import argparse import json import logging import os import time from dataset import PeptidePairDataset, PeptidePairPicDataset from network import DMutaPeptide, DMutaPeptideCNN from sklearn.model_selection import KFold from torchmetrics import MeanAbsoluteError, RelativeSquaredError, PearsonCorrCoef, KendallRankCorrCoef, F1Score, Accuracy, AveragePrecision, AUROC import torch import torch.nn as nn from torch.utils.data import DataLoader, Subset import torchvision.transforms.v2 as T import numpy as np from loss import MLCE, SuperLoss, LogCoshLoss, BMCLoss from utils import set_seed parser = argparse.ArgumentParser(description='resnet26') # model setting parser.add_argument('--model', type=str, default='resnet34', help='resnet34 resnet50 densenet') parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='lstm', help='lstm mamba mla') parser.add_argument("--side-enc", dest='side_enc', type=str, default=None, help="use side features") parser.add_argument('--channels', type=int, default=256) parser.add_argument('--fusion', type=str, default='att', help='mlp att diff') parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False, help="use global features") parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False, help="use non-siamese architecture") # task & dataset setting parser.add_argument('--task', type=str, default='reg', help='reg or cls') parser.add_argument('--one-way', action='store_true', dest='one_way', default=False, help='use one-way constructed dataset') parser.add_argument('--max-length', dest='max_length', type=int, default=30, help='Max length for sequence filtering') parser.add_argument('--split', type=int, default=5, help="Split k fold in cross validation (default: 5)") parser.add_argument('--seed', type=int, default=42, help="Seed (default: 1)") parser.add_argument('--pcs', action='store_true', default=False, help='Consider protease cleavage site') parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False, help='Consider protease cleavage site') parser.add_argument('--resize', type=int, default=[768], nargs='+', help='resize the image') # parser.add_argument('--llm-data', action='store_true', default=False, # help='Use LLM augmentation data') # training setting parser.add_argument('--gpu', type=int, default=0, help='GPU index to use, -1 for CPU (default: 0)') parser.add_argument('--batch-size', type=int, dest='batch_size', default=32, help='input batch size for training (default: 128)') parser.add_argument('--epochs', type=int, default=50, help='number of epochs to train (default: 100)') parser.add_argument('--lr', type=float, default=0.001, help='learning rate (default: 0.001)') parser.add_argument('--decay', type=float, default=0.0005, help='weight decay (default: 0.0005)') parser.add_argument('--pretrain', type=str, dest='pretrain', default='', help='path of the pretrain model') parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro', help='metric average type') parser.add_argument('--loss', type=str, default='mse', help='loss function') parser.add_argument('--dir', action='store_true', default=False, help='use DIR') args = parser.parse_args() if args.mix_pcs: args.pcs = 'mix' def main(): set_seed(args.seed) if args.task == 'reg': args.classes = 1 trainer = train if args.loss == "mse" or args.loss in ['ce']: args.loss = 'mse' criterion = nn.MSELoss() elif args.loss == "smoothl1": criterion = nn.SmoothL1Loss() elif args.loss == "super": criterion = SuperLoss() elif args.loss in ["bmc", "bmc_ln"]: criterion = BMCLoss() else: raise NotImplementedError("unimplemented regression task loss function") elif args.task == 'cls': trainer = train_cls args.classes = 2 if args.loss == 'ce' or args.loss in ['mse', 'smoothl1', 'super']: args.loss = 'ce' criterion = nn.CrossEntropyLoss() else: raise NotImplementedError("unimplemented