def test_padding_batch(self, padding, device, dtype):
torch.manual_seed(42)
batch_size = 2
inp = torch.tensor([[[
[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.]
]]], device=device, dtype=dtype).repeat(batch_size, 1, 3, 1, 1)
expected = torch.tensor([[[
[[0., 1., 2., 10.],
[3., 4., 5., 10.],
[6., 7., 8., 10.]],
[[0., 1., 2., 10.],
[3., 4., 5., 10.],
[6., 7., 8., 10.]],
]], [[
[[3., 4., 5., 10.],
[6., 7., 8., 10.],
[10, 10, 10, 10.]],
[[3., 4., 5., 10.],
[6., 7., 8., 10.],
[10, 10, 10, 10.]],
]]], device=device, dtype=dtype)
f = RandomCrop3D(size=(2, 3, 4), fill=10., padding=padding, align_corners=True, p=1.)
out = f(inp)
assert_allclose(out, expected, atol=1e-4, rtol=1e-4)
def test_no_padding(self, batch_size, device, dtype):
torch.manual_seed(42)
inp = torch.tensor([[[[
[0., 1., 2., 3., 4.],
[5., 6., 7., 8., 9.],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]
]]]], device=device, dtype=dtype).repeat(batch_size, 1, 5, 1, 1)
f = RandomCrop3D(size=(2, 3, 4), padding=None, align_corners=True, p=1.)
out = f(inp)
if batch_size == 1:
expected = torch.tensor([[[[
[11, 12, 13, 14],
[16, 17, 18, 19],
[21, 22, 23, 24]
]]]], device=device, dtype=dtype).repeat(batch_size, 1, 2, 1, 1)
if batch_size == 2:
expected = torch.tensor([
[[[[6.0000, 7.0000, 8.0000, 9.0000],
[11.0000, 12.0000, 13.0000, 14.0000],
[16.0000, 17.0000, 18.0000, 19.0000]],
[[6.0000, 7.0000, 8.0000, 9.0000],
[11.0000, 12.0000, 13.0000, 14.0000],
[16.0000, 17.0000, 18.0000, 19.0000]]]],
[[[[11.0000, 12.0000, 13.0000, 14.0000],
[16.0000, 17.0000, 18.0000, 19.0000],
[21.0000, 22.0000, 23.0000, 24.0000]],
[[11.0000, 12.0000, 13.0000, 14.0000],
[16.0000, 17.0000, 18.0000, 19.0000],
[21.0000, 22.0000, 23.0000, 24.0000]]]]], device=device, dtype=dtype)
assert_allclose(out, expected, atol=1e-4, rtol=1e-4)
def test_smoke(self):
f = RandomCrop3D(size=(2, 3, 4), padding=(0, 1, 2), fill=10, pad_if_needed=False, p=1.0)
repr = (
"RandomCrop3D(crop_size=(2, 3, 4), padding=(0, 1, 2), fill=10, pad_if_needed=False, "
"padding_mode=constant, resample=BILINEAR, p=1.0, p_batch=1.0, same_on_batch=False, "
"return_transform=False)"
)
assert str(f) == repr
def test_same_on_batch(self, device, dtype):
f = RandomCrop3D(size=(2, 3, 4),
padding=None,
align_corners=True,
p=1.0,
same_on_batch=True)
input = torch.eye(6).unsqueeze(dim=0).unsqueeze(dim=0).unsqueeze(
dim=0).repeat(2, 3, 5, 1, 1)
res = f(input)
assert (res[0] == res[1]).all()
def test_pad_if_needed(self, device, dtype):
torch.manual_seed(42)
inp = torch.tensor([[
[0., 1., 2.],
]], device=device, dtype=dtype)
expected = torch.tensor([[[
[[9., 9., 9., 9.],
[9., 9., 9., 9.],
[9., 9., 9., 9.]],
[[0., 1., 2., 9.],
[9., 9., 9., 9.],
[9., 9., 9., 9.]],
]]], device=device, dtype=dtype)
rc = RandomCrop3D(size=(2, 3, 4), pad_if_needed=True, fill=9, align_corners=True, p=1.)
out = rc(inp)
assert_allclose(out, expected, atol=1e-4, rtol=1e-4)
def test_gradcheck(self, device, dtype):
torch.manual_seed(0) # for random reproductibility
inp = torch.rand((3, 3, 3), device=device, dtype=dtype) # 3 x 3
inp = utils.tensor_to_gradcheck_var(inp) # to var
assert gradcheck(RandomCrop3D(size=(3, 3, 3), p=1.), (inp, ), raise_exception=True)
