_path)
def _setup_signals(save_model_fn):
"""Sets up a signal for model saving."""
# Save a model on Ctrl+C.
def sigint_handler(unused_sig, unused_frame):
# Ideally, rather than saving immediately, we would then "wait" for a good
# time to save. In practice this reads from an in-memory checkpoint that
# only saves every 30 seconds or so, so chances of race conditions are very
# small.
save_model_fn()
logging.info(r'Use `Ctrl+\` to save and exit.')
# Exit on `Ctrl+\`, saving a model.
prev_sigquit_handler = signal.getsignal(signal.SIGQUIT)
def sigquit_handler(unused_sig, unused_frame):
# Restore previous handler early, just in case something goes wrong in the
# next lines, so it is possible to press again and exit.
signal.signal(signal.SIGQUIT, prev_sigquit_handler)
save_model_fn()
logging.info(r'Exiting on `Ctrl+\`')
# Re-raise for clean exit.
os.kill(os.getpid(), signal.SIGQUIT)
signal.signal(signal.SIGINT, sigint_handler)
signal.signal(signal.SIGQUIT, sigquit_handler)
def main(argv, experiment_class: experiment.AbstractExperiment):
# Maybe restore a model.
restore_path = FLAGS.config.restore_path
if restore_path:
_restore_state_to_in_memory_checkpointer(restore_path)
# Maybe save a model.
save_dir = os.path.join(FLAGS.config.checkpoint_dir, 'models')
if FLAGS.config.one_off_evaluate:
save_model_fn = lambda: None # No need to save checkpoint in this case.
else:
save_model_fn = functools.partial(
_save_state_from_in_memory_checkpointer, save_dir, experiment_class)
_setup_signals(save_model_fn) # Save on Ctrl+C (continue) or Ctrl+\ (exit).
try:
platform.main(experiment_class, argv)
finally:
save_model_fn() # Save at the end of training or in case of exception.
if __name__ == '__main__':
jax_config.update('jax_debug_nans', False)
flags.mark_flag_as_required('config')
app.run(lambda argv: main(argv, Experiment))
================================================
FILE: ogb_lsc/pcq/generate_conformer_features.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Generate conformer features to be used for training/predictions."""
import multiprocessing as mp
import pickle
from typing import List
from absl import app
from absl import flags
import numpy as np
# pylint: disable=g-bad-import-order
import conformer_utils
import datasets
_SPLITS = flags.DEFINE_spaceseplist(
'splits', ['test'], 'Splits to compute conformer features for.')
_OUTPUT_FILE = flags.DEFINE_string(
'output_file',
None,
required=True,
help='Output file name to write the generated conformer features to.')
_NUM_PROCS = flags.DEFINE_integer(
'num_parallel_procs', 64,
'Number of parallel processes to use for conformer generation.')
def generate_conformer_features(smiles: List[str]) -> List[np.ndarray]:
# Conformer generation is a CPU-bound task and hence can get a boost from
# parallel processing.
# To avoid GIL, we choose multiprocessing instead of the
# simpler multi-threading option here for parallel computing.
with mp.Pool(_NUM_PROCS.value) as pool:
return list(pool.map(conformer_utils.compute_conformer, smiles))
def main(_):
smiles = datasets.load_smile_strings(with_labels=False)
indices = set()
for split in _SPLITS.value:
indices.update(datasets.load_splits()[split])
smiles = [smiles[i] for i in sorted(indices)]
conformers = generate_conformer_features(smiles)
smiles_to_conformers = dict(zip(smiles, conformers))
with open(_OUTPUT_FILE.value, 'wb') as f:
pickle.dump(smiles_to_conformers, f)
if __name__ == '__main__':
app.run(main)
================================================
FILE: ogb_lsc/pcq/generate_validation_splits.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Generates the k-fold validation splits."""
import os
import pickle
from absl import app
from absl import flags
from absl import logging
import numpy as np
# pylint: disable=g-bad-import-order
import datasets
_OUTPUT_DIR = flags.DEFINE_string(
'output_dir', None, required=True,
help='Output directory to write the splits to')
K = 10
def main(argv):
del argv
valid_indices = datasets.load_splits()['valid']
k_splits = np.split(valid_indices, K)
os.makedirs(_OUTPUT_DIR.value, exist_ok=True)
for k_i, split in enumerate(k_splits):
fname = os.path.join(_OUTPUT_DIR.value, f'{k_i}.pkl')
with open(fname, 'wb') as f:
pickle.dump(split, f)
logging.info('Saved: %s', fname)
if __name__ == '__main__':
app.run(main)
================================================
FILE: ogb_lsc/pcq/model.py
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PCQM4M-LSC models."""
import copy
import functools
from typing import Any, Dict, Mapping, Sequence, Tuple
import chex
import haiku as hk
import jax
import jax.numpy as jnp
import jraph
from ml_collections import config_dict
_REDUCER_NAMES = {
"sum": jax.ops.segment_sum,
"mean": jraph.segment_mean,
}
_NUM_EDGE_FEATURES = 13
_NUM_NODE_FEATURES = 173
@chex.dataclass
class RegressionLossConfig:
"""Regression Loss Config."""
# For normalization and denormalization.
std: float
mean: float
kwargs: Mapping[str, Any]
out_size: int = 1
def _sigmoid_cross_entropy(
logits: jax.Array,
labels: jax.Array,
) -> jax.Array:
log_p = jax.nn.log_sigmoid(logits)
log_not_p = jax.nn.log_sigmoid(-logits)
return -labels * log_p - (1. - labels) * log_not_p
def _softmax_cross_entropy(
logits: jax.Array,
targets: jax.Array,
) -> jax.Array:
logits = jax.nn.log_softmax(logits)
return -jnp.sum(targets * logits, axis=-1)
def _regression_loss(
pred: jnp.ndarray,
targets: jnp.ndarray,
exponent: int,
) -> jnp.ndarray:
"""Regression loss."""
error = pred - targets
if exponent == 2:
return error ** 2
elif exponent == 1:
return jnp.abs(error)
else:
raise ValueError(f"Unsupported exponent value {exponent}.")
def _build_mlp(
name: str,
output_sizes: Sequence[int],
use_layer_norm=False,
activation=jax.nn.relu,
):
"""Builds an MLP, optionally with layernorm."""
net = hk.nets.MLP(
output_sizes=output_sizes, name=name + "_mlp", activation=activation)
if use_layer_norm:
layer_norm = hk.LayerNorm(
axis=-1,
create_scale=True,
create_offset=True,
name=name + "_layer_norm")
net = hk.Sequential([net, layer_norm])
return jraph.concatenated_args(net)
def _compute_relative_displacement_and_distance(
graph: jraph.GraphsTuple,
normalization_factor: float,
use_target: bool,
) -> Tuple[jnp.ndarray, jnp.ndarray]:
"""Computes relative displacements and distances."""
if use_target:
node_positions = graph.nodes["positions_targets"]
else:
node_positions = graph.nodes["positions"]
relative_displacement = node_positions[
graph.receivers] - node_positions[graph.senders]
# Note due to the random rotations in space, mean across all nodes across
# all batches is guaranteed to be zero, and the standard deviation is
# guaranteed to be the same for all 3 coordinates, so we only need to scale
# by a single value.
relative_displacement /= normalization_factor
relative_distance = jnp.linalg.norm(
relative_displacement, axis=-1, keepdims=True)
return relative_displacement, relative_distance
def _broadcast_global_to_nodes(
global_feature: jnp.ndarray,
graph: jraph.GraphsTuple,
) -> jnp.ndarray:
graph_idx = jnp.arange(graph.n_node.shape[0])
sum_n_node = jax.tree_leaves(graph.nodes)[0].shape[0]
node_graph_idx = jnp.repeat(
graph_idx, graph.n_node, axis=0, total_repeat_length=sum_n_node)
return global_feature[node_graph_idx]
def _broadcast_global_to_edges(
global_feature: jnp.ndarray,
graph: jraph.GraphsTuple,
) -> jnp.ndarray:
graph_idx = jnp.arange(graph.n_edge.shape[0])
sum_n_edge = graph.senders.shape[0]
edge_graph_idx = jnp.repeat(
graph_idx, graph.n_edge, axis=0, total_repeat_length=sum_n_edge)
return global_feature[edge_graph_idx]
class GraphPropertyEncodeProcessDecode(hk.Module):
"""Encode-process-decode model for graph property prediction."""
def __init__(
self,
loss_config: config_dict.ConfigDict,
mlp_hidden_size: int,
mlp_layers: int,
latent_size: int,
use_layer_norm: bool,
num_message_passing_steps: int,
shared_message_passing_weights: bool,
mask_padding_graph_at_every_step: bool,
loss_config_name: str,
loss_kwargs: config_dict.ConfigDict,
processor_mode: str,
global_reducer: str,
node_reducer: str,
dropedge_rate: float,
dropnode_rate: float,
aux_multiplier: float,
ignore_globals: bool,
ignore_globals_from_final_layer_for_predictions: bool,
add_relative_distance: bool = False,
add_relative_displacement: bool = False,
add_absolute_positions: bool = False,
position_normalization: float = 1.,
relative_displacement_normalization: float = 1.,
add_misc_node_features: bool = None,
name="GraphPropertyEncodeProcessDecode",
):
super(GraphPropertyEncodeProcessDecode, self).__init__()
self._loss_config = loss_config
self._config = config_dict.ConfigDict(dict(
loss_config=loss_config,
mlp_hidden_size=mlp_hidden_size,
mlp_layers=mlp_layers,
latent_size=latent_size,
use_layer_norm=use_layer_norm,
num_message_passing_steps=num_message_passing_steps,
shared_message_passing_weights=shared_message_passing_weights,
mask_padding_graph_at_every_step=mask_padding_graph_at_every_step,
loss_config_name=loss_config_name,
loss_kwargs=loss_kwargs,
processor_mode=processor_mode,
global_reducer=global_reducer,
node_reducer=node_reducer,
dropedge_rate=dropedge_rate,
dropnode_rate=dropnode_rate,
aux_multiplier=aux_multiplier,
ignore_globals=ignore_globals,
ignore_globals_from_final_layer_for_predictions=ignore_globals_from_final_layer_for_predictions,
add_relative_distance=add_relative_distance,
add_relative_displacement=add_relative_displacement,
add_absolute_positions=add_absolute_positions,
position_normalization=position_normalization,
relative_displacement_normalization=relative_displacement_normalization,
add_misc_node_features=add_misc_node_features,
))
def __call__(self, graph: jraph.GraphsTuple) -> chex.ArrayTree:
"""Model inference step."""
out = self._forward(graph, is_training=False)
if isinstance(self._loss_config, RegressionLossConfig):
out["globals"] = out[
"globals"]*self._loss_config.std + self._loss_config.mean
return out
@hk.experimental.name_like("__call__")
def get_loss(
self,
graph: jraph.GraphsTuple,
is_training: bool = True,
) -> Tuple[jnp.ndarray, chex.ArrayTree]:
"""Model loss."""
scalars = get_utilization_scalars(graph)
targets = copy.deepcopy(graph.globals["target"])
if len(targets.shape) == 1:
targets = targets[:, None]
del graph.globals["target"]
target_mask = None
if "target_mask" in graph.globals:
target_mask = copy.deepcopy(graph.globals["target_mask"])
del graph.globals["target_mask"]
out = self._forward(graph, is_training)
if isinstance(self._loss_config, RegressionLossConfig):
normalized_targets = (
(targets - self._loss_config.mean) / self._loss_config.std)
per_graph_and_head_loss = _regression_loss(
out["globals"], normalized_targets, **self._loss_config.kwargs)
else:
raise TypeError(type(self._loss_config))
# Mask out nans
if target_mask is None:
per_graph_and_head_loss = jnp.mean(per_graph_and_head_loss, axis=1)
else:
per_graph_and_head_loss = jnp.sum(
per_graph_and_head_loss * target_mask, axis=1)
per_graph_and_head_loss /= jnp.sum(target_mask + 1e-8, axis=1)
g_mask = jraph.get_graph_padding_mask(graph)
loss = _mean_with_mask(per_graph_and_head_loss, g_mask)
scalars.update({"loss": loss})
if self._config.aux_multiplier > 0:
atom_loss = self._get_node_auxiliary_loss(
graph, out["atom_one_hots"], graph.nodes["atom_one_hots_targets"],
is_regression=False)
bond_loss = self._get_edge_auxiliary_loss(
graph, out["bond_one_hots"], graph.edges["bond_one_hots_targets"],
is_regression=False)
loss += (atom_loss + bond_loss)*self._config.aux_multiplier
scalars.update({"atom_loss": atom_loss, "bond_loss": bond_loss})
scaled_loss = loss / jax.device_count()
scalars.update({"total_loss": loss})
return scaled_loss, scalars
@hk.transparent
def _prepare_features(self, graph: jraph.GraphsTuple) -> jraph.GraphsTuple:
"""Prepares features keys into flat node, edge and global features."""
# Collect edge features.
edge_features_list = [graph.edges["bond_one_hots"]]
if (self._config.add_relative_displacement or
self._config.add_relative_distance):
(relative_displacement, relative_distance
) = _compute_relative_displacement_and_distance(
graph, self._config.relative_displacement_normalization,
use_target=False)
if self._config.add_relative_displacement:
edge_features_list.append(relative_displacement)
if self._config.add_relative_distance:
edge_features_list.append(relative_distance)
mask_at_edges = _broadcast_global_to_edges(
graph.globals["positions_nan_mask"], graph)
edge_features_list.append(mask_at_edges[:, None].astype(jnp.float32))
edge_features = jnp.concatenate(edge_features_list, axis=-1)
# Collect node features
node_features_list = [graph.nodes["atom_one_hots"]]
if self._config.add_absolute_positions:
node_features_list.append(
graph.nodes["positions"] / self._config.position_normalization)
mask_at_nodes = _broadcast_global_to_nodes(
graph.globals["positions_nan_mask"], graph)
node_features_list.append(mask_at_nodes[:, None].astype(jnp.float32))
node_features = jnp.concatenate(node_features_list, axis=-1)
global_features = jnp.zeros((len(graph.n_node), self._config.latent_size))
chex.assert_tree_shape_prefix(global_features, (len(graph.n_node),))
return graph._replace(
nodes=node_features, edges=edge_features, globals=global_features)
@hk.transparent
def _encoder(
self,
graph: jraph.GraphsTuple,
is_training: bool,
) -> jraph.GraphsTuple:
"""Builds the encoder."""
del is_training # unused
graph = self._prepare_features(graph)
# Run encoders in all of the node, edge and global features.
output_sizes = [self._config.mlp_hidden_size] * self._config.mlp_layers
output_sizes += [self._config.latent_size]
build_mlp = functools.partial(
_build_mlp,
output_sizes=output_sizes,
use_layer_norm=self._config.use_layer_norm,
)
gmf = jraph.GraphMapFeatures(
embed_edge_fn=build_mlp("edge_encoder"),
embed_node_fn=build_mlp("node_encoder"),
embed_global_fn=None
if self._config.ignore_globals else build_mlp("global_encoder"),
)
return gmf(graph)
@hk.transparent
def _processor(
self,
graph: jraph.GraphsTuple,
is_training: bool,
) -> jraph.GraphsTuple:
"""Builds the processor."""
output_sizes = [self._config.mlp_hidden_size] * self._config.mlp_layers
output_sizes += [self._config.latent_size]
build_mlp = functools.partial(
_build_mlp,
output_sizes=output_sizes,
use_layer_norm=self._config.use_layer_norm,
)
shared_weights = self._config.shared_message_passing_weights
node_reducer = _REDUCER_NAMES[self._config.node_reducer]
global_reducer = _REDUCER_NAMES[self._config.global_reducer]
def dropout_if_training(fn, dropout_rate: float):
def wrapped(*args):
out = fn(*args)
if is_training:
mask = hk.dropout(hk.next_rng_key(), dropout_rate,
jnp.ones([out.shape[0], 1]))
out = out * mask
return out
return wrapped
num_mps = self._config.num_message_passing_steps
for step in range(num_mps):
if step == 0 or not shared_weights:
suffix = "shared" if shared_weights else step
update_edge_fn = dropout_if_training(
build_mlp(f"edge_processor_{suffix}"),
dropout_rate=self._config.dropedge_rate)
update_node_fn = dropout_if_training(
build_mlp(f"node_processor_{suffix}"),
dropout_rate=self._config.dropnode_rate)
if self._config.ignore_globals:
gnn = jraph.InteractionNetwork(
update_edge_fn=update_edge_fn,
update_node_fn=update_node_fn,
aggregate_edges_for_nodes_fn=node_reducer)
else:
gnn = jraph.GraphNetwork(
update_edge_fn=update_edge_fn,
update_node_fn=update_node_fn,
update_global_fn=build_mlp(f"global_processor_{suffix}"),
aggregate_edges_for_nodes_fn=node_reducer,
aggregate_nodes_for_globals_fn=global_reducer,
aggregate_edges_for_globals_fn=global_reducer,
)
mode = self._config.processor_mode
if mode == "mlp":
graph = gnn(graph)
elif mode == "resnet":
new_graph = gnn(graph)
graph = graph._replace(
nodes=graph.nodes + new_graph.nodes,
edges=graph.edges + new_graph.edges,
globals=graph.globals + new_graph.globals,
)
else:
raise ValueError(f"Unknown processor_mode `{mode}`")
if self._config.mask_padding_graph_at_every_step:
graph = _mask_out_padding_graph(graph)
return graph
@hk.transparent
def _decoder(
self,
graph: jraph.GraphsTuple,
input_graph: jraph.GraphsTuple,
is_training: bool,
) -> chex.ArrayTree:
"""Builds the decoder."""
del is_training # unused.
output_sizes = [self._config.mlp_hidden_size] * self._config.mlp_layers
output_sizes += [self._loss_config.out_size]
net = _build_mlp("regress_out", output_sizes, use_layer_norm=False)
summed_nodes = _aggregate_nodes_to_globals(graph, graph.nodes)
inputs_to_global_decoder = [summed_nodes]
if not self._config.ignore_globals_from_final_layer_for_predictions:
inputs_to_global_decoder.append(graph.globals)
out = net(jnp.concatenate(inputs_to_global_decoder, axis=-1))
out_dict = {}
out_dict["globals"] = out
# Note "linear" names are for compatibility with pre-trained model names.
out_dict["bond_one_hots"] = hk.Linear(
_NUM_EDGE_FEATURES, name="linear")(graph.edges)
out_dict["atom_one_hots"] = hk.Linear(
_NUM_NODE_FEATURES, name="linear_1")(graph.nodes)
return out_dict
@hk.transparent
def _forward(self, graph: jraph.GraphsTuple, is_training: bool):
input_graph = jraph.GraphsTuple(*graph)
with hk.experimental.name_scope("encoder_scope"):
graph = self._encoder(graph, is_training)
with hk.experimental.name_scope("processor_scope"):
graph = self._processor(graph, is_training)
with hk.experimental.name_scope("decoder_scope"):
out = self._decoder(graph, input_graph, is_training)
return out
def _get_node_auxiliary_loss(
self, graph, pred, targets, is_regression, additional_mask=None):
loss = self._get_loss(pred, targets, is_regression)
target_mask = jraph.get_node_padding_mask(graph)
if additional_mask is not None:
loss *= additional_mask
target_mask = jnp.logical_and(target_mask, additional_mask)
return _mean_with_mask(loss, target_mask)
def _get_edge_auxiliary_loss(
self, graph, pred, targets, is_regression, additional_mask=None):
loss = self._get_loss(pred, targets, is_regression)
target_mask = jraph.get_edge_padding_mask(graph)
if additional_mask is not None:
loss *= additional_mask
target_mask = jnp.logical_and(target_mask, additional_mask)
return _mean_with_mask(loss, target_mask)
def _get_loss(self, pred, targets, is_regression):
if is_regression:
loss = ((pred - targets)**2).mean(axis=-1)
else:
targets /= jnp.maximum(1., jnp.sum(targets, axis=-1, keepdims=True))
loss = _softmax_cross_entropy(pred, targets)
return loss
def get_utilization_scalars(
padded_graph: jraph.GraphsTuple) -> Dict[str, float]:
padding_nodes = jraph.get_number_of_padding_with_graphs_nodes(padded_graph)
all_nodes = len(jax.tree_leaves(padded_graph.nodes)[0])
padding_edges = jraph.get_number_of_padding_with_graphs_edges(padded_graph)
all_edges = len(jax.tree_leaves(padded_graph.edges)[0])
padding_graphs = jraph.get_number_of_padding_with_graphs_graphs(padded_graph)
all_graphs = len(padded_graph.n_node)
return {"node_utilization": 1 - (padding_nodes / all_nodes),
"edge_utilization": 1 - (padding_edges / all_edges),
"graph_utilization": 1 - (padding_graphs / all_graphs)}
def sum_with_mask(array: jnp.ndarray, mask: jnp.ndarray) -> jnp.ndarray:
return (mask * array).sum(0)
def _mean_with_mask(array: jnp.ndarray, mask: jnp.ndarray) -> jnp.ndarray:
num_valid_rows = mask.sum(0)
return sum_with_mask(array, mask) / num_valid_rows
def _mask_out_padding_graph(
padded_graph: jraph.GraphsTuple) -> jraph.GraphsTuple:
return padded_graph._replace(
nodes=jnp.where(
jraph.get_node_padding_mask(
padded_graph)[:, None], padded_graph.nodes, 0.),
edges=jnp.where(
jraph.get_edge_padding_mask(
padded_graph)[:, None], padded_graph.edges, 0.),
globals=jnp.where(
jraph.get_graph_padding_mask(
padded_graph)[:, None], padded_graph.globals, 0.),
)
def _aggregate_nodes_to_globals(graph, node_features):
n_graph = graph.n_node.shape[0]
sum_n_node = jax.tree_leaves(graph.nodes)[0].shape[0]
graph_idx = jnp.arange(n_graph)
node_gr_idx = jnp.repeat(
graph_idx, graph.n_node, axis=0, total_repeat_length=sum_n_node)
return jax.ops.segment_sum(node_features, node_gr_idx, num_segments=n_graph)
================================================
FILE: ogb_lsc/pcq/requirements.txt
================================================
wheel
absl-py>=0.12.0
chex>=0.0.7
dill>=0.3.3
dm-haiku>=0.0.4
# jaxline
git+https://github.com/deepmind/jaxline@927695a0b88e480d085590736e2daa36c2f2c68b
optax>=0.0.8
ml_collections
numpy>=1.16.4
tensorflow>=2.5.0
tensorflow-datasets>=4.3.0
dm-tree>=0.1.6
ogb>=1.3.1
# graph_nets
git+git://github.com/deepmind/graph_nets.git@64771dff0d74ca8e77b1f1dcd5a7d26634356d61
rdkit-pypi>=2021.3.2.3
jaxlib>=0.1.67+cuda110
# jraph
git+git://github.com/deepmind/jraph.git@80d2f9e4d82f841a71d56ba44fa9e8781e93ae1f
google-cloud-storage
================================================
FILE: ogb_lsc/pcq/run_preprocessing.sh
================================================
#!/bin/bash
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
set -x
while getopts ":r:" opt; do
case ${opt} in
r )
TASK_ROOT=$OPTARG
;;
\? )
echo "Usage: run_training.sh -r "
;;
: )
echo "Invalid option: $OPTARG requires an argument" 1>&2
;;
esac
done
shift $((OPTIND -1))
# Get this script's directory.
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
DATA_ROOT=${TASK_ROOT}/data/
python "${SCRIPT_DIR}"/generate_validation_splits.py \
--output_dir="${DATA_ROOT}/k_fold_splits"
mkdir -p ${DATA_ROOT}/preprocessed/
python "${SCRIPT_DIR}"/generate_conformer_features.py \
--splits="test valid train" \
--num_parallel_procs=32 \
--output_file="${DATA_ROOT}/preprocessed/smile_to_conformer.pkl"
================================================
FILE: ogb_lsc/pcq/run_pretrained_eval.sh
================================================
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Generates test predictions from trained model checkpoints.
set -e
set -x
while getopts ":r:" opt; do
case ${opt} in
r )
TASK_ROOT=$OPTARG
;;
\? )
echo "Usage: run_pretrained_eval.sh -r "
;;
: )
echo "Invalid option: $OPTARG requires an argument" 1>&2
;;
esac
done
shift $((OPTIND -1))
# Get this script's directory.
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
SPLIT="test"
DATA_ROOT=${TASK_ROOT}/data/ # For valid k-fold splits.
MODELS_ROOT=${TASK_ROOT}/models/
CACHED_CONFORMERS_DIR=${TASK_ROOT}/online_conformers/$SPLIT
CACHED_CONFORMERS_FILE=${CACHED_CONFORMERS_DIR}/smiles_to_conformers.pkl
OUTPUT_DIR=${TASK_ROOT}/predictions/
# First generate and cache the conformer feature data for the test split.
mkdir -p ${CACHED_CONFORMERS_DIR}
time python "${SCRIPT_DIR}"/generate_conformer_features.py \
--splits=$SPLIT \
--num_parallel_procs=32 \
--output_file=${CACHED_CONFORMERS_FILE}
seed=42
# Share GPU between two runs by disabling XLA memory pre-allocation.
export XLA_PYTHON_CLIENT_PREALLOCATE=false
for seed_group in 0 1; do
for k in `seq 0 9`; do
# Conformer
predictions_dir="${OUTPUT_DIR}/predictions/${SPLIT}/conformer/k${k}_seed${seed}"
time python "${SCRIPT_DIR}"/experiment.py \
--jaxline_mode="eval" \
--config="${SCRIPT_DIR}"/config.py \
--config.experiment_kwargs.config.evaluation.split=$SPLIT \
--config.restore_path=${MODELS_ROOT}/conformer/k${k}_seed${seed} \
--config.experiment_kwargs.config.predictions_dir=${predictions_dir} \
--config.experiment_kwargs.config.dataset_config.data_root=${DATA_ROOT} \
--config.experiment_kwargs.config.dataset_config.k_fold_split_id=$k \
--config.experiment_kwargs.config.dataset_config.cached_conformers_file=${CACHED_CONFORMERS_FILE} \
--config.experiment_kwargs.config.dataset_config.filter_in_or_out_samples_with_nans_in_conformers="out" \
--config.experiment_kwargs.config.model.latent_size=256 \
--config.experiment_kwargs.config.model.mlp_hidden_size=1024 \
--config.experiment_kwargs.config.model.num_message_passing_steps=32 \
--config.one_off_evaluate &
# Non-Conformer
predictions_dir="${OUTPUT_DIR}/predictions/${SPLIT}/non_conformer/k${k}_seed${seed}"
time python "${SCRIPT_DIR}"/experiment.py \
--jaxline_mode="eval" \
--config="${SCRIPT_DIR}"/config.py \
--config.experiment_kwargs.config.evaluation.split=$SPLIT \
--config.restore_path=${MODELS_ROOT}/non_conformer/k${k}_seed${seed} \
--config.experiment_kwargs.config.predictions_dir=${predictions_dir} \
--config.experiment_kwargs.config.dataset_config.data_root=${DATA_ROOT} \
--config.experiment_kwargs.config.dataset_config.k_fold_split_id=$k \
--config.experiment_kwargs.config.dataset_config.cached_conformers_file=${CACHED_CONFORMERS_FILE} \
--config.experiment_kwargs.config.dataset_config.filter_in_or_out_samples_with_nans_in_conformers="in" \
--config.experiment_kwargs.config.model.num_message_passing_steps=50 \
--config.experiment_kwargs.config.model.add_relative_distance=false \
--config.experiment_kwargs.config.model.add_relative_displacement=false \
--config.one_off_evaluate &
wait
((seed=seed+1))
done
done
# Ensemble predictions.
time python "${SCRIPT_DIR}"/ensemble_predictions.py \
--seed_start=42 \
--split=$SPLIT \
--conformer_path="${OUTPUT_DIR}/predictions/${SPLIT}/conformer" \
--non_conformer_path="${OUTPUT_DIR}/predictions/${SPLIT}/non_conformer" \
--output_path="${OUTPUT_DIR}/ensembled_predictions/"
================================================
FILE: ogb_lsc/pcq/run_training.sh
================================================
#!/bin/bash
# Copyright 2021 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
set -x
while getopts ":r:" opt; do
case ${opt} in
r )
TASK_ROOT=$OPTARG
;;
\? )
echo "Usage: run_training.sh -r "
;;
: )
echo "Invalid option: $OPTARG requires an argument" 1>&2
;;
esac
done
shift $((OPTIND -1))
# Get this script's directory.
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
echo "
These scripts are provided for illustrative purposes. It is not practical for
actual training since it only uses a single machine, and likely requires
reducing the batch size and/or model size to fit on a single GPU.
For the actual submission we used training distributed in different ways:
* We used 4x Cloud TPU v4s to implement batch parallelism, so batch
size is effectively 8 times larger than the values in the config.
* We ran the online early-stopping evaluator on a separate machine with an
NVIDIA V100 GPU.
* We run identical replicas of all of the above for each of the 40 models
trained.
Using those mechanisms, training runs at ~4 and 6 steps per second, reaching
500k steps in about 1.5 days.
"
echo "
Pre-requisites (See README):
* cd into directory containing the 'ogb_lsc' folder.
* Python dependencies have been installed.
* pre-processed data is available at the hardcoded paths
"
read -p "Press enter to continue"
DATA_ROOT=${TASK_ROOT}/data/
CHECKPOINT_DIR=${TASK_ROOT}/checkpoints/
CACHED_CONFORMERS_FILE="${DATA_ROOT}/preprocessed/smile_to_conformer.pkl"
# We run two seeds for each model of the k=10 k-fold.
BASE_SEED=42
for SEED_OFFSET in 0 10; do
for K_FOLD_INDEX in {0..9}; do
MODEL_SEED=`expr ${BASE_SEED} + ${SEED_OFFSET} + ${K_FOLD_INDEX}`
echo "Running k=${K_FOLD_INDEX} with init seed ${MODEL_SEED}"
# This runs training (each model is trained on train split + 90% of the
# validation split) with early stopping, storing both "latest" model and
# "best" early-stopped model at `--config.checkpoint_dir`.
# Models are early stopped based on accuracy of on 10% of the validation data
# (each K_FOLD_INDEX leaves a different 10% of data out from training) left
# out from training.
# Models are stored at the end of training. Intermediate models can also be
# stored while training by sending a SIGINT signal (Ctrl+C) which will not
# interrupt the training.
# It is possible to interrupt training using (Ctrl+\) and then continue
# passing the corresponding `--config.restore_path=${RESTORE_PATH}` which is
# stored when interrupting.
# Conformer.
SUFFIX="conformer/k${K_FOLD_INDEX}_seed${MODEL_SEED}"
echo "Running ${SUFFIX}"
python "${SCRIPT_DIR}"/experiment.py \
--jaxline_mode="train_eval_multithreaded" \
--config="${SCRIPT_DIR}"/config.py \
--config.random_seed=${MODEL_SEED} \
--config.checkpoint_dir=${CHECKPOINT_DIR}/${SUFFIX} \
--config.experiment_kwargs.config.dataset_config.data_root=${DATA_ROOT} \
--config.experiment_kwargs.config.dataset_config.k_fold_split_id=${K_FOLD_INDEX} \
--config.experiment_kwargs.config.dataset_config.num_k_fold_splits=10 \
--config.experiment_kwargs.config.dataset_config.cached_conformers_file=${CACHED_CONFORMERS_FILE} \
--config.experiment_kwargs.config.model.latent_size=256 \
--config.experiment_kwargs.config.model.mlp_hidden_size=1024 \
--config.experiment_kwargs.config.model.num_message_passing_steps=32 \
--config.experiment_kwargs.config.evaluation.split="valid"
# Non-Conformer.
SUFFIX="non_conformer/k${K_FOLD_INDEX}_seed${MODEL_SEED}"
echo "Running ${SUFFIX}"
python "${SCRIPT_DIR}"/experiment.py \
--jaxline_mode="train_eval_multithreaded" \
--config="${SCRIPT_DIR}"/config.py \
--config.random_seed=${MODEL_SEED} \
--config.checkpoint_dir=${CHECKPOINT_DIR}/${SUFFIX} \
--config.experiment_kwargs.config.dataset_config.data_root=${DATA_ROOT} \
--config.experiment_kwargs.config.dataset_config.k_fold_split_id=${K_FOLD_INDEX} \
--config.experiment_kwargs.config.dataset_config.num_k_fold_splits=10 \
--config.experiment_kwargs.config.dataset_config.cached_conformers_file=${CACHED_CONFORMERS_FILE} \
--config.experiment_kwargs.config.model.num_message_passing_steps=50 \
--config.experiment_kwargs.config.model.add_relative_distance=false \
--config.experiment_kwargs.config.model.add_relative_displacement=false \
--config.experiment_kwargs.config.evaluation.split="valid"
done
done
# Each of the 40 (two for each value of k, and both conformer and non conformer)
# jobs, it will generate paths of the form with the early-stopped models:
# RESTORE_PATH=${--config.checkpoint_dir}/models/best/step_${STEP}_${TIMESTAMP}
# These can then be used as the "RESTORE_PATHS" for `./run_pretrained_eval.sh`.
echo "Done"
================================================
FILE: option_keyboard/README.md
================================================
# The Option Keyboard: Combining Skills in Reinforcement Learning
This directory contains an implementation of the Option Keyboard framework.
From the [abstract](http://papers.nips.cc/paper/9463-the-option-keyboard-combining-skills-in-reinforcement-learning):
> The ability to combine known skills to create new ones may be crucial in the
solution of complex reinforcement learning problems that unfold over extended
periods. We argue that a robust way of combining skills is to define and manipulate
them in the space of pseudo-rewards (or “cumulants”). Based on this premise, we
propose a framework for combining skills using the formalism of options. We show
that every deterministic option can be unambiguously represented as a cumulant
defined in an extended domain. Building on this insight and on previous results
on transfer learning, we show how to approximate options whose cumulants are
linear combinations of the cumulants of known options. This means that, once we
have learned options associated with a set of cumulants, we can instantaneously
synthesise options induced by any linear combination of them, without any learning
involved. We describe how this framework provides a hierarchical interface to the
environment whose abstract actions correspond to combinations of basic skills.
We demonstrate the practical benefits of our approach in a resource management
problem and a navigation task involving a quadrupedal simulated robot.
If you use the code here please cite this paper
> Andre Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan hunt, Shibl Mourad, David Silver, Doina Precup. *The Option Keyboard: Combining Skills in Reinforcement Learning*. Neurips 2019. [\[paper\]](https://papers.nips.cc/paper/9463-the-option-keyboard-combining-skills-in-reinforcement-learning).
## Running the code
### Setup
```
python3 -m venv ok_venv
source ok_venv/bin/activate
pip install -r option_keyboard/requirements.txt
```
### Scavenger Task
All agents are trained on a simple grid-world resource collection task. There
are two types of collectible objects in the world: if the agent collects the
object that is less abundant of the two then it receives a reward of -1,
otherwise it receives a reward of +1 when it collects the object. See section
5.1 in the paper for more details.
### Train the DQN baseline
```
python3 -m option_keyboard.run_dqn
```
This trains a DQN agent on the scavenger task.
### Train the Option Keyboard and agent
```
python3 -m option_keyboard.run_ok
```
This first trains an Option Keyboard on the cumulants in the task environment.
Then it trains a DQN agent on the true task reward using high level abstract
actions provided by the keyboard.
## Disclaimer
This is not an official Google or DeepMind product.
================================================
FILE: option_keyboard/auto_reset_environment.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Auto-resetting environment base class.
The environment API states that stepping an environment after a LAST timestep
should return the first timestep of a new episode.
However, environment authors sometimes don't spot this part or find it awkward
to implement. This module contains a class that helps implement the reset
behaviour.
"""
import abc
import dm_env
class Base(dm_env.Environment):
"""This class implements the required `step()` and `reset()` methods.
It instead requires users to implement `_step()` and `_reset()`. This class
handles the reset behaviour automatically when it detects a LAST timestep.
"""
def __init__(self):
self._reset_next_step = True
@abc.abstractmethod
def _reset(self):
"""Returns a `timestep` namedtuple as per the regular `reset()` method."""
@abc.abstractmethod
def _step(self, action):
"""Returns a `timestep` namedtuple as per the regular `step()` method."""
def reset(self):
self._reset_next_step = False
return self._reset()
def step(self, action):
if self._reset_next_step:
return self.reset()
timestep = self._step(action)
self._reset_next_step = timestep.last()
return timestep
================================================
FILE: option_keyboard/configs.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Environment configurations."""
def get_task_config():
return dict(
arena_size=11,
num_channels=2,
max_num_steps=50, # 50 for the actual task.
num_init_objects=10,
object_priors=[0.5, 0.5],
egocentric=True,
rewarder="BalancedCollectionRewarder",
)
def get_pretrain_config():
return dict(
arena_size=11,
num_channels=2,
max_num_steps=40, # 40 for pretraining.
num_init_objects=10,
object_priors=[0.5, 0.5],
egocentric=True,
default_w=(1, 1),
)
def get_fig4_task_config():
return dict(
arena_size=11,
num_channels=2,
max_num_steps=50, # 50 for the actual task.
num_init_objects=10,
object_priors=[0.5, 0.5],
egocentric=True,
default_w=(1, -1),
)
def get_fig5_task_config(default_w):
return dict(
arena_size=11,
num_channels=2,
max_num_steps=50, # 50 for the actual task.
num_init_objects=10,
object_priors=[0.5, 0.5],
egocentric=True,
default_w=default_w,
)
================================================
FILE: option_keyboard/dqn_agent.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""DQN agent."""
import numpy as np
import sonnet as snt
import tensorflow.compat.v1 as tf
class Agent():
"""A DQN Agent."""
def __init__(
self,
obs_spec,
action_spec,
network_kwargs,
epsilon,
additional_discount,
batch_size,
optimizer_name,
optimizer_kwargs,
):
"""A simple DQN agent.
Args:
obs_spec: The observation spec.
action_spec: The action spec.
network_kwargs: Keyword arguments for snt.nets.MLP
epsilon: Exploration probability.
additional_discount: Discount on returns used by the agent.
batch_size: Size of update batch.
optimizer_name: Name of an optimizer from tf.train
optimizer_kwargs: Keyword arguments for the optimizer.
"""
self._epsilon = epsilon
self._additional_discount = additional_discount
self._batch_size = batch_size
self._n_actions = action_spec.num_values
self._network = ValueNet(self._n_actions, network_kwargs=network_kwargs)
self._replay = []
obs_spec = self._extract_observation(obs_spec)
# Placeholders for policy
o = tf.placeholder(shape=obs_spec.shape, dtype=obs_spec.dtype)
q = self._network(tf.expand_dims(o, axis=0))
# Placeholders for update.
o_tm1 = tf.placeholder(shape=(None,) + obs_spec.shape, dtype=obs_spec.dtype)
a_tm1 = tf.placeholder(shape=(None,), dtype=tf.int32)
r_t = tf.placeholder(shape=(None,), dtype=tf.float32)
d_t = tf.placeholder(shape=(None,), dtype=tf.float32)
o_t = tf.placeholder(shape=(None,) + obs_spec.shape, dtype=obs_spec.dtype)
# Compute values over all options.
q_tm1 = self._network(o_tm1)
q_t = self._network(o_t)
a_t = tf.cast(tf.argmax(q_t, axis=-1), tf.int32)
qa_tm1 = _batched_index(q_tm1, a_tm1)
qa_t = _batched_index(q_t, a_t)
# TD error
g = additional_discount * d_t
td_error = tf.stop_gradient(r_t + g * qa_t) - qa_tm1
loss = tf.reduce_sum(tf.square(td_error) / 2)
with tf.variable_scope("optimizer"):
self._optimizer = getattr(tf.train, optimizer_name)(**optimizer_kwargs)
train_op = self._optimizer.minimize(loss)
# Make session and callables.
session = tf.Session()
self._update_fn = session.make_callable(train_op,
[o_tm1, a_tm1, r_t, d_t, o_t])
self._value_fn = session.make_callable(q, [o])
session.run(tf.global_variables_initializer())
def _extract_observation(self, obs):
return obs["arena"]
def step(self, timestep, is_training=False):
"""Select actions according to epsilon-greedy policy."""
if is_training and np.random.rand() < self._epsilon:
return np.random.randint(self._n_actions)
q_values = self._value_fn(
self._extract_observation(timestep.observation))
return int(np.argmax(q_values))
def update(self, step_tm1, action, step_t):
"""Takes in a transition from the environment."""
transition = [
self._extract_observation(step_tm1.observation),
action,
step_t.reward,
step_t.discount,
self._extract_observation(step_t.observation),
]
self._replay.append(transition)
if len(self._replay) == self._batch_size:
batch = list(zip(*self._replay))
self._update_fn(*batch)
self._replay = [] # Just a queue.
class ValueNet(snt.AbstractModule):
"""Value Network."""
def __init__(self,
n_actions,
network_kwargs,
name="value_network"):
"""Construct a value network sonnet module.
Args:
n_actions: Number of actions.
network_kwargs: Network arguments.
name: Name
"""
super(ValueNet, self).__init__(name=name)
self._n_actions = n_actions
self._network_kwargs = network_kwargs
def _build(self, observation):
flat_obs = snt.BatchFlatten()(observation)
net = snt.nets.MLP(**self._network_kwargs)(flat_obs)
net = snt.Linear(output_size=self._n_actions)(net)
return net
@property
def num_actions(self):
return self._n_actions
def _batched_index(values, indices):
one_hot_indices = tf.one_hot(indices, values.shape[-1], dtype=values.dtype)
return tf.reduce_sum(values * one_hot_indices, axis=-1)
================================================
FILE: option_keyboard/environment_wrappers.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Environment with keyboard."""
import itertools
from absl import logging
import dm_env
import numpy as np
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import tree
from option_keyboard import smart_module
class EnvironmentWithLogging(dm_env.Environment):
"""Wraps an environment with additional logging."""
def __init__(self, env):
self._env = env
self._episode_return = 0
def reset(self):
self._episode_return = 0
return self._env.reset()
def step(self, action):
"""Take action in the environment and do some logging."""
step = self._env.step(action)
if step.first():
step = self._env.step(action)
self._episode_return = 0
self._episode_return += step.reward
return step
@property
def episode_return(self):
return self._episode_return
def action_spec(self):
return self._env.action_spec()
def observation_spec(self):
return self._env.observation_spec()
def __getattr__(self, name):
return getattr(self._env, name)
class EnvironmentWithKeyboard(dm_env.Environment):
"""Wraps an environment with a keyboard."""
def __init__(self,
env,
keyboard,
keyboard_ckpt_path,
n_actions_per_dim,
additional_discount,
call_and_return=False):
self._env = env
self._keyboard = keyboard
self._discount = additional_discount
self._call_and_return = call_and_return
options = _discretize_actions(n_actions_per_dim, keyboard.num_cumulants)
self._options_np = options
options = tf.convert_to_tensor(options, dtype=tf.float32)
self._options = options
obs_spec = self._extract_observation(env.observation_spec())
obs_ph = tf.placeholder(shape=obs_spec.shape, dtype=obs_spec.dtype)
option_ph = tf.placeholder(shape=(), dtype=tf.int32)
gpi_action = self._keyboard.gpi(obs_ph, options[option_ph])
session = tf.Session()
self._gpi_action = session.make_callable(gpi_action, [obs_ph, option_ph])
self._keyboard_action = session.make_callable(
self._keyboard(tf.expand_dims(obs_ph, axis=0))[0], [obs_ph])
session.run(tf.global_variables_initializer())
if keyboard_ckpt_path:
saver = tf.train.Saver(var_list=keyboard.variables)
saver.restore(session, keyboard_ckpt_path)
def _compute_reward(self, option, obs):
return np.sum(self._options_np[option] * obs["cumulants"])
def reset(self):
return self._env.reset()
def step(self, option):
"""Take a step in the keyboard, then the environment."""
step_count = 0
option_step = None
while True:
obs = self._extract_observation(self._env.observation())
action = self._gpi_action(obs, option)
action_step = self._env.step(action)
step_count += 1
if option_step is None:
option_step = action_step
else:
new_discount = (
option_step.discount * self._discount * action_step.discount)
new_reward = (
option_step.reward + new_discount * action_step.reward)
option_step = option_step._replace(
observation=action_step.observation,
reward=new_reward,
discount=new_discount,
step_type=action_step.step_type)
if action_step.last():
break
# Terminate option.
if self._should_terminate(option, action_step.observation):
break
if not self._call_and_return:
break
return option_step
def _should_terminate(self, option, obs):
if self._compute_reward(option, obs) > 0:
return True
elif np.all(self._options_np[option] <= 0):
# TODO(shaobohou) A hack ensure option with non-positive weights
# terminates after one step
return True
else:
return False
def action_spec(self):
return dm_env.specs.DiscreteArray(
num_values=self._options_np.shape[0], name="action")
def _extract_observation(self, obs):
return obs["arena"]
def observation_spec(self):
return self._env.observation_spec()
def __getattr__(self, name):
return getattr(self._env, name)
class EnvironmentWithKeyboardDirect(dm_env.Environment):
"""Wraps an environment with a keyboard.
This is different from EnvironmentWithKeyboard as the actions space is not
discretized.
TODO(shaobohou) Merge the two implementations.
"""
def __init__(self,
env,
keyboard,
keyboard_ckpt_path,
additional_discount,
call_and_return=False):
self._env = env
self._keyboard = keyboard
self._discount = additional_discount
self._call_and_return = call_and_return
obs_spec = self._extract_observation(env.observation_spec())
obs_ph = tf.placeholder(shape=obs_spec.shape, dtype=obs_spec.dtype)
option_ph = tf.placeholder(
shape=(keyboard.num_cumulants,), dtype=tf.float32)
gpi_action = self._keyboard.gpi(obs_ph, option_ph)
session = tf.Session()
self._gpi_action = session.make_callable(gpi_action, [obs_ph, option_ph])
self._keyboard_action = session.make_callable(
self._keyboard(tf.expand_dims(obs_ph, axis=0))[0], [obs_ph])
session.run(tf.global_variables_initializer())
if keyboard_ckpt_path:
saver = tf.train.Saver(var_list=keyboard.variables)
saver.restore(session, keyboard_ckpt_path)
def _compute_reward(self, option, obs):
assert option.shape == obs["cumulants"].shape
return np.sum(option * obs["cumulants"])
def reset(self):
return self._env.reset()
def step(self, option):
"""Take a step in the keyboard, then the environment."""
step_count = 0
option_step = None
while True:
obs = self._extract_observation(self._env.observation())
action = self._gpi_action(obs, option)
action_step = self._env.step(action)
step_count += 1
if option_step is None:
option_step = action_step
else:
new_discount = (
option_step.discount * self._discount * action_step.discount)
new_reward = (
option_step.reward + new_discount * action_step.reward)
option_step = option_step._replace(
observation=action_step.observation,
reward=new_reward,
discount=new_discount,
step_type=action_step.step_type)
if action_step.last():
break
# Terminate option.
if self._should_terminate(option, action_step.observation):
break
if not self._call_and_return:
break
return option_step
def _should_terminate(self, option, obs):
if self._compute_reward(option, obs) > 0:
return True
elif np.all(option <= 0):
# TODO(shaobohou) A hack ensure option with non-positive weights
# terminates after one step
return True
else:
return False
def action_spec(self):
return dm_env.specs.BoundedArray(shape=(self._keyboard.num_cumulants,),
dtype=np.float32,
minimum=-1.0,
maximum=1.0,
name="action")
def _extract_observation(self, obs):
return obs["arena"]
def observation_spec(self):
return self._env.observation_spec()
def __getattr__(self, name):
return getattr(self._env, name)
def _discretize_actions(num_actions_per_dim,
action_space_dim,
min_val=-1.0,
max_val=1.0):
"""Discrete action space."""
if num_actions_per_dim > 1:
discretized_dim_action = np.linspace(
min_val, max_val, num_actions_per_dim, endpoint=True)
discretized_actions = [discretized_dim_action] * action_space_dim
discretized_actions = itertools.product(*discretized_actions)
discretized_actions = list(discretized_actions)
elif num_actions_per_dim == 1:
discretized_actions = [
max_val * np.eye(action_space_dim),
min_val * np.eye(action_space_dim),
]
discretized_actions = np.concatenate(discretized_actions, axis=0)
elif num_actions_per_dim == 0:
discretized_actions = np.eye(action_space_dim)
else:
raise ValueError(
"Unsupported num_actions_per_dim {}".format(num_actions_per_dim))
discretized_actions = np.array(discretized_actions)
# Remove options with all zeros.
non_zero_entries = np.sum(np.square(discretized_actions), axis=-1) != 0.0
discretized_actions = discretized_actions[non_zero_entries]
logging.info("Total number of discretized actions: %s",
len(discretized_actions))
logging.info("Discretized actions: %s", discretized_actions)
return discretized_actions
class EnvironmentWithLearnedPhi(dm_env.Environment):
"""Wraps an environment with learned phi model."""
def __init__(self, env, model_path):
self._env = env
create_ph = lambda x: tf.placeholder(shape=x.shape, dtype=x.dtype)
add_batch = lambda x: tf.expand_dims(x, axis=0)
# Make session and callables.
with tf.Graph().as_default():
model = smart_module.SmartModuleImport(hub.Module(model_path))
obs_spec = env.observation_spec()
obs_ph = tree.map_structure(create_ph, obs_spec)
action_ph = tf.placeholder(shape=(), dtype=tf.int32)
phis = model(tree.map_structure(add_batch, obs_ph), add_batch(action_ph))
self.num_phis = phis.shape.as_list()[-1]
self._last_phis = np.zeros((self.num_phis,), dtype=np.float32)
session = tf.Session()
self._session = session
self._phis_fn = session.make_callable(
phis[0], tree.flatten([obs_ph, action_ph]))
self._session.run(tf.global_variables_initializer())
def reset(self):
self._last_phis = np.zeros((self.num_phis,), dtype=np.float32)
return self._env.reset()
def step(self, action):
"""Take action in the environment and do some logging."""
phis = self._phis_fn(*tree.flatten([self._env.observation(), action]))
step = self._env.step(action)
if step.first():
phis = self._phis_fn(*tree.flatten([self._env.observation(), action]))
step = self._env.step(action)
step.observation["cumulants"] = phis
self._last_phis = phis
return step
def action_spec(self):
return self._env.action_spec()
def observation(self):
obs = self._env.observation()
obs["cumulants"] = self._last_phis
return obs
def observation_spec(self):
obs_spec = self._env.observation_spec()
obs_spec["cumulants"] = dm_env.specs.BoundedArray(
shape=(self.num_phis,),
dtype=np.float32,
minimum=-1e9,
maximum=1e9,
name="collected_resources")
return obs_spec
def __getattr__(self, name):
return getattr(self._env, name)
================================================
FILE: option_keyboard/experiment.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""A simple training loop."""
import csv
from absl import logging
from tensorflow.compat.v1.io import gfile
def _ema(base, val, decay=0.995):
return base * decay + (1 - decay) * val
def run(env, agent, num_episodes, report_every=200, num_eval_reps=1):
"""Runs an agent on an environment.
Args:
env: The environment.
agent: The agent.
num_episodes: Number of episodes to train for.
report_every: Frequency at which training progress are reported (episodes).
num_eval_reps: Number of eval episodes to run per training episode.
Returns:
A list of dicts containing training and evaluation returns, and a list of
reported returns smoothed by EMA.
"""
returns = []
logged_returns = []
train_return_ema = 0.
eval_return_ema = 0.
for episode in range(num_episodes):
returns.append(dict(episode=episode))
# Run a training episode.
train_episode_return = run_episode(env, agent, is_training=True)
train_return_ema = _ema(train_return_ema, train_episode_return)
returns[-1]["train"] = train_episode_return
# Run an evaluation episode.
returns[-1]["eval"] = []
for _ in range(num_eval_reps):
eval_episode_return = run_episode(env, agent, is_training=False)
eval_return_ema = _ema(eval_return_ema, eval_episode_return)
returns[-1]["eval"].append(eval_episode_return)
if ((episode + 1) % report_every) == 0 or episode == 0:
logged_returns.append(
dict(episode=episode, train=train_return_ema, eval=[eval_return_ema]))
logging.info("Episode %s, avg train return %.3f, avg eval return %.3f",
episode + 1, train_return_ema, eval_return_ema)
if hasattr(agent, "get_logs"):
logging.info("Episode %s, agent logs: %s", episode + 1,
agent.get_logs())
return returns, logged_returns
def run_episode(environment, agent, is_training=False):
"""Run a single episode."""
timestep = environment.reset()
while not timestep.last():
action = agent.step(timestep, is_training)
new_timestep = environment.step(action)
if is_training:
agent.update(timestep, action, new_timestep)
timestep = new_timestep
episode_return = environment.episode_return
return episode_return
def write_returns_to_file(path, returns):
"""Write returns to file."""
with gfile.GFile(path, "w") as file:
writer = csv.writer(file, delimiter=" ", quoting=csv.QUOTE_MINIMAL)
writer.writerow(["episode", "train"] +
[f"eval_{idx}" for idx in range(len(returns[0]["eval"]))])
for row in returns:
writer.writerow([row["episode"], row["train"]] + row["eval"])
================================================
FILE: option_keyboard/gpe_gpi_experiments/eval_keyboard_fig5.py
================================================
# pylint: disable=g-bad-file-header
# pylint: disable=line-too-long
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
r"""Run an experiment.
This script generates the raw data for the polar plots used to visualise how
well a trained keyboard covers the space of w.
For example, train 3 separate keyboards with different base policies:
python3 train_keyboard.py --logtostderr --policy_weights_name=12
python3 train_keyboard.py --logtostderr --policy_weights_name=34
python3 train_keyboard.py --logtostderr --policy_weights_name=5
Then generate the polar plot data as follows:
python3 eval_keyboard_fig5.py --logtostderr \
--keyboard_paths=/tmp/option_keyboard/keyboard_12/tfhub,/tmp/option_keyboard/keyboard_34/tfhub,/tmp/option_keyboard/keyboard_5/tfhub \
--num_episodes=1000
Example outout:
[[ 0.11 0.261 -0.933 ]
[ 1.302 3.955 0.54 ]
[ 2.398 4.434 1.2105359 ]
[ 3.459 4.606 2.087 ]
[ 4.09026795 4.60911325 3.06106882]
[ 4.55499485 4.71947818 3.8123229 ]
[ 4.715 4.835 4.395 ]
[ 4.75743564 4.64095528 4.46330207]
[ 4.82518207 4.71232378 4.56190708]
[ 4.831 4.7155 4.5735 ]
[ 4.78074425 4.6754641 4.58312762]
[ 4.70154374 4.5416429 4.47850417]
[ 4.694 4.631 4.427 ]
[ 4.25085125 4.56606664 3.68157677]
[ 3.61726795 4.4838453 2.68154403]
[ 2.714 4.43 1.554 ]
[ 1.69 4.505 0.9635359 ]
[ 0.894 4.043 0.424 ]
[ 0.099 0.349 0.055 ]]
"""
import csv
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1.io import gfile
import tensorflow_hub as hub
from option_keyboard import configs
from option_keyboard import environment_wrappers
from option_keyboard import experiment
from option_keyboard import scavenger
from option_keyboard import smart_module
from option_keyboard.gpe_gpi_experiments import regressed_agent
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_episodes", 1000, "Number of training episodes.")
flags.DEFINE_list("keyboard_paths", [], "Path to keyboard model.")
flags.DEFINE_string("output_path", None, "Path to write out returns.")
def evaluate_keyboard(keyboard_path, weights_to_sweep):
"""Evaluate a keyboard."""
# Load the keyboard.
keyboard = smart_module.SmartModuleImport(hub.Module(keyboard_path))
# Create the task environment.
all_returns = []
for w_to_sweep in weights_to_sweep.tolist():
base_env_config = configs.get_fig5_task_config(w_to_sweep)
base_env = scavenger.Scavenger(**base_env_config)
base_env = environment_wrappers.EnvironmentWithLogging(base_env)
# Wrap the task environment with the keyboard.
with tf.variable_scope(None, default_name="inner_loop"):
additional_discount = 0.9
env = environment_wrappers.EnvironmentWithKeyboardDirect(
env=base_env,
keyboard=keyboard,
keyboard_ckpt_path=None,
additional_discount=additional_discount,
call_and_return=False)
# Create the player agent.
agent = regressed_agent.Agent(
batch_size=10,
optimizer_name="AdamOptimizer",
# Disable training.
optimizer_kwargs=dict(learning_rate=0.0,),
init_w=w_to_sweep)
returns = []
for _ in range(FLAGS.num_episodes):
returns.append(experiment.run_episode(env, agent))
tf.logging.info(f"Task: {w_to_sweep}, mean returns over "
f"{FLAGS.num_episodes} episodes is {np.mean(returns)}")
all_returns.append(returns)
return all_returns
def main(argv):
del argv
angles_to_sweep = np.deg2rad(np.linspace(-90, 180, num=19, endpoint=True))
weights_to_sweep = np.stack(
[np.sin(angles_to_sweep),
np.cos(angles_to_sweep)], axis=-1)
weights_to_sweep /= np.sum(
np.maximum(weights_to_sweep, 0.0), axis=-1, keepdims=True)
weights_to_sweep = np.clip(weights_to_sweep, -1000, 1000)
tf.logging.info(weights_to_sweep)
all_returns = []
for keyboard_path in FLAGS.keyboard_paths:
returns = evaluate_keyboard(keyboard_path, weights_to_sweep)
all_returns.append(returns)
print("Results:")
print(np.mean(all_returns, axis=-1).T)
if FLAGS.output_path:
with gfile.GFile(FLAGS.output_path, "w") as file:
writer = csv.writer(file, delimiter=" ", quoting=csv.QUOTE_MINIMAL)
writer.writerow(["angle", "return", "idx"])
for idx, returns in enumerate(all_returns):
for row in np.array(returns).T.tolist():
assert len(angles_to_sweep) == len(row)
for ang, val in zip(angles_to_sweep, row):
ang = "{:.4g}".format(ang)
val = "{:.4g}".format(val)
writer.writerow([ang, val, idx])
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/gpe_gpi_experiments/generate_figures.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "N1EHYIqxd80m"
},
"source": [
"\n",
"\n",
"##### Copyright 2020 DeepMind Technologies Limited.\n",
"\n",
"```\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"executionInfo": {
"elapsed": 608,
"status": "ok",
"timestamp": 1596666770403,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "BbsG8P6ICv2t"
},
"outputs": [],
"source": [
"#@title Util functions\n",
"\n",
"import csv\n",
"import os\n",
"from matplotlib import pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import tensorflow.compat.v1 as tf\n",
"from tensorflow.compat.v1.io import gfile\n",
"\n",
"\n",
"def read_csv_as_dataframe(path):\n",
" with gfile.GFile(path, \"r\") as file:\n",
" reader = csv.reader(file, delimiter=\" \")\n",
" rows = [row for row in reader]\n",
" rows[1:] = [[float(v) for v in row] for row in rows[1:]]\n",
" cols = rows[0]\n",
" rows = dict(zip(cols, (zip(*rows[1:]))))\n",
"\n",
" return pd.DataFrame(rows)\n",
"\n",
"\n",
"def read_data(path, num_seeds, verbose=False):\n",
" all_dfs = []\n",
" for seed in range(num_seeds):\n",
" seed_path = path.format(seed)\n",
" if verbose:\n",
" print(f\"Reading {seed_path}\")\n",
" df = read_csv_as_dataframe(seed_path)\n",
" df[\"seed\"] = seed \n",
" all_dfs.append(df)\n",
" return pd.concat(all_dfs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "code",
"colab": {},
"colab_type": "code",
"id": "YiiUlbIS42px"
},
"outputs": [],
"source": [
"#@title Unpack precomputed training curves\n",
"!wget -q --no-check-certificate https://storage.googleapis.com/option_keyboard/gpe_gpi_experiments.zip -P -O /tmp\n",
"!unzip -o /tmp/gpe_gpi_experiments.zip -d /tmp\n",
"DATA_DIR = \"/tmp\""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "OFWlVSJdght9"
},
"source": [
"# Figure 4b\n",
"\n",
"To generate the q-learning results:\n",
"```\n",
"python3 ../run_dqn.py --num_episodes=20000 --report_every=5 --output_path=/tmp/fig4_dqn.csv\n",
"```\n",
"\n",
"To generate the regressed w results:\n",
"```\n",
"python3 train_keybooard.py --num_pretrain_episodes=20000 --policy_weights_name=12 --export_path=/tmp/fig4_keyboard\n",
"python3 run_regressed_w_fig4b.py --num_episodes=4000 --report_every=5 --keyboard_path=/tmp/fig6_keyboard/tfhub \\\n",
" --output_path=/tmp/fig4b_regressed_w.csv\n",
"```\n",
"\n",
"To generate the results with true w:\n",
"```\n",
"# Make use of a pretrained keyboard.\n",
"python3 run_true_w_fig4.py --num_episodes=1000 --keyboard_path=/tmp/fig4_keyboard/tfhub -- output_path=/tmp/fig4b_true_w.csv\n",
"```\n",
"\n",
"Repeat the above steps for multiple runs. Below shows the results for 10 runs."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"executionInfo": {
"elapsed": 1412,
"status": "ok",
"timestamp": 1596666774254,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "d694MYF5ght_"
},
"outputs": [],
"source": [
"#@title Load Data\n",
"\n",
"dqn_path = os.path.join(DATA_DIR, \"fig4_dqn_{}.csv\")\n",
"dqn_df = read_data(dqn_path, num_seeds=10)\n",
"dqn_df[\"method\"] = \"Q-Learning\"\n",
"\n",
"regressed_w_path = os.path.join(DATA_DIR, \"fig4_regressed_w_{}.csv\")\n",
"regressed_w_df = read_data(regressed_w_path, num_seeds=10)\n",
"regressed_w_df[\"method\"] = \"GPE+GPI with regressed w\"\n",
"\n",
"true_w_path = os.path.join(DATA_DIR, \"fig4_true_w_{}.csv\")\n",
"true_w_df = read_data(true_w_path, num_seeds=10)\n",
"true_w_df[\"method\"] = \"GPE+GPI with true w\"\n",
"\n",
"fig4b_df = pd.concat([dqn_df, regressed_w_df])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"cellView": "form",
"colab": {
"height": 400
},
"colab_type": "code",
"executionInfo": {
"elapsed": 6285,
"status": "ok",
"timestamp": 1596666780546,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "dO0W-e43ghuD",
"outputId": "d35efafb-bd43-4c40-9a0f-4b1ac5118298"
},
"outputs": [
{
"data": {
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1DhZrvrpa7PrvaP/t6elhzZq1C27nnZVlGffeew9vf/sfz/j8tNNewFVXXcEdd9zOc5+7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NyBf1fWBm0voKAtIiIyGipmZNv1wFY1EtmGmVyWbFsrbrF/0M41r6b9zzeTb27G19BA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\n9h4LJgAAAABJRU5ErkJggg==\n",
"text/plain": [
"\u003cFigure size 1200x600 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#@title Plot\n",
"\n",
"fig, ax = plt.subplots(figsize=(12,6))\n",
"sns.tsplot(fig4b_df, time=\"episode\", unit=\"seed\", value=\"eval_0\", condition=\"method\", ci=95, color=[\"r\", \"b\"], linestyle=\"--\", ax=ax)\n",
"\n",
"ax.axhline(dqn_df.groupby(\"seed\").tail(1).mean()[\"eval_0\"], color='r', linestyle='--')\n",
"ax.axhline(true_w_df.mean()[\"return\"], color='b', linestyle='-', label=\"GPE+GPI with true w\")\n",
"\n",
"ax.text(\n",
" 800,\n",
" 4.3,\n",
" r\"$Q$-learning after $10^6$ sample transitions\",\n",
" fontdict=dict(fontsize=15))\n",
"\n",
"ax.set_xlim([0, 3000])\n",
"ax.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4ruEpAxS56s5"
},
"source": [
"# Figure 4c\n",
"\n",
"To generate the regressed w results:\n",
"```\n",
"python3 train_keybooard.py --num_pretrain_episodes=20000 --policy_weights_name=12 --export_path=/tmp/fig4_keyboard\n",
"python3 run_regressed_w_fig4c.py --num_episodes=100 --report_every=1 --keyboard_path=/tmp/fig6_keyboard/tfhub \\\n",
" --output_path=/tmp/fig4b_regressed_w.csv\n",
"```\n",
"\n",
"To generate the regressed w with learned phi results:\n",
"```\n",
"# First train a phi model. Change num_phis to phi of different dimensions e.g. 3 or 4.\n",
"python3 train_phi_model.py --export_path=/tmp/phi_model_2d --num_phis=2\n",
"# Then train a keyboard.\n",
"python3 train_keybooard_with_phi.py --num_pretrain_episodes=20000 --phi_model_phi=/tmp/phi_model_2d \\\n",
" --export_path=/tmp/fig4_keyboard_with_phi\n",
"# Finally regress w with both models.\n",
"python3 run_regressed_w_with_phi_fig4c.py --num_episodes=100 --report_every=1 --keyboard_path=/tmp/fig4_keyboard_with_phi/tfhub \\\n",
" --output_path=/tmp/fig4c_regressed_w.csv\n",
"```\n",
"\n",
"(Note that training of the phi model can converge to a poor local minima, so it maybe necessary to rerun it if the eval loss is too high, or use a larger set of random training tasks.)\n",
"\n",
"Repeat the above steps for multiple runs. Below shows the results for 10 runs."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"executionInfo": {
"elapsed": 1313,
"status": "ok",
"timestamp": 1596666781870,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "Lp22-gE856s9"
},
"outputs": [],
"source": [
"#@title Load Data\n",
"\n",
"dqn_path = os.path.join(DATA_DIR, \"fig4_dqn_{}.csv\")\n",
"dqn_df = read_data(dqn_path, num_seeds=10)\n",
"dqn_df[\"method\"] = \"Q-Learning\"\n",
"\n",
"true_w_path = os.path.join(DATA_DIR, \"fig4_true_w_{}.csv\")\n",
"true_w_df = read_data(true_w_path, num_seeds=10)\n",
"true_w_df[\"method\"] = \"GPE+GPI with true w\"\n",
"\n",
"regressed_w_path = os.path.join(DATA_DIR, \"fig4c_regressed_w_{}.csv\")\n",
"regressed_w_df = read_data(regressed_w_path, num_seeds=10)\n",
"regressed_w_df[\"method\"] = \"GPE+GPI with regressed w\"\n",
"\n",
"regressed_w_with_phi_2d_path = os.path.join(DATA_DIR, \"fig4c_regressed_w_with_phi_{}_2d.csv\")\n",
"regressed_w_with_phi_2d_df = read_data(regressed_w_with_phi_2d_path, num_seeds=10)\n",
"regressed_w_with_phi_2d_df[\"method\"] = \"GPE+GPI with regressed w and 2d phi\"\n",
"\n",
"regressed_w_with_phi_3d_path = os.path.join(DATA_DIR, \"fig4c_regressed_w_with_phi_{}_3d.csv\")\n",
"regressed_w_with_phi_3d_df = read_data(regressed_w_with_phi_3d_path, num_seeds=10)\n",
"regressed_w_with_phi_3d_df[\"method\"] = \"GPE+GPI with regressed w and 3d phi\"\n",
"\n",
"regressed_w_with_phi_4d_path = os.path.join(DATA_DIR, \"fig4c_regressed_w_with_phi_{}_4d.csv\")\n",
"regressed_w_with_phi_4d_df = read_data(regressed_w_with_phi_4d_path, num_seeds=10)\n",
"regressed_w_with_phi_4d_df[\"method\"] = \"GPE+GPI with regressed w and 4d phi\"\n",
"\n",
"fig4c_df = pd.concat([regressed_w_df, regressed_w_with_phi_2d_df, regressed_w_with_phi_3d_df, regressed_w_with_phi_4d_df])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"cellView": "form",
"colab": {
"height": 400
},
"colab_type": "code",
"executionInfo": {
"elapsed": 1769,
"status": "ok",
"timestamp": 1596666783655,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "qxaeiWVz56tC",
"outputId": "538644f7-b0e7-4a31-b0ac-65ef41f11106"
},
"outputs": [
{
"data": {
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bWimKfOPZpEVQC8JxZnWxHAP9grPRwuwd3lVjYlkdRiwrdcjN6yrfejnUi2f0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\n8pIaC334wZk8Gt1NBZOB5YFDhjs3GI3eLDYziMBDV6vIMCCVkiQzee3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N1aRUpRSUgjwi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\n37XXXsXQo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wgdLxgM8vHHi8nL6xZ77UggesRTTz2H39/ihMcqLz/Ivn17ufDCgpM6N8BH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\nEREREamE/wNr76ODwBfe1QAAAABJRU5ErkJggg==\n",
"text/plain": [
"\u003cFigure size 1200x600 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#@title Plot\n",
"\n",
"fig, ax = plt.subplots(figsize=(12,6))\n",
"sns.tsplot(fig4c_df, time=\"episode\", unit=\"seed\", value=\"eval_0\", condition=\"method\", ci=95, ax=ax)\n",
"\n",
"ax.axhline(dqn_df.groupby(\"seed\").tail(1).mean()[\"eval_0\"], color='r', linestyle='--')\n",
"ax.axhline(true_w_df.mean()[\"return\"], color='b', linestyle='-', label=\"GPE+GPI with true w\")\n",
"\n",
"ax.text(\n",
" 8,\n",
" 4.3,\n",
" r\"$Q$-learning after $10^6$ sample transitions\",\n",
" fontdict=dict(fontsize=15))\n",
"\n",
"ax.set_xlim([0, 30])\n",
"ax.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "LqZUN6k-ssNB"
},
"source": [
"# Figure 5a\n",
"\n",
"To generate the result for each set of policies:\n",
"\n",
"```\n",
"# Train a keyboard for a set of policies, i.e. replace {POLICY} with 5, 12 or 34\n",
"python3 train_keyboard.py --num_pretrain_episodes=20000 --policy_weights_name={POLICY} --export_path=/tmp/fig5a_keyboard_{POLICY}\n",
"# Evaluate the trained keyboard at regular interval between [-1, 0] to [0, -1]\n",
"python3 eval_keyboard_fig5.py --num_episodes=1000 --keyboard_paths=/tmp/fig5a_keyboard_{POLICY}/tfhub \\\n",
" --output_path=/tmp/fig5_polar_{POLICY}.csv\n",
"```\n",
"\n",
"Repeat the above steps for multiple runs. Below shows the results for 10 runs."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"executionInfo": {
"elapsed": 7098,
"status": "ok",
"timestamp": 1596666790760,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "pKXVbt_UssNE"
},
"outputs": [],
"source": [
"#@title Load Data\n",
"\n",
"policy_12_path = os.path.join(DATA_DIR, \"fig5_polar_{}_12.csv\")\n",
"policy_12_df = read_data(policy_12_path, num_seeds=10)\n",
"policy_34_path = os.path.join(DATA_DIR, \"fig5_polar_{}_34.csv\")\n",
"policy_34_df = read_data(policy_34_path, num_seeds=10)\n",
"policy_5_path = os.path.join(DATA_DIR, \"fig5_polar_{}_5.csv\")\n",
"policy_5_df = read_data(policy_5_path, num_seeds=10)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"cellView": "form",
"colab": {
"height": 606
},
"colab_type": "code",
"executionInfo": {
"elapsed": 1243,
"status": "ok",
"timestamp": 1596666792009,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "4f-68h2ZssNI",
"outputId": "84b9394c-7d5a-4e0b-c254-ce4674ef7565"
},
"outputs": [
{
"data": {
"image/png": 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IJ9HjQctTv0Lo8MjKpwyHgt74F4gEsbNrN7Yd+xx7fMcg+HPU0NdTDEQSf17M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zFzYQ8OPuu29De3sblixpwH33fdsQTVJVFfv378V///cTOHny5JCvdcstX0yLNsXjMfzoRz/A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\nhDSgoEcsxIkaZjO17pGWcdY6SK8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LQKWSQSaT2L3f0vsvIV2Xx4MexwlbPnEc1/ydCfEy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\nrkJggg==\n",
"text/plain": [
"\u003cFigure size 1000x1000 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#@title Plot\n",
"\n",
"use_polar = True\n",
"plt.figure(figsize=(10, 10))\n",
"ax = plt.subplot(111, polar=use_polar)\n",
"\n",
"policy_5_mean_df = policy_5_df.groupby(\"angle\").mean()\n",
"ax.plot(\n",
" policy_5_mean_df.index,\n",
" policy_5_mean_df[\"return\"],\n",
" \".-\",\n",
" linewidth=5, color='r')\n",
"\n",
"policy_12_mean_df = policy_12_df.groupby(\"angle\").mean()\n",
"ax.plot(\n",
" policy_12_mean_df.index,\n",
" policy_12_mean_df[\"return\"],\n",
" \".-\",\n",
" linewidth=5, color='g')\n",
"\n",
"policy_34_mean_df = policy_34_df.groupby(\"angle\").mean()\n",
"ax.plot(\n",
" policy_34_mean_df.index,\n",
" policy_34_mean_df[\"return\"],\n",
" \".-\",\n",
" linewidth=5, color='b')\n",
"\n",
"legend = ax.legend([\n",
" r\"GPE + GPI with $\\Pi_{5}$\", r\"GPE + GPI with $\\Pi_{12}$\", r\"GPE + GPI with $\\Pi_{34}$\",\n",
" r\"$Q$-learning\"\n",
"],\n",
" fontsize=\"22\",\n",
" loc=\"lower left\")\n",
"\n",
"ax.set_theta_zero_location(\"N\")\n",
"ax.set_theta_direction(-1)\n",
"\n",
"lines, labels = ax.set_thetagrids(\n",
" (0, 45, 90, 135, 315),\n",
" (r\"$\\mathbf{w}_2 = [0,1]$\", r\"$\\mathbf{w}_5 = [1,1]$\",\n",
" r\"$\\qquad \\mathbf{w}_1 = [1,0]$\", r\"$\\mathbf{w}_3 = [1,-1]$\",\n",
" r\"$\\mathbf{w}_4 = [-1,1]$\"),\n",
" fontweight=\"bold\",\n",
" fontsize=15)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "MlOymzIuhQh3"
},
"source": [
"# Figure 5b\n",
"\n",
"To generate the result for each set of policies:\n",
"\n",
"```\n",
"# Train a keyboard for a set of policies, i.e. replace {POLICY} with 4, 42, 425, 4251 or 42513\n",
"python3 train_keyboard.py --num_pretrain_episodes=20000 --policy_weights_name={POLICY} --export_path=/tmp/fig5a_keyboard_{POLICY}\n",
"# Evaluate the trained keyboard at regular interval between [-1, 0] to [0, -1]\n",
"python3 eval_keyboard_fig5.py --num_episodes=1000 --keyboard_paths=/tmp/fig5a_keyboard_{POLICY}/tfhub \\\n",
" --output_path=/tmp/fig5_polar_{POLICY}.csv\n",
"```\n",
"\n",
"Repeat the above steps for multiple runs. Below shows the results for 10 runs."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"executionInfo": {
"elapsed": 12379,
"status": "ok",
"timestamp": 1596666804399,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "lkACArMNhUbl"
},
"outputs": [],
"source": [
"#@title Load Data\n",
"\n",
"policy_42513_path = os.path.join(DATA_DIR, \"fig5_polar_{}_42513.csv\")\n",
"policy_42513_df = read_data(policy_42513_path, num_seeds=10)\n",
"policy_4251_path = os.path.join(DATA_DIR, \"fig5_polar_{}_4251.csv\")\n",
"policy_4251_df = read_data(policy_4251_path, num_seeds=10)\n",
"policy_425_path = os.path.join(DATA_DIR, \"fig5_polar_{}_425.csv\")\n",
"policy_425_df = read_data(policy_425_path, num_seeds=10)\n",
"policy_42_path = os.path.join(DATA_DIR, \"fig5_polar_{}_42.csv\")\n",
"policy_42_df = read_data(policy_42_path, num_seeds=10)\n",
"policy_4_path = os.path.join(DATA_DIR, \"fig5_polar_{}_4.csv\")\n",
"policy_4_df = read_data(policy_4_path, num_seeds=10)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"cellView": "form",
"colab": {
"height": 606
},
"colab_type": "code",
"executionInfo": {
"elapsed": 1288,
"status": "ok",
"timestamp": 1596666805694,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "mm46b10CiThP",
"outputId": "1bad8fe4-fa1d-4134-aaec-1805664229d3"
},
"outputs": [
{
"data": {
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6E6EoEoGA2mnjM/ibCINBxmIxYjYbo29td9F31VVXYTKZol3O3/rWt7j00kuH+lIEAsE5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JWMzx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\n3XU7Egm91UoQRPzhD4/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XQaaJNABQFPX3q15hlOSYdpyO/wNBqAH/2pgmFlP/VHGpCks5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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"\u003cFigure size 1000x1000 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#@title Plot\n",
"\n",
"use_polar = True\n",
"plt.figure(figsize=(10, 10))\n",
"ax = plt.subplot(111, polar=use_polar)\n",
"\n",
"policy_42513_mean_df = policy_42513_df.groupby(\"angle\").mean()\n",
"ax.plot(\n",
" policy_42513_mean_df.index,\n",
" policy_42513_mean_df[\"return\"],\n",
" \".-\",\n",
" linewidth=5, color='y')\n",
"\n",
"policy_4251_mean_df = policy_4251_df.groupby(\"angle\").mean()\n",
"ax.plot(\n",
" policy_4251_mean_df.index,\n",
" policy_4251_mean_df[\"return\"],\n",
" \".-\",\n",
" linewidth=5, color='k')\n",
"\n",
"policy_425_mean_df = policy_425_df.groupby(\"angle\").mean()\n",
"ax.plot(\n",
" policy_425_mean_df.index,\n",
" policy_425_mean_df[\"return\"],\n",
" \".-\",\n",
" linewidth=5, color='b')\n",
"\n",
"policy_42_mean_df = policy_42_df.groupby(\"angle\").mean()\n",
"ax.plot(\n",
" policy_42_mean_df.index,\n",
" policy_42_mean_df[\"return\"],\n",
" \".-\",\n",
" linewidth=5, color='g')\n",
"\n",
"policy_4_mean_df = policy_4_df.groupby(\"angle\").mean()\n",
"ax.plot(\n",
" policy_4_mean_df.index,\n",
" policy_4_mean_df[\"return\"],\n",
" \".-\",\n",
" linewidth=5, color='r')\n",
"\n",
"legend = ax.legend([\n",
" r\"GPE + GPI with $\\Pi_{4}$\",\n",
" r\"GPE + GPI with $\\Pi_{42}$\",\n",
" r\"GPE + GPI with $\\Pi_{425}$\",\n",
" r\"GPE + GPI with $\\Pi_{4251}$\",\n",
" r\"GPE + GPI with $\\Pi_{42513}$\",\n",
" r\"$Q$-learning\",\n",
" ],\n",
" fontsize=\"15\",\n",
" loc=\"best\")\n",
"\n",
"ax.set_theta_zero_location(\"N\")\n",
"ax.set_theta_direction(-1)\n",
"\n",
"lines, labels = ax.set_thetagrids(\n",
" (0, 45, 90, 135, 315),\n",
" (r\"$\\mathbf{w}_2 = [0,1]$\", r\"$\\mathbf{w}_5 = [1,1]$\",\n",
" r\"$\\qquad \\mathbf{w}_1 = [1,0]$\", r\"$\\mathbf{w}_3 = [1,-1]$\",\n",
" r\"$\\mathbf{w}_4 = [-1,1]$\"),\n",
" fontweight=\"bold\",\n",
" fontsize=15)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "vB8evAxHCsEF"
},
"source": [
"# Figure 6\n",
"\n",
"To generate the q-learning results:\n",
"```\n",
"python3 ../run_dqn.py --num_episodes=20000 --report_every=100 --output_path=/tmp/fig6_dqn.csv\n",
"```\n",
"\n",
"To generate the OK results:\n",
"```\n",
"python3 train_keybooard.py --num_pretrain_episodes=20000 --policy_weights_name=12 --export_path=/tmp/fig6_keyboard\n",
"python3 ../run_ok.py --num_episodes=20000 --report_every=100 --keyboard_path=/tmp/fig6_keyboard/tfhub --output_path=/tmp/fig6_ok.csv\n",
"```\n",
"\n",
"To generate the results with fixed w:\n",
"```\n",
"# Make use of a pretrained keyboard. Change test_w to evaluate other policies such as [1,0] and [1,-1].\n",
"python3 run_true_w_fig6.py --num_episodes=1000 --keyboard_path=/tmp/fig6_keyboard/tfhub --test_w=1,1\n",
"```\n",
"\n",
"Repeat the above steps for multiple runs. Below shows the results for 10 runs."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"executionInfo": {
"elapsed": 881,
"status": "ok",
"timestamp": 1596666806583,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "8RubRCQ-EhDf"
},
"outputs": [],
"source": [
"#@title Load Data\n",
"\n",
"dqn_path = os.path.join(DATA_DIR, \"fig6_dqn_{}.csv\")\n",
"dqn_df = read_data(dqn_path, num_seeds=10)\n",
"dqn_df[\"method\"] = \"Q-Learning\"\n",
"ok_path = os.path.join(DATA_DIR, \"fig6_ok_{}.csv\")\n",
"ok_df = read_data(ok_path, num_seeds=10)\n",
"ok_df[\"method\"] = \"GPE + GPI with varying w\"\n",
"fig6_df = pd.concat([dqn_df, ok_df])\n",
"\n",
"test_ws = [[1, 1], [1, 0], [1, -1]]\n",
"test_dfs = []\n",
"for test_w in test_ws:\n",
" test_w_str = \"|\".join([str(x) for x in test_w])\n",
" path = os.path.join(DATA_DIR, \"fig6_true_w=\" + test_w_str + \"_{}.csv\")\n",
" test_dfs.append(read_data(path, num_seeds=10))"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"cellView": "form",
"colab": {
"height": 400
},
"colab_type": "code",
"executionInfo": {
"elapsed": 1491,
"status": "ok",
"timestamp": 1596666808084,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "7PBgQoxELoAz",
"outputId": "497bfb1c-f3d6-4c74-d180-e7b571b9b3e9"
},
"outputs": [
{
"data": {
"image/png": 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fkMpNZqxVQkIlqVcikv56sku1FFIthQyzgkBKovDW1hXcrt8V1rIlBvCTiBff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\nDZBC0KiGNGsxUsBSO6E72Hq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rEpOPUkCMQaiA4OhnNmVGW2PQyRCdDN2I81GEnpACoSKE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\nRAaIwreIiIiIyABR+BYRERERGSD/P4XeUz01vwISAAAAAElFTkSuQmCC\n",
"text/plain": [
"\u003cFigure size 1200x600 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#@title Plot\n",
"\n",
"fig, ax = plt.subplots(figsize=(12,6))\n",
"sns.tsplot(fig6_df, time=\"episode\", unit=\"seed\", value=\"eval_0\", condition=\"method\", ci=95, ax=ax)\n",
"\n",
"ax.axhline(test_dfs[0][\"return\"].mean(), color='g', linestyle='--', label=test_ws[0])\n",
"ax.axhline(test_dfs[1][\"return\"].mean(), color='g', linestyle='-.', label=test_ws[1])\n",
"ax.axhline(test_dfs[2][\"return\"].mean(), color='g', linestyle=':', label=test_ws[2])\n",
"\n",
"ax.legend();"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "//learning/deepmind/applications/hippos/atlantic:light_atlantic_auditable_colab",
"kind": "shared"
},
"name": "generate_figures.ipynb",
"provenance": [
{
"file_id": "1uLGieQXt93jX0ASo-qSCvnjEV67_4ZOO",
"timestamp": 1591027877583
}
],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: option_keyboard/gpe_gpi_experiments/regressed_agent.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Regressed agent."""
import numpy as np
import tensorflow.compat.v1 as tf
class Agent():
"""A DQN Agent."""
def __init__(
self,
batch_size,
optimizer_name,
optimizer_kwargs,
init_w,
):
"""A simple DQN agent.
Args:
batch_size: Size of update batch.
optimizer_name: Name of an optimizer from tf.train
optimizer_kwargs: Keyword arguments for the optimizer.
init_w: The initial cumulant weight.
"""
self._batch_size = batch_size
self._init_w = np.array(init_w)
self._replay = []
# Regress w by gradient descent, could also use closed-form solution.
self._n_cumulants = len(init_w)
self._regressed_w = tf.get_variable(
"regressed_w",
dtype=tf.float32,
initializer=lambda: tf.to_float(init_w))
cumulants_ph = tf.placeholder(
shape=(None, self._n_cumulants), dtype=tf.float32)
rewards_ph = tf.placeholder(shape=(None,), dtype=tf.float32)
predicted_rewards = tf.reduce_sum(
tf.multiply(self._regressed_w, cumulants_ph), axis=-1)
loss = tf.reduce_sum(tf.square(predicted_rewards - rewards_ph))
with tf.variable_scope("optimizer"):
self._optimizer = getattr(tf.train, optimizer_name)(**optimizer_kwargs)
train_op = self._optimizer.minimize(loss)
# Make session and callables.
session = tf.Session()
self._update_fn = session.make_callable(train_op,
[cumulants_ph, rewards_ph])
self._action = session.make_callable(self._regressed_w.read_value(), [])
session.run(tf.global_variables_initializer())
def step(self, timestep, is_training=False):
"""Select actions according to epsilon-greedy policy."""
del timestep
if is_training:
# Can also just use random actions at environment level.
return np.random.uniform(low=-1.0, high=1.0, size=(self._n_cumulants,))
return self._action()
def update(self, step_tm1, action, step_t):
"""Takes in a transition from the environment."""
del step_tm1, action
transition = [
step_t.observation["cumulants"],
step_t.reward,
]
self._replay.append(transition)
if len(self._replay) == self._batch_size:
batch = list(zip(*self._replay))
self._update_fn(*batch)
self._replay = [] # Just a queue.
def get_logs(self):
return dict(regressed=self._action())
================================================
FILE: option_keyboard/gpe_gpi_experiments/run_dqn_fig4b.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Run an experiment.
Run a q-learning agent on task (1, -1).
"""
from absl import app
from absl import flags
import tensorflow.compat.v1 as tf
from option_keyboard import configs
from option_keyboard import dqn_agent
from option_keyboard import environment_wrappers
from option_keyboard import experiment
from option_keyboard import scavenger
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_episodes", 10000, "Number of training episodes.")
flags.DEFINE_integer("report_every", 5,
"Frequency at which metrics are reported.")
flags.DEFINE_string("output_path", None, "Path to write out training curves.")
def main(argv):
del argv
# Create the task environment.
env_config = configs.get_fig4_task_config()
env = scavenger.Scavenger(**env_config)
env = environment_wrappers.EnvironmentWithLogging(env)
# Create the flat agent.
agent = dqn_agent.Agent(
obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
network_kwargs=dict(
output_sizes=(64, 128),
activate_final=True,
),
epsilon=0.1,
additional_discount=0.9,
batch_size=10,
optimizer_name="AdamOptimizer",
optimizer_kwargs=dict(learning_rate=3e-4,))
_, ema_returns = experiment.run(
env,
agent,
num_episodes=FLAGS.num_episodes,
report_every=FLAGS.report_every)
if FLAGS.output_path:
experiment.write_returns_to_file(FLAGS.output_path, ema_returns)
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/gpe_gpi_experiments/run_dqn_fig5.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Run an experiment.
Run a q-learning agent on a task.
"""
from absl import app
from absl import flags
import tensorflow.compat.v1 as tf
from option_keyboard import configs
from option_keyboard import dqn_agent
from option_keyboard import environment_wrappers
from option_keyboard import experiment
from option_keyboard import scavenger
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_episodes", 10000, "Number of training episodes.")
flags.DEFINE_list("test_w", None, "The w to test.")
flags.DEFINE_integer("report_every", 200,
"Frequency at which metrics are reported.")
flags.DEFINE_string("output_path", None, "Path to write out training curves.")
def main(argv):
del argv
# Create the task environment.
test_w = [float(x) for x in FLAGS.test_w]
env_config = configs.get_fig5_task_config(test_w)
env = scavenger.Scavenger(**env_config)
env = environment_wrappers.EnvironmentWithLogging(env)
# Create the flat agent.
agent = dqn_agent.Agent(
obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
network_kwargs=dict(
output_sizes=(64, 128),
activate_final=True,
),
epsilon=0.1,
additional_discount=0.9,
batch_size=10,
optimizer_name="AdamOptimizer",
optimizer_kwargs=dict(learning_rate=3e-4,))
_, ema_returns = experiment.run(
env,
agent,
num_episodes=FLAGS.num_episodes,
report_every=FLAGS.report_every)
if FLAGS.output_path:
experiment.write_returns_to_file(FLAGS.output_path, ema_returns)
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/gpe_gpi_experiments/run_regressed_w_fig4b.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
r"""Run an experiment.
Run GPE/GPI on task (1, -1) with w obtained by regression.
For example, first train a keyboard:
python3 train_keyboard.py -- --logtostderr --policy_weights_name=12 \
--export_path=/tmp/option_keyboard/keyboard
Then, evaluate the keyboard with w by regression.
python3 run_regressed_w_fig4b.py -- --logtostderr \
--keyboard_path=/tmp/option_keyboard/keyboard_12/tfhub
"""
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
from option_keyboard import configs
from option_keyboard import environment_wrappers
from option_keyboard import experiment
from option_keyboard import scavenger
from option_keyboard import smart_module
from option_keyboard.gpe_gpi_experiments import regressed_agent
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_episodes", 4000, "Number of training episodes.")
flags.DEFINE_integer("report_every", 5,
"Frequency at which metrics are reported.")
flags.DEFINE_string("keyboard_path", None, "Path to keyboard model.")
flags.DEFINE_string("output_path", None, "Path to write out training curves.")
def main(argv):
del argv
# Load the keyboard.
keyboard = smart_module.SmartModuleImport(hub.Module(FLAGS.keyboard_path))
# Create the task environment.
base_env_config = configs.get_fig4_task_config()
base_env = scavenger.Scavenger(**base_env_config)
base_env = environment_wrappers.EnvironmentWithLogging(base_env)
# Wrap the task environment with the keyboard.
additional_discount = 0.9
env = environment_wrappers.EnvironmentWithKeyboardDirect(
env=base_env,
keyboard=keyboard,
keyboard_ckpt_path=None,
additional_discount=additional_discount,
call_and_return=False)
# Create the player agent.
agent = regressed_agent.Agent(
batch_size=10,
optimizer_name="AdamOptimizer",
optimizer_kwargs=dict(learning_rate=3e-2,),
init_w=np.random.normal(size=keyboard.num_cumulants) * 0.1,
)
_, ema_returns = experiment.run(
env,
agent,
num_episodes=FLAGS.num_episodes,
report_every=FLAGS.report_every,
num_eval_reps=20)
if FLAGS.output_path:
experiment.write_returns_to_file(FLAGS.output_path, ema_returns)
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/gpe_gpi_experiments/run_regressed_w_fig4c.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
r"""Run an experiment.
Run GPE/GPI on task (1, -1) with w obtained by regression.
For example, first train a keyboard:
python3 train_keyboard.py -- --logtostderr --policy_weights_name=12 \
--export_path=/tmp/option_keyboard/keyboard
Then, evaluate the keyboard with w by regression.
python3 run_regressed_w_fig4c.py -- --logtostderr \
--keyboard_path=/tmp/option_keyboard/keyboard_12/tfhub
"""
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
from option_keyboard import configs
from option_keyboard import environment_wrappers
from option_keyboard import experiment
from option_keyboard import scavenger
from option_keyboard import smart_module
from option_keyboard.gpe_gpi_experiments import regressed_agent
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_episodes", 100, "Number of training episodes.")
flags.DEFINE_integer("report_every", 1,
"Frequency at which metrics are reported.")
flags.DEFINE_string("keyboard_path", None, "Path to keyboard model.")
flags.DEFINE_string("output_path", None, "Path to write out training curves.")
def main(argv):
del argv
# Load the keyboard.
keyboard = smart_module.SmartModuleImport(hub.Module(FLAGS.keyboard_path))
# Create the task environment.
base_env_config = configs.get_fig4_task_config()
base_env = scavenger.Scavenger(**base_env_config)
base_env = environment_wrappers.EnvironmentWithLogging(base_env)
# Wrap the task environment with the keyboard.
additional_discount = 0.9
env = environment_wrappers.EnvironmentWithKeyboardDirect(
env=base_env,
keyboard=keyboard,
keyboard_ckpt_path=None,
additional_discount=additional_discount,
call_and_return=False)
# Create the player agent.
agent = regressed_agent.Agent(
batch_size=10,
optimizer_name="AdamOptimizer",
optimizer_kwargs=dict(learning_rate=3e-2,),
init_w=np.random.normal(size=keyboard.num_cumulants) * 0.1,
)
_, ema_returns = experiment.run(
env,
agent,
num_episodes=FLAGS.num_episodes,
report_every=FLAGS.report_every,
num_eval_reps=100)
if FLAGS.output_path:
experiment.write_returns_to_file(FLAGS.output_path, ema_returns)
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/gpe_gpi_experiments/run_regressed_w_with_phi_fig4c.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
r"""Run an experiment.
Run GPE/GPI on task (1, -1) with a learned phi model and w by regression.
For example, first train a phi model with 3 dimenional phi:
python3 train_phi_model.py -- --logtostderr --use_random_tasks \
--export_path=/tmp/option_keyboard/phi_model_3d --num_phis=3
Then train a keyboard:
python3 train_keyboard_with_phi.py -- --logtostderr \
--export_path=/tmp/option_keyboard/keyboard_3d \
--phi_model_path=/tmp/option_keyboard/phi_model_3d \
--num_phis=2
Finally, evaluate the keyboard with w by regression.
python3 run_regressed_w_with_phi_fig4c.py -- --logtostderr \
--phi_model_path=/tmp/option_keyboard/phi_model_3d \
--keyboard_path=/tmp/option_keyboard/keyboard_3d/tfhub
"""
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
from option_keyboard import configs
from option_keyboard import environment_wrappers
from option_keyboard import experiment
from option_keyboard import scavenger
from option_keyboard import smart_module
from option_keyboard.gpe_gpi_experiments import regressed_agent
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_episodes", 100, "Number of training episodes.")
flags.DEFINE_integer("report_every", 1,
"Frequency at which metrics are reported.")
flags.DEFINE_string("phi_model_path", None, "Path to phi model.")
flags.DEFINE_string("keyboard_path", None, "Path to keyboard model.")
flags.DEFINE_string("output_path", None, "Path to write out training curves.")
def main(argv):
del argv
# Load the keyboard.
keyboard = smart_module.SmartModuleImport(hub.Module(FLAGS.keyboard_path))
# Create the task environment.
base_env_config = configs.get_fig4_task_config()
base_env = scavenger.Scavenger(**base_env_config)
base_env = environment_wrappers.EnvironmentWithLogging(base_env)
base_env = environment_wrappers.EnvironmentWithLearnedPhi(
base_env, FLAGS.phi_model_path)
# Wrap the task environment with the keyboard.
additional_discount = 0.9
env = environment_wrappers.EnvironmentWithKeyboardDirect(
env=base_env,
keyboard=keyboard,
keyboard_ckpt_path=None,
additional_discount=additional_discount,
call_and_return=False)
# Create the player agent.
agent = regressed_agent.Agent(
batch_size=10,
optimizer_name="AdamOptimizer",
optimizer_kwargs=dict(learning_rate=3e-2,),
init_w=np.random.normal(size=keyboard.num_cumulants) * 0.1,
)
_, ema_returns = experiment.run(
env,
agent,
num_episodes=FLAGS.num_episodes,
report_every=FLAGS.report_every,
num_eval_reps=100)
if FLAGS.output_path:
experiment.write_returns_to_file(FLAGS.output_path, ema_returns)
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/gpe_gpi_experiments/run_true_w_fig4.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
r"""Run an experiment.
Run GPE/GPI on task (1, -1) with the groundtruth w.
For example, first train a keyboard:
python3 train_keyboard.py -- --logtostderr --policy_weights_name=12
Then, evaluate the keyboard with groundtruth w.
python3 run_true_w_fig4.py -- --logtostderr \
--keyboard_path=/tmp/option_keyboard/keyboard_12/tfhub
"""
import csv
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1.io import gfile
import tensorflow_hub as hub
from option_keyboard import configs
from option_keyboard import environment_wrappers
from option_keyboard import experiment
from option_keyboard import scavenger
from option_keyboard import smart_module
from option_keyboard.gpe_gpi_experiments import regressed_agent
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_episodes", 1000, "Number of training episodes.")
flags.DEFINE_string("keyboard_path", None, "Path to keyboard model.")
flags.DEFINE_string("output_path", None, "Path to write out returns.")
def main(argv):
del argv
# Load the keyboard.
keyboard = smart_module.SmartModuleImport(hub.Module(FLAGS.keyboard_path))
# Create the task environment.
base_env_config = configs.get_fig4_task_config()
base_env = scavenger.Scavenger(**base_env_config)
base_env = environment_wrappers.EnvironmentWithLogging(base_env)
# Wrap the task environment with the keyboard.
additional_discount = 0.9
env = environment_wrappers.EnvironmentWithKeyboardDirect(
env=base_env,
keyboard=keyboard,
keyboard_ckpt_path=None,
additional_discount=additional_discount,
call_and_return=False)
# Create the player agent.
agent = regressed_agent.Agent(
batch_size=10,
optimizer_name="AdamOptimizer",
# Disable training.
optimizer_kwargs=dict(learning_rate=0.0,),
init_w=[1., -1.])
returns = []
for _ in range(FLAGS.num_episodes):
returns.append(experiment.run_episode(env, agent))
tf.logging.info("#" * 80)
tf.logging.info(
f"Avg. return over {FLAGS.num_episodes} episodes is {np.mean(returns)}")
tf.logging.info("#" * 80)
if FLAGS.output_path:
with gfile.GFile(FLAGS.output_path, "w") as file:
writer = csv.writer(file, delimiter=" ", quoting=csv.QUOTE_MINIMAL)
writer.writerow(["return"])
for val in returns:
writer.writerow([val])
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/gpe_gpi_experiments/run_true_w_fig6.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
r"""Run an experiment.
Run GPE/GPI on the "balancing" task with a fixed w
For example, first train a keyboard:
python3 train_keyboard.py -- --logtostderr --policy_weights_name=12
Then, evaluate the keyboard with a fixed w.
python3 run_true_w_fig6.py -- --logtostderr \
--keyboard_path=/tmp/option_keyboard/keyboard_12/tfhub
"""
import csv
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1.io import gfile
import tensorflow_hub as hub
from option_keyboard import configs
from option_keyboard import environment_wrappers
from option_keyboard import experiment
from option_keyboard import scavenger
from option_keyboard import smart_module
from option_keyboard.gpe_gpi_experiments import regressed_agent
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_episodes", 1000, "Number of training episodes.")
flags.DEFINE_string("keyboard_path", None, "Path to keyboard model.")
flags.DEFINE_list("test_w", None, "The w to test.")
flags.DEFINE_string("output_path", None, "Path to write out returns.")
def main(argv):
del argv
# Load the keyboard.
keyboard = smart_module.SmartModuleImport(hub.Module(FLAGS.keyboard_path))
# Create the task environment.
base_env_config = configs.get_task_config()
base_env = scavenger.Scavenger(**base_env_config)
base_env = environment_wrappers.EnvironmentWithLogging(base_env)
# Wrap the task environment with the keyboard.
additional_discount = 0.9
env = environment_wrappers.EnvironmentWithKeyboardDirect(
env=base_env,
keyboard=keyboard,
keyboard_ckpt_path=None,
additional_discount=additional_discount,
call_and_return=False)
# Create the player agent.
agent = regressed_agent.Agent(
batch_size=10,
optimizer_name="AdamOptimizer",
# Disable training.
optimizer_kwargs=dict(learning_rate=0.0,),
init_w=[float(x) for x in FLAGS.test_w])
returns = []
for _ in range(FLAGS.num_episodes):
returns.append(experiment.run_episode(env, agent))
tf.logging.info("#" * 80)
tf.logging.info(
f"Avg. return over {FLAGS.num_episodes} episodes is {np.mean(returns)}")
tf.logging.info("#" * 80)
if FLAGS.output_path:
with gfile.GFile(FLAGS.output_path, "w") as file:
writer = csv.writer(file, delimiter=" ", quoting=csv.QUOTE_MINIMAL)
writer.writerow(["return"])
for val in returns:
writer.writerow([val])
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/gpe_gpi_experiments/train_keyboard.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Train a keyboard."""
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v1 as tf
from option_keyboard import keyboard_utils
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_pretrain_episodes", 20000,
"Number of pretraining episodes.")
flags.DEFINE_string("export_path", None,
"Where to save the keyboard checkpoints.")
flags.DEFINE_string("policy_weights_name", None,
"A string repsenting the policy weights.")
def main(argv):
del argv
all_policy_weights = {
"1": [1., 0.],
"2": [0., 1.],
"3": [1., -1.],
"4": [-1., 1.],
"5": [1., 1.],
}
if FLAGS.policy_weights_name:
policy_weights = np.array(
[all_policy_weights[v] for v in FLAGS.policy_weights_name])
num_episodes = ((FLAGS.num_pretrain_episodes // 2) *
max(2, len(policy_weights)))
export_path = FLAGS.export_path + "_" + FLAGS.policy_weights_name
else:
policy_weights = None
num_episodes = FLAGS.num_pretrain_episodes
export_path = FLAGS.export_path
keyboard_utils.create_and_train_keyboard(
num_episodes=num_episodes,
policy_weights=policy_weights,
export_path=export_path)
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/gpe_gpi_experiments/train_keyboard_with_phi.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Train a keyboard."""
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v1 as tf
from option_keyboard import keyboard_utils
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_pretrain_episodes", 20000,
"Number of pretraining episodes.")
flags.DEFINE_integer("num_phis", None, "Size of phi")
flags.DEFINE_string("phi_model_path", None,
"Where to load the phi model checkpoints.")
flags.DEFINE_string("export_path", None,
"Where to save the keyboard checkpoints.")
def main(argv):
del argv
keyboard_utils.create_and_train_keyboard_with_phi(
num_episodes=FLAGS.num_pretrain_episodes,
phi_model_path=FLAGS.phi_model_path,
policy_weights=np.eye(FLAGS.num_phis, dtype=np.float32),
export_path=FLAGS.export_path)
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/gpe_gpi_experiments/train_phi_model.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Train simple phi model."""
import collections
import random
from absl import app
from absl import flags
from absl import logging
import numpy as np
import sonnet as snt
import tensorflow.compat.v1 as tf
import tree
from option_keyboard import scavenger
from option_keyboard import smart_module
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_phis", 2, "Dimensionality of phis.")
flags.DEFINE_integer("num_train_steps", 2000, "Number of training steps.")
flags.DEFINE_integer("num_replay_steps", 500, "Number of replay steps.")
flags.DEFINE_integer("min_replay_size", 1000,
"Minimum replay size before starting training.")
flags.DEFINE_integer("num_train_repeats", 10, "Number of training repeats.")
flags.DEFINE_float("learning_rate", 3e-3, "Learning rate.")
flags.DEFINE_bool("use_random_tasks", False, "Use random tasks.")
flags.DEFINE_string("normalisation", "L2",
"Normalisation method for cumulant weights.")
flags.DEFINE_string("export_path", None, "Export path.")
StepOutput = collections.namedtuple("StepOutput",
["obs", "actions", "rewards", "next_obs"])
def collect_experience(env, num_episodes, verbose=False):
"""Collect experience."""
num_actions = env.action_spec().maximum + 1
observations = []
actions = []
rewards = []
next_observations = []
for _ in range(num_episodes):
timestep = env.reset()
episode_return = 0
while not timestep.last():
action = np.random.randint(num_actions)
observations.append(timestep.observation)
actions.append(action)
timestep = env.step(action)
rewards.append(timestep.observation["aux_tasks_reward"])
episode_return += timestep.reward
next_observations.append(timestep.observation)
if verbose:
logging.info("Total return for episode: %f", episode_return)
observation_spec = tree.map_structure(lambda _: None, observations[0])
def stack_observations(obs_list):
obs_list = [
np.stack(obs) for obs in zip(*[tree.flatten(obs) for obs in obs_list])
]
obs_dict = tree.unflatten_as(observation_spec, obs_list)
obs_dict.pop("aux_tasks_reward")
return obs_dict
observations = stack_observations(observations)
actions = np.array(actions, dtype=np.int32)
rewards = np.stack(rewards)
next_observations = stack_observations(next_observations)
return StepOutput(observations, actions, rewards, next_observations)
class PhiModel(snt.AbstractModule):
"""A model for learning phi."""
def __init__(self,
n_actions,
n_phis,
network_kwargs,
final_activation="sigmoid",
name="PhiModel"):
super(PhiModel, self).__init__(name=name)
self._n_actions = n_actions
self._n_phis = n_phis
self._network_kwargs = network_kwargs
self._final_activation = final_activation
def _build(self, observation, actions):
obs = observation["arena"]
n_outputs = self._n_actions * self._n_phis
flat_obs = snt.BatchFlatten()(obs)
net = snt.nets.MLP(**self._network_kwargs)(flat_obs)
net = snt.Linear(output_size=n_outputs)(net)
net = snt.BatchReshape((self._n_actions, self._n_phis))(net)
indices = tf.stack([tf.range(tf.shape(actions)[0]), actions], axis=1)
values = tf.gather_nd(net, indices)
if self._final_activation:
values = getattr(tf.nn, self._final_activation)(values)
return values
def create_ph(tensor):
return tf.placeholder(shape=(None,) + tensor.shape[1:], dtype=tensor.dtype)
def main(argv):
del argv
if FLAGS.use_random_tasks:
tasks = np.random.normal(size=(8, 2))
else:
tasks = [
[1.0, 0.0],
[0.0, 1.0],
[1.0, 1.0],
[-1.0, 1.0],
]
if FLAGS.normalisation == "L1":
tasks /= np.sum(np.abs(tasks), axis=-1, keepdims=True)
elif FLAGS.normalisation == "L2":
tasks /= np.linalg.norm(tasks, axis=-1, keepdims=True)
else:
raise ValueError("Unknown normlisation_method {}".format(
FLAGS.normalisation))
logging.info("Tasks: %s", tasks)
env_config = dict(
arena_size=11,
num_channels=2,
max_num_steps=100,
num_init_objects=10,
object_priors=[1.0, 1.0],
egocentric=True,
default_w=None,
aux_tasks_w=tasks)
env = scavenger.Scavenger(**env_config)
num_actions = env.action_spec().maximum + 1
model_config = dict(
n_actions=num_actions,
n_phis=FLAGS.num_phis,
network_kwargs=dict(
output_sizes=(64, 128),
activate_final=True,
),
)
model = smart_module.SmartModuleExport(lambda: PhiModel(**model_config))
dummy_steps = collect_experience(env, num_episodes=10, verbose=True)
num_rewards = dummy_steps.rewards.shape[-1]
# Placeholders
steps_ph = tree.map_structure(create_ph, dummy_steps)
phis = model(steps_ph.obs, steps_ph.actions)
phis_to_rewards = snt.Linear(
num_rewards, initializers=dict(w=tf.zeros), use_bias=False)
preds = phis_to_rewards(phis)
loss_per_batch = tf.square(preds - steps_ph.rewards)
loss_op = tf.reduce_mean(loss_per_batch)
replay = []
# Optimizer and train op.
with tf.variable_scope("optimizer"):
optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
train_op = optimizer.minimize(loss_op)
# Add normalisation of weights in phis_to_rewards
if FLAGS.normalisation == "L1":
w_norm = tf.reduce_sum(tf.abs(phis_to_rewards.w), axis=0, keepdims=True)
elif FLAGS.normalisation == "L2":
w_norm = tf.norm(phis_to_rewards.w, axis=0, keepdims=True)
else:
raise ValueError("Unknown normlisation_method {}".format(
FLAGS.normalisation))
normalise_w = tf.assign(phis_to_rewards.w,
phis_to_rewards.w / tf.maximum(w_norm, 1e-6))
def filter_steps(steps):
mask = np.sum(np.abs(steps.rewards), axis=-1) > 0.1
nonzero_inds = np.where(mask)[0]
zero_inds = np.where(np.logical_not(mask))[0]
zero_inds = np.random.choice(
zero_inds, size=len(nonzero_inds), replace=False)
selected_inds = np.concatenate([nonzero_inds, zero_inds])
selected_steps = tree.map_structure(lambda x: x[selected_inds], steps)
return selected_steps, selected_inds
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step = 0
while step < FLAGS.num_train_steps:
step += 1
steps_output = collect_experience(env, num_episodes=10)
selected_step_outputs, selected_inds = filter_steps(steps_output)
if len(replay) > FLAGS.min_replay_size:
# Do training.
for _ in range(FLAGS.num_train_repeats):
train_samples = random.choices(replay, k=128)
train_samples = tree.map_structure(
lambda *x: np.stack(x, axis=0), *train_samples)
train_samples = tree.unflatten_as(steps_ph, train_samples)
feed_dict = dict(
zip(tree.flatten(steps_ph), tree.flatten(train_samples)))
_, train_loss = sess.run([train_op, loss_op], feed_dict=feed_dict)
sess.run(normalise_w)
# Do evaluation.
if step % 50 == 0:
feed_dict = dict(
zip(tree.flatten(steps_ph), tree.flatten(selected_step_outputs)))
eval_loss = sess.run(loss_op, feed_dict=feed_dict)
logging.info("Step %d, train loss %f, eval loss %f, replay %s",
step, train_loss, eval_loss, len(replay))
print(sess.run(phis_to_rewards.get_variables())[0].T)
values = dict(step=step, train_loss=train_loss, eval_loss=eval_loss)
logging.info(values)
# Add to replay.
if step <= FLAGS.num_replay_steps:
def select_fn(ind):
return lambda x: x[ind]
for idx in range(len(selected_inds)):
replay.append(
tree.flatten(
tree.map_structure(select_fn(idx), selected_step_outputs)))
# Export trained model.
if FLAGS.export_path:
model.export(FLAGS.export_path, sess, overwrite=True)
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/keyboard_agent.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Keyboard agent."""
import os
import numpy as np
import sonnet as snt
import tensorflow.compat.v1 as tf
from option_keyboard import smart_module
class Agent():
"""An Option Keyboard Agent."""
def __init__(
self,
obs_spec,
action_spec,
policy_weights,
network_kwargs,
epsilon,
additional_discount,
batch_size,
optimizer_name,
optimizer_kwargs,
):
"""A simple DQN agent.
Args:
obs_spec: The observation spec.
action_spec: The action spec.
policy_weights: A list of vectors each representing the cumulant weights
for that particular option/policy.
network_kwargs: Keyword arguments for snt.nets.MLP
epsilon: Exploration probability.
additional_discount: Discount on returns used by the agent.
batch_size: Size of update batch.
optimizer_name: Name of an optimizer from tf.train
optimizer_kwargs: Keyword arguments for the optimizer.
"""
tf.logging.info(policy_weights)
self._policy_weights = tf.convert_to_tensor(
policy_weights, dtype=tf.float32)
self._current_policy = None
self._epsilon = epsilon
self._additional_discount = additional_discount
self._batch_size = batch_size
self._n_actions = action_spec.num_values
self._n_policies, self._n_cumulants = policy_weights.shape
def create_network():
return OptionValueNet(
self._n_policies,
self._n_cumulants,
self._n_actions,
network_kwargs=network_kwargs,
)
self._network = smart_module.SmartModuleExport(create_network)
self._replay = []
obs_spec = self._extract_observation(obs_spec)
def option_values(values, policy):
return tf.tensordot(
values[:, policy, ...], self._policy_weights[policy], axes=[1, 0])
# Placeholders for policy.
o = tf.placeholder(shape=obs_spec.shape, dtype=obs_spec.dtype)
p = tf.placeholder(shape=(), dtype=tf.int32)
q = self._network(tf.expand_dims(o, axis=0))
qo = option_values(q, p)
# Placeholders for update.
o_tm1 = tf.placeholder(shape=(None,) + obs_spec.shape, dtype=obs_spec.dtype)
a_tm1 = tf.placeholder(shape=(None,), dtype=tf.int32)
c_t = tf.placeholder(shape=(None, self._n_cumulants), dtype=tf.float32)
d_t = tf.placeholder(shape=(None,), dtype=tf.float32)
o_t = tf.placeholder(shape=(None,) + obs_spec.shape, dtype=obs_spec.dtype)
# Compute values over all options.
q_tm1 = self._network(o_tm1)
q_t = self._network(o_t)
qo_t = option_values(q_t, p)
a_t = tf.cast(tf.argmax(qo_t, axis=-1), tf.int32)
qa_tm1 = _batched_index(q_tm1[:, p, ...], a_tm1)
qa_t = _batched_index(q_t[:, p, ...], a_t)
# TD error
g = additional_discount * tf.expand_dims(d_t, axis=-1)
td_error = tf.stop_gradient(c_t + g * qa_t) - qa_tm1
loss = tf.reduce_sum(tf.square(td_error) / 2)
# Dummy calls to keyboard for SmartModule
_ = self._network.gpi(o_tm1[0], c_t[0])
_ = self._network.num_cumulants
_ = self._network.num_policies
_ = self._network.num_actions
with tf.variable_scope("optimizer"):
self._optimizer = getattr(tf.train, optimizer_name)(**optimizer_kwargs)
train_op = self._optimizer.minimize(loss)
# Make session and callables.
session = tf.Session()
self._session = session
self._update_fn = session.make_callable(
train_op, [o_tm1, a_tm1, c_t, d_t, o_t, p])
self._value_fn = session.make_callable(qo, [o, p])
session.run(tf.global_variables_initializer())
self._saver = tf.train.Saver(var_list=self._network.variables)
@property
def keyboard(self):
return self._network
def _extract_observation(self, obs):
return obs["arena"]
def step(self, timestep, is_training=False):
"""Select actions according to epsilon-greedy policy."""
if timestep.first():
self._current_policy = np.random.randint(self._n_policies)
if is_training and np.random.rand() < self._epsilon:
return np.random.randint(self._n_actions)
q_values = self._value_fn(
self._extract_observation(timestep.observation), self._current_policy)
return int(np.argmax(q_values))
def update(self, step_tm1, action, step_t):
"""Takes in a transition from the environment."""
transition = [
self._extract_observation(step_tm1.observation),
action,
step_t.observation["cumulants"],
step_t.discount,
self._extract_observation(step_t.observation),
]
self._replay.append(transition)
if len(self._replay) == self._batch_size:
batch = list(zip(*self._replay)) + [self._current_policy]
self._update_fn(*batch)
self._replay = [] # Just a queue.
def export(self, path):
tf.logging.info("Exporting keyboard to %s", path)
self._network.export(
os.path.join(path, "tfhub"), self._session, overwrite=True)
self._saver.save(self._session, os.path.join(path, "checkpoints"))
class OptionValueNet(snt.AbstractModule):
"""Option Value net."""
def __init__(self,
n_policies,
n_cumulants,
n_actions,
network_kwargs,
name="option_keyboard"):
"""Construct an Option Value Net sonnet module.
Args:
n_policies: Number of policies.
n_cumulants: Number of cumulants.
n_actions: Number of actions.
network_kwargs: Network arguments.
name: Name
"""
super(OptionValueNet, self).__init__(name=name)
self._n_policies = n_policies
self._n_cumulants = n_cumulants
self._n_actions = n_actions
self._network_kwargs = network_kwargs
def _build(self, observation):
values = []
flat_obs = snt.BatchFlatten()(observation)
for _ in range(self._n_cumulants):
net = snt.nets.MLP(**self._network_kwargs)(flat_obs)
net = snt.Linear(output_size=self._n_policies * self._n_actions)(net)
net = snt.BatchReshape([self._n_policies, self._n_actions])(net)
values.append(net)
values = tf.stack(values, axis=2)
return values
def gpi(self, observation, cumulant_weights):
q_values = self.__call__(tf.expand_dims(observation, axis=0))[0]
q_w = tf.tensordot(q_values, cumulant_weights, axes=[1, 0]) # [P,a]
q_w_actions = tf.reduce_max(q_w, axis=0)
action = tf.cast(tf.argmax(q_w_actions), tf.int32)
return action
@property
def num_cumulants(self):
return self._n_cumulants
@property
def num_policies(self):
return self._n_policies
@property
def num_actions(self):
return self._n_actions
def _batched_index(values, indices):
one_hot_indices = tf.one_hot(indices, values.shape[-1], dtype=values.dtype)
one_hot_indices = tf.expand_dims(one_hot_indices, axis=1)
return tf.reduce_sum(values * one_hot_indices, axis=-1)
================================================
FILE: option_keyboard/keyboard_utils.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Keyboard utils."""
import numpy as np
from option_keyboard import configs
from option_keyboard import environment_wrappers
from option_keyboard import experiment
from option_keyboard import keyboard_agent
from option_keyboard import scavenger
def create_and_train_keyboard(num_episodes,
policy_weights=None,
export_path=None):
"""Train an option keyboard."""
if policy_weights is None:
policy_weights = np.eye(2, dtype=np.float32)
env_config = configs.get_pretrain_config()
env = scavenger.Scavenger(**env_config)
env = environment_wrappers.EnvironmentWithLogging(env)
agent = keyboard_agent.Agent(
obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
policy_weights=policy_weights,
network_kwargs=dict(
output_sizes=(64, 128),
activate_final=True,
),
epsilon=0.1,
additional_discount=0.9,
batch_size=10,
optimizer_name="AdamOptimizer",
optimizer_kwargs=dict(learning_rate=3e-4,))
if num_episodes:
experiment.run(env, agent, num_episodes=num_episodes)
agent.export(export_path)
return agent
def create_and_train_keyboard_with_phi(num_episodes,
phi_model_path,
policy_weights,
export_path=None):
"""Train an option keyboard."""
env_config = configs.get_pretrain_config()
env = scavenger.Scavenger(**env_config)
env = environment_wrappers.EnvironmentWithLogging(env)
env = environment_wrappers.EnvironmentWithLearnedPhi(env, phi_model_path)
agent = keyboard_agent.Agent(
obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
policy_weights=policy_weights,
network_kwargs=dict(
output_sizes=(64, 128),
activate_final=True,
),
epsilon=0.1,
additional_discount=0.9,
batch_size=10,
optimizer_name="AdamOptimizer",
optimizer_kwargs=dict(learning_rate=3e-4,))
if num_episodes:
experiment.run(env, agent, num_episodes=num_episodes)
agent.export(export_path)
return agent
================================================
FILE: option_keyboard/requirements.txt
================================================
absl-py
dm-env==1.2
dm-sonnet==1.34
dm-tree
numpy==1.16.4
tensorflow==1.13.2
tensorflow_hub==0.7.0
tensorflow_probability==0.6.0
wrapt
================================================
FILE: option_keyboard/run.sh
================================================
#!/bin/sh
# Copyright 2020 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
python3 -m venv /tmp/ok_venv
source /tmp/ok_venv/bin/activate
pip install -U pip
pip install -r option_keyboard/requirements.txt
python3 -m option_keyboard.run_dqn_test
python3 -m option_keyboard.run_ok_test
================================================
FILE: option_keyboard/run_dqn.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Run an experiment."""
from absl import app
from absl import flags
import tensorflow.compat.v1 as tf
from option_keyboard import configs
from option_keyboard import dqn_agent
from option_keyboard import environment_wrappers
from option_keyboard import experiment
from option_keyboard import scavenger
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_episodes", 10000, "Number of training episodes.")
flags.DEFINE_integer("report_every", 200,
"Frequency at which metrics are reported.")
flags.DEFINE_string("output_path", None, "Path to write out training curves.")
def main(argv):
del argv
# Create the task environment.
env_config = configs.get_task_config()
env = scavenger.Scavenger(**env_config)
env = environment_wrappers.EnvironmentWithLogging(env)
# Create the flat agent.
agent = dqn_agent.Agent(
obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
network_kwargs=dict(
output_sizes=(64, 128),
activate_final=True,
),
epsilon=0.1,
additional_discount=0.9,
batch_size=10,
optimizer_name="AdamOptimizer",
optimizer_kwargs=dict(learning_rate=3e-4,))
_, ema_returns = experiment.run(
env,
agent,
num_episodes=FLAGS.num_episodes,
report_every=FLAGS.report_every)
if FLAGS.output_path:
experiment.write_returns_to_file(FLAGS.output_path, ema_returns)
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/run_dqn_test.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Tests for running the simple DQN agent."""
from absl import flags
from absl.testing import absltest
import tensorflow.compat.v1 as tf
from option_keyboard import run_dqn
FLAGS = flags.FLAGS
class RunDQNTest(absltest.TestCase):
def test_run(self):
FLAGS.num_episodes = 200
run_dqn.main(None)
if __name__ == '__main__':
tf.disable_v2_behavior()
absltest.main()
================================================
FILE: option_keyboard/run_ok.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Run an experiment."""
import os
from absl import app
from absl import flags
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
from option_keyboard import configs
from option_keyboard import dqn_agent
from option_keyboard import environment_wrappers
from option_keyboard import experiment
from option_keyboard import keyboard_utils
from option_keyboard import scavenger
from option_keyboard import smart_module
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_episodes", 10000, "Number of training episodes.")
flags.DEFINE_integer("num_pretrain_episodes", 20000,
"Number of pretraining episodes.")
flags.DEFINE_integer("report_every", 200,
"Frequency at which metrics are reported.")
flags.DEFINE_string("keyboard_path", None, "Path to pretrained keyboard model.")
flags.DEFINE_string("output_path", None, "Path to write out training curves.")
def main(argv):
del argv
# Pretrain the keyboard and save a checkpoint.
if FLAGS.keyboard_path:
keyboard_path = FLAGS.keyboard_path
else:
with tf.Graph().as_default():
export_path = "/tmp/option_keyboard/keyboard"
_ = keyboard_utils.create_and_train_keyboard(
num_episodes=FLAGS.num_pretrain_episodes, export_path=export_path)
keyboard_path = os.path.join(export_path, "tfhub")
# Load the keyboard.
keyboard = smart_module.SmartModuleImport(hub.Module(keyboard_path))
# Create the task environment.
base_env_config = configs.get_task_config()
base_env = scavenger.Scavenger(**base_env_config)
base_env = environment_wrappers.EnvironmentWithLogging(base_env)
# Wrap the task environment with the keyboard.
additional_discount = 0.9
env = environment_wrappers.EnvironmentWithKeyboard(
env=base_env,
keyboard=keyboard,
keyboard_ckpt_path=None,
n_actions_per_dim=3,
additional_discount=additional_discount,
call_and_return=False)
# Create the player agent.
agent = dqn_agent.Agent(
obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
network_kwargs=dict(
output_sizes=(64, 128),
activate_final=True,
),
epsilon=0.1,
additional_discount=additional_discount,
batch_size=10,
optimizer_name="AdamOptimizer",
optimizer_kwargs=dict(learning_rate=3e-4,))
_, ema_returns = experiment.run(
env,
agent,
num_episodes=FLAGS.num_episodes,
report_every=FLAGS.report_every)
if FLAGS.output_path:
experiment.write_returns_to_file(FLAGS.output_path, ema_returns)
if __name__ == "__main__":
tf.disable_v2_behavior()
app.run(main)
================================================
FILE: option_keyboard/run_ok_test.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Tests for training a keyboard and then running a DQN agent on top of it."""
from absl import flags
from absl.testing import absltest
import tensorflow.compat.v1 as tf
from option_keyboard import run_ok
FLAGS = flags.FLAGS
class RunDQNTest(absltest.TestCase):
def test_run(self):
FLAGS.num_episodes = 200
FLAGS.num_pretrain_episodes = 200
run_ok.main(None)
if __name__ == '__main__':
tf.disable_v2_behavior()
absltest.main()
================================================
FILE: option_keyboard/scavenger.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Simple Scavenger environment."""
import copy
import enum
import sys
import dm_env
import numpy as np
from option_keyboard import auto_reset_environment
this_module = sys.modules[__name__]
class Action(enum.IntEnum):
"""Actions available to the player."""
UP = 0
DOWN = 1
LEFT = 2
RIGHT = 3
def _one_hot(indices, depth):
return np.eye(depth)[indices]
def _random_pos(arena_size):
return tuple(np.random.randint(0, arena_size, size=[2]).tolist())
class Scavenger(auto_reset_environment.Base):
"""Simple Scavenger."""
def __init__(self,
arena_size,
num_channels,
max_num_steps,
default_w=None,
num_init_objects=15,
object_priors=None,
egocentric=True,
rewarder=None,
aux_tasks_w=None):
self._arena_size = arena_size
self._num_channels = num_channels
self._max_num_steps = max_num_steps
self._num_init_objects = num_init_objects
self._egocentric = egocentric
self._rewarder = (
getattr(this_module, rewarder)() if rewarder is not None else None)
self._aux_tasks_w = aux_tasks_w
if object_priors is None:
self._object_priors = np.ones(num_channels) / num_channels
else:
assert len(object_priors) == num_channels
self._object_priors = np.array(object_priors) / np.sum(object_priors)
if default_w is None:
self._default_w = np.ones(shape=(num_channels,))
else:
self._default_w = default_w
self._num_channels_all = self._num_channels + 2
self._step_in_episode = None
@property
def state(self):
return copy.deepcopy([
self._step_in_episode,
self._walls,
self._objects,
self._player_pos,
self._prev_collected,
])
def set_state(self, state):
state_ = copy.deepcopy(state)
self._step_in_episode = state_[0]
self._walls = state_[1]
self._objects = state_[2]
self._player_pos = state_[3]
self._prev_collected = state_[4]
@property
def player_pos(self):
return self._player_pos
def _reset(self):
self._step_in_episode = 0
# Walls.
self._walls = []
for col in range(self._arena_size):
new_pos = (0, col)
if new_pos not in self._walls:
self._walls.append(new_pos)
for row in range(self._arena_size):
new_pos = (row, 0)
if new_pos not in self._walls:
self._walls.append(new_pos)
# Objects.
self._objects = dict()
for _ in range(self._num_init_objects):
while True:
new_pos = _random_pos(self._arena_size)
if new_pos not in self._objects and new_pos not in self._walls:
self._objects[new_pos] = np.random.multinomial(1, self._object_priors)
break
# Player
self._player_pos = _random_pos(self._arena_size)
while self._player_pos in self._objects or self._player_pos in self._walls:
self._player_pos = _random_pos(self._arena_size)
self._prev_collected = np.zeros(shape=(self._num_channels,))
obs = self.observation()
return dm_env.restart(obs)
def _step(self, action):
self._step_in_episode += 1
if action == Action.UP:
new_player_pos = (self._player_pos[0], self._player_pos[1] + 1)
elif action == Action.DOWN:
new_player_pos = (self._player_pos[0], self._player_pos[1] - 1)
elif action == Action.LEFT:
new_player_pos = (self._player_pos[0] - 1, self._player_pos[1])
elif action == Action.RIGHT:
new_player_pos = (self._player_pos[0] + 1, self._player_pos[1])
else:
raise ValueError("Invalid action `{}`".format(action))
# Toroidal.
new_player_pos = (
(new_player_pos[0] + self._arena_size) % self._arena_size,
(new_player_pos[1] + self._arena_size) % self._arena_size,
)
if new_player_pos not in self._walls:
self._player_pos = new_player_pos
# Compute rewards.
consumed = self._objects.pop(self._player_pos,
np.zeros(shape=(self._num_channels,)))
if self._rewarder is None:
reward = np.dot(consumed, np.array(self._default_w))
else:
reward = self._rewarder.get_reward(self.state, consumed)
self._prev_collected = np.copy(consumed)
assert self._player_pos not in self._objects
assert self._player_pos not in self._walls
# Render everything.
obs = self.observation()
if self._step_in_episode < self._max_num_steps:
return dm_env.transition(reward=reward, observation=obs)
else:
# termination with discount=1.0
return dm_env.truncation(reward=reward, observation=obs)
def observation(self, force_non_egocentric=False):
arena_shape = [self._arena_size] * 2 + [self._num_channels_all]
arena = np.zeros(shape=arena_shape, dtype=np.float32)
def offset_position(pos_):
use_egocentric = self._egocentric and not force_non_egocentric
offset = self._player_pos if use_egocentric else (0, 0)
x = (pos_[0] - offset[0] + self._arena_size) % self._arena_size
y = (pos_[1] - offset[1] + self._arena_size) % self._arena_size
return (x, y)
player_pos = offset_position(self._player_pos)
arena[player_pos] = _one_hot(self._num_channels, self._num_channels_all)
for pos, obj in self._objects.items():
x, y = offset_position(pos)
arena[x, y, :self._num_channels] = obj
for pos in self._walls:
x, y = offset_position(pos)
arena[x, y] = _one_hot(self._num_channels + 1, self._num_channels_all)
collected_resources = np.copy(self._prev_collected).astype(np.float32)
obs = dict(
arena=arena,
cumulants=collected_resources,
)
if self._aux_tasks_w is not None:
obs["aux_tasks_reward"] = np.dot(
np.array(self._aux_tasks_w), self._prev_collected).astype(np.float32)
return obs
def observation_spec(self):
arena = dm_env.specs.BoundedArray(
shape=(self._arena_size, self._arena_size, self._num_channels_all),
dtype=np.float32,
minimum=0.,
maximum=1.,
name="arena")
collected_resources = dm_env.specs.BoundedArray(
shape=(self._num_channels,),
dtype=np.float32,
minimum=-1e9,
maximum=1e9,
name="collected_resources")
obs_spec = dict(
arena=arena,
cumulants=collected_resources,
)
if self._aux_tasks_w is not None:
obs_spec["aux_tasks_reward"] = dm_env.specs.BoundedArray(
shape=(len(self._aux_tasks_w),),
dtype=np.float32,
minimum=-1e9,
maximum=1e9,
name="aux_tasks_reward")
return obs_spec
def action_spec(self):
return dm_env.specs.DiscreteArray(num_values=len(Action), name="action")
class SequentialCollectionRewarder(object):
"""SequentialCollectionRewarder."""
def get_reward(self, state, consumed):
"""Get reward."""
object_counts = sum(list(state[2].values()) + [np.zeros(len(consumed))])
reward = 0.0
if np.sum(consumed) > 0:
for i in range(len(consumed)):
if np.all(object_counts[:i] <= object_counts[i]):
reward += consumed[i]
else:
reward -= consumed[i]
return reward
class BalancedCollectionRewarder(object):
"""BalancedCollectionRewarder."""
def get_reward(self, state, consumed):
"""Get reward."""
object_counts = sum(list(state[2].values()) + [np.zeros(len(consumed))])
reward = 0.0
if np.sum(consumed) > 0:
for i in range(len(consumed)):
if (object_counts[i] + consumed[i]) >= np.max(object_counts):
reward += consumed[i]
else:
reward -= consumed[i]
return reward
================================================
FILE: option_keyboard/smart_module.py
================================================
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Smart module export/import utilities."""
import inspect
import pickle
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1.io import gfile
import tensorflow_hub as hub
import tree as nest
import wrapt
_ALLOWED_TYPES = (bool, float, int, str)
def _getcallargs(signature, *args, **kwargs):
bound_args = signature.bind(*args, **kwargs)
bound_args.apply_defaults()
inputs = bound_args.arguments
inputs.pop("self", None)
return inputs
def _to_placeholder(arg):
if arg is None or isinstance(arg, bool):
return arg
arg = tf.convert_to_tensor(arg)
return tf.placeholder(dtype=arg.dtype, shape=arg.shape)
class SmartModuleExport(object):
"""Helper class for exporting TF-Hub modules."""
def __init__(self, object_factory):
self._object_factory = object_factory
self._wrapped_object = self._object_factory()
self._variable_scope = tf.get_variable_scope()
self._captured_calls = {}
self._captured_attrs = {}
def _create_captured_method(self, method_name):
"""Creates a wrapped method that captures its inputs."""
with tf.variable_scope(self._variable_scope):
method_ = getattr(self._wrapped_object, method_name)
@wrapt.decorator
def wrapper(method, instance, args, kwargs):
"""Wrapped method to capture inputs."""
del instance
specs = inspect.signature(method)
inputs = _getcallargs(specs, *args, **kwargs)
with tf.variable_scope(self._variable_scope):
output = method(*args, **kwargs)
self._captured_calls[method_name] = [inputs, specs]
return output
return wrapper(method_) # pylint: disable=no-value-for-parameter
def __getattr__(self, name):
"""Helper method for accessing an attributes of the wrapped object."""
# if "_wrapped_object" not in self.__dict__:
# return super(ExportableModule, self).__getattr__(name)
with tf.variable_scope(self._variable_scope):
attr = getattr(self._wrapped_object, name)
if inspect.ismethod(attr) or inspect.isfunction(attr):
return self._create_captured_method(name)
else:
if all([isinstance(v, _ALLOWED_TYPES) for v in nest.flatten(attr)]):
self._captured_attrs[name] = attr
return attr
def __call__(self, *args, **kwargs):
return self._create_captured_method("__call__")(*args, **kwargs)
def export(self, path, session, overwrite=False):
"""Build the TF-Hub spec, module and sync ops."""
method_specs = {}
def module_fn():
"""A module_fn for use with hub.create_module_spec()."""
# We will use a copy of the original object to build the graph.
wrapped_object = self._object_factory()
for method_name, method_info in self._captured_calls.items():
captured_inputs, captured_specs = method_info
tensor_inputs = nest.map_structure(_to_placeholder, captured_inputs)
method_to_call = getattr(wrapped_object, method_name)
tensor_outputs = method_to_call(**tensor_inputs)
flat_tensor_inputs = nest.flatten(tensor_inputs)
flat_tensor_inputs = {
str(k): v for k, v in zip(
range(len(flat_tensor_inputs)), flat_tensor_inputs)
}
flat_tensor_outputs = nest.flatten(tensor_outputs)
flat_tensor_outputs = {
str(k): v for k, v in zip(
range(len(flat_tensor_outputs)), flat_tensor_outputs)
}
method_specs[method_name] = dict(
specs=captured_specs,
inputs=nest.map_structure(lambda _: None, tensor_inputs),
outputs=nest.map_structure(lambda _: None, tensor_outputs))
signature_name = ("default"
if method_name == "__call__" else method_name)
hub.add_signature(signature_name, flat_tensor_inputs,
flat_tensor_outputs)
hub.attach_message(
"methods", tf.train.BytesList(value=[pickle.dumps(method_specs)]))
hub.attach_message(
"properties",
tf.train.BytesList(value=[pickle.dumps(self._captured_attrs)]))
# Create the spec that will be later used in export.
hub_spec = hub.create_module_spec(module_fn, drop_collections=["sonnet"])
# Get variables values
module_weights = [
session.run(v) for v in self._wrapped_object.get_all_variables()
]
# create the sync ops
with tf.Graph().as_default():
hub_module = hub.Module(hub_spec, trainable=True, name="hub")
assign_ops = []
assign_phs = []
for _, v in sorted(hub_module.variable_map.items()):
ph = tf.placeholder(shape=v.shape, dtype=v.dtype)
assign_phs.append(ph)
assign_ops.append(tf.assign(v, ph))
with tf.Session() as module_session:
module_session.run(tf.local_variables_initializer())
module_session.run(tf.global_variables_initializer())
module_session.run(
assign_ops, feed_dict=dict(zip(assign_phs, module_weights)))
if overwrite and gfile.exists(path):
gfile.rmtree(path)
gfile.makedirs(path)
hub_module.export(path, module_session)
class SmartModuleImport(object):
"""A class for importing graph building objects from TF-Hub modules."""
def __init__(self, module):
self._module = module
self._method_specs = pickle.loads(
self._module.get_attached_message("methods",
tf.train.BytesList).value[0])
self._properties = pickle.loads(
self._module.get_attached_message("properties",
tf.train.BytesList).value[0])
def _create_wrapped_method(self, method):
"""Creates a wrapped method that converts nested inputs and outputs."""
def wrapped_method(*args, **kwargs):
"""A wrapped method around a TF-Hub module signature."""
inputs = _getcallargs(self._method_specs[method]["specs"], *args,
**kwargs)
nest.assert_same_structure(self._method_specs[method]["inputs"], inputs)
flat_inputs = nest.flatten(inputs)
flat_inputs = {
str(k): v for k, v in zip(range(len(flat_inputs)), flat_inputs)
}
signature = "default" if method == "__call__" else method
flat_outputs = self._module(
flat_inputs, signature=signature, as_dict=True)
flat_outputs = [v for _, v in sorted(flat_outputs.items())]
output_spec = self._method_specs[method]["outputs"]
if output_spec is None:
if len(flat_outputs) != 1:
raise ValueError(
"Expected output containing a single tensor, found {}".format(
flat_outputs))
outputs = flat_outputs[0]
else:
outputs = nest.unflatten_as(output_spec, flat_outputs)
return outputs
return wrapped_method
def __getattr__(self, name):
if name in self._method_specs:
return self._create_wrapped_method(name)
if name in self._properties:
return self._properties[name]
return getattr(self._module, name)
def __call__(self, *args, **kwargs):
return self._create_wrapped_method("__call__")(*args, **kwargs)
================================================
FILE: perceiver/README.md
================================================
# Perceiver and Perceiver IO
Perceiver [1] is a general architecture that works on many kinds of data,
including images, video, audio, 3D point clouds, language and symbolic inputs,
multimodal combinations, etc.
Perceivers can handle new types of data with only minimal modifications.
Perceivers process inputs using domain-agnostic Transformer-style attention.
Unlike Transformers, Perceivers first map inputs to a small latent space where
processing is cheap and doesn't depend on the input size.
This makes it possible to build very deep networks
even when using large inputs like images or videos.
Perceiver IO [2] is a generalization of Perceiver to handle arbitrary *outputs*
in addition to arbitrary inputs.
The original Perceiver only produced a single classification label.
In addition to classification labels,
Perceiver IO can produce (for example) language, optical flow,
and multimodal videos with audio.
This is done using the same building blocks as the original Perceiver.
The computational complexity of Perceiver IO is linear in the input and output
size and the bulk of the processing occurs in the latent space,
allowing us to process inputs and outputs that are much larger
than can be handled by standard Transformers.
This means, for example, Perceiver IO can do BERT-style masked language modeling
directly using *bytes* instead of tokenized inputs.
This directory contains our implementation of Perceiver IO
(encompassing the original Perceiver as a special case).
The `perceiver.py` file contains our implementation of Perceiver IO,
and `io_processors.py` contains domain-specific input and output processors
for the experiments we ran.
We provide example colabs in the `colabs` directory to demonstrate
how our models can be used and show the qualitative performance of Perceiver IO
on a diverse collection of tasks.
## Usage
First, install dependencies following these instructions:
1. Create a virtual env: `python3 -m venv ~/.venv/perceiver`
2. Switch to the virtual env: `source ~/.venv/perceiver/bin/activate`
3. Follow instructions for installing JAX on your platform:
https://github.com/google/jax#installation
4. Install other dependencies: `pip install -f requirements.txt`
After installing dependencies, you can open the notebooks in the `colabs` directory
using Jupyter or Colab, and you can run our example training script.
Our colabs and training script assume that you are running from the
`deepmind_research` directory.
### Colabs
We provide the following colabs:
* [colabs/masked_language_modelling.ipynb](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/perceiver/colabs/masked_language_modelling.ipynb):
Colab for running a pre-trained
Perceiver IO masked-language model (Section 4.1 in [2]).
* [colabs/optical_flow.ipynb](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/perceiver/colabs/optical_flow.ipynb):
Colab for running a pre-trained optical flow
Perceiver IO model and visualizing the output flow (Section 4.2 in [2]).
* [colabs/video_autoencoding.ipynb](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/perceiver/colabs/video_autoencoding.ipynb):
Colab for running a pre-trained
video autoencoding Perceiver IO model and visualizing video reconstructions
(Section 4.3 in [2]).
* [colabs/imagenet_classification.ipynb](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/perceiver/colabs/imagenet_classification.ipynb):
Colab for running three pre-trained
ImageNet classification Perceiver IO models (Section 4.5 in [2]).
### Training scripts
We also provide an example training script to train a Perceiver IO model for
ImageNet classification.
The provided hyperparameters are the settings used to train Perceiver IO
with 2D Fourier position encodings, as described in
section 4.5 and supplemental section H of the paper [2].
To run the script locally and train a miniature Perceiver model,
run: `perceiver/train/launch_local.sh`.
The script would need to be adapted to run on a distributed training setup
in order to train a full-scale model with the full batch size.
## Attributions and Disclaimers
The file `perceiver/train/autoaugment.py` originates from the `tensorflow/tpu`
repository (https://github.com/tensorflow/tpu/blob/b24729de804fdb751b06467d3dce0637fa652060/models/official/efficientnet/autoaugment.py),
copyright (c) The Tensorflow Authors.
Sintel data is provided by and available from Sintel.org (https://durian.blender.org/),
copyright (c) Blender Foundation/www.sintel.org.
The sample image in `imagenet_classification.ipynb` is obtained from
Getty Images under license (https://www.gettyimages.co.uk/eula#RF).
Video content may include clips provided as part of the THUMOS Challenge datasets,
which may be accessed at http://crcv.ucf.edu/THUMOS14/download.html,
copyrights held by the creators.
All data and parameters included with Perceiver are made available
under the terms of the CC BY 4.0 license,
available at https://creativecommons.org/licenses/by/4.0/legalcode.
This is not an officially supported Google product.
## References
[1] Andrew Jaegle, Felix Gimeno, Andrew Brock, Andrew Zisserman, Oriol Vinyals,
João Carreira.
*Perceiver: General Perception with Iterative Attention*. ICML 2021.
https://arxiv.org/abs/2103.03206
[2] Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch,
Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock,
Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman,
Oriol Vinyals, João Carreira.
*Perceiver IO: A General Architecture for Structured Inputs & Outputs*.
arXiv, 2021.
https://arxiv.org/abs/2107.14795
================================================
FILE: perceiver/bytes_tokenizer.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenizer implementation mapping strings to their UTF-8 bytes."""
from typing import Union
import numpy as np
class BytesTokenizer:
"""Tokenizes string to utf-8 bytes."""
