Some cleaning with utils files (#633)

* clean
Co-authored-by: thibaultdvx <[email protected]>

clinicadl/caps_dataset/caps_dataset_config.py

@@ -14,15 +14,15 @@
     PreprocessingConfig,
     T1PreprocessingConfig,
 )
-from clinicadl.transforms.config import TransformsConfig
-from clinicadl.utils.clinica_utils import (
-    FileType,
+from clinicadl.caps_dataset.preprocessing.utils import (
     bids_nii,
     dwi_dti,
     linear_nii,
     pet_linear_nii,
 )
+from clinicadl.transforms.config import TransformsConfig
 from clinicadl.utils.enum import ExtractionMethod, Preprocessing
+from clinicadl.utils.iotools.clinica_utils import FileType
 
 
 def get_extraction(extract_method: ExtractionMethod):

clinicadl/caps_dataset/caps_dataset_utils.py

@@ -1,5 +1,6 @@
+import json
 from pathlib import Path
-from typing import Optional, Tuple
+from typing import Any, Dict, Optional, Tuple
 
 from clinicadl.caps_dataset.caps_dataset_config import CapsDatasetConfig
 from clinicadl.caps_dataset.preprocessing.config import (
@@ -9,14 +10,15 @@
     PETPreprocessingConfig,
     T1PreprocessingConfig,
 )
-from clinicadl.utils.clinica_utils import (
-    FileType,
+from clinicadl.caps_dataset.preprocessing.utils import (
     bids_nii,
     dwi_dti,
     linear_nii,
     pet_linear_nii,
 )
 from clinicadl.utils.enum import Preprocessing
+from clinicadl.utils.exceptions import ClinicaDLArgumentError
+from clinicadl.utils.iotools.clinica_utils import FileType
 
 
 def compute_folder_and_file_type(
@@ -54,3 +56,138 @@ def compute_folder_and_file_type(
                 description="Custom suffix",
             )
     return mod_subfolder, file_type
+
+
+def find_file_type(config: CapsDatasetConfig) -> FileType:
+    if isinstance(config.preprocessing, T1PreprocessingConfig):
+        file_type = linear_nii(config.preprocessing)
+    elif isinstance(config.preprocessing, PETPreprocessingConfig):
+        if (
+            config.preprocessing.tracer is None
+            or config.preprocessing.suvr_reference_region is None
+        ):
+            raise ClinicaDLArgumentError(
+                "`tracer` and `suvr_reference_region` must be defined "
+                "when using `pet-linear` preprocessing."
+            )
+        file_type = pet_linear_nii(config.preprocessing)
+    else:
+        raise NotImplementedError(
+            f"Generation of synthetic data is not implemented for preprocessing {config.preprocessing.preprocessing.value}"
+        )
+
+    return file_type
+
+
+def read_json(json_path: Path) -> Dict[str, Any]:
+    """
+    Ensures retro-compatibility between the different versions of ClinicaDL.
+
+    Parameters
+    ----------
+    json_path: Path
+        path to the JSON file summing the parameters of a MAPS.
+
+    Returns
+    -------
+    A dictionary of training parameters.
+    """
+    from clinicadl.utils.iotools.utils import path_decoder
+
+    with json_path.open(mode="r") as f:
+        parameters = json.load(f, object_hook=path_decoder)
+    # Types of retro-compatibility
+    # Change arg name: ex network --> model
+    # Change arg value: ex for preprocessing: mni --> t1-extensive
+    # New arg with default hard-coded value --> discarded_slice --> 20
+    retro_change_name = {
+        "model": "architecture",
+        "multi": "multi_network",
+        "minmaxnormalization": "normalize",
+        "num_workers": "n_proc",
+        "mode": "extract_method",
+    }
+
+    retro_add = {
+        "optimizer": "Adam",
+        "loss": None,
+    }
+
+    for old_name, new_name in retro_change_name.items():
+        if old_name in parameters:
+            parameters[new_name] = parameters[old_name]
+            del parameters[old_name]
+
+    for name, value in retro_add.items():
+        if name not in parameters:
+            parameters[name] = value
+
+    if "extract_method" in parameters:
+        parameters["mode"] = parameters["extract_method"]
+    # Value changes
+    if "use_cpu" in parameters:
+        parameters["gpu"] = not parameters["use_cpu"]
+        del parameters["use_cpu"]
+    if "nondeterministic" in parameters:
+        parameters["deterministic"] = not parameters["nondeterministic"]
+        del parameters["nondeterministic"]
+
+    # Build preprocessing_dict
+    if "preprocessing_dict" not in parameters:
+        parameters["preprocessing_dict"] = {"mode": parameters["mode"]}
+        preprocessing_options = [
+            "preprocessing",
+            "use_uncropped_image",
+            "prepare_dl",
+            "custom_suffix",
+            "tracer",
+            "suvr_reference_region",
+            "patch_size",
+            "stride_size",
+            "slice_direction",
+            "slice_mode",
+            "discarded_slices",
+            "roi_list",
+            "uncropped_roi",
+            "roi_custom_suffix",
+            "roi_custom_template",
+            "roi_custom_mask_pattern",
+        ]
+        for preprocessing_var in preprocessing_options:
+            if preprocessing_var in parameters:
+                parameters["preprocessing_dict"][preprocessing_var] = parameters[
+                    preprocessing_var
+                ]
+                del parameters[preprocessing_var]
+
+    # Add missing parameters in previous version of extract
+    if "use_uncropped_image" not in parameters["preprocessing_dict"]:
+        parameters["preprocessing_dict"]["use_uncropped_image"] = False
+
+    if (
+        "prepare_dl" not in parameters["preprocessing_dict"]
+        and parameters["mode"] != "image"
+    ):
+        parameters["preprocessing_dict"]["prepare_dl"] = False
+
+    if (
+        parameters["mode"] == "slice"
+        and "slice_mode" not in parameters["preprocessing_dict"]
+    ):
+        parameters["preprocessing_dict"]["slice_mode"] = "rgb"
+
+    if "preprocessing" not in parameters:
+        parameters["preprocessing"] = parameters["preprocessing_dict"]["preprocessing"]
+
+    from clinicadl.caps_dataset.caps_dataset_config import CapsDatasetConfig
+
+    config = CapsDatasetConfig.from_preprocessing_and_extraction_method(
+        extraction=parameters["mode"],
+        preprocessing_type=parameters["preprocessing"],
+        **parameters,
+    )
+    if "file_type" not in parameters["preprocessing_dict"]:
+        _, file_type = compute_folder_and_file_type(config)
+        parameters["preprocessing_dict"]["file_type"] = file_type.model_dump()
+
+    return parameters

