sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data

Pre-requirement

Linux, python 3.6+

Setup

pip install -r requirements.txt

Instruction

Supported CNN architectures and datasets:

Dataset	Architecture(ARCH)
CIFAR-10	vgg19_bn, resnet110, resnet164, mobilenetv2, shufflenetv2
CIFAR-100	vgg19_bn, resnet110, resnet164
ImageNet-1K	vgg19_bn, resnet50

1. Generate groups by running:

For CIFAR-10/CIFAR-100:

python3 group_selection.py --arch $ARCH --resume $pretrained_model --dataset $DATASET --ngroups $number_of_groups --gpu_num $number_of_gpu 

For ImageNet-1K:

python3 group_selection.py --arch $ARCH --dataset imagenet --ngroups $number_of_groups --gpu_num $number_of_gpu --data /{path_to_imagenet_dataset}

Pruning candidate now stored in ./prune_candidate_logs

2. Prune models:

For CIFAR-10/CIFAR-100:

python3 prune_and_get_model.py -a $ARCH --dataset $DATASET --resume $pretrained_model  -c ./prune_candidate_logs/ -s ./{TO_SAVE_PRUNED_MODEL_DIR}

For ImageNet-1K:

python3 prune_and_get_model.py -a $ARCH --dataset imagenet -c ./prune_candidate_logs/ -s ./{TO_SAVE_PRUNED_MODEL_DIR} --pretrained

Pruned models are now saved in ./TO_SAVE_PRUNED_MODEL_DIR/$ARCH

3. Retrain pruned models:

For CIFAR-10/CIFAR-100:

python3 retrain_grouped_model.py -a $ARCH --dataset $DATASET --resume ./{TO_SAVE_PRUNED_MODEL_DIR}/ --train_batch $batch_size --epochs $number_of_epochs --num_gpus $number_of_gpus

For ImageNet-1K:

python3 retrain_grouped_model.py -a $ARCH --dataset imagenet --resume ./{TO_SAVE_PRUNED_MODEL_DIR}/ --epochs $number_of_epochs --num_gpus $number_of_gpus --train_batch $batch_size --data /{path_to_imagenet_dataset}

Retrained models now saved in ./TO_SAVE_PRUNED_MODEL_DIR_retrained/$ARCH/

4. Evaluate

For CIFAR-10/CIFAR-100:

python3 evaluate.py -a $ARCH --dataset=$DATASET --retrained_dir ./{TO_SAVE_PRUNED_MODEL_DIR}_retrained --test-batch $batch_size

For ImageNet-1K:

python3 evaluate.py -d imagenet -a $ARCH --retrained_dir ./{TO_SAVE_PRUNED_MODEL_DIR}_retrained --data /{path_to_imagenet_dataset}

Contributors

Thanks for all the contributors to this repository.

• Brandon Hsieh

• Zhuang Liu

• Kenan Jiang

• Kehan Wang

• Siyuan Zhuang

• Zihao Fan

• Hank O'Brien