FALCON2: Lightweight and Accurate Convolution

This package provides implementations of FALCON2/FALCONBranch convolution with their corresponding CNN model.

Overview

Code structure

FALCON2
  │ 
  ├── src
  │    │     
  │    ├── models
  │    │    ├── vgg.py: VGG model
  │    │    ├── resnet.py: ResNet model
  │    │    ├── model_imagenet.py: Pretrained model (from pytorch) 
  │    │    ├── falcon.py: FALCON
  │    │    └── stconv_branch.py: StConvBranch & FALCONBranch
  │    │      
  │    ├── train_test
  │    │    ├── imagenet.py: train/validate on ImageNet 
  │    │    ├── main.py: train/test on CIFAR10/CIFAR100/SVHN 
  │    │    ├── train.py: training process
  │    │    ├── test.py: testing process
  │    │    └── validation.py: validation process
  │    ├── imagenetutils: this code are from https://github.com/d-li14/mobilenetv2.pytorch
  │    │    ├── dataloaders.py: dataloader for ImageNet 
  │    │    ├── eval.py: evaluate function for ImageNet 
  │    │    ├── logger.py: logger functions 
  │    │    ├── misc.py: helper function
  │    │    └── visualize.py: visualization functions
  │    │     
  │    └── utils
  │         ├── compression_cal.py: calculate the number of parameters and FLOPs
  │         ├── default_param.py: default cfgs 
  │         ├── load_data.py: load datasets
  │         ├── lr_decay.py: control learning rate
  │         ├── optimizer_option.py: choose optimizer 
  │         ├── save_restore.py: save and restore trained model
  │         └── timer.py: timer for inference time
  │
  └── script: shell scripts for execution of training/testing codes

Naming convention

StandardConv: Standard Convolution (baseline)

FALCON2: Lightweight and Accurate Convolution - the new convolution architecture we proposed

Rank: Rank of convolution. Copy the conv layer for k times, run independently and add output together at the end of the layer. This hyperparameter helps balace compression rate/ accelerate rate and accuracy.

FALCON-branch: New version of FALCON - for fitting FALCON into ShuffleUnitV2 architecture.

Data description

• CIFAR-100 datasets
• SVHN
• ImageNet
• Note that:
  • CIFAR and SVHN datasets depend on torchvision (https://pytorch.org/docs/stable/torchvision/datasets.html#cifar). You don't have to download anything. When executing the source code, the datasets will be automaticly downloaded if it is not detected.
  • ImageNet is downloaded from http://www.image-net.org/challenges/LSVRC/

Output

• For CIFAR datasets, the trained model will be saved in train_test/trained_model/ after training.
• For ImageNet, the checkpoint will be saved in train_test/checkpoint
• You can test the model only if there is a trained model in train_test/trained_model/.

Install

Environment

• Unbuntu
• CUDA 10.1
• Python 3.6
• torch
• torchvision
• DALI (NVIDIA Data Loading Library)

Dependence Install

pip install torch torchvision

How to use

CIFAR100/SVHN

Training & Testing

• To train the model on CIFAR-100/SVHN datasets, run script:

  cd src/train_test
  python main.py -train -conv StandardConv -m VGG19 -data cifar100
  python main.py -train -conv FALCON -m VGG19 -data cifar100 -init
  

  The trained model will be saved in src/train_test/trained_model/
• To test the model, run script:

  cd src/train_test
  python main.py -conv StandardConv -m VGG19 -data cifar100
  python main.py -conv FALCON -m VGG19 -data cifar100 -init
  

  The testing accuracy, inference time, number of parameters and number of FLOPs will be printed on the screen.
• Pre-trained model is saved in src/train_test/trained_model/
  • For example:
    • Standard model: conv=StandardConv,model=VGG19,data=cifar100,rank=1,alpha=1,opt=SGD,lr=0.1.pkl
    • FALCON model: conv=FALCON,model=VGG19,data=cifar100,rank=1,alpha=1,init,opt=SGD,lr=0.1.pkl

DEMO

• There are two demo scripts: script/train.sh and script/inference.sh.
• You can change arguments in .sh files to train/test different model.
  • train.sh: Execute training process of the model
    • Accuracy/ loss/ training time for 100 iteration will be printed on the screen during training.
    • Accuracy/ inference time/ number of parameters/ number of FLOPs will be printed on the screen after training.
  • inference.sh: Execute inference process of the model
    • Accuracy/ inference time/ number of parameters/ number of FLOPs will be printed on the screen.
    • You can run this file only when the trained model exist.
    • Sample trained model is provided in src/train_test/trained_model/.

ImageNet

Training & Testing

• To train the model on ImageNet dataset, run script:

  python imagenet.py \
  -a <architecture> \
  -conv <convolution> \
  -b <batch-size> \
  -init \
  -c <checkpoint directory> \
  --pretrained \
  --epochs <number of epochs> \
  --lr-decay <decay strategey> \
  --lr <learning rate> \
  data directory
  

  The trained model will be saved in src/checkpoint directory/

  Note: dataloader function is implemented based on DALI library; hence, you install DALI library before training a model with ImageNet dataset. Our dataloader function is from https://github.com/d-li14/mobilenetv2.pytorch

• To test the modelon ImageNet dataset, run script:

  python imagenet.py \
  -a <architecture> \
  -e \
  -conv <convolution> \
  -b <batch-size> \
  -init \
  --resume <model location> \
  data directory
  

  The testing accuracy, inference time, number of parameters and number of FLOPs will be printed on the screen.

• Pre-trained model is saved in src/checkpoint directory/ for ImageNet dataset

  • For example:
    • FALCON model: checkpoints/model_best.pth.tar

DEMO

• There are four demo scripts: script/train.sh, script/inference.sh, script/imagenet_vgg_train.sh, and script/imagenet_vgg_test.sh.
• You can change arguments in .sh files to train/test different model.
  • imagenet_vgg_train.sh: Execute training process of vgg model for ImageNet
  • imagenet_vgg_test.sh: Execute inference process of vgg model for ImageNet
    • Sample trained model is provided in src/checkpoints/.

License

Licensed under the Apache License, Version 2.0