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Repository: junyanz/pytorch-CycleGAN-and-pix2pix
Branch: master
Commit: 2a7afba2895d
Files: 61
Total size: 266.9 KB
Directory structure:
gitextract_c042vuns/
├── .gitignore
├── .replit
├── CycleGAN.ipynb
├── LICENSE
├── README.md
├── data/
│ ├── __init__.py
│ ├── aligned_dataset.py
│ ├── base_dataset.py
│ ├── colorization_dataset.py
│ ├── image_folder.py
│ ├── single_dataset.py
│ ├── template_dataset.py
│ └── unaligned_dataset.py
├── docs/
│ ├── Dockerfile
│ ├── README_es.md
│ ├── datasets.md
│ ├── docker.md
│ ├── overview.md
│ ├── qa.md
│ └── tips.md
├── environment.yml
├── models/
│ ├── __init__.py
│ ├── base_model.py
│ ├── colorization_model.py
│ ├── cycle_gan_model.py
│ ├── networks.py
│ ├── pix2pix_model.py
│ ├── template_model.py
│ └── test_model.py
├── options/
│ ├── __init__.py
│ ├── base_options.py
│ ├── test_options.py
│ └── train_options.py
├── pix2pix.ipynb
├── scripts/
│ ├── conda_deps.sh
│ ├── download_cyclegan_model.sh
│ ├── download_pix2pix_model.sh
│ ├── edges/
│ │ ├── PostprocessHED.m
│ │ └── batch_hed.py
│ ├── eval_cityscapes/
│ │ ├── caffemodel/
│ │ │ └── deploy.prototxt
│ │ ├── cityscapes.py
│ │ ├── download_fcn8s.sh
│ │ ├── evaluate.py
│ │ └── util.py
│ ├── install_deps.sh
│ ├── test_before_push.py
│ ├── test_colorization.sh
│ ├── test_cyclegan.sh
│ ├── test_pix2pix.sh
│ ├── test_single.sh
│ ├── train_colorization.sh
│ ├── train_cyclegan.sh
│ └── train_pix2pix.sh
├── test.py
├── train.py
└── util/
├── __init__.py
├── get_data.py
├── html.py
├── image_pool.py
├── util.py
└── visualizer.py
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FILE CONTENTS
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FILE: .gitignore
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.DS_Store
debug*
datasets/
checkpoints/
results/
build/
dist/
*.png
torch.egg-info/
*/**/__pycache__
torch/version.py
torch/csrc/generic/TensorMethods.cpp
torch/lib/*.so*
torch/lib/*.dylib*
torch/lib/*.h
torch/lib/build
torch/lib/tmp_install
torch/lib/include
torch/lib/torch_shm_manager
torch/csrc/cudnn/cuDNN.cpp
torch/csrc/nn/THNN.cwrap
torch/csrc/nn/THNN.cpp
torch/csrc/nn/THCUNN.cwrap
torch/csrc/nn/THCUNN.cpp
torch/csrc/nn/THNN_generic.cwrap
torch/csrc/nn/THNN_generic.cpp
torch/csrc/nn/THNN_generic.h
docs/src/**/*
test/data/legacy_modules.t7
test/data/gpu_tensors.pt
test/htmlcov
test/.coverage
*/*.pyc
*/**/*.pyc
*/**/**/*.pyc
*/**/**/**/*.pyc
*/**/**/**/**/*.pyc
*/*.so*
*/**/*.so*
*/**/*.dylib*
test/data/legacy_serialized.pt
*~
.idea
#Ignore Wandb
wandb/
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FILE: .replit
================================================
language = "python3"
run = "<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>"
================================================
FILE: CycleGAN.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/bkkaggle/pytorch-CycleGAN-and-pix2pix/blob/master/CycleGAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5VIGyIus8Vr7"
},
"source": [
"Take a look at the [repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) for more information"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7wNjDKdQy35h"
},
"source": [
"# Install"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "TRm-USlsHgEV"
},
"outputs": [],
"source": [
"!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Pt3igws3eiVp"
},
"outputs": [],
"source": [
"import os\n",
"os.chdir('pytorch-CycleGAN-and-pix2pix/')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "z1EySlOXwwoa"
},
"outputs": [],
"source": [
"!pip install -r requirements.txt"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8daqlgVhw29P"
},
"source": [
"# Datasets\n",
"\n",
"Download one of the official datasets with:\n",
"\n",
"- `bash ./datasets/download_cyclegan_dataset.sh [apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos]`\n",
"\n",
"Or use your own dataset by creating the appropriate folders and adding in the images.\n",
"\n",
"- Create a dataset folder under `/dataset` for your dataset.\n",
"- Create subfolders `testA`, `testB`, `trainA`, and `trainB` under your dataset's folder. Place any images you want to transform from a to b (cat2dog) in the `testA` folder, images you want to transform from b to a (dog2cat) in the `testB` folder, and do the same for the `trainA` and `trainB` folders."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "vrdOettJxaCc"
},
"outputs": [],
"source": [
"!bash ./datasets/download_cyclegan_dataset.sh horse2zebra"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gdUz4116xhpm"
},
"source": [
"# Pretrained models\n",
"\n",
"Download one of the official pretrained models with:\n",
"\n",
"- `bash ./scripts/download_cyclegan_model.sh [apple2orange, orange2apple, summer2winter_yosemite, winter2summer_yosemite, horse2zebra, zebra2horse, monet2photo, style_monet, style_cezanne, style_ukiyoe, style_vangogh, sat2map, map2sat, cityscapes_photo2label, cityscapes_label2photo, facades_photo2label, facades_label2photo, iphone2dslr_flower]`\n",
"\n",
"Or add your own pretrained model to `./checkpoints/{NAME}_pretrained/latest_net_G.pt`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "B75UqtKhxznS"
},
"outputs": [],
"source": [
"!bash ./scripts/download_cyclegan_model.sh horse2zebra"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "yFw1kDQBx3LN"
},
"source": [
"# Training\n",
"\n",
"- `python train.py --dataroot ./datasets/horse2zebra --name horse2zebra --model cycle_gan`\n",
"\n",
"Change the `--dataroot` and `--name` to your own dataset's path and model's name. Use `--gpu_ids 0,1,..` to train on multiple GPUs and `--batch_size` to change the batch size. I've found that a batch size of 16 fits onto 4 V100s and can finish training an epoch in ~90s.\n",
"\n",
"Once your model has trained, copy over the last checkpoint to a format that the testing model can automatically detect:\n",
"\n",
"Use `cp ./checkpoints/horse2zebra/latest_net_G_A.pth ./checkpoints/horse2zebra/latest_net_G.pth` if you want to transform images from class A to class B and `cp ./checkpoints/horse2zebra/latest_net_G_B.pth ./checkpoints/horse2zebra/latest_net_G.pth` if you want to transform images from class B to class A.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "0sp7TCT2x9dB"
},
"outputs": [],
"source": [
"!python train.py --dataroot ./datasets/horse2zebra --name horse2zebra --model cycle_gan --display_id -1"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9UkcaFZiyASl"
},
"source": [
"# Testing\n",
"\n",
"- `python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout`\n",
"\n",
"Change the `--dataroot` and `--name` to be consistent with your trained model's configuration.\n",
"\n",
"> from https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix:\n",
"> 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 requires loading and generating results in both directions, which is sometimes unnecessary. The results will be saved at ./results/. Use --results_dir {directory_path_to_save_result} to specify the results directory.\n",
"\n",
"> For your own experiments, you might want to specify --netG, --norm, --no_dropout to match the generator architecture of the trained model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "uCsKkEq0yGh0"
},
"outputs": [],
"source": [
"!python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "OzSKIPUByfiN"
},
"source": [
"# Visualize"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "9Mgg8raPyizq"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_fake.png')\n",
"plt.imshow(img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "0G3oVH9DyqLQ"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_real.png')\n",
"plt.imshow(img)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"include_colab_link": true,
"name": "CycleGAN",
"provenance": []
},
"environment": {
"name": "tf2-gpu.2-3.m74",
"type": "gcloud",
"uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-3:m74"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: LICENSE
================================================
Copyright (c) 2017, Jun-Yan Zhu and Taesung Park
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
--------------------------- LICENSE FOR pix2pix --------------------------------
BSD License
For pix2pix software
Copyright (c) 2016, Phillip Isola and Jun-Yan Zhu
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
----------------------------- LICENSE FOR DCGAN --------------------------------
BSD License
For dcgan.torch software
Copyright (c) 2015, Facebook, Inc. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name Facebook nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
================================================
FILE: README.md
================================================
<img src='imgs/horse2zebra.gif' align="right" width=384>
<br><br><br>
# CycleGAN and pix2pix in PyTorch
**Udpate in 2025**: we recently updated the code to support Python 3.11 and PyTorch 2.4. It also supports DDP for single-machine multiple-GPU training. (Please use `torchrun --nproc_per_node=4 train.py ...`)
**New**: Please check out [img2img-turbo](https://github.com/GaParmar/img2img-turbo) repo that includes both pix2pix-turbo and CycleGAN-Turbo. Our new one-step image-to-image translation methods can support both paired and unpaired training and produce better results by leveraging the pre-trained StableDiffusion-Turbo model. The inference time for 512x512 image is 0.29 sec on A6000 and 0.11 sec on A100.
Please check out [contrastive-unpaired-translation](https://github.com/taesungp/contrastive-unpaired-translation) (CUT), our new unpaired image-to-image translation model that enables fast and memory-efficient training.
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/taesungp), and supported by [Tongzhou Wang](https://github.com/SsnL).
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 in Lua/Torch.
**Note**: The current software works well with PyTorch 2.4+. 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). To implement custom models and datasets, check out our [templates](#custom-model-and-dataset). To help users better understand and adapt our codebase, we provide an [overview](docs/overview.md) of the code structure of this repository.
**CycleGAN: [Project](https://junyanz.github.io/CycleGAN/) | [Paper](https://arxiv.org/pdf/1703.10593.pdf) | [Torch](https://github.com/junyanz/CycleGAN) |
[Tensorflow Core Tutorial](https://www.tensorflow.org/tutorials/generative/cyclegan) | [PyTorch Colab](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/CycleGAN.ipynb)**
<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) |
[Tensorflow Core Tutorial](https://www.tensorflow.org/tutorials/generative/pix2pix) | [PyTorch Colab](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/pix2pix.ipynb)**
<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.<br>
[Jun-Yan Zhu](https://www.cs.cmu.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.<br>
[Phillip Isola](https://people.eecs.berkeley.edu/~isola), [Jun-Yan Zhu](https://www.cs.cmu.edu/~junyanz/), [Tinghui Zhou](https://people.eecs.berkeley.edu/~tinghuiz), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros). In CVPR 2017. [[Bibtex]](https://www.cs.cmu.edu/~junyanz/projects/pix2pix/pix2pix.bib)
## Talks and Course
pix2pix slides: [keynote](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/pix2pix.key) | [pdf](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/pix2pix.pdf),
CycleGAN slides: [pptx](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/CycleGAN.pptx) | [pdf](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/CycleGAN.pdf)
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.
## Colab Notebook
TensorFlow Core CycleGAN Tutorial: [Google Colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/generative/cyclegan.ipynb) | [Code](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/cyclegan.ipynb)
TensorFlow Core pix2pix Tutorial: [Google Colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/generative/pix2pix.ipynb) | [Code](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/pix2pix.ipynb)
PyTorch Colab notebook: [CycleGAN](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/CycleGAN.ipynb) and [pix2pix](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/pix2pix.ipynb)
ZeroCostDL4Mic Colab notebook: [CycleGAN](https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks_Beta/CycleGAN_ZeroCostDL4Mic.ipynb) and [pix2pix](https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks_Beta/pix2pix_ZeroCostDL4Mic.ipynb)
## 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-2"> [Tensorflow2]</a> (by Zhenliang He),
<a href="https://github.com/luoxier/CycleGAN_Tensorlayer"> [TensorLayer1.0]</a> (by luoxier),
<a href="https://github.com/tensorlayer/cyclegan"> [TensorLayer2.0]</a> (by zsdonghao),
<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),
<a href="https://github.com/simontomaskarlsson/CycleGAN-Keras">[Keras]</a> (by Simon Karlsson),
<a href="https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Oneflow-Python/CycleGAN">[OneFlow]</a> (by Ldpe2G)
</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 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](http://pytorch.org) and other dependencies. For Conda users, you can create a new Conda environment by
```bash
conda env create -f environment.yml
```
and then activate the environment by
```bash
conda activate pytorch-img2img
```
- For Docker users, we provide the pre-built Docker image and Dockerfile. Please refer to our [Docker](docs/docker.md) page.
- For Repl users, please click [](https://repl.it/github/junyanz/pytorch-CycleGAN-and-pix2pix).
### CycleGAN train/test
- Download a CycleGAN dataset (e.g. maps):
```bash
bash ./datasets/download_cyclegan_dataset.sh maps
```
- To log training progress and test images to W&B dashboard, set the `--use_wandb` flag with training script
- Train a model:
```bash
#!./scripts/train_cyclegan.sh
python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan --use_wandb
```
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](http://cmp.felk.cvut.cz/~tylecr1/facade/)):
```bash
bash ./datasets/download_pix2pix_dataset.sh facades
```
- To log training progress and test images to W&B dashboard, set the `--use_wandb` flag with training script
- Train a model:
```bash
#!./scripts/train_pix2pix.sh
python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA --use_wandb
```
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.
- To train and test pix2pix-based colorization models, please add `--model colorization` and `--dataset_mode colorization`. See our training [tips](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md#notes-on-colorization) for more details.
### 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` requires loading and generating results in both directions, which is sometimes unnecessary. The results will be saved at `./results/`. Use `--results_dir {directory_path_to_save_result}` to specify the results directory.
- For pix2pix and your own models, you need to explicitly specify `--netG`, `--norm`, `--no_dropout` to match the generator architecture of the trained model. See this [FAQ](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md#runtimeerror-errors-in-loading-state_dict-812-671461-296) for more details.
### Apply a pre-trained model (pix2pix)
Download a pre-trained model with `./scripts/download_pix2pix_model.sh`.
- Check [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/scripts/download_pix2pix_model.sh#L3) for all the available pix2pix models. For example, if you would like to download label2photo model on the Facades dataset,
```bash
bash ./scripts/download_pix2pix_model.sh facades_label2photo
```
- Download the pix2pix facades datasets:
```bash
bash ./datasets/download_pix2pix_dataset.sh facades
```
- Then generate the results using
```bash
python test.py --dataroot ./datasets/facades/ --direction BtoA --model pix2pix --name facades_label2photo_pretrained
```
- Note that we specified `--direction BtoA` as Facades dataset's A to B direction is photos to labels.
- If you would like to apply a pre-trained model to a collection of input images (rather than image pairs), please use `--model test` option. See `./scripts/test_single.sh` for how to apply a model to Facade label maps (stored in the directory `facades/testB`).
- See a list of currently available models at `./scripts/download_pix2pix_model.sh`
### Multi-GPU training
To train a model on multiple GPUs, please use `torchrun --nproc_per_node=4 train.py ...` instead of `python train.py ...`. We also need to use synchronized batchnorm by setting `--norm sync_batch` (or `--norm sync_instance` for instance normgalization). The `--norm batch` is not compatible with DDP.
## [Docker](docs/docker.md)
We provide the pre-built Docker image and Dockerfile that can run this code repo. See [docker](docs/docker.md).
## [Datasets](docs/datasets.md)
Download pix2pix/CycleGAN datasets and create your own datasets.
## [Training/Test Tips](docs/tips.md)
Best practice for training and testing your models.
## [Frequently Asked Questions](docs/qa.md)
Before you post a new question, please first look at the above Q & A and existing GitHub issues.
## Custom Model and Dataset
If you plan to implement custom models and dataset for your new applications, we provide a dataset [template](data/template_dataset.py) and a model [template](models/template_model.py) as a starting point.
## [Code structure](docs/overview.md)
To help users better understand and use our code, we briefly overview the functionality and implementation of each package and each module.
## Pull Request
You are always welcome to contribute to this repository by sending a [pull request](https://help.github.com/articles/about-pull-requests/).
Please run `flake8 --ignore E501 .` and `pytest scripts/test_before_push.py -v` before you commit the code. Please also update the code structure [overview](docs/overview.md) accordingly if you add or remove files.
## Citation
If you use this code for your research, please cite our papers.
```
@inproceedings{CycleGAN2017,
title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
year={2017}
}
@inproceedings{isola2017image,
title={Image-to-Image Translation with Conditional Adversarial Networks},
author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on},
year={2017}
}
```
## Other Languages
[Spanish](docs/README_es.md)
## Related Projects
[img2img-turbo](https://github.com/GaParmar/img2img-turbo)<br>
[contrastive-unpaired-translation](https://github.com/taesungp/contrastive-unpaired-translation) (CUT)<br>
[CycleGAN-Torch](https://github.com/junyanz/CycleGAN) |
[pix2pix-Torch](https://github.com/phillipi/pix2pix) | [pix2pixHD](https://github.com/NVIDIA/pix2pixHD)|
[BicycleGAN](https://github.com/junyanz/BicycleGAN) | [vid2vid](https://tcwang0509.github.io/vid2vid/) | [SPADE/GauGAN](https://github.com/NVlabs/SPADE)<br>
[iGAN](https://github.com/junyanz/iGAN) | [GAN Dissection](https://github.com/CSAILVision/GANDissect) | [GAN Paint](http://ganpaint.io/)
## Cat Paper Collection
If you love cats, and love reading cool graphics, vision, and learning papers, please check out the Cat Paper [Collection](https://github.com/junyanz/CatPapers).
## Acknowledgments
Our code is inspired by [pytorch-DCGAN](https://github.com/pytorch/examples/tree/master/dcgan).
================================================
FILE: data/__init__.py
================================================
"""This package includes all the modules related to data loading and preprocessing
To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
You need to implement four functions:
-- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt).
-- <__len__>: return the size of dataset.
-- <__getitem__>: get a data point from data loader.
-- <modify_commandline_options>: (optionally) add dataset-specific options and set default options.
Now you can use the dataset class by specifying flag '--dataset_mode dummy'.
See our template dataset class 'template_dataset.py' for more details.
"""
import importlib
import torch.utils.data
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
import os
from data.base_dataset import BaseDataset
def find_dataset_using_name(dataset_name):
"""Import the module "data/[dataset_name]_dataset.py".
In the file, the class called DatasetNameDataset() will
be instantiated. It has to be a subclass of BaseDataset,
and it is case-insensitive.
"""
dataset_filename = "data." + dataset_name + "_dataset"
datasetlib = importlib.import_module(dataset_filename)
dataset = None
target_dataset_name = dataset_name.replace("_", "") + "dataset"
for name, cls in datasetlib.__dict__.items():
if name.lower() == target_dataset_name.lower() and issubclass(cls, BaseDataset):
dataset = cls
if dataset is None:
raise NotImplementedError("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name))
return dataset
def get_option_setter(dataset_name):
"""Return the static method <modify_commandline_options> of the dataset class."""
dataset_class = find_dataset_using_name(dataset_name)
return dataset_class.modify_commandline_options
def create_dataset(opt):
"""Create a dataset given the option.
This function wraps the class CustomDatasetDataLoader.
This is the main interface between this package and 'train.py'/'test.py'
Example:
>>> from data import create_dataset
>>> dataset = create_dataset(opt)
"""
data_loader = CustomDatasetDataLoader(opt)
dataset = data_loader.load_data()
return dataset
class CustomDatasetDataLoader:
"""Wrapper class of Dataset class that performs multi-threaded data loading"""
def __init__(self, opt):
"""Initialize this class
Step 1: create a dataset instance given the name [dataset_mode]
Step 2: create a multi-threaded data loader.
"""
self.opt = opt
dataset_class = find_dataset_using_name(opt.dataset_mode)
self.dataset = dataset_class(opt)
print("dataset [%s] was created" % type(self.dataset).__name__)
# Use DistributedSampler for DDP training
if "LOCAL_RANK" in os.environ:
print(f'create DDP sampler on rank {int(os.environ["LOCAL_RANK"])}')
self.sampler = DistributedSampler(self.dataset, shuffle=not opt.serial_batches)
shuffle = False # DistributedSampler handles shuffling
else:
self.sampler = None
shuffle = not opt.serial_batches
self.dataloader = torch.utils.data.DataLoader(self.dataset, batch_size=opt.batch_size, shuffle=shuffle, sampler=self.sampler, num_workers=int(opt.num_threads))
def load_data(self):
return self
def __len__(self):
"""Return the number of data in the dataset"""
return min(len(self.dataset), self.opt.max_dataset_size)
def __iter__(self):
"""Return a batch of data"""
for i, data in enumerate(self.dataloader):
if i * self.opt.batch_size >= self.opt.max_dataset_size:
break
yield data
def set_epoch(self, epoch):
"""Set epoch for DistributedSampler to ensure proper shuffling"""
if self.sampler is not None:
self.sampler.set_epoch(epoch)
================================================
FILE: data/aligned_dataset.py
================================================
import os
from data.base_dataset import BaseDataset, get_params, get_transform
from data.image_folder import make_dataset
from PIL import Image
class AlignedDataset(BaseDataset):
"""A dataset class for paired image dataset.
