This convert tools is base on TensorRT 2.0 Int8 calibration tools, which use the KL algorithm to find the suitable threshold to quantize the activions from Float32 to Int8(-127 - 127).
We provide the Classification(SqueezeNet_v1.1) and Detection(MobileNet_v1 SSD 300) demo based on ncnn(a high-performance neural network inference framework optimized for the mobile platform) and the community ready to support this implementation.
Using this new ncnn-quantization-tools, you can convert your ncnn model to ncnn int8 model directly. If you just want to deploy your model with ncnn,I suggest you use it.
For details, please read the following PDF:
MXNet quantization implementation:
Quantization module for generating quantized (INT8) models from FP32 models
An introduction to the principles of a Chinese blog written by my friend(bruce.zhang):
The implement of Int8 quantize base on TensorRT
The purpose of this tool(caffe-int8-convert-tool-dev.py) is to test new features, such as mulit-channels quantization depend on group num.
This format is already supported in the ncnn latest version. I will do my best to transform some common network models into classification-dev
python caffe-int8-convert-tool-dev-weight.py -h
usage: caffe-int8-convert-tool-dev-weight.py [-h] [--proto PROTO] [--model MODEL]
[--mean MEAN MEAN MEAN] [--norm NORM]
[--images IMAGES] [--output OUTPUT]
[--group GROUP] [--gpu GPU]
find the pretrained caffemodel int8 quantize scale value
optional arguments:
-h, --help show this help message and exit
--proto PROTO path to deploy prototxt.
--model MODEL path to pretrained caffemodel
--mean MEAN value of mean
--norm NORM value of normalize(scale value or std value)
--images IMAGES path to calibration images
--output OUTPUT path to output calibration table file
--group GROUP enable the group scale(0:disable,1:enable,default:1)
--gpu GPU use gpu to forward(0:disable,1:enable,default:0)
python caffe-int8-convert-tool-dev-weight.py --proto=test/models/mobilenet_v1.prototxt --model=test/models/mobilenet_v1.caffemodel --mean 103.94 116.78 123.68 --norm=0.017 --images=test/images/ output=mobilenet_v1.table --group=1 --gpu=1
For example in MobileNet_v1_dev.table
conv1_param_0 0.0 3779.48337933 482.140562772 1696.53814502
conv2_1/dw_param_0 0 72.129143 149.919382 // the convdw layer's weight scale every group is 0.0 72.129 149.919 ......
......
conv1 49.466518
conv2_1/dw 123.720796 // the convdw layer's bottom blobchannel scale is 123.720
......
Three steps to implement the conv1 layer int8 convolution:
-
Quantize the bottom_blob and weight:
bottom_blob_int8 = bottom_blob_float32 * data_scale(49.466518) weight_int8 = weight_float32 * weight_scale(156.639840) -
Convolution_Int8:
top_blob_int32 = bottom_blob_int8 * weight_int8 -
Dequantize the TopBlob_Int32 and add the bias:
top_blob_float32 = top_blob_int32 / [data_scale(49.466518) * weight_scale(156.639840)] + bias_float32
| Type | Detail |
|---|---|
| Calibration Dataset | ILSVRC2012_img_test 1k |
| Test Dataset | ILSVRC2012_img_val 5k |
| Framework | ncnn |
| Support Layer | Convolution,ConvolutionDepthwise,ReLU |
The following table show the Top1 and Top5 different between Float32 and Int8 inference.
| Models | FP32 | INT8 | Loss | |||
|---|---|---|---|---|---|---|
| Top1 | Top5 | Top1 | Top5 | Diff Top1 | Diff Top5 | |
| SqueezeNet v1.1 | 57.78% | 79.88% | 57.82% | 79.84% | +0.04% | -0.04% |
| MobileNet v1 | 67.26% | 87.92% | 66.74% | 87.43% | -0.52% | -0.49% |
| GoogleNet | 68.50% | 88.84% | 68.62% | 88.68% | +0.12% | -0.16% |
| ResNet18 | 65.49% | 86.56% | 65.30% | 86.52% | -0.19% | -0.04% |
| ResNet50 | 71.80% | 89.90% | 71.76% | 90.06% | -0.04% | +0.16% |
| Type | Detail |
|---|---|
| Test Dataset | VOC2007 |
| Unit | mAP (Class 20) |
| Models | FP32 | INT8 | Loss |
|---|---|---|---|
| SqueezeNet SSD | 61.80 | 61.27 | -0.53 |
| MobileNet_v1 SSD | 70.49 | 68.92 | -1.57 |
The following table show the speedup between Float32 and Int8 inference. It should be noted that the winograd algorithm is enable in the Float32 and Int8 inference. The Hardware Platform is Hisi3519(Cortex-A17@880MHz)
| Uint(ms) | SqueezeNet v1.1 | MobileNet v1 | GoogleNet | ResNet18 | MobileNetv1 SSD | SqueezeNet SSD |
|---|---|---|---|---|---|---|
| Float32 | 282 | 490 | 1107 | 985 | 970 | 610 |
| Int8 | 192 | 369 | 696 | 531 | 605 | 498 |
| Ratio | x1.46 | x1.33 | x1.59 | x1.85 | x1.60 | x1.22 |
Runtime Memory : mbytes
| Models | fp32-wino63 | int8-wino23 | int8-wino43 |
|---|---|---|---|
| squeezenet_v1_1 | 50 | 30 | 32 |
| mobilenet_v1 | 61 | 35 | 35 |
| mobilenet_v1_ssd | 90 | 45 | 45 |
| squeezenet_v1_ssd | 210 | 70 | 94 |
| resnet18 | 335 | 77 | 130 |
| googlenet_v1 | 154 | 72 | 89 |
Storage Memory : mbytes
| Models | fp32 | int8 |
|---|---|---|
| squeezenet_v1_1 | 4.71 | 1.20 |
| mobilenet_v1 | 16.3 | 4.31 |
| mobilenet_v1_ssd | 22.0 | 5.60 |
| squeezenet_v1_ssd | 21.1 | 5.37 |
| resnet18 | 44.6 | 11.2 |
| googlenet_v1 | 26.6 | 6.72 |
Thanks to NVIDIA for providing the principle of correlation entropy and ncnn's author nihui sharing his neural network inference framework.
Thanks to the help from the following friends:
Optimization Instructor : Fugangping, bruce.zhang
Algorithm : xupengfeixupf, JansonZhu, wangxinwei, lengmm
Python : daquexian
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