mikelzc1990 / once-for-all

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

Once for All: Train One Network and Specialize it for Efficient Deployment [arXiv] [Slides] [Video]

@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://arxiv.org/pdf/1908.09791.pdf}
}

[News] The hands-on tutorial of OFA is released!

[News] OFA is available via pip! Run pip install ofa to install the whole OFA codebase.

[News] Fisrt place in the 4th Low-Power Computer Vision Challenge, both classification and detection track.

[News] First place in the 3rd Low-Power Computer Vision Challenge, DSP track at ICCV’19 using the Once-for-all Network.

Train once, specialize for many deployment scenarios

80% top1 ImageNet accuracy under mobile setting

Consistently outperforms MobileNetV3 on Diverse hardware platforms

How to use / evaluate OFA Specialized Networks

Use

""" OFA Specialized Networks.
Example: net, image_size = ofa_specialized('flops@595M_top1@80.0_finetune@75', pretrained=True)
""" 
from ofa.model_zoo import ofa_specialized
net, image_size = ofa_specialized(net_id, pretrained=True)

If the above scripts failed to download, you download it manually from Google Drive and put them under $HOME/.torch/ofa_specialized/.

Evaluate

python eval_specialized_net.py --path 'Your path to imagent' --net flops@595M_top1@80.0_finetune@75

OFA based on FLOPs

• flops@595M_top1@80.0_finetune@75
• flops@482M_top1@79.6_finetune@75
• flops@389M_top1@79.1_finetune@75

OFA for Mobile Phones

LG G8 LG-G8_lat@24ms_top1@76.4_finetune@25 LG-G8_lat@16ms_top1@74.7_finetune@25 LG-G8_lat@11ms_top1@73.0_finetune@25 LG-G8_lat@8ms_top1@71.1_finetune@25	Samsung Note8 note8_lat@65ms_top1@76.1_finetune@25 note8_lat@49ms_top1@74.9_finetune@25 note8_lat@31ms_top1@72.8_finetune@25 note8_lat@22ms_top1@70.4_finetune@25
Google Pixel1 pixel1_lat@143ms_top1@80.1_finetune@75 pixel1_lat@132ms_top1@79.8_finetune@75 pixel1_lat@79ms_top1@78.7_finetune@75 pixel1_lat@58ms_top1@76.9_finetune@75 pixel1_lat@40ms_top1@74.9_finetune@25 pixel1_lat@28ms_top1@73.3_finetune@25 pixel1_lat@20ms_top1@71.4_finetune@25	Samsung Note10 note10_lat@64ms_top1@80.2_finetune@75 note10_lat@50ms_top1@79.7_finetune@75 note10_lat@41ms_top1@79.3_finetune@75 note10_lat@30ms_top1@78.4_finetune@75 note10_lat@22ms_top1@76.6_finetune@25 note10_lat@16ms_top1@75.5_finetune@25 note10_lat@11ms_top1@73.6_finetune@25 note10_lat@8ms_top1@71.4_finetune@25
Google Pixel2 pixel2_lat@62ms_top1@75.8_finetune@25 pixel2_lat@50ms_top1@74.7_finetune@25 pixel2_lat@35ms_top1@73.4_finetune@25 pixel2_lat@25ms_top1@71.5_finetune@25	Samsung S7 Edge s7edge_lat@88ms_top1@76.3_finetune@25 s7edge_lat@58ms_top1@74.7_finetune@25 s7edge_lat@41ms_top1@73.1_finetune@25 s7edge_lat@29ms_top1@70.5_finetune@25

OFA for Desktop (CPUs and GPUs)

1080ti GPU (Batch Size 64) 1080ti_gpu64@27ms_top1@76.4_finetune@25 1080ti_gpu64@22ms_top1@75.3_finetune@25 1080ti_gpu64@15ms_top1@73.8_finetune@25 1080ti_gpu64@12ms_top1@72.6_finetune@25	V100 GPU (Batch Size 64) v100_gpu64@11ms_top1@76.1_finetune@25 v100_gpu64@9ms_top1@75.3_finetune@25 v100_gpu64@6ms_top1@73.0_finetune@25 v100_gpu64@5ms_top1@71.6_finetune@25
Jetson TX2 GPU (Batch Size 16) tx2_gpu16@96ms_top1@75.8_finetune@25 tx2_gpu16@80ms_top1@75.4_finetune@25 tx2_gpu16@47ms_top1@72.9_finetune@25 tx2_gpu16@35ms_top1@70.3_finetune@25	Intel Xeon CPU with MKL-DNN (Batch Size 1) cpu_lat@17ms_top1@75.7_finetune@25 cpu_lat@15ms_top1@74.6_finetune@25 cpu_lat@11ms_top1@72.0_finetune@25 cpu_lat@10ms_top1@71.1_finetune@25

How to use / evaluate OFA Networks

Use

""" OFA Networks.
    Example: ofa_network = ofa_net('ofa_mbv3_d234_e346_k357_w1.0', pretrained=True)
""" 
from ofa.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)

If the above scripts failed to download, you download it manually from Google Drive and put them under $HOME/.torch/ofa_nets/.

Evaluate

python eval_ofa_net.py --path 'Your path to imagenet' --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

Introduction Video

Hands-on Tutorial Video

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)