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Fix coordinate system conventions in renderer
Summary:
## Updates
- Defined the world and camera coordinates according to this figure. The world coordinates are defined as having +Y up, +X left and +Z in.
{F230888499}
- Removed all flipping from blending functions.
- Updated the rasterizer to return images with +Y up and +X left.
- Updated all the mesh rasterizer tests
- The expected values are now defined in terms of the default +Y up, +X left
- Added tests where the triangles in the meshes are non symmetrical so that it is clear which direction +X and +Y are
## Questions:
- Should we have **scene settings** instead of raster settings?
- To be more correct we should be [z clipping in the rasterizer based on the far/near clipping planes](https://github.com/ShichenLiu/SoftRas/blob/master/soft_renderer/cuda/soft_rasterize_cuda_kernel.cu#L400) - these values are also required in the blending functions so should we make these scene level parameters and have a scene settings tuple which is available to the rasterizer and shader?
Reviewed By: gkioxari
Differential Revision: D20208604
fbshipit-source-id: 55787301b1bffa0afa9618f0a0886cc681da51f3
This commit is contained in:
Nikhila Ravi
committed by
Facebook Github Bot
Facebook Github Bot
parent
767d68a3af
commit
15c72be444
@@ -19,7 +19,7 @@ class TestRasterizeMeshes(unittest.TestCase):
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device = torch.device("cpu")
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self._simple_triangle_raster(
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rasterize_meshes_python, device, bin_size=-1
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) # don't set binsize
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)
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self._simple_blurry_raster(rasterize_meshes_python, device, bin_size=-1)
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self._test_behind_camera(rasterize_meshes_python, device, bin_size=-1)
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self._test_perspective_correct(
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@@ -28,10 +28,10 @@ class TestRasterizeMeshes(unittest.TestCase):
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def test_simple_cpu_naive(self):
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device = torch.device("cpu")
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self._simple_triangle_raster(rasterize_meshes, device)
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self._simple_blurry_raster(rasterize_meshes, device)
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self._test_behind_camera(rasterize_meshes, device)
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self._test_perspective_correct(rasterize_meshes, device)
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self._simple_triangle_raster(rasterize_meshes, device, bin_size=0)
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self._simple_blurry_raster(rasterize_meshes, device, bin_size=0)
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self._test_behind_camera(rasterize_meshes, device, bin_size=0)
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self._test_perspective_correct(rasterize_meshes, device, bin_size=0)
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def test_simple_cuda_naive(self):
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device = torch.device("cuda:0")
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@@ -285,14 +285,15 @@ class TestRasterizeMeshes(unittest.TestCase):
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max_faces_per_bin = 20
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device = torch.device("cpu")
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meshes = ico_sphere(2, device)
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meshes = ico_sphere(2, device)
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faces = meshes.faces_packed()
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verts = meshes.verts_packed()
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faces_verts = verts[faces]
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num_faces_per_mesh = meshes.num_faces_per_mesh()
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mesh_to_face_first_idx = meshes.mesh_to_faces_packed_first_idx()
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args = (
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bin_faces_cpu = _C._rasterize_meshes_coarse(
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faces_verts,
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mesh_to_face_first_idx,
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num_faces_per_mesh,
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@@ -301,17 +302,16 @@ class TestRasterizeMeshes(unittest.TestCase):
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bin_size,
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max_faces_per_bin,
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)
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bin_faces_cpu = _C._rasterize_meshes_coarse(*args)
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device = torch.device("cuda:0")
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meshes = ico_sphere(2, device)
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meshes = meshes.clone().to(device)
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faces = meshes.faces_packed()
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verts = meshes.verts_packed()
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faces_verts = verts[faces]
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num_faces_per_mesh = meshes.num_faces_per_mesh()
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mesh_to_face_first_idx = meshes.mesh_to_faces_packed_first_idx()
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args = (
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bin_faces_cuda = _C._rasterize_meshes_coarse(
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faces_verts,
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mesh_to_face_first_idx,
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num_faces_per_mesh,
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@@ -320,11 +320,11 @@ class TestRasterizeMeshes(unittest.TestCase):
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bin_size,
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max_faces_per_bin,
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)
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bin_faces_cuda = _C._rasterize_meshes_coarse(*args)
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# Bin faces might not be the same: CUDA version might write them in
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# any order. But if we sort the non-(-1) elements of the CUDA output
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# then they should be the same.
