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(-128 - 127).
We provide the Classification(SqueezeNet_v1.1) and Detection(MobileNet_v1 SSD 300) demos based on ncnn(It is a high-performance neural network inference framework optimized for the mobile platform )
For details, please read the following PDF:
$ python caffe-int8-convert-tool.py --help
usage: caffe-int8-convert-tool.py [-h] [--proto PROTO] [--model MODEL]
[--mean MEAN MEAN MEAN] [--norm NORM]
[--images IMAGES] [--output OUTPUT]
[--gpu GPU]
find the pretrained caffe models 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 weights
--mean MEAN value of mean
--norm NORM value of normalize
--images IMAGES path to calibration images
--output OUTPUT path to output calibration table file
--gpu GPU use gpu to forward
$ python caffe-int8-convert-tool.py --proto=squeezenet_v1.1.prototxt --model=squeezenet.caffemodel --mean 104 117 123 --images=ILSVRC2012_1k --output=squeezenet_v1.1.table --gpu=1
We used ImageNet2012 Dataset to complete some experiments.
| Type | Detail |
|---|---|
| Calibration Dataset | ILSVRC2012_img_test 1k |
| Test Dataset | ILSVRC2012_img_val 5k |
| Framework | ncnn-int8 |
| Support Layer | Convolution3x3,Convolution1x1,ConvolutionDepthwise3x3 |
The following table show the Top1 and Top5 different between Float32 and Int8 inference.
| FP32 | INT8 | |||
|---|---|---|---|---|
| NETWORK | Top1 | Top5 | Top1 | Top5 |
| SqueezeNet v1.1 | 57.86% | 79.86% | 57.36% | 79.84% |
| MobileNet v1 | 67.78% | 87.62% | 64.92% | 85.22% |
| MobileNet v2 | 70.20% | 89.20% | 69.00% | 88.04% |
| GoogleNet v1 | 67.70% | 88.32% | 67.64% | 88.26% |
| ResNet-18 | 65.50% | 86.46% | 65.48% | 86.44% |
| ResNet-50 | 71.68% | 89.94% | 71.38% | 89.52% |
| NETWORK | Top1 | Top5 | Diff Top1 | Diff Top5 |
| SqueezeNet v1.1 | 57.86% | 79.86% | 0.50% | 0.02% |
| MobileNet v1 | 67.78% | 87.62% | 2.86% | 2.40% |
| MobileNet v2 | 70.20% | 89.20% | 1.06% | 1.16% |
| GoogleNet v1 | 67.70% | 88.32% | 0.06% | 0.06% |
| ResNet-18 | 65.50% | 86.46% | 0.02% | 0.02% |
| ResNet-50 | 71.68% | 89.94% | 0.30% | 0.32% |
The following table show the speedup between Float32 and Int8 inference.It should be noted that the winograd algorithm is not used in the Float32 inference.The Hardware Platform is Hisi3519(Cortex-A17@1.2GHz)
| Uint(ms) | SqueezeNet v1.1 | MobileNet v1 | MobileNet v2 | GoogleNet | ResNet18 | MobileNetv1 SSD |
|---|---|---|---|---|---|---|
| Float32 | 382 | 568 | 392 | 1662 | 1869 | 1120 |
| Int8 | 242 | 369 | 311 | 1159 | 1159 | 701 |
| Ratio | x1.30 | x1.41 | x1.28 | x1.43 | x1.61 | x1.47 |
Thanks to our company SenseNets to support the open source project,and 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:
Algorithm : xupengfeixupf, JansonZhu, wangxinwei, lengmm
Python : daquexian
BSD 3 Clause