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def Dropout(x, params, rate=0.0, mode='train', rng=None, **kwargs):
"""Layer construction function for a dropout layer with given rate.""" |
del params, kwargs
if rng is None:
msg = ('Dropout layer requires apply_fun to be called with a rng keyword '
'argument. That is, instead of `Dropout(params, inputs)`, call '
'it like `Dropout(params, inputs, rng=key)`.')
raise ValueError(msg)
if rate >= 1.0:
raise ValueError('D... |
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def _kernel_shape(self, input_shape):
"""Helper to calculate the kernel shape.""" |
kernel_size_iter = iter(self._kernel_size)
return [self._filters if c == 'O' else
input_shape[self._lhs_spec.index('C')] if c == 'I' else
next(kernel_size_iter) for c in self._rhs_spec] |
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def _conv_shape_tuple(self, lhs_shape, rhs_shape, strides, pads):
"""Compute the shape of a conv given input shapes in canonical order.""" |
if isinstance(pads, str):
pads = padtype_to_pads(lhs_shape[2:], rhs_shape[2:], strides, pads)
if len(pads) != len(lhs_shape) - 2:
msg = 'Wrong number of explicit pads for conv: expected {}, got {}.'
raise TypeError(msg.format(len(lhs_shape) - 2, len(pads)))
lhs_padded = onp.add(lhs_shape[... |
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def _conv_general_permutations(self, dimension_numbers):
"""Utility for convolution dimension permutations relative to Conv HLO.""" |
lhs_spec, rhs_spec, out_spec = dimension_numbers
lhs_char, rhs_char, out_char = ('N', 'C'), ('O', 'I'), ('N', 'C')
charpairs = (lhs_char, rhs_char, out_char)
for i, (a, b) in enumerate(charpairs):
if not (dimension_numbers[i].count(a) == 1 and
dimension_numbers[i].count(b) == 1):
... |
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def _conv_general_shape_tuple(self, lhs_shape, rhs_shape, window_strides, padding, dimension_numbers):
"""Generalized computation of conv shape.""" |
lhs_perm, rhs_perm, out_perm = self._conv_general_permutations(
dimension_numbers)
lhs_trans = onp.take(lhs_shape, lhs_perm)
rhs_trans = onp.take(rhs_shape, rhs_perm)
out_trans = self._conv_shape_tuple(
lhs_trans, rhs_trans, window_strides, padding)
return tuple(onp.take(out_trans, ... |
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def get_create_agent(agent_kwargs):
"""Factory for dopamine agent initialization. Args: agent_kwargs: dict of BatchDQNAgent parameters Returns: Function(sess, en... |
def create_agent(sess, environment, summary_writer=None):
"""Creates a DQN agent.
Simplified version of `dopamine.discrete_domains.train.create_agent`
Args:
sess: a session
environment: an environment
summary_writer: a summary writer.
Returns:
a DQN agent.
"""
retu... |
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def get_create_batch_env_fun(batch_env_fn, time_limit):
"""Factory for dopamine environment initialization function. Args: batch_env_fn: function(in_graph: bool)... |
def create_env_fun(game_name=None, sticky_actions=None):
del game_name, sticky_actions
batch_env = batch_env_fn(in_graph=False)
batch_env = ResizeBatchObservation(batch_env) # pylint: disable=redefined-variable-type
batch_env = DopamineBatchEnv(batch_env, max_episode_steps=time_limit)
return ba... |
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def _parse_hparams(hparams):
"""Split hparams, based on key prefixes. Args: hparams: hyperparameters Returns: Tuple of hparams for respectably: agent, optimizer,... |
prefixes = ["agent_", "optimizer_", "runner_", "replay_buffer_"]
ret = []
for prefix in prefixes:
ret_dict = {}
for key in hparams.values():
if prefix in key:
par_name = key[len(prefix):]
ret_dict[par_name] = hparams.get(key)
ret.append(ret_dict)
return ret |
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def _build_replay_buffer(self, use_staging):
"""Build WrappedReplayBuffer with custom OutOfGraphReplayBuffer.""" |
replay_buffer_kwargs = dict(
observation_shape=dqn_agent.NATURE_DQN_OBSERVATION_SHAPE,
stack_size=dqn_agent.NATURE_DQN_STACK_SIZE,
replay_capacity=self._replay_capacity,
batch_size=self._buffer_batch_size,
update_horizon=self.update_horizon,
gamma=self.gamma,
... |
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def next_frame_glow_hparams():
"""Hparams for next_frame_glow.""" |
hparams = glow.glow_hparams()
# Possible modes are conditional and unconditional
hparams.add_hparam("gen_mode", "conditional")
hparams.add_hparam("learn_top_scale", False)
hparams.add_hparam("condition_all_levels", True)
# For each video, substitutes "num_input_frames + num_output_frames" with a
# random... |
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def next_frame_glow_bair_quant():
"""Hparams to reproduce bits-per-pixel results on BAIR action-free dataset.""" |
hparams = next_frame_glow_hparams()
hparams.video_num_input_frames = 3
hparams.video_num_target_frames = 10
hparams.num_train_frames = 4
hparams.num_cond_latents = 3
hparams.depth = 24
hparams.latent_dist_encoder = "conv3d_net"
hparams.latent_encoder_width = 256
hparams.latent_architecture = "glow_re... |
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def next_frame_glow_bair_qual():
"""Hparams for qualitative video generation results.""" |
hparams = next_frame_glow_bair_quant()
