Using Segmentation model in as pre-processing

Hello,

For pre-processing my images, I use the SAM Large model to work with masks from the original image.

In the _getitem_ of my CustomDataset class, the original image is read then sent to the SAM model, then pre-processed from these masks before being returned.

Here's a pseudocode of the function

```
def __getitem__(self, idx):

img = load_image(idx)

masks = mask_generator.generate(img)

img_preprocessed = preprocess(img, masks)

return img_preprocessed
```

However, when I iterate over loaders I have the following issues

```
RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
```

I solved the problem by adding the line

```
torch.multiprocessing.set_start_method('spawn')
```

However after this, iteration over my DataLoader only works with num_workers=0. When it is strictly positive, the DataLoader iteration does not converge and does not even show an error.

I have absolutly no idea where it can come from. Please, do you have an idea ?

Thank you

The report only provides pseudocode for CustomDataset.__getitem__ and mentions SAM Large, DataLoader, num_workers, and torch.multiprocessing.set_start_method('spawn'); no repository file or test is identified. Start by reproducing the CUDA multiprocessing behavior with num_workers=0 and then compare it with positive worker counts; done would require a confirmed, documented fix or a clearly scoped repository change.

Written by the indexing model from the issue text.