def test_convolutional_transpose():
x = tensor.tensor4('x')
num_channels = 4
num_filters = 3
image_size = (8, 6)
original_image_size = (17, 13)
batch_size = 5
filter_size = (3, 3)
step = (2, 2)
conv = ConvolutionalTranspose(filter_size,
num_filters,
num_channels,
step=step,
original_image_size=original_image_size,
image_size=image_size,
weights_init=Constant(1.),
biases_init=Constant(5.))
conv.initialize()
y = conv.apply(x)
func = function([x], y)
x_val = numpy.ones((batch_size, num_channels) + image_size,
dtype=theano.config.floatX)
expected_value = num_channels * numpy.ones((batch_size, num_filters) +
original_image_size)
expected_value[:, :, 2:-2:2, :] += num_channels
expected_value[:, :, :, 2:-2:2] += num_channels
expected_value[:, :, 2:-2:2, 2:-2:2] += num_channels
assert_allclose(func(x_val), expected_value + 5)
def test_convolutional_transpose():
x = tensor.tensor4('x')
num_channels = 4
num_filters = 3
image_size = (8, 6)
original_image_size = (17, 13)
batch_size = 5
filter_size = (3, 3)
step = (2, 2)
conv = ConvolutionalTranspose(
filter_size, num_filters, num_channels, step=step,
original_image_size=original_image_size,
image_size=image_size, weights_init=Constant(1.),
biases_init=Constant(5.))
conv.initialize()
y = conv.apply(x)
func = function([x], y)
x_val = numpy.ones((batch_size, num_channels) + image_size,
dtype=theano.config.floatX)
expected_value = num_channels * numpy.ones(
(batch_size, num_filters) + original_image_size)
expected_value[:, :, 2:-2:2, :] += num_channels
expected_value[:, :, :, 2:-2:2] += num_channels
expected_value[:, :, 2:-2:2, 2:-2:2] += num_channels
assert_allclose(func(x_val), expected_value + 5)
def test_convolutional_transpose_original_size_inference_full_padding():
brick = ConvolutionalTranspose(filter_size=(4, 5), num_filters=10,
num_channels=5, step=(3, 2),
border_mode='full',
image_size=(6, 9))
brick.allocate()
assert brick.original_image_size == (13, 13)
input_ = tensor.tensor4()
dummy = numpy.empty((4, 5, 6, 9), dtype=theano.config.floatX)
result = brick.apply(input_).eval({input_: dummy})
assert result.shape == (4, 10, 13, 13)
def test_convolutional_transpose_original_size_inference_unused_edge():
brick = ConvolutionalTranspose(filter_size=(3, 3), num_filters=10,
num_channels=5, step=(2, 2),
border_mode=(1, 1), image_size=(4, 4),
unused_edge=(1, 1))
brick.allocate()
assert brick.original_image_size == (8, 8)
input_ = tensor.tensor4()
dummy = numpy.empty((4, 5, 4, 4), dtype=theano.config.floatX)
result = brick.apply(input_).eval({input_: dummy})
assert result.shape == (4, 10, 8, 8)
def test_convolutional_transpose_original_size_inference():
brick = ConvolutionalTranspose(filter_size=(4, 5), num_filters=10,
num_channels=5, step=(3, 2),
image_size=(6, 9))
brick.allocate()
# In x: filter applied 6 times with a step of 3 and filter size of 4
# means 1 dangling pixel, total original image size of 6 * 3 + 1 == 19.
# In y: step of 2, applied 9 times, filter size of 5 means 3
# dangling pixels, so original is 2 * 9 + 3 == 21.
assert brick.original_image_size == (19, 21)
input_ = tensor.tensor4()
dummy = numpy.empty((4, 5, 6, 9), dtype=theano.config.floatX)
result = brick.apply(input_).eval({input_: dummy})
assert result.shape == (4, 10, 19, 21)