# GANomaly

This repository contains PyTorch implementation of the following paper: GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training [[1]](#reference)

##  Table of Contents
- [GANomaly](#ganomaly)
    - [Table of Contents](#table-of-contents)
    - [Prerequisites](#prerequisites)
    - [Experiment](#experiment)
    - [Training](#training)
        - [Training on MNIST](#training-on-mnist)
        - [Training on CIFAR10](#training-on-cifar10)
        - [Train on Custom Dataset](#train-on-custom-dataset)
    - [Citing GANomaly](#citing-ganomaly)
    - [Reference](#reference)


## Prerequisites
Please note that this project has been tested on the following packages versions. Using different version might result in errors when running the code.
1. PyTorch 0.3.0.post4
2. torchvision 0.1.9
3. visdom 0.1.7
4. tqdm 4.15.0

The project is currently being migrated to PyTorch v0.4, and the repository will be updated once the migration is done.



## Experiment

To replicate the results in the paper, run the following commands:

For MNIST experiments:
``` shell
sh experiments/run_mnist.sh
```

For CIFAR experiments:
``` shell
sh experiments/run_cifar.sh
```

## Training
To list the arguments, run the following command:
```
python train.py -h
```

### Training on MNIST
To train the model on MNIST dataset for a given anomaly class, run the following:

``` 
python train.py \
    --dataset mnist             \
    --niter <number-of-epochs>  \
    --anomaly_class <0,1,2,3,4,5,6,7,8,9>
```

### Training on CIFAR10
To train the model on CIFAR10 dataset for a given anomaly class, run the following:

``` 
python train.py \
    --dataset cifar10             \
    --niter <number-of-epochs>    \
    --anomaly_class               \
        <plane, car, bird, cat, deer, dog, frog, horse, ship, truck>
```

### Train on Custom Dataset
To train the model on a custom dataset, the dataset should be copied into `./data` directory, and should have the following directory & file structure:

```
Custom Dataset
├── test
│   ├── 0.normal
│   └── 1.abnormal
└── train
    └── 0.normal

```

Then model training is the same as training MNIST or CIFAR10 datasets explained above.

```
python train.py                     \
    --dataset <name-of-the-data>    \
    --isize <image-size>            \
    --niter <number-of-epochs>
```

For more training options, run `python train.py -h` as shown below:
```
usage: train.py [-h] [--dataset DATASET] [--dataroot DATAROOT]
                [--batchsize BATCHSIZE] [--workers WORKERS] [--droplast]
                [--isize ISIZE] [--nc NC] [--nz NZ] [--ngf NGF] [--ndf NDF]
                [--extralayers EXTRALAYERS] [--gpu_ids GPU_IDS] [--ngpu NGPU]
                [--name NAME] [--model MODEL]
                [--display_server DISPLAY_SERVER]
                [--display_port DISPLAY_PORT] [--display_id DISPLAY_ID]
                [--display] [--outf OUTF] [--manualseed MANUALSEED]
                [--anomaly_class ANOMALY_CLASS] [--print_freq PRINT_FREQ]
                [--save_image_freq SAVE_IMAGE_FREQ] [--save_test_images]
                [--load_weights] [--resume RESUME] [--phase PHASE]
                [--iter ITER] [--niter NITER] [--beta1 BETA1] [--lr LR]
                [--alpha ALPHA]

optional arguments:
  -h, --help            show this help message and exit
  --dataset             folder | cifar10 | mnist (default: cifar10)
  --dataroot            path to dataset (default: '')
  --batchsize           input batch size (default: 64)
  --workers             number of data loading workers (default: 8)
  --droplast            Drop last batch size. (default: True)
  --isize               input image size. (default: 32)
  --nc                  input image channels (default: 3)
  --nz                  size of the latent z vector (default: 100)
  --ngf                 Number of features of the generator network
  --ndf                 Number of features of the discriminator network.
  --extralayers         Number of extra layers on gen and disc (default: 0)
  --gpu_ids             gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU (default: 0)
  --ngpu                number of GPUs to use (default: 1)
  --name                name of the experiment (default: experiment_name)
  --model               chooses which model to use. (default:ganomaly)
  --display_server      visdom server of the web display (default: http://localhost)
  --display_port        visdom port of the web display (default: 8097)
  --display_id          window id of the web display (default: 0)
  --display             Use visdom. (default: False)
  --outf                folder to output images and model checkpoints (default: ./output)
  --manualseed          manual seed (default: None)
  --anomaly_class       Anomaly class idx for mnist and cifar datasets (default: car)
  --print_freq          frequency of showing training results on console (default: 100)
  --save_image_freq     frequency of saving real and fake images (default:100)
  --save_test_images    Save test images for demo. (default: False)
  --load_weights        Load the pretrained weights (default: False)
  --resume              path to checkpoints (to continue training) (default: '')
  --phase               train, val, test, etc (default: train)
  --iter                Start from iteration i (default: 0)
  --niter               number of epochs to train for (default: 15)
  --beta1               momentum term of adam (default: 0.5)
  --lr                  initial learning rate for adam (default: 0.0002)
  --alpha               alpha to weight l1 loss. default=500 (default: 50)

```

## Citing GANomaly
If you use this repository or would like to refer the paper, please use the following BibTeX entry
```
@article{Akcay2018,
    author = {Akcay, S. and Atapour-Abarghouei, A. and Breckon, T.~P.},
    title = "{GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training}",
    journal = {ArXiv e-prints},
    archivePrefix = "arXiv",
    eprint = {1805.06725},
    primaryClass = "cs.CV",
    keywords = {Computer Science - Computer Vision and Pattern Recognition},
    year = 2018,
    month = may,
}
```

## Reference
[[1]  S. Akcay, A. Atapour-Abarghouei, and T. P. Breckon.  GANomaly:  Semi-SupervisedAnomaly Detection via Adversarial Training. ArXiv e-prints, May 2018.](https://arxiv.org/abs/1805.06725)