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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
FROM nvcr.io/nvidia/pytorch:21.08-py3
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1
RUN pip install imageio-ffmpeg==0.4.4 pyspng==0.1.0
WORKDIR /workspace
RUN (printf '#!/bin/bash\nexec \"$@\"\n' >> /entry.sh) && chmod a+x /entry.sh
ENTRYPOINT ["/entry.sh"]

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## Alias-Free Generative Adversarial Networks (StyleGAN3)<br><sub>Official PyTorch implementation of the NeurIPS 2021 paper</sub>
![Teaser image](./docs/stylegan3-teaser-1920x1006.png)
**Alias-Free Generative Adversarial Networks**<br>
Tero Karras, Miika Aittala, Samuli Laine, Erik H&auml;rk&ouml;nen, Janne Hellsten, Jaakko Lehtinen, Timo Aila<br>
https://nvlabs.github.io/stylegan3<br>
Abstract: *We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. This manifests itself as, e.g., detail appearing to be glued to image coordinates instead of the surfaces of depicted objects. We trace the root cause to careless signal processing that causes aliasing in the generator network. Interpreting all signals in the network as continuous, we derive generally applicable, small architectural changes that guarantee that unwanted information cannot leak into the hierarchical synthesis process. The resulting networks match the FID of StyleGAN2 but differ dramatically in their internal representations, and they are fully equivariant to translation and rotation even at subpixel scales. Our results pave the way for generative models better suited for video and animation.*
For business inquiries, please contact [researchinquiries@nvidia.com](mailto:researchinquiries@nvidia.com)<br>
For press and other inquiries, please contact Hector Marinez at [hmarinez@nvidia.com](mailto:hmarinez@nvidia.com)<br>
## Release notes
This repository is an updated version of [stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch), with several new features:
- Alias-free generator architecture and training configurations (`stylegan3-t`, `stylegan3-r`).
- Tools for interactive visualization (`visualizer.py`), spectral analysis (`avg_spectra.py`), and video generation (`gen_video.py`).
- Equivariance metrics (`eqt50k_int`, `eqt50k_frac`, `eqr50k`).
- General improvements: reduced memory usage, slightly faster training, bug fixes.
Compatibility:
- Compatible with old network pickles created using [stylegan2-ada](https://github.com/NVlabs/stylegan2-ada) and [stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch).
- Supports old StyleGAN2 training configurations, including ADA and transfer learning. See [Training configurations](./docs/configs.md) for details.
- Improved compatibility with Ampere GPUs and newer versions of PyTorch, CuDNN, etc.
## Synthetic image detection
While new generator approaches enable new media synthesis capabilities, they may also present a new challenge for AI forensics algorithms for detection and attribution of synthetic media. In collaboration with digital forensic researchers participating in DARPA's SemaFor program, we curated a synthetic image dataset that allowed the researchers to test and validate the performance of their image detectors in advance of the public release. Please see [here](https://github.com/NVlabs/stylegan3-detector) for more details.
## Additional material
- [Result videos](https://nvlabs-fi-cdn.nvidia.com/stylegan3/videos/)
- [Curated example images](https://nvlabs-fi-cdn.nvidia.com/stylegan3/images/)
- [StyleGAN3 pre-trained models](https://ngc.nvidia.com/catalog/models/nvidia:research:stylegan3) for config T (translation equiv.) and config R (translation and rotation equiv.)
> <sub>Access individual networks via `https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/<MODEL>`, where `<MODEL>` is one of:</sub><br>
> <sub>`stylegan3-t-ffhq-1024x1024.pkl`, `stylegan3-t-ffhqu-1024x1024.pkl`, `stylegan3-t-ffhqu-256x256.pkl`</sub><br>
> <sub>`stylegan3-r-ffhq-1024x1024.pkl`, `stylegan3-r-ffhqu-1024x1024.pkl`, `stylegan3-r-ffhqu-256x256.pkl`</sub><br>
> <sub>`stylegan3-t-metfaces-1024x1024.pkl`, `stylegan3-t-metfacesu-1024x1024.pkl`</sub><br>
> <sub>`stylegan3-r-metfaces-1024x1024.pkl`, `stylegan3-r-metfacesu-1024x1024.pkl`</sub><br>
> <sub>`stylegan3-t-afhqv2-512x512.pkl`</sub><br>
> <sub>`stylegan3-r-afhqv2-512x512.pkl`</sub><br>
- [StyleGAN2 pre-trained models](https://ngc.nvidia.com/catalog/models/nvidia:research:stylegan2) compatible with this codebase
> <sub>Access individual networks via `https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/<MODEL>`, where `<MODEL>` is one of:</sub><br>
> <sub>`stylegan2-ffhq-1024x1024.pkl`, `stylegan2-ffhq-512x512.pkl`, `stylegan2-ffhq-256x256.pkl`</sub><br>
> <sub>`stylegan2-ffhqu-1024x1024.pkl`, `stylegan2-ffhqu-256x256.pkl`</sub><br>
> <sub>`stylegan2-metfaces-1024x1024.pkl`, `stylegan2-metfacesu-1024x1024.pkl`</sub><br>
> <sub>`stylegan2-afhqv2-512x512.pkl`</sub><br>
> <sub>`stylegan2-afhqcat-512x512.pkl`, `stylegan2-afhqdog-512x512.pkl`, `stylegan2-afhqwild-512x512.pkl`</sub><br>
> <sub>`stylegan2-brecahad-512x512.pkl`, `stylegan2-cifar10-32x32.pkl`</sub><br>
> <sub>`stylegan2-celebahq-256x256.pkl`, `stylegan2-lsundog-256x256.pkl`</sub><br>
## Requirements
* Linux and Windows are supported, but we recommend Linux for performance and compatibility reasons.
* 1&ndash;8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using Tesla V100 and A100 GPUs.
* 64-bit Python 3.8 and PyTorch 1.9.0 (or later). See https://pytorch.org for PyTorch install instructions.
* CUDA toolkit 11.1 or later. (Why is a separate CUDA toolkit installation required? See [Troubleshooting](./docs/troubleshooting.md#why-is-cuda-toolkit-installation-necessary)).
* Python libraries: see [environment.yml](./environment.yml) for exact library dependencies. You can use the following commands with Miniconda3 to create and activate your StyleGAN3 Python environment:
- `conda env create -f environment.yml`
- `conda activate stylegan3`
* Docker users:
- Ensure you have correctly installed the [NVIDIA container runtime](https://docs.docker.com/config/containers/resource_constraints/#gpu).
- Use the [provided Dockerfile](./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](https://visualstudio.microsoft.com/vs/) and adding it into `PATH` using `"C:\Program Files (x86)\Microsoft Visual Studio\<VERSION>\Community\VC\Auxiliary\Build\vcvars64.bat"`.
See [Troubleshooting](./docs/troubleshooting.md) for help on common installation and run-time problems.
## Getting started
Pre-trained networks are stored as `*.pkl` files that can be referenced using local filenames or URLs:
```.bash
# Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
python gen_images.py --outdir=out --trunc=1 --seeds=2 \
--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
# Render a 4x2 grid of interpolations for seeds 0 through 31.
python gen_video.py --output=lerp.mp4 --trunc=1 --seeds=0-31 --grid=4x2 \
--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.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:
```.bash
# Build the stylegan3:latest image
docker build --tag stylegan3 .
# Run the gen_images.py script using Docker:
docker run --gpus all -it --rm --user $(id -u):$(id -g) \
-v `pwd`:/scratch --workdir /scratch -e HOME=/scratch \
stylegan3 \
python gen_images.py --outdir=out --trunc=1 --seeds=2 \
--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
```
Note: The Docker image requires NVIDIA driver release `r470` or later.
The `docker run` invocation may look daunting, so let's unpack its contents here:
- `--gpus all -it --rm --user $(id -u):$(id -g)`: with all GPUs enabled, run an interactive session with current user's UID/GID to avoid Docker writing files as root.
- ``-v `pwd`:/scratch --workdir /scratch``: mount current running dir (e.g., the top of this git repo on your host machine) to `/scratch` in the container and use that as the current working dir.
- `-e HOME=/scratch`: let PyTorch and StyleGAN3 code know where to cache temporary files such as pre-trained models and custom PyTorch extension build results. Note: if you want more fine-grained control, you can instead set `TORCH_EXTENSIONS_DIR` (for custom extensions build dir) and `DNNLIB_CACHE_DIR` (for pre-trained model download cache). You want these cache dirs to reside on persistent volumes so that their contents are retained across multiple `docker run` invocations.
## Interactive visualization
This release contains an interactive model visualization tool that can be used to explore various characteristics of a trained model. To start it, run:
```.bash
python visualizer.py
```
<a href="./docs/visualizer_screen0.png"><img alt="Visualizer screenshot" src="./docs/visualizer_screen0_half.png"></img></a>
## Using networks from Python
You can use pre-trained networks in your own Python code as follows:
```.python
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], no truncation
```
The above code requires `torch_utils` and `dnnlib` to be accessible via `PYTHONPATH`. It does not need source code for the networks themselves &mdash; 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:
```.python
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 [`gen_images.py`](./gen_images.py) for complete code example.
## 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`](./docs/dataset-tool-help.txt) 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.
**FFHQ**: Download the [Flickr-Faces-HQ dataset](https://github.com/NVlabs/ffhq-dataset) as 1024x1024 images and create a zip archive using `dataset_tool.py`:
```.bash
# Original 1024x1024 resolution.
python dataset_tool.py --source=/tmp/images1024x1024 --dest=~/datasets/ffhq-1024x1024.zip
# Scaled down 256x256 resolution.
python dataset_tool.py --source=/tmp/images1024x1024 --dest=~/datasets/ffhq-256x256.zip \
--width=256 --height=256
```
See the [FFHQ README](https://github.com/NVlabs/ffhq-dataset) for information on how to obtain the unaligned FFHQ dataset images. Use the same steps as above to create a ZIP archive for training and validation.
**MetFaces**: Download the [MetFaces dataset](https://github.com/NVlabs/metfaces-dataset) and create a ZIP archive:
```.bash
python dataset_tool.py --source=~/downloads/metfaces/images --dest=~/datasets/metfaces-1024x1024.zip
```
See the [MetFaces README](https://github.com/NVlabs/metfaces-dataset) for information on how to obtain the unaligned MetFaces dataset images. Use the same steps as above to create a ZIP archive for training and validation.
**AFHQv2**: Download the [AFHQv2 dataset](https://github.com/clovaai/stargan-v2/blob/master/README.md#animal-faces-hq-dataset-afhq) and create a ZIP archive:
```.bash
python dataset_tool.py --source=~/downloads/afhqv2 --dest=~/datasets/afhqv2-512x512.zip
```
Note that the above command creates a single combined dataset using all images of all three classes (cats, dogs, and wild animals), matching the setup used in the StyleGAN3 paper. Alternatively, you can also create a separate dataset for each class:
```.bash
python dataset_tool.py --source=~/downloads/afhqv2/train/cat --dest=~/datasets/afhqv2cat-512x512.zip
python dataset_tool.py --source=~/downloads/afhqv2/train/dog --dest=~/datasets/afhqv2dog-512x512.zip
python dataset_tool.py --source=~/downloads/afhqv2/train/wild --dest=~/datasets/afhqv2wild-512x512.zip
```
## Training
You can train new networks using `train.py`. For example:
```.bash
# Train StyleGAN3-T for AFHQv2 using 8 GPUs.
python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/afhqv2-512x512.zip \
--gpus=8 --batch=32 --gamma=8.2 --mirror=1
# Fine-tune StyleGAN3-R for MetFaces-U using 1 GPU, starting from the pre-trained FFHQ-U pickle.
python train.py --outdir=~/training-runs --cfg=stylegan3-r --data=~/datasets/metfacesu-1024x1024.zip \
--gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=5 \
--resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl
# Train StyleGAN2 for FFHQ at 1024x1024 resolution using 8 GPUs.
python train.py --outdir=~/training-runs --cfg=stylegan2 --data=~/datasets/ffhq-1024x1024.zip \
--gpus=8 --batch=32 --gamma=10 --mirror=1 --aug=noaug
```
Note that the result quality and training time depend heavily on the exact set of options. The most important ones (`--gpus`, `--batch`, and `--gamma`) must be specified explicitly, and they should be selected with care. See [`python train.py --help`](./docs/train-help.txt) for the full list of options and [Training configurations](./docs/configs.md) for general guidelines &amp; recommendations, along with the expected training speed &amp; memory usage in different scenarios.
The results of each training run are saved to a newly created directory, for example `~/training-runs/00000-stylegan3-t-afhqv2-512x512-gpus8-batch32-gamma8.2`. The training loop exports network pickles (`network-snapshot-<KIMG>.pkl`) and random image grids (`fakes<KIMG>.png`) at regular intervals (controlled by `--snap`). For each exported pickle, it evaluates FID (controlled by `--metrics`) and logs the result in `metric-fid50k_full.jsonl`. It also records various statistics in `training_stats.jsonl`, as well as `*.tfevents` if TensorBoard is installed.
## 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.
Additional quality metrics can also be computed after the training:
```.bash
# Previous training run: look up options automatically, save result to JSONL file.
python calc_metrics.py --metrics=eqt50k_int,eqr50k \
--network=~/training-runs/00000-stylegan3-r-mydataset/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-1024x1024.zip --mirror=1 \
--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-ffhq-1024x1024.pkl
```
The first example looks up the training configuration and performs the same operation as if `--metrics=eqt50k_int,eqr50k` had been specified during training. The second example downloads a pre-trained network pickle, in which case the values of `--data` and `--mirror` must be specified explicitly.
Note that the metrics can be quite expensive to compute (up to 1h), and many of them have an additional one-off cost for each 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.
Recommended metrics:
* `fid50k_full`: Fr&eacute;chet inception distance<sup>[1]</sup> against the full dataset.
* `kid50k_full`: Kernel inception distance<sup>[2]</sup> against the full dataset.
* `pr50k3_full`: Precision and recall<sup>[3]</sup> againt the full dataset.
* `ppl2_wend`: Perceptual path length<sup>[4]</sup> in W, endpoints, full image.
* `eqt50k_int`: Equivariance<sup>[5]</sup> w.r.t. integer translation (EQ-T).
* `eqt50k_frac`: Equivariance w.r.t. fractional translation (EQ-T<sub>frac</sub>).
* `eqr50k`: Equivariance w.r.t. rotation (EQ-R).
Legacy metrics:
* `fid50k`: Fr&eacute;chet inception distance against 50k real images.
* `kid50k`: Kernel inception distance against 50k real images.
* `pr50k3`: Precision and recall against 50k real images.
* `is50k`: Inception score<sup>[6]</sup> for CIFAR-10.
References:
1. [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500), Heusel et al. 2017
2. [Demystifying MMD GANs](https://arxiv.org/abs/1801.01401), Bi&nacute;kowski et al. 2018
3. [Improved Precision and Recall Metric for Assessing Generative Models](https://arxiv.org/abs/1904.06991), Kynk&auml;&auml;nniemi et al. 2019
4. [A Style-Based Generator Architecture for Generative Adversarial Networks](https://arxiv.org/abs/1812.04948), Karras et al. 2018
5. [Alias-Free Generative Adversarial Networks](https://nvlabs.github.io/stylegan3), Karras et al. 2021
6. [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498), Salimans et al. 2016
## Spectral analysis
The easiest way to inspect the spectral properties of a given generator is to use the built-in FFT mode in `visualizer.py`. In addition, you can visualize average 2D power spectra (Appendix A, Figure 15) as follows:
```.bash
# Calculate dataset mean and std, needed in subsequent steps.
python avg_spectra.py stats --source=~/datasets/ffhq-1024x1024.zip
# Calculate average spectrum for the training data.
python avg_spectra.py calc --source=~/datasets/ffhq-1024x1024.zip \
--dest=tmp/training-data.npz --mean=112.684 --std=69.509
# Calculate average spectrum for a pre-trained generator.
python avg_spectra.py calc \
--source=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhq-1024x1024.pkl \
--dest=tmp/stylegan3-r.npz --mean=112.684 --std=69.509 --num=70000
# Display results.
python avg_spectra.py heatmap tmp/training-data.npz
python avg_spectra.py heatmap tmp/stylegan3-r.npz
python avg_spectra.py slices tmp/training-data.npz tmp/stylegan3-r.npz
```
<a href="./docs/avg_spectra_screen0.png"><img alt="Average spectra screenshot" src="./docs/avg_spectra_screen0_half.png"></img></a>
## License
Copyright &copy; 2021, NVIDIA Corporation & affiliates. All rights reserved.
This work is made available under the [Nvidia Source Code License](https://github.com/NVlabs/stylegan3/blob/main/LICENSE.txt).
## Citation
```
@inproceedings{Karras2021,
author = {Tero Karras and Miika Aittala and Samuli Laine and Erik H\"ark\"onen and Janne Hellsten and Jaakko Lehtinen and Timo Aila},
title = {Alias-Free Generative Adversarial Networks},
booktitle = {Proc. NeurIPS},
year = {2021}
}
```
## 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, Ming-Yu Liu, Koki Nagano, Tuomas Kynk&auml;&auml;nniemi, and Timo Viitanen for reviewing early drafts and helpful suggestions. Fr&eacute;do Durand for early discussions. Tero Kuosmanen for maintaining our compute infrastructure. AFHQ authors for an updated version of their dataset. Getty Images for the training images in the Beaches dataset. We did not receive external funding or additional revenues for this project.

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Compare average power spectra between real and generated images,
or between multiple generators."""
import os
import numpy as np
import torch
import torch.fft
import scipy.ndimage
import matplotlib.pyplot as plt
import click
import tqdm
import dnnlib
import legacy
from training import dataset
#----------------------------------------------------------------------------
# Setup an iterator for streaming images, in uint8 NCHW format, based on the
# respective command line options.
def stream_source_images(source, num, seed, device, data_loader_kwargs=None): # => num_images, image_size, image_iter
ext = source.split('.')[-1].lower()
if data_loader_kwargs is None:
data_loader_kwargs = dict(pin_memory=True, num_workers=3, prefetch_factor=2)
if ext == 'pkl':
if num is None:
raise click.ClickException('--num is required when --source points to network pickle')
with dnnlib.util.open_url(source) as f:
G = legacy.load_network_pkl(f)['G_ema'].to(device)
def generate_image(seed):
rnd = np.random.RandomState(seed)
z = torch.from_numpy(rnd.randn(1, G.z_dim)).to(device)
c = torch.zeros([1, G.c_dim], device=device)
if G.c_dim > 0:
c[:, rnd.randint(G.c_dim)] = 1
return (G(z=z, c=c) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
_ = generate_image(seed) # warm up
image_iter = (generate_image(seed + idx) for idx in range(num))
return num, G.img_resolution, image_iter
elif ext == 'zip' or os.path.isdir(source):
dataset_obj = dataset.ImageFolderDataset(path=source, max_size=num, random_seed=seed)
if num is not None and num != len(dataset_obj):
raise click.ClickException(f'--source contains fewer than {num} images')
data_loader = torch.utils.data.DataLoader(dataset_obj, batch_size=1, **data_loader_kwargs)
image_iter = (image.to(device) for image, _label in data_loader)
return len(dataset_obj), dataset_obj.resolution, image_iter
else:
raise click.ClickException('--source must point to network pickle, dataset zip, or directory')
#----------------------------------------------------------------------------
# Load average power spectrum from the specified .npz file and construct
# the corresponding heatmap for visualization.
def construct_heatmap(npz_file, smooth):
npz_data = np.load(npz_file)
spectrum = npz_data['spectrum']
image_size = npz_data['image_size']
hmap = np.log10(spectrum) * 10 # dB
hmap = np.fft.fftshift(hmap)
hmap = np.concatenate([hmap, hmap[:1, :]], axis=0)
hmap = np.concatenate([hmap, hmap[:, :1]], axis=1)
if smooth > 0:
sigma = spectrum.shape[0] / image_size * smooth
hmap = scipy.ndimage.gaussian_filter(hmap, sigma=sigma, mode='nearest')
return hmap, image_size
#----------------------------------------------------------------------------
@click.group()
def main():
"""Compare average power spectra between real and generated images,
or between multiple generators.
