5599a6d1a6
* re-implement Batch.stack and add testcases * add doc for Batch.stack * reuse _create_values and refactor stack_ & cat_ * fix pep8 * fix docs * raise exception for stacking with partial keys and axis!=0 * minor fix * minor fix Co-authored-by: Trinkle23897 <463003665@qq.com>
740 lines
28 KiB
Python
740 lines
28 KiB
Python
import torch
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import pprint
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import warnings
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import numpy as np
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from copy import deepcopy
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from numbers import Number
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from typing import Any, List, Tuple, Union, Iterator, Optional
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# Disable pickle warning related to torch, since it has been removed
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# on torch master branch. See Pull Request #39003 for details:
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# https://github.com/pytorch/pytorch/pull/39003
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warnings.filterwarnings(
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"ignore", message="pickle support for Storage will be removed in 1.5.")
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def _is_batch_set(data: Any) -> bool:
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if isinstance(data, (list, tuple)):
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if len(data) > 0 and all(isinstance(e, (dict, Batch)) for e in data):
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return True
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elif isinstance(data, np.ndarray) and data.dtype == np.object:
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if all(isinstance(e, (dict, Batch)) for e in data.tolist()):
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return True
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return False
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def _create_value(inst: Any, size: int, stack=True) -> Union[
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'Batch', np.ndarray, torch.Tensor]:
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"""
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:param bool stack: whether to stack or to concatenate. E.g. if inst has
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shape of (3, 5), size = 10, stack=True returns an np.ndarry with shape
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of (10, 3, 5), otherwise (10, 5)
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"""
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has_shape = isinstance(inst, (np.ndarray, torch.Tensor))
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is_scalar = \
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isinstance(inst, Number) or \
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issubclass(inst.__class__, np.generic) or \
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(has_shape and not inst.shape)
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if not stack and is_scalar:
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# here we do not consider scalar types, following the
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# behavior of numpy which does not support concatenation
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# of zero-dimensional arrays (scalars)
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raise TypeError(f"cannot cat {inst} with which is scalar")
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if has_shape:
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shape = (size, *inst.shape) if stack else (size, *inst.shape[1:])
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if isinstance(inst, np.ndarray):
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if issubclass(inst.dtype.type, (np.bool_, np.number)):
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target_type = inst.dtype.type
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else:
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target_type = np.object
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return np.full(shape,
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fill_value=None if target_type == np.object else 0,
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dtype=target_type)
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elif isinstance(inst, torch.Tensor):
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return torch.full(shape,
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fill_value=0,
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device=inst.device,
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dtype=inst.dtype)
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elif isinstance(inst, (dict, Batch)):
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zero_batch = Batch()
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for key, val in inst.items():
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zero_batch.__dict__[key] = _create_value(val, size, stack=stack)
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return zero_batch
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elif is_scalar:
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return _create_value(np.asarray(inst), size, stack=stack)
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else: # fall back to np.object
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return np.array([None for _ in range(size)])
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def _assert_type_keys(keys):
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keys = list(keys)
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assert all(isinstance(e, str) for e in keys), \
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f"keys should all be string, but got {keys}"
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class Batch:
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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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>>> # the list will automatically be converted to numpy array
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>>> data.b
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array([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.
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For Numpy arrays, only data types with ``np.object``, bool, and number
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are supported. For strings or other data types, however, they can be
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held in ``np.object`` arrays.
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The 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 returns 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` object can be initialized by a wide variety
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of arguments, ranging from the key/value pairs or dictionary, to list and
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Numpy arrays of :class:`dict` or Batch instances where each element is
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considered as an individual sample and get stacked together:
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::
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>>> data = Batch([{'a': {'b': [0.0, "info"]}}])
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>>> print(data[0])
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Batch(
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a: Batch(
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b: array([0.0, 'info'], dtype=object),
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),
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)
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:class:`~tianshou.data.Batch` has the same API as a native Python
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:class:`dict`. In this regard, one can access stored data using string
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key, or iterate over stored data:
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::
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>>> data = Batch(a=4, b=[5, 5])
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>>> print(data["a"])
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4
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>>> for key, value in data.items():
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>>> print(f"{key}: {value}")
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a: 4
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b: [5, 5]
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:class:`~tianshou.data.Batch` also partially reproduces the Numpy API for
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arrays. It also supports the advanced slicing method, such as batch[:, i],
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if the index is valid. You can access or iterate over the individual
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samples, if any:
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::
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>>> data = Batch(a=np.array([[0.0, 2.0], [1.0, 3.0]]), b=[[5, -5]])
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>>> print(data[0])
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Batch(
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a: array([0., 2.])
