1fce527c77
* Fix Batch to_torch method not updating dtype/device of already converted data. * Fix dtype/device to forwarded by to_tensor for Batch over Batch. * Add Unit test to check to_torch dtype/device recursive forwarding. * Batch UT check accessing data using both dict and class style. * Fix utils to_tensor dtype/device forwarding. Add Unit tests. * Fix UT. Co-authored-by: Alexis Duburcq <alexis.duburcq@wandercraft.eu> Co-authored-by: n+e <463003665@qq.com>
41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
import torch
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import numpy as np
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from typing import Union, Optional
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from tianshou.data import Batch
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def to_numpy(x: Union[
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torch.Tensor, dict, Batch, np.ndarray]) -> Union[
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dict, Batch, np.ndarray]:
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"""Return an object without torch.Tensor."""
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if isinstance(x, torch.Tensor):
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x = x.detach().cpu().numpy()
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elif isinstance(x, dict):
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for k, v in x.items():
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x[k] = to_numpy(v)
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elif isinstance(x, Batch):
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x.to_numpy()
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return x
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def to_torch(x: Union[torch.Tensor, dict, Batch, np.ndarray],
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dtype: Optional[torch.dtype] = None,
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device: Union[str, int] = 'cpu'
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) -> Union[dict, Batch, torch.Tensor]:
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"""Return an object without np.ndarray."""
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if isinstance(x, np.ndarray):
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x = torch.from_numpy(x).to(device)
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if dtype is not None:
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x = x.type(dtype)
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if isinstance(x, torch.Tensor):
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if dtype is not None:
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x = x.type(dtype)
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x = x.to(device)
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elif isinstance(x, dict):
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for k, v in x.items():
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x[k] = to_torch(v, dtype, device)
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elif isinstance(x, Batch):
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x.to_torch(dtype, device)
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return x
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