SYMBOL INDEX (282 symbols across 31 files)

FILE: src/jonasz/cifar10/cifar10_dataset.py
  class DatasetParams (line 16) | class DatasetParams(util.Params):
    method get_allowed_params_with_defaults (line 17) | def get_allowed_params_with_defaults(self):
    method validate (line 39) | def validate(self):
  function _load_from_gcs (line 48) | def _load_from_gcs(gcs_bucket, data_dir, filename):
  function _load_dict (line 59) | def _load_dict(gcs_bucket, data_dir, filename):
  function _load_train_data (line 76) | def _load_train_data(gcs_bucket, data_dir):
  function _write_data_to_tfrecord (line 89) | def _write_data_to_tfrecord(data_dict, path):
  function write_train_data_to_tfrecord (line 99) | def write_train_data_to_tfrecord(target_path, data_dir):
  function write_test_data_to_tfrecord (line 104) | def write_test_data_to_tfrecord(target_path, data_dir):
  function _load_test_data (line 109) | def _load_test_data(gcs_bucket, data_dir):
  function _random_crop (line 113) | def _random_crop(img, params):
  function _random_shift (line 129) | def _random_shift(img, params):
  function _image_noise (line 144) | def _image_noise(params):
  function _shift_pixel_values (line 162) | def _shift_pixel_values(img, params):
  function _example_to_img_and_label (line 168) | def _example_to_img_and_label(serialized_example):
  function get_train_input_fn (line 178) | def get_train_input_fn(params, batch_size=128):
  function get_test_input_fn (line 219) | def get_test_input_fn(params, batch_size=128):
  function _get_class_name (line 244) | def _get_class_name(label):
  function _test_params1 (line 259) | def _test_params1(shuffle=True):
  function _test_params2 (line 273) | def _test_params2():
  function test1 (line 279) | def test1():
  function test2 (line 294) | def test2():
  function test3 (line 309) | def test3():
  function test4 (line 321) | def test4():
  function test5 (line 333) | def test5():

FILE: src/jonasz/experiments/2018_08_28/exp_append_channels.py
  function training_params (line 16) | def training_params(is_gcloud=False, output_dir=None):
  function main (line 107) | def main(*args):

FILE: src/jonasz/experiments/2018_08_28/exp_append_simple.py
  function training_params (line 16) | def training_params(is_gcloud=False, output_dir=None):
  function main (line 106) | def main(*args):

FILE: src/jonasz/experiments/2018_08_28/exp_condition.py
  function training_params (line 16) | def training_params(is_gcloud=False, output_dir=None):
  function main (line 105) | def main(*args):

FILE: src/jonasz/experiments/2018_08_28/exp_condition_4vars.py
  function training_params (line 16) | def training_params(is_gcloud=False, output_dir=None):
  function main (line 106) | def main(*args):

FILE: src/jonasz/experiments/2018_08_28/exp_condition_8vars.py
  function training_params (line 16) | def training_params(is_gcloud=False, output_dir=None):
  function main (line 106) | def main(*args):

FILE: src/jonasz/experiments/2018_08_28/exp_consistency_30.py
  function training_params (line 15) | def training_params(is_gcloud=False, output_dir=None):
  function main (line 109) | def main(*args):

FILE: src/jonasz/experiments/2018_08_28/exp_consistency_300.py
  function training_params (line 15) | def training_params(is_gcloud=False, output_dir=None):
  function main (line 109) | def main(*args):

FILE: src/jonasz/experiments/2018_08_28/exp_gradual_loss.py
  function training_params (line 17) | def training_params(is_gcloud=False, output_dir=None):
  function main (line 116) | def main(*args):

FILE: src/jonasz/experiments/2018_08_28/exp_noinfo.py
  function training_params (line 15) | def training_params(is_gcloud=False, output_dir=None):
  function main (line 93) | def main(*args):

FILE: src/jonasz/experiments/2018_08_28/exp_unmask.py
  function training_params (line 18) | def training_params(is_gcloud=False, output_dir=None):
  function main (line 126) | def main(*args):

FILE: src/jonasz/experiments/2018_08_28/exp_vanilla.py
  function training_params (line 15) | def training_params(is_gcloud=False, output_dir=None):
  function main (line 105) | def main(*args):