Example:
\b
# Calculate dataset mean and std, needed in subsequent steps.
python avg_spectra.py stats --source=~/datasets/ffhq-1024x1024.zip
\b
# Calculate average spectrum for the training data.
python avg_spectra.py calc --source=~/datasets/ffhq-1024x1024.zip \\
--dest=tmp/training-data.npz --mean=112.684 --std=69.509
\b
# Calculate average spectrum for a pre-trained generator.
python avg_spectra.py calc \\
--source=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhq-1024x1024.pkl \\
--dest=tmp/stylegan3-r.npz --mean=112.684 --std=69.509 --num=70000
\b
# Display results.
python avg_spectra.py heatmap tmp/training-data.npz
python avg_spectra.py heatmap tmp/stylegan3-r.npz
python avg_spectra.py slices tmp/training-data.npz tmp/stylegan3-r.npz
\b
# Save as PNG.
python avg_spectra.py heatmap tmp/training-data.npz --save=tmp/training-data.png --dpi=300
python avg_spectra.py heatmap tmp/stylegan3-r.npz --save=tmp/stylegan3-r.png --dpi=300
python avg_spectra.py slices tmp/training-data.npz tmp/stylegan3-r.npz --save=tmp/slices.png --dpi=300
"""
#----------------------------------------------------------------------------
@main.command()
@click.option('--source', help='Network pkl, dataset zip, or directory', metavar='[PKL|ZIP|DIR]', required=True)
@click.option('--num', help='Number of images to process [default: all]', metavar='INT', type=click.IntRange(min=1))
@click.option('--seed', help='Random seed for selecting the images', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True)
def stats(source, num, seed, device=torch.device('cuda')):
"""Calculate dataset mean and standard deviation needed by 'calc'."""
torch.multiprocessing.set_start_method('spawn')
num_images, _image_size, image_iter = stream_source_images(source=source, num=num, seed=seed, device=device)
# Accumulate moments.
moments = torch.zeros([3], dtype=torch.float64, device=device)
for image in tqdm.tqdm(image_iter, total=num_images):
image = image.to(torch.float64)
moments += torch.stack([torch.ones_like(image).sum(), image.sum(), image.square().sum()])
moments = moments / moments[0]
# Compute mean and standard deviation.
mean = moments[1]
std = (moments[2] - moments[1].square()).sqrt()
print(f'--mean={mean:g} --std={std:g}')
#----------------------------------------------------------------------------
@main.command()
@click.option('--source', help='Network pkl, dataset zip, or directory', metavar='[PKL|ZIP|DIR]', required=True)
@click.option('--dest', help='Where to store the result', metavar='NPZ', required=True)
@click.option('--mean', help='Dataset mean for whitening', metavar='FLOAT', type=float, required=True)
@click.option('--std', help='Dataset standard deviation for whitening', metavar='FLOAT', type=click.FloatRange(min=0), required=True)
@click.option('--num', help='Number of images to process [default: all]', metavar='INT', type=click.IntRange(min=1))
@click.option('--seed', help='Random seed for selecting the images', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True)
@click.option('--beta', help='Shape parameter for the Kaiser window', metavar='FLOAT', type=click.FloatRange(min=0), default=8, show_default=True)
@click.option('--interp', help='Frequency-domain interpolation factor', metavar='INT', type=click.IntRange(min=1), default=4, show_default=True)
def calc(source, dest, mean, std, num, seed, beta, interp, device=torch.device('cuda')):
"""Calculate average power spectrum and store it in .npz file."""
torch.multiprocessing.set_start_method('spawn')
num_images, image_size, image_iter = stream_source_images(source=source, num=num, seed=seed, device=device)
spectrum_size = image_size * interp
padding = spectrum_size - image_size
# Setup window function.
window = torch.kaiser_window(image_size, periodic=False, beta=beta, device=device)
window *= window.square().sum().rsqrt()
window = window.ger(window).unsqueeze(0).unsqueeze(1)
# Accumulate power spectrum.
spectrum = torch.zeros([spectrum_size, spectrum_size], dtype=torch.float64, device=device)
for image in tqdm.tqdm(image_iter, total=num_images):
image = (image.to(torch.float64) - mean) / std
image = torch.nn.functional.pad(image * window, [0, padding, 0, padding])
spectrum += torch.fft.fftn(image, dim=[2,3]).abs().square().mean(dim=[0,1])
spectrum /= num_images
# Save result.
if os.path.dirname(dest):
os.makedirs(os.path.dirname(dest), exist_ok=True)
np.savez(dest, spectrum=spectrum.cpu().numpy(), image_size=image_size)
#----------------------------------------------------------------------------
@main.command()
@click.argument('npz-file', nargs=1)
@click.option('--save', help='Save the plot and exit', metavar='[PNG|PDF|...]')
@click.option('--dpi', help='Figure resolution', metavar='FLOAT', type=click.FloatRange(min=1), default=100, show_default=True)
@click.option('--smooth', help='Amount of smoothing', metavar='FLOAT', type=click.FloatRange(min=0), default=1.25, show_default=True)
def heatmap(npz_file, save, smooth, dpi):
"""Visualize 2D heatmap based on the given .npz file."""
hmap, image_size = construct_heatmap(npz_file=npz_file, smooth=smooth)
# Setup plot.
plt.figure(figsize=[6, 4.8], dpi=dpi, tight_layout=True)
freqs = np.linspace(-0.5, 0.5, num=hmap.shape[0], endpoint=True) * image_size
ticks = np.linspace(freqs[0], freqs[-1], num=5, endpoint=True)
levels = np.linspace(-40, 20, num=13, endpoint=True)
# Draw heatmap.
plt.xlim(ticks[0], ticks[-1])
plt.ylim(ticks[0], ticks[-1])
plt.xticks(ticks)
plt.yticks(ticks)
plt.contourf(freqs, freqs, hmap, levels=levels, extend='both', cmap='Blues')
plt.gca().set_aspect('equal')
plt.colorbar(ticks=levels)
plt.contour(freqs, freqs, hmap, levels=levels, extend='both', linestyles='solid', linewidths=1, colors='midnightblue', alpha=0.2)
# Display or save.
if save is None:
plt.show()
else:
if os.path.dirname(save):
os.makedirs(os.path.dirname(save), exist_ok=True)
plt.savefig(save)
#----------------------------------------------------------------------------
@main.command()
@click.argument('npz-files', nargs=-1, required=True)
@click.option('--save', help='Save the plot and exit', metavar='[PNG|PDF|...]')
@click.option('--dpi', help='Figure resolution', metavar='FLOAT', type=click.FloatRange(min=1), default=100, show_default=True)
@click.option('--smooth', help='Amount of smoothing', metavar='FLOAT', type=click.FloatRange(min=0), default=0, show_default=True)
def slices(npz_files, save, dpi, smooth):
"""Visualize 1D slices based on the given .npz files."""
cases = [dnnlib.EasyDict(npz_file=npz_file) for npz_file in npz_files]
for c in cases:
c.hmap, c.image_size = construct_heatmap(npz_file=c.npz_file, smooth=smooth)
c.label = os.path.splitext(os.path.basename(c.npz_file))[0]
# Check consistency.
image_size = cases[0].image_size
hmap_size = cases[0].hmap.shape[0]
if any(c.image_size != image_size or c.hmap.shape[0] != hmap_size for c in cases):
raise click.ClickException('All .npz must have the same resolution')
# Setup plot.
plt.figure(figsize=[12, 4.6], dpi=dpi, tight_layout=True)
hmap_center = hmap_size // 2
hmap_range = np.arange(hmap_center, hmap_size)
freqs0 = np.linspace(0, image_size / 2, num=(hmap_size // 2 + 1), endpoint=True)
freqs45 = np.linspace(0, image_size / np.sqrt(2), num=(hmap_size // 2 + 1), endpoint=True)
xticks0 = np.linspace(freqs0[0], freqs0[-1], num=9, endpoint=True)
xticks45 = np.round(np.linspace(freqs45[0], freqs45[-1], num=9, endpoint=True))
yticks = np.linspace(-50, 30, num=9, endpoint=True)
# Draw 0 degree slice.
plt.subplot(1, 2, 1)
plt.title('0\u00b0 slice')
plt.xlim(xticks0[0], xticks0[-1])
plt.ylim(yticks[0], yticks[-1])
plt.xticks(xticks0)
plt.yticks(yticks)
for c in cases:
plt.plot(freqs0, c.hmap[hmap_center, hmap_range], label=c.label)
plt.grid()
plt.legend(loc='upper right')
# Draw 45 degree slice.
plt.subplot(1, 2, 2)
plt.title('45\u00b0 slice')
plt.xlim(xticks45[0], xticks45[-1])
plt.ylim(yticks[0], yticks[-1])
plt.xticks(xticks45)
plt.yticks(yticks)
for c in cases:
plt.plot(freqs45, c.hmap[hmap_range, hmap_range], label=c.label)
plt.grid()
plt.legend(loc='upper right')
# Display or save.
if save is None:
plt.show()
else:
if os.path.dirname(save):
os.makedirs(os.path.dirname(save), exist_ok=True)
plt.savefig(save)
#----------------------------------------------------------------------------
if __name__ == "__main__":
main() # pylint: disable=no-value-for-parameter
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Calculate quality metrics for previous training run or pretrained network pickle."""
import os
import click
import json
import tempfile
import copy
import torch
import dnnlib
import legacy
from metrics import metric_main
from metrics import metric_utils
from torch_utils import training_stats
from torch_utils import custom_ops
from torch_utils import misc
from torch_utils.ops import conv2d_gradfix
#----------------------------------------------------------------------------
def subprocess_fn(rank, args, temp_dir):
dnnlib.util.Logger(should_flush=True)
# Init torch.distributed.
if args.num_gpus > 1:
init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
if os.name == 'nt':
init_method = 'file:///' + init_file.replace('\\', '/')
torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=args.num_gpus)
else:
init_method = f'file://{init_file}'
torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=args.num_gpus)
# Init torch_utils.
sync_device = torch.device('cuda', rank) if args.num_gpus > 1 else None
training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
if rank != 0 or not args.verbose:
custom_ops.verbosity = 'none'
# Configure torch.
device = torch.device('cuda', rank)
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False
conv2d_gradfix.enabled = True
# Print network summary.
G = copy.deepcopy(args.G).eval().requires_grad_(False).to(device)
if rank == 0 and args.verbose:
z = torch.empty([1, G.z_dim], device=device)
c = torch.empty([1, G.c_dim], device=device)
misc.print_module_summary(G, [z, c])
# Calculate each metric.
for metric in args.metrics:
if rank == 0 and args.verbose:
print(f'Calculating {metric}...')
progress = metric_utils.ProgressMonitor(verbose=args.verbose)
result_dict = metric_main.calc_metric(metric=metric, G=G, dataset_kwargs=args.dataset_kwargs,
num_gpus=args.num_gpus, rank=rank, device=device, progress=progress)
if rank == 0:
metric_main.report_metric(result_dict, run_dir=args.run_dir, snapshot_pkl=args.network_pkl)
if rank == 0 and args.verbose:
print()
# Done.
if rank == 0 and args.verbose:
print('Exiting...')
#----------------------------------------------------------------------------
def parse_comma_separated_list(s):
if isinstance(s, list):
return s
if s is None or s.lower() == 'none' or s == '':
return []
return s.split(',')
#----------------------------------------------------------------------------
@click.command()
@click.pass_context
@click.option('network_pkl', '--network', help='Network pickle filename or URL', metavar='PATH', required=True)
@click.option('--metrics', help='Quality metrics', metavar='[NAME|A,B,C|none]', type=parse_comma_separated_list, default='fid50k_full', show_default=True)
@click.option('--data', help='Dataset to evaluate against [default: look up]', metavar='[ZIP|DIR]')
@click.option('--mirror', help='Enable dataset x-flips [default: look up]', type=bool, metavar='BOOL')
@click.option('--gpus', help='Number of GPUs to use', type=int, default=1, metavar='INT', show_default=True)
@click.option('--verbose', help='Print optional information', type=bool, default=True, metavar='BOOL', show_default=True)
def calc_metrics(ctx, network_pkl, metrics, data, mirror, gpus, verbose):
"""Calculate quality metrics for previous training run or pretrained network pickle.
Examples:
\b
# Previous training run: look up options automatically, save result to JSONL file.
python calc_metrics.py --metrics=eqt50k_int,eqr50k \\
--network=~/training-runs/00000-stylegan3-r-mydataset/network-snapshot-000000.pkl
\b
# Pre-trained network pickle: specify dataset explicitly, print result to stdout.
python calc_metrics.py --metrics=fid50k_full --data=~/datasets/ffhq-1024x1024.zip --mirror=1 \\
--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-ffhq-1024x1024.pkl
\b
Recommended metrics:
fid50k_full Frechet inception distance against the full dataset.
kid50k_full Kernel inception distance against the full dataset.
pr50k3_full Precision and recall againt the full dataset.
ppl2_wend Perceptual path length in W, endpoints, full image.
eqt50k_int Equivariance w.r.t. integer translation (EQ-T).
eqt50k_frac Equivariance w.r.t. fractional translation (EQ-T_frac).
eqr50k Equivariance w.r.t. rotation (EQ-R).
\b
Legacy metrics:
fid50k Frechet inception distance against 50k real images.
kid50k Kernel inception distance against 50k real images.
pr50k3 Precision and recall against 50k real images.
is50k Inception score for CIFAR-10.
"""
dnnlib.util.Logger(should_flush=True)
# Validate arguments.
args = dnnlib.EasyDict(metrics=metrics, num_gpus=gpus, network_pkl=network_pkl, verbose=verbose)
if not all(metric_main.is_valid_metric(metric) for metric in args.metrics):
ctx.fail('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics()))
if not args.num_gpus >= 1:
ctx.fail('--gpus must be at least 1')
# Load network.
if not dnnlib.util.is_url(network_pkl, allow_file_urls=True) and not os.path.isfile(network_pkl):
ctx.fail('--network must point to a file or URL')
if args.verbose:
print(f'Loading network from "{network_pkl}"...')
with dnnlib.util.open_url(network_pkl, verbose=args.verbose) as f:
network_dict = legacy.load_network_pkl(f)
args.G = network_dict['G_ema'] # subclass of torch.nn.Module
# Initialize dataset options.
if data is not None:
args.dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data)
elif network_dict['training_set_kwargs'] is not None:
args.dataset_kwargs = dnnlib.EasyDict(network_dict['training_set_kwargs'])
else:
ctx.fail('Could not look up dataset options; please specify --data')
# Finalize dataset options.
args.dataset_kwargs.resolution = args.G.img_resolution
args.dataset_kwargs.use_labels = (args.G.c_dim != 0)
if mirror is not None:
args.dataset_kwargs.xflip = mirror
# Print dataset options.
if args.verbose:
print('Dataset options:')
print(json.dumps(args.dataset_kwargs, indent=2))
# Locate run dir.
args.run_dir = None
if os.path.isfile(network_pkl):
pkl_dir = os.path.dirname(network_pkl)
if os.path.isfile(os.path.join(pkl_dir, 'training_options.json')):
args.run_dir = pkl_dir
# Launch processes.
if args.verbose:
print('Launching processes...')
torch.multiprocessing.set_start_method('spawn')
with tempfile.TemporaryDirectory() as temp_dir:
if args.num_gpus == 1:
subprocess_fn(rank=0, args=args, temp_dir=temp_dir)
else:
torch.multiprocessing.spawn(fn=subprocess_fn, args=(args, temp_dir), nprocs=args.num_gpus)
#----------------------------------------------------------------------------
if __name__ == "__main__":
calc_metrics() # pylint: disable=no-value-for-parameter
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Tool for creating ZIP/PNG based datasets."""
import functools
import gzip
import io
import json
import os
import pickle
import re
import sys
import tarfile
import zipfile
from pathlib import Path
from typing import Callable, Optional, Tuple, Union
import click
import numpy as np
import PIL.Image
from tqdm import tqdm
#----------------------------------------------------------------------------
def error(msg):
print('Error: ' + msg)
sys.exit(1)
#----------------------------------------------------------------------------
def parse_tuple(s: str) -> Tuple[int, int]:
'''Parse a 'M,N' or 'MxN' integer tuple.
Example:
'4x2' returns (4,2)
'0,1' returns (0,1)
'''
if m := re.match(r'^(\d+)[x,](\d+)$', s):
return (int(m.group(1)), int(m.group(2)))
raise ValueError(f'cannot parse tuple {s}')
#----------------------------------------------------------------------------
def maybe_min(a: int, b: Optional[int]) -> int:
if b is not None:
return min(a, b)
return a
#----------------------------------------------------------------------------
def file_ext(name: Union[str, Path]) -> str:
return str(name).split('.')[-1]
#----------------------------------------------------------------------------
def is_image_ext(fname: Union[str, Path]) -> bool:
ext = file_ext(fname).lower()
return f'.{ext}' in PIL.Image.EXTENSION # type: ignore
#----------------------------------------------------------------------------
def open_image_folder(source_dir, *, max_images: Optional[int]):
input_images = [str(f) for f in sorted(Path(source_dir).rglob('*')) if is_image_ext(f) and os.path.isfile(f)]
# Load labels.
labels = {}
meta_fname = os.path.join(source_dir, 'dataset.json')
if os.path.isfile(meta_fname):
with open(meta_fname, 'r') as file:
labels = json.load(file)['labels']
if labels is not None:
labels = { x[0]: x[1] for x in labels }
else:
labels = {}
max_idx = maybe_min(len(input_images), max_images)
def iterate_images():
for idx, fname in enumerate(input_images):
arch_fname = os.path.relpath(fname, source_dir)
arch_fname = arch_fname.replace('\\', '/')
img = np.array(PIL.Image.open(fname))
yield dict(img=img, label=labels.get(arch_fname))
if idx >= max_idx-1:
break
return max_idx, iterate_images()
#----------------------------------------------------------------------------
def open_image_zip(source, *, max_images: Optional[int]):
with zipfile.ZipFile(source, mode='r') as z:
input_images = [str(f) for f in sorted(z.namelist()) if is_image_ext(f)]
