| import logging |
| import os |
| import pickle |
| import random |
| import shutil |
| import subprocess |
| import SharedArray |
|
|
| import numpy as np |
| import torch |
| import torch.distributed as dist |
| import torch.multiprocessing as mp |
|
|
|
|
| def check_numpy_to_torch(x): |
| if isinstance(x, np.ndarray): |
| return torch.from_numpy(x).float(), True |
| return x, False |
|
|
|
|
| def limit_period(val, offset=0.5, period=np.pi): |
| val, is_numpy = check_numpy_to_torch(val) |
| ans = val - torch.floor(val / period + offset) * period |
| return ans.numpy() if is_numpy else ans |
|
|
|
|
| def drop_info_with_name(info, name): |
| ret_info = {} |
| keep_indices = [i for i, x in enumerate(info['name']) if x != name] |
| for key in info.keys(): |
| ret_info[key] = info[key][keep_indices] |
| return ret_info |
|
|
|
|
| def rotate_points_along_z(points, angle): |
| """ |
| Args: |
| points: (B, N, 3 + C) |
| angle: (B), angle along z-axis, angle increases x ==> y |
| Returns: |
| |
| """ |
| points, is_numpy = check_numpy_to_torch(points) |
| angle, _ = check_numpy_to_torch(angle) |
|
|
| cosa = torch.cos(angle) |
| sina = torch.sin(angle) |
| zeros = angle.new_zeros(points.shape[0]) |
| ones = angle.new_ones(points.shape[0]) |
| rot_matrix = torch.stack(( |
| cosa, sina, zeros, |
| -sina, cosa, zeros, |
| zeros, zeros, ones |
| ), dim=1).view(-1, 3, 3).float() |
| points_rot = torch.matmul(points[:, :, 0:3], rot_matrix) |
| points_rot = torch.cat((points_rot, points[:, :, 3:]), dim=-1) |
| return points_rot.numpy() if is_numpy else points_rot |
|
|
|
|
| def angle2matrix(angle): |
| """ |
| Args: |
| angle: angle along z-axis, angle increases x ==> y |
| Returns: |
| rot_matrix: (3x3 Tensor) rotation matrix |
| """ |
|
|
| cosa = torch.cos(angle) |
| sina = torch.sin(angle) |
| rot_matrix = torch.tensor([ |
| [cosa, -sina, 0], |
| [sina, cosa, 0], |
| [ 0, 0, 1] |
| ]) |
| return rot_matrix |
|
|
|
|
| def mask_points_by_range(points, limit_range): |
| mask = (points[:, 0] >= limit_range[0]) & (points[:, 0] <= limit_range[3]) \ |
| & (points[:, 1] >= limit_range[1]) & (points[:, 1] <= limit_range[4]) |
| return mask |
|
|
|
|
| def get_voxel_centers(voxel_coords, downsample_times, voxel_size, point_cloud_range): |
| """ |
| Args: |
| voxel_coords: (N, 3) |
| downsample_times: |
| voxel_size: |
| point_cloud_range: |
| |
| Returns: |
| |
| """ |
| assert voxel_coords.shape[1] == 3 |
| voxel_centers = voxel_coords[:, [2, 1, 0]].float() |
| voxel_size = torch.tensor(voxel_size, device=voxel_centers.device).float() * downsample_times |
| pc_range = torch.tensor(point_cloud_range[0:3], device=voxel_centers.device).float() |
| voxel_centers = (voxel_centers + 0.5) * voxel_size + pc_range |
| return voxel_centers |
|
|
|
|
| def create_logger(log_file=None, rank=0, log_level=logging.INFO): |
| logger = logging.getLogger(__name__) |
| logger.setLevel(log_level if rank == 0 else 'ERROR') |
| formatter = logging.Formatter('%(asctime)s %(levelname)5s %(message)s') |
| console = logging.StreamHandler() |
| console.setLevel(log_level if rank == 0 else 'ERROR') |
| console.setFormatter(formatter) |
| logger.addHandler(console) |
| if log_file is not None: |
| file_handler = logging.FileHandler(filename=log_file) |
| file_handler.setLevel(log_level if rank == 0 else 'ERROR') |
| file_handler.setFormatter(formatter) |
| logger.addHandler(file_handler) |
| logger.propagate = False |
| return logger |
|
|
|
|
| def set_random_seed(seed): |
| random.seed(seed) |
| np.random.seed(seed) |
| torch.manual_seed(seed) |
| torch.cuda.manual_seed(seed) |
| torch.backends.cudnn.deterministic = True |
| torch.backends.cudnn.benchmark = False |
|
|
|
|
| def worker_init_fn(worker_id, seed=666): |
| if seed is not None: |
| random.seed(seed + worker_id) |
| np.random.seed(seed + worker_id) |
| torch.manual_seed(seed + worker_id) |
| torch.cuda.manual_seed(seed + worker_id) |
| torch.cuda.manual_seed_all(seed + worker_id) |
|
|
|
|
| def get_pad_params(desired_size, cur_size): |
| """ |
| Get padding parameters for np.pad function |
| Args: |
| desired_size: int, Desired padded output size |
| cur_size: int, Current size. Should always be less than or equal to cur_size |
