first try after rebase and new config

clinicadl/__init__.py

@@ -1,7 +1,5 @@
 from importlib.metadata import version
 
-from .utils.maps_manager import MapsManager
-
 __all__ = ["__version__", "MapsManager"]
 
 __version__ = version("clinicadl")

clinicadl/caps_dataset/caps_dataset_config.py

@@ -1,23 +1,20 @@
-import abc
-from pathlib import Path
-from typing import Any, Dict, Optional, Union
+from typing import Union
 
-import pandas as pd
-from pydantic import BaseModel, computed_field
+from pydantic import BaseModel, ConfigDict
 
-from clinicadl.caps_dataset.data_config import ConfigDict, DataConfig
-from clinicadl.caps_dataset.data_utils import check_multi_cohort_tsv
+from clinicadl.caps_dataset.data_config import DataConfig
 from clinicadl.caps_dataset.dataloader_config import DataLoaderConfig
-from clinicadl.config.config import modality
+from clinicadl.config.config.modality import (
+    CustomModalityConfig,
+    DTIModalityConfig,
+    FlairModalityConfig,
+    ModalityConfig,
+    PETModalityConfig,
+    T1ModalityConfig,
+)
 from clinicadl.generate import generate_config as generate_type
-from clinicadl.generate.generate_config import GenerateConfig
 from clinicadl.preprocessing import config as preprocessing
 from clinicadl.utils.enum import ExtractionMethod, GenerateType, Preprocessing
-from clinicadl.utils.exceptions import (
-    ClinicaDLArgumentError,
-    ClinicaDLTSVError,
-    DownloadError,
-)
 
 
 def get_preprocessing(extract_method: ExtractionMethod):
@@ -38,15 +35,15 @@ def get_modality(preprocessing: Preprocessing):
         preprocessing == Preprocessing.T1_EXTENSIVE
         or preprocessing == Preprocessing.T1_LINEAR
     ):
-        return modality.T1ModalityConfig
+        return T1ModalityConfig
     elif preprocessing == Preprocessing.PET_LINEAR:
-        return modality.PETModalityConfig
+        return PETModalityConfig
     elif preprocessing == Preprocessing.FLAIR_LINEAR:
-        return modality.FlairModalityConfig
+        return FlairModalityConfig
     elif preprocessing == Preprocessing.CUSTOM:
-        return modality.CustomModalityConfig
+        return CustomModalityConfig
     elif preprocessing == Preprocessing.DWI_DTI:
-        return modality.DTIModalityConfig
+        return DTIModalityConfig
     else:
         raise ValueError(f"Preprocessing {preprocessing.value} is not implemented.")
 
@@ -69,7 +66,8 @@ def get_generate(generate: Union[str, GenerateType]):
 
 class CapsDatasetBase(BaseModel):
     data: DataConfig
-    modality: modality.ModalityConfig
+    dataloader: DataLoaderConfig
+    modality: ModalityConfig
     preprocessing: preprocessing.PreprocessingConfig
 
     # pydantic config
@@ -86,29 +84,7 @@ def from_preprocessing_and_extraction_method(
     ):
         return cls(
             data=DataConfig(**kwargs),
+            dataloader=DataLoaderConfig(**kwargs),
             modality=get_modality(Preprocessing(preprocessing_type))(**kwargs),
             preprocessing=get_preprocessing(ExtractionMethod(extraction))(**kwargs),
         )
-
-
-# def create_caps_dataset_config(
-#     preprocessing: Union[str, Preprocessing], extract: Union[str, ExtractionMethod]
-# ):
-#     try:
-#         preprocessing_type = Preprocessing(preprocessing)
-#     except ClinicaDLArgumentError:
-#         print("Invalid preprocessing configuration")
-
-#     try:
-#         extract_method = ExtractionMethod(extract)
-#     except ClinicaDLArgumentError:
-#         print("Invalid preprocessing configuration")
-
-#     class CapsDatasetConfig(CapsDatasetBase):
-#         modality: get_modality(preprocessing_type)
-#         preprocessing: get_preprocessing(extract_method)
-
-#         def __init__(self, **kwargs):
-#             super().__init__(data=kwargs, modality=kwargs, preprocessing=kwargs)
-
-#     return CapsDatasetConfig

