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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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18
examples/vision/detection/paddledetection/sophgo/python/README.md
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@@ -15,13 +15,21 @@ cd FastDeploy/examples/vision/detection/paddledetection/sophgo/python
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# 推理
#ppyoloe推理示例
python3 infer_ppyoloe.py --model_file model/ppyoloe_crn_s_300e_coco_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg
# ppyoloe推理示例
# 指定--auto True，自动完成模型准备、转换和推理，需要指定PaddleDetection路径
python3 infer_ppyoloe.py --auto True --pp_detect_path {Path to PaddleDetection} --model_file '' --config_file '' --image ''
#picodet推理示例
python3 infer_picodet.py --model_file model/picodet_s_416_coco_lcnet_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg
# 指定--auto False，需要用户指定模型、配置文件和图片路径，不需要指定PaddleDetection路径。
python3 infer_ppyoloe.py --auto False --pp_detect_path '' --model_file model/ppyoloe_crn_s_300e_coco_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg
#yolov8推理示例
# picodet推理示例
# 指定--auto True，自动完成模型准备、转换和推理，需要指定PaddleDetection路径
python3 infer_picodet.py --auto True --pp_detect_path {Path to PaddleDetection} --model_file '' --config_file '' --image ''
# 指定--auto False，需要用户指定模型、配置文件和图片路径，不需要指定PaddleDetection路径。
python3 infer_picodet.py --auto False --pp_detect_path '' --model_file model/ppyoloe_crn_s_300e_coco_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg
# yolov8推理示例
python3 infer_yolov8.py --model_file model/yolov8s_s_300e_coco_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg
# 运行完成后返回结果如下所示
可视化结果存储在sophgo_result.jpg中

109
examples/vision/detection/paddledetection/sophgo/python/infer_picodet.py
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@@ -14,12 +14,101 @@
import fastdeploy as fd
import cv2
import os
from subprocess import run
from prepare_npz import prepare
def export_model(args):
PPDetection_path = args.pp_detect_path
export_str = 'python3 tools/export_model.py \
-c configs/picodet/picodet_s_320_coco_lcnet.yml \
--output_dir=output_inference \
-o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams'
cur_path = os.getcwd()
os.chdir(PPDetection_path)
print(export_str)
run(export_str, shell=True)
cp_str = 'cp -r ./output_inference/picodet_s_320_coco_lcnet ' + cur_path
print(cp_str)
run(cp_str, shell=True)
os.chdir(cur_path)
def paddle2onnx():
convert_str = 'paddle2onnx --model_dir picodet_s_320_coco_lcnet/ \
--model_filename model.pdmodel \
--params_filename model.pdiparams \
--save_file picodet_s_320_coco_lcnet.onnx \
--enable_dev_version True'
print(convert_str)
run(convert_str, shell=True)
fix_shape_str = 'python3 -m paddle2onnx.optimize \
--input_model picodet_s_320_coco_lcnet.onnx \
--output_model picodet_s_320_coco_lcnet.onnx \
--input_shape_dict "{\'image\':[1,3,640,640]}"'
print(fix_shape_str)
run(fix_shape_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 picodet',
'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./picodet',
'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./picodet',
'cp picodet_s_320_coco_lcnet.onnx ./picodet',
'mkdir ./picodet/workspace']
for str in mv_str_list:
print(str)
run(str, shell=True)
def image_prepare():
img_str = 'wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg'
if not os.path.exists('000000014439.jpg'):
print(img_str)
run(img_str, shell=True)
prepare('000000014439.jpg', [320, 320])
cp_npz_str = 'cp ./inputs.npz ./picodet'
print(cp_npz_str)
run(cp_npz_str, shell=True)
def onnx2mlir():
transform_str = 'model_transform.py \
--model_name picodet_s_320_coco_lcnet \
--model_def ../picodet_s_320_coco_lcnet.onnx \
--input_shapes [[1,3,320,320],[1,2]] \
--keep_aspect_ratio \
--pixel_format rgb \
--output_names p2o.Div.79,p2o.Concat.9 \
--test_input ../inputs.npz \
--test_result picodet_s_320_coco_lcnet_top_outputs.npz \
--mlir picodet_s_320_coco_lcnet.mlir'
os.chdir('./picodet/workspace')
print(transform_str)
run(transform_str, shell=True)
os.chdir('../../')
def mlir2bmodel():
deploy_str = 'model_deploy.py \
--mlir picodet_s_320_coco_lcnet.mlir \
--quantize F32 \
--chip bm1684x \
--test_input picodet_s_320_coco_lcnet_in_f32.npz \
--test_reference picodet_s_320_coco_lcnet_top_outputs.npz \
--model picodet_s_320_coco_lcnet_1684x_f32.bmodel'
os.chdir('./picodet/workspace')
print(deploy_str)
run(deploy_str, shell=True)
os.chdir('../../')
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(
"--pp_detect_path", default='/workspace/PaddleDetection', help="Path of PaddleDetection folder")
parser.add_argument(
"--model_file", required=True, help="Path of sophgo model.")
