[ { "path": ".gitignore", "content": "*.tar.gz\n\n# PyCharm\n.idea\n\n# Training Result\ncheckpoints\n\n# Dataset\nreplays_infos\nhigh_quality_replays\nparsed_replays\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# dotenv\n.env\n\n# virtualenv\n.venv\nvenv/\nENV/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n" }, { "path": "Baselines/BuildOrderPrediction/test.py", "content": "import os\nimport visdom\nimport pickle\nimport argparse\n\nimport numpy as np\n\nLAMBDA = 0.99\n\ndef calc_action_acc(action_pre, action_gt):\n return np.mean(action_pre == action_gt)\n\ndef calc_weighted_action_acc(action_pre, action_gt, weight):\n return np.sum((action_pre == action_gt) * np.abs(weight)) / np.sum(np.abs(weight))\n\ndef show_test_result(name, phrase, result, steps=10, title=''):\n action_pres, action_gts = result\n\n ################################## Calc Acc #########################################\n weights = [(action_gt[0]*2-1)*LAMBDA**np.arange(len(action_gt)-1, -1, -1) for action_gt in action_gts]\n\n action_pres_np = np.hstack(action_pres)\n action_gts_np = np.hstack(action_gts)\n weights_np = np.hstack(weights)\n\n action_acc = calc_action_acc(action_pres_np, action_gts_np)\n weighted_action_acc = calc_weighted_action_acc(action_pres_np, action_gts_np, weights_np)\n print('\\tAction Accuracy: {}%\\tWeighted Action Accuracy: {}%'.format(action_acc*100, weighted_action_acc * 100))\n ################################### Plot ###################################################\n vis = visdom.Visdom(env=name + '[{}]'.format(phrase))\n\n action_pre_result, action_gt_result = [[] for _ in range(steps)], [[] for _ in range(steps)]\n weight_result = [[] for _ in range(steps)]\n for action_pre, action_gt, weight in zip(action_pres, action_gts, weights):\n if len(action_pre) < steps:\n continue\n\n step = len(action_pre) // steps\n for s in range(steps):\n action_pre_result[s].append(action_pre[s * step:(s + 1) * step])\n action_gt_result[s].append(action_gt[s * step:(s + 1) * step])\n weight_result[s].append(weight[s * step:(s + 1) * step])\n\n legend = ['Action', 'Weighted Action']\n X = np.repeat(np.arange(steps), len(legend), axis=0).reshape(steps, -1)\n Y = np.zeros((steps, len(legend)))\n for idx, (action_pres, action_gts, weights) in enumerate(\n zip(action_pre_result, action_gt_result, weight_result)):\n\n action_pres_np = np.hstack(action_pres)\n action_gts_np = np.hstack(action_gts)\n weights_np = np.hstack(weights)\n\n Y[idx, 0] = calc_action_acc(action_pres_np, action_gts_np)\n Y[idx, 1] = calc_weighted_action_acc(action_pres_np, action_gts_np, weights_np)\n\n vis.line(X=X, Y=Y,\n opts=dict(title='Acc[{}]'.format(title), legend=legend), win=title)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='A2C : Starcraft II')\n parser.add_argument('--name', type=str, default='StarCraft II:TvT[BuildOrder]',\n help='Experiment name. All outputs will be stored in checkpoints/[name]/')\n parser.add_argument('--phrase', type=str, default='test',\n help='val|test (default: test)')\n args = parser.parse_args()\n\n test_path = os.path.join('checkpoints', args.name, args.phrase)\n test_results = sorted([int(os.path.basename(result).split('.')[0].split('_')[-1])\n for result in os.listdir(test_path)])\n\n for idx, name_id in enumerate(test_results):\n result_path = os.path.join(test_path, 'model_iter_{}.pth'.format(name_id))\n print('Processing {}/{} ...'.format(idx+1, len(test_results)))\n print('\\t'+result_path)\n\n with open(result_path, 'rb') as f:\n result = pickle.load(f)\n\n show_test_result(args.name, args.phrase, result, title=idx)" }, { "path": "Baselines/BuildOrderPrediction/train.py", "content": "from __future__ import print_function\n\nimport os\nimport json\nimport time\nimport pickle\nimport argparse\n\nimport visdom\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nfrom torch.autograd import Variable\n\nfrom Baselines.GlobalStateEvaluation.test import show_test_result\n\nfrom data_loader.BatchEnv import BatchGlobalFeatureEnv\n\nclass BuildOrderGRU(torch.nn.Module):\n def __init__(self, num_inputs, num_outputs):\n super(BuildOrderGRU, self).__init__()\n self.linear1 = nn.Linear(num_inputs, 1024)\n self.linear2 = nn.Linear(1024, 2048)\n\n self.rnn1 = nn.GRUCell(input_size=2048, hidden_size=2048)\n self.rnn2 = nn.GRUCell(input_size=2048, hidden_size=512)\n\n self.actor_linear = nn.Linear(512, num_outputs)\n\n self.h1, self.h2 = None, None\n\n def forward(self, states, require_init):\n batch = states.size(1)\n if self.h1 is None or self.h2 is None:\n self.h1 = Variable(states.data.new().resize_((batch, 2048)).zero_())\n self.h2 = Variable(states.data.new().resize_((batch, 512)).zero_())\n elif True in require_init:\n h1, h2 = self.h1.data, self.h2.data\n for idx, init in enumerate(require_init):\n if init:\n h1[idx].zero_()\n h2[idx].zero_()\n self.h1, self.h2 = Variable(h1), Variable(h2)\n else:\n pass\n\n values = []\n for idx, state in enumerate(states):\n x = F.relu(self.linear1(state))\n x = F.relu(self.linear2(x))\n self.h1 = self.rnn1(x, self.h1)\n self.h2 = self.rnn2(self.h1, self.h2)\n\n values.append(self.actor_linear(self.h2))\n\n return values\n\n def detach(self):\n if self.h1 is not None:\n self.h1.detach_()\n if self.h2 is not None:\n self.h2.detach_()\n\ndef train(model, env, args):\n #################################### PLOT ###################################################\n STEPS = 10\n LAMBDA = 0.99\n vis = visdom.Visdom(env=args.name+'[{}]'.format(args.phrase))\n pre_per_replay = [[] for _ in range(args.n_replays)]\n gt_per_replay = [[] for _ in range(args.n_replays)]\n acc = None\n win = vis.line(X=np.zeros(1), Y=np.zeros(1))\n loss_win = vis.line(X=np.zeros(1), Y=np.zeros(1))\n\n #################################### TRAIN ######################################################\n torch.manual_seed(args.seed)\n torch.cuda.manual_seed(args.seed)\n\n gpu_id = args.gpu_id\n with torch.cuda.device(gpu_id):\n model = model.cuda() if gpu_id >= 0 else model\n model.train()\n optimizer = optim.Adam(model.parameters(), lr=args.lr)\n\n epoch = 0\n save = args.save_intervel\n env_return = env.step(reward=False, action=True)\n if env_return is not None:\n (states, actions_gt), require_init = env_return\n with torch.cuda.device(gpu_id):\n states = torch.from_numpy(states).float()\n actions_gt = torch.from_numpy(actions_gt).long().squeeze()\n weight = torch.ones((env.n_actions,))\n weight[-1] = 0.05\n if gpu_id >= 0:\n states = states.cuda()\n actions_gt = actions_gt.cuda()\n weight = weight.cuda()\n\n while True:\n actions = model(Variable(states), require_init)\n\n action_loss = 0\n for action, action_gt in zip(actions, actions_gt):\n action_loss = action_loss + F.cross_entropy(action, Variable(action_gt), weight=weight)\n action_loss = action_loss / len(actions)\n\n model.zero_grad()\n action_loss.backward()\n optimizer.step()\n model.detach()\n\n if env.epoch > epoch:\n epoch = env.epoch\n for p in optimizer.param_groups:\n p['lr'] *= 0.1\n\n ############################ PLOT ##########################################\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray(action_loss.data.cpu().numpy()),\n win=loss_win,\n name='action')\n\n actions_np = np.swapaxes(np.asarray([np.argmax(action.data.cpu().numpy(), axis=1) for action in actions]), 0, 1)\n actions_gt_np = np.swapaxes(actions_gt.cpu().numpy(), 0, 1)\n\n for idx, (action, action_gt, init) in enumerate(zip(actions_np, actions_gt_np, require_init)):\n if init and len(pre_per_replay[idx]) > 0:\n pre_per_replay[idx] = np.asarray(pre_per_replay[idx], dtype=np.uint8)\n gt_per_replay[idx] = np.asarray(gt_per_replay[idx], dtype=np.uint8)\n\n step = len(pre_per_replay[idx]) // STEPS\n if step > 0:\n acc_tmp = []\n for s in range(STEPS):\n action_pre = pre_per_replay[idx][s*step:(s+1)*step]\n action_gt = gt_per_replay[idx][s*step:(s+1)*step]\n acc_tmp.append(np.mean(action_pre == action_gt))\n\n acc_tmp = np.asarray(acc_tmp)\n if acc is None:\n acc = acc_tmp\n else:\n acc = LAMBDA * acc + (1-LAMBDA) * acc_tmp\n\n if acc is None:\n continue\n for s in range(STEPS):\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray([acc[s]]),\n win=win,\n name='{}[{}%~{}%]'.format('action', s*10, (s+1)*10))\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray([np.mean(acc)]),\n win=win,\n name='action[TOTAL]')\n\n pre_per_replay[idx] = []\n gt_per_replay[idx] = []\n\n pre_per_replay[idx].append(action[-1])\n gt_per_replay[idx].append(action_gt[-1])\n\n ####################### NEXT BATCH ###################################\n env_return = env.step(reward=False, action=True)\n if env_return is not None:\n (raw_states, raw_actions_gt), require_init = env_return\n states = states.copy_(torch.from_numpy(raw_states).float())\n actions_gt = actions_gt.copy_(torch.from_numpy(raw_actions_gt).long().squeeze())\n\n if env.step_count() > save or env_return is None:\n save = env.step_count()+args.save_intervel\n torch.save(model.state_dict(),\n os.path.join(args.model_path, 'model_iter_{}.pth'.format(env.step_count())))\n torch.save(model.state_dict(), os.path.join(args.model_path, 'model_latest.pth'))\n if env_return is None:\n env.close()\n break\n\ndef test(model, env, args):\n ######################### SAVE RESULT ############################\n action_pre_per_replay = [[]]\n action_gt_per_replay = [[]]\n\n ######################### TEST ###################################\n torch.manual_seed(args.seed)\n torch.cuda.manual_seed(args.seed)\n\n gpu_id = args.gpu_id\n with torch.cuda.device(gpu_id):\n model = model.cuda() if gpu_id >= 0 else model\n model.eval()\n\n env_return = env.step(reward=False, action=True)\n if env_return is not None:\n (states, actions_gt), require_init = env_return\n with torch.cuda.device(gpu_id):\n states = torch.from_numpy(states).float()\n actions_gt = torch.from_numpy(actions_gt).long().squeeze()\n if gpu_id >= 0:\n states = states.cuda()\n actions_gt = actions_gt.cuda()\n\n while True:\n actions = model(Variable(states), require_init)\n ############################ PLOT ##########################################\n actions_np = np.squeeze(np.vstack([np.argmax(action.data.cpu().numpy(), axis=1) for action in actions]))\n actions_gt_np = np.squeeze(actions_gt.cpu().numpy())\n\n if require_init[-1] and len(action_gt_per_replay[-1]) > 0:\n action_pre_per_replay[-1] = np.ravel(np.hstack(action_pre_per_replay[-1]))\n action_gt_per_replay[-1] = np.ravel(np.hstack(action_gt_per_replay[-1]))\n\n action_pre_per_replay.append([])\n action_gt_per_replay.append([])\n\n action_pre_per_replay[-1].append(actions_np)\n action_gt_per_replay[-1].append(actions_gt_np)\n ########################### NEXT BATCH #############################################\n env_return = env.step(reward=False, action=True)\n if env_return is not None:\n (raw_states, raw_actions), require_init = env_return\n states = states.copy_(torch.from_numpy(raw_states).float())\n actions_gt = actions_gt.copy_(torch.from_numpy(raw_actions).long().squeeze())\n else:\n action_pre_per_replay[-1] = np.ravel(np.hstack(action_pre_per_replay[-1]))\n action_gt_per_replay[-1] = np.ravel(np.hstack(action_gt_per_replay[-1]))\n\n env.close()\n break\n\n return action_pre_per_replay, action_gt_per_replay\n\ndef next_path(model_folder, paths):\n models = {int(os.path.basename(model).split('.')[0].split('_')[-1])\n for model in os.listdir(model_folder) if 'latest' not in model}\n models_not_process = models - paths\n if len(models_not_process) == 0:\n return None\n models_not_process = sorted(models_not_process)\n paths.add(models_not_process[0])\n\n return os.path.join(model_folder, 'model_iter_{}.pth'.format(models_not_process[0]))\n\ndef main():\n # Training settings\n parser = argparse.ArgumentParser(description='Global State Evaluation : StarCraft II')\n parser.add_argument('--name', type=str, default='StarCraft II:TvT[BuildOrder]',\n help='Experiment name. All outputs will be stored in checkpoints/[name]/')\n parser.add_argument('--replays_path', default='train_val_test/Terran_vs_Terran',\n help='Path for training, validation and test set (default: train_val_test/Terran_vs_Terran)')\n parser.add_argument('--race', default='Terran', help='Which race? (default: Terran)')\n parser.add_argument('--enemy_race', default='Terran', help='Which the enemy race? (default: Terran)')\n parser.add_argument('--phrase', type=str, default='train',\n help='train|val|test (default: train)')\n parser.add_argument('--gpu_id', default=0, type=int, help='Which GPU to use [-1 indicate CPU] (default: 0)')\n\n parser.add_argument('--lr', type=float, default=0.001, help='Learning rate (default: 0.001)')\n parser.add_argument('--seed', type=int, default=1, help='Random seed (default: 1)')\n\n parser.add_argument('--n_steps', type=int, default=20, help='# of forward steps (default: 20)')\n parser.add_argument('--n_replays', type=int, default=256, help='# of replays (default: 256)')\n parser.add_argument('--n_epoch', type=int, default=10, help='# of epoches (default: 10)')\n\n parser.add_argument('--save_intervel', type=int, default=1000000,\n help='Frequency of model saving (default: 1000000)')\n args = parser.parse_args()\n\n args.save_path = os.path.join('checkpoints', args.name)\n args.model_path = os.path.join(args.save_path, 'snapshots')\n\n print('------------ Options -------------')\n for k, v in sorted(vars(args).items()):\n print('{}: {}'.format(k, v))\n print('-------------- End ----------------')\n\n if args.phrase == 'train':\n if not os.path.isdir(args.save_path):\n os.makedirs(args.save_path)\n if not os.path.isdir(args.model_path):\n os.makedirs(args.model_path)\n with open(os.path.join(args.save_path, 'config'), 'w') as f:\n f.write(json.dumps(vars(args)))\n\n env = BatchGlobalFeatureEnv()\n env.init(os.path.join(args.replays_path, '{}.json'.format(args.phrase)),\n './', args.race, args.enemy_race, n_steps=args.n_steps, seed=args.seed,\n n_replays=args.n_replays, epochs=args.n_epoch)\n model = BuildOrderGRU(env.n_features, env.n_actions)\n train(model, env, args)\n elif 'val' in args.phrase or 'test' in args.phrase:\n test_result_path = os.path.join(args.save_path, args.phrase)\n if not os.path.isdir(test_result_path):\n os.makedirs(test_result_path)\n\n dataset_path = 'test.json' if 'test' in args.phrase else 'val.json'\n paths = set()\n while True:\n path = next_path(args.model_path, paths)\n if path is not None:\n print('[{}]Testing {} ...'.format(len(paths), path))\n\n env = BatchGlobalFeatureEnv()\n env.init(os.path.join(args.replays_path, dataset_path),\n './', args.race, args.enemy_race, n_steps=args.n_steps,\n seed=args.seed, n_replays=1, epochs=1)\n model = BuildOrderGRU(env.n_features, env.n_actions)\n model.load_state_dict(torch.load(path))\n result = test(model, env, args)\n with open(os.path.join(test_result_path, os.path.basename(path)), 'wb') as f:\n pickle.dump(result, f)\n show_test_result(args.name, args.phrase, result, title=len(paths)-1)\n else:\n time.sleep(60)\n\nif __name__ == '__main__':\n main()" }, { "path": "Baselines/BuildOrderPrediction/train_spatial.py", "content": "from __future__ import print_function\n\nimport os\nimport json\nimport time\nimport pickle\nimport argparse\n\nimport visdom\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nfrom torch.autograd import Variable\n\nfrom Baselines.GlobalStateEvaluation.test import show_test_result\n\nfrom data_loader.BatchEnv import BatchSpatialEnv\n\nclass BuildOrderGRU(torch.nn.Module):\n def __init__(self, n_channels, n_features, n_actions):\n super(BuildOrderGRU, self).