深度学习模型文件mnn量化实践

冷不防 2023-01-03 04:57 243阅读 0赞

转化成mnn模型虽然可以进行推理

不过模型文件可能较大或者运行较慢的情况

特别是在移动设备等边缘设备上，算力和储存空间受限

因此压缩模型是一个急需的工作

mnn自带了量化工具，环境安装很简单，这[文章][Link 1]编译就可以使用量化了

mnn模型文件是使用的是[之前的文章][Link 2]训练并转化的mnn文件

在使用之前需要新建一个json文件，里面配置好内容

preprocessConfig.json

{
        "format":"GRAY",
        "mean":[
            127.5
        ],
        "normal":[
            0.00784314
        ],
        "width":28,
        "height":28,
        "path":"FashionMNIST",
        "used_image_num":50,
        "feature_quantize_method":"KL",
        "weight_quantize_method":"MAX_ABS"
    }

需要说明的是因为之前那篇文件训练的是单通道的图片，因此json文件里面format写的GRAY，实际上可以选择的有："RGB", "BGR", "RGBA", "GRAY"，需要根据自己模型情况进行选择

具体可以看一下文档[https://www.yuque.com/mnn/cn/tool\_quantize][https_www.yuque.com_mnn_cn_tool_quantize]

准备工作都做好了，现在只需要量化即可

/opt/MNN/build/quantized.out FashionMNIST.mnn quan.mnn preprocessConfig.json

量化的结果：

[15:54:55] /opt/MNN/tools/quantization/quantized.cpp:21: >>> modelFile: FashionMNIST.mnn
    [15:54:55] /opt/MNN/tools/quantization/quantized.cpp:22: >>> preTreatConfig: preprocessCo nfig.json
    [15:54:55] /opt/MNN/tools/quantization/quantized.cpp:23: >>> dstFile: quan.mnn
    [15:54:55] /opt/MNN/tools/quantization/quantized.cpp:50: Calibrate the feature and quanti ze model...
    [15:54:55] /opt/MNN/tools/quantization/calibration.cpp:121: Use feature quantization meth od: KL
    [15:54:55] /opt/MNN/tools/quantization/calibration.cpp:122: Use weight quantization metho d: MAX_ABS
    [15:54:55] /opt/MNN/tools/quantization/Helper.cpp:100: used image num: 50
    ComputeFeatureRange: 100.00 %
    CollectFeatureDistribution: 100.00 %
    [15:54:55] /opt/MNN/tools/quantization/quantized.cpp:54: Quantize model done!

389K的文件变成了108K

现在需要测试一下量化和未量化之前的运行速度和运行结果

import time
    import MNN
    import numpy as np
     
    if __name__ == '__main__':
        x=np.ones([1, 1, 28, 28]).astype(np.float32)
        #quan mnn
        start=time.time()
        interpreter = MNN.Interpreter("quan.mnn")
        print("quan mnn load")
        mnn_session = interpreter.createSession()
        input_tensor = interpreter.getSessionInput(mnn_session)
        tmp_input = MNN.Tensor((1, 1, 28, 28),\
        MNN.Halide_Type_Float, x[0], MNN.Tensor_DimensionType_Tensorflow)  
        interpreter.runSession(mnn_session)
        output_tensor = interpreter.getSessionOutput(mnn_session,'output')
        output_data=np.array(output_tensor.getData())
        print('quan mnn result is:',output_data)
        print('quan mnn run time  is ',time.time()-start)

运行结果：

quan mnn load
    quan mnn result is: [ 0.5922392  -0.40196353  0.32656723  0.13848761  0.01854512 -1.11787963
      0.99948055 -0.32638997  0.92734373 -0.93912888]
    quan mnn run time  is  0.0015997886657714844

和之前的运行结果有一定差距，量化必然会带来精度的损失，需要重点注意

这里贴出来之前的结果

![20201230153021529.png][]

