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from typing import List, Union
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torch.autograd import Variable
import torch.optim as optim
import numpy as np
import torch
__all__ = ["accuracy", "AverageMeter"]
def accuracy(
output: torch.Tensor, target: torch.Tensor, topk=(1,)
) -> List[torch.Tensor]:
"""Computes the precision@k for the specified values of k."""
maxk = max(topk)
batch_size = target.shape[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
class AverageMeter(object):
"""Computes and stores the average and current value.
Copied from: https://github.com/pytorch/examples/blob/master/imagenet/main.py
"""
def __init__(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val: Union[torch.Tensor, np.ndarray, float, int], n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
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