RV1126 部署自己训练的yolov8

1)环境：

创建一个 python3.8的虚拟环境

采用rknn_toolkit-1.7.5-cp38-cp38-linux_x86_64.whl

1) pt -> onnx

使用RK的yolov8.

在满足 ./requirements.txt 的环境要求后，执行以下语句导出模型

# 调整 ./ultralytics/cfg/default.yaml 中 model 文件路径，默认为 yolov8n.pt，若自己训练模型，请调接至对应的路径。支持检测、分割模型。
# 如填入 yolov8n.pt 导出检测模型
# 如填入 yolov8-seg.pt 导出分割模型

export PYTHONPATH=./
python ./ultralytics/engine/exporter.py

# 执行完毕后，会生成 ONNX 模型. 假如原始模型为 yolov8n.pt，则生成 yolov8n.onnx 模型。

执行之后，就会在当前目录下生成 对应的ONNX模型。我们打开这个模型看一下，就会发现outputs输出是9个。如果不用这个方式，outputs会是一个，导致后面的无法正常使用。RK的GIT上相关的问题，都没人回答，这是一个坑，被我踩了一次。

2) ONNX->rknn

进入rknn_model_zoo/example/yolov8/python

python3 convert.py best.onnx rv1126

W rknn-toolkit version: 1.7.5
--> Config model
done
--> Loading model
W Catch exception when optimize onnx model, skip it! Exception: Unresolved value references: /model.10/Constant_1_output_0,/model.10/Constant_output_0,/model.13/Constant_1_output_0,/model.13/Constant_output_0,/model.22/Constant_1_output_0,/model.22/Constant_2_output_0,/model.22/Constant_3_output_0,/model.22/Constant_4_output_0,/model.22/Constant_5_output_0,/model.22/Constant_output_0,
/usr/local/lib/python3.8/dist-packages/rknn/api/rknn.py:160: FutureWarning: Possible nested set at position 7
  ret = self.rknn_base.load_onnx(model, inputs, input_size_list, outputs)
--> Building model
W found outlier value, this may affect quantization accuracy
const name                              abs_mean    abs_std     outlier value
Conv_/model.0/conv/Conv_226:weight      2.32        2.26        14.828      
W:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/func_graph.py:981: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.
    
W:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1659: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
done
--> Export rknn model
done

打开生成的rknn看一下

3）进行在线预编译：

在线预编译模型需要板子接入网线或者跟电脑连接。用的是adb connect指令直接连接ip。

#执行指令
adb connect 192.168.2.17
#得到结果
already connected to 192.168.2.17:5555
#连接成功

cd到toolkit的examples->common_function_demos->export_rknn_precompile_model文件夹下，将刚刚得到的best.rknn模型拷贝进去。
执行如下指令：

python export_rknn_precompile_model.py best.rknn yolov8_best_pre.rknn rv1126

运行结束后得到yolov8_best_pre.rknn。如果不使用预编译，运行速度会非常慢。