4ac9bda9d0
Some test methods are disabled, but all tests now have a new "_xla" version of the test for XLA:GPU testing. This will run 2 different tests. One with XLA and one without. PiperOrigin-RevId: 229149574
362 lines
14 KiB
Python
362 lines
14 KiB
Python
# Copyright 2015 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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"""Functional tests for SpacetoDepth op."""
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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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import numpy as np
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_array_ops
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from tensorflow.python.ops import gradient_checker
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import test
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from tensorflow.python.platform import tf_logging
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class SpaceToDepthTest(test.TestCase):
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def _testOne(self, inputs, block_size, outputs, dtype=dtypes.float32):
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input_nhwc = math_ops.cast(inputs, dtype)
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with test_util.force_cpu():
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# test NHWC (default) on CPU
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x_tf = array_ops.space_to_depth(input_nhwc, block_size)
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self.assertAllEqual(self.evaluate(x_tf), outputs)
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if test_util.is_gpu_available():
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with test_util.force_gpu():
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# test NHWC (default) on GPU
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x_tf = array_ops.space_to_depth(input_nhwc, block_size)
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self.assertAllEqual(self.evaluate(x_tf), outputs)
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# test NCHW on GPU
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input_nchw = test_util.NHWCToNCHW(input_nhwc)
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output_nchw = array_ops.space_to_depth(
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input_nchw, block_size, data_format="NCHW")
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output_nhwc = test_util.NCHWToNHWC(output_nchw)
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self.assertAllEqual(self.evaluate(output_nhwc), outputs)
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def testBasic(self):
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x_np = [[[[1], [2]], [[3], [4]]]]
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block_size = 2
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x_out = [[[[1, 2, 3, 4]]]]
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for dtype in [dtypes.float32, dtypes.float16, dtypes.uint8]:
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self._testOne(x_np, block_size, x_out, dtype=dtype)
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# Tests for larger input dimensions. To make sure elements are
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# correctly ordered spatially.
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def testLargerInput2x2(self):
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x_np = [[[[1], [2], [5], [6]], [[3], [4], [7], [8]],
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[[9], [10], [13], [14]], [[11], [12], [15], [16]]]]
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block_size = 2
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x_out = [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12],
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[13, 14, 15, 16]]]]
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self._testOne(x_np, block_size, x_out)
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# Tests for larger input dimensions. To make sure elements are
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# correctly ordered in depth. Here, larger block size.
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def testLargerInput4x4(self):
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x_np = [[[[1], [2], [5], [6]], [[3], [4], [7], [8]],
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[[9], [10], [13], [14]], [[11], [12], [15], [16]]]]
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block_size = 4
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x_out = [[[[1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 13, 14, 11, 12, 15, 16]]]]
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self._testOne(x_np, block_size, x_out)
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# Tests for larger input depths.
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# To make sure elements are properly interleaved in depth.
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def testDepthInterleaved(self):
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x_np = [[[[1, 10], [2, 20]], [[3, 30], [4, 40]]]]
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block_size = 2
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x_out = [[[[1, 10, 2, 20, 3, 30, 4, 40]]]]
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self._testOne(x_np, block_size, x_out)
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# Tests for larger input depths. Here an odd depth.
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# To make sure elements are properly interleaved in depth.
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def testDepthInterleavedDepth3(self):
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x_np = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]]
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block_size = 2
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x_out = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
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self._testOne(x_np, block_size, x_out)
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# Tests for larger input dimensions AND for larger input depths.
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# To make sure elements are properly interleaved in depth and ordered
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# spatially.
