def test_upsampling_3d():
nb_samples = 9
stack_size = 7
input_len_dim1 = 10
input_len_dim2 = 11
input_len_dim3 = 12
for dim_ordering in ['th', 'tf']:
if dim_ordering == 'th':
input = np.random.rand(nb_samples, stack_size, input_len_dim1,
input_len_dim2, input_len_dim3)
else: # tf
input = np.random.rand(nb_samples, input_len_dim1, input_len_dim2,
input_len_dim3, stack_size)
for length_dim1 in [2, 3, 9]:
for length_dim2 in [2, 3, 9]:
for length_dim3 in [2, 3, 9]:
layer = convolutional.UpSampling3D(
size=(length_dim1, length_dim2, length_dim3),
input_shape=input.shape[1:],
dim_ordering=dim_ordering)
layer.input = K.variable(input)
for train in [True, False]:
out = K.eval(layer.get_output(train))
if dim_ordering == 'th':
assert out.shape[2] == length_dim1 * input_len_dim1
assert out.shape[3] == length_dim2 * input_len_dim2
assert out.shape[4] == length_dim3 * input_len_dim3
else: # tf
assert out.shape[1] == length_dim1 * input_len_dim1
assert out.shape[2] == length_dim2 * input_len_dim2
assert out.shape[3] == length_dim3 * input_len_dim3
# compare with numpy
if dim_ordering == 'th':
expected_out = np.repeat(input,
length_dim1,
axis=2)
expected_out = np.repeat(expected_out,
length_dim2,
axis=3)
expected_out = np.repeat(expected_out,
length_dim3,
axis=4)
else: # tf
expected_out = np.repeat(input,
length_dim1,
axis=1)
expected_out = np.repeat(expected_out,
length_dim2,
axis=2)
expected_out = np.repeat(expected_out,
length_dim3,
axis=3)
assert_allclose(out, expected_out)
layer.get_config()
def test_upsampling_3d():
nb_samples = 2
stack_size = 2
input_len_dim1 = 10
input_len_dim2 = 11
input_len_dim3 = 12
for dim_ordering in ['th', 'tf']:
if dim_ordering == 'th':
input = np.random.rand(nb_samples, stack_size, input_len_dim1,
input_len_dim2, input_len_dim3)
else: # tf
input = np.random.rand(nb_samples, input_len_dim1, input_len_dim2,
input_len_dim3, stack_size)
for length_dim1 in [2, 3, 9]:
for length_dim2 in [2, 3, 9]:
for length_dim3 in [2, 3, 9]:
layer = convolutional.UpSampling3D(
size=(length_dim1, length_dim2, length_dim3),
dim_ordering=dim_ordering)
layer.build(input.shape)
output = layer(K.variable(input))
np_output = K.eval(output)
if dim_ordering == 'th':
assert np_output.shape[
2] == length_dim1 * input_len_dim1
assert np_output.shape[
3] == length_dim2 * input_len_dim2
assert np_output.shape[
4] == length_dim3 * input_len_dim3
else: # tf
assert np_output.shape[
1] == length_dim1 * input_len_dim1
assert np_output.shape[
2] == length_dim2 * input_len_dim2
assert np_output.shape[
3] == length_dim3 * input_len_dim3
# compare with numpy
if dim_ordering == 'th':
expected_out = np.repeat(input, length_dim1, axis=2)
expected_out = np.repeat(expected_out,
length_dim2,
axis=3)
expected_out = np.repeat(expected_out,
length_dim3,
axis=4)
else: # tf
expected_out = np.repeat(input, length_dim1, axis=1)
expected_out = np.repeat(expected_out,
length_dim2,
axis=2)
expected_out = np.repeat(expected_out,
length_dim3,
axis=3)
assert_allclose(np_output, expected_out)
def test_upsampling_3d(data_format):
num_samples = 2
stack_size = 2
input_len_dim1 = 10
input_len_dim2 = 11
input_len_dim3 = 12
if data_format == 'channels_first':
inputs = np.random.rand(num_samples, stack_size, input_len_dim1,
input_len_dim2, input_len_dim3)
else: # tf
inputs = np.random.rand(num_samples, input_len_dim1, input_len_dim2,
input_len_dim3, stack_size)
# basic test
layer_test(convolutional.UpSampling3D,
kwargs={
'size': (2, 2, 2),
'data_format': data_format
},
input_shape=inputs.shape)
for length_dim1 in [2, 3]:
for length_dim2 in [2]:
for length_dim3 in [3]:
layer = convolutional.UpSampling3D(size=(length_dim1,
length_dim2,
length_dim3),
data_format=data_format)
layer.build(inputs.shape)
outputs = layer(K.variable(inputs))
np_output = K.eval(outputs)
if data_format == 'channels_first':
assert np_output.shape[2] == length_dim1 * input_len_dim1
assert np_output.shape[3] == length_dim2 * input_len_dim2
assert np_output.shape[4] == length_dim3 * input_len_dim3
else: # tf
assert np_output.shape[1] == length_dim1 * input_len_dim1
assert np_output.shape[2] == length_dim2 * input_len_dim2
assert np_output.shape[3] == length_dim3 * input_len_dim3
# compare with numpy
if data_format == 'channels_first':
expected_out = np.repeat(inputs, length_dim1, axis=2)
expected_out = np.repeat(expected_out, length_dim2, axis=3)
expected_out = np.repeat(expected_out, length_dim3, axis=4)
else: # tf
expected_out = np.repeat(inputs, length_dim1, axis=1)
expected_out = np.repeat(expected_out, length_dim2, axis=2)
expected_out = np.repeat(expected_out, length_dim3, axis=3)
assert_allclose(np_output, expected_out)
