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train | dataset_generator | Generate example dicts. | tensor2tensor/data_generators/gene_expression.py | 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... | 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... | [
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train | to_example_dict | Convert single h5 record to an example dict. | tensor2tensor/data_generators/gene_expression.py | def to_example_dict(encoder, inputs, mask, outputs):
"""Convert single h5 record to an example dict."""
# Inputs
bases = []
input_ids = []
last_idx = -1
for row in np.argwhere(inputs):
idx, base_id = row
idx, base_id = int(idx), int(base_id)
assert idx > last_idx # if not, means 2 True values i... | def to_example_dict(encoder, inputs, mask, outputs):
"""Convert single h5 record to an example dict."""
# Inputs
bases = []
input_ids = []
last_idx = -1
for row in np.argwhere(inputs):
idx, base_id = row
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train | linear_interpolate | 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
interpolations at coeffs[i].
shape=(l... | tensor2tensor/models/research/glow_ops.py | 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... | 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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train | linear_interpolate_rank | 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 floats.
rank: integer.
Returns:
interp_latents: list of in... | tensor2tensor/models/research/glow_ops.py | 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 ... | 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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train | postprocess | Converts x from [-0.5, 0.5], to [0, 255].
Args:
x: 3-D or 4-D Tensor normalized between [-0.5, 0.5]
n_bits_x: Number of bits representing each pixel of the output.
Defaults to 8, to default to 256 possible values.
Returns:
x: 3-D or 4-D Tensor representing images or videos. | tensor2tensor/models/research/glow_ops.py | def postprocess(x, n_bits_x=8):
"""Converts x from [-0.5, 0.5], to [0, 255].
Args:
x: 3-D or 4-D Tensor normalized between [-0.5, 0.5]
n_bits_x: Number of bits representing each pixel of the output.
Defaults to 8, to default to 256 possible values.
Returns:
x: 3-D or 4-D Tensor represen... | def postprocess(x, n_bits_x=8):
"""Converts x from [-0.5, 0.5], to [0, 255].
Args:
x: 3-D or 4-D Tensor normalized between [-0.5, 0.5]
n_bits_x: Number of bits representing each pixel of the output.
Defaults to 8, to default to 256 possible values.
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train | get_cond_latents_at_level | Returns a single or list of conditional latents at level 'level'. | tensor2tensor/models/research/glow_ops.py | 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... | 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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train | check_cond_latents | Shape checking for cond_latents. | tensor2tensor/models/research/glow_ops.py | 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]
exp_num_latents = hparams.num_cond_latents
if hparams.latent_dist_encoder == "conv_net":
exp_num_latents += ... | 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]
exp_num_latents = hparams.num_cond_latents
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exp_num_latents += ... | [
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train | get_variable_ddi | Wrapper for data-dependent initialization. | tensor2tensor/models/research/glow_ops.py | 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... | 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.
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train | get_dropout | 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. | tensor2tensor/models/research/glow_ops.py | 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... | 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... | [
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train | actnorm_3d | 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
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Returns:
x: 5-D Tensor, (NTHWC) with... | tensor2tensor/models/research/glow_ops.py | 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_... | 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)
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train | actnorm | x_{ij} = s x x_{ij} + b. Per-channel scaling and bias.
If init is set to True, the scaling and bias are initialized such
that the mean and variance of the output activations of the first minibatch
are zero and one respectively.
Args:
name: variable scope.
x: input
logscale_factor: Used in actnorm_... | tensor2tensor/models/research/glow_ops.py | def actnorm(name, x, logscale_factor=3., reverse=False, init=False,
trainable=True):
"""x_{ij} = s x x_{ij} + b. Per-channel scaling and bias.
If init is set to True, the scaling and bias are initialized such
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trainable=True):
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train | actnorm_center | 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:
x_center: (x + b), if reverse is True and (x - b) otherwise. | tensor2tensor/models/research/glow_ops.py | 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:
... | 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:
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train | actnorm_scale | Per-channel scaling of x. | tensor2tensor/models/research/glow_ops.py | 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):
# Variance initialization logic.
assert len(x_shape) == 2 or len(x_shape) == 4
if len(x_shape) == 2:
... | 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):
# Variance initialization logic.
assert len(x_shape) == 2 or len(x_shape) == 4
if len(x_shape) == 2:
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train | invertible_1x1_conv | 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.
