865ef6c693
* Enable converting list/tuple back and forth from/to numpy/torch. * Add fallbacks. * Fix PEP8 * Update unit tests. * Type annotation. Robust dtype check. * List of object are converted individually, as a single tensor otherwise. * Improve robustness of _to_array_with_correct_type * Add unit tests. * Do not catch exception at _to_array_with_correct_type level. * Use _parse_value * Fix PEP8 * Fix _parse_value list output type fallback. * Catch torch exception. * Do not convert torch tensor during fallback. * Improve unit tests. * Add unit tests. * FIx missing import * Remove support of numpy arrays of tensors for Batch value parser. * Forbid numpy arrays of tensors. * Fix PEP8. * Fix comment. * Reduce _parse_value branch number. * Fix None value. * Forward error message for debugging purpose. * Fix _is_scalar. * More specific try/catch blocks. * Fix exception chaining. * Fix PEP8. * Fix _is_scalar. * Fix missing corner case. * Fix PEP8. * Allow Batch empty key. * Fix multi-dim array datatype check. Co-authored-by: Alexis Duburcq <alexis.duburcq@wandercraft.eu>
70 lines
2.3 KiB
Python
70 lines
2.3 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.batch import _parse_value, Batch
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def to_numpy(x: Union[
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Batch, dict, list, tuple, np.ndarray, torch.Tensor]) -> Union[
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Batch, dict, list, tuple, np.ndarray, torch.Tensor]:
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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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elif isinstance(x, (list, tuple)):
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try:
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x = to_numpy(_parse_value(x))
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except TypeError:
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x = [to_numpy(e) for e in x]
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else: # fallback
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x = np.asanyarray(x)
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return x
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def to_torch(x: Union[Batch, dict, list, tuple, np.ndarray, torch.Tensor],
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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[Batch, dict, list, tuple, np.ndarray, 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, tuple)):
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try:
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x = to_torch(_parse_value(x), dtype, device)
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except TypeError:
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x = [to_torch(e, dtype, device) for e in x]
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else: # fallback
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x = np.asanyarray(x)
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if issubclass(x.dtype.type, (np.bool_, np.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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else:
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raise TypeError(f"object {x} cannot be converted to torch.")
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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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