YOLOv3 quantization(offline) model v1.0

This repository is YOLOv3 quantization model vertion1.0, include pretrain code on ImageNet, inference with one image as input and save the quantization parameters of inputs,activations,origins,weights and biases of each layer.

Prerequisites

Python environment (>=2.7) for 64 bits windows or Linux
tensorflow-gpu==1.12.0, To run TensorFlow on your GPU as we and most people do, you'll need to follow the directions for installing CUDA and CuDNN. We recommend setting aside at least an hour to make sure you do this right.
other python library: numpy, opencv-python, PIL, easydict, math,absl
hardware environment: NVIDIA Geforce GTX 1080Ti

Document

The file fold contains the core codes of this repository, if you need to change the CNN structure, just modify the backbone in darknet53.py

|--core
|  |--config.py          // definite global hyper-parameter
|  |--darknet53.py       // build the backbone of YOLOv3
|  |--nn_skeleton.py     // CNN library, definite all the model
|  |--save_parameters.py // save the paramenters of each layer
|  |--utils.py           // image preprocess and postprocess
|  |--YOLOv3.py          // build the integrate struture

This file fold provides the input image , anchor size and the information for image post-processing

|--data
|  |--plusAI
|  |  |--baselines.txt     // general anchor size 
|  |  |--coco.names        // classfication numbers of MS COCO dataset 
|  |  |--layer_name.txt    // YOLOv3 layer names
|  |  |--test.jpg
|  |  |--test_1024x772.jpg
|  |  |--result.jpg
|  |  |--YOLO_v1.2.xlxs    // YOLOv3 structure details

Others

|--dataset
|  |--ImageNet.py           // the library to load the imagenet data       
|--log
|  |--checkpoint_imagenet   // the ckpt save path of pretraining on imagenet 
|  |--checkpoint_transfer   // the ckpt path of YOLOv3 inference
|--debug.py                 // for debug 
|--pretrain_on_ImageNet.py  // pretrain code on ImageNet dataset
|--quant_YOLO.py            // YOLOv3 inference

Instructions

Clone this repository.
pip install the library which are required.

run the pretrain code on ImageNet

python pretrain_ImageNet.py --gpu=0,1,2,3

run the YOLOv3 inference

python quant_YOLO.py --gpu=0,1

Acknowledgement

YunYang1994/YOLO v3: https://github.com/YunYang1994/tensorflow-yolov3
XJTUWYD/ShuffleDet: https://github.com/XJTUWYD/ShuffleDet
BichenWuUCB/squeezeDet: https://github.com/BichenWuUCB/squeezeDet

Reference