I want to use a DataLoader that uses a custom sampler you can find at vision/references/classification/sampler.py at main · pytorch/vision · GitHub
When doing :
print(dataset, dataset.sampler) dataset = accelerator.prepare(dataset) print(dataset, dataset.sampler)
I get the following print:
<torch.utils.data.dataloader.DataLoader object at 0x7f987fbc4e50> <utils.imagenet_dataloader.RASampler object at 0x7f987fbc4e20> <accelerate.data_loader.DataLoaderShard object at 0x7f98518b6da0> <torch.utils.data.sampler.SequentialSampler object at 0x7f98518b6b60>
This means that my RASampler got turned into a SequentialSampler.
Is this a normal behaviour? Since it seems I can’t manually restore my sampler afterwhile, this is quite a problem.
Could you tell me how to solve this problem?