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Scale Adaptive Network

Official implementation of Scale Adaptive Network (SAN) as described in Learning to Learn Parameterized Classification Networks for Scalable Input Images (ECCV'20) by Duo Li, Anbang Yao and Qifeng Chen on the ILSVRC 2012 benchmark.

We present a meta learning framework which dynamically parameterizes main networks conditioned on its input resolution at runtime, leading to efficient and flexible inference for arbitrarily switchable input resolutions.

Requirements

Dependency

  • PyTorch 1.0+
  • NVIDIA-DALI (in development, not recommended)

Dataset

Download the ImageNet dataset and move validation images to labeled subfolders. To do this, you can use the following script: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh

Pre-trained Models

Baseline (individually trained on each resolution)

ResNet-18

Resolution Top-1 Acc. Download
224x224 70.974 Google Drive
192x192 69.754 Google Drive
160x160 68.482 Google Drive
128x128 66.360 Google Drive
96x96 62.560 Google Drive

ResNet-50

Resolution Top-1 Acc. Download
224x224 77.150 Google Drive
192x192 76.406 Google Drive
160x160 75.312 Google Drive
128x128 73.526 Google Drive
96x96 70.610 Google Drive

MobileNetV2

Please visit my repository mobilenetv2.pytorch.

SAN

Architecture Download
ResNet-18 Google Drive
ResNet-50 Google Drive
MobileNetV2 Google Drive

Training

ResNet-18/50

python imagenet.py \
    -a meta_resnet18/50 \
    -d <path-to-ILSVRC2012-data> \
    --epochs 120 \
    --lr-decay cos \
    -c <path-to-save-checkpoints> \
    --sizes <list-of-input-resolutions> \ # default is 224, 192, 160, 128, 96
    -j <num-workers>
    --kd

MobileNetV2

python imagenet.py \
    -a meta_mobilenetv2 \
    -d <path-to-ILSVRC2012-data> \
    --epochs 150 \
    --lr-decay cos \
    --lr 0.05 \
    --wd 4e-5 \
    -c <path-to-save-checkpoints> \
    --sizes <list-of-input-resolutions> \ # default is 224, 192, 160, 128, 96
    -j <num-workers>
    --kd

Testing

Proxy Inference (default)

python imagenet.py \
    -a <arch> \
    -d <path-to-ILSVRC2012-data> \
    --resume <checkpoint-file> \
    --sizes <list-of-input-resolutions> \
    -e
    -j <num-workers>

Arguments are:

  • checkpoint-file: previously downloaded checkpoint file from here.
  • list-of-input-resolutions: test resolutions using different privatized BNs.

which gives Table 1 in the main paper and Table 5 in the supplementary materials.

Ideal Inference

Manually set the scale encoding here, which gives the left panel of Table 2 in the main paper.

Uncomment this line in the main script to enable post-hoc BN calibration, which gives the middle panel of Table 2 in the main paper.

Data-Free Ideal Inference

Manually set the scale encoding here and its corresponding shift here, then uncomment this line to replace its above line, which gives Table 6 in the supplementary materials.

Comparison to MutualNet

MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution is accpepted to ECCV 2020 as oral. MutualNet and SAN are contemporary works sharing the similar motivation, regarding to switchable resolution during inference. We provide a comparison of top-1 validation accuracy on ImageNet with the same FLOPs, based on the common MobileNetV2 backbone.

Note that MutualNet training is based on the resolution range of {224, 192, 160, 128} with 4 network widths, while SAN training is based on the resolution range of {224, 192, 160, 128, 96} without tuning the network width, so the configuratuon is different in this comparison.

Method Config (width-resolution) MFLOPs Top-1 Acc.
MutualNet
SAN
1.0-224
1.0-224
300
300
73.0
72.86
MutualNet
SAN
0.9-224
1.0-208
269
270
72.4
72.42
MutualNet
SAN
1.0-192
1.0-192
221
221
71.9
72.22
MutualNet
SAN
0.9-192
1.0-176
198
195
71.5
71.63
MutualNet
SAN
0.75-192
1.0-160
154
154
70.2
71.16
MutualNet
SAN
0.9-160
1.0-144
138
133
69.9
69.80
MutualNet
SAN
1.0-128
1.0-128
99
99
67.8
69.14
MutualNet
SAN
0.85-128
1.0-112
84
82
66.1
66.59
MutualNet
SAN
0.7-128
1.0-96
58
56
64.3
65.07

Citation

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

@InProceedings{Li_2020_ECCV,
author = {Li, Duo and Yao, Anbang and Chen, Qifeng},
title = {Learning to Learn Parameterized Classification Networks for Scalable Input Images},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {August},
year = {2020}
}

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[ECCV 2020] Scale Adaptive Network: Learning to Learn Parameterized Classification Networks for Scalable Input Images

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