This repository modification PyTorch implementation of the following paper: GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training [1]
- First clone the repository
git clone https://github.com/RForestLiu/ganomaly_s.git - Create the virtual environment via conda
conda create -n ganomaly python=3.7 - Activate the virtual environment.
conda activate ganomaly - Install the dependencies.
pip install --user --requirement requirements.txt
To replicate the results in the paper for MNIST and CIFAR10 datasets, run the following commands:
# MNIST
sh experiments/run_mnist_s.sh
# CIFAR
sh experiments/run_cifar_s.sh # CIFAR10To list the arguments, run the following command:
python train.py -h
To train the model on MNIST dataset for a given anomaly class, run the following:
python train.py \
--dataset mnist \
--isize 32 \
--nc 1 \
--niter <number-of-epochs> \
--manualseed 1 \
--nz 256 \
--abnormal_class <0,1,2,3,4,5,6,7,8,9> \
--display # optional if you want to visualize
Wait for first stage finish.Then run the following:
python train_z.py \
--dataset mnist \
--isize 32 \
--load_weights \
--niter <number-of-epochs> \
--strengthen 1 \
--nz 256 \
--classifier \
--nc 1 \
--z_metric roc \
--manualseed 1 \ # optional if you want to set seed
--abnormal_class <0,1,2,3,4,5,6,7,8,9> \
--display # optional if you want to visualize
To train the model on CIFAR10 dataset for a given anomaly class, run the following:
python train.py \
--dataset cifar10 \
--isize 32 \
--niter <number-of-epochs> \
--nz 256 \
--manualseed 1 \ #optional if you want to set seed
--abnormal_class cat \
<plane, car, bird, cat, deer, dog, frog, horse, ship, truck> \
--display # optional if you want to visualize
Wait for first stage finish.Then run the following:
python train_z.py \
--dataset cifar10 \
--isize 32 \
--load_weights \
--strengthen 1 \
--nz 256 \
--classifier \
--z_metric roc \
--manualseed 1 \ #optional if you want to set seed
--abnormal_class cat \
--display # optional if you want to visualize
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
│ │ └── normal_tst_img_0.png
│ │ └── normal_tst_img_1.png
│ │ ...
│ │ └── normal_tst_img_n.png
│ ├── 1.abnormal
│ │ └── abnormal_tst_img_0.png
│ │ └── abnormal_tst_img_1.png
│ │ ...
│ │ └── abnormal_tst_img_m.png
├── train
│ ├── 0.normal
│ │ └── normal_tst_img_0.png
│ │ └── normal_tst_img_1.png
│ │ ...
│ │ └── normal_tst_img_t.png
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> \
--strengthen 1 \
--niter <number-of-epochs> \
--nz 1024 \
--display # optional if you want to visualize
Wait for first stage finish.Then run the following:
python train_z.py \
--dataset <name-of-the-data> \
--isize <image-size> \
--niter <number-of-epochs> \
--load_weights \
--strengthen 1 \
--classifier \
--nz 1024 \
--display # optional if you want to visualize
For more training options, run python train.py -h.