complete merge

clinicadl/data/datasets/caps_dataset.py

@@ -242,10 +242,9 @@ def _get_df_from_input(
         df = self._check_data_instance(data)
         self.df = df
 
-        if not self._check_preprocessing_config():
-            raise ClinicaDLCAPSError(
-                f"The DataFrame does not match the preprocessing configuration: {self.preprocessing.preprocessing.value}"
-            )
+        self.caps_reader.check_preprocessing(
+            self._get_participant_session_couples(), self.preprocessing
+        )
 
         return df
 
@@ -260,11 +259,11 @@ def _check_data_instance(self, data: Optional[Union[pd.DataFrame, Path]] = None)
                     "Please ensure the file path is correct and accessible."
                 )
             df = tsv_to_df(data)
-        if isinstance(data, pd.DataFrame):
+        elif isinstance(data, pd.DataFrame):
             df = check_df(data)
         else:
             raise ValueError(
-                f"'data' must be a Pandas DataFrame, a path to a TSV file or None. Got{data}"
+                f"'data' must be a Pandas DataFrame, a path to a TSV file or None. Got {data}"
             )
 
         return df

clinicadl/predictor/old_predictor.py

@@ -1059,7 +1059,7 @@ def _compute_output_tensors(
         Compute the output tensors and saves them in the MAPS.
 
         Args:
-            dataset (clinicadl.dataset.caps_dataset.CapsDataset): wrapper of the data set.
+            dataset (clinicadl.data.datasets.caps_dataset.CapsDataset): wrapper of the data set.
             data_group (str): name of the data group used for the task.
             split (int): split number.
             selection_metrics (list[str]): metrics used for model selection.

tests/unittests/dataset/test_config.py

@@ -1,6 +1,6 @@
 import pytest
 
-from clinicadl.dataset.config import FileType
+from clinicadl.data.config import FileType
 from clinicadl.utils.enum import PreprocessingMethod
 
 

tests/unittests/dataset/test_datasets.py

@@ -3,8 +3,8 @@
 import pytest
 import torchio as tio
 
-from clinicadl.dataset.datasets.caps_dataset import CapsDataset
-from clinicadl.dataset.preprocessing import PreprocessingPET, PreprocessingT1
+from clinicadl.data.datasets import CapsDataset
+from clinicadl.data.preprocessing import PreprocessingPET, PreprocessingT1
 from clinicadl.transforms import Transforms
 from clinicadl.utils.exceptions import (
     ClinicaDLArgumentError,

tests/unittests/dataset/test_reader.py

@@ -2,8 +2,8 @@
 
 import pytest
 
-from clinicadl.dataset.preprocessing import PreprocessingT1
-from clinicadl.dataset.readers import CapsReader
+from clinicadl.data.preprocessing import PreprocessingT1
+from clinicadl.data.readers import CapsReader
 from clinicadl.utils.enum import PreprocessingMethod
 from clinicadl.utils.exceptions import (
     ClinicaDLArgumentError,

tests/unittests/splitter/test_splitter.py

@@ -1,30 +1,16 @@
-import json
 from pathlib import Path
 
-import nibabel as nib
 import numpy as np
-import pandas as pd
 import pytest
 from pydantic import ValidationError
 
 from clinicadl.data.datasets.caps_dataset import CapsDataset
-from clinicadl.data.preprocessing import PreprocessingT1, PreprocessingT2
-from clinicadl.splitter.split import Split
+from clinicadl.data.preprocessing import PreprocessingT1
 from clinicadl.splitter.splitter.kfold import KFold, KFoldConfig
 from clinicadl.splitter.splitter.single_split import SingleSplit, SingleSplitConfig
-from clinicadl.splitter.splitter.splitter import (
-    Splitter,
-    SplitterConfig,
-    SubjectsSessionsSplit,
-)
+from clinicadl.splitter.splitter.splitter import SubjectsSessionsSplit
 from clinicadl.transforms import Transforms
-from clinicadl.utils.enum import Preprocessing
-from clinicadl.utils.exceptions import (
-    ClinicaDLArgumentError,
-    ClinicaDLCAPSError,
-    ClinicaDLConfigurationError,
-    ClinicaDLTSVError,
-)
+from clinicadl.utils.exceptions import ClinicaDLTSVError
 
 caps_dir = Path(__file__).parents[1] / "ressources" / "caps_example"
 split_dir = caps_dir / "split_test" / "split"

tests/unittests/transforms/test_transforms.py

@@ -71,22 +71,22 @@ def test_get_transforms():
     assert (tio_image.label.tensor == old_tio_image.label.tensor).all()
     assert (tio_image.mask_1.tensor == old_tio_image.mask_1.tensor).all()
 
-    tio_sample = transforms.extraction.extract_tio_sample(tio_image, 0)
+    tio_sample, _ = transforms.extraction.extract_tio_sample(tio_image, 0)
     patch_mask = np.zeros((1, 4, 4, 4))
     patch_mask[:, 1:, 1:, 1:] = 1
     patch_mask = torch.from_numpy(patch_mask)
-    assert (tio_sample.sample.tensor == tio_image.image.tensor[:, :4, :4, :4]).all()
+    assert (tio_sample.image.tensor == tio_image.image.tensor[:, :4, :4, :4]).all()
     assert (tio_sample.label.tensor == tio_image.label.tensor[:, :4, :4, :4]).all()
     assert (tio_sample.mask_1.tensor == patch_mask).all()
 
     tio_sample = sample_transforms(tio_sample)
-    assert tio_sample.sample.tensor.shape == (1, 6, 6, 6)
+    assert tio_sample.image.tensor.shape == (1, 6, 6, 6)
     assert tio_sample.label.tensor.shape == (1, 6, 6, 6)
     assert tio_sample.mask_1.tensor.shape == (1, 6, 6, 6)
 
     tio_sample = sample_augmentations(tio_sample)
-    assert (tio_sample.sample.tensor[:, :2, :2, :2] == 0.0).all()
-    assert (tio_sample.sample.tensor[:, 5:, 5:, 5:] == 0.0).all()
+    assert (tio_sample.image.tensor[:, :2, :2, :2] == 0.0).all()
+    assert (tio_sample.image.tensor[:, 5:, 5:, 5:] == 0.0).all()
 
 
 def test_str():