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https://github.com/PaddlePaddle/FastDeploy.git
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dc2dad62a4
* Improve the usage of fastdeploy tools * Fix quantized YOLOv5,v6 and v7 model deployment
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
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() == "pptrt":
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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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option.enable_paddle_to_trt()
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elif args.backend.lower() == "ort":
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option.use_ort_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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