Code stringlengths 103 85.9k | Summary listlengths 0 94 |
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Please provide a description of the function:def deep_discriminator(x,
batch_norm,
is_training,
filters=64,
filter_size=4,
stride=2,
output_size=1024):
with tf.variable_scope(
... | [
"Discriminator architecture based on InfoGAN."
] |
Please provide a description of the function:def instance_norm(x):
with tf.variable_scope("instance_norm"):
epsilon = 1e-5
mean, var = tf.nn.moments(x, [1, 2], keep_dims=True)
scale = tf.get_variable(
"scale", [x.get_shape()[-1]],
initializer=tf.truncated_normal_initializer(mean=1.0, st... | [
"Instance normalization layer."
] |
Please provide a description of the function:def general_conv(x,
num_filters=64,
filter_size=7,
stride=1,
stddev=0.02,
padding="VALID",
name="conv",
do_norm="instance",
do_relu=True,
... | [
"Generalized convolution layer."
] |
Please provide a description of the function:def patch_discriminator(x, filters=64, filter_size=5, n=4,
name="patch_discrim"):
with tf.variable_scope(name):
x_shape = shape_list(x)
spatial_dims = [x_shape[1] // 4, x_shape[2] // 4]
x = tf.random_crop(x, [x_shape[0]] + spatial_dim... | [
"Patch descriminator."
] |
Please provide a description of the function:def mean_with_attention(x, name, num_heads=4):
with tf.variable_scope(name):
shape = shape_list(x)
m = tf.reduce_mean(x, [1, 2])
a = layers().Dense(num_heads, name="mean_attn")(x)
s = tf.reshape(a, [shape[0], -1, num_heads])
s = tf.nn.softmax(s, axis... | [
"Mean and attention to reduce spatial dimensions."
] |
Please provide a description of the function:def single_discriminator(x, filters=128, kernel_size=8,
strides=4, pure_mean=False):
with tf.variable_scope("discriminator"):
net = layers().Conv2D(
filters, kernel_size, strides=strides, padding="SAME", name="conv1")(x)
if pure_... | [
"A simple single-layer convolutional discriminator."
] |
Please provide a description of the function:def double_discriminator(x, filters1=128, filters2=None,
kernel_size=8, strides=4, pure_mean=False):
if filters2 is None:
filters2 = 4 * filters1
with tf.variable_scope("discriminator"):
batch_size = shape_list(x)[0]
net = layers()... | [
"A convolutional discriminator with 2 layers and concatenated output."
] |
Please provide a description of the function:def upscale(inputs, f, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR):
height, width = shape_list(inputs)[1:3] # pylint: disable=unbalanced-tuple-unpacking
return tf.image.resize_images(inputs, (height * f, width * f), method) | [
"Upscaling the image by a factor of f."
] |
Please provide a description of the function:def cyclegan_upsample(net, num_outputs, stride, method="conv2d_transpose"):
with tf.variable_scope("upconv"):
net_shape = tf.shape(net)
height = net_shape[1]
width = net_shape[2]
# Reflection pad by 1 in spatial dimensions (axes 1, 2 = h, w) to make a
... | [
"Upsamples the given inputs.\n\n Args:\n net: A Tensor of size [batch_size, height, width, filters].\n num_outputs: The number of output filters.\n stride: A list of 2 scalars or a 1x2 Tensor indicating the scale,\n relative to the inputs, of the output dimensions. For example, if kernel\n size ... |
Please provide a description of the function:def weight_targeting(w, k):
k = tf.to_int32(k)
w_shape = shape_list(w)
size = tf.to_int32(tf.reduce_prod(w_shape[:-1]))
w = tf.reshape(w, [size, w_shape[-1]])
transpose_w = tf.transpose(w)
thres = tf.contrib.framework.sort(tf.abs(transpose_w), axis=1)[:, k]
... | [
"Weight-level magnitude pruning."
] |
Please provide a description of the function:def unit_targeting(w, k):
k = tf.to_int32(k)
w_shape = shape_list(w)
size = tf.to_int32(tf.reduce_prod(w_shape[:-1]))
w = tf.reshape(w, [size, w_shape[-1]])
norm = tf.norm(w, axis=0)
thres = tf.contrib.framework.sort(norm, axis=0)[k]
mask = to_float(thres >... | [
"Unit-level magnitude pruning."
] |
Please provide a description of the function:def td_conv(inputs,
filters,
kernel_size,
targeting_count,
targeting_fn,
keep_prob,
is_training,
do_prune=True,
strides=(1, 1),
padding="valid",
data_forma... | [
"Apply targeted dropout to the weights of a convolution."
