| import os |
| import numpy as np |
| import random |
| import shutil |
| import torch |
| import torch.distributed as dist |
|
|
|
|
| def set_seed(seed, disable_deterministic=False): |
| """Set randon seed for pytorch and numpy""" |
| random.seed(seed) |
| np.random.seed(seed) |
| torch.manual_seed(seed) |
|
|
| if disable_deterministic: |
| torch.backends.cudnn.deterministic = False |
| torch.backends.cudnn.benchmark = True |
| else: |
| torch.backends.cudnn.deterministic = True |
| torch.backends.cudnn.benchmark = False |
| os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" |
| torch.use_deterministic_algorithms(True, warn_only=True) |
|
|
|
|
| def update_workdir(cfg, exp_id, gpu_num): |
| cfg.work_dir = os.path.join(cfg.work_dir, f"gpu{gpu_num}_id{exp_id}/") |
| return cfg |
|
|
|
|
| def create_folder(folder_path): |
| dir_name = os.path.expanduser(folder_path) |
| if not os.path.exists(dir_name): |
| os.makedirs(dir_name, mode=0o777, exist_ok=True) |
|
|
|
|
| def save_config(cfg, folder_path): |
| shutil.copy2(cfg, folder_path) |
|
|
|
|
| def reduce_loss(loss_dict): |
| |
| for loss_name, loss_value in loss_dict.items(): |
| loss_value = loss_value.data.clone() |
| dist.all_reduce(loss_value.div_(dist.get_world_size())) |
| loss_dict[loss_name] = loss_value |
| return loss_dict |
|
|
|
|
| class AverageMeter(object): |
| """Computes and stores the average and current value. |
| Used to compute dataset stats from mini-batches |
| """ |
|
|
| def __init__(self): |
| self.initialized = False |
| self.val = None |
| self.avg = None |
| self.sum = None |
| self.count = 0.0 |
|
|
| def initialize(self, val, n): |
| self.val = val |
| self.avg = val |
| self.sum = val * n |
| self.count = n |
| self.initialized = True |
|
|
| def update(self, val, n=1): |
| if not self.initialized: |
| self.initialize(val, n) |
| else: |
| self.add(val, n) |
|
|
| def add(self, val, n): |
| self.val = val |
| self.sum += val * n |
| self.count += n |
| self.avg = self.sum / self.count |
|
|
