In this work, we present a simple yet effective architecture distillation method. The central idea is that subnetworks can learn collaboratively and teach each other throughout the training process, aiming to boost the convergence of individual models. We introduce the concept of prioritized path, which refers to the architecture candidates exhibiting superior performance during training. Distilling knowledge from the prioritized paths is able to boost the training of subnetworks. Since the prioritized paths are changed on the fly depending on their performance and complexity, the final obtained paths are the cream of the crop. The discovered architectures achieve superior performance compared to the recent MobileNetV3 and EfficientNet families under aligned settings.
Top-1 Accuracy on ImageNet. The top-1 accuracy of Cream search algorithm surpasses MobileNetV3 and EfficientNet-B0/B1 on ImageNet. The training with 16 Gpus is a little bit superior than 8 Gpus, as below.
| Model (M Flops) | 8Gpus | 16Gpus |
|---|---|---|
| 14M | 53.7 | 53.8 |
| 43M | 65.8 | 66.5 |
| 114M | 72.1 | 72.8 |
| 287M | 76.7 | 77.6 |
| 481M | 78.9 | 79.2 |
| 604M | 79.4 | 80.0 |
Please run the following scripts in the example folder.
You need to first download the ImageNet-2012 to the folder ./data/imagenet and move the validation set to the subfolder ./data/imagenet/val. To move the validation set, you cloud use the following script .
Put the imagenet data in ./data. It should be like following:
./data/imagenet/train
./data/imagenet/val
...First, build environments for searching.
pip install -r ./requirements
git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cpp_ext --cuda_extTo search for an architecture, you need to configure the parameters FLOPS_MINIMUM and FLOPS_MAXIMUM to specify the desired model flops, such as [0,600]MB flops. You can specify the flops interval by changing these two parameters in ./configs/train.yaml
FLOPS_MINIMUM: 0 # Minimum Flops of Architecture
FLOPS_MAXIMUM: 600 # Maximum Flops of ArchitectureFor example, if you expect to search an architecture with model flops <= 200M, please set the FLOPS_MINIMUM and FLOPS_MAXIMUM to be 0 and 200.
After you specify the flops of the architectures you would like to search, you can search an architecture now by running:
python -m torch.distributed.launch --nproc_per_node=8 ./train.py --cfg ./configs/train.yamlThe searched architectures need to be retrained and obtain the final model. The final model is saved in .pth.tar format. Retraining code will be released soon.
To train searched architectures, you need to configure the parameter MODEL_SELECTION to specify the model Flops. To specify which model to train, you should add MODEL_SELECTION in ./configs/retrain.yaml. You can select one from [14,43,112,287,481,604], which stands for different Flops(MB).
MODEL_SELECTION: 43 # Retrain 43m model
MODEL_SELECTION: 481 # Retrain 481m model
......To train random architectures, you need specify MODEL_SELECTION to -1 and configure the parameter INPUT_ARCH:
MODEL_SELECTION: -1 # Train random architectures
INPUT_ARCH: [[0], [3], [3, 3], [3, 1, 3], [3, 3, 3, 3], [3, 3, 3], [0]] # Random Architectures
......After adding MODEL_SELECTION in ./configs/retrain.yaml, you need to use the following command to train the model.
python -m torch.distributed.launch --nproc_per_node=8 ./retrain.py --cfg ./configs/retrain.yamlTo test our trained of models, you need to use MODEL_SELECTION in ./configs/test.yaml to specify which model to test.
MODEL_SELECTION: 43 # test 43m model
MODEL_SELECTION: 481 # test 470m model
......After specifying the flops of the model, you need to write the path to the resume model in ./test.sh.
RESUME_PATH: './43.pth.tar'
RESUME_PATH: './481.pth.tar'
......We provide 14M/43M/114M/287M/481M/604M pretrained models in google drive or [Models-Baidu Disk (password: wqw6)] .
After downloading the pretrained models and adding MODEL_SELECTION and RESUME_PATH in './configs/test.yaml', you need to use the following command to test the model.
python -m torch.distributed.launch --nproc_per_node=8 ./test.py --cfg ./configs/test.yaml