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AlphaFold3 data_modules 模块的 OpenFoldDataset 类是一个自定义的数据集类，继承自 torch.utils.data.Dataset。它的目的是在训练时实现 随机过滤器（stochastic filters），用于从多个不同的数据集（OpenFoldSingleDataset 或 OpenFoldSingleMultimerDataset）中进行样本选择和过滤。该类具有一些 数据增强 和 过滤 的机制，以提高模型的训练质量。

源代码：

class OpenFoldDataset(torch.utils.data.Dataset):"""Implements the stochastic filters applied during AlphaFold's training.Because samples are selected from constituent datasets randomly, thelength of an OpenFoldFilteredDataset is arbitrary. Samples are selectedand filtered once at initialization."""def __init__(self,datasets: Union[Sequence[OpenFoldSingleDataset], Sequence[OpenFoldSingleMultimerDataset]],probabilities: Sequence[float],epoch_len: int,generator: torch.Generator = None,_roll_at_init: bool = True,):self.datasets = datasetsself.probabilities = probabilitiesself.epoch_len = epoch_lenself.generator = generatorself.datapoints = Noneself._samples = [self.looped_samples(i) for i in range(len(self.datasets))]if _roll_at_init:self.reroll()@staticmethoddef deterministic_train_filter(cache_entry: Any,max_resolution: float = 9.,max_single_aa_prop: float = 0.8,*args, **kwargs) -> bool:# Hard filtersresolution = cache_entry.get("resolution", None)seqs = [cache_entry["seq"]]return all([resolution_filter(resolution=resolution,max_resolution=max_resolution),aa_count_filter(seqs=seqs,max_single_aa_prop=max_single_aa_prop)])@staticmethoddef get_stochastic_train_filter_prob(cache_entry: Any,*args, **kwargs) -> float:# Stochastic filtersprobabilities = []cluster_size = cache_entry.get("cluster_size", None)if cluster_size is not None and cluster_size > 0:probabilities.append(1 / cluster_size)chain_length = len(cache_entry["seq"])