__init__()\n\n self.conv1 = nn.Conv2d(n_channels, 16, 8, stride=4)\n self.conv2 = nn.Conv2d(16, 32, 4, stride=2)\n\n self.linear_g = nn.Linear(n_features, 128)\n\n self.linear = nn.Linear(1280, 512)\n self.rnn = nn.GRUCell(input_size=512, hidden_size=128)\n\n self.actor_linear = nn.Linear(128, n_actions)\n\n self.h = None\n\n def forward(self, states_S, states_G, require_init):\n batch = states_S.size(1)\n if self.h is None:\n self.h = Variable(states_S.data.new().resize_((batch, 128)).zero_())\n elif True in require_init:\n h= self.h.data\n for idx, init in enumerate(require_init):\n if init:\n h[idx].zero_()\n self.h = Variable(h)\n else:\n pass\n\n values = []\n for idx, (state_S, state_G) in enumerate(zip(states_S, states_G)):\n x_s = F.relu(self.conv1(state_S))\n x_s = F.relu(self.conv2(x_s))\n x_s = x_s.view(-1, 1152)\n\n x_g = F.relu(self.linear_g(state_G))\n\n x = torch.cat((x_s, x_g), 1)\n x = F.relu(self.linear(x))\n\n self.h = self.rnn(x, self.h)\n\n values.append(self.actor_linear(self.h))\n\n return values\n\n def detach(self):\n if self.h is not None:\n self.h.detach_()\n\ndef train(model, env, args):\n #################################### PLOT ###################################################\n STEPS = 10\n LAMBDA = 0.99\n vis = visdom.Visdom(env=args.name+'[{}]'.format(args.phrase))\n pre_per_replay = [[] for _ in range(args.n_replays)]\n gt_per_replay = [[] for _ in range(args.n_replays)]\n acc = None\n win = vis.line(X=np.zeros(1), Y=np.zeros(1))\n loss_win = vis.line(X=np.zeros(1), Y=np.zeros(1))\n\n #################################### TRAIN ######################################################\n torch.manual_seed(args.seed)\n torch.cuda.manual_seed(args.seed)\n\n gpu_id = args.gpu_id\n with torch.cuda.device(gpu_id):\n model = model.cuda() if gpu_id >= 0 else model\n model.train()\n optimizer = optim.Adam(model.parameters(), lr=args.lr)\n\n epoch = 0\n save = args.save_intervel\n env_return = env.step(reward=False, action=True)\n if env_return is not None:\n (states_S, states_G, actions_gt), require_init = env_return\n with torch.cuda.device(gpu_id):\n states_S = torch.from_numpy(states_S).float()\n states_G = torch.from_numpy(states_G).float()\n actions_gt = torch.from_numpy(actions_gt).long().squeeze()\n weight = torch.ones((env.n_actions,))\n weight[-1] = 0.05\n if gpu_id >= 0:\n states_S = states_S.cuda()\n states_G = states_G.cuda()\n actions_gt = actions_gt.cuda()\n weight = weight.cuda()\n\n while True:\n actions = model(Variable(states_S), Variable(states_G), require_init)\n action_loss = 0\n for action, action_gt in zip(actions, actions_gt):\n action_loss = action_loss + F.cross_entropy(action, Variable(action_gt), weight=weight)\n action_loss = action_loss / len(actions_gt)\n\n model.zero_grad()\n action_loss.backward()\n optimizer.step()\n model.detach()\n\n if env.epoch > epoch:\n epoch = env.epoch\n for p in optimizer.param_groups:\n p['lr'] *= 0.5\n\n ############################ PLOT ##########################################\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray(action_loss.data.cpu().numpy()),\n win=loss_win,\n name='action')\n\n actions_np = np.swapaxes(np.asarray([np.argmax(action.data.cpu().numpy(), axis=1) for action in actions]), 0, 1)\n actions_gt_np = np.swapaxes(actions_gt.cpu().numpy(), 0, 1)\n\n for idx, (action, action_gt, init) in enumerate(zip(actions_np, actions_gt_np, require_init)):\n if init and len(pre_per_replay[idx]) > 0:\n pre_per_replay[idx] = np.asarray(pre_per_replay[idx], dtype=np.uint8)\n gt_per_replay[idx] = np.asarray(gt_per_replay[idx], dtype=np.uint8)\n\n step = len(pre_per_replay[idx]) // STEPS\n if step > 0:\n acc_tmp = []\n for s in range(STEPS):\n action_pre = pre_per_replay[idx][s*step:(s+1)*step]\n action_gt = gt_per_replay[idx][s*step:(s+1)*step]\n acc_tmp.append(np.mean(action_pre == action_gt))\n\n acc_tmp = np.asarray(acc_tmp)\n if acc is None:\n acc = acc_tmp\n else:\n acc = LAMBDA * acc + (1-LAMBDA) * acc_tmp\n\n if acc is None:\n continue\n for s in range(STEPS):\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray([acc[s]]),\n win=win,\n name='{}[{}%~{}%]'.format('action', s*10, (s+1)*10))\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray([np.mean(acc)]),\n win=win,\n name='action[TOTAL]')\n\n pre_per_replay[idx] = []\n gt_per_replay[idx] = []\n\n pre_per_replay[idx].append(action[-1])\n gt_per_replay[idx].append(action_gt[-1])\n\n ####################### NEXT BATCH ###################################\n env_return = env.step(reward=False, action=True)\n if env_return is not None:\n (raw_states_S, raw_states_G, raw_rewards), require_init = env_return\n states_S = states_S.copy_(torch.from_numpy(raw_states_S).float())\n states_G = states_G.copy_(torch.from_numpy(raw_states_G).float())\n actions_gt = actions_gt.copy_(torch.from_numpy(raw_rewards).long().squeeze())\n\n if env.step_count() > save or env_return is None:\n save = env.step_count()+args.save_intervel\n torch.save(model.state_dict(),\n os.path.join(args.model_path, 'model_iter_{}.pth'.format(env.step_count())))\n torch.save(model.state_dict(), os.path.join(args.model_path, 'model_latest.pth'))\n if env_return is None:\n env.close()\n break\n\ndef test(model, env, args):\n ######################### SAVE RESULT ############################\n action_pre_per_replay = [[]]\n action_gt_per_replay = [[]]\n\n ######################### TEST ###################################\n torch.manual_seed(args.seed)\n torch.cuda.manual_seed(args.seed)\n\n gpu_id = args.gpu_id\n with torch.cuda.device(gpu_id):\n model = model.cuda() if gpu_id >= 0 else model\n model.eval()\n\n env_return = env.step(reward=False, action=True)\n if env_return is not None:\n (states_S, states_G, actions_gt), require_init = env_return\n with torch.cuda.device(gpu_id):\n states_S = torch.from_numpy(states_S).float()\n states_G = torch.from_numpy(states_G).float()\n actions_gt = torch.from_numpy(actions_gt).float()\n if gpu_id >= 0:\n states_S = states_S.cuda()\n states_G = states_G.cuda()\n actions_gt = actions_gt.cuda()\n\n while True:\n actions = model(Variable(states_S), Variable(states_G), require_init)\n ############################ PLOT ##########################################\n actions_np = np.squeeze(np.vstack([np.argmax(action.data.cpu().numpy(), axis=1) for action in actions]))\n actions_gt_np = np.squeeze(actions_gt.cpu().numpy())\n\n if require_init[-1] and len(action_gt_per_replay[-1]) > 0:\n action_pre_per_replay[-1] = np.ravel(np.hstack(action_pre_per_replay[-1]))\n action_gt_per_replay[-1] = np.ravel(np.hstack(action_gt_per_replay[-1]))\n\n action_pre_per_replay.append([])\n action_gt_per_replay.append([])\n\n action_pre_per_replay[-1].append(actions_np)\n action_gt_per_replay[-1].append(actions_gt_np)\n ########################### NEXT BATCH #############################################\n env_return = env.step(reward=False, action=True)\n if env_return is not None:\n (raw_states_S, raw_states_G, raw_actions), require_init = env_return\n states_S = states_S.copy_(torch.from_numpy(raw_states_S).float())\n states_G = states_G.copy_(torch.from_numpy(raw_states_G).float())\n actions_gt = actions_gt.copy_(torch.from_numpy(raw_actions).float())\n else:\n action_pre_per_replay[-1] = np.ravel(np.hstack(action_pre_per_replay[-1]))\n action_gt_per_replay[-1] = np.ravel(np.hstack(action_gt_per_replay[-1]))\n\n env.close()\n break\n\n return action_pre_per_replay, action_gt_per_replay\n\ndef next_path(model_folder, paths):\n models = {int(os.path.basename(model).split('.')[0].split('_')[-1])\n for model in os.listdir(model_folder) if 'latest' not in model}\n models_not_process = models - paths\n if len(models_not_process) == 0:\n return None\n models_not_process = sorted(models_not_process)\n paths.add(models_not_process[0])\n\n return os.path.join(model_folder, 'model_iter_{}.pth'.format(models_not_process[0]))\n\ndef main():\n # Training settings\n parser = argparse.ArgumentParser(description='Global State Evaluation : StarCraft II')\n parser.add_argument('--name', type=str, default='StarCraft II:TvT[BuildOrder:Spatial]',\n help='Experiment name. All outputs will be stored in checkpoints/[name]/')\n parser.add_argument('--replays_path', default='train_val_test/Terran_vs_Terran',\n help='Path for training, validation and test set (default: train_val_test/Terran_vs_Terran)')\n parser.add_argument('--race', default='Terran', help='Which race? (default: Terran)')\n parser.add_argument('--enemy_race', default='Terran', help='Which the enemy race? (default: Terran)')\n parser.add_argument('--phrase', type=str, default='train',\n help='train|val|test (default: train)')\n parser.add_argument('--gpu_id', default=0, type=int, help='Which GPU to use [-1 indicate CPU] (default: 0)')\n\n parser.add_argument('--lr', type=float, default=0.001, help='Learning rate (default: 0.001)')\n parser.add_argument('--seed', type=int, default=1, help='Random seed (default: 1)')\n\n parser.add_argument('--n_steps', type=int, default=20, help='# of forward steps (default: 20)')\n parser.add_argument('--n_replays', type=int, default=32, help='# of replays (default: 32)')\n parser.add_argument('--n_epoch', type=int, default=10, help='# of epoches (default: 10)')\n\n parser.add_argument('--save_intervel', type=int, default=1000000,\n help='Frequency of model saving (default: 1000000)')\n args = parser.parse_args()\n\n args.save_path = os.path.join('checkpoints', args.name)\n args.model_path = os.path.join(args.save_path, 'snapshots')\n\n print('------------ Options -------------')\n for k, v in sorted(vars(args).items()):\n print('{}: {}'.format(k, v))\n print('-------------- End ----------------')\n\n if args.phrase == 'train':\n if not os.path.isdir(args.save_path):\n os.makedirs(args.save_path)\n if not os.path.isdir(args.model_path):\n os.makedirs(args.model_path)\n with open(os.path.join(args.save_path, 'config'), 'w') as f:\n f.write(json.dumps(vars(args)))\n\n env = BatchSpatialEnv()\n env.init(os.path.join(args.replays_path, '{}.json'.format(args.phrase)),\n './', args.race, args.enemy_race, n_steps=args.n_steps, seed=args.seed,\n n_replays=args.n_replays, epochs=args.n_epoch)\n model = BuildOrderGRU(env.n_channels, env.n_features, env.n_actions)\n train(model, env, args)\n elif 'val' in args.phrase or 'test' in args.phrase:\n test_result_path = os.path.join(args.save_path, args.phrase)\n if not os.path.isdir(test_result_path):\n os.makedirs(test_result_path)\n\n dataset_path = 'test.json' if 'test' in args.phrase else 'val.json'\n paths = set()\n while True:\n path = next_path(args.model_path, paths)\n if path is not None:\n print('[{}]Testing {} ...'.format(len(paths), path))\n\n env = BatchSpatialEnv()\n env.init(os.path.join(args.replays_path, dataset_path),\n './', args.race, args.enemy_race, n_steps=args.n_steps,\n seed=args.seed, n_replays=1, epochs=1)\n model = BuildOrderGRU(env.n_channels, env.n_features, env.n_actions)\n model.load_state_dict(torch.load(path))\n result = test(model, env, args)\n with open(os.path.join(test_result_path, os.path.basename(path)), 'wb') as f:\n pickle.dump(result, f)\n show_test_result(args.name, args.phrase, result, title=len(paths)-1)\n else:\n time.sleep(60)\n\nif __name__ == '__main__':\n main()\n" }, { "path": "Baselines/GlobalStateEvaluation/requirements.txt", "content": "numpy == 1.13.0\n\nvisdom == 0.1.4\n\ntorch == 0.1.12.post2" }, { "path": "Baselines/GlobalStateEvaluation/test.py", "content": "import os\nimport visdom\nimport pickle\nimport argparse\n\nimport numpy as np\n\nLAMBDA = 0.99\n\ndef calc_value_acc(value_pre, value_gt):\n return np.mean(value_pre == value_gt)\n\ndef calc_weighted_value_acc(value_pre, value_gt, weight):\n return np.sum((value_pre == value_gt) * np.abs(weight)) / np.sum(np.abs(weight))\n\ndef show_test_result(name, phrase, result, steps=10, title=''):\n value_pres, value_gts = result\n\n ################################## Calc Acc #########################################\n weights = [(value_gt[0]*2-1)*LAMBDA**np.arange(len(value_gt)-1, -1, -1) for value_gt in value_gts]\n\n value_pres_np = np.hstack(value_pres)\n value_gts_np = np.hstack(value_gts)\n weights_np = np.hstack(weights)\n\n value_acc = calc_value_acc(value_pres_np, value_gts_np)\n weighted_value_acc = calc_weighted_value_acc(value_pres_np, value_gts_np, weights_np)\n print('\\tValue Accuracy: {}%\\tWeighted Value Accuracy: {}%'.format(value_acc*100, weighted_value_acc * 100))\n ################################### Plot ###################################################\n vis = visdom.Visdom(env=name + '[{}]'.format(phrase))\n\n value_pre_result, value_gt_result = [[] for _ in range(steps)], [[] for _ in range(steps)]\n weight_result = [[] for _ in range(steps)]\n for value_pre, value_gt, weight in zip(value_pres, value_gts, weights):\n if len(value_pre) < steps:\n continue\n\n step = len(value_pre) // steps\n for s in range(steps):\n value_pre_result[s].append(value_pre[s * step:(s + 1) * step])\n value_gt_result[s].append(value_gt[s * step:(s + 1) * step])\n weight_result[s].append(weight[s * step:(s + 1) * step])\n\n legend = ['Value', 'Weighted Value']\n X = np.repeat(np.arange(steps), len(legend), axis=0).reshape(steps, -1)\n Y = np.zeros((steps, len(legend)))\n for idx, (value_pres, value_gts, weights) in enumerate(\n zip(value_pre_result, value_gt_result, weight_result)):\n\n value_pres_np = np.hstack(value_pres)\n value_gts_np = np.hstack(value_gts)\n weights_np = np.hstack(weights)\n\n Y[idx, 0] = calc_value_acc(value_pres_np, value_gts_np)\n Y[idx, 1] = calc_weighted_value_acc(value_pres_np, value_gts_np, weights_np)\n\n vis.line(X=X, Y=Y,\n opts=dict(title='Acc[{}]'.format(title), legend=legend), win=title)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='A2C : Starcraft II')\n parser.add_argument('--name', type=str, default='StarCraft II:TvT',\n help='Experiment name. All outputs will be stored in checkpoints/[name]/')\n parser.add_argument('--phrase', type=str, default='test',\n help='val|test (default: test)')\n args = parser.parse_args()\n\n test_path = os.path.join('checkpoints', args.name, args.phrase)\n test_results = sorted([int(os.path.basename(result).split('.')[0].split('_')[-1])\n for result in os.listdir(test_path)])\n\n for idx, name_id in enumerate(test_results):\n result_path = os.path.join(test_path, 'model_iter_{}.pth'.format(name_id))\n print('Processing {}/{} ...'.format(idx+1, len(test_results)))\n print('\\t'+result_path)\n\n with open(result_path, 'rb') as f:\n result = pickle.load(f)\n\n show_test_result(args.name, args.phrase, result, title=idx)" }, { "path": "Baselines/GlobalStateEvaluation/train.py", "content": "from __future__ import print_function\n\nimport os\nimport json\nimport time\nimport pickle\nimport argparse\n\nimport visdom\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nfrom torch.autograd import Variable\n\nfrom Baselines.GlobalStateEvaluation.test import show_test_result\n\nfrom data_loader.BatchEnv import BatchGlobalFeatureEnv\n\nclass StateEvaluationGRU(torch.nn.Module):\n def __init__(self, num_inputs):\n super(StateEvaluationGRU, self).