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for n in range(N):
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for by in range(bin_faces_cpu.shape[1]):
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for bx in range(bin_faces_cpu.shape[2]):
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@@ -456,64 +456,68 @@ class TestRasterizeMeshes(unittest.TestCase):
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# Run with and without perspective correction
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idx_f, zbuf_f, bary_f, dists_f = rasterize_meshes_fn(**kwargs)
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kwargs["perspective_correct"] = True
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idx_t, zbuf_t, bary_t, dists_t = rasterize_meshes_fn(**kwargs)
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# Expected output tensors in the format with axes +X left, +Y up, +Z in
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# idx and dists should be the same with or without perspecitve correction
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# fmt: off
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idx_expected = torch.tensor([
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
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[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
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[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
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[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
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[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
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[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
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[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
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[-1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
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[-1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
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[-1, -1, -1, -1, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
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[-1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
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[-1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
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[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
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[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
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[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
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[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
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[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
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[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
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], dtype=torch.int64, device=device).view(1, 11, 11, 1)
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dists_expected = torch.tensor([
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[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, 0.1283, 0.1071, 0.1071, 0.1071, 0.1071, 0.1071, 0.1283, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, 0.1283, 0.0423, 0.0212, 0.0212, 0.0212, 0.0212, 0.0212, 0.0423, 0.1283, -1.0000], # noqa: E241, E201
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[-1.0000, 0.1084, 0.0225, -0.0003, -0.0013, -0.0013, -0.0013, -0.0003, 0.0225, 0.1084, -1.0000], # noqa: E241, E201
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[-1.0000, 0.1523, 0.0518, 0.0042, -0.0095, -0.0476, -0.0095, 0.0042, 0.0518, 0.1523, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, 0.0955, 0.0214, -0.0003, -0.0320, -0.0003, 0.0214, 0.0955, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, 0.1523, 0.0518, 0.0042, -0.0095, 0.0042, 0.0518, 0.1523, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, 0.0955, 0.0214, -0.0003, 0.0214, 0.0955, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, 0.1523, 0.0542, 0.0212, 0.0542, 0.1523, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, -1.0000, 0.1402, 0.1071, 0.1402, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
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[-1., -1., -1., -1., 0.1402, 0.1071, 0.1402, -1., -1., -1., -1.], # noqa: E241, E201
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[-1., -1., - 1., 0.1523, 0.0542, 0.0212, 0.0542, 0.1523, -1., -1., -1.], # noqa: E241, E201
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[-1., -1., -1., 0.0955, 0.0214, -0.0003, 0.0214, 0.0955, -1., -1., -1.], # noqa: E241, E201
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[-1., -1., 0.1523, 0.0518, 0.0042, -0.0095, 0.0042, 0.0518, 0.1523, -1., -1.], # noqa: E241, E201
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[-1., -1., 0.0955, 0.0214, -0.0003, -0.032, -0.0003, 0.0214, 0.0955, -1., -1.], # noqa: E241, E201
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[-1., 0.1523, 0.0518, 0.0042, -0.0095, -0.0476, -0.0095, 0.0042, 0.0518, 0.1523, -1.], # noqa: E241, E201
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[-1., 0.1084, 0.0225, -0.0003, -0.0013, -0.0013, -0.0013, -0.0003, 0.0225, 0.1084, -1.], # noqa: E241, E201
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[-1., 0.1283, 0.0423, 0.0212, 0.0212, 0.0212, 0.0212, 0.0212, 0.0423, 0.1283, -1.], # noqa: E241, E201
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[-1., -1., 0.1283, 0.1071, 0.1071, 0.1071, 0.1071, 0.1071, 0.1283, -1., -1.], # noqa: E241, E201
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[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.] # noqa: E241, E201
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], dtype=torch.float32, device=device).view(1, 11, 11, 1)
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# zbuf and barycentric will be different with perspective correction
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zbuf_f_expected = torch.tensor([
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[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, -1.0000], # noqa: E241, E201
