import torch
import numpy as np
from copy import deepcopy
from numbers import Number
from typing import Union, Optional
from tianshou.data.batch import _parse_value, Batch
[docs]def to_numpy(
x: Optional[Union[Batch, dict, list, tuple, np.number, np.bool_, Number,
np.ndarray, torch.Tensor]]
) -> Union[Batch, dict, list, tuple, np.ndarray]:
"""Return an object without torch.Tensor."""
if isinstance(x, torch.Tensor): # most often case
return x.detach().cpu().numpy()
elif isinstance(x, np.ndarray): # second often case
return x
elif isinstance(x, (np.number, np.bool_, Number)):
return np.asanyarray(x)
elif x is None:
return np.array(None, dtype=np.object)
elif isinstance(x, Batch):
x = deepcopy(x)
x.to_numpy()
return x
elif isinstance(x, dict):
return {k: to_numpy(v) for k, v in x.items()}
elif isinstance(x, (list, tuple)):
try:
return to_numpy(_parse_value(x))
except TypeError:
return [to_numpy(e) for e in x]
else: # fallback
return np.asanyarray(x)
[docs]def to_torch(
x: Union[Batch, dict, list, tuple, np.number, np.bool_, Number, np.ndarray,
torch.Tensor],
dtype: Optional[torch.dtype] = None,
device: Union[str, int, torch.device] = "cpu",
) -> Union[Batch, dict, list, tuple, torch.Tensor]:
"""Return an object without np.ndarray."""
if isinstance(x, np.ndarray) and issubclass(
x.dtype.type, (np.bool_, np.number)
): # most often case
x = torch.from_numpy(x).to(device) # type: ignore
if dtype is not None:
x = x.type(dtype)
return x
elif isinstance(x, torch.Tensor): # second often case
if dtype is not None:
x = x.type(dtype)
return x.to(device) # type: ignore
elif isinstance(x, (np.number, np.bool_, Number)):
return to_torch(np.asanyarray(x), dtype, device)
elif isinstance(x, dict):
return {k: to_torch(v, dtype, device) for k, v in x.items()}
elif isinstance(x, Batch):
x = deepcopy(x)
x.to_torch(dtype, device)
return x
elif isinstance(x, (list, tuple)):
try:
return to_torch(_parse_value(x), dtype, device)
except TypeError:
return [to_torch(e, dtype, device) for e in x]
else: # fallback
raise TypeError(f"object {x} cannot be converted to torch.")
[docs]def to_torch_as(
x: Union[Batch, dict, list, tuple, np.ndarray, torch.Tensor],
y: torch.Tensor,
) -> Union[Batch, dict, list, tuple, torch.Tensor]:
"""Return an object without np.ndarray.
Same as ``to_torch(x, dtype=y.dtype, device=y.device)``.
"""
assert isinstance(y, torch.Tensor)
return to_torch(x, dtype=y.dtype, device=y.device)