__init__()\n self.linear1 = nn.Linear(num_inputs, 1024)\n self.linear2 = nn.Linear(1024, 2048)\n\n self.rnn1 = nn.GRUCell(input_size=2048, hidden_size=2048)\n self.rnn2 = nn.GRUCell(input_size=2048, hidden_size=512)\n\n self.critic_linear = nn.Linear(512, 1)\n\n self.h1, self.h2 = None, None\n\n def forward(self, states, require_init):\n batch = states.size(1)\n if self.h1 is None or self.h2 is None:\n self.h1 = Variable(states.data.new().resize_((batch, 2048)).zero_())\n self.h2 = Variable(states.data.new().resize_((batch, 512)).zero_())\n elif True in require_init:\n h1, h2 = self.h1.data, self.h2.data\n for idx, init in enumerate(require_init):\n if init:\n h1[idx].zero_()\n h2[idx].zero_()\n self.h1, self.h2 = Variable(h1), Variable(h2)\n else:\n pass\n\n values = []\n for idx, state in enumerate(states):\n x = F.relu(self.linear1(state))\n x = F.relu(self.linear2(x))\n self.h1 = self.rnn1(x, self.h1)\n self.h2 = self.rnn2(self.h1, self.h2)\n\n values.append(F.sigmoid(self.critic_linear(self.h2)))\n\n return values\n\n def detach(self):\n if self.h1 is not None:\n self.h1.detach_()\n if self.h2 is not None:\n self.h2.detach_()\n\ndef train(model, env, args):\n #################################### PLOT ###################################################\n STEPS = 10\n LAMBDA = 0.99\n vis = visdom.Visdom(env=args.name+'[{}]'.format(args.phrase))\n pre_per_replay = [[] for _ in range(args.n_replays)]\n gt_per_replay = [[] for _ in range(args.n_replays)]\n acc = None\n win = vis.line(X=np.zeros(1), Y=np.zeros(1))\n loss_win = vis.line(X=np.zeros(1), Y=np.zeros(1))\n\n #################################### TRAIN ######################################################\n torch.manual_seed(args.seed)\n torch.cuda.manual_seed(args.seed)\n\n gpu_id = args.gpu_id\n with torch.cuda.device(gpu_id):\n model = model.cuda() if gpu_id >= 0 else model\n model.train()\n optimizer = optim.Adam(model.parameters(), lr=args.lr)\n\n epoch = 0\n save = args.save_intervel\n env_return = env.step()\n if env_return is not None:\n (states, rewards), require_init = env_return\n with torch.cuda.device(gpu_id):\n states = torch.from_numpy(states).float()\n rewards = torch.from_numpy(rewards).float()\n if gpu_id >= 0:\n states = states.cuda()\n rewards = rewards.cuda()\n\n while True:\n values = model(Variable(states), require_init)\n\n value_loss = 0\n for value, reward in zip(values, rewards):\n value_loss = value_loss + F.binary_cross_entropy(value, Variable(reward))\n\n model.zero_grad()\n value_loss.backward()\n optimizer.step()\n model.detach()\n\n if env.epoch > epoch:\n epoch = env.epoch\n for p in optimizer.param_groups:\n p['lr'] *= 0.1\n\n ############################ PLOT ##########################################\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray(value_loss.data.cpu().numpy()),\n win=loss_win,\n name='value')\n\n values_np = np.swapaxes(np.asarray([value.data.cpu().numpy() for value in values]), 0, 1)\n rewards_np = np.swapaxes(rewards.cpu().numpy(), 0, 1)\n\n for idx, (value, reward, init) in enumerate(zip(values_np, rewards_np, require_init)):\n if init and len(pre_per_replay[idx]) > 0:\n pre_per_replay[idx] = np.asarray(pre_per_replay[idx], dtype=np.uint8)\n gt_per_replay[idx] = np.asarray(gt_per_replay[idx], dtype=np.uint8)\n\n step = len(pre_per_replay[idx]) // STEPS\n if step > 0:\n acc_tmp = []\n for s in range(STEPS):\n value_pre = pre_per_replay[idx][s*step:(s+1)*step]\n value_gt = gt_per_replay[idx][s*step:(s+1)*step]\n acc_tmp.append(np.mean(value_pre == value_gt))\n\n acc_tmp = np.asarray(acc_tmp)\n if acc is None:\n acc = acc_tmp\n else:\n acc = LAMBDA * acc + (1-LAMBDA) * acc_tmp\n\n if acc is None:\n continue\n for s in range(STEPS):\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray([acc[s]]),\n win=win,\n name='{}[{}%~{}%]'.format('value', s*10, (s+1)*10))\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray([np.mean(acc)]),\n win=win,\n name='value[TOTAL]')\n\n pre_per_replay[idx] = []\n gt_per_replay[idx] = []\n\n pre_per_replay[idx].append(int(value[-1] >= 0.5))\n gt_per_replay[idx].append(int(reward[-1]))\n\n ####################### NEXT BATCH ###################################\n env_return = env.step()\n if env_return is not None:\n (raw_states, raw_rewards), require_init = env_return\n states = states.copy_(torch.from_numpy(raw_states).float())\n rewards = rewards.copy_(torch.from_numpy(raw_rewards).float())\n\n if env.step_count() > save or env_return is None:\n save = env.step_count()+args.save_intervel\n torch.save(model.state_dict(),\n os.path.join(args.model_path, 'model_iter_{}.pth'.format(env.step_count())))\n torch.save(model.state_dict(), os.path.join(args.model_path, 'model_latest.pth'))\n if env_return is None:\n env.close()\n break\n\ndef test(model, env, args):\n ######################### SAVE RESULT ############################\n value_pre_per_replay = [[]]\n value_gt_per_replay = [[]]\n\n ######################### TEST ###################################\n torch.manual_seed(args.seed)\n torch.cuda.manual_seed(args.seed)\n\n gpu_id = args.gpu_id\n with torch.cuda.device(gpu_id):\n model = model.cuda() if gpu_id >= 0 else model\n model.eval()\n\n env_return = env.step()\n if env_return is not None:\n (states, rewards), require_init = env_return\n with torch.cuda.device(gpu_id):\n states = torch.from_numpy(states).float()\n rewards = torch.from_numpy(rewards).float()\n if gpu_id >= 0:\n states = states.cuda()\n rewards = rewards.cuda()\n\n while True:\n values = model(Variable(states), require_init)\n ############################ PLOT ##########################################\n values_np = np.squeeze(np.vstack([value.data.cpu().numpy() for value in values]))\n rewards_np = np.squeeze(rewards.cpu().numpy())\n\n if require_init[-1] and len(value_gt_per_replay[-1]) > 0:\n value_pre_per_replay[-1] = np.ravel(np.hstack(value_pre_per_replay[-1]))\n value_gt_per_replay[-1] = np.ravel(np.hstack(value_gt_per_replay[-1]))\n\n value_pre_per_replay.append([])\n value_gt_per_replay.append([])\n\n value_pre_per_replay[-1].append(values_np>=0.5)\n value_gt_per_replay[-1].append(rewards_np)\n\n ########################### NEXT BATCH #############################################\n env_return = env.step()\n if env_return is not None:\n (raw_states, raw_rewards), require_init = env_return\n states = states.copy_(torch.from_numpy(raw_states).float())\n rewards = rewards.copy_(torch.from_numpy(raw_rewards).float())\n else:\n value_pre_per_replay[-1] = np.ravel(np.hstack(value_pre_per_replay[-1]))\n value_gt_per_replay[-1] = np.ravel(np.hstack(value_gt_per_replay[-1]))\n\n env.close()\n break\n\n return value_pre_per_replay, value_gt_per_replay\n\ndef next_path(model_folder, paths):\n models = {int(os.path.basename(model).split('.')[0].split('_')[-1])\n for model in os.listdir(model_folder) if 'latest' not in model}\n models_not_process = models - paths\n if len(models_not_process) == 0:\n return None\n models_not_process = sorted(models_not_process)\n paths.add(models_not_process[0])\n\n return os.path.join(model_folder, 'model_iter_{}.pth'.format(models_not_process[0]))\n\ndef main():\n # Training settings\n parser = argparse.ArgumentParser(description='Global State Evaluation : StarCraft II')\n parser.add_argument('--name', type=str, default='StarCraft II:TvT',\n help='Experiment name. All outputs will be stored in checkpoints/[name]/')\n parser.add_argument('--replays_path', default='train_val_test/Terran_vs_Terran',\n help='Path for training, validation and test set (default: train_val_test/Terran_vs_Terran)')\n parser.add_argument('--race', default='Terran', help='Which race? (default: Terran)')\n parser.add_argument('--enemy_race', default='Terran', help='Which the enemy race? (default: Terran)')\n parser.add_argument('--phrase', type=str, default='train',\n help='train|val|test (default: train)')\n parser.add_argument('--gpu_id', default=0, type=int, help='Which GPU to use [-1 indicate CPU] (default: 0)')\n\n parser.add_argument('--lr', type=float, default=0.001, help='Learning rate (default: 0.001)')\n parser.add_argument('--seed', type=int, default=1, help='Random seed (default: 1)')\n\n parser.add_argument('--n_steps', type=int, default=20, help='# of forward steps (default: 20)')\n parser.add_argument('--n_replays', type=int, default=256, help='# of replays (default: 256)')\n parser.add_argument('--n_epoch', type=int, default=10, help='# of epoches (default: 10)')\n\n parser.add_argument('--save_intervel', type=int, default=1000000,\n help='Frequency of model saving (default: 1000000)')\n args = parser.parse_args()\n\n args.save_path = os.path.join('checkpoints', args.name)\n args.model_path = os.path.join(args.save_path, 'snapshots')\n\n print('------------ Options -------------')\n for k, v in sorted(vars(args).items()):\n print('{}: {}'.format(k, v))\n print('-------------- End ----------------')\n\n if args.phrase == 'train':\n if not os.path.isdir(args.save_path):\n os.makedirs(args.save_path)\n if not os.path.isdir(args.model_path):\n os.makedirs(args.model_path)\n with open(os.path.join(args.save_path, 'config'), 'w') as f:\n f.write(json.dumps(vars(args)))\n\n env = BatchGlobalFeatureEnv()\n env.init(os.path.join(args.replays_path, '{}.json'.format(args.phrase)),\n './', args.race, args.enemy_race, n_steps=args.n_steps, seed=args.seed,\n n_replays=args.n_replays, epochs=args.n_epoch)\n model = StateEvaluationGRU(env.n_features)\n train(model, env, args)\n elif 'val' in args.phrase or 'test' in args.phrase:\n test_result_path = os.path.join(args.save_path, args.phrase)\n if not os.path.isdir(test_result_path):\n os.makedirs(test_result_path)\n\n dataset_path = 'test.json' if 'test' in args.phrase else 'val.json'\n paths = set()\n while True:\n path = next_path(args.model_path, paths)\n if path is not None:\n print('[{}]Testing {} ...'.format(len(paths), path))\n\n env = BatchGlobalFeatureEnv()\n env.init(os.path.join(args.replays_path, dataset_path),\n './', args.race, args.enemy_race, n_steps=args.n_steps,\n seed=args.seed, n_replays=1, epochs=1)\n model = StateEvaluationGRU(env.n_features)\n model.load_state_dict(torch.load(path))\n result = test(model, env, args)\n with open(os.path.join(test_result_path, os.path.basename(path)), 'wb') as f:\n pickle.dump(result, f)\n show_test_result(args.name, args.phrase, result, title=len(paths)-1)\n else:\n time.sleep(60)\n\nif __name__ == '__main__':\n main()" }, { "path": "Baselines/GlobalStateEvaluation/train_spatial.py", "content": "from __future__ import print_function\n\nimport os\nimport json\nimport time\nimport pickle\nimport argparse\n\nimport visdom\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nfrom torch.autograd import Variable\n\nfrom Baselines.GlobalStateEvaluation.test import show_test_result\n\nfrom data_loader.BatchEnv import BatchSpatialEnv\n\nclass StateEvaluationGRU(torch.nn.Module):\n def __init__(self, n_channels, n_features):\n super(StateEvaluationGRU, self).__init__()\n\n self.conv1 = nn.Conv2d(n_channels, 16, 8, stride=4)\n self.conv2 = nn.Conv2d(16, 32, 4, stride=2)\n\n self.linear_g = nn.Linear(n_features, 128)\n\n self.linear = nn.Linear(1280, 512)\n self.rnn = nn.GRUCell(input_size=512, hidden_size=128)\n\n self.critic_linear = nn.Linear(128, 1)\n\n self.h = None\n\n def forward(self, states_S, states_G, require_init):\n batch = states_S.size(1)\n if self.h is None:\n self.h = Variable(states_S.data.new().resize_((batch, 128)).zero_())\n elif True in require_init:\n h= self.h.data\n for idx, init in enumerate(require_init):\n if init:\n h[idx].zero_()\n self.h = Variable(h)\n else:\n pass\n\n values = []\n for idx, (state_S, state_G) in enumerate(zip(states_S, states_G)):\n x_s = F.relu(self.conv1(state_S))\n x_s = F.relu(self.conv2(x_s))\n x_s = x_s.view(-1, 1152)\n\n x_g = F.relu(self.linear_g(state_G))\n\n x = torch.cat((x_s, x_g), 1)\n x = F.relu(self.linear(x))\n\n self.h = self.rnn(x, self.h)\n\n values.append(F.sigmoid(self.critic_linear(self.h)))\n\n return values\n\n def detach(self):\n if self.h is not None:\n self.h.detach_()\n\ndef train(model, env, args):\n #################################### PLOT ###################################################\n STEPS = 10\n LAMBDA = 0.99\n vis = visdom.Visdom(env=args.name+'[{}]'.format(args.phrase))\n pre_per_replay = [[] for _ in range(args.n_replays)]\n gt_per_replay = [[] for _ in range(args.n_replays)]\n acc = None\n win = vis.line(X=np.zeros(1), Y=np.zeros(1))\n loss_win = vis.line(X=np.zeros(1), Y=np.zeros(1))\n\n #################################### TRAIN ######################################################\n torch.manual_seed(args.seed)\n torch.cuda.manual_seed(args.seed)\n\n gpu_id = args.gpu_id\n with torch.cuda.device(gpu_id):\n model = model.cuda() if gpu_id >= 0 else model\n model.train()\n optimizer = optim.Adam(model.parameters(), lr=args.lr)\n\n epoch = 0\n save = args.save_intervel\n env_return = env.step()\n if env_return is not None:\n (states_S, states_G, rewards), require_init = env_return\n with torch.cuda.device(gpu_id):\n states_S = torch.from_numpy(states_S).float()\n states_G = torch.from_numpy(states_G).float()\n rewards = torch.from_numpy(rewards).float()\n if gpu_id >= 0:\n states_S = states_S.cuda()\n states_G = states_G.cuda()\n rewards = rewards.cuda()\n\n while True:\n values = model(Variable(states_S), Variable(states_G), require_init)\n\n value_loss = 0\n for value, reward in zip(values, rewards):\n value_loss = value_loss + F.binary_cross_entropy(value, Variable(reward))\n\n model.zero_grad()\n value_loss.backward()\n optimizer.step()\n model.detach()\n\n if env.epoch > epoch:\n epoch = env.epoch\n for p in optimizer.param_groups:\n p['lr'] *= 0.5\n\n ############################ PLOT ##########################################\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray(value_loss.data.cpu().numpy()),\n win=loss_win,\n name='value')\n\n values_np = np.swapaxes(np.asarray([value.data.cpu().numpy() for value in values]), 0, 1)\n rewards_np = np.swapaxes(rewards.cpu().numpy(), 0, 1)\n\n for idx, (value, reward, init) in enumerate(zip(values_np, rewards_np, require_init)):\n if init and len(pre_per_replay[idx]) > 0:\n pre_per_replay[idx] = np.asarray(pre_per_replay[idx], dtype=np.uint8)\n gt_per_replay[idx] = np.asarray(gt_per_replay[idx], dtype=np.uint8)\n\n step = len(pre_per_replay[idx]) // STEPS\n if step > 0:\n acc_tmp = []\n for s in range(STEPS):\n value_pre = pre_per_replay[idx][s*step:(s+1)*step]\n value_gt = gt_per_replay[idx][s*step:(s+1)*step]\n acc_tmp.append(np.mean(value_pre == value_gt))\n\n acc_tmp = np.asarray(acc_tmp)\n if acc is None:\n acc = acc_tmp\n else:\n acc = LAMBDA * acc + (1-LAMBDA) * acc_tmp\n\n if acc is None:\n continue\n for s in range(STEPS):\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray([acc[s]]),\n win=win,\n name='{}[{}%~{}%]'.format('value', s*10, (s+1)*10))\n vis.updateTrace(X=np.asarray([env.step_count()]),\n Y=np.asarray([np.mean(acc)]),\n win=win,\n name='value[TOTAL]')\n\n pre_per_replay[idx] = []\n gt_per_replay[idx] = []\n\n pre_per_replay[idx].append(int(value[-1] >= 0.5))\n gt_per_replay[idx].append(int(reward[-1]))\n\n ####################### NEXT BATCH ###################################\n env_return = env.step()\n if env_return is not None:\n (raw_states_S, raw_states_G, raw_rewards), require_init = env_return\n states_S = states_S.copy_(torch.from_numpy(raw_states_S).float())\n states_G = states_G.copy_(torch.from_numpy(raw_states_G).float())\n rewards = rewards.copy_(torch.from_numpy(raw_rewards).float())\n\n if env.step_count() > save or env_return is None:\n save = env.step_count()+args.save_intervel\n torch.save(model.state_dict(),\n os.path.join(args.model_path, 'model_iter_{}.pth'.format(env.step_count())))\n torch.save(model.state_dict(), os.path.join(args.model_path, 'model_latest.pth'))\n if env_return is None:\n env.close()\n break\n\ndef test(model, env, args):\n ######################### SAVE RESULT ############################\n value_pre_per_replay = [[]]\n value_gt_per_replay = [[]]\n\n ######################### TEST ###################################\n torch.manual_seed(args.seed)\n torch.cuda.manual_seed(args.seed)\n\n gpu_id = args.gpu_id\n with torch.cuda.device(gpu_id):\n model = model.cuda() if gpu_id >= 0 else model\n model.eval()\n\n env_return = env.step()\n if env_return is not None:\n (states_S, states_G, rewards), require_init = env_return\n with torch.cuda.device(gpu_id):\n states_S = torch.from_numpy(states_S).float()\n states_G = torch.from_numpy(states_G).float()\n rewards = torch.from_numpy(rewards).float()\n if gpu_id >= 0:\n states_S = states_S.cuda()\n states_G = states_G.cuda()\n rewards = rewards.cuda()\n\n while True:\n values = model(Variable(states_S), Variable(states_G), require_init)\n ############################ PLOT ##########################################\n values_np = np.squeeze(np.vstack([value.data.cpu().numpy() for value in values]))\n rewards_np = np.squeeze(rewards.cpu().numpy())\n\n if require_init[-1] and len(value_gt_per_replay[-1]) > 0:\n value_pre_per_replay[-1] = np.ravel(np.hstack(value_pre_per_replay[-1]))\n value_gt_per_replay[-1] = np.ravel(np.hstack(value_gt_per_replay[-1]))\n\n value_pre_per_replay.append([])\n value_gt_per_replay.append([])\n\n value_pre_per_replay[-1].append(values_np>=0.5)\n value_gt_per_replay[-1].append(rewards_np)\n\n ########################### NEXT BATCH #############################################\n env_return = env.step()\n if env_return is not None:\n (raw_states_S, raw_states_G, raw_rewards), require_init = env_return\n states_S = states_S.copy_(torch.from_numpy(raw_states_S).float())\n states_G = states_G.copy_(torch.from_numpy(raw_states_G).float())\n rewards = rewards.copy_(torch.from_numpy(raw_rewards).float())\n else:\n value_pre_per_replay[-1] = np.ravel(np.hstack(value_pre_per_replay[-1]))\n value_gt_per_replay[-1] = np.ravel(np.hstack(value_gt_per_replay[-1]))\n\n env.close()\n break\n\n return value_pre_per_replay, value_gt_per_replay\n\ndef next_path(model_folder, paths):\n models = {int(os.path.basename(model).split('.')