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tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki.py | _page_to_title | def _page_to_title(page):
"""Extract the title from a page.
Args:
page: a unicode string
Returns:
a unicode string
"""
# print("page=%s" % page)
start_tag = u"<title>"
end_tag = u"</title>"
start_pos = page.find(start_tag)
end_pos = page.find(end_tag)
assert start_pos != -1
assert end_pos... | python | def _page_to_title(page):
"""Extract the title from a page.
Args:
page: a unicode string
Returns:
a unicode string
"""
# print("page=%s" % page)
start_tag = u"<title>"
end_tag = u"</title>"
start_pos = page.find(start_tag)
end_pos = page.find(end_tag)
assert start_pos != -1
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tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki.py | _page_to_text | def _page_to_text(page):
"""Extract the text from a page.
Args:
page: a unicode string
Returns:
a unicode string
"""
# text start tag looks like "<text ..otherstuff>"
start_pos = page.find(u"<text")
assert start_pos != -1
end_tag_pos = page.find(u">", start_pos)
assert end_tag_pos != -1
end... | python | def _page_to_text(page):
"""Extract the text from a page.
Args:
page: a unicode string
Returns:
a unicode string
"""
# text start tag looks like "<text ..otherstuff>"
start_pos = page.find(u"<text")
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tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki.py | _find_and_replace | def _find_and_replace(text, start_string, end_string, replace_fn):
"""Remove everything found between instances of start_string and end_string.
Replace each such instance with replace_fn(removed_text)
e.g. _find_and_replace(u"the [[fat]] cat [[sat]]", u"[[", u"]]", lambda x: x)
= u"the fat cat sat"
Args:... | python | def _find_and_replace(text, start_string, end_string, replace_fn):
"""Remove everything found between instances of start_string and end_string.
Replace each such instance with replace_fn(removed_text)
e.g. _find_and_replace(u"the [[fat]] cat [[sat]]", u"[[", u"]]", lambda x: x)
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tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki.py | _remove_double_brackets | def _remove_double_brackets(text):
"""Remove double brackets (internal links) but leave the viewable text.
Args:
text: a unicode string
Returns:
a unicode string
"""
def replacement_fn(s):
if u":" in s:
# this is probably a category or something like that.
return ""
# keep the par... | python | def _remove_double_brackets(text):
"""Remove double brackets (internal links) but leave the viewable text.
Args:
text: a unicode string
Returns:
a unicode string
"""
def replacement_fn(s):
if u":" in s:
# this is probably a category or something like that.
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tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | image_encoder | def image_encoder(image_feat,
hparams,
name="image_encoder",
save_weights_to=None,
make_image_summary=True):
"""A stack of self attention layers."""
x = image_feat
image_hidden_size = hparams.image_hidden_size or hparams.hidden_size
image_... | python | def image_encoder(image_feat,
hparams,
name="image_encoder",
save_weights_to=None,
make_image_summary=True):
"""A stack of self attention layers."""
x = image_feat
image_hidden_size = hparams.image_hidden_size or hparams.hidden_size
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tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | prepare_question_encoder | def prepare_question_encoder(inputs, hparams):
"""Prepare question encoder.
Args:
inputs: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
encoder_self_attention_bias: a bias tensor for use in encoder self-attention
"""
encoder_input = inputs
... | python | def prepare_question_encoder(inputs, hparams):
"""Prepare question encoder.
Args:
inputs: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
encoder_self_attention_bias: a bias tensor for use in encoder self-attention
"""
encoder_input = inputs
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tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | question_encoder | def question_encoder(question,
question_self_attention_bias,
hparams,
name="question_encoder",
save_weights_to=None,
make_image_summary=True):
"""A stack of self attention layers."""
x = question
with tf.varia... | python | def question_encoder(question,
question_self_attention_bias,
hparams,
name="question_encoder",
save_weights_to=None,
make_image_summary=True):
"""A stack of self attention layers."""
x = question
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tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | attn | def attn(image_feat,
query,
hparams,
name="attn",
save_weights_to=None,
make_image_summary=True):
"""Attention on image feature with question as query."""
with tf.variable_scope(name, "attn", values=[image_feat, query]):
total_key_depth = hparams.attention_key_channe... | python | def attn(image_feat,
query,
hparams,
name="attn",
save_weights_to=None,
make_image_summary=True):
"""Attention on image feature with question as query."""
with tf.variable_scope(name, "attn", values=[image_feat, query]):
total_key_depth = hparams.attention_key_channe... | [
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tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | mlp | def mlp(feature, hparams, name="mlp"):
"""Multi layer perceptron with dropout and relu activation."""
with tf.variable_scope(name, "mlp", values=[feature]):
num_mlp_layers = hparams.num_mlp_layers
mlp_size = hparams.mlp_size
for _ in range(num_mlp_layers):
feature = common_layers.dense(feature, ml... | python | def mlp(feature, hparams, name="mlp"):
"""Multi layer perceptron with dropout and relu activation."""
with tf.variable_scope(name, "mlp", values=[feature]):
num_mlp_layers = hparams.num_mlp_layers
mlp_size = hparams.mlp_size
for _ in range(num_mlp_layers):
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tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | prepare_image_question_encoder | def prepare_image_question_encoder(image_feat, question, hparams):
"""Prepare encoder.
