F-ANOGAN使用Gan改进了AE进行异常检测

# f-AnoGAN f-AnoGAN is a GAN for anomaly detection. One of the features of this GAN is that two adversarial networks (Generator and Discriminator) and Encoder are trained separately. In addition, an anomaly score is computed by both a discriminator feature residual error and an image reconstruction error. [Japanese version](./README.ja.md) ## References Papar - [f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks](https://www.sciencedirect.com/science/article/pii/S1361841518302640) Github - [tSchlegl/f-AnoGAN: Code for reproducing f-AnoGAN training and anomaly scoring](https://github.com/tSchlegl/f-AnoGAN) - [PyTorch-GAN/wgan_gp.py at master · eriklindernoren/PyTorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/wgan_gp/wgan_gp.py) - [PyTorch-GAN/dcgan.py at master · eriklindernoren/PyTorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/dcgan/dcgan.py) `mnist/model.py`, `fanogan/train_wgangp.py` and `fanogan/train_encoder_izif.py` are modified eriklindernoren's `wgan_gp.py` for f-AnoGAN. `mvtec_ad/model.py` is modified eriklindernoren's `dcgan.py` for f-AnoGAN. ## Requirements Python 3.6 or later PyTorch 1.x Matplotlib Numpy pandas Pillow scikit-learn ## Usage for training and test a dataset ### MNIST Please run below in order on the CPU. #### Step: 0 ``` python setup.py install cd mnist ``` #### Step: 1 ``` python train_wgangp.py --training_label 1 --seed 2 --n_epochs 20 ``` #### Step: 2 ``` python train_encoder_izif.py --training_label 1 --seed 2 --n_epochs 20 ``` #### Step: 3 ``` python test_anomaly_detection.py --training_label 1 ``` After Step: 3, `score.csv` will be generated in the directory `results`. See [f-AnoGAN_MNIST.ipynb](https://colab.research.google.com/drive/1mnuMH2gZH5RR47haP9r8Rv568G1mjJ1T?usp=sharing) about data visualization for `score.csv`. #### Step: 4 ``` python save_compared_images.py --seed 4 --n_iters 0 --n_grid_lines 10 ``` Compared images are saved under `f-AnoGAN/mnist/results/images_diff`. #### Visualization     ### Your own dataset Please run below in order. #### Step: 0 ``` python setup.py install cd your_own_dataset ``` Add your own dataset under `f-AnoGAN/your_own_dataset` #### Step: 1 Please replace `your_own_dataset_dir_name/train_dir_name` with a relative path of your own training data. ``` python train_wgangp.py "your_own_dataset_dir_name/train_dir_name" ``` #### Step: 2 Please replace `your_own_dataset_dir_name/train_dir_name` with a relative path of your own training data. ``` python train_encoder_izif.py "your_own_dataset_dir_name/train_dir_name" ``` #### Step: 3 Please replace `your_own_dataset_dir_name/test_dir_name` with a relative path of your own test data. ``` python test_anomaly_detection.py "your_own_dataset_dir_name/test_dir_name" ``` After Step: 3, `score.csv` will be generated in the directory `results`. #### Step: 4 Please replace `your_own_dataset_dir_name/test_dir_name` with a relative path of your own test data. ``` python save_compared_images.py "your_own_dataset_dir_name/test_dir_name" --n_iters 0 --n_grid_lines 10 ``` Compared images are saved under `f-AnoGAN/your_own_dataset/results/images_diff`. ## Colaboratory [f-AnoGAN_MNIST.ipynb](https://colab.research.google.com/drive/1mnuMH2gZH5RR47haP9r8Rv568G1mjJ1T?usp=sharing) [f-AnoGAN_MVTecAD.ipynb](https://colab.research.google.com/drive/1_mIcP6k-706O6Bo70iIAoEIhYGYO4wAe?usp=sharing)