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 = "https://miladeepgraphlearningproteindata.s3.us-east-2.amazonaws.com/data/proteinnet.tar.gz"
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 = utils.download(self.url, 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 = self.data[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