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21,800 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | linear_interpolate_rank | def linear_interpolate_rank(tensor1, tensor2, coeffs, rank=1):
"""Linearly interpolate channel at "rank" between two tensors.
The channels are ranked according to their L2 norm between tensor1[channel]
and tensor2[channel].
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.
The channels are ranked according to their L2 norm between tensor1[channel]
and tensor2[channel].
Args:
tensor1: 4-D Tensor, NHWC
tensor2: 4-D Tensor, NHWC
coeffs: list of ... | [
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21,801 | 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'."""
if cond_latents:
if hparams.latent_dist_encoder in ["conv_net", "conv3d_net"]:
return [cond_latent[level] for cond_latent in cond_latents]
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21,802 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | check_cond_latents | def check_cond_latents(cond_latents, hparams):
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if not isinstance(cond_latents[0], list):
cond_latents = [cond_latents]
exp_num_latents = hparams.num_cond_latents
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exp_num_latents += ... | python | def check_cond_latents(cond_latents, hparams):
"""Shape checking for cond_latents."""
if cond_latents is None:
return
if not isinstance(cond_latents[0], list):
cond_latents = [cond_latents]
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21,803 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | get_variable_ddi | def get_variable_ddi(name, shape, initial_value, dtype=tf.float32, init=False,
trainable=True):
"""Wrapper for data-dependent initialization."""
# If init is a tf bool: w is assigned dynamically at runtime.
# If init is a python bool: then w is determined during graph construction.
w = tf.g... | python | def get_variable_ddi(name, shape, initial_value, dtype=tf.float32, init=False,
trainable=True):
"""Wrapper for data-dependent initialization."""
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21,804 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | get_dropout | def get_dropout(x, rate=0.0, init=True):
"""Dropout x with dropout_rate = rate.
Apply zero dropout during init or prediction time.
Args:
x: 4-D Tensor, shape=(NHWC).
rate: Dropout rate.
init: Initialization.
Returns:
x: activations after dropout.
"""
if init or rate == 0:
return x
re... | python | def get_dropout(x, rate=0.0, init=True):
"""Dropout x with dropout_rate = rate.
Apply zero dropout during init or prediction time.
Args:
x: 4-D Tensor, shape=(NHWC).
rate: Dropout rate.
init: Initialization.
Returns:
x: activations after dropout.
"""
if init or rate == 0:
return x
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21,805 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | actnorm_3d | def actnorm_3d(name, x, logscale_factor=3.):
"""Applies actnorm to each time-step independently.
There are a total of 2*n_channels*n_steps parameters learnt.
Args:
name: variable scope.
x: 5-D Tensor, (NTHWC)
logscale_factor: Increases the learning rate of the scale by
logscale_... | python | def actnorm_3d(name, x, logscale_factor=3.):
"""Applies actnorm to each time-step independently.
There are a total of 2*n_channels*n_steps parameters learnt.
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name: variable scope.
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21,806 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | actnorm_center | def actnorm_center(name, x, reverse=False, init=False):
"""Add a bias to x.
Initialize such that the output of the first minibatch is zero centered
per channel.
Args:
name: scope
x: 2-D or 4-D Tensor.
reverse: Forward or backward operation.
init: data-dependent initialization.
Returns:
... | python | def actnorm_center(name, x, reverse=False, init=False):
"""Add a bias to x.
Initialize such that the output of the first minibatch is zero centered
per channel.
Args:
name: scope
x: 2-D or 4-D Tensor.
reverse: Forward or backward operation.
init: data-dependent initialization.
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21,807 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | actnorm_scale | def actnorm_scale(name, x, logscale_factor=3., reverse=False, init=False):
"""Per-channel scaling of x."""
x_shape = common_layers.shape_list(x)
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# Variance initialization logic.
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... | python | def actnorm_scale(name, x, logscale_factor=3., reverse=False, init=False):
"""Per-channel scaling of x."""
x_shape = common_layers.shape_list(x)
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
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21,808 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | invertible_1x1_conv | def invertible_1x1_conv(name, x, reverse=False):
"""1X1 convolution on x.
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1. P is a permutation matrix.
2. L is a lower triangular matrix with diagonal entries unity.
3. U is a upper triangular matrix where the diagonal entries zero.
... | python | def invertible_1x1_conv(name, x, reverse=False):
"""1X1 convolution on x.
The 1X1 convolution is parametrized as P*L*(U + sign(s)*exp(log(s))) where
1. P is a permutation matrix.
2. L is a lower triangular matrix with diagonal entries unity.
3. U is a upper triangular matrix where the diagonal entries zero.
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21,809 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | add_edge_bias | def add_edge_bias(x, filter_size):
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The edge bias can be thought of as a binary feature which is unity when
the filter is being convolved over an edge and zero otherwise.
Args:
x: Input tensor, shape (NHWC)
filter_size: filter_size to determ... | python | def add_edge_bias(x, filter_size):
"""Pad x and concatenates an edge bias across the depth of x.
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x: Input tensor, shape (NHWC)
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21,810 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | time_pad | def time_pad(x, filter_size, dilations):
"""Pad left across time and pad valid across the spatial components.
Also concats a binary feature that indicates if a feature is padded or not.
Args:
x: 5-D Tensor, (NTHWC)
filter_size: list of ints
dilations: list of ints, dilations - 1 specifies the number... | python | def time_pad(x, filter_size, dilations):
"""Pad left across time and pad valid across the spatial components.
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21,811 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | conv | def conv(name, x, output_channels, filter_size=None, stride=None,
logscale_factor=3.0, apply_actnorm=True, conv_init="default",
dilations=None):
"""Convolutional layer with edge bias padding and optional actnorm.