# Load labels.
labels = {}
if 'dataset.json' in z.namelist():
with z.open('dataset.json', 'r') as file:
labels = json.load(file)['labels']
if labels is not None:
labels = { x[0]: x[1] for x in labels }
else:
labels = {}
max_idx = maybe_min(len(input_images), max_images)
def iterate_images():
with zipfile.ZipFile(source, mode='r') as z:
for idx, fname in enumerate(input_images):
with z.open(fname, 'r') as file:
img = PIL.Image.open(file) # type: ignore
img = np.array(img)
yield dict(img=img, label=labels.get(fname))
if idx >= max_idx-1:
break
return max_idx, iterate_images()
#----------------------------------------------------------------------------
def open_lmdb(lmdb_dir: str, *, max_images: Optional[int]):
import cv2 # pip install opencv-python # pylint: disable=import-error
import lmdb # pip install lmdb # pylint: disable=import-error
with lmdb.open(lmdb_dir, readonly=True, lock=False).begin(write=False) as txn:
max_idx = maybe_min(txn.stat()['entries'], max_images)
def iterate_images():
with lmdb.open(lmdb_dir, readonly=True, lock=False).begin(write=False) as txn:
for idx, (_key, value) in enumerate(txn.cursor()):
try:
try:
img = cv2.imdecode(np.frombuffer(value, dtype=np.uint8), 1)
if img is None:
raise IOError('cv2.imdecode failed')
img = img[:, :, ::-1] # BGR => RGB
except IOError:
img = np.array(PIL.Image.open(io.BytesIO(value)))
yield dict(img=img, label=None)
if idx >= max_idx-1:
break
except:
print(sys.exc_info()[1])
return max_idx, iterate_images()
#----------------------------------------------------------------------------
def open_cifar10(tarball: str, *, max_images: Optional[int]):
images = []
labels = []
with tarfile.open(tarball, 'r:gz') as tar:
for batch in range(1, 6):
member = tar.getmember(f'cifar-10-batches-py/data_batch_{batch}')
with tar.extractfile(member) as file:
data = pickle.load(file, encoding='latin1')
images.append(data['data'].reshape(-1, 3, 32, 32))
labels.append(data['labels'])
images = np.concatenate(images)
labels = np.concatenate(labels)
images = images.transpose([0, 2, 3, 1]) # NCHW -> NHWC
assert images.shape == (50000, 32, 32, 3) and images.dtype == np.uint8
assert labels.shape == (50000,) and labels.dtype in [np.int32, np.int64]
assert np.min(images) == 0 and np.max(images) == 255
assert np.min(labels) == 0 and np.max(labels) == 9
max_idx = maybe_min(len(images), max_images)
def iterate_images():
for idx, img in enumerate(images):
yield dict(img=img, label=int(labels[idx]))
if idx >= max_idx-1:
break
return max_idx, iterate_images()
#----------------------------------------------------------------------------
def open_mnist(images_gz: str, *, max_images: Optional[int]):
labels_gz = images_gz.replace('-images-idx3-ubyte.gz', '-labels-idx1-ubyte.gz')
assert labels_gz != images_gz
images = []
labels = []
with gzip.open(images_gz, 'rb') as f:
images = np.frombuffer(f.read(), np.uint8, offset=16)
with gzip.open(labels_gz, 'rb') as f:
labels = np.frombuffer(f.read(), np.uint8, offset=8)
images = images.reshape(-1, 28, 28)
images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0)
assert images.shape == (60000, 32, 32) and images.dtype == np.uint8
assert labels.shape == (60000,) and labels.dtype == np.uint8
assert np.min(images) == 0 and np.max(images) == 255
assert np.min(labels) == 0 and np.max(labels) == 9
max_idx = maybe_min(len(images), max_images)
def iterate_images():
for idx, img in enumerate(images):
yield dict(img=img, label=int(labels[idx]))
if idx >= max_idx-1:
break
return max_idx, iterate_images()
#----------------------------------------------------------------------------
def make_transform(
transform: Optional[str],
output_width: Optional[int],
output_height: Optional[int]
) -> Callable[[np.ndarray], Optional[np.ndarray]]:
def scale(width, height, img):
w = img.shape[1]
h = img.shape[0]
if width == w and height == h:
return img
img = PIL.Image.fromarray(img)
ww = width if width is not None else w
hh = height if height is not None else h
img = img.resize((ww, hh), PIL.Image.LANCZOS)
return np.array(img)
def center_crop(width, height, img):
crop = np.min(img.shape[:2])
img = img[(img.shape[0] - crop) // 2 : (img.shape[0] + crop) // 2, (img.shape[1] - crop) // 2 : (img.shape[1] + crop) // 2]
img = PIL.Image.fromarray(img, 'RGB')
img = img.resize((width, height), PIL.Image.LANCZOS)
return np.array(img)
def center_crop_wide(width, height, img):
ch = int(np.round(width * img.shape[0] / img.shape[1]))
if img.shape[1] < width or ch < height:
return None
img = img[(img.shape[0] - ch) // 2 : (img.shape[0] + ch) // 2]
img = PIL.Image.fromarray(img, 'RGB')
img = img.resize((width, height), PIL.Image.LANCZOS)
img = np.array(img)
canvas = np.zeros([width, width, 3], dtype=np.uint8)
canvas[(width - height) // 2 : (width + height) // 2, :] = img
return canvas
if transform is None:
return functools.partial(scale, output_width, output_height)
if transform == 'center-crop':
if (output_width is None) or (output_height is None):
error ('must specify --resolution=WxH when using ' + transform + 'transform')
return functools.partial(center_crop, output_width, output_height)
if transform == 'center-crop-wide':
if (output_width is None) or (output_height is None):
error ('must specify --resolution=WxH when using ' + transform + ' transform')
return functools.partial(center_crop_wide, output_width, output_height)
assert False, 'unknown transform'
#----------------------------------------------------------------------------
def open_dataset(source, *, max_images: Optional[int]):
if os.path.isdir(source):
if source.rstrip('/').endswith('_lmdb'):
return open_lmdb(source, max_images=max_images)
else:
return open_image_folder(source, max_images=max_images)
elif os.path.isfile(source):
if os.path.basename(source) == 'cifar-10-python.tar.gz':
return open_cifar10(source, max_images=max_images)
elif os.path.basename(source) == 'train-images-idx3-ubyte.gz':
return open_mnist(source, max_images=max_images)
elif file_ext(source) == 'zip':
return open_image_zip(source, max_images=max_images)
else:
assert False, 'unknown archive type'
else:
error(f'Missing input file or directory: {source}')
#----------------------------------------------------------------------------
def open_dest(dest: str) -> Tuple[str, Callable[[str, Union[bytes, str]], None], Callable[[], None]]:
dest_ext = file_ext(dest)
if dest_ext == 'zip':
if os.path.dirname(dest) != '':
os.makedirs(os.path.dirname(dest), exist_ok=True)
zf = zipfile.ZipFile(file=dest, mode='w', compression=zipfile.ZIP_STORED)
def zip_write_bytes(fname: str, data: Union[bytes, str]):
zf.writestr(fname, data)
return '', zip_write_bytes, zf.close
else:
# If the output folder already exists, check that is is
# empty.
#
# Note: creating the output directory is not strictly
# necessary as folder_write_bytes() also mkdirs, but it's better
# to give an error message earlier in case the dest folder
# somehow cannot be created.
if os.path.isdir(dest) and len(os.listdir(dest)) != 0:
error('--dest folder must be empty')
os.makedirs(dest, exist_ok=True)
def folder_write_bytes(fname: str, data: Union[bytes, str]):
os.makedirs(os.path.dirname(fname), exist_ok=True)
with open(fname, 'wb') as fout:
if isinstance(data, str):
data = data.encode('utf8')
fout.write(data)
return dest, folder_write_bytes, lambda: None
#----------------------------------------------------------------------------
@click.command()
@click.pass_context
@click.option('--source', help='Directory or archive name for input dataset', required=True, metavar='PATH')
@click.option('--dest', help='Output directory or archive name for output dataset', required=True, metavar='PATH')
@click.option('--max-images', help='Output only up to `max-images` images', type=int, default=None)
@click.option('--transform', help='Input crop/resize mode', type=click.Choice(['center-crop', 'center-crop-wide']))
@click.option('--resolution', help='Output resolution (e.g., \'512x512\')', metavar='WxH', type=parse_tuple)
def convert_dataset(
ctx: click.Context,
source: str,
dest: str,
max_images: Optional[int],
transform: Optional[str],
resolution: Optional[Tuple[int, int]]
):
"""Convert an image dataset into a dataset archive usable with StyleGAN2 ADA PyTorch.
The input dataset format is guessed from the --source argument:
\b
--source *_lmdb/ Load LSUN dataset
--source cifar-10-python.tar.gz Load CIFAR-10 dataset
--source train-images-idx3-ubyte.gz Load MNIST dataset
--source path/ Recursively load all images from path/
--source dataset.zip Recursively load all images from dataset.zip
Specifying the output format and path:
\b
--dest /path/to/dir Save output files under /path/to/dir
--dest /path/to/dataset.zip Save output files into /path/to/dataset.zip
The output dataset format can be either an image folder or an uncompressed zip archive.
Zip archives makes it easier to move datasets around file servers and clusters, and may
offer better training performance on network file systems.
Images within the dataset archive will be stored as uncompressed PNG.
Uncompresed PNGs can be efficiently decoded in the training loop.
Class labels are stored in a file called 'dataset.json' that is stored at the
dataset root folder. This file has the following structure:
\b
{
"labels": [
["00000/img00000000.png",6],
["00000/img00000001.png",9],
... repeated for every image in the datase
["00049/img00049999.png",1]
]
}
If the 'dataset.json' file cannot be found, the dataset is interpreted as
not containing class labels.
Image scale/crop and resolution requirements:
Output images must be square-shaped and they must all have the same power-of-two
dimensions.
To scale arbitrary input image size to a specific width and height, use the
--resolution option. Output resolution will be either the original
input resolution (if resolution was not specified) or the one specified with
--resolution option.
Use the --transform=center-crop or --transform=center-crop-wide options to apply a
center crop transform on the input image. These options should be used with the
--resolution option. For example:
\b
python dataset_tool.py --source LSUN/raw/cat_lmdb --dest /tmp/lsun_cat \\
--transform=center-crop-wide --resolution=512x384
"""
PIL.Image.init() # type: ignore
if dest == '':
ctx.fail('--dest output filename or directory must not be an empty string')
num_files, input_iter = open_dataset(source, max_images=max_images)
archive_root_dir, save_bytes, close_dest = open_dest(dest)
if resolution is None: resolution = (None, None)
transform_image = make_transform(transform, *resolution)
dataset_attrs = None
labels = []
for idx, image in tqdm(enumerate(input_iter), total=num_files):
idx_str = f'{idx:08d}'
archive_fname = f'{idx_str[:5]}/img{idx_str}.png'
# Apply crop and resize.
img = transform_image(image['img'])
# Transform may drop images.
if img is None:
continue
# Error check to require uniform image attributes across
# the whole dataset.
channels = img.shape[2] if img.ndim == 3 else 1
cur_image_attrs = {
'width': img.shape[1],
'height': img.shape[0],
'channels': channels
}
if dataset_attrs is None:
dataset_attrs = cur_image_attrs
width = dataset_attrs['width']
height = dataset_attrs['height']
if width != height:
error(f'Image dimensions after scale and crop are required to be square. Got {width}x{height}')
if dataset_attrs['channels'] not in [1, 3]:
error('Input images must be stored as RGB or grayscale')
if width != 2 ** int(np.floor(np.log2(width))):
error('Image width/height after scale and crop are required to be power-of-two')
elif dataset_attrs != cur_image_attrs:
err = [f' dataset {k}/cur image {k}: {dataset_attrs[k]}/{cur_image_attrs[k]}' for k in dataset_attrs.keys()] # pylint: disable=unsubscriptable-object
error(f'Image {archive_fname} attributes must be equal across all images of the dataset. Got:\n' + '\n'.join(err))
# Save the image as an uncompressed PNG.
img = PIL.Image.fromarray(img, { 1: 'L', 3: 'RGB' }[channels])
image_bits = io.BytesIO()
img.save(image_bits, format='png', compress_level=0, optimize=False)
save_bytes(os.path.join(archive_root_dir, archive_fname), image_bits.getbuffer())
labels.append([archive_fname, image['label']] if image['label'] is not None else None)
metadata = {
'labels': labels if all(x is not None for x in labels) else None
}
save_bytes(os.path.join(archive_root_dir, 'dataset.json'), json.dumps(metadata))
close_dest()
#----------------------------------------------------------------------------
if __name__ == "__main__":
convert_dataset() # pylint: disable=no-value-for-parameter

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from .util import EasyDict, make_cache_dir_path

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Miscellaneous utility classes and functions."""
import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
from typing import Any, List, Tuple, Union
# Util classes
# ------------------------------------------------------------------------------------------
class EasyDict(dict):
"""Convenience class that behaves like a dict but allows access with the attribute syntax."""
def __getattr__(self, name: str) -> Any:
try:
return self[name]
except KeyError:
raise AttributeError(name)
def __setattr__(self, name: str, value: Any) -> None:
self[name] = value
def __delattr__(self, name: str) -> None:
del self[name]
class Logger(object):
"""Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
self.file = None
if file_name is not None:
self.file = open(file_name, file_mode)
self.should_flush = should_flush
self.stdout = sys.stdout
self.stderr = sys.stderr
sys.stdout = self
sys.stderr = self
def __enter__(self) -> "Logger":
return self
def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
self.close()
def write(self, text: Union[str, bytes]) -> None:
"""Write text to stdout (and a file) and optionally flush."""
if isinstance(text, bytes):
text = text.decode()
if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
return
if self.file is not None:
self.file.write(text)
self.stdout.write(text)
if self.should_flush:
self.flush()
def flush(self) -> None:
"""Flush written text to both stdout and a file, if open."""
if self.file is not None:
self.file.flush()
self.stdout.flush()
def close(self) -> None:
"""Flush, close possible files, and remove stdout/stderr mirroring."""
self.flush()
# if using multiple loggers, prevent closing in wrong order
if sys.stdout is self:
sys.stdout = self.stdout
if sys.stderr is self:
sys.stderr = self.stderr
if self.file is not None:
self.file.close()
self.file = None
# Cache directories
# ------------------------------------------------------------------------------------------
_dnnlib_cache_dir = None
def set_cache_dir(path: str) -> None:
global _dnnlib_cache_dir
_dnnlib_cache_dir = path
def make_cache_dir_path(*paths: str) -> str:
if _dnnlib_cache_dir is not None:
return os.path.join(_dnnlib_cache_dir, *paths)
if 'DNNLIB_CACHE_DIR' in os.environ:
return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths)
if 'HOME' in os.environ:
return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths)
if 'USERPROFILE' in os.environ:
return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths)
return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths)
# Small util functions
# ------------------------------------------------------------------------------------------
def format_time(seconds: Union[int, float]) -> str:
"""Convert the seconds to human readable string with days, hours, minutes and seconds."""
s = int(np.rint(seconds))
if s < 60:
return "{0}s".format(s)
elif s < 60 * 60:
return "{0}m {1:02}s".format(s // 60, s % 60)
elif s < 24 * 60 * 60:
return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
else:
return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
def format_time_brief(seconds: Union[int, float]) -> str:
"""Convert the seconds to human readable string with days, hours, minutes and seconds."""
s = int(np.rint(seconds))
if s < 60:
return "{0}s".format(s)
elif s < 60 * 60:
return "{0}m {1:02}s".format(s // 60, s % 60)
elif s < 24 * 60 * 60:
return "{0}h {1:02}m".format(s // (60 * 60), (s // 60) % 60)
else:
return "{0}d {1:02}h".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24)
def ask_yes_no(question: str) -> bool:
"""Ask the user the question until the user inputs a valid answer."""
while True:
try:
print("{0} [y/n]".format(question))
return strtobool(input().lower())
except ValueError:
pass
def tuple_product(t: Tuple) -> Any:
"""Calculate the product of the tuple elements."""
result = 1
for v in t:
result *= v
return result
_str_to_ctype = {
"uint8": ctypes.c_ubyte,
"uint16": ctypes.c_uint16,
"uint32": ctypes.c_uint32,
"uint64": ctypes.c_uint64,
"int8": ctypes.c_byte,
"int16": ctypes.c_int16,
"int32": ctypes.c_int32,
"int64": ctypes.c_int64,
"float32": ctypes.c_float,
"float64": ctypes.c_double
}
def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
"""Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
type_str = None
if isinstance(type_obj, str):
type_str = type_obj
elif hasattr(type_obj, "__name__"):
type_str = type_obj.__name__
elif hasattr(type_obj, "name"):
type_str = type_obj.name
else:
raise RuntimeError("Cannot infer type name from input")
assert type_str in _str_to_ctype.keys()
my_dtype = np.dtype(type_str)
my_ctype = _str_to_ctype[type_str]
assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
return my_dtype, my_ctype
def is_pickleable(obj: Any) -> bool:
try:
with io.BytesIO() as stream:
pickle.dump(obj, stream)
return True
except:
return False
# Functionality to import modules/objects by name, and call functions by name
# ------------------------------------------------------------------------------------------
def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
"""Searches for the underlying module behind the name to some python object.
Returns the module and the object name (original name with module part removed)."""
# allow convenience shorthands, substitute them by full names
obj_name = re.sub("^np.", "numpy.", obj_name)
obj_name = re.sub("^tf.", "tensorflow.", obj_name)
# list alternatives for (module_name, local_obj_name)
parts = obj_name.split(".")
name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)]
# try each alternative in turn
for module_name, local_obj_name in name_pairs:
try:
module = importlib.import_module(module_name) # may raise ImportError
get_obj_from_module(module, local_obj_name) # may raise AttributeError
return module, local_obj_name
except:
pass
# maybe some of the modules themselves contain errors?
for module_name, _local_obj_name in name_pairs:
try:
importlib.import_module(module_name) # may raise ImportError
except ImportError:
if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
raise
# maybe the requested attribute is missing?
for module_name, local_obj_name in name_pairs:
try:
module = importlib.import_module(module_name) # may raise ImportError
get_obj_from_module(module, local_obj_name) # may raise AttributeError
except ImportError:
pass
# we are out of luck, but we have no idea why
raise ImportError(obj_name)
def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
"""Traverses the object name and returns the last (rightmost) python object."""
if obj_name == '':
return module
obj = module
for part in obj_name.split("."):
obj = getattr(obj, part)
return obj
def get_obj_by_name(name: str) -> Any:
"""Finds the python object with the given name."""
module, obj_name = get_module_from_obj_name(name)
return get_obj_from_module(module, obj_name)
def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
"""Finds the python object with the given name and calls it as a function."""
assert func_name is not None
func_obj = get_obj_by_name(func_name)
assert callable(func_obj)
return func_obj(*args, **kwargs)
def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any:
"""Finds the python class with the given name and constructs it with the given arguments."""
return call_func_by_name(*args, func_name=class_name, **kwargs)
def get_module_dir_by_obj_name(obj_name: str) -> str:
"""Get the directory path of the module containing the given object name."""
module, _ = get_module_from_obj_name(obj_name)
return os.path.dirname(inspect.getfile(module))
def is_top_level_function(obj: Any) -> bool:
"""Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
def get_top_level_function_name(obj: Any) -> str:
"""Return the fully-qualified name of a top-level function."""
assert is_top_level_function(obj)
module = obj.__module__
if module == '__main__':
module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0]
return module + "." + obj.__name__
# File system helpers
# ------------------------------------------------------------------------------------------
def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
"""List all files recursively in a given directory while ignoring given file and directory names.
Returns list of tuples containing both absolute and relative paths."""
assert os.path.isdir(dir_path)
base_name = os.path.basename(os.path.normpath(dir_path))
if ignores is None:
ignores = []
result = []
for root, dirs, files in os.walk(dir_path, topdown=True):
for ignore_ in ignores:
dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
# dirs need to be edited in-place
for d in dirs_to_remove:
dirs.remove(d)
files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
absolute_paths = [os.path.join(root, f) for f in files]
relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
if add_base_to_relative:
relative_paths = [os.path.join(base_name, p) for p in relative_paths]
assert len(absolute_paths) == len(relative_paths)
result += zip(absolute_paths, relative_paths)
return result
def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
"""Takes in a list of tuples of (src, dst) paths and copies files.
