Moving mutiplication by 100 to accuracy computation, training log fix

src/sparseml/pytorch/torchvision/train.py

@@ -138,11 +138,13 @@ def train_one_epoch(
                 # Reset ema buffer to keep copying weights during warmup period
                 model_ema.n_averaged.fill_(0)
 
-        acc1, _1, acc5, _5 = utils.accuracy(output, target, topk=(1, 5))
+        acc1, num_correct_1, acc5, num_correct_5 = utils.accuracy(output, target, topk=(1, 5))
         batch_size = image.shape[0]
         metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"])
-        metric_logger.meters["acc1"].update(acc1.item(), n=batch_size)
-        metric_logger.meters["acc5"].update(acc5.item(), n=batch_size)
+        metric_logger.meters["acc1"].update(
+                acc1.item(), n=batch_size, total=num_correct_1)
+        metric_logger.meters["acc5"].update(
+            acc5.item(), n=batch_size, total=num_correct_5)
         metric_logger.meters["imgs_per_sec"].update(
             batch_size / (time.time() - start_time)
         )

src/sparseml/pytorch/torchvision/utils.py

@@ -57,7 +57,7 @@ def avg(self):
 
     @property
     def global_avg(self):
-        return 100.0 * self.total / self.count
+        return self.total / self.count
 
     @property
     def max(self):
@@ -213,7 +213,7 @@ def accuracy(output, target, topk=(1,)):
         for k in topk:
             correct_k = correct[:k].flatten().sum(dtype=torch.float32)
             res.append(correct_k * (100.0 / batch_size))
-            res.append(correct_k)
+            res.append(correct_k *  100.0)
         return res