If x is 5-dimensional, actnorm is applied independently across every
time-step.
... | python | def conv(name, x, output_channels, filter_size=None, stride=None,
logscale_factor=3.0, apply_actnorm=True, conv_init="default",
dilations=None):
"""Convolutional layer with edge bias padding and optional actnorm.
If x is 5-dimensional, actnorm is applied independently across every
time-step.
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21,812 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | conv_block | def conv_block(name, x, mid_channels, dilations=None, activation="relu",
dropout=0.0):
"""2 layer conv block used in the affine coupling layer.
Args:
name: variable scope.
x: 4-D or 5-D Tensor.
mid_channels: Output channels of the second layer.
dilations: Optional, list of integers.
... | python | def conv_block(name, x, mid_channels, dilations=None, activation="relu",
dropout=0.0):
"""2 layer conv block used in the affine coupling layer.
Args:
name: variable scope.
x: 4-D or 5-D Tensor.
mid_channels: Output channels of the second layer.
dilations: Optional, list of integers.
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21,813 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | dilated_conv_stack | def dilated_conv_stack(name, x, mid_channels, output_channels,
dilation_rates, activation="relu",
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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dropout=0.0):
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Features at different rates are computed independently using a 3 layer
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21,814 | 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):
"""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.
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21,815 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | additive_coupling | def additive_coupling(name, x, mid_channels=512, reverse=False,
activation="relu", dropout=0.0):
"""Reversible additive coupling layer.
Args:
name: variable scope.
x: 4-D Tensor, shape=(NHWC).
mid_channels: number of channels in the coupling layer.
reverse: Forward or reverse ... | python | def additive_coupling(name, x, mid_channels=512, reverse=False,
activation="relu", dropout=0.0):
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name: variable scope.
x: 4-D Tensor, shape=(NHWC).
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21,816 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | affine_coupling | def affine_coupling(name, x, mid_channels=512, activation="relu",
reverse=False, dropout=0.0):
"""Reversible affine coupling layer.
Args:
name: variable scope.
x: 4-D Tensor.
mid_channels: number of channels in the coupling layer.
activation: Can be either "relu" or "gatu".
... | python | def affine_coupling(name, x, mid_channels=512, activation="relu",
reverse=False, dropout=0.0):
"""Reversible affine coupling layer.
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name: variable scope.
x: 4-D Tensor.
mid_channels: number of channels in the coupling layer.
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21,817 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | squeeze | def squeeze(name, x, factor=2, reverse=True):
"""Block-wise spatial squeezing of x to increase the number of channels.
Args:
name: Used for variable scoping.
x: 4-D Tensor of shape (batch_size X H X W X C)
factor: Factor by which the spatial dimensions should be squeezed.
reverse: Squueze or unsque... | python | def squeeze(name, x, factor=2, reverse=True):
"""Block-wise spatial squeezing of x to increase the number of channels.
Args:
name: Used for variable scoping.
x: 4-D Tensor of shape (batch_size X H X W X C)
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21,818 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | get_dilation_rates | def get_dilation_rates(hparams, width):
"""Get a list of valid dilation rates.
Args:
hparams: HParams.
width: spatial dimension. Ensures that the effective filter size is
not larger than the spatial dimension.
Returns:
allowed_dilations: A list of dilation rates.
"""
# dil_rate=1 means... | python | def get_dilation_rates(hparams, width):
"""Get a list of valid dilation rates.
Args:
hparams: HParams.
width: spatial dimension. Ensures that the effective filter size is
not larger than the spatial dimension.
Returns:
allowed_dilations: A list of dilation rates.
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21,819 | 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.
output_channels: int, Number of channels of th... | python | 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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21,820 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | single_conv_dist | def single_conv_dist(name, x, output_channels=None):
"""A 3x3 convolution mapping x to a standard normal distribution at init.
Args:
name: variable scope.
x: 4-D Tensor.
output_channels: number of channels of the mean and std.
"""
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
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"""A 3x3 convolution mapping x to a standard normal distribution at init.
Args:
name: variable scope.
x: 4-D Tensor.
output_channels: number of channels of the mean and std.
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21,821 | 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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Args:
name: variable scope.
x: 4-D Tensor of shape (NHWC)
hparams: HParams.
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defaul... | python | def latent_to_dist(name, x, hparams, output_channels=None):
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Args:
name: variable scope.
x: 4-D Tensor of shape (NHWC)
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21,822 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | noise_op | 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.
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.
Returns:
latents: latents with isotropic gaussian noise appended.
"""
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21,823 | 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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Args:
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"""Merge level_dist and latent_dist.
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21,824 | 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.
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.
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21,825 | 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
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reverse: forward or re... | python | 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
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21,826 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | revnet | def revnet(name, x, hparams, reverse=True):
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Args:
name: variable scope for the revnet block.
x: 4-D Tensor, shape=(NHWC).
hparams: HParams.
reverse: bool, forward or backward pass.
Returns:
x: 4-D Tensor, shape=(NHWC).
objective: float.
"""
... | python | def revnet(name, x, hparams, reverse=True):
"""'hparams.depth' steps of generative flow.
Args:
name: variable scope for the revnet block.
x: 4-D Tensor, shape=(NHWC).
hparams: HParams.
reverse: bool, forward or backward pass.
Returns:
x: 4-D Tensor, shape=(NHWC).
objective: float.
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21,827 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | top_prior | def top_prior(name, z_shape, learn_prior="normal", temperature=1.0):
"""Unconditional prior distribution.
Args:
name: variable scope
z_shape: Shape of the mean / scale of the prior distribution.
learn_prior: Possible options are "normal" and "single_conv".