| Returns: |
| pad_params: tuple(int), Number of values padded to the edges (before, after) |
| """ |
| assert desired_size >= cur_size |
|
|
| |
| diff = desired_size - cur_size |
| pad_params = (0, diff) |
|
|
| return pad_params |
|
|
|
|
| def keep_arrays_by_name(gt_names, used_classes): |
| inds = [i for i, x in enumerate(gt_names) if x in used_classes] |
| inds = np.array(inds, dtype=np.int64) |
| return inds |
|
|
|
|
| def init_dist_slurm(tcp_port, local_rank, backend='nccl'): |
| """ |
| modified from https://github.com/open-mmlab/mmdetection |
| Args: |
| tcp_port: |
| backend: |
| |
| Returns: |
| |
| """ |
| proc_id = int(os.environ['SLURM_PROCID']) |
| ntasks = int(os.environ['SLURM_NTASKS']) |
| node_list = os.environ['SLURM_NODELIST'] |
| num_gpus = torch.cuda.device_count() |
| torch.cuda.set_device(proc_id % num_gpus) |
| addr = subprocess.getoutput('scontrol show hostname {} | head -n1'.format(node_list)) |
| os.environ['MASTER_PORT'] = str(tcp_port) |
| os.environ['MASTER_ADDR'] = addr |
| os.environ['WORLD_SIZE'] = str(ntasks) |
| os.environ['RANK'] = str(proc_id) |
| dist.init_process_group(backend=backend) |
|
|
| total_gpus = dist.get_world_size() |
| rank = dist.get_rank() |
| return total_gpus, rank |
|
|
|
|
| def init_dist_pytorch(tcp_port, local_rank, backend='nccl'): |
| if mp.get_start_method(allow_none=True) is None: |
| mp.set_start_method('spawn') |
| |
| |
| num_gpus = torch.cuda.device_count() |
| torch.cuda.set_device(local_rank % num_gpus) |
|
|
| dist.init_process_group( |
| backend=backend, |
| |
| |
| |
| ) |
| rank = dist.get_rank() |
| return num_gpus, rank |
|
|
|
|
| def get_dist_info(return_gpu_per_machine=False): |
| if torch.__version__ < '1.0': |
| initialized = dist._initialized |
| else: |
| if dist.is_available(): |
| initialized = dist.is_initialized() |
| else: |
| initialized = False |
| if initialized: |
| rank = dist.get_rank() |
| world_size = dist.get_world_size() |
| else: |
| rank = 0 |
| world_size = 1 |
|
|
| if return_gpu_per_machine: |
| gpu_per_machine = torch.cuda.device_count() |
| return rank, world_size, gpu_per_machine |
|
|
| return rank, world_size |
|
|
|
|
| def merge_results_dist(result_part, size, tmpdir): |
| rank, world_size = get_dist_info() |
| os.makedirs(tmpdir, exist_ok=True) |
|
|
| dist.barrier() |
| pickle.dump(result_part, open(os.path.join(tmpdir, 'result_part_{}.pkl'.format(rank)), 'wb')) |
| dist.barrier() |
|
|
| if rank != 0: |
| return None |
|
|
| part_list = [] |
| for i in range(world_size): |
| part_file = os.path.join(tmpdir, 'result_part_{}.pkl'.format(i)) |
| part_list.append(pickle.load(open(part_file, 'rb'))) |
|
|
| ordered_results = [] |
| for res in zip(*part_list): |
| ordered_results.extend(list(res)) |
| ordered_results = ordered_results[:size] |
| shutil.rmtree(tmpdir) |
| return ordered_results |
|
|
|
|
| def scatter_point_inds(indices, point_inds, shape): |
| ret = -1 * torch.ones(*shape, dtype=point_inds.dtype, device=point_inds.device) |
| ndim = indices.shape[-1] |
| flattened_indices = indices.view(-1, ndim) |
| slices = [flattened_indices[:, i] for i in range(ndim)] |
| ret[slices] = point_inds |
| return ret |
|
|
|
|
| def generate_voxel2pinds(sparse_tensor): |
| device = sparse_tensor.indices.device |
| batch_size = sparse_tensor.batch_size |
| spatial_shape = sparse_tensor.spatial_shape |
| indices = sparse_tensor.indices.long() |
| point_indices = torch.arange(indices.shape[0], device=device, dtype=torch.int32) |
| output_shape = [batch_size] + list(spatial_shape) |
| v2pinds_tensor = scatter_point_inds(indices, point_indices, output_shape) |
| return v2pinds_tensor |
|
|
|
|
| def sa_create(name, var): |
| x = SharedArray.create(name, var.shape, dtype=var.dtype) |
| x[...] = var[...] |
| x.flags.writeable = False |
| return x |
|
|
|
|
| class AverageMeter(object): |
| """Computes and stores the average and current value""" |
| def __init__(self): |
| self.reset() |
|
|
| def reset(self): |
| self.val = 0 |
| self.avg = 0 |
| self.sum = 0 |
| self.count = 0 |
|
|
| def update(self, val, n=1): |
| self.val = val |
| self.sum += val * n |
| self.count += n |
| self.avg = self.sum / self.count |
|
|
