def test_zero_padding_3d():
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))
# basic test
for data_format in ['channels_first', 'channels_last']:
layer_test(convolutional.ZeroPadding3D,
kwargs={'padding': (2, 2, 2), 'data_format': data_format},
input_shape=inputs.shape)
layer_test(convolutional.ZeroPadding3D,
kwargs={'padding': ((1, 2), (3, 4), (0, 2)), 'data_format': data_format},
input_shape=inputs.shape)
# correctness test
layer = convolutional.ZeroPadding3D(padding=(2, 2, 2),
data_format=data_format)
layer.build(inputs.shape)
outputs = layer(K.variable(inputs))
np_output = K.eval(outputs)
if data_format == 'channels_last':
for offset in [0, 1, -1, -2]:
assert_allclose(np_output[:, offset, :, :, :], 0.)
assert_allclose(np_output[:, :, offset, :, :], 0.)
assert_allclose(np_output[:, :, :, offset, :], 0.)
assert_allclose(np_output[:, 2:-2, 2:-2, 2:-2, :], 1.)
elif data_format == 'channels_first':
for offset in [0, 1, -1, -2]:
assert_allclose(np_output[:, :, offset, :, :], 0.)
assert_allclose(np_output[:, :, :, offset, :], 0.)
assert_allclose(np_output[:, :, :, :, offset], 0.)
assert_allclose(np_output[:, :, 2:-2, 2:-2, 2:-2], 1.)
layer = convolutional.ZeroPadding3D(padding=((1, 2), (3, 4), (0, 2)),
data_format=data_format)
layer.build(inputs.shape)
outputs = layer(K.variable(inputs))
np_output = K.eval(outputs)
if data_format == 'channels_last':
for dim1_offset in [0, -1, -2]:
assert_allclose(np_output[:, dim1_offset, :, :, :], 0.)
for dim2_offset in [0, 1, 2, -1, -2, -3, -4]:
assert_allclose(np_output[:, :, dim2_offset, :, :], 0.)
for dim3_offset in [-1, -2]:
assert_allclose(np_output[:, :, :, dim3_offset, :], 0.)
assert_allclose(np_output[:, 1:-2, 3:-4, 0:-2, :], 1.)
elif data_format == 'channels_first':
for dim1_offset in [0, -1, -2]:
assert_allclose(np_output[:, :, dim1_offset, :, :], 0.)
for dim2_offset in [0, 1, 2, -1, -2, -3, -4]:
assert_allclose(np_output[:, :, :, dim2_offset, :], 0.)
for dim3_offset in [-1, -2]:
assert_allclose(np_output[:, :, :, :, dim3_offset], 0.)
assert_allclose(np_output[:, :, 1:-2, 3:-4, 0:-2], 1.)
def test_zero_padding_3d():
nb_samples = 2
stack_size = 2
input_len_dim1 = 4
input_len_dim2 = 5
input_len_dim3 = 3
input = np.ones((nb_samples,
input_len_dim1, input_len_dim2, input_len_dim3,
stack_size))
# basic test
layer_test(convolutional.ZeroPadding3D,
kwargs={'padding': (2, 2, 2)},
input_shape=input.shape)
# correctness test
layer = convolutional.ZeroPadding3D(padding=(2, 2, 2))
layer.build(input.shape)
output = layer(K.variable(input))
np_output = K.eval(output)
for offset in [0, 1, -1, -2]:
assert_allclose(np_output[:, offset, :, :, :], 0.)
assert_allclose(np_output[:, :, offset, :, :], 0.)
assert_allclose(np_output[:, :, :, offset, :], 0.)
assert_allclose(np_output[:, 2:-2, 2:-2, 2:-2, :], 1.)
layer.get_config()
def test_zero_padding_3d():
nb_samples = 9
stack_size = 7
input_len_dim1 = 10
input_len_dim2 = 11
input_len_dim3 = 12
input = np.ones((nb_samples, stack_size, input_len_dim1, input_len_dim2,
input_len_dim3))
# basic test
layer_test(convolutional.ZeroPadding3D,
kwargs={'padding': (2, 2, 2)},
input_shape=input.shape)
# correctness test
layer = convolutional.ZeroPadding3D(padding=(2, 2, 2))
layer.set_input(K.variable(input), shape=input.shape)
out = K.eval(layer.output)
for offset in [0, 1, -1, -2]:
assert_allclose(out[:, :, offset, :, :], 0.)
assert_allclose(out[:, :, :, offset, :], 0.)
assert_allclose(out[:, :, :, :, offset], 0.)
assert_allclose(out[:, :, 2:-2, 2:-2, 2:-2], 1.)
layer.get_config()
def test_zero_padding_3d():
nb_samples = 9
stack_size = 7
input_len_dim1 = 10
input_len_dim2 = 11
input_len_dim3 = 12
input = np.ones((nb_samples, stack_size, input_len_dim1, input_len_dim2,
input_len_dim3))
layer = convolutional.ZeroPadding3D(padding=(2, 2, 2))
layer.input = K.variable(input)
for train in [True, False]:
out = K.eval(layer.get_output(train))
for offset in [0, 1, -1, -2]:
assert_allclose(out[:, :, offset, :, :], 0.)
assert_allclose(out[:, :, :, offset, :], 0.)
assert_allclose(out[:, :, :, :, offset], 0.)
assert_allclose(out[:, :, 2:-2, 2:-2, 2:-2], 1.)
layer.get_config()
