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100 lines
4.6 KiB
Markdown
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100 lines
4.6 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# YOLOv7 Python Deployment Demo
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Two steps before deployment:
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- 1. The hardware and software environment meets the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Please refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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This doc provides a quick `infer.py` demo of YOLOv7 deployment on CPU/GPU, and accelerated GPU deployment by TensorRT. Run the following command:
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```bash
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# Download sample deployment code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/detection/yolov7/python/
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7_infer.tar
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tar -xf yolov7_infer.tar
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# CPU
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python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device cpu
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# GPU
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python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device gpu
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# KunlunXin XPU
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python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device kunlunxin
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# Huawei Ascend
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python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device ascend
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```
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If you want to test ONNX model:
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```bash
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# Download yolov7 model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# CPU Inference
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python infer.py --model yolov7.onnx --image 000000014439.jpg --device cpu
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# GPU
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python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu
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# Infer with TensorRT on GPU
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python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu --use_trt True
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```
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The visualisation of the results is as follows.
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<img width="640" src="https://user-images.githubusercontent.com/67993288/183847558-abcd9a57-9cd9-4891-b09a-710963c99b74.jpg">
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## YOLOv7 Python Interface
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```python
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fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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YOLOv7 model loading and initialisation, with model_file being the exported ONNX model format.
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**Parameters**
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path. If the model format is ONNX, the parameter can be filled with an empty string.
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> * **runtime_option**(RuntimeOption): Back-end inference configuration. The default is None, i.e. the default is applied
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> * **model_format**(ModelFormat): Model format. The default is ONNX format
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### Predict Function
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> ```python
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> YOLOv7.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
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> ```
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> Model prediction interface with direct output of detection results from the image input.
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>
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> **Parameters**
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>
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> > * **image_data**(np.ndarray): Input image. Images need to be in HWC or BGR format
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> > * **conf_threshold**(float): Filter threshold for detection box confidence
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> > * **nms_iou_threshold**(float): iou thresholds during NMS processing
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> **Return**
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>
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> > Return to`fastdeploy.vision.DetectionResult`Struct. For more details, please refer to [Vision Model Results](../../../../../docs/api/vision_results/)
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### Class Member Variables
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#### Pre-processing parameters
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Users can modify the following pre-processing parameters for their needs. This will affect the final reasoning and deployment results
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> > * **size**(list[int]): This parameter modifies the 'resize' during preprocessing and contains two integer elements representing [width, height]. The default value is [640, 640].
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> > * **padding_value**(list[float]): This parameter modifies the value of the padding when resizing the image. It contains three floating-point elements, representing the values of the three channels. The default value is [114, 114, 114].
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> > * **is_no_pad**(bool): This parameter determines whether the image is resized by padding, `is_no_pad=ture` means no padding is used. The default value is `is_no_pad=false`.
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> > * **is_mini_pad**(bool): This parameter allows the width and height of the image after resize to be the closest value to the `size` member variable, which the pixel size of the padding can be divided by the `stride` member variable. The default value is `is_mini_pad=false`.
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> > * **stride**(int): Used with`stris_mini_pad` member value. The default value is`stride=32`
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## Related files
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- [YOLOv7 Model Introduction](..)
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- [YOLOv7 C++ Deployment](../cpp)
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- [Vision Model Results](../../../../../docs/api/vision_results/)
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- [how to change inference backend](../../../../../docs/en/faq/how_to_change_backend.md)
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