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Align_corners switch in Volumes

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Summary:
Porting this commit by davnov134 .
93a3a62800 (diff-a8e107ebe039de52ca112ac6ddfba6ebccd53b4f53030b986e13f019fe57a378)

Capability to interpret world/local coordinates with various align_corners semantics.

Reviewed By: bottler

Differential Revision: D51855420

fbshipit-source-id: 834cd220c25d7f0143d8a55ba880da5977099dd6
This commit is contained in:
Roman Shapovalov
2023-12-07 03:07:41 -08:00
committed by Facebook GitHub Bot
parent fbc6725f03
commit 94da8841af
5 changed files with 91 additions and 9 deletions
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43
tests/test_volumes.py
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@@ -312,6 +312,49 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
).permute(0, 2, 3, 4, 1)
self.assertClose(grid_world_resampled, grid_world, atol=1e-7)
for align_corners in [True, False]:
v_trivial = Volumes(densities=densities, align_corners=align_corners)
# check the case with x_world=(0,0,0)
pts_world = torch.zeros(
num_volumes, 1, 3, device=device, dtype=torch.float32
)
pts_local = v_trivial.world_to_local_coords(pts_world)
pts_local_expected = torch.zeros_like(pts_local)
self.assertClose(pts_local, pts_local_expected)
# check the case with x_world=(-2, 3, -2)
pts_world_tuple = [-2, 3, -2]
pts_world = torch.tensor(
pts_world_tuple, device=device, dtype=torch.float32
)[None, None].repeat(num_volumes, 1, 1)
pts_local = v_trivial.world_to_local_coords(pts_world)
pts_local_expected = torch.tensor(
[-1, 1, -1], device=device, dtype=torch.float32
)[None, None].repeat(num_volumes, 1, 1)
self.assertClose(pts_local, pts_local_expected)
# # check that the central voxel has coords x_world=(0, 0, 0) and x_local(0, 0, 0)
grid_world = v_trivial.get_coord_grid(world_coordinates=True)
grid_local = v_trivial.get_coord_grid(world_coordinates=False)
for grid in (grid_world, grid_local):
x0 = grid[0, :, :, 2, 0]
y0 = grid[0, :, 3, :, 1]
z0 = grid[0, 2, :, :, 2]
for coord_line in (x0, y0, z0):
self.assertClose(
coord_line, torch.zeros_like(coord_line), atol=1e-7
)
# resample grid_world using grid_sampler with local coords
# -> make sure the resampled version is the same as original
grid_world_resampled = torch.nn.functional.grid_sample(
grid_world.permute(0, 4, 1, 2, 3),
grid_local,
align_corners=align_corners,
).permute(0, 2, 3, 4, 1)
self.assertClose(grid_world_resampled, grid_world, atol=1e-7)
def test_coord_grid_convention_heterogeneous(
self, num_channels=4, dtype=torch.float32
):