def test_crop(self, device, dtype):
inp = torch.arange(0., 64., device=device,
dtype=dtype).view(1, 1, 4, 4, 4)
depth, height, width = 2, 2, 2
expected = torch.tensor([[[[[25.1667, 27.1667], [30.5000, 32.5000]],
[[46.5000, 48.5000], [51.8333, 53.8333]]]]],
device=device,
dtype=dtype)
boxes = torch.tensor([[
[0, 0, 1],
[3, 0, 1],
[3, 2, 1],
[0, 2, 1],
[0, 0, 3],
[3, 0, 3],
[3, 2, 3],
[0, 2, 3],
]],
device=device,
dtype=dtype) # 1x8x3
patches = kornia.crop_and_resize3d(inp, boxes, (depth, height, width))
assert_allclose(patches, expected)
def test_crop_batch(self, device, dtype):
inp = torch.cat(
[
torch.arange(0.0, 64.0, device=device, dtype=dtype).view(1, 1, 4, 4, 4),
torch.arange(0.0, 128.0, step=2, device=device, dtype=dtype).view(1, 1, 4, 4, 4),
],
dim=0,
)
depth, height, width = 2, 2, 2
expected = torch.tensor(
[
[[[[16.0000, 19.0000], [24.0000, 27.0000]], [[48.0000, 51.0000], [56.0000, 59.0000]]]],
[[[[0.0000, 6.0000], [16.0000, 22.0000]], [[64.0000, 70.0000], [80.0000, 86.0000]]]],
],
device=device,
dtype=dtype,
)
boxes = torch.tensor(
[
[[0, 0, 1], [3, 0, 1], [3, 2, 1], [0, 2, 1], [0, 0, 3], [3, 0, 3], [3, 2, 3], [0, 2, 3]],
[[0, 0, 0], [3, 0, 0], [3, 2, 0], [0, 2, 0], [0, 0, 2], [3, 0, 2], [3, 2, 2], [0, 2, 2]],
],
device=device,
dtype=dtype,
) # 2x8x3
patches = kornia.crop_and_resize3d(inp, boxes, (depth, height, width), align_corners=True)
assert_allclose(patches, expected)
