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def sequence_edit_distance(predictions, labels, weights_fn=common_layers.weights_nonzero):
"""Average edit distance, ignoring padding 0s. The score returned is t... |
if weights_fn is not common_layers.weights_nonzero:
raise ValueError("Only weights_nonzero can be used for this metric.")
with tf.variable_scope("edit_distance", values=[predictions, labels]):
# Transform logits into sequence classes by taking max at every step.
predictions = tf.to_int32(
tf.s... |
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def padded_neg_log_perplexity(predictions, labels, weights_fn=common_layers.weights_nonzero):
"""Average log-perplexity exluding padding 0s. No smoothing.""" |
num, den = common_layers.padded_cross_entropy(
predictions, labels, 0.0, weights_fn=weights_fn, reduce_sum=False)
return (-num, den) |
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def padded_neg_log_perplexity_with_masking( predictions, labels, features, weights_fn=None):
"""Average log-perplexity with custom targets_mask.""" |
del weights_fn
if "targets_mask" not in features:
raise ValueError("masked_neg_log_perplexity requires targets_mask feature")
# Features are 4 dimensional, so we need to reshape the targets_mask to match
# the shape of the labels. A lot of models rely on these features being 4D,
# so it's best to update... |
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def multilabel_accuracy_matchk(predictions, labels, k, weights_fn=common_layers.weights_nonzero):
"""Used to evaluate the VQA accuracy. Let n be the times that p... |
predictions = tf.to_int32(tf.argmax(predictions, axis=-1))
scores = tf.to_float(tf.equal(predictions, labels))
# those label == 0 do not count
weights = weights_fn(labels)
scores *= weights
scores = tf.reduce_sum(scores, axis=[1, 2, 3])
scores = tf.minimum(scores / tf.to_float(k), 1)
# every sample cou... |
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def set_precision(predictions, labels, weights_fn=common_layers.weights_nonzero):
"""Precision of set predictions. Args: predictions : A Tensor of scores of shap... |
with tf.variable_scope("set_precision", values=[predictions, labels]):
labels = tf.squeeze(labels, [2, 3])
weights = weights_fn(labels)
labels = tf.one_hot(labels, predictions.shape[-1])
labels = tf.reduce_max(labels, axis=1)
labels = tf.cast(labels, tf.bool)
return tf.to_float(tf.equal(label... |
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def image_summary(predictions, targets, hparams):
"""Reshapes predictions and passes it to tensorboard. Args: predictions : The predicted image (logits). targets... |
del hparams
results = tf.cast(tf.argmax(predictions, axis=-1), tf.uint8)
gold = tf.cast(targets, tf.uint8)
summary1 = tf.summary.image("prediction", results, max_outputs=2)
summary2 = tf.summary.image("data", gold, max_outputs=2)
summary = tf.summary.merge([summary1, summary2])
return summary, tf.zeros_l... |
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def softmax_cross_entropy_one_hot(logits, labels, weights_fn=None):
"""Calculate softmax cross entropy given one-hot labels and logits. Args: logits: Tensor of s... |
with tf.variable_scope("softmax_cross_entropy_one_hot",
values=[logits, labels]):
del weights_fn
cross_entropy = tf.losses.softmax_cross_entropy(
onehot_labels=labels, logits=logits)
return cross_entropy, tf.constant(1.0) |
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def sigmoid_accuracy_one_hot(logits, labels, weights_fn=None):
"""Calculate accuracy for a set, given one-hot labels and logits. Args: logits: Tensor of size [ba... |
with tf.variable_scope("sigmoid_accuracy_one_hot", values=[logits, labels]):
del weights_fn
predictions = tf.nn.sigmoid(logits)
labels = tf.argmax(labels, -1)
predictions = tf.argmax(predictions, -1)
_, accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions)
return accuracy, tf... |
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def sigmoid_recall_one_hot(logits, labels, weights_fn=None):
"""Calculate recall for a set, given one-hot labels and logits. Predictions are converted to one-hot... |
with tf.variable_scope("sigmoid_recall_one_hot", values=[logits, labels]):
del weights_fn
num_classes = logits.shape[-1]
predictions = tf.nn.sigmoid(logits)
predictions = tf.argmax(predictions, -1)
predictions = tf.one_hot(predictions, num_classes)
_, recall = tf.metrics.recall(labels=labels,... |
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def sigmoid_cross_entropy_one_hot(logits, labels, weights_fn=None):
"""Calculate sigmoid cross entropy for one-hot lanels and logits. Args: logits: Tensor of siz... |
with tf.variable_scope("sigmoid_cross_entropy_one_hot",
values=[logits, labels]):
del weights_fn
cross_entropy = tf.losses.sigmoid_cross_entropy(
multi_class_labels=labels, logits=logits)
return cross_entropy, tf.constant(1.0) |
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def roc_auc(logits, labels, weights_fn=None):
"""Calculate ROC AUC. Requires binary classes. Args: logits: Tensor of size [batch_size, 1, 1, num_classes] labels:... |
del weights_fn
with tf.variable_scope("roc_auc", values=[logits, labels]):
predictions = tf.argmax(logits, axis=-1)
_, auc = tf.metrics.auc(labels, predictions, curve="ROC")
return auc, tf.constant(1.0) |
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def create_evaluation_metrics(problems, model_hparams):
"""Creates the evaluation metrics for the model. Args: problems: List of Problem instances. model_hparams... |
def reduce_dimensions(predictions, labels):
"""Reduce dimensions for high-dimensional predictions and labels."""
