mirror of
https://github.com/facebookresearch/pytorch3d.git
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e6a32bfc37
Reviewed By: zertosh Differential Revision: D25800514 fbshipit-source-id: 191b2753b8fcfbe2386c761241aaeb58939a973e
387 lines
14 KiB
Python
387 lines
14 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import unittest
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from typing import Tuple
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import numpy as np
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import torch
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from common_testing import TestCaseMixin
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from pytorch3d.ops import add_pointclouds_to_volumes
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from pytorch3d.ops.sample_points_from_meshes import sample_points_from_meshes
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from pytorch3d.structures.meshes import Meshes
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from pytorch3d.structures.pointclouds import Pointclouds
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from pytorch3d.structures.volumes import Volumes
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from pytorch3d.transforms.so3 import so3_exponential_map
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DEBUG = False
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if DEBUG:
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import os
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import tempfile
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from PIL import Image
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def init_cube_point_cloud(
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batch_size: int = 10, n_points: int = 100000, rotate_y: bool = True
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):
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"""
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Generate a random point cloud of `n_points` whose points of
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which are sampled from faces of a 3D cube.
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"""
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# create the cube mesh batch_size times
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meshes = TestPointsToVolumes.init_cube_mesh(batch_size)
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# generate point clouds by sampling points from the meshes
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pcl = sample_points_from_meshes(meshes, num_samples=n_points, return_normals=False)
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# colors of the cube sides
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clrs = [
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[1.0, 0.0, 0.0],
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[1.0, 1.0, 0.0],
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[0.0, 1.0, 0.0],
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[0.0, 1.0, 1.0],
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[1.0, 1.0, 1.0],
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[1.0, 0.0, 1.0],
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]
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# init the color tensor "rgb"
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rgb = torch.zeros_like(pcl)
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# color each side of the cube with a constant color
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clri = 0
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for dim in (0, 1, 2):
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for offs in (0.0, 1.0):
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current_face_verts = (pcl[:, :, dim] - offs).abs() <= 1e-2
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for bi in range(batch_size):
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rgb[bi, current_face_verts[bi], :] = torch.tensor(clrs[clri]).type_as(
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pcl
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)
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clri += 1
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if rotate_y:
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# uniformly spaced rotations around y axis
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R = init_uniform_y_rotations(batch_size=batch_size)
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# rotate the point clouds around y axis
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pcl = torch.bmm(pcl - 0.5, R) + 0.5
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return pcl, rgb
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def init_volume_boundary_pointcloud(
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batch_size: int,
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volume_size: Tuple[int, int, int],
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n_points: int,
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interp_mode: str,
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require_grad: bool = False,
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):
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"""
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Initialize a point cloud that closely follows a boundary of
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a volume with a given size. The volume buffer is initialized as well.
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"""
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# generate a 3D point cloud sampled from sides of a [0,1] cube
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xyz, rgb = init_cube_point_cloud(batch_size, n_points=n_points, rotate_y=True)
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# make volume_size tensor
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volume_size_t = torch.tensor(volume_size, dtype=xyz.dtype, device=xyz.device)
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if interp_mode == "trilinear":
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# make the xyz locations fall on the boundary of the
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# first/last two voxels along each spatial dimension of the
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# volume - this properly checks the correctness of the
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# trilinear interpolation scheme
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xyz = (xyz - 0.5) * ((volume_size_t - 2) / (volume_size_t - 1))[[2, 1, 0]] + 0.5
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# rescale the cube pointcloud to overlap with the volume sides
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# of the volume
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rel_scale = volume_size_t / volume_size[0]
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xyz = xyz * rel_scale[[2, 1, 0]][None, None]
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# enable grad accumulation for the differentiability check
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xyz.requires_grad = require_grad
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rgb.requires_grad = require_grad
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# create the pointclouds structure
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pointclouds = Pointclouds(xyz, features=rgb)
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# set the volume translation so that the point cloud is centered
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# around 0
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volume_translation = -0.5 * rel_scale[[2, 1, 0]]
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# set the voxel size to 1 / (volume_size-1)
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volume_voxel_size = 1 / (volume_size[0] - 1.0)
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# instantiate the volumes
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initial_volumes = Volumes(
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features=xyz.new_zeros(batch_size, 3, *volume_size),
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densities=xyz.new_zeros(batch_size, 1, *volume_size),
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volume_translation=volume_translation,
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voxel_size=volume_voxel_size,
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)
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return pointclouds, initial_volumes
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def init_uniform_y_rotations(batch_size: int = 10):
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"""
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Generate a batch of `batch_size` 3x3 rotation matrices around y-axis
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whose angles are uniformly distributed between 0 and 2 pi.
