Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/Tianxiaomo/pytorch-YOLOv4
PyTorch ,ONNX and TensorRT implementation of YOLOv4
https://github.com/Tianxiaomo/pytorch-YOLOv4
darknet2onnx darknet2pytorch onnx pytorch pytorch-yolov4 tensorrt yolov3 yolov4 yolov4-tiny
Last synced: about 2 months ago
JSON representation
PyTorch ,ONNX and TensorRT implementation of YOLOv4
- Host: GitHub
- URL: https://github.com/Tianxiaomo/pytorch-YOLOv4
- Owner: Tianxiaomo
- License: apache-2.0
- Created: 2020-04-25T10:11:33.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-06-19T20:43:20.000Z (6 months ago)
- Last Synced: 2024-10-29T14:59:42.126Z (about 2 months ago)
- Topics: darknet2onnx, darknet2pytorch, onnx, pytorch, pytorch-yolov4, tensorrt, yolov3, yolov4, yolov4-tiny
- Language: Python
- Homepage:
- Size: 2.33 MB
- Stars: 4,478
- Watchers: 52
- Forks: 1,489
- Open Issues: 346
-
Metadata Files:
- Readme: README.md
- License: License.txt
Awesome Lists containing this project
- awesome-yolo-object-detection - Tianxiaomo/pytorch-YOLOv4 - YOLOv4?style=social"/> : PyTorch ,ONNX and TensorRT implementation of YOLOv4. (Other Versions of YOLO)
README
# Pytorch-YOLOv4
[](./License.txt)
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/
- [x] Inference
- [x] Train
- [x] Mosaic
```
├── 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_annotation.py coco dataset generator
│ ├── config.py
│ ├── darknet2pytorch.py
│ ├── region_loss.py
│ ├── utils.py
│ └── yolo_layer.py
```
# 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](Use_yolov4_to_train_your_own_data.md)
1. Download weight
2. Transform data
For coco dataset,you can use tool/coco_annotation.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
## 2.1 Performance on MS COCO dataset (using pretrained DarknetWeights from )
**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)
| Model type | AP | AP50 | AP75 | APS | APM | APL |
| ------------------- | ----------: | ----------: | ----------: | ----------: | ----------: | ----------: |
| DarkNet (YOLOv4 paper)| 0.471 | 0.710 | 0.510 | 0.278 | 0.525 | 0.636 |
| Pytorch (TianXiaomo)| 0.466 | 0.704 | 0.505 | 0.267 | 0.524 | 0.629 |
| TensorRT FP32 + BatchedNMSPlugin | 0.472| 0.708 | 0.511 | 0.273 | 0.530 | 0.637 |
| TensorRT FP16 + BatchedNMSPlugin | 0.472| 0.708 | 0.511 | 0.273 | 0.530 | 0.636 |
- testdev2017 dataset (input size: 416x416)
| Model type | AP | AP50 | AP75 | APS | APM | APL |
| ------------------- | ----------: | ----------: | ----------: | ----------: | ----------: | ----------: |
| DarkNet (YOLOv4 paper)| 0.412 | 0.628 | 0.443 | 0.204 | 0.444 | 0.560 |
| Pytorch (TianXiaomo)| 0.404 | 0.615 | 0.436 | 0.196 | 0.438 | 0.552 |
| TensorRT FP32 + BatchedNMSPlugin | 0.412| 0.625 | 0.445 | 0.200 | 0.446 | 0.564 |
| TensorRT FP16 + BatchedNMSPlugin | 0.412| 0.625 | 0.445 | 0.200 | 0.446 | 0.563 |
## 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.
```py
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)
```sh
python demo.py -cfgfile -weightfile -imgfile
```
- Load pytorch weights (pth file) to do the inference
```sh
python models.py
```
- 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
- **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**
```sh
pip install onnxruntime
```
- **Run python script to generate ONNX model and run the demo**
```sh
python demo_darknet2onnx.py
```
## 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
- **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**
```sh
pip install onnxruntime
```
- **Run python script to generate ONNX model and run the demo**
```sh
python demo_pytorch2onnx.py
```
For example:
```sh
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
- **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**
```sh
trtexec --onnx= --explicitBatch --saveEngine= --workspace= --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**
```sh
trtexec --onnx= \
--minShapes=input: --optShapes=input: --maxShapes=input: \
--workspace= --saveEngine= --fp16
```
- For example:
```sh
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
```sh
python demo_trt.py
```
- 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= --explicitBatch --saveEngine= --workspace= --fp16
```
For multi-batch,
```
trtexec --onnx= --explicitBatch --shapes=input:Xx3xHxW --optShapes=input:Xx3xHxW --maxShapes=input:Xx3xHxW --minShape=input:1x3xHxW --saveEngine= --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}
}
```