7f9a1f1328
This PR does the following: - improvement: dramatic reduce of the call to _is_batch_set - bugfix: list(Batch()) fail; Batch(a=[torch.ones(3), torch.ones(3)]) fail; - misc: add type check for each element rather than the first element; add test case; _create_value with torch.Tensor does not have np.object type;
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
import torch
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import numpy as np
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from numbers import Number
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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, torch.device] = '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, 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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elif isinstance(x, (np.number, np.bool_, Number)):
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x = to_torch(np.asanyarray(x), dtype, device)
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elif isinstance(x, list) and len(x) > 0 and \
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all(isinstance(e, (np.number, np.bool_, Number)) for e in x):
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x = to_torch(np.asanyarray(x), dtype, device)
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elif isinstance(x, np.ndarray) and \
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isinstance(x.item(0), (np.number, np.bool_, Number)):
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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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return x
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def to_torch_as(x: Union[torch.Tensor, dict, Batch, np.ndarray],
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y: torch.Tensor
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) -> Union[dict, Batch, torch.Tensor]:
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"""Return an object without np.ndarray. Same as
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``to_torch(x, dtype=y.dtype, device=y.device)``.
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"""
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assert isinstance(y, torch.Tensor)
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return to_torch(x, dtype=y.dtype, device=y.device)
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