mirror of
https://github.com/facebookresearch/pytorch3d.git
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ff19c642cb
Summary: Updates to the Renderer to enable barycentric clipping. This is important when there is blurring in the rasterization step. Also added support for flat shading. Reviewed By: jcjohnson Differential Revision: D19934259 fbshipit-source-id: 036e48636cd80d28a04405d7a29fcc71a2982904
226 lines
8.2 KiB
Python
226 lines
8.2 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import unittest
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import torch
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import torch.nn.functional as F
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from pytorch3d.renderer.mesh.rasterizer import Fragments
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from pytorch3d.renderer.mesh.texturing import (
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interpolate_face_attributes,
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interpolate_texture_map,
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interpolate_vertex_colors,
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)
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from pytorch3d.structures import Meshes, Textures
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from common_testing import TestCaseMixin
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from test_meshes import TestMeshes
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class TestTexturing(TestCaseMixin, unittest.TestCase):
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def test_interpolate_attributes(self):
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"""
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This tests both interpolate_vertex_colors as well as
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interpolate_face_attributes.
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"""
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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 = Textures(verts_rgb=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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texels = interpolate_vertex_colors(fragments, mesh)
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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 = Textures(verts_rgb=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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[
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[0.3, 0.3, 0.3],
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[0.9, 0.9, 0.9],
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[0.5, 0.5, 0.5],
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[0.3, 0.3, 0.3],
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],
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dtype=torch.float32,
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)
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texels = interpolate_vertex_colors(fragments, mesh)
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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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def test_interpolate_texture_map(self):
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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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dummy_verts = torch.zeros(4, 3)
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vert_uvs = torch.tensor(
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[[1, 0], [0, 1], [1, 1], [0, 0]], dtype=torch.float32
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)
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face_uvs = torch.tensor([[0, 1, 2], [1, 2, 3]], dtype=torch.int64)
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interpolated_uvs = torch.tensor(
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[[0.5 + 0.2, 0.3 + 0.2], [0.6, 0.3 + 0.6]], dtype=torch.float32
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)
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# Create a dummy texture map
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H = 2
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W = 2
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x = torch.linspace(0, 1, W).view(1, W).expand(H, W)
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y = torch.linspace(0, 1, H).view(H, 1).expand(H, W)
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tex_map = torch.stack([x, y], dim=2).view(1, H, W, 2)
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pix_to_face = torch.tensor([0, 1], dtype=torch.int64).view(1, 1, 1, 2)
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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=pix_to_face,
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dists=pix_to_face,
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)
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tex = Textures(
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maps=tex_map,
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faces_uvs=face_uvs[None, ...],
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verts_uvs=vert_uvs[None, ...],
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)
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meshes = Meshes(verts=[dummy_verts], faces=[face_uvs], textures=tex)
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texels = interpolate_texture_map(fragments, meshes)
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# Expected output
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pixel_uvs = interpolated_uvs * 2.0 - 1.0
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pixel_uvs = pixel_uvs.view(2, 1, 1, 2)
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tex_map = torch.flip(tex_map, [1])
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tex_map = tex_map.permute(0, 3, 1, 2)
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tex_map = torch.cat([tex_map, tex_map], dim=0)
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expected_out = F.grid_sample(tex_map, pixel_uvs, align_corners=False)
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self.assertTrue(
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torch.allclose(texels.squeeze(), expected_out.squeeze())
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)
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def test_clone(self):
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V = 20
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tex = Textures(
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maps=torch.ones((5, 16, 16, 3)),
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faces_uvs=torch.randint(size=(5, 10, 3), low=0, high=V),
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verts_uvs=torch.ones((5, V, 2)),
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)
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tex_cloned = tex.clone()
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self.assertSeparate(tex._faces_uvs_padded, tex_cloned._faces_uvs_padded)
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self.assertSeparate(tex._verts_uvs_padded, tex_cloned._verts_uvs_padded)
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self.assertSeparate(tex._maps_padded, tex_cloned._maps_padded)
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def test_to(self):
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V = 20
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tex = Textures(
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maps=torch.ones((5, 16, 16, 3)),
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faces_uvs=torch.randint(size=(5, 10, 3), low=0, high=V),
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verts_uvs=torch.ones((5, V, 2)),
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)
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device = torch.device("cuda:0")
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tex = tex.to(device)
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self.assertTrue(tex._faces_uvs_padded.device == device)
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self.assertTrue(tex._verts_uvs_padded.device == device)
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self.assertTrue(tex._maps_padded.device == device)
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def test_extend(self):
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B = 10
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mesh = TestMeshes.init_mesh(B, 30, 50)
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V = mesh._V
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F = mesh._F
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tex = Textures(
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maps=torch.randn((B, 16, 16, 3)),
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faces_uvs=torch.randint(size=(B, F, 3), low=0, high=V),
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verts_uvs=torch.randn((B, V, 2)),
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)
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tex_mesh = Meshes(
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verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tex
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)
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N = 20
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new_mesh = tex_mesh.extend(N)
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self.assertEqual(len(tex_mesh) * N, len(new_mesh))
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tex_init = tex_mesh.textures
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new_tex = new_mesh.textures
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for i in range(len(tex_mesh)):
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for n in range(N):
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self.assertClose(
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tex_init.faces_uvs_list()[i],
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new_tex.faces_uvs_list()[i * N + n],
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)
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self.assertClose(
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tex_init.verts_uvs_list()[i],
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new_tex.verts_uvs_list()[i * N + n],
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)
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self.assertAllSeparate(
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[
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tex_init.faces_uvs_padded(),
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new_tex.faces_uvs_padded(),
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tex_init.verts_uvs_padded(),
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new_tex.verts_uvs_padded(),
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tex_init.maps_padded(),
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new_tex.maps_padded(),
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]
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)
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with self.assertRaises(ValueError):
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tex_mesh.extend(N=-1)
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