forked from mindspore-Ecosystem/mindspore
!12776 add unet 310 mindir infer
From: @lihongkang1 Reviewed-by: Signed-off-by:
This commit is contained in:
commit
b7e977f590
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@ -4,7 +4,7 @@
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- [Model Architecture](#model-architecture)
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- [Model Architecture](#model-architecture)
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- [Dataset](#dataset)
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- [Dataset](#dataset)
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- [Environment Requirements](#environment-requirements)
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- [Environment Requirements](#environment-requirements)
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- [Quick Start](#quick-start)
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- [Quick Start](#quick-start)
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- [Script Description](#script-description)
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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Script and Sample Code](#script-and-sample-code)
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- [Script Parameters](#script-parameters)
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- [Script Parameters](#script-parameters)
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@ -17,97 +17,89 @@
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- [Performance](#performance)
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- [Performance](#performance)
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- [Evaluation Performance](#evaluation-performance)
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- [Evaluation Performance](#evaluation-performance)
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- [How to use](#how-to-use)
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- [How to use](#how-to-use)
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- [Inference](#inference)
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- [Inference](#inference)
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- [Continue Training on the Pretrained Model](#continue-training-on-the-pretrained-model)
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- [Continue Training on the Pretrained Model](#continue-training-on-the-pretrained-model)
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- [Description of Random Situation](#description-of-random-situation)
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- [Description of Random Situation](#description-of-random-situation)
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- [ModelZoo Homepage](#modelzoo-homepage)
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- [ModelZoo Homepage](#modelzoo-homepage)
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## [Unet Description](#contents)
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# [Unet Description](#contents)
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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.
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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.
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[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.
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[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.
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# [Model Architecture](#contents)
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# [Model Architecture](#contents)
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Specifically, the U network structure is proposed in UNET, which can better extract and fuse high-level features and obtain context information and spatial location information. The U network structure is composed of encoder and decoder. The encoder is composed of two 3x3 conv and a 2x2 max pooling iteration. The number of channels is doubled after each down sampling. The decoder is composed of a 2x2 deconv, concat layer and two 3x3 convolutions, and then outputs after a 1x1 convolution.
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Specifically, the U network structure is proposed in UNET, which can better extract and fuse high-level features and obtain context information and spatial location information. The U network structure is composed of encoder and decoder. The encoder is composed of two 3x3 conv and a 2x2 max pooling iteration. The number of channels is doubled after each down sampling. The decoder is composed of a 2x2 deconv, concat layer and two 3x3 convolutions, and then outputs after a 1x1 convolution.
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# [Dataset](#contents)
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# [Dataset](#contents)
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Dataset used: [ISBI Challenge](http://brainiac2.mit.edu/isbi_challenge/home)
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Dataset used: [ISBI Challenge](http://brainiac2.mit.edu/isbi_challenge/home)
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- Description: The training and test datasets are two stacks of 30 sections from a serial section Transmission Electron Microscopy (ssTEM) data set of the Drosophila first instar larva ventral nerve cord (VNC). The microcube measures 2 x 2 x 1.5 microns approx., with a resolution of 4x4x50 nm/pixel.
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- Description: The training and test datasets are two stacks of 30 sections from a serial section Transmission Electron Microscopy (ssTEM) data set of the Drosophila first instar larva ventral nerve cord (VNC). The microcube measures 2 x 2 x 1.5 microns approx., with a resolution of 4x4x50 nm/pixel.
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- License: You are free to use this data set for the purpose of generating or testing non-commercial image segmentation software. If any scientific publications derive from the usage of this data set, you must cite TrakEM2 and the following publication: Cardona A, Saalfeld S, Preibisch S, Schmid B, Cheng A, Pulokas J, Tomancak P, Hartenstein V. 2010. An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy. PLoS Biol 8(10): e1000502. doi:10.1371/journal.pbio.1000502.
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- License: You are free to use this data set for the purpose of generating or testing non-commercial image segmentation software. If any scientific publications derive from the usage of this data set, you must cite TrakEM2 and the following publication: Cardona A, Saalfeld S, Preibisch S, Schmid B, Cheng A, Pulokas J, Tomancak P, Hartenstein V. 2010. An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy. PLoS Biol 8(10): e1000502. doi:10.1371/journal.pbio.1000502.
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- Dataset size:22.5M,
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- Dataset size:22.5M,
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- Train:15M, 30 images (Training data contains 2 multi-page TIF files, each containing 30 2D-images. train-volume.tif and train-labels.tif respectly contain data and label.)
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- Train:15M, 30 images (Training data contains 2 multi-page TIF files, each containing 30 2D-images. train-volume.tif and train-labels.tif respectly contain data and label.)
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- Val:(We randomly divde the training data into 5-fold and evaluate the model by across 5-fold cross-validation.)
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- Val:(We randomly divide the training data into 5-fold and evaluate the model by across 5-fold cross-validation.)
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- Test:7.5M, 30 images (Testing data contains 1 multi-page TIF files, each containing 30 2D-images. test-volume.tif respectly contain data.)
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- Test:7.5M, 30 images (Testing data contains 1 multi-page TIF files, each containing 30 2D-images. test-volume.tif respectly contain data.)
