100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Miscellaneous functions useful for nD-LSTM models.
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Some of these functions duplicate functionality in tfslim with
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slightly different interfaces.
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Tensors in this library generally have the shape (num_images, height, width,
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depth).
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.layers.python.layers import layers
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import sparse_ops
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def _shape(tensor):
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"""Get the shape of a tensor as an int list."""
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return tensor.get_shape().as_list()
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def pixels_as_vector(images, scope=None):
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"""Reduce images to vectors by combining all pixels."""
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with ops.name_scope(scope, "PixelsAsVector", [images]):
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batch_size, height, width, depth = _shape(images)
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return array_ops.reshape(images, [batch_size, height * width * depth])
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def pool_as_vector(images, scope=None):
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"""Reduce images to vectors by averaging all pixels."""
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with ops.name_scope(scope, "PoolAsVector", [images]):
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return math_ops.reduce_mean(images, [1, 2])
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def one_hot_planes(labels, num_classes, scope=None):
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"""Compute 1-hot encodings for planes.
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Given a label, this computes a label image that contains
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1 at all pixels in the plane corresponding to the target
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class and 0 in all other planes.
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Args:
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labels: (batch_size,) tensor
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num_classes: number of classes
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scope: optional scope name
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Returns:
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Tensor of shape (batch_size, 1, 1, num_classes) with a 1-hot encoding.
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"""
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with ops.name_scope(scope, "OneHotPlanes", [labels]):
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batch_size, = _shape(labels)
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batched = layers.one_hot_encoding(labels, num_classes)
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return array_ops.reshape(batched, [batch_size, 1, 1, num_classes])
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def one_hot_mask(labels, num_classes, scope=None):
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"""Compute 1-hot encodings for masks.
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Given a label image, this computes the one hot encoding at
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each pixel.
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Args:
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labels: (batch_size, width, height, 1) tensor containing labels.
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num_classes: number of classes
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scope: optional scope name
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Returns:
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Tensor of shape (batch_size, width, height, num_classes) with
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a 1-hot encoding.
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"""
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with ops.name_scope(scope, "OneHotMask", [labels]):
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height, width, depth = _shape(labels)
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assert depth == 1
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sparse_labels = math_ops.to_int32(array_ops.reshape(labels, [-1, 1]))
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sparse_size, _ = _shape(sparse_labels)
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indices = array_ops.reshape(math_ops.range(0, sparse_size, 1), [-1, 1])
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concated = array_ops.concat([indices, sparse_labels], 1)
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dense_result = sparse_ops.sparse_to_dense(concated,
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[sparse_size, num_classes], 1.0,
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0.0)
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result = array_ops.reshape(dense_result, [height, width, num_classes])
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return result
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