FILE: src/jonasz/gan/evaluation.py
  class EvalParams (line 14) | class EvalParams(util.Params):
    method get_allowed_params_with_defaults (line 15) | def get_allowed_params_with_defaults(self):
  function _inception_logits (line 27) | def _inception_logits(gan):
  function _inception_final_pool (line 36) | def _inception_final_pool(gan):
  function _write_summaries_internal (line 51) | def _write_summaries_internal(eval_dir, sess, cur_global_step, summaries...
  function _write_summaries (line 61) | def _write_summaries(eval_dir, sess, cur_global_step, summaries=None,
  function _accumulate_n (line 72) | def _accumulate_n(tensor, sess, n):
  function _maybe_calc_inception_score (line 92) | def _maybe_calc_inception_score(gan, sess, params):
  function _maybe_calc_frechet_inception_distance (line 107) | def _maybe_calc_frechet_inception_distance(gan, sess, eval_params):
  function calc_mean (line 125) | def calc_mean(vals):
  function _maybe_calc_infogan_isolation (line 131) | def _maybe_calc_infogan_isolation(gan, sess, training_params, version='m...
  function block_to_corresponding_cont_coords (line 232) | def block_to_corresponding_cont_coords(tp, block_id):
  function _maybe_calc_infogan_metrics (line 253) | def _maybe_calc_infogan_metrics(gan_model_dict, sess, training_params):
  class _RestoreGANSession (line 322) | class _RestoreGANSession(object):
    method __init__ (line 323) | def __init__(self, model_dir = None, vars_to_restore=None,
    method __enter__ (line 339) | def __enter__(self):
    method __exit__ (line 358) | def __exit__(self, *args):
  function _get_side_for_dynamic_mse (line 364) | def _get_side_for_dynamic_mse(i, steps, img_side):
  class maybe_gpu_tower_scope (line 370) | class maybe_gpu_tower_scope(object):
    method __init__ (line 371) | def __init__(self, use_gpu_tower_scope):
    method __enter__ (line 380) | def __enter__(self):
    method __exit__ (line 384) | def __exit__(self, *args):
  function _draw_images (line 389) | def _draw_images(training_params, gan_fn, how_many=32, type_='real',
  function _calc_msssim_diversity_from_images (line 428) | def _calc_msssim_diversity_from_images(images):
  function _maybe_calc_msssim_diversity (line 449) | def _maybe_calc_msssim_diversity(gan_fn, training_params):
  function evaluate (line 496) | def evaluate(gan_fn, training_params):

FILE: src/jonasz/lib/datasets.py
  class DummyDatasetParams (line 8) | class DummyDatasetParams(util.Params):
    method get_allowed_params_with_defaults (line 9) | def get_allowed_params_with_defaults(self):
  function get_dummy_dataset_input_fn (line 17) | def get_dummy_dataset_input_fn(params, batch_size):
  function get_input_fn (line 34) | def get_input_fn(dataset_params, batch_size):

FILE: src/jonasz/lib/progressive_infogan_ipynb_utils.py
  function restart_server (line 26) | def restart_server():
  function get_images (line 45) | def get_images(model, gen_inputs):
  function manual_animation (line 56) | def manual_animation(model, gen_inp, coords=range(64,80)):
  function interpolate (line 102) | def interpolate(model, inp, coord, steps=3):
  function blur (line 112) | def blur(a, stddev):
  function diff2 (line 120) | def diff2(a, b):
  function avg_change (line 128) | def avg_change(model, coord,

FILE: src/jonasz/lib/tensor_util.py
  function nchw_to_nhwc (line 4) | def nchw_to_nhwc(images):
  function nhwc_to_nchw (line 8) | def nhwc_to_nchw(images):
  function nchw_to_nhwc_single (line 12) | def nchw_to_nhwc_single(images):
  function nhwc_to_nchw_single (line 16) | def nhwc_to_nchw_single(images):

