pytorch-image-models/validate.py

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 models import create_model, load_checkpoint, TestTimePoolHead
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()

    # create model
    num_classes = 1000
    model = create_model(
        args.model,
        num_classes=num_classes,
        pretrained=args.pretrained)

    print('Model %s created, param count: %d' %
          (args.model, sum([m.numel() for m in model.parameters()])))

    # load a checkpoint
    if not args.pretrained:
        if not load_checkpoint(model, args.checkpoint):
            exit(1)

    test_time_pool = False
    # FIXME make this work for networks with default img size != 224 and default pool k != 7
    if args.img_size > 224 and not args.no_test_pool:
        model = TestTimePoolHead(model)
        test_time_pool = True

    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,
        crop_pct=1.0 if test_time_pool else None)

    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()