nnUnet使用_unet cascade

作者：笔触狂放9 | 2024-08-09 20:31:27

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unet cascade

nnUnet使用

MedicalSeg/nnUnet组件介绍

组件结构

在这里插入图片描述

使用流程

准备工作：

#1.1获取PaddleSeg组件
#cd ~表示进入用户主目录
%cd ~/
!git clone https://gitee.com/PaddlePaddle/PaddleSeg.git
!git pull
%cd ~/PaddleSeg/
!git checkout develop
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%cd ~/进入用户根目录，该指令不论当前工作目录是什么，均会返回根目录

%cd ~/data/进入根目录下的data目录

%cd data进入当前文件夹下的data目录，注意与~/data区别

%cd …/返回上一根目录

#1.2解压数据集
%cd ~/
!mkdir ~/PaddleSeg/contrib/MedicalSeg/msd_lung
!tar -xf ~/data/data125872/Task06_Lung.tar -C ~/PaddleSeg/contrib/MedicalSeg/msd_lung
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#1.3安装依赖
%cd ~/PaddleSeg/contrib/MedicalSeg
!pip install -r requirements.txt
!pip install medpy
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#1.4(可选)解压2D-UNet和Cascade UNet-lowres训练好的权重
%cd ~/
!mkdir ~/PaddleSeg/contrib/MedicalSeg/output
!unzip -oq ~/data/data169512/2d_and_cascade_lowres.zip -d ~/PaddleSeg/contrib/MedicalSeg/output/
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开始训练

# 2D-UNet 训练————5折训练
%cd ~/PaddleSeg/contrib/MedicalSeg/
!python train.py --config configs/nnunet/msd_lung/nnunet_2d_msd_lung_fold0.yml --log_iters 20 --precision fp16 --nnunet --save_dir output/2d_unet/fold0 --save_interval 1000 --use_vdl
'''
修改fold1/2/3/4分别训练即可
考虑到内存问题，可增大save_interval
--precison fp16表示Use AMP (Auto mixed precision)自动混合精度训练
'''
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# 3D-UNet训练
%cd ~/PaddleSeg/contrib/MedicalSeg/
!python train.py --config configs/nnunet/msd_lung/nnunet_3d_fullres_msd_lung_fold0.yml --log_iters 20 --precision fp16 --nnunet --save_dir output/3d_unet/fold0 --save_interval 1000 --use_vdl
'''
同样分别修改fold1/2/3/4进行5折训练
'''
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train.py的可设置参数，具体查看train.py脚本

—— log_iters记录训练的loss、cost等参数的轮数

—— use_vdl记录训练参数，结束后可用于可视化

—— resume_model=‘path’中断训练和恢复重新训练

—— save_interval保存训练模型的轮数

验证——由于分为5折训练，故验证时5个模型分别在各自验证集验证，之后整合验证，即6次验证

# 2D UNet验证-提供权重
%cd ~/PaddleSeg/contrib/MedicalSeg/
!python nnunet/single_fold_eval.py --config configs/nnunet/msd_lung/nnunet_2d_msd_lung_fold0.yml --model_path output/2d_unet/fold0/iter_30000/model.pdparams --val_save_folder output/2d_val --precision fp16
'''
single_fold_eval需进行5次
'''
!python nnunet/all_folds_eval.py --gt_dir output/2d_val/gt_niftis --val_pred_dir output/2d_val
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# 3D UNet 验证
%cd ~/PaddleSeg/contrib/MedicalSeg/
!python nnunet/single_fold_eval.py --config configs/nnunet/msd_lung/nnunet_3d_fullres_msd_lung_fold0.yml --model_path output/3d_unet/fold0/iter_30000/model.pdparams --val_save_folder output/3dfullres_val --precision fp16
'''
single_fold_eval需进行5次
'''
!python nnunet/all_folds_eval.py --gt_dir output/3dfullres_val/gt_niftis --val_pred_dir output/3dfullres_val
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Cascade UNet训练、验证

