摘要：这是发表于CVPR 2020的一篇论文的复现模型。
本文分享自华为云社区《Panoptic Deeplab(全景分割PyTorch)》，作者：HWCloudAI 。
这是发表于CVPR 2020的一篇论文的复现模型，B. Cheng et al, “Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation”, CVPR 2020，此模型在原论文的基础上，使用HRNet作为backbone，得到了高于原论文的精度，PQ达到了63.7%，mIoU达到了80.3%，AP达到了37.3%。该算法会载入Cityscapes上的预训练模型（HRNet），我们提供了训练代码和可用于训练的模型，用于实际场景的微调训练。训练后生成的模型可直接在ModelArts平台部署成在线服务。
具体算法介绍：AI Gallery_算法_模型_云市场-华为云
注意事项：
1.本案例使用框架:PyTorch1.4.0
2.本案例使用硬件:GPU: 1*NVIDIA-V100NV32(32GB) | CPU: 8 核 64GB
3.运行代码方法: 点击本页面顶部菜单栏的三角形运行按钮或按Ctrl+Enter键 运行每个方块中的代码
4.JupyterLab的详细用法: 请参考《ModelAtrs JupyterLab使用指导》
5.碰到问题的解决办法: 请参考《ModelAtrs JupyterLab常见问题解决办法》
1.下载数据和代码
运行下面代码，进行数据和代码的下载
本案例使用cityscapes数据集。
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import os
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import moxing
as mox
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# 数据代码下载
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mox.file.copy_parallel('s3://obs-aigallery-zc/algorithm/panoptic-deeplab','./panoptic-deeplab')
2.模型训练
2.1依赖库加载
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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from __future__
import print_function
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import os
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root_path = './panoptic-deeplab/'
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os.chdir(root_path)
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# 获取当前目录结构信息，以便进行代码调试
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print('os.getcwd():', os.getcwd())
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import time
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import argparse
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import time
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import datetime
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import math
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import sys
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import shutil
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import moxing
as mox # ModelArts上专用的moxing模块，可用于与OBS的数据交互，API文档请查看：https://github.com/huaweicloud/ModelArts-Lab/tree/master/docs/moxing_api_doc
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from
PIL
import ImageFile
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ImageFile.
LOAD_TRUNCATED_IMAGES =
True
2.2训练参数设置
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parser = argparse.ArgumentParser(description=
'Panoptic Deeplab')
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parser.add_argument(
'--training_dataset', default=
'/home/ma-user/work/panoptic-deeplab/',
help=
'Training dataset directory')
# 在ModelArts中创建算法时，必须进行输入路径映射配置，输入映射路径的前缀必须是/home/work/modelarts/inputs/，作用是在启动训练时，将OBS的数据拷贝到这个本地路径中供本地代码使用。
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parser.add_argument(
'--train_url', default=
'./output',
help=
'the path to save training outputs')
# 在ModelArts中创建训练作业时，必须指定OBS上的一个训练输出位置，训练结束时，会将输出映射路径拷贝到该位置
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parser.add_argument(
'--num_gpus', default=
1,
type=
int,
help=
'num of GPUs to train')
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parser.add_argument(
'--eval', default=
'False',
help=
'whether to eval')
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parser.add_argument(
'--load_weight', default=
'trained_model/model/model_final.pth',
type=
str)
# obs路径 断点模型 pth文件 如果是评估 则是相对于src的路径
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parser.add_argument(
'--iteration', default=
100,
type=
int)
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parser.add_argument(
'--learning_rate', default=
0.001,
type=
float)
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parser.add_argument(
'--ims_per_batch', default=
8,
type=
int)
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args, unknown = parser.parse_known_args()
# 必须将parse_args改成parse_known_args，因为在ModelArts训练作业中运行时平台会传入一个额外的init_method的参数
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# dir
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fname = os.getcwd()
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project_dir = os.path.join(fname,
"panoptic-deeplab")
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detectron2_dir = os.path.join(fname,
"detectron2-0.3+cu102-cp36-cp36m-linux_x86_64.whl")
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panopticapi_dir = os.path.join(fname,
"panopticapi-0.1-py3-none-any.whl")
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cityscapesscripts_dir = os.path.join(fname,
"cityscapesScripts-2.1.7-py3-none-any.whl")
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requirements_dir = os.path.join(project_dir,
"requirements.txt")
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output_dir =
"/home/work/modelarts/outputs/train_output"
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# config strings
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evalpath =
''
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MAX_ITER =
'SOLVER.MAX_ITER ' +
str(args.iteration+
90000)
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BASE_LR =
'SOLVER.BASE_LR ' +
str(args.learning_rate)
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IMS_PER_BATCH =
'SOLVER.IMS_PER_BATCH ' +
str(args.ims_per_batch)
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SCRIPT_PATH = os.path.join(project_dir,
"tools_d2/train_panoptic_deeplab.py")
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CONFIG_PATH = os.path.join(fname,
"configs/config.yaml")
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CONFIG_CMD =
'--config-file ' + CONFIG_PATH
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EVAL_CMD =
''
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GPU_CMD =
''
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OPTS_CMD = MAX_ITER +
' ' + BASE_LR +
' ' + IMS_PER_BATCH
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RESUME_CMD =
''
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#functions
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def
merge_cmd(
scirpt_path, config_cmd, gpu_cmd, eval_cmd, resume_cmd, opts_cmd):
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return
"python " + scirpt_path +
" "+ config_cmd +
" " + gpu_cmd +
" " + eval_cmd +
" " + resume_cmd +
" " + OPTS_CMD
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if args.