] |
Please provide a description of the function:def targeted_dropout(inputs,
k,
keep_prob,
targeting_fn,
is_training,
do_prune=False):
if not is_training and do_prune:
k = tf.round(to_float(k) * to_float(1. - ... | [
"Applies targeted dropout.\n\n Applies dropout at a rate of `1 - keep_prob` to only those elements of\n `inputs` marked by `targeting_fn`. See below and paper for more detail:\n\n \"Targeted Dropout for Posthoc Pruning\" Aidan N. Gomez, Ivan Zhang,\n Kevin Swersky, Yarin Gal, and Geoffrey E. Hinton.\n\n Args... |
Please provide a description of the function:def kl_divergence(mu, log_var, mu_p=0.0, log_var_p=0.0):
batch_size = shape_list(mu)[0]
prior_distribution = tfp.distributions.Normal(
mu_p, tf.exp(tf.multiply(0.5, log_var_p)))
posterior_distribution = tfp.distributions.Normal(
mu, tf.exp(tf.multiply(0... | [
"KL divergence of diagonal gaussian N(mu,exp(log_var)) and N(0,1).\n\n Args:\n mu: mu parameter of the distribution.\n log_var: log(var) parameter of the distribution.\n mu_p: optional mu from a learned prior distribution\n log_var_p: optional log(var) from a learned prior distribution\n Returns:\n ... |
Please provide a description of the function:def to_tensor(self):
a_shape = shape_list(self.a)
b_shape = shape_list(self.b)
inner_dim = b_shape[1]
result_dim = b_shape[0]
flat_a = tf.reshape(self.a, [-1, inner_dim])
product = tf.matmul(flat_a, self.b, transpose_b=True)
product_shape = a... | [
"Convert to Tensor."
] |
Please provide a description of the function:def _compute_weights(self):
with tf.variable_scope("compute_weights"):
self.layer.kernel = tf.nn.l2_normalize(
self.layer.v, axis=self.norm_axes) * self.layer.g | [
"Generate weights with normalization."
] |
Please provide a description of the function:def _init_norm(self, weights):
with tf.variable_scope("init_norm"):
flat = tf.reshape(weights, [-1, self.layer_depth])
return tf.reshape(tf.norm(flat, axis=0), (self.layer_depth,)) | [
"Set the norm of the weight vector."
] |
Please provide a description of the function:def _data_dep_init(self, inputs):
with tf.variable_scope("data_dep_init"):
# Generate data dependent init values
activation = self.layer.activation
self.layer.activation = None
x_init = self.layer.call(inputs)
m_init, v_init = tf.momen... | [
"Data dependent initialization for eager execution."
] |
Please provide a description of the function:def build(self, input_shape=None):
input_shape = tf.TensorShape(input_shape).as_list()
self.input_spec = layers().InputSpec(shape=input_shape)
if not self.layer.built:
self.layer.build(input_shape)
self.layer.built = False
if not hasattr(... | [
"Build `Layer`."
] |
Please provide a description of the function:def call(self, inputs):
# if context.executing_eagerly():
# if not self.initialized:
# self._data_dep_init(inputs)
self._compute_weights() # Recompute weights for each forward pass
output = self.layer.call(inputs)
return output | [
"Call `Layer`."
] |
Please provide a description of the function:def compute_mean_reward(rollouts, clipped):
reward_name = "reward" if clipped else "unclipped_reward"
rewards = []
for rollout in rollouts:
if rollout[-1].done:
rollout_reward = sum(getattr(frame, reward_name) for frame in rollout)
rewards.append(rol... | [
"Calculate mean rewards from given epoch."
] |
Please provide a description of the function:def evaluate_single_config(
hparams, sampling_temp, max_num_noops, agent_model_dir,
eval_fn=_eval_fn_with_learner
):
tf.logging.info("Evaluating metric %s", get_metric_name(
sampling_temp, max_num_noops, clipped=False
))
eval_hparams = trainer_lib.crea... | [
"Evaluate the PPO agent in the real environment."
] |
Please provide a description of the function:def evaluate_all_configs(
hparams, agent_model_dir, eval_fn=_eval_fn_with_learner
):
metrics = {}
# Iterate over all combinations of sampling temperatures and whether to do
# initial no-ops.
for sampling_temp in hparams.eval_sampling_temps:
# Iterate over ... | [
"Evaluate the agent with multiple eval configurations."
] |
Please provide a description of the function:def evaluate_world_model(
real_env, hparams, world_model_dir, debug_video_path,
split=tf.estimator.ModeKeys.EVAL,
):
frame_stack_size = hparams.frame_stack_size
rollout_subsequences = []
def initial_frame_chooser(batch_size):
assert batch_size == len(rol... | [
"Evaluate the world model (reward accuracy).",
"Add a debug frame."
] |
Please provide a description of the function:def summarize_metrics(eval_metrics_writer, metrics, epoch):
for (name, value) in six.iteritems(metrics):
summary = tf.Summary()
summary.value.add(tag=name, simple_value=value)
eval_metrics_writer.add_summary(summary, epoch)
eval_metrics_writer.flush() | [
"Write metrics to summary."
] |
Please provide a description of the function:def full_game_name(short_name):
camel_game_name = misc_utils.snakecase_to_camelcase(short_name)
full_name = camel_game_name + ATARI_GAME_MODE
return full_name | [
"CamelCase game name with mode suffix.\n\n Args:\n short_name: snake_case name without mode e.g \"crazy_climber\"\n\n Returns:\n full game name e.g. \"CrazyClimberNoFrameskip-v4\"\n "
] |
Please provide a description of the function:def setup_env(hparams,
batch_size,
max_num_noops,
rl_env_max_episode_steps=-1,
env_name=None):
if not env_name:
env_name = full_game_name(hparams.game)
maxskip_envs = should_apply_max_and_skip_env(hparams)
... | [
"Setup."
