def test_cropping_3d():
num_samples = 2
stack_size = 2
input_len_dim1 = 8
input_len_dim2 = 8
input_len_dim3 = 8
cropping = ((2, 2), (3, 3), (2, 3))
for data_format in ['channels_last', 'channels_first']:
if data_format == 'channels_first':
inputs = np.random.rand(num_samples, stack_size,
input_len_dim1, input_len_dim2, input_len_dim3)
else:
inputs = np.random.rand(num_samples,
input_len_dim1, input_len_dim2,
input_len_dim3, stack_size)
# basic test
layer_test(convolutional.Cropping3D,
kwargs={'cropping': cropping,
'data_format': data_format},
input_shape=inputs.shape)
# correctness test
layer = convolutional.Cropping3D(cropping=cropping,
data_format=data_format)
layer.build(inputs.shape)
outputs = layer(K.variable(inputs))
np_output = K.eval(outputs)
# compare with numpy
if data_format == 'channels_first':
expected_out = inputs[:, :, cropping[0][0]: -cropping[0][1], cropping[1][0]: -cropping[1][1], cropping[2][0]: -cropping[2][1]]
else:
expected_out = inputs[:, cropping[0][0]: -cropping[0][1], cropping[1][0]: -cropping[1][1], cropping[2][0]: -cropping[2][1], :]
assert_allclose(np_output, expected_out)
for data_format in ['channels_last', 'channels_first']:
if data_format == 'channels_first':
inputs = np.random.rand(num_samples, stack_size,
input_len_dim1, input_len_dim2, input_len_dim3)
else:
inputs = np.random.rand(num_samples,
input_len_dim1, input_len_dim2,
input_len_dim3, stack_size)
# another correctness test (no cropping)
cropping = ((0, 0), (0, 0), (0, 0))
layer = convolutional.Cropping3D(cropping=cropping,
data_format=data_format)
layer.build(inputs.shape)
outputs = layer(K.variable(inputs))
np_output = K.eval(outputs)
# compare with input
assert_allclose(np_output, inputs)
# Test invalid use cases
with pytest.raises(ValueError):
layer = convolutional.Cropping3D(cropping=((1, 1),))
with pytest.raises(ValueError):
layer = convolutional.Cropping3D(cropping=lambda x: x)
def test_cropping_3d():
nb_samples = 2
stack_size = 2
input_len_dim1 = 8
input_len_dim2 = 8
input_len_dim3 = 8
cropping = ((2, 2), (3, 3), (2, 3))
dim_ordering = K.image_dim_ordering()
if dim_ordering == 'th':
input = np.random.rand(nb_samples, stack_size,
input_len_dim1, input_len_dim2, input_len_dim3)
else:
input = np.random.rand(nb_samples,
input_len_dim1, input_len_dim2,
input_len_dim3, stack_size)
# basic test
layer_test(convolutional.Cropping3D,
kwargs={'cropping': cropping,
'dim_ordering': dim_ordering},
input_shape=input.shape)
# correctness test
layer = convolutional.Cropping3D(cropping=cropping,
dim_ordering=dim_ordering)
layer.build(input.shape)
output = layer(K.variable(input))
np_output = K.eval(output)
# compare with numpy
if dim_ordering == 'th':
expected_out = input[:,
:,
cropping[0][0]: -cropping[0][1],
cropping[1][0]: -cropping[1][1],
cropping[2][0]: -cropping[2][1]]
else:
expected_out = input[:,
cropping[0][0]: -cropping[0][1],
cropping[1][0]: -cropping[1][1],
cropping[2][0]: -cropping[2][1],
:]
assert_allclose(np_output, expected_out)
# another correctness test (no cropping)
cropping = ((0, 0), (0, 0), (0, 0))
layer = convolutional.Cropping3D(cropping=cropping,
dim_ordering=dim_ordering)
layer.build(input.shape)
output = layer(K.variable(input))
np_output = K.eval(output)
# compare with input
assert_allclose(np_output, input)