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[-1.0000, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, -1.0000], # noqa: E241, E201
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[-1.0000, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, 15.0000, 15.0000, 15.0000, 15.0000, 15.0000, 15.0000, 15.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, 19.5455, 19.5455, 19.5455, 19.5455, 19.5455, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, 21.8182, 21.8182, 21.8182, 21.8182, 21.8182, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, -1.0000, 24.0909, 24.0909, 24.0909, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
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[-1., -1., -1., -1., 24.0909, 24.0909, 24.0909, -1., -1., -1., -1.], # noqa: E241, E201
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[-1., -1., -1., 21.8182, 21.8182, 21.8182, 21.8182, 21.8182, -1., -1., -1.], # noqa: E241, E201
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[-1., -1., -1., 19.5455, 19.5455, 19.5455, 19.5455, 19.5455, -1., -1., -1.], # noqa: E241, E201
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[-1., -1., 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, -1., -1.], # noqa: E241, E201
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[-1., -1., 15., 15., 15., 15., 15., 15., 15., -1., -1.], # noqa: E241, E201
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[-1., 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, -1.], # noqa: E241, E201
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[-1., 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, -1.], # noqa: E241, E201
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[-1., 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, -1.], # noqa: E241, E201
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[-1., -1., 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, -1., -1.], # noqa: E241, E201
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[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
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], dtype=torch.float32, device=device).view(1, 11, 11, 1)
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zbuf_t_expected = torch.tensor([
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[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, -1.0000], # noqa: E241, E201
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[-1.0000, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, -1.0000], # noqa: E241, E201
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[-1.0000, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, 19.1304, 19.1304, 19.1304, 19.1304, 19.1304, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, 24.4444, 24.4444, 24.4444, 24.4444, 24.4444, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, -1.0000, 33.8462, 33.8462, 33.8461, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
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[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
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[-1., -1., -1., -1., 33.8461, 33.8462, 33.8462, -1., -1., -1., -1.], # noqa: E241, E201
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[-1., -1., -1., 24.4444, 24.4444, 24.4444, 24.4444, 24.4444, -1., -1., -1.], # noqa: E241, E201
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[-1., -1., -1., 19.1304, 19.1304, 19.1304, 19.1304, 19.1304, -1., -1., -1.], # noqa: E241, E201
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[-1., -1., 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, -1., -1.], # noqa: E241, E201
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[-1., -1., 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, -1., -1.], # noqa: E241, E201
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[-1., 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, -1.], # noqa: E241, E201
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[-1., 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, -1.], # noqa: E241, E201
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[-1., 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, -1.], # noqa: E241, E201
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[-1., -1., 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, -1., -1.], # noqa: E241, E201
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[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.] # noqa: E241, E201
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], dtype=torch.float32, device=device).view(1, 11, 11, 1)
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# fmt: on
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@@ -618,9 +622,10 @@ class TestRasterizeMeshes(unittest.TestCase):
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def _simple_triangle_raster(self, raster_fn, device, bin_size=None):
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image_size = 10
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# Mesh with a single face.
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# Mesh with a single non-symmetrical face - this will help
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# check that the XY directions are correctly oriented.
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verts0 = torch.tensor(
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[[-0.7, -0.4, 0.1], [0.0, 0.6, 0.1], [0.7, -0.4, 0.1]],
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[[-0.3, -0.4, 0.1], [0.0, 0.6, 0.1], [0.9, -0.4, 0.1]],
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dtype=torch.float32,
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device=device,
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)
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@@ -630,7 +635,7 @@ class TestRasterizeMeshes(unittest.TestCase):
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# fmt: off
|
||||
verts1 = torch.tensor(
|
||||
[
|
||||
[-0.7, -0.4, 0.1], # noqa: E241, E201
|
||||
[-0.9, -0.2, 0.1], # noqa: E241, E201
|
||||
[ 0.0, 0.6, 0.1], # noqa: E241, E201
|
||||
[ 0.7, -0.4, 0.1], # noqa: E241, E201
|
||||
[-0.7, 0.4, 0.5], # noqa: E241, E201
|
||||
@@ -645,6 +650,8 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
[[1, 0, 2], [3, 4, 5]], dtype=torch.int64, device=device
|
||||
)
