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