From da91db20aaf94ab33dfa3de752474a00270e5a2e Mon Sep 17 00:00:00 2001 From: zhaoting Date: Wed, 10 Mar 2021 10:22:08 +0800 Subject: [PATCH] update unet readme --- model_zoo/official/cv/unet/README.md | 72 +++++++++++++++++++----- model_zoo/official/cv/unet/README_CN.md | 74 ++++++++++++++++++++----- 2 files changed, 119 insertions(+), 27 deletions(-) diff --git a/model_zoo/official/cv/unet/README.md b/model_zoo/official/cv/unet/README.md index e965aef425..5f3ef1b509 100644 --- a/model_zoo/official/cv/unet/README.md +++ b/model_zoo/official/cv/unet/README.md @@ -30,7 +30,11 @@ Unet Medical model for 2D image segmentation. This implementation is as described in the original paper [UNet: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597). Unet, in the 2015 ISBI cell tracking competition, many of the best are obtained. In this paper, a network model for medical image segmentation is proposed, and a data enhancement method is proposed to effectively use the annotation data to solve the problem of insufficient annotation data in the medical field. A U-shaped network structure is also used to extract the context and location information. -[Paper](https://arxiv.org/abs/1505.04597): Olaf Ronneberger, Philipp Fischer, Thomas Brox. "U-Net: Convolutional Networks for Biomedical Image Segmentation." *conditionally accepted at MICCAI 2015*. 2015. +UNet++ is a neural architecture for semantic and instance segmentation with re-designed skip pathways and deep supervision. + +[U-Net Paper](https://arxiv.org/abs/1505.04597): Olaf Ronneberger, Philipp Fischer, Thomas Brox. "U-Net: Convolutional Networks for Biomedical Image Segmentation." *conditionally accepted at MICCAI 2015*. 2015. + +[UNet++ Paper](https://arxiv.org/abs/1912.05074): Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh and J. Liang, "UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation," in IEEE Transactions on Medical Imaging, vol. 39, no. 6, pp. 1856-1867, June 2020, doi: 10.1109/TMI.2019.2959609. ## [Model Architecture](#contents) @@ -49,6 +53,8 @@ Dataset used: [ISBI Challenge](http://brainiac2.mit.edu/isbi_challenge/home) - Data format：binary files(TIF file) - Note：Data will be processed in src/data_loader.py +We also support cell nuclei dataset which is used in [Unet++ original paper](https://arxiv.org/abs/1912.05074). If you want to use the dataset, please add `'dataset': 'Cell_nuclei'` in `src/config.py`. + ## [Environment Requirements](#contents) - Hardware（Ascend） @@ -63,6 +69,10 @@ Dataset used: [ISBI Challenge](http://brainiac2.mit.edu/isbi_challenge/home) After installing MindSpore via the official website, you can start training and evaluation as follows: +- Select the network and dataset to use + +Refer to `src/config.py`. We support some parameter configurations for quick start. You can set `'model'` to `'unet_medical'`,`'unet_nested'` or `'unet_simple'` to select which net to use. We support `ISBI` and `Cell_nuclei` two dataset, you can set `'dataset'` to `'Cell_nuclei'` to use `Cell_nuclei` dataset, default is `ISBI`. + - Run on Ascend ```python @@ -107,21 +117,32 @@ Then you can run everything just like on ascend. ├── README.md // descriptions about all the models ├── unet ├── README.md // descriptions about Unet + ├── ascend310_infer // code of infer on ascend 310 ├── scripts - │ ├──run_standalone_train.sh // shell script for distributed on Ascend + │ ├──docker_start.sh // shell script for quick docker start + │ ├──run_disribute_train.sh // shell script for distributed on Ascend + │ ├──run_infer_310.sh // shell script for infer on ascend 310 + │ ├──run_standalone_train.sh // shell script for standalone on Ascend │ ├──run_standalone_eval.sh // shell script for evaluation on Ascend ├── src │ ├──config.py // parameter configuration │ ├──data_loader.py // creating dataset │ ├──loss.py // loss │ ├──utils.py // General components (callback function) - │ ├──unet.py // Unet architecture + │ ├──unet_medical // Unet medical architecture + ├──__init__.py // init file + ├──unet_model.py // unet model + ├──unet_parts.py // unet part + │ ├──unet_nested // Unet++ architecture ├──__init__.py // init file ├──unet_model.py // unet model ├──unet_parts.py // unet part ├── train.py // training script - ├──launch_8p.py // training 8P script - ├── eval.py // evaluation script + ├── eval.py // evaluation script + ├── export.py // export script + ├── mindspore_hub_conf.py // hub config file + ├── postprocess.py // unet 310 infer postprocess. + ├── preprocess.py // unet 310 infer preprocess dataset ``` ### [Script Parameters](#contents) @@ -149,6 +170,31 @@ Parameters for both training and evaluation can be set in config.py 'filter_weight': ["final.weight"] # weight name to filter while doing transfer training ``` +- config for Unet++, cell nuclei dataset + + ```python + 'model': 'unet_nested', # model name + 'dataset': 'Cell_nuclei', # dataset name + 'img_size': [96, 96], # image size + 'lr': 3e-4, # learning rate + 'epochs': 200, # total training epochs when run 1p + 'distribute_epochs': 1600, # total training epochs when run 8p + 'batchsize': 16, # batch size + 'num_classes': 2, # the number of classes in the dataset + 'num_channels': 3, # the number of input image channels + 'keep_checkpoint_max': 10, # only keep the last keep_checkpoint_max checkpoint + 'weight_decay': 0.0005, # weight decay value + 'loss_scale': 1024.0, # loss scale + 'FixedLossScaleManager': 1024.0, # loss scale + 'use_bn': True, # whether to use BN + 'use_ds': True, # whether to use deep supervisio + 'use_deconv': True, # whether to use Conv2dTranspose + 'resume': False, # whether training with pretrain model + 'resume_ckpt': './', # pretrain model path + 'transfer_training': False # whether do transfer training + 'filter_weight': ['final1.weight', 'final2.weight', 'final3.weight', 'final4.weight'] # weight name to filter while doing transfer training + ``` + ## [Training Process](#contents) ### Training @@ -283,10 +329,10 @@ Cross valid dice coeff is: 0.9054352151297033 Set options `resume` to True in `config.py`, and set `resume_ckpt` to the path of your checkpoint. e.g. ```python - 'resume': True, - 'resume_ckpt': 'ckpt_0/ckpt_unet_medical_adam_1-1_600.ckpt', - 'transfer_training': False, - 'filter_weight': ["final.weight"] + 'resume': True, + 'resume_ckpt': 'ckpt_0/ckpt_unet_medical_adam_1-1_600.ckpt', + 'transfer_training': False, + 'filter_weight': ["final.weight"] ``` #### Transfer training @@ -294,10 +340,10 @@ Set options `resume` to True in `config.py`, and set `resume_ckpt` to the path o Do the same thing as resuming traing above. In addition, set `transfer_training` to True. The `filter_weight` shows the weights which will be filtered for different dataset. Usually, the default value of `filter_weight` don't need to be changed. The default values includes the weights which depends on the class number. e.g. ```python - 'resume': True, - 'resume_ckpt': 'ckpt_0/ckpt_unet_medical_adam_1-1_600.ckpt', - 'transfer_training': True, - 'filter_weight': ["final.weight"] + 'resume': True, + 'resume_ckpt': 'ckpt_0/ckpt_unet_medical_adam_1-1_600.ckpt', + 'transfer_training': True, + 'filter_weight': ["final.weight"] ``` ## [Description of Random Situation](#contents) diff --git a/model_zoo/official/cv/unet/README_CN.md b/model_zoo/official/cv/unet/README_CN.md index 3485e51f97..0b839c7a09 100644 --- a/model_zoo/official/cv/unet/README_CN.md +++ b/model_zoo/official/cv/unet/README_CN.md @@ -33,7 +33,11 @@ U-Net医学模型基于二维图像分割。实现方式见论文[UNet：Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)。在2015年ISBI细胞跟踪竞赛中，U-Net获得了许多最佳奖项。论文中提出了一种用于医学图像分割的网络模型和数据增强方法，有效利用标注数据来解决医学领域标注数据不足的问题。U型网络结构也用于提取上下文和位置信息。 -[论文](https://arxiv.org/abs/1505.04597)： Olaf Ronneberger, Philipp Fischer, Thomas Brox. "U-Net: Convolutional Networks for Biomedical Image Segmentation." *conditionally accepted at MICCAI 2015*. 