If set to "single_conv", the ... | python | def top_prior(name, z_shape, learn_prior="normal", temperature=1.0):
"""Unconditional prior distribution.
Args:
name: variable scope
z_shape: Shape of the mean / scale of the prior distribution.
learn_prior: Possible options are "normal" and "single_conv".
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21,828 | tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | bfloat16_activations_var_getter | def bfloat16_activations_var_getter(getter, *args, **kwargs):
"""A custom getter function for float32 parameters and bfloat16 activations.
Args:
getter: custom getter
*args: arguments
**kwargs: keyword arguments
Returns:
variables with the correct dtype.
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KeyError: if "dtype" is not ... | python | def bfloat16_activations_var_getter(getter, *args, **kwargs):
"""A custom getter function for float32 parameters and bfloat16 activations.
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getter: custom getter
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**kwargs: keyword arguments
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21,829 | tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | float16_activations_var_getter | def float16_activations_var_getter(getter, *args, **kwargs):
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21,830 | tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | simulated_quantize | def simulated_quantize(x, num_bits, noise):
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num_bits is the number of bits used to store each value.
noise is a float32 Tensor containing values in [0, 1).
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"""Simulate quantization to num_bits bits, with externally-stored scale.
num_bits is the number of bits used to store each value.
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21,831 | tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | _randomized_roundoff_to_bfloat16 | def _randomized_roundoff_to_bfloat16(x, noise, cand1, cand2):
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Cand1 and cand2 are the same shape as x.
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cand1 an... | python | def _randomized_roundoff_to_bfloat16(x, noise, cand1, cand2):
"""Round-off x to cand1 or to cand2 in an unbiased way.
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21,832 | tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | _to_bfloat16_unbiased | def _to_bfloat16_unbiased(x, noise):
"""Convert a float32 to a bfloat16 using randomized roundoff.
Args:
x: A float32 Tensor.
noise: a float32 Tensor with values in [0, 1), broadcastable to tf.shape(x)
Returns:
A float32 Tensor.
"""
x_sign = tf.sign(x)
# Make sure x is positive. If it is zero,... | python | def _to_bfloat16_unbiased(x, noise):
"""Convert a float32 to a bfloat16 using randomized roundoff.
Args:
x: A float32 Tensor.
noise: a float32 Tensor with values in [0, 1), broadcastable to tf.shape(x)
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21,833 | tensorflow/tensor2tensor | tensor2tensor/utils/quantization.py | ParameterEncoding.custom_getter | def custom_getter(self, activation_dtype=tf.bfloat16):
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21,834 | 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.
dataset_len: number of the item... | python | 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.
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dataset: the tf dataset frame by frame.
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21,835 | 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,)
"""... | python | 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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21,836 | 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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21,837 | 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.
Returns:
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)
reduce_func: callable, np.argmax or np.argmin.
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best_metrics: 2-D numpy array, shape=(num_sample... | [
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21,838 | 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.
Each array has shape=(num_decodes, num_samples, num_frames).
Returns:
statistics: dict of 1-D numpy arrays, shape=(num_frames).
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21,839 | tensorflow/tensor2tensor | tensor2tensor/utils/video_metrics.py | compute_video_metrics_from_predictions | def compute_video_metrics_from_predictions(predictions, decode_hparams):
"""Computes metrics from predictions.
Args:
predictions: list of list of dicts.
outer length: num_decodes, inner_length: num_samples
decode_hparams: Decode hparams. instance of HParams.
Returns:
statistics: dict... | python | def compute_video_metrics_from_predictions(predictions, decode_hparams):
"""Computes metrics from predictions.
Args:
predictions: list of list of dicts.
outer length: num_decodes, inner_length: num_samples
decode_hparams: Decode hparams. instance of HParams.
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21,840 | 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(
output_dirs, problem_name, video_length, frame_shape)
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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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21,841 | tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | basic_lstm | def basic_lstm(inputs, state, num_units, name=None):
"""Basic LSTM."""
input_shape = common_layers.shape_list(inputs)
# reuse parameters across time-steps.
cell = tf.nn.rnn_cell.BasicLSTMCell(
num_units, name=name, reuse=tf.AUTO_REUSE)
if state is None:
state = cell.zero_state(input_shape[0], tf.flo... | python | def basic_lstm(inputs, state, num_units, name=None):
"""Basic LSTM."""
input_shape = common_layers.shape_list(inputs)
# reuse parameters across time-steps.
cell = tf.nn.rnn_cell.BasicLSTMCell(
num_units, name=name, reuse=tf.AUTO_REUSE)
if state is None:
state = cell.zero_state(input_shape[0], tf.flo... | [
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21,842 | tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | lstm_cell | def lstm_cell(inputs,
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cell_clip=0.0,
initializer=None,
num_proj=None,
num_unit_shards=None,
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name=None):
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cell_clip=0.0,
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21,843 | 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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21,844 | 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.
Args:
ground_truth_x: tensor of ground-truth data points.
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21,845 | 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.
mode: how the infor should be added to the layer:
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"""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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21,846 | 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.
Args:
ground_truth_x: tensor of ground-truth data points.
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21,847 | 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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21,848 | 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.
cdna_input: hidden lyaer to be used for computing CDNA kern... | python | def cdna_transformation(prev_image, cdna_input, num_masks, color_channels,
dna_kernel_size, relu_shift):
"""Apply convolutional dynamic neural advection to previous image.
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prev_image: previous image to be transformed.
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21,849 | 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.
Args:
inputs: image tensor
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21,850 | 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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21,851 | 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
Returns:
The encoded gif string.