clinicadl/caps_dataset/caps_dataset_utils.py

@@ -0,0 +1,68 @@
+from pathlib import Path
+from typing import Dict, Optional, Tuple, Union
+
+from pydantic import BaseModel, ConfigDict
+
+from clinicadl.caps_dataset.caps_dataset_config import CapsDatasetConfig
+from clinicadl.utils.enum import LinearModality, Preprocessing
+
+
+def compute_folder_and_file_type(
+    config: CapsDatasetConfig, from_bids: Optional[Path] = None
+) -> Tuple[str, Dict[str, str]]:
+    from clinicadl.utils.clinica_utils import (
+        bids_nii,
+        dwi_dti,
+        linear_nii,
+        pet_linear_nii,
+    )
+
+    preprocessing = Preprocessing(
+        config.preprocessing.preprocessing
+    )  # replace("-", "_")
+    if from_bids is not None:
+        if preprocessing == Preprocessing.CUSTOM:
+            mod_subfolder = Preprocessing.CUSTOM.value
+            file_type = {
+                "pattern": f"*{config.modality.custom_suffix}",
+                "description": "Custom suffix",
+            }
+        else:
+            mod_subfolder = preprocessing
+            file_type = bids_nii(preprocessing)
+
+    elif preprocessing not in Preprocessing:
+        raise NotImplementedError(
+            f"Extraction of preprocessing {config.preprocessing.preprocessing.value} is not implemented from CAPS directory."
+        )
+    else:
+        mod_subfolder = preprocessing.value.replace("-", "_")
+        if preprocessing == Preprocessing.T1_LINEAR:
+            file_type = linear_nii(
+                LinearModality.T1W, config.preprocessing.use_uncropped_image
+            )
+
+        elif preprocessing == Preprocessing.FLAIR_LINEAR:
+            file_type = linear_nii(
+                LinearModality.FLAIR, config.preprocessing.use_uncropped_image
+            )
+
+        elif preprocessing == Preprocessing.PET_LINEAR:
+            file_type = pet_linear_nii(
+                config.modality.tracer,
+                config.modality.suvr_reference_region,
+                config.preprocessing.use_uncropped_image,
+            )
+        elif preprocessing == Preprocessing.DWI_DTI:
+            file_type = dwi_dti(
+                config.modality.dti_measure,
+                config.modality.dti_space,
+            )
+        elif preprocessing == Preprocessing.CUSTOM:
+            file_type = {
+                "pattern": f"*{config.modality.custom_suffix}",
+                "description": "Custom suffix",
+            }
+            # custom_suffix["use_uncropped_image"] = None
+
+    return mod_subfolder, file_type

clinicadl/caps_dataset/data.py

@@ -10,6 +10,7 @@
 import torch
 from torch.utils.data import Dataset
 
+from clinicadl.caps_dataset.caps_dataset_utils import compute_folder_and_file_type
 from clinicadl.prepare_data.prepare_data_config import (
     PrepareDataConfig,
     PrepareDataImageConfig,
@@ -19,7 +20,6 @@
 )
 from clinicadl.prepare_data.prepare_data_utils import (
     compute_discarded_slices,
-    compute_folder_and_file_type,
     extract_patch_path,
     extract_patch_tensor,
     extract_roi_path,
@@ -142,30 +142,6 @@ def domain_fn(self, target: Union[str, float, int]) -> Union[float, int]:
     def __len__(self) -> int:
         return len(self.df) * self.elem_per_image
 