It assumes that the directory '/path/to/data/train' contains image pairs in the form of {A,B}.
During test time, you need to prepare a directory '/path/to/data/test'.
"""
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.dir_AB = os.path.join(opt.dataroot, opt.phase) # get the image directory
self.AB_paths = sorted(make_dataset(self.dir_AB, opt.max_dataset_size)) # get image paths
assert self.opt.load_size >= self.opt.crop_size # crop_size should be smaller than the size of loaded image
self.input_nc = self.opt.output_nc if self.opt.direction == "BtoA" else self.opt.input_nc
self.output_nc = self.opt.input_nc if self.opt.direction == "BtoA" else self.opt.output_nc
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index - - a random integer for data indexing
Returns a dictionary that contains A, B, A_paths and B_paths
A (tensor) - - an image in the input domain
B (tensor) - - its corresponding image in the target domain
A_paths (str) - - image paths
B_paths (str) - - image paths (same as A_paths)
"""
# read a image given a random integer index
AB_path = self.AB_paths[index]
AB = Image.open(AB_path).convert("RGB")
# split AB image into A and B
w, h = AB.size
w2 = int(w / 2)
A = AB.crop((0, 0, w2, h))
B = AB.crop((w2, 0, w, h))
# apply the same transform to both A and B
transform_params = get_params(self.opt, A.size)
A_transform = get_transform(self.opt, transform_params, grayscale=(self.input_nc == 1))
B_transform = get_transform(self.opt, transform_params, grayscale=(self.output_nc == 1))
A = A_transform(A)
B = B_transform(B)
return {"A": A, "B": B, "A_paths": AB_path, "B_paths": AB_path}
def __len__(self):
"""Return the total number of images in the dataset."""
return len(self.AB_paths)
================================================
FILE: data/base_dataset.py
================================================
"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
"""
import random
import numpy as np
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
from abc import ABC, abstractmethod
class BaseDataset(data.Dataset, ABC):
"""This class is an abstract base class (ABC) for datasets.
To create a subclass, you need to implement the following four functions:
-- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt).
-- <__len__>: return the size of dataset.
-- <__getitem__>: get a data point.
-- <modify_commandline_options>: (optionally) add dataset-specific options and set default options.
"""
def __init__(self, opt):
"""Initialize the class; save the options in the class
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
self.opt = opt
self.root = opt.dataroot
@staticmethod
def modify_commandline_options(parser, is_train):
"""Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
"""
return parser
@abstractmethod
def __len__(self):
"""Return the total number of images in the dataset."""
return 0
@abstractmethod
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index - - a random integer for data indexing
Returns:
a dictionary of data with their names. It ususally contains the data itself and its metadata information.
"""
pass
def get_params(opt, size):
w, h = size
new_h = h
new_w = w
if opt.preprocess == "resize_and_crop":
new_h = new_w = opt.load_size
elif opt.preprocess == "scale_width_and_crop":
new_w = opt.load_size
new_h = opt.load_size * h // w
x = random.randint(0, np.maximum(0, new_w - opt.crop_size))
y = random.randint(0, np.maximum(0, new_h - opt.crop_size))
flip = random.random() > 0.5
return {"crop_pos": (x, y), "flip": flip}
def get_transform(opt, params=None, grayscale=False, method=transforms.InterpolationMode.BICUBIC, convert=True):
transform_list = []
if grayscale:
transform_list.append(transforms.Grayscale(1))
if "resize" in opt.preprocess:
osize = [opt.load_size, opt.load_size]
transform_list.append(transforms.Resize(osize, method))
elif "scale_width" in opt.preprocess:
transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.load_size, opt.crop_size, method)))
if "crop" in opt.preprocess:
if params is None:
transform_list.append(transforms.RandomCrop(opt.crop_size))
else:
transform_list.append(transforms.Lambda(lambda img: __crop(img, params["crop_pos"], opt.crop_size)))
if opt.preprocess == "none":
transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base=4, method=method)))
if not opt.no_flip:
if params is None:
transform_list.append(transforms.RandomHorizontalFlip())
elif params["flip"]:
transform_list.append(transforms.Lambda(lambda img: __flip(img, params["flip"])))
if convert:
transform_list += [transforms.ToTensor()]
if grayscale:
transform_list += [transforms.Normalize((0.5,), (0.5,))]
else:
transform_list += [transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
return transforms.Compose(transform_list)
def __transforms2pil_resize(method):
mapper = {
transforms.InterpolationMode.BILINEAR: Image.BILINEAR,
transforms.InterpolationMode.BICUBIC: Image.BICUBIC,
transforms.InterpolationMode.NEAREST: Image.NEAREST,
transforms.InterpolationMode.LANCZOS: Image.LANCZOS,
}
return mapper[method]
def __make_power_2(img, base, method=transforms.InterpolationMode.BICUBIC):
method = __transforms2pil_resize(method)
ow, oh = img.size
h = int(round(oh / base) * base)
w = int(round(ow / base) * base)
if h == oh and w == ow:
return img
__print_size_warning(ow, oh, w, h)
return img.resize((w, h), method)
def __scale_width(img, target_size, crop_size, method=transforms.InterpolationMode.BICUBIC):
method = __transforms2pil_resize(method)
ow, oh = img.size
if ow == target_size and oh >= crop_size:
return img
w = target_size
h = int(max(target_size * oh / ow, crop_size))
return img.resize((w, h), method)
def __crop(img, pos, size):
ow, oh = img.size
x1, y1 = pos
tw = th = size
if ow > tw or oh > th:
return img.crop((x1, y1, x1 + tw, y1 + th))
return img
def __flip(img, flip):
if flip:
return img.transpose(Image.FLIP_LEFT_RIGHT)
return img
def __print_size_warning(ow, oh, w, h):
"""Print warning information about image size(only print once)"""
if not hasattr(__print_size_warning, "has_printed"):
print("The image size needs to be a multiple of 4. " "The loaded image size was (%d, %d), so it was adjusted to " "(%d, %d). This adjustment will be done to all images " "whose sizes are not multiples of 4" % (ow, oh, w, h))
__print_size_warning.has_printed = True
================================================
FILE: data/colorization_dataset.py
================================================
import os
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
from skimage import color # require skimage
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
class ColorizationDataset(BaseDataset):
"""This dataset class can load a set of natural images in RGB, and convert RGB format into (L, ab) pairs in Lab color space.
This dataset is required by pix2pix-based colorization model ('--model colorization')
"""
@staticmethod
def modify_commandline_options(parser, is_train):
"""Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
By default, the number of channels for input image is 1 (L) and
the number of channels for output image is 2 (ab). The direction is from A to B
"""
parser.set_defaults(input_nc=1, output_nc=2, direction="AtoB")
return parser
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.dir = os.path.join(opt.dataroot, opt.phase)
self.AB_paths = sorted(make_dataset(self.dir, opt.max_dataset_size))
assert opt.input_nc == 1 and opt.output_nc == 2 and opt.direction == "AtoB"
self.transform = get_transform(self.opt, convert=False)
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index - - a random integer for data indexing
Returns a dictionary that contains A, B, A_paths and B_paths
A (tensor) - - the L channel of an image
B (tensor) - - the ab channels of the same image
A_paths (str) - - image paths
B_paths (str) - - image paths (same as A_paths)
"""
path = self.AB_paths[index]
im = Image.open(path).convert("RGB")
im = self.transform(im)
im = np.array(im)
lab = color.rgb2lab(im).astype(np.float32)
lab_t = transforms.ToTensor()(lab)
A = lab_t[[0], ...] / 50.0 - 1.0
B = lab_t[[1, 2], ...] / 110.0
return {"A": A, "B": B, "A_paths": path, "B_paths": path}
def __len__(self):
"""Return the total number of images in the dataset."""
return len(self.AB_paths)
================================================
FILE: data/image_folder.py
================================================
"""A modified image folder class
We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py)
so that this class can load images from both current directory and its subdirectories.
"""
import torch.utils.data as data
from pathlib import Path
from PIL import Image
IMG_EXTENSIONS = [
".jpg",
".JPG",
".jpeg",
".JPEG",
".png",
".PNG",
".ppm",
".PPM",
".bmp",
".BMP",
".tif",
".TIF",
".tiff",
".TIFF",
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def make_dataset(dir, max_dataset_size=float("inf")):
images = []
dir_path = Path(dir)
assert dir_path.is_dir(), f"{dir} is not a valid directory"
for path in sorted(dir_path.rglob("*")):
if path.is_file() and is_image_file(path.name):
images.append(str(path))
return images[: min(max_dataset_size, len(images))]
def default_loader(path):
return Image.open(path).convert("RGB")
class ImageFolder(data.Dataset):
def __init__(self, root, transform=None, return_paths=False, loader=default_loader):
imgs = make_dataset(root)
if len(imgs) == 0:
raise (RuntimeError("Found 0 images in: " + root + "\n" "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
self.root = root
self.imgs = imgs
self.transform = transform
self.return_paths = return_paths
self.loader = loader
def __getitem__(self, index):
path = self.imgs[index]
img = self.loader(path)
if self.transform is not None:
img = self.transform(img)
if self.return_paths:
return img, path
else:
return img
def __len__(self):
return len(self.imgs)
================================================
FILE: data/single_dataset.py
================================================
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
from PIL import Image
class SingleDataset(BaseDataset):
"""This dataset class can load a set of images specified by the path --dataroot /path/to/data.
It can be used for generating CycleGAN results only for one side with the model option '-model test'.
"""
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size))
input_nc = self.opt.output_nc if self.opt.direction == "BtoA" else self.opt.input_nc
self.transform = get_transform(opt, grayscale=(input_nc == 1))
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index - - a random integer for data indexing
Returns a dictionary that contains A and A_paths
A(tensor) - - an image in one domain
A_paths(str) - - the path of the image
"""
A_path = self.A_paths[index]
A_img = Image.open(A_path).convert("RGB")
A = self.transform(A_img)
return {"A": A, "A_paths": A_path}
def __len__(self):
"""Return the total number of images in the dataset."""
return len(self.A_paths)
================================================
FILE: data/template_dataset.py
================================================
"""Dataset class template
This module provides a template for users to implement custom datasets.
You can specify '--dataset_mode template' to use this dataset.
The class name should be consistent with both the filename and its dataset_mode option.
The filename should be <dataset_mode>_dataset.py
The class name should be <Dataset_mode>Dataset.py
You need to implement the following functions:
-- <modify_commandline_options>: Add dataset-specific options and rewrite default values for existing options.
-- <__init__>: Initialize this dataset class.
-- <__getitem__>: Return a data point and its metadata information.
-- <__len__>: Return the number of images.
"""
from data.base_dataset import BaseDataset, get_transform
# from data.image_folder import make_dataset
# from PIL import Image
class TemplateDataset(BaseDataset):
"""A template dataset class for you to implement custom datasets."""
@staticmethod
def modify_commandline_options(parser, is_train):
"""Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
"""
parser.add_argument("--new_dataset_option", type=float, default=1.0, help="new dataset option")
parser.set_defaults(max_dataset_size=10, new_dataset_option=2.0) # specify dataset-specific default values
return parser
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
A few things can be done here.
- save the options (have been done in BaseDataset)
- get image paths and meta information of the dataset.
- define the image transformation.
"""
# save the option and dataset root
BaseDataset.__init__(self, opt)
# get the image paths of your dataset;
self.image_paths = [] # You can call sorted(make_dataset(self.root, opt.max_dataset_size)) to get all the image paths under the directory self.root
# define the default transform function. You can use <base_dataset.get_transform>; You can also define your custom transform function
self.transform = get_transform(opt)
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index -- a random integer for data indexing
Returns:
a dictionary of data with their names. It usually contains the data itself and its metadata information.
Step 1: get a random image path: e.g., path = self.image_paths[index]
Step 2: load your data from the disk: e.g., image = Image.open(path).convert('RGB').
Step 3: convert your data to a PyTorch tensor. You can use helpder functions such as self.transform. e.g., data = self.transform(image)
Step 4: return a data point as a dictionary.
"""
path = "temp" # needs to be a string
data_A = None # needs to be a tensor
data_B = None # needs to be a tensor
return {"data_A": data_A, "data_B": data_B, "path": path}
def __len__(self):
"""Return the total number of images."""
return len(self.image_paths)
================================================
FILE: data/unaligned_dataset.py
================================================
import os
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
from PIL import Image
import random
class UnalignedDataset(BaseDataset):
"""
This dataset class can load unaligned/unpaired datasets.
It requires two directories to host training images from domain A '/path/to/data/trainA'
and from domain B '/path/to/data/trainB' respectively.
You can train the model with the dataset flag '--dataroot /path/to/data'.
Similarly, you need to prepare two directories:
'/path/to/data/testA' and '/path/to/data/testB' during test time.
"""
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.dir_A = os.path.join(opt.dataroot, opt.phase + "A") # create a path '/path/to/data/trainA'
self.dir_B = os.path.join(opt.dataroot, opt.phase + "B") # create a path '/path/to/data/trainB'
self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA'
self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size)) # load images from '/path/to/data/trainB'
self.A_size = len(self.A_paths) # get the size of dataset A
self.B_size = len(self.B_paths) # get the size of dataset B
btoA = self.opt.direction == "BtoA"
input_nc = self.opt.output_nc if btoA else self.opt.input_nc # get the number of channels of input image
output_nc = self.opt.input_nc if btoA else self.opt.output_nc # get the number of channels of output image
self.transform_A = get_transform(self.opt, grayscale=(input_nc == 1))
self.transform_B = get_transform(self.opt, grayscale=(output_nc == 1))
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index (int) -- a random integer for data indexing
Returns a dictionary that contains A, B, A_paths and B_paths
A (tensor) -- an image in the input domain
B (tensor) -- its corresponding image in the target domain
A_paths (str) -- image paths
B_paths (str) -- image paths
"""
A_path = self.A_paths[index % self.A_size] # make sure index is within then range
if self.opt.serial_batches: # make sure index is within then range
index_B = index % self.B_size
else: # randomize the index for domain B to avoid fixed pairs.
index_B = random.randint(0, self.B_size - 1)
B_path = self.B_paths[index_B]
A_img = Image.open(A_path).convert("RGB")
B_img = Image.open(B_path).convert("RGB")
# apply image transformation
A = self.transform_A(A_img)
B = self.transform_B(B_img)
return {"A": A, "B": B, "A_paths": A_path, "B_paths": B_path}
def __len__(self):
"""Return the total number of images in the dataset.
As we have two datasets with potentially different number of images,
we take a maximum of
"""
return max(self.A_size, self.B_size)
================================================
FILE: docs/Dockerfile
================================================
FROM nvidia/cuda:10.1-base
#Nvidia Public GPG Key
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
RUN apt update && apt install -y wget unzip curl bzip2 git
RUN curl -LO http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
RUN bash Miniconda3-latest-Linux-x86_64.sh -p /miniconda -b
RUN rm Miniconda3-latest-Linux-x86_64.sh
ENV PATH=/miniconda/bin:${PATH}
RUN conda update -y conda
RUN conda install -y pytorch torchvision -c pytorch
RUN mkdir /workspace/ && cd /workspace/ && git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git && cd pytorch-CycleGAN-and-pix2pix && pip install -r requirements.txt
WORKDIR /workspace
================================================
FILE: docs/README_es.md
================================================
<img src='https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/raw/master/imgs/horse2zebra.gif' align="right" width=384>
<br><br><br>
# CycleGAN y pix2pix en PyTorch
Implementacion en PyTorch de Unpaired Image-to-Image Translation.
Este codigo fue escrito por [Jun-Yan Zhu](https://github.com/junyanz) y [Taesung Park](https://github.com/taesung), y con ayuda de [Tongzhou Wang](https://ssnl.github.io/).
Esta implementacion de PyTorch produce resultados comparables o mejores que nuestros original software de Torch. Si te gustaria producir los mismos resultados que en documento oficial, echa un vistazo al codigo original [CycleGAN Torch](https://github.com/junyanz/CycleGAN) y [pix2pix Torch](https://github.com/phillipi/pix2pix)
**Aviso**: El software actual funciona correctamente en PyTorch 2.4+. Para soporte en PyTorch 0.1-0.3: [branch](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/tree/pytorch0.3.1).
Puede encontrar información útil en [training/test tips](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md) y [preguntas frecuentes](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md). Para implementar modelos y conjuntos de datos personalizados, consulte nuestro [templates](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/README_es.md#modelo-y-dataset-personalizado). Para ayudar a los usuarios a comprender y adaptar mejor nuestra base de código, proporcionamos un [overview](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/overview.md) de la estructura de código de este repositorio.
**CycleGAN: [Proyecto](https://junyanz.github.io/CycleGAN/) | [PDF](https://arxiv.org/pdf/1703.10593.pdf) | [Torch](https://github.com/junyanz/CycleGAN) |
[Guia de Tensorflow Core](https://www.tensorflow.org/tutorials/generative/cyclegan) | [PyTorch Colab](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/CycleGAN.ipynb)**
<img src="https://junyanz.github.io/CycleGAN/images/teaser_high_res.jpg" width="800"/>
**Pix2pix: [Proyeto](https://phillipi.github.io/pix2pix/) | [PDF](https://arxiv.org/pdf/1611.07004.pdf) | [Torch](https://github.com/phillipi/pix2pix) |
[Guia de Tensorflow Core](https://www.tensorflow.org/tutorials/generative/cyclegan) | [PyTorch Colab](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/pix2pix.ipynb)**
<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) | por [Christopher Hesse](https://twitter.com/christophrhesse)**
<img src='https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/imgs/edges2cats.jpg' width="400px"/>
Si usa este código para su investigación, cite:
Unpaired Image-to-Image Translation usando Cycle-Consistent Adversarial Networks.<br>
[Jun-Yan Zhu](https://www.cs.cmu.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. (* contribucion igualitaria) [[Bibtex]](https://junyanz.github.io/CycleGAN/CycleGAN.txt)
Image-to-Image Translation usando Conditional Adversarial Networks.<br>
[Phillip Isola](https://people.eecs.berkeley.edu/~isola), [Jun-Yan Zhu](https://www.cs.cmu.edu/~junyanz/), [Tinghui Zhou](https://people.eecs.berkeley.edu/~tinghuiz), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros). In CVPR 2017. [[Bibtex]](https://www.cs.cmu.edu/~junyanz/projects/pix2pix/pix2pix.bib)
## Charlas y curso
Presentacion en PowerPoint de Pix2pix: [keynote](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/pix2pix.key) | [pdf](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/pix2pix.pdf),
Presentacion en PowerPoint de CycleGAN: [pptx](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/CycleGAN.pptx) | [pdf](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/CycleGAN.pdf)
Asignación del curso CycleGAN [codigo](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/assignments/a4-code.zip) y [handout](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/assignments/a4-handout.pdf) diseñado por el 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" en la universidad de Toronto. Póngase en contacto con el instructor si desea adoptarlo en su curso.
