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# Licensed under the MIT License.
import json
from pytorch_lightning import LightningDataModule
# import torch_geometric
from torch.utils.data import DataLoader, Dataset
from pathlib import Path
class ProtQACollater(object):
def __init__(self, tokenizer, prot_tokenizer, q_max_len, a_max_len, prot_max_len):
self.tokenizer = tokenizer
self.prot_tokenizer = prot_tokenizer
self.q_max_len = q_max_len
self.a_max_len = a_max_len
self.prot_max_len = prot_max_len
def __call__(self, batch):
prot_seqs, questions, answers, _, _ = zip(*batch)
answers = [a + '\n' for a in answers]
prot_batch = self.prot_tokenizer(prot_seqs,
truncation=True,
padding='max_length',
max_length=self.prot_max_len,
return_tensors="pt",
return_attention_mask=True,
return_token_type_ids=False)
if False:
self.tokenizer.padding_side = 'left'
q_batch = self.tokenizer(questions,
truncation=True,
padding='max_length',
add_special_tokens=True,
max_length=self.q_max_len,
return_tensors='pt',
return_attention_mask=True,
return_token_type_ids=False)
self.tokenizer.padding_side = 'right'
a_batch = self.tokenizer(answers,
truncation=True,
padding='max_length',
add_special_tokens=True,
max_length=self.a_max_len,
return_tensors='pt',
return_attention_mask=True,
return_token_type_ids=False)
return prot_batch, q_batch, a_batch
else:
self.tokenizer.padding_side = 'right'
qa_pair = [[q, a] for q, a in zip(questions, answers)]
qa_batch = self.tokenizer(qa_pair,
truncation=True,
padding='max_length',
add_special_tokens=True,
max_length=self.q_max_len + self.a_max_len,
return_tensors='pt',
return_attention_mask=True,
return_token_type_ids=True)
return prot_batch, qa_batch
class InferenceCollater(object):
def __init__(self, tokenizer, prot_tokenizer, q_max_len, a_max_len, prot_max_len):
self.tokenizer = tokenizer
self.prot_tokenizer = prot_tokenizer
self.q_max_len = q_max_len
self.a_max_len = a_max_len
self.prot_max_len = prot_max_len
def __call__(self, batch):
prot_seqs, questions, answers, q_types, indices = zip(*batch)
answers = [a + '\n' for a in answers]
prot_batch = self.prot_tokenizer(prot_seqs,
truncation=True,
padding='max_length',
max_length=self.prot_max_len,
return_tensors="pt",
return_attention_mask=True,
return_token_type_ids=False)
self.tokenizer.padding_side = 'left'
q_batch = self.tokenizer(questions,
truncation=True,
padding='max_length',
add_special_tokens=True,
max_length=self.q_max_len,
return_tensors='pt',
return_attention_mask=True,
return_token_type_ids=False)
target_dict = {'targets': answers, 'q_types': q_types, 'indices': indices}
return prot_batch, q_batch, target_dict
class ProtQADM(LightningDataModule):
def __init__(
self,
root: str = 'data/',
args=None,
):
super().__init__()
self.args = args
self.batch_size = args.batch_size
self.inference_batch_size = args.inference_batch_size
self.num_workers = args.num_workers
self.q_max_len = args.q_max_len
self.a_max_len = args.a_max_len
self.prot_max_len = args.prot_max_len
self.prompt = args.prompt
self.train_dataset = PDBQADataset(root, 'train.txt', prompt=self.prompt, filter_side_qa=args.filter_side_qa)
self.val_dataset = PDBQADataset(root, 'val.txt', prompt=self.prompt, filter_side_qa=args.filter_side_qa)
self.test_dataset = PDBQADataset(root, 'test.txt', prompt=self.prompt, filter_side_qa=args.filter_side_qa)
self.tokenizer = None
self.prot_tokenizer = None
def init_tokenizer(self, tokenizer, prot_tokenizer):
self.tokenizer = tokenizer
self.prot_tokenizer = prot_tokenizer
def train_dataloader(self):
loader = DataLoader(
self.train_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers,
pin_memory=False,
drop_last=True,
persistent_workers=False,
collate_fn=ProtQACollater(self.tokenizer, self.prot_tokenizer, self.q_max_len, self.a_max_len, self.prot_max_len),
)
return loader
def val_dataloader(self):
val_loader = DataLoader(
self.val_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
pin_memory=False,
drop_last=False,
persistent_workers=False,
collate_fn=ProtQACollater(self.tokenizer, self.prot_tokenizer, self.q_max_len, self.a_max_len, self.prot_max_len),
)
test_loader = DataLoader(
self.test_dataset,
batch_size=self.inference_batch_size,
shuffle=False,
num_workers=self.num_workers,
pin_memory=False,
drop_last=False,
persistent_workers=False,
collate_fn=InferenceCollater(self.tokenizer, self.prot_tokenizer, self.q_max_len, self.a_max_len, self.prot_max_len),
)
return [val_loader, test_loader]
def add_model_specific_args(parent_parser):
parser = parent_parser.add_argument_group("Data module")
parser.add_argument('--num_workers', type=int, default=2)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--inference_batch_size', type=int, default=4)