# We will treat first dimensions as batch. One example are video frames.
if len(predictions.get_shape()) > 5:
predictions_shape = common_layers.shape_list(predictions)
predictions ... |
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def create_eager_metrics_for_problem(problem, model_hparams):
"""See create_eager_metrics.""" |
metric_fns = problem.eval_metric_fns(model_hparams)
problem_hparams = problem.get_hparams(model_hparams)
target_modality = problem_hparams.modality["targets"]
weights_fn = model_hparams.weights_fn.get(
"targets",
modalities.get_weights_fn(target_modality))
return create_eager_metrics_internal(met... |
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def word_error_rate(raw_predictions, labels, lookup=None, weights_fn=common_layers.weights_nonzero):
"""Calculate word error rate. Args: raw_predictions: The raw... |
def from_tokens(raw, lookup_):
gathered = tf.gather(lookup_, tf.cast(raw, tf.int32))
joined = tf.regex_replace(tf.reduce_join(gathered, axis=1), b"<EOS>.*", b"")
cleaned = tf.regex_replace(joined, b"_", b" ")
tokens = tf.string_split(cleaned, " ")
return tokens
def from_characters(raw, lookup... |
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def pearson_correlation_coefficient(predictions, labels, weights_fn=None):
"""Calculate pearson correlation coefficient. Args: predictions: The raw predictions. ... |
del weights_fn
_, pearson = tf.contrib.metrics.streaming_pearson_correlation(predictions,
labels)
return pearson, tf.constant(1.0) |
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def attention_lm_decoder(decoder_input, decoder_self_attention_bias, hparams, name="decoder"):
"""A stack of attention_lm layers. Args: decoder_input: a Tensor d... |
x = decoder_input
with tf.variable_scope(name):
for layer in range(hparams.num_hidden_layers):
with tf.variable_scope("layer_%d" % layer):
with tf.variable_scope("self_attention"):
y = common_attention.multihead_attention(
common_layers.layer_preprocess(
... |
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def attention_lm_small():
"""Cheap model. on lm1b_32k: 45M params 2 steps/sec on [GeForce GTX TITAN X] Returns: an hparams object. """ |
hparams = attention_lm_base()
hparams.num_hidden_layers = 4
hparams.hidden_size = 512
hparams.filter_size = 2048
hparams.layer_prepostprocess_dropout = 0.5
return hparams |
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def bleu_score(predictions, labels, **unused_kwargs):
"""BLEU score computation between labels and predictions. An approximate BLEU scoring method since we do no... |
outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
# Convert the outputs and labels to a [batch_size, input_length] tensor.
outputs = tf.squeeze(outputs, axis=[-1, -2])
labels = tf.squeeze(labels, axis=[-1, -2])
bleu = tf.py_func(compute_bleu, (labels, outputs), tf.float32)
return bleu, tf.constant(1.... |
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def bleu_tokenize(string):
r"""Tokenize a string following the official BLEU implementation. See https://github.com/moses-smt/mosesdecoder/" "blob/master/scripts... |
string = uregex.nondigit_punct_re.sub(r"\1 \2 ", string)
string = uregex.punct_nondigit_re.sub(r" \1 \2", string)
string = uregex.symbol_re.sub(r" \1 ", string)
return string.split() |
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def _try_twice_tf_glob(pattern):
"""Glob twice, first time possibly catching `NotFoundError`. tf.gfile.Glob may crash with ``` tensorflow.python.framework.errors... |
try:
return tf.gfile.Glob(pattern)
except tf.errors.NotFoundError:
return tf.gfile.Glob(pattern) |
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def _read_stepfiles_list(path_prefix, path_suffix=".index", min_steps=0):
"""Return list of StepFiles sorted by step from files at path_prefix.""" |
stepfiles = []
for filename in _try_twice_tf_glob(path_prefix + "*-[0-9]*" + path_suffix):
basename = filename[:-len(path_suffix)] if path_suffix else filename
try:
steps = int(basename.rsplit("-")[-1])
except ValueError: # The -[0-9]* part is not an integer.
continue
if steps < min_st... |
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def stepfiles_iterator(path_prefix, wait_minutes=0, min_steps=0, path_suffix=".index", sleep_sec=10):