Args:
image_feat: a Tensor.
question: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
encoder_self_attention_bias: a bias tensor for use in encoder self-att... | python | def prepare_image_question_encoder(image_feat, question, hparams):
"""Prepare encoder.
Args:
image_feat: a Tensor.
question: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
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tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | image_question_encoder | def image_question_encoder(encoder_inputs,
encoder_self_attention_bias,
hparams,
query=None,
name="image_question_encoder",
save_weights_to=None,
make_image_s... | python | def image_question_encoder(encoder_inputs,
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hparams,
query=None,
name="image_question_encoder",
save_weights_to=None,
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tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | decoder | def decoder(decoder_input,
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encoder_decoder_attention_bias,
hparams,
name="decoder",
save_weights_to=None,
make_image_summary=True,):
"""A stack of transformer layers.
Args:
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encoder_output,
decoder_self_attention_bias,
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tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | iterative_encoder_decoder | def iterative_encoder_decoder(encoder_input,
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encoder_decoder_attention_bias,
query,
hparams):
"""Iterative encoder decoder."""
for _ in range(hparams.num_rec_steps):
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encoder_decoder_attention_bias,
query,
hparams):
"""Iterative encoder decoder."""
for _ in range(hparams.num_rec_steps):
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tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | vqa_self_attention_base | def vqa_self_attention_base():
"""VQA attention baseline hparams."""
hparams = common_hparams.basic_params1()
hparams.batch_size = 128
hparams.use_fixed_batch_size = True,
hparams.optimizer = "adam"
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.997
hparams.optimizer_adam_epsilon = ... | python | def vqa_self_attention_base():
"""VQA attention baseline hparams."""
hparams = common_hparams.basic_params1()
hparams.batch_size = 128
hparams.use_fixed_batch_size = True,
hparams.optimizer = "adam"
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.997
hparams.optimizer_adam_epsilon = ... | [
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tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | vqa_self_attention_feature_batch1024_big | def vqa_self_attention_feature_batch1024_big():
"""Big model."""
hparams = vqa_self_attention_feature_batch1024()
hparams.learning_rate_constant = 7e-4
hparams.batch_size = 256
hparams.hidden_size = 1024
hparams.filter_size = 4096
hparams.num_heads = 16
hparams.layer_prepostprocess_dropout = 0.3
hpara... | python | def vqa_self_attention_feature_batch1024_big():
"""Big model."""
hparams = vqa_self_attention_feature_batch1024()
hparams.learning_rate_constant = 7e-4
hparams.batch_size = 256
hparams.hidden_size = 1024
hparams.filter_size = 4096
hparams.num_heads = 16
hparams.layer_prepostprocess_dropout = 0.3
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tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | _bucket_boundaries | def _bucket_boundaries(max_length, min_length=8, length_bucket_step=1.1):
"""A default set of length-bucket boundaries."""
assert length_bucket_step > 1.0
x = min_length
boundaries = []
while x < max_length:
boundaries.append(x)
x = max(x + 1, int(x * length_bucket_step))
return boundaries | python | def _bucket_boundaries(max_length, min_length=8, length_bucket_step=1.1):
"""A default set of length-bucket boundaries."""
assert length_bucket_step > 1.0
x = min_length
boundaries = []
while x < max_length:
boundaries.append(x)
x = max(x + 1, int(x * length_bucket_step))
return boundaries | [
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tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | batching_scheme | def batching_scheme(batch_size,
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length_bucket_step,
drop_long_sequences=False,
shard_multiplier=1,
length_multiplier=1,
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max_length,
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drop_long_sequences=False,
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length_multiplier=1,
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tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | hparams_to_batching_scheme | def hparams_to_batching_scheme(hparams,
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length_multiplier=1):
"""Wrapper around _batching_scheme with hparams."""
return batching_scheme(
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... | python | def hparams_to_batching_scheme(hparams,
drop_long_sequences=False,
shard_multiplier=1,
length_multiplier=1):
"""Wrapper around _batching_scheme with hparams."""
return batching_scheme(
batch_size=hparams.batch_size,
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tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | pad_for_tpu | def pad_for_tpu(shapes_dict, hparams, max_length):
"""Pads unknown features' dimensions for TPU."""
padded_shapes = {}
def get_filler(specified_max_length):
if not specified_max_length:
return max_length
return min(specified_max_length, max_length)
inputs_none_filler = get_filler(hparams.max_inp... | python | def pad_for_tpu(shapes_dict, hparams, max_length):
"""Pads unknown features' dimensions for TPU."""