## Colab Notebook
TensorFlow Core CycleGAN Tutorial: [Google Colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/generative/cyclegan.ipynb) | [Codigo](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/cyclegan.ipynb)
Guia de TensorFlow Core pix2pix : [Google Colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/generative/pix2pix.ipynb) | [Codigo](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/pix2pix.ipynb)
PyTorch Colab notebook: [CycleGAN](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/CycleGAN.ipynb) y [pix2pix](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/pix2pix.ipynb)
## Otras implementaciones
### CycleGAN
<p><a href="https://github.com/leehomyc/cyclegan-1"> [Tensorflow]</a> (por Harry Yang),
<a href="https://github.com/architrathore/CycleGAN/">[Tensorflow]</a> (por Archit Rathore),
<a href="https://github.com/vanhuyz/CycleGAN-TensorFlow">[Tensorflow]</a> (por Van Huy),
<a href="https://github.com/XHUJOY/CycleGAN-tensorflow">[Tensorflow]</a> (por Xiaowei Hu),
<a href="https://github.com/LynnHo/CycleGAN-Tensorflow-Simple"> [Tensorflow-simple]</a> (por Zhenliang He),
<a href="https://github.com/luoxier/CycleGAN_Tensorlayer"> [TensorLayer]</a> (por luoxier),
<a href="https://github.com/Aixile/chainer-cyclegan">[Chainer]</a> (por Yanghua Jin),
<a href="https://github.com/yunjey/mnist-svhn-transfer">[Minimal PyTorch]</a> (por yunjey),
<a href="https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/CycleGAN">[Mxnet]</a> (por Ldpe2G),
<a href="https://github.com/tjwei/GANotebooks">[lasagne/Keras]</a> (por tjwei),
<a href="https://github.com/simontomaskarlsson/CycleGAN-Keras">[Keras]</a> (por Simon Karlsson)
</p>
</ul>
### pix2pix
<p><a href="https://github.com/affinelayer/pix2pix-tensorflow"> [Tensorflow]</a> (por Christopher Hesse),
<a href="https://github.com/Eyyub/tensorflow-pix2pix">[Tensorflow]</a> (por Eyyüb Sariu),
<a href="https://github.com/datitran/face2face-demo"> [Tensorflow (face2face)]</a> (por Dat Tran),
<a href="https://github.com/awjuliani/Pix2Pix-Film"> [Tensorflow (film)]</a> (por Arthur Juliani),
<a href="https://github.com/kaonashi-tyc/zi2zi">[Tensorflow (zi2zi)]</a> (por Yuchen Tian),
<a href="https://github.com/pfnet-research/chainer-pix2pix">[Chainer]</a> (por mattya),
<a href="https://github.com/tjwei/GANotebooks">[tf/torch/keras/lasagne]</a> (por tjwei),
<a href="https://github.com/taey16/pix2pixBEGAN.pytorch">[Pytorch]</a> (por taey16)
</p>
</ul>
## Requerimientos
- Linux o macOS
- Python 3
- CPU o NVIDIA GPU usando CUDA CuDNN
## Inicio
### Instalación
- Clone este repositorio:
```bash
git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
cd pytorch-CycleGAN-and-pix2pix
```
- Instale [PyTorch](http://pytorch.org) 0.4+ y sus otras dependencias (e.g., torchvision, [visdom](https://github.com/facebookresearch/visdom) y [dominate](https://github.com/Knio/dominate)).
- Para uso de pip, por favor escriba el comando `pip install -r requirements.txt`.
- Para uso de Conda, proporcionamos un script de instalación `./scripts/conda_deps.sh`. De forma alterna, puede crear un nuevo entorno Conda usando `conda env create -f environment.yml`.
- Para uso de Docker, Proporcionamos la imagen Docker y el archivo Docker preconstruidos. Por favor, consulte nuestra página
[Docker](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/docker.md).
### CycleGAN entreanimiento/test
- Descargar el dataset de CycleGAN (e.g. maps):
```bash
bash ./datasets/download_cyclegan_dataset.sh maps
```
- Para ver los resultados del entrenamiento y las gráficas de pérdidas, `python -m visdom.server` y haga clic en la URL
http://localhost:8097.
- Entrenar el modelo:
```bash
#!./scripts/train_cyclegan.sh
python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
```
Para ver más resultados intermedios, consulte `./checkpoints/maps_cyclegan/web/index.html`.
- Pruebe el modelo:
```bash
#!./scripts/test_cyclegan.sh
python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
```
-Los resultados de la prueba se guardarán en un archivo html aquí: `./results/maps_cyclegan/latest_test/index.html`.
### pix2pix entrenamiento/test
- Descargue el dataset de pix2pix (e.g.[facades](http://cmp.felk.cvut.cz/~tylecr1/facade/)):
```bash
bash ./datasets/download_pix2pix_dataset.sh facades
```
- Para ver los resultados del entrenamiento y las gráficas de pérdidas `python -m visdom.server`, haga clic en la URL http://localhost:8097.
- Para entrenar el modelo:
```bash
#!./scripts/train_pix2pix.sh
python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
```
Para ver más resultados intermedios, consulte `./checkpoints/facades_pix2pix/web/index.html`.
- Pruebe el modelo (`bash ./scripts/test_pix2pix.sh`):
```bash
#!./scripts/test_pix2pix.sh
python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
```
- Los resultados de la prueba se guardarán en un archivo html aquí: `./results/facades_pix2pix/test_latest/index.html`. Puede encontrar más scripts en `scripts` directory.
- Para entrenar y probar modelos de colorización basados en pix2pix, agregue la linea `--model colorization` y `--dataset_mode colorization`. Para más detalles de nuestro entrenamiento [tips](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md#notes-on-colorization).
### Aplicar un modelo pre-entrenado (CycleGAN)
- Puedes descargar un modelo previamente entrenado (e.g. horse2zebra) con el siguiente script:
```bash
bash ./scripts/download_cyclegan_model.sh horse2zebra
```
- El modelo pre-entrenado se guarda en `./checkpoints/{name}_pretrained/latest_net_G.pth`. Revise [aqui](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/scripts/download_cyclegan_model.sh#L3) para todos los modelos CycleGAN disponibles.
- Para probar el modelo, también debe descargar el dataset horse2zebra:
```bash
bash ./datasets/download_cyclegan_dataset.sh horse2zebra
```
- Luego genere los resultados usando:
```bash
python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout
```
- La opcion `--model test` ise usa para generar resultados de CycleGAN de un solo lado. Esta opción configurará automáticamente
`--dataset_mode single`, carga solo las imágenes de un conjunto. Por el contrario, el uso de `--model cycle_gan` requiere cargar y generar resultados en ambas direcciones, lo que a veces es innecesario. Los resultados se guardarán en `./results/`. Use `--results_dir {directory_path_to_save_result}` para especificar el directorio de resultados.
- Para sus propios experimentos, es posible que desee especificar `--netG`, `--norm`, `--no_dropout` para que coincida con la arquitectura del generador del modelo entrenado.
### Aplicar un modelo pre-entrenado (pix2pix)
Descargue un modelo pre-entrenado con `./scripts/download_pix2pix_model.sh`.
- Revise [aqui](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/scripts/download_pix2pix_model.sh#L3) para todos los modelos pix2pix disponibles. Por ejemplo, si desea descargar el modelo label2photo en el dataset:
```bash
bash ./scripts/download_pix2pix_model.sh facades_label2photo
```
- Descarga el dataset facades de pix2pix:
```bash
bash ./datasets/download_pix2pix_dataset.sh facades
```
- Luego genere los resultados usando:
```bash
python test.py --dataroot ./datasets/facades/ --direction BtoA --model pix2pix --name facades_label2photo_pretrained
```
- Tenga en cuenta que `--direction BtoA` como Facades dataset's, son direcciones A o B para etiquetado de fotos.
- Si desea aplicar un modelo previamente entrenado a una colección de imágenes de entrada (en lugar de pares de imágenes), use la opcion `--model test`. Vea `./scripts/test_single.sh` obre cómo aplicar un modelo a Facade label maps (almacenados en el directorio `facades/testB`).
- Vea una lista de los modelos disponibles actualmente en `./scripts/download_pix2pix_model.sh`
## [Docker](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/docker.md)
Proporcionamos la imagen Docker y el archivo Docker preconstruidos que pueden ejecutar este repositorio de código. Ver [docker](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/docker.md).
## [Datasets](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/datasets.md)
Descargue los conjuntos de datos pix2pix / CycleGAN y cree sus propios conjuntos de datos.
## [Entretanimiento/Test Tips](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md)
Las mejores prácticas para entrenar y probar sus modelos.
## [Preguntas frecuentes](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md)
Antes de publicar una nueva pregunta, primero mire las preguntas y respuestas anteriores y los problemas existentes de GitHub.
## Modelo y Dataset personalizado
Si planea implementar modelos y conjuntos de datos personalizados para sus nuevas aplicaciones, proporcionamos un conjunto de datos [template](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/data/template_dataset.py) y un modelo [template](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/template_model.py) como punto de partida.
## [Estructura de codigo](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/overview.md)
Para ayudar a los usuarios a comprender mejor y usar nuestro código, presentamos brevemente la funcionalidad e implementación de cada paquete y cada módulo.
## Solicitud de Pull
Siempre puede contribuir a este repositorio enviando un [pull request](https://help.github.com/articles/about-pull-requests/).
Por favor ejecute `flake8 --ignore E501 .` y `python ./scripts/test_before_push.py` antes de realizar un Pull en el código, asegure de también actualizar la estructura del código [overview](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/overview.md) en consecuencia si agrega o elimina archivos.
## Citación
Si utiliza este código para su investigación, cite nuestros documentos.
```
@inproceedings{CycleGAN2017,
title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkss},
author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
year={2017}
}
@inproceedings{isola2017image,
title={Image-to-Image Translation with Conditional Adversarial Networks},
author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on},
year={2017}
}
```
## Proyectos relacionados
**[CycleGAN-Torch](https://github.com/junyanz/CycleGAN) |
[pix2pix-Torch](https://github.com/phillipi/pix2pix) | [pix2pixHD](https://github.com/NVIDIA/pix2pixHD)|
[BicycleGAN](https://github.com/junyanz/BicycleGAN) | [vid2vid](https://tcwang0509.github.io/vid2vid/) | [SPADE/GauGAN](https://github.com/NVlabs/SPADE)**<br>
**[iGAN](https://github.com/junyanz/iGAN) | [GAN Dissection](https://github.com/CSAILVision/GANDissect) | [GAN Paint](http://ganpaint.io/)**
## Cat Paper Collection
Si amas a los gatos y te encanta leer gráficos geniales, computer vision y documentos de aprendizaje, echa un vistazo a Cat Paper [Collection](https://github.com/junyanz/CatPapers).
## Agradecimientos
Nuestro código fue inspirado en [pytorch-DCGAN](https://github.com/pytorch/examples/tree/master/dcgan).
================================================
FILE: docs/datasets.md
================================================
### CycleGAN Datasets
Download the CycleGAN datasets using the following script. Some of the datasets are collected by other researchers. Please cite their papers if you use the data.
```bash
bash ./datasets/download_cyclegan_dataset.sh dataset_name
```
- `facades`: 400 images from the [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade). [[Citation](../datasets/bibtex/facades.tex)]
- `cityscapes`: 2975 images from the [Cityscapes training set](https://www.cityscapes-dataset.com). [[Citation](../datasets/bibtex/cityscapes.tex)]. Note: Due to license issue, we cannot directly provide the Cityscapes dataset. Please download the Cityscapes dataset from [https://cityscapes-dataset.com](https://cityscapes-dataset.com) and use the script `./datasets/prepare_cityscapes_dataset.py`.
- `maps`: 1096 training images scraped from Google Maps.
- `horse2zebra`: 939 horse images and 1177 zebra images downloaded from [ImageNet](http://www.image-net.org) using keywords `wild horse` and `zebra`
- `apple2orange`: 996 apple images and 1020 orange images downloaded from [ImageNet](http://www.image-net.org) using keywords `apple` and `navel orange`.
- `summer2winter_yosemite`: 1273 summer Yosemite images and 854 winter Yosemite images were downloaded using Flickr API. See more details in our paper.
- `monet2photo`, `vangogh2photo`, `ukiyoe2photo`, `cezanne2photo`: The art images were downloaded from [Wikiart](https://www.wikiart.org/). The real photos are downloaded from Flickr using the combination of the tags *landscape* and *landscapephotography*. The training set size of each class is Monet:1074, Cezanne:584, Van Gogh:401, Ukiyo-e:1433, Photographs:6853.
- `iphone2dslr_flower`: both classes of images were downlaoded from Flickr. The training set size of each class is iPhone:1813, DSLR:3316. See more details in our paper.
To train a model on your own datasets, you need to create a data folder with two subdirectories `trainA` and `trainB` that contain images from domain A and B. You can test your model on your training set by setting `--phase train` in `test.py`. You can also create subdirectories `testA` and `testB` if you have test data.
You should **not** expect our method to work on just any random combination of input and output datasets (e.g. `cats<->keyboards`). From our experiments, we find it works better if two datasets share similar visual content. For example, `landscape painting<->landscape photographs` works much better than `portrait painting <-> landscape photographs`. `zebras<->horses` achieves compelling results while `cats<->dogs` completely fails.
### pix2pix datasets
Download the pix2pix datasets using the following script. Some of the datasets are collected by other researchers. Please cite their papers if you use the data.
```bash
bash ./datasets/download_pix2pix_dataset.sh dataset_name
```
- `facades`: 400 images from [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade). [[Citation](../datasets/bibtex/facades.tex)]
- `cityscapes`: 2975 images from the [Cityscapes training set](https://www.cityscapes-dataset.com). [[Citation](../datasets/bibtex/cityscapes.tex)]
- `maps`: 1096 training images scraped from Google Maps
- `edges2shoes`: 50k training images from [UT Zappos50K dataset](http://vision.cs.utexas.edu/projects/finegrained/utzap50k). Edges are computed by [HED](https://github.com/s9xie/hed) edge detector + post-processing. [[Citation](datasets/bibtex/shoes.tex)]
- `edges2handbags`: 137K Amazon Handbag images from [iGAN project](https://github.com/junyanz/iGAN). Edges are computed by [HED](https://github.com/s9xie/hed) edge detector + post-processing. [[Citation](datasets/bibtex/handbags.tex)]
- `night2day`: around 20K natural scene images from [Transient Attributes dataset](http://transattr.cs.brown.edu/) [[Citation](datasets/bibtex/transattr.tex)]. To train a `day2night` pix2pix model, you need to add `--direction BtoA`.
We provide a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene. For example, these might be pairs {label map, photo} or {bw image, color image}. Then we can learn to translate A to B or B to A:
Create folder `/path/to/data` with subfolders `A` and `B`. `A` and `B` should each have their own subfolders `train`, `val`, `test`, etc. In `/path/to/data/A/train`, put training images in style A. In `/path/to/data/B/train`, put the corresponding images in style B. Repeat same for other data splits (`val`, `test`, etc).
Corresponding images in a pair {A,B} must be the same size and have the same filename, e.g., `/path/to/data/A/train/1.jpg` is considered to correspond to `/path/to/data/B/train/1.jpg`.
Once the data is formatted this way, call:
```bash
python datasets/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data
```
This will combine each pair of images (A,B) into a single image file, ready for training.
================================================
FILE: docs/docker.md
================================================
# Docker image with pytorch-CycleGAN-and-pix2pix
We provide both Dockerfile and pre-built Docker container that can run this code repo.
## Prerequisite
- Install [docker-ce](https://docs.docker.com/install/linux/docker-ce/ubuntu/)
- Install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker#quickstart)
## Running pre-built Dockerfile
- Pull the pre-built docker file
```bash
docker pull taesungp/pytorch-cyclegan-and-pix2pix
```
- Start an interactive docker session. `-p 8097:8097` option is needed if you want to run `visdom` server on the Docker container.
```bash
nvidia-docker run -it -p 8097:8097 taesungp/pytorch-cyclegan-and-pix2pix
```
- Now you are in the Docker environment. Go to our code repo and start running things.
```bash
cd /workspace/pytorch-CycleGAN-and-pix2pix
bash datasets/download_pix2pix_dataset.sh facades
python -m visdom.server &
bash scripts/train_pix2pix.sh
```
## Running with Dockerfile
We also posted the [Dockerfile](Dockerfile). To generate the pre-built file, download the Dockerfile in this directory and run
```bash
docker build -t [target_tag] .
```
in the directory that contains the Dockerfile.
================================================
FILE: docs/overview.md
================================================
## Overview of Code Structure
To help users better understand and use our codebase, we briefly overview the functionality and implementation of each package and each module. Please see the documentation in each file for more details. If you have questions, you may find useful information in [training/test tips](tips.md) and [frequently asked questions](qa.md).
[train.py](../train.py) is a general-purpose training script. It works for various models (with option `--model`: e.g., `pix2pix`, `cyclegan`, `colorization`) and different datasets (with option `--dataset_mode`: e.g., `aligned`, `unaligned`, `single`, `colorization`). See the main [README](.../README.md) and [training/test tips](tips.md) for more details.
[test.py](../test.py) is a general-purpose test script. Once you have trained your model with `train.py`, you can use this script to test the model. It will load a saved model from `--checkpoints_dir` and save the results to `--results_dir`. See the main [README](.../README.md) and [training/test tips](tips.md) for more details.
[data](../data) directory contains all the modules related to data loading and preprocessing. To add a custom dataset class called `dummy`, you need to add a file called `dummy_dataset.py` and define a subclass `DummyDataset` inherited from `BaseDataset`. You need to implement four functions: `__init__` (initialize the class, you need to first call `BaseDataset.__init__(self, opt)`), `__len__` (return the size of dataset), `__getitem__` (get a data point), and optionally `modify_commandline_options` (add dataset-specific options and set default options). Now you can use the dataset class by specifying flag `--dataset_mode dummy`. See our template dataset [class](../data/template_dataset.py) for an example. Below we explain each file in details.
* [\_\_init\_\_.py](../data/__init__.py) implements the interface between this package and training and test scripts. `train.py` and `test.py` call `from data import create_dataset` and `dataset = create_dataset(opt)` to create a dataset given the option `opt`.
* [base_dataset.py](../data/base_dataset.py) implements an abstract base class ([ABC](https://docs.python.org/3/library/abc.html)) for datasets. It also includes common transformation functions (e.g., `get_transform`, `__scale_width`), which can be later used in subclasses.
* [image_folder.py](../data/image_folder.py) implements an image folder class. We modify the official PyTorch image folder [code](https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py) so that this class can load images from both the current directory and its subdirectories.
* [template_dataset.py](../data/template_dataset.py) provides a dataset template with detailed documentation. Check out this file if you plan to implement your own dataset.
* [aligned_dataset.py](../data/aligned_dataset.py) includes a dataset class that can load image pairs. It assumes a single image directory `/path/to/data/train`, which contains image pairs in the form of {A,B}. See [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md#prepare-your-own-datasets-for-pix2pix) on how to prepare aligned datasets. During test time, you need to prepare a directory `/path/to/data/test` as test data.
* [unaligned_dataset.py](../data/unaligned_dataset.py) includes a dataset class that can load unaligned/unpaired datasets. It assumes that two directories to host training images from domain A `/path/to/data/trainA` and from domain B `/path/to/data/trainB` respectively. Then you can train the model with the dataset flag `--dataroot /path/to/data`. Similarly, you need to prepare two directories `/path/to/data/testA` and `/path/to/data/testB` during test time.
* [single_dataset.py](../data/single_dataset.py) includes a dataset class that can load a set of single images specified by the path `--dataroot /path/to/data`. It can be used for generating CycleGAN results only for one side with the model option `-model test`.
* [colorization_dataset.py](../data/colorization_dataset.py) implements a dataset class that can load a set of nature images in RGB, and convert RGB format into (L, ab) pairs in [Lab](https://en.wikipedia.org/wiki/CIELAB_color_space) color space. It is required by pix2pix-based colorization model (`--model colorization`).
[models](../models) directory contains modules related to objective functions, optimizations, and network architectures. To add a custom model class called `dummy`, you need to add a file called `dummy_model.py` and define a subclass `DummyModel` inherited from `BaseModel`. You need to implement four functions: `__init__` (initialize the class; you need to first call `BaseModel.__init__(self, opt)`), `set_input` (unpack data from dataset and apply preprocessing), `forward` (generate intermediate results), `optimize_parameters` (calculate loss, gradients, and update network weights), and optionally `modify_commandline_options` (add model-specific options and set default options). Now you can use the model class by specifying flag `--model dummy`. See our template model [class](../models/template_model.py) for an example. Below we explain each file in details.
* [\_\_init\_\_.py](../models/__init__.py) implements the interface between this package and training and test scripts. `train.py` and `test.py` call `from models import create_model` and `model = create_model(opt)` to create a model given the option `opt`. You also need to call `model.setup(opt)` to properly initialize the model.
* [base_model.py](../models/base_model.py) implements an abstract base class ([ABC](https://docs.python.org/3/library/abc.html)) for models. It also includes commonly used helper functions (e.g., `setup`, `test`, `update_learning_rate`, `save_networks`, `load_networks`), which can be later used in subclasses.
* [template_model.py](../models/template_model.py) provides a model template with detailed documentation. Check out this file if you plan to implement your own model.
* [pix2pix_model.py](../models/pix2pix_model.py) implements the pix2pix [model](https://phillipi.github.io/pix2pix/), for learning a mapping from input images to output images given paired data. The model training requires `--dataset_mode aligned` dataset. By default, it uses a `--netG unet256` [U-Net](https://arxiv.org/pdf/1505.04597.pdf) generator, a `--netD basic` discriminator (PatchGAN), and a `--gan_mode vanilla` GAN loss (standard cross-entropy objective).