再使用mnn自带的时间测试工具测试一下

/opt/MNN/build/timeProfile.out quan.mnn 10 0

运行结果：

Use extra forward type: 0
    
    Open Model quan.mnn
    Sort by node name !
    Node Name                                       Op Type         Avg(ms)         %               Flops Rate
    11                                              ConvInt8        0.572700        64.814415       5.521849
    13                                              Pooling         0.015200        1.720236        0.585116
    13___FloatToInt8___0                            FloatToInt8     0.011900        1.346764        0.146279
    14                                              ConvInt8        0.066800        7.559983        44.174789
    16                                              Pooling         0.010500        1.188321        0.292558
    16___FloatToInt8___0                            FloatToInt8     0.007600        0.860118        0.073140
    17                                              ConvInt8        0.048200        5.454958        44.174789
    19                                              Pooling         0.008000        0.905387        0.107470
    23                                              BinaryOp        0.007800        0.882753        0.005971
    MatMul15                                        ConvInt8        0.004400        0.497963        3.606106
    MatMul19                                        ConvInt8        0.001000        0.113173        0.062606
    Raster12                                        Raster          0.014000        1.584428        0.026868
    Raster16                                        Raster          0.009200        1.041195        0.005971
    Raster18                                        Raster          0.006700        0.758262        0.005971
    Raster22                                        Raster          0.006900        0.780897        0.000466
    Reshape14___tr4MatMul15___FloatToInt8___0       FloatToInt8     0.009000        1.018561        0.026868
    Reshape19___tr4MatMul19___FloatToInt8___0       FloatToInt8     0.006600        0.746944        0.005971
    ___Int8ToFloat___For_130                        Int8ToFloat     0.021800        2.467180        0.585116
    ___Int8ToFloat___For_160                        Int8ToFloat     0.007400        0.837483        0.292558
    ___Int8ToFloat___For_190                        Int8ToFloat     0.008200        0.928022        0.146279
    ___Int8ToFloat___For_MatMul15___tr4Reshape160   Int8ToFloat     0.019600        2.218199        0.005971
    ___Int8ToFloat___For_MatMul19___tr4Reshape210   Int8ToFloat     0.010800        1.222273        0.000560
    input___FloatToInt8___0                         FloatToInt8     0.004100        0.464011        0.146279
    output                                          BinaryOp        0.005200        0.588502        0.000466
    Sort by time cost !
    Node Type       Avg(ms)         %               Called times    Flops Rate
    BinaryOp        0.013000        1.471254        2.000000        0.006437
    Pooling         0.033700        3.813944        3.000000        0.985145
    Raster          0.036800        4.164783        4.000000        0.039275
    FloatToInt8     0.039200        4.436398        5.000000        0.398536
    Int8ToFloat     0.067800        7.673157        5.000000        1.030484
    ConvInt8        0.693100        78.440491       5.000000        97.540123
    total time : 0.883600 ms, total mflops : 2.044533
    main, 113, cost time: 13.603001 ms

未量化的时间测试

/opt/MNN/build/timeProfile.out FashionMNIST.mnn 10 0

运行结果：

Use extra forward type: 0
    
    Open Model FashionMNIST.mnn
    Sort by node name !
    Node Name       Op Type         Avg(ms)         %               Flops Rate
    11              Convolution     0.485900        54.638489       5.601901
    13              Pooling         0.017400        1.956595        0.593599
    14              Convolution     0.089500        10.064097       44.815208
    16              Pooling         0.012000        1.349376        0.296799
    17              Convolution     0.048400        5.442484        44.815208
    19              Pooling         0.153200        17.227037       0.109028
    23              BinaryOp        0.006300        0.708422        0.006057
    MatMul15        Convolution     0.025700        2.889914        3.658385
    MatMul19        Convolution     0.009000        1.012032        0.063514
    Raster10        Raster          0.006400        0.719667        0.006057
    Raster12        Raster          0.005400        0.607219        0.000473
    Raster6         Raster          0.014200        1.596762        0.027257
    Raster8         Raster          0.009700        1.090746        0.006057
    output          BinaryOp        0.006200        0.697178        0.000473
    Sort by time cost !
    Node Type       Avg(ms)         %               Called times    Flops Rate
    BinaryOp        0.012500        1.405600        2.000000        0.006530
    Raster          0.035700        4.014394        4.000000        0.039845
    Pooling         0.182600        20.533010       3.000000        0.999427
    Convolution     0.658500        74.047020       5.000000        98.954201
    total time : 0.889300 ms, total mflops : 2.015316
    main, 113, cost time: 12.360001 ms

[Link 1]: https://blog.csdn.net/zhou_438/article/details/112318839
[Link 2]: https://blog.csdn.net/zhou_438/article/details/111991619
[https_www.yuque.com_mnn_cn_tool_quantize]: https://www.yuque.com/mnn/cn/tool_quantize
[20201230153021529.png]: /images/20221119/fd2c7d2ca39847e48d78c1e48c22bc56.png