4. s is a vector.
sign(s) and P are fixed and the... | tensor2tensor/models/research/glow_ops.py | 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.
... | 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.
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train | conv | Convolutional layer with edge bias padding and optional actnorm.
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Args:
name: variable scope.
x: 4-D Tensor or 5-D Tensor of shape NHWC or NTHWC
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dilations=None):
"""Convolutional layer with edge bias padding and optional actnorm.
If x is 5-dimensional, actnorm is applied independently across every
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train | conv_block | 2 layer conv block used in the affine coupling layer.
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mid_channels: Output channels of the second layer.
dilations: Optional, list of integers.
activation: relu or gatu.
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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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name: variable scope.
x: 4-D or 5-D Tensor.
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train | dilated_conv_stack | Dilated convolutional stack.
Features at different rates are computed independently using a 3 layer
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Args:
name: variable scope.
x: 5-D Tensor.
mid_channels: Number of output channels of the first layer in the conv
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"""Dilated convolutional stack.
Features at different rates are computed independently using a 3 layer
convolutional stack and added.
Args:
name: va... | def dilated_conv_stack(name, x, mid_channels, output_channels,
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Features at different rates are computed independently using a 3 layer
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train | conv_stack | 3-layer convolutional stack.
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x: 5-D Tensor.
mid_channels: Number of output channels of the first layer.
output_channels: Number of output channels.
dilations: Dilations to apply in the first 3x3 layer and the last 3x3 layer.
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"""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.
... | def conv_stack(name, x, mid_channels, output_channels, dilations=None,
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"""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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train | additive_coupling | Reversible additive coupling layer.
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name: variable scope.
x: 4-D Tensor, shape=(NHWC).
mid_channels: number of channels in the coupling layer.
reverse: Forward or reverse operation.
activation: "relu" or "gatu"
dropout: default, 0.0
Returns:
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ob... | tensor2tensor/models/research/glow_ops.py | def additive_coupling(name, x, mid_channels=512, reverse=False,
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"""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 ... | 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.
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train | affine_coupling | Reversible affine coupling layer.
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name: variable scope.
x: 4-D Tensor.
mid_channels: number of channels in the coupling layer.
activation: Can be either "relu" or "gatu".
reverse: Forward or reverse operation.
dropout: default, 0.0
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output: x shifted and scaled by an affin... | tensor2tensor/models/research/glow_ops.py | 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".
... | def affine_coupling(name, x, mid_channels=512, activation="relu",
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"""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".
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train | squeeze | Block-wise spatial squeezing of x to increase the number of channels.
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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 unsqueeze operation.
Returns:
x: 4-D Tensor of sha... | tensor2tensor/models/research/glow_ops.py | 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... | def squeeze(name, x, factor=2, reverse=True):
"""Block-wise spatial squeezing of x to increase the number of channels.
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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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train | get_dilation_rates | 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. | tensor2tensor/models/research/glow_ops.py | 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... | 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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train | temporal_latent_to_dist | 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 the output gaussian mean.
Returns:
dist: tfp.distributions.Normal | tensor2tensor/models/research/glow_ops.py | 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... | 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.
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name: variable scope.
x: List of 4-D Tensors indexed by time, (NHWC)
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train | single_conv_dist | 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. | tensor2tensor/models/research/glow_ops.py | 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):
x_shape = com... | def single_conv_dist(name, x, output_channels=None):
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Args:
name: variable scope.
x: 4-D Tensor.
output_channels: number of channels of the mean and std.
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train | latent_to_dist | Map latent to the mean and log-scale of a Gaussian.
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x: 4-D Tensor of shape (NHWC)
hparams: HParams.
latent_architecture - can be "single_conv", "glow_nn" or "glow_resnet",
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latent_encoder_depth - int, depth of archit... | tensor2tensor/models/research/glow_ops.py | 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.
latent_architecture - can be "single_conv", "glow_nn" or "glow_resnet",
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name: variable scope.
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train | noise_op | 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. | tensor2tensor/models/research/glow_ops.py | 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... | 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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train | merge_level_and_latent_dist | Merge level_dist and latent_dist.
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train | level_cond_prior | Returns a conditional prior for each level.
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z: Tensor, output of the previous levels.
latent: Tensor or a list of tensors to condition the latent_distribution.
