adapt without preprocessing

clinicadl/caps_dataset/extraction/config.py

@@ -1,4 +1,5 @@
 from logging import getLogger
+from pathlib import Path
 from time import time
 from typing import List, Optional, Tuple, Union
 
@@ -31,6 +32,10 @@ class ExtractionConfig(BaseModel):
 
     @field_validator("extract_json", mode="before")
     def compute_extract_json(cls, v: str):
+        if isinstance(v, Path):
+            v = str(v)
+        elif isinstance(v, bool):
+            v = None
         if v is None:
             return f"extract_{int(time())}.json"
         elif not v.endswith(".json"):
@@ -75,3 +80,11 @@ class ExtractionROIConfig(ExtractionConfig):
     roi_custom_mask_pattern: str = ""
     roi_background_value: int = 0
     extract_method: ExtractionMethod = ExtractionMethod.ROI
+
+
+ALL_EXTRACTION_TYPES = Union[
+    ExtractionImageConfig,
+    ExtractionROIConfig,
+    ExtractionSliceConfig,
+    ExtractionPatchConfig,
+]

clinicadl/caps_dataset/preprocessing/config.py

@@ -1,7 +1,7 @@
 import abc
 from logging import getLogger
 from pathlib import Path
-from typing import Optional, Tuple
+from typing import Optional, Tuple, Union
 
 from pydantic import BaseModel, ConfigDict
 
@@ -207,3 +207,13 @@ def caps_nii(self) -> tuple:
 
     def get_filetype(self) -> FileType:
         return self.linear_nii()
+
+
+ALL_PREPROCESSING_TYPES = Union[
+    T1PreprocessingConfig,
+    T2PreprocessingConfig,
+    FlairPreprocessingConfig,
+    PETPreprocessingConfig,
+    CustomPreprocessingConfig,
+    DTIPreprocessingConfig,
+]

clinicadl/caps_dataset/utils.py

@@ -61,8 +61,9 @@ def get_preprocessing_and_mode_from_parameters(**kwargs):
     if "preprocessing_dict" in kwargs:
         kwargs = kwargs["preprocessing_dict"]
 
+    print(kwargs)
     preprocessing = Preprocessing(kwargs["preprocessing"])
-    mode = ExtractionMethod(kwargs["mode"])
+    mode = ExtractionMethod(kwargs["extract_method"])
     return get_preprocessing(preprocessing)(**kwargs), get_extraction(mode)(**kwargs)
 
 

clinicadl/maps_manager/maps_manager.py

@@ -154,7 +154,6 @@ def _check_args(self, parameters):
         mandatory_arguments = [
             "caps_directory",
             "tsv_path",
-            "mode",
             "network_task",
         ]
         for arg in mandatory_arguments:

clinicadl/trainer/config/train.py

@@ -12,6 +12,8 @@
 from clinicadl.callbacks.config import CallbacksConfig
 from clinicadl.caps_dataset.data_config import DataConfig
 from clinicadl.caps_dataset.dataloader_config import DataLoaderConfig
+from clinicadl.caps_dataset.extraction.config import ALL_EXTRACTION_TYPES
+from clinicadl.caps_dataset.preprocessing.config import ALL_PREPROCESSING_TYPES
 from clinicadl.config.config.lr_scheduler import LRschedulerConfig
 from clinicadl.config.config.reproducibility import ReproducibilityConfig
 from clinicadl.maps_manager.config import MapsManagerConfig
@@ -42,9 +44,11 @@ class TrainConfig(BaseModel, ABC):
     data: DataConfig
     dataloader: DataLoaderConfig
     early_stopping: EarlyStoppingConfig
+    extraction: ALL_EXTRACTION_TYPES
     lr_scheduler: LRschedulerConfig
     maps_manager: MapsManagerConfig
     model: NetworkConfig
+    preprocessing: ALL_PREPROCESSING_TYPES
     optimization: OptimizationConfig
     optimizer: OptimizerConfig
     reproducibility: ReproducibilityConfig
@@ -55,6 +59,9 @@ class TrainConfig(BaseModel, ABC):
     # pydantic config
     model_config = ConfigDict(validate_assignment=True)
 
+    # @field_validator("preprocessing", mode="before")
+    # def check_preprocessing(cls, v: str):
+
     @computed_field
     @property
     @abstractmethod
@@ -68,9 +75,11 @@ def __init__(self, **kwargs):
             data=kwargs,
             dataloader=kwargs,
             early_stopping=kwargs,
+            extraction=kwargs,
             lr_scheduler=kwargs,
             maps_manager=kwargs,
             model=kwargs,
+            preprocessing=kwargs,
             optimization=kwargs,
             optimizer=kwargs,
             reproducibility=kwargs,
@@ -87,9 +96,11 @@ def _update(self, config_dict: Dict[str, Any]) -> None:
         self.data.__dict__.update(config_dict)
         self.dataloader.__dict__.update(config_dict)
         self.early_stopping.__dict__.update(config_dict)
+        self.extraction.__dict__.update(config_dict)
         self.lr_scheduler.__dict__.update(config_dict)
         self.maps_manager.__dict__.update(config_dict)
         self.model.__dict__.update(config_dict)
+        self.preprocessing.__dict__.update(config_dict)
         self.optimization.__dict__.update(config_dict)
         self.optimizer.__dict__.update(config_dict)
         self.reproducibility.__dict__.update(config_dict)

clinicadl/trainer/trainer.py

@@ -125,8 +125,11 @@ def from_json(
         config_dict = patch_to_read_json(read_json(config_file))  # TODO : remove patch
         config_dict["maps_dir"] = maps_path
         config_dict["split"] = split if split else ()
+
+        from clinicadl.utils.iotools.trainer_utils import read_multi_level_dict
+
         config_object = create_training_config(config_dict["network_task"])(
-            **config_dict
+            **read_multi_level_dict(config_dict)
         )
         return cls(config_object)
 

clinicadl/utils/iotools/trainer_utils.py

@@ -1,6 +1,16 @@
 from pathlib import Path
 
 
+def read_multi_level_dict(dict_):
+    parameters = {}
+    for key in dict_:
+        if isinstance(dict_[key], dict):
+            parameters.update(dict_[key])
+        else:
+            parameters[key] = dict_[key]
+    return parameters
+
+
 def create_parameters_dict(config):
     parameters = {}
     config_dict = config.model_dump()
@@ -20,7 +30,6 @@ def create_parameters_dict(config):
     if parameters["data_augmentation"] == ():
         parameters["data_augmentation"] = False
 
-    del parameters["preprocessing_json"]
     # if "tsv_path" in parameters:
     #     parameters["tsv_path"] = parameters["tsv_path"]
     #     del parameters["tsv_path"]