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- Data format:binary files(TIF file)
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- Data format:binary files(TIF file)
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- Note:Data will be processed in src/data_loader.py
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- Note:Data will be processed in src/data_loader.py
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# [Environment Requirements](#contents)
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# [Environment Requirements](#contents)
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- Hardware(Ascend)
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- Hardware(Ascend)
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- Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
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- Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
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- Framework
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- Framework
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- [MindSpore](https://www.mindspore.cn/install/en)
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- [MindSpore](https://www.mindspore.cn/install/en)
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- For more information, please check the resources below:
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- For more information, please check the resources below:
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- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
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- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
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# [Quick Start](#contents)
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# [Quick Start](#contents)
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After installing MindSpore via the official website, you can start training and evaluation as follows:
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After installing MindSpore via the official website, you can start training and evaluation as follows:
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- running on Ascend
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- running on Ascend
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```python
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```python
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# run training example
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# run training example
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python train.py --data_url=/path/to/data/ > train.log 2>&1 &
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python train.py --data_url=/path/to/data/ > train.log 2>&1 &
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OR
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OR
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bash scripts/run_standalone_train.sh [DATASET]
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bash scripts/run_standalone_train.sh [DATASET]
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# run distributed training example
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# run distributed training example
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bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET]
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bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET]
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# run evaluation example
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# run evaluation example
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python eval.py --data_url=/path/to/data/ --ckpt_path=/path/to/checkpoint/ > eval.log 2>&1 &
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python eval.py --data_url=/path/to/data/ --ckpt_path=/path/to/checkpoint/ > eval.log 2>&1 &
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OR
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OR
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bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT]
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bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT]
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```
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```
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# [Script Description](#contents)
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# [Script Description](#contents)
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## [Script and Sample Code](#contents)
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## [Script and Sample Code](#contents)
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```
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```text
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├── model_zoo
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├── model_zoo
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├── README.md // descriptions about all the models
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├── README.md // descriptions about all the models
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├── unet
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├── unet
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├── README.md // descriptions about Unet
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├── README.md // descriptions about Unet
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├── scripts
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├── scripts
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│ ├──run_standalone_train.sh // shell script for distributed on Ascend
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│ ├──run_standalone_train.sh // shell script for distributed on Ascend
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│ ├──run_standalone_eval.sh // shell script for evaluation on Ascend
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│ ├──run_standalone_eval.sh // shell script for evaluation on Ascend
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├── src
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├── src
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│ ├──config.py // parameter configuration
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│ ├──config.py // parameter configuration
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│ ├──data_loader.py // creating dataset
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│ ├──data_loader.py // creating dataset
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│ ├──loss.py // loss
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│ ├──loss.py // loss
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│ ├──utils.py // General components (callback function)
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│ ├──utils.py // General components (callback function)
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│ ├──unet.py // Unet architecture
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│ ├──unet.py // Unet architecture
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├──__init__.py // init file
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├──__init__.py // init file
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├──unet_model.py // unet model
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├──unet_model.py // unet model
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├──unet_parts.py // unet part
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├──unet_parts.py // unet part
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├── train.py // training script
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├── train.py // training script
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├──launch_8p.py // training 8P script
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├──launch_8p.py // training 8P script
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├── eval.py // evaluation script
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├── eval.py // evaluation script
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```
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```
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## [Script Parameters](#contents)
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## [Script Parameters](#contents)
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@ -133,24 +125,24 @@ Parameters for both training and evaluation can be set in config.py
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'resume_ckpt': './', # pretrain model path
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'resume_ckpt': './', # pretrain model path
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```
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```
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## [Training Process](#contents)
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## [Training Process](#contents)
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### Training
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### Training
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- running on Ascend
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- running on Ascend
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```
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```shell
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python train.py --data_url=/path/to/data/ > train.log 2>&1 &
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python train.py --data_url=/path/to/data/ > train.log 2>&1 &
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OR
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OR
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bash scripts/run_standalone_train.sh [DATASET]
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bash scripts/run_standalone_train.sh [DATASET]
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```
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```
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The python command above will run in the background, you can view the results through the file `train.log`.
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The python command above will run in the background, you can view the results through the file `train.log`.
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After training, you'll get some checkpoint files under the script folder by default. The loss value will be achieved as follows:
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After training, you'll get some checkpoint files under the script folder by default. The loss value will be achieved as follows:
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```
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```shell
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# grep "loss is " train.log
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# grep "loss is " train.log
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step: 1, loss is 0.7011719, fps is 0.25025035060906264
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step: 1, loss is 0.7011719, fps is 0.25025035060906264
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step: 2, loss is 0.69433594, fps is 56.77693756377044
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step: 2, loss is 0.69433594, fps is 56.77693756377044
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@ -163,19 +155,20 @@ Parameters for both training and evaluation can be set in config.py
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step: 598, loss is 0.19958496, fps is 57.95493929352674
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step: 598, loss is 0.19958496, fps is 57.95493929352674
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step: 599, loss is 0.18371582, fps is 58.04039977720966
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step: 599, loss is 0.18371582, fps is 58.04039977720966
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step: 600, loss is 0.22070312, fps is 56.99692546024671
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step: 600, loss is 0.22070312, fps is 56.99692546024671
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```
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```
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The model checkpoint will be saved in the current directory.
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The model checkpoint will be saved in the current directory.
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### Distributed Training
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### Distributed Training
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```
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```shell
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bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET]
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bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET]
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```
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```
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The above shell script will run distribute training in the background. You can view the results through the file `logs/device[X]/log.log`. The loss value will be achieved as follows:
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The above shell script will run distribute training in the background. You can view the results through the file `logs/device[X]/log.log`. The loss value will be achieved as follows:
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```
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```shell
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# grep "loss is" logs/device0/log.log
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# grep "loss is" logs/device0/log.log
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step: 1, loss is 0.70524895, fps is 0.15914689861221412
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step: 1, loss is 0.70524895, fps is 0.15914689861221412
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step: 2, loss is 0.6925452, fps is 56.43668656967454
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step: 2, loss is 0.6925452, fps is 56.43668656967454
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@ -191,27 +184,27 @@ step: 300, loss is 0.18949677, fps is 57.63118508760329
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- evaluation on ISBI dataset when running on Ascend
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- evaluation on ISBI dataset when running on Ascend
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Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/unet/ckpt_unet_medical_adam-48_600.ckpt".