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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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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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train | revnet_step | One step of glow generative flow.
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x: 4-D Tensor, shape=(NHWC).
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reverse: bool, forward or backward pass.
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name: variable scope for the revnet block.
x: 4-D Tensor, shape=(NHWC).
hparams: HParams.
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x: 4-D Tensor, shape=(NHWC).
objective: float.
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x: 4-D Tensor, shape=(NHWC).
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train | scale_gaussian_prior | 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.
std^i is optionally learnable with identity initialization.
Args:
name: variable scope.
z: input_tensor
logscale_factor: equivalent to scaling up the learning_rate by a facto... | tensor2tensor/models/research/glow_ops.py | def scale_gaussian_prior(name, z, logscale_factor=3.0, trainable=True):
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s^i is a learnable parameter with identity initialization.
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Args:
name: variable scope.
z: input_tens... | def scale_gaussian_prior(name, z, logscale_factor=3.0, trainable=True):
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train | top_prior | Unconditional prior distribution.
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learn_prior: Possible options are "normal" and "single_conv".
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z_shape: Shape of the mean / scale of the prior distribution.
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train | uniform_binning_correction | 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:
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objective: Equivalent to -q(x)*log(q(x)). | tensor2tensor/models/research/glow_ops.py | def uniform_binning_correction(x, n_bits=8):
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Args:
x: 4-D Tensor of shape (NHWC)
n_bits: optional.
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objective: Equivalent to -q(x)*log(q(x)).
"""
n_bins = 2**n_bits
batch_size, height, width, n_channels ... | def uniform_binning_correction(x, n_bits=8):
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x: 4-D Tensor of shape (NHWC)
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train | encoder_decoder | Glow encoder-decoder. n_levels of (Squeeze + Flow + Split.) operations.
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hparams: HParams.
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Args:
name: variable scope.
x: 4-D Tensor, shape=(NHWC).
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train | bfloat16_activations_var_getter | A custom getter function for float32 parameters and bfloat16 activations.
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getter: custom getter
*args: arguments
**kwargs: keyword arguments
Returns:
variables with the correct dtype.
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Args:
getter: custom getter
*args: arguments
**kwargs: keyword arguments
Returns:
variables with the correct dtype.
Raises:
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getter: custom getter
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**kwargs: keyword arguments
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train | float16_activations_var_getter | A custom getter function for float32 parameters and float16 activations.
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2. All variables requested with type fp32 are returned as type fp16.
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This function ensures the following:
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2. All variables requested with type fp32 are returned as type fp1... | def float16_activations_var_getter(getter, *args, **kwargs):
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train | simulated_quantize | Simulate quantization to num_bits bits, with externally-stored scale.
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Each value in noise should take different values across
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num_bits is the number of bits used to store each value.
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train | noise_from_step_num | Quantization noise equal to (phi * (step_num + 1)) mod 1.0.
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a float32 scalar | tensor2tensor/utils/quantization.py | def noise_from_step_num():
"""Quantization noise equal to (phi * (step_num + 1)) mod 1.0.
Not using random_uniform here due to a problem on TPU in that random seeds
are not respected, which may cause the parameters on different replicas
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Returns:
a float32 scalar
"""
step = tf.to_i... | def noise_from_step_num():
"""Quantization noise equal to (phi * (step_num + 1)) mod 1.0.
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train | _randomized_roundoff_to_bfloat16 | Round-off x to cand1 or to cand2 in an unbiased way.
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For every element of x, the corresponding elements of cand1 and cand2 should
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cand1 an... | def _randomized_roundoff_to_bfloat16(x, noise, cand1, cand2):
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train | _to_bfloat16_unbiased | 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:
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"""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,... | 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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A float32 Tensor.
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train | ParameterEncoding.custom_getter | A custom getter that uses the encoding for bfloat16 and float32 vars.
When a bfloat16 or float32 variable is requsted, an encoded float16
varaible is created, which is then decoded and cast to a bfloat16
activation.
Args:
activation_dtype: a dtype to which to convert the decoded value.
Retu... | tensor2tensor/utils/quantization.py | def custom_getter(self, activation_dtype=tf.bfloat16):
"""A custom getter that uses the encoding for bfloat16 and float32 vars.
