def test_unresample_nearest_real_sampling_cpu():
# Basic Unresample layer
layer = Unresample(InterpolationType.NEAREST)
# Run a forward and backward pass
output, forward_time, backward_time, gradcheck_res = utils.mapped_resample_test(
layer,
input=params.input_3x4().repeat(bs, channels, 1, 1),
sample_map=params.sample_map7(),
cuda=False)
# Manually computed correct result
correct_output = torch.tensor([[1, 6], [10, 0], [0, 11]]).double()
# Assert gradient check has passed
assert gradcheck_res
# Assert outputs match
testing.assert_allclose(output, correct_output)
def test_unresample_bispherical_real_sampling_cuda():
# Basic Unresample layer
layer = Unresample(InterpolationType.BISPHERICAL)
# Run a forward and backward pass
output, forward_time, backward_time, gradcheck_res = utils.mapped_resample_test(
layer,
input=params.input_3x4().repeat(bs, channels, 1, 1),
sample_map=params.sample_map7(),
cuda=True)
# Manually computed correct result
correct_output = torch.tensor([[0.5, 5.5], [9.5, 9.5],
[1.5, 10.5]]).double().cuda()
# Assert gradient check has passed
assert gradcheck_res
# Assert outputs match
testing.assert_allclose(output, correct_output)