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.
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.
During training, the current results can be viewed using two methods. First, if you set --display_id > 0, the results and loss plot will appear on a local graphics web server launched by visdom. To do this, you should have visdom installed and a server running by the command python -m visdom.server. The default server URL is http://localhost:8097. display_id corresponds to the window ID that is displayed on the visdom server. The visdom display functionality is turned on by default. To avoid the extra overhead of communicating with visdom set --display_id -1. Second, the intermediate results are saved to [opt.checkpoints_dir]/[opt.name]/web/ as an HTML file. To avoid this, set --no_html.
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_datset.py do see how all these were implemented.
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.
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.
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:
python datasets/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/dataThis will combine each pair of images (A,B) into a single image file, ready for training.
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.
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 --crop_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.
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.
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 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.
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.
We provide python and Matlab scripts to extract coarse edges from photos. Run scripts/edges/batch_hed.py to compute HED edges. Run scripts/edges/PostprocessHED.m to simplify edges with additional post-processing steps. Check the code documentation for more details.
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 for installation instructions. Once caffe is successfully installed, download the pre-trained FCN-8s semantic segmentation model (512MB) by running
bash ./scripts/eval_cityscapes/download_fcn8s.shThen make sure ./scripts/eval_cityscapes/ is in your system's python path. If not, run the following command to add it
export PYTHONPATH=${PYTHONPATH}:./scripts/eval_cityscapes/Now you can run the following command to evaluate your predictions:
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/By default, images in your prediction result directory have the same naming convention as the Cityscapes dataset (e.g., frankfurt_000001_038418_leftImg8bit.png). The script will output a text file under --output_dir containing the metric.
Further notes: The pre-trained model does not work well on Cityscapes in the original resolution (1024x2048) as it was trained on 256x256 images that are resized to 1024x2048. The purpose of the resizing 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 for Cityscapes. To get the ground-truth numbers in the pix2pix paper, you need to resize the original Cityscapes images to 256x256 before running the evaluation code.