Will create all necessary directories."""
for file in files:
target_dir_name = os.path.dirname(file[1])
# will create all intermediate-level directories
if not os.path.exists(target_dir_name):
os.makedirs(target_dir_name)
shutil.copyfile(file[0], file[1])
# URL helpers
# ------------------------------------------------------------------------------------------
def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
"""Determine whether the given object is a valid URL string."""
if not isinstance(obj, str) or not "://" in obj:
return False
if allow_file_urls and obj.startswith('file://'):
return True
try:
res = requests.compat.urlparse(obj)
if not res.scheme or not res.netloc or not "." in res.netloc:
return False
res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
if not res.scheme or not res.netloc or not "." in res.netloc:
return False
except:
return False
return True
def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any:
"""Download the given URL and return a binary-mode file object to access the data."""
assert num_attempts >= 1
assert not (return_filename and (not cache))
# Doesn't look like an URL scheme so interpret it as a local filename.
if not re.match('^[a-z]+://', url):
return url if return_filename else open(url, "rb")
# Handle file URLs. This code handles unusual file:// patterns that
# arise on Windows:
#
# file:///c:/foo.txt
#
# which would translate to a local '/c:/foo.txt' filename that's
# invalid. Drop the forward slash for such pathnames.
#
# If you touch this code path, you should test it on both Linux and
# Windows.
#
# Some internet resources suggest using urllib.request.url2pathname() but
# but that converts forward slashes to backslashes and this causes
# its own set of problems.
if url.startswith('file://'):
filename = urllib.parse.urlparse(url).path
if re.match(r'^/[a-zA-Z]:', filename):
filename = filename[1:]
return filename if return_filename else open(filename, "rb")
assert is_url(url)
# Lookup from cache.
if cache_dir is None:
cache_dir = make_cache_dir_path('downloads')
url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
if cache:
cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
if len(cache_files) == 1:
filename = cache_files[0]
return filename if return_filename else open(filename, "rb")
# Download.
url_name = None
url_data = None
with requests.Session() as session:
if verbose:
print("Downloading %s ..." % url, end="", flush=True)
for attempts_left in reversed(range(num_attempts)):
try:
with session.get(url) as res:
res.raise_for_status()
if len(res.content) == 0:
raise IOError("No data received")
if len(res.content) < 8192:
content_str = res.content.decode("utf-8")
if "download_warning" in res.headers.get("Set-Cookie", ""):
links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link]
if len(links) == 1:
url = requests.compat.urljoin(url, links[0])
raise IOError("Google Drive virus checker nag")
if "Google Drive - Quota exceeded" in content_str:
raise IOError("Google Drive download quota exceeded -- please try again later")
match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
url_name = match[1] if match else url
url_data = res.content
if verbose:
print(" done")
break
except KeyboardInterrupt:
raise
except:
if not attempts_left:
if verbose:
print(" failed")
raise
if verbose:
print(".", end="", flush=True)
# Save to cache.
if cache:
safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
os.makedirs(cache_dir, exist_ok=True)
with open(temp_file, "wb") as f:
f.write(url_data)
os.replace(temp_file, cache_file) # atomic
if return_filename:
return cache_file
# Return data as file object.
assert not return_filename
return io.BytesIO(url_data)

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# Training configurations
This document provides guidelines for selecting appropriate training options for various scenarios, as well as an extensive list of recommended configurations.
#### Example
In the remainder of this document, we summarize each configuration as follows:
| <sub>Config</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :--------------------------- | :--------------: | :--------------: | :------------: | :--
| <sub>StyleGAN3&#8209;T</sub> | <sub>18.47</sub> | <sub>12.29</sub> | <sub>4.3</sub> | <sub>`--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=8.2 --mirror=1`</sub>
This corresponds to the following command line:
```.bash
# Train StyleGAN3-T for AFHQv2 using 8 GPUs.
python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/afhqv2-512x512.zip \
--gpus=8 --batch=32 --gamma=8.2 --mirror=1
```
Explanation of the columns:
- **Config**: StyleGAN3-T (translation equiv.), StyleGAN3-R (translation and rotation equiv.), or StyleGAN2. Reflects the value of `--cfg`.
- **s/kimg**: Raw training speed, measured separately on Tesla V100 and A100 using our recommended Docker image. The number indicates how many seconds, on average, it takes to process 1000 images from the training set. The number tends to vary slightly over the course of training; typically by no more than &plusmn;20%.
- **GPU mem**: Maximum GPU memory usage observed during training, reported in gigabytes per GPU. The above example uses 8 GPUs, which means that the total GPU memory usage is around 34.4 GB.
- **Options**: Command line options for `train.py`, excluding `--outdir` and `--data`.
#### Total training time
In addition the raw s/kimg number, the training time also depends on the `--kimg` and `--metric` options. `--kimg` controls the total number of training iterations and is set to 25000 by default. This is long enough to reach convergence in typical cases, but in practice the results should already look quite reasonable around 5000 kimg. `--metrics` determines which quality metrics are computed periodically during training. The default is `fid50k_full`, which increases the training time slightly; typically by no more than 5%. The automatic computation can be disabled by specifying `--metrics=none`.
In the above example, the total training time on V100 is approximately 18.47 s/kimg * 25000 kimg * 1.05 &thickapprox; 485,000 seconds &thickapprox; 5 days and 14 hours. Disabling metric computation (`--metrics=none`) reduces this to approximately 5 days and 8 hours.
## General guidelines
The most important hyperparameter that needs to be tuned on a per-dataset basis is the R<sub>1</sub> regularization weight, `--gamma`, that must be specified explicitly for `train.py`. As a rule of thumb, the value of `--gamma` scales quadratically with respect to the training set resolution: doubling the resolution (e.g., 256x256 &rarr; 512x512) means that `--gamma` should be multiplied by 4 (e.g., 2 &rarr; 8). The optimal value is usually the same for `--cfg=stylegan3-t` and `--cfg=stylegan3-r`, but considerably lower for `--cfg=stylegan2`.
In practice, we recommend selecting the value of `--gamma` as follows:
- Find the closest match for your specific case in this document (config, resolution, and GPU count).
- Try training with the same `--gamma` first.
- Then, try increasing the value by 2x and 4x, and also decreasing it by 2x and 4x.
- Pick the value that yields the lowest FID.
The results may also be improved by adjusting `--mirror` and `--aug`, depending on the training data. Specifying `--mirror=1` augments the dataset with random *x*-flips, which effectively doubles the number of images. This is generally beneficial with datasets that are horizontally symmetric (e.g., FFHQ), but it can be harmful if the images contain noticeable asymmetric features (e.g., text or letters). Specifying `--aug=noaug` disables adaptive discriminator augmentation (ADA), which may improve the results slightly if the training set is large enough (at least 100k images when accounting for *x*-flips). With small datasets (less than 30k images), it is generally a good idea to leave the augmentations enabled.
It is possible to speed up the training by decreasing network capacity, i.e., `--cbase=16384`. This typically leads to lower quality results, but the difference is less pronounced with low-resolution datasets (e.g., 256x256).
#### Scaling to different number of GPUs
You can select the number of GPUs by changing the value of `--gpu`; this does not affect the convergence curves or training dynamics in any way. By default, the total batch size (`--batch`) is divided evenly among the GPUs, which means that decreasing the number of GPUs yields higher per-GPU memory usage. To avoid running out of memory, you can decrease the per-GPU batch size by specifying `--batch-gpu`, which performs the same computation in multiple passes using gradient accumulation.
By default, `train.py` exports network snapshots once every 200 kimg, i.e., the product of `--snap=50` and `--tick=4`. When using few GPUs (e.g., 1&ndash;2), this means that it may take a very long time for the first snapshot to appear. We recommend increasing the snapshot frequency in such cases by specifying `--snap=20`, `--snap=10`, or `--snap=5`.
Note that the configurations listed in this document have been specifically tuned for 8 GPUs. The safest way to scale them to different GPU counts is to adjust `--gpu`, `--batch-gpu`, and `--snap` as described above, but it may be possible to reach faster convergence by adjusting some of the other hyperparameters as well. Note, however, that adjusting the total batch size (`--batch`) requires some experimentation; decreasing `--batch` usually necessitates increasing regularization (`--gamma`) and/or decreasing the learning rates (most importantly `--dlr`).
#### Transfer learning
Transfer learning makes it possible to reach very good results very quickly, especially when the training set is small and/or the images resemble the ones produced by a pre-trained model. To enable transfer learning, you can point `--resume` to one of the pre-trained models that we provide for [StyleGAN3](https://ngc.nvidia.com/catalog/models/nvidia:research:stylegan3) and [StyleGAN2](https://ngc.nvidia.com/catalog/models/nvidia:research:stylegan2). For example:
```.bash
# Fine-tune StyleGAN3-R for MetFaces-U using 1 GPU, starting from the pre-trained FFHQ-U pickle.
python train.py --outdir=~/training-runs --cfg=stylegan3-r --data=~/datasets/metfacesu-1024x1024.zip \
--gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=5 \
--resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl
```
The pre-trained model should be selected to match the specified config, resolution, and architecture-related hyperparameters (e.g., `--cbase`, `--map-depth`, and `--mbstd-group`). You check this by looking at the `fakes_init.png` exported by `train.py` at the beginning; if the configuration is correct, the images should look reasonable.
With transfer learning, the results may be improved slightly by adjusting `--freezed`, in addition to the above guidelines for `--gamma`, `--mirror`, and `--aug`. In our experience, `--freezed=10` and `--freezed=13` tend to work reasonably well.
## Recommended configurations
This section lists recommended settings for StyleGAN3-T and StyleGAN3-R for different resolutions and GPU counts, selected according to the above guidelines. These are intended to provide a good starting point when experimenting with a new dataset. Please note that many of the options (e.g., `--gamma`, `--mirror`, and `--aug`) are still worth adjusting on a case-by-case basis.
#### 128x128 resolution
| <sub>Config</sub><br><br> | <sub>GPUs</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :--------------------------- | :----------: | :--------------: | :--------------: | :------------: | :--
| <sub>StyleGAN3&#8209;T</sub> | <sub>1</sub> | <sub>73.68</sub> | <sub>27.20</sub> | <sub>7.2</sub> | <sub>`--cfg=stylegan3-t --gpus=1 --batch=32 --gamma=0.5 --batch-gpu=16 --snap=10`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>2</sub> | <sub>37.30</sub> | <sub>13.74</sub> | <sub>7.1</sub> | <sub>`--cfg=stylegan3-t --gpus=2 --batch=32 --gamma=0.5 --snap=20`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>4</sub> | <sub>20.66</sub> | <sub>7.52</sub> | <sub>4.1</sub> | <sub>`--cfg=stylegan3-t --gpus=4 --batch=32 --gamma=0.5`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>8</sub> | <sub>11.31</sub> | <sub>4.40</sub> | <sub>2.6</sub> | <sub>`--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=0.5`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>1</sub> | <sub>58.44</sub> | <sub>34.23</sub> | <sub>8.3</sub> | <sub>`--cfg=stylegan3-r --gpus=1 --batch=32 --gamma=0.5 --batch-gpu=16 --snap=10`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>2</sub> | <sub>29.92</sub> | <sub>17.29</sub> | <sub>8.2</sub> | <sub>`--cfg=stylegan3-r --gpus=2 --batch=32 --gamma=0.5 --snap=20`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>4</sub> | <sub>15.49</sub> | <sub>9.53</sub> | <sub>4.5</sub> | <sub>`--cfg=stylegan3-r --gpus=4 --batch=32 --gamma=0.5`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>8</sub> | <sub>8.43</sub> | <sub>5.69</sub> | <sub>2.7</sub> | <sub>`--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=0.5`</sub>
#### 256x256 resolution
| <sub>Config</sub><br><br> | <sub>GPUs</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :--------------------------- | :----------: | :--------------: | :--------------: | :------------: | :--
| <sub>StyleGAN3&#8209;T</sub> | <sub>1</sub> | <sub>89.15</sub> | <sub>49.81</sub> | <sub>9.5</sub> | <sub>`--cfg=stylegan3-t --gpus=1 --batch=32 --gamma=2 --batch-gpu=16 --snap=10`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>2</sub> | <sub>45.45</sub> | <sub>25.05</sub> | <sub>9.3</sub> | <sub>`--cfg=stylegan3-t --gpus=2 --batch=32 --gamma=2 --snap=20`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>4</sub> | <sub>23.94</sub> | <sub>13.26</sub> | <sub>5.2</sub> | <sub>`--cfg=stylegan3-t --gpus=4 --batch=32 --gamma=2`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>8</sub> | <sub>13.04</sub> | <sub>7.32</sub> | <sub>3.1</sub> | <sub>`--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=2`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>1</sub> | <sub>87.37</sub> | <sub>56.73</sub> | <sub>6.7</sub> | <sub>`--cfg=stylegan3-r --gpus=1 --batch=32 --gamma=2 --batch-gpu=8 --snap=10`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>2</sub> | <sub>44.12</sub> | <sub>28.60</sub> | <sub>6.7</sub> | <sub>`--cfg=stylegan3-r --gpus=2 --batch=32 --gamma=2 --batch-gpu=8 --snap=20`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>4</sub> | <sub>22.42</sub> | <sub>14.39</sub> | <sub>6.6</sub> | <sub>`--cfg=stylegan3-r --gpus=4 --batch=32 --gamma=2`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>8</sub> | <sub>11.88</sub> | <sub>8.03</sub> | <sub>3.7</sub> | <sub>`--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=2`</sub>
#### 512x512 resolution
| <sub>Config</sub><br><br> | <sub>GPUs</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :--------------------------- | :----------: | :---------------: | :---------------: | :------------: | :--
| <sub>StyleGAN3&#8209;T</sub> | <sub>1</sub> | <sub>137.33</sub> | <sub>90.25</sub> | <sub>7.8</sub> | <sub>`--cfg=stylegan3-t --gpus=1 --batch=32 --gamma=8 --batch-gpu=8 --snap=10`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>2</sub> | <sub>69.65</sub> | <sub>45.42</sub> | <sub>7.7</sub> | <sub>`--cfg=stylegan3-t --gpus=2 --batch=32 --gamma=8 --batch-gpu=8 --snap=20`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>4</sub> | <sub>34.88</sub> | <sub>22.81</sub> | <sub>7.6</sub> | <sub>`--cfg=stylegan3-t --gpus=4 --batch=32 --gamma=8`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>8</sub> | <sub>18.47</sub> | <sub>12.29</sub> | <sub>4.3</sub> | <sub>`--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=8`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>1</sub> | <sub>158.91</sub> | <sub>110.13</sub> | <sub>6.0</sub> | <sub>`--cfg=stylegan3-r --gpus=1 --batch=32 --gamma=8 --batch-gpu=4 --snap=10`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>2</sub> | <sub>79.96</sub> | <sub>55.18</sub> | <sub>6.0</sub> | <sub>`--cfg=stylegan3-r --gpus=2 --batch=32 --gamma=8 --batch-gpu=4 --snap=20`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>4</sub> | <sub>40.86</sub> | <sub>27.99</sub> | <sub>5.9</sub> | <sub>`--cfg=stylegan3-r --gpus=4 --batch=32 --gamma=8 --batch-gpu=4`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>8</sub> | <sub>20.44</sub> | <sub>14.04</sub> | <sub>5.9</sub> | <sub>`--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=8`</sub>
#### 1024x1024 resolution
| <sub>Config</sub><br><br> | <sub>GPUs</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :--------------------------- | :----------: | :---------------: | :---------------: | :-------------: | :--
| <sub>StyleGAN3&#8209;T</sub> | <sub>1</sub> | <sub>221.85</sub> | <sub>156.91</sub> | <sub>7.0</sub> | <sub>`--cfg=stylegan3-t --gpus=1 --batch=32 --gamma=32 --batch-gpu=4 --snap=5`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>2</sub> | <sub>113.44</sub> | <sub>79.16</sub> | <sub>6.8</sub> | <sub>`--cfg=stylegan3-t --gpus=2 --batch=32 --gamma=32 --batch-gpu=4 --snap=10`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>4</sub> | <sub>57.04</sub> | <sub>39.62</sub> | <sub>6.7</sub> | <sub>`--cfg=stylegan3-t --gpus=4 --batch=32 --gamma=32 --batch-gpu=4 --snap=20`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>8</sub> | <sub>28.71</sub> | <sub>20.01</sub> | <sub>6.6</sub> | <sub>`--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=32`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>1</sub> | <sub>263.44</sub> | <sub>184.81</sub> | <sub>10.2</sub> | <sub>`--cfg=stylegan3-r --gpus=1 --batch=32 --gamma=32 --batch-gpu=4 --snap=5`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>2</sub> | <sub>134.22</sub> | <sub>92.58</sub> | <sub>10.1</sub> | <sub>`--cfg=stylegan3-r --gpus=2 --batch=32 --gamma=32 --batch-gpu=4 --snap=10`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>4</sub> | <sub>67.33</sub> | <sub>46.53</sub> | <sub>10.0</sub> | <sub>`--cfg=stylegan3-r --gpus=4 --batch=32 --gamma=32 --batch-gpu=4 --snap=20`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>8</sub> | <sub>34.12</sub> | <sub>23.42</sub> | <sub>9.9</sub> | <sub>`--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=32`</sub>
## Configurations used in StyleGAN3 paper
This section lists the exact settings that we used in the "Alias-Free Generative Adversarial Networks" paper.
#### FFHQ-U and FFHQ at 1024x1024 resolution
| <sub>Config</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :--------------------------- | :--------------: | :--------------: | :------------: | :--
| <sub>StyleGAN2</sub> | <sub>17.55</sub> | <sub>14.57</sub> | <sub>6.2</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=32 --gamma=10 --mirror=1 --aug=noaug`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>28.71</sub> | <sub>20.01</sub> | <sub>6.6</sub> | <sub>`--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=32.8 --mirror=1 --aug=noaug`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>34.12</sub> | <sub>23.42</sub> | <sub>9.9</sub> | <sub>`--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=32.8 --mirror=1 --aug=noaug`</sub>
#### MetFaces-U at 1024x1024 resolution
| <sub>Config</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :--------------------------- | :--------------: | :--------------: | :-------------: | :--
| <sub>StyleGAN2</sub> | <sub>18.74</sub> | <sub>11.80</sub> | <sub>7.4</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=32 --gamma=10 --mirror=1 --kimg=5000 --snap=10 --resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-ffhqu-1024x1024.pkl`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>29.84</sub> | <sub>21.06</sub> | <sub>7.7</sub> | <sub>`--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=16.4 --mirror=1 --kimg=5000 --snap=10 --resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-ffhqu-1024x1024.pkl`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>35.10</sub> | <sub>24.32</sub> | <sub>10.9</sub> | <sub>`--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=10 --resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl`</sub>
#### MetFaces at 1024x1024 resolution
| <sub>Config</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :--------------------------- | :--------------: | :--------------: | :-------------: | :--
| <sub>StyleGAN2</sub> | <sub>18.74</sub> | <sub>11.80</sub> | <sub>7.4</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=32 --gamma=5 --mirror=1 --kimg=5000 --snap=10 --resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-ffhq-1024x1024.pkl`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>29.84</sub> | <sub>21.06</sub> | <sub>7.7</sub> | <sub>`--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=10 --resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-ffhq-1024x1024.pkl`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>35.10</sub> | <sub>24.32</sub> | <sub>10.9</sub> | <sub>`--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=3.3 --mirror=1 --kimg=5000 --snap=10 --resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhq-1024x1024.pkl`</sub>
#### AFHQv2 at 512x512 resolution
| <sub>Config</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :--------------------------- | :--------------: | :--------------: | :------------: | :--
| <sub>StyleGAN2</sub> | <sub>10.90</sub> | <sub>6.60</sub> | <sub>3.9</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=32 --gamma=5 --mirror=1`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>18.47</sub> | <sub>12.29</sub> | <sub>4.3</sub> | <sub>`--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=8.2 --mirror=1`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>20.44</sub> | <sub>14.04</sub> | <sub>5.9</sub> | <sub>`--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=16.4 --mirror=1`</sub>
#### FFHQ-U ablations at 256x256 resolution
| <sub>Config</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :--------------------------- | :-------------: | :-------------: | :------------: | :--
| <sub>StyleGAN2</sub> | <sub>3.61</sub> | <sub>2.19</sub> | <sub>2.7</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=64 --gamma=1 --mirror=1 --aug=noaug --cbase=16384 --glr=0.0025 --dlr=0.0025 --mbstd-group=8`</sub>
| <sub>StyleGAN3&#8209;T</sub> | <sub>7.40</sub> | <sub>3.74</sub> | <sub>3.5</sub> | <sub>`--cfg=stylegan3-t --gpus=8 --batch=64 --gamma=1 --mirror=1 --aug=noaug --cbase=16384 --dlr=0.0025`</sub>
| <sub>StyleGAN3&#8209;R</sub> | <sub>6.71</sub> | <sub>4.81</sub> | <sub>4.2</sub> | <sub>`--cfg=stylegan3-r --gpus=8 --batch=64 --gamma=1 --mirror=1 --aug=noaug --cbase=16384 --dlr=0.0025`</sub>
## Old StyleGAN2-ADA configurations
This section lists command lines that can be used to match the configurations provided by our previous [StyleGAN2-ADA](https://github.com/NVlabs/stylegan2-ada-pytorch) codebase. The first table corresponds to `--cfg=auto` (default) for different resolutions and GPU counts, while the second table lists the remaining alternatives.