hparams.coupling = "additive"
hparams.temperature = 0.5
hparams.coupling_width = 392
return hparams |
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def next_frame_glow_shapes():
"""Hparams for qualitative and quantitative results on shapes dataset.""" |
hparams = next_frame_glow_bair_quant()
hparams.video_num_input_frames = 1
hparams.video_num_target_frames = 2
hparams.num_train_frames = 2
hparams.num_cond_latents = 1
hparams.coupling = "additive"
hparams.coupling_width = 512
hparams.latent_encoder_depth = 10
hparams.latent_skip = False
hparams.le... |
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def basic_fc_small():
"""Small fully connected model.""" |
hparams = common_hparams.basic_params1()
hparams.learning_rate = 0.1
hparams.batch_size = 128
hparams.hidden_size = 256
hparams.num_hidden_layers = 2
hparams.initializer = "uniform_unit_scaling"
hparams.initializer_gain = 1.0
hparams.weight_decay = 0.0
hparams.dropout = 0.0
return hparams |
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def imagenet_pixelrnn_generator(tmp_dir, training, size=_IMAGENET_SMALL_IMAGE_SIZE):
"""Image generator for Imagenet 64x64 downsampled images. It assumes that th... |
if size == _IMAGENET_SMALL_IMAGE_SIZE:
train_prefix = _IMAGENET_SMALL_TRAIN_PREFIX
eval_prefix = _IMAGENET_SMALL_EVAL_PREFIX
else:
train_prefix = _IMAGENET_MEDIUM_TRAIN_PREFIX
eval_prefix = _IMAGENET_MEDIUM_EVAL_PREFIX
prefix = train_prefix if training else eval_prefix
images_filepath = os.path... |
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def imagenet_preprocess_example(example, mode, resize_size=None, normalize=True):
"""Preprocessing used for Imagenet and similar problems.""" |
resize_size = resize_size or [299, 299]
assert resize_size[0] == resize_size[1]
image = example["inputs"]
if mode == tf.estimator.ModeKeys.TRAIN:
image = preprocess_for_train(image, image_size=resize_size[0],
normalize=normalize)
else:
image = preprocess_for_eval(ima... |
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def _crop(image, offset_height, offset_width, crop_height, crop_width):
"""Crops the given image using the provided offsets and sizes. Note that the method doesn... |
original_shape = tf.shape(image)
rank_assertion = tf.Assert(
tf.equal(tf.rank(image), 3), ["Rank of image must be equal to 3."])
with tf.control_dependencies([rank_assertion]):
cropped_shape = tf.stack([crop_height, crop_width, original_shape[2]])
size_assertion = tf.Assert(
tf.logical_and(
... |
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def distorted_bounding_box_crop(image, bbox, min_object_covered=0.1, aspect_ratio_range=(0.75, 1.33), area_range=(0.05, 1.0), max_attempts=100, scope=None):
"""G... |
with tf.name_scope(scope, default_name="distorted_bounding_box_crop",
values=[image, bbox]):
# Each bounding box has shape [1, num_boxes, box coords] and
# the coordinates are ordered [ymin, xmin, ymax, xmax].
# A large fraction of image datasets contain a human-annotated bounding
... |
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def _at_least_x_are_true(a, b, x):
"""At least `x` of `a` and `b` `Tensors` are true.""" |
match = tf.equal(a, b)
match = tf.cast(match, tf.int32)
return tf.greater_equal(tf.reduce_sum(match), x) |
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def _do_scale(image, size):
"""Rescale the image by scaling the smaller spatial dimension to `size`.""" |
shape = tf.cast(tf.shape(image), tf.float32)
w_greater = tf.greater(shape[0], shape[1])
shape = tf.cond(w_greater,
lambda: tf.cast([shape[0] / shape[1] * size, size], tf.int32),
lambda: tf.cast([size, shape[1] / shape[0] * size], tf.int32))
return tf.image.resize_bicubic([i... |
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def _center_crop(image, size):
"""Crops to center of image with specified `size`.""" |
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
offset_height = ((image_height - size) + 1) / 2
offset_width = ((image_width - size) + 1) / 2
image = _crop(image, offset_height, offset_width, size, size)
return image |
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def _normalize(image):
"""Normalize the image to zero mean and unit variance.""" |
offset = tf.constant(MEAN_RGB, shape=[1, 1, 3])
image -= offset
scale = tf.constant(STDDEV_RGB, shape=[1, 1, 3])
image /= scale
return image |
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def MultifactorSchedule(history=None, factors="constant * linear_warmup * rsqrt_decay", constant=0.1, warmup_steps=100, decay_factor=0.5, steps_per_decay=20000):
... |
del history
cache_args = (factors, constant, warmup_steps)
if cache_args in _memoized_multifactor_schedules:
return _memoized_multifactor_schedules[cache_args]
factors = [n.strip() for n in factors.split("*")]
def learning_rate(step): # pylint: disable=invalid-name
"""Step to learning rate functi... |
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def EvalAdjustingSchedule(history, constant=0.1, steps_to_decrease=20, improvement_margin=0.001, decrease_rate=1.5, history_mode="eval", metric="metrics/accuracy"... |
metrics = history.get(history_mode, metric)
adjusted = constant
if len(metrics) < 2:
return MultifactorSchedule(history, constant=adjusted)
steps_without_improvement = 0
cur = metrics.pop()[1] # The most-recent value of the metric.