"""Continuously yield new files with steps in filename as t... |
# Wildcard D*-[0-9]* does not match D/x-1, so if D is a directory let
# path_prefix="D/".
if not path_prefix.endswith(os.sep) and os.path.isdir(path_prefix):
path_prefix += os.sep
stepfiles = _read_stepfiles_list(path_prefix, path_suffix, min_steps)
tf.logging.info("Found %d files with steps: %s",
... |
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def _get_vqa_v2_annotations(directory, annotation_url, annotation_filename="vqa_v2.tar.gz"):
"""Extract the VQA V2 annotation files to directory unless it's ther... |
annotation_file = generator_utils.maybe_download_from_drive(
directory, annotation_filename, annotation_url)
with tarfile.open(annotation_file, "r:gz") as annotation_tar:
annotation_tar.extractall(directory) |
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def _get_vqa_v2_image_raw_dataset(directory, image_root_url, image_urls):
"""Extract the VQA V2 image data set to directory unless it's there.""" |
for url in image_urls:
filename = os.path.basename(url)
download_url = os.path.join(image_root_url, url)
path = generator_utils.maybe_download(directory, filename, download_url)
unzip_dir = os.path.join(directory, filename.strip(".zip"))
if not tf.gfile.Exists(unzip_dir):
zipfile.ZipFile(pa... |
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def _get_vqa_v2_image_feature_dataset( directory, feature_url, feature_filename="mscoco_feat.tar.gz"):
"""Extract the VQA V2 feature data set to directory unless... |
feature_file = generator_utils.maybe_download_from_drive(
directory, feature_filename, feature_url)
with tarfile.open(feature_file, "r:gz") as feature_tar:
feature_tar.extractall(directory) |
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def _parse_fail(name, var_type, value, values):
"""Helper function for raising a value error for bad assignment.""" |
raise ValueError(
'Could not parse hparam \'%s\' of type \'%s\' with value \'%s\' in %s' %
(name, var_type.__name__, value, values)) |
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def _process_scalar_value(name, parse_fn, var_type, m_dict, values, results_dictionary):
"""Update results_dictionary with a scalar value. Used to update the res... |
try:
parsed_value = parse_fn(m_dict['val'])
except ValueError:
_parse_fail(name, var_type, m_dict['val'], values)
# If no index is provided
if not m_dict['index']:
if name in results_dictionary:
_reuse_fail(name, values)
results_dictionary[name] = parsed_value
else:
if name in resu... |
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def _process_list_value(name, parse_fn, var_type, m_dict, values, results_dictionary):
"""Update results_dictionary from a list of values. Used to update results... |
if m_dict['index'] is not None:
raise ValueError('Assignment of a list to a list index.')
elements = filter(None, re.split('[ ,]', m_dict['vals']))
# Make sure the name hasn't already been assigned a value
if name in results_dictionary:
raise _reuse_fail(name, values)
try:
results_dictionary[name... |
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def _cast_to_type_if_compatible(name, param_type, value):
"""Cast hparam to the provided type, if compatible. Args: name: Name of the hparam to be cast. param_ty... |
fail_msg = (
"Could not cast hparam '%s' of type '%s' from value %r" %
(name, param_type, value))
# Some callers use None, for which we can't do any casting/checking. :(
if issubclass(param_type, type(None)):
return value
# Avoid converting a non-string type to a string.
if (issubclass(para... |
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def parse_values(values, type_map, ignore_unknown=False):
"""Parses hyperparameter values from a string into a python map. `values` is a string containing comma-... |
results_dictionary = {}
pos = 0
while pos < len(values):
m = PARAM_RE.match(values, pos)
if not m:
raise ValueError('Malformed hyperparameter value: %s' % values[pos:])
# Check that there is a comma between parameters and move past it.
pos = m.end()
# Parse the values.
m_dict = m.gr... |
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def set_hparam(self, name, value):
"""Set the value of an existing hyperparameter. This function verifies that the type of the value matches the type of the exis... |
param_type, is_list = self._hparam_types[name]
if isinstance(value, list):
if not is_list:
raise ValueError(
'Must not pass a list for single-valued parameter: %s' % name)
setattr(self, name, [
_cast_to_type_if_compatible(name, param_type, v) for v in value])
else:... |
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def del_hparam(self, name):
"""Removes the hyperparameter with key 'name'. Does nothing if it isn't present. Args: name: Name of the hyperparameter. """ |
if hasattr(self, name):
delattr(self, name)
del self._hparam_types[name] |
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def parse(self, values):
"""Override existing hyperparameter values, parsing new values from a string. See parse_values for more detail on the allowed format for... |
type_map = {}
for name, t in self._hparam_types.items():
param_type, _ = t
type_map[name] = param_type
values_map = parse_values(values, type_map)
return self.override_from_dict(values_map) |
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def override_from_dict(self, values_dict):
"""Override existing hyperparameter values, parsing new values from a dictionary. Args: values_dict: Dictionary of nam... |
for name, value in values_dict.items():
self.set_hparam(name, value)
return self |
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def to_json(self, indent=None, separators=None, sort_keys=False):
"""Serializes the hyperparameters into JSON. Args: indent: If a non-negative integer, JSON arra... |
def remove_callables(x):
"""Omit callable elements from input with arbitrary nesting."""