parser.add_argument("--config_file", required=True, help="Path of config.")
@@ -31,10 +120,18 @@ def parse_arguments():
if __name__ == "__main__":
args = parse_arguments()
model_file = args.model_file
params_file = ""
config_file = args.config_file
if args.auto:
export_model()
paddle2onnx()
mlir_prepare()
image_prepare()
onnx2mlir()
mlir2bmodel()
model_file = './picodet/workspace/picodet_s_320_coco_lcnet_1684x_f32.bmodel' if args.auto else args.model_file
params_file = ""
config_file = './picodet_s_320_coco_lcnet/infer_cfg.yml' if args.auto else args.config_file
img_file = './000000014439.jpg' if args.auto else args.image
# 配置runtime，加载模型
runtime_option = fd.RuntimeOption()
runtime_option.use_sophgo()
@@ -46,14 +143,14 @@ if __name__ == "__main__":
runtime_option=runtime_option,
model_format=fd.ModelFormat.SOPHGO)
model.postprocessor.apply_decode_and_nms()
model.postprocessor.apply_nms()
# 预测图片分割结果
im = cv2.imread(args.image)
im = cv2.imread(img_file)
result = model.predict(im)
print(result)
# 可视化结果
vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
cv2.imwrite("sophgo_result.jpg", vis_im)
print("Visualized result save in ./sophgo_result.jpg")
print("Visualized result save in ./sophgo_result_picodet.jpg")

106
examples/vision/detection/paddledetection/sophgo/python/infer_ppyoloe.py
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@@ -14,12 +14,17 @@
import fastdeploy as fd
import cv2
import os
from subprocess import run
from prepare_npz import prepare
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(
"--pp_detect_path", default='/workspace/PaddleDetection', help="Path of PaddleDetection folder")
parser.add_argument(
"--model_file", required=True, help="Path of sophgo model.")
parser.add_argument("--config_file", required=True, help="Path of config.")
@@ -27,13 +32,102 @@ def parse_arguments():
"--image", type=str, required=True, help="Path of test image file.")
