Code style

examples/torch/classification/main.py

@@ -728,20 +728,19 @@ def validate(val_loader, model, criterion, config, epoch=0, log_validation_info=
                     )
                 )
 
-        if is_main_process():
-            if log_validation_info:
-                config.tb.add_scalar("val/loss", losses.avg, len(val_loader) * epoch)
-                config.tb.add_scalar("val/top1", top1.avg, len(val_loader) * epoch)
-                config.tb.add_scalar("val/top5", top5.avg, len(val_loader) * epoch)
-                config.mlflow.safe_call("log_metric", "val/loss", float(losses.avg), epoch)
-                config.mlflow.safe_call("log_metric", "val/top1", float(top1.avg), epoch)
-                config.mlflow.safe_call("log_metric", "val/top5", float(top5.avg), epoch)
-
-                logger.info(" * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}\n".format(top1=top1, top5=top5))
-
-            if config.metrics_dump is not None:
-                acc = top1.avg / 100
-                write_metrics(acc, config.metrics_dump)
+        if is_main_process() and log_validation_info:
+            config.tb.add_scalar("val/loss", losses.avg, len(val_loader) * epoch)
+            config.tb.add_scalar("val/top1", top1.avg, len(val_loader) * epoch)
+            config.tb.add_scalar("val/top5", top5.avg, len(val_loader) * epoch)
+            config.mlflow.safe_call("log_metric", "val/loss", float(losses.avg), epoch)
+            config.mlflow.safe_call("log_metric", "val/top1", float(top1.avg), epoch)
+            config.mlflow.safe_call("log_metric", "val/top5", float(top5.avg), epoch)
+
+            logger.info(" * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}\n".format(top1=top1, top5=top5))
+
+        if is_main_process() and config.metrics_dump is not None:
+            acc = top1.avg / 100
+            write_metrics(acc, config.metrics_dump)
 
     return top1.avg, top5.avg, losses.avg