204 lines
7.3 KiB
Python
204 lines
7.3 KiB
Python
import torch
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import pprint
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import numpy as np
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from typing import Any, List, Union, Iterator, Optional
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class Batch(object):
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"""Tianshou provides :class:`~tianshou.data.Batch` as the internal data
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structure to pass any kind of data to other methods, for example, a
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collector gives a :class:`~tianshou.data.Batch` to policy for learning.
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Here is the usage:
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::
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>>> import numpy as np
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>>> from tianshou.data import Batch
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>>> data = Batch(a=4, b=[5, 5], c='2312312')
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>>> data.b
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[5, 5]
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>>> data.b = np.array([3, 4, 5])
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>>> print(data)
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Batch(
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a: 4,
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b: array([3, 4, 5]),
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c: '2312312',
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)
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In short, you can define a :class:`Batch` with any key-value pair. The
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current implementation of Tianshou typically use 7 reserved keys in
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:class:`~tianshou.data.Batch`:
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* ``obs`` the observation of step :math:`t` ;
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* ``act`` the action of step :math:`t` ;
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* ``rew`` the reward of step :math:`t` ;
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* ``done`` the done flag of step :math:`t` ;
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* ``obs_next`` the observation of step :math:`t+1` ;
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* ``info`` the info of step :math:`t` (in ``gym.Env``, the ``env.step()``\
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function return 4 arguments, and the last one is ``info``);
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* ``policy`` the data computed by policy in step :math:`t`;
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:class:`~tianshou.data.Batch` has other methods, including
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:meth:`~tianshou.data.Batch.__getitem__`,
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:meth:`~tianshou.data.Batch.__len__`,
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:meth:`~tianshou.data.Batch.append`,
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and :meth:`~tianshou.data.Batch.split`:
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::
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>>> data = Batch(obs=np.array([0, 11, 22]), rew=np.array([6, 6, 6]))
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>>> # here we test __getitem__
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>>> index = [2, 1]
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>>> data[index].obs
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array([22, 11])
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>>> # here we test __len__
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>>> len(data)
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3
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>>> data.append(data) # similar to list.append
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>>> data.obs
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array([0, 11, 22, 0, 11, 22])
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>>> # split whole data into multiple small batch
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>>> for d in data.split(size=2, shuffle=False):
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... print(d.obs, d.rew)
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[ 0 11] [6 6]
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[22 0] [6 6]
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[11 22] [6 6]
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"""
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def __init__(self, **kwargs) -> None:
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super().__init__()
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self._meta = {}
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for k, v in kwargs.items():
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if isinstance(v, (list, np.ndarray)) \
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and len(v) > 0 and isinstance(v[0], dict) and k != 'info':
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self._meta[k] = list(v[0].keys())
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for k_ in v[0].keys():
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k__ = '_' + k + '@' + k_
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self.__dict__[k__] = np.array([
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v[i][k_] for i in range(len(v))
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])
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elif isinstance(v, dict):
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self._meta[k] = list(v.keys())
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for k_ in v.keys():
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k__ = '_' + k + '@' + k_
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self.__dict__[k__] = v[k_]
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else:
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self.__dict__[k] = kwargs[k]
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def __getitem__(self, index: Union[str, slice]) -> Union['Batch', dict]:
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"""Return self[index]."""
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if isinstance(index, str):
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return self.__getattr__(index)
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b = Batch()
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for k in self.__dict__:
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if k != '_meta' and self.__dict__[k] is not None:
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b.__dict__.update(**{k: self.__dict__[k][index]})
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b._meta = self._meta
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return b
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def __getattr__(self, key: str) -> Union['Batch', Any]:
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"""Return self.key"""
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if key not in self._meta:
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if key not in self.__dict__:
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raise AttributeError(key)
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return self.__dict__[key]
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d = {}
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for k_ in self._meta[key]:
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k__ = '_' + key + '@' + k_
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d[k_] = self.__dict__[k__]
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return Batch(**d)
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def __repr__(self) -> str:
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"""Return str(self)."""
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s = self.__class__.__name__ + '(\n'
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flag = False
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for k in sorted(list(self.__dict__) + list(self._meta)):
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if k[0] != '_' and (self.__dict__.get(k, None) is not None or
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k in self._meta):
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rpl = '\n' + ' ' * (6 + len(k))
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obj = pprint.pformat(self.__getattr__(k)).replace('\n', rpl)
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s += f' {k}: {obj},\n'
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flag = True
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if flag:
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s += ')'
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else:
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s = self.__class__.__name__ + '()'
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return s
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def keys(self) -> List[str]:
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"""Return self.keys()."""
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return sorted([
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i for i in self.__dict__ if i[0] != '_'] + list(self._meta))
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def values(self) -> List[Any]:
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"""Return self.values()."""
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return [self[k] for k in self.keys()]
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def get(self, k: str, d: Optional[Any] = None) -> Union['Batch', Any]:
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"""Return self[k] if k in self else d. d defaults to None."""
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if k in self.__dict__ or k in self._meta:
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return self.__getattr__(k)
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return d
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def to_numpy(self) -> np.ndarray:
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"""Change all torch.Tensor to numpy.ndarray. This is an inplace
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operation.
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"""
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for k in self.__dict__:
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if isinstance(self.__dict__[k], torch.Tensor):
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self.__dict__[k] = self.__dict__[k].cpu().numpy()
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def append(self, batch: 'Batch') -> None:
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"""Append a :class:`~tianshou.data.Batch` object to current batch."""
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assert isinstance(batch, Batch), 'Only append Batch is allowed!'
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for k in batch.__dict__:
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if k == '_meta':
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self._meta.update(batch._meta)
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continue
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if batch.__dict__[k] is None:
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continue
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if not hasattr(self, k) or self.__dict__[k] is None:
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self.__dict__[k] = batch.__dict__[k]
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elif isinstance(batch.__dict__[k], np.ndarray):
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self.__dict__[k] = np.concatenate([
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self.__dict__[k], batch.__dict__[k]])
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elif isinstance(batch.__dict__[k], torch.Tensor):
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self.__dict__[k] = torch.cat([
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self.__dict__[k], batch.__dict__[k]])
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elif isinstance(batch.__dict__[k], list):
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self.__dict__[k] += batch.__dict__[k]
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else:
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s = 'No support for append with type' \
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+ str(type(batch.__dict__[k])) \
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+ 'in class Batch.'
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raise TypeError(s)
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def __len__(self) -> int:
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"""Return len(self)."""
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return min([
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len(self.__dict__[k]) for k in self.__dict__
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if k != '_meta' and self.__dict__[k] is not None])
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def split(self, size: Optional[int] = None,
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shuffle: bool = True) -> Iterator['Batch']:
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"""Split whole data into multiple small batch.
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:param int size: if it is ``None``, it does not split the data batch;
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otherwise it will divide the data batch with the given size.
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Default to ``None``.
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:param bool shuffle: randomly shuffle the entire data batch if it is
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``True``, otherwise remain in the same. Default to ``True``.
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"""
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length = len(self)
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if size is None:
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size = length
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temp = 0
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if shuffle:
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index = np.random.permutation(length)
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else:
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index = np.arange(length)
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while temp < length:
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yield self[index[temp:temp + size]]
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temp += size
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