def __init__(self):
self._num_reserved_tokens = 6 # PAD, BOS, EOS, MASK, CLS, SEP
def to_string(self, inputs: np.ndarray) -> str:
inputs_no_special = (
inputs[inputs >= self._num_reserved_tokens] - self._num_reserved_tokens)
decoded_bytes = inputs_no_special.astype(np.uint8).tobytes()
return decoded_bytes.decode('utf-8', errors='replace')
def to_int(self, inputs: Union[str, bytes]) -> np.ndarray:
if isinstance(inputs, str):
inputs = inputs.encode('utf-8')
encoded = np.frombuffer(inputs, np.uint8).astype(np.int32)
encoded = encoded + self._num_reserved_tokens
return encoded.astype(np.int32)
@property
def vocab_size(self) -> int:
return 256 + self._num_reserved_tokens
@property
def pad_token(self) -> int:
return 0
@property
def bos_token(self) -> int:
return 1
@property
def eos_token(self) -> int:
return 2
@property
def mask_token(self) -> int:
return 3
@property
def cls_token(self) -> int:
return 4
@property
def sep_token(self) -> int:
return 5
================================================
FILE: perceiver/colabs/imagenet_classification.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "532jUiWVFvuK"
},
"outputs": [],
"source": [
"# Copyright 2021 DeepMind Technologies Limited\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TJtDrQ8nxlbs"
},
"outputs": [],
"source": [
"# Install dependencies for Google Colab.\n",
"# If you want to run this notebook on your own machine, you can skip this cell\n",
"!pip install dm-haiku\n",
"!pip install einops\n",
"\n",
"!mkdir /content/perceiver\n",
"!touch /content/perceiver/__init__.py\n",
"!wget -O /content/perceiver/io_processors.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/io_processors.py\n",
"!wget -O /content/perceiver/perceiver.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/perceiver.py\n",
"!wget -O /content/perceiver/position_encoding.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/position_encoding.py"
]
},
{
"cell_type": "code",
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"metadata": {
"cellView": "form",
"id": "VHzUTH5KqNEt"
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"outputs": [],
"source": [
"#@title Imports\n",
"\n",
"import functools\n",
"import itertools\n",
"import pickle\n",
"\n",
"import cv2\n",
"import haiku as hk\n",
"import imageio\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"from perceiver import perceiver, io_processors\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "uxeP5yit7hJg"
},
"outputs": [],
"source": [
"#@title Model construction\n",
"\n",
"# One of learned_position_encoding, fourier_position_encoding, or conv_preprocessing'\n",
"# learned_position_encoding: Uses a learned position encoding over the image\n",
"# and 1x1 convolution over the pixels\n",
"# fourier_position_encoding: Uses a 2D fourier position encoding\n",
"# and the raw pixels\n",
"# conv_preprocessing: Uses a 2D fourier position encoding\n",
"# and a 2D conv-net as preprocessing\n",
"model_type = 'conv_preprocessing' #@param ['learned_position_encoding', 'fourier_position_encoding', 'conv_preprocessing']\n",
"\n",
"IMAGE_SIZE = (224, 224)\n",
"\n",
"learned_pos_configs = dict(\n",
" input_preprocessor=dict(\n",
" position_encoding_type='trainable',\n",
" trainable_position_encoding_kwargs=dict(\n",
" init_scale=0.02,\n",
" num_channels=256,\n",
" ),\n",
" prep_type='conv1x1',\n",
" project_pos_dim=256,\n",
" num_channels=256,\n",
" spatial_downsample=1,\n",
" concat_or_add_pos='concat',\n",
" ),\n",
" encoder=dict(\n",
" cross_attend_widening_factor=1,\n",
" cross_attention_shape_for_attn='kv',\n",
" dropout_prob=0,\n",
" num_blocks=8,\n",
" num_cross_attend_heads=1,\n",
" num_self_attend_heads=8,\n",
" num_self_attends_per_block=6,\n",
" num_z_channels=1024,\n",
" self_attend_widening_factor=1,\n",
" use_query_residual=True,\n",
" z_index_dim=512,\n",
" z_pos_enc_init_scale=0.02\n",
" ),\n",
" decoder=dict(\n",
" num_z_channels=1024,\n",
" position_encoding_type='trainable',\n",
" trainable_position_encoding_kwargs=dict(\n",
" init_scale=0.02,\n",
" num_channels=1024,\n",
" ),\n",
" use_query_residual=False,\n",
" )\n",
")\n",
"\n",
"fourier_pos_configs = dict(\n",
" input_preprocessor=dict(\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" concat_pos=True,\n",
" max_resolution=(224, 224),\n",
" num_bands=64,\n",
" sine_only=False\n",
" ),\n",
" prep_type='pixels',\n",
" spatial_downsample=1,\n",
" ),\n",
" encoder=dict(\n",
" cross_attend_widening_factor=1,\n",
" cross_attention_shape_for_attn='kv',\n",
" dropout_prob=0,\n",
" num_blocks=8,\n",
" num_cross_attend_heads=1,\n",
" num_self_attend_heads=8,\n",
" num_self_attends_per_block=6,\n",
" num_z_channels=1024,\n",
" self_attend_widening_factor=1,\n",
" use_query_residual=True,\n",
" z_index_dim=512,\n",
" z_pos_enc_init_scale=0.02\n",
" ),\n",
" decoder=dict(\n",
" num_z_channels=1024,\n",
" position_encoding_type='trainable',\n",
" trainable_position_encoding_kwargs=dict(\n",
" init_scale=0.02,\n",
" num_channels=1024,\n",
" ),\n",
" use_query_residual=True,\n",
" )\n",
")\n",
"\n",
"conv_maxpool_configs = dict(\n",
" input_preprocessor=dict(\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" concat_pos=True,\n",
" max_resolution=(56, 56),\n",
" num_bands=64,\n",
" sine_only=False\n",
" ),\n",
" prep_type='conv',\n",
" ),\n",
" encoder=dict(\n",
" cross_attend_widening_factor=1,\n",
" cross_attention_shape_for_attn='kv',\n",
" dropout_prob=0,\n",
" num_blocks=8,\n",
" num_cross_attend_heads=1,\n",
" num_self_attend_heads=8,\n",
" num_self_attends_per_block=6,\n",
" num_z_channels=1024,\n",
" self_attend_widening_factor=1,\n",
" use_query_residual=True,\n",
" z_index_dim=512,\n",
" z_pos_enc_init_scale=0.02\n",
" ),\n",
" decoder=dict(\n",
" num_z_channels=1024,\n",
" position_encoding_type='trainable',\n",
" trainable_position_encoding_kwargs=dict(\n",
" init_scale=0.02,\n",
" num_channels=1024,\n",
" ),\n",
" use_query_residual=True,\n",
" )\n",
")\n",
"\n",
"CONFIGS = {\n",
" 'learned_position_encoding': learned_pos_configs,\n",
" 'fourier_position_encoding': fourier_pos_configs,\n",
" 'conv_preprocessing': conv_maxpool_configs,\n",
"}\n",
"\n",
"def imagenet_classifier(config, images):\n",
" input_preprocessor = io_processors.ImagePreprocessor(\n",
" **config['input_preprocessor'])\n",
" encoder = perceiver.PerceiverEncoder(**config['encoder'])\n",
" decoder = perceiver.ClassificationDecoder(\n",
" 1000,\n",
" **config['decoder'])\n",
" model = perceiver.Perceiver(\n",
" encoder=encoder,\n",
" decoder=decoder,\n",
" input_preprocessor=input_preprocessor)\n",
" logits = model(images, is_training=False)\n",
" return logits\n",
"\n",
"\n",
"imagenet_classifier = hk.transform_with_state(imagenet_classifier)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "EVRWatw4LXFx"
},
"outputs": [],
"source": [
"#@title Load parameters from checkpoint\n",
"\n",
"rng = jax.random.PRNGKey(42)\n",
"\n",
"CHECKPOINT_URLS = {\n",
" 'conv_preprocessing': 'https://storage.googleapis.com/perceiver_io/imagenet_conv_preprocessing.pystate',\n",
" 'fourier_position_encoding': 'https://storage.googleapis.com/perceiver_io/imagenet_fourier_position_encoding.pystate',\n",
" 'learned_position_encoding': 'https://storage.googleapis.com/perceiver_io/imagenet_learned_position_encoding.pystate',\n",
"}\n",
"url = CHECKPOINT_URLS[model_type]\n",
"!wget -O imagenet_checkpoint.pystate $url\n",
"\n",
"rng = jax.random.PRNGKey(42)\n",
"with open('imagenet_checkpoint.pystate', 'rb') as f:\n",
" ckpt = pickle.loads(f.read())\n",
"\n",
"params = ckpt['params']\n",
"state = ckpt['state']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "NJ_9qoQvRFPY"
},
"outputs": [],
"source": [
"#@title Imagenet labels\n",
"\n",
"# ImageNet labels were obtained from the ImageNet dataset: https://image-net.org/.\n",
"# ImageNet is provided for non-commercial research and educational purposes.\n",
"# See https://image-net.org/download for details.\n",
"\n",
"IMAGENET_LABELS = [\n",
" \"tench, Tinca tinca\",\n",
" \"goldfish, Carassius auratus\",\n",
" \"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\",\n",
" \"tiger shark, Galeocerdo cuvieri\",\n",
" \"hammerhead, hammerhead shark\",\n",
" \"electric ray, crampfish, numbfish, torpedo\",\n",
" \"stingray\",\n",
" \"cock\",\n",
" \"hen\",\n",
" \"ostrich, Struthio camelus\",\n",
" \"brambling, Fringilla montifringilla\",\n",
" \"goldfinch, Carduelis carduelis\",\n",
" \"house finch, linnet, Carpodacus mexicanus\",\n",
" \"junco, snowbird\",\n",
" \"indigo bunting, indigo finch, indigo bird, Passerina cyanea\",\n",
" \"robin, American robin, Turdus migratorius\",\n",
" \"bulbul\",\n",
" \"jay\",\n",
" \"magpie\",\n",
" \"chickadee\",\n",
" \"water ouzel, dipper\",\n",
" \"kite\",\n",
" \"bald eagle, American eagle, Haliaeetus leucocephalus\",\n",
" \"vulture\",\n",
" \"great grey owl, great gray owl, Strix nebulosa\",\n",
" \"European fire salamander, Salamandra salamandra\",\n",
" \"common newt, Triturus vulgaris\",\n",
" \"eft\",\n",
" \"spotted salamander, Ambystoma maculatum\",\n",
" \"axolotl, mud puppy, Ambystoma mexicanum\",\n",
" \"bullfrog, Rana catesbeiana\",\n",
" \"tree frog, tree-frog\",\n",
" \"tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui\",\n",
" \"loggerhead, loggerhead turtle, Caretta caretta\",\n",
" \"leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea\",\n",
" \"mud turtle\",\n",
" \"terrapin\",\n",
" \"box turtle, box tortoise\",\n",
" \"banded gecko\",\n",
" \"common iguana, iguana, Iguana iguana\",\n",
" \"American chameleon, anole, Anolis carolinensis\",\n",
" \"whiptail, whiptail lizard\",\n",
" \"agama\",\n",
" \"frilled lizard, Chlamydosaurus kingi\",\n",
" \"alligator lizard\",\n",
" \"Gila monster, Heloderma suspectum\",\n",
" \"green lizard, Lacerta viridis\",\n",
" \"African chameleon, Chamaeleo chamaeleon\",\n",
" \"Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis\",\n",
" \"African crocodile, Nile crocodile, Crocodylus niloticus\",\n",
" \"American alligator, Alligator mississipiensis\",\n",
" \"triceratops\",\n",
" \"thunder snake, worm snake, Carphophis amoenus\",\n",
" \"ringneck snake, ring-necked snake, ring snake\",\n",
" \"hognose snake, puff adder, sand viper\",\n",
" \"green snake, grass snake\",\n",
" \"king snake, kingsnake\",\n",
" \"garter snake, grass snake\",\n",
" \"water snake\",\n",
" \"vine snake\",\n",
" \"night snake, Hypsiglena torquata\",\n",
" \"boa constrictor, Constrictor constrictor\",\n",
" \"rock python, rock snake, Python sebae\",\n",
" \"Indian cobra, Naja naja\",\n",
" \"green mamba\",\n",
" \"sea snake\",\n",
" \"horned viper, cerastes, sand viper, horned asp, Cerastes cornutus\",\n",
" \"diamondback, diamondback rattlesnake, Crotalus adamanteus\",\n",
" \"sidewinder, horned rattlesnake, Crotalus cerastes\",\n",
" \"trilobite\",\n",
" \"harvestman, daddy longlegs, Phalangium opilio\",\n",
" \"scorpion\",\n",
" \"black and gold garden spider, Argiope aurantia\",\n",
" \"barn spider, Araneus cavaticus\",\n",
" \"garden spider, Aranea diademata\",\n",
" \"black widow, Latrodectus mactans\",\n",
" \"tarantula\",\n",
" \"wolf spider, hunting spider\",\n",
" \"tick\",\n",
" \"centipede\",\n",
" \"black grouse\",\n",
" \"ptarmigan\",\n",
" \"ruffed grouse, partridge, Bonasa umbellus\",\n",
" \"prairie chicken, prairie grouse, prairie fowl\",\n",
" \"peacock\",\n",
" \"quail\",\n",
" \"partridge\",\n",
" \"African grey, African gray, Psittacus erithacus\",\n",
" \"macaw\",\n",
" \"sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita\",\n",
" \"lorikeet\",\n",
" \"coucal\",\n",
" \"bee eater\",\n",
" \"hornbill\",\n",
" \"hummingbird\",\n",
" \"jacamar\",\n",
" \"toucan\",\n",
" \"drake\",\n",
" \"red-breasted merganser, Mergus serrator\",\n",
" \"goose\",\n",
" \"black swan, Cygnus atratus\",\n",
" \"tusker\",\n",
" \"echidna, spiny anteater, anteater\",\n",
" \"platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus\",\n",
" \"wallaby, brush kangaroo\",\n",
" \"koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus\",\n",
" \"wombat\",\n",
" \"jellyfish\",\n",
" \"sea anemone, anemone\",\n",
" \"brain coral\",\n",
" \"flatworm, platyhelminth\",\n",
" \"nematode, nematode worm, roundworm\",\n",
" \"conch\",\n",
" \"snail\",\n",
" \"slug\",\n",
" \"sea slug, nudibranch\",\n",
" \"chiton, coat-of-mail shell, sea cradle, polyplacophore\",\n",
" \"chambered nautilus, pearly nautilus, nautilus\",\n",
" \"Dungeness crab, Cancer magister\",\n",
" \"rock crab, Cancer irroratus\",\n",
" \"fiddler crab\",\n",
" \"king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica\",\n",
" \"American lobster, Northern lobster, Maine lobster, Homarus americanus\",\n",
" \"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish\",\n",
" \"crayfish, crawfish, crawdad, crawdaddy\",\n",
" \"hermit crab\",\n",
" \"isopod\",\n",
" \"white stork, Ciconia ciconia\",\n",
" \"black stork, Ciconia nigra\",\n",
" \"spoonbill\",\n",
" \"flamingo\",\n",
" \"little blue heron, Egretta caerulea\",\n",
" \"American egret, great white heron, Egretta albus\",\n",
" \"bittern\",\n",
" \"crane\",\n",
" \"limpkin, Aramus pictus\",\n",
" \"European gallinule, Porphyrio porphyrio\",\n",
" \"American coot, marsh hen, mud hen, water hen, Fulica americana\",\n",
" \"bustard\",\n",
" \"ruddy turnstone, Arenaria interpres\",\n",
" \"red-backed sandpiper, dunlin, Erolia alpina\",\n",
" \"redshank, Tringa totanus\",\n",
" \"dowitcher\",\n",
" \"oystercatcher, oyster catcher\",\n",
" \"pelican\",\n",
" \"king penguin, Aptenodytes patagonica\",\n",
" \"albatross, mollymawk\",\n",
" \"grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus\",\n",
" \"killer whale, killer, orca, grampus, sea wolf, Orcinus orca\",\n",
" \"dugong, Dugong dugon\",\n",
" \"sea lion\",\n",
" \"Chihuahua\",\n",
" \"Japanese spaniel\",\n",
" \"Maltese dog, Maltese terrier, Maltese\",\n",
" \"Pekinese, Pekingese, Peke\",\n",
" \"Shih-Tzu\",\n",
" \"Blenheim spaniel\",\n",
" \"papillon\",\n",
" \"toy terrier\",\n",
" \"Rhodesian ridgeback\",\n",
" \"Afghan hound, Afghan\",\n",
" \"basset, basset hound\",\n",
" \"beagle\",\n",
" \"bloodhound, sleuthhound\",\n",
" \"bluetick\",\n",
" \"black-and-tan coonhound\",\n",
" \"Walker hound, Walker foxhound\",\n",
" \"English foxhound\",\n",
" \"redbone\",\n",
" \"borzoi, Russian wolfhound\",\n",
" \"Irish wolfhound\",\n",
" \"Italian greyhound\",\n",
" \"whippet\",\n",
" \"Ibizan hound, Ibizan Podenco\",\n",
" \"Norwegian elkhound, elkhound\",\n",
" \"otterhound, otter hound\",\n",
" \"Saluki, gazelle hound\",\n",
" \"Scottish deerhound, deerhound\",\n",
" \"Weimaraner\",\n",
" \"Staffordshire bullterrier, Staffordshire bull terrier\",\n",
" \"American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier\",\n",
" \"Bedlington terrier\",\n",
" \"Border terrier\",\n",
" \"Kerry blue terrier\",\n",
" \"Irish terrier\",\n",
" \"Norfolk terrier\",\n",
" \"Norwich terrier\",\n",
" \"Yorkshire terrier\",\n",
" \"wire-haired fox terrier\",\n",
" \"Lakeland terrier\",\n",
" \"Sealyham terrier, Sealyham\",\n",
" \"Airedale, Airedale terrier\",\n",
" \"cairn, cairn terrier\",\n",
" \"Australian terrier\",\n",
" \"Dandie Dinmont, Dandie Dinmont terrier\",\n",
" \"Boston bull, Boston terrier\",\n",
" \"miniature schnauzer\",\n",
" \"giant schnauzer\",\n",
" \"standard schnauzer\",\n",
" \"Scotch terrier, Scottish terrier, Scottie\",\n",
" \"Tibetan terrier, chrysanthemum dog\",\n",
" \"silky terrier, Sydney silky\",\n",
" \"soft-coated wheaten terrier\",\n",
" \"West Highland white terrier\",\n",
" \"Lhasa, Lhasa apso\",\n",
" \"flat-coated retriever\",\n",
" \"curly-coated retriever\",\n",
" \"golden retriever\",\n",
" \"Labrador retriever\",\n",
" \"Chesapeake Bay retriever\",\n",
" \"German short-haired pointer\",\n",
" \"vizsla, Hungarian pointer\",\n",
" \"English setter\",\n",
" \"Irish setter, red setter\",\n",
" \"Gordon setter\",\n",
" \"Brittany spaniel\",\n",
" \"clumber, clumber spaniel\",\n",
" \"English springer, English springer spaniel\",\n",
" \"Welsh springer spaniel\",\n",
" \"cocker spaniel, English cocker spaniel, cocker\",\n",
" \"Sussex spaniel\",\n",
" \"Irish water spaniel\",\n",
" \"kuvasz\",\n",
" \"schipperke\",\n",
" \"groenendael\",\n",
" \"malinois\",\n",
" \"briard\",\n",
" \"kelpie\",\n",
" \"komondor\",\n",
" \"Old English sheepdog, bobtail\",\n",
" \"Shetland sheepdog, Shetland sheep dog, Shetland\",\n",
" \"collie\",\n",
" \"Border collie\",\n",
" \"Bouvier des Flandres, Bouviers des Flandres\",\n",
" \"Rottweiler\",\n",
" \"German shepherd, German shepherd dog, German police dog, alsatian\",\n",
" \"Doberman, Doberman pinscher\",\n",
" \"miniature pinscher\",\n",
" \"Greater Swiss Mountain dog\",\n",
" \"Bernese mountain dog\",\n",
" \"Appenzeller\",\n",
" \"EntleBucher\",\n",
" \"boxer\",\n",
" \"bull mastiff\",\n",
" \"Tibetan mastiff\",\n",
" \"French bulldog\",\n",
" \"Great Dane\",\n",
" \"Saint Bernard, St Bernard\",\n",
" \"Eskimo dog, husky\",\n",
" \"malamute, malemute, Alaskan malamute\",\n",
" \"Siberian husky\",\n",
" \"dalmatian, coach dog, carriage dog\",\n",
" \"affenpinscher, monkey pinscher, monkey dog\",\n",
" \"basenji\",\n",
" \"pug, pug-dog\",\n",
" \"Leonberg\",\n",
" \"Newfoundland, Newfoundland dog\",\n",
" \"Great Pyrenees\",\n",
" \"Samoyed, Samoyede\",\n",
" \"Pomeranian\",\n",
" \"chow, chow chow\",\n",
" \"keeshond\",\n",
" \"Brabancon griffon\",\n",
" \"Pembroke, Pembroke Welsh corgi\",\n",
" \"Cardigan, Cardigan Welsh corgi\",\n",
" \"toy poodle\",\n",
" \"miniature poodle\",\n",
" \"standard poodle\",\n",
" \"Mexican hairless\",\n",
" \"timber wolf, grey wolf, gray wolf, Canis lupus\",\n",
" \"white wolf, Arctic wolf, Canis lupus tundrarum\",\n",
" \"red wolf, maned wolf, Canis rufus, Canis niger\",\n",
" \"coyote, prairie wolf, brush wolf, Canis latrans\",\n",
" \"dingo, warrigal, warragal, Canis dingo\",\n",
" \"dhole, Cuon alpinus\",\n",
" \"African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus\",\n",
" \"hyena, hyaena\",\n",
" \"red fox, Vulpes vulpes\",\n",
" \"kit fox, Vulpes macrotis\",\n",
" \"Arctic fox, white fox, Alopex lagopus\",\n",
" \"grey fox, gray fox, Urocyon cinereoargenteus\",\n",
" \"tabby, tabby cat\",\n",
" \"tiger cat\",\n",
" \"Persian cat\",\n",
" \"Siamese cat, Siamese\",\n",
" \"Egyptian cat\",\n",
" \"cougar, puma, catamount, mountain lion, painter, panther, Felis concolor\",\n",
" \"lynx, catamount\",\n",
" \"leopard, Panthera pardus\",\n",
" \"snow leopard, ounce, Panthera uncia\",\n",
" \"jaguar, panther, Panthera onca, Felis onca\",\n",
" \"lion, king of beasts, Panthera leo\",\n",
" \"tiger, Panthera tigris\",\n",
" \"cheetah, chetah, Acinonyx jubatus\",\n",
" \"brown bear, bruin, Ursus arctos\",\n",
" \"American black bear, black bear, Ursus americanus, Euarctos americanus\",\n",
" \"ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus\",\n",
" \"sloth bear, Melursus ursinus, Ursus ursinus\",\n",
" \"mongoose\",\n",
" \"meerkat, mierkat\",\n",
" \"tiger beetle\",\n",
" \"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle\",\n",
" \"ground beetle, carabid beetle\",\n",
" \"long-horned beetle, longicorn, longicorn beetle\",\n",
" \"leaf beetle, chrysomelid\",\n",
" \"dung beetle\",\n",
" \"rhinoceros beetle\",\n",
" \"weevil\",\n",
" \"fly\",\n",
" \"bee\",\n",
" \"ant, emmet, pismire\",\n",
" \"grasshopper, hopper\",\n",
" \"cricket\",\n",
" \"walking stick, walkingstick, stick insect\",\n",
" \"cockroach, roach\",\n",
" \"mantis, mantid\",\n",
" \"cicada, cicala\",\n",
" \"leafhopper\",\n",
" \"lacewing, lacewing fly\",\n",
" \"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\",\n",
" \"damselfly\",\n",
" \"admiral\",\n",
" \"ringlet, ringlet butterfly\",\n",
" \"monarch, monarch butterfly, milkweed butterfly, Danaus plexippus\",\n",
" \"cabbage butterfly\",\n",
" \"sulphur butterfly, sulfur butterfly\",\n",
" \"lycaenid, lycaenid butterfly\",\n",
" \"starfish, sea star\",\n",
" \"sea urchin\",\n",
" \"sea cucumber, holothurian\",\n",
" \"wood rabbit, cottontail, cottontail rabbit\",\n",
" \"hare\",\n",
" \"Angora, Angora rabbit\",\n",
" \"hamster\",\n",
" \"porcupine, hedgehog\",\n",
" \"fox squirrel, eastern fox squirrel, Sciurus niger\",\n",
" \"marmot\",\n",
" \"beaver\",\n",
" \"guinea pig, Cavia cobaya\",\n",
" \"sorrel\",\n",
" \"zebra\",\n",
" \"hog, pig, grunter, squealer, Sus scrofa\",\n",
" \"wild boar, boar, Sus scrofa\",\n",
" \"warthog\",\n",
" \"hippopotamus, hippo, river horse, Hippopotamus amphibius\",\n",
" \"ox\",\n",
" \"water buffalo, water ox, Asiatic buffalo, Bubalus bubalis\",\n",
" \"bison\",\n",
" \"ram, tup\",\n",
" \"bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis\",\n",
" \"ibex, Capra ibex\",\n",
" \"hartebeest\",\n",
" \"impala, Aepyceros melampus\",\n",
" \"gazelle\",\n",
" \"Arabian camel, dromedary, Camelus dromedarius\",\n",
" \"llama\",\n",
" \"weasel\",\n",
" \"mink\",\n",
" \"polecat, fitch, foulmart, foumart, Mustela putorius\",\n",
" \"black-footed ferret, ferret, Mustela nigripes\",\n",
" \"otter\",\n",
" \"skunk, polecat, wood pussy\",\n",
" \"badger\",\n",
" \"armadillo\",\n",
" \"three-toed sloth, ai, Bradypus tridactylus\",\n",
" \"orangutan, orang, orangutang, Pongo pygmaeus\",\n",
" \"gorilla, Gorilla gorilla\",\n",
" \"chimpanzee, chimp, Pan troglodytes\",\n",
" \"gibbon, Hylobates lar\",\n",
" \"siamang, Hylobates syndactylus, Symphalangus syndactylus\",\n",
" \"guenon, guenon monkey\",\n",
" \"patas, hussar monkey, Erythrocebus patas\",\n",
" \"baboon\",\n",
" \"macaque\",\n",
" \"langur\",\n",
" \"colobus, colobus monkey\",\n",
" \"proboscis monkey, Nasalis larvatus\",\n",
" \"marmoset\",\n",
" \"capuchin, ringtail, Cebus capucinus\",\n",
" \"howler monkey, howler\",\n",
" \"titi, titi monkey\",\n",
" \"spider monkey, Ateles geoffroyi\",\n",
" \"squirrel monkey, Saimiri sciureus\",\n",
" \"Madagascar cat, ring-tailed lemur, Lemur catta\",\n",
" \"indri, indris, Indri indri, Indri brevicaudatus\",\n",
" \"Indian elephant, Elephas maximus\",\n",
" \"African elephant, Loxodonta africana\",\n",
" \"lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens\",\n",
" \"giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca\",\n",
" \"barracouta, snoek\",\n",
" \"eel\",\n",
" \"coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch\",\n",
" \"rock beauty, Holocanthus tricolor\",\n",
" \"anemone fish\",\n",
" \"sturgeon\",\n",
" \"gar, garfish, garpike, billfish, Lepisosteus osseus\",\n",
" \"lionfish\",\n",
" \"puffer, pufferfish, blowfish, globefish\",\n",
" \"abacus\",\n",
" \"abaya\",\n",
" \"academic gown, academic robe, judge's robe\",\n",
" \"accordion, piano accordion, squeeze box\",\n",
" \"acoustic guitar\",\n",
" \"aircraft carrier, carrier, flattop, attack aircraft carrier\",\n",
" \"airliner\",\n",
" \"airship, dirigible\",\n",
" \"altar\",\n",
" \"ambulance\",\n",
" \"amphibian, amphibious vehicle\",\n",
" \"analog clock\",\n",
" \"apiary, bee house\",\n",
" \"apron\",\n",
" \"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin\",\n",
" \"assault rifle, assault gun\",\n",
" \"backpack, back pack, knapsack, packsack, rucksack, haversack\",\n",
" \"bakery, bakeshop, bakehouse\",\n",
" \"balance beam, beam\",\n",
" \"balloon\",\n",
" \"ballpoint, ballpoint pen, ballpen, Biro\",\n",
" \"Band Aid\",\n",
" \"banjo\",\n",
" \"bannister, banister, balustrade, balusters, handrail\",\n",
" \"barbell\",\n",
" \"barber chair\",\n",
" \"barbershop\",\n",
" \"barn\",\n",
" \"barometer\",\n",
" \"barrel, cask\",\n",
" \"barrow, garden cart, lawn cart, wheelbarrow\",\n",
" \"baseball\",\n",
" \"basketball\",\n",
" \"bassinet\",\n",
" \"bassoon\",\n",
" \"bathing cap, swimming cap\",\n",
" \"bath towel\",\n",
" \"bathtub, bathing tub, bath, tub\",\n",
" \"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon\",\n",
" \"beacon, lighthouse, beacon light, pharos\",\n",
" \"beaker\",\n",
" \"bearskin, busby, shako\",\n",
" \"beer bottle\",\n",
" \"beer glass\",\n",
" \"bell cote, bell cot\",\n",
" \"bib\",\n",
" \"bicycle-built-for-two, tandem bicycle, tandem\",\n",
" \"bikini, two-piece\",\n",
" \"binder, ring-binder\",\n",
" \"binoculars, field glasses, opera glasses\",\n",
" \"birdhouse\",\n",
" \"boathouse\",\n",
" \"bobsled, bobsleigh, bob\",\n",
" \"bolo tie, bolo, bola tie, bola\",\n",
" \"bonnet, poke bonnet\",\n",
" \"bookcase\",\n",
" \"bookshop, bookstore, bookstall\",\n",
" \"bottlecap\",\n",
" \"bow\",\n",
" \"bow tie, bow-tie, bowtie\",\n",
" \"brass, memorial tablet, plaque\",\n",
" \"brassiere, bra, bandeau\",\n",
" \"breakwater, groin, groyne, mole, bulwark, seawall, jetty\",\n",
" \"breastplate, aegis, egis\",\n",
" \"broom\",\n",
" \"bucket, pail\",\n",
" \"buckle\",\n",
" \"bulletproof vest\",\n",
" \"bullet train, bullet\",\n",
" \"butcher shop, meat market\",\n",
" \"cab, hack, taxi, taxicab\",\n",
" \"caldron, cauldron\",\n",
" \"candle, taper, wax light\",\n",
" \"cannon\",\n",
" \"canoe\",\n",
" \"can opener, tin opener\",\n",
" \"cardigan\",\n",
" \"car mirror\",\n",
" \"carousel, carrousel, merry-go-round, roundabout, whirligig\",\n",
" \"carpenter's kit, tool kit\",\n",
" \"carton\",\n",
" \"car wheel\",\n",
" \"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM\",\n",
" \"cassette\",\n",
" \"cassette player\",\n",
" \"castle\",\n",
" \"catamaran\",\n",
" \"CD player\",\n",
" \"cello, violoncello\",\n",
" \"cellular telephone, cellular phone, cellphone, cell, mobile phone\",\n",
" \"chain\",\n",
" \"chainlink fence\",\n",
" \"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour\",\n",
" \"chain saw, chainsaw\",\n",
" \"chest\",\n",
" \"chiffonier, commode\",\n",
" \"chime, bell, gong\",\n",
" \"china cabinet, china closet\",\n",
" \"Christmas stocking\",\n",
" \"church, church building\",\n",
" \"cinema, movie theater, movie theatre, movie house, picture palace\",\n",
" \"cleaver, meat cleaver, chopper\",\n",
" \"cliff dwelling\",\n",
" \"cloak\",\n",
" \"clog, geta, patten, sabot\",\n",
" \"cocktail shaker\",\n",
" \"coffee mug\",\n",
" \"coffeepot\",\n",
" \"coil, spiral, volute, whorl, helix\",\n",
" \"combination lock\",\n",
" \"computer keyboard, keypad\",\n",
" \"confectionery, confectionary, candy store\",\n",
" \"container ship, containership, container vessel\",\n",
" \"convertible\",\n",
" \"corkscrew, bottle screw\",\n",
" \"cornet, horn, trumpet, trump\",\n",
" \"cowboy boot\",\n",
" \"cowboy hat, ten-gallon hat\",\n",
" \"cradle\",\n",
" \"crane\",\n",
" \"crash helmet\",\n",
" \"crate\",\n",
" \"crib, cot\",\n",
" \"Crock Pot\",\n",
" \"croquet ball\",\n",
" \"crutch\",\n",
" \"cuirass\",\n",
" \"dam, dike, dyke\",\n",
" \"desk\",\n",
" \"desktop computer\",\n",
" \"dial telephone, dial phone\",\n",
" \"diaper, nappy, napkin\",\n",
" \"digital clock\",\n",
" \"digital watch\",\n",
" \"dining table, board\",\n",
" \"dishrag, dishcloth\",\n",
" \"dishwasher, dish washer, dishwashing machine\",\n",
" \"disk brake, disc brake\",\n",
" \"dock, dockage, docking facility\",\n",
" \"dogsled, dog sled, dog sleigh\",\n",
" \"dome\",\n",
" \"doormat, welcome mat\",\n",
" \"drilling platform, offshore rig\",\n",
" \"drum, membranophone, tympan\",\n",
" \"drumstick\",\n",
" \"dumbbell\",\n",
" \"Dutch oven\",\n",
" \"electric fan, blower\",\n",
" \"electric guitar\",\n",
" \"electric locomotive\",\n",
" \"entertainment center\",\n",
" \"envelope\",\n",
" \"espresso maker\",\n",
" \"face powder\",\n",
" \"feather boa, boa\",\n",
" \"file, file cabinet, filing cabinet\",\n",
" \"fireboat\",\n",
" \"fire engine, fire truck\",\n",
" \"fire screen, fireguard\",\n",
" \"flagpole, flagstaff\",\n",
" \"flute, transverse flute\",\n",
" \"folding chair\",\n",
" \"football helmet\",\n",
" \"forklift\",\n",
" \"fountain\",\n",
" \"fountain pen\",\n",
" \"four-poster\",\n",
" \"freight car\",\n",
" \"French horn, horn\",\n",
" \"frying pan, frypan, skillet\",\n",
" \"fur coat\",\n",
" \"garbage truck, dustcart\",\n",
" \"gasmask, respirator, gas helmet\",\n",
" \"gas pump, gasoline pump, petrol pump, island dispenser\",\n",
" \"goblet\",\n",
" \"go-kart\",\n",
" \"golf ball\",\n",
" \"golfcart, golf cart\",\n",
" \"gondola\",\n",
" \"gong, tam-tam\",\n",
" \"gown\",\n",
" \"grand piano, grand\",\n",
" \"greenhouse, nursery, glasshouse\",\n",
" \"grille, radiator grille\",\n",
" \"grocery store, grocery, food market, market\",\n",
" \"guillotine\",\n",
" \"hair slide\",\n",
" \"hair spray\",\n",
" \"half track\",\n",
" \"hammer\",\n",
" \"hamper\",\n",
" \"hand blower, blow dryer, blow drier, hair dryer, hair drier\",\n",
" \"hand-held computer, hand-held microcomputer\",\n",
" \"handkerchief, hankie, hanky, hankey\",\n",
" \"hard disc, hard disk, fixed disk\",\n",
" \"harmonica, mouth organ, harp, mouth harp\",\n",
" \"harp\",\n",
" \"harvester, reaper\",\n",
" \"hatchet\",\n",
" \"holster\",\n",
" \"home theater, home theatre\",\n",
" \"honeycomb\",\n",
" \"hook, claw\",\n",
" \"hoopskirt, crinoline\",\n",
" \"horizontal bar, high bar\",\n",
" \"horse cart, horse-cart\",\n",
" \"hourglass\",\n",
" \"iPod\",\n",
" \"iron, smoothing iron\",\n",
" \"jack-o'-lantern\",\n",
" \"jean, blue jean, denim\",\n",
" \"jeep, landrover\",\n",
" \"jersey, T-shirt, tee shirt\",\n",
" \"jigsaw puzzle\",\n",
" \"jinrikisha, ricksha, rickshaw\",\n",
" \"joystick\",\n",
" \"kimono\",\n",
" \"knee pad\",\n",
" \"knot\",\n",
" \"lab coat, laboratory coat\",\n",
" \"ladle\",\n",
" \"lampshade, lamp shade\",\n",
" \"laptop, laptop computer\",\n",
" \"lawn mower, mower\",\n",
" \"lens cap, lens cover\",\n",
" \"letter opener, paper knife, paperknife\",\n",
" \"library\",\n",
" \"lifeboat\",\n",
" \"lighter, light, igniter, ignitor\",\n",
" \"limousine, limo\",\n",
" \"liner, ocean liner\",\n",
" \"lipstick, lip rouge\",\n",
" \"Loafer\",\n",
" \"lotion\",\n",
" \"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system\",\n",
" \"loupe, jeweler's loupe\",\n",
" \"lumbermill, sawmill\",\n",
" \"magnetic compass\",\n",
" \"mailbag, postbag\",\n",
" \"mailbox, letter box\",\n",
" \"maillot\",\n",
" \"maillot, tank suit\",\n",
" \"manhole cover\",\n",
" \"maraca\",\n",
" \"marimba, xylophone\",\n",
" \"mask\",\n",
" \"matchstick\",\n",
" \"maypole\",\n",
" \"maze, labyrinth\",\n",
" \"measuring cup\",\n",
" \"medicine chest, medicine cabinet\",\n",
" \"megalith, megalithic structure\",\n",
" \"microphone, mike\",\n",
" \"microwave, microwave oven\",\n",
" \"military uniform\",\n",
" \"milk can\",\n",
" \"minibus\",\n",
" \"miniskirt, mini\",\n",
" \"minivan\",\n",
" \"missile\",\n",
" \"mitten\",\n",
" \"mixing bowl\",\n",
" \"mobile home, manufactured home\",\n",
" \"Model T\",\n",
" \"modem\",\n",
" \"monastery\",\n",
" \"monitor\",\n",
" \"moped\",\n",
" \"mortar\",\n",
" \"mortarboard\",\n",
" \"mosque\",\n",
" \"mosquito net\",\n",
" \"motor scooter, scooter\",\n",
" \"mountain bike, all-terrain bike, off-roader\",\n",
" \"mountain tent\",\n",
" \"mouse, computer mouse\",\n",
" \"mousetrap\",\n",
" \"moving van\",\n",
" \"muzzle\",\n",
" \"nail\",\n",
" \"neck brace\",\n",
" \"necklace\",\n",
" \"nipple\",\n",
" \"notebook, notebook computer\",\n",
" \"obelisk\",\n",
" \"oboe, hautboy, hautbois\",\n",
" \"ocarina, sweet potato\",\n",
" \"odometer, hodometer, mileometer, milometer\",\n",
" \"oil filter\",\n",
" \"organ, pipe organ\",\n",
" \"oscilloscope, scope, cathode-ray oscilloscope, CRO\",\n",
" \"overskirt\",\n",
" \"oxcart\",\n",
" \"oxygen mask\",\n",
" \"packet\",\n",
" \"paddle, boat paddle\",\n",
" \"paddlewheel, paddle wheel\",\n",
" \"padlock\",\n",
" \"paintbrush\",\n",
" \"pajama, pyjama, pj's, jammies\",\n",
" \"palace\",\n",
" \"panpipe, pandean pipe, syrinx\",\n",
" \"paper towel\",\n",
" \"parachute, chute\",\n",
" \"parallel bars, bars\",\n",
" \"park bench\",\n",
" \"parking meter\",\n",
" \"passenger car, coach, carriage\",\n",
" \"patio, terrace\",\n",
" \"pay-phone, pay-station\",\n",
" \"pedestal, plinth, footstall\",\n",
" \"pencil box, pencil case\",\n",
" \"pencil sharpener\",\n",
" \"perfume, essence\",\n",
" \"Petri dish\",\n",
" \"photocopier\",\n",
" \"pick, plectrum, plectron\",\n",
" \"pickelhaube\",\n",
" \"picket fence, paling\",\n",
" \"pickup, pickup truck\",\n",
" \"pier\",\n",
" \"piggy bank, penny bank\",\n",
" \"pill bottle\",\n",
" \"pillow\",\n",
" \"ping-pong ball\",\n",
" \"pinwheel\",\n",
" \"pirate, pirate ship\",\n",
" \"pitcher, ewer\",\n",
" \"plane, carpenter's plane, woodworking plane\",\n",
" \"planetarium\",\n",
" \"plastic bag\",\n",
" \"plate rack\",\n",
" \"plow, plough\",\n",
" \"plunger, plumber's helper\",\n",
" \"Polaroid camera, Polaroid Land camera\",\n",
" \"pole\",\n",
" \"police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria\",\n",
" \"poncho\",\n",
" \"pool table, billiard table, snooker table\",\n",
" \"pop bottle, soda bottle\",\n",
" \"pot, flowerpot\",\n",
" \"potter's wheel\",\n",
" \"power drill\",\n",
" \"prayer rug, prayer mat\",\n",
" \"printer\",\n",
" \"prison, prison house\",\n",
" \"projectile, missile\",\n",
" \"projector\",\n",
" \"puck, hockey puck\",\n",
" \"punching bag, punch bag, punching ball, punchball\",\n",
" \"purse\",\n",
" \"quill, quill pen\",\n",
" \"quilt, comforter, comfort, puff\",\n",
" \"racer, race car, racing car\",\n",
" \"racket, racquet\",\n",
" \"radiator\",\n",
" \"radio, wireless\",\n",
" \"radio telescope, radio reflector\",\n",
" \"rain barrel\",\n",
" \"recreational vehicle, RV, R.V.\",\n",
" \"reel\",\n",
" \"reflex camera\",\n",
" \"refrigerator, icebox\",\n",
" \"remote control, remote\",\n",
" \"restaurant, eating house, eating place, eatery\",\n",
" \"revolver, six-gun, six-shooter\",\n",
" \"rifle\",\n",
" \"rocking chair, rocker\",\n",
" \"rotisserie\",\n",
" \"rubber eraser, rubber, pencil eraser\",\n",
" \"rugby ball\",\n",
" \"rule, ruler\",\n",
" \"running shoe\",\n",
" \"safe\",\n",
" \"safety pin\",\n",
" \"saltshaker, salt shaker\",\n",
" \"sandal\",\n",
" \"sarong\",\n",
" \"sax, saxophone\",\n",
" \"scabbard\",\n",
" \"scale, weighing machine\",\n",
" \"school bus\",\n",
" \"schooner\",\n",
" \"scoreboard\",\n",
" \"screen, CRT screen\",\n",
" \"screw\",\n",
" \"screwdriver\",\n",
" \"seat belt, seatbelt\",\n",
" \"sewing machine\",\n",
" \"shield, buckler\",\n",
" \"shoe shop, shoe-shop, shoe store\",\n",
" \"shoji\",\n",
" \"shopping basket\",\n",
" \"shopping cart\",\n",
" \"shovel\",\n",
" \"shower cap\",\n",
" \"shower curtain\",\n",
" \"ski\",\n",
" \"ski mask\",\n",
" \"sleeping bag\",\n",
" \"slide rule, slipstick\",\n",
" \"sliding door\",\n",
" \"slot, one-armed bandit\",\n",
" \"snorkel\",\n",
" \"snowmobile\",\n",
" \"snowplow, snowplough\",\n",
" \"soap dispenser\",\n",
" \"soccer ball\",\n",
" \"sock\",\n",
" \"solar dish, solar collector, solar furnace\",\n",
" \"sombrero\",\n",
" \"soup bowl\",\n",
" \"space bar\",\n",
" \"space heater\",\n",
" \"space shuttle\",\n",
" \"spatula\",\n",
" \"speedboat\",\n",
" \"spider web, spider's web\",\n",
" \"spindle\",\n",
" \"sports car, sport car\",\n",
" \"spotlight, spot\",\n",
" \"stage\",\n",
" \"steam locomotive\",\n",
" \"steel arch bridge\",\n",
" \"steel drum\",\n",
" \"stethoscope\",\n",
" \"stole\",\n",
" \"stone wall\",\n",
" \"stopwatch, stop watch\",\n",
" \"stove\",\n",
" \"strainer\",\n",
" \"streetcar, tram, tramcar, trolley, trolley car\",\n",
" \"stretcher\",\n",
" \"studio couch, day bed\",\n",
" \"stupa, tope\",\n",
" \"submarine, pigboat, sub, U-boat\",\n",
" \"suit, suit of clothes\",\n",
" \"sundial\",\n",
" \"sunglass\",\n",
" \"sunglasses, dark glasses, shades\",\n",
" \"sunscreen, sunblock, sun blocker\",\n",
" \"suspension bridge\",\n",
" \"swab, swob, mop\",\n",
" \"sweatshirt\",\n",
" \"swimming trunks, bathing trunks\",\n",
" \"swing\",\n",
" \"switch, electric switch, electrical switch\",\n",
" \"syringe\",\n",
" \"table lamp\",\n",
" \"tank, army tank, armored combat vehicle, armoured combat vehicle\",\n",
" \"tape player\",\n",
" \"teapot\",\n",
" \"teddy, teddy bear\",\n",
" \"television, television system\",\n",
" \"tennis ball\",\n",
" \"thatch, thatched roof\",\n",
" \"theater curtain, theatre curtain\",\n",
" \"thimble\",\n",
" \"thresher, thrasher, threshing machine\",\n",
" \"throne\",\n",
" \"tile roof\",\n",
" \"toaster\",\n",
" \"tobacco shop, tobacconist shop, tobacconist\",\n",
" \"toilet seat\",\n",
" \"torch\",\n",
" \"totem pole\",\n",
" \"tow truck, tow car, wrecker\",\n",
" \"toyshop\",\n",
" \"tractor\",\n",
" \"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi\",\n",
" \"tray\",\n",
" \"trench coat\",\n",
" \"tricycle, trike, velocipede\",\n",
" \"trimaran\",\n",
" \"tripod\",\n",
" \"triumphal arch\",\n",
" \"trolleybus, trolley coach, trackless trolley\",\n",
" \"trombone\",\n",
" \"tub, vat\",\n",
" \"turnstile\",\n",
" \"typewriter keyboard\",\n",
" \"umbrella\",\n",
" \"unicycle, monocycle\",\n",
" \"upright, upright piano\",\n",
" \"vacuum, vacuum cleaner\",\n",
" \"vase\",\n",
" \"vault\",\n",
" \"velvet\",\n",
" \"vending machine\",\n",
" \"vestment\",\n",
" \"viaduct\",\n",
" \"violin, fiddle\",\n",
" \"volleyball\",\n",
" \"waffle iron\",\n",
" \"wall clock\",\n",
" \"wallet, billfold, notecase, pocketbook\",\n",
" \"wardrobe, closet, press\",\n",
" \"warplane, military plane\",\n",
" \"washbasin, handbasin, washbowl, lavabo, wash-hand basin\",\n",
" \"washer, automatic washer, washing machine\",\n",
" \"water bottle\",\n",
" \"water jug\",\n",
" \"water tower\",\n",
" \"whiskey jug\",\n",
" \"whistle\",\n",
" \"wig\",\n",
" \"window screen\",\n",
" \"window shade\",\n",
" \"Windsor tie\",\n",
" \"wine bottle\",\n",
" \"wing\",\n",
" \"wok\",\n",
" \"wooden spoon\",\n",
" \"wool, woolen, woollen\",\n",
" \"worm fence, snake fence, snake-rail fence, Virginia fence\",\n",
" \"wreck\",\n",
" \"yawl\",\n",
" \"yurt\",\n",
" \"web site, website, internet site, site\",\n",
" \"comic book\",\n",
" \"crossword puzzle, crossword\",\n",
" \"street sign\",\n",
" \"traffic light, traffic signal, stoplight\",\n",
" \"book jacket, dust cover, dust jacket, dust wrapper\",\n",
" \"menu\",\n",
" \"plate\",\n",
" \"guacamole\",\n",
" \"consomme\",\n",
" \"hot pot, hotpot\",\n",
" \"trifle\",\n",
" \"ice cream, icecream\",\n",
" \"ice lolly, lolly, lollipop, popsicle\",\n",
" \"French loaf\",\n",
" \"bagel, beigel\",\n",
" \"pretzel\",\n",
" \"cheeseburger\",\n",
" \"hotdog, hot dog, red hot\",\n",
" \"mashed potato\",\n",
" \"head cabbage\",\n",
" \"broccoli\",\n",
" \"cauliflower\",\n",
" \"zucchini, courgette\",\n",
" \"spaghetti squash\",\n",
" \"acorn squash\",\n",
" \"butternut squash\",\n",
" \"cucumber, cuke\",\n",
" \"artichoke, globe artichoke\",\n",
" \"bell pepper\",\n",
" \"cardoon\",\n",
" \"mushroom\",\n",
" \"Granny Smith\",\n",
" \"strawberry\",\n",
" \"orange\",\n",
" \"lemon\",\n",
" \"fig\",\n",
" \"pineapple, ananas\",\n",
" \"banana\",\n",
" \"jackfruit, jak, jack\",\n",
" \"custard apple\",\n",
" \"pomegranate\",\n",
" \"hay\",\n",
" \"carbonara\",\n",
" \"chocolate sauce, chocolate syrup\",\n",
" \"dough\",\n",
" \"meat loaf, meatloaf\",\n",
" \"pizza, pizza pie\",\n",
" \"potpie\",\n",
" \"burrito\",\n",
" \"red wine\",\n",
" \"espresso\",\n",
" \"cup\",\n",
" \"eggnog\",\n",
" \"alp\",\n",
" \"bubble\",\n",
" \"cliff, drop, drop-off\",\n",
" \"coral reef\",\n",
" \"geyser\",\n",
" \"lakeside, lakeshore\",\n",
" \"promontory, headland, head, foreland\",\n",
" \"sandbar, sand bar\",\n",
" \"seashore, coast, seacoast, sea-coast\",\n",
" \"valley, vale\",\n",
" \"volcano\",\n",
" \"ballplayer, baseball player\",\n",
" \"groom, bridegroom\",\n",
" \"scuba diver\",\n",
" \"rapeseed\",\n",
" \"daisy\",\n",
" \"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum\",\n",
" \"corn\",\n",
" \"acorn\",\n",
" \"hip, rose hip, rosehip\",\n",
" \"buckeye, horse chestnut, conker\",\n",
" \"coral fungus\",\n",
" \"agaric\",\n",
" \"gyromitra\",\n",
" \"stinkhorn, carrion fungus\",\n",
" \"earthstar\",\n",
" \"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa\",\n",
" \"bolete\",\n",
" \"ear, spike, capitulum\",\n",
" \"toilet tissue, toilet paper, bathroom tissue\",\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MAfLTtEXeE3-"
},
"outputs": [],
"source": [
"# dalmation.jpg is obtained from Getty Images under license\n",
"# (https://www.gettyimages.co.uk/eula#RF).\n",
"\n",
"!wget -O dog.jpg https://storage.googleapis.com/perceiver_io/dalmation.jpg\n",
"with open('dog.jpg', 'rb') as f:\n",
" img = imageio.imread(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "Z7ZQJ2auy4Lt"
},
"outputs": [],
"source": [
"#@title Image Utility Functions\n",
"\n",
"MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)\n",
"STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)\n",
"\n",
"def normalize(im):\n",
" return (im - np.array(MEAN_RGB)) / np.array(STDDEV_RGB)\n",
"\n",
"def resize_and_center_crop(image):\n",
" \"\"\"Crops to center of image with padding then scales.\"\"\"\n",
" shape = image.shape\n",
"\n",
" image_height = shape[0]\n",
" image_width = shape[1]\n",
"\n",
" padded_center_crop_size = ((224 / (224 + 32)) *\n",
" np.minimum(image_height, image_width).astype(np.float32)).astype(np.int32)\n",
"\n",
" offset_height = ((image_height - padded_center_crop_size) + 1) // 2\n",
" offset_width = ((image_width - padded_center_crop_size) + 1) // 2\n",
" crop_window = [offset_height, offset_width,\n",
" padded_center_crop_size, padded_center_crop_size]\n",
"\n",
" # image = tf.image.crop_to_bounding_box(image_bytes, *crop_window)\n",
" image = image[crop_window[0]:crop_window[0] + crop_window[2], crop_window[1]:crop_window[1]+crop_window[3]]\n",
" return cv2.resize(image, (224, 224), interpolation=cv2.INTER_CUBIC)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FfWpBNZJLib4"
},
"outputs": [],
"source": [
"# Imagenet classification\n",
"\n",
"# Obtain a [224, 224] crop of the image while preserving aspect ratio.\n",
"# With Fourier position encoding, no resize is needed -- the model can\n",
"# generalize to image sizes it never saw in training\n",
"centered_img = resize_and_center_crop(img) # img\n",
"logits, _ = imagenet_classifier.apply(params, state, rng, CONFIGS[model_type], normalize(centered_img)[None])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vy75nEs6PdYO"
},
"outputs": [],
"source": [
"_, indices = jax.lax.top_k(logits[0], 5)\n",
"probs = jax.nn.softmax(logits[0])\n",
"\n",
"plt.imshow(img)\n",
"plt.axis('off')\n",
"print('Top 5 labels:')\n",
"for i in list(indices):\n",
" print(f'{IMAGENET_LABELS[i]}: {probs[i]}')"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "//learning/deepmind/dm_python:dm_notebook3",
"kind": "private"
},
"name": "Perceiver IO: ImageNet Classification.ipynb",
"private_outputs": true,
"provenance": [
{
"file_id": "13GmNq76sKk9-gQSxpiaYg-ASC4rFQkIk",
"timestamp": 1627599827239
},
{
"file_id": "1bt4J3-jS7C-xZQrtx0AgAeKSBxOUoOPy",
"timestamp": 1627592566036
}
],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: perceiver/colabs/masked_language_modelling.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uFsBlyxwmRq2"
},
"outputs": [],
"source": [
"# Copyright 2021 DeepMind Technologies Limited\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rmw6HSr7xZvZ"
},
"outputs": [],
"source": [
"# Install dependencies for Google Colab.\n",
"# If you want to run this notebook on your own machine, you can skip this cell\n",
"!pip install dm-haiku\n",
"!pip install einops\n",
"\n",
"!mkdir /content/perceiver\n",
"!touch /content/perceiver/__init__.py\n",
"!wget -O /content/perceiver/bytes_tokenizer.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/bytes_tokenizer.py\n",
"!wget -O /content/perceiver/io_processors.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/io_processors.py\n",
"!wget -O /content/perceiver/perceiver.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/perceiver.py\n",
"!wget -O /content/perceiver/position_encoding.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/position_encoding.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "IBVh06qUojjm"
},
"outputs": [],
"source": [
"#@title Import\n",
"from typing import Union\n",
"\n",
"import haiku as hk\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import numpy as np\n",
"import pickle\n",
"\n",
"from perceiver import perceiver, position_encoding, io_processors, bytes_tokenizer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "sFa-lRuVfKZt"
},
"outputs": [],
"source": [
"#@title Load parameters from checkpoint\n",
"!wget -O language_perceiver_io_bytes.pickle https://storage.googleapis.com/perceiver_io/language_perceiver_io_bytes.pickle\n",
"\n",
"with open(\"language_perceiver_io_bytes.pickle\", \"rb\") as f:\n",
" params = pickle.loads(f.read())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "LBzQQ7t_VCBo"
},
"outputs": [],
"source": [
"#@title Model config\n",
"D_MODEL = 768\n",
"D_LATENTS = 1280\n",
"MAX_SEQ_LEN = 2048\n",
"\n",
"encoder_config = dict(\n",
" num_self_attends_per_block=26,\n",
" num_blocks=1,\n",
" z_index_dim=256,\n",
" num_z_channels=D_LATENTS,\n",
" num_self_attend_heads=8,\n",
" num_cross_attend_heads=8,\n",
" qk_channels=8 * 32,\n",
" v_channels=D_LATENTS,\n",
" use_query_residual=True,\n",
" cross_attend_widening_factor=1,\n",
" self_attend_widening_factor=1)\n",
"\n",
"decoder_config = dict(\n",
" output_num_channels=D_LATENTS,\n",
" position_encoding_type='trainable',\n",
" output_index_dims=MAX_SEQ_LEN,\n",
" num_z_channels=D_LATENTS,\n",
" qk_channels=8 * 32,\n",
" v_channels=D_MODEL,\n",
" num_heads=8,\n",
" final_project=False,\n",
" use_query_residual=False,\n",
" trainable_position_encoding_kwargs=dict(num_channels=D_MODEL))\n",
"\n",
"# The tokenizer is just UTF-8 encoding (with an offset)\n",
"tokenizer = bytes_tokenizer.BytesTokenizer()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "EWOeFoF0aCaT"
},
"outputs": [],
"source": [
"#@title Decoding Perceiver Model\n",
"def apply_perceiver(\n",
" inputs: jnp.ndarray, input_mask: jnp.ndarray) -\u003e jnp.ndarray:\n",
" \"\"\"Runs a forward pass on the Perceiver.\n",
"\n",
" Args:\n",
" inputs: input bytes, an int array of shape [B, T]\n",
" input_mask: Array of shape indicating which entries are valid and which are\n",
" masked. A truthy value indicates that the entry is valid.\n",
"\n",
" Returns:\n",
" The output logits, an array of shape [B, T, vocab_size].\n",
" \"\"\"\n",
" assert inputs.shape[1] == MAX_SEQ_LEN\n",
"\n",
" embedding_layer = hk.Embed(\n",
" vocab_size=tokenizer.vocab_size,\n",
" embed_dim=D_MODEL)\n",
" embedded_inputs = embedding_layer(inputs)\n",
"\n",
" batch_size = embedded_inputs.shape[0]\n",
"\n",
" input_pos_encoding = perceiver.position_encoding.TrainablePositionEncoding(\n",
" index_dim=MAX_SEQ_LEN, num_channels=D_MODEL)\n",
" embedded_inputs = embedded_inputs + input_pos_encoding(batch_size)\n",
" perceiver_mod = perceiver.Perceiver(\n",
" encoder=perceiver.PerceiverEncoder(**encoder_config),\n",
" decoder=perceiver.BasicDecoder(**decoder_config))\n",
" output_embeddings = perceiver_mod(\n",
" embedded_inputs, is_training=False, input_mask=input_mask, query_mask=input_mask)\n",
"\n",
" logits = io_processors.EmbeddingDecoder(\n",
" embedding_matrix=embedding_layer.embeddings)(output_embeddings)\n",
" return logits\n",
"\n",
"apply_perceiver = hk.transform(apply_perceiver).apply"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Pna1ZXEyOJZb"
},
"outputs": [],
"source": [
"input_str = \"This is an incomplete sentence where some words are missing.\"\n",
"input_tokens = tokenizer.to_int(input_str)\n",
"\n",
"# Mask \" missing.\". Note that the model performs much better if the masked chunk\n",
"# starts with a space.\n",
"input_tokens[51:60] = tokenizer.mask_token\n",
"print(\"Tokenized string without masked bytes:\")\n",
"print(tokenizer.to_string(input_tokens))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "6TCMuVUabnTg"
},
"outputs": [],
"source": [
"#@title Pad and reshape inputs\n",
"inputs = input_tokens[None]\n",
"input_mask = np.ones_like(inputs)\n",
"\n",
"def pad(max_sequence_length: int, inputs, input_mask):\n",
" input_len = inputs.shape[1]\n",
" assert input_len \u003c= max_sequence_length\n",
" pad_len = max_sequence_length - input_len\n",
" padded_inputs = np.pad(\n",
" inputs,\n",
" pad_width=((0, 0), (0, pad_len)),\n",
" constant_values=tokenizer.pad_token)\n",
" padded_mask = np.pad(\n",
" input_mask,\n",
" pad_width=((0, 0), (0, pad_len)),\n",
" constant_values=0)\n",
" return padded_inputs, padded_mask\n",
"\n",
"inputs, input_mask = pad(MAX_SEQ_LEN, inputs, input_mask)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ipZs6p0Xk3lb"
},
"outputs": [],
"source": [
"rng = jax.random.PRNGKey(1) # Unused\n",
"\n",
"out = apply_perceiver(params, rng=rng, inputs=inputs, input_mask=input_mask)\n",
"\n",
"masked_tokens_predictions = out[0, 51:60].argmax(axis=-1)\n",
"print(\"Greedy predictions:\")\n",
"print(masked_tokens_predictions)\n",
"print()\n",
"print(\"Predicted string:\")\n",
"print(tokenizer.to_string(masked_tokens_predictions))"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "//learning/deepmind/dm_python:dm_notebook3",
"kind": "private"
},
"name": "Perceiver IO: Masked Language Modelling",
"private_outputs": true,
"provenance": [
{
"file_id": "1N31dQM-SzjG-_acz405i3jCR_m6D4bq8",
"timestamp": 1627567455889
}
],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: perceiver/colabs/optical_flow.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "AEigJ-mOGOk9"
},
"outputs": [],
"source": [
"# Copyright 2021 DeepMind Technologies Limited\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YyGBRVPJxLzo"
},
"outputs": [],
"source": [
"# Install dependencies for Google Colab.\n",
"# If you want to run this notebook on your own machine, you can skip this cell\n",
"!pip install dm-haiku\n",
"!pip install einops\n",
"\n",
"!mkdir /content/perceiver\n",
"!touch /content/perceiver/__init__.py\n",
"!wget -O /content/perceiver/io_processors.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/io_processors.py\n",
"!wget -O /content/perceiver/perceiver.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/perceiver.py\n",
"!wget -O /content/perceiver/position_encoding.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/position_encoding.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "VHzUTH5KqNEt"
},
"outputs": [],
"source": [
"#@title Imports\n",
"\n",
"import functools\n",
"import itertools\n",
"import pickle\n",
"\n",
"import haiku as hk\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"import cv2\n",
"import imageio\n",
"\n",
"from perceiver import perceiver, io_processors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "uxeP5yit7hJg"
},
"outputs": [],
"source": [
"#@title Model construction\n",
"\n",
"FLOW_SCALE_FACTOR = 20\n",
"# The network assumes images are of the following size\n",
"TRAIN_SIZE = (368, 496)\n",
"\n",
"def optical_flow(images):\n",
" \"\"\"Perceiver IO model for optical flow.\n",
"\n",
" Args:\n",
" images: Array of two stacked images, of shape [B, 2, H, W, C]\n",
" Returns:\n",
" Optical flow field, of shape [B, H, W, 2].\n",
" \"\"\"\n",
" input_preprocessor = io_processors.ImagePreprocessor(\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" num_bands=64,\n",
" max_resolution=TRAIN_SIZE,\n",
" sine_only=False,\n",
" concat_pos=True,\n",
" ),\n",
" n_extra_pos_mlp=0,\n",
" prep_type='patches',\n",
" spatial_downsample=1,\n",
" conv_after_patching=True,\n",
" temporal_downsample=2)\n",
"\n",
" encoder = encoder = perceiver.PerceiverEncoder(\n",
" num_self_attends_per_block=24,\n",
" # Weights won't be shared if num_blocks is set to 1.\n",
" num_blocks=1,\n",
" z_index_dim=2048,\n",
" num_cross_attend_heads=1,\n",
" num_z_channels=512,\n",
" num_self_attend_heads=16,\n",
" cross_attend_widening_factor=1,\n",
" self_attend_widening_factor=1,\n",
" dropout_prob=0.0,\n",
" z_pos_enc_init_scale=0.02,\n",
" cross_attention_shape_for_attn='kv',\n",
" name='perceiver_encoder')\n",
"\n",
" decoder = perceiver.FlowDecoder(\n",
" TRAIN_SIZE,\n",
" rescale_factor=100.0,\n",
" use_query_residual=False,\n",
" output_num_channels=2,\n",
" output_w_init=jnp.zeros,\n",
" # We query the decoder using the first frame features\n",
" # rather than a standard decoder position encoding.\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" concat_pos=True,\n",
" max_resolution=TRAIN_SIZE,\n",
" num_bands=64,\n",
" sine_only=False\n",
" )\n",
" )\n",
"\n",
" model = perceiver.Perceiver(\n",
" input_preprocessor=input_preprocessor,\n",
" encoder=encoder,\n",
" decoder=decoder,\n",
" output_postprocessor=None)\n",
"\n",
" return model(io_processors.patches_for_flow(images),\n",
" is_training=False) * FLOW_SCALE_FACTOR\n",
"\n",
"\n",
"optical_flow = hk.transform(optical_flow)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "dmvRv3o-6ASw"
},
"outputs": [],
"source": [
"#@title Function to compute flow between pairs of images\n",
"\n",
"# If you encounter GPU memory errors while running the function below,\n",
"# you can run it on the CPU instead:\n",
"# _apply_optical_flow_model = jax.jit(optical_flow.apply, backend=\"cpu\")\n",
"_apply_optical_flow_model = jax.jit(optical_flow.apply)\n",
"\n",
"def compute_grid_indices(image_shape, patch_size=TRAIN_SIZE, min_overlap=20):\n",
" if min_overlap \u003e= TRAIN_SIZE[0] or min_overlap \u003e= TRAIN_SIZE[1]:\n",
" raise ValueError(\n",
" f\"Overlap should be less than size of patch (got {min_overlap}\"\n",
" f\"for patch size {patch_size}).\")\n",
" ys = list(range(0, image_shape[0], TRAIN_SIZE[0] - min_overlap))\n",
" xs = list(range(0, image_shape[1], TRAIN_SIZE[1] - min_overlap))\n",
" # Make sure the final patch is flush with the image boundary\n",
" ys[-1] = image_shape[0] - patch_size[0]\n",
" xs[-1] = image_shape[1] - patch_size[1]\n",
" return itertools.product(ys, xs)\n",
"\n",
"def compute_optical_flow(params, rng, img1, img2, grid_indices,\n",
" patch_size=TRAIN_SIZE):\n",
" \"\"\"Function to compute optical flow between two images.\n",
"\n",
" To compute the flow between images of arbitrary sizes, we divide the image\n",
" into patches, compute the flow for each patch, and stitch the flows together.\n",
"\n",
" Args:\n",
" params: model parameters\n",
" rng: jax.random.PRNGKey, not used in this model\n",
" img1: first image\n",
" img2: second image\n",
" grid_indices: indices of the upper left corner for each patch.\n",
" patch_size: size of patch, should be TRAIN_SIZE.\n",
" \"\"\"\n",
" imgs = jnp.stack([img1, img2], axis=0)[None]\n",
" height = imgs.shape[-3]\n",
" width = imgs.shape[-2]\n",
"\n",
" if height \u003c patch_size[0]:\n",
" raise ValueError(\n",
" f\"Height of image (shape: {imgs.shape}) must be at least {patch_size[0]}.\"\n",
" \"Please pad or resize your image to the minimum dimension.\"\n",
" )\n",
" if width \u003c patch_size[1]:\n",
" raise ValueError(\n",
" f\"Width of image (shape: {imgs.shape}) must be at least {patch_size[1]}.\"\n",
" \"Please pad or resize your image to the minimum dimension.\"\n",
" )\n",
"\n",
" flows = 0\n",
" flow_count = 0\n",
"\n",
" for y, x in grid_indices:\n",
" inp_piece = imgs[..., y : y + patch_size[0],\n",
" x : x + patch_size[1], :]\n",
" flow_piece = _apply_optical_flow_model(params, rng, inp_piece)\n",
" weights_x, weights_y = jnp.meshgrid(\n",
" jnp.arange(patch_size[1]), jnp.arange(patch_size[0]))\n",
"\n",
" weights_x = jnp.minimum(weights_x + 1, patch_size[1] - weights_x)\n",
" weights_y = jnp.minimum(weights_y + 1, patch_size[0] - weights_y)\n",
" weights = jnp.minimum(weights_x, weights_y)[jnp.newaxis, :, :,\n",
" jnp.newaxis]\n",
" padding = [(0, 0), (y, height - y - patch_size[0]),\n",
" (x, width - x - patch_size[1]), (0, 0)]\n",
" flows += jnp.pad(flow_piece * weights, padding)\n",
" flow_count += jnp.pad(weights, padding)\n",
"\n",
" flows /= flow_count\n",
" return flows"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "EVRWatw4LXFx"
},
"outputs": [],
"source": [
"#@title Load parameters from checkpoint\n",
"\n",
"!wget -O optical_flow_checkpoint.pystate https://storage.googleapis.com/perceiver_io/optical_flow_checkpoint.pystate\n",
"\n",
"rng = jax.random.PRNGKey(42)\n",
"with open(\"optical_flow_checkpoint.pystate\", \"rb\") as f:\n",
" params = pickle.loads(f.read())\n",
"\n",
"state = {}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MAfLTtEXeE3-"
},
"outputs": [],
"source": [
"# Download two example frames from the Sintel dataset.\n",
"# These files are obtained from the Sintel dataset test split,\n",
"# downloaded from http://sintel.is.tue.mpg.de/downloads.\n",
"# They correspond to MPI-Sintel-testing/test/clean/cave_3/frame_0001.png\n",
"# and MPI-Sintel-testing/test/clean/cave_3/frame_0002.png.\n",
"#\n",
"# Citation for Sintel dataset:\n",
"# D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black.\n",
"# A naturalistic open source movie for optical flow evaluation.\n",
"# European Conf. on Computer Vision (ECCV), 2012.\n",
"# https://files.is.tue.mpg.de/black/papers/ButlerECCV2012.pdf\n",
"#\n",
"# The Sintel images are originally generated for the Durian Open Movie project\n",
"# and are licensed under the Creative Commons Attribution 3.0 license (https://durian.blender.org/sharing/).\n",
"# The images are copyrighted by the Blender Foundation (https://durian.blender.org).\n",
"\n",
"\n",
"!wget -O sintel_frame1.png https://storage.googleapis.com/perceiver_io/sintel_frame1.png\n",
"!wget -O sintel_frame2.png https://storage.googleapis.com/perceiver_io/sintel_frame2.png\n",
"\n",
"with open(\"sintel_frame1.png\", \"rb\") as f:\n",
" im1 = imageio.imread(f)\n",
"with open(\"sintel_frame2.png\", \"rb\") as f:\n",
" im2 = imageio.imread(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "Z7ZQJ2auy4Lt"
},
"outputs": [],
"source": [
"#@title Image Utility Functions\n",
"\n",
"def normalize(im):\n",
" return im / 255.0 * 2 - 1\n",
"\n",
"def visualize_flow(flow):\n",
" flow = np.array(flow)\n",
" # Use Hue, Saturation, Value colour model \n",
" hsv = np.zeros((flow.shape[0], flow.shape[1], 3), dtype=np.uint8)\n",
" hsv[..., 2] = 255\n",
"\n",
" mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])\n",
" hsv[..., 0] = ang / np.pi / 2 * 180\n",
" hsv[..., 1] = np.clip(mag * 255 / 24, 0, 255)\n",
" bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\n",
" plt.imshow(bgr)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FfWpBNZJLib4"
},
"outputs": [],
"source": [
"# Compute optical flow\n",
"\n",
"# Divide images into patches, compute flow between corresponding patches\n",
"# of both images, and stitch the flows together\n",
"grid_indices = compute_grid_indices(im1.shape)\n",
"flow = compute_optical_flow(params, rng, normalize(im1), normalize(im2), grid_indices)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Bz7G04rmtHVI"
},
"outputs": [],
"source": [
"# Visualize the computed flow\n",
"visualize_flow(flow[0])"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "Perceiver IO: Optical Flow Visualization.ipynb",
"private_outputs": true,
"provenance": [
{
"file_id": "1bt4J3-jS7C-xZQrtx0AgAeKSBxOUoOPy",
"timestamp": 1627577366926
}
],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: perceiver/colabs/video_autoencoding.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6hVqbkgBFVKB"
},
"outputs": [],
"source": [
"# Copyright 2021 DeepMind Technologies Limited\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sm0mo9ciwhKu"
},
"outputs": [],
"source": [
"# Install dependencies for Google Colab.\n",
"# If you want to run this notebook on your own machine, you can skip this cell\n",
"!pip install dm-haiku\n",
"!pip install einops\n",
"\n",
"!mkdir /content/perceiver\n",
"!touch /content/perceiver/__init__.py\n",
"!wget -O /content/perceiver/io_processors.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/io_processors.py\n",
"!wget -O /content/perceiver/perceiver.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/perceiver.py\n",
"!wget -O /content/perceiver/position_encoding.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/position_encoding.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "VHzUTH5KqNEt"
},
"outputs": [],
"source": [
"#@title Imports\n",
"\n",
"import base64\n",
"import functools\n",
"import os\n",
"import pickle\n",
"import ssl\n",
"import re\n",
"import tempfile\n",
"\n",
"from urllib import request\n",
"\n",
"import cv2\n",
"import haiku as hk\n",
"import imageio\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import numpy as np\n",
"import scipy.io.wavfile\n",
"\n",
"from IPython.display import HTML\n",
"\n",
"from perceiver import perceiver, io_processors\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "Bn1jTwkv3gHf"
},
"outputs": [],
"source": [
"#@title Helper functions for the UCF101 dataset\n",
"\n",
"# Utilities to fetch videos from UCF101 dataset\n",
"UCF_ROOT = 'https://www.crcv.ucf.edu/THUMOS14/UCF101/UCF101/'\n",
"_VIDEO_LIST = None\n",
"_CACHE_DIR = tempfile.mkdtemp()\n",
"# As of July 2020, crcv.ucf.edu doesn't use a certificate accepted by the\n",
"# default Colab environment anymore.\n",
"unverified_context = ssl._create_unverified_context()\n",
"\n",
"def list_ucf_videos():\n",
" \"\"\"Lists videos available in UCF101 dataset.\"\"\"\n",
" global _VIDEO_LIST\n",
" if not _VIDEO_LIST:\n",
" index = request.urlopen(UCF_ROOT, context=unverified_context).read().decode('utf-8')\n",
" videos = re.findall('(v_[\\w_]+\\.avi)', index)\n",
" _VIDEO_LIST = sorted(set(videos))\n",
" return list(_VIDEO_LIST)\n",
"\n",
"def fetch_ucf_video(video):\n",
" \"\"\"Fetchs a video and cache into local filesystem.\"\"\"\n",
" cache_path = os.path.join(_CACHE_DIR, video)\n",
" if not os.path.exists(cache_path):\n",
" urlpath = request.urljoin(UCF_ROOT, video)\n",
" print('Fetching %s =\u003e %s' % (urlpath, cache_path))\n",
" data = request.urlopen(urlpath, context=unverified_context).read()\n",
" open(cache_path, \"wb\").write(data)\n",
" return cache_path\n",
"\n",
"# Utilities to open video files using CV2\n",
"def crop_center_square(frame):\n",
" y, x = frame.shape[0:2]\n",
" min_dim = min(y, x)\n",
" start_x = (x // 2) - (min_dim // 2)\n",
" start_y = (y // 2) - (min_dim // 2)\n",
" return frame[start_y:start_y+min_dim,start_x:start_x+min_dim]\n",
"\n",
"def load_video(path, max_frames=0, resize=(224, 224)):\n",
" cap = cv2.VideoCapture(path)\n",
" frames = []\n",
" try:\n",
" while True:\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" break\n",
" frame = crop_center_square(frame)\n",
" frame = cv2.resize(frame, resize)\n",
" frame = frame[:, :, [2, 1, 0]]\n",
" frames.append(frame)\n",
" \n",
" if len(frames) == max_frames:\n",
" break\n",
" finally:\n",
" cap.release()\n",
" return np.array(frames) / 255.0\n",
"\n",
"def to_gif(images):\n",
" converted_images = np.clip(images * 255, 0, 255).astype(np.uint8)\n",
" imageio.mimsave('./animation.gif', converted_images, fps=25)\n",
" with open('./animation.gif', 'rb') as f:\n",
" gif_64 = base64.b64encode(f.read()).decode('utf-8')\n",
" return HTML('\u003cimg src=\"data:image/gif;base64,%s\"/\u003e' % gif_64)\n",
"\n",
"def play_audio(data, sample_rate=48000):\n",
" scipy.io.wavfile.write('tmp_audio.wav', sample_rate, data)\n",
"\n",
" with open('./tmp_audio.wav', 'rb') as f:\n",
" audio_64 = base64.b64encode(f.read()).decode('utf-8')\n",
" return HTML('\u003caudio controls src=\"data:audio/wav;base64,%s\"/\u003e' % audio_64)\n",
"\n",
"def table(elements):\n",
" row = ['\u003ctd\u003e%s\u003c/td\u003e' % el.data for el in elements]\n",
" return HTML('\u003ctable\u003e\u003ctr\u003e%s\u003c/tr\u003e\u003c/table\u003e' % ''.join(row))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "QqXUfdsF3iZ6"
},
"outputs": [],
"source": [
"#@title Load video and audio from UCF\n",
"\n",
"video_names = list_ucf_videos()\n",
"video_path = fetch_ucf_video(video_names[0])\n",
"\n",
"# Extract audio using FFMPEG and encode as pcm float wavfile (only format readable by scipy.io.wavfile).\n",
"!yes | ffmpeg -i \"$video_path\" -c copy -f wav -map 0:a pcm_f32le -ar 48000 output.wav\n",
"\n",
"sample_rate, audio = scipy.io.wavfile.read(\"output.wav\")\n",
"if audio.dtype == np.int16:\n",
" audio = audio.astype(np.float32) / 2**15\n",
"elif audio.dtype != np.float32:\n",
" raise ValueError('Unexpected datatype. Model expects sound samples to lie in [-1, 1]')\n",
"\n",
"video = load_video(video_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "6hxYUvnpqD8Y"
},
"outputs": [],
"source": [
"#@title Kinetics 700 Classes\n",
"KINETICS_CLASSES = [\"abseiling\", \"acting in play\", \"adjusting glasses\", \"air drumming\", \n",
"\"alligator wrestling\", \"answering questions\", \"applauding\", \"applying cream\", \n",
"\"archaeological excavation\", \"archery\", \"arguing\", \"arm wrestling\", \n",
"\"arranging flowers\", \"arresting\", \"assembling bicycle\", \"assembling computer\", \n",
"\"attending conference\", \"auctioning\", \"baby waking up\", \"backflip (human)\", \n",
"\"baking cookies\", \"bandaging\", \"barbequing\", \"bartending\", \n",
"\"base jumping\", \"bathing dog\", \"battle rope training\", \"beatboxing\", \n",
"\"bee keeping\", \"being excited\", \"being in zero gravity\", \"belly dancing\", \n",
"\"bench pressing\", \"bending back\", \"bending metal\", \"biking through snow\", \n",
"\"blasting sand\", \"blending fruit\", \"blowdrying hair\", \"blowing bubble gum\", \n",
"\"blowing glass\", \"blowing leaves\", \"blowing nose\", \"blowing out candles\", \n",
"\"bobsledding\", \"bodysurfing\", \"bookbinding\", \"bottling\", \n",
"\"bouncing ball (not juggling)\", \"bouncing on bouncy castle\", \"bouncing on trampoline\", \"bowling\", \n",
"\"braiding hair\", \"breading or breadcrumbing\", \"breakdancing\", \"breaking boards\", \n",
"\"breaking glass\", \"breathing fire\", \"brush painting\", \"brushing floor\", \n",
"\"brushing hair\", \"brushing teeth\", \"building cabinet\", \"building lego\", \n",
"\"building sandcastle\", \"building shed\", \"bulldozing\", \"bungee jumping\", \n",
"\"burping\", \"busking\", \"calculating\", \"calligraphy\", \n",
"\"canoeing or kayaking\", \"capoeira\", \"capsizing\", \"card stacking\", \n",
"\"card throwing\", \"carrying baby\", \"carrying weight\", \"cartwheeling\", \n",
"\"carving ice\", \"carving marble\", \"carving pumpkin\", \"carving wood with a knife\", \n",
"\"casting fishing line\", \"catching fish\", \"catching or throwing baseball\", \"catching or throwing frisbee\", \n",
"\"catching or throwing softball\", \"celebrating\", \"changing gear in car\", \"changing oil\", \n",
"\"changing wheel (not on bike)\", \"chasing\", \"checking tires\", \"checking watch\", \n",
"\"cheerleading\", \"chewing gum\", \"chiseling stone\", \"chiseling wood\", \n",
"\"chopping meat\", \"chopping wood\", \"clam digging\", \"clapping\", \n",
"\"clay pottery making\", \"clean and jerk\", \"cleaning gutters\", \"cleaning pool\", \n",
"\"cleaning shoes\", \"cleaning toilet\", \"cleaning windows\", \"climbing a rope\", \n",
"\"climbing ladder\", \"climbing tree\", \"closing door\", \"coloring in\", \n",
"\"combing hair\", \"contact juggling\", \"contorting\", \"cooking chicken\", \n",
"\"cooking egg\", \"cooking on campfire\", \"cooking sausages (not on barbeque)\", \"cooking scallops\", \n",
"\"cosplaying\", \"coughing\", \"counting money\", \"country line dancing\", \n",
"\"cracking back\", \"cracking knuckles\", \"cracking neck\", \"crawling baby\", \n",
"\"crocheting\", \"crossing eyes\", \"crossing river\", \"crying\", \n",
"\"cumbia\", \"curling (sport)\", \"curling eyelashes\", \"curling hair\", \n",
"\"cutting apple\", \"cutting cake\", \"cutting nails\", \"cutting orange\", \n",
"\"cutting pineapple\", \"cutting watermelon\", \"dancing ballet\", \"dancing charleston\", \n",
"\"dancing gangnam style\", \"dancing macarena\", \"deadlifting\", \"dealing cards\", \n",
"\"decorating the christmas tree\", \"decoupage\", \"delivering mail\", \"digging\", \n",
"\"dining\", \"directing traffic\", \"disc golfing\", \"diving cliff\", \n",
"\"docking boat\", \"dodgeball\", \"doing aerobics\", \"doing jigsaw puzzle\", \n",
"\"doing laundry\", \"doing nails\", \"doing sudoku\", \"drawing\", \n",
"\"dribbling basketball\", \"drinking shots\", \"driving car\", \"driving tractor\", \n",
"\"drooling\", \"drop kicking\", \"drumming fingers\", \"dumpster diving\", \n",
"\"dunking basketball\", \"dyeing eyebrows\", \"dyeing hair\", \"eating burger\", \n",
"\"eating cake\", \"eating carrots\", \"eating chips\", \"eating doughnuts\", \n",
"\"eating hotdog\", \"eating ice cream\", \"eating nachos\", \"eating spaghetti\", \n",
"\"eating watermelon\", \"egg hunting\", \"embroidering\", \"entering church\", \n",
"\"exercising arm\", \"exercising with an exercise ball\", \"extinguishing fire\", \"faceplanting\", \n",
"\"falling off bike\", \"falling off chair\", \"feeding birds\", \"feeding fish\", \n",
"\"feeding goats\", \"fencing (sport)\", \"fidgeting\", \"filling cake\", \n",
"\"filling eyebrows\", \"finger snapping\", \"fixing bicycle\", \"fixing hair\", \n",
"\"flint knapping\", \"flipping bottle\", \"flipping pancake\", \"fly tying\", \n",
"\"flying kite\", \"folding clothes\", \"folding napkins\", \"folding paper\", \n",
"\"front raises\", \"frying vegetables\", \"gargling\", \"geocaching\", \n",
"\"getting a haircut\", \"getting a piercing\", \"getting a tattoo\", \"giving or receiving award\", \n",
"\"gold panning\", \"golf chipping\", \"golf driving\", \"golf putting\", \n",
"\"gospel singing in church\", \"grinding meat\", \"grooming cat\", \"grooming dog\", \n",
"\"grooming horse\", \"gymnastics tumbling\", \"hammer throw\", \"hand washing clothes\", \n",
"\"head stand\", \"headbanging\", \"headbutting\", \"helmet diving\", \n",
"\"herding cattle\", \"high fiving\", \"high jump\", \"high kick\", \n",
"\"historical reenactment\", \"hitting baseball\", \"hockey stop\", \"holding snake\", \n",
"\"home roasting coffee\", \"hopscotch\", \"hoverboarding\", \"huddling\", \n",
"\"hugging (not baby)\", \"hugging baby\", \"hula hooping\", \"hurdling\", \n",
"\"hurling (sport)\", \"ice climbing\", \"ice fishing\", \"ice skating\", \n",
"\"ice swimming\", \"inflating balloons\", \"installing carpet\", \"ironing\", \n",
"\"ironing hair\", \"javelin throw\", \"jaywalking\", \"jetskiing\", \n",
"\"jogging\", \"juggling balls\", \"juggling fire\", \"juggling soccer ball\", \n",
"\"jumping bicycle\", \"jumping into pool\", \"jumping jacks\", \"jumping sofa\", \n",
"\"jumpstyle dancing\", \"karaoke\", \"kicking field goal\", \"kicking soccer ball\", \n",
"\"kissing\", \"kitesurfing\", \"knitting\", \"krumping\", \n",
"\"land sailing\", \"laughing\", \"lawn mower racing\", \"laying bricks\", \n",
"\"laying concrete\", \"laying decking\", \"laying stone\", \"laying tiles\", \n",
"\"leatherworking\", \"letting go of balloon\", \"licking\", \"lifting hat\", \n",
"\"lighting candle\", \"lighting fire\", \"listening with headphones\", \"lock picking\", \n",
"\"long jump\", \"longboarding\", \"looking at phone\", \"looking in mirror\", \n",
"\"luge\", \"lunge\", \"making a cake\", \"making a sandwich\", \n",
"\"making balloon shapes\", \"making bubbles\", \"making cheese\", \"making horseshoes\", \n",
"\"making jewelry\", \"making latte art\", \"making paper aeroplanes\", \"making pizza\", \n",
"\"making slime\", \"making snowman\", \"making sushi\", \"making tea\", \n",
"\"making the bed\", \"marching\", \"marriage proposal\", \"massaging back\", \n",
"\"massaging feet\", \"massaging legs\", \"massaging neck\", \"massaging person's head\", \n",
"\"metal detecting\", \"milking cow\", \"milking goat\", \"mixing colours\", \n",
"\"moon walking\", \"mopping floor\", \"mosh pit dancing\", \"motorcycling\", \n",
"\"mountain climber (exercise)\", \"moving baby\", \"moving child\", \"moving furniture\", \n",
"\"mowing lawn\", \"mushroom foraging\", \"needle felting\", \"news anchoring\", \n",
"\"opening bottle (not wine)\", \"opening coconuts\", \"opening door\", \"opening present\", \n",
"\"opening refrigerator\", \"opening wine bottle\", \"packing\", \"paragliding\", \n",
"\"parasailing\", \"parkour\", \"passing American football (in game)\", \"passing American football (not in game)\", \n",
"\"passing soccer ball\", \"peeling apples\", \"peeling banana\", \"peeling potatoes\", \n",
"\"person collecting garbage\", \"petting animal (not cat)\", \"petting cat\", \"petting horse\", \n",
"\"photobombing\", \"photocopying\", \"picking apples\", \"picking blueberries\", \n",
"\"pillow fight\", \"pinching\", \"pirouetting\", \"planing wood\", \n",
"\"planting trees\", \"plastering\", \"playing accordion\", \"playing american football\", \n",
"\"playing badminton\", \"playing bagpipes\", \"playing basketball\", \"playing bass guitar\", \n",
"\"playing beer pong\", \"playing billiards\", \"playing blackjack\", \"playing cards\", \n",
"\"playing cello\", \"playing checkers\", \"playing chess\", \"playing clarinet\", \n",
"\"playing controller\", \"playing cricket\", \"playing cymbals\", \"playing darts\", \n",
"\"playing didgeridoo\", \"playing dominoes\", \"playing drums\", \"playing field hockey\", \n",
"\"playing flute\", \"playing gong\", \"playing guitar\", \"playing hand clapping games\", \n",
"\"playing harmonica\", \"playing harp\", \"playing ice hockey\", \"playing keyboard\", \n",
"\"playing kickball\", \"playing laser tag\", \"playing lute\", \"playing mahjong\", \n",
"\"playing maracas\", \"playing marbles\", \"playing monopoly\", \"playing netball\", \n",
"\"playing nose flute\", \"playing oboe\", \"playing ocarina\", \"playing organ\", \n",
"\"playing paintball\", \"playing pan pipes\", \"playing piano\", \"playing piccolo\", \n",
"\"playing pinball\", \"playing ping pong\", \"playing poker\", \"playing polo\", \n",
"\"playing recorder\", \"playing road hockey\", \"playing rounders\", \"playing rubiks cube\", \n",
"\"playing saxophone\", \"playing scrabble\", \"playing shuffleboard\", \"playing slot machine\", \n",
"\"playing squash or racquetball\", \"playing tennis\", \"playing trombone\", \"playing trumpet\", \n",
"\"playing ukulele\", \"playing violin\", \"playing volleyball\", \"playing with trains\", \n",
"\"playing xylophone\", \"poaching eggs\", \"poking bellybutton\", \"pole vault\", \n",
"\"polishing furniture\", \"polishing metal\", \"popping balloons\", \"pouring beer\", \n",
"\"pouring milk\", \"pouring wine\", \"preparing salad\", \"presenting weather forecast\", \n",
"\"pretending to be a statue\", \"pull ups\", \"pulling espresso shot\", \"pulling rope (game)\", \n",
"\"pumping fist\", \"pumping gas\", \"punching bag\", \"punching person (boxing)\", \n",
"\"push up\", \"pushing car\", \"pushing cart\", \"pushing wheelbarrow\", \n",
"\"pushing wheelchair\", \"putting in contact lenses\", \"putting on eyeliner\", \"putting on foundation\", \n",
"\"putting on lipstick\", \"putting on mascara\", \"putting on sari\", \"putting on shoes\", \n",
"\"putting wallpaper on wall\", \"raising eyebrows\", \"reading book\", \"reading newspaper\", \n",
"\"recording music\", \"repairing puncture\", \"riding a bike\", \"riding camel\", \n",
"\"riding elephant\", \"riding mechanical bull\", \"riding mule\", \"riding or walking with horse\", \n",
"\"riding scooter\", \"riding snow blower\", \"riding unicycle\", \"ripping paper\", \n",
"\"roasting marshmallows\", \"roasting pig\", \"robot dancing\", \"rock climbing\", \n",
"\"rock scissors paper\", \"roller skating\", \"rolling eyes\", \"rolling pastry\", \n",
"\"rope pushdown\", \"running on treadmill\", \"sailing\", \"salsa dancing\", \n",
"\"saluting\", \"sanding floor\", \"sanding wood\", \"sausage making\", \n",
"\"sawing wood\", \"scrambling eggs\", \"scrapbooking\", \"scrubbing face\", \n",
"\"scuba diving\", \"seasoning food\", \"separating eggs\", \"setting table\", \n",
"\"sewing\", \"shaking hands\", \"shaking head\", \"shaping bread dough\", \n",
"\"sharpening knives\", \"sharpening pencil\", \"shaving head\", \"shaving legs\", \n",
"\"shearing sheep\", \"shining flashlight\", \"shining shoes\", \"shoot dance\", \n",
"\"shooting basketball\", \"shooting goal (soccer)\", \"shooting off fireworks\", \"shopping\", \n",
"\"shot put\", \"shouting\", \"shoveling snow\", \"shredding paper\", \n",
"\"shucking oysters\", \"shuffling cards\", \"shuffling feet\", \"side kick\", \n",
"\"sieving\", \"sign language interpreting\", \"silent disco\", \"singing\", \n",
"\"sipping cup\", \"situp\", \"skateboarding\", \"ski ballet\", \n",
"\"ski jumping\", \"skiing crosscountry\", \"skiing mono\", \"skiing slalom\", \n",
"\"skipping rope\", \"skipping stone\", \"skydiving\", \"slacklining\", \n",
"\"slapping\", \"sled dog racing\", \"sleeping\", \"slicing onion\", \n",
"\"smashing\", \"smelling feet\", \"smoking\", \"smoking hookah\", \n",
"\"smoking pipe\", \"snatch weight lifting\", \"sneezing\", \"snorkeling\", \n",
"\"snowboarding\", \"snowkiting\", \"snowmobiling\", \"somersaulting\", \n",
"\"spelunking\", \"spinning plates\", \"spinning poi\", \"splashing water\", \n",
"\"spray painting\", \"spraying\", \"springboard diving\", \"square dancing\", \n",
"\"squat\", \"squeezing orange\", \"stacking cups\", \"stacking dice\", \n",
"\"standing on hands\", \"staring\", \"steer roping\", \"steering car\", \n",
"\"sticking tongue out\", \"stomping grapes\", \"stretching arm\", \"stretching leg\", \n",
"\"sucking lolly\", \"surfing crowd\", \"surfing water\", \"surveying\", \n",
"\"sweeping floor\", \"swimming backstroke\", \"swimming breast stroke\", \"swimming butterfly stroke\", \n",
"\"swimming front crawl\", \"swimming with dolphins\", \"swimming with sharks\", \"swing dancing\", \n",
"\"swinging baseball bat\", \"swinging on something\", \"sword fighting\", \"sword swallowing\", \n",
"\"tackling\", \"tagging graffiti\", \"tai chi\", \"taking photo\", \n",
"\"talking on cell phone\", \"tango dancing\", \"tap dancing\", \"tapping guitar\", \n",
"\"tapping pen\", \"tasting beer\", \"tasting food\", \"tasting wine\", \n",
"\"testifying\", \"texting\", \"threading needle\", \"throwing axe\", \n",
"\"throwing ball (not baseball or American football)\", \"throwing discus\", \"throwing knife\", \"throwing snowballs\", \n",
"\"throwing tantrum\", \"throwing water balloon\", \"tickling\", \"tie dying\", \n",
"\"tightrope walking\", \"tiptoeing\", \"tobogganing\", \"tossing coin\", \n",
"\"tossing salad\", \"training dog\", \"trapezing\", \"treating wood\", \n",
"\"trimming or shaving beard\", \"trimming shrubs\", \"trimming trees\", \"triple jump\", \n",
"\"twiddling fingers\", \"tying bow tie\", \"tying knot (not on a tie)\", \"tying necktie\", \n",
"\"tying shoe laces\", \"unboxing\", \"uncorking champagne\", \"unloading truck\", \n",
"\"using a microscope\", \"using a paint roller\", \"using a power drill\", \"using a sledge hammer\", \n",
"\"using a wrench\", \"using atm\", \"using bagging machine\", \"using circular saw\", \n",
"\"using inhaler\", \"using megaphone\", \"using puppets\", \"using remote controller (not gaming)\", \n",
"\"using segway\", \"vacuuming car\", \"vacuuming floor\", \"visiting the zoo\", \n",
"\"wading through mud\", \"wading through water\", \"waiting in line\", \"waking up\", \n",
"\"walking on stilts\", \"walking the dog\", \"walking through snow\", \"walking with crutches\", \n",
"\"washing dishes\", \"washing feet\", \"washing hair\", \"washing hands\", \n",
"\"watching tv\", \"water skiing\", \"water sliding\", \"watering plants\", \n",
"\"waving hand\", \"waxing armpits\", \"waxing back\", \"waxing chest\", \n",
"\"waxing eyebrows\", \"waxing legs\", \"weaving basket\", \"weaving fabric\", \n",
"\"welding\", \"whistling\", \"windsurfing\", \"winking\", \n",
"\"wood burning (art)\", \"wrapping present\", \"wrestling\", \"writing\", \n",
"\"yarn spinning\", \"yawning\", \"yoga\", \"zumba\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bQpSe7DMhuln"
},
"outputs": [],
"source": [
"# Visualize inputs\n",
"table([to_gif(video), play_audio(audio)])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "uxeP5yit7hJg"
},
"outputs": [],
"source": [
"#@title Model construction\n",
"NUM_FRAMES = 16\n",
"AUDIO_SAMPLES_PER_FRAME = 48000 // 25\n",
"SAMPLES_PER_PATCH = 16\n",
"NUM_CLASSES = 700\n",
"IMG_SZ = 56\n",
"\n",
"def video_autoencoder(images, audio, subsampling):\n",
" n_audio_samples = NUM_FRAMES * AUDIO_SAMPLES_PER_FRAME\n",
" input_preprocessor = io_processors.MultimodalPreprocessor(\n",
" min_padding_size=4,\n",
" modalities={\n",
" 'audio': io_processors.AudioPreprocessor(\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" num_bands=192,\n",
" max_resolution=(n_audio_samples,),\n",
" sine_only=False,\n",
" concat_pos=True,\n",
" ),\n",
" n_extra_pos_mlp=0,\n",
" prep_type='patches',\n",
" samples_per_patch=16),\n",
" 'image': io_processors.ImagePreprocessor(\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" num_bands=32,\n",
" max_resolution=(NUM_FRAMES, IMG_SZ, IMG_SZ),\n",
" sine_only=False,\n",
" concat_pos=True,\n",
" ),\n",
" n_extra_pos_mlp=0,\n",
" prep_type='patches',\n",
" spatial_downsample=4,\n",
" temporal_downsample=1),\n",
" 'label': io_processors.OneHotPreprocessor(),\n",
" },\n",
" mask_probs={'image': 0.0, 'audio': 0.0, 'label': 1.0},\n",
" )\n",
"\n",
" output_postprocessor = io_processors.MultimodalPostprocessor(\n",
" modalities={\n",
" 'audio': io_processors.AudioPostprocessor(\n",
" samples_per_patch=SAMPLES_PER_PATCH),\n",
" 'image': io_processors.ProjectionPostprocessor(\n",
" num_outputs=3),\n",
" 'label': io_processors.ClassificationPostprocessor(\n",
" num_classes=NUM_CLASSES),\n",
" })\n",
"\n",
" encoder = encoder = perceiver.PerceiverEncoder(\n",
" num_self_attends_per_block=8,\n",
" # Weights won't be shared if num_blocks is set to 1.\n",
" num_blocks=1,\n",
" z_index_dim=28*28*1,\n",
" num_z_channels=512,\n",
" num_cross_attend_heads=1,\n",
" num_self_attend_heads=8,\n",
" cross_attend_widening_factor=1,\n",
" self_attend_widening_factor=1,\n",
" dropout_prob=0.0,\n",
" z_pos_enc_init_scale=0.02,\n",
" cross_attention_shape_for_attn='kv',\n",
" name='encoder')\n",
"\n",
" subsampled_index_dims = {\n",
" 'audio': subsampling['audio'].shape[0],\n",
" 'image': subsampling['image'].shape[0],\n",
" 'label': 1,\n",
" }\n",
" image_decoder = perceiver.BasicVideoAutoencodingDecoder(\n",
" # Autoencoding, don't pass inputs to the queries.\n",
" concat_preprocessed_input=False,\n",
" subsampled_index_dims=subsampling['image'],\n",
" output_shape=images.shape[:4],\n",
" num_z_channels=1024,\n",
" output_num_channels=512,\n",
" use_query_residual=False,\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" num_bands=32,\n",
" max_resolution=(NUM_FRAMES, IMG_SZ, IMG_SZ),\n",
" sine_only=False,\n",
" concat_pos=True,\n",
" ),\n",
" )\n",
"\n",
" decoder = perceiver.MultimodalDecoder(\n",
" # Autoencoding, don't pass inputs to the queries.\n",
" concat_preprocessed_input=False,\n",
" subsampled_index_dims=subsampled_index_dims,\n",
" # Modality specific decoders are used ONLY to generate queries.\n",
" # All modalties are decoded together using a unified decoder.\n",
" modalities={\n",
" 'audio': perceiver.BasicDecoder(\n",
" # Autoencoding, don't pass inputs to the queries.\n",
" concat_preprocessed_input=False,\n",
" subsampled_index_dims=subsampling['audio'],\n",
" output_index_dims=(n_audio_samples // SAMPLES_PER_PATCH,),\n",
" num_z_channels=1024,\n",
" output_num_channels=512,\n",
" use_query_residual=False,\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" num_bands=192,\n",
" max_resolution=(n_audio_samples,),\n",
" sine_only=False,\n",
" concat_pos=True,\n",
" ),\n",
" ),\n",
" 'image': image_decoder,\n",
" 'label': perceiver.ClassificationDecoder(\n",
" # Autoencoding, don't pass inputs to the queries.\n",
" concat_preprocessed_input=False,\n",
" num_classes=NUM_CLASSES,\n",
" num_z_channels=1024,\n",
" use_query_residual=False,\n",
" position_encoding_type='trainable',\n",
" trainable_position_encoding_kwargs=dict(\n",
" num_channels=1024,\n",
" init_scale=0.02,\n",
" ),\n",
" ),\n",
" },\n",
" num_outputs=None,\n",
" output_num_channels=512,\n",
" use_query_residual=False,)\n",
" \n",
" model = perceiver.Perceiver(\n",
" input_preprocessor=input_preprocessor,\n",
" encoder=encoder,\n",
" decoder=decoder,\n",
" output_postprocessor=output_postprocessor)\n",
" \n",
" return model({'image': images,\n",
" 'audio': audio,\n",
" 'label': np.zeros((images.shape[0], 700))},\n",
" is_training=False, subsampled_output_points=subsampling)\n",
"\n",
"\n",
"video_autoencoder = hk.transform_with_state(video_autoencoder)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "N88EZZ7QHvbu"
},
"outputs": [],
"source": [
"#@title Model application\n",
"\n",
"\n",
"def autoencode_video(params, state, rng, images, audio):\n",
" nchunks = 128\n",
" reconstruction = {}\n",
" for chunk_idx in range(nchunks):\n",
" image_chunk_size = np.prod(images.shape[1:-1]) // nchunks\n",
" audio_chunk_size = audio.shape[1] // SAMPLES_PER_PATCH // nchunks\n",
" subsampling = {\n",
" 'image': jnp.arange(\n",
" image_chunk_size * chunk_idx, image_chunk_size * (chunk_idx + 1)),\n",
" 'audio': jnp.arange(\n",
" audio_chunk_size * chunk_idx, audio_chunk_size * (chunk_idx + 1)),\n",
" 'label': None,\n",
" }\n",
" output, state = video_autoencoder.apply(\n",
" params, state, rng, images, audio, subsampling)\n",
" reconstruction['label'] = output['label']\n",
" if 'image' not in reconstruction:\n",
" reconstruction['image'] = output['image']\n",
" reconstruction['audio'] = output['audio']\n",
" else:\n",
" reconstruction['image'] = jnp.concatenate(\n",
" [reconstruction['image'], output['image']], axis=1)\n",
" reconstruction['audio'] = jnp.concatenate(\n",
" [reconstruction['audio'], output['audio']], axis=1)\n",
" \n",
" reconstruction['image'] = jnp.reshape(reconstruction['image'], images.shape)\n",
" reconstruction['audio'] = jnp.reshape(reconstruction['audio'], audio.shape)\n",
" return reconstruction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "EVRWatw4LXFx"
},
"outputs": [],
"source": [
"#@title Load parameters from checkpoint\n",
"\n",
"!wget -O video_autoencoding_checkpoint.pystate https://storage.googleapis.com/perceiver_io/video_autoencoding_checkpoint.pystate\n",
"\n",
"rng = jax.random.PRNGKey(42)\n",
"with open(\"video_autoencoding_checkpoint.pystate\", \"rb\") as f:\n",
" params = pickle.loads(f.read())\n",
"\n",
"state = {}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FfWpBNZJLib4"
},
"outputs": [],
"source": [
"# Auto-encode the first 16 frames of the video and one of the audio channels\n",
"reconstruction = autoencode_video(params, state, rng, video[None, :16], audio[None, :16*AUDIO_SAMPLES_PER_FRAME, 0:1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cZpggBTO4eO5"
},
"outputs": [],
"source": [
"# Visualize reconstruction of first 16 frames\n",
"table([to_gif(reconstruction[\"image\"][0]), play_audio(np.array(reconstruction[\"audio\"][0]))])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oTJzBsl6xkOP"
},
"outputs": [],
"source": [
"# Kinetics 700 Labels\n",
"scores, indices = jax.lax.top_k(jax.nn.softmax(reconstruction[\"label\"]), 5)\n",
"\n",
"for score, index in zip(scores[0], indices[0]):\n",
" print(\"%s: %s\" % (KINETICS_CLASSES[index], score))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7JZRtQdwC4eE"
},
"outputs": [],
"source": [
"# Auto-encode the entire video, one chunk at a time\n",
"\n",
"# Partial video and audio into 16-frame chunks\n",
"nframes = video.shape[0]\n",
"# Truncate to be divisible by 16\n",
"nframes = nframes - (nframes % 16)\n",
"video_chunks = jnp.reshape(video[:nframes], [nframes // 16, 16, 224, 224, 3])\n",
"audio_chunks = jnp.reshape(audio[:nframes * AUDIO_SAMPLES_PER_FRAME],\n",
" [nframes // 16, 16 * AUDIO_SAMPLES_PER_FRAME, 2])\n",
"\n",
"encode = jax.jit(functools.partial(autoencode_video, params, state, rng))\n",
"\n",
"# Logically, what we do is the following code. We write out the loop to allocate\n",
"# GPU memory for only one chunk\n",
"#\n",
"# reconstruction = jax.vmap(encode, in_axes=1, out_axes=1)(\n",
"# video_chunks[None, :], audio_chunks[None, :, :, 0:1])\n",
"\n",
"chunks = []\n",
"for i in range(nframes // 16):\n",
" reconstruction = encode(video_chunks[None, i], audio_chunks[None, i, :, 0:1])\n",
" chunks.append(jax.tree_map(lambda x: np.array(x), reconstruction))\n",
"\n",
"reconstruction = jax.tree_multimap(lambda *args: np.stack(args, axis=1),\n",
" *chunks)\n",
"\n",
"reconstruction = jax.tree_map(lambda x: np.reshape(x, [-1] + list(x.shape[2:])), reconstruction)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lzwbz1mgES4d"
},
"outputs": [],
"source": [
"# Visualize reconstruction of entire video\n",
"table([to_gif(reconstruction['image'][0]), play_audio(np.array(reconstruction['audio'][0]))])"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "",
"kind": "local"
},
"name": "Perceiver IO: Video Autoencoding.ipynb",
"private_outputs": true,
"provenance": [
{
"file_id": "1qD1lvE-5c4LVw9l7H9XjA3DNtiYIcTgj",
"timestamp": 1626089023488
}
],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: perceiver/io_processors.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""IO pre- and post-processors for Perceiver."""
import functools
import math
from typing import Any, Callable, Mapping, Optional, Sequence, Tuple
import einops
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
from perceiver import position_encoding
ModalitySizeT = Mapping[str, int]
PreprocessorOutputT = Tuple[jnp.ndarray, Optional[jnp.ndarray], jnp.ndarray]
PreprocessorT = Callable[..., PreprocessorOutputT]
PostprocessorT = Callable[..., Any]
def reverse_space_to_depth(
frames: jnp.ndarray,
temporal_block_size: int = 1,
spatial_block_size: int = 1) -> jnp.ndarray:
"""Reverse space to depth transform."""
if len(frames.shape) == 4:
return einops.rearrange(
frames, 'b h w (dh dw c) -> b (h dh) (w dw) c',
dh=spatial_block_size, dw=spatial_block_size)
elif len(frames.shape) == 5:
return einops.rearrange(
frames, 'b t h w (dt dh dw c) -> b (t dt) (h dh) (w dw) c',
dt=temporal_block_size, dh=spatial_block_size, dw=spatial_block_size)
else:
raise ValueError(
'Frames should be of rank 4 (batch, height, width, channels)'
' or rank 5 (batch, time, height, width, channels)')
def space_to_depth(
frames: jnp.ndarray,
temporal_block_size: int = 1,
spatial_block_size: int = 1) -> jnp.ndarray:
"""Space to depth transform."""
if len(frames.shape) == 4:
return einops.rearrange(
frames, 'b (h dh) (w dw) c -> b h w (dh dw c)',
dh=spatial_block_size, dw=spatial_block_size)
elif len(frames.shape) == 5:
return einops.rearrange(
frames, 'b (t dt) (h dh) (w dw) c -> b t h w (dt dh dw c)',
dt=temporal_block_size, dh=spatial_block_size, dw=spatial_block_size)
else:
raise ValueError(
'Frames should be of rank 4 (batch, height, width, channels)'
' or rank 5 (batch, time, height, width, channels)')
def extract_patches(images: jnp.ndarray,
sizes: Sequence[int],
strides: Sequence[int],
rates: Sequence[int],
padding: str = 'VALID') -> jnp.ndarray:
"""Extract patches from images.
This function is a wrapper for jax.lax.conv_general_dilated_patches
to conforms to the same interface as tf.image.extract_patches.
The function extracts patches of shape sizes from the input images in the same
manner as a convolution with kernel of shape sizes, stride equal to strides,
and the given padding scheme.
The patches are stacked in the channel dimension.
Args:
images: input batch of images of shape [B, H, W, C].
sizes: size of extracted patches. Must be [1, size_rows, size_cols, 1].
strides: strides, must be [1, stride_rows, stride_cols, 1].
rates: sampling rate (as in dilated convolutions),
must be [1, rate_rows, rate_cols, 1].
padding: padding algorithm to use.
Returns:
Tensor of shape [B, patch_rows, patch_cols, size_rows * size_cols * C]
"""
if len(sizes) != 4 or sizes[0] != 1 or sizes[3] != 1:
raise ValueError(
f'Shape of sizes must be [1, size_rows, size_cols, 1], got {sizes}.')
if len(strides) != 4 or strides[0] != 1 or strides[3] != 1:
raise ValueError(
f'Shape of strides must be [1, size_rows, size_cols, 1], '
f'got {strides}.')
if len(rates) != 4 or rates[0] != 1 or rates[3] != 1:
raise ValueError(
f'Shape of rates must be [1, size_rows, size_cols, 1], got {rates}.')
if images.ndim != 4:
raise ValueError(
f'Rank of images must be 4 (got tensor of shape {jnp.shape(images)})')
# Rearrange axes of images to NCHW for conv_general_dilated_patches
images = einops.rearrange(images, 'n h w c -> n c h w')
channels = images.shape[1]
patches = jax.lax.conv_general_dilated_patches(
images, sizes[1:-1], strides[1:-1], padding, rhs_dilation=rates[1:-1])
# conv_general_dilated_patches returns patches in channel-major order.
# Rearrange to match interface of tf.image.extract_patches.
patches = einops.rearrange(patches, 'n (c ph pw) h w -> n h w (ph pw c)',
c=channels, ph=sizes[1], pw=sizes[2])
return patches
def patches_for_flow(inputs: jnp.ndarray) -> jnp.ndarray:
"""Extract 3x3x2 image patches for flow inputs."""
def pad_and_extract_patches(inputs):
padded_inputs = jnp.pad(inputs, [[0, 0], [1, 1], [1, 1], [0, 0]],
mode='constant')
return extract_patches(
padded_inputs,
sizes=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='VALID',
rates=[1, 1, 1, 1])
return jax.vmap(pad_and_extract_patches, in_axes=1, out_axes=1)(inputs)
# ------------------------------------------------------------
# ------------------- Up/down-sampling ---------------------
# ------------------------------------------------------------
class Conv2DDownsample(hk.Module):
"""Downsamples 4x by applying a 2D convolution and doing max pooling."""
def __init__(
self,
num_layers: int = 1,
num_channels: int = 64,
use_batchnorm: bool = True,
bn_config: Optional[Mapping[str, float]] = None,
name: Optional[str] = None,
):
"""Constructs a Conv2DDownsample model.
Args:
num_layers: The number of conv->max_pool layers.
num_channels: The number of conv output channels.
use_batchnorm: Whether to use batchnorm.
bn_config: A dictionary of two elements, ``decay_rate`` and ``eps`` to be
passed on to the :class:`~haiku.BatchNorm` layers. By default the
``decay_rate`` is ``0.9`` and ``eps`` is ``1e-5``.
name: Name of the module.
"""
super().__init__(name=name)
self._num_layers = num_layers
self._use_batchnorm = use_batchnorm
bn_config = dict(bn_config or {})
bn_config.setdefault('decay_rate', 0.9)
bn_config.setdefault('eps', 1e-5)
bn_config.setdefault('create_scale', True)
bn_config.setdefault('create_offset', True)
self.layers = []
for _ in range(self._num_layers):
conv = hk.Conv2D(
output_channels=num_channels,
kernel_shape=7,
stride=2,
with_bias=False,
padding='SAME',
name='conv')
if use_batchnorm:
batchnorm = hk.BatchNorm(name='batchnorm', **bn_config)
else:
batchnorm = None
self.layers.append(dict(conv=conv, batchnorm=batchnorm))
def __call__(self, inputs: jnp.ndarray, *,
is_training: bool,
test_local_stats: bool = False) -> jnp.ndarray:
out = inputs
for layer in self.layers:
out = layer['conv'](out)
if layer['batchnorm'] is not None:
out = layer['batchnorm'](out, is_training, test_local_stats)
out = jax.nn.relu(out)
out = hk.max_pool(out,
window_shape=(1, 3, 3, 1),
strides=(1, 2, 2, 1),
padding='SAME')
return out
class Conv2DUpsample(hk.Module):
"""Upsamples 4x using 2 2D transposed convolutions."""
def __init__(
self,
n_outputs: int,
name: Optional[str] = None,
):
"""Constructs a Conv2DUpsample model.
Args:
n_outputs: The number of output channels of the module.
name: Name of the module.
"""
super().__init__(name=name)
self.transp_conv1 = hk.Conv2DTranspose(
output_channels=n_outputs*2,
kernel_shape=4,
stride=2,
with_bias=True,
padding='SAME',
name='transp_conv_1')
self.transp_conv2 = hk.Conv2DTranspose(
output_channels=n_outputs,
kernel_shape=4,
stride=2,
with_bias=True,
padding='SAME',
name='transp_conv_2')
def __call__(self, inputs: jnp.ndarray, *,
is_training: bool,
test_local_stats: bool = False) -> jnp.ndarray:
out = inputs
out = self.transp_conv1(out)
out = jax.nn.relu(out)
out = self.transp_conv2(out)
return out
class Conv3DUpsample(hk.Module):
"""Simple convolutional auto-encoder."""
def __init__(self,
n_outputs: int,
n_time_upsamples: int = 2,
n_space_upsamples: int = 4,
name: Optional[str] = None):
super().__init__(name=name)
self._n_outputs = n_outputs
self._n_time_upsamples = n_time_upsamples
self._n_space_upsamples = n_space_upsamples
def __call__(self, x: jnp.ndarray, *, is_training: bool) -> jnp.ndarray:
n_upsamples = max(self._n_time_upsamples, self._n_space_upsamples)
time_stride = 2
space_stride = 2
for i in range(n_upsamples):
if i >= self._n_time_upsamples:
time_stride = 1
if i >= self._n_space_upsamples:
space_stride = 1
channels = self._n_outputs * pow(2, n_upsamples - 1 - i)
x = hk.Conv3DTranspose(output_channels=channels,
stride=[time_stride, space_stride, space_stride],
kernel_shape=[4, 4, 4],
name=f'conv3d_transpose_{i}')(x)
if i != n_upsamples - 1:
x = jax.nn.relu(x)
return x
class ImagePreprocessor(hk.Module):
"""Image preprocessing for Perceiver Encoder."""
def __init__(
self,
prep_type='conv',
spatial_downsample: int = 4,
temporal_downsample: int = 1,
position_encoding_type: str = 'fourier',
n_extra_pos_mlp: int = 0,
num_channels: int = 64,
conv_after_patching: bool = False,
conv2d_use_batchnorm: bool = True,
concat_or_add_pos: str = 'concat',
name: Optional[str] = None,
**position_encoding_kwargs):
super().__init__(name=name)
if prep_type not in ('conv', 'patches', 'pixels', 'conv1x1'):
raise ValueError('Invalid prep_type!')
if concat_or_add_pos not in ['concat', 'add']:
raise ValueError(
f'Invalid value {concat_or_add_pos} for concat_or_add_pos.')
self._prep_type = prep_type
self._spatial_downsample = spatial_downsample
self._temporal_downsample = temporal_downsample
self._concat_or_add_pos = concat_or_add_pos
self._conv_after_patching = conv_after_patching
self._num_channels = num_channels
if self._prep_type == 'conv':
# Downsampling with conv is currently restricted
convnet_num_layers = math.log(spatial_downsample, 4)
convnet_num_layers_is_int = (
convnet_num_layers == np.round(convnet_num_layers))
if not convnet_num_layers_is_int or temporal_downsample != 1:
raise ValueError('Only powers of 4 expected for spatial '
'and 1 expected for temporal '
'downsampling with conv.')
self.convnet = Conv2DDownsample(
num_layers=int(convnet_num_layers),
num_channels=num_channels,
use_batchnorm=conv2d_use_batchnorm)
elif self._prep_type == 'conv1x1':
assert temporal_downsample == 1, 'conv1x1 does not downsample in time.'
self.convnet_1x1 = hk.Conv2D(
num_channels, kernel_shape=[1, 1],
# spatial_downsample is unconstrained for 1x1 convolutions.
stride=[spatial_downsample, spatial_downsample])
# Partially construct the positional encoding function.
# We fully construct it when we know the input size.
self._positional_encoding_ctor = functools.partial(
position_encoding.build_position_encoding,
position_encoding_type=position_encoding_type,
**position_encoding_kwargs)
# Stack MLPs to get a deeper positional embedding.
self._n_extra_pos_mlp = n_extra_pos_mlp
def _build_network_inputs(
self, inputs: jnp.ndarray, pos: jnp.ndarray,
network_input_is_1d: bool = True) -> Tuple[jnp.ndarray, jnp.ndarray]:
"""Construct the final input, including position encoding."""
batch_size = inputs.shape[0]
index_dims = inputs.shape[1:-1]
# Reshape input features to a 1D index dimension if necessary.
if len(inputs.shape) > 3 and network_input_is_1d:
inputs = jnp.reshape(
inputs, [batch_size, np.prod(index_dims), -1])
# Construct the position encoding.
pos_enc = self._positional_encoding_ctor(
index_dims=index_dims)(batch_size=batch_size, pos=pos)
for i in range(0, self._n_extra_pos_mlp):
pos_enc += hk.Linear(pos_enc.shape[-1])(pos_enc)
if i < (self._n_extra_pos_mlp-1):
pos_enc = jax.nn.relu(pos_enc)
if not network_input_is_1d:
# Reshape pos to match the input feature shape
# if the network takes non-1D inputs
sh = inputs.shape
pos_enc = jnp.reshape(pos_enc, list(sh)[:-1]+[-1])
if self._concat_or_add_pos == 'concat':
inputs_with_pos = jnp.concatenate([inputs, pos_enc], axis=-1)
elif self._concat_or_add_pos == 'add':
inputs_with_pos = inputs + pos_enc
return inputs_with_pos, inputs
def __call__(
self, inputs: jnp.ndarray, *,
is_training: bool,
pos: Optional[jnp.ndarray] = None,
network_input_is_1d: bool = True) -> PreprocessorOutputT:
if self._prep_type == 'conv':
# Convnet image featurization.
# Downsamples spatially by a factor of 4
conv = self.convnet
if len(inputs.shape) == 5:
conv = hk.BatchApply(conv)
inputs = conv(inputs, is_training=is_training)
elif self._prep_type == 'conv1x1':
# maps inputs to 64d
conv = self.convnet_1x1
if len(inputs.shape) == 5:
conv = hk.BatchApply(conv)
inputs = conv(inputs)
elif self._prep_type == 'patches':
# Space2depth featurization.
# Video: B x T x H x W x C
inputs = space_to_depth(
inputs,
temporal_block_size=self._temporal_downsample,
spatial_block_size=self._spatial_downsample)
if inputs.ndim == 5 and inputs.shape[1] == 1:
# for flow
inputs = jnp.squeeze(inputs, axis=1)
if self._conv_after_patching:
inputs = hk.Linear(self._num_channels, name='patches_linear')(inputs)
elif self._prep_type == 'pixels':
# if requested, downsamples in the crudest way
if inputs.ndim == 4:
inputs = inputs[:,
::self._spatial_downsample, ::self._spatial_downsample]
elif inputs.ndim == 5:
inputs = inputs[:, ::self._temporal_downsample,
::self._spatial_downsample, ::self._spatial_downsample]
else:
raise ValueError('Unsupported data format for pixels.')
inputs, inputs_without_pos = self._build_network_inputs(
inputs, pos, network_input_is_1d)
modality_sizes = None # Size for each modality, only needed for multimodal
return inputs, modality_sizes, inputs_without_pos
class ImagePostprocessor(hk.Module):
"""Image postprocessing for Perceiver."""
def __init__(
self,
postproc_type: str = 'pixels',
spatial_upsample: int = 1,
temporal_upsample: int = 1,
n_outputs: int = -1, # only relevant for 'conv1x1', 'conv', and 'raft'
input_reshape_size: Optional[Sequence[int]] = None,
name: Optional[str] = None):
super().__init__(name=name)
if postproc_type not in ('conv', 'patches', 'pixels', 'raft', 'conv1x1'):
raise ValueError('Invalid postproc_type!')
# Architecture parameters:
self._postproc_type = postproc_type
self._temporal_upsample = temporal_upsample
self._spatial_upsample = spatial_upsample
self._input_reshape_size = input_reshape_size
if self._postproc_type == 'pixels':
# No postprocessing.
if self._temporal_upsample != 1 or self._spatial_upsample != 1:
raise ValueError('Pixels postprocessing should not currently upsample.')
elif self._postproc_type == 'conv1x1':
assert self._temporal_upsample == 1, 'conv1x1 does not upsample in time.'
if n_outputs == -1:
raise ValueError('Expected value for n_outputs')
self.conv1x1 = hk.Conv2D(
n_outputs, kernel_shape=[1, 1],
# spatial_downsample is unconstrained for 1x1 convolutions.
stride=[self._spatial_upsample, self._spatial_upsample])
elif self._postproc_type == 'conv':
if n_outputs == -1:
raise ValueError('Expected value for n_outputs')
if self._temporal_upsample != 1:
def int_log2(x):
return int(np.round(np.log(x) / np.log(2)))
self.convnet = Conv3DUpsample(
n_outputs, int_log2(temporal_upsample), int_log2(spatial_upsample))
else:
self.convnet = Conv2DUpsample(n_outputs)
def __call__(
self, inputs: jnp.ndarray, *,
is_training: bool,
pos: Optional[jnp.ndarray] = None,
modality_sizes: Optional[ModalitySizeT] = None) -> jnp.ndarray:
if self._input_reshape_size is not None:
inputs = jnp.reshape(
inputs,
[inputs.shape[0]] + list(self._input_reshape_size)
+ [inputs.shape[-1]])
if self._postproc_type == 'conv' or self._postproc_type == 'raft':
# Convnet image featurization.
conv = self.convnet
if len(inputs.shape) == 5 and self._temporal_upsample == 1:
conv = hk.BatchApply(conv)
inputs = conv(inputs, is_training=is_training)
elif self._postproc_type == 'conv1x1':
inputs = self.conv1x1(inputs)
elif self._postproc_type == 'patches':
inputs = reverse_space_to_depth(
inputs, self._temporal_upsample, self._spatial_upsample)
return inputs
class OneHotPreprocessor(hk.Module):
"""One-hot preprocessor for Perceiver Encoder."""
def __init__(self, name: Optional[str] = None):
super().__init__(name=name)
def __call__(self, inputs: jnp.ndarray, *,
is_training: bool,
pos: Optional[jnp.ndarray] = None,
network_input_is_1d: bool = True) -> PreprocessorOutputT:
# Add a dummy index dimension.
inputs = inputs[:, None, :]
# No position encodings, so the 1st (input) and 3rd (inputs_without_pos)
# outputs are identical.
return inputs, None, inputs
class AudioPreprocessor(hk.Module):
"""Audio preprocessing for Perceiver Encoder."""
def __init__(
self,
prep_type: str = 'patches',
samples_per_patch: int = 96,
position_encoding_type: str = 'fourier',
n_extra_pos_mlp: int = 0,
concat_or_add_pos: str = 'concat',
name: Optional[str] = None,
**position_encoding_kwargs):
super().__init__(name=name)
if prep_type not in ('patches',):
raise ValueError('Invalid prep_type!')
if concat_or_add_pos not in ['concat', 'add']:
raise ValueError(
f'Invalid value {concat_or_add_pos} for concat_or_add_pos.')
self._samples_per_patch = samples_per_patch
self._concat_or_add_pos = concat_or_add_pos
# Partially construct the positional encoding function.
# We fully construct it when we know the input size.
self._positional_encoding_ctor = functools.partial(
position_encoding.build_position_encoding,
position_encoding_type=position_encoding_type,
**position_encoding_kwargs)
# for deeper positional embeddings
self._n_extra_pos_mlp = n_extra_pos_mlp
def _build_network_inputs(
self, inputs: jnp.ndarray,
pos: jnp.ndarray) -> Tuple[jnp.ndarray, jnp.ndarray]:
"""Construct the final input, including position encoding."""
batch_size = inputs.shape[0]
index_dims = inputs.shape[1:-1]
# Construct the position encoding.
pos_enc = self._positional_encoding_ctor(
index_dims=index_dims)(batch_size=batch_size, pos=pos)
for i in range(0, self._n_extra_pos_mlp):
pos_enc += hk.Linear(pos_enc.shape[-1])(pos_enc)
if i < (self._n_extra_pos_mlp-1):
pos_enc = jax.nn.relu(pos_enc)
if self._concat_or_add_pos == 'concat':
inputs_with_pos = jnp.concatenate([inputs, pos_enc], axis=-1)
elif self._concat_or_add_pos == 'add':
inputs_with_pos = inputs + pos_enc
return inputs_with_pos, inputs
def __call__(self, inputs: jnp.ndarray, *,
is_training: bool,
pos: Optional[jnp.ndarray] = None,
network_input_is_1d: bool = True) -> PreprocessorOutputT:
inputs = jnp.reshape(inputs, [inputs.shape[0], -1,
self._samples_per_patch])
inputs, inputs_without_pos = self._build_network_inputs(inputs, pos)
modality_sizes = None # Size for each modality, only needed for multimodal
return inputs, modality_sizes, inputs_without_pos
class AudioPostprocessor(hk.Module):
"""Audio postprocessing for Perceiver."""
def __init__(
self,
postproc_type: str = 'patches', # 'conv', 'patches', 'pixels'
samples_per_patch: int = 96,
name: Optional[str] = None):
super().__init__(name=name)
if postproc_type not in ('patches',):
raise ValueError('Invalid postproc_type!')
self._samples_per_patch = samples_per_patch
# Architecture parameters:
self._postproc_type = postproc_type
def __call__(self, inputs: jnp.ndarray, *,
is_training: bool,
pos: Optional[jnp.ndarray] = None,
modality_sizes: Optional[ModalitySizeT] = None) -> jnp.ndarray:
out = hk.Linear(self._samples_per_patch)(inputs)
return jnp.reshape(out, [inputs.shape[0], -1])
class IdentityPostprocessor(hk.Module):
"""Passes through the inputs unchanged."""
def __init__(self, name: Optional[str] = None):
super().__init__(name=name)
def __call__(self, inputs: jnp.ndarray, *,
is_training: bool,
pos: Optional[jnp.ndarray] = None,
modality_sizes: Optional[ModalitySizeT] = None) -> jnp.ndarray:
return inputs
def restructure(modality_sizes: ModalitySizeT,
inputs: jnp.ndarray) -> Mapping[str, jnp.ndarray]:
"""Partitions a [B, N, C] tensor into tensors for each modality.
Args:
modality_sizes: dict specifying the size of the modality
inputs: input tensor
Returns:
dict mapping name of modality to its associated tensor.
"""
outputs = {}
index = 0
# Apply a predictable ordering to the modalities
for modality in sorted(modality_sizes.keys()):
size = modality_sizes[modality]
inp = inputs[:, index:index + size]
index += size
outputs[modality] = inp
return outputs
class MultimodalPreprocessor(hk.Module):
"""Multimodal preprocessing for Perceiver Encoder.
Inputs for each modality is preprocessed then padded with trainable position
embeddings to have the same number of channels.
"""
def __init__(
self,
modalities: Mapping[str, PreprocessorT],
mask_probs: Optional[Mapping[str, float]] = None,
min_padding_size: int = 2,
name: Optional[str] = None):
"""Constructor.
Args:
modalities: dict mapping modality name to preprocessor
mask_probs: dict mapping modality name to masking probability of that
modality
min_padding_size: the minimum padding size for all modalities.
The final output will have num_channels equal to the maximum channels
across all modalities plus min_padding_size.
name: name of module
"""
super().__init__(name=name)
self._modalities = modalities
self._min_padding_size = min_padding_size
self._mask_probs = mask_probs
def __call__(self, inputs: jnp.ndarray, *,
is_training: bool,
pos: Optional[jnp.ndarray] = None,
network_input_is_1d: bool = True) -> PreprocessorOutputT:
outputs = {}
inputs_without_pos = {}
for modality, preprocessor in self._modalities.items():
outputs[modality], _, inputs_without_pos[modality] = preprocessor(
inputs[modality], is_training=is_training, pos=pos,
network_input_is_1d=network_input_is_1d)
common_channel_size = (max(o.shape[2] for o in outputs.values())
+ self._min_padding_size)
padded = {}
modality_sizes = {}
for modality, output in outputs.items():
pos_enc = position_encoding.TrainablePositionEncoding(
1, num_channels=common_channel_size-output.shape[2],
init_scale=0.02, name=f'{modality}_padding')
padding = jnp.broadcast_to(
pos_enc(batch_size=output.shape[0]),
[output.shape[0], output.shape[1],
common_channel_size-output.shape[2]])
output_padded = jnp.concatenate([output, padding], axis=2)
if self._mask_probs is not None:
# Randomly mask out each token corresponding to this modality
mask_token = position_encoding.TrainablePositionEncoding(
1, num_channels=output_padded.shape[2],
init_scale=0.02, name=f'{modality}_mask_token')(output.shape[0])
mask_prob = self._mask_probs[modality]
rng = hk.next_rng_key()
mask = jax.random.bernoulli(rng, mask_prob,
shape=[output.shape[0], output.shape[1]])
mask = jnp.expand_dims(mask, axis=2)
output_padded = (1 - mask) * output_padded + mask * mask_token
padded[modality] = output_padded
modality_sizes[modality] = output_padded.shape[1]
# Apply a predictable ordering to the modalities
padded_ls = [padded[k] for k in sorted(padded.keys())]
return (jnp.concatenate(padded_ls, axis=1), # pytype: disable=bad-return-type # jax-ndarray
modality_sizes,
inputs_without_pos)
class MultimodalPostprocessor(hk.Module):
"""Multimodal postprocessing for Perceiver."""
def __init__(
self,
modalities: Mapping[str, PostprocessorT],
input_is_dict: bool = False,
name: Optional[str] = None):
"""Constructor.
Args:
modalities: dict mapping modality name to post processor for that modality
input_is_dict: If True, input is assumed to be dictionary structured,
and outputs keep the same dictionary shape. If False, input is a tensor
which is sliced up during postprocessing by `modality_sizes`.
name: name of the module
"""
super().__init__(name=name)
self._modalities = modalities
self._input_is_dict = input_is_dict
def __call__(
self, inputs: jnp.ndarray, *,
is_training: bool,
pos: Optional[jnp.ndarray] = None,
modality_sizes: Optional[ModalitySizeT] = None) -> Mapping[str,
jnp.ndarray]:
if not self._input_is_dict:
# Slice up modalities by their sizes.
assert modality_sizes is not None
inputs = restructure(modality_sizes=modality_sizes, inputs=inputs)
outputs = {modality: postprocessor(
inputs[modality], is_training=is_training, pos=pos, modality_sizes=None)
for modality, postprocessor in self._modalities.items()}
return outputs
class ClassificationPostprocessor(hk.Module):
"""Classification postprocessing for Perceiver."""
def __init__(
self,
num_classes: int,
name: Optional[str] = None):
super().__init__(name=name)
self._num_classes = num_classes
def __call__(self, inputs: jnp.ndarray, *,
is_training: bool,
pos: Optional[jnp.ndarray] = None,
modality_sizes: Optional[ModalitySizeT] = None) -> jnp.ndarray:
logits = hk.Linear(self._num_classes)(inputs)
return logits[:, 0, :]
class ProjectionPostprocessor(hk.Module):
"""Projection postprocessing for Perceiver."""
def __init__(
self,
num_outputs: int,
name: Optional[str] = None):
super().__init__(name=name)
self._num_outputs = num_outputs
def __call__(self, inputs: jnp.ndarray, *,
is_training: bool,
pos: Optional[jnp.ndarray] = None,
modality_sizes: Optional[ModalitySizeT] = None) -> jnp.ndarray:
logits = hk.Linear(self._num_outputs)(inputs)
return logits
class EmbeddingDecoder(hk.Module):
"""Haiku module to decode embeddings."""
def __init__(self, embedding_matrix: jnp.ndarray, name='embedding_decoder'):
"""Constructs the module.
Args:
embedding_matrix: Array of shape [vocab_size, d_model].
name: Name of the module.
"""
super().__init__(name=name)
self._embedding_matrix = embedding_matrix
self._vocab_size, self._d_model = embedding_matrix.shape
def __call__(self, embeddings: jnp.ndarray) -> jnp.ndarray:
batch_size, seq_len, _ = embeddings.shape
output = jnp.matmul(
embeddings.reshape([-1, self._d_model]), # Flatten batch dim
jnp.transpose(self._embedding_matrix))
bias = hk.get_parameter('bias', shape=[self._vocab_size], init=jnp.zeros)
output = output + bias
return output.reshape([batch_size, seq_len, self._vocab_size])
================================================
FILE: perceiver/io_processors_test.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for io_processors."""
import numpy as np
import tensorflow as tf
from perceiver import io_processors
def _create_test_image(shape):
image = np.arange(np.prod(np.array(shape)))
return np.reshape(image, shape)
def test_space_to_depth_image():
image_shape = (2, 3 * 5, 3 * 7, 11)
image = _create_test_image(image_shape)
output = io_processors.space_to_depth(image, spatial_block_size=3)
assert output.shape == (2, 5, 7, 3 * 3 * 11)
def test_space_to_depth_video():
image_shape = (2, 5 * 7, 3 * 11, 3 * 13, 17)
image = _create_test_image(image_shape)
output = io_processors.space_to_depth(image, spatial_block_size=3,
temporal_block_size=5)
assert output.shape == (2, 7, 11, 13, 5 * 3 * 3 * 17)
def test_reverse_space_to_depth_image():
image_shape = (2, 5, 7, 3 * 3 * 11)
image = _create_test_image(image_shape)
output = io_processors.reverse_space_to_depth(image, spatial_block_size=3)
assert output.shape == (2, 3 * 5, 3 * 7, 11)
def test_reverse_space_to_depth_video():
image_shape = (2, 7, 11, 13, 5 * 3 * 3 * 17)
image = _create_test_image(image_shape)
output = io_processors.reverse_space_to_depth(
image, spatial_block_size=3, temporal_block_size=5)
assert output.shape == (2, 5 * 7, 3 * 11, 3 * 13, 17)
def test_extract_patches():
image_shape = (2, 5, 7, 3)
image = _create_test_image(image_shape)
sizes = [1, 2, 3, 1]
strides = [1, 1, 2, 1]
rates = [1, 2, 1, 1]
for padding in ["VALID", "SAME"]:
jax_patches = io_processors.extract_patches(
image, sizes=sizes, strides=strides, rates=rates, padding=padding)
tf_patches = tf.image.extract_patches(
image, sizes=sizes, strides=strides, rates=rates, padding=padding)
assert np.array_equal(
np.array(jax_patches),
tf_patches.numpy())
================================================
FILE: perceiver/perceiver.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Perceiver architecture and components."""
import abc
import math
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
from perceiver import io_processors
from perceiver import position_encoding
# -----------------------------------------------------------
# ---------------------- Primitives -----------------------
# -----------------------------------------------------------
def attend(q, k, v, dropout_prob=0.0, attention_mask=None):
"""Computes multi-head attention using a query, key and value.
Args:
q: Query with shape [batch, q_indices, num_heads, head_dim].
k: Key with shape [batch, kv_indices, num_heads, head_dim].
v: Value with shape [batch, kv_indices, num_heads, head_dim].
dropout_prob: dropout probability on the attention weights.
attention_mask: Array of shape [batch, q_indices, kv_indices] indicating
which attentions are valid
Returns:
Output of the attention with shape [batch, q_indices, hiddens]
"""
batch, q_indices, num_heads, q_head_dim = q.shape
_, _, _, v_head_dim = v.shape
hiddens = num_heads * v_head_dim
attention = jnp.einsum('bthd,bThd->bhtT', q, k)
scale = 1. / math.sqrt(q_head_dim)
attention *= scale
if attention_mask is not None:
# Use large_k instead of np.NINF because np.NINF breaks for causal-masked
# left-padded sampling.
large_k = jnp.array(1e4 if attention.dtype == jnp.float16 else 1e30,
dtype=attention.dtype)
attention = jnp.where(attention_mask[:, None, :, :], attention,
-large_k)
normalized = jax.nn.softmax(attention)
if dropout_prob > 0:
normalized = hk.dropout(hk.next_rng_key(), dropout_prob, normalized)
summed = jnp.einsum('bhtT,bThd->bthd', normalized, v)
summed = jnp.reshape(summed, [batch, q_indices, hiddens])
if attention_mask is not None:
# If all attended tokens are masked, or for masked tokens
# some rows of logits gets completely masked, in which case the softmax
# gives a uniform row and we obtain non-zero outputs where it should be
# zero. We force zeros.
wipe_attn = jnp.all(
attention_mask == 0, axis=2, keepdims=True) # shape (B, T, 1)
summed = jnp.where(wipe_attn, jnp.zeros_like(summed), summed)
return summed
def conv_1d(
output_channels,
init_scale=1.0,
with_bias=True,
name=None):
"""A 1D convolution."""
return hk.Linear(
output_size=output_channels,
with_bias=with_bias,
w_init=hk.initializers.VarianceScaling(init_scale),
name=name)
def layer_norm(x, name=None):
return hk.LayerNorm(axis=-1, create_scale=True, create_offset=True,
name=name)(x)
def make_cross_attention_mask(query_mask, kv_mask):
batch_size, query_len = query_mask.shape
_, key_len = kv_mask.shape
mask = jax.vmap(jnp.outer)(query_mask, kv_mask)
assert mask.shape == (batch_size, query_len, key_len)
return mask
# -----------------------------------------------------------
# ----------------------- Modules -------------------------
# -----------------------------------------------------------
class Attention(hk.Module):
"""Multi-headed {cross, self}-attention."""
def __init__(self,
num_heads=8,
init_scale=1.0,
with_final_bias=True,
final_init_scale_multiplier=1.,
dropout_prob=0.0,
qk_channels=None,
v_channels=None,
output_channels=None,
name=None):
super(Attention, self).__init__(name=name)
self._num_heads = num_heads
self._init_scale = init_scale
self._with_final_bias = with_final_bias
self._final_init_scale = final_init_scale_multiplier * init_scale
self._dropout_prob = dropout_prob
# If none of these are passed, the Q input determines the output shape:
self._qk_channels = qk_channels
self._v_channels = v_channels
self._output_channels = output_channels
def __call__(self, inputs_q, inputs_kv, attention_mask=None):
# Q and K must have the same number of channels.
# Default to preserving Q's input's shape.
if self._qk_channels is None:
self._qk_channels = inputs_q.shape[-1]
# V's num_channels determines the shape of the output of QKV-attention.
# Default to the same number of channels used in the key-query operation.
if self._v_channels is None:
self._v_channels = self._qk_channels
# Project the output of QKV attention to a desired number of channels.
# Default to the same number as the output of the QKV attention operation.
if self._output_channels is None:
self._output_channels = self._v_channels
if self._qk_channels % self._num_heads != 0:
raise ValueError(f'qk_channels ({self._qk_channels}) must be divisible by'
f' num_heads ({self._num_heads}).')
if self._v_channels % self._num_heads != 0:
raise ValueError(f'v_channels ({self._v_channels}) must be divisible by'
f' num_heads ({self._num_heads}).')
qk_channels_per_head = self._qk_channels // self._num_heads
v_channels_per_head = self._v_channels // self._num_heads
# Project QKV to a common feature dimension.
q = conv_1d(self._qk_channels, init_scale=self._init_scale)(inputs_q)
k = conv_1d(self._qk_channels, init_scale=self._init_scale)(inputs_kv)
v = conv_1d(self._v_channels, init_scale=self._init_scale)(inputs_kv)
# Reshape channels for multi-head attention.
batch, q_time, _ = q.shape
_, kv_time, _ = k.shape
q = jnp.reshape(q, [batch, q_time, self._num_heads, qk_channels_per_head])
k = jnp.reshape(k, [batch, kv_time, self._num_heads, qk_channels_per_head])
v = jnp.reshape(v, [batch, kv_time, self._num_heads, v_channels_per_head])
result = attend(q, k, v, dropout_prob=self._dropout_prob,
attention_mask=attention_mask)
return conv_1d(
self._output_channels,
with_bias=self._with_final_bias,
init_scale=self._final_init_scale)(result)
class MLP(hk.Module):
"""A Transformer-style dense module to follow attention."""
def __init__(self,
widening_factor=4,
dropout_prob=0.0,
init_scale=1.,
name=None):
super(MLP, self).__init__(name=name)
self._widening_factor = widening_factor
self._dropout_prob = dropout_prob
self._init_scale = init_scale
def __call__(self, x, *, is_training):
dropout_prob = self._dropout_prob if is_training else 0.0
output_channels = x.shape[-1]
x = conv_1d(
output_channels=self._widening_factor * output_channels,
init_scale=self._init_scale)(x)
x = jax.nn.gelu(x)
x = conv_1d(
output_channels=output_channels,
init_scale=self._init_scale)(x)
return hk.dropout(hk.next_rng_key(), dropout_prob, x)
class SelfAttention(hk.Module):
"""A self-attention module, including a dense block."""
def __init__(self,
widening_factor=4,
dropout_prob=0.0,
dropout_attn_prob=0.0,
num_heads=8,
att_init_scale=1.0,
dense_init_scale=1.0,
qk_channels=None,
v_channels=None,
name=None):
super(SelfAttention, self).__init__(name=name)
self._widening_factor = widening_factor
self._dropout_prob = dropout_prob
self._dropout_attn_prob = dropout_attn_prob
self._num_heads = num_heads
self._att_init_scale = att_init_scale
self._dense_init_scale = dense_init_scale
self._qk_channels = qk_channels
self._v_channels = v_channels
def __call__(self,
inputs,
*,
attention_mask=None,
is_training):
dropout_prob = self._dropout_prob if is_training else 0.0
dropout_attn_prob = self._dropout_attn_prob if is_training else 0.0
x = inputs
qkv_inputs = layer_norm(inputs)
attention = Attention(
num_heads=self._num_heads,
init_scale=self._att_init_scale,
qk_channels=self._qk_channels,
v_channels=self._v_channels,
dropout_prob=dropout_attn_prob)(qkv_inputs, qkv_inputs,
attention_mask=attention_mask)
attention = hk.dropout(hk.next_rng_key(), dropout_prob, attention)
x += attention
x += MLP(
widening_factor=self._widening_factor,
dropout_prob=dropout_prob,
init_scale=self._dense_init_scale)(
layer_norm(x), is_training=is_training)
return x
class CrossAttention(hk.Module):
"""A cross-attention module, including a dense block."""
def __init__(self,
widening_factor=1,
dropout_prob=0.0,
dropout_attn_prob=0.0,
num_heads=8,
att_init_scale=1.0,
dense_init_scale=1.0,
shape_for_attn='kv',
use_query_residual=True,
qk_channels=None,
v_channels=None,
name=None):
super(CrossAttention, self).__init__(name=name)
self._widening_factor = widening_factor
self._dropout_prob = dropout_prob
self._dropout_attn_prob = dropout_attn_prob
self._num_heads = num_heads
self._att_init_scale = att_init_scale
self._dense_init_scale = dense_init_scale
self._shape_for_attn = shape_for_attn
self._use_query_residual = use_query_residual
self._qk_channels = qk_channels
self._v_channels = v_channels
def __call__(self,
inputs_q,
inputs_kv,
*,
attention_mask=None,
is_training):
dropout_prob = self._dropout_prob if is_training else 0.0
dropout_attn_prob = self._dropout_attn_prob if is_training else 0.0
output_channels = inputs_q.shape[-1]
if self._shape_for_attn == 'q':
qk_channels = inputs_q.shape[-1]
elif self._shape_for_attn == 'kv':
qk_channels = inputs_kv.shape[-1]
else:
raise ValueError(f'Unknown value {self._shape_for_attn} for '
'shape_for_attention.')
v_channels = None
if self._qk_channels is not None:
qk_channels = self._qk_channels
if self._v_channels is not None:
v_channels = self._v_channels
attention = Attention(
num_heads=self._num_heads,
init_scale=self._att_init_scale,
dropout_prob=dropout_attn_prob,
qk_channels=qk_channels,
v_channels=v_channels,
output_channels=output_channels)(layer_norm(inputs_q),
layer_norm(inputs_kv),
attention_mask=attention_mask)
attention = hk.dropout(hk.next_rng_key(), dropout_prob, attention)
# Optionally include a residual to the query.
# Consider omitting the residual if the semantics of query and output
# are different, e.g. if queries are positions and outputs are pixels.
if self._use_query_residual:
x = inputs_q + attention
else:
x = attention
x += MLP(
widening_factor=self._widening_factor,
dropout_prob=dropout_prob,
init_scale=self._dense_init_scale)(
layer_norm(x), is_training=is_training)
return x
# -----------------------------------------------------------
# ----------------------- Perceiver -----------------------
# -----------------------------------------------------------
class Perceiver(hk.Module):
"""The Perceiver: a scalable, fully attentional architecture."""
def __init__(
self,
encoder,
decoder,
input_preprocessor=None,
output_postprocessor=None,
name='perceiver'):
super().__init__(name=name)
# Feature and task parameters:
self._input_preprocessor = input_preprocessor
self._output_postprocessor = output_postprocessor
self._decoder = decoder
self._encoder = encoder
def __call__(self, inputs, *, is_training, subsampled_output_points=None,
pos=None, input_mask=None, query_mask=None):
if self._input_preprocessor:
network_input_is_1d = self._encoder._input_is_1d
inputs, modality_sizes, inputs_without_pos = self._input_preprocessor(
inputs, pos=pos, is_training=is_training,
network_input_is_1d=network_input_is_1d)
else:
modality_sizes = None
inputs_without_pos = None
# Get the queries for encoder and decoder cross-attends.
encoder_query = self._encoder.latents(inputs)
decoder_query = self._decoder.decoder_query(
inputs, modality_sizes, inputs_without_pos,
subsampled_points=subsampled_output_points)
# Run the network forward:
z = self._encoder(inputs, encoder_query,
is_training=is_training, input_mask=input_mask)
_, output_modality_sizes = self._decoder.output_shape(
inputs)
output_modality_sizes = output_modality_sizes or modality_sizes
outputs = self._decoder(
decoder_query, z, is_training=is_training, query_mask=query_mask)
if self._output_postprocessor:
outputs = self._output_postprocessor(outputs, is_training=is_training,
modality_sizes=output_modality_sizes)
return outputs
class PerceiverEncoder(hk.Module):
"""The Perceiver Encoder: a scalable, fully attentional encoder."""
def __init__(
self,
# The encoder has a total of
# num_self_attends_per_block * num_blocks
# self-attend layers. We share weights between blocks.
num_self_attends_per_block=6,
num_blocks=8,
z_index_dim=512,
num_z_channels=1024,
qk_channels=None,
v_channels=None,
num_cross_attend_heads=1,
num_self_attend_heads=8,
cross_attend_widening_factor=1,
self_attend_widening_factor=1,
dropout_prob=0.0,
z_pos_enc_init_scale=0.02,
cross_attention_shape_for_attn='kv',
use_query_residual=True,
name='perceiver_encoder'):
super().__init__(name=name)
# Check that we can use multihead-attention with these shapes.
if num_z_channels % num_self_attend_heads != 0:
raise ValueError(f'num_z_channels ({num_z_channels}) must be divisible by'
f' num_self_attend_heads ({num_self_attend_heads}).')
if num_z_channels % num_cross_attend_heads != 0:
raise ValueError(f'num_z_channels ({num_z_channels}) must be divisible by'
f' num_cross_attend_heads ({num_cross_attend_heads}).')
self._input_is_1d = True
self._num_blocks = num_blocks
# Construct the latent array initial state.
self.z_pos_enc = position_encoding.TrainablePositionEncoding(
index_dim=z_index_dim,
num_channels=num_z_channels,
init_scale=z_pos_enc_init_scale)
# Construct the cross attend:
self.cross_attend = CrossAttention(
dropout_prob=dropout_prob,
num_heads=num_cross_attend_heads,
widening_factor=cross_attend_widening_factor,
shape_for_attn=cross_attention_shape_for_attn,
qk_channels=qk_channels,
v_channels=v_channels,
use_query_residual=use_query_residual)
# Construct the block of self-attend layers.
# We get deeper architectures by applying this block more than once.
self.self_attends = []
for _ in range(num_self_attends_per_block):
self_attend = SelfAttention(
num_heads=num_self_attend_heads,
dropout_prob=dropout_prob,
qk_channels=qk_channels,
v_channels=v_channels,
widening_factor=self_attend_widening_factor)
self.self_attends.append(self_attend)
def latents(self, inputs):
# Initialize the latent array for the initial cross-attend.
return self.z_pos_enc(batch_size=inputs.shape[0])
def __call__(self, inputs, z, *, is_training, input_mask=None):
attention_mask = None
if input_mask is not None:
attention_mask = make_cross_attention_mask(
query_mask=jnp.ones(z.shape[:2], dtype=jnp.int32),
kv_mask=input_mask)
z = self.cross_attend(z, inputs, is_training=is_training,
attention_mask=attention_mask)
for _ in range(self._num_blocks):
for self_attend in self.self_attends:
z = self_attend(z, is_training=is_training)
return z
class AbstractPerceiverDecoder(hk.Module, metaclass=abc.ABCMeta):
"""Abstract Perceiver decoder."""
@abc.abstractmethod
def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None,
subsampled_points=None):
raise NotImplementedError
@abc.abstractmethod
def output_shape(self, inputs):
raise NotImplementedError
@abc.abstractmethod
def __call__(self, query, z, *, is_training, query_mask=None):
raise NotImplementedError
class ProjectionDecoder(AbstractPerceiverDecoder):
"""Baseline projection decoder (no cross-attention)."""
def __init__(
self,
num_classes,
final_avg_before_project=False,
name='projection_decoder'):
super().__init__(name=name)
self._final_avg_before_project = final_avg_before_project
self._num_classes = num_classes
self.final_layer = hk.Linear(
num_classes, w_init=jnp.zeros, name='logits')
def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None,
subsampled_points=None):
return None
def output_shape(self, inputs):
return ((inputs.shape[0], self._num_classes), None)
def __call__(self, query, z, *, is_training, query_mask=None):
# b x n_z x c -> b x c
z = jnp.mean(z, axis=1, dtype=z.dtype)
# b x c -> b x n_logits
logits = self.final_layer(z)
return logits
class BasicDecoder(AbstractPerceiverDecoder):
"""Cross-attention-based decoder."""
def __init__(self,
output_num_channels,
position_encoding_type='trainable',
# Ignored if position_encoding_type == 'none':
output_index_dims=None,
subsampled_index_dims=None,
num_z_channels=1024,
qk_channels=None,
v_channels=None,
use_query_residual=False,
output_w_init=None,
concat_preprocessed_input=False,
num_heads=1,
name='basic_decoder',
final_project=True,
**position_encoding_kwargs):
super().__init__(name=name)
self._position_encoding_type = position_encoding_type
# If `none`, the decoder will not construct any position encodings.
# You should construct your own when quering the decoder.
self.output_pos_enc = None
if self._position_encoding_type != 'none':
self.output_pos_enc = position_encoding.build_position_encoding(
position_encoding_type,
index_dims=output_index_dims,
**position_encoding_kwargs)
self._output_index_dim = output_index_dims
if subsampled_index_dims is None:
subsampled_index_dims = output_index_dims
self._subsampled_index_dims = subsampled_index_dims
self._output_num_channels = output_num_channels
self._output_w_init = output_w_init
self._use_query_residual = use_query_residual
self._qk_channels = qk_channels
self._v_channels = v_channels
self._final_project = final_project
self._num_heads = num_heads
self._concat_preprocessed_input = concat_preprocessed_input
def output_shape(self, inputs):
return ((inputs[0], self._subsampled_index_dims, self._output_num_channels),
None)
def decoder_query(self, inputs, modality_sizes=None,
inputs_without_pos=None, subsampled_points=None):
assert self._position_encoding_type != 'none' # Queries come from elsewhere
if subsampled_points is not None:
# unravel_index returns a tuple (x_idx, y_idx, ...)
# stack to get the [n, d] tensor of coordinates
pos = jnp.stack(
jnp.unravel_index(subsampled_points, self._output_index_dim),
axis=1)
# Map these coordinates to [-1, 1]
pos = -1 + 2 * pos / jnp.array(self._output_index_dim)[None, :]
pos = jnp.broadcast_to(pos[None],
[inputs.shape[0], pos.shape[0], pos.shape[1]])
pos_emb = self.output_pos_enc(
batch_size=inputs.shape[0],
pos=pos)
pos_emb = jnp.reshape(pos_emb, [pos_emb.shape[0], -1, pos_emb.shape[-1]])
else:
pos_emb = self.output_pos_enc(batch_size=inputs.shape[0])
if self._concat_preprocessed_input:
if inputs_without_pos is None:
raise ValueError('Value is required for inputs_without_pos if'
' concat_preprocessed_input is True')
pos_emb = jnp.concatenate([inputs_without_pos, pos_emb], axis=-1)
return pos_emb
def __call__(self, query, z, *, is_training,
query_mask=None):
# Cross-attention decoding.
# key, value: B x N x K; query: B x M x K
# Attention maps -> B x N x M
# Output -> B x M x K
# Construct cross attention and linear layer lazily, in case we don't need
# them.
attention_mask = None
if query_mask is not None:
attention_mask = make_cross_attention_mask(
query_mask=query_mask,
kv_mask=jnp.ones(z.shape[:2], dtype=jnp.int32))
decoding_cross_attn = CrossAttention(
dropout_prob=0.0,
num_heads=self._num_heads,
widening_factor=1,
shape_for_attn='kv',
qk_channels=self._qk_channels,
v_channels=self._v_channels,
use_query_residual=self._use_query_residual)
final_layer = hk.Linear(
self._output_num_channels, w_init=self._output_w_init, name='output')
output = decoding_cross_attn(query, z, is_training=is_training,
attention_mask=attention_mask)
if self._final_project:
output = final_layer(output)
return output
class ClassificationDecoder(AbstractPerceiverDecoder):
"""Cross-attention based classification decoder.
Light-weight wrapper of `BasicDecoder` for logit output.
"""
def __init__(self,
num_classes,
name='classification_decoder',
**decoder_kwargs):
super().__init__(name=name)
self._num_classes = num_classes
self.decoder = BasicDecoder(
output_index_dims=(1,), # Predict a single logit array.
output_num_channels=num_classes,
**decoder_kwargs)
def decoder_query(self, inputs, modality_sizes=None,
inputs_without_pos=None, subsampled_points=None):
return self.decoder.decoder_query(inputs, modality_sizes,
inputs_without_pos,
subsampled_points=subsampled_points)
def output_shape(self, inputs):
return (inputs.shape[0], self._num_classes), None
def __call__(self, query, z, *, is_training, query_mask=None):
# B x 1 x num_classes -> B x num_classes
logits = self.decoder(query, z, is_training=is_training)
return logits[:, 0, :]
class MultimodalDecoder(AbstractPerceiverDecoder):
"""Multimodal decoding by composing uni-modal decoders.
The modalities argument of the constructor is a dictionary mapping modality
name to the decoder of that modality. That decoder will be used to construct
queries for that modality. However, there is a shared cross attention across
all modalities, using the concatenated per-modality query vectors.
"""
def __init__(self, modalities, num_outputs, output_num_channels,
min_padding_size=2,
subsampled_index_dims=None,
name='multimodal_decoder', **decoder_kwargs):
super().__init__(name=name)
self._modalities = modalities
self._subsampled_index_dims = subsampled_index_dims
self._min_padding_size = min_padding_size
self._output_num_channels = output_num_channels
self._num_outputs = num_outputs
self._decoder = BasicDecoder(
output_index_dims=(num_outputs,),
output_num_channels=output_num_channels,
position_encoding_type='none',
**decoder_kwargs)
def decoder_query(self, inputs, modality_sizes, inputs_without_pos=None, # pytype: disable=signature-mismatch # overriding-parameter-count-checks
subsampled_points=None):
# Partition the flat inputs among the different modalities
inputs = io_processors.restructure(modality_sizes, inputs)
# Obtain modality-specific decoders' queries
subsampled_points = subsampled_points or dict()
decoder_queries = dict()
for modality, decoder in self._modalities.items():
# Get input_without_pos for this modality if it exists.
input_without_pos = None
if inputs_without_pos is not None:
input_without_pos = inputs_without_pos.get(modality, None)
decoder_queries[modality] = decoder.decoder_query(
inputs=inputs[modality],
modality_sizes=None,
inputs_without_pos=input_without_pos,
subsampled_points=subsampled_points.get(modality, None)
)
# Pad all queries with trainable position encodings to make them
# have the same channels
num_channels = (max(query.shape[2] for query in decoder_queries.values())
+ self._min_padding_size)
def embed(modality, x):
x = jnp.reshape(x, [x.shape[0], np.prod(x.shape[1:-1]), x.shape[-1]])
pos = position_encoding.TrainablePositionEncoding(
1, num_channels=num_channels - x.shape[2],
init_scale=0.02, name=f'{modality}_padding')(x.shape[0])
pos = jnp.broadcast_to(
pos, [x.shape[0], x.shape[1], num_channels - x.shape[2]])
return jnp.concatenate([x, pos], axis=2)
# Apply a predictable ordering to the modalities
return jnp.concatenate([
embed(modality, decoder_queries[modality])
for modality in sorted(self._modalities.keys())
], axis=1)
def output_shape(self, inputs):
if self._subsampled_index_dims is not None:
subsampled_index_dims = sum(self._subsampled_index_dims.values())
else:
subsampled_index_dims = self._num_outputs
return ((inputs.shape[0], subsampled_index_dims, self._output_num_channels),
self._subsampled_index_dims)
def __call__(self, query, z, *, is_training, query_mask=None):
# B x 1 x num_classes -> B x num_classes
return self._decoder(query, z, is_training=is_training)
class BasicVideoAutoencodingDecoder(AbstractPerceiverDecoder):
"""Cross-attention based video-autoencoding decoder.
Light-weight wrapper of `BasicDecoder` with video reshaping logic.
"""
def __init__(self,
output_shape,
position_encoding_type,
name='basic_video_autoencoding_decoder',
**decoder_kwargs):
super().__init__(name=name)
if len(output_shape) != 4: # B, T, H, W
raise ValueError(f'Expected rank 4 output_shape, got {output_shape}.')
# Build the decoder components:
self._output_shape = output_shape
self._output_num_channels = decoder_kwargs['output_num_channels']
self.decoder = BasicDecoder(
output_index_dims=self._output_shape[1:4], # T*H*W
position_encoding_type=position_encoding_type,
**decoder_kwargs)
def decoder_query(self, inputs, modality_sizes=None,
inputs_without_pos=None, subsampled_points=None):
return self.decoder.decoder_query(inputs,
modality_sizes=modality_sizes,
inputs_without_pos=inputs_without_pos,
subsampled_points=subsampled_points)
def output_shape(self, inputs):
return ([inputs.shape[0]] + self._output_shape[1:] +
[self._output_num_channels], None)
def __call__(self, query, z, *, is_training, query_mask=None):
output = self.decoder(query, z, is_training=is_training)
output = jnp.reshape(output, self._output_shape + [output.shape[-1]])
return output
class FlowDecoder(AbstractPerceiverDecoder):
"""Cross-attention based flow decoder."""
def __init__(self,
output_image_shape,
output_num_channels=2,
rescale_factor=100.0,
name='flow_decoder',
**decoder_kwargs):
super().__init__(name=name)
self._output_image_shape = output_image_shape
self._output_num_channels = output_num_channels
self._rescale_factor = rescale_factor
self.decoder = BasicDecoder(
output_num_channels=output_num_channels,
**decoder_kwargs)
def output_shape(self, inputs):
# The channel dimensions of output here don't necessarily correspond to
# (u, v) of flow: they may contain dims needed for the post-processor.
return ((inputs.shape[0],) + tuple(self._output_image_shape) + (
self._output_num_channels,), None)
def decoder_query(
self, inputs, modality_sizes=None, inputs_without_pos=None,
subsampled_points=None):
if subsampled_points is not None:
raise ValueError("FlowDecoder doesn't support subsampling yet.")
# assumes merged in time
return inputs
def __call__(self, query, z, *, is_training, query_mask=None):
# Output flow and rescale.
preds = self.decoder(query, z, is_training=is_training)
preds /= self._rescale_factor
return preds.reshape([preds.shape[0]] + list(self._output_image_shape) +
[preds.shape[-1]])
================================================
FILE: perceiver/position_encoding.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Position encodings and utilities."""
import abc
import functools
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
def generate_fourier_features(
pos, num_bands, max_resolution=(224, 224),
concat_pos=True, sine_only=False):
"""Generate a Fourier frequency position encoding with linear spacing.
Args:
pos: The position of n points in d dimensional space.
A jnp array of shape [n, d].
num_bands: The number of bands (K) to use.
max_resolution: The maximum resolution (i.e. the number of pixels per dim).
A tuple representing resolution for each dimension
concat_pos: Concatenate the input position encoding to the Fourier features?
sine_only: Whether to use a single phase (sin) or two (sin/cos) for each
frequency band.
Returns:
embedding: A 1D jnp array of shape [n, n_channels]. If concat_pos is True
and sine_only is False, output dimensions are ordered as:
[dim_1, dim_2, ..., dim_d,
sin(pi*f_1*dim_1), ..., sin(pi*f_K*dim_1), ...,
sin(pi*f_1*dim_d), ..., sin(pi*f_K*dim_d),
cos(pi*f_1*dim_1), ..., cos(pi*f_K*dim_1), ...,
cos(pi*f_1*dim_d), ..., cos(pi*f_K*dim_d)],
where dim_i is pos[:, i] and f_k is the kth frequency band.
"""
min_freq = 1.0
# Nyquist frequency at the target resolution:
freq_bands = jnp.stack([
jnp.linspace(min_freq, res / 2, num=num_bands, endpoint=True)
for res in max_resolution], axis=0)
# Get frequency bands for each spatial dimension.
# Output is size [n, d * num_bands]
per_pos_features = pos[:, :, None] * freq_bands[None, :, :]
per_pos_features = jnp.reshape(per_pos_features,
[-1, np.prod(per_pos_features.shape[1:])])
if sine_only:
# Output is size [n, d * num_bands]
per_pos_features = jnp.sin(jnp.pi * (per_pos_features))
else:
# Output is size [n, 2 * d * num_bands]
per_pos_features = jnp.concatenate(
[jnp.sin(jnp.pi * per_pos_features),
jnp.cos(jnp.pi * per_pos_features)], axis=-1)
# Concatenate the raw input positions.
if concat_pos:
# Adds d bands to the encoding.
per_pos_features = jnp.concatenate([pos, per_pos_features], axis=-1)
return per_pos_features
def build_linear_positions(index_dims, output_range=(-1.0, 1.0)):
"""Generate an array of position indices for an N-D input array.
Args:
index_dims: The shape of the index dimensions of the input array.
output_range: The min and max values taken by each input index dimension.
Returns:
A jnp array of shape [index_dims[0], index_dims[1], .., index_dims[-1], N].
"""
def _linspace(n_xels_per_dim):
return jnp.linspace(
output_range[0], output_range[1],
num=n_xels_per_dim,
endpoint=True, dtype=jnp.float32)
dim_ranges = [
_linspace(n_xels_per_dim) for n_xels_per_dim in index_dims]
array_index_grid = jnp.meshgrid(*dim_ranges, indexing='ij')
return jnp.stack(array_index_grid, axis=-1)
class AbstractPositionEncoding(hk.Module, metaclass=abc.ABCMeta):
"""Abstract Perceiver decoder."""
@abc.abstractmethod
def __call__(self, batch_size, pos):
raise NotImplementedError
class TrainablePositionEncoding(AbstractPositionEncoding):
"""Trainable position encoding."""
def __init__(self, index_dim, num_channels=128, init_scale=0.02, name=None):
super(TrainablePositionEncoding, self).__init__(name=name)
self._index_dim = index_dim
self._num_channels = num_channels
self._init_scale = init_scale
def __call__(self, batch_size, pos=None):
del pos # Unused.
pos_embs = hk.get_parameter(
'pos_embs', [self._index_dim, self._num_channels],
init=hk.initializers.TruncatedNormal(stddev=self._init_scale))
if batch_size is not None:
pos_embs = jnp.broadcast_to(
pos_embs[None, :, :], (batch_size,) + pos_embs.shape)
return pos_embs
def _check_or_build_spatial_positions(pos, index_dims, batch_size):
"""Checks or builds spatial position features (x, y, ...).
Args:
pos: None, or an array of position features. If None, position features
are built. Otherwise, their size is checked.
index_dims: An iterable giving the spatial/index size of the data to be
featurized.
batch_size: The batch size of the data to be featurized.
Returns:
An array of position features, of shape [batch_size, prod(index_dims)].
"""
if pos is None:
pos = build_linear_positions(index_dims)
pos = jnp.broadcast_to(pos[None], (batch_size,) + pos.shape)
pos = jnp.reshape(pos, [batch_size, np.prod(index_dims), -1])
else:
# Just a warning label: you probably don't want your spatial features to
# have a different spatial layout than your pos coordinate system.
# But feel free to override if you think it'll work!
assert pos.shape[-1] == len(index_dims)
return pos
class FourierPositionEncoding(AbstractPositionEncoding):
"""Fourier (Sinusoidal) position encoding."""
def __init__(self, index_dims, num_bands, concat_pos=True,
max_resolution=None, sine_only=False, name=None):
super(FourierPositionEncoding, self).__init__(name=name)
self._num_bands = num_bands
self._concat_pos = concat_pos
self._sine_only = sine_only
self._index_dims = index_dims
# Use the index dims as the maximum resolution if it's not provided.
self._max_resolution = max_resolution or index_dims
def __call__(self, batch_size, pos=None):
pos = _check_or_build_spatial_positions(pos, self._index_dims, batch_size)
build_ff_fn = functools.partial(
generate_fourier_features,
num_bands=self._num_bands,
max_resolution=self._max_resolution,
concat_pos=self._concat_pos,
sine_only=self._sine_only)
return jax.vmap(build_ff_fn, 0, 0)(pos)
class PositionEncodingProjector(AbstractPositionEncoding):
"""Projects a position encoding to a target size."""
def __init__(self, output_size, base_position_encoding, name=None):
super(PositionEncodingProjector, self).__init__(name=name)
self._output_size = output_size
self._base_position_encoding = base_position_encoding
def __call__(self, batch_size, pos=None):
base_pos = self._base_position_encoding(batch_size, pos)
projected_pos = hk.Linear(output_size=self._output_size)(base_pos)
return projected_pos
def build_position_encoding(
position_encoding_type,
index_dims,
project_pos_dim=-1,
trainable_position_encoding_kwargs=None,
fourier_position_encoding_kwargs=None,
name=None):
"""Builds the position encoding."""
if position_encoding_type == 'trainable':
assert trainable_position_encoding_kwargs is not None
output_pos_enc = TrainablePositionEncoding(
# Construct 1D features:
index_dim=np.prod(index_dims),
name=name,
**trainable_position_encoding_kwargs)
elif position_encoding_type == 'fourier':
assert fourier_position_encoding_kwargs is not None
output_pos_enc = FourierPositionEncoding(
index_dims=index_dims,
name=name,
**fourier_position_encoding_kwargs)
else:
raise ValueError(f'Unknown position encoding: {position_encoding_type}.')
if project_pos_dim > 0:
# Project the position encoding to a target dimension:
output_pos_enc = PositionEncodingProjector(
output_size=project_pos_dim,
base_position_encoding=output_pos_enc)
return output_pos_enc
================================================
FILE: perceiver/requirements.txt
================================================
absl-py==0.13.0
dill==0.3.4
dm-haiku==0.0.4
einops==0.3.0
flatbuffers~=1.12.0
imageio==2.9.0
immutabledict==2.0.0
jaxline==0.0.3
numpy~=1.19.2
opencv-python==4.5.2.54
opt-einsum==3.3.0
optax==0.0.9
Pillow==8.3.1
scipy==1.7.0
six~=1.15.0
tabulate==0.8.9
tensorflow==2.5.0
tensorflow-addons==0.13.0
tensorflow-datasets==4.3.0
tensorflow-probability==0.13.0
================================================
FILE: perceiver/train/autoaugment.py
================================================
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""AutoAugment and RandAugment policies for enhanced image preprocessing.
AutoAugment Reference: https://arxiv.org/abs/1805.09501
RandAugment Reference: https://arxiv.org/abs/1909.13719
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import inspect
import math
from ml_collections import config_dict
import tensorflow.compat.v1 as tf
from tensorflow_addons import image as contrib_image
# pylint: disable=deprecated-method
# This signifies the max integer that the controller RNN could predict for the
# augmentation scheme.
_MAX_LEVEL = 10.
def policy_v0():
"""Autoaugment policy that was used in AutoAugment Paper."""
# Each tuple is an augmentation operation of the form
# (operation, probability, magnitude). Each element in policy is a
# sub-policy that will be applied sequentially on the image.
policy = [
[('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
[('Color', 0.4, 9), ('Equalize', 0.6, 3)],
[('Color', 0.4, 1), ('Rotate', 0.6, 8)],
[('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
[('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
[('Color', 0.2, 0), ('Equalize', 0.8, 8)],
[('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
[('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
[('Color', 0.6, 1), ('Equalize', 1.0, 2)],
[('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
[('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
[('Color', 0.4, 7), ('Equalize', 0.6, 0)],
[('Posterize', 0.4, 6), ('AutoContrast', 0.4, 7)],
[('Solarize', 0.6, 8), ('Color', 0.6, 9)],
[('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
[('Rotate', 1.0, 7), ('TranslateY', 0.8, 9)],
[('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
[('ShearY', 0.8, 0), ('Color', 0.6, 4)],
[('Color', 1.0, 0), ('Rotate', 0.6, 2)],
[('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
[('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
[('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
[('Posterize', 0.8, 2), ('Solarize', 0.6, 10)],
[('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
[('Color', 0.8, 6), ('Rotate', 0.4, 5)],
]
return policy
def policy_vtest():
"""Autoaugment test policy for debugging."""
# Each tuple is an augmentation operation of the form
# (operation, probability, magnitude). Each element in policy is a
# sub-policy that will be applied sequentially on the image.
policy = [
[('TranslateX', 1.0, 4), ('Equalize', 1.0, 10)],
]
return policy
def blend(image1, image2, factor):
"""Blend image1 and image2 using 'factor'.
Factor can be above 0.0. A value of 0.0 means only image1 is used.
A value of 1.0 means only image2 is used. A value between 0.0 and
1.0 means we linearly interpolate the pixel values between the two
images. A value greater than 1.0 "extrapolates" the difference
between the two pixel values, and we clip the results to values
between 0 and 255.
Args:
image1: An image Tensor of type uint8.
image2: An image Tensor of type uint8.
factor: A floating point value above 0.0.
Returns:
A blended image Tensor of type uint8.
"""
if factor == 0.0:
return tf.convert_to_tensor(image1)
if factor == 1.0:
return tf.convert_to_tensor(image2)
image1 = tf.to_float(image1)
image2 = tf.to_float(image2)
difference = image2 - image1
scaled = factor * difference
# Do addition in float.
temp = tf.to_float(image1) + scaled
# Interpolate
if factor > 0.0 and factor < 1.0:
# Interpolation means we always stay within 0 and 255.
return tf.cast(temp, tf.uint8)
# Extrapolate:
#
# We need to clip and then cast.
return tf.cast(tf.clip_by_value(temp, 0.0, 255.0), tf.uint8)
def cutout(image, pad_size, replace=0):
"""Apply cutout (https://arxiv.org/abs/1708.04552) to image.
This operation applies a (2*pad_size x 2*pad_size) mask of zeros to
a random location within `img`. The pixel values filled in will be of the
value `replace`. The located where the mask will be applied is randomly
chosen uniformly over the whole image.
Args:
image: An image Tensor of type uint8.
pad_size: Specifies how big the zero mask that will be generated is that
is applied to the image. The mask will be of size
(2*pad_size x 2*pad_size).
replace: What pixel value to fill in the image in the area that has
the cutout mask applied to it.
Returns:
An image Tensor that is of type uint8.
"""
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
# Sample the center location in the image where the zero mask will be applied.
cutout_center_height = tf.random_uniform(
shape=[], minval=0, maxval=image_height,
dtype=tf.int32)
cutout_center_width = tf.random_uniform(
shape=[], minval=0, maxval=image_width,
dtype=tf.int32)
lower_pad = tf.maximum(0, cutout_center_height - pad_size)
upper_pad = tf.maximum(0, image_height - cutout_center_height - pad_size)
left_pad = tf.maximum(0, cutout_center_width - pad_size)
right_pad = tf.maximum(0, image_width - cutout_center_width - pad_size)
cutout_shape = [image_height - (lower_pad + upper_pad),
image_width - (left_pad + right_pad)]
padding_dims = [[lower_pad, upper_pad], [left_pad, right_pad]]
mask = tf.pad(
tf.zeros(cutout_shape, dtype=image.dtype),
padding_dims, constant_values=1)
mask = tf.expand_dims(mask, -1)
mask = tf.tile(mask, [1, 1, 3])
image = tf.where(
tf.equal(mask, 0),
tf.ones_like(image, dtype=image.dtype) * replace,
image)
return image
def solarize(image, threshold=128):
# For each pixel in the image, select the pixel
# if the value is less than the threshold.
# Otherwise, subtract the pixel from 255.
return tf.where(image < threshold, image, 255 - image)
def solarize_add(image, addition=0, threshold=128):
# For each pixel in the image less than threshold
# we add 'addition' amount to it and then clip the
# pixel value to be between 0 and 255. The value
# of 'addition' is between -128 and 128.
added_image = tf.cast(image, tf.int64) + addition
added_image = tf.cast(tf.clip_by_value(added_image, 0, 255), tf.uint8)
return tf.where(image < threshold, added_image, image)
def color(image, factor):
"""Equivalent of PIL Color."""
degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(image))
return blend(degenerate, image, factor)
def contrast(image, factor):
"""Equivalent of PIL Contrast."""
degenerate = tf.image.rgb_to_grayscale(image)
# Cast before calling tf.histogram.
degenerate = tf.cast(degenerate, tf.int32)
# Compute the grayscale histogram, then compute the mean pixel value,
# and create a constant image size of that value. Use that as the
# blending degenerate target of the original image.
hist = tf.histogram_fixed_width(degenerate, [0, 255], nbins=256)
mean = tf.reduce_sum(tf.cast(hist, tf.float32)) / 256.0
degenerate = tf.ones_like(degenerate, dtype=tf.float32) * mean
degenerate = tf.clip_by_value(degenerate, 0.0, 255.0)
degenerate = tf.image.grayscale_to_rgb(tf.cast(degenerate, tf.uint8))
return blend(degenerate, image, factor)
def brightness(image, factor):
"""Equivalent of PIL Brightness."""
degenerate = tf.zeros_like(image)
return blend(degenerate, image, factor)
def posterize(image, bits):
"""Equivalent of PIL Posterize."""
shift = 8 - bits
return tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift)
def rotate(image, degrees, replace):
"""Rotates the image by degrees either clockwise or counterclockwise.
Args:
image: An image Tensor of type uint8.
degrees: Float, a scalar angle in degrees to rotate all images by. If
degrees is positive the image will be rotated clockwise otherwise it will
be rotated counterclockwise.
replace: A one or three value 1D tensor to fill empty pixels caused by
the rotate operation.
Returns:
The rotated version of image.
"""
# Convert from degrees to radians.
degrees_to_radians = math.pi / 180.0
radians = degrees * degrees_to_radians
# In practice, we should randomize the rotation degrees by flipping
# it negatively half the time, but that's done on 'degrees' outside
# of the function.
image = contrib_image.rotate(wrap(image), radians)
return unwrap(image, replace)
def translate_x(image, pixels, replace):
"""Equivalent of PIL Translate in X dimension."""
image = contrib_image.translate(wrap(image), [-pixels, 0])
return unwrap(image, replace)
def translate_y(image, pixels, replace):
"""Equivalent of PIL Translate in Y dimension."""
image = contrib_image.translate(wrap(image), [0, -pixels])
return unwrap(image, replace)
def shear_x(image, level, replace):
"""Equivalent of PIL Shearing in X dimension."""
# Shear parallel to x axis is a projective transform
# with a matrix form of:
# [1 level
# 0 1].
image = contrib_image.transform(
wrap(image), [1., level, 0., 0., 1., 0., 0., 0.])
return unwrap(image, replace)
def shear_y(image, level, replace):
"""Equivalent of PIL Shearing in Y dimension."""
# Shear parallel to y axis is a projective transform
# with a matrix form of:
# [1 0
# level 1].
image = contrib_image.transform(
wrap(image), [1., 0., 0., level, 1., 0., 0., 0.])
return unwrap(image, replace)
def autocontrast(image):
"""Implements Autocontrast function from PIL using TF ops.
Args:
image: A 3D uint8 tensor.
Returns:
The image after it has had autocontrast applied to it and will be of type
uint8.
"""
def scale_channel(image):
"""Scale the 2D image using the autocontrast rule."""
# A possibly cheaper version can be done using cumsum/unique_with_counts
# over the histogram values, rather than iterating over the entire image.
# to compute mins and maxes.
lo = tf.to_float(tf.reduce_min(image))
hi = tf.to_float(tf.reduce_max(image))
# Scale the image, making the lowest value 0 and the highest value 255.
def scale_values(im):
scale = 255.0 / (hi - lo)
offset = -lo * scale
im = tf.to_float(im) * scale + offset
im = tf.clip_by_value(im, 0.0, 255.0)
return tf.cast(im, tf.uint8)
result = tf.cond(hi > lo, lambda: scale_values(image), lambda: image)
return result
# Assumes RGB for now. Scales each channel independently
# and then stacks the result.
s1 = scale_channel(image[:, :, 0])
s2 = scale_channel(image[:, :, 1])
s3 = scale_channel(image[:, :, 2])
image = tf.stack([s1, s2, s3], 2)
return image
def sharpness(image, factor):
"""Implements Sharpness function from PIL using TF ops."""
orig_image = image
image = tf.cast(image, tf.float32)
# Make image 4D for conv operation.
image = tf.expand_dims(image, 0)
# SMOOTH PIL Kernel.
kernel = tf.constant(
[[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=tf.float32,
shape=[3, 3, 1, 1]) / 13.
# Tile across channel dimension.
kernel = tf.tile(kernel, [1, 1, 3, 1])
strides = [1, 1, 1, 1]
with tf.device('/cpu:0'):
# Some augmentation that uses depth-wise conv will cause crashing when
# training on GPU. See (b/156242594) for details.
degenerate = tf.nn.depthwise_conv2d(
image, kernel, strides, padding='VALID', rate=[1, 1])
degenerate = tf.clip_by_value(degenerate, 0.0, 255.0)
degenerate = tf.squeeze(tf.cast(degenerate, tf.uint8), [0])
# For the borders of the resulting image, fill in the values of the
# original image.
mask = tf.ones_like(degenerate)
padded_mask = tf.pad(mask, [[1, 1], [1, 1], [0, 0]])
padded_degenerate = tf.pad(degenerate, [[1, 1], [1, 1], [0, 0]])
result = tf.where(tf.equal(padded_mask, 1), padded_degenerate, orig_image)
# Blend the final result.
return blend(result, orig_image, factor)
def equalize(image):
"""Implements Equalize function from PIL using TF ops."""
def scale_channel(im, c):
"""Scale the data in the channel to implement equalize."""
im = tf.cast(im[:, :, c], tf.int32)
# Compute the histogram of the image channel.
histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
# For the purposes of computing the step, filter out the nonzeros.
nonzero = tf.where(tf.not_equal(histo, 0))
nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [-1])
step = (tf.reduce_sum(nonzero_histo) - nonzero_histo[-1]) // 255
def build_lut(histo, step):
# Compute the cumulative sum, shifting by step // 2
# and then normalization by step.
lut = (tf.cumsum(histo) + (step // 2)) // step
# Shift lut, prepending with 0.
lut = tf.concat([[0], lut[:-1]], 0)
# Clip the counts to be in range. This is done
# in the C code for image.point.
return tf.clip_by_value(lut, 0, 255)
# If step is zero, return the original image. Otherwise, build
# lut from the full histogram and step and then index from it.
result = tf.cond(tf.equal(step, 0),
lambda: im,
lambda: tf.gather(build_lut(histo, step), im))
return tf.cast(result, tf.uint8)
# Assumes RGB for now. Scales each channel independently
# and then stacks the result.
s1 = scale_channel(image, 0)
s2 = scale_channel(image, 1)
s3 = scale_channel(image, 2)
image = tf.stack([s1, s2, s3], 2)
return image
def invert(image):
"""Inverts the image pixels."""
image = tf.convert_to_tensor(image)
return 255 - image
def wrap(image):
"""Returns 'image' with an extra channel set to all 1s."""
shape = tf.shape(image)
extended_channel = tf.ones([shape[0], shape[1], 1], image.dtype)
extended = tf.concat([image, extended_channel], 2)
return extended
def unwrap(image, replace):
"""Unwraps an image produced by wrap.
Where there is a 0 in the last channel for every spatial position,
the rest of the three channels in that spatial dimension are grayed
(set to 128). Operations like translate and shear on a wrapped
Tensor will leave 0s in empty locations. Some transformations look
at the intensity of values to do preprocessing, and we want these
empty pixels to assume the 'average' value, rather than pure black.
Args:
image: A 3D Image Tensor with 4 channels.
replace: A one or three value 1D tensor to fill empty pixels.
Returns:
image: A 3D image Tensor with 3 channels.
"""
image_shape = tf.shape(image)
# Flatten the spatial dimensions.
flattened_image = tf.reshape(image, [-1, image_shape[2]])
# Find all pixels where the last channel is zero.
alpha_channel = flattened_image[:, 3]
replace = tf.concat([replace, tf.ones([1], image.dtype)], 0)
# Where they are zero, fill them in with 'replace'.
flattened_image = tf.where(
tf.equal(alpha_channel, 0),
tf.ones_like(flattened_image, dtype=image.dtype) * replace,
flattened_image)
image = tf.reshape(flattened_image, image_shape)
image = tf.slice(image, [0, 0, 0], [image_shape[0], image_shape[1], 3])
return image
NAME_TO_FUNC = {
'AutoContrast': autocontrast,
'Equalize': equalize,
'Invert': invert,
'Rotate': rotate,
'Posterize': posterize,
'Solarize': solarize,
'SolarizeAdd': solarize_add,
'Color': color,
'Contrast': contrast,
'Brightness': brightness,
'Sharpness': sharpness,
'ShearX': shear_x,
'ShearY': shear_y,
'TranslateX': translate_x,
'TranslateY': translate_y,
'Cutout': cutout,
}
def _randomly_negate_tensor(tensor):
"""With 50% prob turn the tensor negative."""
should_flip = tf.cast(tf.floor(tf.random_uniform([]) + 0.5), tf.bool)
final_tensor = tf.cond(should_flip, lambda: tensor, lambda: -tensor)
return final_tensor
def _rotate_level_to_arg(level):
level = (level/_MAX_LEVEL) * 30.
level = _randomly_negate_tensor(level)
return (level,)
def _shrink_level_to_arg(level):
"""Converts level to ratio by which we shrink the image content."""
if level == 0:
return (1.0,) # if level is zero, do not shrink the image
# Maximum shrinking ratio is 2.9.
level = 2. / (_MAX_LEVEL / level) + 0.9
return (level,)
def _enhance_level_to_arg(level):
return ((level/_MAX_LEVEL) * 1.8 + 0.1,)
def _shear_level_to_arg(level):
level = (level/_MAX_LEVEL) * 0.3
# Flip level to negative with 50% chance.
level = _randomly_negate_tensor(level)
return (level,)
def _translate_level_to_arg(level, translate_const):
level = (level/_MAX_LEVEL) * float(translate_const)
# Flip level to negative with 50% chance.
level = _randomly_negate_tensor(level)
return (level,)
def level_to_arg(hparams):
return {
'AutoContrast': lambda level: (),
'Equalize': lambda level: (),
'Invert': lambda level: (),
'Rotate': _rotate_level_to_arg,
'Posterize': lambda level: (int((level/_MAX_LEVEL) * 4),),
'Solarize': lambda level: (int((level/_MAX_LEVEL) * 256),),
'SolarizeAdd': lambda level: (int((level/_MAX_LEVEL) * 110),),
'Color': _enhance_level_to_arg,
'Contrast': _enhance_level_to_arg,
'Brightness': _enhance_level_to_arg,
'Sharpness': _enhance_level_to_arg,
'ShearX': _shear_level_to_arg,
'ShearY': _shear_level_to_arg,
'Cutout': lambda level: (int((level/_MAX_LEVEL) * hparams.cutout_const),),
# pylint:disable=g-long-lambda
'TranslateX': lambda level: _translate_level_to_arg(
level, hparams.translate_const),
'TranslateY': lambda level: _translate_level_to_arg(
level, hparams.translate_const),
# pylint:enable=g-long-lambda
}
def _parse_policy_info(name, prob, level, replace_value, augmentation_hparams):
"""Return the function that corresponds to `name` and update `level` param."""
func = NAME_TO_FUNC[name]
args = level_to_arg(augmentation_hparams)[name](level)
# Check to see if prob is passed into function. This is used for operations
# where we alter bboxes independently.
# pytype:disable=wrong-arg-types
if 'prob' in inspect.getargspec(func)[0]:
args = tuple([prob] + list(args))
# pytype:enable=wrong-arg-types
# Add in replace arg if it is required for the function that is being called.
# pytype:disable=wrong-arg-types
if 'replace' in inspect.getargspec(func)[0]:
# Make sure replace is the final argument
assert 'replace' == inspect.getargspec(func)[0][-1]
args = tuple(list(args) + [replace_value])
# pytype:enable=wrong-arg-types
return (func, prob, args)
def _apply_func_with_prob(func, image, args, prob):
"""Apply `func` to image w/ `args` as input with probability `prob`."""
assert isinstance(args, tuple)
# If prob is a function argument, then this randomness is being handled
# inside the function, so make sure it is always called.
# pytype:disable=wrong-arg-types
if 'prob' in inspect.getargspec(func)[0]:
prob = 1.0
# pytype:enable=wrong-arg-types
# Apply the function with probability `prob`.
should_apply_op = tf.cast(
tf.floor(tf.random_uniform([], dtype=tf.float32) + prob), tf.bool)
augmented_image = tf.cond(
should_apply_op,
lambda: func(image, *args),
lambda: image)
return augmented_image
def select_and_apply_random_policy(policies, image):
"""Select a random policy from `policies` and apply it to `image`."""
policy_to_select = tf.random_uniform([], maxval=len(policies), dtype=tf.int32)
# Note that using tf.case instead of tf.conds would result in significantly
# larger graphs and would even break export for some larger policies.
for (i, policy) in enumerate(policies):
image = tf.cond(
tf.equal(i, policy_to_select),
lambda selected_policy=policy: selected_policy(image),
lambda: image)
return image
def build_and_apply_nas_policy(policies, image,
augmentation_hparams):
"""Build a policy from the given policies passed in and apply to image.
Args:
policies: list of lists of tuples in the form `(func, prob, level)`, `func`
is a string name of the augmentation function, `prob` is the probability
of applying the `func` operation, `level` is the input argument for
`func`.
image: tf.Tensor that the resulting policy will be applied to.
augmentation_hparams: Hparams associated with the NAS learned policy.
Returns:
A version of image that now has data augmentation applied to it based on
the `policies` pass into the function.
"""
replace_value = [128, 128, 128]
# func is the string name of the augmentation function, prob is the
# probability of applying the operation and level is the parameter associated
# with the tf op.
# tf_policies are functions that take in an image and return an augmented
# image.
tf_policies = []
for policy in policies:
tf_policy = []
# Link string name to the correct python function and make sure the correct
# argument is passed into that function.
for policy_info in policy:
policy_info = list(policy_info) + [replace_value, augmentation_hparams]
tf_policy.append(_parse_policy_info(*policy_info))
# Now build the tf policy that will apply the augmentation procedue
# on image.
def make_final_policy(tf_policy_):
def final_policy(image_):
for func, prob, args in tf_policy_:
image_ = _apply_func_with_prob(
func, image_, args, prob)
return image_
return final_policy
tf_policies.append(make_final_policy(tf_policy))
augmented_image = select_and_apply_random_policy(
tf_policies, image)
return augmented_image
def distort_image_with_autoaugment(image, augmentation_name):
"""Applies the AutoAugment policy to `image`.
AutoAugment is from the paper: https://arxiv.org/abs/1805.09501.
Args:
image: `Tensor` of shape [height, width, 3] representing an image.
augmentation_name: The name of the AutoAugment policy to use. The available
options are `v0` and `test`. `v0` is the policy used for
all of the results in the paper and was found to achieve the best results
on the COCO dataset. `v1`, `v2` and `v3` are additional good policies
found on the COCO dataset that have slight variation in what operations
were used during the search procedure along with how many operations are
applied in parallel to a single image (2 vs 3).
Returns:
A tuple containing the augmented versions of `image`.
"""
available_policies = {'v0': policy_v0,
'test': policy_vtest}
if augmentation_name not in available_policies:
raise ValueError('Invalid augmentation_name: {}'.format(augmentation_name))
policy = available_policies[augmentation_name]()
# Hparams that will be used for AutoAugment.
augmentation_hparams = config_dict.ConfigDict(dict(
cutout_const=100, translate_const=250))
return build_and_apply_nas_policy(policy, image, augmentation_hparams)
def distort_image_with_randaugment(image, num_layers, magnitude):
"""Applies the RandAugment policy to `image`.
RandAugment is from the paper https://arxiv.org/abs/1909.13719,
Args:
image: `Tensor` of shape [height, width, 3] representing an image.
num_layers: Integer, the number of augmentation transformations to apply
sequentially to an image. Represented as (N) in the paper. Usually best
values will be in the range [1, 3].
magnitude: Integer, shared magnitude across all augmentation operations.
Represented as (M) in the paper. Usually best values are in the range
[5, 30].
Returns:
The augmented version of `image`.
"""
replace_value = [128] * 3
tf.logging.info('Using RandAug.')
augmentation_hparams = config_dict.ConfigDict(dict(
cutout_const=40, translate_const=100))
available_ops = [
'AutoContrast', 'Equalize', 'Invert', 'Rotate', 'Posterize',
'Solarize', 'Color', 'Contrast', 'Brightness', 'Sharpness',
'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Cutout', 'SolarizeAdd']
for layer_num in range(num_layers):
op_to_select = tf.random_uniform(
[], maxval=len(available_ops), dtype=tf.int32)
random_magnitude = float(magnitude)
with tf.name_scope('randaug_layer_{}'.format(layer_num)):
for (i, op_name) in enumerate(available_ops):
prob = tf.random_uniform([], minval=0.2, maxval=0.8, dtype=tf.float32)
func, _, args = _parse_policy_info(op_name, prob, random_magnitude,
replace_value, augmentation_hparams)
image = tf.cond(
tf.equal(i, op_to_select),
# pylint:disable=g-long-lambda
lambda selected_func=func, selected_args=args: selected_func(
image, *selected_args),
# pylint:enable=g-long-lambda
lambda: image)
return image
================================================
FILE: perceiver/train/dataset.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""ImageNet dataset with pre-processing and augmentation.
Deng, et al CVPR 2009 - ImageNet: A large-scale hierarchical image database.
https://image-net.org/
"""
import enum
from typing import Any, Generator, Mapping, Optional, Sequence, Text, Tuple
import jax
import numpy as np
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
import tensorflow_probability as tfp
from perceiver.train import autoaugment
Batch = Mapping[Text, np.ndarray]
MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)
STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)
AUTOTUNE = tf.data.experimental.AUTOTUNE
INPUT_DIM = 224 # The number of pixels in the image resize.
class Split(enum.Enum):
"""ImageNet dataset split."""
TRAIN = 1
TRAIN_AND_VALID = 2
VALID = 3
TEST = 4
@classmethod
def from_string(cls, name: Text) -> 'Split':
return {'TRAIN': Split.TRAIN, 'TRAIN_AND_VALID': Split.TRAIN_AND_VALID,
'VALID': Split.VALID, 'VALIDATION': Split.VALID,
'TEST': Split.TEST}[name.upper()]
@property
def num_examples(self):
return {Split.TRAIN_AND_VALID: 1281167, Split.TRAIN: 1271167,
Split.VALID: 10000, Split.TEST: 50000}[self]
def load(
split: Split,
*,
is_training: bool,
# batch_dims should be:
# [device_count, per_device_batch_size] or [total_batch_size]
batch_dims: Sequence[int],
augmentation_settings: Mapping[str, Any],
# The shape to which images are resized.
im_dim: int = INPUT_DIM,
threadpool_size: int = 48,
max_intra_op_parallelism: int = 1,
) -> Generator[Batch, None, None]:
"""Loads the given split of the dataset."""
start, end = _shard(split, jax.host_id(), jax.host_count())
im_size = (im_dim, im_dim)
total_batch_size = np.prod(batch_dims)
tfds_split = tfds.core.ReadInstruction(_to_tfds_split(split),
from_=start, to=end, unit='abs')
ds = tfds.load('imagenet2012:5.*.*', split=tfds_split,
decoders={'image': tfds.decode.SkipDecoding()})
options = tf.data.Options()
options.experimental_threading.private_threadpool_size = threadpool_size
options.experimental_threading.max_intra_op_parallelism = (
max_intra_op_parallelism)
options.experimental_optimization.map_parallelization = True
if is_training:
options.experimental_deterministic = False
ds = ds.with_options(options)
if is_training:
if jax.host_count() > 1:
# Only cache if we are reading a subset of the dataset.
ds = ds.cache()
ds = ds.repeat()
ds = ds.shuffle(buffer_size=10 * total_batch_size, seed=0)
else:
if split.num_examples % total_batch_size != 0:
raise ValueError(f'Test/valid must be divisible by {total_batch_size}')
def crop_augment_preprocess(example):
image, _ = _preprocess_image(
example['image'], is_training, im_size, augmentation_settings)
label = tf.cast(example['label'], tf.int32)
out = {'images': image, 'labels': label}
if is_training:
if augmentation_settings['cutmix']:
out['mask'] = cutmix_padding(*im_size)
out['cutmix_ratio'] = tf.reduce_mean(out['mask'])
if augmentation_settings['mixup_alpha'] is not None:
beta = tfp.distributions.Beta(
augmentation_settings['mixup_alpha'],
augmentation_settings['mixup_alpha'])
out['mixup_ratio'] = beta.sample()
return out
ds = ds.map(crop_augment_preprocess, num_parallel_calls=AUTOTUNE)
# Mixup/cutmix by temporarily batching (using the per-device batch size):
use_cutmix = augmentation_settings['cutmix']
use_mixup = augmentation_settings['mixup_alpha'] is not None
if is_training and (use_cutmix or use_mixup):
inner_batch_size = batch_dims[-1]
# Apply mixup, cutmix, or mixup + cutmix on batched data.
# We use data from 2 batches to produce 1 mixed batch.
ds = ds.batch(inner_batch_size * 2)
if not use_cutmix and use_mixup:
ds = ds.map(my_mixup, num_parallel_calls=AUTOTUNE)
elif use_cutmix and not use_mixup:
ds = ds.map(my_cutmix, num_parallel_calls=AUTOTUNE)
elif use_cutmix and use_mixup:
ds = ds.map(my_mixup_cutmix, num_parallel_calls=AUTOTUNE)
# Unbatch for further processing.
ds = ds.unbatch()
for batch_size in reversed(batch_dims):
ds = ds.batch(batch_size)
ds = ds.prefetch(AUTOTUNE)
yield from tfds.as_numpy(ds)
# cutmix_padding, my_cutmix, my_mixup, and my_mixup_cutmix taken from:
# https://github.com/deepmind/deepmind-research/blob/master/nfnets/dataset.py
def cutmix_padding(h, w):
"""Returns image mask for CutMix.
Taken from (https://github.com/google/edward2/blob/master/experimental
/marginalization_mixup/data_utils.py#L367)
Args:
h: image height.
w: image width.
"""
r_x = tf.random.uniform([], 0, w, tf.int32)
r_y = tf.random.uniform([], 0, h, tf.int32)
# Beta dist in paper, but they used Beta(1,1) which is just uniform.
image1_proportion = tf.random.uniform([])
patch_length_ratio = tf.math.sqrt(1 - image1_proportion)
r_w = tf.cast(patch_length_ratio * tf.cast(w, tf.float32), tf.int32)
r_h = tf.cast(patch_length_ratio * tf.cast(h, tf.float32), tf.int32)
bbx1 = tf.clip_by_value(tf.cast(r_x - r_w // 2, tf.int32), 0, w)
bby1 = tf.clip_by_value(tf.cast(r_y - r_h // 2, tf.int32), 0, h)
bbx2 = tf.clip_by_value(tf.cast(r_x + r_w // 2, tf.int32), 0, w)
bby2 = tf.clip_by_value(tf.cast(r_y + r_h // 2, tf.int32), 0, h)
# Create the binary mask.
pad_left = bbx1
pad_top = bby1
pad_right = tf.maximum(w - bbx2, 0)
pad_bottom = tf.maximum(h - bby2, 0)
r_h = bby2 - bby1
r_w = bbx2 - bbx1
mask = tf.pad(
tf.ones((r_h, r_w)),
paddings=[[pad_top, pad_bottom], [pad_left, pad_right]],
mode='CONSTANT',
constant_values=0)
mask.set_shape((h, w))
return mask[..., None] # Add channel dim.
def my_cutmix(batch):
"""Apply CutMix: https://arxiv.org/abs/1905.04899."""
batch = dict(**batch)
bs = tf.shape(batch['images'])[0] // 2
mask = batch['mask'][:bs]
images = (mask * batch['images'][:bs] + (1.0 - mask) * batch['images'][bs:])
mix_labels = batch['labels'][bs:]
labels = batch['labels'][:bs]
ratio = batch['cutmix_ratio'][:bs]
return {'images': images, 'labels': labels,
'mix_labels': mix_labels, 'ratio': ratio}
def my_mixup(batch):
"""Apply mixup: https://arxiv.org/abs/1710.09412."""
batch = dict(**batch)
bs = tf.shape(batch['images'])[0] // 2
ratio = batch['mixup_ratio'][:bs, None, None, None]
images = (ratio * batch['images'][:bs] + (1.0 - ratio) * batch['images'][bs:])
mix_labels = batch['labels'][bs:]
labels = batch['labels'][:bs]
ratio = ratio[..., 0, 0, 0] # Unsqueeze
return {'images': images, 'labels': labels,
'mix_labels': mix_labels, 'ratio': ratio}
def my_mixup_cutmix(batch):
"""Apply mixup to half the batch, and cutmix to the other."""
batch = dict(**batch)
bs = tf.shape(batch['images'])[0] // 4
mixup_ratio = batch['mixup_ratio'][:bs, None, None, None]
mixup_images = (mixup_ratio * batch['images'][:bs]
+ (1.0 - mixup_ratio) * batch['images'][bs:2*bs])
mixup_labels = batch['labels'][:bs]
mixup_mix_labels = batch['labels'][bs:2*bs]
cutmix_mask = batch['mask'][2*bs:3*bs]
cutmix_images = (cutmix_mask * batch['images'][2*bs:3*bs]
+ (1.0 - cutmix_mask) * batch['images'][-bs:])
cutmix_labels = batch['labels'][2*bs:3*bs]
cutmix_mix_labels = batch['labels'][-bs:]
cutmix_ratio = batch['cutmix_ratio'][2*bs : 3*bs]
return {'images': tf.concat([mixup_images, cutmix_images], axis=0),
'labels': tf.concat([mixup_labels, cutmix_labels], axis=0),
'mix_labels': tf.concat([mixup_mix_labels, cutmix_mix_labels], 0),
'ratio': tf.concat([mixup_ratio[..., 0, 0, 0], cutmix_ratio], axis=0)}
def _to_tfds_split(split: Split) -> tfds.Split:
"""Returns the TFDS split appropriately sharded."""
# NOTE: Imagenet did not release labels for the test split used in the
# competition, so it has been typical at DeepMind to consider the VALID
# split the TEST split and to reserve 10k images from TRAIN for VALID.
if split in (
Split.TRAIN, Split.TRAIN_AND_VALID, Split.VALID):
return tfds.Split.TRAIN
else:
assert split == Split.TEST
return tfds.Split.VALIDATION
def _shard(
split: Split, shard_index: int, num_shards: int) -> Tuple[int, int]:
"""Returns [start, end) for the given shard index."""
assert shard_index < num_shards
arange = np.arange(split.num_examples)
shard_range = np.array_split(arange, num_shards)[shard_index]
start, end = shard_range[0], (shard_range[-1] + 1)
if split == Split.TRAIN:
# Note that our TRAIN=TFDS_TRAIN[10000:] and VALID=TFDS_TRAIN[:10000].
offset = Split.VALID.num_examples
start += offset
end += offset
return start, end
def _preprocess_image(
image_bytes: tf.Tensor,
is_training: bool,
image_size: Sequence[int],
augmentation_settings: Mapping[str, Any],
) -> Tuple[tf.Tensor, tf.Tensor]:
"""Returns processed and resized images."""
# Get the image crop.
if is_training:
image, im_shape = _decode_and_random_crop(image_bytes)
image = tf.image.random_flip_left_right(image)
else:
image, im_shape = _decode_and_center_crop(image_bytes)
assert image.dtype == tf.uint8
# Optionally apply RandAugment: https://arxiv.org/abs/1909.13719
if is_training:
if augmentation_settings['randaugment'] is not None:
# Input and output images are dtype uint8.
image = autoaugment.distort_image_with_randaugment(
image,
num_layers=augmentation_settings['randaugment']['num_layers'],
magnitude=augmentation_settings['randaugment']['magnitude'])
# Resize and normalize the image crop.
# NOTE: Bicubic resize (1) casts uint8 to float32 and (2) resizes without
# clamping overshoots. This means values returned will be outside the range
# [0.0, 255.0] (e.g. we have observed outputs in the range [-51.1, 336.6]).
image = tf.image.resize(
image, image_size, tf.image.ResizeMethod.BICUBIC)
image = _normalize_image(image)
return image, im_shape
def _normalize_image(image: tf.Tensor) -> tf.Tensor:
"""Normalize the image to zero mean and unit variance."""
image -= tf.constant(MEAN_RGB, shape=[1, 1, 3], dtype=image.dtype)
image /= tf.constant(STDDEV_RGB, shape=[1, 1, 3], dtype=image.dtype)
return image
def _distorted_bounding_box_crop(
image_bytes: tf.Tensor,
*,
jpeg_shape: tf.Tensor,
bbox: tf.Tensor,
min_object_covered: float,
aspect_ratio_range: Tuple[float, float],
area_range: Tuple[float, float],
max_attempts: int,
) -> Tuple[tf.Tensor, tf.Tensor]:
"""Generates cropped_image using one of the bboxes randomly distorted."""
bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
jpeg_shape,
bounding_boxes=bbox,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
max_attempts=max_attempts,
use_image_if_no_bounding_boxes=True)
# Crop the image to the specified bounding box.
offset_y, offset_x, _ = tf.unstack(bbox_begin)
target_height, target_width, _ = tf.unstack(bbox_size)
crop_window = [offset_y, offset_x, target_height, target_width]
if image_bytes.dtype == tf.dtypes.string:
image = tf.image.decode_and_crop_jpeg(image_bytes,
tf.stack(crop_window),
channels=3)
else:
image = tf.image.crop_to_bounding_box(image_bytes, *crop_window)
im_shape = tf.stack([target_height, target_width])
return image, im_shape
def _decode_whole_image(image_bytes: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
image = tf.io.decode_jpeg(image_bytes, channels=3)
im_shape = tf.io.extract_jpeg_shape(image_bytes, output_type=tf.int32)
return image, im_shape
def _decode_and_random_crop(
image_bytes: tf.Tensor
) -> Tuple[tf.Tensor, tf.Tensor]:
"""Make a random crop of INPUT_DIM."""
if image_bytes.dtype == tf.dtypes.string:
jpeg_shape = tf.image.extract_jpeg_shape(image_bytes)
else:
jpeg_shape = tf.shape(image_bytes)
bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
image, im_shape = _distorted_bounding_box_crop(
image_bytes,
jpeg_shape=jpeg_shape,
bbox=bbox,
min_object_covered=0.1,
aspect_ratio_range=(3 / 4, 4 / 3),
area_range=(0.08, 1.0),
max_attempts=10)
if tf.reduce_all(tf.equal(jpeg_shape, tf.shape(image))):
# If the random crop failed fall back to center crop.
image, im_shape = _decode_and_center_crop(image_bytes, jpeg_shape)
return image, im_shape
def _center_crop(image, crop_dim):
"""Center crops an image to a target dimension."""
image_height = image.shape[0]
image_width = image.shape[1]
offset_height = ((image_height - crop_dim) + 1) // 2
offset_width = ((image_width - crop_dim) + 1) // 2
return tf.image.crop_to_bounding_box(
image, offset_height, offset_width, crop_dim, crop_dim)
def _decode_and_center_crop(
image_bytes: tf.Tensor,
jpeg_shape: Optional[tf.Tensor] = None,
) -> Tuple[tf.Tensor, tf.Tensor]:
"""Crops to center of image with padding then scales."""
if jpeg_shape is None:
if image_bytes.dtype == tf.dtypes.string:
jpeg_shape = tf.image.extract_jpeg_shape(image_bytes)
else:
jpeg_shape = tf.shape(image_bytes)
image_height = jpeg_shape[0]
image_width = jpeg_shape[1]
padded_center_crop_size = tf.cast(
((INPUT_DIM / (INPUT_DIM + 32)) *
tf.cast(tf.minimum(image_height, image_width), tf.float32)), tf.int32)
offset_height = ((image_height - padded_center_crop_size) + 1) // 2
offset_width = ((image_width - padded_center_crop_size) + 1) // 2
crop_window = [offset_height, offset_width,
padded_center_crop_size, padded_center_crop_size]
if image_bytes.dtype == tf.dtypes.string:
image = tf.image.decode_and_crop_jpeg(image_bytes,
tf.stack(crop_window),
channels=3)
else:
image = tf.image.crop_to_bounding_box(image_bytes, *crop_window)
im_shape = tf.stack([padded_center_crop_size, padded_center_crop_size])
return image, im_shape
================================================
FILE: perceiver/train/experiment.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A reference training pipeline for Perceiver/Perceiver IO on ImageNet.
We use the Jaxline (https://github.com/deepmind/jaxline) training framework.
Two sets of hyperparameters are provided, the hyperparameters we used for the
Perceiver IO paper, and scaled-down hyperparameters for local testing.
This script should run out-of-the-box with the local hyper parameters.
The scaled-up hyperparameters requires a distributed learning setup to run,
and this script will need to be adapted to your specific setup.
"""
import functools
from typing import Generator, Mapping, Text, Tuple
from absl import app
from absl import flags
from absl import logging
import haiku as hk
import jax
import jax.numpy as jnp
from jaxline import base_config
from jaxline import experiment
from jaxline import platform
from jaxline import utils as jl_utils
from ml_collections import config_dict
import numpy as np
import optax
from perceiver import io_processors
from perceiver import perceiver
from perceiver.train import dataset
from perceiver.train import utils
FLAGS = flags.FLAGS
OptState = Tuple[optax.TraceState, optax.ScaleByScheduleState, optax.ScaleState]
Scalars = Mapping[Text, jnp.ndarray]
N_TRAIN_EXAMPLES = dataset.Split.TRAIN_AND_VALID.num_examples
N_CLASSES = 1000
# Only local/debug parameters are supported out of the box.
# To use the scaled-up hyperparameters, please adapt this script to your
# training setup and set this flag to False
IS_LOCAL = True
def get_training_steps(batch_size, n_epochs):
return (N_TRAIN_EXAMPLES * n_epochs) // batch_size
def get_config():
"""Return config object for training."""
use_debug_settings = IS_LOCAL
config = base_config.get_base_config()
# Experiment config.
local_batch_size = 2
# Modify this to adapt to your custom distributed learning setup
num_devices = 1
config.train_batch_size = local_batch_size * num_devices
config.n_epochs = 110
def _default_or_debug(default_value, debug_value):
return debug_value if use_debug_settings else default_value
n_train_examples = N_TRAIN_EXAMPLES
num_classes = N_CLASSES
config.experiment_kwargs = config_dict.ConfigDict(
dict(
config=dict(
optimizer=dict(
base_lr=5e-4,
max_norm=10.0, # < 0 to turn off.
schedule_type='constant_cosine',
weight_decay=1e-1,
decay_pos_embs=True,
scale_by_batch=True,
cosine_decay_kwargs=dict(
init_value=0.0,
warmup_epochs=0,
end_value=0.0,
),
step_decay_kwargs=dict(
decay_boundaries=[0.5, 0.8, 0.95],
decay_rate=0.1,
),
constant_cosine_decay_kwargs=dict(
constant_fraction=0.5,
end_value=0.0,
),
optimizer='lamb',
# Optimizer-specific kwargs:
adam_kwargs=dict(
b1=0.9,
b2=0.999,
eps=1e-8,
),
lamb_kwargs=dict(
b1=0.9,
b2=0.999,
eps=1e-6,
),
),
# Don't specify output_channels - it's not used for
# classifiers.
model=dict(
perceiver_kwargs=dict(
input_preprocessor=dict(
prep_type='pixels',
# Channels for conv/conv1x1 preprocessing:
num_channels=64,
# -------------------------
# Position encoding arguments:
# -------------------------
position_encoding_type='fourier',
concat_or_add_pos='concat',
spatial_downsample=1,
# If >0, project position to this size:
project_pos_dim=-1,
trainable_position_encoding_kwargs=dict(
num_channels=258, # Match default # for Fourier.
init_scale=0.02,
),
fourier_position_encoding_kwargs=dict(
num_bands=64,
max_resolution=(224, 224),
sine_only=False,
concat_pos=True,
),
),
encoder=dict(
num_self_attends_per_block=_default_or_debug(6, 2),
# Weights won't be shared if num_blocks is set to 1.
num_blocks=_default_or_debug(8, 2),
z_index_dim=512,
num_z_channels=1024,
num_cross_attend_heads=1,
num_self_attend_heads=8,
cross_attend_widening_factor=1,
self_attend_widening_factor=1,
dropout_prob=0.0,
# Position encoding for the latent array.
z_pos_enc_init_scale=0.02,
cross_attention_shape_for_attn='kv',
use_query_residual=True,
),
decoder=dict(
num_z_channels=1024,
use_query_residual=True,
# Position encoding for the output logits.
position_encoding_type='trainable',
trainable_position_encoding_kwargs=dict(
num_channels=1024,
init_scale=0.02,
),
),
),
),
training=dict(
images_per_epoch=n_train_examples,
label_smoothing=0.1,
n_epochs=config.get_oneway_ref('n_epochs'),
batch_size=config.get_oneway_ref('train_batch_size')
),
data=dict(
num_classes=num_classes,
# Run on smaller images to debug.
im_dim=_default_or_debug(224, 32),
augmentation=dict(
# Typical randaug params:
# num_layers in [1, 3]
# magnitude in [5, 30]
# Set randaugment to None to disable.
randaugment=dict(
num_layers=4,
magnitude=5),
cutmix=True,
# Mixup alpha should be in [0, 1].
# Set to None to disable.
mixup_alpha=0.2,
),
),
evaluation=dict(
subset='test',
batch_size=2,
),
)
)
)
# Training loop config.
config.training_steps = get_training_steps(
config.get_oneway_ref('train_batch_size'),
config.get_oneway_ref('n_epochs'))
config.log_train_data_interval = 60
config.log_tensors_interval = 60
config.save_checkpoint_interval = 300
config.eval_specific_checkpoint_dir = ''
config.best_model_eval_metric = 'eval_top_1_acc'
config.checkpoint_dir = '/tmp/perceiver_imagnet_checkpoints'
config.train_checkpoint_all_hosts = False
# Prevents accidentally setting keys that aren't recognized (e.g. in tests).
config.lock()
return config
class Experiment(experiment.AbstractExperiment):
"""ImageNet experiment."""
# A map from object properties that will be checkpointed to their name
# in a checkpoint. Currently we assume that these are all sharded
# device arrays.
CHECKPOINT_ATTRS = {
'_params': 'params',
'_state': 'state',
'_opt_state': 'opt_state',
}
def __init__(self, mode, init_rng, config):
"""Initializes experiment."""
super(Experiment, self).__init__(mode=mode, init_rng=init_rng)
self.mode = mode
self.init_rng = init_rng
self.config = config
# Checkpointed experiment state.
self._params = None
self._state = None
self._opt_state = None
# Input pipelines.
self._train_input = None
self._eval_input = None
self.forward = hk.transform_with_state(self._forward_fn)
# NOTE: We "donate" the `params, state, opt_state` arguments which allows
# JAX (on some backends) to reuse the device memory associated with these
# inputs to store the outputs of our function (which also start with
# `params, state, opt_state`).
self._update_func = jax.pmap(self._update_func, axis_name='i',
donate_argnums=(0, 1, 2))
self._eval_batch = jax.jit(self._eval_batch)
def _forward_fn(
self,
inputs: dataset.Batch,
is_training: bool,
) -> jnp.ndarray:
images = inputs['images']
perceiver_kwargs = self.config.model.perceiver_kwargs
input_preprocessor = io_processors.ImagePreprocessor(
**perceiver_kwargs['input_preprocessor'])
encoder = perceiver.PerceiverEncoder(**perceiver_kwargs['encoder'])
decoder = perceiver.ClassificationDecoder(
self.config.data.num_classes,
**perceiver_kwargs['decoder'])
model = perceiver.Perceiver(
encoder=encoder,
decoder=decoder,
input_preprocessor=input_preprocessor)
return model(images, is_training=is_training)
# _ _
# | |_ _ __ __ _(_)_ __
# | __| '__/ _` | | '_ \
# | |_| | | (_| | | | | |
# \__|_| \__,_|_|_| |_|
#
def step(self, global_step: int, rng: jnp.ndarray, # pytype: disable=signature-mismatch # jax-ndarray
*unused_args, **unused_kwargs):
"""See base class."""
if self._train_input is None:
self._initialize_train()
inputs = next(self._train_input)
self._params, self._state, self._opt_state, scalars = (
self._update_func(
self._params, self._state, self._opt_state, inputs, rng, global_step
))
scalars = jl_utils.get_first(scalars)
return scalars
def _initialize_train(self):
self._train_input = jl_utils.py_prefetch(self._build_train_input)
total_batch_size = self.config.training.batch_size
steps_per_epoch = (
self.config.training.images_per_epoch / self.config.training.batch_size)
total_steps = self.config.training.n_epochs * steps_per_epoch
# Scale by the (negative) learning rate.
self._lr_schedule = utils.get_learning_rate_schedule(
total_batch_size, steps_per_epoch, total_steps, self.config.optimizer)
self._optimizer = utils.make_optimizer(
self.config.optimizer,
self._lr_schedule)
# Check we haven't already restored params
if self._params is None:
logging.info('Initializing parameters.')
inputs = next(self._train_input)
init_net = jax.pmap(lambda *a: self.forward.init(*a, is_training=True))
init_opt = jax.pmap(self._optimizer.init)
# Init uses the same RNG key on all hosts+devices to ensure everyone
# computes the same initial state.
init_rng = jl_utils.bcast_local_devices(self.init_rng)
self._params, self._state = init_net(init_rng, inputs)
self._opt_state = init_opt(self._params)
def _load_data(self, split, is_training, batch_dims):
"""Wrapper for dataset loading."""
return dataset.load(
split=split,
is_training=is_training,
batch_dims=batch_dims,
im_dim=self.config.data.im_dim,
augmentation_settings=self.config.data.augmentation,
)
def _build_train_input(self) -> Generator[dataset.Batch, None, None]:
"""See base class."""
num_devices = jax.device_count()
global_batch_size = self.config.training.batch_size
per_device_batch_size, ragged = divmod(global_batch_size, num_devices)
if ragged:
raise ValueError(
f'Global batch size {global_batch_size} must be divisible by '
f'num devices {num_devices}')
split = dataset.Split.TRAIN_AND_VALID
return self._load_data(
split=split,
is_training=True,
batch_dims=[jax.local_device_count(), per_device_batch_size])
def _one_hot(self, value):
"""One-hot encoding potentially over a sequence of labels."""
y = jax.nn.one_hot(value, self.config.data.num_classes)
return y
def _loss_fn(
self,
params: hk.Params,
state: hk.State,
inputs: dataset.Batch,
rng: jnp.ndarray,
) -> Tuple[jnp.ndarray, Tuple[Scalars, hk.State]]:
logits, state = self.forward.apply(
params, state, rng, inputs, is_training=True)
label = self._one_hot(inputs['labels'])
# Handle cutmix/mixup label mixing:
if 'mix_labels' in inputs:
logging.info('Using mixup or cutmix!')
mix_label = self._one_hot(inputs['mix_labels'])
mix_ratio = inputs['ratio'][:, None]
label = mix_ratio * label + (1. - mix_ratio) * mix_label
# Apply label-smoothing to one-hot labels.
label_smoothing = self.config.training.label_smoothing
if not (label_smoothing >= 0. and label_smoothing < 1.):
raise ValueError(
f"'label_smoothing is {label_smoothing} and should be in [0, 1)")
if label_smoothing > 0:
smooth_positives = 1. - label_smoothing
smooth_negatives = label_smoothing / self.config.data.num_classes
label = smooth_positives * label + smooth_negatives
loss_w_batch = utils.softmax_cross_entropy(logits, label)
loss = jnp.mean(loss_w_batch, dtype=loss_w_batch.dtype)
scaled_loss = loss / jax.device_count()
metrics = utils.topk_correct(logits, inputs['labels'], prefix='')
metrics = jax.tree_map(jnp.mean, metrics)
top_1_acc = metrics['top_1_acc']
top_5_acc = metrics['top_5_acc']
loss_scalars = dict(
loss=loss,
top_1_acc=top_1_acc,
top_5_acc=top_5_acc,
)
return scaled_loss, (loss_scalars, state)
def _update_func(
self,
params: hk.Params,
state: hk.State,
opt_state: OptState,
inputs: dataset.Batch,
rng: jnp.ndarray,
global_step: int,
) -> Tuple[hk.Params, hk.State, OptState, Scalars]:
"""Applies an update to parameters and returns new state."""
# This function computes the gradient of the first output of loss_fn and
# passes through the other arguments unchanged.
grad_loss_fn = jax.grad(self._loss_fn, has_aux=True)
scaled_grads, (loss_scalars, state) = grad_loss_fn(
params, state, inputs, rng)
grads = jax.lax.psum(scaled_grads, axis_name='i')
# Grab the learning rate to log before performing the step.
learning_rate = self._lr_schedule(global_step)
# Compute and apply updates via our optimizer.
updates, opt_state = self._optimizer.update(grads, opt_state, params)
params = optax.apply_updates(params, updates)
n_params = 0
for k in params.keys(): # pytype: disable=attribute-error # numpy-scalars
for l in params[k]:
n_params = n_params + np.prod(params[k][l].shape) # pytype: disable=attribute-error # numpy-scalars
# Scalars to log (note: we log the mean across all hosts/devices).
scalars = {'learning_rate': learning_rate,
'n_params (M)': float(n_params/1e6),
'global_gradient_norm': optax.global_norm(grads)}
loss_scalars = {f'train_{k}': v for k, v in loss_scalars.items()}
scalars.update(loss_scalars)
scalars = jax.lax.pmean(scalars, axis_name='i')
return params, state, opt_state, scalars
# _
# _____ ____ _| |
# / _ \ \ / / _` | |
# | __/\ V / (_| | |
# \___| \_/ \__,_|_|
#
def evaluate(self, global_step, rng, **unused_args):
"""See base class."""
global_step = np.array(jl_utils.get_first(global_step))
scalars = jax.device_get(self._eval_epoch(jl_utils.get_first(rng)))
logging.info('[Step %d] Eval scalars: %s', global_step, scalars)
return scalars
def _eval_batch(
self,
params: hk.Params,
state: hk.State,
inputs: dataset.Batch,
rng: jnp.ndarray,
) -> Scalars:
"""Evaluates a batch."""
logits, _ = self.forward.apply(
params, state, rng, inputs, is_training=False)
labels = self._one_hot(inputs['labels'])
loss = utils.softmax_cross_entropy(logits, labels)
metrics = utils.topk_correct(logits, inputs['labels'], prefix='')
metrics = jax.tree_map(jnp.mean, metrics)
top_1_acc = metrics['top_1_acc']
top_5_acc = metrics['top_5_acc']
bs = logits.shape[0]
top_1_acc = jnp.expand_dims(top_1_acc, axis=0) * bs
top_5_acc = jnp.expand_dims(top_5_acc, axis=0) * bs
# NOTE: Returned values will be summed and finally divided by num_samples.
return {
'eval_loss': loss,
'eval_top_1_acc': top_1_acc, 'eval_top_5_acc': top_5_acc}
def _build_eval_input(self) -> Generator[dataset.Batch, None, None]:
split = dataset.Split.from_string(self.config.evaluation.subset)
return self._load_data(
split=split,
is_training=False,
batch_dims=[self.config.evaluation.batch_size])
def _eval_epoch(self, rng):
"""Evaluates an epoch."""
num_samples = 0.
summed_scalars = None
params = jl_utils.get_first(self._params)
state = jl_utils.get_first(self._state)
for inputs in self._build_eval_input():
num_samples += inputs['labels'].shape[0]
scalars = self._eval_batch(params, state, inputs, rng)
# Accumulate the sum of scalars for each step.
scalars = jax.tree_map(lambda x: jnp.sum(x, axis=0), scalars)
if summed_scalars is None:
summed_scalars = scalars
else:
summed_scalars = jax.tree_map(jnp.add, summed_scalars, scalars)
mean_scalars = jax.tree_map(lambda x: x / num_samples, summed_scalars)
return mean_scalars
if __name__ == '__main__':
flags.mark_flag_as_required('config')
app.run(functools.partial(platform.main, Experiment))
================================================
FILE: perceiver/train/launch_local.sh
================================================
#!/bin/bash
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Command for running training script.
PYTHONPATH=.::$PYTHONPATH python perceiver/train/experiment.py \
--config=perceiver/train/experiment.py --logtostderr
================================================
FILE: perceiver/train/utils.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities."""
from typing import Callable, List, Mapping, NamedTuple, Optional, Tuple, Union
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import optax
Batch = Mapping[str, np.ndarray]
OptState = Tuple[optax.TraceState, optax.ScaleByScheduleState, optax.ScaleState]
Scalars = Mapping[str, jnp.ndarray]
ParamsOrState = Union[hk.Params, hk.State]
NORM_NAMES = ['layer_norm', 'batchnorm']
# any_in and topk_correct taken from
# https://github.com/deepmind/deepmind-research/blob/master/nfnets/utils.py
@jax.vmap
def any_in(prediction, target):
"""For each row in a and b, checks if any element of a is in b."""
return jnp.isin(prediction, target)
def topk_correct(logits, labels, mask=None, prefix='', topk=(1, 5)):
"""Calculate top-k error for multiple k values."""
metrics = {}
argsorted_logits = jnp.argsort(logits)
for k in topk:
pred_labels = argsorted_logits[..., -k:]
# Get the number of examples where the label is in the top-k predictions
correct = any_in(pred_labels, labels).any(axis=-1).astype(jnp.float32)
if mask is not None:
correct *= mask
metrics[f'{prefix}top_{k}_acc'] = correct
return metrics
def softmax_cross_entropy(logits, labels):
"""Computes softmax cross entropy given logits and one-hot class labels.
Args:
logits: Logit output values.
labels: Ground truth one-hot-encoded labels.
Returns:
Loss value with the same shape as `labels`;
"""
return jnp.asarray(optax.softmax_cross_entropy(logits, labels))
def _get_batch_scaled_lr(total_batch_size, lr, scale_by_batch=True):
# This is the linear scaling rule in Section 5.1 of
# https://arxiv.org/pdf/1706.02677.pdf.
if scale_by_batch:
lr = (lr * total_batch_size) / 256
return lr
def get_learning_rate_schedule(
total_batch_size, steps_per_epoch, total_steps, optimizer_config):
"""Build the learning rate schedule function."""
base_lr = _get_batch_scaled_lr(total_batch_size, optimizer_config.base_lr,
optimizer_config.scale_by_batch)
schedule_type = optimizer_config.schedule_type
if schedule_type == 'steps':
boundaries = optimizer_config.step_decay_kwargs.decay_boundaries
boundaries.sort()
decay_rate = optimizer_config.step_decay_kwargs.decay_rate
boundaries_and_scales = {
int(boundary * total_steps): decay_rate for boundary in boundaries}
schedule_fn = optax.piecewise_constant_schedule(
init_value=base_lr, boundaries_and_scales=boundaries_and_scales)
elif schedule_type == 'cosine':
warmup_steps = (optimizer_config.cosine_decay_kwargs.warmup_epochs
* steps_per_epoch)
# Batch scale the other lr values as well:
init_value = _get_batch_scaled_lr(
total_batch_size,
optimizer_config.cosine_decay_kwargs.init_value,
optimizer_config.scale_by_batch)
end_value = _get_batch_scaled_lr(
total_batch_size,
optimizer_config.cosine_decay_kwargs.end_value,
optimizer_config.scale_by_batch)
schedule_fn = optax.warmup_cosine_decay_schedule(
init_value=init_value,
peak_value=base_lr,
warmup_steps=warmup_steps,
decay_steps=total_steps,
end_value=end_value)
elif schedule_type == 'constant_cosine':
# Convert end_value to alpha, used by cosine_decay_schedule.
alpha = optimizer_config.constant_cosine_decay_kwargs.end_value / base_lr
# Number of steps spent in constant phase.
constant_steps = int(
optimizer_config.constant_cosine_decay_kwargs.constant_fraction
* total_steps)
decay_steps = total_steps - constant_steps
constant_phase = optax.constant_schedule(value=base_lr)
decay_phase = optax.cosine_decay_schedule(
init_value=base_lr,
decay_steps=decay_steps,
alpha=alpha)
schedule_fn = optax.join_schedules(
schedules=[constant_phase, decay_phase],
boundaries=[constant_steps])
else:
raise ValueError(f'Unknown learning rate schedule: {schedule_type}')
return schedule_fn
def _weight_decay_exclude(
exclude_names: Optional[List[str]] = None
) -> Callable[[str, str, jnp.ndarray], bool]:
"""Logic for deciding which parameters to include for weight decay..
Args:
exclude_names: an optional list of names to include for weight_decay. ['w']
by default.
Returns:
A predicate that returns True for params that need to be excluded from
weight_decay.
"""
# By default weight_decay the weights but not the biases.
if not exclude_names:
exclude_names = ['b']
def exclude(module_name: str, name: str, value: jnp.array):
del value
# Do not weight decay the parameters of normalization blocks.
if any([norm_name in module_name for norm_name in NORM_NAMES]):
return True
else:
return name in exclude_names
return exclude
class AddWeightDecayState(NamedTuple):
"""Stateless transformation."""
def add_weight_decay(
weight_decay: float,
exclude_names: Optional[List[str]] = None) -> optax.GradientTransformation:
"""Add parameter scaled by `weight_decay` to the `updates`.
Same as optax.add_decayed_weights but can exclude parameters by name.
Args:
weight_decay: weight_decay coefficient.
exclude_names: an optional list of names to exclude for weight_decay. ['b']
by default.
Returns:
An (init_fn, update_fn) tuple.
"""
def init_fn(_):
return AddWeightDecayState()
def update_fn(updates, state, params):
exclude = _weight_decay_exclude(exclude_names=exclude_names)
u_ex, u_in = hk.data_structures.partition(exclude, updates)
_, p_in = hk.data_structures.partition(exclude, params)
u_in = jax.tree_map(lambda g, p: g + weight_decay * p, u_in, p_in)
updates = hk.data_structures.merge(u_ex, u_in)
return updates, state
return optax.GradientTransformation(init_fn, update_fn)
def make_optimizer(optimizer_config, lr_schedule):
"""Construct the optax optimizer with given LR schedule."""
if (optimizer_config.get('decay_pos_embs') is None or
optimizer_config.decay_pos_embs):
# Decay learned position embeddings by default.
weight_decay_exclude_names = ['b']
else:
weight_decay_exclude_names = ['pos_embs', 'b']
optax_chain = []
if optimizer_config.max_norm > 0:
optax_chain.append(
optax.clip_by_global_norm(optimizer_config.max_norm))
if optimizer_config.optimizer == 'adam':
# See: https://arxiv.org/abs/1412.6980
optax_chain.extend([
optax.scale_by_adam(**optimizer_config.adam_kwargs),
add_weight_decay(
optimizer_config.weight_decay,
exclude_names=weight_decay_exclude_names)
])
elif optimizer_config.optimizer == 'lamb':
# See: https://arxiv.org/abs/1904.00962
optax_chain.extend([
optax.scale_by_adam(**optimizer_config.lamb_kwargs),
add_weight_decay(
optimizer_config.weight_decay,
exclude_names=weight_decay_exclude_names),
optax.scale_by_trust_ratio()
])
else:
raise ValueError(f'Undefined optimizer {optimizer_config.optimizer}')
# Scale by the (negative) learning rate.
optax_chain.extend([
optax.scale_by_schedule(lr_schedule),
optax.scale(-1),
])
return optax.chain(*optax_chain)
================================================
FILE: physics_inspired_models/README.md
================================================
# Implementation of multiple physics inspired models for modelling dynamics
This repository contains an implementation of different physics inspired models
used in the papers: **SyMetric: Measuring the Quality of Learnt Hamiltonian
Dynamics Inferred from Vision** and **Which priors matter? Benchmarking models
for learning latent dynamics**.
## Contributing
This is purely research code, provided with no further intentions of support or
any guarantees of backward compatibility.
## Installation
All package requirements are listed in `requirements.txt`.
You will still need to download and setup the datasets from the
[DeepMind Hamiltonian Dynamics Suite] manually.
```shell
git clone git@github.com:deepmind/deepmind-research.git
pip install -r ./deepmind_research/physics_inspired_models/requirements.txt
pip install ./deepmind_research/physics_inspired_models
pip install --upgrade "jax[XXX]"
```
where `XXX` is the correct type of accelerator that you have on your machine.
Note that if you are using a GPU you might need `XXX` to also include the
correct version of CUDA and cuDNN installed on your machine.
For more details please read [here](https://github.com/google/jax#installation).
## Usage
The file `jaxline_configs.py` contains all the configurations specifications for
the experiments in the two papers. To run an experiment, in addition to passing
the location of the configs file, you must provide extra arguments in the
following manner:
`${name_of_configuration},${index_in_sweep},${dataset_name}`
For example to run the second hyper-parameter configuration of the improved
Hamiltonian Generative Network (HGN++) on the mass-spring dataset you should
run in the command line (assuming that you are in the folder of the project):
```shell
python3 jaxline_train.py \
--config="jaxline_configs.py:sym_metric_hgn_plus_plus_sweep,1,toy_physics/mass_spring" \
--jaxline_mode="train" \
--logtostderr
```
## Reference
**SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision**
Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev
URL: https://openreview.net/forum?id=9Qu0U9Fj7IP
**Which priors matter? Benchmarking models for learning latent dynamics**
Aleksandar Botev, Drew Jaegle, Peter Wirnsberger, Daniel Hennes and Irina
Higgins
URL: https://openreview.net/forum?id=qBl8hnwR0px
[DeepMind Hamiltonian Dynamics Suite]: https://github.com/deepmind/dm_hamiltonian_dynamics_suite
================================================
FILE: physics_inspired_models/__init__.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
================================================
FILE: physics_inspired_models/eval_metric.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module containing model evaluation metric."""
import _thread as thread
import sys
import threading
import time
import warnings
from absl import logging
import distrax
import numpy as np
from sklearn import linear_model
from sklearn import model_selection
from sklearn import preprocessing
def quit_function(fn_name):
logging.error('%s took too long', fn_name)
sys.stderr.flush()
thread.interrupt_main()
def exit_after(s):
"""Use as decorator to exit function after s seconds."""
def outer(fn):
def inner(*args, **kwargs):
timer = threading.Timer(s, quit_function, args=[fn.__name__])
timer.start()
try:
result = fn(*args, **kwargs)
finally:
timer.cancel()
return result
return inner
return outer
@exit_after(400)
def do_grid_search(data_x_exp, data_y, clf, parameters, cv):
scoring_choice = 'explained_variance'
regressor = model_selection.GridSearchCV(
clf, parameters, cv=cv, refit=True, scoring=scoring_choice)
regressor.fit(data_x_exp, data_y)
return regressor
def symplectic_matrix(dim):
"""Return anti-symmetric identity matrix of given dimensionality."""
half_dims = int(dim/2)
eye = np.eye(half_dims)
zeros = np.zeros([half_dims, half_dims])
top_rows = np.concatenate([zeros, - eye], axis=1)
bottom_rows = np.concatenate([eye, zeros], axis=1)
return np.concatenate([top_rows, bottom_rows], axis=0)
def create_latent_mask(z0, dist_std_threshold=0.5):
"""Create mask based on informativeness of each latent dimension.
For stochastic models those latent dimensions that are too close to the prior
are likely to be uninformative and can be ignored.
Args:
z0: distribution or array of phase space
dist_std_threshold: informative latents have average inferred stds <
dist_std_threshold
Returns:
latent_mask_final: boolean mask of the same dimensionality as z0
"""
if isinstance(z0, distrax.Normal):
std_vals = np.mean(z0.variance(), axis=0)
elif isinstance(z0, distrax.Distribution):
raise NotImplementedError()
else:
# If the latent is deterministic, pass through all dimensions
return np.array([True]*z0.shape[-1])
tensor_shape = std_vals.shape
half_dims = int(tensor_shape[-1] / 2)
std_vals_q = std_vals[:half_dims]
std_vals_p = std_vals[half_dims:]
# Keep both q and corresponding p as either one is informative
informative_latents_inds = np.array([
x for x in range(len(std_vals_q)) if
std_vals_q[x] < dist_std_threshold or std_vals_p[x] < dist_std_threshold
])
if informative_latents_inds.shape[0] > 0:
latent_mask_final = np.zeros_like(std_vals_q)
latent_mask_final[informative_latents_inds] = 1
latent_mask_final = np.concatenate([latent_mask_final, latent_mask_final])
latent_mask_final = latent_mask_final == 1
return latent_mask_final
else:
return np.array([True]*tensor_shape[-1])
def standardize_data(data):
"""Applies the sklearn standardization to the data."""
scaler = preprocessing.StandardScaler()
scaler.fit(data)
return scaler.transform(data)
def find_best_polynomial(data_x, data_y, max_poly_order, rsq_threshold,
max_dim_n=32,
alpha_sweep=None,
max_iter=1000, cv=2):
"""Find minimal polynomial expansion that is sufficient to explain data using Lasso regression."""
rsq = 0
poly_order = 1
if not np.any(alpha_sweep):
alpha_sweep = [1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2]
# Avoid a large polynomial expansion for large latent sizes
if data_x.shape[-1] > max_dim_n:
print(f'>WARNING! Data is too high dimensional at {data_x.shape[-1]}')
print('>WARNING! Setting max_poly_order = 1')
max_poly_order = 1
while rsq < rsq_threshold and poly_order <= max_poly_order:
time_start = time.perf_counter()
poly = preprocessing.PolynomialFeatures(poly_order, include_bias=False)
data_x_exp = poly.fit_transform(data_x)
time_end = time.perf_counter()
print(
f'Took {time_end-time_start}s to create polynomial features of order '
f'{poly_order} and size {data_x_exp.shape[1]}.')
with warnings.catch_warnings():
warnings.simplefilter('ignore')
time_start = time.perf_counter()
clf = linear_model.Lasso(
random_state=0, max_iter=max_iter, normalize=False, warm_start=False)
parameters = {'alpha': alpha_sweep}
try:
regressor = do_grid_search(data_x_exp, data_y, clf, parameters, cv)
time_end = time.perf_counter()
print(f'Took {time_end-time_start}s to do regression grid search.')
# Get rsq results
time_start = time.perf_counter()
clf = linear_model.Lasso(
random_state=0,
alpha=regressor.best_params_['alpha'],
max_iter=max_iter,
normalize=False,
warm_start=False)
clf.fit(data_x_exp, data_y)
rsq = clf.score(data_x_exp, data_y)
time_end = time.perf_counter()
print(f'Took {time_end-time_start}s to get rsq results.')
old_regressor = regressor
old_poly_order = poly_order
old_poly = poly
old_data_x_exp = data_x_exp
old_rsq = rsq
old_clf = clf
print(f'Polynomial of order {poly_order} with '
f' alpha={regressor.best_params_} RSQ: {rsq}')
poly_order += 1
except KeyboardInterrupt:
time_end = time.perf_counter()
print(f'Timed out after {time_end-time_start}s of doing grid search.')
# pytype: disable=name-error # py39-upgrade
print(f'Continuing with previous poly_order={old_poly_order}...')
regressor = old_regressor
poly_order = old_poly_order
poly = old_poly
data_x_exp = old_data_x_exp
rsq = old_rsq
clf = old_clf
# pytype: enable=name-error # py39-upgrade
print(f'Polynomial of order {poly_order} with '
f' alpha={regressor.best_params_} RSQ: {rsq}')
break
return clf, poly, data_x_exp, rsq
def eval_monomial_grad(feature, x, w, grad_acc):
"""Accumulates gradient from polynomial features and their weights."""
features = feature.split(' ')
variable_indices = []
grads = np.ones(len(features)) * w
for i, feature in enumerate(features):
name_and_power = feature.split('^')
if len(name_and_power) == 1:
name, power = name_and_power[0], 1
else:
name, power = name_and_power
power = int(power)
var_index = int(name[1:])
variable_indices.append(var_index)
new_prod = np.ones_like(grads) * (x[var_index] ** power)
# This needs a special case, for situation where x[index] = 0.0
if power == 1:
new_prod[i] = 1.0
else:
new_prod[i] = power * (x[var_index] ** (power - 1))
grads = grads * new_prod
grad_acc[variable_indices] += grads
return grad_acc
def compute_jacobian_manual(x, polynomial_features, weight_matrix, tolerance):
"""Computes the jacobian manually."""
# Put together the equation for each output var
# polynomial_features = np.array(polynomial_obj.get_feature_names())
weight_mask = np.abs(weight_matrix) > tolerance
weight_matrix = weight_mask * weight_matrix
jacobians = list()
for i in range(weight_matrix.shape[0]):
grad_accumulator = np.zeros_like(x)
for j, feature in enumerate(polynomial_features):
eval_monomial_grad(feature, x, weight_matrix[i, j], grad_accumulator)
jacobians.append(grad_accumulator)
return np.stack(jacobians)
def calculate_jacobian_prod(jacobian, noise_eps=1e-6):
"""Calculates AA*, where A=JEJ^T and A*=JE^TJ^T, which should be I."""
