File size: 3,305 Bytes
3b99abb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import pandas as pd
import json
import numpy as np
from torch.utils.data import Subset
from transformers import EsmTokenizer
from ..lmdb_dataset import *
from ..data_interface import register_dataset
@register_dataset
class SaprotAnnotationDataset(LMDBDataset):
def __init__(self,
tokenizer: str,
bias_feature: bool = False,
max_length: int = 1024,
mask_struc_ratio: float = None,
plddt_threshold: float = None,
**kwargs):
"""
Args:
tokenizer: EsmTokenizer config path
bias_feature: If True, structure information will be used
max_length: Max length of sequence
mask_struc_ratio: Ratio of masked structure tokens, replace structure tokens with "#"
plddt_threshold: If not None, mask structure tokens with pLDDT < threshold
**kwargs: other arguments for LMDBDataset
"""
super().__init__(**kwargs)
self.tokenizer = EsmTokenizer.from_pretrained(tokenizer)
self.bias_feature = bias_feature
self.max_length = max_length
self.mask_struc_ratio = mask_struc_ratio
self.plddt_threshold = plddt_threshold
def __getitem__(self, index):
data = json.loads(self._get(index))
seq = data['seq']
# Mask structure tokens
if self.mask_struc_ratio is not None:
tokens = self.tokenizer.tokenize(seq)
mask_candi = [i for i, t in enumerate(tokens) if t[-1] != "#"]
# Randomly select tokens to mask
mask_num = int(len(mask_candi) * self.mask_struc_ratio)
mask_idx = np.random.choice(mask_candi, mask_num, replace=False)
for i in mask_idx:
tokens[i] = tokens[i][:-1] + "#"
seq = "".join(tokens)
# Mask structure tokens with pLDDT < threshold
if self.plddt_threshold is not None:
plddt = data["plddt"]
tokens = self.tokenizer.tokenize(seq)
seq = ""
for token, score in zip(tokens, plddt):
if score < self.plddt_threshold:
seq += token[:-1] + "#"
else:
seq += token
tokens = self.tokenizer.tokenize(seq)[:self.max_length]
seq = " ".join(tokens)
coords = data['coords'][:self.max_length] if self.bias_feature else None
label = data['label']
if isinstance(label, str):
label = json.loads(label)
return seq, label, coords
def collate_fn(self, batch):
seqs, labels, coords = zip(*batch)
model_inputs = self.tokenizer.batch_encode_plus(seqs, return_tensors='pt', padding=True)
inputs = {"inputs": model_inputs}
# print(self.tokenizer.convert_ids_to_tokens(inputs['inputs']['input_ids'][0]))
if self.bias_feature:
inputs['structure_info'] = (coords,)
labels = {"labels": torch.tensor(labels, dtype=torch.long)}
return inputs, labels
def __len__(self):
return int(self._get('length'))
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