39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright (c) Alibaba, Inc. and its affiliates.
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from collections.abc import Mapping
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import torch
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from modelscope.outputs import ModelOutputBase
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def to_device(batch, device, non_blocking=False):
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"""Put the data to the target cuda device just before the forward function.
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Args:
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batch: The batch data out of the dataloader.
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device: (str | torch.device): The target device for the data.
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Returns: The data to the target device.
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"""
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if isinstance(batch, ModelOutputBase):
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for idx in range(len(batch)):
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batch[idx] = to_device(batch[idx], device)
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return batch
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elif isinstance(batch, dict) or isinstance(batch, Mapping):
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if hasattr(batch, '__setitem__'):
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# Reuse mini-batch to keep attributes for prediction.
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for k, v in batch.items():
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batch[k] = to_device(v, device)
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return batch
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else:
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return type(batch)(
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{k: to_device(v, device)
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for k, v in batch.items()})
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elif isinstance(batch, (tuple, list)):
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return type(batch)(to_device(v, device) for v in batch)
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elif isinstance(batch, torch.Tensor):
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return batch.to(device, non_blocking=non_blocking)
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else:
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return batch
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