] |
Please provide a description of the function:def update_hparams_from_hparams(target_hparams, source_hparams, prefix):
for (param_name, param_value) in six.iteritems(source_hparams.values()):
if param_name.startswith(prefix):
target_hparams.set_hparam(param_name[len(prefix):], param_value) | [
"Copy a subset of hparams to target_hparams."
] |
Please provide a description of the function:def random_rollout_subsequences(rollouts, num_subsequences, subsequence_length):
def choose_subsequence():
# TODO(koz4k): Weigh rollouts by their lengths so sampling is uniform over
# frames and not rollouts.
rollout = random.choice(rollouts)
try:
... | [
"Chooses a random frame sequence of given length from a set of rollouts."
] |
Please provide a description of the function:def make_initial_frame_chooser(
real_env, frame_stack_size, simulation_random_starts,
simulation_flip_first_random_for_beginning,
split=tf.estimator.ModeKeys.TRAIN,
):
initial_frame_rollouts = real_env.current_epoch_rollouts(
split=split, minimal_rollo... | [
"Make frame chooser.\n\n Args:\n real_env: T2TEnv to take initial frames from.\n frame_stack_size (int): Number of consecutive frames to extract.\n simulation_random_starts (bool): Whether to choose frames at random.\n simulation_flip_first_random_for_beginning (bool): Whether to flip the first\n ... |
Please provide a description of the function:def absolute_hinge_difference(arr1, arr2, min_diff=10, dtype=np.uint8):
diff = np.abs(arr1.astype(np.int) - arr2, dtype=np.int)
return np.maximum(diff - min_diff, 0).astype(dtype) | [
"Point-wise, hinge loss-like, difference between arrays.\n\n Args:\n arr1: integer array to compare.\n arr2: integer array to compare.\n min_diff: minimal difference taken into consideration.\n dtype: dtype of returned array.\n\n Returns:\n array\n "
] |
Please provide a description of the function:def augment_observation(
observation, reward, cum_reward, frame_index, bar_color=None,
header_height=27
):
img = PIL_Image().new(
"RGB", (observation.shape[1], header_height,)
)
draw = PIL_ImageDraw().Draw(img)
draw.text(
(1, 0), "c:{:3}, r:{:3... | [
"Augments an observation with debug info."
] |
Please provide a description of the function:def run_rollouts(
env, agent, initial_observations, step_limit=None, discount_factor=1.0,
log_every_steps=None, video_writers=(), color_bar=False,
many_rollouts_from_each_env=False
):
assert step_limit is not None or not many_rollouts_from_each_env, (
... | [
"Runs a batch of rollouts from given initial observations."
] |
Please provide a description of the function:def set_initial_state(self, initial_state, initial_frames):
self.env.set_initial_state(initial_state, initial_frames)
self._initial_frames = initial_frames | [
"Sets the state that will be used on next reset."
] |
Please provide a description of the function:def _maybe_download_corpora(tmp_dir, dataset_split):
cnn_filename = "cnn_stories.tgz"
cnn_finalpath = os.path.join(tmp_dir, "cnn/stories/")
dailymail_filename = "dailymail_stories.tgz"
dailymail_finalpath = os.path.join(tmp_dir, "dailymail/stories/")
if not tf.g... | [
"Download corpora if necessary and unzip them.\n\n Args:\n tmp_dir: directory containing dataset.\n dataset_split: whether we're in train/dev/test mode.\n\n Returns:\n List of all files generated and path to file containing\n train/dev/test split info.\n "
] |
Please provide a description of the function:def example_splits(url_file, all_files):
def generate_hash(inp):
h = hashlib.sha1()
h.update(inp)
return h.hexdigest()
all_files_map = {f.split("/")[-1]: f for f in all_files}
urls = [line.strip().encode("utf-8") for line in tf.gfile.Open(url_fil... | [
"Generate splits of the data.",
"Generate a sha1 hash to match the raw url to the filename extracted."
] |
Please provide a description of the function:def example_generator(all_files, urls_path, sum_token):
def fix_run_on_sents(line):
if u"@highlight" in line:
return line
if not line:
return line
if line[-1] in END_TOKENS:
return line
return line + u"."
filelist = example_splits(u... | [
"Generate examples."
] |
Please provide a description of the function:def write_raw_text_to_files(all_files, urls_path, dataset_split, tmp_dir):
def write_to_file(all_files, urls_path, tmp_dir, filename):
with io.open(
os.path.join(tmp_dir, filename + ".source"), "w",
encoding="utf-8") as fstory:
with io.op... | [
"Write text to files.",
"Write text to files."
] |
Please provide a description of the function:def infer_last_epoch_num(data_dir):
names = os.listdir(data_dir)
epochs_str = [re.findall(pattern=r".*\.(-?\d+)$", string=name)
for name in names]
epochs_str = sum(epochs_str, [])
return max([int(epoch_str) for epoch_str in epochs_str]) | [
"Infer highest epoch number from file names in data_dir."
