FastDeploy/examples/vision/detection/yolov7end2end_trt/cpp/README.md

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YOLOv7End2EndTRT C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment on GPU accelerated by TensorRT. Now only TensorRT deployment is supported.

Two steps before deployment

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. Download the precompiled deployment library and samples code according to your development environment. Refer to FastDeploy Precompiled Library

Taking the inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.

mkdir build
cd build
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j

# Download the official converted yolov7 model files and test images 
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-trt-nms.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

# TensorRT inference on GPU
./infer_demo yolov7-end2end-trt-nms.onnx 000000014439.jpg 2

The visualized result after running is as follows

The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:

• How to use FastDeploy C++ SDK in Windows

Attention: YOLOv7End2EndORT is designed for the inference of End2End models with TRT_NMS among the YOLOv7 exported models. For models without nms, use YOLOv7 class for inference. For End2End models with ORT_NMS, use YOLOv7End2EndTRT for inference.

YOLOv7End2EndTRT C++ Interface

YOLOv7End2EndTRT Class

fastdeploy::vision::detection::YOLOv7End2EndTRT(
        const string& model_file,
        const string& params_file = "",
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::ONNX)

YOLOv7End2EndTRT model loading and initialization, among which model_file is the exported ONNX model format.

Parameter

• model_file(str): Model file path
• params_file(str): Parameter file path. Merely passing an empty string when the model is in ONNX format
• runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
• model_format(ModelFormat): Model format. ONNX format by default

Predict Function

YOLOv7End2EndTRT::Predict(cv::Mat* im, DetectionResult* result,
                          float conf_threshold = 0.25)

Model prediction interface. Input images and output detection results.

Parameter

• im: Input images in HWC or BGR format
• result: Detection results, including detection box and confidence of each box. Refer to Vision Model Prediction Results for DetectionResult
• conf_threshold: Filtering threshold of detection box confidence. But considering that YOLOv7 End2End models have a score threshold specified during ONNX export, this parameter will be effective when being greater than the specified one.

Class Member Variable

Pre-processing Parameter

Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results

• size(vector<int>): This parameter changes resize used during preprocessing, containing two integer elements for [width, height] with default value [640, 640]
• padding_value(vector<float>): This parameter is used to change the padding value of images during resize, containing three floating-point elements that represent the value of three channels. Default value [114, 114, 114]
• is_no_pad(bool): Specify whether to resize the image through padding. is_no_pad=ture represents no paddling. Default is_no_pad=false
• is_mini_pad(bool): This parameter sets the width and height of the image after resize to the value nearest to the size member variable and to the point where the padded pixel size is divisible by the stride member variable. Default is_mini_pad=false
• stride(int): Used with the stris_mini_pad member variable. Default stride=32

• Model Description
• Python Deployment
• Vision Model Prediction Results
• How to switch the model inference backend engine