#### Default configuration
| <sub>Res.</sub><br><br> | <sub>GPUs</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :---------------------- | :----------: | :---------------: | :--------------: | :------------: | :--
| <sub>128&sup2;</sub> | <sub>1</sub> | <sub>12.51</sub> | <sub>6.79</sub> | <sub>6.2</sub> | <sub>`--cfg=stylegan2 --gpus=1 --batch=32 --gamma=0.1024 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384`</sub>
| <sub>128&sup2;</sub> | <sub>2</sub> | <sub>6.43</sub> | <sub>3.45</sub> | <sub>6.2</sub> | <sub>`--cfg=stylegan2 --gpus=2 --batch=64 --gamma=0.0512 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384`</sub>
| <sub>128&sup2;</sub> | <sub>4</sub> | <sub>3.82</sub> | <sub>2.23</sub> | <sub>3.5</sub> | <sub>`--cfg=stylegan2 --gpus=4 --batch=64 --gamma=0.0512 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384`</sub>
| <sub>256&sup2;</sub> | <sub>1</sub> | <sub>20.84</sub> | <sub>12.53</sub> | <sub>4.5</sub> | <sub>`--cfg=stylegan2 --gpus=1 --batch=16 --gamma=0.8192 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384`</sub>
| <sub>256&sup2;</sub> | <sub>2</sub> | <sub>10.93</sub> | <sub>6.36</sub> | <sub>4.5</sub> | <sub>`--cfg=stylegan2 --gpus=2 --batch=32 --gamma=0.4096 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384`</sub>
| <sub>256&sup2;</sub> | <sub>4</sub> | <sub>5.39</sub> | <sub>3.20</sub> | <sub>4.5</sub> | <sub>`--cfg=stylegan2 --gpus=4 --batch=64 --gamma=0.2048 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384`</sub>
| <sub>256&sup2;</sub> | <sub>8</sub> | <sub>3.89</sub> | <sub>2.38</sub> | <sub>2.6</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=64 --gamma=0.2048 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384`</sub>
| <sub>512&sup2;</sub> | <sub>1</sub> | <sub>71.59</sub> | <sub>41.06</sub> | <sub>6.8</sub> | <sub>`--cfg=stylegan2 --gpus=1 --batch=8 --gamma=6.5536 --map-depth=2 --glr=0.0025 --dlr=0.0025`</sub>
| <sub>512&sup2;</sub> | <sub>2</sub> | <sub>36.79</sub> | <sub>20.83</sub> | <sub>6.8</sub> | <sub>`--cfg=stylegan2 --gpus=2 --batch=16 --gamma=3.2768 --map-depth=2 --glr=0.0025 --dlr=0.0025`</sub>
| <sub>512&sup2;</sub> | <sub>4</sub> | <sub>18.12</sub> | <sub>10.45</sub> | <sub>6.7</sub> | <sub>`--cfg=stylegan2 --gpus=4 --batch=32 --gamma=1.6384 --map-depth=2 --glr=0.0025 --dlr=0.0025`</sub>
| <sub>512&sup2;</sub> | <sub>8</sub> | <sub>9.09</sub> | <sub>5.24</sub> | <sub>6.8</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=64 --gamma=0.8192 --map-depth=2 --glr=0.0025 --dlr=0.0025`</sub>
| <sub>1024&sup2;</sub> | <sub>1</sub> | <sub>141.83</sub> | <sub>90.39</sub> | <sub>7.2</sub> | <sub>`--cfg=stylegan2 --gpus=1 --batch=4 --gamma=52.4288 --map-depth=2`</sub>
| <sub>1024&sup2;</sub> | <sub>2</sub> | <sub>73.13</sub> | <sub>46.04</sub> | <sub>7.2</sub> | <sub>`--cfg=stylegan2 --gpus=2 --batch=8 --gamma=26.2144 --map-depth=2`</sub>
| <sub>1024&sup2;</sub> | <sub>4</sub> | <sub>36.95</sub> | <sub>23.15</sub> | <sub>7.0</sub> | <sub>`--cfg=stylegan2 --gpus=4 --batch=16 --gamma=13.1072 --map-depth=2`</sub>
| <sub>1024&sup2;</sub> | <sub>8</sub> | <sub>18.47</sub> | <sub>11.66</sub> | <sub>7.3</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=32 --gamma=6.5536 --map-depth=2`</sub>
#### Repro configurations
| <sub>Name</sub><br><br> | <sub>s/kimg</sub><br><sup>(V100)</sup> | <sub>s/kimg</sub><br><sup>(A100)</sup> | <sub>GPU</sub><br><sup>mem</sup> | <sub>Options</sub><br><br>
| :---------------------- | :--------------: | :--------------: | :------------: | :--
| <sub>`stylegan2`</sub> | <sub>17.55</sub> | <sub>14.57</sub> | <sub>6.2</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=32 --gamma=10`</sub>
| <sub>`paper256`</sub> | <sub>4.01</sub> | <sub>2.47</sub> | <sub>2.7</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=64 --gamma=1 --cbase=16384 --glr=0.0025 --dlr=0.0025 --mbstd-group=8`</sub>
| <sub>`paper512`</sub> | <sub>9.11</sub> | <sub>5.28</sub> | <sub>6.7</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=64 --gamma=0.5 --glr=0.0025 --dlr=0.0025 --mbstd-group=8`</sub>
| <sub>`paper1024`</sub> | <sub>18.56</sub> | <sub>11.75</sub> | <sub>6.9</sub> | <sub>`--cfg=stylegan2 --gpus=8 --batch=32 --gamma=2`</sub>

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Usage: dataset_tool.py [OPTIONS]
Convert an image dataset into a dataset archive usable with StyleGAN2 ADA
PyTorch.
The input dataset format is guessed from the --source argument:
--source *_lmdb/ Load LSUN dataset
--source cifar-10-python.tar.gz Load CIFAR-10 dataset
--source train-images-idx3-ubyte.gz Load MNIST dataset
--source path/ Recursively load all images from path/
--source dataset.zip Recursively load all images from dataset.zip
Specifying the output format and path:
--dest /path/to/dir Save output files under /path/to/dir
--dest /path/to/dataset.zip Save output files into /path/to/dataset.zip
The output dataset format can be either an image folder or an uncompressed
zip archive. Zip archives makes it easier to move datasets around file
servers and clusters, and may offer better training performance on network
file systems.
Images within the dataset archive will be stored as uncompressed PNG.
Uncompresed PNGs can be efficiently decoded in the training loop.
Class labels are stored in a file called 'dataset.json' that is stored at
the dataset root folder. This file has the following structure:
{
"labels": [
["00000/img00000000.png",6],
["00000/img00000001.png",9],
... repeated for every image in the datase
["00049/img00049999.png",1]
]
}
If the 'dataset.json' file cannot be found, the dataset is interpreted as
not containing class labels.
Image scale/crop and resolution requirements:
Output images must be square-shaped and they must all have the same power-
of-two dimensions.
To scale arbitrary input image size to a specific width and height, use
the --resolution option. Output resolution will be either the original
input resolution (if resolution was not specified) or the one specified
with --resolution option.
Use the --transform=center-crop or --transform=center-crop-wide options to
apply a center crop transform on the input image. These options should be
used with the --resolution option. For example:
python dataset_tool.py --source LSUN/raw/cat_lmdb --dest /tmp/lsun_cat \
--transform=center-crop-wide --resolution=512x384
Options:
--source PATH Directory or archive name for input dataset
[required]
--dest PATH Output directory or archive name for output
dataset [required]
--max-images INTEGER Output only up to `max-images` images
--transform [center-crop|center-crop-wide]
Input crop/resize mode
--resolution WxH Output resolution (e.g., '512x512')
--help Show this message and exit.

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Usage: train.py [OPTIONS]
Train a GAN using the techniques described in the paper "Alias-Free
Generative Adversarial Networks".
Examples:
# Train StyleGAN3-T for AFHQv2 using 8 GPUs.
python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/afhqv2-512x512.zip \
--gpus=8 --batch=32 --gamma=8.2 --mirror=1
# Fine-tune StyleGAN3-R for MetFaces-U using 1 GPU, starting from the pre-trained FFHQ-U pickle.
python train.py --outdir=~/training-runs --cfg=stylegan3-r --data=~/datasets/metfacesu-1024x1024.zip \
--gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=5 \
--resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl
# Train StyleGAN2 for FFHQ at 1024x1024 resolution using 8 GPUs.
python train.py --outdir=~/training-runs --cfg=stylegan2 --data=~/datasets/ffhq-1024x1024.zip \
--gpus=8 --batch=32 --gamma=10 --mirror=1 --aug=noaug
Options:
--outdir DIR Where to save the results [required]
--cfg [stylegan3-t|stylegan3-r|stylegan2]
Base configuration [required]
--data [ZIP|DIR] Training data [required]
--gpus INT Number of GPUs to use [required]
--batch INT Total batch size [required]
--gamma FLOAT R1 regularization weight [required]
--cond BOOL Train conditional model [default: False]
--mirror BOOL Enable dataset x-flips [default: False]
--aug [noaug|ada|fixed] Augmentation mode [default: ada]
--resume [PATH|URL] Resume from given network pickle
--freezed INT Freeze first layers of D [default: 0]
--p FLOAT Probability for --aug=fixed [default: 0.2]
--target FLOAT Target value for --aug=ada [default: 0.6]
--batch-gpu INT Limit batch size per GPU
--cbase INT Capacity multiplier [default: 32768]
--cmax INT Max. feature maps [default: 512]
--glr FLOAT G learning rate [default: varies]
--dlr FLOAT D learning rate [default: 0.002]
--map-depth INT Mapping network depth [default: varies]
--mbstd-group INT Minibatch std group size [default: 4]
--desc STR String to include in result dir name
--metrics [NAME|A,B,C|none] Quality metrics [default: fid50k_full]
--kimg KIMG Total training duration [default: 25000]
--tick KIMG How often to print progress [default: 4]
--snap TICKS How often to save snapshots [default: 50]
--seed INT Random seed [default: 0]
--fp32 BOOL Disable mixed-precision [default: False]
--nobench BOOL Disable cuDNN benchmarking [default: False]
--workers INT DataLoader worker processes [default: 3]
-n, --dry-run Print training options and exit
--help Show this message and exit.

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# Troubleshooting
Our PyTorch code uses custom [CUDA extensions](https://pytorch.org/tutorials/advanced/cpp_extension.html) to speed up some of the network layers. Getting these to run can sometimes be a hassle.
This page aims to give guidance on how to diagnose and fix run-time problems related to these extensions.
## Before you start
1. Try Docker first! Ensure you can successfully run our models using the recommended Docker image. Follow the instructions in [README.md](/README.md) to get it running.
2. Can't use Docker? Read on..
## Installing dependencies
Make sure you've installed everything listed on the requirements section in the [README.md](/README.md). The key components w.r.t. custom extensions are:
- **[CUDA toolkit 11.1](https://developer.nvidia.com/cuda-toolkit)** or later (this is not the same as `cudatoolkit` from Conda).
- PyTorch invokes `nvcc` to compile our CUDA kernels.
- **ninja**
- PyTorch uses [Ninja](https://ninja-build.org/) as its build system.
- **GCC** (Linux) or **Visual Studio** (Windows)
#### Why is CUDA toolkit installation necessary?
The PyTorch package contains the required CUDA toolkit libraries needed to run PyTorch, so why is a separate CUDA toolkit installation required? Our models use custom CUDA kernels to implement operations such as efficient resampling of 2D images. PyTorch code invokes the CUDA compiler at run-time to compile these kernels on first-use. The tools and libraries required for this compilation are not bundled in PyTorch and thus a host CUDA toolkit installation is required.
## Things to try
- Completely remove: `$HOME/.cache/torch_extensions` (Linux) or `C:\Users\<username>\AppData\Local\torch_extensions\torch_extensions\Cache` (Windows) and re-run StyleGAN3 python code.
- Run ninja in `$HOME/.cache/torch_extensions` to see that it builds.
- Inspect the `build.ninja` in the build directories under `$HOME/.cache/torch_extensions` and check CUDA tools and versions are consistent with what you intended to use.

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name: stylegan3
channels:
- pytorch
- nvidia
dependencies:
- python >= 3.8
- pip
- numpy>=1.20
- click>=8.0
- pillow=8.3.1
- scipy=1.7.1
- pytorch=1.9.1
- cudatoolkit=11.1
- requests=2.26.0
- tqdm=4.62.2
- ninja=1.10.2
- matplotlib=3.4.2
- imageio=2.9.0
- pip:
- imgui==1.3.0
- glfw==2.2.0
- pyopengl==3.1.5
- imageio-ffmpeg==0.4.3
- pyspng

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Generate images using pretrained network pickle."""
import os
import re
from typing import List, Optional, Tuple, Union
import click
import dnnlib
import numpy as np
import PIL.Image
import torch
import legacy
#----------------------------------------------------------------------------
def parse_range(s: Union[str, List]) -> List[int]:
'''Parse a comma separated list of numbers or ranges and return a list of ints.
Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
'''
if isinstance(s, list): return s
ranges = []
range_re = re.compile(r'^(\d+)-(\d+)$')
for p in s.split(','):
if m := range_re.match(p):
ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
else:
ranges.append(int(p))
return ranges
#----------------------------------------------------------------------------
def parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]:
'''Parse a floating point 2-vector of syntax 'a,b'.
Example:
'0,1' returns (0,1)
'''
if isinstance(s, tuple): return s
parts = s.split(',')
if len(parts) == 2:
return (float(parts[0]), float(parts[1]))
raise ValueError(f'cannot parse 2-vector {s}')
#----------------------------------------------------------------------------
def make_transform(translate: Tuple[float,float], angle: float):
m = np.eye(3)
s = np.sin(angle/360.0*np.pi*2)
c = np.cos(angle/360.0*np.pi*2)
m[0][0] = c
m[0][1] = s
m[0][2] = translate[0]
m[1][0] = -s
m[1][1] = c
m[1][2] = translate[1]
return m
#----------------------------------------------------------------------------
@click.command()
@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
@click.option('--seeds', type=parse_range, help='List of random seeds (e.g., \'0,1,4-6\')', required=True)
@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)')
@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
@click.option('--translate', help='Translate XY-coordinate (e.g. \'0.3,1\')', type=parse_vec2, default='0,0', show_default=True, metavar='VEC2')
@click.option('--rotate', help='Rotation angle in degrees', type=float, default=0, show_default=True, metavar='ANGLE')
@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR')
def generate_images(
network_pkl: str,
seeds: List[int],
truncation_psi: float,
noise_mode: str,
outdir: str,
translate: Tuple[float,float],
rotate: float,
class_idx: Optional[int]
):
"""Generate images using pretrained network pickle.
Examples:
\b
# Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
python gen_images.py --outdir=out --trunc=1 --seeds=2 \\
--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
\b
# Generate uncurated images with truncation using the MetFaces-U dataset
python gen_images.py --outdir=out --trunc=0.7 --seeds=600-605 \\
--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfacesu-1024x1024.pkl
"""
print('Loading networks from "%s"...' % network_pkl)
device = torch.device('cuda')
with dnnlib.util.open_url(network_pkl) as f:
G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
os.makedirs(outdir, exist_ok=True)
# Labels.
label = torch.zeros([1, G.c_dim], device=device)
if G.c_dim != 0:
if class_idx is None:
raise click.ClickException('Must specify class label with --class when using a conditional network')
label[:, class_idx] = 1
else:
if class_idx is not None:
print ('warn: --class=lbl ignored when running on an unconditional network')
# Generate images.
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device)
# Construct an inverse rotation/translation matrix and pass to the generator. The
# generator expects this matrix as an inverse to avoid potentially failing numerical
# operations in the network.
if hasattr(G.synthesis, 'input'):
m = make_transform(translate, rotate)
m = np.linalg.inv(m)
G.synthesis.input.transform.copy_(torch.from_numpy(m))
img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png')
#----------------------------------------------------------------------------
if __name__ == "__main__":
generate_images() # pylint: disable=no-value-for-parameter
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Generate lerp videos using pretrained network pickle."""
import copy
import os
import re
from typing import List, Optional, Tuple, Union
import click
import dnnlib
import imageio
import numpy as np
import scipy.interpolate
import torch
from tqdm import tqdm
import legacy
#----------------------------------------------------------------------------
def layout_grid(img, grid_w=None, grid_h=1, float_to_uint8=True, chw_to_hwc=True, to_numpy=True):
batch_size, channels, img_h, img_w = img.shape
if grid_w is None:
grid_w = batch_size // grid_h
assert batch_size == grid_w * grid_h
if float_to_uint8:
img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)
img = img.reshape(grid_h, grid_w, channels, img_h, img_w)
img = img.permute(2, 0, 3, 1, 4)
img = img.reshape(channels, grid_h * img_h, grid_w * img_w)
if chw_to_hwc:
img = img.permute(1, 2, 0)
if to_numpy:
img = img.cpu().numpy()
return img
#----------------------------------------------------------------------------
def gen_interp_video(G, mp4: str, seeds, shuffle_seed=None, w_frames=60*4, kind='cubic', grid_dims=(1,1), num_keyframes=None, wraps=2, psi=1, device=torch.device('cuda'), **video_kwargs):
grid_w = grid_dims[0]
grid_h = grid_dims[1]
if num_keyframes is None:
if len(seeds) % (grid_w*grid_h) != 0:
raise ValueError('Number of input seeds must be divisible by grid W*H')
num_keyframes = len(seeds) // (grid_w*grid_h)
all_seeds = np.zeros(num_keyframes*grid_h*grid_w, dtype=np.int64)
for idx in range(num_keyframes*grid_h*grid_w):
all_seeds[idx] = seeds[idx % len(seeds)]
if shuffle_seed is not None:
rng = np.random.RandomState(seed=shuffle_seed)
rng.shuffle(all_seeds)
zs = torch.from_numpy(np.stack([np.random.RandomState(seed).randn(G.z_dim) for seed in all_seeds])).to(device)
ws = G.mapping(z=zs, c=None, truncation_psi=psi)
_ = G.synthesis(ws[:1]) # warm up
ws = ws.reshape(grid_h, grid_w, num_keyframes, *ws.shape[1:])
# Interpolation.
grid = []
for yi in range(grid_h):
row = []
for xi in range(grid_w):
x = np.arange(-num_keyframes * wraps, num_keyframes * (wraps + 1))
y = np.tile(ws[yi][xi].cpu().numpy(), [wraps * 2 + 1, 1, 1])
interp = scipy.interpolate.interp1d(x, y, kind=kind, axis=0)
row.append(interp)
grid.append(row)
# Render video.
video_out = imageio.get_writer(mp4, mode='I', fps=60, codec='libx264', **video_kwargs)
for frame_idx in tqdm(range(num_keyframes * w_frames)):
imgs = []
for yi in range(grid_h):
for xi in range(grid_w):
interp = grid[yi][xi]
w = torch.from_numpy(interp(frame_idx / w_frames)).to(device)
img = G.synthesis(ws=w.unsqueeze(0), noise_mode='const')[0]
imgs.append(img)
video_out.append_data(layout_grid(torch.stack(imgs), grid_w=grid_w, grid_h=grid_h))
video_out.close()
#----------------------------------------------------------------------------
def parse_range(s: Union[str, List[int]]) -> List[int]:
'''Parse a comma separated list of numbers or ranges and return a list of ints.
Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
'''
if isinstance(s, list): return s
ranges = []
range_re = re.compile(r'^(\d+)-(\d+)$')
for p in s.split(','):
if m := range_re.match(p):
ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
else:
ranges.append(int(p))
return ranges
#----------------------------------------------------------------------------
def parse_tuple(s: Union[str, Tuple[int,int]]) -> Tuple[int, int]:
'''Parse a 'M,N' or 'MxN' integer tuple.