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21,852 | 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:
_encode_gif(images, 2)
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)
return True
except (IOError, OSError):
return False | [
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21,853 | 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):
"""Builds convolutional latent tower for stochastic model.
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is_training=False, random_latent=False,
tiny_mode=False, small_mode=False):
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21,854 | 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.
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21,855 | 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):
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21,856 | tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | WholeVideoWriter.__init_ffmpeg | def __init_ffmpeg(self, image_shape):
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stream: output stream of the FFMPEG process.
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21,858 | tensorflow/tensor2tensor | tensor2tensor/layers/common_video.py | WholeVideoWriter.finish | def finish(self):
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"""Finishes transconding and returns the video.
Returns:
bytes
Raises:
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21,859 | tensorflow/tensor2tensor | tensor2tensor/serving/query.py | validate_flags | def validate_flags():
"""Validates flags are set to acceptable values."""
if FLAGS.cloud_mlengine_model_name:
assert not FLAGS.server
assert not FLAGS.servable_name
else:
assert FLAGS.server
assert FLAGS.servable_name | python | def validate_flags():
"""Validates flags are set to acceptable values."""
if FLAGS.cloud_mlengine_model_name:
assert not FLAGS.server
assert not FLAGS.servable_name
else:
assert FLAGS.server
assert FLAGS.servable_name | [
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21,860 | tensorflow/tensor2tensor | tensor2tensor/serving/query.py | make_request_fn | def make_request_fn():
"""Returns a request function."""
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credentials=GoogleCredentials.get_application_default(),
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"""Returns a request function."""
if FLAGS.cloud_mlengine_model_name:
request_fn = serving_utils.make_cloud_mlengine_request_fn(
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21,861 | tensorflow/tensor2tensor | tensor2tensor/models/video/savp.py | NextFrameSavpBase.encoder | def encoder(self, inputs, n_layers=3):
"""Convnet that encodes inputs into mean and std of a gaussian.
Args:
inputs: 5-D Tensor, shape (batch_size, num_frames, width, height, channels)
n_layers: Number of layers.
Returns:
z_mu: Mean of the latent gaussians.
z_log_var: log(var) of the l... | python | def encoder(self, inputs, n_layers=3):
"""Convnet that encodes inputs into mean and std of a gaussian.
Args:
inputs: 5-D Tensor, shape (batch_size, num_frames, width, height, channels)
n_layers: Number of layers.
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21,862 | tensorflow/tensor2tensor | tensor2tensor/models/video/savp.py | NextFrameSavpBase.get_fc_dimensions | def get_fc_dimensions(self, strides, kernel_sizes):
"""Get expected fully connected shape after a series of convolutions."""
output_height, output_width, _ = self.hparams.problem.frame_shape
output_steps = self.hparams.video_num_target_frames
output_shape = np.array([output_steps, output_height, output_... | python | def get_fc_dimensions(self, strides, kernel_sizes):
"""Get expected fully connected shape after a series of convolutions."""
output_height, output_width, _ = self.hparams.problem.frame_shape
output_steps = self.hparams.video_num_target_frames
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21,863 | tensorflow/tensor2tensor | tensor2tensor/models/video/savp.py | NextFrameSavpBase.discriminator | def discriminator(self, frames):
"""3-D SNGAN discriminator.
Args:
frames: a list of batch-major tensors indexed by time.
Returns:
logits: 1-D Tensor with shape=batch_size.
Positive logits imply that the discriminator thinks that it
belongs to the true class.
""... | python | def discriminator(self, frames):
"""3-D SNGAN discriminator.
Args:
frames: a list of batch-major tensors indexed by time.
Returns:
logits: 1-D Tensor with shape=batch_size.
Positive logits imply that the discriminator thinks that it
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21,864 | tensorflow/tensor2tensor | tensor2tensor/models/video/savp.py | NextFrameSavpBase.d_step | def d_step(self, true_frames, gen_frames):
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21,865 | tensorflow/tensor2tensor | tensor2tensor/models/video/savp.py | NextFrameSavpBase.g_step | def g_step(self, gen_frames, fake_logits_stop):
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Args:
gen_frames: Generated frames
fake_logits_stop: Logits corresponding to the generated frames as per
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Ret... | python | def g_step(self, gen_frames, fake_logits_stop):
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21,866 | tensorflow/tensor2tensor | tensor2tensor/models/video/savp.py | NextFrameSavpBase.get_gan_loss | def get_gan_loss(self, true_frames, gen_frames, name):
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This performs an 1:1 update of the discriminator and generator at every
step.
Args:
true_frames: 5-D Tensor of shape (num_steps, batch_size, H, W, C)
Assumed to be g... | python | def get_gan_loss(self, true_frames, gen_frames, name):
"""Get the discriminator + generator loss at every step.
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21,867 | tensorflow/tensor2tensor | tensor2tensor/models/video/savp.py | NextFrameSavpBase.get_extra_loss | def get_extra_loss(self, latent_means=None, latent_stds=None,
true_frames=None, gen_frames=None):
"""Gets extra loss from VAE and GAN."""
if not self.is_training:
return 0.0
vae_loss, d_vae_loss, d_gan_loss = 0.0, 0.0, 0.0
# Use sv2p's KL divergence computation.
if self.h... | python | def get_extra_loss(self, latent_means=None, latent_stds=None,
true_frames=None, gen_frames=None):
"""Gets extra loss from VAE and GAN."""
if not self.is_training:
return 0.0
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21,868 | tensorflow/tensor2tensor | tensor2tensor/models/video/savp.py | NextFrameSavpBase.pad_conv3d_lrelu | def pad_conv3d_lrelu(self, activations, n_filters, kernel_size, strides,
scope):
"""Pad, apply 3-D convolution and leaky relu."""
