Revert warning when SubjectsLoader is not used (#1340)

Author: fepegar

src/torchio/data/image.py

@@ -33,7 +33,6 @@
 from ..types import TypeTripletInt
 from ..utils import get_stem
 from ..utils import guess_external_viewer
-from ..utils import in_torch_loader
 from ..utils import is_iterable
 from ..utils import to_tuple
 from .io import check_uint_to_int
@@ -179,7 +178,6 @@ def __init__(
             warnings.warn(message, FutureWarning, stacklevel=2)
 
         super().__init__(**kwargs)
-        self._check_data_loader()
         self.path = self._parse_path(path, verify=verify_path)
 
         self[PATH] = '' if self.path is None else str(self.path)
@@ -238,20 +236,6 @@ def __copy__(self):
         )
         return new_image
 
-    @staticmethod
-    def _check_data_loader() -> None:
-        if torch.__version__ >= '2.3' and in_torch_loader():
-            message = (
-                'Using TorchIO images without a torchio.SubjectsLoader in PyTorch >='
-                ' 2.3 might have unexpected consequences, e.g., the collated batches'
-                ' will be instances of torchio.Subject with 5D images. Replace'
-                ' your PyTorch DataLoader with a torchio.SubjectsLoader so that'
-                ' the collated batch becomes a dictionary, as expected. See'
-                ' https://github.com/TorchIO-project/torchio/issues/1179 for more'
-                ' context about this issue.'
-            )
-            warnings.warn(message, stacklevel=1)
-
     @property
     def data(self) -> torch.Tensor:
         """Tensor data (same as :class:`Image.tensor`)."""

src/torchio/utils.py

@@ -2,7 +2,6 @@
 
 import ast
 import gzip
-import inspect
 import os
 import shutil
 import sys
@@ -15,7 +14,6 @@
 import numpy as np
 import SimpleITK as sitk
 import torch
-import torch.utils.data.dataloader
 from nibabel.nifti1 import Nifti1Image
 from torch.utils.data import DataLoader
 from torch.utils.data._utils.collate import default_collate
@@ -412,24 +410,3 @@ def is_iterable(object: Any) -> bool:
         return True
     except TypeError:
         return False
-
-
-def in_class(classes) -> bool:
-    classes = to_tuple(classes)
-    stack = inspect.stack()
-    for frame_info in stack:
-        instance = frame_info.frame.f_locals.get('self')
-        if instance is None:
-            continue
-        if instance.__class__ in classes:
-            return True
-    else:
-        return False
-
-
-def in_torch_loader() -> bool:
-    classes = (
-        torch.utils.data.dataloader._SingleProcessDataLoaderIter,
-        torch.utils.data.dataloader._MultiProcessingDataLoaderIter,
-    )
-    return in_class(classes)