def get_predictor()

in miscellaneous/distributed_tensorflow_mask_rcnn/container-serving-optimized/resources/predict.py [0:0]
62 lines of code
15 McCabe index (conditional complexity)

    def get_predictor(cls):
        """load trained model"""

        with cls.lock:
            # check if model is already loaded
            if cls.predictor:
                return cls.predictor

            os.environ["TENSORPACK_FP16"] = "true"

            # create a mask r-cnn model
            mask_rcnn_model = ResNetFPNModel(True)

            try:
                model_dir = os.environ["SM_MODEL_DIR"]
            except KeyError:
                model_dir = "/opt/ml/model"
            try:
                resnet_arch = os.environ["RESNET_ARCH"]
            except KeyError:
                resnet_arch = "resnet50"

            # file path to previoulsy trained mask r-cnn model
            latest_trained_model = ""
            model_search_path = os.path.join(model_dir, "model-*.index")
            for model_file in glob.glob(model_search_path):
                if model_file > latest_trained_model:
                    latest_trained_model = model_file

            trained_model = latest_trained_model
            print(f"Using model: {trained_model}")

            # fixed resnet50 backbone weights
            cfg.MODE_FPN = True
            cfg.MODE_MASK = True
            if resnet_arch == "resnet101":
                cfg.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 23, 3]
            else:
                cfg.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 6, 3]

            cfg_prefix = "CONFIG__"
            for key, value in dict(os.environ).items():
                if key.startswith(cfg_prefix):
                    attr_name = key[len(cfg_prefix) :]
                    attr_name = attr_name.replace("__", ".")
                    value = eval(value)
                    print(f"update config: {attr_name}={value}")
                    nested_var = cfg
                    attr_list = attr_name.split(".")
                    for attr in attr_list[0:-1]:
                        nested_var = getattr(nested_var, attr)
                    setattr(nested_var, attr_list[-1], value)

            # calling detection dataset gets the number of coco categories
            # and saves in the configuration
            DetectionDataset()
            finalize_configs(is_training=False)

            # Create an inference model
            # PredictConfig takes a model, input tensors and output tensors
            cls.predictor = OfflinePredictor(
                PredictConfig(
                    model=mask_rcnn_model,
                    session_init=get_model_loader(trained_model),
                    input_names=["images", "orig_image_dims"],
                    output_names=[
                        "generate_{}_proposals_topk_per_image/boxes".format(
                            "fpn" if cfg.MODE_FPN else "rpn"
                        ),
                        "generate_{}_proposals_topk_per_image/scores".format(
                            "fpn" if cfg.MODE_FPN else "rpn"
                        ),
                        "fastrcnn_all_scores",
                        "output/boxes",
                        "output/scores",
                        "output/labels",
                        "output/masks",
                    ],
                )
            )
            return cls.predictor