padding = [[0, 0], [1, 1], [1, 1], [1, 1], [0, 0]]
# tf.nn.conv3d accepts a list of 5 values for strides
# with first and last value equal to 1
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scope):
"""Pad, apply 3-D convolution and leaky relu."""
padding = [[0, 0], [1, 1], [1, 1], [1, 1], [0, 0]]
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21,869 | tensorflow/tensor2tensor | tensor2tensor/utils/pruning_utils.py | sparsify | def sparsify(sess, eval_model, pruning_strategy, pruning_params):
"""Prune the weights of a model and evaluate."""
weights = tf.trainable_variables()
def should_prune(name):
"""Whether to prune a weight or not."""
in_whitelist = not pruning_params.white_list or any(
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"""Prune the weights of a model and evaluate."""
weights = tf.trainable_variables()
def should_prune(name):
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21,870 | tensorflow/tensor2tensor | tensor2tensor/insights/server.py | DebugFrontendApplication.load_config | def load_config(self):
"""Loads the configuration."""
config = dict([(key, value) for key, value in iteritems(self.options)
if key in self.cfg.settings and value is not None])
for key, value in iteritems(config):
self.cfg.set(key.lower(), value) | python | def load_config(self):
"""Loads the configuration."""
config = dict([(key, value) for key, value in iteritems(self.options)
if key in self.cfg.settings and value is not None])
for key, value in iteritems(config):
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21,871 | tensorflow/tensor2tensor | tensor2tensor/models/research/rl.py | ppo_atari_base | def ppo_atari_base():
"""Pong base parameters."""
hparams = ppo_discrete_action_base()
hparams.learning_rate_constant = 1e-4
hparams.epoch_length = 200
hparams.gae_gamma = 0.985
hparams.gae_lambda = 0.985
hparams.entropy_loss_coef = 0.003
hparams.value_loss_coef = 1
hparams.optimization_epochs = 3
h... | python | def ppo_atari_base():
"""Pong base parameters."""
hparams = ppo_discrete_action_base()
hparams.learning_rate_constant = 1e-4
hparams.epoch_length = 200
hparams.gae_gamma = 0.985
hparams.gae_lambda = 0.985
hparams.entropy_loss_coef = 0.003
hparams.value_loss_coef = 1
hparams.optimization_epochs = 3
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21,872 | tensorflow/tensor2tensor | tensor2tensor/models/research/rl.py | ppo_original_params | def ppo_original_params():
"""Parameters based on the original PPO paper."""
hparams = ppo_atari_base()
hparams.learning_rate_constant = 2.5e-4
hparams.gae_gamma = 0.99
hparams.gae_lambda = 0.95
hparams.clipping_coef = 0.1
hparams.value_loss_coef = 1
hparams.entropy_loss_coef = 0.01
hparams.eval_every... | python | def ppo_original_params():
"""Parameters based on the original PPO paper."""
hparams = ppo_atari_base()
hparams.learning_rate_constant = 2.5e-4
hparams.gae_gamma = 0.99
hparams.gae_lambda = 0.95
hparams.clipping_coef = 0.1
hparams.value_loss_coef = 1
hparams.entropy_loss_coef = 0.01
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21,873 | tensorflow/tensor2tensor | tensor2tensor/models/research/rl.py | ppo_original_world_model_stochastic_discrete | def ppo_original_world_model_stochastic_discrete():
"""Atari parameters with stochastic discrete world model as policy."""
hparams = ppo_original_params()
hparams.policy_network = "next_frame_basic_stochastic_discrete"
hparams_keys = hparams.values().keys()
video_hparams = basic_stochastic.next_frame_basic_st... | python | def ppo_original_world_model_stochastic_discrete():
"""Atari parameters with stochastic discrete world model as policy."""
hparams = ppo_original_params()
hparams.policy_network = "next_frame_basic_stochastic_discrete"
hparams_keys = hparams.values().keys()
video_hparams = basic_stochastic.next_frame_basic_st... | [
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21,874 | tensorflow/tensor2tensor | tensor2tensor/models/research/rl.py | make_simulated_env_fn | def make_simulated_env_fn(**env_kwargs):
"""Returns a function creating a simulated env, in or out of graph.
Args:
**env_kwargs: kwargs to pass to the simulated env constructor.
Returns:
Function in_graph -> env.
"""
def env_fn(in_graph):
class_ = SimulatedBatchEnv if in_graph else SimulatedBatc... | python | def make_simulated_env_fn(**env_kwargs):
"""Returns a function creating a simulated env, in or out of graph.
Args:
**env_kwargs: kwargs to pass to the simulated env constructor.
Returns:
Function in_graph -> env.
"""
def env_fn(in_graph):
class_ = SimulatedBatchEnv if in_graph else SimulatedBatc... | [
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21,875 | tensorflow/tensor2tensor | tensor2tensor/models/research/rl.py | make_simulated_env_kwargs | def make_simulated_env_kwargs(real_env, hparams, **extra_kwargs):
"""Extracts simulated env kwargs from real_env and loop hparams."""
objs_and_attrs = [
(real_env, [
"reward_range", "observation_space", "action_space", "frame_height",
"frame_width"
]),
(hparams, ["frame_stack_s... | python | def make_simulated_env_kwargs(real_env, hparams, **extra_kwargs):
"""Extracts simulated env kwargs from real_env and loop hparams."""
objs_and_attrs = [
(real_env, [
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21,876 | tensorflow/tensor2tensor | tensor2tensor/models/research/rl.py | get_policy | def get_policy(observations, hparams, action_space):
"""Get a policy network.