|
||||
|
||||
# Expected output tensors in the format with axes +X left, +Y up, +Z in
|
||||
# k = 0, closest point.
|
||||
# fmt off
|
||||
expected_p2face_k0 = torch.tensor(
|
||||
[
|
||||
@@ -652,10 +659,10 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 0, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 0, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, 0, 0, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
@@ -663,11 +670,11 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
[
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 1, 1, 1, 1, 1, 1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 1, 1, 1, 1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 1, 1, 1, 1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 2, 2, 1, 1, 2, 2, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, 1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 2, 2, 1, 1, 1, 2, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 1, 1, 1, 1, 1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 1, 1, 1, 1, 1, 1, -1], # noqa: E241, E201
|
||||
[-1, -1, 1, 1, 1, 2, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
@@ -677,49 +684,37 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
device=device,
|
||||
)
|
||||
expected_zbuf_k0 = torch.tensor(
|
||||
[
|
||||
[
|
||||
[
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
],
|
||||
[
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 0.5, 0.5, 0.1, 0.1, 0.5, 0.5, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
],
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 0.1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
|
||||
],
|
||||
[
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 0.5, 0.5, 0.1, 0.1, 0.1, 0.5, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1], # noqa: E241, E201
|
||||
[-1, -1, 0.1, 0.1, 0.1, 0.5, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
|
||||
]
|
||||
],
|
||||
device=device,
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
meshes = Meshes(verts=[verts0, verts1], faces=[faces0, faces1])
|
||||
if bin_size == -1:
|
||||
# simple python case with no binning
|
||||
p2face, zbuf, bary, pix_dists = raster_fn(
|
||||
meshes, image_size, 0.0, 2
|
||||
)
|
||||
else:
|
||||
p2face, zbuf, bary, pix_dists = raster_fn(
|
||||
meshes, image_size, 0.0, 2, bin_size
|
||||
)
|
||||
# k = 0, closest point.
|
||||
self.assertTrue(torch.allclose(p2face[..., 0], expected_p2face_k0))
|
||||
self.assertTrue(torch.allclose(zbuf[..., 0], expected_zbuf_k0))
|
||||
|
||||
# k = 1, second closest point.
|
||||
expected_p2face_k1 = expected_p2face_k0.clone()
|
||||
@@ -729,18 +724,18 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
|
||||
# fmt: off
|
||||
expected_p2face_k1[1, :] = torch.tensor(
|
||||
[
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 2, 2, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 2, 2, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
],
|
||||
[
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 2, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
|
||||
],
|
||||
dtype=torch.int64,
|
||||
device=device,
|
||||
)
|
||||
@@ -748,21 +743,35 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
expected_zbuf_k1[0, :] = torch.ones_like(expected_zbuf_k1[0, :]) * -1
|
||||
expected_zbuf_k1[1, :] = torch.tensor(
|
||||
[
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 0.5, 0.5, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0.5, 0.5, 0.5, 0.5, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0.5, 0.5, 0.5, 0.5, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 0.5, 0.5, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., 0.5, 0.5, 0.5, -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., 0.5, 0.5, 0.5, 0.5, -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., 0.5, 0.5, 0.5, 0.5, -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., 0.5, -1., -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.] # noqa: E241, E201
|
||||
],
|
||||
dtype=torch.float32,
|
||||
device=device,
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Coordinate conventions +Y up, +Z in, +X left
|
||||
if bin_size == -1:
|
||||
# simple python, no bin_size
|
||||
p2face, zbuf, bary, pix_dists = raster_fn(
|
||||
meshes, image_size, 0.0, 2
|
||||
)
|
||||
else:
|
||||
p2face, zbuf, bary, pix_dists = raster_fn(
|
||||
meshes, image_size, 0.0, 2, bin_size
|
||||
)
|
||||
|
||||
self.assertTrue(torch.allclose(p2face[..., 0], expected_p2face_k0))
|
||||
self.assertTrue(torch.allclose(zbuf[..., 0], expected_zbuf_k0))
|
||||
self.assertTrue(torch.allclose(p2face[..., 1], expected_p2face_k1))
|
||||
self.assertTrue(torch.allclose(zbuf[..., 1], expected_zbuf_k1))
|
||||
|
||||
@@ -778,9 +787,9 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
# fmt: off
|
||||
verts = torch.tensor(
|
||||
[
|
||||
[ -0.5, 0.0, 0.1], # noqa: E241, E201
|
||||
[ -0.3, 0.0, 0.1], # noqa: E241, E201
|
||||
[ 0.0, 0.6, 0.1], # noqa: E241, E201
|
||||
[ 0.5, 0.0, 0.1], # noqa: E241, E201
|
||||
[ 0.8, 0.0, 0.1], # noqa: E241, E201
|
||||
[-0.25, 0.0, 0.9], # noqa: E241, E201
|
||||
[0.25, 0.5, 0.9], # noqa: E241, E201
|
||||
[0.75, 0.0, 0.9], # noqa: E241, E201
|
||||
@@ -794,6 +803,8 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
dtype=torch.float32,
|
||||
device=device,
|
||||
)