[0].split('_')[-1])\n for model in os.listdir(model_folder) if 'latest' not in model}\n models_not_process = models - paths\n if len(models_not_process) == 0:\n return None\n models_not_process = sorted(models_not_process)\n paths.add(models_not_process[0])\n\n return os.path.join(model_folder, 'model_iter_{}.pth'.format(models_not_process[0]))\n\ndef main():\n # Training settings\n parser = argparse.ArgumentParser(description='Global State Evaluation : StarCraft II')\n parser.add_argument('--name', type=str, default='StarCraft II:TvT',\n help='Experiment name. All outputs will be stored in checkpoints/[name]/')\n parser.add_argument('--replays_path', default='train_val_test/Terran_vs_Terran',\n help='Path for training, validation and test set (default: train_val_test/Terran_vs_Terran)')\n parser.add_argument('--race', default='Terran', help='Which race? (default: Terran)')\n parser.add_argument('--enemy_race', default='Terran', help='Which the enemy race? (default: Terran)')\n parser.add_argument('--phrase', type=str, default='train',\n help='train|val|test (default: train)')\n parser.add_argument('--gpu_id', default=0, type=int, help='Which GPU to use [-1 indicate CPU] (default: 0)')\n\n parser.add_argument('--lr', type=float, default=0.001, help='Learning rate (default: 0.001)')\n parser.add_argument('--seed', type=int, default=1, help='Random seed (default: 1)')\n\n parser.add_argument('--n_steps', type=int, default=20, help='# of forward steps (default: 20)')\n parser.add_argument('--n_replays', type=int, default=32, help='# of replays (default: 32)')\n parser.add_argument('--n_epoch', type=int, default=10, help='# of epoches (default: 10)')\n\n parser.add_argument('--save_intervel', type=int, default=1000000,\n help='Frequency of model saving (default: 1000000)')\n args = parser.parse_args()\n\n args.save_path = os.path.join('checkpoints', args.name)\n args.model_path = os.path.join(args.save_path, 'snapshots')\n\n print('------------ Options -------------')\n for k, v in sorted(vars(args).items()):\n print('{}: {}'.format(k, v))\n print('-------------- End ----------------')\n\n if args.phrase == 'train':\n if not os.path.isdir(args.save_path):\n os.makedirs(args.save_path)\n if not os.path.isdir(args.model_path):\n os.makedirs(args.model_path)\n with open(os.path.join(args.save_path, 'config'), 'w') as f:\n f.write(json.dumps(vars(args)))\n\n env = BatchSpatialEnv()\n env.init(os.path.join(args.replays_path, '{}.json'.format(args.phrase)),\n './', args.race, args.enemy_race, n_steps=args.n_steps, seed=args.seed,\n n_replays=args.n_replays, epochs=args.n_epoch)\n model = StateEvaluationGRU(env.n_channels, env.n_features)\n train(model, env, args)\n elif 'val' in args.phrase or 'test' in args.phrase:\n test_result_path = os.path.join(args.save_path, args.phrase)\n if not os.path.isdir(test_result_path):\n os.makedirs(test_result_path)\n\n dataset_path = 'test.json' if 'test' in args.phrase else 'val.json'\n paths = set()\n while True:\n path = next_path(args.model_path, paths)\n if path is not None:\n print('[{}]Testing {} ...'.format(len(paths), path))\n\n env = BatchSpatialEnv()\n env.init(os.path.join(args.replays_path, dataset_path),\n './', args.race, args.enemy_race, n_steps=args.n_steps,\n seed=args.seed, n_replays=1, epochs=1)\n model = StateEvaluationGRU(env.n_channels, env.n_features)\n model.load_state_dict(torch.load(path))\n result = test(model, env, args)\n with open(os.path.join(test_result_path, os.path.basename(path)), 'wb') as f:\n pickle.dump(result, f)\n show_test_result(args.name, args.phrase, result, title=len(paths)-1)\n else:\n time.sleep(60)\n\nif __name__ == '__main__':\n main()\n" }, { "path": "README.md", "content": "# MSC\n[MSC: A Dataset for Macro-Management in StarCraft II.](https://arxiv.org/pdf/1710.03131.pdf)\n```\n@article{wu2017msc,\n title={MSC: A Dataset for Macro-Management in StarCraft II},\n author={Wu, Huikai and Zhang, Junge and Huang, Kaiqi},\n journal={arXiv preprint arXiv:1710.03131},\n year={2017}\n}\n```\n## Download\n- **Global** features are available [HERE](https://drive.google.com/file/d/0Bybnpq8dvwudNUVOX1FCWnZoSGM/view?usp=sharing&resourcekey=0-GJ3Loydd4k2QaONnVd3B5Q).\n- **[TRAIN|VAL|TEST]** split is available [HERE](train_val_test).\n- **[Stat]** is available [HERE](parsed_replays/Stat). The stat files with postfix **_human.json** are human-readable.\n- **Spatial** features are **NOT** avaiable since I do not have any download server. Please follow the [instructions](#step-by-step-instructions) to generate the spatial features by yourself.\n\n## Baselines\n### Global State Evaluation\n| Method | TvT:T | TvZ:T | TvZ:Z | TvP:T | TvP:P | ZvZ:Z | ZvP:Z | ZvP:P | PvP:P |\n| - | - | - | - | - | - | - | - | - | - |\n| Baseline[Global] | 61.09 | 58.89 | 60.61 | 57.21 | 60.95 | 59.91 | 59.95 | 59.35 | 51.36 |\n| Baseline[Spatial] | 50.85 | 52.35 | 59.82 | 54.90 | 59.15 | 54.65 | 55.02 | 58.78 | 57.76 |\n### Build Order Prediction\n| Method | TvT:T | TvZ:T | TvZ:Z | TvP:T | TvP:P | ZvZ:Z | ZvP:Z | ZvP:P | PvP:P |\n| - | - | - | - | - | - | - | - | - | - |\n| Baseline[Global] | 74.12 | 73.01 | 73.89 | 70.29 | 79.28 | 76.07 | 72.02 | 78.08 | 76.28 |\n| Baseline[Spatial] | 73.07 | 73.71 | 75.92 | 64.15 | 75.09 | 74.88 | 72.32 | 76.12 | 74.22 |\n## Dataset: Global Feature Vector\nEach replay is a **(T, M)** matrix **F**, where **F[t, :]** is the feature vector for time step **t**.\n\nEach **row** of **F** is a **M**-dimensional vector, with **M** varying as **[RACE] v.s. [RACE]**.\n\nThe **M**-dimensional vector is orgnized as follows:\n1. **[0]:** reward, i.e. final result of the game. **0**: DEFEAT, **1:** WIN.\n2. **[1]:** ground truth action, ranging from **[0, #ACTION]**.\n3. **[2-15):** cumulative score **[NOT NORMALIZED]**, which is defined in [Here](https://github.com/wuhuikai/MSC/blob/ebb1a722206e594e1c3a1da7cf21df8c514e5040/parse_replay/replay2global_features.py#L52).\n4. **[15-M):** observation feature vector, which is normalized into **[0, 1]**.\n 1. **[15]:** frame id.\n 2. **[16-27):** player info, including various [resources](https://github.com/wuhuikai/MSC/blob/ebb1a722206e594e1c3a1da7cf21df8c514e5040/parse_replay/replay2global_features.py#L68) and **n_power_source**.\n 3. **[27-#1):** alerts, **boolean**.\n 4. **[#1-#2):** upgrades, **boolean**.\n 5. **[#2-#3):** research count.\n 6. **[#3-#4):** friendly units info, which is defined in [Here](https://github.com/wuhuikai/MSC/blob/ebb1a722206e594e1c3a1da7cf21df8c514e5040/extract_features/game_state.py#L110).\n 7. **[#4-M):** enemy units info, where **M = #4 + #[ENEMY RACE]**.\n \n | V.S. | TvT:T | TvZ:T | TvZ:Z | TvP:T | TvP:P | ZvZ:Z | ZvP:Z | ZvP:P | PvP:P |\n | - | - | - | - | - | - | - | - | - | - |\n | M | 753 | 1131 | 1121 | 663 | 653 | 1499 | 1031 | 1031 | 563 |\n\n | RACE | #1 | #2 | #3 | #4 | #ACTION | #RACE |\n | - | - | - | - | - | - | - |\n | Terran | 29 | 60 | 81 | 417 | 75 | 336|\n | Protoss | 29 | 55 | 71 | 317 | 61 | 246 |\n | Zerg | 29 | 55 | 71 | 785 | 74 | 714 |\nCode for loading **F**:\n```python\nimport numpy as np\nfrom scipy import sparse\nF = np.asarray(sparse.load_npz(PATH).todense())\n```\n## Dataset: Spatial Feature Tensor\nEach replay contains a **(T, 13, 64, 64)** tensor **S** and a **(T, 26)** matrix **G**.\n\nThe specifics for **S[t, :, :, :]** is as follows:\n1. **S[t, 0:8, :, :]:** screen features, roughly normalized into **[0-1]**, which is defined in [Here](https://github.com/wuhuikai/MSC/blob/ebb1a722206e594e1c3a1da7cf21df8c514e5040/extract_features/SpatialFeatures.py#L45).\n2. **S[t, 8:13, :, :]:** minimap features, roughly normalized into **[0-1]**, which is defined in [Here](https://github.com/wuhuikai/MSC/blob/ebb1a722206e594e1c3a1da7cf21df8c514e5040/extract_features/SpatialFeatures.py#L58).\n\n**WARNING**[Cheat Layer]: The last layer **S[t, 12, :, :]** refers to **unit_type**, which could only be obtained in replays.\n\nCode for loading **S**:\n```python\nimport numpy as np\nfrom scipy import sparse\nS = np.asarray(sparse.load_npz(PATH).todense()).reshape([-1, 13, 64, 64])\n```\nThe specifics for **G[t, :]** is as follows:\n1. **[0-11):** frame id + player info, normalized into **[0, 1]**, which is defined [Here](https://github.com/wuhuikai/MSC/blob/ebb1a722206e594e1c3a1da7cf21df8c514e5040/extract_features/SpatialFeatures.py#L97).\n2. **[11-24):** cumulative score **[NOT NORMALIZED]**, which is defined in [Here](https://github.com/wuhuikai/MSC/blob/ebb1a722206e594e1c3a1da7cf21df8c514e5040/extract_features/SpatialFeatures.py#L111).\n3. **[24]:** reward, i.e. final result of the game. **0:** DEFEAT, **1**: WIN\n4. **[25]:** ground truth action, ranging from **[0, #ACTION]**.\n\nCode for loading **G**:\n```python\nimport numpy as np\nfrom scipy import sparse\nG = np.asarray(sparse.load_npz(PATH).todense())\n```\n## Build the Dataset Yourself Step by Step\n### Install [SC2LE](https://github.com/Blizzard/s2client-proto)\n1. Download and **unzip** (Password: iagreetotheeula) StarCraft II Linux Packages [3.16.1](http://blzdistsc2-a.akamaihd.net/Linux/SC2.3.16.1.zip) into **$STAR_CRAFT$**.\n2. Download and **unzip** (Password: iagreetotheeula) Replay Packs ([3.16.1 - Pack 1](http://blzdistsc2-a.akamaihd.net/ReplayPacks/3.16.1-Pack_1-fix.zip), [3.16.1 - Pack 2](http://blzdistsc2-a.akamaihd.net/ReplayPacks/3.16.1-Pack_2.zip) [Currently not used]) into **$STAR_CRAFT$**.\n\nAfter step 1 and step 2, the folder structure is as follows:\n```\n$STAR_CRAFT$\n ├── Battle.net\n ├── Libs\n ├── Maps\n ├── Replays\n ├── SC2Data\n └── Versions\n```\n- **NOTE:**\n 1. **$STAR_CRAFT$/Replays** contains all ***.SC2Replay** files from **3.16.1 - Pack 1** and **3.16.1 - Pack 2** [Currently not used]\n 2. **$STAR_CRAFT$/Battle.net** contains all contents from the folder **Battle.net** in **3.16.1 - Pack 1** and **3.16.1 - Pack 2** [Currently not used]\n### Step-by-Step Instructions\n#### [The Easy Way](instructions/EasyWay.md)\n#### [The Hard Way [Step-by-Step in Details]](instructions/HardWay.md)\n### Requirements\n```\nfuture == 0.16.0\n\nnumpy == 1.13.0\nscipy == 0.19.0\n\npython_gflags == 3.1.1\n\ntqdm == 4.14.0\n\nprotobuf == 3.4.0\npystream_protobuf == 1.4.4\n\nPySC2 == 1.0\ns2clientprotocol == 1.1\n```\n" }, { "path": "_config.yml", "content": "theme: jekyll-theme-hacker" }, { "path": "data_loader/BatchEnv.py", "content": "import os\nimport json\nfrom collections import namedtuple\n\nimport numpy as np\nfrom scipy import sparse\n\nfrom tqdm import tqdm\n\nclass BatchEnv(object):\n def __init__(self):\n pass\n\n def init(self, path, root, race, enemy_race, step_mul=8, n_replays=4, n_steps=5, epochs=10, seed=None):\n np.random.seed(seed)\n\n with open(path) as f:\n replays = json.load(f)\n\n self.replays = self.__generate_replay_list__(replays, root, race)\n\n self.race = race\n self.enemy_race = enemy_race\n\n self.step_mul = step_mul\n self.n_replays = n_replays\n self.n_steps = n_steps\n\n self.epochs = epochs\n self.epoch = -1\n self.steps = 0\n\n self.replay_idx = -1\n self.replay_list = [None for _ in range(self.n_replays)]\n\n ## Display Progress Bar\n self.epoch_pbar = tqdm(total=self.epochs, desc='Epoch')\n self.replay_pbar = None\n\n self.__post_init__()\n\n def __generate_replay_list__(self, replays, race):\n raise NotImplementedError\n\n def __init_epoch__(self):\n self.epoch += 1\n if self.epoch > 0:\n self.epoch_pbar.update(1)\n if self.epoch == self.epochs:\n return False\n\n np.random.shuffle(self.replays)\n ## Display Progress Bar\n if self.replay_pbar is not None:\n self.replay_pbar.close()\n self.replay_pbar = tqdm(total=len(self.replays), desc=' Replays')\n return True\n\n def __reset__(self):\n self.replay_idx += 1\n if self.replay_idx % len(self.replays) == 0:\n has_more = self.__init_epoch__()\n if not has_more:\n return None\n\n path = self.replays[self.replay_idx%len(self.replays)]\n\n return self.__load_replay__(path)\n\n def __load_replay__(self, path):\n raise NotImplementedError\n\n def step(self, **kwargs):\n require_init = [False for _ in range(self.n_replays)]\n for i in range(self.n_replays):\n if self.replay_list[i] is None or self.replay_list[i]['done']:\n if self.replay_list[i] is not None:\n keys = set(self.replay_list[i].keys())\n for k in keys:\n del self.replay_list[i][k]\n self.replay_list[i] = self.__reset__()\n require_init[i] = True\n if self.replay_list[i] is None:\n return None\n\n result = []\n for step in range(self.n_steps):\n result_per_step = []\n for i in range(self.n_replays):\n replay_dict = self.replay_list[i]\n\n features = self.__one_step__(replay_dict, replay_dict['done'])\n\n result_per_step.append(features)\n\n result.append(result_per_step)\n\n return self.