remove split_name parameters

aramis-lab · Oct 3, 2024 · 765f29a · 765f29a
1 parent 7382e97
commit 765f29a
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Showing 18 changed files with 69 additions and 152 deletions.
diff --git a/...dline/modules_options/cross_validation.py → ...cadl/commandline/modules_options/split.py b/...dline/modules_options/cross_validation.py → ...cadl/commandline/modules_options/split.py
diff --git a/clinicadl/commandline/pipelines/predict/cli.py b/clinicadl/commandline/pipelines/predict/cli.py
@@ -3,10 +3,10 @@
 from clinicadl.commandline import arguments
 from clinicadl.commandline.modules_options import (
     computational,
-    cross_validation,
     data,
     dataloader,
     maps_manager,
+    split,
     validation,
 )
 from clinicadl.commandline.pipelines.predict import options
@@ -29,7 +29,7 @@
 @data.diagnoses
 @validation.skip_leak_check
 @validation.selection_metrics
-@cross_validation.split
+@split.split
 @computational.gpu
 @computational.amp
 @dataloader.n_proc

diff --git a/clinicadl/commandline/pipelines/train/classification/cli.py b/clinicadl/commandline/pipelines/train/classification/cli.py
@@ -4,7 +4,6 @@
 from clinicadl.commandline.modules_options import (
     callbacks,
     computational,
-    cross_validation,
     data,
     dataloader,
     early_stopping,
@@ -13,6 +12,7 @@
     optimization,
     optimizer,
     reproducibility,
+    split,
     ssda,
     transforms,
     validation,
@@ -70,8 +70,8 @@
 @ssda.tsv_target_unlab
 @ssda.preprocessing_json_target
 # Cross validation
-@cross_validation.n_splits
-@cross_validation.split
+@split.n_splits
+@split.split
 # Optimization
 @optimizer.optimizer
 @optimizer.weight_decay

diff --git a/clinicadl/commandline/pipelines/train/from_json/cli.py b/clinicadl/commandline/pipelines/train/from_json/cli.py
@@ -4,15 +4,15 @@
 
 from clinicadl.commandline import arguments
 from clinicadl.commandline.modules_options import (
-    cross_validation,
+    split,
 )
 from clinicadl.trainer.trainer import Trainer
 
 
 @click.command(name="from_json", no_args_is_help=True)
 @arguments.config_file
 @arguments.output_maps
-@cross_validation.split
+@split.split
 def cli(**kwargs):
     """
     Replicate a deep learning training based on a previously created JSON file.

diff --git a/clinicadl/commandline/pipelines/train/reconstruction/cli.py b/clinicadl/commandline/pipelines/train/reconstruction/cli.py
@@ -4,7 +4,6 @@
 from clinicadl.commandline.modules_options import (
     callbacks,
     computational,
-    cross_validation,
     data,
     dataloader,
     early_stopping,
@@ -13,6 +12,7 @@
     optimization,
     optimizer,
     reproducibility,
+    split,
     ssda,
     transforms,
     validation,
@@ -70,8 +70,8 @@
 @ssda.tsv_target_unlab
 @ssda.preprocessing_json_target
 # Cross validation
-@cross_validation.n_splits
-@cross_validation.split
+@split.n_splits
+@split.split
 # Optimization
 @optimizer.optimizer
 @optimizer.weight_decay

diff --git a/clinicadl/commandline/pipelines/train/regression/cli.py b/clinicadl/commandline/pipelines/train/regression/cli.py
@@ -4,7 +4,6 @@
 from clinicadl.commandline.modules_options import (
     callbacks,
     computational,
-    cross_validation,
     data,
     dataloader,
     early_stopping,
@@ -13,6 +12,7 @@
     optimization,
     optimizer,
     reproducibility,
+    split,
     ssda,
     transforms,
     validation,
@@ -68,8 +68,8 @@
 @ssda.tsv_target_unlab
 @ssda.preprocessing_json_target
 # Cross validation
-@cross_validation.n_splits
-@cross_validation.split
+@split.n_splits
+@split.split
 # Optimization
 @optimizer.optimizer
 @optimizer.weight_decay

diff --git a/clinicadl/commandline/pipelines/train/resume/cli.py b/clinicadl/commandline/pipelines/train/resume/cli.py
@@ -2,14 +2,14 @@
 
 from clinicadl.commandline import arguments
 from clinicadl.commandline.modules_options import (
-    cross_validation,
+    split,
 )
 from clinicadl.trainer.trainer import Trainer
 
