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Eager execution coverage for image_grad_test.py. Removed run_deprecated_v1 decorators. (Part 2)

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PiperOrigin-RevId: 349505411
Change-Id: Ifeaf7b77d0150339d88eda47984752bb7e3efa98
This commit is contained in:
Hye Soo Yang 2020-12-29 20:43:28 -08:00 committed by TensorFlower Gardener
parent 8ef5acc36d
commit d3bba8c715
1 changed files with 36 additions and 44 deletions

80
tensorflow/python/ops/image_grad_test_base.py
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@ -27,9 +27,7 @@ from tensorflow.python.framework import constant_op
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_image_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import gradient_checker_v2
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import image_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
@ -137,47 +135,45 @@ class ResizeBilinearOpTestBase(test.TestCase, parameterized.TestCase):
dtype=np.float32,
use_gpu=False,
force_gpu=False):
with self.cached_session(use_gpu=use_gpu, force_gpu=force_gpu) as sess:
with self.cached_session(use_gpu=use_gpu, force_gpu=force_gpu):
# Input values should not influence gradients
x = np.arange(np.prod(in_shape)).reshape(in_shape).astype(dtype)
input_tensor = constant_op.constant(x, shape=in_shape)
resized_tensor = image_ops.resize_bilinear(
input_tensor,
out_shape[1:3],
align_corners=align_corners,
half_pixel_centers=half_pixel_centers)
# compute_gradient will use a random tensor as the init value
return gradient_checker.compute_gradient(input_tensor, in_shape,
resized_tensor, out_shape)
@parameterized.parameters({
'batch_size': 1,
'channel_count': 1
}, {
'batch_size': 2,
'channel_count': 3
}, {
'batch_size': 5,
'channel_count': 4
})
@test_util.run_deprecated_v1
def testShapes(self, batch_size, channel_count):
smaller_shape = [batch_size, 2, 3, channel_count]
larger_shape = [batch_size, 4, 6, channel_count]
for in_shape, out_shape, align_corners, half_pixel_centers in \
self._itGen(smaller_shape, larger_shape):
# Input values should not influence shapes
x = np.arange(np.prod(in_shape)).reshape(in_shape).astype(np.float32)
input_tensor = constant_op.constant(x, shape=in_shape)
resized_tensor = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
self.assertEqual(out_shape, list(resized_tensor.get_shape()))
grad_tensor = gradients_impl.gradients(resized_tensor, input_tensor)[0]
self.assertEqual(in_shape, list(grad_tensor.get_shape()))
with self.cached_session():
resized_values = self.evaluate(resized_tensor)
self.assertEqual(out_shape, list(resized_values.shape))
grad_values = self.evaluate(grad_tensor)
self.assertEqual(in_shape, list(grad_values.shape))
def func(in_tensor):
return image_ops.resize_bilinear(
in_tensor,
out_shape[1:3],
align_corners=align_corners,
half_pixel_centers=half_pixel_centers)
return gradient_checker_v2.compute_gradient(func, [input_tensor])
@parameterized.parameters(set((True, context.executing_eagerly())))
def _testShapesParameterized(self, use_tape):
TEST_CASES = [[1, 1], [2, 3], [5, 4]] # pylint: disable=invalid-name
for batch_size, channel_count in TEST_CASES:
smaller_shape = [batch_size, 2, 3, channel_count]
larger_shape = [batch_size, 4, 6, channel_count]
for in_shape, out_shape, _, _ in self._itGen(smaller_shape, larger_shape):
with test_util.AbstractGradientTape(use_tape=use_tape) as tape:
# Input values should not influence shapes
x = np.arange(np.prod(in_shape)).reshape(in_shape).astype(np.float32)
input_tensor = constant_op.constant(x, shape=in_shape)
tape.watch(input_tensor)
resized_tensor = image_ops.resize_bilinear(input_tensor,
out_shape[1:3])
self.assertEqual(out_shape, list(resized_tensor.get_shape()))
grad_tensor = tape.gradient(resized_tensor, input_tensor)
self.assertEqual(in_shape, list(grad_tensor.get_shape()))
with self.cached_session():
resized_values = self.evaluate(resized_tensor)
self.assertEqual(out_shape, list(resized_values.shape))
grad_values = self.evaluate(grad_tensor)
self.assertEqual(in_shape, list(grad_values.shape))
@parameterized.parameters({
'batch_size': 1,
@ -189,7 +185,6 @@ class ResizeBilinearOpTestBase(test.TestCase, parameterized.TestCase):
'batch_size': 3,
'channel_count': 2
})
@test_util.run_deprecated_v1
def testGradients(self, batch_size, channel_count):
smaller_shape = [batch_size, 2, 3, channel_count]
larger_shape = [batch_size, 5, 6, channel_count]
@ -197,10 +192,9 @@ class ResizeBilinearOpTestBase(test.TestCase, parameterized.TestCase):
self._itGen(smaller_shape, larger_shape):
jacob_a, jacob_n = self._getJacobians(in_shape, out_shape, align_corners,
half_pixel_centers)
threshold = 1e-4
threshold = 5e-3
self.assertAllClose(jacob_a, jacob_n, threshold, threshold)
@test_util.run_deprecated_v1
def testTypes(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
@ -260,14 +254,12 @@ class ResizeBilinearOpTestBase(test.TestCase, parameterized.TestCase):
'batch_size': 5,
'channel_count': 4
})
@test_util.run_deprecated_v1
def testCompareGpuVsCpu(self, batch_size, channel_count):
smaller_shape = [batch_size, 4, 6, channel_count]
larger_shape = [batch_size, 8, 16, channel_count]
for params in self._itGen(smaller_shape, larger_shape):
self._gpuVsCpuCase(*params, dtype=np.float32)
@test_util.run_deprecated_v1
def testCompareGpuVsCpuFloat64(self):
in_shape = [1, 5, 7, 1]
out_shape = [1, 9, 11, 1]