SFL/train_LBA.py at main · ispc-lab/SFL · GitHub

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import numpy as np
import torch
from torch.optim import Adam
from torch.optim.lr_scheduler import CosineAnnealingLR
import torch.nn.functional as F
from sklearn import metrics
from tqdm import tqdm
import numpy as np
import os
import argparse
import shutil
from utils.load_utils import load_data, load_model
from utils.dataloader import get_data_loader
from utils.train import set_logger, set_seed
import torch.nn as nn


def train(args):
    model = args.model

    # Define optimizer and learning rate scheduler
    optimizer = Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
    lr_scheduler = CosineAnnealingLR(optimizer, T_max=args.tmax)

    train_data_loader, valid_data_loader = get_data_loader(args=args, mode='train')

    best_score = 1e9
    best_epoch = 0

    criterion = nn.MSELoss()
    for epoch in tqdm(range(args.max_epoch)):
        model.train()
        for batch in tqdm(train_data_loader):
            batch = {k: torch.tensor(v).to(args.device) for k, v in batch.items()}
            label_true = batch['label'].unsqueeze(1)

            output, output_feat = model(batch)
            loss = criterion(output, label_true)

            loss.backward()
            optimizer.step()
            optimizer.zero_grad()

        with torch.no_grad():
            valid_metric = evaluate(args, model, valid_data_loader)

        lr_scheduler.step()

        valid_score = valid_metric['RMSE']
        args.logger.info(f"Epoch: {epoch}, Valid RMSE: {valid_metric['RMSE']:07.4f}")

        if valid_score < best_score:
            torch.save(model.state_dict(), os.path.join(args.save_dir, "best_model.pt"))
            best_score = valid_score
            best_epoch = epoch
            args.logger.info(f'current_best_score: {best_score:.4f}, best_epoch: {best_epoch}')
        elif epoch > best_epoch + args.max_bearable_epoch or epoch == args.max_epoch - 1:
                args.logger.info(f"model_{args.runnername} is Done!!")
                args.logger.info('valid')
                args.logger.info(f'current_best_score: {best_score:.4f}, best_epoch: {best_epoch}')
                torch.save(model.state_dict(), os.path.join(args.save_dir, "last_model.pt"))
                break
        else:
            args.logger.info(f'current_best_score: {best_score:.4f}, best_epoch: {best_epoch}')


def evaluate(args, model, dataloader):
    model.eval()
    y_pred = np.array([])
    y_true = np.array([])

    for batch in tqdm(dataloader):
        batch = {k: v.to(args.device) for k, v in batch.items()}
        compound_class = batch['label']
        output, _ = model(batch)
        y_pred = np.concatenate((y_pred, output[:, 0].detach().cpu().numpy()))
        y_true = np.concatenate((y_true, compound_class.detach().cpu().numpy()))

    rmse = np.sqrt(metrics.mean_squared_error(y_true, y_pred))

    metric_eval = {
        'RMSE': round(rmse, 4),
    }

    return metric_eval


def build_argparse():
    parser = argparse.ArgumentParser()
    parser.add_argument("--use_SFL", default=0, type=int)
    parser.add_argument("--note", default=None, type=str)
    parser.add_argument('--arch_type', default='comenet', type=str,
                            help="deepdta, moltrans, atom3d_cnn3d, atom3d_gnn, comenet, visnet")
    parser.add_argument('--dataset', default='LBA_30', type=str,
                            help="LBA_30, LBA_60")
    parser.add_argument('--save_dir', default='./ckpt_LBA/base', type=str)
    parser.add_argument('--seed', default=2025, type=int)
    parser.add_argument('--device', default='cuda:2', type=str)
    parser.add_argument('--num_workers', default=7, type=int)

    parser.add_argument('--batch_size', default=16, type=int)
    parser.add_argument('--tmax', default=15, type=int)
    parser.add_argument('--weight_decay', default=1e-5, type=float)
    parser.add_argument("--max_bearable_epoch", type=int, default=50)

    args = parser.parse_args()
    args = load_data(args)

    if args.note:
        tag = [args.arch_type + '_' + args.note,  args.dataset, str(args.seed)]
    else:
        tag = [args.arch_type,  args.dataset, str(args.seed)]

    args.runnername = tag[0] + '_' + tag[1] + '/' + tag[2]

    args.save_dir = os.path.join(args.save_dir, args.runnername)
    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)

    return args

def init_experiment():
    args = build_argparse()
    args.logger = set_logger(args)
    set_seed(args.seed)

    args = load_model(args)

    # Backup current python file
    current_file = os.path.abspath(__file__)
    destination = os.path.join(args.save_dir, os.path.basename(current_file))
    shutil.copy(current_file, destination)

    return args


if __name__ == '__main__':

    args = init_experiment()
    train(args)