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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Miscellaneous utilities for TFGAN code and examples.
Includes:
1) Conditioning the value of a Tensor, based on techniques from
https://arxiv.org/abs/1609.03499.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.layers.python.layers import layers
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variable_scope
__all__ = [
'condition_tensor',
'condition_tensor_from_onehot',
]
def _get_shape(tensor):
tensor_shape = array_ops.shape(tensor)
static_tensor_shape = tensor_util.constant_value(tensor_shape)
return (static_tensor_shape if static_tensor_shape is not None else
tensor_shape)
def condition_tensor(tensor, conditioning):
"""Condition the value of a tensor.
Conditioning scheme based on https://arxiv.org/abs/1609.03499.
Args:
tensor: A minibatch tensor to be conditioned.
conditioning: A minibatch Tensor of to condition on. Must be 2D, with first
dimension the same as `tensor`.
Returns:
`tensor` conditioned on `conditioning`.
Raises:
ValueError: If the non-batch dimensions of `tensor` aren't fully defined.
ValueError: If `conditioning` isn't at least 2D.
ValueError: If the batch dimension for the input Tensors don't match.
"""
tensor.shape[1:].assert_is_fully_defined()
num_features = tensor.shape[1:].num_elements()
mapped_conditioning = layers.linear(
layers.flatten(conditioning), num_features)
if not mapped_conditioning.shape.is_compatible_with(tensor.shape):
mapped_conditioning = array_ops.reshape(
mapped_conditioning, _get_shape(tensor))
return tensor + mapped_conditioning
def _one_hot_to_embedding(one_hot, embedding_size):
"""Get a dense embedding vector from a one-hot encoding."""
num_tokens = one_hot.shape[1]
label_id = math_ops.argmax(one_hot, axis=1)
embedding = variable_scope.get_variable(
'embedding', [num_tokens, embedding_size])
return embedding_ops.embedding_lookup(
embedding, label_id, name='token_to_embedding')
def _validate_onehot(one_hot_labels):
one_hot_labels.shape.assert_has_rank(2)
one_hot_labels.shape[1:].assert_is_fully_defined()
def condition_tensor_from_onehot(tensor, one_hot_labels, embedding_size=256):
"""Condition a tensor based on a one-hot tensor.
Conditioning scheme based on https://arxiv.org/abs/1609.03499.
Args:
tensor: Tensor to be conditioned.
one_hot_labels: A Tensor of one-hot labels. Shape is
[batch_size, num_classes].
embedding_size: The size of the class embedding.
Returns:
`tensor` conditioned on `one_hot_labels`.
Raises:
ValueError: `one_hot_labels` isn't 2D, if non-batch dimensions aren't
fully defined, or if batch sizes don't match.
"""
_validate_onehot(one_hot_labels)
conditioning = _one_hot_to_embedding(one_hot_labels, embedding_size)
return condition_tensor(tensor, conditioning)
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