if isinstance(x, dict):
return {k: remove_callables(v) for k, v in six.iteritems(x)
if not callable(v)}
elif isinstance(x, list):
return [remove_callables(i) for i in x if not ... |
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def parse_json(self, values_json):
"""Override existing hyperparameter values, parsing new values from a json object. Args: values_json: String containing a json... |
values_map = json.loads(values_json)
return self.override_from_dict(values_map) |
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def values(self):
"""Return the hyperparameter values as a Python dictionary. Returns: A dictionary with hyperparameter names as keys. The values are the hyperpa... |
return {n: getattr(self, n) for n in self._hparam_types.keys()} |
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def get(self, key, default=None):
"""Returns the value of `key` if it exists, else `default`.""" |
if key in self._hparam_types:
# Ensure that default is compatible with the parameter type.
if default is not None:
param_type, is_param_list = self._hparam_types[key]
type_str = 'list<%s>' % param_type if is_param_list else str(param_type)
fail_msg = ("Hparam '%s' of type '%s' i... |
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def _get_kind_name(param_type, is_list):
"""Returns the field name given parameter type and is_list. Args: param_type: Data type of the hparam. is_list: Whether ... |
if issubclass(param_type, bool):
# This check must happen before issubclass(param_type, six.integer_types),
# since Python considers bool to be a subclass of int.
typename = 'bool'
elif issubclass(param_type, six.integer_types):
# Setting 'int' and 'long' types to be 'int64' to ensure t... |
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def _default_output_dir():
"""Default output directory.""" |
try:
dataset_name = gin.query_parameter("inputs.dataset_name")
except ValueError:
dataset_name = "random"
dir_name = "{model_name}_{dataset_name}_{timestamp}".format(
model_name=gin.query_parameter("train.model").configurable.name,
dataset_name=dataset_name,
timestamp=datetime.datetime.... |
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def _setup_gin():
"""Setup gin configuration.""" |
# Imports for configurables
# pylint: disable=g-import-not-at-top,unused-import,g-bad-import-order,reimported,unused-variable
from tensor2tensor.trax import models as _trax_models
from tensor2tensor.trax import optimizers as _trax_opt
# pylint: disable=g-import-not-at-top,unused-import,g-bad-import-order,rei... |
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def _make_info(shape_list, num_classes):
"""Create an info-like tuple for feature given some shapes and vocab size.""" |
feature_info = collections.namedtuple("FeatureInfo", ["shape", "num_classes"])
cur_shape = list(shape_list[0])
# We need to merge the provided shapes, put None where they disagree.
for shape in shape_list:
if len(shape) != len(cur_shape):
raise ValueError("Shapes need to have the same number of dimen... |
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def _select_features(example, feature_list=None):
"""Select a subset of features from the example dict.""" |
feature_list = feature_list or ["inputs", "targets"]
return {f: example[f] for f in feature_list} |
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def optimize_fn(model, optimizer=None, learning_rate_schedule=None, loss=None, metrics=None):
"""Compile the model in Keras.""" |
learning_rate_schedule = learning_rate_schedule or T2TLearningRateSchedule()
if optimizer:
optimizer = optimizer(learning_rate=learning_rate_schedule)
else: # We use Adam by default with adjusted parameters.
optimizer = tf.keras.optimizers.Adam(
learning_rate=learning_rate_schedule,
beta... |
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def train_fn(data_dir=None, output_dir=None, model_class=gin.REQUIRED, dataset=gin.REQUIRED, input_names=None, target_names=None, train_steps=1000, eval_steps=1, ... |
train_data, eval_data, features_info, keys = train_and_eval_dataset(
dataset, data_dir)
if input_names is None:
input_names = keys[0]
if target_names is None:
target_names = keys[1]
# TODO(lukaszkaiser): The use of distribution strategy below fails like this:
# .../keras/models.py", line 93, ... |
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def t2t_train(model_name, dataset_name, data_dir=None, output_dir=None, config_file=None, config=None):
"""Main function to train the given model on the given da... |
if model_name not in _MODEL_REGISTRY:
raise ValueError("Model %s not in registry. Available models:\n * %s." %
(model_name, "\n * ".join(_MODEL_REGISTRY.keys())))
model_class = _MODEL_REGISTRY[model_name]()
gin.bind_parameter("train_fn.model_class", model_class)
gin.bind_parameter("tra... |
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def decode(estimator, hparams, decode_hp):
"""Decode from estimator. Interactive, from file, or from dataset.""" |
if FLAGS.decode_interactive:
if estimator.config.use_tpu:
raise ValueError("TPU can only decode from dataset.")
decoding.decode_interactively(estimator, hparams, decode_hp,
checkpoint_path=FLAGS.checkpoint_path)
elif FLAGS.decode_from_file:
decoding.decode_from_f... |
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Description:
def score_file(filename):
"""Score each line in a file and return the scores.""" |
# Prepare model.
hparams = create_hparams()
encoders = registry.problem(FLAGS.problem).feature_encoders(FLAGS.data_dir)
has_inputs = "inputs" in encoders
# Prepare features for feeding into the model.
if has_inputs:
inputs_ph = tf.placeholder(dtype=tf.int32) # Just length dimension.
