深度学习基于对抗网络的游戏人物动作模仿与实现python源码.zip

<img src='imgs/horse2zebra.gif' align="right" width=384> <br><br><br> # CycleGAN and pix2pix in PyTorch We provide PyTorch implementations for both unpaired and paired image-to-image translation. The code was written by [Jun-Yan Zhu](https://github.com/junyanz) and [Taesung Park](https://github.com/taesung), and supported by [Tongzhou Wang](https://ssnl.github.io/). This PyTorch implementation produces results comparable to or better than our original Torch software. If you would like to reproduce the same results as in the papers, check out the original [CycleGAN Torch](https://github.com/junyanz/CycleGAN) and [pix2pix Torch](https://github.com/phillipi/pix2pix) code **Note**: The current software works well with PyTorch 0.4+. Check out the older [branch](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/tree/pytorch0.3.1) that supports PyTorch 0.1-0.3. You may find useful information in [training/test tips](docs/tips.md) and [frequently asked questions](docs/qa.md). **CycleGAN: [Project](https://junyanz.github.io/CycleGAN/) | [Paper](https://arxiv.org/pdf/1703.10593.pdf) | [Torch](https://github.com/junyanz/CycleGAN)** <img src="https://junyanz.github.io/CycleGAN/images/teaser_high_res.jpg" width="800"/> **Pix2pix: [Project](https://phillipi.github.io/pix2pix/) | [Paper](https://arxiv.org/pdf/1611.07004.pdf) | [Torch](https://github.com/phillipi/pix2pix)** <img src="https://phillipi.github.io/pix2pix/images/teaser_v3.png" width="800px"/> **[EdgesCats Demo](https://affinelayer.com/pixsrv/) | [pix2pix-tensorflow](https://github.com/affinelayer/pix2pix-tensorflow) | by [Christopher Hesse](https://twitter.com/christophrhesse)** <img src='imgs/edges2cats.jpg' width="400px"/> If you use this code for your research, please cite: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks [Jun-Yan Zhu](https://people.eecs.berkeley.edu/~junyanz/)\*, [Taesung Park](https://taesung.me/)\*, [Phillip Isola](https://people.eecs.berkeley.edu/~isola/), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros) In ICCV 2017. (* equal contributions) [[Bibtex]](https://junyanz.github.io/CycleGAN/CycleGAN.txt) Image-to-Image Translation with Conditional Adversarial Networks [Phillip Isola](https://people.eecs.berkeley.edu/~isola), [Jun-Yan Zhu](https://people.eecs.berkeley.edu/~junyanz), [Tinghui Zhou](https://people.eecs.berkeley.edu/~tinghuiz), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros) In CVPR 2017. [[Bibtex]](http://people.csail.mit.edu/junyanz/projects/pix2pix/pix2pix.bib) ## Course CycleGAN course assignment [code](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/assignments/a4-code.zip) and [handout](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/assignments/a4-handout.pdf) designed by Prof. [Roger Grosse](http://www.cs.toronto.edu/~rgrosse/) for [CSC321](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/) "Intro to Neural Networks and Machine Learning" at University of Toronto. Please contact the instructor if you would like to adopt it in your course. ## Other implementations ### CycleGAN <p><a href="https://github.com/leehomyc/cyclegan-1"> [Tensorflow]</a> (by Harry Yang), <a href="https://github.com/architrathore/CycleGAN/">[Tensorflow]</a> (by Archit Rathore), <a href="https://github.com/vanhuyz/CycleGAN-TensorFlow">[Tensorflow]</a> (by Van Huy), <a href="https://github.com/XHUJOY/CycleGAN-tensorflow">[Tensorflow]</a> (by Xiaowei Hu), <a href="https://github.com/LynnHo/CycleGAN-Tensorflow-Simple"> [Tensorflow-simple]</a> (by Zhenliang He), <a href="https://github.com/luoxier/CycleGAN_Tensorlayer"> [TensorLayer]</a> (by luoxier), <a href="https://github.com/Aixile/chainer-cyclegan">[Chainer]</a> (by Yanghua Jin), <a href="https://github.com/yunjey/mnist-svhn-transfer">[Minimal PyTorch]</a> (by yunjey), <a href="https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/CycleGAN">[Mxnet]</a> (by