# Cascade UNet lowres 训练
%cd ~/PaddleSeg/contrib/MedicalSeg/

!python train.py --config configs/nnunet/msd_lung/nnunet_3dcascade_lowres_msd_lung_fold0.yml --log_iters 20 --precision fp16 --nnunet --save_dir output/cascade_lowres/fold0 --save_interval 1000 --use_vdl
'''
同样是5折训练
'''
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# Cascade UNet lowres验证，并进行推理，得到下一阶段的输入-提供权重
%cd ~/PaddleSeg/contrib/MedicalSeg/

!python nnunet/single_fold_eval.py --config configs/nnunet/msd_lung/nnunet_3dcascade_lowres_msd_lung_fold0.yml --model_path output/cascade_lowres/fold0/iter_30000/model.pdparams --val_save_folder output/cascade_lowres_val --precision fp16 --
'''5折预测'''
!python nnunet/all_folds_eval.py --gt_dir output/cascade_lowres_val/gt_niftis --val_pred_dir output/cascade_lowres_val
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cascade unet二阶段一定会涨点吗？
不一定！二阶段数据处理将数据的体素间距统一成数据集中最小的体素间距，最小的体素间距并不一定是最优的，一阶段可能效果比二阶段好，这个需要选手自己尝试。

# Cascade UNet fullres 训练
%cd ~/PaddleSeg/contrib/MedicalSeg/

!python train.py --config configs/nnunet/msd_lung/nnunet_3dcascade_fullres_msd_lung_fold0.yml --log_iters 20 --precision fp16 --nnunet --save_dir output/cascade_fullres/fold0 --save_interval 1000 --use_vdl
'''5折训练'''
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# Cascade UNet fullres 验证
%cd ~/PaddleSeg/contrib/MedicalSeg/

!python nnunet/single_fold_eval.py --config configs/nnunet/msd_lung/nnunet_3dcascade_fullres_msd_lung_fold0.yml --model_path output/cascade_fullres/fold0/iter_30000/model.pdparams --val_save_folder output/cascade_fullres_val --precision fp16
'''5折预测完成后，需要整合'''
!python nnunet/all_folds_eval.py --gt_dir output/cascade_fullres_val/gt_niftis --val_pred_dir output/cascade_fullres_val
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Ensemble整合模型

def：nnUNet最后会评以下7个策略的精度，挑选精度最高的提交
2D-UNet
3D-UNet
Cascade-UNet-lowres
Cascade-UNet-fullres
2D-UNet + 3D-UNet
2D-UNet + Cascade-UNet-fullres
3D-UNet + Cascade-UNet-fullres

%cd ~/PaddleSeg/contrib/MedicalSeg/
#注意修改路径
!python nnunet/ensemble.py --ensemble_folds \
output/3dfullres_val\
output/cascade_lowres_val\
output/2d_val \
--gt_dir output/cascade_lowres_val/gt_niftis \
--plan_path msd_lung/preprocessed/Task006_Lung/nnUNetPlansv2.1_plans_2D.pkl \
--output_folder output/ensemble

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训练完成后

预测——预测的结果同样支持模型间Ensemble。如果预测的结果需要Ensemble，加上**–save_npz参数**。
比如想要：2D-UNet + 3D-UNet 的预测，就分别得到2个模型的预测结果，使用上述Ensemble命令集成。

#2D预测
%cd ~/PaddleSeg/contrib/MedicalSeg/
!python nnunet/predict.py --image_folder msd_lung/Task006_Lung/imagesTs\
--output_folder output/nnunet_predict/2d_unet \
--plan_path msd_lung/preprocessed/Task006_Lung/nnUNetPlansv2.1_plans_2D.pkl \
--model_paths output/2d_unet/fold0/iter_30000/model.pdparams output/2d_unet/fold1/iter_30000/model.pdparams output/2d_unet/fold2/iter_30000/model.pdparams output/2d_unet/fold3/iter_30000/model.pdparams output/2d_unet/fold4/iter_30000/model.pdparams \
--postprocessing_json_path output/2d_val/postprocessing.json \
--model_type 2d \
--save_npz
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# 3D UNet 预测
%cd ~/PaddleSeg/contrib/MedicalSeg/
!python nnunet/predict.py --image_folder msd_lung/Task006_Lung/imagesTs\
--output_folder output/nnunet_predict/3d_unet \
--plan_path msd_lung/preprocessed/Task006_Lung/nnUNetPlansv2.1_plans_3D.pkl \
--model_paths output/3d_unet/fold0/iter_30000/model.pdparams output/3d_unet/fold1/iter_30000/model.pdparams output/3d_unet/fold2/iter_30000/model.pdparams output/3d_unet/fold3/iter_30000/model.pdparams output/3d_unet/fold4/iter_30000/model.pdparams \
--postprocessing_json_path output/3d_unet/postprocessing.json \
--model_type 3d \
--save_npz
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# Cascade UNet 预测，先使用lowres预测，在fullres预测
%cd ~/PaddleSeg/contrib/MedicalSeg/

