| | |
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| | | # dataloader |
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| | | batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler") |
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| | | batch_sampler_class = registry_tables.batch_sampler_classes.get(batch_sampler.lower()) |
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| | | batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf")) |
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| | | if batch_sampler is not None: |
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| | | batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf")) |
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| | | dataloader_tr = torch.utils.data.DataLoader(dataset_tr, |
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| | | collate_fn=dataset_tr.collator, |
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| | | batch_sampler=batch_sampler, |
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| | |
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| | | pin_memory=True) |
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| | | |
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| | | |
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| | | |
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| | | trainer = Trainer( |
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| | | model=model, |
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| | | optim=optim, |
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