classification task loss function") else: raise NotImplementedError("unimplemented task") if args.q_encoder in ['cnn', 'rn18']: weight_dir = f'./run-{args.task}/{"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}_aug' else: weight_dir = f'./run-{args.task}/{"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}_aug' if not os.path.exists(weight_dir): os.makedirs(weight_dir) logging.basicConfig(handlers=[ logging.FileHandler(filename=os.path.join(weight_dir, "training.log"), encoding='utf-8', mode='w+'), logging.StreamHandler()], format="%(asctime)s: %(message)s", datefmt="%F %T", level=logging.INFO) logging.info(f'saving_dir: {weight_dir}') with open(os.path.join(weight_dir, "config.json"), "w") as f: f.write(json.dumps(vars(args))) device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}") if args.q_encoder in ['cnn', 'rn18']: logging.info('Loading Training Dataset') all_set = PeptidePairPicDataset(mode='train', pad_length=args.max_length, task=args.task, one_way=args.one_way, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize) logging.info('Loading Test Dataset') test_set = PeptidePairPicDataset(mode='test', pad_length=args.max_length, task=args.task, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize) else: logging.info('Loading Train Dataset') all_set = PeptidePairDataset(mode='train', pad_length=args.max_length, task=args.task, one_way=args.one_way, gf=args.glob_feat) logging.info('Loading Test Dataset') test_set = PeptidePairDataset(mode='test', pad_length=args.max_length, task=args.task, gf=args.glob_feat) test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True) best_perform_list = [[] for i in range(5)] test_perform_list = [[] for i in range(5)] kf = KFold(n_splits=5, shuffle=True, random_state=42) for fold, (train_idx, val_idx) in enumerate(kf.split(all_set)): train_set= Subset(all_set, train_idx) valid_set = Subset(all_set, val_idx) train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=8, pin_memory=True) valid_loader = DataLoader(valid_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True) if args.q_encoder in ['cnn', 'rn18']: model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese) else: model = DMutaPeptide(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, non_siamese=args.non_siamese) if len(args.pretrain) != 0: #TODO: load pretrain pass model.to(device) # model.compile() optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.decay) # optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.decay) # scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10], gamma=0.5) if args.q_encoder == 'cnn': scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5) else: scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5) if args.loss == 'bmc_ln': optimizer.add_param_group({'params': criterion.noise_sigma, 'lr': args.lr, 'name': 'noise_sigma'}) weights_path = f"{weight_dir}/model_{fold}.pth" # early_stopping = EarlyStopping(patience=args.patience, path=weights_path) logging.info(f'Running Cross Validation {fold}') logging.info(f'Fold {fold} Train set:{len(train_set)}, Valid set:{len(valid_set)}, Test set: {len(test_set)}') best_metric = -float('inf') best_test = -float('inf') start_time = time.time() if args.task == 'reg': for epoch in range(1, args.epochs + 1): train_loss, mae, rse, pcc, kcc = trainer(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer) logging.info(f'Epoch: {epoch:03d} Train Loss: {train_loss:.3f}, mae: {mae:.3f}, rse: {rse:.3f}, pcc: {pcc:.3f}, kcc: {kcc:.3f}') scheduler.step() avg_metric = (pcc + kcc) - (mae + rse) if avg_metric > best_metric: logging.info(f'Epoch: {epoch:03d} New best VALIDATION metrics') torch.save(model.state_dict(), weights_path) best_metric = avg_metric best_perform_list[fold] = np.asarray([mae, rse, pcc, kcc]) _, test_mae, test_rse, test_pcc, test_kcc = trainer(args, epoch, model, None, test_loader, device, None, None) logging.info(f'Epoch: {epoch:03d} Test results, ap: mae: {test_mae:.3f}, rse: {test_rse:.3f}, pcc: {test_pcc:.3f}, kcc: {test_kcc:.3f}') test_metric = (test_pcc + test_kcc) - (test_mae + test_rse) if test_metric > best_test and epoch > 10: logging.info(f'Epoch: {epoch:03d} New best TEST metrics') best_test = test_metric test_perform_list[fold] = np.asarray([test_mae, test_rse, test_pcc, test_kcc]) torch.save(model.state_dict(), weights_path.replace('.pth', '_test.pth')) elif args.task == 'cls': for epoch in range(1, args.epochs + 1): train_loss, ap, auc, f1, acc = trainer(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer) logging.info(f'Epoch: {epoch:03d} Train Loss: {train_loss:.3f}, ap: {ap:.3f}, auc: {auc:.3f}, f1: {f1:.3f}, acc: {acc:.3f}') scheduler.step() avg_metric = ap + auc #+ f1 + acc if avg_metric > best_metric: logging.info(f'Epoch: {epoch:03d} New best VALIDATION metrics') torch.save(model.state_dict(), weights_path) best_metric = avg_metric best_perform_list[fold] = np.asarray([ap, auc, f1, acc]) _, test_ap, test_auc, test_f1, test_acc = trainer(args, epoch, model, None, test_loader, device, None, None) logging.info(f'Epoch: {epoch:03d} Test results, ap: {test_ap:.3f}, auc: {test_auc:.3f}, f1: {test_f1:.3f}, acc: {test_acc:.3f}') test_metric = test_ap + test_auc #+ test_f1 + test_acc if test_metric > best_test and epoch > 10: logging.info(f'Epoch: {epoch:03d} New best TEST metrics') best_test = test_metric test_perform_list[fold] = np.asarray([test_ap, test_auc, test_f1, test_acc]) torch.save(model.state_dict(), weights_path.replace('.pth', '_test.pth')) torch.save(model.state_dict(), weights_path.replace('.pth', '_last.pth')) logging.info(f'used time {(time.time()-start_time)/3600:.2f}h') logging.info(f'Cross Validation Finished!') best_perform_list = np.asarray(best_perform_list) test_perform_list = np.asarray(test_perform_list) logging.info('Best validation perform list\n%s', best_perform_list) logging.info('mean: %s', np.round(np.mean(best_perform_list, 0), 3)) logging.info('std: %s', np.round(np.std(best_perform_list, 0), 3)) logging.info('Best test perform list\n%s', test_perform_list) logging.info('mean: %s', np.round(np.mean(test_perform_list, 0), 3)) logging.info('std: %s', np.round(np.std(test_perform_list, 0), 3)) perform = open(weight_dir+'/result.txt', 'w') perform.write('Valid\n') perform.write(','.join([str(i) for i in np.mean(best_perform_list, 0)])+'\n') perform.write(','.join([str(i) for i in np.std(best_perform_list, 0)])+'\n') perform.write('Test\n') perform.write(','.join([str(i) for i in np.mean(test_perform_list, 0)])+'\n') perform.write(','.join([str(i) for i in np.std(test_perform_list, 0)])+'\n') def move_to_device(batch, device, non_blocking=False): if isinstance(batch, (list, tuple)): return type(batch)(move_to_device(item, device, non_blocking) for item in batch) return batch.to(device, non_blocking=non_blocking) def move_and_aug(batch, device, transforms, non_blocking=False): batch = move_to_device(batch, device, non_blocking) if not isinstance(batch[0][0], (list, tuple)): return batch for i in range(batch[0][0][0].shape[0]): img_pair = torch.stack((batch[0][0][0][i], batch[0][1][0][i]), dim=0) img_pair = transforms(img_pair) batch[0][0][0][i] = img_pair[0] batch[0][1][0][i] = img_pair[1] return batch class GaussianNoise(nn.Module): def __init__(self, mean=0., sigma=0.15): super(GaussianNoise, self).