56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
import torch
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import numpy as np
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class Batch(object):
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"""Suggested keys: [obs, act, rew, done, obs_next, info]"""
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def __init__(self, **kwargs):
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super().__init__()
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self.__dict__.update(kwargs)
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def __getitem__(self, index):
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b = Batch()
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for k in self.__dict__.keys():
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if self.__dict__[k] is not None:
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b.update(**{k: self.__dict__[k][index]})
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return b
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def update(self, **kwargs):
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self.__dict__.update(kwargs)
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def append(self, batch):
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assert isinstance(batch, Batch), 'Only append Batch is allowed!'
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for k in batch.__dict__.keys():
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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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raise TypeError(
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'No support for append with type {} in class Batch.'
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.format(type(batch.__dict__[k])))
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def split(self, size=None, permute=True):
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length = min([
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len(self.__dict__[k]) for k in self.__dict__.keys()
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if self.__dict__[k] is not None])
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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 permute:
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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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