def test_predicting_with_mock_longer_width(self):
np.random.seed(1337)
height, width = 4, 6
inp = np.random.random((12, 8, 16, 3))
with self.cached_session(use_gpu=True):
layer = image_preprocessing.RandomCrop(height, width)
actual_output = layer(inp, training=0)
resized_inp = image_ops.resize_images_v2(inp, size=[4, 8])
expected_output = resized_inp[:, :, 1:7, :]
self.assertAllClose(expected_output, actual_output)
def test_training_with_mock(self):
np.random.seed(1337)
height, width = 3, 4
height_offset = np.random.randint(low=0, high=3)
width_offset = np.random.randint(low=0, high=5)
mock_offset = [0, height_offset, width_offset, 0]
with test.mock.patch.object(
stateless_random_ops, 'stateless_random_uniform',
return_value=mock_offset):
with self.cached_session(use_gpu=True):
layer = image_preprocessing.RandomCrop(height, width)
inp = np.random.random((12, 5, 8, 3))
actual_output = layer(inp, training=1)
expected_output = inp[:, height_offset:(height_offset + height),
width_offset:(width_offset + width), :]
self.assertAllClose(expected_output, actual_output)
def test_training_with_mock(self):
if test.is_built_with_rocm():
# TODO(rocm):
# re-enable this test once ROCm adds support for
# the StatefulUniformFullInt Op (on the GPU)
self.skipTest('Feature not supported on ROCm')
np.random.seed(1337)
height, width = 3, 4
height_offset = np.random.randint(low=0, high=3)
width_offset = np.random.randint(low=0, high=5)
mock_offset = [0, height_offset, width_offset, 0]
with test.mock.patch.object(stateless_random_ops,
'stateless_random_uniform',
return_value=mock_offset):
with self.cached_session(use_gpu=True):
layer = image_preprocessing.RandomCrop(height, width)
inp = np.random.random((12, 5, 8, 3))
actual_output = layer(inp, training=1)
expected_output = inp[:,
height_offset:(height_offset + height),
width_offset:(width_offset + width), :]
self.assertAllClose(expected_output, actual_output)
def test_config_with_custom_name(self):
layer = image_preprocessing.RandomCrop(5, 5, name='image_preproc')
config = layer.get_config()
layer_1 = image_preprocessing.RandomCrop.from_config(config)
self.assertEqual(layer_1.name, layer.name)