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AlphaFold3 data_modules 模块的 OpenFoldSingleMultimerDataset类继承自torch.utils.data.Dataset，用于处理多肽复合物（Multimer） 数据，提供数据加载、特征提取、模板匹配等功能。该类支持 训练（train）、验证（eval）、推理（predict） 三种模式，并在__getitem__ 方法中读取 .cif 结构文件、MSA 数据、模板特征，最终返回 标准化的蛋白质特征张量。

源代码：

class OpenFoldSingleMultimerDataset(torch.utils.data.Dataset):def __init__(self,data_dir: str,alignment_dir: str,template_mmcif_dir: str,max_template_date: str,config: mlc.ConfigDict,mmcif_data_cache_path: Optional[str] = None,kalign_binary_path: str = '/usr/bin/kalign',max_template_hits: int = 4,obsolete_pdbs_file_path: Optional[str] = None,template_release_dates_cache_path: Optional[str] = None,shuffle_top_k_prefiltered: Optional[int] = None,treat_pdb_as_distillation: bool = True,filter_path: Optional[str] = None,mode: str = "train",alignment_index: Optional[Any] = None,_output_raw: bool = False,_structure_index: Optional[Any] = None,):"""This class check each individual PDB ID and return its chain(s) features/ground truth Args:data_dir:A path to a directory containing mmCIF files (in trainmode) or FASTA files (in inference mode).alignment_dir:A path to a directory containing only data in the format output by an AlignmentRunner (defined in openfold.features.alignment_runner).I.e. a directory of directories named {PDB_ID}_{CHAIN_ID}or simply {PDB_ID}, each containing .a3m, .sto, and .hhrfiles.template_mmcif_dir:Path to a directory containing template mmCIF files.config:A dataset config object. See openfold.configmmcif_data_cache_path:Path to cache of all mmcifs files generated byscripts/generate_mmcif_cache.py It should be a json file which recordswhat PDB ID contains which chain(s)kalign_binary_path:Path to kalign binary.max_template_hits:An upper bound on how many templates are considered. Duringtraining, the templates ultimately used are subsampledfrom this total quantity.template_release_dates_cache_path:Path to the output of scripts/generate_mmcif_cache.obsolete_pdbs_file_path:Path to the file containing replacements for obsolete PDBs.shuffle_top_k_prefiltered:Whether to uniformly shuffle the top k template hits beforeparsing max_template_hits of them. Can be used toapproximate DeepMind's training-time template subsamplingscheme much more performantly.treat_pdb_as_distillation:Whether to assume that .pdb files in the data_dir are fromthe self-distillation set (and should be subjected tospecial distillation set preprocessing steps).mode:"train", "val", or "predict""""super(OpenFoldSingleMultimerDataset, self).__init__()self.data_dir = data_dirself.mmcif_data_cache_path = mmcif_data_cache_pathself.mmcif_data_cache = Noneif self.mmcif_data_cache_path is not None:with open(self.mmcif_data_cache_path, "r") as infile:self.mmcif_data_cache = json.load(infile)assert isinstance(self.mmcif_data_cache, dict)self.alignment_dir = alignment_dirself.config = configself.treat_pdb_as_distillation = treat_pdb_as_distillationself.mode = modeself.alignment_index = alignment_indexself._output_raw = _output_rawself._structure_index = _structure_indexself.supported_exts = [".cif", ".core", ".pdb"]valid_modes = ["train", "eval", "predict"]if mode not in valid_modes:raise ValueError(f'mode must be one of {valid_modes}')if template_release_dates_cache_path is None:logging.warning("Template release dates cache does not exist. Remember to run ""scripts/generate_mmcif_cache.py before running OpenFold")if self.mmcif_data_cache_path is not None:self._mmcifs = list(self.mmcif_data_cache.keys())elif self.alignment_index is not None:self._mmcifs = [i.split("_")[0] for i in list(alignment_index.keys())]elif self.alignment_dir is not None:self._mmcifs = [i.split("_")[0] for i in os.listdir(self.alignment_dir)]else:raise ValueError("You must provide at least one of the mmcif_data_cache or alignment_dir")if filter_path is not None:with open(filter_path, "r") as f:mmcifs_to_include = set([l.strip() for l in f.readlines()])self._mmcifs = [m for m in self._mmcifs if m in mmcifs_to_include]self._mmcif_id_to_idx_dict = {mmcif: i for i, mmcif in enumerate(self._mmcifs)}template_featurizer = templates.HmmsearchHitFeaturizer(mmcif_dir=template_mmcif_dir,