When a bfloat16 or float32 variable is requsted, an encoded float16
varaible is created, which is then decoded and cast to a bfloat16
activation.
Args:
activation_d... | def custom_getter(self, activation_dtype=tf.bfloat16):
"""A custom getter that uses the encoding for bfloat16 and float32 vars.
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train | load_videos | 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 items which is the number of image files.
Raises:
ValueE... | tensor2tensor/utils/video_metrics.py | 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... | def load_videos(template, video_length, frame_shape):
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train | psnr_and_ssim | 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,) | tensor2tensor/utils/video_metrics.py | 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,)
"""... | 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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train | get_zipped_dataset_from_predictions | Creates dataset from in-memory predictions. | tensor2tensor/utils/video_metrics.py | 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... | 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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train | compute_one_decoding_video_metrics | 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.
Returns:
all_psnr: 2-D Numpy array, shape=(num_samples, num_frames)
all_ssim: 2-D Numpy array, shape=(num_samples, num_frames) | tensor2tensor/utils/video_metrics.py | 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.
Returns:
all_psnr: 2-D Numpy array, shape=(num_samples... | 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.
Returns:
all_psnr: 2-D Numpy array, shape=(num_samples... | [
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train | reduce_to_best_decode | 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_samples, num_frames).
best_decode_ind: 1-D numpy array, ... | tensor2tensor/utils/video_metrics.py | 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... | 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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train | compute_all_metrics_statistics | 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).
First the statistic (max/mean/std) is compu... | tensor2tensor/utils/video_metrics.py | 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).
... | 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).
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statistics: dict of 1-D numpy arrays, shape=(num_frames).
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train | compute_video_metrics_from_predictions | 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 of Tensors, key being the metric with each Tensor
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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... | def compute_video_metrics_from_predictions(predictions, decode_hparams):
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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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train | compute_video_metrics_from_png_files | Computes the average of all the metric for one decoding.
This function assumes that all the predicted and target frames
have been saved on the disk and sorting them by name will result
to consecutive frames saved in order.
Args:
output_dirs: directory with all the saved frames.
problem_name: prefix of... | tensor2tensor/utils/video_metrics.py | def compute_video_metrics_from_png_files(
output_dirs, problem_name, video_length, frame_shape):
"""Computes the average of all the metric for one decoding.
This function assumes that all the predicted and target frames
have been saved on the disk and sorting them by name will result
to consecutive frames ... | def compute_video_metrics_from_png_files(
output_dirs, problem_name, video_length, frame_shape):
"""Computes the average of all the metric for one decoding.
This function assumes that all the predicted and target frames
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train | compute_and_save_video_metrics | Compute and saves the video metrics. | tensor2tensor/utils/video_metrics.py | 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)
for results, output_dir in zip(all_results, output_d... | 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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train | swap_time_and_batch_axes | Swaps time and batch axis (the first two axis). | tensor2tensor/layers/common_video.py | 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) | def swap_time_and_batch_axes(inputs):
"""Swaps time and batch axis (the first two axis)."""
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train | encode_to_shape | Encode the given tensor to given image shape. | tensor2tensor/layers/common_video.py | def encode_to_shape(inputs, shape, scope):
"""Encode the given tensor to given image shape."""
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x = tf.reshape(x, (-1, w, h, 1))
... | def encode_to_shape(inputs, shape, scope):
"""Encode the given tensor to given image shape."""
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x = inputs
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train | decode_to_shape | Encode the given tensor to given image shape. | tensor2tensor/layers/common_video.py | def decode_to_shape(inputs, shape, scope):
"""Encode the given tensor to given image shape."""
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"""Encode the given tensor to given image shape."""
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x = inputs
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train | basic_lstm | Basic LSTM. | tensor2tensor/layers/common_video.py | 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(
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if state is None:
state = cell.zero_state(input_shape[0], tf.flo... | 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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train | lstm_cell | Full LSTM cell. | tensor2tensor/layers/common_video.py | def lstm_cell(inputs,
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train | conv_lstm_2d | 2D Convolutional LSTM. | tensor2tensor/layers/common_video.py | 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... | 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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train | scheduled_sample_count | Sample batch with specified mix of groundtruth and generated data points.
Args:
ground_truth_x: tensor of ground-truth data points.
generated_x: tensor of generated data points.
batch_size: batch size
scheduled_sample_var: number of ground-truth examples to include in batch.
