mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 09:07:10 +08:00
9e20dab0d6
* fix infer.py and README * [Example] Merge Download Paddle Model, Paddle->Onnx->Mlir->Bmodel and inference into infer.py. Modify README.md * modify pp_liteseg sophgo infer.py and README.md * fix PPOCR,PPYOLOE,PICODET,LITESEG sophgo infer.py and README.md * fix memory overflow problem while inferring with sophgo backend * fix memory overflow problem while inferring with sophgo backend --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: xuyizhou <yizhou.xu@sophgo.com>
109 lines
3.3 KiB
Python
109 lines
3.3 KiB
Python
import fastdeploy as fd
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import cv2
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import os
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from subprocess import run
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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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"--auto", required=True, help="Auto download, convert, compile and infer if True")
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parser.add_argument("--model", help="Path of model.")
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parser.add_argument(
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"--image", type=str, help="Path of test image file.")
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return parser.parse_args()
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def download():
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download_model_str = 'wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx'
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if not os.path.exists('yolov5s.onnx'):
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print(download_model_str)
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run(download_model_str, shell=True)
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download_img_str = 'wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg'
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if not os.path.exists('000000014439.jpg'):
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print(download_img_str)
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run(download_img_str, shell=True)
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def mlir_prepare():
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mlir_path = os.getenv("MODEL_ZOO_PATH")
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mlir_path = mlir_path[:-13]
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regression_path = os.path.join(mlir_path, 'regression')
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mv_str_list = ['mkdir YOLOv5s',
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'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./YOLOv5s',
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'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./YOLOv5s',
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'cp yolov5s.onnx ./YOLOv5s',
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'mkdir ./YOLOv5s/workspace']
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for str in mv_str_list:
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print(str)
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run(str, shell=True)
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def onnx2mlir():
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transform_str = 'model_transform.py \
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--model_name yolov5s \
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--model_def ../yolov5s.onnx \
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--input_shapes [[1,3,640,640]] \
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--mean 0.0,0.0,0.0 \
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--scale 0.0039216,0.0039216,0.0039216 \
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--keep_aspect_ratio \
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--pixel_format rgb \
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--output_names output0 \
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--test_input ../image/dog.jpg \
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--test_result yolov5s_top_outputs.npz \
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--mlir yolov5s.mlir'
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os.chdir('./YOLOv5s/workspace')
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print(transform_str)
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run(transform_str, shell=True)
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os.chdir('../../')
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def mlir2bmodel():
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deploy_str = 'model_deploy.py \
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--mlir yolov5s.mlir \
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--quantize F32 \
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--chip bm1684x \
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--test_input yolov5s_in_f32.npz \
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--test_reference yolov5s_top_outputs.npz \
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--model yolov5s_1684x_f32.bmodel'
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os.chdir('./YOLOv5s/workspace')
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print(deploy_str)
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run(deploy_str, shell=True)
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os.chdir('../../')
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args = parse_arguments()
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if args.auto:
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download()
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mlir_prepare()
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onnx2mlir()
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mlir2bmodel()
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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runtime_option.use_sophgo()
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model_file = './YOLOv5s/workspace/yolov5s_1684x_f32.bmodel' if args.auto else args.model
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params_file = ""
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img_file = './000000014439.jpg' if args.auto else args.image
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model = fd.vision.detection.YOLOv5(
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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=fd.ModelFormat.SOPHGO)
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# 预测图片分类结果
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im = cv2.imread(img_file)
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result = model.predict(im)
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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("sophgo_result.jpg", vis_im)
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print("Visualized result save in ./sophgo_result.jpg")
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