NVlabs/ SPADE

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Semantic Image Synthesis with SPADE

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Semantic Image Synthesis with SPADE

Name: SPADE
Brand: SPADE
SKU: //NVlabs/SPADE
Rating: 4.6387777777777774 (6749 reviews)

New implementation available at imaginaire repository

We have a reimplementation of the SPADE method that is more performant. It is avaiable at Imaginaire

Project page | Paper | Online Interactive Demo of GauGAN | GTC 2019 demo | Youtube Demo of GauGAN

Semantic Image Synthesis with Spatially-Adaptive Normalization.
Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu.
In CVPR 2019 (Oral).

License

The code is released for academic research use only. For commercial use or business inquiries, please contact [email protected].

For press and other inquiries, please contact Hector Marinez

Installation

Clone this repo.

bash
git clone https://github.com/NVlabs/SPADE.git
cd SPADE/

This code requires PyTorch 1.0 and python 3+. Please install dependencies by

bash
pip install -r requirements.txt

This code also requires the Synchronized-BatchNorm-PyTorch rep.

cd models/networks/
git clone https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
cp -rf Synchronized-BatchNorm-PyTorch/sync_batchnorm .
cd ../../

To reproduce the results reported in the paper, you would need an NVIDIA DGX1 machine with 8 V100 GPUs.

Dataset Preparation

For COCO-Stuff, Cityscapes or ADE20K, the datasets must be downloaded beforehand. Please download them on the respective webpages. In the case of COCO-stuff, we put a few sample images in this code repo.

Preparing COCO-Stuff Dataset. The dataset can be downloaded here. In particular, you will need to download train2017.zip, val2017.zip, stuffthingmapstrainval2017.zip, and annotationstrainval2017.zip. The images, labels, and instance maps should be arranged in the same directory structure as in

datasets/coco_stuff/

. In particular, we used an instance map that combines both the boundaries of "things instance map" and "stuff label map". To do this, we used a simple script

datasets/coco_generate_instance_map.py

. Please install

pycocotools

using

pip install pycocotools

and refer to the script to generate instance maps.

Preparing ADE20K Dataset. The dataset can be downloaded here, which is from MIT Scene Parsing BenchMark. After unzipping the datgaset, put the jpg image files

ADEChallengeData2016/images/

and png label files

ADEChallengeData2016/annotatoins/

in the same directory.

There are different modes to load images by specifying

--preprocess_mode

along with

--load_size

--crop_size

. There are options such as

resize_and_crop

, which resizes the images into square images of side length

load_size

and randomly crops to

crop_size

scale_shortside_and_crop

scales the image to have a short side of length

load_size

and crops to

crop_size

crop_size

square. To see all modes, please use

python train.py --help

and take a look at

data/base_dataset.py

. By default at the training phase, the images are randomly flipped horizontally. To prevent this use

--no_flip

Generating Images Using Pretrained Model

Once the dataset is ready, the result images can be generated using pretrained models.

Download the tar of the pretrained models from the Google Drive Folder, save it in 'checkpoints/', and run
```
cd checkpoints
tar xvf checkpoints.tar.gz
cd ../
```
Generate images using the pretrained model.
```
bash
python test.py --name [type]_pretrained --dataset_mode [dataset] --dataroot [path_to_dataset]
```
```
[type]_pretrained
```
is the directory name of the checkpoint file downloaded in Step 1, which should be one of
```
coco_pretrained
```
,
```
ade20k_pretrained
```
, and
```
cityscapes_pretrained
```
.
```
[dataset]
```
can be one of
```
coco
```
,
```
ade20k
```
, and
```
cityscapes
```
, and
```
[path_to_dataset]
```
, is the path to the dataset. If you are running on CPU mode, append
```
--gpu_ids -1
```
.
The outputs images are stored at
```
./results/[type]_pretrained/
```
by default. You can view them using the autogenerated HTML file in the directory.

Generating Landscape Image using GauGAN

In the paper and the demo video, we showed GauGAN, our interactive app that generates realistic landscape images from the layout users draw. The model was trained on landscape images scraped from Flickr.com. We released an online demo that has the same features. Please visit https://www.nvidia.com/en-us/research/ai-playground/. The model weights are not released.

Training New Models

New models can be trained with the following commands.

Prepare dataset. To train on the datasets shown in the paper, you can download the datasets and use
```
--dataset_mode
```
option, which will choose which subclass of
```
BaseDataset
```
is loaded. 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.
Train.

# To train on the Facades or COCO dataset, for example.
python train.py --name [experiment_name] --dataset_mode facades --dataroot [path_to_facades_dataset]
python train.py --name [experiment_name] --dataset_mode coco --dataroot [path_to_coco_dataset]

To train on your own custom dataset
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 options you can specify. Please use

python train.py --help

. The specified options are printed to the console. To specify the number of GPUs to utilize, use

--gpu_ids

. If you want to use the second and third GPUs for example, use

--gpu_ids 1,2

To log training, use

--tf_log

for Tensorboard. The logs are stored at

[checkpoints_dir]/[name]/logs

Testing

Testing is similar to testing pretrained models.

python test.py --name [name_of_experiment] --dataset_mode [dataset_mode] --dataroot [path_to_dataset]

Use

--results_dir

to specify the output directory.

--how_many

will specify the maximum number of images to generate. By default, it loads the latest checkpoint. It can be changed using

--which_epoch

Code 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 using
```
argparse
```
package. 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.

Options

This code repo contains many options. Some options belong to only one specific model, and some options have different default values depending on other options. To address this, the

BaseOption

class dynamically loads and sets options depending on what model, network, and datasets are used. This is done by calling the static method

modify_commandline_options

of various classes. It takes in the

parser

argparse

package and modifies the list of options. For example, since COCO-stuff dataset contains a special label "unknown", when COCO-stuff dataset is used, it sets

--contain_dontcare_label

automatically at

data/coco_dataset.py

. You can take a look at

def gather_options()

options/base_options.py

, or

models/network/__init__.py

to get a sense of how this works.

VAE-Style Training with an Encoder For Style Control and Multi-Modal Outputs

To train our model along with an image encoder to enable multi-modal outputs as in Figure 15 of the paper, please use

--use_vae

. The model will create

netE

in addition to

netG

and

netD

and train with KL-Divergence loss.

Citation

If you use this code for your research, please cite our papers.

@inproceedings{park2019SPADE,
  title={Semantic Image Synthesis with Spatially-Adaptive Normalization},
  author={Park, Taesung and Liu, Ming-Yu and Wang, Ting-Chun and Zhu, Jun-Yan},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2019}
}

Acknowledgments

This code borrows heavily from pix2pixHD. We thank Jiayuan Mao for his Synchronized Batch Normalization code.