Linux, python 3.6+
pip install -r requirements.txtSupported 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 |
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/
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 $ARCH \
--dataset imagenet \
-c ./prune_candidate_logs/ \
-s ./{TO_SAVE_PRUNED_MODEL_DIR}/ \
--pretrainedPruned models are now saved in ./{TO_SAVE_PRUNED_MODEL_DIR}/$ARCH/
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_gpusFor 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/
For CIFAR-10/CIFAR-100:
python3 evaluate.py \
-a $ARCH \
--dataset=$DATASET \
--retrained_dir ./{TO_SAVE_PRUNED_MODEL_DIR}_retrained/ \
--test-batch $batch_sizeFor ImageNet-1K:
python3 evaluate.py \
-d imagenet \
-a $ARCH \
--retrained_dir ./{TO_SAVE_PRUNED_MODEL_DIR}_retrained/ \
--data /{path_to_imagenet_dataset}/Thanks for all the main contributors to this repository:
And many others Zihao Fan, Hank O'Brien , Yaoqing Yang, Adarsh Karnati, Jichan Chung, Yingxin Kang, Balaji Veeramani, Sahil Rao.
@inproceedings{wang2021sensAI,
author = {Guanhua Wang and Zhuang Liu and Brandon Hsieh and Siyuan Zhuang and Joseph Gonzalez and Trevor Darrell and Ion Stoica},
title = {{sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data}},
booktitle = {Proceedings of Fourth Conference on Machine Learning and Systems (MLSys'21)},
year = {2021}
}