Example:
'4x2' returns (4,2)
'0,1' returns (0,1)
'''
if isinstance(s, tuple): return s
if m := re.match(r'^(\d+)[x,](\d+)$', s):
return (int(m.group(1)), int(m.group(2)))
raise ValueError(f'cannot parse tuple {s}')
#----------------------------------------------------------------------------
@click.command()
@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
@click.option('--seeds', type=parse_range, help='List of random seeds', required=True)
@click.option('--shuffle-seed', type=int, help='Random seed to use for shuffling seed order', default=None)
@click.option('--grid', type=parse_tuple, help='Grid width/height, e.g. \'4x3\' (default: 1x1)', default=(1,1))
@click.option('--num-keyframes', type=int, help='Number of seeds to interpolate through. If not specified, determine based on the length of the seeds array given by --seeds.', default=None)
@click.option('--w-frames', type=int, help='Number of frames to interpolate between latents', default=120)
@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
@click.option('--output', help='Output .mp4 filename', type=str, required=True, metavar='FILE')
def generate_images(
network_pkl: str,
seeds: List[int],
shuffle_seed: Optional[int],
truncation_psi: float,
grid: Tuple[int,int],
num_keyframes: Optional[int],
w_frames: int,
output: str
):
"""Render a latent vector interpolation video.
Examples:
\b
# Render a 4x2 grid of interpolations for seeds 0 through 31.
python gen_video.py --output=lerp.mp4 --trunc=1 --seeds=0-31 --grid=4x2 \\
--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
Animation length and seed keyframes:
The animation length is either determined based on the --seeds value or explicitly
specified using the --num-keyframes option.
When num keyframes is specified with --num-keyframes, the output video length
will be 'num_keyframes*w_frames' frames.
If --num-keyframes is not specified, the number of seeds given with
--seeds must be divisible by grid size W*H (--grid). In this case the
output video length will be '# seeds/(w*h)*w_frames' frames.
"""
print('Loading networks from "%s"...' % network_pkl)
device = torch.device('cuda')
with dnnlib.util.open_url(network_pkl) as f:
G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
gen_interp_video(G=G, mp4=output, bitrate='12M', grid_dims=grid, num_keyframes=num_keyframes, w_frames=w_frames, seeds=seeds, shuffle_seed=shuffle_seed, psi=truncation_psi)
#----------------------------------------------------------------------------
if __name__ == "__main__":
generate_images() # pylint: disable=no-value-for-parameter
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
# empty

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import os
import functools
import contextlib
import numpy as np
import OpenGL.GL as gl
import OpenGL.GL.ARB.texture_float
import dnnlib
#----------------------------------------------------------------------------
def init_egl():
assert os.environ['PYOPENGL_PLATFORM'] == 'egl' # Must be set before importing OpenGL.
import OpenGL.EGL as egl
import ctypes
# Initialize EGL.
display = egl.eglGetDisplay(egl.EGL_DEFAULT_DISPLAY)
assert display != egl.EGL_NO_DISPLAY
major = ctypes.c_int32()
minor = ctypes.c_int32()
ok = egl.eglInitialize(display, major, minor)
assert ok
assert major.value * 10 + minor.value >= 14
# Choose config.
config_attribs = [
egl.EGL_RENDERABLE_TYPE, egl.EGL_OPENGL_BIT,
egl.EGL_SURFACE_TYPE, egl.EGL_PBUFFER_BIT,
egl.EGL_NONE
]
configs = (ctypes.c_int32 * 1)()
num_configs = ctypes.c_int32()
ok = egl.eglChooseConfig(display, config_attribs, configs, 1, num_configs)
assert ok
assert num_configs.value == 1
config = configs[0]
# Create dummy pbuffer surface.
surface_attribs = [
egl.EGL_WIDTH, 1,
egl.EGL_HEIGHT, 1,
egl.EGL_NONE
]
surface = egl.eglCreatePbufferSurface(display, config, surface_attribs)
assert surface != egl.EGL_NO_SURFACE
# Setup GL context.
ok = egl.eglBindAPI(egl.EGL_OPENGL_API)
assert ok
context = egl.eglCreateContext(display, config, egl.EGL_NO_CONTEXT, None)
assert context != egl.EGL_NO_CONTEXT
ok = egl.eglMakeCurrent(display, surface, surface, context)
assert ok
#----------------------------------------------------------------------------
_texture_formats = {
('uint8', 1): dnnlib.EasyDict(type=gl.GL_UNSIGNED_BYTE, format=gl.GL_LUMINANCE, internalformat=gl.GL_LUMINANCE8),
('uint8', 2): dnnlib.EasyDict(type=gl.GL_UNSIGNED_BYTE, format=gl.GL_LUMINANCE_ALPHA, internalformat=gl.GL_LUMINANCE8_ALPHA8),
('uint8', 3): dnnlib.EasyDict(type=gl.GL_UNSIGNED_BYTE, format=gl.GL_RGB, internalformat=gl.GL_RGB8),
('uint8', 4): dnnlib.EasyDict(type=gl.GL_UNSIGNED_BYTE, format=gl.GL_RGBA, internalformat=gl.GL_RGBA8),
('float32', 1): dnnlib.EasyDict(type=gl.GL_FLOAT, format=gl.GL_LUMINANCE, internalformat=OpenGL.GL.ARB.texture_float.GL_LUMINANCE32F_ARB),
('float32', 2): dnnlib.EasyDict(type=gl.GL_FLOAT, format=gl.GL_LUMINANCE_ALPHA, internalformat=OpenGL.GL.ARB.texture_float.GL_LUMINANCE_ALPHA32F_ARB),
('float32', 3): dnnlib.EasyDict(type=gl.GL_FLOAT, format=gl.GL_RGB, internalformat=gl.GL_RGB32F),
('float32', 4): dnnlib.EasyDict(type=gl.GL_FLOAT, format=gl.GL_RGBA, internalformat=gl.GL_RGBA32F),
}
def get_texture_format(dtype, channels):
return _texture_formats[(np.dtype(dtype).name, int(channels))]
#----------------------------------------------------------------------------
def prepare_texture_data(image):
image = np.asarray(image)
if image.ndim == 2:
image = image[:, :, np.newaxis]
if image.dtype.name == 'float64':
image = image.astype('float32')
return image
#----------------------------------------------------------------------------
def draw_pixels(image, *, pos=0, zoom=1, align=0, rint=True):
pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2])
zoom = np.broadcast_to(np.asarray(zoom, dtype='float32'), [2])
align = np.broadcast_to(np.asarray(align, dtype='float32'), [2])
image = prepare_texture_data(image)
height, width, channels = image.shape
size = zoom * [width, height]
pos = pos - size * align
if rint:
pos = np.rint(pos)
fmt = get_texture_format(image.dtype, channels)
gl.glPushAttrib(gl.GL_CURRENT_BIT | gl.GL_PIXEL_MODE_BIT)
gl.glPushClientAttrib(gl.GL_CLIENT_PIXEL_STORE_BIT)
gl.glRasterPos2f(pos[0], pos[1])
gl.glPixelZoom(zoom[0], -zoom[1])
gl.glPixelStorei(gl.GL_UNPACK_ALIGNMENT, 1)
gl.glDrawPixels(width, height, fmt.format, fmt.type, image)
gl.glPopClientAttrib()
gl.glPopAttrib()
#----------------------------------------------------------------------------
def read_pixels(width, height, *, pos=0, dtype='uint8', channels=3):
pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2])
dtype = np.dtype(dtype)
fmt = get_texture_format(dtype, channels)
image = np.empty([height, width, channels], dtype=dtype)
gl.glPushClientAttrib(gl.GL_CLIENT_PIXEL_STORE_BIT)
gl.glPixelStorei(gl.GL_PACK_ALIGNMENT, 1)
gl.glReadPixels(int(np.round(pos[0])), int(np.round(pos[1])), width, height, fmt.format, fmt.type, image)
gl.glPopClientAttrib()
return np.flipud(image)
#----------------------------------------------------------------------------
class Texture:
def __init__(self, *, image=None, width=None, height=None, channels=None, dtype=None, bilinear=True, mipmap=True):
self.gl_id = None
self.bilinear = bilinear
self.mipmap = mipmap
# Determine size and dtype.
if image is not None:
image = prepare_texture_data(image)
self.height, self.width, self.channels = image.shape
self.dtype = image.dtype
else:
assert width is not None and height is not None
self.width = width
self.height = height
self.channels = channels if channels is not None else 3
self.dtype = np.dtype(dtype) if dtype is not None else np.uint8
# Validate size and dtype.
assert isinstance(self.width, int) and self.width >= 0
assert isinstance(self.height, int) and self.height >= 0
assert isinstance(self.channels, int) and self.channels >= 1
assert self.is_compatible(width=width, height=height, channels=channels, dtype=dtype)
# Create texture object.
self.gl_id = gl.glGenTextures(1)
with self.bind():
gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR if self.bilinear else gl.GL_NEAREST)
gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR_MIPMAP_LINEAR if self.mipmap else gl.GL_NEAREST)
self.update(image)
def delete(self):
if self.gl_id is not None:
gl.glDeleteTextures([self.gl_id])
self.gl_id = None
def __del__(self):
try:
self.delete()
except:
pass
@contextlib.contextmanager
def bind(self):
prev_id = gl.glGetInteger(gl.GL_TEXTURE_BINDING_2D)
gl.glBindTexture(gl.GL_TEXTURE_2D, self.gl_id)
yield
gl.glBindTexture(gl.GL_TEXTURE_2D, prev_id)
def update(self, image):
if image is not None:
image = prepare_texture_data(image)
assert self.is_compatible(image=image)
with self.bind():
fmt = get_texture_format(self.dtype, self.channels)
gl.glPushClientAttrib(gl.GL_CLIENT_PIXEL_STORE_BIT)
gl.glPixelStorei(gl.GL_UNPACK_ALIGNMENT, 1)
gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, fmt.internalformat, self.width, self.height, 0, fmt.format, fmt.type, image)
if self.mipmap:
gl.glGenerateMipmap(gl.GL_TEXTURE_2D)
gl.glPopClientAttrib()
def draw(self, *, pos=0, zoom=1, align=0, rint=False, color=1, alpha=1, rounding=0):
zoom = np.broadcast_to(np.asarray(zoom, dtype='float32'), [2])
size = zoom * [self.width, self.height]
with self.bind():
gl.glPushAttrib(gl.GL_ENABLE_BIT)
gl.glEnable(gl.GL_TEXTURE_2D)
draw_rect(pos=pos, size=size, align=align, rint=rint, color=color, alpha=alpha, rounding=rounding)
gl.glPopAttrib()
def is_compatible(self, *, image=None, width=None, height=None, channels=None, dtype=None): # pylint: disable=too-many-return-statements
if image is not None:
if image.ndim != 3:
return False
ih, iw, ic = image.shape
if not self.is_compatible(width=iw, height=ih, channels=ic, dtype=image.dtype):
return False
if width is not None and self.width != width:
return False
if height is not None and self.height != height:
return False
if channels is not None and self.channels != channels:
return False
if dtype is not None and self.dtype != dtype:
return False
return True
#----------------------------------------------------------------------------
class Framebuffer:
def __init__(self, *, texture=None, width=None, height=None, channels=None, dtype=None, msaa=0):
self.texture = texture
self.gl_id = None
self.gl_color = None
self.gl_depth_stencil = None
self.msaa = msaa
# Determine size and dtype.
if texture is not None:
assert isinstance(self.texture, Texture)
self.width = texture.width
self.height = texture.height
self.channels = texture.channels
self.dtype = texture.dtype
else:
assert width is not None and height is not None
self.width = width
self.height = height
self.channels = channels if channels is not None else 4
self.dtype = np.dtype(dtype) if dtype is not None else np.float32
# Validate size and dtype.
assert isinstance(self.width, int) and self.width >= 0
assert isinstance(self.height, int) and self.height >= 0
assert isinstance(self.channels, int) and self.channels >= 1
assert width is None or width == self.width
assert height is None or height == self.height
assert channels is None or channels == self.channels
assert dtype is None or dtype == self.dtype
# Create framebuffer object.
self.gl_id = gl.glGenFramebuffers(1)
with self.bind():
# Setup color buffer.
if self.texture is not None:
assert self.msaa == 0
gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_TEXTURE_2D, self.texture.gl_id, 0)
else:
fmt = get_texture_format(self.dtype, self.channels)
self.gl_color = gl.glGenRenderbuffers(1)
gl.glBindRenderbuffer(gl.GL_RENDERBUFFER, self.gl_color)
gl.glRenderbufferStorageMultisample(gl.GL_RENDERBUFFER, self.msaa, fmt.internalformat, self.width, self.height)
gl.glFramebufferRenderbuffer(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_RENDERBUFFER, self.gl_color)
# Setup depth/stencil buffer.
self.gl_depth_stencil = gl.glGenRenderbuffers(1)
gl.glBindRenderbuffer(gl.GL_RENDERBUFFER, self.gl_depth_stencil)
gl.glRenderbufferStorageMultisample(gl.GL_RENDERBUFFER, self.msaa, gl.GL_DEPTH24_STENCIL8, self.width, self.height)
gl.glFramebufferRenderbuffer(gl.GL_FRAMEBUFFER, gl.GL_DEPTH_STENCIL_ATTACHMENT, gl.GL_RENDERBUFFER, self.gl_depth_stencil)
def delete(self):
if self.gl_id is not None:
gl.glDeleteFramebuffers([self.gl_id])
self.gl_id = None
if self.gl_color is not None:
gl.glDeleteRenderbuffers(1, [self.gl_color])
self.gl_color = None
if self.gl_depth_stencil is not None:
gl.glDeleteRenderbuffers(1, [self.gl_depth_stencil])
self.gl_depth_stencil = None
def __del__(self):
try:
self.delete()
except:
pass
@contextlib.contextmanager
def bind(self):
prev_fbo = gl.glGetInteger(gl.GL_FRAMEBUFFER_BINDING)
prev_rbo = gl.glGetInteger(gl.GL_RENDERBUFFER_BINDING)
gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, self.gl_id)
if self.width is not None and self.height is not None:
gl.glViewport(0, 0, self.width, self.height)
yield
gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, prev_fbo)
gl.glBindRenderbuffer(gl.GL_RENDERBUFFER, prev_rbo)
def blit(self, dst=None):
assert dst is None or isinstance(dst, Framebuffer)
with self.bind():
gl.glBindFramebuffer(gl.GL_DRAW_FRAMEBUFFER, 0 if dst is None else dst.fbo)
gl.glBlitFramebuffer(0, 0, self.width, self.height, 0, 0, self.width, self.height, gl.GL_COLOR_BUFFER_BIT, gl.GL_NEAREST)
#----------------------------------------------------------------------------
def draw_shape(vertices, *, mode=gl.GL_TRIANGLE_FAN, pos=0, size=1, color=1, alpha=1):
assert vertices.ndim == 2 and vertices.shape[1] == 2
pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2])
size = np.broadcast_to(np.asarray(size, dtype='float32'), [2])
color = np.broadcast_to(np.asarray(color, dtype='float32'), [3])
alpha = np.clip(np.broadcast_to(np.asarray(alpha, dtype='float32'), []), 0, 1)
gl.glPushClientAttrib(gl.GL_CLIENT_VERTEX_ARRAY_BIT)
gl.glPushAttrib(gl.GL_CURRENT_BIT | gl.GL_TRANSFORM_BIT)
gl.glMatrixMode(gl.GL_MODELVIEW)
gl.glPushMatrix()
gl.glEnableClientState(gl.GL_VERTEX_ARRAY)
gl.glEnableClientState(gl.GL_TEXTURE_COORD_ARRAY)
gl.glVertexPointer(2, gl.GL_FLOAT, 0, vertices)
gl.glTexCoordPointer(2, gl.GL_FLOAT, 0, vertices)
gl.glTranslate(pos[0], pos[1], 0)
gl.glScale(size[0], size[1], 1)
gl.glColor4f(color[0] * alpha, color[1] * alpha, color[2] * alpha, alpha)
gl.glDrawArrays(mode, 0, vertices.shape[0])
gl.glPopMatrix()
gl.glPopAttrib()
gl.glPopClientAttrib()
#----------------------------------------------------------------------------
def draw_rect(*, pos=0, pos2=None, size=None, align=0, rint=False, color=1, alpha=1, rounding=0):
assert pos2 is None or size is None
pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2])
pos2 = np.broadcast_to(np.asarray(pos2, dtype='float32'), [2]) if pos2 is not None else None
size = np.broadcast_to(np.asarray(size, dtype='float32'), [2]) if size is not None else None
size = size if size is not None else pos2 - pos if pos2 is not None else np.array([1, 1], dtype='float32')
pos = pos - size * align
if rint:
pos = np.rint(pos)
rounding = np.broadcast_to(np.asarray(rounding, dtype='float32'), [2])
rounding = np.minimum(np.abs(rounding) / np.maximum(np.abs(size), 1e-8), 0.5)
if np.min(rounding) == 0:
rounding *= 0
vertices = _setup_rect(float(rounding[0]), float(rounding[1]))
draw_shape(vertices, mode=gl.GL_TRIANGLE_FAN, pos=pos, size=size, color=color, alpha=alpha)
@functools.lru_cache(maxsize=10000)
def _setup_rect(rx, ry):
t = np.linspace(0, np.pi / 2, 1 if max(rx, ry) == 0 else 64)
s = 1 - np.sin(t); c = 1 - np.cos(t)
x = [c * rx, 1 - s * rx, 1 - c * rx, s * rx]
y = [s * ry, c * ry, 1 - s * ry, 1 - c * ry]
v = np.stack([x, y], axis=-1).reshape(-1, 2)
return v.astype('float32')
#----------------------------------------------------------------------------
def draw_circle(*, center=0, radius=100, hole=0, color=1, alpha=1):
hole = np.broadcast_to(np.asarray(hole, dtype='float32'), [])
vertices = _setup_circle(float(hole))
draw_shape(vertices, mode=gl.GL_TRIANGLE_STRIP, pos=center, size=radius, color=color, alpha=alpha)
@functools.lru_cache(maxsize=10000)
def _setup_circle(hole):
t = np.linspace(0, np.pi * 2, 128)
s = np.sin(t); c = np.cos(t)
v = np.stack([c, s, c * hole, s * hole], axis=-1).reshape(-1, 2)
return v.astype('float32')
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import time
import glfw
import OpenGL.GL as gl
from . import gl_utils
#----------------------------------------------------------------------------
class GlfwWindow: # pylint: disable=too-many-public-methods
def __init__(self, *, title='GlfwWindow', window_width=1920, window_height=1080, deferred_show=True, close_on_esc=True):
self._glfw_window = None
self._drawing_frame = False
self._frame_start_time = None
self._frame_delta = 0
self._fps_limit = None
self._vsync = None
self._skip_frames = 0
self._deferred_show = deferred_show
self._close_on_esc = close_on_esc
self._esc_pressed = False
self._drag_and_drop_paths = None
self._capture_next_frame = False
self._captured_frame = None
# Create window.
glfw.init()
glfw.window_hint(glfw.VISIBLE, False)
self._glfw_window = glfw.create_window(width=window_width, height=window_height, title=title, monitor=None, share=None)
self._attach_glfw_callbacks()
self.make_context_current()
# Adjust window.
self.set_vsync(False)
self.set_window_size(window_width, window_height)
if not self._deferred_show:
glfw.show_window(self._glfw_window)
def close(self):
if self._drawing_frame:
self.end_frame()
if self._glfw_window is not None:
glfw.destroy_window(self._glfw_window)
self._glfw_window = None
#glfw.terminate() # Commented out to play it nice with other glfw clients.