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"""
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device = torch.device("cuda:0")
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axis = torch.tensor([0.0, 1.0, 0.0], device=device, dtype=torch.float32)
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angles = torch.linspace(0, 2.0 * np.pi, batch_size + 1, device=device)
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angles = angles[:batch_size]
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log_rots = axis[None, :] * angles[:, None]
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R = so3_exponential_map(log_rots)
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return R
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class TestPointsToVolumes(TestCaseMixin, unittest.TestCase):
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def setUp(self) -> None:
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np.random.seed(42)
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torch.manual_seed(42)
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@staticmethod
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def add_points_to_volumes(
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batch_size: int,
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volume_size: Tuple[int, int, int],
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n_points: int,
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interp_mode: str,
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):
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(pointclouds, initial_volumes) = init_volume_boundary_pointcloud(
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batch_size=batch_size,
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volume_size=volume_size,
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n_points=n_points,
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interp_mode=interp_mode,
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require_grad=False,
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)
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def _add_points_to_volumes():
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add_pointclouds_to_volumes(pointclouds, initial_volumes, mode=interp_mode)
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return _add_points_to_volumes
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@staticmethod
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def stack_4d_tensor_to_3d(arr):
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n = arr.shape[0]
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H = int(np.ceil(np.sqrt(n)))
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W = int(np.ceil(n / H))
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n_add = H * W - n
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arr = torch.cat((arr, torch.zeros_like(arr[:1]).repeat(n_add, 1, 1, 1)))
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rows = torch.chunk(arr, chunks=W, dim=0)
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arr3d = torch.cat([torch.cat(list(row), dim=2) for row in rows], dim=1)
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return arr3d
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@staticmethod
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def init_cube_mesh(batch_size: int = 10):
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"""
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Generate a batch of `batch_size` cube meshes.
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"""
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device = torch.device("cuda:0")
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verts, faces = [], []
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for _ in range(batch_size):
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v = torch.tensor(
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[
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[0.0, 0.0, 0.0],
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[1.0, 0.0, 0.0],
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[1.0, 1.0, 0.0],
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[0.0, 1.0, 0.0],
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[0.0, 1.0, 1.0],
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[1.0, 1.0, 1.0],
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[1.0, 0.0, 1.0],
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[0.0, 0.0, 1.0],
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],
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dtype=torch.float32,
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device=device,
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)
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verts.append(v)
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faces.append(
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torch.tensor(
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[
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[0, 2, 1],
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[0, 3, 2],
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[2, 3, 4],
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[2, 4, 5],
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[1, 2, 5],
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[1, 5, 6],
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[0, 7, 4],
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[0, 4, 3],
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[5, 4, 7],
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[5, 7, 6],
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[0, 6, 7],
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[0, 1, 6],
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],
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dtype=torch.int64,
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device=device,
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)
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)
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faces = torch.stack(faces)
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verts = torch.stack(verts)
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simpleces = Meshes(verts=verts, faces=faces)
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return simpleces
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def test_from_point_cloud(self, interp_mode="trilinear"):
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"""
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Generates a volume from a random point cloud sampled from faces
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of a 3D cube. Since each side of the cube is homogenously colored with
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a different color, this should result in a volume with a
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predefined homogenous color of the cells along its borders
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and black interior. The test is run for both cube and non-cube shaped
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volumes.
