def test_unresample_bilinear_integer_sampling_cpu():
# Basic Unresample layer
layer = Unresample(InterpolationType.BILINEAR)
# 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_map6(),
cuda=False)
# Manually computed correct result
correct_output = torch.tensor([[0, 5], [9, 11], [3, 10]]).double()
# Assert gradient check has passed
assert gradcheck_res
# Assert outputs match
testing.assert_allclose(output, correct_output)
def test_unresample_bispherical_out_of_bounds_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_map8(),
cuda=True)
# Manually computed correct result
correct_output = torch.tensor([[0, 0], [0, 0], [4.75, 11]]).double().cuda()
# Assert gradient check has passed
assert gradcheck_res
# Assert outputs match
testing.assert_allclose(output, correct_output)
def test_unresample_weighted_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_4x7().repeat(bs, channels, 1, 1),
sample_map=params.sample_map5(),
interp_weights=params.interp_weights0(),
cuda=True)
# Manually computed correct result
correct_output = torch.tensor([[14, 15.8], [9.6, 19.1]]).double().cuda()
# Assert gradient check has passed
assert gradcheck_res
# Assert outputs match
testing.assert_allclose(output, correct_output)