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# Copyright 2016 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.
# ==============================================================================
"""Tests for SparseReshape."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
class SparseReshapeTest(tf.test.TestCase):
def _SparseTensorPlaceholder(self):
return tf.SparseTensor(
tf.placeholder(tf.int64), tf.placeholder(tf.float64),
tf.placeholder(tf.int64))
def _SparseTensorValue_5x6(self):
ind = np.array([
[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]
]).astype(np.int64)
val = np.array([0, 10, 13, 14, 32, 33]).astype(np.float64)
shape = np.array([5, 6]).astype(np.int64)
return tf.SparseTensorValue(ind, val, shape)
def _SparseTensorValue_2x3x4(self):
ind = np.array([
[0, 0, 1], [0, 1, 0], [0, 1, 2], [1, 0, 3], [1, 1, 1], [1, 1, 3],
[1, 2, 2]
])
val = np.array([1, 10, 12, 103, 111, 113, 122])
shape = np.array([2, 3, 4])
return tf.SparseTensorValue(ind, val, shape)
def testSameShape(self):
with self.test_session(use_gpu=False) as sess:
input_val = self._SparseTensorValue_5x6()
sp_output = tf.sparse_reshape(input_val, [5, 6])
output_val = sess.run(sp_output)
self.assertAllEqual(output_val.indices, input_val.indices)
self.assertAllEqual(output_val.values, input_val.values)
self.assertAllEqual(output_val.dense_shape, input_val.dense_shape)
def testFeedSameShape(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensorPlaceholder()
input_val = self._SparseTensorValue_5x6()
sp_output = tf.sparse_reshape(sp_input, [5, 6])
output_val = sess.run(sp_output, {sp_input: input_val})
self.assertAllEqual(output_val.indices, input_val.indices)
self.assertAllEqual(output_val.values, input_val.values)
self.assertAllEqual(output_val.dense_shape, input_val.dense_shape)
def testFeedSameShapeWithInferredDim(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensorPlaceholder()
input_val = self._SparseTensorValue_5x6()
sp_output = tf.sparse_reshape(sp_input, [-1, 6])
output_val = sess.run(sp_output, {sp_input: input_val})
self.assertAllEqual(output_val.indices, input_val.indices)
self.assertAllEqual(output_val.values, input_val.values)
self.assertAllEqual(output_val.dense_shape, input_val.dense_shape)
def testFeedNewShapeSameRank(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensorPlaceholder()
input_val = self._SparseTensorValue_5x6()
sp_output = tf.sparse_reshape(sp_input, [3, 10])
output_val = sess.run(sp_output, {sp_input: input_val})
self.assertAllEqual(output_val.indices, np.array([
[0, 0], [0, 6], [0, 9], [1, 0], [2, 0], [2, 1]
]))
self.assertAllEqual(output_val.values, input_val.values)
self.assertAllEqual(output_val.dense_shape, [3, 10])
def testFeedNewShapeSameRankWithInferredDim(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensorPlaceholder()
input_val = self._SparseTensorValue_5x6()
sp_output = tf.sparse_reshape(sp_input, [3, -1])
output_val = sess.run(sp_output, {sp_input: input_val})
self.assertAllEqual(output_val.indices, np.array([
[0, 0], [0, 6], [0, 9], [1, 0], [2, 0], [2, 1]
]))
self.assertAllEqual(output_val.values, input_val.values)
self.assertAllEqual(output_val.dense_shape, [3, 10])
def testUpRank(self):
with self.test_session(use_gpu=False) as sess:
input_val = self._SparseTensorValue_5x6()
sp_output = tf.sparse_reshape(input_val, [2, 3, 5])
output_val = sess.run(sp_output)
self.assertAllEqual(output_val.indices, np.array([
[0, 0, 0], [0, 1, 1], [0, 1, 4], [0, 2, 0], [1, 1, 0], [1, 1, 1]
]))
self.assertAllEqual(output_val.values, input_val.values)
self.assertAllEqual(output_val.dense_shape, [2, 3, 5])
def testFeedUpRank(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensorPlaceholder()
input_val = self._SparseTensorValue_5x6()
sp_output = tf.sparse_reshape(sp_input, [2, 3, 5])
output_val = sess.run(sp_output, {sp_input: input_val})
self.assertAllEqual(output_val.indices, np.array([
[0, 0, 0], [0, 1, 1], [0, 1, 4], [0, 2, 0], [1, 1, 0], [1, 1, 1]
]))
self.assertAllEqual(output_val.values, input_val.values)
self.assertAllEqual(output_val.dense_shape, [2, 3, 5])
def testFeedUpRankWithInferredDim(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensorPlaceholder()
input_val = self._SparseTensorValue_5x6()
sp_output = tf.sparse_reshape(sp_input, [2, -1, 5])
output_val = sess.run(sp_output, {sp_input: input_val})
self.assertAllEqual(output_val.indices, np.array([
[0, 0, 0], [0, 1, 1], [0, 1, 4], [0, 2, 0], [1, 1, 0], [1, 1, 1]
]))
self.assertAllEqual(output_val.values, input_val.values)
