在这个案例中我们演示了从环境适配到模型的转换验证的全流程样例代码，开发者可以通过“Run in ModelArts”一键将Notebook案例在ModelArts控制台快速打开、运行以及进行二次开发等操作。 开发环境部署
使用开发板进行ModelBox AI应用开发有两种方式，一是开发板连接显示器和键盘鼠标，安装Ubuntu桌面，直接在开发板上进行开发；二是使用远程连接工具（如VS Code中的Remote-SSH）从PC端登录开发板进行开发。这里我们推荐第二种方式，因为PC端可以使用功能更丰富、界面更友好的IDE。 1.配置网络
PC连接开发板需要知道开发板的ip，但是开发板默认没有固定ip，我们提供了ModelBox PC Tool，可以自动为开发板配置ip，也可以在推理阶段很方便的进行视频推流拉流。
PC Tool位于SDK的connect_wizard目录中：
# Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
# Basic config
[base]
name = "yolox_post" # The FlowUnit name
device = "cpu" # The flowunit runs on cpu
version = "1.0.0" # The version of the flowunit
type = "python" # Fixed value, do not change
description = "description" # The description of the flowunit
entry = "yolox_post@yolox_postFlowUnit" # Python flowunit entry function
group_type = "generic" # flowunit group attribution, change as input/output/image ...
# Flowunit Type
stream = false # Whether the flowunit is a stream flowunit
condition = true # Whether the flowunit is a condition flowunit
collapse = false # Whether the flowunit is a collapse flowunit
collapse_all = false # Whether the flowunit will collapse all the data
expand = false # Whether the flowunit is a expand flowunit
# The default Flowunit config
[config]
item = "value"
# Input ports description
[input]
[input.input1] # Input port number, the format is input.input[N]
name = "in_image" # Input port name
type = "uint8" # input port data type ,e.g. float or uint8
device = "cpu" # input buffer type
[input.input2] # Input port number, the format is input.input[N]
name = "in_feat" # Input port name
type = "uint8" # input port data type ,e.g. float or uint8
device = "cpu" # input buffer type
# Output ports description
[output]
[output.output1] # Output port number, the format is output.output[N]
name = "has_hand" # Output port name
type = "float" # output port data type ,e.g. float or uint8
[output.output2] # Output port number, the format is output.output[N]
name = "no_hand" # Output port name
type = "float" # output port data type ,e.g. float or uint8
# Input ports description
[input]
[input.input1] # Input port number, the format is input.input[N]
name = "in_image" # Input port name
type = "uint8" # input port data type ,e.g. float or uint8
device = "cpu" # input buffer type
[input.input2] # Input port number, the format is input.input[N]
name = "in_feat" # Input port name
type = "float" # input port data type ,e.g. float or uint8
device = "cpu" # input buffer type
# Output ports description
[output]
[output.output1] # Output port number, the format is output.output[N]
name = "has_hand" # Output port name
type = "uint8" # output port data type ,e.g. float or uint8
[output.output2] # Output port number, the format is output.output[N]
name = "no_hand" # Output port name
type = "uint8" # output port data type ,e.g. float or uint8
接下来，我们查看yolox_post.py，可以看到创建单元时已经生成了基本接口：
# Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import _flowunit as modelbox
class yolox_postFlowUnit(modelbox.FlowUnit):
# Derived from modelbox.FlowUnit
def __init__(self):
super().__init__()
def open(self, config):
# Open the flowunit to obtain configuration information
return modelbox.Status.StatusCode.STATUS_SUCCESS
def process(self, data_context):
# Process the data
in_data = data_context.input("in_1")
out_data = data_context.output("out_1")
# yolox_post process code.
# Remove the following code and add your own code here.
for buffer in in_data:
response = "Hello World " + buffer.as_object()
result = response.encode('utf-8').strip()
add_buffer = modelbox.Buffer(self.get_bind_device(), result)
out_data.push_back(add_buffer)
return modelbox.Status.StatusCode.STATUS_SUCCESS
def close(self):
# Close the flowunit
return modelbox.Status()
def data_pre(self, data_context):
# Before streaming data starts
return modelbox.Status()
def data_post(self, data_context):
# After streaming data ends
return modelbox.Status()
def data_group_pre(self, data_context):
# Before all streaming data starts
return modelbox.Status()
def data_group_post(self, data_context):
# After all streaming data ends
return modelbox.Status()
sdk version is modelbox-rk-aarch64-1.0.8.8
call mb-pkg-tool pack [folder] > [rpm file] to building rpm, waiting...
success: create hand_painting.rpm in /home/rock/lxy/examples/workspace/hand_painting
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发布于：2022-09-06 15:46
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