* [colorization_model.py](../models/colorization_model.py) implements a subclass of `Pix2PixModel` for image colorization (black & white image to colorful image). The model training requires `-dataset_model colorization` dataset. It trains a pix2pix model, mapping from L channel to ab channels in [Lab](https://en.wikipedia.org/wiki/CIELAB_color_space) color space. By default, the `colorization` dataset will automatically set `--input_nc 1` and `--output_nc 2`.
* [cycle_gan_model.py](../models/cycle_gan_model.py) implements the CycleGAN [model](https://junyanz.github.io/CycleGAN/), for learning image-to-image translation without paired data. The model training requires `--dataset_mode unaligned` dataset. By default, it uses a `--netG resnet_9blocks` ResNet generator, a `--netD basic` discriminator (PatchGAN introduced by pix2pix), and a least-square GANs [objective](https://arxiv.org/abs/1611.04076) (`--gan_mode lsgan`).
* [networks.py](../models/networks.py) module implements network architectures (both generators and discriminators), as well as normalization layers, initialization methods, optimization scheduler (i.e., learning rate policy), and GAN objective function (`vanilla`, `lsgan`, `wgangp`).
* [test_model.py](../models/test_model.py) implements a model that can be used to generate CycleGAN results for only one direction. This model will automatically set `--dataset_mode single`, which only loads the images from one set. See the test [instruction](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix#apply-a-pre-trained-model-cyclegan) for more details.
[options](../options) directory includes our option modules: training options, test options, and basic options (used in both training and test). `TrainOptions` and `TestOptions` are both subclasses of `BaseOptions`. They will reuse the options defined in `BaseOptions`.
* [\_\_init\_\_.py](../options/__init__.py) is required to make Python treat the directory `options` as containing packages,
* [base_options.py](../options/base_options.py) includes options that are used in both training and test. It also implements a few helper functions such as parsing, printing, and saving the options. It also gathers additional options defined in `modify_commandline_options` functions in both dataset class and model class.
* [train_options.py](../options/train_options.py) includes options that are only used during training time.
* [test_options.py](../options/test_options.py) includes options that are only used during test time.
[util](../util) directory includes a miscellaneous collection of useful helper functions.
* [\_\_init\_\_.py](../util/__init__.py) is required to make Python treat the directory `util` as containing packages,
* [get_data.py](../util/get_data.py) provides a Python script for downloading CycleGAN and pix2pix datasets. Alternatively, You can also use bash scripts such as [download_pix2pix_model.sh](../scripts/download_pix2pix_model.sh) and [download_cyclegan_model.sh](../scripts/download_cyclegan_model.sh).
* [html.py](../util/html.py) implements a module that saves images into a single HTML file. It consists of functions such as `add_header` (add a text header to the HTML file), `add_images` (add a row of images to the HTML file), `save` (save the HTML to the disk). It is based on Python library `dominate`, a Python library for creating and manipulating HTML documents using a DOM API.
* [image_pool.py](../util/image_pool.py) implements an image buffer that stores previously generated images. This buffer enables us to update discriminators using a history of generated images rather than the ones produced by the latest generators. The original idea was discussed in this [paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Shrivastava_Learning_From_Simulated_CVPR_2017_paper.pdf). The size of the buffer is controlled by the flag `--pool_size`.
* [visualizer.py](../util/visualizer.py) includes several functions that can display/save images and print/save logging information. It uses Weights & Biases for logging and a Python library `dominate` (wrapped in `HTML`) for creating HTML files with images.
* [util.py](../util/util.py) consists of simple helper functions such as `tensor2im` (convert a tensor array to a numpy image array), `diagnose_network` (calculate and print the mean of average absolute value of gradients), and `mkdirs` (create multiple directories).
================================================
FILE: docs/qa.md
================================================
## Frequently Asked Questions
Before you post a new question, please first look at the following Q & A and existing GitHub issues. You may also want to read [Training/Test tips](tips.md) for more suggestions.
#### Connection Error:HTTPConnectionPool ([#230](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/230), [#24](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/24), [#38](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/38))
Similar error messages include “Failed to establish a new connection/Connection refused”.
Please start the visdom server before starting the training:
```bash
python -m visdom.server
```
To install the visdom, you can use the following command:
```bash
pip install visdom
```
You can also disable the visdom by setting `--display_id 0`.
#### My PyTorch errors on CUDA related code.
Try to run the following code snippet to make sure that CUDA is working (assuming using PyTorch >= 0.4):
```python
import torch
torch.cuda.init()
print(torch.randn(1, device='cuda'))
```
If you met an error, it is likely that your PyTorch build does not work with CUDA, e.g., it is installed from the official MacOS binary, or you have a GPU that is too old and not supported anymore. You may run the the code with CPU using `--gpu_ids -1`.
#### TypeError: Object of type 'Tensor' is not JSON serializable ([#258](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/258))
Similar errors: AttributeError: module 'torch' has no attribute 'device' ([#314](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/314))
The current code only works with PyTorch 2.4+. An earlier PyTorch version can often cause the above errors.
#### ValueError: empty range for randrange() ([#390](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/390), [#376](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/376), [#194](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/194))
Similar error messages include "ConnectionRefusedError: [Errno 111] Connection refused"
It is related to the data augmentation step. It often happens when you use `--preprocess crop`. The program will crop random `crop_size x crop_size` patches out of the input training images. But if some of your image sizes (e.g., `256x384`) are smaller than the `crop_size` (e.g., 512), you will get this error. A simple fix will be to use other data augmentation methods such as `resize_and_crop` or `scale_width_and_crop`. Our program will automatically resize the images according to `load_size` before apply `crop_size x crop_size` cropping. Make sure that `load_size >= crop_size`.
#### Can I continue/resume my training? ([#350](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/350), [#275](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/275), [#234](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/234), [#87](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/87))
You can use the option `--continue_train`. Also set `--epoch_count` to specify a different starting epoch count. See more discussion in [training/test tips](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md#trainingtest-tips).
#### Why does my training loss not converge? ([#335](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/335), [#164](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/164), [#30](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/30))
Many GAN losses do not converge (exception: WGAN, WGAN-GP, etc. ) due to the nature of minimax optimization. For DCGAN and LSGAN objective, it is quite normal for the G and D losses to go up and down. It should be fine as long as they do not blow up.
#### How can I make it work for my own data (e.g., 16-bit png, tiff, hyperspectral images)? ([#309](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/309), [#320](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/), [#202](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/202))
The current code only supports RGB and grayscale images. If you would like to train the model on other data types, please follow the following steps:
- change the parameters `--input_nc` and `--output_nc` to the number of channels in your input/output images.
- Write your own custom data loader (It is easy as long as you know how to load your data with python). If you write a new data loader class, you need to change the flag `--dataset_mode` accordingly. Alternatively, you can modify the existing data loader. For aligned datasets, change this [line](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/data/aligned_dataset.py#L41); For unaligned datasets, change these two [lines](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/data/unaligned_dataset.py#L57).
- If you use visdom and HTML to visualize the results, you may also need to change the visualization code.
#### Multi-GPU Training ([#327](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/327), [#292](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/292), [#137](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/137), [#35](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/35))
You can use Multi-GPU training by setting `--gpu_ids` (e.g., `--gpu_ids 0,1,2,3` for the first four GPUs on your machine.) To fully utilize all the GPUs, you need to increase your batch size. Try `--batch_size 4`, `--batch_size 16`, or even a larger batch_size. Each GPU will process batch_size/#GPUs images. The optimal batch size depends on the number of GPUs you have, GPU memory per GPU, and the resolution of your training images.
We also recommend that you use the instance normalization for multi-GPU training by setting `--norm instance`. The current batch normalization might not work for multi-GPUs as the batchnorm parameters are not shared across different GPUs. Advanced users can try [synchronized batchnorm](https://github.com/vacancy/Synchronized-BatchNorm-PyTorch).
#### Can I run the model on CPU? ([#310](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/310))
Yes, you can set `--gpu_ids -1`. See [training/test tips](tips.md) for more details.
#### Are pre-trained models available? ([#10](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/10))
Yes, you can download pretrained models with the bash script `./scripts/download_cyclegan_model.sh`. See [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix#apply-a-pre-trained-model-cyclegan) for more details. We are slowly adding more models to the repo.
#### Out of memory ([#174](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/174))
CycleGAN is more memory-intensive than pix2pix as it requires two generators and two discriminators. If you would like to produce high-resolution images, you can do the following.
- During training, train CycleGAN on cropped images of the training set. Please be careful not to change the aspect ratio or the scale of the original image, as this can lead to the training/test gap. You can usually do this by using `--preprocess crop` option, or `--preprocess scale_width_and_crop`.
- Then at test time, you can load only one generator to produce the results in a single direction. This greatly saves GPU memory as you are not loading the discriminators and the other generator in the opposite direction. You can probably take the whole image as input. You can do this using `--model test --dataroot [path to the directory that contains your test images (e.g., ./datasets/horse2zebra/trainA)] --model_suffix _A --preprocess none`. You can use either `--preprocess none` or `--preprocess scale_width --crop_size [your_desired_image_width]`. Please see the [model_suffix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/test_model.py#L16) and [preprocess](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/data/base_dataset.py#L24) for more details.
#### RuntimeError: Error(s) in loading state_dict ([#812](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/812), [#671](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/671),[#461](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/461), [#296](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/296))
If you get the above errors when loading the generator during test time, you probably have used different network configurations for training and test. There are a few things to check: (1) the network architecture `--netG`: you will get an error if you use `--netG unet256` during training, and use `--netG resnet_6blocks` during test. Make sure that the flag is the same. (2) the normalization parameters `--norm`: we use different default `--norm` parameters for `--model cycle_gan`, `--model pix2pix`, and `--model test`. They might be different from the one you used in your training time. Make sure that you add the `--norm` flag in your test code. (3) If you use dropout during training time, make sure that you use the same Dropout setting in your test. Check the flag `--no_dropout`.
Note that we use different default generators, normalization, and dropout options for different models. The model file can overwrite the default arguments and add new arguments. For example, this [line](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/pix2pix_model.py#L32) adds and changes default arguments for pix2pix. For CycleGAN, the default is `--netG resnet_9blocks --no_dropout --norm instance --dataset_mode unaligned`. For pix2pix, the default is `--netG unet_256 --norm batch --dataset_mode aligned`. For model testing with single direction (`--model test`), the default is `--netG resnet_9blocks --norm instance --dataset_mode single`. To make sure that your training and test follow the same setting, you are encouraged to plicitly specify the `--netG`, `--norm`, `--dataset_mode`, and `--no_dropout` (or not) in your script.
#### NotSupportedError ([#829](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/829))
The error message states that `slicing multiple dimensions at the same time isn't supported yet proposals (Tensor): boxes to be encoded`. It is not related to our repo. It is often caused by incompatibility between the `torhvision` version and `pytorch` version. You need to re-intall or upgrade your `torchvision` to be compatible with the `pytorch` version.
#### What is the identity loss? ([#322](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/322), [#373](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/373), [#362](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/pull/362))
We use the identity loss for our photo to painting application. The identity loss can regularize the generator to be close to an identity mapping when fed with real samples from the _target_ domain. If something already looks like from the target domain, you should preserve the image without making additional changes. The generator trained with this loss will often be more conservative for unknown content. Please see more details in Sec 5.2 ''Photo generation from paintings'' and Figure 12 in the CycleGAN [paper](https://arxiv.org/pdf/1703.10593.pdf). The loss was first proposed in Equation 6 of the prior work [[Taigman et al., 2017]](https://arxiv.org/pdf/1611.02200.pdf).
#### The color gets inverted from the beginning of training ([#249](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/249))
The authors also observe that the generator unnecessarily inverts the color of the input image early in training, and then never learns to undo the inversion. In this case, you can try two things.
- First, try using identity loss `--lambda_identity 1.0` or `--lambda_identity 0.1`. We observe that the identity loss makes the generator to be more conservative and make fewer unnecessary changes. However, because of this, the change may not be as dramatic.
- Second, try smaller variance when initializing weights by changing `--init_gain`. We observe that a smaller variance in weight initialization results in less color inversion.
#### For labels2photo Cityscapes evaluation, why does the pretrained FCN-8s model not work well on the original Cityscapes input images? ([#150](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/150))
The model was trained on 256x256 images that are resized/upsampled to 1024x2048, so expected input images to the network are very blurry. The purpose of the resizing was to 1) keep the label maps in the original high resolution untouched and 2) avoid the need to change the standard FCN training code for Cityscapes.
#### How do I get the `ground-truth` numbers on the labels2photo Cityscapes evaluation? ([#150](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/150))
You need to resize the original Cityscapes images to 256x256 before running the evaluation code.
#### What is a good evaluation metric for CycleGAN? ([#730](https://github.com/pulls), [#716](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/716), [#166](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/166))
The evaluation metric highly depends on your specific task and dataset. There is no single metric that works for all the datasets and tasks.
There are a few popular choices: (1) we often evaluate CycleGAN on paired datasets (e.g., Cityscapes dataset and the meanIOU metric used in the CycleGAN paper), in which the model was trained without pairs. (2) Many researchers have adopted standard GAN metrics such as FID. Note that FID only evaluates the output images, while it ignores the correspondence between output and input. (3) A user study regarding photorealism might be helpful. Please check out the details of a user study in the CycleGAN paper (Section 5.1.1).
In summary, how to automatically evaluate the results is an open research problem for GANs research. But for many creative applications, the results are subjective and hard to quantify without humans in the loop.
#### What dose the CycleGAN loss look like if training goes well? ([#1096](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1096), [#1086](ttps://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1086), [#288](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/288), [#30](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/30))
Typically, the cycle-consistency loss and identity loss decrease during training, while GAN losses oscillate. To evaluate the quality of your results, you need to adopt additional evaluation metrics to your training and test images. See the Q & A above.
#### Using resize-conv to reduce checkerboard artifacts ([#190](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/190), [#64](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/64))
This Distill [blog](https://distill.pub/2016/deconv-checkerboard/) discussed one of the potential causes of the checkerboard artifacts. You can fix that issue by switching from "deconvolution" to nearest-neighbor upsampling, followed by regular convolution. Here is one implementation provided by [@SsnL](https://github.com/SsnL). You can replace the ConvTranspose2d with the following layers.
```python
nn.Upsample(scale_factor = 2, mode='bilinear'),
nn.ReflectionPad2d(1),
nn.Conv2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=1, padding=0),
```
We have also noticed that sometimes the checkboard artifacts will go away if you train long enough. Maybe you can try training your model a bit longer.
#### pix2pix/CycleGAN has no random noise z ([#152](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/152))
The current pix2pix/CycleGAN model does not take z as input. In both pix2pix and CycleGAN, we tried to add z to the generator: e.g., adding z to a latent state, concatenating with a latent state, applying dropout, etc., but often found the output did not vary significantly as a function of z. Conditional GANs do not need noise as long as the input is sufficiently complex so that the input can kind of play the role of noise. Without noise, the mapping is deterministic.
Please check out the following papers that show ways of getting z to actually have a substantial effect: e.g., [BicycleGAN](https://github.com/junyanz/BicycleGAN), [AugmentedCycleGAN](https://arxiv.org/abs/1802.10151), [MUNIT](https://arxiv.org/abs/1804.04732), [DRIT](https://arxiv.org/pdf/1808.00948.pdf), etc.
#### Experiment details (e.g., BW->color) ([#306](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/306))
You can find more training details and hyperparameter settings in the appendix of [CycleGAN](https://arxiv.org/abs/1703.10593) and [pix2pix](https://arxiv.org/abs/1611.07004) papers.
#### Results with [Cycada](https://arxiv.org/pdf/1711.03213.pdf)
We generated the [result of translating GTA images to Cityscapes-style images](https://junyanz.github.io/CycleGAN/) using our Torch repo. Our PyTorch and Torch implementation seemed to produce a little bit different results, although we have not measured the FCN score using the PyTorch-trained model. To reproduce the result of Cycada, please use the Torch repo for now.
#### Loading and using the saved model in your code
You can easily consume the model in your code using the below code snippet:
```python
import torch
from models.networks import define_G
from collections import OrderedDict
model_dict = torch.load("checkpoints/stars_pix2pix/latest_net_G.pth")
new_dict = OrderedDict()
for k, v in model_dict.items():
# load_state_dict expects keys with prefix 'module.'
new_dict["module." + k] = v
# make sure you pass the correct parameters to the define_G method
generator_model = define_G(input_nc=1,output_nc=1,ngf=64,netG="resnet_9blocks",
norm="batch",use_dropout=True,init_gain=0.02,gpu_ids=[0])
generator_model.load_state_dict(new_dict)
```
If everything goes well you should see a '\<All keys matched successfully\>' message.
================================================
FILE: docs/tips.md
================================================
## Training/test Tips
#### Training/test options
Please see `options/train_options.py` and `options/base_options.py` for the training flags; see `options/test_options.py` and `options/base_options.py` for the test flags. There are some model-specific flags as well, which are added in the model files, such as `--lambda_A` option in `model/cycle_gan_model.py`. The default values of these options are also adjusted in the model files.
#### CPU/GPU (default `--gpu_ids 0`)
Please set`--gpu_ids -1` to use CPU mode; set `--gpu_ids 0,1,2` for multi-GPU mode. You need a large batch size (e.g., `--batch_size 32`) to benefit from multiple GPUs.
#### Visualization
During training, the current results can be viewed using two methods. First, the intermediate results are saved to `[opt.checkpoints_dir]/[opt.name]/web/` as an HTML file. To avoid this, set `--no_html`. Second, if you set `--use_wandb`, the results and loss plots will appear on your Weights & Biases dashboard.
#### Preprocessing
Images can be resized and cropped in different ways using `--preprocess` option. The default option `'resize_and_crop'` resizes the image to be of size `(opt.load_size, opt.load_size)` and does a random crop of size `(opt.crop_size, opt.crop_size)`. `'crop'` skips the resizing step and only performs random cropping. `'scale_width'` resizes the image to have width `opt.crop_size` while keeping the aspect ratio. `'scale_width_and_crop'` first resizes the image to have width `opt.load_size` and then does random cropping of size `(opt.crop_size, opt.crop_size)`. `'none'` tries to skip all these preprocessing steps. However, if the image size is not a multiple of some number depending on the number of downsamplings of the generator, you will get an error because the size of the output image may be different from the size of the input image. Therefore, `'none'` option still tries to adjust the image size to be a multiple of 4. You might need a bigger adjustment if you change the generator architecture. Please see `data/base_dataset.py` do see how all these were implemented.
#### Fine-tuning/resume training
To fine-tune a pre-trained model, or resume the previous training, use the `--continue_train` flag. The program will then load the model based on `epoch`. By default, the program will initialize the epoch count as 1. Set `--epoch_count <int>` to specify a different starting epoch count.
#### Prepare your own datasets for CycleGAN
You need to create two directories to host images from domain A `/path/to/data/trainA` and from domain B `/path/to/data/trainB`. Then you can train the model with the dataset flag `--dataroot /path/to/data`. Optionally, you can create hold-out test datasets at `/path/to/data/testA` and `/path/to/data/testB` to test your model on unseen images.
#### Prepare your own datasets for pix2pix
Pix2pix's training requires paired data. We provide a python script to generate training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene. For example, these might be pairs {label map, photo} or {bw image, color image}. Then we can learn to translate A to B or B to A:
Create folder `/path/to/data` with subdirectories `A` and `B`. `A` and `B` should each have their own subdirectories `train`, `val`, `test`, etc. In `/path/to/data/A/train`, put training images in style A. In `/path/to/data/B/train`, put the corresponding images in style B. Repeat same for other data splits (`val`, `test`, etc).
Corresponding images in a pair {A,B} must be the same size and have the same filename, e.g., `/path/to/data/A/train/1.jpg` is considered to correspond to `/path/to/data/B/train/1.jpg`.
Once the data is formatted this way, call:
```bash
python datasets/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data
```
This will combine each pair of images (A,B) into a single image file, ready for training.
#### About image size
Since the generator architecture in CycleGAN involves a series of downsampling / upsampling operations, the size of the input and output image may not match if the input image size is not a multiple of 4. As a result, you may get a runtime error because the L1 identity loss cannot be enforced with images of different size. Therefore, we slightly resize the image to become multiples of 4 even with `--preprocess none` option. For the same reason, `--crop_size` needs to be a multiple of 4.
#### Training/Testing with high res images
CycleGAN is quite memory-intensive as four networks (two generators and two discriminators) need to be loaded on one GPU, so a large image cannot be entirely loaded. In this case, we recommend training with cropped images. For example, to generate 1024px results, you can train with `--preprocess scale_width_and_crop --load_size 1024 --crop_size 360`, and test with `--preprocess scale_width --load_size 1024`. This way makes sure the training and test will be at the same scale. At test time, you can afford higher resolution because you don’t need to load all networks.
