mirror of
https://github.com/facebookresearch/pytorch3d.git
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34bbb3ad32
Summary: Applies new import merging and sorting from µsort v1.0. When merging imports, µsort will make a best-effort to move associated comments to match merged elements, but there are known limitations due to the diynamic nature of Python and developer tooling. These changes should not produce any dangerous runtime changes, but may require touch-ups to satisfy linters and other tooling. Note that µsort uses case-insensitive, lexicographical sorting, which results in a different ordering compared to isort. This provides a more consistent sorting order, matching the case-insensitive order used when sorting import statements by module name, and ensures that "frog", "FROG", and "Frog" always sort next to each other. For details on µsort's sorting and merging semantics, see the user guide: https://usort.readthedocs.io/en/stable/guide.html#sorting Reviewed By: bottler Differential Revision: D35553814 fbshipit-source-id: be49bdb6a4c25264ff8d4db3a601f18736d17be1
194 lines
7.5 KiB
Python
194 lines
7.5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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import torch
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from common_testing import get_random_cuda_device, TestCaseMixin
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from pytorch3d.ops.interp_face_attrs import (
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interpolate_face_attributes,
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interpolate_face_attributes_python,
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)
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from pytorch3d.renderer.mesh import TexturesVertex
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from pytorch3d.renderer.mesh.rasterizer import Fragments
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from pytorch3d.structures import Meshes
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class TestInterpolateFaceAttributes(TestCaseMixin, unittest.TestCase):
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def _test_interp_face_attrs(self, interp_fun, device):
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pix_to_face = [0, 2, -1, 0, 1, -1]
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barycentric_coords = [
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[1.0, 0.0, 0.0],
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[0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0],
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[0.5, 0.5, 0.0],
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[0.8, 0.0, 0.2],
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[0.25, 0.5, 0.25],
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]
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face_attrs = [
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[[1, 2], [3, 4], [5, 6]],
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[[7, 8], [9, 10], [11, 12]],
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[[13, 14], [15, 16], [17, 18]],
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]
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pix_attrs = [
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[1, 2],
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[15, 16],
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[0, 0],
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[2, 3],
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[0.8 * 7 + 0.2 * 11, 0.8 * 8 + 0.2 * 12],
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[0, 0],
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]
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N, H, W, K, D = 1, 2, 1, 3, 2
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pix_to_face = torch.tensor(pix_to_face, dtype=torch.int64, device=device)
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pix_to_face = pix_to_face.view(N, H, W, K)
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barycentric_coords = torch.tensor(
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barycentric_coords, dtype=torch.float32, device=device
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)
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barycentric_coords = barycentric_coords.view(N, H, W, K, 3)
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face_attrs = torch.tensor(face_attrs, dtype=torch.float32, device=device)
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pix_attrs = torch.tensor(pix_attrs, dtype=torch.float32, device=device)
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pix_attrs = pix_attrs.view(N, H, W, K, D)
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args = (pix_to_face, barycentric_coords, face_attrs)
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pix_attrs_actual = interp_fun(*args)
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self.assertClose(pix_attrs_actual, pix_attrs)
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def test_python(self):
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device = torch.device("cuda:0")
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self._test_interp_face_attrs(interpolate_face_attributes_python, device)
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def test_cuda(self):
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device = torch.device("cuda:0")
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self._test_interp_face_attrs(interpolate_face_attributes, device)
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def test_python_vs_cuda(self):
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N, H, W, K = 2, 32, 32, 5
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F = 1000
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D = 3
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device = get_random_cuda_device()
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torch.manual_seed(598)
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pix_to_face = torch.randint(-F, F, (N, H, W, K), device=device)
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barycentric_coords = torch.randn(
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N, H, W, K, 3, device=device, requires_grad=True
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)
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face_attrs = torch.randn(F, 3, D, device=device, requires_grad=True)
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grad_pix_attrs = torch.randn(N, H, W, K, D, device=device)
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args = (pix_to_face, barycentric_coords, face_attrs)
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# Run the python version
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pix_attrs_py = interpolate_face_attributes_python(*args)
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pix_attrs_py.backward(gradient=grad_pix_attrs)
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grad_bary_py = barycentric_coords.grad.clone()
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grad_face_attrs_py = face_attrs.grad.clone()
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# Clear gradients
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barycentric_coords.grad.zero_()
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face_attrs.grad.zero_()
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# Run the CUDA version
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pix_attrs_cu = interpolate_face_attributes(*args)
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pix_attrs_cu.backward(gradient=grad_pix_attrs)
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grad_bary_cu = barycentric_coords.grad.clone()
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grad_face_attrs_cu = face_attrs.grad.clone()
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# Check they are the same
