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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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78
examples/vision/detection/yolov5/sophgo/python/infer.py
View File

@@ -1,27 +1,94 @@
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("--model", required=True, help="Path of model.")
parser.add_argument(
"--image", type=str, required=True, help="Path of test image file.")
"--auto", required=True, help="Auto download, convert, compile and infer if True")
parser.add_argument("--model", help="Path of model.")
parser.add_argument(
"--image", type=str, help="Path of test image file.")
return parser.parse_args()
def download():
download_model_str = 'wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx'
if not os.path.exists('yolov5s.onnx'):
print(download_model_str)
run(download_model_str, shell=True)
download_img_str = 'wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg'
if not os.path.exists('000000014439.jpg'):
print(download_img_str)
run(download_img_str, 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 YOLOv5s',
'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./YOLOv5s',
'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./YOLOv5s',
'cp yolov5s.onnx ./YOLOv5s',
'mkdir ./YOLOv5s/workspace']
for str in mv_str_list:
print(str)
run(str, shell=True)
def onnx2mlir():
transform_str = 'model_transform.py \
--model_name yolov5s \
--model_def ../yolov5s.onnx \
--input_shapes [[1,3,640,640]] \
--mean 0.0,0.0,0.0 \
--scale 0.0039216,0.0039216,0.0039216 \
--keep_aspect_ratio \
--pixel_format rgb \
--output_names output0 \
--test_input ../image/dog.jpg \
--test_result yolov5s_top_outputs.npz \
--mlir yolov5s.mlir'
os.chdir('./YOLOv5s/workspace')
print(transform_str)
run(transform_str, shell=True)
os.chdir('../../')
def mlir2bmodel():
deploy_str = 'model_deploy.py \
--mlir yolov5s.mlir \
--quantize F32 \
--chip bm1684x \
--test_input yolov5s_in_f32.npz \
--test_reference yolov5s_top_outputs.npz \
--model yolov5s_1684x_f32.bmodel'
os.chdir('./YOLOv5s/workspace')
print(deploy_str)
run(deploy_str, shell=True)
os.chdir('../../')
args = parse_arguments()
if args.auto:
download()
mlir_prepare()
onnx2mlir()
mlir2bmodel()
# 配置runtime，加载模型
runtime_option = fd.RuntimeOption()
runtime_option.use_sophgo()
model_file = args.model
model_file = './YOLOv5s/workspace/yolov5s_1684x_f32.bmodel' if args.auto else args.model
params_file = ""
img_file = './000000014439.jpg' if args.auto else args.image
model = fd.vision.detection.YOLOv5(
model_file,
@@ -29,8 +96,9 @@ model = fd.vision.detection.YOLOv5(
runtime_option=runtime_option,
model_format=fd.ModelFormat.SOPHGO)
# 预测图片分类结果
im = cv2.imread(args.image)
im = cv2.imread(img_file)
result = model.predict(im)
print(result)