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https://github.com/phillipi/pix2pix

Image-to-image translation with conditional adversarial nets
https://github.com/phillipi/pix2pix

computer-graphics computer-vision dcgan deep-learning gan generative-adversarial-network image-generation image-manipulation image-to-image-translation pix2pix

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Image-to-image translation with conditional adversarial nets

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README 

        

# pix2pix

[Project](https://phillipi.github.io/pix2pix/) | [Arxiv](https://arxiv.org/abs/1611.07004) |

[PyTorch](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)



Torch implementation for learning a mapping from input images to output images, for example:






Image-to-Image Translation with Conditional Adversarial Networks  

 [Phillip Isola](http://web.mit.edu/phillipi/), [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/)   

 CVPR, 2017.



On some tasks, decent results can be obtained fairly quickly and on small datasets. For example, to learn to generate facades (example shown above), we trained on just 400 images for about 2 hours (on a single Pascal Titan X GPU). However, for harder problems it may be important to train on far larger datasets, and for many hours or even days.



**Note**: Please check out our [PyTorch](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) implementation for pix2pix and CycleGAN. The PyTorch version is under active development and can produce results comparable to or better than this Torch version.



## Setup



### Prerequisites

- Linux or OSX

- NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN may work with minimal modification, but untested)



### Getting Started

- Install torch and dependencies from https://github.com/torch/distro

- Install torch packages `nngraph` and `display`

```bash

luarocks install nngraph

luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec

```

- Clone this repo:

```bash

git clone [email protected]:phillipi/pix2pix.git

cd pix2pix

```

- Download the dataset (e.g., [CMP Facades](http://cmp.felk.cvut.cz/~tylecr1/facade/)):

```bash

bash ./datasets/download_dataset.sh facades

```

- Train the model

```bash

DATA_ROOT=./datasets/facades name=facades_generation which_direction=BtoA th train.lua

```

- (CPU only) The same training command without using a GPU or CUDNN. Setting the environment variables ```gpu=0 cudnn=0``` forces CPU only

```bash

DATA_ROOT=./datasets/facades name=facades_generation which_direction=BtoA gpu=0 cudnn=0 batchSize=10 save_epoch_freq=5 th train.lua

```

- (Optionally) start the display server to view results as the model trains. ( See [Display UI](#display-ui) for more details):

```bash

th -ldisplay.start 8000 0.0.0.0

```



- Finally, test the model:

```bash

DATA_ROOT=./datasets/facades name=facades_generation which_direction=BtoA phase=val th test.lua

```

The test results will be saved to an html file here: `./results/facades_generation/latest_net_G_val/index.html`.



## Train

```bash

DATA_ROOT=/path/to/data/ name=expt_name which_direction=AtoB th train.lua

```

Switch `AtoB` to `BtoA` to train translation in opposite direction.



Models are saved to `./checkpoints/expt_name` (can be changed by passing `checkpoint_dir=your_dir` in train.lua).



See `opt` in train.lua for additional training options.



## Test

```bash

DATA_ROOT=/path/to/data/ name=expt_name which_direction=AtoB phase=val th test.lua

```



This will run the model named `expt_name` in direction `AtoB` on all images in `/path/to/data/val`.



Result images, and a webpage to view them, are saved to `./results/expt_name` (can be changed by passing `results_dir=your_dir` in test.lua).



See `opt` in test.lua for additional testing options.



## Datasets

Download the 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_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 `which_direction=BtoA`.



## Models

Download the pre-trained models with the following script. You need to rename the model (e.g., `facades_label2image` to `/checkpoints/facades/latest_net_G.t7`) after the download has finished.

```bash

bash ./models/download_model.sh model_name

```

- `facades_label2image` (label -> facade): trained on the CMP Facades dataset.

- `cityscapes_label2image` (label -> street scene): trained on the Cityscapes dataset.

- `cityscapes_image2label` (street scene -> label): trained on the Cityscapes dataset.

- `edges2shoes` (edge -> photo): trained on UT Zappos50K dataset.

- `edges2handbags` (edge -> photo): trained on Amazon handbags images.

- `day2night` (daytime scene -> nighttime scene): trained on around 100 [webcams](http://transattr.cs.brown.edu/).



## Setup Training and Test data

### Generating Pairs

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 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 scripts/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.



### Notes on Colorization

No need to run `combine_A_and_B.py` for colorization. Instead, you need to prepare some natural images and set `preprocess=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`.



### 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 to change 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.



## Display UI

Optionally, for displaying images during training and test, use the [display package](https://github.com/szym/display).



- Install it with: `luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec`

- Then start the server with: `th -ldisplay.start`

- Open this URL in your browser: [http://localhost:8000](http://localhost:8000)



By default, the server listens on localhost. Pass `0.0.0.0` to allow external connections on any interface:

```bash

th -ldisplay.start 8000 0.0.0.0

```

Then open `http://(hostname):(port)/` in your browser to load the remote desktop.



L1 error is plotted to the display by default. Set the environment variable `display_plot` to a comma-separated list of values `errL1`, `errG` and `errD` to visualize the L1, generator, and discriminator error respectively. For example, to plot only the generator and discriminator errors to the display instead of the default L1 error, set `display_plot="errG,errD"`.



## Citation

If you use this code for your research, please cite our paper Image-to-Image Translation Using Conditional Adversarial Networks:



```

@article{pix2pix2017,

  title={Image-to-Image Translation with Conditional Adversarial Networks},

  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},

  journal={CVPR},

  year={2017}

}

```



## Cat Paper Collection

If you love cats, and love reading cool graphics, vision, and learning papers, please check out the Cat Paper Collection:  

[[Github]](https://github.com/junyanz/CatPapers) [[Webpage]](https://www.cs.cmu.edu/~junyanz/cat/cat_papers.html)



## Acknowledgments

Code borrows heavily from [DCGAN](https://github.com/soumith/dcgan.torch). The data loader is modified from [DCGAN](https://github.com/soumith/dcgan.torch) and  [Context-Encoder](https://github.com/pathak22/context-encoder).