StyleGAN2-ADA for High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data.

Computer-aided skin disease diagnosis using deep learning and computer vision relies on a large amount of data collected from hospitals and medical professionals. Accessing high volumes of data, especially for facial skin conditions, is challenging due to privacy concerns. Thus, in the computer-aided diagnosis research community, a facial skin condition like Rosacea is lamentably neglected due to limited data availability. This is the major motivation for exploring an alternative approach to computer-aided diagnosis when only a few images are available. Recently, Generative Adversarial Networks, principally the variants of StyleGANs trained on limited datasets, have shown promising performance in generating synthetic facial images. In this study, for the first time, a small dataset of Rosacea with 300 full-face images is utilized and investigated using StyleGAN2-ADA. The preliminary experiments show how fine-tuning the model and varying experimental settings significantly affect the fidelity of the Rosacea features. We demonstrated that $R_1$ Regularization strength helps achieve high-fidelity characteristics. Additionally, this study presents qualitative evaluations of synthetic/generated faces by expert dermatologists and non-specialist participants. The quantitative and qualitative evaluations are presented by validation metric(s) and the opinions of expert dermatologists and non-specialist participants. Followed by these evaluations, a few limitations and future directions are discussed.

Generated Images

300 synthetic rosacea faces generated from this work is available at https://github.com/thinkercache/synth-rff-300

The 300 real images used in this work came from 3 sources such as:

• The Powell Lab, UCD (https://www.ucd.ie/charles/research/researchgroups/thepowelllab/).
• X. Sun, J. Yang, M. Sun, and K. Wang, “A benchmark for automatic visual classification of clinical skin disease images,” in European Conference on Computer Vision. Springer, 2016, pp. 206–222.
• Google Search images, Teledermatology websites.

Steps for preparing a custom Rosacea dataset:

• Handpick the images with full face visibility in which forehead, eyes, cheeks, and chin are present.
• To preserve the high quality of the disease feature, consider orginial images with minimum resolution 250x250 and above.
• In this work, images are resized to 512x512. Please refer python dataset_tool.py --help for more details on dataset preparation.

Running Experiments:

• Please refer stylegan2-ada-pytorch/Config-Exp1-10/ for configuration details of 10 experiments conducted in this study.

Acknowledgements

This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224.

Credits

Please find the original Stylegan2-ada-pytorch on https://github.com/NVlabs/stylegan2-ada-pytorch

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Official README.md

Requirements

• Linux and Windows are supported, but we recommend Linux for performance and compatibility reasons.
• 1–8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using NVIDIA DGX-1 with 8 Tesla V100 GPUs.
• 64-bit Python 3.7 and PyTorch 1.7.1. See https://pytorch.org/ for PyTorch install instructions.
• CUDA toolkit 11.0 or later. Use at least version 11.1 if running on RTX 3090. (Why is a separate CUDA toolkit installation required? See comments in #2.)
• Python libraries: pip install click requests tqdm pyspng ninja imageio-ffmpeg==0.4.3. We use the Anaconda3 2020.11 distribution which installs most of these by default.
• Docker users: use the provided Dockerfile to build an image with the required library dependencies.

The code relies heavily on custom PyTorch extensions that are compiled on the fly using NVCC. On Windows, the compilation requires Microsoft Visual Studio. We recommend installing Visual Studio Community Edition and adding it into PATH using "C:\Program Files (x86)\Microsoft Visual Studio\<VERSION>\Community\VC\Auxiliary\Build\vcvars64.bat".

Preparing datasets

Datasets are stored as uncompressed ZIP archives containing uncompressed PNG files and a metadata file dataset.json for labels.

Custom datasets can be created from a folder containing images; see python dataset_tool.py --help for more information. Alternatively, the folder can also be used directly as a dataset, without running it through dataset_tool.py first, but doing so may lead to suboptimal performance.

Legacy TFRecords datasets are not supported — see below for instructions on how to convert them.

FFHQ:

Step 1: Download the Flickr-Faces-HQ dataset as TFRecords.