batch_inputs ... |
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def time_to_channels(embedded_video):
"""Put time dimension on channels in an embedded video.""" |
video_shape = common_layers.shape_list(embedded_video)
if len(video_shape) != 5:
raise ValueError("Assuming videos given as tensors in the format "
"[batch, time, height, width, channels] but got one "
"of shape: %s" % str(video_shape))
transposed = tf.transpose(embe... |
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Description:
def autoencoder_autoregressive():
"""Autoregressive autoencoder model.""" |
hparams = autoencoder_basic()
hparams.add_hparam("autoregressive_forget_base", False)
hparams.add_hparam("autoregressive_mode", "none")
hparams.add_hparam("autoregressive_decode_steps", 0)
hparams.add_hparam("autoregressive_eval_pure_autoencoder", False)
hparams.add_hparam("autoregressive_gumbel_sample", F... |
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def autoencoder_residual():
"""Residual autoencoder model.""" |
hparams = autoencoder_autoregressive()
hparams.optimizer = "Adafactor"
hparams.clip_grad_norm = 1.0
hparams.learning_rate_constant = 0.5
hparams.learning_rate_warmup_steps = 500
hparams.learning_rate_schedule = "constant * linear_warmup * rsqrt_decay"
hparams.num_hidden_layers = 5
hparams.hidden_size =... |
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def autoencoder_residual_text():
"""Residual autoencoder model for text.""" |
hparams = autoencoder_residual()
hparams.bottleneck_bits = 32
hparams.batch_size = 1024
hparams.hidden_size = 64
hparams.max_hidden_size = 512
hparams.bottleneck_noise = 0.0
hparams.bottom = {
"inputs": modalities.identity_bottom,
"targets": modalities.identity_bottom,
}
hparams.top = {
... |
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def autoencoder_residual_discrete():
"""Residual discrete autoencoder model.""" |
hparams = autoencoder_residual()
hparams.bottleneck_bits = 1024
hparams.bottleneck_noise = 0.05
hparams.add_hparam("discretize_warmup_steps", 16000)
hparams.add_hparam("bottleneck_kind", "tanh_discrete")
hparams.add_hparam("isemhash_noise_dev", 0.5)
hparams.add_hparam("isemhash_mix_prob", 0.5)
hparams.... |
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def autoencoder_residual_discrete_big():
"""Residual discrete autoencoder model, big version.""" |
hparams = autoencoder_residual_discrete()
hparams.hidden_size = 128
hparams.max_hidden_size = 4096
hparams.bottleneck_noise = 0.1
hparams.residual_dropout = 0.4
return hparams |
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def autoencoder_ordered_text():
"""Ordered discrete autoencoder model for text.""" |
hparams = autoencoder_ordered_discrete()
hparams.bottleneck_bits = 1024
hparams.bottleneck_shared_bits = 1024-64
hparams.bottleneck_shared_bits_start_warmup = 75000
hparams.bottleneck_shared_bits_stop_warmup = 275000
hparams.num_hidden_layers = 7
hparams.batch_size = 1024
hparams.autoregressive_mode = ... |
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def autoencoder_ordered_text_small():
"""Ordered discrete autoencoder model for text, small version.""" |
hparams = autoencoder_ordered_text()
hparams.bottleneck_bits = 32
hparams.num_hidden_layers = 3
hparams.hidden_size = 64
hparams.max_hidden_size = 512
hparams.bottleneck_noise = 0.0
hparams.autoregressive_mode = "conv5"
hparams.sample_height = 4
return hparams |
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def autoencoder_discrete_pong():
"""Discrete autoencoder model for compressing pong frames.""" |
hparams = autoencoder_ordered_discrete()
hparams.num_hidden_layers = 3
hparams.bottleneck_bits = 24
hparams.batch_size = 2
hparams.gan_loss_factor = 0.01
hparams.bottleneck_l2_factor = 0.001
hparams.add_hparam("video_modality_loss_cutoff", 0.02)
return hparams |
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def autoencoder_discrete_tiny():
"""Discrete autoencoder model for compressing pong frames for testing.""" |
hparams = autoencoder_ordered_discrete()
hparams.num_hidden_layers = 2
hparams.bottleneck_bits = 24
hparams.batch_size = 2
hparams.gan_loss_factor = 0.
hparams.bottleneck_l2_factor = 0.001
hparams.add_hparam("video_modality_loss_cutoff", 0.02)
hparams.num_residual_layers = 1
hparams.hidden_size = 32
... |
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def autoencoder_discrete_cifar():
"""Discrete autoencoder model for compressing cifar.""" |
hparams = autoencoder_ordered_discrete()
hparams.bottleneck_noise = 0.0
hparams.bottleneck_bits = 90
hparams.num_hidden_layers = 2
hparams.hidden_size = 256
hparams.num_residual_layers = 4
hparams.batch_size = 32
hparams.learning_rate_constant = 1.0
return hparams |
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def autoencoder_range(rhp):
"""Tuning grid of the main autoencoder params.""" |
rhp.set_float("dropout", 0.01, 0.3)
rhp.set_float("gan_loss_factor", 0.01, 0.1)
rhp.set_float("bottleneck_l2_factor", 0.001, 0.1, scale=rhp.LOG_SCALE)
rhp.set_discrete("bottleneck_warmup_steps", [200, 2000])
rhp.set_float("gumbel_temperature", 0, 1)
rhp.set_float("gumbel_noise_factor", 0, 0.5) |
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def question_encoder(question, hparams, name="encoder"):
"""Question encoder, run LSTM encoder and get the last output as encoding.""" |
with tf.variable_scope(name, "encoder", values=[question]):
question = common_layers.flatten4d3d(question)
padding = common_attention.embedding_to_padding(question)
length = common_attention.padding_to_length(padding)
max_question_length = hparams.max_question_length
question = question[:, :max_... |
