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[Example] Merge Download Paddle Model, Paddle->ONNX->MLIR->BModel (#1643)

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* 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>
This commit is contained in:
Yi-sir
2023-03-31 15:08:01 +08:00
committed by GitHub
parent 8deb2ed179
commit 9e20dab0d6
15 changed files with 629 additions and 42 deletions
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205
examples/vision/ocr/PP-OCR/sophgo/python/infer.py
View File

@@ -1,12 +1,15 @@
import fastdeploy as fd
import cv2
import os
from subprocess import run
def parse_arguments():
import argparse
import ast
parser = argparse.ArgumentParser()
parser.add_argument(
"--auto", required=True, help="Auto download, convert, compile and infer if True")
parser.add_argument(
"--det_model", required=True, help="Path of Detection model of PPOCR.")
parser.add_argument(
@@ -27,22 +30,210 @@ def parse_arguments():
return parser.parse_args()
def getPPOCRv3():
cmd_str_det = 'wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar'
tar_str_det = 'tar xvf ch_PP-OCRv3_det_infer.tar'
cmd_str_cls = 'wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar'
tar_str_cls = 'tar xvf ch_ppocr_mobile_v2.0_cls_infer.tar'
cmd_str_rec = 'wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar'
tar_str_rec = 'tar xvf ch_PP-OCRv3_rec_infer.tar'
cmd_str_img = 'wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg'
cmd_str_label = 'wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt'
script_str = 'wget https://raw.githubusercontent.com/PaddlePaddle/Paddle2ONNX/develop/tools/paddle/paddle_infer_shape.py'
if not os.path.exists('ch_PP-OCRv3_det_infer.tar'):
print(cmd_str_det, tar_str_det)
run(cmd_str_det, shell=True)
run(tar_str_det, shell=True)
if not os.path.exists('ch_ppocr_mobile_v2.0_cls_infer.tar'):
print(cmd_str_cls, tar_str_cls)
run(cmd_str_cls, shell=True)
run(tar_str_cls, shell=True)
if not os.path.exists('ch_PP-OCRv3_rec_infer.tar'):
print(cmd_str_rec, tar_str_rec)
run(cmd_str_rec, shell=True)
run(tar_str_rec, shell=True)
if not os.path.exists('12.jpg'):
print(cmd_str_img)
run(cmd_str_img, shell=True)
if not os.path.exists('ppocr_keys_v1.txt'):
print(cmd_str_label)
run(cmd_str_label, shell=True)
if not os.path.exists('paddle_infer_shape.py'):
print(script_str)
run(script_str, shell=True)
def fix_input_shape():
fix_det_str = 'python paddle_infer_shape.py --model_dir ch_PP-OCRv3_det_infer \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--save_dir ch_PP-OCRv3_det_infer_fix \
--input_shape_dict="{\'x\':[1,3,960,608]}"'
fix_rec_str = 'python paddle_infer_shape.py --model_dir ch_PP-OCRv3_rec_infer \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--save_dir ch_PP-OCRv3_rec_infer_fix \
--input_shape_dict="{\'x\':[1,3,48,320]}"'
fix_cls_str = 'python paddle_infer_shape.py --model_dir ch_ppocr_mobile_v2.0_cls_infer \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--save_dir ch_PP-OCRv3_cls_infer_fix \
--input_shape_dict="{\'x\':[1,3,48,192]}"'
print(fix_det_str)
run(fix_det_str, shell=True)
print(fix_rec_str)
run(fix_rec_str, shell=True)
print(fix_cls_str)
run(fix_cls_str, shell=True)
def paddle2onnx():
cmd_str_det = 'paddle2onnx --model_dir ch_PP-OCRv3_det_infer_fix \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--save_file ch_PP-OCRv3_det_infer.onnx \
--enable_dev_version True'
cmd_str_cls = 'paddle2onnx --model_dir ch_PP-OCRv3_cls_infer_fix \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--save_file ch_PP-OCRv3_cls_infer.onnx \
--enable_dev_version True'
cmd_str_rec = 'paddle2onnx --model_dir ch_PP-OCRv3_rec_infer_fix \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--save_file ch_PP-OCRv3_rec_infer.onnx \
--enable_dev_version True'
print(cmd_str_det)
run(cmd_str_det, shell=True)
print(cmd_str_cls)
run(cmd_str_cls, shell=True)
print(cmd_str_rec)
run(cmd_str_rec, shell=True)
def mlir_prepare():
mlir_path = os.getenv("MODEL_ZOO_PATH")
mlir_path = mlir_path[:-13]
regression_path = os.path.join(mlir_path, 'regression')
mv_str_list = ['mkdir ch_PP-OCRv3',
'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./ch_PP-OCRv3',
'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./ch_PP-OCRv3',
'mv ch_PP-OCRv3_det_infer.onnx ./ch_PP-OCRv3',
