def main(args): np.random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) cudnn.benchmark = True # Redirect print to both console and log file if not args.evaluate: sys.stdout = Logger(osp.join(args.logs_dir, 'log.txt')) # Create data loaders if args.loss == 'triplet': assert args.num_instances > 1, 'TripletLoss requires num_instances > 1' assert args.batch_size % args.num_instances == 0, \ 'num_instances should divide batch_size' dataset, num_classes, train_loader, val_loader, test_loader = \ get_data(args.dataset, args.split, args.data_dir, args.batch_size, args.workers, args.num_instances, combine_trainval=args.combine_trainval) # Create model if args.loss == 'xentropy': model = InceptionNet(num_classes=num_classes, num_features=args.features, dropout=args.dropout) elif args.loss == 'oim': model = InceptionNet(num_features=args.features, norm=True, dropout=args.dropout) elif args.loss == 'triplet': model = InceptionNet(num_features=args.features, dropout=args.dropout) else: raise ValueError("Cannot recognize loss type:", args.loss) model = torch.nn.DataParallel(model).cuda() # Load from checkpoint if args.resume: checkpoint = load_checkpoint(args.resume) model.load_state_dict(checkpoint['state_dict']) args.start_epoch = checkpoint['epoch'] best_top1 = checkpoint['best_top1'] print("=> start epoch {} best top1 {:.1%}".format( args.start_epoch, best_top1)) else: best_top1 = 0 # Distance metric metric = DistanceMetric(algorithm=args.dist_metric) # Evaluator evaluator = Evaluator(model) if args.evaluate: metric.train(model, train_loader) print("Validation:") evaluator.evaluate(val_loader, dataset.val, dataset.val, metric) print("Test:") evaluator.evaluate(test_loader, dataset.query, dataset.gallery, metric) return # Criterion if args.loss == 'xentropy': criterion = torch.nn.CrossEntropyLoss() elif args.loss == 'oim': criterion = OIMLoss(model.module.num_features, num_classes, scalar=args.oim_scalar, momentum=args.oim_momentum) elif args.loss == 'triplet': criterion = TripletLoss(margin=args.triplet_margin) else: raise ValueError("Cannot recognize loss type:", args.loss) criterion.cuda() # Optimizer if args.optimizer == 'sgd': optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) elif args.optimizer == 'adam': optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) else: raise ValueError("Cannot recognize optimizer type:", args.optimizer) # Trainer trainer = Trainer(model, criterion) # Schedule learning rate def adjust_lr(epoch): if args.optimizer == 'sgd': lr = args.lr * (0.1**(epoch // 60)) elif args.optimizer == 'adam': lr = args.lr if epoch <= 100 else \ args.lr * (0.001 ** (epoch - 100) / 50) else: raise ValueError("Cannot recognize optimizer type:", args.optimizer) for g in optimizer.param_groups: g['lr'] = lr # Start training for epoch in range(args.start_epoch, args.epochs): adjust_lr(epoch) trainer.train(epoch, train_loader, optimizer) top1 = evaluator.evaluate(val_loader, dataset.val, dataset.val) is_best = top1 > best_top1 best_top1 = max(top1, best_top1) save_checkpoint( { 'state_dict': model.state_dict(), 'epoch': epoch + 1, 'best_top1': best_top1, }, is_best, fpath=osp.join(args.logs_dir, 'checkpoint.pth.tar')) print('\n * Finished epoch {:3d} top1: {:5.1%} best: {:5.1%}{}\n'. format(epoch, top1, best_top1, ' *' if is_best else '')) # Final test print('Test with best model:') checkpoint = load_checkpoint(osp.join(args.logs_dir, 'model_best.pth.tar')) model.load_state_dict(checkpoint['state_dict']) metric.train(model, train_loader) evaluator.evaluate(test_loader, dataset.query, dataset.gallery, metric)
def main(args): np.random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) cudnn.benchmark = True # Redirect print to both console and log file # All the print infomration are stored in the logs_dir sys.stdout = Logger(osp.join(args.logs_dir, 'log.txt')) # Create data loaders if args.loss == 'triplet': assert args.num_instances > 1, 'TripletLoss requires num_instances > 1' assert args.batch_size % args.num_instances == 0, \ 'num_instances should divide batch_size' dataset, num_classes, train_loader, val_loader, test_loader = \ get_data(args.dataset, args.split, args.data_dir, args.batch_size, args.seq_len, args.seq_srd, args.workers, args.num_instances, combine_trainval=True) # Create model if args.loss == 'xentropy': model = ResNetLSTM_btfu(args.depth, pretrained=True, num_features=args.features, dropout=args.dropout) elif args.loss == 'oim': model = ResNetLSTM_btfu(args.depth, pretrained=True, num_features=args.features, norm=True, dropout=args.dropout) elif args.loss == 'triplet': model = ResNetLSTM_btfu(args.depth, pretrained=True, num_features=args.features, dropout=args.dropout) else: raise ValueError("cannot recognize loss type:", args.loss) model = torch.nn.DataParallel(model).cuda() # Load from checkpoint # TODO is not necessary currently # Distance metric metric = DistanceMetric(algorithm=args.dist_metric) # Evaluator evaluator = Evaluator(model) # Criterion if args.loss == 'xentropy': criterion = torch.nn.CrossEntropyLoss() elif args.loss == 'oim': criterion = OIMLoss(model.module.num_features, num_classes, scalar=args.oim_scalar, momentum=args.oim_momentum) elif args.loss == 'triplet': criterion = TripletLoss(margin=args.triplet_margin) else: raise ValueError("Cannot recognize loss type:", args.loss) criterion.cuda() # Optimizer if args.optimizer == 'sgd': if args.loss == 'xentropy': base_param_ids = set(map(id, model.module.base.parameters())) new_params = [ p for p in model.parameters() if id(p) not in base_param_ids ] param_groups = [{ 'params': model.module.base.parameters(), 'lr_mult': 0.1 }, { 'params': new_params, 'lr_mult': 1.0 }] else: param_groups = model.parameters() optimizer = torch.optim.SGD(param_groups, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True) elif args.optimizer == 'adam': optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) else: raise ValueError("Cannot recognize optimizer type:", args.optimizer) # Trainer trainer = SeqTrainer(model, criterion) # Schedule learning rate def adjust_lr(epoch): if args.optimizer == 'sgd': lr = args.lr * (0.1**(epoch // 40)) elif args.optimizer == 'adam': lr = args.lr if epoch <= 100 else \ args.lr * (0.001 ** (epoch - 100) / 50) else: raise ValueError("Cannot recognize optimizer type:", args.optimizer) for g in optimizer.param_groups: g['lr'] = lr * g.get('lr_mult', 1) # Starting training for epoch in range(args.start_epoch, args.epochs): adjust_lr(epoch) trainer.train(epoch, train_loader, optimizer) top1 = evaluator.evaluate(test_loader, dataset.query, dataset.gallery, multi_shot=True)