utils.py

import torch
import numpy as np
import random
import torch.backends.cudnn as cudnn

def clip_gradient(optimizer, grad_clip):
    if grad_clip <= 0:
        return
    for group in optimizer.param_groups:
        for param in group['params']:
            if param.grad is not None:
                param.grad.data.clamp_(-grad_clip, grad_clip)


def adjust_lr(optimizer, init_lr, epoch, decay_rate=0.1, decay_epoch=60):
    decay = decay_rate ** (epoch // decay_epoch)
    for param_group in optimizer.param_groups:
        param_group['lr'] = decay * init_lr
        lr = param_group['lr']
    return lr


def str2bool(v):
    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False


def setup_seed(seed=2021):
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    random.seed(seed)
    cudnn.deterministic = True
    torch.backends.cudnn.enabled = False