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self.assertClose(pix_attrs_py, pix_attrs_cu, rtol=2e-3)
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self.assertClose(grad_bary_py, grad_bary_cu, rtol=1e-4)
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self.assertClose(grad_face_attrs_py, grad_face_attrs_cu, rtol=1e-3)
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def test_interpolate_attributes(self):
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verts = torch.randn((4, 3), dtype=torch.float32)
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faces = torch.tensor([[2, 1, 0], [3, 1, 0]], dtype=torch.int64)
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vert_tex = torch.tensor(
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[[0, 1, 0], [0, 1, 1], [1, 1, 0], [1, 1, 1]], dtype=torch.float32
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)
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tex = TexturesVertex(verts_features=vert_tex[None, :])
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mesh = Meshes(verts=[verts], faces=[faces], textures=tex)
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pix_to_face = torch.tensor([0, 1], dtype=torch.int64).view(1, 1, 1, 2)
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barycentric_coords = torch.tensor(
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[[0.5, 0.3, 0.2], [0.3, 0.6, 0.1]], dtype=torch.float32
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).view(1, 1, 1, 2, -1)
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expected_vals = torch.tensor(
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[[0.5, 1.0, 0.3], [0.3, 1.0, 0.9]], dtype=torch.float32
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).view(1, 1, 1, 2, -1)
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fragments = Fragments(
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pix_to_face=pix_to_face,
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bary_coords=barycentric_coords,
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zbuf=torch.ones_like(pix_to_face),
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dists=torch.ones_like(pix_to_face),
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)
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verts_features_packed = mesh.textures.verts_features_packed()
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faces_verts_features = verts_features_packed[mesh.faces_packed()]
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texels = interpolate_face_attributes(
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fragments.pix_to_face, fragments.bary_coords, faces_verts_features
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)
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self.assertTrue(torch.allclose(texels, expected_vals[None, :]))
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def test_interpolate_attributes_grad(self):
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verts = torch.randn((4, 3), dtype=torch.float32)
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faces = torch.tensor([[2, 1, 0], [3, 1, 0]], dtype=torch.int64)
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vert_tex = torch.tensor(
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[[0, 1, 0], [0, 1, 1], [1, 1, 0], [1, 1, 1]],
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dtype=torch.float32,
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requires_grad=True,
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)
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tex = TexturesVertex(verts_features=vert_tex[None, :])
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mesh = Meshes(verts=[verts], faces=[faces], textures=tex)
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pix_to_face = torch.tensor([0, 1], dtype=torch.int64).view(1, 1, 1, 2)
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barycentric_coords = torch.tensor(
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[[0.5, 0.3, 0.2], [0.3, 0.6, 0.1]], dtype=torch.float32
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).view(1, 1, 1, 2, -1)
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fragments = Fragments(
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pix_to_face=pix_to_face,
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bary_coords=barycentric_coords,
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zbuf=torch.ones_like(pix_to_face),
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dists=torch.ones_like(pix_to_face),
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)
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grad_vert_tex = torch.tensor(
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[[0.3, 0.3, 0.3], [0.9, 0.9, 0.9], [0.5, 0.5, 0.5], [0.3, 0.3, 0.3]],
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dtype=torch.float32,
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)
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verts_features_packed = mesh.textures.verts_features_packed()
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faces_verts_features = verts_features_packed[mesh.faces_packed()]
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texels = interpolate_face_attributes(
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fragments.pix_to_face, fragments.bary_coords, faces_verts_features
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)
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texels.sum().backward()
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self.assertTrue(hasattr(vert_tex, "grad"))
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self.assertTrue(torch.allclose(vert_tex.grad, grad_vert_tex[None, :]))
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def test_interpolate_face_attributes_fail(self):
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# 1. A face can only have 3 verts
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# i.e. face_attributes must have shape (F, 3, D)
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face_attributes = torch.ones(1, 4, 3)
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pix_to_face = torch.ones((1, 1, 1, 1))
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fragments = Fragments(
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pix_to_face=pix_to_face,
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bary_coords=pix_to_face[..., None].expand(-1, -1, -1, -1, 3),
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zbuf=pix_to_face,
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dists=pix_to_face,
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)
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with self.assertRaises(ValueError):
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interpolate_face_attributes(
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fragments.pix_to_face, fragments.bary_coords, face_attributes
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)
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# 2. pix_to_face must have shape (N, H, W, K)
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pix_to_face = torch.ones((1, 1, 1, 1, 3))
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fragments = Fragments(
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pix_to_face=pix_to_face,
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bary_coords=pix_to_face,
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zbuf=pix_to_face,
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dists=pix_to_face,
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)
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with self.assertRaises(ValueError):
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interpolate_face_attributes(
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fragments.pix_to_face, fragments.bary_coords, face_attributes
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)
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