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FastDeploy/examples/vision/segmentation/paddleseg/rknpu2/python/infer.py
Zheng_Bicheng ce828ecb38 [Backend And DOC] 改进ppseg文档 + 为RKNPU2后端新增对多输入模型的支持 (#491)
* 11-02/14:35
* 新增输入数据format错误判断
* 优化推理过程,减少内存分配次数
* 支持多输入rknn模型
* rknn模型输出shape为三维时,输出将被强制对齐为4纬。现在将直接抹除rknn补充的shape,方便部分对输出shape进行判断的模型进行正确的后处理。

* 11-03/17:25
* 支持导出多输入RKNN模型
* 更新各种文档
* ppseg改用Fastdeploy中的模型进行转换

* 11-03/17:25
* 新增开源头

* 11-03/21:48
* 删除无用debug代码,补充注释
2022-11-04 09:39:23 +08:00

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import fastdeploy as fd
import cv2
import os
def parse_arguments():
import argparse
import ast
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_file", required=True, help="Path of PaddleSeg model.")
parser.add_argument(
"--config_file", required=True, help="Path of PaddleSeg config.")
parser.add_argument(
"--image", type=str, required=True, help="Path of test image file.")
return parser.parse_args()
def build_option(args):
option = fd.RuntimeOption()
option.use_rknpu2()
return option
args = parse_arguments()
# 配置runtime加载模型
runtime_option = build_option(args)
model_file = args.model_file
params_file = ""
config_file = args.config_file
model = fd.vision.segmentation.PaddleSegModel(
model_file,
params_file,
config_file,
runtime_option=runtime_option,
model_format=fd.ModelFormat.RKNN)
model.disable_normalize_and_permute()
# 预测图片分割结果
im = cv2.imread(args.image)
result = model.predict(im.copy())
print(result)
# 可视化结果
vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
cv2.imwrite("vis_img.png", vis_im)