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Patching Weak Convolutional Neural Network Models through Modularization and Composition

Abstract

This repository includes the code and experimental data in our paper entitled "Patching Weak Convolutional Neural Network Models through Modularization and Composition".

In this paper, we propose a structured modularization approach, CNNSplitter, which decomposes a strong CNN model for $N$-class classification into $N$ CNN modules. Each module is a sub-model containing a part of the convolution kernels of the strong model. To patch a weak CNN model with low performance on a target class, we compose the weak CNN model with the corresponding module obtained from a strong CNN model. The ability of the weak CNN model to recognize the target class can thus be improved through patching.

Requirements

  • python 3.8.10
  • pytorch 1.8.1
  • numpy 1.19.2
  • tqdm 4.61.0
  • matplotlib 3.4.2
  • seaborn 0.11.1
  • GPU with CUDA support is also needed

How to install

Install the dependent packages via pip:

$ pip install numpy==1.19.2 tqdm==4.61.0 matplotlib==3.4.2 seaborn==0.11.1

Install pytorch according to your environment, see https://pytorch.org/.

How to modularize a trained CNN model

  1. modify global_configure.py to set the root_dir.
  2. run python train.py --model simcnn --dataset cifar10 to get the pre-trained model SimCNN-CIFAR.
  3. run python kernel_importance_analyzer.py --model simcnn --dataset cifar10 in directory preprocess/ to get the importance of each kernel in SimCNN-CIFAR.
  4. run python run_layer_sensitivity_analyzer.py --model simcnn --dataset cifar10 in directory scripts/ to analyze the sensitivity of SimCNN-CIFAR.
  5. modify configures/simcnn_cifar10.py to set the configures of GA searching.
  6. run python module_recorder.py --model simcnn --dataset cifar10.
  7. run python module_explorer.py --model simcnn --dataset cifar10 --target_class 0 with 10 instances in parallel (--target_class from 0 to 9), each of which searches for one class.

We provide the four trained CNN models and the corresponding modules, as well as the weak models.
One can download data/ from here and reuse a module to patch a weak CNN model following the description below.

How to patch a weak CNN model

preparing

  1. run python module_output_collector.py --model simcnn --dataset cifar10 in directory preprocess/ to collect the outputs of 10 modules.

Patching a simple model

  1. run python train.py --model simcnn --dataset cifar10 in directory experiments/patch/patch_for_weak_model to train an overly simple SimCNN-CIFAR.
  2. run python apply_patch.py --model simcnn --dataset cifar10 --exp_type weak --target_class 0 --target_epoch 99 in directory experiments/patch to patch the simple SimCNN-CIFAR.

Patching an overfitting/underfitting model

  1. run python train.py --model simcnn --dataset cifar10 in directory experiments/patch/patch_for_poor_model to train an overfitting/underfitting SimCNN-CIFAR.
  2. run python apply_patch.py --model simcnn --dataset cifar10 --exp_type poor_fit --target_class 0 --target_epoch 169 in directory experiments/patch to patch the overfitting SimCNN-CIFAR.
  3. run python apply_patch.py --model simcnn --dataset cifar10 --exp_type poor_fit --target_class 0 --target_epoch 84 in directory experiments/patch to patch the underfitting SimCNN-CIFAR.