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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):
# reduce loss when distributed training, only for logging
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
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