code/train.py

from utils import *
from modules import *
import os, sys
import numpy as np
from tqdm import tqdm
import random
import torch
from torch import nn
from config import CFG
from dataset import *
import torch.utils.data
import copy, json, pickle
import itertools as it
import loss

loss_func = loss.infoNCE_loss2

def make_next_record_dir(basedir, prefix=''):
    path = '%s/%%s001/' %basedir
    n = 2
    while os.path.exists(path %prefix):
        path = '%s/%%s%.3d/' %(basedir, n)
        n += 1

    pth = path %prefix
    os.makedirs(pth)
    return pth

def setup_seed(seed):
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    torch.backends.cudnn.deterministic = True

def my_collate(batch):
    batch = list(filter(lambda x:(x is not None), batch))
    msbinl, molfpl, molfml, vl, al, msl = [], [], [], [], [], []
    bat = {}

    for b in batch:
        if 'ms_bins' in b:
            msbinl.append(b['ms_bins'])
        if 'mol_fps' in b:
            molfpl.append(b['mol_fps'])
        if 'mol_fmvec' in b:
            molfml.append(b['mol_fmvec'])
        if 'V' in b:
            vl.append(b['V'])
        if 'A' in b:
            al.append(b['A'])
        if 'mol_size' in b:
            msl.append(b['mol_size'])

    if msbinl:
        bat['ms_bins'] = torch.stack(msbinl)
    if molfpl:
        bat['mol_fps'] = torch.stack(molfpl)
    if molfml:
        bat['mol_fmvec'] = torch.stack(molfml)
    if vl and al and msl:
        max_n = max(map(lambda x:x.shape[0], vl))
        vl1, al1 = [], []
        for v in vl:
            vl1.append(pad_V(v, max_n))
        for a in al:
            al1.append(pad_A(a, max_n))

        bat['V'] = torch.stack(vl1)
        bat['A'] = torch.stack(al1)
        bat['mol_size'] = torch.cat(msl, dim=0)

    #return torch.utils.data.dataloader.default_collate(batch)
    return bat

def make_train_valid(data, valid_ratio, seed=1234):
    idxs = np.arange(len(data))
    np.random.seed(seed)
    np.random.shuffle(idxs)

    lenval = int(valid_ratio*len(data))

    valid_set = [ data[i] for i in idxs[:lenval] ]
    train_set = [ data[i] for i in idxs[lenval:] ]

    return train_set, valid_set

def build_loaders(inp, mode, cfg, num_workers):
    if type(inp[0]) is dict:
        dataset = Dataset(inp, cfg)
    else:
        dataset = PathDataset(inp, cfg)
    dataloader = torch.utils.data.DataLoader(
        dataset,
        batch_size=cfg.batch_size,
        num_workers=num_workers,
        shuffle=True if mode == "train" else False,
        collate_fn=my_collate
    )
    return dataloader

def train_epoch(model, train_loader, optimizer, lr_scheduler, step):
    model.train()
    loss_meter = AvgMeter()
    tqdm_object = tqdm(train_loader, total=len(train_loader))
    total_cos_sim = 0

    for batch in tqdm_object:
        for k, v in batch.items():
            batch[k] = v.to(CFG.device)

        optimizer.zero_grad()

        mol_features, ms_features = model(batch)

        loss = loss_func(mol_features, ms_features)

        loss.backward()
        optimizer.step()

        with torch.no_grad():
            cos_sim = F.cosine_similarity(
                mol_features.detach(), 
                ms_features.detach()
            ).mean().item()
            total_cos_sim += cos_sim

        if step == "batch":
            lr_scheduler.step()

        count = batch["ms_bins"].size(0)
        loss_meter.update(loss.item(), count)

        tqdm_object.set_postfix(train_loss=loss_meter.avg, train_cos_sim=round(cos_sim, 4), lr=get_lr(optimizer))

        del mol_features, ms_features, loss, cos_sim
        
        for k in list(batch.keys()):
            del batch[k]
        del batch

    return loss_meter, total_cos_sim / len(train_loader)

def valid_epoch(model, valid_loader):
    model.eval()
    loss_meter = AvgMeter()
    total_cos_sim = 0

    with torch.no_grad():
        tqdm_object = tqdm(valid_loader, total=len(valid_loader))
        for batch in tqdm_object:
            for k, v in batch.items():
                batch[k] = v.to(CFG.device)

            mol_features, ms_features = model(batch)

            loss = loss_func(mol_features, ms_features)

            count = batch["ms_bins"].size(0)
            loss_meter.update(loss.item(), count)
            cos_sim = F.cosine_similarity(mol_features.detach(), ms_features.detach()).mean().item()
            total_cos_sim += cos_sim

            tqdm_object.set_postfix(valid_loss=loss_meter.avg, valid_cos_sim=round(cos_sim, 4))

            del mol_features, ms_features, loss, cos_sim
            
            for k in list(batch.keys()):
                del batch[k]
            del batch

    return loss_meter, total_cos_sim / len(valid_loader)

def main(data, cfg=CFG, savedir='data/train', model_path=None, ratio=1):
    setup_seed(cfg.seed)

    train_set, valid_set = make_train_valid(data, valid_ratio=cfg.valid_ratio, seed=cfg.seed)

    log_file = f'{savedir}/trainlog.txt'

    n = len(train_set)
    if ratio < 1:
        train_set = random.sample(train_set, int(n*ratio))
        print(f'Ratio {ratio}, lenall {n}, newtrainset {len(train_set)}')

    train_loader = build_loaders(train_set, "train", cfg, 1)
    valid_loader = build_loaders(valid_set, "valid", cfg, 1)

    step = "epoch"

    best_loss = float('inf')
    best_model_fn = ''
    best_model_fns = []

    model = FragSimiModel(cfg).to(cfg.device)

    print(model)

    optimizer = torch.optim.AdamW(
        model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay
    )

    lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
        optimizer, mode="min", patience=cfg.patience, factor=cfg.factor
    )