# Add noise as 0 in jacobian creates issues in calculations later
jacobian = jacobian + noise_eps
sym_matrix = symplectic_matrix(jacobian.shape[1])
pred = np.matmul(jacobian, sym_matrix)
pred = np.matmul(pred, np.transpose(jacobian))
pred_t = np.matmul(jacobian, np.transpose(sym_matrix))
pred_t = np.matmul(pred_t, np.transpose(jacobian))
pred_id = np.matmul(pred, pred_t)
return pred_id
def normalise_jacobian_prods(jacobian_preds):
"""Normalises Jacobians evaluated at various points by a constant."""
stacked_preds = np.stack(jacobian_preds)
# For each attempt at estimating E, get the max term, and take their average
normalisation_factor = np.mean(np.max(np.abs(stacked_preds), axis=(1, 2)))
if normalisation_factor != 0:
stacked_preds = stacked_preds/normalisation_factor
return stacked_preds
def calculate_symetric_score(
gt_data,
model_data,
max_poly_order,
max_sym_score,
rsq_threshold,
sym_threshold,
evaluation_point_n,
trajectory_n=1,
weight_tolerance=1e-5,
alpha_sweep=None,
max_iter=1000,
cv=2):
"""Finds minimal polynomial expansion to explain data using Lasso regression, gets the Jacobian of the mapping and calculates how symplectic the map is."""
model_data = model_data[..., :gt_data.shape[0], :]
# Fing polynomial expansion that explains enough variance in the gt data
print('Finding best polynomial expansion...')
time_start = time.perf_counter()
# Clean up model data to ensure it doesn't contain NaN, infinity
# or values too large for dtype('float32')
model_data = np.nan_to_num(model_data)
model_data = np.clip(model_data, -999999, 999999)
clf, poly, model_data_exp, best_rsq = find_best_polynomial(
model_data, gt_data, max_poly_order, rsq_threshold,
32, alpha_sweep, max_iter, cv)
time_end = time.perf_counter()
print(f'Took {time_end - time_start}s to find best polynomial.')
# Calculate Symplecticity score
all_raw_scores = []
features = np.array(poly.get_feature_names())
points_per_trajectory = int(len(gt_data) / trajectory_n)
for trajectory in range(trajectory_n):
random_data_inds = np.random.permutation(
range(points_per_trajectory))[:evaluation_point_n]
jacobian_preds = []
for point_ind in random_data_inds:
input_data_point = model_data[points_per_trajectory * trajectory +
point_ind]
time_start = time.perf_counter()
jacobian = compute_jacobian_manual(input_data_point, features,
clf.coef_, weight_tolerance)
pred = calculate_jacobian_prod(jacobian)
jacobian_preds.append(pred)
time_end = time.perf_counter()
print(f'Took {time_end - time_start}s to evaluate jacobian '
f'around point {point_ind}.')
# Normalise
normalised_jacobian_preds = normalise_jacobian_prods(jacobian_preds)
# The score is measured as the deviation from I
identity = np.eye(normalised_jacobian_preds.shape[-1])
scores = np.mean(np.power(normalised_jacobian_preds - identity, 2),
axis=(1, 2))
all_raw_scores.append(scores)
sym_score = np.min([np.mean(all_raw_scores), max_sym_score])
# Calculate final SyMetric score
if best_rsq > rsq_threshold and sym_score < sym_threshold:
sy_metric = 1.0
else:
sy_metric = 0.0
results = {
'poly_exp_order': poly.get_params()['degree'],
'rsq': best_rsq,
'sym': sym_score,
'SyMetric': sy_metric,
}
with np.printoptions(precision=4, suppress=True):
print(f'----------------FINAL RESULTS FOR {trajectory_n} '
'TRAJECTORIES------------------')
print(f'BEST POLYNOMIAL EXPANSION ORDER: {results["poly_exp_order"]}')
print(f'BEST RSQ (1-best): {results["rsq"]}')
print(f'SYMPLECTICITY SCORE AROUND ALL POINTS AND ALL '
f'TRAJECTORIES (0-best): {sym_score}')
print(f'SyMETRIC SCORE: {sy_metric}')
print(f'----------------FINAL RESULTS FOR {trajectory_n} '
f'TRAJECTORIES------------------')
return results, clf, poly, model_data_exp
================================================
FILE: physics_inspired_models/integrators.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module containing the implementations of the various numerical integrators.
Higher order methods mostly taken from [1].
References:
[1] Leimkuhler, Benedict and Sebastian Reich. Simulating hamiltonian dynamics.
Vol. 14. Cambridge university press, 2004.
[2] Forest, Etienne and Ronald D. Ruth. Fourth-order symplectic integration.
Physica D: Nonlinear Phenomena 43.1 (1990): 105-117.
[3] Blanes, Sergio and Per Christian Moan. Practical symplectic partitioned
Runge–Kutta and Runge–Kutta–Nyström methods. Journal of Computational and
Applied Mathematics 142.2 (2002): 313-330.
[4] McLachlan, Robert I. On the numerical integration of ordinary differential
equations by symmetric composition methods. SIAM Journal on Scientific
Computing 16.1 (1995): 151-168.
[5] Yoshida, Haruo. Construction of higher order symplectic integrators.
Physics letters A 150.5-7 (1990): 262-268.
[6] Süli, Endre; Mayers, David (2003), An Introduction to Numerical Analysis,
Cambridge University Press, ISBN 0-521-00794-1.
[7] Hairer, Ernst; Nørsett, Syvert Paul; Wanner, Gerhard (1993), Solving
ordinary differential equations I: Nonstiff problems, Berlin, New York:
Springer-Verlag, ISBN 978-3-540-56670-0.
"""
from typing import Callable, Dict, Optional, Sequence, Tuple, TypeVar, Union
from dm_hamiltonian_dynamics_suite.hamiltonian_systems import phase_space
import jax
from jax import lax
from jax.experimental import ode
import jax.numpy as jnp
import numpy as np
M = TypeVar("M")
TM = TypeVar("TM")
TimeInterval = Union[jnp.ndarray, Tuple[float, float]]
# _____ _
# / ____| | |
# | | __ ___ _ __ ___ _ __ __ _| |
# | | |_ |/ _ \ '_ \ / _ \ '__/ _` | |
# | |__| | __/ | | | __/ | | (_| | |
# \_____|\___|_| |_|\___|_| \__,_|_|
# _____ _ _ _
# |_ _| | | | | (_)
# | | _ __ | |_ ___ __ _ _ __ __ _| |_ _ ___ _ __
# | | | '_ \| __/ _ \/ _` | '__/ _` | __| |/ _ \| '_ \
# _| |_| | | | || __/ (_| | | | (_| | |_| | (_) | | | |
# |_____|_| |_|\__\___|\__, |_| \__,_|\__|_|\___/|_| |_|
# __/ |
# |___/
GeneralTangentFunction = Callable[
[
Optional[Union[float, jnp.ndarray]], # t
M # y
],
TM # dy_dt
]
GeneralIntegrator = Callable[
[
GeneralTangentFunction,
Optional[Union[float, jnp.ndarray]], # t
M, # y
jnp.ndarray, # dt
],
M # y_next
]
def solve_ivp_dt(
fun: GeneralTangentFunction,
y0: M,
t0: Union[float, jnp.ndarray],
dt: Union[float, jnp.ndarray],
method: Union[str, GeneralIntegrator],
num_steps: Optional[int] = None,
steps_per_dt: int = 1,
use_scan: bool = True,
ode_int_kwargs: Optional[Dict[str, Union[float, int]]] = None
) -> Tuple[jnp.ndarray, M]:
"""Solve an initial value problem for a system of ODEs using explicit method.
This function numerically integrates a system of ordinary differential
equations given an initial value::
dy / dt = f(t, y)
y(t0) = y0
Here t is a one-dimensional independent variable (time), y(t) is an
n-dimensional vector-valued function (state), and an n-dimensional
vector-valued function f(t, y) determines the differential equations.
The goal is to find y(t) approximately satisfying the differential
equations, given an initial value y(t0)=y0.
All of the solvers supported here are explicit and non-adaptive. This makes
them easy to run with a fixed amount of computation and ensures solutions are
easily differentiable.
Args:
fun: callable
Right-hand side of the system. The calling signature is ``fun(t, y)``.
Here `t` is a scalar representing the time instance. `y` can be any
type `M`, including a flat array, that is registered as a
pytree. In addition, there is a type denoted as `TM` that represents
the tangent space to `M`. It is assumed that any element of `TM` can be
multiplied by arrays and scalars, can be added to other `TM` instances
as well as they can be right added to an element of `M`, that is
add(M, TM) exists. The function should return an element of `TM` that
defines the time derivative of `y`.
y0: an instance of `M`
Initial state at `t_span[0]`.
t0: float or array.
The initial time point of integration.
dt: array
Array containing all consecutive increments in time, at which the integral
to be evaluated. The size of this array along axis 0 defines the number of
steps that the integrator would do.
method: string or `GeneralIntegrator`
The integrator method to use. Possible values for string are:
* general_euler - see `GeneralEuler`
* rk2 - see `RungaKutta2`
* rk4 - see `RungaKutta4`
* rk38 - see `RungaKutta38`
num_steps: Optional int.
If provided the `dt` will be treated as the same per step time interval,
applied for this many steps. In other words setting this argument is
equivalent to replicating `dt` num_steps times and stacking over axis=0.
steps_per_dt: int
This determines the overall step size. Between any two values of t_eval
the step size is `dt = (t_eval[i+1] - t_eval[i]) / steps_per_dt.
use_scan: bool
Whether for the loop to use `lax.scan` or a python loop
ode_int_kwargs: dict
Extra arguments to be passed to `ode.odeint` when method="adaptive"
Returns:
t: array
Time points at which the solution is evaluated.
y : an instance of M
Values of the solution at `t`.
"""
if method == "adaptive":
ndim = y0.q.ndim if isinstance(y0, phase_space.PhaseSpace) else y0.ndim
signs = jnp.asarray(jnp.sign(dt))
signs = signs.reshape([-1] + [1] * (ndim - 1))
if isinstance(dt, float) or dt.ndim == 0:
true_t_eval = t0 + dt * np.arange(1, num_steps + 1)
else:
true_t_eval = t0 + dt[None] * np.arange(1, num_steps + 1)[:, None]
if isinstance(dt, float):
dt = np.asarray(dt)
if isinstance(dt, np.ndarray) and dt.ndim > 0:
if np.all(np.abs(dt) != np.abs(dt[0])):
raise ValueError("Not all values of `dt` where the same.")
elif isinstance(dt, jnp.ndarray) and dt.ndim > 0:
raise ValueError("The code here works only when `dy_dt` is time "
"independent and `np.abs(dt)` is the same. For this we "
"allow calling this only with numpy (not jax.numpy) "
"arrays.")
dt: jnp.ndarray = jnp.abs(jnp.asarray(dt))
dt = dt.reshape([-1])[0]
t_eval = t0 + dt * np.arange(num_steps + 1)
outputs = ode.odeint(
func=lambda y_, t_: fun(None, y_) * signs,
y0=y0,
t=jnp.abs(t_eval - t0),
**(ode_int_kwargs or dict())
)
# Note that we do not return the initial point
return true_t_eval, jax.tree_map(lambda x: x[1:], outputs)
method = get_integrator(method)
if num_steps is not None:
dt = jnp.repeat(jnp.asarray(dt)[None], repeats=num_steps, axis=0)
t_eval = t0 + jnp.cumsum(dt, axis=0)
t0 = jnp.ones_like(t_eval[..., :1]) * t0
t = jnp.concatenate([t0, t_eval[..., :-1]], axis=-1)
def loop_body(y_: M, t_dt: Tuple[jnp.ndarray, jnp.ndarray]) -> Tuple[M, M]:
t_, dt_ = t_dt
dt_: jnp.ndarray = dt_ / steps_per_dt
for _ in range(steps_per_dt):
y_ = method(fun, t_, y_, dt_)
t_ = t_ + dt_
return y_, y_
if use_scan:
return t_eval, lax.scan(loop_body, init=y0, xs=(t, dt))[1]
else:
y = [y0]
for t_and_dt_i in zip(t, dt):
y.append(loop_body(y[-1], t_and_dt_i)[0])
# Note that we do not return the initial point
return t_eval, jax.tree_map(lambda *args: jnp.stack(args, axis=0),
*y[1:])
def solve_ivp_dt_two_directions(
fun: GeneralTangentFunction,
y0: M,
t0: Union[float, jnp.ndarray],
dt: Union[float, jnp.ndarray],
method: Union[str, GeneralIntegrator],
num_steps_forward: int,
num_steps_backward: int,
include_y0: bool = True,
steps_per_dt: int = 1,
use_scan: bool = True,
ode_int_kwargs: Optional[Dict[str, Union[float, int]]] = None
) -> M:
"""Equivalent to `solve_ivp_dt` but you can specify unrolling the problem for a fixed number of steps in both time directions."""
yt = []
if num_steps_backward > 0:
yt_bck = solve_ivp_dt(
fun=fun,
y0=y0,
t0=t0,
dt=- dt,
method=method,
num_steps=num_steps_backward,
steps_per_dt=steps_per_dt,
use_scan=use_scan,
ode_int_kwargs=ode_int_kwargs
)[1]
yt.append(jax.tree_map(lambda x: jnp.flip(x, axis=0), yt_bck))
if include_y0:
yt.append(jax.tree_map(lambda x: x[None], y0))
if num_steps_forward > 0:
yt_fwd = solve_ivp_dt(
fun=fun,
y0=y0,
t0=t0,
dt=dt,
method=method,
num_steps=num_steps_forward,
steps_per_dt=steps_per_dt,
use_scan=use_scan,
ode_int_kwargs=ode_int_kwargs
)[1]
yt.append(yt_fwd)
if len(yt) > 1:
return jax.tree_map(lambda *a: jnp.concatenate(a, axis=0), *yt)
else:
return yt[0]
def solve_ivp_t_eval(
fun: GeneralTangentFunction,
t_span: TimeInterval,
y0: M,
method: Union[str, GeneralIntegrator],
t_eval: Optional[jnp.ndarray] = None,
steps_per_dt: int = 1,
use_scan: bool = True,
ode_int_kwargs: Optional[Dict[str, Union[float, int]]] = None
) -> Tuple[jnp.ndarray, M]:
"""Solve an initial value problem for a system of ODEs using an explicit method.
This function numerically integrates a system of ordinary differential
equations given an initial value::
dy / dt = f(t, y)
y(t0) = y0
Here t is a one-dimensional independent variable (time), y(t) is an
n-dimensional vector-valued function (state), and an n-dimensional
vector-valued function f(t, y) determines the differential equations.
The goal is to find y(t) approximately satisfying the differential
equations, given an initial value y(t0)=y0.
All of the solvers supported here are explicit and non-adaptive. This in
terms makes them easy to run with fixed amount of computation and
the solutions to be easily differentiable.
Args:
fun: callable
Right-hand side of the system. The calling signature is ``fun(t, y)``.
Here `t` is a scalar representing the time instance. `y` can be any
type `M`, including a flat array, that is registered as a
pytree. In addition, there is a type denoted as `TM` that represents
the tangent space to `M`. It is assumed that any element of `TM` can be
multiplied by arrays and scalars, can be added to other `TM` instances
as well as they can be right added to an element of `M`, that is
add(M, TM) exists. The function should return an element of `TM` that
defines the time derivative of `y`.
t_span: 2-tuple of floats
Interval of integration (t0, tf). The solver starts with t=t0 and
integrates until it reaches t=tf.
y0: an instance of `M`
Initial state at `t_span[0]`.
method: string or `GeneralIntegrator`
The integrator method to use. Possible values for string are:
* general_euler - see `GeneralEuler`
* rk2 - see `RungaKutta2`
* rk4 - see `RungaKutta4`
* rk38 - see `RungaKutta38`
t_eval: array or None.
Times at which to store the computed solution. Must be sorted and lie
within `t_span`. If None then t_eval = [t_span[-1]]
steps_per_dt: int
This determines the overall step size. Between any two values of t_eval
the step size is `dt = (t_eval[i+1] - t_eval[i]) / steps_per_dt.
use_scan: bool
Whether for the loop to use `lax.scan` or a python loop
ode_int_kwargs: dict
Extra arguments to be passed to `ode.odeint` when method="adaptive"
Returns:
t: array
Time points at which the solution is evaluated.
y : an instance of M
Values of the solution at `t`.
"""
# Check for t_eval
if t_eval is None:
t_eval = np.asarray([t_span[-1]])
if isinstance(t_span[0], float) and isinstance(t_span[1], float):
t_span = np.asarray(t_span)
elif isinstance(t_span[0], float) and isinstance(t_span[1], jnp.ndarray):
t_span = (np.full_like(t_span[1], t_span[0]), t_span[1])
t_span = np.stack(t_span, axis=0)
elif isinstance(t_span[1], float) and isinstance(t_span[0], jnp.ndarray):
t_span = (t_span[0], jnp.full_like(t_span[0], t_span[1]))
t_span = np.stack(t_span, axis=0)
else:
t_span = np.stack(t_span, axis=0)
def check_span(span, ts):
# Verify t_span and t_eval
if span[0] < span[1]:
# Forward in time
if not np.all(np.logical_and(span[0] <= ts, ts <= span[1])):
raise ValueError("Values in `t_eval` are not within `t_span`.")
if not np.all(ts[:-1] < ts[1:]):
raise ValueError("Values in `t_eval` are not properly sorted.")
else:
# Backward in time
if not np.all(np.logical_and(span[0] >= ts, ts >= span[1])):
raise ValueError("Values in `t_eval` are not within `t_span`.")
if not np.all(ts[:-1] > ts[1:]):
raise ValueError("Values in `t_eval` are not properly sorted.")
if t_span.ndim == 1:
check_span(t_span, t_eval)
elif t_span.ndim == 2:
if t_eval.ndim != 2:
raise ValueError("t_eval should have rank 2.")
for i in range(t_span.shape[1]):
check_span(t_span[:, i], t_eval[:, i])
t = np.concatenate([t_span[:1], t_eval[:-1]], axis=0)
return solve_ivp_dt(
fun=fun,
y0=y0,
t0=t_span[0],
dt=t_eval - t,
method=method,
steps_per_dt=steps_per_dt,
use_scan=use_scan,
ode_int_kwargs=ode_int_kwargs
)
class RungaKutta(GeneralIntegrator):
"""A general Runga-Kutta integrator defined using a Butcher tableau."""
def __init__(
self,
a_tableau: Sequence[Sequence[float]],
b_tableau: Sequence[float],
c_tableau: Sequence[float],
order: int):
if len(b_tableau) != len(c_tableau) + 1:
raise ValueError("The length of b_tableau should be exactly one more than"
" the length of c_tableau.")
if len(b_tableau) != len(a_tableau) + 1:
raise ValueError("The length of b_tableau should be exactly one more than"
" the length of a_tableau.")
self.a_tableau = a_tableau
self.b_tableau = b_tableau
self.c_tableau = c_tableau
self.order = order
def __call__(
self,
tangent_func: GeneralTangentFunction,
t: jnp.ndarray,
y: M,
dt: jnp.ndarray
) -> M: # pytype: disable=invalid-annotation
k = [tangent_func(t, y)]
zero = jax.tree_map(jnp.zeros_like, k[0])
# We always broadcast opposite to numpy (e.g. leading dims (batch) count)
if dt.ndim > 0:
dt = dt.reshape(dt.shape + (1,) * (y.ndim - dt.ndim))
if t.ndim > 0:
t = t.reshape(t.shape + (1,) * (y.ndim - t.ndim))
for c_n, a_n_row in zip(self.c_tableau, self.a_tableau):
t_n = t + dt * c_n
products = [a_i * k_i for a_i, k_i in zip(a_n_row, k) if a_i != 0.0]
delta_n = sum(products, zero)
y_n = y + dt * delta_n
k.append(tangent_func(t_n, y_n))
products = [b_i * k_i for b_i, k_i in zip(self.b_tableau, k) if b_i != 0.0]
delta = sum(products, zero)
return y + dt * delta
class GeneralEuler(RungaKutta):
"""The standard Euler method (for general ODE problems)."""
def __init__(self):
super().__init__(
a_tableau=[],
b_tableau=[1.0],
c_tableau=[],
order=1
)
class RungaKutta2(RungaKutta):
"""The second order Runga-Kutta method corresponding to the mid-point rule."""
def __init__(self):
super().__init__(
a_tableau=[[1.0 / 2.0]],
b_tableau=[0.0, 1.0],
c_tableau=[1.0 / 2.0],
order=2
)
class RungaKutta4(RungaKutta):
"""The fourth order Runga-Kutta method from [6]."""
def __init__(self):
super().__init__(
a_tableau=[[1.0 / 2.0],
[0.0, 1.0 / 2.0],
[0.0, 0.0, 1.0]],
b_tableau=[1.0 / 6.0, 1.0 / 3.0, 1.0 / 3.0, 1.0 / 6.0],
c_tableau=[1.0 / 2.0, 1.0 / 2.0, 1.0],
order=4
)
class RungaKutta38(RungaKutta):
"""The fourth order 3/8 rule Runga-Kutta method from [7]."""
def __init__(self):
super().__init__(
a_tableau=[[1.0 / 3.0],
[-1.0 / 3.0, 1.0],
[1.0, -1.0, 1.0]],
b_tableau=[1.0 / 8.0, 3.0 / 8.0, 3.0 / 8.0, 1.0 / 8.0],
c_tableau=[1.0 / 3.0, 2.0 / 3.0, 1.0],
order=4
)
# _____ _ _ _
# / ____| | | | | (_)
# | (___ _ _ _ __ ___ _ __ | | ___ ___| |_ _ ___
# \___ \| | | | '_ ` _ \| '_ \| |/ _ \/ __| __| |/ __|
# ____) | |_| | | | | | | |_) | | __/ (__| |_| | (__
# |_____/ \__, |_| |_| |_| .__/|_|\___|\___|\__|_|\___|
# __/ | | |
# |___/ |_|
# _____ _ _ _
# |_ _| | | | | (_)
# | | _ __ | |_ ___ __ _ _ __ __ _| |_ _ ___ _ __
# | | | '_ \| __/ _ \/ _` | '__/ _` | __| |/ _ \| '_ \
# _| |_| | | | || __/ (_| | | | (_| | |_| | (_) | | | |
# |_____|_| |_|\__\___|\__, |_| \__,_|\__|_|\___/|_| |_|
# __/ |
# |___/
SymplecticIntegrator = Callable[
[
phase_space.SymplecticTangentFunction,
jnp.ndarray, # t
phase_space.PhaseSpace, # (q, p)
jnp.ndarray, # dt
],
phase_space.PhaseSpace # (q_next, p_next)
]
def solve_hamiltonian_ivp_dt(
hamiltonian: phase_space.HamiltonianFunction,
y0: phase_space.PhaseSpace,
t0: Union[float, jnp.ndarray],
dt: Union[float, jnp.ndarray],
method: Union[str, SymplecticIntegrator],
num_steps: Optional[int] = None,
steps_per_dt: int = 1,
use_scan: bool = True,
ode_int_kwargs: Optional[Dict[str, Union[float, int]]] = None
) -> Tuple[jnp.ndarray, phase_space.PhaseSpace]:
"""Solve an initial value problem for a Hamiltonian system.
This function numerically integrates a Hamiltonian system given an
initial value::
dq / dt = dH / dp
dp / dt = - dH / dq
q(t0), p(t0) = y0.q, y0.p
Here t is a one-dimensional independent variable (time), y(t) is an
n-dimensional vector-valued function (state), and an n-dimensional
vector-valued function H(t, q, p) determines the value of the Hamiltonian.
The goal is to find q(t) and p(t) approximately satisfying the differential
equations, given an initial values q(t0), p(t0) = y0.q, y0.p
All of the solvers supported here are explicit and non-adaptive. This in
terms makes them easy to run with fixed amount of computation and
the solutions to be easily differentiable.
Args:
hamiltonian: callable
The Hamiltonian function. The calling signature is ``h(t, s)``, where
`s` is an instance of `PhaseSpace`.
y0: an instance of `M`
Initial state at t=t0.
t0: float or array.
The initial time point of integration.
dt: array
Array containing all consecutive increments in time, at which the integral
to be evaluated. The size of this array along axis 0 defines the number of
steps that the integrator would do.
method: string or `GeneralIntegrator`
The integrator method to use. Possible values for string are:
* symp_euler - see `SymplecticEuler`
* symp_euler_q - a `SymplecticEuler` with position_first=True
* symp_euler_p - a `SymplecticEuler` with position_first=False
* leap_frog - see `LeapFrog`
* leap_frog_q - a `LeapFrog` with position_first=True
* leap_frog_p - a `LeapFrog` with position_first=False
* stormer_verlet - same as leap_frog
* stormer_verlet_q - same as leap_frog_q
* stormer_verlet_p - same as leap_frog_p
* ruth4 - see `Ruth4`,
* sym4 - see `Symmetric4`
* sym6 - see `Symmetric6`
* so4 - see `SymmetricSo4`
* so4_q - a `SymmetricSo4` with position_first=True
* so4_p - a `SymmetricSo4` with position_first=False
* so6 - see `SymmetricSo6`
* so6_q - a `SymmetricSo6` with position_first=True
* so6_p - a `SymmetricSo6` with position_first=False
* so8 - see `SymmetricSo8`
* so8_q - a `SymmetricSo8` with position_first=True
* so8_p - a `SymmetricSo8` with position_first=False
num_steps: Optional int.
If provided the `dt` will be treated as the same per step time interval,
applied for this many steps. In other words setting this argument is
equivalent to replicating `dt` num_steps times and stacking over axis=0.
steps_per_dt: int
This determines the overall step size. Between any two values of t_eval
the step size is `dt = (t_eval[i+1] - t_eval[i]) / steps_per_dt.
use_scan: bool
Whether for the loop to use `lax.scan` or a python loop
ode_int_kwargs: dict
Extra arguments to be passed to `ode.odeint` when method="adaptive"
Returns:
t: array
Time points at which the solution is evaluated.
y : an instance of M
Values of the solution at `t`.
"""
if not isinstance(y0, phase_space.PhaseSpace):
raise ValueError("The initial state must be an instance of `PhaseSpace`.")
dy_dt = phase_space.poisson_bracket_with_q_and_p(hamiltonian)
return solve_ivp_dt(
fun=dy_dt,
y0=y0,
t0=t0,
dt=dt,
method=method,
num_steps=num_steps,
steps_per_dt=steps_per_dt,
use_scan=use_scan,
ode_int_kwargs=ode_int_kwargs
)
def solve_hamiltonian_ivp_t_eval(
hamiltonian: phase_space.HamiltonianFunction,
t_span: TimeInterval,
y0: phase_space.PhaseSpace,
method: Union[str, SymplecticIntegrator],
t_eval: Optional[jnp.ndarray] = None,
steps_per_dt: int = 1,
use_scan: bool = True,
ode_int_kwargs: Optional[Dict[str, Union[float, int]]] = None
) -> Tuple[jnp.ndarray, phase_space.PhaseSpace]:
"""Solve an initial value problem for a Hamiltonian system.
This function numerically integrates a Hamiltonian system given an
initial value::
dq / dt = dH / dp
dp / dt = - dH / dq
q(t0), p(t0) = y0.q, y0.p
Here t is a one-dimensional independent variable (time), y(t) is an
n-dimensional vector-valued function (state), and an n-dimensional
vector-valued function H(t, q, p) determines the value of the Hamiltonian.
The goal is to find q(t) and p(t) approximately satisfying the differential
equations, given an initial values q(t0), p(t0) = y0.q, y0.p
All of the solvers supported here are explicit and non-adaptive. This in
terms makes them easy to run with fixed amount of computation and
the solutions to be easily differentiable.
Args:
hamiltonian: callable
The Hamiltonian function. The calling signature is ``h(t, s)``, where
`s` is an instance of `PhaseSpace`.
t_span: 2-tuple of floats
Interval of integration (t0, tf). The solver starts with t=t0 and
integrates until it reaches t=tf.
y0: an instance of `M`
Initial state at `t_span[0]`.
method: string or `GeneralIntegrator`
The integrator method to use. Possible values for string are:
* symp_euler - see `SymplecticEuler`
* symp_euler_q - a `SymplecticEuler` with position_first=True
* symp_euler_p - a `SymplecticEuler` with position_first=False
* leap_frog - see `LeapFrog`
* leap_frog_q - a `LeapFrog` with position_first=True
* leap_frog_p - a `LeapFrog` with position_first=False
* stormer_verlet - same as leap_frog
* stormer_verlet_q - same as leap_frog_q
* stormer_verlet_p - same as leap_frog_p
* ruth4 - see `Ruth4`,
* sym4 - see `Symmetric4`
* sym6 - see `Symmetric6`
* so4 - see `SymmetricSo4`
* so4_q - a `SymmetricSo4` with position_first=True
* so4_p - a `SymmetricSo4` with position_first=False
* so6 - see `SymmetricSo6`
* so6_q - a `SymmetricSo6` with position_first=True
* so6_p - a `SymmetricSo6` with position_first=False
* so8 - see `SymmetricSo8`
* so8_q - a `SymmetricSo8` with position_first=True
* so8_p - a `SymmetricSo8` with position_first=False
t_eval: array or None.
Times at which to store the computed solution. Must be sorted and lie
within `t_span`. If None then t_eval = [t_span[-1]]
steps_per_dt: int
This determines the overall step size. Between any two values of t_eval
the step size is `dt = (t_eval[i+1] - t_eval[i]) / steps_per_dt.
use_scan: bool
Whether for the loop to use `lax.scan` or a python loop
ode_int_kwargs: dict
Extra argumrnts to be passed to `ode.odeint` when method="adaptive"
Returns:
t: array
Time points at which the solution is evaluated.
y : an instance of M
Values of the solution at `t`.
"""
if not isinstance(y0, phase_space.PhaseSpace):
raise ValueError("The initial state must be an instance of `PhaseSpace`.")
dy_dt = phase_space.poisson_bracket_with_q_and_p(hamiltonian)
if method == "adaptive":
dy_dt = phase_space.transform_symplectic_tangent_function_using_array(dy_dt)
return solve_ivp_t_eval( # pytype: disable=bad-return-type # jax-ndarray
fun=dy_dt,
t_span=t_span,
y0=y0,
method=method,
t_eval=t_eval,
steps_per_dt=steps_per_dt,
use_scan=use_scan,
ode_int_kwargs=ode_int_kwargs
)
class CompositionSymplectic(SymplecticIntegrator):
"""A generalized symplectic integrator based on compositions.
Simulates Hamiltonian dynamics using a composition of symplectic steps:
q_{0} = q_init, p_{0} = p_init
for i in [1, n]:
p_{i+1} = p_{i} - c_{i} * dH/dq(q_{i}) * dt
q_{i+1} = q_{i} + d_{i} * dH/dp(p_{i+1}) * dt
q_next = q_{n}, p_next = p_{n}
This integrator always starts with updating the momentum.
The order argument is used mainly for testing to estimate the error when
integrating various systems.
"""
def __init__(
self,
momentum_coefficients: Sequence[float],
position_coefficients: Sequence[float],
order: int):
if len(position_coefficients) != len(momentum_coefficients):
raise ValueError("The number of momentum_coefficients and "
"position_coefficients must be the same.")
if not np.allclose(sum(position_coefficients), 1.0):
raise ValueError("The sum of the position_coefficients "
"must be equal to 1.")
if not np.allclose(sum(momentum_coefficients), 1.0):
raise ValueError("The sum of the momentum_coefficients "
"must be equal to 1.")
self.momentum_coefficients = momentum_coefficients
self.position_coefficients = position_coefficients
self.order = order
def __call__(
self,
tangent_func: phase_space.SymplecticTangentFunction,
t: jnp.ndarray,
y: phase_space.PhaseSpace,
dt: jnp.ndarray
) -> phase_space.PhaseSpace:
q, p = y.q, y.p
# This is intentional to prevent a bug where one uses y later
del y
# We always broadcast opposite to numpy (e.g. leading dims (batch) count)
if dt.ndim > 0:
dt = dt.reshape(dt.shape + (1,) * (q.ndim - dt.ndim))
if t.ndim > 0:
t = t.reshape(t.shape + (1,) * (q.ndim - t.ndim))
t_q = t
t_p = t
for c, d in zip(self.momentum_coefficients, self.position_coefficients):
# Update momentum
if c != 0.0:
dp_dt = tangent_func(t_p, phase_space.PhaseSpace(q, p)).p
p = p + c * dt * dp_dt
t_p = t_p + c * dt
# Update position
if d != 0.0:
dq_dt = tangent_func(t_q, phase_space.PhaseSpace(q, p)).q
q = q + d * dt * dq_dt
t_q = t_q + d * dt
return phase_space.PhaseSpace(position=q, momentum=p)
class SymplecticEuler(CompositionSymplectic):
"""The symplectic Euler method (for Hamiltonian systems).
If position_first = True:
q_{t+1} = q_{t} + dH/dp(p_{t}) * dt
p_{t+1} = p_{t} - dH/dq(q_{t+1}) * dt
else:
p_{t+1} = p_{t} - dH/dq(q_{t}) * dt
q_{t+1} = q_{t} + dH/dp(p_{t+1}) * dt
"""
def __init__(self, position_first=True):
if position_first:
super().__init__(
momentum_coefficients=[0.0, 1.0],
position_coefficients=[1.0, 0.0],
order=1
)
else:
super().__init__(
momentum_coefficients=[1.0],
position_coefficients=[1.0],
order=1
)
class SymmetricCompositionSymplectic(CompositionSymplectic):
"""A generalized composition integrator that is symmetric.
The integrators produced are always of the form:
[update_q, update_p, ..., update_p, update_q]
or
[update_p, update_q, ..., update_q, update_p]
based on the position_first argument. The method will expect which ever is
updated first to have one more coefficient.
"""
def __init__(
self,
momentum_coefficients: Sequence[float],
position_coefficients: Sequence[float],
position_first: bool,
order: int):
position_coefficients = list(position_coefficients)
momentum_coefficients = list(momentum_coefficients)
if position_first:
if len(position_coefficients) != len(momentum_coefficients) + 1:
raise ValueError("The number of position_coefficients must be one more "
"than momentum_coefficients when position_first=True.")
momentum_coefficients = [0.0] + momentum_coefficients
else:
if len(position_coefficients) + 1 != len(momentum_coefficients):
raise ValueError("The number of momentum_coefficients must be one more "
"than position_coefficients when position_first=True.")
position_coefficients = position_coefficients + [0.0]
super().__init__(
position_coefficients=position_coefficients,
momentum_coefficients=momentum_coefficients,
order=order
)
def symmetrize_coefficients(
coefficients: Sequence[float],
odd_number: bool
) -> Sequence[float]:
"""Symmetrizes the coefficients for an integrator."""
coefficients = list(coefficients)
if odd_number:
final = 1.0 - 2.0 * sum(coefficients)
return coefficients + [final] + coefficients[::-1]
else:
final = 0.5 - sum(coefficients)
return coefficients + [final, final] + coefficients[::-1]
class LeapFrog(SymmetricCompositionSymplectic):
"""The standard Leap-Frog method (also known as Stormer-Verlet).
If position_first = True:
q_half = q_{t} + dH/dp(p_{t}) * dt / 2
p_{t+1} = p_{t} - dH/dq(q_half) * dt
q_{t+1} = q_half + dH/dp(p_{t+1}) * dt / 2
else:
p_half = p_{t} - dH/dq(q_{t}) * dt / 2
q_{t+1} = q_{t} + dH/dp(p_half) * dt
p_{t+1} = p_half - dH/dq(q_{t+1}) * dt / 2
"""
def __init__(self, position_first=False):
if position_first:
super().__init__(
position_coefficients=[0.5, 0.5],
momentum_coefficients=[1.0],
position_first=True,
order=2
)
else:
super().__init__(
position_coefficients=[1.0],
momentum_coefficients=[0.5, 0.5],
position_first=False,
order=2
)
class Ruth4(SymmetricCompositionSymplectic):
"""The Fourth order method from [2]."""
def __init__(self):
cbrt_2 = float(np.cbrt(2.0))
c = [1.0 / (2.0 - cbrt_2)]
# 3: [c1, 1.0 - 2*c1, c1]
c = symmetrize_coefficients(c, odd_number=True)
d = [1.0 / (4.0 - 2.0 * cbrt_2)]
# 4: [d1, 0.5 - d1, 0.5 - d1, d1]
d = symmetrize_coefficients(d, odd_number=False)
super().__init__(
position_coefficients=d,
momentum_coefficients=c,
position_first=True,
order=4
)
class Symmetric4(SymmetricCompositionSymplectic):
"""The fourth order method from Table 6.1 in [1] (originally from [3])."""
def __init__(self):
c = [0.0792036964311957, 0.353172906049774, -0.0420650803577195]
# 7 : [c1, c2, c3, 1.0 - c1 - c2 - c3, c3, c2, c1]
c = symmetrize_coefficients(c, odd_number=True)
d = [0.209515106613362, -0.143851773179818]
# 6: [d1, d2, 0.5 - d1, 0.5 - d1, d2, d1]
d = symmetrize_coefficients(d, odd_number=False)
super().__init__(
position_coefficients=d,
momentum_coefficients=c,
position_first=False,
order=4
)
class Symmetric6(SymmetricCompositionSymplectic):
"""The sixth order method from Table 6.1 in [1] (originally from [3])."""
def __init__(self):
c = [0.0502627644003922, 0.413514300428344, 0.0450798897943977,
-0.188054853819569, 0.541960678450780]
# 11 : [c1, c2, c3, c4, c5, 1.0 - sum(ci), c5, c4, c3, c2, c1]
c = symmetrize_coefficients(c, odd_number=True)
d = [0.148816447901042, -0.132385865767784, 0.067307604692185,
0.432666402578175]
# 10: [d1, d2, d3, d4, 0.5 - sum(di), 0.5 - sum(di), d4, d3, d2, d1]
d = symmetrize_coefficients(d, odd_number=False)
super().__init__(
position_coefficients=d,
momentum_coefficients=c,
position_first=False,
order=4
)
def coefficients_based_on_composing_second_order(
weights: Sequence[float]
) -> Tuple[Sequence[float], Sequence[float]]:
"""Constructs the coefficients for methods based on second-order schemes."""
coefficients_0 = []
coefficients_1 = []
coefficients_0.append(weights[0] / 2.0)
for i in range(len(weights) - 1):
coefficients_1.append(weights[i])
coefficients_0.append((weights[i] + weights[i + 1]) / 2.0)
coefficients_1.append(weights[-1])
coefficients_0.append(weights[-1] / 2.0)
return coefficients_0, coefficients_1
class SymmetricSo4(SymmetricCompositionSymplectic):
"""The fourth order method from Table 6.2 in [1] (originally from [4])."""
def __init__(self, position_first: bool = False):
w = [0.28, 0.62546642846767004501]
# 5
w = symmetrize_coefficients(w, odd_number=True)
c0, c1 = coefficients_based_on_composing_second_order(w)
c_q, c_p = (c0, c1) if position_first else (c1, c0)
super().__init__(
position_coefficients=c_q,
momentum_coefficients=c_p,
position_first=position_first,
order=4
)
class SymmetricSo6(SymmetricCompositionSymplectic):
"""The sixth order method from Table 6.2 in [1] (originally from [5])."""
def __init__(self, position_first: bool = False):
w = [0.78451361047755726382, 0.23557321335935813368,
-1.17767998417887100695]
# 7
w = symmetrize_coefficients(w, odd_number=True)
c0, c1 = coefficients_based_on_composing_second_order(w)
c_q, c_p = (c0, c1) if position_first else (c1, c0)
super().__init__(
position_coefficients=c_q,
momentum_coefficients=c_p,
position_first=position_first,
order=6
)
class SymmetricSo8(SymmetricCompositionSymplectic):
"""The eighth order method from Table 6.2 in [1] (originally from [4])."""
def __init__(self, position_first: bool = False):
w = [0.74167036435061295345, -0.40910082580003159400,
0.19075471029623837995, -0.57386247111608226666,
0.29906418130365592384, 0.33462491824529818378,
0.31529309239676659663]
# 15
w = symmetrize_coefficients(w, odd_number=True)
c0, c1 = coefficients_based_on_composing_second_order(w)
c_q, c_p = (c0, c1) if position_first else (c1, c0)
super().__init__(
position_coefficients=c_q,
momentum_coefficients=c_p,
position_first=position_first,
order=8
)
general_integrators = dict(
general_euler=GeneralEuler(),
rk2=RungaKutta2(),
rk4=RungaKutta4(),
rk38=RungaKutta38()
)
symplectic_integrators = dict(
symp_euler=SymplecticEuler(position_first=True),
symp_euler_q=SymplecticEuler(position_first=True),
symp_euler_p=SymplecticEuler(position_first=False),
leap_frog=LeapFrog(position_first=False),
leap_frog_q=LeapFrog(position_first=True),
leap_frog_p=LeapFrog(position_first=False),
stormer_verlet=LeapFrog(position_first=False),
stormer_verlet_q=LeapFrog(position_first=True),
stormer_verlet_p=LeapFrog(position_first=False),
ruth4=Ruth4(),
sym4=Symmetric4(),
sym6=Symmetric6(),
so4=SymmetricSo4(position_first=False),
so4_q=SymmetricSo4(position_first=True),
so4_p=SymmetricSo4(position_first=False),
so6=SymmetricSo6(position_first=False),
so6_q=SymmetricSo6(position_first=True),
so6_p=SymmetricSo6(position_first=False),
so8=SymmetricSo8(position_first=False),
so8_q=SymmetricSo8(position_first=True),
so8_p=SymmetricSo8(position_first=False),
)
def get_integrator(
name_or_callable: Union[str, GeneralIntegrator]
) -> GeneralIntegrator:
"""Returns any integrator with the provided name or the argument."""
if isinstance(name_or_callable, str):
if name_or_callable in general_integrators:
return general_integrators[name_or_callable]
elif name_or_callable in symplectic_integrators:
return symplectic_integrators[name_or_callable]
else:
raise ValueError(f"Unrecognized integrator with name {name_or_callable}.")
if not callable(name_or_callable):
raise ValueError(f"Expected a callable, but got {type(name_or_callable)}.")
return name_or_callable
================================================
FILE: physics_inspired_models/jaxline_configs.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module containing all of the configurations for various models."""
import copy
import os
from jaxline import base_config
import ml_collections as collections
_DATASETS_PATH_VAR_NAME = "DM_HAMILTONIAN_DYNAMICS_SUITE_DATASETS"
def get_config(arg_string):
"""Return config object for training."""
args = arg_string.split(",")
if len(args) != 3:
raise ValueError("You must provide exactly three arguments separated by a "
"comma - model_config_name,sweep_index,dataset_name.")
model_config_name, sweep_index, dataset_name = args
sweep_index = int(sweep_index)
config = base_config.get_base_config()
config.random_seed = 123109801
config.eval_modes = ("eval", "eval_metric")
# Get the model config and the sweeps
if model_config_name not in globals():
raise ValueError(f"The config name {model_config_name} does not exist in "
f"jaxline_configs.py")
config_and_sweep_fn = globals()[model_config_name]
model_config, sweeps = config_and_sweep_fn()
if not os.environ.get(_DATASETS_PATH_VAR_NAME, None):
raise ValueError(f"You need to set the {_DATASETS_PATH_VAR_NAME}")
dm_hamiltonian_suite_path = os.environ[_DATASETS_PATH_VAR_NAME]
dataset_folder = os.path.join(dm_hamiltonian_suite_path, dataset_name)
# Experiment config. Note that batch_size is per device.
# In the experiments we run on 4 GPUs, so the effective batch size was 128.
config.experiment_kwargs = collections.ConfigDict(
dict(
config=dict(
dataset_folder=dataset_folder,
model_kwargs=model_config,
num_extrapolation_steps=60,
drop_stats_containing=("neg_log_p_x", "l2_over_time", "neg_elbo"),
optimizer=dict(
name="adam",
kwargs=dict(
learning_rate=1.5e-4,
b1=0.9,
b2=0.999,
)
),
training=dict(
batch_size=32,
burnin_steps=5,
num_epochs=None,
lagging_vae=False
),
evaluation=dict(
batch_size=64,
),
evaluation_metric=dict(
batch_size=5,
batch_n=20,
num_eval_metric_steps=60,
max_poly_order=5,
max_jacobian_score=1000,
rsq_threshold=0.9,
sym_threshold=0.05,
evaluation_point_n=10,
weight_tolerance=1e-03,
max_iter=1000,
cv=2,
alpha_min_logspace=-4,
alpha_max_logspace=-0.5,
alpha_step_n=10,
calculate_fully_after_steps=40000,
),
evaluation_metric_mlp=dict(
batch_size=64,
batch_n=10000,
datapoint_param_multiplier=1000,
num_eval_metric_steps=60,
evaluation_point_n=10,
evaluation_trajectory_n=50,
rsq_threshold=0.9,
sym_threshold=0.05,
ridge_lambda=0.01,
model=dict(
num_units=4,
num_layers=4,
activation="tanh",
),
optimizer=dict(
name="adam",
kwargs=dict(
learning_rate=1.5e-3,
)
),
),
evaluation_vpt=dict(
batch_size=5,
batch_n=2,
vpt_threshold=0.025,
)
)
)
)
# Training loop config.
config.training_steps = int(500000)
config.interval_type = "steps"
config.log_tensors_interval = 50
config.log_train_data_interval = 50
config.log_all_train_data = False
config.save_checkpoint_interval = 100
config.checkpoint_dir = "/tmp/physics_inspired_models/"
config.train_checkpoint_all_hosts = False
config.eval_specific_checkpoint_dir = ""
config.update_from_flattened_dict(sweeps[sweep_index])
return config
config_prefix = "experiment_kwargs.config."
model_prefix = config_prefix + "model_kwargs."
default_encoder_kwargs = collections.ConfigDict(dict(
conv_channels=64,
num_blocks=3,
blocks_depth=2,
activation="leaky_relu",
))
default_decoder_kwargs = collections.ConfigDict(dict(
conv_channels=64,
num_blocks=3,
blocks_depth=2,
activation="leaky_relu",
))
default_latent_system_net_kwargs = collections.ConfigDict(dict(
conv_channels=64,
num_units=250,
num_layers=5,
activation="swish",
))
default_latent_system_kwargs = collections.ConfigDict(dict(
# Physics model arguments
input_space=collections.config_dict.placeholder(str),
simulation_space=collections.config_dict.placeholder(str),
potential_func_form="separable_net",
kinetic_func_form=collections.config_dict.placeholder(str),
hgn_kinetic_func_form="separable_net",
lgn_kinetic_func_form="matrix_dep_quad",
parametrize_mass_matrix=collections.config_dict.placeholder(bool),
hgn_parametrize_mass_matrix=False,
lgn_parametrize_mass_matrix=True,
mass_eps=1.0,
# ODE model arguments
integrator_method=collections.config_dict.placeholder(str),
# RGN model arguments
residual=collections.config_dict.placeholder(bool),
# General arguments
net_kwargs=default_latent_system_net_kwargs
))
default_config_dict = collections.ConfigDict(dict(
name=collections.config_dict.placeholder(str),
latent_system_dim=32,
latent_system_net_type="mlp",
latent_system_kwargs=default_latent_system_kwargs,
encoder_aggregation_type="linear_projection",
decoder_de_aggregation_type=collections.config_dict.placeholder(str),
encoder_kwargs=default_encoder_kwargs,
decoder_kwargs=default_decoder_kwargs,
has_latent_transform=False,
num_inference_steps=5,
num_target_steps=60,
latent_training_type="forward",
# Choices: overlap_by_one, no_overlap, include_inference
training_data_split="overlap_by_one",
objective_type="ELBO",
elbo_beta_delay=0,
elbo_beta_final=1.0,
geco_kappa=0.001,
geco_alpha=0.0,
dt=0.125,
))
hgn_paper_encoder_kwargs = collections.ConfigDict(dict(
conv_channels=[[32, 64], [64, 64], [64]],
num_blocks=3,
blocks_depth=2,
activation="relu",
kernel_shapes=[2, 4],
padding=["VALID", "SAME"],
))
hgn_paper_decoder_kwargs = collections.ConfigDict(dict(
conv_channels=64,
num_blocks=3,
blocks_depth=2,
activation="tf_leaky_relu",
))
hgn_paper_latent_net_kwargs = collections.ConfigDict(dict(
conv_channels=[32, 64, 64, 64],
num_units=250,
num_layers=5,
activation="softplus",
kernel_shapes=[3, 2, 2, 2, 2],
strides=[1, 2, 1, 2, 1],
padding=["SAME", "VALID", "SAME", "VALID", "SAME"]
))
hgn_paper_latent_system_kwargs = collections.ConfigDict(dict(
potential_func_form="separable_net",
kinetic_func_form="separable_net",
parametrize_mass_matrix=False,
net_kwargs=hgn_paper_latent_net_kwargs
))
hgn_paper_latent_transform_kwargs = collections.ConfigDict(dict(
num_layers=5,
conv_channels=64,
num_units=64,
activation="relu",
))
hgn_paper_config = copy.deepcopy(default_config_dict)
hgn_paper_config.training_data_split = "include_inference"
hgn_paper_config.latent_system_net_type = "conv"
hgn_paper_config.encoder_aggregation_type = (collections.config_dict.
placeholder(str))
hgn_paper_config.decoder_de_aggregation_type = (collections.config_dict.
placeholder(str))
hgn_paper_config.latent_system_kwargs = hgn_paper_latent_system_kwargs
hgn_paper_config.encoder_kwargs = hgn_paper_encoder_kwargs
hgn_paper_config.decoder_kwargs = hgn_paper_decoder_kwargs
hgn_paper_config.has_latent_transform = True
hgn_paper_config.latent_transform_kwargs = hgn_paper_latent_transform_kwargs
hgn_paper_config.num_inference_steps = 31
hgn_paper_config.num_target_steps = 0
hgn_paper_config.objective_type = "GECO"
forward_overlap_by_one = {
model_prefix + "latent_training_type": "forward",
model_prefix + "training_data_split": "overlap_by_one",
}
forward_backward_include_inference = {
model_prefix + "latent_training_type": "forward_backward",
model_prefix + "training_data_split": "include_inference",
}
latent_training_sweep = [
forward_overlap_by_one,
forward_backward_include_inference,
]
def sym_metric_hgn_plus_plus_sweep():
"""HGN++ experimental sweep for the SyMetric paper."""
model_config = copy.deepcopy(default_config_dict)
model_config.name = "HGN"
sweeps = list()
for elbo_beta_final in [0.001, 0.1, 1.0, 2.0]:
sweeps.append({
config_prefix + "optimizer.kwargs.learning_rate": 1.5e-4,
model_prefix + "latent_training_type": "forward",
model_prefix + "training_data_split": "overlap_by_one",
model_prefix + "elbo_beta_final": elbo_beta_final,
})
for elbo_beta_final in [0.001, 0.1, 1.0, 2.0]:
sweeps.append({
config_prefix + "optimizer.kwargs.learning_rate": 1.5e-4,
model_prefix + "latent_training_type": "forward_backward",
model_prefix + "training_data_split": "include_inference",
model_prefix + "elbo_beta_final": elbo_beta_final,
})
return model_config, sweeps
def sym_metric_hgn_sweep():
"""HGN experimental sweep for the SyMetric paper."""
model_config = copy.deepcopy(hgn_paper_config)
model_config.name = "HGN"
return model_config, list(dict())
def benchmark_hgn_overlap_sweep():
"""HGN++ sweep for the benchmark paper."""
model_config = copy.deepcopy(default_config_dict)
model_config.name = "HGN"
sweeps = list()
for elbo_beta_final in [0.001, 0.1, 1.0, 2.0]:
for train_dict in latent_training_sweep:
sweeps.append({
config_prefix + "optimizer.kwargs.learning_rate": 1.5e-4,
model_prefix + "elbo_beta_final": elbo_beta_final,
})
sweeps[-1].update(train_dict)
return model_config, sweeps
def benchmark_lgn_sweep():
"""LGN sweep for the benchmark paper."""
model_config = copy.deepcopy(default_config_dict)
model_config.name = "LGN"
sweeps = list()
for elbo_beta_final in [0.001, 0.1, 1.0, 2.0]:
for train_dict in latent_training_sweep:
sweeps.append({
config_prefix + "optimizer.kwargs.learning_rate": 1.5e-4,
model_prefix + "latent_system_kwargs.kinetic_func_form":
"matrix_dep_pure_quad",
model_prefix + "elbo_beta_final": elbo_beta_final,
})
sweeps[-1].update(train_dict)
return model_config, sweeps
def benchmark_ode_sweep():
"""Neural ODE sweep for the benchmark paper."""
model_config = copy.deepcopy(default_config_dict)
model_config.name = "ODE"
sweeps = list()
for elbo_beta_final in [0.001, 0.1, 1.0, 2.0]:
for integrator in ("adaptive", "rk2"):
for train_dict in latent_training_sweep:
sweeps.append({
config_prefix + "optimizer.kwargs.learning_rate": 1.5e-4,
model_prefix + "integrator_method": integrator,
model_prefix + "elbo_beta_final": elbo_beta_final,
})
sweeps[-1].update(train_dict)
return model_config, sweeps
def benchmark_rgn_sweep():
"""RGN sweep for the benchmark paper."""
model_config = copy.deepcopy(default_config_dict)
model_config.name = "RGN"
sweeps = list()
for elbo_beta_final in [0.001, 0.1, 1.0, 2.0]:
for residual in (True, False):
sweeps.append({
config_prefix + "optimizer.kwargs.learning_rate": 1.5e-4,
model_prefix + "latent_system_kwargs.residual": residual,
model_prefix + "elbo_beta_final": elbo_beta_final,
})
return model_config, sweeps
def benchmark_ar_sweep():
"""AR sweep for the benchmark paper."""
model_config = copy.deepcopy(default_config_dict)
model_config.name = "AR"
model_config.latent_dynamics_type = "vanilla"
sweeps = list()
for elbo_beta_final in [0.001, 0.1, 1.0, 2.0]:
for ar_type in ("vanilla", "lstm", "gru"):
sweeps.append({
config_prefix + "optimizer.kwargs.learning_rate": 1.5e-4,
model_prefix + "latent_dynamics_type": ar_type,
model_prefix + "elbo_beta_final": elbo_beta_final,
})
return model_config, sweeps
================================================
FILE: physics_inspired_models/jaxline_train.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""The training script for the HGN models."""
import functools
from absl import app
from absl import flags
from absl import logging
from dm_hamiltonian_dynamics_suite import load_datasets
import haiku as hk
import jax
import jax.numpy as jnp
from jaxline import experiment
from jaxline import platform
import numpy as np
import optax
from physics_inspired_models import eval_metric
from physics_inspired_models import utils
from physics_inspired_models.models import common
AutoregressiveModel = common.autoregressive.TeacherForcingAutoregressiveModel
class HGNExperiment(experiment.AbstractExperiment):
"""HGN experiment."""
CHECKPOINT_ATTRS = {
"_params": "params",
"_state": "state",
"_opt_state": "opt_state",
}
NON_BROADCAST_CHECKPOINT_ATTRS = {
"_python_step": "python_step"
}
def __init__(self, mode, init_rng, config):
super().__init__(mode=mode)
self.mode = mode
self.init_rng = init_rng
self.config = config
# Checkpointed experiment state.
self._python_step = None
self._params = None
self._state = None
self._opt_state = None
# Input pipelines.
self._train_input = None
self._step_fn = None
self._burnin_fn = None
self._eval_input = None
self._eval_batch = None
self._eval_input_metric = None
self._eval_input_vpt = None
self._compute_gt_state_and_latents = None
self._get_reconstructions = None
self._get_samples = None
# Construct the model
model_kwargs = dict(**self.config.model_kwargs)
self.model = common.construct_model(**model_kwargs)
# Construct the optimizer
optimizer_ctor = getattr(optax, self.config.optimizer.name)
self.optimizer = optimizer_ctor(**self.config.optimizer.kwargs)
self.model_init = jax.pmap(self.model.init)
self.opt_init = jax.pmap(self.optimizer.init)
logging.info("Number of hosts: %d/%d",
jax.process_index(), jax.process_count())
logging.info("Number of local devices: %d/%d", jax.local_device_count(),
jax.device_count())
def _process_stats(self, stats, axis_name=None):
keys_to_remove = list()
for key in stats.keys():
for dropped_keys in self.config.drop_stats_containing:
if dropped_keys in key:
keys_to_remove.append(key)
break
for key in keys_to_remove:
stats.pop(key)
# Take average statistics
stats = jax.tree_map(utils.mean_if_not_scalar, stats)
stats = utils.filter_only_scalar_stats(stats)
if axis_name is not None:
stats = utils.pmean_if_pmap(stats, axis_name="i")
return stats
# _ _
# | |_ _ __ __ _(_)_ __
# | __| '__/ _` | | '_ \
# | |_| | | (_| | | | | |
# \__|_| \__,_|_|_| |_|
#
def step(self, global_step, rng, **unused_args):
"""See base class."""
if self._train_input is None:
self._initialize_train()
# Do a small burnin to accumulate any persistent network state
if self._python_step == 0 and self._state:
for _ in range(self.config.training.burnin_steps):
rng, key = utils.p_split(rng, 2)
batch = next(self._train_input)
self._state = self._burnin_fn(self._params, self._state, key, batch)
self._state = jax.tree_map(
lambda x: x / self.config.training.burnin_steps, self._state)
batch = next(self._train_input)
self._params, self._state, self._opt_state, stats = self._step_fn(
self._params, self._state, self._opt_state, rng, batch, global_step)
self._python_step += 1
stats = utils.get_first(stats)
logging.info("global_step: %d, %s", self._python_step,
jax.tree_map(float, stats))
return stats
def _initialize_train(self):
self._train_input = utils.py_prefetch(
load_datasets.dataset_as_iter(self._build_train_input))
self._burnin_fn = jax.pmap(
self._jax_burnin_fn, axis_name="i", donate_argnums=list(range(1, 4)))
self._step_fn = jax.pmap(
self._jax_train_step_fn, axis_name="i", donate_argnums=list(range(5)))
if self._params is not None:
logging.info("Not running initialization - loaded from checkpoint.")
assert self._opt_state is not None
return
logging.info("Initializing parameters - NOT loading from checkpoint.")
# Use the same rng on all devices, so that the initialization is identical
init_rng = utils.bcast_local_devices(self.init_rng)
# Initialize the parameters and the optimizer
batch = next(self._train_input)
self._params, self._state = self.model_init(init_rng, batch)
self._python_step = 0
self._opt_state = self.opt_init(self._params)
def _build_train_input(self):
batch_size = self.config.training.batch_size
return load_datasets.load_dataset(
path=self.config.dataset_folder,
tfrecord_prefix="train",
sub_sample_length=self.model.train_sequence_length,
per_device_batch_size=batch_size,
num_epochs=self.config.training.num_epochs,
drop_remainder=True,
multi_device=True,
shuffle=True,
shuffle_buffer=100 * batch_size,
cache=False,
keys_to_preserve=["image"],
)
def _jax_train_step_fn(self, params, state, opt_state, rng_key, batch, step):
# The loss and the stats are averaged over the batch
def loss_func(*args):
outs = self.model.training_objectives(*args, is_training=True)
# Average everything over the batch
return jax.tree_map(utils.mean_if_not_scalar, outs)
# Compute gradients
grad_fn = jax.grad(loss_func, has_aux=True)
grads, (state, stats, _) = grad_fn(params, state, rng_key, batch, step)
# Average everything over the devices (e.g. average and sync)
grads, state = utils.pmean_if_pmap((grads, state), axis_name="i")
# Apply updates
updates, opt_state = self.optimizer.update(grads, opt_state)
params = optax.apply_updates(params, updates)
return params, state, opt_state, self._process_stats(stats, axis_name="i")
def _jax_burnin_fn(self, params, state, rng_key, batch):
_, (new_state, _, _) = self.model.training_objectives(
params, state, rng_key, batch, jnp.zeros([]), is_training=True)
new_state = jax.tree_map(utils.mean_if_not_scalar, new_state)
new_state = utils.pmean_if_pmap(new_state, axis_name="i")
new_state = hk.data_structures.to_mutable_dict(new_state)
new_state = hk.data_structures.to_immutable_dict(new_state)
return jax.tree_map(jnp.add, new_state, state)
# _
# _____ ____ _| |
# / _ \ \ / / _` | |
# | __/\ V / (_| | |
# \___| \_/ \__,_|_|
#
def evaluate(self, global_step, rng, writer):
"""See base class."""
logging.info("Starting evaluation.")
if self.mode == "eval":
if self._eval_input is None:
self._initialize_eval()
self._initialize_eval_vpt()
key1, _ = utils.p_split(rng, 2)
stats = utils.to_numpy(self._eval_epoch(global_step, key1))
stats.update(utils.to_numpy(self._eval_epoch_vpt(global_step, rng)))
elif self.mode == "eval_metric":
if self._eval_input_metric is None:
self._initialize_eval_metric()
stats = utils.to_numpy(self._eval_epoch_metric(global_step, rng))
else:
raise NotImplementedError()
logging.info("Finished evaluation.")
return stats
def _eval_epoch(self, step, rng):
"""Evaluates an epoch."""
accumulator = utils.MultiBatchAccumulator()
for batch in self._eval_input():
rng, key = utils.p_split(rng, 2)
stats, num_samples = utils.get_first(
self._eval_batch(self._params, self._state, key, batch, step)
)
accumulator.add(stats, num_samples)
return accumulator.value()
def _eval_epoch_metric(self, step, rng):
"""Evaluates an epoch."""
# To prevent from calculating SyMetric early on in training where a large
# polynomial expansion is likely to be required and the score is likely
# to be bad anyway, we only compute using a single batch to save compute
if step[0] > self.config.evaluation_metric.calculate_fully_after_steps:
batch_n = self.config.evaluation_metric.batch_n
else:
batch_n = 1
logging.info("Step: %d, batch_n: %d", step[0], batch_n)
accumulator = utils.MultiBatchAccumulator()
for _ in range(self.config.evaluation_metric.batch_n):
batch = next(self._eval_input_metric)
rng, key = utils.p_split(rng, 2)
stats = self._eval_batch_metric(
self._params, key, batch,
eval_seq_len=self.config.evaluation_metric.num_eval_metric_steps,
)
accumulator.add(stats, 1)
stats = utils.flatten_dict(accumulator.value())
max_keys = ("sym", "SyMetric")
for k, v in utils.flatten_dict(accumulator.max()).items():
if any(m in k for m in max_keys):
stats[k + "_max"] = v
min_keys = ("sym", "SyMetric")
for k, v in utils.flatten_dict(accumulator.min()).items():
if any(m in k for m in min_keys):
stats[k + "_min"] = v
sum_keys = ("sym", "SyMetric")
for k, v in utils.flatten_dict(accumulator.sum()).items():
if any(m in k for m in sum_keys):
stats[k + "_sum"] = v
return stats
def _eval_epoch_vpt(self, step, rng):
"""Evaluates an epoch."""
accumulator = utils.MultiBatchAccumulator()
for _ in range(self.config.evaluation_vpt.batch_n):
batch = next(self._eval_input_vpt)
rng, key = utils.p_split(rng, 2)
stats = self._eval_batch_vpt(self._params, self._state, key, batch)
accumulator.add(stats, 1)
stats = utils.flatten_dict(accumulator.value())
return stats
def _reconstruct_and_align(self, rng_key, full_trajectory, prefix, suffix):
if hasattr(self.model, "training_data_split"):
if self.model.training_data_split == "overlap_by_one":
reconstruction_skip = self.model.num_inference_steps - 1
elif self.model.training_data_split == "no_overlap":
reconstruction_skip = self.model.num_inference_steps
elif self.model.training_data_split == "include_inference":
reconstruction_skip = 0
else:
raise NotImplementedError()
else:
reconstruction_skip = 1
full_forward_targets = jax.tree_map(
lambda x: x[:, :, reconstruction_skip:], full_trajectory)
full_backward_targets = jax.tree_map(
lambda x: x[:, :, :x.shape[2] - reconstruction_skip], full_trajectory)
train_targets_length = (self.model.train_sequence_length -
reconstruction_skip)
full_targets_length = full_forward_targets.shape[2]
# Fully unroll the model and reconstruct the whole sequence, take the mean
full_prediction = self._get_reconstructions(self._params, full_trajectory,
rng_key, prefix == "forward",
True).mean()
full_targets = (full_forward_targets if prefix == "forward" else
full_backward_targets)
# In cases where the model can run backwards it is possible to reconstruct
# parts which were indented to be skipped, so here we take care of that.
if full_prediction.mean().shape[2] > full_targets_length:
if prefix == "forward":
full_prediction = jax.tree_map(
lambda x: x[:, :, -full_targets_length:], full_prediction)
else:
full_prediction = jax.tree_map(
lambda x: x[:, :, :full_targets_length], full_prediction)
# Based on the prefix and suffix fetch correct predictions and targets
if prefix == "forward" and suffix == "train":
predict, targets = jax.tree_map(
lambda x: x[:, :, :train_targets_length],
(full_prediction, full_targets))
elif prefix == "forward" and suffix == "extrapolation":
predict, targets = jax.tree_map(
lambda x: x[:, :, train_targets_length:],
(full_prediction, full_targets))
elif prefix == "backward" and suffix == "train":
predict, targets = jax.tree_map(
lambda x: x[:, :, -train_targets_length:],
(full_prediction, full_targets))
elif prefix == "backward" and suffix == "extrapolation":
predict, targets = jax.tree_map(
lambda x: x[:, :, :-train_targets_length],
(full_prediction, full_targets))
else:
predict, targets = full_prediction, full_targets
return predict, targets
def _initialize_eval(self):
length = (self.model.train_sequence_length +
self.config.num_extrapolation_steps)
batch_size = self.config.evaluation.batch_size
self._eval_input = load_datasets.dataset_as_iter(
load_datasets.load_dataset,
path=self.config.dataset_folder,
tfrecord_prefix="test",
sub_sample_length=length,
per_device_batch_size=batch_size,
num_epochs=1,
drop_remainder=False,
shuffle=False,
cache=False,
keys_to_preserve=["image"]
)
self._eval_batch = jax.pmap(
self._jax_eval_step_fn, axis_name="i")
self._get_reconstructions = jax.pmap(
self.model.reconstruct, axis_name="i",
static_broadcasted_argnums=(3, 4))
if isinstance(self.model,
common.deterministic_vae.DeterministicLatentsGenerativeModel):
self._get_samples = jax.pmap(
self.model.sample_trajectories_from_prior,
static_broadcasted_argnums=(1, 3, 4))
def _initialize_eval_metric(self):
self._eval_input_metric = utils.py_prefetch(
load_datasets.dataset_as_iter(
load_datasets.load_dataset,
path=self.config.dataset_folder,
tfrecord_prefix="test",
sub_sample_length=None,
per_device_batch_size=self.config.evaluation_metric.batch_size,
num_epochs=None,
drop_remainder=False,
cache=False,
shuffle=False,
keys_to_preserve=["image", "x"]
)
)
def compute_gt_state_and_latents(*args):
# Note that the `dt` has to be passed as a kwargs argument
if len(args) == 4:
return self.model.gt_state_and_latents(*args[:4])
elif len(args) == 5:
return self.model.gt_state_and_latents(*args[:4], dt=args[4])
else:
raise NotImplementedError()
self._compute_gt_state_and_latents = jax.pmap(
compute_gt_state_and_latents, static_broadcasted_argnums=3)
def _initialize_eval_vpt(self):
dataset_name = self.config.dataset_folder.split("/")[-1]
dataset_folder = self.config.dataset_folder
if dataset_name in ("hnn_mass_spring_dt_0_05",
"mass_spring_colors_v1_dt_0_05",
"hnn_pendulum_dt_0_05",
"pendulum_colors_v1_dt_0_05",
"matrix_rps_dt_0_1",
"matrix_mp_dt_0_1"):
dataset_folder += "_long_trajectory"
self._eval_input_vpt = utils.py_prefetch(
load_datasets.dataset_as_iter(
load_datasets.load_dataset,
path=dataset_folder,
tfrecord_prefix="test",
sub_sample_length=None,
per_device_batch_size=self.config.evaluation_vpt.batch_size,
num_epochs=None,
drop_remainder=False,
cache=False,
shuffle=False,
keys_to_preserve=["image", "x"]
)
)
self._get_reconstructions = jax.pmap(
self.model.reconstruct, axis_name="i",
static_broadcasted_argnums=(3, 4))
def _jax_eval_step_fn(self, params, state, rng_key, batch, step):
# We care only about the statistics
_, (_, stats, _) = self.model.training_objectives(params, state, rng_key,
batch, step,
is_training=False)
# Compute the full batch size
batch_size = jax.tree_flatten(batch)[0][0].shape[0]
batch_size = utils.psum_if_pmap(batch_size, axis_name="i")
return self._process_stats(stats, axis_name="i"), batch_size
def _eval_batch_vpt(self, params, state, rng_key, batch):
full_trajectory = utils.extract_image(batch)
prefixes = ("forward",
"backward") if self.model.can_run_backwards else ("forward",)
stats = dict()
vpt_abs_scores = []
vpt_rel_scores = []
seq_length = None
for prefix in prefixes:
reconstruction, gt_images = self._reconstruct_and_align(
rng_key, full_trajectory, prefix, "extrapolation")
seq_length = gt_images.shape[2]
mse_norm = np.mean(
(gt_images - reconstruction)**2, axis=(3, 4, 5)) / np.mean(
gt_images**2, axis=(3, 4, 5))
vpt_scores = []
for i in range(mse_norm.shape[1]):
vpt_ind = np.argwhere(
mse_norm[:, i:i + 1, :] > self.config.evaluation_vpt.vpt_threshold)
if vpt_ind.shape[0] > 0:
vpt_ind = vpt_ind[0][2]
else:
vpt_ind = mse_norm.shape[-1]
vpt_scores.append(vpt_ind)
vpt_abs_scores.append(np.median(vpt_scores))
vpt_rel_scores.append(np.median(vpt_scores) / seq_length)
scores = {"vpt_abs": vpt_abs_scores[-1], "vpt_rel": vpt_rel_scores[-1]}
scores = utils.to_numpy(scores)
scores = utils.filter_only_scalar_stats(scores)
stats[prefix] = scores
stats["vpt_abs"] = utils.to_numpy(np.mean(vpt_abs_scores))
stats["vpt_rel"] = utils.to_numpy(np.mean(vpt_rel_scores))
logging.info("vpt_abs: %s, seq_length: %d}",
str(vpt_abs_scores), seq_length)
return stats
def _eval_batch_metric(self, params, rng, batch, eval_seq_len=200):
# Initialise alpha values for Lasso regression
alpha_sweep = np.logspace(self.config.evaluation_metric.alpha_min_logspace,
self.config.evaluation_metric.alpha_max_logspace,
self.config.evaluation_metric.alpha_step_n)
trajectory_n = self.config.evaluation_metric.batch_size
subsection = f"{trajectory_n}tr"
stats = dict()
# Get data
(gt_trajectory,
model_trajectory,
informative_dim_n) = self._get_gt_and_model_phase_space_for_eval(
params, rng, batch, eval_seq_len)
# Calculate SyMetric scores
if informative_dim_n > 1:
scores, *_ = eval_metric.calculate_symetric_score(
gt_trajectory,
model_trajectory,
self.config.evaluation_metric.max_poly_order,
self.config.evaluation_metric.max_jacobian_score,
self.config.evaluation_metric.rsq_threshold,
self.config.evaluation_metric.sym_threshold,
self.config.evaluation_metric.evaluation_point_n,
trajectory_n=trajectory_n,
weight_tolerance=self.config.evaluation_metric.weight_tolerance,
alpha_sweep=alpha_sweep,
max_iter=self.config.evaluation_metric.max_iter,
cv=self.config.evaluation_metric.cv)
scores["unmasked_latents"] = informative_dim_n
scores = utils.to_numpy(scores)
scores = utils.filter_only_scalar_stats(scores)
stats[subsection] = scores
else:
scores = {
"poly_exp_order":
self.config.evaluation_metric.max_poly_order,
"rsq":
0,
"sym":
self.config.evaluation_metric.max_jacobian_score,
"SyMetric": 0.0,
"unmasked_latents":
informative_dim_n
}
scores = utils.to_numpy(scores)
scores = utils.filter_only_scalar_stats(scores)
stats[subsection] = scores
return stats
def _get_gt_and_model_phase_space_for_eval(self, params, rng, batch,
eval_seq_len):
# Get data
gt_data, model_data, z0 = utils.stack_device_dim_into_batch(
self._compute_gt_state_and_latents(params, rng, batch, eval_seq_len)
)
if isinstance(self.model, AutoregressiveModel):
# These models return the `z` for the whole sequence
z0 = z0[:, 0]
# If latent space is image like, reshape it down to vector
if self.model.latent_system_net_type == "conv":
z0 = jax.tree_map(utils.reshape_latents_conv_to_flat, z0)
model_data = jax.tree_map(
lambda x: utils.reshape_latents_conv_to_flat(x, axis_n_to_keep=2),
model_data)
# Create mask to get rid of uninformative latents
latent_mask = eval_metric.create_latent_mask(z0)
informative_dim_n = np.sum(latent_mask)
model_data = model_data[:, :, latent_mask]
logging.info("Masking out model data, leaving dim_n=%d dimensions.",
model_data.shape[-1])
gt_trajectory = np.reshape(
gt_data,
[np.product(gt_data.shape[:-1]), gt_data.shape[-1]]
)
model_trajectory = np.reshape(model_data, [
np.product(model_data.shape[:-1]), model_data.shape[-1]
])
# Standardize data
gt_trajectory = eval_metric.standardize_data(gt_trajectory)
model_trajectory = eval_metric.standardize_data(model_trajectory)
return gt_trajectory, model_trajectory, informative_dim_n
if __name__ == "__main__":
flags.mark_flag_as_required("config")
logging.set_stderrthreshold(logging.INFO)
app.run(functools.partial(platform.main, HGNExperiment))
================================================
FILE: physics_inspired_models/launch_all.sh
================================================
#!/bin/bash
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Script to execute a single configuration on all datasets.
if [[ "$#" -eq 2 ]]; then
readonly CONFIG_NAME="$1"
readonly NUM_SWEEPS="$2"
else
echo "You must provide exactly two arguments - the configuration name and " \
"how many sweeps it contains. For example:"
echo "./launch_all.sh sym_metric_hgn_plus_plus_sweep 1"
exit 2
fi
DATASETS=(
"toy_physics/mass_spring"
"toy_physics/mass_spring_colors"
"toy_physics/mass_spring_colors_friction"
"toy_physics/pendulum"
"toy_physics/pendulum_colors"
"toy_physics/pendulum_colors_friction"
"toy_physics/two_body"
"toy_physics/two_body_colors"
"toy_physics/double_pendulum"
"toy_physics/double_pendulum_colors"
"toy_physics/double_pendulum_colors_friction"
"molecular_dynamics/lj_4"
"molecular_dynamics/lj_16"
"multi_agent/rock_paper_scissors"
"multi_agent/matching_pennies"
"mujoco_room/circle"
"mujoco_room/spiral"
)
readonly DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
for dataset in "${DATASETS[@]}"; do
"${DIR}/launch_local.sh" "${CONFIG_NAME}" "${NUM_SWEEPS}" "${dataset}"
done
================================================
FILE: physics_inspired_models/launch_local.sh
================================================
#!/bin/bash
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# A script to execute a single configuration name on a given dataset.
if [[ "$#" -eq 3 ]]; then
readonly CONFIG_NAME="$1"
readonly NUM_SWEEPS="$2"
readonly DATASET="$3"
else
echo "You must provide exactly three arguments - the configuration name, " \
"the number of sweeps it contains and the dataset name. For example:"
echo "./launch_local.sh sym_metric_hgn_plus_plus_sweep 1 " \
"toy_physics/mass_spring"
exit 2
fi
echo "Running with config ${CONFIG_NAME} on ${DATASET}."
readonly EXPERIMENT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
readonly TRAIN_FILE="${EXPERIMENT_DIR}/jaxline_train.py"
readonly CONFIG_FILE="${EXPERIMENT_DIR}/jaxline_configs.py"
for sweep_id in $(seq 0 $((NUM_SWEEPS - 1))); do
python3 "${TRAIN_FILE}" \
--config="${CONFIG_FILE}:${CONFIG_NAME},${sweep_id},${DATASET}" \
--jaxline_mode="train" \
--logtostderr
done
================================================
FILE: physics_inspired_models/metrics.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module containing code for computing various metrics for training and evaluation."""
from typing import Callable, Dict, Optional
import distrax
import haiku as hk
import jax
import jax.nn as nn
import jax.numpy as jnp
import numpy as np
import physics_inspired_models.utils as utils
_ReconstructFunc = Callable[[utils.Params, jnp.ndarray, jnp.ndarray, bool],
distrax.Distribution]
def calculate_small_latents(dist, threshold=0.5):
"""Calculates the number of active latents by thresholding the variance of their distribution."""
if not isinstance(dist, distrax.Normal):
raise NotImplementedError()
latent_means = dist.mean()
latent_stddevs = dist.variance()
small_latents = jnp.sum(
(latent_stddevs < threshold) & (jnp.abs(latent_means) > 0.1), axis=1)
return jnp.mean(small_latents)
def compute_scale(
targets: jnp.ndarray,
rescale_by: str
) -> jnp.ndarray:
"""Compute a scaling factor based on targets shape and the rescale_by argument."""
if rescale_by == "pixels_and_time":
return jnp.asarray(np.prod(targets.shape[-4:]))
elif rescale_by is not None:
raise ValueError(f"Unrecognized rescale_by={rescale_by}.")
else:
return jnp.ones([])
def compute_data_domain_stats(
p_x: distrax.Distribution,
targets: jnp.ndarray
) -> Dict[str, jnp.ndarray]:
"""Compute several statistics in the data domain, such as L2 and negative log likelihood."""
axis = tuple(range(2, targets.ndim))
l2_over_time = jnp.sum((p_x.mean() - targets) ** 2, axis=axis)
l2 = jnp.sum(l2_over_time, axis=1)
# Calculate relative L2 normalised by image "length"
norm_factor = jnp.sum(targets**2, axis=(2, 3, 4))
l2_over_time_norm = l2_over_time / norm_factor
l2_norm = jnp.sum(l2_over_time_norm, axis=1)
# Compute negative log-likelihood under p(x)
neg_log_p_x_over_time = - np.sum(p_x.log_prob(targets), axis=axis)
neg_log_p_x = jnp.sum(neg_log_p_x_over_time, axis=1)
return dict(
neg_log_p_x_over_time=neg_log_p_x_over_time,
neg_log_p_x=neg_log_p_x,
l2_over_time=l2_over_time,
l2=l2,
l2_over_time_norm=l2_over_time_norm,
l2_norm=l2_norm,
)
def compute_vae_stats(
neg_log_p_x: jnp.ndarray,
rng: jnp.ndarray,
q_z: distrax.Distribution,
prior: distrax.Distribution
) -> Dict[str, jnp.ndarray]:
"""Compute the KL(q(z|x)||p(z)) and the negative ELBO, which are used for VAE models."""
# Compute the KL
kl = distrax.estimate_kl_best_effort(q_z, prior, rng_key=rng, num_samples=1)
kl = np.sum(kl, axis=list(range(1, kl.ndim)))
# Sanity check
assert kl.shape == neg_log_p_x.shape
return dict(
kl=kl,
neg_elbo=neg_log_p_x + kl,
)
def training_statistics(
p_x: distrax.Distribution,
targets: jnp.ndarray,
rescale_by: Optional[str],
rng: Optional[jnp.ndarray] = None,
q_z: Optional[distrax.Distribution] = None,
prior: Optional[distrax.Distribution] = None,
p_x_learned_sigma: bool = False
) -> Dict[str, jnp.ndarray]:
"""Computes various statistics we track during training."""
stats = compute_data_domain_stats(p_x, targets)
if rng is not None and q_z is not None and prior is not None:
stats.update(compute_vae_stats(stats["neg_log_p_x"], rng, q_z, prior))
else:
assert rng is None and q_z is None and prior is None
# Rescale these stats accordingly
scale = compute_scale(targets, rescale_by)
# Note that "_over_time" stats are getting normalised by time here
stats = jax.tree_map(lambda x: x / scale, stats)
if p_x_learned_sigma:
stats["p_x_sigma"] = p_x.variance().reshape([-1])[0] # pytype: disable=attribute-error # numpy-scalars
if q_z is not None:
stats["small_latents"] = calculate_small_latents(q_z)
return stats
def evaluation_only_statistics(
reconstruct_func: _ReconstructFunc,
params: hk.Params,
inputs: jnp.ndarray,
rng: jnp.ndarray,
rescale_by: str,
can_run_backwards: bool,
train_sequence_length: int,
reconstruction_skip: int,
p_x_learned_sigma: bool = False,
) -> Dict[str, jnp.ndarray]:
"""Computes various statistics we track only during evaluation."""
full_trajectory = utils.extract_image(inputs)
prefixes = ("forward", "backward") if can_run_backwards else ("forward",)
full_forward_targets = jax.tree_map(
lambda x: x[:, reconstruction_skip:], full_trajectory)
full_backward_targets = jax.tree_map(
lambda x: x[:, :x.shape[1]-reconstruction_skip], full_trajectory)
train_targets_length = train_sequence_length - reconstruction_skip
full_targets_length = full_forward_targets.shape[1]
stats = dict()
keys = ()
for prefix in prefixes:
# Fully unroll the model and reconstruct the whole sequence
full_prediction = reconstruct_func(params, full_trajectory, rng,
prefix == "forward")
assert isinstance(full_prediction, distrax.Normal)
full_targets = (full_forward_targets if prefix == "forward" else
full_backward_targets)
# In cases where the model can run backwards it is possible to reconstruct
# parts which were indented to be skipped, so here we take care of that.
if full_prediction.mean().shape[1] > full_targets_length:
if prefix == "forward":
full_prediction = jax.tree_map(lambda x: x[:, -full_targets_length:],
full_prediction)
else:
full_prediction = jax.tree_map(lambda x: x[:, :full_targets_length],
full_prediction)
# Based on the prefix and suffix fetch correct predictions and targets
for suffix in ("train", "extrapolation", "full"):
if prefix == "forward" and suffix == "train":
predict, targets = jax.tree_map(lambda x: x[:, :train_targets_length],
(full_prediction, full_targets))
elif prefix == "forward" and suffix == "extrapolation":
predict, targets = jax.tree_map(lambda x: x[:, train_targets_length:],
(full_prediction, full_targets))
elif prefix == "backward" and suffix == "train":
predict, targets = jax.tree_map(lambda x: x[:, -train_targets_length:],
(full_prediction, full_targets))
elif prefix == "backward" and suffix == "extrapolation":
predict, targets = jax.tree_map(lambda x: x[:, :-train_targets_length],
(full_prediction, full_targets))
else:
predict, targets = full_prediction, full_targets
# Compute train statistics
train_stats = training_statistics(predict, targets, rescale_by,
p_x_learned_sigma=p_x_learned_sigma)
for key, value in train_stats.items():
stats[prefix + "_" + suffix + "_" + key] = value
# Copy all stats keys
keys = tuple(train_stats.keys())
# Make a combined metric summing forward and backward
if can_run_backwards:
# Also compute
for suffix in ("train", "extrapolation", "full"):
for key in keys:
forward = stats["forward_" + suffix + "_" + key]
backward = stats["backward_" + suffix + "_" + key]
combined = (forward + backward) / 2
stats["combined_" + suffix + "_" + key] = combined
return stats
def geco_objective(
l2_loss,
kl,
alpha,
kappa,
constraint_ema,
lambda_var,
is_training
) -> Dict[str, jnp.ndarray]:
"""Computes the objective for GECO and some of it statistics used ofr updates."""
# C_t
constraint_t = l2_loss - kappa
if is_training:
# We update C_ma only during training
constraint_ema = alpha * constraint_ema + (1 - alpha) * constraint_t
lagrange = nn.softplus(lambda_var)
lagrange = jnp.broadcast_to(lagrange, constraint_ema.shape)
# Add this special op for getting all gradients correct
loss = utils.geco_lagrange_product(lagrange, constraint_ema, constraint_t)
return dict(
loss=loss + kl,
geco_multiplier=lagrange,
geco_constraint=constraint_t,
geco_constraint_ema=constraint_ema
)
def elbo_objective(neg_log_p_x, kl, final_beta, beta_delay, step):
"""Computes objective for optimizing the Evidence Lower Bound (ELBO)."""
if beta_delay == 0:
beta = final_beta
else:
delayed_beta = jnp.minimum(float(step) / float(beta_delay), 1.0)
beta = delayed_beta * final_beta
return dict(
loss=neg_log_p_x + beta * kl,
elbo_beta=beta
)
================================================
FILE: physics_inspired_models/models/__init__.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
================================================
FILE: physics_inspired_models/models/autoregressive.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module for all autoregressive models."""
import functools
from typing import Any, Dict, Mapping, Optional, Sequence, Tuple, Union
import distrax
import haiku as hk
from jax import lax
import jax.numpy as jnp
import jax.random as jnr
import physics_inspired_models.metrics as metrics
import physics_inspired_models.models.base as base
import physics_inspired_models.models.networks as nets
import physics_inspired_models.utils as utils
class TeacherForcingAutoregressiveModel(base.SequenceModel):
"""A standard autoregressive model trained via teacher forcing."""
def __init__(
self,
latent_system_dim: int,
latent_system_net_type: str,
latent_system_kwargs: Dict[str, Any],
latent_dynamics_type: str,
encoder_aggregation_type: Optional[str],
decoder_de_aggregation_type: Optional[str],
encoder_kwargs: Dict[str, Any],
decoder_kwargs: Dict[str, Any],
num_inference_steps: int,
num_target_steps: int,
name: Optional[str] = None,
**kwargs
):
# Remove any parameters from vae models
encoder_kwargs = dict(**encoder_kwargs)
encoder_kwargs["distribution_name"] = None
if kwargs.get("has_latent_transform", False):
raise ValueError("We do not support AR models with latent transform.")
super().__init__(
can_run_backwards=False,
latent_system_dim=latent_system_dim,
latent_system_net_type=latent_system_net_type,
latent_system_kwargs=latent_system_kwargs,
encoder_aggregation_type=encoder_aggregation_type,
decoder_de_aggregation_type=decoder_de_aggregation_type,
encoder_kwargs=encoder_kwargs,
decoder_kwargs=decoder_kwargs,
num_inference_steps=num_inference_steps,
num_target_steps=num_target_steps,
name=name,
**kwargs
)
self.latent_dynamics_type = latent_dynamics_type
# Arguments checks
if self.latent_system_net_type != "mlp":
raise ValueError("Currently we do not support non-mlp AR models.")
def recurrence_function(sequence, initial_state=None):
core = nets.make_flexible_recurrent_net(
core_type=latent_dynamics_type,
net_type=latent_system_net_type,
output_dims=self.latent_system_dim,
**self.latent_system_kwargs["net_kwargs"])
initial_state = initial_state or core.initial_state(sequence.shape[1])
core(sequence[0], initial_state)
return hk.dynamic_unroll(core, sequence, initial_state)
self.recurrence = hk.transform(recurrence_function)
def process_inputs_for_encoder(self, x: jnp.ndarray) -> jnp.ndarray:
return x
def process_latents_for_dynamics(self, z: jnp.ndarray) -> jnp.ndarray:
return z
def process_latents_for_decoder(self, z: jnp.ndarray) -> jnp.ndarray:
return z
@property
def inferred_index(self) -> int:
return self.num_inference_steps - 1
@property
def train_sequence_length(self) -> int:
return self.num_target_steps
def train_data_split(
self,
images: jnp.ndarray
) -> Tuple[jnp.ndarray, jnp.ndarray, Mapping[str, Any]]:
images = images[:, :self.train_sequence_length]
inference_data = images[:, :-1]
target_data = images[:, 1:]
return inference_data, target_data, dict(
num_steps_forward=1,
num_steps_backward=0,
include_z0=False)
def unroll_without_inputs(
self,
params: utils.Params,
rng: jnp.ndarray,
x_init: jnp.ndarray,
h_init: jnp.ndarray,
num_steps: int,
is_training: bool
) -> Tuple[Tuple[distrax.Distribution, jnp.ndarray], Any]:
if num_steps < 1:
raise ValueError("`num_steps` must be at least 1.")
def step_fn(carry, key):
x_last, h_last = carry
enc_key, dec_key = jnr.split(key)
z_in_next = self.encoder.apply(params, enc_key, x_last,
is_training=is_training)
z_next, h_next = self.recurrence.apply(params, None, z_in_next[None],
h_last)
p_x_next = self.decode_latents(params, dec_key, z_next[0],
is_training=is_training)
return (p_x_next.mean(), h_next), (p_x_next, z_next[0])
return lax.scan(
step_fn,
init=(x_init, h_init),
xs=jnr.split(rng, num_steps)
)
def unroll_latent_dynamics(
self,
z: jnp.ndarray,
params: utils.Params,
key: jnp.ndarray,
num_steps_forward: int,
num_steps_backward: int,
include_z0: bool,
is_training: bool,
**kwargs: Any
) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:
init_key, unroll_key, dec_key = jnr.split(key, 3)
if num_steps_backward != 0:
raise ValueError("This model can not run backwards.")
# Change 'z' time dimension to be first
z = jnp.swapaxes(z, 0, 1)
# Run recurrent model on inputs
z_0, h_0 = self.recurrence.apply(params, init_key, z)
if num_steps_forward == 1:
z_t = z_0
elif num_steps_forward > 1:
p_x_0 = self.decode_latents(params, dec_key, z_0[-1], is_training=False)
_, (_, z_t) = self.unroll_without_inputs(
params=params,
rng=unroll_key,
x_init=p_x_0.mean(),
h_init=h_0,
num_steps=num_steps_forward-1,
is_training=is_training
)
z_t = jnp.concatenate([z_0, z_t], axis=0)
else:
raise ValueError("num_steps_forward should be at least 1.")
# Make time dimension second
return jnp.swapaxes(z_t, 0, 1), dict()
def _models_core(
self,
params: utils.Params,
keys: jnp.ndarray,
image_data: jnp.ndarray,
is_training: bool,
**unroll_kwargs: Any
) -> Tuple[distrax.Distribution, jnp.ndarray, jnp.ndarray]:
enc_key, _, transform_key, unroll_key, dec_key, _ = keys
# Calculate latent input representation
inference_data = self.process_inputs_for_encoder(image_data)
z_raw = self.encoder.apply(params, enc_key, inference_data,
is_training=is_training)
# Apply latent transformation (should be identity)
z0 = self.apply_latent_transform(params, transform_key, z_raw,
is_training=is_training)
z0 = self.process_latents_for_dynamics(z0)
# Calculate latent output representation
decoder_z, _ = self.unroll_latent_dynamics(
z=z0,
params=params,
key=unroll_key,
is_training=is_training,
**unroll_kwargs
)
decoder_z = self.process_latents_for_decoder(decoder_z)
# Compute p(x|z)
p_x = self.decode_latents(params, dec_key, decoder_z,
is_training=is_training)
return p_x, z0, decoder_z
def training_objectives( # pytype: disable=signature-mismatch # jax-ndarray
self,
params: hk.Params,
state: hk.State,
rng: jnp.ndarray,
inputs: jnp.ndarray,
step: jnp.ndarray,
is_training: bool = True,
use_mean_for_eval_stats: bool = True
) -> Tuple[jnp.ndarray, Sequence[Dict[str, jnp.ndarray]]]:
"""Computes the training objective and any supporting stats."""
# Split all rng keys
keys = jnr.split(rng, 6)
# Process training data
images = utils.extract_image(inputs)
image_data, target_data, unroll_kwargs = self.train_data_split(images)
p_x, _, _ = self._models_core(
params=params,
keys=keys,
image_data=image_data,
is_training=is_training,
**unroll_kwargs
)
# Compute training statistics
stats = metrics.training_statistics(
p_x=p_x,
targets=target_data,
rescale_by=self.rescale_by,
p_x_learned_sigma=self.decoder_kwargs.get("learned_sigma", False)
)
# The loss is just the negative log-likelihood (e.g. the L2 loss)
stats["loss"] = stats["neg_log_p_x"]
if not is_training:
# Optionally add the evaluation stats when not training
# Add also the evaluation statistics
# We need to be able to set `use_mean = False` for some of the tests
stats.update(metrics.evaluation_only_statistics(
reconstruct_func=functools.partial(
self.reconstruct, use_mean=use_mean_for_eval_stats),
params=params,
inputs=inputs,
rng=rng,
rescale_by=self.rescale_by,
can_run_backwards=self.can_run_backwards,
train_sequence_length=self.train_sequence_length,
reconstruction_skip=1,
p_x_learned_sigma=self.decoder_kwargs.get("learned_sigma", False)
))
return stats["loss"], (dict(), stats, dict())
def reconstruct(
self,
params: utils.Params,
inputs: jnp.ndarray,
rng: jnp.ndarray,
forward: bool,
use_mean: bool = True,
) -> distrax.Distribution:
"""Reconstructs the input sequence."""
if not forward:
raise ValueError("This model can not run backwards.")
images = utils.extract_image(inputs)
image_data = images[:, :self.num_inference_steps]
return self._models_core(
params=params,
keys=jnr.split(rng, 6),
image_data=image_data,
is_training=False,
num_steps_forward=images.shape[1] - self.num_inference_steps,
num_steps_backward=0,
include_z0=False,
)[0]
def gt_state_and_latents( # pytype: disable=signature-mismatch # jax-ndarray
self,
params: hk.Params,
rng: jnp.ndarray,
inputs: Dict[str, jnp.ndarray],
seq_length: int,
is_training: bool = False,
unroll_direction: str = "forward",
**kwargs: Dict[str, Any]
) -> Tuple[jnp.ndarray, jnp.ndarray,
Union[distrax.Distribution, jnp.ndarray]]:
"""Computes the ground state and matching latents."""
assert unroll_direction == "forward"
images = utils.extract_image(inputs)
gt_state = utils.extract_gt_state(inputs)
image_data = images[:, :self.num_inference_steps]
gt_state = gt_state[:, 1:seq_length + 1]
_, z_in, z_out = self._models_core(
params=params,
keys=jnr.split(rng, 6),
image_data=image_data,
is_training=False,
num_steps_forward=images.shape[1] - self.num_inference_steps,
num_steps_backward=0,
include_z0=False,
)
return gt_state, z_out, z_in
def _init_non_model_params_and_state(
self,
rng: jnp.ndarray
) -> Tuple[Dict[str, jnp.ndarray], Dict[str, jnp.ndarray]]:
return dict(), dict()
def _init_latent_system( # pytype: disable=signature-mismatch # jax-ndarray
self,
rng: jnp.ndarray,
z: jnp.ndarray,
**kwargs: Any
) -> utils.Params:
return self.recurrence.init(rng, z)
================================================
FILE: physics_inspired_models/models/base.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module containing the base abstract classes for sequence models."""
import abc
from typing import Any, Dict, Generic, Mapping, Optional, Sequence, Tuple, TypeVar, Union
from absl import logging
import distrax
import haiku as hk
import jax
import jax.numpy as jnp
import jax.random as jnr
from physics_inspired_models import utils
from physics_inspired_models.models import networks
T = TypeVar("T")
class SequenceModel(abc.ABC, Generic[T]):
"""An abstract class for sequence models."""
def __init__(
self,
can_run_backwards: bool,
latent_system_dim: int,
latent_system_net_type: str,
latent_system_kwargs: Dict[str, Any],
encoder_aggregation_type: Optional[str],
decoder_de_aggregation_type: Optional[str],
encoder_kwargs: Dict[str, Any],
decoder_kwargs: Dict[str, Any],
num_inference_steps: int,
num_target_steps: int,
name: str,
latent_spatial_shape: Optional[Tuple[int, int]] = (4, 4),
has_latent_transform: bool = False,
latent_transform_kwargs: Optional[Dict[str, Any]] = None,
rescale_by: Optional[str] = "pixels_and_time",
data_format: str = "NHWC",
**unused_kwargs
):
# Arguments checks
encoder_kwargs = encoder_kwargs or dict()
decoder_kwargs = decoder_kwargs or dict()
# Set the decoder de-aggregation type the "same" type as the encoder if not
# provided
if (decoder_de_aggregation_type is None and
encoder_aggregation_type is not None):
if encoder_aggregation_type == "linear_projection":
decoder_de_aggregation_type = "linear_projection"
elif encoder_aggregation_type in ("mean", "max"):
decoder_de_aggregation_type = "tile"
else:
raise ValueError(f"Unrecognized encoder_aggregation_type="
f"{encoder_aggregation_type}")
if latent_system_net_type == "conv":
if encoder_aggregation_type is not None:
raise ValueError("When the latent system is convolutional, the encoder "
"aggregation type should be None.")
if decoder_de_aggregation_type is not None:
raise ValueError("When the latent system is convolutional, the decoder "
"aggregation type should be None.")
else:
if encoder_aggregation_type is None:
raise ValueError("When the latent system is not convolutional, the "
"you must provide an encoder aggregation type.")
if decoder_de_aggregation_type is None:
raise ValueError("When the latent system is not convolutional, the "
"you must provide an decoder aggregation type.")
if has_latent_transform and latent_transform_kwargs is None:
raise ValueError("When using latent transformation you have to provide "
"the latent_transform_kwargs argument.")
if unused_kwargs:
logging.warning("Unused kwargs: %s", str(unused_kwargs))
super().__init__(**unused_kwargs)
self.can_run_backwards = can_run_backwards
self.latent_system_dim = latent_system_dim
self.latent_system_kwargs = latent_system_kwargs
self.latent_system_net_type = latent_system_net_type
self.latent_spatial_shape = latent_spatial_shape
self.num_inference_steps = num_inference_steps
self.num_target_steps = num_target_steps
self.rescale_by = rescale_by
self.data_format = data_format
self.name = name
# Encoder
self.encoder_kwargs = encoder_kwargs
self.encoder = hk.transform(
lambda *args, **kwargs: networks.SpatialConvEncoder( # pylint: disable=unnecessary-lambda,g-long-lambda
latent_dim=latent_system_dim,
aggregation_type=encoder_aggregation_type,
data_format=data_format,
name="Encoder",
**encoder_kwargs
)(*args, **kwargs))
# Decoder
self.decoder_kwargs = decoder_kwargs
self.decoder = hk.transform(
lambda *args, **kwargs: networks.SpatialConvDecoder( # pylint: disable=unnecessary-lambda,g-long-lambda
initial_spatial_shape=self.latent_spatial_shape,
de_aggregation_type=decoder_de_aggregation_type,
data_format=data_format,
max_de_aggregation_dims=self.latent_system_dim // 2,
name="Decoder",
**decoder_kwargs,
)(*args, **kwargs))
self.has_latent_transform = has_latent_transform
if has_latent_transform:
self.latent_transform = hk.transform(
lambda *args, **kwargs: networks.make_flexible_net( # pylint: disable=unnecessary-lambda,g-long-lambda
net_type=latent_system_net_type,
output_dims=latent_system_dim,
name="LatentTransform",
**latent_transform_kwargs
)(*args, **kwargs))
else:
self.latent_transform = None
self._jit_init = None
@property
@abc.abstractmethod
def train_sequence_length(self) -> int:
"""Computes the total length of a sequence needed for training or evaluation."""
pass
@abc.abstractmethod
def train_data_split(
self,
images: jnp.ndarray,
) -> Tuple[jnp.ndarray, jnp.ndarray, Mapping[str, Any]]:
"""Extracts from the inputs the data splits for training."""
pass
def decode_latents(
self,
params: hk.Params,
rng: jnp.ndarray,
z: jnp.ndarray,
**kwargs: Any
) -> distrax.Distribution:
"""Decodes the latent variable given the parameters of the model."""
# Allow to run with both the full parameters and only the decoders
if self.latent_system_net_type == "mlp":
fixed_dims = 1
elif self.latent_system_net_type == "conv":
fixed_dims = 1 + len(self.latent_spatial_shape)
else:
raise NotImplementedError()
n_shape = z.shape[:-fixed_dims]
z = z.reshape((-1,) + z.shape[-fixed_dims:])
x = self.decoder.apply(params, rng, z, **kwargs)
return jax.tree_map(lambda a: a.reshape(n_shape + a.shape[1:]), x)
def apply_latent_transform(
self,
params: hk.Params,
key: jnp.ndarray,
z: jnp.ndarray,
**kwargs: Any
) -> jnp.ndarray:
if self.latent_transform is not None:
return self.latent_transform.apply(params, key, z, **kwargs)
else:
return z
@abc.abstractmethod
def process_inputs_for_encoder(self, x: jnp.ndarray) -> jnp.ndarray:
pass
@abc.abstractmethod
def process_latents_for_dynamics(self, z: jnp.ndarray) -> T:
pass
@abc.abstractmethod
def process_latents_for_decoder(self, z: T) -> jnp.ndarray:
pass
@abc.abstractmethod
def unroll_latent_dynamics(
self,
z: T,
params: utils.Params,
key: jnp.ndarray,
num_steps_forward: int,
num_steps_backward: int,
include_z0: bool,
is_training: bool,
**kwargs: Any
) -> Tuple[T, Mapping[str, jnp.ndarray]]:
"""Unrolls the latent dynamics starting from z and pre-processing for the decoder."""
pass
@abc.abstractmethod
def reconstruct(
self,
params: utils.Params,
inputs: jnp.ndarray,
rng_key: Optional[jnp.ndarray],
forward: bool,
) -> distrax.Distribution:
"""Using the first `num_inference_steps` parts of inputs reconstructs the rest."""
pass
@abc.abstractmethod
def training_objectives(
self,
params: utils.Params,
state: hk.State,
rng: jnp.ndarray,
inputs: Union[Dict[str, jnp.ndarray], jnp.ndarray],
step: jnp.ndarray,
is_training: bool = True,
use_mean_for_eval_stats: bool = True
) -> Tuple[jnp.ndarray, Sequence[Dict[str, jnp.ndarray]]]:
"""Returns all training objectives statistics and update states."""
pass
@property
@abc.abstractmethod
def inferred_index(self):
"""Returns the time index in the input sequence, for which the encoder infers.
If the encoder takes as input the sequence x[0:n-1], where
`n = self.num_inference_steps`, then this outputs the index `k` relative to
the begging of the input sequence `x_0`, which the encoder infers.
"""
pass
@property
def inferred_right_offset(self):
return self.num_inference_steps - 1 - self.inferred_index
@abc.abstractmethod
def gt_state_and_latents(
self,
params: hk.Params,
rng: jnp.ndarray,
inputs: Dict[str, jnp.ndarray],
seq_len: int,
is_training: bool = False,
unroll_direction: str = "forward",
**kwargs: Dict[str, Any]
) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
"""Computes the ground state and matching latents."""
pass
@abc.abstractmethod
def _init_non_model_params_and_state(
self,
rng: jnp.ndarray
) -> Tuple[utils.Params, utils.Params]:
"""Initializes any non-model parameters and state."""
pass
@abc.abstractmethod
def _init_latent_system(
self,
rng: jnp.ndarray,
z: jnp.ndarray,
**kwargs: Any
) -> hk.Params:
"""Initializes the parameters of the latent system."""
pass
def _init(
self,
rng: jnp.ndarray,
images: jnp.ndarray
) -> Tuple[hk.Params, hk.State]:
"""Initializes the whole model parameters and state."""
inference_data, _, _ = self.train_data_split(images)
# Initialize parameters and state for the vae training
rng, key = jnr.split(rng)
params, state = self._init_non_model_params_and_state(key)
# Initialize and run encoder
inference_data = self.process_inputs_for_encoder(inference_data)
rng, key = jnr.split(rng)
encoder_params = self.encoder.init(key, inference_data, is_training=True)
rng, key = jnr.split(rng)
z_in = self.encoder.apply(encoder_params, key, inference_data,
is_training=True)
# For probabilistic models this will be a distribution
if isinstance(z_in, distrax.Distribution):
z_in = z_in.mean()
# Initialize and run the optional latent transform
if self.latent_transform is not None:
rng, key = jnr.split(rng)
transform_params = self.latent_transform.init(key, z_in, is_training=True)
rng, key = jnr.split(rng)
z_in = self.latent_transform.apply(transform_params, key, z_in,
is_training=True)
else:
transform_params = dict()
# Initialize and run the latent system
z_in = self.process_latents_for_dynamics(z_in)
rng, key = jnr.split(rng)
latent_params = self._init_latent_system(key, z_in, is_training=True)
rng, key = jnr.split(rng)
z_out, _ = self.unroll_latent_dynamics(
z=z_in,
params=latent_params,
key=key,
num_steps_forward=1,
num_steps_backward=0,
include_z0=False,
is_training=True
)
z_out = self.process_latents_for_decoder(z_out)
# Initialize and run the decoder
rng, key = jnr.split(rng)
decoder_params = self.decoder.init(key, z_out[:, 0], is_training=True)
_ = self.decoder.apply(decoder_params, rng, z_out[:, 0], is_training=True)
# Combine all and make immutable
params = hk.data_structures.merge(params, encoder_params, transform_params,
latent_params, decoder_params)
params = hk.data_structures.to_immutable_dict(params)
state = hk.data_structures.to_immutable_dict(state)
return params, state # pytype: disable=bad-return-type # jax-ndarray
def init(
self,
rng: jnp.ndarray,
inputs_or_shape: Union[jnp.ndarray, Mapping[str, jnp.ndarray],
Sequence[int]],
) -> Tuple[utils.Params, hk.State]:
"""Initializes the whole model parameters and state."""
if (isinstance(inputs_or_shape, (tuple, list))
and isinstance(inputs_or_shape[0], int)):
images = jnp.zeros(inputs_or_shape)
else:
images = utils.extract_image(inputs_or_shape)
if self._jit_init is None:
self._jit_init = jax.jit(self._init)
return self._jit_init(rng, images)
================================================
FILE: physics_inspired_models/models/common.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module for all models."""
from typing import Any, Dict, Optional
import physics_inspired_models.models.autoregressive as autoregressive
import physics_inspired_models.models.deterministic_vae as deterministic_vae
_physics_arguments = (
"input_space", "simulation_space", "potential_func_form",
"kinetic_func_form", "hgn_kinetic_func_form", "lgn_kinetic_func_form",
"parametrize_mass_matrix", "hgn_parametrize_mass_matrix",
"lgn_parametrize_mass_matrix", "mass_eps"
)
def construct_model(
name: str,
*args,
**kwargs: Dict[str, Any]
):
"""Constructs the correct instance of a model given the short name."""
latent_dynamics_type: Optional[str] = kwargs.pop("latent_dynamics_type", None) # pytype: disable=annotation-type-mismatch
latent_system_kwargs = dict(**kwargs.pop("latent_system_kwargs", dict()))
if name == "AR":
assert latent_dynamics_type in ("vanilla", "lstm", "gru")
# This arguments are not part of the AR models
for k in _physics_arguments + ("integrator_method", "residual"):
latent_system_kwargs.pop(k, None)
return autoregressive.TeacherForcingAutoregressiveModel(
*args,
latent_dynamics_type=latent_dynamics_type,
latent_system_kwargs=latent_system_kwargs,
**kwargs
)
elif name == "RGN":
assert latent_dynamics_type in ("Discrete", None)
latent_dynamics_type = "Discrete"
# This arguments are not part of the RGN models
for k in _physics_arguments + ("integrator_method",):
latent_system_kwargs.pop(k, None)
elif name == "ODE":
assert latent_dynamics_type in ("ODE", None)
latent_dynamics_type = "ODE"
# This arguments are not part of the ODE models
for k in _physics_arguments + ("residual",):
latent_system_kwargs.pop(k, None)
elif name == "HGN":
assert latent_dynamics_type in ("Physics", None)
latent_dynamics_type = "Physics"
assert latent_system_kwargs.get("input_space", None) in ("momentum", None)
latent_system_kwargs["input_space"] = "momentum"
assert (latent_system_kwargs.get("simulation_space", None)
in ("momentum", None))
latent_system_kwargs["simulation_space"] = "momentum"
# Kinetic func form
hgn_specific = latent_system_kwargs.pop("hgn_kinetic_func_form", None)
if hgn_specific is not None:
latent_system_kwargs["kinetic_func_form"] = hgn_specific
# Mass matrix
hgn_specific = latent_system_kwargs.pop("hgn_parametrize_mass_matrix",
None)
if hgn_specific is not None:
latent_system_kwargs["parametrize_mass_matrix"] = hgn_specific
# This arguments are not part of the HGN models
latent_system_kwargs.pop("residual", None)
latent_system_kwargs.pop("lgn_kinetic_func_form", None)
latent_system_kwargs.pop("lgn_parametrize_mass_matrix", None)
elif name == "LGN":
assert latent_dynamics_type in ("Physics", None)
latent_dynamics_type = "Physics"
assert latent_system_kwargs.get("input_space", None) in ("velocity", None)
latent_system_kwargs["input_space"] = "velocity"
assert (latent_system_kwargs.get("simulation_space", None) in
("velocity", None))
latent_system_kwargs["simulation_space"] = "velocity"
# Kinetic func form
lgn_specific = latent_system_kwargs.pop("lgn_kinetic_func_form", None)
if lgn_specific is not None:
latent_system_kwargs["kinetic_func_form"] = lgn_specific
# Mass matrix
lgn_specific = latent_system_kwargs.pop("lgn_parametrize_mass_matrix",
None)
if lgn_specific is not None:
latent_system_kwargs["parametrize_mass_matrix"] = lgn_specific
# This arguments are not part of the HGN models
latent_system_kwargs.pop("residual", None)
latent_system_kwargs.pop("hgn_kinetic_func_form", None)
latent_system_kwargs.pop("hgn_parametrize_mass_matrix", None)
elif name == "PGN":
assert latent_dynamics_type in ("Physics", None)
latent_dynamics_type = "Physics"
# This arguments are not part of the PGN models
latent_system_kwargs.pop("residual")
latent_system_kwargs.pop("hgn_kinetic_func_form", None)
latent_system_kwargs.pop("hgn_parametrize_mass_matrix", None)
latent_system_kwargs.pop("lgn_kinetic_func_form", None)
latent_system_kwargs.pop("lgn_parametrize_mass_matrix", None)
else:
raise NotImplementedError()
return deterministic_vae.DeterministicLatentsGenerativeModel(
*args,
latent_dynamics_type=latent_dynamics_type,
latent_system_kwargs=latent_system_kwargs,
**kwargs)
================================================
FILE: physics_inspired_models/models/deterministic_vae.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module containing the main models code."""
import functools
from typing import Any, Dict, Mapping, Optional, Sequence, Tuple, Union
import distrax
from dm_hamiltonian_dynamics_suite.hamiltonian_systems import phase_space
import haiku as hk
import jax.numpy as jnp
import jax.random as jnr
import numpy as np
from physics_inspired_models import metrics
from physics_inspired_models import utils
from physics_inspired_models.models import base
from physics_inspired_models.models import dynamics
_ArrayOrPhase = Union[jnp.ndarray, phase_space.PhaseSpace]
class DeterministicLatentsGenerativeModel(base.SequenceModel[_ArrayOrPhase]):
"""Common class for generative models with deterministic latent dynamics."""
def __init__(
self,
latent_system_dim: int,
latent_system_net_type: str,
latent_system_kwargs: Dict[str, Any],
latent_dynamics_type: str,
encoder_aggregation_type: Optional[str],
decoder_de_aggregation_type: Optional[str],
encoder_kwargs: Dict[str, Any],
decoder_kwargs: Dict[str, Any],
num_inference_steps: int,
num_target_steps: int,
latent_training_type: str,
training_data_split: str,
objective_type: str,
dt: float = 0.125,
render_from_q_only: bool = True,
prior_type: str = "standard_normal",
use_analytical_kl: bool = True,
geco_kappa: float = 0.001,
geco_alpha: Optional[float] = 0.0,
elbo_beta_delay: int = 0,
elbo_beta_final: float = 1.0,
name: Optional[str] = None,
**kwargs
):
can_run_backwards = latent_dynamics_type in ("ODE", "Physics")
# Verify arguments
if objective_type not in ("GECO", "ELBO", "NON-PROB"):
raise ValueError(f"Unrecognized training type - {objective_type}")
if geco_alpha is None:
geco_alpha = 0
if geco_alpha < 0 or geco_alpha >= 1:
raise ValueError("GECO alpha parameter must be in [0, 1).")
if prior_type not in ("standard_normal", "made", "made_gated"):
raise ValueError(f"Unrecognized prior_type='{prior_type}.")
if (latent_training_type == "forward_backward" and
training_data_split != "include_inference"):
raise ValueError("Training forward_backward works only when "
"training_data_split=include_inference.")
if (latent_training_type == "forward_backward" and
num_inference_steps % 2 == 0):
raise ValueError("Training forward_backward works only when "
"num_inference_steps are odd.")
if latent_training_type == "forward_backward" and not can_run_backwards:
raise ValueError("Training forward_backward works only when the model can"
" be run backwards.")
if prior_type != "standard_normal":
raise ValueError("For now we support only `standard_normal`.")
super().__init__(
can_run_backwards=can_run_backwards,
latent_system_dim=latent_system_dim,
latent_system_net_type=latent_system_net_type,
latent_system_kwargs=latent_system_kwargs,
encoder_aggregation_type=encoder_aggregation_type,
decoder_de_aggregation_type=decoder_de_aggregation_type,
encoder_kwargs=encoder_kwargs,
decoder_kwargs=decoder_kwargs,
num_inference_steps=num_inference_steps,
num_target_steps=num_target_steps,
name=name,
**kwargs
)
# VAE specific arguments
self.prior_type = prior_type
self.objective_type = objective_type
self.use_analytical_kl = use_analytical_kl
self.geco_kappa = geco_kappa
self.geco_alpha = geco_alpha
self.elbo_beta_delay = elbo_beta_delay
self.elbo_beta_final = jnp.asarray(elbo_beta_final)
# The dynamics module and arguments
self.latent_dynamics_type = latent_dynamics_type
self.latent_training_type = latent_training_type
self.training_data_split = training_data_split
self.dt = dt
self.render_from_q_only = render_from_q_only
latent_system_kwargs["net_kwargs"] = dict(
latent_system_kwargs["net_kwargs"])
latent_system_kwargs["net_kwargs"]["net_type"] = self.latent_system_net_type
if self.latent_dynamics_type == "Physics":
# Note that here system_dim means the dimensionality of `q` and `p`.
model_constructor = functools.partial(
dynamics.PhysicsSimulationNetwork,
system_dim=self.latent_system_dim // 2,
name="Physics",
**latent_system_kwargs
)
elif self.latent_dynamics_type == "ODE":
model_constructor = functools.partial(
dynamics.OdeNetwork,
system_dim=self.latent_system_dim,
name="ODE",
**latent_system_kwargs
)
elif self.latent_dynamics_type == "Discrete":
model_constructor = functools.partial(
dynamics.DiscreteDynamicsNetwork,
system_dim=self.latent_system_dim,
name="Discrete",
**latent_system_kwargs
)
else:
raise NotImplementedError()
self.dynamics = hk.transform(
lambda *args, **kwargs_: model_constructor()(*args, **kwargs_)) # pylint: disable=unnecessary-lambda
def process_inputs_for_encoder(self, x: jnp.ndarray) -> jnp.ndarray:
return utils.stack_time_into_channels(x, self.data_format)
def process_latents_for_dynamics(self, z: jnp.ndarray) -> _ArrayOrPhase:
if self.latent_dynamics_type == "Physics":
return phase_space.PhaseSpace.from_state(z)
return z
def process_latents_for_decoder(self, z: _ArrayOrPhase) -> jnp.ndarray:
if self.latent_dynamics_type == "Physics":
return z.q if self.render_from_q_only else z.single_state # pytype: disable=attribute-error # jax-ndarray
return z # pytype: disable=bad-return-type # jax-ndarray
@property
def inferred_index(self) -> int:
if self.latent_training_type == "forward":
return self.num_inference_steps - 1
elif self.latent_training_type == "forward_backward":
assert self.num_inference_steps % 2 == 1
return self.num_inference_steps // 2
else:
raise NotImplementedError()
@property
def targets_index_offset(self) -> int:
if self.training_data_split == "overlap_by_one":
return -1
elif self.training_data_split == "no_overlap":
return 0
elif self.training_data_split == "include_inference":
return - self.num_inference_steps
else:
raise NotImplementedError()
@property
def targets_length(self) -> int:
if self.training_data_split == "include_inference":
return self.num_inference_steps + self.num_target_steps
return self.num_target_steps
@property
def train_sequence_length(self) -> int:
"""Computes the total length of a sequence needed for training."""
if self.training_data_split == "overlap_by_one":
# Input - [-------------------------------------------------]
# Inference - [---------------]
# Targets - [---------------------------------]
return self.num_inference_steps + self.num_target_steps - 1
elif self.training_data_split == "no_overlap":
# Input - [-------------------------------------------------]
# Inference - [---------------]
# Targets - [--------------------------------]
return self.num_inference_steps + self.num_target_steps
elif self.training_data_split == "include_inference":
# Input - [-------------------------------------------------]
# Inference - [---------------]
# Targets - [-------------------------------------------------]
return self.num_inference_steps + self.num_target_steps
else:
raise NotImplementedError()
def train_data_split(
self,
images: jnp.ndarray
) -> Tuple[jnp.ndarray, jnp.ndarray, Mapping[str, Any]]:
images = images[:, :self.train_sequence_length]
inf_idx = self.num_inference_steps
t_idx = self.num_inference_steps + self.targets_index_offset
if self.latent_training_type == "forward":
inference_data = images[:, :inf_idx]
target_data = images[:, t_idx:]
if self.training_data_split == "include_inference":
num_steps_backward = self.inferred_index
else:
num_steps_backward = 0
num_steps_forward = self.num_target_steps
if self.training_data_split == "overlap_by_one":
num_steps_forward -= 1
unroll_kwargs = dict(
num_steps_backward=num_steps_backward,
include_z0=self.training_data_split != "no_overlap",
num_steps_forward=num_steps_forward,
dt=self.dt
)
elif self.latent_training_type == "forward_backward":
assert self.training_data_split == "include_inference"
n_fwd = images.shape[0] // 2
inference_fwd = images[:n_fwd, :inf_idx]
targets_fwd = images[:n_fwd, t_idx:]
inference_bckwd = images[n_fwd:, -inf_idx:]
targets_bckwd = jnp.flip(images[n_fwd:, :images.shape[1] - t_idx], axis=1)
inference_data = jnp.concatenate([inference_fwd, inference_bckwd], axis=0)
target_data = jnp.concatenate([targets_fwd, targets_bckwd], axis=0)
# This needs to by numpy rather than jax.numpy, because we make some
# verification checks in `integrators.py:149-161`.
dt_fwd = np.full([n_fwd], self.dt)
dt_bckwd = np.full([images.shape[0] - n_fwd], self.dt)
dt = np.concatenate([dt_fwd, -dt_bckwd], axis=0)
unroll_kwargs = dict(
num_steps_backward=self.inferred_index,
include_z0=True,
num_steps_forward=self.targets_length - self.inferred_index - 1,
dt=dt
)
else:
raise NotImplementedError()
return inference_data, target_data, unroll_kwargs
def prior(self) -> distrax.Distribution:
"""Given the parameters returns the prior distribution of the model."""
# Allow to run with both the full parameters and only the priors
if self.prior_type == "standard_normal":
# assert self.prior_nets is None and self.gated_made is None
if self.latent_system_net_type == "mlp":
event_shape = (self.latent_system_dim,)
elif self.latent_system_net_type == "conv":
if self.data_format == "NHWC":
event_shape = self.latent_spatial_shape + (self.latent_system_dim,)
else:
event_shape = (self.latent_system_dim,) + self.latent_spatial_shape
else:
raise NotImplementedError()
return distrax.Normal(jnp.zeros(event_shape), jnp.ones(event_shape))
else:
raise ValueError(f"Unrecognized prior_type='{self.prior_type}'.")
def sample_latent_from_prior(
self,
params: utils.Params,
rng: jnp.ndarray,
num_samples: int = 1,
**kwargs: Any) -> jnp.ndarray:
"""Takes sample from the prior (and optionally puts them through the latent transform function."""
_, sample_key, transf_key = jnr.split(rng, 3)
prior = self.prior()
z_raw = prior.sample(seed=sample_key, sample_shape=[num_samples])
return self.apply_latent_transform(params, transf_key, z_raw, **kwargs)
def sample_trajectories_from_prior(
self,
params: utils.Params,
num_steps: int,
rng: jnp.ndarray,
num_samples: int = 1,
is_training: bool = False,
**kwargs
) -> distrax.Distribution:
"""Generates samples from the prior (unconditional generation)."""
sample_key, unroll_key, dec_key = jnr.split(rng, 3)
z0 = self.sample_latent_from_prior(params, sample_key, num_samples,
is_training=is_training)
z, _ = self.unroll_latent_dynamics(
z=self.process_latents_for_dynamics(z0),
params=params,
key=unroll_key,
num_steps_forward=num_steps,
num_steps_backward=0,
include_z0=True,
is_training=is_training,
**kwargs
)
z = self.process_latents_for_decoder(z)
return self.decode_latents(params, dec_key, z, is_training=is_training)
def verify_unroll_args(
self,
num_steps_forward: int,
num_steps_backward: int,
include_z0: bool
) -> None:
if num_steps_forward < 0 or num_steps_backward < 0:
raise ValueError("num_steps_forward and num_steps_backward can not be "
"negative.")
if num_steps_forward == 0 and num_steps_backward == 0:
raise ValueError("You need one of num_steps_forward or "
"num_of_steps_backward to be positive.")
if num_steps_forward > 0 and num_steps_backward > 0 and not include_z0:
raise ValueError("When both num_steps_forward and num_steps_backward are "
"positive include_t0 should be True.")
if num_steps_backward > 0 and not self.can_run_backwards:
raise ValueError("This model can not be unrolled backward in time.")
def unroll_latent_dynamics( # pytype: disable=signature-mismatch # jax-ndarray
self,
z: phase_space.PhaseSpace,
params: hk.Params,
key: jnp.ndarray,
num_steps_forward: int,
num_steps_backward: int,
include_z0: bool,
is_training: bool,
**kwargs: Any
) -> Tuple[_ArrayOrPhase, Mapping[str, jnp.ndarray]]:
self.verify_unroll_args(num_steps_forward, num_steps_backward, include_z0)
return self.dynamics.apply(
params,
key,
y0=z,
dt=kwargs.pop("dt", self.dt),
num_steps_forward=num_steps_forward,
num_steps_backward=num_steps_backward,
include_y0=include_z0,
return_stats=True,
is_training=is_training
)
def _models_core(
self,
params: utils.Params,
keys: jnp.ndarray,
image_data: jnp.ndarray,
use_mean: bool,
is_training: bool,
**unroll_kwargs: Any
) -> Tuple[distrax.Distribution, distrax.Distribution, distrax.Distribution,
jnp.ndarray, jnp.ndarray, Mapping[str, jnp.ndarray]]:
enc_key, sample_key, transform_key, unroll_key, dec_key, _ = keys
# Calculate the approximate posterior q(z|x)
inference_data = self.process_inputs_for_encoder(image_data)
q_z: distrax.Distribution = self.encoder.apply(params, enc_key,
inference_data,
is_training=is_training)
# Sample latent variables or take the mean
z_raw = q_z.mean() if use_mean else q_z.sample(seed=sample_key)
# Apply latent transformation
z0 = self.apply_latent_transform(params, transform_key, z_raw,
is_training=is_training)
# Unroll the latent variable
z, dyn_stats = self.unroll_latent_dynamics(
z=self.process_latents_for_dynamics(z0),
params=params,
key=unroll_key,
is_training=is_training,
**unroll_kwargs
)
decoder_z = self.process_latents_for_decoder(z)
# Compute p(x|z)
p_x = self.decode_latents(params, dec_key, decoder_z,
is_training=is_training)
z = z.single_state if isinstance(z, phase_space.PhaseSpace) else z
return p_x, q_z, self.prior(), z0, z, dyn_stats
def training_objectives( # pytype: disable=signature-mismatch # jax-ndarray
self,
params: utils.Params,
state: hk.State,
rng: jnp.ndarray,
inputs: jnp.ndarray,
step: jnp.ndarray,
is_training: bool = True,
use_mean_for_eval_stats: bool = True
) -> Tuple[jnp.ndarray, Sequence[Dict[str, jnp.ndarray]]]:
# Split all rng keys
keys = jnr.split(rng, 6)
# Process training data
images = utils.extract_image(inputs)
image_data, target_data, unroll_kwargs = self.train_data_split(images)
p_x, q_z, prior, _, _, dyn_stats = self._models_core(
params=params,
keys=keys,
image_data=image_data,
use_mean=False,
is_training=is_training,
**unroll_kwargs
)
# Note: we reuse the rng key used to sample the latent variable here
# so that it can be reused to evaluate a (non-analytical) KL at that sample.
stats = metrics.training_statistics(
p_x=p_x,
targets=target_data,
rescale_by=self.rescale_by,
rng=keys[1],
q_z=q_z,
prior=prior,
p_x_learned_sigma=self.decoder_kwargs.get("learned_sigma", False)
)
stats.update(dyn_stats)
# Compute other (non-reported statistics)
z_stats = dict()
other_stats = dict(x_reconstruct=p_x.mean(), z_stats=z_stats)
# The loss computation and GECO state update
new_state = dict()
if self.objective_type == "GECO":
geco_stats = metrics.geco_objective(
l2_loss=stats["l2"],
kl=stats["kl"],
alpha=self.geco_alpha,
kappa=self.geco_kappa,
constraint_ema=state["GECO"]["geco_constraint_ema"],
lambda_var=params["GECO"]["geco_lambda_var"],
is_training=is_training
)
new_state["GECO"] = dict(
geco_constraint_ema=geco_stats["geco_constraint_ema"])
stats.update(geco_stats)
elif self.objective_type == "ELBO":
elbo_stats = metrics.elbo_objective(
neg_log_p_x=stats["neg_log_p_x"],
kl=stats["kl"],
final_beta=self.elbo_beta_final,
beta_delay=self.elbo_beta_delay,
step=step
)
stats.update(elbo_stats)
elif self.objective_type == "NON-PROB":
stats["loss"] = stats["neg_log_p_x"]
else:
raise ValueError()
if not is_training:
if self.training_data_split == "overlap_by_one":
reconstruction_skip = self.num_inference_steps - 1
elif self.training_data_split == "no_overlap":
reconstruction_skip = self.num_inference_steps
elif self.training_data_split == "include_inference":
reconstruction_skip = 0
else:
raise NotImplementedError()
# We intentionally reuse the same rng as the training, in order to be able
# to run tests and verify that the evaluation and reconstruction work
# correctly.
# We need to be able to set `use_mean = False` for some of the tests
stats.update(metrics.evaluation_only_statistics(
reconstruct_func=functools.partial(
self.reconstruct, use_mean=use_mean_for_eval_stats),
params=params,
inputs=inputs,
rng=rng,
rescale_by=self.rescale_by,
can_run_backwards=self.can_run_backwards,
train_sequence_length=self.train_sequence_length,
reconstruction_skip=reconstruction_skip,
p_x_learned_sigma=self.decoder_kwargs.get("learned_sigma", False)
))
# Make new state the same type as state
new_state = utils.convert_to_pytype(new_state, state)
return stats["loss"], (new_state, stats, other_stats)
def reconstruct(
self,
params: utils.Params,
inputs: jnp.ndarray,
rng: Optional[jnp.ndarray],
forward: bool,
use_mean: bool = True,
) -> distrax.Distribution:
if not self.can_run_backwards and not forward:
raise ValueError("This model can not be run backwards.")
images = utils.extract_image(inputs)
# This is intentionally matching the split for the training stats
if forward:
num_steps_backward = self.inferred_index
num_steps_forward = images.shape[1] - num_steps_backward - 1
else:
num_steps_forward = self.num_inference_steps - self.inferred_index - 1
num_steps_backward = images.shape[1] - num_steps_forward - 1
if not self.can_run_backwards:
num_steps_backward = 0
if forward:
image_data = images[:, :self.num_inference_steps]
else:
image_data = images[:, -self.num_inference_steps:]
return self._models_core(
params=params,
keys=jnr.split(rng, 6),
image_data=image_data,
use_mean=use_mean,
is_training=False,
num_steps_forward=num_steps_forward,
num_steps_backward=num_steps_backward,
include_z0=True,
)[0]
def gt_state_and_latents( # pytype: disable=signature-mismatch # jax-ndarray
self,
params: hk.Params,
rng: jnp.ndarray,
inputs: Dict[str, jnp.ndarray],
seq_length: int,
is_training: bool = False,
unroll_direction: str = "forward",
**kwargs: Dict[str, Any]
) -> Tuple[jnp.ndarray, jnp.ndarray,
Union[distrax.Distribution, jnp.ndarray]]:
"""Computes the ground state and matching latents."""
assert unroll_direction in ("forward", "backward")
if unroll_direction == "backward" and not self.can_run_backwards:
raise ValueError("This model can not be unrolled backwards.")
images = utils.extract_image(inputs)
gt_state = utils.extract_gt_state(inputs)
if unroll_direction == "forward":
image_data = images[:, :self.num_inference_steps]
if self.can_run_backwards:
num_steps_backward = self.inferred_index
gt_start_idx = 0
else:
num_steps_backward = 0
gt_start_idx = self.inferred_index
num_steps_forward = seq_length - num_steps_backward - 1
gt_state = gt_state[:, gt_start_idx: seq_length + gt_start_idx]
elif unroll_direction == "backward":
inference_start_idx = seq_length - self.num_inference_steps
image_data = images[:, inference_start_idx: seq_length]
num_steps_forward = self.num_inference_steps - self.inferred_index - 1
num_steps_backward = seq_length - num_steps_forward - 1
gt_state = gt_state[:, :seq_length]
else:
raise NotImplementedError()
_, q_z, _, z0, z, _ = self._models_core(
params=params,
keys=jnr.split(rng, 6),
image_data=image_data,
use_mean=True,
is_training=False,
num_steps_forward=num_steps_forward,
num_steps_backward=num_steps_backward,
include_z0=True,
)
if self.has_latent_transform:
return gt_state, z, z0
else:
return gt_state, z, q_z
def _init_non_model_params_and_state(
self,
rng: jnp.ndarray
) -> Tuple[utils.Params, utils.Params]:
if self.objective_type == "GECO":
# Initialize such that softplus(lambda_var) = 1
geco_lambda_var = jnp.asarray(jnp.log(jnp.e - 1.0))
geco_constraint_ema = jnp.asarray(0.0)
return (dict(GECO=dict(geco_lambda_var=geco_lambda_var)),
dict(GECO=dict(geco_constraint_ema=geco_constraint_ema)))
else:
return dict(), dict()
def _init_latent_system(
self,
rng: jnp.ndarray,
z: jnp.ndarray,
**kwargs: Mapping[str, Any]
) -> hk.Params:
"""Initializes the parameters of the latent system."""
return self.dynamics.init(
rng,
y0=z,
dt=self.dt,
num_steps_forward=1,
num_steps_backward=0,
include_y0=True,
**kwargs
)
================================================
FILE: physics_inspired_models/models/dynamics.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module containing all of the networks as Haiku modules."""
from typing import Any, Mapping, Optional, Tuple, Union
from dm_hamiltonian_dynamics_suite.hamiltonian_systems import phase_space
import haiku as hk
import jax
import jax.numpy as jnp
from physics_inspired_models import integrators
from physics_inspired_models import utils
from physics_inspired_models.models import networks
_PhysicsSimulationOutput = Union[
phase_space.PhaseSpace,
Tuple[phase_space.PhaseSpace, Mapping[str, jnp.ndarray]]
]
class PhysicsSimulationNetwork(hk.Module):
"""A model for simulating an abstract physical system, whose energy is defined by a neural network."""
def __init__(
self,
system_dim: int,
input_space: str,
simulation_space: str,
potential_func_form: str,
kinetic_func_form: str,
parametrize_mass_matrix: bool,
net_kwargs: Mapping[str, Any],
mass_eps: float = 1.0,
integrator_method: Optional[str] = None,
steps_per_dt: int = 1,
ode_int_kwargs: Optional[Mapping[str, float]] = None,
use_scan: bool = True,
feature_axis: int = -1,
features_extra_dims: Optional[int] = None,
network_creation_func=networks.make_flexible_net,
name: Optional[str] = None
):
"""Initializes the model.
Args:
system_dim: The number of system dimensions. Note that this specifies the
number of dimensions only of the position vectors, not of position and
momentum. Hence the generalized coordinates would be of dimension
`2 * system_dim`.
input_space: Either `velocity` or `momentum`. Specifies whether the inputs
to the model are to be interpreted as `(position, velocity)` or as
`(position, momentum)`.
simulation_space: Either `velocity` or `momentum`. Specifies whether the
model should simulate the dynamics in `(position, velocity)` space
using the Lagrangian formulation or in `(position, momentum)` space
using the Hamiltonian formulation. If this is different than the value
of `input_space` then `kinetic_func_form` must be one of pure_quad,
matrix_diag_quad, matrix_quad, matrix_dep_diag_quad, matrix_dep_quad.
In all other cases one can not compute analytically the form of the
functional (Lagrangian or Hamiltonian) from the other.
potential_func_form: String specifying the form of the potential energy:
* separable_net - The network uses only the position:
U(q, q_dot/p) = f(q) f: R^d -> R
* dep_net - The network uses both the position and velocity/momentum:
U(q, q_dot/p) = f(q, q_dot/p) f: R^d x R^d -> R
* embed_quad - A quadratic of the embedding of a network embedding of
the velocity/momentum:
U(q, q_dot/p) = f(q)^T f(q) / 2 f: R^d -> R^d
kinetic_func_form: String specifying the form of the potential energy:
* separable_net - The network uses only the velocity/momentum:
K(q, q_dot/p) = f(q_dot/p) f: R^d -> R
* dep_net - The network uses both the position and velocity/momentum:
K(q, q_dot/p) = f(q, q_dot/p) f: R^d x R^d -> R
* pure_quad - A quadratic function of the velocity/momentum:
K(q, q_dot/p) = (q_dot/p)^T (q_dot/p) / 2
* matrix_diag_quad - A quadratic function of the velocity/momentum,
where there is diagonal mass matrix, whose log `P` is a parameter:
K(q, q_dot) = q_dot^T M q_dot / 2
K(q, p) = p^T M^-1 p / 2
[if `parameterize_mass_matrix`]
M = diag(exp(P) + mass_eps)
[else]
M^-1 = diag(exp(P) + mass_eps)
* matrix_quad - A quadratic function of the velocity/momentum, where
there is a full mass matrix, whose Cholesky factor L is a parameter:
K(q, q_dot) = q_dot^T M q_dot / 2
K(q, p) = p^T M^-1 p / 2
[if `parameterize_mass_matrix`]
M = LL^T + mass_eps * I
[else]
M^-1 = LL^T + mass_eps * I
* matrix_dep_quad - A quadratic function of the velocity/momentum, where
there is a full mass matrix defined as a function of the position:
K(q, q_dot) = q_dot^T M(q) q_dot / 2
K(q, p) = p^T M(q)^-1 p / 2
[if `parameterize_mass_matrix`]
M(q) = g(q) g(q)^T + mass_eps * I g: R^d -> R^(d(d+1)/2)
[else]
M(q)^-1 = g(q) g(q)^T + mass_eps * I g: R^d -> R^(d(d+1)/2)
* embed_quad - A quadratic of the embedding of a network embedding of
the velocity/momentum:
K(q, q_dot/p) = f(q_dot/p)^T f(q_dot/p) / 2 f: R^d -> R^d
* matrix_dep_diag_embed_quad - A quadratic of the embedding of a network
embedding of the velocity/momentum where there is diagonal mass matrix
defined as a function of the position:
K(q, q_dot) = f(q_dot)^T M(q) f(q_dot) / 2 f: R^d -> R^d
K(q, p) = f(p)^T M(q)^-1 f(p) / 2 f: R^d -> R^d
[if `parameterize_mass_matrix`]
M(q) = diag(exp(g(q)) + mass_eps * I g: R^d -> R^d
[else]
M(q)^-1 = diag(exp(g(q)) + mass_eps * I g: R^d -> R^d
* matrix_dep_embed_quad - A quadratic of the embedding of a network
embedding of the velocity/momentum where there is a full mass matrix
defined as a function of the position:
K(q, q_dot) = f(q_dot)^T M(q) f(q_dot) / 2 f: R^d -> R^d
K(q, p) = f(p)^T M(q)^-1 f(p) / 2 f: R^d -> R^d
[if `parameterize_mass_matrix`]
M(q) = g(q) g(q)^T + mass_eps * I g: R^d -> R^(d(d+1)/2)
[else]
M(q)^-1 = g(q) g(q)^T + mass_eps * I g: R^d -> R^(d(d+1)/2)
For any of the function forms with mass matrices, if we have a
convolutional input it is assumed that the matrix is shared across all
spatial locations.
parametrize_mass_matrix: Defines for the kinetic functional form, whether
the network output defines the mass or the inverse of the mass matrix.
net_kwargs: Any keyword arguments to pass down to the networks.
mass_eps: The additional weight of the identity added to the mass matrix,
when relevant.
integrator_method: What method to use for integrating the system.
steps_per_dt: How many internal steps per a single `dt` step to do.
ode_int_kwargs: Extra arguments when using "implicit" integrator method.
use_scan: Whether to use `lax.scan` for explicit integrators.
feature_axis: The number of the features axis in the inputs.
features_extra_dims: If the inputs have extra features (like spatial for
convolutions) this specifies how many of them there are.
network_creation_func: A function that creates the networks. Should have a
signature `network_creation_func(output_dims, name, **net_kwargs)`.
name: The name of this Haiku module.
"""
super().__init__(name=name)
if input_space not in ("velocity", "momentum"):
raise ValueError("input_space must be either velocity or momentum.")
if simulation_space not in ("velocity", "momentum"):
raise ValueError("simulation_space must be either velocity or momentum.")
if potential_func_form not in ("separable_net", "dep_net", "embed_quad"):
raise ValueError("The potential network can be only a network.")
if kinetic_func_form not in ("separable_net", "dep_net", "pure_quad",
"matrix_diag_quad", "matrix_quad",
"matrix_dep_diag_quad", "matrix_dep_quad",
"embed_quad", "matrix_dep_diag_embed_quad",
"matrix_dep_embed_quad"):
raise ValueError(f"Unrecognized kinetic func form {kinetic_func_form}.")
if input_space != simulation_space:
if kinetic_func_form not in (
"pure_quad", "matrix_diag_quad", "matrix_quad",
"matrix_dep_diag_quad", "matrix_dep_quad"):
raise ValueError(
"When the input and simulation space are not the same, it is "
"possible to simulate the physical system only if kinetic_func_form"
" is one of pure_quad, matrix_diag_quad, matrix_quad, "
"matrix_dep_diag_quad, matrix_dep_quad. In all other cases one can"
"not compute analytically the form of the functional (Lagrangian or"
" Hamiltonian) from the other.")
if feature_axis != -1:
raise ValueError("Currently we only support features_axis=-1.")
if integrator_method is None:
if simulation_space == "velocity":
integrator_method = "rk2"
else:
integrator_method = "leap_frog"
if features_extra_dims is None:
if net_kwargs["net_type"] == "mlp":
features_extra_dims = 0
elif net_kwargs["net_type"] == "conv":
features_extra_dims = 2
else:
raise NotImplementedError()
ode_int_kwargs = dict(ode_int_kwargs or {})
ode_int_kwargs.setdefault("rtol", 1e-6)
ode_int_kwargs.setdefault("atol", 1e-6)
ode_int_kwargs.setdefault("mxstep", 50)
self.system_dim = system_dim
self.input_space = input_space
self.simulation_space = simulation_space
self.potential_func_form = potential_func_form
self.kinetic_func_form = kinetic_func_form
self.parametrize_mass_matrix = parametrize_mass_matrix
self.features_axis = feature_axis
self.features_extra_dims = features_extra_dims
self.integrator_method = integrator_method
self.steps_per_dt = steps_per_dt
self.ode_int_kwargs = ode_int_kwargs
self.net_kwargs = net_kwargs
self.mass_eps = mass_eps
self.use_scan = use_scan
self.name = name
self.potential_net = network_creation_func(
output_dims=1, name="PotentialNet", **net_kwargs)
if kinetic_func_form in ("separable_net", "dep_net"):
self.kinetic_net = network_creation_func(
output_dims=1, name="KineticNet", **net_kwargs)
else:
self.kinetic_net = None
if kinetic_func_form in ("matrix_dep_quad", "matrix_dep_embed_quad"):
output_dims = (system_dim * (system_dim + 1)) // 2
name = "MatrixNet" if parametrize_mass_matrix else "InvMatrixNet"
self.mass_matrix_net = network_creation_func(
output_dims=output_dims, name=name, **net_kwargs)
elif kinetic_func_form in ("matrix_dep_diag_quad",
"matrix_dep_diag_embed_quad",
"matrix_dep_embed_quad"):
name = "MatrixNet" if parametrize_mass_matrix else "InvMatrixNet"
self.mass_matrix_net = network_creation_func(
output_dims=system_dim, name=name, **net_kwargs)
else:
self.mass_matrix_net = None
if kinetic_func_form in ("embed_quad", "matrix_dep_diag_embed_quad",
"matrix_dep_embed_quad"):
self.kinetic_embed_net = network_creation_func(
output_dims=system_dim, name="KineticEmbed", **net_kwargs)
else:
self.kinetic_embed_net = None
def sum_per_dim_energy(self, energy: jnp.ndarray) -> jnp.ndarray:
"""Sums the per dimension energy."""
axis = [-i-1 for i in range(self.features_extra_dims + 1)]
return jnp.sum(energy, axis=axis)
def feature_matrix_vector(self, m, v):
"""A utility function to compute the product of a matrix and vector in the features axis."""
v = jnp.expand_dims(v, axis=self.features_axis-1)
return jnp.sum(m * v, axis=self.features_axis)
def mass_matrix_mul(
self,
q: jnp.ndarray,
v: jnp.ndarray,
**kwargs
) -> jnp.ndarray:
"""Computes the product of the mass matrix with a vector and throws an error if not applicable."""
if self.kinetic_func_form in ("separable_net", "dep_net"):
raise ValueError("It is not possible to compute `M q_dot` when using a "
"network for the kinetic energy.")
if self.kinetic_func_form in ("pure_quad", "embed_quad"):
return v
if self.kinetic_func_form == "matrix_diag_quad":
if self.parametrize_mass_matrix:
m_diag_log = hk.get_parameter("MassMatrixDiagLog",
shape=[self.system_dim],
init=hk.initializers.Constant(0.0))
m_diag = jnp.exp(m_diag_log) + self.mass_eps
else:
m_inv_diag_log = hk.get_parameter("InvMassMatrixDiagLog",
shape=[self.system_dim],
init=hk.initializers.Constant(0.0))
m_diag = 1.0 / (jnp.exp(m_inv_diag_log) + self.mass_eps)
return m_diag * v
if self.kinetic_func_form == "matrix_quad":
if self.parametrize_mass_matrix:
m_triu = hk.get_parameter("MassMatrixU",
shape=[self.system_dim, self.system_dim],
init=hk.initializers.Identity())
m_triu = jnp.triu(m_triu)
m = jnp.matmul(m_triu.T, m_triu)
m = m + self.mass_eps * jnp.eye(self.system_dim)
return self.feature_matrix_vector(m, v)
else:
m_inv_triu = hk.get_parameter("InvMassMatrixU",
shape=[self.system_dim, self.system_dim],
init=hk.initializers.Identity())
m_inv_triu = jnp.triu(m_inv_triu)
m_inv = jnp.matmul(m_inv_triu.T, m_inv_triu)
m_inv = m_inv + self.mass_eps * jnp.eye(self.system_dim)
solve = jnp.linalg.solve
for _ in range(v.ndim + 1 - m_inv.ndim):
solve = jax.vmap(solve, in_axes=(None, 0))
return solve(m_inv, v)
if self.kinetic_func_form in ("matrix_dep_diag_quad",
"matrix_dep_diag_embed_quad"):
if self.parametrize_mass_matrix:
m_diag_log = self.mass_matrix_net(q, **kwargs)
m_diag = jnp.exp(m_diag_log) + self.mass_eps
else:
m_inv_diag_log = self.mass_matrix_net(q, **kwargs)
m_diag = 1.0 / (jnp.exp(m_inv_diag_log) + self.mass_eps)
return m_diag * v
if self.kinetic_func_form in ("matrix_dep_quad",
"matrix_dep_embed_quad"):
if self.parametrize_mass_matrix:
m_triu = self.mass_matrix_net(q, **kwargs)
m_triu = utils.triu_matrix_from_v(m_triu, self.system_dim)
m = jnp.matmul(jnp.swapaxes(m_triu, -1, -2), m_triu)
m = m + self.mass_eps * jnp.eye(self.system_dim)
return self.feature_matrix_vector(m, v)
else:
m_inv_triu = self.mass_matrix_net(q, **kwargs)
m_inv_triu = utils.triu_matrix_from_v(m_inv_triu, self.system_dim)
m_inv = jnp.matmul(jnp.swapaxes(m_inv_triu, -1, -2), m_inv_triu)
m_inv = m_inv + self.mass_eps * jnp.eye(self.system_dim)
return jnp.linalg.solve(m_inv, v)
raise NotImplementedError()
def mass_matrix_inv_mul(
self,
q: jnp.ndarray,
v: jnp.ndarray,
**kwargs
) -> jnp.ndarray:
"""Computes the product of the inverse mass matrix with a vector."""
if self.kinetic_func_form in ("separable_net", "dep_net"):
raise ValueError("It is not possible to compute `M^-1 p` when using a "
"network for the kinetic energy.")
if self.kinetic_func_form in ("pure_quad", "embed_quad"):
return v
if self.kinetic_func_form == "matrix_diag_quad":
if self.parametrize_mass_matrix:
m_diag_log = hk.get_parameter("MassMatrixDiagLog",
shape=[self.system_dim],
init=hk.initializers.Constant(0.0))
m_inv_diag = 1.0 / (jnp.exp(m_diag_log) + self.mass_eps)
else:
m_inv_diag_log = hk.get_parameter("InvMassMatrixDiagLog",
shape=[self.system_dim],
init=hk.initializers.Constant(0.0))
m_inv_diag = jnp.exp(m_inv_diag_log) + self.mass_eps
return m_inv_diag * v
if self.kinetic_func_form == "matrix_quad":
if self.parametrize_mass_matrix:
m_triu = hk.get_parameter("MassMatrixU",
shape=[self.system_dim, self.system_dim],
init=hk.initializers.Identity())
m_triu = jnp.triu(m_triu)
m = jnp.matmul(m_triu.T, m_triu)
m = m + self.mass_eps * jnp.eye(self.system_dim)
solve = jnp.linalg.solve
for _ in range(v.ndim + 1 - m.ndim):
solve = jax.vmap(solve, in_axes=(None, 0))
return solve(m, v)
else:
m_inv_triu = hk.get_parameter("InvMassMatrixU",
shape=[self.system_dim, self.system_dim],
init=hk.initializers.Identity())
m_inv_triu = jnp.triu(m_inv_triu)
m_inv = jnp.matmul(m_inv_triu.T, m_inv_triu)
m_inv = m_inv + self.mass_eps * jnp.eye(self.system_dim)
return self.feature_matrix_vector(m_inv, v)
if self.kinetic_func_form in ("matrix_dep_diag_quad",
"matrix_dep_diag_embed_quad"):
if self.parametrize_mass_matrix:
m_diag_log = self.mass_matrix_net(q, **kwargs)
m_inv_diag = 1.0 / (jnp.exp(m_diag_log) + self.mass_eps)
else:
m_inv_diag_log = self.mass_matrix_net(q, **kwargs)
m_inv_diag = jnp.exp(m_inv_diag_log) + self.mass_eps
return m_inv_diag * v
if self.kinetic_func_form in ("matrix_dep_quad",
"matrix_dep_embed_quad"):
if self.parametrize_mass_matrix:
m_triu = self.mass_matrix_net(q, **kwargs)
m_triu = utils.triu_matrix_from_v(m_triu, self.system_dim)
m = jnp.matmul(jnp.swapaxes(m_triu, -2, -1), m_triu)
m = m + self.mass_eps * jnp.eye(self.system_dim)
return jnp.linalg.solve(m, v)
else:
m_inv_triu = self.mass_matrix_net(q, **kwargs)
m_inv_triu = utils.triu_matrix_from_v(m_inv_triu, self.system_dim)
m_inv = jnp.matmul(jnp.swapaxes(m_inv_triu, -2, -1), m_inv_triu)
m_inv = m_inv + self.mass_eps * jnp.eye(self.system_dim)
return self.feature_matrix_vector(m_inv, v)
raise NotImplementedError()
def momentum_from_velocity(
self,
q: jnp.ndarray,
q_dot: jnp.ndarray,
**kwargs
) -> jnp.ndarray:
"""Computes the momentum from position and velocity."""
def local_lagrangian(q_dot_):
# We take the sum so we can easily take gradients
return jnp.sum(self.lagrangian(
phase_space.PhaseSpace(q, q_dot_), **kwargs))
return jax.grad(local_lagrangian)(q_dot)
def velocity_from_momentum(
self,
q: jnp.ndarray,
p: jnp.ndarray,
**kwargs
) -> jnp.ndarray:
"""Computes the velocity from position and momentum."""
def local_hamiltonian(p_):
# We take the sum so we can easily take gradients
return jnp.sum(self.hamiltonian(
phase_space.PhaseSpace(q, p_), **kwargs))
return jax.grad(local_hamiltonian)(p)
def kinetic_energy_velocity(
self,
q: jnp.ndarray,
q_dot: jnp.ndarray,
**kwargs
) -> jnp.ndarray:
"""Computes the kinetic energy in velocity coordinates."""
if self.kinetic_func_form in ("separable_net", "dep_net"):
if self.input_space != "velocity":
raise ValueError("Can not evaluate the Kinetic energy from velocity, "
"when the input space is momentum and "
"kinetic_func_form is separable_net or dep_net.")
if self.kinetic_func_form == "separable_net":
s = q_dot
else:
s = jnp.concatenate([q, q_dot], axis=-1)
per_dim_energy = self.kinetic_net(s, **kwargs)
else:
if self.kinetic_embed_net is not None:
if self.input_space != "velocity":
raise ValueError("Can not evaluate the Kinetic energy from velocity, "
"when the input space is momentum and "
"kinetic_func_form is embed_quad, "
"matrix_dep_diag_embed_quad or "
"matrix_dep_embed_quad.")
q_dot = self.kinetic_embed_net(q_dot, **kwargs)
m_q_dot = self.mass_matrix_mul(q, q_dot, **kwargs)
per_dim_energy = q_dot * m_q_dot / 2
return self.sum_per_dim_energy(per_dim_energy)
def kinetic_energy_momentum(
self,
q: jnp.ndarray,
p: jnp.ndarray,
**kwargs
) -> jnp.ndarray:
"""Computes the kinetic energy in momentum coordinates."""
if self.kinetic_func_form in ("separable_net", "dep_net"):
if self.input_space != "momentum":
raise ValueError("Can not evaluate the Kinetic energy from momentum, "
"when the input space is velocity and "
"kinetic_func_form is separable_net or dep_net.")
if self.kinetic_func_form == "separable_net":
s = p
else:
s = jnp.concatenate([q, p], axis=-1)
per_dim_energy = self.kinetic_net(s, **kwargs)
else:
if self.kinetic_embed_net is not None:
if self.input_space != "momentum":
raise ValueError("Can not evaluate the Kinetic energy from momentum, "
"when the input space is velocity and "
"kinetic_func_form is embed_quad, "
"matrix_dep_diag_embed_quad or "
"matrix_dep_embed_quad.")
p = self.kinetic_embed_net(p, **kwargs)
m_inv_p = self.mass_matrix_inv_mul(q, p, **kwargs)
per_dim_energy = p * m_inv_p / 2
return self.sum_per_dim_energy(per_dim_energy)
def potential_energy_velocity(
self,
q: jnp.ndarray,
q_dot: jnp.ndarray,
**kwargs
) -> jnp.ndarray:
"""Computes the potential energy in velocity coordinates."""
if self.potential_func_form == "separable_net":
per_dim_energy = self.potential_net(q, **kwargs)
elif self.input_space != "momentum":
raise ValueError("Can not evaluate the Potential energy from velocity, "
"when the input space is momentum and "
"potential_func_form is dep_net.")
else:
s = jnp.concatenate([q, q_dot], axis=-1)
per_dim_energy = self.potential_net(s, **kwargs)
return self.sum_per_dim_energy(per_dim_energy)
def potential_energy_momentum(
self,
q: jnp.ndarray,
p: jnp.ndarray,
**kwargs
) -> jnp.ndarray:
"""Computes the potential energy in momentum coordinates."""
if self.potential_func_form == "separable_net":
per_dim_energy = self.potential_net(q, **kwargs)
elif self.input_space != "momentum":
raise ValueError("Can not evaluate the Potential energy from momentum, "
"when the input space is velocity and "
"potential_func_form is dep_net.")
else:
s = jnp.concatenate([q, p], axis=-1)
per_dim_energy = self.potential_net(s, **kwargs)
return self.sum_per_dim_energy(per_dim_energy)
def hamiltonian(
self,
s: phase_space.PhaseSpace,
**kwargs
) -> jnp.ndarray:
"""Computes the Hamiltonian in momentum coordinates."""
potential = self.potential_energy_momentum(s.q, s.p, **kwargs)
kinetic = self.kinetic_energy_momentum(s.q, s.p, **kwargs)
# Sanity check
assert potential.shape == kinetic.shape
return kinetic + potential
def lagrangian(
self,
s: phase_space.PhaseSpace,
**kwargs
) -> jnp.ndarray:
"""Computes the Lagrangian in velocity coordinates."""
potential = self.potential_energy_velocity(s.q, s.p, **kwargs)
kinetic = self.kinetic_energy_velocity(s.q, s.p, **kwargs)
# Sanity check
assert potential.shape == kinetic.shape
return kinetic - potential
def energy_from_momentum(
self,
s: phase_space.PhaseSpace,
**kwargs
) -> jnp.ndarray:
"""Computes the energy of the system in momentum coordinates."""
return self.hamiltonian(s, **kwargs)
def energy_from_velocity(
self,
s: phase_space.PhaseSpace,
**kwargs
) -> jnp.ndarray:
"""Computes the energy of the system in velocity coordinates."""
q, q_dot = s.q, s.p
p = self.momentum_from_velocity(q, q_dot, **kwargs)
q_dot_p = jnp.sum(q_dot * p, self.features_axis)
return q_dot_p - self.lagrangian(s, **kwargs)
def velocity_and_acceleration(
self,
q: jnp.ndarray,
q_dot: jnp.ndarray,
**kwargs
) -> phase_space.TangentPhaseSpace:
"""Computes the velocity and acceleration of the system in velocity coordinates."""
def local_lagrangian(*q_and_q_dot):
# We take the sum so we can easily take gradients
return jnp.sum(self.lagrangian(
phase_space.PhaseSpace(*q_and_q_dot), **kwargs))
grad_q = jax.grad(local_lagrangian, 0)(q, q_dot)
grad_q_dot_func = jax.grad(local_lagrangian, 1)
_, grad_q_dot_grad_q_times_q_dot = jax.jvp(grad_q_dot_func, (q, q_dot),
(q_dot, jnp.zeros_like(q_dot)))
pre_acc_vector = grad_q - grad_q_dot_grad_q_times_q_dot
if self.kinetic_func_form in ("pure_quad", "matrix_diag_quad",
"matrix_quad", "matrix_dep_diag_quad",
"matrix_dep_quad"):
q_dot_dot = self.mass_matrix_inv_mul(q, pre_acc_vector, **kwargs)
else:
hess_q_dot = jax.vmap(jax.hessian(local_lagrangian, 1))(q, q_dot)
q_dot_dot = jnp.linalg.solve(hess_q_dot, pre_acc_vector)
return phase_space.TangentPhaseSpace(q_dot, q_dot_dot)
def simulate(
self,
y0: phase_space.PhaseSpace,
dt: Union[float, jnp.ndarray],
num_steps_forward: int,
num_steps_backward: int,
include_y0: bool,
return_stats: bool = True,
**nets_kwargs
) -> _PhysicsSimulationOutput:
"""Simulates the continuous dynamics of the physical system.
Args:
y0: Initial state of the system.
dt: The size of the time intervals at which to evolve the system.
num_steps_forward: Number of steps to make into the future.
num_steps_backward: Number of steps to make into the past.
include_y0: Whether to include the initial state in the result.
return_stats: Whether to return additional statistics.
**nets_kwargs: Keyword arguments to pass to the networks.
Returns:
* The state of the system evolved as many steps as specified by the
arguments into the past and future, all in chronological order.
* Optionally return a dictionary of additional statistics. For the moment
this only returns the energy of the system at each evaluation point.
"""
# Define the dynamics
if self.simulation_space == "velocity":
dy_dt = lambda t_, y: self.velocity_and_acceleration( # pylint: disable=g-long-lambda
y.q, y.p, **nets_kwargs)
# Special Haiku magic to avoid tracer issues
if hk.running_init():
return self.lagrangian(y0, **nets_kwargs) # pytype: disable=bad-return-type # jax-ndarray
else:
hamiltonian = lambda t_, y: self.hamiltonian(y, **nets_kwargs)
dy_dt = phase_space.poisson_bracket_with_q_and_p(hamiltonian)
if hk.running_init():
return self.hamiltonian(y0, **nets_kwargs) # pytype: disable=bad-return-type # jax-ndarray
# Optionally switch coordinate frame
if self.input_space == "velocity" and self.simulation_space == "momentum":
p = self.momentum_from_velocity(y0.q, y0.p, **nets_kwargs)
y0 = phase_space.PhaseSpace(y0.q, p)
if self.input_space == "momentum" and self.simulation_space == "velocity":
q_dot = self.velocity_from_momentum(y0.q, y0.p, **nets_kwargs)
y0 = phase_space.PhaseSpace(y0.q, q_dot)
yt = integrators.solve_ivp_dt_two_directions(
fun=dy_dt,
y0=y0,
t0=0.0,
dt=dt,
method=self.integrator_method,
num_steps_forward=num_steps_forward,
num_steps_backward=num_steps_backward,
include_y0=include_y0,
steps_per_dt=self.steps_per_dt,
ode_int_kwargs=self.ode_int_kwargs
)
# Make time axis second
yt = jax.tree_map(lambda x: jnp.swapaxes(x, 0, 1), yt)
# Compute energies for the full trajectory
yt_energy = jax.tree_map(utils.merge_first_dims, yt)
if self.simulation_space == "momentum":
energy = self.energy_from_momentum(yt_energy, **nets_kwargs)
else:
energy = self.energy_from_velocity(yt_energy, **nets_kwargs)
energy = energy.reshape(yt.q.shape[:2])
# Optionally switch back to input coordinate frame
if self.input_space == "velocity" and self.simulation_space == "momentum":
q_dot = self.velocity_from_momentum(yt.q, yt.p, **nets_kwargs)
yt = phase_space.PhaseSpace(yt.q, q_dot)
if self.input_space == "momentum" and self.simulation_space == "velocity":
p = self.momentum_from_velocity(yt.q, yt.p, **nets_kwargs)
yt = phase_space.PhaseSpace(yt.q, p)
# Compute energy deficit
t = energy.shape[-1]
non_zero_diffs = float((t * (t - 1)) // 2)
energy_deficits = jnp.abs(energy[..., None, :] - energy[..., None])
avg_deficit = jnp.sum(energy_deficits, axis=(-2, -1)) / non_zero_diffs
max_deficit = jnp.max(energy_deficits)
# Return the states and energies
if return_stats:
return yt, dict(avg_energy_deficit=avg_deficit,
max_energy_deficit=max_deficit)
else:
return yt
def __call__(self, *args, **kwargs):
return self.simulate(*args, **kwargs)
class OdeNetwork(hk.Module):
"""A simple haiku module for constructing a NeuralODE."""
def __init__(
self,
system_dim: int,
net_kwargs: Mapping[str, Any],
integrator_method: Optional[str] = None,
steps_per_dt: int = 1,
ode_int_kwargs: Optional[Mapping[str, float]] = None,
use_scan: bool = True,
network_creation_func=networks.make_flexible_net,
name: Optional[str] = None,
):
super().__init__(name=name)
ode_int_kwargs = dict(ode_int_kwargs or {})
ode_int_kwargs.setdefault("rtol", 1e-6)
ode_int_kwargs.setdefault("atol", 1e-6)
ode_int_kwargs.setdefault("mxstep", 50)
self.system_dim = system_dim
self.integrator_method = integrator_method or "adaptive"
self.steps_per_dt = steps_per_dt
self.ode_int_kwargs = ode_int_kwargs
self.net_kwargs = net_kwargs
self.use_scan = use_scan
self.core = network_creation_func(
output_dims=system_dim, name="Net", **net_kwargs)
def simulate(
self,
y0: jnp.ndarray,
dt: Union[float, jnp.ndarray],
num_steps_forward: int,
num_steps_backward: int,
include_y0: bool,
return_stats: bool = True,
**nets_kwargs
) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]]:
"""Simulates the continuous dynamics of the ODE specified by the network.
Args:
y0: Initial state of the system.
dt: The size of the time intervals at which to evolve the system.
num_steps_forward: Number of steps to make into the future.
num_steps_backward: Number of steps to make into the past.
include_y0: Whether to include the initial state in the result.
return_stats: Whether to return additional statistics.
**nets_kwargs: Keyword arguments to pass to the networks.
Returns:
* The state of the system evolved as many steps as specified by the
arguments into the past and future, all in chronological order.
* Optionally return a dictionary of additional statistics. For the moment
this is just an empty dictionary.
"""
if hk.running_init():
return self.core(y0, **nets_kwargs)
yt = integrators.solve_ivp_dt_two_directions(
fun=lambda t, y: self.core(y, **nets_kwargs),
y0=y0,
t0=0.0,
dt=dt,
method=self.integrator_method,
num_steps_forward=num_steps_forward,
num_steps_backward=num_steps_backward,
include_y0=include_y0,
steps_per_dt=self.steps_per_dt,
ode_int_kwargs=self.ode_int_kwargs
)
# Make time axis second
yt = jax.tree_map(lambda x: jnp.swapaxes(x, 0, 1), yt)
if return_stats:
return yt, dict()
else:
return yt
def __call__(self, *args, **kwargs):
return self.simulate(*args, **kwargs)
class DiscreteDynamicsNetwork(hk.Module):
"""A simple haiku module for constructing a discrete dynamics network."""
def __init__(
self,
system_dim: int,
residual: bool,
net_kwargs: Mapping[str, Any],
use_scan: bool = True,
network_creation_func=networks.make_flexible_net,
name: Optional[str] = None,
):
super().__init__(name=name)
self.system_dim = system_dim
self.residual = residual
self.net_kwargs = net_kwargs
self.use_scan = use_scan
self.core = network_creation_func(
output_dims=system_dim, name="Net", **net_kwargs)
def simulate(
self,
y0: jnp.ndarray,
num_steps_forward: int,
include_y0: bool,
return_stats: bool = True,
**nets_kwargs
) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]]:
"""Simulates the dynamics of the discrete system.
Args:
y0: Initial state of the system.
num_steps_forward: Number of steps to make into the future.
include_y0: Whether to include the initial state in the result.
return_stats: Whether to return additional statistics.
**nets_kwargs: Keyword arguments to pass to the networks.
Returns:
* The state of the system evolved as many steps as specified by the
arguments into the past and future, all in chronological order.
* Optionally return a dictionary of additional statistics. For the moment
this is just an empty dictionary.
"""
if num_steps_forward < 0:
raise ValueError("It is required to unroll at least one step.")
nets_kwargs.pop("dt", None)
nets_kwargs.pop("num_steps_backward", None)
if hk.running_init():
return self.core(y0, **nets_kwargs)
def step(*args):
y, _ = args
if self.residual:
y_next = y + self.core(y, **nets_kwargs)
else:
y_next = self.core(y, **nets_kwargs)
return y_next, y_next
if self.use_scan:
_, yt = jax.lax.scan(step, init=y0, xs=None, length=num_steps_forward)
if include_y0:
yt = jnp.concatenate([y0[None], yt], axis=0)
# Make time axis second
yt = jax.tree_map(lambda x: jnp.swapaxes(x, 0, 1), yt)
else:
yt = [y0]
for _ in range(num_steps_forward):
yt.append(step(yt[-1], None)[0])
if not include_y0:
yt = yt[1:]
if len(yt) == 1:
yt = yt[0][:, None]
else:
yt = jax.tree_map(lambda args: jnp.stack(args, 1), yt)
if return_stats:
return yt, dict()
else:
return yt
def __call__(self, *args, **kwargs):
return self.simulate(*args, **kwargs)
================================================
FILE: physics_inspired_models/models/networks.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module containing all of the networks as Haiku modules."""
from typing import Any, Callable, Mapping, Optional, Sequence, Union
from absl import logging
import distrax
import haiku as hk
import jax.numpy as jnp
from physics_inspired_models import utils
Activation = Union[str, Callable[[jnp.ndarray], jnp.ndarray]]
class DenseNet(hk.Module):
"""A feed forward network (MLP)."""
def __init__(
self,
num_units: Sequence[int],
activate_final: bool = False,
activation: Activation = "leaky_relu",
name: Optional[str] = None):
super().__init__(name=name)
self.num_units = num_units
self.num_layers = len(self.num_units)
self.activate_final = activate_final
self.activation = utils.get_activation(activation)
self.linear_modules = []
for i in range(self.num_layers):
self.linear_modules.append(
hk.Linear(
output_size=self.num_units[i],
name=f"ff_{i}"
)
)
def __call__(self, inputs: jnp.ndarray, is_training: bool):
net = inputs
for i, linear in enumerate(self.linear_modules):
net = linear(net)
if i < self.num_layers - 1 or self.activate_final:
net = self.activation(net)
return net
class Conv2DNet(hk.Module):
"""Convolutional Network."""
def __init__(
self,
output_channels: Sequence[int],
kernel_shapes: Union[int, Sequence[int]] = 3,
strides: Union[int, Sequence[int]] = 1,
padding: Union[str, Sequence[str]] = "SAME",
data_format: str = "NHWC",
with_batch_norm: bool = False,
activate_final: bool = False,
activation: Activation = "leaky_relu",
name: Optional[str] = None):
super().__init__(name=name)
self.output_channels = tuple(output_channels)
self.num_layers = len(self.output_channels)
self.kernel_shapes = utils.bcast_if(kernel_shapes, int, self.num_layers)
self.strides = utils.bcast_if(strides, int, self.num_layers)
self.padding = utils.bcast_if(padding, str, self.num_layers)
self.data_format = data_format
self.with_batch_norm = with_batch_norm
self.activate_final = activate_final
self.activation = utils.get_activation(activation)
if len(self.kernel_shapes) != self.num_layers:
raise ValueError(f"Kernel shapes is of size {len(self.kernel_shapes)}, "
f"while output_channels is of size{self.num_layers}.")
if len(self.strides) != self.num_layers:
raise ValueError(f"Strides is of size {len(self.kernel_shapes)}, while "
f"output_channels is of size{self.num_layers}.")
if len(self.padding) != self.num_layers:
raise ValueError(f"Padding is of size {len(self.padding)}, while "
f"output_channels is of size{self.num_layers}.")
self.conv_modules = []
self.bn_modules = []
for i in range(self.num_layers):
self.conv_modules.append(
hk.Conv2D(
output_channels=self.output_channels[i],
kernel_shape=self.kernel_shapes[i],
stride=self.strides[i],
padding=self.padding[i],
data_format=data_format,
name=f"conv_2d_{i}")
)
if with_batch_norm:
self.bn_modules.append(
hk.BatchNorm(
create_offset=True,
create_scale=False,
decay_rate=0.999,
name=f"batch_norm_{i}")
)
else:
self.bn_modules.append(None)
def __call__(self, inputs: jnp.ndarray, is_training: bool):
assert inputs.ndim == 4
net = inputs
for i, (conv, bn) in enumerate(zip(self.conv_modules, self.bn_modules)):
net = conv(net)
# Batch norm
if bn is not None:
net = bn(net, is_training=is_training)
if i < self.num_layers - 1 or self.activate_final:
net = self.activation(net)
return net
class SpatialConvEncoder(hk.Module):
"""Spatial Convolutional Encoder for learning the Hamiltonian."""
def __init__(
self,
latent_dim: int,
conv_channels: Union[Sequence[int], int],
num_blocks: int,
blocks_depth: int = 2,
distribution_name: str = "diagonal_normal",
aggregation_type: Optional[str] = None,
data_format: str = "NHWC",
activation: Activation = "leaky_relu",
scale_factor: int = 2,
kernel_shapes: Union[Sequence[int], int] = 3,
padding: Union[Sequence[str], str] = "SAME",
name: Optional[str] = None):
super().__init__(name=name)
if aggregation_type not in (None, "max", "mean", "linear_projection"):
raise ValueError(f"Unrecognized aggregation_type={aggregation_type}.")
self.latent_dim = latent_dim
self.conv_channels = conv_channels
self.num_blocks = num_blocks
self.scale_factor = scale_factor
self.data_format = data_format
self.distribution_name = distribution_name
self.aggregation_type = aggregation_type
# Compute the required size of the output
if distribution_name is None:
self.output_dim = latent_dim
elif distribution_name == "diagonal_normal":
self.output_dim = 2 * latent_dim
else:
raise ValueError(f"Unrecognized distribution_name={distribution_name}.")
if isinstance(conv_channels, int):
conv_channels = [[conv_channels] * blocks_depth
for _ in range(num_blocks)]
conv_channels[-1] += [self.output_dim]
else:
assert isinstance(conv_channels, (list, tuple))
assert len(conv_channels) == num_blocks
conv_channels = list(list(c) for c in conv_channels)
conv_channels[-1].append(self.output_dim)
if isinstance(kernel_shapes, tuple):
kernel_shapes = list(kernel_shapes)
# Convolutional blocks
self.blocks = []
for i, channels in enumerate(conv_channels):
if isinstance(kernel_shapes, int):
extra_kernel_shapes = 0
else:
extra_kernel_shapes = [3] * (len(channels) - len(kernel_shapes))
self.blocks.append(Conv2DNet(
output_channels=channels,
kernel_shapes=kernel_shapes + extra_kernel_shapes,
strides=[self.scale_factor] + [1] * (len(channels) - 1),
padding=padding,
data_format=data_format,
with_batch_norm=False,
activate_final=i < num_blocks - 1,
activation=activation,
name=f"block_{i}"
))
def spatial_aggregation(self, x: jnp.ndarray) -> jnp.ndarray:
if self.aggregation_type is None:
return x
axis = (1, 2) if self.data_format == "NHWC" else (2, 3)
if self.aggregation_type == "max":
return jnp.max(x, axis=axis)
if self.aggregation_type == "mean":
return jnp.mean(x, axis=axis)
if self.aggregation_type == "linear_projection":
x = x.reshape(x.shape[:-3] + (-1,))
return hk.Linear(self.output_dim, name="LinearProjection")(x)
raise NotImplementedError()
def make_distribution(self, net_output: jnp.ndarray) -> distrax.Distribution:
if self.distribution_name is None:
return net_output
elif self.distribution_name == "diagonal_normal":
if self.aggregation_type is None:
split_axis, num_axes = self.data_format.index("C"), 3
else:
split_axis, num_axes = 1, 1
# Add an extra axis if the input has more than 1 batch dimension
split_axis += net_output.ndim - num_axes - 1
loc, log_scale = jnp.split(net_output, 2, axis=split_axis)
return distrax.Normal(loc, jnp.exp(log_scale))
else:
raise NotImplementedError()
def __call__(
self,
inputs: jnp.ndarray,
is_training: bool
) -> Union[jnp.ndarray, distrax.Distribution]:
# Treat any extra dimensions (like time) as the batch
batched_shape = inputs.shape[:-3]
net = jnp.reshape(inputs, (-1,) + inputs.shape[-3:])
# Apply all blocks in sequence
for block in self.blocks:
net = block(net, is_training=is_training)
# Final projection
net = self.spatial_aggregation(net)
# Reshape back to correct dimensions (like batch + time)
net = jnp.reshape(net, batched_shape + net.shape[1:])
# Return a distribution over the observations
return self.make_distribution(net)
class SpatialConvDecoder(hk.Module):
"""Spatial Convolutional Decoder for learning the Hamiltonian."""
def __init__(
self,
initial_spatial_shape: Sequence[int],
conv_channels: Union[Sequence[int], int],
num_blocks: int,
max_de_aggregation_dims: int,
blocks_depth: int = 2,
scale_factor: int = 2,
output_channels: int = 3,
h_const_channels: int = 2,
data_format: str = "NHWC",
activation: Activation = "leaky_relu",
learned_sigma: bool = False,
de_aggregation_type: Optional[str] = None,
final_activation: Activation = "sigmoid",
discard_half_de_aggregated: bool = False,
kernel_shapes: Union[Sequence[int], int] = 3,
padding: Union[Sequence[str], str] = "SAME",
name: Optional[str] = None):
super().__init__(name=name)
if de_aggregation_type not in (None, "tile", "linear_projection"):
raise ValueError(f"Unrecognized de_aggregation_type="
f"{de_aggregation_type}.")
self.num_blocks = num_blocks
self.scale_factor = scale_factor
self.h_const_channels = h_const_channels
self.data_format = data_format
self.learned_sigma = learned_sigma
self.initial_spatial_shape = tuple(initial_spatial_shape)
self.final_activation = utils.get_activation(final_activation)
self.de_aggregation_type = de_aggregation_type
self.max_de_aggregation_dims = max_de_aggregation_dims
self.discard_half_de_aggregated = discard_half_de_aggregated
if isinstance(conv_channels, int):
conv_channels = [[conv_channels] * blocks_depth
for _ in range(num_blocks)]
conv_channels[-1] += [output_channels]
else:
assert isinstance(conv_channels, (list, tuple))
assert len(conv_channels) == num_blocks
conv_channels = list(list(c) for c in conv_channels)
conv_channels[-1].append(output_channels)
# Convolutional blocks
self.blocks = []
for i, channels in enumerate(conv_channels):
is_final_block = i == num_blocks - 1
self.blocks.append(
Conv2DNet( # pylint: disable=g-complex-comprehension
output_channels=channels,
kernel_shapes=kernel_shapes,
strides=1,
padding=padding,
data_format=data_format,
with_batch_norm=False,
activate_final=not is_final_block,
activation=activation,
name=f"block_{i}"
))
def spatial_de_aggregation(self, x: jnp.ndarray) -> jnp.ndarray:
if self.de_aggregation_type is None:
assert x.ndim >= 4
if self.data_format == "NHWC":
assert x.shape[1:3] == self.initial_spatial_shape
elif self.data_format == "NCHW":
assert x.shape[2:4] == self.initial_spatial_shape
return x
elif self.de_aggregation_type == "linear_projection":
assert x.ndim == 2
n, d = x.shape
d = min(d, self.max_de_aggregation_dims or d)
out_d = d * self.initial_spatial_shape[0] * self.initial_spatial_shape[1]
x = hk.Linear(out_d, name="LinearProjection")(x)
if self.data_format == "NHWC":
shape = (n,) + self.initial_spatial_shape + (d,)
else:
shape = (n, d) + self.initial_spatial_shape
return x.reshape(shape)
elif self.de_aggregation_type == "tile":
assert x.ndim == 2
if self.data_format == "NHWC":
repeats = (1,) + self.initial_spatial_shape + (1,)
x = x[:, None, None, :]
else:
repeats = (1, 1) + self.initial_spatial_shape
x = x[:, :, None, None]
return jnp.tile(x, repeats)
else:
raise NotImplementedError()
def add_constant_channels(self, inputs: jnp.ndarray) -> jnp.ndarray:
# --------------------------------------------
# This is purely for TF compatibility purposes
if self.discard_half_de_aggregated:
axis = self.data_format.index("C")
inputs, _ = jnp.split(inputs, 2, axis=axis)
# --------------------------------------------
# An extra constant channels
if self.data_format == "NHWC":
h_shape = self.initial_spatial_shape + (self.h_const_channels,)
else:
h_shape = (self.h_const_channels,) + self.initial_spatial_shape
h_const = hk.get_parameter("h", h_shape, dtype=inputs.dtype,
init=hk.initializers.Constant(1))
h_const = jnp.tile(h_const, reps=[inputs.shape[0], 1, 1, 1])
return jnp.concatenate([h_const, inputs], axis=self.data_format.index("C"))
def make_distribution(self, net_output: jnp.ndarray) -> distrax.Distribution:
if self.learned_sigma:
init = hk.initializers.Constant(- jnp.log(2.0) / 2.0)
log_scale = hk.get_parameter("log_scale", shape=(),
dtype=net_output.dtype, init=init)
scale = jnp.full_like(net_output, jnp.exp(log_scale))
else:
scale = jnp.full_like(net_output, 1 / jnp.sqrt(2.0))
return distrax.Normal(net_output, scale)
def __call__(
self,
inputs: jnp.ndarray,
is_training: bool
) -> distrax.Distribution:
# Apply the spatial de-aggregation
inputs = self.spatial_de_aggregation(inputs)
# Add the parameterized constant channels
net = self.add_constant_channels(inputs)
# Apply all the blocks
for block in self.blocks:
# Up-sample the image
net = utils.nearest_neighbour_upsampling(net, self.scale_factor)
# Apply the convolutional block
net = block(net, is_training=is_training)
# Apply any specific output nonlinearity
net = self.final_activation(net)
# Construct the distribution over the observations
return self.make_distribution(net)
def make_flexible_net(
net_type: str,
output_dims: int,
conv_channels: Union[Sequence[int], int],
num_units: Union[Sequence[int], int],
num_layers: Optional[int],
activation: Activation,
activate_final: bool = False,
kernel_shapes: Union[Sequence[int], int] = 3,
strides: Union[Sequence[int], int] = 1,
padding: Union[Sequence[str], str] = "SAME",
name: Optional[str] = None,
**unused_kwargs: Mapping[str, Any]
):
"""Commonly used for creating a flexible network."""
if unused_kwargs:
logging.warning("Unused kwargs of `make_flexible_net`: %s",
str(unused_kwargs))
if net_type == "mlp":
if isinstance(num_units, int):
assert num_layers is not None
num_units = [num_units] * (num_layers - 1) + [output_dims]
else:
num_units = list(num_units) + [output_dims]
return DenseNet(
num_units=num_units,
activation=activation,
activate_final=activate_final,
name=name
)
elif net_type == "conv":
if isinstance(conv_channels, int):
assert num_layers is not None
conv_channels = [conv_channels] * (num_layers - 1) + [output_dims]
else:
conv_channels = list(conv_channels) + [output_dims]
return Conv2DNet(
output_channels=conv_channels,
kernel_shapes=kernel_shapes,
strides=strides,
padding=padding,
activation=activation,
activate_final=activate_final,
name=name
)
elif net_type == "transformer":
raise NotImplementedError()
else:
raise ValueError(f"Unrecognized net_type={net_type}.")
def make_flexible_recurrent_net(
core_type: str,
net_type: str,
output_dims: int,
num_units: Union[Sequence[int], int],
num_layers: Optional[int],
activation: Activation,
activate_final: bool = False,
name: Optional[str] = None,
**unused_kwargs
):
"""Commonly used for creating a flexible recurrences."""
if net_type != "mlp":
raise ValueError("We do not support convolutional recurrent nets atm.")
if unused_kwargs:
logging.warning("Unused kwargs of `make_flexible_recurrent_net`: %s",
str(unused_kwargs))
if isinstance(num_units, (list, tuple)):
num_units = list(num_units) + [output_dims]
num_layers = len(num_units)
else:
assert num_layers is not None
num_units = [num_units] * (num_layers - 1) + [output_dims]
name = name or f"{core_type.upper()}"
activation = utils.get_activation(activation)
core_list = []
for i, n in enumerate(num_units):
if core_type.lower() == "vanilla":
core_list.append(hk.VanillaRNN(hidden_size=n, name=f"{name}_{i}"))
elif core_type.lower() == "lstm":
core_list.append(hk.LSTM(hidden_size=n, name=f"{name}_{i}"))
elif core_type.lower() == "gru":
core_list.append(hk.GRU(hidden_size=n, name=f"{name}_{i}"))
else:
raise ValueError(f"Unrecognized core_type={core_type}.")
if i != num_layers - 1:
core_list.append(activation)
if activate_final:
core_list.append(activation)
return hk.DeepRNN(core_list, name="RNN")
================================================
FILE: physics_inspired_models/requirements.txt
================================================
git+https://github.com/deepmind/dm_hamiltonian_dynamics_suite@main#egg=dm_hamiltonian_dynamics_suite
absl-py==0.12.0
numpy>=1.16.4
scikit-learn>=1.0
typing>=3.7.4.3
jax==0.2.20
jaxline==0.0.3
distrax==0.0.2
optax==0.0.6
dm-haiku==0.0.3
================================================
FILE: physics_inspired_models/setup.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Setup for pip package."""
from setuptools import setup
REQUIRED_PACKAGES = (
"dm_hamiltonian_dynamics_suite@git+https://github.com/deepmind/dm_hamiltonian_dynamics_suite", # pylint: disable=line-too-long.
"absl-py>=0.12.0",
"numpy>=1.16.4",
"scikit-learn>=1.0",
"typing>=3.7.4.3",
"jax==0.2.20",
"jaxline==0.0.3",
"distrax==0.0.2",
"optax==0.0.6",
"dm-haiku==0.0.3",
)
LONG_DESCRIPTION = "\n".join([
"A codebase containing the implementation of the following models:",
"Hamiltonian Generative Network (HGN)",
"Lagrangian Generative Network (LGN)",
"Neural ODE",
"Recurrent Generative Network (RGN)",
"and RNN, LSTM and GRU.",
"This is code accompanying the publication of:"
])
setup(
name="physics_inspired_models",
version="0.0.1",
description="Implementation of multiple physically inspired models.",
long_description=LONG_DESCRIPTION,
url="https://github.com/deepmind/deepmind-research/physics_inspired_models",
author="DeepMind",
package_dir={"physics_inspired_models": "."},
packages=["physics_inspired_models", "physics_inspired_models.models"],
install_requires=REQUIRED_PACKAGES,
platforms=["any"],
license="Apache License, Version 2.0",
)
================================================
FILE: physics_inspired_models/utils.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities functions for Jax."""
from collections import abc
import functools
from typing import Any, Callable, Dict, Mapping, Union
import distrax
import jax
from jax import core
from jax import lax
from jax import nn
import jax.numpy as jnp
from jax.tree_util import register_pytree_node
from jaxline import utils
import numpy as np
HaikuParams = Mapping[str, Mapping[str, jnp.ndarray]]
Params = Union[Mapping[str, jnp.ndarray], HaikuParams, jnp.ndarray]
_Activation = Callable[[jnp.ndarray], jnp.ndarray]
tf_leaky_relu = functools.partial(nn.leaky_relu, negative_slope=0.2)
def filter_only_scalar_stats(stats):
return {k: v for k, v in stats.items() if v.size == 1}
def to_numpy(obj):
return jax.tree_map(np.array, obj)
@jax.custom_gradient
def geco_lagrange_product(lagrange_multiplier, constraint_ema, constraint_t):
"""Modifies the gradients so that they work as described in GECO.
The evaluation gives:
lagrange * C_ema
The gradient w.r.t lagrange:
- g * C_t
The gradient w.r.t constraint_ema:
0.0
The gradient w.r.t constraint_t:
g * lagrange
Note that if you pass the same value for `constraint_ema` and `constraint_t`
this would only flip the gradient for the lagrange multiplier.
Args:
lagrange_multiplier: The lagrange multiplier
constraint_ema: The moving average of the constraint
constraint_t: The current constraint
Returns:
"""
def grad(gradient):
return (- gradient * constraint_t,
jnp.zeros_like(constraint_ema),
gradient * lagrange_multiplier)
return lagrange_multiplier * constraint_ema, grad
def bcast_if(x, t, n):
return [x] * n if isinstance(x, t) else x
def stack_time_into_channels(
images: jnp.ndarray,
data_format: str
) -> jnp.ndarray:
axis = data_format.index("C")
list_of_time = [jnp.squeeze(v, axis=1) for v in
jnp.split(images, images.shape[1], axis=1)]
return jnp.concatenate(list_of_time, axis)
def stack_device_dim_into_batch(obj):
return jax.tree_map(lambda x: x.reshape((-1,) + x.shape[2:]), obj)
def nearest_neighbour_upsampling(x, scale, data_format="NHWC"):
"""Performs nearest-neighbour upsampling."""
if data_format == "NCHW":
b, c, h, w = x.shape
x = jnp.reshape(x, [b, c, h, 1, w, 1])
ones = jnp.ones([1, 1, 1, scale, 1, scale], dtype=x.dtype)
return jnp.reshape(x * ones, [b, c, scale * h, scale * w])
elif data_format == "NHWC":
b, h, w, c = x.shape
x = jnp.reshape(x, [b, h, 1, w, 1, c])
ones = jnp.ones([1, 1, scale, 1, scale, 1], dtype=x.dtype)
return jnp.reshape(x * ones, [b, scale * h, scale * w, c])
else:
raise ValueError(f"Unrecognized data_format={data_format}.")
def get_activation(arg: Union[_Activation, str]) -> _Activation:
"""Returns an activation from provided string."""
if isinstance(arg, str):
# Try fetch in order - [this module, jax.nn, jax.numpy]
if arg in globals():
return globals()[arg]
if hasattr(nn, arg):
return getattr(nn, arg)
elif hasattr(jnp, arg):
return getattr(jnp, arg)
else:
raise ValueError(f"Unrecognized activation with name {arg}.")
if not callable(arg):
raise ValueError(f"Expected a callable, but got {type(arg)}")
return arg
def merge_first_dims(x: jnp.ndarray, num_dims_to_merge: int = 2) -> jnp.ndarray:
return x.reshape((-1,) + x.shape[num_dims_to_merge:])
def extract_image(
inputs: Union[jnp.ndarray, Mapping[str, jnp.ndarray]]
) -> jnp.ndarray:
"""Extracts a tensor with key `image` or `x_image` if it is a dict, otherwise returns the inputs."""
if isinstance(inputs, dict):
if "image" in inputs:
return inputs["image"]
else:
return inputs["x_image"]
elif isinstance(inputs, jnp.ndarray):
return inputs
raise NotImplementedError(f"Not implemented of inputs of type"
f" {type(inputs)}.")
def extract_gt_state(inputs: Any) -> jnp.ndarray:
if isinstance(inputs, dict):
return inputs["x"]
elif not isinstance(inputs, jnp.ndarray):
raise NotImplementedError(f"Not implemented of inputs of type"
f" {type(inputs)}.")
return inputs
def reshape_latents_conv_to_flat(conv_latents, axis_n_to_keep=1):
q, p = jnp.split(conv_latents, 2, axis=-1)
q = jax.tree_map(lambda x: x.reshape(x.shape[:axis_n_to_keep] + (-1,)), q)
p = jax.tree_map(lambda x: x.reshape(x.shape[:axis_n_to_keep] + (-1,)), p)
flat_latents = jnp.concatenate([q, p], axis=-1)
return flat_latents
def triu_matrix_from_v(x, ndim):
assert x.shape[-1] == (ndim * (ndim + 1)) // 2
matrix = jnp.zeros(x.shape[:-1] + (ndim, ndim))
idx = jnp.triu_indices(ndim)
index_update = lambda x, idx, y: x.at[idx].set(y)
for _ in range(x.ndim - 1):
index_update = jax.vmap(index_update, in_axes=(0, None, 0))
return index_update(matrix, idx, x)
def flatten_dict(d, parent_key: str = "", sep: str = "_") -> Dict[str, Any]:
items = []
for k, v in d.items():
new_key = parent_key + sep + k if parent_key else k
if isinstance(v, abc.MutableMapping):
items.extend(flatten_dict(v, new_key, sep=sep).items())
else:
items.append((new_key, v))
return dict(items)
def convert_to_pytype(target, reference):
"""Makes target the same pytype as reference, by jax.tree_flatten."""
_, pytree = jax.tree_flatten(reference)
leaves, _ = jax.tree_flatten(target)
return jax.tree_unflatten(pytree, leaves)
def func_if_not_scalar(func):
"""Makes a function that uses func only on non-scalar values."""
@functools.wraps(func)
def wrapped(array, axis=0):
if array.ndim == 0:
return array
return func(array, axis=axis)
return wrapped
mean_if_not_scalar = func_if_not_scalar(jnp.mean)
class MultiBatchAccumulator(object):
"""Class for abstracting statistics accumulation over multiple batches."""
def __init__(self):
self._obj = None
self._obj_max = None
self._obj_min = None
self._num_samples = None
def add(self, averaged_values, num_samples):
"""Adds an element to the moving average and the max."""
if self._obj is None:
self._obj_max = jax.tree_map(lambda y: y * 1.0, averaged_values)
self._obj_min = jax.tree_map(lambda y: y * 1.0, averaged_values)
self._obj = jax.tree_map(lambda y: y * num_samples, averaged_values)
self._num_samples = num_samples
else:
self._obj_max = jax.tree_map(jnp.maximum, self._obj_max,
averaged_values)
self._obj_min = jax.tree_map(jnp.minimum, self._obj_min,
averaged_values)
self._obj = jax.tree_map(lambda x, y: x + y * num_samples, self._obj,
averaged_values)
self._num_samples += num_samples
def value(self):
return jax.tree_map(lambda x: x / self._num_samples, self._obj)
def max(self):
return jax.tree_map(float, self._obj_max)
def min(self):
return jax.tree_map(float, self._obj_min)
def sum(self):
return self._obj
register_pytree_node(
distrax.Normal,
lambda instance: ([instance.loc, instance.scale], None),
lambda _, args: distrax.Normal(*args)
)
def inner_product(x: Any, y: Any) -> jnp.ndarray:
products = jax.tree_map(lambda x_, y_: jnp.sum(x_ * y_), x, y)
return sum(jax.tree_leaves(products))
get_first = utils.get_first
bcast_local_devices = utils.bcast_local_devices
py_prefetch = utils.py_prefetch
p_split = jax.pmap(lambda x, num: list(jax.random.split(x, num)),
static_broadcasted_argnums=1)
def wrap_if_pmap(p_func):
def p_func_if_pmap(obj, axis_name):
try:
core.axis_frame(axis_name)
return p_func(obj, axis_name)
except NameError:
return obj
return p_func_if_pmap
pmean_if_pmap = wrap_if_pmap(lax.pmean)
psum_if_pmap = wrap_if_pmap(lax.psum)
================================================
FILE: physics_planning_games/README.md
================================================
# Physically Embedded Planning Environments
This repository contains the three environments introduced in
'Physically Embedded Planning Problems: New Challenges for Reinforcement
Learning'
If you use this package, please cite our accompanying [tech report]:
```
@misc{mirza2020physically,
title={Physically Embedded Planning Problems: New Challenges for Reinforcement Learning},
author={Mehdi Mirza and Andrew Jaegle and Jonathan J. Hunt and Arthur Guez and Saran Tunyasuvunakool and Alistair Muldal and Théophane Weber and Peter Karkus and Sébastien Racanière and Lars Buesing and Timothy Lillicrap and Nicolas Heess},
year={2020},
eprint={2009.05524},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## Requirements and Installation
This repository is divided into 'mujoban' and 'board_games' folders.
