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DenseNAS

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Densely Connected Search Space for More Flexible Neural Architecture Search (CVPR2020)

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최종 버전 다운로드 (.zip)

- __init__.py
- imagenet_search_cfg_mbv2.yaml
- imagenet_search_cfg_resnet.yaml
- imagenet_train_cfg.py
- imagenet_val_cfg.py
- search_config.py
- __init__.py
- imagenet_data.py
- img2lmdb.py
- lmdb_dataset.py
- mk_img_list.py
- mk_split_img_list.py
- prefetch_data.py
- torchvision_extension.py
- archs.png
- mbv2_comp.png
- mbv2_results.png
- res_comp.png
- res_results.png
- search_space.png
- lat_list_densenas_mbv2_xp32
- lat_list_resv2_32
- lat_list_resv2_32_reg10
- __init__.py
- dropped_model.py
- model_derived.py
- operations.py
- search_space_base.py
- search_space_mbv2.py
- search_space_res.py
- __init__.py
- derive_arch.py
- derive_arch_mbv2.py
- derive_arch_res.py
- latency_measure.py
- optimizer.py
- retrain.py
- search.py
- trainer.py
- validation.py
- __init__.py
- collections.py
- config_yaml.py
- io.py
- lr_scheduler.py
- multadds_count.py
- utils.py
.gitignore
LICENSE
README.md

// repository documentation

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# DenseNAS

The code of the CVPR2020 paper [Densely Connected Search Space for More Flexible Neural Architecture Search](https://arxiv.org/abs/1906.09607).

Neural architecture search (NAS) has dramatically advanced the development of neural network design. We revisit the search space design in most previous NAS methods and find the number of blocks and the widths of blocks are set manually. However, block counts and block widths determine the network scale (depth and width) and make a great influence on both the accuracy and the model cost (FLOPs/latency).

We propose to search block counts and block widths by designing a densely connected search space, i.e., DenseNAS. The new search space is represented as a dense super network, which is built upon our designed routing blocks. In the super network, routing blocks are densely connected and we search for the best path between them to derive the final architecture. We further propose a chained cost estimation algorithm to approximate the model cost during the search. Both the accuracy and model cost are optimized in DenseNAS.
![search_space](https://raw.githubusercontent.com/JaminFong/DenseNAS/master/imgs/search_space.png)

## Updates
* 2020.6 The search code is released, including both MobileNetV2- and ResNet- based search space.

## Requirements

* pytorch >= 1.0.1
* python >= 3.6

## Search

1. Prepare the image set for search which contains 100 classes of the original ImageNet dataset. And 20% images are used as the validation set and 80% are used as the training set.

1). Generate the split list of the image data.<br>
    `python dataset/mk_split_img_list.py --image_path 'the path of your ImageNet data' --output_path 'the path to output the list file'`

2). Use the image list obtained above to make the lmdb file.<br>
    `python dataset/img2lmdb.py --image_path 'the path of your ImageNet data' --list_path 'the path of your image list generated above' --output_path 'the path to output the lmdb file' --split 'split folder (train/val)'`

2. Build the latency lookup table (lut) of the search space using the following script or directly use the ones provided in `./latency_list/`.<br>
`python -m run_apis.latency_measure --save 'output path' --input_size 'the input image size' --meas_times 'the times of op measurement' --list_name 'the name of the output lut' --device 'gpu or cpu' --config 'the path of the yaml config'`

3. Search for the architectures. (We perform the search process on 4 32G V100 GPUs.)<br>
For MobileNetV2 search:<br>
`python -m run_apis.search --data_path 'the path of the split dataset' --config configs/imagenet_search_cfg_mbv2.yaml`<br>
For ResNet search:<br>
`python -m run_apis.search --data_path 'the path of the split dataset' --config configs/imagenet_search_cfg_resnet.yaml`

## Train

1. (Optional) We pack the ImageNet data as the lmdb file for faster IO. The lmdb files can be made as follows. If you don't want to use lmdb data, just set `__C.data.train_data_type='img'` in the training config file `imagenet_train_cfg.py`.

1). Generate the list of the image data.<br>
    `python dataset/mk_img_list.py --image_path 'the path of your image data' --output_path 'the path to output the list file'`

2). Use the image list obtained above to make the lmdb file.<br>
    `python dataset/img2lmdb.py --image_path 'the path of your image data' --list_path 'the path of your image list' --output_path 'the path to output the lmdb file' --split 'split folder (train/val)'`

2. Train the searched model with the following script by assigning `__C.net_config` with the architecture obtained in the above search process. You can also train your customized model by redefine the variable `model` in `retrain.py`.<br>
`python -m run_apis.retrain --data_path 'The path of ImageNet data' --load_path 'The path you put the net_config of the model'`

## Evaluate

1. Download the related files of the pretrained model and put `net_config` and `weights.pt` into the `model_path`
2. `python -m run_apis.validation --data_path 'The path of ImageNet data' --load_path 'The path you put the pre-trained model'`

## Results

For experiments on the MobileNetV2-based search space, DenseNAS achieves 75.3\% top-1 accuracy on ImageNet with only 361MB FLOPs and 17.9ms latency on a single TITAN-XP. The larger model searched by DenseNAS achieves 76.1\% accuracy with only 479M FLOPs. DenseNAS further promotes the ImageNet classification accuracies of ResNet-18, -34 and -50-B by 1.5\%, 0.5\% and 0.3\% with 200M, 600M and 680M FLOPs reduction respectively.

The comparison of model performance on ImageNet under the MobileNetV2-based search spaces.

<p align="center">
<img src="imgs/mbv2_results.png" width="45%">
<img src="imgs/mbv2_comp.png" width="40%">
</p>

The comparison of model performance on ImageNet under the ResNet-based search spaces.

<p align="center">
<img src="imgs/res_results.png" width="45%">
<img src="imgs/res_comp.png" width="40%">
</p>

Our pre-trained models can be downloaded in the following links. The complete list of the models can be found in [DenseNAS_modelzoo](https://drive.google.com/open?id=183oIMF6IowZrj81kenVBkQoIMlip9kLo).

| Model                | FLOPs | Latency | Top-1(%)| 
|----------------------|-------|---------|---------| 
| [DenseNAS-Large](https://drive.google.com/open?id=14Zgc-IlxjaRtGyDHJSdMpLHVvOd0Km1u) | 479M  | 28.9ms  | 76.1    | 
| [DenseNAS-A](https://drive.google.com/open?id=1ZdephrAY4GVRqv9SvOXoJDUmO-kWhhml) | 251M  | 13.6ms  | 73.1    | 
| [DenseNAS-B](https://drive.google.com/open?id=1djhL5P1vsWVqWuT5lR7UCxEhw4cET__7) | 314M  | 15.4ms  | 74.6    | 
| [DenseNAS-C](https://drive.google.com/open?id=1L2mqir89b1UiBkePmrtjG6QLi9MqzRdQ) | 361M  | 17.9ms  | 75.3    | 
| [DenseNAS-R1](https://drive.google.com/open?id=1YaMWb1LKpgSS5mgBcB3CthTGtTIOtxWw) | 1.61B | 12.0ms  | 73.5    | 
| [DenseNAS-R2](https://drive.google.com/open?id=1Qawst3E2hqdam2TiTFo2BhBXS-M6AWdh) | 3.06B | 22.2ms  | 75.8    | 
| [DenseNAS-R3](https://drive.google.com/open?id=14RwIGWsurNvevhxL9AcnlngU0KR8WeX-) | 3.41B | 41.7ms  | 78.0    |

![archs](https://raw.githubusercontent.com/JaminFong/DenseNAS/master/imgs/archs.png)

## Citation
If you find this repository/work helpful in your research, welcome to cite it.
```
@inproceedings{fang2019densely,
  title={Densely connected search space for more flexible neural architecture search},
  author={Fang, Jiemin and Sun, Yuzhu and Zhang, Qian and Li, Yuan and Liu, Wenyu and Wang, Xinggang},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2020}
}
```