 
 @click.command(name="resume", no_args_is_help=True)
 @arguments.input_maps
-@cross_validation.split
+@split.split
 def cli(input_maps_directory, split):
     """Resume training job in specified maps.
 

diff --git a/clinicadl/maps_manager/maps_manager.py b/clinicadl/maps_manager/maps_manager.py
@@ -105,17 +105,11 @@ def __init__(
                     f"Please choose between classification, regression and reconstruction."
                 )
 
-            self.split_name = (
-                self._check_split_wording()
-            )  # Used only for retro-compatibility
-
         # Initiate MAPS
         else:
-            print(parameters)
             self._check_args(parameters)
             parameters["tsv_path"] = Path(parameters["tsv_path"])
 
-            self.split_name = "split"  # Used only for retro-compatibility
             if cluster.master:
                 if (maps_path.is_dir() and maps_path.is_file()) or (  # Non-folder file
                     maps_path.is_dir() and list(maps_path.iterdir())  # Non empty folder
@@ -326,12 +320,7 @@ def _write_train_val_groups(self):
         for split in split_manager.split_iterator():
             for data_group in ["train", "validation"]:
                 df = split_manager[split][data_group]
-                group_path = (
-                    self.maps_path
-                    / "groups"
-                    / data_group
-                    / f"{self.split_name}-{split}"
-                )
+                group_path = self.maps_path / "groups" / data_group / f"split-{split}"
                 group_path.mkdir(parents=True, exist_ok=True)
 
                 columns = ["participant_id", "session_id", "cohort"]
@@ -422,10 +411,7 @@ def _mode_level_to_tsv(
             data_group: the name referring to the data group on which evaluation is performed.
         """
         performance_dir = (
-            self.maps_path
-            / f"{self.split_name}-{split}"
-            / f"best-{selection}"
-            / data_group
+            self.maps_path / f"split-{split}" / f"best-{selection}" / data_group
         )
         performance_dir.mkdir(parents=True, exist_ok=True)
         performance_path = (
@@ -482,7 +468,6 @@ def _ensemble_to_tsv(
             validation_dataset = "validation"
         test_df = get_prediction(
             self.maps_path,
-            self.split_name,
             data_group,
             split,
             selection,
@@ -491,7 +476,6 @@ def _ensemble_to_tsv(
         )
         validation_df = get_prediction(
             self.maps_path,
-            self.split_name,
             validation_dataset,
             split,
             selection,
@@ -500,10 +484,7 @@ def _ensemble_to_tsv(
         )
 
         performance_dir = (
-            self.maps_path
-            / f"{self.split_name}-{split}"
-            / f"best-{selection}"
-            / data_group
+            self.maps_path / f"split-{split}" / f"best-{selection}" / data_group
         )
 
         performance_dir.mkdir(parents=True, exist_ok=True)
@@ -551,7 +532,6 @@ def _mode_to_image_tsv(
         """
         sub_df = get_prediction(
             self.maps_path,
-            self.split_name,
             data_group,
             split,
             selection,
@@ -561,10 +541,7 @@ def _mode_to_image_tsv(
         sub_df.rename(columns={f"{self.mode}_id": "image_id"}, inplace=True)
 
         performance_dir = (
-            self.maps_path
-            / f"{self.split_name}-{split}"
-            / f"best-{selection}"
-            / data_group
+            self.maps_path / f"split-{split}" / f"best-{selection}" / data_group
         )
         sub_df.to_csv(
             performance_dir / f"{data_group}_image_level_prediction.tsv",
@@ -638,10 +615,7 @@ def _init_model(
 