__post_process__(result, **kwargs), require_init\n\n def __one_step__(self, replay_dict, done):\n raise NotImplementedError\n\n def __post_process__(self, result, **kwargs):\n raise NotImplementedError\n\n def step_count(self):\n return self.steps\n\n def close(self):\n if self.epoch_pbar is not None:\n self.epoch_pbar.close()\n if self.replay_pbar is not None:\n self.replay_pbar.close()\n\nclass BatchGlobalFeatureEnv(BatchEnv):\n n_features_dic = {'Terran': {'Terran': 738, 'Protoss': 648, 'Zerg': 1116},\n 'Protoss': {'Terran': 638, 'Protoss': 548, 'Zerg': 1016},\n 'Zerg': {'Terran': 1106, 'Protoss': 1016, 'Zerg': 1484}}\n n_actions_dic = {'Terran': 75, 'Protoss': 61, 'Zerg': 74}\n\n def __post_init__(self):\n self.n_features = self.n_features_dic[self.race][self.enemy_race]\n self.n_actions = self.n_actions_dic[self.race]\n\n def __generate_replay_list__(self, replays, root, race):\n result = []\n for path_dict in replays:\n for player_path in path_dict[race]:\n result.append(os.path.join(root, player_path['global_path']))\n\n return result\n\n def __load_replay__(self, path):\n replay_dict = {}\n replay_dict['ptr'] = 0\n replay_dict['done'] = False\n replay_dict['states'] = np.asarray(sparse.load_npz(path).todense())\n\n return replay_dict\n\n def __one_step__(self, replay_dict, done):\n states = replay_dict['states']\n feature_shape = states.shape[1:]\n if done:\n return np.zeros(feature_shape)\n\n self.steps += 1\n state = states[replay_dict['ptr']]\n replay_dict['ptr'] += 1\n if replay_dict['ptr'] == states.shape[0]:\n self.replay_pbar.update(1)\n replay_dict['done'] = True\n\n return state\n\n def __post_process__(self, result, reward=True, action=False, score=False):\n result = np.asarray(result)\n\n result_return = [result[:, :, 15:]]\n if reward:\n result_return.append(result[:, :, 0:1])\n if action:\n result_return.append(result[:, :, 1:2])\n if score:\n result_return.append(result[:, :, 2:15])\n\n return result_return\n\nclass BatchSpatialEnv(BatchEnv):\n n_channels = 5\n n_features = 11\n n_actions_dic = {'Terran': 75, 'Protoss': 61, 'Zerg': 74}\n Feature = namedtuple('Feature', ['S', 'G'])\n\n def __post_init__(self):\n self.n_actions = self.n_actions_dic[self.race]\n\n def __generate_replay_list__(self, replays, root, race):\n result = []\n for path_dict in replays:\n for player_path in path_dict[race]:\n result.append([os.path.join(root, player_path['spatial_path_S']),\n os.path.join(root, player_path['spatial_path_G'])])\n return result\n\n def __load_replay__(self, path):\n replay_dict = {}\n replay_dict['ptr'] = 0\n replay_dict['done'] = False\n replay_dict['states_S'] = np.asarray(sparse.load_npz(path[0]).todense()).reshape([-1, 13, 64, 64])\n replay_dict['states_G'] = np.asarray(sparse.load_npz(path[1]).todense())\n\n return replay_dict\n\n def __one_step__(self, replay_dict, done):\n states_S = replay_dict['states_S']\n states_G = replay_dict['states_G']\n feature_shape_S = states_S.shape[1:]\n feature_shape_G = states_G.shape[1:]\n if done:\n return self.Feature(np.zeros(feature_shape_S), np.zeros(feature_shape_G))\n\n self.steps += 1\n state_S = states_S[replay_dict['ptr']]\n state_G = states_G[replay_dict['ptr']]\n replay_dict['ptr'] += 1\n if replay_dict['ptr'] == states_S.shape[0]:\n self.replay_pbar.update(1)\n replay_dict['done'] = True\n\n return self.Feature(state_S, state_G)\n\n def __post_process__(self, result, reward=True, action=False, score=False):\n result = self.Feature(*zip(*[self.Feature(*zip(*result_per_step)) for result_per_step in result]))\n\n S = np.asarray(result.S)\n G = np.asarray(result.G)\n\n result_return = [S[:, :, 8:13, :, :], G[:,:, :11]]\n if reward:\n result_return.append(G[:, :, 24:25])\n if action:\n result_return.append(G[:, :, 25:26])\n if score:\n result_return.append(G[:, :, 11:24])\n\n return result_return\n\nif __name__ == '__main__':\n env = BatchSpatialEnv()\n env.init('../train_val_test/Terran_vs_Terran/train.json', '../', 'Terran', 'Terran')\n while True:\n r = env.step()\n if r is None:\n break\n" }, { "path": "extract_features/SpatialFeatures.py", "content": "from pysc2.lib.features import *\nfrom pysc2.lib import stopwatch\n\nsw = stopwatch.sw\n\nclass ScreenFeatures(collections.namedtuple(\"ScreenFeatures\", [\"height_map\", \"visibility_map\",\n \"creep\", \"power\", \"player_relative\", \"unit_type\", \"unit_density\", \"unit_density_aa\"])):\n \"\"\"The set of screen feature layers.\"\"\"\n __slots__ = ()\n\n def __new__(cls, **kwargs):\n feats = {}\n for name, (scale, type_, palette, clip) in six.iteritems(kwargs):\n feats[name] = Feature(\n index=ScreenFeatures._fields.index(name),\n name=name,\n layer_set=\"renders\",\n full_name=\"screen \" + name,\n scale=scale,\n type=type_,\n palette=palette(scale) if callable(palette) else palette,\n clip=clip)\n return super(ScreenFeatures, cls).__new__(cls, **feats)\n\n\nclass MinimapFeatures(collections.namedtuple(\"MinimapFeatures\", [\n \"height_map\", \"visibility_map\", \"creep\", \"player_relative\", \"unit_type\"])):\n \"\"\"The set of minimap feature layers.\"\"\"\n __slots__ = ()\n\n def __new__(cls, **kwargs):\n feats = {}\n for name, (scale, type_, palette) in six.iteritems(kwargs):\n feats[name] = Feature(\n index=MinimapFeatures._fields.index(name),\n name=name,\n layer_set=\"minimap_renders\",\n full_name=\"minimap \" + name,\n scale=scale,\n type=type_,\n palette=palette(scale) if callable(palette) else palette,\n clip=False)\n return super(MinimapFeatures, cls).__new__(cls, **feats)\n\nSCREEN_FEATURES = ScreenFeatures(\n height_map=(256, FeatureType.SCALAR, colors.winter, False),\n visibility_map=(4, FeatureType.CATEGORICAL,\n colors.VISIBILITY_PALETTE, False),\n creep=(2, FeatureType.CATEGORICAL, colors.CREEP_PALETTE, False),\n power=(2, FeatureType.CATEGORICAL, colors.POWER_PALETTE, False),\n player_relative=(5, FeatureType.CATEGORICAL,\n colors.PLAYER_RELATIVE_PALETTE, False),\n unit_type=(1850, FeatureType.CATEGORICAL, colors.unit_type, False),\n unit_density=(16, FeatureType.SCALAR, colors.hot, False),\n unit_density_aa=(256, FeatureType.SCALAR, colors.hot, False),\n)\n\nMINIMAP_FEATURES = MinimapFeatures(\n height_map=(256, FeatureType.SCALAR, colors.winter),\n visibility_map=(4, FeatureType.CATEGORICAL, colors.VISIBILITY_PALETTE),\n creep=(2, FeatureType.CATEGORICAL, colors.CREEP_PALETTE),\n player_relative=(5, FeatureType.CATEGORICAL,\n colors.PLAYER_RELATIVE_PALETTE),\n unit_type=(1850, FeatureType.CATEGORICAL, colors.unit_type)\n)\n\nclass SpatialFeatures(Features):\n def observation_spec(self):\n \"\"\"The observation spec for the SC2 environment.\n Returns:\n The dict of observation names to their tensor shapes. Shapes with a 0 can\n vary in length, for example the number of valid actions depends on which\n units you have selected.\n \"\"\"\n return {\n \"screen\": (len(SCREEN_FEATURES),\n self._screen_size_px.y,\n self._screen_size_px.x),\n \"minimap\": (len(MINIMAP_FEATURES),\n self._minimap_size_px.y,\n self._minimap_size_px.x),\n \"player\": (11,),\n \"score\": (13,)\n }\n\n @sw.decorate\n def transform_obs(self, obs):\n \"\"\"Render some SC2 observations into something an agent can handle.\"\"\"\n out = {}\n\n with sw(\"feature_layers\"):\n out[\"screen\"] = np.stack(\n f.unpack(obs)/f.scale for f in SCREEN_FEATURES).astype(np.float32, copy=False)\n out[\"minimap\"] = np.stack(\n f.unpack(obs)/f.scale for f in MINIMAP_FEATURES).astype(np.float32, copy=False)\n\n out[\"player\"] = np.array([\n obs.game_loop - 1,\n obs.player_common.minerals,\n obs.player_common.vespene,\n obs.player_common.food_used,\n obs.player_common.food_cap,\n obs.player_common.food_army,\n obs.player_common.food_workers,\n obs.player_common.idle_worker_count,\n obs.player_common.army_count,\n obs.player_common.warp_gate_count,\n obs.player_common.larva_count,\n ], dtype=np.int32)\n\n out[\"score\"] = np.array([\n obs.score.score,\n obs.score.score_details.idle_production_time,\n obs.score.score_details.idle_worker_time,\n obs.score.score_details.total_value_units,\n obs.score.score_details.total_value_structures,\n obs.score.score_details.killed_value_units,\n obs.score.score_details.killed_value_structures,\n obs.score.score_details.collected_minerals,\n obs.score.score_details.collected_vespene,\n obs.score.score_details.collection_rate_minerals,\n obs.score.score_details.collection_rate_vespene,\n obs.score.score_details.spent_minerals,\n obs.score.score_details.spent_vespene,\n ], dtype=np.int32)\n\n return out" }, { "path": "extract_features/compute_stat.sh", "content": "python replay_stat.py --race $1" }, { "path": "extract_features/extract_features.sh", "content": "python global_feature_vector.py --hq_replay_set $1 &&\npython spatial_feature_tensor.py --hq_replay_set $1 &&\npython split.py --hq_replay_set $1" }, { "path": "extract_features/game_state.py", "content": "import os\nimport json\nimport pprint\nimport numpy as np\n\ndef load_stat(path):\n def dict_key_to_int(obj):\n def str2int(s):\n try:\n return int(s)\n except:\n return s\n\n if not isinstance(obj, dict):\n return str2int(obj)\n\n return {str2int(k): dict_key_to_int(v) for k, v in obj.items()}\n\n with open(path) as f:\n stat = json.load(f)\n stat = dict_key_to_int(stat)\n stat['action_id'][-1] = len(stat['action_id'])\n\n return stat\n\nclass GameState(object):\n ## Starcraft Stat\n eps = 1e-9\n\n stat_path, stat = None, None\n enemy_stat_path, enemy_stat = None, None\n\n max_vars = ['frame_id', 'minerals', 'vespene', 'food_cap',\n 'food_used', 'food_army', 'food_workers', 'idle_worker_count',\n 'army_count', 'warp_gate_count', 'larva_count', 'n_power_source']\n max_keys = ['frame_id', 'minerals', 'vespene', 'food_cap',\n 'food_cap', 'food_cap', 'food_cap', 'idle_worker_count',\n 'army_count', 'warp_gate_count', 'larva_count', 'n_power_source']\n\n int_vars = max_vars\n\n def __init__(self, stat_path, enemy_stat_path):\n if self.stat_path is None or not os.path.samefile(self.stat_path, stat_path):\n self.stat_path = stat_path\n self.stat = load_stat(stat_path)\n if self.enemy_stat_path is None or not os.path.samefile(self.enemy_stat_path, enemy_stat_path):\n self.enemy_stat_path = enemy_stat_path\n self.enemy_stat = load_stat(enemy_stat_path)\n\n for k in self.int_vars:\n setattr(self, k, -1)\n # Reward\n self.reward = -1\n self.score = None\n # Alerts\n self.alert = set()\n # Upgrades\n self.upgrades = set()\n # Actions\n self.action = -1\n self.research = {}\n # Units\n self.friendly_units = {}\n self.enemy_units = {}\n\n def update(self, state):\n for k in self.int_vars:\n setattr(self, k, state[k])\n # Reward\n self.reward = 2 - state['reward']\n self.score = state['score_cumulative']\n # Alert\n self.alert = set(state['alert'])\n # Upgrades\n self.upgrades = set(state['upgrades'])\n # Actions\n self.__set_action__(-1 if state['action'] is None else state['action'][0])\n ## Units\n # Friendly units\n self.friendly_units = self.__set_units__(state['friendly_units'])\n # Enemy units\n self.enemy_units = self.__set_units__(state['enemy_units'])\n\n def get_action(self):\n return self.stat['action_id'][self.action]\n\n def __set_units__(self, units):\n results = {}\n for unit_type_id, unit in units.items():\n unit_type_id = int(unit_type_id)\n if unit_type_id not in results:\n results[unit_type_id] = {'built': [], 'building': []}\n for unit_instance in unit['units']:\n if unit_instance['build_progress'] >= 1:\n results[unit_type_id]['built'].append(unit_instance)\n else:\n results[unit_type_id]['building'].append(unit_instance)\n return results\n\n def __set_action__(self, action):\n self.action = action\n if action == -1:\n return\n if self.stat['action_name'][action].startswith('Research'):\n if action not in self.research:\n self.research[action] = 0\n self.research[action] += 1\n\n def __units2vec__(self, units, stat):\n name2id = {'total_num': 0, 'finished_num': 1, 'building_num': 2,\n 'max_building_progress': 3, 'min_building_progress': 4,\n 'avg_building_progress': 5}\n units_stat = stat['units_type']\n\n result = np.zeros(len(units_stat)*len(name2id))\n for k, unit in units.items():\n if k not in units_stat:\n continue\n start = units_stat[k] * len(name2id)\n result[start + name2id['total_num']] = len(unit['built'])+len(unit['building'])\n if result[start + name2id['total_num']] == 0:\n continue\n\n result[start + name2id['finished_num']] = len(unit['built'])\n result[start + name2id['building_num']] = len(unit['building'])\n\n if result[start + name2id['building_num']] > 0:\n ## Init min value\n result[start+name2id['min_building_progress']] = 1.0\n\n for u in unit['building']:\n result[start + name2id['max_building_progress']] = \\\n max(result[start + name2id['max_building_progress']], u['build_progress'])\n result[start + name2id['min_building_progress']] = \\\n min(result[start + name2id['min_building_progress']], u['build_progress'])\n result[start + name2id['avg_building_progress']] += u['build_progress']\n\n result[start+name2id['avg_building_progress']] /= result[start+name2id['building_num']]\n\n for name in {'total_num', 'finished_num', 'building_num'}:\n result[start+name2id[name]] /= stat['max_unit_num']\n\n return result\n\n def __set_to_array__(self, set_var, key2id):\n result = np.zeros(len(key2id))\n for key in set_var:\n result[key2id[key]] = 1\n return result\n\n def __dict_to_array__(self, dict_var, key2id, scale=1):\n result = np.zeros(len(key2id))\n for key, value in dict_var.items():\n result[key2id[key]] = value/scale\n return result\n\n def to_vector(self):\n result = []\n # Frame_id; Resources\n max_result = []\n for k, v in zip(self.max_keys, self.max_vars):\n max_result.append(self.__dict__[v]/(self.stat['max_'+k]+self.eps))\n result.append(max_result)\n # Alerts\n result.append(self.__set_to_array__(self.alert, self.stat['alert']))\n # Upgrades\n result.append(self.__set_to_array__(self.upgrades, self.stat['upgrades']))\n # Research\n result.append(self.__dict_to_array__(self.research, self.stat['research_id'],\n self.stat['max_research_num']))\n ## Units\n result.append(self.__units2vec__(self.friendly_units, self.stat))\n result.append(self.__units2vec__(self.enemy_units, self.enemy_stat))\n\n return np.hstack(result)\n\n def __str__(self):\n return pprint.pformat(self.__dict__)\n" }, { "path": "extract_features/global_feature_vector.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport json\nfrom absl import flags\n\nimport numpy as np\nfrom scipy import sparse\n\nfrom tqdm import tqdm\n\nfrom google.protobuf.json_format import Parse\n\nfrom s2clientprotocol import sc2api_pb2 as sc_pb\n\nfrom game_state import GameState\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string(name='hq_replay_set', default='../high_quality_replays/Terran_vs_Terran.json',\n help='File storing replays list')\nflags.DEFINE_string(name='parsed_replay_path', default='../parsed_replays',\n help='Path storing parsed replays')\n\ndef parse_replay(replay_player_path, reward, race, enemy_race):\n with open(os.path.join(FLAGS.parsed_replay_path, 'GlobalFeatures', replay_player_path)) as f:\n states = json.load(f)\n\n states_np = []\n game_state = GameState(os.path.join(FLAGS.parsed_replay_path, 'Stat', '{}.json'.format(race)),\n os.path.join(FLAGS.parsed_replay_path, 'Stat', '{}.json'.format(enemy_race)))\n for state in states:\n game_state.update(state)\n states_np.append(np.hstack([game_state.reward, game_state.get_action(),\n game_state.score, game_state.to_vector()]))\n states_np = np.asarray(states_np)\n\n sparse.save_npz(os.path.join(FLAGS.parsed_replay_path, 'GlobalFeatureVector',\n replay_player_path), sparse.csc_matrix(states_np))\n\ndef main():\n with open(FLAGS.hq_replay_set) as f:\n replay_list = sorted(json.load(f))\n\n race_vs_race = os.path.basename(FLAGS.hq_replay_set).split('.')[0]\n global_feature_vec_path = os.path.join(FLAGS.parsed_replay_path, 'GlobalFeatureVector', race_vs_race)\n races = set(race_vs_race.split('_vs_'))\n for race in races:\n path = os.path.join(global_feature_vec_path, race)\n if not os.path.isdir(path):\n os.makedirs(path)\n\n pbar = tqdm(total=len(replay_list), desc='#Replay')\n for replay_path, replay_info_path in replay_list:\n with open(replay_info_path) as f:\n info = json.load(f)\n info = Parse(info['info'], sc_pb.ResponseReplayInfo())\n\n replay_name = os.path.basename(replay_path)\n for player_info in info.player_info:\n race = sc_pb.Race.Name(player_info.player_info.race_actual)\n player_id = player_info.player_info.player_id\n reward = player_info.player_result.result\n\n replay_player_path = os.path.join(race_vs_race, race, '{}@{}'.format(player_id, replay_name))\n parse_replay(replay_player_path, reward, race, race if len(races) == 1 else list(races - {race})[0])\n\n pbar.update()\n\nif __name__ == '__main__':\n main()\n" }, { "path": "extract_features/replay_stat.