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b: array([ 5, -5]),
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)
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>>> for sample in data:
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>>> print(sample.a)
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[0., 2.]
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>>> print(data.shape)
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[1, 2]
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>>> data[:, 1] += 1
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>>> print(data)
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Batch(
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a: array([[0., 3.],
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[1., 4.]]),
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b: array([[ 5, -4]]),
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)
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Similarly, one can also perform simple algebra on it, and stack, split or
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concatenate multiple instances:
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::
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>>> data_1 = Batch(a=np.array([0.0, 2.0]), b=5)
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>>> data_2 = Batch(a=np.array([1.0, 3.0]), b=-5)
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>>> data = Batch.stack((data_1, data_2))
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>>> print(data)
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Batch(
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b: array([ 5, -5]),
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a: array([[0., 2.],
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[1., 3.]]),
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)
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>>> print(np.mean(data))
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Batch(
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b: 0.0,
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a: array([0.5, 2.5]),
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)
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>>> data_split = list(data.split(1, False))
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>>> print(list(data.split(1, False)))
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[Batch(
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b: array([5]),
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a: array([[0., 2.]]),
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), Batch(
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b: array([-5]),
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a: array([[1., 3.]]),
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)]
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>>> data_cat = Batch.cat(data_split)
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>>> print(data_cat)
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Batch(
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b: array([ 5, -5]),
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a: array([[0., 2.],
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[1., 3.]]),
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)
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Note that stacking of inconsistent data is also supported. In which case,
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``None`` is added in list or :class:`np.ndarray` of objects, 0 otherwise.
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::
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>>> data_1 = Batch(a=np.array([0.0, 2.0]))
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>>> data_2 = Batch(a=np.array([1.0, 3.0]), b='done')
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>>> data = Batch.stack((data_1, data_2))
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>>> print(data)
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Batch(
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a: array([[0., 2.],
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[1., 3.]]),
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b: array([None, 'done'], dtype=object),
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)
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Method ``empty_`` sets elements to 0 or ``None`` for ``np.object``.
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::
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>>> data.empty_()
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>>> print(data)
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Batch(
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a: array([[0., 0.],
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[0., 0.]]),
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b: array([None, None], dtype=object),
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)
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>>> data = Batch(a=[False, True], b={'c': [2., 'st'], 'd': [1., 0.]})
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>>> data[0] = Batch.empty(data[1])
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>>> data
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Batch(
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a: array([False, True]),
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b: Batch(
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c: array([None, 'st']),
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d: array([0., 0.]),
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),
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)
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:meth:`~tianshou.data.Batch.shape` and :meth:`~tianshou.data.Batch.__len__`
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methods are also provided to respectively get the shape and the length of
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a :class:`Batch` instance. It mimics the Numpy API for Numpy arrays, which
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means that getting the length of a scalar Batch raises an exception.
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::
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>>> data = Batch(a=[5., 4.], b=np.zeros((2, 3, 4)))
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>>> data.shape
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[2]
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>>> len(data)
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2
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>>> data[0].shape
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[]
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>>> len(data[0])
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TypeError: Object of type 'Batch' has no len()
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Convenience helpers are available to convert in-place the stored data into
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Numpy arrays or Torch tensors.
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Finally, note that :class:`~tianshou.data.Batch` is serializable and
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therefore Pickle compatible. This is especially important for distributed
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sampling.
