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人工智能系统实战第三期/实战代码/计算机视觉/diffusion变体/README.md

+# Pytorch-CycleGAN
+# Pytorch-CycleGAN
+A clean and readable Pytorch implementation of CycleGAN (https://arxiv.org/abs/1703.10593)
+
+## Prerequisites
+Code is intended to work with ```Python 3.6.x```, it hasn't been tested with previous versions
+
+### [PyTorch & torchvision](http://pytorch.org/)
+Follow the instructions in [pytorch.org](http://pytorch.org) for your current setup
+
+### [Visdom](https://github.com/facebookresearch/visdom)
+To plot loss graphs and draw images in a nice web browser view
+```
+pip3 install visdom
+```
+
+## Training
+### 1. Setup the dataset
+First, you will need to download and setup a dataset. The easiest way is to use one of the already existing datasets on UC Berkeley's repository:
+```
+./download_dataset <dataset_name>
+```
+Valid <dataset_name> are: apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos
+
+Alternatively you can build your own dataset by setting up the following directory structure:
+
+    .
+    ├── datasets                   
+    |   ├── <dataset_name>         # i.e. brucewayne2batman
+    |   |   ├── train              # Training
+    |   |   |   ├── A              # Contains domain A images (i.e. Bruce Wayne)
+    |   |   |   └── B              # Contains domain B images (i.e. Batman)
+    |   |   └── test               # Testing
+    |   |   |   ├── A              # Contains domain A images (i.e. Bruce Wayne)
+    |   |   |   └── B              # Contains domain B images (i.e. Batman)
+    
+### 2. Train!
+```
+./train --dataroot datasets/<dataset_name>/ --cuda
+```
+This command will start a training session using the images under the *dataroot/train* directory with the hyperparameters that showed best results according to CycleGAN authors. You are free to change those hyperparameters, see ```./train --help``` for a description of those.
+
+Both generators and discriminators weights will be saved under the output directory.
+
+If you don't own a GPU remove the --cuda option, although I advise you to get one!
+
+You can also view the training progress as well as live output images by running ```python3 -m visdom``` in another terminal and opening [http://localhost:8097/](http://localhost:8097/) in your favourite web browser. This should generate training loss progress as shown below (default params, horse2zebra dataset):
+
+![Generator loss](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/loss_G.png)
+![Discriminator loss](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/loss_D.png)
+![Generator GAN loss](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/loss_G_GAN.png)
+![Generator identity loss](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/loss_G_identity.png)
+![Generator cycle loss](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/loss_G_cycle.png)
+
+## Testing
+```
+./test --dataroot datasets/<dataset_name>/ --cuda
+```
+This command will take the images under the *dataroot/test* directory, run them through the generators and save the output under the *output/A* and *output/B* directories. As with train, some parameters like the weights to load, can be tweaked, see ```./test --help``` for more information.
+
+Examples of the generated outputs (default params, horse2zebra dataset):
+
+![Real horse](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/real_A.jpg)
+![Fake zebra](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/fake_B.png)
+![Real zebra](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/real_B.jpg)
+![Fake horse](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/fake_A.png)
+
+## License
+This project is licensed under the GPL v3 License - see the [LICENSE.md](LICENSE.md) file for details
+
+## Acknowledgments
+Code is basically a cleaner and less obscured implementation of [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix). All credit goes to the authors of [CycleGAN](https://arxiv.org/abs/1703.10593), Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.