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def get(self, mode, metric):
"""Get the history for the given metric and mode.""" |
if mode not in self._values:
logging.info("Metric %s not found for mode %s", metric, mode)
return []
return list(self._values[mode][metric]) |
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def metrics_for_mode(self, mode):
"""Metrics available for a given mode.""" |
if mode not in self._values:
logging.info("Mode %s not found", mode)
return []
return sorted(list(self._values[mode].keys())) |
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def batch_norm_relu(inputs, is_training, relu=True, init_zero=False, data_format="channels_first"):
"""Performs a batch normalization followed by a ReLU. Args: i... |
if init_zero:
gamma_initializer = tf.zeros_initializer()
else:
gamma_initializer = tf.ones_initializer()
if data_format == "channels_first":
axis = 1
else:
axis = 3
inputs = layers().BatchNormalization(
axis=axis,
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON,
... |
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def residual_block(inputs, filters, is_training, projection_shortcut, strides, final_block, data_format="channels_first", use_td=False, targeting_rate=None, keep_... |
del final_block
shortcut = inputs
inputs = batch_norm_relu(inputs, is_training, data_format=data_format)
if projection_shortcut is not None:
shortcut = projection_shortcut(inputs)
inputs = conv2d_fixed_padding(
inputs=inputs,
filters=filters,
kernel_size=3,
strides=strides,
... |
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def resnet_v2(inputs, block_fn, layer_blocks, filters, data_format="channels_first", is_training=False, is_cifar=False, use_td=False, targeting_rate=None, keep_pr... |
inputs = block_layer(
inputs=inputs,
filters=filters[1],
block_fn=block_fn,
blocks=layer_blocks[0],
strides=1,
is_training=is_training,
name="block_layer1",
data_format=data_format,
use_td=use_td,
targeting_rate=targeting_rate,
keep_prob=keep_prob)
... |
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def _len_lcs(x, y):
"""Returns the length of the Longest Common Subsequence between two seqs. Source: http://www.algorithmist.com/index.php/Longest_Common_Subseq... |
table = _lcs(x, y)
n, m = len(x), len(y)
return table[n, m] |
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def _lcs(x, y):
"""Computes the length of the LCS between two seqs. The implementation below uses a DP programming algorithm and runs in O(nm) time where n = len... |
n, m = len(x), len(y)
table = {}
for i in range(n + 1):
for j in range(m + 1):
if i == 0 or j == 0:
table[i, j] = 0
elif x[i - 1] == y[j - 1]:
table[i, j] = table[i - 1, j - 1] + 1
else:
table[i, j] = max(table[i - 1, j], table[i, j - 1])
return table |
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def _get_ngrams(n, text):
"""Calculates n-grams. Args: n: which n-grams to calculate text: An array of tokens Returns: A set of n-grams """ |
ngram_set = set()
text_length = len(text)
max_index_ngram_start = text_length - n
for i in range(max_index_ngram_start + 1):
ngram_set.add(tuple(text[i:i + n]))
return ngram_set |
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def flatten_zip_dataset(*args):
"""A list of examples to a dataset containing mixed examples. Given a list of `n` dataset examples, flatten them by converting ea... |
flattened = tf.data.Dataset.from_tensors(args[0])
for ex in args[1:]:
flattened = flattened.concatenate(tf.data.Dataset.from_tensors(ex))
return flattened |
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def aggregate_task_lm_losses(hparams, problem_hparams, logits, feature_name, feature):
"""LM loss for multiproblems.""" |
summaries = []
vocab_size = problem_hparams.vocab_size[feature_name]
if vocab_size is not None and hasattr(hparams, "vocab_divisor"):
vocab_size += (-vocab_size) % hparams.vocab_divisor
modality = problem_hparams.modality[feature_name]
loss = hparams.loss.get(feature_name, modalities.get_loss(modality))
... |
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def update_task_ids(self, encoder_vocab_size):
"""Generate task_ids for each problem. These ids correspond to the index of the task in the task_list. Args: encod... |
for idx, task in enumerate(self.task_list):
task.set_task_id(idx + encoder_vocab_size)
tf.logging.info("Task %d (%s) has id %d." %
(idx, task.name, task.task_id)) |
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def get_max_num_classes(self):
"""Compute the maximum number of classes any subtask has. This is useful for modifying the size of the softmax to include the outp... |
num = 0
for task in self.task_list:
if hasattr(task, "num_classes"):
if num < task.num_classes:
num = task.num_classes
return num |
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def _norm(self, x):
"""Compute the safe norm.""" |
return tf.sqrt(tf.reduce_sum(tf.square(x), keepdims=True, axis=-1) + 1e-7) |
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def _address_content(self, x):
"""Address the memory based on content similarity. Args: x: a tensor in the shape of [batch_size, length, depth]. Returns: the log... |
mem_keys = tf.layers.dense(self.mem_vals, self.key_depth,
bias_initializer=tf.constant_initializer(1.0),
name="mem_key")
mem_query = tf.layers.dense(x, self.key_depth,
bias_initializer=tf.constant_initializer(1.0),
... |
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def read(self, x):
"""Read from the memory. An external component can use the results via a simple MLP, e.g., fn(x W_x + retrieved_mem W_m). Args: x: a tensor in... |
access_logits = self._address_content(x)
