tmp_test.py

import argparse
import json
import logging
import os
import time

from dataset import PeptidePairDataset, PeptidePairPicDataset, SimplePairClsDataset, AA_to_index
from network import DMutaPeptide, DMutaPeptideCNN#, DMutaPeptideWiden
from sklearn.model_selection import KFold
from train import move_to_device
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
from infer_case import FasterModelForCase, CustomDataset


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='rn18',
                    help='lstm mamba mla')
parser.add_argument("--side-enc", dest='side_enc', type=str, default='mamba',
                    help="use side features")
parser.add_argument('--channels', type=int, default=16)
parser.add_argument('--fusion', type=str, default='diff',
                    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=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('--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='train', pad_length=args.max_length, task=args.task, one_way=True, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
    # test_set = CustomDataset(case='r2', pad_length=args.max_length, side_enc=args.side_enc, pcs=True, resize=args.resize, 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):
    for fold in [0]:
        logging.info(f'Finetuning Fold {fold}')
        logging.info(f'Fold {fold}, 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:
        # model = FasterModelForCase(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).to(device).eval()
        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).to(device).eval()
        weights_path = f"{weight_dir}/model_{fold}.pth"

        model.load_state_dict(torch.load(weights_path, map_location=device))

        # model.load_state_dict(torch.load(weights_path.replace('.pth', '_test.pth'), map_location=device), strict=False)


        if args.task == 'cls':
            train_cls(args, None, model, None, test_loader, device, criterion, None)

def train_cls(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer):
    num_labels = model.classes

    model.eval()
    seqs_t, seqs_d = [], []
    preds = []
    gt_list_valid = []
    with torch.no_grad():
        for data in valid_loader:
            x, gt = data
            seqs_1, seqs_2 = get_seq_from_batched_data(x)
            seqs_t.extend(seqs_1)
            seqs_d.extend(seqs_2)
            # x1, x2 = move_to_device(x, device)
            x = move_to_device(x, device)
            # model.cache_temp_vector(x1)
            gt_list_valid.append(gt.to(device))
            # out = model(x2)
            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()

    preds = (preds[:, 1] > 0.5).int()

    wrong_preds = (preds != gt_list_valid)

    for i in range(len(wrong_preds)):
        if wrong_preds[i]:
            print(f"{seqs_t[i]} {seqs_d[i]} {preds[i]} {gt_list_valid[i]}")

index_to_aa = {v: k for k, v in AA_to_index.items()}

def get_seq_from_batched_data(x):
    seq_encs_1, seq_encs_2 = x[0][1], x[1][1]
    seqs_1 = get_seq_from_enc(seq_encs_1)
    seqs_2 = get_seq_from_enc(seq_encs_2)
    return seqs_1, seqs_2

def get_seq_from_enc(enc:torch.Tensor):
    encs = enc.cpu().numpy()
    seqs = []
    for enc in encs:
        seq = ''
        d_indicator = enc[:, 0].astype(bool)
        enc[:, 0] = 0.
        index = np.argmax(enc, axis=-1) - 1
        for d, i in zip(d_indicator, index):
            if i < 0:
                break
            if d:
                seq += index_to_aa[i].lower()
            else:
                seq += index_to_aa[i]
        seqs.append(seq)
    return seqs

if __name__ == "__main__":
    main()