Pytorch-YOLOv4

A minimal PyTorch implementation of YOLOv4.

• Paper Yolo v4: https://arxiv.org/abs/2004.10934

• Source code:https://github.com/AlexeyAB/darknet

• More details: http://pjreddie.com/darknet/yolo/

• Inference

• Train

  • Mocaic

├── README.md
├── dataset.py            dataset
├── demo.py               demo to run pytorch --> tool/darknet2pytorch
├── demo_darknet2onnx.py  tool to convert into onnx --> tool/darknet2pytorch
├── demo_pytorch2onnx.py  tool to convert into onnx
├── models.py             model for pytorch
├── train.py              train models.py
├── cfg.py                cfg.py for train
├── cfg                   cfg --> darknet2pytorch
├── data            
├── weight                --> darknet2pytorch
├── tool
│   ├── camera.py           a demo camera
│   ├── coco_annotatin.py       coco dataset generator
│   ├── config.py
│   ├── darknet2pytorch.py
│   ├── region_loss.py
│   ├── utils.py
│   └── yolo_layer.py

Wildflower Forked Version

Changes

• Run using a CLI (yolov4)
• Automatically download weights
• PyPi ready so project can be used as a library

0. Weights Download

0.1 darknet

• baidu(https://pan.baidu.com/s/1dAGEW8cm-dqK14TbhhVetA Extraction code:dm5b)
• google(https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT)

0.2 pytorch

you can use darknet2pytorch to convert it yourself, or download my converted model.

• baidu
  • yolov4.pth(https://pan.baidu.com/s/1ZroDvoGScDgtE1ja_QqJVw Extraction code:xrq9)
  • yolov4.conv.137.pth(https://pan.baidu.com/s/1ovBie4YyVQQoUrC3AY0joA Extraction code:kcel)
• google
  • yolov4.pth(https://drive.google.com/open?id=1wv_LiFeCRYwtpkqREPeI13-gPELBDwuJ)
  • yolov4.conv.137.pth(https://drive.google.com/open?id=1fcbR0bWzYfIEdLJPzOsn4R5mlvR6IQyA)

1. Train

use yolov4 to train your own data

1. Download weight

2. Transform data

   For coco dataset,you can use tool/coco_annotatin.py.

   # train.txt
   image_path1 x1,y1,x2,y2,id x1,y1,x2,y2,id x1,y1,x2,y2,id ...
   image_path2 x1,y1,x2,y2,id x1,y1,x2,y2,id x1,y1,x2,y2,id ...
   ...
   ...
   

3. Train

   you can set parameters in cfg.py.

    python train.py -g [GPU_ID] -dir [Dataset direction] ...
   

2. Inference (Evolving)

2.1 Performance on MS COCO dataset (using pretrained DarknetWeights from https://github.com/AlexeyAB/darknet)

ONNX and TensorRT models are converted from Pytorch (TianXiaomo): Pytorch->ONNX->TensorRT. See following sections for more details of conversions.

• val2017 dataset (input size: 416x416)

• testdev2017 dataset (input size: 416x416)

2.2 Image input size for inference

Image input size is NOT restricted in 320 * 320, 416 * 416, 512 * 512 and 608 * 608. You can adjust your input sizes for a different input ratio, for example: 320 * 608. Larger input size could help detect smaller targets, but may be slower and GPU memory exhausting.

height = 320 + 96 * n, n in {0, 1, 2, 3, ...}
width  = 320 + 96 * m, m in {0, 1, 2, 3, ...}

2.3 Different inference options

• Load the pretrained darknet model and darknet weights to do the inference (image size is configured in cfg file already)

  python demo.py -cfgfile <cfgFile> -weightfile <weightFile> -imgfile <imgFile>
  

• Load pytorch weights (pth file) to do the inference

  python models.py <num_classes> <weightfile> <imgfile> <IN_IMAGE_H> <IN_IMAGE_W> <namefile(optional)>
  

• Load converted ONNX file to do inference (See section 3 and 4)

• Load converted TensorRT engine file to do inference (See section 5)

2.4 Inference output

There are 2 inference outputs.

• One is locations of bounding boxes, its shape is [batch, num_boxes, 1, 4] which represents x1, y1, x2, y2 of each bounding box.
• The other one is scores of bounding boxes which is of shape [batch, num_boxes, num_classes] indicating scores of all classes for each bounding box.

Until now, still a small piece of post-processing including NMS is required. We are trying to minimize time and complexity of post-processing.