Returns:
New batch ... | tensor2tensor/layers/common_video.py | 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.
... | 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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train | inject_additional_input | 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:
"concat" concats as additional channels.
"multiplicative" broadcasts inputs and multi... | tensor2tensor/layers/common_video.py | 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:
"concat" concats as a... | 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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train | scheduled_sample_prob | Probability based scheduled sampling.
Args:
ground_truth_x: tensor of ground-truth data points.
generated_x: tensor of generated data points.
batch_size: batch size
scheduled_sample_var: probability of choosing from ground_truth.
Returns:
New batch with randomly selected data points. | tensor2tensor/layers/common_video.py | def scheduled_sample_prob(ground_truth_x,
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scheduled_sample_var):
"""Probability based scheduled sampling.
Args:
ground_truth_x: tensor of ground-truth data points.
generated_x: tensor of generated data po... | def scheduled_sample_prob(ground_truth_x,
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"""Probability based scheduled sampling.
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ground_truth_x: tensor of ground-truth data points.
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train | dna_transformation | 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: shift for ReLU function.
Returns:
List of images transformed by the predicted ... | tensor2tensor/layers/common_video.py | 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... | 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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train | cdna_transformation | Apply convolutional dynamic neural advection to previous image.
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prev_image: previous image to be transformed.
cdna_input: hidden lyaer to be used for computing CDNA kernels.
num_masks: number of masks and hence the number of CDNA transformations.
color_channels: the number of color channels in ... | tensor2tensor/layers/common_video.py | def cdna_transformation(prev_image, cdna_input, num_masks, color_channels,
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"""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... | def cdna_transformation(prev_image, cdna_input, num_masks, color_channels,
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prev_image: previous image to be transformed.
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train | vgg_layer | A layer of VGG network with batch norm.
Args:
inputs: image tensor
nout: number of output channels
kernel_size: size of the kernel
activation: activation function
padding: padding of the image
is_training: whether it is training mode or not
has_batchnorm: whether batchnorm is applied or n... | tensor2tensor/layers/common_video.py | 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
... | def vgg_layer(inputs,
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kernel_size=3,
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has_batchnorm=False,
scope=None):
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train | tile_and_concat | 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:
concat_latent: 4-D Tensor, (batch_size X height X width X channe... | tensor2tensor/layers/common_video.py | 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... | 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.
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train | _encode_gif | Encodes numpy images into gif string.
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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:
IOError: If the ffmpeg command ret... | tensor2tensor/layers/common_video.py | 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:
... | 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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train | ffmpeg_works | Tries to encode images with ffmpeg to check if it works. | tensor2tensor/layers/common_video.py | 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 | def ffmpeg_works():
"""Tries to encode images with ffmpeg to check if it works."""
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try:
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train | py_gif_summary | Outputs a `Summary` protocol buffer with gif animations.
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.
max_outputs: Max number of batch elements to generate gifs for.
fps: frames per second of ... | tensor2tensor/layers/common_video.py | def py_gif_summary(tag, images, max_outputs, fps, return_summary_value=False):
"""Outputs a `Summary` protocol buffer with gif animations.
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.
max_output... | def py_gif_summary(tag, images, max_outputs, fps, return_summary_value=False):
"""Outputs a `Summary` protocol buffer with gif animations.
Args:
tag: Name of the summary.
images: A 5-D `uint8` `np.array` of shape `[batch_size, time, height, width,
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train | gif_summary | Outputs a `Summary` protocol buffer with gif animations.
Args:
name: Name of the summary.
tensor: A 5-D `uint8` `Tensor` of shape `[batch_size, time, height, width,
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family=None):
"""Outputs a `Summary` protocol buffer with gif animations.
Args:
name: Name of the summary.
tensor: A 5-D `uint8` `Tensor` of shape `[batch_size, time, height, width,
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family=None):
"""Outputs a `Summary` protocol buffer with gif animations.
Args:
name: Name of the summary.
tensor: A 5-D `uint8` `Tensor` of shape `[batch_size, time, height, width,
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train | conv_latent_tower | Builds convolutional latent tower for stochastic model.
At training time this tower generates a latent distribution (mean and std)
conditioned on the entire video. This latent variable will be fed to the
main tower as an extra variable to be used for future frames prediction.
