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Remove @test_util.run_deprecated_v1 in morphological_ops_test.py

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PiperOrigin-RevId: 324093550
Change-Id: Ib909ed17fab6b6b4b17ad862d8118e5e805951fb
This commit is contained in:
Kibeom Kim 2020-07-30 15:15:23 -07:00 committed by TensorFlower Gardener
parent 5f68356af4
commit c111987612
1 changed files with 33 additions and 33 deletions
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66
tensorflow/python/kernel_tests/morphological_ops_test.py
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@ -22,7 +22,7 @@ import numpy as np
from tensorflow.python.framework import constant_op from tensorflow.python.framework import constant_op
from tensorflow.python.framework import test_util from tensorflow.python.framework import test_util
from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import gradient_checker_v2
from tensorflow.python.ops import nn_ops from tensorflow.python.ops import nn_ops
import tensorflow.python.ops.nn_grad # pylint: disable=unused-import import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
from tensorflow.python.platform import test from tensorflow.python.platform import test
@ -199,32 +199,33 @@ class DilationTest(test.TestCase):
np.random.seed(1) # Make it reproducible. np.random.seed(1) # Make it reproducible.
image = np.random.random_sample(image_shape).astype(np.float32) image = np.random.random_sample(image_shape).astype(np.float32)
kernel = np.random.random_sample(kernel_shape).astype(np.float32) kernel = np.random.random_sample(kernel_shape).astype(np.float32)
image_init = np.random.random_sample(image_shape).astype(np.float32)
kernel_init = np.random.random_sample(kernel_shape).astype(np.float32)
strides = [1] + strides + [1] strides = [1] + strides + [1]
rates = [1] + rates + [1] rates = [1] + rates + [1]
with self.cached_session(use_gpu=use_gpu): image_tensor = constant_op.constant(image, shape=image_shape, name="input")
image_tensor = constant_op.constant( kernel_tensor = constant_op.constant(
image, shape=image_shape, name="input") kernel, shape=kernel_shape, name="filter")
kernel_tensor = constant_op.constant(
kernel, shape=kernel_shape, name="filter") def compute_dilation2d(image_tensor, kernel_tensor):
out_tensor = nn_ops.dilation2d( return nn_ops.dilation2d(
image_tensor, image_tensor,
kernel_tensor, kernel_tensor,
strides=strides, strides=strides,
rates=rates, rates=rates,
padding=padding, padding=padding,
name="dilation2d") name="dilation2d")
out_shape = self.evaluate(out_tensor).shape
# Small delta is necessary for argmax to remain the same. with test_util.device(use_gpu=use_gpu):
err = gradient_checker.compute_gradient_error( with self.cached_session():
[image_tensor, kernel_tensor], [image_shape, kernel_shape], # Small delta is necessary for argmax to remain the same.
out_tensor, err1 = gradient_checker_v2.max_error(
out_shape, [image_init, kernel_init], *gradient_checker_v2.compute_gradient(
delta=1e-3) lambda x: compute_dilation2d(x, kernel_tensor), [image_tensor]))
err2 = gradient_checker_v2.max_error(
*gradient_checker_v2.compute_gradient(
lambda x: compute_dilation2d(image_tensor, x), [kernel_tensor]))
err = max(err1, err2)
print("Dilation gradient error = %f" % err) print("Dilation gradient error = %f" % err)
self.assertLess(err, 1e-4) self.assertLess(err, 1e-4)
@ -292,7 +293,6 @@ class DilationTest(test.TestCase):
padding="SAME", padding="SAME",
use_gpu=use_gpu) use_gpu=use_gpu)
@test_util.run_deprecated_v1
def testDilationGrad(self): def testDilationGrad(self):
for use_gpu in True, False: for use_gpu in True, False:
self._testDilationGradValidPadding_1x1x1(use_gpu) self._testDilationGradValidPadding_1x1x1(use_gpu)
@ -475,32 +475,33 @@ class ErosionTest(test.TestCase):
np.random.seed(1) # Make it reproducible. np.random.seed(1) # Make it reproducible.
image = np.random.random_sample(image_shape).astype(np.float32) image = np.random.random_sample(image_shape).astype(np.float32)
kernel = np.random.random_sample(kernel_shape).astype(np.float32) kernel = np.random.random_sample(kernel_shape).astype(np.float32)
image_init = np.random.random_sample(image_shape).astype(np.float32)
kernel_init = np.random.random_sample(kernel_shape).astype(np.float32)
strides = [1] + strides + [1] strides = [1] + strides + [1]
rates = [1] + rates + [1] rates = [1] + rates + [1]
with self.cached_session(use_gpu=use_gpu): image_tensor = constant_op.constant(image, shape=image_shape, name="input")
image_tensor = constant_op.constant( kernel_tensor = constant_op.constant(
image, shape=image_shape, name="input") kernel, shape=kernel_shape, name="filter")
kernel_tensor = constant_op.constant(
kernel, shape=kernel_shape, name="filter") def compute_erosion2d(image_tensor, kernel_tensor):
out_tensor = nn_ops.erosion2d( return nn_ops.erosion2d(
image_tensor, image_tensor,
kernel_tensor, kernel_tensor,
strides=strides, strides=strides,
rates=rates, rates=rates,
padding=padding, padding=padding,
name="erosion2d") name="erosion2d")
out_shape = self.evaluate(out_tensor).shape
# Small delta is necessary for argmax to remain the same. with test_util.device(use_gpu=use_gpu):
err = gradient_checker.compute_gradient_error( with self.cached_session():
[image_tensor, kernel_tensor], [image_shape, kernel_shape], # Small delta is necessary for argmax to remain the same.
out_tensor, err1 = gradient_checker_v2.max_error(
out_shape, [image_init, kernel_init], *gradient_checker_v2.compute_gradient(
delta=1e-3) lambda x: compute_erosion2d(x, kernel_tensor), [image_tensor]))
err2 = gradient_checker_v2.max_error(
*gradient_checker_v2.compute_gradient(
lambda x: compute_erosion2d(image_tensor, x), [kernel_tensor]))
err = max(err1, err2)
print("Erosion gradient error = %f" % err) print("Erosion gradient error = %f" % err)
self.assertLess(err, 1e-4) self.assertLess(err, 1e-4)
@ -568,7 +569,6 @@ class ErosionTest(test.TestCase):
padding="SAME", padding="SAME",
use_gpu=use_gpu) use_gpu=use_gpu)
@test_util.run_deprecated_v1
def testErosionGrad(self): def testErosionGrad(self):
for use_gpu in True, False: for use_gpu in True, False:
self._testErosionGradValidPadding_1x1x1(use_gpu) self._testErosionGradValidPadding_1x1x1(use_gpu)