-    @staticmethod
-    def create_caps_dict(caps_directory: Path, multi_cohort: bool) -> Dict[str, Path]:
-        from clinicadl.utils.clinica_utils import check_caps_folder
-
-        if multi_cohort:
-            if not caps_directory.suffix == ".tsv":
-                raise ClinicaDLArgumentError(
-                    "If multi_cohort is True, the CAPS_DIRECTORY argument should be a path to a TSV file."
-                )
-            else:
-                caps_df = pd.read_csv(caps_directory, sep="\t")
-                check_multi_cohort_tsv(caps_df, "CAPS")
-                caps_dict = dict()
-                for idx in range(len(caps_df)):
-                    cohort = caps_df.loc[idx, "cohort"]
-                    caps_path = Path(caps_df.loc[idx, "path"])
-                    check_caps_folder(caps_path)
-                    caps_dict[cohort] = caps_path
-        else:
-            check_caps_folder(caps_directory)
-            caps_dict = {"single": caps_directory}
-
-        return caps_dict
-
     def _get_image_path(self, participant: str, session: str, cohort: str) -> Path:
         """
         Gets the path to the tensor image (*.pt)
@@ -804,3 +780,94 @@ def num_elem_per_image(self):
             - self.discarded_slices[0]
             - self.discarded_slices[1]
         )
+
+
+def return_dataset(
+    input_dir: Path,
+    data_df: pd.DataFrame,
+    preprocessing_dict: Dict[str, Any],
+    all_transformations: Optional[Callable],
+    label: str = None,
+    label_code: Dict[str, int] = None,
+    train_transformations: Optional[Callable] = None,
+    cnn_index: int = None,
+    label_presence: bool = True,
+    multi_cohort: bool = False,
+) -> CapsDataset:
+    """
+    Return appropriate Dataset according to given options.
+    Args:
+        input_dir: path to a directory containing a CAPS structure.
+        data_df: List subjects, sessions and diagnoses.
+        preprocessing_dict: preprocessing dict contained in the JSON file of prepare_data.
+        train_transformations: Optional transform to be applied during training only.
+        all_transformations: Optional transform to be applied during training and evaluation.
+        label: Name of the column in data_df containing the label.
+        label_code: label code that links the output node number to label value.
+        cnn_index: Index of the CNN in a multi-CNN paradigm (optional).
+        label_presence: If True the diagnosis will be extracted from the given DataFrame.
+        multi_cohort: If True caps_directory is the path to a TSV file linking cohort names and paths.
+
+    Returns:
+         the corresponding dataset.
+    """
+    if cnn_index is not None and preprocessing_dict["mode"] == "image":
+        raise NotImplementedError(
+            f"Multi-CNN is not implemented for {preprocessing_dict['mode']} mode."
+        )
+
+    if preprocessing_dict["mode"] == "image":
+        return CapsDatasetImage(
+            input_dir,
+            data_df,
+            preprocessing_dict,
+            train_transformations=train_transformations,
+            all_transformations=all_transformations,
+            label_presence=label_presence,
+            label=label,
+            label_code=label_code,
+            multi_cohort=multi_cohort,
+        )
+    elif preprocessing_dict["mode"] == "patch":
+        return CapsDatasetPatch(
+            input_dir,
+            data_df,
+            preprocessing_dict,
+            train_transformations=train_transformations,
+            all_transformations=all_transformations,
+            patch_index=cnn_index,
+            label_presence=label_presence,
+            label=label,
+            label_code=label_code,
+            multi_cohort=multi_cohort,
+        )
+    elif preprocessing_dict["mode"] == "roi":
+        return CapsDatasetRoi(
+            input_dir,
+            data_df,
+            preprocessing_dict,
+            train_transformations=train_transformations,
+            all_transformations=all_transformations,
+            roi_index=cnn_index,
+            label_presence=label_presence,
+            label=label,
+            label_code=label_code,
+            multi_cohort=multi_cohort,
+        )
+    elif preprocessing_dict["mode"] == "slice":
+        return CapsDatasetSlice(
+            input_dir,
+            data_df,
+            preprocessing_dict,
+            train_transformations=train_transformations,
+            all_transformations=all_transformations,
+            slice_index=cnn_index,
+            label_presence=label_presence,
+            label=label,
+            label_code=label_code,
+            multi_cohort=multi_cohort,
+        )
+    else:
+        raise NotImplementedError(
+            f"Mode {preprocessing_dict['mode']} is not implemented."
+        )

clinicadl/caps_dataset/data_config.py

@@ -1,4 +1,3 @@
-import tarfile
 from logging import getLogger
 from pathlib import Path
 from typing import Any, Dict, Optional, Tuple, Union
@@ -8,19 +7,11 @@
 
 from clinicadl.caps_dataset.data_utils import check_multi_cohort_tsv, load_data_test
 from clinicadl.preprocessing.preprocessing import read_preprocessing
-from clinicadl.utils.clinica_utils import (
-    RemoteFileStructure,
-    clinicadl_file_reader,
-    fetch_file,
-)
-from clinicadl.utils.enum import MaskChecksum, Mode, Pathology
+from clinicadl.utils.enum import Mode
 from clinicadl.utils.exceptions import (
     ClinicaDLArgumentError,
     ClinicaDLTSVError,
-    DownloadError,
 )
-from clinicadl.utils.maps_manager.maps_manager import MapsManager
-from clinicadl.utils.read_utils import get_mask_checksum_and_filename
 
 logger = getLogger("clinicadl.data_config")
 
@@ -52,11 +43,6 @@ def validator_diagnoses(cls, v):
             return tuple(v)
         return v  # TODO : check if columns are in tsv
 
-    def adapt_data_with_maps_manager_info(self, maps_manager: MapsManager):
-        # TEMPORARY
-        if self.diagnoses is None or len(self.diagnoses) == 0:
-            self.diagnoses = maps_manager.diagnoses
-
     def create_groupe_df(self):
         group_df = None
         if self.data_tsv is not None and self.data_tsv.is_file():