return parser.parse_args()
def export_model(args):
PPDetection_path = args.pp_detect_path
export_str = 'python3 tools/export_model.py \
-c configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml \
-output_dir=output_inference \
-o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams'
cur_path = os.getcwd()
os.chdir(PPDetection_path)
print(export_str)
run(export_str, shell=True)
cp_str = 'cp -r ./output_inference/ppyoloe_crn_s_300e_coco ' + cur_path
print(cp_str)
run(cp_str, shell=True)
os.chdir(cur_path)
def paddle2onnx():
convert_str = 'paddle2onnx --model_dir ppyoloe_crn_s_300e_coco \
--model_filename model.pdmodel \
--params_filename model.pdiparams \
--save_file ppyoloe_crn_s_300e_coco.onnx \
--enable_dev_version True'
print(convert_str)
run(convert_str, shell=True)
fix_shape_str = 'python3 -m paddle2onnx.optimize --input_model ppyoloe_crn_s_300e_coco.onnx \
--output_model ppyoloe_crn_s_300e_coco.onnx \
--input_shape_dict "{\'image\':[1,3,640,640]}"'
print(fix_shape_str)
run(fix_shape_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 ppyoloe',
'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./ppyoloe',
'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./ppyoloe',
'cp ppyoloe_crn_s_300e_coco.onnx ./ppyoloe',
'mkdir ./ppyoloe/workspace']
for str in mv_str_list:
print(str)
run(str, shell=True)
def image_prepare():
img_str = 'wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg'
if not os.path.exists('000000014439.jpg'):
print(img_str)
run(img_str, shell=True)
prepare('000000014439.jpg', [640, 640])
cp_npz_str = 'cp ./inputs.npz ./ppyoloe'
print(cp_npz_str)
run(cp_npz_str, shell=True)
def onnx2mlir():
transform_str = 'model_transform.py \
--model_name ppyoloe_crn_s_300e_coco \
--model_def ../ppyoloe_crn_s_300e_coco.onnx \
--input_shapes [[1,3,640,640],[1,2]] \
--keep_aspect_ratio \
--pixel_format rgb \
--output_names p2o.Div.1,p2o.Concat.29 \
--test_input ../inputs.npz \
--test_result ppyoloe_crn_s_300e_coco_top_outputs.npz \
--mlir ppyoloe_crn_s_300e_coco.mlir'
os.chdir('./ppyoloe/workspace')
print(transform_str)
run(transform_str, shell=True)
os.chdir('../../')
def mlir2bmodel():
deploy_str = 'model_deploy.py \
--mlir ppyoloe_crn_s_300e_coco.mlir \
--quantize F32 \
--chip bm1684x \
--test_input ppyoloe_crn_s_300e_coco_in_f32.npz \
--test_reference ppyoloe_crn_s_300e_coco_top_outputs.npz \
--model ppyoloe_crn_s_300e_coco_1684x_f32.bmodel'
os.chdir('./ppyoloe/workspace')
print(deploy_str)
run(deploy_str, shell=True)
os.chdir('../../')
if __name__ == "__main__":
args = parse_arguments()
model_file = args.model_file
if args.auto:
export_model(args)
paddle2onnx()
mlir_prepare()
image_prepare()
onnx2mlir()
mlir2bmodel()
model_file = './ppyoloe/workspace/ppyoloe_crn_s_300e_coco_1684x_f32.bmodel' if args.auto else args.model_file
params_file = ""
config_file = args.config_file
config_file = './ppyoloe_crn_s_300e_coco/infer_cfg.yml' if args.auto else args.config_file
image_file = './000000014439.jpg' if args.auto else args.image
# 配置runtime，加载模型
runtime_option = fd.RuntimeOption()
@@ -46,14 +140,14 @@ if __name__ == "__main__":
runtime_option=runtime_option,
model_format=fd.ModelFormat.SOPHGO)
model.postprocessor.apply_decode_and_nms()
model.postprocessor.apply_nms()
# 预测图片分割结果
im = cv2.imread(args.image)
im = cv2.imread(image_file)
result = model.predict(im)
print(result)
# 可视化结果
vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
cv2.imwrite("sophgo_result.jpg", vis_im)
print("Visualized result save in ./sophgo_result.jpg")
print("Visualized result save in ./sophgo_result_ppyoloe.jpg")

17
examples/vision/detection/paddledetection/sophgo/python/prepare_npz.py Normal file
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@@ -0,0 +1,17 @@
import cv2
import numpy as np
def prepare(img_path, sz):
im = cv2.imread(img_path)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
im = cv2.resize(im, sz)
im = im.transpose((2,0,1))
im = im[np.newaxis,...]
im_scale_y = sz[0] / float(im.shape[2])
im_scale_x = sz[1] / float(im.shape[3])
inputs = {}
inputs['image'] = np.array(im).astype('float32')
# scale = np.array([im_scale_y, im_scale_x])
# scale = scale[np.newaxis,...]
inputs['scale_factor'] = np.array(([im_scale_y, im_scale_x], )).astype('float32')
np.savez('inputs.npz', image=inputs['image'], scale_factor=inputs['scale_factor'])