weights = tf.nn.softmax(access_logits)
retrieved_mem = tf.reduce_sum(
tf.multiply(tf.expand_dims(weights, 3),
tf.expand_dims(self.mem_vals, axis=1)), axis=2)
return access_logits, retrieved_mem |
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def write(self, x, access_logits):
"""Write to the memory based on a combination of similarity and least used. Based on arXiv:1607.00036v2 [cs.LG]. Args: x: a te... |
gamma = tf.layers.dense(x, 1, activation=tf.sigmoid, name="gamma")
write_logits = access_logits - gamma * tf.expand_dims(self.mean_logits, 1)
candidate_value = tf.layers.dense(x, self.val_depth,
activation=tf.nn.relu,
name="candida... |
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def reset(self, entries_to_reset):
"""Reset the entries in the memory. Args: entries_to_reset: a 1D tensor. Returns: the reset op. """ |
num_updates = tf.size(entries_to_reset)
update_vals = tf.scatter_update(
self.mem_vals, entries_to_reset,
tf.tile(tf.expand_dims(
tf.fill([self.memory_size, self.val_depth], .0), 0),
[num_updates, 1, 1]))
update_logits = tf.scatter_update(
self.mean_logit... |
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def _define_train( train_env, ppo_hparams, eval_env_fn=None, sampling_temp=1.0, **collect_kwargs ):
"""Define the training setup.""" |
memory, collect_summary, train_initialization = (
_define_collect(
train_env,
ppo_hparams,
"ppo_train",
eval_phase=False,
sampling_temp=sampling_temp,
**collect_kwargs))
ppo_summary = ppo.define_ppo_epoch(
memory, ppo_hparams, train_env.action... |
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def _rollout_metadata(batch_env):
"""Metadata for rollouts.""" |
batch_env_shape = batch_env.observ.get_shape().as_list()
batch_size = [batch_env_shape[0]]
shapes_types_names = [
# TODO(piotrmilos): possibly retrieve the observation type for batch_env
(batch_size + batch_env_shape[1:], batch_env.observ_dtype, "observation"),
(batch_size, tf.float32, "reward"... |
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def sliced_gan():
"""Basic parameters for a vanilla_gan.""" |
hparams = common_hparams.basic_params1()
hparams.optimizer = "adam"
hparams.learning_rate_constant = 0.0002
hparams.learning_rate_warmup_steps = 500
hparams.learning_rate_schedule = "constant * linear_warmup"
hparams.label_smoothing = 0.0
hparams.batch_size = 128
hparams.hidden_size = 128
hparams.ini... |
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def body(self, features):
"""Body of the model. Args: features: a dictionary with the tensors. Returns: A pair (predictions, losses) where predictions is the gen... |
features["targets"] = features["inputs"]
is_training = self.hparams.mode == tf.estimator.ModeKeys.TRAIN
# Input images.
inputs = tf.to_float(features["targets_raw"])
# Noise vector.
z = tf.random_uniform([self.hparams.batch_size,
self.hparams.bottleneck_bits],
... |
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def inputs(num_devices, dataset_name, data_dir=None, input_name=None, num_chunks=0, append_targets=False):
"""Make Inputs for built-in datasets. Args: num_device... |
assert data_dir, "Must provide a data directory"
data_dir = os.path.expanduser(data_dir)
(train_batches, train_eval_batches, eval_batches,
input_name, input_shape) = _train_and_eval_batches(
dataset_name, data_dir, input_name, num_devices)
def numpy_stream(dataset):
return dataset_to_stream(
... |
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def random_inputs( num_devices, input_shape=gin.REQUIRED, input_dtype=np.int32, input_range=(0, 255), output_shape=gin.REQUIRED, output_dtype=np.int32, output_ran... |
if input_shape[0] % num_devices != 0:
tf.logging.fatal(
"num_devices[%d] should divide the first dimension of input_shape[%s]",
num_devices, input_shape)
if output_shape[0] % num_devices != 0:
tf.logging.fatal(
"num_devices[%d] should divide the first dimension of output_shape[%s]",... |
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def dataset_to_stream(dataset, input_name, num_chunks=0, append_targets=False):
"""Takes a tf.Dataset and creates a numpy stream of ready batches.""" |
for example in tfds.as_numpy(dataset):
inp, out = example[0][input_name], example[1]
if len(out.shape) > 1 and out.shape[-1] == 1:
out = np.squeeze(out, axis=-1)
if num_chunks > 0:
inp = np.split(inp, num_chunks, axis=1)
out = np.split(out, num_chunks, axis=1)
if append_targets:
... |
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def _train_and_eval_batches(dataset, data_dir, input_name, num_devices):
"""Return train and eval batches with input name and shape.""" |
(train_data, eval_data, features_info, keys) = train_and_eval_dataset(
dataset, data_dir)
input_names, target_names = keys[0], keys[1]
train_batches = shuffle_and_batch_data(
train_data, target_names, features_info, training=True,
num_devices=num_devices)
train_eval_batches = shuffle_and_batc... |
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def get_multi_dataset(datasets, pmf=None):
"""Returns a Dataset that samples records from one or more Datasets. Args: datasets: A list of one or more Dataset obj... |
pmf = tf.fill([len(datasets)], 1.0 / len(datasets)) if pmf is None else pmf
samplers = [d.repeat().make_one_shot_iterator().get_next for d in datasets]
sample = lambda _: categorical_case(pmf, samplers)
return tf.data.Dataset.from_tensors([]).repeat().map(sample) |
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def get_schedule_distribution(schedule, global_step=None):
"""Computes the pmf of a schedule given the global_step. Args: schedule: A schedule tuple, see encode_... |
interpolation, steps, pmfs = schedule
if len(pmfs) == 1:
