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To support int8 model deployment on AIoT devices, we provide some universal post training quantization tools which can convert the Float32 tmfile model to Int8/UInt8 tmfile model.
| Type | Note |
|---|---|
| Adaptive | TENGINE_MODE_INT8 |
| Activation data | Int8 |
| Weight date | Int8 |
| Bias date | Int32 |
| Example | tm_classification_int8.c |
| Execution environment | Ubuntu 18.04 |
Currently, only provide the per-compiled file, you can get it from quant_tool_int8.
sudo apt install libopencv-dev
$ ./quant_tool_int8 -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)
Before use the quant tool, you need Float32 tmfile and Calibration Dataset, the image num of calibration dataset we suggest to use 500-1000.
$ .quant_tool_int8 -m ./mobilenet_fp32.tmfile -i ./dataset -o ./mobilenet_int8.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, 17:32:30 Dec 24 2020
Status : int8, per-channel, symmetric
Input model : ./mobilenet_fp32.tmfile
Output model: ./mobilenet_int8.tmfile
Calib images: ./dataset
Algorithm : KL
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, find original calibration table done, output ./table_minmax.scale
[Quant Tools Info]: Step 2, find calibration table.
[Quant Tools Info]: Step 2, find calibration table done, output ./table_kl.scale
[Quant Tools Info]: Thread 1, image nums 55, total time 1964.24 ms, avg time 35.71 ms
[Quant Tools Info]: Calibration file is using table_kl.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_kl.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_int8.tmfile
---- Tengine Int8 tmfile create success, best wish for your INT8 inference has a low accuracy loss...\(^0^)/ ----
| 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 |
Currently, only provide the per-compiled file, you can get it from quant_tool_uint8.
$ ./quant_tool_int8 -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)
Before use the quant tool, you need Float32 tmfile and Calibration Dataset, the image num of calibration dataset we suggest to use 500-1000.
$ .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^)/ ----
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