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)