First upload

README.md

@@ -90,6 +90,114 @@ Besides, it can also check if the code is ready to run.
 At the beginning of training, the resultant `.pth` files will be directly loaded into RAM to accelerate the dataset loading during training.
 
 
+## 🔬 Training and Evaluation
+
+Run the following command to train and evaluate a model:
+```
+bash scripts/run/train_and_eval.sh {GPU_IDX} {MODEL_NAME} {EXP_NAME}
+```
+Command-line arguments are as follows:
+1. `{GPU_IDX}`: The index of GPU which you want to adopt. (*default*: `0`)
+2. `{MODEL_NAME}`: The model used for training and evaluation. (*default*: `RepMode`)
+3. `{EXP_NAME}`: The directory to save the experimental data (logs, checkpoint, *etc.*). (*default*: `exps/test` | we recommend setting it to a sub-directory of `exps/`)
+
+For example, to train and evaluate `RepMode` with the GPU `1` and save the experimental data in `exps/benchmark_RepMode`, please run:
+```
+bash scripts/run/train_and_eval.sh 1 RepMode exps/benchmark_RepMode
+```
+
+If you only want to evaluate a trained model, please run:
+```
+bash scripts/run/only_eval.sh {GPU_IDX} {MODEL_NAME} {EXP_NAME} {MODEL_PATH}
+```
+Command-line arguments are as follows:
+1. `{GPU_IDX}`, `{MODEL_NAME}`, `{EXP_NAME}`: The same as above.
+2. `{MODEL_PATH}`: The path to the trained model used for evaluation. (*default*: `exps/test/checkpoints/model_best_test.p`)
+
+If you would like to customize this code, check out `scripts\`, `main.py`, `eval.py`, and `config.py` for more details!
+
+## 📑 Citation
+If you find that our work is helpful in your research, please consider citing our paper:
+```latex
+@misc{zhou2022repmode,
+      title={RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction},
+      author={Zhou, Donghao and Gu, Chunbin and Xu, Junde and Liu, Furui and Wang, Qiong and Chen, Guangyong and Heng, Pheng-Ann},
+      journal={arXiv preprint arXiv:2212.10066},
+      year={2022}
+}
+```
+
+## ✉️ Contact
+Feel free to contact me (Donghao Zhou: [dh.zhou@siat.ac.cn](mailto:dh.zhou@siat.ac.cn)) if anything is unclear or you are interested in potential collaboration.
+
+## 🤝 Acknowledgement
+Our code is built upon the repository of [pytorch_fnet](https://github.com/AllenCellModeling/pytorch_fnet/). We would like to thank its authors for their excellent work. If you want to use and redistribe our code, please follow [this license](https://github.com/Correr-Zhou/RepMode/blob/LICENSE) as well.
+
+<<<<<<< HEAD
+- 2023.03: This paper is selected as a **CVPR Highlight (Top 2.5% of 9155 submissions)**!
+- 2023.02: We are delight to announce that this paper is accepted by CVPR 2023!
+
+
+## 💻 Device Requirements
+To run this code, please make sure that you have:
+1. A computer with >50GB RAM (for loading the dataset)
+2. An NVIDIA GPU with >20GB memory (we used an NVIDIA V100 GPU with 32GB memory)
+3. A Linux operating system (we used Ubuntu 18.04.5)
+
+
+## 🛠️ Installation
+1. Create a Conda environment for the code:
+```
+conda create --name SSP python=3.9.12
+```
+2. Activate the environment:
+```
+conda activate SSP
+```
+3. Install the dependencies:
+```
+pip install -r requirements.txt
+```
+4. Following [this guide](https://docs.wandb.ai/quickstart#set-up-wb) to set up [W&B](https://wandb.ai/) (a tool for experimental logging).
+
+
+## 💽 Preparing Datasets
+
+We adopt a dataset collection (released by [Allen Institute for Cell Science](https://www.allencell.org/)) containing twelve partially labeled datasets, each of which corresponds to one category of subcellular structures (*i.e.* one single label prediction task).
+
+### Downloading Data
+Change the current directory of the terminal to the path to this code:
+```
+cd {PATH-TO-THIS-CODE}
+```
+Download the dataset collection:
+```
+bash scripts/dataset/download_all_data.sh
+```
+
+### Spliting Data
+First, conduct train/val/test dataset spliting for eleven datasets:
+```
+bash scripts/dataset/split_train_set.sh
+bash scripts/dataset/split_val_set.sh
+```
+Then, construct the DNA dataset based on the existing eleven datasets:
+```
+bash scripts/dataset/make_dna_dataset.sh
+```
+> ❗❗❗ Notably, each transmitted-light image in this collection have an extra DNA annotation but there are not a specialized DNA dataset.
+> Therefore, the samples of the DNA dataset are collected from the other datasets.
+
+### Integrating data
+The last step is to format these twelve datasets and integate them:
+```
+bash scripts/dataset/integ_dataset.sh
+```
+The above command would run `main.py` to process the data and finally generate three `.pth` files for train/val/test, respectively.
+Besides, it can also check if the code is ready to run.
+At the beginning of training, the resultant `.pth` files will be directly loaded into RAM to accelerate the dataset loading during training.
+
+
 ## 🔬 Training and Evaluation
 
 Run the following command to train and evaluate a model: