mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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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>
308 lines
12 KiB
Python
308 lines
12 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(
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"--det_model", required=True, help="Path of Detection model of PPOCR.")
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parser.add_argument(
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"--cls_model",
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required=True,
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help="Path of Classification model of PPOCR.")
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parser.add_argument(
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"--rec_model",
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required=True,
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help="Path of Recognization model of PPOCR.")
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parser.add_argument(
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"--rec_label_file",
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required=True,
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help="Path of Recognization label of PPOCR.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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return parser.parse_args()
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def getPPOCRv3():
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cmd_str_det = 'wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar'
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tar_str_det = 'tar xvf ch_PP-OCRv3_det_infer.tar'
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cmd_str_cls = 'wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar'
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tar_str_cls = 'tar xvf ch_ppocr_mobile_v2.0_cls_infer.tar'
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cmd_str_rec = 'wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar'
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tar_str_rec = 'tar xvf ch_PP-OCRv3_rec_infer.tar'
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cmd_str_img = 'wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg'
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cmd_str_label = 'wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt'
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script_str = 'wget https://raw.githubusercontent.com/PaddlePaddle/Paddle2ONNX/develop/tools/paddle/paddle_infer_shape.py'
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if not os.path.exists('ch_PP-OCRv3_det_infer.tar'):
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print(cmd_str_det, tar_str_det)
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run(cmd_str_det, shell=True)
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run(tar_str_det, shell=True)
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if not os.path.exists('ch_ppocr_mobile_v2.0_cls_infer.tar'):
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print(cmd_str_cls, tar_str_cls)
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run(cmd_str_cls, shell=True)
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run(tar_str_cls, shell=True)
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if not os.path.exists('ch_PP-OCRv3_rec_infer.tar'):
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print(cmd_str_rec, tar_str_rec)
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run(cmd_str_rec, shell=True)
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run(tar_str_rec, shell=True)
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if not os.path.exists('12.jpg'):
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print(cmd_str_img)
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run(cmd_str_img, shell=True)
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if not os.path.exists('ppocr_keys_v1.txt'):
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print(cmd_str_label)
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run(cmd_str_label, shell=True)
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if not os.path.exists('paddle_infer_shape.py'):
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print(script_str)
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run(script_str, shell=True)
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def fix_input_shape():
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fix_det_str = 'python paddle_infer_shape.py --model_dir ch_PP-OCRv3_det_infer \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_dir ch_PP-OCRv3_det_infer_fix \
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--input_shape_dict="{\'x\':[1,3,960,608]}"'
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fix_rec_str = 'python paddle_infer_shape.py --model_dir ch_PP-OCRv3_rec_infer \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_dir ch_PP-OCRv3_rec_infer_fix \
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--input_shape_dict="{\'x\':[1,3,48,320]}"'
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fix_cls_str = 'python paddle_infer_shape.py --model_dir ch_ppocr_mobile_v2.0_cls_infer \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_dir ch_PP-OCRv3_cls_infer_fix \
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--input_shape_dict="{\'x\':[1,3,48,192]}"'
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print(fix_det_str)
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run(fix_det_str, shell=True)
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print(fix_rec_str)
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run(fix_rec_str, shell=True)
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print(fix_cls_str)
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run(fix_cls_str, shell=True)
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def paddle2onnx():
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cmd_str_det = 'paddle2onnx --model_dir ch_PP-OCRv3_det_infer_fix \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_file ch_PP-OCRv3_det_infer.onnx \
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--enable_dev_version True'
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cmd_str_cls = 'paddle2onnx --model_dir ch_PP-OCRv3_cls_infer_fix \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_file ch_PP-OCRv3_cls_infer.onnx \
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--enable_dev_version True'
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cmd_str_rec = 'paddle2onnx --model_dir ch_PP-OCRv3_rec_infer_fix \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_file ch_PP-OCRv3_rec_infer.onnx \
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--enable_dev_version True'
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print(cmd_str_det)
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run(cmd_str_det, shell=True)
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print(cmd_str_cls)
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run(cmd_str_cls, shell=True)
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print(cmd_str_rec)
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run(cmd_str_rec, 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 ch_PP-OCRv3',
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'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./ch_PP-OCRv3',
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'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./ch_PP-OCRv3',
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'mv ch_PP-OCRv3_det_infer.onnx ./ch_PP-OCRv3',
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'mv ch_PP-OCRv3_rec_infer.onnx ./ch_PP-OCRv3',
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'mv ch_PP-OCRv3_cls_infer.onnx ./ch_PP-OCRv3',
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'mkdir ./ch_PP-OCRv3/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_det = 'model_transform.py \
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--model_name ch_PP-OCRv3_det \
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--model_def ../ch_PP-OCRv3_det_infer.onnx \
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--input_shapes [[1,3,960,608]] \
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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 sigmoid_0.tmp_0 \
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--test_input ../image/dog.jpg \
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--test_result ch_PP-OCRv3_det_top_outputs.npz \
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--mlir ./ch_PP-OCRv3_det.mlir'
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transform_str_rec = 'model_transform.py \
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--model_name ch_PP-OCRv3_rec \
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--model_def ../ch_PP-OCRv3_rec_infer.onnx \
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--input_shapes [[1,3,48,320]] \
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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 softmax_5.tmp_0 \
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--test_input ../image/dog.jpg \
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--test_result ch_PP-OCRv3_rec_top_outputs.npz \
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--mlir ./ch_PP-OCRv3_rec.mlir'
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transform_str_cls = 'model_transform.py \
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--model_name ch_PP-OCRv3_cls \
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--model_def ../ch_PP-OCRv3_cls_infer.onnx \
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--input_shapes [[1,3,48,192]] \
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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 softmax_0.tmp_0 \
