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+# Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets
+This code is for MobileNetV3 Search Space experiments
+
+
+## Prerequisites
+- Python 3.6 (Anaconda)
+- PyTorch 1.6.0
+- CUDA 10.2
+- python-igraph==0.8.2
+- tqdm==4.50.2
+- torchvision==0.7.0
+- python-igraph==0.8.2
+- scipy==1.5.2
+- ofa==0.0.4-2007200808
+
+
+## MobileNetV3 Search Space
+Go to the folder for MobileNetV3 experiments (i.e. ```MetaD2A_mobilenetV3```)
+
+The overall flow is summarized as follows:
+- Building database for Predictor
+- Meta-Training Predictor
+- Building database for Generator with trained Predictor
+- Meta-Training Generator
+- Meta-Testing (Searching)
+- Evaluating the Searched architecture
+
+
+## Data Preparation
+To download preprocessed data files, run ```get_files/get_preprocessed_data.py```: 
+```shell script
+$ python get_files/get_preprocessed_data.py
+```
+It will take some time to download and preprocess each dataset.
+
+
+## Meta Test and Evaluation
+### Meta-Test
+
+You can download trained checkpoint files for generator and predictor
+```shell script
+$ python get_files/get_generator_checkpoint.py
+$ python get_files/get_predictor_checkpoint.py
+```
+
+If you want to meta-test with your own dataset, please first make your own preprocessed data, 
+by modifying  ```process_dataset.py``` .
+```shell script
+$ process_dataset.py
+```
+
+This code automatically generates neural architecturess and then 
+selects high-performing architectures among the candidates.
+By setting ```--data-name``` as the name of dataset (i.e. ```cifar10```, ```cifar100```, ```aircraft100```, ```pets```), 
+you can evaluate the specific dataset.
+
+```shell script
+# Meta-testing
+$ python main.py --gpu 0 --model generator --hs 56 --nz 56 --test --load-epoch 120 --num-gen-arch 200 --data-name {DATASET_NAME}
+```
+
+### Arhictecture Evaluation (MetaD2A vs NSGANetV2)
+##### Dataset Preparation
+You need to download Oxford-IIIT Pet dataset to evaluate on ```--data-name pets```
+```shell script
+$ python get_files/get_pets.py
+```
+Every others ```cifar10```, ```cifar100```, ```aircraft100``` will be downloaded automatically.
+
+##### evaluation
+You can run the searched architecture by running ```evaluation/main```. Codes are based on NSGANetV2.
+
+Go to the evaluation folder (i.e. ```evaluation```)
+```shell script
+$ cd evaluation
+```
+
+This automatically run the top 1 predicted architecture derived by MetaD2A. 
+```shell script
+python main.py --data-name cifar10 --num-gen-arch 200
+```
+You can also give flop constraint by using ```bound``` option. 
+```shell script
+python main.py --data-name cifar10 --num-gen-arch 200 --bound 300
+```
+
+You can compare MetaD2A with NSGANetV2 
+but you need to download some files provided 
+by [NSGANetV2](https://github.com/human-analysis/nsganetv2)
+
+```shell script
+python main.py --data-name cifar10 --num-gen-arch 200 --model-config flops@232
+```
+
+
+## Meta-Training MetaD2A Model
+To build database for Meta-training, you need to set ```IMGNET_PATH```, which is a directory of ILSVRC2021.
+
+### Database Building for Predictor
+We recommend you to run the multiple ```create_database.sh``` simultaneously to build fast. 
+You need to set ```IMGNET_PATH``` in the shell script.
+```shell script
+# Examples
+bash create_database.sh 0,1,2,3 0 49 predictor
+bash create_database.sh all 50 99 predictor
+...
+```
+After enough dataset is gathered, run ```build_database.py``` to collect them as one file. 
+```shell script
+python build_database.py --model_name predictor --collect
+```
+
+We also provide the database we use. To download database, run ```get_files/get_predictor_database.py```: 
+```shell script
+$ python get_files/get_predictor_database.py
+```
+
+### Meta-Train Predictor
+You can train the predictor as follows
+```shell script
+# Meta-training for predictor
+$ python main.py --gpu 0 --model predictor --hs 512 --nz 56 
+```
+### Database Building for Generator
+We recommend you to run the multiple ```create_database.sh``` simultaneously to build fast.
+```shell script
+# Examples
+bash create_database.sh 4,5,6,7 0 49 generator
+bash create_database.sh all 50 99 generator
+...
+```
+After enough dataset is gathered, run ```build_database.py``` to collect them as one. 
+```shell script
+python build_database.py --model_name generator --collect
+```
+
+We also provide the database we use. To download database, run ```get_files/get_generator_database.py``` 
+```shell script
+$ python get_files/get_generator_database.py
+```
+
+
+### Meta-Train Generator
+You can train the generator as follows
+```shell script
+# Meta-training for generator
+$ python main.py --gpu 0 --model generator --hs 56 --nz 56 
+```
+
+
+
+## Citation
+If you found the provided code useful, please cite our work.
+```
+@inproceedings{
+    lee2021rapid,
+    title={Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets},
+    author={Hayeon Lee and Eunyoung Hyung and Sung Ju Hwang},
+    booktitle={ICLR},
+    year={2021}
+}
+```
+
+## Reference
+- [Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks (ICML2019)](https://github.com/juho-lee/set_transformer)
+- [D-VAE: A Variational Autoencoder for Directed Acyclic Graphs, Advances in Neural Information Processing Systems (NeurIPS2019)](https://github.com/muhanzhang/D-VAE)
+- [Once for All: Train One Network and Specialize it for Efficient Deployment (ICLR2020)](https://github.com/mit-han-lab/once-for-all)
+- [NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search (ECCV2020)](https://github.com/human-analysis/nsganetv2)