padded_shapes = {}
def get_filler(specified_max_length):
if not specified_max_length:
return max_length
return min(specified_max_length, max_length)
inputs_none_filler = get_filler(hparams.max_inp... | [
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tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | standardize_shapes | def standardize_shapes(features, batch_size=None):
"""Set the right shapes for the features."""
for fname in ["inputs", "targets"]:
if fname not in features:
continue
f = features[fname]
while len(f.get_shape()) < 4:
f = tf.expand_dims(f, axis=-1)
features[fname] = f
if batch_size:
... | python | def standardize_shapes(features, batch_size=None):
"""Set the right shapes for the features."""
for fname in ["inputs", "targets"]:
if fname not in features:
continue
f = features[fname]
while len(f.get_shape()) < 4:
f = tf.expand_dims(f, axis=-1)
features[fname] = f
if batch_size:
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tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | _file_num_records_cached | def _file_num_records_cached(filename):
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# Cache the result, as this is expensive to compute
if filename in _file_num_records_cache:
return _file_num_records_cache[filename]
ret = 0
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ret += 1
_file_num_... | python | def _file_num_records_cached(filename):
"""Return the number of TFRecords in a file."""
# Cache the result, as this is expensive to compute
if filename in _file_num_records_cache:
return _file_num_records_cache[filename]
ret = 0
for _ in tf.python_io.tf_record_iterator(filename):
ret += 1
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tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | pad_batch | def pad_batch(features, batch_multiple):
"""Pad batch dim of features to nearest multiple of batch_multiple."""
feature = list(features.items())[0][1]
batch_size = tf.shape(feature)[0]
mod = batch_size % batch_multiple
has_mod = tf.cast(tf.cast(mod, tf.bool), tf.int32)
batch_padding = batch_multiple * has_m... | python | def pad_batch(features, batch_multiple):
"""Pad batch dim of features to nearest multiple of batch_multiple."""
feature = list(features.items())[0][1]
batch_size = tf.shape(feature)[0]
mod = batch_size % batch_multiple
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tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | input_fn | def input_fn(dataset,
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data_dir=None,
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batch_size_multiplier,
max_length,
mode,
hparams,
data_dir=None,
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tensorflow/tensor2tensor | tensor2tensor/data_generators/gene_expression.py | generate_shard_args | def generate_shard_args(outfiles, num_examples):
"""Generate start and end indices per outfile."""
num_shards = len(outfiles)
num_examples_per_shard = num_examples // num_shards
start_idxs = [i * num_examples_per_shard for i in range(num_shards)]
end_idxs = list(start_idxs)
end_idxs.pop(0)
end_idxs.append... | python | def generate_shard_args(outfiles, num_examples):
"""Generate start and end indices per outfile."""
num_shards = len(outfiles)
num_examples_per_shard = num_examples // num_shards
start_idxs = [i * num_examples_per_shard for i in range(num_shards)]
end_idxs = list(start_idxs)
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tensorflow/tensor2tensor | tensor2tensor/data_generators/gene_expression.py | dataset_generator | def dataset_generator(filepath,
dataset,
chunk_size=1,
start_idx=None,
end_idx=None):
"""Generate example dicts."""
encoder = dna_encoder.DNAEncoder(chunk_size=chunk_size)
with h5py.File(filepath, "r") as h5_file:
# Get in... | python | def dataset_generator(filepath,
dataset,
chunk_size=1,
start_idx=None,
end_idx=None):
"""Generate example dicts."""
encoder = dna_encoder.DNAEncoder(chunk_size=chunk_size)
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tensorflow/tensor2tensor | tensor2tensor/data_generators/gene_expression.py | to_example_dict | def to_example_dict(encoder, inputs, mask, outputs):
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bases = []
input_ids = []
last_idx = -1
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# Inputs
bases = []
input_ids = []
last_idx = -1
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | linear_interpolate | def linear_interpolate(tensor1, tensor2, coeffs):
"""Linearly interpolate between two tensors at coeff.
Args:
tensor1: 4-D Tensor, shape=(NHWC)
tensor2: 4-D Tensor, shape=(NHWC)
coeffs: list of floats.
Returns:
interp_latents: 5-D Tensor, with interp_latents[i] representing
in... | python | def linear_interpolate(tensor1, tensor2, coeffs):
"""Linearly interpolate between two tensors at coeff.
Args:
tensor1: 4-D Tensor, shape=(NHWC)
tensor2: 4-D Tensor, shape=(NHWC)
coeffs: list of floats.