2015. +UNet++是U-Net的增强版本，使用了新的跨层链接方式和深层监督，可以用于语义分割和实例分割。 + +[U-Net 论文](https://arxiv.org/abs/1505.04597): Olaf Ronneberger, Philipp Fischer, Thomas Brox. "U-Net: Convolutional Networks for Biomedical Image Segmentation." *conditionally accepted at MICCAI 2015*. 2015. + +[UNet++ 论文](https://arxiv.org/abs/1912.05074): Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh and J. Liang, "UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation," in IEEE Transactions on Medical Imaging, vol. 39, no. 6, pp. 1856-1867, June 2020, doi: 10.1109/TMI.2019.2959609. ## 模型架构 @@ -53,6 +57,8 @@ U-Net医学模型基于二维图像分割。实现方式见论文[UNet：Convolu - 数据格式：二进制文件（TIF） - 注意：数据在src/data_loader.py中处理 +我们也支持一个在 [Unet++](https://arxiv.org/abs/1912.05074) 原论文中使用的数据集 `Cell_nuclei`。可以通过修改`src/config.py`中`'dataset': 'Cell_nuclei'`配置使用. + ## 环境要求 - 硬件（Ascend） @@ -67,6 +73,10 @@ U-Net医学模型基于二维图像分割。实现方式见论文[UNet：Convolu 通过官方网站安装MindSpore后，您可以按照如下步骤进行训练和评估： +- 选择模型及数据集 + +我们在`src/config.py`预备了一些网络及数据集的参数配置用于快速体验。也可以通过设置`'model'` 为 `'unet_medical'`,`'unet_nested'` 或者 `'unet_simple'` 来选择使用什么网络结构。我们支持`ISBI` 和 `Cell_nuclei`两种数据集处理，默认使用`ISBI`，可以设置`'dataset'` 为 `'Cell_nuclei'`使用`Cell_nuclei`数据集。 + - Ascend处理器环境运行 ```python @@ -108,24 +118,35 @@ bash scripts/docker_start.sh unet:20.1.0 [DATA_DIR] [MODEL_DIR] ```path ├── model_zoo - ├── README.md // 所有模型相关说明 + ├── README.md // 模型描述 ├── unet - ├── README.md // U-Net相关说明 + ├── README.md // Unet描述 + ├── ascend310_infer // Ascend 310 推理代码 ├── scripts - │ ├──run_standalone_train.sh // Ascend分布式shell脚本 - │ ├──run_standalone_eval.sh // Ascend评估shell脚本 + │ ├──docker_start.sh // docker 脚本 + │ ├──run_disribute_train.sh // Ascend 上分布式训练脚本 + │ ├──run_infer_310.sh // Ascend 310 推理脚本 + │ ├──run_standalone_train.sh // Ascend 上单卡训练脚本 + │ ├──run_standalone_eval.sh // Ascend 上推理脚本 ├── src │ ├──config.py // 参数配置 - │ ├──data_loader.py // 创建数据集 - │ ├──loss.py // 损失 + │ ├──data_loader.py // 数据处理 + │ ├──loss.py // 损失函数 │ ├──utils.py // 通用组件（回调函数） - │ ├──unet.py // U-Net架构 - ├──__init__.py // 初始化文件 - ├──unet_model.py // U-Net模型 - ├──unet_parts.py // U-Net部分 + │ ├──unet_medical // 医学图像处理Unet结构 + ├──__init__.py + ├──unet_model.py // Unet 网络结构 + ├──unet_parts.py // Unet 子网 + │ ├──unet_nested // Unet++ + ├──__init__.py + ├──unet_model.py // Unet++ 网络结构 + ├──unet_parts.py // Unet++ 子网 ├── train.py // 训练脚本 - ├──launch_8p.py // 训练8P脚本 - ├── eval.py // 评估脚本 + ├── eval.py // 推理脚本 + ├── export.py // 导出脚本 + ├── mindspore_hub_conf.py // hub 配置脚本 + ├── postprocess.py // 310 推理后处理脚本 + ├── preprocess.py // 310 推理前处理脚本 ``` ### 脚本参数 @@ -143,7 +164,7 @@ bash scripts/docker_start.sh unet:20.1.0 [DATA_DIR] [MODEL_DIR] 'cross_valid_ind': 1, # 交叉验证指标 'num_classes': 2, # 数据集类数 'num_channels': 1, # 通道数 - 'keep_checkpoint_max': 10, # 只保留最后一个keep_checkpoint_max检查点 + 'keep_checkpoint_max': 10, # 保留checkpoint检查个数 'weight_decay': 0.0005, # 权重衰减值 'loss_scale': 1024.0, # 损失放大 'FixedLossScaleManager': 1024.0, # 固定损失放大 @@ -151,6 +172,31 @@ bash scripts/docker_start.sh unet:20.1.0 [DATA_DIR] [MODEL_DIR] 'resume_ckpt': './', # 预训练模型路径 ``` +- Unet++配置, cell nuclei数据集 + + ```python + 'model': 'unet_nested', # 模型名称 + 'dataset': 'Cell_nuclei', # 数据集名称 + 'img_size': [96, 96], # 输入图像大小 + 'lr': 3e-4, # 学习率 + 'epochs': 200, # 运行1p时的总训练轮次 + 'distribute_epochs': 1600, # 运行8p时的总训练轮次 + 'batchsize': 16, # 训练批次大小 + 'num_classes': 2, # 数据集类数 + 'num_channels': 3, # 输入图像通道数 + 'keep_checkpoint_max': 10, # 保留checkpoint检查个数 + 'weight_decay': 0.0005, # 权重衰减值 + 'loss_scale': 1024.0, # 损失放大 + 'FixedLossScaleManager': 1024.0, # 损失放大 + 'use_bn': True, # 是否使用BN + 'use_ds': True, # 是否使用深层监督 + 'use_deconv': True, # 是否使用反卷积 + 'resume': False, # 是否使用预训练模型训练 + 'resume_ckpt': './', # 预训练模型路径 + 'transfer_training': False # 是否使用迁移学习 + 'filter_weight': ['final1.weight', 'final2.weight', 'final3.weight', 'final4.weight'] # 迁移学习过滤参数名 + ``` + ## 训练过程 ### 用法