Improve handling of test_split_mode='none' and val_split_mode='none'

anomalib/config/config.py

@@ -14,6 +14,8 @@
 
 from omegaconf import DictConfig, ListConfig, OmegaConf
 
+from anomalib.data.utils import TestSplitMode, ValSplitMode
+
 
 def _get_now_str(timestamp: float) -> str:
     """Standard format for datetimes is defined here."""
@@ -176,10 +178,24 @@ def update_datasets_config(config: Union[DictConfig, ListConfig]) -> Union[DictC
         warn(
             DeprecationWarning(
                 "The 'split_ratio' parameter is deprecated and will be removed in a future release. Please use "
-                "'normal_split_ratio' instead."
+                "'test_split_ratio' instead."
             )
         )
-        config.dataset.normal_split_ratio = config.dataset.split_ratio
+        config.dataset.test_split_ratio = config.dataset.split_ratio
+
+    if config.dataset.get("test_split_mode") == TestSplitMode.NONE and config.dataset.get("val_split_mode") in [
+        ValSplitMode.SAME_AS_TEST,
+        ValSplitMode.FROM_TEST,
+    ]:
+        warn(
+            f"val_split_mode {config.dataset.val_split_mode} not allowed for test_split_mode = 'none'. "
+            "Setting val_split_mode to 'none'."
+        )
+        config.dataset.val_split_mode = ValSplitMode.NONE
+
+    if config.dataset.get("val_split_mode") == ValSplitMode.NONE and config.trainer.limit_val_batches != 0.0:
+        warn("Running without validation set. Setting trainer.limit_val_batches to 0.")
+        config.trainer.limit_val_batches = 0.0
     return config
 
 

anomalib/data/base/datamodule.py

@@ -120,8 +120,9 @@ def _create_test_split(self):
         if self.test_data.has_normal:
             # split the test data into normal and anomalous so these can be processed separately
             normal_test_data, self.test_data = split_by_label(self.test_data)
-        else:
-            # when the user did not provide any normal images for testing, we sample some from the training set
+        elif self.test_split_mode != TestSplitMode.NONE:
+            # when the user did not provide any normal images for testing, we sample some from the training set,
+            # except when the user explicitly requested no test splitting.
             logger.info(
                 "No normal test images found. Sampling from training set using a split ratio of %d",
                 self.test_split_ratio,
@@ -132,7 +133,7 @@ def _create_test_split(self):
             self.test_data += normal_test_data
         elif self.test_split_mode == TestSplitMode.SYNTHETIC:
             self.test_data = SyntheticAnomalyDataset.from_dataset(normal_test_data)
-        else:
+        elif self.test_split_mode != TestSplitMode.NONE:
             raise ValueError(f"Unsupported Test Split Mode: {self.test_split_mode}")
 
     def _create_val_split(self):

tools/train.py

@@ -16,6 +16,7 @@
 
 from anomalib.config import get_configurable_parameters
 from anomalib.data import get_datamodule
+from anomalib.data.utils import TestSplitMode
 from anomalib.models import get_model
 from anomalib.utils.callbacks import LoadModelCallback, get_callbacks
 from anomalib.utils.loggers import configure_logger, get_experiment_logger
@@ -63,11 +64,11 @@ def train():
     load_model_callback = LoadModelCallback(weights_path=trainer.checkpoint_callback.best_model_path)
     trainer.callbacks.insert(0, load_model_callback)
 
-    if len(datamodule.test_data) != 0:
+    if config.dataset.test_split_mode == TestSplitMode.NONE:
+        logger.info("No test set provided. Skipping test stage.")
+    else:
         logger.info("Testing the model.")
         trainer.test(model=model, datamodule=datamodule)
-    else:
-        logger.info("No anomalous images found in dataset. Skipping test stage.")
 
 
 if __name__ == "__main__":