Update all prec occurrences to acc (pytorch#420)

Author: vipinpillai

imagenet/main.py

@@ -62,11 +62,11 @@
 parser.add_argument('--gpu', default=None, type=int,
                     help='GPU id to use.')
 
-best_prec1 = 0
+best_acc1 = 0
 
 
 def main():
-    global args, best_prec1
+    global args, best_acc1
     args = parser.parse_args()
 
     if args.seed is not None:
@@ -122,7 +122,7 @@ def main():
             print("=> loading checkpoint '{}'".format(args.resume))
             checkpoint = torch.load(args.resume)
             args.start_epoch = checkpoint['epoch']
-            best_prec1 = checkpoint['best_prec1']
+            best_acc1 = checkpoint['best_acc1']
             model.load_state_dict(checkpoint['state_dict'])
             optimizer.load_state_dict(checkpoint['optimizer'])
             print("=> loaded checkpoint '{}' (epoch {})"
@@ -179,16 +179,16 @@ def main():
         train(train_loader, model, criterion, optimizer, epoch)
 
         # evaluate on validation set
-        prec1 = validate(val_loader, model, criterion)
+        acc1 = validate(val_loader, model, criterion)
 
-        # remember best prec@1 and save checkpoint
-        is_best = prec1 > best_prec1
-        best_prec1 = max(prec1, best_prec1)
+        # remember best acc@1 and save checkpoint
+        is_best = acc1 > best_acc1
+        best_acc1 = max(acc1, best_acc1)
         save_checkpoint({
             'epoch': epoch + 1,
             'arch': args.arch,
             'state_dict': model.state_dict(),
-            'best_prec1': best_prec1,
+            'best_acc1': best_acc1,
             'optimizer' : optimizer.state_dict(),
         }, is_best)
 
@@ -217,10 +217,10 @@ def train(train_loader, model, criterion, optimizer, epoch):
         loss = criterion(output, target)
 
         # measure accuracy and record loss
-        prec1, prec5 = accuracy(output, target, topk=(1, 5))
+        acc1, acc5 = accuracy(output, target, topk=(1, 5))
         losses.update(loss.item(), input.size(0))
-        top1.update(prec1[0], input.size(0))
-        top5.update(prec5[0], input.size(0))
+        top1.update(acc1[0], input.size(0))
+        top5.update(acc5[0], input.size(0))
 
         # compute gradient and do SGD step
         optimizer.zero_grad()
@@ -236,8 +236,8 @@ def train(train_loader, model, criterion, optimizer, epoch):
                   'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                   'Data {data_time.val:.3f} ({data_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(
+                  'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
+                  'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                    epoch, i, len(train_loader), batch_time=batch_time,
                    data_time=data_time, loss=losses, top1=top1, top5=top5))
 
@@ -263,10 +263,10 @@ def validate(val_loader, model, criterion):
             loss = criterion(output, target)
 
             # measure accuracy and record loss
-            prec1, prec5 = accuracy(output, target, topk=(1, 5))
+            acc1, acc5 = accuracy(output, target, topk=(1, 5))
             losses.update(loss.item(), input.size(0))
-            top1.update(prec1[0], input.size(0))
-            top5.update(prec5[0], input.size(0))
+            top1.update(acc1[0], input.size(0))
+            top5.update(acc5[0], input.size(0))
 
             # measure elapsed time
             batch_time.update(time.time() - end)
@@ -276,12 +276,12 @@ def validate(val_loader, model, criterion):
                 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(
+                      'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
+                      'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                        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}'
+        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
               .format(top1=top1, top5=top5))
 
     return top1.avg