PyTorch implementation of two ensemble paper

Requirements

First create a new virtual environment (conda or virtualenv).

Then

pip install -r requirements.txt

Depending on your cuda version, you may need to follow homepage to install pytorch.

Our code is tested under pytorch 1.8.1, cuda/10.2.

Dataset

For CIFAR, the data will automatically be downloaded in ./data directory.
For ImageNet, download the dataset from ImageNet
- Then, and move validation images to labeled subfolders, using the following shell script

Training

Sample commands to run on CIFAR

#  MobileNetV2 baseline on CIFAR 
python modens_all.py --dataset cifar10 --steps 160 200 --epochs 240 -b 128 --lr 0.1 \
     --suffix 'baseline'  -a mv2
# Group Ensemble, --boost_groups specify the group for each stage
python modens_all.py --dataset cifar10 --steps 160 200 --epochs 240 -b 128 --lr 0.1 \
     --suffix 'mdens'  -a mv2 --boost_groups 1 1 1 1 1 3 --avg-losses
# Modular Ensemble, --split_groups specify the group for each stage
python modens_all.py --dataset cifar10 --steps 160 200 --epochs 240 -b 128 --lr 0.1 \
     --suffix 'mdens'  -a mv2 --split_groups 1 1 1 1 1 3 --avg-losses
#  ResNet with 56 layers baseline on CIFAR 
python modens_all.py --dataset cifar10 --steps 160 200 --epochs 240 -b 128 --lr 0.1 \
     --suffix 'baseline'  -a rex_56_64_1
#  ResNeXt-32x4 with 29 layers baseline on CIFAR 
python modens_all.py --dataset cifar10 --steps 160 200 --epochs 240 -b 128 --lr 0.1 \
     --suffix 'baseline'  -a rex_29_32_4

Sample commands to run on ImageNet

#  MobileNetV2 on ImageNet 
python modens_all.py -a mv2 --suffix 'baseline' -j 16 --width-mult 2.5  \
    --dataset imagenet --epochs 150 --steps 75 120 145  -b 256 --lr 0.05 --cos-lr
# Group ensemble 
python modens_all.py -a mv2 --suffix 'gpens' -j 16 --width-mult 2.5  \
    --dataset imagenet --epochs 150 --steps 75 120 145  -b 256 --lr 0.05 --cos-lr \
    --boost_groups 1 1 1 1 2
# Modular ensemble 
python modens_all.py -a mv2 --suffix 'mdens' -j 16 --width-mult 2.5  \
    --dataset imagenet --epochs 150 --steps 75 120 145  -b 256 --lr 0.05 --cos-lr \
    --split_groups 1 1 1 1 2
# ResNeXt-32x4 with 50 layers on ImageNet
python dse_all.py  -a rex_50_32_4 --suffix 'baseline'  -j 16 --wd 1e-4 \
    --dataset imagenet --epochs 90 --steps 30 60  --lr 0.1 -b 256
python dse_all.py  -a rex_50_32_4 --suffix 'mdens'  -j 16 --wd 1e-4 \
    --dataset imagenet --epochs 90 --steps 30 60  --lr 0.1 -b 256 --split_groups 1 1 1 3

Cite

Please cite our papers if they help your research