|
||||
# Face with index 0 is non symmetric about the X and Y axis to
|
||||
# test that the positive Y and X directions are correct in the output.
|
||||
faces_packed = torch.tensor(
|
||||
[[1, 0, 2], [4, 3, 5], [7, 6, 8], [10, 9, 11]],
|
||||
dtype=torch.int64,
|
||||
@@ -803,12 +814,12 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
[
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, 2, 2, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, 2, 0, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, 0, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, 0, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 0, 0, 0, 0, 0, 0, 2, -1], # noqa: E241, E201
|
||||
[-1, -1, 0, 0, 0, 0, 0, 0, 2, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0, 0, 0, 0, 2, 2, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 0, 0, 2, 2, 2, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
],
|
||||
@@ -817,16 +828,16 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
)
|
||||
expected_zbuf = torch.tensor(
|
||||
[
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.5, -1], # noqa: E241, E201
|
||||
[-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.5, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, 0.5, 0.5, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, 0.1, 0.1, 0.5, 0.5, 0.5, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., 0.5, 0.5, 0.1, 0.1, 0.1, -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., 0.5, 0.1, 0.1, 0.1, 0.1, -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
|
||||
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.] # noqa: E241, E201
|
||||
],
|
||||
dtype=torch.float32,
|
||||
device=device,
|
||||
@@ -837,7 +848,7 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
faces = faces_packed[order] # rearrange order of faces.
|
||||
mesh = Meshes(verts=[verts], faces=[faces])
|
||||
if bin_size == -1:
|
||||
# simple python case with no binning
|
||||
# simple python, no bin size arg
|
||||
pix_to_face, zbuf, bary_coords, dists = raster_fn(
|
||||
mesh, image_size, blur_radius, faces_per_pixel
|
||||
)
|
||||
@@ -845,7 +856,6 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
pix_to_face, zbuf, bary_coords, dists = raster_fn(
|
||||
mesh, image_size, blur_radius, faces_per_pixel, bin_size
|
||||
)
|
||||
|
||||
if i == 0:
|
||||
expected_dists = dists
|
||||
p2f = expected_p2f.clone()
|
||||
@@ -861,33 +871,37 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
|
||||
def _test_coarse_rasterize(self, device):
