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README.md

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YOLOv7End2EndTRT Python Deployment Example

Two steps before deployment

This directory provides examples that infer.py fast finishes the deployment of YOLOv7End2EndTRT accelerated by TensorRT. The script is as follows

# Download the example code for deployment
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/detection/yolov7end2end_trt/python/

# Download 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 
python infer.py --model yolov7-end2end-trt-nms.onnx --image 000000014439.jpg --device gpu --use_trt True
# If it is not supported by the python package, compile the latest FastDeploy Python Wheel package from the source code in develop branch and install it.

The visualized result after running is as follows

image

Attention: YOLOv7End2EndTRT 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 Python Interface

fastdeploy.vision.detection.YOLOv7End2EndTRT(model_file, params_file=None, runtime_option=None, 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. No need to set 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(image_data, conf_threshold=0.25)

Model prediction interface. Input images and output detection results.

Parameter

  • image_data(np.ndarray): Input data in HWC or BGR format
  • conf_threshold(float): 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.

Return

Return fastdeploy.vision.DetectionResult structure. Refer to Vision Model Prediction Results for its description.

Class Member Property

Pre-processing Parameter

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

  • size(list[int]): This parameter changes resize used during preprocessing, containing two integer elements for [width, height] with default value [640, 640]
  • padding_value(list[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=True 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_padide member variable. Default stride=32

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