while len(metrics) > 1:
# The one-before value of metrics as .pop... |
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def project_hidden(x, projection_tensors, hidden_size, num_blocks):
"""Project encoder hidden state under num_blocks using projection tensors. Args: x: Encoder h... |
batch_size, latent_dim, _ = common_layers.shape_list(x)
x = tf.reshape(x, shape=[1, -1, hidden_size])
x_tiled = tf.reshape(
tf.tile(x, multiples=[num_blocks, 1, 1]),
shape=[num_blocks, -1, hidden_size])
x_projected = tf.matmul(x_tiled, projection_tensors)
x_projected = tf.transpose(x_projected, p... |
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def slice_hidden(x, hidden_size, num_blocks):
"""Slice encoder hidden state under num_blocks. Args: x: Encoder hidden state of shape [batch_size, latent_dim, hid... |
batch_size, latent_dim, _ = common_layers.shape_list(x)
block_dim = hidden_size // num_blocks
x_sliced = tf.reshape(x,
shape=[batch_size, latent_dim, num_blocks, block_dim])
return x_sliced |
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def embedding_lookup(x, means, num_blocks, block_v_size, bottleneck_kind="dvq", random_top_k=1, soft_em=False, num_samples=1, do_hard_gumbel_softmax=False, temper... |
if bottleneck_kind == "gumbel-softmax-dvq":
x_means_hot, neg_q_entropy = gumbel_softmax_nearest_neighbor_dvq(
x,
means,
block_v_size,
hard=do_hard_gumbel_softmax,
num_samples=num_samples,
temperature_warmup_steps=temperature_warmup_steps,
num_flows=num_flow... |
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def vae(x, z_size, name=None):
"""Simple variational autoencoder without discretization. Args: x: Input to the discretization bottleneck. z_size: Number of bits,... |
with tf.variable_scope(name, default_name="vae"):
mu = tf.layers.dense(x, z_size, name="mu")
log_sigma = tf.layers.dense(x, z_size, name="log_sigma")
shape = common_layers.shape_list(x)
epsilon = tf.random_normal([shape[0], shape[1], 1, z_size])
z = mu + tf.exp(log_sigma / 2) * epsilon
kl = 0... |
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def gumbel_sample(shape):
"""Sample from the Gumbel distribution, protect from overflows. Args: shape: Shape of Gumbel samples. Returns: Noise drawn from Gumbel ... |
uniform_samples = tf.random_uniform(shape, minval=0.00001, maxval=0.99998)
return -tf.log(-tf.log(uniform_samples)) |
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def gumbel_softmax(x, z_size, mode, softmax_k=0, temperature_warmup_steps=150000, summary=True, name=None):
"""Gumbel softmax discretization bottleneck. Args: x:... |
with tf.variable_scope(name, default_name="gumbel_softmax"):
m = tf.layers.dense(x, 2**z_size, name="mask")
if softmax_k > 0:
m, kl = top_k_softmax(m, softmax_k)
return m, m, 1.0 - tf.reduce_mean(kl)
logsm = tf.nn.log_softmax(m)
# Gumbel-softmax sample.
gumbel_samples = gumbel_sample... |
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def vq_body(x, codebook_size, beta=0.25, decay=0.999, epsilon=1e-5, soft_em=False, num_samples=10, temperature=None, do_update=True):
"""Discretize each x into o... |
x_shape = common_layers.shape_list(x)
hidden_size = x_shape[-1]
means, ema_means, ema_count = get_vq_codebook(codebook_size, hidden_size)
x = tf.reshape(x, [-1, hidden_size])
x_means_hot, e_loss, distances = vq_nearest_neighbor(
x, means, soft_em=soft_em, num_samples=num_samples,
temperature=temp... |
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def vq_loss(x, targets, codebook_size, beta=0.25, decay=0.999, epsilon=1e-5, soft_em=False, num_samples=10, temperature=None, do_update=True):
"""Compute the los... |
x_shape = common_layers.shape_list(x)
target_shape = common_layers.shape_list(targets)
hidden_size = x_shape[-1]
means, _, _ = get_vq_codebook(codebook_size, hidden_size)
x = tf.reshape(x, [-1, hidden_size])
targets = tf.reshape(targets, [-1])
one_hot_targets = tf.one_hot(targets, codebook_size)
target... |
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def tanh_discrete_bottleneck(x, bottleneck_bits, bottleneck_noise, discretize_warmup_steps, mode):
"""Simple discretization through tanh, flip bottleneck_noise m... |
x = tf.layers.dense(x, bottleneck_bits, name="tanh_discrete_bottleneck")
d0 = tf.stop_gradient(2.0 * tf.to_float(tf.less(0.0, x))) - 1.0
if mode == tf.estimator.ModeKeys.TRAIN:
x += tf.truncated_normal(
common_layers.shape_list(x), mean=0.0, stddev=0.2)
x = tf.tanh(x)
d = x + tf.stop_gradient(2.0... |
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def tanh_discrete_unbottleneck(x, hidden_size):
"""Simple un-discretization from tanh.""" |
x = tf.layers.dense(x, hidden_size, name="tanh_discrete_unbottleneck")
return x |
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def isemhash_bottleneck(x, bottleneck_bits, bottleneck_noise, discretize_warmup_steps, mode, isemhash_noise_dev=0.5, isemhash_mix_prob=0.5):
"""Improved semantic... |
with tf.variable_scope("isemhash_bottleneck"):
x = tf.layers.dense(x, bottleneck_bits, name="dense")
y = common_layers.saturating_sigmoid(x)
if isemhash_noise_dev > 0 and mode == tf.estimator.ModeKeys.TRAIN:
noise = tf.truncated_normal(
common_layers.shape_list(x), mean=0.0, stddev=isemha... |
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def isemhash_unbottleneck(x, hidden_size, isemhash_filter_size_multiplier=1.0):
"""Improved semantic hashing un-bottleneck.""" |
filter_size = int(hidden_size * isemhash_filter_size_multiplier)
x = 0.5 * (x - 1.0) # Move from [-1, 1] to [0, 1].
