yolov7-tiny.weigths转onnx模型

# 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 <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) | 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 <cfgFile> -weightfile <weightFile> -imgfile <imgFile> ``` - Load pytorch weights (pth file) to do the inference ```sh 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 - **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 <cfgFile> <namesFile> <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 - **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 <weight_file> <image_path> <batch_size> <n_classes> <IN_IMAGE_H> <IN_IMAGE_W> ``` 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=<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 fol