# Copyright (c) OpenMMLab. All rights reserved. import numpy as np import pytest import torch from mmdet3d.core.voxel.voxel_generator import VoxelGenerator from mmdet3d.datasets.pipelines import LoadPointsFromFile from mmdet3d.ops.voxel.voxelize import Voxelization def _get_voxel_points_indices(points, coors, voxel): result_form = np.equal(coors, voxel) return result_form[:, 0] & result_form[:, 1] & result_form[:, 2] def test_voxelization(): voxel_size = [0.5, 0.5, 0.5] point_cloud_range = [0, -40, -3, 70.4, 40, 1] max_num_points = 1000 self = VoxelGenerator(voxel_size, point_cloud_range, max_num_points) data_path = './tests/data/kitti/training/velodyne_reduced/000000.bin' load_points_from_file = LoadPointsFromFile( coord_type='LIDAR', load_dim=4, use_dim=4) results = dict() results['pts_filename'] = data_path results = load_points_from_file(results) points = results['points'].tensor.numpy() voxels_generator = self.generate(points) coors, voxels, num_points_per_voxel = voxels_generator expected_coors = coors expected_voxels = voxels expected_num_points_per_voxel = num_points_per_voxel points = torch.tensor(points) max_num_points = -1 dynamic_voxelization = Voxelization(voxel_size, point_cloud_range, max_num_points) max_num_points = 1000 hard_voxelization = Voxelization(voxel_size, point_cloud_range, max_num_points) # test hard_voxelization on cpu coors, voxels, num_points_per_voxel = hard_voxelization.forward(points) coors = coors.detach().numpy() voxels = voxels.detach().numpy() num_points_per_voxel = num_points_per_voxel.detach().numpy() assert np.all(coors == expected_coors) assert np.all(voxels == expected_voxels) assert np.all(num_points_per_voxel == expected_num_points_per_voxel) # test dynamic_voxelization on cpu coors = dynamic_voxelization.forward(points) coors = coors.detach().numpy() points = points.detach().numpy() for i in range(expected_voxels.shape[0]): indices = _get_voxel_points_indices(points, coors, expected_voxels[i]) num_points_current_voxel = points[indices].shape[0] assert num_points_current_voxel > 0 assert np.all( points[indices] == expected_coors[i][:num_points_current_voxel]) assert num_points_current_voxel == expected_num_points_per_voxel[i] if not torch.cuda.is_available(): pytest.skip('test requires GPU and torch+cuda') # test hard_voxelization on gpu points = torch.tensor(points).contiguous().to(device='cuda:0') coors, voxels, num_points_per_voxel = hard_voxelization.forward(points) coors = coors.cpu().detach().numpy() voxels = voxels.cpu().detach().numpy() num_points_per_voxel = num_points_per_voxel.cpu().detach().numpy() assert np.all(coors == expected_coors) assert np.all(voxels == expected_voxels) assert np.all(num_points_per_voxel == expected_num_points_per_voxel) # test dynamic_voxelization on gpu coors = dynamic_voxelization.forward(points) coors = coors.cpu().detach().numpy() points = points.cpu().detach().numpy() for i in range(expected_voxels.shape[0]): indices = _get_voxel_points_indices(points, coors, expected_voxels[i]) num_points_current_voxel = points[indices].shape[0] assert num_points_current_voxel > 0 assert np.all( points[indices] == expected_coors[i][:num_points_current_voxel]) assert num_points_current_voxel == expected_num_points_per_voxel[i] def test_voxelization_nondeterministic(): if not torch.cuda.is_available(): pytest.skip('test requires GPU and torch+cuda') voxel_size = [0.5, 0.5, 0.5] point_cloud_range = [0, -40, -3, 70.4, 40, 1] data_path = './tests/data/kitti/training/velodyne_reduced/000000.bin' load_points_from_file = LoadPointsFromFile( coord_type='LIDAR', load_dim=4, use_dim=4) results = dict() results['pts_filename'] = data_path results = load_points_from_file(results) points = results['points'].tensor.numpy() points = torch.tensor(points) max_num_points = -1 dynamic_voxelization = Voxelization(voxel_size, point_cloud_range, max_num_points) max_num_points = 10 max_voxels = 50 hard_voxelization = Voxelization( voxel_size, point_cloud_range, max_num_points, max_voxels, deterministic=False) # test hard_voxelization (non-deterministic version) on gpu points = torch.tensor(points).contiguous().to(device='cuda:0') voxels, coors, num_points_per_voxel = hard_voxelization.forward(points) coors = coors.cpu().detach().numpy().tolist() voxels = voxels.cpu().detach().numpy().tolist() num_points_per_voxel = num_points_per_voxel.cpu().detach().numpy().tolist() coors_all = dynamic_voxelization.forward(points) coors_all = coors_all.cpu().detach().numpy().tolist() coors_set = set([tuple(c) for c in coors]) coors_all_set = set([tuple(c) for c in coors_all]) assert len(coors_set) == len(coors) assert len(coors_set - coors_all_set) == 0 points = points.cpu().detach().numpy().tolist() coors_points_dict = {} for c, ps in zip(coors_all, points): if tuple(c) not in coors_points_dict: coors_points_dict[tuple(c)] = set() coors_points_dict[tuple(c)].add(tuple(ps)) for c, ps, n in zip(coors, voxels, num_points_per_voxel): ideal_voxel_points_set = coors_points_dict[tuple(c)] voxel_points_set = set([tuple(p) for p in ps[:n]]) assert len(voxel_points_set) == n if n < max_num_points: assert voxel_points_set == ideal_voxel_points_set for p in ps[n:]: assert max(p) == min(p) == 0 else: assert len(voxel_points_set - ideal_voxel_points_set) == 0 # test hard_voxelization (non-deterministic version) on gpu # with all input point in range points = torch.tensor(points).contiguous().to(device='cuda:0')[:max_voxels] coors_all = dynamic_voxelization.forward(points) valid_mask = coors_all.ge(0).all(-1) points = points[valid_mask] coors_all = coors_all[valid_mask] coors_all = coors_all.cpu().detach().numpy().tolist() voxels, coors, num_points_per_voxel = hard_voxelization.forward(points) coors = coors.cpu().detach().numpy().tolist() coors_set = set([tuple(c) for c in coors]) coors_all_set = set([tuple(c) for c in coors_all]) assert len(coors_set) == len(coors) == len(coors_all_set)