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport glob\nimport pprint\nimport numpy as np\n\nimport os\nimport json\nfrom absl import flags\n\nfrom tqdm import tqdm\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string(name='hq_replay_path', default='../high_quality_replays',\n help='Path for replays lists')\nflags.DEFINE_string(name='parsed_replay_path', default='../parsed_replays',\n help='Path storing parsed replays')\nflags.DEFINE_string(name='race', default='Terran',\n help='Race name')\n# MAX stat\nmax_keys = {'frame_id', 'minerals', 'vespene', 'food_cap',\n 'idle_worker_count', 'army_count', 'warp_gate_count',\n 'larva_count', 'n_power_source'}\n# SET stat\nset_keys = {'alert', 'upgrades'}\n\ndef update(replay_path, stat):\n with open(replay_path) as f:\n states = json.load(f)\n\n research_count = {}\n for state in states:\n # MAX stat\n for key in max_keys:\n stat['max_' + key] = max(state[key], stat['max_'+key])\n # SET stat\n for key in set_keys:\n stat[key].update(set(state[key]))\n # max_score_cumulative\n stat['max_score_cumulative'] = max(stat['max_score_cumulative'], state['score_cumulative'][0])\n ## Units stat\n units = state['friendly_units']\n for unit_type, unit in units.items():\n stat['units_type'].add(unit_type)\n stat['units_name'][unit_type] = unit['name']\n stat['max_unit_num'] = max(stat['max_unit_num'], len(unit['units']))\n ## Actions\n if state['action'] is None:\n continue\n\n id, name = state['action']\n stat['action_id'].add(id)\n stat['action_name'][id] = name\n\n if name.startswith('Research_'):\n stat['research_id'].add(id)\n if id not in research_count:\n research_count[id] = 0\n research_count[id] += 1\n if len(research_count) > 0:\n stat['max_research_num'] = max(stat['max_research_num'], max(research_count.values()))\n\ndef post_process(stat):\n for key in set_keys | {'action_id', 'research_id', 'units_type'}:\n values = np.asarray(list(stat[key]))\n idx = np.argsort(values)\n values = values[idx]\n stat[key] = {k: v for k, v in zip(values, idx)}\n\n # Turn all keys into str\n def dict_key_to_str(obj):\n if not isinstance(obj, dict):\n return str(obj)\n return {str(k):dict_key_to_str(v) for k, v in obj.items()}\n\n return dict_key_to_str(stat)\n\ndef main():\n replay_lists = sorted(glob.glob(os.path.join(FLAGS.hq_replay_path, '*{}*.json'.format(FLAGS.race))))\n save_path = os.path.join(FLAGS.parsed_replay_path, 'Stat')\n if not os.path.isdir(save_path):\n os.makedirs(save_path)\n\n ## Init Stat Dict\n stat = {}\n # MAX stat\n for key in max_keys:\n stat['max_'+key] = 0\n # SET stat\n for key in set_keys:\n stat[key] = set()\n # score_cumulative\n stat['max_score_cumulative'] = 0\n ## Units stat\n stat['units_type'] = set()\n stat['units_name'] = {}\n stat['max_unit_num'] = 0\n ## Actions\n stat['action_id'] = set()\n stat['action_name'] = {}\n stat['research_id'] = set()\n stat['max_research_num'] = 0\n\n set_bar = tqdm(total=len(replay_lists), desc='#SET')\n for replay_list_path in replay_lists:\n race_vs_race = os.path.basename(replay_list_path).split('.')[0]\n replays = glob.glob(os.path.join(FLAGS.parsed_replay_path, 'GlobalFeatures',\n race_vs_race, FLAGS.race, '*.SC2Replay'))\n\n pbar = tqdm(total=len(replays), desc='\\t#Replay')\n for replay in replays:\n update(replay, stat)\n pbar.update()\n\n set_bar.update()\n\n stat = post_process(stat)\n\n with open(os.path.join(save_path, '{}_human.json'.format(FLAGS.race)), 'w') as f:\n f.write(pprint.pformat(stat))\n with open(os.path.join(save_path, '{}.json'.format(FLAGS.race)), 'w') as f:\n json.dump(stat, f)\n\nif __name__ == '__main__':\n main()\n" }, { "path": "extract_features/spatial_feature_tensor.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport json\nimport stream\nfrom absl import flags\nfrom multiprocessing import Pool\n\nimport numpy as np\nfrom scipy import sparse\n\nfrom tqdm import tqdm\n\nfrom google.protobuf.json_format import Parse\n\nfrom pysc2.lib import FUNCTIONS\nfrom s2clientprotocol import sc2api_pb2 as sc_pb\n\nfrom game_state import load_stat\nfrom SpatialFeatures import SpatialFeatures\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string(name='hq_replay_set', default='../high_quality_replays/Terran_vs_Terran.json',\n help='File storing replays list')\nflags.DEFINE_string(name='parsed_replay_path', default='../parsed_replays',\n help='Path storing parsed replays')\nflags.DEFINE_integer(name='step_mul', default=8,\n help='step size')\nflags.DEFINE_integer(name='n_workers', default=16,\n help='#processes')\n\ndef parse_replay(replay_player_path, sampled_action_path, reward, race, enemy_race, stat):\n with open(os.path.join(FLAGS.parsed_replay_path, 'GlobalInfos', replay_player_path)) as f:\n global_info = json.load(f)\n feat = SpatialFeatures(Parse(global_info['game_info'], sc_pb.ResponseGameInfo()))\n\n states = [obs for obs in stream.parse(os.path.join(FLAGS.parsed_replay_path,\n 'SampledObservations', replay_player_path), sc_pb.ResponseObservation)]\n\n # Sampled Actions\n with open(sampled_action_path) as f:\n sampled_action = json.load(f)\n sampled_action_id = [id // FLAGS.step_mul + 1 for id in sampled_action]\n # Actions\n with open(os.path.join(FLAGS.parsed_replay_path, 'Actions', replay_player_path)) as f:\n actions = json.load(f)\n actions = [None if len(actions[idx]) == 0 else Parse(actions[idx][0], sc_pb.Action())\n for idx in sampled_action_id]\n\n assert len(states) == len(actions)\n\n spatial_states_np, global_states_np = [], []\n for state, action in zip(states, actions):\n action_id = -1\n if action is not None:\n try:\n func_id = feat.reverse_action(action).function\n func_name = FUNCTIONS[func_id].name\n if func_name.split('_')[0] in {'Build', 'Train', 'Research', 'Morph', 'Cancel', 'Halt', 'Stop'}:\n action_id = func_id\n except:\n pass\n\n obs = feat.transform_obs(state.observation)\n spatial_states_np.append(np.concatenate([obs['screen'], obs['minimap']], axis=0))\n\n global_states_np.append(np.hstack([obs['player']/(stat['max']+1e-5), obs['score'], [reward],\n [stat['action_id'][action_id]]]))\n\n spatial_states_np = np.asarray(spatial_states_np)\n global_states_np = np.asarray(global_states_np)\n\n spatial_states_np = spatial_states_np.reshape([len(states), -1])\n sparse.save_npz(os.path.join(FLAGS.parsed_replay_path, 'SpatialFeatureTensor',\n replay_player_path+'@S'), sparse.csc_matrix(spatial_states_np))\n sparse.save_npz(os.path.join(FLAGS.parsed_replay_path, 'SpatialFeatureTensor',\n replay_player_path+'@G'), sparse.csc_matrix(global_states_np))\n\nclass Parser(object):\n def __init__(self, race_vs_race, races, stats):\n self.race_vs_race = race_vs_race\n self.races = races\n self.stats = stats\n\n def __call__(self, line):\n replay_path, replay_info_path = line\n with open(replay_info_path) as f:\n info = json.load(f)\n info = Parse(info['info'], sc_pb.ResponseReplayInfo())\n\n replay_name = os.path.basename(replay_path)\n sampled_action_path = os.path.join(FLAGS.parsed_replay_path, 'SampledActions', self.race_vs_race, replay_name)\n for player_info in info.player_info:\n race = sc_pb.Race.Name(player_info.player_info.race_actual)\n player_id = player_info.player_info.player_id\n reward = 2 - player_info.player_result.result\n\n replay_player_path = os.path.join(self.race_vs_race, race, '{}@{}'.format(player_id, replay_name))\n parse_replay(replay_player_path, sampled_action_path, reward, race,\n race if len(self.races) == 1 else list(self.races - {race})[0], self.stats[race])\n\nmax_keys = ['frame_id', 'minerals', 'vespene', 'food_cap',\n 'food_cap', 'food_cap', 'food_cap', 'idle_worker_count',\n 'army_count', 'warp_gate_count', 'larva_count']\n\ndef main():\n with open(FLAGS.hq_replay_set) as f:\n replay_list = sorted(json.load(f))\n\n race_vs_race = os.path.basename(FLAGS.hq_replay_set).split('.')[0]\n global_feature_vec_path = os.path.join(FLAGS.parsed_replay_path, 'SpatialFeatureTensor', race_vs_race)\n races = set(race_vs_race.split('_vs_'))\n stats = {}\n for race in races:\n path = os.path.join(global_feature_vec_path, race)\n if not os.path.isdir(path):\n os.makedirs(path)\n\n stat = load_stat(os.path.join(FLAGS.parsed_replay_path, 'Stat', '{}.json'.format(race)))\n stats[race] = {'max': np.asarray([stat['max_'+k] for k in max_keys]),\n 'action_id': stat['action_id']}\n\n pbar = tqdm(total=len(replay_list), desc='#Replay')\n with Pool(FLAGS.n_workers) as p:\n for _ in p.imap(Parser(race_vs_race, races, stats), replay_list):\n pbar.update()\n\nif __name__ == '__main__':\n main()\n" }, { "path": "extract_features/split.py", "content": "import os\nimport json\nimport numpy as np\nfrom absl import flags\n\nfrom google.protobuf.json_format import Parse\nfrom s2clientprotocol import sc2api_pb2 as sc_pb\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string(name='hq_replay_set', default='../high_quality_replays/Terran_vs_Terran.json',\n help='File storing replays list')\nflags.DEFINE_string(name='root', default='..',\n help='Root for parsed replays')\nflags.DEFINE_string(name='parsed_replay_path', default='parsed_replays',\n help='Path for parsed replays')\nflags.DEFINE_string(name='save_path', default='../train_val_test',\n help='Path for saving results')\nflags.DEFINE_string(name='ratio', default='7:1:2',\n help='train:val:test')\nflags.DEFINE_integer(name='seed', default=1,\n help='random seed')\n\ndef save(replays, prefix, folder):\n print('{}/{}: {}'.format(folder, prefix, len(replays)))\n with open(os.path.join(folder, prefix+'.json'), 'w') as f:\n json.dump(replays, f)\n\ndef main():\n np.random.seed(FLAGS.seed)\n ratio = np.asarray([float(i) for i in FLAGS.ratio.split(':')])\n ratio /= np.sum(ratio)\n\n if not os.path.isdir(FLAGS.save_path):\n os.makedirs(FLAGS.save_path)\n\n with open(FLAGS.hq_replay_set) as f:\n replays_list = json.load(f)\n\n result = []\n race_vs_race = os.path.basename(FLAGS.hq_replay_set).split('.')[0]\n for replay_path, info_path in replays_list:\n replay_path_dict = {}\n replay_name = os.path.basename(replay_path)\n\n ## Parsed Replay\n for race in set(race_vs_race.split('_vs_')):\n replay_path_dict[race] = []\n\n with open(info_path) as f:\n info = json.load(f)\n proto = Parse(info['info'], sc_pb.ResponseReplayInfo())\n for p in proto.player_info:\n player_id = p.player_info.player_id\n race = sc_pb.Race.Name(p.player_info.race_actual)\n\n parsed_replays_info = {}\n ## Global Feature\n global_path = os.path.join(FLAGS.parsed_replay_path, 'GlobalFeatureVector', race_vs_race, race,\n '{}@{}.npz'.format(player_id, replay_name))\n\n if os.path.isfile(os.path.join(FLAGS.root, global_path)):\n parsed_replays_info['global_path'] = global_path\n ## Spatial Feature\n spatial_path_S = os.path.join(FLAGS.parsed_replay_path, 'SpatialFeatureTensor', race_vs_race, race,\n '{}@{}@S.npz'.format(player_id, replay_name))\n if os.path.isfile(os.path.join(FLAGS.root, spatial_path_S)):\n parsed_replays_info['spatial_path_S'] = spatial_path_S\n\n spatial_path_G = os.path.join(FLAGS.parsed_replay_path, 'SpatialFeatureTensor', race_vs_race, race,\n '{}@{}@G.npz'.format(player_id, replay_name))\n if os.path.isfile(os.path.join(FLAGS.root, spatial_path_G)):\n parsed_replays_info['spatial_path_G'] = spatial_path_G\n\n replay_path_dict[race].append(parsed_replays_info)\n\n result.append(replay_path_dict)\n\n FLAGS.save_path = os.path.join(FLAGS.save_path, race_vs_race)\n if not os.path.isdir(FLAGS.save_path):\n os.makedirs(FLAGS.save_path)\n\n train_end = int(len(result)*ratio[0])\n val_end = int(len(result)*(ratio[0]+ratio[1]))\n\n np.random.shuffle(result)\n save(result[:train_end], 'train', FLAGS.save_path)\n save(result[train_end:val_end], 'val', FLAGS.save_path)\n save(result[val_end:], 'test', FLAGS.save_path)\n\nif __name__ == '__main__':\n main()" }, { "path": "instructions/EasyWay.md", "content": "# Step-by-Step: The Easy Way\n## Preprocess\n```sh\ncd preprocess\nbash preprocess.sh\n```\n## Parse Replay\n```sh\ncd parse_replay\nbash parse_replay.sh $HQ_REPLAY_LIST$ [N_PROCESSES]\n```\nFor example:\n```sh\ncd parse_replay\nbash parse_replay.sh ../high_quality_replays/Protoss_vs_Terran.json 32\n```\n## Build Dataset\n```sh\ncd extract_features\n```\n### Compute Stat\n```sh\nbash compute_stat.sh [RACE]\n```\nFor example:\n```sh\nbash compute_stat.sh Terran\n```\n### Extract Features\n```sh\nbash extract_features.sh $HQ_REPLAY_LIST$\n```\nFor example:\n```sh\nbash extract_features.sh ../high_quality_replays/Protoss_vs_Terran.json\n```" }, { "path": "instructions/HardWay.md", "content": "# Step-by-Step: Details\n## Preprocessing Replays\n```sh\ncd preprocess\n```\n### Parse Replay Info\n```sh\npython parse_replay_info.py\n --replays_paths $REPLAY_FOLDER_PATH_1$;$REPLAY_FOLDER_PATH_2$;...;$REPLAY_FOLDER_PATH_N$\n --save_path $SAVE_PATH$\n --n_instance [N_PROCESSES]\n --batch_size [BATCH_SIZE]\n```\n- **Code for reading processed files:**\n ```python\n import json\n from google.protobuf.json_format import Parse\n from s2clientprotocol import sc2api_pb2 as sc_pb\n\n with open(REPLAY_INFO_PATH) as f:\n info = json.load(f)\n REPLAY_PATH = info['path']\n REPLAY_INFO_PROTO = Parse(info['info'], sc_pb.ResponseReplayInfo())\n ```\n **ResponseReplayInfo** is defined [Here](https://github.com/Blizzard/s2client-proto/blob/4028f80aac30120f541e0e103efd63e921f1b7d5/s2clientprotocol/sc2api.proto#L398).\n### Filter Replays\n```sh\npython preprocess.py\n --infos_path $REPLAY_INFO_PATH$\n --save_path $SAVE_PATH$\n --min_duration [MIN_DURATION]\n --max_duration [MAX_DURATION]\n --min_apm [MIN_APM]\n --min_mmr [MIN_MMR]\n```\n- **Format of processed files [JSON]:**\n ```python\n [[REPLAY_PATH_1, REPLAY_INFO_PATH_1],\n [REPLAY_PATH_2, REPLAY_INFO_PATH_2],\n ...,\n [REPLAY_PATH_N, REPLAY_INFO_PATH_N]]\n ```\n## Parsing Replays\n```sh\ncd parse_replay\n```\n### Extract Actions\n```sh\npython extract_actions.py\n --hq_replay_set $PREFILTERED_REPLAY_LIST$\n --save_path $SAVE_PATH$\n --n_instance [N_PROCESSES]\n --batch_size [BATCH_SIZE]\n --step_mul [STEP_SIZE]\n --width [WORLD_WIDTH]\n --map_size [MAP_SIZE]\n```\n- **Code for reading processed files:**\n ```python\n import json\n from google.protobuf.json_format import Parse\n from s2clientprotocol import sc2api_pb2 as sc_pb\n\n with open(ACTION_PATH) as f:\n actions = json.load(f)\n\n for actions_per_frame in actions:\n for action_str in actions_per_frame:\n action = Parse(action_str, sc_pb.Action())\n ```\n **Action** is defined [Here](https://github.com/Blizzard/s2client-proto/blob/4028f80aac30120f541e0e103efd63e921f1b7d5/s2clientprotocol/sc2api.proto#L553).\n### Sample Actions\n```sh\npython sample_actions.py\n --hq_replay_set $PREFILTERED_REPLAY_LIST$\n --parsed_replays $PARSED_REPLAYS$\n --infos_path $REPLAY_INFOS$\n --step_mul [STEP_SIZE]\n --skip [SKIP_FRAMES]\n```\n- **Format of processed files [JSON]:**\n ```python\n [FRAME_ID_1, FRAME_ID_2, ..., FRAME_ID_N]\n ```\n### Extract Sampled Observations\n```sh\npython parse_replay.py\n --hq_replay_set $PREFILTERED_REPLAY_LIST$\n --save_path $SAVE_PATH$\n --n_instance [N_PROCESSES]\n --batch_size [BATCH_SIZE]\n --width [WORLD_WIDTH]\n --map_size [MAP_SIZE]\n```\n- **Code for reading GlobalInfos files:**\n ```python\n import json\n from google.protobuf.json_format import Parse\n from s2clientprotocol import sc2api_pb2 as sc_pb\n\n with open(GLOBAL_INFO_PATH) as f:\n global_info = json.load(f)\n GAME_INFO = Parse(global_info['game_info'], sc_pb.ResponseGameInfo())\n DATA_RAW = Parse(global_info['data_raw'], sc_pb.ResponseData())\n ```\n **ResponseGameInfo** is defined [Here](https://github.com/Blizzard/s2client-proto/blob/4028f80aac30120f541e0e103efd63e921f1b7d5/s2clientprotocol/sc2api.proto#L315) while **ResponseData** is defined [Here](https://github.com/Blizzard/s2client-proto/blob/4028f80aac30120f541e0e103efd63e921f1b7d5/s2clientprotocol/sc2api.proto#L367).\n- **Code for reading SampledObservations files**\n ```python\n import stream\n from s2clientprotocol import sc2api_pb2 as sc_pb\n\n OBS = [obs for obs in stream.parse(SAMPLED_OBSERVATION_PATH), sc_pb.ResponseObservation)]\n ```\n **ResponseObsevation** is defined [Here](https://github.com/Blizzard/s2client-proto/blob/4028f80aac30120f541e0e103efd63e921f1b7d5/s2clientprotocol/sc2api.proto#L329).