'mv ch_PP-OCRv3_rec_infer.onnx ./ch_PP-OCRv3',
'mv ch_PP-OCRv3_cls_infer.onnx ./ch_PP-OCRv3',
'mkdir ./ch_PP-OCRv3/workspace']
for str in mv_str_list:
print(str)
run(str, shell=True)
def onnx2mlir():
transform_str_det = 'model_transform.py \
--model_name ch_PP-OCRv3_det \
--model_def ../ch_PP-OCRv3_det_infer.onnx \
--input_shapes [[1,3,960,608]] \
--mean 0.0,0.0,0.0 \
--scale 0.0039216,0.0039216,0.0039216 \
--keep_aspect_ratio \
--pixel_format rgb \
--output_names sigmoid_0.tmp_0 \
--test_input ../image/dog.jpg \
--test_result ch_PP-OCRv3_det_top_outputs.npz \
--mlir ./ch_PP-OCRv3_det.mlir'
transform_str_rec = 'model_transform.py \
--model_name ch_PP-OCRv3_rec \
--model_def ../ch_PP-OCRv3_rec_infer.onnx \
--input_shapes [[1,3,48,320]] \
--mean 0.0,0.0,0.0 \
--scale 0.0039216,0.0039216,0.0039216 \
--keep_aspect_ratio \
--pixel_format rgb \
--output_names softmax_5.tmp_0 \
--test_input ../image/dog.jpg \
--test_result ch_PP-OCRv3_rec_top_outputs.npz \
--mlir ./ch_PP-OCRv3_rec.mlir'
transform_str_cls = 'model_transform.py \
--model_name ch_PP-OCRv3_cls \
--model_def ../ch_PP-OCRv3_cls_infer.onnx \
--input_shapes [[1,3,48,192]] \
--mean 0.0,0.0,0.0 \
--scale 0.0039216,0.0039216,0.0039216 \
--keep_aspect_ratio \
--pixel_format rgb \
--output_names softmax_0.tmp_0 \
--test_input ../image/dog.jpg \
--test_result ch_PP-OCRv3_cls_top_outputs.npz \
--mlir ./ch_PP-OCRv3_cls.mlir'
os.chdir('./ch_PP-OCRv3/workspace/')
print(transform_str_det)
run(transform_str_det, shell=True)
print(transform_str_rec)
run(transform_str_rec, shell=True)
print(transform_str_cls)
run(transform_str_cls, shell=True)
os.chdir('../../')
def mlir2bmodel():
det_str = 'model_deploy.py \
--mlir ./ch_PP-OCRv3_det.mlir \
--quantize F32 \
--chip bm1684x \
--test_input ./ch_PP-OCRv3_det_in_f32.npz \
--test_reference ./ch_PP-OCRv3_det_top_outputs.npz \
--model ./ch_PP-OCRv3_det_1684x_f32.bmodel'
rec_str = 'model_deploy.py \
--mlir ./ch_PP-OCRv3_rec.mlir \
--quantize F32 \
--chip bm1684x \
--test_input ./ch_PP-OCRv3_rec_in_f32.npz \
--test_reference ./ch_PP-OCRv3_rec_top_outputs.npz \
--model ./ch_PP-OCRv3_rec_1684x_f32.bmodel'
cls_str = 'model_deploy.py \
--mlir ./ch_PP-OCRv3_cls.mlir \
--quantize F32 \
--chip bm1684x \
--test_input ./ch_PP-OCRv3_cls_in_f32.npz \
--test_reference ./ch_PP-OCRv3_cls_top_outputs.npz \
--model ./ch_PP-OCRv3_cls_1684x_f32.bmodel'
os.chdir('./ch_PP-OCRv3/workspace/')
print(det_str)
run(det_str, shell=True)
print(rec_str)
run(rec_str, shell=True)
print(cls_str)
run(cls_str, shell=True)
os.chdir('../../')
args = parse_arguments()
if (args.auto):
getPPOCRv3()
fix_input_shape()
paddle2onnx()
mlir_prepare()
onnx2mlir()
mlir2bmodel()
# 配置runtime，加载模型
runtime_option = fd.RuntimeOption()
runtime_option.use_sophgo()
# Detection模型, 检测文字框
det_model_file = args.det_model
det_model_file = './ch_PP-OCRv3/workspace/ch_PP-OCRv3_det_1684x_f32.bmodel' if args.auto else args.det_model
det_params_file = ""
# Classification模型，方向分类，可选
cls_model_file = args.cls_model
cls_model_file = './ch_PP-OCRv3/workspace/ch_PP-OCRv3_cls_1684x_f32.bmodel' if args.auto else args.cls_model
cls_params_file = ""
# Recognition模型，文字识别模型
rec_model_file = args.rec_model
rec_model_file = './ch_PP-OCRv3/workspace/ch_PP-OCRv3_rec_1684x_f32.bmodel' if args.auto else args.rec_model
rec_params_file = ""
rec_label_file = args.rec_label_file
rec_label_file = './ppocr_keys_v1.txt' if args.auto else args.rec_label_file
image_file = './12.jpg' if args.auto else args.image
# PPOCR的cls和rec模型现在已经支持推理一个Batch的数据
# 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
@@ -92,9 +283,9 @@ rec_model = fd.vision.ocr.Recognizer(
ppocr_v3 = fd.vision.ocr.PPOCRv3(
det_model=det_model, cls_model=cls_model, rec_model=rec_model)
# 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, 584]
# 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]
rec_model.preprocessor.static_shape_infer = True
rec_model.preprocessor.rec_image_shape = [3, 48, 584]
rec_model.preprocessor.rec_image_shape = [3, 48, 320]
# 给cls和rec模型设置推理时的batch size
# 此值能为-1, 和1到正无穷
@@ -103,7 +294,7 @@ ppocr_v3.cls_batch_size = cls_batch_size
ppocr_v3.rec_batch_size = rec_batch_size
# 预测图片准备
im = cv2.imread(args.image)
im = cv2.imread(image_file)
#预测并打印结果
result = ppocr_v3.predict(im)