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Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/unet/ckpt_unet_medical_adam-48_600.ckpt".
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```
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```shell
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python eval.py --data_url=/path/to/data/ --ckpt_path=/path/to/checkpoint/ > eval.log 2>&1 &
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python eval.py --data_url=/path/to/data/ --ckpt_path=/path/to/checkpoint/ > eval.log 2>&1 &
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OR
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OR
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bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT]
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bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT]
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```
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```
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The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
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The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
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```
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```shell
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# grep "Cross valid dice coeff is:" eval.log
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# grep "Cross valid dice coeff is:" eval.log
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============== Cross valid dice coeff is: {'dice_coeff': 0.9085704886070473}
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============== Cross valid dice coeff is: {'dice_coeff': 0.9085704886070473}
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```
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```
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# [Model Description](#contents)
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# [Model Description](#contents)
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## [Performance](#contents)
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### Evaluation Performance
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## Performance
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### Evaluation Performance
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| Parameters | Ascend |
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| Parameters | Ascend |
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| -------------------------- | ------------------------------------------------------------ |
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| -------------------------- | ------------------------------------------------------------ |
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@ -227,45 +220,74 @@ step: 300, loss is 0.18949677, fps is 57.63118508760329
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| outputs | probability |
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| outputs | probability |
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| Loss | 0.22070312 |
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| Loss | 0.22070312 |
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| Speed | 1pc: 267 ms/step; 8pc: 280 ms/step; |
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| Speed | 1pc: 267 ms/step; 8pc: 280 ms/step; |
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| Total time | 1pc: 2.67 mins; 8pc: 1.40 mins |
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| Total time | 1pc: 2.67 mins; 8pc: 1.40 mins |
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| Parameters (M) | 93M |
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| Parameters (M) | 93M |
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| Checkpoint for Fine tuning | 355.11M (.ckpt file) |
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| Checkpoint for Fine tuning | 355.11M (.ckpt file) |
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| Scripts | [unet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/unet) |
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| Scripts | [unet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/unet) |
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## [How to use](#contents)
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## [How to use](#contents)
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### Inference
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### Inference
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If you need to use the trained model to perform inference on multiple hardware platforms, such as Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/migrate_3rd_scripts.html). Following the steps below, this is a simple example:
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If you need to use the trained model to perform inference on multiple hardware platforms, such as Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/migrate_3rd_scripts.html). Following the steps below, this is a simple example:
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- Running on Ascend
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- Running on Ascend
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```
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```python
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# Set context
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# Set context
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device_id = int(os.getenv('DEVICE_ID'))
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",save_graphs=True,device_id=device_id)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",save_graphs=True,device_id=device_id)
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# Load unseen dataset for inference
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# Load unseen dataset for inference
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_, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False)
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_, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False)
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# Define model and Load pre-trained model
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# Define model and Load pre-trained model
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net = UNet(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
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net = UNet(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
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param_dict= load_checkpoint(ckpt_path)
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param_dict= load_checkpoint(ckpt_path)
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load_param_into_net(net , param_dict)
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load_param_into_net(net , param_dict)
|
||||||
criterion = CrossEntropyWithLogits()
|
criterion = CrossEntropyWithLogits()
|
||||||
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
|
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
|
||||||
|
|
||||||
# Make predictions on the unseen dataset
|
# Make predictions on the unseen dataset
|
||||||
print("============== Starting Evaluating ============")
|
print("============== Starting Evaluating ============")
|
||||||
dice_score = model.eval(valid_dataset, dataset_sink_mode=False)
|
dice_score = model.eval(valid_dataset, dataset_sink_mode=False)
|
||||||
print("============== Cross valid dice coeff is:", dice_score)
|
print("============== Cross valid dice coeff is:", dice_score)
|
||||||
|
|
||||||
```
|
```
|
||||||
|
|
||||||
### Continue Training on the Pretrained Model
|
- Running on Ascend 310
|
||||||
|
|
||||||
|
Export MindIR
|
||||||
|
|
||||||
|
```shell
|
||||||
|
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
||||||
|
```
|
||||||
|
|
||||||
|
The ckpt_file parameter is required,
|
||||||
|
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
|
||||||
|
|
||||||
|
Before performing inference, the MINDIR file must be exported by export script on the 910 environment.
|
||||||
|
Current batch_size can only be set to 1.
|
||||||
|
|
||||||
|
```shell
|
||||||
|
# Ascend310 inference
|
||||||
|
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
|
||||||
|
```
|
||||||
|
|
||||||
|
`DEVICE_ID` is optional, default value is 0.
|
||||||
|
|
||||||
|
Inference result is saved in current path, you can find result in acc.log file.