Args:
observations: observations
hparams: parameters
action_space: action space
Returns:
Tuple (action logits, value).
"""
if not isinstance(action_space, gym.spaces.Discrete):
raise ValueError("Expecting discr... | python | def get_policy(observations, hparams, action_space):
"""Get a policy network.
Args:
observations: observations
hparams: parameters
action_space: action space
Returns:
Tuple (action logits, value).
"""
if not isinstance(action_space, gym.spaces.Discrete):
raise ValueError("Expecting discr... | [
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21,877 | tensorflow/tensor2tensor | tensor2tensor/models/research/rl.py | rlmf_tictactoe | def rlmf_tictactoe():
"""Base set of hparams for model-free PPO."""
hparams = rlmf_original()
hparams.game = "tictactoe"
hparams.rl_env_name = "T2TEnv-TicTacToeEnv-v0"
# Since we don't have any no-op actions, otherwise we have to have an
# attribute called `get_action_meanings`.
hparams.eval_max_num_noops... | python | def rlmf_tictactoe():
"""Base set of hparams for model-free PPO."""
hparams = rlmf_original()
hparams.game = "tictactoe"
hparams.rl_env_name = "T2TEnv-TicTacToeEnv-v0"
# Since we don't have any no-op actions, otherwise we have to have an
# attribute called `get_action_meanings`.
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21,878 | tensorflow/tensor2tensor | tensor2tensor/models/research/rl.py | rlmf_tiny | def rlmf_tiny():
"""Tiny set of hparams for model-free PPO."""
hparams = rlmf_original()
hparams = hparams.override_from_dict(rlmf_tiny_overrides())
hparams.batch_size = 2
hparams.base_algo_params = "ppo_original_tiny"
hparams.add_hparam("ppo_epochs_num", 3)
hparams.add_hparam("ppo_epoch_length", 2)
ret... | python | def rlmf_tiny():
"""Tiny set of hparams for model-free PPO."""
hparams = rlmf_original()
hparams = hparams.override_from_dict(rlmf_tiny_overrides())
hparams.batch_size = 2
hparams.base_algo_params = "ppo_original_tiny"
hparams.add_hparam("ppo_epochs_num", 3)
hparams.add_hparam("ppo_epoch_length", 2)
ret... | [
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21,879 | tensorflow/tensor2tensor | tensor2tensor/models/research/rl.py | rlmf_dqn_tiny | def rlmf_dqn_tiny():
"""Tiny DQN params."""
hparams = rlmf_original()
hparams = hparams.override_from_dict(rlmf_tiny_overrides())
hparams.batch_size = 1
hparams.base_algo = "dqn"
hparams.base_algo_params = "dqn_original_params"
hparams.add_hparam("dqn_num_frames", 128)
hparams.add_hparam("dqn_save_every... | python | def rlmf_dqn_tiny():
"""Tiny DQN params."""
hparams = rlmf_original()
hparams = hparams.override_from_dict(rlmf_tiny_overrides())
hparams.batch_size = 1
hparams.base_algo = "dqn"
hparams.base_algo_params = "dqn_original_params"
hparams.add_hparam("dqn_num_frames", 128)
hparams.add_hparam("dqn_save_every... | [
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21,880 | tensorflow/tensor2tensor | tensor2tensor/models/research/rl.py | rlmf_eval | def rlmf_eval():
"""Eval set of hparams for model-free PPO."""
hparams = rlmf_original()
hparams.batch_size = 8
hparams.eval_sampling_temps = [0.0, 0.5, 1.0]
hparams.eval_rl_env_max_episode_steps = -1
hparams.add_hparam("ppo_epoch_length", 128)
hparams.add_hparam("ppo_optimization_batch_size", 32)
hpara... | python | def rlmf_eval():
"""Eval set of hparams for model-free PPO."""
hparams = rlmf_original()
hparams.batch_size = 8
hparams.eval_sampling_temps = [0.0, 0.5, 1.0]
hparams.eval_rl_env_max_episode_steps = -1
hparams.add_hparam("ppo_epoch_length", 128)
hparams.add_hparam("ppo_optimization_batch_size", 32)
hpara... | [
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21,881 | tensorflow/tensor2tensor | tensor2tensor/models/research/rl.py | feed_forward_gaussian_fun | def feed_forward_gaussian_fun(action_space, config, observations):
"""Feed-forward Gaussian."""
if not isinstance(action_space, gym.spaces.box.Box):
raise ValueError("Expecting continuous action space.")
mean_weights_initializer = tf.initializers.variance_scaling(
scale=config.init_mean_factor)
logst... | python | def feed_forward_gaussian_fun(action_space, config, observations):
"""Feed-forward Gaussian."""
if not isinstance(action_space, gym.spaces.box.Box):
raise ValueError("Expecting continuous action space.")
mean_weights_initializer = tf.initializers.variance_scaling(
scale=config.init_mean_factor)
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21,882 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer._curvature_range | def _curvature_range(self):
"""Curvature range.
Returns:
h_max_t, h_min_t ops
"""
self._curv_win = tf.get_variable("curv_win",
dtype=tf.float32,
trainable=False,
shape=[self.curvature_wi... | python | def _curvature_range(self):
"""Curvature range.
Returns:
h_max_t, h_min_t ops
"""
self._curv_win = tf.get_variable("curv_win",
dtype=tf.float32,
trainable=False,
shape=[self.curvature_wi... | [
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21,883 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer._grad_variance | def _grad_variance(self):
"""Estimate of gradient Variance.
Returns:
C_t ops.
