FastDeploy/docs/cn/faq/horizon/export.md

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导出模型指南

简介

地平线的模型转换以及量化工具均封装在提供的docker镜像中，在进行模型转换前请根据Horizon导航文档将环境安装成功。

转换流程

由于地平线不支持直接从paddle模型到horizon模型的转换，因此，首先要将paddle的模型转换为ONNX模型，地平线目前主要支持的opset版本是opset10和opset11，ir_version <= 7，转换过程需特别注意，具体可参考地平线提供的官方文档。

paddle模型转ONNX模型可以运行以下命令

## 转为ONNX
paddle2onnx --model_dir model/ \
            --model_filename inference.pdmodel \
            --params_filename inference.pdiparams \
            --save_file model.onnx \
            --enable_dev_version True \
            --opset_version 11

更改ir_version，可参考以下代码

import onnx
model = onnx.load("model.onnx")
model.ir_version = 7
onnx.save(model, "model.onnx")

转换为ONNX模型之后，开始进行地平线模型的转换，可参考官方文档进行转换，此处给出MobileNetv2的模型转换示例。

进入docker实例，cd至如下目录:

cd ddk/samples/ai_toolchain/horizon_model_convert_sample/03_classification/04_mobilenet_onnx/mapper/

该目录下有01_check.sh，02_preprocess.sh，03_build.sh，在模型转换阶段主要用这三个脚本就可完成，下面详细介绍使用这三个脚本的注意事项。

01_check.sh，对模型以及运行环境进行检查，只需要修改caffe_model为自己的ONNX模型路径就可以完成。

set -ex
cd $(dirname $0) || exit

model_type="onnx"
caffe_model="../../../01_common/model_zoo/mapper/classification/mobilenet_onnx/mobilenetv2.onnx"
march="bernoulli2"

hb_mapper checker --model-type ${model_type} \
                  --model ${caffe_model} \
                  --march ${march}

02_preprocess.sh，准备量化所需的数据格式，FastDeploy选择以下的配置。

python3 ../../../data_preprocess.py \
  --src_dir ../../../01_common/calibration_data/imagenet \
  --dst_dir ./calibration_data_rgb \
  --pic_ext .rgb \
  --read_mode opencv \
  --saved_data_type uint8

03_build.sh，将ONNX模型转换为Horizon可运行的模型，其中转换需要进行参数的配置，包括输入数据格式等。 FastDeploy对模型路径的配置如下：

model_parameters:
  # the model file of floating-point ONNX neural network data
  onnx_model: '../../../01_common/model_zoo/mapper/classification/mobilenet_onnx/mobilenetv2.onnx'
 
  # the applicable BPU architecture
  march: "bernoulli2"

  # specifies whether or not to dump the intermediate results of all layers in conversion
  # if set to True, then the intermediate results of all layers shall be dumped
  layer_out_dump: False

  # the directory in which model conversion results are stored
  working_dir: 'model_output_rgb'

  # model conversion generated name prefix of those model files used for dev board execution
  output_model_file_prefix: 'mobilenetv2_224x224_rgb'

对模型输入格式的配置如下：

input_parameters:

  # (Optional) node name of model input,
  # it shall be the same as the name of model file, otherwise an error will be reported,
  # the node name of model file will be used when left blank
  input_name: ""

  # the data formats to be passed into neural network when actually performing neural network
  # available options: nv12/rgb/bgr/yuv444/gray/featuremap,
  input_type_rt: 'rgb'

  # the data layout formats to be passed into neural network when actually performing neural network, available options: NHWC/NCHW
  # If input_type_rt is configured as nv12, then this parameter does not need to be configured
  input_layout_rt: 'NHWC'

  # the data formats in network training
  # available options: rgb/bgr/gray/featuremap/yuv444
  input_type_train: 'rgb'

  # the data layout in network training, available options: NHWC/NCHW
  input_layout_train: 'NCHW'

  # (Optional)the input size of model network, seperated by 'x'
  # note that the network input size of model file will be used if left blank
  # otherwise it will overwrite the input size of model file
  input_shape: ''

  # the data batch_size to be passed into neural network when actually performing neural network, default value: 1
  #input_batch: 1
  
  # preprocessing methods of network input, available options:
  # 'no_preprocess' indicates that no preprocess will be made 
  # 'data_mean' indicates that to minus the channel mean, i.e. mean_value
  # 'data_scale' indicates that image pixels to multiply data_scale ratio
  # 'data_mean_and_scale' indicates that to multiply scale ratio after channel mean is minused
  norm_type: 'data_mean_and_scale'

  # the mean value minused by image
  # note that values must be seperated by space if channel mean value is used
  mean_value: 123.675 116.28 103.53

  # scale value of image preprocess
  # note that values must be seperated by space if channel scale value is used
  scale_value: 0.01712 0.0175 0.01743

对模型量化参数的配置如下：

calibration_parameters:

  # the directory where reference images of model quantization are stored
  # image formats include JPEG, BMP etc.
  # should be classic application scenarios, usually 20~100 images are picked out from test datasets
  # in addition, note that input images should cover typical scenarios
  # and try to avoid those overexposed, oversaturated, vague, 
  # pure blank or pure white images
  # use ';' to seperate when there are multiple input nodes
  cal_data_dir: './calibration_data_rgb'

  # calibration data binary file save type, available options: float32, uint8
#   cal_data_type: 'float32'

  # In case the size of input image file is different from that of in model training
  # and that preprocess_on is set to True,
  # shall the default preprocess method(skimage resize) be used
  # i.e., to resize or crop input image into specified size
  # otherwise user must keep image size as that of in training in advance
  # preprocess_on: False

  # The algorithm type of model quantization, support default, mix, kl, max, load, usually use default can meet the requirements.
  # If it does not meet the expectation, you can try to change it to mix first. If there is still no expectation, try kl or max again.
  # When using QAT to export the model, this parameter should be set to load.
  # For more details of the parameters, please refer to the parameter details in PTQ Principle And Steps section of the user manual.
  calibration_type: 'max'

  # this is the parameter of the 'max' calibration method and it is used for adjusting the intercept point of the 'max' calibration.
  # this parameter will only become valid when the calibration_type is specified as 'max'.
  # RANGE: 0.0 - 1.0. Typical options includes: 0.99999/0.99995/0.99990/0.99950/0.99900.
  max_percentile: 0.9999

其余参数选择默认值，运行03_build.sh,

config_file="./mobilenetv2_config.yaml"
model_type="onnx"
# build model
hb_mapper makertbin --config ${config_file}  \
                    --model-type  ${model_type}

至此，在同路径下model_output_rgb会生成转换完成的模型文件（后缀为.bin）