clinicadl/caps_dataset/data.py

@@ -3,15 +3,14 @@
 import abc
 from logging import getLogger
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
 import torch
 from torch.utils.data import Dataset
 
 from clinicadl.caps_dataset.caps_dataset_config import CapsDatasetConfig
-from clinicadl.caps_dataset.caps_dataset_utils import compute_folder_and_file_type
 from clinicadl.caps_dataset.extraction.config import (
     ExtractionImageConfig,
     ExtractionPatchConfig,
@@ -30,15 +29,13 @@
 )
 from clinicadl.transforms.config import TransformsConfig
 from clinicadl.utils.enum import (
-    ExtractionMethod,
     Pattern,
     Preprocessing,
     SliceDirection,
     SliceMode,
     Template,
 )
 from clinicadl.utils.exceptions import (
-    ClinicaDLArgumentError,
     ClinicaDLCAPSError,
     ClinicaDLTSVError,
 )
@@ -133,7 +130,7 @@ def _get_image_path(self, participant: str, session: str, cohort: str) -> Path:
         Returns:
             image_path: path to the tensor containing the whole image.
         """
-        from clinicadl.utils.clinica_utils import clinicadl_file_reader
+        from clinicadl.utils.iotools.clinica_utils import clinicadl_file_reader
 
         # Try to find .nii.gz file
         try:
@@ -221,7 +218,7 @@ def _get_full_image(self) -> torch.Tensor:
         """
         import nibabel as nib
 
-        from clinicadl.utils.clinica_utils import clinicadl_file_reader
+        from clinicadl.utils.iotools.clinica_utils import clinicadl_file_reader
 
         participant_id = self.df.loc[0, "participant_id"]
         session_id = self.df.loc[0, "session_id"]

clinicadl/caps_dataset/data_config.py

@@ -5,13 +5,13 @@
 import pandas as pd
 from pydantic import BaseModel, ConfigDict, computed_field, field_validator
 
-from clinicadl.caps_dataset.data_utils import check_multi_cohort_tsv, load_data_test
-from clinicadl.caps_dataset.extraction.utils import read_preprocessing
 from clinicadl.utils.enum import Mode
 from clinicadl.utils.exceptions import (
     ClinicaDLArgumentError,
     ClinicaDLTSVError,
 )
+from clinicadl.utils.iotools.data_utils import check_multi_cohort_tsv, load_data_test
+from clinicadl.utils.iotools.utils import read_preprocessing
 
 logger = getLogger("clinicadl.data_config")
 
@@ -85,7 +85,7 @@ def check_data_tsv(cls, v) -> Path:
     @computed_field
     @property
     def caps_dict(self) -> Dict[str, Path]:
-        from clinicadl.utils.clinica_utils import check_caps_folder
+        from clinicadl.utils.iotools.clinica_utils import check_caps_folder
 
         if self.multi_cohort:
             if self.caps_directory.suffix != ".tsv":

clinicadl/caps_dataset/extraction/config.py

@@ -5,12 +5,12 @@
 from pydantic import BaseModel, ConfigDict, field_validator
 from pydantic.types import NonNegativeInt
 
-from clinicadl.utils.clinica_utils import FileType
 from clinicadl.utils.enum import (
     ExtractionMethod,
     SliceDirection,
     SliceMode,
 )
+from clinicadl.utils.iotools.clinica_utils import FileType
 
 logger = getLogger("clinicadl.preprocessing_config")