def mots_challenge_train(): return get_kiti_mots_dicts(ims_path, annots_path, is_train=True, train_percentage=1., image_extension='jpg')
MetadataCatalog.get(cfg.DATASETS.TRAIN[0]).set( crop=opts.crop, hflip=opts.hflip, change_contrast=opts.contrast) cfg.OUTPUT_DIR = output_dir os.makedirs(cfg.OUTPUT_DIR, exist_ok=True) trainer = TrainerDA(cfg) val_loss = ValidationLoss(cfg) trainer.register_hooks([val_loss]) trainer._hooks = trainer._hooks[:-2] + trainer._hooks[-2:][::-1] trainer.resume_or_load(resume=True) trainer.train() if not opts.train_only: evaluator = COCOEvaluator("kitti_mots_test", cfg, False, output_dir=output_dir) trainer.test(cfg, trainer.model, evaluators=[evaluator]) plot_losses(cfg) predictor = DefaultPredictor(cfg) predictor.model.load_state_dict(trainer.model.state_dict()) dataset_dicts = get_kiti_mots_dicts( "../datasets/KITTI-MOTS/training/image_02", "../datasets/KITTI-MOTS/instances_txt", is_train=False, image_extension='png') show_results(cfg, dataset_dicts, predictor, samples=10)
def kitti_mots_test(): return get_kiti_mots_dicts(ims_path_kitti, annots_path_kitti, is_train=False, train_percentage=train_percent_kitti_mots, image_extension='png')