] |
Please provide a description of the function:def setup_and_load_epoch(hparams, data_dir, which_epoch_data=None):
t2t_env = rl_utils.setup_env(
hparams, batch_size=hparams.real_batch_size,
max_num_noops=hparams.max_num_noops
)
# Load data.
if which_epoch_data is not None:
if which_epoch_data =... | [
"Load T2TGymEnv with data from one epoch.\n\n Args:\n hparams: hparams.\n data_dir: data directory.\n which_epoch_data: data from which epoch to load.\n\n Returns:\n env.\n "
] |
Please provide a description of the function:def infer_game_name_from_filenames(data_dir, snake_case=True):
names = os.listdir(data_dir)
game_names = [re.findall(pattern=r"^Gym(.*)NoFrameskip", string=name)
for name in names]
assert game_names, "No data files found in {}".format(data_dir)
gam... | [
"Infer name from filenames."
] |
Please provide a description of the function:def wrap_with_monitor(env, video_dir):
env = ExtendToEvenDimentions(env)
env = RenderObservations(env) # pylint: disable=redefined-variable-type
env = gym.wrappers.Monitor(env, video_dir, force=True,
video_callable=lambda idx: True,
... | [
"Wrap environment with gym.Monitor.\n\n Video recording provided by Monitor requires\n 1) both height and width of observation to be even numbers.\n 2) rendering of environment\n\n Args:\n env: environment.\n video_dir: video directory.\n\n Returns:\n wrapped environment.\n "
] |
Please provide a description of the function:def create_simulated_env(
output_dir, grayscale, resize_width_factor, resize_height_factor,
frame_stack_size, generative_model, generative_model_params,
random_starts=True, which_epoch_data="last", **other_hparams
):
# We need these, to initialize T2TGymEnv,... | [
"\"Create SimulatedEnv with minimal subset of hparams."
] |
Please provide a description of the function:def infer_paths(output_dir, **subdirs):
directories = {}
for name, path in six.iteritems(subdirs):
directories[name] = path if path else os.path.join(output_dir, name)
directories["output_dir"] = output_dir
return directories | [
"Infers standard paths to policy and model directories.\n\n Example:\n >>> infer_paths(\"/some/output/dir/\", policy=\"\", model=\"custom/path\")\n {\"policy\": \"/some/output/dir/policy\", \"model\": \"custom/path\",\n \"output_dir\":\"/some/output/dir/\"}\n\n Args:\n output_dir: output directory.\n ... |
Please provide a description of the function:def add_to_initial_stack(self, frame):
if not self._setable_initial_frames:
raise ValueError(
"This instance does not allow to manually set initial frame stack.")
assert_msg = "{}, {}".format(frame.shape, self._initial_frames.shape[:1])
asser... | [
"Adds new frame to (initial) frame stack, removes last one."
] |
Please provide a description of the function:def observation(self, frame):
if frame.shape == self.observation_space.shape:
return frame
else:
extended_frame = np.zeros(self.observation_space.shape,
self.observation_space.dtype)
assert self.HW_AXES == (0, 1)... | [
"Add single zero row/column to observation if needed."
] |
Please provide a description of the function:def infer(self, ob):
self._add_to_stack(ob)
logits, vf = self.infer_from_frame_stack(self._frame_stack)
return logits, vf | [
"Add new observation to frame stack and infer policy.\n\n Args:\n ob: array of shape (height, width, channels)\n\n Returns:\n logits and vf.\n "
] |
Please provide a description of the function:def infer_from_frame_stack(self, ob_stack):
logits, vf = self.sess.run([self.logits_t, self.value_function_t],
feed_dict={self.obs_t: ob_stack})
return logits, vf | [
"Infer policy from stack of observations.\n\n Args:\n ob_stack: array of shape (1, frame_stack_size, height, width, channels)\n\n Returns:\n logits and vf.\n "
] |
Please provide a description of the function:def _normalize_string(raw_str):
return " ".join(
token.strip()
for token in tokenizer.encode(text_encoder.native_to_unicode(raw_str))) | [
"Normalizes the string using tokenizer.encode.\n\n Args:\n raw_str: the input string\n\n Returns:\n A string which is ready to be tokenized using split()\n "
] |
Please provide a description of the function:def _prepare_babi_data(tmp_dir, data_dir):
if not tf.gfile.Exists(data_dir):
tf.gfile.MakeDirs(data_dir)
file_path = os.path.join(tmp_dir, _TAR)
headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_1) "
"AppleWebKit/... | [
"Downloads and extracts the dataset.\n\n Args:\n tmp_dir: temp directory to download and extract the dataset\n data_dir: The base directory where data and vocab files are stored.\n\n Returns:\n tmp_dir: temp directory containing the raw data.\n "
] |
Please provide a description of the function:def _babi_parser(tmp_dir,
babi_task_id,
subset,
dataset_split,
joint_training=True):
def _data_file(mode, task_id):
file_name = (_TASKS[task_id] + "_{}.txt")
return os.path.join(_DIR_NAME,... | [
"Parsing the bAbi dataset (train and test).\n\n Args:\n tmp_dir: temp directory to download and extract the dataset\n babi_task_id: babi task id\n subset: babi subset\n dataset_split: dataset split (train or eval)\n joint_training: if training the model on all tasks.\n\n Returns:\n babi_instanc... |
Please provide a description of the function:def _register_babi_problems():
for (subset, subset_suffix) in [("en", "_1k"), ("en-10k", "_10k")]:
for problem_name, babi_task_id in six.iteritems(_problems_to_register()):
problem_class = type("BabiQaConcat" + problem_name + subset_suffix,
... | [
"It dynamically instantiates a class for each babi subsets-tasks.\n\n @registry.register_problem\n class BabiQaConcatAllTasks_10k(EditSequenceRegexProblem):\n @property\n def babi_task_id(self):\n return \"qa0\"\n @property\n def babi_subset(self):\n return \"en-10k\"\n\n It does not... |
Please provide a description of the function:def get_labels_encoder(self, data_dir):
label_filepath = os.path.join(data_dir, self.vocab_filename)
return text_encoder.TokenTextEncoder(label_filepath) | [
"Builds encoder for the given class labels.\n\n Args:\n data_dir: data directory\n\n Returns:\n An encoder for class labels.\n "
] |
Please provide a description of the function:def generate_encoded_samples(self, data_dir, tmp_dir, dataset_split):
generator = self.generate_samples(data_dir, tmp_dir, dataset_split)
encoder = self.get_or_create_vocab(data_dir, tmp_dir)
label_encoder = self.get_labels_encoder(data_dir)
for sample i... | [
"A generator that generates samples that are encoded.\n\n Args:\n data_dir: data directory\n tmp_dir: temp directory\n dataset_split: dataset split\n\n Yields:\n A dict.\n\n "
] |
Please provide a description of the function:def feature_encoders(self, data_dir):
encoders = (super(BabiQa, self).feature_encoders(data_dir))
label_encoder = self.get_labels_encoder(data_dir)
encoders["targets"] = label_encoder # bAbi as a classification task
return encoders | [
"Return a dict for encoding and decoding inference input/output.\n\n Args:\n data_dir: data directory\n\n Returns:\n A dict of <feature name, TextEncoder>.\n\n "
] |
Please provide a description of the function:def hparams(self, defaults, unused_model_hparams):
(super(BabiQa, self).hparams(defaults, unused_model_hparams))
p = defaults
num_classes = self._encoders["targets"].vocab_size
p.modality = {"targets": modalities.ModalityType.CLASS_LABEL}
p.vocab_siz... | [
"Returns problem_hparams.\n\n Args:\n defaults: default hyperparameters\n unused_model_hparams: model hyperparameters\n\n "
] |
Please provide a description of the function:def dataset_splits(self):
return [{
"split": problem.DatasetSplit.TRAIN,
"shards": self.num_train_shards,
}, {
"split": problem.DatasetSplit.EVAL,
"shards": self.num_eval_shards,
}, {
"split": problem.DatasetSplit.TEST... | [
"Splits of data to produce and number the output shards for each."
] |
Please provide a description of the function:def _collect_data(directory, input_ext, transcription_ext):
# Directory from string to tuple pair of strings
# key: the filepath to a datafile including the datafile's basename. Example,
# if the datafile was "/path/to/datafile.wav" then the key would be
# "/p... | [
"Traverses directory collecting input and target files."
] |
Please provide a description of the function:def add_librispeech_hparams(hparams):
hparams.batch_size = 36
hparams.audio_compression = 8
hparams.hidden_size = 2048
hparams.max_input_seq_length = 600000
hparams.max_target_seq_length = 350
hparams.max_length = hparams.max_input_seq_length
hparams.min_len... | [
"Adding to base hparams the attributes for for librispeech."
] |
Please provide a description of the function:def words_and_tags_from_wsj_tree(tree_string):
stack, tags, words = [], [], []
for tok in tree_string.strip().split():
if tok[0] == "(":
symbol = tok[1:]
tags.append(symbol)
stack.append(symbol)
else:
assert tok[-1] == ")"
stack.p... | [
"Generates linearized trees and tokens from the wsj tree format.\n\n It uses the linearized algorithm described in https://arxiv.org/abs/1412.7449.\n\n Args:\n tree_string: tree in wsj format\n\n Returns:\n tuple: (words, linearized tree)\n "
] |
Please provide a description of the function:def token_generator(tree_path, source_token_vocab, target_token_vocab,
eos=None):
eos_list = [] if eos is None else [eos]
with tf.gfile.GFile(tree_path, mode="r") as tree_file:
tree_line = tree_file.readline()
while tree_line:
source,... | [
"Generator for parsing as a sequence-to-sequence task that uses tokens.\n\n This generator assumes the files at source_path and target_path have\n the same number of lines and yields dictionaries of \"inputs\" and \"targets\"\n where inputs and targets are token ids from source and target lines\n converted to i... |
Please provide a description of the function:def parsing_token_generator(data_dir, tmp_dir, train, source_vocab_size,
target_vocab_size):
# TODO(lukaszkaiser): Correct these calls to generate vocabularies. No data
# sources are being passed.
del (data_dir, tmp_dir, train, source_voc... | [
"Generator for parsing as a sequence-to-sequence task that uses tokens.\n\n This generator assumes the files parsing_{train,dev}.trees, which contain\n trees in WSJ format.\n\n Args:\n data_dir: path to the data directory.\n tmp_dir: path to temporary storage directory.\n train: whether we're training o... |
Please provide a description of the function:def aggregate_stats(stats_files):
all_stats = {}
for fname in stats_files:
with tf.gfile.Open(fname) as f:
stats = json.loads(f.read())
for k, v in stats.iteritems():
if k not in all_stats:
if isinstance(v, list):
all_stat... | [
"Aggregate stats in per-shard stats files."
