def test_integer_sampling_non_square_cpu():
# Basic MappedTransposedConvolution layer
layer = MappedTransposedConvolution(in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size).double()
# Run a forward and backward pass
output, forward_time, backward_time, gradcheck_res = utils.mapped_transposed_conv_test(
layer,
weight=params.transposed_weights_unit(in_channels, out_channels),
input=params.transposed_input_2x3().repeat(bs, in_channels, 1, 1),
oh=4,
ow=5,
sample_map=params.sample_map25(),
cuda=False)
# Manually computed correct result
correct_output = 2 + params.in_channels * torch.tensor([[
2, 0, 3, 0, 13
], [8, 0, 5, 6, 0], [8, 0, 8, 0, 4], [0, 0, 3, 0, 0]]).double()
# Assert gradient check has passed
assert gradcheck_res
# Assert outputs match
testing.assert_allclose(output, correct_output)
def test_bilinear_interpolation_sampling_cuda():
# Basic MappedTransposedConvolution layer
layer = MappedTransposedConvolution(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size).double().cuda()
# Run a forward and backward pass
output, forward_time, backward_time, gradcheck_res = utils.mapped_transposed_conv_test(
layer,
weight=params.transposed_weights_unit(in_channels, out_channels),
input=params.transposed_input_2x2().repeat(bs, in_channels, 1, 1),
oh=4,
ow=5,
sample_map=params.sample_map3(),
cuda=True)
# Manually computed correct result
correct_output = 2 + params.in_channels * torch.tensor([
[1.0, 0.0, 0.0, 2.5, 2.5], [1.0, 0.0, 0.5, 0.5, 0.0],
[0.0, 0.5, 1.0, 0.5, 0.0], [1.5, 2.0, 2.0, 4.0, 2.5]
]).double().cuda()
# Assert gradient check has passed
assert gradcheck_res
# Assert outputs match
testing.assert_allclose(output, correct_output)