def test_conv3d_transpose_invalid():
filters = 2
stack_size = 3
num_depth = 7
num_row = 5
num_col = 6
padding = 'valid'
# Test invalid use case
with pytest.raises(ValueError):
model = Sequential([
convolutional.Conv3DTranspose(filters=filters,
kernel_size=3,
padding=padding,
batch_input_shape=(None, None, 5,
None, None))
])
# Test invalid output padding for given stride. Output padding equal
# to stride
with pytest.raises(ValueError):
model = Sequential([
convolutional.Conv3DTranspose(filters=filters,
kernel_size=3,
padding=padding,
output_padding=(0, 3, 3),
strides=(1, 3, 4),
batch_input_shape=(None, num_depth,
num_row, num_col,
stack_size))
])
# Output padding greater than stride
with pytest.raises(ValueError):
model = Sequential([
convolutional.Conv3DTranspose(filters=filters,
kernel_size=3,
padding=padding,
output_padding=(2, 2, 3),
strides=(1, 3, 4),
batch_input_shape=(None, num_depth,
num_row, num_col,
stack_size))
])
def test_conv3d_transpose():
filters = 2
stack_size = 3
num_depth = 7
num_row = 5
num_col = 6
for padding in _convolution_paddings:
for strides in [(1, 1, 1), (2, 2, 2)]:
for data_format in ['channels_first', 'channels_last']:
if padding == 'same' and strides != (1, 1, 1):
continue
layer_test(convolutional.Conv3DTranspose,
kwargs={
'filters': filters,
'kernel_size': 3,
'padding': padding,
'strides': strides,
'data_format': data_format
},
input_shape=(None, num_depth, num_row, num_col,
stack_size),
fixed_batch_size=True)
layer_test(convolutional.Conv3DTranspose,
kwargs={
'filters': filters,
'kernel_size': 3,
'padding': padding,
'data_format': 'channels_first',
'activation': None,
'kernel_regularizer': 'l2',
'bias_regularizer': 'l2',
'activity_regularizer': 'l2',
'kernel_constraint': 'max_norm',
'bias_constraint': 'max_norm',
'use_bias': True,
'strides': strides
},
input_shape=(None, stack_size, num_depth, num_row, num_col),
fixed_batch_size=True)
# Test invalid use case
with pytest.raises(ValueError):
model = Sequential([
convolutional.Conv3DTranspose(filters=filters,
kernel_size=3,
padding=padding,
batch_input_shape=(None, None, 5,
None, None))
])