Returns:
interp_latents: 5-D Tensor, with interp_latents[i] representing
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | linear_interpolate_rank | def linear_interpolate_rank(tensor1, tensor2, coeffs, rank=1):
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The channels are ranked according to their L2 norm between tensor1[channel]
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Args:
tensor1: 4-D Tensor, NHWC
tensor2: 4-D Tensor, NHWC
coeffs: list of ... | python | def linear_interpolate_rank(tensor1, tensor2, coeffs, rank=1):
"""Linearly interpolate channel at "rank" between two tensors.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | postprocess | def postprocess(x, n_bits_x=8):
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Args:
x: 3-D or 4-D Tensor normalized between [-0.5, 0.5]
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"""Converts x from [-0.5, 0.5], to [0, 255].
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x: 3-D or 4-D Tensor normalized between [-0.5, 0.5]
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | get_cond_latents_at_level | def get_cond_latents_at_level(cond_latents, level, hparams):
"""Returns a single or list of conditional latents at level 'level'."""
if cond_latents:
if hparams.latent_dist_encoder in ["conv_net", "conv3d_net"]:
return [cond_latent[level] for cond_latent in cond_latents]
elif hparams.latent_dist_encod... | python | def get_cond_latents_at_level(cond_latents, level, hparams):
"""Returns a single or list of conditional latents at level 'level'."""
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if hparams.latent_dist_encoder in ["conv_net", "conv3d_net"]:
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | check_cond_latents | def check_cond_latents(cond_latents, hparams):
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | get_variable_ddi | def get_variable_ddi(name, shape, initial_value, dtype=tf.float32, init=False,
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | get_dropout | def get_dropout(x, rate=0.0, init=True):
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x: 4-D Tensor, shape=(NHWC).
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x: activations after dropout.
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if init or rate == 0:
return x
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | actnorm_3d | def actnorm_3d(name, x, logscale_factor=3.):
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name: variable scope.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | actnorm | def actnorm(name, x, logscale_factor=3., reverse=False, init=False,
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | actnorm_center | def actnorm_center(name, x, reverse=False, init=False):
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per channel.
Args:
name: scope
x: 2-D or 4-D Tensor.
reverse: Forward or backward operation.
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"""Add a bias to x.
Initialize such that the output of the first minibatch is zero centered
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name: scope
x: 2-D or 4-D Tensor.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | actnorm_scale | def actnorm_scale(name, x, logscale_factor=3., reverse=False, init=False):
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | invertible_1x1_conv | def invertible_1x1_conv(name, x, reverse=False):
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | add_edge_bias | def add_edge_bias(x, filter_size):
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Args:
x: Input tensor, shape (NHWC)
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"""Pad x and concatenates an edge bias across the depth of x.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | time_pad | def time_pad(x, filter_size, dilations):
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Args:
x: 5-D Tensor, (NTHWC)
filter_size: list of ints
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | conv | def conv(name, x, output_channels, filter_size=None, stride=None,
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dilations=None):
"""Convolutional layer with edge bias padding and optional actnorm.
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time-step.
... | python | def conv(name, x, output_channels, filter_size=None, stride=None,
logscale_factor=3.0, apply_actnorm=True, conv_init="default",
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"""Convolutional layer with edge bias padding and optional actnorm.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | conv_block | def conv_block(name, x, mid_channels, dilations=None, activation="relu",
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Args:
name: variable scope.
x: 4-D or 5-D Tensor.
mid_channels: Output channels of the second layer.
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"""2 layer conv block used in the affine coupling layer.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | dilated_conv_stack | def dilated_conv_stack(name, x, mid_channels, output_channels,
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dropout=0.0):
"""Dilated convolutional stack.
Features at different rates are computed independently using a 3 layer
convolutional stack and added.
Args:
name: va... | python | def dilated_conv_stack(name, x, mid_channels, output_channels,
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | conv_stack | def conv_stack(name, x, mid_channels, output_channels, dilations=None,
activation="relu", dropout=0.0):
"""3-layer convolutional stack.
Args:
name: variable scope.
x: 5-D Tensor.
mid_channels: Number of output channels of the first layer.
output_channels: Number of output channels.
... | python | def conv_stack(name, x, mid_channels, output_channels, dilations=None,
activation="relu", dropout=0.0):
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name: variable scope.
x: 5-D Tensor.
mid_channels: Number of output channels of the first layer.
output_channels: Number of output channels.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | additive_coupling | def additive_coupling(name, x, mid_channels=512, reverse=False,
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name: variable scope.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | affine_coupling | def affine_coupling(name, x, mid_channels=512, activation="relu",
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name: variable scope.
x: 4-D Tensor.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | squeeze | def squeeze(name, x, factor=2, reverse=True):
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name: Used for variable scoping.
x: 4-D Tensor of shape (batch_size X H X W X C)
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | get_dilation_rates | def get_dilation_rates(hparams, width):
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Args:
hparams: HParams.
width: spatial dimension. Ensures that the effective filter size is
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Returns:
allowed_dilations: A list of dilation rates.
"""
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Args:
hparams: HParams.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | temporal_latent_to_dist | def temporal_latent_to_dist(name, x, hparams, output_channels=None):
"""Network that maps a time-indexed list of 3-D latents to a gaussian.
