RS-Nets: Resolution Switchable Networks

By Yikai Wang, Fuchun Sun, Duo Li and Anbang Yao.

This repository is an official implementation of "Resolution Switchable Networks for Runtime Efficient Image Recognition", accepted by ECCV 2020. We provide PyTorch implementation for non-quantization and TensorFlow implementation for quantization. Experiments are conducted on the ILSVRC 2012 benchmark.

Dataset

Following this repository,

• Download the ImageNet dataset from http://www.image-net.org/.
• Then move validation images to labeled subfolders, using the following script.

Non-Quantization

Requirements

• Python >= 3.6
• PyTorch >= 1.0
• torchvision
• tensorboardX

Pretrained Models

Basic results comparison on ImageNet. Performance is measured by top-1 / top-5 accuracies (%).

Training / Testing Scripts

First move to the non-quantization folder,

cd non-quantization

For training ResNet18 or ResNet50,

python imagenet.py \
    --arch parallel_resnet18 \  # or parallel_resnet50
    --data <path-to-data> \  # e.g., ../data/ILSVRC2012
    --workers <num-workers>  # e.g., 16
    --epochs 120 \
    --checkpoint <path-to-checkpoint> \
    --sizes 224 192 160 128 96 \
    --kd \
    --kd-type ens_topdown

For training MobileNetV2,

python imagenet.py \
    --arch parallel_mobilenetv2 \
    --data <path-to-data> \
    --workers <num-workers>
    --epochs 150 \
    --lr 0.05 \
    --wd 4e-5 \
    --checkpoint <path-to-checkpoint> \
    --sizes 224 192 160 128 96 \
    --kd \
    --kd-type ens_topdown

For testing ResNet18, ResNet50 or MobileNetV2,

python imagenet.py \
    --arch <model-architecture> \  # e.g., parallel_resnet18
    --data <path-to-data> \
    --workers <num-workers>
    --eval \
    --resume <path-to-checkpoint> \
    --sizes 224 192 160 128 96

Quantization

This part of code is built on the repository of LQ-Nets.

Requirements

• Python >= 3.6
• Python bindings for OpenCV
• TensorFlow >= 1.3
• TensorPack

Pretrained Models

Results comparison for quantization tasks on ImageNet, including two kinds of bit-widths (W / A). Performance is measured by top-1 / top-5 accuracies (%).

Training / Testing Scripts

First move to the quantization folder,

cd quantization

For training quantization on weights, e.g., 2 / 32,

python imagenet.py \
    --gpu <select-gpus> \  # e.g., 0,1,2,3
    --data <path-to-data> \
    --depth <select-ResNet-depth> \  # e.g., 18 or 50
    --mode resnet \
    --qw 2 \
    --sizes 224 192 160 128 96 \
    --kd

For training quantization on both weights and activations, e.g., 2 / 2,

python imagenet.py \
    --gpu <select-gpu> \
    --data <path-to-data> \
    --depth <select-ResNet-depth> \
    --mode preact \
    --qw 2 \
    --qa 2 \
    --sizes 224 192 160 128 96 \
    --kd

For testing quantization on weights,

python imagenet.py \
    --gpu <select-gpu> \  # e.g., 0
    --data <path-to-data> \
    --depth <select-ResNet-depth> \
    --mode resnet \
    --qw 2 \
    --eval \
    -ec <path-to-checkpoint-folder> \
    -es <select-one-testing-size>  # e.g., 224

For testing quantization on both weights and activations,

python imagenet.py \
    --gpu <select-gpu> \
    --data <path-to-data> \
    --depth <select-ResNet-depth> \
    --mode preact \
    --qw 2 \
    --qa 2 \
    --eval \
    -ec <path-to-checkpoint-folder> \
    -es <select-one-testing-size>

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{wang2020rsnets,
  title={Resolution Switchable Networks for Runtime Efficient Image Recognition},
  author={Wang, Yikai and Sun, Fuchun and Li, Duo and Yao, Anbang},
  booktitle = {The European Conference on Computer Vision (ECCV)},
  year={2020}
}