FALCON: Lightweight and Accurate Convolution === This package provides implementations of FALCON/FALCONBranch convolution with their corresponding CNN model. ## Overview #### Code structure ``` unicode FALCON │ ├── 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) **FALCON**: 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)](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html) #### 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 \ -conv \ -b \ -init \ -c \ --pretrained \ --epochs \ --lr-decay \ --lr \ 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 \ -e \ -conv \ -b \ -init \ --resume \ 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

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