def evaluate_rcnn(model_name, paper_arxiv_id, cfg_list, model_file): evaluator = COCOEvaluator( root=COCO_ROOT, model_name=model_name, paper_arxiv_id=paper_arxiv_id ) category_id_to_coco_id = { v: k for k, v in COCODetection.COCO_id_to_category_id.items() } cfg.update_config_from_args(cfg_list) # TODO backup/restore config finalize_configs(False) MODEL = ResNetFPNModel() if cfg.MODE_FPN else ResNetC4Model() predcfg = PredictConfig( model=MODEL, session_init=SmartInit(model_file), input_names=MODEL.get_inference_tensor_names()[0], output_names=MODEL.get_inference_tensor_names()[1], ) predictor = OfflinePredictor(predcfg) def xyxy_to_xywh(box): box[2] -= box[0] box[3] -= box[1] return box df = get_eval_dataflow("coco_val2017") df.reset_state() for img, img_id in tqdm.tqdm(df, total=len(df)): results = predict_image(img, predictor) res = [ { "image_id": img_id, "category_id": category_id_to_coco_id.get( int(r.class_id), int(r.class_id) ), "bbox": xyxy_to_xywh([round(float(x), 4) for x in r.box]), "score": round(float(r.score), 3), } for r in results ] evaluator.add(res) if evaluator.cache_exists: break evaluator.save()
parser.add_argument( '--benchmark', action='store_true', help="Benchmark the speed of the model + postprocessing") parser.add_argument( '--config', help="A list of KEY=VALUE to overwrite those defined in config.py", nargs='+') parser.add_argument('--compact', help='Save a model to .pb') parser.add_argument('--serving', help='Save a model to serving file') args = parser.parse_args() if args.config: cfg.update_args(args.config) register_coco(cfg.DATA.BASEDIR) # add COCO datasets to the registry MODEL = ResNetFPNModel() if cfg.MODE_FPN else ResNetC4Model() if not tf.test.is_gpu_available(): from tensorflow.python.framework import test_util assert get_tf_version_tuple() >= (1, 7) and test_util.IsMklEnabled(), \ "Inference requires either GPU support or MKL support!" assert args.load finalize_configs(is_training=False) if args.predict or args.visualize: cfg.TEST.RESULT_SCORE_THRESH = cfg.TEST.RESULT_SCORE_THRESH_VIS if args.visualize: do_visualize(MODEL, args.load) else: predcfg = PredictConfig(