def test_bilinear_interpolation_cpu():
# Basic MappedConvolution layer
layer = MappedConvolution(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_conv_test(
layer,
weight=params.weights_unit(in_channels, out_channels),
input=params.input_4x5().repeat(bs, in_channels, 1, 1),
sample_map=params.sample_map1(),
cuda=False)
# Manually computed correct result
correct_output = 2 + params.in_channels * torch.tensor(
[[35.75, 16.00, 28.00, 39.25, 45.00], [
32.25, 32.00, 23.00, 36.75, 29.00
], [34.50, 47.00, 31.00, 34.25, 33.75],
[39.00, 27.00, 35.25, 40.75, 39.50]]).double()
# Assert gradient check has passed
assert gradcheck_res
# Assert outputs match
testing.assert_allclose(output, correct_output)
def test_max_pool_bilinear_interpolation_sampling_cpu():
# Basic MappedTransposedConvolution layer
layer = MappedMaxPool(kernel_size=kernel_size).double()
# Run a forward and backward pass
output, forward_time, backward_time, gradcheck_res = utils.mapped_pool_test(
layer,
input=params.input_4x5().repeat(bs, 1, 1, 1),
sample_map=params.sample_map1(),
cuda=False)
# Manually computed correct result
correct_output = torch.tensor([[14, 5, 8.25, 13, 16],
[9.25, 9, 9.25, 13,
13], [13, 15, 15, 13, 11],
[13, 13, 13, 13, 15]]).double()
# Assert gradient check has passed
assert gradcheck_res
# Assert outputs match
testing.assert_allclose(output, correct_output)