Python GeneralizedRCNN примеры использования

Пример #1

Файл: extract_features_frcnn.py Проект: hivestrung/mmf

 def _build_detection_model(self):
     if self.args.config_file:
         frcnn_cfg = Config.from_pretrained(self.args.config_file)
     else:
         frcnn_cfg = Config.from_pretrained(
             self.MODEL_URL.get(self.args.model_name, self.args.model_name))
     if self.args.model_file:
         frcnn = GeneralizedRCNN.from_pretrained(self.args.model_file,
                                                 config=frcnn_cfg)
     else:
         frcnn = GeneralizedRCNN.from_pretrained(
             self.MODEL_URL.get(self.args.model_name, self.args.model_name),
             config=frcnn_cfg,
         )
     return frcnn, frcnn_cfg

Пример #2

Файл: torchray_visual_interface.py Проект: yongkangzzz/mmfgroup

def torchRay_feat_extract(img_tensor):
	Args = get_parser().parse_args(
		["--config_file", "/Users/louitech_zero/Desktop/Imperial College London/CS/GroupProject/group_project_draft/mmf/tools/scripts/features/frcnn/config.yaml",
		"--model_file", "/Users/louitech_zero/Desktop/Imperial College London/CS/GroupProject/group_project_draft/mmf/tools/scripts/features/frcnn/model_finetuned.bin"])
	feature_extraction_model = GeneralizedRCNN.from_pretrained(Args.model_file, config=Config.from_pretrained(Args.config_file))

	features = feature_extraction_model(
			img_tensor,
			torch.tensor([[224, 224]]),
			scales_yx=torch.tensor([[1.0, 1.0]]),
			padding=None,
			max_detections=Config.from_pretrained(Args.config_file).max_detections,
			return_tensors="pt",

		)
	single_features, feat_list, info_list = process_features(features, 0, Args)

	return feat_list, info_list

Пример #3

def torchRay_feat_extract(img_tensor):
    Args = get_parser().parse_args([
        "--config_file",
        loadFromCache("frcnn_config"),
        "--model_file",
        loadFromCache("frcnn_model"),
    ])
    feature_extraction_model = GeneralizedRCNN.from_pretrained(
        Args.model_file, config=Config.from_pretrained(Args.config_file))

    feature_extraction_model.to(
        torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))

    features = feature_extraction_model(
        img_tensor,
        torch.tensor([[224, 224]]),
        scales_yx=torch.tensor([[1.0, 1.0]]),
        padding=None,
        max_detections=Config.from_pretrained(Args.config_file).max_detections,
        return_tensors="pt",
    )
    single_features, feat_list, info_list = process_features(features, 0, Args)

    return feat_list, info_list

Пример #4

Файл: extract_features_frcnn.py Проект: yongkangzzz/mmfgroup

    def _build_detection_model(self):
        frcnn_cfg = Config.from_pretrained(self.args.config_file)
        frcnn = GeneralizedRCNN.from_pretrained(self.args.model_file,
                                                config=frcnn_cfg)

        return frcnn, frcnn_cfg