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--test_input ../image/dog.jpg \
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--test_result ch_PP-OCRv3_cls_top_outputs.npz \
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--mlir ./ch_PP-OCRv3_cls.mlir'
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os.chdir('./ch_PP-OCRv3/workspace/')
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print(transform_str_det)
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run(transform_str_det, shell=True)
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print(transform_str_rec)
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run(transform_str_rec, shell=True)
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print(transform_str_cls)
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run(transform_str_cls, shell=True)
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os.chdir('../../')
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def mlir2bmodel():
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det_str = 'model_deploy.py \
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--mlir ./ch_PP-OCRv3_det.mlir \
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--quantize F32 \
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--chip bm1684x \
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--test_input ./ch_PP-OCRv3_det_in_f32.npz \
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--test_reference ./ch_PP-OCRv3_det_top_outputs.npz \
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--model ./ch_PP-OCRv3_det_1684x_f32.bmodel'
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rec_str = 'model_deploy.py \
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--mlir ./ch_PP-OCRv3_rec.mlir \
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--quantize F32 \
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--chip bm1684x \
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--test_input ./ch_PP-OCRv3_rec_in_f32.npz \
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--test_reference ./ch_PP-OCRv3_rec_top_outputs.npz \
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--model ./ch_PP-OCRv3_rec_1684x_f32.bmodel'
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cls_str = 'model_deploy.py \
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--mlir ./ch_PP-OCRv3_cls.mlir \
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--quantize F32 \
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--chip bm1684x \
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--test_input ./ch_PP-OCRv3_cls_in_f32.npz \
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--test_reference ./ch_PP-OCRv3_cls_top_outputs.npz \
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--model ./ch_PP-OCRv3_cls_1684x_f32.bmodel'
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os.chdir('./ch_PP-OCRv3/workspace/')
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print(det_str)
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run(det_str, shell=True)
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print(rec_str)
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run(rec_str, shell=True)
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print(cls_str)
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run(cls_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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getPPOCRv3()
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fix_input_shape()
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paddle2onnx()
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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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# Detection模型, 检测文字框
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det_model_file = './ch_PP-OCRv3/workspace/ch_PP-OCRv3_det_1684x_f32.bmodel' if args.auto else args.det_model
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det_params_file = ""
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# Classification模型，方向分类，可选
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cls_model_file = './ch_PP-OCRv3/workspace/ch_PP-OCRv3_cls_1684x_f32.bmodel' if args.auto else args.cls_model
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cls_params_file = ""
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# Recognition模型，文字识别模型
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rec_model_file = './ch_PP-OCRv3/workspace/ch_PP-OCRv3_rec_1684x_f32.bmodel' if args.auto else args.rec_model
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rec_params_file = ""
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rec_label_file = './ppocr_keys_v1.txt' if args.auto else args.rec_label_file
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image_file = './12.jpg' if args.auto else args.image
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# PPOCR的cls和rec模型现在已经支持推理一个Batch的数据
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# 定义下面两个变量后, 可用于设置trt输入shape, 并在PPOCR模型初始化后, 完成Batch推理设置
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cls_batch_size = 1
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rec_batch_size = 1
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# 当使用TRT时，分别给三个模型的runtime设置动态shape,并完成模型的创建.
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# 注意: 需要在检测模型创建完成后，再设置分类模型的动态输入并创建分类模型, 识别模型同理.
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# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
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det_option = runtime_option
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det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
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[1, 3, 960, 960])
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# 用户可以把TRT引擎文件保存至本地
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# det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
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det_model = fd.vision.ocr.DBDetector(
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det_model_file,
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det_params_file,
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runtime_option=det_option,
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model_format=fd.ModelFormat.SOPHGO)
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cls_option = runtime_option
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cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
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[cls_batch_size, 3, 48, 320],
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[cls_batch_size, 3, 48, 1024])
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# 用户可以把TRT引擎文件保存至本地
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# cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
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cls_model = fd.vision.ocr.Classifier(
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cls_model_file,
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cls_params_file,
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runtime_option=cls_option,
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model_format=fd.ModelFormat.SOPHGO)
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rec_option = runtime_option
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rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
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[rec_batch_size, 3, 48, 320],
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[rec_batch_size, 3, 48, 2304])
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# 用户可以把TRT引擎文件保存至本地
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# rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
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rec_model = fd.vision.ocr.Recognizer(
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rec_model_file,
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rec_params_file,
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rec_label_file,
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runtime_option=rec_option,
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model_format=fd.ModelFormat.SOPHGO)
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# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
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ppocr_v3 = fd.vision.ocr.PPOCRv3(
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det_model=det_model, cls_model=cls_model, rec_model=rec_model)
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# 需要使用下行代码, 来启用rec模型的静态shape推理，这里rec模型的静态输入为[3, 48, 320]
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rec_model.preprocessor.static_shape_infer = True
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rec_model.preprocessor.rec_image_shape = [3, 48, 320]
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# 给cls和rec模型设置推理时的batch size
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# 此值能为-1, 和1到正无穷
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# 当此值为-1时, cls和rec模型的batch size将默认和det模型检测出的框的数量相同
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ppocr_v3.cls_batch_size = cls_batch_size
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ppocr_v3.rec_batch_size = rec_batch_size
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# 预测图片准备
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im = cv2.imread(image_file)
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#预测并打印结果
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result = ppocr_v3.predict(im)
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print(result)
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# 可视化结果
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vis_im = fd.vision.vis_ppocr(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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