tests

clinicadl/predict/predict_manager.py

@@ -39,7 +39,7 @@ class PredictManager:
     def __init__(self, _config: Union[PredictConfig, InterpretConfig]) -> None:
         self.maps_manager = MapsManager(_config.maps_dir)
         self._config = _config
-        self.validator = Validator()
+        self.validator = Validator(**_config.model_dump())
 
     def predict(
         self,
@@ -48,49 +48,8 @@ def predict(
         """Performs the prediction task on a subset of caps_directory defined in a TSV file.
         Parameters
         ----------
-        data_group : str
-            name of the data group tested.
-        caps_directory : Path (optional, default=None)
-            path to the CAPS folder. For more information please refer to
-            [clinica documentation](https://aramislab.paris.inria.fr/clinica/docs/public/latest/CAPS/Introduction/).
-            Default will load the value of an existing data group
-        tsv_path : Path (optional, default=None)
-            path to a TSV file containing the list of participants and sessions to test.
-            Default will load the DataFrame of an existing data group
-        split_list : List[int] (optional, default=None)
-            list of splits to test. Default perform prediction on all splits available.
-        selection_metrics : List[str] (optional, default=None)
-            list of selection metrics to test.
-                Default performs the prediction on all selection metrics available.
-        multi_cohort : bool (optional, default=False)
-            If True considers that tsv_path is the path to a multi-cohort TSV.
-        diagnoses : List[str] (optional, default=())
-            List of diagnoses to load if tsv_path is a split_directory.
-            Default uses the same as in training step.
-        use_labels : bool (optional, default=True)
-            If True, the labels must exist in test meta-data and metrics are computed.
-        batch_size : int (optional, default=None)
-            If given, sets the value of batch_size, else use the same as in training step.
-        n_proc : int (optional, default=None)
-            If given, sets the value of num_workers, else use the same as in training step.
-        gpu : bool (optional, default=None)
-            If given, a new value for the device of the model will be computed.
-        amp : bool (optional, default=False)
-            If enabled, uses Automatic Mixed Precision (requires GPU usage).
-        overwrite : bool (optional, default=False)
-            If True erase the occurrences of data_group.
-        label : str (optional, default=None)
-            Target label used for training (if network_task in [`regression`, `classification`]).
-        label_code : Optional[Dict[str, int]] (optional, default="default")
-            dictionary linking the target values to a node number.
-        save_tensor : bool (optional, default=False)
-            If true, save the tensor predicted for reconstruction task
-        save_nifti : bool (optional, default=False)
-            If true, save the nifti associated to the prediction for reconstruction task.
-        save_latent_tensor : bool (optional, default=False)
-            If true, save the tensor from the latent space for reconstruction task.
-        skip_leak_check : bool (optional, default=False)
-            If true, skip the leak check (not recommended).
+        label_code :
+
         Examples
         --------
         >>> _input_
@@ -189,8 +148,6 @@ def predict(
                     self.maps_manager,
                     self._config.data_group,
                     split,
-                    self._config.selection_metrics,
-                    self._config.use_labels,
                     self._config.skip_leak_check,
                 )
 
@@ -437,8 +394,8 @@ def _predict_single(
                 data_test,
                 self._config.data_group,
                 split,
-                self._config.selection_metrics,
-                gpu=self._config.gpu,
+                # self._config.selection_metrics,
+                # gpu=self._config.gpu,
             )
         if self._config.save_nifti:
             self._compute_output_nifti(

clinicadl/trainer/trainer.py

@@ -226,8 +226,8 @@ def train(
             n_classes=self.maps_manager.n_classes,
             network_task=self.maps_manager.network_task,
             amp=self.maps_manager.std_amp,
-            use_labels=use_labels,
-            report_ci=report_ci,
+            use_labels=self.maps_manager.use_labels,
+            report_ci=self.maps_manager.report_ci,
             selection_metrics=self.config.validation.selection_metrics,
         )