utils.py

import numpy as np
import random
import torch
import torch.nn.functional as F

def set_global_seeds(i):
    random.seed(i)
    np.random.seed(i)
    torch.manual_seed(i)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(i)


def set_device():
    if torch.cuda.is_available():
        _device = torch.device("cuda")
    else:
        _device = torch.device("cpu")
    print(f'Current device is {_device}', flush=True)
    return _device


# Adjust learning rate and for SGD Optimizer
def adjust_learning_rate(optimizer, epoch,alpha_plan):
    for param_group in optimizer.param_groups:
        param_group['lr']=alpha_plan[epoch]
        

def accuracy(logit, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    output = F.softmax(logit, dim=1)
    maxk = max(topk)
    batch_size = target.size(0)

    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.reshape(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res