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* add horizon backend and PPYOLOE examples * 更改horizon头文件编码规范 * 更改horizon头文件编码规范 * 更改horizon头文件编码规范 * 增加horizon packages下载及自动安装 * Add UseHorizonNPUBackend Method * 删除编译FD SDK后多余的头文件,同时更改部分规范 * Update horizon.md * Update horizon.md --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
180 lines
7.4 KiB
Markdown
180 lines
7.4 KiB
Markdown
[English](../../../en/faq/horizon/export.md) | 中文
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# 导出模型指南
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## 简介
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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](../../build_and_install/horizon.md)将环境安装成功。
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## 转换流程
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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](https://developer.horizon.ai/api/v1/fileData/doc/ddk_doc/navigation/ai_toolchain/docs_cn/horizon_ai_toolchain_user_guide/model_conversion.html#fp-model-preparation)。
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paddle模型转ONNX模型可以运行以下命令
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```bash
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## 转为ONNX
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paddle2onnx --model_dir model/ \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_file model.onnx \
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--enable_dev_version True \
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--opset_version 11
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```
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更改ir_version，可参考以下代码
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```python
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import onnx
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model = onnx.load("model.onnx")
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model.ir_version = 7
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onnx.save(model, "model.onnx")
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```
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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。
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进入docker实例，cd至如下目录:
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```bash
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cd ddk/samples/ai_toolchain/horizon_model_convert_sample/03_classification/04_mobilenet_onnx/mapper/
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```
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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。
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`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。
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```bash
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set -ex
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cd $(dirname $0) || exit
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model_type="onnx"
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caffe_model="../../../01_common/model_zoo/mapper/classification/mobilenet_onnx/mobilenetv2.onnx"
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march="bernoulli2"
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hb_mapper checker --model-type ${model_type} \
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--model ${caffe_model} \
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--march ${march}
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```
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`02_preprocess.sh`，准备量化所需的数据格式，FastDeploy选择以下的配置。
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```bash
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python3 ../../../data_preprocess.py \
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--src_dir ../../../01_common/calibration_data/imagenet \
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--dst_dir ./calibration_data_rgb \
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--pic_ext .rgb \
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--read_mode opencv \
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--saved_data_type uint8
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```
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`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。
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FastDeploy对模型路径的配置如下：
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```yaml
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model_parameters:
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# the model file of floating-point ONNX neural network data
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onnx_model: '../../../01_common/model_zoo/mapper/classification/mobilenet_onnx/mobilenetv2.onnx'
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# the applicable BPU architecture
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march: "bernoulli2"
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# specifies whether or not to dump the intermediate results of all layers in conversion
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# if set to True, then the intermediate results of all layers shall be dumped
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layer_out_dump: False
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# the directory in which model conversion results are stored
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working_dir: 'model_output_rgb'
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# model conversion generated name prefix of those model files used for dev board execution
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output_model_file_prefix: 'mobilenetv2_224x224_rgb'
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```
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对模型输入格式的配置如下：
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```yaml
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input_parameters:
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# (Optional) node name of model input,
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# it shall be the same as the name of model file, otherwise an error will be reported,
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# the node name of model file will be used when left blank
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input_name: ""
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# the data formats to be passed into neural network when actually performing neural network
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# available options: nv12/rgb/bgr/yuv444/gray/featuremap,
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input_type_rt: 'rgb'
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# the data layout formats to be passed into neural network when actually performing neural network, available options: NHWC/NCHW
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# If input_type_rt is configured as nv12, then this parameter does not need to be configured
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input_layout_rt: 'NHWC'
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# the data formats in network training
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# available options: rgb/bgr/gray/featuremap/yuv444
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input_type_train: 'rgb'
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# the data layout in network training, available options: NHWC/NCHW
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input_layout_train: 'NCHW'
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# (Optional)the input size of model network, seperated by 'x'
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# note that the network input size of model file will be used if left blank
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# otherwise it will overwrite the input size of model file
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input_shape: ''
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# the data batch_size to be passed into neural network when actually performing neural network, default value: 1
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#input_batch: 1
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# preprocessing methods of network input, available options:
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# 'no_preprocess' indicates that no preprocess will be made
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# 'data_mean' indicates that to minus the channel mean, i.e. mean_value
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# 'data_scale' indicates that image pixels to multiply data_scale ratio
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# 'data_mean_and_scale' indicates that to multiply scale ratio after channel mean is minused
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norm_type: 'data_mean_and_scale'
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# the mean value minused by image
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# note that values must be seperated by space if channel mean value is used
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mean_value: 123.675 116.28 103.53
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# scale value of image preprocess
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# note that values must be seperated by space if channel scale value is used
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scale_value: 0.01712 0.0175 0.01743
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```
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对模型量化参数的配置如下：
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```yaml
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calibration_parameters:
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# the directory where reference images of model quantization are stored
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# image formats include JPEG, BMP etc.
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# should be classic application scenarios, usually 20~100 images are picked out from test datasets
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# in addition, note that input images should cover typical scenarios
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# and try to avoid those overexposed, oversaturated, vague,
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# pure blank or pure white images
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# use ';' to seperate when there are multiple input nodes
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cal_data_dir: './calibration_data_rgb'
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# calibration data binary file save type, available options: float32, uint8
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# cal_data_type: 'float32'
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# In case the size of input image file is different from that of in model training
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# and that preprocess_on is set to True,
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# shall the default preprocess method(skimage resize) be used
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# i.e., to resize or crop input image into specified size
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# otherwise user must keep image size as that of in training in advance
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# preprocess_on: False
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# The algorithm type of model quantization, support default, mix, kl, max, load, usually use default can meet the requirements.
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# 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.
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# When using QAT to export the model, this parameter should be set to load.
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# For more details of the parameters, please refer to the parameter details in PTQ Principle And Steps section of the user manual.
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calibration_type: 'max'
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# this is the parameter of the 'max' calibration method and it is used for adjusting the intercept point of the 'max' calibration.
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# this parameter will only become valid when the calibration_type is specified as 'max'.
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# RANGE: 0.0 - 1.0. Typical options includes: 0.99999/0.99995/0.99990/0.99950/0.99900.
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max_percentile: 0.9999
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```
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其余参数选择默认值，运行`03_build.sh`,
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```bash
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config_file="./mobilenetv2_config.yaml"
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model_type="onnx"
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# build model
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hb_mapper makertbin --config ${config_file} \
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--model-type ${model_type}
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```
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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）
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