FILE: src/jonasz/lib/util.py
  function CHW_to_HWC (line 23) | def CHW_to_HWC(img):
  function imshow (line 30) | def imshow(img, minv=None, maxv=None, turn_off_axis=True):
  function show_imgs (line 50) | def show_imgs(imgs, data_type='HWC', cols=5, minv=None, maxv=None,
  function random_crop (line 71) | def random_crop(img, max_shift=7, can_flip=False, data_type='CHW'):
  class Params (line 98) | class Params(object):
    method __init__ (line 99) | def __init__(self, **kwargs):
    method validate (line 107) | def validate(self):
    method overwrite (line 110) | def overwrite(self, **kwargs):
    method __getattr__ (line 113) | def __getattr__(self, name):
    method default_param (line 116) | def default_param(self, key, val):
    method __getstate__ (line 119) | def __getstate__(self):
    method __setstate__ (line 122) | def __setstate__(self, vals):
    method __repr__ (line 125) | def __repr__(self):
    method get_allowed_params_with_defaults (line 136) | def get_allowed_params_with_defaults(self):
    method __deepcopy__ (line 140) | def __deepcopy__(self, memo):
  function reset_timer (line 145) | def reset_timer():
  function time_elapsed (line 149) | def time_elapsed(log=True):
  function assert_set_covered (line 160) | def assert_set_covered(covered, covered_by):
  function get_time_str (line 165) | def get_time_str(microseconds=False):
  function get_new_dir (line 171) | def get_new_dir(base_path, suffix):
  function serialized_example (line 175) | def serialized_example(float_features, int_features):
  function _chunks (line 186) | def _chunks(iterable, chunk_size):
  class _DummyCtxMgr (line 193) | class _DummyCtxMgr():
    method __init__ (line 194) | def __init__(self, *args):
    method __enter__ (line 196) | def __enter__(self):
    method __exit__ (line 198) | def __exit__(self, exc_type, exc_value, traceback):
  function tensorflow_model_server_predict (line 202) | def tensorflow_model_server_predict(host_port=None,
  function _create_model_config_file (line 294) | def _create_model_config_file(models):
  function start_tensorflow_model_server (line 326) | def start_tensorflow_model_server(port=8100, models=None, interruptible=...
  class TFModelServer (line 353) | class TFModelServer(object):
    method __init__ (line 354) | def __init__(self, port=8100, models=None, initial_sleep=None,
    method __enter__ (line 366) | def __enter__(self):
    method __exit__ (line 374) | def __exit__(self, exc_type, exc_value, traceback):
  class _TFLoggingFilter (line 378) | class _TFLoggingFilter(logging.Filter):
    method filter (line 382) | def filter(self, record):
  class TFFileLogger (line 388) | class TFFileLogger(object):
    method __init__ (line 396) | def __init__(self, dir_path, is_gcloud):
    method __enter__ (line 401) | def __enter__(self):
    method __exit__ (line 417) | def __exit__(self, *args):
  function tf_logging_decorator (line 424) | def tf_logging_decorator(f):
  function get_optimizer (line 435) | def get_optimizer(params, scope, global_step=None):
  function construct_experiment_output_dir (line 475) | def construct_experiment_output_dir(fname):

FILE: src/jonasz/nvidia_celeb/celeba_align_dataset.py
  class CelebADatasetParams (line 10) | class CelebADatasetParams(util.Params):
    method get_allowed_params_with_defaults (line 11) | def get_allowed_params_with_defaults(self):
  function get_dataset (line 23) | def get_dataset(is_gcloud=False, **kwargs):
  function open_img (line 33) | def open_img(path):
  function img_generator (line 40) | def img_generator(imgs_path, gcs_bucket=None):
  function process (line 49) | def process(img, params):
  function get_train_input_fn (line 74) | def get_train_input_fn(params, batch_size=128):

FILE: src/jonasz/nvidia_celeb/celeba_hq_dataset.py
  class CelebAHQDatasetParams (line 12) | class CelebAHQDatasetParams(util.Params):
    method get_allowed_params_with_defaults (line 13) | def get_allowed_params_with_defaults(self):
    method validate (line 27) | def validate(self):
  function get_dataset_params (line 31) | def get_dataset_params(
  function process (line 47) | def process(img, params, input_img_size):
  function get_train_input_fn (line 75) | def get_train_input_fn(params, batch_size=128):