3. Darknet2ONNX (Evolving)

• This script is to convert the official pretrained darknet model into ONNX

• Pytorch version Recommended:

  • Pytorch 1.4.0 for TensorRT 7.0 and higher
  • Pytorch 1.5.0 and 1.6.0 for TensorRT 7.1.2 and higher

• Install onnxruntime

  pip install onnxruntime
  

• Run python script to generate ONNX model and run the demo

  python demo_darknet2onnx.py <cfgFile> <weightFile> <imageFile> <batchSize>
  

3.1 Dynamic or static batch size

• Positive batch size will generate ONNX model of static batch size, otherwise, batch size will be dynamic
  • Dynamic batch size will generate only one ONNX model
  • Static batch size will generate 2 ONNX models, one is for running the demo (batch_size=1)

4. Pytorch2ONNX (Evolving)

• You can convert your trained pytorch model into ONNX using this script

• Pytorch version Recommended:

  • Pytorch 1.4.0 for TensorRT 7.0 and higher
  • Pytorch 1.5.0 and 1.6.0 for TensorRT 7.1.2 and higher

• Install onnxruntime

  pip install onnxruntime
  

• Run python script to generate ONNX model and run the demo

  python demo_pytorch2onnx.py <weight_file> <image_path> <batch_size> <n_classes> <IN_IMAGE_H> <IN_IMAGE_W>
  

  For example:

  python demo_pytorch2onnx.py yolov4.pth dog.jpg 8 80 416 416
  

4.1 Dynamic or static batch size

• Positive batch size will generate ONNX model of static batch size, otherwise, batch size will be dynamic
  • Dynamic batch size will generate only one ONNX model
  • Static batch size will generate 2 ONNX models, one is for running the demo (batch_size=1)

5. ONNX2TensorRT (Evolving)

• TensorRT version Recommended: 7.0, 7.1

5.1 Convert from ONNX of static Batch size

• Run the following command to convert YOLOv4 ONNX model into TensorRT engine

  trtexec --onnx=<onnx_file> --explicitBatch --saveEngine=<tensorRT_engine_file> --workspace=<size_in_megabytes> --fp16
  

  • Note: If you want to use int8 mode in conversion, extra int8 calibration is needed.

5.2 Convert from ONNX of dynamic Batch size

• Run the following command to convert YOLOv4 ONNX model into TensorRT engine

  trtexec --onnx=<onnx_file> \
  --minShapes=input:<shape_of_min_batch> --optShapes=input:<shape_of_opt_batch> --maxShapes=input:<shape_of_max_batch> \
  --workspace=<size_in_megabytes> --saveEngine=<engine_file> --fp16
  

• For example:

  trtexec --onnx=yolov4_-1_3_320_512_dynamic.onnx \
  --minShapes=input:1x3x320x512 --optShapes=input:4x3x320x512 --maxShapes=input:8x3x320x512 \
  --workspace=2048 --saveEngine=yolov4_-1_3_320_512_dynamic.engine --fp16
  

5.3 Run the demo

python demo_trt.py <tensorRT_engine_file> <input_image> <input_H> <input_W>

• This demo here only works when batchSize is dynamic (1 should be within dynamic range) or batchSize=1, but you can update this demo a little for other dynamic or static batch sizes.

• Note1: input_H and input_W should agree with the input size in the original ONNX file.

• Note2: extra NMS operations are needed for the tensorRT output. This demo uses python NMS code from tool/utils.py.

6. ONNX2Tensorflow

• First:Conversion to ONNX

  tensorflow >=2.0

  1: Thanks:github:https://github.com/onnx/onnx-tensorflow

  2: Run git clone https://github.com/onnx/onnx-tensorflow.git && cd onnx-tensorflow Run pip install -e .

  Note:Errors will occur when using "pip install onnx-tf", at least for me,it is recommended to use source code installation

7. ONNX2TensorRT and DeepStream Inference

1. Compile the DeepStream Nvinfer Plugin

    cd DeepStream
    make 

2. Build a TRT Engine.

For single batch,

trtexec --onnx=<onnx_file> --explicitBatch --saveEngine=<tensorRT_engine_file> --workspace=<size_in_megabytes> --fp16

For multi-batch,

trtexec --onnx=<onnx_file> --explicitBatch --shapes=input:Xx3xHxW --optShapes=input:Xx3xHxW --maxShapes=input:Xx3xHxW --minShape=input:1x3xHxW --saveEngine=<tensorRT_engine_file> --fp16

Note :The maxShapes could not be larger than model original shape.

3. Write the deepstream config file for the TRT Engine.

Reference:

• https://github.com/eriklindernoren/PyTorch-YOLOv3
• https://github.com/marvis/pytorch-caffe-darknet-convert
• https://github.com/marvis/pytorch-yolo3

@article{yolov4,
  title={YOLOv4: YOLOv4: Optimal Speed and Accuracy of Object Detection},
  author={Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao},
  journal = {arXiv},
  year={2020}
}