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"""
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def __init__(self,
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batch_dict: Optional[Union[
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dict, 'Batch', Tuple[Union[dict, 'Batch']],
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List[Union[dict, 'Batch']], np.ndarray]] = None,
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copy: bool = False,
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**kwargs) -> None:
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if copy:
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batch_dict = deepcopy(batch_dict)
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if batch_dict is not None:
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if isinstance(batch_dict, (dict, Batch)):
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_assert_type_keys(batch_dict.keys())
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for k, v in batch_dict.items():
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if isinstance(v, (list, tuple, np.ndarray)):
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v_ = None
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if not isinstance(v, np.ndarray) and \
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all(isinstance(e, torch.Tensor) for e in v):
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self.__dict__[k] = torch.stack(v)
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continue
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else:
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v_ = np.asanyarray(v)
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if v_.dtype != np.object:
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v = v_ # normal data list, this is the main case
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if not issubclass(v.dtype.type,
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(np.bool_, np.number)):
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v = v.astype(np.object)
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else:
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if _is_batch_set(v):
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v = Batch(v) # list of dict / Batch
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else:
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# this is actually a data list with objects
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v = v_
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self.__dict__[k] = v
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elif isinstance(v, dict):
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self.__dict__[k] = Batch(v)
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else:
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self.__dict__[k] = v
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elif _is_batch_set(batch_dict):
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self.stack_(batch_dict)
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if len(kwargs) > 0:
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self.__init__(kwargs, copy=copy)
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def __setattr__(self, key: str, value: Any):
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"""self[key] = value"""
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if isinstance(value, list):
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if _is_batch_set(value):
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value = Batch(value)
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else:
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value = np.array(value)
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if not issubclass(value.dtype.type, (np.bool_, np.number)):
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value = value.astype(np.object)
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elif isinstance(value, dict) or isinstance(value, np.ndarray) \
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and value.dtype == np.object and _is_batch_set(value):
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value = Batch(value)
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self.__dict__[key] = value
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def __getstate__(self):
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"""Pickling interface. Only the actual data are serialized for both
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efficiency and simplicity.
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"""
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state = {}
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for k, v in self.items():
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if isinstance(v, Batch):
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v = v.__getstate__()
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state[k] = v
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return state
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def __setstate__(self, state):
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"""Unpickling interface. At this point, self is an empty Batch instance
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that has not been initialized, so it can safely be initialized by the
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pickle state.
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"""
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self.__init__(**state)
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def __getitem__(self, index: Union[
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str, slice, int, np.integer, np.ndarray, List[int]]) -> 'Batch':
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"""Return self[index]."""
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if isinstance(index, str):
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return self.__dict__[index]
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batch_items = self.items()
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if len(batch_items) > 0:
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b = Batch()
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for k, v in batch_items:
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if isinstance(v, Batch) and len(v.__dict__) == 0:
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b.__dict__[k] = Batch()
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else:
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b.__dict__[k] = v[index]
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return b
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else:
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raise IndexError("Cannot access item from empty Batch object.")
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def __setitem__(self, index: Union[
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str, slice, int, np.integer, np.ndarray, List[int]],
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value: Any) -> None:
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"""Assign value to self[index]."""
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if isinstance(value, np.ndarray):
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if not issubclass(value.dtype.type, (np.bool_, np.number)):
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value = value.astype(np.object)
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if isinstance(index, str):
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self.__dict__[index] = value
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return
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if not isinstance(value, (dict, Batch)):
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raise TypeError("Batch does not supported value type "
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f"{type(value)} for item assignment.")
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if not set(value.keys()).issubset(self.__dict__.keys()):
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raise KeyError(
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"Creating keys is not supported by item assignment.")
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for key, val in self.items():
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try:
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self.__dict__[key][index] = value[key]
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except KeyError:
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if isinstance(val, Batch):
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self.__dict__[key][index] = Batch()
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elif isinstance(val, np.ndarray) and \
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issubclass(val.dtype.type, (np.bool_, np.number)):
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self.__dict__[key][index] = 0
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else:
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self.__dict__[key][index] = None
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def __iadd__(self, other: Union['Batch', Number, np.number]):
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"""Algebraic addition with another :class:`~tianshou.data.Batch`
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instance in-place."""
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if isinstance(other, Batch):
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for (k, r), v in zip(self.__dict__.items(),
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other.__dict__.values()):
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# TODO are keys consistent?
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if r is None:
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continue
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else:
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self.__dict__[k] += v
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return self
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elif isinstance(other, (Number, np.number)):
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for k, r in self.items():
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if r is None:
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continue
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else:
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self.__dict__[k] += other
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return self
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else:
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raise TypeError("Only addition of Batch or number is supported.")
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def __add__(self, other: Union['Batch', Number, np.number]):
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"""Algebraic addition with another :class:`~tianshou.data.Batch`
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instance out-of-place."""
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return deepcopy(self).__iadd__(other)
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def __imul__(self, val: Union[Number, np.number]):
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"""Algebraic multiplication with a scalar value in-place."""
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assert isinstance(val, (Number, np.number)), \
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"Only multiplication by a number is supported."
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for k in self.__dict__.keys():
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self.__dict__[k] *= val
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return self
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def __mul__(self, val: Union[Number, np.number]):
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"""Algebraic multiplication with a scalar value out-of-place."""
