Source code for torchdrug.datasets.proteinnet

import os

import torch
from torch.utils import data as torch_data

from torchdrug import data, utils
from torchdrug.core import Registry as R

[docs]@R.register("datasets.ProteinNet") @utils.copy_args(data.ProteinDataset.load_lmdbs, ignore=("target_fields",)) class ProteinNet(data.ProteinDataset): """ A set of proteins with 3D structures for the contact prediction task. Statistics: - #Train: 25,299 - #Valid: 224 - #Test: 40 Parameters: path (str): the path to store the dataset verbose (int, optional): output verbose level **kwargs """ url = "" md5 = "ab44ab201b1570c0171a2bba9eb4d389" splits = ["train", "valid", "test"] target_fields = ["tertiary", "valid_mask"] def __init__(self, path, verbose=1, **kwargs): path = os.path.expanduser(path) if not os.path.exists(path): os.makedirs(path) self.path = path zip_file =, path, md5=self.md5) data_path = utils.extract(zip_file) lmdb_files = [os.path.join(data_path, "proteinnet/proteinnet_%s.lmdb" % split) for split in self.splits] self.load_lmdbs(lmdb_files, target_fields=self.target_fields, verbose=verbose, **kwargs) def get_item(self, index): if self.lazy: graph = data.Protein.from_sequence(self.sequences[index], **self.kwargs) else: graph =[index] with graph.residue(): residue_position = torch.as_tensor(self.targets["tertiary"][index], dtype=torch.float) graph.residue_position = residue_position mask = torch.as_tensor(self.targets["valid_mask"][index], dtype=torch.bool) graph.mask = mask item = {"graph": graph} if self.transform: item = self.transform(item) return item def split(self, keys=None): keys = keys or self.splits offset = 0 splits = [] for split_name, num_sample in zip(self.splits, self.num_samples): if split_name in keys: split = torch_data.Subset(self, range(offset, offset + num_sample)) splits.append(split) offset += num_sample return splits