cuteboyqq / Skip-SelfAttension-GANomaly-Pytorch

Skip-SelfAttension-GANomaly-Pytorch

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Generator + Discriminator model

Table of contents

• Skip-SelfAttension-GANomaly-Pytorch
• Requirement
• implement
• Unet-Network
• Train-on-custom-dataset
• Train
• Test
• Lose-value-distribution
• Reference

Requirement

pip install -r requirements.txt

implement

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1. Encoder-Decoder use Unet
2. Use self-Attension

Unet-Network

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Train-on-custom-dataset

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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

Train

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python train.py --img-dir "[train dataset dir]" --batch-size 64 --img-size 32 --epoch 20

Test

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python test.py --nomal-dir "[test normal dataset dir]" --abnormal-dir "[test abnormal dataset dir]" --view-img --img-size 32

Example : Train dataset : factory line only

dataset :factory line , top: input images, bottom: reconstruct images

dataset :factory noline , top: input images, bottom: reconstruct images

Lose-value-distribution

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Blue : normal dataset

Orange : abnormal dataset

Reference

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https://arxiv.org/abs/1805.06725

https://arxiv.org/pdf/1901.08954.pdf