|
||||||
|
|
||||||
|
```text
|
||||||
|
Cross valid dice coeff is: 0.9054352151297033
|
||||||
|
```
|
||||||
|
|
||||||
|
### Continue Training on the Pretrained Model
|
||||||
|
|
||||||
- running on Ascend
|
- running on Ascend
|
||||||
|
|
||||||
```
|
```python
|
||||||
# Define model
|
# Define model
|
||||||
net = UNet(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
|
net = UNet(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
|
||||||
# Continue training if set 'resume' to be True
|
# Continue training if set 'resume' to be True
|
||||||
|
@ -276,33 +298,32 @@ If you need to use the trained model to perform inference on multiple hardware p
|
||||||
# Load dataset
|
# Load dataset
|
||||||
train_dataset, _ = create_dataset(data_dir, epochs, batch_size, True, cross_valid_ind, run_distribute)
|
train_dataset, _ = create_dataset(data_dir, epochs, batch_size, True, cross_valid_ind, run_distribute)
|
||||||
train_data_size = train_dataset.get_dataset_size()
|
train_data_size = train_dataset.get_dataset_size()
|
||||||
|
|
||||||
optimizer = nn.Adam(params=net.trainable_params(), learning_rate=lr, weight_decay=cfg['weight_decay'],
|
optimizer = nn.Adam(params=net.trainable_params(), learning_rate=lr, weight_decay=cfg['weight_decay'],
|
||||||
loss_scale=cfg['loss_scale'])
|
loss_scale=cfg['loss_scale'])
|
||||||
criterion = CrossEntropyWithLogits()
|
criterion = CrossEntropyWithLogits()
|
||||||
loss_scale_manager = mindspore.train.loss_scale_manager.FixedLossScaleManager(cfg['FixedLossScaleManager'], False)
|
loss_scale_manager = mindspore.train.loss_scale_manager.FixedLossScaleManager(cfg['FixedLossScaleManager'], False)
|
||||||
|
|
||||||
model = Model(net, loss_fn=criterion, loss_scale_manager=loss_scale_manager, optimizer=optimizer, amp_level="O3")
|
model = Model(net, loss_fn=criterion, loss_scale_manager=loss_scale_manager, optimizer=optimizer, amp_level="O3")
|
||||||
|
|
||||||
|
|
||||||
# Set callbacks
|
# Set callbacks
|
||||||
ckpt_config = CheckpointConfig(save_checkpoint_steps=train_data_size,
|
ckpt_config = CheckpointConfig(save_checkpoint_steps=train_data_size,
|
||||||
keep_checkpoint_max=cfg['keep_checkpoint_max'])
|
keep_checkpoint_max=cfg['keep_checkpoint_max'])
|
||||||
ckpoint_cb = ModelCheckpoint(prefix='ckpt_unet_medical_adam',
|
ckpoint_cb = ModelCheckpoint(prefix='ckpt_unet_medical_adam',
|
||||||
directory='./ckpt_{}/'.format(device_id),
|
directory='./ckpt_{}/'.format(device_id),
|
||||||
config=ckpt_config)
|
config=ckpt_config)
|
||||||
|
|
||||||
print("============== Starting Training ==============")
|
print("============== Starting Training ==============")
|
||||||
model.train(1, train_dataset, callbacks=[StepLossTimeMonitor(batch_size=batch_size), ckpoint_cb],
|
model.train(1, train_dataset, callbacks=[StepLossTimeMonitor(batch_size=batch_size), ckpoint_cb],
|
||||||
dataset_sink_mode=False)
|
dataset_sink_mode=False)
|
||||||
print("============== End Training ==============")
|
print("============== End Training ==============")
|
||||||
```
|
```
|
||||||
|
|
||||||
|
|
||||||
# [Description of Random Situation](#contents)
|
# [Description of Random Situation](#contents)
|
||||||
|
|
||||||
In data_loader.py, we set the seed inside “_get_val_train_indices" function. We also use random seed in train.py.
|
In data_loader.py, we set the seed inside “_get_val_train_indices" function. We also use random seed in train.py.
|
||||||
|
|
||||||
|
|
||||||
# [ModelZoo Homepage](#contents)
|
# [ModelZoo Homepage](#contents)
|
||||||
|
|
||||||
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
|
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
|
||||||
|
|
|
@ -254,6 +254,33 @@ step: 300, loss is 0.18949677, fps is 57.63118508760329
|
||||||
print("============== Starting Evaluating ============")
|
print("============== Starting Evaluating ============")
|
||||||
dice_score = model.eval(valid_dataset, dataset_sink_mode=False)
|
dice_score = model.eval(valid_dataset, dataset_sink_mode=False)
|
||||||
print("============== Cross valid dice coeff is:", dice_score)
|
print("============== Cross valid dice coeff is:", dice_score)
|
||||||
|
|
||||||
|
```
|
||||||
|
|
||||||
|
- Ascend 310环境运行
|
||||||
|
|
||||||
|
导出mindir模型
|
||||||
|
|
||||||
|
```shell
|
||||||
|
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
||||||
|
```
|
||||||
|
|
||||||
|
参数`ckpt_file` 是必需的,`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中进行选择。
|
||||||
|
|
||||||
|
在执行推理前,MINDIR文件必须在910上通过export.py文件导出。
|
||||||
|
目前仅可处理batch_Size为1。
|
||||||
|
|
||||||
|
```shell
|
||||||
|
# Ascend310 推理
|
||||||
|
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
|
||||||
|
```
|
||||||
|
|
||||||
|
`DEVICE_ID` 可选,默认值为 0。
|
||||||
|
|
||||||
|
推理结果保存在当前路径,可在acc.log中看到最终精度结果。
|
||||||
|
|
||||||
|
```text
|
||||||
|
Cross valid dice coeff is: 0.9054352151297033
|
||||||
```
|
```
|
||||||
|
|
||||||
### 继续训练预训练模型
|
### 继续训练预训练模型
|
||||||
|
|
|
@ -0,0 +1,32 @@
|
||||||
|
/**
|
||||||
|
* Copyright 2021 Huawei Technologies Co., Ltd
|
||||||
|
*
|
||||||
|
* 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.