FILE: src/jonasz/progressive_infogan/create_animation.py
  function map_channel (line 64) | def map_channel(ch, ref):
  function adjust_img_to_ref (line 73) | def adjust_img_to_ref(img, ref):
  function _animate (line 80) | def _animate(imgs, interval=20, size=8, export_path=None, resolution=800):
  function _write_on_img (line 115) | def _write_on_img((img, text)):
  function _animate_single_image (line 126) | def _animate_single_image(
  function _make_grid_img (line 210) | def _make_grid_img(imgs, grid_size):
  function create_animation (line 218) | def create_animation(
  function _make_batches (line 292) | def _make_batches(data, batch_size):
  function main (line 306) | def main(*args):

FILE: src/jonasz/progressive_infogan/export_utils.py
  function _define_flags (line 12) | def _define_flags():
  function _restore_average_from_multiple_checkpoints (line 31) | def _restore_average_from_multiple_checkpoints(saver_for_restore,
  function export_newest_savedmodel (line 51) | def export_newest_savedmodel(training_params,
  function _maybe_restore_from_path (line 167) | def _maybe_restore_from_path(sess, path, vars_to_restore=None,
  function _handle_legacy_vars_to_restore (line 183) | def _handle_legacy_vars_to_restore(vars_to_restore, path):
  function maybe_restore (line 205) | def maybe_restore(sess, training_params=None, vars_to_restore=None,
  function checkpoint (line 239) | def checkpoint(sess, cur_global_step, training_params):
  function should_checkpoint (line 254) | def should_checkpoint(training_params, steps_passed, secs_passed):
  function write_summaries (line 265) | def write_summaries(model_dir, sess, cur_global_step, summaries=None,
  function _extract_step (line 284) | def _extract_step(path):
  function _newest_checkpoints (line 290) | def _newest_checkpoints(training_params, num_checkpoints=5,
  function main (line 301) | def main(*args):

FILE: src/jonasz/progressive_infogan/gcloud_training/determine_config_yaml.py
  function main (line 6) | def main(*args):

FILE: src/jonasz/progressive_infogan/gcloud_training/test.py
  function main (line 4) | def main(*args):

FILE: src/jonasz/progressive_infogan/info_utils.py
  class InfoGanSummary (line 8) | class InfoGanSummary(object):
    method __init__ (line 9) | def __init__(self, training_params, generator_inputs, sample_t, reps=3):
    method _feed_dict_from_gen_inputs (line 36) | def _feed_dict_from_gen_inputs(self, cur_generator_inputs):
    method _construct_cont_infogan_images (line 44) | def _construct_cont_infogan_images(self, sess, coord, cur_generator_in...
    method _construct_cat_infogan_images (line 60) | def _construct_cat_infogan_images(self, sess, coord, cur_generator_inp...
    method _fetch_generator_inputs (line 80) | def _fetch_generator_inputs(self, sess, generator_inputs):
    method construct_feed_dict (line 96) | def construct_feed_dict(self, sess):
    method _infogan_images_summary (line 112) | def _infogan_images_summary(self, num_coords, prefix, grid_side,

FILE: src/jonasz/progressive_infogan/network_utils.py
  function upscale2d (line 10) | def upscale2d(x, factor=2, data_format='NCHW'):
  function downscale2d (line 26) | def downscale2d(x, factor=2, data_format='NCHW'):
  function downgrade2d (line 39) | def downgrade2d(x, factor=2, data_format='NCHW'):
  function concat_features_stddev (line 45) | def concat_features_stddev(layer):
  function pixel_norm (line 61) | def pixel_norm(x, axis, epsilon=1e-8, scope='pixel_norm'):
  function append_one_hot_to_tensor (line 67) | def append_one_hot_to_tensor(tensor, one_hot):
  function batch_norm (line 94) | def batch_norm(net, axis, scope='batch_norm'):
  function batch_norm_in_place (line 109) | def batch_norm_in_place(net, axis, scope='batch_norm_in_place',
  function layer_norm (line 129) | def layer_norm(net, axis, scope='layer_norm'):
  function dense (line 152) | def dense(inp, units=None, scope='linear', weight_norm=None):
  function _prod (line 180) | def _prod(s):
  function get_weights (line 185) | def get_weights(shape, weight_norm):
  function conv (line 204) | def conv(net, kernel_size=3, strides=1, filters=None, scope='conv',
  function conv_trans (line 225) | def conv_trans(*args, **kwargs):
  function _norm (line 230) | def _norm(net, axis, version, scope='norm', is_training=None):
  function norm (line 247) | def norm(net, axis, version, scope='norm', is_training=None, gpu_id=None,
  function _get_shape (line 259) | def _get_shape(tensor):
  function condition_tensor (line 268) | def condition_tensor(tensor, conditioning, act=None):

