423A35C7/pytorch3d
1
0
Fork 0
mirror of https://github.com/facebookresearch/pytorch3d.git synced 2025-12-18 21:30:35 +08:00
Code Issues Packages Projects Releases Wiki Activity

Heterogeneous raysampling -> RayBundleHeterogeneous

Browse Source 
Summary:
Added heterogeneous raysampling to pytorch3d raysampler, different cameras are sampled different number of times.

 It now returns RayBundle if heterogeneous raysampling is off and new RayBundleHeterogeneous (with added fields `camera_ids` and `camera_counts`).  Heterogeneous raysampling is on if `n_rays_total` is not None.

Reviewed By: bottler

Differential Revision: D39542222

fbshipit-source-id: d3d88d822ec7696e856007c088dc36a1cfa8c625
This commit is contained in:
Darijan Gudelj
2022-09-30 04:03:01 -07:00
committed by Facebook GitHub Bot
parent 9a0f9ae572
commit 6ae863f301
6 changed files with 325 additions and 48 deletions
Download Patch File Download Diff File Expand all files Collapse all files

62
tests/test_raysampling.py
View File

@@ -152,6 +152,8 @@ class TestRaysampling(TestCaseMixin, unittest.TestCase):
min_depth=1.0,
max_depth=10.0,
n_pts_per_ray=10,
n_rays_total=None,
n_rays_per_image=None,
):
raysampler_params = {
"min_x": min_x,
@@ -161,6 +163,8 @@ class TestRaysampling(TestCaseMixin, unittest.TestCase):
"n_pts_per_ray": n_pts_per_ray,
"min_depth": min_depth,
"max_depth": max_depth,
"n_rays_total": n_rays_total,
"n_rays_per_image": n_rays_per_image,
}
if issubclass(raysampler_type, MultinomialRaysampler):
@@ -168,7 +172,11 @@ class TestRaysampling(TestCaseMixin, unittest.TestCase):
{"image_width": image_width, "image_height": image_height}
)
elif issubclass(raysampler_type, MonteCarloRaysampler):
raysampler_params["n_rays_per_image"] = image_width * image_height
raysampler_params["n_rays_per_image"] = (
image_width * image_height
if (n_rays_total is None) and (n_rays_per_image is None)
else n_rays_per_image
)
else:
raise ValueError(str(raysampler_type))
@@ -580,3 +588,55 @@ class TestRaysampling(TestCaseMixin, unittest.TestCase):
# samples[3] has enough sources, so must contain 3 distinct values.
self.assertLessEqual(samples[3].max(), 3)
self.assertEqual(len(set(samples[3].tolist())), 3)
def test_heterogeneous_sampling(self, batch_size=8):
"""
Test that the output of heterogeneous sampling has the first dimension equal
to n_rays_total and second to 1 and that ray_bundle elements from different
sampled cameras are different and equal for same sampled cameras.
"""
cameras = init_random_cameras(PerspectiveCameras, batch_size, random_z=True)
for n_rays_total in [2, 3, 17, 21, 32]:
for cls in (MultinomialRaysampler, MonteCarloRaysampler):
with self.subTest(cls.__name__ + ", n_rays_total=" + str(n_rays_total)):
raysampler = self.init_raysampler(
cls, n_rays_total=n_rays_total, n_rays_per_image=None
)
ray_bundle = raysampler(cameras)
# test weather they are of the correct shape
for attr in ("origins", "directions", "lengths", "xys"):
tensor = getattr(ray_bundle, attr)
assert tensor.shape[:2] == torch.Size(
(n_rays_total, 1)
), tensor.shape
# if two camera ids are same than origins should also be the same
# directions and xys are always different and lengths equal
for i1, (origin1, dir1, len1, id1) in enumerate(
zip(
ray_bundle.origins,
ray_bundle.directions,
ray_bundle.lengths,
torch.repeat_interleave(
ray_bundle.camera_ids, ray_bundle.camera_counts
),
)
):
for i2, (origin2, dir2, len2, id2) in enumerate(
zip(
ray_bundle.origins,
ray_bundle.directions,
ray_bundle.lengths,
torch.repeat_interleave(
ray_bundle.camera_ids, ray_bundle.camera_counts
),
)
):
if i1 == i2:
continue
assert torch.allclose(
origin1, origin2, rtol=1e-4, atol=1e-4
) == (id1 == id2), (origin1, origin2, id1, id2)
assert not torch.allclose(dir1, dir2), (dir1, dir2)
self.assertClose(len1, len2), (len1, len2)