#### Training/Testing with rectangular images
Both pix2pix and CycleGAN can work for rectangular images. To make them work, you need to use different preprocessing flags. Let's say that you are working with `360x256` images. During training, you can specify `--preprocess crop` and `--crop_size 256`. This will allow your model to be trained on randomly cropped `256x256` images during training time. During test time, you can apply the model on `360x256` images with the flag `--preprocess none`.
There are practical restrictions regarding image sizes for each generator architecture. For `unet256`, it only supports images whose width and height are divisible by 256. For `unet128`, the width and height need to be divisible by 128. For `resnet_6blocks` and `resnet_9blocks`, the width and height need to be divisible by 4.
#### About loss curve
Unfortunately, the loss curve does not reveal much information in training GANs, and CycleGAN is no exception. To check whether the training has converged or not, we recommend periodically generating a few samples and looking at them.
#### About batch size
For all experiments in the paper, we set the batch size to be 1. If there is room for memory, you can use higher batch size with batch norm or instance norm. (Note that the default batchnorm does not work well with multi-GPU training. You may consider using [synchronized batchnorm](https://github.com/vacancy/Synchronized-BatchNorm-PyTorch) instead). But please be aware that it can impact the training. In particular, even with Instance Normalization, different batch sizes can lead to different results. Moreover, increasing `--crop_size` may be a good alternative to increasing the batch size.
#### Notes on Colorization
No need to run `combine_A_and_B.py` for colorization. Instead, you need to prepare natural images and set `--dataset_mode colorization` and `--model colorization` in the script. The program will automatically convert each RGB image into Lab color space, and create `L -> ab` image pair during the training. Also set `--input_nc 1` and `--output_nc 2`. The training and test directory should be organized as `/your/data/train` and `your/data/test`. See example scripts `scripts/train_colorization.sh` and `scripts/test_colorization` for more details.
#### Notes on Extracting Edges
We provide python and Matlab scripts to extract coarse edges from photos. Run `scripts/edges/batch_hed.py` to compute [HED](https://github.com/s9xie/hed) edges. Run `scripts/edges/PostprocessHED.m` to simplify edges with additional post-processing steps. Check the code documentation for more details.
#### Evaluating Labels2Photos on Cityscapes
We provide scripts for running the evaluation of the Labels2Photos task on the Cityscapes **validation** set. We assume that you have installed `caffe` (and `pycaffe`) in your system. If not, see the [official website](http://caffe.berkeleyvision.org/installation.html) for installation instructions. Once `caffe` is successfully installed, download the pre-trained FCN-8s semantic segmentation model (512MB) by running
```bash
bash ./scripts/eval_cityscapes/download_fcn8s.sh
```
Then make sure `./scripts/eval_cityscapes/` is in your system's python path. If not, run the following command to add it
```bash
export PYTHONPATH=${PYTHONPATH}:./scripts/eval_cityscapes/
```
Now you can run the following command to evaluate your predictions:
```bash
python ./scripts/eval_cityscapes/evaluate.py --cityscapes_dir /path/to/original/cityscapes/dataset/ --result_dir /path/to/your/predictions/ --output_dir /path/to/output/directory/
```
Images stored under `--result_dir` should contain your model predictions on the Cityscapes **validation** split, and have the original Cityscapes naming convention (e.g., `frankfurt_000001_038418_leftImg8bit.png`). The script will output a text file under `--output_dir` containing the metric.
**Further notes**: Our pre-trained FCN model is **not** supposed to work on Cityscapes in the original resolution (1024x2048) as it was trained on 256x256 images that are then upsampled to 1024x2048 during training. The purpose of the resizing during training was to 1) keep the label maps in the original high resolution untouched and 2) avoid the need of changing the standard FCN training code and the architecture for Cityscapes. During test time, you need to synthesize 256x256 results. Our test code will automatically upsample your results to 1024x2048 before feeding them to the pre-trained FCN model. The output is at 1024x2048 resolution and will be compared to 1024x2048 ground truth labels. You do not need to resize the ground truth labels. The best way to verify whether everything is correct is to reproduce the numbers for real images in the paper first. To achieve it, you need to resize the original/real Cityscapes images (**not** labels) to 256x256 and feed them to the evaluation code.
================================================
FILE: environment.yml
================================================
name: pytorch-img2img
channels:
- pytorch
- conda-forge
- nvidia
dependencies:
- python=3.11
- pytorch=2.4.0
- torchvision=0.19.0
- pytorch-cuda=12.1
- numpy=1.24.3
- scikit-image
- pip
- pip:
- dominate>=2.8.0
- Pillow>=10.0.0
- wandb>=0.16.0
================================================
FILE: models/__init__.py
================================================
"""This package contains modules related to objective functions, optimizations, and network architectures.
To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel.
You need to implement the following five functions:
-- <__init__>: initialize the class; first call BaseModel.__init__(self, opt).
-- <set_input>: unpack data from dataset and apply preprocessing.
-- <forward>: produce intermediate results.
-- <optimize_parameters>: calculate loss, gradients, and update network weights.
-- <modify_commandline_options>: (optionally) add model-specific options and set default options.
In the function <__init__>, you need to define four lists:
-- self.loss_names (str list): specify the training losses that you want to plot and save.
-- self.model_names (str list): define networks used in our training.
-- self.visual_names (str list): specify the images that you want to display and save.
-- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an usage.
Now you can use the model class by specifying flag '--model dummy'.
See our template model class 'template_model.py' for more details.
"""
import importlib
from models.base_model import BaseModel
def find_model_using_name(model_name: str):
"""Import the module "models/[model_name]_model.py".
In the file, the class called DatasetNameModel() will
be instantiated. It has to be a subclass of BaseModel,
and it is case-insensitive.
"""
model_filename = "models." + model_name + "_model"
modellib = importlib.import_module(model_filename)
model = None
target_model_name = model_name.replace("_", "") + "model"
for name, cls in modellib.__dict__.items():
if name.lower() == target_model_name.lower() and issubclass(cls, BaseModel):
model = cls
if model is None:
print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name))
exit(0)
return model
def get_option_setter(model_name: str):
"""Return the static method <modify_commandline_options> of the model class."""
model_class = find_model_using_name(model_name)
return model_class.modify_commandline_options
def create_model(opt):
"""Create a model given the option."""
model = find_model_using_name(opt.model)
instance = model(opt)
print(f"model [{type(instance).__name__}] was created")
return instance
================================================
FILE: models/base_model.py
================================================
import os
import torch
import torch.distributed as dist
from pathlib import Path
from collections import OrderedDict
from abc import ABC, abstractmethod
from . import networks
class BaseModel(ABC):
"""This class is an abstract base class (ABC) for models.
To create a subclass, you need to implement the following five functions:
-- <__init__>: initialize the class; first call BaseModel.__init__(self, opt).
-- <set_input>: unpack data from dataset and apply preprocessing.
-- <forward>: produce intermediate results.
-- <optimize_parameters>: calculate losses, gradients, and update network weights.
-- <modify_commandline_options>: (optionally) add model-specific options and set default options.
"""
def __init__(self, opt):
"""Initialize the BaseModel class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
When creating your custom class, you need to implement your own initialization.
In this function, you should first call <BaseModel.__init__(self, opt)>
Then, you need to define four lists:
-- self.loss_names (str list): specify the training losses that you want to plot and save.
-- self.model_names (str list): define networks used in our training.
-- self.visual_names (str list): specify the images that you want to display and save.
-- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
"""
self.opt = opt
self.isTrain = opt.isTrain
self.save_dir = Path(opt.checkpoints_dir) / opt.name # save all the checkpoints to save_dir
self.device = opt.device
# with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark.
if opt.preprocess != "scale_width":
torch.backends.cudnn.benchmark = True
self.loss_names = []
self.model_names = []
self.visual_names = []
self.optimizers = []
self.image_paths = []
self.metric = 0 # used for learning rate policy 'plateau'
@staticmethod
def modify_commandline_options(parser, is_train):
"""Add new model-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
"""
return parser
@abstractmethod
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input (dict): includes the data itself and its metadata information.
"""
pass
@abstractmethod
def forward(self):
"""Run forward pass; called by both functions <optimize_parameters> and <test>."""
pass
@abstractmethod
def optimize_parameters(self):
"""Calculate losses, gradients, and update network weights; called in every training iteration"""
pass
def setup(self, opt):
"""Load and print networks; create schedulers
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
# Initialize all networks and load if needed
for name in self.model_names:
if isinstance(name, str):
net = getattr(self, "net" + name)
net = networks.init_net(net, opt.init_type, opt.init_gain)
# Load networks if needed
if not self.isTrain or opt.continue_train:
load_suffix = f"iter_{opt.load_iter}" if opt.load_iter > 0 else opt.epoch
load_filename = f"{load_suffix}_net_{name}.pth"
load_path = self.save_dir / load_filename
if isinstance(net, torch.nn.parallel.DistributedDataParallel):
net = net.module
print(f"loading the model from {load_path}")
state_dict = torch.load(load_path, map_location=str(self.device), weights_only=True)
if hasattr(state_dict, "_metadata"):
del state_dict._metadata
# patch InstanceNorm checkpoints
for key in list(state_dict.keys()):
self.__patch_instance_norm_state_dict(state_dict, net, key.split("."))
net.load_state_dict(state_dict)
# Move network to device
net.to(self.device)
# Wrap networks with DDP after loading
if dist.is_initialized():
# Check if using syncbatch normalization for DDP
if self.opt.norm == "syncbatch":
raise ValueError(f"For distributed training, opt.norm must be 'syncbatch' or 'inst', but got '{self.opt.norm}'. " "Please set --norm syncbatch for multi-GPU training.")
net = torch.nn.parallel.DistributedDataParallel(net, device_ids=[self.device.index])
# Sync all processes after DDP wrapping
dist.barrier()
setattr(self, "net" + name, net)
self.print_networks(opt.verbose)
if self.isTrain:
self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
def eval(self):
"""Make models eval mode during test time"""
for name in self.model_names:
if isinstance(name, str):
net = getattr(self, "net" + name)
net.eval()
def test(self):
"""Forward function used in test time.
This function wraps <forward> function in no_grad() so we don't save intermediate steps for backprop
It also calls <compute_visuals> to produce additional visualization results
"""
with torch.no_grad():
self.forward()
self.compute_visuals()
def compute_visuals(self):
"""Calculate additional output images for visdom and HTML visualization"""
pass
def get_image_paths(self):
"""Return image paths that are used to load current data"""
return self.image_paths
def update_learning_rate(self):
"""Update learning rates for all the networks; called at the end of every epoch"""
old_lr = self.optimizers[0].param_groups[0]["lr"]
for scheduler in self.schedulers:
if self.opt.lr_policy == "plateau":
scheduler.step(self.metric)
else:
scheduler.step()
lr = self.optimizers[0].param_groups[0]["lr"]
print(f"learning rate {old_lr:.7f} -> {lr:.7f}")
def get_current_visuals(self):
"""Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
visual_ret = OrderedDict()
for name in self.visual_names:
if isinstance(name, str):
visual_ret[name] = getattr(self, name)
return visual_ret
def get_current_losses(self):
"""Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
errors_ret = OrderedDict()
for name in self.loss_names:
if isinstance(name, str):
errors_ret[name] = float(getattr(self, "loss_" + name)) # float(...) works for both scalar tensor and float number
return errors_ret
def save_networks(self, epoch):
"""Save all the networks to the disk, unwrapping them first."""
# Only allow the main process (rank 0) to save the checkpoint
if not dist.is_initialized() or dist.get_rank() == 0:
for name in self.model_names:
if isinstance(name, str):
save_filename = f"{epoch}_net_{name}.pth"
save_path = self.save_dir / save_filename
net = getattr(self, "net" + name)
# 1. First, unwrap from DDP if it exists
if hasattr(net, "module"):
model_to_save = net.module
else:
model_to_save = net
# 2. Second, unwrap from torch.compile if it exists
if hasattr(model_to_save, "_orig_mod"):
model_to_save = model_to_save._orig_mod
# 3. Save the final, clean state_dict
torch.save(model_to_save.state_dict(), save_path)
def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
"""Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
key = keys[i]
if i + 1 == len(keys): # at the end, pointing to a parameter/buffer
if module.__class__.__name__.startswith("InstanceNorm") and (key == "running_mean" or key == "running_var"):
if getattr(module, key) is None:
state_dict.pop(".".join(keys))
if module.__class__.__name__.startswith("InstanceNorm") and (key == "num_batches_tracked"):
state_dict.pop(".".join(keys))
else:
self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
def load_networks(self, epoch):
"""Load all networks from the disk for DDP."""
for name in self.model_names:
if isinstance(name, str):
load_filename = f"{epoch}_net_{name}.pth"
load_path = self.save_dir / load_filename
net = getattr(self, "net" + name)
if isinstance(net, torch.nn.parallel.DistributedDataParallel):
net = net.module
print(f"loading the model from {load_path}")
state_dict = torch.load(load_path, map_location=str(self.device), weights_only=True)
if hasattr(state_dict, "_metadata"):
del state_dict._metadata
# patch InstanceNorm checkpoints
for key in list(state_dict.keys()):
self.__patch_instance_norm_state_dict(state_dict, net, key.split("."))
net.load_state_dict(state_dict)
# Add a barrier to sync all processes before continuing
if dist.is_initialized():
dist.barrier()
def print_networks(self, verbose):
"""Print the total number of parameters in the network and (if verbose) network architecture
Parameters:
verbose (bool) -- if verbose: print the network architecture
"""
print("---------- Networks initialized -------------")
for name in self.model_names:
if isinstance(name, str):
net = getattr(self, "net" + name)
num_params = 0
for param in net.parameters():
num_params += param.numel()
if verbose:
print(net)
print(f"[Network {name}] Total number of parameters : {num_params / 1e6:.3f} M")
print("-----------------------------------------------")
def set_requires_grad(self, nets, requires_grad=False):
"""Set requies_grad=Fasle for all the networks to avoid unnecessary computations
Parameters:
nets (network list) -- a list of networks
requires_grad (bool) -- whether the networks require gradients or not
"""
if not isinstance(nets, list):
nets = [nets]
for net in nets:
if net is not None:
for param in net.parameters():
param.requires_grad = requires_grad
def init_networks(self, init_type="normal", init_gain=0.02):
"""Initialize all networks: 1. move to device; 2. initialize weights
Parameters:
init_type (str) -- initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal
"""
import os
for name in self.model_names:
if isinstance(name, str):
net = getattr(self, "net" + name)
# Move to device
if torch.cuda.is_available():
if "LOCAL_RANK" in os.environ:
local_rank = int(os.environ["LOCAL_RANK"])
net.to(local_rank)
print(f"Initialized network {name} with device cuda:{local_rank}")
else:
net.to(0)
print(f"Initialized network {name} with device cuda:0")
else:
net.to("cpu")
print(f"Initialized network {name} with device cpu")
# Initialize weights using networks function
networks.init_weights(net, init_type, init_gain)
================================================
FILE: models/colorization_model.py
================================================
from .pix2pix_model import Pix2PixModel
import torch
from skimage import color # used for lab2rgb
import numpy as np
class ColorizationModel(Pix2PixModel):
"""This is a subclass of Pix2PixModel for image colorization (black & white image -> colorful images).
The model training requires '-dataset_model colorization' dataset.
It trains a pix2pix model, mapping from L channel to ab channels in Lab color space.
By default, the colorization dataset will automatically set '--input_nc 1' and '--output_nc 2'.
"""
@staticmethod
def modify_commandline_options(parser, is_train=True):
"""Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
By default, we use 'colorization' dataset for this model.
See the original pix2pix paper (https://arxiv.org/pdf/1611.07004.pdf) and colorization results (Figure 9 in the paper)
"""
Pix2PixModel.modify_commandline_options(parser, is_train)
parser.set_defaults(dataset_mode="colorization")
return parser
def __init__(self, opt):
"""Initialize the class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
For visualization, we set 'visual_names' as 'real_A' (input real image),
'real_B_rgb' (ground truth RGB image), and 'fake_B_rgb' (predicted RGB image)
We convert the Lab image 'real_B' (inherited from Pix2pixModel) to a RGB image 'real_B_rgb'.
we convert the Lab image 'fake_B' (inherited from Pix2pixModel) to a RGB image 'fake_B_rgb'.
"""
# reuse the pix2pix model
Pix2PixModel.__init__(self, opt)
# specify the images to be visualized.
self.visual_names = ["real_A", "real_B_rgb", "fake_B_rgb"]
def lab2rgb(self, L, AB):
"""Convert an Lab tensor image to a RGB numpy output
Parameters:
L (1-channel tensor array): L channel images (range: [-1, 1], torch tensor array)
AB (2-channel tensor array): ab channel images (range: [-1, 1], torch tensor array)
Returns:
rgb (RGB numpy image): rgb output images (range: [0, 255], numpy array)
"""
AB2 = AB * 110.0
L2 = (L + 1.0) * 50.0
Lab = torch.cat([L2, AB2], dim=1)
Lab = Lab[0].data.cpu().float().numpy()
Lab = np.transpose(Lab.astype(np.float64), (1, 2, 0))
rgb = color.lab2rgb(Lab) * 255
return rgb
def compute_visuals(self):
"""Calculate additional output images for visdom and HTML visualization"""
self.real_B_rgb = self.lab2rgb(self.real_A, self.real_B)
self.fake_B_rgb = self.lab2rgb(self.real_A, self.fake_B)
================================================
FILE: models/cycle_gan_model.py
================================================
import torch
import itertools
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
class CycleGANModel(BaseModel):
"""
This class implements the CycleGAN model, for learning image-to-image translation without paired data.
The model training requires '--dataset_mode unaligned' dataset.
By default, it uses a '--netG resnet_9blocks' ResNet generator,
a '--netD basic' discriminator (PatchGAN introduced by pix2pix),
and a least-square GANs objective ('--gan_mode lsgan').
CycleGAN paper: https://arxiv.org/pdf/1703.10593.pdf
"""
@staticmethod
def modify_commandline_options(parser, is_train=True):
"""Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
For CycleGAN, in addition to GAN losses, we introduce lambda_A, lambda_B, and lambda_identity for the following losses.
A (source domain), B (target domain).
Generators: G_A: A -> B; G_B: B -> A.
Discriminators: D_A: G_A(A) vs. B; D_B: G_B(B) vs. A.
Forward cycle loss: lambda_A * ||G_B(G_A(A)) - A|| (Eqn. (2) in the paper)
Backward cycle loss: lambda_B * ||G_A(G_B(B)) - B|| (Eqn. (2) in the paper)
Identity loss (optional): lambda_identity * (||G_A(B) - B|| * lambda_B + ||G_B(A) - A|| * lambda_A) (Sec 5.2 "Photo generation from paintings" in the paper)
Dropout is not used in the original CycleGAN paper.
"""
parser.set_defaults(no_dropout=True) # default CycleGAN did not use dropout
if is_train:
parser.add_argument("--lambda_A", type=float, default=10.0, help="weight for cycle loss (A -> B -> A)")
parser.add_argument("--lambda_B", type=float, default=10.0, help="weight for cycle loss (B -> A -> B)")
parser.add_argument(
"--lambda_identity",
type=float,
default=0.5,
help="use identity mapping. Setting lambda_identity other than 0 has an effect of scaling the weight of the identity mapping loss. For example, if the weight of the identity loss should be 10 times smaller than the weight of the reconstruction loss, please set lambda_identity = 0.1",
)
return parser
def __init__(self, opt):
"""Initialize the CycleGAN class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseModel.__init__(self, opt)
# specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
self.loss_names = ["D_A", "G_A", "cycle_A", "idt_A", "D_B", "G_B", "cycle_B", "idt_B"]
# specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
visual_names_A = ["real_A", "fake_B", "rec_A"]
visual_names_B = ["real_B", "fake_A", "rec_B"]
if self.isTrain and self.opt.lambda_identity > 0.0: # if identity loss is used, we also visualize idt_B=G_A(B) ad idt_A=G_B(A)
visual_names_A.append("idt_B")
visual_names_B.append("idt_A")
self.visual_names = visual_names_A + visual_names_B # combine visualizations for A and B
# specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>.
if self.isTrain:
self.model_names = ["G_A", "G_B", "D_A", "D_B"]
else: # during test time, only load Gs
self.model_names = ["G_A", "G_B"]
# define networks (both Generators and discriminators)
# The naming is different from those used in the paper.
# Code (vs. paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)
self.netG_A = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain)
self.netG_B = networks.define_G(opt.output_nc, opt.input_nc, opt.ngf, opt.netG, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain)
if self.isTrain: # define discriminators
self.netD_A = networks.define_D(opt.output_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain)
self.netD_B = networks.define_D(opt.input_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain)
if self.isTrain:
if opt.lambda_identity > 0.0: # only works when input and output images have the same number of channels
assert opt.input_nc == opt.output_nc
self.fake_A_pool = ImagePool(opt.pool_size) # create image buffer to store previously generated images
self.fake_B_pool = ImagePool(opt.pool_size) # create image buffer to store previously generated images
# define loss functions
self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device) # define GAN loss.
self.criterionCycle = torch.nn.L1Loss()
self.criterionIdt = torch.nn.L1Loss()
# initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
self.optimizer_G = torch.optim.Adam(itertools.chain(self.netG_A.parameters(), self.netG_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_D = torch.optim.Adam(itertools.chain(self.netD_A.parameters(), self.netD_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D)
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input (dict): include the data itself and its metadata information.
