94bfb32cc1
1. add policy.eval() in all test scripts' "watch performance" 2. remove dict return support for collector preprocess_fn 3. add `__contains__` and `pop` in batch: `key in batch`, `batch.pop(key, deft)` 4. exact n_episode for a list of n_episode limitation and save fake data in cache_buffer when self.buffer is None (#184) 5. fix tensorboard logging: h-axis stands for env step instead of gradient step; add test results into tensorboard 6. add test_returns (both GAE and nstep) 7. change the type-checking order in batch.py and converter.py in order to meet the most often case first 8. fix shape inconsistency for torch.Tensor in replay buffer 9. remove `**kwargs` in ReplayBuffer 10. remove default value in batch.split() and add merge_last argument (#185) 11. improve nstep efficiency 12. add max_batchsize in onpolicy algorithms 13. potential bugfix for subproc.wait 14. fix RecurrentActorProb 15. improve the code-coverage (from 90% to 95%) and remove the dead code 16. fix some incorrect type annotation The above improvement also increases the training FPS: on my computer, the previous version is only ~1800 FPS and after that, it can reach ~2050 (faster than v0.2.4.post1).
75 lines
2.6 KiB
Python
75 lines
2.6 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): # most often case
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x = x.detach().cpu().numpy()
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elif isinstance(x, np.ndarray): # second often case
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pass
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elif isinstance(x, (np.number, np.bool_, Number)):
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x = np.asanyarray(x)
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elif x is None:
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x = np.array(None, dtype=np.object)
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elif isinstance(x, Batch):
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x.to_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, (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, np.ndarray) and \
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issubclass(x.dtype.type, (np.bool_, np.number)): # most often case
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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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elif isinstance(x, torch.Tensor): # second often case
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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, (np.number, np.bool_, Number)):
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x = to_torch(np.asanyarray(x), dtype, 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, (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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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[Batch, dict, list, tuple, np.ndarray, torch.Tensor],
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y: torch.Tensor
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) -> Union[Batch, dict, list, tuple, np.ndarray, 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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