定义sample_scene

“sample_scene.py”定义了生成Stream业务流配置、准备模型、后处理配置文件等流程。

编写要点

定义推理服务生成的函数。

get_pipeline_name：返回流程编排的主函数sample_scene_pipeline。

def get_pipeline_name():
     return "sample_scene_pipeline"

流程编排主函数：实现自定义场景业务流程编排，通过上述绑定的映射关系调用流程编排主函数。

def sample_scene_pipeline(task_name, scene_data, output_path):
    # 将样例的模型名与继承的Deployer对应，以下用ssd举例说明
    deployers_map = {
        "ssd_mobilenetv1_fpn": NewSceneDeployer
    }
    model_list = scene_data['model_list']
    # 创建文件夹
    model_path = create_scene_dir(task_name, scene_data, output_path)
    # 添加解码等插件
    input_plugins = {}
    add_decode_plugins(input_plugins)
    # 添加检测模型
    det_plugins = detection_process(model_list, model_path, deployers_map, input_plugins)
    serial_keys = det_plugins[-1].plugin_name()
    props_dataserialize = {
        "outputDataKeys": serial_keys
    }
    # 添加序列化等插件
    dataserialize0 = PluginNode("mxpi_dataserialize", props_dataserialize)(det_plugins[-1])
    appsink0 = PluginNode("appsink")(dataserialize0)

    f = FunctionalStream('stream', [input_plugins.get('appsrc')], [appsink0])
    f.set_device_id('0')
    f.build()

    pipeline = {}
    pipeline_key = f'{scene_data["project_name"]}_{task_name}'
    pipeline[pipeline_key] = json.loads(f.to_json())
    pipeline_file = write_pipeline(pipeline, model_path)
    return pipeline_file

定义自定义场景类。

通过继承现有场景Deployer实现generate_plugins函数，该函数是实现Stream业务流的主要步骤。

若不继承已有场景，则需要继承ModelDeployer，同时还需实现generate_deploy_files函数，该函数用于模型准备、生成后处理配置文件。

如下示例通过继承现有场景SSDDeployer定义了新场景NewSceneDeployer类实现Stream业务流，实现了新场景的关键插件串联关系。

class NewSceneDeployer(SSDDeployer): # 继承已有场景SSDDeployer
    def generate_plugins(self):
        props_imageresize = {
            "dataSource": "mxpi_imagedecoder0",
            "handleMethod": "opencv"
        }
        props_tensorinfer = {
            "dataSource": "mxpi_imageresize" + str(self.index),
            "modelPath": self.deploy_files.get('om_file')
        }
        props_post_process = {
            "dataSource": self.plugin_name + str(self.index),
            "postProcessConfigPath": self.deploy_files.get('post_process_file'),
            "labelPath": self.deploy_files.get('label_file'),
            "postProcessLibPath": "libSsdMobilenetFpn_MindsporePost.so"
        }
        imageresize0 = PluginNode("mxpi_imageresize", props_imageresize)(self.prev_plugins['mxpi_imagedecoder'])
        self.plugins.append(imageresize0)
        tensorinfer0 = PluginNode("mxpi_tensorinfer", props_tensorinfer,
                                  name=self.plugin_name + str(self.index))(imageresize0)
        self.plugins.append(tensorinfer0)
        objectpostprocessor0 = PluginNode("mxpi_objectpostprocessor", props_post_process,
                                          name=self.plugin_name + str(self.index) + '_post_process')(tensorinfer0)
        self.plugins.append(objectpostprocessor0)

定义get_model_with_type(model_list: str, application: str)：key值与模型训练场景定义中的“model_name”字段的值保持一致，value值与自定义场景类保持一致，该map是为了将模型训练场景定义的模型名字与用户自定义场景类做映射，便于调用用户自定义场景类。
如下示例将新场景类标签与模型训练场景定义中的“model_name”字段的值保持一致。
```
deployers_map = {yers_map = {
     "ssd_mobilenetv1_fpn": NewSceneDeployer
 }
```

定义get_model_with_type(model_list: str, application: str)：第二个入参必须要与模型训练场景定义中的“application”字段的值保持一致性，该函数是为了获取推理模型标签，方便后续更进一步获取对应模型的训练配置信息。

如下示例通过上述deployers_map调用新场景类。

def detection_process(model_list, model_path, deployers_map, prev_plugins):
     detection_models = get_model_with_number(model_list, 'det')
     if len(detection_models) == 0:
         print(f'Error: find application model failed.')
         return False
     detection_model = detection_models[0]
     json_data = parse_train_params(detection_model['result_path'])
     if json_data == False:
         return False
     deployer = deployers_map.get(json_data['model'])
     if deployer is None:
         print('Error: unsupported model to deploy, model name: %s' % detection_model['alias_model_name'])
         return False
     dep = deployer(detection_model, 'detection_model', json_data, 0, prev_plugins)
     if not dep.run(model_path):
         return False
     return dep.plugins

归档推理模型及配置文件，与平台下发的项目配置json文件中的“result_path”字段保持一致。

|-- mxAOIService/scripts/om_cfg/
|-- model_service_param.json
|-- det/
     |-- ssd_mobilenetv1_fpn_best.om
     |-- train_params.config

调试方法

安装自定义推理服务包后，执行命令python mxAOIService/scripts/infer_service_generation.py --model_params=./scripts/om_cfg/ model_service_param.json，检查文件内容。

|-- mxAOIService/config/models/project_det/
|-- model_configs.json
|-- project_det/
     |-- assm1-2
          |-- 1
               |-- label_0.names
               |-- post_process_0.cfg
               |-- mindx_sdk.pipeline
               |-- ssd_mobilenetv1_fpn_best.om

父主题： 样例说明