The option 'direction' can be used to swap domain A and domain B.
"""
AtoB = self.opt.direction == "AtoB"
self.real_A = input["A" if AtoB else "B"].to(self.device)
self.real_B = input["B" if AtoB else "A"].to(self.device)
self.image_paths = input["A_paths" if AtoB else "B_paths"]
def forward(self):
"""Run forward pass; called by both functions <optimize_parameters> and <test>."""
self.fake_B = self.netG_A(self.real_A) # G_A(A)
self.rec_A = self.netG_B(self.fake_B) # G_B(G_A(A))
self.fake_A = self.netG_B(self.real_B) # G_B(B)
self.rec_B = self.netG_A(self.fake_A) # G_A(G_B(B))
def backward_D_basic(self, netD, real, fake):
"""Calculate GAN loss for the discriminator
Parameters:
netD (network) -- the discriminator D
real (tensor array) -- real images
fake (tensor array) -- images generated by a generator
Return the discriminator loss.
We also call loss_D.backward() to calculate the gradients.
"""
# Real
pred_real = netD(real)
loss_D_real = self.criterionGAN(pred_real, True)
# Fake
pred_fake = netD(fake.detach())
loss_D_fake = self.criterionGAN(pred_fake, False)
# Combined loss and calculate gradients
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
return loss_D
def backward_D_A(self):
"""Calculate GAN loss for discriminator D_A"""
fake_B = self.fake_B_pool.query(self.fake_B)
self.loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, fake_B)
def backward_D_B(self):
"""Calculate GAN loss for discriminator D_B"""
fake_A = self.fake_A_pool.query(self.fake_A)
self.loss_D_B = self.backward_D_basic(self.netD_B, self.real_A, fake_A)
def backward_G(self):
"""Calculate the loss for generators G_A and G_B"""
lambda_idt = self.opt.lambda_identity
lambda_A = self.opt.lambda_A
lambda_B = self.opt.lambda_B
# Identity loss
if lambda_idt > 0:
# G_A should be identity if real_B is fed: ||G_A(B) - B||
self.idt_A = self.netG_A(self.real_B)
self.loss_idt_A = self.criterionIdt(self.idt_A, self.real_B) * lambda_B * lambda_idt
# G_B should be identity if real_A is fed: ||G_B(A) - A||
self.idt_B = self.netG_B(self.real_A)
self.loss_idt_B = self.criterionIdt(self.idt_B, self.real_A) * lambda_A * lambda_idt
else:
self.loss_idt_A = 0
self.loss_idt_B = 0
# GAN loss D_A(G_A(A))
self.loss_G_A = self.criterionGAN(self.netD_A(self.fake_B), True)
# GAN loss D_B(G_B(B))
self.loss_G_B = self.criterionGAN(self.netD_B(self.fake_A), True)
# Forward cycle loss || G_B(G_A(A)) - A||
self.loss_cycle_A = self.criterionCycle(self.rec_A, self.real_A) * lambda_A
# Backward cycle loss || G_A(G_B(B)) - B||
self.loss_cycle_B = self.criterionCycle(self.rec_B, self.real_B) * lambda_B
# combined loss and calculate gradients
self.loss_G = self.loss_G_A + self.loss_G_B + self.loss_cycle_A + self.loss_cycle_B + self.loss_idt_A + self.loss_idt_B
self.loss_G.backward()
def optimize_parameters(self):
"""Calculate losses, gradients, and update network weights; called in every training iteration"""
# forward
self.forward() # compute fake images and reconstruction images.
# G_A and G_B
self.set_requires_grad([self.netD_A, self.netD_B], False) # Ds require no gradients when optimizing Gs
self.optimizer_G.zero_grad() # set G_A and G_B's gradients to zero
self.backward_G() # calculate gradients for G_A and G_B
self.optimizer_G.step() # update G_A and G_B's weights
# D_A and D_B
self.set_requires_grad([self.netD_A, self.netD_B], True)
self.optimizer_D.zero_grad() # set D_A and D_B's gradients to zero
self.backward_D_A() # calculate gradients for D_A
self.backward_D_B() # calculate graidents for D_B
self.optimizer_D.step() # update D_A and D_B's weights
================================================
FILE: models/networks.py
================================================
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
###############################################################################
# Helper Functions
###############################################################################
class Identity(nn.Module):
def forward(self, x):
return x
def get_norm_layer(norm_type="instance"):
"""Return a normalization layer
Parameters:
norm_type (str) -- the name of the normalization layer: batch | instance | none
For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
"""
if norm_type == "batch":
norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
elif norm_type == "syncbatch":
norm_layer = functools.partial(nn.SyncBatchNorm, affine=True, track_running_stats=True)
elif norm_type == "instance":
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
elif norm_type == "none":
def norm_layer(x):
return Identity()
else:
raise NotImplementedError("normalization layer [%s] is not found" % norm_type)
return norm_layer
def get_scheduler(optimizer, opt):
"""Return a learning rate scheduler
Parameters:
optimizer -- the optimizer of the network
opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．
opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
For 'linear', we keep the same learning rate for the first <opt.n_epochs> epochs
and linearly decay the rate to zero over the next <opt.n_epochs_decay> epochs.
For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
See https://pytorch.org/docs/stable/optim.html for more details.
"""
if opt.lr_policy == "linear":
def lambda_rule(epoch):
lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)
return lr_l
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
elif opt.lr_policy == "step":
scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
elif opt.lr_policy == "plateau":
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.2, threshold=0.01, patience=5)
elif opt.lr_policy == "cosine":
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
else:
return NotImplementedError("learning rate policy [%s] is not implemented", opt.lr_policy)
return scheduler
def init_weights(net, init_type="normal", init_gain=0.02):
"""Initialize network weights.
Parameters:
net (network) -- network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
def init_func(m): # define the initialization function
classname = m.__class__.__name__
if hasattr(m, "weight") and (classname.find("Conv") != -1 or classname.find("Linear") != -1):
if init_type == "normal":
init.normal_(m.weight.data, 0.0, init_gain)
elif init_type == "xavier":
init.xavier_normal_(m.weight.data, gain=init_gain)
elif init_type == "kaiming":
init.kaiming_normal_(m.weight.data, a=0, mode="fan_in")
elif init_type == "orthogonal":
init.orthogonal_(m.weight.data, gain=init_gain)
else:
raise NotImplementedError("initialization method [%s] is not implemented" % init_type)
if hasattr(m, "bias") and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find("BatchNorm2d") != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
init.normal_(m.weight.data, 1.0, init_gain)
init.constant_(m.bias.data, 0.0)
print("initialize network with %s" % init_type)
net.apply(init_func) # apply the initialization function <init_func>
def init_net(net, init_type="normal", init_gain=0.02):
"""Initialize a network: 1. register CPU/GPU device; 2. initialize the network weights
Parameters:
net (network) -- the network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
gain (float) -- scaling factor for normal, xavier and orthogonal.
Return an initialized network.
"""
import os
if torch.cuda.is_available():
if "LOCAL_RANK" in os.environ:
local_rank = int(os.environ["LOCAL_RANK"])
net.to(local_rank)
print(f"Initialized with device cuda:{local_rank}")
else:
net.to(0)
print("Initialized with device cuda:0")
init_weights(net, init_type, init_gain=init_gain)
return net
def define_G(input_nc, output_nc, ngf, netG, norm="batch", use_dropout=False, init_type="normal", init_gain=0.02):
"""Create a generator
Parameters:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
ngf (int) -- the number of filters in the last conv layer
netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_128 | unet_256
norm (str) -- the name of normalization layers used in the network: batch | instance | none
use_dropout (bool) -- if use dropout layers.
init_type (str) -- the name of our initialization method.
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
Returns a generator
"""
net = None
norm_layer = get_norm_layer(norm_type=norm)
if netG == "resnet_9blocks":
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
elif netG == "resnet_6blocks":
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
elif netG == "unet_128":
net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
elif netG == "unet_256":
net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
else:
raise NotImplementedError("Generator model name [%s] is not recognized" % netG)
return net
def define_D(input_nc, ndf, netD, n_layers_D=3, norm="batch", init_type="normal", init_gain=0.02):
"""Create a discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the first conv layer
netD (str) -- the architecture's name: basic | n_layers | pixel
n_layers_D (int) -- the number of conv layers in the discriminator; effective when netD=='n_layers'
norm (str) -- the type of normalization layers used in the network.
init_type (str) -- the name of the initialization method.
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
Returns a discriminator
Our current implementation provides three types of discriminators:
[basic]: 'PatchGAN' classifier described in the original pix2pix paper.
It can classify whether 70×70 overlapping patches are real or fake.
Such a patch-level discriminator architecture has fewer parameters
than a full-image discriminator and can work on arbitrarily-sized images
in a fully convolutional fashion.
[n_layers]: With this mode, you can specify the number of conv layers in the discriminator
with the parameter <n_layers_D> (default=3 as used in [basic] (PatchGAN).)
[pixel]: 1x1 PixelGAN discriminator can classify whether a pixel is real or not.
It encourages greater color diversity but has no effect on spatial statistics.
The discriminator has been initialized by <init_net>. It uses Leakly RELU for non-linearity.
"""
net = None
norm_layer = get_norm_layer(norm_type=norm)
if netD == "basic": # default PatchGAN classifier
net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer)
elif netD == "n_layers": # more options
net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer)
elif netD == "pixel": # classify if each pixel is real or fake
net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer)
else:
raise NotImplementedError("Discriminator model name [%s] is not recognized" % netD)
return net
##############################################################################
# Classes
##############################################################################
class GANLoss(nn.Module):
"""Define different GAN objectives.
The GANLoss class abstracts away the need to create the target label tensor
that has the same size as the input.
"""
def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
"""Initialize the GANLoss class.
Parameters:
gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
target_real_label (bool) - - label for a real image
target_fake_label (bool) - - label of a fake image
Note: Do not use sigmoid as the last layer of Discriminator.
LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
"""
super(GANLoss, self).__init__()
self.register_buffer("real_label", torch.tensor(target_real_label))
self.register_buffer("fake_label", torch.tensor(target_fake_label))
self.gan_mode = gan_mode
if gan_mode == "lsgan":
self.loss = nn.MSELoss()
elif gan_mode == "vanilla":
self.loss = nn.BCEWithLogitsLoss()
elif gan_mode in ["wgangp"]:
self.loss = None
else:
raise NotImplementedError("gan mode %s not implemented" % gan_mode)
def get_target_tensor(self, prediction, target_is_real):
"""Create label tensors with the same size as the input.
Parameters:
prediction (tensor) - - tpyically the prediction from a discriminator
target_is_real (bool) - - if the ground truth label is for real images or fake images
Returns:
A label tensor filled with ground truth label, and with the size of the input
"""
if target_is_real:
target_tensor = self.real_label
else:
target_tensor = self.fake_label
return target_tensor.expand_as(prediction)
def __call__(self, prediction, target_is_real):
"""Calculate loss given Discriminator's output and grount truth labels.
Parameters:
prediction (tensor) - - tpyically the prediction output from a discriminator
target_is_real (bool) - - if the ground truth label is for real images or fake images
Returns:
the calculated loss.
"""
if self.gan_mode in ["lsgan", "vanilla"]:
target_tensor = self.get_target_tensor(prediction, target_is_real)
loss = self.loss(prediction, target_tensor)
elif self.gan_mode == "wgangp":
if target_is_real:
loss = -prediction.mean()
else:
loss = prediction.mean()
return loss
def cal_gradient_penalty(netD, real_data, fake_data, device, type="mixed", constant=1.0, lambda_gp=10.0):
"""Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028
Arguments:
netD (network) -- discriminator network
real_data (tensor array) -- real images
fake_data (tensor array) -- generated images from the generator
device (str) -- GPU / CPU
type (str) -- if we mix real and fake data or not [real | fake | mixed].
constant (float) -- the constant used in formula ( ||gradient||_2 - constant)^2
lambda_gp (float) -- weight for this loss
Returns the gradient penalty loss
"""
if lambda_gp > 0.0:
if type == "real": # either use real images, fake images, or a linear interpolation of two.
interpolatesv = real_data
elif type == "fake":
interpolatesv = fake_data
elif type == "mixed":
alpha = torch.rand(real_data.shape[0], 1, device=device)
alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(*real_data.shape)
interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
else:
raise NotImplementedError(f"{type} not implemented")
interpolatesv.requires_grad_(True)
disc_interpolates = netD(interpolatesv)
gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv, grad_outputs=torch.ones(disc_interpolates.size()).to(device), create_graph=True, retain_graph=True, only_inputs=True)
gradients = gradients[0].view(real_data.size(0), -1) # flat the data
gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp # added eps
return gradient_penalty, gradients
else:
return 0.0, None
class ResnetGenerator(nn.Module):
"""Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
"""
def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type="reflect"):
"""Construct a Resnet-based generator
Parameters:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
ngf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers
n_blocks (int) -- the number of ResNet blocks
padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero
"""
assert n_blocks >= 0
super(ResnetGenerator, self).__init__()
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias), norm_layer(ngf), nn.ReLU(True)]
n_downsampling = 2
for i in range(n_downsampling): # add downsampling layers
mult = 2**i
model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias), norm_layer(ngf * mult * 2), nn.ReLU(True)]
mult = 2**n_downsampling
for i in range(n_blocks): # add ResNet blocks
model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
for i in range(n_downsampling): # add upsampling layers
mult = 2 ** (n_downsampling - i)
model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1, bias=use_bias), norm_layer(int(ngf * mult / 2)), nn.ReLU(True)]
model += [nn.ReflectionPad2d(3)]
model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
model += [nn.Tanh()]
self.model = nn.Sequential(*model)
def forward(self, input):
"""Standard forward"""
return self.model(input)
class ResnetBlock(nn.Module):
"""Define a Resnet block"""
def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
"""Initialize the Resnet block
A resnet block is a conv block with skip connections
We construct a conv block with build_conv_block function,
and implement skip connections in <forward> function.
Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
"""
super(ResnetBlock, self).__init__()
self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)
def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
"""Construct a convolutional block.
Parameters:
dim (int) -- the number of channels in the conv layer.
padding_type (str) -- the name of padding layer: reflect | replicate | zero
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers.
use_bias (bool) -- if the conv layer uses bias or not
Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
"""
conv_block = []
p = 0
if padding_type == "reflect":
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == "replicate":
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == "zero":
p = 1
else:
raise NotImplementedError("padding [%s] is not implemented" % padding_type)
conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]
if use_dropout:
conv_block += [nn.Dropout(0.5)]
p = 0
if padding_type == "reflect":
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == "replicate":
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == "zero":
p = 1
else:
raise NotImplementedError("padding [%s] is not implemented" % padding_type)
conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)]
return nn.Sequential(*conv_block)
def forward(self, x):
"""Forward function (with skip connections)"""
out = x + self.conv_block(x) # add skip connections
return out
class UnetGenerator(nn.Module):
"""Create a Unet-based generator"""
def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False):
"""Construct a Unet generator
Parameters:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7,
image of size 128x128 will become of size 1x1 # at the bottleneck
ngf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
We construct the U-Net from the innermost layer to the outermost layer.
It is a recursive process.
"""
super(UnetGenerator, self).__init__()
# construct unet structure
unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # add the innermost layer
for i in range(num_downs - 5): # add intermediate layers with ngf * 8 filters
unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
# gradually reduce the number of filters from ngf * 8 to ngf
unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # add the outermost layer
def forward(self, input):
"""Standard forward"""
return self.model(input)
class UnetSkipConnectionBlock(nn.Module):
"""Defines the Unet submodule with skip connection.
X -------------------identity----------------------
|-- downsampling -- |submodule| -- upsampling --|
"""
def __init__(self, outer_nc, inner_nc, input_nc=None, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
"""Construct a Unet submodule with skip connections.
Parameters:
outer_nc (int) -- the number of filters in the outer conv layer
inner_nc (int) -- the number of filters in the inner conv layer
input_nc (int) -- the number of channels in input images/features
submodule (UnetSkipConnectionBlock) -- previously defined submodules
outermost (bool) -- if this module is the outermost module
innermost (bool) -- if this module is the innermost module
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers.
"""
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
if input_nc is None:
input_nc = outer_nc
downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
downrelu = nn.LeakyReLU(0.2, True)
downnorm = norm_layer(inner_nc)
uprelu = nn.ReLU(True)
upnorm = norm_layer(outer_nc)
if outermost:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1)
down = [downconv]
up = [uprelu, upconv, nn.Tanh()]
model = down + [submodule] + up
elif innermost:
upconv = nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
down = [downrelu, downconv]
up = [uprelu, upconv, upnorm]
model = down + up
else:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
down = [downrelu, downconv, downnorm]
up = [uprelu, upconv, upnorm]
if use_dropout:
model = down + [submodule] + up + [nn.Dropout(0.5)]
else:
model = down + [submodule] + up
self.model = nn.Sequential(*model)
def forward(self, x):
if self.outermost:
return self.model(x)
else: # add skip connections
return torch.cat([x, self.model(x)], 1)
class NLayerDiscriminator(nn.Module):
"""Defines a PatchGAN discriminator"""
def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d):
"""Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
norm_layer -- normalization layer
"""
super(NLayerDiscriminator, self).__init__()
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
kw = 4
padw = 1
sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
nf_mult = 1
nf_mult_prev = 1
for n in range(1, n_layers): # gradually increase the number of filters
nf_mult_prev = nf_mult
nf_mult = min(2**n, 8)
sequence += [nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True)]
nf_mult_prev = nf_mult
nf_mult = min(2**n_layers, 8)
sequence += [nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True)]
sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map
self.model = nn.Sequential(*sequence)
def forward(self, input):
"""Standard forward."""
return self.model(input)
class PixelDiscriminator(nn.Module):
"""Defines a 1x1 PatchGAN discriminator (pixelGAN)"""
def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d):
"""Construct a 1x1 PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
"""
super(PixelDiscriminator, self).__init__()
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
self.net = [
nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias),
norm_layer(ndf * 2),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias),
]
self.net = nn.Sequential(*self.net)
def forward(self, input):
"""Standard forward."""
return self.net(input)
================================================
FILE: models/pix2pix_model.py
================================================
import torch
from .base_model import BaseModel
from . import networks
class Pix2PixModel(BaseModel):
"""This class implements the pix2pix model, for learning a mapping from input images to output images given paired data.
The model training requires '--dataset_mode aligned' dataset.
By default, it uses a '--netG unet256' U-Net generator,
a '--netD basic' discriminator (PatchGAN),
and a '--gan_mode' vanilla GAN loss (the cross-entropy objective used in the orignal GAN paper).
pix2pix paper: https://arxiv.org/pdf/1611.07004.pdf
"""
@staticmethod
def modify_commandline_options(parser, is_train=True):
"""Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
For pix2pix, we do not use image buffer
The training objective is: GAN Loss + lambda_L1 * ||G(A)-B||_1
By default, we use vanilla GAN loss, UNet with batchnorm, and aligned datasets.
"""
# changing the default values to match the pix2pix paper (https://phillipi.github.io/pix2pix/)
parser.set_defaults(norm="batch", netG="unet_256", dataset_mode="aligned")
if is_train:
parser.set_defaults(pool_size=0, gan_mode="vanilla")
parser.add_argument("--lambda_L1", type=float, default=100.0, help="weight for L1 loss")
return parser
def __init__(self, opt):
"""Initialize the pix2pix class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseModel.__init__(self, opt)
# specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
self.loss_names = ["G_GAN", "G_L1", "D_real", "D_fake"]
# specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
self.visual_names = ["real_A", "fake_B", "real_B"]
# specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>
if self.isTrain:
self.model_names = ["G", "D"]
else: # during test time, only load G
self.model_names = ["G"]
self.device = opt.device
# define networks (both generator and discriminator)
self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain)
if self.isTrain: # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
self.netD = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain)
if self.isTrain:
# define loss functions
self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device) # move to the device for custom loss
self.criterionL1 = torch.nn.L1Loss()
# initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D)
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input (dict): include the data itself and its metadata information.