|
||||||
|
*/
|
||||||
|
|
||||||
|
#ifndef MINDSPORE_INFERENCE_UTILS_H_
|
||||||
|
#define MINDSPORE_INFERENCE_UTILS_H_
|
||||||
|
|
||||||
|
#include <sys/stat.h>
|
||||||
|
#include <dirent.h>
|
||||||
|
#include <vector>
|
||||||
|
#include <string>
|
||||||
|
#include <memory>
|
||||||
|
#include "include/api/types.h"
|
||||||
|
|
||||||
|
std::vector<std::string> GetAllFiles(std::string_view dirName);
|
||||||
|
DIR *OpenDir(std::string_view dirName);
|
||||||
|
std::string RealPath(std::string_view path);
|
||||||
|
mindspore::MSTensor ReadFileToTensor(const std::string &file);
|
||||||
|
int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
|
||||||
|
#endif
|
|
@ -0,0 +1,14 @@
|
||||||
|
cmake_minimum_required(VERSION 3.14.1)
|
||||||
|
project(MindSporeCxxTestcase[CXX])
|
||||||
|
add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
|
||||||
|
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
|
||||||
|
set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
|
||||||
|
option(MINDSPORE_PATH "mindspore install path" "")
|
||||||
|
include_directories(${MINDSPORE_PATH})
|
||||||
|
include_directories(${MINDSPORE_PATH}/include)
|
||||||
|
include_directories(${PROJECT_SRC_ROOT}/../inc)
|
||||||
|
find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
|
||||||
|
file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
|
||||||
|
|
||||||
|
add_executable(main main.cc utils.cc)
|
||||||
|
target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
|
|
@ -0,0 +1,18 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||||
|
#
|
||||||
|
# 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.
|
||||||
|
# ============================================================================
|
||||||
|
|
||||||
|
cmake . -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
|
||||||
|
make
|
|
@ -0,0 +1,123 @@
|
||||||
|
/**
|
||||||
|
* Copyright 2021 Huawei Technologies Co., Ltd
|
||||||
|
*
|
||||||
|
* 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.
|
||||||
|
*/
|
||||||
|
|
||||||
|
#include <sys/time.h>
|
||||||
|
#include <gflags/gflags.h>
|
||||||
|
#include <dirent.h>
|
||||||
|
#include <iostream>
|
||||||
|
#include <string>
|
||||||
|
#include <algorithm>
|
||||||
|
#include <iosfwd>
|
||||||
|
#include <vector>
|
||||||
|
#include <fstream>
|
||||||
|
|
||||||
|
#include "include/api/model.h"
|
||||||
|
#include "include/api/serialization.h"
|
||||||
|
#include "include/api/context.h"
|
||||||
|
#include "include/minddata/dataset/include/execute.h"
|
||||||
|
#include "include/minddata/dataset/include/vision.h"
|
||||||
|
#include "../inc/utils.h"
|
||||||
|
#include "include/api/types.h"
|
||||||
|
|
||||||
|
|
||||||
|
using mindspore::Context;
|
||||||
|
using mindspore::GlobalContext;
|
||||||
|
using mindspore::ModelContext;
|
||||||
|
using mindspore::Serialization;
|
||||||
|
using mindspore::Model;
|
||||||
|
using mindspore::Status;
|
||||||
|
using mindspore::dataset::Execute;
|
||||||
|
using mindspore::MSTensor;
|
||||||
|
using mindspore::ModelType;
|
||||||
|
using mindspore::GraphCell;
|
||||||
|
using mindspore::kSuccess;
|
||||||
|
|
||||||
|
|
||||||
|
DEFINE_string(mindir_path, "", "mindir path");
|
||||||
|
DEFINE_string(dataset_path, ".", "dataset path");
|
||||||
|
DEFINE_int32(device_id, 0, "device id");
|
||||||
|
|
||||||
|
int main(int argc, char **argv) {
|
||||||
|
gflags::ParseCommandLineFlags(&argc, &argv, true);
|
||||||
|
if (RealPath(FLAGS_mindir_path).empty()) {
|
||||||
|
std::cout << "Invalid mindir" << std::endl;
|
||||||
|
return 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
GlobalContext::SetGlobalDeviceTarget(mindspore::kDeviceTypeAscend310);
|
||||||
|
GlobalContext::SetGlobalDeviceID(FLAGS_device_id);
|
||||||
|
auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
|
||||||
|
auto model_context = std::make_shared<Context>();
|
||||||
|
Model model(GraphCell(graph), model_context);
|
||||||
|
|
||||||
|
Status ret = model.Build();
|
||||||
|
if (ret != kSuccess) {
|
||||||
|
std::cout << "EEEEEEEERROR Build failed." << std::endl;
|
||||||
|
return 1;
|
||||||
|
}
|
||||||
|
std::vector<MSTensor> model_inputs = model.GetInputs();
|
||||||
|
|
||||||
|
auto all_files = GetAllFiles(FLAGS_dataset_path);
|
||||||
|
if (all_files.empty()) {
|
||||||
|
std::cout << "ERROR: no input data." << std::endl;
|
||||||
|
return 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::map<double, double> costTime_map;
|
||||||
|
size_t size = all_files.size();
|
||||||
|
for (size_t i = 0; i < size; ++i) {
|
||||||
|
struct timeval start = {0};
|
||||||
|
struct timeval end = {0};
|
||||||
|
double startTime_ms;
|
||||||
|
double endTime_ms;
|
||||||
|
std::vector<MSTensor> inputs;
|
||||||
|
std::vector<MSTensor> outputs;
|
||||||
|
std::cout << "Start predict input files:" << all_files[i] << std::endl;
|
||||||
|
auto img = ReadFileToTensor(all_files[i]);
|
||||||
|
|
||||||
|
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
|
||||||
|
img.Data().get(), img.DataSize());
|
||||||
|
gettimeofday(&start, NULL);
|
||||||
|
ret = model.Predict(inputs, &outputs);
|
||||||
|
gettimeofday(&end, NULL);
