install requirements:
pip install -r requirements.txtThe code is heavily based on the official implementation of SPADE, and therefore has the saome structure:
train.py,test.py: the entry point for training and testing.trainers/pix2pix_trainer.py: harnesses and reports the progress of training.models/pix2pix_model.py: creates the networks, and compute the losses.models/networks/: defines the architecture of all models.options/: creates option lists usingargparsepackage. More individuals are dynamically added in other files as well. Please see the section below.data/: defines the class for loading images and label maps.
The ASAPNets generator is implementaed in:
models/networks/generator: defines the architecture of the ASAPNets generator.
run:
cd data
bash facadesHR_download_and_extract.sh
This will extract the facades full resolution images into datasets/facadesHR.
download the dataset into datasets/cityscapes and arrange in folders: train_images, train_labels, val_images, val_labels
Pretraned models can be downloaded from here.
Save the models under the checkpoints/ folder.
Images can be generated using the command:
# Facades 512
bash test_facades512.sh
# Facades 1024
bash test_facades512.sh
# Cityscapes
bash test_cityscapes.sh
The outputs images will appear at the./results/ folder.
New models can be trained with the following commands.
Prepare dataset in the ./datasets/ folder. Arrange in folders: train_images, train_labels, val_images, val_labels .
For custom datasets, the easiest way is to use ./data/custom_dataset.py by specifying the option --dataset_mode custom, along with --label_dir [path_to_labels] --image_dir [path_to_images].
You also need to specify options such as --label_nc for the number of label classes in the dataset, --contain_dontcare_label to specify whether it has an unknown label, or --no_instance to denote the dataset doesn't have instance maps.
Run:
python train.py --name [experiment_name] --dataset_mode custom --label_dir [path_to_labels] -- image_dir [path_to_images] --label_nc [num_labels]
There are many additional options you can specify, please explore the ./options files.
To specify the number of GPUs to utilize, use --gpu_ids.
Testing is similar to testing pretrained models.
python test.py --name [name_of_experiment] --dataset_mode [dataset_mode] --dataroot [path_to_dataset]
you can load the parameters used from training by specifying --load_from_opt_file.
This code is heavily based on the official implementation of SPADE. We thank the authors for sharing their code publicly!
Attribution-NonCommercial-ShareAlike 4.0 International (see file).
@inproceedings{RottShaham2020ASAP,
title={Spatially-Adaptive Pixelwise Networks for Fast Image Translation},
author={Rott Shaham, Tamar and Gharbi, Michael and Zhang, Richard and Shechtman, Eli and Michaeli, Tomer},
booktitle={Computer Vision and Pattern Recognition (CVPR)},
year={2021}
}
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