def test_valid_param_combinations(self, batch_size, depth, height, width,
size, device, dtype):
center_crop_generator3d(batch_size=batch_size,
depth=depth,
height=height,
width=width,
size=size)
def test_invalid_param_combinations(self, depth, height, width, size,
device, dtype):
with pytest.raises(Exception):
center_crop_generator3d(batch_size=2,
depth=depth,
height=height,
width=width,
size=size)
def test_random_gen(self, device, dtype):
torch.manual_seed(42)
res = center_crop_generator3d(batch_size=2,
depth=200,
height=200,
width=200,
size=(120, 150, 100))
expected = dict(
src=torch.tensor([[[50, 25, 40], [149, 25, 40], [149, 174, 40],
[50, 174, 40], [50, 25, 159], [149, 25, 159],
[149, 174, 159], [50, 174, 159]]],
device=device,
dtype=torch.long).repeat(2, 1, 1),
dst=torch.tensor(
[[[0, 0, 0], [99, 0, 0], [99, 149, 0], [0, 149, 0],
[0, 0, 119], [99, 0, 119], [99, 149, 119], [0, 149, 119]]],
device=device,
dtype=torch.long).repeat(2, 1, 1),
)
assert res.keys() == expected.keys()
assert_allclose(res['src'].to(device=device),
expected['src'],
atol=1e-4,
rtol=1e-4)
assert_allclose(res['dst'].to(device=device),
expected['dst'],
atol=1e-4,
rtol=1e-4)
def generate_parameters(self, batch_shape: Size) -> Dict[str, Tensor]:
return rg.center_crop_generator3d(batch_shape[0],
batch_shape[-3],
batch_shape[-2],
batch_shape[-1],
self.flags["size"],
device=self.device)