self.assertAllEqual(output_val.dense_shape, [2, 3, 5])
def testFeedDownRank(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensorPlaceholder()
input_val = self._SparseTensorValue_2x3x4()
sp_output = tf.sparse_reshape(sp_input, [6, 4])
output_val = sess.run(sp_output, {sp_input: input_val})
self.assertAllEqual(output_val.indices, np.array([
[0, 1], [1, 0], [1, 2], [3, 3], [4, 1], [4, 3], [5, 2]
]))
self.assertAllEqual(output_val.values, input_val.values)
self.assertAllEqual(output_val.dense_shape, [6, 4])
def testFeedDownRankWithInferredDim(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensorPlaceholder()
input_val = self._SparseTensorValue_2x3x4()
sp_output = tf.sparse_reshape(sp_input, [6, -1])
output_val = sess.run(sp_output, {sp_input: input_val})
self.assertAllEqual(output_val.indices, np.array([
[0, 1], [1, 0], [1, 2], [3, 3], [4, 1], [4, 3], [5, 2]
]))
self.assertAllEqual(output_val.values, input_val.values)
self.assertAllEqual(output_val.dense_shape, [6, 4])
def testFeedMultipleInferredDims(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensorPlaceholder()
input_val = self._SparseTensorValue_5x6()
sp_output = tf.sparse_reshape(sp_input, [4, -1, -1])
with self.assertRaisesOpError("only one output shape size may be -1"):
sess.run(sp_output, {sp_input: input_val})
def testFeedMismatchedSizes(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensorPlaceholder()
input_val = self._SparseTensorValue_5x6()
sp_output = tf.sparse_reshape(sp_input, [4, 7])
with self.assertRaisesOpError(
"Input to reshape is a tensor with 30 dense values"):
sess.run(sp_output, {sp_input: input_val})
def testFeedMismatchedSizesWithInferredDim(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensorPlaceholder()
input_val = self._SparseTensorValue_5x6()
sp_output = tf.sparse_reshape(sp_input, [4, -1])
with self.assertRaisesOpError("requested shape requires a multiple"):
sess.run(sp_output, {sp_input: input_val})
def testFeedPartialShapes(self):
with self.test_session(use_gpu=False):
# Incorporate new rank into shape information if known
sp_input = self._SparseTensorPlaceholder()
sp_output = tf.sparse_reshape(sp_input, [2, 3, 5])
self.assertListEqual(sp_output.indices.get_shape().as_list(), [None, 3])
self.assertListEqual(sp_output.dense_shape.get_shape().as_list(), [3])
# Incorporate known shape information about input indices in output
# indices
sp_input = self._SparseTensorPlaceholder()
sp_input.indices.set_shape([5, None])
sp_output = tf.sparse_reshape(sp_input, [2, 3, 5])
self.assertListEqual(sp_output.indices.get_shape().as_list(), [5, 3])
self.assertListEqual(sp_output.dense_shape.get_shape().as_list(), [3])
# Even if new_shape has no shape information, we know the ranks of
# output indices and shape
sp_input = self._SparseTensorPlaceholder()
sp_input.indices.set_shape([5, None])
new_shape = tf.placeholder(tf.int64)
sp_output = tf.sparse_reshape(sp_input, new_shape)
self.assertListEqual(sp_output.indices.get_shape().as_list(), [5, None])
self.assertListEqual(sp_output.dense_shape.get_shape().as_list(), [None])
def testFeedDenseReshapeSemantics(self):
with self.test_session(use_gpu=False) as sess:
# Compute a random rank-5 initial shape and new shape, randomly sparsify
# it, and check that the output of SparseReshape has the same semantics
# as a dense reshape.
factors = np.array([2] * 4 + [3] * 4 + [5] * 4) # 810k total elements
orig_rank = np.random.randint(2, 7)
orig_map = np.random.randint(orig_rank, size=factors.shape)
orig_shape = [np.prod(factors[orig_map == d]) for d in range(orig_rank)]
new_rank = np.random.randint(2, 7)
new_map = np.random.randint(new_rank, size=factors.shape)
new_shape = [np.prod(factors[new_map == d]) for d in range(new_rank)]
orig_dense = np.random.uniform(size=orig_shape)
orig_indices = np.transpose(np.nonzero(orig_dense < 0.5))
orig_values = orig_dense[orig_dense < 0.5]
new_dense = np.reshape(orig_dense, new_shape)
new_indices = np.transpose(np.nonzero(new_dense < 0.5))
new_values = new_dense[new_dense < 0.5]
sp_input = self._SparseTensorPlaceholder()
input_val = tf.SparseTensorValue(orig_indices, orig_values, orig_shape)
sp_output = tf.sparse_reshape(sp_input, new_shape)
output_val = sess.run(sp_output, {sp_input: input_val})
self.assertAllEqual(output_val.indices, new_indices)
self.assertAllEqual(output_val.values, new_values)
self.assertAllEqual(output_val.dense_shape, new_shape)
if __name__ == "__main__":
tf.test.main()
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