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def testDepthInterleavedLarge(self):
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x_np = [[[[1, 10], [2, 20], [5, 50], [6, 60]],
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[[3, 30], [4, 40], [7, 70], [8, 80]],
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[[9, 90], [10, 100], [13, 130], [14, 140]],
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[[11, 110], [12, 120], [15, 150], [16, 160]]]]
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block_size = 2
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x_out = [[[[1, 10, 2, 20, 3, 30, 4, 40], [5, 50, 6, 60, 7, 70, 8, 80]],
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[[9, 90, 10, 100, 11, 110, 12, 120],
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[13, 130, 14, 140, 15, 150, 16, 160]]]]
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self._testOne(x_np, block_size, x_out)
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def testBlockSize2Batch10(self):
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block_size = 2
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def batch_input_elt(i):
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return [[[1 * i], [2 * i], [5 * i], [6 * i]],
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[[3 * i], [4 * i], [7 * i], [8 * i]],
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[[9 * i], [10 * i], [13 * i], [14 * i]],
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[[11 * i], [12 * i], [15 * i], [16 * i]]]
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def batch_output_elt(i):
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return [[[1 * i, 2 * i, 3 * i, 4 * i], [5 * i, 6 * i, 7 * i, 8 * i]],
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[[9 * i, 10 * i, 11 * i, 12 * i],
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[13 * i, 14 * i, 15 * i, 16 * i]]]
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batch_size = 10
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x_np = [batch_input_elt(i) for i in range(batch_size)]
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x_out = [batch_output_elt(i) for i in range(batch_size)]
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self._testOne(x_np, block_size, x_out)
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def testBatchSize0(self):
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block_size = 2
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batch_size = 0
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input_nhwc = array_ops.ones([batch_size, 4, 6, 3])
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x_out = array_ops.ones([batch_size, 2, 3, 12])
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with test_util.force_cpu():
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# test NHWC (default) on CPU
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x_tf = array_ops.space_to_depth(input_nhwc, block_size)
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self.assertAllEqual(x_tf.shape, x_out.shape)
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self.evaluate(x_tf)
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if test.is_gpu_available():
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with test_util.use_gpu():
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# test NHWC (default) on GPU
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x_tf = array_ops.space_to_depth(input_nhwc, block_size)
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self.assertAllEqual(x_tf.shape, x_out.shape)
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self.evaluate(x_tf)
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# Tests for different width and height.
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def testNonSquare(self):
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x_np = [[[[1, 10], [2, 20]], [[3, 30], [4, 40]], [[5, 50], [6, 60]],
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[[7, 70], [8, 80]], [[9, 90], [10, 100]], [[11, 110], [12, 120]]]]
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block_size = 2
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x_out = [[[[1, 10, 2, 20, 3, 30, 4, 40]], [[5, 50, 6, 60, 7, 70, 8, 80]],
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[[9, 90, 10, 100, 11, 110, 12, 120]]]]
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self._testOne(x_np, block_size, x_out)
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# Error handling:
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@test_util.run_deprecated_v1
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def testInputWrongDimMissingDepth(self):
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# The input is missing the last dimension ("depth")
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x_np = [[[1, 2], [3, 4]]]
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block_size = 2
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with self.assertRaises(ValueError):
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out_tf = array_ops.space_to_depth(x_np, block_size)
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self.evaluate(out_tf)
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@test_util.run_deprecated_v1
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def testInputWrongDimMissingBatch(self):
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# The input is missing the first dimension ("batch")
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x_np = [[[1], [2]], [[3], [4]]]
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block_size = 2
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with self.assertRaises(ValueError):
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_ = array_ops.space_to_depth(x_np, block_size)
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@test_util.run_deprecated_v1
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def testBlockSize0(self):
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# The block size is 0.
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x_np = [[[[1], [2]], [[3], [4]]]]
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block_size = 0
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with self.assertRaises(ValueError):
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out_tf = array_ops.space_to_depth(x_np, block_size)
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self.evaluate(out_tf)
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@test_util.run_deprecated_v1
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def testBlockSizeOne(self):
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# The block size is 1. The block size needs to be > 1.
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x_np = [[[[1], [2]], [[3], [4]]]]
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block_size = 1
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with self.assertRaises(ValueError):
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out_tf = array_ops.space_to_depth(x_np, block_size)
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self.evaluate(out_tf)
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@test_util.run_deprecated_v1
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def testBlockSizeLarger(self):
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# The block size is too large for this input.