Both of them are built on top of [dm_control] which requires MuJoCo. Please
follow [these] instructions to install MuJoCo.
Other dependencies can be installed
by:
```
pip3 install -r requirements.txt
```
### Board games
The game logic is based on [open_spiel]. Please install as instructed [here].
[gnugo] is required to play the game of Go against a non-random opponent. [gnugo] can be installed in Ubuntu by:
```
apt install gnugo
```
Board game scripts expect gnugo binary to be at: `/usr/games/gnugo`. Users can
change this path inside `board_games/go_logic.py`
This library has only been tested on Ubuntu.
## Example usage
The code snippets below show examples of instantiating each of the environments.
### Mujoban
```python
from dm_control import composer
from dm_control.locomotion import walkers
from physics_planning_games.mujoban.mujoban import Mujoban
from physics_planning_games.mujoban.mujoban_level import MujobanLevel
from physics_planning_games.mujoban.boxoban import boxoban_level_generator
walker = walkers.JumpingBallWithHead(add_ears=True, camera_height=0.25)
maze = MujobanLevel(boxoban_level_generator)
task = Mujoban(walker=walker,
maze=maze,
control_timestep=0.1,
top_camera_height=96,
top_camera_width=96)
env = composer.Environment(time_limit=1000, task=task)
```
### Board games
```python
from physics_planning_games import board_games
environment_name = 'go_7x7'
env = board_games.load(environment_name=environment_name)
```
### Stepping through environment.
The returned environments are of type of `dm_env.Environment` and can be stepped
through as shown here with random actions:
```python
import numpy as np
timestep = env.reset()
action_spec = env.action_spec()
while True:
action = np.stack([
np.random.uniform(low=minimum, high=maximum)
for minimum, maximum in zip(action_spec.minimum, action_spec.maximum)
])
timestep = env.step(action)
```
### Visualization
For visualization of the environments `explore.py` loads them using the [viewer]
from [dm_control].
## More details
For more details please refer to the [tech report], [dm_control] and [dm_env].
[tech report]: https://arxiv.org/abs/2009.05524
[dm_control]: https://github.com/deepmind/dm_control
[dm_env]: https://github.com/deepmind/dm_env
[gnugo]: https://www.gnu.org/software/gnugo/
[open_spiel]: https://github.com/deepmind/open_spiel
[here]: https://github.com/deepmind/open_spiel/blob/master/docs/install.md
[these]: https://github.com/deepmind/dm_control#requirements-and-installation
[viewer]: https://github.com/deepmind/dm_control/tree/master/dm_control/viewer
================================================
FILE: physics_planning_games/board_games/__init__.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Physically-grounded board game environments."""
from dm_control import composer as _composer
from physics_planning_games.board_games import go as _go
from physics_planning_games.board_games import tic_tac_toe as _tic_tac_toe
from physics_planning_games.board_games._internal import registry as _registry
_registry.done_importing_tasks()
ALL = tuple(_registry.get_all_names())
TAGS = tuple(_registry.get_tags())
def get_environments_by_tag(tag):
"""Returns the names of all environments matching a given tag.
Args:
tag: A string from `TAGS`.
Returns:
A tuple of environment names.
"""
return tuple(_registry.get_names_by_tag(tag))
def load(environment_name,
env_kwargs=None,
seed=None,
time_limit=float('inf'),
strip_singleton_obs_buffer_dim=False):
"""Loads an environment from board_games.
Args:
environment_name: String, the name of the environment to load. Must be in
`ALL`.
env_kwargs: extra params to pass to task creation.
seed: Optional, either an int seed or an `np.random.RandomState`
object. If None (default), the random number generator will self-seed
from a platform-dependent source of entropy.
time_limit: (optional) A float, the time limit in seconds beyond which an
episode is forced to terminate.
strip_singleton_obs_buffer_dim: (optional) A boolean, if `True`,
the array shape of observations with `buffer_size == 1` will not have a
leading buffer dimension.
Returns:
An instance of `composer.Environment`.
"""
if env_kwargs is not None:
task = _registry.get_constructor(environment_name)(**env_kwargs)
else:
task = _registry.get_constructor(environment_name)()
return _composer.Environment(
task=task,
time_limit=time_limit,
strip_singleton_obs_buffer_dim=strip_singleton_obs_buffer_dim,
random_state=seed)
================================================
FILE: physics_planning_games/board_games/_internal/arenas.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Board game-specific arena classes."""
from dm_control import composer
from dm_control.composer.observation import observable
from dm_control.mujoco import wrapper
# Robot geoms will be assigned to this group in order to disable their
# visibility to the top-down camera.
ROBOT_GEOM_GROUP = 1
class Standard(composer.Arena):
""""Board game-specific arena class."""
def _build(self, name=None):
"""Initializes this arena.
Args:
name: (optional) A string, the name of this arena. If `None`, use the
model name defined in the MJCF file.
"""
super(Standard, self)._build(name=name)
# Add visual assets.
self.mjcf_model.asset.add(
'texture',
type='skybox',
builtin='gradient',
rgb1=(0.4, 0.6, 0.8),
rgb2=(0., 0., 0.),
width=100,
height=100)
groundplane_texture = self.mjcf_model.asset.add(
'texture',
name='groundplane',
type='2d',
builtin='checker',
rgb1=(0.2, 0.3, 0.4),
rgb2=(0.1, 0.2, 0.3),
width=300,
height=300,
mark='edge',
markrgb=(.8, .8, .8))
groundplane_material = self.mjcf_model.asset.add(
'material',
name='groundplane',
texture=groundplane_texture,
texrepeat=(5, 5),
texuniform='true',
reflectance=0.2)
# Add ground plane.
self.mjcf_model.worldbody.add(
'geom',
name='ground',
type='plane',
material=groundplane_material,
size=(1, 1, 0.1),
friction=(0.4,),
solimp=(0.95, 0.99, 0.001),
solref=(0.002, 1))
# Add lighting
self.mjcf_model.worldbody.add(
'light',
pos=(0, 0, 1.5),
dir=(0, 0, -1),
diffuse=(0.7, 0.7, 0.7),
specular=(.3, .3, .3),
directional='false',
castshadow='true')
# Add some fixed cameras to the arena.
self._front_camera = self.mjcf_model.worldbody.add(
'camera',
name='front',
pos=(0., -0.6, 0.75),
xyaxes=(1., 0., 0., 0., 0.7, 0.75))
# Ensures a 7x7 go board fits into the view from camera
self._front_camera_2 = self.mjcf_model.worldbody.add(
'camera',
name='front_2',
pos=(0., -0.65, 0.85),
xyaxes=(1., 0., 0., 0., 0.85, 0.6))
self._top_down_camera = self.mjcf_model.worldbody.add(
'camera',
name='top_down',
pos=(0., 0., 0.5),
xyaxes=(1., 0., 0., 0., 1., 0.))
# Always initialize the free camera so that it points at the origin.
self.mjcf_model.statistic.center = (0., 0., 0.)
def _build_observables(self):
return ArenaObservables(self)
@property
def front_camera(self):
return self._front_camera
@property
def front_camera_2(self):
return self._front_camera_2
@property
def top_down_camera(self):
return self._top_down_camera
def attach_offset(self, entity, offset, attach_site=None):
"""Attaches another entity at a position offset from the attachment site.
Args:
entity: The `Entity` to attach.
offset: A length 3 array-like object representing the XYZ offset.
attach_site: (optional) The site to which to attach the entity's model.
If not set, defaults to self.attachment_site.
Returns:
The frame of the attached model.
"""
frame = self.attach(entity, attach_site=attach_site)
frame.pos = offset
return frame
class ArenaObservables(composer.Observables):
"""Observables belonging to the arena."""
@composer.observable
def front_camera(self):
return observable.MJCFCamera(mjcf_element=self._entity.front_camera)
@composer.observable
def front_camera_2(self):
return observable.MJCFCamera(mjcf_element=self._entity.front_camera_2)
@composer.observable
def top_down_camera(self):
return observable.MJCFCamera(mjcf_element=self._entity.top_down_camera)
@composer.observable
def top_down_camera_invisible_robot(self):
# Custom scene options for making robot geoms invisible.
robot_geoms_invisible = wrapper.MjvOption()
robot_geoms_invisible.geomgroup[ROBOT_GEOM_GROUP] = 0
return observable.MJCFCamera(mjcf_element=self._entity.top_down_camera,
scene_option=robot_geoms_invisible)
================================================
FILE: physics_planning_games/board_games/_internal/boards.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Composer entities corresponding to game boards."""
import copy
import os
from dm_control import composer
from dm_control import mjcf
import numpy as np
from dm_control.utils import io as resources
_TOUCH_THRESHOLD = 1e-3 # Activation threshold for touch sensors (N).
# whether to display underlying sensors for Goboard (useful to align texture)
_SHOW_DEBUG_GRID = False
_TEXTURE_PATH = os.path.join(os.path.dirname(__file__), 'goboard_7x7.png')
def _make_checkerboard(rows,
columns,
square_halfwidth,
height=0.01,
sensor_size=0.7,
name='checkerboard'):
"""Builds a checkerboard with touch sensors centered on each square."""
root = mjcf.RootElement(model=name)
black_mat = root.asset.add('material', name='black', rgba=(0.2, 0.2, 0.2, 1))
white_mat = root.asset.add('material', name='white', rgba=(0.8, 0.8, 0.8, 1))
sensor_mat = root.asset.add('material', name='sensor', rgba=(0, 1, 0, 0.3))
root.default.geom.set_attributes(
type='box', size=(square_halfwidth, square_halfwidth, height))
root.default.site.set_attributes(
type='box',
size=(sensor_size * square_halfwidth,) * 2 + (0.5 * height,),
material=sensor_mat, group=composer.SENSOR_SITES_GROUP)
xpos = (np.arange(columns) - 0.5*(columns - 1)) * 2 * square_halfwidth
ypos = (np.arange(rows) - 0.5*(rows - 1)) * 2 * square_halfwidth
geoms = []
touch_sensors = []
for i in range(rows):
for j in range(columns):
geom_mat = black_mat if ((i % 2) == (j % 2)) else white_mat
name = '{}_{}'.format(i, j)
geoms.append(
root.worldbody.add(
'geom',
pos=(xpos[j], ypos[i], height),
name=name,
material=geom_mat))
site = root.worldbody.add('site', pos=(xpos[j], ypos[i], 2*height),
name=name)
touch_sensors.append(root.sensor.add('touch', site=site, name=name))
return root, geoms, touch_sensors
def _make_goboard(boardsize,
square_halfwidth,
height=0.01,
sensor_size=0.7,
name='goboard'):
"""Builds a Go with touch sensors centered on each intersection."""
y_offset = -0.08
rows = boardsize
columns = boardsize
root = mjcf.RootElement(model=name)
if _SHOW_DEBUG_GRID:
black_mat = root.asset.add('material', name='black',
rgba=(0.2, 0.2, 0.2, 0.5))
white_mat = root.asset.add('material', name='white',
rgba=(0.8, 0.8, 0.8, 0.5))
else:
transparent_mat = root.asset.add('material', name='intersection',
rgba=(0, 1, 0, 0.0))
sensor_mat = root.asset.add('material', name='sensor', rgba=(0, 1, 0, 0.3))
contents = resources.GetResource(_TEXTURE_PATH)
root.asset.add('texture', name='goboard', type='2d',
file=mjcf.Asset(contents, '.png'))
board_mat = root.asset.add(
'material', name='goboard', texture='goboard',
texrepeat=[0.97, 0.97])
root.default.geom.set_attributes(
type='box', size=(square_halfwidth, square_halfwidth, height))
root.default.site.set_attributes(
type='box',
size=(sensor_size * square_halfwidth,) * 2 + (0.5 * height,),
material=sensor_mat, group=composer.SENSOR_SITES_GROUP)
board_height = height
if _SHOW_DEBUG_GRID:
board_height = 0.5*height
root.worldbody.add(
'geom',
pos=(0, 0+y_offset, height),
type='box',
size=(square_halfwidth * boardsize,) * 2 + (board_height,),
name=name,
material=board_mat)
xpos = (np.arange(columns) - 0.5*(columns - 1)) * 2 * square_halfwidth
ypos = (np.arange(rows) - 0.5*(rows - 1)) * 2 * square_halfwidth + y_offset
geoms = []
touch_sensors = []
for i in range(rows):
for j in range(columns):
name = '{}_{}'.format(i, j)
if _SHOW_DEBUG_GRID:
transparent_mat = black_mat if ((i % 2) == (j % 2)) else white_mat
geoms.append(
root.worldbody.add(
'geom',
pos=(xpos[j], ypos[i], height),
name=name,
material=transparent_mat))
site = root.worldbody.add('site', pos=(xpos[j], ypos[i], 2*height),
name=name)
touch_sensors.append(root.sensor.add('touch', site=site, name=name))
pass_geom = root.worldbody.add(
'geom',
pos=(0, y_offset, 0.0),
size=(square_halfwidth*boardsize*2,
square_halfwidth*boardsize) + (0.5 * height,),
name='pass',
material=transparent_mat)
site = root.worldbody.add('site', pos=(0, y_offset, 0.0),
size=(square_halfwidth*boardsize*2,
square_halfwidth*boardsize) + (0.5 * height,),
name='pass')
pass_sensor = root.sensor.add('touch', site=site, name='pass')
return root, geoms, touch_sensors, pass_geom, pass_sensor
class CheckerBoard(composer.Entity):
"""An entity representing a checkerboard."""
def __init__(self, *args, **kwargs):
super(CheckerBoard, self).__init__(*args, **kwargs)
self._contact_from_before_substep = None
def _build(self, rows=3, columns=3, square_halfwidth=0.05):
"""Builds a `CheckerBoard` entity.
Args:
rows: Integer, the number of rows.
columns: Integer, the number of columns.
square_halfwidth: Float, the halfwidth of the squares on the board.
"""
root, geoms, touch_sensors = _make_checkerboard(
rows=rows, columns=columns, square_halfwidth=square_halfwidth)
self._mjcf_model = root
self._geoms = np.array(geoms).reshape(rows, columns)
self._touch_sensors = np.array(touch_sensors).reshape(rows, columns)
@property
def mjcf_model(self):
return self._mjcf_model
def before_substep(self, physics, random_state):
del random_state # Unused.
# Cache a copy of the array of active contacts before each substep.
self._contact_from_before_substep = [
copy.copy(c) for c in physics.data.contact
]
def validate_finger_touch(self, physics, row, col, hand):
# Geom for the board square
geom_id = physics.bind(self._geoms[row, col]).element_id
# finger geoms
finger_geoms_ids = set(physics.bind(hand.finger_geoms).element_id)
contacts = self._contact_from_before_substep
set1, set2 = set([geom_id]), finger_geoms_ids
for contact in contacts:
finger_tile_contact = ((contact.geom1 in set1 and
contact.geom2 in set2) or
(contact.geom1 in set2 and contact.geom2 in set1))
if finger_tile_contact:
return True
return False
def get_contact_pos(self, physics, row, col):
geom_id = physics.bind(self._geoms[row, col]).element_id
# Here we use the array of active contacts from the previous substep, rather
# than the current values in `physics.data.contact`. This is because we use
# touch sensors to detect when a square on the board is being pressed, and
# the pressure readings are based on forces that were calculated at the end
# of the previous substep. It's possible that `physics.data.contact` no
# longer contains any active contacts involving the board geoms, even though
# the touch sensors are telling us that one of the squares on the board is
# being pressed.
contacts = self._contact_from_before_substep
relevant_contacts = [
c for c in contacts if c.geom1 == geom_id or c.geom2 == geom_id
]
if relevant_contacts:
# If there are multiple contacts involving this square of the board, just
# pick the first one.
return relevant_contacts[0].pos.copy()
else:
print("Touch sensor at ({},{}) doesn't have any active contacts!".format(
row, col))
return False
def get_contact_indices(self, physics):
pressures = physics.bind(self._touch_sensors.ravel()).sensordata
# If any of the touch sensors exceed the threshold, return the (row, col)
# indices of the most strongly activated sensor.
if np.any(pressures > _TOUCH_THRESHOLD):
return np.unravel_index(np.argmax(pressures), self._touch_sensors.shape)
else:
return None
def sample_pos_inside_touch_sensor(self, physics, random_state, row, col):
bound_site = physics.bind(self._touch_sensors[row, col].site)
jitter = bound_site.size * np.array([1., 1., 0.])
return bound_site.xpos + random_state.uniform(-jitter, jitter)
class GoBoard(CheckerBoard):
"""An entity representing a Goboard."""
def _build(self, boardsize=7, square_halfwidth=0.05): # pytype: disable=signature-mismatch # overriding-default-value-checks
"""Builds a `GoBoard` entity.
Args:
boardsize: Integer, the size of the board (boardsize x boardsize).
square_halfwidth: Float, the halfwidth of the squares on the board.
"""
if boardsize != 7:
raise ValueError('Only boardsize of 7x7 is implemented at the moment')
root, geoms, touch_sensors, pass_geom, pass_sensor = _make_goboard(
boardsize=boardsize, square_halfwidth=square_halfwidth)
self._mjcf_model = root
self._geoms = np.array(geoms).reshape(boardsize, boardsize)
self._touch_sensors = np.array(touch_sensors).reshape(boardsize, boardsize)
self._pass_geom = pass_geom
self._pass_sensor = pass_sensor
def get_contact_indices(self, physics):
pressures = physics.bind(self._touch_sensors.ravel()).sensordata
# Deal with pass first
pass_pressure = physics.bind(self._pass_sensor).sensordata
if pass_pressure > np.max(pressures) and pass_pressure > _TOUCH_THRESHOLD:
return -1, -1
# If any of the other touch sensors exceed the threshold, return the
# (row, col) indices of the most strongly activated sensor.
if np.any(pressures > _TOUCH_THRESHOLD):
return np.unravel_index(np.argmax(pressures), self._touch_sensors.shape)
else:
return None
def validate_finger_touch(self, physics, row, col, hand):
# Geom for the board square
if row == -1 and col == -1:
geom_id = physics.bind(self._pass_geom).element_id
else:
geom_id = physics.bind(self._geoms[row, col]).element_id
# finger geoms
finger_geoms_ids = set(physics.bind(hand.finger_geoms).element_id)
contacts = self._contact_from_before_substep
set1, set2 = set([geom_id]), finger_geoms_ids
for contact in contacts:
finger_tile_contact = ((contact.geom1 in set1 and
contact.geom2 in set2) or
(contact.geom1 in set2 and contact.geom2 in set1))
if finger_tile_contact:
return True
return False
def sample_pos_inside_touch_sensor(self, physics, random_state, row, col):
bound_site = physics.bind(self._touch_sensors[row, col].site)
jitter = bound_site.size * np.array([0.25, 0.25, 0.])
return bound_site.xpos + random_state.uniform(-jitter, jitter)
================================================
FILE: physics_planning_games/board_games/_internal/observations.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Configuration for observations."""
import collections
import numpy as np
class ObservableSpec(collections.namedtuple(
'ObservableSpec',
['enabled', 'update_interval', 'buffer_size', 'delay', 'aggregator',
'corruptor'])):
"""Configuration options for generic observables."""
__slots__ = ()
class CameraObservableSpec(collections.namedtuple(
'CameraObservableSpec', ('height', 'width') + ObservableSpec._fields)):
"""Configuration options for camera observables."""
__slots__ = ()
class ObservationSettings(collections.namedtuple(
'ObservationSettings',
['proprio', 'ftt', 'prop_pose', 'board_state', 'camera'])):
"""Container of `ObservableSpecs` grouped by category."""
__slots__ = ()
class ObservableNames(collections.namedtuple(
'ObservableNames',
['proprio', 'ftt', 'prop_pose', 'board_state', 'camera'])):
"""Container that groups the names of observables by category."""
__slots__ = ()
def __new__(cls, proprio=(), ftt=(), prop_pose=(), board_state=(), camera=()):
return super(ObservableNames, cls).__new__(
cls,
proprio=proprio,
ftt=ftt,
prop_pose=prop_pose,
board_state=board_state,
camera=camera)
# Global defaults for "feature" observables (i.e. anything that isn't a camera).
_DISABLED_FEATURE = ObservableSpec(
enabled=False,
update_interval=1,
buffer_size=1,
delay=0,
aggregator=None,
corruptor=None)
_ENABLED_FEATURE = _DISABLED_FEATURE._replace(enabled=True)
# Force, torque and touch-sensor readings are scaled using a symmetric
# logarithmic transformation that handles 0 and negative values.
_symlog1p = lambda x, random_state: np.sign(x) * np.log1p(abs(x))
_DISABLED_FTT = _DISABLED_FEATURE._replace(corruptor=_symlog1p)
_ENABLED_FTT = _ENABLED_FEATURE._replace(corruptor=_symlog1p)
# Global defaults for camera observables.
_DISABLED_CAMERA = CameraObservableSpec(
height=84,
width=84,
enabled=False,
update_interval=1,
buffer_size=1,
delay=0,
aggregator=None,
corruptor=None)
_ENABLED_CAMERA = _DISABLED_CAMERA._replace(enabled=True)
# Predefined sets of configurations options to apply to each category of
# observable.
PERFECT_FEATURES = ObservationSettings(
proprio=_ENABLED_FEATURE,
ftt=_ENABLED_FTT,
prop_pose=_ENABLED_FEATURE,
board_state=_ENABLED_FEATURE,
camera=_ENABLED_CAMERA)
ARENA_OBSERVABLES = ObservableNames(camera=['front_camera', 'front_camera_2'])
JACO_ARM_OBSERVABLES = ObservableNames(
proprio=['joints_pos', 'joints_vel'], ftt=['joints_torque'])
JACO_HAND_OBSERVABLES = ObservableNames(
proprio=['joints_pos', 'joints_vel', 'pinch_site_pos', 'pinch_site_rmat'])
MARKER_OBSERVABLES = ObservableNames(prop_pose=['position'])
def make_options(obs_settings, obs_names):
"""Constructs a dict of configuration options for a set of named observables.
Args:
obs_settings: An `ObservationSettings` instance.
obs_names: An `ObservableNames` instance.
Returns:
A nested dict containing `{observable_name: {option_name: value}}`.
"""
observable_options = {}
for category, spec in obs_settings._asdict().items():
for observable_name in getattr(obs_names, category):
observable_options[observable_name] = spec._asdict()
return observable_options
================================================
FILE: physics_planning_games/board_games/_internal/pieces.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Entities representing board game pieces."""
import itertools
from dm_control import composer
from dm_control import mjcf
from dm_control.composer.observation import observable
import numpy as np
_VISIBLE_SITE_GROUP = 0
_INVISIBLE_SITE_GROUP = 3
_RED = (1., 0., 0., 0.5)
_BLUE = (0., 0, 1., 0.5)
_INVALID_PLAYER_ID = '`player_id` must be between 0 and {}, got {}.'
_NO_MORE_MARKERS_AVAILABLE = (
'All {} markers for player {} have already been placed.')
class Markers(composer.Entity):
"""A collection of non-physical entities for marking board positions."""
def _build(self,
num_per_player,
player_colors=(_RED, _BLUE),
halfwidth=0.025,
height=0.01,
board_size=7):
"""Builds a `Markers` entity.
Args:
num_per_player: Integer, the total number of markers to create per player.
player_colors: Sequence of (R, G, B, A) values specifying the marker
colors for each player.
halfwidth: Scalar, the halfwidth of each marker.
height: Scalar, height of each marker.
board_size: Integer, optional if using the integer indexing.
"""
root = mjcf.RootElement(model='markers')
root.default.site.set_attributes(type='cylinder', size=(halfwidth, height))
all_markers = []
for i, color in enumerate(player_colors):
player_name = 'player_{}'.format(i)
# TODO(alimuldal): Would look cool if these were textured.
material = root.asset.add('material', name=player_name, rgba=color)
player_markers = []
for j in range(num_per_player):
player_markers.append(
root.worldbody.add(
'site',
name='player_{}_move_{}'.format(i, j),
material=material))
all_markers.append(player_markers)
self._num_players = len(player_colors)
self._mjcf_model = root
self._all_markers = all_markers
self._move_counts = [0] * self._num_players
# To go from integer position to marker index in the all_markers array
self._marker_ids = np.zeros((2, board_size, board_size))
self._board_size = board_size
def _build_observables(self):
return MarkersObservables(self)
@property
def mjcf_model(self):
"""`mjcf.RootElement` for this entity."""
return self._mjcf_model
@property
def markers(self):
"""Marker sites belonging to all players.
Returns:
A nested list, where `markers[i][j]` contains the `mjcf.Element`
corresponding to player i's jth marker.
"""
return self._all_markers
def initialize_episode(self, physics, random_state):
"""Resets the markers at the start of an episode."""
del random_state # Unused.
self._reset(physics)
def _reset(self, physics):
for player_markers in self._all_markers:
for marker in player_markers:
bound_marker = physics.bind(marker)
bound_marker.pos = 0. # Markers are initially placed at the origin.
bound_marker.group = _INVISIBLE_SITE_GROUP
self._move_counts = [0] * self._num_players
self._marker_ids = np.zeros((2, self._board_size, self._board_size),
dtype=np.int32)
def make_all_invisible(self, physics):
for player_markers in self._all_markers:
for marker in player_markers:
bound_marker = physics.bind(marker)
bound_marker.group = _INVISIBLE_SITE_GROUP
def make_visible_by_bpos(self, physics, player_id, all_bpos):
for bpos in all_bpos:
marker_id = self._marker_ids[player_id][bpos[0]][bpos[1]]
marker = self._all_markers[player_id][marker_id]
bound_marker = physics.bind(marker)
bound_marker.group = _VISIBLE_SITE_GROUP
def mark(self, physics, player_id, pos, bpos=None):
"""Enables the visibility of a marker, moves it to the specified position.
Args:
physics: `mjcf.Physics` instance.
player_id: Integer specifying the ID of the player whose marker to use.
pos: Array-like object specifying the cartesian position of the marker.
bpos: Board position, optional integer coordinates to index the markers.
Raises:
ValueError: If `player_id` is invalid.
RuntimeError: If `player_id` has no more available markers.
"""
if not 0 <= player_id < self._num_players:
raise ValueError(
_INVALID_PLAYER_ID.format(self._num_players - 1, player_id))
markers = self._all_markers[player_id]
move_count = self._move_counts[player_id]
if move_count >= len(markers):
raise RuntimeError(
_NO_MORE_MARKERS_AVAILABLE.format(move_count, player_id))
bound_marker = physics.bind(markers[move_count])
bound_marker.pos = pos
# TODO(alimuldal): Set orientation as well (random? same as contact frame?)
bound_marker.group = _VISIBLE_SITE_GROUP
self._move_counts[player_id] += 1
if bpos:
self._marker_ids[player_id][bpos[0]][bpos[1]] = move_count
class MarkersObservables(composer.Observables):
"""Observables for a `Markers` entity."""
@composer.observable
def position(self):
"""Cartesian positions of all marker sites.
Returns:
An `observable.MJCFFeature` instance. When called with an instance of
`physics` as the argument, this will return a numpy float64 array of shape
(num_players * num_markers, 3) where each row contains the cartesian
position of a marker. Unplaced markers will have position (0, 0, 0).
"""
return observable.MJCFFeature(
'xpos', list(itertools.chain.from_iterable(self._entity.markers)))
================================================
FILE: physics_planning_games/board_games/_internal/pieces_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Tests for physics_planning_games.board_games._internal.pieces.py."""
from absl.testing import absltest
from dm_control import mjcf
import numpy as np
from physics_planning_games.board_games._internal import pieces
class MarkersTest(absltest.TestCase):
def test_position_observable(self):
num_per_player = 3
markers = pieces.Markers(num_per_player=num_per_player)
physics = mjcf.Physics.from_mjcf_model(markers.mjcf_model)
all_positions = [
[(0, 1, 2), (3, 4, 5), (6, 7, 8)], # Player 0
[(-1, 2, -3), (4, -5, 6)], # Player 1
]
for player_id, positions in enumerate(all_positions):
for marker_pos in positions:
markers.mark(physics=physics, player_id=player_id, pos=marker_pos)
expected_positions = np.zeros((2, num_per_player, 3), dtype=np.double)
expected_positions[0, :len(all_positions[0])] = all_positions[0]
expected_positions[1, :len(all_positions[1])] = all_positions[1]
observed_positions = markers.observables.position(physics)
np.testing.assert_array_equal(
expected_positions.reshape(-1, 3), observed_positions)
def test_invalid_player_id(self):
markers = pieces.Markers(num_per_player=5)
physics = mjcf.Physics.from_mjcf_model(markers.mjcf_model)
invalid_player_id = 99
with self.assertRaisesWithLiteralMatch(
ValueError, pieces._INVALID_PLAYER_ID.format(1, 99)):
markers.mark(physics=physics, player_id=invalid_player_id, pos=(1, 2, 3))
def test_too_many_moves(self):
num_per_player = 5
player_id = 0
markers = pieces.Markers(num_per_player=num_per_player)
physics = mjcf.Physics.from_mjcf_model(markers.mjcf_model)
for _ in range(num_per_player):
markers.mark(physics=physics, player_id=player_id, pos=(1, 2, 3))
with self.assertRaisesWithLiteralMatch(
RuntimeError,
pieces._NO_MORE_MARKERS_AVAILABLE.format(num_per_player, player_id)):
markers.mark(physics=physics, player_id=player_id, pos=(1, 2, 3))
if __name__ == '__main__':
absltest.main()
================================================
FILE: physics_planning_games/board_games/_internal/registry.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""A global registry of constructors for board game environments."""
from dm_control.utils import containers
_ALL_CONSTRUCTORS = containers.TaggedTasks(allow_overriding_keys=False)
add = _ALL_CONSTRUCTORS.add
get_constructor = _ALL_CONSTRUCTORS.__getitem__
get_all_names = _ALL_CONSTRUCTORS.keys
get_tags = _ALL_CONSTRUCTORS.tags
get_names_by_tag = _ALL_CONSTRUCTORS.tagged
# This disables the check that prevents the same task constructor name from
# being added to the container more than once. This is done in order to allow
# individual task modules to be reloaded without also reloading `registry.py`
# first (e.g. when "hot-reloading" environments in domain explorer).
def done_importing_tasks():
_ALL_CONSTRUCTORS.allow_overriding_keys = True
================================================
FILE: physics_planning_games/board_games/_internal/tags.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""String constants used to annotate task constructors."""
FEATURES = 'features'
VISION = 'vision'
EASY = 'easy'
MED = 'medium'
HARD = 'hard'
================================================
FILE: physics_planning_games/board_games/board_games_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Basic smoke test for board_games environments."""
from absl.testing import absltest
from dm_env import test_utils
from physics_planning_games import board_games
class GoTest(test_utils.EnvironmentTestMixin, absltest.TestCase):
def make_object_under_test(self):
return board_games.load(environment_name='go_7x7', seed=0)
class TicTacToeTest(test_utils.EnvironmentTestMixin, absltest.TestCase):
def make_object_under_test(self):
return board_games.load(
environment_name='tic_tac_toe_mixture_opponent_markers_features',
seed=0)
if __name__ == '__main__':
absltest.main()
================================================
FILE: physics_planning_games/board_games/go.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""A Go board game."""
from dm_control.composer.observation import observable
import numpy as np
from physics_planning_games.board_games import go_logic
from physics_planning_games.board_games import jaco_arm_board_game
from physics_planning_games.board_games._internal import boards
from physics_planning_games.board_games._internal import observations
from physics_planning_games.board_games._internal import pieces
from physics_planning_games.board_games._internal import registry
from physics_planning_games.board_games._internal import tags
_BLACK = (0., 0., 0., 0.75)
_WHITE = (1., 1., 1., 0.75)
_GO_PIECE_SIZE = 0.04
_DEFAULT_OPPONENT_MIXTURE = 0.2
class Go(jaco_arm_board_game.JacoArmBoardGame):
"""Single-player Go of configurable size."""
def __init__(self, board_size, observation_settings, opponent=None,
reset_arm_after_move=True):
"""Initializes a `Go` task.
Args:
board_size: board size
observation_settings: An `observations.ObservationSettings` namedtuple
specifying configuration options for each category of observation.
opponent: Go opponent to use for the opponent player actions.
reset_arm_after_move: Whether to reset arm to random position after every
piece being placed on the board.
"""
game_logic = go_logic.GoGameLogic(board_size=board_size)
if opponent is None:
opponent = go_logic.GoGTPOpponent(board_size=board_size,
mixture_p=_DEFAULT_OPPONENT_MIXTURE)
self._last_valid_move_is_pass = False
super(Go, self).__init__(observation_settings=observation_settings,
opponent=opponent,
game_logic=game_logic,
board=boards.GoBoard(boardsize=board_size),
markers=pieces.Markers(
player_colors=(_BLACK, _WHITE),
halfwidth=_GO_PIECE_SIZE,
num_per_player=board_size*board_size*2,
observable_options=observations.make_options(
observation_settings,
observations.MARKER_OBSERVABLES),
board_size=board_size))
self._reset_arm_after_move = reset_arm_after_move
# Add an observable exposing the move history (to reconstruct game states)
move_history_observable = observable.Generic(
lambda physics: self._game_logic.get_move_history())
move_history_observable.configure(
**observation_settings.board_state._asdict())
self._task_observables['move_history'] = move_history_observable
@property
def name(self):
return 'Go'
@property
def control_timestep(self):
return 0.05
def after_substep(self, physics, random_state):
if not self._made_move_this_step:
# which board square received the most contact pressure
indices = self._board.get_contact_indices(physics)
if not indices:
return
row, col = indices
# Makes sure that contact with that board square involved a finger
finger_touch = self._board.validate_finger_touch(physics,
row, col, self._hand)
if not finger_touch:
return
pass_action = True if (row == -1 and col == -1) else False
if pass_action and self._last_valid_move_is_pass:
# Don't allow two passes in a row (otherwise hard to only pass once)
valid_move = False
else:
valid_move = self._game_logic.apply(
player=jaco_arm_board_game.SELF,
action=go_logic.GoMarkerAction(row=int(row), col=int(col),
pass_action=pass_action))
if valid_move:
self._made_move_this_step = True
if not pass_action:
self._last_valid_move_is_pass = False
marker_pos = self._board.get_contact_pos(
physics=physics, row=row, col=col)
self._markers.mark(physics=physics,
player_id=jaco_arm_board_game.SELF,
pos=marker_pos,
bpos=(row, col))
else:
self._last_valid_move_is_pass = True
if not self._game_logic.is_game_over:
opponent_move = self._game_opponent.policy(
game_logic=self._game_logic, player=jaco_arm_board_game.OPPONENT,
random_state=random_state)
assert opponent_move
assert self._game_logic.apply(player=jaco_arm_board_game.OPPONENT,
action=opponent_move)
marker_pos = self._board.sample_pos_inside_touch_sensor(
physics=physics,
random_state=random_state,
row=opponent_move.row,
col=opponent_move.col)
self._markers.mark(physics=physics,
player_id=jaco_arm_board_game.OPPONENT,
pos=marker_pos,
bpos=(opponent_move.row,
opponent_move.col))
if self._reset_arm_after_move:
self._tcp_initializer(physics, random_state)
# Redraw all markers that are on the board (after captures)
self._markers.make_all_invisible(physics)
board = self._game_logic.get_board_state()
black_stones = np.transpose(np.nonzero(board[:, :, 1]))
white_stones = np.transpose(np.nonzero(board[:, :, 2]))
if black_stones.size > 0:
self._markers.make_visible_by_bpos(physics, 0, black_stones)
if white_stones.size > 0:
self._markers.make_visible_by_bpos(physics, 1, white_stones)
@registry.add(tags.EASY, tags.FEATURES)
def go_7x7():
return Go(board_size=7,
observation_settings=observations.PERFECT_FEATURES)
================================================
FILE: physics_planning_games/board_games/go_logic.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Logic for the Go Game."""
import abc
import collections
import enum
import shutil
import subprocess
from absl import logging
import numpy as np
from dm_control.utils import io as resources
from physics_planning_games.board_games import logic_base
import pyspiel
GNUGO_PATH = '/usr/games/gnugo'
GoMarkerAction = collections.namedtuple('GoMarkerAction',
['row', 'col', 'pass_action'])
# Note that there is no 'i' in these Go board coordinates
# (cf https://senseis.xmp.net/?Coordinates)
_X_CHARS = 'abcdefghjklmnopqrstuvwxyz'
_X_MAP = {c: x for c, x in zip(_X_CHARS, range(len(_X_CHARS)))}
def _go_marker_to_int(go_marker, board_size):
"""Convert GoMarkerAction into GoPoint integer encoding of move.
Args:
go_marker: GoMarkerAction.
board_size: Board size of the go board (e.g. 9 or 19).
Returns:
GoPoint int value.
"""
if go_marker.pass_action:
return board_size * board_size
else:
return int((go_marker.row) * board_size + go_marker.col)
def _int_to_go_marker(move_int, board_size):
"""Decode the integer move encoding to a GoMarkerAction.
Args:
move_int: Integer encoding the go move.
board_size: Board size of the go board (e.g. 9 or 19).
Returns:
GoMarkerAction encoding of move.
"""
if move_int == board_size * board_size:
go_marker_action = GoMarkerAction(row=-1, col=-1, pass_action=True)
else:
row = move_int // board_size
col = move_int % board_size
go_marker_action = GoMarkerAction(row=row, col=col, pass_action=False)
return go_marker_action
def _go_marker_to_str(go_marker):
if go_marker.pass_action:
return 'PASS'
else:
move_str = _X_CHARS[go_marker.col] + str(go_marker.row + 1)
return move_str
def _str_to_go_marker(move_str):
"""Convert from a 2-letter Go move str (e.g.
a3) to a GoMarker.
Args:
move_str: String describing the move (e.g. a3).
Returns:
GoMarkerAction encoding of move.
"""
move_str = move_str.lower()
if move_str == 'pass':
action = GoMarkerAction(row=-1, col=-1, pass_action=True)
elif move_str == 'resign':
raise NotImplementedError('Not dealing with resign')
else:
assert len(move_str) == 2
col, row = move_str[0], move_str[1]
col = _X_MAP[col]
row = int(row) - 1
action = GoMarkerAction(row=row, col=col, pass_action=False)
return action
def _get_gnugo_ref_config(level=1, binary_path=None):
"""Reference config for GnuGo.
Args:
level: GnuGo level
binary_path: string pointing to GnuGo binary
Returns:
Config dict that can be passed to gtp engine
"""
try:
gnugo_binary_path = resources.GetResourceFilename(binary_path)
except FileNotFoundError:
gnugo_binary_path = shutil.which('gnugo')
if not gnugo_binary_path:
raise FileNotFoundError('Not able to locate gnugo library. ',
'Try installing it by: apt install gnugo')
gnugo_extra_flags = ['--mode', 'gtp']
gnugo_extra_flags += ['--chinese-rules', '--capture-all-dead']
gtp_player_cfg = {
'name': 'gnugo',
'binary_path': gnugo_binary_path,
'level': level,
'extra_flags': gnugo_extra_flags,
}
return gtp_player_cfg
class Stone(enum.Enum):
EMPTY = 1
WHITE = 2
BLACK = 3
def __lt__(self, other):
value = int(self.value)
return value < other.value
def gtp_to_sgf_point(gtp_point, board_size):
"""Format a GTP point according to the SGF format."""
if gtp_point.lower() == 'pass' or gtp_point.lower() == 'resign':
return 'tt'
column, row = gtp_point[0], gtp_point[1:]
# GTP doesn't use i, but SGF does, so we need to convert.
gtp_columns = 'abcdefghjklmnopqrstuvwxyz'
sgf_columns = 'abcdefghijklmnopqrstuvwxyz'
x = gtp_columns.find(column.lower())
y = board_size - int(row)
return '%s%s' % (sgf_columns[x], sgf_columns[y])
class Gtp(object):
"""Wrapper around Go playing program that communicates using GTP."""
__metaclass__ = abc.ABCMeta
def __init__(self, checkpoint_file=None):
self.stones = {
'.': Stone.EMPTY,
'+': Stone.EMPTY,
'O': Stone.WHITE,
'X': Stone.BLACK
}
self.moves = []
self.comments = []
self.handicap = 0
self.board_size = 19
self.komi = 0
self.free_handicap = None
self.byo_yomi_time = None
self.checkpoint_file = checkpoint_file
self.stderr = None
def set_board_size(self, size):
self.board_size = size
self.gtp_command('boardsize %d' % size)
self.gtp_command('clear_board')
def set_komi(self, komi):
self.komi = komi
self.gtp_command('komi %s' % komi)
def set_free_handicap(self, vertices):
self.free_handicap = vertices
self.gtp_command('set_free_handicap %s' % vertices)
def place_free_handicap(self, n):
self.free_handicap = self.gtp_command('place_free_handicap %d' % n)
return self.free_handicap
def make_move(self, move, record=True):
self.gtp_command('play %s' % move)
if record:
self._record_move(move)
def set_byo_yomi_time(self, t):
self.byo_yomi_time = t
def num_moves(self):
return len(self.moves)
def clear_board(self):
self.moves = []
self.comments = []
self.gtp_command('clear_board')
def generate_move(self, color):
if self.byo_yomi_time is not None:
self.gtp_command('time_left %s %d 1' % (color, self.byo_yomi_time))
move = '%s %s' % (color, self.gtp_command(
'genmove %s' % color).split(' ')[-1].lower())
self._record_move(move, stderr=self.stderr)
return move
def board(self):
raw_board = self.gtp_command('showboard', log=False)[1:].strip()
rows = [line.strip().split(' ')[0] for line in raw_board.split('\n')][1:-1]
rows = [''.join(row.split(' ')[1:-1]) for row in rows]
return [[self.stones[cell] for cell in row] for row in rows]
def quit(self):
self.gtp_command('quit')
def final_status(self, status):
return self.gtp_command('final_status_list %s' % status)[2:].replace(
'\n', ' ').split(' ')
def fixed_handicap(self, handicap):
self.handicap = handicap
self.gtp_command('fixed_handicap %d' % handicap)
def undo(self, num_moves):
self.gtp_command('gg-undo %d' % num_moves)
for _ in range(num_moves):
self.moves.pop()
self.comments.pop()
def _record_move(self, move, stderr=None):
self.moves.append(move)
self.comments.append(stderr)
if self.checkpoint_file:
with open(self.checkpoint_file, 'w') as f:
f.write(self.to_sgf())
def to_sgf(self):
sgf = '(;PB[Black]PW[White]KM[%.1f]HA[%d]SZ[19]' % (self.komi,
self.handicap)
for i, move in enumerate(self.moves):
sgf += '\n;' + self._format_sgf_move(move)
if self.comments[i]:
sgf += 'C[' + self._sgf_escape(self.comments[i]) + ']'
return sgf + ')'
def _format_sgf_move(self, move):
"""Format a move according to the SGF format."""
color, vertex = str(move).split(' ')
return '%s[%s]' % (color[0].upper(),
gtp_to_sgf_point(vertex, self.board_size))
def _sgf_escape(self, text):
return ''.join(['\\' + t if t == ']' or t == '\\' else t for t in text])
@abc.abstractmethod
def gtp_command(self, command, log=True):
"""Executes a GTP command and returns its response.
Args:
command: The GTP command to run, no trailing newline.
log: Whether to log command and response to INFO.
Returns:
The GTP response.
Raises:
GtpError: if the response is not ok (doesn't start with '=').
"""
pass
class GtpError(Exception):
def __init__(self, response):
super(GtpError, self).__init__()
self.response = response
def __str__(self):
return self.response
class GoEngine(Gtp):
"""GTP-based Go engine.
Supports at least GnuGo and Pachi.
For GnuGo, at least specify ['--mode', 'gtp'] in extra_flags.
"""
def __init__(self, command='', checkpoint_file=None, extra_flags=None):
super(GoEngine, self).__init__(checkpoint_file)
if extra_flags:
command = [command] + extra_flags
self.p = subprocess.Popen(
command,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
universal_newlines=True)
def gtp_command(self, command, log=True):
if log:
logging.info('GTP: %s', command)
self.p.stdin.write(command)
self.p.stdin.write('\n')
self.p.stdin.flush()
response = [self.p.stdout.readline()]
while response[-1] != '\n':
response.append(self.p.stdout.readline())
response = ''.join(response).strip()
if log:
logging.info('GTP: %s', response)
if response[0][0] != '=':
raise GtpError(response)
return response
class GoGameLogic(logic_base.OpenSpielBasedLogic):
"""Logic for Go game."""
def __init__(self, board_size, gnugo_level=1, komi=5.5):
self._board_size = board_size
self._komi = komi
gtp_player_cfg = _get_gnugo_ref_config(
level=gnugo_level,
binary_path=GNUGO_PATH)
self._gtp_player = GoEngine(
command=gtp_player_cfg['binary_path'],
extra_flags=gtp_player_cfg['extra_flags'])
self._gtp_player.set_board_size(board_size)
self.reset()
def board_size(self):
return self._board_size
def get_gtp_player(self):
return self._gtp_player
def reset(self):
"""Resets the game state."""
# For now we always assume we are the starting player and use a random
# opponent.
self._gtp_player.gtp_command('clear_board', log=False)
self._gtp_player.set_board_size(self._board_size)
self._gtp_player.set_komi(self._komi)
game = pyspiel.load_game('go', {'board_size': self._board_size})
self._open_spiel_state = game.new_initial_state()
self._moves = np.ones(
(self._board_size * self._board_size * 2,), dtype=np.int32) * -1
self._move_id = 0
def show_board(self):
self._gtp_player.gtp_command('showboard')
def get_gtp_reward(self):
self._gtp_player.gtp_command('final_score')
def get_board_state(self):
"""Returns the logical board state as a numpy array.
Returns: A boolean array of shape (H, W, C), where H=3, W=3 (height and
width of the board) and C=4 for the 4 planes. The 4 planes are, in order,
unmarked, black (player 0), white (player 1) and komi (this layer is
always all the same value indicating whether white is to play).
"""
board_state = np.reshape(
np.array(self._open_spiel_state.observation_tensor(0), dtype=bool),
[4, self._board_size, self._board_size])
board_state = np.transpose(board_state, [1, 2, 0])
board_state = board_state[:, :, [2, 0, 1, 3]]
return board_state
def set_state_from_history(self, move_history):
self.reset()
move_history = np.squeeze(move_history.numpy())
for t in range(move_history.size):
if move_history[t] < 0:
break
else:
self.apply(t % 2, move_history[t])
# self.show_board()
def get_move_history(self):
"""Returns the move history as padded numpy array."""
return self._moves
def apply(self, player, action):
"""Checks whether action is valid, and if so applies it to the game state.
Args:
player: Integer specifying the player ID; either 0 or 1.
action: A `GoMarkerAction` instance (or numpy.int32) which represent the
action in the board of size `board_size`.
Returns:
True if the action was valid, else False.
"""
if isinstance(action, GoMarkerAction):
action = _go_marker_to_int(action, self._board_size)
if self._open_spiel_state.current_player() != player:
return False
legal_actions = self._open_spiel_state.legal_actions()
if np.isin(action, legal_actions):
self._open_spiel_state.apply_action(action)
was_valid_move = True
else:
was_valid_move = False
if not was_valid_move:
return False
self._moves[self._move_id] = action
self._move_id += 1
# Apply to the Go program
player_color = 'B' if player == 0 else 'W'
action_str = _go_marker_to_str(_int_to_go_marker(action, self._board_size))
self._gtp_player.gtp_command('play {} {}'.format(player_color, action_str))
return was_valid_move
def gen_move(game_logic, player):
"""Generate move from GTP player and game state defined in game_logic."""
player_color = 'B' if player == 0 else 'W'
gtp_player = game_logic.get_gtp_player()
move_str = gtp_player.gtp_command(
'reg_genmove {}'.format(player_color), log=True)
move_str = move_str[2:].lower()
action = _str_to_go_marker(move_str)
return action
def gen_random_move(game_logic, random_state):
"""Generate random move for current state in game logic."""
if game_logic.is_game_over:
return None
valid_moves = game_logic.open_spiel_state.legal_actions()
assert valid_moves
move = random_state.choice(valid_moves)
go_action = _int_to_go_marker(move, board_size=game_logic.board_size())
return go_action
class GoGTPOpponent(logic_base.Opponent):
"""Use external binary Pachi to generate opponent moves."""
def __init__(self, board_size, mixture_p=0.0):
"""Initialize Go opponent.
Args:
board_size: Go board size (int)
mixture_p: Probability of playing a random move (amongst legal moves).
"""
self._board_size = board_size
self._mixture_p = mixture_p
def reset(self):
pass
def policy(self, game_logic, player, random_state):
"""Return policy action.
Args:
game_logic: Go game logic state.
player: Integer specifying the player ID; either 0 or 1.
random_state: Numpy random state object.
Returns:
GoMarkerAction indicating opponent move.
"""
if random_state.rand() < self._mixture_p:
return gen_random_move(game_logic, random_state)
else:
return gen_move(game_logic, player)
class GoRandomOpponent(logic_base.Opponent):
"""An easy opponent for Go."""
def __init__(self, board_size):
self._board_size = board_size
def reset(self):
"""Resets the opponent's internal state (not implemented)."""
pass
def policy(self, game_logic, player, random_state):
"""Return a random, valid move.
Args:
game_logic: TicTacToeGameLogic state of the game.
player: Integer specifying the player ID; either 0 or 1.
random_state: An instance of `np.random.RandomState`
Returns:
GoMarkerAction of opponent.
"""
return gen_random_move(game_logic, random_state)
================================================
FILE: physics_planning_games/board_games/go_logic_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
from physics_planning_games.board_games import go_logic
class GoGameLogicTest(parameterized.TestCase):
def setUp(self):
super(GoGameLogicTest, self).setUp()
self.logic = go_logic.GoGameLogic(board_size=5)
self.expected_board_state = np.zeros((5, 5, 4), dtype=bool)
self.expected_board_state[:, :, 0] = True
def test_valid_move_sequence(self):
np.testing.assert_array_equal(self.logic.get_board_state(),
self.expected_board_state)
action = go_logic.GoMarkerAction(col=1, row=2, pass_action=False)
self.assertTrue(self.logic.apply(player=0, action=action),
msg='Invalid action: {}'.format(action))
def test_pass(self):
action = go_logic.GoMarkerAction(col=0, row=0, pass_action=True)
self.assertTrue(self.logic.apply(player=0, action=action),
msg='Invalid action: {}'.format(action))
self.expected_board_state[:, :, 3] = True
np.testing.assert_array_equal(self.logic.get_board_state(),
self.expected_board_state)
def test_invalid_move_sequence(self):
np.testing.assert_array_equal(self.logic.get_board_state(),
self.expected_board_state)
action = go_logic.GoMarkerAction(col=1, row=2, pass_action=False)
self.assertTrue(self.logic.apply(player=0, action=action),
msg='Invalid action: {}'.format(action))
self.expected_board_state[action.row, action.col, 0] = False
self.expected_board_state[action.row, action.col, 1] = True
self.expected_board_state[:, :, 3] = True
np.testing.assert_array_equal(self.logic.get_board_state(),
self.expected_board_state)
action = go_logic.GoMarkerAction(col=1, row=2, pass_action=False)
self.assertFalse(self.logic.apply(player=0, action=action),
msg='Invalid action was accepted: {}'.format(action))
# Player 1 tries to move in the same location as player 0.
self.assertFalse(self.logic.apply(player=1, action=action),
msg='Invalid action was accepted: {}'.format(action))
# The board state should not have changed as a result of invalid actions.
np.testing.assert_array_equal(self.logic.get_board_state(),
self.expected_board_state)
def test_random_opponent_vs_gnugo(self):
"""Play random v gnugo opponents and check that optimal largely wins.
"""
board_size = 9
rand_state = np.random.RandomState(42)
pachi_opponent = go_logic.GoGTPOpponent(board_size)
random_opponent = go_logic.GoRandomOpponent(board_size)
players = [pachi_opponent, random_opponent]
pachi_returns = []
random_returns = []
for _ in range(3):
logic = go_logic.GoGameLogic(board_size)
pachi_opponent.reset()
random_opponent.reset()
rand_state.shuffle(players)
current_player_idx = 0
while not logic.is_game_over:
current_player = players[current_player_idx]
action = current_player.policy(logic, current_player_idx, rand_state)
valid_action = logic.apply(current_player_idx, action)
self.assertTrue(valid_action,
msg='Opponent {} selected invalid action {}'.format(
current_player, action))
current_player_idx = (current_player_idx + 1) % 2
# Record the winner.
reward = logic.get_reward
if players[0] == pachi_opponent:
pachi_return = reward[0]
random_return = reward[1]
else:
pachi_return = reward[1]
random_return = reward[0]
pachi_returns.append(pachi_return)
random_returns.append(random_return)
mean_pachi_returns = np.mean(pachi_returns)
mean_random_returns = np.mean(random_returns)
self.assertGreater(mean_pachi_returns, 0.95)
self.assertLess(mean_random_returns, 0.05)
@parameterized.named_parameters([
dict(testcase_name='00',
row=0, col=0),
dict(testcase_name='01',
row=1, col=0)])
def test_go_marker_to_int(self, row, col):
go_marker = go_logic.GoMarkerAction(row=row, col=col, pass_action=False)
int_action = go_logic._go_marker_to_int(go_marker, board_size=19)
recovered_go_marker = go_logic._int_to_go_marker(int_action, board_size=19)
self.assertEqual(go_marker, recovered_go_marker,
msg='Initial go marker {}, recovered {}'.format(
go_marker, recovered_go_marker))
@parameterized.named_parameters([
dict(testcase_name='00',
row=0, col=0),
dict(testcase_name='01',
row=1, col=0)])
def test_go_marker_to_str(self, row, col):
go_marker = go_logic.GoMarkerAction(row=row, col=col, pass_action=False)
str_action = go_logic._go_marker_to_str(go_marker)
recovered_go_marker = go_logic._str_to_go_marker(str_action)
self.assertEqual(go_marker,
recovered_go_marker,
msg='Initial go marker {}, recovered {}, '
'str_action {}'.format(go_marker, recovered_go_marker,
str_action))
if __name__ == '__main__':
absltest.main()
================================================
FILE: physics_planning_games/board_games/jaco_arm_board_game.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Shared base class for two-player Jaco arm board games.
"""
import functools
from dm_control import composer
from dm_control.composer import initializers
from dm_control.composer.observation import observable
from dm_control.composer.variation import distributions
from dm_control.composer.variation import rotations
from dm_control.entities.manipulators import base
from dm_control.entities.manipulators import kinova
import numpy as np
from physics_planning_games.board_games._internal import arenas
from physics_planning_games.board_games._internal import observations
_ARM_Y_OFFSET = 0.4
_TCP_LOWER_BOUNDS = (-0.1, -0.1, 0.2)
_TCP_UPPER_BOUNDS = (0.1, 0.1, 0.4)
# Player IDs
SELF = 0
OPPONENT = 1
def _uniform_downward_rotation():
angle = distributions.Uniform(-np.pi, np.pi, single_sample=True)
quaternion = rotations.QuaternionFromAxisAngle(axis=(0., 0., 1.), angle=angle)
return functools.partial(rotations.QuaternionPreMultiply(quaternion),
initial_value=base.DOWN_QUATERNION)
class JacoArmBoardGame(composer.Task):
"""Base class for two-player checker-like board games."""
def __init__(self, observation_settings, opponent, game_logic, board,
markers):
"""Initializes the task.
Args:
observation_settings: An `observations.ObservationSettings` namedtuple
specifying configuration options for each category of observation.
opponent: Opponent used for generating opponent moves.
game_logic: Logic for keeping track of the logical state of the board.
board: Board to use.
markers: Markers to use.
"""
self._game_logic = game_logic
self._game_opponent = opponent
arena = arenas.Standard(observable_options=observations.make_options(
observation_settings, observations.ARENA_OBSERVABLES))
arena.attach(board)
arm = kinova.JacoArm(observable_options=observations.make_options(
observation_settings, observations.JACO_ARM_OBSERVABLES))
hand = kinova.JacoHand(observable_options=observations.make_options(
observation_settings, observations.JACO_HAND_OBSERVABLES))
arm.attach(hand)
arena.attach_offset(arm, offset=(0, _ARM_Y_OFFSET, 0))
arena.attach(markers)
# Geoms belonging to the arm and hand are placed in a custom group in order
# to disable their visibility to the top-down camera. NB: we assume that
# there are no other geoms in ROBOT_GEOM_GROUP that don't belong to the
# robot (this is usually the case since the default geom group is 0). If
# there are then these will also be invisible to the top-down camera.
for robot_geom in arm.mjcf_model.find_all('geom'):
robot_geom.group = arenas.ROBOT_GEOM_GROUP
self._arena = arena
self._board = board
self._arm = arm
self._hand = hand
self._markers = markers
self._tcp_initializer = initializers.ToolCenterPointInitializer(
hand=hand, arm=arm,
position=distributions.Uniform(_TCP_LOWER_BOUNDS, _TCP_UPPER_BOUNDS),
quaternion=_uniform_downward_rotation())
# Add an observable exposing the logical state of the board.
board_state_observable = observable.Generic(
lambda physics: self._game_logic.get_board_state())
board_state_observable.configure(
**observation_settings.board_state._asdict())
self._task_observables = {'board_state': board_state_observable}
@property
def root_entity(self):
return self._arena
@property
def arm(self):
return self._arm
@property
def hand(self):
return self._hand
@property
def task_observables(self):
return self._task_observables
def get_reward(self, physics):
del physics # Unused.
return self._game_logic.get_reward[SELF]
def should_terminate_episode(self, physics):
return self._game_logic.is_game_over
def initialize_episode(self, physics, random_state):
self._tcp_initializer(physics, random_state)
self._game_logic.reset()
self._game_opponent.reset()
def before_step(self, physics, action, random_state):
super(JacoArmBoardGame, self).before_step(physics, action, random_state)
self._made_move_this_step = False
def after_substep(self, physics, random_state):
raise NotImplementedError('Subclass must implement after_substep.')
================================================
FILE: physics_planning_games/board_games/logic_base.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Abstract base classes and utility functions for logical aspects of the games.
"""
import abc
ABC = abc.ABCMeta('ABC', (object,), {'__slots__': ()})
class GameLogic(ABC):
"""Define the abstrat game logic class.
"""
@abc.abstractmethod
def __init__(self):
pass
@abc.abstractmethod
def reset(self):
pass
@abc.abstractproperty
def is_game_over(self):
"""Boolean specifying whether the current game has ended."""
@abc.abstractproperty
def get_reward(self):
pass
@abc.abstractmethod
def get_board_state(self):
"""Returns the logical board state as a numpy array."""
@abc.abstractmethod
def apply(self, player, action):
"""Checks whether action is valid, and if so applies it to the game state.
Args:
player: Integer specifying the player ID; either 0 or 1.
action: A `GoMarkerAction` instance.
Returns:
True if the action was valid, else False.
"""
class OpenSpielBasedLogic(GameLogic):
"""GameLogic using OpenSpiel for tracking game state.
"""
@property
def is_game_over(self):
"""Boolean specifying whether the current game has ended."""
return self._open_spiel_state.is_terminal()
@property
def get_reward(self):
"""Returns a dictionary that maps from `{player_id: player_reward}`."""
if self.is_game_over:
player0_return = self._open_spiel_state.player_return(0)
# Translate from OpenSpiel returns to 0.5 for draw, -1 for loss,
# +1 for win.
if player0_return == 0.:
reward = {0: 0.5, 1: 0.5}
elif player0_return == 1.:
reward = {0: 1., 1: 0.}
else:
assert player0_return == -1.
reward = {0: 0., 1: 1.}
else:
reward = {0: 0.,
1: 0.}
return reward
@property
def open_spiel_state(self):
"""OpenSpiel object representing the underlying game state."""
return self._open_spiel_state
class Opponent(ABC):
"""Abstract Opponent class."""
@abc.abstractmethod
def __init__(self):
pass
@abc.abstractmethod
def reset(self):
pass
@abc.abstractmethod
def policy(self, game_logic, random_state):
"""Return policy action.
Args:
game_logic: Go game logic state.
random_state: Numpy random state object.
Returns:
NamedTuple indicating opponent move.
"""
================================================
FILE: physics_planning_games/board_games/tic_tac_toe.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""A Tic Tac Toe task."""
from physics_planning_games.board_games import jaco_arm_board_game
from physics_planning_games.board_games import tic_tac_toe_logic
from physics_planning_games.board_games._internal import boards
from physics_planning_games.board_games._internal import observations
from physics_planning_games.board_games._internal import pieces
from physics_planning_games.board_games._internal import registry
from physics_planning_games.board_games._internal import tags
class TicTacToe(jaco_arm_board_game.JacoArmBoardGame):
"""Single-player Tic Tac Toe."""
def __init__(self, observation_settings, opponent=None,
reset_arm_after_move=True):
"""Initializes a `TicTacToe` task.
Args:
observation_settings: An `observations.ObservationSettings` namedtuple
specifying configuration options for each category of observation.
opponent: TicTacToeOpponent used for generating opponent moves.
reset_arm_after_move: Whether to reset arm to random position after every
piece being placed on the board.
"""
game_logic = tic_tac_toe_logic.TicTacToeGameLogic()
if opponent is None:
opponent = tic_tac_toe_logic.TicTacToeRandomOpponent()
markers = pieces.Markers(num_per_player=5,
observable_options=observations.make_options(
observation_settings,
observations.MARKER_OBSERVABLES))
self._reset_arm_after_move = reset_arm_after_move
super(TicTacToe, self).__init__(observation_settings=observation_settings,
opponent=opponent,
game_logic=game_logic,
board=boards.CheckerBoard(),
markers=markers)
@property
def control_timestep(self):
return 0.05
def after_substep(self, physics, random_state):
if not self._made_move_this_step:
indices = self._board.get_contact_indices(physics)
if not indices:
return
row, col = indices
valid_move = self._game_logic.apply(
player=jaco_arm_board_game.SELF,
action=tic_tac_toe_logic.SingleMarkerAction(row=row, col=col))
if valid_move:
self._made_move_this_step = True
marker_pos = self._board.get_contact_pos(
physics=physics, row=row, col=col)
self._markers.mark(physics=physics, player_id=jaco_arm_board_game.SELF,
pos=marker_pos)
if not self._game_logic.is_game_over:
opponent_move = self._game_opponent.policy(
game_logic=self._game_logic, random_state=random_state)
assert opponent_move
assert self._game_logic.apply(player=jaco_arm_board_game.OPPONENT,
action=opponent_move)
marker_pos = self._board.sample_pos_inside_touch_sensor(
physics=physics,
random_state=random_state,
row=opponent_move.row,
col=opponent_move.col)
self._markers.mark(physics=physics,
player_id=jaco_arm_board_game.OPPONENT,
pos=marker_pos)
if self._reset_arm_after_move:
self._tcp_initializer(physics, random_state)
@registry.add(tags.EASY, tags.FEATURES)
def tic_tac_toe_markers_features(**unused_kwargs):
return TicTacToe(observation_settings=observations.PERFECT_FEATURES)
@registry.add(tags.MED, tags.FEATURES)
def tic_tac_toe_mixture_opponent_markers_features(mixture_p=0.25):
print('Creating tictactoe task with random/optimal opponent mixture, p={}'
.format(mixture_p))
return TicTacToe(
observation_settings=observations.PERFECT_FEATURES,
opponent=tic_tac_toe_logic.TicTacToeMixtureOpponent(mixture_p))
@registry.add(tags.HARD, tags.FEATURES)
def tic_tac_toe_optimal_opponent_markers_features(**unused_kwargs):
return TicTacToe(observation_settings=observations.PERFECT_FEATURES,
opponent=tic_tac_toe_logic.TicTacToeOptimalOpponent())
================================================
FILE: physics_planning_games/board_games/tic_tac_toe_logic.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""TicTacToe logic wrapper for use in manipulation tasks."""
import collections
import itertools
import numpy as np
from physics_planning_games.board_games import logic_base
from open_spiel.python.algorithms import minimax
import pyspiel
SingleMarkerAction = collections.namedtuple('SingleMarkerAction',
['row', 'col'])
force_random_start_position = False
class TicTacToeGameLogic(logic_base.OpenSpielBasedLogic):
"""Logic for TicTacToe game."""
def __init__(self):
self.reset()
def reset(self):
"""Resets the game state."""
# For now we always assume we are the starting player.
game = pyspiel.load_game('tic_tac_toe')
self._open_spiel_state = game.new_initial_state()
if force_random_start_position:
# For debugging purposes only, force some random moves
rand_state = np.random.RandomState(46)
rand_player = TicTacToeRandomOpponent()
num_moves = 4
for _ in range(num_moves):
action = rand_player.policy(self, rand_state)
action_1d = np.ravel_multi_index(action, (3, 3))
self._open_spiel_state.apply_action(action_1d)
def get_board_state(self):
"""Returns the logical board state as a numpy array.
Returns:
A boolean array of shape (H, W, C), where H=3, W=3 (height and width
of the board) and C=3 for the 3 planes. The 3 planes are, in order,
unmarked squares, x's (player 0) and y's (player 1).
"""
board_state = np.reshape(
np.array(self._open_spiel_state.observation_tensor(0), dtype=bool),
[3, 3, 3])
board_state = np.transpose(board_state, [1, 2, 0])
board_state = board_state[:, :, [0, 2, 1]]
return board_state
def apply(self, player, action):
"""Checks whether action is valid, and if so applies it to the game state.
Args:
player: Integer specifying the player ID; either 0 or 1.
action: A `SingleMarkerAction` instance.
Returns:
True if the action was valid, else False.
"""
action_value = np.ravel_multi_index((action.row, action.col), (3, 3))
if self._open_spiel_state.current_player() != player:
return False
try:
self._open_spiel_state.apply_action(action_value)
was_valid_move = True
except RuntimeError:
was_valid_move = False
return was_valid_move
class TicTacToeRandomOpponent(logic_base.Opponent):
"""An easy opponent for TicTacToe."""
def __init__(self):
pass
def reset(self):
"""Resets the opponent's internal state (not implemented)."""
pass
def policy(self, game_logic, random_state):
"""Return a random, valid move.
Args:
game_logic: TicTacToeGameLogic state of the game.
random_state: An instance of `np.random.RandomState`
Returns:
SingleMarkerAction of opponent.
"""
if game_logic.is_game_over:
return None
valid_moves = game_logic.open_spiel_state.legal_actions()
assert valid_moves
move = random_state.choice(valid_moves)
row, col = np.unravel_index(move, shape=(3, 3))
return SingleMarkerAction(row=row, col=col)
class TicTacToeMixtureOpponent(logic_base.Opponent):
"""A TicTacToe opponent which makes a mixture of optimal and random moves.
The optimal mixture component uses minimax search.
"""
def __init__(self, mixture_p):
"""Initialize the mixture opponent.
Args:
mixture_p: The mixture probability. We choose moves from the random
opponent with probability mixture_p and moves from the optimal
opponent with probability 1 - mixture_p.
"""
self._random_opponent = TicTacToeRandomOpponent()
self._optimal_opponent = TicTacToeOptimalOpponent()
self._mixture_p = mixture_p
def reset(self):
pass
def policy(self, game_logic, random_state):
if random_state.rand() < self._mixture_p:
return self._random_opponent.policy(game_logic, random_state)
else:
return self._optimal_opponent.policy(game_logic, random_state)
class TicTacToeOptimalOpponent(logic_base.Opponent):
"""A TicTacToe opponent which makes perfect moves.
Uses minimax search.
"""
def __init__(self):
pass
def reset(self):
pass
def policy(self, game_logic, random_state):
action = tic_tac_toe_minimax(game_logic.open_spiel_state, random_state)
return action
def numpy_array_to_open_spiel_state(board_state):
"""Take a numpy observation [3x3x3] bool area and create an OpenSpiel state.
Args:
board_state: 3x3x3 bool array with [col, row, c] with c indexing, in order,
empty squares, x moves, y moves.
Returns:
open_spiel_state: OpenSpiel state of this position.
"""
game = pyspiel.load_game('tic_tac_toe')
open_spiel_state = game.new_initial_state()
x_moves = np.flatnonzero(board_state[:, :, 1])
y_moves = np.flatnonzero(board_state[:, :, 2])
for x_m, y_m in itertools.zip_longest(x_moves, y_moves):
if open_spiel_state.is_terminal():
break
open_spiel_state.apply_action(x_m)
if open_spiel_state.is_terminal():
break
if y_m is not None:
open_spiel_state.apply_action(y_m)
return open_spiel_state
def open_spiel_move_to_single_marker_action(action):
row, col = np.unravel_index(action, shape=(3, 3))
return SingleMarkerAction(row=row, col=col)
def tic_tac_toe_random_move(state, random_state):
"""Returns a legal move at random from current state.
Args:
state: World state of the game. Either an OpenSpiel state
or a numpy encoding of the board.
random_state: numpy random state used for choosing randomly if there is more
than one optimal action.
Returns:
action: SingleMarkerAction of a random move.
"""
if isinstance(state, np.ndarray):
spiel_state = numpy_array_to_open_spiel_state(state)
else:
spiel_state = state
if spiel_state.is_terminal():
return False
legal_actions = spiel_state.legal_actions()
action = random_state.choice(legal_actions)
return open_spiel_move_to_single_marker_action(action)
def tic_tac_toe_minimax(state, random_state):
"""Tree search from the world_state in order to find the optimal action.
Args:
state: World state of the game. Either an OpenSpiel state
or a numpy encoding of the board.
random_state: numpy random state used for choosing randomly if there is more
than one optimal action.
Returns:
action: SingleMarkerAction of an optimal move.
"""
if isinstance(state, np.ndarray):
spiel_state = numpy_array_to_open_spiel_state(state)
else:
spiel_state = state
if spiel_state.is_terminal():
return False
current_player = spiel_state.current_player()
legal_actions = spiel_state.legal_actions()
best_actions = []
best_value = -100
for action in legal_actions:
state_after_action = spiel_state.clone()
state_after_action.apply_action(action)
value, _ = minimax.expectiminimax(state_after_action, 100, None,
current_player)
if value > best_value:
best_value = value
best_actions = [action]
elif value == best_value:
best_actions.append(action)
assert best_actions
action = random_state.choice(best_actions)
return open_spiel_move_to_single_marker_action(action)
================================================
FILE: physics_planning_games/board_games/tic_tac_toe_logic_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
from physics_planning_games.board_games import tic_tac_toe_logic
class TicTacToeGameLogicTest(parameterized.TestCase):
def setUp(self):
super(TicTacToeGameLogicTest, self).setUp()
self.logic = tic_tac_toe_logic.TicTacToeGameLogic()
self.expected_board_state = np.zeros((3, 3, 3), dtype=bool)
self.expected_board_state[..., 0] = True # All positions initially empty.
def test_valid_move_sequence(self):
np.testing.assert_array_equal(self.logic.get_board_state(),
self.expected_board_state)
action = tic_tac_toe_logic.SingleMarkerAction(col=1, row=2)
self.assertTrue(self.logic.apply(player=0, action=action),
msg='Invalid action: {}'.format(action))
self.expected_board_state[action.row, action.col, 0] = False
self.expected_board_state[action.row, action.col, 1] = True
np.testing.assert_array_equal(self.logic.get_board_state(),
self.expected_board_state)
action = tic_tac_toe_logic.SingleMarkerAction(col=0, row=1)
self.assertTrue(self.logic.apply(player=1, action=action),
msg='Invalid action: {}'.format(action))
self.expected_board_state[action.row, action.col, 0] = False
self.expected_board_state[action.row, action.col, 2] = True
np.testing.assert_array_equal(self.logic.get_board_state(),
self.expected_board_state)
def test_invalid_move_sequence(self):
np.testing.assert_array_equal(self.logic.get_board_state(),
self.expected_board_state)
action = tic_tac_toe_logic.SingleMarkerAction(col=1, row=2)
self.assertTrue(self.logic.apply(player=0, action=action),
msg='Invalid action: {}'.format(action))
self.expected_board_state[action.row, action.col, 0] = False
self.expected_board_state[action.row, action.col, 1] = True
np.testing.assert_array_equal(self.logic.get_board_state(),
self.expected_board_state)
# Player 0 tries to move again in the same location.
action = tic_tac_toe_logic.SingleMarkerAction(col=1, row=2)
self.assertFalse(self.logic.apply(player=0, action=action),
msg='Invalid action was accepted: {}'.format(action))
# Player 1 tries to move in the same location as player 0.
self.assertFalse(self.logic.apply(player=1, action=action),
msg='Invalid action was accepted: {}'.format(action))
# The board state should not have changed as a result of invalid actions.
np.testing.assert_array_equal(self.logic.get_board_state(),
self.expected_board_state)
@parameterized.named_parameters([
dict(testcase_name='player_0_win',
move_sequence=((0, 0, 0),
(1, 0, 1),
(0, 1, 0),
(1, 2, 1),
(0, 2, 0)),
winner_id=0),
dict(testcase_name='player_1_win',
move_sequence=((0, 0, 0),
(1, 0, 2),
(0, 1, 0),
(1, 1, 1),
(0, 0, 1),
(1, 2, 0)),
winner_id=1),
dict(testcase_name='draw',
move_sequence=((0, 0, 0),
(1, 1, 1),
(0, 1, 0),
(1, 2, 0),
(0, 0, 2),
(1, 0, 1),
(0, 2, 1),
(1, 2, 2),
(0, 1, 2)),
winner_id=None)])
def test_reward_and_termination(self, move_sequence, winner_id):
for (player_id, row, col) in move_sequence:
self.assertFalse(self.logic.is_game_over)
self.assertDictEqual(self.logic.get_reward, {0: 0.0, 1: 0.0})
action = tic_tac_toe_logic.SingleMarkerAction(col=col, row=row)
self.assertTrue(self.logic.apply(player=player_id, action=action),
msg='Invalid action: {}'.format(action))
self.assertTrue(self.logic.is_game_over)
rewards = self.logic.get_reward
if winner_id is not None:
loser_id = 1 - winner_id
self.assertDictEqual(rewards, {winner_id: 1.0, loser_id: 0.0})
else: # Draw
self.assertDictEqual(rewards, {0: 0.5, 1: 0.5})
def test_random_opponent_vs_optimal(self):
"""Play random v optimal opponents and check that optimal largely wins.
"""
rand_state = np.random.RandomState(42)
optimal_opponent = tic_tac_toe_logic.TicTacToeOptimalOpponent()
random_opponent = tic_tac_toe_logic.TicTacToeRandomOpponent()
players = [optimal_opponent, random_opponent]
optimal_returns = []
random_returns = []
for _ in range(20):
logic = tic_tac_toe_logic.TicTacToeGameLogic()
optimal_opponent.reset()
random_opponent.reset()
rand_state.shuffle(players)
current_player_idx = 0
while not logic.is_game_over:
current_player = players[current_player_idx]
action = current_player.policy(logic, rand_state)
self.assertTrue(logic.apply(current_player_idx, action),
msg='Opponent {} selected invalid action {}'.format(
current_player, action))
current_player_idx = (current_player_idx + 1) % 2
# Record the winner.
reward = logic.get_reward
if players[0] == optimal_opponent:
optimal_return = reward[0]
random_return = reward[1]
else:
optimal_return = reward[1]
random_return = reward[0]
optimal_returns.append(optimal_return)
random_returns.append(random_return)
mean_optimal_returns = np.mean(optimal_returns)
mean_random_returns = np.mean(random_returns)
self.assertGreater(mean_optimal_returns, 0.9)
self.assertLess(mean_random_returns, 0.1)
@parameterized.named_parameters([
dict(testcase_name='pos0',
move_sequence=((0, 0, 1),
(1, 1, 1),
(0, 0, 2),
(1, 1, 2)),
optimal_move=(0, 0)),
dict(testcase_name='pos1',
move_sequence=((0, 0, 1),
(1, 1, 2),
(0, 0, 2),
(1, 1, 1)),
optimal_move=(0, 0)),
dict(testcase_name='pos2',
move_sequence=((0, 2, 1),
(1, 1, 2),
(0, 2, 2),
(1, 1, 1)),
optimal_move=(2, 0)),
])
def test_minimax_policy(self, move_sequence, optimal_move):
rand_state = np.random.RandomState(42)
for (player_id, row, col) in move_sequence:
action = tic_tac_toe_logic.SingleMarkerAction(col=col, row=row)
self.assertTrue(self.logic.apply(player=player_id, action=action),
msg='Invalid action: {}'.format(action))
state = self.logic.open_spiel_state
planner_action = tic_tac_toe_logic.tic_tac_toe_minimax(state,
rand_state)
self.assertEqual(planner_action, optimal_move)
# Do the same but with np array as input
self.logic = tic_tac_toe_logic.TicTacToeGameLogic()
for (player_id, row, col) in move_sequence:
action = tic_tac_toe_logic.SingleMarkerAction(col=col, row=row)
self.assertTrue(self.logic.apply(player=player_id, action=action),
msg='Invalid action: {}'.format(action))
board = self.logic.get_board_state()
planner_action = tic_tac_toe_logic.tic_tac_toe_minimax(board,
rand_state)
self.assertEqual(planner_action, optimal_move)
if __name__ == '__main__':
absltest.main()
================================================
FILE: physics_planning_games/explore.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Visualize physical planning games in Domain Explorer.
"""
import functools
from absl import app
from absl import flags
from dm_control import composer
from dm_control import viewer
from dm_control.locomotion import walkers
from physics_planning_games import board_games
from physics_planning_games.mujoban.boxoban import boxoban_level_generator
from physics_planning_games.mujoban.mujoban import Mujoban
from physics_planning_games.mujoban.mujoban_level import MujobanLevel
flags.DEFINE_enum('environment_name', 'mujoban', [
'mujoban', 'go_7x7', 'tic_tac_toe_markers_features',
'tic_tac_toe_mixture_opponent_markers_features',
'tic_tac_toe_optimal_opponent_markers_features'],
'Name of an environment to load.')
FLAGS = flags.FLAGS
TIME_LIMIT = 1000
CONTROL_TIMESTEP = .1
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
environment_name = FLAGS.environment_name
if environment_name == 'mujoban':
walker = walkers.JumpingBallWithHead(add_ears=True, camera_height=0.25)
arena = MujobanLevel(boxoban_level_generator)
task = Mujoban(
walker=walker,
maze=arena,
control_timestep=CONTROL_TIMESTEP,
top_camera_height=64,
top_camera_width=48)
env = composer.Environment(
time_limit=TIME_LIMIT, task=task, strip_singleton_obs_buffer_dim=True)
else:
env = functools.partial(
board_games.load, environment_name=environment_name)
viewer.launch(env)
if __name__ == '__main__':
app.run(main)
================================================
FILE: physics_planning_games/mujoban/__init__.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Top-level module definitions for mujoban."""
from physics_planning_games.mujoban.mujoban import Mujoban
from physics_planning_games.mujoban.mujoban_level import MujobanLevel
================================================
FILE: physics_planning_games/mujoban/boxoban.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Level generator for Mujoban based on levels from follwing dataset.
https://github.com/deepmind/boxoban-levels/
"""
import glob
import os
import zipfile
import numpy as np
import requests
BOXOBAN_URL = "https://github.com/deepmind/boxoban-levels/archive/master.zip"
def boxoban_level_generator(levels_set="unfiltered", data_split="valid"):
env = Boxoban(levels_set=levels_set, data_split=data_split)
while True:
index = np.random.randint(0, env.num_levels-1)
yield env.levels[index]
class Boxoban(object):
"""Class for loading and generatting Boxoban levels."""
def __init__(self,
levels_set="unfiltered",
data_split="valid"):
self._levels_set = levels_set
self._data_split = data_split
self._levels = []
data_file_path_local = os.path.join(os.path.dirname(__file__),
"boxoban_cache",
"{}_{}.npz".format(self._levels_set,
self._data_split))
data_file_path_global = os.path.join("/tmp/boxoban_cache",
"{}_{}.npz".format(self._levels_set,
self._data_split))
if os.path.exists(data_file_path_local):
self.levels = np.load(data_file_path_local)["levels"]
elif os.path.exists(data_file_path_global):
self.levels = np.load(data_file_path_global)["levels"]
else:
self.levels = self.get_data()
self.num_levels = len(self.levels)
def get_data(self):
"""Downloads and cache the data."""
try:
cache_path = os.path.join(
os.path.dirname(__file__), "boxoban_cache")
os.makedirs(cache_path, exist_ok=True)
except PermissionError:
cache_path = os.path.join("/tmp/boxoban_cache")
if not os.path.exists(cache_path):
os.makedirs(cache_path, exist_ok=True)
# Get the zip file
zip_file_path = os.path.join(cache_path, "master.zip")
if not os.path.exists(zip_file_path):
response = requests.get(BOXOBAN_URL, stream=True)
handle = open(zip_file_path, "wb")
for chunk in response.iter_content(chunk_size=512):
if chunk:
handle.write(chunk)
handle.close()
with zipfile.ZipFile(zip_file_path, "r") as zipref:
zipref.extractall(cache_path)
# convert to npz
path = os.path.join(cache_path, "boxoban-levels-master",
self._levels_set,
self._data_split)
files = glob.glob(path + "/*.txt")
levels = "".join([open(f, "r").read() for f in files])
levels = levels.split("\n;")
levels = ["\n".join(item.split("\n")[1:]) for item in levels]
levels = np.asarray(levels)
data_file_path = os.path.join(
cache_path, "{}_{}.npz".format(self._levels_set, self._data_split))
np.savez(data_file_path, levels=levels)
return levels
================================================
FILE: physics_planning_games/mujoban/mujoban.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""MuJoban task.
Mujoban is a single player puzzle-solving game embedded in the MuJoCo
simulation environment. The puzzle is based on the 2D game of Sokoban,
where an agent situated on a grid has to push boxes onto target locations.
"""
import collections
from dm_control import composer
from dm_control.composer.observation import observable as observable_lib
from dm_control.locomotion.arenas import labmaze_textures
from dm_control.locomotion.arenas.mazes import MazeWithTargets
from dm_env import specs
import numpy as np
from six.moves import range
from six.moves import zip
from physics_planning_games.mujoban import mujoban_level
from physics_planning_games.mujoban.mujoban_pad import MujobanPad
from physics_planning_games.mujoban.props import BoxWithSites
_FLOOR_GAP_CHAR = '#'
_AMBIENT_HEADLIGHT = 0.8
_BOX_SIZE = 0.4
_BOX_HEIGHT = 0.15
_BOX_MASS = 2.5
_BOX_FRICTION = [0.5, 0.005, 0.0001]
_BOX_RGBA = [173. / 255., 179. / 255., 60. / 255., 1.]
_BOX_PRESSED_RGBA = [0, 0, 1, 1]
_TARGET_RGBA = [1.0, 0., 0., 1.]
_PRESSED_TARGET_RGBA = [0., 1., 0., 1.]
_PEG_SIZE = 0.05
_PEG_HEIGHT = 0.25
_PEG_RGBA = [0.5, 0.5, 0.5, 1]
_PEG_ANGLE = np.pi / 4
# Aliveness in [-1., 0.].
_ALIVE_THRESHOLD = -0.5
# Constants used by the full entity layer
_WALL_LAYER = 0
_TARGET_LAYER = 1
_SOKOBAN_LAYER = 2
_BOX_LAYER = 3
def _round_positions(boxes, walker, last_round_walker):
"""Round float positions to snap objects to grid."""
round_walker = np.round(walker).astype('int32')
round_boxes = [np.round(box).astype('int32') for box in boxes]
for box in round_boxes:
if np.array_equal(box, round_walker):
round_walker = last_round_walker
return round_boxes, round_walker
class Mujoban(composer.Task):
"""Requires objects to be moved onto matching-colored floor pads.
Agent only receives instantaneous rewards of +1 for the
timestep in which a box first enters a target, and -1 for the
timestep in which a box leaves the target. There is an additional reward of
+10 when all the boxes are put on targets, at which point the episode
terminates.
"""
def __init__(self,
walker,
maze,
target_height=0,
box_prop=None,
box_size=None,
box_mass=None,
with_grid_pegs=False,
detection_tolerance=0.0,
physics_timestep=0.001,
control_timestep=0.025,
top_camera_height=128,
top_camera_width=128,
box_on_target_reward=1.0,
level_solved_reward=10.0):
"""Initializes this task.
Args:
walker: A `Walker` object.
maze: A `BaseMaze` object.
target_height: The height of the target pads above the ground, in meters.
box_prop: An optional `Primitive` prop to use as the box.
box_size: An optional three element sequence defining the half lengths of
the sides of the box.
box_mass: Box mass. If this is a list or tuple, a random value is sampled
from the truncated exponential distribution in [a, b) where a =
box_mass[0] and b = box_mass[1], with scale factor box_mass[2] * (b -
a).
with_grid_pegs: Whether to add solid pegs at the corners of the maze
grid cells. This helps to enforce the usual Sokoban rules where
diagonal movements are forbidden.
detection_tolerance: A maximum length scale (in metres) within which a
box is allowed to stick outside a target pad while still activating it.
For example, if this is set to 0.1 then a box will activate a pad if it
sticks out of the pad by no more than 10 centimetres.
physics_timestep: The time step of the physics simulation.
control_timestep: Should be an integer multiple of the physics time step.
top_camera_height: An int; the height of the top camera in the
observation. Setting this to 0 will disable the top camera.
top_camera_width: An int; the width of the top camera in the observation.
Setting this to 0 will disable the top camera.
box_on_target_reward: A float; reward for putting a box on a target.
level_solved_reward: A float: reward for solving the level.
"""
skybox_texture = labmaze_textures.SkyBox(style='sky_03')
wall_textures = labmaze_textures.WallTextures(style='style_01')
floor_textures = labmaze_textures.FloorTextures(style='style_01')
self._detection_tolerance = detection_tolerance
self._box_prop = box_prop
self._box_on_target_reward = box_on_target_reward
self._level_solved_reward = level_solved_reward
self._maze = maze
self._arena = MazeWithTargets(
maze=maze,
xy_scale=1,
z_height=1,
skybox_texture=skybox_texture,
wall_textures=wall_textures,
floor_textures=floor_textures)
self._walker = walker
self._arena.mjcf_model.visual.headlight.ambient = [_AMBIENT_HEADLIGHT] * 3
self._arena.text_maze_regenerated_hook = self._regenerate_positions
self._first_step = True
# Targets.
self._targets = []
self._target_positions = []
# Boxes.
self._box_size = box_size or [_BOX_SIZE] * 2 + [_BOX_HEIGHT]
self._box_mass = box_mass or _BOX_MASS
self._boxes = []
self._box_positions = []
self._with_grid_pegs = with_grid_pegs
self._peg_body = None
self._last_walker_position = None
# Create walkers and corresponding observables.
self._walker.create_root_joints(self._arena.attach(self._walker))
enabled_observables = [self._walker.observables.sensors_touch,
self._walker.observables.orientation]
enabled_observables += self._walker.observables.proprioception
enabled_observables += self._walker.observables.kinematic_sensors
for observable in enabled_observables:
observable.enabled = True
if top_camera_width and top_camera_height:
self._arena.observables.top_camera.enabled = True
self._arena.observables.top_camera.width = top_camera_width
self._arena.observables.top_camera.height = top_camera_height
# symbolic entity repenstaion in labyrinth format.
self._entity_layer = self._maze.entity_layer
# pixel layer is same as pixel rendering of symbolic sokoban.
self._pixel_layer = np.zeros(self._entity_layer.shape + (3,), dtype='uint8')
self._full_entity_layer = np.zeros(self._entity_layer.shape + (4,),
dtype='bool')
pixel_layer_obs = observable_lib.Generic(lambda _: self._pixel_layer)
pixel_layer_obs.enabled = True
full_entity_layer_obs = observable_lib.Generic(
lambda _: self._full_entity_layer)
full_entity_layer_obs.enabled = True
self._task_observables = collections.OrderedDict({
'pixel_layer': pixel_layer_obs,
'full_entity_layer': full_entity_layer_obs,
})
# Set time steps.
self.set_timesteps(
physics_timestep=physics_timestep, control_timestep=control_timestep)
self._discount = 1.
@property
def name(self):
return 'Mujoban'
@property
def root_entity(self):
return self._arena
def _regenerate_positions(self):
self._object_positions = self._arena.find_token_grid_positions(
[mujoban_level.TARGET_CHAR, mujoban_level.BOX_CHAR])
self._box_positions = self._arena.grid_to_world_positions(
self._object_positions[mujoban_level.BOX_CHAR])
target_grid_positions = self._object_positions[mujoban_level.TARGET_CHAR]
self._target_positions = self._arena.grid_to_world_positions(
target_grid_positions)
for idx in range(len(self._target_positions)):
target_grid_position = target_grid_positions[idx]
grid_y, grid_x = target_grid_position
self._arena.maze.variations_layer[grid_y, grid_x] = _FLOOR_GAP_CHAR
def initialize_episode_mjcf(self, random_state):
self._arena.regenerate()
# Clear existing targets and boxes
for target in self._targets:
target.detach()
self._targets = []
for box in self._boxes:
box.detach()
self._boxes = []
self._arena.mjcf_model.contact.remove('pair')
for _ in range(self._maze.num_targets):
target = MujobanPad(
size=self._arena.xy_scale,
height=0,
detection_tolerance=self._detection_tolerance)
self._arena.attach(target)
self._targets.append(target)
for _ in range(self._maze.num_boxes):
box = self._box_prop
if not box:
box = BoxWithSites(half_lengths=self._box_size)
box.geom.mass = _BOX_MASS
box.geom.rgba = [0, 0, 0, 1] # Will be randomized for each episode.
frame = self._arena.attach(box)
frame.add('joint', type='slide', axis=[1, 0, 0], name='x_slider')
frame.add('joint', type='slide', axis=[0, 1, 0], name='y_slider')
frame.add('joint', type='slide', axis=[0, 0, 1], name='z_slider')
self._boxes.append(box)
for target in self._targets:
target.register_box(box)
# Reduce the friction between box and ground.
ground_geom = self._arena.mjcf_model.find('geom', 'ground')
self._arena.mjcf_model.contact.add(
'pair',
geom1=box.geom,
geom2=ground_geom,
condim=6,
friction=[
_BOX_FRICTION[0], _BOX_FRICTION[0], _BOX_FRICTION[1],
_BOX_FRICTION[2], _BOX_FRICTION[2]
])
# Set box masses.
for box in self._boxes:
box.geom.mass = _BOX_MASS
box.geom.rgba[:] = _BOX_RGBA
for target in self._targets:
target.rgba[:] = _TARGET_RGBA
target.pressed_rgba[:] = _PRESSED_TARGET_RGBA
if self._with_grid_pegs:
if self._peg_body is not None:
self._peg_body.remove()
self._peg_body = self._arena.mjcf_model.worldbody.add('body')
for y in range(self._arena.maze.height - 1):
for x in range(self._arena.maze.width - 1):
peg_x, peg_y, _ = self._arena.grid_to_world_positions(
[[x + 0.5, y + 0.5]])[0]
self._peg_body.add(
'geom', type='box',
size=[_PEG_SIZE / np.sqrt(2),
_PEG_SIZE / np.sqrt(2),
_PEG_HEIGHT / 2],
pos=[peg_x, peg_y, _PEG_HEIGHT / 2],
quat=[np.cos(_PEG_ANGLE / 2), 0, 0, np.sin(_PEG_ANGLE / 2)],
rgba=_PEG_RGBA)
def initialize_episode(self, physics, random_state):
self._first_step = True
self._was_activated = [False] * len(self._targets)
self._is_solved = False
self._discount = 1.
self._walker.reinitialize_pose(physics, random_state)
spawn_position = self._arena.spawn_positions[0]
spawn_rotation = random_state.uniform(-np.pi, np.pi)
spawn_quat = np.array(
[np.cos(spawn_rotation / 2), 0, 0,
np.sin(spawn_rotation / 2)])
self._walker.shift_pose(
physics, [spawn_position[0], spawn_position[1], 0.0], spawn_quat)
for box, box_xy_position in zip(self._boxes, self._box_positions):
# Position at the middle of a maze cell.
box_position = np.array(
[box_xy_position[0], box_xy_position[1], self._box_size[2]])
# Commit the box's final pose.
box.set_pose(physics, position=box_position, quaternion=[1., 0., 0., 0.])
for target, target_position in zip(self._targets, self._target_positions):
target.set_pose(physics, position=target_position)
target.reset(physics)
self._update_entity_pixel_layers(physics)
def before_step(self, physics, actions, random_state):
if isinstance(actions, list):
actions = np.concatenate(actions)
super(Mujoban, self).before_step(physics, actions, random_state)
if self._first_step:
self._first_step = False
else:
self._was_activated = [target.activated for target in self._targets]
def _get_object_positions_in_grid(self, physics):
box_positions = self._arena.world_to_grid_positions(
[physics.bind(box.geom).xpos for box in self._boxes])
walker_position = self._arena.world_to_grid_positions(
[physics.bind(self._walker.root_body).xpos])[0]
return box_positions, walker_position
def _update_entity_pixel_layers(self, physics):
"""Updates the pixel observation and both layered representations.
Mujoban offers 3 grid representations of the world:
* the pixel layer: this is a grid representations with an RGB value at
each grid point;
* the entity layer: this is a grid representation with a character at
each grid point. This representation hides information since if Sokoban
or a box are over a target, then the target is occluded. This is the
official entity layer used by arenas which is based on dm_control labmaze;
* the full entity layer: this is a grid represention with a boolean vector
of length 4 at each grid point. The first value is `True` iff there is a
wall at this location. The second value is `True` iff there is a target at
this location. The third value is for Sokoban, and fourth value is for
boxes. Note that this is not a one-hot encoding since Sokoban or a box
can share the same location as a target.
Args:
physics: a Mujoco physics object.
Raises:
RuntimeError: if a box or walker are overlapping with a wall.
"""
# The entity layer from the maze is a string that shows the maze at the
# *beginning* of the level. This is fixed throughout an episode.
entity_layer = self._maze.entity_layer.copy()
box_positions, walker_position = self._get_object_positions_in_grid(physics)
# round positions to snap to grid.
box_positions, walker_position = _round_positions(
box_positions, walker_position, self._last_walker_position)
# setup pixel layer
map_size = entity_layer.shape
pixel_layer = np.ndarray(map_size + (3,), dtype='uint8')
pixel_layer.fill(128)
# setup full entity layer
full_entity_layer = np.zeros(map_size + (4,), dtype='bool')
# remove boxes and agent
entity_layer[entity_layer == mujoban_level.BOX_CHAR] = '.'
entity_layer[entity_layer == 'P'] = '.'
# draw empty space and goals
pixel_layer[entity_layer == '.'] = [0, 0, 0]
pixel_layer[entity_layer == 'G'] = [255, 0, 0]
full_entity_layer[:, :, _WALL_LAYER] = True
full_entity_layer[:, :, _WALL_LAYER][entity_layer == '.'] = False
full_entity_layer[:, :, _WALL_LAYER][entity_layer == 'G'] = False
full_entity_layer[:, :, _TARGET_LAYER][entity_layer == 'G'] = True
# update boxes
for pos in box_positions:
# to ensure we are not changing the walls.
if entity_layer[pos[0], pos[1]] == '*':
raise RuntimeError('Box and wall positions are overlapping and this ',
'should not happen. It requires investigation and ',
'and fixing.')
# the entity layer has no representation of box on goal.
entity_layer[pos[0], pos[1]] = mujoban_level.BOX_CHAR
if np.array_equal(pixel_layer[pos[0], pos[1]], [255, 0, 0]):
pixel_layer[pos[0], pos[1]] = [0, 255, 0] # box on goal
else:
pixel_layer[pos[0], pos[1]] = [255, 255, 0]
full_entity_layer[pos[0], pos[1], _BOX_LAYER] = True
# update player
if entity_layer[walker_position[0], walker_position[1]] == '*':
raise RuntimeError('Walker and wall positions are overlapping and this ',
'should have not happen. It requires investigation ',
'and fixing.')
entity_layer[walker_position[0], walker_position[1]] = 'P'
pixel_layer[walker_position[0], walker_position[1]] = 0, 0, 255
full_entity_layer[
walker_position[0], walker_position[1], _SOKOBAN_LAYER] = True
self._last_walker_position = walker_position
self._entity_layer = entity_layer
self._pixel_layer = pixel_layer
self._full_entity_layer = full_entity_layer
def after_step(self, physics, random_state):
super(Mujoban, self).after_step(physics, random_state)
for box in self._boxes:
physics.bind(box.geom).rgba = _BOX_RGBA
for target in self._targets:
if target.activated:
target.activator.rgba = _BOX_PRESSED_RGBA
self._update_entity_pixel_layers(physics)
self._is_solved = all([target.activated for target in self._targets])
if self._is_solved:
self._discount = 0.
def get_reward(self, physics):
reward = 0.0
for target, was_activated in zip(self._targets, self._was_activated):
if target.activated and not was_activated:
reward += self._box_on_target_reward
elif was_activated and not target.activated:
reward -= self._box_on_target_reward
if self._is_solved:
reward += self._level_solved_reward
return reward
def get_discount(self, physics):
return self._discount
def should_terminate_episode(self, physics):
is_dead = self._walker.aliveness(physics) < _ALIVE_THRESHOLD
return self._is_solved or is_dead
def get_reward_spec(self):
return specs.ArraySpec(shape=[], dtype=np.float32)
@property
def task_observables(self):
return self._task_observables
================================================
FILE: physics_planning_games/mujoban/mujoban_level.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Module for generating Mujoban level.
"""
import labmaze
BOX_CHAR = 'B'
TARGET_CHAR = labmaze.defaults.OBJECT_TOKEN
_DEFAULT_LEVEL = """
#####
# #
#### # #
# . .# #
# . #
# .## $##
## #$$ #
## $@#
## ###
####"""
# The meaning of symbols here are the same as defined in
# http://sneezingtiger.com/sokoban/levels/sasquatch5Text.html. These are the
# same symbols as used by the Sokoban community.
EMPTY_CELL = ' '
GOAL = '.'
PLAYER = '@'
PLAYER_ON_GOAL = '+'
BOX = '$'
BOX_ON_GOAL = '*'
WALL = '#'
_SOKOBAN_SYMBOLS = [
EMPTY_CELL, GOAL, PLAYER, PLAYER_ON_GOAL, BOX, BOX_ON_GOAL, WALL
]
def single_level_generator(level=_DEFAULT_LEVEL):
while True:
yield level
def _ascii_to_text_grid_level(ascii_level):
"""Goes from official Sokoban ASCII art to string understood by Mujoban.
Args:
ascii_level: a multiline string; each character is a location in a
gridworld.
Returns:
A string.
"""
level = ascii_level
if level.startswith('\n'):
level = level[1:]
level = level.replace('$', BOX_CHAR)
level = level.replace('.', TARGET_CHAR)
level = level.replace(' ', '.')
level = level.replace('#', '*')
level = level.replace('@', 'P')
if level[-1] == '\n':
level = level[:-1]
# Pad
all_rows = level.split('\n')
width = max(len(row) for row in all_rows)
padded_rows = []
for row in all_rows:
row += '*' * (width - len(row))
padded_rows.append(row)
level = '\n'.join(padded_rows)
return level + '\n'
class MujobanLevel(labmaze.BaseMaze):
"""A maze that represents a level in Mujoban."""
def __init__(self, ascii_level_generator=single_level_generator):
"""Constructor.
Args:
ascii_level_generator: a Python generator. At each iteration, this should
return a string representing a level. The symbols in the string should be
those of http://sneezingtiger.com/sokoban/levels/sasquatch5Text.html.
These are the same symbols as used by the Sokoban community.
"""
self._level_iterator = ascii_level_generator()
self.regenerate()
def regenerate(self):
"""Regenerates the maze if required."""
level = next(self._level_iterator)
self._entity_layer = labmaze.TextGrid(_ascii_to_text_grid_level(level))
self._variation_layer = self._entity_layer.copy()
self._variation_layer[:] = '.'
self._num_boxes = (self._entity_layer == BOX_CHAR).sum()
num_targets = (self._entity_layer == TARGET_CHAR).sum()
if num_targets != self._num_boxes:
raise ValueError('Number of targets {} should equal number of boxes {}.'
.format(num_targets, self._num_boxes))
@property
def num_boxes(self):
return self._num_boxes
@property
def num_targets(self):
return self._num_boxes
@property
def entity_layer(self):
return self._entity_layer
@property
def variations_layer(self):
return self._variation_layer
@property
def height(self):
return self._entity_layer.shape[0]
@property
def width(self):
return self._entity_layer.shape[1]
================================================
FILE: physics_planning_games/mujoban/mujoban_level_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Tests for mujoban_level."""
from absl.testing import absltest
from physics_planning_games.mujoban import mujoban_level
_LEVEL = """
#####
# @####
# $. #
###$.# #
# $.# #
# #$. #
# ###
######"""
_GRID_LEVEL = """********
*..P****
*..BG..*
***BG*.*
*..BG*.*
*.*BG..*
*....***
********
"""
class MujobanLevelTest(absltest.TestCase):
def test_ascii_to_text_grid_level(self):
grid_level = mujoban_level._ascii_to_text_grid_level(_LEVEL)
self.assertEqual(_GRID_LEVEL, grid_level)
if __name__ == '__main__':
absltest.main()
================================================
FILE: physics_planning_games/mujoban/mujoban_pad.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""A floor pad that is activated through touch."""
import weakref
from dm_control import composer
from dm_control import mjcf
import numpy as np
def _get_activator_box(pad_xpos, pad_size, boxes, tolerance=0.0):
"""Returns the activator box, if any. Otherwise returns None."""
# Ignore the height
pad_min = pad_xpos[0:2] - pad_size[0:2]
pad_max = pad_xpos[0:2] + pad_size[0:2]
for box in boxes:
box_xpos = np.array(box.xpos[0:2])
box_size = np.array(box.size[0:2])
min_ = pad_min + box_size - tolerance
max_ = pad_max - box_size + tolerance
in_range = np.logical_and(box_xpos >= min_, box_xpos <= max_).all()
if in_range:
return box
# No activator box was found
return None
class MujobanPad(composer.Entity):
"""A less sensitive floor pad for Mujoban."""
def _build(self, rgba=None, pressed_rgba=None,
size=1, height=0.02, detection_tolerance=0.0, name='mujoban_pad'):
rgba = tuple(rgba or (1, 0, 0, 1))
pressed_rgba = tuple(pressed_rgba or (0.2, 0, 0, 1))
self._mjcf_root = mjcf.RootElement(model=name)
self._site = self._mjcf_root.worldbody.add(
'site', type='box', name='site',
pos=[0, 0, (height / 2 or -0.001)],
size=[size / 2, size / 2, (height / 2 or 0.001)], rgba=rgba)
self._activated = False
self._rgba = np.array(rgba, dtype=float)
self._pressed_rgba = np.array(pressed_rgba, dtype=float)
self._activator = None
self._detection_tolerance = detection_tolerance
self._boxes = []
@property
def rgba(self):
return self._rgba
@property
def pressed_rgba(self):
return self._pressed_rgba
def register_box(self, box_entity):
self._boxes.append(weakref.proxy(box_entity))
@property
def site(self):
return self._site
@property
def boxes(self):
return self._boxes
@property
def activator(self):
return self._activator if self._activated else None
@property
def mjcf_model(self):
return self._mjcf_root
def initialize_episode_mjcf(self, unused_random_state):
self._activated = False
def initialize_episode(self, physics, unused_random_state):
self._update_activation(physics)
def _update_activation(self, physics):
# Note: we get the physically bound box, not an object from self._boxes.
# That's because the generator expression below generates bound objects.
box = _get_activator_box(
pad_xpos=np.array(physics.bind(self._site).xpos),
pad_size=np.array(physics.bind(self._site).size),
boxes=(physics.bind(box.geom) for box in self._boxes),
tolerance=self._detection_tolerance,)
if box:
self._activated = True
self._activator = box
else:
self._activated = False
self._activator = None
if self._activated:
physics.bind(self._site).rgba = self._pressed_rgba
else:
physics.bind(self._site).rgba = self._rgba
def before_step(self, physics, unused_random_state):
self._update_activation(physics)
def after_substep(self, physics, unused_random_state):
self._update_activation(physics)
@property
def activated(self):
"""Whether this floor pad is pressed at the moment."""
return self._activated
def reset(self, physics):
self._activated = False
physics.bind(self._site).rgba = self._rgba
================================================
FILE: physics_planning_games/mujoban/mujoban_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Tests for Mujoban."""
from absl.testing import absltest
from dm_control import composer
from dm_control.locomotion import walkers
import dm_env as environment
import numpy as np
from physics_planning_games.mujoban.mujoban import Mujoban
from physics_planning_games.mujoban.mujoban_level import MujobanLevel
TIME_LIMIT = 5
CONTROL_TIMESTEP = .1
class MujobanTest(absltest.TestCase):
def test(self):
walker = walkers.JumpingBallWithHead(add_ears=True, camera_height=0.25)
arena = MujobanLevel()
task = Mujoban(
walker=walker,
maze=arena,
control_timestep=CONTROL_TIMESTEP,
top_camera_height=64,
top_camera_width=48)
env = composer.Environment(
time_limit=TIME_LIMIT,
task=task,
strip_singleton_obs_buffer_dim=True)
time_step = env.reset()
self.assertEqual(
set([
'pixel_layer', 'full_entity_layer', 'top_camera',
'walker/body_height', 'walker/end_effectors_pos',
'walker/joints_pos', 'walker/joints_vel',
'walker/sensors_accelerometer', 'walker/sensors_gyro',
'walker/sensors_touch', 'walker/sensors_velocimeter',
'walker/world_zaxis', 'walker/orientation',
]), set(time_step.observation.keys()))
top_camera = time_step.observation['top_camera']
self.assertEqual(np.uint8, top_camera.dtype)
self.assertEqual((64, 48, 3), top_camera.shape)
all_step_types = []
# Run enough actions that we are guaranteed to have restarted the
# episode at least once.
for _ in range(int(2*TIME_LIMIT/CONTROL_TIMESTEP)):
action = 2*np.random.random(env.action_spec().shape) - 1
time_step = env.step(action)
all_step_types.append(time_step.step_type)
self.assertEqual(set([environment.StepType.FIRST,
environment.StepType.MID,
environment.StepType.LAST]),
set(all_step_types))
if __name__ == '__main__':
absltest.main()
================================================
FILE: physics_planning_games/mujoban/props.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Box props used in Mujoban that the agent pushes.
"""
import itertools
from dm_control import composer
from dm_control.entities import props
class Box(props.Primitive):
"""A class representing a box prop."""
def _build(self, half_lengths=None, mass=None, name='box'):
half_lengths = half_lengths or [0.05, 0.1, 0.15]
super(Box, self)._build(geom_type='box',
size=half_lengths,
mass=mass,
name=name)
class BoxWithSites(Box):
"""A class representing a box prop with sites on the corners."""
def _build(self, half_lengths=None, mass=None, name='box'):
half_lengths = half_lengths or [0.05, 0.1, 0.15]
super(BoxWithSites, self)._build(half_lengths=half_lengths, mass=mass,
name=name)
corner_positions = itertools.product([half_lengths[0], -half_lengths[0]],
[half_lengths[1], -half_lengths[1]],
[half_lengths[2], -half_lengths[2]])
corner_sites = []
for i, corner_pos in enumerate(corner_positions):
corner_sites.append(
self.mjcf_model.worldbody.add(
'site',
type='sphere',
name='corner_{}'.format(i),
size=[0.1],
pos=corner_pos,
rgba=[1, 0, 0, 1.0],
group=composer.SENSOR_SITES_GROUP))
self._corner_sites = tuple(corner_sites)
@property
def corner_sites(self):
return self._corner_sites
================================================
FILE: physics_planning_games/requirements.txt
================================================
absl-py == 0.9.0
dm-control == 0.0.403778684
dm-env
labmaze
numpy == 1.19.1
requests == 2.24.0
================================================
FILE: pitfalls_static_language_models/README.md
================================================
# Mind the Gap: Assessing Temporal Generalization in Neural Language Models
This repository contains the dataset splits used in
[Mind the Gap: Assessing Temporal Generalization in Neural Language Models](https://arxiv.org/abs/2102.01951)
(Lazaridou, Kuncoro, Gribovskaya et al., 2021).
## Datasets
We provide splits of two public datasets used in the paper:
WMT News Crawl and arXiv abstracts.
Each subset is stored on Google Cloud Storage as gzipped text file specifying publication dates (in the `YYYYMMDD` format) and IDs of documents contained in the subset.
### arXiv
arXiv abstracts and publication dates were obtained through arXiv's [OAI-PMH service](https://arxiv.org/help/oa) on January 2, 2021. We used the value in the `created` field as the article's publication date. The arXiv dataset can also be downloaded from [Kaggle](https://www.kaggle.com/Cornell-University/arxiv).
### WMT
We downloaded document-split versions of the English and German [WMT News Crawl dataset](http://data.statmt.org/news-crawl/README). As the dataset does not provide document IDs, we used SHA256 hashes of the Base64 encoded unsplit texts of articles as their IDs, i.e.:
```python
import gzip
import hashlib
with gzip.open('news-docs.2007.en.filtered.gz', 'rb') as gz_file:
for line in gz_file:
date, sentence_split_text, unsplit_text = line.decode('utf-8').strip().split('\t')
docid = hashlib.sha256(unsplit_text.encode('utf-8')).hexdigest()
yield docid, (date, sentence_split_text, unsplit_text)
```
We trained models on sentence split article texts. Some articles may appear multiple times in the dataset with different publication dates; we used each article's earliest publication date.
## Splits used in experiments
| Experiments | Dataset | Splits |
|---|:---:|:---:|
| Sections 3-5 | WMT | control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2017-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2017-09-30/control_validation.gz)
time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2017-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2017-09-30/time_stratified_validation.gz)
[test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2017-09-30/test.gz)| || arXiv | control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/arxiv/time_stratified_control/split_2017-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/arxiv/time_stratified_control/split_2017-09-30/control_validation.gz)
time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/arxiv/time_stratified_control/split_2017-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/arxiv/time_stratified_control/split_2017-09-30/time_stratified_validation.gz)
[test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/arxiv/time_stratified_control/split_2017-09-30/test.gz)| | Appendix B: The effect of outdated models persists beyond the 2018/2019 test period | WMT | test period 2017/2018: control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2016-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2016-09-30/control_validation.gz); time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2016-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2016-09-30/time_stratified_validation.gz); [test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2016-09-30/test.gz)
test period 2016/2017: control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2015-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2015-09-30/control_validation.gz); time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2015-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2015-09-30/time_stratified_validation.gz); [test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2015-09-30/test.gz)
test period 2015/2016: control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2014-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2014-09-30/control_validation.gz); time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2014-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2014-09-30/time_stratified_validation.gz); [test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2014-09-30/test.gz)
test period 2014/2015: control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2013-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2013-09-30/control_validation.gz); time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2013-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2013-09-30/time_stratified_validation.gz); [test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2013-09-30/test.gz)
test period 2013/2014: control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2012-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2012-09-30/control_validation.gz); time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2012-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2012-09-30/time_stratified_validation.gz); [test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2012-09-30/test.gz) | | Appendix C: The effect of outdated models persists beyond the two-year gap | WMT | test: same as the one for Sections 3-5
validation: same as the one for the time-stratified setup for Sections 3-5
train until: [2017-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2017-09-30/train.gz), [2017-03-31](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2017-03-31/train.gz), [2016-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2016-09-30/train.gz), [2016-03-31](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2016-03-31/train.gz), [2015-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2015-09-30/train.gz), [2015-03-31](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2015-03-31/train.gz), [2014-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2014-09-30/train.gz), [2014-03-31](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2014-03-31/train.gz), [2013-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2013-09-30/train.gz), [2013-03-31](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2013-03-31/train.gz), [2012-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2012-09-30/train.gz) ================================================ FILE: polygen/README.md ================================================ # PolyGen: An Autoregressive Generative Model of 3D Meshes  This package provides an implementation of PolyGen as described in: > **PolyGen: An Autoregressive Generative Model of 3D Meshes**, *Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. Battaglia*, ICML, 2020. ([abs](https://arxiv.org/abs/2002.10880)) PolyGen is a generative model of 3D meshes that sequentially outputs mesh vertices and faces. PolyGen consists of two parts: A vertex model, that unconditionally models mesh vertices, and a face model, that models the mesh faces conditioned on input vertices. The vertex model uses a masked Transformer decoder to express a distribution over the vertex sequences. For the face model we combine Transformers with [pointer networks](https://arxiv.org/abs/1506.03134) to express a distribution over variable length vertex sequences. In this repository we provide model code in `modules.py`, as well as data processing utilities in `data_utils.py`. We also provide Colabs that demo training PolyGen from scratch on a toy dataset, as well as sampling from a pre-trained model. There are some minor differences between this implementation and the paper: * We add global information (e.g. class label embeddings) as an additional input in the first sequence position rather than project it at each layer. This reduces parameters, but does not significantly impact performance. * We use [ReZero](https://arxiv.org/abs/2003.04887) which improves training speed. * We train with only shifting augmentations, which we find to be as effective as the combination of augmentations described in the paper. This helps to simplify the data pre-processing pipeline. ## Training Colab [](https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/polygen/training.ipynb) To train a PolyGen model from scratch on a collection of simple meshes use this colab. This demonstrates the data pre-processing required to create inputs for the vertex and face models. ## Sampling pre-trained model Colab [](https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/polygen/sample-pretrained.ipynb) To sample a model pre-trained on [ShapeNet](https://www.shapenet.org/) use this colab. The model is class-conditional, and is trained on longer sequence lengths than those described in the paper. This colab uses the following checkpoints: ([Google Cloud Storage bucket](https://console.cloud.google.com/storage/browser/deepmind-research-polygen)). ## Installation To install the package locally run: ```bash git clone https://github.com/deepmind/deepmind-research.git . cd deepmind-research/polygen pip install -e . ``` ## Giving Credit If you use this code in your work, we ask you to cite this paper: ``` @article{nash2020polygen, author={Charlie Nash and Yaroslav Ganin and S. M. Ali Eslami and Peter W. Battaglia}, title={PolyGen: An Autoregressive Generative Model of 3D Meshes}, journal={ICML}, year={2020} } ``` ## Disclaimer This is not an official Google product. ================================================ FILE: polygen/data_utils.py ================================================ # Copyright 2020 Deepmind Technologies Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Mesh data utilities.""" import matplotlib.pyplot as plt from mpl_toolkits import mplot3d # pylint: disable=unused-import from mpl_toolkits.mplot3d.art3d import Poly3DCollection import networkx as nx import numpy as np import six from six.moves import range import tensorflow.compat.v1 as tf import tensorflow_probability as tfp tfd = tfp.distributions def random_shift(vertices, shift_factor=0.25): """Apply random shift to vertices.""" max_shift_pos = tf.cast(255 - tf.reduce_max(vertices, axis=0), tf.float32) max_shift_pos = tf.maximum(max_shift_pos, 1e-9) max_shift_neg = tf.cast(tf.reduce_min(vertices, axis=0), tf.float32) max_shift_neg = tf.maximum(max_shift_neg, 1e-9) shift = tfd.TruncatedNormal( tf.zeros([1, 3]), shift_factor*255, -max_shift_neg, max_shift_pos).sample() shift = tf.cast(shift, tf.int32) vertices += shift return vertices def make_vertex_model_dataset(ds, apply_random_shift=False): """Prepare dataset for vertex model training.""" def _vertex_model_map_fn(example): vertices = example['vertices'] # Randomly shift vertices if apply_random_shift: vertices = random_shift(vertices) # Re-order vertex coordinates as (z, y, x). vertices_permuted = tf.stack( [vertices[:, 2], vertices[:, 1], vertices[:, 0]], axis=-1) # Flatten quantized vertices, reindex starting from 1, and pad with a # zero stopping token. vertices_flat = tf.reshape(vertices_permuted, [-1]) example['vertices_flat'] = tf.pad(vertices_flat + 1, [[0, 1]]) # Create mask to indicate valid tokens after padding and batching. example['vertices_flat_mask'] = tf.ones_like( example['vertices_flat'], dtype=tf.float32) return example return ds.map(_vertex_model_map_fn) def make_face_model_dataset( ds, apply_random_shift=False, shuffle_vertices=True, quantization_bits=8): """Prepare dataset for face model training.""" def _face_model_map_fn(example): vertices = example['vertices'] # Randomly shift vertices if apply_random_shift: vertices = random_shift(vertices) example['num_vertices'] = tf.shape(vertices)[0] # Optionally shuffle vertices and re-order faces to match if shuffle_vertices: permutation = tf.random_shuffle(tf.range(example['num_vertices'])) vertices = tf.gather(vertices, permutation) face_permutation = tf.concat( [tf.constant([0, 1], dtype=tf.int32), tf.argsort(permutation) + 2], axis=0) example['faces'] = tf.cast( tf.gather(face_permutation, example['faces']), tf.int64) def _dequantize_verts(verts, n_bits): min_range = -0.5 max_range = 0.5 range_quantize = 2**n_bits - 1 verts = tf.cast(verts, tf.float32) verts = verts * (max_range - min_range) / range_quantize + min_range return verts # Vertices are quantized. So convert to floats for input to face model example['vertices'] = _dequantize_verts(vertices, quantization_bits) example['vertices_mask'] = tf.ones_like( example['vertices'][..., 0], dtype=tf.float32) example['faces_mask'] = tf.ones_like(example['faces'], dtype=tf.float32) return example return ds.map(_face_model_map_fn) def read_obj_file(obj_file): """Read vertices and faces from already opened file.""" vertex_list = [] flat_vertices_list = [] flat_vertices_indices = {} flat_triangles = [] for line in obj_file: tokens = line.split() if not tokens: continue line_type = tokens[0] # We skip lines not starting with v or f. if line_type == 'v': vertex_list.append([float(x) for x in tokens[1:]]) elif line_type == 'f': triangle = [] for i in range(len(tokens) - 1): vertex_name = tokens[i + 1] if vertex_name in flat_vertices_indices: triangle.append(flat_vertices_indices[vertex_name]) continue flat_vertex = [] for index in six.ensure_str(vertex_name).split('/'): if not index: continue # obj triangle indices are 1 indexed, so subtract 1 here. flat_vertex += vertex_list[int(index) - 1] flat_vertex_index = len(flat_vertices_list) flat_vertices_list.append(flat_vertex) flat_vertices_indices[vertex_name] = flat_vertex_index triangle.append(flat_vertex_index) flat_triangles.append(triangle) return np.array(flat_vertices_list, dtype=np.float32), flat_triangles def read_obj(obj_path): """Open .obj file from the path provided and read vertices and faces.""" with open(obj_path) as obj_file: return read_obj_file(obj_file) def write_obj(vertices, faces, file_path, transpose=True, scale=1.): """Write vertices and faces to obj.""" if transpose: vertices = vertices[:, [1, 2, 0]] vertices *= scale if faces is not None: if min(min(faces)) == 0: f_add = 1 else: f_add = 0 with open(file_path, 'w') as f: for v in vertices: f.write('v {} {} {}\n'.format(v[0], v[1], v[2])) for face in faces: line = 'f' for i in face: line += ' {}'.format(i + f_add) line += '\n' f.write(line) def quantize_verts(verts, n_bits=8): """Convert vertices in [-1., 1.] to discrete values in [0, n_bits**2 - 1].""" min_range = -0.5 max_range = 0.5 range_quantize = 2**n_bits - 1 verts_quantize = (verts - min_range) * range_quantize / ( max_range - min_range) return verts_quantize.astype('int32') def dequantize_verts(verts, n_bits=8, add_noise=False): """Convert quantized vertices to floats.""" min_range = -0.5 max_range = 0.5 range_quantize = 2**n_bits - 1 verts = verts.astype('float32') verts = verts * (max_range - min_range) / range_quantize + min_range if add_noise: verts += np.random.uniform(size=verts.shape) * (1 / range_quantize) return verts def face_to_cycles(face): """Find cycles in face.""" g = nx.Graph() for v in range(len(face) - 1): g.add_edge(face[v], face[v + 1]) g.add_edge(face[-1], face[0]) return list(nx.cycle_basis(g)) def flatten_faces(faces): """Converts from list of faces to flat face array with stopping indices.""" if not faces: return np.array([0]) else: l = [f + [-1] for f in faces[:-1]] l += [faces[-1] + [-2]] return np.array([item for sublist in l for item in sublist]) + 2 # pylint: disable=g-complex-comprehension def unflatten_faces(flat_faces): """Converts from flat face sequence to a list of separate faces.""" def group(seq): g = [] for el in seq: if el == 0 or el == -1: yield g g = [] else: g.append(el - 1) yield g outputs = list(group(flat_faces - 1))[:-1] # Remove empty faces return [o for o in outputs if len(o) > 2] def center_vertices(vertices): """Translate the vertices so that bounding box is centered at zero.""" vert_min = vertices.min(axis=0) vert_max = vertices.max(axis=0) vert_center = 0.5 * (vert_min + vert_max) return vertices - vert_center def normalize_vertices_scale(vertices): """Scale the vertices so that the long diagonal of the bounding box is one.""" vert_min = vertices.min(axis=0) vert_max = vertices.max(axis=0) extents = vert_max - vert_min scale = np.sqrt(np.sum(extents**2)) return vertices / scale def quantize_process_mesh(vertices, faces, tris=None, quantization_bits=8): """Quantize vertices, remove resulting duplicates and reindex faces.""" vertices = quantize_verts(vertices, quantization_bits) vertices, inv = np.unique(vertices, axis=0, return_inverse=True) # Sort vertices by z then y then x. sort_inds = np.lexsort(vertices.T) vertices = vertices[sort_inds] # Re-index faces and tris to re-ordered vertices. faces = [np.argsort(sort_inds)[inv[f]] for f in faces] if tris is not None: tris = np.array([np.argsort(sort_inds)[inv[t]] for t in tris]) # Merging duplicate vertices and re-indexing the faces causes some faces to # contain loops (e.g [2, 3, 5, 2, 4]). Split these faces into distinct # sub-faces. sub_faces = [] for f in faces: cliques = face_to_cycles(f) for c in cliques: c_length = len(c) # Only append faces with more than two verts. if c_length > 2: d = np.argmin(c) # Cyclically permute faces just that first index is the smallest. sub_faces.append([c[(d + i) % c_length] for i in range(c_length)]) faces = sub_faces if tris is not None: tris = np.array([v for v in tris if len(set(v)) == len(v)]) # Sort faces by lowest vertex indices. If two faces have the same lowest # index then sort by next lowest and so on. faces.sort(key=lambda f: tuple(sorted(f))) if tris is not None: tris = tris.tolist() tris.sort(key=lambda f: tuple(sorted(f))) tris = np.array(tris) # After removing degenerate faces some vertices are now unreferenced. # Remove these. num_verts = vertices.shape[0] vert_connected = np.equal( np.arange(num_verts)[:, None], np.hstack(faces)[None]).any(axis=-1) vertices = vertices[vert_connected] # Re-index faces and tris to re-ordered vertices. vert_indices = ( np.arange(num_verts) - np.cumsum(1 - vert_connected.astype('int'))) faces = [vert_indices[f].tolist() for f in faces] if tris is not None: tris = np.array([vert_indices[t].tolist() for t in tris]) return vertices, faces, tris def process_mesh(vertices, faces, quantization_bits=8): """Process mesh vertices and faces.""" # Transpose so that z-axis is vertical. vertices = vertices[:, [2, 0, 1]] # Translate the vertices so that bounding box is centered at zero. vertices = center_vertices(vertices) # Scale the vertices so that the long diagonal of the bounding box is equal # to one. vertices = normalize_vertices_scale(vertices) # Quantize and sort vertices, remove resulting duplicates, sort and reindex # faces. vertices, faces, _ = quantize_process_mesh( vertices, faces, quantization_bits=quantization_bits) # Flatten faces and add 'new face' = 1 and 'stop' = 0 tokens. faces = flatten_faces(faces) # Discard degenerate meshes without faces. return { 'vertices': vertices, 'faces': faces, } def load_process_mesh(mesh_obj_path, quantization_bits=8): """Load obj file and process.""" # Load mesh vertices, faces = read_obj(mesh_obj_path) return process_mesh(vertices, faces, quantization_bits) def plot_meshes(mesh_list, ax_lims=0.3, fig_size=4, el=30, rot_start=120, vert_size=10, vert_alpha=0.75, n_cols=4): """Plots mesh data using matplotlib.""" n_plot = len(mesh_list) n_cols = np.minimum(n_plot, n_cols) n_rows = np.ceil(n_plot / n_cols).astype('int') fig = plt.figure(figsize=(fig_size * n_cols, fig_size * n_rows)) for p_inc, mesh in enumerate(mesh_list): for key in [ 'vertices', 'faces', 'vertices_conditional', 'pointcloud', 'class_name' ]: if key not in list(mesh.keys()): mesh[key] = None ax = fig.add_subplot(n_rows, n_cols, p_inc + 1, projection='3d') if mesh['faces'] is not None: if mesh['vertices_conditional'] is not None: face_verts = np.concatenate( [mesh['vertices_conditional'], mesh['vertices']], axis=0) else: face_verts = mesh['vertices'] collection = [] for f in mesh['faces']: collection.append(face_verts[f]) plt_mesh = Poly3DCollection(collection) plt_mesh.set_edgecolor((0., 0., 0., 0.3)) plt_mesh.set_facecolor((1, 0, 0, 0.2)) ax.add_collection3d(plt_mesh) if mesh['vertices'] is not None: ax.scatter3D( mesh['vertices'][:, 0], mesh['vertices'][:, 1], mesh['vertices'][:, 2], lw=0., s=vert_size, c='g', alpha=vert_alpha) if mesh['vertices_conditional'] is not None: ax.scatter3D( mesh['vertices_conditional'][:, 0], mesh['vertices_conditional'][:, 1], mesh['vertices_conditional'][:, 2], lw=0., s=vert_size, c='b', alpha=vert_alpha) if mesh['pointcloud'] is not None: ax.scatter3D( mesh['pointcloud'][:, 0], mesh['pointcloud'][:, 1], mesh['pointcloud'][:, 2], lw=0., s=2.5 * vert_size, c='b', alpha=1.) ax.set_xlim(-ax_lims, ax_lims) ax.set_ylim(-ax_lims, ax_lims) ax.set_zlim(-ax_lims, ax_lims) ax.view_init(el, rot_start) display_string = '' if mesh['faces'] is not None: display_string += 'Num. faces: {}\n'.format(len(collection)) if mesh['vertices'] is not None: num_verts = mesh['vertices'].shape[0] if mesh['vertices_conditional'] is not None: num_verts += mesh['vertices_conditional'].shape[0] display_string += 'Num. verts: {}\n'.format(num_verts) if mesh['class_name'] is not None: display_string += 'Synset: {}'.format(mesh['class_name']) if mesh['pointcloud'] is not None: display_string += 'Num. pointcloud: {}\n'.format( mesh['pointcloud'].shape[0]) ax.text2D(0.05, 0.8, display_string, transform=ax.transAxes) plt.subplots_adjust( left=0., right=1., bottom=0., top=1., wspace=0.025, hspace=0.025) plt.show() ================================================ FILE: polygen/meshes/cone.obj ================================================ v 0.000000 -1.000000 -1.000000 v 0.195090 -1.000000 -0.980785 v 0.382683 -1.000000 -0.923880 v 0.555570 -1.000000 -0.831470 v 0.707107 -1.000000 -0.707107 v 0.831470 -1.000000 -0.555570 v 0.923880 -1.000000 -0.382683 v 0.980785 -1.000000 -0.195090 v 1.000000 -1.000000 -0.000000 v 0.980785 -1.000000 0.195090 v 0.923880 -1.000000 0.382683 v 0.831470 -1.000000 0.555570 v 0.707107 -1.000000 0.707107 v 0.555570 -1.000000 0.831470 v 0.382683 -1.000000 0.923880 v 0.195090 -1.000000 0.980785 v -0.000000 -1.000000 1.000000 v -0.195091 -1.000000 0.980785 v -0.382684 -1.000000 0.923879 v -0.555571 -1.000000 0.831469 v -0.707107 -1.000000 0.707106 v -0.831470 -1.000000 0.555570 v -0.923880 -1.000000 0.382683 v 0.000000 1.000000 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f 18 19 4 f 18 13 19 f 13 3 19 f 16 17 6 f 16 14 17 f 14 1 17 f 13 15 3 f 13 14 15 f 14 2 15 ================================================ FILE: polygen/model_test.py ================================================ # Copyright 2020 Deepmind Technologies Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for the PolyGen open-source version.""" from modules import FaceModel from modules import VertexModel import numpy as np import tensorflow as tf _BATCH_SIZE = 4 _TRANSFORMER_CONFIG = { 'num_layers': 2, 'hidden_size': 64, 'fc_size': 256 } _CLASS_CONDITIONAL = True _NUM_CLASSES = 4 _NUM_INPUT_VERTS = 50 _NUM_PAD_VERTS = 10 _NUM_INPUT_FACE_INDICES = 200 _QUANTIZATION_BITS = 8 _VERTEX_MODEL_USE_DISCRETE_EMBEDDINGS = True _FACE_MODEL_DECODER_CROSS_ATTENTION = True _FACE_MODEL_DISCRETE_EMBEDDINGS = True _MAX_SAMPLE_LENGTH_VERTS = 10 _MAX_SAMPLE_LENGTH_FACES = 10 def _get_vertex_model_batch(): """Returns batch with placeholders for vertex model inputs.""" return { 'class_label': tf.range(_BATCH_SIZE), 'vertices_flat': tf.placeholder( dtype=tf.int32, shape=[_BATCH_SIZE, None]), } def _get_face_model_batch(): """Returns batch with placeholders for face model inputs.""" return { 'vertices': tf.placeholder( dtype=tf.float32, shape=[_BATCH_SIZE, None, 3]), 'vertices_mask': tf.placeholder( dtype=tf.float32, shape=[_BATCH_SIZE, None]), 'faces': tf.placeholder( dtype=tf.int32, shape=[_BATCH_SIZE, None]), } class VertexModelTest(tf.test.TestCase): def setUp(self): """Defines a vertex model.""" super(VertexModelTest, self).setUp() self.model = VertexModel( decoder_config=_TRANSFORMER_CONFIG, class_conditional=_CLASS_CONDITIONAL, num_classes=_NUM_CLASSES, max_num_input_verts=_NUM_INPUT_VERTS, quantization_bits=_QUANTIZATION_BITS, use_discrete_embeddings=_VERTEX_MODEL_USE_DISCRETE_EMBEDDINGS) def test_model_runs(self): """Tests if the model runs without crashing.""" batch = _get_vertex_model_batch() pred_dist = self.model(batch, is_training=False) logits = pred_dist.logits with self.session() as sess: sess.run(tf.global_variables_initializer()) vertices_flat = np.random.randint( 2**_QUANTIZATION_BITS + 1, size=[_BATCH_SIZE, _NUM_INPUT_VERTS * 3 + 1]) sess.run(logits, {batch['vertices_flat']: vertices_flat}) def test_sample_outputs_range(self): """Tests if the model produces samples in the correct range.""" context = {'class_label': tf.zeros((_BATCH_SIZE,), dtype=tf.int32)} sample_dict = self.model.sample( _BATCH_SIZE, max_sample_length=_MAX_SAMPLE_LENGTH_VERTS, context=context) with self.session() as sess: sess.run(tf.global_variables_initializer()) sample_dict_np = sess.run(sample_dict) in_range = np.logical_and( 0 <= sample_dict_np['vertices'], sample_dict_np['vertices'] <= 2**_QUANTIZATION_BITS).all() self.assertTrue(in_range) class FaceModelTest(tf.test.TestCase): def setUp(self): """Defines a face model.""" super(FaceModelTest, self).setUp() self.model = FaceModel( encoder_config=_TRANSFORMER_CONFIG, decoder_config=_TRANSFORMER_CONFIG, class_conditional=False, max_seq_length=_NUM_INPUT_FACE_INDICES, decoder_cross_attention=_FACE_MODEL_DECODER_CROSS_ATTENTION, use_discrete_vertex_embeddings=_FACE_MODEL_DISCRETE_EMBEDDINGS, quantization_bits=_QUANTIZATION_BITS) def test_model_runs(self): """Tests if the model runs without crashing.""" batch = _get_face_model_batch() pred_dist = self.model(batch, is_training=False) logits = pred_dist.logits with self.session() as sess: sess.run(tf.global_variables_initializer()) vertices = np.random.rand(_BATCH_SIZE, _NUM_INPUT_VERTS, 3) - 0.5 vertices_mask = np.ones([_BATCH_SIZE, _NUM_INPUT_VERTS]) faces = np.random.randint( _NUM_INPUT_VERTS + 2, size=[_BATCH_SIZE, _NUM_INPUT_FACE_INDICES]) sess.run( logits, {batch['vertices']: vertices, batch['vertices_mask']: vertices_mask, batch['faces']: faces} ) def test_sample_outputs_range(self): """Tests if the model produces samples in the correct range.""" context = _get_face_model_batch() del context['faces'] sample_dict = self.model.sample( context, max_sample_length=_MAX_SAMPLE_LENGTH_FACES) with self.session() as sess: sess.run(tf.global_variables_initializer()) # Pad the vertices in order to test that the face model only outputs # vertex indices in the unpadded range vertices = np.pad( np.random.rand(_BATCH_SIZE, _NUM_INPUT_VERTS, 3) - 0.5, [[0, 0], [0, _NUM_PAD_VERTS], [0, 0]], mode='constant') vertices_mask = np.pad( np.ones([_BATCH_SIZE, _NUM_INPUT_VERTS]), [[0, 0], [0, _NUM_PAD_VERTS]], mode='constant') sample_dict_np = sess.run( sample_dict, {context['vertices']: vertices, context['vertices_mask']: vertices_mask}) in_range = np.logical_and( 0 <= sample_dict_np['faces'], sample_dict_np['faces'] <= _NUM_INPUT_VERTS + 1).all() self.assertTrue(in_range) if __name__ == '__main__': tf.test.main() ================================================ FILE: polygen/modules.py ================================================ # Copyright 2020 Deepmind Technologies Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Modules and networks for mesh generation.""" import sonnet as snt from tensor2tensor.layers import common_attention from tensor2tensor.layers import common_layers import tensorflow.compat.v1 as tf from tensorflow.python.framework import function import tensorflow_probability as tfp tfd = tfp.distributions tfb = tfp.bijectors def dequantize_verts(verts, n_bits, add_noise=False): """Quantizes vertices and outputs integers with specified n_bits.""" min_range = -0.5 max_range = 0.5 range_quantize = 2**n_bits - 1 verts = tf.cast(verts, tf.float32) verts = verts * (max_range - min_range) / range_quantize + min_range if add_noise: verts += tf.random_uniform(tf.shape(verts)) * (1 / float(range_quantize)) return verts def quantize_verts(verts, n_bits): """Dequantizes integer vertices to floats.""" min_range = -0.5 max_range = 0.5 range_quantize = 2**n_bits - 1 verts_quantize = ( (verts - min_range) * range_quantize / (max_range - min_range)) return tf.cast(verts_quantize, tf.int32) def top_k_logits(logits, k): """Masks logits such that logits not in top-k are small.""" if k == 0: return logits else: values, _ = tf.math.top_k(logits, k=k) k_largest = tf.reduce_min(values) logits = tf.where(tf.less_equal(logits, k_largest), tf.ones_like(logits)*-1e9, logits) return logits def top_p_logits(logits, p): """Masks logits using nucleus (top-p) sampling.""" if p == 1: return logits else: logit_shape = tf.shape(logits) seq, dim = logit_shape[1], logit_shape[2] logits = tf.reshape(logits, [-1, dim]) sort_indices = tf.argsort(logits, axis=-1, direction='DESCENDING') probs = tf.gather(tf.nn.softmax(logits), sort_indices, batch_dims=1) cumprobs = tf.cumsum(probs, axis=-1, exclusive=True) # The top 1 candidate always will not be masked. # This way ensures at least 1 indices will be selected. sort_mask = tf.cast(tf.greater(cumprobs, p), logits.dtype) batch_indices = tf.tile( tf.expand_dims(tf.range(tf.shape(logits)[0]), axis=-1), [1, dim]) top_p_mask = tf.scatter_nd( tf.stack([batch_indices, sort_indices], axis=-1), sort_mask, tf.shape(logits)) logits -= top_p_mask * 1e9 return tf.reshape(logits, [-1, seq, dim]) _function_cache = {} # For multihead_self_attention_memory_efficient def multihead_self_attention_memory_efficient(x, bias, num_heads, head_size=None, cache=None, epsilon=1e-6, forget=True, test_vars=None, name=None): """Memory-efficient Multihead scaled-dot-product self-attention. Based on Tensor2Tensor version but adds optional caching. Returns multihead-self-attention(layer_norm(x)) Computes one attention head at a time to avoid exhausting memory. If forget=True, then forget all forwards activations and recompute on the backwards pass. Args: x: a Tensor with shape [batch, length, input_size] bias: an attention bias tensor broadcastable to [batch, 1, length, length] num_heads: an integer head_size: an optional integer - defaults to input_size/num_heads cache: Optional dict containing tensors which are the results of previous attentions, used for fast decoding. Expects the dict to contain two keys ('k' and 'v'), for the initial call the values for these keys should be empty Tensors of the appropriate shape. 'k' [batch_size, 0, key_channels] 'v' [batch_size, 0, value_channels] epsilon: a float, for layer norm forget: a boolean - forget forwards activations and recompute on backprop test_vars: optional tuple of variables for testing purposes name: an optional string Returns: A Tensor. """ io_size = x.get_shape().as_list()[-1] if head_size is None: assert io_size % num_heads == 0 head_size = io_size / num_heads def forward_internal(x, wqkv, wo, attention_bias, norm_scale, norm_bias): """Forward function.""" n = common_layers.layer_norm_compute(x, epsilon, norm_scale, norm_bias) wqkv_split = tf.unstack(wqkv, num=num_heads) wo_split = tf.unstack(wo, num=num_heads) y = 0 if cache is not None: cache_k = [] cache_v = [] for h in range(num_heads): with tf.control_dependencies([y] if h > 0 else []): combined = tf.nn.conv1d(n, wqkv_split[h], 1, 'SAME') q, k, v = tf.split(combined, 3, axis=2) if cache is not None: k = tf.concat([cache['k'][:, h], k], axis=1) v = tf.concat([cache['v'][:, h], v], axis=1) cache_k.append(k) cache_v.append(v) o = common_attention.scaled_dot_product_attention_simple( q, k, v, attention_bias) y += tf.nn.conv1d(o, wo_split[h], 1, 'SAME') if cache is not None: cache['k'] = tf.stack(cache_k, axis=1) cache['v'] = tf.stack(cache_v, axis=1) return y key = ( 'multihead_self_attention_memory_efficient %s %s' % (num_heads, epsilon)) if not forget: forward_fn = forward_internal elif key in _function_cache: forward_fn = _function_cache[key] else: @function.Defun(compiled=True) def grad_fn(x, wqkv, wo, attention_bias, norm_scale, norm_bias, dy): """Custom gradient function.""" with tf.control_dependencies([dy]): n = common_layers.layer_norm_compute(x, epsilon, norm_scale, norm_bias) wqkv_split = tf.unstack(wqkv, num=num_heads) wo_split = tf.unstack(wo, num=num_heads) deps = [] dwqkvs = [] dwos = [] dn = 0 for h in range(num_heads): with tf.control_dependencies(deps): combined = tf.nn.conv1d(n, wqkv_split[h], 1, 'SAME') q, k, v = tf.split(combined, 3, axis=2) o = common_attention.scaled_dot_product_attention_simple( q, k, v, attention_bias) partial_y = tf.nn.conv1d(o, wo_split[h], 1, 'SAME') pdn, dwqkvh, dwoh = tf.gradients( ys=[partial_y], xs=[n, wqkv_split[h], wo_split[h]], grad_ys=[dy]) dn += pdn dwqkvs.append(dwqkvh) dwos.append(dwoh) deps = [dn, dwqkvh, dwoh] dwqkv = tf.stack(dwqkvs) dwo = tf.stack(dwos) with tf.control_dependencies(deps): dx, dnorm_scale, dnorm_bias = tf.gradients( ys=[n], xs=[x, norm_scale, norm_bias], grad_ys=[dn]) return (dx, dwqkv, dwo, tf.zeros_like(attention_bias), dnorm_scale, dnorm_bias) @function.Defun( grad_func=grad_fn, compiled=True, separate_compiled_gradients=True) def forward_fn(x, wqkv, wo, attention_bias, norm_scale, norm_bias): return forward_internal(x, wqkv, wo, attention_bias, norm_scale, norm_bias) _function_cache[key] = forward_fn if bias is not None: bias = tf.squeeze(bias, 1) with tf.variable_scope(name, default_name='multihead_attention', values=[x]): if test_vars is not None: wqkv, wo, norm_scale, norm_bias = list(test_vars) else: wqkv = tf.get_variable( 'wqkv', [num_heads, 1, io_size, 3 * head_size], initializer=tf.random_normal_initializer(stddev=io_size**-0.5)) wo = tf.get_variable( 'wo', [num_heads, 1, head_size, io_size], initializer=tf.random_normal_initializer( stddev=(head_size * num_heads)**-0.5)) norm_scale, norm_bias = common_layers.layer_norm_vars(io_size) y = forward_fn(x, wqkv, wo, bias, norm_scale, norm_bias) y.set_shape(x.get_shape()) # pytype: disable=attribute-error return y class TransformerEncoder(snt.AbstractModule): """Transformer encoder. Sonnet Transformer encoder module as described in Vaswani et al. 2017. Uses the Tensor2Tensor multihead_attention function for full self attention (no masking). Layer norm is applied inside the residual path as in sparse transformers (Child 2019). This module expects inputs to be already embedded, and does not add position embeddings. """ def __init__(self, hidden_size=256, fc_size=1024, num_heads=4, layer_norm=True, num_layers=8, dropout_rate=0.2, re_zero=True, memory_efficient=False, name='transformer_encoder'): """Initializes TransformerEncoder. Args: hidden_size: Size of embedding vectors. fc_size: Size of fully connected layer. num_heads: Number of attention heads. layer_norm: If True, apply layer normalization num_layers: Number of Transformer blocks, where each block contains a multi-head attention layer and a MLP. dropout_rate: Dropout rate applied immediately after the ReLU in each fully-connected layer. re_zero: If True, alpha scale residuals with zero init. memory_efficient: If True, recompute gradients for memory savings. name: Name of variable scope """ super(TransformerEncoder, self).__init__(name=name) self.hidden_size = hidden_size self.num_heads = num_heads self.layer_norm = layer_norm self.fc_size = fc_size self.num_layers = num_layers self.dropout_rate = dropout_rate self.re_zero = re_zero self.memory_efficient = memory_efficient def _build(self, inputs, is_training=False): """Passes inputs through Transformer encoder network. Args: inputs: Tensor of shape [batch_size, sequence_length, embed_size]. Zero embeddings are masked in self-attention. is_training: If True, dropout is applied. Returns: output: Tensor of shape [batch_size, sequence_length, embed_size]. """ if is_training: dropout_rate = self.dropout_rate else: dropout_rate = 0. # Identify elements with all zeros as padding, and create bias to mask # out padding elements in self attention. encoder_padding = common_attention.embedding_to_padding(inputs) encoder_self_attention_bias = ( common_attention.attention_bias_ignore_padding(encoder_padding)) x = inputs for layer_num in range(self.num_layers): with tf.variable_scope('layer_{}'.format(layer_num)): # Multihead self-attention from Tensor2Tensor. res = x if self.memory_efficient: res = multihead_self_attention_memory_efficient( res, bias=encoder_self_attention_bias, num_heads=self.num_heads, head_size=self.hidden_size // self.num_heads, forget=True if is_training else False, name='self_attention' ) else: if self.layer_norm: res = common_layers.layer_norm(res, name='self_attention') res = common_attention.multihead_attention( res, memory_antecedent=None, bias=encoder_self_attention_bias, total_key_depth=self.hidden_size, total_value_depth=self.hidden_size, output_depth=self.hidden_size, num_heads=self.num_heads, dropout_rate=0., make_image_summary=False, name='self_attention') if self.re_zero: res *= tf.get_variable('self_attention/alpha', initializer=0.) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) x += res # MLP res = x if self.layer_norm: res = common_layers.layer_norm(res, name='fc') res = tf.layers.dense( res, self.fc_size, activation=tf.nn.relu, name='fc_1') res = tf.layers.dense(res, self.hidden_size, name='fc_2') if self.re_zero: res *= tf.get_variable('fc/alpha', initializer=0.) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) x += res if self.layer_norm: output = common_layers.layer_norm(x, name='output') else: output = x return output class TransformerDecoder(snt.AbstractModule): """Transformer decoder. Sonnet Transformer decoder module as described in Vaswani et al. 2017. Uses the Tensor2Tensor multihead_attention function for masked self attention, and non-masked cross attention attention. Layer norm is applied inside the residual path as in sparse transformers (Child 2019). This module expects inputs to be already embedded, and does not add position embeddings. """ def __init__(self, hidden_size=256, fc_size=1024, num_heads=4, layer_norm=True, num_layers=8, dropout_rate=0.2, re_zero=True, memory_efficient=False, name='transformer_decoder'): """Initializes TransformerDecoder. Args: hidden_size: Size of embedding vectors. fc_size: Size of fully connected layer. num_heads: Number of attention heads. layer_norm: If True, apply layer normalization. If mem_efficient_attention is True, then layer norm is always applied. num_layers: Number of Transformer blocks, where each block contains a multi-head attention layer and a MLP. dropout_rate: Dropout rate applied immediately after the ReLU in each fully-connected layer. re_zero: If True, alpha scale residuals with zero init. memory_efficient: If True, recompute gradients for memory savings. name: Name of variable scope """ super(TransformerDecoder, self).__init__(name=name) self.hidden_size = hidden_size self.num_heads = num_heads self.layer_norm = layer_norm self.fc_size = fc_size self.num_layers = num_layers self.dropout_rate = dropout_rate self.re_zero = re_zero self.memory_efficient = memory_efficient def _build(self, inputs, sequential_context_embeddings=None, is_training=False, cache=None): """Passes inputs through Transformer decoder network. Args: inputs: Tensor of shape [batch_size, sequence_length, embed_size]. Zero embeddings are masked in self-attention. sequential_context_embeddings: Optional tensor with global context (e.g image embeddings) of shape [batch_size, context_seq_length, context_embed_size]. is_training: If True, dropout is applied. cache: Optional dict containing tensors which are the results of previous attentions, used for fast decoding. Expects the dict to contain two keys ('k' and 'v'), for the initial call the values for these keys should be empty Tensors of the appropriate shape. 'k' [batch_size, 0, key_channels] 'v' [batch_size, 0, value_channels] Returns: output: Tensor of shape [batch_size, sequence_length, embed_size]. """ if is_training: dropout_rate = self.dropout_rate else: dropout_rate = 0. # create bias to mask future elements for causal self-attention. seq_length = tf.shape(inputs)[1] decoder_self_attention_bias = common_attention.attention_bias_lower_triangle( seq_length) # If using sequential_context, identify elements with all zeros as padding, # and create bias to mask out padding elements in self attention. if sequential_context_embeddings is not None: encoder_padding = common_attention.embedding_to_padding( sequential_context_embeddings) encoder_decoder_attention_bias = ( common_attention.attention_bias_ignore_padding(encoder_padding)) x = inputs for layer_num in range(self.num_layers): with tf.variable_scope('layer_{}'.format(layer_num)): # If using cached decoding, access cache for current layer, and create # bias that enables un-masked attention into the cache if cache is not None: layer_cache = cache[layer_num] layer_decoder_bias = tf.zeros([1, 1, 1, 1]) # Otherwise use standard masked bias else: layer_cache = None layer_decoder_bias = decoder_self_attention_bias # Multihead self-attention from Tensor2Tensor. res = x if self.memory_efficient: res = multihead_self_attention_memory_efficient( res, bias=layer_decoder_bias, cache=layer_cache, num_heads=self.num_heads, head_size=self.hidden_size // self.num_heads, forget=True if is_training else False, name='self_attention' ) else: if self.layer_norm: res = common_layers.layer_norm(res, name='self_attention') res = common_attention.multihead_attention( res, memory_antecedent=None, bias=layer_decoder_bias, total_key_depth=self.hidden_size, total_value_depth=self.hidden_size, output_depth=self.hidden_size, num_heads=self.num_heads, cache=layer_cache, dropout_rate=0., make_image_summary=False, name='self_attention') if self.re_zero: res *= tf.get_variable('self_attention/alpha', initializer=0.) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) x += res # Optional cross attention into sequential context if sequential_context_embeddings is not None: res = x if self.layer_norm: res = common_layers.layer_norm(res, name='cross_attention') res = common_attention.multihead_attention( res, memory_antecedent=sequential_context_embeddings, bias=encoder_decoder_attention_bias, total_key_depth=self.hidden_size, total_value_depth=self.hidden_size, output_depth=self.hidden_size, num_heads=self.num_heads, dropout_rate=0., make_image_summary=False, name='cross_attention') if self.re_zero: res *= tf.get_variable('cross_attention/alpha', initializer=0.) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) x += res # FC layers res = x if self.layer_norm: res = common_layers.layer_norm(res, name='fc') res = tf.layers.dense( res, self.fc_size, activation=tf.nn.relu, name='fc_1') res = tf.layers.dense(res, self.hidden_size, name='fc_2') if self.re_zero: res *= tf.get_variable('fc/alpha', initializer=0.) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) x += res if self.layer_norm: output = common_layers.layer_norm(x, name='output') else: output = x return output def create_init_cache(self, batch_size): """Creates empty cache dictionary for use in fast decoding.""" def compute_cache_shape_invariants(tensor): """Helper function to get dynamic shapes for cache tensors.""" shape_list = tensor.shape.as_list() if len(shape_list) == 4: return tf.TensorShape( [shape_list[0], shape_list[1], None, shape_list[3]]) elif len(shape_list) == 3: return tf.TensorShape([shape_list[0], None, shape_list[2]]) # Build cache k = common_attention.split_heads( tf.zeros([batch_size, 0, self.hidden_size]), self.num_heads) v = common_attention.split_heads( tf.zeros([batch_size, 0, self.hidden_size]), self.num_heads) cache = [{'k': k, 'v': v} for _ in range(self.num_layers)] shape_invariants = tf.nest.map_structure( compute_cache_shape_invariants, cache) return cache, shape_invariants def conv_residual_block(inputs, output_channels=None, downsample=False, kernel_size=3, re_zero=True, dropout_rate=0., name='conv_residual_block'): """Convolutional block with residual connections for 2D or 3D inputs. Args: inputs: Input tensor of shape [batch_size, height, width, channels] or [batch_size, height, width, depth, channels]. output_channels: Number of output channels. downsample: If True, downsample by 1/2 in this block. kernel_size: Spatial size of convolutional kernels. re_zero: If True, alpha scale residuals with zero init. dropout_rate: Dropout rate applied after second ReLU in residual path. name: Name for variable scope. Returns: outputs: Output tensor of shape [batch_size, height, width, output_channels] or [batch_size, height, width, depth, output_channels]. """ with tf.variable_scope(name): input_shape = inputs.get_shape().as_list() num_dims = len(input_shape) - 2 if num_dims == 2: conv = tf.layers.conv2d elif num_dims == 3: conv = tf.layers.conv3d input_channels = input_shape[-1] if output_channels is None: output_channels = input_channels if downsample: shortcut = conv( inputs, filters=output_channels, strides=2, kernel_size=kernel_size, padding='same', name='conv_shortcut') else: shortcut = inputs res = inputs res = tf.nn.relu(res) res = conv( res, filters=input_channels, kernel_size=kernel_size, padding='same', name='conv_1') res = tf.nn.relu(res) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) if downsample: out_strides = 2 else: out_strides = 1 res = conv( res, filters=output_channels, kernel_size=kernel_size, padding='same', strides=out_strides, name='conv_2') if re_zero: res *= tf.get_variable('alpha', initializer=0.) return shortcut + res class ResNet(snt.AbstractModule): """ResNet architecture for 2D image or 3D voxel inputs.""" def __init__(self, num_dims, hidden_sizes=(64, 256), num_blocks=(2, 2), dropout_rate=0.1, re_zero=True, name='res_net'): """Initializes ResNet. Args: num_dims: Number of spatial dimensions. 2 for images or 3 for voxels. hidden_sizes: Sizes of hidden layers in resnet blocks. num_blocks: Number of resnet blocks at each size. dropout_rate: Dropout rate applied immediately after the ReLU in each fully-connected layer. re_zero: If True, alpha scale residuals with zero init. name: Name of variable scope """ super(ResNet, self).__init__(name=name) self.num_dims = num_dims self.hidden_sizes = hidden_sizes self.num_blocks = num_blocks self.dropout_rate = dropout_rate self.re_zero = re_zero def _build(self, inputs, is_training=False): """Passes inputs through resnet. Args: inputs: Tensor of shape [batch_size, height, width, channels] or [batch_size, height, width, depth, channels]. is_training: If True, dropout is applied. Returns: output: Tensor of shape [batch_size, height, width, depth, output_size]. """ if is_training: dropout_rate = self.dropout_rate else: dropout_rate = 0. # Initial projection with large kernel as in original resnet architecture if self.num_dims == 3: conv = tf.layers.conv3d elif self.num_dims == 2: conv = tf.layers.conv2d x = conv( inputs, filters=self.hidden_sizes[0], kernel_size=7, strides=2, padding='same', name='conv_input') if self.num_dims == 2: x = tf.layers.max_pooling2d( x, strides=2, pool_size=3, padding='same', name='pool_input') for d, (hidden_size, blocks) in enumerate(zip(self.hidden_sizes, self.num_blocks)): with tf.variable_scope('resolution_{}'.format(d)): # Downsample at the start of each collection of blocks x = conv_residual_block( x, downsample=False if d == 0 else True, dropout_rate=dropout_rate, output_channels=hidden_size, re_zero=self.re_zero, name='block_1_downsample') for i in range(blocks - 1): x = conv_residual_block( x, dropout_rate=dropout_rate, output_channels=hidden_size, re_zero=self.re_zero, name='block_{}'.format(i + 2)) return x class VertexModel(snt.AbstractModule): """Autoregressive generative model of quantized mesh vertices. Operates on flattened vertex sequences with a stopping token: [z_0, y_0, x_0, z_1, y_1, x_1, ..., z_n, y_n, z_n, STOP] Input vertex coordinates are embedded and tagged with learned coordinate and position indicators. A transformer decoder outputs logits for a quantized vertex distribution. """ def __init__(self, decoder_config, quantization_bits, class_conditional=False, num_classes=55, max_num_input_verts=2500, use_discrete_embeddings=True, name='vertex_model'): """Initializes VertexModel. Args: decoder_config: Dictionary with TransformerDecoder config quantization_bits: Number of quantization used in mesh preprocessing. class_conditional: If True, then condition on learned class embeddings. num_classes: Number of classes to condition on. max_num_input_verts: Maximum number of vertices. Used for learned position embeddings. use_discrete_embeddings: If True, use discrete rather than continuous vertex embeddings. name: Name of variable scope """ super(VertexModel, self).__init__(name=name) self.embedding_dim = decoder_config['hidden_size'] self.class_conditional = class_conditional self.num_classes = num_classes self.max_num_input_verts = max_num_input_verts self.quantization_bits = quantization_bits self.use_discrete_embeddings = use_discrete_embeddings with self._enter_variable_scope(): self.decoder = TransformerDecoder(**decoder_config) @snt.reuse_variables def _embed_class_label(self, labels): """Embeds class label with learned embedding matrix.""" init_dict = {'embeddings': tf.glorot_uniform_initializer} return snt.Embed( vocab_size=self.num_classes, embed_dim=self.embedding_dim, initializers=init_dict, densify_gradients=True, name='class_label')(labels) @snt.reuse_variables def _prepare_context(self, context, is_training=False): """Prepare class label context.""" if self.class_conditional: global_context_embedding = self._embed_class_label(context['class_label']) else: global_context_embedding = None return global_context_embedding, None @snt.reuse_variables def _embed_inputs(self, vertices, global_context_embedding=None): """Embeds flat vertices and adds position and coordinate information.""" # Dequantize inputs and get shapes input_shape = tf.shape(vertices) batch_size, seq_length = input_shape[0], input_shape[1] # Coord indicators (x, y, z) coord_embeddings = snt.Embed( vocab_size=3, embed_dim=self.embedding_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='coord_embeddings')(tf.mod(tf.range(seq_length), 3)) # Position embeddings pos_embeddings = snt.Embed( vocab_size=self.max_num_input_verts, embed_dim=self.embedding_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='coord_embeddings')(tf.floordiv(tf.range(seq_length), 3)) # Discrete vertex value embeddings if self.use_discrete_embeddings: vert_embeddings = snt.Embed( vocab_size=2**self.quantization_bits + 1, embed_dim=self.embedding_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='value_embeddings')(vertices) # Continuous vertex value embeddings else: vert_embeddings = tf.layers.dense( dequantize_verts(vertices[..., None], self.quantization_bits), self.embedding_dim, use_bias=True, name='value_embeddings') # Step zero embeddings if global_context_embedding is None: zero_embed = tf.get_variable( 'embed_zero', shape=[1, 1, self.embedding_dim]) zero_embed_tiled = tf.tile(zero_embed, [batch_size, 1, 1]) else: zero_embed_tiled = global_context_embedding[:, None] # Aggregate embeddings embeddings = vert_embeddings + (coord_embeddings + pos_embeddings)[None] embeddings = tf.concat([zero_embed_tiled, embeddings], axis=1) return embeddings @snt.reuse_variables def _project_to_logits(self, inputs): """Projects transformer outputs to logits for predictive distribution.""" return tf.layers.dense( inputs, 2**self.quantization_bits + 1, # + 1 for stopping token use_bias=True, kernel_initializer=tf.zeros_initializer(), name='project_to_logits') @snt.reuse_variables def _create_dist(self, vertices, global_context_embedding=None, sequential_context_embeddings=None, temperature=1., top_k=0, top_p=1., is_training=False, cache=None): """Outputs categorical dist for quantized vertex coordinates.""" # Embed inputs decoder_inputs = self._embed_inputs(vertices, global_context_embedding) if cache is not None: decoder_inputs = decoder_inputs[:, -1:] # pass through decoder outputs = self.decoder( decoder_inputs, cache=cache, sequential_context_embeddings=sequential_context_embeddings, is_training=is_training) # Get logits and optionally process for sampling logits = self._project_to_logits(outputs) logits /= temperature logits = top_k_logits(logits, top_k) logits = top_p_logits(logits, top_p) cat_dist = tfd.Categorical(logits=logits) return cat_dist def _build(self, batch, is_training=False): """Pass batch through vertex model and get log probabilities under model. Args: batch: Dictionary containing: 'vertices_flat': int32 vertex tensors of shape [batch_size, seq_length]. is_training: If True, use dropout. Returns: pred_dist: tfd.Categorical predictive distribution with batch shape [batch_size, seq_length]. """ global_context, seq_context = self._prepare_context( batch, is_training=is_training) pred_dist = self._create_dist( batch['vertices_flat'][:, :-1], # Last element not used for preds global_context_embedding=global_context, sequential_context_embeddings=seq_context, is_training=is_training) return pred_dist def sample(self, num_samples, context=None, max_sample_length=None, temperature=1., top_k=0, top_p=1., recenter_verts=True, only_return_complete=True): """Autoregressive sampling with caching. Args: num_samples: Number of samples to produce. context: Dictionary of context, such as class labels. See _prepare_context for details. max_sample_length: Maximum length of sampled vertex sequences. Sequences that do not complete are truncated. temperature: Scalar softmax temperature > 0. top_k: Number of tokens to keep for top-k sampling. top_p: Proportion of probability mass to keep for top-p sampling. recenter_verts: If True, center vertex samples around origin. This should be used if model is trained using shift augmentations. only_return_complete: If True, only return completed samples. Otherwise return all samples along with completed indicator. Returns: outputs: Output dictionary with fields: 'completed': Boolean tensor of shape [num_samples]. If True then corresponding sample completed within max_sample_length. 'vertices': Tensor of samples with shape [num_samples, num_verts, 3]. 'num_vertices': Tensor indicating number of vertices for each example in padded vertex samples. 'vertices_mask': Tensor of shape [num_samples, num_verts] that masks corresponding invalid elements in 'vertices'. """ # Obtain context for decoder global_context, seq_context = self._prepare_context( context, is_training=False) # num_samples is the minimum value of num_samples and the batch size of # context inputs (if present). if global_context is not None: num_samples = tf.minimum(num_samples, tf.shape(global_context)[0]) global_context = global_context[:num_samples] if seq_context is not None: seq_context = seq_context[:num_samples] elif seq_context is not None: num_samples = tf.minimum(num_samples, tf.shape(seq_context)[0]) seq_context = seq_context[:num_samples] def _loop_body(i, samples, cache): """While-loop body for autoregression calculation.""" cat_dist = self._create_dist( samples, global_context_embedding=global_context, sequential_context_embeddings=seq_context, cache=cache, temperature=temperature, top_k=top_k, top_p=top_p) next_sample = cat_dist.sample() samples = tf.concat([samples, next_sample], axis=1) return i + 1, samples, cache def _stopping_cond(i, samples, cache): """Stopping condition for sampling while-loop.""" del i, cache # Unused return tf.reduce_any(tf.reduce_all(tf.not_equal(samples, 0), axis=-1)) # Initial values for loop variables samples = tf.zeros([num_samples, 0], dtype=tf.int32) max_sample_length = max_sample_length or self.max_num_input_verts cache, cache_shape_invariants = self.decoder.create_init_cache(num_samples) _, v, _ = tf.while_loop( cond=_stopping_cond, body=_loop_body, loop_vars=(0, samples, cache), shape_invariants=(tf.TensorShape([]), tf.TensorShape([None, None]), cache_shape_invariants), maximum_iterations=max_sample_length * 3 + 1, back_prop=False, parallel_iterations=1) # Check if samples completed. Samples are complete if the stopping token # is produced. completed = tf.reduce_any(tf.equal(v, 0), axis=-1) # Get the number of vertices in the sample. This requires finding the # index of the stopping token. For complete samples use to argmax to get # first nonzero index. stop_index_completed = tf.argmax( tf.cast(tf.equal(v, 0), tf.int32), axis=-1, output_type=tf.int32) # For incomplete samples the stopping index is just the maximum index. stop_index_incomplete = ( max_sample_length * 3 * tf.ones_like(stop_index_completed)) stop_index = tf.where( completed, stop_index_completed, stop_index_incomplete) num_vertices = tf.floordiv(stop_index, 3) # Convert to 3D vertices by reshaping and re-ordering x -> y -> z v = v[:, :(tf.reduce_max(num_vertices) * 3)] - 1 verts_dequantized = dequantize_verts(v, self.quantization_bits) vertices = tf.reshape(verts_dequantized, [num_samples, -1, 3]) vertices = tf.stack( [vertices[..., 2], vertices[..., 1], vertices[..., 0]], axis=-1) # Pad samples to max sample length. This is required in order to concatenate # Samples across different replicator instances. Pad with stopping tokens # for incomplete samples. pad_size = max_sample_length - tf.shape(vertices)[1] vertices = tf.pad(vertices, [[0, 0], [0, pad_size], [0, 0]]) # 3D Vertex mask vertices_mask = tf.cast( tf.range(max_sample_length)[None] < num_vertices[:, None], tf.float32) if recenter_verts: vert_max = tf.reduce_max( vertices - 1e10 * (1. - vertices_mask)[..., None], axis=1, keepdims=True) vert_min = tf.reduce_min( vertices + 1e10 * (1. - vertices_mask)[..., None], axis=1, keepdims=True) vert_centers = 0.5 * (vert_max + vert_min) vertices -= vert_centers vertices *= vertices_mask[..., None] if only_return_complete: vertices = tf.boolean_mask(vertices, completed) num_vertices = tf.boolean_mask(num_vertices, completed) vertices_mask = tf.boolean_mask(vertices_mask, completed) completed = tf.boolean_mask(completed, completed) # Outputs outputs = { 'completed': completed, 'vertices': vertices, 'num_vertices': num_vertices, 'vertices_mask': vertices_mask, } return outputs class ImageToVertexModel(VertexModel): """Generative model of quantized mesh vertices with image conditioning. Operates on flattened vertex sequences with a stopping token: [z_0, y_0, x_0, z_1, y_1, x_1, ..., z_n, y_n, z_n, STOP] Input vertex coordinates are embedded and tagged with learned coordinate and position indicators. A transformer decoder outputs logits for a quantized vertex distribution. Image inputs are encoded and used to condition the vertex decoder. """ def __init__(self, res_net_config, decoder_config, quantization_bits, use_discrete_embeddings=True, max_num_input_verts=2500, name='image_to_vertex_model'): """Initializes VoxelToVertexModel. Args: res_net_config: Dictionary with ResNet config. decoder_config: Dictionary with TransformerDecoder config. quantization_bits: Number of quantization used in mesh preprocessing. use_discrete_embeddings: If True, use discrete rather than continuous vertex embeddings. max_num_input_verts: Maximum number of vertices. Used for learned position embeddings. name: Name of variable scope """ super(ImageToVertexModel, self).__init__( decoder_config=decoder_config, quantization_bits=quantization_bits, max_num_input_verts=max_num_input_verts, use_discrete_embeddings=use_discrete_embeddings, name=name) with self._enter_variable_scope(): self.res_net = ResNet(num_dims=2, **res_net_config) @snt.reuse_variables def _prepare_context(self, context, is_training=False): # Pass images through encoder image_embeddings = self.res_net( context['image'] - 0.5, is_training=is_training) # Add 2D coordinate grid embedding processed_image_resolution = tf.shape(image_embeddings)[1] x = tf.linspace(-1., 1., processed_image_resolution) image_coords = tf.stack(tf.meshgrid(x, x), axis=-1) image_coord_embeddings = tf.layers.dense( image_coords, self.embedding_dim, use_bias=True, name='image_coord_embeddings') image_embeddings += image_coord_embeddings[None] # Reshape spatial grid to sequence batch_size = tf.shape(image_embeddings)[0] sequential_context_embedding = tf.reshape( image_embeddings, [batch_size, -1, self.embedding_dim]) return None, sequential_context_embedding class VoxelToVertexModel(VertexModel): """Generative model of quantized mesh vertices with voxel conditioning. Operates on flattened vertex sequences with a stopping token: [z_0, y_0, x_0, z_1, y_1, x_1, ..., z_n, y_n, z_n, STOP] Input vertex coordinates are embedded and tagged with learned coordinate and position indicators. A transformer decoder outputs logits for a quantized vertex distribution. Image inputs are encoded and used to condition the vertex decoder. """ def __init__(self, res_net_config, decoder_config, quantization_bits, use_discrete_embeddings=True, max_num_input_verts=2500, name='voxel_to_vertex_model'): """Initializes VoxelToVertexModel. Args: res_net_config: Dictionary with ResNet config. decoder_config: Dictionary with TransformerDecoder config. quantization_bits: Integer number of bits used for vertex quantization. use_discrete_embeddings: If True, use discrete rather than continuous vertex embeddings. max_num_input_verts: Maximum number of vertices. Used for learned position embeddings. name: Name of variable scope """ super(VoxelToVertexModel, self).__init__( decoder_config=decoder_config, quantization_bits=quantization_bits, max_num_input_verts=max_num_input_verts, use_discrete_embeddings=use_discrete_embeddings, name=name) with self._enter_variable_scope(): self.res_net = ResNet(num_dims=3, **res_net_config) @snt.reuse_variables def _prepare_context(self, context, is_training=False): # Embed binary input voxels voxel_embeddings = snt.Embed( vocab_size=2, embed_dim=self.pre_embed_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='voxel_embeddings')(context['voxels']) # Pass embedded voxels through voxel encoder voxel_embeddings = self.res_net( voxel_embeddings, is_training=is_training) # Add 3D coordinate grid embedding processed_voxel_resolution = tf.shape(voxel_embeddings)[1] x = tf.linspace(-1., 1., processed_voxel_resolution) voxel_coords = tf.stack(tf.meshgrid(x, x, x), axis=-1) voxel_coord_embeddings = tf.layers.dense( voxel_coords, self.embedding_dim, use_bias=True, name='voxel_coord_embeddings') voxel_embeddings += voxel_coord_embeddings[None] # Reshape spatial grid to sequence batch_size = tf.shape(voxel_embeddings)[0] sequential_context_embedding = tf.reshape( voxel_embeddings, [batch_size, -1, self.embedding_dim]) return None, sequential_context_embedding class FaceModel(snt.AbstractModule): """Autoregressive generative model of n-gon meshes. Operates on sets of input vertices as well as flattened face sequences with new face and stopping tokens: [f_0^0, f_0^1, f_0^2, NEW, f_1^0, f_1^1, ..., STOP] Input vertices are encoded using a Transformer encoder. Input face sequences are embedded and tagged with learned position indicators, as well as their corresponding vertex embeddings. A transformer decoder outputs a pointer which is compared to each vertex embedding to obtain a distribution over vertex indices. """ def __init__(self, encoder_config, decoder_config, class_conditional=True, num_classes=55, decoder_cross_attention=True, use_discrete_vertex_embeddings=True, quantization_bits=8, max_seq_length=5000, name='face_model'): """Initializes FaceModel. Args: encoder_config: Dictionary with TransformerEncoder config. decoder_config: Dictionary with TransformerDecoder config. class_conditional: If True, then condition on learned class embeddings. num_classes: Number of classes to condition on. decoder_cross_attention: If True, the use cross attention from decoder querys into encoder outputs. use_discrete_vertex_embeddings: If True, use discrete vertex embeddings. quantization_bits: Number of quantization bits for discrete vertex embeddings. max_seq_length: Maximum face sequence length. Used for learned position embeddings. name: Name of variable scope """ super(FaceModel, self).__init__(name=name) self.embedding_dim = decoder_config['hidden_size'] self.class_conditional = class_conditional self.num_classes = num_classes self.max_seq_length = max_seq_length self.decoder_cross_attention = decoder_cross_attention self.use_discrete_vertex_embeddings = use_discrete_vertex_embeddings self.quantization_bits = quantization_bits with self._enter_variable_scope(): self.decoder = TransformerDecoder(**decoder_config) self.encoder = TransformerEncoder(**encoder_config) @snt.reuse_variables def _embed_class_label(self, labels): """Embeds class label with learned embedding matrix.""" init_dict = {'embeddings': tf.glorot_uniform_initializer} return snt.Embed( vocab_size=self.num_classes, embed_dim=self.embedding_dim, initializers=init_dict, densify_gradients=True, name='class_label')(labels) @snt.reuse_variables def _prepare_context(self, context, is_training=False): """Prepare class label and vertex context.""" if self.class_conditional: global_context_embedding = self._embed_class_label(context['class_label']) else: global_context_embedding = None vertex_embeddings = self._embed_vertices( context['vertices'], context['vertices_mask'], is_training=is_training) if self.decoder_cross_attention: sequential_context_embeddings = ( vertex_embeddings * tf.pad(context['vertices_mask'], [[0, 0], [2, 0]], constant_values=1)[..., None]) else: sequential_context_embeddings = None return (vertex_embeddings, global_context_embedding, sequential_context_embeddings) @snt.reuse_variables def _embed_vertices(self, vertices, vertices_mask, is_training=False): """Embeds vertices with transformer encoder.""" # num_verts = tf.shape(vertices)[1] if self.use_discrete_vertex_embeddings: vertex_embeddings = 0. verts_quantized = quantize_verts(vertices, self.quantization_bits) for c in range(3): vertex_embeddings += snt.Embed( vocab_size=256, embed_dim=self.embedding_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='coord_{}'.format(c))(verts_quantized[..., c]) else: vertex_embeddings = tf.layers.dense( vertices, self.embedding_dim, use_bias=True, name='vertex_embeddings') vertex_embeddings *= vertices_mask[..., None] # Pad vertex embeddings with learned embeddings for stopping and new face # tokens stopping_embeddings = tf.get_variable( 'stopping_embeddings', shape=[1, 2, self.embedding_dim]) stopping_embeddings = tf.tile(stopping_embeddings, [tf.shape(vertices)[0], 1, 1]) vertex_embeddings = tf.concat( [stopping_embeddings, vertex_embeddings], axis=1) # Pass through Transformer encoder vertex_embeddings = self.encoder(vertex_embeddings, is_training=is_training) return vertex_embeddings @snt.reuse_variables def _embed_inputs(self, faces_long, vertex_embeddings, global_context_embedding=None): """Embeds face sequences and adds within and between face positions.""" # Face value embeddings are gathered vertex embeddings face_embeddings = tf.gather(vertex_embeddings, faces_long, batch_dims=1) # Position embeddings pos_embeddings = snt.Embed( vocab_size=self.max_seq_length, embed_dim=self.embedding_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='coord_embeddings')(tf.range(tf.shape(faces_long)[1])) # Step zero embeddings batch_size = tf.shape(face_embeddings)[0] if global_context_embedding is None: zero_embed = tf.get_variable( 'embed_zero', shape=[1, 1, self.embedding_dim]) zero_embed_tiled = tf.tile(zero_embed, [batch_size, 1, 1]) else: zero_embed_tiled = global_context_embedding[:, None] # Aggregate embeddings embeddings = face_embeddings + pos_embeddings[None] embeddings = tf.concat([zero_embed_tiled, embeddings], axis=1) return embeddings @snt.reuse_variables def _project_to_pointers(self, inputs): """Projects transformer outputs to pointer vectors.""" return tf.layers.dense( inputs, self.embedding_dim, use_bias=True, kernel_initializer=tf.zeros_initializer(), name='project_to_pointers' ) @snt.reuse_variables def _create_dist(self, vertex_embeddings, vertices_mask, faces_long, global_context_embedding=None, sequential_context_embeddings=None, temperature=1., top_k=0, top_p=1., is_training=False, cache=None): """Outputs categorical dist for vertex indices.""" # Embed inputs decoder_inputs = self._embed_inputs( faces_long, vertex_embeddings, global_context_embedding) # Pass through Transformer decoder if cache is not None: decoder_inputs = decoder_inputs[:, -1:] decoder_outputs = self.decoder( decoder_inputs, cache=cache, sequential_context_embeddings=sequential_context_embeddings, is_training=is_training) # Get pointers pred_pointers = self._project_to_pointers(decoder_outputs) # Get logits and mask logits = tf.matmul(pred_pointers, vertex_embeddings, transpose_b=True) logits /= tf.sqrt(float(self.embedding_dim)) f_verts_mask = tf.pad( vertices_mask, [[0, 0], [2, 0]], constant_values=1.)[:, None] logits *= f_verts_mask logits -= (1. - f_verts_mask) * 1e9 logits /= temperature logits = top_k_logits(logits, top_k) logits = top_p_logits(logits, top_p) return tfd.Categorical(logits=logits) def _build(self, batch, is_training=False): """Pass batch through face model and get log probabilities. Args: batch: Dictionary containing: 'vertices_dequantized': Tensor of shape [batch_size, num_vertices, 3]. 'faces': int32 tensor of shape [batch_size, seq_length] with flattened faces. 'vertices_mask': float32 tensor with shape [batch_size, num_vertices] that masks padded elements in 'vertices'. is_training: If True, use dropout. Returns: pred_dist: tfd.Categorical predictive distribution with batch shape [batch_size, seq_length]. """ vertex_embeddings, global_context, seq_context = self._prepare_context( batch, is_training=is_training) pred_dist = self._create_dist( vertex_embeddings, batch['vertices_mask'], batch['faces'][:, :-1], global_context_embedding=global_context, sequential_context_embeddings=seq_context, is_training=is_training) return pred_dist def sample(self, context, max_sample_length=None, temperature=1., top_k=0, top_p=1., only_return_complete=True): """Sample from face model using caching. Args: context: Dictionary of context, including 'vertices' and 'vertices_mask'. See _prepare_context for details. max_sample_length: Maximum length of sampled vertex sequences. Sequences that do not complete are truncated. temperature: Scalar softmax temperature > 0. top_k: Number of tokens to keep for top-k sampling. top_p: Proportion of probability mass to keep for top-p sampling. only_return_complete: If True, only return completed samples. Otherwise return all samples along with completed indicator. Returns: outputs: Output dictionary with fields: 'completed': Boolean tensor of shape [num_samples]. If True then corresponding sample completed within max_sample_length. 'faces': Tensor of samples with shape [num_samples, num_verts, 3]. 'num_face_indices': Tensor indicating number of vertices for each example in padded vertex samples. """ vertex_embeddings, global_context, seq_context = self._prepare_context( context, is_training=False) num_samples = tf.shape(vertex_embeddings)[0] def _loop_body(i, samples, cache): """While-loop body for autoregression calculation.""" pred_dist = self._create_dist( vertex_embeddings, context['vertices_mask'], samples, global_context_embedding=global_context, sequential_context_embeddings=seq_context, cache=cache, temperature=temperature, top_k=top_k, top_p=top_p) next_sample = pred_dist.sample()[:, -1:] samples = tf.concat([samples, next_sample], axis=1) return i + 1, samples, cache def _stopping_cond(i, samples, cache): """Stopping conditions for autoregressive calculation.""" del i, cache # Unused return tf.reduce_any(tf.reduce_all(tf.not_equal(samples, 0), axis=-1)) # While loop sampling with caching samples = tf.zeros([num_samples, 0], dtype=tf.int32) max_sample_length = max_sample_length or self.max_seq_length cache, cache_shape_invariants = self.decoder.create_init_cache(num_samples) _, f, _ = tf.while_loop( cond=_stopping_cond, body=_loop_body, loop_vars=(0, samples, cache), shape_invariants=(tf.TensorShape([]), tf.TensorShape([None, None]), cache_shape_invariants), back_prop=False, parallel_iterations=1, maximum_iterations=max_sample_length) # Record completed samples complete_samples = tf.reduce_any(tf.equal(f, 0), axis=-1) # Find number of faces sample_length = tf.shape(f)[-1] # Get largest new face (1) index as stopping point for incomplete samples. max_one_ind = tf.reduce_max( tf.range(sample_length)[None] * tf.cast(tf.equal(f, 1), tf.int32), axis=-1) zero_inds = tf.cast( tf.argmax(tf.cast(tf.equal(f, 0), tf.int32), axis=-1), tf.int32) num_face_indices = tf.where(complete_samples, zero_inds, max_one_ind) + 1 # Mask faces beyond stopping token with zeros # This mask has a -1 in order to replace the last new face token with zero faces_mask = tf.cast( tf.range(sample_length)[None] < num_face_indices[:, None] - 1, tf.int32) f *= faces_mask # This is the real mask faces_mask = tf.cast( tf.range(sample_length)[None] < num_face_indices[:, None], tf.int32) # Pad to maximum size with zeros pad_size = max_sample_length - sample_length f = tf.pad(f, [[0, 0], [0, pad_size]]) if only_return_complete: f = tf.boolean_mask(f, complete_samples) num_face_indices = tf.boolean_mask(num_face_indices, complete_samples) context = tf.nest.map_structure( lambda x: tf.boolean_mask(x, complete_samples), context) complete_samples = tf.boolean_mask(complete_samples, complete_samples) # outputs outputs = { 'context': context, 'completed': complete_samples, 'faces': f, 'num_face_indices': num_face_indices, } return outputs ================================================ FILE: polygen/run.sh ================================================ #!/bin/sh # Copyright 2020 Deepmind Technologies Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. python3 -m venv polygen source polygen/bin/activate pip3 install . python3 model_test.py deactivate ================================================ FILE: polygen/sample-pretrained.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "lUh_eWpealmh" }, "source": [ "Copyright 2020 DeepMind Technologies Limited\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "\n", " https://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "kYd9gIfGJYZ8" }, "source": [ "## Clone repo and import dependencies" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "Ux33ZDQ_tqUV" }, "outputs": [], "source": [ "!pip install tensorflow==1.15 dm-sonnet==1.36 tensor2tensor==1.14\n", "\n", "import time\n", "import numpy as np\n", "import tensorflow.compat.v1 as tf\n", "tf.logging.set_verbosity(tf.logging.ERROR) # Hide TF deprecation messages\n", "import matplotlib.pyplot as plt\n", "\n", "%cd /tmp\n", "%rm -rf /tmp/deepmind_research\n", "!git clone https://github.com/deepmind/deepmind-research.git \\\n", " /tmp/deepmind_research\n", "%cd /tmp/deepmind_research/polygen\n", "import modules\n", "import data_utils" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "YUZNqHTVJbm3" }, "source": [ "## Download pre-trained model weights from Google Cloud Storage" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "LpZjBUmq10gX" }, "outputs": [], "source": [ "!mkdir /tmp/vertex_model\n", "!mkdir /tmp/face_model\n", "!gsutil cp gs://deepmind-research-polygen/vertex_model.tar.gz /tmp/vertex_model/\n", "!gsutil cp gs://deepmind-research-polygen/face_model.tar.gz /tmp/face_model/\n", "!tar xvfz /tmp/vertex_model/vertex_model.tar.gz -C /tmp/vertex_model/\n", "!tar xvfz /tmp/face_model/face_model.tar.gz -C /tmp/face_model/" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "vXhMOoxaJ3Xb" }, "source": [ "## Pre-trained model config" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "7VGSSS9vJSn-" }, "outputs": [], "source": [ "vertex_module_config=dict(\n", " decoder_config=dict(\n", " hidden_size=512,\n", " fc_size=2048,\n", " num_heads=8,\n", " layer_norm=True,\n", " num_layers=24,\n", " dropout_rate=0.4,\n", " re_zero=True,\n", " memory_efficient=True\n", " ),\n", " quantization_bits=8,\n", " class_conditional=True,\n", " max_num_input_verts=5000,\n", " use_discrete_embeddings=True,\n", " )\n", "\n", "face_module_config=dict(\n", " encoder_config=dict(\n", " hidden_size=512,\n", " fc_size=2048,\n", " num_heads=8,\n", " layer_norm=True,\n", " num_layers=10,\n", " dropout_rate=0.2,\n", " re_zero=True,\n", " memory_efficient=True,\n", " ),\n", " decoder_config=dict(\n", " hidden_size=512,\n", " fc_size=2048,\n", " num_heads=8,\n", " layer_norm=True,\n", " num_layers=14,\n", " dropout_rate=0.2,\n", " re_zero=True,\n", " memory_efficient=True,\n", " ),\n", " class_conditional=False,\n", " decoder_cross_attention=True,\n", " use_discrete_vertex_embeddings=True,\n", " max_seq_length=8000,\n", " )" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WNXf_XbuKW4S" }, "source": [ "## Generate class-conditional samples\n", "\n", "Try varying the `class_id` parameter to generate meshes from different object categories. Good classes to try are tables (49), lamps (30), and cabinets (32). \n", "\n", "We can also specify the maximum number of vertices / face indices we want to see in the generated meshes using `max_num_vertices` and `max_num_face_indices`. The code will keep generating batches of samples until there are at least `num_samples_min` complete samples with the required number of vertices / faces.\n", "\n", "`top_p_vertex_model` and `top_p_face_model` control how varied the outputs are, with `1.` being the most varied, and `0.` the least varied. `0.9` is a good value for both the vertex and face models.\n", "\n", "Sampling should take around 2-5 minutes with a colab GPU using the default settings depending on the object class." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "kqKMbPJJu3lk" }, "outputs": [], "source": [ "class_id = '49) table' #@param ['0) airplane,aeroplane,plane','1) ashcan,trash can,garbage can,wastebin,ash bin,ash-bin,ashbin,dustbin,trash barrel,trash bin','2) bag,traveling bag,travelling bag,grip,suitcase','3) basket,handbasket','4) bathtub,bathing tub,bath,tub','5) bed','6) bench','7) birdhouse','8) bookshelf','9) bottle','10) bowl','11) bus,autobus,coach,charabanc,double-decker,jitney,motorbus,motorcoach,omnibus,passenger vehi','12) cabinet','13) camera,photographic camera','14) can,tin,tin can','15) cap','16) car,auto,automobile,machine,motorcar','17) cellular telephone,cellular phone,cellphone,cell,mobile phone','18) chair','19) clock','20) computer keyboard,keypad','21) dishwasher,dish washer,dishwashing machine','22) display,video display','23) earphone,earpiece,headphone,phone','24) faucet,spigot','25) file,file cabinet,filing cabinet','26) guitar','27) helmet','28) jar','29) knife','30) lamp','31) laptop,laptop computer','32) loudspeaker,speaker,speaker unit,loudspeaker system,speaker system','33) mailbox,letter box','34) microphone,mike','35) microwave,microwave oven','36) motorcycle,bike','37) mug','38) piano,pianoforte,forte-piano','39) pillow','40) pistol,handgun,side arm,shooting iron','41) pot,flowerpot','42) printer,printing machine','43) remote control,remote','44) rifle','45) rocket,projectile','46) skateboard','47) sofa,couch,lounge','48) stove','49) table','50) telephone,phone,telephone set','51) tower','52) train,railroad train','53) vessel,watercraft','54) washer,automatic washer,washing machine']\n", "num_samples_min = 1 #@param\n", "num_samples_batch = 8 #@param\n", "max_num_vertices = 400 #@param\n", "max_num_face_indices = 2000 #@param\n", "top_p_vertex_model = 0.9 #@param\n", "top_p_face_model = 0.9 #@param\n", "\n", "tf.reset_default_graph()\n", "\n", "# Build models\n", "vertex_model = modules.VertexModel(**vertex_module_config)\n", "face_model = modules.FaceModel(**face_module_config)\n", "\n", "# Tile out class label to every element in batch\n", "class_id = int(class_id.split(')')[0])\n", "vertex_model_context = {'class_label': tf.fill([num_samples_batch,], class_id)}\n", "vertex_samples = vertex_model.sample(\n", " num_samples_batch, context=vertex_model_context, \n", " max_sample_length=max_num_vertices, top_p=top_p_vertex_model, \n", " recenter_verts=True, only_return_complete=True)\n", "vertex_model_saver = tf.train.Saver(var_list=vertex_model.variables)\n", "\n", "# The face model generates samples conditioned on a context, which here is\n", "# the vertex model samples\n", "face_samples = face_model.sample(\n", " vertex_samples, max_sample_length=max_num_face_indices, \n", " top_p=top_p_face_model, only_return_complete=True)\n", "face_model_saver = tf.train.Saver(var_list=face_model.variables)\n", "\n", "# Start sampling\n", "start = time.time()\n", "print('Generating samples...')\n", "with tf.Session() as sess:\n", " vertex_model_saver.restore(sess, '/tmp/vertex_model/model')\n", " face_model_saver.restore(sess, '/tmp/face_model/model')\n", " mesh_list = []\n", " num_samples_complete = 0\n", " while num_samples_complete \u003c num_samples_min:\n", " v_samples_np = sess.run(vertex_samples)\n", " if v_samples_np['completed'].size == 0:\n", " print('No vertex samples completed in this batch. Try increasing ' +\n", " 'max_num_vertices.')\n", " continue\n", " f_samples_np = sess.run(\n", " face_samples,\n", " {vertex_samples[k]: v_samples_np[k] for k in vertex_samples.keys()})\n", " v_samples_np = f_samples_np['context']\n", " num_samples_complete_batch = f_samples_np['completed'].sum()\n", " num_samples_complete += num_samples_complete_batch\n", " print('Num. samples complete: {}'.format(num_samples_complete))\n", " for k in range(num_samples_complete_batch):\n", " verts = v_samples_np['vertices'][k][:v_samples_np['num_vertices'][k]]\n", " faces = data_utils.unflatten_faces(\n", " f_samples_np['faces'][k][:f_samples_np['num_face_indices'][k]])\n", " mesh_list.append({'vertices': verts, 'faces': faces})\n", "end = time.time()\n", "print('sampling time: {}'.format(end - start))\n", "\n", "data_utils.plot_meshes(mesh_list, ax_lims=0.4) " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "OOQV6pMvSymz" }, "source": [ "## Export meshes as `.obj` files\n", "Pick a `mesh_id` (starting at 0) corresponding to the samples generated above. Refresh the colab file browser to find an `.obj` file with the mesh data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "fO0Klbq2Sx0m" }, "outputs": [], "source": [ "mesh_id = 4 #@param\n", "data_utils.write_obj(\n", " mesh_list[mesh_id]['vertices'], mesh_list[mesh_id]['faces'], \n", " 'mesh-{}.obj'.format(mesh_id))" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "sampling-pretrained.ipynb", "provenance": [ { "file_id": "1yj-oHYqCnwYVGSM22coa68NqnSoNdyVr", "timestamp": 1591622616999 }, { "file_id": "1v_7DtLnpXrEhVbwZhzDiVQW7ghroi11Y", "timestamp": 1591264007511 } ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: polygen/setup.py ================================================ # Copyright 2020 DeepMind Technologies Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Setup for pip package.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from setuptools import find_packages from setuptools import setup REQUIRED_PACKAGES = ['numpy', 'dm-sonnet==1.36', 'tensorflow==1.14', 'tensor2tensor==1.15', 'networkx', 'matplotlib', 'six'] setup( name='polygen', version='0.1', description='A library for PolyGen: An Autoregressive Generative Model of 3D Meshes.', url='https://github.com/deepmind/deepmind-research/polygen', author='DeepMind', author_email='no-reply@google.com', # Contained modules and scripts. packages=find_packages(), install_requires=REQUIRED_PACKAGES, platforms=['any'], license='Apache 2.0', ) ================================================ FILE: polygen/training.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "szgmaK1HajOc" }, "source": [ "Copyright 2020 DeepMind Technologies Limited\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "\n", " https://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_dv0afOrKheU" }, "source": [ "## Clone repo and import dependencies" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "Ux33ZDQ_tqUV" }, "outputs": [], "source": [ "!pip install tensorflow==1.15 dm-sonnet==1.36 tensor2tensor==1.14\n", "\n", "import os\n", "import numpy as np\n", "import tensorflow.compat.v1 as tf\n", "tf.logging.set_verbosity(tf.logging.ERROR) # Hide TF deprecation messages\n", "import matplotlib.pyplot as plt\n", "\n", "%cd /tmp\n", "%rm -rf /tmp/deepmind_research\n", "!git clone https://github.com/deepmind/deepmind-research.git \\\n", " /tmp/deepmind_research\n", "%cd /tmp/deepmind_research/polygen\n", "import modules\n", "import data_utils" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "U3GDZhJ5wGOf" }, "source": [ "## Prepare a synthetic dataset\n", "We prepare a dataset of meshes using four simple geometric primitives.\n", "\n", "The important function here is `data_utils.load_process_mesh`, which loads the raw `.obj` file, normalizes and centers the meshes, and applies quantization to the vertex positions. The mesh faces are flattened and treated as a long sequence, with a new-face token (`=1`) separating the faces. For each of the four synthetic meshes, we associate a unique class label, so we can train class-conditional models.\n", "\n", "After processing the raw mesh data into numpy arrays, we create a `tf.data.Dataset` that we can use to feed data to our models. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "3QAqwyZjtOdC" }, "outputs": [], "source": [ "# Prepare synthetic dataset\n", "ex_list = []\n", "for k, mesh in enumerate(['cube', 'cylinder', 'cone', 'icosphere']):\n", " mesh_dict = data_utils.load_process_mesh(\n", " os.path.join('meshes', '{}.obj'.format(mesh)))\n", " mesh_dict['class_label'] = k\n", " ex_list.append(mesh_dict)\n", "synthetic_dataset = tf.data.Dataset.from_generator(\n", " lambda: ex_list, \n", " output_types={\n", " 'vertices': tf.int32, 'faces': tf.int32, 'class_label': tf.int32},\n", " output_shapes={\n", " 'vertices': tf.TensorShape([None, 3]), 'faces': tf.TensorShape([None]), \n", " 'class_label': tf.TensorShape(())}\n", " )\n", "ex = synthetic_dataset.make_one_shot_iterator().get_next()\n", "\n", "# Inspect the first mesh\n", "with tf.Session() as sess:\n", " ex_np = sess.run(ex)\n", "print(ex_np)\n", "\n", "# Plot the meshes\n", "mesh_list = []\n", "with tf.Session() as sess:\n", " for i in range(4):\n", " ex_np = sess.run(ex)\n", " mesh_list.append(\n", " {'vertices': data_utils.dequantize_verts(ex_np['vertices']), \n", " 'faces': data_utils.unflatten_faces(ex_np['faces'])})\n", "data_utils.plot_meshes(mesh_list, ax_lims=0.4)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9G2FCQQyyTXw" }, "source": [ "## Vertex model\n", "\n", "#### Prepare the dataset for vertex model training\n", "We need to perform some additional processing to make the dataset ready for vertex model training. In particular, `data_utils.make_vertex_model_dataset` flattens the `[V, 3]` vertex arrays, ordering by `Z-\u003eY-\u003eX` coordinates. It also creates masks, which are used to mask padded elements in data batches. We also add random shifts to make the modelling task more challenging.\n", "\n", "#### Create a vertex model\n", "`modules.VertexModel` is a Sonnet module that. Calling the module on a batch of data will produce outputs which are the sequential predictions for each vertex coordinate. The basis of the vertex model is a Transformer decoder, and we specify it's parameters in `decoder_config`. \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "o2KCoDeeFP8C" }, "outputs": [], "source": [ "# Prepare the dataset for vertex model training\n", "vertex_model_dataset = data_utils.make_vertex_model_dataset(\n", " synthetic_dataset, apply_random_shift=False)\n", "vertex_model_dataset = vertex_model_dataset.repeat()\n", "vertex_model_dataset = vertex_model_dataset.padded_batch(\n", " 4, padded_shapes=vertex_model_dataset.output_shapes)\n", "vertex_model_dataset = vertex_model_dataset.prefetch(1)\n", "vertex_model_batch = vertex_model_dataset.make_one_shot_iterator().get_next()\n", "\n", "# Create vertex model\n", "vertex_model = modules.VertexModel(\n", " decoder_config={\n", " 'hidden_size': 128,\n", " 'fc_size': 512, \n", " 'num_layers': 3,\n", " 'dropout_rate': 0.\n", " },\n", " class_conditional=True,\n", " num_classes=4,\n", " max_num_input_verts=250,\n", " quantization_bits=8,\n", ")\n", "vertex_model_pred_dist = vertex_model(vertex_model_batch)\n", "vertex_model_loss = -tf.reduce_sum(\n", " vertex_model_pred_dist.log_prob(vertex_model_batch['vertices_flat']) * \n", " vertex_model_batch['vertices_flat_mask'])\n", "vertex_samples = vertex_model.sample(\n", " 4, context=vertex_model_batch, max_sample_length=200, top_p=0.95,\n", " recenter_verts=False, only_return_complete=False)\n", "\n", "print(vertex_model_batch)\n", "print(vertex_model_pred_dist)\n", "print(vertex_samples)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "-9RNYr5x1jov" }, "source": [ "## Face model\n", "\n", "#### Prepare the dataset for face model training\n", "We need to perform some additional processing to make the dataset ready for vertex model training. In particular, `data_utils.make_vertex_model_dataset` flattens the `[V, 3]` vertex arrays, ordering by `Z-\u003eY-\u003eX` coordinates. It also creates masks, which are used to mask padded elements in data batches. We also add random shifts to make the modelling task more challenging.\n", "\n", "#### Create a face model\n", "`modules.VertexModel` is a Sonnet module that. Calling the module on a batch of data will produce outputs which are the sequential predictions for each vertex coordinate. The basis of the vertex model is a Transformer decoder, and we specify it's parameters in `decoder_config`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "a2yO6dOGzn8c" }, "outputs": [], "source": [ "face_model_dataset = data_utils.make_face_model_dataset(\n", " synthetic_dataset, apply_random_shift=False)\n", "face_model_dataset = face_model_dataset.repeat()\n", "face_model_dataset = face_model_dataset.padded_batch(\n", " 4, padded_shapes=face_model_dataset.output_shapes)\n", "face_model_dataset = face_model_dataset.prefetch(1)\n", "face_model_batch = face_model_dataset.make_one_shot_iterator().get_next()\n", "\n", "# Create face model\n", "face_model = modules.FaceModel(\n", " encoder_config={\n", " 'hidden_size': 128,\n", " 'fc_size': 512, \n", " 'num_layers': 3,\n", " 'dropout_rate': 0.\n", " },\n", " decoder_config={\n", " 'hidden_size': 128,\n", " 'fc_size': 512, \n", " 'num_layers': 3,\n", " 'dropout_rate': 0.\n", " },\n", " class_conditional=False,\n", " max_seq_length=500,\n", " quantization_bits=8,\n", " decoder_cross_attention=True,\n", " use_discrete_vertex_embeddings=True,\n", ")\n", "face_model_pred_dist = face_model(face_model_batch)\n", "face_model_loss = -tf.reduce_sum(\n", " face_model_pred_dist.log_prob(face_model_batch['faces']) * \n", " face_model_batch['faces_mask'])\n", "face_samples = face_model.sample(\n", " context=vertex_samples, max_sample_length=500, top_p=0.95,\n", " only_return_complete=False)\n", "print(face_model_batch)\n", "print(face_model_pred_dist)\n", "print(face_samples)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "hL7yloXB1pUb" }, "source": [ "## Train on the synthetic data\n", "\n", "Now that we've created vertex and face models and their respective data loaders, we can train them and look at some outputs. While we train the models together here, they can be trained seperately and recombined later if required. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "hjrbofa8zqQt" }, "outputs": [], "source": [ "# Optimization settings\n", "learning_rate = 5e-4\n", "training_steps = 500\n", "check_step = 5\n", "\n", "# Create an optimizer an minimize the summed log probability of the mesh \n", "# sequences\n", "optimizer = tf.train.AdamOptimizer(learning_rate)\n", "vertex_model_optim_op = optimizer.minimize(vertex_model_loss)\n", "face_model_optim_op = optimizer.minimize(face_model_loss)\n", "\n", "# Training loop\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for n in range(training_steps):\n", " if n % check_step == 0:\n", " v_loss, f_loss = sess.run((vertex_model_loss, face_model_loss))\n", " print('Step {}'.format(n))\n", " print('Loss (vertices) {}'.format(v_loss))\n", " print('Loss (faces) {}'.format(f_loss))\n", " v_samples_np, f_samples_np, b_np = sess.run(\n", " (vertex_samples, face_samples, vertex_model_batch))\n", " mesh_list = []\n", " for n in range(4):\n", " mesh_list.append(\n", " {\n", " 'vertices': v_samples_np['vertices'][n][:v_samples_np['num_vertices'][n]],\n", " 'faces': data_utils.unflatten_faces(\n", " f_samples_np['faces'][n][:f_samples_np['num_face_indices'][n]])\n", " }\n", " )\n", " data_utils.plot_meshes(mesh_list, ax_lims=0.5)\n", " sess.run((vertex_model_optim_op, face_model_optim_op))" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "training.ipynb", "provenance": [ { "file_id": "1QL8ib2FKPGUWFQbuX8AttUk-H34Al8Ue", "timestamp": 1591364245034 }, { "file_id": "1v_7DtLnpXrEhVbwZhzDiVQW7ghroi11Y", "timestamp": 1591355096822 } ], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: powerpropagation/README.md ================================================ # Powerpropagation This notebook contains code to accompany the NeurIPS 2021 paper on Powerpropagation. [](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/powerpropagation/powerpropagation.ipynb) If you find this code useful, please consider citing this paper: ``` @article{schwarz2021powerpropagation, title={Powerpropagation: A sparsity inducing weight reparameterisation}, author={Schwarz, Jonathan and Jayakumar, Siddhant M and Pascanu, Razvan and Latham, Peter and Teh, Yee Whye}, year={2021}, journal={Neural Information Processing Systems (NeurIPS)}, keywords={conference}, } ``` ================================================ FILE: powerpropagation/powerpropagation.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "V7L7Byz8qQZd" }, "source": [ "Copyright 2021 The Powerpropagation Authors. All rights reserved\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", "\n", "https://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "SuSveOpsxQuA" }, "outputs": [], "source": [ "#@title Imports\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "\n", "import tensorflow as tf\n", "import tensorflow_probability as tfp\n", "\n", "#@title Installing and Importing Dependencies\n", "\n", "print('Installing necessary libraries...')\n", "\n", "def install_libraries():\n", " !pip install dm-sonnet\n", "\n", "import IPython\n", "\n", "with IPython.utils.io.capture_output() as captured:\n", " install_libraries()\n", "\n", "import sonnet as snt" ] }, { "cell_type": "markdown", "metadata": { "id": "zNZE9eU_poe0" }, "source": [ "### Implementation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pQBoLdNFL7fE" }, "outputs": [], "source": [ "#@title Training \u0026 Pruning functions\n", "\n", "@tf.function\n", "def train_fn(model, train_x, train_y, optimizer_to_use):\n", " with tf.GradientTape() as tape:\n", " loss, stats = model.loss(train_x, train_y)\n", "\n", " train_vars = model.trainable_variables()\n", " train_grads = tape.gradient(loss, train_vars)\n", " optimizer_to_use.apply(train_grads, train_vars)\n", "\n", " return stats\n", "\n", "@tf.function\n", "def eval_fn(model, eval_x, eval_y):\n", " _, stats = model.loss(eval_x, eval_y)\n", "\n", " return stats\n", "\n", "def _bottom_k_mask(percent_to_keep, condition):\n", " how_many = int(percent_to_keep * condition.size)\n", " top_k = tf.nn.top_k(condition, k=how_many)\n", "\n", " mask = np.zeros(shape=condition.shape, dtype=np.float32)\n", " mask[top_k.indices.numpy()] = 1\n", "\n", " assert np.sum(mask) == how_many\n", "\n", " return mask\n", "\n", "def prune_by_magnitude(percent_to_keep, weight):\n", " mask = _bottom_k_mask(percent_to_keep, np.abs(weight.flatten()))\n", "\n", " return mask.reshape(weight.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uAPa99KakJDB" }, "outputs": [], "source": [ "#@title Initialisers\n", "\n", "class PowerPropVarianceScaling(snt.initializers.VarianceScaling):\n", "\n", " def __init__(self, alpha, *args, **kwargs):\n", " super(PowerPropVarianceScaling, self).__init__(*args, **kwargs)\n", " self._alpha = alpha\n", "\n", " def __call__(self, shape, dtype):\n", " u = super(PowerPropVarianceScaling, self).__call__(shape, dtype).numpy()\n", "\n", " return tf.sign(u) * tf.pow(tf.abs(u), 1.0/self._alpha)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UXtgeSdPgufJ" }, "outputs": [], "source": [ "#@title Models\n", "\n", "class PowerPropLinear(snt.Linear):\n", " \"\"\"Powerpropagation Linear module.\"\"\"\n", " def __init__(self, alpha, *args, **kwargs):\n", " super(PowerPropLinear, self).__init__(*args, **kwargs)\n", " self._alpha = alpha\n", "\n", " def get_weights(self):\n", " return tf.sign(self.w) * tf.pow(tf.abs(self.w), self._alpha)\n", "\n", " def __call__(self, inputs, mask=None):\n", " self._initialize(inputs)\n", " params = self.w * tf.pow(tf.abs(self.w), self._alpha-1)\n", "\n", " if mask is not None:\n", " params *= mask\n", "\n", " outputs = tf.matmul(inputs, params) + self.b\n", "\n", " return outputs\n", "\n", "class MLP(snt.Module):\n", " \"\"\"A multi-layer perceptron module.\"\"\"\n", "\n", " def __init__(self, alpha, w_init, output_sizes=[300, 100, 10], name='MLP'):\n", "\n", " super(MLP, self).__init__(name=name)\n", " self._alpha = alpha\n", " self._w_init = w_init\n", "\n", " self._layers = []\n", " for index, output_size in enumerate(output_sizes):\n", " self._layers.append(\n", " PowerPropLinear(\n", " output_size=output_size,\n", " alpha=alpha,\n", " w_init=w_init,\n", " name=\"linear_{}\".format(index)))\n", "\n", " def get_weights(self):\n", " return [l.get_weights().numpy() for l in self._layers]\n", "\n", " def __call__(self, inputs, masks=None):\n", " num_layers = len(self._layers)\n", "\n", " for i, layer in enumerate(self._layers):\n", " if masks is not None:\n", " inputs = layer(inputs, masks[i])\n", " else:\n", " inputs = layer(inputs)\n", " if i \u003c (num_layers - 1):\n", " inputs = tf.nn.relu(inputs)\n", "\n", " return inputs\n", "\n", "\n", "class DensityNetwork(snt.Module):\n", " \"\"\"Produces categorical distribution.\"\"\"\n", "\n", " def __init__(self, network=None, name=\"DensityNetwork\", *args, **kwargs):\n", " super(DensityNetwork, self).__init__(name=name)\n", " self._network = network\n", "\n", " def __call__(self, inputs, masks=None, *args, **kwargs):\n", " outputs = self._network(inputs, masks, *args, **kwargs)\n", "\n", " return tfp.distributions.Categorical(logits=outputs), outputs\n", "\n", " def trainable_variables(self):\n", " return self._network.trainable_variables\n", "\n", " def get_weights(self):\n", " return self._network.get_weights()\n", "\n", " def loss(self, inputs, targets, masks=None, *args, **kwargs):\n", " dist, logits = self.__call__(\n", " inputs, masks, *args, **kwargs)\n", " loss = -tf.reduce_mean(dist.log_prob(targets))\n", "\n", " accuracy = tf.reduce_mean(\n", " tf.cast(tf.equal(tf.argmax(logits, axis=1), targets), tf.float32))\n", "\n", " return loss, {'loss': loss, 'acc': accuracy}" ] }, { "cell_type": "markdown", "metadata": { "id": "J8M6PJ4oNK0X" }, "source": [ "### Training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9TwBkiSmKWCz" }, "outputs": [], "source": [ "#@title Training configuration\n", "\n", "model_seed = 0 #@param\n", "\n", "alphas = [1.0, 2.0, 3.0, 4.0, 5.0] #@param\n", "init_distribution = 'truncated_normal'\n", "init_mode = 'fan_in'\n", "init_scale = 1.0\n", "\n", "# Fixed values taken from the Lottery Ticket Hypothesis paper\n", "train_batch_size = 60\n", "num_train_steps = 50000\n", "learning_rate = 0.1\n", "\n", "report_interval = 2500\n", "\n", "tf.random.set_seed(model_seed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "JGFVxUjCRe3v" }, "outputs": [], "source": [ "#@title Get data\n", "\n", "(train_x, train_y), (test_x, test_y) = tf.keras.datasets.mnist.load_data()\n", "\n", "train_x = train_x.reshape([60000, 784]).astype(np.float32) / 255.0\n", "test_x = test_x.reshape([10000, 784]).astype(np.float32) / 255.0\n", "\n", "train_y = train_y.astype(np.int64)\n", "test_y = test_y.astype(np.int64)\n", "\n", "# Reserve some data for a validation set\n", "valid_x = train_x[-5000:]\n", "valid_y = train_y[-5000:]\n", "train_x = train_x[:-5000]\n", "train_y = train_y[:-5000]\n", "\n", "train_dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y))\n", "\n", "# Sample random batches of data from the entire training set\n", "train_iterator = iter(\n", " train_dataset.repeat().shuffle(10000).batch(train_batch_size))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "nWemRI-x4FEa" }, "outputs": [], "source": [ "#@title Training set-up\n", "\n", "model_types = []\n", "models = []\n", "n_models = len(alphas)\n", "\n", "for alpha in alphas:\n", " w_init = PowerPropVarianceScaling(alpha)\n", " models.append(DensityNetwork(MLP(alpha=alpha, w_init=w_init)))\n", " if alpha \u003e 1.0:\n", " model_types.append('Powerprop. ($\\\\alpha={}$)'.format(alpha))\n", " else:\n", " model_types.append('Baseline')\n", "\n", "# Initalise variables\n", "for m in models:\n", " m(valid_x)\n", "\n", "initial_weights = [m.get_weights() for m in models]\n", "\n", "\n", "optimizers = [snt.optimizers.SGD(learning_rate=learning_rate)\n", " for _ in range(n_models)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "xvtAESbqFGaQ" }, "outputs": [], "source": [ "#@title Training loop\n", "\n", "all_train_stats = [[] for _ in range(n_models)]\n", "all_valid_stats = [[] for _ in range(n_models)]\n", "\n", "for step in range(num_train_steps+1):\n", " train_x_batch, train_y_batch = next(train_iterator)\n", "\n", " for m_id, model in enumerate(models):\n", " all_train_stats[m_id].append(\n", " train_fn(model, train_x_batch, train_y_batch, optimizers[m_id]))\n", "\n", " if step % report_interval == 0:\n", " for m_id, model in enumerate(models):\n", " print('[Train Step {}, Alpha {}] Loss: {:1.3f}. Acc: {:1.3f}'.format(\n", " step, \n", " alphas[m_id], \n", " all_train_stats[m_id][-1]['loss'], \n", " all_train_stats[m_id][-1]['acc']))\n", " all_valid_stats[m_id].append(eval_fn(model, valid_x, valid_y))\n", "\n", " print('[Eval Step {}, Alpha {}] Loss: {:1.3f}. Acc: {:1.3f}'.format(\n", " step, \n", " alphas[m_id], \n", " all_valid_stats[m_id][-1]['loss'], \n", " all_valid_stats[m_id][-1]['acc']))\n", " print('---')" ] }, { "cell_type": "markdown", "metadata": { "id": "NDBNpC_4piOg" }, "source": [ "### Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "dG7tyvmOQxeX" }, "outputs": [], "source": [ "#@title Pruning\n", "\n", "final_weights = [m.get_weights() for m in models]\n", "\n", "eval_at_sparsity_level = np.geomspace(0.01, 1.0, 20).tolist()\n", "acc_at_sparsity = [[] for _ in range(n_models)]\n", "\n", "for p_to_use in eval_at_sparsity_level:\n", "\n", " # Half the sparsity at output layer\n", " percent = 2*[p_to_use] + [min(1.0, p_to_use*2)]\n", "\n", " for m_id, model_to_use in enumerate(models):\n", " masks = []\n", " for i, w in enumerate(final_weights[m_id]):\n", " masks.append(prune_by_magnitude(percent[i], w))\n", "\n", " _, stats = model_to_use.loss(test_x, test_y, masks=masks)\n", "\n", " acc_at_sparsity[m_id].append(stats['acc'].numpy())\n", " print(' Performance @ {:1.0f}% of weights [Alpha {}]: Acc {:1.3f} NLL {:1.3f} '.format(\n", " 100*p_to_use, alphas[m_id], stats['acc'], stats['loss']))\n", " print('---')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "nv8u662-4yug" }, "outputs": [], "source": [ "sns.set_style(\"whitegrid\")\n", "sns.set_context(\"paper\")\n", "\n", "#@title Plot\n", "f, ax = plt.subplots(1, 1, figsize=(7,5))\n", "\n", "for acc, label in zip(acc_at_sparsity, model_types):\n", " ax.plot(eval_at_sparsity_level, acc, label=label, marker='o', lw=2)\n", "\n", "ax.set_xscale('log')\n", "ax.set_xlim([1.0, 0.01])\n", "ax.set_ylim([0.0, 1.0])\n", "ax.legend(frameon=False)\n", "ax.set_xlabel('Weights Remaining (%)')\n", "ax.set_ylabel('Test Accuracy (%)')\n", "\n", "sns.despine()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-NEnDx52w9NL" }, "outputs": [], "source": [ "" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "zNZE9eU_poe0", "J8M6PJ4oNK0X", "NDBNpC_4piOg" ], "last_runtime": { "build_target": "//learning/deepmind/dm_python:dm_notebook3", "kind": "private" }, "name": "Powerpropagation", "private_outputs": true, "provenance": [ { "file_id": "12kDm1gRUsFQaQUfmf1AzONNtg2Oxm96h", "timestamp": 1621155153435 }, { "file_id": "1m7j2OKVrTR0YkvMXwDIZRmZdvFlB_HMk", "timestamp": 1615900826226 }, { "file_id": "11JbbynSJgkt4n92oEKrrgfHzCQo-4sRp", "timestamp": 1613740982607 }, { "file_id": "1Pa7Szt1OuHZXczMcMxLE_UctRRtMIbkO", "timestamp": 1612795019221 } ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: rapid_task_solving/README.md ================================================ # One-shot StreetLearn and Memory & Planning Game This repository contains code for the environments used in the paper ["Rapid Task-Solving in Novel Environments"](https://arxiv.org/abs/2006.03662) by Sam Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matt Botvinick and David Raposo. It was published as a conference paper at ICLR 2021. To cite this work: ``` @inproceedings{ ritter2021rapid, title={Rapid Task-Solving in Novel Environments}, author={Samuel Ritter and Ryan Faulkner and Laurent Sartran and Adam Santoro and Matthew Botvinick and David Raposo}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=F-mvpFpn_0q} } ``` ### Memory&Planning Game The _Memory&Planning Game_ is a simple variation of the well-known _Memory Game_, wherein players must remember the locations of cards in a grid. This variation extends the challenge to require planning as well as remembering. In the _Memory&Planning Game_, the agent occupies an environment consisting of a grid of symbols (e.g. 4x4). The observation consists of two symbols — one which corresponds to the agent's current location, and another that corresponds to the "goal" the agent is tasked with navigating to. The agent can not see its relative location with respect to other symbols in the grid. At each step the agent selects one of 5 possible actions: _move left_, _move right_, _move up_, _move down_, and _collect_. If the agent chooses the "collect" action when its current location symbol matches the goal symbol, a reward of +1 is received. Otherwise, the agent receives a reward of 0. At the beginning of each episode, a new set of symbols is sampled, effectively inducing a new transition function. The agent is allowed a fixed number of steps (e.g. 100) per episode to "collect" as many goals as possible. Each time the agent collects a goal — which corresponds to completing a task —, a new goal is sampled in and the transition function stays fixed. #### Example The following code snipped shows an example of how to load the environment, start a new episode and take a few random steps. A plot representing the current state of the environment is displayed for each step. ``` import memory_planning_game env = memory_planning_game.MemoryPlanningGame(4, seed=123) _ = env.reset() for _ in range(5): timestep = env.take_random_action() fig, ax = env.draw_maze() ```  ### One-Shot StreetLearn The _One-Shot StreetLearn_ is a domain wherein environments are sampled as neighborhoods from the _StreetLearn_ dataset (Mirowski et al., 2019). Tasks are then sampled by selecting a position and orientation that the agent must navigate to from its current location. In _One-Shot StreetLearn_, the agent's observations consist of a representation of the current state and a representation of the goal state. The agent receives no other information from the environment. The available actions are _turn right_, which orients the agent clockwise toward the next available direction of motion from its current location; _turn left_, which does the same in the other direction; and _move forward_, which moves the agent along the direction it is facing to the next available location. In each episode, we sample a new neighborhood with 5 intersections from one of 12 cities. To reduce the exploration problem while keeping the planning difficulty constant, we removed all the locations that were not intersections (i.e. that corresponded to degree-2 nodes in the connectivity graph of the sampled neighbourhood). Note: In the paper we used images to represent each location, taken from the _StreetLearn_ dataset. In this codebase, to simplify the public release process, we replace the images with one-hot vectors, to represent each location. Every time the agent reaches a goal, a new starting- and goal-state pair is sampled, initiating a new task, until the fixed episode step limit is reached (e.g. 200 steps). A step that takes the agent to the goal state results in a reward of +1. Any other step results in a reward of 0. #### City graph datasets In this [link] (https://console.cloud.google.com/storage/browser/one_shot_streetlearn_graphs) you can download the datasets containing the connectivity graphs of different cities. You will need at least one of these datasets in order to build the environment. #### Example The following code snipped shows an example of how to load the environment for an example city (in this case, Madrid), start a new episode and take a few random steps. A plot representing the current state of the environment is displayed for each step. ``` import numpy as np import one_shot_streetlearn region = 'madrid' sl_config = { 'dataset_path': f'./datasets/{region}_full_graph.gexf', 'max_episode_steps': 200, 'num_junctions': 5, } env = one_shot_streetlearn.OneShotStreetLearn(**sl_config) _ = env.reset() for _ in range(4): rnd_action = np.random.randint(env.NUM_ACTIONS) timestep = env.step(rnd_action) fig, ax = env.draw_subgraph() ```  ================================================ FILE: rapid_task_solving/memory_planning_game.py ================================================ # Copyright 2021 DeepMind Technologies Limited. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Memory & Planning Game environment.""" import string import dm_env import matplotlib.pyplot as plt import networkx as nx import numpy as np class MemoryPlanningGame(dm_env.Environment): """Memory & Planning Game environment.""" ACTION_NAMES = ['Up', 'Down', 'Left', 'Right', 'Collect'] NUM_ACTIONS = len(ACTION_NAMES) DIRECTIONS = [ (0, 1), # Up (0, -1), # Down (-1, 0), # Left (1, 0), # Right (0, 0), # Collect ] def __init__(self, maze_size=4, max_episode_steps=100, target_reward=1., per_step_reward=0., random_respawn=False, seed=None): """The Memory & Planning Game environment. Args: maze_size: (int) size of the maze dimension. max_episode_steps: (int) number of steps per episode. target_reward: (float) reward value of the target. per_step_reward: (float) reward/cost of taking a step. random_respawn: (bool) whether the agent respawns in a random location upon collecting the goal. seed: (int or None) seed for random number generator. """ self._maze_size = maze_size self._num_labels = maze_size * maze_size # The graph itself is the same across episodes, but the node labels will be # randomly sampled in each episode. self._graph = nx.grid_2d_graph( self._maze_size, self._maze_size, periodic=True) self._max_episode_steps = max_episode_steps self._target_reward = target_reward self._per_step_reward = per_step_reward self._random_respawn = random_respawn self._rng = np.random.RandomState(seed) def _one_hot(self, node): one_hot_vector = np.zeros([self._num_labels], dtype=np.int32) one_hot_vector[self._labels[node]] = 1 return one_hot_vector def step(self, action): # If previous step was the last step of an episode, reset. if self._needs_reset: return self.reset() # Increment step count and check if it's the last step of the episode. self._episode_steps += 1 if self._episode_steps >= self._max_episode_steps: self._needs_reset = True transition = dm_env.termination else: transition = dm_env.transition # Recompute agent's position given the selected action. direction = self.DIRECTIONS[action] self._position = tuple( (np.array(self._position) + np.array(direction)) % self._maze_size) self._previous_action = self.ACTION_NAMES[action] # Get reward if agent is over the goal location and the selected action is # `collect`. if self._position == self._goal and self.ACTION_NAMES[action] == 'Collect': reward = self._target_reward self._set_new_goal() else: reward = self._per_step_reward self._episode_reward += reward return transition(reward, self._observation()) def _observation(self): return { 'position': np.array(self._one_hot(self.position), dtype=np.int32), 'goal': np.array(self._one_hot(self.goal), dtype=np.int32), } def observation_spec(self): return { 'position': dm_env.specs.Array( shape=(self._num_labels,), dtype=np.int32, name='position'), 'goal': dm_env.specs.Array( shape=(self._num_labels,), dtype=np.int32, name='goal'), } def action_spec(self): return dm_env.specs.DiscreteArray(self.NUM_ACTIONS) def take_random_action(self): return self.step(self._rng.randint(self.NUM_ACTIONS)) def reset(self): self._previous_action = '' self._episode_reward = 0. self._episode_steps = 0 self._needs_reset = False random_labels = self._rng.permutation(self._num_labels) self._labels = {n: random_labels[i] for i, n in enumerate(self._graph.nodes())} self._respawn() self._set_new_goal() return dm_env.restart(self._observation()) def _respawn(self): random_idx = self._rng.randint(self._num_labels) self._position = list(self._graph.nodes())[random_idx] def _set_new_goal(self): if self._random_respawn: self._respawn() goal = self._position while goal == self._position: random_idx = self._rng.randint(self._num_labels) goal = list(self._graph.nodes())[random_idx] self._goal = goal @property def position(self): return self._position @property def goal(self): return self._goal @property def previous_action(self): return self._previous_action @property def episode_reward(self): return self._episode_reward def draw_maze(self, ax=None): if ax is None: plt.figure() ax = plt.gca() node_positions = {(x, y): (x, y) for x, y in self._graph.nodes()} letters = string.ascii_uppercase + string.ascii_lowercase labels = {n: letters[self._labels[n]] for n in self._graph.nodes()} node_list = list(self._graph.nodes()) colors = [] for n in node_list: if n == self.position: colors.append('lightblue') elif n == self.goal: colors.append('lightgreen') else: colors.append('pink') nx.draw(self._graph, pos=node_positions, nodelist=node_list, ax=ax, node_color=colors, with_labels=True, node_size=200, labels=labels) ax.set_title('{}\nEpisode reward={:.1f}'.format( self.previous_action, self.episode_reward)) ax.margins(.1) return plt.gcf(), ax ================================================ FILE: rapid_task_solving/one_shot_streetlearn.py ================================================ # Copyright 2021 DeepMind Technologies Limited. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """One-shot StreetLearn environment.""" import dm_env import matplotlib.pyplot as plt import networkx as nx import numpy as np def deg_to_rad(x): """Convert degrees to radians.""" return x / 180. * np.pi def rad_to_deg(x): """Convert radians to degrees.""" return x * 180. / np.pi class OneShotStreetLearn(dm_env.Environment): """One-shot Streetlearn environment.""" ACTION_NAMES = [ 'Forward', 'Left', 'Right', 'Collect', ] NUM_ACTIONS = len(ACTION_NAMES) def __init__(self, dataset_path, max_episode_steps, num_junctions=8, target_reward=1., per_step_reward=0., observation_length=60, seed=None): self._graph = nx.read_gexf(dataset_path) self._node_attrs = self._graph.nodes(data=True) self._num_junctions = num_junctions self._observation_length = observation_length self._max_episode_steps = max_episode_steps self._target_reward = target_reward self._per_step_reward = per_step_reward self._rng = np.random.RandomState(seed) self.reset() def reset(self): self._previous_action = '' self._episode_reward = 0. self._episode_steps = 0 self._needs_reset = False self._subgraph = self.get_random_subgraph() self._observation_map = self.randomize_observations(self._subgraph) self._position = self._rng.choice(list(self._subgraph.nodes())) neighbours = self._neighbors_bearings(self._subgraph, self._position) self._neighbour = neighbours[self._rng.randint(len(neighbours))] self._set_new_goal() return dm_env.restart(self._observation()) @property def _current_edge(self): return (self._position, self._neighbour['neighbour']) def _set_new_goal(self): goal = None edges = list(self._observation_map.keys()) while goal is None or goal == self._current_edge: goal = edges[self._rng.randint(len(edges))] self._goal = goal def _one_hot(self, edge): one_hot_vector = np.zeros([self._observation_length], dtype=np.int32) one_hot_vector[self._observation_map[edge]] = 1 return one_hot_vector def _observation(self): return { 'position': np.array(self._one_hot(self._current_edge), dtype=np.int32), 'goal': np.array(self._one_hot(self._goal), dtype=np.int32), } def observation_spec(self): return { 'position': dm_env.specs.Array( shape=(self._observation_length,), dtype=np.int32, name='position'), 'goal': dm_env.specs.Array( shape=(self._observation_length,), dtype=np.int32, name='goal'), } def action_spec(self): return dm_env.specs.DiscreteArray(self.NUM_ACTIONS) def step(self, action): # If previous step was the last step of an episode, reset. if self._needs_reset: return self.reset() # Increment step count and check if it's the last step of the episode. self._episode_steps += 1 if self._episode_steps >= self._max_episode_steps: self._needs_reset = True transition = dm_env.termination else: transition = dm_env.transition # Recompute agent's position self._move(action) self._previous_action = self.ACTION_NAMES[action] # Get reward if agent is at the goal location and the selected action is # `collect`. if (self._current_edge == self._goal and self.ACTION_NAMES[action] == 'Collect'): reward = self._target_reward self._set_new_goal() else: reward = self._per_step_reward self._episode_reward += reward return transition(reward, self._observation()) def randomize_observations(self, subgraph): edges = list(subgraph.edges()) edges.extend([(y, x) for (x, y) in edges]) obs_permutation = self._rng.permutation(self._observation_length) return {e: obs_permutation[i] for i, e in enumerate(edges)} def _calculate_bearing(self, node, neighbor): lat1 = deg_to_rad(self._node_attrs[node]['lat']) lng1 = deg_to_rad(self._node_attrs[node]['lng']) lat2 = deg_to_rad(self._node_attrs[neighbor]['lat']) lng2 = deg_to_rad(self._node_attrs[neighbor]['lng']) delta_lng = lng2 - lng1 theta = np.arctan2( np.sin(delta_lng) * np.cos(lat2), np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(delta_lng)) return theta def _neighbors_bearings(self, subgraph, node): bearings = [] for neighbor in list(subgraph[node]): orientation = self._calculate_bearing(node, neighbor) bearings.append({'neighbour': neighbor, 'orientation': orientation}) bearings.sort(key=lambda x: x['orientation']) return bearings def _sort_neighbors(self, node, neighbour): bearings = self._neighbors_bearings(self._subgraph, node) bs = [x['orientation'] for x in bearings] idx = np.argmin(np.abs(bs - neighbour['orientation'])) return { 'forward': bearings[idx], 'right': bearings[idx-1], 'left': bearings[(idx+1) % len(bearings)], } def _move(self, action): neighbours = self._sort_neighbors(self._position, self._neighbour) if action == 0: new_node = self._neighbour['neighbour'] neighbours = self._sort_neighbors(new_node, neighbours['forward']) new_neighbour = neighbours['forward'] else: new_node = self._position if action == 1: new_neighbour = neighbours['left'] elif action == 2: new_neighbour = neighbours['right'] else: new_neighbour = self._neighbour self._position = new_node self._neighbour = new_neighbour def _all_next_junctions(self, subgraph, node): neighbors = list(subgraph[node]) edges = [self._get_next_junction(subgraph, node, nb) for nb in neighbors] nodes = [y for (_, y) in edges] return nodes, edges def _get_next_junction(self, subgraph, initial_node, next_node): node = initial_node while subgraph.degree(next_node) == 2: neighbours = list(subgraph.neighbors(next_node)) neighbours.remove(node) node = next_node next_node = neighbours.pop() return (initial_node, next_node) def get_random_subgraph(self): graph = self._graph num_nodes = len(graph) rnd_index = self._rng.randint(num_nodes) center_node = list(graph.nodes())[rnd_index] while graph.degree(center_node) <= 2: rnd_index = self._rng.randint(num_nodes) center_node = list(graph.nodes())[rnd_index] to_visit = [center_node] visited = [] subgraph = nx.Graph() while to_visit: node = to_visit.pop(0) visited.append(node) new_nodes, new_edges = self._all_next_junctions(graph, node) subgraph.add_edges_from(new_edges) node_degrees = [subgraph.degree(n) for n in subgraph.nodes()] count_junctions = len(list(filter(lambda x: x > 2, node_degrees))) if count_junctions >= self._num_junctions: break new_nodes = filter(lambda x: x not in visited + to_visit, new_nodes) to_visit.extend(new_nodes) return subgraph def draw_subgraph(self, ax=None): if ax is None: _ = plt.figure(figsize=(3, 3)) ax = plt.gca() node_ids = list(self._subgraph.nodes()) pos = { x: (self._node_attrs[x]['lat'], self._node_attrs[x]['lng']) for x in node_ids } labels = {} nc = 'pink' ec = 'black' ns = 50 nshape = 'o' # Draw the current subgraph nx.draw(self._subgraph, pos=pos, node_color=nc, with_labels=False, node_size=ns, labels=labels, edgecolors=ec, node_shape=nshape, ax=ax) max_xy = np.array([np.array(x) for x in pos.values()]).max(0) min_xy = np.array([np.array(x) for x in pos.values()]).min(0) delta_xy = (max_xy - min_xy) / 6. ax.set_xlim([min_xy[0] - delta_xy[0], max_xy[0] + delta_xy[0]]) ax.set_ylim([min_xy[1] - delta_xy[1], max_xy[1] + delta_xy[1]]) # Draw goal position and orientation x = self._node_attrs[self._goal[0]]['lat'] y = self._node_attrs[self._goal[0]]['lng'] rotation = rad_to_deg(self._calculate_bearing(*self._goal)) _ = ax.plot(x, y, marker=(3, 0, rotation - 90), color=(0, 0, 0), markersize=14, markerfacecolor='white') _ = ax.plot(x, y, marker=(2, 0, rotation - 90), color=(0, 0, 0), markersize=12, markerfacecolor='None') # Draw current position and orientation x = self._node_attrs[self._position]['lat'] y = self._node_attrs[self._position]['lng'] rotation = rad_to_deg(self._neighbour['orientation']) _ = ax.plot(x, y, marker=(3, 0, rotation - 90), color=(0, 0, 0), markersize=14, markerfacecolor='lightgreen') _ = ax.plot(x, y, marker=(2, 0, rotation - 90), color=(0, 0, 0), markersize=12, markerfacecolor='None') ax.set_title('{}\nEpisode reward = {}'.format( self._previous_action, self._episode_reward)) return plt.gcf(), ax ================================================ FILE: rapid_task_solving/requirements.txt ================================================ dm-env>=1.2 dm-haiku>=0.0.3 jax>=0.2.8 matplotlib>=3.1.2 networkx>=2.3 numpy>=1.18.0 ================================================ FILE: regal/README.md ================================================ # REGAL This dataset contains dataflow computational graphs generated procedurally, intended for training and evaluating algorithms that optimize execution (e.g. placement and scheduling), in [TensorFlow's CostGraphDef](https://github.com/tensorflow/tensorflow/blob/59ee7f9138482d85cd93c004aca961bea35820c7/tensorflow/core/framework/cost_graph.proto#L12) [protocol buffer](https://en.wikipedia.org/wiki/Protocol_Buffers) format and encoded as [text](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.text_format). Original paper [REGAL: Transfer Learning For Fast Optimization of Computation Graphs](https://arxiv.org/abs/1905.02494) (Paliwal, Gimeno, Nair, Li, Lubin, Kohli, Vinyals) ## Folder structure There are 10000 training graphs, 1000 validation graphs and 1000 test graphs. The file names follow the format of "graph_" plus a hash of the graph topology plus ".pbtxt". ## Filtering For each set (train, valid, test) there are not two graphs with the same topology. We used the Biased Random Key Genetic Algorithm (BRKGA) to filter out graphs that did not have "room for improvement" "Room for improvement" was defined as the union of two conditions: * if BRKGA with a low fitness evaluation limit (number of calls to fitness function) did not fit the hardware constraints and BRKGA with a high number did. * if BRKGA with a high fitness evaluation limit was 20% faster in running time that BRKGA with a low number. ## Example Graph ```protobuf node { name: "_SOURCE" } node { name: "node_0" id: 1 control_input: 0 } node { name: "node_1" id: 2 output_info { size: 70 alias_input_port: -1 } control_input: 0 compute_cost: 58 } node { name: "node_2" id: 3 output_info { size: 52 alias_input_port: -1 } control_input: 0 compute_cost: 47 } node { name: "node_3" id: 4 input_info { preceding_node: 2 } output_info { size: 55 alias_input_port: -1 } control_input: 0 compute_cost: 58 } ``` ## Dataset Location The dataset is available in the following [link](https://storage.googleapis.com/synthetic-graphs-dataset/synthetic-graphs.tar.gz) ## Dataset Metadata The following table is necessary for this dataset to be indexed by search engines such as Google Dataset Search.
