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# Copyright 2016 Google Inc. 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.
# ==============================================================================
"""Functional tests for DepthToSpace op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
class DepthToSpaceTest(tf.test.TestCase):
def testBasic(self):
x_np = [[[[1, 2, 3, 4]]]]
with self.test_session(use_gpu=False):
block_size = 2
x_tf = tf.depth_to_space(x_np, block_size)
self.assertAllEqual(x_tf.eval(), [[[[1], [2]], [[3], [4]]]])
# Tests for larger input dimensions. To make sure elements are
# correctly ordered spatially.
def testBlockSize2(self):
x_np = [[[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[9, 10, 11, 12],
[13, 14, 15, 16]]]]
block_size = 2
with self.test_session(use_gpu=False):
x_tf = tf.depth_to_space(x_np, block_size)
self.assertAllEqual(x_tf.eval(), [[[[1], [2], [5], [6]],
[[3], [4], [7], [8]],
[[9], [10], [13], [14]],
[[11], [12], [15], [16]]]])
# Tests for different width and height.
def testNonSquare(self):
x_np = [[[[1, 10, 2, 20, 3, 30, 4, 40]],
[[5, 50, 6, 60, 7, 70, 8, 80]],
[[9, 90, 10, 100, 11, 110, 12, 120]]]]
block_size = 2
with self.test_session(use_gpu=False):
x_tf = tf.depth_to_space(x_np, block_size)
self.assertAllEqual(x_tf.eval(), [[[[1, 10], [2, 20]],
[[3, 30], [4, 40]],
[[5, 50], [6, 60]],
[[7, 70], [8, 80]],
[[9, 90], [10, 100]],
[[11, 110], [12, 120]]]])
# Tests for larger input dimensions. To make sure elements are
# correctly ordered spatially.
def testBlockSize4FlatInput(self):
x_np = [[[[1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 13, 14, 11, 12, 15, 16]]]]
block_size = 4
with self.test_session(use_gpu=False):
x_tf = tf.depth_to_space(x_np, block_size)
self.assertAllEqual(x_tf.eval(), [[[[1], [2], [5], [6]],
[[3], [4], [7], [8]],
[[9], [10], [13], [14]],
[[11], [12], [15], [16]]]])
# Tests for larger input depths.
# To make sure elements are properly interleaved in depth.
def testDepthInterleaved(self):
x_np = [[[[1, 10, 2, 20, 3, 30, 4, 40]]]]
block_size = 2
with self.test_session(use_gpu=False):
x_tf = tf.depth_to_space(x_np, block_size)
self.assertAllEqual(x_tf.eval(), [[[[1, 10], [2, 20]],
[[3, 30], [4, 40]]]])
# Tests for larger input depths. Here an odd depth.
# To make sure elements are properly interleaved in depth.
def testDepthInterleavedDepth3(self):
x_np = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
block_size = 2
with self.test_session(use_gpu=False):
x_tf = tf.depth_to_space(x_np, block_size)
self.assertAllEqual(x_tf.eval(), [[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]])
# Tests for larger input depths.
# To make sure elements are properly interleaved in depth.
def testDepthInterleavedLarger(self):
x_np = [[[[1, 10, 2, 20, 3, 30, 4, 40],
[5, 50, 6, 60, 7, 70, 8, 80]],
[[9, 90, 10, 100, 11, 110, 12, 120],
[13, 130, 14, 140, 15, 150, 16, 160]]]]
block_size = 2
with self.test_session(use_gpu=False):
x_tf = tf.depth_to_space(x_np, block_size)
self.assertAllEqual(x_tf.eval(),
[[[[1, 10], [2, 20], [5, 50], [6, 60]],
[[3, 30], [4, 40], [7, 70], [8, 80]],
[[9, 90], [10, 100], [13, 130], [14, 140]],
[[11, 110], [12, 120], [15, 150], [16, 160]]]])
# Error handling:
# Tests for a block larger for the depth. In this case should raise an
# exception.
def testBlockSizeTooLarge(self):
x_np = [[[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[9, 10, 11, 12],
[13, 14, 15, 16]]]]
block_size = 4
# Raise an exception, since th depth is only 4 and needs to be
# divisible by 16.
with self.assertRaises(IndexError):
out_tf = tf.depth_to_space(x_np, block_size)
out_tf.eval()
# Test when the block size is 0.
def testBlockSize0(self):
x_np = [[[[1], [2]],
[[3], [4]]]]
block_size = 0
with self.assertRaises(ValueError):
out_tf = tf.depth_to_space(x_np, block_size)
out_tf.eval()
# Test when the block size is 1. The block size should be > 1.
def testBlockSizeOne(self):
x_np = [[[[1, 1, 1, 1],
[2, 2, 2, 2]],
[[3, 3, 3, 3],
[4, 4, 4, 4]]]]
block_size = 1
with self.assertRaises(ValueError):
out_tf = tf.depth_to_space(x_np, block_size)
out_tf.eval()
def testBlockSizeLargerThanInput(self):
# The block size is too large for this input.
x_np = [[[[1], [2]],
[[3], [4]]]]
block_size = 10
with self.assertRaises(IndexError):
out_tf = tf.space_to_depth(x_np, block_size)
out_tf.eval()
def testBlockSizeNotDivisibleDepth(self):
# The the depth is not divisible by the square of the block size.
x_np = [[[[1, 1, 1, 1],
[2, 2, 2, 2]],
[[3, 3, 3, 3],
[4, 4, 4, 4]]]]
block_size = 3
with self.assertRaises(IndexError):
_ = tf.space_to_depth(x_np, block_size)
class DepthToSpaceGradientTest(tf.test.TestCase):
# Check the gradients.
def _checkGrad(self, x, block_size):
assert 4 == x.ndim
with self.test_session():
tf_x = tf.convert_to_tensor(x)
tf_y = tf.depth_to_space(tf_x, block_size)
epsilon = 1e-2
((x_jacob_t, x_jacob_n)) = tf.test.compute_gradient(
tf_x,
x.shape,
tf_y,
tf_y.get_shape().as_list(),
x_init_value=x,
delta=epsilon)
self.assertAllClose(x_jacob_t, x_jacob_n, rtol=1e-2, atol=epsilon)
# Tests a gradient for depth_to_space of x which is a four dimensional
# tensor of shape [b, h, w, d * block_size * block_size].
def _compare(self, b, h, w, d, block_size):
block_size_sq = block_size * block_size
x = np.random.normal(
0, 1, b * h * w * d * block_size_sq).astype(np.float32).reshape(
[b, h, w, d * block_size_sq])
self._checkGrad(x, block_size)
# Don't use very large numbers as dimensions here, as the result is tensor
# with cartesian product of the dimensions.
def testSmall(self):
block_size = 2
self._compare(3, 2, 5, 3, block_size)
def testSmall2(self):
block_size = 3
self._compare(1, 2, 3, 2, block_size)
if __name__ == "__main__":
tf.test.main()
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