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Add Examples to deploy quantized models (#342)
* Add PaddleOCR Support * Add PaddleOCR Support * Add PaddleOCRv3 Support * Add PaddleOCRv3 Support * Update README.md * Update README.md * Update README.md * Update README.md * Add PaddleOCRv3 Support * Add PaddleOCRv3 Supports * Add PaddleOCRv3 Suport * Fix Rec diff * Remove useless functions * Remove useless comments * Add PaddleOCRv2 Support * Add PaddleOCRv3 & PaddleOCRv2 Support * remove useless parameters * Add utils of sorting det boxes * Fix code naming convention * Fix code naming convention * Fix code naming convention * Fix bug in the Classify process * Imporve OCR Readme * Fix diff in Cls model * Update Model Download Link in Readme * Fix diff in PPOCRv2 * Improve OCR readme * Imporve OCR readme * Improve OCR readme * Improve OCR readme * Imporve OCR readme * Improve OCR readme * Fix conflict * Add readme for OCRResult * Improve OCR readme * Add OCRResult readme * Improve OCR readme * Improve OCR readme * Add Model Quantization Demo * Fix Model Quantization Readme * Fix Model Quantization Readme * Add the function to do PTQ quantization * Improve quant tools readme * Improve quant tool readme * Improve quant tool readme * Add PaddleInference-GPU for OCR Rec model * Add QAT method to fastdeploy-quantization tool * Remove examples/slim for now * Move configs folder * Add Quantization Support for Classification Model * Imporve ways of importing preprocess * Upload YOLO Benchmark on readme * Upload YOLO Benchmark on readme * Upload YOLO Benchmark on readme * Improve Quantization configs and readme * Add support for multi-inputs model * Add backends and params file for YOLOv7 * Add quantized model deployment support for YOLO series * Fix YOLOv5 quantize readme * Fix YOLO quantize readme * Fix YOLO quantize readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Fix bug, change Fronted to ModelFormat * Change Fronted to ModelFormat * Add examples to deploy quantized paddleclas models * Fix readme * Add quantize Readme * Add quantize Readme * Add quantize Readme * Modify readme of quantization tools * Modify readme of quantization tools * Improve quantization tools readme * Improve quantization readme * Improve PaddleClas quantized model deployment readme * Add PPYOLOE-l quantized deployment examples * Improve quantization tools readme
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examples/vision/detection/yolov6/quantize/python/README.md
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examples/vision/detection/yolov6/quantize/python/README.md
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# YOLOv6量化模型 Python部署示例
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本目录下提供的`infer.py`,可以帮助用户快速完成YOLOv6量化模型在CPU/GPU上的部署推理加速.
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## 部署准备
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### FastDeploy环境准备
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/environment.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/quick_start)
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### 量化模型准备
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- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
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- 2. 用户可以使用FastDeploy提供的[一键模型量化工具](../../../../../../tools/quantization/),自行进行模型量化, 并使用产出的量化模型进行部署.
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## 以量化后的YOLOv6s模型为例, 进行部署
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/slim/yolov6/python
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#下载FastDeloy提供的yolov6s量化模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s_quant.tar
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tar -xvf yolov6s_quant.tar
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# 在CPU上使用Paddle-Inference推理量化模型
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python infer.py --model yolov6s_quant --image 000000014439.jpg --device cpu --backend paddle
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# 在GPU上使用TensorRT推理量化模型
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python infer.py --model yolov6s_quant --image 000000014439.jpg --device gpu --backend trt
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```
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examples/vision/detection/yolov6/quantize/python/infer.py
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examples/vision/detection/yolov6/quantize/python/infer.py
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import fastdeploy as fd
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import cv2
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import os
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from fastdeploy import ModelFormat
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model", required=True, help="Path of yolov6 onnx model.")
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parser.add_argument(
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"--image", required=True, help="Path of test image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--backend",
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type=str,
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default="default",
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help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
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)
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parser.add_argument(
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"--device_id",
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type=int,
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default=0,
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help="Define which GPU card used to run model.")
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parser.add_argument(
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"--cpu_thread_num",
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type=int,
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default=9,
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help="Number of threads while inference on CPU.")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu(0)
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option.set_cpu_thread_num(args.cpu_thread_num)
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if args.backend.lower() == "trt":
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assert args.device.lower(
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) == "gpu", "TensorRT backend require inference on device GPU."
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option.use_trt_backend()
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elif args.backend.lower() == "ort":
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option.use_ort_backend()
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elif args.backend.lower() == "paddle":
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option.use_paddle_backend()
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elif args.backend.lower() == "openvino":
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assert args.device.lower(
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) == "cpu", "OpenVINO backend require inference on device CPU."
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option.use_openvino_backend()
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return option
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args = parse_arguments()
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model_file = os.path.join(args.model, "model.pdmodel")
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params_file = os.path.join(args.model, "model.pdiparams")
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model = fd.vision.detection.YOLOv6(
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model_file,
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params_file,
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runtime_option=runtime_option,
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model_format=ModelFormat.PADDLE)
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# 预测图片检测结果
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im = cv2.imread(args.image)
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result = model.predict(im.copy())
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print(result)
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# 预测结果可视化
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vis_im = fd.vision.vis_detection(im, result)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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