classRandomSampler(Sampler[int]):r"""Samples elements randomly. If without replacement, then sample from a shuffled dataset.If with replacement, then user can specify :attr:`num_samples` to draw.Args:data_source (Dataset): dataset to sample fromreplacement (bool): samples are drawn on-demand ...
replacement: 若为True,则表示可以重复采样,即同一个样本可以重复采样,这样可能导致有的样本采样不到。所以此时我们可以设置num_samples来增加采样数量使得每个样本都可能被采样到。class RandomSampler(Sampler): r"""Samples elements randomly. If without replacement, then sample from a shuffled dataset. If with...
class SubsetRandomSampler(Sampler[int]): r"""Samples elements randomly from a given list of indices, without replacement. Args: indices (sequence): a sequence of indices generator (Generator): Generator used in sampling. """ indices: Sequence[int] def __init__(self, indices: Sequence[int...
batch_sampler (Sample, optional) - 和sampler类似,返回批中的索引。 num_workers (int, optional) - 用于数据加载的子进程数。 collate_fn (callable, optional) - 合并样本列表以形成小批量。 pin_memory (bool, optional) - 如果为True,数据加载器在返回去将张量复制到CUDA固定内存中。 drop_last (bool,...
(frames_per_batch // sub_batch_size):subdata = replay_buffer.sample(sub_batch_size)loss_vals = loss_module(subdata.to(device))loss_value = (loss_vals["loss_objective"]+ loss_vals["loss_critic"]+ loss_vals["loss_entropy"])# Optimization: backward, grad clipping and optimization step...
ReplayMemory- 一个有界大小的循环缓冲区,保存最近观察到的转换。它还实现了一个.sample()方法,用于选择用于训练的随机批次的转换。 Transition = namedtuple('Transition',('state', 'action', 'next_state', 'reward'))class ReplayMemory(object):def __init__(self, capacity):self.memory = deque([], ...
r"""Samples elements randomly, without replacement. Arguments: data_source (Dataset): dataset to sample from """ def __init__(self, data_source): self.data_source = data_source def __iter__(self): return iter(torch.randperm(len(self.data_source)).tolist()) ...
If without replacement, then sample from a shuffled dataset. If with replacement, then user can specify ``num_samples`` to draw. Arguments: data_source (Dataset): dataset to sample from num_samples (int): number of samples to draw, default=len(dataset) replacement (bool): samples are draw...
replacement: 若为True,则表示可以重复采样,即同一个样本可以重复采样,这样可能导致有的样本采样不到。所以此时我们可以设置num_samples来增加采样数量使得每个样本都可能被采样到。 class RandomSampler(Sampler): r"""Samples elements randomly. If without replacement, then sample from a shuffled dataset. ...
classRandomSampler(Sampler):r"""Samples elements randomly, without replacement. Arguments: data_source (Dataset): dataset to sample from """def__init__(self, data_source): self.data_source = data_sourcedef__iter__(self):returniter(torch.randperm(len(self.data_source)).tolist())def__len...