Fix: Enforce dataloader params to have shuffle=True (#259)

### Description

- **What**: It seems that in the `CAREamics` `TrainDataModule` the
dataloader does not have shuffle set to `True`.
- **Why**: Not shuffling the data during training can result in worse
training, e.g. overfitting.
- **How**: Allow users to explicitly pass shuffle=False with a warning,
otherwise `{"shuffle": True}` is added to the param dictionary, if the
dataset is not a subclass of `IterableDataset`.`

### Changes Made

- **Modified**: `TrainDataModule.train_dataloader`

### Additional Notes and Examples

See the discussion in #258 for details.

---

**Please ensure your PR meets the following requirements:**

- [x] Code builds and passes tests locally, including doctests
- [x] New tests have been added (for bug fixes/features)
- [x] Pre-commit passes
- [ ] PR to the documentation exists (for bug fixes / features)

---------

Co-authored-by: Joran Deschamps <[email protected]>

src/careamics/lightning/train_data_module.py

@@ -2,11 +2,12 @@
 
 from pathlib import Path
 from typing import Any, Callable, Literal, Optional, Union
+from warnings import warn
 
 import numpy as np
 import pytorch_lightning as L
 from numpy.typing import NDArray
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, IterableDataset
 
 from careamics.config import DataConfig
 from careamics.config.support import SupportedData
@@ -446,6 +447,19 @@ def train_dataloader(self) -> Any:
         Any
             Training dataloader.
         """
+        # check because iterable dataset cannot be shuffled
+        if not isinstance(self.train_dataset, IterableDataset):
+            if ("shuffle" in self.dataloader_params) and (
+                not self.dataloader_params["shuffle"]
+            ):
+                warn(
+                    "Dataloader parameters include `shuffle=False`, this will be "
+                    "passed to the training dataloader and may result in bad results.",
+                    stacklevel=1,
+                )
+            else:
+                self.dataloader_params["shuffle"] = True
+
         return DataLoader(
             self.train_dataset, batch_size=self.batch_size, **self.dataloader_params
         )