Step 2: Extract images from TFRecords using dataset_tool.py from the TensorFlow version of StyleGAN2-ADA:

# Using dataset_tool.py from TensorFlow version at
# https://github.com/NVlabs/stylegan2-ada/
python ../stylegan2-ada/dataset_tool.py unpack \
    --tfrecord_dir=~/ffhq-dataset/tfrecords/ffhq --output_dir=/tmp/ffhq-unpacked

Step 3: Create ZIP archive using dataset_tool.py from this repository:

# Original 1024x1024 resolution.
python dataset_tool.py --source=/tmp/ffhq-unpacked --dest=~/datasets/ffhq.zip

# Scaled down 256x256 resolution.
python dataset_tool.py --source=/tmp/ffhq-unpacked --dest=~/datasets/ffhq256x256.zip \
    --width=256 --height=256

MetFaces: Download the MetFaces dataset and create ZIP archive:

python dataset_tool.py --source=~/downloads/metfaces/images --dest=~/datasets/metfaces.zip

AFHQ: Download the AFHQ dataset and create ZIP archive:

python dataset_tool.py --source=~/downloads/afhq/train/cat --dest=~/datasets/afhqcat.zip
python dataset_tool.py --source=~/downloads/afhq/train/dog --dest=~/datasets/afhqdog.zip
python dataset_tool.py --source=~/downloads/afhq/train/wild --dest=~/datasets/afhqwild.zip

CIFAR-10: Download the CIFAR-10 python version and convert to ZIP archive:

python dataset_tool.py --source=~/downloads/cifar-10-python.tar.gz --dest=~/datasets/cifar10.zip

LSUN: Download the desired categories from the LSUN project page and convert to ZIP archive:

python dataset_tool.py --source=~/downloads/lsun/raw/cat_lmdb --dest=~/datasets/lsuncat200k.zip \
    --transform=center-crop --width=256 --height=256 --max_images=200000

python dataset_tool.py --source=~/downloads/lsun/raw/car_lmdb --dest=~/datasets/lsuncar200k.zip \
    --transform=center-crop-wide --width=512 --height=384 --max_images=200000

BreCaHAD:

Step 1: Download the BreCaHAD dataset.

Step 2: Extract 512x512 resolution crops using dataset_tool.py from the TensorFlow version of StyleGAN2-ADA:

# Using dataset_tool.py from TensorFlow version at
# https://github.com/NVlabs/stylegan2-ada/
python dataset_tool.py extract_brecahad_crops --cropsize=512 \
    --output_dir=/tmp/brecahad-crops --brecahad_dir=~/downloads/brecahad/images

Step 3: Create ZIP archive using dataset_tool.py from this repository:

python dataset_tool.py --source=/tmp/brecahad-crops --dest=~/datasets/brecahad.zip

Training new networks

In its most basic form, training new networks boils down to:

python train.py --outdir=~/training-runs --data=~/mydataset.zip --gpus=1 --dry-run
python train.py --outdir=~/training-runs --data=~/mydataset.zip --gpus=1

The first command is optional; it validates the arguments, prints out the training configuration, and exits. The second command kicks off the actual training.

In this example, the results are saved to a newly created directory ~/training-runs/<ID>-mydataset-auto1, controlled by --outdir. The training exports network pickles (network-snapshot-<INT>.pkl) and example images (fakes<INT>.png) at regular intervals (controlled by --snap). For each pickle, it also evaluates FID (controlled by --metrics) and logs the resulting scores in metric-fid50k_full.jsonl (as well as TFEvents if TensorBoard is installed).

The name of the output directory reflects the training configuration. For example, 00000-mydataset-auto1 indicates that the base configuration was auto1, meaning that the hyperparameters were selected automatically for training on one GPU. The base configuration is controlled by --cfg:

Base config	Description
auto (default)	Automatically select reasonable defaults based on resolution and GPU count. Serves as a good starting point for new datasets but does not necessarily lead to optimal results.
stylegan2	Reproduce results for StyleGAN2 config F at 1024x1024 using 1, 2, 4, or 8 GPUs.
paper256	Reproduce results for FFHQ and LSUN Cat at 256x256 using 1, 2, 4, or 8 GPUs.
paper512	Reproduce results for BreCaHAD and AFHQ at 512x512 using 1, 2, 4, or 8 GPUs.
paper1024	Reproduce results for MetFaces at 1024x1024 using 1, 2, 4, or 8 GPUs.
cifar	Reproduce results for CIFAR-10 (tuned configuration) using 1 or 2 GPUs.

Please refer to python train.py --help for the full list.

Pre-trained networks

Pre-trained networks are stored as *.pkl files that can be referenced using local filenames or URLs:

# Generate curated MetFaces images without truncation (Fig.10 left)
python generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl

# Generate uncurated MetFaces images with truncation (Fig.12 upper left)
python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl

# Generate class conditional CIFAR-10 images (Fig.17 left, Car)
python generate.py --outdir=out --seeds=0-35 --class=1 \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/cifar10.pkl

# Style mixing example
python style_mixing.py --outdir=out --rows=85,100,75,458,1500 --cols=55,821,1789,293 \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl

Outputs from the above commands are placed under out/*.png, controlled by --outdir. Downloaded network pickles are cached under $HOME/.cache/dnnlib, which can be overridden by setting the DNNLIB_CACHE_DIR environment variable. The default PyTorch extension build directory is $HOME/.cache/torch_extensions, which can be overridden by setting TORCH_EXTENSIONS_DIR.