## Disclaimer
This is not an officially supported Google product.
================================================
FILE: rl_unplugged/README.md
================================================
# RL Unplugged: Benchmarks for Offline Reinforcement Learning
RL Unplugged is suite of benchmarks for offline reinforcement learning. The RL
Unplugged is designed around the following considerations: to facilitate ease of
use, we provide the datasets with a unified API which makes it easy for the
practitioner to work with all data in the suite once a general pipeline has been
established. This is a dataset accompanying the paper
[RL Unplugged: Benchmarks for Offline Reinforcement Learning]([https://arxiv.org/abs/2006.13888]).
In this suite of benchmarks, we try to focus on the following problems:
- High dimensional action spaces, for example the locomotion humanoid domains,
we have 56 dimensional actions.
- High dimensional observations.
- Partial observability, observations have egocentric vision.
- Difficulty of exploration, using states of the art algorithms and imitation
to generate data for difficult environments.
- Real world challenges.
The data is available under
[RL Unplugged GCP bucket](https://console.cloud.google.com/storage/browser/rl_unplugged).
## Atari Dataset
We are releasing a large and diverse dataset of gameplay following the protocol
described by [Agarwal et al., 2020], which can be used to evaluate several
discrete offline RL algorithms. The dataset is generated by running an online
DQN agent and recording transitions from its replay during training with sticky
actions [Machado et al., 2018]. As stated in [Agarwal et al., 2020], for each
game we use data from five runs with 50 million transitions each. States in each
transition include stacks of four frames to be able to do frame-stacking with
our baselines. We release datasets for 46 Atari games. For details on how the
dataset was generated, please refer to the paper.
Atari is a standard RL benchmark. We recommend you to try offline RL methods
on Atari if you are interested in comparing your approach to other state of the
art offline RL methods with discrete actions.
## DeepMind Locomotion Dataset
These tasks are made up of the corridor locomotion tasks involving the CMU
Humanoid, for which prior efforts have either used motion capture data [Merel et
al., 2019a], [Merel et al., 2019b] or training from scratch [Song et al., 2020].
In addition, the DM Locomotion repository contains a set of tasks adapted to be
suited to a virtual rodent [Merel et al., 2020]. We emphasize that the DM
Locomotion tasks feature the combination of challenging high-DoF continuous
control along with perception from rich egocentric observations. For details on
how the dataset was generated, please refer to the paper.
We recommend you to try offline RL methods on DeepMind Locomotion dataset, if
you are interested in very challenging offline RL dataset with continuous
action space.
## DeepMind Control Suite Dataset
DeepMind Control Suite [Tassa et al., 2018] is a set of control tasks
implemented in MuJoCo [Todorov et al., 2012]. We consider a subset of the tasks
provided in the suite that cover a wide range of difficulties.
Most of the datasets in this domain are generated using D4PG. For the
environments Manipulator insert ball and Manipulator insert peg we use V-MPO
[Song et al., 2020] to generate the data as D4PG is unable to solve these tasks.
We release datasets for 9 control suite tasks. For details on how the dataset
was generated, please refer to the paper.
DeepMind Control Suite is a traditional continuous action RL benchmark. In
particular, we recommend you test your approach in DeepMind Control Suite if
you are interested in comparing against other state of the art offline RL
methods.
## Realworld RL Dataset
Examples in the dataset represent SARS transitions stored when running a
partially online trained agent as described in
[RWRL](https://arxiv.org/abs/2003.11881).
We release 8 datasets in total -- with no combined challenge and easy combined
challenge on the cartpole, walker, quadruped, and humanoid tasks. For details on
how the dataset was generated, please refer to the paper.
## DeepMind Lab Dataset
DeepMind Lab dataset has several levels from the challenging, partially
observable [Deepmind Lab suite](https://github.com/deepmind/lab). DeepMind Lab
dataset is collected by training distributed R2D2 by [Kapturowski et al., 2018]
agents from scratch on individual tasks. We recorded the experience across all
actors during entire training runs a few times for every task. The details of
the dataset generation process is described in [Gulcehre et al., 2021].
We release datasets for five different DeepMind Lab levels: `seekavoid_arena_01`,
`explore_rewards_few`, `explore_rewards_many`, `rooms_watermaze`,
`rooms_select_nonmatching_object`. We also release the snapshot datasets for
`seekavoid_arena_01` level that we generated the datasets from a trained R2D2
snapshot with different levels of epsilons for the epsilon-greedy algorithm
when evaluating the agent in the environment.
DeepMind Lab dataset is fairly large-scale. We recommend you to try it if you
are interested in large-scale offline RL models with memory.
## bsuite Dataset
[bsuite](https://github.com/deepmind/bsuite) data was collected by training DQN
agents with the default setting in [Acme](https://github.com/deepmind/acme) from
scratch in each one of the following three tasks: cartpole, catch, and
mountain_car.
We converted the originally deterministic environments into stochastic ones by
randomly replacing the agent action with a uniformly sampled action with a
probability of {0, 0.1, 0.2, 0.3, 0.4, 0.5}. In this case, probability of 0
corresponds to original environment. The details of
the dataset generation process is described in [Gulcehre et al., 2021].
bsuite datasets are fairly light-weight and running experiments doesn't require
too much compute. We recommend you to try bsuite, if you are interested in
small-scale and easy to run offline RL datasets generated by stochastic
environments where the stochasticity of the environment is easy to control.
## Running the code
### Installation
* Install dependencies: `pip install -r requirements.txt`
* (Optional) Setup MuJoCo license key for DM Control environments
([instructions](https://github.com/deepmind/dm_control#requirements-and-installation)).
* (Optional) Install
[realworldrl_suite](https://github.com/google-research/realworldrl_suite#installation).
### Atari example
```
mkdir -p /tmp/dataset/Asterix
gsutil cp gs://rl_unplugged/atari/Asterix/run_1-00000-of-00100 \
/tmp/dataset/Asterix/run_1-00000-of-00001
python atari_example.py --path=/tmp/dataset --game=Asterix
```
This copies a single shard from one of the Asterix datasets from GCP to a local
folder, and then runs a script that loads a single example and runs a step on
the Atari environment.
## Citation
Please use the following bibtex for citations:
```
@misc{gulcehre2020rl,
title={RL Unplugged: Benchmarks for Offline Reinforcement Learning},
author={Caglar Gulcehre and Ziyu Wang and Alexander Novikov and Tom Le Paine
and Sergio Gómez Colmenarejo and Konrad Zolna and Rishabh Agarwal and
Josh Merel and Daniel Mankowitz and Cosmin Paduraru and Gabriel
Dulac-Arnold and Jerry Li and Mohammad Norouzi and Matt Hoffman and
Ofir Nachum and George Tucker and Nicolas Heess and Nando deFreitas},
year={2020},
eprint={2006.13888},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
# Dataset Metadata
The following table is necessary for this dataset to be indexed by search
engines such as Google Dataset Search.
[Agarwal et al., 2020]: https://arxiv.org/abs/1907.04543
[Machado et al., 2018]: https://arxiv.org/abs/1709.06009
[Merel et al., 2019a]: https://arxiv.org/abs/1811.09656
[Merel et al., 2019b]: https://arxiv.org/abs/1811.11711
[Merel et al., 2020]: https://arxiv.org/abs/1911.09451
[Song et al., 2020]: https://arxiv.org/abs/1909.12238
[Tassa et al., 2018]: https://arxiv.org/abs/1801.00690
[Todorov et al., 2012]: https://homes.cs.washington.edu/~todorov/papers/TodorovIROS12.pdf
[Kapturowski et al., 2018]: https://openreview.net/forum?id=r1lyTjAqYX
[Gulcehre et al., 2021]: https://arxiv.org/abs/2103.09575
================================================
FILE: rl_unplugged/atari.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Atari RL Unplugged datasets.
Examples in the dataset represent SARSA transitions stored during a
DQN training run as described in https://arxiv.org/pdf/1907.04543.
For every training run we have recorded all 50 million transitions corresponding
to 200 million environment steps (4x factor because of frame skipping). There
are 5 separate datasets for each of the 45 games.
Every transition in the dataset is a tuple containing the following features:
* o_t: Observation at time t. Observations have been processed using the
canonical Atari frame processing, including 4x frame stacking. The shape
of a single observation is [84, 84, 4].
* a_t: Action taken at time t.
* r_t: Reward after a_t.
* d_t: Discount after a_t.
* o_tp1: Observation at time t+1.
* a_tp1: Action at time t+1.
* extras:
* episode_id: Episode identifier.
* episode_return: Total episode return computed using per-step [-1, 1]
clipping.
"""
import functools
import os
from typing import Dict
from acme import wrappers
import dm_env
from dm_env import specs
from dopamine.discrete_domains import atari_lib
import reverb
import tensorflow as tf
# 9 tuning games.
TUNING_SUITE = [
'BeamRider',
'DemonAttack',
'DoubleDunk',
'IceHockey',
'MsPacman',
'Pooyan',
'RoadRunner',
'Robotank',
'Zaxxon',
]
# 36 testing games.
TESTING_SUITE = [
'Alien',
'Amidar',
'Assault',
'Asterix',
'Atlantis',
'BankHeist',
'BattleZone',
'Boxing',
'Breakout',
'Carnival',
'Centipede',
'ChopperCommand',
'CrazyClimber',
'Enduro',
'FishingDerby',
'Freeway',
'Frostbite',
'Gopher',
'Gravitar',
'Hero',
'Jamesbond',
'Kangaroo',
'Krull',
'KungFuMaster',
'NameThisGame',
'Phoenix',
'Pong',
'Qbert',
'Riverraid',
'Seaquest',
'SpaceInvaders',
'StarGunner',
'TimePilot',
'UpNDown',
'VideoPinball',
'WizardOfWor',
'YarsRevenge',
]
# Total of 45 games.
ALL = TUNING_SUITE + TESTING_SUITE
def _decode_frames(pngs: tf.Tensor):
"""Decode PNGs.
Args:
pngs: String Tensor of size (4,) containing PNG encoded images.
Returns:
4 84x84 grayscale images packed in a (84, 84, 4) uint8 Tensor.
"""
# Statically unroll png decoding
frames = [tf.image.decode_png(pngs[i], channels=1) for i in range(4)]
frames = tf.concat(frames, axis=2)
frames.set_shape((84, 84, 4))
return frames
def _make_reverb_sample(o_t: tf.Tensor,
a_t: tf.Tensor,
r_t: tf.Tensor,
d_t: tf.Tensor,
o_tp1: tf.Tensor,
a_tp1: tf.Tensor,
extras: Dict[str, tf.Tensor]) -> reverb.ReplaySample:
"""Create Reverb sample with offline data.
Args:
o_t: Observation at time t.
a_t: Action at time t.
r_t: Reward at time t.
d_t: Discount at time t.
o_tp1: Observation at time t+1.
a_tp1: Action at time t+1.
extras: Dictionary with extra features.
Returns:
Replay sample with fake info: key=0, probability=1, table_size=0.
"""
info = reverb.SampleInfo(
key=tf.constant(0, tf.uint64),
probability=tf.constant(1.0, tf.float64),
table_size=tf.constant(0, tf.int64),
priority=tf.constant(1.0, tf.float64),
times_sampled=tf.constant(1, tf.int32))
data = (o_t, a_t, r_t, d_t, o_tp1, a_tp1, extras)
return reverb.ReplaySample(info=info, data=data)
def _tf_example_to_reverb_sample(tf_example: tf.train.Example
) -> reverb.ReplaySample:
"""Create a Reverb replay sample from a TF example."""
# Parse tf.Example.
feature_description = {
'o_t': tf.io.FixedLenFeature([4], tf.string),
'o_tp1': tf.io.FixedLenFeature([4], tf.string),
'a_t': tf.io.FixedLenFeature([], tf.int64),
'a_tp1': tf.io.FixedLenFeature([], tf.int64),
'r_t': tf.io.FixedLenFeature([], tf.float32),
'd_t': tf.io.FixedLenFeature([], tf.float32),
'episode_id': tf.io.FixedLenFeature([], tf.int64),
'episode_return': tf.io.FixedLenFeature([], tf.float32),
}
data = tf.io.parse_single_example(tf_example, feature_description)
# Process data.
o_t = _decode_frames(data['o_t'])
o_tp1 = _decode_frames(data['o_tp1'])
a_t = tf.cast(data['a_t'], tf.int32)
a_tp1 = tf.cast(data['a_tp1'], tf.int32)
episode_id = tf.bitcast(data['episode_id'], tf.uint64)
# Build Reverb replay sample.
extras = {
'episode_id': episode_id,
'return': data['episode_return']
}
return _make_reverb_sample(o_t, a_t, data['r_t'], data['d_t'], o_tp1, a_tp1,
extras)
def dataset(path: str,
game: str,
run: int,
num_shards: int = 100,
shuffle_buffer_size: int = 100000) -> tf.data.Dataset:
"""TF dataset of Atari SARSA tuples."""
path = os.path.join(path, f'{game}/run_{run}')
filenames = [f'{path}-{i:05d}-of-{num_shards:05d}' for i in range(num_shards)]
file_ds = tf.data.Dataset.from_tensor_slices(filenames)
file_ds = file_ds.repeat().shuffle(num_shards)
example_ds = file_ds.interleave(
functools.partial(tf.data.TFRecordDataset, compression_type='GZIP'),
cycle_length=tf.data.experimental.AUTOTUNE,
block_length=5)
example_ds = example_ds.shuffle(shuffle_buffer_size)
return example_ds.map(_tf_example_to_reverb_sample,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
class AtariDopamineWrapper(dm_env.Environment):
"""Wrapper for Atari Dopamine environmnet."""
def __init__(self, env, max_episode_steps=108000):
self._env = env
self._max_episode_steps = max_episode_steps
self._episode_steps = 0
self._reset_next_episode = True
def reset(self):
self._episode_steps = 0
self._reset_next_step = False
observation = self._env.reset()
return dm_env.restart(observation.squeeze(-1))
def step(self, action):
if self._reset_next_step:
return self.reset()
observation, reward, terminal, _ = self._env.step(action.item())
observation = observation.squeeze(-1)
discount = 1 - float(terminal)
self._episode_steps += 1
if terminal:
self._reset_next_episode = True
return dm_env.termination(reward, observation)
elif self._episode_steps == self._max_episode_steps:
self._reset_next_episode = True
return dm_env.truncation(reward, observation, discount)
else:
return dm_env.transition(reward, observation, discount)
def observation_spec(self):
space = self._env.observation_space
return specs.Array(space.shape[:-1], space.dtype)
def action_spec(self):
return specs.DiscreteArray(self._env.action_space.n)
def environment(game: str) -> dm_env.Environment:
"""Atari environment."""
env = atari_lib.create_atari_environment(game_name=game,
sticky_actions=True)
env = AtariDopamineWrapper(env)
env = wrappers.FrameStackingWrapper(env, num_frames=4)
return wrappers.SinglePrecisionWrapper(env)
================================================
FILE: rl_unplugged/atari_dqn.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KDiJzbb8KFvP"
},
"source": [
"Copyright 2020 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use\n",
"this file except in compliance with the License. You may obtain a copy of the\n",
"License at\n",
"\n",
"[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed\n",
"under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n",
"CONDITIONS OF ANY KIND, either express or implied. See the License for the\n",
"specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ULdrhOaVbsdO"
},
"source": [
"# RL Unplugged: Offline DQN - Atari\n",
"## Guide to training an Acme DQN agent on Atari data.\n",
"# \u003ca href=\"https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/rl_unplugged/atari_dqn.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "xaJxoatMhJ71"
},
"source": [
"## Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "KH3O0zcXUeun"
},
"outputs": [],
"source": [
"!pip install dm-acme\n",
"!pip install dm-acme[reverb]\n",
"!pip install dm-acme[tf]\n",
"!pip install dm-sonnet\n",
"!pip install dopamine-rl==3.1.2\n",
"!pip install atari-py\n",
"!git clone https://github.com/deepmind/deepmind-research.git\n",
"%cd deepmind-research"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "c-H2d6UZi7Sf"
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "HJ74Id-8MERq"
},
"outputs": [],
"source": [
"import copy\n",
"\n",
"import acme\n",
"from acme.agents.tf import actors\n",
"from acme.agents.tf.dqn import learning as dqn\n",
"from acme.tf import utils as acme_utils\n",
"from acme.utils import loggers\n",
"from rl_unplugged import atari\n",
"import sonnet as snt\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "JrOSnoWiY4Xl"
},
"source": [
"## Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Vi3_H_h1zy_0"
},
"outputs": [],
"source": [
"game = 'Pong' #@param\n",
"run = 1 #@param\n",
"\n",
"tmp_path = '/tmp/atari'\n",
"gs_path = 'gs://rl_unplugged/atari'\n",
"\n",
"!mkdir -p {tmp_path}/{game}\n",
"\n",
"src = f'{gs_path}/{game}/run_{run}-00000-of-00100'\n",
"dest = f'{tmp_path}/{game}/run_{run}-00000-of-00001'\n",
"!gsutil cp {src} {dest}"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "a9vF7LtYvLzy"
},
"source": [
"## Dataset and environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "01AHHNd9cEX2"
},
"outputs": [],
"source": [
"batch_size = 10 #@param\n",
"\n",
"def discard_extras(sample):\n",
" return sample._replace(data=sample.data[:5])\n",
"\n",
"dataset = atari.dataset(path=tmp_path, game='Pong', run=1, num_shards=1)\n",
"# Small batch size, experiments in the paper were run with batch size 256.\n",
"dataset = dataset.map(discard_extras).batch(batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KoYBhjPtI_N6"
},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4b4_rHwCmQg-"
},
"outputs": [],
"source": [
"environment = atari.environment(game='Pong')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BukOfOsmtSQn"
},
"source": [
"## DQN learner"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 34
},
"colab_type": "code",
"executionInfo": {
"elapsed": 83,
"status": "ok",
"timestamp": 1593614657342,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "3Jcjk1w6oHVX",
"outputId": "1746b0bb-5a5c-45dd-b5a1-c77852545e12"
},
"outputs": [
{
"data": {
"text/plain": [
"TensorSpec(shape=(6,), dtype=tf.float32, name=None)"
]
},
"execution_count": 20,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"# Get total number of actions.\n",
"num_actions = environment.action_spec().num_values\n",
"\n",
"# Create the Q network.\n",
"network = snt.Sequential([\n",
" lambda x: tf.image.convert_image_dtype(x, tf.float32),\n",
" snt.Conv2D(32, [8, 8], [4, 4]),\n",
" tf.nn.relu,\n",
" snt.Conv2D(64, [4, 4], [2, 2]),\n",
" tf.nn.relu,\n",
" snt.Conv2D(64, [3, 3], [1, 1]),\n",
" tf.nn.relu,\n",
" snt.Flatten(),\n",
" snt.nets.MLP([512, num_actions])\n",
"])\n",
"acme_utils.create_variables(network, [environment.observation_spec()])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "9CD2sNK-oA9S"
},
"outputs": [],
"source": [
"# Create a logger.\n",
"logger = loggers.TerminalLogger(label='learner', time_delta=1.)\n",
"\n",
"# Create the DQN learner.\n",
"learner = dqn.DQNLearner(\n",
" network=network,\n",
" target_network=copy.deepcopy(network),\n",
" discount=0.99,\n",
" learning_rate=3e-4,\n",
" importance_sampling_exponent=0.2,\n",
" target_update_period=2500,\n",
" dataset=dataset,\n",
" logger=logger)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "oKeGQxzitXYC"
},
"source": [
"## Training loop"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 51
},
"colab_type": "code",
"executionInfo": {
"elapsed": 4694,
"status": "ok",
"timestamp": 1593614662237,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "VWZd5N-Qoz82",
"outputId": "5ee2ce7c-b3fe-483b-8893-5a6e13519f48"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Learner] Loss = 0.003 | Steps = 1 | Walltime = 0\n",
"[Learner] Loss = 0.004 | Steps = 54 | Walltime = 1.126\n"
]
}
],
"source": [
"for _ in range(100):\n",
" learner.step()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "qFQDrp0CgIzU"
},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 102
},
"colab_type": "code",
"executionInfo": {
"elapsed": 15099,
"status": "ok",
"timestamp": 1593614677360,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "DWYHBalygIDF",
"outputId": "4ec412c3-810a-4208-b521-919a8ece40df"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Evaluation] Episode Length = 842 | Episode Return = -20.000 | Episodes = 1 | Steps = 842 | Steps Per Second = 265.850\n",
"[Evaluation] Episode Length = 792 | Episode Return = -21.000 | Episodes = 2 | Steps = 1634 | Steps Per Second = 270.043\n",
"[Evaluation] Episode Length = 812 | Episode Return = -21.000 | Episodes = 3 | Steps = 2446 | Steps Per Second = 274.792\n",
"[Evaluation] Episode Length = 812 | Episode Return = -21.000 | Episodes = 4 | Steps = 3258 | Steps Per Second = 270.967\n",
"[Evaluation] Episode Length = 812 | Episode Return = -21.000 | Episodes = 5 | Steps = 4070 | Steps Per Second = 274.253\n"
]
}
],
"source": [
"# Create a logger.\n",
"logger = loggers.TerminalLogger(label='evaluation', time_delta=1.)\n",
"\n",
"# Create an environment loop.\n",
"policy_network = snt.Sequential([\n",
" network,\n",
" lambda q: tf.argmax(q, axis=-1),\n",
"])\n",
"loop = acme.EnvironmentLoop(\n",
" environment=environment,\n",
" actor=actors.DeprecatedFeedForwardActor(policy_network=policy_network),\n",
" logger=logger)\n",
"\n",
"loop.run(5)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "//learning/deepmind/dm_python:dm_notebook3",
"kind": "private"
},
"name": "RL Unplugged: Offline DQN - Atari",
"provenance": [
{
"file_id": "1g9yTbTuk9aeERxWflOWqUGpx2M3osx0l",
"timestamp": 1593685504110
}
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: rl_unplugged/atari_example.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""Atari dataset example.
Instructions:
> mkdir -p /tmp/dataset/Asterix
> gsutil cp gs://rl_unplugged/atari/Asterix/run_1-00000-of-00100 \
/tmp/dataset/Asterix/run_1-00000-of-00001
> python atari_example.py --path=/tmp/dataset --game=Asterix
"""
from absl import app
from absl import flags
from acme import specs
import tree
from rl_unplugged import atari
flags.DEFINE_string('path', '/tmp/dataset', 'Path to dataset.')
flags.DEFINE_string('game', 'Asterix', 'Game.')
FLAGS = flags.FLAGS
def main(_):
ds = atari.dataset(FLAGS.path, FLAGS.game, 1,
num_shards=1,
shuffle_buffer_size=1)
for sample in ds.take(1):
print('Data spec')
print(tree.map_structure(lambda x: (x.dtype, x.shape), sample.data))
env = atari.environment(FLAGS.game)
print('Environment spec')
print(specs.make_environment_spec(env))
print('Environment observation')
timestep = env.reset()
print(tree.map_structure(lambda x: (x.dtype, x.shape), timestep.observation))
if __name__ == '__main__':
app.run(main)
================================================
FILE: rl_unplugged/bsuite.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "KDiJzbb8KFvP"
},
"source": [
"Copyright 2021 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use\n",
"this file except in compliance with the License. You may obtain a copy of the\n",
"License at\n",
"\n",
"[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed\n",
"under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n",
"CONDITIONS OF ANY KIND, either express or implied. See the License for the\n",
"specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ULdrhOaVbsdO"
},
"source": [
"# RL Unplugged: Offline DQN - Bsuite\n",
"## Guide to training an Acme DQN agent on Bsuite data.\n",
"# \u003ca href=\"https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/rl_unplugged/atari_dqn.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"collapsed": true,
"id": "KH3O0zcXUeun"
},
"outputs": [],
"source": [
"# @title Installation\n",
"!pip install dm-acme\n",
"!pip install dm-acme[reverb]\n",
"!pip install dm-acme[tf]\n",
"!pip install dm-sonnet\n",
"!pip install dopamine-rl==3.1.2\n",
"!pip install atari-py\n",
"!pip install dm_env\n",
"!git clone https://github.com/deepmind/deepmind-research.git\n",
"%cd deepmind-research\n",
"\n",
"!git clone https://github.com/deepmind/bsuite.git\n",
"!pip install -q bsuite/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "HJ74Id-8MERq"
},
"outputs": [],
"source": [
"# @title Imports\n",
"import copy\n",
"import functools\n",
"from typing import Dict, Tuple\n",
"\n",
"\n",
"import acme\n",
"from acme.agents.tf import actors\n",
"from acme.agents.tf.dqn import learning as dqn\n",
"from acme.tf import utils as acme_utils\n",
"from acme.utils import loggers\n",
"\n",
"import sonnet as snt\n",
"import tensorflow as tf\n",
"\n",
"import numpy as np\n",
"import tree\n",
"import dm_env\n",
"import reverb\n",
"from acme.wrappers import base as wrapper_base\n",
"from acme.wrappers import single_precision\n",
"\n",
"import bsuite"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "j0Aahizs2ff-"
},
"outputs": [],
"source": [
"# @title Data Loading Utilities\n",
"def _parse_seq_tf_example(example, shapes, dtypes):\n",
" \"\"\"Parse tf.Example containing one or two episode steps.\"\"\"\n",
"\n",
" def to_feature(shape, dtype):\n",
" if np.issubdtype(dtype, np.floating):\n",
" return tf.io.FixedLenSequenceFeature(\n",
" shape=shape, dtype=tf.float32, allow_missing=True)\n",
" elif dtype == np.bool or np.issubdtype(dtype, np.integer):\n",
" return tf.io.FixedLenSequenceFeature(\n",
" shape=shape, dtype=tf.int64, allow_missing=True)\n",
" else:\n",
" raise ValueError(f'Unsupported type {dtype} to '\n",
" f'convert from TF Example.')\n",
"\n",
" feature_map = {}\n",
" for k, v in shapes.items():\n",
" feature_map[k] = to_feature(v, dtypes[k])\n",
"\n",
" parsed = tf.io.parse_single_example(example, features=feature_map)\n",
"\n",
" restructured = {}\n",
" for k, v in parsed.items():\n",
" dtype = tf.as_dtype(dtypes[k])\n",
" if v.dtype == dtype:\n",
" restructured[k] = parsed[k]\n",
" else:\n",
" restructured[k] = tf.cast(parsed[k], dtype)\n",
"\n",
" return restructured\n",
"\n",
"\n",
"def _build_sars_example(sequences):\n",
" \"\"\"Convert raw sequences into a Reverb SARS' sample.\"\"\"\n",
"\n",
" o_tm1 = tree.map_structure(lambda t: t[0], sequences['observation'])\n",
" o_t = tree.map_structure(lambda t: t[1], sequences['observation'])\n",
" a_tm1 = tree.map_structure(lambda t: t[0], sequences['action'])\n",
" r_t = tree.map_structure(lambda t: t[0], sequences['reward'])\n",
" p_t = tree.map_structure(\n",
" lambda d, st: d[0] * tf.cast(st[1] != dm_env.StepType.LAST, d.dtype),\n",
" sequences['discount'], sequences['step_type'])\n",
"\n",
" info = reverb.SampleInfo(key=tf.constant(0, tf.uint64),\n",
" probability=tf.constant(1.0, tf.float64),\n",
" table_size=tf.constant(0, tf.int64),\n",
" priority=tf.constant(1.0, tf.float64))\n",
" return reverb.ReplaySample(info=info, data=(\n",
" o_tm1, a_tm1, r_t, p_t, o_t))\n",
"\n",
"\n",
"def bsuite_dataset_params(env):\n",
" \"\"\"Return shapes and dtypes parameters for bsuite offline dataset.\"\"\"\n",
" shapes = {\n",
" 'observation': env.observation_spec().shape,\n",
" 'action': env.action_spec().shape,\n",
" 'discount': env.discount_spec().shape,\n",
" 'reward': env.reward_spec().shape,\n",
" 'episodic_reward': env.reward_spec().shape,\n",
" 'step_type': (),\n",
" }\n",
"\n",
" dtypes = {\n",
" 'observation': env.observation_spec().dtype,\n",
" 'action': env.action_spec().dtype,\n",
" 'discount': env.discount_spec().dtype,\n",
" 'reward': env.reward_spec().dtype,\n",
" 'episodic_reward': env.reward_spec().dtype,\n",
" 'step_type': np.int64,\n",
" }\n",
"\n",
" return {'shapes': shapes, 'dtypes': dtypes}\n",
"\n",
"\n",
"def bsuite_dataset(path: str,\n",
" shapes: Dict[str, Tuple[int]],\n",
" dtypes: Dict[str, type], # pylint:disable=g-bare-generic\n",
" num_threads: int,\n",
" batch_size: int,\n",
" num_shards: int,\n",
" shuffle_buffer_size: int = 100000,\n",
" shuffle: bool = True) -\u003e tf.data.Dataset:\n",
" \"\"\"Create tf dataset for training.\"\"\"\n",
"\n",
" filenames = [f'{path}-{i:05d}-of-{num_shards:05d}' for i in range(\n",
" num_shards)]\n",
" file_ds = tf.data.Dataset.from_tensor_slices(filenames)\n",
" if shuffle:\n",
" file_ds = file_ds.repeat().shuffle(num_shards)\n",
"\n",
" example_ds = file_ds.interleave(\n",
" functools.partial(tf.data.TFRecordDataset, compression_type='GZIP'),\n",
" cycle_length=tf.data.experimental.AUTOTUNE,\n",
" block_length=5)\n",
" if shuffle:\n",
" example_ds = example_ds.shuffle(shuffle_buffer_size)\n",
"\n",
" def map_func(example):\n",
" example = _parse_seq_tf_example(example, shapes, dtypes)\n",
" return example\n",
"\n",
" example_ds = example_ds.map(map_func, num_parallel_calls=num_threads)\n",
" if shuffle:\n",
" example_ds = example_ds.repeat().shuffle(batch_size * 10)\n",
"\n",
" example_ds = example_ds.map(\n",
" _build_sars_example,\n",
" num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
" example_ds = example_ds.batch(batch_size, drop_remainder=True)\n",
"\n",
" example_ds = example_ds.prefetch(tf.data.experimental.AUTOTUNE)\n",
"\n",
" return example_ds\n",
"\n",
"\n",
"def load_offline_bsuite_dataset(\n",
" bsuite_id: str,\n",
" path: str,\n",
" batch_size: int,\n",
" num_shards: int = 1,\n",
" num_threads: int = 1,\n",
" single_precision_wrapper: bool = True,\n",
" shuffle: bool = True) -\u003e Tuple[tf.data.Dataset,\n",
" dm_env.Environment]:\n",
" \"\"\"Load bsuite offline dataset.\"\"\"\n",
" # Data file path format: {path}-?????-of-{num_shards:05d}\n",
" # The dataset is not deterministic and not repeated if shuffle = False.\n",
" environment = bsuite.load_from_id(bsuite_id)\n",
" if single_precision_wrapper:\n",
" environment = single_precision.SinglePrecisionWrapper(environment)\n",
" params = bsuite_dataset_params(environment)\n",
" dataset = bsuite_dataset(path=path,\n",
" num_threads=num_threads,\n",
" batch_size=batch_size,\n",
" num_shards=num_shards,\n",
" shuffle_buffer_size=2,\n",
" shuffle=shuffle,\n",
" **params)\n",
" return dataset, environment"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a9vF7LtYvLzy"
},
"source": [
"## Dataset and environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2I4lHR5jLeXm"
},
"outputs": [],
"source": [
"tmp_path = 'gs://rl_unplugged/bsuite'\n",
"level = 'catch'\n",
"dir = '0_0.0'\n",
"filename = '0_full'\n",
"path = f'{tmp_path}/{level}/{dir}/{filename}'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"id": "01AHHNd9cEX2"
},
"outputs": [],
"source": [
"batch_size = 2 #@param\n",
"bsuite_id = level + '/0'\n",
"dataset, environment = load_offline_bsuite_dataset(bsuite_id=bsuite_id,\n",
" path=path,\n",
" batch_size=batch_size)\n",
"dataset = dataset.prefetch(1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BukOfOsmtSQn"
},
"source": [
"## DQN learner"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3Jcjk1w6oHVX"
},
"outputs": [],
"source": [
"# Get total number of actions.\n",
"num_actions = environment.action_spec().num_values\n",
"obs_spec = environment.observation_spec()\n",
"print(environment.observation_spec())\n",
"# Create the Q network.\n",
"network = snt.Sequential([\n",
" snt.flatten,\n",
" snt.nets.MLP([56, 56]),\n",
" snt.nets.MLP([num_actions])\n",
" ])\n",
"acme_utils.create_variables(network, [environment.observation_spec()])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9CD2sNK-oA9S"
},
"outputs": [],
"source": [
"# Create a logger.\n",
"logger = loggers.TerminalLogger(label='learner', time_delta=1.)\n",
"\n",
"# Create the DQN learner.\n",
"learner = dqn.DQNLearner(\n",
" network=network,\n",
" target_network=copy.deepcopy(network),\n",
" discount=0.99,\n",
" learning_rate=3e-4,\n",
" importance_sampling_exponent=0.2,\n",
" target_update_period=2500,\n",
" dataset=dataset,\n",
" logger=logger)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oKeGQxzitXYC"
},
"source": [
"## Training loop"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VWZd5N-Qoz82"
},
"outputs": [],
"source": [
"for _ in range(10000):\n",
" learner.step()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qFQDrp0CgIzU"
},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DWYHBalygIDF"
},
"outputs": [],
"source": [
"# Create a logger.\n",
"logger = loggers.TerminalLogger(label='evaluation', time_delta=1.)\n",
"\n",
"# Create an environment loop.\n",
"policy_network = snt.Sequential([\n",
" network,\n",
" lambda q: tf.argmax(q, axis=-1),\n",
"])\n",
"loop = acme.EnvironmentLoop(\n",
" environment=environment,\n",
" actor=actors.DeprecatedFeedForwardActor(policy_network=policy_network),\n",
" logger=logger)\n",
"\n",
"loop.run(400)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "RL_Unplugged_Offline_DQN_Bsuite.ipynb",
"provenance": [
{
"file_id": "1rhoeTFuan8B_q8eBeCEtQPiIeMux1pWU",
"timestamp": 1613499802780
}
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: rl_unplugged/dm_control_suite.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Control RL Unplugged datasets.
Examples in the dataset represent sequences stored when running a partially
trained agent (trained in online way) as described in
https://arxiv.org/abs/2006.13888.
Every dataset has a SARSA version, and datasets for environments for solving
which we believe one may need a recurrent agent also include a version of the
dataset with overlapping sequences of length 40.
Datasets for the dm_control_suite environments only include proprio
observations, while datasets for dm_locomotion include both pixel and proprio
observations.
"""
import collections
import functools
import os
from typing import Dict, Optional, Tuple, Set
from acme import wrappers
from acme.adders import reverb as adders
from dm_control import composer
from dm_control import suite
from dm_control.composer.variation import colors
from dm_control.composer.variation import distributions
from dm_control.locomotion import arenas
from dm_control.locomotion import props
from dm_control.locomotion import tasks
from dm_control.locomotion import walkers
from dm_env import specs
import numpy as np
import reverb
import tensorflow as tf
import tree
def _build_rodent_escape_env():
"""Build environment where a rodent escapes from a bowl."""
walker = walkers.Rat(
observable_options={'egocentric_camera': dict(enabled=True)},
)
arena = arenas.bowl.Bowl(
size=(20., 20.),
aesthetic='outdoor_natural')
locomotion_task = tasks.escape.Escape(
walker=walker,
arena=arena,
physics_timestep=0.001,
control_timestep=.02)
raw_env = composer.Environment(
time_limit=20,
task=locomotion_task,
strip_singleton_obs_buffer_dim=True)
return raw_env
def _build_rodent_maze_env():
"""Build environment where a rodent runs to targets."""
walker = walkers.Rat(
observable_options={'egocentric_camera': dict(enabled=True)},
)
wall_textures = arenas.labmaze_textures.WallTextures(
style='style_01')
arena = arenas.mazes.RandomMazeWithTargets(
x_cells=11,
y_cells=11,
xy_scale=.5,
z_height=.3,
max_rooms=4,
room_min_size=4,
room_max_size=5,
spawns_per_room=1,
targets_per_room=3,
wall_textures=wall_textures,
aesthetic='outdoor_natural')
rodent_task = tasks.random_goal_maze.ManyGoalsMaze(
walker=walker,
maze_arena=arena,
target_builder=functools.partial(
props.target_sphere.TargetSphere,
radius=0.05,
height_above_ground=.125,
rgb1=(0, 0, 0.4),
rgb2=(0, 0, 0.7)),
target_reward_scale=50.,
contact_termination=False,
control_timestep=.02,
physics_timestep=0.001)
raw_env = composer.Environment(
time_limit=30,
task=rodent_task,
strip_singleton_obs_buffer_dim=True)
return raw_env
def _build_rodent_corridor_gaps():
"""Build environment where a rodent runs over gaps."""
walker = walkers.Rat(
observable_options={'egocentric_camera': dict(enabled=True)},
)
platform_length = distributions.Uniform(low=0.4, high=0.8)
gap_length = distributions.Uniform(low=0.05, high=0.2)
arena = arenas.corridors.GapsCorridor(
corridor_width=2,
platform_length=platform_length,
gap_length=gap_length,
corridor_length=40,
aesthetic='outdoor_natural')
rodent_task = tasks.corridors.RunThroughCorridor(
walker=walker,
arena=arena,
walker_spawn_position=(5, 0, 0),
walker_spawn_rotation=0,
target_velocity=1.0,
contact_termination=False,
terminate_at_height=-0.3,
physics_timestep=0.001,
control_timestep=.02)
raw_env = composer.Environment(
time_limit=30,
task=rodent_task,
strip_singleton_obs_buffer_dim=True)
return raw_env
def _build_rodent_two_touch_env():
"""Build environment where a rodent touches targets."""
walker = walkers.Rat(
observable_options={'egocentric_camera': dict(enabled=True)},
)
arena_floor = arenas.floors.Floor(
size=(10., 10.), aesthetic='outdoor_natural')
task_reach = tasks.reach.TwoTouch(
walker=walker,
arena=arena_floor,
target_builders=[
functools.partial(
props.target_sphere.TargetSphereTwoTouch,
radius=0.025),
],
randomize_spawn_rotation=True,
target_type_rewards=[25.],
shuffle_target_builders=False,
target_area=(1.5, 1.5),
physics_timestep=0.001,
control_timestep=.02)
raw_env = composer.Environment(
time_limit=30,
task=task_reach,
strip_singleton_obs_buffer_dim=True)
return raw_env
def _build_humanoid_walls_env():
"""Build humanoid walker walls environment."""
walker = walkers.CMUHumanoidPositionControlled(
name='walker',
observable_options={'egocentric_camera': dict(enabled=True)},
)
wall_width = distributions.Uniform(low=1, high=7)
wall_height = distributions.Uniform(low=2.5, high=4.0)
swap_wall_side = distributions.Bernoulli(prob=0.5)
wall_r = distributions.Uniform(low=0.5, high=0.6)
wall_g = distributions.Uniform(low=0.21, high=0.41)
wall_rgba = colors.RgbVariation(r=wall_r, g=wall_g, b=0, alpha=1)
arena = arenas.WallsCorridor(
wall_gap=5.0,
wall_width=wall_width,
wall_height=wall_height,
swap_wall_side=swap_wall_side,
wall_rgba=wall_rgba,
corridor_width=10,
corridor_length=100)
humanoid_task = tasks.RunThroughCorridor(
walker=walker,
arena=arena,
walker_spawn_rotation=1.57, # pi / 2
physics_timestep=0.005,
control_timestep=0.03)
raw_env = composer.Environment(
time_limit=30,
task=humanoid_task,
strip_singleton_obs_buffer_dim=True)
return raw_env
def _build_humanoid_corridor_env():
"""Build humanoid walker walls environment."""
walker = walkers.CMUHumanoidPositionControlled(
name='walker',
observable_options={'egocentric_camera': dict(enabled=True)},
)
arena = arenas.EmptyCorridor(
corridor_width=10,
corridor_length=100)
humanoid_task = tasks.RunThroughCorridor(
walker=walker,
arena=arena,
walker_spawn_rotation=1.57, # pi / 2
physics_timestep=0.005,
control_timestep=0.03)
raw_env = composer.Environment(
time_limit=30,
task=humanoid_task,
strip_singleton_obs_buffer_dim=True)
return raw_env
def _build_humanoid_corridor_gaps():
"""Build humanoid walker walls environment."""
walker = walkers.CMUHumanoidPositionControlled(
name='walker',
observable_options={'egocentric_camera': dict(enabled=True)},
)
platform_length = distributions.Uniform(low=0.3, high=2.5)
gap_length = distributions.Uniform(low=0.75, high=1.25)
arena = arenas.GapsCorridor(
corridor_width=10,
platform_length=platform_length,
gap_length=gap_length,
corridor_length=100)
humanoid_task = tasks.RunThroughCorridor(
walker=walker,
arena=arena,
walker_spawn_position=(2, 0, 0),
walker_spawn_rotation=1.57, # pi / 2
physics_timestep=0.005,
control_timestep=0.03)
raw_env = composer.Environment(
time_limit=30,
task=humanoid_task,
strip_singleton_obs_buffer_dim=True)
return raw_env
class MujocoActionNormalizer(wrappers.EnvironmentWrapper):
"""Rescale actions to [-1, 1] range for mujoco physics engine.
For control environments whose actions have bounded range in [-1, 1], this
adaptor rescale actions to the desired range. This allows actor network to
output unscaled actions for better gradient dynamics.
"""
def __init__(self, environment, rescale='clip'):
super().__init__(environment)
self._rescale = rescale
def step(self, action):
"""Rescale actions to [-1, 1] range before stepping wrapped environment."""
if self._rescale == 'tanh':
scaled_actions = tree.map_structure(np.tanh, action)
elif self._rescale == 'clip':
scaled_actions = tree.map_structure(lambda a: np.clip(a, -1., 1.), action)
else:
raise ValueError('Unrecognized scaling option: %s' % self._rescale)
return self._environment.step(scaled_actions)
class NormilizeActionSpecWrapper(wrappers.EnvironmentWrapper):
"""Turn each dimension of the actions into the range of [-1, 1]."""
def __init__(self, environment):
super().__init__(environment)
action_spec = environment.action_spec()
# pytype: disable=attribute-error # always-use-return-annotations
self._scale = action_spec.maximum - action_spec.minimum
self._offset = action_spec.minimum
minimum = action_spec.minimum * 0 - 1.
maximum = action_spec.minimum * 0 + 1.
self._action_spec = specs.BoundedArray(
action_spec.shape,
action_spec.dtype,
minimum,
maximum,
name=action_spec.name)
# pytype: enable=attribute-error # always-use-return-annotations
def _from_normal_actions(self, actions):
actions = 0.5 * (actions + 1.0) # a_t is now in the range [0, 1]
# scale range to [minimum, maximum]
return actions * self._scale + self._offset
def step(self, action):
action = self._from_normal_actions(action)
return self._environment.step(action)
def action_spec(self):
return self._action_spec
class FilterObservationsWrapper(wrappers.EnvironmentWrapper):
"""Filter out all the observations not specified to this wrapper."""
def __init__(self, environment, observations_to_keep):
super().__init__(environment)
self._observations_to_keep = observations_to_keep
spec = self._environment.observation_spec()
filtered = [(k, spec[k]) for k in observations_to_keep]
self._observation_spec = collections.OrderedDict(filtered)
def _filter_observation(self, timestep):
observation = timestep.observation
filtered = [(k, observation[k]) for k in self._observations_to_keep]
return timestep._replace(observation=collections.OrderedDict(filtered))
def step(self, action):
return self._filter_observation(self._environment.step(action))
def reset(self):
return self._filter_observation(self._environment.reset())
def observation_spec(self):
return self._observation_spec
class ControlSuite:
"""Create bits needed to run agents on an Control Suite dataset."""
def __init__(self, task_name='humanoid_run'):
"""Initializes datasets/environments for the Deepmind Control suite.
Args:
task_name: take name. Must be one of,
finger_turn_hard, manipulator_insert_peg, humanoid_run,
cartpole_swingup, cheetah_run, fish_swim, manipulator_insert_ball,
walker_stand, walker_walk
"""
self.task_name = task_name
self._uint8_features = set([])
self._environment = None
if task_name == 'swim':
self._domain_name = 'fish'
self._task_name = 'swim'
self._shapes = {
'observation/target': (3,),
'observation/velocity': (13,),
'observation/upright': (1,),
'observation/joint_angles': (7,),
'action': (5,),
'discount': (),
'reward': (),
'episodic_reward': (),
'step_type': ()
}
elif task_name == 'humanoid_run':
self._domain_name = 'humanoid'
self._task_name = 'run'
self._shapes = {
'observation/velocity': (27,),
'observation/com_velocity': (3,),
'observation/torso_vertical': (3,),
'observation/extremities': (12,),
'observation/head_height': (1,),
'observation/joint_angles': (21,),
'action': (21,),
'discount': (),
'reward': (),
'episodic_reward': (),
'step_type': ()
}
elif task_name == 'manipulator_insert_ball':
self._domain_name = 'manipulator'
self._task_name = 'insert_ball'
self._shapes = {
'observation/arm_pos': (16,),
'observation/arm_vel': (8,),
'observation/touch': (5,),
'observation/hand_pos': (4,),
'observation/object_pos': (4,),
'observation/object_vel': (3,),
'observation/target_pos': (4,),
'action': (5,),
'discount': (),
'reward': (),
'episodic_reward': (),
'step_type': ()}
elif task_name == 'manipulator_insert_peg':
self._domain_name = 'manipulator'
self._task_name = 'insert_peg'
self._shapes = {
'observation/arm_pos': (16,),
'observation/arm_vel': (8,),
'observation/touch': (5,),
'observation/hand_pos': (4,),
'observation/object_pos': (4,),
'observation/object_vel': (3,),
'observation/target_pos': (4,),
'episodic_reward': (),
'action': (5,),
'discount': (),
'reward': (),
'step_type': ()}
elif task_name == 'cartpole_swingup':
self._domain_name = 'cartpole'
self._task_name = 'swingup'
self._shapes = {
'observation/position': (3,),
'observation/velocity': (2,),
'action': (1,),
'discount': (),
'reward': (),
'episodic_reward': (),
'step_type': ()}
elif task_name == 'walker_walk':
self._domain_name = 'walker'
self._task_name = 'walk'
self._shapes = {
'observation/orientations': (14,),
'observation/velocity': (9,),
'observation/height': (1,),
'action': (6,),
'discount': (),
'reward': (),
'episodic_reward': (),
'step_type': ()}
elif task_name == 'walker_stand':
self._domain_name = 'walker'
self._task_name = 'stand'
self._shapes = {
'observation/orientations': (14,),
'observation/velocity': (9,),
'observation/height': (1,),
'action': (6,),
'discount': (),
'reward': (),
'episodic_reward': (),
'step_type': ()}
elif task_name == 'cheetah_run':
self._domain_name = 'cheetah'
self._task_name = 'run'
self._shapes = {
'observation/position': (8,),
'observation/velocity': (9,),
'action': (6,),
'discount': (),
'reward': (),
'episodic_reward': (),
'step_type': ()}
elif task_name == 'finger_turn_hard':
self._domain_name = 'finger'
self._task_name = 'turn_hard'
self._shapes = {
'observation/position': (4,),
'observation/velocity': (3,),
'observation/touch': (2,),
'observation/target_position': (2,),
'observation/dist_to_target': (1,),
'action': (2,),
'discount': (),
'reward': (),
'episodic_reward': (),
'step_type': ()}
else:
raise ValueError('Task \'{}\' not found.'.format(task_name))
self._data_path = 'dm_control_suite/{}/train'.format(task_name)
@property
def shapes(self):
return self._shapes
@property
def data_path(self):
return self._data_path
@property
def uint8_features(self):
return self._uint8_features
@property
def environment(self):
"""Build and return the environment."""
if self._environment is not None:
return self._environment
self._environment = suite.load(
domain_name=self._domain_name,
task_name=self._task_name)
self._environment = wrappers.SinglePrecisionWrapper(self._environment)
self._environment = NormilizeActionSpecWrapper(self._environment)
return self._environment
class CmuThirdParty:
"""Create bits needed to run agents on an locomotion humanoid dataset."""
def __init__(self, task_name='humanoid_walls'):
# 'humanoid_corridor|humanoid_gaps|humanoid_walls'
self._task_name = task_name
self._pixel_keys = self.get_pixel_keys()
self._uint8_features = set(['observation/walker/egocentric_camera'])
self.additional_paths = {}
self._proprio_keys = [
'walker/joints_vel',
'walker/sensors_velocimeter',
'walker/sensors_gyro',
'walker/joints_pos',
'walker/world_zaxis',
'walker/body_height',
'walker/sensors_accelerometer',
'walker/end_effectors_pos'
]
self._shapes = {
'observation/walker/joints_vel': (56,),
'observation/walker/sensors_velocimeter': (3,),
'observation/walker/sensors_gyro': (3,),
'observation/walker/joints_pos': (56,),
'observation/walker/world_zaxis': (3,),
'observation/walker/body_height': (1,),
'observation/walker/sensors_accelerometer': (3,),
'observation/walker/end_effectors_pos': (12,),
'observation/walker/egocentric_camera': (
64,
64,
3,
),
'action': (56,),
'discount': (),
'reward': (),
'episodic_reward': (),
'step_type': ()
}
if task_name == 'humanoid_corridor':
self._data_path = 'dm_locomotion/humanoid_corridor/seq2/train'
elif task_name == 'humanoid_gaps':
self._data_path = 'dm_locomotion/humanoid_gaps/seq2/train'
elif task_name == 'humanoid_walls':
self._data_path = 'dm_locomotion/humanoid_walls/seq40/train'
else:
raise ValueError('Task \'{}\' not found.'.format(task_name))
@staticmethod
def get_pixel_keys():
return ('walker/egocentric_camera',)
@property
def uint8_features(self):
return self._uint8_features
@property
def shapes(self):
return self._shapes
@property
def data_path(self):
return self._data_path
@property
def environment(self):
"""Build and return the environment."""
if self._task_name == 'humanoid_corridor':
self._environment = _build_humanoid_corridor_env()
elif self._task_name == 'humanoid_gaps':
self._environment = _build_humanoid_corridor_gaps()
elif self._task_name == 'humanoid_walls':
self._environment = _build_humanoid_walls_env()
self._environment = NormilizeActionSpecWrapper(self._environment)
self._environment = MujocoActionNormalizer(
environment=self._environment, rescale='clip')
self._environment = wrappers.SinglePrecisionWrapper(self._environment)
all_observations = list(self._proprio_keys) + list(self._pixel_keys)
self._environment = FilterObservationsWrapper(self._environment,
all_observations)
return self._environment
class Rodent:
"""Create bits needed to run agents on an Rodent dataset."""
def __init__(self, task_name='rodent_gaps'):
# 'rodent_escape|rodent_two_touch|rodent_gaps|rodent_mazes'
self._task_name = task_name
self._pixel_keys = self.get_pixel_keys()
self._uint8_features = set(['observation/walker/egocentric_camera'])
self._proprio_keys = [
'walker/joints_pos', 'walker/joints_vel', 'walker/tendons_pos',
'walker/tendons_vel', 'walker/appendages_pos', 'walker/world_zaxis',
'walker/sensors_accelerometer', 'walker/sensors_velocimeter',
'walker/sensors_gyro', 'walker/sensors_touch',
]
self._shapes = {
'observation/walker/joints_pos': (30,),
'observation/walker/joints_vel': (30,),
'observation/walker/tendons_pos': (8,),
'observation/walker/tendons_vel': (8,),
'observation/walker/appendages_pos': (15,),
'observation/walker/world_zaxis': (3,),
'observation/walker/sensors_accelerometer': (3,),
'observation/walker/sensors_velocimeter': (3,),
'observation/walker/sensors_gyro': (3,),
'observation/walker/sensors_touch': (4,),
'observation/walker/egocentric_camera': (64, 64, 3),
'action': (38,),
'discount': (),
'reward': (),
'step_type': ()
}
if task_name == 'rodent_gaps':
self._data_path = 'dm_locomotion/rodent_gaps/seq2/train'
elif task_name == 'rodent_escape':
self._data_path = 'dm_locomotion/rodent_bowl_escape/seq2/train'
elif task_name == 'rodent_two_touch':
self._data_path = 'dm_locomotion/rodent_two_touch/seq40/train'
elif task_name == 'rodent_mazes':
self._data_path = 'dm_locomotion/rodent_mazes/seq40/train'
else:
raise ValueError('Task \'{}\' not found.'.format(task_name))
@staticmethod
def get_pixel_keys():
return ('walker/egocentric_camera',)
@property
def shapes(self):
return self._shapes
@property
def uint8_features(self):
return self._uint8_features
@property
def data_path(self):
return self._data_path
@property
def environment(self):
"""Return environment."""
if self._task_name == 'rodent_escape':
self._environment = _build_rodent_escape_env()
elif self._task_name == 'rodent_gaps':
self._environment = _build_rodent_corridor_gaps()
elif self._task_name == 'rodent_two_touch':
self._environment = _build_rodent_two_touch_env()
elif self._task_name == 'rodent_mazes':
self._environment = _build_rodent_maze_env()
self._environment = NormilizeActionSpecWrapper(self._environment)
self._environment = MujocoActionNormalizer(
environment=self._environment, rescale='clip')
self._environment = wrappers.SinglePrecisionWrapper(self._environment)
all_observations = list(self._proprio_keys) + list(self._pixel_keys)
self._environment = FilterObservationsWrapper(self._environment,
all_observations)
return self._environment
def _parse_seq_tf_example(example, uint8_features, shapes):
"""Parse tf.Example containing one or two episode steps."""
def to_feature(key, shape):
if key in uint8_features:
return tf.io.FixedLenSequenceFeature(
shape=[], dtype=tf.string, allow_missing=True)
else:
return tf.io.FixedLenSequenceFeature(
shape=shape, dtype=tf.float32, allow_missing=True)
feature_map = {}
for k, v in shapes.items():
feature_map[k] = to_feature(k, v)
parsed = tf.io.parse_single_example(example, features=feature_map)
observation = {}
restructured = {}
for k in parsed.keys():
if 'observation' not in k:
restructured[k] = parsed[k]
continue
if k in uint8_features:
observation[k.replace('observation/', '')] = tf.reshape(
tf.io.decode_raw(parsed[k], out_type=tf.uint8), (-1,) + shapes[k])
else:
observation[k.replace('observation/', '')] = parsed[k]
restructured['observation'] = observation
restructured['length'] = tf.shape(restructured['action'])[0]
return restructured
def _build_sequence_example(sequences):
"""Convert raw sequences into a Reverb sequence sample."""
data = adders.Step(
observation=sequences['observation'],
action=sequences['action'],
reward=sequences['reward'],
discount=sequences['discount'],
start_of_episode=(),
extras=())
info = reverb.SampleInfo(
key=tf.constant(0, tf.uint64),
probability=tf.constant(1.0, tf.float64),
table_size=tf.constant(0, tf.int64),
priority=tf.constant(1.0, tf.float64),
times_sampled=tf.constant(1.0, tf.int32))
return reverb.ReplaySample(info=info, data=data)
def _build_sarsa_example(sequences):
"""Convert raw sequences into a Reverb n-step SARSA sample."""
o_tm1 = tree.map_structure(lambda t: t[0], sequences['observation'])
o_t = tree.map_structure(lambda t: t[1], sequences['observation'])
a_tm1 = tree.map_structure(lambda t: t[0], sequences['action'])
a_t = tree.map_structure(lambda t: t[1], sequences['action'])
r_t = tree.map_structure(lambda t: t[0], sequences['reward'])
p_t = tree.map_structure(lambda t: t[0], sequences['discount'])
info = reverb.SampleInfo(
key=tf.constant(0, tf.uint64),
probability=tf.constant(1.0, tf.float64),
table_size=tf.constant(0, tf.int64),
priority=tf.constant(1.0, tf.float64),
times_sampled=tf.constant(1.0, tf.int32))
return reverb.ReplaySample(info=info, data=(o_tm1, a_tm1, r_t, p_t, o_t, a_t))
def _padded_batch(example_ds, batch_size, shapes, drop_remainder=False):
"""Batch data while handling unequal lengths."""
padded_shapes = {}
padded_shapes['observation'] = {}
for k, v in shapes.items():
if 'observation' in k:
padded_shapes['observation'][
k.replace('observation/', '')] = (-1,) + v
else:
padded_shapes[k] = (-1,) + v
padded_shapes['length'] = ()
return example_ds.padded_batch(batch_size,
padded_shapes=padded_shapes,
drop_remainder=drop_remainder)
def dataset(root_path: str,
data_path: str,
shapes: Dict[str, Tuple[int]],
num_threads: int,
batch_size: int,
uint8_features: Optional[Set[str]] = None,
num_shards: int = 100,
shuffle_buffer_size: int = 100000,
sarsa: bool = True) -> tf.data.Dataset:
"""Create tf dataset for training."""
uint8_features = uint8_features if uint8_features else {}
path = os.path.join(root_path, data_path)
filenames = [f'{path}-{i:05d}-of-{num_shards:05d}' for i in range(num_shards)]
file_ds = tf.data.Dataset.from_tensor_slices(filenames)
file_ds = file_ds.repeat().shuffle(num_shards)
example_ds = file_ds.interleave(
functools.partial(tf.data.TFRecordDataset, compression_type='GZIP'),
cycle_length=tf.data.experimental.AUTOTUNE,
block_length=5)
example_ds = example_ds.shuffle(shuffle_buffer_size)
def map_func(example):
example = _parse_seq_tf_example(example, uint8_features, shapes)
return example
example_ds = example_ds.map(map_func, num_parallel_calls=num_threads)
example_ds = example_ds.repeat().shuffle(batch_size * 10)
if sarsa:
example_ds = example_ds.map(
_build_sarsa_example,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
example_ds.batch(batch_size)
else:
example_ds = _padded_batch(
example_ds, batch_size, shapes, drop_remainder=True)
example_ds = example_ds.map(
_build_sequence_example,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
example_ds = example_ds.prefetch(tf.data.experimental.AUTOTUNE)
return example_ds
================================================
FILE: rl_unplugged/dm_control_suite_crr.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KDiJzbb8KFvP"
},
"source": [
"Copyright 2020 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use\n",
"this file except in compliance with the License. You may obtain a copy of the\n",
"License at\n",
"\n",
"[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed\n",
"under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n",
"CONDITIONS OF ANY KIND, either express or implied. See the License for the\n",
"specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zzJlIvx4tnrM"
},
"source": [
"# RL Unplugged: CRR agent with GPU/TPU support - DM control\n",
"\n",
"## Guide to training an Acme CRR agent on DM control data.\n",
"# \u003ca href=\"https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/rl_unplugged/dm_control_suite_crr.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "o1eig5zGEL4y"
},
"source": [
"## Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "WbpMoLbgEL41"
},
"outputs": [],
"source": [
"!pip install git+https://github.com/deepmind/acme.git#egg=dm-acme\n",
"!pip install dm-acme[reverb]\n",
"!pip install dm-acme[tf]\n",
"!pip install dm-sonnet\n",
"!git clone https://github.com/deepmind/deepmind-research.git\n",
"%cd deepmind-research"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "04bMANoeEPM3"
},
"source": [
"### dm_control\n",
"\n",
"More detailed instructions in [this tutorial](https://colab.research.google.com/github/deepmind/dm_control/blob/master/tutorial.ipynb#scrollTo=YvyGCsgSCxHQ)."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "VEEj3Qw60y73"
},
"source": [
"#### Institutional MuJoCo license."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "IbZxYDxzoz5R"
},
"outputs": [],
"source": [
"#@title Edit and run\n",
"mjkey = \"\"\"\n",
"\n",
"REPLACE THIS LINE WITH YOUR MUJOCO LICENSE KEY\n",
"\n",
"\"\"\".strip()\n",
"\n",
"mujoco_dir = \"$HOME/.mujoco\"\n",
"\n",
"# Install OpenGL deps\n",
"!apt-get update \u0026\u0026 apt-get install -y --no-install-recommends \\\n",
" libgl1-mesa-glx libosmesa6 libglew2.0\n",
"\n",
"# Fetch MuJoCo binaries from Roboti\n",
"!wget -q https://www.roboti.us/download/mujoco200_linux.zip -O mujoco.zip\n",
"!unzip -o -q mujoco.zip -d \"$mujoco_dir\"\n",
"\n",
"# Copy over MuJoCo license\n",
"!echo \"$mjkey\" \u003e \"$mujoco_dir/mjkey.txt\"\n",
"\n",
"\n",
"# Configure dm_control to use the OSMesa rendering backend\n",
"%env MUJOCO_GL=osmesa\n",
"\n",
"# Install dm_control\n",
"!pip install dm_control"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "-_7tVg-zzjzW"
},
"source": [
"#### Machine-locked MuJoCo license."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "OvMLEDE-D9oF"
},
"outputs": [],
"source": [
"#@title Add your MuJoCo License and run\n",
"mjkey = \"\"\"\n",
"\"\"\".strip()\n",
"\n",
"mujoco_dir = \"$HOME/.mujoco\"\n",
"\n",
"# Install OpenGL dependencies\n",
"!apt-get update \u0026\u0026 apt-get install -y --no-install-recommends \\\n",
" libgl1-mesa-glx libosmesa6 libglew2.0\n",
"\n",
"# Get MuJoCo binaries\n",
"!wget -q https://www.roboti.us/download/mujoco200_linux.zip -O mujoco.zip\n",
"!unzip -o -q mujoco.zip -d \"$mujoco_dir\"\n",
"\n",
"# Copy over MuJoCo license\n",
"!echo \"$mjkey\" \u003e \"$mujoco_dir/mjkey.txt\"\n",
"\n",
"# Install dm_control\n",
"!pip install dm_control[locomotion_mazes]\n",
"\n",
"# Configure dm_control to use the OSMesa rendering backend\n",
"%env MUJOCO_GL=osmesa"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "IE2nV9Hivnv5"
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "RI7NgnJIvs4s"
},
"outputs": [],
"source": [
"import copy\n",
"from typing import Sequence\n",
"import acme\n",
"from acme import specs\n",
"from acme.agents.tf import actors\n",
"from acme.agents.tf import crr\n",
"from acme.tf import networks as acme_networks\n",
"from acme.tf import utils as tf2_utils\n",
"from acme.utils import loggers\n",
"import numpy as np\n",
"from rl_unplugged import dm_control_suite\n",
"from rl_unplugged import networks\n",
"import sonnet as snt\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "a2PCwF3bwBII"
},
"source": [
"## Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "VaEJbXjampPy"
},
"outputs": [],
"source": [
"task_name = 'cartpole_swingup' #@param\n",
"gs_path = 'gs://rl_unplugged/dm_control_suite'\n",
"\n",
"num_shards_str, = !gsutil ls {gs_path}/{task_name}/* | wc -l\n",
"num_shards = int(num_shards_str)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "mQ1as51Mww7X"
},
"source": [
"## Dataset and environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "5kHzJpfcw306"
},
"outputs": [],
"source": [
"batch_size = 256 #@param\n",
"\n",
"task = dm_control_suite.ControlSuite(task_name)\n",
"\n",
"environment = task.environment\n",
"environment_spec = specs.make_environment_spec(environment)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "T2wd9sHeGrD-"
},
"source": [
"## Networks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Gc2Qsdi7GpCI"
},
"outputs": [],
"source": [
"def make_networks(\n",
" action_spec: specs.BoundedArray,\n",
" policy_lstm_sizes: Sequence[int] = None,\n",
" critic_lstm_sizes: Sequence[int] = None,\n",
" num_components: int = 5,\n",
" vmin: float = 0.,\n",
" vmax: float = 100.,\n",
" num_atoms: int = 21,\n",
"):\n",
" \"\"\"Creates recurrent networks with GMM head used by the agents.\"\"\"\n",
"\n",
" action_size = np.prod(action_spec.shape, dtype=int)\n",
" actor_head = acme_networks.MultivariateGaussianMixture(\n",
" num_components=num_components, num_dimensions=action_size)\n",
"\n",
" if policy_lstm_sizes is None:\n",
" policy_lstm_sizes = [1024, 1024]\n",
" if critic_lstm_sizes is None:\n",
" critic_lstm_sizes = [1024, 1024]\n",
"\n",
" actor_neck = acme_networks.LayerNormAndResidualMLP(hidden_size=1024,\n",
" num_blocks=4)\n",
" actor_encoder = networks.ControlNetwork(\n",
" proprio_encoder_size=300,\n",
" activation=tf.nn.relu)\n",
"\n",
" policy_lstms = [snt.LSTM(s) for s in policy_lstm_sizes]\n",
"\n",
" policy_network = snt.DeepRNN([actor_encoder, actor_neck] + policy_lstms +\n",
" [actor_head])\n",
"\n",
" critic_encoder = networks.ControlNetwork(\n",
" proprio_encoder_size=400,\n",
" activation=tf.nn.relu)\n",
" critic_neck = acme_networks.LayerNormAndResidualMLP(\n",
" hidden_size=1024, num_blocks=4)\n",
" distributional_head = acme_networks.DiscreteValuedHead(\n",
" vmin=vmin, vmax=vmax, num_atoms=num_atoms)\n",
" critic_lstms = [snt.LSTM(s) for s in critic_lstm_sizes]\n",
" critic_network = acme_networks.CriticDeepRNN([critic_encoder, critic_neck] +\n",
" critic_lstms + [\n",
" distributional_head,\n",
" ])\n",
"\n",
" return {\n",
" 'policy': policy_network,\n",
" 'critic': critic_network,\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zL1fAYN8GvCf"
},
"source": [
"## Set up TPU if present"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "V3fQxmeiGtrQ"
},
"outputs": [],
"source": [
"try:\n",
" tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection\n",
" tf.config.experimental_connect_to_cluster(tpu)\n",
" tf.tpu.experimental.initialize_tpu_system(tpu)\n",
" accelerator_strategy = snt.distribute.TpuReplicator()\n",
" print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])\n",
"except ValueError:\n",
" print('Running on CPU or GPU (no TPUs available)')\n",
" accelerator_strategy = snt.distribute.Replicator()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "adb0cyE5qu9G"
},
"source": [
"## CRR learner"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "83naOY7a_A4I"
},
"outputs": [],
"source": [
"action_spec = environment_spec.actions\n",
"action_size = np.prod(action_spec.shape, dtype=int)\n",
"\n",
"with accelerator_strategy.scope():\n",
" dataset = dm_control_suite.dataset(\n",
" 'gs://rl_unplugged/',\n",
" data_path=task.data_path,\n",
" shapes=task.shapes,\n",
" uint8_features=task.uint8_features,\n",
" num_threads=1,\n",
" batch_size=batch_size,\n",
" num_shards=num_shards,\n",
" sarsa=False)\n",
" # CRR learner assumes that the dataset samples don't have metadata,\n",
" # so let's remove it here.\n",
" dataset = dataset.map(lambda sample: sample.data)\n",
" nets = make_networks(action_spec)\n",
" policy_network, critic_network = nets['policy'], nets['critic']\n",
"\n",
" # Create the target networks\n",
" target_policy_network = copy.deepcopy(policy_network)\n",
" target_critic_network = copy.deepcopy(critic_network)\n",
"\n",
" # Create variables.\n",
" tf2_utils.create_variables(network=policy_network,\n",
" input_spec=[environment_spec.observations])\n",
" tf2_utils.create_variables(network=critic_network,\n",
" input_spec=[environment_spec.observations,\n",
" environment_spec.actions])\n",
" tf2_utils.create_variables(network=target_policy_network,\n",
" input_spec=[environment_spec.observations])\n",
" tf2_utils.create_variables(network=target_critic_network,\n",
" input_spec=[environment_spec.observations,\n",
" environment_spec.actions])\n",
"\n",
"# The learner updates the parameters (and initializes them).\n",
"learner = crr.RCRRLearner(\n",
" policy_network=policy_network,\n",
" critic_network=critic_network,\n",
" accelerator_strategy=accelerator_strategy,\n",
" target_policy_network=target_policy_network,\n",
" target_critic_network=target_critic_network,\n",
" dataset=dataset,\n",
" discount=0.99,\n",
" target_update_period=100)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PYkjKaduy_xj"
},
"source": [
"## Training loop"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "HbQOyCG4zCwa"
},
"outputs": [],
"source": [
"# Run\n",
"# tf.config.run_functions_eagerly(True)\n",
"# if you want to debug the code in eager mode.\n",
"\n",
"for _ in range(100):\n",
" learner.step()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "LJ_XsuQSzFSV"
},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "blvNCANKb22J"
},
"outputs": [],
"source": [
"# Create a logger.\n",
"logger = loggers.TerminalLogger(label='evaluation', time_delta=1.)\n",
"\n",
"# Create an environment loop.\n",
"loop = acme.EnvironmentLoop(\n",
" environment=environment,\n",
" actor=actors.DeprecatedRecurrentActor(policy_network),\n",
" logger=logger)\n",
"\n",
"loop.run(5)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "",
"kind": "local"
},
"name": "RL Unplugged: Offline CRR - DM control",
"provenance": [
{
"file_id": "1OerSIsTjv4d3rQCjAsi0ljPaLan87juJ",
"timestamp": 1593080049369
}
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: rl_unplugged/dm_control_suite_d4pg.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KDiJzbb8KFvP"
},
"source": [
"Copyright 2020 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use\n",
"this file except in compliance with the License. You may obtain a copy of the\n",
"License at\n",
"\n",
"[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed\n",
"under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n",
"CONDITIONS OF ANY KIND, either express or implied. See the License for the\n",
"specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zzJlIvx4tnrM"
},
"source": [
"# RL Unplugged: Offline D4PG - DM control\n",
"\n",
"## Guide to training an Acme D4PG agent on DM control data.\n",
"# \u003ca href=\"https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/rl_unplugged/dm_control_suite_d4pg.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "o1eig5zGEL4y"
},
"source": [
"## Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "WbpMoLbgEL41"
},
"outputs": [],
"source": [
"!pip install dm-acme\n",
"!pip install dm-acme[reverb]\n",
"!pip install dm-acme[tf]\n",
"!pip install dm-sonnet\n",
"!git clone https://github.com/deepmind/deepmind-research.git\n",
"%cd deepmind-research"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "04bMANoeEPM3"
},
"source": [
"### dm_control\n",
"\n",
"More detailed instructions in [this tutorial](https://colab.research.google.com/github/deepmind/dm_control/blob/master/tutorial.ipynb#scrollTo=YvyGCsgSCxHQ)."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "VEEj3Qw60y73"
},
"source": [
"#### Institutional MuJoCo license."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "IbZxYDxzoz5R"
},
"outputs": [],
"source": [
"#@title Edit and run\n",
"mjkey = \"\"\"\n",
"\n",
"REPLACE THIS LINE WITH YOUR MUJOCO LICENSE KEY\n",
"\n",
"\"\"\".strip()\n",
"\n",
"mujoco_dir = \"$HOME/.mujoco\"\n",
"\n",
"# Install OpenGL deps\n",
"!apt-get update \u0026\u0026 apt-get install -y --no-install-recommends \\\n",
" libgl1-mesa-glx libosmesa6 libglew2.0\n",
"\n",
"# Fetch MuJoCo binaries from Roboti\n",
"!wget -q https://www.roboti.us/download/mujoco200_linux.zip -O mujoco.zip\n",
"!unzip -o -q mujoco.zip -d \"$mujoco_dir\"\n",
"\n",
"# Copy over MuJoCo license\n",
"!echo \"$mjkey\" \u003e \"$mujoco_dir/mjkey.txt\"\n",
"\n",
"\n",
"# Configure dm_control to use the OSMesa rendering backend\n",
"%env MUJOCO_GL=osmesa\n",
"\n",
"# Install dm_control\n",
"!pip install dm_control"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "-_7tVg-zzjzW"
},
"source": [
"#### Machine-locked MuJoCo license."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "OvMLEDE-D9oF"
},
"outputs": [],
"source": [
"#@title Add your MuJoCo License and run\n",
"mjkey = \"\"\"\n",
"\"\"\".strip()\n",
"\n",
"mujoco_dir = \"$HOME/.mujoco\"\n",
"\n",
"# Install OpenGL dependencies\n",
"!apt-get update \u0026\u0026 apt-get install -y --no-install-recommends \\\n",
" libgl1-mesa-glx libosmesa6 libglew2.0\n",
"\n",
"# Get MuJoCo binaries\n",
"!wget -q https://www.roboti.us/download/mujoco200_linux.zip -O mujoco.zip\n",
"!unzip -o -q mujoco.zip -d \"$mujoco_dir\"\n",
"\n",
"# Copy over MuJoCo license\n",
"!echo \"$mjkey\" \u003e \"$mujoco_dir/mjkey.txt\"\n",
"\n",
"# Install dm_control\n",
"!pip install dm_control[locomotion_mazes]\n",
"\n",
"# Configure dm_control to use the OSMesa rendering backend\n",
"%env MUJOCO_GL=osmesa"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "IE2nV9Hivnv5"
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "RI7NgnJIvs4s"
},
"outputs": [],
"source": [
"import collections\n",
"import copy\n",
"from typing import Mapping, Sequence\n",
"\n",
"import acme\n",
"from acme import specs\n",
"from acme.agents.tf import actors\n",
"from acme.agents.tf import d4pg\n",
"from acme.tf import networks\n",
"from acme.tf import utils as tf2_utils\n",
"from acme.utils import loggers\n",
"from acme.wrappers import single_precision\n",
"from acme.tf import utils as tf2_utils\n",
"import numpy as np\n",
"from rl_unplugged import dm_control_suite\n",
"import sonnet as snt\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "a2PCwF3bwBII"
},
"source": [
"## Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "VaEJbXjampPy"
},
"outputs": [],
"source": [
"task_name = 'cartpole_swingup' #@param\n",
"tmp_path = '/tmp/dm_control_suite'\n",
"gs_path = 'gs://rl_unplugged/dm_control_suite'\n",
"\n",
"!mkdir -p {tmp_path}/{task_name}\n",
"!gsutil cp {gs_path}/{task_name}/* {tmp_path}/{task_name}\n",
"\n",
"num_shards_str, = !ls {tmp_path}/{task_name}/* | wc -l\n",
"num_shards = int(num_shards_str)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "mQ1as51Mww7X"
},
"source": [
"## Dataset and environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "5kHzJpfcw306"
},
"outputs": [],
"source": [
"batch_size = 10 #@param\n",
"\n",
"task = dm_control_suite.ControlSuite(task_name)\n",
"\n",
"environment = task.environment\n",
"environment_spec = specs.make_environment_spec(environment)\n",
"\n",
"dataset = dm_control_suite.dataset(\n",
" '/tmp',\n",
" data_path=task.data_path,\n",
" shapes=task.shapes,\n",
" uint8_features=task.uint8_features,\n",
" num_threads=1,\n",
" batch_size=batch_size,\n",
" num_shards=num_shards)\n",
"\n",
"def discard_extras(sample):\n",
" return sample._replace(data=sample.data[:5])\n",
"\n",
"dataset = dataset.map(discard_extras).batch(batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "adb0cyE5qu9G"
},
"source": [
"## D4PG learner"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "83naOY7a_A4I"
},
"outputs": [],
"source": [
"# Create the networks to optimize.\n",
"action_spec = environment_spec.actions\n",
"action_size = np.prod(action_spec.shape, dtype=int)\n",
"\n",
"policy_network = snt.Sequential([\n",
" tf2_utils.batch_concat,\n",
" networks.LayerNormMLP(layer_sizes=(300, 200, action_size)),\n",
" networks.TanhToSpec(spec=environment_spec.actions)])\n",
"\n",
"critic_network = snt.Sequential([\n",
" networks.CriticMultiplexer(\n",
" observation_network=tf2_utils.batch_concat,\n",
" action_network=tf.identity,\n",
" critic_network=networks.LayerNormMLP(\n",
" layer_sizes=(400, 300),\n",
" activate_final=True)),\n",
" # Value-head gives a 51-atomed delta distribution over state-action values.\n",
" networks.DiscreteValuedHead(vmin=-150., vmax=150., num_atoms=51)])\n",
"\n",
"# Create the target networks\n",
"target_policy_network = copy.deepcopy(policy_network)\n",
"target_critic_network = copy.deepcopy(critic_network)\n",
"\n",
"# Create variables.\n",
"tf2_utils.create_variables(network=policy_network,\n",
" input_spec=[environment_spec.observations])\n",
"tf2_utils.create_variables(network=critic_network,\n",
" input_spec=[environment_spec.observations,\n",
" environment_spec.actions])\n",
"tf2_utils.create_variables(network=target_policy_network,\n",
" input_spec=[environment_spec.observations])\n",
"tf2_utils.create_variables(network=target_critic_network,\n",
" input_spec=[environment_spec.observations,\n",
" environment_spec.actions])\n",
"\n",
"# The learner updates the parameters (and initializes them).\n",
"learner = d4pg.D4PGLearner(\n",
" policy_network=policy_network,\n",
" critic_network=critic_network,\n",
" target_policy_network=target_policy_network,\n",
" target_critic_network=target_critic_network,\n",
" dataset=dataset,\n",
" discount=0.99,\n",
" target_update_period=100)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PYkjKaduy_xj"
},
"source": [
"## Training loop"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 34
},
"colab_type": "code",
"executionInfo": {
"elapsed": 3493,
"status": "ok",
"timestamp": 1593622068277,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "HbQOyCG4zCwa",
"outputId": "cfb99d00-da2d-4ce8-e010-034a26e2ada0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Learner] Critic Loss = 3.919 | Policy Loss = 0.326 | Steps = 1 | Walltime = 0\n"
]
}
],
"source": [
"for _ in range(100):\n",
" learner.step()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "LJ_XsuQSzFSV"
},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 51
},
"colab_type": "code",
"executionInfo": {
"elapsed": 4197,
"status": "ok",
"timestamp": 1593620604870,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "blvNCANKb22J",
"outputId": "af5ae073-9847-45cc-e51e-a803fc2148b0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Evaluation] Episode Length = 1000 | Episode Return = 129.717 | Episodes = 2 | Steps = 2000 | Steps Per Second = 1480.399\n",
"[Evaluation] Episode Length = 1000 | Episode Return = 34.790 | Episodes = 4 | Steps = 4000 | Steps Per Second = 1449.009\n"
]
}
],
"source": [
"# Create a logger.\n",
"logger = loggers.TerminalLogger(label='evaluation', time_delta=1.)\n",
"\n",
"# Create an environment loop.\n",
"loop = acme.EnvironmentLoop(\n",
" environment=environment,\n",
" actor=actors.DeprecatedFeedForwardActor(policy_network),\n",
" logger=logger)\n",
"\n",
"loop.run(5)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "",
"kind": "local"
},
"name": "RL Unplugged: Offline D4PG - DM control",
"provenance": [
{
"file_id": "1OerSIsTjv4d3rQCjAsi0ljPaLan87juJ",
"timestamp": 1593080049369
}
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: rl_unplugged/dm_control_suite_example.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""DM control suite and locomotion dataset examples.
Example:
Instructions:
> export TMP_PATH=/tmp/dataset
> export TASK_NAME=humanoid_run
> mkdir -p $TMP_PATH/$TASK_NAME
> gsutil cp gs://rl_unplugged/dm_control_suite/$TASK_NAME/train-00000-of-00100 \
$TMP_PATH/dm_control_suite/$TASK_NAME/train-00000-of-00001
> python dm_control_suite_example.py --path=$TMP_PATH \
--task_class=control_suite --task_name=$TASK_NAME
"""
from absl import app
from absl import flags
import tree
from rl_unplugged import dm_control_suite
flags.DEFINE_string('path', '/tmp/dataset', 'Path to dataset.')
flags.DEFINE_string('task_name', 'humanoid_run', 'Game.')
flags.DEFINE_enum('task_class', 'control_suite',
['humanoid', 'rodent', 'control_suite'],
'Task classes.')
FLAGS = flags.FLAGS
def main(_):
if FLAGS.task_class == 'control_suite':
task = dm_control_suite.ControlSuite(task_name=FLAGS.task_name)
elif FLAGS.task_class == 'humanoid':
task = dm_control_suite.CmuThirdParty(task_name=FLAGS.task_name)
elif FLAGS.task_class == 'rodent':
task = dm_control_suite.Rodent(task_name=FLAGS.task_name)
ds = dm_control_suite.dataset(root_path=FLAGS.path,
data_path=task.data_path,
shapes=task.shapes,
num_threads=1,
batch_size=2,
uint8_features=task.uint8_features,
num_shards=1,
shuffle_buffer_size=10)
for sample in ds.take(1):
print('Data spec')
print(tree.map_structure(lambda x: (x.dtype, x.shape), sample.data))
environment = task.environment
timestep = environment.reset()
print(tree.map_structure(lambda x: (x.dtype, x.shape), timestep.observation))
if __name__ == '__main__':
app.run(main)
================================================
FILE: rl_unplugged/dmlab_r2d2.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "pdgOfM42e7in"
},
"source": [
"Copyright 2021 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use\n",
"this file except in compliance with the License. You may obtain a copy of the\n",
"License at\n",
"\n",
"[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed\n",
"under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n",
"CONDITIONS OF ANY KIND, either express or implied. See the License for the\n",
"specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WOzmAie8e-NK"
},
"source": [
"# RL Unplugged: Offline R2D2 - DeepMind Lab\n",
"\n",
"## A Colab example of an Acme R2D2 agent on DeepMind Lab data.\n",
"# \u003ca href=\"https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/rl_unplugged/dmlab_r2d2.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tr2MoADAQepq"
},
"source": [
"## Installation\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SvWeEWGd5Nx_"
},
"source": [
"### External dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fTuqZxDv4v0y"
},
"outputs": [],
"source": [
"!apt-get install libsdl2-dev\n",
"!apt-get install libosmesa6-dev\n",
"!apt-get install libffi-dev\n",
"!apt-get install gettext\n",
"!apt-get install python3-numpy-dev python3-dev"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ewPoBUDd04xh"
},
"source": [
"### Bazel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ewVV3-Oh0sBm"
},
"outputs": [],
"source": [
"BAZEL_VERSION = '3.6.0'\n",
"!wget https://github.com/bazelbuild/bazel/releases/download/{BAZEL_VERSION}/bazel-{BAZEL_VERSION}-installer-linux-x86_64.sh\n",
"!chmod +x bazel-{BAZEL_VERSION}-installer-linux-x86_64.sh\n",
"!./bazel-{BAZEL_VERSION}-installer-linux-x86_64.sh\n",
"!bazel --version"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fwdmJWW3KB7g"
},
"source": [
"### DeepMind Lab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ng9xopirzVYA"
},
"outputs": [],
"source": [
"!git clone https://github.com/deepmind/lab.git"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NeO57QYqDG-L"
},
"outputs": [],
"source": [
"%%writefile lab/bazel/python.BUILD\n",
"\n",
"# Description:\n",
"# Build rule for Python and Numpy.\n",
"# This rule works for Debian and Ubuntu. Other platforms might keep the\n",
"# headers in different places, cf. 'How to build DeepMind Lab' in build.md.\n",
"\n",
"cc_library(\n",
" name = \"python\",\n",
" hdrs = select(\n",
" {\n",
" \"@bazel_tools//tools/python:PY3\": glob([\n",
" \"usr/include/python3.6m/*.h\",\n",
" \"usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/*.h\",\n",
" ]),\n",
" },\n",
" no_match_error = \"Internal error, Python version should be one of PY2 or PY3\",\n",
" ),\n",
" includes = select(\n",
" {\n",
" \"@bazel_tools//tools/python:PY3\": [\n",
" \"usr/include/python3.6m\",\n",
" \"usr/local/lib/python3.6/dist-packages/numpy/core/include\",\n",
" ],\n",
" },\n",
" no_match_error = \"Internal error, Python version should be one of PY2 or PY3\",\n",
" ),\n",
" visibility = [\"//visibility:public\"],\n",
")\n",
"\n",
"alias(\n",
" name = \"python_headers\",\n",
" actual = \":python\",\n",
" visibility = [\"//visibility:public\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pRuLCRzpzX8E"
},
"outputs": [],
"source": [
"!cd lab \u0026\u0026 bazel build -c opt --python_version=PY3 //python/pip_package:build_pip_package"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Oen2E99T0E58"
},
"outputs": [],
"source": [
"!cd lab \u0026\u0026 ./bazel-bin/python/pip_package/build_pip_package /tmp/dmlab_pkg"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "okrzzmrC0H_O"
},
"outputs": [],
"source": [
"!pip install /tmp/dmlab_pkg/deepmind_lab-1.0-py3-none-any.whl --force-reinstall"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hq-VGgvbRKSI"
},
"source": [
"### Python dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8Fme7zxOKejg"
},
"outputs": [],
"source": [
"!pip install dm_env\n",
"!pip install dm-acme[reverb]\n",
"!pip install dm-acme[tf]\n",
"!pip install dm-sonnet"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Rfd4jQGFt-HB"
},
"outputs": [],
"source": [
"# Upgrade to recent commit for latest R2D2 learner.\n",
"!pip install --upgrade git+https://github.com/deepmind/acme.git@3dfda9d392312d948906e6c567c7f56d8c911de5"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DvicrJPBqemz"
},
"source": [
"## Imports and Utils"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "_8qxA0KLU468"
},
"outputs": [],
"source": [
"# @title Imports\n",
"import copy\n",
"import functools\n",
"\n",
"from acme import environment_loop\n",
"from acme import specs\n",
"from acme.adders import reverb as acme_reverb\n",
"from acme.agents.tf import actors\n",
"from acme.agents.tf.r2d2 import learning as r2d2\n",
"from acme.tf import utils as tf_utils\n",
"from acme.tf import networks\n",
"from acme.utils import loggers\n",
"from acme.wrappers import observation_action_reward\n",
"import tree\n",
"\n",
"import deepmind_lab\n",
"import dm_env\n",
"import numpy as np\n",
"import reverb\n",
"import sonnet as snt\n",
"import tensorflow as tf\n",
"import trfl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "becmQVMMuCRU"
},
"outputs": [],
"source": [
"# @title Environment\n",
"\n",
"_ACTION_MAP = {\n",
" 0: (0, 0, 0, 1, 0, 0, 0),\n",
" 1: (0, 0, 0, -1, 0, 0, 0),\n",
" 2: (0, 0, -1, 0, 0, 0, 0),\n",
" 3: (0, 0, 1, 0, 0, 0, 0),\n",
" 4: (-10, 0, 0, 0, 0, 0, 0),\n",
" 5: (10, 0, 0, 0, 0, 0, 0),\n",
" 6: (-60, 0, 0, 0, 0, 0, 0),\n",
" 7: (60, 0, 0, 0, 0, 0, 0),\n",
" 8: (0, 10, 0, 0, 0, 0, 0),\n",
" 9: (0, -10, 0, 0, 0, 0, 0),\n",
" 10: (-10, 0, 0, 1, 0, 0, 0),\n",
" 11: (10, 0, 0, 1, 0, 0, 0),\n",
" 12: (-60, 0, 0, 1, 0, 0, 0),\n",
" 13: (60, 0, 0, 1, 0, 0, 0),\n",
" 14: (0, 0, 0, 0, 1, 0, 0),\n",
"}\n",
"\n",
"class DeepMindLabEnvironment(dm_env.Environment):\n",
" \"\"\"DeepMind Lab environment.\"\"\"\n",
"\n",
" def __init__(self, level_name: str, action_repeats: int = 4):\n",
" \"\"\"Construct environment.\n",
"\n",
" Args:\n",
" level_name: DeepMind lab level name (e.g. 'rooms_watermaze').\n",
" action_repeats: Number of times the same action is repeated on every\n",
" step().\n",
" \"\"\"\n",
" config = dict(fps='30',\n",
" height='72',\n",
" width='96',\n",
" maxAltCameraHeight='1',\n",
" maxAltCameraWidth='1',\n",
" hasAltCameras='false')\n",
"\n",
" # seekavoid_arena_01 is not part of dmlab30.\n",
" if level_name != 'seekavoid_arena_01':\n",
" level_name = 'contributed/dmlab30/{}'.format(level_name)\n",
"\n",
" self._lab = deepmind_lab.Lab(level_name, ['RGB_INTERLEAVED'], config)\n",
" self._action_repeats = action_repeats\n",
" self._reward = 0\n",
"\n",
" def _observation(self):\n",
" last_action = getattr(self, '_action', 0)\n",
" last_reward = getattr(self, '_reward', 0)\n",
" self._last_observation = observation_action_reward.OAR(\n",
" observation=self._lab.observations()['RGB_INTERLEAVED'],\n",
" action=np.array(last_action, dtype=np.int64),\n",
" reward=np.array(last_reward, dtype=np.float32))\n",
" return self._last_observation\n",
"\n",
" def reset(self):\n",
" self._lab.reset()\n",
" return dm_env.restart(self._observation())\n",
"\n",
" def step(self, action):\n",
" if not self._lab.is_running():\n",
" return dm_env.restart(self.reset())\n",
"\n",
" self._action = action.item()\n",
" if self._action not in _ACTION_MAP:\n",
" raise ValueError('Action not available')\n",
" lab_action = np.array(_ACTION_MAP[self._action], dtype=np.intc)\n",
" self._reward = self._lab.step(lab_action, num_steps=self._action_repeats)\n",
"\n",
" if self._lab.is_running():\n",
" return dm_env.transition(self._reward, self._observation())\n",
" return dm_env.termination(self._reward, self._last_observation)\n",
"\n",
" def observation_spec(self):\n",
" return observation_action_reward.OAR(\n",
" observation=dm_env.specs.Array(shape=(72, 96, 3), dtype=np.uint8),\n",
" action=dm_env.specs.Array(shape=(), dtype=np.int64),\n",
" reward=dm_env.specs.Array(shape=(), dtype=np.float32))\n",
"\n",
" def action_spec(self):\n",
" return dm_env.specs.DiscreteArray(num_values=15, dtype=np.int64)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "4ms1TBjDSXr0"
},
"outputs": [],
"source": [
"# @title Dataset\n",
"\n",
"def _decode_images(pngs):\n",
" \"\"\"Decode tensor of PNGs.\"\"\"\n",
" decode_rgb_png = functools.partial(tf.io.decode_png, channels=3)\n",
" images = tf.map_fn(decode_rgb_png, pngs, dtype=tf.uint8,\n",
" parallel_iterations=10)\n",
" # [N, 72, 96, 3]\n",
" images.set_shape((pngs.shape[0], 72, 96, 3))\n",
" return images\n",
"\n",
"def _tf_example_to_step_ds(tf_example: tf.train.Example,\n",
" episode_length: int) -\u003e reverb.ReplaySample:\n",
" \"\"\"Create a Reverb replay sample from a TF example.\"\"\"\n",
"\n",
" # Parse tf.Example.\n",
" def sequence_feature(shape, dtype=tf.float32):\n",
" return tf.io.FixedLenFeature(shape=[episode_length] + shape, dtype=dtype)\n",
"\n",
" feature_description = {\n",
" 'episode_id': tf.io.FixedLenFeature([], tf.int64),\n",
" 'start_idx': tf.io.FixedLenFeature([], tf.int64),\n",
" 'episode_return': tf.io.FixedLenFeature([], tf.float32),\n",
" 'observations_pixels': sequence_feature([], tf.string),\n",
" 'observations_reward': sequence_feature([]),\n",
" # actions are one-hot arrays.\n",
" 'observations_action': sequence_feature([15]),\n",
" 'actions': sequence_feature([], tf.int64),\n",
" 'rewards': sequence_feature([]),\n",
" 'discounted_rewards': sequence_feature([]),\n",
" 'discounts': sequence_feature([]),\n",
" }\n",
"\n",
" data = tf.io.parse_single_example(tf_example, feature_description)\n",
" pixels = _decode_images(data['observations_pixels'])\n",
"\n",
" observation = observation_action_reward.OAR(\n",
" observation=pixels,\n",
" action=tf.argmax(data['observations_action'],\n",
" axis=1, output_type=tf.int64),\n",
" reward=data['observations_reward'])\n",
"\n",
" data = acme_reverb.Step(\n",
" observation=observation,\n",
" action=data['actions'],\n",
" reward=data['rewards'],\n",
" discount=data['discounts'],\n",
" start_of_episode=tf.zeros((episode_length,), tf.bool),\n",
" extras={})\n",
"\n",
" # Keys are all zero and probabilities are all one.\n",
" info = reverb.SampleInfo(key=tf.zeros((episode_length,), tf.int64),\n",
" probability=tf.ones((episode_length,), tf.float32),\n",
" table_size=tf.zeros((episode_length,), tf.int64),\n",
" priority=tf.ones((episode_length,), tf.float32))\n",
" sample = reverb.ReplaySample(info=info, data=data)\n",
" return tf.data.Dataset.from_tensor_slices(sample)\n",
"\n",
"def subsequences(step_ds: tf.data.Dataset,\n",
" length: int, shift: int = 1\n",
" ) -\u003e tf.data.Dataset:\n",
" \"\"\"Dataset of subsequences from a dataset of episode steps.\"\"\"\n",
" window_ds = step_ds.window(length, shift=shift, stride=1)\n",
" return window_ds.interleave(_nest_ds).batch(length, drop_remainder=True)\n",
"\n",
"\n",
"def _nest_ds(nested_ds: tf.data.Dataset) -\u003e tf.data.Dataset:\n",
" \"\"\"Produces a dataset of nests from a nest of datasets of the same size.\"\"\"\n",
" flattened_ds = tuple(tree.flatten(nested_ds))\n",
" zipped_ds = tf.data.Dataset.zip(flattened_ds)\n",
" return zipped_ds.map(lambda *x: tree.unflatten_as(nested_ds, x))\n",
"\n",
"\n",
"def make_dataset(path: str,\n",
" episode_length: int,\n",
" sequence_length: int,\n",
" sequence_shift: int,\n",
" num_shards: int = 500) -\u003e tf.data.Dataset:\n",
" \"\"\"Create dataset of DeepMind Lab sequences.\"\"\"\n",
"\n",
" filenames = [f'{path}/tfrecord-{i:05d}-of-{num_shards:05d}'\n",
" for i in range(num_shards)]\n",
" file_ds = tf.data.Dataset.from_tensor_slices(filenames)\n",
" file_ds = file_ds.repeat().shuffle(num_shards)\n",
" tfrecord_dataset = functools.partial(tf.data.TFRecordDataset,\n",
" compression_type='GZIP')\n",
"\n",
" # Dataset of tf.Examples containing full episodes.\n",
" example_ds = file_ds.interleave(tfrecord_dataset)\n",
"\n",
" # Dataset of episodes, each represented as a dataset of steps.\n",
" _tf_example_to_step_ds_with_length = functools.partial(\n",
" _tf_example_to_step_ds, episode_length=episode_length)\n",
" episode_ds = example_ds.map(_tf_example_to_step_ds_with_length,\n",
" num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
"\n",
" # Dataset of sequences.\n",
" training_sequences = functools.partial(subsequences, length=sequence_length,\n",
" shift=sequence_shift)\n",
" return episode_ds.interleave(training_sequences)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sV2vXWAsU5Zg"
},
"source": [
"## Experiment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0F-l-4LolX1c"
},
"outputs": [],
"source": [
"# task | episode length | run\n",
"# ----------------------------------------------------------------------------\n",
"# seekavoid_arena_01 | 301 | training_{0..2}\n",
"# seekavoid_arena_01 | 301 | snapshot_{0..1}_eps_0.0\n",
"# seekavoid_arena_01 | 301 | snapshot_{0..1}_eps_0.01\n",
"# seekavoid_arena_01 | 301 | snapshot_{0..1}_eps_0.1\n",
"# seekavoid_arena_01 | 301 | snapshot_{0..1}_eps_0.25\n",
"# explore_object_rewards_few | 1351 | training_{0..2}\n",
"# explore_object_rewards_many | 1801 | training_{0..2}\n",
"# rooms_select_nonmatching_object | 181 | training_{0..2}\n",
"# rooms_watermaze | 1801 | training_{0..2}\n",
"\n",
"TASK = 'seekavoid_arena_01'\n",
"RUN = 'training_0'\n",
"EPISODE_LENGTH = 301\n",
"BATCH_SIZE = 1\n",
"DATASET_PATH = f'gs://rl_unplugged/dmlab/{TASK}/{RUN}'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "H7YN_qwDVPqQ"
},
"outputs": [],
"source": [
"environment = DeepMindLabEnvironment(TASK, action_repeats=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-B25Lcgt8JD4"
},
"outputs": [],
"source": [
"dataset = make_dataset(DATASET_PATH, num_shards=500,\n",
" episode_length=EPISODE_LENGTH,\n",
" sequence_length=120,\n",
" sequence_shift=40)\n",
"dataset = dataset.padded_batch(BATCH_SIZE, drop_remainder=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gTO61WolqkzG"
},
"source": [
"### Learning"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cBFmIYxTtBg4"
},
"outputs": [],
"source": [
"# Create network.\n",
"def process_observations(x):\n",
" return x._replace(observation=tf.image.convert_image_dtype(x.observation, tf.float32))\n",
"\n",
"environment_spec = specs.make_environment_spec(environment)\n",
"num_actions = environment_spec.actions.maximum + 1\n",
"network = snt.DeepRNN([\n",
" process_observations,\n",
" networks.R2D2AtariNetwork(num_actions=num_actions)\n",
"])\n",
"tf_utils.create_variables(network, [environment_spec.observations])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QLoAU2zwwi3X"
},
"outputs": [],
"source": [
"# Create a logger.\n",
"logger = loggers.TerminalLogger(label='learner', time_delta=1.)\n",
"\n",
"# Create the R2D2 learner.\n",
"learner = r2d2.R2D2Learner(\n",
" environment_spec=environment_spec,\n",
" network=network,\n",
" target_network=copy.deepcopy(network),\n",
" discount=0.99,\n",
" learning_rate=1e-4,\n",
" importance_sampling_exponent=0.2,\n",
" target_update_period=100,\n",
" burn_in_length=0,\n",
" sequence_length=120,\n",
" store_lstm_state=False,\n",
" dataset=dataset,\n",
" logger=logger)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lqMgZS9UWfWl"
},
"outputs": [],
"source": [
"for _ in range(5):\n",
" learner.step()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eMpO7eBeqmZn"
},
"source": [
"### Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "orUDJVmpA0lU"
},
"outputs": [],
"source": [
"# Create a logger.\n",
"logger = loggers.TerminalLogger(label='evaluator', time_delta=1.)\n",
"\n",
"# Create evaluation loop.\n",
"eval_network = snt.DeepRNN([\n",
" network,\n",
" lambda q: trfl.epsilon_greedy(q, epsilon=0.4**8).sample(),\n",
"])\n",
"eval_loop = environment_loop.EnvironmentLoop(\n",
" environment=environment,\n",
" actor=actors.DeprecatedRecurrentActor(policy_network=eval_network),\n",
" logger=logger)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6FDsfWVXCcYZ"
},
"outputs": [],
"source": [
"eval_loop.run(2)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [
"tr2MoADAQepq",
"SvWeEWGd5Nx_",
"ewPoBUDd04xh",
"fwdmJWW3KB7g"
],
"name": "RL Unplugged: Offline R2D2 - DeepMind Lab",
"provenance": [
{
"file_id": "1vgfEtkThYTNWHhi3pisuRFxgmoMniuQz",
"timestamp": 1605722818242
},
{
"file_id": "/v2/external/notebooks/intro.ipynb",
"timestamp": 1602763830869
}
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: rl_unplugged/networks.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Networks used for training agents.
"""
from acme.tf import networks as acme_networks
from acme.tf import utils as tf2_utils
import numpy as np
import sonnet as snt
import tensorflow as tf
def instance_norm_and_elu(x):
mean = tf.reduce_mean(x, axis=[1, 2], keepdims=True)
x_ = x - mean
var = tf.reduce_mean(x_**2, axis=[1, 2], keepdims=True)
x_norm = x_ / (var + 1e-6)
return tf.nn.elu(x_norm)
class ControlNetwork(snt.Module):
"""Image, proprio and optionally action encoder used for actors and critics.
"""
def __init__(self,
proprio_encoder_size: int,
proprio_keys=None,
activation=tf.nn.elu):
"""Creates a ControlNetwork.
Args:
proprio_encoder_size: Size of the linear layer for the proprio encoder.
proprio_keys: Optional list of names of proprioceptive observations.
Defaults to all observations. Note that if this is specified, any
observation not contained in proprio_keys will be ignored by the agent.
activation: Linear layer activation function.
"""
super().__init__(name='control_network')
self._activation = activation
self._proprio_keys = proprio_keys
self._proprio_encoder = acme_networks.LayerNormMLP([proprio_encoder_size])
def __call__(self, inputs, action: tf.Tensor = None, task=None):
"""Evaluates the ControlNetwork.
Args:
inputs: A dictionary of agent observation tensors.
action: Agent actions.
task: Optional encoding of the task.
Raises:
ValueError: if neither proprio_input is provided.
ValueError: if some proprio input looks suspiciously like pixel inputs.
Returns:
Processed network output.
"""
if not isinstance(inputs, dict):
inputs = {'inputs': inputs}
proprio_input = []
# By default, treat all observations as proprioceptive.
if self._proprio_keys is None:
self._proprio_keys = list(sorted(inputs.keys()))
for key in self._proprio_keys:
proprio_input.append(snt.Flatten()(inputs[key]))
if np.prod(inputs[key].shape[1:]) > 32*32*3:
raise ValueError(
'This input does not resemble a proprioceptive '
'state: {} with shape {}'.format(
key, inputs[key].shape))
# Append optional action input (i.e. for critic networks).
if action is not None:
proprio_input.append(action)
proprio_input = tf2_utils.batch_concat(proprio_input)
proprio_state = self._proprio_encoder(proprio_input)
return proprio_state
================================================
FILE: rl_unplugged/requirements.txt
================================================
absl-py==0.9.0
astunparse==1.6.3
atari-py==0.2.6
cachetools==4.1.1
certifi==2020.6.20
chardet==3.0.4
cloudpickle==1.3.0
decorator==4.4.2
dm-acme==0.1.7
dm-control==0.0.319497192
dm-env==1.2
dm-reverb-nightly==0.1.0.dev20200616
dm-sonnet==2.0.0
dm-tree==0.1.5
dopamine-rl==3.1.2
future==0.18.2
gast==0.3.3
gin-config==0.3.0
glfw==1.11.2
google-auth==1.18.0
google-auth-oauthlib==0.4.1
google-pasta==0.2.0
grpcio==1.30.0
gym==0.17.2
h5py==2.10.0
idna==2.10
Keras-Preprocessing==1.1.2
lxml==4.5.1
Markdown==3.2.2
numpy==1.19.0
oauthlib==3.1.0
opencv-python==4.3.0.36
opt-einsum==3.2.1
Pillow==7.2.0
pkg-resources==0.0.0
portpicker==1.3.1
protobuf==3.12.2
pyasn1==0.4.8
pyasn1-modules==0.2.8
pyglet==1.5.0
PyOpenGL==3.1.5
pyparsing==2.4.7
requests==2.24.0
requests-oauthlib==1.3.0
rsa==4.6
scipy==1.4.1
six==1.15.0
tabulate==0.8.7
tb-nightly==2.3.0a20200706
tensorboard-plugin-wit==1.7.0
termcolor==1.1.0
tf-estimator-nightly==2.4.0.dev2020070701
tf-nightly==2.3.0.dev20200616
tfp-nightly==0.11.0.dev20200707
trfl==1.1.0
urllib3==1.25.9
Werkzeug==1.0.1
wrapt==1.12.1
================================================
FILE: rl_unplugged/rwrl.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Real World RL for RL Unplugged datasets.
Examples in the dataset represent SARS transitions stored when running a
partially online trained agent as described in https://arxiv.org/abs/1904.12901.
We release 8 datasets in total -- with no combined challenge and easy combined
challenge on the cartpole, walker, quadruped, and humanoid tasks. For details
on how the dataset was generated, please refer to the paper.
Every transition in the dataset is a tuple containing the following features:
* o_t: Observation at time t. Observations have been processed using the
canonical
* a_t: Action taken at time t.
* r_t: Reward at time t.
* d_t: Discount at time t.
* o_tp1: Observation at time t+1.
* a_tp1: Action taken at time t+1. This is set to equal to the last action
for the last timestep.
Note that this serves as an example. For optimal data loading speed, consider
separating out data preprocessing from the data loading loop during training,
e.g. saving the preprocessed data.
"""
import collections
import functools
import os
from typing import Any, Dict, Optional, Sequence
from acme import wrappers
import dm_env
import realworldrl_suite.environments as rwrl_envs
import reverb
import tensorflow as tf
import tree
DELIMITER = ':'
# Control suite tasks have 1000 timesteps per episode. One additional timestep
# accounts for the very first observation where no action has been taken yet.
DEFAULT_NUM_TIMESTEPS = 1001
def _decombine_key(k: str, delimiter: str = DELIMITER) -> Sequence[str]:
return k.split(delimiter)
def tf_example_to_feature_description(example,
num_timesteps=DEFAULT_NUM_TIMESTEPS):
"""Takes a string tensor encoding an tf example and returns its features."""
if not tf.executing_eagerly():
raise AssertionError(
'tf_example_to_reverb_sample() only works under eager mode.')
example = tf.train.Example.FromString(example.numpy())
ret = {}
for k, v in example.features.feature.items():
l = len(v.float_list.value)
if l % num_timesteps:
raise ValueError('Unexpected feature length %d. It should be divisible '
'by num_timesteps: %d' % (l, num_timesteps))
size = l // num_timesteps
ret[k] = tf.io.FixedLenFeature([num_timesteps, size], tf.float32)
return ret
def tree_deflatten_with_delimiter(
flat_dict: Dict[str, Any], delimiter: str = DELIMITER) -> Dict[str, Any]:
"""De-flattens a dict to its originally nested structure.
Does the opposite of {combine_nested_keys(k) :v
for k, v in tree.flatten_with_path(nested_dicts)}
Example: {'a:b': 1} -> {'a': {'b': 1}}
Args:
flat_dict: the keys of which equals the `path` separated by `delimiter`.
delimiter: the delimiter that separates the keys of the nested dict.
Returns:
An un-flattened dict.
"""
root = collections.defaultdict(dict)
for delimited_key, v in flat_dict.items():
keys = _decombine_key(delimited_key, delimiter=delimiter)
node = root
for k in keys[:-1]:
node = node[k]
node[keys[-1]] = v
return dict(root)
def get_slice_of_nested(nested: Dict[str, Any], start: int,
end: int) -> Dict[str, Any]:
return tree.map_structure(lambda item: item[start:end], nested)
def repeat_last_and_append_to_nested(nested: Dict[str, Any]) -> Dict[str, Any]:
return tree.map_structure(
lambda item: tf.concat((item, item[-1:]), axis=0), nested)
def tf_example_to_reverb_sample(example,
feature_description,
num_timesteps=DEFAULT_NUM_TIMESTEPS):
"""Converts the episode encoded as a tf example into SARSA reverb samples."""
example = tf.io.parse_single_example(example, feature_description)
kv = tree_deflatten_with_delimiter(example)
output = (
get_slice_of_nested(kv['observation'], 0, num_timesteps - 1),
get_slice_of_nested(kv['action'], 1, num_timesteps),
kv['reward'][1:num_timesteps],
# The two fields below aren't needed for learning,
# but are kept here to be compatible with acme learner format.
kv['discount'][1:num_timesteps],
get_slice_of_nested(kv['observation'], 1, num_timesteps),
repeat_last_and_append_to_nested(
get_slice_of_nested(kv['action'], 2, num_timesteps)))
ret = tf.data.Dataset.from_tensor_slices(output)
ret = ret.map(lambda *x: reverb.ReplaySample(info=b'None', data=x)) # pytype: disable=wrong-arg-types
return ret
def dataset(path: str,
combined_challenge: str,
domain: str,
task: str,
difficulty: str,
num_shards: int = 100,
shuffle_buffer_size: int = 100000) -> tf.data.Dataset:
"""TF dataset of RWRL SARSA tuples."""
path = os.path.join(
path,
f'combined_challenge_{combined_challenge}/{domain}/{task}/'
f'offline_rl_challenge_{difficulty}'
)
filenames = [
f'{path}/episodes.tfrecord-{i:05d}-of-{num_shards:05d}'
for i in range(num_shards)
]
file_ds = tf.data.Dataset.from_tensor_slices(filenames)
file_ds = file_ds.repeat().shuffle(num_shards)
tf_example_ds = file_ds.interleave(
tf.data.TFRecordDataset,
cycle_length=tf.data.experimental.AUTOTUNE,
block_length=5)
# Take one item to get the output types and shapes.
example_item = None
for example_item in tf.data.TFRecordDataset(filenames[:1]).take(1):
break
if example_item is None:
raise ValueError('Empty dataset')
feature_description = tf_example_to_feature_description(example_item)
reverb_ds = tf_example_ds.interleave(
functools.partial(
tf_example_to_reverb_sample, feature_description=feature_description),
num_parallel_calls=tf.data.experimental.AUTOTUNE,
deterministic=False)
reverb_ds = reverb_ds.prefetch(100)
reverb_ds = reverb_ds.shuffle(shuffle_buffer_size)
return reverb_ds
def environment(
combined_challenge: str,
domain: str,
task: str,
log_output: Optional[str] = None,
environment_kwargs: Optional[Dict[str, Any]] = None) -> dm_env.Environment:
"""RWRL environment."""
env = rwrl_envs.load(
domain_name=domain,
task_name=task,
log_output=log_output,
environment_kwargs=environment_kwargs,
combined_challenge=combined_challenge)
return wrappers.SinglePrecisionWrapper(env)
================================================
FILE: rl_unplugged/rwrl_d4pg.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KDiJzbb8KFvP"
},
"source": [
"Copyright 2020 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use\n",
"this file except in compliance with the License. You may obtain a copy of the\n",
"License at\n",
"\n",
"[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed\n",
"under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n",
"CONDITIONS OF ANY KIND, either express or implied. See the License for the\n",
"specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zzJlIvx4tnrM"
},
"source": [
"# RL Unplugged: Offline D4PG - RWRL\n",
"\n",
"## Guide to training an Acme D4PG agent on RWRL data.\n",
"# \u003ca href=\"https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/rl_unplugged/rwrl_d4pg.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "L6EObDZat-6b"
},
"source": [
"## Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "GxZogj3guCzN"
},
"outputs": [],
"source": [
"!pip install dm-acme\n",
"!pip install dm-acme[reverb]\n",
"!pip install dm-acme[tf]\n",
"!pip install dm-sonnet"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "pDil_q8cDjov"
},
"source": [
"### MuJoCo\n",
"\n",
"More detailed instructions in [this tutorial](https://colab.research.google.com/github/deepmind/dm_control/blob/master/tutorial.ipynb#scrollTo=YvyGCsgSCxHQ)."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "VEEj3Qw60y73"
},
"source": [
"#### Institutional MuJoCo license."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "IbZxYDxzoz5R"
},
"outputs": [],
"source": [
"#@title Edit and run\n",
"mjkey = \"\"\"\n",
"\n",
"REPLACE THIS LINE WITH YOUR MUJOCO LICENSE KEY\n",
"\n",
"\"\"\".strip()\n",
"\n",
"mujoco_dir = \"$HOME/.mujoco\"\n",
"\n",
"# Install OpenGL deps\n",
"!apt-get update \u0026\u0026 apt-get install -y --no-install-recommends \\\n",
" libgl1-mesa-glx libosmesa6 libglew2.0\n",
"\n",
"# Fetch MuJoCo binaries from Roboti\n",
"!wget -q https://www.roboti.us/download/mujoco200_linux.zip -O mujoco.zip\n",
"!unzip -o -q mujoco.zip -d \"$mujoco_dir\"\n",
"\n",
"# Copy over MuJoCo license\n",
"!echo \"$mjkey\" \u003e \"$mujoco_dir/mjkey.txt\"\n",
"\n",
"# Configure dm_control to use the OSMesa rendering backend\n",
"%env MUJOCO_GL=osmesa\n",
"\n",
"# Install dm_control\n",
"!pip install dm_control"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "-_7tVg-zzjzW"
},
"source": [
"#### Machine-locked MuJoCo license."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "ZvCGB5LSr-30"
},
"outputs": [],
"source": [
"#@title Add your MuJoCo License and run\n",
"mjkey = \"\"\"\n",
"\"\"\".strip()\n",
"\n",
"mujoco_dir = \"$HOME/.mujoco\"\n",
"\n",
"# Install OpenGL dependencies\n",
"!apt-get update \u0026\u0026 apt-get install -y --no-install-recommends \\\n",
" libgl1-mesa-glx libosmesa6 libglew2.0\n",
"\n",
"# Get MuJoCo binaries\n",
"!wget -q https://www.roboti.us/download/mujoco200_linux.zip -O mujoco.zip\n",
"!unzip -o -q mujoco.zip -d \"$mujoco_dir\"\n",
"\n",
"# Copy over MuJoCo license\n",
"!echo \"$mjkey\" \u003e \"$mujoco_dir/mjkey.txt\"\n",
"\n",
"# Configure dm_control to use the OSMesa rendering backend\n",
"%env MUJOCO_GL=osmesa\n",
"\n",
"# Install dm_control, including extra dependencies needed for the locomotion\n",
"# mazes.\n",
"!pip install dm_control[locomotion_mazes]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ZrszOaSSMGiF"
},
"source": [
"### RWRL"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Oe5D7pEQAq82"
},
"outputs": [],
"source": [
"!git clone https://github.com/google-research/realworldrl_suite.git\n",
"!pip install realworldrl_suite/"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "jSr-8p2BA1wl"
},
"source": [
"### RL Unplugged"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "DGCc6SBaAnyb"
},
"outputs": [],
"source": [
"!git clone https://github.com/deepmind/deepmind-research.git\n",
"%cd deepmind-research"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "IE2nV9Hivnv5"
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "RI7NgnJIvs4s"
},
"outputs": [],
"source": [
"import collections\n",
"import copy\n",
"from typing import Mapping, Sequence\n",
"\n",
"import acme\n",
"from acme import specs\n",
"from acme.agents.tf import actors\n",
"from acme.agents.tf import d4pg\n",
"from acme.tf import networks\n",
"from acme.tf import utils as tf2_utils\n",
"from acme.utils import loggers\n",
"from acme.wrappers import single_precision\n",
"from acme.tf import utils as tf2_utils\n",
"import numpy as np\n",
"import realworldrl_suite.environments as rwrl_envs\n",
"from reverb import replay_sample\n",
"import six\n",
"from rl_unplugged import rwrl\n",
"import sonnet as snt\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "a2PCwF3bwBII"
},
"source": [
"## Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "VaEJbXjampPy"
},
"outputs": [],
"source": [
"domain_name = 'cartpole' #@param\n",
"task_name = 'swingup' #@param\n",
"difficulty = 'easy' #@param\n",
"combined_challenge = 'easy' #@param\n",
"combined_challenge_str = str(combined_challenge).lower()\n",
"\n",
"tmp_path = '/tmp/rwrl'\n",
"gs_path = f'gs://rl_unplugged/rwrl'\n",
"data_path = (f'combined_challenge_{combined_challenge_str}/{domain_name}/'\n",
" f'{task_name}/offline_rl_challenge_{difficulty}')\n",
"\n",
"!mkdir -p {tmp_path}/{data_path}\n",
"!gsutil cp -r {gs_path}/{data_path}/* {tmp_path}/{data_path}\n",
"\n",
"num_shards_str, = !ls {tmp_path}/{data_path}/* | wc -l\n",
"num_shards = int(num_shards_str)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "mQ1as51Mww7X"
},
"source": [
"## Dataset and environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "K8HnIIi3ywYC"
},
"outputs": [],
"source": [
"#@title Auxiliary functions\n",
"\n",
"def flatten_observation(observation):\n",
" \"\"\"Flattens multiple observation arrays into a single tensor.\n",
"\n",
" Args:\n",
" observation: A mutable mapping from observation names to tensors.\n",
"\n",
" Returns:\n",
" A flattened and concatenated observation array.\n",
"\n",
" Raises:\n",
" ValueError: If `observation` is not a `collections.MutableMapping`.\n",
" \"\"\"\n",
" if not isinstance(observation, collections.MutableMapping):\n",
" raise ValueError('Can only flatten dict-like observations.')\n",
"\n",
" if isinstance(observation, collections.OrderedDict):\n",
" keys = six.iterkeys(observation)\n",
" else:\n",
" # Keep a consistent ordering for other mappings.\n",
" keys = sorted(six.iterkeys(observation))\n",
"\n",
" observation_arrays = [tf.reshape(observation[key], [-1]) for key in keys]\n",
" return tf.concat(observation_arrays, 0)\n",
"\n",
"\n",
"def preprocess_fn(sample):\n",
" o_tm1, a_tm1, r_t, d_t, o_t = sample.data[:5]\n",
" o_tm1 = flatten_observation(o_tm1)\n",
" o_t = flatten_observation(o_t)\n",
" return replay_sample.ReplaySample(\n",
" info=sample.info, data=(o_tm1, a_tm1, r_t, d_t, o_t))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "5kHzJpfcw306"
},
"outputs": [],
"source": [
"batch_size = 10 #@param\n",
"\n",
"environment = rwrl_envs.load(\n",
" domain_name=domain_name,\n",
" task_name=f'realworld_{task_name}',\n",
" environment_kwargs=dict(log_safety_vars=False, flat_observation=True),\n",
" combined_challenge=combined_challenge)\n",
"environment = single_precision.SinglePrecisionWrapper(environment)\n",
"environment_spec = specs.make_environment_spec(environment)\n",
"act_spec = environment_spec.actions\n",
"obs_spec = environment_spec.observations\n",
"\n",
"dataset = rwrl.dataset(\n",
" tmp_path,\n",
" combined_challenge=combined_challenge_str,\n",
" domain=domain_name,\n",
" task=task_name,\n",
" difficulty=difficulty,\n",
" num_shards=num_shards,\n",
" shuffle_buffer_size=10)\n",
"dataset = dataset.map(preprocess_fn).batch(batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "adb0cyE5qu9G"
},
"source": [
"## D4PG learner"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "CriIaelxqwQg"
},
"outputs": [],
"source": [
"#@title Auxiliary functions\n",
"\n",
"def make_networks(\n",
" action_spec: specs.BoundedArray,\n",
" hidden_size: int = 1024,\n",
" num_blocks: int = 4,\n",
" num_mixtures: int = 5,\n",
" vmin: float = -150.,\n",
" vmax: float = 150.,\n",
" num_atoms: int = 51,\n",
"):\n",
" \"\"\"Creates networks used by the agent.\"\"\"\n",
" num_dimensions = np.prod(action_spec.shape, dtype=int)\n",
"\n",
" policy_network = snt.Sequential([\n",
" networks.LayerNormAndResidualMLP(\n",
" hidden_size=hidden_size, num_blocks=num_blocks),\n",
" # Converts the policy output into the same shape as the action spec.\n",
" snt.Linear(num_dimensions),\n",
" # Note that TanhToSpec applies tanh to the input.\n",
" networks.TanhToSpec(action_spec)\n",
" ])\n",
" # The multiplexer concatenates the (maybe transformed) observations/actions.\n",
" critic_network = snt.Sequential([\n",
" networks.CriticMultiplexer(\n",
" critic_network=networks.LayerNormAndResidualMLP(\n",
" hidden_size=hidden_size, num_blocks=num_blocks),\n",
" observation_network=tf2_utils.batch_concat),\n",
" networks.DiscreteValuedHead(vmin, vmax, num_atoms)\n",
" ])\n",
"\n",
" return {\n",
" 'policy': policy_network,\n",
" 'critic': critic_network,\n",
" }\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "83naOY7a_A4I"
},
"outputs": [],
"source": [
"# Create the networks to optimize.\n",
"online_networks = make_networks(act_spec)\n",
"target_networks = copy.deepcopy(online_networks)\n",
"\n",
"# Create variables.\n",
"tf2_utils.create_variables(online_networks['policy'], [obs_spec])\n",
"tf2_utils.create_variables(online_networks['critic'], [obs_spec, act_spec])\n",
"tf2_utils.create_variables(target_networks['policy'], [obs_spec])\n",
"tf2_utils.create_variables(target_networks['critic'], [obs_spec, act_spec])\n",
"\n",
"# The learner updates the parameters (and initializes them).\n",
"learner = d4pg.D4PGLearner(\n",
" policy_network=online_networks['policy'],\n",
" critic_network=online_networks['critic'],\n",
" target_policy_network=target_networks['policy'],\n",
" target_critic_network=target_networks['critic'],\n",
" dataset=dataset,\n",
" discount=0.99,\n",
" target_update_period=100)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PYkjKaduy_xj"
},
"source": [
"## Training loop"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 136
},
"colab_type": "code",
"executionInfo": {
"elapsed": 15293,
"status": "ok",
"timestamp": 1593617156917,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "HbQOyCG4zCwa",
"outputId": "7a1055c4-4256-41b9-92a6-6fb05af89725"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Learner] Critic Loss = 4.016 | Policy Loss = 0.500 | Steps = 1 | Walltime = 0\n",
"[Learner] Critic Loss = 3.851 | Policy Loss = 0.279 | Steps = 16 | Walltime = 2.165\n",
"[Learner] Critic Loss = 3.832 | Policy Loss = 0.190 | Steps = 32 | Walltime = 3.216\n",
"[Learner] Critic Loss = 3.744 | Policy Loss = 0.223 | Steps = 48 | Walltime = 4.262\n",
"[Learner] Critic Loss = 3.782 | Policy Loss = 0.287 | Steps = 64 | Walltime = 5.305\n",
"[Learner] Critic Loss = 3.799 | Policy Loss = 0.315 | Steps = 80 | Walltime = 6.353\n",
"[Learner] Critic Loss = 3.796 | Policy Loss = 0.230 | Steps = 96 | Walltime = 7.397\n"
]
}
],
"source": [
"for _ in range(100):\n",
" learner.step()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "LJ_XsuQSzFSV"
},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 102
},
"colab_type": "code",
"executionInfo": {
"elapsed": 21830,
"status": "ok",
"timestamp": 1593617178762,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "blvNCANKb22J",
"outputId": "b05b7272-78cc-4bef-fe64-b95cc6fe5688"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Evaluation] Episode Length = 1000 | Episode Return = 135.244 | Episodes = 1 | Steps = 1000 | Steps Per Second = 222.692\n",
"[Evaluation] Episode Length = 1000 | Episode Return = 136.972 | Episodes = 2 | Steps = 2000 | Steps Per Second = 233.214\n",
"[Evaluation] Episode Length = 1000 | Episode Return = 136.173 | Episodes = 3 | Steps = 3000 | Steps Per Second = 229.496\n",
"[Evaluation] Episode Length = 1000 | Episode Return = 146.131 | Episodes = 4 | Steps = 4000 | Steps Per Second = 230.199\n",
"[Evaluation] Episode Length = 1000 | Episode Return = 137.818 | Episodes = 5 | Steps = 5000 | Steps Per Second = 232.834\n"
]
}
],
"source": [
"# Create a logger.\n",
"logger = loggers.TerminalLogger(label='evaluation', time_delta=1.)\n",
"\n",
"# Create an environment loop.\n",
"loop = acme.EnvironmentLoop(\n",
" environment=environment,\n",
" actor=actors.DeprecatedFeedForwardActor(online_networks['policy']),\n",
" logger=logger)\n",
"\n",
"loop.run(5)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"VEEj3Qw60y73",
"-_7tVg-zzjzW"
],
"last_runtime": {
"build_target": "",
"kind": "local"
},
"name": "RL Unplugged: Offline D4PG - RWRL",
"provenance": [
{
"file_id": "1OerSIsTjv4d3rQCjAsi0ljPaLan87juJ",
"timestamp": 1593080049369
}
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: rl_unplugged/rwrl_example.py
================================================
# pylint: disable=line-too-long
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""RWRL dataset example.
Instructions:
> export TMP_PATH=/tmp/dataset/rwrl
> export DATA_PATH=combined_challenge_easy/quadruped/walk/offline_rl_challenge_easy
> mkdir -p $TMP_PATH/$DATA_PATH
> gsutil cp gs://rl_unplugged/rwrl/$DATA_PATH/episodes.tfrecord-00001-of-00015 \
$TMP_PATH/$DATA_PATH/episodes.tfrecord-00000-of-00001
> python rwrl_example.py --path=$TMP_PATH
"""
# pylint: enable=line-too-long
from absl import app
from absl import flags
import tree
from rl_unplugged import rwrl
flags.DEFINE_string('path', '/tmp/dataset', 'Path to dataset.')
def main(_):
ds = rwrl.dataset(
flags.FLAGS.path,
combined_challenge='easy',
domain='quadruped',
task='walk',
difficulty='easy',
num_shards=1,
shuffle_buffer_size=1)
for replay_sample in ds.take(1):
print(tree.map_structure(lambda x: (x.dtype, x.shape), replay_sample.data))
break
if __name__ == '__main__':
app.run(main)
================================================
FILE: satore/Clause.rkt
================================================
#lang racket/base
;**************************************************************************************;
;**** Clause: Clauses With Additional Properties In A Struct ****;
;**************************************************************************************;
(require define2
define2/define-wrapper
racket/format
racket/list
racket/string
satore/clause
satore/clause-format
satore/misc
satore/unification
text-table)
(provide (all-defined-out))
;==============;
;=== Clause ===;
;==============;
;; TODO: A lot of space is wasted in Clause (boolean flags?)
;; TODO: What's the best way to gain space without losing time or readability?
;; idx : exact-nonnegative-integer? ; unique id of the Clause.
;; parents : (listof Clause?) ; The first parent is the 'mother'.
;; clause : clause? ; the list of literals.
;; type : symbol? ; How the Clause was generated (loaded from file, input clause, rewrite, resolution,
;; factor, etc.)
;; binary-rewrite-rule? : boolean? ; Initially #false, set to #true if the clause has been added
;; (at some point) to the binary rewrite rules (but may not be in the set anymore if subsumed).
;; candidate? : boolean? ; Whether the clause is currently a candidate (see `saturation` in
;; saturation.rkt).
;; discarded? : boolean? ; whether the Clause has been discarded (see `saturation` in saturation.rkt).
;; n-literals : exact-nonnegative-integer? ; number of literals in the clause.
;; size : number? ; tree-size of the clause.
;; depth : exact-nonnegative-integer? : Number of parents up to the input clauses, when following
;; resolutions and factorings.
;; cost : number? ; Used to sort Clauses in `saturation` (in saturation.rkt).
(struct Clause (idx
parents
clause
type
[binary-rewrite-rule? #:mutable]
[candidate? #:mutable]
[discarded? #:mutable]
n-literals
size
depth
[cost #:mutable])
#:prefab)
;; Unique clause index. No two Clauses should have the same index.
(define-counter clause-index 0)
;; Clause constructor. See the struct Clause for more information.
;;
;; parents : (listof Clause?)
;; candidate? : boolean?
;; n-literals : exact-nonnegative-integer?
;; size : number?
;; depth : exact-nonnegative-integer?
(define (make-Clause cl
[parents '()]
#:? [type '?]
#:? [candidate? #false]
#:? [n-literals (length cl)]
#:? [size (clause-size cl)]
#:? [depth (if (empty? parents) 1 (+ 1 (Clause-depth (first parents))))])
(++clause-index)
(when-debug>= steps
(define cl2 (clause-normalize cl)) ; costly, hence done only in debug mode
(unless (= (tree-size cl) (tree-size cl2))
(displayln "Assertion failed: clause is in normal form")
(printf "Clause (type: ~a):\n~a\n" type (clause->string cl))
(displayln "Parents:")
(print-Clauses parents)
(error (format "Assertion failed: (= (tree-size cl) (tree-size cl2)): ~a ~a"
(tree-size cl) (tree-size cl2)))))
; Notice: Variables are ASSUMED freshed. Freshing is not performed here.
(Clause clause-index
parents
cl
type
#false ; binary-rewrite-rule
candidate?
#false ; discarded?
n-literals
size
depth ; depth (C0 is of depth 0, axioms are of depth 1)
0. ; cost
))
;; Sets the Clause as discarded. Used in `saturation`.
;;
;; Clause? -> void?
(define (discard-Clause! C) (set-Clause-discarded?! C #true))
;; A tautological clause used for as parent of the converse of a unit Clause.
(define true-Clause (make-Clause (list ltrue)))
;; Returns a converse Clause of a unit or binary Clause.
;; These are meant to be temporary.
;;
;; C : Clause?
;; candidate? : boolean?
;; -> Clause?
(define (make-converse-Clause C #:? [candidate? #false])
(if (unit-Clause? C)
true-Clause ; If C has 1 literal A, then C = A | false, and converse is ~A | true = true
(make-Clause (fresh (clause-converse (Clause-clause C)))
(list C)
#:type 'converse
#:candidate? candidate?)))
;; List of possible fields for output formatting.
(define Clause->string-all-fields '(idx parents clause type binary-rw? depth size cost))
;; Returns a tree representation of the Clause, for human reading.
;; If what is a list, each element is printed (possibly multiple times).
;; If what is 'all, all fields are printed.
;;
;; Clause? (or/c 'all (listof symbol?)) -> list?
(define (Clause->list C [what '(idx parents clause)])
(when (eq? what 'all)
(set! what Clause->string-all-fields))
(for/list ([w (in-list what)])
(case w
[(idx) (~a (Clause-idx C))]
[(parents) (~a (map Clause-idx (Clause-parents C)))]
[(clause) (clause->string (Clause-clause C))]
[(clause-pretty) (clause->string/pretty (Clause-clause C))]
[(type) (~a (Clause-type C))]
[(binary-rw?) (~a (Clause-binary-rewrite-rule? C))]
[(depth) (~r (Clause-depth C))]
[(size) (~r (Clause-size C))]
[(cost) (~r2 (Clause-cost C))])))
;; Returns a string representation of a Clause.
;;
;; Clause? (or/c 'all (listof symbol?)) -> string?
(define (Clause->string C [what '(idx parents clause)])
(string-join (Clause->list C what) " "))
;; Returns a string representation of a Clause, for displaying a single Clause.
;;
;; Clause? (listof symbol?) -> string?
(define (Clause->string/alone C [what '(idx parents clause)])
(when (eq? what 'all)
(set! what Clause->string-all-fields))
(string-join (map (λ (f w) (format "~a: ~a " w f))
(Clause->list C what)
what)
" "))
;; Outputs the Clauses `Cs` in a table for human reading.
;;
;; (listof Clause?) (or/c 'all (listof symbol?)) -> void?
(define (print-Clauses Cs [what '(idx parents clause)])
(when (eq? what 'all)
(set! what Clause->string-all-fields))
(print-simple-table
(cons what
(map (λ (C) (Clause->list C what)) Cs))))
;; Returns a substitution if C1 subsumes C2 and the number of literals of C1 is no larger
;; than that of C2, #false otherwise.
;; Indeed, even when the clauses are safely factored, there can still be issues, for example,
;; this prevents cases infinite chains such as:
;; p(A, A) subsumed by p(A, B) | p(B, A) subsumed by p(A, B) | p(B, C) | p(C, A) subsumed by…
;; Notice: This is an approximation of the correct subsumption based on multisets.
;;
;; Clause? Clause? -> (or/c #false subst?)
(define (Clause-subsumes C1 C2)
(and (<= (Clause-n-literals C1) (Clause-n-literals C2))
(clause-subsumes (Clause-clause C1) (Clause-clause C2))))
;; Like Clause-subsumes but first takes the converse of C1.
;; Useful for rewrite rules.
;;
;; Clause? Clause? -> (or/c #false subst?)
(define (Clause-converse-subsumes C1 C2)
(and (<= (Clause-n-literals C1) (Clause-n-literals C2))
(clause-subsumes (clause-converse (Clause-clause C1))
(Clause-clause C2))))
;; Clause? -> boolean?
(define (unit-Clause? C)
(= 1 (Clause-n-literals C)))
;; Clause? -> boolean?
(define (binary-Clause? C)
(= 2 (Clause-n-literals C)))
;; Clause? -> boolean?
(define (Clause-tautology? C)
(clause-tautology? (Clause-clause C)))
;; Returns whether C1 and C2 are α-equivalences, that is,
;; if there exists a renaming substitution α such that C1α = C2
;; and C2α⁻¹ = C1.
;;
;; Clause? Clause? -> boolean?
(define (Clause-equivalence? C1 C2)
(and (Clause-subsumes C1 C2)
(Clause-subsumes C2 C1)))
;================;
;=== Printing ===;
;================;
;; Returns the tree of ancestor Clauses of C up to init Clauses,
;; but each Clause appears only once in the tree.
;; (The full tree can be further retrieved from the Clause-parents.)
;; Used for proofs.
;;
;; C : Clause?
;; dmax : number?
;; -> (treeof Clause?)
(define (Clause-ancestor-graph C #:depth [dmax +inf.0])
(define h (make-hasheq))
(let loop ([C C] [depth 0])
(cond
[(or (> depth dmax)
(hash-has-key? h C))
#false]
[else
(hash-set! h C #true)
(cons C (filter-map (λ (C2) (loop C2 (+ depth 1)))
(Clause-parents C)))])))
;; Like `Clause-ancestor-graph` but represented as a string for printing.
;;
;; C : Clause?
;; prefix : string? ; a prefix before each line
;; tab : string? ; tabulation string to show the tree-like structure
;; what : (or/c 'all (listof symbol?)) ; see `Clause->string`
;; -> string?
(define (Clause-ancestor-graph-string C
#:? [depth +inf.0]
#:? [prefix ""]
#:? [tab " "]
#:? [what '(idx parents type clause)])
(define h (make-hasheq))
(define str-out "")
(let loop ([C C] [d 0])
(unless (or (> d depth)
(hash-has-key? h C))
(set! str-out (string-append str-out
prefix
(string-append* (make-list d tab))
(Clause->string C what)
"\n"))
(hash-set! h C #true)
(for ([P (in-list (Clause-parents C))])
(loop P (+ d 1)))))
str-out)
;; Like `Clause-ancestor-graph-string` but directly outputs it.
(define-wrapper (display-Clause-ancestor-graph
(Clause-ancestor-graph-string C #:? depth #:? prefix #:? tab #:? what))
#:call-wrapped call
(display (call)))
;; Returns #true if C1 was generated before C2
;;
;; Clause? Clause? -> boolean?
(define (Clause-age>= C1 C2)
(<= (Clause-idx C1) (Clause-idx C2)))
;=================;
;=== Save/load ===;
;=================;
;; Saves the Clauses `Cs` to the file `f`.
;;
;; Cs : (listof Clause?)
;; f : file?
;; exists : symbol? ; see `with-output-to-file`
;; -> void?
(define (save-Clauses! Cs f #:? exists)
(save-clauses! (map Clause-clause Cs) f #:exists exists))
;; Loads Clauses from a file. If `sort?` is not #false, Clauses are sorted by Clause-size.
;; The type defaults to `'load` and can be changed with `type`.
;;
;; f : file?
;; sort? : boolean?
;; type : symbol?
;; -> (listof Clause?)
(define (load-Clauses f #:? [sort? #true] #:? [type 'load])
(define Cs (map (λ (c) (make-Clause c #:type type))
(load-clauses f)))
(if sort?
(sort Cs <= #:key Clause-size)
Cs))
;======================;
;=== Test utilities ===;
;======================;
;; Provides testing utilities. Use with `(require (submod satore/Clause test))`.
(module+ test
(require rackunit)
(provide Clausify
check-Clause-set-equivalent?)
;; Takes a symbol tree, turns symbol variables into actual `Var`s, freshes them,
;; sorts the literals and makes a new Clause.
;;
;; tree? -> Clause?
(define Clausify (compose make-Clause clausify))
;; Returns whether for every clause of Cs1 there is an α-equivalent clause in Cs2.
;;
;; (listof Clause?) (listof Clause?) -> any/c
(define-check (check-Clause-set-equivalent? Cs1 Cs2)
(unless (= (length Cs1) (length Cs2))
(fail-check "not ="))
(for/fold ([Cs2 Cs2])
([C1 (in-list Cs1)])
(define C1b
(for/first ([C2 (in-list Cs2)] #:when (Clause-equivalence? C1 C2))
C2))
(unless C1b
(printf "Cannot find equivalence Clause for ~a\n" (Clause->string C1))
(print-Clauses Cs1)
(print-Clauses Cs2)
(fail-check))
(remq C1b Cs2))))
================================================
FILE: satore/README.md
================================================
# Satore: First-order logic saturation with atom rewriting
Satore is a first-order logic resolution based theorem prover in CNF without
equality, but with atom rewrite rules. New rewrite rules can be
discovered during the proof search, potentially reducing exponentially the
search space.
Satore stands for Saturation with Atom Rewriting.
## Installation
First, install the **Racket** programming language (Apache2/MIT):
https://download.racket-lang.org
Note for Linux users: Do read the comments on the download page.
You may need to
[configure the PATH environment variable](https://github.com/racket/racket/wiki/Configure-Command-Line-for-Racket)
to include the directory containing the `racket` and `raco` executables.
To install **satore** and its dependencies (all are Apache2/MIT licensed),
type:
```shell
raco pkg install --auto --update-deps satore
```
## Running Satore
First, you can check that satore is correctly installed by typing:
```shell
racket -l- satore --help
```
There are some examples in the `examples` subdirectory of the `satore`
directory, and its location can be found with
```shell
racket -e '(displayln (collection-file-path "examples" "satore"))'
```
Run a trivial example:
```shell
racket -l- satore -p path-to-satore/examples/socrates.p --proof
```
The `.p` file is assumed to be a standalone file with only comments and
`cnf(…).` lines without equality, where the logic clause must be surrounded by
parentheses. All axioms must be included. (This will likely be improved soon.)
## Results on TPTP 7.2.0 (corrected)
Corrected results on TPTP 7.2.0, on a 2-2.5GHz CPU, with a limit of 5 minutes
and 32GB per problem, either with the default settings, or with
`--no-discover-online` to disable atom rewriting:
```
┌────────────────────────────┬───────┬───────┬───────┬───────┬───────┬────────┐
│Strategy │ UEQ │ CNE │ CEQ │ FNE │ FEQ │ All │
│ Class size→│ 1092 │ 2212 │ 4436 │ 1784 │ 5860 │ 15384 │
├────────────────────────────┼───────┼───────┼───────┼───────┼───────┼────────┤
│Satore (all) │ 177 │ 1140 │ 956 │ 822 │ 1324 │ 4419 │
│ (proof) │ 177 │ 1078 │ 952 │ 766 │ 1324 │ 4297 │
│ (sat) │ 0 │ 62 │ 4 │ 56 │ 0 │ 122 │
├────────────────────────────┼───────┼───────┼───────┼───────┼───────┼────────┤
│Satore w/o atom rw (all) │ 128 │ 967 │ 674 │ 711 │ 843 │ 3323 │
│ (proof) │ 128 │ 913 │ 670 │ 658 │ 843 │ 3212 │
│ (sat) │ 0 │ 54 │ 4 │ 53 │ 0 │ 111 │
└────────────────────────────┴───────┴───────┴───────┴───────┴───────┴────────┘
```
================================================
FILE: satore/clause-format.rkt
================================================
#lang racket/base
;***************************************************************************************;
;**** Clause <-> String Conversions ****;
;***************************************************************************************;
;;; In a separate file because of cyclic dependencies with "tptp.rkt" if in "clause.rkt"
(require define2
racket/format
racket/list
racket/pretty
satore/tptp
satore/unification
text-table)
(provide (all-defined-out))
;; Returns a string representation of the clause.
;;
;; clause? -> string?
(define (clause->string cl)
((if (*tptp-out?*)
clause->tptp-string
~a)
(Vars->symbols cl)))
;; Same as clause->string but pretty prints the result for better reading.
;;
;; clause? -> string?
(define (clause->string/pretty cl)
(pretty-format (Vars->symbols cl)))
;; clause? -> void?
(define (print-clause cl)
(displayln (clause->string cl)))
;; Prints the list of clauses in a table, possibly sothing them first.
;;
;; cls : (listof clause?)
;; sort? : boolean?
(define (print-clauses cls #:? [sort? #false])
(unless (empty? cls)
(print-table
(for/list ([cl (in-list (if sort?
(sort cls < #:key tree-size #:cache-keys? #true)
cls))]
[i (in-naturals)])
(cons i (Vars->symbols cl)))
#:border-style 'space
#:row-sep? #false
#:framed? #false)))
================================================
FILE: satore/clause.rkt
================================================
#lang racket/base
;***************************************************************************************;
;**** Operations on clauses ****;
;***************************************************************************************;
(require bazaar/cond-else
bazaar/list
bazaar/loop
bazaar/mutation
(except-in bazaar/order atom<=>)
define2
global
racket/file
racket/list
satore/misc
satore/trie
satore/unification
syntax/parse/define)
(provide (all-defined-out))
(define-global *subsumes-iter-limit* 0
'("Number of iterations in the θ-subsumption loop before failing."
"May help in cases where subsumption take far too long."
"0 = no limit.")
exact-nonnegative-integer?
string->number)
(define-counter n-tautologies 0)
;; Returns a new clause where the literals have been sorted according to `literal (listof literal?)
(define (sort-clause cl)
(sort cl literal (listof literal?)
(define (clause-normalize cl)
; fresh the variables just to make sure
(fresh (safe-factoring (sort-clause cl))))
;; Takes a tree of symbols and returns a clause, after turning symbol variables into `Var`s.
;; Used to turn human-readable clauses into computer-friendly clauses.
;;
;; tree? -> clause?
(define (clausify l)
(clause-normalize (Varify l)))
;; clause? -> boolean?
(define (empty-clause? cl)
(empty? cl))
;; Returns whether the clause `cl` is a tautologie.
;; cl is a tautology if it contains the literals `l` and `(not l)`.
;; Assumes that the clause cl is sorted according to `sort-clause`.
;;
;; clause? -> boolean?
(define (clause-tautology? cl)
(define-values (neg pos) (partition lnot? cl))
(define pneg (map lnot neg))
(and
(or
(memq ltrue pos)
(memq lfalse pneg)
(let loop ([pos pos] [pneg pneg])
(cond/else
[(or (empty? pos) (empty? pneg)) #false]
#:else
(define p (first pos))
(define n (first pneg))
(define c (literal<=> p n))
#:cond
[(order? c) (loop pos (rest pneg))]
[(literal==? p n)]
#:else (error "uh?"))))
(begin (++n-tautologies) #true)))
;; Returns the converse clause of `cl`.
;; Notice: This does *not* rename the variables.
;;
;; clause? -> clause?
(define (clause-converse cl)
(sort-clause (map lnot cl)))
;; Returns the pair of (predicate-symbol . arity) of the literal.
;;
;; literal? -> (cons/c symbol? exact-nonnegative-integer?)
(define (predicate.arity lit)
(let ([lit (depolarize lit)])
(cond [(list? lit) (cons (first lit) (length lit))]
[else (cons lit 0)])))
;; Several counters to keep track of statistics.
(define-counter n-subsumes-checks 0)
(define-counter n-subsumes-steps 0)
(define-counter n-subsumes-breaks 0)
(define (reset-subsumes-stats!)
(reset-n-subsumes-checks!)
(reset-n-subsumes-steps!)
(reset-n-subsumes-breaks!))
;; θ-subsumption. Returns a (unreduced) most-general unifier θ such that caθ ⊆ cb, in the sense
;; of set inclusion.
;; Assumes vars(ca) ∩ vars(cb) = ∅.
;; Note that this function does not check for multiset inclusion. A length check is performed in
;; Clause-subsumes?.
;;
;; clause? clause? -> subst?
(define (clause-subsumes ca cb)
(++n-subsumes-checks)
; For every each la of ca with current substitution β, we need to find a literal lb of cb
; such that we can extend β to β' so that la β' = lb.
(define cbtrie (make-trie #:variable? Var?))
(for ([litb (in-list cb)])
; the key must be a list, but a literal may be just a constant, so we need to `list` it.
(trie-insert! cbtrie (list litb) litb))
;; Each literal lita of ca is paired with a list of potential literals in cb that lita matches,
;; for subsequent left-unification.
;; We sort the groups by smallest size first, to fail fast.
(define groups
(sort
(for/list ([lita (in-list ca)])
; lita must match litb, hence inverse-ref
(cons lita (append* (trie-inverse-ref cbtrie (list lita)))))
< #:key length #:cache-keys? #true))
;; Depth-first search while trying to find a substitution that works for all literals of ca.
(define n-iter-max (*subsumes-iter-limit*))
(define n-iter 0)
(let/ec return
(let loop ([groups groups] [subst '()])
(++ n-iter)
; Abort when we have reached the step limit
(when (= n-iter n-iter-max) ; if n-iter-max = 0 then no limit
(++n-subsumes-breaks)
(return #false))
(++n-subsumes-steps)
(cond
[(empty? groups) subst]
[else
(define gp (first groups))
(define lita (car gp))
(define litbs (cdr gp))
(for/or ([litb (in-list litbs)])
; We use a immutable substitution to let racket handle copies when needed.
(define new-subst (left-unify/assoc lita litb subst))
(and new-subst (loop (rest groups) new-subst)))]))))
;; Returns the shortest clause `cl2` such that `cl2` subsumes `cl`.
;; Since `cl` subsumes each of its factors (safe or unsafe, and in the sense of
;; non-multiset subsumption above), this means that `cl2` is equivalent to `cl`
;; (hence no information is lost in `cl2`, it's a 'safe' factor).
;; Assumes that the clause cl is sorted according to `sort-clause`.
;; - The return value is eq? to the argument cl if no safe-factoring is possible.
;; - Applies safe-factoring as much as possible.
;;
;; clause? -> clause?
(define (safe-factoring cl)
(let/ec return
(zip-loop ([(l x r) cl])
(define pax (predicate.arity x))
(zip-loop ([(l2 y r2) r] #:break (not (equal? pax (predicate.arity y))))
; To avoid code duplication:
(define-simple-macro (attempt a b)
(begin
(define s (left-unify a b))
(when s
(define new-cl
(sort-clause
(fresh ; required for clause-subsumes below
(left-substitute (rev-append l (rev-append l2 (cons a r2))) ; remove b
s))))
(when (clause-subsumes new-cl cl)
; Try one more time with new-cl.
(return (safe-factoring new-cl))))))
(attempt x y)
(attempt y x)))
cl))
;; Returns whether the two clauses subsume each other,
;; in the sense of (non-multiset) subsumption above.
;;
;; clause? clause? -> boolean?
(define (clause-equivalence? cl1 cl2)
(and (clause-subsumes cl1 cl2)
(clause-subsumes cl2 cl1)))
;=================;
;=== Save/load ===;
;=================;
;; Save the clauses `cls` to the file `f`.
;;
;; cls : (listof clause?)
;; f : file?
;; exists : symbol? ; See `with-output-to-file`.
(define (save-clauses! cls f #:? [exists 'replace])
(with-output-to-file f #:exists exists
(λ () (for-each writeln cls))))
;; Returns the list of clauses loaded from the file `f`.
;;
;; file? -> (listof clause?)
(define (load-clauses f)
(map clausify (file->list f)))
================================================
FILE: satore/examples/binary30.p
================================================
cnf(i_0, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a))).
cnf(i_1, plain, (~num(b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27, X28) | num(a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27, X28))).
cnf(i_2, plain, (num(b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27, X28) | ~num(a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27, X28))).
cnf(i_3, plain, (~num(a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27) | num(b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27))).
cnf(i_4, plain, (num(a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27) | ~num(b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27))).
cnf(i_5, plain, (~num(a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26) | num(b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26))).
cnf(i_6, plain, (num(a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26) | ~num(b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26))).
cnf(i_7, plain, (~num(a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25) | num(b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25))).
cnf(i_8, plain, (num(a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25) | ~num(b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25))).
cnf(i_9, plain, (~num(a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24) | num(b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24))).
cnf(i_10, plain, (num(a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24) | ~num(b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24))).
cnf(i_11, plain, (~num(a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23) | num(b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23))).
cnf(i_12, plain, (num(a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23) | ~num(b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23))).
cnf(i_13, plain, (~num(a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22) | num(b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22))).
cnf(i_14, plain, (num(a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22) | ~num(b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22))).
cnf(i_15, plain, (~num(a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21) | num(b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21))).
cnf(i_16, plain, (num(a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21) | ~num(b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21))).
cnf(i_17, plain, (~num(a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20) | num(b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20))).
cnf(i_18, plain, (num(a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20) | ~num(b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20))).
cnf(i_19, plain, (~num(a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19) | num(b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19))).
cnf(i_20, plain, (num(a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19) | ~num(b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19))).
cnf(i_21, plain, (~num(a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18) | num(b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18))).
cnf(i_22, plain, (num(a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18) | ~num(b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18))).
cnf(i_23, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17) | num(b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17))).
cnf(i_24, plain, (num(a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17) | ~num(b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17))).
cnf(i_25, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16) | num(b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16))).
cnf(i_26, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16))).
cnf(i_27, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15))).
cnf(i_28, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15))).
cnf(i_29, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14))).
cnf(i_30, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14))).
cnf(i_31, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13))).
cnf(i_32, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13))).
cnf(i_33, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12))).
cnf(i_34, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12))).
cnf(i_35, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11))).
cnf(i_36, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11))).
cnf(i_37, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10))).
cnf(i_38, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10))).
cnf(i_39, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9))).
cnf(i_40, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8, X9))).
cnf(i_41, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8))).
cnf(i_42, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7, X8) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7, X8))).
cnf(i_43, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7))).
cnf(i_44, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6, X7) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6, X7))).
cnf(i_45, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6))).
cnf(i_46, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5, X6) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5, X6))).
cnf(i_47, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5))).
cnf(i_48, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4, X5) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4, X5))).
cnf(i_49, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4))).
cnf(i_50, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3, X4) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3, X4))).
cnf(i_51, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3))).
cnf(i_52, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2, X3) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2, X3))).
cnf(i_53, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2))).
cnf(i_54, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1, X2) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1, X2))).
cnf(i_55, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1))).
cnf(i_56, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0, X1) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0, X1))).
cnf(i_57, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0))).
cnf(i_58, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b, X0) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a, X0))).
cnf(i_59, plain, (~num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b) | num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a))).
cnf(i_60, plain, (num(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, b) | ~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, a))).
cnf(i_0, negated_conjecture, (~num(b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b, b))).
================================================
FILE: satore/examples/socrates.p
================================================
cnf(humans_are_mortal, axiom, (~human(X) | mortal(X))).
cnf(socrates_is_human, hypothesis, (human(socrates))).
cnf(socrates_is_mortal, negated_conjecture, (~mortal(socrates))).