    # Load pre-trained model if path is provided
    if model_path and os.path.exists(model_path):
        print(f"Loading model from {model_path}")
        checkpoint = torch.load(model_path, map_location=cfg.device)
        model.load_state_dict(checkpoint['state_dict'])

        '''if 'optimizer' in checkpoint:

            optimizer.load_state_dict(checkpoint['optimizer'])

            print("Loaded optimizer state")'''

        print(f"Resuming training")
        del checkpoint

    # write training log
    with open(log_file, 'a', encoding='utf8') as f:
        f.write(f'Start training:\n')
        f.write(f'Data path: {cfg.dataset_path}, valid ratio: {cfg.valid_ratio}\n')
        if model_path:
            f.write(f'Resuming from: {model_path}\n')
        print(model, file=f)
        f.write(f'\n')

    for epoch in range(cfg.epochs):
        print(f"Epoch: {epoch + 1}/{cfg.epochs}")
        train_loss, t_cos_sim = train_epoch(model, train_loader, optimizer, lr_scheduler, step)
        valid_loss, v_cos_sim = valid_epoch(model, valid_loader)

        txt = f"Train Loss: {train_loss.avg:.4f} | Val Loss: {valid_loss.avg:.4f} | Train cos sim: {t_cos_sim:.4f}  | Val cos sim: {v_cos_sim:.4f}"
        print(txt)
        open(log_file, 'a').write(f"Epoch {epoch + 1}/{cfg.epochs}: {txt}\n")
        
        if True: #valid_loss.avg < best_loss:
            best_loss = valid_loss.avg
            best_model_fn = f"{savedir}/model-tloss{round(train_loss.avg, 3)}-vloss{round(valid_loss.avg, 3)}-tcos{round(t_cos_sim, 3)}-vcos{round(v_cos_sim, 3)}-epoch{epoch}.pth"
            best_model_fn_base = best_model_fn.replace('.pth', '')
            n = 1
            while os.path.exists(best_model_fn):
                best_model_fn = best_model_fn_base + f'-{n}.pth'
                n += 1

            best_model_fns.append(best_model_fn)

            torch.save({'epoch': epoch,
                        'state_dict': model.state_dict(),
                        'optimizer': optimizer.state_dict(), 
                        'config': dict(CFG),
                        'train_loss': train_loss.avg,
                        'valid_loss': valid_loss.avg,
                        'train_cos_sim': t_cos_sim,
                        'val_cos_sim': v_cos_sim
                       }, best_model_fn)

            print("Saved new best model!")

    best_model_fnl = []
    for fn in best_model_fns:
        if os.path.exists(fn):
            best_model_fnl.append(fn)

    for fn in best_model_fnl[:-cfg.keep_best_models_num]:
        os.remove(fn)

    best_model_fnl = best_model_fnl[-cfg.keep_best_models_num:]

    print(best_model_fnl, best_loss)
    return best_model_fnl, best_loss

if __name__ == "__main__":
    import pickle
    from tqdm import tqdm
    try:
        conffn = sys.argv[1]
        if conffn.endswith('.json'):
            CFG.load(sys.argv[1])
        elif conffn.endswith('.pth'):
            dpath = CFG.dataset_path
            d = torch.load(conffn)
            CFG.load(d['config'])
            CFG.dataset_path = dpath
        print('Use config from', conffn)
    except:
        pass

    try:
        savedir = sys.argv[2]
    except:
        savedir = 'data/'

    os.system('mkdir -p %s' %savedir)

    try:
        prev_model_pth = sys.argv[3]
    except:
        prev_model_pth = None

    print(CFG)

    if os.path.isdir(CFG.dataset_path):
        data = [os.path.join(CFG.dataset_path, i) for i in os.listdir(CFG.dataset_path) if i.endswith('mgf')]
    elif os.path.isfile(CFG.dataset_path):
        if CFG.dataset_path.endswith('.pkl'):
            print(f'loading data from {CFG.dataset_path} ...')
            data = pickle.load(open(CFG.dataset_path, 'rb'))
        else:
            data = json.load(open(CFG.dataset_path))
            pklfn = CFG.dataset_path.replace('.json', '.pkl')
            if not os.path.exists(pklfn):
                pickle.dump(data, open(pklfn, 'wb'))

    subdir = make_next_record_dir(savedir, f'train-neg-')
    os.system(f'cp -a *py {subdir}; cp -a GNN {subdir}')
    CFG.save(f'{subdir}/config.json')

    modelfnl, _ = main(data, CFG, subdir, prev_model_pth)