Fixes to ImageNet training script (#19)

- Fix call to top-k which had produced incorrect prec@1 calculation
- Fix checkpoint loading. Create the DataParallel container before
  resuming checkpoints so that the structure matches.
- Weight average precision by batch size
- Add '--evaluate FILE' option to only run on validation set

Author: colesbury

imagenet/main.py

@@ -16,10 +16,10 @@
 
 
 parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
-parser.add_argument('--data', metavar='PATH', required=True,
+parser.add_argument('data', metavar='DIR',
                     help='path to dataset')
 parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18',
-                    help='model architecture: resnet18 | resnet34 | ...'
+                    help='model architecture: resnet18 | resnet34 | ... '
                          '(default: resnet18)')
 parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                     help='number of data loading workers (default: 4)')
@@ -39,6 +39,8 @@
                     metavar='N', help='print frequency (default: 10)')
 parser.add_argument('--resume', default='', type=str, metavar='PATH',
                     help='path to latest checkpoint (default: none)')
+parser.add_argument('-e', '--evaluate', type=str, metavar='FILE',
+                    help='evaluate model FILE on validation set')
 
 best_prec1 = 0
 
@@ -50,20 +52,28 @@ def main():
     # create model
     if args.arch.startswith('resnet'):
         print("=> creating model '{}'".format(args.arch))
-        model = resnet.__dict__[args.arch]()
+        model = torch.nn.DataParallel(resnet.__dict__[args.arch]())
         model.cuda()
     else:
         parser.error('invalid architecture: {}'.format(args.arch))
 
     # optionally resume from a checkpoint
-    if args.resume:
+    if args.evaluate:
+        if not os.path.isfile(args.evaluate):
+            parser.error('invalid checkpoint: {}'.format(args.evaluate))
+        checkpoint = torch.load(args.evaluate)
+        model.load_state_dict(checkpoint['state_dict'])
+        print("=> loaded checkpoint '{}' (epoch {})"
+              .format(args.evaluate, checkpoint['epoch']))
+    elif args.resume:
         if os.path.isfile(args.resume):
             print("=> loading checkpoint '{}'".format(args.resume))
             checkpoint = torch.load(args.resume)
             args.start_epoch = checkpoint['epoch']
             best_prec1 = checkpoint['best_prec1']
             model.load_state_dict(checkpoint['state_dict'])
-            print(" | resuming from epoch {}".format(args.start_epoch))
+            print("=> loaded checkpoint '{}' (epoch {})"
+                  .format(args.evaluate, checkpoint['epoch']))
         else:
             print("=> no checkpoint found at '{}'".format(args.resume))
 
@@ -95,32 +105,31 @@ def main():
         batch_size=args.batch_size, shuffle=False,
         num_workers=args.workers, pin_memory=True)
 
-    # parallelize model across all visible GPUs
-    model = torch.nn.DataParallel(model)
-
     # define loss function (criterion) and pptimizer
     criterion = nn.CrossEntropyLoss().cuda()
 
     optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                 momentum=args.momentum,
                                 weight_decay=args.weight_decay)
 
+    if args.evaluate:
+        validate(val_loader, model, criterion)
+        return
+
     for epoch in range(args.start_epoch, args.epochs):
         adjust_learning_rate(optimizer, epoch)
 
         # train for one epoch
-        model.train()
         train(train_loader, model, criterion, optimizer, epoch)
 
         # evaluate on validation set
-        model.eval()
         prec1 = validate(val_loader, model, criterion)
 
         # remember best prec@1 and save checkpoint
         is_best = prec1 > best_prec1
         best_prec1 = max(prec1, best_prec1)
         save_checkpoint({
-            'epoch': epoch,
+            'epoch': epoch + 1,
             'arch': args.arch,
             'state_dict': model.state_dict(),
             'best_prec1': best_prec1,
@@ -134,6 +143,9 @@ def train(train_loader, model, criterion, optimizer, epoch):
     top1 = AverageMeter()
     top5 = AverageMeter()
 
+    # switch to train mode
+    model.train()
+
     end = time.time()
     for i, (input, target) in enumerate(train_loader):
         # measure data loading time
@@ -149,9 +161,9 @@ def train(train_loader, model, criterion, optimizer, epoch):
 
         # measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
-        losses.update(loss.data[0])
-        top1.update(prec1[0])
-        top5.update(prec5[0])
+        losses.update(loss.data[0], input.size(0))
+        top1.update(prec1[0], input.size(0))
+        top5.update(prec5[0], input.size(0))
 
         # compute gradient and do SGD step
         optimizer.zero_grad()
@@ -179,6 +191,9 @@ def validate(val_loader, model, criterion):
     top1 = AverageMeter()
     top5 = AverageMeter()
 
+    # switch to evaluate mode
+    model.eval()
+
     end = time.time()
     for i, (input, target) in enumerate(val_loader):
         target = target.cuda(async=True)
@@ -191,9 +206,9 @@ def validate(val_loader, model, criterion):
 
         # measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
-        losses.update(loss.data[0])
-        top1.update(prec1[0])
-        top5.update(prec5[0])
+        losses.update(loss.data[0], input.size(0))
+        top1.update(prec1[0], input.size(0))
+        top5.update(prec5[0], input.size(0))
 
         # measure elapsed time
         batch_time.update(time.time() - end)
@@ -208,6 +223,9 @@ def validate(val_loader, model, criterion):
                    i, len(val_loader), batch_time=batch_time, loss=losses,
                    top1=top1, top5=top5))
 
+    print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
+          .format(top1=top1, top5=top5))
+
     return top1.avg
 
 
@@ -226,13 +244,13 @@ def reset(self):
         self.val = 0
         self.avg = 0
         self.sum = 0
-        self.n = 0
+        self.count = 0
 
-    def update(self, val):
+    def update(self, val, n=1):
         self.val = val
-        self.sum += val
-        self.n += 1
-        self.avg = self.sum / self.n
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
 
 
 def adjust_learning_rate(optimizer, epoch):
@@ -247,7 +265,7 @@ def accuracy(output, target, topk=(1,)):
     maxk = max(topk)
     batch_size = target.size(0)
 
-    _, pred = output.topk(maxk, True, True)
+    _, pred = output.topk(maxk, 1, True, True)
     pred = pred.t()
     correct = pred.eq(target.view(1, -1).expand_as(pred))