# Copyright 2021 Yan Yan # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from cumm import tensorview as tv from spconv.utils import Point2VoxelCPU3d from spconv.pytorch.utils import PointToVoxel, gather_features_by_pc_voxel_id import torch def main_pytorch_voxel_gen(): np.random.seed(50051) # voxel gen source code: spconv/csrc/sparse/pointops.py gen = PointToVoxel(vsize_xyz=[0.1, 0.1, 0.1], coors_range_xyz=[-80, -80, -6, 80, 80, 6], num_point_features=3, max_num_voxels=5000, max_num_points_per_voxel=5) pc = np.random.uniform(-4, 4, size=[1000, 3]) pc_th = torch.from_numpy(pc) voxels_th, indices_th, num_p_in_vx_th = gen(pc_th) voxels_np = voxels_th.numpy() indices_np = indices_th.numpy() num_p_in_vx_np = num_p_in_vx_th.numpy() print(f"------Raw Voxels {voxels_np.shape[0]}-------") print(voxels_np[0]) # run voxel gen and FILL MEAN VALUE to voxel remain voxels_th, indices_th, num_p_in_vx_th = gen(pc_th, empty_mean=True) voxels_np = voxels_th.numpy() indices_np = indices_th.numpy() num_p_in_vx_np = num_p_in_vx_th.numpy() print("------Voxels with mean filled-------") print(voxels_np[0]) voxels_th, indices_th, num_p_in_vx_th, pc_voxel_id = gen.generate_voxel_with_id(pc_th, empty_mean=True) print("------Voxel ids for every point-------") print(pc_voxel_id[:10]) def main_pytorch_voxel_gen_cuda(): np.random.seed(50051) # voxel gen source code: spconv/csrc/sparse/pointops.py pc = np.random.uniform(-2, 8, size=[1000, 3]).astype(np.float32) for device in [torch.device("cuda:0"), torch.device("cpu:0")]: gen = PointToVoxel(vsize_xyz=[0.25, 0.25, 0.25], coors_range_xyz=[0, 0, 0, 10, 10, 10], num_point_features=3, max_num_voxels=5000, max_num_points_per_voxel=5, device=device) pc_th = torch.from_numpy(pc).to(device) voxels_th, indices_th, num_p_in_vx_th = gen(pc_th) voxels_np = voxels_th.cpu().numpy() indices_np = indices_th.cpu().numpy() num_p_in_vx_np = num_p_in_vx_th.cpu().numpy() print(f"------{device} Raw Voxels {voxels_np.shape[0]}-------") print(voxels_np[0]) # run voxel gen and FILL MEAN VALUE to voxel remain voxels_tv, indices_tv, num_p_in_vx_tv = gen(pc_th, empty_mean=True) voxels_np = voxels_tv.cpu().numpy() indices_np = indices_tv.cpu().numpy() num_p_in_vx_np = num_p_in_vx_tv.cpu().numpy() print(f"------{device} Voxels with mean filled-------") print(voxels_np[0]) voxels_th, indices_th, num_p_in_vx_th, pc_voxel_id = gen.generate_voxel_with_id(pc_th, empty_mean=True) print(f"------{device} Reconstruct Indices From Voxel ids for every point-------") indices_th_float = indices_th.float() # we gather indices by voxel_id to see correctness of voxel id. indices_th_voxel_id = gather_features_by_pc_voxel_id(indices_th_float, pc_voxel_id) indices_th_voxel_id_np = indices_th_voxel_id[:10].cpu().numpy() print(pc[:10]) print(indices_th_voxel_id_np[:, ::-1] / 4) def main_gather_features_by_pc_voxel_id(): np.random.seed(50051) # voxel gen source code: spconv/csrc/sparse/pointops.py device = torch.device("cuda:0") gen = PointToVoxel(vsize_xyz=[0.25, 0.25, 0.25], coors_range_xyz=[-10, -10, -10, 10, 10, 10], num_point_features=3, max_num_voxels=2000, max_num_points_per_voxel=5, device=device) pc = np.random.uniform(-8, 8, size=[5000, 3]).astype(np.float32) pc_th = torch.from_numpy(pc).to(device) voxels_th, indices_th, num_p_in_vx_th, pc_voxel_id = gen.generate_voxel_with_id(pc_th, empty_mean=True) res_features_from_seg = torch.zeros((voxels_th.shape[0], 