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"""
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# batch_size = 4 sides of the cube
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batch_size = 4
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for volume_size in ([25, 25, 25], [30, 25, 15]):
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for interp_mode in ("trilinear", "nearest"):
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(pointclouds, initial_volumes) = init_volume_boundary_pointcloud(
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volume_size=volume_size,
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n_points=int(1e5),
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interp_mode=interp_mode,
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batch_size=batch_size,
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require_grad=True,
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)
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volumes = add_pointclouds_to_volumes(
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pointclouds, initial_volumes, mode=interp_mode
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)
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V_color, V_density = volumes.features(), volumes.densities()
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# expected colors of different cube sides
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clr_sides = torch.tensor(
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[
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[[1.0, 1.0, 1.0], [1.0, 0.0, 1.0]],
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[[1.0, 0.0, 0.0], [1.0, 1.0, 0.0]],
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[[1.0, 0.0, 1.0], [1.0, 1.0, 1.0]],
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[[1.0, 1.0, 0.0], [1.0, 0.0, 0.0]],
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],
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dtype=V_color.dtype,
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device=V_color.device,
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)
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clr_ambient = torch.tensor(
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[0.0, 0.0, 0.0], dtype=V_color.dtype, device=V_color.device
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)
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clr_top_bot = torch.tensor(
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[[0.0, 1.0, 0.0], [0.0, 1.0, 1.0]],
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dtype=V_color.dtype,
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device=V_color.device,
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)
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if DEBUG:
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outdir = tempfile.gettempdir() + "/test_points_to_volumes"
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os.makedirs(outdir, exist_ok=True)
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for slice_dim in (1, 2):
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for vidx in range(V_color.shape[0]):
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vim = V_color.detach()[vidx].split(1, dim=slice_dim)
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vim = torch.stack([v.squeeze() for v in vim])
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vim = TestPointsToVolumes.stack_4d_tensor_to_3d(vim.cpu())
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im = Image.fromarray(
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(vim.numpy() * 255.0)
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.astype(np.uint8)
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.transpose(1, 2, 0)
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)
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outfile = (
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outdir
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+ f"/rgb_{interp_mode}"
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+ f"_{str(volume_size).replace(' ','')}"
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+ f"_{vidx:003d}_sldim{slice_dim}.png"
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)
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im.save(outfile)
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print("exported %s" % outfile)
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# check the density V_density
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# first binarize the density
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V_density_bin = (V_density > 1e-4).type_as(V_density)
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d_one = V_density.new_ones(1)
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d_zero = V_density.new_zeros(1)
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for vidx in range(V_color.shape[0]):
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# the first/last depth-wise slice has to be filled with 1.0
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self._check_volume_slice_color_density(
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V_density_bin[vidx], 1, interp_mode, d_one, "first"
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)
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self._check_volume_slice_color_density(
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V_density_bin[vidx], 1, interp_mode, d_one, "last"
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)
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# the middle depth-wise slices have to be empty
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self._check_volume_slice_color_density(
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V_density_bin[vidx], 1, interp_mode, d_zero, "middle"
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)
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# the top/bottom slices have to be filled with 1.0
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self._check_volume_slice_color_density(
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V_density_bin[vidx], 2, interp_mode, d_one, "first"
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)
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self._check_volume_slice_color_density(
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V_density_bin[vidx], 2, interp_mode, d_one, "last"
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)
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# check the colors
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for vidx in range(V_color.shape[0]):
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self._check_volume_slice_color_density(
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V_color[vidx], 1, interp_mode, clr_sides[vidx][0], "first"
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)
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self._check_volume_slice_color_density(
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V_color[vidx], 1, interp_mode, clr_sides[vidx][1], "last"
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)
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self._check_volume_slice_color_density(
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V_color[vidx], 1, interp_mode, clr_ambient, "middle"
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)
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self._check_volume_slice_color_density(
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V_color[vidx], 2, interp_mode, clr_top_bot[0], "first"
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)
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self._check_volume_slice_color_density(
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V_color[vidx], 2, interp_mode, clr_top_bot[1], "last"
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)
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# check differentiability
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loss = V_color.mean() + V_density.mean()
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loss.backward()
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rgb = pointclouds.features_padded()
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xyz = pointclouds.points_padded()
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for field in (xyz, rgb):
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if interp_mode == "nearest" and (field is xyz):
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# this does not produce grads w.r.t. xyz
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self.assertIsNone(field.grad)
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else:
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self.assertTrue(field.grad.data.isfinite().all())
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def _check_volume_slice_color_density(
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self, V, split_dim, interp_mode, clr_gt, slice_type, border=3
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):
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# decompose the volume to individual slices along split_dim
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vim = V.detach().split(1, dim=split_dim)
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vim = torch.stack([v.squeeze(split_dim) for v in vim])
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# determine which slices should be compared to clr_gt based on
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# the 'slice_type' input
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if slice_type == "first":
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slice_dims = (0, 1) if interp_mode == "trilinear" else (0,)
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elif slice_type == "last":
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slice_dims = (-1, -2) if interp_mode == "trilinear" else (-1,)
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elif slice_type == "middle":
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internal_border = 2 if interp_mode == "trilinear" else 1
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slice_dims = torch.arange(internal_border, vim.shape[0] - internal_border)
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else:
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raise ValueError(slice_type)
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# compute the average error within each slice
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clr_diff = (
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vim[slice_dims, :, border:-border, border:-border]
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- clr_gt[None, :, None, None]
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)
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clr_diff = clr_diff.abs().mean(dim=(2, 3)).view(-1)
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# check that all per-slice avg errors vanish
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self.assertClose(clr_diff, torch.zeros_like(clr_diff), atol=1e-2)
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