] |
Please provide a description of the function:def filename_to_task_id(fname):
# This matches the order and size in WikisumBase.out_filepaths
fname = os.path.basename(fname)
shard_id_increment = {
"train": 0,
"dev": 800,
"test": 900,
}
parts = fname.split("-")
split = parts[1]
shard_id ... | [
"Map filename to the task id that created it assuming 1k tasks."
] |
Please provide a description of the function:def validate_data_files(problem, data_files, min_size):
# Check that all files are present
data_dir = os.path.split(data_files[0])[0]
out_filepaths = problem.out_filepaths(data_dir)
missing_filepaths = set(out_filepaths) - set(data_files)
if missing_filepaths:
... | [
"Validate presence and minimum size of files."
] |
Please provide a description of the function:def distill_resnet_32_to_15_cifar20x5():
hparams = distill_base()
hparams.teacher_model = "resnet"
hparams.teacher_hparams = "resnet_cifar_32"
hparams.student_model = "resnet"
hparams.student_hparams = "resnet_cifar_15"
hparams.optimizer_momentum_nesterov = T... | [
"Set of hyperparameters."
] |
Please provide a description of the function:def _prepare_lambada_data(tmp_dir, data_dir, vocab_size, vocab_filename):
if not tf.gfile.Exists(data_dir):
tf.gfile.MakeDirs(data_dir)
file_path = generator_utils.maybe_download(tmp_dir, _TAR, _URL)
tar_all = tarfile.open(file_path)
tar_all.extractall(tmp_d... | [
"Downloading and preparing the dataset.\n\n Args:\n tmp_dir: tem directory\n data_dir: data directory\n vocab_size: size of vocabulary\n vocab_filename: name of vocab file\n\n "
] |
Please provide a description of the function:def get_dataset_split(tmp_dir, split, use_control_set):
if not use_control_set:
dataset_split = {
problem.DatasetSplit.TRAIN: [
f for f in tf.gfile.Glob(
os.path.join(tmp_dir, "train-novels/*/*.txt"))
],
problem.Da... | [
"Gives the file paths with regards to the given split.\n\n Args:\n tmp_dir: temp directory\n split: dataset split\n use_control_set: uses control dataset if true.\n\n Returns:\n list of file paths.\n\n "
] |
Please provide a description of the function:def min_sequence_length(self, dataset_split):
return {
problem.DatasetSplit.TRAIN: 8,
problem.DatasetSplit.EVAL: 65,
problem.DatasetSplit.TEST: 65
}[dataset_split] | [
"Determine the minimum sequence length given a dataset_split.\n\n Args:\n dataset_split: A problem.DatasetSplit.\n\n Returns:\n The minimum length that a sequence can be for this dataset_split.\n "
] |
Please provide a description of the function:def max_sequence_length(self, dataset_split):
return {
problem.DatasetSplit.TRAIN: 64,
problem.DatasetSplit.EVAL: 128,
problem.DatasetSplit.TEST: 128
}[dataset_split] | [
"Determine the maximum sequence length given a dataset_split.\n\n Args:\n dataset_split: A problem.DatasetSplit.\n\n Returns:\n The maximum length that a sequence can be for this dataset_split.\n "
] |
Please provide a description of the function:def num_samples(self, dataset_split):
return {
problem.DatasetSplit.TRAIN: 1000000,
problem.DatasetSplit.EVAL: 10000,
problem.DatasetSplit.TEST: 10000
}[dataset_split] | [
"Determine the dataset sized given a dataset_split.\n\n Args:\n dataset_split: A problem.DatasetSplit.\n\n Returns:\n The desired number of samples for this dataset_split.\n "
] |
Please provide a description of the function:def next_checkpoint(model_dir, timeout_mins=240):
last_ckpt = None
timeout_secs = None
if timeout_mins != -1:
timeout_secs = timeout_mins * 60
while True:
last_ckpt = tf.contrib.training.wait_for_new_checkpoint(
model_dir, last_ckpt, seconds_to_sle... | [
"Yields successive checkpoints from model_dir.\n\n Args:\n model_dir: The directory in which checkpoints are saved.\n timeout_mins: The maximum amount of time in minutes to wait\n between checkpoints. Set this to -1 to wait indefinitely.\n Yields:\n last_ckpt: a new checkpoint path, or N... |
Please provide a description of the function:def next_undecoded_checkpoint(model_dir, timeout_mins=240):
last_ckpt = None
last_step = 0
while True:
# Get the latest checkpoint.
last_ckpt = tf.contrib.training.wait_for_new_checkpoint(
model_dir, last_ckpt, seconds_to_sleep=60, timeout=60 * timeo... | [
"Yields successive checkpoints from model_dir."