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x_np = [[[[1], [2]], [[3], [4]]]]
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block_size = 10
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with self.assertRaises(ValueError):
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out_tf = array_ops.space_to_depth(x_np, block_size)
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self.evaluate(out_tf)
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@test_util.run_deprecated_v1
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def testBlockSizeNotDivisibleWidth(self):
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# The block size divides width but not height.
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x_np = [[[[1], [2], [3]], [[3], [4], [7]]]]
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block_size = 3
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with self.assertRaises(ValueError):
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_ = array_ops.space_to_depth(x_np, block_size)
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@test_util.run_deprecated_v1
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def testBlockSizeNotDivisibleHeight(self):
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# The block size divides height but not width.
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x_np = [[[[1], [2]], [[3], [4]], [[5], [6]]]]
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block_size = 3
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with self.assertRaises(ValueError):
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_ = array_ops.space_to_depth(x_np, block_size)
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@test_util.run_deprecated_v1
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def testBlockSizeNotDivisibleBoth(self):
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# The block size does not divide neither width or height.
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x_np = [[[[1], [2]], [[3], [4]]]]
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block_size = 3
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with self.assertRaises(ValueError):
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_ = array_ops.space_to_depth(x_np, block_size)
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@test_util.run_deprecated_v1
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def testUnknownShape(self):
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t = array_ops.space_to_depth(
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array_ops.placeholder(dtypes.float32), block_size=4)
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self.assertEqual(4, t.get_shape().ndims)
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def spaceToDepthUsingTranspose(self, tensor, block_size, data_format):
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block_size_sq = block_size * block_size
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if data_format == "NHWC":
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b, ih, iw, ic = tensor.shape.as_list()
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assert ih % block_size == 0, (ih, block_size)
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assert iw % block_size == 0, (iw, block_size)
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ow, oh, oc = iw // block_size, ih // block_size, ic * block_size_sq
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tensor = array_ops.reshape(tensor,
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[b, oh, block_size, ow, block_size, ic])
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tensor = array_ops.transpose(tensor, [0, 1, 3, 2, 4, 5])
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tensor = array_ops.reshape(tensor, [b, oh, ow, oc])
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elif data_format == "NCHW":
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b, ic, ih, iw = tensor.shape.as_list()
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assert ih % block_size == 0, (ih, block_size)
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assert iw % block_size == 0, (iw, block_size)
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ow, oh, oc = iw // block_size, ih // block_size, ic * block_size_sq
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tensor = array_ops.reshape(tensor,
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[b, ic, oh, block_size, ow, block_size])
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tensor = array_ops.transpose(tensor, [0, 3, 5, 1, 2, 4])
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tensor = array_ops.reshape(tensor, [b, oc, oh, ow])
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return tensor
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def compareToTranspose(self, batch_size, out_height, out_width, in_channels,
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block_size, data_format, use_gpu):
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in_height = out_height * block_size
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in_width = out_width * block_size
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nhwc_input_shape = [batch_size, in_height, in_width, in_channels]
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nchw_input_shape = [batch_size, in_channels, in_height, in_width]
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total_size = np.prod(nhwc_input_shape)
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if data_format == "NCHW_VECT_C":
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# Initialize the input tensor with qint8 values that circle -127..127.
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x = [((f + 128) % 255) - 127 for f in range(total_size)]
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t = constant_op.constant(x, shape=nhwc_input_shape, dtype=dtypes.float32)
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expected = self.spaceToDepthUsingTranspose(t, block_size, "NHWC")
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t = test_util.NHWCToNCHW_VECT_C(t)
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t, _, _ = gen_array_ops.quantize_v2(t, -128.0, 127.0, dtypes.qint8)
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t = array_ops.space_to_depth(t, block_size, data_format="NCHW_VECT_C")
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t = gen_array_ops.dequantize(t, -128, 127)
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actual = test_util.NCHW_VECT_CToNHWC(t)
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else:
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# Initialize the input tensor with ascending whole numbers as floats.