with tf.variable_scope("isemhash_unbottleneck"):
h1a = tf.layers.dense(x, filter_size, name="hidden1a")
h1b = tf.layers.dense(1.0 - x, filter_size, name="hidden1b")
h2 = tf.layers.dens... |
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def parametrized_bottleneck(x, hparams):
"""Meta-function calling all the above bottlenecks with hparams.""" |
if hparams.bottleneck_kind == "tanh_discrete":
d, _ = tanh_discrete_bottleneck(
x, hparams.bottleneck_bits, hparams.bottleneck_noise * 0.5,
hparams.discretize_warmup_steps, hparams.mode)
return d, 0.0
if hparams.bottleneck_kind == "isemhash":
return isemhash_bottleneck(
x, hpara... |
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def parametrized_unbottleneck(x, hidden_size, hparams):
"""Meta-function calling all the above un-bottlenecks with hparams.""" |
if hparams.bottleneck_kind == "tanh_discrete":
return tanh_discrete_unbottleneck(x, hidden_size)
if hparams.bottleneck_kind == "isemhash":
return isemhash_unbottleneck(x, hidden_size,
hparams.isemhash_filter_size_multiplier)
if hparams.bottleneck_kind in ["vq", "em", "gum... |
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def iaf_hparams(hidden_size=512, filter_size=4096):
"""Create hyperpameters for inverse autoregressive flows. Args: hidden_size: Width of attention layers and ne... |
hparams = common_hparams.basic_params1()
# Attention hyperparameters.
hparams.hidden_size = hidden_size
hparams.add_hparam("attention_key_channels", None)
hparams.add_hparam("attention_value_channels", None)
hparams.add_hparam("num_heads", 4)
hparams.add_hparam("attention_dropout", 0.1)
hparams.add_hp... |
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def _original_vocab(tmp_dir):
"""Returns a set containing the original vocabulary. This is important for comparing with published results. Args: tmp_dir: directo... |
vocab_url = ("http://download.tensorflow.org/models/LM_LSTM_CNN/"
"vocab-2016-09-10.txt")
vocab_filename = os.path.basename(vocab_url + ".en")
vocab_filepath = os.path.join(tmp_dir, vocab_filename)
if not os.path.exists(vocab_filepath):
generator_utils.maybe_download(tmp_dir, vocab_filename,... |
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def _replace_oov(original_vocab, line):
"""Replace out-of-vocab words with "UNK". This maintains compatibility with published results. Args: original_vocab: a se... |
return u" ".join(
[word if word in original_vocab else u"UNK" for word in line.split()]) |
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def lossfn(real_input, fake_input, compress, hparams, lsgan, name):
"""Loss function.""" |
eps = 1e-12
with tf.variable_scope(name):
d1 = discriminator(real_input, compress, hparams, "discriminator")
d2 = discriminator(fake_input, compress, hparams, "discriminator",
reuse=True)
if lsgan:
dloss = tf.reduce_mean(
tf.squared_difference(d1, 0.9)) + tf.reduc... |
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def cycle_gan_internal(inputs, targets, _, hparams):
"""Cycle GAN, main step used for training.""" |
with tf.variable_scope("cycle_gan"):
# Embed inputs and targets.
inputs_orig, targets_orig = tf.to_int32(inputs), tf.to_int32(targets)
inputs = common_layers.embedding(
inputs_orig, hparams.vocab_size, hparams.hidden_size, "embed")
targets = common_layers.embedding(
targets_orig, hpar... |
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def decode_hparams(overrides=""):
"""Hparams for decoding.""" |
hparams = decoding.decode_hparams()
# Number of interpolations between [0.0, 1.0].
hparams.add_hparam("num_interp", 11)
# Which level(s) to interpolate.
hparams.add_hparam("level_interp", [0, 1, 2])