The option 'direction' can be used to swap images in domain A and domain B.
"""
AtoB = self.opt.direction == "AtoB"
self.real_A = input["A" if AtoB else "B"].to(self.device)
self.real_B = input["B" if AtoB else "A"].to(self.device)
self.image_paths = input["A_paths" if AtoB else "B_paths"]
def forward(self):
"""Run forward pass; called by both functions <optimize_parameters> and <test>."""
self.fake_B = self.netG(self.real_A) # G(A)
def backward_D(self):
"""Calculate GAN loss for the discriminator"""
# Fake; stop backprop to the generator by detaching fake_B
fake_AB = torch.cat((self.real_A, self.fake_B), 1) # we use conditional GANs; we need to feed both input and output to the discriminator
pred_fake = self.netD(fake_AB.detach())
self.loss_D_fake = self.criterionGAN(pred_fake, False)
# Real
real_AB = torch.cat((self.real_A, self.real_B), 1)
pred_real = self.netD(real_AB)
self.loss_D_real = self.criterionGAN(pred_real, True)
# combine loss and calculate gradients
self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5
self.loss_D.backward()
def backward_G(self):
"""Calculate GAN and L1 loss for the generator"""
# First, G(A) should fake the discriminator
fake_AB = torch.cat((self.real_A, self.fake_B), 1)
pred_fake = self.netD(fake_AB)
self.loss_G_GAN = self.criterionGAN(pred_fake, True)
# Second, G(A) = B
self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1
# combine loss and calculate gradients
self.loss_G = self.loss_G_GAN + self.loss_G_L1
self.loss_G.backward()
def optimize_parameters(self):
self.forward() # compute fake images: G(A)
# update D
self.set_requires_grad(self.netD, True) # enable backprop for D
self.optimizer_D.zero_grad() # set D's gradients to zero
self.backward_D() # calculate gradients for D
self.optimizer_D.step() # update D's weights
# update G
self.set_requires_grad(self.netD, False) # D requires no gradients when optimizing G
self.optimizer_G.zero_grad() # set G's gradients to zero
self.backward_G() # calculate graidents for G
self.optimizer_G.step() # update G's weights
================================================
FILE: models/template_model.py
================================================
"""Model class template
This module provides a template for users to implement custom models.
You can specify '--model template' to use this model.
The class name should be consistent with both the filename and its model option.
The filename should be <model>_dataset.py
The class name should be <Model>Dataset.py
It implements a simple image-to-image translation baseline based on regression loss.
Given input-output pairs (data_A, data_B), it learns a network netG that can minimize the following L1 loss:
min_<netG> ||netG(data_A) - data_B||_1
You need to implement the following functions:
<modify_commandline_options>: Add model-specific options and rewrite default values for existing options.
<__init__>: Initialize this model class.
<set_input>: Unpack input data and perform data pre-processing.
<forward>: Run forward pass. This will be called by both <optimize_parameters> and <test>.
<optimize_parameters>: Update network weights; it will be called in every training iteration.
"""
import torch
from .base_model import BaseModel
from . import networks
class TemplateModel(BaseModel):
@staticmethod
def modify_commandline_options(parser, is_train=True):
"""Add new model-specific options and rewrite default values for existing options.
Parameters:
parser -- the option parser
is_train -- if it is training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
"""
parser.set_defaults(dataset_mode="aligned") # You can rewrite default values for this model. For example, this model usually uses aligned dataset as its dataset.
if is_train:
parser.add_argument("--lambda_regression", type=float, default=1.0, help="weight for the regression loss") # You can define new arguments for this model.
return parser
def __init__(self, opt):
"""Initialize this model class.
Parameters:
opt -- training/test options
A few things can be done here.
- (required) call the initialization function of BaseModel
- define loss function, visualization images, model names, and optimizers
"""
BaseModel.__init__(self, opt) # call the initialization method of BaseModel
# specify the training losses you want to print out. The program will call base_model.get_current_losses to plot the losses to the console and save them to the disk.
self.loss_names = ["G"]
# specify the images you want to save and display. The program will call base_model.get_current_visuals to save and display these images.
self.visual_names = ["data_A", "data_B", "output"]
# specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks to save and load networks.
# you can use opt.isTrain to specify different behaviors for training and test. For example, some networks will not be used during test, and you don't need to load them.
self.model_names = ["G"]
# define networks; you can use opt.isTrain to specify different behaviors for training and test.
self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG)
if self.isTrain: # only defined during training time
# define your loss functions. You can use losses provided by torch.nn such as torch.nn.L1Loss.
# We also provide a GANLoss class "networks.GANLoss". self.criterionGAN = networks.GANLoss().to(self.device)
self.criterionLoss = torch.nn.L1Loss()
# define and initialize optimizers. You can define one optimizer for each network.
# If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
self.optimizer = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizers = [self.optimizer]
# Our program will automatically call <model.setup> to define schedulers, load networks, and print networks
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input: a dictionary that contains the data itself and its metadata information.
"""
AtoB = self.opt.direction == "AtoB" # use <direction> to swap data_A and data_B
self.data_A = input["A" if AtoB else "B"].to(self.device) # get image data A
self.data_B = input["B" if AtoB else "A"].to(self.device) # get image data B
self.image_paths = input["A_paths" if AtoB else "B_paths"] # get image paths
def forward(self):
"""Run forward pass. This will be called by both functions <optimize_parameters> and <test>."""
self.output = self.netG(self.data_A) # generate output image given the input data_A
def backward(self):
"""Calculate losses, gradients, and update network weights; called in every training iteration"""
# caculate the intermediate results if necessary; here self.output has been computed during function <forward>
# calculate loss given the input and intermediate results
self.loss_G = self.criterionLoss(self.output, self.data_B) * self.opt.lambda_regression
self.loss_G.backward() # calculate gradients of network G w.r.t. loss_G
def optimize_parameters(self):
"""Update network weights; it will be called in every training iteration."""
self.forward() # first call forward to calculate intermediate results
self.optimizer.zero_grad() # clear network G's existing gradients
self.backward() # calculate gradients for network G
self.optimizer.step() # update gradients for network G
================================================
FILE: models/test_model.py
================================================
from .base_model import BaseModel
from . import networks
class TestModel(BaseModel):
"""This TesteModel can be used to generate CycleGAN results for only one direction.
This model will automatically set '--dataset_mode single', which only loads the images from one collection.
See the test instruction for more details.
"""
@staticmethod
def modify_commandline_options(parser, is_train=True):
"""Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
The model can only be used during test time. It requires '--dataset_mode single'.
You need to specify the network using the option '--model_suffix'.
"""
assert not is_train, "TestModel cannot be used during training time"
parser.set_defaults(dataset_mode="single")
parser.add_argument("--model_suffix", type=str, default="", help="In checkpoints_dir, [epoch]_net_G[model_suffix].pth will be loaded as the generator.")
return parser
def __init__(self, opt):
"""Initialize the pix2pix class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
assert not opt.isTrain
BaseModel.__init__(self, opt)
# specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
self.loss_names = []
# specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
self.visual_names = ["real", "fake"]
# specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>
self.model_names = ["G" + opt.model_suffix] # only generator is needed.
self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain)
# assigns the model to self.netG_[suffix] so that it can be loaded
# please see <BaseModel.load_networks>
setattr(self, "netG" + opt.model_suffix, self.netG) # store netG in self.
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input: a dictionary that contains the data itself and its metadata information.
We need to use 'single_dataset' dataset mode. It only load images from one domain.
"""
self.real = input["A"].to(self.device)
self.image_paths = input["A_paths"]
def forward(self):
"""Run forward pass."""
self.fake = self.netG(self.real) # G(real)
def optimize_parameters(self):
"""No optimization for test model."""
pass
================================================
FILE: options/__init__.py
================================================
"""This package options includes option modules: training options, test options, and basic options (used in both training and test)."""
================================================
FILE: options/base_options.py
================================================
import argparse
from pathlib import Path
from util import util
import torch
import models
import data
class BaseOptions:
"""This class defines options used during both training and test time.
It also implements several helper functions such as parsing, printing, and saving the options.
It also gathers additional options defined in <modify_commandline_options> functions in both dataset class and model class.
"""
def __init__(self):
"""Reset the class; indicates the class hasn't been initailized"""
self.initialized = False
def initialize(self, parser):
"""Define the common options that are used in both training and test."""
# basic parameters
parser.add_argument("--dataroot", required=True, help="path to images (should have subfolders trainA, trainB, valA, valB, etc)")
parser.add_argument("--name", type=str, default="experiment_name", help="name of the experiment. It decides where to store samples and models")
parser.add_argument("--checkpoints_dir", type=str, default="./checkpoints", help="models are saved here")
# model parameters
parser.add_argument("--model", type=str, default="cycle_gan", help="chooses which model to use. [cycle_gan | pix2pix | test | colorization]")
parser.add_argument("--input_nc", type=int, default=3, help="# of input image channels: 3 for RGB and 1 for grayscale")
parser.add_argument("--output_nc", type=int, default=3, help="# of output image channels: 3 for RGB and 1 for grayscale")
parser.add_argument("--ngf", type=int, default=64, help="# of gen filters in the last conv layer")
parser.add_argument("--ndf", type=int, default=64, help="# of discrim filters in the first conv layer")
parser.add_argument("--netD", type=str, default="basic", help="specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator")
parser.add_argument("--netG", type=str, default="resnet_9blocks", help="specify generator architecture [resnet_9blocks | resnet_6blocks | unet_256 | unet_128]")
parser.add_argument("--n_layers_D", type=int, default=3, help="only used if netD==n_layers")
parser.add_argument("--norm", type=str, default="instance", help="instance normalization or batch normalization [instance | batch | none | syncbatch]")
parser.add_argument("--init_type", type=str, default="normal", help="network initialization [normal | xavier | kaiming | orthogonal]")
parser.add_argument("--init_gain", type=float, default=0.02, help="scaling factor for normal, xavier and orthogonal.")
parser.add_argument("--no_dropout", action="store_true", help="no dropout for the generator")
# dataset parameters
parser.add_argument("--dataset_mode", type=str, default="unaligned", help="chooses how datasets are loaded. [unaligned | aligned | single | colorization]")
parser.add_argument("--direction", type=str, default="AtoB", help="AtoB or BtoA")
parser.add_argument("--serial_batches", action="store_true", help="if true, takes images in order to make batches, otherwise takes them randomly")
parser.add_argument("--num_threads", default=4, type=int, help="# threads for loading data")
parser.add_argument("--batch_size", type=int, default=1, help="input batch size")
parser.add_argument("--load_size", type=int, default=286, help="scale images to this size")
parser.add_argument("--crop_size", type=int, default=256, help="then crop to this size")
parser.add_argument("--max_dataset_size", type=int, default=float("inf"), help="Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.")
parser.add_argument("--preprocess", type=str, default="resize_and_crop", help="scaling and cropping of images at load time [resize_and_crop | crop | scale_width | scale_width_and_crop | none]")
parser.add_argument("--no_flip", action="store_true", help="if specified, do not flip the images for data augmentation")
parser.add_argument("--display_winsize", type=int, default=256, help="display window size for both visdom and HTML")
# additional parameters
parser.add_argument("--epoch", type=str, default="latest", help="which epoch to load? set to latest to use latest cached model")
parser.add_argument("--load_iter", type=int, default="0", help="which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]")
parser.add_argument("--verbose", action="store_true", help="if specified, print more debugging information")
parser.add_argument("--suffix", default="", type=str, help="customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}")
# wandb parameters
parser.add_argument("--use_wandb", action="store_true", help="if specified, then init wandb logging")
parser.add_argument("--wandb_project_name", type=str, default="CycleGAN-and-pix2pix", help="specify wandb project name")
self.initialized = True
return parser
def gather_options(self):
"""Initialize our parser with basic options(only once).
Add additional model-specific and dataset-specific options.
These options are defined in the <modify_commandline_options> function
in model and dataset classes.
"""
if not self.initialized: # check if it has been initialized
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = self.initialize(parser)
# get the basic options
opt, _ = parser.parse_known_args()
# modify model-related parser options
model_name = opt.model
model_option_setter = models.get_option_setter(model_name)
parser = model_option_setter(parser, self.isTrain)
opt, _ = parser.parse_known_args() # parse again with new defaults
# mod
gitextract_c042vuns/
├── .gitignore
├── .replit
├── CycleGAN.ipynb
├── LICENSE
├── README.md
├── data/
│ ├── __init__.py
│ ├── aligned_dataset.py
│ ├── base_dataset.py
│ ├── colorization_dataset.py
│ ├── image_folder.py
│ ├── single_dataset.py
│ ├── template_dataset.py
│ └── unaligned_dataset.py
├── docs/
│ ├── Dockerfile
│ ├── README_es.md
│ ├── datasets.md
│ ├── docker.md
│ ├── overview.md
│ ├── qa.md
│ └── tips.md
├── environment.yml
├── models/
│ ├── __init__.py
│ ├── base_model.py
│ ├── colorization_model.py
│ ├── cycle_gan_model.py
│ ├── networks.py
│ ├── pix2pix_model.py
│ ├── template_model.py
│ └── test_model.py
├── options/
│ ├── __init__.py
│ ├── base_options.py
│ ├── test_options.py
│ └── train_options.py
├── pix2pix.ipynb
├── scripts/
│ ├── conda_deps.sh
│ ├── download_cyclegan_model.sh
│ ├── download_pix2pix_model.sh
│ ├── edges/
│ │ ├── PostprocessHED.m
│ │ └── batch_hed.py
│ ├── eval_cityscapes/
│ │ ├── caffemodel/
│ │ │ └── deploy.prototxt
│ │ ├── cityscapes.py
│ │ ├── download_fcn8s.sh
│ │ ├── evaluate.py
│ │ └── util.py
│ ├── install_deps.sh
│ ├── test_before_push.py
│ ├── test_colorization.sh
│ ├── test_cyclegan.sh
│ ├── test_pix2pix.sh
│ ├── test_single.sh
│ ├── train_colorization.sh
│ ├── train_cyclegan.sh
│ └── train_pix2pix.sh
├── test.py
├── train.py
└── util/
├── __init__.py
├── get_data.py
├── html.py
├── image_pool.py
├── util.py
└── visualizer.py
SYMBOL INDEX (211 symbols across 29 files)
FILE: data/__init__.py
function find_dataset_using_name (line 22) | def find_dataset_using_name(dataset_name):
function get_option_setter (line 44) | def get_option_setter(dataset_name):
function create_dataset (line 50) | def create_dataset(opt):
class CustomDatasetDataLoader (line 65) | class CustomDatasetDataLoader:
method __init__ (line 68) | def __init__(self, opt):
method load_data (line 90) | def load_data(self):
method __len__ (line 93) | def __len__(self):
method __iter__ (line 97) | def __iter__(self):
method set_epoch (line 104) | def set_epoch(self, epoch):
FILE: data/aligned_dataset.py
class AlignedDataset (line 7) | class AlignedDataset(BaseDataset):
method __init__ (line 14) | def __init__(self, opt):
method __getitem__ (line 27) | def __getitem__(self, index):
method __len__ (line 58) | def __len__(self):
FILE: data/base_dataset.py
class BaseDataset (line 14) | class BaseDataset(data.Dataset, ABC):
method __init__ (line 24) | def __init__(self, opt):
method modify_commandline_options (line 34) | def modify_commandline_options(parser, is_train):
method __len__ (line 47) | def __len__(self):
method __getitem__ (line 52) | def __getitem__(self, index):
function get_params (line 64) | def get_params(opt, size):
function get_transform (line 82) | def get_transform(opt, params=None, grayscale=False, method=transforms.I...
function __transforms2pil_resize (line 116) | def __transforms2pil_resize(method):
function __make_power_2 (line 126) | def __make_power_2(img, base, method=transforms.InterpolationMode.BICUBIC):
function __scale_width (line 138) | def __scale_width(img, target_size, crop_size, method=transforms.Interpo...
function __crop (line 148) | def __crop(img, pos, size):
function __flip (line 157) | def __flip(img, flip):
function __print_size_warning (line 163) | def __print_size_warning(ow, oh, w, h):
FILE: data/colorization_dataset.py
class ColorizationDataset (line 10) | class ColorizationDataset(BaseDataset):
method modify_commandline_options (line 17) | def modify_commandline_options(parser, is_train):
method __init__ (line 33) | def __init__(self, opt):
method __getitem__ (line 45) | def __getitem__(self, index):
method __len__ (line 67) | def __len__(self):
FILE: data/image_folder.py
function is_image_file (line 29) | def is_image_file(filename):
function make_dataset (line 33) | def make_dataset(dir, max_dataset_size=float("inf")):
function default_loader (line 44) | def default_loader(path):
class ImageFolder (line 48) | class ImageFolder(data.Dataset):
method __init__ (line 50) | def __init__(self, root, transform=None, return_paths=False, loader=de...
method __getitem__ (line 61) | def __getitem__(self, index):
method __len__ (line 71) | def __len__(self):
FILE: data/single_dataset.py
class SingleDataset (line 6) | class SingleDataset(BaseDataset):
method __init__ (line 12) | def __init__(self, opt):
method __getitem__ (line 23) | def __getitem__(self, index):
method __len__ (line 38) | def __len__(self):
FILE: data/template_dataset.py
class TemplateDataset (line 21) | class TemplateDataset(BaseDataset):
method modify_commandline_options (line 25) | def modify_commandline_options(parser, is_train):
method __init__ (line 39) | def __init__(self, opt):
method __getitem__ (line 57) | def __getitem__(self, index):
method __len__ (line 76) | def __len__(self):
FILE: data/unaligned_dataset.py
class UnalignedDataset (line 8) | class UnalignedDataset(BaseDataset):
method __init__ (line 19) | def __init__(self, opt):
method __getitem__ (line 39) | def __getitem__(self, index):
method __len__ (line 65) | def __len__(self):
FILE: models/__init__.py
function find_model_using_name (line 25) | def find_model_using_name(model_name: str):
function get_option_setter (line 47) | def get_option_setter(model_name: str):
function create_model (line 53) | def create_model(opt):
FILE: models/base_model.py
class BaseModel (line 10) | class BaseModel(ABC):
method __init__ (line 20) | def __init__(self, opt):
method modify_commandline_options (line 49) | def modify_commandline_options(parser, is_train):
method set_input (line 62) | def set_input(self, input):
method forward (line 71) | def forward(self):
method optimize_parameters (line 76) | def optimize_parameters(self):
method setup (line 80) | def setup(self, opt):
method eval (line 132) | def eval(self):
method test (line 139) | def test(self):
method compute_visuals (line 149) | def compute_visuals(self):
method get_image_paths (line 153) | def get_image_paths(self):
method update_learning_rate (line 157) | def update_learning_rate(self):
method get_current_visuals (line 169) | def get_current_visuals(self):
method get_current_losses (line 177) | def get_current_losses(self):
method save_networks (line 185) | def save_networks(self, epoch):
method __patch_instance_norm_state_dict (line 209) | def __patch_instance_norm_state_dict(self, state_dict, module, keys, i...
method load_networks (line 221) | def load_networks(self, epoch):
method print_networks (line 248) | def print_networks(self, verbose):
method set_requires_grad (line 266) | def set_requires_grad(self, nets, requires_grad=False):
method init_networks (line 279) | def init_networks(self, init_type="normal", init_gain=0.02):
FILE: models/colorization_model.py
class ColorizationModel (line 7) | class ColorizationModel(Pix2PixModel):
method modify_commandline_options (line 16) | def modify_commandline_options(parser, is_train=True):
method __init__ (line 33) | def __init__(self, opt):
method lab2rgb (line 49) | def lab2rgb(self, L, AB):
method compute_visuals (line 66) | def compute_visuals(self):
FILE: models/cycle_gan_model.py
class CycleGANModel (line 8) | class CycleGANModel(BaseModel):
method modify_commandline_options (line 21) | def modify_commandline_options(parser, is_train=True):
method __init__ (line 53) | def __init__(self, opt):
method set_input (line 101) | def set_input(self, input):
method forward (line 114) | def forward(self):
method backward_D_basic (line 121) | def backward_D_basic(self, netD, real, fake):
method backward_D_A (line 143) | def backward_D_A(self):
method backward_D_B (line 148) | def backward_D_B(self):
method backward_G (line 153) | def backward_G(self):
method optimize_parameters (line 182) | def optimize_parameters(self):
FILE: models/networks.py
class Identity (line 13) | class Identity(nn.Module):
method forward (line 14) | def forward(self, x):
function get_norm_layer (line 18) | def get_norm_layer(norm_type="instance"):
function get_scheduler (line 43) | def get_scheduler(optimizer, opt):
function init_weights (line 74) | def init_weights(net, init_type="normal", init_gain=0.02):
function init_net (line 109) | def init_net(net, init_type="normal", init_gain=0.02):
function define_G (line 132) | def define_G(input_nc, output_nc, ngf, netG, norm="batch", use_dropout=F...
