143 lines
5.8 KiB
Python
143 lines
5.8 KiB
Python
# Copyright 2020 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 deterministic image op gradient functions."""
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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 os
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import numpy as np
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from absl.testing import parameterized
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from tensorflow.python.eager import backprop
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from tensorflow.python.eager import context
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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 test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gradients_impl
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from tensorflow.python.ops import image_grad_test_base as test_base
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from tensorflow.python.ops import image_ops
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from tensorflow.python.platform import test
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class ResizeBilinearOpDeterministicTest(test_base.ResizeBilinearOpTestBase):
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def _randomNDArray(self, shape):
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return 2 * np.random.random_sample(shape) - 1
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def _randomDataOp(self, shape, data_type):
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return constant_op.constant(self._randomNDArray(shape), dtype=data_type)
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@parameterized.parameters(
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# Note that there is no 16-bit floating point format registered for GPU
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{
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'align_corners': False,
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'half_pixel_centers': False,
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'data_type': dtypes.float32
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},
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{
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'align_corners': False,
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'half_pixel_centers': False,
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'data_type': dtypes.float64
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},
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{
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'align_corners': True,
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'half_pixel_centers': False,
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'data_type': dtypes.float32
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},
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{
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'align_corners': False,
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'half_pixel_centers': True,
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'data_type': dtypes.float32
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})
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@test_util.run_in_graph_and_eager_modes
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@test_util.run_cuda_only
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def testDeterministicGradients(self, align_corners, half_pixel_centers,
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data_type):
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if not align_corners and test_util.is_xla_enabled():
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# Align corners is deprecated in TF2.0, but align_corners==False is not
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# supported by XLA.
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self.skipTest('align_corners==False not currently supported by XLA')
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with self.session(force_gpu=True):
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seed = (
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hash(align_corners) % 256 + hash(half_pixel_centers) % 256 +
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hash(data_type) % 256)
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np.random.seed(seed)
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input_shape = (1, 25, 12, 3) # NHWC
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output_shape = (1, 200, 250, 3)
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input_image = self._randomDataOp(input_shape, data_type)
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repeat_count = 3
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if context.executing_eagerly():
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def resize_bilinear_gradients(local_seed):
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np.random.seed(local_seed)
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upstream_gradients = self._randomDataOp(output_shape, dtypes.float32)
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with backprop.GradientTape(persistent=True) as tape:
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tape.watch(input_image)
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output_image = image_ops.resize_bilinear(
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input_image,
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output_shape[1:3],
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align_corners=align_corners,
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half_pixel_centers=half_pixel_centers)
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gradient_injector_output = output_image * upstream_gradients
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return tape.gradient(gradient_injector_output, input_image)
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for i in range(repeat_count):
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local_seed = seed + i # select different upstream gradients
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result_a = resize_bilinear_gradients(local_seed)
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result_b = resize_bilinear_gradients(local_seed)
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self.assertAllEqual(result_a, result_b)
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else: # graph mode
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upstream_gradients = array_ops.placeholder(
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dtypes.float32, shape=output_shape, name='upstream_gradients')
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output_image = image_ops.resize_bilinear(
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input_image,
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output_shape[1:3],
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align_corners=align_corners,
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half_pixel_centers=half_pixel_centers)
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gradient_injector_output = output_image * upstream_gradients
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# The gradient function behaves as if grad_ys is multiplied by the op
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# gradient result, not passing the upstram gradients through the op's
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# gradient generation graph. This is the reason for using the
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# gradient injector
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resize_bilinear_gradients = gradients_impl.gradients(
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gradient_injector_output,
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input_image,
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grad_ys=None,
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colocate_gradients_with_ops=True)[0]
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for i in range(repeat_count):
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feed_dict = {upstream_gradients: self._randomNDArray(output_shape)}
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result_a = resize_bilinear_gradients.eval(feed_dict=feed_dict)
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result_b = resize_bilinear_gradients.eval(feed_dict=feed_dict)
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self.assertAllEqual(result_a, result_b)
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if __name__ == '__main__':
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# Note that the effect of setting the following environment variable to
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# 'true' is not tested. Unless we can find a simpler pattern for testing these
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# environment variables, it would require this file to be made into a base
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# and then two more test files to be created.
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#
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# When deterministic op functionality can be enabled and disabled between test
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# cases in the same process, then the tests for deterministic op
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# functionality, for this op and for other ops, will be able to be included in
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# the same file with the regular tests, simplifying the organization of tests
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# and test files.
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os.environ['TF_DETERMINISTIC_OPS'] = '1'
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test.main()
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