eval.py

import torch
import os
import random
import numpy as np
import time

def seed_all(seed=1029):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU.
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        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 __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        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].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res
    

@torch.no_grad()
def validate_model(val_loader, model, criterion, device=None, print_freq=100, to_print=True):
    if device is None:
        device = next(model.parameters()).device
    else:
        model.to(device)
    batch_time = AverageMeter('Time', ':6.3f')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    loss = AverageMeter('Loss', ':6.3f')
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, top1, top5],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    end = time.time()
    for i, (images, target) in enumerate(val_loader):
        images = images.to(device)
        target = target.to(device)

        # compute output
        output = model(images)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        top1.update(acc1[0], images.size(0))
        top5.update(acc5[0], images.size(0))
        loss1 = criterion(output, target)
        loss.update(loss1, images.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if to_print and i % print_freq == 0:
            progress.display(i)

    if to_print:
        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f} Loss {loss.avg:.4f}'.format(top1=top1, top5=top5, loss=loss))

    return top1.avg