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Eager execution coverage for extract_image_patches_grad_test.py. Removed run_deprecated_v1 decorators.

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PiperOrigin-RevId: 349505498
Change-Id: I4a43b1be685d1fef4a9e7ed91e265617c7187feb
This commit is contained in:
Hye Soo Yang 2020-12-29 20:44:36 -08:00 committed by TensorFlower Gardener
parent d3bba8c715
commit 3821342892
2 changed files with 71 additions and 55 deletions
tensorflow/python/kernel_tests
BUILDextract_image_patches_grad_test.py

2
tensorflow/python/kernel_tests/BUILD
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@ -3229,7 +3229,7 @@ cuda_py_test(
name = "extract_image_patches_grad_test",
size = "medium",
srcs = ["extract_image_patches_grad_test.py"],
shard_count = 3,
shard_count = 9,
tags = ["notap"], # http://b/31080670
deps = [
"//tensorflow/python:array_ops",

124
tensorflow/python/kernel_tests/extract_image_patches_grad_test.py
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@ -18,20 +18,23 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
import numpy as np
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed as random_seed_lib
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import gradient_checker_v2
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
class ExtractImagePatchesGradTest(test.TestCase):
class ExtractImagePatchesGradTest(test.TestCase, parameterized.TestCase):
"""Gradient-checking for ExtractImagePatches op."""
_TEST_CASES = [
@ -79,7 +82,6 @@ class ExtractImagePatchesGradTest(test.TestCase):
},
]
@test_util.run_deprecated_v1
def testGradient(self):
# Set graph seed for determinism.
random_seed = 42
@ -91,80 +93,94 @@ class ExtractImagePatchesGradTest(test.TestCase):
in_shape = test_case['in_shape']
in_val = constant_op.constant(
np.random.random(in_shape), dtype=dtypes.float32)
# Avoid `dangerous-default-value` pylint error by creating default
# args to `extract` as tuples.
ksizes = tuple(test_case['ksizes'])
strides = tuple(test_case['strides'])
rates = tuple(test_case['rates'])
for padding in ['VALID', 'SAME']:
out_val = array_ops.extract_image_patches(in_val, test_case['ksizes'],
test_case['strides'],
test_case['rates'], padding)
out_shape = out_val.get_shape().as_list()
err = gradient_checker.compute_gradient_error(in_val, in_shape,
out_val, out_shape)
def extract(in_val,
ksizes=ksizes,
strides=strides,
rates=rates,
padding=padding):
return array_ops.extract_image_patches(in_val, ksizes, strides,
rates, padding)
err = gradient_checker_v2.max_error(
*gradient_checker_v2.compute_gradient(extract, [in_val]))
self.assertLess(err, 1e-4)
@test_util.run_deprecated_v1
def testConstructGradientWithLargeImages(self):
batch_size = 4
height = 1024
width = 1024
ksize = 5
images = variable_scope.get_variable('inputs',
(batch_size, height, width, 1))
patches = array_ops.extract_image_patches(images,
ksizes=[1, ksize, ksize, 1],
strides=[1, 1, 1, 1],
rates=[1, 1, 1, 1],
padding='SAME')
# Github issue: #20146
# tf.image.extract_image_patches() gradient very slow at graph construction
# time
gradients = gradients_impl.gradients(patches, images)
# Won't time out.
self.assertIsNotNone(gradients)
@parameterized.parameters(set((True, context.executing_eagerly())))
def testConstructGradientWithLargeImages(self, use_tape):
with test_util.AbstractGradientTape(use_tape=use_tape) as tape:
batch_size = 4
# Prevent OOM by setting reasonably large image size (b/171808681).
height = 512
width = 512
ksize = 5
shape = (batch_size, height, width, 1)
images = variables.Variable(
np.random.uniform(size=np.prod(shape)).reshape(shape), name='inputs')
tape.watch(images)
patches = array_ops.extract_image_patches(images,
ksizes=[1, ksize, ksize, 1],
strides=[1, 1, 1, 1],
rates=[1, 1, 1, 1],
padding='SAME')
# Github issue: #20146
# tf.image.extract_image_patches() gradient very slow at graph
# construction time.
gradients = tape.gradient(patches, images)
# Won't time out.
self.assertIsNotNone(gradients)
def _VariableShapeGradient(self, test_shape_pattern):
"""Use test_shape_pattern to infer which dimensions are of
variable size.
"""
# Set graph seed for determinism.
random_seed = 42
random_seed_lib.set_random_seed(random_seed)
# Testing shape gradient requires graph mode.
with ops.Graph().as_default():
# Set graph seed for determinism.
random_seed = 42
random_seed_lib.set_random_seed(random_seed)
with self.test_session():
for test_case in self._TEST_CASES:
np.random.seed(random_seed)
in_shape = test_case['in_shape']
test_shape = [
x if x is None else y for x, y in zip(test_shape_pattern, in_shape)
]
in_val = array_ops.placeholder(shape=test_shape, dtype=dtypes.float32)
with self.test_session():
for test_case in self._TEST_CASES:
np.random.seed(random_seed)
in_shape = test_case['in_shape']
test_shape = [
x if x is None else y
for x, y in zip(test_shape_pattern, in_shape)
]
in_val = array_ops.placeholder(shape=test_shape, dtype=dtypes.float32)
feed_dict = {in_val: np.random.random(in_shape)}
for padding in ['VALID', 'SAME']:
out_val = array_ops.extract_image_patches(in_val, test_case['ksizes'],
test_case['strides'],
test_case['rates'], padding)
out_val_tmp = out_val.eval(feed_dict=feed_dict)
out_shape = out_val_tmp.shape
feed_dict = {in_val: np.random.random(in_shape)}
for padding in ['VALID', 'SAME']:
out_val = array_ops.extract_image_patches(in_val,
test_case['ksizes'],
test_case['strides'],
test_case['rates'],
padding)
out_val_tmp = out_val.eval(feed_dict=feed_dict)
out_shape = out_val_tmp.shape
err = gradient_checker.compute_gradient_error(in_val, in_shape,
out_val, out_shape)
self.assertLess(err, 1e-4)
err = gradient_checker.compute_gradient_error(
in_val, in_shape, out_val, out_shape)
self.assertLess(err, 1e-4)
@test_util.run_deprecated_v1
def test_BxxC_Gradient(self):
self._VariableShapeGradient([-1, None, None, -1])
@test_util.run_deprecated_v1
def test_xHWx_Gradient(self):
self._VariableShapeGradient([None, -1, -1, None])
@test_util.run_deprecated_v1
def test_BHWC_Gradient(self):
self._VariableShapeGradient([-1, -1, -1, -1])
@test_util.run_deprecated_v1
def test_AllNone_Gradient(self):
self._VariableShapeGradient([None, None, None, None])