# "all" or "ranked", interpolate all channels or a "ranked".
hparams.add_hparam("channel_interp", "all")
... |
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def preprocess_frame(frame):
"""Preprocess frame. 1. Converts [0, 255] to [-0.5, 0.5] 2. Adds uniform noise. Args: frame: 3-D Tensor representing pixels. Returns... |
# Normalize from [0.0, 1.0] -> [-0.5, 0.5]
frame = common_layers.convert_rgb_to_real(frame)
frame = frame - 0.5
frame, _ = glow_ops.uniform_binning_correction(frame)
return frame |
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def frame_to_latents(frame, hparams):
"""Encode frames to latents.""" |
# Preprocess
frame = preprocess_frame(frame)
# Encode [X_t] to [z^1_t, z^2_t .. z^l_t]
glow_vals = glow_ops.encoder_decoder(
"codec", frame, hparams, eps=None, reverse=False)
z_top, _, level_eps, _, _ = glow_vals
return z_top, level_eps |
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def latents_to_frames(z_top_interp, level_eps_interp, hparams):
"""Decodes latents to frames.""" |
# Decode [z^1_t, z^2_t .. z^l_t] to [X_t]
images, _, _, _ = glow_ops.encoder_decoder(
"codec", z_top_interp, hparams, eps=level_eps_interp, reverse=True)
images = glow_ops.postprocess(images)
return images |
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def interpolate(features, hparams, decode_hp):
"""Interpolate between the first input frame and last target frame. Args: features: dict of tensors hparams: HPara... |
inputs, targets = features["inputs"], features["targets"]
inputs = tf.unstack(inputs, axis=1)
targets = tf.unstack(targets, axis=1)
coeffs = np.linspace(0.0, 1.0, decode_hp.num_interp)
# (X_1, X_t) -> (z_1, z_t)
first_frame, last_frame = inputs[0], targets[-1]
first_top_z, first_level_eps = frame_to_lat... |
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def get_summaries_log_dir(decode_hp, output_dir, dataset_split):
"""Get nested summaries_log_dir based on decode_hp.""" |
child_dir = decode_hp.summaries_log_dir
level_dir = "".join([str(level) for level in decode_hp.level_interp])
if decode_hp.channel_interp == "all":
rank_dir = "all"
else:
rank_dir = "rank_%d" % decode_hp.rank_interp
child_dir = "%s/%s_%s" % (child_dir, level_dir, rank_dir)
if dataset_split is not N... |
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def interpolations_to_summary(sample_ind, interpolations, first_frame, last_frame, hparams, decode_hp):
"""Converts interpolated frames into tf summaries. The su... |
parent_tag = "sample_%d" % sample_ind
frame_shape = hparams.problem.frame_shape
interp_shape = [hparams.batch_size, decode_hp.num_interp] + frame_shape
interpolations = np.reshape(interpolations, interp_shape)
interp_tag = "%s/interp/%s" % (parent_tag, decode_hp.channel_interp)
if decode_hp.channel_interp ... |
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def next_frame_epva():
"""EPVA hparams.""" |
hparams = basic_deterministic_params.next_frame_basic_deterministic()
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 4
hparams.bottom = {
"inputs": modalities.video_raw_bottom,
"targets": modalities.video_raw_targets_bottom,
}
hparams.loss = {
"targets": modalities.v... |
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def _create_slots(self, var_list):
"""Create slot variables for Adam with accumulated gradients.""" |
super(MultistepAdamOptimizer, self)._create_slots(var_list)
first_var = min(var_list, key=lambda x: x.name)
self._create_non_slot_variable(initial_value=0 if self._n == 1 else 1,
name="iter",
colocate_with=first_var)
for v in var_lis... |
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def _apply_cond(self, apply_fn, grad, var, *args, **kwargs):
"""Apply conditionally if counter is zero.""" |
grad_acc = self.get_slot(var, "grad_acc")
def apply_adam(grad_acc, apply_fn, grad, var, *args, **kwargs):
total_grad = (grad_acc + grad) / tf.cast(self._n_t, grad.dtype)
adam_op = apply_fn(total_grad, var, *args, **kwargs)
with tf.control_dependencies([adam_op]):
grad_acc_to_zero_op ... |
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def _finish(self, update_ops, name_scope):
"""Updates beta_power variables every n batches and incrs counter.""" |
iter_ = self._get_iter_variable()
beta1_power, beta2_power = self._get_beta_accumulators()
with tf.control_dependencies(update_ops):
with tf.colocate_with(iter_):
def update_beta_op():
update_beta1 = beta1_power.assign(
beta1_power * self._beta1_t,
use_l... |
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def data_parallelism_from_flags(daisy_chain_variables=True, all_workers=False):
"""Over which devices do we split each training batch. In old-fashioned async mod... |
dp_arg_names = inspect.getargspec(data_parallelism).args
blacklist = ["daisy_chain_variables", "all_workers"]
kwargs = {}
for arg in dp_arg_names:
if arg in blacklist:
continue
kwargs[arg] = getattr(tf.flags.FLAGS, arg)
return data_parallelism(
daisy_chain_variables=daisy_chain_variabl... |
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def concat_generator(filename, up_threshold, low_threshold=10):
"""Generate concatenated lines from file upto up_threshold characters.""" |
txt = ""
for line in tf.gfile.Open(filename):
line = line.strip()
if len(txt) + len(line) + 1 >= up_threshold:
ret = txt
txt = ""
# We don't yield very short long parts to prevent noisy examples.
if len(ret) > low_threshold and len(ret) < up_threshold:
yield {"targets": ret}... |
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def mix_generators(generator_list):
"""Given python generators, generate from one, then from another, etc.""" |
i = 0
l = len(generator_list)
stopiters_seen = 0
while stopiters_seen <= l:
try:
yield six.next(generator_list[i % l])
i += 1
stopiters_seen = 0
except StopIteration:
i += 1
stopiters_seen += 1 |
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def compute_bleu_summaries(hook_args):
"""Compute BLEU core summaries using the decoder output. Args: hook_args: DecodeHookArgs namedtuple Returns: A list of tf.... |
decode_hparams = hook_args.decode_hparams
if not (decode_hparams.decode_reference and decode_hparams.decode_to_file):
return None
values = []
bleu = 100 * bleu_hook.bleu_wrapper(
decode_hparams.decode_reference, decode_hparams.decode_to_file)
values.append(tf.Summary.Value(tag="BLEU", simple_valu... |
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def _preprocess_sgm(line, is_sgm):
"""Preprocessing to strip tags in SGM files.""" |
if not is_sgm:
return line
# In SGM files, remove <srcset ...>, <p>, <doc ...> lines.
if line.startswith("<srcset") or line.startswith("</srcset"):
return ""
if line.startswith("<doc") or line.startswith("</doc"):
return ""
if line.startswith("<p>") or line.startswith("</p>"):
return ""
# S... |
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def get_or_create_vocab(self, data_dir, tmp_dir, force_get=False):
"""Get vocab for distill problems.""" |
# We assume that vocab file is present in data_dir directory where the
# data generated will be stored.
vocab_filepath = os.path.join(data_dir, self.vocab_filename)
encoder = text_encoder.SubwordTextEncoder(vocab_filepath)
return encoder |
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def set_hparams_from_args(args):
"""Set hparams overrides from unparsed args list.""" |
if not args:
return
hp_prefix = "--hp_"
tf.logging.info("Found unparsed command-line arguments. Checking if any "
"start with %s and interpreting those as hparams "
"settings.", hp_prefix)
pairs = []
i = 0
while i < len(args):
arg = args[i]
if arg.startswit... |
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def create_hparams():
"""Create hparams.""" |
if FLAGS.use_tpu and "tpu" not in FLAGS.hparams_set:
tf.logging.warn("Not all hyperparameter sets work on TPU. "
"Prefer hparams_sets with a '_tpu' suffix, "
"e.g. transformer_tpu, if available for your model.")