At inference time, the tower is di... | tensor2tensor/layers/common_video.py | def conv_latent_tower(images, time_axis, latent_channels=1, min_logvar=-5,
is_training=False, random_latent=False,
tiny_mode=False, small_mode=False):
"""Builds convolutional latent tower for stochastic model.
At training time this tower generates a latent distribution (... | 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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train | beta_schedule | Get KL multiplier (beta) based on the schedule. | tensor2tensor/layers/common_video.py | def beta_schedule(schedule, global_step, final_beta, decay_start, decay_end):
"""Get KL multiplier (beta) based on the schedule."""
if decay_start > decay_end:
raise ValueError("decay_end is smaller than decay_end.")
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train | extract_random_video_patch | 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... | tensor2tensor/layers/common_video.py | 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... | 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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train | VideoWriter.write_multi | Writes multiple video frames. | tensor2tensor/layers/common_video.py | 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) | 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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train | WholeVideoWriter.__init_ffmpeg | Initializes ffmpeg to write frames. | tensor2tensor/layers/common_video.py | 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... | 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
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train | WholeVideoWriter._start_reader_thread | Starts a thread for reading output from FFMPEG.
The thread reads consecutive chunks from the stream and saves them in
the given list.
Args:
stream: output stream of the FFMPEG process.
chunks: list to save output chunks to.
Returns:
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"""Starts a thread for reading output from FFMPEG.
The thread reads consecutive chunks from the stream and saves them in
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Args:
stream: output stream of the FFMPEG process.
chunks: list to save output chunks to.
Returns:
... | def _start_reader_thread(self, stream, chunks):
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Args:
stream: output stream of the FFMPEG process.
chunks: list to save output chunks to.
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train | WholeVideoWriter.finish | Finishes transconding and returns the video.
Returns:
bytes
Raises:
IOError: in case of transcoding error. | tensor2tensor/layers/common_video.py | def finish(self):
"""Finishes transconding and returns the video.
Returns:
bytes
Raises:
IOError: in case of transcoding error.
"""
if self.proc is None:
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train | validate_flags | Validates flags are set to acceptable values. | tensor2tensor/serving/query.py | 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 | def validate_flags():
"""Validates flags are set to acceptable values."""
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train | make_request_fn | Returns a request function. | tensor2tensor/serving/query.py | def make_request_fn():
"""Returns a request function."""
if FLAGS.cloud_mlengine_model_name:
request_fn = serving_utils.make_cloud_mlengine_request_fn(
credentials=GoogleCredentials.get_application_default(),
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train | NextFrameSavpBase.encoder | 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 latent gaussians.
Raises:
ValueError... | tensor2tensor/models/video/savp.py | 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... | 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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z_mu: Mean of the latent gaussians.
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train | NextFrameSavpBase.get_fc_dimensions | Get expected fully connected shape after a series of convolutions. | tensor2tensor/models/video/savp.py | 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_... | 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
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train | NextFrameSavpBase.discriminator | 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. | tensor2tensor/models/video/savp.py | 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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""... | def discriminator(self, frames):
"""3-D SNGAN discriminator.
Args:
frames: a list of batch-major tensors indexed by time.
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logits: 1-D Tensor with shape=batch_size.
Positive logits imply that the discriminator thinks that it
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train | NextFrameSavpBase.d_step | Performs the discriminator step in computing the GAN loss.
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train | NextFrameSavpBase.get_gan_loss | Get the discriminator + generator loss at every step.
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true_frames: 5-D Tensor of shape (num_steps, batch_size, H, W, C)
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step.
Args:
true_frames: 5-D Tensor of shape (num_steps, batch_size, H, W, C)
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train | NextFrameSavpBase.get_extra_loss | Gets extra loss from VAE and GAN. | tensor2tensor/models/video/savp.py | def get_extra_loss(self, latent_means=None, latent_stds=None,
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"""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
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if self.h... | def get_extra_loss(self, latent_means=None, latent_stds=None,
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train | NextFrameSavpBase.pad_conv3d_lrelu | Pad, apply 3-D convolution and leaky relu. | tensor2tensor/models/video/savp.py | 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
if... | 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]]
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train | weight | Weight-level magnitude pruning. | tensor2tensor/utils/pruning_utils.py | def weight(w, sparsity):
"""Weight-level magnitude pruning."""