================================================
FILE: satore/info.rkt
================================================
#lang info
(define collection "satore")
(define deps '("bazaar"
"data-lib"
"define2"
"global"
"math-lib"
"text-table"
"base"))
(define build-deps '("rackunit-lib"
"scribble-lib"
))
(define scribblings '(("scribblings/satore.scrbl" ())))
(define pkg-desc "First-order logic prover in CNF without equality, but with atom rewrite rules")
(define version "0.1")
(define pkg-authors '(orseau))
(define racket-launcher-names '("satore"))
(define racket-launcher-libraries '("satore.rkt"))
(define test-omit-paths '("info.rkt"
"last-results.rkt"
"parse-log.rkt"
"in-progress/"
"find-rules.rkt"
"print-rules.rkt"
"run-eprover.rkt"
"rules/"
"logs/"
"scribblings/"))
================================================
FILE: satore/interact.rkt
================================================
#lang racket/base
;***************************************************************************************;
;**** User Interaction Commands ****;
;***************************************************************************************;
(require (for-syntax racket/base syntax/parse)
racket/format
racket/list
racket/match
racket/port)
(provide (all-defined-out))
;; Notice: variables set via eval or only set locally, in the local namespace,
;; and not in the main namespace.
;; variables set via the (list 'var val) pattern are set in the main namespace.
;; Even though the namespace is at the module level, the variables
;; are set in the namespace with their value so they can be used with eval.
(define-syntax (interact stx)
(syntax-parse stx
#:literals (list)
[(_ (~alt (~optional (~seq #:prompt prompt:expr)) ; must evaluate to a string, default "> "
(~optional (~seq #:command command:expr))
(~optional (~seq #:namespace-anchor ns-anchor:expr)) ; default #false
(~optional (~seq #:variables (var:id ...))) ; must be bound identifiers
(~optional (~seq #:readline? readline?:expr))) ; start with readline enabled? (#false)
...
[(list pat ...) help-string body ...+] ...) ; match patterns
(with-syntax ([(var ...) #'(~? (var ...) ())])
#'(begin
(define names (list 'var ...))
(define nsa (~? ns-anchor #false))
(define ns (and nsa (namespace-anchor->namespace nsa)))
(when (~? readline? #false) (eval '(require readline) ns))
(when ns
(namespace-set-variable-value! 'var var #false ns) ...
(void)) ; to avoid bad 'when' form if no variable
(define the-prompt (~? prompt "> "))
(let loop ()
(with-handlers ([exn:fail? (λ (e)
(displayln (exn-message e))
(loop))])
(define cmd (~? command #false))
(when (and cmd (not (string? cmd)))
(error "command must be a string"))
(unless cmd (display the-prompt))
(define cmd-str (or cmd (read-line)))
(unless (eof-object? cmd-str)
(define cmd (with-input-from-string (string-append "(" cmd-str ")") read))
(match cmd
['() (void)]
['(help)
(unless (empty? names)
(printf "Available variables: ~a\n" names))
(displayln "Other commands:")
(parameterize ([print-reader-abbreviations #true]
[print-as-expression #false])
(void)
(begin
(displayln (string-append " " (apply ~v '(pat ...) #:separator " ")))
(displayln (string-append " " help-string)))
...)
(when ns
(displayln " eval expr")
(displayln
" Evaluate expr in a namespace that is local to this interaction loop."))
(loop)]
[(list 'eval cmd)
(if ns
(call-with-values (λ () (eval cmd ns))
(λ l (if (= 1 (length l))
(unless (void? (first l))
(displayln (first l)))
(for-each displayln l))))
(displayln "Cannot use eval without a namespace-anchor"))
(loop)]
['(var) (println var) (loop)] ...
[(list 'var val) (set! var val) (loop)] ...
[(list pat ...) body ... (loop)] ...
[else (printf "Unknown command: ~a\n" cmd)
(loop)]))))))]))
;; For manual testing in DrRacket
(module+ drracket
(define-namespace-anchor ns-anchor) ; optional, to use the eval command
(let ([x 3] [y 'a])
(interact
#:prompt ">> "
#:namespace-anchor ns-anchor
#:variables (x y)
;; All patterns must be of the form (list ....)
[(list 'yo) "prints yo" (displayln "yo")]
[(list 'yo (? number? n)) "prints multiple yos" (displayln (make-list n 'yo))])))
================================================
FILE: satore/json-output.rkt
================================================
#lang racket/base
;***************************************************************************************;
;**** Json Output ****;
;***************************************************************************************;
(require racket/dict
racket/string)
(provide (all-defined-out))
;; Correspondance with Eprover output values
(define status-dict
'((running . UNSPECIFIED_PROOF_STATUS)
(refuted . REFUTATION_FOUND)
(time . TIME_LIMIT_REACHED)
(memory . MEMORY_LIMIT_REACHED)
(steps . STEP_LIMIT_REACHED)
(saturated . COUNTER_SATISFIABLE)))
;; Take a result dictionary from `saturation` and returns a JSON string representation of it.
;;
;; dict? -> string?
(define (saturation-result->json res)
(define d
(let* ([res (dict-remove res 'name)]
[res (dict-remove res 'file)]
[res (dict-update res 'status (λ (v) (dict-ref status-dict v)))])
res))
(string-join
#:before-first "{\n "
(for/list ([(k v) (in-dict d)])
(define kstr (regexp-replace* #px"-|:" (symbol->string k) "_"))
(format "~s: ~s" kstr (if (symbol? v) (symbol->string v) v)))
",\n "
#:after-last "\n}"))
;; Simple visual test.
(module+ drracket
(define res
'((name . "GEO170+1.p")
(file . "data/tptp_geo/GEO170+1.p")
(status . refuted)
(steps . 205)
(generated . 3186)
(actives . 106)
(candidates . 2651)
(priority-remaining . 0)
(tautologies . 156)
(rules . 30)
(unit-rules . 24)
(binary-rules . 6)
(binary-rules-static . 0)
(binary-rules-dynamic . 6)
(binary-rewrites . 164)
(forward-subsumed . 96)
(backward-subsumed . 0)
(subsumes-checks . 7654)
(subsumes-steps . 13268)
(subsumes-breaks . 0)
(L-resolvent-pruning . 0)
(memory . 181509744)
(time . 196)
(proof-length . 12)
(proof-inferences . 5)
(proof-type:in . 7)
(proof-type:res . 4)
(proof-type:rw . 1)))
(displayln (saturation-result->json res)))
================================================
FILE: satore/log.rkt
================================================
#lang racket/base
;***************************************************************************************;
;**** Logging To File With Consistent Debugging Information ****;
;***************************************************************************************;
(require bazaar/date
bazaar/debug
define2
global
racket/file
racket/port
racket/pretty
racket/string
racket/system)
(provide call-with-log
*log*)
(define-global:boolean *log* #false
"Output to a log file?")
(define-global:boolean *git?* #false
"Commit to git if needed and include the last git commit hash in the globals.")
;; Calls thunk. Outputs to a log file if `log?` is not #false.
;; When `git?` is not #false, also commits to git to ensure consistency of the code base
;; with the experiment, and adds the git commit number to the global variables.
;;
;; thunk : thunk?
;; dir : path-string?
;; filename : string?
;; filepath : path-string?
;; log? : boolean?
;; git? : boolean?
;; quiet? : boolean?
(define (call-with-log thunk
#:? [dir "logs"]
#:? [filename (string-append "log-" (date-iso-file) ".txt")]
; if given, dir and filename have no effect:
#:? [filepath (build-path dir filename)]
#:? [log? (*log*)]
#:? [git? (*git?*)]
#:? [quiet? #false])
(when git?
(define cmd "git commit -am \".\" ")
(displayln cmd)
(system cmd))
;; Non-quiet mode.
(define (thunk2)
; Also write the last commit hash for easy retrieval.
(pretty-write
(list* `(cmd-line . ,(current-command-line-arguments))
`(git-commit . ,(and git?
(string-normalize-spaces
(with-output-to-string (λ () (system "git rev-parse HEAD"))))))
(globals->assoc)))
(thunk))
(cond [log?
(make-parent-directory* filepath)
(assert (not (file-exists? filepath)) filepath)
(printf "Logging to: ~a\n" filepath)
(pretty-write (globals->assoc))
(with-output-to-file filepath thunk2)]
[quiet? (thunk)]
[else (thunk2)]))
================================================
FILE: satore/main.rkt
================================================
#!/usr/bin/env racket
#lang racket/base
;**************************************************************************************;
;**** Satore ****;
;**************************************************************************************;
;;; Try:
;;; racket -l- satore --help
;;; to see all the available flags.
(module+ main
(require global
racket/file
racket/port
satore/misc
satore/rewrite-tree
satore/saturation
satore/unification)
(define-global *prog* #false
'("Data file containing a single TPTP program."
"If not provided, reads from the input port.")
file-exists?
values
'("-p"))
;; If -p is not specified, reads from current-input-port
(void (globals->command-line #:program "satore"))
;; No validation here yet.
(define program
(if (*prog*)
(file->string (*prog*))
(port->string)))
(iterative-saturation
(λ (#:clauses input-clauses #:cpu-limit cpu-limit #:rwtree-in rwtree-in #:rwtree-out rwtree-out)
(saturation input-clauses
#:cpu-limit cpu-limit
#:rwtree rwtree-in
#:rwtree-out rwtree-out))
#:tptp-program program
#:rwtree-in (make-rewrite-tree #:atom<=> (get-atom<=>)
#:dynamic-ok? (*dynamic-rules?*)
#:rules-file (*input-rules*))))
================================================
FILE: satore/misc.rkt
================================================
#lang racket/base
;***************************************************************************************;
;**** Various Utilities ****;
;***************************************************************************************;
(require (for-syntax racket/base racket/port racket/syntax)
global
racket/format
racket/port)
(provide (all-defined-out))
;; Prints #true and #false
(print-boolean-long-form #true)
(define-syntax-rule (begin-for-both e)
(begin
e
(begin-for-syntax e)))
(begin-for-both
;; (or/c string? symbol? number?) -> number?
(define (debug-level->number lev)
(cond
[(number? lev) lev]
[(string? lev) (debug-level->number (with-input-from-string lev read))]
[else
(case lev
[(none) 0]
[(init) 1]
[(step steps) 2]
[(interact) 3]
[else (error "unknown debug level" lev)])])))
(define-global *debug-level* 0
"Number or one of (none=0 init=1 steps interact)."
exact-nonnegative-integer?
debug-level->number
'("--debug"))
;; ;; (or/c string? symbol? number?) -> boolean?
(define (debug>= lev)
(>= (*debug-level*) (debug-level->number lev)))
;; Do a sequence of actions only when debug-level is greater than a given level.
(define-syntax (when-debug>= stx)
(syntax-case stx ()
[(_ lev body ...)
(with-syntax ([levv (debug-level->number (syntax-e #'lev))])
#'(when (>= (*debug-level*) levv)
body ...))]))
;; any/c -> boolean?
(define (thunk? p)
(and (procedure? p)
(procedure-arity-includes? p 0)))
;; Defines a counter with a reset function and an increment function.
;; Ex:
;; (define-counter num 0)
;; (++num)
;; (++num 3)
;; (reset-num!)
(define-syntax (define-counter stx)
(syntax-case stx ()
[(_ name init)
(with-syntax ([reset (format-id stx #:source stx "reset-~a!" (syntax-e #'name))]
[++ (format-id stx #:source stx "++~a" (syntax-e #'name))])
#'(begin
(define name init)
(define (reset)
(set! name init))
(define (++ [n 1])
(set! name (+ name n)))))]))
;; -> number?
(define (current-inexact-seconds)
(* 0.001 (current-inexact-milliseconds)))
;; -> exact-nonnegative-integere?
(define (current-memory-use-MB)
(arithmetic-shift (current-memory-use) -20))
;; Prints `x` with a given precision if it is a rational, otherwise formants it with `~a`.
;;
;; x : any/c
;; precision : (or/c exact-nonnegative-integer? (list/c '= exact-nonnegative-integer?))
(define (~r2 x #:precision [precision 2])
(if (rational? x)
(~r x #:precision precision)
(~a x)))
================================================
FILE: satore/rewrite-tree.rkt
================================================
#lang racket/base
;***************************************************************************************;
;**** Binary Rewrite Tree ****;
;***************************************************************************************;
(require bazaar/cond-else
bazaar/mutation
(except-in bazaar/order atom<=>)
define2
define2/define-wrapper
global
racket/file
racket/list
racket/string
satore/Clause
satore/clause-format
satore/clause
satore/misc
satore/trie
satore/unification)
(provide (all-defined-out)
(all-from-out satore/trie))
;===============;
;=== Globals ===;
;===============;
(define-counter n-rule-added 0)
(define-counter n-binary-rewrites 0)
(define-global:boolean *bounded-confluence?* #true
'("When performing confluence, should the size of the critical pairs "
"be bounded by the size of parent rules?"))
(define-global *confluence-max-steps* 10
"Maximum number of confluence steps"
number?
string->number)
;====================;
;=== Rewrite tree ===;
;====================;
;; atom<=> : comparator?
;; dynamic-ok? : boolean? ; whether we keep dynamic rules
;; rules-file : file? ; if not #false, loads rules from the given file
(struct rewrite-tree trie (atom<=> dynamic?))
;; rewrite-tree constructor.
;;
;; constructor: procedure?
;; atom<=> : comparator?
;; dynamic-ok? : boolean?
;; rules-file : file?
;; other-args : list?
;; -> rewrite-tree?
(define (make-rewrite-tree #:? [constructor rewrite-tree]
#:! atom<=>
#:? [dynamic-ok? #true]
#:? [rules-file #false]
. other-args)
(define rwtree
(apply make-trie
#:constructor constructor
#:variable? Var?
atom<=>
dynamic-ok?
other-args))
(when rules-file
(load-rules! rwtree #:rules-file rules-file #:rewrite? #true))
rwtree)
;; Returns a new rewrite-tree.
;; Duplicates the structure of rwtree, but the Clauses and rules are shared.
;; This means that the Clause ancestors of the rules are preserved.
;;
;; rwtree : rewrite-tree?
;; -> rewrite-tree?
(define (rewrite-tree-shallow-copy rwtree)
(define new-rwtree (make-rewrite-tree #:atom<=> (rewrite-tree-atom<=> rwtree)
#:dynamic-ok? (rewrite-tree-dynamic? rwtree)))
(for ([rl (in-list (rewrite-tree-rules rwtree))])
(add-rule! new-rwtree rl))
new-rwtree)
;; Returns the list of values that match the literal t.
;;
;; rwtree : rewrite-tree?
;; t : any/c
;; -> list?
(define (rewrite-tree-ref rwtree t)
; Node values are lists of rules, and trie-ref returns a list of node-values,
; hence the append*.
(append* (trie-ref rwtree t)))
;; Clause : The Clause from which this rule originates
;; NOTICE: Clause may subsume the rule, and can be *more* general if the rule is asymmetric.
;; from-literal : heavy-side
;; to-literal : light side
;; to-fresh : variables of to-literal not in from-literal that need to be freshed
;; after applying the rule
;;
;; Clause : Clause?
;; rule-group : rule-group?
;; from-literal? : literal?
;; to-literal? : literal?
;; to-fresh : unused
;; static? : boolean?
(struct rule (Clause rule-group from-literal to-literal to-fresh static?)
#:prefab)
;; A rule group holds all the rules that have been created by a single call to
;; Clause->rules.
;; A rule also has a reference to its rule group, so it makes it easy to find
;; the other rules of the same group from a given group.
;; A single group can have 2 or 4 rules: 2 for static rules or dynamic self-converse rules,
;; and 4 for dynamic non-self-converse rules.
;;
;; Clause : (or/c false/c Clause?)
;; Converse : Clause?
;; rules: (listof rule?)
(struct rule-group (Clause Converse [rules #:mutable]) #:prefab)
;; Rule constructor.
;;
;; C : Clause?
;; rule-gp : rule-group?
;; from : literal?
;; to : literal?
;; static? : boolean?
;; -> rule?
(define (make-rule C rule-gp from to static?)
(define-values (lit1 lit2) (apply values (fresh (list from to))))
(rule C rule-gp lit1 lit2 (variables-minus lit2 lit1) static?))
;; Returns whether rule is a unit rule, that is, if the `to` literal
;; is either ltrue or lfalse.
;;
;; rl : rule?
;; -> any/c
(define (unit-rule? rl)
(memq (rule-to-literal rl) (list lfalse ltrue)))
;; Returns whether rl is a 'tautology', that is, if its `to` and `from`
;; literals are syntactically equal.
;;
;; rl : rule?
;; -> boolean?
(define (rule-tautology? rl)
(equal? (rule-from-literal rl) (rule-to-literal rl)))
;; Returns whether the rule is left linear, that is, if each variable
;; occurs at most once in the `from` literal.
;;
;; rl : rule?
;; -> boolean?
(define (left-linear? rl)
(define occs (var-occs (rule-from-literal rl)))
(for/and ([(v n) (in-hash occs)])
(<= n 1)))
;; Returns whether rl1 subsumes rl2.
;;
;; rl1 : rule?
;; rl2 : rule?
;; -> any/c
(define (rule-subsumes rl1 rl2)
(define subst (left-unify (rule-from-literal rl1) (rule-from-literal rl2)))
(and subst (left-unify (rule-to-literal rl1) (rule-to-literal rl2) subst)))
;; A binary Clause [lit1 lit2] is treated as an *equivalence* (4 implications)
;; rather than two implications,
;; which means that a converse Clause or one that subsumes the latter MUST have been proven too
;; before adding the rules.
;; Thus: (Clause->rules C <=>) == (Clause->rules (make-converse-Clause C) <=>).
;; That is, if C = ~q | p, the converse C' = q | ~p MUST have been proven.
;; We MUST add the four rules at the same time, because the if we add just the (max 2) rules
;; corresponding to the implications of C only, and then add the rules for C',
;; then C' is going to be rewritten to a tautology before it has a chance to propose its rules
;; (since resolution(C, converse(C)) = tautology).
;; The number of returned rules is either 2 or 4.
;; Conv is either the converse Clause of C, or a Clause that subsumes it, and is used
;; as a parent reference (for proofs), even if converse(C) is more specific than Conv.
;; Conv is *not* used to generate the rules themselves (only C), so if Conv is more general than C
;; (as for asymmetric rules) the generated rules will *not* be more general than those of C.
;;
;; C : Clause?
;; Conv : Clause?
;; atom<=> : comparator?
;; dynamic-ok? : boolean?
;; -> (listof rule?)
(define (Clause->rules C Conv #:! atom<=> #:? [dynamic-ok? #true])
(define cl (Clause-clause C))
(define unit? (unit-Clause? C))
(define lit1 (first cl))
(define lit2 (if unit? lfalse (second cl)))
(define rg (rule-group C Conv #false))
; Not the most efficient. The last 2 cases can be deduced from the first 2,
; as long as atom<=> does not count polarity (which SHOULD be the case).
(define rules
(for/list ([parent
(in-list (cond [unit?
(list C #false C)]
[(eq? C Conv) ; self-converse, no need to duplicate the rules
(list C C)]
[else
(list C C Conv Conv)]))]
[from (in-list (list (lnot lit1) (lnot lit2) lit1 lit2))]
[to (in-list (list lit2 lit1 (lnot lit2) (lnot lit1)))]
; if Conv is #false for example, skip it (see force-add-binary-Clause!)
; also useful for unit?
#:when parent
[c (in-value (atom<=> from to))]
#:when (and (not (order? c))))
; rule cannot be oriented to → from,
; so the rule from → to is valid.
; It is 'static' if it can be oriented from → to, 'dynamic' otherwise.
(make-rule parent rg from to (order>? c))))
(set-rule-group-rules! rg rules)
rules)
;; a is left-unify< to b if a left-unifies with b.
;;
;; comparator?
;; literal? literal? -> (one-of '< '> '= #false)
(define left-unify<=> (make<=> left-unify) ; left-unify is <=?
; equivalent to:
#;(if (left-unify c1 c2)
(if (left-unify c2 c1)
'=
'<)
(if (left-unify c2 c1)
'>
#false)))
;;; What we want to assess is whether
;;; when applying rl1 to any clause c to get c1,
;;; c1 is always necessarily better than c2 when applying rl2 to c.
;; Compares 2 rules. May help decide if one should be discarded.
;; rl1 <= rl2 if the heavyside of rl1 subsumes that of rl2,
;; and its light side is no heavier than that of rl2.
;; NOTICE: A return value of '= does not mean that rl1 and rl2 are equal or equivalent;
;; it only means that either can be used. It does mean that their heavy sides are equivalent though.
;;
;; rl1 : rule?
;; rl2 : rule?
;; atom<=> : comparator?
;; -> (one-of '< '> '= #false)
(define (rule<=> rl1 rl2 atom<=>)
(chain-comparisons
(left-unify<=> (rule-from-literal rl1) (rule-from-literal rl2))
(atom<=> (rule-to-literal rl1) (rule-to-literal rl2))))
;; Returns a rule of rwtree that subsumes rl if any.
;;
;; rwtree : rewrite-tree?
;; rl : rule?
;; -> (or/c #false rule?)
(define (find-subsuming-rule rwtree rl)
(for/or ([rl2 (in-list (rewrite-tree-ref rwtree (rule-from-literal rl)))])
(and (rule-subsumes rl2 rl) rl2)))
;; If there is a substitution α such that rule-from α = lit, then rule-to α is returned,
;; otherwise #false is returned.
;; If the rule introduces variables, these are freshed.
;;
;; rl : rule?
;; lit : literal?
;; -> literal?
(define (rule-rewrite-literal rl lit)
(define subst (left-unify (rule-from-literal rl) lit))
(cond [subst
; NOTICE: We need to fresh the variables
; that are introduced by the rule.
; Ex:
; clause: [(p a) (p b)] rule: (p X) -> (q Y)
; Then this must be rewritten to:
; [(q A) (q B)] and NOT [(q Y) (q Y)]
(for ([v (in-list (rule-to-fresh rl))])
(subst-set!/name subst v (new-Var)))
(left-substitute (rule-to-literal rl) subst)]
[else #false]))
;; Recursively rewrites the given literal lit from the rules in rwtree.
;; Returns the new literal and the sequence of rules used to rewrite it
;; (may contain duplicate rules).
;; lit: literal?
;; C: The Clause containing the literal lit. Used to avoid backward rewriting C to a tautology.
;;
;; rwtree : rewrite-tree?
;; lit : literal?
;; C : Clause?
(define (binary-rewrite-literal rwtree lit C)
(define atom<=> (rewrite-tree-atom<=> rwtree))
(let loop ([lit lit] [rules '()])
(define candidate-rules (rewrite-tree-ref rwtree lit))
; Find the best rewrite of lit according to atom<=>.
; Q: Can we rewrite more greedily, that is, applied each rewrite asap?
; A: if the set of rules is confluent, yes, since they are all equivalent (although
; choosing the one that leads to the smallest literal may still be faster)
; But if the rules are not confluent then we may have a problem.
(for/fold ([best-lit lit]
[best-rule #false]
#:result (if best-rule
(loop best-lit (cons best-rule rules)) ; try once more
(values best-lit (reverse rules)))) ; no more rewrites
([r (in-list candidate-rules)]
#:unless (let ([g (rule-rule-group r)])
; Don't rewrite yourself or your converse!
(or (eq? C (rule-group-Clause g))
(eq? C (rule-group-Converse g)))))
(define new-lit (rule-rewrite-literal r lit))
(if (and new-lit (order new-lit best-lit)))
(values new-lit r)
(values best-lit best-rule)))))
;; Returns a rewritten clause and the set (without duplicates) of rules used for rewriting.
;; Rewriting a clause is a simple as rewriting each literal, because
;; only left-unification is used, so the substitution cannot apply to the rest of the clause.
;; NOTICE: The variables of the resulting clause are not freshed.
;;
;; rwtree : rewrite-tree?
;; cl : clause?
;; C : Clause?
(define (binary-rewrite-clause rwtree cl C)
(let/ec return
(for/fold ([lits '()]
[rules '()]
#:result (values (sort-clause lits)
(remove-duplicates rules eq?)))
([lit (in-list cl)])
(define-values (new-lit new-rules) (binary-rewrite-literal rwtree lit C))
(values (cond [(ltrue? new-lit) (return (list new-lit) new-rules)] ; tautology shortcut
[(lfalse? new-lit) lits]
[else (cons new-lit lits)])
(append new-rules rules)))))
(define Clause-type-rw 'rw)
;; Clause? -> boolean?
(define (Clause-type-rw? C)
(eq? (Clause-type C) Clause-type-rw))
;; Returns a new Clause if C can be rewritten, C otherwise.
;; The parents of the new Clause are C followed by the set of clauses from which the rules
;; used for rewriting C originate.
;;
;; rwtree : rewrite-tree?
;; C : Clause?
;; -> Clause?
(define (binary-rewrite-Clause rwtree C)
(define-values (new-cl rules) (binary-rewrite-clause rwtree (Clause-clause C) C))
(cond [(empty? rules)
C]
[else
(++n-binary-rewrites)
(make-Clause (clause-normalize new-cl)
(cons C (remove-duplicates (map rule-Clause rules) eq?))
#:type Clause-type-rw)]))
;; Returns whether the clause cl would be rewritten. Does not perform the rewriting.
;;
;; rwtree : rewrite-tree?
;; cl : clause?
;; C : Clause?
;; -> boolean?
(define (binary-rewrite-clause? rwtree cl C)
(for/or ([lit (in-list cl)])
; We need to perform the literal rewriting anyway, because
; we need to check if the result is order boolean?
(define (binary-rewrite-Clause? rwtree C)
(binary-rewrite-clause? rwtree (Clause-clause C) C))
;; Unconditionally adds the rule rl to the rewrite-tree rwtree,
;; and removes from rwtree the rules that are subsumed by rl
;;
;; rwtree : rewrite-tree?
;; rl : rule?
;; -> void?
(define (add-rule! rwtree rl)
(define atom<=> (rewrite-tree-atom<=> rwtree))
(unless (or (rule-tautology? rl)
(find-subsuming-rule rwtree rl))
;; Remove existing rules that are subsumed by rl.
(define from-lit (rule-from-literal rl))
(trie-inverse-find rwtree from-lit
(λ (nd)
(define matches (trie-node-value nd))
(when (list? matches) ; o.w. no-value
(define new-value
(for/list ([rl2 (in-list matches)]
#:unless (rule-subsumes rl rl2))
rl2))
;; TODO: If new-value is '(), the node should be removed from the trie,
;; TODO: along with any similar parent. (this could be made automatic?)
(set-trie-node-value! nd new-value))))
;; NOTICE: No need to backward-rewrite the rules.
;; Resolution (in the saturation loop) will take care of generating new rules
;; and rewriting both their heavy sides and the light sides,
;; while add-rule! takes care of subsumption on the heavy side.
;; Letting the resolution loop take care of this is much safer, as it ensures
;; that rwtree and utree are in sync, and that rwtree is not going to rewrite
;; clauses before utree has a chance to generate new candidates via resolution.
;; That is, if we only *remove* stuff from rwtree, and not do the job of resolution
;; (apart from forward rewriting), then we should be fine?
(++n-rule-added)
(trie-insert! rwtree from-lit rl)))
;; Unconditionally removes a single (oriented) rule from the tree.
;; Use with caution and see instead remove-rule-group!.
;;
;; rwtree : rewrite-tree?
;; rl : rule?
;; -> void?
(define (remove-rule! rwtree rl)
(trie-find rwtree (rule-from-literal rl)
(λ (nd)
(define old-value (trie-node-value nd))
(when (list? old-value) ; o.w. no-value
;; TODO: If new-value is '(), the node should be removed from the trie,
;; TODO: along with any similar parent. (this could be made automatic?)
(set-trie-node-value! nd (remove rl old-value eq?))))))
;; Removes a group of rules that were added at the same time via Clause->rules
;; (via add-binary-Clause!).
;;
;; rwtree : rewrite-tree?
;; gp : rule-group?
;; -> void?
(define (remove-rule-group! rwtree gp)
(for ([rl (in-list (rule-group-rules gp))])
(remove-rule! rwtree rl)))
;; Turn the unit Clause into a binary Clause before adding it.
;; As for self-converses, we say that C is its own converse but that's a lie;
;; This is to avoid problems and specific cases when loading/saving rules.
;; A self-converse rule is necessarily dynamic (unless commutativity can be handled statically?),.
;; A unit rule is necessarily static (since $false is the bottom element).
;; Hence a 'self-converse' static rule is necessarily a unit rule (for now).
;;
;; rwtree : rewrite-tree?
;; C : Clause?
;; rewrite? : boolean?
;; -> void?
(define (rewrite-tree-add-unit-Clause! rwtree C #:? rewrite?)
(unless (unit-Clause? C) (error "Non-unit Clause v" C))
(rewrite-tree-add-binary-Clause! rwtree C C #:rewrite? rewrite?))
;; Rewriting of the clause C must be done prior to calling this function.
;; Conv: Converse of C. See Clause->rules.
;; Returns the new rules (use these to obtain the rewritten Clauses).
;;
;; rwtree : rewrite-tree?
;; C : Clause?
;; Conv : Clause?
;; rewrite? : boolean?
;; -> void?
(define (rewrite-tree-add-binary-Clause! rwtree C Conv #:? [rewrite? #true])
(cond
[(Clause-binary-rewrite-rule? C) ; already added as a rule in the past?
(when-debug>= steps
(displayln "Clause has already been added before. Skipping."))
'()]
[else
(let-values
([(C Conv)
(cond/else
[(not rewrite?) (values C Conv)]
#:else ; Rewriting
(define C2 (binary-rewrite-Clause rwtree C))
(when-debug>= steps
(displayln "Considering binary Clause for a rule (before rewriting):")
(display-Clause-ancestor-graph C #:depth 0)
(displayln "After rewriting:")
(display-Clause-ancestor-graph C2)
(when (Clause-tautology? C2)
(displayln "...but tautology.")))
#:cond
[(Clause-tautology? C2)
(values #false #false)]
#:else
; Rewritten rule can still be used, rewrite the converse too. Not very efficient.
(define Conv2 (if (eq? C Conv) Conv (binary-rewrite-Clause rwtree Conv)))
(values C2 Conv2))])
(cond
[(not C) '()]
[(empty-clause? (Clause-clause C))
; Refutation found!
(when-debug>= steps
(displayln "Refutation found while adding rules!")
(display-Clause-ancestor-graph C))
; TODO: Let's just discard it for now. A refutation will probably be found very early
; TODO: at the next saturation iteration.
'()]
[else
(define atom<=> (rewrite-tree-atom<=> rwtree))
(define dynamic-ok? (rewrite-tree-dynamic? rwtree))
(define rls (Clause->rules C Conv #:atom<=> atom<=> #:dynamic-ok? dynamic-ok?))
(when-debug>= steps
(displayln "Adding the following rules:")
(display-rules rls))
(for ([rl (in-list rls)])
(add-rule! rwtree rl))
; We set the bit to #true, *even if* no rule has been added,
; because the purpose of this bit is to avoid considering
; C later again to save time.
; TODO: We could set Conv as a rewrite-rule too but ONLY if C also subsumes the converse
; TODO: of conv.
(set-Clause-binary-rewrite-rule?! C #true)
rls]))]))
;; Adds the binary Clauses Cs as rules and returns the corresponding rules.
;; If rewrite? is not #false, the clauses are rewritten beforehand using the rules in the tree
;; (the order of the rules in Cs does matter as of now)
;; and tautologies are filtered out.
;; The default rewrite = #true is 'safe' because when considering A->B, Clause->rules also considers
;; B->A because we provide it with the converse equivalence (that is, the proof that B->A is valid).
;; Hence even converse implications can safely be rewritten to tautologies without losing rules.
;; Conv: MUST Subsumes the converse clause of each of Cs.
;;
;; rwtree : rewrite-tree?
;; Cs : (listof Clause?)
;; Conv : Clause?
;; rewrite? : boolean?
;; -> void?
(define (rewrite-tree-add-binary-Clauses! rwtree Cs Conv #:? rewrite?)
(for/fold ([rules '()])
([C (in-list Cs)])
(define rls (rewrite-tree-add-binary-Clause! rwtree C Conv #:rewrite? rewrite?))
(append rls rules)))
;; Returns the list of rules (without duplicates) of rwtree.
;;
;; rwtree : rewrite-tree?
;; -> (listof rule?)
(define (rewrite-tree-rules rwtree)
(remove-duplicates (append* (trie-values rwtree)) eq?))
;; Returns the list of rule groups (without duplicates) of rwtree.
;;
;; rwtree : rewrite-tree?
;; -> (listof rule-group?)
(define (rewrite-tree-rule-groups rwtree)
(remove-duplicates (map rule-rule-group (append* (trie-values rwtree))) eq?))
;; Returns the list of unique Clauses that have been used to create the rules
;; held by the given rule groups.
;;
;; groups : (listof rule-group?)
;; -> (listof Clause?)
(define (rule-groups-original-Clauses groups)
(remove-duplicates (append (map rule-group-Clause groups)
(map rule-group-Converse groups))
eq?))
;; Returns the list of unique Clauses that have been used to create the rules.
;;
;; rules : (listof rule?)
;; -> (listof Clause?)
(define (rules-original-Clauses rules)
(rule-groups-original-Clauses (map rule-rule-group rules)))
;; Returns the list of unique Clauses that have been used to create the rules
;; of the rewrite tree.
;;
;; rwtree : rewrite-tree?
;; -> (listof Clause?)
(define (rewrite-tree-original-Clauses rwtree)
(rule-groups-original-Clauses (rewrite-tree-rule-groups rwtree)))
;; Returns the number of rules in the rewrite tree.
;;
;; rwtree : rewrite-tree?
;; -> exact-nonnegative-integer?
(define (rewrite-tree-count rwtree)
(length (rewrite-tree-rules rwtree))) ; not efficient
;; Returns a dictionary of statistics about the rewrite tree.
;;
;; rwtree : rewrite-tree?
;; -> list?
(define (rewrite-tree-stats rwtree)
(define rules (rewrite-tree-rules rwtree))
(define n-rules (length rules))
(define n-dyn (count (λ (r) (not (rule-static? r))) rules))
(define n-unit (count unit-rule? rules))
(define n-bin (- n-rules n-unit))
`((rules . ,n-rules)
(unit-rules . ,n-unit)
(binary-rules . ,n-bin)
(binary-rules-static . ,(- n-bin n-dyn))
(binary-rules-dynamic . ,n-dyn)))
;; Attempts to simplify the rules by successively removing each rule,
;; rewrite its underlying Clause and add the rule again—checking for subsumption.
;; Since all rules are processed, the new set of rules can be obtained via rewrite-tree-rules.
;; Notice: Not (currently) suitable for use *inside* the saturation loop because the new Clauses
;; (as per `eq?` are not part of the active set or candidates.
;;
;; rwtree : rewrite-tree?
;; -> void?
(define (re-add-rules! rwtree)
(define groups (rewrite-tree-rule-groups rwtree))
(for ([gp (in-list groups)])
(remove-rule-group! rwtree gp)
(define C (rule-group-Clause gp))
(set-Clause-binary-rewrite-rule?! C #false) ; otherwise will not be added
(rewrite-tree-add-binary-Clause! rwtree C (rule-group-Converse gp) #:rewrite? #true)))
;===================;
;=== Save / load ===;
;===================;
;; Save the rules to file f (as clauses).
;;
;; rwtree : rewrite-tree?
;; rules-file : file?
;; exists : symbol? ; see `display-to-file`.
;; -> void?
(define (save-rules! rwtree #:! rules-file #:? [exists 'replace])
(define groups (rewrite-tree-rule-groups rwtree))
; We use hash to avoid duplicating clauses, so that we also avoid loading
; the same clause under different names.
(define-counter idx 0)
(define h (make-hasheq))
(define (get-clause C)
(if (hash-has-key? h C)
(hash-ref h C)
(begin0 (Clause-clause C)
(++idx)
(hash-set! h C idx))))
(make-parent-directory* rules-file) ; ensure the path exists
(with-output-to-file rules-file #:exists exists
(λ () (for ([gp (in-list groups)])
(define C (rule-group-Clause gp))
(define Conv (rule-group-Converse gp))
(writeln (if (eq? C Conv)
(list (get-clause C)) ; self-converse
(list (get-clause C)
(get-clause Conv))))))))
;; Private.
;;
;; rules-file : file?
;; -> (listof Clause?)
(define (load-rule-Clause-lists #:! rules-file)
(define-counter idx 0)
; Each clause has a number (local to the file) and if a number appears while reading
; then we must load the saved clause of the same number.
(define h (make-hasheqv))
(define (get-Clause! x)
(cond [(number? x)
(hash-ref h x)]
[else
(++idx)
(define C (make-Clause x #:type 'load))
(hash-set! h idx C)
C]))
(for/list ([cls (in-list (file->list rules-file))])
(map get-Clause! cls)))
;; Load the rules from file into the rewrite-tree.
;; Optionally: rewrites the rules before adding them
;; Returns the set of new rules.
;;
;; rwtree : rewrite-tree?
;; rules-file : file?
;; rewrite? : boolean?
;; -> (listof rule?)
(define (load-rules! rwtree #:! rules-file #:? [rewrite? #true])
(define Crules (load-rule-Clause-lists #:rules-file rules-file))
(for/fold ([rules '()])
([Cs (in-list Crules)])
(define C (first Cs))
(define Conv
(if (= (length Cs) 1)
C ; self-converse
(second Cs)))
(define new-rules (rewrite-tree-add-binary-Clause! rwtree C Conv #:rewrite? rewrite?))
(append new-rules rules)))
;=======================;
;=== Filtering rules ===;
;=======================;
;; Returns a new rwtree containing only the filtered rules
;;
;; rwtree : rewrite-tree?
;; proc : rule-group? -> boolean?
;; -> rewrite-tree?
(define (filter-rule-groups rwtree proc)
(define rwtree2 (make-rewrite-tree #:atom<=> (rewrite-tree-atom<=> rwtree)
#:dynamic-ok? (rewrite-tree-dynamic? rwtree)))
(define groups (rewrite-tree-rule-groups rwtree))
(for ([gp (in-list groups)])
(when (proc gp)
; Copy the clauses (shallow)
(define C (make-Clause (Clause-clause (rule-group-Clause gp)) #:type 'filter-rule-groups))
(define Conv (make-Clause (Clause-clause (rule-group-Converse gp)) #:type 'filter-rule-groups))
(rewrite-tree-add-binary-Clause! rwtree2 C Conv #:rewrite? #true)))
rwtree2)
;==================;
;=== Confluence ===;
;==================;
;; Unifies (resolves) only with the lhs of rules and returns
;; the new set of Clauses (and converse Clauses).
;; If bounded? is not #false, then Clauses which have a literal
;; that is heavier (in literal-size) than a `from' literal of its parent rules
;; are discarded.
;;
;; rwtree : rewrite-tree?
;; rl : rule?
;; bounded? : boolean?
;; -> (listof Clause?)
(define (find-critical-pairs rwtree rl #:? [bounded? (*bounded-confluence?*)])
(define lnot-from-lit1 (lnot (rule-from-literal rl)))
(define to-lit1 (rule-to-literal rl))
(define new-Clauses '())
; Resolution (like unification with the converse)
; to ensure that the Clause parents are correct
(trie-both-find rwtree lnot-from-lit1
(λ (nd)
(define rls (trie-node-value nd))
(when (list? rls)
(for ([rl2 (in-list rls)]
#:unless (eq? rl rl2))
(define from-lit2 (rule-from-literal rl2))
(define to-lit2 (rule-to-literal rl2))
(define s (unify lnot-from-lit1 from-lit2))
(when s
(define cl (clause-normalize (substitute (list to-lit1 to-lit2) s)))
(define C (make-Clause cl (list (rule-Clause rl) (rule-Clause rl2))
#:type 'c-p)) ; critical-pair
(define max-size (max (literal-size (rule-from-literal rl))
(literal-size from-lit2)))
(unless (or (Clause-tautology? C)
(and bounded?
(> (max (literal-size (first cl))
(literal-size (second cl)))
max-size)))
(cons! C new-Clauses)))))))
new-Clauses)
;; This is a bad fix. Instead we should find the correct converse Clause (via rule groups)
;; Returns the list of rules that were added, or '() if none.
;;
;; rwtree : rewrite-tree?
;; C : Clause?
;; -> void?
(define (force-add-binary-Clause! rwtree C)
(define Conv (make-converse-Clause C))
(rewrite-tree-add-binary-Clause! rwtree C (if (Clause-equivalence? C Conv) C Conv)))
;; Add all critical pairs between existing rules.
;; This procedure may need to be called several times, but such a loop may not terminate
;; as some rules can be inductive.
;; It is recommended to call `simplify-rewrite-tree!` before and after calling this procedure.
;; Returns #true if any rules has been added.
;;
;; rwtree : rewrite-tree?
;; bounded? : boolean?
;; -> boolean?
(define (rewrite-tree-confluence-step! rwtree #:? bounded?)
(define rules (rewrite-tree-rules rwtree))
(for/fold ([any-change? #false])
([rl (in-list rules)])
(define pairs (find-critical-pairs rwtree rl #:bounded? bounded?))
(for/fold ([any-change? any-change?])
([C (in-list pairs)])
(define change? (force-add-binary-Clause! rwtree C))
(or any-change? (and change? #true)))))
;; Performs several steps of confluence until no more change happens.
;; Returns whether any change has been made.
;; Notice: if bounded? is #false, this may loop forever.
;; If it does halt however, the system is confluent, but may not be minimal;
;; call re-add-rules! to remove unnecessary rules and simplify rhs of rules
;; (this should help make rewriting faster).
;; It is advised to call re-add-rules! once before and once after rewrite-tree-confluence!.
;;
;; rwtree : rewrite-tree?
;; bounded? : boolean?
;; max-steps : exact-nonnegative-integer?
;; -> boolean?
(define (rewrite-tree-confluence! rwtree #:? bounded? #:? [max-steps (*confluence-max-steps*)])
(let loop ([step 1] [any-change? #false])
(define changed? (rewrite-tree-confluence-step! rwtree #:bounded? bounded?))
(cond [(or (>= step max-steps)
(not changed?))
any-change?]
[else (loop (+ step 1) #true)])))
;================;
;=== Printing ===;
;================;
;; Returns a list representing the rule.
;;
;; rule? -> list?
(define (rule->list rl)
(cons (Clause-idx (rule-Clause rl))
(Vars->symbols (list (rule-from-literal rl)
(rule-to-literal rl)))))
;; A comparator to sort the rules for printing
(define display-rule<=>
(make-chain<=> boolean<=> rule-static?
boolean<=> unit-rule?
boolean<=> (λ (r) (lnot? (rule-from-literal r)))
number<=> (λ (r) (+ (tree-size (rule-from-literal r))
(tree-size (rule-to-literal r))))
number<=> (λ (r) (tree-size (rule-to-literal r)))
length<=> rule-to-fresh))
;; (listof rule?) -> (listof rule?)
(define (sort-rules rls)
(sort rls (λ (a b) (order>? (display-rule<=> a b)))))
;; Human-readable output
;; Notice: Since unit rules are asymmetric (only one rule per unit clause),
;; if positive-only is not #false it may not display some unit rules.
;;
;; rls : (listof rule?)
;; sort? : boolean?
;; positive-only? : boolean?
;; unit? : boolean?
(define (rules->string rls #:? [sort? #true] #:? [positive-only? #false] #:? [unit? #true])
(string-join
(for/list ([rl (in-list (if sort? (sort-rules rls) rls))]
#:unless (or (and (not unit?) (unit-rule? rl))
(and positive-only? (lnot? (rule-from-literal rl)))))
; abusing clause->string:
(clause->string (list (if (rule-static? rl) "static " "dynamic")
(map Var (rule-to-fresh rl))
':
(rule-from-literal rl)
'→
(rule-to-literal rl))))
"\n"))
;; Like rules->string but directly displays the result.
;;
;; rls : (listof rule?)
;; sort? : boolean?
;; positive-only? : boolean?
;; unit? : boolean?
(define-wrapper (display-rules (rules->string rls #:? sort? #:? positive-only? #:? unit?))
#:call-wrapped call
(displayln (call)))
================================================
FILE: satore/saturation.rkt
================================================
#lang racket/base
;**************************************************************************************;
;**** Saturation Algorithm ****;
;**************************************************************************************;
(require bazaar/cond-else
bazaar/date
bazaar/debug
bazaar/dict
bazaar/mutation
data/heap/unsafe
data/queue
define2
global
racket/block
racket/dict
racket/format
racket/list
racket/math
racket/pretty
racket/string
satore/Clause
satore/clause-format
satore/clause
satore/interact
satore/json-output
satore/misc
satore/rewrite-tree
satore/tptp
satore/unification-tree
satore/unification)
(provide (all-defined-out))
;===============;
;=== Globals ===;
;===============;
(define-global:boolean *quiet-json?* #false
'("JSON output format and silent mode. Deactivates some verbose options."))
(define-global *memory-limit* 4096
"Memory limit in MB, including the Racket VM."
exact-positive-integer?
string->number)
(define-global:boolean *find-unit-rules-in-candidates?* #false
'("Search for unit rewrite rules in the condidate set?"
"This may speed up the search significantly, or slow it down significantly,"
"depending on how many unit rules can be generated."))
(define-global:boolean *backward-rw?* #true
'("Use binary-clause rewriting? (aka θ-equivalences)."
"The iterative mode does not use backward rewrites."))
(define-global:boolean *dynamic-rules?* #false
"Use dynamic rules? Experimental.")
(define-global:boolean *proof?* #false
"Display the proof when found?")
(define-global *age:cost* '(1 9)
"Age:cost ratio. Format: :, e.g., '1:3'"
(λ (p) (and (list? p) (= 2 (length p)) (andmap exact-nonnegative-integer? p)))
(λ (s) (map string->number (string-split s ":"))))
(define-global:category *cost-type* 'weight-fair
'(weight-fair weight)
"Cost type.")
(define-global *cost-depth-factor* 1.5
"Cost = weight + cost-depth-factor * depth"
(λ (x) #true)
string->number)
(define-global *cost-noise* 0.
"Noise factor to add to costs"
(λ (x) (and (real? x) (>= x 0)))
string->number)
(define-global:boolean *parent-discard?* #false
"Discard clauses when at least one parent has been discarded?")
(define-global:boolean *discover-online?* #true
'("Use rewrite rules as soon as they are discovered?"
"The rules will not be made confluence until the next restart"))
(define-global:boolean *negative-literal-selection?* #true
"When resolving, if a clause has negative literals, only select one of them.")
(define-global *cpu-limit* +inf.0
"CPU limit in seconds per problem."
exact-positive-integer?
string->number)
(define-global *cpu-first-limit* +inf.0
"CPU limit in seconds per problem."
exact-positive-integer?
string->number)
(define-global *cpu-limit-factor* 3
'("Increase of cpu limit of cpu-first-limit once an iteration has failed."
"Set to 0 to avoid restarting."
"Assumes --cpu-limit.")
(λ (x) (and (real? x) (>= x 0)))
string->number)
(define-global *input-rules* #false
'("File to read rewrite rules from.")
file-exists?
values)
(define-global *output-rules* #false
'("File to write rewrite rules to. If 'auto', a unique name is chosen automatically.")
(λ (x) #true)
(λ (str)
(if (string=? str "auto")
(build-path "rules" (string-append "rules-" (date-iso-file) ".txt"))
str)))
;======================;
;=== Rule discovery ===;
;======================;
;; Finds new binary equivalences between `C` and the clauses of `utree`,
;; and adds and returns the set of resulting new rules that can be added to `rwtree`.
;;
;; rwtree-out : rewrite-tree?
;; C : Clause?
;; utree : unification-tree?
(define (discover-new-rules! rwtree-out C utree)
(cond/else
[(not rwtree-out) '()]
;; FIND UNIT REWRITE RULES
;; Unit rewrite using the binary rewrite tree
[(unit-Clause? C)
(when-debug>= steps (displayln "Found unit clause"))
;; Add the new unit clause to the set of unit rewrite rules
(rewrite-tree-add-unit-Clause! rwtree-out C #:rewrite? #false)]
[(not (binary-Clause? C))
'()]
;; FIND BINARY REWRITE RULES
#:else
(when-debug>= steps (displayln "Found binary clause"))
;; We search for the converse implication in the active set.
;; This takes care of clauses that have converse clauses but not the converse.
;; Ex: C1 = p(X) | q(X) ; C2 = ~p(a) | ~q(a)
;; C2 has a converse clause, but not C1. Hence C2 can be added as a binary rewrite rule
;; but not C1.
(define Conv (make-converse-Clause C))
(define self-conv? (Clause-subsumes C Conv))
#:cond
[self-conv?
; Self-converse Clause, so only one Clause to add (but will lead to 2 rules).
(when-debug>= steps (displayln "Self-converse"))
(rewrite-tree-add-binary-Clause! rwtree-out C C #:rewrite? #false)]
#:else
(define Subs (utree-find/any utree Conv (λ (a Conv) (and (binary-Clause? a)
(Clause-subsumes a Conv)))))
#:cond
[Subs
; We found a converse Clause in the active set, or a Clause that subsumes the
; converse Clause, so we can add selected-Clause as a rule and also its
; converse Clause.
; We can't add Subs, since Subs may not itself have a converse
; clause in the active set. Ex:
; selected-Clause = p(a) | q(a), so Conv = ~p(a) | ~q(a) and Subs = ~p(X) | ~q(X)
; Subs cannot be added as a rule because (supposedly) we haven't found a converse
; clause yet, but we can still add Conv as a rule.
; If Subs is aleardy a rule, Conv will be rewritten to a tautology and discarded.
(when-debug>= steps (printf "Found converse subsuming clause: ~a\n"
(Clause->string Subs)))
; Since C has already been rewritten, there is no need to do it again.
(rewrite-tree-add-binary-Clause! rwtree-out C Subs #:rewrite? #false)]
#:else
; Asymmetric rules.
; Even when selected-Clause is not a rule (yet?), it may still enable
; other more specific Clauses of the active set to be added as rules too.
; Eg, selected-Clause = p(X) | q(X) and some other active clause is ~p(a) | ~q(a).
(define Subsd (utree-find/all utree Conv (λ (a Conv) (and (binary-Clause? a)
(Clause-subsumes Conv a)))))
#:cond
[(not (empty? Subsd))
(when-debug>= steps
(displayln "Found converse subsumed clauses:")
(print-Clauses Subsd))
; rewrite=#true is ok here because Clause->rules already considers both directions
; of the implication. Hence if a potential rule is rewritten to a tautology,
; we know it's already redundant anyway.
; TODO: Rename this to add-asymmetric-rules ?
(rewrite-tree-add-binary-Clauses! rwtree-out Subsd C #:rewrite? #true)]
#:else '()))
;====================;
;=== Clause costs ===;
;====================;
;; Clause comparison for the cost queue.
;;
;; Clause? Clause? -> boolean?
(define (Candidate<= C1 C2)
(<= (Clause-cost C1) (Clause-cost C2)))
;; Sets the cost of a list of Clauses that all have the same parent `parent`.
;; Some noise can be added to the cost via *cost-noise*.
;;
;; Cs : (listof Clause?)
;; cost-type: symbol?
;; parent : Clause?
;; cost-depth-factor : number?
;; -> void?
(define (Clauses-calculate-cost! Cs
cost-type
parent
#:! cost-depth-factor)
(case cost-type
;; Very simple cost function that uses the weight of the Clause. May not be fair.
[(weight)
(for ([C (in-list Cs)])
(set-Clause-cost! C
(if (empty? (Clause-clause C))
-inf.0 ; empty clause should always be top of the list
(Clause-size C))))]
;; Very simple cost function that is fair
[(weight-fair)
(for ([C (in-list Cs)])
(set-Clause-cost! C
(if (empty? (Clause-clause C))
-inf.0 ; empty clause should always be top of the list
(+ (* (Clause-depth C) cost-depth-factor)
(Clause-size C)))))])
;; Add noise to the cost so as to potentially solve more later.
;; To combine with a slowly increasing step-limit-factor in iterative saturation
(unless (zero? (*cost-noise*))
(define ε (*cost-noise*))
(for ([C (in-list Cs)])
(set-Clause-cost! C
(* (+ (- 1. ε) (* ε (random)))
(Clause-cost C))))))
;==================;
;=== Saturation ===;
;==================;
;; List of possible status values. Used to prevent mistakes.
(define statuses '(refuted saturated time memory steps running))
;; Returns whether the result dictionary `res` has the status `status`.
;;
;; res : dict?
;; status : symbol?
(define (check-status res status)
(define res-status (dict-ref res 'status #false))
; To avoid silent typo bugs.
(assert (memq status statuses) status)
(assert (memq res-status statuses) res-status)
(eq? status res-status))
;; The main algorithm. Saturates the formula given by the input clauses
;; by adding new clauses (either resolutions or factors) until either
;; the empty clause is produced, or a resource limit is reached (steps, time, memory).
;;
;; input-clauses : (listof clause?)
;; step-limit : number?
;; memory-limit : number?
;; cpu-limit : number?
;; rwtree : (or/c #false rewrite-tree?)
;; rwtree-out : (or/c #false rewrite-tree?)
;; backward-rewrite? : boolean?
;; parent-discard? : boolean?
;; age:cost : (list/c exact-nonnegative-integer? exact-nonnegative-integer?)
;; cost-type : symbol?
;; disp-proof? : boolean?
;; L-resolvent-pruning : boolean?
;; find-unit-rules-in-candidates? : boolean?
;; negative-literal-selection? : boolean?
;; -> dict?
(define (saturation input-clauses
#:? [step-limit +inf.0]
#:? [memory-limit (*memory-limit*)] ; in MB
#:? [cpu-limit +inf.0] ; in seconds
; The rewrite tree holding the binary rules.
; Set it to #false to deactivate it.
#:? [rwtree (make-rewrite-tree #:atom<=> (get-atom<=>)
#:dynamic-ok? (*dynamic-rules?*)
#:rules-file (*input-rules*))]
; rewrite-tree where new rules found during saturation are stored.
; If rwtree-out is different from rwtree, new rules are not used but only stored,
; and backward rewriting is deactivated.
#:? [rwtree-out rwtree] ; or #false if don't care
; Only effective if (eq? rwtree rwtree-out)
#:? [backward-rewrite? (*backward-rw?*)]
#:? [parent-discard? (*parent-discard?*)]
#:? [age:cost (*age:cost*)] ; chooses age if (< (modulo t (+ age cost)) age)
#:? [cost-type (*cost-type*)]
#:? [disp-proof? (*proof?*)]
#:? [L-resolvent-pruning? (*L-resolvent-pruning?*)]
#:? [find-unit-rules-in-candidates? (*find-unit-rules-in-candidates?*)]
#:? [negative-literal-selection? (*negative-literal-selection?*)])
;; Do NOT reset the clause-index, in particular if rwtree is kept over several calls to saturation.
#;(reset-clause-index!)
;; Tree containing the active Clauses
(define utree (make-unification-tree))
;; Clauses are pulled from priority first, unless empty.
;; INVARIANT: active-Clauses U priority is (should be!) equisatisfiable with input-Clauses.
;; In other words, if priority is empty, then the set of active-Clauses
;; is equisatisfiable with the input-Clauses.
;; Some active clauses may be removed from utree and pushed back into priority for further
;; processing like 'backward' rewriting. In that case, the active Clauses (utree) is not
;; 'complete'.
(define priority (make-queue))
;; Both heaps contain the candidate clauses (there may be duplicates between the two,
;; but this is checked when extracting Clauses from either heap).
(define candidates (make-heap Candidate<=))
(define age-queue (make-heap Clause-age>=))
;; Frequency of extracting Clauses from either heap.
(define age-freq (first age:cost))
(define cost-freq (second age:cost))
(define age+cost-freq (+ age-freq cost-freq))
;; Add the Clauses Cs to the priority queue for priority processing.
(define (add-priority-Clauses! Cs)
(for ([C (in-list Cs)])
; Need to set candidate? to #true otherwise it may be skipped.
; (or maybe we should not skip clauses loaded from `priority`?)
(set-Clause-candidate?! C #true)
(enqueue! priority C)))
(define cost-depth-factor (*cost-depth-factor*)) ; immutable value
(define (add-candidates! parent Cs)
;; Calculate costs and add to candidate heap.
(unless (empty? Cs)
(Clauses-calculate-cost! Cs cost-type parent #:cost-depth-factor cost-depth-factor)
(for ([C (in-list Cs)])
(set-Clause-candidate?! C #true))
(unless (= 0 cost-freq) (heap-add-all! candidates Cs))
(unless (= 0 age-freq) (heap-add-all! age-queue Cs))
(when-debug>= steps
(printf "#new candidates: ~a #candidates: ~a\n"
(length Cs)
(heap-count candidates)))))
(define input-Clauses
(map (λ (c) (make-Clause c '() #:type 'in)) input-clauses))
;; This maintains the invariant: If priority is empty, then the set of active-Clauses
;; is equisatisfiable with the input-Clauses.
;; In other words active-Clauses U priority is (should be!) equisatisfiable with input-Clauses.
(add-priority-Clauses! input-Clauses)
;; We add the Clauses of the binary rules as candidates, so as to not cluter the active set
;; in case there are many rules.
;; Another option is to add them to the priority queue because they can be seen as possibly
;; useful lemmas.
(when rwtree
; A mock root clause parent of all input rules
(define C0rules (make-Clause (list ltrue) '() #:type 'rules-root))
; rewrite=#false should not be necessary (since rewriting checks if a clause is from the original
(add-candidates! C0rules (rewrite-tree-original-Clauses rwtree)))
(define step 0)
(reset-n-tautologies!)
(define n-parent-discard 0)
(define n-forward-subsumed 0)
(define n-backward-subsumed 0)
(reset-n-binary-rewrites!)
(reset-n-rule-added!)
(reset-subsumes-stats!)
(reset-n-L-resolvent-pruning!)
(define start-time (current-milliseconds))
;; TODO: Some calls are very slow...
(define (make-return-dict status [other '()])
(assert (memq status statuses) status)
(define stop-time (current-milliseconds))
`((status . ,status)
(steps . ,step)
(generated . ,clause-index) ; includes all input clauses and rules and intermediate steps
(actives . ,(length (unification-tree-Clauses utree)))
(candidates . ,(heap-count candidates))
(priority-remaining . ,(queue-length priority))
(tautologies . ,n-tautologies) ; counted in generated, (mostly) not in candidates
,@(rewrite-tree-stats rwtree)
(binary-rewrites . ,n-binary-rewrites)
(forward-subsumed . ,n-forward-subsumed)
(backward-subsumed . ,n-backward-subsumed)
(subsumes-checks . ,n-subsumes-checks)
(subsumes-steps . ,n-subsumes-steps)
(subsumes-breaks . ,n-subsumes-breaks)
(parent-discard . ,n-parent-discard)
(L-resolvent-pruning . ,n-L-resolvent-pruning)
(memory . ,(current-memory-use)) ; doesn't account for GC---this would take too much time
(time . ,(- stop-time start-time))
. ,other))
(define (make-refuted-dict C)
(define proof (Clause-ancestor-graph C)) ; no duplicates
(define flat-proof (flatten proof))
(define type-occs (occurrences flat-proof #:key Clause-type))
(when disp-proof?
(displayln "#| begin-proof")
(display-Clause-ancestor-graph C #:tab " ")
(displayln "end-proof |#"))
(make-return-dict 'refuted
`((proof-length . ,(length flat-proof)) ; doesn't account for compound rewrites
(proof-steps . ,(for/sum ([C2 (in-list flat-proof)])
(define n (length (Clause-parents C2)))
(if (< n 2) n (- n 1))))
(proof-inferences . ,(count (λ (C2) (not (empty? (Clause-parents C2))))
flat-proof))
,@(for/list ([(t o) (in-dict type-occs)])
(cons (string->symbol (format "proof-type:~a" t)) o)))))
;:::::::::::::::::::::;
;:: Saturation Loop ::;
;:::::::::::::::::::::;
(define result
(let loop ()
(++ step)
(define time-passed (- (current-milliseconds) start-time)) ; this is fast
(define mem (current-memory-use-MB)) ; mflatt says it's fast
(when-debug>= steps
(printf "\nstep: ~a generated: ~a processed/s: ~a generated/s: ~a\n"
step
clause-index
(quotient (* 1000 step) (+ 1 time-passed))
(quotient (* 1000 clause-index) (+ 1 time-passed))))
(cond/else
[(and (= 0 (heap-count candidates))
(= 0 (heap-count age-queue))
(= 0 (queue-length priority)))
(when-debug>= steps (displayln "Saturated"))
(make-return-dict 'saturated)]
[(> step step-limit) (make-return-dict 'steps)]
[(> time-passed (* 1000 cpu-limit)) (make-return-dict 'time)]
[(and (> mem memory-limit)
(block
(define pre (current-milliseconds))
;; Memory is full, but try to collect garbage first.
(unless (*quiet-json?*)
(printf "; before GC: memory-limit: ~a memory-use: ~a\n" memory-limit mem))
(collect-garbage)
(collect-garbage)
(define mem2 (current-memory-use-MB))
(define post (current-milliseconds))
(unless (*quiet-json?*)
(printf "; after GC: memory-limit: ~a memory-use: ~a gc-time: ~a\n"
memory-limit mem2 (* 0.001 (- post pre))))
(> mem2 memory-limit)))
; mem is full even after GC, so exit
(make-return-dict 'memory)]
#:else
;; Choose a queue/heap to extract the selected-Clause from.
(define queue
(cond [(> (queue-length priority) 0)
; Always has priority.
priority]
[(or (= 0 (heap-count candidates))
(and (> (heap-count age-queue) 0)
(< (modulo step age+cost-freq) age-freq)))
; TODO: This is somewhat defeated by the `priority` queue.
age-queue]
[else candidates]))
(when-debug>= steps
(printf "Selected queue: ~a\n" (cond [(eq? queue priority) "priority"]
[(eq? queue candidates) "candidates"]
[else "age queue"])))
(define selected-Clause
(if (heap? queue)
(begin0 (heap-min queue)
(heap-remove-min! queue))
(dequeue! queue)))
#:cond
;; ALREADY PROCESSED
[(not (Clause-candidate? selected-Clause))
(when-debug>= steps (displayln "Clause already processed. Skipping."))
(-- step) ; don't count this as a step
(loop)]
;; ONE PARENT DISCARDED
[(and parent-discard? (ormap Clause-discarded? (Clause-parents selected-Clause)))
(when-debug>= steps (displayln "At least one parent has been discarded. Discard too."))
(discard-Clause! selected-Clause)
(++ n-parent-discard)
(loop)]
#:else
(set-Clause-candidate?! selected-Clause #false)
;; FORWARD REWRITE
;; BINARY CLAUSE REWRITE OF SELECTED
;; NOTICE: We do binary rewrites first because if we did unit then binary
;; we would need to attempt a second unit-rewrite after that.
;; (This may lead to unnecessary binary rewrites, but it's cleaner this way.)
(define selected-Clause-brw
(if rwtree
(binary-rewrite-Clause rwtree selected-Clause)
selected-Clause))
(when-debug>= steps
(printf "|\nstep ~a: selected: ~a\n"
step (Clause->string/alone selected-Clause 'all))
(define binary-rewritten? (not (eq? selected-Clause-brw selected-Clause)))
(when binary-rewritten?
(displayln "Binary rewritten:")
(display-Clause-ancestor-graph selected-Clause-brw #:depth 1))
(unless (eq? selected-Clause-brw selected-Clause-brw)
(displayln "Unit rewritten:")
(display-Clause-ancestor-graph selected-Clause-brw #:depth 1))
(when-debug>= interact
(interact-saturation
(priority utree rwtree selected-Clause make-return-dict)
selected-Clause-brw selected-Clause-brw)))
(set! selected-Clause selected-Clause-brw)
;;; From now on, only selected-Clause should be used
(define selected-clause (Clause-clause selected-Clause))
#:cond
;; REFUTED?
[(empty-clause? selected-clause)
(make-refuted-dict selected-Clause)]
;; TAUTOLOGY?
[(clause-tautology? selected-clause)
(when-debug>= steps (displayln "Tautology."))
(discard-Clause! selected-Clause)
(loop)] ; skip clause
;; FORWARD SUBSUMPTION
[(utree-find/any utree selected-Clause Clause-subsumes)
;; TODO: Tests
=>
(λ (C2)
(++ n-forward-subsumed)
(when-debug>= steps (printf "Subsumed by ~a\n" (Clause->string C2 'all)))
(discard-Clause! selected-Clause)
(loop))] ; skip clause
#:else
;; Clause is being processed.
;; BACKWARD SUBSUMPTION
(define removed (utree-inverse-find/remove! utree selected-Clause Clause-subsumes))
(for-each discard-Clause! removed)
(+= n-backward-subsumed (length removed))
(when-debug>= steps
(define n-removed (length removed))
(when (> n-removed 0)
(printf "#backward subsumed: ~a\n" n-removed)
(when-debug>= interact
(print-Clauses removed 'all))))
;; FIND NEW REWRITE RULES
(define clause-index-before-discover clause-index)
(define new-rules (discover-new-rules! rwtree-out selected-Clause utree))
(define new-rule-Clauses (rules-original-Clauses new-rules))
;; NOTICE: We MUST add Clauses that are newly generated to the set of active rules
;; (via priority) otherwise we may miss some resolutions.
;; Only the Clauses that have been created during the discovery process need to be added.
;; Notice: To prevent the clauses from which the rules have originated to be rewritten to
;; tautologies, a test is performed in binary-rewrite-literal.
;; But this applies *only* to the `eq?`-Clause of the rule, hence beware of copies or
;; rewrites.
(add-priority-Clauses!
(filter (λ (C) (> (Clause-idx C) clause-index-before-discover))
new-rule-Clauses))
;; BACKWARD BINARY REWRITING
;; We don't need to backward rewrite if the new rules are not stored in rwtree,
;; as this means the set of used rules does not change during the whole saturation.
(when (and backward-rewrite?
rwtree
(eq? rwtree rwtree-out) ; not storing new rules in a different rwtree
(not (empty? new-rules)))
; Remove active Clauses that can be rewritten, and push them into priority.
; We must check whether the clauses we remove will be rewritten,
; otherwise we might add all the same candidates again when the removed Clause
; is popped from priority.
(define removed-active-Clauses
;; TODO: This is inefficient. We should modify utree-inverse-find/remove!
;; TODO: to handle multiple rule-C so as to take advantage of its hash/cache.
(remove-duplicates
(flatten
(for/list ([rule-C (in-list new-rule-Clauses)])
(utree-inverse-find/remove! utree rule-C
(λ (_rule-C C2)
(binary-rewrite-Clause? rwtree C2)))))
eq?))
(unless (empty? removed-active-Clauses)
(when-debug>= steps
(displayln "Some active Clauses can be backward binary rewritten:")
(print-Clauses removed-active-Clauses))
(add-priority-Clauses! removed-active-Clauses)))
;; Note that backward-rewritable Clauses are not yet discarded. They may be discarded
;; when they are pulled from priority and deemed discardable.
;;; Even if the selected Clause is a unit/binary rewrite rule, we must continue processing it
;;; and generate resolutions (because rewriting is only left-unification, not full unification)
;;; NEW CANDIDATES
(define L-resolvent-pruning-allowed?
(and L-resolvent-pruning?
; As per the invariant, if no Clause is in the priority queue,
; then the set of active Clauses of utree is equisatisfiable with the input clauses.
(= 0 (queue-length priority))))
(define new-Candidates
(if negative-literal-selection?
(utree-resolve+unsafe-factors/select utree selected-Clause
#:rewriter (λ (C) (binary-rewrite-Clause rwtree C)))
(utree-resolve+unsafe-factors utree selected-Clause
#:rewriter (λ (C) (binary-rewrite-Clause rwtree C))
#:L-resolvent-pruning? L-resolvent-pruning-allowed?)))
(when-debug>= interact
(displayln "New candidates:")
(print-Clauses new-Candidates))
;; If a clause has no resolvent with the active set (when complete)
;; then it will never resolve with anything and can thus be discarded.
#:cond
[(and L-resolvent-pruning-allowed?
(empty? new-Candidates))
(discard-Clause! selected-Clause)
(when-debug>= steps
(printf "No resolvent (L-resolvent-pruning?=~a). Clause discarded. \n"
L-resolvent-pruning?))
(loop)]
#:else
;; ADD CLAUSE TO ACTIVES
;; Rewrite the candidates with unit and binary rules, filter out tautologies,
;; calculate their costs and add them to the queues.
(add-candidates! selected-Clause new-Candidates)
;; UNIT RULE DISCOVERY IN CANDIDATES
;; Look for unit rewrite rules in the candidate set.
;; (Looking for binary rules would be too costly here)
(when find-unit-rules-in-candidates?
(when rwtree-out
(for ([C (in-list new-Candidates)])
(when (unit-Clause? C)
;; TODO: Should be calling/merged with discover-rules! to avoid inconsistencies
(rewrite-tree-add-unit-Clause! rwtree-out C #:rewrite? #false)))))
(add-Clause! utree selected-Clause)
(when-debug>= steps
(displayln "Adding clause.")
(print-active-Clauses utree #false))
(loop))))
(when-debug>= interact
(displayln "Saturation loop finished.")
(pretty-print result)
(define selected-Clause #false) ; mock up
(interact-saturation
(priority utree rwtree selected-Clause make-return-dict)))
result)
;========================;
;=== User interaction ===;
;========================;
;; Some commands to use with '--debug interact'. Type 'help' for information.
(define interact-commands '())
(define-namespace-anchor ns-anchor)
(define-syntax-rule (interact-saturation
(priority utree rwtree selected-Clause make-return-dict)
more ...)
(begin
(define what '(idx parents clause-pretty))
(interact
#:command (and (not (empty? interact-commands))
(begin0 (first interact-commands)
(rest! interact-commands)))
#:variables (priority utree rwtree what more ...)
#:namespace-anchor ns-anchor
#:readline? #true
[(list 'steps (? number? n))
"skips n steps"
(when (> n 0)
(cons! "" interact-commands)
(cons! (format "steps ~a" (- n 1)) interact-commands))]
[(list (or 'ancestors 'ancestor-graph 'graph))
"display the ancestor graph of the selected Clause."
(display-Clause-ancestor-graph selected-Clause)]
[(list (or 'ancestors 'ancestor-graph 'graph) (? number? depth))
"display the ancestor graph of the selected Clause down to the given depth."
(display-Clause-ancestor-graph selected-Clause #:depth depth)]
[(list 'what-fields)
(string-append
"Prints which fields are available for 'what,\n"
"which is used for printing clause information.")
(displayln Clause->string-all-fields)]
[(list 'selected)
"Selected clause"
(print-Clauses (list selected-Clause) what)]
[(list 'active)
"Active clauses"
(print-active-Clauses utree #true what)]
[(list (or 'binary 'rules))
"Found binary rules"
(print-binary-rules rwtree #true)]
[(list 'stats)
"Return-dictionary-like stats"
(pretty-print (make-return-dict 'running))]
[(list 'save-rules)
"Save the binary rules from the default rules-file"
(save-rules! rwtree #:rules-file (*output-rules*))])))
;; Prints the set of active Clauses (held in utree).
;;
;; utree : unification-tree?
;; long? : boolean?
;; what : (or/c 'all (listof symbol?))
;; -> void?
(define (print-active-Clauses utree long? [what 'all])
(define actives (unification-tree-Clauses utree))
(printf "#active clauses: ~a\n" (length actives))
(when long?
(displayln "Active clauses:")
(print-Clauses (sort actives < #:key Clause-idx) what)))
;; Prints the set of binary rules.
;;
;; rewrite-tree? boolean? -> void?
(define (print-binary-rules rwtree long?)
(define rules (rewrite-tree-rules rwtree))
(printf "#binary rules: ~a #original clauses: ~a\n"
(length rules)
(length (remove-duplicates (map rule-Clause rules) eq?)))
(when long?
(display-rules (rewrite-tree-rules rwtree))))
;============================;
;=== Iterative saturation ===;
;============================;
;; A struct holding information about a given input formula.
(struct problem (file name clauses [time-used #:mutable] [last-time #:mutable]))
;; file? (or/ #false string?) (listof clause?) -> problem?
(define (make-problem file name clauses)
(problem file name clauses 0 0))
;; Returns the same values as body ... but evaluates time-body ... before returning.
;; The result of time-body ... is discarded.
(define-syntax-rule (with-time-result [(cpu real gc) time-body ...] body ...)
(let-values ([(res cpu real gc) (time-apply (λ () body ...) '())])
time-body ...
(apply values res)))
;; Calls saturation for a set of problems in a loop.
;; Tries again each unsolved problem after multiplying the step-limit by step-limit-factor
;; and so on untill all problems are solved.
;; Loading time from files is *not* taken into account.
;;
;; problems : (listof problem?)
;; saturate : procedure?
;; memory-limit : number?
;; cpu-limit : number?
;; cpu-first-limit : number?
;; cpu-limit-factor? : number?
;; -> void?
(define (iterative-saturation/problem-set problems
saturate
#:! memory-limit
#:! cpu-limit ; in second
#:! cpu-first-limit ; in seconds
#:! cpu-limit-factor) ; in seconds
(define n-problems (length problems))
(define n-attempted 0)
(define n-solved 0)
(with-time-result [(cpu real gc)
(unless (*quiet-json?*)
(printf "; Total time: cpu: ~a real: ~a gc: ~a\n" cpu real gc))]
(let loop ([problems problems] [iter 0])
(define n-unsolved (length problems))
(define n-solved-iter 0)
(define n-attempted-iter 0)
(define new-unsolved
(for/fold ([unsolved '()]
[cumu-time 0]
#:result (reverse unsolved))
([prob (in-list problems)])
(define input-clauses (problem-clauses prob))
(when-debug>= init (for-each (compose displayln clause->string) input-clauses))
;; Collecting garbage can take time even when there's nothing to collect,
;; and can take a significant proportion of the time when solving is fast,
;; hence it's better to trigger GC only if needed.
(when (>= (current-memory-use-MB) (* 0.8 memory-limit))
(collect-garbage)
(collect-garbage))
;; Main call
(define cpu-limit-problem (min (max cpu-first-limit
(* cpu-limit-factor (problem-last-time prob)))
(- cpu-limit (problem-time-used prob))))
(define res (saturate input-clauses cpu-limit-problem))
(set! res (append `((name . ,(problem-name prob))
(file . ,(problem-file prob)))
res))
(++ n-attempted-iter)
(when (= 0 iter) (++ n-attempted))
(define solved? (or (check-status res 'refuted)
(check-status res 'saturated)))
(when solved?
(++ n-solved-iter)
(++ n-solved))
(define last-time (* 0.001 (dict-ref res 'time)))
(set-problem-last-time! prob last-time)
(set-problem-time-used! prob (+ (problem-time-used prob) last-time))
(set! res (dict-set res 'cumulative-time (exact-ceiling (* 1000 (problem-time-used prob)))))
(define remove-problem?
(or solved?
(check-status res 'memory) ; more time won't help if status=memory
(>= (problem-time-used prob) cpu-limit))) ; cpu exhausted for this problem
; Don't pretty-print to keep it on a single line which is simpler for parsing.
; Only print the last iteration of a problem for borg.
(cond
[(*quiet-json?*)
(when remove-problem? (displayln (saturation-result->json res)))]
[else
(pretty-write res)
(printf "; ~a/~a solved (iter: ~a/~a/~a success: ~a% avg-time: ~as ETA: ~as)\n"
n-solved n-attempted
n-solved-iter n-attempted-iter n-unsolved
(~r (* 100 (/ n-solved-iter n-attempted-iter)) #:precision '(= 1))
(~r (/ cumu-time n-attempted-iter 1000.) #:precision '(= 3))
(~r (* (/ cumu-time n-attempted-iter 1000.)
(- n-unsolved n-attempted-iter)) #:precision '(= 2)))])
(flush-output)
(values
(if remove-problem?
unsolved
(cons prob unsolved))
(+ cumu-time last-time))))
(unless (or (empty? new-unsolved)
(= cpu-limit-factor 0))
(loop new-unsolved (+ iter 1))))))
;; Calls saturate on a single set of clauses, first with a time limit of cpu-first-limit,
;; then restarts and doubles it until the cumulative time reaches cpu-limit.
;; Loading time is taken into account.
;; During a call to saturate, the new rewrite rules are saved in a separate tree,
;; which means that no new rule is introduced until the next restart—and thus the first
;; call to saturate uses no rewrite rule.
;;
;; NOTICE: In this mode the unit rewrites are gathered only for the next round, but this is
;; likely not necessary!
;;
;; saturate : procedure?
;; tptp-program : string?
;; rwtree-in : rewrite-tree?
;; discover-online? : boolean?
;; cpu-limit : number?
;; cpu-first-limit : number?
;; cpu-limit-factor? : number?
;; -> void?
(define (iterative-saturation saturate
#:! tptp-program
#:! rwtree-in
#:? [discover-online? (*discover-online?*)]
#:? [cpu-limit (*cpu-limit*)]
#:? [cpu-first-limit (*cpu-first-limit*)]
#:? [cpu-limit-factor (*cpu-limit-factor*)])
(define cpu-start (current-inexact-seconds))
; Don't make new Clauses here, they need to be created at each `saturation` call.
(define clauses (tptp-prog->clauses tptp-program))
(define quiet? (*quiet-json?*))
(define n-rules-init (rewrite-tree-count rwtree-in))
(let loop ([iter 1] [uncapped-current-cpu-limit cpu-first-limit] [rwtree-in rwtree-in])
(define remaining-cpu (- cpu-limit (- (current-inexact-seconds) cpu-start)))
(define current-cpu-limit (min remaining-cpu uncapped-current-cpu-limit))
(unless quiet?
(printf "; iter: ~a remaining-cpu: ~a current-cpu-limit: ~a\n"
iter
remaining-cpu
current-cpu-limit))
; Simplify the set of rules (only once)
(unless (and (= 1 iter)
(= 0 n-rules-init)) ; don't do this if no restarting
; Note that these steps destroy the Clause ancestry, and proofs will be incomplete.
(unless quiet?
(printf "; Rules stats: ~v\n" (rewrite-tree-stats rwtree-in))
(displayln "; Simplifying the rules via re-add-rules!"))
;; Rewrite lhs and rhs of rules, remove subsumed and tautologies.
(re-add-rules! rwtree-in)
(unless quiet?
(printf "; Rules stats: ~v\n" (rewrite-tree-stats rwtree-in))
(printf "; Confluence! bounded? = ~a\n" (*bounded-confluence?*)))
;; Unify rhs of rules to produce new rules.
(rewrite-tree-confluence! rwtree-in)
(unless quiet?
(printf "; Rules stats: ~v\n" (rewrite-tree-stats rwtree-in))
(displayln "; Simplifying the rules via re-add-rules! (again)"))
;; Rewrite and simplify again.
(re-add-rules! rwtree-in)
(unless quiet? (printf "; Rules stats: ~v\n" (rewrite-tree-stats rwtree-in))))
(flush-output)
(define rwtree-out (if discover-online? rwtree-in (rewrite-tree-shallow-copy rwtree-in)))
(define res (saturate #:clauses clauses
#:cpu-limit current-cpu-limit
#:rwtree-in rwtree-in
#:rwtree-out rwtree-out))
(define new-cumulative-cpu (- (current-inexact-seconds) cpu-start))
(set! res (dict-set res 'cumulative-time (exact-ceiling (* 1000. new-cumulative-cpu)))) ; ms
(set! res (dict-set res 'saturation-iter iter))
(define solved? (or (check-status res 'refuted)
(check-status res 'saturated)))
;; We exit also if memory limit has been reached, but we could instead restart
;; if new rules have been found.
(define finished? (or solved?
(check-status res 'memory)
(> new-cumulative-cpu cpu-limit)))
(cond
[(*quiet-json?*)
(when finished? (displayln (saturation-result->json res)))]
[else
(pretty-write res)])
(flush-output)
(cond
[finished?
(when (*output-rules*)
(unless quiet?
(printf "Saving rules to ~a\n"
(if (string? (*output-rules*))
(*output-rules*)
(path->string (*output-rules*)))))
(save-rules! rwtree-out #:rules-file (*output-rules*)))]
[else
(loop (+ iter 1)
(* uncapped-current-cpu-limit cpu-limit-factor)
rwtree-out)])))
================================================
FILE: satore/scribblings/satore.scrbl
================================================
#lang scribble/manual
@title{First-order logic saturation with atom rewriting}
See the @hyperlink["https://github.com/deepmind/deepmind-research/tree/master/satore"]{readme}.
================================================
FILE: satore/tests/Clause.rkt
================================================
#lang racket/base
(require racket/list
rackunit
(submod satore/Clause test)
satore/Clause
satore/unification)
;; Polarity should not count for the 'weight' cost function because otherwise it will be harder
;; to prove ~A | ~B than A | B.
(check-equal? (Clause-size (make-Clause '[p q]))
(Clause-size (make-Clause '[(not p) (not q)])))
(check-equal? (Clause-size (make-Clause '[p q]))
(Clause-size (make-Clause '[(not p) q])))
(let ()
(define Cs1 (map Clausify '([(p A B) (p B C) (p D E)]
[(q A B C) (q B A C)]
[(r X Y)])))
(define Cs2 (shuffle (map (λ (C) (make-Clause (fresh (Clause-clause C)))) Cs1)))
(check-Clause-set-equivalent? Cs1 Cs2)
(check-Clause-set-equivalent? Cs2 Cs1))
================================================
FILE: satore/tests/clause.rkt
================================================
#lang racket/base
(require racket/dict
rackunit
satore/clause
satore/misc
satore/unification)
(*subsumes-iter-limit* 0)
(begin
(define-simple-check (check-tautology cl res)
(check-equal? (clause-tautology? (sort-clause (Varify cl))) res))
(check-tautology '[] #false)
(check-tautology `[,ltrue] #true)
(check-tautology `[,(lnot lfalse)] #true)
(check-tautology '[a] #false)
(check-tautology '[a a] #false)
(check-tautology '[a (not a)] #true)
(check-tautology '[a b (not c)] #false)
(check-tautology '[a b (not a)] #true)
(check-tautology '[a (not (a a)) (a b) (not (a (not a)))] #false)
(check-tautology '[a (a a) b c (not (a a))] #true)
(check-tautology `[(a b) b (not (b a)) (not (b b)) (not (a c)) (not (a ,(Var 'b)))] #false)
)
(begin
;; Equivalences
(for ([(A B) (in-dict '(([] . [] ) ; if empty clause #true, everything is #true
([p] . [p] )
([(p X)] . [(p X)] )
([(p X)] . [(p Y)] )
([(not (p X))] . [(not (p X))] )
([(p X) (q X)] . [(p X) (q X) (q Y)] )
))])
(define cl1 (sort-clause (Varify A)))
(define cl2 (sort-clause (fresh (Varify B))))
(check-not-false (clause-subsumes cl1 cl2)
(format "cl1: ~a\ncl2: ~a" cl1 cl2))
(check-not-false (clause-subsumes cl2 cl1)
(format "cl1: ~a\ncl2: ~a" cl1 cl2))
)
;; One-way implication (not equivalence)
(for ([(A B) (in-dict '(([] . [p] ) ; if empty clause #true, everything is #true
([p] . [p q] )
([(p X)] . [(p c)] )
([(p X) (p X) (p Y)] . [(p c)] )
([(p X)] . [(p X) (q X)] )
([(p X)] . [(p X) (q Y)] )
([(p X Y)] . [(p X X)] )
([(p X) (q Y)] . [(p X) (p Y) (q Y)] )
([(p X) (p Y) (q Y)] . [(p Y) (q Y) c] )
([(p X Y) (p Y X)] . [(p X X)] )
([(q X X) (q X Y) (q Y Z)] . [(q a a) (q b b)])
([(f (q X)) (p X)] . [(p c) (f (q c))])
; A θ-subsumes B, but does not θ-subsume it 'strictly'
([(p X Y) (p Y X)] . [(p X X) (r)])
))])
(define cl1 (sort-clause (Varify A)))
(define cl2 (sort-clause (fresh (Varify B))))
(check-not-false (clause-subsumes cl1 cl2))
(check-false (clause-subsumes cl2 cl1)))
; Not implications, both ways. Actually, this is independence
(for ([(A B) (in-dict '(([p] . [q])
([(p X)] . [(q X)])
([p] . [(not p)])
([(p X c)] . [(p d Y)])
([(p X) (q X)] . [(p c)])
([(p X) (f (q X))] . [(p c)])
([(eq X X)] . [(eq (mul X0 X1) (mul X2 X3))
(not (eq X0 X2)) (not (eq X1 X3))])
; A implies B, but there is no θ-subsumption
; https://www.doc.ic.ac.uk/~kb/MACTHINGS/SLIDES/2013Notes/6LSub4up13.pdf
([(p (f X)) (not (p X))] . [(p (f (f Y))) (not (p Y))])
))])
(define cl1 (sort-clause (Varify A)))
(define cl2 (sort-clause (fresh (Varify B))))
(check-false (clause-subsumes cl1 cl2)
(list (list 'A= A) (list 'B= B)))
(check-false (clause-subsumes cl2 cl1)
(list A B)))
(let* ()
(define cl
(Varify
`((not (incident X Y))
(not (incident ab Y))
(not (incident ab Z))
(not (incident ab Z))
(not (incident ac Y))
(not (incident ac Z))
(not (incident ac Z))
(not (incident bc a1b1))
(not (line_equal Z Z))
(not (point_equal bc X)))))
(define cl2
(sort-clause (fresh (left-substitute cl (hasheq (symbol->Var-name 'X) 'bc
(symbol->Var-name 'Y) 'a1b1)))))
(check-not-false (clause-subsumes cl cl2))))
#;
(begin
; This case SHOULD pass, according to the standard definition of clause subsumption based on
; multisets, but our current definition of subsumption is more general (not necessarily in a
; good way.)
; Our definition is based on sets, with a constraint on the number of literals (in
; Clause-subsumes).
; This makes it more general, but also not well-founded (though I'm not sure yet whether this is
; really bad).
(check-false (clause-subsumes (clausify '[(p A A) (q X Y) (q Y Z)])
(clausify '[(p a a) (p b b) (q C C)]))))
(begin
(*debug-level* (debug-level->number 'steps))
(define-simple-check (check-safe-factoring cl res)
(define got (safe-factoring (sort-clause (Varify cl))))
(set! res (sort-clause (Varify res)))
; Check equivalence
(check-not-false (clause-subsumes res got))
(check-not-false (clause-subsumes got res)))
(check-safe-factoring '[(p a b) (p A B)]
'[(p a b)]) ; Note that [(p a b) (p A B)] ≠> (p A B)
(check-safe-factoring '[(p X) (p Y)]
'[(p Y)])
(check-safe-factoring '[(p Y) (p Y)]
'[(p Y)])
(check-safe-factoring '[(p X) (q X) (p Y) (q Y)]
'[(p Y) (q Y)])
(check-safe-factoring '[(p X Y) (p A X)]
'[(p X Y) (p A X)])
(check-safe-factoring '[(p X Y) (p X X)]
'[(p X X)]) ; is a subset of above, so necessarily no less general
(check-safe-factoring '[(p X Y) (p A X) (p Y A)]
'[(p X Y) (p A X) (p Y A)]) ; cannot be safely factored?
(check-safe-factoring '[(p X) (p Y) (q X Y)]
'[(p X) (p Y) (q X Y)]) ; Cannot be safely factored (proven)
(check-safe-factoring '[(leq B A) (leq A B) (not (def B)) (not (def A))]
'[(leq B A) (leq A B) (not (def B)) (not (def A))]) ; no safe factor
(check-safe-factoring '[(p X) (p (f X))]
'[(p X) (p (f X))])
(check-safe-factoring
(fresh '((not (incident #s(Var 5343) #s(Var 5344)))
(not (incident ab #s(Var 5344)))
(not (incident ab #s(Var 5345)))
(not (incident ab #s(Var 5345)))
(not (incident ac #s(Var 5344)))
(not (incident ac #s(Var 5345)))
(not (incident ac #s(Var 5345)))
(not (incident bc a1b1))
(not (line_equal #s(Var 5345) #s(Var 5345)))
(not (point_equal bc #s(Var 5343)))))
(fresh
'((not (incident #s(Var 148) #s(Var 149)))
(not (incident ab #s(Var 149)))
(not (incident ab #s(Var 150)))
(not (incident ac #s(Var 149)))
(not (incident ac #s(Var 150)))
(not (incident bc a1b1))
(not (line_equal #s(Var 150) #s(Var 150)))
(not (point_equal bc #s(Var 148))))))
(check-not-exn (λ () (safe-factoring
(fresh '((not (incident #s(Var 5343) #s(Var 5344)))
(not (incident ab #s(Var 5344)))
(not (incident ab #s(Var 5345)))
(not (incident ab #s(Var 5345)))
(not (incident ac #s(Var 5344)))
(not (incident ac #s(Var 5345)))
(not (incident ac #s(Var 5345)))
(not (incident bc a1b1))
(not (line_equal #s(Var 5345) #s(Var 5345)))
(not (point_equal bc #s(Var 5343))))))))
)
================================================
FILE: satore/tests/confluence.rkt
================================================
#lang racket/base
(require (for-syntax racket/base syntax/parse)
define2
global
racket/list
racket/pretty
rackunit
satore/Clause
satore/clause
satore/rewrite-tree
satore/unification)
(define-global:boolean *dynamic-ok?* #true
"Use dynamic rules?")
(define (take-at-most l n)
(take l (min (length l) n)))
(define (display-rwtree rwtree #:? [n-max 100])
(define rules (rewrite-tree-rules rwtree))
(define-values (statics dyns)
(partition rule-static?
(filter-not (λ (rl) (lnot? (rule-from-literal rl)))
rules)))
(display-rules (take-at-most (reverse (sort-rules statics)) n-max))
(display-rules (take-at-most (reverse (sort-rules dyns)) n-max))
(when (or (> (length statics) n-max) (> (length dyns) n-max))
(displayln "(output truncated because there are too many rules)"))
(pretty-write (rewrite-tree-stats rwtree)))
;; Adds an equivalence as rules.
;; For testing purposes.
(define (add-equiv! rwtree equiv)
(define C (make-Clause (clausify (list (lnot (first equiv)) (second equiv)))))
(force-add-binary-Clause! rwtree C))
(define (rewrite-literal rwt lit)
(define-values (new-lit rls) (binary-rewrite-literal rwt lit #false))
new-lit)
;; Given a set of implications, generate equivalence
(define (equivs->rwtree equivs
#:? [dynamic-ok? (*dynamic-ok?*)]
#:? [atom<=> (get-atom<=>)])
(define rwt (make-rewrite-tree #:atom<=> atom<=> #:dynamic-ok? dynamic-ok?))
(for ([equiv (in-list equivs)])
(add-equiv! rwt equiv)
(add-equiv! rwt (map lnot equiv)))
rwt)
(define-syntax (test-confluence stx)
(syntax-parse stx
[(_ equivs expected-stats #:with rwt body ...)
#'(let ()
(define rwt (equivs->rwtree equivs))
(rewrite-tree-confluence! rwt)
(define stats (rewrite-tree-stats rwt))
(unless (equal? stats expected-stats)
(display-rwtree rwt))
(check-equal? stats expected-stats)
body ...)]
[(_ equivs expected-stats)
#'(test-confluence equivs expected-stats #:with _rwt)]))
(with-globals ([*bounded-confluence?* #true]
[*dynamic-ok?* #false])
;; This induction does work and is not subsumed.
;; This is possibly the minimal induction scheme (that doesn't lead to subsumed rules).
(test-confluence
'([(p A (f B)) (p A B)]
[(p C C) d]) ; not left linear
; Should not produce longer rules than the parents!
'((rules . 6)
(unit-rules . 0)
(binary-rules . 6)
(binary-rules-static . 6)
(binary-rules-dynamic . 0)))
(test-confluence
'([(p (f (f (f z))) (f (f (f z)))) (g (g (g b)))] ; should -> b
[(p (f (f (f z))) (f (f (f X)))) b]
[(p (f (f z)) (f (f X))) c]
[(p (f z) (f X)) d]
[(p X X) (q X)])
'((rules . 18) ; 16 also ok
(unit-rules . 0)
(binary-rules . 18)
(binary-rules-static . 18)
(binary-rules-dynamic . 0))
#:with rwt
(check-equal? (rewrite-literal rwt '(p z z)) '(q z))
(check-equal? (rewrite-literal rwt '(p (f (f (f z))) (f (f (f a))))) 'b)
(check-equal? (rewrite-literal rwt '(p (f (f (f z))) (f (f (f z))))) 'b))
(test-confluence
'([(p a X) q]
[(p X a) f]
[(p a a) (g b)])
'((rules . 8)
(unit-rules . 0)
(binary-rules . 8)
(binary-rules-static . 8)
(binary-rules-dynamic . 0))
#:with rwt
(check-equal? (rewrite-literal rwt '(p a a)) 'f)
(check-equal? (rewrite-literal rwt '(p a b)) 'f)
(check-equal? (rewrite-literal rwt '(g b)) 'f)
(check-equal? (rewrite-literal rwt 'q) 'f))
)
================================================
FILE: satore/tests/interact.rkt
================================================
#lang racket/base
(require racket/list
racket/port
rackunit
satore/interact)
(define-syntax-rule (check-interact in out args ...)
(check-equal?
(with-output-to-string
(λ ()
(with-input-from-string (string-append in "\n\n") ; ensure no read loop
(λ () (interact args ...)))))
out))
(define-namespace-anchor ns-anchor) ; optional, to use the eval command
(let ([x 2] [y 'a])
(check-interact
"x\ny\nx 3\nx"
"2\n'a\n3\n"
#:prompt ""
#:variables (x y)))
(let ([x 3] [y 'a])
(check-interact
"yo\nyo 4\nx\nx 2\nx"
"yo\n(yo yo yo yo)\n3\n2\n"
#:prompt ""
#:namespace-anchor ns-anchor
#:variables (x y)
;; All patterns must be of the form (list ....)
[(list 'yo) "prints yo" (displayln "yo")]
[(list 'yo (? number? n)) "prints multiple yos" (displayln (make-list n 'yo))]))
================================================
FILE: satore/tests/misc.rkt
================================================
#lang racket/base
(require rackunit
satore/misc)
(define-counter num 0)
(check-equal? num 0)
(++num)
(check-equal? num 1)
(++num 3)
(check-equal? num 4)
(reset-num!)
(check-equal? num 0)
================================================
FILE: satore/tests/rewrite-tree.rkt
================================================
#lang racket/base
(require bazaar/debug
(except-in bazaar/order atom<=>)
racket/file
racket/list
racket/random
rackunit
(submod satore/Clause test)
satore/Clause
satore/clause
satore/misc
satore/rewrite-tree
satore/unification)
(*debug-level* 0)
(define-check (check-rewrite rwtree c crw)
(define C (Clausify c))
(define Crw (binary-rewrite-Clause rwtree C))
(define crw-sorted (sort-clause crw))
(unless (equal? (Clause-clause Crw) crw-sorted)
(eprintf "c-sorted : ~a\ncrw-sorted: ~a\n" (Clause-clause C) crw-sorted)
(eprintf (Clause-ancestor-graph-string Crw))
(fail-check)))
(define-simple-check (check-not-rewrite rwtree c)
(check-rewrite rwtree c c))
;;; Self-equivalence
(let ()
(define rwtree (make-rewrite-tree #:atom<=> atom1<=>))
(define C1 (make-Clause (clausify '[(not (eq A B)) (eq B A)])))
(define rls1 (Clause->rules C1 C1 #:atom<=> atom1<=>))
(rewrite-tree-add-binary-Clause! rwtree C1 C1)
(check-equal? (rewrite-tree-count rwtree) 2)
; Rewrite clause in lexicographical order
(check-rewrite rwtree '[(eq b a)] '[(eq a b)]))
;;; Adding two converse implications
(let ()
(define rwtree (make-rewrite-tree #:atom<=> atom1<=>))
(define C1 (make-Clause (clausify '[(not (p A A)) (q A)])))
(define C2 (make-Clause (clausify '[(not (q A)) (p A A)])))
(define rls1 (Clause->rules C1 C2 #:atom<=> atom1<=>))
(rewrite-tree-add-binary-Clause! rwtree C1 C2)
; This is not needed because both polarities are considered by Clause->rules:
(rewrite-tree-add-binary-Clauses! rwtree (list C2) C1 #:rewrite? #true)
(check-equal? (rewrite-tree-count rwtree) 2)
(check-rewrite rwtree '[(p a a) (z c)] '[(q a) (z c)])
(check-rewrite rwtree '[(not (p a a)) (z c)] '[(not (q a)) (z c)]))
;;; Adding rules where the converse implication is more general
(let ()
(define rwtree (make-rewrite-tree #:atom<=> atom1<=>))
(define Crules
(map (compose make-Clause clausify)
'([(not (p A A)) (q A)]
[(not (q a)) (p a a)]
[(not (q b)) (p b b)])))
(for ([C (in-list (rest Crules))])
(rewrite-tree-add-binary-Clause! rwtree C (first Crules)))
(check-equal? (rewrite-tree-count rwtree) 4)
(check-rewrite rwtree '[(p a a) (z c)] '[(q a) (z c)])
(check-rewrite rwtree '[(not (p a a)) (z c)] '[(not (q a)) (z c)])
(check-rewrite rwtree '[(p b b) (z c)] '[(q b) (z c)])
(check-rewrite rwtree '[(not (p b b)) (z c)] '[(not (q b)) (z c)])
(check-not-rewrite rwtree '[(p x x) (z c)]))
;;; The same with add-binary-Clauses
(let ()
(define rwtree (make-rewrite-tree #:atom<=> atom1<=>))
(define Crules
(map (compose make-Clause clausify)
'([(not (p A A)) (q A)]
[(not (q a)) (p a a)]
[(not (q b)) (p b b)])))
(rewrite-tree-add-binary-Clauses! rwtree (rest Crules) (first Crules))
(check-equal? (rewrite-tree-count rwtree) 4)
(check-rewrite rwtree '[(p a a) (z c)] '[(q a) (z c)])
(check-rewrite rwtree '[(not (p a a)) (z c)] '[(not (q a)) (z c)])
(check-not-rewrite rwtree '[(p x x) (z c)]))
;;; Dynamic, non-self-converse Clauses, leading to 4 rules
(let ()
(define rwtree (make-rewrite-tree #:atom<=> atom1<=>))
(define C1 (make-Clause (clausify '[(not (p A B C)) (p C A B)])))
(define C2 (make-converse-Clause C1))
(define rls1 (Clause->rules C1 C2 #:atom<=> atom1<=>))
(rewrite-tree-add-binary-Clause! rwtree C1 C2)
(check-equal? (rewrite-tree-count rwtree) 4)
(check-rewrite rwtree '[(p a b c)] '[(p a b c)])
(check-rewrite rwtree '[(p c a b)] '[(p a b c)])
(check-rewrite rwtree '[(p b c a)] '[(p a b c)])
(check-rewrite rwtree '[(p b a c)] '[(p a c b)]))
;;; Some random testing to make sure atom<=> has the Groundedness property.
(define (random-atom)
(define syms '(aaa a p q r z zzz))
(define choices (append syms syms '(NV OV L L))) ; reduce proba of NV and OV
(define vars '())
(let loop ()
(define r (random-ref choices))
(case r
[(NV) ; new var
(define v (new-Var))
(set! vars (cons v vars))
v]
[(OV) (if (empty? vars) (loop) (random-ref vars))] ; old vars
[(L) (cons (random-ref syms) ; first element must be a symbol
(build-list (random 4) (λ (i) (loop))))]
[else r])))
(define random-atom-bank
(remove-duplicates (build-list 1000 (λ _ (random-atom)))))
(debug-vars (length random-atom-bank))
(define (check-groundedness atom<=> lita litb)
(define from<=>to (atom<=> lita litb))
; no point in testing groundedness if we don't have from literal< to
(assert (orderto) lita litb from<=>to)
(define vs (vars (list lita litb)))
(define s (make-subst))
(for ([v (in-list vs)])
(subst-set!/name s v (random-ref random-atom-bank)))
(define lita2 (substitute lita s))
(define litb2 (substitute litb s))
(check-equal? (atom<=> lita2 litb2) '<))
(for ([i 10000])
(apply check-groundedness atom1<=> (Varify '[(eq A B) (eq A (mul B a))]))
(apply check-groundedness atom1<=> (Varify '[(eq A B a) (eq A (mul B a))])))
; IMPORTANT CASE: Check circularity of the rules
; Imagine we have two clauses:
; c1 = p | q
; c2 = ~p | ~q
; They are converse implications.
; From c1 we can generate the rules:
; r1 = ~p → q
; r2 = ~q → p
; and from c2 we can generate:
; r3 = p → ~q
; r4 = q → ~p
; If we choose {r1, r4} or {r2, r3} we run in circles!
; Hence the valid choices are {r2, r4} and {r1, r2}
; {r2, r4} is justified by removing negations and considering 'p < 'q.
; {r1, r2} is justified by considering that the negated atoms 'weigh' more.
;
; Now if the two clauses are:
; c3 = ~p | q with rules p → q and ~q → ~p
; c4 = p | ~q with rules ~p → ~q and q → p
; Now we should choose q → p and ~q → ~p to avoid running in circles.
(for* ([lits (in-list '( (p q) ; + ((not p) (not q))
(p (not q))
((distinct_points A B) (equal_points A B))
((distinct_points A B) (not (equal_points A B)))
))]
[r1 (in-list
(Clause->rules (Clausify lits) #false #:atom<=> atom1<=>))]
[r2 (in-list
(Clause->rules (Clausify (map lnot lits)) #false #:atom<=> atom1<=>))])
; The rules should NOT be circular!
(check-not-equal?
(Vars->symbols (list (rule-from-literal r1) (rule-to-literal r1)))
(Vars->symbols (list (rule-to-literal r2) (rule-from-literal r2)))))
;; Saving and loading rules, especially with asymmetric rules.
(let ()
(define rwtree (make-rewrite-tree #:atom<=> atom1<=>))
(define rwtree2 (make-rewrite-tree #:atom<=> atom1<=>))
;; Asymmetric rules
(let ([Conv (Clausify '[(not (p A A)) (q A)])])
(rewrite-tree-add-binary-Clauses! rwtree
(map Clausify
'([(not (q a)) (p a a)]
[(not (q b)) (p b b)]
[(not (q c)) (p c c)]))
Conv))
; Self-converse
(let ([C (Clausify '[(not (eq A B)) (eq B A)])])
(rewrite-tree-add-binary-Clause! rwtree C C))
; Symmetric
(rewrite-tree-add-binary-Clause! rwtree
(Clausify '[(not (pp A A)) (qq A)])
(Clausify '[(pp A A) (not (qq A))]))
(define Crules (rewrite-tree-original-Clauses rwtree))
(define f (make-temporary-file))
(save-rules! rwtree #:rules-file f)
(load-rules! rwtree2 #:rules-file f)
(define Crules2 (rewrite-tree-original-Clauses rwtree2))
(check-equal? (length Crules2) (length Crules))
;; not efficient
(for ([C (in-list Crules)])
(define cl (Clause-clause C))
(check-not-false (for/or ([C2 (in-list Crules2)])
(Clause-equivalence? C C2))
cl)))
;; Tautology reduction
(let ()
(define rwtree (make-rewrite-tree #:atom<=> atom1<=>))
(rewrite-tree-add-binary-Clause! rwtree
(make-Clause (clausify '[(not (p (p X))) (p X)]))
(make-Clause (clausify '[(p (p X)) (not (p X))])))
(check-equal? (rewrite-tree-stats rwtree)
'((rules . 2)
(unit-rules . 0)
(binary-rules . 2)
(binary-rules-static . 2)
(binary-rules-dynamic . 0)))
; These should be reduced to tautologies and thus not added
(rewrite-tree-add-binary-Clause! rwtree
(make-Clause (clausify '[(not (p (p (p X)))) (p X)]))
(make-Clause (clausify '[(p (p (p X))) (not (p X))])))
(check-equal? (rewrite-tree-stats rwtree)
'((rules . 2)
(unit-rules . 0)
(binary-rules . 2)
(binary-rules-static . 2)
(binary-rules-dynamic . 0))))
;; Tautology reduction by dynamic rule
;; Currently fails
#;
(let ()
(define rwtree (make-rewrite-tree #:atom<=> atom1<=>))
(define Cp (Clausify '[(not (p A B)) (p B A)]))
(rewrite-tree-add-binary-Clause! rwtree Cp Cp)
; What should we do?
; The dynamic rule *can* reduce this to a tautology, but doesn't because
; it can't be ground-oriented.
(check Clause-equivalence?
(binary-rewrite-Clause rwtree (Clausify '[(p A B) (p B A) q]))
(Clausify '[(p A B) q]))
; Same, but after a rewrite
(rewrite-tree-add-binary-Clause! rwtree
(Clausify '[(not (p (f A) B)) (p A B)])
(Clausify '[(p (f A) B) (not (p A B))]))
(check Clause-equivalence?
(binary-rewrite-Clause rwtree (Clausify '[(p (f A) B) (p B A) q]))
(Clausify '[(p A B) q])))
================================================
FILE: satore/tests/saturation.rkt
================================================
#lang racket/base
(require (for-syntax racket/base)
define2
define2/define-wrapper
global
racket/dict
racket/pretty
rackunit
(prefix-in sat: satore/saturation)
satore/clause
satore/misc
satore/rewrite-tree
satore/unification
syntax/parse/define)
(define-global *cpu-limit* 10
"Time limit in seconds for tests"
number?
string->number)
(define (Vars+clausify-list l)
(map clausify
(symbol-variables->Vars l)))
(define current-saturation-args #false)
(define-syntax (for-in-list* stx)
(syntax-parse stx
[(_ ([var x ...] ... clauses ...) body ...)
#:with (name ...) (generate-temporaries #'(var ...))
#'(for ((~@ [var (in-list (list x ...))]
[name (in-list '(x ...))]
#:when #true)
...)
(set! current-saturation-args (list (cons 'var name) ...))
body ...)]))
;; Print additional information
(define old-check-handler (current-check-handler))
(current-check-handler
(λ (e)
(eprintf (pretty-format current-saturation-args))
(eprintf "\n")
(old-check-handler e)))
;; USE THIS FOR DEBUGGING
(define-simple-macro (replay-on-failure body ...)
(let ([old-check-handler (current-check-handler)])
(parameterize ([current-check-handler
(λ (e)
(old-check-handler e)
(eprintf
"Some checks have failed. Replaying in interactive mode for debugging.\n")
(*debug-level* 3)
(*cpu-limit* +inf.0)
(let () body ...))])
; encapsulated to avoid collisions
(let () body ...))))
(for-in-list* ([neg-lit-select? #true #false]
[l-res-pruning? #true #false]
[atom<=> #false KBO1lex<=> atom1<=>]
[dynamic-ok? #;#true #false]
[parent-discard? #false]
; notice: if parent-discard? is #true, then can't have both rewriting and
; neg-lit-select.
)
(define-wrapper (saturation
(sat:saturation input-clauses
#:? [step-limit 200]
#:? [memory-limit 4096] ; in MB
#:? [cpu-limit (*cpu-limit*)] ; in seconds
#:? [rwtree (make-rewrite-tree #:atom<=> atom<=>
#:dynamic-ok? dynamic-ok?)]
#:? [rwtree-out (and atom<=> rwtree)]
#:? backward-rewrite?
#:? age:cost
#:? cost-type
#:? [parent-discard? parent-discard?]
#:? [disp-proof? #false]
#:? [L-resolvent-pruning? l-res-pruning?]
#:? [negative-literal-selection? neg-lit-select?]))
#:call-wrapped call
(define res (call))
(unless l-res-pruning?
(check-equal? (dict-ref res 'L-resolvent-pruning) 0))
(unless dynamic-ok?
(check-equal? (dict-ref res 'binary-rules-dynamic) 0))
(unless atom<=>
(check-equal? (dict-ref res 'binary-rules) 0)
(check-true (= (dict-ref res 'binary-rewrites) 0)))
res)
;; Some refutation tests
(check-equal?
(dict-ref (saturation (Vars+clausify-list '( [] )))
'status)
'refuted)
(check-equal?
(dict-ref (saturation (Vars+clausify-list '( [p] )))
'status)
'saturated)
(check-equal?
(dict-ref (saturation (Vars+clausify-list '( [p]
[(not p)])))
'status)
'refuted)
(check-equal?
(dict-ref (saturation (Vars+clausify-list '( [p]
[(not q)])))
'status)
'saturated)
;; To do: If L-resolvents-pruning applied to input clauses too,
;; it would discard the 2nd clause immediately and would saturate.
(replay-on-failure
(check-equal?
(dict-ref (saturation (Vars+clausify-list '( [(p z)]
[(not (p X)) (p (s X))]
[(not q)])))
'status)
'steps))
;; Russell's 'paradox', requires factoring:
(check-equal?
(dict-ref (saturation (Vars+clausify-list
'( [(s X X) (s b X)]
[(not (s X X)) (not (s b X))])))
'status)
'refuted)
;; Second version
(check-equal?
(dict-ref (saturation (Vars+clausify-list
'( [(s X b) (s b X)]
[(not (s X b)) (not (s b X))])))
'status)
'refuted)
(check-equal?
(dict-ref (saturation (Vars+clausify-list
'( [(big_f T0_0 T0_1) (big_g T0_0 T0_2)]
[(big_f T1_0 T1_1) (not (big_g T1_0 T1_0))]
[(big_g T2_0 T2_1) (not (big_f T2_0 T2_2))]
[(not (big_f T3_0 T3_1)) (not (big_g T3_0 (esk1_1 T3_0)))])))
'status)
'refuted)
(check-equal?
(dict-ref (saturation (Vars+clausify-list '( [p1 p2]
[p1 (not p2)]
[p2 (not p1)]
[(not p1) (not p2)])))
'status)
'refuted)
(check-equal?
(dict-ref (saturation
'((p1 p2 p3)
(p1 p3 (not p2))
(p2 p3 (not p1))
(p1 p2 (not p3))
(p1 (not p2) (not p3))
(p2 (not p1) (not p3))
(p3 (not p1) (not p2))
((not p1) (not p2) (not p3))))
'status)
'refuted)
(check-equal?
(dict-ref (saturation
(Vars+clausify-list
'( [(big_f T0_0 T0_1 (esk3_2 T0_0 T0_1))]
[(big_f esk1_0 esk2_0 esk2_0) (not (big_f esk1_0 esk1_0 esk2_0))]
[(big_f esk1_0 esk1_0 esk2_0)
(big_f esk1_0 esk2_0 esk2_0)
(not (big_f esk2_0 esk2_0 T2_0))]
[(big_f esk1_0 esk1_0 esk2_0)
(big_f esk2_0 T3_0 T3_1)
(not (big_f esk1_0 esk2_0 esk2_0))]
[(not (big_f esk1_0 esk1_0 esk2_0))
(not (big_f esk1_0 esk2_0 esk2_0))
(not (big_f esk2_0 esk2_0 T4_0))]
[(big_f esk1_0 esk2_0 esk2_0)
(not (big_f T5_0 T5_1 (esk3_2 T5_1 T5_0)))
(not (big_f esk1_0 esk1_0 esk2_0))]
[(big_f esk1_0 esk1_0 esk2_0)
(not (big_f T6_0 (esk3_2 T6_0 T6_1) (esk3_2 T6_0 T6_1)))
(not (big_f esk1_0 esk2_0 esk2_0))] )))
'status)
'refuted)
(check-equal?
(dict-ref
(saturation
(Vars+clausify-list
'([(p X) (not (p (p X)))]
[(not (p a))]
[(not (q a))]
[(q X) (not (q (q (q X))))]
[(q (q (q (q (q (q (q (q (q (q (q (q (q (q (q a)))))))))))))))])))
'status)
'refuted)
;; This problem shows there may be some loops with implication-removal and factoring!
(check-equal?
(dict-ref
(saturation
(Vars+clausify-list
'([(not (p X Y)) (p X Z) (p Z Y)]
[(p x x)]
[(not (q a a a a b b b b c c c c))]
[(q A A A A B B B B C C C C) (not (q (q A A A A) (q B B B B) (q C C C C)))]
[(q (q a a a a) (q b b b b) (q c c c c))])))
'status)
'refuted)
;; Binary rewrite
;; This example shows that *not* backward rewriting rules can be a problem:
;; Around step 19, there should be immediate resolution to '() with an active clause.
;; But because [(not (p a A))] has not been rewritten to [(notp a A)],
;; it cannot unify to '() immediately, and must wait for a *resolution* between
;; the rule and the clause to pop up from the queue.
(replay-on-failure
(define res
(saturation
(map clausify
'(((notp A B) (p A B)) ; axiom, binary clause
((not (notp A B)) (not (p A B))) ; axiom, converse binary clause
((p a A) (q b B) (r c C) (s d D)) ; these two clauses should resolve to '() immediately
((not (p A a))) ; Note that 'a A' is to prevent unit-clause rewrites
((not (q B b)))
((not (r C c)))
((not (s D d)))
))))
(check-equal? (dict-ref res 'status) 'refuted)
(when atom<=>
(check > (dict-ref res 'unit-rules) 0)
(check-equal? (dict-ref res 'binary-rules) 2)
(check > (dict-ref res 'binary-rewrites) 0)))
;; 'Asymmetric' rules
(replay-on-failure
(define res
(saturation
(map clausify
'([(not (p A A)) (q A)] ; Not a rule in itself (too general), but enables the next ones
[(p a a) (not (q a))] ; rule (p a a) <-> (q a)
[(p b b) (not (q b))] ; rule (p b b) <-> (q b)
[(p a a) (p b b) (p c c)]
[(not (q a)) (remove-me x Y)]
[(not (q b)) (remove-me x Y)]
[(not (p c c)) (remove-me x Y)]
[(not (remove-me X y))] ; defeats urw
))))
(check-equal? (dict-ref res 'status) 'refuted)
(when atom<=>
(check-equal? (dict-ref res 'binary-rules) 4)
(check-true (> (dict-ref res 'binary-rewrites) 0))))
;; TODO: Same test but with rules loaded from a file
;; Greedy selection of binary rewrites can lead to failure
(replay-on-failure
(define res
(saturation
(map clausify
'(; equivalences
[(not (q A B C D)) (p A B C)] ; (q A B C D) <=> (p A B C)
[(q A B C D) (not (p A B C))]
[(not (p A b C)) (t a)] ; (p A b C) <=> (t a)
[(p A b C) (not (t a))]
[(not (q A B c D)) (s b c)] ; (q A b c D) <=> (s b c)
[(q A B c D) (not (s b c))]
; inputs
; may be rewritten to (s b c)
[(q a b c d) (remove-me x Y) (remove-me y Y) (remove-me z Y)]
[(not (t a)) (remove-me x Y) (remove-me y Y) (remove-me z Y)]
;
[(not (remove-me X y))] ; defeats urw
))))
(check-equal? (dict-ref res 'status) 'refuted)
(when atom<=>
(check-equal? (dict-ref res 'binary-rules) 6)
(check-true (> (dict-ref res 'binary-rewrites) 0))))
;; Overlapping rewrites can lead to failures (but not without rewrites)
(replay-on-failure
(define res
(saturation
(map clausify
'(; equivalences
[(not (q A B C D)) (p A B C)] ; (q A B C D) <=> (p A B C)
[(q A B C D) (not(p A B C))]
[(not (p A b C)) (t a)] ; (p A b C) <=> (t a)
[(p A b C) (not (t a))]
[(not (q A b c D)) (s b c)] ; (q A b c D) <=> (s b c)
[(q A b c D) (not (s b c))]
; inputs
[(s b c) (remove-me x Y) (remove-me y Y) (remove-me z Y)]
[(not (t a)) (remove-me x Y) (remove-me y Y) (remove-me z Y)]
;
[(not (remove-me X y))] ; defeats urw
))))
(check-equal? (dict-ref res 'status) 'refuted)
(when atom<=>
(check-equal? (dict-ref res 'binary-rules) 6)
(check-true (> (dict-ref res 'binary-rewrites) 0))))
;; Greedy selection of overlapping rewrites can lead to failures
(replay-on-failure
(define res
(saturation
(map clausify
'(; equivalences
[(not (q A B C D)) (p A B C)] ; (q A B C D) <=> (p A B C)
[(q A B C D) (not(p A B C))]
[(not (p A b C)) (r A C)] ; (p A b C) <=> (r A C)
[(p A b C) (not (r A C))]
[(not (r A c)) (t a)] ; (r A c) <=> (t a)
[(r A c) (not (t a))]
[(not (q A b c D)) (s b c)] ; (q A b c D) <=> (s b c)
[(q A b c D) (not (s b c))]
; inputs
[(s b c) (remove-me x Y) (remove-me y Y) (remove-me z Y)]
[(not (t a)) (remove-me x Y) (remove-me y Y) (remove-me z Y)]
;
[(not (remove-me X y))] ; defeats urw
))))
(check-equal? (dict-ref res 'status) 'refuted)
(when atom<=>
(check-equal? (dict-ref res 'binary-rules) 8)
(check-true (> (dict-ref res 'binary-rewrites) 0)))))
================================================
FILE: satore/tests/stress-test1.rkt
================================================
#lang racket/base
(require satore/clause
satore/unification)
(define cms current-milliseconds)
;;; Stress test.
;;; There's only one predicate of two arguments.
;;; This takes basically exponential time with n.
;;; All the time is taken by clausify (safe-factoring, which includes subsumption check)
(define (stress n)
(define pre (cms))
(define cl1
(time
(clausify
(fresh ; ensures the names are adequate variable names
(for/list ([i n])
`(eq #s(Var ,i) #s(Var ,(+ i 1))))))))
(define cl2
(time
(clausify
(fresh
(for/list ([i (+ n 1)])
`(eq #s(Var ,i) #s(Var ,(+ i 1))))))))
(void (time (clause-subsumes cl1 cl2)))
(void (time (clause-subsumes cl2 cl1)))
(- (cms) pre))
;; Takes about 10s on my desktop machine for n=40 (subsumes-iter-limit=0).
(for/list ([n (in-list '(10 20 30 40))])
(printf "n = ~a\n" n)
(stress n))
================================================
FILE: satore/tests/trie.rkt
================================================
#lang racket/base
(require rackunit
satore/trie
(only-in satore/unification symbol-variable?))
(let ([atrie (make-trie #:variable? symbol-variable?)])
(trie-set! atrie
'(a X (f Y) c)
'A)
(trie-set! atrie
'(a b (f Y) c)
'B)
(trie-set! atrie
'(a b (f Y) E)
'C)
(check-equal?
(sort (trie-ref atrie '(a b (f (g e)) c)) symbolimsubst s)
(cond [(subst? s)
(subst->list s)]
[(list? s)
(sort (map (λ (p) (cons (Var-name (Varify (car p)))
(Varify (cdr p))))
s)
Var-namesymbol (symbol->Var-name 'C)) 'C)
(check-eq? (Var-name->symbol (symbol->Var-name 'X0)) 'X0)
(check-eq? (Var-name->symbol (symbol->Var-name 'X1353)) 'X1353)
(check-equal?
(find-var-names (Varify '(p D C A B)))
(map Var-name (Varify '(D C A B))))
(check-equal?
(find-var-names (Varify '(p (q D E) C A B E D D A)))
(map Var-name (Varify '(D E C A B))))
(let ()
(define-check (check/fail-var-occs<=> p q c)
(let ([res (var-occs<=> p q)])
(unless (eq? res c)
(fail-check (format "Params: ~a ~a \nExpected: ~a\nGot: ~a\n" p q c res)))))
(define (check-var-occs<=> p q c)
(let ([p (Varify p)] [q (Varify q)])
(check/fail-var-occs<=> p q c)
(case c
[(<) (check/fail-var-occs<=> q p '>)]
[(>) (check/fail-var-occs<=> q p '<)]
[(= #false) (check/fail-var-occs<=> q p c)])))
(check-var-occs<=> '(p) '(q) '=)
(check-var-occs<=> '(p X) '(q) '>)
(check-var-occs<=> '(p X) '(q X) '=)
(check-var-occs<=> '(p X X) '(q X) '>)
(check-var-occs<=> '(p X Y) '(q X) '>)
(check-var-occs<=> '(p X Y) '(q X Z) #false)
(check-var-occs<=> '(p X X Y) '(q X Z) #false)
(check-var-occs<=> '(p X X Y) '(q X Y) '>)
(check-var-occs<=> '(p X X Y) '(q X Y Y) #false))
(let ()
(check equal? (lnot 'auie) `(not auie))
(check equal? (lnot (lnot 'auie)) 'auie)
(check equal? (lnot lfalse) ltrue)
(check equal? (lnot `(not ,lfalse)) lfalse) ; to fix non-reduced values
(check equal? (lnot `(not ,ltrue)) ltrue) ; to fix non-reduced values
(check equal? (lnot ltrue) lfalse)
(check equal? (lnot (lnot ltrue)) ltrue)
(check equal? (lnot (lnot lfalse)) lfalse)
(check<=> polarity<=> 'a '(not a) '<))
(let ()
(define-simple-check (check-atom1<=> a b res)
(check<=> atom1<=> (Varify a) (Varify b) res))
(check-atom1<=> lfalse 'a '<)
(check-atom1<=> lfalse lfalse '=)
(check-atom1<=> '() '() '=)
(check-atom1<=> '(eq b a) '(eq a b) '>) ; lexicographical order
(check-atom1<=> '(p X Y) '(p Y X) '=) ; no lex order between variables
(check-atom1<=> '(p a X) '(p X a) '=) ; no lex order between variable and symbol
(check-atom1<=> '(p A (q B))
'(p (q A) B)
'=) ; no lex order when variable is involved
(check-atom1<=> '(p A (q b))
'(p (q A) b)
'=) ; ????
(check-atom1<=> '(not (eq X0 X1 X1)) ; var-occs=
'(not (eq X0 X1 X1))
'=)
(check-atom1<=> '(eq X0 X1 X1)
'(not (eq X0 X1 X1))
'=) ; negation should NOT count
;;; This is very important, otherwise the following problem can end with 'saturated:
;;; ((notp A B) (p A B)) ; axiom, binary clause
;;; ((not (notp A B)) (not (p A B))) ; axiom, converse binary clause
;;; ((p a A)) ; these two clauses should resolve to '() immediately
;;; ((not (p A a))) ; Note that 'a A' is to prevent unit-clause rewrites
(check-atom1<=> 'p 'q '<)
(check-atom1<=> '(not p) '(not q) '<)
(check-atom1<=> '(not (eq X0 X1 X1))
'(not (eq X1 X0))
'>)
(check-atom1<=> '(p X Y Z)
'(p X Y one)
'>)
(check-atom1<=> '(p X A one)
'(p X Y one)
'#false)
(check-atom1<=> '(p X one one)
'(p X one)
'>)
(check-atom1<=> '(p X one (q one))
'(p X one one)
'>)
)
;; Tests for KBO
(let ()
(define-simple-check (check-KBO1lex<=> a b res)
(check<=> KBO1lex<=> (Varify a) (Varify b) res))
(check-KBO1lex<=> lfalse 'a '<)
(check-KBO1lex<=> lfalse lfalse '=)
;(check-KBO1lex<=> '() '() '=) ; not a term
(check-KBO1lex<=> '(eq b a) '(eq a b) '>) ; lexicographical order
(check-KBO1lex<=> '(p X Y) '(p Y X) #false) ; commutativity cannot be oriented
(check-KBO1lex<=> '(p a X) '(p X a) #false) ; left->right order: a <=> X -> #false
(check-KBO1lex<=> '(p A (q B))
'(p (q A) B)
'<) ; left->right order: A < (q A)
(check-KBO1lex<=> '(p A (q b))
'(p (q A) b)
'<) ; left->right order: A < (q A)
(check-KBO1lex<=> '(not (eq X0 X1 X1)) ; var-occs=
'(not (eq X0 X1 X1))
'=)
(check-KBO1lex<=> '(eq X0 X1 X1)
'(not (eq X0 X1 X1))
'=) ; negation should NOT count
;;; This is very important, otherwise the following problem can end with 'saturated:
;;; ((notp A B) (p A B)) ; axiom, binary clause
;;; ((not (notp A B)) (not (p A B))) ; axiom, converse binary clause
;;; ((p a A)) ; these two clauses should resolve to '() immediately
;;; ((not (p A a))) ; Note that 'a A' is to prevent unit-clause rewrites
(check-KBO1lex<=> 'p 'q '<) ; lex
(check-KBO1lex<=> '(not p) '(not q) '<) ; lex
(check-KBO1lex<=> '(not (eq X0 X1 X1))
'(not (eq X1 X0))
'>) ; by var-occs and weight
(check-KBO1lex<=> '(p X Y Z)
'(p X Y one)
#false) ; var-occs incomparable
(check-KBO1lex<=> '(p X Y (f Z))
'(p X (f Y) one)
#false) ; var-occs incomparable
(check-KBO1lex<=> '(p X Y (f Z))
'(p X (f Y) Z)
'<) ; same weight, Y < (f Y)
(check-KBO1lex<=> '(p X A one)
'(p X Y one)
#false)
(check-KBO1lex<=> '(p X one one)
'(p X one)
'>)
(check-KBO1lex<=> '(p X one (q one))
'(p X one one)
'>)
(check-KBO1lex<=> '(p A (p B C))
'(p (p A B) C)
'<) ; associativity ok: A < (p A B)
)
(let ()
(check-equal? (term-lex2<=> '(p a) '(p b)) '<))
(let ()
(define-simple-check (check-term-lex<=> a b res)
(let-values ([(a b) (apply values (fresh (Varify (list a b))))])
(check<=> term-lex<=> (Varify a) (Varify b) res)))
(check-term-lex<=> 'a (Var 'X) '>)
(check-term-lex<=> (Var 'X) (Var 'X) '=)
(check-term-lex<=> 'a 'a '=)
(check-term-lex<=> 'a 'b '<)
(define-simple-check (check-literal<=> a b res)
(let-values ([(a b) (apply values (fresh (Varify (list a b))))])
(check<=> literal<=> (Varify a) (Varify b) res)))
(check-literal<=> 'a '(not a) '<)
(check-literal<=> 'a 'b '<)
(check-literal<=> 'z '(not a) '<)
(check-literal<=> '(z b) '(not a) '<)
(check-literal<=> 'a 'a '=)
(check-literal<=> 'z '(a a) '>)
(check-literal<=> '(z z) '(z (a a)) '<))
(let ()
(check-true (literal==? 'a 'a))
(check-true (literal==? (Var 'X) (Var 'X)))
(check-true (literal==? (Var 'X) #s(Var X))) ; prefab
(check-false (literal==? (Var 'X) (Var 'Y)))
(check-false (literal==? (fresh (Var 'X)) (Var 'X)))
(check-false (literal==? 'X (Var 'X))) ; not considered the same??
(check-true (literal==? `(p (f ,(Var 'X) ,(Var 'X)) y) `(p (f ,(Var 'X) ,(Var 'X)) y))))
(let ()
(define-check (test-unify t1 t2 subst)
(let ([t1 (Varify t1)] [t2 (Varify t2)])
(set! subst (subst/#false->imsubst subst))
(define sh (unify t1 t2))
(define sl (subst/#false->imsubst
(and sh
(for/list ([(k v) (in-subst sh)])
(cons (Var k)
(if (already-substed? v)
(already-substed-term v)
v))))))
(unless (equal? sl subst)
(fail-check (format "Expected ~a. Got: ~a\nt1 = ~a\nt2 = ~a\n"
subst sl
t1 t2)))
(when sh
(define r1 (substitute t1 sh))
(define r2 (substitute t2 sh))
(unless (equal? r1 r2)
(fail-check "r1≠r2" sh r1 r2)))))
(test-unify '(p X)
'(p X)
'())
(test-unify '(p (f X) X)
'(p (f a) a)
'((X . a)))
(test-unify '(p (f c) (g X))
'(p Y Y)
#false)
(test-unify '(p X (f X))
'(p a Y)
'((X . a) (Y . (f a))))
(test-unify '(p (f X Y) (f Y Z))
'(p (f (f a) (f b)) (f (f b) c))
'((X . (f a)) (Y . (f b)) (Z . c)))
(test-unify '(p X (p X) a)
'(p Y (p (p Z)) Z)
(if reduce-mgu?
'((Z . a) (X . (p a)) (Y . (p a)))
'((X . Y) (Y . (p Z)) (Z . a))))
(test-unify '(p X (p X) (p (p X)))
'(p a Y Z)
'((X . a) (Y . (p a)) (Z . (p (p a)))))
(test-unify '(p X (p X) (p (p X)))
'(p a (p Y) (p (p Z)))
'((X . a) (Y . a) (Z . a)))
(test-unify '(p (p X) (p X) a)
'(p Y (p (p Z)) Z)
(if reduce-mgu?
'((Z . a) (X . (p a)) (Y . (p (p a))))
'((Y . (p X)) (X . (p Z)) (Z . a))))
(test-unify '(p X X)
'(p a Y)
'((X . a) (Y . a)))
(test-unify '(p X X)
'(p (f Y) Z)
'((X . (f Y)) (Z . (f Y))))
(test-unify '(p X X) '(p (f Y) Y) #false)
(test-unify '(p (f X Y) (g Z Z))
'(p (f (f W U) V) W)
(if reduce-mgu?
'((W . (g Z Z)) (Y . V) (X . (f (g Z Z) U)))
'((X . (f W U)) (Y . V) (W . (g Z Z)))))
(test-unify '(eq X30 (mul X31 (mul X32 (inv (mul X31 X32)))))
'(eq (mul X25 one) X26)
`((X26 . (mul X31 (mul X32 (inv (mul X31 X32)))))
(X30 . (mul X25 one))))
(test-unify '(p A B)
'(p B A)
'((A . B)))
)
(let ()
(define (test-suite-left-unify left-unify)
(define-simple-check (test-left-unify t1 t2 subst)
(let ([t1 (Varify t1)] [t2 (Varify t2)])
(set! subst (subst/#false->imsubst subst))
(define sh (left-unify t1 t2))
(define sl (subst/#false->imsubst sh))
(check-equal? sl subst
(format "Expected ~a. Got: ~at1 = ~a\nt2 = ~a\n"
subst sl
t1 t2))
(when sh
(define r1 (left-substitute t1 sh))
(check-equal? r1 t2 (format "r1≠t2\nsh=~a\nr1=~a\nt2=~a\n" sh r1 t2)))))
(test-left-unify '(p (f X) X)
'(p (f a) a)
'((X . a)))
(test-left-unify '(p (f c) (g X))
'(p Y Y)
#false)
(test-left-unify '(p X (f X)) '(p a Y) #false)
(test-left-unify '(p (f X Y) (f Y Z))
'(p (f (f a) (f b)) (f (f b) c))
'((Z . c) (Y . (f b)) (X . (f a))))
(test-left-unify '(p X X) '(p a Y) #false)
(test-left-unify '(p X X) '(p (f Y) Z) #false)
(test-left-unify '(p X X) '(p (f Y) Y) #false)
(test-left-unify '(p (f X Y) (g Z Z))
'(p (f (f W U) V) W)
#false)
(test-left-unify '(p X X)
'(p A B)
#false)
; This MUST return false because of the circularity.
; The found substitution must be specializing, that is C2σ = C2 (and C1σ = C2),
; otherwise safe factoring can fail, in particular.
; Hence we must ensure that vars(C2) ∩ dom(σ) = ø.
(test-left-unify '(p A B)
'(p B A)
#false)
(test-left-unify '(p B A)
'(p A B)
#false)
(test-left-unify '(p A A)
'(p B B)
'((A . B)))
(test-left-unify '(p A)
'(p A)
'())
(test-left-unify '(p a)
'(p a)
'())
(test-left-unify '(p A X)
'(p X Y)
#false))
(test-suite-left-unify left-unify)
(test-suite-left-unify (λ (t1 t2) (define subst-assoc (left-unify/assoc t1 t2))
(and subst-assoc (make-subst subst-assoc)))))
(let ([pat '(_not_ (_not_ #s(Var A)))]
[t (fresh (Varify '(q (p (_not_ (_not_ (f A B)))))))])
(define s
(left-unify-anywhere pat t))
(check-equal? (left-substitute pat s)
(cadadr t)))
(let ([t '(q (p (_not_ (_not_ (f A B)))))])
(check-equal?
(match-anywhere (match-lambda [`(_not_ (_not_ ,x)) `([x . ,x])] [else #false])
t)
'([x . (f A B)])))
(let ([c1 (clausify '((theorem (or A B)) (not (theorem (or A (or (or A B) B))))))]
[c2 (clausify '((theorem (or (_not_ A) (or B A)))))])
(define s1 (unify (first c2) (lnot (second c1))))
(define s2 (unify (lnot (second c1)) (first c2)))
(list c1
c2
s1
(substitute (first c1) s1)
s2
(substitute (first c1) s2))
(check clause-equivalence?
(substitute (list (first c1)) s1)
(substitute (list (first c1)) s2)))
;; Stress test for unification
;; This should take 0ms
;; See https://en.wikipedia.org/wiki/Unification_(computer_science)
;; #Examples_of_syntactic_unification_of_first-order_terms
(let ()
(define last-var? #true)
(define (stress-unify n)
(define A
(let left ([d 1])
(if (>= d n)
(list '* (Var d) (if last-var? (Var (+ d 1)) 'a))
(list '* (left (+ d 1)) (Var d)))))
(define B
(let right ([d 1])
(if (>= d n)
(list '* (if last-var? (Var (+ d 1)) 'a) (Var d))
(list '* (Var d) (right (+ d 1))))))
(define subst (time (unify A B)))
; Verify that there's only 1 variable in the each rhs
(when (and reduce-mgu? last-var?)
(check-equal? (length (Vars (map cdr (subst->list subst))))
1
subst))
(time (substitute A subst)))
(for ([n (in-range 30 50)])
(printf "~a: " n)
(stress-unify n)))
================================================
FILE: satore/tptp.rkt
================================================
#lang racket/base
;**************************************************************************************;
;**** Tptp Input/Output Format ****;
;**************************************************************************************;
(require bazaar/debug
bazaar/string
global
racket/dict
racket/file
racket/format
racket/list
racket/match
racket/port
racket/string
satore/clause
satore/unification)
(provide (all-defined-out))
(define-global:boolean *tptp-out?* #false
"Output is in TPTP format?")
#|
File formats:
.rktd: Racket data, one Racket clause per line.
.p: Prolog format, with Prolog clauses that contain tptp (FOL) clauses.
.tptp: only the tptp clauses, one per line.
|#
;; Reads a .p file and returns a list of clauses.
;;
;; program-file : file?
;; -> (listof clause?)
(define (tptp-program-file->clauses program-file)
; Not efficient: Loads the whole program as a string then parses it.
; It would be more efficient to read it as a stream with an actual parser.
; Another possibility is to read it line by line and parse each line as a cnf(…)
; but that will file if the cnf(…) is multiline.
(tptp-prog->clauses (file->string program-file)))
;; Helper function
(define (tptp-pre-clauses->clauses pre-clauses)
(define clauses
(for/list ([cl (in-list pre-clauses)])
(let loop ([t cl])
(match t
[(? symbol? x) x]
[(? string? x)
(string->symbol (string-append "_str_" x))] ; to avoid being interpreted as a variable
['() '()]
[(list '~ (? symbol? pred) (list a ...) r ...)
(cons (list 'not (cons (loop pred) (loop a)))
(loop r))]
[(list (? symbol? pred) (list a ...) r ...)
(cons (cons (loop pred) (loop a))
(loop r))]
[(list '~ x r ...)
(cons (list 'not (loop x))
(loop r))]
[(list x a ...)
(cons (loop x) (loop a))]
[else (error "Unrecognized token: " t)]))))
(map (compose clausify symbol-variables->Vars) clauses))
;; Reads the .p program given as a string and returns a list of clauses.
;;
;; str : string?
;; -> (listof clause?)
(define (tptp-prog->clauses str)
; hardly tested and not strict enough
; It should be mostly robust to line breaking though.
; Doesn't parse strings properly (will remove lines that look like comments in multiline strings)
(define l
(filter
(λ (x)
(if (list? x)
x
(begin
(assert (memq x '(cnf end_cnf))
x)
#false)))
; Ensure operators are surrounded with spaces
; turn racket special symbols (| and ,) into normal symbols.
; then use racket's reader to parse it like an s-expression
(string->data
(regexp-replaces
str
(list*
'[#px"(?:^|\n)\\s*[%#][^\n]*" "\n"] ; prolog and shell/python/eprover full-line comments
'[#px"\\bnot\\b" "_not_"] ;; To do: Use $not for `lnot` instead? (as in TPTP)
(map (λ (p) (list (regexp-quote (first p))
(string-append " " (regexp-replace-quote (second p)) " ")))
'(["|" "" ]
["&" "" ]
["," "" ]
["$false" ""] ; empty literal
["~" "~"]
["." "end_cnf"]
["'" "\""])))))))
; first is name, second is type, third is clause, rest is comments(?)
(define pre-clauses (map third l))
(tptp-pre-clauses->clauses pre-clauses))
;; Simple parser for the proposer output into s-exp clauses.
;; The format is expected to be in cnf.
;;
;; str : string?
;; -> (listof clause?)
(define (tptp-string->clauses str)
; To do: Optimize. This can be very slow for large conjectures.
(define pre-clauses
(append*
; split first to avoid regenerating the whole string after each substitution?
(for/list ([str (in-list (string-split str #px"&|\n"))]) ; & and \n play the same role
(with-handlers ([exn? (λ (e) (displayln str) (raise e))])
(string->data
; Ensure operators are surrounded with spaces
; turn racket special symbols (| and ,) into normal symbols
(regexp-replaces
str
(list*
'[#px"\\bnot\\b" "_not_"] ;; To do: use $not for `lnot` instead? (as TPTP)
(map (λ (p) (list (regexp-quote (first p))
(string-append " " (regexp-replace-quote (second p)) " ")))
'(["|" ""]
["," ""]
["~" "~"]
["'" "\""])))))))))
(tptp-pre-clauses->clauses pre-clauses))
;; Returns a string representing the literal lit.
;;
;; lit : literal?
;; -> string?
(define (literal->tptp-string lit)
(cond
[(lnot? lit)
(string-append "~ " (literal->tptp-string (second lit)))]
[(empty? lit)
"$false"]
[(list? lit)
(string-append (literal->tptp-string (first lit))
"("
(string-join (map literal->tptp-string (rest lit)) ", ")
")")]
[(Var? lit) (symbol->string (Var-name->symbol lit))]
[else (format "~a" lit)]))
;; Returns a string representing the clause cl.
;;
;; cl : clause?
;; ->string?
(define (clause->tptp-string cl)
(string-join
(map literal->tptp-string (Vars->symbols cl))
" | "))
;; Returns a string representing the clauses cls.
;;
;; cls : (listof clause?)
;; -> string?
(define (clauses->tptp-string cls)
(string-join (map clause->tptp-string cls) "\n"))
;; String replacement of tptp names with shorter ones to improve readability
;;
;; str : string?
;; -> string?
(define (tptp-shortener str)
(define substs
(sort
(map (λ (p) (cons (~a (car p)) (~a (cdr p))))
; fld_1
(append
'((multiplicative_identity . _1)
(additive_identity . _0)
(less_or_equal . ≤)
(additive_inverse . –)
(multiplicative_inverse . /)
(equalish . ≃)
(multiply . ×)
(product . ×=)
(inverse . /)
(add . +)
)
;grp_5
'((equalish . ≃)
(multiply . ×)
(product . ×=)
(inverse . /)
(identity . _1)
)
; geo
'((convergent_lines . /\\)
(unorthogonal_lines . ¬⊥)
(orthogonal_through_point . ⊥_thru_pt)
(parallel_through_point . //_thru_pt)
(distinct_lines . ≠_ln)
(apart_point_and_line . ≠_pt_ln)
(orthogonal_lines . ⊥)
(distinct_points . ≠_pt)
(parallel_lines . //)
(equal_lines . =_ln)
(equal_points . =_pt)))
)
; forces prefixes to appear later to match longer strings first:
> #:key (compose string-length car)))
(string-join
(for/list ([line (in-lines (open-input-string str))])
(for/fold ([line line])
([(from to) (in-dict substs)])
(string-replace line from to #:all? #true)))
"\n"))
;; Helper: Surround any printing operation with this macro
;; to automatically replace the output with shortened names.
(define-syntax-rule (with-tptp-shortener body ...)
(let ([str (with-output-to-string (λ () body ...))])
(displayln (tptp-shortener str))))
;============;
;=== Main ===;
;============;
(module+ main
(require global
racket/file)
(define-global *rktd-file* #false
"file in rktd format to output in tptp format"
file-exists?
values)
(void (globals->command-line))
(when (*rktd-file*)
(displayln (clauses->tptp-string (file->list (*rktd-file*))))))
================================================
FILE: satore/trie.rkt
================================================
#lang racket/base
;***************************************************************************************;
;**** Trie: Imperfect Discrimination Tree ****;
;***************************************************************************************;
;;; A discrimination tree is like a hashtable where the key is a list of elements.
;;; The keys are organized in a tree structure so that to retrieving an object
;;; takes at most O(A×l) steps, where l is the length of the key and A is the size of
;;; the alphabet. In practice it will be closer to O(l) since a hash table is used
;;; at each node to store the branches.
;;;
;;; A key is a actually tree (a list of lists of ...), which is flattened to a list
;;; where parenthesis are replaced with symbols.
;;; Variables are considered to be unnamed and there is no unification/matching.
;;; The only dependency on first-order logic specifics is `variable?`.
;;;
;;; An imperfect discrimination tree does not differentiate variable names.
;;; Hence p(X Y) is stored in the same node as p(A A). An additional tests
;;; is required to tell them apart.
(require bazaar/cond-else
define2
racket/list
racket/match
satore/misc)
(provide (except-out (all-defined-out) no-value)
(rename-out [no-value trie-no-value]))
;; Default value at the leaves. Should not be visible to the user.
(define no-value (string->uninterned-symbol "no-value"))
; Tokens used in the keys of the tree
(define anyvar (string->uninterned-symbol "¿"))
(define sublist-begin (string->uninterned-symbol "<<"))
(define sublist-end (string->uninterned-symbol ">>"))
;; edges : hasheq(key . node?)
;; value : any/c
(struct trie-node (edges value)
#:transparent
#:mutable)
(define (make-node)
(trie-node (make-hasheq) no-value))
;; Trie structure with variables.
;;
;; root : trie-node?
;; variable? : any/c -> boolean?
(struct trie (root variable?))
;; Trie constructor.
;;
;; constructor : procedure?
;; variable? : any/c -> boolean?
;; other-args : (listof any/c)
;; -> trie?
(define (make-trie #:? [constructor trie]
#:? [variable? (λ (x) #false)]
. other-args)
(apply constructor (make-node) variable? other-args))
;; Updates the value of the node for the given key (or sets one if none exists).
;; If default-val/proc is a procedure of arity 0, then it is applied to produce the
;; default value when requested, otherwise default-val/proc is used itself as the
;; default value.
;;
;; atrie : trie?
;; key : any/c
;; update : any/c -> any/c
;; default-val/proc : (or/c thunk? any/c)
;; -> void?
(define (trie-update! atrie key update default-val/proc)
(match-define (trie root variable?) atrie)
; The key is `list`ed because we need a list, and this allows the given key to not be a list.
(let node-insert! ([nd root] [key (list key)])
(cond/else
[(empty? key)
; Stop here.
(define old-value (trie-node-value nd))
(set-trie-node-value! nd (update (if (eq? old-value no-value)
(if (thunk? default-val/proc)
(default-val/proc)
default-val/proc)
old-value)))]
#:else ; key is a list
(define k (car key))
(define edges (trie-node-edges nd))
#:cond
[(pair? k)
; Linearize the tree structure of the key.
(define key2 (cons sublist-begin (append k (cons sublist-end (cdr key)))))
(node-insert! nd key2)]
#:else ; nil, atom, variable
(let ([k (if (variable? k) anyvar k)])
(define nd2 (hash-ref! edges k make-node))
(node-insert! nd2 (cdr key))))))
;; Keeps a list of values at the leaves.
;; If `trie-insert!` is used, any use of `trie-update!` should be consistent with values being lists.
;;
;; atrie : trie?
;; key : any/c
;; val : any/c
;; -> void?
(define (trie-insert! atrie key val)
(trie-update! atrie key (λ (old) (cons val old)) '()))
;; Replacing the current value (if any) for key with val.
;;
;; atrie : trie?
;; key : any/c
;; val : any/C
(define (trie-set! atrie key val)
(trie-update! atrie key (λ _ val) #false))
;; Applies on-leaf at each node that match with key.
;; The matching keys of the trie are necessarily no less general than the given key.
;; `on-leaf` may be effectful.
;;
;; atrie : trie?
;; key : any/c
;; on-leaf : trie-node? -> any
;; -> void?
(define (trie-find atrie key on-leaf)
(define variable? (trie-variable? atrie))
(let node-ref ([nd (trie-root atrie)] [key (list key)])
(cond/else
[(empty? key)
; Leaf found.
(unless (eq? no-value (trie-node-value nd))
(on-leaf nd))]
#:else
(define k (car key))
(define var-nd (hash-ref (trie-node-edges nd) anyvar #false))
#:cond
[(variable? k)
(when var-nd
; both the key and the node are variables
(node-ref var-nd (cdr key)))]
#:else
(when var-nd
; If a variable matches, consider the two paths.
(node-ref var-nd (cdr key)))
#:cond
[(pair? k)
; Linearize the tree structure of the key.
(define key2 (cons sublist-begin (append k (cons sublist-end (cdr key)))))
(node-ref nd key2)]
#:else
(define nd2 (hash-ref (trie-node-edges nd) k #false))
(when nd2
(node-ref nd2 (cdr key))))))
;; Applies the procedure `on-leaf` to any node for which the key is matched by the given key.
;; The matching keys of the trie are necessarily no more general than the given key.
;; `on-leaf` may be effectful.
;;
;; atrie : trie?
;; key : any/c
;; on-leaf : trie-node -> any/c
;; -> void?
(define (trie-inverse-find atrie key on-leaf)
(define variable? (trie-variable? atrie))
(let node-find ([nd (trie-root atrie)] [key (list key)] [depth 0])
(define edges (trie-node-edges nd))
(cond/else
[(> depth 0)
; If the depth is positive, that means we are currently matching a variable.
; We need to continue through every branch and decrease the depth only if we encounter
; a sublist-end, and increase the counter if we encounter a sublist-begin.
; Note that key can be empty while depth > 0.
(for([(k2 nd2) (in-hash edges)])
(node-find nd2 key
(cond [(eq? k2 sublist-begin) (+ depth 1)]
[(eq? k2 sublist-end) (- depth 1)]
[else depth])))]
[(empty? key)
; Leaf found.
(unless (eq? no-value (trie-node-value nd))
(on-leaf nd))]
#:else
(define k (car key))
#:cond
[(variable? k)
;; Anything matches. For sublist we need to keep track of the depth.
;; Note that variables in the tree can only be matched if k is a variable.
(for ([(k2 nd2) (in-hash edges)])
(node-find nd2 (cdr key) (if (eq? k2 sublist-begin) 1 0)))]
[(pair? k)
; Linearize the tree structure of the key.
(define key2 (cons sublist-begin (append k (cons sublist-end (cdr key)))))
(node-find nd key2 0)]
#:else
(define nd2 (hash-ref edges k #false))
(when nd2
(node-find nd2 (cdr key) 0)))))
;; Both find and inverse-find at the same time.
;; Useful when (full) unification must be performed afterwards.
;; `on-leaf` may be effectful.
;;
;; atrie : trie?
;; key : any/c
;; on-leaf : trie-node? -> any
;; -> void?
(define (trie-both-find atrie key on-leaf)
(define variable? (trie-variable? atrie))
(let node-find ([nd (trie-root atrie)] [key (list key)] [depth 0])
(define edges (trie-node-edges nd))
(cond/else
[(> depth 0)
; If the depth is positive, that means we are currently matching a variable.
; Consume everything until we find a sublist-end at depth 1.
; We need to continue through every branch and decrease the depth only if we encounter
; a sublist-end, and increase the counter if we encounter a sublist-begin.
; Note that key can be empty while depth > 0.
(for([(k2 nd2) (in-hash edges)])
(node-find nd2 key
(cond [(eq? k2 sublist-begin) (+ depth 1)]
[(eq? k2 sublist-end) (- depth 1)]
[else depth])))]
[(empty? key)
; Leaf found.
(unless (eq? no-value (trie-node-value nd))
(on-leaf nd))]
#:else
(define k (car key))
(define var-nd (hash-ref (trie-node-edges nd) anyvar #false))
#:cond
[(variable? k)
;; Anything matches. For sublist we need to keep track of the depth.
;; Note that variables in the tree can only be matched if k is a variable.
(for ([(k2 nd2) (in-hash edges)])
(node-find nd2 (cdr key) (if (eq? k2 sublist-begin) 1 0)))]
#:else
(when var-nd
; The node contains a variable, which thus matches the key.
(node-find var-nd (cdr key) 0))
#:cond
[(pair? k)
; Linearize the tree structure of the key.
(define key2 (cons sublist-begin (append k (cons sublist-end (cdr key)))))
(node-find nd key2 0)]
#:else
(define nd2 (hash-ref edges k #false))
(when nd2
(node-find nd2 (cdr key) 0)))))
;; Helper function
(define ((make-proc-tree-ref proc) atrie key)
(define res '())
(proc atrie
key
(λ (nd) (set! res (cons (trie-node-value nd) res))))
res)
;; Returns a list of values which keys are matched by the given key.
;; The matching keys of the trie are necessarily no more general than the given key.
;; These functions do not have side effects.
;;
;; Each function takes as input:
;; atrie : trie?
;; key : any/c
;; -> list?
(define trie-inverse-ref (make-proc-tree-ref trie-inverse-find))
(define trie-ref (make-proc-tree-ref trie-find))
(define trie-both-ref (make-proc-tree-ref trie-both-find))
;; Returns the list of all values in all nodes.
;;
;; atrie : trie?
;; -> list?
(define (trie-values atrie)
(let loop ([nd (trie-root atrie)] [res '()])
(define edges (trie-node-edges nd))
(define val (trie-node-value nd))
(for/fold ([res (if (eq? val no-value) res (cons val res))])
([(key nd2) (in-hash edges)])
(loop nd2 res))))
================================================
FILE: satore/unification-tree.rkt
================================================
#lang racket/base
;**************************************************************************************;
;**** Unification Tree ****;
;**************************************************************************************;
;;; An imperfect discrimination tree specialized for unifying literals and other
;;; operations. This is *different* from "substitution trees"
;;; (https://link.springer.com/chapter/10.1007%2F3-540-59200-8_52)
;;; Note: This should be probably named a Clause-trie instead, since the
;;; major difference with the trie is that we are dealing with Clauses, which
;;; are lists of literals, and the same Clause can appear in different leaves
;;; of the trie. Unification is only one of the operations performed on Clauses.
(require bazaar/list
bazaar/loop
bazaar/mutation
define2
global
racket/list
satore/Clause
satore/clause
satore/misc
satore/trie
satore/unification)
(provide (all-defined-out)
(all-from-out satore/trie))
(module+ test
(require rackunit))
;; Experimental.
;; Based on "A Unifying Principle for Clause Elimination in First-Order Logic",
;; by Kiesl, Benjamin and Suda, Martin, CADE 26, 2017.
;;
;; Notice: This cannot be applied to input clauses.
;; Notice: To pass Russell's problem, we must
;; do 1-to-N resolution (non-binary resolution), OR, maybe,
;; binary resolution + unsafe factoring, but the 'resolutions'
;; for factoring must be taken into account too.
(define-global:boolean *L-resolvent-pruning?* #false
'("Discard clauses for which a literal leads to 0 resolvents."
"Currently doesn't apply to input clauses."))
(define-counter n-L-resolvent-pruning 0)
;========================;
;=== Unification Tree ===;
;========================;
;; A unification tree is simply a discrimination tree where trie-node-values
;; are lists of `utree-leaf`.
(struct unification-tree trie () #:transparent)
;; A leaf value.
;; Several leaves may have the same clause-idx but different clauses——well, the same clauses
;; but ordered differently. It's named `uclause` to make it clear it's not a well-formed clause
;; (stands for unordered-clause).
(struct utree-leaf (Clause uclause) #:transparent)
;; Returns a new unification tree (or a substruct).
;;
;; constructor : procedure?
;; other-args : list?
;; -> unification-tree?
(define (make-unification-tree #:constructor [constructor unification-tree]
. other-args)
(apply make-trie
#:constructor constructor
#:variable? Var?
other-args))
;; Inserts a Clause into the unification tree.
;; Each literal of the clause cl is added to the tree, and the leaf value at each literal lite is the
;; clause, but where the first literal is lit.
;; Notice: Thus the clause is *not* sorted according to `sort-clause`.
;; Note: We could also keep the clause unchanged and cons the index of the literal,
;; that would avoid using up new cons cells, while keeping the clause intact.
;;
;; unification-tree? Clause? -> void?
(define (add-Clause! utree C)
(define cl (Clause-clause C))
(zip-loop ([(left lit right) cl])
(define reordered-clause (cons lit (rev-append left right)))
(trie-insert! utree lit (utree-leaf C reordered-clause))))
;; Returns the list of unique Clauses present in the utree.
;;
;; unification-tree? -> (listof Clause?)
(define (unification-tree-Clauses utree)
(remove-duplicates (map utree-leaf-Clause (append* (trie-values utree))) eq?))
;; Calls `on-unified` for each literal of each clause of utree that unifies with `lit`.
;; If a clause `cl` has n literals that unify with `lit`, then `on-unified` is called n times.
;;
;; utree : unification-tree?
;; lit : literal?
;; on-unified : utree-leaf? subst lit1 lit2 other-lit2s -> void?
;; -> void?
(define (find-unifiers utree lit on-unified)
(trie-both-find utree lit
(λ (nd)
(define val (trie-node-value nd))
(when (list? val)
(for ([lf (in-list val)])
(define cl (utree-leaf-uclause lf))
; Unify only with the first literal, assuming clauses in node-values
; are so that the first literal corresponds to the key
; (the path from the root)
(define lit2 (first cl))
(define subst (unify lit2 lit))
(when subst
(on-unified lf subst lit lit2 (rest cl))))))))
;; Returns the set of Clauses that *may* left-unify with lit.
;; The returned clauses are sorted according to `sort-clause` and duplicate clauses are removed.
;;
;; unification-tree? literal? -> (listof Clause?)
(define (unification-tree-ref utree lit)
; Node values are lists of rules, and trie-ref returns a list of node-values,
; hence the append*.
(remove-duplicates (append* (map utree-leaf-Clause (trie-ref utree lit))) eq?))
;; Helper for the resolve/factors functions below.
;; Defines a new set of Clauses, and a helper function that creates new Clauses,
;; rewrites them, checks for tautologies and add them to the new-Clauses.
(define-syntax-rule (define-add-Clause! C new-Clauses add-Clause! rewriter)
(begin
(define new-Clauses '())
(define (add-Clause! lits subst type parents)
(define cl (clause-normalize (substitute lits subst)))
(define new-C (make-Clause cl (cons C parents) #:type type))
; Rewrite
(let ([new-C (rewriter new-C)])
(unless (Clause-tautology? new-C)
(cons! new-C new-Clauses))))))
;; Resolves the Clause `C` with all the Clauses in `utree` and returns the list of resolvents.
;; Uses negative literal selection based on `literal-cost`:
;; If `C` has at least one negative literal, the costliest one is selected using `literal-cost`,
;; and only this literal is used for resolution with other Clauses.
;; The resolvents are immediately rewritten with `rewriter`.
;;
;; utree : unification-tree?
;; C : Clause?
;; rewriter : Clause? -> Clause?
;; literal-cost : literal? -> number?
;; -> (listof Clause?)
(define (utree-resolve/select-literal utree C
#:? [rewriter (λ (C) C)]
#:? [literal-cost literal-size])
(define cl (Clause-clause C))
;; Choose the costliest negative literal if any (for elimination)
(define selected-idx
(for/fold ([best-idx #false]
[best-cost -inf.0]
#:result best-idx)
([lit (in-list cl)]
[idx (in-naturals)]
#:when (lnot? lit)) ; negative literals only
(define c (literal-cost lit))
(if (> c best-cost)
(values idx c)
(values best-idx best-cost))))
(zip-loop ([(left lit right) cl]
[resolvents '()]
[lit-idx 0]
#:result (or resolvents '()))
(cond
[(or (not selected-idx)
(= lit-idx selected-idx))
(define-add-Clause! C new-Clauses add-Clause! rewriter)
; Find resolvents
(find-unifiers utree
(lnot lit)
(λ (lf subst nlit lit2 rcl2)
(add-Clause! (rev-append left (rev-append right rcl2))
subst
'res
(list (utree-leaf-Clause lf)))))
(values (rev-append new-Clauses resolvents)
(+ 1 lit-idx))]
[else
(values resolvents
(+ 1 lit-idx))])))
;; Returns the list of unsafe factors of `C`.
;; Factors are immediately rewritten with `rewriter`.
;;
;; C : Clause?
;; rewriter : Clause? -> Clause?
;; -> (listof Clause?)
(define (unsafe-factors C #:? [rewriter (λ (C) C)])
(define-add-Clause! C factors add-Clause! rewriter)
(define cl (Clause-clause C))
(zip-loop ([(left lit1 right) cl])
(define pax (predicate.arity lit1))
(zip-loop ([(left2 lit2 right2) right]
; Literals are sorted, so no need to go further.
#:break (not (equal? pax (predicate.arity lit2))))
(define subst (unify lit1 lit2))
; We could do left-unify instead, but then we need to do both sides,
; at the risk of generating twice as many clauses, so may not be worth it.
(when subst
(add-Clause! (rev-append left right) ; remove lit1
subst
'fac
'()))))
factors)
;; Appends and returns the results of `unsafe-factors` and `utree-resolve/select-literal`.
;;
;; utree : unification-tree?
;; C : Clause?
;; rewriter : Clause? -> Clause?
;; literal-cost : literal? -> number?
;; -> (listof Clause?)
(define (utree-resolve+unsafe-factors/select utree C #:? rewriter #:? literal-cost)
(rev-append
(unsafe-factors C #:rewriter rewriter)
(utree-resolve/select-literal utree C
#:rewriter rewriter
#:literal-cost literal-cost)))
;; Like `utree-resolve+unsafe-factors/select` but without negative literal selection,
;; and with L-resolvent pruning.
;; Returns the set of Clauses from resolutions between cl and the clauses in utree,
;; as well as the factors.
;; Clauses are immediately rewritten with `rewriter`.
;;
;; utree : unification-tree?
;; C : Clause?
;; rewriter : Clause? -> Clause?
;; L-resolvent-pruning? : boolean?
;; -> (listof Clause?)
(define (utree-resolve+unsafe-factors utree C
#:? [rewriter (λ (C) C)]
#:! L-resolvent-pruning?)
;; Used to prevent pruning by L-resolvent-discard.
;; This is used to mark the second literals in unsafe factors.
(define lit-marks (make-vector (Clause-n-literals C) #false))
(define (mark-literal! idx) (vector-set! lit-marks idx #true))
(define (literal-marked? idx) (vector-ref lit-marks idx))
(zip-loop ([(left lit right) (Clause-clause C)]
[resolvents+factors '()]
[lit-idx 0]
#:break (not resolvents+factors) ; shortcut
#:result (or resolvents+factors '()))
(define-add-Clause! C new-Clauses add-Clause! rewriter)
;; Resolutions
(find-unifiers utree
(lnot lit)
(λ (lf subst nlit lit2 rcl2)
(add-Clause! (rev-append left (rev-append right rcl2))
subst
'res
(list (utree-leaf-Clause lf)))))
;; Unsafe binary factors
;; Somewhat efficient implementation since the literals are sorted by predicate.arity.
(define pax (predicate.arity lit))
(zip-loop ([(left2 lit2 right2) right]
[lit2-idx (+ 1 lit-idx)]
#:break (not (equal? pax (predicate.arity lit2))))
(define subst (unify lit lit2))
(when subst
(mark-literal! lit2-idx) ; prevents pruning
(add-Clause! (rev-append left right) ; remove lit
subst
'fac
'()))
(+ 1 lit2-idx))
;; L-resolvent 'pruning'
;; See the principle of implication modulo resolution:
;; "A unifying principle for clause elimination in first-order logic", CADE 26.
;; which contains other techniques and short proofs of their soundness.
;; We return the empty set of resolution, meaning that the selected clause
;; can (must) be discarded, i.e., not added to the active set.
(cond [(and L-resolvent-pruning?
(empty? new-Clauses)
(not (literal-marked? lit-idx)))
(++n-L-resolvent-pruning)
(values #false (+ 1 lit-idx))]
[else
(values (rev-append new-Clauses resolvents+factors)
(+ 1 lit-idx))])))
;; Returns the first (in any order) Clause C2 such that
;; there is a literal of C2 that imperfectly matches a literal of C,
;; and (pred C C2).
;;
;; utree : unification-tree?
;; C2 : Clause?
;; pred : Clause? Clause? -> boolean?
(define (utree-find/any utree C2 pred)
(define tested (make-hasheq)) ; don't test the same C2 twice
(define cl2 (Clause-clause C2))
(let/ec return
(for ([lit (in-list cl2)])
(trie-find utree lit
(λ (nd)
(define val (trie-node-value nd))
(when (list? val)
(for ([lf (in-list val)])
(define C (utree-leaf-Clause lf))
(hash-ref! tested
C
(λ ()
(when (pred C C2)
(return C))
#true)))))))
#false))
;; Return all Clauses C that left-subunify on at least one literal and for which (pred C C2).
;;
;; utree : unification-tree?
;; C2 : Clause?
;; pred : Clause? Clause? -> boolean?
(define (utree-find/all utree C2 pred)
(define tested (make-hasheq)) ; don't test the same C2 twice
(define cl2 (Clause-clause C2))
(define res '())
(for ([lit (in-list cl2)])
(trie-find utree lit
(λ (nd)
(define val (trie-node-value nd))
(when (list? val)
(for ([lf (in-list val)])
(define C (utree-leaf-Clause lf))
(hash-ref! tested
C
(λ ()
(when (pred C C2)
(set! res (cons C res)))
#true)))))))
res)
;; Removes the Clause C from the utree.
;;
;; utree : unification-tree?
;; C : Clause?
(define (utree-remove-Clause! utree C)
(define cl (Clause-clause C))
(for ([lit (in-list cl)])
(trie-find utree lit
(λ (nd)
(define val (trie-node-value nd))
(when (list? val)
(set-trie-node-value! nd
(filter-not (λ (lf2) (eq? C (utree-leaf-Clause lf2)))
val)))))))
;; Finds the leaves for which C loosely left-unifies on some literal and remove those which clause C2
;; where (pred C C2).
;; Returns the set of Clauses that have been removed.
;;
;; utree : unification-tree?
;; C2 : Clause?
;; pred: Clause? Clause? -> boolean
(define (utree-inverse-find/remove! utree C pred)
; Since the same Clause may match multiple times,
; We use a hash to remember which clauses have already been tested (and if the result
; was #true or #false).
; Then remove all the leaves of each clause to remove.
(define tested (make-hasheq))
(define Clauses-to-remove '())
(define cl (Clause-clause C))
(for ([lit (in-list cl)])
(trie-inverse-find utree lit
(λ (nd)
(define val (trie-node-value nd))
(when (list? val)
(for ([lf (in-list (trie-node-value nd))])
(define C2 (utree-leaf-Clause lf))
(hash-ref! tested
C2
(λ ()
(cond [(pred C C2)
(cons! C2 Clauses-to-remove)
#true]
[else #false]))))))))
(for ([C2 (in-list Clauses-to-remove)])
(utree-remove-Clause! utree C2))
Clauses-to-remove)
================================================
FILE: satore/unification.rkt
================================================
#lang racket/base
;***************************************************************************************;
;**** Operations On Literals: Unification And Friends ****;
;***************************************************************************************;
;;; * A literal A unifies with a literal B iff there exists a substitution σ s.t. Aσ = Bσ.
;;; * A literal A left-unifies (= matches) with a literal B iff there exists a substitution
;;; σ s.t. Aσ = B.
;;; Note that left-unifies => unifies.
(require bazaar/cond-else
bazaar/list
bazaar/mutation
(except-in bazaar/order atom<=>)
define2
global
racket/dict
racket/list
racket/match
(submod racket/performance-hint begin-encourage-inline))
(provide (all-defined-out))
;===============;
;=== Globals ===;
;===============;
(define-global:category *atom-order* 'atom1
'(atom1 KBO1lex)
"Atom comparison function for rewrite rules.")
(define (get-atom<=> #:? [atom-order (*atom-order*)])
(case atom-order
[(KBO1lex) KBO1lex<=>]
[(atom1) atom1<=>]
[else (error "Unknown atom order: ~a" (*atom-order*))]))
;=================;
;=== Variables ===;
;=================;
;; The name of a variable is a number.
(struct Var (name)
#:prefab)
;; Comparisons between variables
(begin-encourage-inline
(define Var-name number<=>)
(define (Var=? v1 v2) (Var-name=? (Var-name v1) (Var-name v2)))
(define (Var v1 v2) (Var-name<=> (Var-name v1) (Var-name v2)))
; Ensures: (order=? (Var<=> v1 v2)) = (Vars=? v1 v2)
;:::::::::::::::::::::::::::::::::::;
;:: Basic operations on Variables ::;
;:::::::::::::::::::::::::::::::::::;
;; All symbols starting with a capitale letter are considered as variables.
;;
;; any/c -> boolean?
(define (symbol-variable? t)
(and (symbol? t)
(char<=? #\A (string-ref (symbol->string t) 0) #\Z)))
;; Returns a variable 'name' corresponding to the given symbol.
;; Currently accepts only symbols like X1, X2, … and A, B, C, …
;; The same symbol is always mapped to the same Var-name, globally.
;;
;; symbol? -> exact-nonnegative-integer?
(define (symbol->Var-name s)
(define str (symbol->string s))
(cond [(regexp-match #px"^X(\\d+)$" str)
=> (λ (m) (+ 26 (string->number (second m))))]
[(regexp-match #px"^[A-Z]$" str)
=> (λ (m) (- (char->integer (string-ref str 0))
(char->integer #\A)))]
[else
(error 'Varify "Unknown variable format: ~a" s)]))
;; Inverse operation of symbol->Var-name.
;; The same Var-name is always mapped to the same symbol, globally.
;;
;; exact-nonnegative-integer? -> symbol?
(define (Var-name->symbol n)
(cond [(symbol-variable? n) n]
[(number? n)
(if (< n 26)
(string->symbol (string (integer->char (+ (char->integer #\A) n))))
(string->symbol (format "X~a" (- n 26))))]
[else (error 'Var-name->symbol "Don't know what to do with ~a" n)]))
;; Returns a new atom like t where all symbol-variables have been turned into `Var?`s.
;; Notice: Does *not* ensure unicity of the variables across clauses.
;;
;; tree? -> atom?
(define (Varify t)
(cond [(pair? t)
; Works also in assocs
(cons (Varify (car t))
(Varify (cdr t)))]
[(symbol-variable? t)
(Var (symbol->Var-name t))]
[else t]))
;====================================;
;=== Substitutions data structure ===;
;====================================;
;; Basic substitution operations.
;; Simply put, a substitution is a `hasheqv`, where the keys are variables names,
;; and the values are terms.
(begin-encourage-inline
(define make-subst make-hasheqv)
(define subst? hash?)
(define in-subst in-hash)
(define subst-count hash-count)
(define subst-ref/name hash-ref) ; for when the name is retrieved from the subst
(define subst-set!/name hash-set!)
(define subst-copy hash-copy)
;; Modifies the substitution to bind `t` to `var`.
;; Returns the substitution to mimick the immutable update behaviour.
;;
;; subst? Var? term? -> subst?
(define (subst-set! subst V t)
(hash-set! subst (Var-name V) t)
subst)
;; Returns the binding for the variable `V` in `subst`, or `default` if it doesn't exist.
;;
;; susbt? Var? term? -> term?
(define (subst-ref subst V [default #false])
(hash-ref subst (Var-name V) default))
;; Returns the binding for the variable `V` in `susbt` if it exists,
;; otherwise sets it to `default` and returns `default`.
;;
;; subst? Var? term? -> term?
(define (subst-ref! subst V default)
(hash-ref! subst (Var-name V) default))
;; Updates the binding for the variable `V` with `update`
;; Returns the modified substitution
;;
;; subst : subst?
;; V : Var?
;; update : term? -> term?
;; default : term
(define (subst-update! subst V update default)
(hash-update! subst (Var-name V) update default)
subst)
;; Returns the substitution as an association list sorted by `Var-name list?
(define (subst->list s)
(sort (hash->list s) Var-name list?
(define (make-imsubst [pairs '()]) pairs)
;; Like subst-ref for immutable substitutions.
;;
;; imsubst? Var? term? -> term?
(define (imsubst-ref subst V default)
(define p (assoc (Var-name V) subst Var-name=?))
(if p (cdr p) default)))
;; like subst-set!, but does not modify the substitution and returns a new substitution.
;;
;; subst : imsubst?
;; V : var?
;; t : term?
(define (imsubst-set subst V t)
(define name (Var-name V))
(let loop ([s subst] [left '()])
(cond/else
[(empty? s)
(cons (cons name t) subst)]
#:else
(define p (car s))
#:cond
[(Var-name=? (car p) name)
(rev-append left (cons (cons name t) (cdr s)))]
#:else
(loop (cdr s) (cons p left)))))
;===============================;
;=== Operations on Variables ===;
;===============================;
;; Global index to ensure unicity of variable names.
(define fresh-idx 0)
;; Returns a fresh variable with a unique name.
;;
;; -> Var?
(define (new-Var)
(++ fresh-idx)
(Var fresh-idx))
;; Renames all variables with fresh names to avoid collisions.
;;
;; term? -> term?
(define (fresh t)
(define h (make-subst))
(let loop ([t t])
(cond [(pair? t)
(cons (loop (car t)) (loop (cdr t)))]
[(Var? t)
(subst-ref! h t new-Var)]
[else t])))
;; Variables names are mapped to a unique symbol, but the resulting Var-name is unpredictable,
;; and this mapping is guaranteed to be consistent only locally to the term t.
;; Used mostly to turn human-readable expressions into terms, without needing to worry about
;; the actual names of the variables.
;;
;; tree? -> term?
(define (symbol-variables->Vars t)
(define h (make-hasheq))
(let loop ([t t])
(cond [(pair? t)
(cons (loop (car t)) (loop (cdr t)))]
[(symbol-variable? t)
(hash-ref! h t new-Var)]
[else t])))
;; Variables are replaced with symbols by order of appearence. Mostly for ease of reading by humans.
;;
;; term? -> tree?
(define (Vars->symbols t)
(define h (make-subst))
(define idx -1)
(let loop ([t t])
(cond [(pair? t)
(cons (loop (car t)) (loop (cdr t)))]
[(Var? t)
(subst-ref! h t (λ () (++ idx) (Var-name->symbol idx)))]
[else t])))
;; Returns a subst of the number of occurrences of the variables *names* in the term `t`.
;;
;; term? -> subst?
(define (var-occs t)
(define h (make-subst))
(let loop ([t t])
(cond [(pair? t)
(loop (car t))
(loop (cdr t))]
[(Var? t)
(subst-update! h t add1 0)]))
h)
;; Returns the variable names of the term `t`.
;;
;; term? -> list?
(define (vars t)
(map car (subst->list (var-occs t))))
;; Returns the variables of the term `t`.
;;
;; term? -> (listof Var?)
(define (Vars t)
(map Var (vars t)))
;; Returns the set of variables *names* that appear in `t1` but not in `t2`.
;;
;; term? term? -> list?
(define (variables-minus t1 t2)
(define h2 (var-occs t2))
(for/list ([(v n) (in-hash (var-occs t1))]
#:unless (hash-has-key? h2 v))
v))
;; Returns the lexicographical index of each occurrence of the variable names of `t`,
;; in depth-first order.
;;
;; term? -> list?
(define (find-var-names t)
(define h (make-subst))
(let loop ([t t] [idx 0])
(cond [(pair? t)
(loop (cdr t) (loop (car t) idx))]
[(Var? t)
(subst-update! h t min idx)
(+ idx 1)]
[else idx]))
(map car (sort (subst->list h) < #:key cdr)))
;; Returns '< if each variable of t1 appears no more times in t1
;; than the same variable in t2,
;; and at least one variable appears strictly fewer times.
;; Returns '= if the occurrences are equal.
;; Returns #false otherwise.
;; This can be seen as a kind of Pareto dominance.
;; This is used for KBO in particular.
;; Note: (var-occs<=> t1 t2) == (var-occs<=> t2 t1)
;; Note: t1 and t2 may have variables in common if they are two subterms of the same clause.
;;
;; term? term? -> (or/c '< '> '= #false)
(define (var-occs<=> t1 t2)
(define h1 (var-occs t1)) ; assumes does not contain 0s
(define h2 (var-occs t2)) ; assumes does not contain 0s
(define n-common 0)
(define cmp
(for/fold ([cmp '=])
([(v1 n1) (in-subst h1)])
(define n2 (subst-ref/name h2 v1 0))
(cond
[(> n2 0)
(++ n-common)
(define c (number<=> n1 n2))
(cond [(eq? cmp '=) c]
[(eq? c '=) cmp]
[(eq? cmp c) c]
[else #false])] ; incomparable
[else cmp])))
(define n1 (subst-count h1))
(define n2 (subst-count h2))
(cond [(and (< n-common n1)
(< n-common n2))
#false]
[(< n-common n2)
(case cmp [(< =) '<] [else #false])]
[(< n-common n1)
(case cmp [(> =) '>] [else #false])]
[else cmp]))
;=====================;
;=== Boolean logic ===;
;=====================;
(begin-encourage-inline
;; Logical false
(define lfalse '$false)
;; any/c -> boolean
(define (lfalse? x) (eq? lfalse x))
;; lfalse must be the bottom element for the various atom orders.
;;
;; any/c any/c -> (or/c '< '> '= #false)
(define (lfalse<=> a b)
(define afalse? (lfalse? a))
(define bfalse? (lfalse? b))
(cond [(and afalse? bfalse?) '=]
[afalse? '<]
[bfalse? '>]
[else #false]))
(define ltrue '$true)
;; any/c -> boolean?
(define (ltrue? x) (eq? x ltrue))
;; Returns whether the literal `lit` has negative polarity.
;;
;; literal? -> boolean?
(define (lnot? lit)
(and (pair? lit)
(eq? 'not (car lit))))
;; Inverses the polarity of the literal.
;; NOTICE: Always use `lnot`, do not construct negated atoms yourself.
;;
;; literal? -> literal?
(define (lnot x)
(cond [(lnot? x) (cadr x)]
[(lfalse? x) ltrue]
[(ltrue? x) lfalse]
[else (list 'not x)]))
;; Compares the polarities of the two literals.
;; (polarity<=> 'a '(not a)) returns '<
;;
;; literal? literal? -> (or/c '< '> '= #false)
(define (polarity<=> lit1 lit2)
(boolean<=> (lnot? lit1) (lnot? lit2))))
;=================================;
;=== Literals, atoms, terms, … ===;
;=================================;
#|
literal = atom | (not atom)
atom = constant | (predicate term ...)
term = (funtion term ...) | variable | constant
predicate = symbol?
function = symbol?
constant = symbol?
variable = (Var number?)
For simplicity, we sometimes use 'term' to mean 'atom or term', or even
'literal, atom or term'.
|#
;; Returns the number of nodes in the tree representing the term `t` (or literal, atom).
;;
;; term? -> exact-nonnegative-integer?
(define (tree-size t)
(let loop ([t t] [s 0])
(cond [(Var? t) (+ s 1)]
[(pair? t)
(loop (cdr t) (loop (car t) s))]
[else (+ s 1)])))
;; The literals are depolarized first, because negation should not count.
;;
;; literal? -> exact-nonnegative-integer?
(define (literal-size lit)
(tree-size (depolarize lit)))
;; In particular, it should be as easy to prove A | B as ~A | ~B, otherwise finding equivalences
;; can be more difficult.
;;
;; clause? -> exact-nonnegative-integer?
(define (clause-size cl)
(for/sum ([lit (in-list cl)])
(literal-size lit)))
;; Comparison of atoms (or literals) for atom rewriting.
;; Returns < if for every substitution α, (atom1<=> t1α t2α) returns <.
;; (Can this be calculated given a base atom1<=> ?)
;; - Rk: variables of t2 that don't appear in t1 are not a problem since they are not instanciated
;; in t2α.
;; - Equality is loose and is based only on *some* properties of the atoms.
;; - This is a good first comparator, but not good enough (e.g., does not associativity)
;; Notice: (order=? (atom<=> t1 t2)) does NOT necessarily mean that t1 and t2 are syntactically equal.
;;
;; literal? literal? -> (or/c '< '> '= #false)
(define (atom1<=> lit1 lit2)
(let ([t1 (depolarize lit1)]
[t2 (depolarize lit2)])
(cond/else
[(lfalse<=> t1 t2)] ; continue if neither is lfalse
#:else
(define size (number<=> (tree-size t1) (tree-size t2)))
(define vs (var-occs<=> t1 t2))
#:cond
[(and (order=? vs) (order=? size)) (or (term-lex2<=> t1 t2) '=)] ; for commutativity
[(and (order≤? vs) (order≤? size)) '<] ; one is necessarily '<
[(and (order≥? vs) (order≥? size)) '>]
#:else #false)))
;; For KBO.
;; fun-weight is also for constants, hence it's more like symbol-weight
;; (but the name 'function' is commonly used for constants too).
;;
;; t : term?
;; var-weight : number?
;; fun-weight : symbol? -> number?
;; -> number?
(define (term-weight t #:? [var-weight 1] #:? [fun-weight (λ (f) 1)])
(let loop ([t t])
(cond [(Var? t) var-weight]
[(symbol? t) (fun-weight t)]
[(list? t) (for/sum ([s (in-list t)]) (loop s))]
[else (error "Unknown term ~a" t)])))
;; Knuth-Bendix Ordering, naive version.
;; Can be used for atom rewriting.
;; To do: Implement a faster version.
;; See "Things to know when implementing KB", Löchner, 2006.
;; var-weight MUST be ≤ to all fun-weights of constants.
;; Simple version for clarity and proximity to the specifications.
;;
;; var-weight : number?
;; fun-weight : symbol? -> number?
;; fun<=> : symbol? symbol? -> (or/c '< '> '= #false)
;; -> (term? term? -> (or/c '< '> '= #false))
(define (make-KBO<=> #:? var-weight #:? fun-weight #:? [fun<=> symbol<=>])
(define (weight t)
(term-weight t #:var-weight var-weight #:fun-weight fun-weight))
(define (KBO<=> t1 t2)
(cond
[(and (Var? t1) (Var? t2)) (and (Var=? t1 t2) '=)] ; not specified, but surely right?
[(Var? t1) (and (occurs? t1 t2) '<)]
[(Var? t2) (and (occurs? t2 t1) '>)]
[else ; both are fun apps or constants
(define v (var-occs<=> t1 t2))
(and v
(let ([t-cmp (sub-KBO<=> (if (list? t1) t1 (list t1)) ; turn constants into fun apps.
(if (list? t2) t2 (list t2)))])
(case v
[(<) (and (order<=? t-cmp) t-cmp)]
[(>) (and (order>=? t-cmp) t-cmp)]
[(=) t-cmp])))]))
;; t1 and t2 MUST be lists.
(define (sub-KBO<=> t1 t2)
(chain-comparisons
(number<=> (weight t1) (weight t2))
(fun<=> (first t1) (first t2))
;; Chain on subterms.
(<=>map KBO<=> (rest t1) (rest t2))))
(λ (t1 t2)
(let ([t1 (depolarize t1)]
[t2 (depolarize t2)])
(or (lfalse<=> t1 t2)
(KBO<=> t1 t2)))))
;; Default KBO comparator.
;;
;; term? term? -> (or/c '< '> '= #false)
(define KBO1lex<=> (make-KBO<=>))
;; Returns the atom of the literal.
;;
;; literal? -> atom?
(define (depolarize lit)
(match lit
[`(not ,x) x]
[else lit]))
;; Returns the number of arguments of the predicate of the literal lit, after depolarizing it.
;;
;; literal? -> exact-nonnegative-integer?
(define (literal-arity lit)
(let ([lit (depolarize lit)])
(if (list? lit)
(length lit)
0)))
;; Returns the name of the predicate (or constant) of the literal.
;;
;; literal? -> symbol?
(define (literal-symbol lit)
(match lit
[`(not (,p . ,r)) p]
[`(not ,a) a]
[`(,p . ,r) p]
[else lit]))
;; Lexicographical comparison.
;; Used in literal<=> to sort literals within a clause. NOT used for rewriting.
;; Guarantees: (order=? (term-lex<=> t1 t2)) = (term==? t1 t2) (but maybe a slightly slower?)
;;
;; term? term? -> (or/c '< '> '= #false)
(define (term-lex<=> t1 t2)
(cond [(eq? t1 t2) '=] ; takes care of '()
[(and (pair? t1) (pair? t2))
(chain-comparisons (term-lex<=> (car t1) (car t2))
(term-lex<=> (cdr t1) (cdr t2)))]
[(pair? t1) '>]
[(pair? t2) '<]
[(and (Var? t1) (Var? t2))
(Var<=> t1 t2)]
[(Var? t1) '<]
[(Var? t2) '>]
[(and (symbol? t1) (symbol? t2))
(symbol<=> t1 t2)]
[else
(error 'term-lex<=> "Unknown term kind for: ~a, ~a" t1 t2)]))
;; Comparator for terms used in atom1<=> for atom rewriting.
;; Can't compare vars with symbols, or vars with vars. Can only compare ground symbols:
;; A binary rule can't be oriented with variables
;;
;; term? term? -> (or/c '< '> '= #false)
(define (term-lex2<=> t1 t2)
(cond [(eq? t1 t2) '=] ; takes care of '()
[(and (Var? t1) (Var? t2) (Var=? t1 t2)) '=]
[(or (Var? t1) (Var? t2)) #false] ; incomparable, cannot be oriented
[(and (pair? t1) (pair? t2))
(chain-comparisons (term-lex2<=> (car t1) (car t2))
(term-lex2<=> (cdr t1) (cdr t2)))]
[(pair? t1) '>]
[(pair? t2) '<]
[(and (symbol? t1) (symbol? t2))
(symbol<=> t1 t2)]
[else
(error 'term-lex2<=> "Unknown term kind for: ~a, ~a" t1 t2)]))
;; Depth-first lexicographical order (df-lex)
;; Used for literal ordering in clauses. Not used for atom rewriting.
;; Guarantees: (order=? (literal<=> lit1 lit2)) = (literal==? lit1 lit2). (or it's a bug)
;;
;; literal? literal? -> (or/c '< '> '= #false)
(define (literal<=> lit1 lit2)
(chain-comparisons
(polarity<=> lit1 lit2)
(symbol<=> (literal-symbol lit1) (literal-symbol lit2)) ; A literal cannot be a variable
(cond [(and (list? lit1) (list? lit2))
; this also checks arity
(<=>map term-lex<=> (rest lit1) (rest lit2))]
[(list? lit2) '<]
[(list? lit1) '>]
[else '=])))
;; Used to sort literals in a clause.
;;
;; literal? literal? -> boolean?
(define (literal lit1 lit2)))
;; Syntactic comparison of terms and literals.
;; This works because variables are transparent (prefab), hence equal? traverses the Var struct too.
;; We use `==` to denote syntactic equivalence.
;;
;; term? term? -> boolean?
(define term==? equal?)
;; literal? literal? -> boolean?
(define literal==? equal?)
;==================================;
;=== Substitution / Unification ===;
;==================================;
;; Notice: Setting this to #true forces the mgu substitutions to ensure
;; dom(σ)\cap vran(σ) = ø
;; but can be exponentially slow in some rare cases.
;; Also, it's not necessary.
(define reduce-mgu? #false)
;; Returns a term where the substitution s is applied to the term t.
;; The substitution `s` may not be 'reduced' in the sense that variables
;; of the domain may appear in the range.
;;
;; term? subst? -> term?
(define (substitute/slow t s)
(define t-orig t)
(let loop ([t t])
(cond
[(null? t) t]
[(pair? t)
(cons (loop (car t))
(loop (cdr t)))]
[(and (Var? t)
(subst-ref s t #false))
; Recur into the substitution.
=> loop]
[else t])))
;; A simple box to signify that there is no need to attempt to substitute
;; inside `term` as this has already been done.
(struct already-substed (term) #:prefab)
;; Like `substitute/slow` but avoids unnecessary work.
;; Such substitutions are performed 'on-demand', if needed.
;; Once a substitution has been applied recursively to a rhs, the resulting
;; term is marked with `already-substed` to avoid attempting it again.
;;
;; Notice: This function can only be used if `s` is *not* going to be extended,
;; otherwise it may not produce the correct result.
;;
;; term? subst? -> term?
(define (substitute t s)
(define t-orig t)
(let loop ([t t])
(cond
[(null? t) t]
[(pair? t)
(cons (loop (car t))
(loop (cdr t)))]
[(and (Var? t)
(subst-ref s t #false))
; Recur into the substitution.
; This avoids recurring many times inside the same substitution.
=>
(λ (rhs)
(cond [(already-substed? rhs)
; No need to loop inside the new term.
(already-substed-term rhs)]
[else
(define new-rhs (loop rhs))
(subst-set! s t (already-substed new-rhs))
new-rhs]))]
[else t])))
;; Checks whether the variable `V` occurs un `t`.
;;
;; Var? term? -> boolean?
(define (occurs? V t)
(cond [(Var? t) (Var=? V t)]
[(pair? t)
(or (occurs? V (car t))
(occurs? V (cdr t)))]
[else #false]))
;; Returns #false if `V` occurs in `t2`, otherwise binds `t2` to `V` in `subst` and returns `subst`.
;;
;; Var? term? subst? -> (or/c #false subst?)
(define (occurs?/extend V t2 subst)
(define t2c (substitute/slow t2 subst))
(if (occurs? V t2c)
#false
(begin
(subst-set! subst V t2c)
subst)))
;; Returns one most general unifier α such that t1α = t2α.
;;
;; term? term? subst? -> subst?
(define (unify t1 t2 [subst (make-subst)])
(define success?
(let loop ([t1 t1] [t2 t2])
(cond/else
[(eq? t1 t2) ; takes care of both null?
subst]
[(and (pair? t1) (pair? t2))
(and (loop (car t1) (car t2))
(loop (cdr t1) (cdr t2)))]
#:else
(define v1? (Var? t1))
(define v2? (Var? t2))
#:cond
[(and (not v1?) (not v2?)) ; since they are not `eq?`
#false]
[(and v1? v2? (Var=? t1 t2)) ; since at least one is a Var
; Same variable, no need to substitute, and should not fail occurs?/extend.
subst]
#:else
(define t1b (and v1? (subst-ref subst t1 #false)))
(define t2b (and v2? (subst-ref subst t2 #false)))
#:cond
[(or t1b t2b)
; rec
(loop (or t1b t1) (or t2b t2))]
[v1? ; t2 may also be a variable
(occurs?/extend t1 t2 subst)]
[v2? ; v2? but not v1?
(occurs?/extend t2 t1 subst)]
#:else (void))))
; Make sure we return a most general unifier
; NOTICE: This can take a lot of time (see strest tests), but may prevent issues too.
(and success?
(if reduce-mgu?
(let ([s2 (make-subst)])
(for ([(k v) (in-subst subst)])
(subst-set!/name s2 k (substitute v subst)))
s2)
subst)))
;; Creates a procedure that returns the substitution α such that t1α = t2, of #false if none exists.
;; t2 is assumed to not contain any variable of t1.
;; Also known as matching
;; - The optional argument is useful to chain left-unify over several literals, say.
;; - Works with both mutable and immutable substitutions.
;; NOTICE:
;; The found substitution must be specializing, that is C2σ = C2 (and C1σ = C2),
;; otherwise safe factoring can fail, in particular.
;; Hence we must ensure that vars(C2) ∩ dom(σ) = ø.
(define-syntax-rule
(define-left-subst+unify left-substitute left-unify make-subst subst-ref subst-set)
(begin
;; Returns a term like `t` where the substitution `s` has been applied.
;;
;; term? subst? -> term?
(define (left-substitute t s)
(let loop ([t t])
(cond
[(null? t) t]
[(pair? t)
(cons (loop (car t))
(loop (cdr t)))]
[(and (Var? t)
(subst-ref s t #false))]
[else t])))
;; Returns a substitution α such that t1α = t2, if it exists, #false otherwise.
;;
;; term? term? subst? -> (or/c #false subst?)
(define (left-unify t1 t2 [subst (make-subst)])
(cond/else
[(eq? t1 t2) ; takes care of both null?
subst]
[(and (pair? t1) (pair? t2))
(define new-subst (left-unify (car t1) (car t2) subst))
(and new-subst
(left-unify (cdr t1) (cdr t2) new-subst))]
[(term==? t1 t2) subst] ; To do: This is costly
[(not (Var? t1)) #false]
#:else
(define t1b (subst-ref subst t1 #false))
#:cond
[t1b (and (term==? t1b t2) subst)]
; This ensures that vars(C2) ∩ dom(σ) = ø:
; if var, t1 must not occur in rhs of subst
; and any lhs of subst and t1 must not occur in t2
[(or (occurs? t1 t2)
(for/or ([(var-name val) (in-dict subst)])
(or (occurs? t1 val)
(occurs? (Var var-name) t2))))
#false]
#:else
(subst-set subst t1 t2)))))
;; Mutable substitutions
(define-left-subst+unify left-substitute left-unify make-subst subst-ref subst-set!)
;; Immutable substitutions
(define-left-subst+unify left-substitute/assoc left-unify/assoc make-imsubst imsubst-ref imsubst-set)
;; Returns #true if `pat` left-unifies with any subterm of `t`.
;;
;; term? term? -> (or/c #false term?)
(define (left-unify-anywhere pat t)
(let loop ([t t])
(cond [(left-unify pat t)]
[(list? t) (ormap loop t)]
[else #false])))
;; Returns #true if `(filt tt)` is true for any subterm `tt` of `t`.
;;
;; (term? -> boolean?) term? -> boolean?
(define (match-anywhere filt t)
(let loop ([t t])
(cond [(filt t)]
[(list? t) (ormap loop (rest t))]
[else #false])))
================================================
FILE: scratchgan/README.md
================================================
# ScratchGAN
This is the example code for the following NeurIPS 2019 paper. If you use the
code here please cite this paper:
@article{DBLP:journals/corr/abs-1905-09922,
author = {Cyprien de Masson d'Autume and
Mihaela Rosca and
Jack W. Rae and
Shakir Mohamed},
title = {Training language GANs from Scratch},
journal = {CoRR},
volume = {abs/1905.09922},
year = {2019},
url = {http://arxiv.org/abs/1905.09922},
archivePrefix = {arXiv},
eprint = {1905.09922},
timestamp = {Wed, 29 May 2019 11:27:50 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1905-09922},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
## Contents
The code contains:
* `generators.py`: implementation of the generator.