"""
grad_var_ops = []
tensor_to_avg = []
for t, g in zip(self._vars, self._grad):
if isinstance(g, tf.IndexedSlices):
tensor_to_avg.append(
tf.reshape(tf.unsorted_segment_sum(g.values,
... | python | def _grad_variance(self):
"""Estimate of gradient Variance.
Returns:
C_t ops.
"""
grad_var_ops = []
tensor_to_avg = []
for t, g in zip(self._vars, self._grad):
if isinstance(g, tf.IndexedSlices):
tensor_to_avg.append(
tf.reshape(tf.unsorted_segment_sum(g.values,
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21,884 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer._dist_to_opt | def _dist_to_opt(self):
"""Distance to optimum.
Returns:
D_t ops
"""
dist_to_opt_ops = []
# Running average of the norm of gradient
self._grad_norm = tf.sqrt(self._grad_norm_squared)
avg_op = self._moving_averager.apply([self._grad_norm,])
dist_to_opt_ops.append(avg_op)
with t... | python | def _dist_to_opt(self):
"""Distance to optimum.
Returns:
D_t ops
"""
dist_to_opt_ops = []
# Running average of the norm of gradient
self._grad_norm = tf.sqrt(self._grad_norm_squared)
avg_op = self._moving_averager.apply([self._grad_norm,])
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21,885 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer._grad_sparsity | def _grad_sparsity(self):
"""Gradient sparsity."""
# If the sparse minibatch gradient has 10 percent of its entries
# non-zero, its sparsity is 0.1.
# The norm of dense gradient averaged from full dataset
# are roughly estimated norm of minibatch
# sparse gradient norm * sqrt(sparsity)
# An ... | python | def _grad_sparsity(self):
"""Gradient sparsity."""
# If the sparse minibatch gradient has 10 percent of its entries
# non-zero, its sparsity is 0.1.
# The norm of dense gradient averaged from full dataset
# are roughly estimated norm of minibatch
# sparse gradient norm * sqrt(sparsity)
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21,886 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer._prepare_variables | def _prepare_variables(self):
"""Prepare Variables for YellowFin.
Returns:
Grad**2, Norm, Norm**2, Mean(Norm**2) ops
"""
self._moving_averager = tf.train.ExponentialMovingAverage(
decay=self._beta, zero_debias=self._zero_debias)
# assert self._grad is not None and len(self._grad) > 0
... | python | def _prepare_variables(self):
"""Prepare Variables for YellowFin.
Returns:
Grad**2, Norm, Norm**2, Mean(Norm**2) ops
"""
self._moving_averager = tf.train.ExponentialMovingAverage(
decay=self._beta, zero_debias=self._zero_debias)
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21,887 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer._get_cubic_root | def _get_cubic_root(self):
"""Get the cubic root."""
# We have the equation x^2 D^2 + (1-x)^4 * C / h_min^2
# where x = sqrt(mu).
# We substitute x, which is sqrt(mu), with x = y + 1.
# It gives y^3 + py = q
# where p = (D^2 h_min^2)/(2*C) and q = -p.
# We use the Vieta's substitution to com... | python | def _get_cubic_root(self):
"""Get the cubic root."""
# We have the equation x^2 D^2 + (1-x)^4 * C / h_min^2
# where x = sqrt(mu).
# We substitute x, which is sqrt(mu), with x = y + 1.
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21,888 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer._get_lr_tensor | def _get_lr_tensor(self):
"""Get lr minimizing the surrogate.
Returns:
The lr_t.
"""
lr = tf.squared_difference(1.0, tf.sqrt(self._mu)) / self._h_min
return lr | python | def _get_lr_tensor(self):
"""Get lr minimizing the surrogate.
Returns:
The lr_t.
"""
lr = tf.squared_difference(1.0, tf.sqrt(self._mu)) / self._h_min
return lr | [
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21,889 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer._get_mu_tensor | def _get_mu_tensor(self):
"""Get the min mu which minimize the surrogate.
Returns:
The mu_t.
"""
root = self._get_cubic_root()
dr = self._h_max / self._h_min
mu = tf.maximum(
root**2, ((tf.sqrt(dr) - 1) / (tf.sqrt(dr) + 1))**2)
return mu | python | def _get_mu_tensor(self):
"""Get the min mu which minimize the surrogate.
Returns:
The mu_t.
"""
root = self._get_cubic_root()
dr = self._h_max / self._h_min
mu = tf.maximum(
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21,890 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer._yellowfin | def _yellowfin(self):
"""YellowFin auto-tuning optimizer based on momentum SGD.
Returns:
YF ops
(Curvature range,
Grad_variance,
Dist_to_opt,
Single-Step,
Auto-Tuning)
"""
# List for the returned Operations.
yellowfin_ops = []
# Curvature range... | python | def _yellowfin(self):
"""YellowFin auto-tuning optimizer based on momentum SGD.
Returns:
YF ops
(Curvature range,
Grad_variance,
Dist_to_opt,
Single-Step,
Auto-Tuning)
"""
# List for the returned Operations.
yellowfin_ops = []
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21,891 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer.apply_gradients | def apply_gradients(self, grads_and_vars, global_step=None, name=None):
"""Applying gradients and tune hyperparams with YellowFin.
Args:
grads_and_vars: List of (gradient, variable) pairs as returned by
compute_gradients().
global_step: Optional Variable to increment by one after the
... | python | def apply_gradients(self, grads_and_vars, global_step=None, name=None):
"""Applying gradients and tune hyperparams with YellowFin.