\n### Extract Global Features\n```sh\npython replay2global_features.py\n --hq_replay_set $PREFILTERED_REPLAY_LIST$\n --parsed_replay_path: $PARSED_REPLAYS$\n --step_mul [STEP_SIZE]\n```\n- **Format of processed files [JSON]:**\n ```python\n [state_1, state_2, ..., state_N]\n state_t = {...} [READ THE CODE or PRINT]\n ```\n## Build Dataset\n```sh\ncd extract_features\n```\n### Compute Stat\n```sh\npython replay_stat.py\n --hq_replay_path $PREFILTERED_REPLAY_FOLDER$\n --parsed_replay_path $PARSED_REPLAYS$\n --race [RACE]\n```\nThe stat files with postfix **_human.json** is human-readable.\n### Extract Features\n- Global Feature Vector\n ```sh\n python global_feature_vector.py\n --hq_replay_set $PREFILTERED_REPLAY_LIST$\n --parsed_replay_path: $PARSED_REPLAYS$\n ```\n- Spatial Feature Tensor\n ```sh\n python spatial_feature_tensor.py\n --hq_replay_set $PREFILTERED_REPLAY_LIST$\n --parsed_replay_path: $PARSED_REPLAYS$\n --step_mul [STEP_SIZE]\n --n_workers [#PROCESSES]\n ```\n### Split Training, Validation and Test sets\n```sh\npython split.py\n --hq_replay_set $PREFILTERED_REPLAY_LIST$\n --root $ROOT_PARSED_REPLAYS$\n --parsed_replay_path $PARSED_REPLAYS$\n --save_path $SAVE_PATH$\n --ratio [TRAIN:VAL:TEST]\n --seed [RANDOM_SEED]\n```\n- **Format of processed files [JSON]:**\n ```python\n [{RACE_1: [{\"global__path\": GLOBAL_FEATURE_PATH,\n \"spatial_path_S\": SPATIAL_FEATURE_PATH_S,\n \"spatial_path_G\": SPATIAL_FEATURE_PATH_G}, ...],\n RACE_2: [{...}, ...]}, {...}, ...]\n ```\n- **NOTE:** The pre-split training, validation and test sets are available in [**Here**](https://github.com/wuhuikai/MSC/tree/master/train_val_test)." }, { "path": "parse_replay/extract_actions.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport json\nimport time\nimport signal\nimport threading\nimport queue as Queue\nimport multiprocessing\nfrom absl import flags\nfrom future.builtins import range\n\nfrom google.protobuf.json_format import MessageToJson\n\nfrom pysc2 import run_configs\nfrom pysc2.lib import point\nfrom s2clientprotocol import sc2api_pb2 as sc_pb\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string(name='hq_replay_set', default='../high_quality_replays/Terran_vs_Terran.json',\n help='File storing replays list')\nflags.DEFINE_string(name='save_path', default='../parsed_replays',\n help='Path for saving results')\n\nflags.DEFINE_integer(name='n_instance', default=16,\n help='# of processes to run')\nflags.DEFINE_integer(name='step_mul', default=8,\n help='step size')\nflags.DEFINE_integer(name='batch_size', default=10,\n help='# of replays to process in one iter')\nflags.DEFINE_integer(name='width', default=24,\n help='World width')\nflags.DEFINE_integer(name='map_size', default=64,\n help='Map size')\n\nFLAGS(sys.argv)\nsize = point.Point(FLAGS.map_size, FLAGS.map_size)\ninterface = sc_pb.InterfaceOptions(raw=True, score=False,\n feature_layer=sc_pb.SpatialCameraSetup(width=FLAGS.width))\nsize.assign_to(interface.feature_layer.resolution)\nsize.assign_to(interface.feature_layer.minimap_resolution)\n\nclass ReplayProcessor(multiprocessing.Process):\n \"\"\"A Process that pulls replays and processes them.\"\"\"\n def __init__(self, run_config, replay_queue, counter, total_num):\n super(ReplayProcessor, self).__init__()\n self.run_config = run_config\n self.replay_queue = replay_queue\n self.counter = counter\n self.total_num = total_num\n\n def run(self):\n signal.signal(signal.SIGTERM, lambda a, b: sys.exit()) # Exit quietly.\n while True:\n with self.run_config.start() as controller:\n for _ in range(FLAGS.batch_size):\n try:\n replay_path = self.replay_queue.get()\n except Queue.Empty:\n return\n\n try:\n with self.counter.get_lock():\n self.counter.value += 1\n print('Processing {}/{} ...'.format(self.counter.value, self.total_num))\n\n replay_data = self.run_config.replay_data(replay_path)\n info = controller.replay_info(replay_data)\n map_data = None\n if info.local_map_path:\n map_data = self.run_config.map_data(info.local_map_path)\n\n for player_info in info.player_info:\n race = sc_pb.Race.Name(player_info.player_info.race_actual)\n player_id = player_info.player_info.player_id\n\n if os.path.isfile(os.path.join(FLAGS.save_path, race,\n '{}@{}'.format(player_id, os.path.basename(replay_path)))):\n continue\n\n self.process_replay(controller, replay_data, map_data, player_id, race, replay_path)\n except Exception as e:\n print(e)\n break\n finally:\n self.replay_queue.task_done()\n\n def process_replay(self, controller, replay_data, map_data, player_id, race, replay_path):\n controller.start_replay(sc_pb.RequestStartReplay(\n replay_data=replay_data,\n map_data=map_data,\n options=interface,\n observed_player_id=player_id))\n\n save_folder = os.path.join(FLAGS.save_path, race)\n actions = []\n controller.step()\n while True:\n obs = controller.observe()\n actions.append([MessageToJson(a) for a in obs.actions])\n\n if obs.player_result:\n with open(os.path.join(save_folder, '{}@{}'.format(\n player_id, os.path.basename(replay_path))), 'w') as f:\n json.dump(actions, f)\n return\n controller.step(FLAGS.step_mul)\n\ndef replay_queue_filler(replay_queue, replay_list):\n \"\"\"A thread that fills the replay_queue with replay filenames.\"\"\"\n for replay_path in replay_list:\n replay_queue.put(replay_path)\n\ndef main():\n race_vs_race = os.path.basename(FLAGS.hq_replay_set).split('.')[0]\n FLAGS.save_path = os.path.join(FLAGS.save_path, 'Actions', race_vs_race)\n\n for race in set(race_vs_race.split('_vs_')):\n path = os.path.join(FLAGS.save_path, race)\n if not os.path.isdir(path):\n os.makedirs(path)\n\n run_config = run_configs.get()\n try:\n with open(FLAGS.hq_replay_set) as f:\n replay_list = json.load(f)\n replay_list = sorted([p for p, _ in replay_list])\n\n replay_queue = multiprocessing.JoinableQueue(FLAGS.n_instance * 10)\n replay_queue_thread = threading.Thread(target=replay_queue_filler,\n args=(replay_queue, replay_list))\n replay_queue_thread.daemon = True\n replay_queue_thread.start()\n\n counter = multiprocessing.Value('i', 0)\n for i in range(FLAGS.n_instance):\n p = ReplayProcessor(run_config, replay_queue, counter, len(replay_list))\n p.daemon = True\n p.start()\n time.sleep(1) # Stagger startups, otherwise they seem to conflict somehow\n\n replay_queue.join() # Wait for the queue to empty.\n except KeyboardInterrupt:\n print(\"Caught KeyboardInterrupt, exiting.\")\n\nif __name__ == '__main__':\n main()\n" }, { "path": "parse_replay/parse_replay.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport json\nimport time\nimport signal\nimport threading\nimport queue as Queue\nimport multiprocessing\nfrom absl import flags\nfrom future.builtins import range\n\nfrom google.protobuf.json_format import MessageToJson\n\nfrom pysc2 import run_configs\nfrom pysc2.lib import point\nfrom s2clientprotocol import sc2api_pb2 as sc_pb\n\nimport stream\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string(name='hq_replay_set', default='../high_quality_replays/Terran_vs_Terran.json',\n help='File storing replays list')\nflags.DEFINE_string(name='save_path', default='../parsed_replays',\n help='Path for saving results')\n\nflags.DEFINE_integer(name='n_instance', default=16,\n help='# of processes to run')\nflags.DEFINE_integer(name='batch_size', default=10,\n help='# of replays to process in one iter')\n\nflags.DEFINE_integer(name='width', default=24,\n help='World width')\nflags.DEFINE_integer(name='map_size', default=64,\n help='Map size')\n\nFLAGS(sys.argv)\nsize = point.Point(FLAGS.map_size, FLAGS.map_size)\ninterface = sc_pb.InterfaceOptions(raw=True, score=True,\n feature_layer=sc_pb.SpatialCameraSetup(width=FLAGS.width))\nsize.assign_to(interface.feature_layer.resolution)\nsize.assign_to(interface.feature_layer.minimap_resolution)\n\nclass ReplayProcessor(multiprocessing.Process):\n \"\"\"A Process that pulls replays and processes them.\"\"\"\n def __init__(self, run_config, replay_queue, counter, total_num):\n super(ReplayProcessor, self).__init__()\n self.run_config = run_config\n self.replay_queue = replay_queue\n self.counter = counter\n self.total_num = total_num\n\n def run(self):\n signal.signal(signal.SIGTERM, lambda a, b: sys.exit()) # Exit quietly.\n while True:\n with self.run_config.start() as controller:\n for _ in range(FLAGS.batch_size):\n try:\n replay_path = self.replay_queue.get()\n except Queue.Empty:\n return\n try:\n with self.counter.get_lock():\n self.counter.value += 1\n print('Processing {}/{} ...'.format(self.counter.value, self.total_num))\n\n sampled_action_path = os.path.join(FLAGS.save_path.replace(\n 'SampledObservations', 'SampledActions'), os.path.basename(replay_path))\n if not os.path.isfile(sampled_action_path):\n return\n\n with open(sampled_action_path) as f:\n actions = json.load(f)\n actions.insert(0, 0)\n\n replay_data = self.run_config.replay_data(replay_path)\n info = controller.replay_info(replay_data)\n map_data = None\n if info.local_map_path:\n map_data = self.run_config.map_data(info.local_map_path)\n\n for player_info in info.player_info:\n race = sc_pb.Race.Name(player_info.player_info.race_actual)\n player_id = player_info.player_info.player_id\n\n observation_path = os.path.join(FLAGS.save_path, race,\n '{}@{}'.format(player_id, os.path.basename(replay_path)))\n global_info_path = observation_path.replace('SampledObservations', 'GlobalInfos')\n\n if os.path.isfile(observation_path) and os.path.isfile(global_info_path):\n continue\n\n ostream = stream.open(observation_path, 'wb', buffer_size=1000)\n self.process_replay(controller, replay_data, map_data, player_id, actions,\n ostream, global_info_path)\n ostream.close()\n except Exception as e:\n try:\n ostream.close()\n if os.path.isfile(observation_path):\n os.remove(observation_path)\n\n if os.path.isfile(global_info_path):\n os.remove(global_info_path)\n except:\n pass\n\n print(e)\n break\n finally:\n self.replay_queue.task_done()\n\n def process_replay(self, controller, replay_data, map_data, player_id, actions, ostream, global_info_path):\n controller.start_replay(sc_pb.RequestStartReplay(\n replay_data=replay_data,\n map_data=map_data,\n options=interface,\n observed_player_id=player_id))\n\n global_info = {'game_info': controller.game_info(),\n 'data_raw': controller.data_raw()}\n with open(global_info_path, 'w') as f:\n json.dump({k:MessageToJson(v) for k, v in global_info.items()}, f)\n\n controller.step()\n for pre_id, id in zip(actions[:-1], actions[1:]):\n controller.step(id - pre_id)\n obs = controller.observe()\n ostream.write(obs)\n\ndef replay_queue_filler(replay_queue, replay_list):\n \"\"\"A thread that fills the replay_queue with replay filenames.\"\"\"\n for replay_path in replay_list:\n replay_queue.put(replay_path)\n\ndef main():\n race_vs_race = os.path.basename(FLAGS.hq_replay_set).split('.')[0]\n FLAGS.save_path = os.path.join(FLAGS.save_path, 'SampledObservations', race_vs_race)\n\n for race in set(race_vs_race.split('_vs_')):\n path = os.path.join(FLAGS.save_path, race)\n if not os.path.isdir(path):\n os.makedirs(path)\n\n path = path.replace('SampledObservations', 'GlobalInfos')\n if not os.path.isdir(path):\n os.makedirs(path)\n\n run_config = run_configs.get()\n try:\n with open(FLAGS.hq_replay_set) as f:\n replay_list = json.load(f)\n replay_list = sorted([p for p, _ in replay_list])\n\n replay_queue = multiprocessing.JoinableQueue(FLAGS.n_instance * 10)\n replay_queue_thread = threading.Thread(target=replay_queue_filler,\n args=(replay_queue, replay_list))\n replay_queue_thread.daemon = True\n replay_queue_thread.start()\n\n counter = multiprocessing.Value('i', 0)\n for i in range(FLAGS.n_instance):\n p = ReplayProcessor(run_config, replay_queue, counter, len(replay_list))\n p.daemon = True\n p.start()\n time.sleep(1) # Stagger startups, otherwise they seem to conflict somehow\n\n replay_queue.join() # Wait for the queue to empty.\n except KeyboardInterrupt:\n print(\"Caught KeyboardInterrupt, exiting.\")\n\nif __name__ == '__main__':\n main()\n" }, { "path": "parse_replay/parse_replay.sh", "content": "python extract_actions.py --hq_replay_set $1 --n_instance $2 &&\npython sample_actions.py --hq_replay_set $1 &&\npython parse_replay.py --hq_replay_set $1 --n_instance $2 &&\npython replay2global_features.py --hq_replay_set $1" }, { "path": "parse_replay/replay2global_features.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport json\nimport stream\nfrom absl import flags\n\nfrom tqdm import tqdm\n\nfrom google.protobuf.json_format import Parse\n\nfrom pysc2.lib import features\nfrom pysc2.lib import FUNCTIONS\nfrom pysc2.lib import static_data\nfrom s2clientprotocol import sc2api_pb2 as sc_pb\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string(name='hq_replay_set', default='../high_quality_replays/Terran_vs_Terran.json',\n help='File storing replays list')\nflags.DEFINE_string(name='parsed_replay_path', default='../parsed_replays',\n help='Path storing parsed replays')\nflags.DEFINE_integer(name='step_mul', default=8,\n help='step size')\n\ndef process_replay(sampled_action, actions, observations, feat, units_info, reward):\n states = []\n\n for frame_id, action, obs in zip(sampled_action, actions, observations):\n state = {}\n # actions\n state['action'] = None\n if action is not None:\n try:\n func_id = feat.reverse_action(action).function\n func_name = FUNCTIONS[func_id].name\n if func_name.split('_')[0] in {'Build', 'Train', 'Research', 'Morph', 'Cancel', 'Halt', 'Stop'}:\n state['action'] = (func_id, func_name)\n except ValueError:\n pass\n\n observation = obs.observation\n #####################################################\n # frame_id\n assert frame_id == observation.game_loop-1\n state['frame_id'] = frame_id\n # reward\n state['reward'] = reward\n\n state['score_cumulative'] = [\n observation.score.score,\n observation.score.score_details.idle_production_time,\n observation.score.score_details.idle_worker_time,\n observation.score.score_details.total_value_units,\n observation.score.score_details.total_value_structures,\n observation.score.score_details.killed_value_units,\n observation.score.score_details.killed_value_structures,\n observation.score.score_details.collected_minerals,\n observation.score.score_details.collected_vespene,\n observation.score.score_details.collection_rate_minerals,\n observation.score.score_details.collection_rate_vespene,\n observation.score.score_details.spent_minerals,\n observation.score.score_details.spent_vespene,\n ]\n # resources\n resources = observation.player_common\n state['minerals'] = resources.minerals\n state['vespene'] = resources.vespene\n state['food_cap'] = resources.food_cap\n state['food_used'] = resources.food_used\n state['food_army'] = resources.food_army\n state['food_workers'] = resources.food_workers\n state['idle_worker_count'] = resources.idle_worker_count\n state['army_count'] = resources.army_count\n state['warp_gate_count'] = resources.warp_gate_count\n state['larva_count'] = resources.larva_count\n #####################################################\n # alert\n state['alert'] = list(observation.alerts)\n\n #####################################################\n ### raw data\n raw_data = observation.raw_data\n ## player\n player = raw_data.player\n # upgrades\n state['upgrades'] = list(player.upgrade_ids)\n # power\n state['n_power_source'] = len(player.power_sources)\n #####################################################\n ## units\n state['friendly_units'] = {}\n state['enemy_units'] = {}\n for unit in raw_data.units:\n if unit.display_type == 3:\n continue\n if unit.alliance != 1 and unit.alliance != 4:\n continue\n # Friendly or Enemy\n units = state['friendly_units'] if unit.alliance == 1 else state['enemy_units']\n # Already have this unit_type ?