Args:
name: variable scope.
x: List of 4-D Tensors indexed by time, (NHWC)
hparams: tf.contrib.training.Hparams.
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"""Network that maps a time-indexed list of 3-D latents to a gaussian.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | single_conv_dist | def single_conv_dist(name, x, output_channels=None):
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name: variable scope.
x: 4-D Tensor.
output_channels: number of channels of the mean and std.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | latent_to_dist | def latent_to_dist(name, x, hparams, output_channels=None):
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name: variable scope.
x: 4-D Tensor of shape (NHWC)
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | noise_op | def noise_op(latents, hparams):
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Args:
latents: 4-D or 5-D tensor, shape=(NTHWC) or (NHWC).
hparams: HParams.
Returns:
latents: latents with isotropic gaussian noise appended.
"""
if hparams.latent_noise == 0 or hparams.mode != tf.estimator.ModeKeys... | python | def noise_op(latents, hparams):
"""Adds isotropic gaussian-noise to each latent.
Args:
latents: 4-D or 5-D tensor, shape=(NTHWC) or (NHWC).
hparams: HParams.
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latents: latents with isotropic gaussian noise appended.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | merge_level_and_latent_dist | def merge_level_and_latent_dist(level_dist, latent_dist,
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | level_cond_prior | def level_cond_prior(prior_dist, z, latent, hparams, state):
"""Returns a conditional prior for each level.
Args:
prior_dist: Distribution conditioned on the previous levels.
z: Tensor, output of the previous levels.
latent: Tensor or a list of tensors to condition the latent_distribution.
hparams:... | python | def level_cond_prior(prior_dist, z, latent, hparams, state):
"""Returns a conditional prior for each level.
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prior_dist: Distribution conditioned on the previous levels.
z: Tensor, output of the previous levels.
latent: Tensor or a list of tensors to condition the latent_distribution.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | compute_prior | def compute_prior(name, z, latent, hparams, condition=False, state=None,
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name: variable scope.
z: 4-D Tensor.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | split | def split(name, x, reverse=False, eps=None, eps_std=None, cond_latents=None,
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | revnet_step | def revnet_step(name, x, hparams, reverse=True):
"""One step of glow generative flow.
Actnorm + invertible 1X1 conv + affine_coupling.
Args:
name: used for variable scope.
x: input
hparams: coupling_width is the only hparam that is being used in
this function.
reverse: forward or re... | python | def revnet_step(name, x, hparams, reverse=True):
"""One step of glow generative flow.
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x: input
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | revnet | def revnet(name, x, hparams, reverse=True):
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | scale_gaussian_prior | def scale_gaussian_prior(name, z, logscale_factor=3.0, trainable=True):
"""Returns N(s^i * z^i, std^i) where s^i and std^i are pre-component.
s^i is a learnable parameter with identity initialization.
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Args:
name: variable scope.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | top_prior | def top_prior(name, z_shape, learn_prior="normal", temperature=1.0):
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name: variable scope
z_shape: Shape of the mean / scale of the prior distribution.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | uniform_binning_correction | def uniform_binning_correction(x, n_bits=8):
"""Replaces x^i with q^i(x) = U(x, x + 1.0 / 256.0).
Args:
x: 4-D Tensor of shape (NHWC)
n_bits: optional.
Returns:
x: x ~ U(x, x + 1.0 / 256)
objective: Equivalent to -q(x)*log(q(x)).
"""
n_bins = 2**n_bits
batch_size, height, width, n_channels ... | python | def uniform_binning_correction(x, n_bits=8):
"""Replaces x^i with q^i(x) = U(x, x + 1.0 / 256.0).
Args:
x: 4-D Tensor of shape (NHWC)
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n_bins = 2**n_bits
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | encoder_decoder | def encoder_decoder(name, x, hparams, eps=None, reverse=False,
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temperature=1.0):
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name: variable scope.
x: 4-D Tensor, shape=(NHWC).
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tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | bfloat16_activations_var_getter | def bfloat16_activations_var_getter(getter, *args, **kwargs):
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Args:
getter: custom getter
*args: arguments
**kwargs: keyword arguments
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tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | float16_activations_var_getter | def float16_activations_var_getter(getter, *args, **kwargs):
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tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | simulated_quantize | def simulated_quantize(x, num_bits, noise):
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tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | noise_from_step_num | def noise_from_step_num():
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a float32 scalar
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tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | _randomized_roundoff_to_bfloat16 | def _randomized_roundoff_to_bfloat16(x, noise, cand1, cand2):
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tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | _to_bfloat16_unbiased | def _to_bfloat16_unbiased(x, noise):
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Args:
x: A float32 Tensor.
noise: a float32 Tensor with values in [0, 1), broadcastable to tf.shape(x)
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x_sign = tf.sign(x)
# Make sure x is positive. If it is zero,... | python | def _to_bfloat16_unbiased(x, noise):
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x: A float32 Tensor.
noise: a float32 Tensor with values in [0, 1), broadcastable to tf.shape(x)
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tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | ParameterEncoding.custom_getter | def custom_getter(self, activation_dtype=tf.bfloat16):
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tensorflow/tensor2tensor | tensor2tensor/utils/video_metrics.py | load_videos | def load_videos(template, video_length, frame_shape):
"""Loads videos from files.