# py_func doesn't seem to work on TPU - at least get the constant case to
# run.
# TODO(noam): get the general case working.
return pmfs[0]
if global_step is None:
global_step = tf.train.get_or_create_global_step()
if interpolation ==... |
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def linear_interpolation(x, xp, fp, **kwargs):
"""Multi-dimensional linear interpolation. Returns the multi-dimensional piecewise linear interpolant to a functio... |
yp = fp.reshape([fp.shape[0], -1]).transpose()
y = np.stack([np.interp(x, xp, zp, **kwargs) for zp in yp]).transpose()
return y.reshape(x.shape[:1] + fp.shape[1:]).astype(np.float32) |
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def step_interpolation(x, xp, fp, **kwargs):
"""Multi-dimensional step interpolation. Returns the multi-dimensional step interpolant to a function with given dis... |
del kwargs # Unused.
xp = np.expand_dims(xp, -1)
lower, upper = xp[:-1], xp[1:]
conditions = (x >= lower) & (x < upper)
# Underflow and overflow conditions and values. Values default to fp[0] and
# fp[-1] respectively.
conditions = np.concatenate([[x < xp[0]], conditions, [x >= xp[-1]]])
values = np.c... |
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def epoch_rates_to_pmf(problems, epoch_rates=None):
"""Create a probability-mass-function based on relative epoch rates. if epoch_rates=None, then we use uniform... |
if epoch_rates is None:
epoch_rates = [1.0] * len(problems)
example_rates = [epoch_rate * p.num_training_examples
for p, epoch_rate in zip(problems, epoch_rates)]
return example_rates_to_pmf(example_rates) |
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def encode_schedule(schedule):
"""Encodes a schedule tuple into a string. Args: schedule: A tuple containing (interpolation, steps, pmfs), where interpolation is... |
interpolation, steps, pmfs = schedule
return interpolation + ' ' + ' '.join(
'@' + str(s) + ' ' + ' '.join(map(str, p)) for s, p in zip(steps, pmfs)) |
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def decode_schedule(string):
"""Decodes a string into a schedule tuple. Args: string: The string encoding of a schedule tuple. Returns: A schedule tuple, see enc... |
splits = string.split()
steps = [int(x[1:]) for x in splits[1:] if x[0] == '@']
pmfs = np.reshape(
[float(x) for x in splits[1:] if x[0] != '@'], [len(steps), -1])
return splits[0], tuplize(steps), tuplize(pmfs) |
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def tuplize(nested):
"""Recursively converts iterables into tuples. Args: nested: A nested structure of items and iterables. Returns: A nested structure of items... |
if isinstance(nested, str):
return nested
try:
return tuple(map(tuplize, nested))
except TypeError:
return nested |
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def filepattern(self, *args, **kwargs):
"""Returns a list of filepatterns, one for each problem.""" |
return [p.filepattern(*args, **kwargs) for p in self.problems] |
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def generate_data(self, *args, **kwargs):
"""Generates data for each problem.""" |
for p in self.problems:
p.generate_data(*args, **kwargs) |
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def dataset(self, mode, hparams=None, global_step=None, **kwargs):
"""Returns a dataset containing examples from multiple problems. Args: mode: A member of probl... |
datasets = [p.dataset(mode, **kwargs) for p in self.problems]
datasets = [
d.map(lambda x, i=j: self.normalize_example( # pylint: disable=g-long-lambda
dict(x, problem_id=tf.constant([i])), hparams))
for j, d in enumerate(datasets) # Tag examples with a problem_id.
]
if mo... |
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def normalize_example(self, example, hparams):
"""Assumes that example contains both inputs and targets.""" |
length = self.max_length(hparams)
def _to_constant_shape(tensor):
tensor = tensor[:length]
tensor = tf.pad(tensor, [(0, length - tf.shape(tensor)[0])])
return tf.reshape(tensor, [length])
if self.has_inputs:
example['inputs'] = _to_constant_shape(example['inputs'])
example['... |
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def generate_data_with_shared_vocab(self, data_dir, tmp_dir, task_id=-1):
"""Generates TF-Records for problems using a global vocabulary file.""" |
global_vocab_filename = os.path.join(data_dir, self.vocab_filename)
if not tf.gfile.Exists(global_vocab_filename):
raise ValueError(
'Global vocabulary file: %s does not exist, '
'please create one using build_vocab.py' % global_vocab_filename)
# Before generating data, we copy th... |
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def lengths_to_area_mask(feature_length, length, max_area_size):
"""Generates a non-padding mask for areas based on lengths. Args: feature_length: a tensor of [b... |
paddings = tf.cast(tf.expand_dims(
tf.logical_not(
tf.sequence_mask(feature_length, maxlen=length)), 2), tf.float32)
_, _, area_sum, _, _ = compute_area_features(paddings,
max_area_width=max_area_size)
mask = tf.squeeze(tf.logical_not(tf.cast(area_s... |
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