| import pandas as pd | |
| import torch | |
| from torch.utils.data import Dataset, DataLoader | |
| from torch.nn.utils.rnn import pad_sequence | |
| from esm_utils import get_latents, load_esm2_model | |
| import config | |
| class ProteinDataset(Dataset): | |
| def __init__(self, csv_file, tokenizer, model): | |
| self.data = pd.read_csv(csv_file).head(4) | |
| self.tokenizer = tokenizer | |
| self.model = model | |
| def __len__(self): | |
| return len(self.data) | |
| def __getitem__(self, idx): | |
| sequence = self.data.iloc[idx]['Sequence'] | |
| latents = get_latents(self.model, self.tokenizer, sequence) | |
| attention_mask = torch.ones_like(latents) | |
| attention_mask = torch.mean(attention_mask, dim=-1) | |
| return latents, attention_mask | |
| def collate_fn(batch): | |
| latents, attention_mask = zip(*batch) | |
| latents_padded = pad_sequence([torch.tensor(latent) for latent in latents], batch_first=True, padding_value=0) | |
| attention_mask_padded = pad_sequence([torch.tensor(mask) for mask in attention_mask], batch_first=True, padding_value=0) | |
| return latents_padded, attention_mask_padded | |
| def get_dataloaders(config): | |
| tokenizer, masked_model, embedding_model = load_esm2_model(config.MODEL_NAME) | |
| train_dataset = ProteinDataset(config.Loader.DATA_PATH + "/train.csv", tokenizer, embedding_model) | |
| val_dataset = ProteinDataset(config.Loader.DATA_PATH + "/val.csv", tokenizer, embedding_model) | |
| test_dataset = ProteinDataset(config.Loader.DATA_PATH + "/test.csv", tokenizer, embedding_model) | |
| train_loader = DataLoader(train_dataset, batch_size=config.Loader.BATCH_SIZE, num_workers=0, shuffle=True, collate_fn=collate_fn) | |
| val_loader = DataLoader(val_dataset, batch_size=config.Loader.BATCH_SIZE, num_workers=0, shuffle=False, collate_fn=collate_fn) | |
| test_loader = DataLoader(test_dataset, batch_size=config.Loader.BATCH_SIZE, num_workers=0, shuffle=False, collate_fn=collate_fn) | |
| return train_loader, val_loader, test_loader |