from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import time import torch import torch.backends.cudnn as cudnn import torch.nn as nn import torch.nn.parallel from collections import OrderedDict from models import create_model from data import Dataset, create_loader, get_model_meanstd parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation') parser.add_argument('data', metavar='DIR', help='path to dataset') parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92', help='model architecture (default: dpn92)') parser.add_argument('-j', '--workers', default=2, type=int, metavar='N', help='number of data loading workers (default: 2)') parser.add_argument('-b', '--batch-size', default=256, type=int, metavar='N', help='mini-batch size (default: 256)') parser.add_argument('--img-size', default=224, type=int, metavar='N', help='Input image dimension') parser.add_argument('--print-freq', '-p', default=10, type=int, metavar='N', help='print frequency (default: 10)') parser.add_argument('--checkpoint', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model') parser.add_argument('--num-gpu', type=int, default=1, help='Number of GPUS to use') parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true', help='disable test time pool for DPN models') def main(): args = parser.parse_args() test_time_pool = False if 'dpn' in args.model and args.img_size > 224 and not args.no_test_pool: test_time_pool = True # create model num_classes = 1000 model = create_model( args.model, num_classes=num_classes, pretrained=args.pretrained, test_time_pool=test_time_pool) print('Model %s created, param count: %d' % (args.model, sum([m.numel() for m in model.parameters()]))) # optionally resume from a checkpoint if args.checkpoint and os.path.isfile(args.checkpoint): print("=> loading checkpoint '{}'".format(args.checkpoint)) checkpoint = torch.load(args.checkpoint) if isinstance(checkpoint, dict) and 'state_dict' in checkpoint: new_state_dict = OrderedDict() for k, v in checkpoint['state_dict'].items(): if k.startswith('module'): name = k[7:] # remove `module.` else: name = k new_state_dict[name] = v model.load_state_dict(new_state_dict) else: model.load_state_dict(checkpoint) print("=> loaded checkpoint '{}'".format(args.checkpoint)) elif not args.pretrained: print("=> no checkpoint found at '{}'".format(args.checkpoint)) exit(1) if args.num_gpu > 1: model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda() else: model = model.cuda() # define loss function (criterion) and optimizer criterion = nn.CrossEntropyLoss().cuda() cudnn.benchmark = True data_mean, data_std = get_model_meanstd(args.model) loader = create_loader( Dataset(args.data), img_size=args.img_size, batch_size=args.batch_size, use_prefetcher=True, mean=data_mean, std=data_std, num_workers=args.workers) batch_time = AverageMeter() losses = AverageMeter() top1 = AverageMeter() top5 = AverageMeter() # switch to evaluate mode model.eval() end = time.time() with torch.no_grad(): for i, (input, target) in enumerate(loader): target = target.cuda() input = input.cuda() # compute output output = model(input) loss = criterion(output, target) # measure accuracy and record loss prec1, prec5 = accuracy(output.data, target, topk=(1, 5)) losses.update(loss.item(), input.size(0)) top1.update(prec1.item(), input.size(0)) top5.update(prec5.item(), input.size(0)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % args.print_freq == 0: print('Test: [{0}/{1}]\t' 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t' 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format( i, len(loader), batch_time=batch_time, loss=losses, top1=top1, top5=top5)) print(' * Prec@1 {top1.avg:.3f} ({top1a:.3f}) Prec@5 {top5.avg:.3f} ({top5a:.3f})'.format( top1=top1, top1a=100-top1.avg, top5=top5, top5a=100.-top5.avg)) class AverageMeter(object): """Computes and stores the average and current value""" 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 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 if __name__ == '__main__': main()