|
||||
image_size = 16
|
||||
blur_radius = 0.2 ** 2
|
||||
# No blurring. This test checks that the XY directions are
|
||||
# correctly oriented.
|
||||
blur_radius = 0.0
|
||||
bin_size = 8
|
||||
max_faces_per_bin = 3
|
||||
|
||||
# fmt: off
|
||||
verts = torch.tensor(
|
||||
[
|
||||
[-0.5, 0.0, 0.1], # noqa: E241, E201
|
||||
[ 0.0, 0.6, 0.1], # noqa: E241, E201
|
||||
[ 0.5, 0.0, 0.1], # noqa: E241, E201
|
||||
[-0.3, 0.0, 0.4], # noqa: E241, E201
|
||||
[ 0.3, 0.5, 0.4], # noqa: E241, E201
|
||||
[0.75, 0.0, 0.4], # noqa: E241, E201
|
||||
[-0.4, -0.3, 0.9], # noqa: E241, E201
|
||||
[ 0.2, -0.7, 0.9], # noqa: E241, E201
|
||||
[ 0.4, -0.3, 0.9], # noqa: E241, E201
|
||||
[-0.4, 0.0, -1.5], # noqa: E241, E201
|
||||
[ 0.6, 0.6, -1.5], # noqa: E241, E201
|
||||
[ 0.8, 0.0, -1.5], # noqa: E241, E201
|
||||
[-0.5, 0.1, 0.1], # noqa: E241, E201
|
||||
[-0.3, 0.6, 0.1], # noqa: E241, E201
|
||||
[-0.1, 0.1, 0.1], # noqa: E241, E201
|
||||
[-0.3, -0.1, 0.4], # noqa: E241, E201
|
||||
[ 0.3, 0.5, 0.4], # noqa: E241, E201
|
||||
[0.75, -0.1, 0.4], # noqa: E241, E201
|
||||
[ 0.2, -0.3, 0.9], # noqa: E241, E201
|
||||
[ 0.3, -0.7, 0.9], # noqa: E241, E201
|
||||
[ 0.6, -0.3, 0.9], # noqa: E241, E201
|
||||
[-0.4, 0.0, -1.5], # noqa: E241, E201
|
||||
[ 0.6, 0.6, -1.5], # noqa: E241, E201
|
||||
[ 0.8, 0.0, -1.5], # noqa: E241, E201
|
||||
],
|
||||
device=device,
|
||||
)
|
||||
# Expected faces using axes convention +Y down, + X right, +Z in
|
||||
# Non symmetrical triangles i.e face 0 and 3 are in one bin only
|
||||
faces = torch.tensor(
|
||||
[
|
||||
[ 1, 0, 2], # noqa: E241, E201 bin 00 and bin 01
|
||||
[ 4, 3, 5], # noqa: E241, E201 bin 00 and bin 01
|
||||
[ 7, 6, 8], # noqa: E241, E201 bin 10 and bin 11
|
||||
[ 1, 0, 2], # noqa: E241, E201 bin 01 only
|
||||
[ 4, 3, 5], # noqa: E241, E201 all bins
|
||||
[ 7, 6, 8], # noqa: E241, E201 bin 10 only
|
||||
[10, 9, 11], # noqa: E241, E201 negative z, should not appear.
|
||||
],
|
||||
dtype=torch.int64,
|
||||
@@ -900,16 +914,19 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
num_faces_per_mesh = meshes.num_faces_per_mesh()
|
||||
mesh_to_face_first_idx = meshes.mesh_to_faces_packed_first_idx()
|
||||
|
||||
# Expected faces using axes convention +Y down, + X right, + Z in
|
||||
bin_faces_expected = (
|
||||
torch.ones(
|
||||
(1, 2, 2, max_faces_per_bin), dtype=torch.int32, device=device
|
||||
)
|
||||
* -1
|
||||
)
|
||||
bin_faces_expected[0, 0, 0, 0:2] = torch.tensor([0, 1])
|
||||
bin_faces_expected[0, 0, 0, 0] = torch.tensor([1])
|
||||
bin_faces_expected[0, 1, 0, 0:2] = torch.tensor([1, 2])
|
||||
bin_faces_expected[0, 0, 1, 0:2] = torch.tensor([0, 1])
|
||||
bin_faces_expected[0, 1, 0, 0:3] = torch.tensor([0, 1, 2])
|
||||
bin_faces_expected[0, 1, 1, 0:3] = torch.tensor([0, 1, 2])
|
||||
bin_faces_expected[0, 1, 1, 0] = torch.tensor([1])
|
||||
|
||||
# +Y up, +X left, +Z in
|
||||
bin_faces = _C._rasterize_meshes_coarse(
|
||||
faces_verts,
|
||||
mesh_to_face_first_idx,
|
||||
@@ -919,9 +936,8 @@ class TestRasterizeMeshes(unittest.TestCase):
|
||||
bin_size,
|
||||
max_faces_per_bin,
|
||||
)
|
||||
bin_faces_same = (
|
||||
bin_faces.squeeze().flip(dims=[0]) == bin_faces_expected
|
||||
).all()
|
||||
# Flip x and y axis of output before comparing to expected
|
||||
bin_faces_same = (bin_faces.squeeze() == bin_faces_expected).all()
|
||||
self.assertTrue(bin_faces_same.item() == 1)
|
||||
|
||||
@staticmethod
|
||||
|
||||
Reference in New Issue
Block a user