parser.add_argument('--root', type=str, default='data/SwissProtV3')
parser.add_argument('--text_max_len', type=int, default=128)
parser.add_argument('--q_max_len', type=int, default=34)
parser.add_argument('--a_max_len', type=int, default=36)
parser.add_argument('--prot_max_len', type=int, default=1024)
parser.add_argument('--prompt', type=str, default='The protein has the following properties: ')
parser.add_argument('--filter_side_qa', action='store_true', default=False)
return parent_parser
class PDBQADataset(Dataset):
def __init__(self, root_path, subset, prompt="Question: {} Answer:", filter_side_qa=False):
super(PDBQADataset, self).__init__()
self.data_path = Path(root_path) / subset
self.qa_path = Path(root_path) / 'qa_all.json'
self.q_type_path = Path(root_path) / 'q_types.txt'
self.prompt = prompt
## load dataset
with open(self.qa_path, 'r') as f:
qa_data = json.load(f)
with open(self.data_path, 'r') as f:
lines = f.readlines()
pdb2seq = [line.strip().split('\t') for line in lines]
## load q types
with open(self.q_type_path, 'r') as f:
q_types = [line.strip().split('\t') for line in f.readlines()]
self.q_type_dict = {q: t for q, t in q_types}
## process dataset
pdb_set = set(i[0] for i in pdb2seq)
## filter qa data
qa_data = {k: v for k, v in qa_data.items() if k in pdb_set}
assert len(qa_data) == len(pdb_set), print(len(qa_data), len(pdb_set))
## generate qa data
self.data_list = []
for pdb_id, seq in pdb2seq:
qa_list = qa_data[pdb_id]
for qa in qa_list:
q = qa['Q']
a = str(qa['A'])
if filter_side_qa:
q_type = self.q_type_dict[q]
if q_type.find('side information') >= 0:
continue
self.data_list.append((seq, q, a))
def __len__(self):
return len(self.data_list)
def __getitem__(self, index):
seq, q, a = self.data_list[index]
q_type = self.q_type_dict[q]
q = self.prompt.format(q)
return seq, q, a, q_type, index
if __name__ == '__main__':
import numpy as np
from collections import defaultdict, Counter
train_dataset = PDBQADataset('../data/PDBDataset', 'train.txt', filter_side_qa=True)
val_dataset = PDBQADataset('../data/PDBDataset', 'val.txt', filter_side_qa=True)
test_dataset = PDBQADataset('../data/PDBDataset', 'test.txt', filter_side_qa=True)
if True:
# print(len(train_dataset), len(val_dataset), len(test_dataset))
# train_protein_lens = np.asarray([len(p) for p in train_dataset.protein_list])
# val_protein_lens = np.asarray([len(p) for p in val_dataset.protein_list])
# test_protein_lens = np.asarray([len(p) for p in test_dataset.protein_list])
q_lens = []
a_lens = []
for seq, q, a in train_dataset.data_list:
q_lens.append(len(q.split()))
a_lens.append(len(a.split()))
print(np.asarray(q_lens).min(), np.asarray(q_lens).max(), np.asarray(q_lens).mean())
print(np.asarray(a_lens).min(), np.asarray(a_lens).max(), np.asarray(a_lens).mean())
q_lens = []
a_lens = []
for seq, q, a in val_dataset.data_list:
q_lens.append(len(q.split()))
a_lens.append(len(a.split()))
print(np.asarray(q_lens).min(), np.asarray(q_lens).max(), np.asarray(q_lens).mean())
print(np.asarray(a_lens).min(), np.asarray(a_lens).max(), np.asarray(a_lens).mean())
q_lens = []
a_lens = []
for seq, q, a in test_dataset.data_list:
q_lens.append(len(q.split()))
a_lens.append(len(a.split()))
print(np.asarray(q_lens).min(), np.asarray(q_lens).max(), np.asarray(q_lens).mean())
print(np.asarray(a_lens).min(), np.asarray(a_lens).max(), np.asarray(a_lens).mean())
elif False:
## construct the guess for prediction by number
train_counter = defaultdict(Counter)
for _, q, a in train_dataset.data_list:
train_counter[q.lower()][a] += 1
## get the most common answer
q2a = {}
for q, counter in train_counter.items():
q2a[q] = counter.most_common(1)[0][0]
## test the guess
acc = 0
for _, q, a in test_dataset.data_list:
if q.lower() in q2a:
predict = q2a[q.lower()]
if predict.lower() == a.lower():
acc += 1
print(acc / len(test_dataset.data_list))
elif False:
from transformers import AutoTokenizer, EsmTokenizer
llm_tokenizer = AutoTokenizer.from_pretrained('facebook/galactica-1.3b', use_fast=False, padding_side='right')
plm_tokenizer = EsmTokenizer.from_pretrained('facebook/esm2_t30_150M_UR50D')
llm_tokenizer.add_special_tokens({'pad_token': '<pad>'})
loader = DataLoader(
train_dataset,
batch_size=32,
shuffle=True,
num_workers=4,
pin_memory=False,
drop_last=True,
persistent_workers=False,
collate_fn=ProtQACollater(llm_tokenizer, plm_tokenizer, 40, 40, 1024),
)
else:
print(len(train_dataset.data_list))
print(len(val_dataset.data_list))
print(len(test_dataset.data_list))
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