import copy import math import os from functools import partial import wandb import torch torch.multiprocessing.set_sharing_strategy('file_system') import resource rlimit = resource.getrlimit(resource.RLIMIT_NOFILE) resource.setrlimit(resource.RLIMIT_NOFILE, (64000, rlimit[1])) import yaml from utils.diffusion_utils import t_to_sigma as t_to_sigma_compl from datasets.pdbbind import construct_loader from utils.parsing import parse_train_args from utils.training import train_epoch, test_epoch, loss_function, inference_epoch from utils.utils import save_yaml_file, get_optimizer_and_scheduler, get_model, ExponentialMovingAverage def train(args, model, optimizer, scheduler, ema_weights, train_loader, val_loader, t_to_sigma, run_dir): best_val_loss = math.inf best_val_inference_value = math.inf if args.inference_earlystop_goal == 'min' else 0 best_epoch = 0 best_val_inference_epoch = 0 loss_fn = partial(loss_function, tr_weight=args.tr_weight, rot_weight=args.rot_weight, tor_weight=args.tor_weight, no_torsion=args.no_torsion) print("Starting training...") for epoch in range(args.n_epochs): if epoch % 5 == 0: print("Run name: ", args.run_name) logs = {} train_losses = train_epoch(model, train_loader, optimizer, device, t_to_sigma, loss_fn, ema_weights) print("Epoch {}: Training loss {:.4f} tr {:.4f} rot {:.4f} tor {:.4f}" .format(epoch, train_losses['loss'], train_losses['tr_loss'], train_losses['rot_loss'], train_losses['tor_loss'])) ema_weights.store(model.parameters()) if args.use_ema: ema_weights.copy_to(model.parameters()) # load ema parameters into model for running validation and inference val_losses = test_epoch(model, val_loader, device, t_to_sigma, loss_fn, args.test_sigma_intervals) print("Epoch {}: Validation loss {:.4f} tr {:.4f} rot {:.4f} tor {:.4f}" .format(epoch, val_losses['loss'], val_losses['tr_loss'], val_losses['rot_loss'], val_losses['tor_loss'])) if args.val_inference_freq != None and (epoch + 1) % args.val_inference_freq == 0: inf_metrics = inference_epoch(model, val_loader.dataset.complex_graphs[:args.num_inference_complexes], device, t_to_sigma, args) print("Epoch {}: Val inference rmsds_lt2 {:.3f} rmsds_lt5 {:.3f}" .format(epoch, inf_metrics['rmsds_lt2'], inf_metrics['rmsds_lt5'])) logs.update({'valinf_' + k: v for k, v in inf_metrics.items()}, step=epoch + 1) if not args.use_ema: ema_weights.copy_to(model.parameters()) ema_state_dict = copy.deepcopy(model.module.state_dict() if device.type == 'cuda' else model.state_dict()) ema_weights.restore(model.parameters()) if args.wandb: logs.update({'train_' + k: v for k, v in train_losses.items()}) logs.update({'val_' + k: v for k, v in val_losses.items()}) logs['current_lr'] = optimizer.param_groups[0]['lr'] wandb.log(logs, step=epoch + 1) state_dict = model.module.state_dict() if device.type == 'cuda' else model.state_dict() if args.inference_earlystop_metric in logs.keys() and \ (args.inference_earlystop_goal == 'min' and logs[args.inference_earlystop_metric] <= best_val_inference_value or args.inference_earlystop_goal == 'max' and logs[args.inference_earlystop_metric] >= best_val_inference_value): best_val_inference_value = logs[args.inference_earlystop_metric] best_val_inference_epoch = epoch torch.save(state_dict, os.path.join(run_dir, 'best_inference_epoch_model.pt')) torch.save(ema_state_dict, os.path.join(run_dir, 'best_ema_inference_epoch_model.pt')) if val_losses['loss'] <= best_val_loss: best_val_loss = val_losses['loss'] best_epoch = epoch torch.save(state_dict, os.path.join(run_dir, 'best_model.pt')) torch.save(ema_state_dict, os.path.join(run_dir, 'best_ema_model.pt')) if scheduler: if args.val_inference_freq is not None: scheduler.step(best_val_inference_value) else: scheduler.step(val_losses['loss']) torch.save({ 'epoch': epoch, 'model': state_dict, 'optimizer': optimizer.state_dict(), 'ema_weights': ema_weights.state_dict(), }, os.path.join(run_dir, 'last_model.pt')) print("Best Validation Loss {} on Epoch {}".format(best_val_loss, best_epoch)) print("Best inference metric {} on Epoch {}".format(best_val_inference_value, best_val_inference_epoch)) def main_function(): args = parse_train_args() if args.config: config_dict = yaml.load(args.config, Loader=yaml.FullLoader) arg_dict = args.__dict__ for key, value in config_dict.items(): if isinstance(value, list): for v in value: arg_dict[key].append(v) else: arg_dict[key] = value args.config = args.config.name assert (args.inference_earlystop_goal == 'max' or args.inference_earlystop_goal == 'min') if args.val_inference_freq is not None and args.scheduler is not None: assert (args.scheduler_patience > args.val_inference_freq) # otherwise we will just stop training after args.scheduler_patience epochs if args.cudnn_benchmark: torch.backends.cudnn.benchmark = True # construct loader t_to_sigma = partial(t_to_sigma_compl, args=args) train_loader, val_loader = construct_loader(args, t_to_sigma) model = get_model(args, device, t_to_sigma=t_to_sigma) optimizer, scheduler = get_optimizer_and_scheduler(args, model, scheduler_mode=args.inference_earlystop_goal if args.val_inference_freq is not None else 'min') ema_weights = ExponentialMovingAverage(model.parameters(),decay=args.ema_rate) if args.restart_dir: try: dict = torch.load(f'{args.restart_dir}/last_model.pt', map_location=torch.device('cpu')) if args.restart_lr is not None: dict['optimizer']['param_groups'][0]['lr'] = args.restart_lr optimizer.load_state_dict(dict['optimizer']) model.module.load_state_dict(dict['model'], strict=True) if hasattr(args, 'ema_rate'): ema_weights.load_state_dict(dict['ema_weights'], device=device) print("Restarting from epoch", dict['epoch']) except Exception as e: print("Exception", e) dict = torch.load(f'{args.restart_dir}/best_model.pt', map_location=torch.device('cpu')) model.module.load_state_dict(dict, strict=True) print("Due to exception had to take the best epoch and no optimiser") numel = sum([p.numel() for p in model.parameters()]) print('Model with', numel, 'parameters') if args.wandb: wandb.init( entity='entity', settings=wandb.Settings(start_method="fork"), project=args.project, name=args.run_name, config=args ) wandb.log({'numel': numel}) # record parameters run_dir = os.path.join(args.log_dir, args.run_name) yaml_file_name = os.path.join(run_dir, 'model_parameters.yml') save_yaml_file(yaml_file_name, args.__dict__) args.device = device train(args, model, optimizer, scheduler, ema_weights, train_loader, val_loader, t_to_sigma, run_dir) if __name__ == '__main__': device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') main_function()