] |
Please provide a description of the function:def create_session_config(log_device_placement=False,
enable_graph_rewriter=False,
gpu_mem_fraction=0.95,
use_tpu=False,
xla_jit_level=tf.OptimizerOptions.OFF,
... | [
"The TensorFlow Session config to use."
] |
Please provide a description of the function:def create_run_config(model_name,
master="",
model_dir=None,
iterations_per_loop=1000,
num_shards=8,
log_device_placement=False,
save_checkpoin... | [
"Create RunConfig, TPUConfig, and Parallelism object."
] |
Please provide a description of the function:def create_estimator(model_name,
hparams,
run_config,
schedule="train_and_evaluate",
decode_hparams=None,
use_tpu=False,
use_tpu_estimator=False,
... | [
"Create a T2T Estimator."
] |
Please provide a description of the function:def create_hooks(use_tfdbg=False,
use_dbgprofile=False,
dbgprofile_kwargs=None,
use_validation_monitor=False,
validation_monitor_kwargs=None,
use_early_stopping=False,
early... | [
"Create train and eval hooks for Experiment."
] |
Please provide a description of the function:def create_experiment(
run_config,
hparams,
model_name,
problem_name,
data_dir,
train_steps,
eval_steps,
min_eval_frequency=2000,
eval_throttle_seconds=600,
schedule="train_and_evaluate",
export=False,
decode_hparams=None,
... | [
"Create Experiment."
] |
Please provide a description of the function:def create_experiment_fn(*args, **kwargs):
def experiment_fn(run_config, hparams):
return create_experiment(run_config, hparams, *args, **kwargs)
return experiment_fn | [
"Wrapper for canonical experiment_fn. See create_experiment."
] |
Please provide a description of the function:def restore_checkpoint(ckpt_dir, saver, sess, must_restore=False):
ckpt = tf.train.get_checkpoint_state(ckpt_dir)
if must_restore and not ckpt:
raise ValueError("No checkpoint found in %s" % ckpt_dir)
if not ckpt:
return 0
path = ckpt.model_checkpoint_pat... | [
"Restore from a checkpoint."
] |
Please provide a description of the function:def train_eval_and_decode(self):
eval_steps = self._hparams.eval_freq_in_steps
packed_dataset = "_packed" in self._hparams.problem.name
mlperf_log.transformer_print(key=mlperf_log.TRAIN_LOOP)
for i in range(0, self._train_spec.max_steps, eval_steps):
... | [
"Does eval and decode after training every eval_freq_in_steps."
] |
Please provide a description of the function:def continuous_eval(self):
for ckpt_path in next_checkpoint(self._hparams.model_dir,
self._hparams.eval_timeout_mins):
# Skip zero'th step.
train_step = decoding.get_step_from_ckpt_path(ckpt_path)
if train_step ... | [
"Evaluate until checkpoints stop being produced."
] |
Please provide a description of the function:def continuous_eval_on_train_data(self):
for ckpt_path in next_checkpoint(self._hparams.model_dir,
self._hparams.eval_timeout_mins):
# Skip zero'th step.
train_step = decoding.get_step_from_ckpt_path(ckpt_path)
... | [
"Evaluate on train data until checkpoints stop being produced."
] |
Please provide a description of the function:def run_std_server(self):
config = tf.estimator.RunConfig()
server = tf.train.Server(
config.cluster_spec,
job_name=config.task_type,
task_index=config.task_id,
protocol=config.protocol)
server.join() | [
"Starts a TensorFlow server and joins the serving thread.\n\n Typically used for parameter servers.\n\n Raises:\n ValueError: if not enough information is available in the estimator's\n config to create a server.\n "
] |
Please provide a description of the function:def decode(self,
dataset_split=None,
decode_from_file=False,
checkpoint_path=None):
if decode_from_file:
decoding.decode_from_file(self._estimator,
self._decode_hparams.decode_from_file,
... | [
"Decodes from dataset or file."
] |
Please provide a description of the function:def continuous_decode(self):
for _ in next_checkpoint(self._hparams.model_dir,
self._decode_hparams.decode_timeout_mins):
self.decode() | [
"Decode from dataset on new checkpoint."
] |
Please provide a description of the function:def continuous_decode_on_train_data(self):
for _ in next_checkpoint(self._hparams.model_dir,
self._decode_hparams.decode_timeout_mins):
self.decode(dataset_split=tf.estimator.ModeKeys.TRAIN) | [
"Decode from dataset on new checkpoint."
] |
Please provide a description of the function:def continuous_decode_on_eval_data(self):
if self._hparams.mlperf_mode:
ckpt_generator = next_undecoded_checkpoint(
self._hparams.model_dir, self._decode_hparams.decode_timeout_mins)
else:
ckpt_generator = next_checkpoint(self._hparams.mode... | [
"Decode from dataset on new checkpoint."
] |
Please provide a description of the function:def continuous_decode_from_file(self):
for _ in next_checkpoint(self._hparams.model_dir,
self._decode_hparams.decode_timeout_mins):
self.decode(decode_from_file=True) | [
"Decode from file on new checkpoint."