128), dtype=torch.float32, device=device) pc_features = gather_features_by_pc_voxel_id(res_features_from_seg, pc_voxel_id) print(pc.shape, pc_features.shape) def main(): np.random.seed(50051) # voxel gen source code: spconv/csrc/sparse/pointops.py gen = Point2VoxelCPU3d(vsize_xyz=[0.1, 0.1, 0.1], coors_range_xyz=[-80, -80, -2, 80, 80, 6], num_point_features=3, max_num_voxels=5000, max_num_points_per_voxel=5) pc = np.random.uniform(-10, 10, size=[1000, 3]) pc_tv = tv.from_numpy(pc) # generate voxels, note that voxels_tv reference to a persistent buffer in generator, # so we can't run it in multi-thread. voxels_tv, indices_tv, num_p_in_vx_tv = gen.point_to_voxel(pc_tv) voxels_np = voxels_tv.numpy_view() indices_np = indices_tv.numpy_view() num_p_in_vx_np = num_p_in_vx_tv.numpy_view() print(f"------Raw Voxels {voxels_np.shape[0]}-------") print(voxels_np[0]) # run voxel gen and FILL MEAN VALUE to voxel remain voxels_tv, indices_tv, num_p_in_vx_tv = gen.point_to_voxel_empty_mean( pc_tv) voxels_np = voxels_tv.numpy_view() indices_np = indices_tv.numpy_view() num_p_in_vx_np = num_p_in_vx_tv.numpy_view() print("------Voxels with mean filled-------") print(voxels_np[0]) def main_point_with_features(): np.random.seed(50051) # voxel gen source code: spconv/csrc/sparse/pointops.py gen = Point2VoxelCPU3d( vsize_xyz=[0.1, 0.1, 0.1], coors_range_xyz=[-80, -80, -2, 80, 80, 6], num_point_features= 4, # here num_point_features must equal to pc.shape[1] max_num_voxels=5000, max_num_points_per_voxel=5) pc = np.random.uniform(-10, 10, size=[1000, 3]) other_pc_feature = np.random.uniform(-1, 1, size=[1000, 1]) pc_with_feature = np.concatenate([pc, other_pc_feature], axis=1) pc_tv = tv.from_numpy(pc_with_feature) # generate voxels, note that voxels_tv reference to a persistent buffer in generator, # so we can't run it in multi-thread. voxels_tv, indices_tv, num_p_in_vx_tv = gen.point_to_voxel(pc_tv) voxels_np = voxels_tv.numpy_view() indices_np = indices_tv.numpy_view() num_p_in_vx_np = num_p_in_vx_tv.numpy_view() print(f"------Raw Voxels {voxels_np.shape[0]}-------") print(voxels_np[0]) # run voxel gen and FILL MEAN VALUE to voxel remain voxels_tv, indices_tv, num_p_in_vx_tv = gen.point_to_voxel_empty_mean( pc_tv) voxels_np = voxels_tv.numpy_view() indices_np = indices_tv.numpy_view() num_p_in_vx_np = num_p_in_vx_tv.numpy_view() print("------Voxels with mean filled-------") print(voxels_np[0]) def main_cuda(): np.random.seed(50051) from spconv.utils import Point2VoxelGPU3d # voxel gen source code: spconv/csrc/sparse/pointops.py gen = Point2VoxelGPU3d(vsize_xyz=[0.1, 0.1, 0.1], coors_range_xyz=[-80, -80, -2, 80, 80, 6], num_point_features=3, max_num_voxels=5000, max_num_points_per_voxel=5) pc = np.random.uniform(-10, 10, size=[100000, 3]).astype(np.float32) pc_tv = tv.from_numpy(pc).cuda() # generate voxels, note that voxels_tv reference to a persistent buffer in generator, # so we can't run it in multi-thread. voxels_tv, indices_tv, num_p_in_vx_tv = gen.point_to_voxel_hash(pc_tv) voxels_np = voxels_tv.cpu().numpy() indices_np = indices_tv.cpu().numpy() num_p_in_vx_np = num_p_in_vx_tv.cpu().numpy() print(f"------CUDA Raw Voxels {voxels_np.shape[0]}-------") print(voxels_np[0]) if __name__ == "__main__": main() main_point_with_features() main_pytorch_voxel_gen() if torch.cuda.is_available(): main_cuda() main_pytorch_voxel_gen_cuda() main_gather_features_by_pc_voxel_id()