utils.py

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import shutil
import numpy as np
import torch


class AverageMeter(object):
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def count_parameters_in_MB(model):
    return sum(np.prod(v.size()) for name, v in model.named_parameters()) / 1e6


def create_exp_dir(path):
    if not os.path.exists(path):
        os.makedirs(path)
        print(f"Created {path}")


def save_checkpoint(state, save_inter_chkpoints, is_best, save_root, model_name):

    save_path = os.path.join(save_root, "chkp_" + model_name + ".pth.tar")
    torch.save(state, save_path)

    if is_best:
        print("Checkpoint saved!!!", flush=True)
        best_save_path = os.path.join(save_root, "model_" + model_name + ".pth.tar")
        shutil.copyfile(save_path, best_save_path)


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

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

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