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import gc
import os
import sys
import argparse
import pandas as pd
import torch
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
sys.path.append(".")
from utils import seed_everything
from models import PLTNUM_PreTrainedModel
from datasets import PLTNUMDataset
from predict import predict_fn
def parse_args():
parser = argparse.ArgumentParser(
description="Prediction script for protein sequence classification/regression."
)
parser.add_argument(
"--data_path",
type=str,
required=True,
help="Path to the input data.",
)
parser.add_argument(
"--model",
type=str,
default="westlake-repl/SaProt_650M_AF2",
help="Pretrained model name or path.",
)
parser.add_argument(
"--architecture",
type=str,
default="SaProt",
help="Model architecture: 'ESM2', 'SaProt', or 'LSTM'.",
)
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the model for prediction.",
)
parser.add_argument("--batch_size", type=int, default=4, help="Batch size.")
parser.add_argument(
"--seed",
type=int,
default=42,
help="Seed for reproducibility.",
)
parser.add_argument(
"--use_amp",
action="store_true",
default=False,
help="Use AMP for mixed precision prediction.",
)
parser.add_argument(
"--num_workers",
type=int,
default=4,
help="Number of workers for data loading.",
)
parser.add_argument(
"--max_length",
type=int,
default=512,
help="Maximum input sequence length. Two tokens are used fo <cls> and <eos> tokens. So the actual length of input sequence is max_length - 2. Padding or truncation is applied to make the length of input sequence equal to max_length.",
)
parser.add_argument(
"--used_sequence",
type=str,
default="left",
help="Which part of the sequence to use: 'left', 'right', 'both', or 'internal'.",
)
parser.add_argument(
"--padding_side",
type=str,
default="right",
help="Padding side: 'right' or 'left'.",
)
parser.add_argument(
"--output_dir",
type=str,
default="./output",
help="Output directory.",
)
parser.add_argument(
"--task",
type=str,
default="classification",
help="Task type: 'classification' or 'regression'.",
)
parser.add_argument(
"--sequence_col",
type=str,
default="aa_foldseek",
help="Column name fot the input sequence.",
)
return parser.parse_args()
def predict(folds, model_path, cfg):
dataset = PLTNUMDataset(cfg, folds, train=False)
loader = DataLoader(
dataset,
batch_size=cfg.batch_size,
shuffle=False,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=False,
)
model = PLTNUM_PreTrainedModel.from_pretrained(model_path, cfg=cfg)
# model.load_state_dict(torch.load(os.path.join(model_path, "pytorch_model.bin"), map_location=cfg.device))
model.to(cfg.device)
predictions = predict_fn(loader, model, cfg)
folds["prediction"] = predictions
torch.cuda.empty_cache()
gc.collect()
return folds
if __name__ == "__main__":
config = parse_args()
config.token_length = 2 if config.architecture == "SaProt" else 1
config.device = "cuda" if torch.cuda.is_available() else "cpu"
if not os.path.exists(config.output_dir):
os.makedirs(config.output_dir)
if config.used_sequence == "both":
config.max_length += 1
seed_everything(config.seed)
df = pd.read_csv(config.data_path)
tokenizer = AutoTokenizer.from_pretrained(
config.model_path, padding_side=config.padding_side
)
config.tokenizer = tokenizer
result = predict(df, config.model_path, config)
result.to_csv(os.path.join(config.output_dir, "result.csv"), index=False)
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