mycosavant / dery

[NeurIPS2022] Deep Model Reassembly

Deep Model Reassembly 🏭 -> 🧱 -> 🏭

😎 Introduction

This repository contains the offical implementation for our paper

Deep Model Reassembly (NeurIPS2022)

[arxiv] [project page] [code]

Xingyi Yang, Daquan Zhou, Songhua Liu, Jingwen Ye, Xinchao Wang

In this work, we explore a novel knowledge-transfer task, termed as Deep Model Reassembly (DeRy), for general-purpose model reuse. DeRy first dissect each model into distinctive building blocks, and then selectively reassemble the derived blocks to produce customized networks under both the hardware resource and performance constraints.

• 2022/12/07 Code Updated for better usage.

📚 File Orgnization

DeRy/
├── blocklize/block_meta.py         [Meta Information & Node Defnition]

├── similarity/
│   ├── get_rep.py                  [Compute and save the feature embeddings]
│   ├── get_sim.py                  [Compute representation similarity given the saved features]
|   ├── partition.py                [Network partition by cover set problem]
|   ├── zeroshot_reassembly.py      [Network reassembly by solving integer program]

├── configs/
|   ├── compute_sim/                [Model configs in the model zoo to compute the feature similarity]
|   ├── dery/XXX/$ModelSize_$DataSet_$BatchSize_$TrainTime_dery_$Optimizor.py   [Config files for transfer experiments]

├── mmcls_addon/
|   ├── datasets/                   [Dataset definitions]
|   ├── models/backbones/dery.py    [DeRy backbone definition]

├── third_package/timm              [Modified timm package]

🛠 Installation

The model training part is based on mmclassification. Some of the pre-trained weights are from timm.

# Create python env
conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab

# Install mmcv and mmcls
pip3 install openmim
mim install mmcv-full==1.4.8
mim install mmcls==0.18.0

# Install timm
pip3 install timm

Note: Our code needs torch.fx to support the computational graph extraction from the torch model. Therefore, please install the torch > 1.10.

🚀 Getting Started

To run the code for DeRy, we need to go through 4 steps

1. [Model Zoo Preparation] Compute the model feature embeddings and representation similarity. We first write model configuration and its weight path, and run the configs in configs/compute_sim

    PYTHONPATH="$PWD" python simlarity/get_rep.py \
    $Config_file \              # configs in `configs/compute_sim`
    --out $Feature_path \       # Save feature embeddings in *.pth* files
    [--checkpoint $Checkpoint]  # download checkpoint if any
   

   All feature embeddings need to be saved in .pth files in the same $Feat_dictionary. We then load them and compute the feature similarity. Similarity will be saved as net1.net2.pkl files.

    PYTHONPATH="$PWD" python simlarity/compute_sim.py /
    --feat_path $Feat_dictionary /
    --sim_func $Similarity_function [cka, rbf_cka, lr]
   

   Pre-computed similarity on ImageNet for Linear CKA and Linear Regression.

   We also need to compute the feature size (input-output feature dimensions). It can be done by running

    PYTHONPATH="$PWD" python simlarity/count_inout_size.py /
    --root $Feat_dictionary
   

   The results is a json file containing the input-output shape for all network layers, like MODEL_INOUT_SHAPE.json.

2. [Network Partition] Solve the cover set optimization to get the network partition. The results is an assignment file in .pkl.

    PYTHONPATH="$PWD" python simlarity/partition.py /
    --sim_path $Feat_similarity_path /
    --K        $Num_partition /             # default=4
    --trial    $Num_repeat_runs /           # default=200
    --eps      $Size_ratio_each_block /     # default=0.2
    --num_iter $Maximum_num_iter_eachrun    # default=200
   

3. [Reassemby] Reassemble the partitioned building blocks into a full model, by solving a integer program with training-free proxy. The results are a series of model configs in .py.

    PYTHONPATH="$PWD" python simlarity/zeroshot_reassembly.py \
    --path          $Block_partition_file [Saved in the partition step] \
    --C             $Maximum_parameter_num \
    --minC          $Minimum_parameter_num \
    --flop_C        $Maximum_FLOPs_num \
    --minflop_C     $Minimum_FLOPs_num \
    --num_batch     $Number_batch_average_to_compute_score \
    --batch_size    $Number_sample_each_batch \
    --trial         $Search_time \
    --zero_proxy    $Type_train_free_proxy [Default NASWOT] \
    --data_config   $Config_target_data
   

4. [Fune-tuning] Train the reassembled model on target data. You may refers to mmclassification for the model training.

Note: Partitioning and reassembling results may not be identical because of the algorithmatic stochaticity. It may slightly affect the performance.

🚛 Other Resources

1. We use several pre-trained models not included in timm and mmcls, listed in Pre-trained.

Thanks for the support

✍ Citation

@article{yang2022dery,
    author    = {Xingyi Yang, Daquan Zhou, Songhua Liu, Jingwen Ye, Xinchao Wang},
    title     = {Deep Model Reassembly},
    journal   = {NeurIPS},
    year      = {2022},
}

Extensions

1. Extension on Efficienr and Parallel Training: Deep-Incubation
2. Extension for Efficient Model Zoo Training: Stitchable Neural Networks