FILE: src/jonasz/progressive_infogan/networks.py
  class GeneratorParams (line 13) | class GeneratorParams(util.Params):
    method get_allowed_params_with_defaults (line 14) | def get_allowed_params_with_defaults(self):
    method validate (line 55) | def validate(self):
  class DiscriminatorParams (line 60) | class DiscriminatorParams(util.Params):
    method get_allowed_params_with_defaults (line 61) | def get_allowed_params_with_defaults(self):
    method validate (line 91) | def validate(self):
  function phase_progress_to_alpha (line 95) | def phase_progress_to_alpha(block, phase, progress):
  function generator_fn (line 111) | def generator_fn(generator_inputs,
  function discriminator_fn (line 414) | def discriminator_fn(image,
  function _img_summary (line 683) | def _img_summary(name, img):
  function _flatten (line 692) | def _flatten(net):
  function _get_progressive_mask (line 698) | def _get_progressive_mask(training_params, phase_t, phase_progress_t):
  function get_gpu_batch_size (line 723) | def get_gpu_batch_size(tensor, training_params):
  function _split_structured_input (line 729) | def _split_structured_input(tp, inp, num_vars, var_name_prefix):
  function _split_structured_continuous_input (line 740) | def _split_structured_continuous_input(tp, inp, phase, phase_progress):
  function _split_structured_categorical_input (line 749) | def _split_structured_categorical_input(tp, inp):
  function _block_id_to_structured_vars_internal (line 767) | def _block_id_to_structured_vars_internal(phase_to_coords, coord_to_tens...
  function _block_id_to_structured_vars (line 785) | def _block_id_to_structured_vars(generator_inputs, tp, phase, phase_prog...

FILE: src/jonasz/progressive_infogan/progressive_infogan_lib.py
  class GenInput (line 11) | class GenInput(object):
    method __init__ (line 13) | def __init__(self, noise, cont_structured_input, cat_structured_input):
    method random (line 19) | def random(cls, noise_stddev):
    method copy (line 26) | def copy(self):
    method as_dict_key (line 33) | def as_dict_key(self):
    method _create_noise (line 39) | def _create_noise(cls, noise_size, noise_stddev):
    method _create_cat_structured_input (line 43) | def _create_cat_structured_input(cls):
    method as_serialized_example (line 46) | def as_serialized_example(self):
  function random_request (line 58) | def random_request(num_images=None, noise_stddev=None, seed=None):
  function _query_tf_model_server (line 66) | def _query_tf_model_server(gen_inputs,
  function gen_inputs_to_images (line 86) | def gen_inputs_to_images(gen_inputs,

FILE: src/jonasz/progressive_infogan/progressive_infogan_losses.py
  function _flatten (line 14) | def _flatten(net):
  function _vanilla_consistency_loss (line 20) | def _vanilla_consistency_loss(cur_rgb, prev_rgb, block_id, tp):
  function _msssim256_consistency_loss (line 34) | def _msssim256_consistency_loss(cur_rgb, prev_rgb, block_id, tp):
  function _add_consistency_loss (line 56) | def _add_consistency_loss(gan_loss, gan_model_dict, training_params):
  function _add_categorical_mutual_information_penalty (line 106) | def _add_categorical_mutual_information_penalty(gan_loss,
  function unweighted_mutual_information_penalty_per_coord (line 145) | def unweighted_mutual_information_penalty_per_coord(gan_model_dict,
  function _add_continuous_mutual_information_penalty (line 169) | def _add_continuous_mutual_information_penalty(gan_loss,
  function _add_drift_loss (line 238) | def _add_drift_loss(gan_loss, gan_model_dict, training_params):
  function construct_gan_loss (line 251) | def construct_gan_loss(training_params, gan_model_dict):