w_shape = common_layers.shape_list(w)
k = int(np.prod(w_shape[:-1]))
count = tf.to_int32(k * sparsity)
mask = common_layers.weight_targeting(w, count)
return (1 - mask) * w | def weight(w, sparsity):
"""Weight-level magnitude pruning."""
w_shape = common_layers.shape_list(w)
k = int(np.prod(w_shape[:-1]))
count = tf.to_int32(k * sparsity)
mask = common_layers.weight_targeting(w, count)
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train | unit | Unit-level magnitude pruning. | tensor2tensor/utils/pruning_utils.py | def unit(w, sparsity):
"""Unit-level magnitude pruning."""
w_shape = common_layers.shape_list(w)
count = tf.to_int32(w_shape[-1] * sparsity)
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return (1 - mask) * w | def unit(w, sparsity):
"""Unit-level magnitude pruning."""
w_shape = common_layers.shape_list(w)
count = tf.to_int32(w_shape[-1] * sparsity)
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train | sparsify | Prune the weights of a model and evaluate. | tensor2tensor/utils/pruning_utils.py | 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):
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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()
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train | DebugFrontendApplication.load_config | Loads the configuration. | tensor2tensor/insights/server.py | 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) | def load_config(self):
"""Loads the configuration."""
config = dict([(key, value) for key, value in iteritems(self.options)
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train | ppo_base_v1 | Set of hyperparameters. | tensor2tensor/models/research/rl.py | def ppo_base_v1():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.learning_rate_schedule = "constant"
hparams.learning_rate_constant = 1e-4
hparams.clip_grad_norm = 0.5
hparams.weight_decay = 0
# If set, extends the LR warmup to all epochs except the final one.
hparams.ad... | def ppo_base_v1():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.learning_rate_schedule = "constant"
hparams.learning_rate_constant = 1e-4
hparams.clip_grad_norm = 0.5
hparams.weight_decay = 0
# If set, extends the LR warmup to all epochs except the final one.
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train | ppo_atari_base | Pong base parameters. | tensor2tensor/models/research/rl.py | 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... | 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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train | ppo_original_params | Parameters based on the original PPO paper. | tensor2tensor/models/research/rl.py | 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... | 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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train | ppo_original_world_model | Atari parameters with world model as policy. | tensor2tensor/models/research/rl.py | def ppo_original_world_model():
"""Atari parameters with world model as policy."""
hparams = ppo_original_params()
hparams.policy_network = "next_frame_basic_deterministic"
hparams_keys = hparams.values().keys()
video_hparams = basic_deterministic_params.next_frame_basic_deterministic()
for (name, value) in... | def ppo_original_world_model():
"""Atari parameters with world model as policy."""
hparams = ppo_original_params()
hparams.policy_network = "next_frame_basic_deterministic"
hparams_keys = hparams.values().keys()
video_hparams = basic_deterministic_params.next_frame_basic_deterministic()
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train | ppo_tiny_world_model | Atari parameters with world model as policy. | tensor2tensor/models/research/rl.py | def ppo_tiny_world_model():
"""Atari parameters with world model as policy."""
hparams = ppo_original_params()
hparams.policy_network = "next_frame_basic_deterministic"
hparams_keys = hparams.values().keys()
video_hparams = basic_deterministic_params.next_frame_tiny()
for (name, value) in six.iteritems(vide... | def ppo_tiny_world_model():
"""Atari parameters with world model as policy."""
hparams = ppo_original_params()
hparams.policy_network = "next_frame_basic_deterministic"
hparams_keys = hparams.values().keys()
video_hparams = basic_deterministic_params.next_frame_tiny()
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train | ppo_original_world_model_stochastic_discrete | Atari parameters with stochastic discrete world model as policy. | tensor2tensor/models/research/rl.py | 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... | 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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"as",
"policy",
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/rl.py#L205-L219 | [
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train | make_simulated_env_fn | 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. | tensor2tensor/models/research/rl.py | 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... | 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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train | make_simulated_env_kwargs | Extracts simulated env kwargs from real_env and loop hparams. | tensor2tensor/models/research/rl.py | 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... | 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"
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/rl.py#L250-L270 | [
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... | 272500b6efe353aeb638d2745ed56e519462ca31 |
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