Ldpe2G), <a href="https://github.com/tjwei/GANotebooks">[lasagne/keras]</a> (by tjwei)</p> </ul> ### pix2pix <p><a href="https://github.com/affinelayer/pix2pix-tensorflow"> [Tensorflow]</a> (by Christopher Hesse), <a href="https://github.com/Eyyub/tensorflow-pix2pix">[Tensorflow]</a> (by Eyyüb Sariu), <a href="https://github.com/datitran/face2face-demo"> [Tensorflow (face2face)]</a> (by Dat Tran), <a href="https://github.com/awjuliani/Pix2Pix-Film"> [Tensorflow (film)]</a> (by Arthur Juliani), <a href="https://github.com/kaonashi-tyc/zi2zi">[Tensorflow (zi2zi)]</a> (by Yuchen Tian), <a href="https://github.com/pfnet-research/chainer-pix2pix">[Chainer]</a> (by mattya), <a href="https://github.com/tjwei/GANotebooks">[tf/torch/keras/lasagne]</a> (by tjwei), <a href="https://github.com/taey16/pix2pixBEGAN.pytorch">[Pytorch]</a> (by taey16) </p> </ul> ## Prerequisites - Linux or macOS - Python 2 or 3 - CPU or NVIDIA GPU + CUDA CuDNN ## Getting Started ### Installation - Clone this repo: ```bash git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix cd pytorch-CycleGAN-and-pix2pix ``` - Install PyTorch 0.4+ and torchvision from http://pytorch.org and other dependencies (e.g., [visdom](https://github.com/facebookresearch/visdom) and [dominate](https://github.com/Knio/dominate)). You can install all the dependencies by ```bash pip install -r requirements.txt ``` - For Conda users, we include a script `./scripts/conda_deps.sh` to install PyTorch and other libraries. ### CycleGAN train/test - Download a CycleGAN dataset (e.g. maps): ```bash bash ./datasets/download_cyclegan_dataset.sh maps ``` - Train a model: ```bash #!./scripts/train_cyclegan.sh python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan ``` - To view training results and loss plots, run `python -m visdom.server` and click the URL http://localhost:8097. To see more intermediate results, check out `./checkpoints/maps_cyclegan/web/index.html`. - Test the model: ```bash #!./scripts/test_cyclegan.sh python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan ``` - The test results will be saved to a html file here: `./results/maps_cyclegan/latest_test/index.html`. ### pix2pix train/test - Download a pix2pix dataset (e.g.facades): ```bash bash ./datasets/download_pix2pix_dataset.sh facades ``` - Train a model: ```bash #!./scripts/train_pix2pix.sh python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA ``` - To view training results and loss plots, run `python -m visdom.server` and click the URL http://localhost:8097. To see more intermediate results, check out `./checkpoints/facades_pix2pix/web/index.html`. - Test the model (`bash ./scripts/test_pix2pix.sh`): ```bash #!./scripts/test_pix2pix.sh python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA ``` - The test results will be saved to a html file here: `./results/facades_pix2pix/test_latest/index.html`. You can find more scripts at `scripts` directory. ### Apply a pre-trained model (CycleGAN) - You can download a pretrained model (e.g. horse2zebra) with the following script: ```bash bash ./scripts/download_cyclegan_model.sh horse2zebra ``` - The pretrained model is saved at `./checkpoints/{name}_pretrained/latest_net_G.pth`. Check [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/scripts/download_cyclegan_model.sh#L3) for all the available CycleGAN models. - To test the model, you also need to download the horse2zebra dataset: ```bash bash ./datasets/download_cyclegan_dataset.sh horse2zebra ``` - Then generate the results using ```bash python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout ``` - The option `--model test` is used for generating results of CycleGAN only for one side. This option will automatically set `--dataset_mode single`, which only loads the images from one set. On the contrary, using `--model cycle_gan` requ