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TST add tests for consolidated version #70

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TST add tests for consolidated version #70

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6f3ba4b
TST adjust shapes to be tested
eickenberg Oct 6, 2023
2ad17ab
TST backward tests passing in 1D as well
eickenberg Oct 6, 2023
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52 changes: 51 additions & 1 deletion tests/test_1d/test_backward_1d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@

torch.set_default_tensor_type(torch.DoubleTensor)
torch.set_default_dtype(torch.float64)

torch.manual_seed(0)

######################################################################
# APPLY WRAPPERS
Expand Down Expand Up @@ -116,6 +116,56 @@ def test_t1_backward_CPU_values(
assert gradcheck(apply_finufft1d1(modifier, fftshift, isign), inputs)



@pytest.mark.parametrize("N", Ns)
@pytest.mark.parametrize("modifier", length_modifiers)
@pytest.mark.parametrize("fftshift", [False, True])
@pytest.mark.parametrize("isign", [-1, 1])
def test_t1_consolidated_backward_CPU_values(N: int, modifier: int, fftshift: bool, isign: int) -> None:

points = torch.rand((1, N), dtype=torch.float64) * 2 * np.pi
values = torch.randn(N, dtype=torch.complex128)

points.requires_grad = False
values.requires_grad = True

inputs = (points, values)

def func(points, values):
return pytorch_finufft.functional.finufft_type1.apply(
points, values, (N + modifier,), None, fftshift, dict(isign=isign)
)

assert gradcheck(func, inputs)


@pytest.mark.parametrize("N", Ns)
@pytest.mark.parametrize("modifier", length_modifiers)
@pytest.mark.parametrize("fftshift", [False, True])
@pytest.mark.parametrize("isign", [-1, 1])
def test_t1_consolidated_backward_CPU_points(N: int, modifier: int, fftshift: bool, isign: int) -> None:

points = torch.rand((1, N), dtype=torch.float64) * 2 * np.pi
values = torch.randn(N, dtype=torch.complex128)

points.requires_grad = True
values.requires_grad = False

inputs = (points, values)

def func(points, values):
return pytorch_finufft.functional.finufft_type1.apply(
points, values, (N + modifier,), None, fftshift, dict(isign=isign)
)

assert gradcheck(func, inputs, atol=1e-5 * N)







######################################################################
# TYPE 2 TESTS
######################################################################
Expand Down
14 changes: 7 additions & 7 deletions tests/test_2d/test_backward_2d.py
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Expand Up @@ -55,18 +55,18 @@ def f(

Ns = [
3,
# 5,
# 8,
# 10,
# 15,
# 16,
5,
#8,
#10,
#15,
#16,
]

length_modifiers = [
0,
1,
# 4,
-1,
#2,
#-1,
]


Expand Down
58 changes: 54 additions & 4 deletions tests/test_3d/test_backward_3d.py
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Expand Up @@ -65,14 +65,15 @@ def f(

Ns = [
3,
# 5,
# 8,
5,
8,
17,
Comment on lines +68 to +70
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This PR is running pretty slowly - I assume because of these new cases in 3d

]

length_modifiers = [
# -1,
#-1,
0,
# 1,
1,
# 4
]

Expand Down Expand Up @@ -190,6 +191,55 @@ def test_t1_backward_CPU_points_z(
assert gradcheck(apply_finufft3d1(modifier, fftshift, isign), inputs)




@pytest.mark.parametrize("N", Ns)
@pytest.mark.parametrize("modifier", length_modifiers)
@pytest.mark.parametrize("fftshift", [False, True])
@pytest.mark.parametrize("isign", [-1, 1])
def test_t1_consolidated_backward_CPU_values(N: int, modifier: int, fftshift: bool, isign: int) -> None:

points = torch.rand((3, N), dtype=torch.float64) * 2 * np.pi
values = torch.randn(N, dtype=torch.complex128)

points.requires_grad = False
values.requires_grad = True

inputs = (points, values)

def func(points, values):
return pytorch_finufft.functional.finufft_type1.apply(
points, values, (N,N + modifier, N + 2 * modifier), None, fftshift, dict(isign=isign)
)

assert gradcheck(func, inputs)


@pytest.mark.parametrize("N", Ns)
@pytest.mark.parametrize("modifier", length_modifiers)
@pytest.mark.parametrize("fftshift", [False, True])
@pytest.mark.parametrize("isign", [-1, 1])
def test_t1_consolidated_backward_CPU_points(N: int, modifier: int, fftshift: bool, isign: int) -> None:

points = torch.rand((3, N), dtype=torch.float64) * 2 * np.pi
values = torch.randn(N, dtype=torch.complex128)

points.requires_grad = True
values.requires_grad = False

inputs = (points, values)

def func(points, values):
return pytorch_finufft.functional.finufft_type1.apply(
points, values, (N,N + modifier, N + 2 * modifier), None, fftshift, dict(isign=isign)
)

assert gradcheck(func, inputs, atol=1e-5 * N)





######################################################################
# TYPE 2 TESTS
######################################################################
Expand Down
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