def __del__(self):
try:
self.close()
except:
pass
@property
def window_width(self):
return self.content_width
@property
def window_height(self):
return self.content_height + self.title_bar_height
@property
def content_width(self):
width, _height = glfw.get_window_size(self._glfw_window)
return width
@property
def content_height(self):
_width, height = glfw.get_window_size(self._glfw_window)
return height
@property
def title_bar_height(self):
_left, top, _right, _bottom = glfw.get_window_frame_size(self._glfw_window)
return top
@property
def monitor_width(self):
_, _, width, _height = glfw.get_monitor_workarea(glfw.get_primary_monitor())
return width
@property
def monitor_height(self):
_, _, _width, height = glfw.get_monitor_workarea(glfw.get_primary_monitor())
return height
@property
def frame_delta(self):
return self._frame_delta
def set_title(self, title):
glfw.set_window_title(self._glfw_window, title)
def set_window_size(self, width, height):
width = min(width, self.monitor_width)
height = min(height, self.monitor_height)
glfw.set_window_size(self._glfw_window, width, max(height - self.title_bar_height, 0))
if width == self.monitor_width and height == self.monitor_height:
self.maximize()
def set_content_size(self, width, height):
self.set_window_size(width, height + self.title_bar_height)
def maximize(self):
glfw.maximize_window(self._glfw_window)
def set_position(self, x, y):
glfw.set_window_pos(self._glfw_window, x, y + self.title_bar_height)
def center(self):
self.set_position((self.monitor_width - self.window_width) // 2, (self.monitor_height - self.window_height) // 2)
def set_vsync(self, vsync):
vsync = bool(vsync)
if vsync != self._vsync:
glfw.swap_interval(1 if vsync else 0)
self._vsync = vsync
def set_fps_limit(self, fps_limit):
self._fps_limit = int(fps_limit)
def should_close(self):
return glfw.window_should_close(self._glfw_window) or (self._close_on_esc and self._esc_pressed)
def skip_frame(self):
self.skip_frames(1)
def skip_frames(self, num): # Do not update window for the next N frames.
self._skip_frames = max(self._skip_frames, int(num))
def is_skipping_frames(self):
return self._skip_frames > 0
def capture_next_frame(self):
self._capture_next_frame = True
def pop_captured_frame(self):
frame = self._captured_frame
self._captured_frame = None
return frame
def pop_drag_and_drop_paths(self):
paths = self._drag_and_drop_paths
self._drag_and_drop_paths = None
return paths
def draw_frame(self): # To be overridden by subclass.
self.begin_frame()
# Rendering code goes here.
self.end_frame()
def make_context_current(self):
if self._glfw_window is not None:
glfw.make_context_current(self._glfw_window)
def begin_frame(self):
# End previous frame.
if self._drawing_frame:
self.end_frame()
# Apply FPS limit.
if self._frame_start_time is not None and self._fps_limit is not None:
delay = self._frame_start_time - time.perf_counter() + 1 / self._fps_limit
if delay > 0:
time.sleep(delay)
cur_time = time.perf_counter()
if self._frame_start_time is not None:
self._frame_delta = cur_time - self._frame_start_time
self._frame_start_time = cur_time
# Process events.
glfw.poll_events()
# Begin frame.
self._drawing_frame = True
self.make_context_current()
# Initialize GL state.
gl.glViewport(0, 0, self.content_width, self.content_height)
gl.glMatrixMode(gl.GL_PROJECTION)
gl.glLoadIdentity()
gl.glTranslate(-1, 1, 0)
gl.glScale(2 / max(self.content_width, 1), -2 / max(self.content_height, 1), 1)
gl.glMatrixMode(gl.GL_MODELVIEW)
gl.glLoadIdentity()
gl.glEnable(gl.GL_BLEND)
gl.glBlendFunc(gl.GL_ONE, gl.GL_ONE_MINUS_SRC_ALPHA) # Pre-multiplied alpha.
# Clear.
gl.glClearColor(0, 0, 0, 1)
gl.glClear(gl.GL_COLOR_BUFFER_BIT | gl.GL_DEPTH_BUFFER_BIT)
def end_frame(self):
assert self._drawing_frame
self._drawing_frame = False
# Skip frames if requested.
if self._skip_frames > 0:
self._skip_frames -= 1
return
# Capture frame if requested.
if self._capture_next_frame:
self._captured_frame = gl_utils.read_pixels(self.content_width, self.content_height)
self._capture_next_frame = False
# Update window.
if self._deferred_show:
glfw.show_window(self._glfw_window)
self._deferred_show = False
glfw.swap_buffers(self._glfw_window)
def _attach_glfw_callbacks(self):
glfw.set_key_callback(self._glfw_window, self._glfw_key_callback)
glfw.set_drop_callback(self._glfw_window, self._glfw_drop_callback)
def _glfw_key_callback(self, _window, key, _scancode, action, _mods):
if action == glfw.PRESS and key == glfw.KEY_ESCAPE:
self._esc_pressed = True
def _glfw_drop_callback(self, _window, paths):
self._drag_and_drop_paths = paths
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import contextlib
import imgui
#----------------------------------------------------------------------------
def set_default_style(color_scheme='dark', spacing=9, indent=23, scrollbar=27):
s = imgui.get_style()
s.window_padding = [spacing, spacing]
s.item_spacing = [spacing, spacing]
s.item_inner_spacing = [spacing, spacing]
s.columns_min_spacing = spacing
s.indent_spacing = indent
s.scrollbar_size = scrollbar
s.frame_padding = [4, 3]
s.window_border_size = 1
s.child_border_size = 1
s.popup_border_size = 1
s.frame_border_size = 1
s.window_rounding = 0
s.child_rounding = 0
s.popup_rounding = 3
s.frame_rounding = 3
s.scrollbar_rounding = 3
s.grab_rounding = 3
getattr(imgui, f'style_colors_{color_scheme}')(s)
c0 = s.colors[imgui.COLOR_MENUBAR_BACKGROUND]
c1 = s.colors[imgui.COLOR_FRAME_BACKGROUND]
s.colors[imgui.COLOR_POPUP_BACKGROUND] = [x * 0.7 + y * 0.3 for x, y in zip(c0, c1)][:3] + [1]
#----------------------------------------------------------------------------
@contextlib.contextmanager
def grayed_out(cond=True):
if cond:
s = imgui.get_style()
text = s.colors[imgui.COLOR_TEXT_DISABLED]
grab = s.colors[imgui.COLOR_SCROLLBAR_GRAB]
back = s.colors[imgui.COLOR_MENUBAR_BACKGROUND]
imgui.push_style_color(imgui.COLOR_TEXT, *text)
imgui.push_style_color(imgui.COLOR_CHECK_MARK, *grab)
imgui.push_style_color(imgui.COLOR_SLIDER_GRAB, *grab)
imgui.push_style_color(imgui.COLOR_SLIDER_GRAB_ACTIVE, *grab)
imgui.push_style_color(imgui.COLOR_FRAME_BACKGROUND, *back)
imgui.push_style_color(imgui.COLOR_FRAME_BACKGROUND_HOVERED, *back)
imgui.push_style_color(imgui.COLOR_FRAME_BACKGROUND_ACTIVE, *back)
imgui.push_style_color(imgui.COLOR_BUTTON, *back)
imgui.push_style_color(imgui.COLOR_BUTTON_HOVERED, *back)
imgui.push_style_color(imgui.COLOR_BUTTON_ACTIVE, *back)
imgui.push_style_color(imgui.COLOR_HEADER, *back)
imgui.push_style_color(imgui.COLOR_HEADER_HOVERED, *back)
imgui.push_style_color(imgui.COLOR_HEADER_ACTIVE, *back)
imgui.push_style_color(imgui.COLOR_POPUP_BACKGROUND, *back)
yield
imgui.pop_style_color(14)
else:
yield
#----------------------------------------------------------------------------
@contextlib.contextmanager
def item_width(width=None):
if width is not None:
imgui.push_item_width(width)
yield
imgui.pop_item_width()
else:
yield
#----------------------------------------------------------------------------
def scoped_by_object_id(method):
def decorator(self, *args, **kwargs):
imgui.push_id(str(id(self)))
res = method(self, *args, **kwargs)
imgui.pop_id()
return res
return decorator
#----------------------------------------------------------------------------
def button(label, width=0, enabled=True):
with grayed_out(not enabled):
clicked = imgui.button(label, width=width)
clicked = clicked and enabled
return clicked
#----------------------------------------------------------------------------
def collapsing_header(text, visible=None, flags=0, default=False, enabled=True, show=True):
expanded = False
if show:
if default:
flags |= imgui.TREE_NODE_DEFAULT_OPEN
if not enabled:
flags |= imgui.TREE_NODE_LEAF
with grayed_out(not enabled):
expanded, visible = imgui.collapsing_header(text, visible=visible, flags=flags)
expanded = expanded and enabled
return expanded, visible
#----------------------------------------------------------------------------
def popup_button(label, width=0, enabled=True):
if button(label, width, enabled):
imgui.open_popup(label)
opened = imgui.begin_popup(label)
return opened
#----------------------------------------------------------------------------
def input_text(label, value, buffer_length, flags, width=None, help_text=''):
old_value = value
color = list(imgui.get_style().colors[imgui.COLOR_TEXT])
if value == '':
color[-1] *= 0.5
with item_width(width):
imgui.push_style_color(imgui.COLOR_TEXT, *color)
value = value if value != '' else help_text
changed, value = imgui.input_text(label, value, buffer_length, flags)
value = value if value != help_text else ''
imgui.pop_style_color(1)
if not flags & imgui.INPUT_TEXT_ENTER_RETURNS_TRUE:
changed = (value != old_value)
return changed, value
#----------------------------------------------------------------------------
def drag_previous_control(enabled=True):
dragging = False
dx = 0
dy = 0
if imgui.begin_drag_drop_source(imgui.DRAG_DROP_SOURCE_NO_PREVIEW_TOOLTIP):
if enabled:
dragging = True
dx, dy = imgui.get_mouse_drag_delta()
imgui.reset_mouse_drag_delta()
imgui.end_drag_drop_source()
return dragging, dx, dy
#----------------------------------------------------------------------------
def drag_button(label, width=0, enabled=True):
clicked = button(label, width=width, enabled=enabled)
dragging, dx, dy = drag_previous_control(enabled=enabled)
return clicked, dragging, dx, dy
#----------------------------------------------------------------------------
def drag_hidden_window(label, x, y, width, height, enabled=True):
imgui.push_style_color(imgui.COLOR_WINDOW_BACKGROUND, 0, 0, 0, 0)
imgui.push_style_color(imgui.COLOR_BORDER, 0, 0, 0, 0)
imgui.set_next_window_position(x, y)
imgui.set_next_window_size(width, height)
imgui.begin(label, closable=False, flags=(imgui.WINDOW_NO_TITLE_BAR | imgui.WINDOW_NO_RESIZE | imgui.WINDOW_NO_MOVE))
dragging, dx, dy = drag_previous_control(enabled=enabled)
imgui.end()
imgui.pop_style_color(2)
return dragging, dx, dy
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import os
import imgui
import imgui.integrations.glfw
from . import glfw_window
from . import imgui_utils
from . import text_utils
#----------------------------------------------------------------------------
class ImguiWindow(glfw_window.GlfwWindow):
def __init__(self, *, title='ImguiWindow', font=None, font_sizes=range(14,24), **glfw_kwargs):
if font is None:
font = text_utils.get_default_font()
font_sizes = {int(size) for size in font_sizes}
super().__init__(title=title, **glfw_kwargs)
# Init fields.
self._imgui_context = None
self._imgui_renderer = None
self._imgui_fonts = None
self._cur_font_size = max(font_sizes)
# Delete leftover imgui.ini to avoid unexpected behavior.
if os.path.isfile('imgui.ini'):
os.remove('imgui.ini')
# Init ImGui.
self._imgui_context = imgui.create_context()
self._imgui_renderer = _GlfwRenderer(self._glfw_window)
self._attach_glfw_callbacks()
imgui.get_io().ini_saving_rate = 0 # Disable creating imgui.ini at runtime.
imgui.get_io().mouse_drag_threshold = 0 # Improve behavior with imgui_utils.drag_custom().
self._imgui_fonts = {size: imgui.get_io().fonts.add_font_from_file_ttf(font, size) for size in font_sizes}
self._imgui_renderer.refresh_font_texture()
def close(self):
self.make_context_current()
self._imgui_fonts = None
if self._imgui_renderer is not None:
self._imgui_renderer.shutdown()
self._imgui_renderer = None
if self._imgui_context is not None:
#imgui.destroy_context(self._imgui_context) # Commented out to avoid creating imgui.ini at the end.
self._imgui_context = None
super().close()
def _glfw_key_callback(self, *args):
super()._glfw_key_callback(*args)
self._imgui_renderer.keyboard_callback(*args)
@property
def font_size(self):
return self._cur_font_size
@property
def spacing(self):
return round(self._cur_font_size * 0.4)
def set_font_size(self, target): # Applied on next frame.
self._cur_font_size = min((abs(key - target), key) for key in self._imgui_fonts.keys())[1]
def begin_frame(self):
# Begin glfw frame.
super().begin_frame()
# Process imgui events.
self._imgui_renderer.mouse_wheel_multiplier = self._cur_font_size / 10
if self.content_width > 0 and self.content_height > 0:
self._imgui_renderer.process_inputs()
# Begin imgui frame.
imgui.new_frame()
imgui.push_font(self._imgui_fonts[self._cur_font_size])
imgui_utils.set_default_style(spacing=self.spacing, indent=self.font_size, scrollbar=self.font_size+4)
def end_frame(self):
imgui.pop_font()
imgui.render()
imgui.end_frame()
self._imgui_renderer.render(imgui.get_draw_data())
super().end_frame()
#----------------------------------------------------------------------------
# Wrapper class for GlfwRenderer to fix a mouse wheel bug on Linux.
class _GlfwRenderer(imgui.integrations.glfw.GlfwRenderer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.mouse_wheel_multiplier = 1
def scroll_callback(self, window, x_offset, y_offset):
self.io.mouse_wheel += y_offset * self.mouse_wheel_multiplier
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import functools
from typing import Optional
import dnnlib
import numpy as np
import PIL.Image
import PIL.ImageFont
import scipy.ndimage
from . import gl_utils
#----------------------------------------------------------------------------
def get_default_font():
url = 'http://fonts.gstatic.com/s/opensans/v17/mem8YaGs126MiZpBA-U1UpcaXcl0Aw.ttf' # Open Sans regular
return dnnlib.util.open_url(url, return_filename=True)
#----------------------------------------------------------------------------
@functools.lru_cache(maxsize=None)
def get_pil_font(font=None, size=32):
if font is None:
font = get_default_font()
return PIL.ImageFont.truetype(font=font, size=size)
#----------------------------------------------------------------------------
def get_array(string, *, dropshadow_radius: int=None, **kwargs):
if dropshadow_radius is not None:
offset_x = int(np.ceil(dropshadow_radius*2/3))
offset_y = int(np.ceil(dropshadow_radius*2/3))
return _get_array_priv(string, dropshadow_radius=dropshadow_radius, offset_x=offset_x, offset_y=offset_y, **kwargs)
else:
return _get_array_priv(string, **kwargs)
@functools.lru_cache(maxsize=10000)
def _get_array_priv(
string: str, *,
size: int = 32,
max_width: Optional[int]=None,
max_height: Optional[int]=None,
min_size=10,
shrink_coef=0.8,
dropshadow_radius: int=None,
offset_x: int=None,
offset_y: int=None,
**kwargs
):
cur_size = size
array = None
while True:
if dropshadow_radius is not None:
# separate implementation for dropshadow text rendering
array = _get_array_impl_dropshadow(string, size=cur_size, radius=dropshadow_radius, offset_x=offset_x, offset_y=offset_y, **kwargs)
else:
array = _get_array_impl(string, size=cur_size, **kwargs)
height, width, _ = array.shape
if (max_width is None or width <= max_width) and (max_height is None or height <= max_height) or (cur_size <= min_size):
break
cur_size = max(int(cur_size * shrink_coef), min_size)
return array
#----------------------------------------------------------------------------
@functools.lru_cache(maxsize=10000)
def _get_array_impl(string, *, font=None, size=32, outline=0, outline_pad=3, outline_coef=3, outline_exp=2, line_pad: int=None):
pil_font = get_pil_font(font=font, size=size)
lines = [pil_font.getmask(line, 'L') for line in string.split('\n')]
lines = [np.array(line, dtype=np.uint8).reshape([line.size[1], line.size[0]]) for line in lines]
width = max(line.shape[1] for line in lines)
lines = [np.pad(line, ((0, 0), (0, width - line.shape[1])), mode='constant') for line in lines]
line_spacing = line_pad if line_pad is not None else size // 2
lines = [np.pad(line, ((0, line_spacing), (0, 0)), mode='constant') for line in lines[:-1]] + lines[-1:]
mask = np.concatenate(lines, axis=0)
alpha = mask
if outline > 0:
mask = np.pad(mask, int(np.ceil(outline * outline_pad)), mode='constant', constant_values=0)
alpha = mask.astype(np.float32) / 255
alpha = scipy.ndimage.gaussian_filter(alpha, outline)
alpha = 1 - np.maximum(1 - alpha * outline_coef, 0) ** outline_exp
alpha = (alpha * 255 + 0.5).clip(0, 255).astype(np.uint8)
alpha = np.maximum(alpha, mask)
return np.stack([mask, alpha], axis=-1)
#----------------------------------------------------------------------------
@functools.lru_cache(maxsize=10000)
def _get_array_impl_dropshadow(string, *, font=None, size=32, radius: int, offset_x: int, offset_y: int, line_pad: int=None, **kwargs):
assert (offset_x > 0) and (offset_y > 0)
pil_font = get_pil_font(font=font, size=size)
lines = [pil_font.getmask(line, 'L') for line in string.split('\n')]
lines = [np.array(line, dtype=np.uint8).reshape([line.size[1], line.size[0]]) for line in lines]
width = max(line.shape[1] for line in lines)
lines = [np.pad(line, ((0, 0), (0, width - line.shape[1])), mode='constant') for line in lines]
line_spacing = line_pad if line_pad is not None else size // 2
lines = [np.pad(line, ((0, line_spacing), (0, 0)), mode='constant') for line in lines[:-1]] + lines[-1:]
mask = np.concatenate(lines, axis=0)
alpha = mask
mask = np.pad(mask, 2*radius + max(abs(offset_x), abs(offset_y)), mode='constant', constant_values=0)
alpha = mask.astype(np.float32) / 255
alpha = scipy.ndimage.gaussian_filter(alpha, radius)
alpha = 1 - np.maximum(1 - alpha * 1.5, 0) ** 1.4
alpha = (alpha * 255 + 0.5).clip(0, 255).astype(np.uint8)
alpha = np.pad(alpha, [(offset_y, 0), (offset_x, 0)], mode='constant')[:-offset_y, :-offset_x]
alpha = np.maximum(alpha, mask)
return np.stack([mask, alpha], axis=-1)
#----------------------------------------------------------------------------
@functools.lru_cache(maxsize=10000)
def get_texture(string, bilinear=True, mipmap=True, **kwargs):
return gl_utils.Texture(image=get_array(string, **kwargs), bilinear=bilinear, mipmap=mipmap)
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Converting legacy network pickle into the new format."""