Args:
template: template string for listing the image files.
video_length: length of the video.
frame_shape: shape of each frame.
Returns:
dataset: the tf dataset frame by frame.
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"""Loads videos from files.
Args:
template: template string for listing the image files.
video_length: length of the video.
frame_shape: shape of each frame.
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tensorflow/tensor2tensor | tensor2tensor/utils/video_metrics.py | psnr_and_ssim | def psnr_and_ssim(output, target):
"""Compute the PSNR and SSIM.
Args:
output: 4-D Tensor, shape=(num_frames, height, width, num_channels)
target: 4-D Tensor, shape=(num_frames, height, width, num_channels)
Returns:
psnr: 1-D Tensor, shape=(num_frames,)
ssim: 1-D Tensor, shape=(num_frames,)
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"""Compute the PSNR and SSIM.
Args:
output: 4-D Tensor, shape=(num_frames, height, width, num_channels)
target: 4-D Tensor, shape=(num_frames, height, width, num_channels)
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psnr: 1-D Tensor, shape=(num_frames,)
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tensorflow/tensor2tensor | tensor2tensor/utils/video_metrics.py | get_zipped_dataset_from_predictions | def get_zipped_dataset_from_predictions(predictions):
"""Creates dataset from in-memory predictions."""
targets = stack_data_given_key(predictions, "targets")
outputs = stack_data_given_key(predictions, "outputs")
num_videos, num_steps = targets.shape[:2]
# Truncate output time-steps to match target time-ste... | python | def get_zipped_dataset_from_predictions(predictions):
"""Creates dataset from in-memory predictions."""
targets = stack_data_given_key(predictions, "targets")
outputs = stack_data_given_key(predictions, "outputs")
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tensorflow/tensor2tensor | tensor2tensor/utils/video_metrics.py | compute_one_decoding_video_metrics | def compute_one_decoding_video_metrics(iterator, feed_dict, num_videos):
"""Computes the average of all the metric for one decoding.
Args:
iterator: dataset iterator.
feed_dict: feed dict to initialize iterator.
num_videos: number of videos.
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all_psnr: 2-D Numpy array, shape=(num_samples... | python | def compute_one_decoding_video_metrics(iterator, feed_dict, num_videos):
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iterator: dataset iterator.
feed_dict: feed dict to initialize iterator.
num_videos: number of videos.
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tensorflow/tensor2tensor | tensor2tensor/utils/video_metrics.py | reduce_to_best_decode | def reduce_to_best_decode(metrics, reduce_func):
"""Extracts the best-decode from the metrics according to reduce_func.
Args:
metrics: 3-D numpy array, shape=(num_decodes, num_samples, num_frames)
reduce_func: callable, np.argmax or np.argmin.
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best_metrics: 2-D numpy array, shape=(num_sample... | python | def reduce_to_best_decode(metrics, reduce_func):
"""Extracts the best-decode from the metrics according to reduce_func.
Args:
metrics: 3-D numpy array, shape=(num_decodes, num_samples, num_frames)
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tensorflow/tensor2tensor | tensor2tensor/utils/video_metrics.py | compute_all_metrics_statistics | def compute_all_metrics_statistics(all_results):
"""Computes statistics of metrics across multiple decodings.
Args:
all_results: dict of 3-D numpy arrays.
Each array has shape=(num_decodes, num_samples, num_frames).
Returns:
statistics: dict of 1-D numpy arrays, shape=(num_frames).
... | python | def compute_all_metrics_statistics(all_results):
"""Computes statistics of metrics across multiple decodings.
Args:
all_results: dict of 3-D numpy arrays.
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statistics: dict of 1-D numpy arrays, shape=(num_frames).
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tensorflow/tensor2tensor | tensor2tensor/utils/video_metrics.py | compute_video_metrics_from_predictions | def compute_video_metrics_from_predictions(predictions, decode_hparams):
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predictions: list of list of dicts.
outer length: num_decodes, inner_length: num_samples
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statistics: dict... | python | def compute_video_metrics_from_predictions(predictions, decode_hparams):
"""Computes metrics from predictions.
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predictions: list of list of dicts.
outer length: num_decodes, inner_length: num_samples
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tensorflow/tensor2tensor | tensor2tensor/utils/video_metrics.py | compute_video_metrics_from_png_files | def compute_video_metrics_from_png_files(
output_dirs, problem_name, video_length, frame_shape):
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tensorflow/tensor2tensor | tensor2tensor/utils/video_metrics.py | compute_and_save_video_metrics | def compute_and_save_video_metrics(
output_dirs, problem_name, video_length, frame_shape):
"""Compute and saves the video metrics."""
statistics, all_results = compute_video_metrics_from_png_files(
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output_dirs, problem_name, video_length, frame_shape):
"""Compute and saves the video metrics."""