__init__() self.mean = mean self.sigma = sigma def forward(self, x): return x + torch.randn_like(x) * self.sigma + self.mean Transforms = T.Compose([ T.RandomResizedCrop(args.resize, scale=(0.9, 1.0)), T.RandomRotation(degrees=30), GaussianNoise(0., 0.05), ]) def train(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer): train_loss = 0 num_labels = model.classes metric_mae = MeanAbsoluteError().to(device) metric_rse = RelativeSquaredError(num_outputs=num_labels).to(device) metric_pcc = PearsonCorrCoef(num_outputs=num_labels).to(device) metric_kcc = KendallRankCorrCoef(num_outputs=num_labels).to(device) if args.dir: encodings, labels = [], [] if train_loader is not None: model.train() for data in train_loader: x, gt = data x = move_and_aug(x, device, Transforms) if args.dir: out, features = model(x, gt.to(device), epoch) encodings.append(features.detach().cpu()) labels.append(gt.cpu()) else: out = model(x) loss = criterion(out, gt.to(device)) loss.backward() optimizer.step() optimizer.zero_grad() train_loss += loss.item() train_loss /= len(train_loader) if args.dir: encodings, labels = torch.cat(encodings), torch.cat(labels) model.FDS.update_last_epoch_stats(epoch) model.FDS.update_running_stats(encodings, labels, epoch) encodings, labels = [], [] model.eval() preds = [] gt_list_valid = [] with torch.no_grad(): for data in valid_loader: x, gt = data x = move_to_device(x, device) gt_list_valid.append(gt.to(device)) out = model(x) if args.dir: out, _ = out preds.append(out) # calculate metrics preds = torch.cat(preds, dim=0) gt_list_valid = torch.cat(gt_list_valid, dim=0) mae = metric_mae(preds, gt_list_valid).item() rse = metric_rse(preds, gt_list_valid).item() pcc = metric_pcc(preds.squeeze(), gt_list_valid.squeeze()).mean().item() kcc = metric_kcc(preds.squeeze(), gt_list_valid.squeeze()).mean().item() return train_loss, mae, rse, pcc, kcc def update_ce_loss_weight(loss_fn: torch.nn.CrossEntropyLoss, gt: torch.Tensor, num_classes: int, device): """ 根据当前 batch 的 ground truth 标签更新 nn.CrossEntropyLoss 对象中的 weight 缓冲区， 使用逆频率方法计算新权重，并通过 register_buffer 进行原地更新。 参数: loss_fn (nn.CrossEntropyLoss): 已初始化的 nn.CrossEntropyLoss 对象， 要求在初始化时已经注册了 weight 缓冲区。 gt (torch.Tensor): 当前 batch 的 ground truth 标签，1D整数张量，标签取值范围 [0, num_classes-1]。 """ class_counts = torch.bincount(gt, minlength=num_classes).float() epsilon = 1e-6 new_weights = 1.0 / (class_counts + epsilon) new_weights = new_weights / new_weights.sum() * num_classes # 使用 register_buffer 来更新 loss_fn 内部的 weight 缓冲区 loss_fn.register_buffer('weight', new_weights.to(device)) def train_cls(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer): train_loss = 0 num_labels = model.classes avg = args.metric_avg if num_labels == 1 or num_labels == 2: task = 'binary' else: task = 'multiclass' metric_acc = Accuracy(average=avg, task=task, num_classes=num_labels).to(device) metric_f1 = F1Score(average=avg, task=task, num_classes=num_labels).to(device) metric_ap = AveragePrecision(average=avg, task=task, num_classes=num_labels).to(device) metric_auc = AUROC(average=avg, task=task, num_classes=num_labels).to(device) if train_loader is not None: model.train() for data in train_loader: x, gt = data x = move_to_device(x, device) out = model(x) update_ce_loss_weight(criterion, gt, num_classes=num_labels, device=device) loss = criterion(out, gt.to(device)) loss.backward() optimizer.step() optimizer.zero_grad() train_loss += loss.item() train_loss /= len(train_loader) model.eval() preds = [] gt_list_valid = [] with torch.no_grad(): for data in valid_loader: x, gt = data x = move_to_device(x, device) gt_list_valid.append(gt.to(device)) out = model(x) preds.append(out) # calculate metrics preds = torch.softmax(torch.cat(preds, dim=0), dim=-1).squeeze() gt_list_valid = torch.cat(gt_list_valid, dim=0).int().squeeze() if num_labels == 2: preds = preds[:, 1] ap = metric_ap(preds, gt_list_valid).item() auc = metric_auc(preds, gt_list_valid).item() f1 = metric_f1(preds, gt_list_valid).item() acc = metric_acc(preds, gt_list_valid).item() return train_loss, ap, auc, f1, acc if __name__ == "__main__": main()