Skip to content

Latest commit

 

History

History

l1-norm

README.md

L1-norm pruning

Pruning Filters for Efficient ConvNets.

Implementation

L1-norm pruning is a classical filter pruning algorithm. It prunes filers(channels) according to the l1-norm of the weight of a conv layer.

We use ItePruneAlgorithm and L1MutableChannelUnit to implement l1-norm pruning. Please refer to Pruning User Guide for more configuration detail.

Model Top-1 Gap Flop(G) Pruned Parameters Pruned Config Download
ResNet34 73.62 - 3.68 - 2.18 - mmcls model | log
ResNet34_Pruned_A 73.61 -0.01 3.10 15.8% 2.01 7.8% config model | log
ResNet34_Pruned_B 73.20 -0.42 2.79 24.2% 1.95 10.6% config model | log
ResNet34_Pruned_C 73.89 +0.27 3.40 7.6% 2.02 7.3% config model | log

Note: There is a different implementation from the original paper. We pruned the layers related to the shortcut with a shared pruning decision, while the original paper pruned them separately in Pruned C. This may be why our Pruned C outperforms Prune A and Prune B, while Pruned C is worst in the original paper.

Getting Started

Prune

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 PORT=29500 ./tools/dist_train.sh \
  {prune_config_path}.py 8 --work-dir $WORK_DIR

after the pruning process, you can get a checkpoint file in the work_dir. This checkpoint file including all parameters of the original model. In the next step, we will use the checkpoint to export a pruned checkpoint.

Get the pruned model

python ./tools/pruning/get_static_model_from_algorithm.py \
  {prune_config_path}.py \
  {checkpoint_file}.pth \
  --o {output_folder}

This step will export a pruned checkpoint and a json file which records the pruning structure. This two file will be used to deploy the pruned model.

Deploy

For a pruned model, you only need to use the pruning deploy config instead of the pretrain config to deploy the pruned version of your model. If you are not fimilar with MMDeploy, please refer to mmdeploy.

python {mmdeploy}/tools/deploy.py \
    {mmdeploy}/{mmdeploy_config}.py \
    {pruning_deploy_config}.py \
    {pruned_checkpoint}.pth \
    {mmdeploy}/tests/data/tiger.jpeg

Get the Flops and Parameters of a Pruned Model

python ./tools/pruning/get_flops.py \
    {pruning_deploy_config}.py