] |
Please provide a description of the function:def _flatten_dict(original_dict):
flat_dict = {}
for key, value in original_dict.items():
if isinstance(value, dict):
for name, tensor in value.items():
if isinstance(tensor, dict):
raise ValueError("flatten_dict only handles 2 levels of ne... | [
"Flatten dict of dicts into a single dict with appropriate prefixes.\n\n Handles only 2 levels of nesting in the original dict.\n\n Args:\n original_dict: Dict which may contain one or more dicts.\n Returns:\n flat_dict: Dict without any nesting. Any dicts in the original dict have\n their keys as pre... |
Please provide a description of the function:def _unflatten_dict(flat_dict, prefixes):
original_dict = {}
for key, value in flat_dict.items():
prefix_found = False
for prefix in prefixes:
full_prefix = "__" + prefix + "_"
if key.startswith(full_prefix):
# Add a dict to the original di... | [
"Returns a dict of dicts if any prefixes match keys in the flat dict.\n\n The function handles the case where the prefix may not be a dict.\n\n Args:\n flat_dict: A dict without any nesting.\n prefixes: A list of strings which may have been dicts in the\n original structure.\n\n "
] |
Please provide a description of the function:def create_dummy_vars():
var_names = set([v.name for v in tf.global_variables()])
if "losses_avg/problem_0/total_loss:0" in var_names:
return
with tf.variable_scope("losses_avg"):
with tf.variable_scope("problem_0"):
for var_name in ["total", "extra", ... | [
"Dummy vars for restore to work when not using TPU codepath."
] |
Please provide a description of the function:def create_tpu_eval_metrics_fn(problem, model_hparams):
metric_fns = []
eval_metrics = problem.eval_metric_fns(model_hparams)
tm = _create_target_modality(problem.get_hparams(model_hparams).modality)
if isinstance(tm, dict):
for k, v in six.iteritems(tm):
... | [
"Create the metrics_fn that TPUEstimatorSpec expects.",
"Construct metrics dictionary."
] |
Please provide a description of the function:def remove_summaries():
g = tf.get_default_graph()
key = tf.GraphKeys.SUMMARIES
log_debug("Remove summaries %s" % str(g.get_collection(key)))
del g.get_collection_ref(key)[:]
assert not g.get_collection(key) | [
"Remove summaries from the default graph."
] |
Please provide a description of the function:def create_host_call(model_dir):
graph = tf.get_default_graph()
summaries = graph.get_collection(tf.GraphKeys.SUMMARIES)
gs_t = tf.reshape(tf.to_int32(tf.train.get_global_step()), [1])
summary_kwargs = collections.OrderedDict()
for t in summaries:
# TODO(aid... | [
"Construct a host_call writing scalar summaries.\n\n Args:\n model_dir: String containing path to train\n\n Returns:\n (fn, args) Pair to be called by TPUEstimator as the host_call.\n ",
"Training host call. Creates summaries for training metrics.\n\n Args:\n **kwargs: Dict of {str: Tensor} , wit... |
Please provide a description of the function:def average_sharded_losses(sharded_losses):
losses = {}
for loss_name in sorted(sharded_losses[0]):
all_shards = [shard_losses[loss_name] for shard_losses in sharded_losses]
if isinstance(all_shards[0], tuple):
sharded_num, sharded_den = zip(*all_shards)... | [
"Average losses across datashards.\n\n Args:\n sharded_losses: list<dict<str loss_name, Tensor loss>>. The loss\n can be a single Tensor or a 2-tuple (numerator and denominator).\n\n Returns:\n losses: dict<str loss_name, Tensor avg_loss>\n "
] |
Please provide a description of the function:def summarize_features(features, num_shards=1):
if not common_layers.should_generate_summaries():
return
with tf.name_scope("input_stats"):
for (k, v) in sorted(six.iteritems(features)):
if (isinstance(v, tf.Tensor) and (v.get_shape().ndims > 1) and
... | [
"Generate summaries for features."
] |
Please provide a description of the function:def _compose_custom_getters(getter_a, getter_b):
if not getter_a:
return getter_b
if not getter_b:
return getter_a
def getter_fn(getter, *args, **kwargs):
return getter_b(functools.partial(getter_a, getter), *args, **kwargs)
return getter_fn | [
"Compose two custom getters.\n\n Example use:\n tf.get_variable_scope().set_custom_getter(\n compose_custom_getters(tf.get_variable_scope().custom_getter, new_getter))\n\n This composes getters in the same way as creating a new variable scope with\n the new_getter, but it does not actually create a new varia... |
Please provide a description of the function:def set_custom_getter_compose(custom_getter):
tf.get_variable_scope().set_custom_getter(
_compose_custom_getters(tf.get_variable_scope().custom_getter,
custom_getter)) | [
"Set a custom getter in the current variable scope.\n\n Do not overwrite the existing custom getter - rather compose with it.\n\n Args:\n custom_getter: a custom getter.\n "
] |
Please provide a description of the function:def initialize_from_ckpt(ckpt_dir, hparams):
model_dir = hparams.get("model_dir", None)
already_has_ckpt = (
model_dir and tf.train.latest_checkpoint(model_dir) is not None)
if already_has_ckpt:
return
tf.logging.info("Checkpoint dir: %s", ckpt_dir)
r... | [
"Initialize variables from given directory."
] |
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