[nnUNet/TF2] Log best metric at the end of training

michal2409 · michal2409 · commit 359ec7144ba8 · 2022-11-04T14:05:57.000+01:00
diff --git a/TensorFlow2/Segmentation/nnUNet/runtime/run.py b/TensorFlow2/Segmentation/nnUNet/runtime/run.py
@@ -207,7 +207,7 @@ def train_step_fn(features, labels, warmup_batch=False):
         metrics = {
             "train_loss": round(total_train_loss / steps_per_epoch, 5),
             "val_loss": round(1 - float(dice_score), 5),
-            "dice": round(float(dice_metrics.metrics["value"]), 5),
+            "dice": round(float(dice_metrics.metrics["max"].result()), 5),
         }
         logger.log_metrics(metrics=metrics)
         logger.flush()

Original file line number	Diff line number	Diff line change
`@@ -207,7 +207,7 @@ def train_step_fn(features, labels, warmup_batch=False):`
`207`	`207`	`metrics = {`
`208`	`208`	`"train_loss": round(total_train_loss / steps_per_epoch, 5),`
`209`	`209`	`"val_loss": round(1 - float(dice_score), 5),`
`210`		`- "dice": round(float(dice_metrics.metrics["value"]), 5),`
	`210`	`+ "dice": round(float(dice_metrics.metrics["max"].result()), 5),`
`211`	`211`	`}`
`212`	`212`	`logger.log_metrics(metrics=metrics)`
`213`	`213`	`logger.flush()`