模型量化-非对称量化
为了支持在 AIoT 设备上部署 uint8 模型，我们提供了一些通用的 post training quantization 工具，可以将 Float32 tmfile 模型转换为 int8 tmfile 模型。
非对称分层量化
| Type | Note |
|---|---|
| Adaptive | TENGINE_MODE_UINT8 |
| Activation data | UInt8 |
| Weight date | UInt8 |
| Bias date | Int32 |
| Example | tm_classification_uint8.c |
| Execution environment | Ubuntu 18.04 |
适配硬件
CPU Uint8 mode
TIM-VX NPU（such as A311D、i.MX8M Plus、RV1126？）
下载
当前我们提供预编译好的可执行文件, 您可以从这里获取 quant_tool_uint8.
安装依赖库
sudo apt install libopencv-dev
运行参数
$ ./quant_tool_uint8 -h
[Quant Tools Info]: optional arguments:
-h help show this help message and exit
-m input model path to input float32 tmfile
-i image dir path to calibration images folder
-o output model path to output int8 tmfile
-a algorithm the type of quant algorithm(0:min-max, 1:kl, default is 1)
-g size the size of input image(using the resize the original image,default is 3,224,224
-w mean value of mean (mean value, default is 104.0,117.0,123.0
-s scale value of normalize (scale value, default is 1.0,1.0,1.0)
-b swapRB flag which indicates that swap first and last channels in 3-channel image is necessary(0:OFF, 1:ON, default is 1)
-c center crop flag which indicates that center crop process image is necessary(0:OFF, 1:ON, default is 0)
-y letter box flag which indicates that letter box process image is necessary(maybe using for YOLO, 0:OFF, 1:ON, default is 0)
-t num thread count of processing threads(default is 4)
示例
使用量化工具前, 你需要 Float32 tmfile 和 Calibration Dataset（量化校准数据集）。
校准数据内容，尽可能的覆盖该模型的所有应用场景，一般我们的经验是从训练集中随机抽取；
校准数据张数，根据经验我们建议使用 500-1000 张。
$ .quant_tool_uint8 -m ./mobilenet_fp32.tmfile -i ./dataset -o ./mobilenet_uint8.tmfile -g 3,224,224 -w 104.007,116.669,122.679 -s 0.017,0.017,0.017
---- Tengine Post Training Quantization Tool ----
Version : v1.0, 18:06:10 Mar 4 2021
Status : uint8, per-layer, asymmetric
Input model : ./mobilenet_fp32.tmfile
Output model: ./mobilenet_uint8.tmfile
Calib images: ./dataset
Algorithm : MIN MAX
Dims : 3 224 224
Mean : 104.007 116.669 122.679
Scale : 0.017 0.017 0.017
BGR2RGB : ON
Center crop : OFF
Letter box : OFF
Thread num : 1
[Quant Tools Info]: Step 0, load FP32 tmfile.
[Quant Tools Info]: Step 0, load FP32 tmfile done.
[Quant Tools Info]: Step 0, load calibration image files.
[Quant Tools Info]: Step 0, load calibration image files done, image num is 55.
[Quant Tools Info]: Step 1, find original calibration table.
[Quant Tools Info]: Step 1, images 00055 / 00055
[Quant Tools Info]: Step 1, find original calibration table done, output ./table_minmax.scale
[Quant Tools Info]: Step 2, find calibration table.
[Quant Tools Info]: Step 2, images 00001 / 00055
[Quant Tools Info]: Step 2, find calibration table done, output ./table_kl.scale
[Quant Tools Info]: Thread 1, image nums 55, total time 1195.07 ms, avg time 21.73 ms
[Quant Tools Info]: Calibration file is using table_minmax.scale
[Quant Tools Info]: Step 3, load FP32 tmfile once again
[Quant Tools Info]: Step 3, load FP32 tmfile once again done.
[Quant Tools Info]: Step 3, load calibration table file table_minmax.scale.
[Quant Tools Info]: Step 4, optimize the calibration table.
[Quant Tools Info]: Step 4, quantize activation tensor done.
[Quant Tools Info]: Step 5, quantize weight tensor done.
[Quant Tools Info]: Step 6, save Int8 tmfile done, ./mobilenet_uint8.tmfile
---- Tengine Int8 tmfile create success, best wish for your INT8 inference has a low accuracy loss...\(^0^)/ ----