!python nnunet/predict.py--image_folder msd_lung/Task006_Lung/imagesTs\
--output_folder output/nnunet_predict/lowres_pred \
--plan_path msd_lung/preprocessed/Task006_Lung/nnUNetPlansv2.1_plans_3D.pkl \
--model_paths output/cascade_lowres/fold0/iter_30000/model.pdparams output/cascade_lowres/fold1/iter_30000/model.pdparams output/cascade_lowres/fold2/iter_30000/model.pdparams output/cascade_lowres/fold3/iter_30000/model.pdparams output/cascade_lowres/fold4/iter_30000/model.pdparams \
--postprocessing_json_path output/cascade_lowres_val/postprocessing.json \
--model_type cascade_lowres

#使用2阶段
!python nnunet/predict.py --image_folder msd_lung/Task006_Lung/imagesTs\
--output_folder output/nnunet_predict/fullres_pred \
--plan_path msd_lung/preprocessed/Task006_Lung/nnUNetPlansv2.1_plans_3D.pkl \
--model_paths output/cascade_fullres/fold0/iter_30000/model.pdparams output/cascade_fullres/fold1/iter_30000/model.pdparams output/cascade_fullres/fold2/iter_30000/model.pdparams output/cascade_fullres/fold3/iter_30000/model.pdparams output/cascade_fullres/fold4/iter_30000/model.pdparams \
--postprocessing_json_path output/cascade_lowres_val/postprocessing.json\
--model_type cascade_fullres \
--save_npz \
--lowres_segmentations output/nnunet_predict/lowres_pred
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部署

仅以2D UNet为示例，演示模型的部署过程。

导出静态图模型（五折训练对应五个模型）；

静态图推理（五折训练就对应五折推理）。
PS：静态图推理的模型也支持Ensemble，需要在推理时加上–save_npz参数，Ensemble命令和5一致。

# 静态图导出
%cd ~/PaddleSeg/contrib/MedicalSeg/
'''导出5个模型'''
!python nnunet/export.py --config configs/nnunet/msd_lung/nnunet_2d_msd_lung_fold0.yml \
--save_dir output/static/2d_unet/fold0 \
--model_path output/2d_unet/fold0/iter_30000/model.pdparams
#fold1 2 3 4
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# 静态图多折推理
%cd ~/PaddleSeg/contrib/MedicalSeg/

!python nnunet/infer.py --image_folder msd_lung/Task006_Lung/imagesTs \
--output_folder output/nnunet_static/2d_unet \
--plan_path msd_lung/preprocessed/Task006_Lung/nnUNetPlansv2.1_plans_2D.pkl \
--model_paths output/static/2d_unet/fold0/model.pdmodel output/static/2d_unet/fold1/model.pdmodel output/static/2d_unet/fold2/model.pdmodel output/static/2d_unet/fold3/model.pdmodel output/static/2d_unet/fold4/model.pdmodel \
--param_paths output/static/2d_unet/fold0/model.pdiparams output/static/2d_unet/fold1/model.pdiparams output/static/2d_unet/fold2/model.pdiparams output/static/2d_unet/fold3/model.pdiparams output/static/2d_unet/fold4/model.pdiparams \
--postprocessing_json_path output/2d_unet_val/postprocessing.json \
--model_type 2d \
--disable_postprocessing  \
--save_npz


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