Docker: You can run the above curated image example using Docker as follows:

docker build --tag sg2ada:latest .
./docker_run.sh python3 generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl

Note: The Docker image requires NVIDIA driver release r455.23 or later.

Legacy networks: The above commands can load most of the network pickles created using the previous TensorFlow versions of StyleGAN2 and StyleGAN2-ADA. However, for future compatibility, we recommend converting such legacy pickles into the new format used by the PyTorch version:

python legacy.py \
    --source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \
    --dest=stylegan2-cat-config-f.pkl

Projecting images to latent space

To find the matching latent vector for a given image file, run:

python projector.py --outdir=out --target=~/mytargetimg.png \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl

For optimal results, the target image should be cropped and aligned similar to the FFHQ dataset. The above command saves the projection target out/target.png, result out/proj.png, latent vector out/projected_w.npz, and progression video out/proj.mp4. You can render the resulting latent vector by specifying --projected_w for generate.py:

python generate.py --outdir=out --projected_w=out/projected_w.npz \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl

Using networks from Python

You can use pre-trained networks in your own Python code as follows:

with open('ffhq.pkl', 'rb') as f:
    G = pickle.load(f)['G_ema'].cuda()  # torch.nn.Module
z = torch.randn([1, G.z_dim]).cuda()    # latent codes
c = None                                # class labels (not used in this example)
img = G(z, c)                           # NCHW, float32, dynamic range [-1, +1]

The above code requires torch_utils and dnnlib to be accessible via PYTHONPATH. It does not need source code for the networks themselves — their class definitions are loaded from the pickle via torch_utils.persistence.

The pickle contains three networks. 'G' and 'D' are instantaneous snapshots taken during training, and 'G_ema' represents a moving average of the generator weights over several training steps. The networks are regular instances of torch.nn.Module, with all of their parameters and buffers placed on the CPU at import and gradient computation disabled by default.

The generator consists of two submodules, G.mapping and G.synthesis, that can be executed separately. They also support various additional options:

w = G.mapping(z, c, truncation_psi=0.5, truncation_cutoff=8)
img = G.synthesis(w, noise_mode='const', force_fp32=True)

Please refer to generate.py, style_mixing.py, and projector.py for further examples.

Expected training time

The total training time depends heavily on resolution, number of GPUs, dataset, desired quality, and hyperparameters. The following table lists expected wallclock times to reach different points in the training, measured in thousands of real images shown to the discriminator ("kimg"):

Resolution	GPUs	1000 kimg	25000 kimg	sec/kimg	GPU mem	CPU mem
128x128	1	4h 05m	4d 06h	12.8–13.7	7.2 GB	3.9 GB
128x128	2	2h 06m	2d 04h	6.5–6.8	7.4 GB	7.9 GB
128x128	4	1h 20m	1d 09h	4.1–4.6	4.2 GB	16.3 GB
128x128	8	1h 13m	1d 06h	3.9–4.9	2.6 GB	31.9 GB
256x256	1	6h 36m	6d 21h	21.6–24.2	5.0 GB	4.5 GB
256x256	2	3h 27m	3d 14h	11.2–11.8	5.2 GB	9.0 GB
256x256	4	1h 45m	1d 20h	5.6–5.9	5.2 GB	17.8 GB
256x256	8	1h 24m	1d 11h	4.4–5.5	3.2 GB	34.7 GB
512x512	1	21h 03m	21d 22h	72.5–74.9	7.6 GB	5.0 GB
512x512	2	10h 59m	11d 10h	37.7–40.0	7.8 GB	9.8 GB
512x512	4	5h 29m	5d 17h	18.7–19.1	7.9 GB	17.7 GB
512x512	8	2h 48m	2d 22h	9.5–9.7	7.8 GB	38.2 GB
1024x1024	1	1d 20h	46d 03h	154.3–161.6	8.1 GB	5.3 GB
1024x1024	2	23h 09m	24d 02h	80.6–86.2	8.6 GB	11.9 GB
1024x1024	4	11h 36m	12d 02h	40.1–40.8	8.4 GB	21.9 GB
1024x1024	8	5h 54m	6d 03h	20.2–20.6	8.3 GB	44.7 GB

The above measurements were done using NVIDIA Tesla V100 GPUs with default settings (--cfg=auto --aug=ada --metrics=fid50k_full). "sec/kimg" shows the expected range of variation in raw training performance, as reported in log.txt. "GPU mem" and "CPU mem" show the highest observed memory consumption, excluding the peak at the beginning caused by torch.backends.cudnn.benchmark.

