/ Machine Learning

Train One Network and Specialize it for Efficient Deployment

Train One Network and Specialize it for Efficient Deployment

Once for All

[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment.

@inproceedings{
  cai2020once,
  title={Once for All: Train One Network and Specialize it for Efficient Deployment},
  author={Han Cai and Chuang Gan and Tianzhe Wang and Zhekai Zhang and Song Han},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=HylxE1HKwS}
}

Train once, specialize for many deployment scenarios

80% top1 ImageNet accuracy under mobile setting

Consistently outperforms MobileNetV3

Diverse hardware platforms

How to use / evaluate OFA Specialized Networks

Use

""" OFA Specialized Networks.
Example: net, image_size = ofa_specialized('[email protected][email protected][email protected]', pretrained=True)
""" 
from model_zoo import ofa_specialized
net, image_size = ofa_specialized(net_id, pretrained=True)

Evaluate

python eval_specialized_net.py --path 'Your path to imagent' --net [email protected][email protected][email protected]

OFA based on FLOPs

OFA for Mobile Phones

LG G8

Samsung Note8

Google Pixel1

Samsung Note10

Google Pixel2

Samsung S7 Edge

OFA for Desktop (CPUs and GPUs)

1080ti GPU (Batch Size 64)

V100 GPU (Batch Size 64)

Jetson TX2 GPU (Batch Size 16)

Intel Xeon CPU with MKL-DNN (Batch Size 1)

How to use / evaluate OFA Networks

Use

""" OFA Networks.
    Example: ofa_network = ofa_net('ofa_mbv3_d234_e346_k357_w1.0', pretrained=True)
""" 
from model_zoo import ofa_net
ofa_network = ofa_net(net_id, pretrained=True)
    
# Randomly sample sub-networks from OFA network
ofa_network.sample_active_subnet()
random_subnet = ofa_network.get_active_subnet(preserve_weight=True)
    
# Manually set the sub-network
ofa_network.set_active_subnet(ks=7, e=6, d=4)
manual_subnet = ofa_network.get_active_subnet(preserve_weight=True)

Evaluate

python eval_ofa_net.py --path 'Your path to imagent' --net ofa_mbv3_d234_e346_k357_w1.0

How to train OFA Networks

mpirun -np 32 -H <server1_ip>:8,<server2_ip>:8,<server3_ip>:8,<server4_ip>:8 \
    -bind-to none -map-by slot \
    -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH \
    python train_ofa_net.py

or

horovodrun -np 32 -H <server1_ip>:8,<server2_ip>:8,<server3_ip>:8,<server4_ip>:8 \
    python train_ofa_net.py

Requirement

  • Python 3.6
  • Pytorch 1.0.0
  • ImageNet Dataset
  • Horovod

Related work on automated and efficient deep learning:

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware (ICLR’19)

AutoML for Architecting Efficient and Specialized Neural Networks (IEEE Micro)

AMC: AutoML for Model Compression and Acceleration on Mobile Devices (ECCV’18)

HAQ: Hardware-Aware Automated Quantization (CVPR’19, oral)

GitHub