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128 lines
4.9 KiB
128 lines
4.9 KiB
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import os
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import time
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import torch
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import torch.nn as nn
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import torch.nn.parallel
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from models import create_model, apply_test_time_pool
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from data import Dataset, create_loader, resolve_data_config
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from utils import accuracy, AverageMeter
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torch.backends.cudnn.benchmark = True
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parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
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parser.add_argument('data', metavar='DIR',
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help='path to dataset')
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parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
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help='model architecture (default: dpn92)')
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parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
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help='number of data loading workers (default: 2)')
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parser.add_argument('-b', '--batch-size', default=256, type=int,
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metavar='N', help='mini-batch size (default: 256)')
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parser.add_argument('--img-size', default=None, type=int,
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metavar='N', help='Input image dimension, uses model default if empty')
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parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
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help='Override mean pixel value of dataset')
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parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
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help='Override std deviation of of dataset')
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parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
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help='Image resize interpolation type (overrides model)')
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parser.add_argument('--num-classes', type=int, default=1000,
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help='Number classes in dataset')
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parser.add_argument('--print-freq', '-p', default=10, type=int,
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metavar='N', help='print frequency (default: 10)')
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parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
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help='path to latest checkpoint (default: none)')
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parser.add_argument('--pretrained', dest='pretrained', action='store_true',
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help='use pre-trained model')
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parser.add_argument('--num-gpu', type=int, default=1,
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help='Number of GPUS to use')
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parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
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help='disable test time pool')
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def main():
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args = parser.parse_args()
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# create model
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model = create_model(
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args.model,
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num_classes=args.num_classes,
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in_chans=3,
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pretrained=args.pretrained,
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checkpoint_path=args.checkpoint)
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print('Model %s created, param count: %d' %
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(args.model, sum([m.numel() for m in model.parameters()])))
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data_config = resolve_data_config(model, args)
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model, test_time_pool = apply_test_time_pool(model, data_config, args)
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if args.num_gpu > 1:
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model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
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else:
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model = model.cuda()
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# define loss function (criterion) and optimizer
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criterion = nn.CrossEntropyLoss().cuda()
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loader = create_loader(
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Dataset(args.data),
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input_size=data_config['input_size'],
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batch_size=args.batch_size,
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use_prefetcher=True,
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interpolation=data_config['interpolation'],
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mean=data_config['mean'],
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std=data_config['std'],
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num_workers=args.workers,
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crop_pct=1.0 if test_time_pool else data_config['crop_pct'])
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batch_time = AverageMeter()
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losses = AverageMeter()
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top1 = AverageMeter()
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top5 = AverageMeter()
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# switch to evaluate mode
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model.eval()
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end = time.time()
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with torch.no_grad():
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for i, (input, target) in enumerate(loader):
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target = target.cuda()
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input = input.cuda()
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# compute output
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output = model(input)
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loss = criterion(output, target)
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# measure accuracy and record loss
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prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
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losses.update(loss.item(), input.size(0))
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top1.update(prec1.item(), input.size(0))
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top5.update(prec5.item(), input.size(0))
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# measure elapsed time
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batch_time.update(time.time() - end)
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end = time.time()
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if i % args.print_freq == 0:
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print('Test: [{0}/{1}]\t'
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'Time {batch_time.val:.3f} ({batch_time.avg:.3f}, {rate_avg:.3f}/s) \t'
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'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
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'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
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'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
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i, len(loader), batch_time=batch_time,
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rate_avg=input.size(0) / batch_time.avg,
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loss=losses, top1=top1, top5=top5))
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print(' * Prec@1 {top1.avg:.3f} ({top1a:.3f}) Prec@5 {top5.avg:.3f} ({top5a:.3f})'.format(
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top1=top1, top1a=100-top1.avg, top5=top5, top5a=100.-top5.avg))
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if __name__ == '__main__':
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main()
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