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | swap_time_and_batch_axes | def swap_time_and_batch_axes(inputs):
"""Swaps time and batch axis (the first two axis)."""
transposed_axes = tf.concat([[1, 0], tf.range(2, tf.rank(inputs))], axis=0)
return tf.transpose(inputs, transposed_axes) | python | def swap_time_and_batch_axes(inputs):
"""Swaps time and batch axis (the first two axis)."""
transposed_axes = tf.concat([[1, 0], tf.range(2, tf.rank(inputs))], axis=0)
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | encode_to_shape | def encode_to_shape(inputs, shape, scope):
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | decode_to_shape | def decode_to_shape(inputs, shape, scope):
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | basic_lstm | def basic_lstm(inputs, state, num_units, name=None):
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"""Basic LSTM."""
input_shape = common_layers.shape_list(inputs)
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | conv_lstm_2d | def conv_lstm_2d(inputs, state, output_channels,
kernel_size=5, name=None, spatial_dims=None):
"""2D Convolutional LSTM."""
input_shape = common_layers.shape_list(inputs)
batch_size, input_channels = input_shape[0], input_shape[-1]
if spatial_dims is None:
input_shape = input_shape[1:]
el... | python | def conv_lstm_2d(inputs, state, output_channels,
kernel_size=5, name=None, spatial_dims=None):
"""2D Convolutional LSTM."""
input_shape = common_layers.shape_list(inputs)
batch_size, input_channels = input_shape[0], input_shape[-1]
if spatial_dims is None:
input_shape = input_shape[1:]
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | scheduled_sample_count | def scheduled_sample_count(ground_truth_x,
generated_x,
batch_size,
scheduled_sample_var):
"""Sample batch with specified mix of groundtruth and generated data points.
Args:
ground_truth_x: tensor of ground-truth data points.
... | python | def scheduled_sample_count(ground_truth_x,
generated_x,
batch_size,
scheduled_sample_var):
"""Sample batch with specified mix of groundtruth and generated data points.
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ground_truth_x: tensor of ground-truth data points.
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | inject_additional_input | def inject_additional_input(layer, inputs, name, mode="concat"):
"""Injects the additional input into the layer.
Args:
layer: layer that the input should be injected to.
inputs: inputs to be injected.
name: TF scope name.
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"""Injects the additional input into the layer.
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layer: layer that the input should be injected to.
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name: TF scope name.
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | scheduled_sample_prob | def scheduled_sample_prob(ground_truth_x,
generated_x,
batch_size,
scheduled_sample_var):
"""Probability based scheduled sampling.
Args:
ground_truth_x: tensor of ground-truth data points.
generated_x: tensor of generated data po... | python | def scheduled_sample_prob(ground_truth_x,
generated_x,
batch_size,
scheduled_sample_var):
"""Probability based scheduled sampling.
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ground_truth_x: tensor of ground-truth data points.
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | dna_transformation | def dna_transformation(prev_image, dna_input, dna_kernel_size, relu_shift):
"""Apply dynamic neural advection to previous image.
Args:
prev_image: previous image to be transformed.
dna_input: hidden lyaer to be used for computing DNA transformation.
dna_kernel_size: dna kernel size.
relu_shift: shi... | python | def dna_transformation(prev_image, dna_input, dna_kernel_size, relu_shift):
"""Apply dynamic neural advection to previous image.
Args:
prev_image: previous image to be transformed.
dna_input: hidden lyaer to be used for computing DNA transformation.
dna_kernel_size: dna kernel size.
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | cdna_transformation | def cdna_transformation(prev_image, cdna_input, num_masks, color_channels,
dna_kernel_size, relu_shift):
"""Apply convolutional dynamic neural advection to previous image.
Args:
prev_image: previous image to be transformed.
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dna_kernel_size, relu_shift):
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prev_image: previous image to be transformed.
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | vgg_layer | def vgg_layer(inputs,
nout,
kernel_size=3,
activation=tf.nn.leaky_relu,
padding="SAME",
is_training=True,
has_batchnorm=False,
scope=None):
"""A layer of VGG network with batch norm.
Args:
inputs: image tensor
... | python | def vgg_layer(inputs,
nout,
kernel_size=3,
activation=tf.nn.leaky_relu,
padding="SAME",
is_training=True,
has_batchnorm=False,
scope=None):
"""A layer of VGG network with batch norm.
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inputs: image tensor
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | tile_and_concat | def tile_and_concat(image, latent, concat_latent=True):
"""Tile latent and concatenate to image across depth.
Args:
image: 4-D Tensor, (batch_size X height X width X channels)
latent: 2-D Tensor, (batch_size X latent_dims)
concat_latent: If set to False, the image is returned as is.
