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