function define_D (line 163) | def define_D(input_nc, ndf, netD, n_layers_D=3, norm="batch", init_type=...
class GANLoss (line 209) | class GANLoss(nn.Module):
method __init__ (line 216) | def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=...
method get_target_tensor (line 240) | def get_target_tensor(self, prediction, target_is_real):
method __call__ (line 257) | def __call__(self, prediction, target_is_real):
function cal_gradient_penalty (line 278) | def cal_gradient_penalty(netD, real_data, fake_data, device, type="mixed...
class ResnetGenerator (line 313) | class ResnetGenerator(nn.Module):
method __init__ (line 319) | def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNor...
method forward (line 359) | def forward(self, input):
class ResnetBlock (line 364) | class ResnetBlock(nn.Module):
method __init__ (line 367) | def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
method build_conv_block (line 378) | def build_conv_block(self, dim, padding_type, norm_layer, use_dropout,...
method forward (line 418) | def forward(self, x):
class UnetGenerator (line 424) | class UnetGenerator(nn.Module):
method __init__ (line 427) | def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=...
method forward (line 451) | def forward(self, input):
class UnetSkipConnectionBlock (line 456) | class UnetSkipConnectionBlock(nn.Module):
method __init__ (line 462) | def __init__(self, outer_nc, inner_nc, input_nc=None, submodule=None, ...
method forward (line 511) | def forward(self, x):
class NLayerDiscriminator (line 518) | class NLayerDiscriminator(nn.Module):
method __init__ (line 521) | def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNo...
method forward (line 553) | def forward(self, input):
class PixelDiscriminator (line 558) | class PixelDiscriminator(nn.Module):
method __init__ (line 561) | def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d):
method forward (line 586) | def forward(self, input):
FILE: models/pix2pix_model.py
class Pix2PixModel (line 6) | class Pix2PixModel(BaseModel):
method modify_commandline_options (line 18) | def modify_commandline_options(parser, is_train=True):
method __init__ (line 40) | def __init__(self, opt):
method set_input (line 73) | def set_input(self, input):
method forward (line 86) | def forward(self):
method backward_D (line 90) | def backward_D(self):
method backward_G (line 104) | def backward_G(self):
method optimize_parameters (line 116) | def optimize_parameters(self):
FILE: models/template_model.py
class TemplateModel (line 24) | class TemplateModel(BaseModel):
method modify_commandline_options (line 26) | def modify_commandline_options(parser, is_train=True):
method __init__ (line 42) | def __init__(self, opt):
method set_input (line 73) | def set_input(self, input):
method forward (line 84) | def forward(self):
method backward (line 88) | def backward(self):
method optimize_parameters (line 95) | def optimize_parameters(self):
FILE: models/test_model.py
class TestModel (line 5) | class TestModel(BaseModel):
method modify_commandline_options (line 13) | def modify_commandline_options(parser, is_train=True):
method __init__ (line 32) | def __init__(self, opt):
method set_input (line 52) | def set_input(self, input):
method forward (line 63) | def forward(self):
method optimize_parameters (line 67) | def optimize_parameters(self):
FILE: options/base_options.py
class BaseOptions (line 9) | class BaseOptions:
method __init__ (line 16) | def __init__(self):
method initialize (line 20) | def initialize(self, parser):
method gather_options (line 62) | def gather_options(self):
method print_options (line 90) | def print_options(self, opt):
method parse (line 115) | def parse(self):
FILE: options/test_options.py
class TestOptions (line 4) | class TestOptions(BaseOptions):
method initialize (line 10) | def initialize(self, parser):
FILE: options/train_options.py
class TrainOptions (line 4) | class TrainOptions(BaseOptions):
method initialize (line 10) | def initialize(self, parser):
FILE: scripts/edges/batch_hed.py
function parse_args (line 21) | def parse_args():
FILE: scripts/eval_cityscapes/cityscapes.py
class cityscapes (line 9) | class cityscapes:
method __init__ (line 10) | def __init__(self, data_path):
method get_dset (line 24) | def get_dset(self, split):
method load_image (line 37) | def load_image(self, split, city, idx):
method assign_trainIds (line 41) | def assign_trainIds(self, label):
method load_label (line 55) | def load_label(self, split, city, idx):
method preprocess (line 66) | def preprocess(self, im):
method palette (line 80) | def palette(self, label):
method make_boundaries (line 95) | def make_boundaries(label, thickness=None):
method list_label_frames (line 110) | def list_label_frames(self, split):
method collect_frame_sequence (line 126) | def collect_frame_sequence(self, split, idx, length):
FILE: scripts/eval_cityscapes/evaluate.py
function main (line 21) | def main():
FILE: scripts/eval_cityscapes/util.py
function get_out_scoremap (line 5) | def get_out_scoremap(net):
function feed_net (line 9) | def feed_net(net, in_):
function segrun (line 17) | def segrun(net, in_):
function fast_hist (line 23) | def fast_hist(a, b, n):
function get_scores (line 32) | def get_scores(hist):
FILE: scripts/test_before_push.py
class TestBeforePush (line 7) | class TestBeforePush:
method setup_datasets (line 11) | def setup_datasets(self):
method test_pretrained_cyclegan_model (line 22) | def test_pretrained_cyclegan_model(self):
method test_pretrained_pix2pix_model (line 34) | def test_pretrained_pix2pix_model(self):
method test_cyclegan_train_test (line 49) | def test_cyclegan_train_test(self):
method test_pix2pix_train_test (line 68) | def test_pix2pix_train_test(self):
method test_template_train_test (line 87) | def test_template_train_test(self):
method test_colorization_train_test (line 106) | def test_colorization_train_test(self):
FILE: util/get_data.py
class GetData (line 10) | class GetData(object):
method __init__ (line 26) | def __init__(self, technique="cyclegan", verbose=True):
method _print (line 34) | def _print(self, text):
method _get_options (line 39) | def _get_options(r):
method _present_options (line 44) | def _present_options(self):
method _download_data (line 53) | def _download_data(self, dataset_url, save_path):
method get (line 77) | def get(self, save_path, dataset=None):
FILE: util/html.py
class HTML (line 6) | class HTML:
method __init__ (line 14) | def __init__(self, web_dir, title, refresh=0):
method get_image_dir (line 34) | def get_image_dir(self):
method add_header (line 38) | def add_header(self, text):
method add_images (line 47) | def add_images(self, ims, txts, links, width=400):
method save (line 67) | def save(self):
FILE: util/image_pool.py
class ImagePool (line 5) | class ImagePool:
method __init__ (line 12) | def __init__(self, pool_size):
method query (line 23) | def query(self, images):
FILE: util/util.py
function tensor2im (line 12) | def tensor2im(input_image, imtype=np.uint8):
function diagnose_network (line 33) | def diagnose_network(net, name="network"):
function init_ddp (line 53) | def init_ddp():
function cleanup_ddp (line 73) | def cleanup_ddp():
function save_image (line 78) | def save_image(image_numpy, image_path, aspect_ratio=1.0):
function print_numpy (line 96) | def print_numpy(x, val=True, shp=False):
function mkdirs (line 111) | def mkdirs(paths):
function mkdir (line 124) | def mkdir(path):
FILE: util/visualizer.py
function save_images (line 12) | def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
class Visualizer (line 40) | class Visualizer:
method __init__ (line 46) | def __init__(self, opt):
method reset (line 85) | def reset(self):
method set_dataset_size (line 89) | def set_dataset_size(self, dataset_size):
method _calculate_global_step (line 93) | def _calculate_global_step(self, epoch, epoch_iter):
method display_current_results (line 98) | def display_current_results(self, visuals, epoch: int, total_iters: in...
method plot_current_losses (line 134) | def plot_current_losses(self, total_iters, losses):
method print_current_losses (line 148) | def print_current_losses(self, epoch, iters, losses, t_comp, t_data):
Condensed preview — 61 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (287K chars).
[
{
"path": ".gitignore",
"chars": 768,
"preview": ".DS_Store\ndebug*\ndatasets/\ncheckpoints/\nresults/\nbuild/\ndist/\n*.png\ntorch.egg-info/\n*/**/__pycache__\ntorch/version.py\nto"
},
{
"path": ".replit",
"chars": 794,
"preview": "language = \"python3\"\nrun = \"<p><a href=\\\"https://github.com/affinelayer/pix2pix-tensorflow\\\"> [Tensorflow]</a> (by Chris"
},
{
"path": "CycleGAN.ipynb",
"chars": 8001,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"view-in-github\"\n }"
},
{
"path": "LICENSE",
"chars": 3565,
"preview": "Copyright (c) 2017, Jun-Yan Zhu and Taesung Park\nAll rights reserved.\n\nRedistribution and use in source and binary forms"
},
{
"path": "README.md",
"chars": 16742,
"preview": "<img src='imgs/horse2zebra.gif' align=\"right\" width=384>\n\n<br><br><br>\n\n# CycleGAN and pix2pix in PyTorch\n\n**Udpate in 2"
},
{
"path": "data/__init__.py",
"chars": 4210,
"preview": "\"\"\"This package includes all the modules related to data loading and preprocessing\n\n To add a custom dataset class calle"
},
{
"path": "data/aligned_dataset.py",
"chars": 2495,
"preview": "import os\nfrom data.base_dataset import BaseDataset, get_params, get_transform\nfrom data.image_folder import make_datase"
},
{
"path": "data/base_dataset.py",
"chars": 5848,
"preview": "\"\"\"This module implements an abstract base class (ABC) 'BaseDataset' for datasets.\n\nIt also includes common transformati"
},
{
"path": "data/colorization_dataset.py",
"chars": 2717,
"preview": "import os\nfrom data.base_dataset import BaseDataset, get_transform\nfrom data.image_folder import make_dataset\nfrom skima"
},
{
"path": "data/image_folder.py",
"chars": 1857,
"preview": "\"\"\"A modified image folder class\n\nWe modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/ma"
},
{
"path": "data/single_dataset.py",
"chars": 1495,
"preview": "from data.base_dataset import BaseDataset, get_transform\nfrom data.image_folder import make_dataset\nfrom PIL import Imag"
},
{
"path": "data/template_dataset.py",
"chars": 3503,
"preview": "\"\"\"Dataset class template\n\nThis module provides a template for users to implement custom datasets.\nYou can specify '--da"
},
{
"path": "data/unaligned_dataset.py",
"chars": 3285,
"preview": "import os\nfrom data.base_dataset import BaseDataset, get_transform\nfrom data.image_folder import make_dataset\nfrom PIL i"
},
{
"path": "docs/Dockerfile",
"chars": 725,
"preview": "FROM nvidia/cuda:10.1-base\n\n#Nvidia Public GPG Key\nRUN apt-key adv --fetch-keys https://developer.download.nvidia.com/co"
},
{
"path": "docs/README_es.md",
"chars": 16397,
"preview": "<img src='https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/raw/master/imgs/horse2zebra.gif' align=\"right\" width=3"
},
{
"path": "docs/datasets.md",
"chars": 4998,
"preview": "\n\n### CycleGAN Datasets\nDownload the CycleGAN datasets using the following script. Some of the datasets are collected by"
},
{
"path": "docs/docker.md",
"chars": 1156,
"preview": "# Docker image with pytorch-CycleGAN-and-pix2pix\n\nWe provide both Dockerfile and pre-built Docker container that can run"
},
{
"path": "docs/overview.md",
"chars": 10930,
"preview": "## Overview of Code Structure\nTo help users better understand and use our codebase, we briefly overview the functionalit"
},
{
"path": "docs/qa.md",
"chars": 18242,
"preview": "## Frequently Asked Questions\n\nBefore you post a new question, please first look at the following Q & A and existing Git"
},
{
"path": "docs/tips.md",
"chars": 9982,
"preview": "## Training/test Tips\n#### Training/test options\nPlease see `options/train_options.py` and `options/base_options.py` for"
},
{
"path": "environment.yml",
"chars": 284,
"preview": "name: pytorch-img2img\nchannels:\n - pytorch\n - conda-forge\n - nvidia\ndependencies:\n - python=3.11\n - pytorch=2.4.0\n "
},
{
"path": "models/__init__.py",
"chars": 2829,
"preview": "\"\"\"This package contains modules related to objective functions, optimizations, and network architectures.\n\nTo add a cus"
},
{
"path": "models/base_model.py",
"chars": 13422,
"preview": "import os\nimport torch\nimport torch.distributed as dist\nfrom pathlib import Path\nfrom collections import OrderedDict\nfro"
},
{
"path": "models/colorization_model.py",
"chars": 3014,
"preview": "from .pix2pix_model import Pix2PixModel\nimport torch\nfrom skimage import color # used for lab2rgb\nimport numpy as np\n\n\n"
},
{
"path": "models/cycle_gan_model.py",
"chars": 10381,
"preview": "import torch\nimport itertools\nfrom util.image_pool import ImagePool\nfrom .base_model import BaseModel\nfrom . import netw"
},
{
"path": "models/networks.py",
"chars": 26797,
"preview": "import torch\nimport torch.nn as nn\nfrom torch.nn import init\nimport functools\nfrom torch.optim import lr_scheduler\n\n\n###"
},
{
"path": "models/pix2pix_model.py",
"chars": 6406,
"preview": "import torch\nfrom .base_model import BaseModel\nfrom . import networks\n\n\nclass Pix2PixModel(BaseModel):\n \"\"\"This class"
},
{
"path": "models/template_model.py",
"chars": 5888,
"preview": "\"\"\"Model class template\n\nThis module provides a template for users to implement custom models.\nYou can specify '--model "
},
{
"path": "models/test_model.py",
"chars": 3098,
"preview": "from .base_model import BaseModel\nfrom . import networks\n\n\nclass TestModel(BaseModel):\n \"\"\"This TesteModel can be use"
},
{
"path": "options/__init__.py",
"chars": 136,
"preview": "\"\"\"This package options includes option modules: training options, test options, and basic options (used in both trainin"
},
{
"path": "options/base_options.py",
"chars": 7911,
"preview": "import argparse\nfrom pathlib import Path\nfrom util import util\nimport torch\nimport models\nimport data\n\n\nclass BaseOption"
},
{
"path": "options/test_options.py",
"chars": 1158,
"preview": "from .base_options import BaseOptions\n\n\nclass TestOptions(BaseOptions):\n \"\"\"This class includes test options.\n\n It"
},
{
"path": "options/train_options.py",
"chars": 2791,
"preview": "from .base_options import BaseOptions\n\n\nclass TrainOptions(BaseOptions):\n \"\"\"This class includes training options.\n\n "
},
{
"path": "pix2pix.ipynb",
"chars": 7122,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"view-in-github\"\n }"
},
{
"path": "scripts/conda_deps.sh",
"chars": 223,
"preview": "set -ex\nconda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing\nconda install pytorch torchvision -c pyt"
},
{
"path": "scripts/download_cyclegan_model.sh",
"chars": 577,
"preview": "FILE=$1\n\necho \"Note: available models are apple2orange, orange2apple, summer2winter_yosemite, winter2summer_yosemite, ho"
},
{
"path": "scripts/download_pix2pix_model.sh",
"chars": 339,
"preview": "FILE=$1\n\necho \"Note: available models are edges2shoes, sat2map, map2sat, facades_label2photo, and day2night\"\necho \"Speci"
},
{
"path": "scripts/edges/PostprocessHED.m",
"chars": 2063,
"preview": "%%% Prerequisites\n% You need to get the cpp file edgesNmsMex.cpp from https://raw.githubusercontent.com/pdollar/edges/ma"
},
{
"path": "scripts/edges/batch_hed.py",
"chars": 3521,
"preview": "# HED batch processing script; modified from https://github.com/s9xie/hed/blob/master/examples/hed/HED-tutorial.ipynb\n# "
},
{
"path": "scripts/eval_cityscapes/caffemodel/deploy.prototxt",
"chars": 10843,
"preview": "layer {\n name: \"data\"\n type: \"Input\"\n top: \"data\"\n input_param {\n shape {\n dim: 1\n dim: 3\n dim: 50"
},
{
"path": "scripts/eval_cityscapes/cityscapes.py",
"chars": 5772,
"preview": "# The following code is modified from https://github.com/shelhamer/clockwork-fcn\nimport sys\nimport os\nimport glob\nimport"
},
{
"path": "scripts/eval_cityscapes/download_fcn8s.sh",
"chars": 189,
"preview": "URL=http://efrosgans.eecs.berkeley.edu/pix2pix_extra/fcn-8s-cityscapes.caffemodel\nOUTPUT_FILE=./scripts/eval_cityscapes/"
},
{
"path": "scripts/eval_cityscapes/evaluate.py",
"chars": 3403,
"preview": "import os\nimport caffe\nimport argparse\nimport numpy as np\nimport scipy.misc\nfrom PIL import Image\nfrom util import segru"
},
{
"path": "scripts/eval_cityscapes/util.py",
"chars": 1051,
"preview": "# The following code is modified from https://github.com/shelhamer/clockwork-fcn\nimport numpy as np\n\n\ndef get_out_scorem"
},
{
"path": "scripts/install_deps.sh",
"chars": 48,
"preview": "set -ex\npip install visdom\npip install dominate\n"
},
{
"path": "scripts/test_before_push.py",
"chars": 5886,
"preview": "import pytest\nimport os\nimport subprocess\nfrom pathlib import Path\n\n\nclass TestBeforePush:\n \"\"\"Test suite to ensure b"
},
{
"path": "scripts/test_colorization.sh",
"chars": 100,
"preview": "set -ex\npython test.py --dataroot ./datasets/colorization --name color_pix2pix --model colorization\n"
},
{
"path": "scripts/test_cyclegan.sh",
"chars": 115,
"preview": "set -ex\npython test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan --phase test --no_dropout\n"
},
{
"path": "scripts/test_pix2pix.sh",
"chars": 161,
"preview": "set -ex\npython test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --netG unet_256 --direction "
},
{
"path": "scripts/test_single.sh",
"chars": 164,
"preview": "set -ex\npython test.py --dataroot ./datasets/facades/testB/ --name facades_pix2pix --model test --netG unet_256 --direct"
},
{
"path": "scripts/train_colorization.sh",
"chars": 118,
"preview": "set -ex\npython train.py --dataroot ./datasets/colorization --name color_pix2pix --model colorization --use_wandb\n```\n"
},
{
"path": "scripts/train_cyclegan.sh",
"chars": 135,
"preview": "set -ex\npython train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan --pool_size 50 --no_dropout -"
},
{
"path": "scripts/train_pix2pix.sh",
"chars": 209,
"preview": "set -ex\npython train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --netG unet_256 --direction"
},
{
"path": "test.py",
"chars": 4258,
"preview": "\"\"\"General-purpose test script for image-to-image translation.\n\nOnce you have trained your model with train.py, you can "
},
{
"path": "train.py",
"chars": 4777,
"preview": "\"\"\"General-purpose training script for image-to-image translation.\n\nThis script works for various models (with option '-"
},
{
"path": "util/__init__.py",
"chars": 83,
"preview": "\"\"\"This package includes a miscellaneous collection of useful helper functions.\"\"\"\n"
},
{
"path": "util/get_data.py",
"chars": 3569,
"preview": "from __future__ import print_function\nfrom pathlib import Path\nimport tarfile\nimport requests\nfrom warnings import warn\n"
},
{
"path": "util/html.py",
"chars": 3143,
"preview": "import dominate\nfrom dominate.tags import meta, h3, table, tr, td, p, a, img, br\nfrom pathlib import Path\n\n\nclass HTML:\n"
},
{
"path": "util/image_pool.py",
"chars": 2217,
"preview": "import random\nimport torch\n\n\nclass ImagePool:\n \"\"\"This class implements an image buffer that stores previously genera"
},
{
"path": "util/util.py",
"chars": 3941,
"preview": "\"\"\"This module contains simple helper functions\"\"\"\n\nfrom __future__ import print_function\nimport torch\nimport numpy as n"
},
{
"path": "util/visualizer.py",
"chars": 7462,
"preview": "import numpy as np\nimport sys\nimport ntpath\nimport time\nfrom . import util, html\nfrom pathlib import Path\nimport wandb\ni"
}
]
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