\n unit_type = unit.unit_type\n if unit_type not in units:\n units[unit_type] = {'units': [], 'name': units_info[unit_type]}\n # Basic info\n unit_info = {'tag': unit.tag,\n 'build_progress': unit.build_progress}\n\n units[unit_type]['units'].append(unit_info)\n\n states.append(state)\n\n return states\n\ndef parse_replay(replay_player_path, sampled_action_path, reward):\n if os.path.isfile(os.path.join(FLAGS.parsed_replay_path, 'GlobalFeatures', replay_player_path)):\n return\n\n # Global Info\n with open(os.path.join(FLAGS.parsed_replay_path, 'GlobalInfos', replay_player_path)) as f:\n global_info = json.load(f)\n units_info = static_data.StaticData(Parse(global_info['data_raw'], sc_pb.ResponseData())).units\n feat = features.Features(Parse(global_info['game_info'], sc_pb.ResponseGameInfo()))\n\n # Sampled Actions\n with open(sampled_action_path) as f:\n sampled_action = json.load(f)\n sampled_action_id = [id // FLAGS.step_mul + 1 for id in sampled_action]\n\n # Actions\n with open(os.path.join(FLAGS.parsed_replay_path, 'Actions', replay_player_path)) as f:\n actions = json.load(f)\n actions = [None if len(actions[idx]) == 0 else Parse(actions[idx][0], sc_pb.Action())\n for idx in sampled_action_id]\n\n # Observations\n observations = [obs for obs in stream.parse(os.path.join(FLAGS.parsed_replay_path,\n 'SampledObservations', replay_player_path), sc_pb.ResponseObservation)]\n\n assert len(sampled_action) == len(sampled_action_id) == len(actions) == len(observations)\n\n states = process_replay(sampled_action, actions, observations, feat, units_info, reward)\n\n with open(os.path.join(FLAGS.parsed_replay_path, 'GlobalFeatures', replay_player_path), 'w') as f:\n json.dump(states, f)\n\ndef main():\n with open(FLAGS.hq_replay_set) as f:\n replay_list = sorted(json.load(f))\n\n race_vs_race = os.path.basename(FLAGS.hq_replay_set).split('.')[0]\n global_feature_path = os.path.join(FLAGS.parsed_replay_path, 'GlobalFeatures', race_vs_race)\n for race in set(race_vs_race.split('_vs_')):\n path = os.path.join(global_feature_path, race)\n if not os.path.isdir(path):\n os.makedirs(path)\n\n pbar = tqdm(total=len(replay_list), desc='#Replay')\n for replay_path, replay_info_path in replay_list:\n with open(replay_info_path) as f:\n info = json.load(f)\n info = Parse(info['info'], sc_pb.ResponseReplayInfo())\n\n replay_name = os.path.basename(replay_path)\n sampled_action_path = os.path.join(FLAGS.parsed_replay_path, 'SampledActions', race_vs_race, replay_name)\n for player_info in info.player_info:\n race = sc_pb.Race.Name(player_info.player_info.race_actual)\n player_id = player_info.player_info.player_id\n reward = player_info.player_result.result\n\n replay_player_path = os.path.join(race_vs_race, race, '{}@{}'.format(player_id, replay_name))\n parse_replay(replay_player_path, sampled_action_path, reward)\n\n pbar.update()\n\nif __name__ == '__main__':\n main()\n" }, { "path": "parse_replay/sample_actions.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport json\nfrom absl import flags\nfrom tqdm import tqdm\n\nfrom google.protobuf.json_format import Parse\n\nfrom pysc2.lib import features\nfrom pysc2.lib import FUNCTIONS\nfrom s2clientprotocol import sc2api_pb2 as sc_pb\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string(name='hq_replay_set', default='../high_quality_replays/Terran_vs_Terran.json',\n help='File storing replays list')\nflags.DEFINE_string(name='parsed_replays', default='../parsed_replays',\n help='Path for parsed actions')\nflags.DEFINE_string(name='infos_path', default='../replays_infos',\n help='Paths for infos of replays')\nflags.DEFINE_integer(name='step_mul', default=8,\n help='step size')\nflags.DEFINE_integer(name='skip', default=96,\n help='# of skipped frames')\n\ndef sample_action_from_player(action_path):\n feat = features.Features(screen_size_px=(1, 1), minimap_size_px=(1, 1))\n with open(action_path) as f:\n actions = json.load(f)\n\n frame_id = 0\n result_frames = []\n for action_strs in actions:\n action_name = None\n for action_str in action_strs:\n action = Parse(action_str, sc_pb.Action())\n try:\n func_id = feat.reverse_action(action).function\n func_name = FUNCTIONS[func_id].name\n if func_name.split('_')[0] in {'Build', 'Train', 'Research', 'Morph', 'Cancel', 'Halt', 'Stop'}:\n action_name = func_name\n break\n except:\n pass\n if frame_id > 0 and (action_name is not None or frame_id%FLAGS.skip == 0):\n result_frames.append(frame_id-FLAGS.step_mul)\n\n frame_id += FLAGS.step_mul\n\n return result_frames\n\ndef sample_action(replay_path, action_path, sampled_path):\n replay_info = os.path.join(FLAGS.infos_path, replay_path)\n if not os.path.isfile(replay_info):\n return\n with open(replay_info) as f:\n info = json.load(f)\n\n result = []\n proto = Parse(info['info'], sc_pb.ResponseReplayInfo())\n for p in proto.player_info:\n player_id = p.player_info.player_id\n race = sc_pb.Race.Name(p.player_info.race_actual)\n\n action_file = os.path.join(action_path, race, '{}@{}'.format(player_id, replay_path))\n if not os.path.isfile(action_file):\n return\n\n result.append(sample_action_from_player(action_file))\n\n assert len(result) == 2\n sampled_actions = sorted(set(result[0]) | set(result[1]))\n\n with open(os.path.join(sampled_path, replay_path), 'w') as f:\n json.dump(sampled_actions, f)\n\ndef main():\n with open(FLAGS.hq_replay_set) as f:\n replay_list = json.load(f)\n replay_list = sorted([p for p, _ in replay_list])\n\n race_vs_race = os.path.basename(FLAGS.hq_replay_set).split('.')[0]\n sampled_path = os.path.join(FLAGS.parsed_replays, 'SampledActions', race_vs_race)\n if not os.path.isdir(sampled_path):\n os.makedirs(sampled_path)\n action_path = os.path.join(FLAGS.parsed_replays, 'Actions', race_vs_race)\n\n pbar = tqdm(total=len(replay_list), desc='#Replay')\n for replay_path in replay_list:\n sample_action(os.path.basename(replay_path), action_path, sampled_path)\n pbar.update()\n\nif __name__ == '__main__':\n main()\n" }, { "path": "preprocess/parse_replay_info.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport time\nimport json\nimport signal\nimport threading\nimport queue as Queue\nfrom absl import flags\nimport multiprocessing\nfrom itertools import chain\nfrom future.builtins import range\nfrom google.protobuf.json_format import MessageToJson\n\nfrom pysc2 import run_configs\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string(name='replays_paths', default='./;',\n help='Paths for replays, split by ;')\nflags.DEFINE_string(name='save_path', default='../replays_infos',\n help='Path for saving results')\n\nflags.DEFINE_integer(name='n_instance', default=4,\n help='# of processes to run')\nflags.DEFINE_integer(name='batch_size', default=300,\n help='# of replays to process in one iter')\n\nclass ReplayProcessor(multiprocessing.Process):\n \"\"\"A Process that pulls replays and processes them.\"\"\"\n def __init__(self, run_config, replay_queue, counter, total_num):\n super(ReplayProcessor, self).__init__()\n self.run_config = run_config\n self.replay_queue = replay_queue\n self.counter = counter\n self.total_num = total_num\n\n def run(self):\n signal.signal(signal.SIGTERM, lambda a, b: sys.exit()) # Exit quietly.\n while True:\n with self.run_config.start() as controller:\n for _ in range(FLAGS.batch_size):\n try:\n replay_path = self.replay_queue.get()\n except Queue.Empty:\n return\n try:\n replay_data = self.run_config.replay_data(replay_path)\n info = controller.replay_info(replay_data)\n\n info_json = MessageToJson(info)\n with open(os.path.join(FLAGS.save_path, os.path.basename(replay_path)), 'w') as f:\n json.dump({'info': info_json, 'path':replay_path}, f)\n with self.counter.get_lock():\n self.counter.value += 1\n print('Processing {}/{} ...'.format(self.counter.value, self.total_num))\n finally:\n self.replay_queue.task_done()\n\ndef replay_queue_filler(replay_queue, replay_list):\n \"\"\"A thread that fills the replay_queue with replay paths.\"\"\"\n for replay_path in replay_list:\n replay_queue.put(replay_path)\n\ndef main():\n if not os.path.isdir(FLAGS.save_path):\n os.makedirs(FLAGS.save_path)\n\n run_config = run_configs.get()\n\n try:\n replay_list = sorted(chain(*[run_config.replay_paths(path)\n for path in FLAGS.replays_paths.split(';')\n if len(path.strip()) > 0]))\n replay_queue = multiprocessing.JoinableQueue(FLAGS.n_instance * 10)\n replay_queue_thread = threading.Thread(target=replay_queue_filler,\n args=(replay_queue, replay_list))\n replay_queue_thread.daemon = True\n replay_queue_thread.start()\n\n counter = multiprocessing.Value('i', 0)\n for i in range(FLAGS.n_instance):\n p = ReplayProcessor(run_config, replay_queue, counter, len(replay_list))\n p.daemon = True\n p.start()\n time.sleep(1) # Stagger startups, otherwise they seem to conflict somehow\n\n replay_queue.join() # Wait for the queue to empty.\n except KeyboardInterrupt:\n print(\"Caught KeyboardInterrupt, exiting.\")\n\nif __name__ == '__main__':\n main()\n" }, { "path": "preprocess/preprocess.py", "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport json\nimport glob\nfrom absl import flags\nfrom tqdm import tqdm\n\nfrom google.protobuf.json_format import Parse\n\nfrom pysc2 import run_configs\nfrom s2clientprotocol import sc2api_pb2 as sc_pb\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string(name='infos_path', default='../replays_infos',\n help='Paths for infos of replays')\nflags.DEFINE_string(name='save_path', default='../high_quality_replays',\n help='Path for saving results')\n\nflags.DEFINE_integer(name='min_duration', default=10000,\n help='Min duration')\nflags.DEFINE_integer(name='max_duration', default=None,\n help='Max duration')\nflags.DEFINE_integer(name='min_apm', default=10,\n help='Min APM')\nflags.DEFINE_integer(name='min_mmr', default=1000,\n help='Min MMR')\n\ndef valid_replay(info, ping):\n \"\"\"Make sure the replay isn't corrupt, and is worth looking at.\"\"\"\n if info.HasField(\"error\"):\n return False\n if info.base_build != ping.base_build:\n return False\n if info.game_duration_loops < FLAGS.min_duration:\n return False\n if FLAGS.max_duration is not None and info.game_duration_loops > FLAGS.max_duration:\n return False\n if len(info.player_info) != 2:\n return False\n\n for p in info.player_info:\n if p.player_apm < FLAGS.min_apm or p.player_mmr < FLAGS.min_mmr:\n # Low APM = player just standing around.\n # Low MMR = corrupt replay or player who is weak.\n return False\n if p.player_result.result not in {1, 2}:\n return False\n\n return True\n\ndef main():\n if not os.path.isdir(FLAGS.save_path):\n os.makedirs(FLAGS.save_path)\n replay_infos = glob.glob(os.path.join(FLAGS.infos_path, '*.SC2Replay'))\n\n run_config = run_configs.get()\n with run_config.start() as controller:\n ping = controller.ping()\n\n result = {}\n pbar = tqdm(total=len(replay_infos), desc='#Replay')\n for info_path in replay_infos:\n with open(info_path) as f:\n info = json.load(f)\n\n proto = Parse(info['info'], sc_pb.ResponseReplayInfo())\n if valid_replay(proto, ping):\n players_info = proto.player_info\n races = '_vs_'.join(sorted(sc_pb.Race.Name(player_info.player_info.race_actual)\n for player_info in players_info))\n if races not in result:\n result[races] = []\n result[races].append((info['path'], info_path))\n pbar.update()\n\n for k, v in result.items():\n with open(os.path.join(FLAGS.save_path, k+'.json'), 'w') as f:\n json.dump(v, f)\n\nif __name__ == '__main__':\n main()\n" }, { "path": "preprocess/preprocess.sh", "content": "python parse_replay_info.py --n_instance 32 && python preprocess.py\n" }, { "path": "requirements.txt", "content": "future == 0.16.0\n\nnumpy == 1.13.0\nscipy == 0.19.0\n\npython_gflags == 3.1.1\n\ntqdm == 4.14.0\n\nprotobuf == 3.4.0\npystream_protobuf == 1.4.4\n\nPySC2 == 1.0\ns2clientprotocol == 1.1" }, { "path": "train_val_test/Protoss_vs_Protoss/test.json", "content": "[{\"Protoss\": [{\"global_path\": \"parsed_replays/GlobalFeatureVector/Protoss_vs_Protoss/Protoss/1@567732f6b9495759f6ee5cfd9748093a68f701b3e520a73929816f66a84266f2.SC2Replay.npz\", \"spatial_path_S\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/1@567732f6b9495759f6ee5cfd9748093a68f701b3e520a73929816f66a84266f2.SC2Replay@S.npz\", \"spatial_path_G\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/1@567732f6b9495759f6ee5cfd9748093a68f701b3e520a73929816f66a84266f2.SC2Replay@G.npz\"}, {\"global_path\": \"parsed_replays/GlobalFeatureVector/Protoss_vs_Protoss/Protoss/2@567732f6b9495759f6ee5cfd9748093a68f701b3e520a73929816f66a84266f2.SC2Replay.npz\", \"spatial_path_S\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/2@567732f6b9495759f6ee5cfd9748093a68f701b3e520a73929816f66a84266f2.SC2Replay@S.npz\", \"spatial_path_G\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/2@567732f6b9495759f6ee5cfd9748093a68f701b3e520a73929816f66a84266f2.SC2Replay@G.npz\"}]}, {\"Protoss\": [{\"global_path\": \"parsed_replays/GlobalFeatureVector/Protoss_vs_Protoss/Protoss/1@a6ad08a95f78fb7bde13e0961d98053584ce6cd6e1d5d21687c7198a225fc416.SC2Replay.npz\", \"spatial_path_S\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/1@a6ad08a95f78fb7bde13e0961d98053584ce6cd6e1d5d21687c7198a225fc416.SC2Replay@S.npz\", \"spatial_path_G\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/1@a6ad08a95f78fb7bde13e0961d98053584ce6cd6e1d5d21687c7198a225fc416.SC2Replay@G.npz\"}, {\"global_path\": \"parsed_replays/GlobalFeatureVector/Protoss_vs_Protoss/Protoss/2@a6ad08a95f78fb7bde13e0961d98053584ce6cd6e1d5d21687c7198a225fc416.SC2Replay.npz\", \"spatial_path_S\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/2@a6ad08a95f78fb7bde13e0961d98053584ce6cd6e1d5d21687c7198a225fc416.SC2Replay@S.npz\", \"spatial_path_G\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/2@a6ad08a95f78fb7bde13e0961d98053584ce6cd6e1d5d21687c7198a225fc416.SC2Replay@G.npz\"}]}, {\"Protoss\": [{\"global_path\": \"parsed_replays/GlobalFeatureVector/Protoss_vs_Protoss/Protoss/1@4b9484f2ea9718ed81b5ea12a489875c3110c87d4ed41c1fe72d34493e8a89b7.SC2Replay.npz\", \"spatial_path_S\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/1@4b9484f2ea9718ed81b5ea12a489875c3110c87d4ed41c1fe72d34493e8a89b7.SC2Replay@S.npz\", \"spatial_path_G\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/1@4b9484f2ea9718ed81b5ea12a489875c3110c87d4ed41c1fe72d34493e8a89b7.SC2Replay@G.npz\"}, {\"global_path\": \"parsed_replays/GlobalFeatureVector/Protoss_vs_Protoss/Protoss/2@4b9484f2ea9718ed81b5ea12a489875c3110c87d4ed41c1fe72d34493e8a89b7.SC2Replay.npz\", \"spatial_path_S\": 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\"parsed_replays/GlobalFeatureVector/Protoss_vs_Protoss/Protoss/2@39e0cbf0ed3cfb77d08ab30e27ee7447ecc54baa85e8db4c1aaf35f6f1e19284.SC2Replay.npz\", \"spatial_path_S\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/2@39e0cbf0ed3cfb77d08ab30e27ee7447ecc54baa85e8db4c1aaf35f6f1e19284.SC2Replay@S.npz\", \"spatial_path_G\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/2@39e0cbf0ed3cfb77d08ab30e27ee7447ecc54baa85e8db4c1aaf35f6f1e19284.SC2Replay@G.npz\"}]}, {\"Protoss\": [{\"global_path\": \"parsed_replays/GlobalFeatureVector/Protoss_vs_Protoss/Protoss/1@e267850865a92fc396648572fec520e74262f5db7bbec9aeee85912b9cb3f77a.SC2Replay.npz\", \"spatial_path_S\": \"parsed_replays/SpatialFeatureTensor/Protoss_vs_Protoss/Protoss/1@e267850865a92fc396648572fec520e74262f5db7bbec9aeee85912b9cb3f77a.SC2Replay@S.npz\", \"spatial_path_G\": 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