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acbef3a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | import argparse
import json
import logging
import os
import time
from dataset import PeptidePairDataset, PeptidePairPicDataset, SimplePairClsDataset
from network import DMutaPeptide, DMutaPeptideCNN#, DMutaPeptideWiden
from sklearn.model_selection import KFold
from train import train_cls
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, WeightedRandomSampler, RandomSampler, Subset
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='cnn',
help='lstm mamba mla')
parser.add_argument("--side-enc", dest='side_enc', type=str, default='lstm',
help="use side features")
parser.add_argument('--channels', type=int, default=16)
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")
parser.add_argument('--widen', action='store_true', default=False,
help='use widen non-siamese architecture')
# task & dataset setting
parser.add_argument('--task', type=str, default='cls',
help='reg or cls')
parser.add_argument('--pdb-src', type=str, dest='pdb_src', default='af',
help='af or hf')
parser.add_argument('--data-ver', type=str, dest='data_ver', default='250228',
help='data version')
parser.add_argument('--one-way', action='store_true', dest='one_way', default=True,
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('--run-folds', type=int, dest='run_folds', nargs='+', default=-1,
help='specify which folds to run')
parser.add_argument('--seed', type=int, default=1,
help="Seed (default: 1)")
parser.add_argument('--pcs', action='store_true', default=False,
help='Consider protease cut site')
parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
help='Consider protease cut 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('--warm-steps', type=int, dest='warm_steps', default=0,
help='number of warm start steps for learning rate (default: 10)')
parser.add_argument('--patience', type=int, default=10,
help='patience for early stopping (default: 10)')
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='ce',
help='loss function')
parser.add_argument('--dir', action='store_true', default=False,
help='use DIR')
parser.add_argument('--bias-curri', dest='bias_curri', action='store_true', default=False,
help='directly use loss as the training data (biased) or not (unbiased)')
parser.add_argument('--anti-curri', dest='anti_curri', action='store_true', default=False,
help='easy to hard (curri), hard to easy (anti)')
parser.add_argument('--std-coff', dest='std_coff', type=float, default=1,
help='the hyper-parameter of std')
parser.add_argument('--ft-epochs', dest='ft_epochs', type=int, default=15,
help='fine-tune epochs')
parser.add_argument('--ft-lr', dest='ft_lr', type=float, default=0.0002,
help='fine-tune learning rate')
parser.add_argument('--simple', dest='simple', action='store_true', default=False)
args = parser.parse_args()
if args.llm_data:
args.simple = True
if args.simple:
args.one_way = True
if args.run_folds == -1:
args.run_folds = list(range(args.split))
def main():
set_seed(args.seed)
if args.task == 'reg':
args.classes = 1
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':
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}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{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 ""}{"-simple" if args.simple else ""}{"-llm" if args.llm_data 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)}'
else:
weight_dir = f'./run-{args.task}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{"-simple" if args.simple else ""}{"-llm" if args.llm_data 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)}'
logging.basicConfig(handlers=[
logging.FileHandler(filename=os.path.join(weight_dir, "finetune.log"), encoding='utf-8', mode='w+'),
logging.StreamHandler()],
format="%(asctime)s: %(message)s", datefmt="%F %T", level=logging.INFO)
logging.info(f'Finetuning: {weight_dir}')
device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
logging.info(f'Loading Training Dataset')
train_set = SimplePairClsDataset(pad_length=args.max_length, ftr2=True, gf=args.glob_feat, q_encoder=args.q_encoder, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
logging.info('Loading Test Dataset')
if args.q_encoder in ['cnn', 'rn18']:
test_set = PeptidePairPicDataset(mode='r2_case', pad_length=args.max_length, task=args.task, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
else:
test_set = PeptidePairDataset(mode='r2_case', pad_length=args.max_length, task=args.task, gf=args.glob_feat)
train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=8, pin_memory=True)
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)]
for fold in range(args.split):
logging.info(f'Finetuning Fold {fold}')
logging.info(f'Fold {fold} Train set:{len(train_set)}, Test set: {len(test_set)}')
# if args.widen:
# model = DMutaPeptideWiden(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, side_enc=args.side_enc)
# else:
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)
weights_path = f"{weight_dir}/model_{fold}.pth"
model.to(device)
# model.load_state_dict(torch.load(weights_path.replace('.pth', '_test.pth'), map_location=device), strict=False)
model.load_state_dict(torch.load(weights_path, map_location=device), strict=False)
optimizer = torch.optim.AdamW(model.parameters(), lr=args.ft_lr)
best_metric = -float('inf')
if args.task == 'cls':
for epoch in range(1, args.ft_epochs + 1):
train_loss, ap, auc, f1, acc = train_cls(args, epoch, model, train_loader, test_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}')
avg_metric = ap + auc #+ f1 + acc
if avg_metric > best_metric:
logging.info(f'Epoch: {epoch:03d} New best VALIDATION metrics')
best_metric = avg_metric
best_perform_list[fold] = np.asarray([ap, auc, f1, acc])
torch.save(model.state_dict(), weights_path.replace('.pth', '_ft.pth'))
if __name__ == "__main__":
main()
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