Args:
grads_and_vars: List of (gradient, variable) pairs as returned by
compute_gradients().
global_step: Optional Variable to increment by one after the
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21,892 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer.compute_gradients | def compute_gradients(self,
loss,
var_list,
global_step=None,
gate_gradients=GATE_OP,
aggregation_method=None,
colocate_gradients_with_ops=False,
name=N... | python | def compute_gradients(self,
loss,
var_list,
global_step=None,
gate_gradients=GATE_OP,
aggregation_method=None,
colocate_gradients_with_ops=False,
name=N... | [
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21,893 | tensorflow/tensor2tensor | tensor2tensor/utils/yellowfin.py | YellowFinOptimizer.minimize | def minimize(self,
loss,
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var_list=None,
gate_gradients=GATE_OP,
aggregation_method=None,
colocate_gradients_with_ops=False,
name=None,
grad_loss=None):
"""Adapted from TensorFlow... | python | def minimize(self,
loss,
global_step=None,
var_list=None,
gate_gradients=GATE_OP,
aggregation_method=None,
colocate_gradients_with_ops=False,
name=None,
grad_loss=None):
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21,894 | tensorflow/tensor2tensor | tensor2tensor/models/bytenet.py | bytenet_internal | def bytenet_internal(inputs, targets, hparams):
"""ByteNet, main step used for training."""
with tf.variable_scope("bytenet"):
# Flatten inputs and extend length by 50%.
inputs = tf.expand_dims(common_layers.flatten4d3d(inputs), axis=2)
extend_length = tf.to_int32(0.5 * tf.to_float(tf.shape(inputs)[1]))... | python | def bytenet_internal(inputs, targets, hparams):
"""ByteNet, main step used for training."""
with tf.variable_scope("bytenet"):
# Flatten inputs and extend length by 50%.
inputs = tf.expand_dims(common_layers.flatten4d3d(inputs), axis=2)
extend_length = tf.to_int32(0.5 * tf.to_float(tf.shape(inputs)[1]))... | [
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21,895 | tensorflow/tensor2tensor | tensor2tensor/data_generators/snli.py | _download_and_parse_dataset | def _download_and_parse_dataset(tmp_dir, train):
"""Downloads and prepairs the dataset to be parsed by the data_generator."""
file_path = generator_utils.maybe_download(tmp_dir, _SNLI_ZIP, _SNLI_URL)
zip_ref = zipfile.ZipFile(file_path, 'r')
zip_ref.extractall(tmp_dir)
zip_ref.close()
file_name = 'train' i... | python | def _download_and_parse_dataset(tmp_dir, train):
"""Downloads and prepairs the dataset to be parsed by the data_generator."""
file_path = generator_utils.maybe_download(tmp_dir, _SNLI_ZIP, _SNLI_URL)
zip_ref = zipfile.ZipFile(file_path, 'r')
zip_ref.extractall(tmp_dir)
zip_ref.close()
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21,896 | tensorflow/tensor2tensor | tensor2tensor/data_generators/snli.py | _get_tokens_and_tags | def _get_tokens_and_tags(parse_str):
"""Parse str to tokens and pos tags."""
tokens = []
parse_split = parse_str.split(' ')
for p in parse_split:
assert p.startswith('(') or p.endswith(')')
if p.endswith(')'):
token = p.replace(')', '')
tokens.append(token)
return tokens | python | def _get_tokens_and_tags(parse_str):
"""Parse str to tokens and pos tags."""
tokens = []
parse_split = parse_str.split(' ')
for p in parse_split:
assert p.startswith('(') or p.endswith(')')
if p.endswith(')'):
token = p.replace(')', '')
tokens.append(token)
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21,897 | tensorflow/tensor2tensor | tensor2tensor/data_generators/snli.py | _parse_dataset | def _parse_dataset(file_path, tmp_dir, train):
"""Convert the dataset in to a simpler format.
This function creates two files. One for being processed to produce a vocab
and another to generate the data.
Args:
file_path: string, path to the file to parse.
tmp_dir: string, path to the directory to outp... | python | def _parse_dataset(file_path, tmp_dir, train):
"""Convert the dataset in to a simpler format.
This function creates two files. One for being processed to produce a vocab
and another to generate the data.
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file_path: string, path to the file to parse.
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21,898 | tensorflow/tensor2tensor | tensor2tensor/data_generators/snli.py | _get_or_generate_vocab | def _get_or_generate_vocab(tmp_dir, vocab_filename, vocab_size):
"""Read or create vocabulary."""
vocab_filepath = os.path.join(tmp_dir, vocab_filename)
print('Vocab file written to: ' + vocab_filepath)
if tf.gfile.Exists(vocab_filepath):
gs = text_encoder.SubwordTextEncoder(vocab_filepath)
return gs
... | python | def _get_or_generate_vocab(tmp_dir, vocab_filename, vocab_size):
"""Read or create vocabulary."""
vocab_filepath = os.path.join(tmp_dir, vocab_filename)
print('Vocab file written to: ' + vocab_filepath)
if tf.gfile.Exists(vocab_filepath):
gs = text_encoder.SubwordTextEncoder(vocab_filepath)
return gs
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21,899 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/get_references_web_single_group.py | shard | def shard(items, num_shards):
"""Split items into num_shards groups."""
sharded = []
num_per_shard = len(items) // num_shards
start = 0
for _ in range(num_shards):
sharded.append(items[start:start + num_per_shard])
start += num_per_shard
remainder = len(items) % num_shards
start = len(items) - re... | python | def shard(items, num_shards):
"""Split items into num_shards groups."""
sharded = []
num_per_shard = len(items) // num_shards
start = 0
for _ in range(num_shards):
sharded.append(items[start:start + num_per_shard])
start += num_per_shard
remainder = len(items) % num_shards
start = len(items) - re... | [
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"appen... | Split items into num_shards groups. | [
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] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/wikisum/get_references_web_single_group.py#L87-L102 |
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