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return deepcopy(self).__imul__(val)
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def __itruediv__(self, val: Union[Number, np.number]):
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"""Algebraic division with a scalar value in-place."""
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assert isinstance(val, (Number, np.number)), \
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"Only division by a number is supported."
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for k in self.__dict__.keys():
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self.__dict__[k] /= val
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return self
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def __truediv__(self, val: Union[Number, np.number]):
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"""Algebraic division with a scalar value out-of-place."""
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return deepcopy(self).__itruediv__(val)
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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, v in self.items():
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rpl = '\n' + ' ' * (6 + len(k))
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obj = pprint.pformat(v).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 self.__dict__.keys()
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def values(self) -> List[Any]:
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"""Return self.values()."""
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return self.__dict__.values()
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def items(self) -> List[Tuple[str, Any]]:
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"""Return self.items()."""
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return self.__dict__.items()
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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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return self.__dict__.get(k, d)
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def to_numpy(self) -> None:
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"""Change all torch.Tensor to numpy.ndarray. This is an in-place
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operation.
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"""
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for k, v in self.items():
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if isinstance(v, torch.Tensor):
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self.__dict__[k] = v.detach().cpu().numpy()
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elif isinstance(v, Batch):
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v.to_numpy()
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def to_torch(self, dtype: Optional[torch.dtype] = None,
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device: Union[str, int, torch.device] = 'cpu') -> None:
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"""Change all numpy.ndarray to torch.Tensor. This is an in-place
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operation.
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"""
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if not isinstance(device, torch.device):
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device = torch.device(device)
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for k, v in self.items():
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if isinstance(v, (np.number, np.bool_, Number, np.ndarray)):
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if isinstance(v, (np.number, np.bool_, Number)):
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v = np.asanyarray(v)
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v = torch.from_numpy(v).to(device)
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if dtype is not None:
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v = v.type(dtype)
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|
self.__dict__[k] = v
|
|
elif isinstance(v, torch.Tensor):
|
|
if dtype is not None and v.dtype != dtype or \
|
|
v.device.type != device.type or \
|
|
device.index is not None and \
|
|
device.index != v.device.index:
|
|
if dtype is not None:
|
|
v = v.type(dtype)
|
|
self.__dict__[k] = v.to(device)
|
|
elif isinstance(v, Batch):
|
|
v.to_torch(dtype, device)
|
|
|
|
def cat_(self,
|
|
batches: Union['Batch', List[Union[dict, 'Batch']]]) -> None:
|
|
"""Concatenate a list of (or one) :class:`~tianshou.data.Batch` objects
|
|
into current batch.
|
|
"""
|
|
if isinstance(batches, Batch):
|
|
batches = [batches]
|
|
if len(batches) == 0:
|
|
return
|
|
batches = [x if isinstance(x, Batch) else Batch(x) for x in batches]
|
|
if len(self.__dict__) > 0:
|
|
batches = [self] + list(batches)
|
|
# partial keys will be padded by zeros
|
|
# with the shape of [len, rest_shape]
|
|
lens = [len(x) for x in batches]
|
|
sum_lens = [0]
|
|
for x in lens:
|
|
sum_lens.append(sum_lens[-1] + x)
|
|
keys_map = list(map(lambda e: set(e.keys()), batches))
|
|
keys_shared = set.intersection(*keys_map)
|
|
values_shared = [[e[k] for e in batches] for k in keys_shared]
|
|
_assert_type_keys(keys_shared)
|
|
for k, v in zip(keys_shared, values_shared):
|
|
if all(isinstance(e, (dict, Batch)) for e in v):
|
|
self.__dict__[k] = Batch.cat(v)
|
|
elif all(isinstance(e, torch.Tensor) for e in v):
|
|
self.__dict__[k] = torch.cat(v)
|
|
else:
|
|
v = np.concatenate(v)
|
|
if not issubclass(v.dtype.type, (np.bool_, np.number)):
|
|
v = v.astype(np.object)
|
|
self.__dict__[k] = v
|
|
keys_partial = set.union(*keys_map) - keys_shared
|
|
_assert_type_keys(keys_partial)
|
|
for k in keys_partial:
|
|
for i, e in enumerate(batches):
|
|
val = e.get(k, None)
|
|
if val is not None:
|
|
try:
|
|
self.__dict__[k][sum_lens[i]:sum_lens[i + 1]] = val
|
|
except KeyError:
|
|
self.__dict__[k] = \
|
|
_create_value(val, sum_lens[-1], stack=False)
|
|
self.__dict__[k][sum_lens[i]:sum_lens[i + 1]] = val
|
|
|
|
@staticmethod
|
|
def cat(batches: List[Union[dict, 'Batch']]) -> 'Batch':
|
|
"""Concatenate a list of :class:`~tianshou.data.Batch` object into a
|
|
single new batch. For keys that are not shared across all batches,
|
|
batches that do not have these keys will be padded by zeros with
|
|
appropriate shapes. E.g.