hparams_path = os.path.join(FLAGS.output_dir, "hparams.json... |
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def save_metadata(hparams):
"""Saves FLAGS and hparams to output_dir.""" |
output_dir = os.path.expanduser(FLAGS.output_dir)
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
# Save FLAGS in txt file
if hasattr(FLAGS, "flags_into_string"):
flags_str = FLAGS.flags_into_string()
t2t_flags_str = "\n".join([
"--%s=%s" % (f.name, f.value)
for f... |
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def residual_block(x, hparams):
"""A stack of convolution blocks with residual connection.""" |
k = (hparams.kernel_height, hparams.kernel_width)
dilations_and_kernels = [((1, 1), k) for _ in range(3)]
y = common_layers.subseparable_conv_block(
x,
hparams.hidden_size,
dilations_and_kernels,
padding="SAME",
separability=0,
name="residual_block")
x = common_layers.layer_... |
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def xception_internal(inputs, hparams):
"""Xception body.""" |
with tf.variable_scope("xception"):
cur = inputs
if cur.get_shape().as_list()[1] > 200:
# Large image, Xception entry flow
cur = xception_entry(cur, hparams.hidden_size)
else:
# Small image, conv
cur = common_layers.conv_block(
cur,
hparams.hidden_size, [((1, ... |
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def xception_entry(inputs, hidden_dim):
"""Xception entry flow.""" |
with tf.variable_scope("xception_entry"):
def xnet_resblock(x, filters, res_relu, name):
"""Resblock."""
with tf.variable_scope(name):
y = common_layers.separable_conv_block(
x,
filters, [((1, 1), (3, 3)), ((1, 1), (3, 3))],
first_relu=True,
pa... |
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def xception_exit(inputs):
"""Xception exit flow.""" |
with tf.variable_scope("xception_exit"):
x = inputs
x_shape = x.get_shape().as_list()
if x_shape[1] is None or x_shape[2] is None:
length_float = tf.to_float(tf.shape(x)[1])
length_float *= tf.to_float(tf.shape(x)[2])
spatial_dim_float = tf.sqrt(length_float)
spatial_dim = tf.to_i... |
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def get_text_from_html(html):
"""Returns a plaintext representation of HTML content.""" |
try:
soup = bs4.BeautifulSoup(html, "html.parser")
except: # pylint: disable=bare-except
# Some docs don't parse
return ""
# Remove script and style tags
for s in soup(["script", "style"]):
s.decompose()
return "\n".join([s for s in _soup_strings(soup)]) |
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def _soup_strings(soup):
"""Return text strings in soup.""" |
paragraph_tags = set([
"caption", "details", "h1", "h2", "h3", "h4", "h5", "h6", "li", "p", "td",
"div", "span"
])
skip_children = None
for descendant in soup.descendants:
# If we've treated a tag as a contiguous paragraph, don't re-emit the
# children (see below).
if skip_children is ... |
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def imagetransformerpp_base_14l_8h_big_uncond_dr03_dan_p():
"""Gets to 2.92 in just under 4 days on 8 p100s.""" |
hparams = imagetransformerpp_base_12l_8h_big_uncond_dr03_dan_l()
hparams.num_decoder_layers = 14
hparams.batch_size = 8
hparams.layer_prepostprocess_dropout = 0.2
return hparams |
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def imagetransformerpp_base_5l_8h_big_uncond_dr00_dan_g_bs1():
"""For 256x256.""" |
hparams = imagetransformerpp_base_10l_8h_big_uncond_dr03_dan_g()
# TODO(trandustin): I forgot to set this in the runs! Maybe it's not used in
# image transformer training implementation?
# hparams.img_len = 256
hparams.max_length = 66000 # allow for 256x256
hparams.batch_size = 1
hparams.num_decoder_lay... |
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def imagetransformer_base_8l_8h_big_cond_dr03_dan_dilated():
"""Dilated hparams.""" |
hparams = imagetransformer_base_8l_8h_big_cond_dr03_dan()
hparams.gap_sizes = [0, 16, 64, 0, 16, 64, 128, 0]
hparams.dec_attention_type = cia.AttentionType.DILATED
hparams.block_length = 128
hparams.block_width = 128
hparams.add_hparam("num_memory_blocks", 1)
return hparams |
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def imagetransformer1d_base_8l_64by64():
"""hparams fo 12 layer big 1d model for imagenet 64x64.""" |
hparams = image_transformer_base()
hparams.num_heads = 8
hparams.hidden_size = 512
hparams.filter_size = 2048
hparams.num_decoder_layers = 8
hparams.batch_size = 1
hparams.block_length = 512
hparams.block_width = 768
hparams.layer_prepostprocess_dropout = 0.1
hparams.max_length = 14000
hparams.un... |
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def imagetransformer_moe_tiny():
"""Set of hyperparameters for a very small imagetransformer with MoE.""" |
hparams = imagetransformer_tiny()
hparams.hidden_size = 64
hparams.batch_size = 1
hparams.num_hidden_layers = 3
hparams.dec_attention_type = cia.AttentionType.MOE_LOCAL_1D
hparams.add_hparam("moe_layers_decoder", "1") # Which layer is MoE.