         if resume:
             checkpoint_path = (
-                self.maps_path
-                / f"{self.split_name}-{split}"
-                / "tmp"
-                / "checkpoint.pth.tar"
+                self.maps_path / f"split-{split}" / "tmp" / "checkpoint.pth.tar"
             )
             checkpoint_state = torch.load(
                 checkpoint_path, map_location=device, weights_only=True
@@ -694,10 +668,7 @@ def _print_description_log(
             selection_metric (str): Metric used for best weights selection.
         """
         log_dir = (
-            self.maps_path
-            / f"{self.split_name}-{split}"
-            / f"best-{selection_metric}"
-            / data_group
+            self.maps_path / f"split-{split}" / f"best-{selection_metric}" / data_group
         )
         log_path = log_dir / "description.log"
         with log_path.open(mode="r") as f:
@@ -767,7 +738,7 @@ def get_state_dict(
             (Dict): dictionary of results (weights, epoch number, metrics values)
         """
         selection_metric = check_selection_metric(
-            self.maps_path, self.split_name, split, selection_metric
+            self.maps_path, split, selection_metric
         )
         if self.multi_network:
             if network is None:
@@ -777,14 +748,14 @@ def get_state_dict(
             else:
                 model_path = (
                     self.maps_path
-                    / f"{self.split_name}-{split}"
+                    / f"split-{split}"
                     / f"best-{selection_metric}"
                     / f"network-{network}_model.pth.tar"
                 )
         else:
             model_path = (
                 self.maps_path
-                / f"{self.split_name}-{split}"
+                / f"split-{split}"
                 / f"best-{selection_metric}"
                 / "model.pth.tar"
             )

diff --git a/clinicadl/metrics/utils.py b/clinicadl/metrics/utils.py
@@ -7,10 +7,10 @@
 from clinicadl.utils.exceptions import ClinicaDLArgumentError, MAPSError
 
 
-def find_selection_metrics(maps_path: Path, split_name: str, split):
+def find_selection_metrics(maps_path: Path, split):
     """Find which selection metrics are available in MAPS for a given split."""
 
-    split_path = maps_path / f"{split_name}-{split}"
+    split_path = maps_path / f"split-{split}"
     if not split_path.is_dir():
         raise KeyError(
             f"Training of split {split} was not performed."
@@ -24,11 +24,9 @@ def find_selection_metrics(maps_path: Path, split_name: str, split):
     ]
 
 
-def check_selection_metric(
-    maps_path: Path, split_name: str, split, selection_metric=None
-):
+def check_selection_metric(maps_path: Path, split, selection_metric=None):
     """Check that a given selection metric is available for a given split."""
-    available_metrics = find_selection_metrics(maps_path, split_name, split)
+    available_metrics = find_selection_metrics(maps_path, split)
 
     if not selection_metric:
         if len(available_metrics) > 1:
@@ -49,7 +47,6 @@ def check_selection_metric(
 
 def get_metrics(
     maps_path: Path,
-    split_name: str,
     data_group: str,
     split: int = 0,
     selection_metric: Optional[str] = None,
@@ -68,15 +65,11 @@ def get_metrics(
     Returns:
         (dict[str:float]): Values of the metrics
     """
-    selection_metric = check_selection_metric(
-        maps_path, split_name, split, selection_metric
-    )
+    selection_metric = check_selection_metric(maps_path, split, selection_metric)
     if verbose:
-        print_description_log(
-            maps_path, split_name, data_group, split, selection_metric
-        )
+        print_description_log(maps_path, data_group, split, selection_metric)
     prediction_dir = (
-        maps_path / f"{split_name}-{split}" / f"best-{selection_metric}" / data_group
+        maps_path / f"split-{split}" / f"best-{selection_metric}" / data_group
     )
     if not prediction_dir.is_dir():
         raise MAPSError(