|
|
::
|
|
|
|
>>> a = Batch(a=np.zeros([3, 4]), common=Batch(c=np.zeros([3, 5])))
|
|
>>> b = Batch(b=np.zeros([4, 3]), common=Batch(c=np.zeros([4, 5])))
|
|
>>> c = Batch.cat([a, b])
|
|
>>> c.a.shape
|
|
(7, 4)
|
|
>>> c.b.shape
|
|
(7, 3)
|
|
>>> c.common.c.shape
|
|
(7, 5)
|
|
"""
|
|
batch = Batch()
|
|
batch.cat_(batches)
|
|
return batch
|
|
|
|
def stack_(self,
|
|
batches: List[Union[dict, 'Batch']],
|
|
axis: int = 0) -> None:
|
|
"""Stack a list of :class:`~tianshou.data.Batch` object into current
|
|
batch.
|
|
"""
|
|
if len(batches) == 0:
|
|
return
|
|
batches = [x if isinstance(x, Batch) else Batch(x) for x in batches]
|
|
if len(self.__dict__) > 0:
|
|
batches = [self] + list(batches)
|
|
keys_map = list(map(lambda e: set(e.keys()), batches))
|
|
keys_shared = set.intersection(*keys_map)
|
|
values_shared = [[e[k] for e in batches] for k in keys_shared]
|
|
_assert_type_keys(keys_shared)
|
|
for k, v in zip(keys_shared, values_shared):
|
|
if all(isinstance(e, (dict, Batch)) for e in v):
|
|
self.__dict__[k] = Batch.stack(v, axis)
|
|
elif all(isinstance(e, torch.Tensor) for e in v):
|
|
self.__dict__[k] = torch.stack(v, axis)
|
|
else:
|
|
v = np.stack(v, axis)
|
|
if not issubclass(v.dtype.type, (np.bool_, np.number)):
|
|
v = v.astype(np.object)
|
|
self.__dict__[k] = v
|
|
keys_partial = set.difference(set.union(*keys_map), keys_shared)
|
|
if keys_partial and axis != 0:
|
|
raise ValueError(
|
|
f"Stack of Batch with non-shared keys {keys_partial} "
|
|
f"is only supported with axis=0, but got axis={axis}!")
|
|
_assert_type_keys(keys_partial)
|
|
for k in keys_partial:
|
|
for i, e in enumerate(batches):
|
|
val = e.get(k, None)
|
|
if val is not None:
|
|
try:
|
|
self.__dict__[k][i] = val
|
|
except KeyError:
|
|
self.__dict__[k] = \
|
|
_create_value(val, len(batches))
|
|
self.__dict__[k][i] = val
|
|
|
|
@staticmethod
|
|
def stack(batches: List[Union[dict, 'Batch']], axis: int = 0) -> 'Batch':
|
|
"""Stack a list of :class:`~tianshou.data.Batch` object into a single
|
|
new batch. For keys that are not shared across all batches,
|
|
batches that do not have these keys will be padded by zeros. E.g.
|
|
::
|
|
|
|
>>> a = Batch(a=np.zeros([4, 4]), common=Batch(c=np.zeros([4, 5])))
|
|
>>> b = Batch(b=np.zeros([4, 6]), common=Batch(c=np.zeros([4, 5])))
|
|
>>> c = Batch.stack([a, b])
|
|
>>> c.a.shape
|
|
(2, 4, 4)
|
|
>>> c.b.shape
|
|
(2, 4, 6)
|
|
>>> c.common.c.shape
|
|
(2, 4, 5)
|
|
|
|
.. note::
|
|
|
|
If there are keys that are not shared across all batches, ``stack``
|
|
with ``axis != 0`` is undefined, and will cause an exception.