hparams.moe_hidden_sizes = "1024" # Hidden layer sizes (comma-s... |
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def imagetransformer_sep_channels_8l_tpu():
"""Hparams for training imagetransformer on tpu.""" |
hparams = imagetransformer_sep_channels_8l()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.num_heads = 4 # heads are expensive on tpu
hparams.shared_embedding_and_softmax_weights = False
return hparams |
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def imagetransformer_b10l_4h_big_uncond_dr03_tpu():
"""Small model for tpu cifar 10.""" |
hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.num_heads = 4 # heads are expensive on tpu
hparams.num_decoder_layers = 10
hparams.block_length = 128
hparams.hidden_size = 512
hparams.filter_size = 1024
hparams.learning_rat... |
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def imagetransformer_b10l_dr03_moe_tpu():
"""Moe tpu params.""" |
hparams = imagetransformer_b10l_4h_big_uncond_dr03_tpu()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.num_heads = 4 # heads are expensive on tpu
hparams.num_decoder_layers = 10
hparams.layer_preprocess_sequence = "none"
hparams.layer_postprocess_sequence = "dan"
hparams.ffn_layer = ... |
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def imagetransformer_cifar_tpu_range(rhp):
"""Range of hyperparameters for vizier.""" |
# After starting from base, set intervals for some parameters.
rhp.set_float("learning_rate", 0.01, 1.0, scale=rhp.LOG_SCALE)
rhp.set_discrete("num_decoder_layers", [8, 10, 12, 14, 16])
rhp.set_discrete("hidden_size", [256, 512, 1024])
rhp.set_discrete("block_length", [128, 256, 512])
rhp.set_categorical("... |
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def imagetransformer_b12l_4h_b128_h512_uncond_dr01_im():
"""TPU related imagenet model.""" |
hparams = imagetransformer_b12l_4h_b256_uncond_dr03_tpu()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.optimizer = "Adafactor"
hparams.learning_rate_schedule = "rsqrt_decay"
hparams.learning_rate_warmup_steps = 6000
hparams.layer_prepostprocess_dropout = 0.1
return hparams |
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def imagetransformer_b12l_4h_b128_uncond_dr03_tpu():
"""TPU config for cifar 10.""" |
hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
update_hparams_for_tpu(hparams)
hparams.batch_size = 2
hparams.num_heads = 4 # heads are expensive on tpu
hparams.num_decoder_layers = 12
hparams.block_length = 128
hparams.hidden_size = 256
hparams.filter_size = 2048
hparams.layer_prepro... |
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def imagetransformer_b12l_8h_b256_uncond_dr03_tpu():
"""TPU related 12 layer 8 heads model.""" |
hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
update_hparams_for_tpu(hparams)
hparams.batch_size = 2
hparams.num_heads = 8 # heads are expensive on tpu
hparams.num_decoder_layers = 12
hparams.block_length = 256
hparams.hidden_size = 512
hparams.filter_size = 2048
hparams.layer_prepro... |
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def training_loop(self):
"""Context manager wrapping the training loop, updates step counters.""" |
if not self.restarting:
self._write_counters(self._local_step_at_start, self._global_step)
tf.logging.info(
"Training %s up to %d, %d to go", self.model_mode,
self.target_local_step, self.steps_to_go
)
yield
self._write_counters(self.target_local_step, -1) |
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def _read_words(filename):
"""Reads words from a file.""" |
with tf.gfile.GFile(filename, "r") as f:
if sys.version_info[0] >= 3:
return f.read().replace("\n", " %s " % EOS).split()
else:
return f.read().decode("utf-8").replace("\n", " %s " % EOS).split() |
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def _build_vocab(filename, vocab_path, vocab_size):
"""Reads a file to build a vocabulary of `vocab_size` most common words. The vocabulary is sorted by occurren... |
data = _read_words(filename)
counter = collections.Counter(data)
count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))
words, _ = list(zip(*count_pairs))
words = words[:vocab_size]
with open(vocab_path, "w") as f:
f.write("\n".join(words)) |
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def _get_token_encoder(vocab_dir, vocab_name, filename):
"""Reads from file and returns a `TokenTextEncoder` for the vocabulary.""" |
vocab_path = os.path.join(vocab_dir, vocab_name)
if not tf.gfile.Exists(vocab_path):
_build_vocab(filename, vocab_path, 10000)
return text_encoder.TokenTextEncoder(vocab_path) |
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def resize(att_mat, max_length=None):
"""Normalize attention matrices and reshape as necessary.""" |
for i, att in enumerate(att_mat):
# Add extra batch dim for viz code to work.
if att.ndim == 3:
att = np.expand_dims(att, axis=0)
if max_length is not None:
# Sum across different attention values for each token.
att = att[:, :, :max_length, :max_length]
row_sums = np.sum(att, axi... |
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def _get_attention(inp_text, out_text, enc_atts, dec_atts, encdec_atts):
"""Compute representation of the attention ready for the d3 visualization. Args: inp_tex... |
def get_full_attention(layer):
"""Get the full input+output - input+output attentions."""
