import numpy as np import pytest import torch from mmcv.ops 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] @pytest.mark.parametrize('device_type', [ 'cpu', pytest.param( 'cuda:0', marks=pytest.mark.skipif( not torch.cuda.is_available(), reason='requires CUDA support')) ]) def test_voxelization(device_type): voxel_size = [0.5, 0.5, 0.5] point_cloud_range = [0, -40, -3, 70.4, 40, 1] voxel_dict = np.load( 'tests/data/for_3d_ops/test_voxel.npy', allow_pickle=True).item() expected_coors = voxel_dict['coors'] expected_voxels = voxel_dict['voxels'] expected_num_points_per_voxel = voxel_dict['num_points_per_voxel'] points = voxel_dict['points'] 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) device = torch.device(device_type) # test hard_voxelization on cpu/gpu points = points.contiguous().to(device) 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 cpu/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]