|
||||||
|
if (ret != kSuccess) {
|
||||||
|
std::cout << "Predict " << all_files[i] << " failed." << std::endl;
|
||||||
|
return 1;
|
||||||
|
}
|
||||||
|
startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
|
||||||
|
endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
|
||||||
|
costTime_map.insert(std::pair<double, double>(startTime_ms, endTime_ms));
|
||||||
|
WriteResult(all_files[i], outputs);
|
||||||
|
}
|
||||||
|
double average = 0.0;
|
||||||
|
int infer_cnt = 0;
|
||||||
|
char tmpCh[256] = {0};
|
||||||
|
for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
|
||||||
|
double diff = 0.0;
|
||||||
|
diff = iter->second - iter->first;
|
||||||
|
average += diff;
|
||||||
|
infer_cnt++;
|
||||||
|
}
|
||||||
|
average = average/infer_cnt;
|
||||||
|
snprintf(tmpCh, sizeof(tmpCh), "NN inference cost average time: %4.3f ms of infer_count %d \n", average, infer_cnt);
|
||||||
|
std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
|
||||||
|
std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
|
||||||
|
std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
|
||||||
|
file_stream << tmpCh;
|
||||||
|
file_stream.close();
|
||||||
|
costTime_map.clear();
|
||||||
|
return 0;
|
||||||
|
}
|
|
@ -0,0 +1,136 @@
|
||||||
|
/**
|
||||||
|
* Copyright 2021 Huawei Technologies Co., Ltd
|
||||||
|
*
|
||||||
|
* 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.
|
||||||
|
*/
|
||||||
|
|
||||||
|
#include "../inc/utils.h"
|
||||||
|
#include <fstream>
|
||||||
|
#include <algorithm>
|
||||||
|
#include <iostream>
|
||||||
|
|
||||||
|
using mindspore::MSTensor;
|
||||||
|
using mindspore::DataType;
|
||||||
|
|
||||||
|
std::vector<std::string> GetAllFiles(std::string_view dirName) {
|
||||||
|
struct dirent *filename;
|
||||||
|
DIR *dir = OpenDir(dirName);
|
||||||
|
if (dir == nullptr) {
|
||||||
|
return {};
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<std::string> res;
|
||||||
|
while ((filename = readdir(dir)) != nullptr) {
|
||||||
|
std::string dName = std::string(filename->d_name);
|
||||||
|
if (dName == "." ||
|
||||||
|
dName == ".." ||
|
||||||
|
filename->d_type != DT_REG)
|
||||||
|
continue;
|
||||||
|
res.emplace_back(std::string(dirName) + "/" + filename->d_name);
|
||||||
|
}
|
||||||
|
|
||||||
|
std::sort(res.begin(), res.end());
|
||||||
|
for (auto &f : res) {
|
||||||
|
std::cout << "image file: " << f << std::endl;
|
||||||
|
}
|
||||||
|
return res;
|
||||||
|
}
|
||||||
|
|
||||||
|
int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
|
||||||
|
std::string homePath = "./result_Files";
|
||||||
|
for (size_t i = 0; i < outputs.size(); ++i) {
|
||||||
|
size_t outputSize;
|
||||||
|
std::shared_ptr<const void> netOutput;
|
||||||
|
netOutput = outputs[i].Data();
|
||||||
|
outputSize = outputs[i].DataSize();
|
||||||
|
|
||||||
|
int pos = imageFile.rfind('/');
|
||||||
|
std::string fileName(imageFile, pos + 1);
|
||||||
|
fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
|
||||||
|
std::string outFileName = homePath + "/" + fileName;
|
||||||
|
FILE * outputFile = fopen(outFileName.c_str(), "wb");
|
||||||
|
fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
|
||||||
|
|
||||||
|
fclose(outputFile);
|
||||||
|
outputFile = nullptr;
|
||||||
|
}
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
MSTensor ReadFileToTensor(const std::string &file) {
|
||||||
|
if (file.empty()) {
|
||||||
|
std::cout << "Pointer file is nullptr" << std::endl;
|
||||||
|
return MSTensor();
|
||||||
|
}
|
||||||
|
|
||||||
|
std::ifstream ifs(file);
|
||||||
|
if (!ifs.good()) {
|
||||||
|
std::cout << "File: " << file << " is not exist" << std::endl;
|
||||||
|
return MSTensor();
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!ifs.is_open()) {
|
||||||
|
std::cout << "File: " << file << "open failed" << std::endl;
|
||||||
|
return MSTensor();
|
||||||
|
}
|
||||||
|
|
||||||
|
ifs.seekg(0, std::ios::end);
|
||||||
|
size_t size = ifs.tellg();
|
||||||
|
MSTensor buffer(file, DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
|
||||||
|
|
||||||
|
ifs.seekg(0, std::ios::beg);
|
||||||
|
ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
|
||||||
|
ifs.close();
|
||||||
|
|
||||||
|
return buffer;
|
||||||
|
}
|
||||||
|
|
||||||
|
DIR *OpenDir(std::string_view dirName) {
|
||||||
|
if (dirName.empty()) {
|
||||||
|
std::cout << " dirName is null ! " << std::endl;
|
||||||
|
return nullptr;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::string realPath = RealPath(dirName);
|
||||||
|
|
||||||
|
struct stat s;
|
||||||
|
lstat(realPath.c_str(), &s);
|
||||||
|
if (!S_ISDIR(s.st_mode)) {
|
||||||
|
std::cout << "dirName is not a valid directory !" << std::endl;
|
||||||
|
return nullptr;
|
||||||
|
}
|
||||||
|
|
||||||
|
DIR *dir;
|
||||||
|
dir = opendir(realPath.c_str());
|
||||||
|
if (dir == nullptr) {
|
||||||
|
std::cout << "Can not open dir " << dirName << std::endl;
|
||||||
|
return nullptr;
|
||||||
|
}
|
||||||
|
std::cout << "Successfully opened the dir " << dirName << std::endl;
|
||||||
|
return dir;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::string RealPath(std::string_view path) {