enc_att = enc_atts[layer][0]
dec_att = dec_atts[layer][0]
encdec_att = encdec_atts[layer][0]
enc_att = np.transpose(enc_att, [0, 2, 1])
dec_att = np.transpose(dec_att, [0, 2, 1])
encdec_att = np.tran... |
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def decode(tokens):
"""Decode a list of tokens to a unicode string. Args: tokens: a list of Unicode strings Returns: a unicode string """ |
token_is_alnum = [t[0] in _ALPHANUMERIC_CHAR_SET for t in tokens]
ret = []
for i, token in enumerate(tokens):
if i > 0 and token_is_alnum[i - 1] and token_is_alnum[i]:
ret.append(u" ")
ret.append(token)
return "".join(ret) |
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def _read_filepattern(filepattern, max_lines=None, split_on_newlines=True):
"""Reads files matching a wildcard pattern, yielding the contents. Args: filepattern:... |
filenames = sorted(tf.gfile.Glob(filepattern))
lines_read = 0
for filename in filenames:
with tf.gfile.Open(filename) as f:
if split_on_newlines:
for line in f:
yield line.strip()
lines_read += 1
if max_lines and lines_read >= max_lines:
return
e... |
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def corpus_token_counts( text_filepattern, corpus_max_lines, split_on_newlines=True):
"""Read the corpus and compute a dictionary of token counts. Args: text_fil... |
counts = collections.Counter()
for doc in _read_filepattern(
text_filepattern,
max_lines=corpus_max_lines,
split_on_newlines=split_on_newlines):
counts.update(encode(_native_to_unicode(doc)))
mlperf_log.transformer_print(
key=mlperf_log.PREPROC_VOCAB_SIZE, value=len(counts))
return... |
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def vocab_token_counts(text_filepattern, max_lines):
"""Read a vocab file and return a dictionary of token counts. Reads a two-column CSV file of tokens and thei... |
ret = {}
for i, line in enumerate(
_read_filepattern(text_filepattern, max_lines=max_lines)):
if "," not in line:
tf.logging.warning("Malformed vocab line #%d '%s'", i, line)
continue
token, count = line.rsplit(",", 1)
ret[_native_to_unicode(token)] = int(count)
return ret |
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def _make_example(input_ids, problem, input_feature_name="inputs"):
"""Make a tf.train.Example for the problem. features[input_feature_name] = input_ids Also fil... |
features = {
input_feature_name:
tf.train.Feature(int64_list=tf.train.Int64List(value=input_ids))
}
# Fill in dummy values for any other required features that presumably
# will not actually be used for prediction.
data_fields, _ = problem.example_reading_spec()
for fname, ftype in data_fi... |
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def make_grpc_request_fn(servable_name, server, timeout_secs):
"""Wraps function to make grpc requests with runtime args.""" |
stub = _create_stub(server)
def _make_grpc_request(examples):
"""Builds and sends request to TensorFlow model server."""
request = predict_pb2.PredictRequest()
request.model_spec.name = servable_name
request.inputs["input"].CopyFrom(
tf.make_tensor_proto(
[ex.SerializeToString(... |
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def make_cloud_mlengine_request_fn(credentials, model_name, version):
"""Wraps function to make CloudML Engine requests with runtime args.""" |
def _make_cloud_mlengine_request(examples):
"""Builds and sends requests to Cloud ML Engine."""
api = discovery.build("ml", "v1", credentials=credentials)
parent = "projects/%s/models/%s/versions/%s" % (cloud.default_project(),
model_name, version)
... |
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def predict(inputs_list, problem, request_fn):
"""Encodes inputs, makes request to deployed TF model, and decodes outputs.""" |
assert isinstance(inputs_list, list)
fname = "inputs" if problem.has_inputs else "targets"
input_encoder = problem.feature_info[fname].encoder
input_ids_list = [
_encode(inputs, input_encoder, add_eos=problem.has_inputs)
for inputs in inputs_list
]
examples = [_make_example(input_ids, problem, ... |
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def next_frame_basic_recurrent():
"""Basic 2-frame recurrent model with stochastic tower.""" |
hparams = basic_stochastic.next_frame_basic_stochastic_discrete()
hparams.filter_double_steps = 2
hparams.hidden_size = 64
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 4
hparams.concat_internal_states = False
hparams.add_hparam("num_lstm_layers", 2)
hparams.add_hparam("num_lstm_... |
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def create_teacher_experiment(run_config, hparams, argv):
"""Creates experiment function.""" |
tf.logging.info("training teacher")
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.cloud_mlengine:
return cloud_mlengine.launch()
if FLAGS.generate_data:
t2t_tr... |
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def masked_mean(inputs, targets, mask_id=None):
"""Mean of the inputs but counting only those where targets != mask_id.""" |
inputs = [x.astype(np.float32) for x in inputs]
# We assume all elements in the list contribute equally.
# TODO(lukaszkaiser): remove this assumption (e.g., when masks differ).
length = len(inputs)
if mask_id is None:
# TODO(lukaszkaiser): can we just divide the sum by length? XLA optimizes?
return s... |
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def neg_log_perplexity(batch, model_predictions):
"""Calculate negative log perplexity.""" |
_, targets = batch
model_predictions, targets = _make_list(model_predictions, targets)
xent = []
for (prediction, target) in zip(model_predictions, targets):
hot_target = layers.one_hot(target, prediction.shape[-1])
xent.append(np.sum(prediction * hot_target, axis=-1))
return masked_mean(xent, target... |
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def loss(params, batch, model_predict, rng):
"""Calculate loss.""" |
inputs, targets = batch
predictions = model_predict(inputs, params, rng=rng)
predictions, targets = _make_list(predictions, targets)
xent = []
for (pred, target) in zip(predictions, targets):
xent.append(np.sum(pred * layers.one_hot(target, pred.shape[-1]), axis=-1))
return - masked_mean(xent, targets) |
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