* `discriminator_nets.py`: implementation of the discriminator.
* `eval_metrics.py`: implementation of the FED metric.
* `losses.py`: implementation of the RL loss for the generator.
* `reader.py`: data reader / tokenizer.
* `experiment.py`: main training script.
The data contains:
* `{train,valid,test}.json`: the EMNLP2017 News dataset.
* `glove_emnlp2017.txt`: the relevant subset of GloVe embeddings.
## Running
./scratchgan/run.sh
================================================
FILE: scratchgan/__init__.py
================================================
# Copyright 2019 DeepMind Technologies Limited and Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
================================================
FILE: scratchgan/discriminator_nets.py
================================================
# Copyright 2019 DeepMind Technologies Limited and Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Discriminator networks for text data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sonnet as snt
import tensorflow.compat.v1 as tf
from scratchgan import utils
class LSTMEmbedDiscNet(snt.AbstractModule):
"""An LSTM discriminator that operates on word indexes."""
def __init__(self,
feature_sizes,
vocab_size,
use_layer_norm,
trainable_embedding_size,
dropout,
pad_token,
embedding_source=None,
vocab_file=None,
name='LSTMEmbedDiscNet'):
super(LSTMEmbedDiscNet, self).__init__(name=name)
self._feature_sizes = feature_sizes
self._vocab_size = vocab_size
self._use_layer_norm = use_layer_norm
self._trainable_embedding_size = trainable_embedding_size
self._embedding_source = embedding_source
self._vocab_file = vocab_file
self._dropout = dropout
self._pad_token = pad_token
if self._embedding_source:
assert vocab_file
def _build(self, sequence, sequence_length, is_training=True):
"""Connect to the graph.
Args:
sequence: A [batch_size, max_sequence_length] tensor of int. For example
the indices of words as sampled by the generator.
sequence_length: A [batch_size] tensor of int. Length of the sequence.
is_training: Boolean, False to disable dropout.
Returns:
A [batch_size, max_sequence_length, feature_size] tensor of floats. For
each sequence in the batch, the features should (hopefully) allow to
distinguish if the value at each timestep is real or generated.
"""
batch_size, max_sequence_length = sequence.shape.as_list()
keep_prob = (1.0 - self._dropout) if is_training else 1.0
if self._embedding_source:
all_embeddings = utils.make_partially_trainable_embeddings(
self._vocab_file, self._embedding_source, self._vocab_size,
self._trainable_embedding_size)
else:
all_embeddings = tf.get_variable(
'trainable_embedding',
shape=[self._vocab_size, self._trainable_embedding_size],
trainable=True)
_, self._embedding_size = all_embeddings.shape.as_list()
input_embeddings = tf.nn.dropout(all_embeddings, keep_prob=keep_prob)
embeddings = tf.nn.embedding_lookup(input_embeddings, sequence)
embeddings.shape.assert_is_compatible_with(
[batch_size, max_sequence_length, self._embedding_size])
position_dim = 8
embeddings_pos = utils.append_position_signal(embeddings, position_dim)
embeddings_pos = tf.reshape(
embeddings_pos,
[batch_size * max_sequence_length, self._embedding_size + position_dim])
lstm_inputs = snt.Linear(self._feature_sizes[0])(embeddings_pos)
lstm_inputs = tf.reshape(
lstm_inputs, [batch_size, max_sequence_length, self._feature_sizes[0]])
lstm_inputs.shape.assert_is_compatible_with(
[batch_size, max_sequence_length, self._feature_sizes[0]])
encoder_cells = []
for feature_size in self._feature_sizes:
encoder_cells += [
snt.LSTM(feature_size, use_layer_norm=self._use_layer_norm)
]
encoder_cell = snt.DeepRNN(encoder_cells)
initial_state = encoder_cell.initial_state(batch_size)
hidden_states, _ = tf.nn.dynamic_rnn(
cell=encoder_cell,
inputs=lstm_inputs,
sequence_length=sequence_length,
initial_state=initial_state,
swap_memory=True)
hidden_states.shape.assert_is_compatible_with(
[batch_size, max_sequence_length,
sum(self._feature_sizes)])
logits = snt.BatchApply(snt.Linear(1))(hidden_states)
logits.shape.assert_is_compatible_with([batch_size, max_sequence_length, 1])
logits_flat = tf.reshape(logits, [batch_size, max_sequence_length])
# Mask past first PAD symbol
#
# Note that we still rely on tf.nn.bidirectional_dynamic_rnn taking
# into account the sequence_length properly, because otherwise
# the logits at a given timestep will depend on the inputs for all other
# timesteps, including the ones that should be masked.
mask = utils.get_mask_past_symbol(sequence, self._pad_token)
masked_logits_flat = logits_flat * tf.cast(mask, tf.float32)
return masked_logits_flat
================================================
FILE: scratchgan/eval_metrics.py
================================================
# Copyright 2019 DeepMind Technologies Limited and Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Evaluation metrics."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tensorflow_gan as tfgan
import tensorflow_hub as hub
def fid(generated_sentences, real_sentences):
"""Compute FID rn sentences using pretrained universal sentence encoder.
Args:
generated_sentences: list of N strings.
real_sentences: list of N strings.
Returns:
Frechet distance between activations.
"""
embed = hub.Module("https://tfhub.dev/google/universal-sentence-encoder/2")
real_embed = embed(real_sentences)
generated_embed = embed(generated_sentences)
distance = tfgan.eval.frechet_classifier_distance_from_activations(
real_embed, generated_embed)
# Restrict the thread pool size to prevent excessive CPU usage.
config = tf.ConfigProto()
config.intra_op_parallelism_threads = 16
config.inter_op_parallelism_threads = 16
with tf.Session(config=config) as session:
session.run(tf.global_variables_initializer())
session.run(tf.tables_initializer())
distance_np = session.run(distance)
return distance_np
================================================
FILE: scratchgan/experiment.py
================================================
# Copyright 2019 DeepMind Technologies Limited and Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Training script for ScratchGAN."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from absl import app
from absl import flags
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1.io import gfile
from scratchgan import discriminator_nets
from scratchgan import eval_metrics
from scratchgan import generators
from scratchgan import losses
from scratchgan import reader
from scratchgan import utils
flags.DEFINE_string("dataset", "emnlp2017", "Dataset.")
flags.DEFINE_integer("batch_size", 512, "Batch size")
flags.DEFINE_string("gen_type", "lstm", "Generator type.")
flags.DEFINE_string("disc_type", "lstm", "Discriminator type.")
flags.DEFINE_string("disc_loss_type", "ce", "Loss type.")
flags.DEFINE_integer("gen_feature_size", 512, "Generator feature size.")
flags.DEFINE_integer("disc_feature_size", 512, "Discriminator feature size.")
flags.DEFINE_integer("num_layers_gen", 2, "Number of generator layers.")
flags.DEFINE_integer("num_layers_disc", 1, "Number of discriminator layers.")
flags.DEFINE_bool("layer_norm_gen", False, "Layer norm generator.")
flags.DEFINE_bool("layer_norm_disc", True, "Layer norm discriminator.")
flags.DEFINE_float("gen_input_dropout", 0.0, "Input dropout generator.")
flags.DEFINE_float("gen_output_dropout", 0.0, "Input dropout discriminator.")
flags.DEFINE_float("l2_gen", 0.0, "L2 regularization generator.")
flags.DEFINE_float("l2_disc", 1e-6, "L2 regularization discriminator.")
flags.DEFINE_float("disc_dropout", 0.1, "Dropout discriminator")
flags.DEFINE_integer("trainable_embedding_size", 64,
"Size of trainable embedding.")
flags.DEFINE_bool("use_pretrained_embedding", True, "Use pretrained embedding.")
flags.DEFINE_integer("num_steps", int(200 * 1000), "Number of training steps.")
flags.DEFINE_integer("num_disc_updates", 1, "Number of discriminator updates.")
flags.DEFINE_integer("num_gen_updates", 1, "Number of generator updates.")
flags.DEFINE_string("data_dir", "/tmp/emnlp2017", "Directory where data is.")
flags.DEFINE_float("gen_lr", 9.59e-5, "Learning rate generator.")
flags.DEFINE_float("disc_lr", 9.38e-3, "Learning rate discriminator.")
flags.DEFINE_float("gen_beta1", 0.5, "Beta1 for generator.")
flags.DEFINE_float("disc_beta1", 0.5, "Beta1 for discriminator.")
flags.DEFINE_float("gamma", 0.23, "Discount factor.")
flags.DEFINE_float("baseline_decay", 0.08, "Baseline decay rate.")
flags.DEFINE_string("mode", "train", "train or evaluate_pair.")
flags.DEFINE_string("checkpoint_dir", "/tmp/emnlp2017/checkpoints/",
"Directory for checkpoints.")
flags.DEFINE_integer("export_every", 1000, "Frequency of checkpoint exports.")
flags.DEFINE_integer("num_examples_for_eval", int(1e4),
"Number of examples for evaluation")
EVALUATOR_SLEEP_PERIOD = 60 # Seconds evaluator sleeps if nothing to do.
def main(_):
config = flags.FLAGS
gfile.makedirs(config.checkpoint_dir)
if config.mode == "train":
train(config)
elif config.mode == "evaluate_pair":
while True:
checkpoint_path = utils.maybe_pick_models_to_evaluate(
checkpoint_dir=config.checkpoint_dir)
if checkpoint_path:
evaluate_pair(
config=config,
batch_size=config.batch_size,
checkpoint_path=checkpoint_path,
data_dir=config.data_dir,
dataset=config.dataset,
num_examples_for_eval=config.num_examples_for_eval)
else:
logging.info("No models to evaluate found, sleeping for %d seconds",
EVALUATOR_SLEEP_PERIOD)
time.sleep(EVALUATOR_SLEEP_PERIOD)
else:
raise Exception(
"Unexpected mode %s, supported modes are \"train\" or \"evaluate_pair\""
% (config.mode))
def train(config):
"""Train."""
logging.info("Training.")
tf.reset_default_graph()
np.set_printoptions(precision=4)
# Get data.
raw_data = reader.get_raw_data(
data_path=config.data_dir, dataset=config.dataset)
train_data, valid_data, word_to_id = raw_data
id_to_word = {v: k for k, v in word_to_id.items()}
vocab_size = len(word_to_id)
max_length = reader.MAX_TOKENS_SEQUENCE[config.dataset]
logging.info("Vocabulary size: %d", vocab_size)
iterator = reader.iterator(raw_data=train_data, batch_size=config.batch_size)
iterator_valid = reader.iterator(
raw_data=valid_data, batch_size=config.batch_size)
real_sequence = tf.placeholder(
dtype=tf.int32,
shape=[config.batch_size, max_length],
name="real_sequence")
real_sequence_length = tf.placeholder(
dtype=tf.int32, shape=[config.batch_size], name="real_sequence_length")
first_batch_np = next(iterator)
valid_batch_np = next(iterator_valid)
test_real_batch = {k: tf.constant(v) for k, v in first_batch_np.items()}
test_fake_batch = {
"sequence":
tf.constant(
np.random.choice(
vocab_size, size=[config.batch_size,
max_length]).astype(np.int32)),
"sequence_length":
tf.constant(
np.random.choice(max_length,
size=[config.batch_size]).astype(np.int32)),
}
valid_batch = {k: tf.constant(v) for k, v in valid_batch_np.items()}
# Create generator.
if config.use_pretrained_embedding:
embedding_source = utils.get_embedding_path(config.data_dir, config.dataset)
vocab_file = "/tmp/vocab.txt"
with gfile.GFile(vocab_file, "w") as f:
for i in range(len(id_to_word)):
f.write(id_to_word[i] + "\n")
logging.info("Temporary vocab file: %s", vocab_file)
else:
embedding_source = None
vocab_file = None
gen = generators.LSTMGen(
vocab_size=vocab_size,
feature_sizes=[config.gen_feature_size] * config.num_layers_gen,
max_sequence_length=reader.MAX_TOKENS_SEQUENCE[config.dataset],
batch_size=config.batch_size,
use_layer_norm=config.layer_norm_gen,
trainable_embedding_size=config.trainable_embedding_size,
input_dropout=config.gen_input_dropout,
output_dropout=config.gen_output_dropout,
pad_token=reader.PAD_INT,
embedding_source=embedding_source,
vocab_file=vocab_file,
)
gen_outputs = gen()
# Create discriminator.
disc = discriminator_nets.LSTMEmbedDiscNet(
vocab_size=vocab_size,
feature_sizes=[config.disc_feature_size] * config.num_layers_disc,
trainable_embedding_size=config.trainable_embedding_size,
embedding_source=embedding_source,
use_layer_norm=config.layer_norm_disc,
pad_token=reader.PAD_INT,
vocab_file=vocab_file,
dropout=config.disc_dropout,
)
disc_logits_real = disc(
sequence=real_sequence, sequence_length=real_sequence_length)
disc_logits_fake = disc(
sequence=gen_outputs["sequence"],
sequence_length=gen_outputs["sequence_length"])
# Loss of the discriminator.
if config.disc_loss_type == "ce":
targets_real = tf.ones(
[config.batch_size, reader.MAX_TOKENS_SEQUENCE[config.dataset]])
targets_fake = tf.zeros(
[config.batch_size, reader.MAX_TOKENS_SEQUENCE[config.dataset]])
loss_real = losses.sequential_cross_entropy_loss(disc_logits_real,
targets_real)
loss_fake = losses.sequential_cross_entropy_loss(disc_logits_fake,
targets_fake)
disc_loss = 0.5 * loss_real + 0.5 * loss_fake
# Loss of the generator.
gen_loss, cumulative_rewards, baseline = losses.reinforce_loss(
disc_logits=disc_logits_fake,
gen_logprobs=gen_outputs["logprobs"],
gamma=config.gamma,
decay=config.baseline_decay)
# Optimizers
disc_optimizer = tf.train.AdamOptimizer(
learning_rate=config.disc_lr, beta1=config.disc_beta1)
gen_optimizer = tf.train.AdamOptimizer(
learning_rate=config.gen_lr, beta1=config.gen_beta1)
# Get losses and variables.
disc_vars = disc.get_all_variables()
gen_vars = gen.get_all_variables()
l2_disc = tf.reduce_sum(tf.add_n([tf.nn.l2_loss(v) for v in disc_vars]))
l2_gen = tf.reduce_sum(tf.add_n([tf.nn.l2_loss(v) for v in gen_vars]))
scalar_disc_loss = tf.reduce_mean(disc_loss) + config.l2_disc * l2_disc
scalar_gen_loss = tf.reduce_mean(gen_loss) + config.l2_gen * l2_gen
# Update ops.
global_step = tf.train.get_or_create_global_step()
disc_update = disc_optimizer.minimize(
scalar_disc_loss, var_list=disc_vars, global_step=global_step)
gen_update = gen_optimizer.minimize(
scalar_gen_loss, var_list=gen_vars, global_step=global_step)
# Saver.
saver = tf.train.Saver()
# Metrics
test_disc_logits_real = disc(**test_real_batch)
test_disc_logits_fake = disc(**test_fake_batch)
valid_disc_logits = disc(**valid_batch)
disc_predictions_real = tf.nn.sigmoid(disc_logits_real)
disc_predictions_fake = tf.nn.sigmoid(disc_logits_fake)
valid_disc_predictions = tf.reduce_mean(
tf.nn.sigmoid(valid_disc_logits), axis=0)
test_disc_predictions_real = tf.reduce_mean(
tf.nn.sigmoid(test_disc_logits_real), axis=0)
test_disc_predictions_fake = tf.reduce_mean(
tf.nn.sigmoid(test_disc_logits_fake), axis=0)
# Only log results for the first element of the batch.
metrics = {
"scalar_gen_loss": scalar_gen_loss,
"scalar_disc_loss": scalar_disc_loss,
"disc_predictions_real": tf.reduce_mean(disc_predictions_real),
"disc_predictions_fake": tf.reduce_mean(disc_predictions_fake),
"test_disc_predictions_real": tf.reduce_mean(test_disc_predictions_real),
"test_disc_predictions_fake": tf.reduce_mean(test_disc_predictions_fake),
"valid_disc_predictions": tf.reduce_mean(valid_disc_predictions),
"cumulative_rewards": tf.reduce_mean(cumulative_rewards),
"baseline": tf.reduce_mean(baseline),
}
# Training.
logging.info("Starting training")
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
latest_ckpt = tf.train.latest_checkpoint(config.checkpoint_dir)
if latest_ckpt:
saver.restore(sess, latest_ckpt)
for step in range(config.num_steps):
real_data_np = next(iterator)
train_feed = {
real_sequence: real_data_np["sequence"],
real_sequence_length: real_data_np["sequence_length"],
}
# Update generator and discriminator.
for _ in range(config.num_disc_updates):
sess.run(disc_update, feed_dict=train_feed)
for _ in range(config.num_gen_updates):
sess.run(gen_update, feed_dict=train_feed)
# Reporting
if step % config.export_every == 0:
gen_sequence_np, metrics_np = sess.run(
[gen_outputs["sequence"], metrics], feed_dict=train_feed)
metrics_np["gen_sentence"] = utils.sequence_to_sentence(
gen_sequence_np[0, :], id_to_word)
saver.save(
sess,
save_path=config.checkpoint_dir + "scratchgan",
global_step=global_step)
metrics_np["model_path"] = tf.train.latest_checkpoint(
config.checkpoint_dir)
logging.info(metrics_np)
# After training, export models.
saver.save(
sess,
save_path=config.checkpoint_dir + "scratchgan",
global_step=global_step)
logging.info("Saved final model at %s.",
tf.train.latest_checkpoint(config.checkpoint_dir))
def evaluate_pair(config, batch_size, checkpoint_path, data_dir, dataset,
num_examples_for_eval):
"""Evaluates a pair generator discriminator.
This function loads a discriminator from disk, a generator, and evaluates the
discriminator against the generator.
It returns the mean probability of the discriminator against several batches,
and the FID of the generator against the validation data.
It also writes evaluation samples to disk.
Args:
config: dict, the config file.
batch_size: int, size of the batch.
checkpoint_path: string, full path to the TF checkpoint on disk.
data_dir: string, path to a directory containing the dataset.
dataset: string, "emnlp2017", to select the right dataset.
num_examples_for_eval: int, number of examples for evaluation.
"""
tf.reset_default_graph()
logging.info("Evaluating checkpoint %s.", checkpoint_path)
# Build graph.
train_data, valid_data, word_to_id = reader.get_raw_data(
data_dir, dataset=dataset)
id_to_word = {v: k for k, v in word_to_id.items()}
vocab_size = len(word_to_id)
train_iterator = reader.iterator(raw_data=train_data, batch_size=batch_size)
valid_iterator = reader.iterator(raw_data=valid_data, batch_size=batch_size)
train_sequence = tf.placeholder(
dtype=tf.int32,
shape=[batch_size, reader.MAX_TOKENS_SEQUENCE[dataset]],
name="train_sequence")
train_sequence_length = tf.placeholder(
dtype=tf.int32, shape=[batch_size], name="train_sequence_length")
valid_sequence = tf.placeholder(
dtype=tf.int32,
shape=[batch_size, reader.MAX_TOKENS_SEQUENCE[dataset]],
name="valid_sequence")
valid_sequence_length = tf.placeholder(
dtype=tf.int32, shape=[batch_size], name="valid_sequence_length")
disc_inputs_train = {
"sequence": train_sequence,
"sequence_length": train_sequence_length,
}
disc_inputs_valid = {
"sequence": valid_sequence,
"sequence_length": valid_sequence_length,
}
if config.use_pretrained_embedding:
embedding_source = utils.get_embedding_path(config.data_dir, config.dataset)
vocab_file = "/tmp/vocab.txt"
with gfile.GFile(vocab_file, "w") as f:
for i in range(len(id_to_word)):
f.write(id_to_word[i] + "\n")
logging.info("Temporary vocab file: %s", vocab_file)
else:
embedding_source = None
vocab_file = None
gen = generators.LSTMGen(
vocab_size=vocab_size,
feature_sizes=[config.gen_feature_size] * config.num_layers_gen,
max_sequence_length=reader.MAX_TOKENS_SEQUENCE[config.dataset],
batch_size=config.batch_size,
use_layer_norm=config.layer_norm_gen,
trainable_embedding_size=config.trainable_embedding_size,
input_dropout=config.gen_input_dropout,
output_dropout=config.gen_output_dropout,
pad_token=reader.PAD_INT,
embedding_source=embedding_source,
vocab_file=vocab_file,
)
gen_outputs = gen()
disc = discriminator_nets.LSTMEmbedDiscNet(
vocab_size=vocab_size,
feature_sizes=[config.disc_feature_size] * config.num_layers_disc,
trainable_embedding_size=config.trainable_embedding_size,
embedding_source=embedding_source,
use_layer_norm=config.layer_norm_disc,
pad_token=reader.PAD_INT,
vocab_file=vocab_file,
dropout=config.disc_dropout,
)
disc_inputs = {
"sequence": gen_outputs["sequence"],
"sequence_length": gen_outputs["sequence_length"],
}
gen_logits = disc(**disc_inputs)
train_logits = disc(**disc_inputs_train)
valid_logits = disc(**disc_inputs_valid)
# Saver.
saver = tf.train.Saver()
# Reduce over time and batch.
train_probs = tf.reduce_mean(tf.nn.sigmoid(train_logits))
valid_probs = tf.reduce_mean(tf.nn.sigmoid(valid_logits))
gen_probs = tf.reduce_mean(tf.nn.sigmoid(gen_logits))
outputs = {
"train_probs": train_probs,
"valid_probs": valid_probs,
"gen_probs": gen_probs,
"gen_sequences": gen_outputs["sequence"],
"valid_sequences": valid_sequence
}
# Get average discriminator score and store generated sequences.
all_valid_sentences = []
all_gen_sentences = []
all_gen_sequences = []
mean_train_prob = 0.0
mean_valid_prob = 0.0
mean_gen_prob = 0.0
logging.info("Graph constructed, generating batches.")
num_batches = num_examples_for_eval // batch_size + 1
# Restrict the thread pool size to prevent excessive GCU usage on Borg.
tf_config = tf.ConfigProto()
tf_config.intra_op_parallelism_threads = 16
tf_config.inter_op_parallelism_threads = 16
with tf.Session(config=tf_config) as sess:
# Restore variables from checkpoints.
logging.info("Restoring variables.")
saver.restore(sess, checkpoint_path)
for i in range(num_batches):
logging.info("Batch %d / %d", i, num_batches)
train_data_np = next(train_iterator)
valid_data_np = next(valid_iterator)
feed_dict = {
train_sequence: train_data_np["sequence"],
train_sequence_length: train_data_np["sequence_length"],
valid_sequence: valid_data_np["sequence"],
valid_sequence_length: valid_data_np["sequence_length"],
}
outputs_np = sess.run(outputs, feed_dict=feed_dict)
all_gen_sequences.extend(outputs_np["gen_sequences"])
gen_sentences = utils.batch_sequences_to_sentences(
outputs_np["gen_sequences"], id_to_word)
valid_sentences = utils.batch_sequences_to_sentences(
outputs_np["valid_sequences"], id_to_word)
all_valid_sentences.extend(valid_sentences)
all_gen_sentences.extend(gen_sentences)
mean_train_prob += outputs_np["train_probs"] / batch_size
mean_valid_prob += outputs_np["valid_probs"] / batch_size
mean_gen_prob += outputs_np["gen_probs"] / batch_size
logging.info("Evaluating FID.")
# Compute FID
fid = eval_metrics.fid(
generated_sentences=all_gen_sentences[:num_examples_for_eval],
real_sentences=all_valid_sentences[:num_examples_for_eval])
utils.write_eval_results(config.checkpoint_dir, all_gen_sentences,
os.path.basename(checkpoint_path), mean_train_prob,
mean_valid_prob, mean_gen_prob, fid)
if __name__ == "__main__":
app.run(main)
================================================
FILE: scratchgan/generators.py
================================================
# Copyright 2019 DeepMind Technologies Limited and Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Generators for text data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
import sonnet as snt
import tensorflow.compat.v1 as tf
import tensorflow_probability as tfp
from scratchgan import utils
class LSTMGen(snt.AbstractModule):
"""A multi-layer LSTM language model.
Uses tied input/output embedding weights.
"""
def __init__(self,
vocab_size,
feature_sizes,
max_sequence_length,
batch_size,
use_layer_norm,
trainable_embedding_size,
input_dropout,
output_dropout,
pad_token,
embedding_source=None,
vocab_file=None,
name='lstm_gen'):
super(LSTMGen, self).__init__(name=name)
self._feature_sizes = feature_sizes
self._max_sequence_length = max_sequence_length
self._vocab_size = vocab_size
self._batch_size = batch_size
self._use_layer_norm = use_layer_norm
self._trainable_embedding_size = trainable_embedding_size
self._embedding_source = embedding_source
self._vocab_file = vocab_file
self._input_dropout = input_dropout
self._output_dropout = output_dropout
self._pad_token = pad_token
if self._embedding_source:
assert vocab_file
def _build(self, is_training=True, temperature=1.0):
input_keep_prob = (1. - self._input_dropout) if is_training else 1.0
output_keep_prob = (1. - self._output_dropout) if is_training else 1.0
batch_size = self._batch_size
max_sequence_length = self._max_sequence_length
if self._embedding_source:
all_embeddings = utils.make_partially_trainable_embeddings(
self._vocab_file, self._embedding_source, self._vocab_size,
self._trainable_embedding_size)
else:
all_embeddings = tf.get_variable(
'trainable_embeddings',
shape=[self._vocab_size, self._trainable_embedding_size],
trainable=True)
_, self._embedding_size = all_embeddings.shape.as_list()
input_embeddings = tf.nn.dropout(all_embeddings, keep_prob=input_keep_prob)
output_embeddings = tf.nn.dropout(
all_embeddings, keep_prob=output_keep_prob)
out_bias = tf.get_variable(
'out_bias', shape=[1, self._vocab_size], dtype=tf.float32)
in_proj = tf.get_variable(
'in_proj', shape=[self._embedding_size, self._feature_sizes[0]])
# If more than 1 layer, then output has dim sum(self._feature_sizes),
# which is different from input dim == self._feature_sizes[0]
# So we need a different projection matrix for input and output.
if len(self._feature_sizes) > 1:
out_proj = tf.get_variable(
'out_proj', shape=[self._embedding_size,
sum(self._feature_sizes)])
else:
out_proj = in_proj
encoder_cells = []
for feature_size in self._feature_sizes:
encoder_cells += [
snt.LSTM(feature_size, use_layer_norm=self._use_layer_norm)
]
encoder_cell = snt.DeepRNN(encoder_cells)
state = encoder_cell.initial_state(batch_size)
# Manual unrolling.
samples_list, logits_list, logprobs_list, embeddings_list = [], [], [], []
sample = tf.tile(
tf.constant(self._pad_token, dtype=tf.int32)[None], [batch_size])
logging.info('Unrolling over %d steps.', max_sequence_length)
for _ in range(max_sequence_length):
# Input is sampled word at t-1.
embedding = tf.nn.embedding_lookup(input_embeddings, sample)
embedding.shape.assert_is_compatible_with(
[batch_size, self._embedding_size])
embedding_proj = tf.matmul(embedding, in_proj)
embedding_proj.shape.assert_is_compatible_with(
[batch_size, self._feature_sizes[0]])
outputs, state = encoder_cell(embedding_proj, state)
outputs_proj = tf.matmul(outputs, out_proj, transpose_b=True)
logits = tf.matmul(
outputs_proj, output_embeddings, transpose_b=True) + out_bias
categorical = tfp.distributions.Categorical(logits=logits/temperature)
sample = categorical.sample()
logprobs = categorical.log_prob(sample)
samples_list.append(sample)
logits_list.append(logits)
logprobs_list.append(logprobs)
embeddings_list.append(embedding)
# Create an op to retrieve embeddings for full sequence, useful for testing.
embeddings = tf.stack( # pylint: disable=unused-variable
embeddings_list,
axis=1,
name='embeddings')
sequence = tf.stack(samples_list, axis=1)
logprobs = tf.stack(logprobs_list, axis=1)
# The sequence stops after the first occurrence of a PAD token.
sequence_length = utils.get_first_occurrence_indices(
sequence, self._pad_token)
mask = utils.get_mask_past_symbol(sequence, self._pad_token)
masked_sequence = sequence * tf.cast(mask, tf.int32)
masked_logprobs = logprobs * tf.cast(mask, tf.float32)
return {
'sequence': masked_sequence,
'sequence_length': sequence_length,
'logprobs': masked_logprobs
}
================================================
FILE: scratchgan/losses.py
================================================
# Copyright 2019 DeepMind Technologies Limited and Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Losses for sequential GANs."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
def sequential_cross_entropy_loss(logits, expected):
"""The cross entropy loss for binary classification.
Used to train the discriminator when not using WGAN loss.
Assume logits is the log probability of classifying as 1. (real).
Args:
logits: a `tf.Tensor`, the model produced logits, shape [batch_size,
sequence_length].
expected: a `tf.Tensor`, the expected output, shape [batch_size,
sequence_length].
Returns:
A scalar `tf.Tensor`, the average loss obtained on the given inputs.
"""
batch_size, sequence_length = logits.shape.as_list()
expected = tf.cast(expected, tf.float32)
ce = tf.nn.sigmoid_cross_entropy_with_logits(labels=expected, logits=logits)
return tf.reshape(ce, [batch_size, sequence_length])
def reinforce_loss(disc_logits, gen_logprobs, gamma, decay):
"""The REINFORCE loss.
Args:
disc_logits: float tensor, shape [batch_size, sequence_length].
gen_logprobs: float32 tensor, shape [batch_size, sequence_length]
gamma: a float, discount factor for cumulative reward.
decay: a float, decay rate for the EWMA baseline of REINFORCE.
Returns:
Float tensor, shape [batch_size, sequence_length], the REINFORCE loss for
each timestep.
"""
# Assume 1 logit for each timestep.
batch_size, sequence_length = disc_logits.shape.as_list()
gen_logprobs.shape.assert_is_compatible_with([batch_size, sequence_length])
disc_predictions = tf.nn.sigmoid(disc_logits)
# MaskGAN uses log(D), but this is more stable empirically.
rewards = 2.0 * disc_predictions - 1
# Compute cumulative rewards.
rewards_list = tf.unstack(rewards, axis=1)
cumulative_rewards = []
for t in range(sequence_length):
cum_value = tf.zeros(shape=[batch_size])
for s in range(t, sequence_length):
cum_value += np.power(gamma, (s - t)) * rewards_list[s]
cumulative_rewards.append(cum_value)
cumulative_rewards = tf.stack(cumulative_rewards, axis=1)
cumulative_rewards.shape.assert_is_compatible_with(
[batch_size, sequence_length])
with tf.variable_scope("reinforce", reuse=tf.AUTO_REUSE):
ewma_reward = tf.get_variable("ewma_reward", initializer=0.0)
mean_reward = tf.reduce_mean(cumulative_rewards)
new_ewma_reward = decay * ewma_reward + (1.0 - decay) * mean_reward
update_op = tf.assign(ewma_reward, new_ewma_reward)
# REINFORCE
with tf.control_dependencies([update_op]):
advantage = cumulative_rewards - ewma_reward
loss = -tf.stop_gradient(advantage) * gen_logprobs
loss.shape.assert_is_compatible_with([batch_size, sequence_length])
return loss, cumulative_rewards, ewma_reward
================================================
FILE: scratchgan/reader.py
================================================
# Copyright 2019 DeepMind Technologies Limited and Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities for parsing text files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import os
from absl import logging
import numpy as np
from tensorflow.compat.v1.io import gfile
# sequences: [N, MAX_TOKENS_SEQUENCE] array of int32
# lengths: [N, 2] array of int32, such that
# lengths[i, 0] is the number of non-pad tokens in sequences[i, :]
FILENAMES = {
"emnlp2017": ("train.json", "valid.json", "test.json"),
}
# EMNLP2017 sentences have max length 50, add one for a PAD token so that all
# sentences end with PAD.
MAX_TOKENS_SEQUENCE = {"emnlp2017": 52}
UNK = ""
PAD = " "
PAD_INT = 0
def tokenize(sentence):
"""Split a string into words."""
return sentence.split(" ") + [PAD]
def _build_vocab(json_data):
"""Builds full vocab from json data."""
vocab = collections.Counter()
for sentence in json_data:
tokens = tokenize(sentence["s"])
vocab.update(tokens)
for title in sentence["t"]:
title_tokens = tokenize(title)
vocab.update(title_tokens)
# Most common words first.
count_pairs = sorted(list(vocab.items()), key=lambda x: (-x[1], x[0]))
words, _ = list(zip(*count_pairs))
words = list(words)
if UNK not in words:
words = [UNK] + words
word_to_id = dict(list(zip(words, list(range(len(words))))))
# Tokens are now sorted by frequency. There's no guarantee that `PAD` will
# end up at `PAD_INT` index. Enforce it by swapping whatever token is
# currently at the `PAD_INT` index with the `PAD` token.
word = list(word_to_id.keys())[list(word_to_id.values()).index(PAD_INT)]
word_to_id[PAD], word_to_id[word] = word_to_id[word], word_to_id[PAD]
assert word_to_id[PAD] == PAD_INT
return word_to_id
def string_sequence_to_sequence(string_sequence, word_to_id):
result = []
for word in string_sequence:
if word in word_to_id:
result.append(word_to_id[word])
else:
result.append(word_to_id[UNK])
return result
def _integerize(json_data, word_to_id, dataset):
"""Transform words into integers."""
sequences = np.full((len(json_data), MAX_TOKENS_SEQUENCE[dataset]),
word_to_id[PAD], np.int32)
sequence_lengths = np.zeros(shape=(len(json_data)), dtype=np.int32)
for i, sentence in enumerate(json_data):
sequence_i = string_sequence_to_sequence(
tokenize(sentence["s"]), word_to_id)
sequence_lengths[i] = len(sequence_i)
sequences[i, :sequence_lengths[i]] = np.array(sequence_i)
return {
"sequences": sequences,
"sequence_lengths": sequence_lengths,
}
def get_raw_data(data_path, dataset, truncate_vocab=20000):
"""Load raw data from data directory "data_path".
Reads text files, converts strings to integer ids,
and performs mini-batching of the inputs.
Args:
data_path: string path to the directory where simple-examples.tgz has been
extracted.
dataset: one of ["emnlp2017"]
truncate_vocab: int, number of words to keep in the vocabulary.
Returns:
tuple (train_data, valid_data, vocabulary) where each of the data
objects can be passed to iterator.
Raises:
ValueError: dataset not in ["emnlp2017"].
"""
if dataset not in FILENAMES:
raise ValueError("Invalid dataset {}. Valid datasets: {}".format(
dataset, list(FILENAMES.keys())))
train_file, valid_file, _ = FILENAMES[dataset]
train_path = os.path.join(data_path, train_file)
valid_path = os.path.join(data_path, valid_file)
with gfile.GFile(train_path, "r") as json_file:
json_data_train = json.load(json_file)
with gfile.GFile(valid_path, "r") as json_file:
json_data_valid = json.load(json_file)
word_to_id = _build_vocab(json_data_train)
logging.info("Full vocab length: %d", len(word_to_id))
# Assume the vocab is sorted by frequency.
word_to_id_truncated = {
k: v for k, v in word_to_id.items() if v < truncate_vocab
}
logging.info("Truncated vocab length: %d", len(word_to_id_truncated))
train_data = _integerize(json_data_train, word_to_id_truncated, dataset)
valid_data = _integerize(json_data_valid, word_to_id_truncated, dataset)
return train_data, valid_data, word_to_id_truncated
def iterator(raw_data, batch_size, random=False):
"""Looping iterators on the raw data."""
sequences = raw_data["sequences"]
sequence_lengths = raw_data["sequence_lengths"]
num_examples = sequences.shape[0]
indice_range = np.arange(num_examples)
if random:
while True:
indices = np.random.choice(indice_range, size=batch_size, replace=True)
yield {
"sequence": sequences[indices, :],
"sequence_length": sequence_lengths[indices],
}
else:
start = 0
while True:
sequence = sequences[start:(start + batch_size), :]
sequence_length = sequence_lengths[start:(start + batch_size)]
start += batch_size
if start + batch_size > num_examples:
start = (start + batch_size) % num_examples
yield {
"sequence": sequence,
"sequence_length": sequence_length,
}
================================================
FILE: scratchgan/requirements.txt
================================================
absl-py==0.7.1
dm-sonnet==1.34
numpy==1.16.4
tensorflow==1.15
tensorflow-probability==0.7.0
tensorflow-gan == 1.0.0.dev0
tensorflow-hub == 0.6.0
tensorflow-io == 0.8.0
tensorflow-gpu==1.15
================================================
FILE: scratchgan/run.sh
================================================
#!/bin/sh
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Get EMNLP data.
mkdir -p /tmp/emnlp2017
curl https://storage.googleapis.com/deepmind-scratchgan-data/train.json --output /tmp/emnlp2017/train.json
curl https://storage.googleapis.com/deepmind-scratchgan-data/valid.json --output /tmp/emnlp2017/valid.json
curl https://storage.googleapis.com/deepmind-scratchgan-data/test.json --output /tmp/emnlp2017/test.json
curl https://storage.googleapis.com/deepmind-scratchgan-data/glove_emnlp2017.txt --output /tmp/emnlp2017/glove_emnlp2017.txt
# Install python3.5
which python3.5
if [ $? -eq 1 ]; then
echo 'Installing python3.5'
(cd /usr/src/
sudo wget https://www.python.org/ftp/python/3.5.6/Python-3.5.6.tgz
tar -xvzf Python-3.5.6.tgz
sudo tar -xvzf Python-3.5.6.tgz
cd Python-3.5.6
./configure --enable-loadable-sqlite-extensions --enable-optimizations
sudo make altinstall)
fi
# Fail on any error.
set -e
python3.5 -m venv scratchgan-venv
echo 'Created venv'
source scratchgan-venv/bin/activate
echo 'Installing pip'
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python3.5 get-pip.py pip==20.2.3
echo 'Getting requirements.'
pip install -r scratchgan/requirements.txt
echo 'Starting training...'
python3.5 -m scratchgan.experiment --mode="train" &
python3.5 -m scratchgan.experiment --mode="evaluate_pair" &
================================================
FILE: scratchgan/utils.py
================================================
# Copyright 2019 DeepMind Technologies Limited and Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1.io import gfile
from scratchgan import reader
EVAL_FILENAME = "evaluated_checkpoints.csv"
GLOVE_DIM = 300
GLOVE_STD = 0.3836 # Standard dev. of GloVe embeddings.
def _get_embedding_initializer(vocab_file, embedding_source, vocab_size):
"""Loads pretrained embeddings from a file in GloVe format."""
with gfile.GFile(embedding_source, "r") as f:
embedding_lines = f.readlines()
# First line contains embedding dim.
_, embedding_dim = list(map(int, embedding_lines[0].split()))
# Get the tokens as strings.
tokens = [line.split()[0] for line in embedding_lines[1:]]
# Get the actual embedding matrix.
unsorted_emb = np.array(
[[float(x) for x in line.split()[1:]] for line in embedding_lines[1:]])
# Get the expected vocab order.
with gfile.GFile(vocab_file, "r") as f:
tokens_order = [l.strip() for l in f.readlines()]
assert vocab_size == len(tokens_order)
# Put the embeddings in the order.
sorted_emb = np.zeros((vocab_size, embedding_dim))
for i, token in enumerate(tokens_order):
if token in tokens:
sorted_emb[i, :] = unsorted_emb[tokens.index(token), :]
else: # If we don't have a pretrained embedding, initialize randomly.
sorted_emb[i, :] = np.random.normal(
loc=0.0, scale=GLOVE_STD, size=(GLOVE_DIM,))
return sorted_emb.astype(np.float32)
def append_position_signal(embeddings, position_dim=8):
"""Append position signal. See get_position_signal."""
batch_size, sequence_length, embedding_dim = embeddings.get_shape().as_list()
positions = get_position_signal(sequence_length, position_dim)
# Append to embeddings.
position_inputs = tf.tile(positions[None, :, :], [batch_size, 1, 1])
embeddings_pos = tf.concat([embeddings, position_inputs], axis=2)
embeddings_pos.shape.assert_is_compatible_with(
[batch_size, sequence_length, embedding_dim + position_dim])
return embeddings_pos
def get_position_signal(sequence_length, position_dim=8):
"""Return fixed position signal as sine waves.
Sine waves frequencies are linearly spaced so that shortest is 2 and
longest is half the maximum length. That way the longest frequency
is long enough to be monotonous over the whole sequence length.
Sine waves are also shifted so that they don't all start with the same
value.
We don't use learned positional embeddings because these embeddings are
projected linearly along with the original embeddings, and the projection is
learned.
Args:
sequence_length: int, T, length of the sequence..
position_dim: int, P, number of sine waves.
Returns:
A [T, P] tensor, position embeddings.
"""
# Compute the frequencies.
periods = tf.exp(
tf.lin_space(
tf.log(2.0), tf.log(tf.to_float(sequence_length)), position_dim))
frequencies = 1.0 / periods # Shape [T, P].
# Compute the sine waves.
xs = frequencies[None, :] * tf.to_float(tf.range(sequence_length)[:, None])
shifts = tf.lin_space(0.0, 2.0, position_dim)[None, :] # [1, P]
positions = tf.math.cos(math.pi * (xs + shifts)) # [T, P]
positions.shape.assert_is_compatible_with([sequence_length, position_dim])
return positions
def get_mask_by_length(lengths, max_length):
"""Returns a mask where x[i , j] = (j < lengths[i]).
Args:
lengths: [B] tensor of int32 such that 0 <= lengths[i] <= max_length.
max_length: scalar tensor of int32.
Returns:
[B, max_length] tensor of booleans such that x[i, j] is True
if and only if j < lengths[i].
"""
batch_size = lengths.get_shape().as_list()[0]
indices = tf.range(start=0, limit=max_length)
all_indices = tf.tile(indices[None, :], [batch_size, 1])
all_lengths = tf.tile(lengths[:, None], [1, max_length])
mask = (all_indices < all_lengths)
mask_boolean = tf.cast(mask, tf.bool)
return mask_boolean
def get_mask_past_symbol(reference, symbol, optimize_for_tpu=False):
"""For each row, mask is True before and at the first occurrence of symbol."""
batch_size, max_length = reference.get_shape().as_list()
symbol = tf.convert_to_tensor(symbol)
symbol.shape.assert_is_compatible_with([])
first_indices = get_first_occurrence_indices(reference, symbol,
optimize_for_tpu)
first_indices.shape.assert_is_compatible_with([batch_size])
keep_lengths = tf.minimum(first_indices, max_length)
mask = get_mask_by_length(keep_lengths, max_length)
mask.shape.assert_is_compatible_with([batch_size, max_length])
mask.set_shape([batch_size, max_length])
return mask
def get_first_occurrence_indices(reference, symbol, optimize_for_tpu=False):
"""For each row in reference, get index after the first occurrence of symbol.
If symbol is not present on a row, return reference.shape[1] instead.
Args:
reference: [B, T] tensor of elements of the same type as symbol.
symbol: int or [] scalar tensor of the same dtype as symbol.
optimize_for_tpu: bool, whether to use a TPU-capable variant.
Returns:
A [B] reference of tf.int32 where x[i] is such that
reference[i, x[i]-1] == symbol, and reference[i, j] != symbol
for j= batch size since there can be
# several `symbol` in one row of tensor. We need to take only the position
# of the first occurrence for each row. `segment_min` does that, taking the
# lowest column index for each row index.
index_first_occurrences = tf.segment_min(index_all_occurrences[:, 1],
index_all_occurrences[:, 0])
index_first_occurrences.set_shape([batch_size])
index_first_occurrences = tf.minimum(index_first_occurrences + 1, max_length)
return index_first_occurrences
def sequence_to_sentence(sequence, id_to_word):
"""Turn a sequence into a sentence , inverse of sentence_to_sequence."""
words = []
for token_index in sequence:
if token_index in id_to_word:
words.append(id_to_word[token_index])
else:
words.append(reader.UNK)
return " ".join(words)
def batch_sequences_to_sentences(sequences, id_to_word):
return [sequence_to_sentence(sequence, id_to_word) for sequence in sequences]
def write_eval_results(checkpoint_dir, all_gen_sentences, checkpoint_name,
mean_train_prob, mean_valid_prob, mean_gen_prob, fid):
"""Write evaluation results to disk."""
to_write = ",".join(
map(str, [
checkpoint_name, mean_train_prob, mean_valid_prob, mean_gen_prob, fid
]))
eval_filepath = os.path.join(checkpoint_dir, EVAL_FILENAME)
previous_eval_content = ""
if gfile.exists(eval_filepath):
with gfile.GFile(eval_filepath, "r") as f:
previous_eval_content = f.read()
with gfile.GFile(eval_filepath, "w") as f:
f.write(previous_eval_content + to_write + "\n")
with gfile.GFile(
os.path.join(checkpoint_dir, checkpoint_name + "_sentences.txt"),
"w") as f:
f.write("\n".join(all_gen_sentences))
def maybe_pick_models_to_evaluate(checkpoint_dir):
"""Pick a checkpoint to evaluate that has not been evaluated already."""
logging.info("Picking checkpoint to evaluate from %s.", checkpoint_dir)
filenames = gfile.listdir(checkpoint_dir)
filenames = [f[:-5] for f in filenames if f[-5:] == ".meta"]
logging.info("Found existing checkpoints: %s", filenames)
evaluated_filenames = []
if gfile.exists(os.path.join(checkpoint_dir, EVAL_FILENAME)):
with gfile.GFile(os.path.join(checkpoint_dir, EVAL_FILENAME), "r") as f:
evaluated_filenames = [l.strip().split(",")[0] for l in f.readlines()]
logging.info("Found already evaluated checkpoints: %s", evaluated_filenames)
checkpoints_to_evaluate = [
f for f in filenames if f not in evaluated_filenames
]
logging.info("Remaining potential checkpoints: %s", checkpoints_to_evaluate)
if checkpoints_to_evaluate:
return os.path.join(checkpoint_dir, checkpoints_to_evaluate[0])
else:
return None
def get_embedding_path(data_dir, dataset):
"""By convention, this is where we store the embedding."""
return os.path.join(data_dir, "glove_%s.txt" % dataset)
def make_partially_trainable_embeddings(vocab_file, embedding_source,
vocab_size, trainable_embedding_size):
"""Makes embedding matrix with pretrained GloVe [1] part and trainable part.
[1] Pennington, J., Socher, R., & Manning, C. (2014, October). Glove: Global
vectors for word representation. In Proceedings of the 2014 conference on
empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
Args:
vocab_file: vocabulary file.
embedding_source: path to the actual embeddings.
vocab_size: number of words in vocabulary.
trainable_embedding_size: size of the trainable part of the embeddings.
Returns:
A matrix of partially pretrained embeddings.
"""
# Our embeddings have 2 parts: a pre-trained, frozen, GloVe part,
# and a trainable, randomly initialized part.
# The standard deviation of the GloVe part is used to initialize
# the trainable part, so that both part have roughly the same distribution.
#
# Let g_ij be the j-th coordinates of the GloVe embedding of the i-th word.
# So that 0 < i < |vocab| and 0 < j < 300.
# Then sum_ij (g_ij - sum_kl g_kl)^2 = (0.3836)^2
#
# In reality g_ij follows a truncated normal distribution
# min(max(N(0, s), -4.2), 4.2) but we approximate it by N(0, 0.3836).
embedding_initializer = _get_embedding_initializer(
vocab_file=vocab_file,
embedding_source=embedding_source,
vocab_size=vocab_size)
pretrained_embedding = tf.get_variable(
"pretrained_embedding",
initializer=embedding_initializer,
dtype=tf.float32)
trainable_embedding = tf.get_variable(
"trainable_embedding",
shape=[vocab_size, trainable_embedding_size],
initializer=tf.initializers.random_normal(mean=0.0, stddev=GLOVE_STD))
# We just concatenate embeddings, they will pass through a projection
# matrix afterwards.
embedding = tf.concat([pretrained_embedding, trainable_embedding], axis=1)
return embedding
================================================
FILE: side_effects_penalties/README.md
================================================
# Side effects penalties
Side effects are unnecessary disruptions to the agent's environment while completing a task. Instead of trying to explicitly penalize all possible side effects, we give the agent a general penalty for impacting the environment, defined as a deviation from some baseline state. For example, a reversibility penalty measures unreachability (deviation) of the starting state (baseline). This code implements a tabular Q-learning agent with different impact penalties. Each penalty consists of a deviation measure (none, unreachability, relative reachability, or attainable utility), a baseline (starting state, inaction, or stepwise inaction), and some other design choices. This is the code for the paper [Penalizing side effects using stepwise relative reachability](https://arxiv.org/abs/1806.01186) by Krakovna et al (2019).
In our latest paper "Avoiding Side Effects By Considering Future Tasks" by Krakovna et al (NeurIPS 2020), the agent receives an auxiliary reward for preserving the ability to perform future tasks. This approach is equivalent to relative reachability with an inaction baseline in deterministic environments. The UVFA approximation for the auxiliary reward is included as an option for the deviation measure.
## Instructions
Clone the repository:
`git clone https://github.com/deepmind/deepmind-research/side_effects_penalties.git`
### Running an agent with a side effects penalty
Run the agent with a given penalty on an AI Safety Gridworlds environment:
`python -m side_effects_penalties.run_experiment -baseline -dev_measure -env_name -suffix `
The following settings can be specified for the side effects penalty:
* Baseline state (`-baseline`): starting state (`start`), inaction (`inaction`),
stepwise inaction with rollouts (`stepwise`), stepwise inaction without
rollouts (`step_noroll`)
* Deviation measure (`-dev_measure`): none (`none`), unreachability (`reach`),
relative reachability (`rel_reach`), attainable utility (`att_util`),
UVFA approximation of relative reachability (`uvfa_rel_reach`)
* Summary function to apply to the relative reachability or attainable utility
deviation measure (`-dev_fun`): max (0, x) (`truncation`) or |x| (`absolute`)
* Discount factor for rewards (`discount`). We use `discount=0.95` for the UVFA
approximation of relative reachability.
* Discount factor for the deviation measure value function (`-value_discount`).
Should be the same as `discount` unless using an undiscounted reachability
measure.
* Weight for the side effects penalty relative to the reward (`-beta`)
* Penalty for nonterminal states relative to terminal states (`-nonterminal'):
1 (`full`) is used in the stepwise relative reachability paper, while
(1-discount) (`disc`) is used in the future tasks paper.
Other settings include:
* Number of episodes (`-num_episodes`)
* AI Safety Gridworlds environment name (`-env_name`)
* Filename suffix for saving result files (`-suffix`)
### Plotting the results
Make a summary data frame from the result files generated by `run_experiment`:
`python -m side_effects_penalties.results_summary -compare_penalties -input_suffix `
Arguments:
* -bar_plot: make a data frame for a bar plot (True) or learning curve plot (False)
* -compare_penalties: compare different penalties using the best beta value for
each penalty (True), or compare different beta values for a given penalty (False)
* If compare_penalties=False, specify the penalty parameters (`-dev_measure`,
`-dev_fun` and `-value_discount`)
* Environment name (`-env_name`)
* Filename suffix for loading result files (`-input_suffix`)
* Filename suffix for the summary data frame (`-output_suffix`)
Import the summary data frame into `plot_results.ipynb` and make a bar plot or
learning curve plot.
## Dependencies
* Python 2.7 or 3 (tested with Python 2.7.15 and 3.6.7)
* [AI Safety Gridworlds](https://github.com/deepmind/ai-safety-gridworlds) suite
of safety environments
* [Abseil](https://github.com/abseil/abseil-py) Python common libraries
* Numpy
* Tensorflow 1
* Sonnet
* Pandas
* Six
* Matplotlib
* Seaborn
## Citing this work
If you use this code in your work, please cite one of the accompanying papers:
`@article{srr2019,
title = {Penalizing Side Effects using Stepwise Relative Reachability},
author = {Victoria Krakovna and Laurent Orseau and Ramana Kumar and Miljan Martic and Shane Legg},
journal = {CoRR},
volume = {abs/1806.01186},
year = {2019}
}`
`@inproceedings{ft2020,
title = {Avoiding Side Effects By Considering Future Tasks},
author = {Victoria Krakovna and Laurent Orseau and Richard Ngo and Miljan Martic and Shane Legg},
booktitle = {Neural Information Processing Systems},
year = {2020}
}`
================================================
FILE: side_effects_penalties/__init__.py
================================================
# Copyright 2019 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
================================================
FILE: side_effects_penalties/agent.py
================================================
# Copyright 2019 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Vanilla Q-Learning agent."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from collections import abc
import numpy as np
from six.moves import range
class EpsilonGreedyPolicy(object):
"""Epsilon greedy policy for table value function lookup."""
def __init__(self, value_function, actions):
"""Construct an epsilon greedy policy object.
Args:
value_function: agent value function as a dict.
actions: list of possible actions.
Raises:
ValueError: if `actions` agument is not an iterable.
"""
if not isinstance(actions, abc.Iterable):
raise ValueError('`actions` argument must be an iterable.')
self._value_function = value_function
self._actions = actions
def get_action(self, epsilon, state):
"""Get action following the e-greedy policy.
Args:
epsilon: probability of selecting a random action
state: current state of the game as a state/action tuple.
Returns:
Chosen action.
"""
if np.random.random() < epsilon:
return np.random.choice(self._actions)
else:
values = [self._value_function[(state, action)]
for action in self._actions]
max_value = max(values)
max_indices = [i for i, value in enumerate(values) if value == max_value]
return self._actions[np.random.choice(max_indices)]
class QLearning(object):
"""Q-learning agent."""
def __init__(self, actions, alpha=0.1, epsilon=0.1, q_initialisation=0.0,
discount=0.99):
"""Create a Q-learning agent.
Args:
actions: a BoundedArraySpec that specifes full discrete action spec.
alpha: agent learning rate.
epsilon: agent exploration rate.
q_initialisation: float, used to initialise the value function.
discount: discount factor for rewards.
"""
self._value_function = collections.defaultdict(lambda: q_initialisation)
self._valid_actions = list(range(actions.minimum, actions.maximum + 1))
self._policy = EpsilonGreedyPolicy(self._value_function,
self._valid_actions)
# Hyperparameters.
self.alpha = alpha
self.epsilon = epsilon
self.discount = discount
# Episode internal variables.
self._current_action = None
self._current_state = None
def begin_episode(self):
"""Perform episode initialisation."""
self._current_state = None
self._current_action = None
def _timestep_to_state(self, timestep):
return tuple(map(tuple, np.copy(timestep.observation['board'])))
def step(self, timestep):
"""Perform a single step in the environment."""
# Get state observations.
state = self._timestep_to_state(timestep)
# This is one of the follow up states (i.e. not the initial state).
if self._current_state is not None:
self._update(timestep, state)
self._current_state = state
# Determine action.
self._current_action = self._policy.get_action(self.epsilon, state)
# Emit action.
return self._current_action
def _calculate_reward(self, timestep, unused_state):
"""Calculate reward: to be extended when impact penalty is added."""
reward = timestep.reward
return reward
def _update(self, timestep, state):
"""Perform value function update."""
reward = self._calculate_reward(timestep, state)
# Terminal state.
if not state:
delta = (reward - self._value_function[(self._current_state,
self._current_action)])
# Non-terminal state.
else:
max_action = self._policy.get_action(0, state)
delta = (
reward + self.discount * self._value_function[(state, max_action)] -
self._value_function[(self._current_state, self._current_action)])
self._value_function[(self._current_state,
self._current_action)] += self.alpha * delta
def end_episode(self, timestep):
"""Performs episode cleanup."""
# Update for the terminal state.
self._update(timestep, None)
@property
def value_function(self):
return self._value_function
================================================
FILE: side_effects_penalties/agent_with_penalties.py
================================================
# Copyright 2019 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Q-learning with side effects penalties."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from side_effects_penalties import agent
from side_effects_penalties import side_effects_penalty as sep
class QLearningSE(agent.QLearning):
"""Q-learning agent with side-effects penalties."""
def __init__(
self, actions, alpha=0.1, epsilon=0.1, q_initialisation=0.0,
baseline='start', dev_measure='none', dev_fun='truncation',
discount=0.99, value_discount=1.0, beta=1.0, num_util_funs=10,
exact_baseline=False, baseline_env=None, start_timestep=None,
state_size=None, nonterminal_weight=0.01):
"""Create a Q-learning agent with a side effects penalty.
Args:
actions: full discrete action spec.
alpha: agent learning rate.
epsilon: agent exploration rate.
q_initialisation: float, used to initialise the value function.
baseline: which baseline state to use ('start', 'inaction', 'stepwise').
dev_measure: deviation measure:
- "none" for no penalty,
- "reach" for unreachability,
- "rel_reach" for relative reachability,
- "att_util" for attainable utility,
dev_fun: what function to apply in the deviation measure ('truncation' or
'absolute' (for 'rel_reach' and 'att_util'), or 'none' (otherwise)).
discount: discount factor for rewards.
value_discount: discount factor for value functions in penalties.
beta: side effects penalty weight.
num_util_funs: number of random utility functions for attainable utility.
exact_baseline: whether to use an exact or approximate baseline.
baseline_env: copy of environment (with noops) for the exact baseline.
start_timestep: copy of starting timestep for the baseline.
state_size: the size of each state (flattened) for NN reachability.
nonterminal_weight: penalty weight on nonterminal states.
Raises:
ValueError: for incorrect baseline, dev_measure, or dev_fun
"""
super(QLearningSE, self).__init__(actions, alpha, epsilon, q_initialisation,
discount)
# Impact penalty: set dev_fun (f)
if 'rel_reach' in dev_measure or 'att_util' in dev_measure:
if dev_fun == 'truncation':
dev_fun = lambda diff: np.maximum(0, diff)
elif dev_fun == 'absolute':
dev_fun = np.abs
else:
raise ValueError('Deviation function not recognized')
else:
assert dev_fun == 'none'
dev_fun = None
# Impact penalty: create deviation measure
if dev_measure in {'reach', 'rel_reach'}:
deviation = sep.Reachability(value_discount, dev_fun, discount)
elif dev_measure == 'uvfa_rel_reach':
deviation = sep.UVFAReachability(value_discount, dev_fun, discount,
state_size)
elif dev_measure == 'att_util':
deviation = sep.AttainableUtility(value_discount, dev_fun, num_util_funs,
discount)
elif dev_measure == 'none':
deviation = sep.NoDeviation()
else:
raise ValueError('Deviation measure not recognized')
use_inseparable_rollout = (
dev_measure == 'reach' and baseline == 'stepwise')
# Impact penalty: create baseline
if baseline in {'start', 'inaction', 'stepwise'}:
baseline_class = getattr(sep, baseline.capitalize() + 'Baseline')
baseline = baseline_class(start_timestep, exact_baseline, baseline_env,
self._timestep_to_state)
elif baseline == 'step_noroll':
baseline_class = getattr(sep, 'StepwiseBaseline')
baseline = baseline_class(start_timestep, exact_baseline, baseline_env,
self._timestep_to_state, False)
else:
raise ValueError('Baseline not recognized')
self._impact_penalty = sep.SideEffectPenalty(
baseline, deviation, beta, nonterminal_weight, use_inseparable_rollout)
def begin_episode(self):
"""Perform episode initialisation."""
super(QLearningSE, self).begin_episode()
self._impact_penalty.reset()
def _calculate_reward(self, timestep, state):
reward = super(QLearningSE, self)._calculate_reward(timestep, state)
return (reward - self._impact_penalty.calculate(
self._current_state, self._current_action, state))
================================================
FILE: side_effects_penalties/file_loading.py
================================================
# Copyright 2019 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Helper functions for loading files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import pandas as pd
def filename(env_name, noops, dev_measure, dev_fun, baseline, beta,
value_discount, seed, path='', suffix=''):
"""Generate filename for the given set of parameters."""
noop_str = 'noops' if noops else 'nonoops'
seed_str = '_' + str(seed) if seed else ''
filename_template = ('{env_name}_{noop_str}_{dev_measure}_{dev_fun}' +
'_{baseline}_beta_{beta}_vd_{value_discount}' +
'{suffix}{seed_str}.csv')
full_path = os.path.join(path, filename_template.format(
env_name=env_name, noop_str=noop_str, dev_measure=dev_measure,
dev_fun=dev_fun, baseline=baseline, beta=beta,
value_discount=value_discount, suffix=suffix, seed_str=seed_str))
return full_path
def load_files(baseline, dev_measure, dev_fun, value_discount, beta, env_name,
noops, path, suffix, seed_list, final=True):
"""Load result files generated by run_experiment with the given parameters."""
def try_loading(f, final):
if os.path.isfile(f):
df = pd.read_csv(f, index_col=0)
if final:
last_episode = max(df['episode'])
return df[df.episode == last_episode]
else:
return df
else:
return pd.DataFrame()
dataframes = []
for seed in seed_list:
f = filename(baseline=baseline, dev_measure=dev_measure, dev_fun=dev_fun,
value_discount=value_discount, beta=beta, env_name=env_name,
noops=noops, path=path, suffix=suffix, seed=int(seed))
df_part = try_loading(f, final)
dataframes.append(df_part)
df = pd.concat(dataframes)
return df
================================================
FILE: side_effects_penalties/plot_results.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "rINYEKJlYpQU"
},
"source": [
"Copyright 2019 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
"https://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "KbCarv91XChI"
},
"outputs": [],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"from google.colab import files\n",
"import io\n",
"import pandas as pd\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "irahOycBZM1E"
},
"source": [
"### Plot parameters (edit as needed)\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "VeaB-Y9TYWCP"
},
"outputs": [],
"source": [
"# Make a bar plot for average results from the final 100 episodes (True),\n",
"# or make a learning curve plot (False)\n",
"bar_plot = False\n",
"\n",
"# Compare different penalties using the best beta value for each penalty (True),\n",
"# or compare different beta values for the same penalty (False):\n",
"compare_penalties = False\n",
"\n",
"# If compare_penalties is False, specify the penalty parameters:\n",
"dev_measure = 'rel_reach'\n",
"dev_fun = 'truncation'\n",
"value_discount = 0.99\n",
"\n",
"# Environment name\n",
"env_name = 'box'\n",
"\n",
"# Filename suffix\n",
"suffix = '' "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "t8hYO3f2ZaHq"
},
"source": [
"### Plot settings"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "foyP_qrUeTsx"
},
"outputs": [],
"source": [
"final_str = '_final' if bar_plot else ''\n",
"if compare_penalties:\n",
" var = 'label'\n",
" x_label = 'deviation_measure'\n",
" legend_title = 'penalty'\n",
" palette = sns.color_palette()\n",
" filename = ('df_summary_penalties_' + env_name + final_str + suffix\n",
" + '.csv')\n",
"else:\n",
" var = 'beta'\n",
" x_label = 'beta'\n",
" legend_title = 'beta'\n",
" palette = sns.cubehelix_palette()\n",
" filename = ('df_summary_betas_' + env_name + '_' + dev_measure + '_' + dev_fun \n",
" + '_' + str(value_discount) + final_str + suffix + '.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5RakwQsFZc0V"
},
"source": [
"### Load summary data output by results_summary.py"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "PQ615uuGYLF3"
},
"outputs": [],
"source": [
"uploaded = files.upload()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "-FoI7u8BtYol"
},
"outputs": [],
"source": [
"df = pd.read_csv(io.BytesIO(uploaded[filename]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "A7BGierpZi6t"
},
"source": [
"### Make bar plots"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ch06DzzeXlGK"
},
"outputs": [],
"source": [
"plot = sns.catplot(data=df, col='baseline', x=var, y='performance_smooth',\n",
" kind='bar', height=4, aspect=1.3)\n",
"axes = plot.axes.flatten()\n",
"for ax in axes:\n",
" title = ax.get_title().split()\n",
" ax.set_title(title[2] + ' baseline')\n",
" ax.set_ylabel('performance')\n",
" ax.set_xlabel(x_label)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BU9PYyzOZlRu"
},
"source": [
"### Make learning curve plots"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "RRfU_2iIX-jo"
},
"outputs": [],
"source": [
"plot = sns.FacetGrid(df, col='baseline', size=5, aspect=1.3,\n",
" sharey=False, sharex=False)\n",
"plot.map_dataframe(sns.tsplot, time='episode', unit='seed', condition=var,\n",
" value='performance_smooth', n_boot=100, color=palette,\n",
" alpha=1.0, linewidth=1)\n",
"plot.add_legend(title=legend_title)\n",
"axes = plot.axes.flatten()\n",
"for ax in axes:\n",
" title = ax.get_title().split()\n",
" ax.set_title(title[2] + ' baseline')\n",
" ax.set_ylabel('performance')\n",
" ax.set_xlabel('episode')"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "plot_results.ipynb",
"provenance": [
{
"file_id": "1a8ub19XYD4M-r5mGm0lKYTrNwTo1zF7Z",
"timestamp": 1569850224175
}
]
},
"kernelspec": {
"display_name": "Python 2",
"name": "python2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: side_effects_penalties/requirements.txt
================================================
absl-py==0.10.0
apparmor==2.13.4
asn1crypto==0.24.0
attrs==19.3.0
bcrypt==3.1.7
beautifulsoup4==4.9.1
blinker==1.4
Brlapi==0.7.0
certifi==2020.4.5.1
chardet==3.0.4
chrome-gnome-shell==0.0.0
cloudpickle==1.6.0
CredentialKit==0.7
credentialkit-client==1
cryptography==2.8
cupshelpers==1.0
dbus-python==1.2.16
decorator==4.4.2
distro==1.5.0
distro-info==0.23
dm-tree==0.1.5
duplicity==0.8.12.0
entrypoints==0.3
enum34==1.1.10
evdev==1.3.0
extras==1.0.0
fasteners==0.14.1
fixtures==3.0.0
future==0.18.2
gast==0.4.0
gbulb==0.6.1
gpg===1.13.1-unknown
hg-evolve==10.1.0.dev0
html5lib==1.0.1
httplib2==0.18.1
idna==2.9
iniparse==0.4
IPy==1.0
jeepney==0.4.3
keyring==18.0.1
keyrings.alt==3.4.0
LibAppArmor==2.13.4
linecache2==1.0.0
lockfile==0.12.2
louis==3.14.0
lxml==4.5.0
lz4==3.0.2+dfsg
mercurial==5.5.1+348.80bf7b1ada15
monotonic==1.5
mox3==1.0.0
networkx==1.8.1
numpy==1.18.4
oauthlib==3.1.0
obno==39
olefile==0.46
onboard==1.4.1
PAM==0.4.2
pandas==1.1.3
paramiko==2.6.0
pbr==5.4.5
pexpect==4.6.0
Pillow==7.2.0
protobuf==3.11.4
psutil==5.6.7
pyasn1==0.4.2
pyasn1-modules==0.2.1
pycairo==1.16.2
pycrypto==2.6.1
pycups==1.9.73
pycurl==7.43.0.2
Pygments==2.3.1
PyGObject==3.36.0
pyinotify==0.9.6
PyJWT==1.7.1
PyKCS11==1.5.8
PyNaCl==1.4.0
pyOpenSSL==19.1.0
pyparsing==2.4.7
pysmbc==1.0.22
python-apt==2.1.3
python-augeas==0.5.0
python-dateutil==2.8.1
python-debian==0.1.37
python-mimeparse==1.6.0
python-networkmanager==2.1
python-pam==1.8.4
python-xapp==2.0.1
python-xlib==0.27
pytz==2020.1
pyudev==0.21.0
pyusb==1.0.2
pyxattr==0.6.1
pyxdg==0.26
PyYAML==5.3.1
reboot-enforcer==0.1
reconfigure==0.1.81
rekey==1
requests==2.23.0
scour==0.37
SecretStorage==3.1.2
setproctitle==1.1.10
six==1.15.0
sonnet==0.1.6
soupsieve==2.0.1
tensorflow-probability==0.11.1
testtools==2.3.0
tinycss==0.4
tinycss2==1.0.2
traceback2==1.4.0
ufw==0.36
unittest2==1.1.0
urllib3==1.25.9
webencodings==0.5.1
youtube-dl==2020.6.16.1
================================================
FILE: side_effects_penalties/results_summary.py
================================================
# Copyright 2019 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Plot results for different side effects penalties.
Loads csv result files generated by `run_experiment' and outputs a summary data
frame in a csv file to be used for plotting by plot_results.ipynb.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
from absl import app
from absl import flags
import pandas as pd
from side_effects_penalties.file_loading import load_files
FLAGS = flags.FLAGS
if __name__ == '__main__': # Avoid defining flags when used as a library.
flags.DEFINE_string('path', '', 'File path.')
flags.DEFINE_string('input_suffix', '',
'Filename suffix to use when loading data files.')
flags.DEFINE_string('output_suffix', '',
'Filename suffix to use when saving files.')
flags.DEFINE_bool('bar_plot', True,
'Make a data frame for a bar plot (True) ' +
'or learning curves (False)')
flags.DEFINE_string('env_name', 'box', 'Environment name.')
flags.DEFINE_bool('noops', True, 'Whether the environment includes noops.')
flags.DEFINE_list('beta_list', [0.1, 0.3, 1.0, 3.0, 10.0, 30.0, 100.0],
'List of beta values.')
flags.DEFINE_list('seed_list', [1], 'List of random seeds.')
flags.DEFINE_bool('compare_penalties', True,
'Compare different penalties using the best beta value ' +
'for each penalty (True), or compare different beta values '
+ 'for the same penalty (False).')
flags.DEFINE_enum('dev_measure', 'rel_reach',
['none', 'reach', 'rel_reach', 'att_util'],
'Deviation measure (used if compare_penalties=False).')
flags.DEFINE_enum('dev_fun', 'truncation', ['truncation', 'absolute'],
'Summary function for the deviation measure ' +
'(used if compare_penalties=False)')
flags.DEFINE_float('value_discount', 0.99,
'Discount factor for deviation measure value function ' +
'(used if compare_penalties=False)')
def beta_choice(baseline, dev_measure, dev_fun, value_discount, env_name,
beta_list, seed_list, noops=False, path='', suffix=''):
"""Choose beta value that gives the highest final performance."""
if dev_measure == 'none':
return 0.1
perf_max = float('-inf')
best_beta = 0.0
for beta in beta_list:
df = load_files(baseline=baseline, dev_measure=dev_measure,
dev_fun=dev_fun, value_discount=value_discount, beta=beta,
env_name=env_name, noops=noops, path=path, suffix=suffix,
seed_list=seed_list)
if df.empty:
perf = float('-inf')
else:
perf = df['performance_smooth'].mean()
if perf > perf_max:
perf_max = perf
best_beta = beta
return best_beta
def penalty_label(dev_measure, dev_fun, value_discount):
"""Penalty label specifying design choices."""
dev_measure_labels = {
'none': 'None', 'rel_reach': 'RR', 'att_util': 'AU', 'reach': 'UR'}
label = dev_measure_labels[dev_measure]
disc_lab = 'u' if value_discount == 1.0 else 'd'
dev_lab = ''
if dev_measure in ['rel_reach', 'att_util']:
dev_lab = 't' if dev_fun == 'truncation' else 'a'
if dev_measure != 'none':
label = label + '(' + disc_lab + dev_lab + ')'
return label
def make_summary_data_frame(
env_name, beta_list, seed_list, final=True, baseline=None, dev_measure=None,
dev_fun=None, value_discount=None, noops=False, compare_penalties=True,
path='', input_suffix='', output_suffix=''):
"""Make summary dataframe from multiple csv result files and output to csv."""
# For each of the penalty parameters (baseline, dev_measure, dev_fun, and
# value_discount), compare a list of multiple values if the parameter is None,
# or use the provided parameter value if it is not None
baseline_list = ['start', 'inaction', 'stepwise', 'step_noroll']
if dev_measure is not None:
dev_measure_list = [dev_measure]
else:
dev_measure_list = ['none', 'reach', 'rel_reach', 'att_util']
dataframes = []
for dev_measure in dev_measure_list:
# These deviation measures don't have a deviation function:
if dev_measure in ['reach', 'none']:
dev_fun_list = ['none']
elif dev_fun is not None:
dev_fun_list = [dev_fun]
else:
dev_fun_list = ['truncation', 'absolute']
# These deviation measures must be discounted:
if dev_measure in ['none', 'att_util']:
value_discount_list = [0.99]
elif value_discount is not None:
value_discount_list = [value_discount]
else:
value_discount_list = [0.99, 1.0]
for baseline in baseline_list:
for vd in value_discount_list:
for devf in dev_fun_list:
# Choose the best beta for this set of penalty parameters if
# compare_penalties=True, or compare all betas otherwise
if compare_penalties:
beta = beta_choice(
baseline=baseline, dev_measure=dev_measure, dev_fun=devf,
value_discount=vd, env_name=env_name, noops=noops,
beta_list=beta_list, seed_list=seed_list, path=path,
suffix=input_suffix)
betas = [beta]
else:
betas = beta_list
for beta in betas:
label = penalty_label(
dev_measure=dev_measure, dev_fun=devf, value_discount=vd)
df_part = load_files(
baseline=baseline, dev_measure=dev_measure, dev_fun=devf,
value_discount=vd, beta=beta, env_name=env_name,
noops=noops, path=path, suffix=input_suffix, final=final,
seed_list=seed_list)
df_part = df_part.assign(
baseline=baseline, dev_measure=dev_measure, dev_fun=devf,
value_discount=vd, beta=beta, env_name=env_name, label=label)
dataframes.append(df_part)
df = pd.concat(dataframes, sort=False)
# Output summary data frame
final_str = '_final' if final else ''
if compare_penalties:
filename = ('df_summary_penalties_' + env_name + final_str +
output_suffix + '.csv')
else:
filename = ('df_summary_betas_' + env_name + '_' + dev_measure + '_' +
dev_fun + '_' + str(value_discount) + final_str + output_suffix
+ '.csv')
f = os.path.join(path, filename)
df.to_csv(f)
return df
def main(unused_argv):
compare_penalties = FLAGS.compare_penalties
dev_measure = None if compare_penalties else FLAGS.dev_measure
dev_fun = None if compare_penalties else FLAGS.dev_fun
value_discount = None if compare_penalties else FLAGS.value_discount
make_summary_data_frame(
compare_penalties=compare_penalties, env_name=FLAGS.env_name,
noops=FLAGS.noops, final=FLAGS.bar_plot, dev_measure=dev_measure,
value_discount=value_discount, dev_fun=dev_fun, path=FLAGS.path,
input_suffix=FLAGS.input_suffix, output_suffix=FLAGS.output_suffix,
beta_list=FLAGS.beta_list, seed_list=FLAGS.seed_list)
if __name__ == '__main__':
app.run(main)
================================================
FILE: side_effects_penalties/run_experiment.py
================================================
# Copyright 2019 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Run a Q-learning agent with a side effects penalty."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import pandas as pd
from six.moves import range
from six.moves import zip
from side_effects_penalties import agent_with_penalties
from side_effects_penalties import training
from side_effects_penalties.file_loading import filename
FLAGS = flags.FLAGS
if __name__ == '__main__': # Avoid defining flags when used as a library.
# Side effects penalty settings
flags.DEFINE_enum('baseline', 'inaction',
['start', 'inaction', 'stepwise', 'step_noroll'],
'Baseline.')
flags.DEFINE_enum('dev_measure', 'rel_reach',
['none', 'reach', 'rel_reach',
'uvfa_rel_reach', 'att_util'],
'Deviation measure.')
flags.DEFINE_enum('dev_fun', 'truncation', ['truncation', 'absolute'],
'Summary function for the deviation measure.')
flags.DEFINE_float('discount', 0.99, 'Discount factor for rewards.')
flags.DEFINE_float('value_discount', 0.99,
'Discount factor for deviation measure value function.')
flags.DEFINE_float('beta', 30.0, 'Weight for side effects penalty.')
flags.DEFINE_string('nonterminal', 'disc',
'Penalty for nonterminal states relative to terminal'
'states: none (0), full (1), or disc (1-discount).')
flags.DEFINE_bool('exact_baseline', False,
'Compute the exact baseline using an environment copy.')
# Agent settings
flags.DEFINE_bool('anneal', True,
'Whether to anneal the exploration rate from 1 to 0.')
flags.DEFINE_integer('num_episodes', 10000, 'Number of episodes.')
flags.DEFINE_integer('num_episodes_noexp', 0,
'Number of episodes with no exploration.')
flags.DEFINE_integer('seed', 1, 'Random seed.')
# Environment settings
flags.DEFINE_string('env_name', 'box', 'Environment name.')
flags.DEFINE_bool('noops', True, 'Whether the environment includes noops.')
flags.DEFINE_integer('movement_reward', 0, 'Movement reward.')
flags.DEFINE_integer('goal_reward', 1, 'Reward for reaching a goal state.')
flags.DEFINE_integer('side_effect_reward', -1,
'Hidden reward for causing side effects.')
# Settings for outputting results
flags.DEFINE_enum('mode', 'save', ['print', 'save'],
'Print results or save to file.')
flags.DEFINE_string('path', '', 'File path.')
flags.DEFINE_string('suffix', '', 'Filename suffix.')
def run_experiment(
baseline, dev_measure, dev_fun, discount, value_discount, beta, nonterminal,
exact_baseline, anneal, num_episodes, num_episodes_noexp, seed,
env_name, noops, movement_reward, goal_reward, side_effect_reward,
mode, path, suffix):
"""Run agent and save or print the results."""
performances = []
rewards = []
seeds = []
episodes = []
if 'rel_reach' not in dev_measure and 'att_util' not in dev_measure:
dev_fun = 'none'
nonterminal_weights = {'none': 0.0, 'disc': 1.0-discount, 'full': 1.0}
nonterminal_weight = nonterminal_weights[nonterminal]
reward, performance = training.run_agent(
baseline=baseline,
dev_measure=dev_measure,
dev_fun=dev_fun,
discount=discount,
value_discount=value_discount,
beta=beta,
nonterminal_weight=nonterminal_weight,
exact_baseline=exact_baseline,
anneal=anneal,
num_episodes=num_episodes,
num_episodes_noexp=num_episodes_noexp,
seed=seed,
env_name=env_name,
noops=noops,
movement_reward=movement_reward,
goal_reward=goal_reward,
side_effect_reward=side_effect_reward,
agent_class=agent_with_penalties.QLearningSE)
rewards.extend(reward)
performances.extend(performance)
seeds.extend([seed] * (num_episodes + num_episodes_noexp))
episodes.extend(list(range(num_episodes + num_episodes_noexp)))
if mode == 'save':
d = {'reward': rewards, 'performance': performances,
'seed': seeds, 'episode': episodes}
df = pd.DataFrame(d)
df1 = add_smoothed_data(df)
f = filename(env_name, noops, dev_measure, dev_fun, baseline, beta,
value_discount, path=path, suffix=suffix, seed=seed)
df1.to_csv(f)
return reward, performance
def _smooth(values, window=100):
return values.rolling(window,).mean()
def add_smoothed_data(df, groupby='seed', window=100):
grouped = df.groupby(groupby)[['reward', 'performance']]
grouped = grouped.apply(_smooth, window=window).rename(columns={
'performance': 'performance_smooth', 'reward': 'reward_smooth'})
temp = pd.concat([df, grouped], axis=1)
return temp
def main(unused_argv):
reward, performance = run_experiment(
baseline=FLAGS.baseline,
dev_measure=FLAGS.dev_measure,
dev_fun=FLAGS.dev_fun,
discount=FLAGS.discount,
value_discount=FLAGS.value_discount,
beta=FLAGS.beta,
nonterminal=FLAGS.nonterminal,
exact_baseline=FLAGS.exact_baseline,
anneal=FLAGS.anneal,
num_episodes=FLAGS.num_episodes,
num_episodes_noexp=FLAGS.num_episodes_noexp,
seed=FLAGS.seed,
env_name=FLAGS.env_name,
noops=FLAGS.noops,
movement_reward=FLAGS.movement_reward,
goal_reward=FLAGS.goal_reward,
side_effect_reward=FLAGS.side_effect_reward,
mode=FLAGS.mode,
path=FLAGS.path,
suffix=FLAGS.suffix)
if FLAGS.mode == 'print':
print('Performance and reward in the last 10 steps:')
print(list(zip(performance, reward))[-10:-1])
if __name__ == '__main__':
app.run(main)
================================================
FILE: side_effects_penalties/side_effects_penalty.py
================================================
[File too large to display: 25.0 KB]
================================================
FILE: side_effects_penalties/side_effects_penalty_test.py
================================================
# Copyright 2019 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Tests for side_effects_penalty."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
from six.moves import range
from side_effects_penalties import side_effects_penalty
from side_effects_penalties import training
from side_effects_penalties.side_effects_penalty import Actions
environments = ['box', 'vase', 'sushi_goal']
class SideEffectsTestCase(parameterized.TestCase):
def _timestep_to_state(self, timestep):
return tuple(map(tuple, np.copy(timestep.observation['board'])))
def _env_to_action_range(self, env):
action_spec = env.action_spec()
action_range = list(range(action_spec.minimum, action_spec.maximum + 1))
return action_range
class BaselineTestCase(SideEffectsTestCase):
def _create_baseline(self, env_name):
self._env, _ = training.get_env(env_name, True)
self._baseline_env, _ = training.get_env(env_name, True)
baseline_class = getattr(side_effects_penalty,
self.__class__.__name__[:-4]) # remove 'Test'
self._baseline = baseline_class(
self._env.reset(), True, self._baseline_env, self._timestep_to_state)
def _test_trajectory(self, actions, key):
init_state = self._timestep_to_state(self._env.reset())
self._baseline.reset()
current_state = init_state
for action in actions:
timestep = self._env.step(action)
next_state = self._timestep_to_state(timestep)
baseline_state = self._baseline.calculate(current_state, action,
next_state)
comparison_dict = {
'current_state': current_state,
'next_state': next_state,
'init_state': init_state
}
self.assertEqual(baseline_state, comparison_dict[key])
current_state = next_state
if timestep.last():
return
class StartBaselineTest(BaselineTestCase):
@parameterized.parameters(*environments)
def testInit(self, env_name):
self._create_baseline(env_name)
self._test_trajectory([Actions.NOOP], 'init_state')
@parameterized.parameters(*environments)
def testTenNoops(self, env_name):
self._create_baseline(env_name)
self._test_trajectory([Actions.NOOP for _ in range(10)], 'init_state')
class InactionBaselineTest(BaselineTestCase):
box_env, _ = training.get_env('box', True)
box_action_spec = box_env.action_spec()
@parameterized.parameters(
*list(range(box_action_spec.minimum, box_action_spec.maximum + 1)))
def testStaticEnvOneAction(self, action):
self._create_baseline('box')
self._test_trajectory([action], 'init_state')
def testStaticEnvRandomActions(self):
self._create_baseline('box')
num_steps = np.random.randint(low=1, high=20)
action_range = self._env_to_action_range(self._env)
actions = [np.random.choice(action_range) for _ in range(num_steps)]
self._test_trajectory(actions, 'init_state')
@parameterized.parameters(*environments)
def testInactionPolicy(self, env_name):
self._create_baseline(env_name)
num_steps = np.random.randint(low=1, high=20)
self._test_trajectory([Actions.NOOP for _ in range(num_steps)],
'next_state')
class StepwiseBaselineTest(BaselineTestCase):
def testStaticEnvRandomActions(self):
self._create_baseline('box')
action_range = self._env_to_action_range(self._env)
num_steps = np.random.randint(low=1, high=20)
actions = [np.random.choice(action_range) for _ in range(num_steps)]
self._test_trajectory(actions, 'current_state')
@parameterized.parameters(*environments)
def testInactionPolicy(self, env_name):
self._create_baseline(env_name)
num_steps = np.random.randint(low=1, high=20)
self._test_trajectory([Actions.NOOP for _ in range(num_steps)],
'next_state')
@parameterized.parameters(*environments)
def testInactionRollout(self, env_name):
self._create_baseline(env_name)
init_state = self._timestep_to_state(self._env.reset())
self._baseline.reset()
action = Actions.NOOP
state1 = init_state
trajectory = [init_state]
for _ in range(10):
trajectory.append(self._timestep_to_state(self._env.step(action)))
state2 = trajectory[-1]
self._baseline.calculate(state1, action, state2)
state1 = state2
chain = self._baseline.rollout_func(init_state)
self.assertEqual(chain, trajectory[:len(chain)])
if len(chain) < len(trajectory):
self.assertEqual(trajectory[len(chain) - 1], trajectory[len(chain)])
def testStaticRollouts(self):
self._create_baseline('box')
action_range = self._env_to_action_range(self._env)
num_steps = np.random.randint(low=1, high=20)
actions = [np.random.choice(action_range) for _ in range(num_steps)]
state1 = self._timestep_to_state(self._env.reset())
states = [state1]
self._baseline.reset()
for action in actions:
state2 = self._timestep_to_state(self._env.step(action))
states.append(state2)
self._baseline.calculate(state1, action, state2)
state1 = state2
i1, i2 = np.random.choice(len(states), 2)
chain = self._baseline.parallel_inaction_rollouts(states[i1], states[i2])
self.assertLen(chain, 1)
chain1 = self._baseline.rollout_func(states[i1])
self.assertLen(chain1, 1)
chain2 = self._baseline.rollout_func(states[i2])
self.assertLen(chain2, 1)
@parameterized.parameters(('parallel', 'vase'), ('parallel', 'sushi'),
('inaction', 'vase'), ('inaction', 'sushi'))
def testConveyorRollouts(self, which_rollout, env_name):
self._create_baseline(env_name)
init_state = self._timestep_to_state(self._env.reset())
self._baseline.reset()
action = Actions.NOOP
state1 = init_state
init_state_next = self._timestep_to_state(self._env.step(action))
state2 = init_state_next
self._baseline.calculate(state1, action, state2)
state1 = state2
for _ in range(10):
state2 = self._timestep_to_state(self._env.step(action))
self._baseline.calculate(state1, action, state2)
state1 = state2
if which_rollout == 'parallel':
chain = self._baseline.parallel_inaction_rollouts(init_state,
init_state_next)
else:
chain = self._baseline.rollout_func(init_state)
self.assertLen(chain, 5)
class NoDeviationTest(SideEffectsTestCase):
def _random_initial_transition(self):
env_name = np.random.choice(environments)
noops = np.random.choice([True, False])
env, _ = training.get_env(env_name, noops)
action_range = self._env_to_action_range(env)
action = np.random.choice(action_range)
state1 = self._timestep_to_state(env.reset())
state2 = self._timestep_to_state(env.step(action))
return (state1, state2)
def testNoDeviation(self):
deviation = side_effects_penalty.NoDeviation()
state1, state2 = self._random_initial_transition()
self.assertEqual(deviation.calculate(state1, state2), 0)
def testNoDeviationUpdate(self):
deviation = side_effects_penalty.NoDeviation()
state1, state2 = self._random_initial_transition()
deviation.update(state1, state2)
self.assertEqual(deviation.calculate(state1, state2), 0)
class UnreachabilityTest(SideEffectsTestCase):
@parameterized.named_parameters(('Discounted', 0.99), ('Undiscounted', 1.0))
def testUnreachabilityCycle(self, gamma):
# Reachability with no dev_fun means unreachability
deviation = side_effects_penalty.Reachability(value_discount=gamma)
env, _ = training.get_env('box', False)
state0 = self._timestep_to_state(env.reset())
state1 = self._timestep_to_state(env.step(Actions.LEFT))
# deviation should not be calculated before calling update
deviation.update(state0, state1)
self.assertEqual(deviation.calculate(state0, state0), 1.0 - 1.0)
self.assertEqual(deviation.calculate(state0, state1), 1.0 - gamma)
self.assertEqual(deviation.calculate(state1, state0), 1.0 - 0.0)
state2 = self._timestep_to_state(env.step(Actions.RIGHT))
self.assertEqual(state0, state2)
deviation.update(state1, state2)
self.assertEqual(deviation.calculate(state0, state0), 1.0 - 1.0)
self.assertEqual(deviation.calculate(state0, state1), 1.0 - gamma)
self.assertEqual(deviation.calculate(state1, state0), 1.0 - gamma)
self.assertEqual(deviation.calculate(state1, state1), 1.0 - 1.0)
if __name__ == '__main__':
absltest.main()
================================================
FILE: side_effects_penalties/training.py
================================================
[File too large to display: 5.6 KB]
================================================
FILE: sketchy/README.md
================================================
# Sketchy data
This is a dataset accompanying the paper
[Scaling data-driven robotics with reward sketching and batch reinforcement learning](https://arxiv.org/abs/1909.12200).
If you use this dataset in your research please cite
```
@article{cabi2019,
title={Scaling data-driven robotics with reward sketching and batch reinforcement learning},
author={Serkan Cabi and
Sergio G{\'o}mez Colmenarejo and
Alexander Novikov and
Ksenia Konyushkova and
Scott Reed and
Rae Jeong and
Konrad Zolna and
Yusuf Aytar and
David Budden and
Mel Vecerik and
Oleg Sushkov and
David Barker and
Jonathan Scholz and
Misha Denil and
Nando de Freitas and
Ziyu Wang},
journal={arXiv preprint arXiv:1909.12200},
year={2019}
}
```
## See example data
There is a small amount of example data included in this repository. To examine
it, run the following commands from the repository root (i.e. one level up from
this folder):
```
python3 -m venv .sketchy_env
source .sketchy_env/bin/activate
pip install --upgrade pip
pip install -r sketchy/requirements.txt
python -m sketchy.dataset_example --show_images
```
For an example of loading rewards for episodes see `reward_example.py`.
## Download the full dataset
Run `./download.sh path/to/download/folder` to download the full dataset. The
full dataset requires ~5.0TB of disk space to download, and extracts to approximately the same size.
You can edit `download.sh` to download subsets of the data.
Once the dataset has been downloaded it can be extracted wtih
`./extract.sh path/to/download/folder`.
### Named subsets
We provide several named subsets of the full dataset, which can be easily
downloaded on their own. See `download.sh` for a description of the subsets
that are provided.
The episodes in each of these named subsets are identified by a tag in the
metadata.
If you would like to curate your own subset you can download the metadata
file and inspect the `ArchiveFiles` table (see below) to figure out which
archive files contain the episodes you want.
# Dataset Contents
The dataset is distribted as a *metadata file* (`metadata.sqlite`) and a
collection of *archive files* (with names ending in `.tar.bz2`).
The metadata file contains information about the episodes, including annotated
rewards for a subset of the episodes.
Each archive file contains several *episode files*, which have names like
`10000313341320364033_b615a417-ce34-41a8-8411-2a1ce3f3bd07`.
Each episode file is a
[tfrecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) file,
containing a sequence of *timesteps* for a single episode.
Each timestep is a `tf.train.Example` proto containing features corresponding to
the observations and actions from a particular point in time.
## Metadata
The metadata file, `metadata.sqlite`, is a sqlite database containing metadata
describing the contents of the files in the dataset.
The following sections describe the important metadata tables. You can find the
full schema by running
```
sqlite3 metadata.sqlite <<< .schema
```
### Episodes
- `EpisodeId`: A string of digits that uniquely identifies the episode.
- `TaskId`: A human readable name for the task corresponding to the behavior
that generated the episode.
- `DataPath`: The name of the episode file holding the data for this episode.
- `EpisodeType`: A string describing the type of policy that generated the
episode. Possible values are:
- `EPISODE_ROBOT_AGENT`: The behavior policy is a learned or scripted
controller.
- `EPISODE_ROBOT_TELEOPERATION`: The behavior policy is a human teleoperating
the robot.
- `EPISODE_ROBOT_DAGGER`: The behavior policy is a mix of controller and human
generated actions.
- `Timestamp`: A unix timestamp recording when the episode was generated.
### EpisodeTags
- `EpisodeId`: Foreign key into the `Episodes` table.
- `Tag`: A human readable identifier for some aspect of the episode (e.g. which
object set is used).
### RewardSequences
- `EpisodeId`: Foreign key into the `Episodes` table.
- `RewardSequenceId`: Distinguishes multiple rewards for the same episode.
- `RewardTaskId`: A human readable name of the task for this reward signal.
Typically the same as the corresponding `TaskId` in the `Episodes` table.
- `Type`: A string describing the type of reward signal. Currently the only
value is `REWARD_SKETCH`.
- `Values`: A sequence of float32 values, packed as a binary blob. There is one
float value for each frame of the episode, corresponding to the annotated
reward.
### ArchiveFiles
- `EpisodeId`: Foreign key into the `Episodes` table.
- `ArchiveFile`: Name of the archive file containing the corresponding episode.
## Episodes
Each episode file is a
[tfrecords](https://www.tensorflow.org/tutorials/load_data/tfrecord) file
containing a sequence of timesteps, encoded as
[`tf.train.Example`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto)
protos.
Each episode file contains a single episode, and each timestep within an episode
contains all of the observations and actions associated with a that timestep as
a single `tf.train.Example`. Within each episode file the timesteps are
temporally ordered, so reading a file from beginning to end will visit all of
the timesteps from the episode in the order they occurred.
Observations and actions occur at 10Hz.
## Timesteps
Each timestep is a collection of observations and actions. Actions stored with a
timestep correspond to actions taken in response to the observations they are
stored with.
For a description of the shapes and types of the timestep data, see the data
loader in `sketchy.py`.
# Dataset Metadata
The following table is necessary for this dataset to be indexed by search
engines such as Google Dataset Search.
================================================
FILE: sketchy/__init__.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
================================================
FILE: sketchy/dataset_example.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of loading sketchy data in tensorflow."""
from absl import app
from absl import flags
import matplotlib.pyplot as plt
import tensorflow.compat.v2 as tf
from sketchy import sketchy
flags.DEFINE_boolean('show_images', False, 'Enable to show example images.')
FLAGS = flags.FLAGS
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
tf.enable_v2_behavior()
# The example file contains only a few timesteps from a single episode.
dataset = sketchy.load_frames('sketchy/example_data.tfrecords')
dataset = dataset.prefetch(5)
for example in dataset:
print('---')
for name, value in sorted(example.items()):
print(name, value.dtype, value.shape)
if FLAGS.show_images:
plt.imshow(example['pixels/basket_front_left'])
plt.show()
if __name__ == '__main__':
app.run(main)
================================================
FILE: sketchy/download.sh
================================================
#!/bin/bash
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Use this script to download the sketchy dataset.
#
# You will need to extract the archive files before using them. Each archive
# (except for the last in each group) contains 100 episodes.
if [ -z "$1" ]; then
echo "Usage: $(basename "$0") download_folder" >&2
exit 1
fi
DOWNLOAD_FOLDER="$1"
NUM_PARALLEL_DOWNLOADS="4" # set the number of download workers
DATA_URL="https://storage.googleapis.com/sketchy-data"
function download_shards {
# Usage: download_shards prefix num_shards
local PREFIX="$1"
local LIMIT="$(printf "%05d" "$2")"
# Avoid leading zeros or this will be interpreted as an octal number.
local MAX="$(("$2"-1))"
(
for IDX in $(seq -f'%05.0f' 0 "$MAX"); do
echo "${PREFIX}-${IDX}-of-${LIMIT}.tar.bz2"
done
) | xargs -I{} -n1 -P"${NUM_PARALLEL_DOWNLOADS}" \
curl "${DATA_URL}/{}" --output "${DOWNLOAD_FOLDER}/{}"
}
# This is the metadata. This file is small, you always want it.
curl "${DATA_URL}/metadata.sqlite" --output "${DOWNLOAD_FOLDER}/metadata.sqlite"
# Download these files if you want all and only the episodes with an associated
# reward sequence.
#
# sqlite3 metadata.sqlite <&2
exit 1
fi
DOWNLOAD_FOLDER="$1"
NUM_PARALLEL_WORKERS="$(grep processor /proc/cpuinfo | wc -l)"
cd "$DOWNLOAD_FOLDER"
find . -name '*.tar.bz2' -print0 \
| xargs -0 -n1 -P"$NUM_PARALLEL_WORKERS" tar xf
================================================
FILE: sketchy/metadata_schema.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Sqlalchemy schema for the metadata db."""
import sqlalchemy
from sqlalchemy.ext import declarative
Column = sqlalchemy.Column
Integer = sqlalchemy.Integer
String = sqlalchemy.String
LargeBinary = sqlalchemy.LargeBinary
ForeignKey = sqlalchemy.ForeignKey
# pylint: disable=invalid-name
# https://docs.sqlalchemy.org/en/13/orm/tutorial.html
Base = declarative.declarative_base()
EpisodeTag = sqlalchemy.Table(
'EpisodeTags', Base.metadata,
Column(
'EpisodeId', String, ForeignKey('Episodes.EpisodeId'),
primary_key=True),
Column('Tag', String, ForeignKey('Tags.Name'), primary_key=True))
"""Table relating episodes and tags.
Attributes:
EpisodeId: A string of digits that uniquely identifies the episode.
Tag: Human readable tag name.
"""
class Episode(Base):
"""Table describing individual episodes.
Attributes:
EpisodeId: A string of digits that uniquely identifies the episode.
TaskId: A human readable name for the task corresponding to the behavior
that generated the episode.
DataPath: The name of the episode file holding the data for this episode.
Timestamp: A unix timestamp recording when the episode was generated.
EpisodeType: A string describing the type of policy that generated the
episode. Possible values are:
- `EPISODE_ROBOT_AGENT`: The behavior policy is a learned or scripted
controller.
- `EPISODE_ROBOT_TELEOPERATION`: The behavior policy is a human
teleoperating the robot.
- `EPISODE_ROBOT_DAGGER`: The behavior policy is a mix of controller
and human generated actions.
Tags: A list of tags attached to this episode.
Rewards: A list of `RewardSequence`s containing sketched rewards for this
episode.
"""
__tablename__ = 'Episodes'
EpisodeId = Column(String, primary_key=True)
TaskId = Column(String)
DataPath = Column(String)
Timestamp = Column(Integer)
EpisodeType = Column(String)
Tags = sqlalchemy.orm.relationship(
'Tag', secondary=EpisodeTag, back_populates='Episodes')
Rewards = sqlalchemy.orm.relationship(
'RewardSequence', backref='Episode')
class Tag(Base):
"""Table of tags that can be attached to episodes.
Attributes:
Name: Human readable tag name.
Episodes: The epsidoes that have been annotated with this tag.
"""
__tablename__ = 'Tags'
Name = Column(String, primary_key=True)
Episodes = sqlalchemy.orm.relationship(
'Episode', secondary=EpisodeTag, back_populates='Tags')
class RewardSequence(Base):
"""Table describing reward sequences for episodes.
Attributes:
EpisodeId: Foreign key into the `Episodes` table.
RewardSequenceId: Distinguishes multiple rewards for the same episode.
RewardTaskId: A human readable name of the task for this reward signal.
Typically the same as the corresponding `TaskId` in the `Episodes`
table.
Type: A string describing the type of reward signal. Currently the only
value is `REWARD_SKETCH`.
User: The name of the user who produced this reward sequence.
Values: A sequence of float32 values, packed as a binary blob. There is one
float value for each frame of the episode, corresponding to the
annotated reward.
"""
__tablename__ = 'RewardSequences'
EpisodeId = Column(
'EpisodeId', String, ForeignKey('Episodes.EpisodeId'), primary_key=True)
RewardSequenceId = Column(String, primary_key=True)
RewardTaskId = Column('RewardTaskId', String)
Type = Column(String)
User = Column(String)
Values = Column(LargeBinary)
class ArchiveFile(Base):
"""Table describing where episodes are stored in archives.
This information is relevant if you want to download or extract a specific
episode from the archives they are distributed in.
Attributes:
EpisodeId: Foreign key into the `Episodes` table.
ArchiveFile: Name of the archive file containing the corresponding episode.
"""
__tablename__ = 'ArchiveFiles'
EpisodeId = Column(
'EpisodeId', String, ForeignKey('Episodes.EpisodeId'), primary_key=True)
ArchiveFile = Column(String)
# pylint: enable=invalid-name
================================================
FILE: sketchy/requirements.txt
================================================
absl-py==0.9.0
astor==0.8.1
cachetools==4.0.0
certifi==2019.11.28
chardet==3.0.4
cycler==0.10.0
gast==0.2.2
google-auth==1.10.0
google-auth-oauthlib==0.4.1
google-pasta==0.1.8
grpcio==1.26.0
h5py==2.10.0
idna==2.8
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
kiwisolver==1.1.0
Markdown==3.1.1
matplotlib==3.1.2
numpy==1.18.1
oauthlib==3.1.0
opt-einsum==3.1.0
protobuf==3.11.2
pyasn1==0.4.8
pyasn1-modules==0.2.7
pyparsing==2.4.6
python-dateutil==2.8.1
requests==2.22.0
requests-oauthlib==1.3.0
rsa==4.0
six==1.13.0
SQLAlchemy==1.3.12
tensorboard==2.0.2
tensorflow==2.0.0
tensorflow-estimator==2.0.1
termcolor==1.1.0
urllib3==1.25.7
Werkzeug==0.16.0
wrapt==1.11.2
================================================
FILE: sketchy/reward_example.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of loading rewards from the metadata file."""
from absl import app
from absl import flags
import numpy as np
import sqlalchemy
from sketchy import metadata_schema
flags.DEFINE_string(
'metadata', '/tmp/metadata.sqlite', 'Path to metadata file.')
FLAGS = flags.FLAGS
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
engine = sqlalchemy.create_engine('sqlite:///' + FLAGS.metadata)
session = sqlalchemy.orm.sessionmaker(bind=engine)()
episodes = session.query(metadata_schema.Episode).join(
metadata_schema.RewardSequence).limit(5)
for episode in episodes:
rewards = np.frombuffer(episode.Rewards[0].Values, dtype=np.float32)
print('---')
print(f'Episode: {episode.EpisodeId}')
print(f'Episode file: {episode.DataPath}')
print(f'Reward type: {episode.Rewards[0].Type}')
print(f'Reward values: {rewards}')
if __name__ == '__main__':
app.run(main)
================================================
FILE: sketchy/run.sh
================================================
#!/bin/bash
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
python3 -m venv /tmp/sketchy_env
source /tmp/sketchy_env/bin/activate
pip install --upgrade pip
pip install -r sketchy/requirements.txt
python -m sketchy.dataset_example --noshow_images
================================================
FILE: sketchy/sketchy.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Interface for loading sketchy data into tensorflow."""
import tensorflow.compat.v2 as tf
def load_frames(filenames, num_parallel_reads=1, num_map_threads=None):
if not num_map_threads:
num_map_threads = num_parallel_reads
dataset = tf.data.TFRecordDataset(
filenames, num_parallel_reads=num_parallel_reads)
return dataset.map(_parse_example, num_parallel_calls=num_map_threads)
_FEATURES = {
# Actions
'actions':
tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
# Observations
'gripper/joints/velocity':
tf.io.FixedLenFeature(shape=1, dtype=tf.float32),
'gripper/joints/torque':
tf.io.FixedLenFeature(shape=1, dtype=tf.float32),
'gripper/grasp':
tf.io.FixedLenFeature(shape=1, dtype=tf.int64),
'gripper/joints/angle':
tf.io.FixedLenFeature(shape=1, dtype=tf.float32),
'sawyer/joints/velocity':
tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
'sawyer/pinch/pose':
tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
'sawyer/tcp/pose':
tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
'sawyer/tcp/effort':
tf.io.FixedLenFeature(shape=6, dtype=tf.float32),
'sawyer/joints/torque':
tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
'sawyer/tcp/velocity':
tf.io.FixedLenFeature(shape=6, dtype=tf.float32),
'sawyer/joints/angle':
tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
'wrist/torque':
tf.io.FixedLenFeature(shape=3, dtype=tf.float32),
'wrist/force':
tf.io.FixedLenFeature(shape=3, dtype=tf.float32),
'pixels/basket_front_left':
tf.io.FixedLenFeature(shape=1, dtype=tf.string),
'pixels/basket_back_left':
tf.io.FixedLenFeature(shape=1, dtype=tf.string),
'pixels/basket_front_right':
tf.io.FixedLenFeature(shape=1, dtype=tf.string),
'pixels/royale_camera_driver_depth':
tf.io.FixedLenFeature(shape=(171, 224, 1), dtype=tf.float32),
'pixels/royale_camera_driver_gray':
tf.io.FixedLenFeature(shape=1, dtype=tf.string),
'pixels/usbcam0':
tf.io.FixedLenFeature(shape=1, dtype=tf.string),
'pixels/usbcam1':
tf.io.FixedLenFeature(shape=1, dtype=tf.string),
}
def _parse_example(example):
return _decode_images(tf.io.parse_single_example(example, _FEATURES))
def _decode_images(record):
for name, value in list(record.items()):
if value.dtype == tf.string:
record[name] = tf.io.decode_jpeg(value[0])
return record
================================================
FILE: synthetic_returns/README.md
================================================
# Code for Synthetic Returns
This repository contains code for the arXiv preprint
["Synthetic Returns for Long-Term Credit Assignment"](https://arxiv.org/abs/2102.12425)
by David Raposo, Sam Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt
Botvinick, Hado van Hasselt, and Francis Song.
To cite this work:
```
@article{raposo2021synthetic,
title={Rapid Task-Solving in Novel Environments},
author={Raposo, David and Ritter, Sam and Santoro, Adam and Wayne, Greg and
Weber, Theophane and Botvinick, Matt and van Hasselt, Hado and Song, Francis},
journal={arXiv preprint arXiv:2102.12425},
year={2021}
}
```
### Agent core wrapper
We implemented the Synthetic Returns module as a wrapper to a recurrent neural
network (RNN), so it should be compatible with any Deep-RL agent with an
arbitrary RNN core, whose inputs consist of batches of vectors. This could be an
LSTM as in the example below, or a more sophisticated core as long as it
implements an `hk.RNNCore`.
```python
agent_core = hk.LSTM(128)
```
To build the SR wrapper, simply pass the existing agent core to the constructor,
along with the SR configuration:
```python
sr_config = {
"memory_size": 128,
"capacity": 300,
"hidden_layers": (128, 128),
"alpha": 0.3,
"beta": 1.0,
}
sr_agent_core = hk.ResetCore(
SyntheticReturnsCoreWrapper(core=agent_core, **sr_config))
```
Typically, the SR wrapper should itself be wrapped in a `hk.ResetCore` in order
to reset the core state in the beginning of a new episode. This will reset not
only the episodic memory but also the original agent core that was passed to the
SR wrapper constructor.
### Learner
Consider the distributed setting, wherein a learner receives mini-batches of
trajectories of length `T` produced by the actors.
`trajectory` is a nested structure of tensors of size `[T,B,...]` (where `B` is
the batch size) containing observations, agent states, rewards and step type
indicators.
We start by producing inputs to the SR core, which consist of tuples of current
state embeddings and return targets. The current state embeddings can be
produced by a ConvNet, for example. In our experiments we used the current step
reward as target. Note that the current step reward correspond to the rewards in
the trajectory shifted by one, relative to the observations:
```python
observations = jax.tree_map(lambda x: x[:-1], trajectory.observation)
vision_output = hk.BatchApply(vision_net)(observations)
return_targets = trajectory.reward[1:]
sr_core_inputs = (vision_output, return_targets)
```
For purposes of core resetting at the beginning of a new episode, we also need
to pass an indicator of which steps correspond to the first step of an episode.
```python
should_reset = jnp.equal(
trajectory.step_type[:-1], int(dm_env.StepType.FIRST))
core_inputs = (sr_core_inputs, should_reset)
```
We can now produce an unroll using `hk.dynamic_unroll` and passing it the SR
core, the core inputs we produced, and the initial state of the unroll, which
corresponds to the agent state in the first step of the trajectory:
```python
state = jax.tree_map(lambda t: t[0], trajectory.agent_state)
core_output, state = hk.dynamic_unroll(
sr_agent_core, core_inputs, state)
```
The SR wrapper produces 4 output tensors: the output of the agent core, the
synthetic returns, the SR-augmented return, and the SR loss.
The synthetic returns are taken into account when computing the augmented return
and the SR loss. Therefore they are not needed anymore and can be discarded or
used for logging purposes.
The agent core outputs should be used, as usual, for producing a policy. In an
actor-critic, policy gradient set-up, like IMPALA, we would produce policy
logits and values:
```python
policy_logits = hk.BatchApply(policy_net)(core_output.output)
value = hk.BatchApply(baseline_net)(core_output.output)
```
Similarly, in a Q-learning setting we would use the agent core outputs to
produce q-values.
The SR-augmented returns should be used in place of the environment rewards for
the policy updates (e.g. when computing the policy gradient and baseline
losses):
```python
rewards = core_output.augmented_return
```
Finally, the SR loss, summed over batch and time dimensions, should be added to
the total learner loss to be minimized:
```python
total_loss += jnp.sum(core_output.sr_loss)
```
================================================
FILE: synthetic_returns/requirements.txt
================================================
dm-haiku>=0.0.3
jax>=0.2.8
================================================
FILE: synthetic_returns/synthetic_returns.py
================================================
# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Episodic Memory and Synthetic Returns Core Wrapper modules."""
import collections
import haiku as hk
import jax
import jax.numpy as jnp
SRCoreWrapperOutput = collections.namedtuple(
"SRCoreWrapperOutput", ["output", "synthetic_return", "augmented_return",
"sr_loss"])
class EpisodicMemory(hk.RNNCore):
"""Episodic Memory module."""
def __init__(self, memory_size, capacity, name="episodic_memory"):
"""Constructor.
Args:
memory_size: Integer. The size of the vectors to be stored.
capacity: Integer. The maximum number of memories to store before it
becomes necessary to overwrite old memories.
name: String. A name for this Haiku module instance.
"""
super().__init__(name=name)
self._memory_size = memory_size
self._capacity = capacity
def __call__(self, inputs, prev_state):
"""Writes a new memory into the episodic memory.
Args:
inputs: A Tensor of shape ``[batch_size, memory_size]``.
prev_state: The previous state of the episodic memory, which is a tuple
with a (i) counter of shape ``[batch_size, 1]`` indicating how many
memories have been written so far, and (ii) a tensor of shape
``[batch_size, capacity, memory_size]`` with the full content of the
episodic memory.
Returns:
A tuple with (i) a tensor of shape ``[batch_size, capacity, memory_size]``
with the full content of the episodic memory, including the newly
written memory, and (ii) the new state of the episodic memory.
"""
inputs = jax.lax.stop_gradient(inputs)
counter, memories = prev_state
counter_mod = jnp.mod(counter, self._capacity)
slot_selector = jnp.expand_dims(
jax.nn.one_hot(counter_mod, self._capacity), axis=2)
memories = memories * (1 - slot_selector) + (
slot_selector * jnp.expand_dims(inputs, 1))
counter = counter + 1
return memories, (counter, memories)
def initial_state(self, batch_size):
"""Creates the initial state of the episodic memory.
Args:
batch_size: Integer. The batch size of the episodic memory.
Returns:
A tuple with (i) a counter of shape ``[batch_size, 1]`` and (ii) a tensor
of shape ``[batch_size, capacity, memory_size]`` with the full content
of the episodic memory.
"""
if batch_size is None:
shape = []
else:
shape = [batch_size]
counter = jnp.zeros(shape)
memories = jnp.zeros(shape + [self._capacity, self._memory_size])
return (counter, memories)
class SyntheticReturnsCoreWrapper(hk.RNNCore):
"""Synthetic Returns core wrapper."""
def __init__(self, core, memory_size, capacity, hidden_layers, alpha, beta,
loss_func=(lambda x, y: 0.5 * jnp.square(x - y)),
apply_core_to_input=False, name="synthetic_returns_wrapper"):
"""Constructor.
Args:
core: hk.RNNCore. The recurrent core of the agent. E.g. an LSTM.
memory_size: Integer. The size of the vectors to be stored in the episodic
memory.
capacity: Integer. The maximum number of memories to store before it
becomes necessary to overwrite old memories.
hidden_layers: Tuple or list of integers, indicating the size of the
hidden layers of the MLPs used to produce synthetic returns, current
state bias, and gate.
alpha: The multiplier of the synthetic returns term in the augmented
return.
beta: The multiplier of the environment returns term in the augmented
return.
loss_func: A function of two arguments (predictions and targets) to
compute the SR loss.
apply_core_to_input: Boolean. Whether to apply the core on the inputs. If
true, the synthetic returns will be computed from the outputs of the
RNN core passed to the constructor. If false, the RNN core will be
applied only at the output of this wrapper, and the synthetic returns
will be computed from the inputs.
name: String. A name for this Haiku module instance.
"""
super().__init__(name=name)
self._em = EpisodicMemory(memory_size, capacity)
self._capacity = capacity
hidden_layers = list(hidden_layers)
self._synthetic_return = hk.nets.MLP(hidden_layers + [1])
self._bias = hk.nets.MLP(hidden_layers + [1])
self._gate = hk.Sequential([
hk.nets.MLP(hidden_layers + [1]),
jax.nn.sigmoid,
])
self._apply_core_to_input = apply_core_to_input
self._core = core
self._alpha = alpha
self._beta = beta
self._loss = loss_func
def initial_state(self, batch_size):
return (
self._em.initial_state(batch_size),
self._core.initial_state(batch_size)
)
def __call__(self, inputs, prev_state):
current_input, return_target = inputs
em_state, core_state = prev_state
(counter, memories) = em_state
if self._apply_core_to_input:
current_input, core_state = self._core(current_input, core_state)
# Synthetic return for the current state
synth_return = jnp.squeeze(self._synthetic_return(current_input), -1)
# Current state bias term
bias = self._bias(current_input)
# Gate computed from current state
gate = self._gate(current_input)
# When counter > capacity, mask will be all ones
mask = 1 - jnp.cumsum(jax.nn.one_hot(counter, self._capacity), axis=1)
mask = jnp.expand_dims(mask, axis=2)
# Synthetic returns for each state in memory
past_synth_returns = hk.BatchApply(self._synthetic_return)(memories)
# Sum of synthetic returns from previous states
sr_sum = jnp.sum(past_synth_returns * mask, axis=1)
prediction = jnp.squeeze(sr_sum * gate + bias, -1)
sr_loss = self._loss(prediction, return_target)
augmented_return = jax.lax.stop_gradient(
self._alpha * synth_return + self._beta * return_target)
# Write current state to memory
_, em_state = self._em(current_input, em_state)
if not self._apply_core_to_input:
output, core_state = self._core(current_input, core_state)
else:
output = current_input
output = SRCoreWrapperOutput(
output=output,
synthetic_return=synth_return,
augmented_return=augmented_return,
sr_loss=sr_loss,
)
return output, (em_state, core_state)
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FILE: tandem_dqn/agent.py
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FILE: tandem_dqn/atari_data.py
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FILE: tandem_dqn/gym_atari.py
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# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""dm_env environment wrapper around Gym Atari configured to be like Xitari.
Gym Atari is built on the Arcade Learning Environment (ALE), whereas Xitari is
an old fork of the ALE.
"""
# pylint: disable=g-bad-import-order
from typing import Optional, Tuple
import atari_py # pylint: disable=unused-import for gym to load Atari games.
import dm_env
from dm_env import specs
import gym
import numpy as np
from tandem_dqn import atari_data
_GYM_ID_SUFFIX = '-xitari-v1'
_SA_SUFFIX = '-sa'
def _game_id(game, sticky_actions):
return game + (_SA_SUFFIX if sticky_actions else '') + _GYM_ID_SUFFIX
def _register_atari_environments():
"""Registers Atari environments in Gym to be as similar to Xitari as possible.
Main difference from PongNoFrameSkip-v4, etc. is max_episode_steps is unset
and only the usual 57 Atari games are registered.
Additionally, sticky-actions variants of the environments are registered
with an '-sa' suffix.
"""
for sticky_actions in [False, True]:
for game in atari_data.ATARI_GAMES:
repeat_action_probability = 0.25 if sticky_actions else 0.0
gym.envs.registration.register(
id=_game_id(game, sticky_actions),
entry_point='gym.envs.atari:AtariEnv',
kwargs={ # Explicitly set all known arguments.
'game': game,
'mode': None, # Not necessarily the same as 0.
'difficulty': None, # Not necessarily the same as 0.
'obs_type': 'image',
'frameskip': 1, # Get every frame.
'repeat_action_probability': repeat_action_probability,
'full_action_space': False,
},
max_episode_steps=None, # No time limit, handled in run loop.
nondeterministic=False, # Xitari is deterministic.
)
_register_atari_environments()
class GymAtari(dm_env.Environment):
"""Gym Atari with a `dm_env.Environment` interface."""
def __init__(self, game, sticky_actions, seed):
self._gym_env = gym.make(_game_id(game, sticky_actions))
self._gym_env.seed(seed)
self._start_of_episode = True
def reset(self) -> dm_env.TimeStep:
"""Resets the environment and starts a new episode."""
observation = self._gym_env.reset()
lives = np.int32(self._gym_env.ale.lives())
timestep = dm_env.restart((observation, lives))
self._start_of_episode = False
return timestep
def step(self, action: np.int32) -> dm_env.TimeStep:
"""Updates the environment given an action and returns a timestep."""
# If the previous timestep was LAST then we call reset() on the Gym
# environment, otherwise step(). Although Gym environments allow you to step
# through episode boundaries (similar to dm_env) they emit a warning.
if self._start_of_episode:
step_type = dm_env.StepType.FIRST
observation = self._gym_env.reset()
discount = None
reward = None
done = False
else:
observation, reward, done, info = self._gym_env.step(action)
if done:
assert 'TimeLimit.truncated' not in info, 'Should never truncate.'
step_type = dm_env.StepType.LAST
discount = 0.
else:
step_type = dm_env.StepType.MID
discount = 1.
lives = np.int32(self._gym_env.ale.lives())
timestep = dm_env.TimeStep(
step_type=step_type,
observation=(observation, lives),
reward=reward,
discount=discount,
)
self._start_of_episode = done
return timestep
def observation_spec(self) -> Tuple[specs.Array, specs.Array]:
space = self._gym_env.observation_space
return (specs.Array(shape=space.shape, dtype=space.dtype, name='rgb'),
specs.Array(shape=(), dtype=np.int32, name='lives'))
def action_spec(self) -> specs.DiscreteArray:
space = self._gym_env.action_space
return specs.DiscreteArray(
num_values=space.n, dtype=np.int32, name='action')
def close(self):
self._gym_env.close()
class RandomNoopsEnvironmentWrapper(dm_env.Environment):
"""Adds a random number of noop actions at the beginning of each episode."""
def __init__(self,
environment: dm_env.Environment,
max_noop_steps: int,
min_noop_steps: int = 0,
noop_action: int = 0,
seed: Optional[int] = None):
"""Initializes the random noops environment wrapper."""
self._environment = environment
if max_noop_steps < min_noop_steps:
raise ValueError('max_noop_steps must be greater or equal min_noop_steps')
self._min_noop_steps = min_noop_steps
self._max_noop_steps = max_noop_steps
self._noop_action = noop_action
self._rng = np.random.RandomState(seed)
def reset(self):
"""Begins new episode.
This method resets the wrapped environment and applies a random number
of noop actions before returning the last resulting observation
as the first episode timestep. Intermediate timesteps emitted by the inner
environment (including all rewards and discounts) are discarded.
Returns:
First episode timestep corresponding to the timestep after a random number
of noop actions are applied to the inner environment.
Raises:
RuntimeError: if an episode end occurs while the inner environment
is being stepped through with the noop action.
"""
return self._apply_random_noops(initial_timestep=self._environment.reset())
def step(self, action):
"""Steps environment given action.
If beginning a new episode then random noops are applied as in `reset()`.
Args:
action: action to pass to environment conforming to action spec.
Returns:
`Timestep` from the inner environment unless beginning a new episode, in
which case this is the timestep after a random number of noop actions
are applied to the inner environment.
"""
timestep = self._environment.step(action)
if timestep.first():
return self._apply_random_noops(initial_timestep=timestep)
else:
return timestep
def _apply_random_noops(self, initial_timestep):
assert initial_timestep.first()
num_steps = self._rng.randint(self._min_noop_steps,
self._max_noop_steps + 1)
timestep = initial_timestep
for _ in range(num_steps):
timestep = self._environment.step(self._noop_action)
if timestep.last():
raise RuntimeError('Episode ended while applying %s noop actions.' %
num_steps)
# We make sure to return a FIRST timestep, i.e. discard rewards & discounts.
return dm_env.restart(timestep.observation)
## All methods except for reset and step redirect to the underlying env.
def observation_spec(self):
return self._environment.observation_spec()
def action_spec(self):
return self._environment.action_spec()
def reward_spec(self):
return self._environment.reward_spec()
def discount_spec(self):
return self._environment.discount_spec()
def close(self):
return self._environment.close()
================================================
FILE: tandem_dqn/losses.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Losses for TandemDQN."""
from typing import Any, Callable
import chex
import jax
import jax.numpy as jnp
import rlax
from tandem_dqn import networks
# Batch variants of double_q_learning and SARSA.
batch_double_q_learning = jax.vmap(rlax.double_q_learning)
batch_sarsa_learning = jax.vmap(rlax.sarsa)
# Batch variant of quantile_q_learning with fixed tau input across batch.
batch_quantile_q_learning = jax.vmap(
rlax.quantile_q_learning, in_axes=(0, None, 0, 0, 0, 0, 0, None))
def _mc_learning(
q_tm1: chex.Array,
a_tm1: chex.Numeric,
mc_return_tm1: chex.Array,
) -> chex.Numeric:
"""Calculates the MC return error."""
chex.assert_rank([q_tm1, a_tm1], [1, 0])
chex.assert_type([q_tm1, a_tm1], [float, int])
return mc_return_tm1 - q_tm1[a_tm1]
# Batch variant of MC learning.
batch_mc_learning = jax.vmap(_mc_learning)
def _qr_loss(q_tm1, q_t, q_target_t, transitions, rng_key):
"""Calculates QR-Learning loss from network outputs and transitions."""
del q_t, rng_key # Unused.
# Compute Q value distributions.
huber_param = 1.
quantiles = networks.make_quantiles()
losses = batch_quantile_q_learning(
q_tm1.q_dist,
quantiles,
transitions.a_tm1,
transitions.r_t,
transitions.discount_t,
q_target_t.q_dist, # No double Q-learning here.
q_target_t.q_dist,
huber_param,
)
loss = jnp.mean(losses)
return loss
def _sarsa_loss(q_tm1, q_t, transitions, rng_key):
"""Calculates SARSA loss from network outputs and transitions."""
del rng_key # Unused.
grad_error_bound = 1. / 32
td_errors = batch_sarsa_learning(
q_tm1.q_values,
transitions.a_tm1,
transitions.r_t,
transitions.discount_t,
q_t.q_values,
transitions.a_t
)
td_errors = rlax.clip_gradient(td_errors, -grad_error_bound, grad_error_bound)
losses = rlax.l2_loss(td_errors)
loss = jnp.mean(losses)
return loss
def _mc_loss(q_tm1, transitions, rng_key):
"""Calculates Monte-Carlo return loss, i.e. regression towards MC return."""
del rng_key # Unused.
errors = batch_mc_learning(q_tm1.q_values, transitions.a_tm1,
transitions.mc_return_tm1)
loss = jnp.mean(rlax.l2_loss(errors))
return loss
def _double_q_loss(q_tm1, q_t, q_target_t, transitions, rng_key):
"""Calculates Double Q-Learning loss from network outputs and transitions."""
del rng_key # Unused.
grad_error_bound = 1. / 32
td_errors = batch_double_q_learning(
q_tm1.q_values,
transitions.a_tm1,
transitions.r_t,
transitions.discount_t,
q_target_t.q_values,
q_t.q_values,
)
td_errors = rlax.clip_gradient(td_errors, -grad_error_bound, grad_error_bound)
losses = rlax.l2_loss(td_errors)
loss = jnp.mean(losses)
return loss
def _q_regression_loss(q_tm1, q_tm1_target):
"""Loss for regression of all action values towards targets."""
errors = q_tm1.q_values - jax.lax.stop_gradient(q_tm1_target.q_values)
loss = jnp.mean(rlax.l2_loss(errors))
return loss
def make_loss_fn(loss_type: str, active: bool) -> Callable[..., Any]:
"""Create active or passive loss function of given type."""
if active:
primary = lambda x: x.active
secondary = lambda x: x.passive
else:
primary = lambda x: x.passive
secondary = lambda x: x.active
def sarsa_loss_fn(q_tm1, q_t, q_target_t, transitions, rng_key):
"""SARSA loss using own networks."""
del q_t # Unused.
return _sarsa_loss(primary(q_tm1), primary(q_target_t), transitions,
rng_key)
def mc_loss_fn(q_tm1, q_t, q_target_t, transitions, rng_key):
"""MonteCarlo loss."""
del q_t, q_target_t
return _mc_loss(primary(q_tm1), transitions, rng_key)
def double_q_loss_fn(q_tm1, q_t, q_target_t, transitions, rng_key):
"""Regular DoubleQ loss using own networks."""
return _double_q_loss(primary(q_tm1), primary(q_t), primary(q_target_t),
transitions, rng_key)
def double_q_loss_v_fn(q_tm1, q_t, q_target_t, transitions, rng_key):
"""DoubleQ loss using other network's (target) value function."""
return _double_q_loss(primary(q_tm1), primary(q_t), secondary(q_target_t),
transitions, rng_key)
def double_q_loss_p_fn(q_tm1, q_t, q_target_t, transitions, rng_key):
"""DoubleQ loss using other network's (online) argmax policy."""
return _double_q_loss(primary(q_tm1), secondary(q_t), primary(q_target_t),
transitions, rng_key)
def double_q_loss_pv_fn(q_tm1, q_t, q_target_t, transitions, rng_key):
"""DoubleQ loss using other network's argmax policy & target value fn."""
return _double_q_loss(primary(q_tm1), secondary(q_t), secondary(q_target_t),
transitions, rng_key)
# Pure regression.
def q_regression_loss_fn(q_tm1, q_t, q_target_t, transitions, rng_key):
"""Pure regression of q_tm1(self) towards q_tm1(other)."""
del q_t, q_target_t, transitions, rng_key # Unused.
return _q_regression_loss(primary(q_tm1), secondary(q_tm1))
# QR loss.
def qr_loss_fn(q_tm1, q_t, q_target_t, transitions, rng_key):
"""QR-Q loss using own networks."""
return _qr_loss(primary(q_tm1), primary(q_t), primary(q_target_t),
transitions, rng_key)
if loss_type == 'double_q':
return double_q_loss_fn
elif loss_type == 'sarsa':
return sarsa_loss_fn
elif loss_type == 'mc_return':
return mc_loss_fn
elif loss_type == 'double_q_v':
return double_q_loss_v_fn
elif loss_type == 'double_q_p':
return double_q_loss_p_fn
elif loss_type == 'double_q_pv':
return double_q_loss_pv_fn
elif loss_type == 'q_regression':
return q_regression_loss_fn
elif loss_type == 'qr':
return qr_loss_fn
else:
raise ValueError('Unknown loss "{}"'.format(loss_type))
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FILE: tandem_dqn/losses_test.py
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================================================
FILE: tandem_dqn/networks.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""DQN agent network components and implementation."""
import typing
from typing import Any, Callable, Tuple, Union
import chex
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
Network = hk.Transformed
Params = hk.Params
NetworkFn = Callable[..., Any]
class QNetworkOutputs(typing.NamedTuple):
q_values: jnp.ndarray
class QRNetworkOutputs(typing.NamedTuple):
q_values: jnp.ndarray
q_dist: jnp.ndarray
NUM_QUANTILES = 201
def _dqn_default_initializer(
num_input_units: int) -> hk.initializers.Initializer:
"""Default initialization scheme inherited from past implementations of DQN.
This scheme was historically used to initialize all weights and biases
in convolutional and linear layers of DQN-type agents' networks.
It initializes each weight as an independent uniform sample from [`-c`, `c`],
where `c = 1 / np.sqrt(num_input_units)`, and `num_input_units` is the number
of input units affecting a single output unit in the given layer, i.e. the
total number of inputs in the case of linear (dense) layers, and
`num_input_channels * kernel_width * kernel_height` in the case of
convolutional layers.
Args:
num_input_units: number of input units to a single output unit of the layer.
Returns:
Haiku weight initializer.
"""
max_val = np.sqrt(1 / num_input_units)
return hk.initializers.RandomUniform(-max_val, max_val)
def make_quantiles():
"""Quantiles for QR-DQN."""
return (jnp.arange(0, NUM_QUANTILES) + 0.5) / float(NUM_QUANTILES)
def conv(
num_features: int,
kernel_shape: Union[int, Tuple[int, int]],
stride: Union[int, Tuple[int, int]],
name=None,
) -> NetworkFn:
"""Convolutional layer with DQN's legacy weight initialization scheme."""
def net_fn(inputs):
"""Function representing conv layer with DQN's legacy initialization."""
num_input_units = inputs.shape[-1] * kernel_shape[0] * kernel_shape[1]
initializer = _dqn_default_initializer(num_input_units)
layer = hk.Conv2D(
num_features,
kernel_shape=kernel_shape,
stride=stride,
w_init=initializer,
b_init=initializer,
padding='VALID',
name=name)
return layer(inputs)
return net_fn
def linear(num_outputs: int, with_bias=True, name=None) -> NetworkFn:
"""Linear layer with DQN's legacy weight initialization scheme."""
def net_fn(inputs):
"""Function representing linear layer with DQN's legacy initialization."""
initializer = _dqn_default_initializer(inputs.shape[-1])
layer = hk.Linear(
num_outputs,
with_bias=with_bias,
w_init=initializer,
b_init=initializer,
name=name)
return layer(inputs)
return net_fn
def linear_with_shared_bias(num_outputs: int, name=None) -> NetworkFn:
"""Linear layer with single shared bias instead of one bias per output."""
def layer_fn(inputs):
"""Function representing a linear layer with single shared bias."""
initializer = _dqn_default_initializer(inputs.shape[-1])
bias_free_linear = hk.Linear(
num_outputs, with_bias=False, w_init=initializer, name=name)
linear_output = bias_free_linear(inputs)
bias = hk.get_parameter('b', [1], inputs.dtype, init=initializer)
bias = jnp.broadcast_to(bias, linear_output.shape)
return linear_output + bias
return layer_fn
def dqn_torso() -> NetworkFn:
"""DQN convolutional torso.
Includes scaling from [`0`, `255`] (`uint8`) to [`0`, `1`] (`float32`)`.
Returns:
Network function that `haiku.transform` can be called on.
"""
def net_fn(inputs):
"""Function representing convolutional torso for a DQN Q-network."""
network = hk.Sequential([
lambda x: x.astype(jnp.float32) / 255.,
conv(32, kernel_shape=(8, 8), stride=(4, 4), name='conv1'),
jax.nn.relu,
conv(64, kernel_shape=(4, 4), stride=(2, 2), name='conv2'),
jax.nn.relu,
conv(64, kernel_shape=(3, 3), stride=(1, 1), name='conv3'),
jax.nn.relu,
hk.Flatten(),
])
return network(inputs)
return net_fn
def dqn_value_head(num_actions: int, shared_bias: bool = False) -> NetworkFn:
"""Regular DQN Q-value head with single hidden layer."""
last_layer = linear_with_shared_bias if shared_bias else linear
def net_fn(inputs):
"""Function representing value head for a DQN Q-network."""
network = hk.Sequential([
linear(512, name='linear1'),
jax.nn.relu,
last_layer(num_actions, name='output'),
])
return network(inputs)
return net_fn
def qr_atari_network(num_actions: int, quantiles: jnp.ndarray) -> NetworkFn:
"""QR-DQN network, expects `uint8` input."""
chex.assert_rank(quantiles, 1)
num_quantiles = len(quantiles)
def net_fn(inputs):
"""Function representing QR-DQN Q-network."""
network = hk.Sequential([
dqn_torso(),
dqn_value_head(num_quantiles * num_actions),
])
network_output = network(inputs)
q_dist = jnp.reshape(network_output, (-1, num_quantiles, num_actions))
q_values = jnp.mean(q_dist, axis=1)
q_values = jax.lax.stop_gradient(q_values)
return QRNetworkOutputs(q_dist=q_dist, q_values=q_values)
return net_fn
def double_dqn_atari_network(num_actions: int) -> NetworkFn:
"""DQN network with shared bias in final layer, expects `uint8` input."""
def net_fn(inputs):
"""Function representing DQN Q-network with shared bias output layer."""
network = hk.Sequential([
dqn_torso(),
dqn_value_head(num_actions, shared_bias=True),
])
return QNetworkOutputs(q_values=network(inputs))
return net_fn
def make_network(network_type: str, num_actions: int) -> Network:
"""Constructs network."""
if network_type == 'double_q':
network_fn = double_dqn_atari_network(num_actions)
elif network_type == 'qr':
quantiles = make_quantiles()
network_fn = qr_atari_network(num_actions, quantiles)
else:
raise ValueError('Unknown network "{}"'.format(network_type))
return hk.transform(network_fn)
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FILE: tandem_dqn/parts.py
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================================================
FILE: tandem_dqn/processors.py
================================================
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Composable timestep processing, for DQN Atari preprocessing.
Aims:
* Be self-contained.
* Easy to have the preprocessing on the agent side or on the environment side.
* Easy to swap out and modify parts of the processing.
Conventions:
* The term "processor" is used to refer to any callable that could also have
a `reset()` function to clear any internal state. E.g. a plain function. Or an
instance of a class with `__call__` method, with or without a `reset()`
method.
* `None` means no output when subsampling inputs.
"""
import collections
from typing import Any, Callable, List, Iterable, Optional, Sequence, Text, Tuple
import dm_env
from dm_env import specs
import numpy as np
from PIL import Image
Processor = Callable # Actually a callable that may also have a reset() method.
Nest = Any # Recursive types are not yet supported by pytype.
NamedTuple = Any
StepType = dm_env.StepType
def reset(processor: Processor[[Any], Any]) -> None:
"""Calls `reset()` on a `Processor` or function if the method exists."""
if hasattr(processor, 'reset'):
processor.reset()
identity = lambda v: v
def trailing_zero_pad(
length: int) -> Processor[[List[np.ndarray]], List[np.ndarray]]:
"""Adds trailing zero padding to array lists to ensure a minimum length."""
def trailing_zero_pad_fn(arrays):
padding_length = length - len(arrays)
if padding_length <= 0:
return arrays
zero = np.zeros_like(arrays[0])
return arrays + [zero] * padding_length
return trailing_zero_pad_fn
def none_to_zero_pad(values: List[Optional[NamedTuple]]) -> List[NamedTuple]:
"""Replaces `None`s in a list of named tuples with zeros of same structure."""
actual_values = [n for n in values if n is not None]
if not actual_values:
raise ValueError('Must have at least one value which is not None.')
if len(actual_values) == len(values):
return values
example = actual_values[0]
zero = type(example)(*(np.zeros_like(x) for x in example))
return [zero if v is None else v for v in values]
def named_tuple_sequence_stack(values: Sequence[NamedTuple]) -> NamedTuple:
"""Converts a sequence of named tuples into a named tuple of tuples."""
# [T(1, 2), T(3, 4), T(5, 6)].
transposed = zip(*values)
# ((1, 3, 5), (2, 4, 6)).
return type(values[0])(*transposed)
# T((1, 3, 5), (2, 4, 6)).
class Deque:
"""Double ended queue with a maximum length and initial values."""
def __init__(self, max_length: int, initial_values=None):
self._deque = collections.deque(maxlen=max_length)
self._initial_values = initial_values or []
def reset(self) -> None:
self._deque.clear()
self._deque.extend(self._initial_values)
def __call__(self, value: Any) -> collections.deque:
self._deque.append(value)
return self._deque
class FixedPaddedBuffer:
"""Fixed size `None`-padded buffer which is cleared after it is filled.
E.g. with `length = 3`, `initial_index = 2` and values `[0, 1, 2, 3, 4, 5, 6]`
this will return `~~0`, `1~~`, `12~`, `123`, `4~~`, `45~`, `456`, where `~`
represents `None`. Used to concatenate timesteps for action repeats.
Action repeat requirements are:
* Fixed size buffer of timesteps.
* The `FIRST` timestep should return immediately to get the first action of
the episode, as there is no preceding action to repeat. Prefix with padding.
* For `MID` timesteps, the timestep buffer is periodically returned when full.
* When a `LAST` timestep is encountered, the current buffer of timesteps is
returned, suffixed with padding, as buffers should not cross episode
boundaries.
The requirements can be fulfilled by conditionally subsampling the output of
this processor.
"""
def __init__(self, length: int, initial_index: int):
self._length = length
self._initial_index = initial_index % length
self._index = self._initial_index
self._buffer = [None] * self._length
def reset(self) -> None:
self._index = self._initial_index
self._buffer = [None] * self._length
def __call__(self, value: Any) -> Sequence[Any]:
if self._index >= self._length:
assert self._index == self._length
self._index = 0
self._buffer = [None] * self._length
self._buffer[self._index] = value
self._index += 1
return self._buffer
class ConditionallySubsample:
"""Conditionally passes through input, returning `None` otherwise."""
def __init__(self, condition: Processor[[Any], bool]):
self._condition = condition
def reset(self) -> None:
reset(self._condition)
def __call__(self, value: Any) -> Optional[Any]:
return value if self._condition(value) else None
class TimestepBufferCondition:
"""Returns `True` when an iterable of timesteps should be passed on.
Specifically returns `True`:
* If timesteps contain a `FIRST`.
* If timesteps contain a `LAST`.
* If number of steps passed since `FIRST` timestep modulo `period` is `0`.
Returns `False` otherwise. Used for action repeats in Atari preprocessing.
"""
def __init__(self, period: int):
self._period = period
self._steps_since_first_timestep = None
self._should_reset = False
def reset(self):
self._should_reset = False
self._steps_since_first_timestep = None
def __call__(self, timesteps: Iterable[dm_env.TimeStep]) -> bool:
if self._should_reset:
raise RuntimeError('Should have reset.')
# Find the main step type, FIRST and LAST take precedence over MID.
main_step_type = StepType.MID
precedent_step_types = (StepType.FIRST, StepType.LAST)
for timestep in timesteps:
if timestep is None:
continue
if timestep.step_type in precedent_step_types:
if main_step_type in precedent_step_types:
raise RuntimeError('Expected at most one FIRST or LAST.')
main_step_type = timestep.step_type
# Must have FIRST timestep after a reset.
if self._steps_since_first_timestep is None:
if main_step_type != StepType.FIRST:
raise RuntimeError('After reset first timestep should be FIRST.')
# pytype: disable=unsupported-operands
if main_step_type == StepType.FIRST:
self._steps_since_first_timestep = 0
return True
elif main_step_type == StepType.LAST:
self._steps_since_first_timestep = None
self._should_reset = True
return True
elif (self._steps_since_first_timestep + 1) % self._period == 0:
self._steps_since_first_timestep += 1
return True
else:
self._steps_since_first_timestep += 1
return False
# pytype: enable=unsupported-operands
class ApplyToNamedTupleField:
"""Runs processors on a particular field of a named tuple."""
def __init__(self, field: Text, *processors: Processor[[Any], Any]):
self._field = field
self._processors = processors
def reset(self) -> None:
for processor in self._processors:
reset(processor)
def __call__(self, value: NamedTuple) -> NamedTuple:
attr_value = getattr(value, self._field)
for processor in self._processors:
attr_value = processor(attr_value)
return value._replace(**{self._field: attr_value})
class Maybe:
"""Wraps another processor so that `None` is returned when `None` is input."""
def __init__(self, processor: Processor[[Any], Any]):
self._processor = processor
def reset(self) -> None:
reset(self._processor)
def __call__(self, value: Optional[Any]) -> Optional[Any]:
if value is None:
return None
else:
return self._processor(value)
class Sequential:
"""Chains together multiple processors."""
def __init__(self, *processors: Processor[[Any], Any]):
self._processors = processors
def reset(self) -> None:
for processor in self._processors:
reset(processor)
def __call__(self, value: Any) -> Any:
for processor in self._processors:
value = processor(value)
return value
class ZeroDiscountOnLifeLoss:
"""Sets discount to zero on timestep if number of lives has decreased.
This processor assumes observations to be tuples whose second entry is a
scalar indicating the remaining number of lives.
"""
def __init__(self):
self._num_lives_on_prev_step = None
def reset(self) -> None:
self._num_lives_on_prev_step = None
def __call__(self, timestep: dm_env.TimeStep) -> dm_env.TimeStep:
# We have a life loss when the timestep is a regular transition and lives
# have decreased since the previous timestep.
num_lives = timestep.observation[1]
life_lost = timestep.mid() and (num_lives < self._num_lives_on_prev_step)
self._num_lives_on_prev_step = num_lives
return timestep._replace(discount=0.) if life_lost else timestep
def reduce_step_type(step_types: Sequence[StepType],
debug: bool = False) -> StepType:
"""Outputs a representative step type from an array of step types."""
# Zero padding will appear to be FIRST. Padding should only be seen before the
# FIRST (e.g. 000F) or after LAST (e.g. ML00).
if debug:
np_step_types = np.array(step_types)
output_step_type = StepType.MID
for i, step_type in enumerate(step_types):
if step_type == 0: # step_type not actually FIRST, but we do expect 000F.
if debug and not (np_step_types == 0).all():
raise ValueError('Expected zero padding followed by FIRST.')
output_step_type = StepType.FIRST
break
elif step_type == StepType.LAST:
output_step_type = StepType.LAST
if debug and not (np_step_types[i + 1:] == 0).all():
raise ValueError('Expected LAST to be followed by zero padding.')
break
else:
if step_type != StepType.MID:
raise ValueError('Expected MID if not FIRST or LAST.')
return output_step_type
def aggregate_rewards(rewards: Sequence[Optional[float]],
debug: bool = False) -> Optional[float]:
"""Sums up rewards, assumes discount is 1."""
if None in rewards:
if debug:
np_rewards = np.array(rewards)
if not (np_rewards[-1] is None and (np_rewards[:-1] == 0).all()):
# Should only ever have [0, 0, 0, None] due to zero padding.
raise ValueError('Should only have a None reward for FIRST.')
return None
else:
# Faster than np.sum for a list of floats.
return sum(rewards)
def aggregate_discounts(discounts: Sequence[Optional[float]],
debug: bool = False) -> Optional[float]:
"""Aggregates array of discounts into a scalar, expects `0`, `1` or `None`."""
if debug:
np_discounts = np.array(discounts)
if not np.isin(np_discounts, [0., 1., None]).all():
raise ValueError('All discounts should be 0 or 1, got: %s.' %
np_discounts)
if None in discounts:
if debug:
if not (np_discounts[-1] is None and (np_discounts[:-1] == 0).all()):
# Should have [0, 0, 0, None] due to zero padding.
raise ValueError('Should only have a None discount for FIRST.')
return None
else:
# Faster than np.prod for a list of floats.
result = 1
for d in discounts:
result *= d
return result
def rgb2y(array: np.ndarray) -> np.ndarray:
"""Converts RGB image array into grayscale."""
if array.ndim != 3:
raise ValueError('Input array should be 3D, got %s.' % array.ndim)
output = np.tensordot(array, [0.299, 0.587, 1 - (0.299 + 0.587)], (-1, 0))
return output.astype(np.uint8)
def resize(shape: Tuple[int, ...]) -> Processor[[np.ndarray], np.ndarray]:
"""Resizes array to the given shape."""
if len(shape) != 2:
raise ValueError('Resize shape has to be 2D, given: %s.' % str(shape))
# Image.resize takes (width, height) as output_shape argument.
image_shape = (shape[1], shape[0])
def resize_fn(array):
image = Image.fromarray(array).resize(image_shape, Image.BILINEAR)
return np.array(image, dtype=np.uint8)
return resize_fn
def select_rgb_observation(timestep: dm_env.TimeStep) -> dm_env.TimeStep:
"""Replaces an observation tuple by its first entry (the RGB observation)."""
return timestep._replace(observation=timestep.observation[0])
def apply_additional_discount(
additional_discount: float) -> Processor[[float], float]:
"""Returns a function that scales its non-`None` input by a constant."""
return lambda d: None if d is None else additional_discount * d
def clip_reward(bound: float) -> Processor[[Optional[float]], Optional[float]]:
"""Returns a function that clips non-`None` inputs to (`-bound`, `bound`)."""
def clip_reward_fn(reward):
return None if reward is None else max(min(reward, bound), -bound)
return clip_reward_fn
def show(prefix: Text) -> Processor[[Any], Any]:
"""Prints value and passes through, for debugging."""
def show_fn(value):
print('%s: %s' % (prefix, value))
return value
return show_fn
def atari(
additional_discount: float = 0.99,
max_abs_reward: Optional[float] = 1.0,
resize_shape: Optional[Tuple[int, int]] = (84, 84),
num_action_repeats: int = 4,
num_pooled_frames: int = 2,
zero_discount_on_life_loss: bool = True,
num_stacked_frames: int = 4,
grayscaling: bool = True,
) -> Processor[[dm_env.TimeStep], Optional[dm_env.TimeStep]]:
"""Standard DQN preprocessing on Atari."""
# This processor does the following to a sequence of timesteps.
#
# 1. Zeroes discount on loss of life.
# 2. Repeats actions (previous action should be repeated if None is returned).
# 3. Max pools action repeated observations.
# 4. Grayscales observations.
# 5. Resizes observations.
# 6. Stacks observations.
# 7. Clips rewards.
# 8. Applies an additional discount.
#
# For more detail see the annotations in the processors below.
# The FixedPaddedBuffer, ConditionallySubsample, none_to_zero_pad, stack and
# max_pool on the observation collectively does this (step types: F = FIRST,
# M = MID, L = LAST, ~ is None):
#
# Type: F | M M M M | M M L | F |
# Frames: A | B C D E | F G H | I |
# Output: max[0A]| ~ ~ ~ max[DE]| ~ ~ max[H0]|max[0I]|
return Sequential(
# When the number of lives decreases, set discount to 0.
ZeroDiscountOnLifeLoss() if zero_discount_on_life_loss else identity,
# Select the RGB observation as the main observation, dropping lives.
select_rgb_observation,
# obs: 1, 2, 3, 4, 5, 6, 7, 8, 9, ...
# Write timesteps into a fixed-sized buffer padded with None.
FixedPaddedBuffer(length=num_action_repeats, initial_index=-1),
# obs: ~~~1, 2~~~, 23~~, 234~, 2345, 6~~~, 67~~, 678~, 6789, ...
# Periodically return the deque of timesteps, when the current timestep is
# FIRST, after that every 4 steps, and when the current timestep is LAST.
ConditionallySubsample(TimestepBufferCondition(num_action_repeats)),
# obs: ~~~1, ~, ~, ~, 2345, ~, ~, ~, 6789, ...
# If None pass through, otherwise apply the processor.
Maybe(
Sequential(
# Replace Nones with zero padding in each buffer.
none_to_zero_pad,
# obs: 0001, ~, ~, ~, 2345, ~, ~, ~, 6789, ...
# Convert sequence of nests into a nest of sequences.
named_tuple_sequence_stack,
# Choose representative step type from an array of step types.
ApplyToNamedTupleField('step_type', reduce_step_type),
# Rewards: sum then clip.
ApplyToNamedTupleField(
'reward',
aggregate_rewards,
clip_reward(max_abs_reward) if max_abs_reward else identity,
),
# Discounts: take product and scale by an additional discount.
ApplyToNamedTupleField(
'discount',
aggregate_discounts,
apply_additional_discount(additional_discount),
),
# Observations: max pool, grayscale, resize, and stack.
ApplyToNamedTupleField(
'observation',
lambda obs: np.stack(obs[-num_pooled_frames:], axis=0),
lambda obs: np.max(obs, axis=0),
# obs: max[01], ~, ~, ~, max[45], ~, ~, ~, max[89], ...
# obs: A, ~, ~, ~, B, ~, ~, ~, C, ...
rgb2y if grayscaling else identity,
resize(resize_shape) if resize_shape else identity,
Deque(max_length=num_stacked_frames),
# obs: A, ~, ~, ~, AB, ~, ~, ~, ABC, ~, ~, ~, ABCD, ~, ~, ~,
# BCDE, ~, ~, ~, CDEF, ...
list,
trailing_zero_pad(length=num_stacked_frames),
# obs: A000, ~, ~, ~, AB00, ~, ~, ~, ABC0, ~, ~, ~, ABCD,
# ~, ~, ~, BCDE, ...
lambda obs: np.stack(obs, axis=-1),
),
)),
)
class AtariEnvironmentWrapper(dm_env.Environment):
"""Python environment wrapper that provides DQN Atari preprocessing.
This is a thin wrapper around the Atari processor.
Expects underlying Atari environment to have interleaved pixels (HWC) and
zero-indexed actions.
"""
def __init__(
self,
environment: dm_env.Environment,
additional_discount: float = 0.99,
max_abs_reward: Optional[float] = 1.0,
resize_shape: Optional[Tuple[int, int]] = (84, 84),
num_action_repeats: int = 4,
num_pooled_frames: int = 2,
zero_discount_on_life_loss: bool = True,
num_stacked_frames: int = 4,
grayscaling: bool = True,
):
rgb_spec, unused_lives_spec = environment.observation_spec()
if rgb_spec.shape[2] != 3:
raise ValueError(
'This wrapper assumes interleaved pixel observations with shape '
'(height, width, channels).')
if int(environment.action_spec().minimum) != 0:
raise ValueError('This wrapper assumes zero-indexed actions.')
self._environment = environment
self._processor = atari(
additional_discount=additional_discount,
max_abs_reward=max_abs_reward,
resize_shape=resize_shape,
num_action_repeats=num_action_repeats,
num_pooled_frames=num_pooled_frames,
zero_discount_on_life_loss=zero_discount_on_life_loss,
num_stacked_frames=num_stacked_frames,
grayscaling=grayscaling,
)
if grayscaling:
self._observation_shape = resize_shape + (num_stacked_frames,)
self._observation_spec_name = 'grayscale'
else:
self._observation_shape = resize_shape + (3, num_stacked_frames)
self._observation_spec_name = 'RGB'
self._reset_next_step = True
def reset(self) -> dm_env.TimeStep:
"""Resets environment and provides the first processed timestep."""
reset(self._processor)
timestep = self._environment.reset()
processed_timestep = self._processor(timestep)
assert processed_timestep is not None
self._reset_next_step = False
return processed_timestep
def step(self, action: int) -> dm_env.TimeStep:
"""Steps up to `num_action_repeat` times, returns a processed timestep."""
# This implements the action repeat by repeatedly passing in the last action
# until an actual timestep is returned by the processor.
if self._reset_next_step:
return self.reset() # Ignore action.
processed_timestep = None
while processed_timestep is None:
timestep = self._environment.step(action)
processed_timestep = self._processor(timestep)
if timestep.last():
self._reset_next_step = True
assert processed_timestep is not None
return processed_timestep
def action_spec(self) -> specs.DiscreteArray:
return self._environment.action_spec()
def observation_spec(self) -> specs.Array:
return specs.Array(
shape=self._observation_shape,
dtype=np.uint8,
name=self._observation_spec_name)
class AtariSimpleActionEnvironmentWrapper(dm_env.Environment):
"""Python environment wrapper for Atari so it takes integer actions.
Use this when processing is done on the agent side.
"""
def __init__(self, environment: dm_env.Environment):
self._environment = environment
if int(environment.action_spec()[0].minimum) != 0:
raise ValueError(
'This wrapper assumes zero-indexed actions. Use the Atari setting '
'zero_indexed_actions=\"true\" to get actions in this format.')
def reset(self) -> dm_env.TimeStep:
return self._environment.reset()
def step(self, action: int) -> dm_env.TimeStep:
return self._environment.step([np.array(action).reshape((1,))])
def action_spec(self) -> specs.DiscreteArray:
action_spec = self._environment.action_spec()[0]
return specs.DiscreteArray(
num_values=action_spec.maximum.item() + 1,
dtype=action_spec.dtype,
name='action_spec')
def observation_spec(self) -> specs.Array:
return self._environment.observation_spec()
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We also make available the code for linear evaluation of a pre-trained model
in UCF101 and the JAX checkpoints for our best models.
We use different parameters for video compression in UCF101 than the ones
used in `tensorflow_datasets`. We provide the code to download and
preprocess the dataset. The eval_ucf101.py script reproduces the results we
report in Table 2 of the paper, using the checkpoints provided below.
Visual Backbone | Training Dataset | Results on Linear UCF101
------- | -------- | --------
S3D-G | AudioSet + HowTo | 89.6
Resnet TSM-50 | AudioSet + HowTo | 91.5
Resnet TSM-50 (x2) | AudioSet + HowTo | 91.8
## Setup
To set up a Python virtual environment with the required dependencies, run:
```shell
python3 -m venv mmv_env
source mmv_env/bin/activate
pip install --upgrade pip setuptools wheel
pip install -r mmv/requirements.txt --use-feature=2020-resolver
```
### Linear evaluation
The linear evaluation on UCF101 can be run using:
```shell
python -m mmv.eval_ucf101 \
--checkpoint_path= \
--dataset_folder=
```
## Checkpoints
We provide three checkpoints containing the best pre-trained weights for each
of the visual backbones we use in the paper, i. e., S3D-G, Resnet-50 TSM,
and Resnet-50 TSM x 2.
- [S3D-G](https://storage.googleapis.com/deepmind-research-mmv/mmv_s3d.pkl)
- [Resnet-50 TSM](https://storage.googleapis.com/deepmind-research-mmv/mmv_tsm_resnet_x1.pkl)
- [Resnet-50 TSMx2](https://storage.googleapis.com/deepmind-research-mmv/mmv_tsm_resnet_x2.pkl)
## References
### Citing our work
If you use that code for your research, please consider citing our paper:
```bibtex
@inproceedings{alayrac2020self,
title={{S}elf-{S}upervised {M}ulti{M}odal {V}ersatile {N}etworks},
author={Alayrac, Jean-Baptiste and Recasens, Adri{\`a} and Schneider, Rosalia and Arandjelovi{\'c}, Relja and Ramapuram, Jason and De Fauw, Jeffrey and Smaira, Lucas and Dieleman, Sander and Zisserman, Andrew},
booktitle={NeurIPS},
year={2020}
}
```
### Models in TF
You may also be interested in using our TF-Hub release models available at:
- [S3D-G](https://tfhub.dev/deepmind/mmv/s3d/1)
- [Resnet-50 TSM](https://tfhub.dev/deepmind/mmv/tsm-resnet50/1)
- [Resnet-50 TSMx2](https://tfhub.dev/deepmind/mmv/tsm-resnet50x2/1)
## License
While the code is licensed under the Apache 2.0 License, the checkpoints weights
are made available for non-commercial use only under the terms of the
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
license. You can find details at:
https://creativecommons.org/licenses/by-nc/4.0/legalcode.
================================================
FILE: mmv/config.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Configuration parameters for MMV."""
def get_model_config(ckpt_path):
"""Returns the model configuration to be used with each checkpoint."""
config = {
'audio_backbone': 'resnet50',
'audio_model_kwargs': {
'bn_config': {
'create_offset': True,
'create_scale': True,
'decay_rate': 0.9,
'eps': 1.0e-5
}
},
'bn_config_proj': {
'create_offset': True,
'create_scale': True,
'decay_rate': 0.9,
'eps': 1.0e-5
},
'config_audio_text': {
'embedding_dim': 512,
'toaud_bn_after_proj': False,
'toaud_head_mode': 'linear',
'totxt_bn_after_proj': False,
'totxt_head_mode': 'linear'
},
'config_video_audio': {
'embedding_dim': 512,
'toaud_bn_after_proj': True,
'toaud_head_mode': 'mlp@512',
'tovid_bn_after_proj': False,
'tovid_head_mode': 'linear'
},
'config_video_text': {
'embedding_dim': 256,
'totxt_bn_after_proj': True,
'totxt_head_mode': 'linear',
'tovid_bn_after_proj': False,
'tovid_head_mode': 'linear'
},
'mm_embedding_graph': 'fac_relu',
'name': 'text_audio_video',
'sentence_dim': 2048,
'use_xreplica_bn': True,
'vision_model_kwargs': {
'bn_config': {
'create_offset': True,
'create_scale': True,
'decay_rate': 0.9,
'eps': 1.0e-5
},
'n_frames': 32,
'width_mult': 1,
},
}
if 's3d' in ckpt_path:
config['visual_backbone'] = 's3d'
if 'tsm_resnet_x1' in ckpt_path:
config['visual_backbone'] = 'resnet50tsm'
if 'tsm_resnet_x2' in ckpt_path:
config['visual_backbone'] = 'resnet50tsm'
config['vision_model_kwargs']['width_mult'] = 2
return config
================================================
FILE: mmv/eval_ucf101.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""UCF101 linear evaluation."""
import functools
from typing import Any, Dict, Optional
from absl import app
from absl import flags
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import sklearn
from sklearn import preprocessing
import sklearn.linear_model
import sklearn.svm
import tensorflow as tf
import tensorflow_datasets as tfds
from mmv import config
from mmv.models import mm_embeddings
from mmv.utils import checkpoint
from mmv.utils import ucf101_dataset
flags.DEFINE_string('checkpoint_path', '~/tmp/mmv_s3d.pkl',
'The directory to load pre-trained weights from.')
flags.DEFINE_string('dataset_folder', '/tmp/ucf101',
'The directory with the ucf101 dataset.')
flags.DEFINE_integer('eval_batch_size', 1,
'The batch size for evaluation.')
flags.DEFINE_integer('train_batch_size', 16,
'The batch size for training.')
flags.DEFINE_integer('num_train_epochs', 10,
'How many epochs to collect features during training.')
flags.DEFINE_integer('num_test_windows', 10,
'How many windows to average on during test.')
flags.DEFINE_integer('min_resize', 224,
'Min value to resize images to during preprocessing.')
flags.DEFINE_integer('crop_size', 224,
'Value to resize images to during preprocessing.')
flags.DEFINE_integer('num_frames', 32,
'Number of video frames.')
flags.DEFINE_integer('stride', 2,
'Stride for video frames.')
flags.DEFINE_integer('ucf101_split', 1,
'Which split of ucf101 to use.')
FLAGS = flags.FLAGS
def get_sampling_offset(sequence: tf.Tensor,
num_steps: Optional[int],
is_training: bool,
stride: int = 1,
seed: Optional[int] = None) -> tf.Tensor:
"""Calculates the initial offset for a sequence where all steps will fit.
Args:
sequence: any tensor where the first dimension is timesteps.
num_steps: The number of timesteps we will output. If None,
deterministically start at the first frame.
is_training: A boolean indicates whether the graph is for training or not.
If False, the starting frame always the first frame.
stride: distance to sample between timesteps.
seed: a deterministic seed to use when sampling.
Returns:
The first index to begin sampling from. A best effort is made to provide a
starting index such that all requested steps fit within the sequence (i.e.
offset + 1 + (num_steps - 1) * stride < len(sequence)). If this is not
satisfied, the starting index is chosen randomly from the full sequence.
"""
if num_steps is None or not is_training:
return tf.constant(0)
sequence_length = tf.shape(sequence)[0]
max_offset = tf.cond(
tf.greater(sequence_length, (num_steps - 1) * stride),
lambda: sequence_length - (num_steps - 1) * stride,
lambda: sequence_length)
offset = tf.random.uniform(
(),
maxval=tf.cast(max_offset, tf.int32),
dtype=tf.int32,
seed=seed)
return offset
def sample_or_pad_sequence_indices(sequence: tf.Tensor,
num_steps: Optional[int],
is_training: bool,
repeat_sequence: bool = True,
stride: int = 1,
offset: Optional[int] = None) -> tf.Tensor:
"""Returns indices to take for sampling or padding a sequence to fixed size.
Samples num_steps from the sequence. If the sequence is shorter than
num_steps, the sequence loops. If the sequence is longer than num_steps and
is_training is True, then we seek to a random offset before sampling. If
offset is provided, we use that deterministic offset.
This method is appropriate for sampling from a tensor where you want every
timestep between a start and end time. See sample_stacked_sequence_indices for
more flexibility.
Args:
sequence: any tensor where the first dimension is timesteps.
num_steps: how many steps (e.g. frames) to take. If None, all steps from
start to end are considered and `is_training` has no effect.
is_training: A boolean indicates whether the graph is for training or not.
If False, the starting frame is deterministic.
repeat_sequence: A boolean indicates whether the sequence will repeat to
have enough steps for sampling. If False, a runtime error is thrown if
num_steps * stride is longer than sequence length.
stride: distance to sample between timesteps.
offset: a deterministic offset to use regardless of the is_training value.
Returns:
Indices to gather from the sequence Tensor to get a fixed size sequence.
"""
sequence_length = tf.shape(sequence)[0]
sel_idx = tf.range(sequence_length)
if num_steps:
if offset is None:
offset = get_sampling_offset(sequence, num_steps, is_training, stride)
if repeat_sequence:
# Repeats sequence until num_steps are available in total.
num_repeats = tf.cast(
tf.math.ceil(
tf.math.divide(
tf.cast(num_steps * stride + offset, tf.float32),
tf.cast(sequence_length, tf.float32)
)), tf.int32)
sel_idx = tf.tile(sel_idx, [num_repeats])
steps = tf.range(offset, offset + num_steps * stride, stride)
else:
steps = tf.range(0, sequence_length, stride)
return tf.gather(sel_idx, steps)
def random_sample_sequence(sequence: tf.Tensor,
num_steps: int,
stride: int = 1) -> tf.Tensor:
"""Randomly sample a segment of size num_steps from a given sequence."""
indices = sample_or_pad_sequence_indices(
sequence=sequence,
num_steps=num_steps,
is_training=True, # Random sample.
repeat_sequence=True, # Will repeat the sequence if request more.
stride=stride,
offset=None)
indices.set_shape((num_steps,))
output = tf.gather(sequence, indices)
return output
def sample_linspace_sequence(sequence: tf.Tensor,
num_windows: int,
num_steps: int,
stride: int = 1) -> tf.Tensor:
"""Samples num_windows segments from sequence with linearly spaced offsets.
The samples are concatenated in a single Tensor in order to have the same
format structure per timestep (e.g. a single frame). If num_steps * stride is
bigger than the number of timesteps, the sequence is repeated. This function
can be used in evaluation in order to extract enough segments in order to span
the entire sequence.
Args:
sequence: Any tensor where the first dimension is timesteps.
num_windows: Number of windows retrieved from the sequence.
num_steps: Number of steps (e.g. frames) to take.
stride: Distance to sample between timesteps.
Returns:
A single Tensor with first dimension num_windows * num_steps. The Tensor
contains the concatenated list of num_windows tensors which offsets have
been linearly spaced from input.
"""
sequence_length = tf.shape(sequence)[0]
max_offset = tf.maximum(0, sequence_length - num_steps * stride)
offsets = tf.linspace(0.0, tf.cast(max_offset, tf.float32), num_windows)
offsets = tf.cast(offsets, tf.int32)
all_indices = []
for i in range(num_windows):
all_indices.append(
sample_or_pad_sequence_indices(
sequence=sequence,
num_steps=num_steps,
is_training=False,
repeat_sequence=True, # Will repeat the sequence if request more.
stride=stride,
offset=offsets[i]))
indices = tf.concat(all_indices, axis=0)
indices.set_shape((num_windows * num_steps,))
output = tf.gather(sequence, indices)
return output
def resize_smallest(frames: tf.Tensor, min_resize: int) -> tf.Tensor:
"""Resizes frames so that min(height, width) is equal to min_resize.
This function will not do anything if the min(height, width) is already equal
to min_resize. This allows to save compute time.
Args:
frames: A Tensor of dimension [timesteps, input_h, input_w, channels].
min_resize: Minimum size of the final image dimensions.
Returns:
A Tensor of shape [timesteps, output_h, output_w, channels] of type
frames.dtype where min(output_h, output_w) = min_resize.
"""
shape = tf.shape(frames)
input_h = shape[1]
input_w = shape[2]
output_h = tf.maximum(min_resize, (input_h * min_resize) // input_w)
output_w = tf.maximum(min_resize, (input_w * min_resize) // input_h)
def resize_fn():
frames_resized = tf.image.resize(frames, (output_h, output_w))
return tf.cast(frames_resized, frames.dtype)
should_resize = tf.math.logical_or(tf.not_equal(input_w, output_w),
tf.not_equal(input_h, output_h))
frames = tf.cond(should_resize, resize_fn, lambda: frames)
return frames
def process_samples(features_dict, num_frames=32, stride=1, is_training=True,
min_resize=224, crop_size=224, num_windows=1):
"""Process video frames."""
video = features_dict['video']
if is_training:
assert num_windows == 1
video = random_sample_sequence(video, num_frames, stride)
is_flipped = tf.random.uniform((), minval=0, maxval=2, dtype=tf.int32)
video = tf.cond(tf.equal(is_flipped, 1),
true_fn=lambda: tf.image.flip_left_right(video),
false_fn=lambda: video)
else:
video = sample_linspace_sequence(video, num_windows, num_frames, stride)
# Resize smallest side.
video = resize_smallest(video, min_resize)
if is_training:
# Random crop.
video = tf.image.random_crop(video, [num_frames, crop_size, crop_size, 3])
else:
# Central crop.
video = tf.image.resize_with_crop_or_pad(video, crop_size, crop_size)
video = tf.cast(video, tf.float32)
video /= 255.0 # Set between [0, 1].
features_dict['video'] = video
return features_dict
def space_to_depth_batch(features_dict):
images = features_dict['video']
_, l, h, w, c = images.shape
images = tf.reshape(images, [-1, l // 2, 2, h // 2, 2, w // 2, 2, c])
images = tf.transpose(images, [0, 1, 3, 5, 2, 4, 6, 7])
images = tf.reshape(images, [-1, l // 2, h // 2, w // 2, 8 * c])
features_dict['video'] = images
return features_dict
def reshape_windows(features_dict, num_frames):
x = features_dict['video']
x = tf.reshape(x, (-1, num_frames, x.shape[2], x.shape[3], x.shape[4]))
features_dict['video'] = x
return features_dict
def compute_accuracy_metrics(pred, gt, prefix=''):
order_pred = np.argsort(pred, axis=1)
assert len(gt.shape) == len(order_pred.shape) == 2
top1_pred = order_pred[:, -1:]
top5_pred = order_pred[:, -5:]
top1_acc = np.mean(top1_pred == gt)
top5_acc = np.mean(np.max(top5_pred == gt, 1))
return {prefix + 'top1': top1_acc,
prefix + 'top5': top5_acc}
def forward_fn(images: jnp.ndarray,
audio_spectrogram: jnp.ndarray,
word_ids: jnp.ndarray,
is_training: bool,
model_config: Dict[str, Any]):
"""Forward pass of the model."""
# This should contain the pre-trained weights. We set it to zero because it
# will be loaded from the checkpoint.
language_model_vocab_size = 65536
word_embedding_dim = 300
dummy_embedding_matrix = jnp.zeros(shape=(language_model_vocab_size,
word_embedding_dim))
module = mm_embeddings.AudioTextVideoEmbedding(
**model_config,
word_embedding_matrix=dummy_embedding_matrix)
return module(images=images,
audio_spectrogram=audio_spectrogram,
word_ids=word_ids,
is_training=is_training)['vid_repr']
def main(argv):
del argv
sklearn_reg = 0.001
model_config = config.get_model_config(FLAGS.checkpoint_path)
forward = hk.without_apply_rng(hk.transform_with_state(forward_fn))
forward_apply = jax.jit(functools.partial(forward.apply,
is_training=False,
model_config=model_config))
# Get the UCF101 config.
dset_config = tfds.video.ucf101.Ucf101.BUILDER_CONFIGS[FLAGS.ucf101_split]
builder = ucf101_dataset.ModUcf101(
data_dir=FLAGS.dataset_folder,
config=dset_config)
# Create the tfrecord files (no-op if already exists)
dl_config = tfds.download.DownloadConfig(verify_ssl=False)
builder.download_and_prepare(download_config=dl_config)
# Generate the training dataset.
train_ds = builder.as_dataset(split='train', shuffle_files=False)
train_ds = train_ds.map(lambda x: process_samples( # pylint: disable=g-long-lambda
x, num_frames=FLAGS.num_frames, stride=FLAGS.stride, is_training=True,
min_resize=FLAGS.min_resize, crop_size=FLAGS.crop_size))
train_ds = train_ds.batch(batch_size=FLAGS.train_batch_size)
if model_config['visual_backbone'] == 's3d':
train_ds = train_ds.map(space_to_depth_batch)
train_ds = train_ds.repeat(FLAGS.num_train_epochs)
# Generate the test dataset.
test_ds = builder.as_dataset(split='test', shuffle_files=False)
test_ds = test_ds.map(lambda x: process_samples( # pylint: disable=g-long-lambda
x, num_frames=FLAGS.num_frames, stride=FLAGS.stride, is_training=False,
min_resize=FLAGS.min_resize, crop_size=FLAGS.crop_size,
num_windows=FLAGS.num_test_windows))
test_ds = test_ds.batch(batch_size=FLAGS.eval_batch_size)
test_ds = test_ds.map(lambda x: reshape_windows( # pylint: disable=g-long-lambda
x, num_frames=FLAGS.num_frames))
if model_config['visual_backbone'] == 's3d':
test_ds = test_ds.map(space_to_depth_batch)
test_ds = test_ds.repeat(1)
pretrained_weights = checkpoint.load_checkpoint(FLAGS.checkpoint_path)
params = pretrained_weights['params']
state = pretrained_weights['state']
# Collect training samples.
audio_frames = 96
mel_filters = 40
num_tokens = 16
dummy_audio = jnp.zeros(
shape=(FLAGS.train_batch_size, audio_frames, mel_filters, 1))
dummy_word_ids = jnp.zeros(
shape=(FLAGS.train_batch_size, num_tokens), dtype=jnp.int32)
train_features = []
train_labels = []
print('Computing features on train')
training_examples = iter(tfds.as_numpy(train_ds))
for train_ex in training_examples:
vid_representation, _ = forward_apply(params=params,
state=state,
images=train_ex['video'],
audio_spectrogram=dummy_audio,
word_ids=dummy_word_ids)
train_labels.append(train_ex['label'])
train_features.append(vid_representation)
if len(train_labels) % 50 == 0:
print(f'Processed {len(train_labels)} examples.')
train_labels = np.concatenate(train_labels, axis=0)
train_features = np.concatenate(train_features, axis=0)
print(f'Finish collecting train features of shape {train_features.shape}')
# Collect test samples.
dummy_audio = jnp.zeros(
shape=(FLAGS.eval_batch_size, audio_frames, mel_filters, 1))
dummy_word_ids = jnp.zeros(
shape=(FLAGS.eval_batch_size, num_tokens), dtype=jnp.int32)
test_features = []
test_labels = []
print('Computing features on test')
test_examples = iter(tfds.as_numpy(test_ds))
for test_ex in test_examples:
vid_representation_test, _ = forward_apply(params=params,
state=state,
images=test_ex['video'],
audio_spectrogram=dummy_audio,
word_ids=dummy_word_ids)
test_labels.append(test_ex['label'])
test_features.append(vid_representation_test)
if len(test_labels) % 50 == 0:
print(f'Processed {len(test_labels)} examples.')
test_features = np.concatenate(test_features, axis=0)
test_labels = np.concatenate(test_labels, axis=0)
print(f'Finish collecting test features of shape {test_features.shape}')
# Train classifier
print('Training linear classifier!')
classifier = sklearn.svm.LinearSVC(C=sklearn_reg)
scaler = preprocessing.StandardScaler().fit(train_features)
train_features = scaler.transform(train_features)
classifier.fit(train_features, train_labels.ravel())
print('Training done !')
# Evaluation.
test_features = scaler.transform(test_features)
print('Running inference on train')
pred_train = classifier.decision_function(train_features)
print('Running inference on test')
pred_test = classifier.decision_function(test_features)
if FLAGS.num_test_windows > 1:
pred_test = np.reshape(
pred_test, (test_labels.shape[0], -1, pred_test.shape[1]))
pred_test = pred_test.mean(axis=1)
# Compute accuracies.
metrics = compute_accuracy_metrics(pred_train, train_labels[:, None],
prefix='train_')
metrics.update(
compute_accuracy_metrics(pred_test, test_labels[:, None], prefix='test_'))
print(metrics)
if __name__ == '__main__':
app.run(main)
================================================
FILE: mmv/models/mm_embeddings.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Model for text-video-audio embeddings."""
from typing import Any, Dict, Optional
import haiku as hk
import jax
import jax.numpy as jnp
from mmv.models import normalization
from mmv.models import resnet
from mmv.models import s3d
from mmv.models import tsm_resnet
_DEFAULT_CFG_AUDTXT = {
"totxt_head_mode": "linear",
"toaud_head_mode": "linear",
"toaud_bn_after_proj": False,
"totxt_bn_after_proj": False,
"embedding_dim": 512}
_DEFAULT_CFG_VIDAUD = {
"tovid_head_mode": "linear",
"toaud_head_mode": "mlp@512",
"tovid_bn_after_proj": False,
"toaud_bn_after_proj": True,
"embedding_dim": 512}
_DEFAULT_CFG_VIDTXT = {
"tovid_head_mode": "linear",
"totxt_head_mode": "mlp@512",
"tovid_bn_after_proj": False,
"totxt_bn_after_proj": True,
"embedding_dim": 512}
_DEFAULT_CFG_BN = {"decay_rate": 0.9, "eps": 1e-5,
"create_scale": True, "create_offset": True}
def _setkey_if_not_exists(d, key, value):
if key not in d:
d[key] = value
class AudioTextVideoEmbedding(hk.Module):
"""Module to fuse audio, text and video for joint embedding learning."""
def __init__(
self,
# Language parameters.
word_embedding_matrix,
sentence_dim=2048,
# Audio parameters.
audio_backbone="resnet18",
audio_model_kwargs=None,
# Vision parameters.
visual_backbone="s3d",
vision_model_kwargs=None,
# Common parameters.
mm_embedding_graph="fac_relu",
use_xreplica_bn=True,
bn_config_proj=None,
config_video_text=None,
config_video_audio=None,
config_audio_text=None,
use_audio_text=False,
name="audio_text_video_model"):
"""Initialize the AudioTextVideoEmbedding class.
Args:
word_embedding_matrix: 2d matrix [vocab_size, embed_size] to embed words.
sentence_dim: The dimension of the sentence representation.
audio_backbone: Backbone for audio.
audio_model_kwargs: Other specific parameters to pass to the audio
module.
visual_backbone: The video backbone.
vision_model_kwargs: Other specific parameters to pass to the vision
module.
mm_embedding_graph: Embedding graph merging strategy.
Can be `shared`, `disjoint` or `fac` (fac can be followed by an
activation function name e.g. `fac_relu`).
use_xreplica_bn: Whether or not to use the cross replica batch norm.
bn_config_proj: BN config of the projection heads.
config_video_text: Config for the video and the text branches.
config_video_audio: Config for the video and the audio branches.
config_audio_text: Config for the audio and the text branches.
use_audio_text: Whether or not the audio text branch is used during
training.
name: graph name.
"""
super(AudioTextVideoEmbedding, self).__init__(name=name)
# Audio parameters.
self._audio_backbone = audio_backbone
self._audio_model_kwargs = audio_model_kwargs
# Language parameters.
self._sentence_dim = sentence_dim
self._word_embedding_matrix = word_embedding_matrix
# Vision parameters.
self._visual_backbone = visual_backbone
self._vision_model_kwargs = vision_model_kwargs
# Joint parameters.
self._use_xreplica_bn = use_xreplica_bn
if self._use_xreplica_bn:
self._normalizer_name = "cross_replica_batch_norm"
else:
self._normalizer_name = "batch_norm"
# Projection head parameters.
if config_video_text is None:
config_video_text = _DEFAULT_CFG_VIDTXT
for k, v in _DEFAULT_CFG_VIDTXT.items():
_setkey_if_not_exists(config_video_text, k, v)
self._cfg_vid_txt = config_video_text
if config_video_audio is None:
config_video_audio = _DEFAULT_CFG_VIDAUD
for k, v in _DEFAULT_CFG_VIDAUD.items():
_setkey_if_not_exists(config_video_audio, k, v)
self._cfg_vid_aud = config_video_audio
if config_audio_text is None:
config_audio_text = _DEFAULT_CFG_AUDTXT
for k, v in _DEFAULT_CFG_AUDTXT.items():
_setkey_if_not_exists(config_audio_text, k, v)
self._cfg_aud_txt = config_audio_text
self._use_audio_text = use_audio_text
self._mm_embedding_graph = mm_embedding_graph
self._use_separate_heads = (
mm_embedding_graph == "disjoint" or
mm_embedding_graph.startswith("fac"))
self._bn_config_proj = bn_config_proj or _DEFAULT_CFG_BN
def _get_pair_embedding_heads(self,
embedding_dim_1, embedding_dim_2,
mode1, mode2,
use_bn_out1, use_bn_out2,
name1, name2):
embd1_module = EmbeddingModule(
embedding_dim_1,
mode=mode1,
use_bn_out=use_bn_out1,
bn_config=self._bn_config_proj,
use_xreplica_bn=self._use_xreplica_bn,
name=name1)
if self._use_separate_heads:
embd2_module = EmbeddingModule(
embedding_dim_2,
mode=mode2,
use_bn_out=use_bn_out2,
use_xreplica_bn=self._use_xreplica_bn,
bn_config=self._bn_config_proj,
name=name2)
else:
assert embedding_dim_1 == embedding_dim_2, (
"Using shared heads but inconsistent embedding dims where provided.")
assert mode1 == mode2, (
"Using shared heads but inconsistent modes where provided.")
assert use_bn_out1 == use_bn_out2, (
"Using shared heads but inconsistent bn conf where provided.")
embd2_module = embd1_module
return embd1_module, embd2_module
def _activate_interaction(self, inputs, activation_fn, is_training,
activation_module=None):
"""Activation function for the interaction modules."""
if activation_fn == "relu":
inputs = jax.nn.relu(inputs)
elif activation_fn == "bnrelu":
if activation_module is None:
activation_module = normalization.get_normalize_fn(
normalizer_name=self._normalizer_name,
normalizer_kwargs=self._bn_config_proj)
inputs = activation_module(inputs, is_training=is_training)
inputs = jax.nn.relu(inputs)
else:
raise ValueError(f"{activation_fn} not supported.")
return inputs, activation_module
def __call__(self,
images,
audio_spectrogram,
word_ids,
is_training,
return_intermediate_audio=False):
"""Computes video, text and audio embeddings.
Args:
images: The videos tensor of shape [B1, T, H, W, 3] where B1 is the batch
size, T is the number of frames per clip, H the height, W the width
and 3 the rgb channels.
audio_spectrogram: The audio tensor of shape [B2, T', F] where B2 is the
batch size, T' is the number of temporal frames, F is the number of
frequency frames.
word_ids: If words_embeddings is set to None, it will use the word indices
input instead so that we can compute the word embeddings within the
model graph. The expected shape is [B3, N, D] where B3 is the batch size
and N the maximum number of words per sentence.
is_training: Whether or not to activate the graph in training mode.
return_intermediate_audio: Return audio intermediate representation.
Returns:
if return_intermediate_audio = True
audio_representation: the 4-dim audio representation taken before
averaging over spatial dims in the Resnet.
else
visual_embd: a dict containing the video embeddings in audio and text
of shape [B1, d_embd].
audio_embd: a dict containing the audio embeddings in video and text
of shape [B2, d_embd].
txt_embd: a dict containing the text embeddings in video and audio
of shape[B3, d_embd].
visual_representation: the video rep of shape [B1, d_visual].
audio_representation: the audio rep of shape [B2, d_audio].
"""
# Computes the visual representation.
video_cnn = VisualModule(backbone=self._visual_backbone,
use_xreplica_bn=self._use_xreplica_bn,
model_kwargs=self._vision_model_kwargs)
visual_representation = video_cnn(images, is_training=is_training)
# Projection heads: Video -> Text and Video -> Audio.
vid2txt_embd_module, vid2aud_embd_module = self._get_pair_embedding_heads(
embedding_dim_1=self._cfg_vid_txt["embedding_dim"],
embedding_dim_2=self._cfg_vid_aud["embedding_dim"],
mode1=self._cfg_vid_txt["totxt_head_mode"],
mode2=self._cfg_vid_aud["toaud_head_mode"],
use_bn_out1=self._cfg_vid_txt["totxt_bn_after_proj"],
use_bn_out2=self._cfg_vid_aud["toaud_bn_after_proj"],
name1="vis_embd",
name2="vid2audio_embd")
video_embd = {}
if self._mm_embedding_graph in ["shared", "disjoint"]:
video_embd["toaud"] = vid2aud_embd_module(visual_representation,
is_training=is_training)
video_embd["totxt"] = vid2txt_embd_module(visual_representation,
is_training=is_training)
elif self._mm_embedding_graph.startswith("fac"):
# Activation function if specificed in the name, e.g. fac_relu.
activation_fn = None
if len(self._mm_embedding_graph.split("_")) == 2:
activation_fn = self._mm_embedding_graph.split("_")[1]
video_embd["toaud"] = vid2aud_embd_module(visual_representation,
is_training=is_training)
fine_rep = video_embd["toaud"]
# Eventually activate the fine grained representation.
if activation_fn:
fine_rep, activation_module = self._activate_interaction(
inputs=fine_rep, activation_fn=activation_fn,
is_training=is_training)
video_embd["totxt"] = vid2txt_embd_module(fine_rep,
is_training=is_training)
else:
raise ValueError(
f"{self._mm_embedding_graph} is not a valid MM embedding graph.")
# Computes the audio representation.
audio_cnn = AudioModule(backbone=self._audio_backbone,
use_xreplica_bn=self._use_xreplica_bn,
model_kwargs=self._audio_model_kwargs)
if return_intermediate_audio:
return audio_cnn(audio_spectrogram,
is_training=is_training,
return_intermediate=True)
audio_representation = audio_cnn(audio_spectrogram, is_training=is_training)
# Projection heads: Audio -> Video and Audio -> Text.
aud2vid_embd_module, aud2txt_embd_module = self._get_pair_embedding_heads(
embedding_dim_1=self._cfg_vid_aud["embedding_dim"],
embedding_dim_2=self._cfg_aud_txt["embedding_dim"],
mode1=self._cfg_vid_aud["tovid_head_mode"],
mode2=self._cfg_aud_txt["totxt_head_mode"],
use_bn_out1=self._cfg_vid_aud["tovid_bn_after_proj"],
use_bn_out2=self._cfg_aud_txt["totxt_bn_after_proj"],
name1="audio_embd",
name2="audio2txt_embd")
audio_embd = {}
audio_embd["tovid"] = aud2vid_embd_module(audio_representation,
is_training=is_training)
# Computes the projection to the text domain depending on the MM graph mode.
if (self._mm_embedding_graph.startswith("fac") and
(self._use_audio_text or (not is_training))):
# In case the audio text branch is not used during training, we do that
# only at eval time (is_training=False) in order to not pollute the BN
# stats in vid2txt_embd_module with audio features during training.
fine_rep_audio = audio_embd["tovid"]
if activation_fn:
fine_rep_audio, _ = self._activate_interaction(
inputs=fine_rep_audio, activation_fn=activation_fn,
is_training=is_training, activation_module=activation_module)
audio_embd["totxt"] = vid2txt_embd_module(fine_rep_audio,
is_training=is_training)
else:
audio_embd["totxt"] = aud2txt_embd_module(audio_representation,
is_training=is_training)
# Computes the text representation.
txt_representation = TextModule(
sentence_dim=self._sentence_dim,
word_embedding_matrix=self._word_embedding_matrix)(
word_ids, is_training=is_training)
# Projection heads: Text -> Video and Text -> Audio.
txt2vid_embd_module, txt2aud_embd_module = self._get_pair_embedding_heads(
embedding_dim_1=self._cfg_vid_txt["embedding_dim"],
embedding_dim_2=self._cfg_aud_txt["embedding_dim"],
mode1=self._cfg_vid_txt["tovid_head_mode"],
mode2=self._cfg_aud_txt["toaud_head_mode"],
use_bn_out1=self._cfg_vid_txt["tovid_bn_after_proj"],
use_bn_out2=self._cfg_aud_txt["toaud_bn_after_proj"],
name1="txt_embd",
name2="txt2audio_embd")
txt_embd = {}
txt_embd["tovid"] = txt2vid_embd_module(txt_representation,
is_training=is_training)
txt_embd["toaud"] = txt2aud_embd_module(txt_representation,
is_training=is_training)
return {
"vid_embd": video_embd,
"aud_embd": audio_embd,
"txt_embd": txt_embd,
"vid_repr": visual_representation,
"aud_repr": audio_representation,
}
class EmbeddingModule(hk.Module):
"""Final Embedding module."""
def __init__(self,
embedding_dim: int,
mode: str = "linear",
use_bn_out: bool = False,
bn_config: Optional[Dict[str, Any]] = None,
use_xreplica_bn: bool = True,
name="embedding_module"):
self._embedding_dim = embedding_dim
self._use_bn_out = use_bn_out
self._mode = mode
# Set default BN config.
bn_config = bn_config or _DEFAULT_CFG_BN
if use_xreplica_bn:
normalizer_name = "cross_replica_batch_norm"
else:
normalizer_name = "batch_norm"
self._batch_norm = normalization.get_normalize_fn(
normalizer_name=normalizer_name,
normalizer_kwargs=bn_config)
super(EmbeddingModule, self).__init__(name=name)
def __call__(self, input_feature, is_training):
if self._mode == "linear":
proj = hk.Linear(self._embedding_dim, name="final_projection")
embedding = proj(input_feature)
elif self._mode.startswith("mlp"):
if "@" not in self._mode:
raise ValueError(
("Please specify the inner dimensions of the MLP with `@` symbol"
"e.g. mlp@512 or mlp@512@256 for a 2 layer MLP."))
inner_dims = [int(dim) for dim in self._mode.split("@")[1:]]
embedding = input_feature
for inner_dim in inner_dims:
embedding = hk.Linear(inner_dim, with_bias=True,
name="final_projection_inner")(embedding)
if not self._mode.startswith("mlp_nobn"):
embedding = self._batch_norm(embedding, is_training=is_training)
embedding = jax.nn.relu(embedding)
# Final projection.
embedding = hk.Linear(self._embedding_dim, name="final_projection",
with_bias=not self._use_bn_out)(embedding)
else:
raise NotImplementedError
if self._use_bn_out:
embedding = self._batch_norm(embedding, is_training=is_training)
return embedding
class VisualModule(hk.Module):
"""The visual module selects which CNN backbone to connect to the graph."""
def __init__(self,
use_xreplica_bn=True,
backbone="s3d",
model_kwargs=None,
name="visual_module"):
self._backbone = backbone
super(VisualModule, self).__init__(name=name)
if model_kwargs is None:
model_kwargs = {}
bn_config = model_kwargs.get("bn_config", _DEFAULT_CFG_BN)
if use_xreplica_bn:
normalizer_name = "cross_replica_batch_norm"
else:
normalizer_name = "batch_norm"
normalize_fn = normalization.get_normalize_fn(
normalizer_name=normalizer_name,
normalizer_kwargs=bn_config)
if backbone == "s3d":
self._cnn = s3d.S3D(normalize_fn=normalize_fn)
elif backbone == "resnet50tsm":
width_mult = model_kwargs.get("width_mult", 1)
self._cnn = tsm_resnet.TSMResNetV2(
normalize_fn=normalize_fn,
depth=50,
num_frames=model_kwargs["n_frames"],
width_mult=width_mult)
else:
raise NotImplementedError
def __call__(self, images, is_training):
"""Connects graph to images."""
features = self._cnn(images, is_training=is_training)
return features
class AudioModule(hk.Module):
"""The audio module selects which CNN backbone to connect to the graph."""
def __init__(self,
backbone="resnet18",
use_xreplica_bn=True,
model_kwargs=None,
name="audio_module"):
super(AudioModule, self).__init__(name=name)
model_kwargs = model_kwargs or {}
bn_config = model_kwargs.get("bn_config", _DEFAULT_CFG_BN)
backbone_to_depth = {
"resnet18": 18,
"resnet34": 34,
"resnet50": 50,
"resnet101": 101
}
assert backbone in backbone_to_depth, (
f"backbone should be in {backbone_to_depth.keys()}")
if use_xreplica_bn:
normalizer_name = "cross_replica_batch_norm"
else:
normalizer_name = "batch_norm"
self._cnn = resnet.ResNetV2(
depth=backbone_to_depth[backbone],
normalize_fn=normalization.get_normalize_fn(
normalizer_name=normalizer_name,
normalizer_kwargs=bn_config),
num_classes=None)
def __call__(self,
audio_spectrogram,
is_training,
return_intermediate=False):
"""Connects graph to audio spectrogram."""
final_endpoint = "output"
if return_intermediate:
final_endpoint = "last_conv"
return self._cnn(audio_spectrogram,
is_training=is_training,
final_endpoint=final_endpoint)
class TextModule(hk.Module):
"""Text module computes the sentences representation."""
def __init__(self,
word_embedding_matrix,
sentence_dim=1024,
name="text_module"):
"""Initialize text module.
Args:
word_embedding_matrix: 2d matrix [vocab_size, embed_size] to embed words.
sentence_dim: dimension of sentence representation.
name: module name.
"""
super(TextModule, self).__init__(name=name)
self._word_embedding_module = hk.Embed(
embedding_matrix=word_embedding_matrix)
self._conv1d_module = hk.Conv1D(sentence_dim, 1, name="text_conv1")
def __call__(self, word_ids, is_training):
"""Connects graph to sentence representation."""
word_embeddings = self._word_embedding_module(word_ids)
word_embeddings = jax.lax.stop_gradient(word_embeddings)
output = self._conv1d_module(word_embeddings)
output = jax.nn.relu(output)
output = jnp.amax(output, axis=1)
return output
================================================
FILE: mmv/models/normalization.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Normalize functions constructors."""
from typing import Any, Dict, Optional, Sequence, Union
import haiku as hk
from jax import numpy as jnp
from mmv.models import types
class _BatchNorm(hk.BatchNorm):
"""A `hk.BatchNorm` with adapted default arguments."""
def __init__(self,
create_scale: bool = True,
create_offset: bool = True,
decay_rate: float = 0.9,
eps: float = 1e-5,
test_local_stats: bool = False,
**kwargs):
# Check args.
if kwargs.get('cross_replica_axis', None) is not None:
raise ValueError(
'Attempting to use \'batch_norm\' normalizer, but specifying '
'`cross_replica_axis`. If you want this behavior use '
'`normalizer=\'cross_replica_batch_norm\'` directly.')
self._test_local_stats = test_local_stats
super().__init__(create_scale=create_scale,
create_offset=create_offset,
decay_rate=decay_rate,
eps=eps,
**kwargs)
def __call__(self,
x: types.TensorLike,
is_training: bool) -> jnp.ndarray:
return super().__call__(x, is_training,
test_local_stats=self._test_local_stats)
class _CrossReplicaBatchNorm(hk.BatchNorm):
"""A `hk.BatchNorm` with adapted default arguments for cross replica."""
def __init__(self,
create_scale: bool = True,
create_offset: bool = True,
decay_rate: float = 0.9,
eps: float = 1e-5,
test_local_stats: bool = False,
**kwargs):
# Check args.
if 'cross_replica_axis' in kwargs and kwargs['cross_replica_axis'] is None:
raise ValueError(
'Attempting to use \'cross_replica_batch_norm\' normalizer, but '
'specifying `cross_replica_axis` to be None. If you want this '
'behavior use `normalizer=\'batch_norm\'` directly.')
self._test_local_stats = test_local_stats
kwargs['cross_replica_axis'] = kwargs.get('cross_replica_axis', 'i')
super().__init__(create_scale=create_scale,
create_offset=create_offset,
decay_rate=decay_rate,
eps=eps,
**kwargs)
def __call__(self,
x: types.TensorLike,
is_training: bool) -> jnp.ndarray:
return super().__call__(x, is_training,
test_local_stats=self._test_local_stats)
class _LayerNorm(hk.LayerNorm):
"""A `hk.LayerNorm` accepting (and discarding) an `is_training` argument."""
def __init__(self,
axis: Union[int, Sequence[int]] = (1, 2),
create_scale: bool = True,
create_offset: bool = True,
**kwargs):
super().__init__(axis=axis,
create_scale=create_scale,
create_offset=create_offset,
**kwargs)
def __call__(self, # pytype: disable=signature-mismatch # overriding-parameter-count-checks
x: types.TensorLike,
is_training: bool) -> jnp.ndarray:
del is_training # Unused.
return super().__call__(x)
_NORMALIZER_NAME_TO_CLASS = {
'batch_norm': _BatchNorm,
'cross_replica_batch_norm': _CrossReplicaBatchNorm,
'layer_norm': _LayerNorm,
}
def get_normalize_fn(
normalizer_name: str = 'batch_norm',
normalizer_kwargs: Optional[Dict[str, Any]] = None,
) -> types.NormalizeFn:
"""Handles NormalizeFn creation.