|
||||||
|
char real_path_mem[PATH_MAX] = {0};
|
||||||
|
char *real_path_ret = nullptr;
|
||||||
|
real_path_ret = realpath(path.data(), real_path_mem);
|
||||||
|
|
||||||
|
if (real_path_ret == nullptr) {
|
||||||
|
std::cout << "File: " << path << " is not exist.";
|
||||||
|
return "";
|
||||||
|
}
|
||||||
|
|
||||||
|
std::string real_path(real_path_mem);
|
||||||
|
std::cout << path << " realpath is: " << real_path << std::endl;
|
||||||
|
return real_path;
|
||||||
|
}
|
|
@ -0,0 +1,97 @@
|
||||||
|
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||||
|
#
|
||||||
|
# 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""unet 310 infer."""
|
||||||
|
import os
|
||||||
|
import argparse
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
from src.data_loader import create_dataset
|
||||||
|
from src.config import cfg_unet
|
||||||
|
from scipy.special import softmax
|
||||||
|
|
||||||
|
|
||||||
|
class dice_coeff():
|
||||||
|
def __init__(self):
|
||||||
|
self.clear()
|
||||||
|
|
||||||
|
def clear(self):
|
||||||
|
self._dice_coeff_sum = 0
|
||||||
|
self._samples_num = 0
|
||||||
|
|
||||||
|
def update(self, *inputs):
|
||||||
|
if len(inputs) != 2:
|
||||||
|
raise ValueError('Mean dice coefficient need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
|
||||||
|
|
||||||
|
y_pred = inputs[0]
|
||||||
|
y = np.array(inputs[1])
|
||||||
|
|
||||||
|
self._samples_num += y.shape[0]
|
||||||
|
y_pred = y_pred.transpose(0, 2, 3, 1)
|
||||||
|
y = y.transpose(0, 2, 3, 1)
|
||||||
|
y_pred = softmax(y_pred, axis=3)
|
||||||
|
|
||||||
|
inter = np.dot(y_pred.flatten(), y.flatten())
|
||||||
|
union = np.dot(y_pred.flatten(), y_pred.flatten()) + np.dot(y.flatten(), y.flatten())
|
||||||
|
|
||||||
|
single_dice_coeff = 2*float(inter)/float(union+1e-6)
|
||||||
|
print("single dice coeff is:", single_dice_coeff)
|
||||||
|
self._dice_coeff_sum += single_dice_coeff
|
||||||
|
|
||||||
|
def eval(self):
|
||||||
|
if self._samples_num == 0:
|
||||||
|
raise RuntimeError('Total samples num must not be 0.')
|
||||||
|
|
||||||
|
return self._dice_coeff_sum / float(self._samples_num)
|
||||||
|
|
||||||
|
|
||||||
|
def test_net(data_dir,
|
||||||
|
cross_valid_ind=1,
|
||||||
|
cfg=None):
|
||||||
|
|
||||||
|
_, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False)
|
||||||
|
labels_list = []
|
||||||
|
|
||||||
|
for data in valid_dataset:
|
||||||
|
labels_list.append(data[1].asnumpy())
|
||||||
|
|
||||||
|
return labels_list
|
||||||
|
|
||||||
|
|
||||||
|
def get_args():
|
||||||
|
parser = argparse.ArgumentParser(description='Test the UNet on images and target masks',
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||||
|
parser.add_argument('-d', '--data_url', dest='data_url', type=str, default='data/',
|
||||||
|
help='data directory')
|
||||||
|
parser.add_argument('-p', '--rst_path', dest='rst_path', type=str, default='./result_Files/',
|
||||||
|
help='infer result path')
|
||||||
|
|
||||||
|
return parser.parse_args()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
|
||||||
|
args = get_args()
|
||||||
|
|
||||||
|
label_list = test_net(data_dir=args.data_url, cross_valid_ind=cfg_unet['cross_valid_ind'], cfg=cfg_unet)
|
||||||
|
rst_path = args.rst_path
|
||||||
|
metrics = dice_coeff()
|
||||||
|
|
||||||
|
for j in range(len(os.listdir(rst_path))):
|
||||||
|
file_name = rst_path + "ISBI_test_bs_1_" + str(j) + "_0" + ".bin"
|
||||||
|
output = np.fromfile(file_name, np.float32).reshape(1, 2, 388, 388)
|
||||||
|
label = label_list[j]
|
||||||
|
metrics.update(output, label)
|
||||||
|
|
||||||
|
print("Cross valid dice coeff is: ", metrics.eval())
|
|
@ -0,0 +1,45 @@
|
||||||
|
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||||
|
#
|
||||||
|
# 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""unet 310 infer preprocess dataset"""
|
||||||
|
import argparse
|
||||||
|
from src.data_loader import create_dataset
|
||||||
|
from src.config import cfg_unet
|
||||||
|
|
||||||
|
|
||||||
|
def preprocess_dataset(data_dir, result_path, cross_valid_ind=1, cfg=None):
|
||||||
|
|
||||||
|
_, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False)
|
||||||
|
|
||||||
|
for i, data in enumerate(valid_dataset):
|
||||||
|
file_name = "ISBI_test_bs_1_" + str(i) + ".bin"
|
||||||
|
file_path = result_path + file_name
|
||||||
|
data[0].asnumpy().tofile(file_path)
|
||||||
|
|
||||||
|
|
||||||
|
def get_args():
|
||||||
|
parser = argparse.ArgumentParser(description='Preprocess the UNet dataset ',
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||||
|
parser.add_argument('-d', '--data_url', dest='data_url', type=str, default='data/',
|
||||||
|
help='data directory')
|
||||||
|
parser.add_argument('-p', '--result_path', dest='result_path', type=str, default='./preprocess_Result/',
|
||||||
|
help='result path')
|
||||||
|
return parser.parse_args()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
args = get_args()
|
||||||
|
|
||||||
|
preprocess_dataset(data_dir=args.data_url, cross_valid_ind=cfg_unet['cross_valid_ind'], cfg=cfg_unet, result_path=
|
||||||
|
args.result_path)
