def save_model(model_ema, is_best, best_mAP, mid, num_ids): save_checkpoint({ 'state_dict': model_ema.state_dict(), 'epoch': epoch + 1, 'best_mAP': best_mAP, 'num_ids': num_ids }, is_best, fpath=osp.join(args.logs_dir, 'model'+str(mid)+'_checkpoint.pth.tar'))
def save_model(model_ema, is_best, best_mAP, phase): save_checkpoint( { 'state_dict': model_ema.state_dict(), 'phase': phase, 'epoch': epoch + 1, 'best_mAP': best_mAP, }, is_best, fpath=osp.join( args.logs_dir, 'phase{}_model_checkpoint.pth.tar'.format(phase)))
def main_worker(args): global start_epoch, best_mAP cudnn.benchmark = True if not args.evaluate: sys.stdout = Logger(osp.join(args.logs_dir, 'log.txt')) else: log_dir = osp.dirname(args.resume) sys.stdout = Logger(osp.join(log_dir, 'log_test.txt')) print("==========\nArgs:{}\n==========".format(args)) # Create data loaders iters = args.iters if (args.iters>0) else None dataset_source, num_classes, train_loader_source, test_loader_source = \ get_data(args.dataset_source, args.data_dir, args.height, args.width, args.batch_size, args.workers, args.num_instances, iters) dataset_target, _, train_loader_target, test_loader_target = \ get_data(args.dataset_target, args.data_dir, args.height, args.width, args.batch_size, args.workers, 0, iters) # Create model model = models.create(args.arch, num_features=args.features, dropout=args.dropout, num_classes=num_classes) model.cuda() model = nn.DataParallel(model) # Load from checkpoint if args.resume: checkpoint = load_checkpoint(args.resume) copy_state_dict(checkpoint['state_dict'], model) start_epoch = checkpoint['epoch'] best_mAP = checkpoint['best_mAP'] print("=> Start epoch {} best mAP {:.1%}" .format(start_epoch, best_mAP)) # Evaluator evaluator = Evaluator(model) if args.evaluate: print("Test on source domain:") evaluator.evaluate(test_loader_source, dataset_source.query, dataset_source.gallery, cmc_flag=True, rerank=args.rerank) print("Test on target domain:") evaluator.evaluate(test_loader_target, dataset_target.query, dataset_target.gallery, cmc_flag=True, rerank=args.rerank) return params = [] for key, value in model.named_parameters(): if not value.requires_grad: continue params += [{"params": [value], "lr": args.lr, "weight_decay": args.weight_decay}] optimizer = torch.optim.Adam(params) lr_scheduler = WarmupMultiStepLR(optimizer, args.milestones, gamma=0.1, warmup_factor=0.01, warmup_iters=args.warmup_step) # Trainer trainer = ABMTPreTrainer(model, num_classes, margin=args.margin) # Start training for epoch in range(start_epoch, args.epochs): train_loader_source.new_epoch() train_loader_target.new_epoch() trainer.train(epoch, train_loader_source, train_loader_target, optimizer, train_iters=len(train_loader_source), print_freq=args.print_freq) lr_scheduler.step() if ((epoch+1)%args.eval_step==0 or (epoch==args.epochs-1)): _, mAP = evaluator.evaluate(test_loader_source, dataset_source.query, dataset_source.gallery, cmc_flag=True) is_best = mAP > best_mAP best_mAP = max(mAP, best_mAP) save_checkpoint({ 'state_dict': model.state_dict(), 'epoch': epoch + 1, 'best_mAP': best_mAP, }, is_best, fpath=osp.join(args.logs_dir, 'checkpoint.pth.tar')) print('\n * Finished epoch {:3d} source mAP: {:5.1%} best: {:5.1%}{}\n'. format(epoch, mAP, best_mAP, ' *' if is_best else '')) print("Test on target domain:") evaluator.evaluate(test_loader_target, dataset_target.query, dataset_target.gallery, cmc_flag=True, rerank=args.rerank)