Changed dataset class returns

Former-commit-id: 3c6d7f5

.gitignore

@@ -201,4 +201,5 @@ examples/process_one.py
 
 tests/temp_folder*
 examples/data_test
-data
+data/
+demo.py

imgtools/io/dataset.py

@@ -4,8 +4,6 @@
 from typing import List, Sequence, Optional, Callable, Iterable, Dict,Tuple
 import torchio as tio
 import pandas as pd
-# from . import file_name_convention
-# from ..ops import StructureSetToSegmentation, ImageAutoInput, Resample, BaseOp
 from imgtools.io import file_name_convention
 from imgtools.ops import StructureSetToSegmentation, ImageAutoInput, Resample, BaseOp
 from tqdm import tqdm
@@ -15,28 +13,26 @@
 from imgtools.pipeline import Pipeline
 import datetime
 
-class Dataset(tio.SubjectsDataset):
+class Dataset(List):
     """
-    This class takes in medical dataset in the form of nrrds or directly from the dataset and converts the data into torchio.Subject object, which can be loaded into 
-    torchio.SubjectDataset object.
-    This class inherits from torchio.SubjectDataset object, which can support transforms and torch.Dataloader.
+    This class takes in medical dataset in the form of nrrds or directly from the dataset and converts the data into list of torchio.Subject object, which can be later loaded into 
+    torchio.SubjectDataset object separately
     Read more about torchio from https://torchio.readthedocs.io/quickstart.html and torchio.SubjectDataset from https://github.com/fepegar/torchio/blob/3e07b78da16d6db4da7193325b3f9cb31fc0911a/torchio/data/dataset.py#L101
     """
     def __init__(
         self,
         subjects: Sequence[tio.Subject],
-        transform: Optional[Callable] = None,
-        load_getitem: bool = True
-        ) -> tio.SubjectsDataset:
-        super().__init__(subjects,transform,load_getitem)
-
+        path: str,
+        ) -> List[tio.Subject]:
+        super().__init__(subjects)
+        self.subjects = subjects
+        self.path = path
+
     @classmethod
     def load_from_nrrd(
             cls,
             path:str,
-            transform: Optional[Callable] = None,
             ignore_multi: bool = True,
-            load_getitem: bool = True
             ) -> List[tio.Subject]:
         """
         Based on the given path, passess the processed nrrd files present in the directory and the metadata associated with it and creates a list of Subject instances
@@ -88,7 +84,7 @@ def load_from_nrrd(
                         #torch dataloader doesnt accept None type
                         temp[metadata_name] = {}
             subjects.append(tio.Subject(temp))
-        return cls(subjects,transform,load_getitem)
+        return cls(subjects,path)
 
     @classmethod
     def load_directly(
@@ -97,9 +93,7 @@ def load_directly(
             modalities: str,
             n_jobs: int = -1,
             spacing: Tuple = (1., 1., 0.),
-            transform: Optional[Callable] = None,
             ignore_multi: bool = True,
-            load_getitem: bool = True
             ) -> List[tio.Subject]:
         """
         Based on the given path, imgtools crawls through the directory, forms datagraph and picks the user defined modalities. These paths are processed into sitk.Image.
@@ -119,7 +113,7 @@ def load_directly(
         resample = Resample(spacing=spacing)
         make_binary_mask = StructureSetToSegmentation(roi_names=[], continuous=False)
         subjects =  Parallel(n_jobs=n_jobs)(delayed(cls.process_one_subject)(input,subject_id,output_streams,resample,make_binary_mask) for subject_id in tqdm(subject_id_list))
-        return cls(subjects,transform,load_getitem)
+        return cls(subjects,path)
 
     @staticmethod
     def process_one_subject(
@@ -187,16 +181,4 @@ def process_one_subject(
                     pet = read_results[i]
                 temp[f"mod_{colname}"] = tio.ScalarImage.from_sitk(pet)
                 temp[f"metadata_{colname}"] = read_results[i].get_metadata()
-        return tio.Subject(temp)
-
-if __name__=="__main__":
-    from torch.utils.data import DataLoader
-    output_path = "/cluster/projects/radiomics/Temp/vishwesh/HN-ctptdose_test2"
-    # input_path = "/cluster/home/ramanav/imgtools/examples/data_test"
-    transform = tio.Compose([tio.Resize(256)])
-    subjects_dataset = Dataset.load_from_nrrd(output_path,transform=transform)
-    # subjects_dataset = Dataset.load_directly(input_path,modalities="CT,RTDOSE,PT",n_jobs=4,transform=transform)
-    print(len(subjects_dataset))
-    training_loader = DataLoader(subjects_dataset, batch_size=4)
-    items = next(iter(training_loader))
-    print(items["mod_RTDOSE_CT"])
+        return tio.Subject(temp)