|
|
@ -0,0 +1,115 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||||
|
#
|
||||||
|
# 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.
|
||||||
|
# ============================================================================
|
||||||
|
|
||||||
|
if [[ $# -lt 2 || $# -gt 3 ]]; then
|
||||||
|
echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
|
||||||
|
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
get_real_path(){
|
||||||
|
if [ "${1:0:1}" == "/" ]; then
|
||||||
|
echo "$1"
|
||||||
|
else
|
||||||
|
echo "$(realpath -m $PWD/$1)"
|
||||||
|
fi
|
||||||
|
}
|
||||||
|
model=$(get_real_path $1)
|
||||||
|
data_path=$(get_real_path $2)
|
||||||
|
if [ $# == 3 ]; then
|
||||||
|
device_id=$3
|
||||||
|
if [ -z $device_id ]; then
|
||||||
|
device_id=0
|
||||||
|
else
|
||||||
|
device_id=$device_id
|
||||||
|
fi
|
||||||
|
fi
|
||||||
|
|
||||||
|
echo "mindir name: "$model
|
||||||
|
echo "dataset path: "$data_path
|
||||||
|
echo "device id: "$device_id
|
||||||
|
|
||||||
|
export ASCEND_HOME=/usr/local/Ascend/
|
||||||
|
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
|
||||||
|
export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||||
|
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||||
|
export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||||
|
export PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||||
|
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||||
|
else
|
||||||
|
export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||||
|
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||||
|
export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages/te.egg:$ASCEND_HOME/atc/python/site-packages/topi.egg:$ASCEND_HOME/atc/python/site-packages/auto_tune.egg::$ASCEND_HOME/atc/python/site-packages/schedule_search.egg:$PYTHONPATH
|
||||||
|
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||||
|
fi
|
||||||
|
|
||||||
|
function preprocess_data()
|
||||||
|
{
|
||||||
|
if [ -d preprocess_Result ]; then
|
||||||
|
rm -rf ./preprocess_Result
|
||||||
|
fi
|
||||||
|
mkdir preprocess_Result
|
||||||
|
python3.7 ../preprocess.py --data_url=$data_path --result_path=./preprocess_Result/
|
||||||
|
}
|
||||||
|
|
||||||
|
function compile_app()
|
||||||
|
{
|
||||||
|
cd ../ascend310_infer/src
|
||||||
|
if [ -f "Makefile" ]; then
|
||||||
|
make clean
|
||||||
|
fi
|
||||||
|
sh build.sh &> build.log
|
||||||
|
}
|
||||||
|
|
||||||
|
function infer()
|
||||||
|
{
|
||||||
|
cd -
|
||||||
|
if [ -d result_Files ]; then
|
||||||
|
rm -rf ./result_Files
|
||||||
|
fi
|
||||||
|
if [ -d time_Result ]; then
|
||||||
|
rm -rf ./time_Result
|
||||||
|
fi
|
||||||
|
mkdir result_Files
|
||||||
|
mkdir time_Result
|
||||||
|
../ascend310_infer/src/main --mindir_path=$model --dataset_path=./preprocess_Result/ --device_id=$device_id &> infer.log
|
||||||
|
}
|
||||||
|
|
||||||
|
function cal_acc()
|
||||||
|
{
|
||||||
|
python3.7 ../postprocess.py --data_url=$data_path --rst_path=./result_Files/ &> acc.log &
|
||||||
|
}
|
||||||
|
|
||||||
|
preprocess_data
|
||||||
|
if [ $? -ne 0 ]; then
|
||||||
|
echo "preprocess dataset failed"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
compile_app
|
||||||
|
if [ $? -ne 0 ]; then
|
||||||
|
echo "compile app code failed"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
infer
|
||||||
|
if [ $? -ne 0 ]; then
|
||||||
|
echo "execute inference failed"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
cal_acc
|
||||||
|
if [ $? -ne 0 ]; then
|
||||||
|
echo "calculate accuracy failed"
|
||||||
|
exit 1
|
||||||
|
fi
|
Loading…
Reference in New Issue