From 38213428929862120bdb32b963a600792ec6feb2 Mon Sep 17 00:00:00 2001
From: Hye Soo Yang <hyey@google.com>
Date: Tue, 29 Dec 2020 20:44:36 -0800
Subject: [PATCH] Eager execution coverage for
extract_image_patches_grad_test.py. Removed run_deprecated_v1 decorators.
PiperOrigin-RevId: 349505498
Change-Id: I4a43b1be685d1fef4a9e7ed91e265617c7187feb
---
tensorflow/python/kernel_tests/BUILD | 2 +-
.../extract_image_patches_grad_test.py | 124 ++++++++++--------
2 files changed, 71 insertions(+), 55 deletions(-)
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index dc00408b9d8..e934d5c4134 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -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",
diff --git a/tensorflow/python/kernel_tests/extract_image_patches_grad_test.py b/tensorflow/python/kernel_tests/extract_image_patches_grad_test.py
index 694bd056037..88bcebde4a7 100644
--- a/tensorflow/python/kernel_tests/extract_image_patches_grad_test.py
+++ b/tensorflow/python/kernel_tests/extract_image_patches_grad_test.py
@@ -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])