def test_cropping_2d():
num_samples = 2
stack_size = 2
input_len_dim1 = 9
input_len_dim2 = 9
cropping = ((2, 2), (3, 3))
for data_format in ['channels_first', 'channels_last']:
if data_format == 'channels_first':
inputs = np.random.rand(num_samples, stack_size, input_len_dim1,
input_len_dim2)
else:
inputs = np.random.rand(num_samples, input_len_dim1,
input_len_dim2, stack_size)
# basic test
layer_test(convolutional.Cropping2D,
kwargs={
'cropping': cropping,
'data_format': data_format
},
input_shape=inputs.shape)
# correctness test
layer = convolutional.Cropping2D(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]]
else:
expected_out = inputs[:, cropping[0][0]:-cropping[0][1],
cropping[1][0]:-cropping[1][1], :]
assert_allclose(np_output, expected_out)
for data_format in ['channels_first', 'channels_last']:
if data_format == 'channels_first':
inputs = np.random.rand(num_samples, stack_size, input_len_dim1,
input_len_dim2)
else:
inputs = np.random.rand(num_samples, input_len_dim1,
input_len_dim2, stack_size)
# another correctness test (no cropping)
cropping = ((0, 0), (0, 0))
layer = convolutional.Cropping2D(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.Cropping2D(cropping=((1, 1), ))
with pytest.raises(ValueError):
layer = convolutional.Cropping2D(cropping=lambda x: x)
def make_model():
network = Sequential()
network.add(
Lambda(lambda pixel: pixel / 255 - 0.5, input_shape=(160, 320, 3)))
network.add(convolutional.Cropping2D(cropping=((70, 25), (0, 0))))
network.add(
convolutional.Convolution2D(24,
5,
5,
activation='relu',
subsample=(2, 2)))
network.add(
convolutional.Convolution2D(36,
5,
5,
activation='relu',
subsample=(2, 2)))
network.add(
convolutional.Convolution2D(48,
5,
5,
activation='relu',
subsample=(2, 2)))
network.add(convolutional.Convolution2D(64, 3, 3, activation='relu'))
network.add(convolutional.Convolution2D(64, 3, 3, activation='relu'))
network.add(Flatten())
network.add(Dense(100))
network.add(Dense(50))
network.add(Dense(10))
network.add(Dense(1))
return network
def test_cropping_2d():
nb_samples = 2
stack_size = 2
input_len_dim1 = 8
input_len_dim2 = 8
cropping = ((2, 2), (3, 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)
else:
input = np.random.rand(nb_samples, input_len_dim1, input_len_dim2,
stack_size)
# basic test
layer_test(convolutional.Cropping2D,
kwargs={
'cropping': cropping,
'dim_ordering': dim_ordering
},
input_shape=input.shape)
# correctness test
layer = convolutional.Cropping2D(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]]
else:
expected_out = input[:, cropping[0][0]:-cropping[0][1],
cropping[1][0]:-cropping[1][1], :]
assert_allclose(np_output, expected_out)