Returns:
con... | python | def tile_and_concat(image, latent, concat_latent=True):
"""Tile latent and concatenate to image across depth.
Args:
image: 4-D Tensor, (batch_size X height X width X channels)
latent: 2-D Tensor, (batch_size X latent_dims)
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | _encode_gif | def _encode_gif(images, fps):
"""Encodes numpy images into gif string.
Args:
images: A 4-D `uint8` `np.array` (or a list of 3-D images) of shape
`[time, height, width, channels]` where `channels` is 1 or 3.
fps: frames per second of the animation
Returns:
The encoded gif string.
Raises:
... | python | def _encode_gif(images, fps):
"""Encodes numpy images into gif string.
Args:
images: A 4-D `uint8` `np.array` (or a list of 3-D images) of shape
`[time, height, width, channels]` where `channels` is 1 or 3.
fps: frames per second of the animation
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | ffmpeg_works | def ffmpeg_works():
"""Tries to encode images with ffmpeg to check if it works."""
images = np.zeros((2, 32, 32, 3), dtype=np.uint8)
try:
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return True
except (IOError, OSError):
return False | python | def ffmpeg_works():
"""Tries to encode images with ffmpeg to check if it works."""
images = np.zeros((2, 32, 32, 3), dtype=np.uint8)
try:
_encode_gif(images, 2)
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | py_gif_summary | def py_gif_summary(tag, images, max_outputs, fps, return_summary_value=False):
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Args:
tag: Name of the summary.
images: A 5-D `uint8` `np.array` of shape `[batch_size, time, height, width,
channels]` where `channels` is 1 or 3.
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"""Outputs a `Summary` protocol buffer with gif animations.
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tag: Name of the summary.
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | gif_summary | def gif_summary(name, tensor, max_outputs=3, fps=10, collections=None,
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Args:
name: Name of the summary.
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | conv_latent_tower | def conv_latent_tower(images, time_axis, latent_channels=1, min_logvar=-5,
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tiny_mode=False, small_mode=False):
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | beta_schedule | def beta_schedule(schedule, global_step, final_beta, decay_start, decay_end):
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if decay_start > decay_end:
raise ValueError("decay_end is smaller than decay_end.")
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tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | extract_random_video_patch | def extract_random_video_patch(videos, num_frames=-1):
"""For every video, extract a random consecutive patch of num_frames.
Args:
videos: 5-D Tensor, (NTHWC)
num_frames: Integer, if -1 then the entire video is returned.
Returns:
video_patch: 5-D Tensor, (NTHWC) with T = num_frames.
Raises:
Val... | python | def extract_random_video_patch(videos, num_frames=-1):
"""For every video, extract a random consecutive patch of num_frames.
Args:
videos: 5-D Tensor, (NTHWC)
num_frames: Integer, if -1 then the entire video is returned.
Returns:
video_patch: 5-D Tensor, (NTHWC) with T = num_frames.
Raises:
Val... | [
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"... | For every video, extract a random consecutive patch of num_frames.
Args:
videos: 5-D Tensor, (NTHWC)
num_frames: Integer, if -1 then the entire video is returned.
Returns:
video_patch: 5-D Tensor, (NTHWC) with T = num_frames.
Raises:
ValueError: If num_frames is greater than the number of total f... | [
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] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_video.py#L621-L658 | train |
tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | VideoWriter.write_multi | def write_multi(self, frames, encoded_frames=None):
"""Writes multiple video frames."""
if encoded_frames is None:
# Infinite iterator.
encoded_frames = iter(lambda: None, 1)
for (frame, encoded_frame) in zip(frames, encoded_frames):
self.write(frame, encoded_frame) | python | def write_multi(self, frames, encoded_frames=None):
"""Writes multiple video frames."""
if encoded_frames is None:
# Infinite iterator.
encoded_frames = iter(lambda: None, 1)
for (frame, encoded_frame) in zip(frames, encoded_frames):
self.write(frame, encoded_frame) | [
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"frame"... | Writes multiple video frames. | [
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] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_video.py#L668-L674 | train |
tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | WholeVideoWriter.__init_ffmpeg | def __init_ffmpeg(self, image_shape):
"""Initializes ffmpeg to write frames."""
import itertools # pylint: disable=g-import-not-at-top
from subprocess import Popen, PIPE # pylint: disable=g-import-not-at-top,g-multiple-import,g-importing-member
ffmpeg = "ffmpeg"
height, width, channels = image_sha... | python | def __init_ffmpeg(self, image_shape):
"""Initializes ffmpeg to write frames."""
import itertools # pylint: disable=g-import-not-at-top
from subprocess import Popen, PIPE # pylint: disable=g-import-not-at-top,g-multiple-import,g-importing-member
ffmpeg = "ffmpeg"
height, width, channels = image_sha... | [
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] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_video.py#L715-L744 | train |
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