/Factorized-Bilinear-Network

FBN: Factorized Bilinear Models for Image Recognition (ICCV 2017)

Primary LanguagePython

Factorized-Bilinear-Network

This repository holds the MXNet code for the paper:

Factorized Bilinear Models for Image Recognition, Yanghao Li, Naiyan Wang, Jiaying Liu, and Xiaodi Hou, arXiv preprint arXiv:1611.05709, 2016

[Arxiv Preprint]

Introduction

Factorized-Bilinear-Network proposes a novel Factorized Bilinear (FB) layer to model the pairwise feature interactions by considering the quadratic terms in the transformations. The factorized parameterization makes our FB layer only require a linear increase of parameters and affordable computational cost. A specific remedy called DropFactor is also devised during the training process to further reduce the risk of overfitting of the FB layer.

CIFAR Results:

Method params CIFAR-10 CIFAR-100
Inception-BN 1.7M 5.82 24.70
ResNet-164 (ours) 1.7M 5.30 23.64
ResNet-1001 (ours) 10.2M 4.04 20.50
Inception-BN-FBN 2.4M 5.58 21.98
ResNet-164-FBN 2.2M 5.00 22.50
ResNet-1001-FBN 10.7M 4.09 19.67

ImageNet Results:

Method Top-1(%) Top-5 (%)
Inception-BN 27.5 9.2
Inception-BN-FBN 26.4 8.4
ResNet-34 27.7 9.1
ResNet-34-FBN 26.3 8.4
ResNet-50 24.7 7.4
ResNet-50-FBN 24.0 7.1

Prepare Datasets

Prepare the corresponding datasets (CIFAR-10, CIFAR-100 or ImageNet) before training FBNs. In our experiments, we use RecordIO data format to generate .rec files for different datasets. Please refer to MXNet Example.

For example, you should first prepare the train.lst and test.lst, then generate the .rec file using im2rec tool. An example for ImageNet:

$im2rec_path train.lst train/ data/imagenet/train_480_q90.rec resize=480 quality=90

Usage

Download this repo recursively:

git clone --recursive https://github.com/lyttonhao/Factorized-Bilinear-Network.git

We implement two versions of factorized bilinear layer:

  1. mx.symbol.FMConvolution1(): similar to Convolution layer with additional FB parameters:

    • num_factor: the factor number for the FB layer. We use 20 in CIFAR dataset. For ImageNet, larger value (50) may improve the performance.
    • p: drop rate for DropFactor

  2. mx.symbol.FMConvolution3(): speed-up version of FMConvolution1 using batch_dot, but cost more memory. (The GPU memory may not be enough for large model.)

CIFAR

The original fully connected layer can be replaced by a FMConvolution3 layer:

bilinear = mx.symbol.FMConvolution3(data=in5b, num_filter=num_classes, num_factor=args.fmconv_factor,
                                    kernel=(1, 1), stride=(1, 1),
                                    pad=(0, 0), p=args.fmconv_drop, name='bilinear1')

The training command:

python train_cifar.py --gpus 2,3 --data-dir /mnt/hdd/lytton/mx_data/cifar-100 --lr 0.1 --wd 0.0001 --batch-size 128 --data-shape 32 --num-epoches 100 --network resnet-small-fmconv --num-classes 100 --lr-step 50,75 --model-prefix cifar100_res18_fmconv22-3  --res-module-num 18  --fmconv-slowstart 3 --fmconv-drop 0.5 --fmconv-factor 20

More examples can be seen in the cifar/run.sh and you can run python cifar/train_cifar.py with -h to see more options.

ImageNet

Our training policy and augmentation methods follow the MXNet ResNet which reproduced ResNet in MXNet. Please using specific settings (like lr schedulers and training epochs) in MXNet ResNet if compares with its original MXNet ResNet results.

An example of training command:

python train_imagenet.py --gpus 0,1,2,3,4,5,6,7  --model-prefix resnet34-fmconv --network resnet-fmconv --batch-size 256 --aug-level 2  --num-epoches 120 --frequent 50  --lr 0.1 --wd 0.0001   --lr-step 60,75,90  --fmconv-drop 0.5  --fmconv-slowstart 1 --fmconv-factor 50 --depth 34

Usually, we will cancel the scale/color/aspect augmentation during training (set --aug-level=1 for around additional 10 epochs) for better result.