experiments/STT-tensorflow
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Merge pull request from autoih:t_raise

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PiperOrigin-RevId: 274856319
Change-Id: Id566d74b067b88354a77cbc213e18a3a8ef634e1
This commit is contained in:
TensorFlower Gardener 2019-10-15 12:35:31 -07:00
commit e3241b8b51
1 changed files with 21 additions and 29 deletions

50
tensorflow/python/ops/image_grad_test.py
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@ -46,12 +46,11 @@ class ResizeNearestNeighborOpTest(test.TestCase):
for nptype in self.TYPES:
x = np.arange(0, 4).reshape(in_shape).astype(nptype)
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_nearest_neighbor(input_tensor,
out_shape[1:3])
with self.cached_session(use_gpu=True):
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_nearest_neighbor(input_tensor,
out_shape[1:3])
self.assertEqual(out_shape, list(resize_out.get_shape()))
resize_out = self.evaluate(resize_out)
self.assertEqual(out_shape, list(resize_out.shape))
@ -119,11 +118,10 @@ class ResizeBilinearOpTest(test.TestCase):
x = np.arange(0, 4).reshape(in_shape).astype(np.float32)
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
with self.cached_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
self.assertEqual(out_shape, list(resize_out.get_shape()))
resize_out = self.evaluate(resize_out)
self.assertEqual(out_shape, list(resize_out.shape))
@ -210,12 +208,11 @@ class ResizeBicubicOpTest(test.TestCase):
x = np.arange(0, 4).reshape(in_shape).astype(np.float32)
for align_corners in [True, False]:
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(
input_tensor, out_shape[1:3], align_corners=align_corners)
with self.cached_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3],
align_corners=align_corners)
self.assertEqual(out_shape, list(resize_out.get_shape()))
resize_out = self.evaluate(resize_out)
self.assertEqual(out_shape, list(resize_out.shape))
@ -243,10 +240,10 @@ class ResizeBicubicOpTest(test.TestCase):
x = np.arange(0, 24).reshape(in_shape).astype(np.float32)
for align_corners in [True, False]:
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(
input_tensor, out_shape[1:3], align_corners=align_corners)
with self.cached_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3],
align_corners=align_corners)
err = gradient_checker.compute_gradient_error(
input_tensor, in_shape, resize_out, out_shape, x_init_value=x)
self.assertLess(err, 1e-3)
@ -258,9 +255,9 @@ class ResizeBicubicOpTest(test.TestCase):
x = np.arange(0, 24).reshape(in_shape).astype(np.uint8)
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3])
with self.cached_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3])
grad = gradients_impl.gradients(input_tensor, [resize_out])
self.assertEqual([None], grad)
@ -349,16 +346,12 @@ class CropAndResizeOpTest(test.TestCase):
boxes = np.array([[0, 0, 1, 1], [.1, .2, .7, .8]], dtype=np.float32)
box_ind = np.array([0, 1], dtype=np.int32)
crops = image_ops.crop_and_resize(
constant_op.constant(image, shape=image_shape),
constant_op.constant(boxes, shape=[num_boxes, 4]),
constant_op.constant(box_ind, shape=[num_boxes]),
constant_op.constant(crop_size, shape=[2]))
with self.session(use_gpu=True) as sess:
crops = image_ops.crop_and_resize(
constant_op.constant(
image, shape=image_shape),
constant_op.constant(
boxes, shape=[num_boxes, 4]),
constant_op.constant(
box_ind, shape=[num_boxes]),
constant_op.constant(
crop_size, shape=[2]))
self.assertEqual(crops_shape, list(crops.get_shape()))
crops = self.evaluate(crops)
self.assertEqual(crops_shape, list(crops.shape))
@ -472,12 +465,11 @@ class RGBToHSVOpTest(test.TestCase):
for nptype in self.TYPES:
x = np.random.randint(0, high=255, size=[2, 20, 30, 3]).astype(nptype)
rgb_input_tensor = constant_op.constant(x, shape=in_shape)
hsv_out = gen_image_ops.rgb_to_hsv(rgb_input_tensor)
with self.cached_session(use_gpu=True):
rgb_input_tensor = constant_op.constant(x, shape=in_shape)
hsv_out = gen_image_ops.rgb_to_hsv(rgb_input_tensor)
self.assertEqual(out_shape, list(hsv_out.get_shape()))
hsv_out = self.evaluate(hsv_out)
hsv_out = self.evaluate(hsv_out)
self.assertEqual(out_shape, list(hsv_out.shape))
def testRGBToHSVGradSimpleCase(self):