718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
72 lines
2.5 KiB
Markdown
72 lines
2.5 KiB
Markdown
# Post Training Quantization Tools
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To support int8 model deployment on mobile devices,we provide the universal post training quantization tools which can convert the float32 model to int8 model.
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## User Guide
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Example with mobilenet, just need three steps.
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### 1. Optimize model
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```shell
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./ncnnoptimize mobilenet.param mobilenet.bin mobilenet-opt.param mobilenet-opt.bin 0
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```
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### 2. Create the calibration table file
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We suggest that using the verification dataset for calibration, which is more than 5000 images.
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Some imagenet sample images here https://github.com/nihui/imagenet-sample-images
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```shell
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find images/ -type f > imagelist.txt
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./ncnn2table mobilenet-opt.param mobilenet-opt.bin imagelist.txt mobilenet.table mean=[104,117,123] norm=[0.017,0.017,0.017] shape=[224,224,3] pixel=BGR thread=8 method=kl
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```
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* mean and norm are the values you passed to ```Mat::substract_mean_normalize()```
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* shape is the blob shape of your model, [w,h] or [w,h,c]
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>
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* if w and h both are given, image will be resized to exactly size.
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* if w and h both are zero or negative, image will not be resized.
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* if only h is zero or negative, image's width will scaled resize to w, keeping aspect ratio.
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* if only w is zero or negative, image's height will scaled resize to h
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* pixel is the pixel format of your model, image pixels will be converted to this type before ```Extractor::input()```
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* thread is the CPU thread count that could be used for parallel inference
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* method is the post training quantization algorithm, kl and aciq are currently supported
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If your model has multiple input nodes, you can use multiple list files and other parameters
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```shell
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./ncnn2table mobilenet-opt.param mobilenet-opt.bin imagelist-bgr.txt,imagelist-depth.txt mobilenet.table mean=[104,117,123],[128] norm=[0.017,0.017,0.017],[0.0078125] shape=[224,224,3],[224,224,1] pixel=BGR,GRAY thread=8 method=kl
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```
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### 3. Quantize model
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```shell
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./ncnn2int8 mobilenet-opt.param mobilenet-opt.bin mobilenet-int8.param mobilenet-int8.bin mobilenet.table
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```
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## use ncnn int8 inference
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the ncnn library would use int8 inference automatically, nothing changed in your code
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```cpp
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ncnn::Net mobilenet;
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mobilenet.load_param("mobilenet-int8.param");
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mobilenet.load_model("mobilenet-int8.bin");
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```
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## mixed precision inference
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Before quantize your model, comment the layer weight scale line in table file, then the layer will do the float32 inference
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```
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conv1_param_0 156.639840536
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```
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```
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#conv1_param_0 156.639840536
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```
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