|
|
"""
|
|
batch = Batch()
|
|
batch.stack_(batches, axis)
|
|
return batch
|
|
|
|
def empty_(self, index: Union[
|
|
str, slice, int, np.integer, np.ndarray, List[int]] = None
|
|
) -> 'Batch':
|
|
"""Return an empty a :class:`~tianshou.data.Batch` object with 0 or
|
|
``None`` filled. If ``index`` is specified, it will only reset the
|
|
specific indexed-data.
|
|
"""
|
|
for k, v in self.items():
|
|
if v is None:
|
|
continue
|
|
if isinstance(v, Batch):
|
|
self.__dict__[k].empty_(index=index)
|
|
elif isinstance(v, torch.Tensor):
|
|
self.__dict__[k][index] = 0
|
|
elif isinstance(v, np.ndarray):
|
|
if v.dtype == np.object:
|
|
self.__dict__[k][index] = None
|
|
else:
|
|
self.__dict__[k][index] = 0
|
|
else: # scalar value
|
|
warnings.warn('You are calling Batch.empty on a NumPy scalar, '
|
|
'which may cause undefined behaviors.')
|
|
if isinstance(v, (np.number, np.bool_, Number)):
|
|
self.__dict__[k] = v.__class__(0)
|
|
else:
|
|
self.__dict__[k] = None
|
|
return self
|
|
|
|
@staticmethod
|
|
def empty(batch: 'Batch', index: Union[
|
|
str, slice, int, np.integer, np.ndarray, List[int]] = None
|
|
) -> 'Batch':
|
|
"""Return an empty :class:`~tianshou.data.Batch` object with 0 or
|
|
``None`` filled, the shape is the same as the given
|
|
:class:`~tianshou.data.Batch`.
|
|
"""
|
|
return deepcopy(batch).empty_(index)
|
|
|
|
def update(self, batch: Optional[Union[dict, 'Batch']] = None,
|
|
**kwargs) -> None:
|
|
"""Update this batch from another dict/Batch."""
|
|
if batch is None:
|
|
self.update(kwargs)
|
|
return
|
|
if isinstance(batch, dict):
|
|
batch = Batch(batch)
|
|
for k, v in batch.items():
|
|
self.__dict__[k] = v
|
|
if kwargs:
|
|
self.update(kwargs)
|
|
|
|
def __len__(self) -> int:
|
|
"""Return len(self)."""
|
|
r = []
|
|
for v in self.__dict__.values():
|
|
if isinstance(v, Batch) and v.is_empty():
|
|
continue
|
|
elif hasattr(v, '__len__') and (not isinstance(
|
|
v, (np.ndarray, torch.Tensor)) or v.ndim > 0):
|
|
r.append(len(v))
|
|
else:
|
|
raise TypeError("Object of type 'Batch' has no len()")
|
|
if len(r) == 0:
|
|
raise TypeError("Object of type 'Batch' has no len()")
|
|
return min(r)
|
|
|
|
def is_empty(self):
|
|
return not any(
|
|
not x.is_empty() if isinstance(x, Batch)
|
|
else hasattr(x, '__len__') and len(x) > 0 for x in self.values())
|
|
|
|
@property
|
|
def shape(self) -> List[int]:
|
|
"""Return self.shape."""
|
|
if len(self.__dict__.keys()) == 0:
|
|
return []
|
|
else:
|
|
data_shape = []
|
|
for v in self.__dict__.values():
|
|
try:
|
|
data_shape.append(v.shape)
|
|
except AttributeError:
|
|
raise TypeError("No support for 'shape' method with "
|
|
f"type {type(v)} in class Batch.")
|
|
return list(map(min, zip(*data_shape))) if len(data_shape) > 1 \
|
|
else data_shape[0]
|
|
|
|
def split(self, size: Optional[int] = None,
|
|
shuffle: bool = True) -> Iterator['Batch']:
|
|
"""Split whole data into multiple small batches.
|
|
|
|
:param int size: if it is ``None``, it does not split the data batch;
|
|
otherwise it will divide the data batch with the given size.
|
|
Default to ``None``.
|
|
:param bool shuffle: randomly shuffle the entire data batch if it is
|
|
``True``, otherwise remain in the same. Default to ``True``.
|
|
"""
|
|
length = len(self)
|
|
if size is None:
|
|
size = length
|
|
if shuffle:
|
|
indices = np.random.permutation(length)
|
|
else:
|
|
indices = np.arange(length)
|
|
for idx in np.arange(0, length, size):
|
|
yield self[indices[idx:(idx + size)]]
|