FILE: src/jonasz/progressive_infogan/run_evaluation.py
  function _find_phase (line 35) | def _find_phase(training_params, checkpoint_num):
  function _run_evaluation_with_logging (line 47) | def _run_evaluation_with_logging(training_params, step, original_output_...
  function get_output_dir (line 63) | def get_output_dir(output_dir_base, job_id, step):
  function run_evaluation (line 68) | def run_evaluation(training_module_name, job_id, step, output_dir_base,

FILE: src/jonasz/progressive_infogan/train.py
  class TrainingParams (line 32) | class TrainingParams(util.Params):
    method get_allowed_params_with_defaults (line 33) | def get_allowed_params_with_defaults(self):
    method validate (line 104) | def validate(self):
    method max_steps (line 139) | def max_steps(self):
    method batch_size (line 148) | def batch_size(self):
    method image_side (line 154) | def image_side(self):
    method phase (line 160) | def phase(self):
    method batch_size_per_gpu (line 165) | def batch_size_per_gpu(self):
    method infogan_cont_depth_to_vars (line 170) | def infogan_cont_depth_to_vars(self):
    method infogan_cont_num_vars (line 185) | def infogan_cont_num_vars(self):
    method target_side_log (line 194) | def target_side_log(self):
    method block_ids (line 200) | def block_ids(self):
  function _specialize_training_params_for_phase (line 204) | def _specialize_training_params_for_phase(tp, phase):
  function _gan_inputs (line 217) | def _gan_inputs(training_params):
  function _slice_gan_inputs (line 258) | def _slice_gan_inputs(generator_inputs, images, num_gpus=1):
  function _get_phase (line 271) | def _get_phase(training_params, global_step):
  function _gan_fn (line 283) | def _gan_fn(training_params, is_training=True, return_dict=False, gpu_id...
  function _gan_fn_from_inputs (line 300) | def _gan_fn_from_inputs(training_params,
  function _should_do_eval (line 382) | def _should_do_eval(params, time_passed, steps_passed):
  function generator_params_ema (line 392) | def generator_params_ema(training_params, gan_model, gen_train_step):
  class GradsAccumulator (line 405) | class GradsAccumulator(object):
    method __init__ (line 406) | def __init__(self):
    method add (line 410) | def add(self, grads_and_vars):
    method _summarize_grad_health (line 415) | def _summarize_grad_health(self, t, name):
    method _check_gradient_health (line 424) | def _check_gradient_health(self, grad, name):
    method avg_grads_and_vars (line 436) | def avg_grads_and_vars(self):
  function _get_update_ops (line 459) | def _get_update_ops(gen_scope, dis_scope, check_for_unused_ops=True):
  function _assign_to_device (line 478) | def _assign_to_device(device, ps_device=None):
  function _get_cur_global_step (line 488) | def _get_cur_global_step(training_params):
  function get_vars_device (line 499) | def get_vars_device(training_params):
  function _train_gan_multi_gpu (line 510) | def _train_gan_multi_gpu(training_params):
  function _download_checkpoint (line 718) | def _download_checkpoint(src_path, dst_path, num_checkpoint=None):
  function _single_gpu_training_params (line 752) | def _single_gpu_training_params(training_params):
  function _create_animation (line 761) | def _create_animation(training_params, saved_model_subdir, seeds):
  function _evaluation (line 793) | def _evaluation(training_params):
  function _run_single_phase_training (line 801) | def _run_single_phase_training(training_params, phase):
  function _train_and_evaluate_gan (line 826) | def _train_and_evaluate_gan(training_params):
  function _create_animations (line 848) | def _create_animations(training_params):
  function run_training (line 858) | def run_training(training_params):
  function _make_grid (line 864) | def _make_grid(imgs, grid_side):
  function _flatten (line 870) | def _flatten(net):
  function _default_dataset_params (line 876) | def _default_dataset_params():

FILE: src/jonasz/tools/tensorboard_gcloud.py
  function syscmd (line 7) | def syscmd(cmd):
  function parse_lines (line 11) | def parse_lines(content):
  function promptuser (line 17) | def promptuser(lines):
  function get_job_ids (line 40) | def get_job_ids():
  function download_last_events (line 59) | def download_last_events(base_dir, job_id):
  function main (line 79) | def main():