Remove poorly named metrics from torch imagenet example origins. Use …

…top1/top5 in csv output for consistency with existing validation results files, acc elsewhere. Fixes huggingface#111

timm/utils.py

@@ -170,10 +170,9 @@ def update(self, val, n=1):
 
 
 def accuracy(output, target, topk=(1,)):
-    """Computes the precision@k for the specified values of k"""
+    """Computes the accuracy over the k top predictions 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))

train.py

@@ -193,8 +193,8 @@
                     help='disable fast prefetcher')
 parser.add_argument('--output', default='', type=str, metavar='PATH',
                     help='path to output folder (default: none, current dir)')
-parser.add_argument('--eval-metric', default='prec1', type=str, metavar='EVAL_METRIC',
-                    help='Best metric (default: "prec1"')
+parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
+                    help='Best metric (default: "top1"')
 parser.add_argument('--tta', type=int, default=0, metavar='N',
                     help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
 parser.add_argument("--local_rank", default=0, type=int)
@@ -596,8 +596,8 @@ def train_epoch(
 def validate(model, loader, loss_fn, args, log_suffix=''):
     batch_time_m = AverageMeter()
     losses_m = AverageMeter()
-    prec1_m = AverageMeter()
-    prec5_m = AverageMeter()
+    top1_m = AverageMeter()
+    top5_m = AverageMeter()
 
     model.eval()
 
@@ -621,20 +621,20 @@ def validate(model, loader, loss_fn, args, log_suffix=''):
                 target = target[0:target.size(0):reduce_factor]
 
             loss = loss_fn(output, target)
-            prec1, prec5 = accuracy(output, target, topk=(1, 5))
+            acc1, acc5 = accuracy(output, target, topk=(1, 5))
 
             if args.distributed:
                 reduced_loss = reduce_tensor(loss.data, args.world_size)
-                prec1 = reduce_tensor(prec1, args.world_size)
-                prec5 = reduce_tensor(prec5, args.world_size)
+                acc1 = reduce_tensor(acc1, args.world_size)
+                acc5 = reduce_tensor(acc5, args.world_size)
             else:
                 reduced_loss = loss.data
 
             torch.cuda.synchronize()
 
             losses_m.update(reduced_loss.item(), input.size(0))
-            prec1_m.update(prec1.item(), output.size(0))
-            prec5_m.update(prec5.item(), output.size(0))
+            top1_m.update(acc1.item(), output.size(0))
+            top5_m.update(acc5.item(), output.size(0))
 
             batch_time_m.update(time.time() - end)
             end = time.time()
@@ -644,13 +644,12 @@ def validate(model, loader, loss_fn, args, log_suffix=''):
                     '{0}: [{1:>4d}/{2}]  '
                     'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})  '
                     'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  '
-                    'Prec@1: {top1.val:>7.4f} ({top1.avg:>7.4f})  '
-                    'Prec@5: {top5.val:>7.4f} ({top5.avg:>7.4f})'.format(
-                        log_name, batch_idx, last_idx,
-                        batch_time=batch_time_m, loss=losses_m,
-                        top1=prec1_m, top5=prec5_m))
+                    'Acc@1: {top1.val:>7.4f} ({top1.avg:>7.4f})  '
+                    'Acc@5: {top5.val:>7.4f} ({top5.avg:>7.4f})'.format(
+                        log_name, batch_idx, last_idx, batch_time=batch_time_m,
+                        loss=losses_m, top1=top1_m, top5=top5_m))
 
-    metrics = OrderedDict([('loss', losses_m.avg), ('prec1', prec1_m.avg), ('prec5', prec5_m.avg)])
+    metrics = OrderedDict([('loss', losses_m.avg), ('top1', top1_m.avg), ('top5', top5_m.avg)])
 
     return metrics
 

validate.py

@@ -150,10 +150,10 @@ def validate(args):
             loss = criterion(output, target)
 
             # measure accuracy and record loss
-            prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
+            acc1, acc5 = 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))
+            top1.update(acc1.item(), input.size(0))
+            top5.update(acc5.item(), input.size(0))
 
             # measure elapsed time
             batch_time.update(time.time() - end)
@@ -164,8 +164,8 @@ def validate(args):
                     'Test: [{0:>4d}/{1}]  '
                     'Time: {batch_time.val:.3f}s ({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s)  '
                     'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  '
-                    'Prec@1: {top1.val:>7.3f} ({top1.avg:>7.3f})  '
-                    'Prec@5: {top5.val:>7.3f} ({top5.avg:>7.3f})'.format(
+                    'Acc@1: {top1.val:>7.3f} ({top1.avg:>7.3f})  '
+                    'Acc@5: {top5.val:>7.3f} ({top5.avg:>7.3f})'.format(
                         i, len(loader), batch_time=batch_time,
                         rate_avg=input.size(0) / batch_time.avg,
                         loss=losses, top1=top1, top5=top5))
@@ -178,7 +178,7 @@ def validate(args):
         cropt_pct=crop_pct,
         interpolation=data_config['interpolation'])
 
-    logging.info(' * Prec@1 {:.3f} ({:.3f}) Prec@5 {:.3f} ({:.3f})'.format(
+    logging.info(' * Acc@1 {:.3f} ({:.3f}) Acc@5 {:.3f} ({:.3f})'.format(
        results['top1'], results['top1_err'], results['top5'], results['top5_err']))
 
     return results