These functions are expected to be used as part of Haiku model. On each
application of the returned normalization_fn, a new Haiku layer will be added
to the model.
Args:
normalizer_name: The name of the normalizer to be constructed.
normalizer_kwargs: The kwargs passed to the normalizer constructor.
Returns:
A `types.NormalizeFn` that when applied will create a new layer.
Raises:
ValueError: If `normalizer_name` is unknown.
"""
# Check args.
if normalizer_name not in _NORMALIZER_NAME_TO_CLASS:
raise ValueError(f'Unrecognized `normalizer_name` {normalizer_name}.')
normalizer_class = _NORMALIZER_NAME_TO_CLASS[normalizer_name]
normalizer_kwargs = normalizer_kwargs or dict()
return lambda *a, **k: normalizer_class(**normalizer_kwargs)(*a, **k) # pylint: disable=unnecessary-lambda
================================================
FILE: mmv/models/resnet.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""ResNet V2 modules.
Equivalent to hk.Resnet except accepting a final_endpoint to return
intermediate activations.
"""
from typing import Optional, Sequence, Text, Type, Union
import haiku as hk
import jax
import jax.numpy as jnp
from mmv.models import types
class BottleneckBlock(hk.Module):
"""Implements a bottleneck residual block (ResNet50 and ResNet101)."""
# pylint:disable=g-bare-generic
def __init__(self,
channels: int,
stride: Union[int, Sequence[int]],
use_projection: bool,
normalize_fn: Optional[types.NormalizeFn] = None,
name: Optional[Text] = None):
super(BottleneckBlock, self).__init__(name=name)
self._channels = channels
self._stride = stride
self._use_projection = use_projection
self._normalize_fn = normalize_fn
if self._use_projection:
self._proj_conv = hk.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=stride,
with_bias=False,
padding='SAME',
name='shortcut_conv')
self._conv_0 = hk.Conv2D(
output_channels=channels // 4,
kernel_shape=1,
stride=1,
with_bias=False,
padding='SAME',
name='conv_0')
self._conv_1 = hk.Conv2D(
output_channels=channels // 4,
kernel_shape=3,
stride=stride,
with_bias=False,
padding='SAME',
name='conv_1')
self._conv_2 = hk.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding='SAME',
name='conv_2')
def __call__(self,
inputs,
is_training):
net = inputs
shortcut = inputs
for i, conv_i in enumerate([self._conv_0, self._conv_1, self._conv_2]):
if self._normalize_fn is not None:
net = self._normalize_fn(net, is_training=is_training)
net = jax.nn.relu(net)
if i == 0 and self._use_projection:
shortcut = self._proj_conv(net)
# Now do the convs.
net = conv_i(net)
return net + shortcut
class BasicBlock(hk.Module):
"""Implements a basic residual block (ResNet18 and ResNet34)."""
# pylint:disable=g-bare-generic
def __init__(self,
channels: int,
stride: Union[int, Sequence[int]],
use_projection: bool,
normalize_fn: Optional[types.NormalizeFn] = None,
name: Optional[Text] = None):
super(BasicBlock, self).__init__(name=name)
self._channels = channels
self._stride = stride
self._use_projection = use_projection
self._normalize_fn = normalize_fn
if self._use_projection:
self._proj_conv = hk.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=stride,
with_bias=False,
padding='SAME',
name='shortcut_conv')
self._conv_0 = hk.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding='SAME',
name='conv_0')
self._conv_1 = hk.Conv2D(
output_channels=channels,
kernel_shape=3,
stride=stride,
with_bias=False,
padding='SAME',
name='conv_1')
def __call__(self,
inputs,
is_training):
net = inputs
shortcut = inputs
for i, conv_i in enumerate([self._conv_0, self._conv_1]):
if self._normalize_fn is not None:
net = self._normalize_fn(net, is_training=is_training)
net = jax.nn.relu(net)
if i == 0 and self._use_projection:
shortcut = self._proj_conv(net)
# Now do the convs.
net = conv_i(net)
return net + shortcut
class ResNetUnit(hk.Module):
"""Unit (group of blocks) for ResNet."""
# pylint:disable=g-bare-generic
def __init__(self,
channels: int,
num_blocks: int,
stride: Union[int, Sequence[int]],
block_module: Type[BottleneckBlock],
normalize_fn: Optional[types.NormalizeFn] = None,
name: Optional[Text] = None,
remat: bool = False):
super(ResNetUnit, self).__init__(name=name)
self._channels = channels
self._num_blocks = num_blocks
self._stride = stride
self._normalize_fn = normalize_fn
self._block_module = block_module
self._remat = remat
def __call__(self,
inputs,
is_training):
input_channels = inputs.shape[-1]
self._blocks = []
for id_block in range(self._num_blocks):
use_projection = id_block == 0 and self._channels != input_channels
self._blocks.append(
self._block_module(
channels=self._channels,
stride=self._stride if id_block == 0 else 1,
use_projection=use_projection,
normalize_fn=self._normalize_fn,
name='block_%d' % id_block))
net = inputs
for block in self._blocks:
if self._remat:
# Note: we can ignore cell-var-from-loop because the lambda is evaluated
# inside every iteration of the loop. This is needed to go around the
# way variables are passed to jax.remat.
net = hk.remat(lambda x: block(x, is_training=is_training))(net) # pylint: disable=cell-var-from-loop
else:
net = block(net, is_training=is_training)
return net
class ResNetV2(hk.Module):
"""ResNetV2 model."""
# Endpoints of the model in order.
VALID_ENDPOINTS = (
'resnet_stem',
'resnet_unit_0',
'resnet_unit_1',
'resnet_unit_2',
'resnet_unit_3',
'last_conv',
'output',
)
# pylint:disable=g-bare-generic
def __init__(self,
depth=50,
num_classes: Optional[int] = 1000,
width_mult: int = 1,
normalize_fn: Optional[types.NormalizeFn] = None,
name: Optional[Text] = None,
remat: bool = False):
"""Creates ResNetV2 Haiku module.
Args:
depth: depth of the desired ResNet (18, 34, 50, 101, 152 or 202).
num_classes: (int) Number of outputs in final layer. If None will not add
a classification head and will return the output embedding.
width_mult: multiplier for channel width.
normalize_fn: normalization function, see helpers/utils.py
name: Name of the module.
remat: Whether to rematerialize intermediate activations (saves memory).
"""
super(ResNetV2, self).__init__(name=name)
self._normalize_fn = normalize_fn
self._num_classes = num_classes
self._width_mult = width_mult
self._strides = [1, 2, 2, 2]
num_blocks = {
18: [2, 2, 2, 2],
34: [3, 4, 6, 3],
50: [3, 4, 6, 3],
101: [3, 4, 23, 3],
152: [3, 8, 36, 3],
200: [3, 24, 36, 3],
}
if depth not in num_blocks:
raise ValueError(
f'`depth` should be in {list(num_blocks.keys())} ({depth} given).')
self._num_blocks = num_blocks[depth]
if depth >= 50:
self._block_module = BottleneckBlock
self._channels = [256, 512, 1024, 2048]
else:
self._block_module = BasicBlock
self._channels = [64, 128, 256, 512]
self._initial_conv = hk.Conv2D(
output_channels=64 * self._width_mult,
kernel_shape=7,
stride=2,
with_bias=False,
padding='SAME',
name='initial_conv')
if remat:
self._initial_conv = hk.remat(self._initial_conv)
self._block_groups = []
for i in range(4):
self._block_groups.append(
ResNetUnit(
channels=self._channels[i] * self._width_mult,
num_blocks=self._num_blocks[i],
block_module=self._block_module,
stride=self._strides[i],
normalize_fn=self._normalize_fn,
name='block_group_%d' % i,
remat=remat))
if num_classes is not None:
self._logits_layer = hk.Linear(
output_size=num_classes, w_init=jnp.zeros, name='logits')
def __call__(self, inputs, is_training, final_endpoint='output'):
self._final_endpoint = final_endpoint
net = self._initial_conv(inputs)
net = hk.max_pool(
net, window_shape=(1, 3, 3, 1),
strides=(1, 2, 2, 1),
padding='SAME')
end_point = 'resnet_stem'
if self._final_endpoint == end_point:
return net
for i_group, block_group in enumerate(self._block_groups):
net = block_group(net, is_training=is_training)
end_point = f'resnet_unit_{i_group}'
if self._final_endpoint == end_point:
return net
end_point = 'last_conv'
if self._final_endpoint == end_point:
return net
if self._normalize_fn is not None:
net = self._normalize_fn(net, is_training=is_training)
net = jax.nn.relu(net)
# The actual representation
net = jnp.mean(net, axis=[1, 2])
assert self._final_endpoint == 'output'
if self._num_classes is None:
# If num_classes was None, we just return the output
# of the last block, without fully connected layer.
return net
return self._logits_layer(net)
================================================
FILE: mmv/models/s3d.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A Haiku S3D model."""
import collections
from typing import Optional, Sequence
import haiku as hk
import jax
from jax import numpy as jnp
from mmv.models import types
class _MaxPool(hk.MaxPool):
"""A `hk.MaxPool` accepting (and discarding) an `is_training` argument."""
def __call__(self,
x: types.TensorLike,
is_training: bool = True) -> jnp.ndarray:
del is_training # Unused.
return super().__call__(x)
def self_gating(inputs: types.TensorLike) -> jnp.ndarray:
"""Feature gating as used in S3D-G.
Transforms the input features by aggregating features from all spatial and
temporal locations, and applying gating conditioned on the aggregated
features. More details can be found at: https://arxiv.org/abs/1712.04851.
Args:
inputs: A 5-D float array of shape `[B, T, H, W, C]`.
Returns:
A tensor with the same shape as input_tensor.
Raises:
ValueError: If `inputs` has the wrong shape.
"""
if inputs.ndim != 5:
raise ValueError(
f'Expected an input of shape `[B, T, H, W, C]` but got {inputs.shape}.')
input_shape = inputs.shape
num_channels = input_shape[4]
spatiotemporal_average = jnp.mean(inputs, axis=(1, 2, 3))
weights = hk.Linear(num_channels, name='self_gating')(spatiotemporal_average)
weights = jax.nn.sigmoid(weights)
return jnp.multiply(weights[:, None, None, None, :], inputs)
class SUnit3D(hk.Module):
"""Base 3d Unit combining Conv3d + Batch Norm + non-linearity."""
def __init__(
self,
output_channels: int,
kernel_shape: Sequence[int] = (1, 1, 1),
stride: Sequence[int] = (1, 1, 1),
with_bias: bool = False,
separable: bool = False,
normalize_fn: Optional[types.NormalizeFn] = None,
activation_fn: Optional[types.ActivationFn] = jax.nn.relu,
self_gating_fn: Optional[types.GatingFn] = None,
name='SUnit3D'):
"""Initializes the SUnit3D module.
Args:
output_channels: Number of output channels.
kernel_shape: The shape of the kernel. A sequence of length 3.
stride: Stride for the kernel. A sequence of length 3.
with_bias: Whether to add a bias to the convolution.
separable: Whether to use separable.
normalize_fn: Function used for normalization.
activation_fn: Function used as non-linearity.
self_gating_fn: Function used for self-gating.
name: The name of the module.
Raises:
ValueError: If `kernel_shape` or `stride` has the wrong shape.
"""
super().__init__(name=name)
# Check args.
if len(kernel_shape) != 3:
raise ValueError(
'Given `kernel_shape` must have length 3 but has length '
f'{len(kernel_shape)}.')
if len(stride) != 3:
raise ValueError(
f'Given `stride` must have length 3 but has length {len(stride)}.')
self._normalize_fn = normalize_fn
self._activation_fn = activation_fn
self._self_gating_fn = self_gating_fn
k0, k1, k2 = kernel_shape
if separable and k1 != 1:
spatial_kernel_shape = [1, k1, k2]
temporal_kernel_shape = [k0, 1, 1]
s0, s1, s2 = stride
spatial_stride = [1, s1, s2]
temporal_stride = [s0, 1, 1]
self._convolutions = [
hk.Conv3D(
output_channels=output_channels,
kernel_shape=spatial_kernel_shape,
stride=spatial_stride,
padding='SAME',
with_bias=with_bias),
hk.Conv3D(
output_channels=output_channels,
kernel_shape=temporal_kernel_shape,
stride=temporal_stride,
padding='SAME',
with_bias=with_bias)
]
else:
self._convolutions = [
hk.Conv3D(
output_channels=output_channels,
kernel_shape=kernel_shape,
stride=stride,
padding='SAME',
with_bias=with_bias)]
def __call__(
self,
inputs: types.TensorLike,
is_training: bool) -> jnp.ndarray:
"""Connects the module to inputs.
Args:
inputs: A 5-D float array of shape `[B, T, H, W, C]`.
is_training: Whether to use training mode.
Returns:
A 5-D float array of shape `[B, new_t, new_h, new_w, output_channels]`.
"""
x = inputs
for conv in self._convolutions:
x = conv(x)
if self._normalize_fn is not None:
x = self._normalize_fn(x, is_training=is_training)
if self._activation_fn is not None:
x = self._activation_fn(x)
if self._self_gating_fn:
x = self._self_gating_fn(x)
return x # pytype: disable=bad-return-type # jax-devicearray
class InceptionBlockV13D(hk.Module):
"""A 3D Inception v1 block.
This allows use of separable 3D convolutions and self-gating, as described in:
Rethinking Spatiotemporal Feature Learning For Video Understanding.
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu and Kevin Murphy.
https://arxiv.org/abs/1712.04851.
"""
def __init__(self,
output_channels: Sequence[int],
normalize_fn: Optional[types.NormalizeFn],
temporal_kernel_size: int = 3,
self_gating_fn: Optional[types.GatingFn] = None,
name: str = 'InceptionBlockV13D'):
"""Initializes the InceptionBlockV13D module.
Args:
output_channels: The size of the output channels of each block, ordered as
[Conv2d_0a_1x1, Conv2d_0a_1x1, Conv2d_0b_3x3, Conv2d_0a_1x1,
Conv2d_0b_3x3, Conv2d_0b_1x1]
normalize_fn: Function used for normalization.
temporal_kernel_size: The size of the temporal convolutional filters in
the conv3d_spatiotemporal blocks.
self_gating_fn: Function which optionally performs self-gating. If `None`,
no self-gating is applied.
name: The name of the module.
Raises:
ValueError: If `output_channels` has the wrong shape.
"""
super().__init__(name=name)
# Check args.
if len(output_channels) != 6:
raise ValueError(
'Given `output_channels` must have length 6 but has length '
f'{len(output_channels)}.')
self._output_channels = output_channels
self._normalize_fn = normalize_fn
self._temporal_kernel_size = temporal_kernel_size
if self_gating_fn is None:
self._self_gating_fn = lambda x: x
else:
self._self_gating_fn = self_gating_fn
def __call__(
self,
inputs: types.TensorLike,
is_training: bool) -> jnp.ndarray:
"""Connects the module to inputs.
Args:
inputs: A 5-D float array of shape `[B, T, H, W, C]`.
is_training: Whether to use training mode.
Returns:
A 5-D float array of shape
`[B, new_t, new_h, new_w, sum(output_channels)]`.
"""
# Branch 0
branch_0 = SUnit3D(
output_channels=self._output_channels[0],
kernel_shape=(1, 1, 1),
separable=False,
normalize_fn=self._normalize_fn,
self_gating_fn=self._self_gating_fn,
name='Branch_0_Conv2d_0a_1x1')(
inputs, is_training=is_training)
# Branch 1
branch_1 = SUnit3D(
output_channels=self._output_channels[1],
kernel_shape=(1, 1, 1),
separable=False,
normalize_fn=self._normalize_fn,
self_gating_fn=None,
name='Branch_1_Conv2d_0a_1x1')(
inputs, is_training=is_training)
branch_1 = SUnit3D(
output_channels=self._output_channels[2],
kernel_shape=(self._temporal_kernel_size, 3, 3),
separable=True,
normalize_fn=self._normalize_fn,
self_gating_fn=self._self_gating_fn,
name='Branch_1_Conv2d_0b_3x3')(
branch_1, is_training=is_training)
# Branch 2
branch_2 = SUnit3D(
output_channels=self._output_channels[3],
kernel_shape=(1, 1, 1),
separable=False,
normalize_fn=self._normalize_fn,
self_gating_fn=None,
name='Branch_2_Conv2d_0a_1x1')(
inputs, is_training=is_training)
branch_2 = SUnit3D(
output_channels=self._output_channels[4],
kernel_shape=(self._temporal_kernel_size, 3, 3),
separable=True,
normalize_fn=self._normalize_fn,
self_gating_fn=self._self_gating_fn,
name='Branch_2_Conv2d_0b_3x3')(
branch_2, is_training=is_training)
# Branch 3
branch_3 = hk.MaxPool(
window_shape=(1, 3, 3, 3, 1),
strides=(1, 1, 1, 1, 1),
padding='SAME',
name='Branch_3_MaxPool_0a_3x3')(
inputs)
branch_3 = SUnit3D(
output_channels=self._output_channels[5],
kernel_shape=(1, 1, 1),
separable=False,
normalize_fn=self._normalize_fn,
self_gating_fn=self._self_gating_fn,
name='Branch_3_Conv2d_0b_1x1')(
branch_3, is_training=is_training)
return jnp.concatenate((branch_0, branch_1, branch_2, branch_3), axis=4)
_Layer = collections.namedtuple('_Layer', ('name', 'module', 'kwargs'))
class S3D(hk.Module):
"""S3D architecture.
Any intermediary representation can be obtained by choosing one of the valid
`final_endpoint`s. The final value returned by this model (when 'Embeddings'
is used as `final_endpoint`) is a single 1-D representation for each video in
the batch. Another layer can be externally added on top of that to obtain
logits.
"""
# Endpoints of the model in order.
VALID_ENDPOINTS = (
'Conv2d_1a_7x7',
'MaxPool_2a_3x3',
'Conv2d_2b_1x1',
'Conv2d_2c_3x3',
'MaxPool_3a_3x3',
'Mixed_3b',
'Mixed_3c',
'MaxPool_4a_3x3',
'Mixed_4b',
'Mixed_4c',
'Mixed_4d',
'Mixed_4e',
'Mixed_4f',
'MaxPool_5a_2x2',
'Mixed_5b',
'Mixed_5c',
'Embeddings',
)
def __init__(self,
normalize_fn: Optional[types.NormalizeFn] = None,
first_temporal_kernel_size: int = 7,
temporal_conv_startat: Optional[str] = 'Conv2d_2c_3x3',
gating_startat: Optional[str] = 'Conv2d_2c_3x3',
name='S3D'):
"""Initializes the S3D module.
Args:
normalize_fn: Function used for normalization.
first_temporal_kernel_size: Specifies the temporal kernel size for the
first conv3d filter. A larger value slows down the model but provides
little accuracy improvement. Must be set to one of 1, 3, 5 or 7.
temporal_conv_startat: Specifies the first conv block to use separable 3D
convs rather than 2D convs (implemented as [1, k, k] 3D conv). This is
used to construct the inverted pyramid models. 'Conv2d_2c_3x3' is the
first valid block to use separable 3D convs. If provided block name is
not present, all valid blocks will use separable 3D convs.
gating_startat: Specifies the first conv block to use self gating.
'Conv2d_2c_3x3' is the first valid block to use self gating. If provided
block name is not present, all valid blocks will use separable 3D convs.
name: The name of the module.
Raises:
ValueError: If `temporal_conv_startat`, `gating_startat` or
`first_temporal_kernel_size` is not recognized.
"""
super().__init__(name=name)
self._first_temporal_kernel_size = first_temporal_kernel_size
self._temporal_conv_startat = temporal_conv_startat
self._gating_startat = gating_startat
self._normalize_fn = normalize_fn
if (temporal_conv_startat not in self.VALID_ENDPOINTS
and temporal_conv_startat is not None):
raise ValueError(
f'Provided `temporal_conv_startat`: {temporal_conv_startat} not '
f'valid. It must be one of: {self.VALID_ENDPOINTS}, or `None`.')
if (gating_startat not in self.VALID_ENDPOINTS
and gating_startat is not None):
raise ValueError(
f'Provided `gating_startat`: {gating_startat} not valid. '
f'It must be one of: {self.VALID_ENDPOINTS}, or `None`.')
if first_temporal_kernel_size not in [1, 3, 5, 7]:
raise ValueError('`first_temporal_kernel_size` can only be 1, 3, 5 or 7.')
def __call__(self,
inputs: types.TensorLike,
is_training: bool,
final_endpoint: str = 'Embeddings') -> jnp.ndarray:
"""Connects the model to inputs.
Args:
inputs: A 5-D float array of shape `[B, T, H, W, C]`.
is_training: Whether to use training mode.
final_endpoint: Up to which endpoint to run / return.
Returns:
A 5-D float array of shape
`[B, new_t, new_h, new_w, sum(output_channels)]`.
Returns:
Network output at location `final_endpoint`. A float array which shape
depends on `final_endpoint`.
Raises:
ValueError: If `final_endpoint` is not recognized.
"""
if final_endpoint not in self.VALID_ENDPOINTS:
raise ValueError(f'Provided final_endpoint: {final_endpoint} not valid.'
f' It must be one of: {self.VALID_ENDPOINTS}')
x = inputs
# We define layers with tuples (name, module, kwargs)
# Not all kwargs are present, as we will need to fill in certain properties
# as we move down the network.
layers = []
# The first layer is conditional on the input data shape: the channel size
# is used to identify whether the `space_to_depth` transformation has been
# applied to the input. This is used to speed up computation on TPUs.
if x.shape[-1] == 3:
layers.append(
_Layer('Conv2d_1a_7x7', SUnit3D,
dict(output_channels=64, stride=(2, 2, 2), separable=False,
kernel_shape=(self._first_temporal_kernel_size, 7, 7),
normalize_fn=self._normalize_fn)))
else:
layers.append(
_Layer('Conv2d_1a_7x7', SUnit3D,
dict(output_channels=64, kernel_shape=(2, 4, 4),
stride=(1, 1, 1), separable=False,
normalize_fn=self._normalize_fn)))
layers.extend([
_Layer('MaxPool_2a_3x3', _MaxPool,
dict(window_shape=(1, 1, 3, 3, 1), strides=(1, 1, 2, 2, 1),
padding='SAME')),
_Layer('Conv2d_2b_1x1', SUnit3D,
dict(output_channels=64, kernel_shape=(1, 1, 1),
normalize_fn=self._normalize_fn)),
_Layer('Conv2d_2c_3x3', SUnit3D,
dict(output_channels=192, separable=True,
normalize_fn=self._normalize_fn)),
_Layer('MaxPool_3a_3x3', _MaxPool,
dict(window_shape=(1, 1, 3, 3, 1), strides=(1, 1, 2, 2, 1),
padding='SAME')),
_Layer('Mixed_3b', InceptionBlockV13D,
dict(output_channels=(64, 96, 128, 16, 32, 32),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_3c', InceptionBlockV13D,
dict(output_channels=(128, 128, 192, 32, 96, 64),
normalize_fn=self._normalize_fn)),
_Layer('MaxPool_4a_3x3', _MaxPool,
dict(window_shape=(1, 3, 3, 3, 1), strides=(1, 2, 2, 2, 1),
padding='SAME')),
_Layer('Mixed_4b', InceptionBlockV13D,
dict(output_channels=(192, 96, 208, 16, 48, 64),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_4c', InceptionBlockV13D,
dict(output_channels=(160, 112, 224, 24, 64, 64),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_4d', InceptionBlockV13D,
dict(output_channels=(128, 128, 256, 24, 64, 64),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_4e', InceptionBlockV13D,
dict(output_channels=(112, 144, 288, 32, 64, 64),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_4f', InceptionBlockV13D,
dict(output_channels=(256, 160, 320, 32, 128, 128),
normalize_fn=self._normalize_fn)),
_Layer('MaxPool_5a_2x2', _MaxPool,
dict(window_shape=(1, 2, 2, 2, 1), strides=(1, 2, 2, 2, 1),
padding='SAME')),
_Layer('Mixed_5b', InceptionBlockV13D,
dict(output_channels=(256, 160, 320, 32, 128, 128),
normalize_fn=self._normalize_fn)),
_Layer('Mixed_5c', InceptionBlockV13D,
dict(output_channels=(384, 192, 384, 48, 128, 128),
normalize_fn=self._normalize_fn)),
])
# These parameters may change thoughout the computation.
self_gating_fn = None
temporal_kernel_size = 1
# Iterate over layers.
for layer in layers:
# Update
if layer.name == self._gating_startat:
self_gating_fn = self_gating
if layer.name == self._temporal_conv_startat:
temporal_kernel_size = 3
kwargs = layer.kwargs
if layer.module is SUnit3D:
kwargs['self_gating_fn'] = self_gating_fn
if 'kernel_shape' not in kwargs:
kwargs['kernel_shape'] = (temporal_kernel_size, 3, 3)
elif layer.module is InceptionBlockV13D:
kwargs['self_gating_fn'] = self_gating_fn
kwargs['temporal_kernel_size'] = temporal_kernel_size
module = layer.module(name=layer.name, **kwargs)
x = module(x, is_training=is_training)
if final_endpoint == layer.name:
return x
assert final_endpoint == 'Embeddings'
return jnp.mean(x, axis=(1, 2, 3))
================================================
FILE: mmv/models/s3d_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for s3d."""
from absl.testing import absltest
from absl.testing import parameterized
import haiku as hk
import jax
import numpy as np
from mmv.models import normalization
from mmv.models import s3d
class _CallableS3D:
"""Wrapper around S3D that take care of parameter book keeping."""
def __init__(self, *args, **kwargs):
self._model = hk.transform_with_state(
lambda *a, **k: # pylint: disable=g-long-lambda,unnecessary-lambda
s3d.S3D(
normalize_fn=normalization.get_normalize_fn(),
*args, **kwargs)(*a, **k))
self._rng = jax.random.PRNGKey(42)
self._params, self._state = None, None
def init(self, inputs, **kwargs):
self._params, self._state = self._model.init(
self._rng, inputs, is_training=True, **kwargs)
def __call__(self, inputs, **kwargs):
if self._params is None:
self.init(inputs)
output, _ = self._model.apply(
self._params, self._state, self._rng, inputs, **kwargs)
return output
class S3DTest(parameterized.TestCase):
# Testing all layers is quite slow, added in comments for completeness.
@parameterized.parameters(
# dict(endpoint='Conv2d_1a_7x7', expected_size=(2, 8, 112, 112, 64)),
# dict(endpoint='MaxPool_2a_3x3', expected_size=(2, 8, 56, 56, 64)),
# dict(endpoint='Conv2d_2b_1x1', expected_size=(2, 8, 56, 56, 64)),
# dict(endpoint='Conv2d_2c_3x3', expected_size=(2, 8, 56, 56, 192)),
# dict(endpoint='MaxPool_3a_3x3', expected_size=(2, 8, 28, 28, 192)),
# dict(endpoint='Mixed_3b', expected_size=(2, 8, 28, 28, 256)),
# dict(endpoint='Mixed_3c', expected_size=(2, 8, 28, 28, 480)),
# dict(endpoint='MaxPool_4a_3x3', expected_size=(2, 4, 14, 14, 480)),
# dict(endpoint='Mixed_4b', expected_size=(2, 4, 14, 14, 512)),
# dict(endpoint='Mixed_4c', expected_size=(2, 4, 14, 14, 512)),
# dict(endpoint='Mixed_4d', expected_size=(2, 4, 14, 14, 512)),
# dict(endpoint='Mixed_4e', expected_size=(2, 4, 14, 14, 528)),
# dict(endpoint='Mixed_4f', expected_size=(2, 4, 14, 14, 832)),
# dict(endpoint='MaxPool_5a_2x2', expected_size=(2, 2, 7, 7, 832)),
# dict(endpoint='Mixed_5b', expected_size=(2, 2, 7, 7, 832)),
# dict(endpoint='Mixed_5c', expected_size=(2, 2, 7, 7, 1024)),
dict(endpoint='Embeddings', expected_size=(2, 1024)),
)
def test_endpoint_expected_output_dimensions(self, endpoint, expected_size):
inputs = np.random.normal(size=(2, 16, 224, 224, 3))
model = _CallableS3D()
output = model(inputs, is_training=False, final_endpoint=endpoint)
self.assertSameElements(output.shape, expected_size)
def test_space_to_depth(self):
inputs = np.random.normal(size=(2, 16//2, 224//2, 224//2, 3*2*2*2))
model = _CallableS3D()
output = model(inputs, is_training=False, final_endpoint='Conv2d_1a_7x7')
self.assertSameElements(output.shape, (2, 8, 112, 112, 64))
if __name__ == '__main__':
absltest.main()
================================================
FILE: mmv/models/tsm_resnet.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Temporal Shift Module w/ ResNet-50 and ResNet-101.
Based on:
TSM: Temporal Shift Module for Efficient Video Understanding
Ji Lin, Chuang Gan, Song Han
https://arxiv.org/pdf/1811.08383.pdf.
"""
from typing import Optional
import haiku as hk
import jax
import jax.numpy as jnp
from mmv.models import tsm_utils as tsmu
from mmv.models import types
class TSMResNetBlock(hk.Module):
"""A ResNet subblock with Temporal Channel Shifting.
Combines a typical ResNetV2 block implementation
(see https://arxiv.org/abs/1512.03385) with a pre-convolution Temporal
Shift Module (see https://arxiv.org/pdf/1811.08383.pdf) in the residual.
"""
def __init__(self,
output_channels: int,
stride: int,
use_projection: bool,
tsm_mode: str,
normalize_fn: Optional[types.NormalizeFn] = None,
channel_shift_fraction: float = 0.125,
num_frames: int = 8,
name: str = 'TSMResNetBlock'):
"""Initializes the TSMResNetBlock module.
Args:
output_channels: Number of output channels.
stride: Stride used in convolutions.
use_projection: Whether to use a projection for the shortcut.
tsm_mode: Mode for TSM ('gpu' or 'tpu').
normalize_fn: Function used for normalization.
channel_shift_fraction: The fraction of temporally shifted channels. If
`channel_shift_fraction` is 0, the block is the same as a normal ResNet
block.
num_frames: Size of frame dimension in a single batch example
name: The name of the module.
"""
super().__init__(name=name)
self._output_channels = output_channels
self._bottleneck_channels = output_channels // 4
self._stride = stride
self._use_projection = use_projection
self._normalize_fn = normalize_fn
self._tsm_mode = tsm_mode
self._channel_shift_fraction = channel_shift_fraction
self._num_frames = num_frames
def __call__(self,
inputs: types.TensorLike,
is_training: bool = True) -> jnp.ndarray:
"""Connects the ResNetBlock module into the graph.
Args:
inputs: A 4-D float array of shape `[B, H, W, C]`.
is_training: Whether to use training mode.
Returns:
A 4-D float array of shape
`[B * num_frames, new_h, new_w, output_channels]`.
"""
# ResNet V2 uses pre-activation, where the batch norm and relu are before
# convolutions, rather than after as in ResNet V1.
preact = inputs
if self._normalize_fn is not None:
preact = self._normalize_fn(preact, is_training=is_training)
preact = jax.nn.relu(preact)
if self._use_projection:
shortcut = hk.Conv2D(
output_channels=self._output_channels,
kernel_shape=1,
stride=self._stride,
with_bias=False,
padding='SAME',
name='shortcut_conv')(
preact)
else:
shortcut = inputs
# Eventually applies Temporal Shift Module.
if self._channel_shift_fraction != 0:
preact = tsmu.apply_temporal_shift(
preact, tsm_mode=self._tsm_mode, num_frames=self._num_frames,
channel_shift_fraction=self._channel_shift_fraction)
# First convolution.
residual = hk.Conv2D(
self._bottleneck_channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding='SAME',
name='conv_0')(
preact)
# Second convolution.
if self._normalize_fn is not None:
residual = self._normalize_fn(residual, is_training=is_training)
residual = jax.nn.relu(residual)
residual = hk.Conv2D(
output_channels=self._bottleneck_channels,
kernel_shape=3,
stride=self._stride,
with_bias=False,
padding='SAME',
name='conv_1')(
residual)
# Third convolution.
if self._normalize_fn is not None:
residual = self._normalize_fn(residual, is_training=is_training)
residual = jax.nn.relu(residual)
residual = hk.Conv2D(
output_channels=self._output_channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding='SAME',
name='conv_2')(
residual)
# NOTE: we do not use block multiplier.
output = shortcut + residual
return output
class TSMResNetUnit(hk.Module):
"""Block group for TSM ResNet."""
def __init__(self,
output_channels: int,
num_blocks: int,
stride: int,
tsm_mode: str,
num_frames: int,
normalize_fn: Optional[types.NormalizeFn] = None,
channel_shift_fraction: float = 0.125,
name: str = 'tsm_resnet_unit'):
"""Creates a TSMResNet Unit.
Args:
output_channels: Number of output channels.
num_blocks: Number of ResNet blocks in the unit.
stride: Stride of the unit.
tsm_mode: Which temporal shift module to use.
num_frames: Size of frame dimension in a single batch example.
normalize_fn: Function used for normalization.
channel_shift_fraction: The fraction of temporally shifted channels. If
`channel_shift_fraction` is 0, the block is the same as a normal ResNet
block.
name: The name of the module.
"""
super().__init__(name=name)
self._output_channels = output_channels
self._num_blocks = num_blocks
self._normalize_fn = normalize_fn
self._stride = stride
self._tsm_mode = tsm_mode
self._channel_shift_fraction = channel_shift_fraction
self._num_frames = num_frames
def __call__(self,
inputs: types.TensorLike,
is_training: bool) -> jnp.ndarray:
"""Connects the module to inputs.
Args:
inputs: A 4-D float array of shape `[B * num_frames, H, W, C]`.
is_training: Whether to use training mode.
Returns:
A 4-D float array of shape
`[B * num_frames, H // stride, W // stride, output_channels]`.
"""
net = inputs
for idx_block in range(self._num_blocks):
net = TSMResNetBlock(
self._output_channels,
stride=self._stride if idx_block == 0 else 1,
use_projection=idx_block == 0,
normalize_fn=self._normalize_fn,
tsm_mode=self._tsm_mode,
channel_shift_fraction=self._channel_shift_fraction,
num_frames=self._num_frames,
name=f'block_{idx_block}')(
net, is_training=is_training)
return net # pytype: disable=bad-return-type # jax-devicearray
class TSMResNetV2(hk.Module):
"""TSM based on ResNet V2 as described in https://arxiv.org/abs/1603.05027."""
# Endpoints of the model in order.
VALID_ENDPOINTS = (
'tsm_resnet_stem',
'tsm_resnet_unit_0',
'tsm_resnet_unit_1',
'tsm_resnet_unit_2',
'tsm_resnet_unit_3',
'last_conv',
'Embeddings',
)
def __init__(self,
normalize_fn: Optional[types.NormalizeFn] = None,
depth: int = 50,
num_frames: int = 16,
channel_shift_fraction: float = 0.125,
width_mult: int = 1,
name: str = 'TSMResNetV2'):
"""Constructs a ResNet model.
Args:
normalize_fn: Function used for normalization.
depth: Depth of the desired ResNet.
num_frames: Number of frames (used in TPU mode).
channel_shift_fraction: Fraction of channels that are temporally shifted,
if `channel_shift_fraction` is 0, a regular ResNet is returned.
width_mult: Whether or not to use a width multiplier.
name: The name of the module.
Raises:
ValueError: If `channel_shift_fraction` or `depth` has invalid value.
"""
super().__init__(name=name)
if not 0. <= channel_shift_fraction <= 1.0:
raise ValueError(
f'channel_shift_fraction ({channel_shift_fraction})'
' has to be in [0, 1].')
self._num_frames = num_frames
self._channels = (256, 512, 1024, 2048)
self._strides = (1, 2, 2, 2)
num_blocks = {
50: (3, 4, 6, 3),
101: (3, 4, 23, 3),
152: (3, 8, 36, 3),
200: (3, 24, 36, 3),
}
if depth not in num_blocks:
raise ValueError(
f'`depth` should be in {list(num_blocks.keys())} ({depth} given).')
self._num_blocks = num_blocks[depth]
self._width_mult = width_mult
self._channel_shift_fraction = channel_shift_fraction
self._normalize_fn = normalize_fn
def __call__(
self,
inputs: types.TensorLike,
is_training: bool = True,
final_endpoint: str = 'Embeddings') -> jnp.ndarray:
"""Connects the TSM ResNetV2 module into the graph.
Args:
inputs: A 4-D float array of shape `[B, H, W, C]`.
is_training: Whether to use training mode.
final_endpoint: Up to which endpoint to run / return.
Returns:
Network output at location `final_endpoint`. A float array which shape
depends on `final_endpoint`.
Raises:
ValueError: If `final_endpoint` is not recognized.
"""
# Prepare inputs for TSM.
inputs, tsm_mode, num_frames = tsmu.prepare_inputs(inputs)
num_frames = num_frames or self._num_frames
self._final_endpoint = final_endpoint
if self._final_endpoint not in self.VALID_ENDPOINTS:
raise ValueError(f'Unknown final endpoint {self._final_endpoint}')
# Stem convolution.
end_point = 'tsm_resnet_stem'
net = hk.Conv2D(
output_channels=64 * self._width_mult,
kernel_shape=7,
stride=2,
with_bias=False,
name=end_point,
padding='SAME')(
inputs)
net = hk.MaxPool(
window_shape=(1, 3, 3, 1),
strides=(1, 2, 2, 1),
padding='SAME')(
net)
if self._final_endpoint == end_point:
return net
# Residual block.
for unit_id, (channels, num_blocks, stride) in enumerate(
zip(self._channels, self._num_blocks, self._strides)):
end_point = f'tsm_resnet_unit_{unit_id}'
net = TSMResNetUnit(
output_channels=channels * self._width_mult,
num_blocks=num_blocks,
stride=stride,
normalize_fn=self._normalize_fn,
channel_shift_fraction=self._channel_shift_fraction,
num_frames=num_frames,
tsm_mode=tsm_mode,
name=end_point)(
net, is_training=is_training)
if self._final_endpoint == end_point:
return net
if self._normalize_fn is not None:
net = self._normalize_fn(net, is_training=is_training)
net = jax.nn.relu(net)
end_point = 'last_conv'
if self._final_endpoint == end_point:
return net
net = jnp.mean(net, axis=(1, 2))
# Prepare embedding outputs for TSM (temporal average of features).
net = tsmu.prepare_outputs(net, tsm_mode, num_frames)
assert self._final_endpoint == 'Embeddings'
return net
================================================
FILE: mmv/models/tsm_resnet_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for TSM ResNet model."""
from absl.testing import absltest
from absl.testing import parameterized
import haiku as hk
import jax
import jax.numpy as jnp
from mmv.models import tsm_resnet
class TSMResNetTest(parameterized.TestCase):
@parameterized.parameters(
('tsm_resnet_stem', (2 * 32, 56, 56, 64)),
('tsm_resnet_unit_0', (2 * 32, 56, 56, 256)),
('tsm_resnet_unit_1', (2 * 32, 28, 28, 512)),
('tsm_resnet_unit_2', (2 * 32, 14, 14, 1024)),
('tsm_resnet_unit_3', (2 * 32, 7, 7, 2048)),
('last_conv', (2 * 32, 7, 7, 2048)),
('Embeddings', (2, 2048)),
)
def test_output_dimension(self, final_endpoint, expected_shape):
input_shape = (2, 32, 224, 224, 3)
def f():
data = jnp.zeros(input_shape)
net = tsm_resnet.TSMResNetV2()
return net(data, final_endpoint=final_endpoint)
init_fn, apply_fn = hk.transform(f)
out = apply_fn(init_fn(jax.random.PRNGKey(42)), None)
self.assertEqual(out.shape, expected_shape)
def test_tpu_mode(self):
input_shape = (32 * 2, 224, 224, 3)
def f():
data = jnp.zeros(input_shape)
net = tsm_resnet.TSMResNetV2(num_frames=32)
return net(data, final_endpoint='Embeddings')
init_fn, apply_fn = hk.transform(f)
out = apply_fn(init_fn(jax.random.PRNGKey(42)), None)
self.assertEqual(out.shape, (2, 2048))
if __name__ == '__main__':
absltest.main()
================================================
FILE: mmv/models/tsm_utils.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utils functions for TSM."""
from typing import Tuple
import jax
import jax.numpy as jnp
from mmv.models import types
def prepare_inputs(
inputs: types.TensorLike) -> Tuple[jnp.ndarray, str, int]:
"""Deduces input mode for TSM."""
# Deduce if we run on TPU based on input shape.
if len(inputs.shape) == 5:
# Input is given in the standard [B, T, H, W, 3] format.
tsm_mode = 'gpu'
num_frames = inputs.shape[1]
inputs = jnp.reshape(inputs, [-1] + list(inputs.shape[2:]))
else:
# Input is given in the [T * B, H, W, 3] format.
tsm_mode = 'tpu'
num_frames = None
return inputs, tsm_mode, num_frames
def prepare_outputs(outputs: types.TensorLike,
tsm_mode: str,
num_frames: int) -> jnp.ndarray:
"""Processes output of TSM by averaging representations over time axis."""
n_channels = outputs.shape[-1]
if tsm_mode == 'tpu':
outputs = jnp.reshape(outputs, [num_frames, -1, n_channels])
outputs = jnp.mean(outputs, axis=0)
elif tsm_mode == 'gpu':
outputs = jnp.reshape(outputs, [-1, num_frames, n_channels])
outputs = jnp.mean(outputs, axis=1)
else:
raise ValueError(
f'`tsm_mode` should be \'tpu\' or \'gpu\' ({tsm_mode} given)')
return outputs
def apply_temporal_shift(
x: types.TensorLike,
tsm_mode: str,
num_frames: int,
channel_shift_fraction: float = 0.125) -> jnp.ndarray:
"""Performs a temporal shift: https://arxiv.org/abs/1811.08383 with mode."""
if tsm_mode == 'tpu':
outputs = temporal_shift_tpu(x, num_frames, channel_shift_fraction)
elif tsm_mode == 'gpu':
outputs = temporal_shift_gpu(x, num_frames, channel_shift_fraction)
else:
raise ValueError(
f'`tsm_mode` should be \'tpu\' or \'gpu\' ({tsm_mode} given)')
return outputs
def temporal_shift_gpu(
x: types.TensorLike,
num_frames: int,
channel_shift_fraction: float = 0.125) -> jnp.ndarray:
"""Performs a temporal shift: https://arxiv.org/abs/1811.08383."""
# B, T, H, W, C = batch_size, num_frames, im_height, im_width, channels
# Input is (B * T, H, W, C)
orig_shp = tuple(x.shape)
reshaped_x = jnp.reshape(x, (-1, num_frames) + orig_shp[1:])
n_channels = orig_shp[-1]
n_shift = int(n_channels * channel_shift_fraction)
new_shp = tuple(reshaped_x.shape)
# shifted_backward = reshaped_x[:, 1:, :, :, -n_shift:]
shifted_backward = jax.lax.slice(
reshaped_x, (0, 1, 0, 0, new_shp[4] - n_shift),
(new_shp[0], new_shp[1], new_shp[2], new_shp[3], new_shp[4]))
shifted_backward_padding = ((0, 0), (0, 1), (0, 0), (0, 0), (0, 0))
shifted_backward = jnp.pad(shifted_backward, shifted_backward_padding)
# shifted_forward = reshaped_x[:, :-1, :, :, :n_shift]
shifted_forward = jax.lax.slice(
reshaped_x, (0, 0, 0, 0, 0),
(new_shp[0], new_shp[1] - 1, new_shp[2], new_shp[3], n_shift))
shifted_forward_padding = ((0, 0), (1, 0), (0, 0), (0, 0), (0, 0))
shifted_forward = jnp.pad(shifted_forward, shifted_forward_padding)
no_shift = reshaped_x[:, :, :, :, n_shift:-n_shift]
shifted_x = jnp.concatenate([shifted_backward, no_shift, shifted_forward],
axis=4)
return jnp.reshape(shifted_x, (-1,) + orig_shp[1:])
def temporal_shift_tpu(
x: types.TensorLike,
num_frames: int,
channel_shift_fraction: float = 0.125) -> jnp.ndarray:
"""Performs a temporal shift: https://arxiv.org/abs/1811.08383.
TPU optimized version of TSM. Reshape is avoided by having the images
reshaped in [T * B, :] so that frames corresponding to same time frame in
videos are contiguous in memory. Thanks to cr/288510308 which allows to fuse
pad->slice into convolution, we reformulate the slice pad into a pad then
slice. Finally, to avoid concatenate that prevent some fusion from happening
we simply sum masked version of the features.
Args:
x: Input expected to be [T * B, H, W, C] (where the batch has been reshaped
from a time major version of the input).
num_frames: number of frames T per video.
channel_shift_fraction: fraction of the channel to shift forward and
backward.
Returns:
The temporal shifted version of x.
"""
# B, T, H, W, C = batch_size, num_frames, im_height, im_width, channels
# Input is (T * B, H, W, C)
original_shape = list(x.shape)
batch_size = int(original_shape[0] / num_frames)
n_channels = int(original_shape[-1])
n_shift = int(n_channels * channel_shift_fraction)
# Cast to bfloat16.
x = x.astype(jnp.bfloat16)
# For the following, assume that x has 3 channels [x1, x2, x3] and n_shift=1.
# Shift backward, we first pad by zeros [x1, x2, x3, 0, 0].
orig_shp = list(x.shape)
shifted_backward_padding = ((0, batch_size, 0), (0, 0, 0), (0, 0, 0),
(0, n_channels - n_shift, 0))
x_backward_padding = jax.lax.pad(
x,
padding_value=jnp.bfloat16(0.),
padding_config=shifted_backward_padding)
# The following shift gets to [x3^+1, 0, 0] (where +1 means from the future).
shifted_backward = jax.lax.slice(x_backward_padding,
(batch_size, 0, 0, n_channels - n_shift),
(orig_shp[0] + batch_size, orig_shp[1],
orig_shp[2], 2 * n_channels - n_shift))
# Shift forward, we first pad by zeros [0, 0, x1, x2, x3].
shifted_forward_padding = ((batch_size, 0, 0), (0, 0, 0), (0, 0, 0),
(n_channels - n_shift, 0, 0))
x_forward_padding = jax.lax.pad(
x,
padding_value=jnp.bfloat16(0.),
padding_config=shifted_forward_padding)
# The following shift gets to [0, 0, x1^-1] (where -1 means from the past).
shifted_forward = jax.lax.slice(
x_forward_padding, (0, 0, 0, 0),
(orig_shp[0], orig_shp[1], orig_shp[2], n_channels))
# No shift is in the middle, this gets [0, x2, 0].
mask_noshift = (jnp.reshape((jnp.arange(n_channels) >= n_shift) &
(jnp.arange(n_channels) < n_channels - n_shift),
(1, 1, 1, -1))).astype(jnp.bfloat16)
no_shift = mask_noshift * x
# By summing everything together, we end up with [x3^+1, x2, x1^-1].
# Note: channels have been reordered but that doesn't matter for the model.
shifted_x = shifted_backward + shifted_forward + no_shift
return shifted_x.astype(jnp.float32)
================================================
FILE: mmv/models/tsm_utils_test.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for tsm_utils."""
from absl.testing import absltest
from absl.testing import parameterized
import jax.numpy as jnp
import numpy as np
from mmv.models import tsm_utils
class TsmUtilsTest(parameterized.TestCase):
@parameterized.parameters(
((2, 32, 224, 224, 3), 'gpu', (2 * 32, 224, 224, 3), 32),
((32, 224, 224, 3), 'tpu', (32, 224, 224, 3), None),
)
def test_prepare_inputs(self, input_shape, expected_mode, expected_shape,
expected_num_frames):
data = jnp.zeros(input_shape)
out, mode, num_frames = tsm_utils.prepare_inputs(data)
self.assertEqual(out.shape, expected_shape)
self.assertEqual(mode, expected_mode)
self.assertEqual(num_frames, expected_num_frames)
def test_prepare_outputs(self):
data = jnp.concatenate([jnp.zeros(4), jnp.ones(4)]).reshape(4, 2)
out_gpu = tsm_utils.prepare_outputs(data, 'gpu', 2)
out_tpu = tsm_utils.prepare_outputs(data, 'tpu', 2)
expected_gpu = np.concatenate([np.zeros(2), np.ones(2)]).reshape(2, 2)
expected_tpu = 0.5 * jnp.ones((2, 2))
np.testing.assert_allclose(out_gpu, expected_gpu)
np.testing.assert_allclose(out_tpu, expected_tpu)
def test_apply_tsm(self):
shape = (32, 224, 224, 16)
data = jnp.zeros(shape)
out_gpu = tsm_utils.apply_temporal_shift(data, 'gpu', 16)
out_tpu = tsm_utils.apply_temporal_shift(data, 'tpu', 16)
self.assertEqual(out_gpu.shape, shape)
self.assertEqual(out_tpu.shape, shape)
if __name__ == '__main__':
absltest.main()
================================================
FILE: mmv/models/types.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Type Aliases."""
from typing import Callable, Tuple, Union
import jax
import numpy as np
import optax
TensorLike = Union[np.ndarray, jax.Array]
ActivationFn = Callable[[TensorLike], TensorLike]
GatingFn = Callable[[TensorLike], TensorLike]
NetworkFn = Callable[[TensorLike], TensorLike]
# Callable doesn't allow kwargs to be used, and we often want to
# pass in is_training=..., so ignore the arguments for the sake of pytype.
NormalizeFn = Callable[..., TensorLike]
OptState = Tuple[optax.TraceState, optax.ScaleByScheduleState, optax.ScaleState]
================================================
FILE: mmv/requirements.txt
================================================
dm-haiku
dm-tree
jax
jaxlib
numpy>=1.16
optax
sklearn
tensorflow
tensorflow_datasets
================================================
FILE: mmv/utils/checkpoint.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Checkpoint restoring utilities."""
from absl import logging
import dill
def load_checkpoint(checkpoint_path):
try:
with open(checkpoint_path, 'rb') as checkpoint_file:
checkpoint_data = dill.load(checkpoint_file)
logging.info('Loading checkpoint from %s', checkpoint_path)
return checkpoint_data
except FileNotFoundError:
return None
================================================
FILE: mmv/utils/ucf101_dataset.py
================================================
# Copyright 2020 DeepMind Technologies Limited.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Ucf101 with custom decoding params."""
import tensorflow as tf
import tensorflow_datasets as tfds
# Utilities functions.
tf.compat.v1.enable_eager_execution()
_CITATION = """\
@article{DBLP:journals/corr/abs-1212-0402,
author = {Khurram Soomro and
Amir Roshan Zamir and
Mubarak Shah},
title = {{UCF101:} {A} Dataset of 101 Human Actions Classes From Videos in
The Wild},
journal = {CoRR},
volume = {abs/1212.0402},
year = {2012},
url = {http://arxiv.org/abs/1212.0402},
archivePrefix = {arXiv},
eprint = {1212.0402},
timestamp = {Mon, 13 Aug 2018 16:47:45 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1212-0402},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_LABELS_FNAME = 'video/ucf101_labels.txt'
class ModUcf101(tfds.video.Ucf101):
"""Ucf101 action recognition dataset with better quality.
"""
def _info(self):
ffmpeg_extra_args = ('-qscale:v', '2', '-r', '25', '-t', '00:00:20')
video_shape = (
None, self.builder_config.height, self.builder_config.width, 3)
labels_names_file = tfds.core.tfds_path(_LABELS_FNAME)
features = tfds.features.FeaturesDict({
'video': tfds.features.Video(video_shape,
ffmpeg_extra_args=ffmpeg_extra_args,
encoding_format='jpeg'), # pytype: disable=wrong-arg-types # gen-stub-imports
'label': tfds.features.ClassLabel(names_file=labels_names_file),
})
return tfds.core.DatasetInfo(
builder=self,
description='A 101-label video classification dataset.',
features=features,
homepage='https://www.crcv.ucf.edu/data-sets/ucf101/',
citation=_CITATION,
)
================================================
FILE: neural_mip_solving/README.md
================================================
# Neural MIP Solving - NN Verification Dataset
This is the “Neural Network Verification” dataset used in the paper
[Solving Mixed Integer Programs Using Neural Networks (Nair et al., 2020)](https://arxiv.org/abs/2012.13349).
It contains a set of mixed integer programs (MIPs) for the problem of verifying
a neural network’s robustness to perturbations to its inputs. The MIP
formulation is described in the paper
[On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models (Gowal et al., 2018)](https://arxiv.org/abs/1810.12715).
This dataset corresponds to MIPs defined for
verifying a neural network with the architecture labelled as “small” in Table 1
of Gowal et al., 2018, and trained on the MNIST image dataset. The code used to
train the neural network to be verified is available at
https://github.com/deepmind/interval-bound-propagation. The MIPs are split into
the same training, validation, and test sets as that in Nair et al., 2020.
## Dataset Location
The dataset is available in the following
[link](https://storage.cloud.google.com/neural-mip-solving/nn_verification.tar.gz)
## Dataset Metadata
The following table is necessary for this dataset to be indexed by search
engines such as Google Dataset Search.
| property | value | ||||||
|---|---|---|---|---|---|---|---|
| name | Neural Network Verification Dataset |
||||||
| url | https://github.com/deepmind/deepmind-research/tree/master/neural_mip_solving |
||||||
| sameAs | https://github.com/deepmind/deepmind-research/tree/master/neural_mip_solving |
||||||
| description |
This dataset contains a set of mixed integer programs (MIPs) for the
problem of verifying a neural network’s robustness to perturbations of its
inputs. The MIPs are encoded in LP format. |
||||||
| license | https://creativecommons.org/licenses/by/4.0/legalcode
|
||||||
| provider |
|
||||||
| citation | https://arxiv.org/abs/2012.13349 |
# An Instance-Dependent Simulation Framework for Learning with Label Noise
We propose a simulation framework for generating instance-dependent
noisy labels via a pseudo-labeling paradigm. We show that this framework
generates synthetic noisy labels whose distribution is closer to human labels
compared to independent and class-conditional random flipping.
Equipped with controllable label noise, we study the negative impact of
noisy labels across a few practical settings to
understand when label noise is more problematic. Additionally, with the
availability of annotator information from our simulation framework, we propose
a new technique, Label Quality Model (LQM), that leverages annotator features to
predict and correct against noisy labels. We show that by adding LQM as a label
correction step before applying existing noisy label techniques, we can further
improve the models' performance.
[An Instance-Dependent Simulation Framework for Learning with Label Noise](https://arxiv.org/pdf/2107.11413.pdf).
In this repository, we provide the link to the datasets that we used in Sections
4 and 5 of the above paper, along with a colab that demonstrates how to load the
data and rater features.
We consider 4 tasks:
[CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html),
[CIFAR100](https://www.cs.toronto.edu/~kriz/cifar.html),
[Patch Camelyon](https://patchcamelyon.grand-challenge.org/),
and
[Cats vs Dogs](https://www.microsoft.com/en-us/download/details.aspx?id=54765).
For each task, we generate three synthetic noisy label
datasets, named as "low", "medium", and "high" according to the amount of label
noise. The data are stored as TFRecords and the rater features are stored as
json files.
The data is available under
[noisy label synthetic dataset GCP bucket](https://console.cloud.google.com/storage/browser/noisy_label_synthetic_datasets).
The colab that contains details of the datasets and examples for data loading
is at
[this colab example](https://github.com/deepmind/deepmind-research/blob/master/noisy_label/noisy_label_datasets_and_rater_features.ipynb)
## License
The noisy labels and rater features in our datasets are under the
[CC0 License](https://choosealicense.com/licenses/cc0-1.0/).
Other parts of the datasets are under the original license of the datasets.
When using the datasets based on CIFAR10/CIFAR100, users are required to
attribute the following paper:
Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009
When using the datasets based on Patch Camelyon, users are required to
attribute the following paper:
Rotation Equivariant CNNs for Digital Pathology, Bastiaan S. Veeling,
Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling, arXiv:1806.03962.
When using the datasets based on Cats vs Dogs, users are required to
attribute the following paper:
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization,
Jeremy Elson, John R. Douceur, Jon Howell, and Jared Saul, ACM Conference on
Computer and Communications Security, 2007.
The colab example is provided under the Apache License, Version 2.0.
## Citation
Please use the following bibtex for citations to our paper:
```
@article{gu2021instance,
title={An Instance-Dependent Simulation Framework for Learning with Label Noise},
author={Gu, Keren and Masotto, Xander and Bachani, Vandana and Lakshminarayanan, Balaji and Nikodem, Jack and Yin, Dong},
year={2021}
}
```
# Dataset Metadata
The following table is necessary for this dataset to be indexed by search
engines such as Google Dataset Search.
| property | value | ||||||
|---|---|---|---|---|---|---|---|
| name | Noisy Label Synthetic Datasets |
||||||
| url | https://github.com/deepmind/deepmind-research/tree/master/noisy_label |
||||||
| sameAs | https://github.com/deepmind/deepmind-research/tree/master/noisy_label |
||||||
| description |
Data accompanying
[An Instance-Dependent Simulation Framework for Learning with Label Noise]().
|
||||||
| provider |
|
||||||
| citation | https://identifiers.org/arxiv:2107.11413 |
time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2017-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2017-09-30/time_stratified_validation.gz)
[test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2017-09-30/test.gz)| || arXiv | control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/arxiv/time_stratified_control/split_2017-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/arxiv/time_stratified_control/split_2017-09-30/control_validation.gz)
time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/arxiv/time_stratified_control/split_2017-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/arxiv/time_stratified_control/split_2017-09-30/time_stratified_validation.gz)
[test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/arxiv/time_stratified_control/split_2017-09-30/test.gz)| | Appendix B: The effect of outdated models persists beyond the 2018/2019 test period | WMT | test period 2017/2018: control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2016-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2016-09-30/control_validation.gz); time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2016-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2016-09-30/time_stratified_validation.gz); [test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2016-09-30/test.gz)
test period 2016/2017: control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2015-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2015-09-30/control_validation.gz); time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2015-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2015-09-30/time_stratified_validation.gz); [test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2015-09-30/test.gz)
test period 2015/2016: control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2014-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2014-09-30/control_validation.gz); time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2014-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2014-09-30/time_stratified_validation.gz); [test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2014-09-30/test.gz)
test period 2014/2015: control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2013-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2013-09-30/control_validation.gz); time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2013-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2013-09-30/time_stratified_validation.gz); [test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2013-09-30/test.gz)
test period 2013/2014: control: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2012-09-30/control_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2012-09-30/control_validation.gz); time-stratified: [train](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2012-09-30/time_stratified_train.gz), [validation](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2012-09-30/time_stratified_validation.gz); [test](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/time_stratified_control/split_2012-09-30/test.gz) | | Appendix C: The effect of outdated models persists beyond the two-year gap | WMT | test: same as the one for Sections 3-5
validation: same as the one for the time-stratified setup for Sections 3-5
train until: [2017-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2017-09-30/train.gz), [2017-03-31](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2017-03-31/train.gz), [2016-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2016-09-30/train.gz), [2016-03-31](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2016-03-31/train.gz), [2015-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2015-09-30/train.gz), [2015-03-31](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2015-03-31/train.gz), [2014-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2014-09-30/train.gz), [2014-03-31](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2014-03-31/train.gz), [2013-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2013-09-30/train.gz), [2013-03-31](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2013-03-31/train.gz), [2012-09-30](https://storage.googleapis.com/deepmind-pitfalls-static-language-models/wmt/increasing_distance/split_2012-09-30/train.gz) ================================================ FILE: polygen/README.md ================================================ # PolyGen: An Autoregressive Generative Model of 3D Meshes  This package provides an implementation of PolyGen as described in: > **PolyGen: An Autoregressive Generative Model of 3D Meshes**, *Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. Battaglia*, ICML, 2020. ([abs](https://arxiv.org/abs/2002.10880)) PolyGen is a generative model of 3D meshes that sequentially outputs mesh vertices and faces. PolyGen consists of two parts: A vertex model, that unconditionally models mesh vertices, and a face model, that models the mesh faces conditioned on input vertices. The vertex model uses a masked Transformer decoder to express a distribution over the vertex sequences. For the face model we combine Transformers with [pointer networks](https://arxiv.org/abs/1506.03134) to express a distribution over variable length vertex sequences. In this repository we provide model code in `modules.py`, as well as data processing utilities in `data_utils.py`. We also provide Colabs that demo training PolyGen from scratch on a toy dataset, as well as sampling from a pre-trained model. There are some minor differences between this implementation and the paper: * We add global information (e.g. class label embeddings) as an additional input in the first sequence position rather than project it at each layer. This reduces parameters, but does not significantly impact performance. * We use [ReZero](https://arxiv.org/abs/2003.04887) which improves training speed. * We train with only shifting augmentations, which we find to be as effective as the combination of augmentations described in the paper. This helps to simplify the data pre-processing pipeline. ## Training Colab [](https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/polygen/training.ipynb) To train a PolyGen model from scratch on a collection of simple meshes use this colab. This demonstrates the data pre-processing required to create inputs for the vertex and face models. ## Sampling pre-trained model Colab [](https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/polygen/sample-pretrained.ipynb) To sample a model pre-trained on [ShapeNet](https://www.shapenet.org/) use this colab. The model is class-conditional, and is trained on longer sequence lengths than those described in the paper. This colab uses the following checkpoints: ([Google Cloud Storage bucket](https://console.cloud.google.com/storage/browser/deepmind-research-polygen)). ## Installation To install the package locally run: ```bash git clone https://github.com/deepmind/deepmind-research.git . cd deepmind-research/polygen pip install -e . ``` ## Giving Credit If you use this code in your work, we ask you to cite this paper: ``` @article{nash2020polygen, author={Charlie Nash and Yaroslav Ganin and S. M. Ali Eslami and Peter W. Battaglia}, title={PolyGen: An Autoregressive Generative Model of 3D Meshes}, journal={ICML}, year={2020} } ``` ## Disclaimer This is not an official Google product. ================================================ FILE: polygen/data_utils.py ================================================ # Copyright 2020 Deepmind Technologies Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Mesh data utilities.""" import matplotlib.pyplot as plt from mpl_toolkits import mplot3d # pylint: disable=unused-import from mpl_toolkits.mplot3d.art3d import Poly3DCollection import networkx as nx import numpy as np import six from six.moves import range import tensorflow.compat.v1 as tf import tensorflow_probability as tfp tfd = tfp.distributions def random_shift(vertices, shift_factor=0.25): """Apply random shift to vertices.""" max_shift_pos = tf.cast(255 - tf.reduce_max(vertices, axis=0), tf.float32) max_shift_pos = tf.maximum(max_shift_pos, 1e-9) max_shift_neg = tf.cast(tf.reduce_min(vertices, axis=0), tf.float32) max_shift_neg = tf.maximum(max_shift_neg, 1e-9) shift = tfd.TruncatedNormal( tf.zeros([1, 3]), shift_factor*255, -max_shift_neg, max_shift_pos).sample() shift = tf.cast(shift, tf.int32) vertices += shift return vertices def make_vertex_model_dataset(ds, apply_random_shift=False): """Prepare dataset for vertex model training.""" def _vertex_model_map_fn(example): vertices = example['vertices'] # Randomly shift vertices if apply_random_shift: vertices = random_shift(vertices) # Re-order vertex coordinates as (z, y, x). vertices_permuted = tf.stack( [vertices[:, 2], vertices[:, 1], vertices[:, 0]], axis=-1) # Flatten quantized vertices, reindex starting from 1, and pad with a # zero stopping token. vertices_flat = tf.reshape(vertices_permuted, [-1]) example['vertices_flat'] = tf.pad(vertices_flat + 1, [[0, 1]]) # Create mask to indicate valid tokens after padding and batching. example['vertices_flat_mask'] = tf.ones_like( example['vertices_flat'], dtype=tf.float32) return example return ds.map(_vertex_model_map_fn) def make_face_model_dataset( ds, apply_random_shift=False, shuffle_vertices=True, quantization_bits=8): """Prepare dataset for face model training.""" def _face_model_map_fn(example): vertices = example['vertices'] # Randomly shift vertices if apply_random_shift: vertices = random_shift(vertices) example['num_vertices'] = tf.shape(vertices)[0] # Optionally shuffle vertices and re-order faces to match if shuffle_vertices: permutation = tf.random_shuffle(tf.range(example['num_vertices'])) vertices = tf.gather(vertices, permutation) face_permutation = tf.concat( [tf.constant([0, 1], dtype=tf.int32), tf.argsort(permutation) + 2], axis=0) example['faces'] = tf.cast( tf.gather(face_permutation, example['faces']), tf.int64) def _dequantize_verts(verts, n_bits): min_range = -0.5 max_range = 0.5 range_quantize = 2**n_bits - 1 verts = tf.cast(verts, tf.float32) verts = verts * (max_range - min_range) / range_quantize + min_range return verts # Vertices are quantized. So convert to floats for input to face model example['vertices'] = _dequantize_verts(vertices, quantization_bits) example['vertices_mask'] = tf.ones_like( example['vertices'][..., 0], dtype=tf.float32) example['faces_mask'] = tf.ones_like(example['faces'], dtype=tf.float32) return example return ds.map(_face_model_map_fn) def read_obj_file(obj_file): """Read vertices and faces from already opened file.""" vertex_list = [] flat_vertices_list = [] flat_vertices_indices = {} flat_triangles = [] for line in obj_file: tokens = line.split() if not tokens: continue line_type = tokens[0] # We skip lines not starting with v or f. if line_type == 'v': vertex_list.append([float(x) for x in tokens[1:]]) elif line_type == 'f': triangle = [] for i in range(len(tokens) - 1): vertex_name = tokens[i + 1] if vertex_name in flat_vertices_indices: triangle.append(flat_vertices_indices[vertex_name]) continue flat_vertex = [] for index in six.ensure_str(vertex_name).split('/'): if not index: continue # obj triangle indices are 1 indexed, so subtract 1 here. flat_vertex += vertex_list[int(index) - 1] flat_vertex_index = len(flat_vertices_list) flat_vertices_list.append(flat_vertex) flat_vertices_indices[vertex_name] = flat_vertex_index triangle.append(flat_vertex_index) flat_triangles.append(triangle) return np.array(flat_vertices_list, dtype=np.float32), flat_triangles def read_obj(obj_path): """Open .obj file from the path provided and read vertices and faces.""" with open(obj_path) as obj_file: return read_obj_file(obj_file) def write_obj(vertices, faces, file_path, transpose=True, scale=1.): """Write vertices and faces to obj.""" if transpose: vertices = vertices[:, [1, 2, 0]] vertices *= scale if faces is not None: if min(min(faces)) == 0: f_add = 1 else: f_add = 0 with open(file_path, 'w') as f: for v in vertices: f.write('v {} {} {}\n'.format(v[0], v[1], v[2])) for face in faces: line = 'f' for i in face: line += ' {}'.format(i + f_add) line += '\n' f.write(line) def quantize_verts(verts, n_bits=8): """Convert vertices in [-1., 1.] to discrete values in [0, n_bits**2 - 1].""" min_range = -0.5 max_range = 0.5 range_quantize = 2**n_bits - 1 verts_quantize = (verts - min_range) * range_quantize / ( max_range - min_range) return verts_quantize.astype('int32') def dequantize_verts(verts, n_bits=8, add_noise=False): """Convert quantized vertices to floats.""" min_range = -0.5 max_range = 0.5 range_quantize = 2**n_bits - 1 verts = verts.astype('float32') verts = verts * (max_range - min_range) / range_quantize + min_range if add_noise: verts += np.random.uniform(size=verts.shape) * (1 / range_quantize) return verts def face_to_cycles(face): """Find cycles in face.""" g = nx.Graph() for v in range(len(face) - 1): g.add_edge(face[v], face[v + 1]) g.add_edge(face[-1], face[0]) return list(nx.cycle_basis(g)) def flatten_faces(faces): """Converts from list of faces to flat face array with stopping indices.""" if not faces: return np.array([0]) else: l = [f + [-1] for f in faces[:-1]] l += [faces[-1] + [-2]] return np.array([item for sublist in l for item in sublist]) + 2 # pylint: disable=g-complex-comprehension def unflatten_faces(flat_faces): """Converts from flat face sequence to a list of separate faces.""" def group(seq): g = [] for el in seq: if el == 0 or el == -1: yield g g = [] else: g.append(el - 1) yield g outputs = list(group(flat_faces - 1))[:-1] # Remove empty faces return [o for o in outputs if len(o) > 2] def center_vertices(vertices): """Translate the vertices so that bounding box is centered at zero.""" vert_min = vertices.min(axis=0) vert_max = vertices.max(axis=0) vert_center = 0.5 * (vert_min + vert_max) return vertices - vert_center def normalize_vertices_scale(vertices): """Scale the vertices so that the long diagonal of the bounding box is one.""" vert_min = vertices.min(axis=0) vert_max = vertices.max(axis=0) extents = vert_max - vert_min scale = np.sqrt(np.sum(extents**2)) return vertices / scale def quantize_process_mesh(vertices, faces, tris=None, quantization_bits=8): """Quantize vertices, remove resulting duplicates and reindex faces.""" vertices = quantize_verts(vertices, quantization_bits) vertices, inv = np.unique(vertices, axis=0, return_inverse=True) # Sort vertices by z then y then x. sort_inds = np.lexsort(vertices.T) vertices = vertices[sort_inds] # Re-index faces and tris to re-ordered vertices. faces = [np.argsort(sort_inds)[inv[f]] for f in faces] if tris is not None: tris = np.array([np.argsort(sort_inds)[inv[t]] for t in tris]) # Merging duplicate vertices and re-indexing the faces causes some faces to # contain loops (e.g [2, 3, 5, 2, 4]). Split these faces into distinct # sub-faces. sub_faces = [] for f in faces: cliques = face_to_cycles(f) for c in cliques: c_length = len(c) # Only append faces with more than two verts. if c_length > 2: d = np.argmin(c) # Cyclically permute faces just that first index is the smallest. sub_faces.append([c[(d + i) % c_length] for i in range(c_length)]) faces = sub_faces if tris is not None: tris = np.array([v for v in tris if len(set(v)) == len(v)]) # Sort faces by lowest vertex indices. If two faces have the same lowest # index then sort by next lowest and so on. faces.sort(key=lambda f: tuple(sorted(f))) if tris is not None: tris = tris.tolist() tris.sort(key=lambda f: tuple(sorted(f))) tris = np.array(tris) # After removing degenerate faces some vertices are now unreferenced. # Remove these. num_verts = vertices.shape[0] vert_connected = np.equal( np.arange(num_verts)[:, None], np.hstack(faces)[None]).any(axis=-1) vertices = vertices[vert_connected] # Re-index faces and tris to re-ordered vertices. vert_indices = ( np.arange(num_verts) - np.cumsum(1 - vert_connected.astype('int'))) faces = [vert_indices[f].tolist() for f in faces] if tris is not None: tris = np.array([vert_indices[t].tolist() for t in tris]) return vertices, faces, tris def process_mesh(vertices, faces, quantization_bits=8): """Process mesh vertices and faces.""" # Transpose so that z-axis is vertical. vertices = vertices[:, [2, 0, 1]] # Translate the vertices so that bounding box is centered at zero. vertices = center_vertices(vertices) # Scale the vertices so that the long diagonal of the bounding box is equal # to one. vertices = normalize_vertices_scale(vertices) # Quantize and sort vertices, remove resulting duplicates, sort and reindex # faces. vertices, faces, _ = quantize_process_mesh( vertices, faces, quantization_bits=quantization_bits) # Flatten faces and add 'new face' = 1 and 'stop' = 0 tokens. faces = flatten_faces(faces) # Discard degenerate meshes without faces. return { 'vertices': vertices, 'faces': faces, } def load_process_mesh(mesh_obj_path, quantization_bits=8): """Load obj file and process.""" # Load mesh vertices, faces = read_obj(mesh_obj_path) return process_mesh(vertices, faces, quantization_bits) def plot_meshes(mesh_list, ax_lims=0.3, fig_size=4, el=30, rot_start=120, vert_size=10, vert_alpha=0.75, n_cols=4): """Plots mesh data using matplotlib.""" n_plot = len(mesh_list) n_cols = np.minimum(n_plot, n_cols) n_rows = np.ceil(n_plot / n_cols).astype('int') fig = plt.figure(figsize=(fig_size * n_cols, fig_size * n_rows)) for p_inc, mesh in enumerate(mesh_list): for key in [ 'vertices', 'faces', 'vertices_conditional', 'pointcloud', 'class_name' ]: if key not in list(mesh.keys()): mesh[key] = None ax = fig.add_subplot(n_rows, n_cols, p_inc + 1, projection='3d') if mesh['faces'] is not None: if mesh['vertices_conditional'] is not None: face_verts = np.concatenate( [mesh['vertices_conditional'], mesh['vertices']], axis=0) else: face_verts = mesh['vertices'] collection = [] for f in mesh['faces']: collection.append(face_verts[f]) plt_mesh = Poly3DCollection(collection) plt_mesh.set_edgecolor((0., 0., 0., 0.3)) plt_mesh.set_facecolor((1, 0, 0, 0.2)) ax.add_collection3d(plt_mesh) if mesh['vertices'] is not None: ax.scatter3D( mesh['vertices'][:, 0], mesh['vertices'][:, 1], mesh['vertices'][:, 2], lw=0., s=vert_size, c='g', alpha=vert_alpha) if mesh['vertices_conditional'] is not None: ax.scatter3D( mesh['vertices_conditional'][:, 0], mesh['vertices_conditional'][:, 1], mesh['vertices_conditional'][:, 2], lw=0., s=vert_size, c='b', alpha=vert_alpha) if mesh['pointcloud'] is not None: ax.scatter3D( mesh['pointcloud'][:, 0], mesh['pointcloud'][:, 1], mesh['pointcloud'][:, 2], lw=0., s=2.5 * vert_size, c='b', alpha=1.) ax.set_xlim(-ax_lims, ax_lims) ax.set_ylim(-ax_lims, ax_lims) ax.set_zlim(-ax_lims, ax_lims) ax.view_init(el, rot_start) display_string = '' if mesh['faces'] is not None: display_string += 'Num. faces: {}\n'.format(len(collection)) if mesh['vertices'] is not None: num_verts = mesh['vertices'].shape[0] if mesh['vertices_conditional'] is not None: num_verts += mesh['vertices_conditional'].shape[0] display_string += 'Num. verts: {}\n'.format(num_verts) if mesh['class_name'] is not None: display_string += 'Synset: {}'.format(mesh['class_name']) if mesh['pointcloud'] is not None: display_string += 'Num. pointcloud: {}\n'.format( mesh['pointcloud'].shape[0]) ax.text2D(0.05, 0.8, display_string, transform=ax.transAxes) plt.subplots_adjust( left=0., right=1., bottom=0., top=1., wspace=0.025, hspace=0.025) plt.show() ================================================ FILE: polygen/meshes/cone.obj ================================================ v 0.000000 -1.000000 -1.000000 v 0.195090 -1.000000 -0.980785 v 0.382683 -1.000000 -0.923880 v 0.555570 -1.000000 -0.831470 v 0.707107 -1.000000 -0.707107 v 0.831470 -1.000000 -0.555570 v 0.923880 -1.000000 -0.382683 v 0.980785 -1.000000 -0.195090 v 1.000000 -1.000000 -0.000000 v 0.980785 -1.000000 0.195090 v 0.923880 -1.000000 0.382683 v 0.831470 -1.000000 0.555570 v 0.707107 -1.000000 0.707107 v 0.555570 -1.000000 0.831470 v 0.382683 -1.000000 0.923880 v 0.195090 -1.000000 0.980785 v -0.000000 -1.000000 1.000000 v -0.195091 -1.000000 0.980785 v -0.382684 -1.000000 0.923879 v -0.555571 -1.000000 0.831469 v -0.707107 -1.000000 0.707106 v -0.831470 -1.000000 0.555570 v -0.923880 -1.000000 0.382683 v 0.000000 1.000000 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0.525738 0.809012 v -0.850648 0.525736 0.000000 v -0.262869 0.525738 -0.809012 v 0.688189 0.525736 -0.499997 v 0.162456 0.850654 0.499995 v 0.525730 0.850652 0.000000 v -0.425323 0.850654 0.309011 v -0.425323 0.850654 -0.309011 v 0.162456 0.850654 -0.499995 f 1 14 13 f 2 14 16 f 1 13 18 f 1 18 20 f 1 20 17 f 2 16 23 f 3 15 25 f 4 19 27 f 5 21 29 f 6 22 31 f 2 23 26 f 3 25 28 f 4 27 30 f 5 29 32 f 6 31 24 f 7 33 38 f 8 34 40 f 9 35 41 f 10 36 42 f 11 37 39 f 39 42 12 f 39 37 42 f 37 10 42 f 42 41 12 f 42 36 41 f 36 9 41 f 41 40 12 f 41 35 40 f 35 8 40 f 40 38 12 f 40 34 38 f 34 7 38 f 38 39 12 f 38 33 39 f 33 11 39 f 24 37 11 f 24 31 37 f 31 10 37 f 32 36 10 f 32 29 36 f 29 9 36 f 30 35 9 f 30 27 35 f 27 8 35 f 28 34 8 f 28 25 34 f 25 7 34 f 26 33 7 f 26 23 33 f 23 11 33 f 31 32 10 f 31 22 32 f 22 5 32 f 29 30 9 f 29 21 30 f 21 4 30 f 27 28 8 f 27 19 28 f 19 3 28 f 25 26 7 f 25 15 26 f 15 2 26 f 23 24 11 f 23 16 24 f 16 6 24 f 17 22 6 f 17 20 22 f 20 5 22 f 20 21 5 f 20 18 21 f 18 4 21 f 18 19 4 f 18 13 19 f 13 3 19 f 16 17 6 f 16 14 17 f 14 1 17 f 13 15 3 f 13 14 15 f 14 2 15 ================================================ FILE: polygen/model_test.py ================================================ # Copyright 2020 Deepmind Technologies Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for the PolyGen open-source version.""" from modules import FaceModel from modules import VertexModel import numpy as np import tensorflow as tf _BATCH_SIZE = 4 _TRANSFORMER_CONFIG = { 'num_layers': 2, 'hidden_size': 64, 'fc_size': 256 } _CLASS_CONDITIONAL = True _NUM_CLASSES = 4 _NUM_INPUT_VERTS = 50 _NUM_PAD_VERTS = 10 _NUM_INPUT_FACE_INDICES = 200 _QUANTIZATION_BITS = 8 _VERTEX_MODEL_USE_DISCRETE_EMBEDDINGS = True _FACE_MODEL_DECODER_CROSS_ATTENTION = True _FACE_MODEL_DISCRETE_EMBEDDINGS = True _MAX_SAMPLE_LENGTH_VERTS = 10 _MAX_SAMPLE_LENGTH_FACES = 10 def _get_vertex_model_batch(): """Returns batch with placeholders for vertex model inputs.""" return { 'class_label': tf.range(_BATCH_SIZE), 'vertices_flat': tf.placeholder( dtype=tf.int32, shape=[_BATCH_SIZE, None]), } def _get_face_model_batch(): """Returns batch with placeholders for face model inputs.""" return { 'vertices': tf.placeholder( dtype=tf.float32, shape=[_BATCH_SIZE, None, 3]), 'vertices_mask': tf.placeholder( dtype=tf.float32, shape=[_BATCH_SIZE, None]), 'faces': tf.placeholder( dtype=tf.int32, shape=[_BATCH_SIZE, None]), } class VertexModelTest(tf.test.TestCase): def setUp(self): """Defines a vertex model.""" super(VertexModelTest, self).setUp() self.model = VertexModel( decoder_config=_TRANSFORMER_CONFIG, class_conditional=_CLASS_CONDITIONAL, num_classes=_NUM_CLASSES, max_num_input_verts=_NUM_INPUT_VERTS, quantization_bits=_QUANTIZATION_BITS, use_discrete_embeddings=_VERTEX_MODEL_USE_DISCRETE_EMBEDDINGS) def test_model_runs(self): """Tests if the model runs without crashing.""" batch = _get_vertex_model_batch() pred_dist = self.model(batch, is_training=False) logits = pred_dist.logits with self.session() as sess: sess.run(tf.global_variables_initializer()) vertices_flat = np.random.randint( 2**_QUANTIZATION_BITS + 1, size=[_BATCH_SIZE, _NUM_INPUT_VERTS * 3 + 1]) sess.run(logits, {batch['vertices_flat']: vertices_flat}) def test_sample_outputs_range(self): """Tests if the model produces samples in the correct range.""" context = {'class_label': tf.zeros((_BATCH_SIZE,), dtype=tf.int32)} sample_dict = self.model.sample( _BATCH_SIZE, max_sample_length=_MAX_SAMPLE_LENGTH_VERTS, context=context) with self.session() as sess: sess.run(tf.global_variables_initializer()) sample_dict_np = sess.run(sample_dict) in_range = np.logical_and( 0 <= sample_dict_np['vertices'], sample_dict_np['vertices'] <= 2**_QUANTIZATION_BITS).all() self.assertTrue(in_range) class FaceModelTest(tf.test.TestCase): def setUp(self): """Defines a face model.""" super(FaceModelTest, self).setUp() self.model = FaceModel( encoder_config=_TRANSFORMER_CONFIG, decoder_config=_TRANSFORMER_CONFIG, class_conditional=False, max_seq_length=_NUM_INPUT_FACE_INDICES, decoder_cross_attention=_FACE_MODEL_DECODER_CROSS_ATTENTION, use_discrete_vertex_embeddings=_FACE_MODEL_DISCRETE_EMBEDDINGS, quantization_bits=_QUANTIZATION_BITS) def test_model_runs(self): """Tests if the model runs without crashing.""" batch = _get_face_model_batch() pred_dist = self.model(batch, is_training=False) logits = pred_dist.logits with self.session() as sess: sess.run(tf.global_variables_initializer()) vertices = np.random.rand(_BATCH_SIZE, _NUM_INPUT_VERTS, 3) - 0.5 vertices_mask = np.ones([_BATCH_SIZE, _NUM_INPUT_VERTS]) faces = np.random.randint( _NUM_INPUT_VERTS + 2, size=[_BATCH_SIZE, _NUM_INPUT_FACE_INDICES]) sess.run( logits, {batch['vertices']: vertices, batch['vertices_mask']: vertices_mask, batch['faces']: faces} ) def test_sample_outputs_range(self): """Tests if the model produces samples in the correct range.""" context = _get_face_model_batch() del context['faces'] sample_dict = self.model.sample( context, max_sample_length=_MAX_SAMPLE_LENGTH_FACES) with self.session() as sess: sess.run(tf.global_variables_initializer()) # Pad the vertices in order to test that the face model only outputs # vertex indices in the unpadded range vertices = np.pad( np.random.rand(_BATCH_SIZE, _NUM_INPUT_VERTS, 3) - 0.5, [[0, 0], [0, _NUM_PAD_VERTS], [0, 0]], mode='constant') vertices_mask = np.pad( np.ones([_BATCH_SIZE, _NUM_INPUT_VERTS]), [[0, 0], [0, _NUM_PAD_VERTS]], mode='constant') sample_dict_np = sess.run( sample_dict, {context['vertices']: vertices, context['vertices_mask']: vertices_mask}) in_range = np.logical_and( 0 <= sample_dict_np['faces'], sample_dict_np['faces'] <= _NUM_INPUT_VERTS + 1).all() self.assertTrue(in_range) if __name__ == '__main__': tf.test.main() ================================================ FILE: polygen/modules.py ================================================ # Copyright 2020 Deepmind Technologies Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Modules and networks for mesh generation.""" import sonnet as snt from tensor2tensor.layers import common_attention from tensor2tensor.layers import common_layers import tensorflow.compat.v1 as tf from tensorflow.python.framework import function import tensorflow_probability as tfp tfd = tfp.distributions tfb = tfp.bijectors def dequantize_verts(verts, n_bits, add_noise=False): """Quantizes vertices and outputs integers with specified n_bits.""" min_range = -0.5 max_range = 0.5 range_quantize = 2**n_bits - 1 verts = tf.cast(verts, tf.float32) verts = verts * (max_range - min_range) / range_quantize + min_range if add_noise: verts += tf.random_uniform(tf.shape(verts)) * (1 / float(range_quantize)) return verts def quantize_verts(verts, n_bits): """Dequantizes integer vertices to floats.""" min_range = -0.5 max_range = 0.5 range_quantize = 2**n_bits - 1 verts_quantize = ( (verts - min_range) * range_quantize / (max_range - min_range)) return tf.cast(verts_quantize, tf.int32) def top_k_logits(logits, k): """Masks logits such that logits not in top-k are small.""" if k == 0: return logits else: values, _ = tf.math.top_k(logits, k=k) k_largest = tf.reduce_min(values) logits = tf.where(tf.less_equal(logits, k_largest), tf.ones_like(logits)*-1e9, logits) return logits def top_p_logits(logits, p): """Masks logits using nucleus (top-p) sampling.""" if p == 1: return logits else: logit_shape = tf.shape(logits) seq, dim = logit_shape[1], logit_shape[2] logits = tf.reshape(logits, [-1, dim]) sort_indices = tf.argsort(logits, axis=-1, direction='DESCENDING') probs = tf.gather(tf.nn.softmax(logits), sort_indices, batch_dims=1) cumprobs = tf.cumsum(probs, axis=-1, exclusive=True) # The top 1 candidate always will not be masked. # This way ensures at least 1 indices will be selected. sort_mask = tf.cast(tf.greater(cumprobs, p), logits.dtype) batch_indices = tf.tile( tf.expand_dims(tf.range(tf.shape(logits)[0]), axis=-1), [1, dim]) top_p_mask = tf.scatter_nd( tf.stack([batch_indices, sort_indices], axis=-1), sort_mask, tf.shape(logits)) logits -= top_p_mask * 1e9 return tf.reshape(logits, [-1, seq, dim]) _function_cache = {} # For multihead_self_attention_memory_efficient def multihead_self_attention_memory_efficient(x, bias, num_heads, head_size=None, cache=None, epsilon=1e-6, forget=True, test_vars=None, name=None): """Memory-efficient Multihead scaled-dot-product self-attention. Based on Tensor2Tensor version but adds optional caching. Returns multihead-self-attention(layer_norm(x)) Computes one attention head at a time to avoid exhausting memory. If forget=True, then forget all forwards activations and recompute on the backwards pass. Args: x: a Tensor with shape [batch, length, input_size] bias: an attention bias tensor broadcastable to [batch, 1, length, length] num_heads: an integer head_size: an optional integer - defaults to input_size/num_heads cache: Optional dict containing tensors which are the results of previous attentions, used for fast decoding. Expects the dict to contain two keys ('k' and 'v'), for the initial call the values for these keys should be empty Tensors of the appropriate shape. 'k' [batch_size, 0, key_channels] 'v' [batch_size, 0, value_channels] epsilon: a float, for layer norm forget: a boolean - forget forwards activations and recompute on backprop test_vars: optional tuple of variables for testing purposes name: an optional string Returns: A Tensor. """ io_size = x.get_shape().as_list()[-1] if head_size is None: assert io_size % num_heads == 0 head_size = io_size / num_heads def forward_internal(x, wqkv, wo, attention_bias, norm_scale, norm_bias): """Forward function.""" n = common_layers.layer_norm_compute(x, epsilon, norm_scale, norm_bias) wqkv_split = tf.unstack(wqkv, num=num_heads) wo_split = tf.unstack(wo, num=num_heads) y = 0 if cache is not None: cache_k = [] cache_v = [] for h in range(num_heads): with tf.control_dependencies([y] if h > 0 else []): combined = tf.nn.conv1d(n, wqkv_split[h], 1, 'SAME') q, k, v = tf.split(combined, 3, axis=2) if cache is not None: k = tf.concat([cache['k'][:, h], k], axis=1) v = tf.concat([cache['v'][:, h], v], axis=1) cache_k.append(k) cache_v.append(v) o = common_attention.scaled_dot_product_attention_simple( q, k, v, attention_bias) y += tf.nn.conv1d(o, wo_split[h], 1, 'SAME') if cache is not None: cache['k'] = tf.stack(cache_k, axis=1) cache['v'] = tf.stack(cache_v, axis=1) return y key = ( 'multihead_self_attention_memory_efficient %s %s' % (num_heads, epsilon)) if not forget: forward_fn = forward_internal elif key in _function_cache: forward_fn = _function_cache[key] else: @function.Defun(compiled=True) def grad_fn(x, wqkv, wo, attention_bias, norm_scale, norm_bias, dy): """Custom gradient function.""" with tf.control_dependencies([dy]): n = common_layers.layer_norm_compute(x, epsilon, norm_scale, norm_bias) wqkv_split = tf.unstack(wqkv, num=num_heads) wo_split = tf.unstack(wo, num=num_heads) deps = [] dwqkvs = [] dwos = [] dn = 0 for h in range(num_heads): with tf.control_dependencies(deps): combined = tf.nn.conv1d(n, wqkv_split[h], 1, 'SAME') q, k, v = tf.split(combined, 3, axis=2) o = common_attention.scaled_dot_product_attention_simple( q, k, v, attention_bias) partial_y = tf.nn.conv1d(o, wo_split[h], 1, 'SAME') pdn, dwqkvh, dwoh = tf.gradients( ys=[partial_y], xs=[n, wqkv_split[h], wo_split[h]], grad_ys=[dy]) dn += pdn dwqkvs.append(dwqkvh) dwos.append(dwoh) deps = [dn, dwqkvh, dwoh] dwqkv = tf.stack(dwqkvs) dwo = tf.stack(dwos) with tf.control_dependencies(deps): dx, dnorm_scale, dnorm_bias = tf.gradients( ys=[n], xs=[x, norm_scale, norm_bias], grad_ys=[dn]) return (dx, dwqkv, dwo, tf.zeros_like(attention_bias), dnorm_scale, dnorm_bias) @function.Defun( grad_func=grad_fn, compiled=True, separate_compiled_gradients=True) def forward_fn(x, wqkv, wo, attention_bias, norm_scale, norm_bias): return forward_internal(x, wqkv, wo, attention_bias, norm_scale, norm_bias) _function_cache[key] = forward_fn if bias is not None: bias = tf.squeeze(bias, 1) with tf.variable_scope(name, default_name='multihead_attention', values=[x]): if test_vars is not None: wqkv, wo, norm_scale, norm_bias = list(test_vars) else: wqkv = tf.get_variable( 'wqkv', [num_heads, 1, io_size, 3 * head_size], initializer=tf.random_normal_initializer(stddev=io_size**-0.5)) wo = tf.get_variable( 'wo', [num_heads, 1, head_size, io_size], initializer=tf.random_normal_initializer( stddev=(head_size * num_heads)**-0.5)) norm_scale, norm_bias = common_layers.layer_norm_vars(io_size) y = forward_fn(x, wqkv, wo, bias, norm_scale, norm_bias) y.set_shape(x.get_shape()) # pytype: disable=attribute-error return y class TransformerEncoder(snt.AbstractModule): """Transformer encoder. Sonnet Transformer encoder module as described in Vaswani et al. 2017. Uses the Tensor2Tensor multihead_attention function for full self attention (no masking). Layer norm is applied inside the residual path as in sparse transformers (Child 2019). This module expects inputs to be already embedded, and does not add position embeddings. """ def __init__(self, hidden_size=256, fc_size=1024, num_heads=4, layer_norm=True, num_layers=8, dropout_rate=0.2, re_zero=True, memory_efficient=False, name='transformer_encoder'): """Initializes TransformerEncoder. Args: hidden_size: Size of embedding vectors. fc_size: Size of fully connected layer. num_heads: Number of attention heads. layer_norm: If True, apply layer normalization num_layers: Number of Transformer blocks, where each block contains a multi-head attention layer and a MLP. dropout_rate: Dropout rate applied immediately after the ReLU in each fully-connected layer. re_zero: If True, alpha scale residuals with zero init. memory_efficient: If True, recompute gradients for memory savings. name: Name of variable scope """ super(TransformerEncoder, self).__init__(name=name) self.hidden_size = hidden_size self.num_heads = num_heads self.layer_norm = layer_norm self.fc_size = fc_size self.num_layers = num_layers self.dropout_rate = dropout_rate self.re_zero = re_zero self.memory_efficient = memory_efficient def _build(self, inputs, is_training=False): """Passes inputs through Transformer encoder network. Args: inputs: Tensor of shape [batch_size, sequence_length, embed_size]. Zero embeddings are masked in self-attention. is_training: If True, dropout is applied. Returns: output: Tensor of shape [batch_size, sequence_length, embed_size]. """ if is_training: dropout_rate = self.dropout_rate else: dropout_rate = 0. # Identify elements with all zeros as padding, and create bias to mask # out padding elements in self attention. encoder_padding = common_attention.embedding_to_padding(inputs) encoder_self_attention_bias = ( common_attention.attention_bias_ignore_padding(encoder_padding)) x = inputs for layer_num in range(self.num_layers): with tf.variable_scope('layer_{}'.format(layer_num)): # Multihead self-attention from Tensor2Tensor. res = x if self.memory_efficient: res = multihead_self_attention_memory_efficient( res, bias=encoder_self_attention_bias, num_heads=self.num_heads, head_size=self.hidden_size // self.num_heads, forget=True if is_training else False, name='self_attention' ) else: if self.layer_norm: res = common_layers.layer_norm(res, name='self_attention') res = common_attention.multihead_attention( res, memory_antecedent=None, bias=encoder_self_attention_bias, total_key_depth=self.hidden_size, total_value_depth=self.hidden_size, output_depth=self.hidden_size, num_heads=self.num_heads, dropout_rate=0., make_image_summary=False, name='self_attention') if self.re_zero: res *= tf.get_variable('self_attention/alpha', initializer=0.) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) x += res # MLP res = x if self.layer_norm: res = common_layers.layer_norm(res, name='fc') res = tf.layers.dense( res, self.fc_size, activation=tf.nn.relu, name='fc_1') res = tf.layers.dense(res, self.hidden_size, name='fc_2') if self.re_zero: res *= tf.get_variable('fc/alpha', initializer=0.) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) x += res if self.layer_norm: output = common_layers.layer_norm(x, name='output') else: output = x return output class TransformerDecoder(snt.AbstractModule): """Transformer decoder. Sonnet Transformer decoder module as described in Vaswani et al. 2017. Uses the Tensor2Tensor multihead_attention function for masked self attention, and non-masked cross attention attention. Layer norm is applied inside the residual path as in sparse transformers (Child 2019). This module expects inputs to be already embedded, and does not add position embeddings. """ def __init__(self, hidden_size=256, fc_size=1024, num_heads=4, layer_norm=True, num_layers=8, dropout_rate=0.2, re_zero=True, memory_efficient=False, name='transformer_decoder'): """Initializes TransformerDecoder. Args: hidden_size: Size of embedding vectors. fc_size: Size of fully connected layer. num_heads: Number of attention heads. layer_norm: If True, apply layer normalization. If mem_efficient_attention is True, then layer norm is always applied. num_layers: Number of Transformer blocks, where each block contains a multi-head attention layer and a MLP. dropout_rate: Dropout rate applied immediately after the ReLU in each fully-connected layer. re_zero: If True, alpha scale residuals with zero init. memory_efficient: If True, recompute gradients for memory savings. name: Name of variable scope """ super(TransformerDecoder, self).__init__(name=name) self.hidden_size = hidden_size self.num_heads = num_heads self.layer_norm = layer_norm self.fc_size = fc_size self.num_layers = num_layers self.dropout_rate = dropout_rate self.re_zero = re_zero self.memory_efficient = memory_efficient def _build(self, inputs, sequential_context_embeddings=None, is_training=False, cache=None): """Passes inputs through Transformer decoder network. Args: inputs: Tensor of shape [batch_size, sequence_length, embed_size]. Zero embeddings are masked in self-attention. sequential_context_embeddings: Optional tensor with global context (e.g image embeddings) of shape [batch_size, context_seq_length, context_embed_size]. is_training: If True, dropout is applied. cache: Optional dict containing tensors which are the results of previous attentions, used for fast decoding. Expects the dict to contain two keys ('k' and 'v'), for the initial call the values for these keys should be empty Tensors of the appropriate shape. 'k' [batch_size, 0, key_channels] 'v' [batch_size, 0, value_channels] Returns: output: Tensor of shape [batch_size, sequence_length, embed_size]. """ if is_training: dropout_rate = self.dropout_rate else: dropout_rate = 0. # create bias to mask future elements for causal self-attention. seq_length = tf.shape(inputs)[1] decoder_self_attention_bias = common_attention.attention_bias_lower_triangle( seq_length) # If using sequential_context, identify elements with all zeros as padding, # and create bias to mask out padding elements in self attention. if sequential_context_embeddings is not None: encoder_padding = common_attention.embedding_to_padding( sequential_context_embeddings) encoder_decoder_attention_bias = ( common_attention.attention_bias_ignore_padding(encoder_padding)) x = inputs for layer_num in range(self.num_layers): with tf.variable_scope('layer_{}'.format(layer_num)): # If using cached decoding, access cache for current layer, and create # bias that enables un-masked attention into the cache if cache is not None: layer_cache = cache[layer_num] layer_decoder_bias = tf.zeros([1, 1, 1, 1]) # Otherwise use standard masked bias else: layer_cache = None layer_decoder_bias = decoder_self_attention_bias # Multihead self-attention from Tensor2Tensor. res = x if self.memory_efficient: res = multihead_self_attention_memory_efficient( res, bias=layer_decoder_bias, cache=layer_cache, num_heads=self.num_heads, head_size=self.hidden_size // self.num_heads, forget=True if is_training else False, name='self_attention' ) else: if self.layer_norm: res = common_layers.layer_norm(res, name='self_attention') res = common_attention.multihead_attention( res, memory_antecedent=None, bias=layer_decoder_bias, total_key_depth=self.hidden_size, total_value_depth=self.hidden_size, output_depth=self.hidden_size, num_heads=self.num_heads, cache=layer_cache, dropout_rate=0., make_image_summary=False, name='self_attention') if self.re_zero: res *= tf.get_variable('self_attention/alpha', initializer=0.) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) x += res # Optional cross attention into sequential context if sequential_context_embeddings is not None: res = x if self.layer_norm: res = common_layers.layer_norm(res, name='cross_attention') res = common_attention.multihead_attention( res, memory_antecedent=sequential_context_embeddings, bias=encoder_decoder_attention_bias, total_key_depth=self.hidden_size, total_value_depth=self.hidden_size, output_depth=self.hidden_size, num_heads=self.num_heads, dropout_rate=0., make_image_summary=False, name='cross_attention') if self.re_zero: res *= tf.get_variable('cross_attention/alpha', initializer=0.) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) x += res # FC layers res = x if self.layer_norm: res = common_layers.layer_norm(res, name='fc') res = tf.layers.dense( res, self.fc_size, activation=tf.nn.relu, name='fc_1') res = tf.layers.dense(res, self.hidden_size, name='fc_2') if self.re_zero: res *= tf.get_variable('fc/alpha', initializer=0.) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) x += res if self.layer_norm: output = common_layers.layer_norm(x, name='output') else: output = x return output def create_init_cache(self, batch_size): """Creates empty cache dictionary for use in fast decoding.""" def compute_cache_shape_invariants(tensor): """Helper function to get dynamic shapes for cache tensors.""" shape_list = tensor.shape.as_list() if len(shape_list) == 4: return tf.TensorShape( [shape_list[0], shape_list[1], None, shape_list[3]]) elif len(shape_list) == 3: return tf.TensorShape([shape_list[0], None, shape_list[2]]) # Build cache k = common_attention.split_heads( tf.zeros([batch_size, 0, self.hidden_size]), self.num_heads) v = common_attention.split_heads( tf.zeros([batch_size, 0, self.hidden_size]), self.num_heads) cache = [{'k': k, 'v': v} for _ in range(self.num_layers)] shape_invariants = tf.nest.map_structure( compute_cache_shape_invariants, cache) return cache, shape_invariants def conv_residual_block(inputs, output_channels=None, downsample=False, kernel_size=3, re_zero=True, dropout_rate=0., name='conv_residual_block'): """Convolutional block with residual connections for 2D or 3D inputs. Args: inputs: Input tensor of shape [batch_size, height, width, channels] or [batch_size, height, width, depth, channels]. output_channels: Number of output channels. downsample: If True, downsample by 1/2 in this block. kernel_size: Spatial size of convolutional kernels. re_zero: If True, alpha scale residuals with zero init. dropout_rate: Dropout rate applied after second ReLU in residual path. name: Name for variable scope. Returns: outputs: Output tensor of shape [batch_size, height, width, output_channels] or [batch_size, height, width, depth, output_channels]. """ with tf.variable_scope(name): input_shape = inputs.get_shape().as_list() num_dims = len(input_shape) - 2 if num_dims == 2: conv = tf.layers.conv2d elif num_dims == 3: conv = tf.layers.conv3d input_channels = input_shape[-1] if output_channels is None: output_channels = input_channels if downsample: shortcut = conv( inputs, filters=output_channels, strides=2, kernel_size=kernel_size, padding='same', name='conv_shortcut') else: shortcut = inputs res = inputs res = tf.nn.relu(res) res = conv( res, filters=input_channels, kernel_size=kernel_size, padding='same', name='conv_1') res = tf.nn.relu(res) if dropout_rate: res = tf.nn.dropout(res, rate=dropout_rate) if downsample: out_strides = 2 else: out_strides = 1 res = conv( res, filters=output_channels, kernel_size=kernel_size, padding='same', strides=out_strides, name='conv_2') if re_zero: res *= tf.get_variable('alpha', initializer=0.) return shortcut + res class ResNet(snt.AbstractModule): """ResNet architecture for 2D image or 3D voxel inputs.""" def __init__(self, num_dims, hidden_sizes=(64, 256), num_blocks=(2, 2), dropout_rate=0.1, re_zero=True, name='res_net'): """Initializes ResNet. Args: num_dims: Number of spatial dimensions. 2 for images or 3 for voxels. hidden_sizes: Sizes of hidden layers in resnet blocks. num_blocks: Number of resnet blocks at each size. dropout_rate: Dropout rate applied immediately after the ReLU in each fully-connected layer. re_zero: If True, alpha scale residuals with zero init. name: Name of variable scope """ super(ResNet, self).__init__(name=name) self.num_dims = num_dims self.hidden_sizes = hidden_sizes self.num_blocks = num_blocks self.dropout_rate = dropout_rate self.re_zero = re_zero def _build(self, inputs, is_training=False): """Passes inputs through resnet. Args: inputs: Tensor of shape [batch_size, height, width, channels] or [batch_size, height, width, depth, channels]. is_training: If True, dropout is applied. Returns: output: Tensor of shape [batch_size, height, width, depth, output_size]. """ if is_training: dropout_rate = self.dropout_rate else: dropout_rate = 0. # Initial projection with large kernel as in original resnet architecture if self.num_dims == 3: conv = tf.layers.conv3d elif self.num_dims == 2: conv = tf.layers.conv2d x = conv( inputs, filters=self.hidden_sizes[0], kernel_size=7, strides=2, padding='same', name='conv_input') if self.num_dims == 2: x = tf.layers.max_pooling2d( x, strides=2, pool_size=3, padding='same', name='pool_input') for d, (hidden_size, blocks) in enumerate(zip(self.hidden_sizes, self.num_blocks)): with tf.variable_scope('resolution_{}'.format(d)): # Downsample at the start of each collection of blocks x = conv_residual_block( x, downsample=False if d == 0 else True, dropout_rate=dropout_rate, output_channels=hidden_size, re_zero=self.re_zero, name='block_1_downsample') for i in range(blocks - 1): x = conv_residual_block( x, dropout_rate=dropout_rate, output_channels=hidden_size, re_zero=self.re_zero, name='block_{}'.format(i + 2)) return x class VertexModel(snt.AbstractModule): """Autoregressive generative model of quantized mesh vertices. Operates on flattened vertex sequences with a stopping token: [z_0, y_0, x_0, z_1, y_1, x_1, ..., z_n, y_n, z_n, STOP] Input vertex coordinates are embedded and tagged with learned coordinate and position indicators. A transformer decoder outputs logits for a quantized vertex distribution. """ def __init__(self, decoder_config, quantization_bits, class_conditional=False, num_classes=55, max_num_input_verts=2500, use_discrete_embeddings=True, name='vertex_model'): """Initializes VertexModel. Args: decoder_config: Dictionary with TransformerDecoder config quantization_bits: Number of quantization used in mesh preprocessing. class_conditional: If True, then condition on learned class embeddings. num_classes: Number of classes to condition on. max_num_input_verts: Maximum number of vertices. Used for learned position embeddings. use_discrete_embeddings: If True, use discrete rather than continuous vertex embeddings. name: Name of variable scope """ super(VertexModel, self).__init__(name=name) self.embedding_dim = decoder_config['hidden_size'] self.class_conditional = class_conditional self.num_classes = num_classes self.max_num_input_verts = max_num_input_verts self.quantization_bits = quantization_bits self.use_discrete_embeddings = use_discrete_embeddings with self._enter_variable_scope(): self.decoder = TransformerDecoder(**decoder_config) @snt.reuse_variables def _embed_class_label(self, labels): """Embeds class label with learned embedding matrix.""" init_dict = {'embeddings': tf.glorot_uniform_initializer} return snt.Embed( vocab_size=self.num_classes, embed_dim=self.embedding_dim, initializers=init_dict, densify_gradients=True, name='class_label')(labels) @snt.reuse_variables def _prepare_context(self, context, is_training=False): """Prepare class label context.""" if self.class_conditional: global_context_embedding = self._embed_class_label(context['class_label']) else: global_context_embedding = None return global_context_embedding, None @snt.reuse_variables def _embed_inputs(self, vertices, global_context_embedding=None): """Embeds flat vertices and adds position and coordinate information.""" # Dequantize inputs and get shapes input_shape = tf.shape(vertices) batch_size, seq_length = input_shape[0], input_shape[1] # Coord indicators (x, y, z) coord_embeddings = snt.Embed( vocab_size=3, embed_dim=self.embedding_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='coord_embeddings')(tf.mod(tf.range(seq_length), 3)) # Position embeddings pos_embeddings = snt.Embed( vocab_size=self.max_num_input_verts, embed_dim=self.embedding_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='coord_embeddings')(tf.floordiv(tf.range(seq_length), 3)) # Discrete vertex value embeddings if self.use_discrete_embeddings: vert_embeddings = snt.Embed( vocab_size=2**self.quantization_bits + 1, embed_dim=self.embedding_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='value_embeddings')(vertices) # Continuous vertex value embeddings else: vert_embeddings = tf.layers.dense( dequantize_verts(vertices[..., None], self.quantization_bits), self.embedding_dim, use_bias=True, name='value_embeddings') # Step zero embeddings if global_context_embedding is None: zero_embed = tf.get_variable( 'embed_zero', shape=[1, 1, self.embedding_dim]) zero_embed_tiled = tf.tile(zero_embed, [batch_size, 1, 1]) else: zero_embed_tiled = global_context_embedding[:, None] # Aggregate embeddings embeddings = vert_embeddings + (coord_embeddings + pos_embeddings)[None] embeddings = tf.concat([zero_embed_tiled, embeddings], axis=1) return embeddings @snt.reuse_variables def _project_to_logits(self, inputs): """Projects transformer outputs to logits for predictive distribution.""" return tf.layers.dense( inputs, 2**self.quantization_bits + 1, # + 1 for stopping token use_bias=True, kernel_initializer=tf.zeros_initializer(), name='project_to_logits') @snt.reuse_variables def _create_dist(self, vertices, global_context_embedding=None, sequential_context_embeddings=None, temperature=1., top_k=0, top_p=1., is_training=False, cache=None): """Outputs categorical dist for quantized vertex coordinates.""" # Embed inputs decoder_inputs = self._embed_inputs(vertices, global_context_embedding) if cache is not None: decoder_inputs = decoder_inputs[:, -1:] # pass through decoder outputs = self.decoder( decoder_inputs, cache=cache, sequential_context_embeddings=sequential_context_embeddings, is_training=is_training) # Get logits and optionally process for sampling logits = self._project_to_logits(outputs) logits /= temperature logits = top_k_logits(logits, top_k) logits = top_p_logits(logits, top_p) cat_dist = tfd.Categorical(logits=logits) return cat_dist def _build(self, batch, is_training=False): """Pass batch through vertex model and get log probabilities under model. Args: batch: Dictionary containing: 'vertices_flat': int32 vertex tensors of shape [batch_size, seq_length]. is_training: If True, use dropout. Returns: pred_dist: tfd.Categorical predictive distribution with batch shape [batch_size, seq_length]. """ global_context, seq_context = self._prepare_context( batch, is_training=is_training) pred_dist = self._create_dist( batch['vertices_flat'][:, :-1], # Last element not used for preds global_context_embedding=global_context, sequential_context_embeddings=seq_context, is_training=is_training) return pred_dist def sample(self, num_samples, context=None, max_sample_length=None, temperature=1., top_k=0, top_p=1., recenter_verts=True, only_return_complete=True): """Autoregressive sampling with caching. Args: num_samples: Number of samples to produce. context: Dictionary of context, such as class labels. See _prepare_context for details. max_sample_length: Maximum length of sampled vertex sequences. Sequences that do not complete are truncated. temperature: Scalar softmax temperature > 0. top_k: Number of tokens to keep for top-k sampling. top_p: Proportion of probability mass to keep for top-p sampling. recenter_verts: If True, center vertex samples around origin. This should be used if model is trained using shift augmentations. only_return_complete: If True, only return completed samples. Otherwise return all samples along with completed indicator. Returns: outputs: Output dictionary with fields: 'completed': Boolean tensor of shape [num_samples]. If True then corresponding sample completed within max_sample_length. 'vertices': Tensor of samples with shape [num_samples, num_verts, 3]. 'num_vertices': Tensor indicating number of vertices for each example in padded vertex samples. 'vertices_mask': Tensor of shape [num_samples, num_verts] that masks corresponding invalid elements in 'vertices'. """ # Obtain context for decoder global_context, seq_context = self._prepare_context( context, is_training=False) # num_samples is the minimum value of num_samples and the batch size of # context inputs (if present). if global_context is not None: num_samples = tf.minimum(num_samples, tf.shape(global_context)[0]) global_context = global_context[:num_samples] if seq_context is not None: seq_context = seq_context[:num_samples] elif seq_context is not None: num_samples = tf.minimum(num_samples, tf.shape(seq_context)[0]) seq_context = seq_context[:num_samples] def _loop_body(i, samples, cache): """While-loop body for autoregression calculation.""" cat_dist = self._create_dist( samples, global_context_embedding=global_context, sequential_context_embeddings=seq_context, cache=cache, temperature=temperature, top_k=top_k, top_p=top_p) next_sample = cat_dist.sample() samples = tf.concat([samples, next_sample], axis=1) return i + 1, samples, cache def _stopping_cond(i, samples, cache): """Stopping condition for sampling while-loop.""" del i, cache # Unused return tf.reduce_any(tf.reduce_all(tf.not_equal(samples, 0), axis=-1)) # Initial values for loop variables samples = tf.zeros([num_samples, 0], dtype=tf.int32) max_sample_length = max_sample_length or self.max_num_input_verts cache, cache_shape_invariants = self.decoder.create_init_cache(num_samples) _, v, _ = tf.while_loop( cond=_stopping_cond, body=_loop_body, loop_vars=(0, samples, cache), shape_invariants=(tf.TensorShape([]), tf.TensorShape([None, None]), cache_shape_invariants), maximum_iterations=max_sample_length * 3 + 1, back_prop=False, parallel_iterations=1) # Check if samples completed. Samples are complete if the stopping token # is produced. completed = tf.reduce_any(tf.equal(v, 0), axis=-1) # Get the number of vertices in the sample. This requires finding the # index of the stopping token. For complete samples use to argmax to get # first nonzero index. stop_index_completed = tf.argmax( tf.cast(tf.equal(v, 0), tf.int32), axis=-1, output_type=tf.int32) # For incomplete samples the stopping index is just the maximum index. stop_index_incomplete = ( max_sample_length * 3 * tf.ones_like(stop_index_completed)) stop_index = tf.where( completed, stop_index_completed, stop_index_incomplete) num_vertices = tf.floordiv(stop_index, 3) # Convert to 3D vertices by reshaping and re-ordering x -> y -> z v = v[:, :(tf.reduce_max(num_vertices) * 3)] - 1 verts_dequantized = dequantize_verts(v, self.quantization_bits) vertices = tf.reshape(verts_dequantized, [num_samples, -1, 3]) vertices = tf.stack( [vertices[..., 2], vertices[..., 1], vertices[..., 0]], axis=-1) # Pad samples to max sample length. This is required in order to concatenate # Samples across different replicator instances. Pad with stopping tokens # for incomplete samples. pad_size = max_sample_length - tf.shape(vertices)[1] vertices = tf.pad(vertices, [[0, 0], [0, pad_size], [0, 0]]) # 3D Vertex mask vertices_mask = tf.cast( tf.range(max_sample_length)[None] < num_vertices[:, None], tf.float32) if recenter_verts: vert_max = tf.reduce_max( vertices - 1e10 * (1. - vertices_mask)[..., None], axis=1, keepdims=True) vert_min = tf.reduce_min( vertices + 1e10 * (1. - vertices_mask)[..., None], axis=1, keepdims=True) vert_centers = 0.5 * (vert_max + vert_min) vertices -= vert_centers vertices *= vertices_mask[..., None] if only_return_complete: vertices = tf.boolean_mask(vertices, completed) num_vertices = tf.boolean_mask(num_vertices, completed) vertices_mask = tf.boolean_mask(vertices_mask, completed) completed = tf.boolean_mask(completed, completed) # Outputs outputs = { 'completed': completed, 'vertices': vertices, 'num_vertices': num_vertices, 'vertices_mask': vertices_mask, } return outputs class ImageToVertexModel(VertexModel): """Generative model of quantized mesh vertices with image conditioning. Operates on flattened vertex sequences with a stopping token: [z_0, y_0, x_0, z_1, y_1, x_1, ..., z_n, y_n, z_n, STOP] Input vertex coordinates are embedded and tagged with learned coordinate and position indicators. A transformer decoder outputs logits for a quantized vertex distribution. Image inputs are encoded and used to condition the vertex decoder. """ def __init__(self, res_net_config, decoder_config, quantization_bits, use_discrete_embeddings=True, max_num_input_verts=2500, name='image_to_vertex_model'): """Initializes VoxelToVertexModel. Args: res_net_config: Dictionary with ResNet config. decoder_config: Dictionary with TransformerDecoder config. quantization_bits: Number of quantization used in mesh preprocessing. use_discrete_embeddings: If True, use discrete rather than continuous vertex embeddings. max_num_input_verts: Maximum number of vertices. Used for learned position embeddings. name: Name of variable scope """ super(ImageToVertexModel, self).__init__( decoder_config=decoder_config, quantization_bits=quantization_bits, max_num_input_verts=max_num_input_verts, use_discrete_embeddings=use_discrete_embeddings, name=name) with self._enter_variable_scope(): self.res_net = ResNet(num_dims=2, **res_net_config) @snt.reuse_variables def _prepare_context(self, context, is_training=False): # Pass images through encoder image_embeddings = self.res_net( context['image'] - 0.5, is_training=is_training) # Add 2D coordinate grid embedding processed_image_resolution = tf.shape(image_embeddings)[1] x = tf.linspace(-1., 1., processed_image_resolution) image_coords = tf.stack(tf.meshgrid(x, x), axis=-1) image_coord_embeddings = tf.layers.dense( image_coords, self.embedding_dim, use_bias=True, name='image_coord_embeddings') image_embeddings += image_coord_embeddings[None] # Reshape spatial grid to sequence batch_size = tf.shape(image_embeddings)[0] sequential_context_embedding = tf.reshape( image_embeddings, [batch_size, -1, self.embedding_dim]) return None, sequential_context_embedding class VoxelToVertexModel(VertexModel): """Generative model of quantized mesh vertices with voxel conditioning. Operates on flattened vertex sequences with a stopping token: [z_0, y_0, x_0, z_1, y_1, x_1, ..., z_n, y_n, z_n, STOP] Input vertex coordinates are embedded and tagged with learned coordinate and position indicators. A transformer decoder outputs logits for a quantized vertex distribution. Image inputs are encoded and used to condition the vertex decoder. """ def __init__(self, res_net_config, decoder_config, quantization_bits, use_discrete_embeddings=True, max_num_input_verts=2500, name='voxel_to_vertex_model'): """Initializes VoxelToVertexModel. Args: res_net_config: Dictionary with ResNet config. decoder_config: Dictionary with TransformerDecoder config. quantization_bits: Integer number of bits used for vertex quantization. use_discrete_embeddings: If True, use discrete rather than continuous vertex embeddings. max_num_input_verts: Maximum number of vertices. Used for learned position embeddings. name: Name of variable scope """ super(VoxelToVertexModel, self).__init__( decoder_config=decoder_config, quantization_bits=quantization_bits, max_num_input_verts=max_num_input_verts, use_discrete_embeddings=use_discrete_embeddings, name=name) with self._enter_variable_scope(): self.res_net = ResNet(num_dims=3, **res_net_config) @snt.reuse_variables def _prepare_context(self, context, is_training=False): # Embed binary input voxels voxel_embeddings = snt.Embed( vocab_size=2, embed_dim=self.pre_embed_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='voxel_embeddings')(context['voxels']) # Pass embedded voxels through voxel encoder voxel_embeddings = self.res_net( voxel_embeddings, is_training=is_training) # Add 3D coordinate grid embedding processed_voxel_resolution = tf.shape(voxel_embeddings)[1] x = tf.linspace(-1., 1., processed_voxel_resolution) voxel_coords = tf.stack(tf.meshgrid(x, x, x), axis=-1) voxel_coord_embeddings = tf.layers.dense( voxel_coords, self.embedding_dim, use_bias=True, name='voxel_coord_embeddings') voxel_embeddings += voxel_coord_embeddings[None] # Reshape spatial grid to sequence batch_size = tf.shape(voxel_embeddings)[0] sequential_context_embedding = tf.reshape( voxel_embeddings, [batch_size, -1, self.embedding_dim]) return None, sequential_context_embedding class FaceModel(snt.AbstractModule): """Autoregressive generative model of n-gon meshes. Operates on sets of input vertices as well as flattened face sequences with new face and stopping tokens: [f_0^0, f_0^1, f_0^2, NEW, f_1^0, f_1^1, ..., STOP] Input vertices are encoded using a Transformer encoder. Input face sequences are embedded and tagged with learned position indicators, as well as their corresponding vertex embeddings. A transformer decoder outputs a pointer which is compared to each vertex embedding to obtain a distribution over vertex indices. """ def __init__(self, encoder_config, decoder_config, class_conditional=True, num_classes=55, decoder_cross_attention=True, use_discrete_vertex_embeddings=True, quantization_bits=8, max_seq_length=5000, name='face_model'): """Initializes FaceModel. Args: encoder_config: Dictionary with TransformerEncoder config. decoder_config: Dictionary with TransformerDecoder config. class_conditional: If True, then condition on learned class embeddings. num_classes: Number of classes to condition on. decoder_cross_attention: If True, the use cross attention from decoder querys into encoder outputs. use_discrete_vertex_embeddings: If True, use discrete vertex embeddings. quantization_bits: Number of quantization bits for discrete vertex embeddings. max_seq_length: Maximum face sequence length. Used for learned position embeddings. name: Name of variable scope """ super(FaceModel, self).__init__(name=name) self.embedding_dim = decoder_config['hidden_size'] self.class_conditional = class_conditional self.num_classes = num_classes self.max_seq_length = max_seq_length self.decoder_cross_attention = decoder_cross_attention self.use_discrete_vertex_embeddings = use_discrete_vertex_embeddings self.quantization_bits = quantization_bits with self._enter_variable_scope(): self.decoder = TransformerDecoder(**decoder_config) self.encoder = TransformerEncoder(**encoder_config) @snt.reuse_variables def _embed_class_label(self, labels): """Embeds class label with learned embedding matrix.""" init_dict = {'embeddings': tf.glorot_uniform_initializer} return snt.Embed( vocab_size=self.num_classes, embed_dim=self.embedding_dim, initializers=init_dict, densify_gradients=True, name='class_label')(labels) @snt.reuse_variables def _prepare_context(self, context, is_training=False): """Prepare class label and vertex context.""" if self.class_conditional: global_context_embedding = self._embed_class_label(context['class_label']) else: global_context_embedding = None vertex_embeddings = self._embed_vertices( context['vertices'], context['vertices_mask'], is_training=is_training) if self.decoder_cross_attention: sequential_context_embeddings = ( vertex_embeddings * tf.pad(context['vertices_mask'], [[0, 0], [2, 0]], constant_values=1)[..., None]) else: sequential_context_embeddings = None return (vertex_embeddings, global_context_embedding, sequential_context_embeddings) @snt.reuse_variables def _embed_vertices(self, vertices, vertices_mask, is_training=False): """Embeds vertices with transformer encoder.""" # num_verts = tf.shape(vertices)[1] if self.use_discrete_vertex_embeddings: vertex_embeddings = 0. verts_quantized = quantize_verts(vertices, self.quantization_bits) for c in range(3): vertex_embeddings += snt.Embed( vocab_size=256, embed_dim=self.embedding_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='coord_{}'.format(c))(verts_quantized[..., c]) else: vertex_embeddings = tf.layers.dense( vertices, self.embedding_dim, use_bias=True, name='vertex_embeddings') vertex_embeddings *= vertices_mask[..., None] # Pad vertex embeddings with learned embeddings for stopping and new face # tokens stopping_embeddings = tf.get_variable( 'stopping_embeddings', shape=[1, 2, self.embedding_dim]) stopping_embeddings = tf.tile(stopping_embeddings, [tf.shape(vertices)[0], 1, 1]) vertex_embeddings = tf.concat( [stopping_embeddings, vertex_embeddings], axis=1) # Pass through Transformer encoder vertex_embeddings = self.encoder(vertex_embeddings, is_training=is_training) return vertex_embeddings @snt.reuse_variables def _embed_inputs(self, faces_long, vertex_embeddings, global_context_embedding=None): """Embeds face sequences and adds within and between face positions.""" # Face value embeddings are gathered vertex embeddings face_embeddings = tf.gather(vertex_embeddings, faces_long, batch_dims=1) # Position embeddings pos_embeddings = snt.Embed( vocab_size=self.max_seq_length, embed_dim=self.embedding_dim, initializers={'embeddings': tf.glorot_uniform_initializer}, densify_gradients=True, name='coord_embeddings')(tf.range(tf.shape(faces_long)[1])) # Step zero embeddings batch_size = tf.shape(face_embeddings)[0] if global_context_embedding is None: zero_embed = tf.get_variable( 'embed_zero', shape=[1, 1, self.embedding_dim]) zero_embed_tiled = tf.tile(zero_embed, [batch_size, 1, 1]) else: zero_embed_tiled = global_context_embedding[:, None] # Aggregate embeddings embeddings = face_embeddings + pos_embeddings[None] embeddings = tf.concat([zero_embed_tiled, embeddings], axis=1) return embeddings @snt.reuse_variables def _project_to_pointers(self, inputs): """Projects transformer outputs to pointer vectors.""" return tf.layers.dense( inputs, self.embedding_dim, use_bias=True, kernel_initializer=tf.zeros_initializer(), name='project_to_pointers' ) @snt.reuse_variables def _create_dist(self, vertex_embeddings, vertices_mask, faces_long, global_context_embedding=None, sequential_context_embeddings=None, temperature=1., top_k=0, top_p=1., is_training=False, cache=None): """Outputs categorical dist for vertex indices.""" # Embed inputs decoder_inputs = self._embed_inputs( faces_long, vertex_embeddings, global_context_embedding) # Pass through Transformer decoder if cache is not None: decoder_inputs = decoder_inputs[:, -1:] decoder_outputs = self.decoder( decoder_inputs, cache=cache, sequential_context_embeddings=sequential_context_embeddings, is_training=is_training) # Get pointers pred_pointers = self._project_to_pointers(decoder_outputs) # Get logits and mask logits = tf.matmul(pred_pointers, vertex_embeddings, transpose_b=True) logits /= tf.sqrt(float(self.embedding_dim)) f_verts_mask = tf.pad( vertices_mask, [[0, 0], [2, 0]], constant_values=1.)[:, None] logits *= f_verts_mask logits -= (1. - f_verts_mask) * 1e9 logits /= temperature logits = top_k_logits(logits, top_k) logits = top_p_logits(logits, top_p) return tfd.Categorical(logits=logits) def _build(self, batch, is_training=False): """Pass batch through face model and get log probabilities. Args: batch: Dictionary containing: 'vertices_dequantized': Tensor of shape [batch_size, num_vertices, 3]. 'faces': int32 tensor of shape [batch_size, seq_length] with flattened faces. 'vertices_mask': float32 tensor with shape [batch_size, num_vertices] that masks padded elements in 'vertices'. is_training: If True, use dropout. Returns: pred_dist: tfd.Categorical predictive distribution with batch shape [batch_size, seq_length]. """ vertex_embeddings, global_context, seq_context = self._prepare_context( batch, is_training=is_training) pred_dist = self._create_dist( vertex_embeddings, batch['vertices_mask'], batch['faces'][:, :-1], global_context_embedding=global_context, sequential_context_embeddings=seq_context, is_training=is_training) return pred_dist def sample(self, context, max_sample_length=None, temperature=1., top_k=0, top_p=1., only_return_complete=True): """Sample from face model using caching. Args: context: Dictionary of context, including 'vertices' and 'vertices_mask'. See _prepare_context for details. max_sample_length: Maximum length of sampled vertex sequences. Sequences that do not complete are truncated. temperature: Scalar softmax temperature > 0. top_k: Number of tokens to keep for top-k sampling. top_p: Proportion of probability mass to keep for top-p sampling. only_return_complete: If True, only return completed samples. Otherwise return all samples along with completed indicator. Returns: outputs: Output dictionary with fields: 'completed': Boolean tensor of shape [num_samples]. If True then corresponding sample completed within max_sample_length. 'faces': Tensor of samples with shape [num_samples, num_verts, 3]. 'num_face_indices': Tensor indicating number of vertices for each example in padded vertex samples. """ vertex_embeddings, global_context, seq_context = self._prepare_context( context, is_training=False) num_samples = tf.shape(vertex_embeddings)[0] def _loop_body(i, samples, cache): """While-loop body for autoregression calculation.""" pred_dist = self._create_dist( vertex_embeddings, context['vertices_mask'], samples, global_context_embedding=global_context, sequential_context_embeddings=seq_context, cache=cache, temperature=temperature, top_k=top_k, top_p=top_p) next_sample = pred_dist.sample()[:, -1:] samples = tf.concat([samples, next_sample], axis=1) return i + 1, samples, cache def _stopping_cond(i, samples, cache): """Stopping conditions for autoregressive calculation.""" del i, cache # Unused return tf.reduce_any(tf.reduce_all(tf.not_equal(samples, 0), axis=-1)) # While loop sampling with caching samples = tf.zeros([num_samples, 0], dtype=tf.int32) max_sample_length = max_sample_length or self.max_seq_length cache, cache_shape_invariants = self.decoder.create_init_cache(num_samples) _, f, _ = tf.while_loop( cond=_stopping_cond, body=_loop_body, loop_vars=(0, samples, cache), shape_invariants=(tf.TensorShape([]), tf.TensorShape([None, None]), cache_shape_invariants), back_prop=False, parallel_iterations=1, maximum_iterations=max_sample_length) # Record completed samples complete_samples = tf.reduce_any(tf.equal(f, 0), axis=-1) # Find number of faces sample_length = tf.shape(f)[-1] # Get largest new face (1) index as stopping point for incomplete samples. max_one_ind = tf.reduce_max( tf.range(sample_length)[None] * tf.cast(tf.equal(f, 1), tf.int32), axis=-1) zero_inds = tf.cast( tf.argmax(tf.cast(tf.equal(f, 0), tf.int32), axis=-1), tf.int32) num_face_indices = tf.where(complete_samples, zero_inds, max_one_ind) + 1 # Mask faces beyond stopping token with zeros # This mask has a -1 in order to replace the last new face token with zero faces_mask = tf.cast( tf.range(sample_length)[None] < num_face_indices[:, None] - 1, tf.int32) f *= faces_mask # This is the real mask faces_mask = tf.cast( tf.range(sample_length)[None] < num_face_indices[:, None], tf.int32) # Pad to maximum size with zeros pad_size = max_sample_length - sample_length f = tf.pad(f, [[0, 0], [0, pad_size]]) if only_return_complete: f = tf.boolean_mask(f, complete_samples) num_face_indices = tf.boolean_mask(num_face_indices, complete_samples) context = tf.nest.map_structure( lambda x: tf.boolean_mask(x, complete_samples), context) complete_samples = tf.boolean_mask(complete_samples, complete_samples) # outputs outputs = { 'context': context, 'completed': complete_samples, 'faces': f, 'num_face_indices': num_face_indices, } return outputs ================================================ FILE: polygen/run.sh ================================================ #!/bin/sh # Copyright 2020 Deepmind Technologies Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. python3 -m venv polygen source polygen/bin/activate pip3 install . python3 model_test.py deactivate ================================================ FILE: polygen/sample-pretrained.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "lUh_eWpealmh" }, "source": [ "Copyright 2020 DeepMind Technologies Limited\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "\n", " https://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "kYd9gIfGJYZ8" }, "source": [ "## Clone repo and import dependencies" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "Ux33ZDQ_tqUV" }, "outputs": [], "source": [ "!pip install tensorflow==1.15 dm-sonnet==1.36 tensor2tensor==1.14\n", "\n", "import time\n", "import numpy as np\n", "import tensorflow.compat.v1 as tf\n", "tf.logging.set_verbosity(tf.logging.ERROR) # Hide TF deprecation messages\n", "import matplotlib.pyplot as plt\n", "\n", "%cd /tmp\n", "%rm -rf /tmp/deepmind_research\n", "!git clone https://github.com/deepmind/deepmind-research.git \\\n", " /tmp/deepmind_research\n", "%cd /tmp/deepmind_research/polygen\n", "import modules\n", "import data_utils" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "YUZNqHTVJbm3" }, "source": [ "## Download pre-trained model weights from Google Cloud Storage" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "LpZjBUmq10gX" }, "outputs": [], "source": [ "!mkdir /tmp/vertex_model\n", "!mkdir /tmp/face_model\n", "!gsutil cp gs://deepmind-research-polygen/vertex_model.tar.gz /tmp/vertex_model/\n", "!gsutil cp gs://deepmind-research-polygen/face_model.tar.gz /tmp/face_model/\n", "!tar xvfz /tmp/vertex_model/vertex_model.tar.gz -C /tmp/vertex_model/\n", "!tar xvfz /tmp/face_model/face_model.tar.gz -C /tmp/face_model/" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "vXhMOoxaJ3Xb" }, "source": [ "## Pre-trained model config" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "7VGSSS9vJSn-" }, "outputs": [], "source": [ "vertex_module_config=dict(\n", " decoder_config=dict(\n", " hidden_size=512,\n", " fc_size=2048,\n", " num_heads=8,\n", " layer_norm=True,\n", " num_layers=24,\n", " dropout_rate=0.4,\n", " re_zero=True,\n", " memory_efficient=True\n", " ),\n", " quantization_bits=8,\n", " class_conditional=True,\n", " max_num_input_verts=5000,\n", " use_discrete_embeddings=True,\n", " )\n", "\n", "face_module_config=dict(\n", " encoder_config=dict(\n", " hidden_size=512,\n", " fc_size=2048,\n", " num_heads=8,\n", " layer_norm=True,\n", " num_layers=10,\n", " dropout_rate=0.2,\n", " re_zero=True,\n", " memory_efficient=True,\n", " ),\n", " decoder_config=dict(\n", " hidden_size=512,\n", " fc_size=2048,\n", " num_heads=8,\n", " layer_norm=True,\n", " num_layers=14,\n", " dropout_rate=0.2,\n", " re_zero=True,\n", " memory_efficient=True,\n", " ),\n", " class_conditional=False,\n", " decoder_cross_attention=True,\n", " use_discrete_vertex_embeddings=True,\n", " max_seq_length=8000,\n", " )" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WNXf_XbuKW4S" }, "source": [ "## Generate class-conditional samples\n", "\n", "Try varying the `class_id` parameter to generate meshes from different object categories. Good classes to try are tables (49), lamps (30), and cabinets (32). \n", "\n", "We can also specify the maximum number of vertices / face indices we want to see in the generated meshes using `max_num_vertices` and `max_num_face_indices`. The code will keep generating batches of samples until there are at least `num_samples_min` complete samples with the required number of vertices / faces.\n", "\n", "`top_p_vertex_model` and `top_p_face_model` control how varied the outputs are, with `1.` being the most varied, and `0.` the least varied. `0.9` is a good value for both the vertex and face models.\n", "\n", "Sampling should take around 2-5 minutes with a colab GPU using the default settings depending on the object class." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "kqKMbPJJu3lk" }, "outputs": [], "source": [ "class_id = '49) table' #@param ['0) airplane,aeroplane,plane','1) ashcan,trash can,garbage can,wastebin,ash bin,ash-bin,ashbin,dustbin,trash barrel,trash bin','2) bag,traveling bag,travelling bag,grip,suitcase','3) basket,handbasket','4) bathtub,bathing tub,bath,tub','5) bed','6) bench','7) birdhouse','8) bookshelf','9) bottle','10) bowl','11) bus,autobus,coach,charabanc,double-decker,jitney,motorbus,motorcoach,omnibus,passenger vehi','12) cabinet','13) camera,photographic camera','14) can,tin,tin can','15) cap','16) car,auto,automobile,machine,motorcar','17) cellular telephone,cellular phone,cellphone,cell,mobile phone','18) chair','19) clock','20) computer keyboard,keypad','21) dishwasher,dish washer,dishwashing machine','22) display,video display','23) earphone,earpiece,headphone,phone','24) faucet,spigot','25) file,file cabinet,filing cabinet','26) guitar','27) helmet','28) jar','29) knife','30) lamp','31) laptop,laptop computer','32) loudspeaker,speaker,speaker unit,loudspeaker system,speaker system','33) mailbox,letter box','34) microphone,mike','35) microwave,microwave oven','36) motorcycle,bike','37) mug','38) piano,pianoforte,forte-piano','39) pillow','40) pistol,handgun,side arm,shooting iron','41) pot,flowerpot','42) printer,printing machine','43) remote control,remote','44) rifle','45) rocket,projectile','46) skateboard','47) sofa,couch,lounge','48) stove','49) table','50) telephone,phone,telephone set','51) tower','52) train,railroad train','53) vessel,watercraft','54) washer,automatic washer,washing machine']\n", "num_samples_min = 1 #@param\n", "num_samples_batch = 8 #@param\n", "max_num_vertices = 400 #@param\n", "max_num_face_indices = 2000 #@param\n", "top_p_vertex_model = 0.9 #@param\n", "top_p_face_model = 0.9 #@param\n", "\n", "tf.reset_default_graph()\n", "\n", "# Build models\n", "vertex_model = modules.VertexModel(**vertex_module_config)\n", "face_model = modules.FaceModel(**face_module_config)\n", "\n", "# Tile out class label to every element in batch\n", "class_id = int(class_id.split(')')[0])\n", "vertex_model_context = {'class_label': tf.fill([num_samples_batch,], class_id)}\n", "vertex_samples = vertex_model.sample(\n", " num_samples_batch, context=vertex_model_context, \n", " max_sample_length=max_num_vertices, top_p=top_p_vertex_model, \n", " recenter_verts=True, only_return_complete=True)\n", "vertex_model_saver = tf.train.Saver(var_list=vertex_model.variables)\n", "\n", "# The face model generates samples conditioned on a context, which here is\n", "# the vertex model samples\n", "face_samples = face_model.sample(\n", " vertex_samples, max_sample_length=max_num_face_indices, \n", " top_p=top_p_face_model, only_return_complete=True)\n", "face_model_saver = tf.train.Saver(var_list=face_model.variables)\n", "\n", "# Start sampling\n", "start = time.time()\n", "print('Generating samples...')\n", "with tf.Session() as sess:\n", " vertex_model_saver.restore(sess, '/tmp/vertex_model/model')\n", " face_model_saver.restore(sess, '/tmp/face_model/model')\n", " mesh_list = []\n", " num_samples_complete = 0\n", " while num_samples_complete \u003c num_samples_min:\n", " v_samples_np = sess.run(vertex_samples)\n", " if v_samples_np['completed'].size == 0:\n", " print('No vertex samples completed in this batch. Try increasing ' +\n", " 'max_num_vertices.')\n", " continue\n", " f_samples_np = sess.run(\n", " face_samples,\n", " {vertex_samples[k]: v_samples_np[k] for k in vertex_samples.keys()})\n", " v_samples_np = f_samples_np['context']\n", " num_samples_complete_batch = f_samples_np['completed'].sum()\n", " num_samples_complete += num_samples_complete_batch\n", " print('Num. samples complete: {}'.format(num_samples_complete))\n", " for k in range(num_samples_complete_batch):\n", " verts = v_samples_np['vertices'][k][:v_samples_np['num_vertices'][k]]\n", " faces = data_utils.unflatten_faces(\n", " f_samples_np['faces'][k][:f_samples_np['num_face_indices'][k]])\n", " mesh_list.append({'vertices': verts, 'faces': faces})\n", "end = time.time()\n", "print('sampling time: {}'.format(end - start))\n", "\n", "data_utils.plot_meshes(mesh_list, ax_lims=0.4) " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "OOQV6pMvSymz" }, "source": [ "## Export meshes as `.obj` files\n", "Pick a `mesh_id` (starting at 0) corresponding to the samples generated above. Refresh the colab file browser to find an `.obj` file with the mesh data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "fO0Klbq2Sx0m" }, "outputs": [], "source": [ "mesh_id = 4 #@param\n", "data_utils.write_obj(\n", " mesh_list[mesh_id]['vertices'], mesh_list[mesh_id]['faces'], \n", " 'mesh-{}.obj'.format(mesh_id))" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "sampling-pretrained.ipynb", "provenance": [ { "file_id": "1yj-oHYqCnwYVGSM22coa68NqnSoNdyVr", "timestamp": 1591622616999 }, { "file_id": "1v_7DtLnpXrEhVbwZhzDiVQW7ghroi11Y", "timestamp": 1591264007511 } ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: polygen/setup.py ================================================ # Copyright 2020 DeepMind Technologies Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Setup for pip package.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from setuptools import find_packages from setuptools import setup REQUIRED_PACKAGES = ['numpy', 'dm-sonnet==1.36', 'tensorflow==1.14', 'tensor2tensor==1.15', 'networkx', 'matplotlib', 'six'] setup( name='polygen', version='0.1', description='A library for PolyGen: An Autoregressive Generative Model of 3D Meshes.', url='https://github.com/deepmind/deepmind-research/polygen', author='DeepMind', author_email='no-reply@google.com', # Contained modules and scripts. packages=find_packages(), install_requires=REQUIRED_PACKAGES, platforms=['any'], license='Apache 2.0', ) ================================================ FILE: polygen/training.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "szgmaK1HajOc" }, "source": [ "Copyright 2020 DeepMind Technologies Limited\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "\n", " https://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_dv0afOrKheU" }, "source": [ "## Clone repo and import dependencies" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "Ux33ZDQ_tqUV" }, "outputs": [], "source": [ "!pip install tensorflow==1.15 dm-sonnet==1.36 tensor2tensor==1.14\n", "\n", "import os\n", "import numpy as np\n", "import tensorflow.compat.v1 as tf\n", "tf.logging.set_verbosity(tf.logging.ERROR) # Hide TF deprecation messages\n", "import matplotlib.pyplot as plt\n", "\n", "%cd /tmp\n", "%rm -rf /tmp/deepmind_research\n", "!git clone https://github.com/deepmind/deepmind-research.git \\\n", " /tmp/deepmind_research\n", "%cd /tmp/deepmind_research/polygen\n", "import modules\n", "import data_utils" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "U3GDZhJ5wGOf" }, "source": [ "## Prepare a synthetic dataset\n", "We prepare a dataset of meshes using four simple geometric primitives.\n", "\n", "The important function here is `data_utils.load_process_mesh`, which loads the raw `.obj` file, normalizes and centers the meshes, and applies quantization to the vertex positions. The mesh faces are flattened and treated as a long sequence, with a new-face token (`=1`) separating the faces. For each of the four synthetic meshes, we associate a unique class label, so we can train class-conditional models.\n", "\n", "After processing the raw mesh data into numpy arrays, we create a `tf.data.Dataset` that we can use to feed data to our models. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "3QAqwyZjtOdC" }, "outputs": [], "source": [ "# Prepare synthetic dataset\n", "ex_list = []\n", "for k, mesh in enumerate(['cube', 'cylinder', 'cone', 'icosphere']):\n", " mesh_dict = data_utils.load_process_mesh(\n", " os.path.join('meshes', '{}.obj'.format(mesh)))\n", " mesh_dict['class_label'] = k\n", " ex_list.append(mesh_dict)\n", "synthetic_dataset = tf.data.Dataset.from_generator(\n", " lambda: ex_list, \n", " output_types={\n", " 'vertices': tf.int32, 'faces': tf.int32, 'class_label': tf.int32},\n", " output_shapes={\n", " 'vertices': tf.TensorShape([None, 3]), 'faces': tf.TensorShape([None]), \n", " 'class_label': tf.TensorShape(())}\n", " )\n", "ex = synthetic_dataset.make_one_shot_iterator().get_next()\n", "\n", "# Inspect the first mesh\n", "with tf.Session() as sess:\n", " ex_np = sess.run(ex)\n", "print(ex_np)\n", "\n", "# Plot the meshes\n", "mesh_list = []\n", "with tf.Session() as sess:\n", " for i in range(4):\n", " ex_np = sess.run(ex)\n", " mesh_list.append(\n", " {'vertices': data_utils.dequantize_verts(ex_np['vertices']), \n", " 'faces': data_utils.unflatten_faces(ex_np['faces'])})\n", "data_utils.plot_meshes(mesh_list, ax_lims=0.4)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9G2FCQQyyTXw" }, "source": [ "## Vertex model\n", "\n", "#### Prepare the dataset for vertex model training\n", "We need to perform some additional processing to make the dataset ready for vertex model training. In particular, `data_utils.make_vertex_model_dataset` flattens the `[V, 3]` vertex arrays, ordering by `Z-\u003eY-\u003eX` coordinates. It also creates masks, which are used to mask padded elements in data batches. We also add random shifts to make the modelling task more challenging.\n", "\n", "#### Create a vertex model\n", "`modules.VertexModel` is a Sonnet module that. Calling the module on a batch of data will produce outputs which are the sequential predictions for each vertex coordinate. The basis of the vertex model is a Transformer decoder, and we specify it's parameters in `decoder_config`. \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "o2KCoDeeFP8C" }, "outputs": [], "source": [ "# Prepare the dataset for vertex model training\n", "vertex_model_dataset = data_utils.make_vertex_model_dataset(\n", " synthetic_dataset, apply_random_shift=False)\n", "vertex_model_dataset = vertex_model_dataset.repeat()\n", "vertex_model_dataset = vertex_model_dataset.padded_batch(\n", " 4, padded_shapes=vertex_model_dataset.output_shapes)\n", "vertex_model_dataset = vertex_model_dataset.prefetch(1)\n", "vertex_model_batch = vertex_model_dataset.make_one_shot_iterator().get_next()\n", "\n", "# Create vertex model\n", "vertex_model = modules.VertexModel(\n", " decoder_config={\n", " 'hidden_size': 128,\n", " 'fc_size': 512, \n", " 'num_layers': 3,\n", " 'dropout_rate': 0.\n", " },\n", " class_conditional=True,\n", " num_classes=4,\n", " max_num_input_verts=250,\n", " quantization_bits=8,\n", ")\n", "vertex_model_pred_dist = vertex_model(vertex_model_batch)\n", "vertex_model_loss = -tf.reduce_sum(\n", " vertex_model_pred_dist.log_prob(vertex_model_batch['vertices_flat']) * \n", " vertex_model_batch['vertices_flat_mask'])\n", "vertex_samples = vertex_model.sample(\n", " 4, context=vertex_model_batch, max_sample_length=200, top_p=0.95,\n", " recenter_verts=False, only_return_complete=False)\n", "\n", "print(vertex_model_batch)\n", "print(vertex_model_pred_dist)\n", "print(vertex_samples)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "-9RNYr5x1jov" }, "source": [ "## Face model\n", "\n", "#### Prepare the dataset for face model training\n", "We need to perform some additional processing to make the dataset ready for vertex model training. In particular, `data_utils.make_vertex_model_dataset` flattens the `[V, 3]` vertex arrays, ordering by `Z-\u003eY-\u003eX` coordinates. It also creates masks, which are used to mask padded elements in data batches. We also add random shifts to make the modelling task more challenging.\n", "\n", "#### Create a face model\n", "`modules.VertexModel` is a Sonnet module that. Calling the module on a batch of data will produce outputs which are the sequential predictions for each vertex coordinate. The basis of the vertex model is a Transformer decoder, and we specify it's parameters in `decoder_config`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "a2yO6dOGzn8c" }, "outputs": [], "source": [ "face_model_dataset = data_utils.make_face_model_dataset(\n", " synthetic_dataset, apply_random_shift=False)\n", "face_model_dataset = face_model_dataset.repeat()\n", "face_model_dataset = face_model_dataset.padded_batch(\n", " 4, padded_shapes=face_model_dataset.output_shapes)\n", "face_model_dataset = face_model_dataset.prefetch(1)\n", "face_model_batch = face_model_dataset.make_one_shot_iterator().get_next()\n", "\n", "# Create face model\n", "face_model = modules.FaceModel(\n", " encoder_config={\n", " 'hidden_size': 128,\n", " 'fc_size': 512, \n", " 'num_layers': 3,\n", " 'dropout_rate': 0.\n", " },\n", " decoder_config={\n", " 'hidden_size': 128,\n", " 'fc_size': 512, \n", " 'num_layers': 3,\n", " 'dropout_rate': 0.\n", " },\n", " class_conditional=False,\n", " max_seq_length=500,\n", " quantization_bits=8,\n", " decoder_cross_attention=True,\n", " use_discrete_vertex_embeddings=True,\n", ")\n", "face_model_pred_dist = face_model(face_model_batch)\n", "face_model_loss = -tf.reduce_sum(\n", " face_model_pred_dist.log_prob(face_model_batch['faces']) * \n", " face_model_batch['faces_mask'])\n", "face_samples = face_model.sample(\n", " context=vertex_samples, max_sample_length=500, top_p=0.95,\n", " only_return_complete=False)\n", "print(face_model_batch)\n", "print(face_model_pred_dist)\n", "print(face_samples)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "hL7yloXB1pUb" }, "source": [ "## Train on the synthetic data\n", "\n", "Now that we've created vertex and face models and their respective data loaders, we can train them and look at some outputs. While we train the models together here, they can be trained seperately and recombined later if required. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "hjrbofa8zqQt" }, "outputs": [], "source": [ "# Optimization settings\n", "learning_rate = 5e-4\n", "training_steps = 500\n", "check_step = 5\n", "\n", "# Create an optimizer an minimize the summed log probability of the mesh \n", "# sequences\n", "optimizer = tf.train.AdamOptimizer(learning_rate)\n", "vertex_model_optim_op = optimizer.minimize(vertex_model_loss)\n", "face_model_optim_op = optimizer.minimize(face_model_loss)\n", "\n", "# Training loop\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for n in range(training_steps):\n", " if n % check_step == 0:\n", " v_loss, f_loss = sess.run((vertex_model_loss, face_model_loss))\n", " print('Step {}'.format(n))\n", " print('Loss (vertices) {}'.format(v_loss))\n", " print('Loss (faces) {}'.format(f_loss))\n", " v_samples_np, f_samples_np, b_np = sess.run(\n", " (vertex_samples, face_samples, vertex_model_batch))\n", " mesh_list = []\n", " for n in range(4):\n", " mesh_list.append(\n", " {\n", " 'vertices': v_samples_np['vertices'][n][:v_samples_np['num_vertices'][n]],\n", " 'faces': data_utils.unflatten_faces(\n", " f_samples_np['faces'][n][:f_samples_np['num_face_indices'][n]])\n", " }\n", " )\n", " data_utils.plot_meshes(mesh_list, ax_lims=0.5)\n", " sess.run((vertex_model_optim_op, face_model_optim_op))" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "training.ipynb", "provenance": [ { "file_id": "1QL8ib2FKPGUWFQbuX8AttUk-H34Al8Ue", "timestamp": 1591364245034 }, { "file_id": "1v_7DtLnpXrEhVbwZhzDiVQW7ghroi11Y", "timestamp": 1591355096822 } ], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: powerpropagation/README.md ================================================ # Powerpropagation This notebook contains code to accompany the NeurIPS 2021 paper on Powerpropagation. [](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/powerpropagation/powerpropagation.ipynb) If you find this code useful, please consider citing this paper: ``` @article{schwarz2021powerpropagation, title={Powerpropagation: A sparsity inducing weight reparameterisation}, author={Schwarz, Jonathan and Jayakumar, Siddhant M and Pascanu, Razvan and Latham, Peter and Teh, Yee Whye}, year={2021}, journal={Neural Information Processing Systems (NeurIPS)}, keywords={conference}, } ``` ================================================ FILE: powerpropagation/powerpropagation.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "V7L7Byz8qQZd" }, "source": [ "Copyright 2021 The Powerpropagation Authors. All rights reserved\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", "\n", "https://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "SuSveOpsxQuA" }, "outputs": [], "source": [ "#@title Imports\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "\n", "import tensorflow as tf\n", "import tensorflow_probability as tfp\n", "\n", "#@title Installing and Importing Dependencies\n", "\n", "print('Installing necessary libraries...')\n", "\n", "def install_libraries():\n", " !pip install dm-sonnet\n", "\n", "import IPython\n", "\n", "with IPython.utils.io.capture_output() as captured:\n", " install_libraries()\n", "\n", "import sonnet as snt" ] }, { "cell_type": "markdown", "metadata": { "id": "zNZE9eU_poe0" }, "source": [ "### Implementation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pQBoLdNFL7fE" }, "outputs": [], "source": [ "#@title Training \u0026 Pruning functions\n", "\n", "@tf.function\n", "def train_fn(model, train_x, train_y, optimizer_to_use):\n", " with tf.GradientTape() as tape:\n", " loss, stats = model.loss(train_x, train_y)\n", "\n", " train_vars = model.trainable_variables()\n", " train_grads = tape.gradient(loss, train_vars)\n", " optimizer_to_use.apply(train_grads, train_vars)\n", "\n", " return stats\n", "\n", "@tf.function\n", "def eval_fn(model, eval_x, eval_y):\n", " _, stats = model.loss(eval_x, eval_y)\n", "\n", " return stats\n", "\n", "def _bottom_k_mask(percent_to_keep, condition):\n", " how_many = int(percent_to_keep * condition.size)\n", " top_k = tf.nn.top_k(condition, k=how_many)\n", "\n", " mask = np.zeros(shape=condition.shape, dtype=np.float32)\n", " mask[top_k.indices.numpy()] = 1\n", "\n", " assert np.sum(mask) == how_many\n", "\n", " return mask\n", "\n", "def prune_by_magnitude(percent_to_keep, weight):\n", " mask = _bottom_k_mask(percent_to_keep, np.abs(weight.flatten()))\n", "\n", " return mask.reshape(weight.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uAPa99KakJDB" }, "outputs": [], "source": [ "#@title Initialisers\n", "\n", "class PowerPropVarianceScaling(snt.initializers.VarianceScaling):\n", "\n", " def __init__(self, alpha, *args, **kwargs):\n", " super(PowerPropVarianceScaling, self).__init__(*args, **kwargs)\n", " self._alpha = alpha\n", "\n", " def __call__(self, shape, dtype):\n", " u = super(PowerPropVarianceScaling, self).__call__(shape, dtype).numpy()\n", "\n", " return tf.sign(u) * tf.pow(tf.abs(u), 1.0/self._alpha)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UXtgeSdPgufJ" }, "outputs": [], "source": [ "#@title Models\n", "\n", "class PowerPropLinear(snt.Linear):\n", " \"\"\"Powerpropagation Linear module.\"\"\"\n", " def __init__(self, alpha, *args, **kwargs):\n", " super(PowerPropLinear, self).__init__(*args, **kwargs)\n", " self._alpha = alpha\n", "\n", " def get_weights(self):\n", " return tf.sign(self.w) * tf.pow(tf.abs(self.w), self._alpha)\n", "\n", " def __call__(self, inputs, mask=None):\n", " self._initialize(inputs)\n", " params = self.w * tf.pow(tf.abs(self.w), self._alpha-1)\n", "\n", " if mask is not None:\n", " params *= mask\n", "\n", " outputs = tf.matmul(inputs, params) + self.b\n", "\n", " return outputs\n", "\n", "class MLP(snt.Module):\n", " \"\"\"A multi-layer perceptron module.\"\"\"\n", "\n", " def __init__(self, alpha, w_init, output_sizes=[300, 100, 10], name='MLP'):\n", "\n", " super(MLP, self).__init__(name=name)\n", " self._alpha = alpha\n", " self._w_init = w_init\n", "\n", " self._layers = []\n", " for index, output_size in enumerate(output_sizes):\n", " self._layers.append(\n", " PowerPropLinear(\n", " output_size=output_size,\n", " alpha=alpha,\n", " w_init=w_init,\n", " name=\"linear_{}\".format(index)))\n", "\n", " def get_weights(self):\n", " return [l.get_weights().numpy() for l in self._layers]\n", "\n", " def __call__(self, inputs, masks=None):\n", " num_layers = len(self._layers)\n", "\n", " for i, layer in enumerate(self._layers):\n", " if masks is not None:\n", " inputs = layer(inputs, masks[i])\n", " else:\n", " inputs = layer(inputs)\n", " if i \u003c (num_layers - 1):\n", " inputs = tf.nn.relu(inputs)\n", "\n", " return inputs\n", "\n", "\n", "class DensityNetwork(snt.Module):\n", " \"\"\"Produces categorical distribution.\"\"\"\n", "\n", " def __init__(self, network=None, name=\"DensityNetwork\", *args, **kwargs):\n", " super(DensityNetwork, self).__init__(name=name)\n", " self._network = network\n", "\n", " def __call__(self, inputs, masks=None, *args, **kwargs):\n", " outputs = self._network(inputs, masks, *args, **kwargs)\n", "\n", " return tfp.distributions.Categorical(logits=outputs), outputs\n", "\n", " def trainable_variables(self):\n", " return self._network.trainable_variables\n", "\n", " def get_weights(self):\n", " return self._network.get_weights()\n", "\n", " def loss(self, inputs, targets, masks=None, *args, **kwargs):\n", " dist, logits = self.__call__(\n", " inputs, masks, *args, **kwargs)\n", " loss = -tf.reduce_mean(dist.log_prob(targets))\n", "\n", " accuracy = tf.reduce_mean(\n", " tf.cast(tf.equal(tf.argmax(logits, axis=1), targets), tf.float32))\n", "\n", " return loss, {'loss': loss, 'acc': accuracy}" ] }, { "cell_type": "markdown", "metadata": { "id": "J8M6PJ4oNK0X" }, "source": [ "### Training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9TwBkiSmKWCz" }, "outputs": [], "source": [ "#@title Training configuration\n", "\n", "model_seed = 0 #@param\n", "\n", "alphas = [1.0, 2.0, 3.0, 4.0, 5.0] #@param\n", "init_distribution = 'truncated_normal'\n", "init_mode = 'fan_in'\n", "init_scale = 1.0\n", "\n", "# Fixed values taken from the Lottery Ticket Hypothesis paper\n", "train_batch_size = 60\n", "num_train_steps = 50000\n", "learning_rate = 0.1\n", "\n", "report_interval = 2500\n", "\n", "tf.random.set_seed(model_seed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "JGFVxUjCRe3v" }, "outputs": [], "source": [ "#@title Get data\n", "\n", "(train_x, train_y), (test_x, test_y) = tf.keras.datasets.mnist.load_data()\n", "\n", "train_x = train_x.reshape([60000, 784]).astype(np.float32) / 255.0\n", "test_x = test_x.reshape([10000, 784]).astype(np.float32) / 255.0\n", "\n", "train_y = train_y.astype(np.int64)\n", "test_y = test_y.astype(np.int64)\n", "\n", "# Reserve some data for a validation set\n", "valid_x = train_x[-5000:]\n", "valid_y = train_y[-5000:]\n", "train_x = train_x[:-5000]\n", "train_y = train_y[:-5000]\n", "\n", "train_dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y))\n", "\n", "# Sample random batches of data from the entire training set\n", "train_iterator = iter(\n", " train_dataset.repeat().shuffle(10000).batch(train_batch_size))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "nWemRI-x4FEa" }, "outputs": [], "source": [ "#@title Training set-up\n", "\n", "model_types = []\n", "models = []\n", "n_models = len(alphas)\n", "\n", "for alpha in alphas:\n", " w_init = PowerPropVarianceScaling(alpha)\n", " models.append(DensityNetwork(MLP(alpha=alpha, w_init=w_init)))\n", " if alpha \u003e 1.0:\n", " model_types.append('Powerprop. ($\\\\alpha={}$)'.format(alpha))\n", " else:\n", " model_types.append('Baseline')\n", "\n", "# Initalise variables\n", "for m in models:\n", " m(valid_x)\n", "\n", "initial_weights = [m.get_weights() for m in models]\n", "\n", "\n", "optimizers = [snt.optimizers.SGD(learning_rate=learning_rate)\n", " for _ in range(n_models)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "xvtAESbqFGaQ" }, "outputs": [], "source": [ "#@title Training loop\n", "\n", "all_train_stats = [[] for _ in range(n_models)]\n", "all_valid_stats = [[] for _ in range(n_models)]\n", "\n", "for step in range(num_train_steps+1):\n", " train_x_batch, train_y_batch = next(train_iterator)\n", "\n", " for m_id, model in enumerate(models):\n", " all_train_stats[m_id].append(\n", " train_fn(model, train_x_batch, train_y_batch, optimizers[m_id]))\n", "\n", " if step % report_interval == 0:\n", " for m_id, model in enumerate(models):\n", " print('[Train Step {}, Alpha {}] Loss: {:1.3f}. Acc: {:1.3f}'.format(\n", " step, \n", " alphas[m_id], \n", " all_train_stats[m_id][-1]['loss'], \n", " all_train_stats[m_id][-1]['acc']))\n", " all_valid_stats[m_id].append(eval_fn(model, valid_x, valid_y))\n", "\n", " print('[Eval Step {}, Alpha {}] Loss: {:1.3f}. Acc: {:1.3f}'.format(\n", " step, \n", " alphas[m_id], \n", " all_valid_stats[m_id][-1]['loss'], \n", " all_valid_stats[m_id][-1]['acc']))\n", " print('---')" ] }, { "cell_type": "markdown", "metadata": { "id": "NDBNpC_4piOg" }, "source": [ "### Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "dG7tyvmOQxeX" }, "outputs": [], "source": [ "#@title Pruning\n", "\n", "final_weights = [m.get_weights() for m in models]\n", "\n", "eval_at_sparsity_level = np.geomspace(0.01, 1.0, 20).tolist()\n", "acc_at_sparsity = [[] for _ in range(n_models)]\n", "\n", "for p_to_use in eval_at_sparsity_level:\n", "\n", " # Half the sparsity at output layer\n", " percent = 2*[p_to_use] + [min(1.0, p_to_use*2)]\n", "\n", " for m_id, model_to_use in enumerate(models):\n", " masks = []\n", " for i, w in enumerate(final_weights[m_id]):\n", " masks.append(prune_by_magnitude(percent[i], w))\n", "\n", " _, stats = model_to_use.loss(test_x, test_y, masks=masks)\n", "\n", " acc_at_sparsity[m_id].append(stats['acc'].numpy())\n", " print(' Performance @ {:1.0f}% of weights [Alpha {}]: Acc {:1.3f} NLL {:1.3f} '.format(\n", " 100*p_to_use, alphas[m_id], stats['acc'], stats['loss']))\n", " print('---')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "nv8u662-4yug" }, "outputs": [], "source": [ "sns.set_style(\"whitegrid\")\n", "sns.set_context(\"paper\")\n", "\n", "#@title Plot\n", "f, ax = plt.subplots(1, 1, figsize=(7,5))\n", "\n", "for acc, label in zip(acc_at_sparsity, model_types):\n", " ax.plot(eval_at_sparsity_level, acc, label=label, marker='o', lw=2)\n", "\n", "ax.set_xscale('log')\n", "ax.set_xlim([1.0, 0.01])\n", "ax.set_ylim([0.0, 1.0])\n", "ax.legend(frameon=False)\n", "ax.set_xlabel('Weights Remaining (%)')\n", "ax.set_ylabel('Test Accuracy (%)')\n", "\n", "sns.despine()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-NEnDx52w9NL" }, "outputs": [], "source": [ "" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "zNZE9eU_poe0", "J8M6PJ4oNK0X", "NDBNpC_4piOg" ], "last_runtime": { "build_target": "//learning/deepmind/dm_python:dm_notebook3", "kind": "private" }, "name": "Powerpropagation", "private_outputs": true, "provenance": [ { "file_id": "12kDm1gRUsFQaQUfmf1AzONNtg2Oxm96h", "timestamp": 1621155153435 }, { "file_id": "1m7j2OKVrTR0YkvMXwDIZRmZdvFlB_HMk", "timestamp": 1615900826226 }, { "file_id": "11JbbynSJgkt4n92oEKrrgfHzCQo-4sRp", "timestamp": 1613740982607 }, { "file_id": "1Pa7Szt1OuHZXczMcMxLE_UctRRtMIbkO", "timestamp": 1612795019221 } ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: rapid_task_solving/README.md ================================================ # One-shot StreetLearn and Memory & Planning Game This repository contains code for the environments used in the paper ["Rapid Task-Solving in Novel Environments"](https://arxiv.org/abs/2006.03662) by Sam Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matt Botvinick and David Raposo. It was published as a conference paper at ICLR 2021. To cite this work: ``` @inproceedings{ ritter2021rapid, title={Rapid Task-Solving in Novel Environments}, author={Samuel Ritter and Ryan Faulkner and Laurent Sartran and Adam Santoro and Matthew Botvinick and David Raposo}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=F-mvpFpn_0q} } ``` ### Memory&Planning Game The _Memory&Planning Game_ is a simple variation of the well-known _Memory Game_, wherein players must remember the locations of cards in a grid. This variation extends the challenge to require planning as well as remembering. In the _Memory&Planning Game_, the agent occupies an environment consisting of a grid of symbols (e.g. 4x4). The observation consists of two symbols — one which corresponds to the agent's current location, and another that corresponds to the "goal" the agent is tasked with navigating to. The agent can not see its relative location with respect to other symbols in the grid. At each step the agent selects one of 5 possible actions: _move left_, _move right_, _move up_, _move down_, and _collect_. If the agent chooses the "collect" action when its current location symbol matches the goal symbol, a reward of +1 is received. Otherwise, the agent receives a reward of 0. At the beginning of each episode, a new set of symbols is sampled, effectively inducing a new transition function. The agent is allowed a fixed number of steps (e.g. 100) per episode to "collect" as many goals as possible. Each time the agent collects a goal — which corresponds to completing a task —, a new goal is sampled in and the transition function stays fixed. #### Example The following code snipped shows an example of how to load the environment, start a new episode and take a few random steps. A plot representing the current state of the environment is displayed for each step. ``` import memory_planning_game env = memory_planning_game.MemoryPlanningGame(4, seed=123) _ = env.reset() for _ in range(5): timestep = env.take_random_action() fig, ax = env.draw_maze() ```  ### One-Shot StreetLearn The _One-Shot StreetLearn_ is a domain wherein environments are sampled as neighborhoods from the _StreetLearn_ dataset (Mirowski et al., 2019). Tasks are then sampled by selecting a position and orientation that the agent must navigate to from its current location. In _One-Shot StreetLearn_, the agent's observations consist of a representation of the current state and a representation of the goal state. The agent receives no other information from the environment. The available actions are _turn right_, which orients the agent clockwise toward the next available direction of motion from its current location; _turn left_, which does the same in the other direction; and _move forward_, which moves the agent along the direction it is facing to the next available location. In each episode, we sample a new neighborhood with 5 intersections from one of 12 cities. To reduce the exploration problem while keeping the planning difficulty constant, we removed all the locations that were not intersections (i.e. that corresponded to degree-2 nodes in the connectivity graph of the sampled neighbourhood). Note: In the paper we used images to represent each location, taken from the _StreetLearn_ dataset. In this codebase, to simplify the public release process, we replace the images with one-hot vectors, to represent each location. Every time the agent reaches a goal, a new starting- and goal-state pair is sampled, initiating a new task, until the fixed episode step limit is reached (e.g. 200 steps). A step that takes the agent to the goal state results in a reward of +1. Any other step results in a reward of 0. #### City graph datasets In this [link] (https://console.cloud.google.com/storage/browser/one_shot_streetlearn_graphs) you can download the datasets containing the connectivity graphs of different cities. You will need at least one of these datasets in order to build the environment. #### Example The following code snipped shows an example of how to load the environment for an example city (in this case, Madrid), start a new episode and take a few random steps. A plot representing the current state of the environment is displayed for each step. ``` import numpy as np import one_shot_streetlearn region = 'madrid' sl_config = { 'dataset_path': f'./datasets/{region}_full_graph.gexf', 'max_episode_steps': 200, 'num_junctions': 5, } env = one_shot_streetlearn.OneShotStreetLearn(**sl_config) _ = env.reset() for _ in range(4): rnd_action = np.random.randint(env.NUM_ACTIONS) timestep = env.step(rnd_action) fig, ax = env.draw_subgraph() ```  ================================================ FILE: rapid_task_solving/memory_planning_game.py ================================================ # Copyright 2021 DeepMind Technologies Limited. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Memory & Planning Game environment.""" import string import dm_env import matplotlib.pyplot as plt import networkx as nx import numpy as np class MemoryPlanningGame(dm_env.Environment): """Memory & Planning Game environment.""" ACTION_NAMES = ['Up', 'Down', 'Left', 'Right', 'Collect'] NUM_ACTIONS = len(ACTION_NAMES) DIRECTIONS = [ (0, 1), # Up (0, -1), # Down (-1, 0), # Left (1, 0), # Right (0, 0), # Collect ] def __init__(self, maze_size=4, max_episode_steps=100, target_reward=1., per_step_reward=0., random_respawn=False, seed=None): """The Memory & Planning Game environment. Args: maze_size: (int) size of the maze dimension. max_episode_steps: (int) number of steps per episode. target_reward: (float) reward value of the target. per_step_reward: (float) reward/cost of taking a step. random_respawn: (bool) whether the agent respawns in a random location upon collecting the goal. seed: (int or None) seed for random number generator. """ self._maze_size = maze_size self._num_labels = maze_size * maze_size # The graph itself is the same across episodes, but the node labels will be # randomly sampled in each episode. self._graph = nx.grid_2d_graph( self._maze_size, self._maze_size, periodic=True) self._max_episode_steps = max_episode_steps self._target_reward = target_reward self._per_step_reward = per_step_reward self._random_respawn = random_respawn self._rng = np.random.RandomState(seed) def _one_hot(self, node): one_hot_vector = np.zeros([self._num_labels], dtype=np.int32) one_hot_vector[self._labels[node]] = 1 return one_hot_vector def step(self, action): # If previous step was the last step of an episode, reset. if self._needs_reset: return self.reset() # Increment step count and check if it's the last step of the episode. self._episode_steps += 1 if self._episode_steps >= self._max_episode_steps: self._needs_reset = True transition = dm_env.termination else: transition = dm_env.transition # Recompute agent's position given the selected action. direction = self.DIRECTIONS[action] self._position = tuple( (np.array(self._position) + np.array(direction)) % self._maze_size) self._previous_action = self.ACTION_NAMES[action] # Get reward if agent is over the goal location and the selected action is # `collect`. if self._position == self._goal and self.ACTION_NAMES[action] == 'Collect': reward = self._target_reward self._set_new_goal() else: reward = self._per_step_reward self._episode_reward += reward return transition(reward, self._observation()) def _observation(self): return { 'position': np.array(self._one_hot(self.position), dtype=np.int32), 'goal': np.array(self._one_hot(self.goal), dtype=np.int32), } def observation_spec(self): return { 'position': dm_env.specs.Array( shape=(self._num_labels,), dtype=np.int32, name='position'), 'goal': dm_env.specs.Array( shape=(self._num_labels,), dtype=np.int32, name='goal'), } def action_spec(self): return dm_env.specs.DiscreteArray(self.NUM_ACTIONS) def take_random_action(self): return self.step(self._rng.randint(self.NUM_ACTIONS)) def reset(self): self._previous_action = '' self._episode_reward = 0. self._episode_steps = 0 self._needs_reset = False random_labels = self._rng.permutation(self._num_labels) self._labels = {n: random_labels[i] for i, n in enumerate(self._graph.nodes())} self._respawn() self._set_new_goal() return dm_env.restart(self._observation()) def _respawn(self): random_idx = self._rng.randint(self._num_labels) self._position = list(self._graph.nodes())[random_idx] def _set_new_goal(self): if self._random_respawn: self._respawn() goal = self._position while goal == self._position: random_idx = self._rng.randint(self._num_labels) goal = list(self._graph.nodes())[random_idx] self._goal = goal @property def position(self): return self._position @property def goal(self): return self._goal @property def previous_action(self): return self._previous_action @property def episode_reward(self): return self._episode_reward def draw_maze(self, ax=None): if ax is None: plt.figure() ax = plt.gca() node_positions = {(x, y): (x, y) for x, y in self._graph.nodes()} letters = string.ascii_uppercase + string.ascii_lowercase labels = {n: letters[self._labels[n]] for n in self._graph.nodes()} node_list = list(self._graph.nodes()) colors = [] for n in node_list: if n == self.position: colors.append('lightblue') elif n == self.goal: colors.append('lightgreen') else: colors.append('pink') nx.draw(self._graph, pos=node_positions, nodelist=node_list, ax=ax, node_color=colors, with_labels=True, node_size=200, labels=labels) ax.set_title('{}\nEpisode reward={:.1f}'.format( self.previous_action, self.episode_reward)) ax.margins(.1) return plt.gcf(), ax ================================================ FILE: rapid_task_solving/one_shot_streetlearn.py ================================================ # Copyright 2021 DeepMind Technologies Limited. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """One-shot StreetLearn environment.""" import dm_env import matplotlib.pyplot as plt import networkx as nx import numpy as np def deg_to_rad(x): """Convert degrees to radians.""" return x / 180. * np.pi def rad_to_deg(x): """Convert radians to degrees.""" return x * 180. / np.pi class OneShotStreetLearn(dm_env.Environment): """One-shot Streetlearn environment.""" ACTION_NAMES = [ 'Forward', 'Left', 'Right', 'Collect', ] NUM_ACTIONS = len(ACTION_NAMES) def __init__(self, dataset_path, max_episode_steps, num_junctions=8, target_reward=1., per_step_reward=0., observation_length=60, seed=None): self._graph = nx.read_gexf(dataset_path) self._node_attrs = self._graph.nodes(data=True) self._num_junctions = num_junctions self._observation_length = observation_length self._max_episode_steps = max_episode_steps self._target_reward = target_reward self._per_step_reward = per_step_reward self._rng = np.random.RandomState(seed) self.reset() def reset(self): self._previous_action = '' self._episode_reward = 0. self._episode_steps = 0 self._needs_reset = False self._subgraph = self.get_random_subgraph() self._observation_map = self.randomize_observations(self._subgraph) self._position = self._rng.choice(list(self._subgraph.nodes())) neighbours = self._neighbors_bearings(self._subgraph, self._position) self._neighbour = neighbours[self._rng.randint(len(neighbours))] self._set_new_goal() return dm_env.restart(self._observation()) @property def _current_edge(self): return (self._position, self._neighbour['neighbour']) def _set_new_goal(self): goal = None edges = list(self._observation_map.keys()) while goal is None or goal == self._current_edge: goal = edges[self._rng.randint(len(edges))] self._goal = goal def _one_hot(self, edge): one_hot_vector = np.zeros([self._observation_length], dtype=np.int32) one_hot_vector[self._observation_map[edge]] = 1 return one_hot_vector def _observation(self): return { 'position': np.array(self._one_hot(self._current_edge), dtype=np.int32), 'goal': np.array(self._one_hot(self._goal), dtype=np.int32), } def observation_spec(self): return { 'position': dm_env.specs.Array( shape=(self._observation_length,), dtype=np.int32, name='position'), 'goal': dm_env.specs.Array( shape=(self._observation_length,), dtype=np.int32, name='goal'), } def action_spec(self): return dm_env.specs.DiscreteArray(self.NUM_ACTIONS) def step(self, action): # If previous step was the last step of an episode, reset. if self._needs_reset: return self.reset() # Increment step count and check if it's the last step of the episode. self._episode_steps += 1 if self._episode_steps >= self._max_episode_steps: self._needs_reset = True transition = dm_env.termination else: transition = dm_env.transition # Recompute agent's position self._move(action) self._previous_action = self.ACTION_NAMES[action] # Get reward if agent is at the goal location and the selected action is # `collect`. if (self._current_edge == self._goal and self.ACTION_NAMES[action] == 'Collect'): reward = self._target_reward self._set_new_goal() else: reward = self._per_step_reward self._episode_reward += reward return transition(reward, self._observation()) def randomize_observations(self, subgraph): edges = list(subgraph.edges()) edges.extend([(y, x) for (x, y) in edges]) obs_permutation = self._rng.permutation(self._observation_length) return {e: obs_permutation[i] for i, e in enumerate(edges)} def _calculate_bearing(self, node, neighbor): lat1 = deg_to_rad(self._node_attrs[node]['lat']) lng1 = deg_to_rad(self._node_attrs[node]['lng']) lat2 = deg_to_rad(self._node_attrs[neighbor]['lat']) lng2 = deg_to_rad(self._node_attrs[neighbor]['lng']) delta_lng = lng2 - lng1 theta = np.arctan2( np.sin(delta_lng) * np.cos(lat2), np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(delta_lng)) return theta def _neighbors_bearings(self, subgraph, node): bearings = [] for neighbor in list(subgraph[node]): orientation = self._calculate_bearing(node, neighbor) bearings.append({'neighbour': neighbor, 'orientation': orientation}) bearings.sort(key=lambda x: x['orientation']) return bearings def _sort_neighbors(self, node, neighbour): bearings = self._neighbors_bearings(self._subgraph, node) bs = [x['orientation'] for x in bearings] idx = np.argmin(np.abs(bs - neighbour['orientation'])) return { 'forward': bearings[idx], 'right': bearings[idx-1], 'left': bearings[(idx+1) % len(bearings)], } def _move(self, action): neighbours = self._sort_neighbors(self._position, self._neighbour) if action == 0: new_node = self._neighbour['neighbour'] neighbours = self._sort_neighbors(new_node, neighbours['forward']) new_neighbour = neighbours['forward'] else: new_node = self._position if action == 1: new_neighbour = neighbours['left'] elif action == 2: new_neighbour = neighbours['right'] else: new_neighbour = self._neighbour self._position = new_node self._neighbour = new_neighbour def _all_next_junctions(self, subgraph, node): neighbors = list(subgraph[node]) edges = [self._get_next_junction(subgraph, node, nb) for nb in neighbors] nodes = [y for (_, y) in edges] return nodes, edges def _get_next_junction(self, subgraph, initial_node, next_node): node = initial_node while subgraph.degree(next_node) == 2: neighbours = list(subgraph.neighbors(next_node)) neighbours.remove(node) node = next_node next_node = neighbours.pop() return (initial_node, next_node) def get_random_subgraph(self): graph = self._graph num_nodes = len(graph) rnd_index = self._rng.randint(num_nodes) center_node = list(graph.nodes())[rnd_index] while graph.degree(center_node) <= 2: rnd_index = self._rng.randint(num_nodes) center_node = list(graph.nodes())[rnd_index] to_visit = [center_node] visited = [] subgraph = nx.Graph() while to_visit: node = to_visit.pop(0) visited.append(node) new_nodes, new_edges = self._all_next_junctions(graph, node) subgraph.add_edges_from(new_edges) node_degrees = [subgraph.degree(n) for n in subgraph.nodes()] count_junctions = len(list(filter(lambda x: x > 2, node_degrees))) if count_junctions >= self._num_junctions: break new_nodes = filter(lambda x: x not in visited + to_visit, new_nodes) to_visit.extend(new_nodes) return subgraph def draw_subgraph(self, ax=None): if ax is None: _ = plt.figure(figsize=(3, 3)) ax = plt.gca() node_ids = list(self._subgraph.nodes()) pos = { x: (self._node_attrs[x]['lat'], self._node_attrs[x]['lng']) for x in node_ids } labels = {} nc = 'pink' ec = 'black' ns = 50 nshape = 'o' # Draw the current subgraph nx.draw(self._subgraph, pos=pos, node_color=nc, with_labels=False, node_size=ns, labels=labels, edgecolors=ec, node_shape=nshape, ax=ax) max_xy = np.array([np.array(x) for x in pos.values()]).max(0) min_xy = np.array([np.array(x) for x in pos.values()]).min(0) delta_xy = (max_xy - min_xy) / 6. ax.set_xlim([min_xy[0] - delta_xy[0], max_xy[0] + delta_xy[0]]) ax.set_ylim([min_xy[1] - delta_xy[1], max_xy[1] + delta_xy[1]]) # Draw goal position and orientation x = self._node_attrs[self._goal[0]]['lat'] y = self._node_attrs[self._goal[0]]['lng'] rotation = rad_to_deg(self._calculate_bearing(*self._goal)) _ = ax.plot(x, y, marker=(3, 0, rotation - 90), color=(0, 0, 0), markersize=14, markerfacecolor='white') _ = ax.plot(x, y, marker=(2, 0, rotation - 90), color=(0, 0, 0), markersize=12, markerfacecolor='None') # Draw current position and orientation x = self._node_attrs[self._position]['lat'] y = self._node_attrs[self._position]['lng'] rotation = rad_to_deg(self._neighbour['orientation']) _ = ax.plot(x, y, marker=(3, 0, rotation - 90), color=(0, 0, 0), markersize=14, markerfacecolor='lightgreen') _ = ax.plot(x, y, marker=(2, 0, rotation - 90), color=(0, 0, 0), markersize=12, markerfacecolor='None') ax.set_title('{}\nEpisode reward = {}'.format( self._previous_action, self._episode_reward)) return plt.gcf(), ax ================================================ FILE: rapid_task_solving/requirements.txt ================================================ dm-env>=1.2 dm-haiku>=0.0.3 jax>=0.2.8 matplotlib>=3.1.2 networkx>=2.3 numpy>=1.18.0 ================================================ FILE: regal/README.md ================================================ # REGAL This dataset contains dataflow computational graphs generated procedurally, intended for training and evaluating algorithms that optimize execution (e.g. placement and scheduling), in [TensorFlow's CostGraphDef](https://github.com/tensorflow/tensorflow/blob/59ee7f9138482d85cd93c004aca961bea35820c7/tensorflow/core/framework/cost_graph.proto#L12) [protocol buffer](https://en.wikipedia.org/wiki/Protocol_Buffers) format and encoded as [text](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.text_format). Original paper [REGAL: Transfer Learning For Fast Optimization of Computation Graphs](https://arxiv.org/abs/1905.02494) (Paliwal, Gimeno, Nair, Li, Lubin, Kohli, Vinyals) ## Folder structure There are 10000 training graphs, 1000 validation graphs and 1000 test graphs. The file names follow the format of "graph_" plus a hash of the graph topology plus ".pbtxt". ## Filtering For each set (train, valid, test) there are not two graphs with the same topology. We used the Biased Random Key Genetic Algorithm (BRKGA) to filter out graphs that did not have "room for improvement" "Room for improvement" was defined as the union of two conditions: * if BRKGA with a low fitness evaluation limit (number of calls to fitness function) did not fit the hardware constraints and BRKGA with a high number did. * if BRKGA with a high fitness evaluation limit was 20% faster in running time that BRKGA with a low number. ## Example Graph ```protobuf node { name: "_SOURCE" } node { name: "node_0" id: 1 control_input: 0 } node { name: "node_1" id: 2 output_info { size: 70 alias_input_port: -1 } control_input: 0 compute_cost: 58 } node { name: "node_2" id: 3 output_info { size: 52 alias_input_port: -1 } control_input: 0 compute_cost: 47 } node { name: "node_3" id: 4 input_info { preceding_node: 2 } output_info { size: 55 alias_input_port: -1 } control_input: 0 compute_cost: 58 } ``` ## Dataset Location The dataset is available in the following [link](https://storage.googleapis.com/synthetic-graphs-dataset/synthetic-graphs.tar.gz) ## Dataset Metadata The following table is necessary for this dataset to be indexed by search engines such as Google Dataset Search.
| property | value | ||||||
|---|---|---|---|---|---|---|---|
| name | REGAL CostGraphDef Synthetic Dataset |
||||||
| url | https://github.com/deepmind/deepmind-research/tree/master/regal |
||||||
| sameAs | https://github.com/deepmind/deepmind-research/tree/master/regal |
||||||
| description |
This dataset contains dataflow computational graphs generated
procedurally, intended for training and evaluating algorithms that
optimize execution (e.g. placement and scheduling), in
[TensorFlow's CostGraphDef](https://github.com/tensorflow/tensorflow/blob/59ee7f9138482d85cd93c004aca961bea35820c7/tensorflow/core/framework/cost_graph.proto#L12)
[protocol buffer](https://en.wikipedia.org/wiki/Protocol_Buffers)
format and encoded as
[text](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.text_format).
|
||||||
| provider |
|
||||||
| citation | https://identifiers.org/arxiv:1905.02494 |
# RL Unplugged: Benchmarks for Offline Reinforcement Learning
RL Unplugged is suite of benchmarks for offline reinforcement learning. The RL
Unplugged is designed around the following considerations: to facilitate ease of
use, we provide the datasets with a unified API which makes it easy for the
practitioner to work with all data in the suite once a general pipeline has been
established. This is a dataset accompanying the paper
[RL Unplugged: Benchmarks for Offline Reinforcement Learning]([https://arxiv.org/abs/2006.13888]).
In this suite of benchmarks, we try to focus on the following problems:
- High dimensional action spaces, for example the locomotion humanoid domains,
we have 56 dimensional actions.
- High dimensional observations.
- Partial observability, observations have egocentric vision.
- Difficulty of exploration, using states of the art algorithms and imitation
to generate data for difficult environments.
- Real world challenges.
The data is available under
[RL Unplugged GCP bucket](https://console.cloud.google.com/storage/browser/rl_unplugged).
## Atari Dataset
We are releasing a large and diverse dataset of gameplay following the protocol
described by [Agarwal et al., 2020], which can be used to evaluate several
discrete offline RL algorithms. The dataset is generated by running an online
DQN agent and recording transitions from its replay during training with sticky
actions [Machado et al., 2018]. As stated in [Agarwal et al., 2020], for each
game we use data from five runs with 50 million transitions each. States in each
transition include stacks of four frames to be able to do frame-stacking with
our baselines. We release datasets for 46 Atari games. For details on how the
dataset was generated, please refer to the paper.
Atari is a standard RL benchmark. We recommend you to try offline RL methods
on Atari if you are interested in comparing your approach to other state of the
art offline RL methods with discrete actions.
## DeepMind Locomotion Dataset
These tasks are made up of the corridor locomotion tasks involving the CMU
Humanoid, for which prior efforts have either used motion capture data [Merel et
al., 2019a], [Merel et al., 2019b] or training from scratch [Song et al., 2020].
In addition, the DM Locomotion repository contains a set of tasks adapted to be
suited to a virtual rodent [Merel et al., 2020]. We emphasize that the DM
Locomotion tasks feature the combination of challenging high-DoF continuous
control along with perception from rich egocentric observations. For details on
how the dataset was generated, please refer to the paper.
We recommend you to try offline RL methods on DeepMind Locomotion dataset, if
you are interested in very challenging offline RL dataset with continuous
action space.
## DeepMind Control Suite Dataset
DeepMind Control Suite [Tassa et al., 2018] is a set of control tasks
implemented in MuJoCo [Todorov et al., 2012]. We consider a subset of the tasks
provided in the suite that cover a wide range of difficulties.
Most of the datasets in this domain are generated using D4PG. For the
environments Manipulator insert ball and Manipulator insert peg we use V-MPO
[Song et al., 2020] to generate the data as D4PG is unable to solve these tasks.
We release datasets for 9 control suite tasks. For details on how the dataset
was generated, please refer to the paper.
DeepMind Control Suite is a traditional continuous action RL benchmark. In
particular, we recommend you test your approach in DeepMind Control Suite if
you are interested in comparing against other state of the art offline RL
methods.
## Realworld RL Dataset
Examples in the dataset represent SARS transitions stored when running a
partially online trained agent as described in
[RWRL](https://arxiv.org/abs/2003.11881).
We release 8 datasets in total -- with no combined challenge and easy combined
challenge on the cartpole, walker, quadruped, and humanoid tasks. For details on
how the dataset was generated, please refer to the paper.
## DeepMind Lab Dataset
DeepMind Lab dataset has several levels from the challenging, partially
observable [Deepmind Lab suite](https://github.com/deepmind/lab). DeepMind Lab
dataset is collected by training distributed R2D2 by [Kapturowski et al., 2018]
agents from scratch on individual tasks. We recorded the experience across all
actors during entire training runs a few times for every task. The details of
the dataset generation process is described in [Gulcehre et al., 2021].
We release datasets for five different DeepMind Lab levels: `seekavoid_arena_01`,
`explore_rewards_few`, `explore_rewards_many`, `rooms_watermaze`,
`rooms_select_nonmatching_object`. We also release the snapshot datasets for
`seekavoid_arena_01` level that we generated the datasets from a trained R2D2
snapshot with different levels of epsilons for the epsilon-greedy algorithm
when evaluating the agent in the environment.
DeepMind Lab dataset is fairly large-scale. We recommend you to try it if you
are interested in large-scale offline RL models with memory.
## bsuite Dataset
[bsuite](https://github.com/deepmind/bsuite) data was collected by training DQN
agents with the default setting in [Acme](https://github.com/deepmind/acme) from
scratch in each one of the following three tasks: cartpole, catch, and
mountain_car.
We converted the originally deterministic environments into stochastic ones by
randomly replacing the agent action with a uniformly sampled action with a
probability of {0, 0.1, 0.2, 0.3, 0.4, 0.5}. In this case, probability of 0
corresponds to original environment. The details of
the dataset generation process is described in [Gulcehre et al., 2021].
bsuite datasets are fairly light-weight and running experiments doesn't require
too much compute. We recommend you to try bsuite, if you are interested in
small-scale and easy to run offline RL datasets generated by stochastic
environments where the stochasticity of the environment is easy to control.
## Running the code
### Installation
* Install dependencies: `pip install -r requirements.txt`
* (Optional) Setup MuJoCo license key for DM Control environments
([instructions](https://github.com/deepmind/dm_control#requirements-and-installation)).
* (Optional) Install
[realworldrl_suite](https://github.com/google-research/realworldrl_suite#installation).
### Atari example
```
mkdir -p /tmp/dataset/Asterix
gsutil cp gs://rl_unplugged/atari/Asterix/run_1-00000-of-00100 \
/tmp/dataset/Asterix/run_1-00000-of-00001
python atari_example.py --path=/tmp/dataset --game=Asterix
```
This copies a single shard from one of the Asterix datasets from GCP to a local
folder, and then runs a script that loads a single example and runs a step on
the Atari environment.
## Citation
Please use the following bibtex for citations:
```
@misc{gulcehre2020rl,
title={RL Unplugged: Benchmarks for Offline Reinforcement Learning},
author={Caglar Gulcehre and Ziyu Wang and Alexander Novikov and Tom Le Paine
and Sergio Gómez Colmenarejo and Konrad Zolna and Rishabh Agarwal and
Josh Merel and Daniel Mankowitz and Cosmin Paduraru and Gabriel
Dulac-Arnold and Jerry Li and Mohammad Norouzi and Matt Hoffman and
Ofir Nachum and George Tucker and Nicolas Heess and Nando deFreitas},
year={2020},
eprint={2006.13888},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
# Dataset Metadata
The following table is necessary for this dataset to be indexed by search
engines such as Google Dataset Search.
| property | value | ||||||
|---|---|---|---|---|---|---|---|
| name | RL Unplugged |
||||||
| url | https://github.com/deepmind/deepmind-research/tree/master/rl_unplugged |
||||||
| sameAs | https://github.com/deepmind/deepmind-research/tree/master/rl_unplugged |
||||||
| description |
Data accompanying
[RL Unplugged: Benchmarks for Offline Reinforcement Learning]().
|
||||||
| provider |
|
||||||
| citation | https://identifiers.org/arxiv:2006.13888 |
| property | value | ||||||
|---|---|---|---|---|---|---|---|
| name | Sketchy |
||||||
| url | https://github.com/deepmind/deepmind-research/tree/master/sketchy |
||||||
| sameAs | https://github.com/deepmind/deepmind-research/tree/master/sketchy |
||||||
| description |
Data accompanying
[Scaling data-driven robotics with reward sketching and batch reinforcement learning](https://arxiv.org/abs/1909.12200).
|
||||||
| provider |
|
||||||
| citation | https://identifiers.org/arxiv:1909.12200 |