In typical cases, 25000 kimg or more is needed to reach convergence, but the results are already quite reasonable around 5000 kimg. 1000 kimg is often enough for transfer learning, which tends to converge significantly faster. The following figure shows example convergence curves for different datasets as a function of wallclock time, using the same settings as above:

Note: --cfg=auto serves as a reasonable first guess for the hyperparameters but it does not necessarily lead to optimal results for a given dataset. For example, --cfg=stylegan2 yields considerably better FID for FFHQ-140k at 1024x1024 than illustrated above. We recommend trying out at least a few different values of --gamma for each new dataset.

Quality metrics

By default, train.py automatically computes FID for each network pickle exported during training. We recommend inspecting metric-fid50k_full.jsonl (or TensorBoard) at regular intervals to monitor the training progress. When desired, the automatic computation can be disabled with --metrics=none to speed up the training slightly (3%–9%).

Additional quality metrics can also be computed after the training:

# Previous training run: look up options automatically, save result to JSONL file.
python calc_metrics.py --metrics=pr50k3_full \
    --network=~/training-runs/00000-ffhq10k-res64-auto1/network-snapshot-000000.pkl

# Pre-trained network pickle: specify dataset explicitly, print result to stdout.
python calc_metrics.py --metrics=fid50k_full --data=~/datasets/ffhq.zip --mirror=1 \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl

The first example looks up the training configuration and performs the same operation as if --metrics=pr50k3_full had been specified during training. The second example downloads a pre-trained network pickle, in which case the values of --mirror and --data must be specified explicitly.

Note that many of the metrics have a significant one-off cost when calculating them for the first time for a new dataset (up to 30min). Also note that the evaluation is done using a different random seed each time, so the results will vary if the same metric is computed multiple times.

We employ the following metrics in the ADA paper. Execution time and GPU memory usage is reported for one NVIDIA Tesla V100 GPU at 1024x1024 resolution:

Metric	Time	GPU mem	Description
fid50k_full	13 min	1.8 GB	Fréchet inception distance[1] against the full dataset
kid50k_full	13 min	1.8 GB	Kernel inception distance[2] against the full dataset
pr50k3_full	13 min	4.1 GB	Precision and recall[3] againt the full dataset
is50k	13 min	1.8 GB	Inception score[4] for CIFAR-10

In addition, the following metrics from the StyleGAN and StyleGAN2 papers are also supported:

Metric	Time	GPU mem	Description
fid50k	13 min	1.8 GB	Fréchet inception distance against 50k real images
kid50k	13 min	1.8 GB	Kernel inception distance against 50k real images
pr50k3	13 min	4.1 GB	Precision and recall against 50k real images
ppl2_wend	36 min	2.4 GB	Perceptual path length[5] in W, endpoints, full image
ppl_zfull	36 min	2.4 GB	Perceptual path length in Z, full paths, cropped image
ppl_wfull	36 min	2.4 GB	Perceptual path length in W, full paths, cropped image
ppl_zend	36 min	2.4 GB	Perceptual path length in Z, endpoints, cropped image
ppl_wend	36 min	2.4 GB	Perceptual path length in W, endpoints, cropped image

References:

1. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, Heusel et al. 2017
2. Demystifying MMD GANs, Bińkowski et al. 2018
3. Improved Precision and Recall Metric for Assessing Generative Models, Kynkäänniemi et al. 2019
4. Improved Techniques for Training GANs, Salimans et al. 2016
5. A Style-Based Generator Architecture for Generative Adversarial Networks, Karras et al. 2018

License

Copyright © 2021, NVIDIA Corporation. All rights reserved.

This work is made available under the Nvidia Source Code License.

Citation

@inproceedings{Karras2020ada,
  title     = {Training Generative Adversarial Networks with Limited Data},
  author    = {Tero Karras and Miika Aittala and Janne Hellsten and Samuli Laine and Jaakko Lehtinen and Timo Aila},
  booktitle = {Proc. NeurIPS},
  year      = {2020}
}

Development

This is a research reference implementation and is treated as a one-time code drop. As such, we do not accept outside code contributions in the form of pull requests.

Acknowledgements

We thank David Luebke for helpful comments; Tero Kuosmanen and Sabu Nadarajan for their support with compute infrastructure; and Edgar Schönfeld for guidance on setting up unconditional BigGAN.