Project-MONAI
diff --git a/‎automl/DiNTS/Figures/arch_ram-cost-0.8.png
111 KB b/‎automl/DiNTS/Figures/arch_ram-cost-0.8.png
111 KB
diff --git a/‎automl/DiNTS/Figures/search_space.png
251 KB b/‎automl/DiNTS/Figures/search_space.png
251 KB
diff --git a/‎automl/DiNTS/Figures/training_loss.png
139 KB b/‎automl/DiNTS/Figures/training_loss.png
139 KB
diff --git a/‎automl/DiNTS/Figures/validation_metric.png
185 KB b/‎automl/DiNTS/Figures/validation_metric.png
185 KB
diff --git a/‎automl/DiNTS/README.md
Lines changed: 76 additions & 0 deletions b/‎automl/DiNTS/README.md
Lines changed: 76 additions & 0 deletions
diff --git a/‎automl/DiNTS/arch_code.pth
2.91 KB b/‎automl/DiNTS/arch_code.pth
2.91 KB
diff --git a/‎automl/DiNTS/install.sh
Lines changed: 12 additions & 0 deletions b/‎automl/DiNTS/install.sh
Lines changed: 12 additions & 0 deletions
@@ -0,0 +1,76 @@
+# Examples of DiNTS: Differentiable neural network topology search
+
+In this tutorial, we present a novel neural architecture search algorithm for 3D medical image segmentation. The datasets used in this tutorial are Task07 Pancreas (CT images) and Task09 Spleen (CT images) from [Medical Segmentation Decathlon](http://medicaldecathlon.com/). The implementation is based on:
+
+Yufan He, Dong Yang, Holger Roth, Can Zhao, Daguang Xu: "[DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation.](https://openaccess.thecvf.com/content/CVPR2021/papers/He_DiNTS_Differentiable_Neural_Network_Topology_Search_for_3D_Medical_Image_CVPR_2021_paper.pdf)" In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5841-5850. 2021.
+
+![0.8](./Figures/arch_ram-cost-0.8.png)
+![space](./Figures/search_space.png)
+
+## Requirements
+The script is tested with:
+- `Ubuntu 20.04` and  `CUDA 11`
+- The searching and training stage requires at least two 16GB GPUs.
+
+## Dependencies and installation
+### Download and install Nvidia PyTorch Docker
+```bash
+docker pull nvcr.io/nvidia/pytorch:21.10-py3
+```
+### Download the repository
+```bash
+git clone https://github.com/Project-MONAI/tutorials.git
+```
+### Run into Docker
+```
+sudo docker run -it --gpus all --pid=host --shm-size 16G -v /location/to/tutorials/automl/DiNTS/:/workspace/DiNTS/  nvcr.io/nvidia/pytorch:21.10-py3
+```
+### Install MONAI and dependencies
+```bash
+bash install.sh
+```
+
+## Data
+[Spleen CT dataset](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2) and [Pancreas MRI dataset](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2)
+from [Medical Segmentation Decathlon](http://medicaldecathlon.com/) is used. You can manually download it and save it to args.root. Otherwise, the script will automatic
+download the dataset.
+
+## Examples
+The tutorial contains two stages: searching stage and training stage. An architecture is searched and saved into a `.pth` file using `search_dints.py`.
+The searched architecture will be loaded by `train_dints.py` and re-trained for spleen segmentation.
+
+Check all possible options:
+```bash
+cd ./DiNTS/
+python search_dints.py -h
+python train_dints.py -h
+```
+
+### Searching
+- Add the following script to the commands of running into docker (optional)
+```
+-v /your_downloaded_data_root/Task07_Pancreas/:/workspace/data_msd/Task07_Pancreas/
+```
+- Change ``NUM_GPUS_PER_NODE`` to your number of GPUs.
+- Run `bash search_dints.sh`
+
+### Training
+- Add the following script to the commands of running into docker (Optional)
+```
+-v /your_downloaded_data_root/Task09_Spleen/:/workspace/data_msd/Task09_Spleen/
+```
+- Change ``ARCH_CKPT`` to point to the architecture file (.pth) from the searching stage.
+- Change ``NUM_GPUS_PER_NODE`` to your number of GPUs.
+- Run `bash train_dints.sh`
+
+Training loss and validation metric curves are shown as follows. The experiments utilized 8 NVIDIA A100 GPUs.
+
+![training_loss](./Figures/training_loss.png)
+
+![validation_metric](./Figures/validation_metric.png)
+
+## Questions and bugs
+
+- For questions relating to the use of MONAI, please use our [Discussions tab](https://github.com/Project-MONAI/MONAI/discussions) on the main repository of MONAI.
+- For bugs relating to MONAI functionality, please create an issue on the [main repository](https://github.com/Project-MONAI/MONAI/issues).
+- For bugs relating to the running of a tutorial, please create an issue in [this repository](https://github.com/Project-MONAI/Tutorials/issues).
@@ -0,0 +1,12 @@
+#!/bin/bash
+clear
+
+pip install nibabel
+pip install pandas
+
+# Update pip
+python -m pip install -U pip
+# Install scikit-image
+python -m pip install -U scikit-image
+
+pip install git+https://github.com/Project-MONAI/MONAI#egg=monai