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x = [f * 1.0 for f in range(total_size)]
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shape = nchw_input_shape if data_format == "NCHW" else nhwc_input_shape
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t = constant_op.constant(x, shape=shape, dtype=dtypes.float32)
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expected = self.spaceToDepthUsingTranspose(t, block_size, data_format)
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actual = array_ops.space_to_depth(t, block_size, data_format=data_format)
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with self.cached_session(use_gpu=use_gpu) as sess:
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actual_vals, expected_vals = self.evaluate([actual, expected])
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self.assertTrue(np.array_equal(actual_vals, expected_vals))
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@test_util.disable_xla("This test never passed for XLA")
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def testAgainstTranspose(self):
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self.compareToTranspose(3, 2, 3, 1, 2, "NHWC", False)
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self.compareToTranspose(1, 2, 3, 2, 2, "NHWC", False)
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self.compareToTranspose(1, 2, 3, 2, 3, "NHWC", False)
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if not test.is_gpu_available():
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tf_logging.info("skipping gpu tests since gpu not available")
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return
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self.compareToTranspose(3, 2, 3, 1, 2, "NHWC", True)
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self.compareToTranspose(3, 2, 3, 2, 2, "NHWC", True)
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self.compareToTranspose(3, 2, 3, 1, 2, "NCHW", True)
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self.compareToTranspose(3, 2, 3, 2, 3, "NCHW", True)
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self.compareToTranspose(5, 7, 11, 3, 2, "NCHW", True)
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self.compareToTranspose(3, 2, 3, 4, 2, "NCHW_VECT_C", True)
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self.compareToTranspose(3, 2, 3, 8, 3, "NCHW_VECT_C", True)
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self.compareToTranspose(5, 7, 11, 12, 2, "NCHW_VECT_C", True)
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class SpaceToDepthGradientTest(test.TestCase):
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# Check the gradients.
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def _checkGrad(self, x, block_size, data_format):
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# NCHW is implemented for only GPU.
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if data_format == "NCHW" and not test.is_gpu_available():
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return
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assert 4 == x.ndim
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with self.cached_session(use_gpu=True):
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tf_x = ops.convert_to_tensor(x)
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tf_y = array_ops.space_to_depth(tf_x, block_size, data_format=data_format)
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epsilon = 1e-2
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((x_jacob_t, x_jacob_n)) = gradient_checker.compute_gradient(
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tf_x,
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x.shape,
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tf_y,
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tf_y.get_shape().as_list(),
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x_init_value=x,
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delta=epsilon)
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self.assertAllClose(x_jacob_t, x_jacob_n, rtol=1e-2, atol=epsilon)
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# Tests a gradient for space_to_depth of x which is a four dimensional
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# tensor of shape [b, h * block_size, w * block_size, d].
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def _compare(self, b, h, w, d, block_size, data_format):
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block_size_sq = block_size * block_size
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data = np.random.normal(0, 1, b * h * w * d * block_size_sq).astype(
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np.float32)
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if data_format == "NHWC":
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x = data.reshape([b, h * block_size, w * block_size, d])
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else:
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x = data.reshape([b, d, h * block_size, w * block_size])
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self._checkGrad(x, block_size, data_format)
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# Don't use very large numbers as dimensions here as the result is tensor
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# with cartesian product of the dimensions.
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@test_util.run_deprecated_v1
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def testSmall(self):
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block_size = 2
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self._compare(1, 2, 3, 5, block_size, "NHWC")
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self._compare(1, 2, 3, 5, block_size, "NCHW")
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@test_util.run_deprecated_v1
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def testSmall2(self):
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block_size = 2
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self._compare(2, 4, 3, 2, block_size, "NHWC")
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self._compare(2, 4, 3, 2, block_size, "NCHW")
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if __name__ == "__main__":
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test.main()
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