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pytorch-image-models/validate.py

122 lines
4.4 KiB

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import time
import torch
import torch.nn as nn
import torch.nn.parallel
from models import create_model, apply_test_time_pool
from data import Dataset, create_loader, get_mean_and_std
from utils import accuracy, AverageMeter
torch.backends.cudnn.benchmark = True
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('--num-classes', type=int, default=1000,
help='Number classes in dataset')
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
model = create_model(
args.model,
num_classes=args.num_classes,
in_chans=3,
pretrained=args.pretrained,
checkpoint_path=args.checkpoint)
print('Model %s created, param count: %d' %
(args.model, sum([m.numel() for m in model.parameters()])))
data_mean, data_std = get_mean_and_std(model, args)
model, test_time_pool = apply_test_time_pool(model, args)
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()
loader = create_loader(
Dataset(args.data),
img_size=args.img_size,
batch_size=args.batch_size,
use_prefetcher=False,
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}, {rate_avg:.3f}/s) \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,
rate_avg=input.size(0) / batch_time.avg,
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))
if __name__ == '__main__':
main()