eval ==
'True':
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assert args.load_weight,
'load_weight empty when trying to evaluate'
# 如果评估时为空，则报错
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if args.load_weight !=
'trained_model/model/model_final.pth':
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#将model拷贝到本地，并获取模型路径
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modelpath, modelname = os.path.split(args.load_weight)
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mox.file.copy_parallel(args.load_weight, os.path.join(fname, modelname))
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evalpath = os.path.join(fname,modelname)
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else:
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evalpath = os.path.join(fname,
'trained_model/model/model_final.pth')
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EVAL_CMD =
'--eval-only MODEL.WEIGHTS ' + evalpath
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else:
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GPU_CMD =
'--num-gpus ' +
str(args.num_gpus)
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if args.load_weight:
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RESUME_CMD =
'--resume'
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if args.load_weight !=
'trained_model/model/model_final.pth':
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modelpath, modelname = os.path.split(args.load_weight)
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mox.file.copy_parallel(args.load_weight, os.path.join(
'/cache',modelname))
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with
open(
'/cache/last_checkpoint',
'w')
as f:
#创建last_checkpoint文件
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f.write(modelname)
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f.close()
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else:
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os.system(
'cp ' + os.path.join(fname,
'trained_model/model/model_final.pth') +
' /cache/model_final.pth')
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with
open(
'/cache/last_checkpoint',
'w')
as f:
#创建last_checkpoint文件
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f.write(
'model_final.pth')
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f.close()
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os.environ[
'DETECTRON2_DATASETS'] = args.training_dataset
#添加数据库路径环境变量
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cmd = merge_cmd(SCRIPT_PATH, CONFIG_CMD, GPU_CMD, EVAL_CMD, RESUME_CMD, OPTS_CMD)
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# os.system('mkdir -p ' + args.train_url)
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print(
'*********Train Information*********')
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print(
'Run Command: ' + cmd)
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print(
'Num of GPUs: ' +
str(args.num_gpus))
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print(
'Evaluation: ' + args.
eval)
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if args.load_weight:
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print(
'Load Weight: ' + args.load_weight)
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else:
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print(
'Load Weight: None (train from scratch)')
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print(
'Iteration: ' +
str(args.iteration))
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print(
'Learning Rate: ' +
str(args.learning_rate))
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print(
'Images Per Batch: ' +
str(args.ims_per_batch))
2.3安装依赖库
安装依赖库需要几分钟，请耐心等待
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def
install_dependecies(r,d, p, c):
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os.
system(
'pip uninstall pytorch> out1.txt')
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os.
system(
'pip install torch==1.7.0> out2.txt')
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os.
system(
'pip install --upgrade pip')
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os.
system(
'pip install --upgrade numpy')
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os.
system(
'pip install torchvision==1.7.0> out3.txt')
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os.
system(
'pip install pydot')
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os.
system(
'pip install --upgrade pycocotools')
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os.
system(
'pip install tensorboard')
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os.
system(
'pip install -r ' + r +
' --ignore-installed PyYAML')
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os.
system(
'pip install ' + d)
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os.
system(
'pip install ' + p)
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os.
system(
'pip install ' + c)
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os.
system(
'pip install pyyaml ==5.1.0')
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# 安装依赖
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print(
'*********Installing Dependencies*********')
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install_dependecies(requirements_dir,detectron2_dir, panopticapi_dir, cityscapesscripts_dir)
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*********Installing Dependencies*********
2.4开始训练
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print(
'*********Training Begin*********')
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print(cmd)
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start =
time.
time()
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ret =
os.system(cmd+
" >out.txt")
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if ret ==
0:
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print(
"success")
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else:
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print(
'fail')
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end_time=
time.
time()
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print(
'done')
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print(end_time-start)
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if args.eval ==
'False':
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os.system(
'mv /cache/model_final.pth ' +
os.
path.join(fname,
'output/model_final.pth')) #/cache模型移动到输出文件夹
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if
os.
path.exists(
os.
path.join(fname,
'pred_results')):
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os.system(
'mv ' +
os.
path.join(fname,
'pred_results') +
' ' + args.train_url)
训练完成之后，可以在out.txt中看运行日志
在./panoptic-deeplab/output/pred_results/文件目录下，有该模型全景分割，实例分割，语义分割的评估结果
3.模型测试
3.1加载测试函数
from test import *3.2开始预测
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if __name__ ==
'__main__':
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img_path =
r'/home/ma-user/work/panoptic-deeplab/cityscapes/leftImg8bit/val/frankfurt/frankfurt_000000_003920_leftImg8bit.png'
# TODO 修改测试图片路径
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model_path =
r'/home/ma-user/work/panoptic-deeplab/output/model_final.pth'
# TODO 修改模型路径
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my_model = ModelClass(model_path)
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result = my_model.predict(img_path)
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print(result)
转载：https://blog.csdn.net/devcloud/article/details/128013436
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