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Merge pull request #40691 from xingyu-long:fix_zero_padding_3d

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PiperOrigin-RevId: 318843722
Change-Id: I5f30799f78f948a12ac0707bb4ba05bf0619dea7
This commit is contained in:
TensorFlower Gardener 2020-06-29 10:41:35 -07:00
commit 177f89fb3c
2 changed files with 74 additions and 16 deletions
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12
tensorflow/python/keras/layers/convolutional.py
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@ -2977,30 +2977,30 @@ class ZeroPadding3D(Layer):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
if self.data_format == 'channels_first':
if input_shape[2] is not None:
dim1 = input_shape[2] + 2 * self.padding[0][0]
dim1 = input_shape[2] + self.padding[0][0] + self.padding[0][1]
else:
dim1 = None
if input_shape[3] is not None:
dim2 = input_shape[3] + 2 * self.padding[1][0]
dim2 = input_shape[3] + self.padding[1][0] + self.padding[1][1]
else:
dim2 = None
if input_shape[4] is not None:
dim3 = input_shape[4] + 2 * self.padding[2][0]
dim3 = input_shape[4] + self.padding[2][0] + self.padding[2][1]
else:
dim3 = None
return tensor_shape.TensorShape(
[input_shape[0], input_shape[1], dim1, dim2, dim3])
elif self.data_format == 'channels_last':
if input_shape[1] is not None:
dim1 = input_shape[1] + 2 * self.padding[0][1]
dim1 = input_shape[1] + self.padding[0][0] + self.padding[0][1]
else:
dim1 = None
if input_shape[2] is not None:
dim2 = input_shape[2] + 2 * self.padding[1][1]
dim2 = input_shape[2] + self.padding[1][0] + self.padding[1][1]
else:
dim2 = None
if input_shape[3] is not None:
dim3 = input_shape[3] + 2 * self.padding[2][1]
dim3 = input_shape[3] + self.padding[2][0] + self.padding[2][1]
else:
dim3 = None
return tensor_shape.TensorShape(

78
tensorflow/python/keras/layers/convolutional_test.py
View File

@ -723,36 +723,94 @@ class ZeroPaddingTest(keras_parameterized.TestCase):
with self.assertRaises(ValueError):
keras.layers.ZeroPadding2D(padding=None)
def test_zero_padding_3d(self):
@parameterized.named_parameters(('channels_first', 'channels_first'),
('channels_last', 'channels_last'))
def test_zero_padding_3d(self, data_format):
num_samples = 2
stack_size = 2
input_len_dim1 = 4
input_len_dim2 = 5
input_len_dim3 = 3
inputs = np.ones((num_samples, input_len_dim1, input_len_dim2,
input_len_dim3, stack_size))
if data_format == 'channels_first':
inputs = np.ones((num_samples, stack_size, input_len_dim1, input_len_dim2,
input_len_dim3))
elif data_format == 'channels_last':
inputs = np.ones((num_samples, input_len_dim1, input_len_dim2,
input_len_dim3, stack_size))
with self.cached_session(use_gpu=True):
# basic test
testing_utils.layer_test(
keras.layers.ZeroPadding3D,
kwargs={'padding': (2, 2, 2)},
kwargs={
'padding': (2, 2, 2),
'data_format': data_format
},
input_shape=inputs.shape)
testing_utils.layer_test(
keras.layers.ZeroPadding3D,
kwargs={
'padding': ((1, 2), (3, 4), (0, 2)),
'data_format': data_format
},
input_shape=inputs.shape)
with self.cached_session(use_gpu=True):
# correctness test
layer = keras.layers.ZeroPadding3D(padding=(2, 2, 2))
layer = keras.layers.ZeroPadding3D(
padding=(2, 2, 2), data_format=data_format)
layer.build(inputs.shape)
output = layer(keras.backend.variable(inputs))
if context.executing_eagerly():
np_output = output.numpy()
else:
np_output = keras.backend.eval(output)
for offset in [0, 1, -1, -2]:
np.testing.assert_allclose(np_output[:, offset, :, :, :], 0.)
np.testing.assert_allclose(np_output[:, :, offset, :, :], 0.)
np.testing.assert_allclose(np_output[:, :, :, offset, :], 0.)
np.testing.assert_allclose(np_output[:, 2:-2, 2:-2, 2:-2, :], 1.)
if data_format == 'channels_last':
for offset in [0, 1, -1, -2]:
np.testing.assert_allclose(np_output[:, offset, :, :, :], 0.)
np.testing.assert_allclose(np_output[:, :, offset, :, :], 0.)
np.testing.assert_allclose(np_output[:, :, :, offset, :], 0.)
np.testing.assert_allclose(np_output[:, 2:-2, 2:-2, 2:-2, :], 1.)
elif data_format == 'channels_first':
for offset in [0, 1, -1, -2]:
np.testing.assert_allclose(np_output[:, :, offset, :, :], 0.)
np.testing.assert_allclose(np_output[:, :, :, offset, :], 0.)
np.testing.assert_allclose(np_output[:, :, :, :, offset], 0.)
np.testing.assert_allclose(np_output[:, :, 2:-2, 2:-2, 2:-2], 1.)
layer = keras.layers.ZeroPadding3D(
padding=((1, 2), (3, 4), (0, 2)), data_format=data_format)
layer.build(inputs.shape)
output = layer(keras.backend.variable(inputs))
if context.executing_eagerly():
np_output = output.numpy()
else:
np_output = keras.backend.eval(output)
if data_format == 'channels_last':
for offset in [0]:
np.testing.assert_allclose(np_output[:, offset, :, :, :], 0.)
for offset in [-1, -2]:
np.testing.assert_allclose(np_output[:, offset, :, :, :], 0.)
for offset in [0, 1, 2]:
np.testing.assert_allclose(np_output[:, :, offset, :, :], 0.)
for offset in [-1, -2, -3, -4]:
np.testing.assert_allclose(np_output[:, :, offset, :, :], 0.)
for offset in [-1, -2]:
np.testing.assert_allclose(np_output[:, :, :, offset, :], 0.)
np.testing.assert_allclose(np_output[:, 1:-2, 3:-4, 0:-2, :], 1.)
elif data_format == 'channels_first':
for offset in [0]:
np.testing.assert_allclose(np_output[:, :, offset, :, :], 0.)
for offset in [-1, -2]:
np.testing.assert_allclose(np_output[:, :, offset, :, :], 0.)
for offset in [0, 1, 2]:
np.testing.assert_allclose(np_output[:, :, :, offset, :], 0.)
for offset in [-1, -2, -3, -4]:
np.testing.assert_allclose(np_output[:, :, :, offset, :], 0.)
for offset in [-1, -2]:
np.testing.assert_allclose(np_output[:, :, :, :, offset], 0.)
np.testing.assert_allclose(np_output[:, :, 1:-2, 3:-4, 0:-2], 1.)
# test incorrect use
with self.assertRaises(ValueError):