import click
import pickle
import re
import copy
import numpy as np
import torch
import dnnlib
from torch_utils import misc
#----------------------------------------------------------------------------
def load_network_pkl(f, force_fp16=False):
data = _LegacyUnpickler(f).load()
# Legacy TensorFlow pickle => convert.
if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
tf_G, tf_D, tf_Gs = data
G = convert_tf_generator(tf_G)
D = convert_tf_discriminator(tf_D)
G_ema = convert_tf_generator(tf_Gs)
data = dict(G=G, D=D, G_ema=G_ema)
# Add missing fields.
if 'training_set_kwargs' not in data:
data['training_set_kwargs'] = None
if 'augment_pipe' not in data:
data['augment_pipe'] = None
# Validate contents.
assert isinstance(data['G'], torch.nn.Module)
assert isinstance(data['D'], torch.nn.Module)
assert isinstance(data['G_ema'], torch.nn.Module)
assert isinstance(data['training_set_kwargs'], (dict, type(None)))
assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))
# Force FP16.
if force_fp16:
for key in ['G', 'D', 'G_ema']:
old = data[key]
kwargs = copy.deepcopy(old.init_kwargs)
fp16_kwargs = kwargs.get('synthesis_kwargs', kwargs)
fp16_kwargs.num_fp16_res = 4
fp16_kwargs.conv_clamp = 256
if kwargs != old.init_kwargs:
new = type(old)(**kwargs).eval().requires_grad_(False)
misc.copy_params_and_buffers(old, new, require_all=True)
data[key] = new
return data
#----------------------------------------------------------------------------
class _TFNetworkStub(dnnlib.EasyDict):
pass
class _LegacyUnpickler(pickle.Unpickler):
def find_class(self, module, name):
if module == 'dnnlib.tflib.network' and name == 'Network':
return _TFNetworkStub
return super().find_class(module, name)
#----------------------------------------------------------------------------
def _collect_tf_params(tf_net):
# pylint: disable=protected-access
tf_params = dict()
def recurse(prefix, tf_net):
for name, value in tf_net.variables:
tf_params[prefix + name] = value
for name, comp in tf_net.components.items():
recurse(prefix + name + '/', comp)
recurse('', tf_net)
return tf_params
#----------------------------------------------------------------------------
def _populate_module_params(module, *patterns):
for name, tensor in misc.named_params_and_buffers(module):
found = False
value = None
for pattern, value_fn in zip(patterns[0::2], patterns[1::2]):
match = re.fullmatch(pattern, name)
if match:
found = True
if value_fn is not None:
value = value_fn(*match.groups())
break
try:
assert found
if value is not None:
tensor.copy_(torch.from_numpy(np.array(value)))
except:
print(name, list(tensor.shape))
raise
#----------------------------------------------------------------------------
def convert_tf_generator(tf_G):
if tf_G.version < 4:
raise ValueError('TensorFlow pickle version too low')
# Collect kwargs.
tf_kwargs = tf_G.static_kwargs
known_kwargs = set()
def kwarg(tf_name, default=None, none=None):
known_kwargs.add(tf_name)
val = tf_kwargs.get(tf_name, default)
return val if val is not None else none
# Convert kwargs.
from training import networks_stylegan2
network_class = networks_stylegan2.Generator
kwargs = dnnlib.EasyDict(
z_dim = kwarg('latent_size', 512),
c_dim = kwarg('label_size', 0),
w_dim = kwarg('dlatent_size', 512),
img_resolution = kwarg('resolution', 1024),
img_channels = kwarg('num_channels', 3),
channel_base = kwarg('fmap_base', 16384) * 2,
channel_max = kwarg('fmap_max', 512),
num_fp16_res = kwarg('num_fp16_res', 0),
conv_clamp = kwarg('conv_clamp', None),
architecture = kwarg('architecture', 'skip'),
resample_filter = kwarg('resample_kernel', [1,3,3,1]),
use_noise = kwarg('use_noise', True),
activation = kwarg('nonlinearity', 'lrelu'),
mapping_kwargs = dnnlib.EasyDict(
num_layers = kwarg('mapping_layers', 8),
embed_features = kwarg('label_fmaps', None),
layer_features = kwarg('mapping_fmaps', None),
activation = kwarg('mapping_nonlinearity', 'lrelu'),
lr_multiplier = kwarg('mapping_lrmul', 0.01),
w_avg_beta = kwarg('w_avg_beta', 0.995, none=1),
),
)
# Check for unknown kwargs.
kwarg('truncation_psi')
kwarg('truncation_cutoff')
kwarg('style_mixing_prob')
kwarg('structure')
kwarg('conditioning')
kwarg('fused_modconv')
unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
if len(unknown_kwargs) > 0:
raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
# Collect params.
tf_params = _collect_tf_params(tf_G)
for name, value in list(tf_params.items()):
match = re.fullmatch(r'ToRGB_lod(\d+)/(.*)', name)
if match:
r = kwargs.img_resolution // (2 ** int(match.group(1)))
tf_params[f'{r}x{r}/ToRGB/{match.group(2)}'] = value
kwargs.synthesis.kwargs.architecture = 'orig'
#for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
# Convert params.
G = network_class(**kwargs).eval().requires_grad_(False)
# pylint: disable=unnecessary-lambda
# pylint: disable=f-string-without-interpolation
_populate_module_params(G,
r'mapping\.w_avg', lambda: tf_params[f'dlatent_avg'],
r'mapping\.embed\.weight', lambda: tf_params[f'mapping/LabelEmbed/weight'].transpose(),
r'mapping\.embed\.bias', lambda: tf_params[f'mapping/LabelEmbed/bias'],
r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'mapping/Dense{i}/weight'].transpose(),
r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'mapping/Dense{i}/bias'],
r'synthesis\.b4\.const', lambda: tf_params[f'synthesis/4x4/Const/const'][0],
r'synthesis\.b4\.conv1\.weight', lambda: tf_params[f'synthesis/4x4/Conv/weight'].transpose(3, 2, 0, 1),
r'synthesis\.b4\.conv1\.bias', lambda: tf_params[f'synthesis/4x4/Conv/bias'],
r'synthesis\.b4\.conv1\.noise_const', lambda: tf_params[f'synthesis/noise0'][0, 0],
r'synthesis\.b4\.conv1\.noise_strength', lambda: tf_params[f'synthesis/4x4/Conv/noise_strength'],
r'synthesis\.b4\.conv1\.affine\.weight', lambda: tf_params[f'synthesis/4x4/Conv/mod_weight'].transpose(),
r'synthesis\.b4\.conv1\.affine\.bias', lambda: tf_params[f'synthesis/4x4/Conv/mod_bias'] + 1,
r'synthesis\.b(\d+)\.conv0\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
r'synthesis\.b(\d+)\.conv0\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/bias'],
r'synthesis\.b(\d+)\.conv0\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0],
r'synthesis\.b(\d+)\.conv0\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/noise_strength'],
r'synthesis\.b(\d+)\.conv0\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose(),
r'synthesis\.b(\d+)\.conv0\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1,
r'synthesis\.b(\d+)\.conv1\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose(3, 2, 0, 1),
r'synthesis\.b(\d+)\.conv1\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/bias'],
r'synthesis\.b(\d+)\.conv1\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0],
r'synthesis\.b(\d+)\.conv1\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/noise_strength'],
r'synthesis\.b(\d+)\.conv1\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose(),
r'synthesis\.b(\d+)\.conv1\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1,
r'synthesis\.b(\d+)\.torgb\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose(3, 2, 0, 1),
r'synthesis\.b(\d+)\.torgb\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/bias'],
r'synthesis\.b(\d+)\.torgb\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose(),
r'synthesis\.b(\d+)\.torgb\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1,
r'synthesis\.b(\d+)\.skip\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
r'.*\.resample_filter', None,
r'.*\.act_filter', None,
)
return G
#----------------------------------------------------------------------------
def convert_tf_discriminator(tf_D):
if tf_D.version < 4:
raise ValueError('TensorFlow pickle version too low')
# Collect kwargs.
tf_kwargs = tf_D.static_kwargs
known_kwargs = set()
def kwarg(tf_name, default=None):
known_kwargs.add(tf_name)
return tf_kwargs.get(tf_name, default)
# Convert kwargs.
kwargs = dnnlib.EasyDict(
c_dim = kwarg('label_size', 0),
img_resolution = kwarg('resolution', 1024),
img_channels = kwarg('num_channels', 3),
architecture = kwarg('architecture', 'resnet'),
channel_base = kwarg('fmap_base', 16384) * 2,
channel_max = kwarg('fmap_max', 512),
num_fp16_res = kwarg('num_fp16_res', 0),
conv_clamp = kwarg('conv_clamp', None),
cmap_dim = kwarg('mapping_fmaps', None),
block_kwargs = dnnlib.EasyDict(
activation = kwarg('nonlinearity', 'lrelu'),
resample_filter = kwarg('resample_kernel', [1,3,3,1]),
freeze_layers = kwarg('freeze_layers', 0),
),
mapping_kwargs = dnnlib.EasyDict(
num_layers = kwarg('mapping_layers', 0),
embed_features = kwarg('mapping_fmaps', None),
layer_features = kwarg('mapping_fmaps', None),
activation = kwarg('nonlinearity', 'lrelu'),
lr_multiplier = kwarg('mapping_lrmul', 0.1),
),
epilogue_kwargs = dnnlib.EasyDict(
mbstd_group_size = kwarg('mbstd_group_size', None),
mbstd_num_channels = kwarg('mbstd_num_features', 1),
activation = kwarg('nonlinearity', 'lrelu'),
),
)
# Check for unknown kwargs.
kwarg('structure')
kwarg('conditioning')
unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
if len(unknown_kwargs) > 0:
raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
# Collect params.
tf_params = _collect_tf_params(tf_D)
for name, value in list(tf_params.items()):
match = re.fullmatch(r'FromRGB_lod(\d+)/(.*)', name)
if match:
r = kwargs.img_resolution // (2 ** int(match.group(1)))
tf_params[f'{r}x{r}/FromRGB/{match.group(2)}'] = value
kwargs.architecture = 'orig'
#for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
# Convert params.
from training import networks_stylegan2
D = networks_stylegan2.Discriminator(**kwargs).eval().requires_grad_(False)
# pylint: disable=unnecessary-lambda
# pylint: disable=f-string-without-interpolation
_populate_module_params(D,
r'b(\d+)\.fromrgb\.weight', lambda r: tf_params[f'{r}x{r}/FromRGB/weight'].transpose(3, 2, 0, 1),
r'b(\d+)\.fromrgb\.bias', lambda r: tf_params[f'{r}x{r}/FromRGB/bias'],
r'b(\d+)\.conv(\d+)\.weight', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight'].transpose(3, 2, 0, 1),
r'b(\d+)\.conv(\d+)\.bias', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'],
r'b(\d+)\.skip\.weight', lambda r: tf_params[f'{r}x{r}/Skip/weight'].transpose(3, 2, 0, 1),
r'mapping\.embed\.weight', lambda: tf_params[f'LabelEmbed/weight'].transpose(),
r'mapping\.embed\.bias', lambda: tf_params[f'LabelEmbed/bias'],
r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'Mapping{i}/weight'].transpose(),
r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'Mapping{i}/bias'],
r'b4\.conv\.weight', lambda: tf_params[f'4x4/Conv/weight'].transpose(3, 2, 0, 1),
r'b4\.conv\.bias', lambda: tf_params[f'4x4/Conv/bias'],
r'b4\.fc\.weight', lambda: tf_params[f'4x4/Dense0/weight'].transpose(),
r'b4\.fc\.bias', lambda: tf_params[f'4x4/Dense0/bias'],
r'b4\.out\.weight', lambda: tf_params[f'Output/weight'].transpose(),
r'b4\.out\.bias', lambda: tf_params[f'Output/bias'],
r'.*\.resample_filter', None,
)
return D
#----------------------------------------------------------------------------
@click.command()
@click.option('--source', help='Input pickle', required=True, metavar='PATH')
@click.option('--dest', help='Output pickle', required=True, metavar='PATH')
@click.option('--force-fp16', help='Force the networks to use FP16', type=bool, default=False, metavar='BOOL', show_default=True)
def convert_network_pickle(source, dest, force_fp16):
"""Convert legacy network pickle into the native PyTorch format.
The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA.
It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks.
Example:
\b
python legacy.py \\
--source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \\
--dest=stylegan2-cat-config-f.pkl
"""
print(f'Loading "{source}"...')
with dnnlib.util.open_url(source) as f:
data = load_network_pkl(f, force_fp16=force_fp16)
print(f'Saving "{dest}"...')
with open(dest, 'wb') as f:
pickle.dump(data, f)
print('Done.')
#----------------------------------------------------------------------------
if __name__ == "__main__":
convert_network_pickle() # pylint: disable=no-value-for-parameter
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
# empty

267
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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Equivariance metrics (EQ-T, EQ-T_frac, and EQ-R) from the paper
"Alias-Free Generative Adversarial Networks"."""
import copy
import numpy as np
import torch
import torch.fft
from torch_utils.ops import upfirdn2d
from . import metric_utils
#----------------------------------------------------------------------------
# Utilities.
def sinc(x):
y = (x * np.pi).abs()
z = torch.sin(y) / y.clamp(1e-30, float('inf'))
return torch.where(y < 1e-30, torch.ones_like(x), z)
def lanczos_window(x, a):
x = x.abs() / a
return torch.where(x < 1, sinc(x), torch.zeros_like(x))
def rotation_matrix(angle):
angle = torch.as_tensor(angle).to(torch.float32)
mat = torch.eye(3, device=angle.device)
mat[0, 0] = angle.cos()
mat[0, 1] = angle.sin()
mat[1, 0] = -angle.sin()
mat[1, 1] = angle.cos()
return mat
#----------------------------------------------------------------------------
# Apply integer translation to a batch of 2D images. Corresponds to the
# operator T_x in Appendix E.1.
def apply_integer_translation(x, tx, ty):
_N, _C, H, W = x.shape
tx = torch.as_tensor(tx * W).to(dtype=torch.float32, device=x.device)
ty = torch.as_tensor(ty * H).to(dtype=torch.float32, device=x.device)
ix = tx.round().to(torch.int64)
iy = ty.round().to(torch.int64)
z = torch.zeros_like(x)
m = torch.zeros_like(x)
if abs(ix) < W and abs(iy) < H:
y = x[:, :, max(-iy,0) : H+min(-iy,0), max(-ix,0) : W+min(-ix,0)]
z[:, :, max(iy,0) : H+min(iy,0), max(ix,0) : W+min(ix,0)] = y
m[:, :, max(iy,0) : H+min(iy,0), max(ix,0) : W+min(ix,0)] = 1
return z, m
#----------------------------------------------------------------------------
# Apply integer translation to a batch of 2D images. Corresponds to the
# operator T_x in Appendix E.2.
def apply_fractional_translation(x, tx, ty, a=3):
_N, _C, H, W = x.shape
tx = torch.as_tensor(tx * W).to(dtype=torch.float32, device=x.device)
ty = torch.as_tensor(ty * H).to(dtype=torch.float32, device=x.device)
ix = tx.floor().to(torch.int64)
iy = ty.floor().to(torch.int64)
fx = tx - ix
fy = ty - iy
b = a - 1
z = torch.zeros_like(x)
zx0 = max(ix - b, 0)
zy0 = max(iy - b, 0)
zx1 = min(ix + a, 0) + W
zy1 = min(iy + a, 0) + H
if zx0 < zx1 and zy0 < zy1:
taps = torch.arange(a * 2, device=x.device) - b
filter_x = (sinc(taps - fx) * sinc((taps - fx) / a)).unsqueeze(0)
filter_y = (sinc(taps - fy) * sinc((taps - fy) / a)).unsqueeze(1)
y = x
y = upfirdn2d.filter2d(y, filter_x / filter_x.sum(), padding=[b,a,0,0])
y = upfirdn2d.filter2d(y, filter_y / filter_y.sum(), padding=[0,0,b,a])
y = y[:, :, max(b-iy,0) : H+b+a+min(-iy-a,0), max(b-ix,0) : W+b+a+min(-ix-a,0)]
z[:, :, zy0:zy1, zx0:zx1] = y
m = torch.zeros_like(x)
mx0 = max(ix + a, 0)
my0 = max(iy + a, 0)
mx1 = min(ix - b, 0) + W
my1 = min(iy - b, 0) + H
if mx0 < mx1 and my0 < my1:
m[:, :, my0:my1, mx0:mx1] = 1
return z, m
#----------------------------------------------------------------------------
# Construct an oriented low-pass filter that applies the appropriate
# bandlimit with respect to the input and output of the given affine 2D
# image transformation.
def construct_affine_bandlimit_filter(mat, a=3, amax=16, aflt=64, up=4, cutoff_in=1, cutoff_out=1):
assert a <= amax < aflt
mat = torch.as_tensor(mat).to(torch.float32)
# Construct 2D filter taps in input & output coordinate spaces.
taps = ((torch.arange(aflt * up * 2 - 1, device=mat.device) + 1) / up - aflt).roll(1 - aflt * up)
yi, xi = torch.meshgrid(taps, taps)
xo, yo = (torch.stack([xi, yi], dim=2) @ mat[:2, :2].t()).unbind(2)
# Convolution of two oriented 2D sinc filters.
fi = sinc(xi * cutoff_in) * sinc(yi * cutoff_in)
fo = sinc(xo * cutoff_out) * sinc(yo * cutoff_out)
f = torch.fft.ifftn(torch.fft.fftn(fi) * torch.fft.fftn(fo)).real
# Convolution of two oriented 2D Lanczos windows.
wi = lanczos_window(xi, a) * lanczos_window(yi, a)
wo = lanczos_window(xo, a) * lanczos_window(yo, a)
w = torch.fft.ifftn(torch.fft.fftn(wi) * torch.fft.fftn(wo)).real
# Construct windowed FIR filter.
f = f * w
# Finalize.
c = (aflt - amax) * up
f = f.roll([aflt * up - 1] * 2, dims=[0,1])[c:-c, c:-c]
f = torch.nn.functional.pad(f, [0, 1, 0, 1]).reshape(amax * 2, up, amax * 2, up)
f = f / f.sum([0,2], keepdim=True) / (up ** 2)
f = f.reshape(amax * 2 * up, amax * 2 * up)[:-1, :-1]
return f
#----------------------------------------------------------------------------
# Apply the given affine transformation to a batch of 2D images.
def apply_affine_transformation(x, mat, up=4, **filter_kwargs):
_N, _C, H, W = x.shape
mat = torch.as_tensor(mat).to(dtype=torch.float32, device=x.device)
# Construct filter.
f = construct_affine_bandlimit_filter(mat, up=up, **filter_kwargs)
assert f.ndim == 2 and f.shape[0] == f.shape[1] and f.shape[0] % 2 == 1
p = f.shape[0] // 2
# Construct sampling grid.
theta = mat.inverse()
theta[:2, 2] *= 2
theta[0, 2] += 1 / up / W
theta[1, 2] += 1 / up / H
theta[0, :] *= W / (W + p / up * 2)
theta[1, :] *= H / (H + p / up * 2)
theta = theta[:2, :3].unsqueeze(0).repeat([x.shape[0], 1, 1])
g = torch.nn.functional.affine_grid(theta, x.shape, align_corners=False)
# Resample image.
y = upfirdn2d.upsample2d(x=x, f=f, up=up, padding=p)
z = torch.nn.functional.grid_sample(y, g, mode='bilinear', padding_mode='zeros', align_corners=False)
# Form mask.
m = torch.zeros_like(y)
c = p * 2 + 1
m[:, :, c:-c, c:-c] = 1
m = torch.nn.functional.grid_sample(m, g, mode='nearest', padding_mode='zeros', align_corners=False)
return z, m
#----------------------------------------------------------------------------
# Apply fractional rotation to a batch of 2D images. Corresponds to the
# operator R_\alpha in Appendix E.3.
def apply_fractional_rotation(x, angle, a=3, **filter_kwargs):
angle = torch.as_tensor(angle).to(dtype=torch.float32, device=x.device)
mat = rotation_matrix(angle)
return apply_affine_transformation(x, mat, a=a, amax=a*2, **filter_kwargs)
#----------------------------------------------------------------------------
# Modify the frequency content of a batch of 2D images as if they had undergo
# fractional rotation -- but without actually rotating them. Corresponds to
# the operator R^*_\alpha in Appendix E.3.
def apply_fractional_pseudo_rotation(x, angle, a=3, **filter_kwargs):
angle = torch.as_tensor(angle).to(dtype=torch.float32, device=x.device)
mat = rotation_matrix(-angle)
f = construct_affine_bandlimit_filter(mat, a=a, amax=a*2, up=1, **filter_kwargs)
y = upfirdn2d.filter2d(x=x, f=f)
m = torch.zeros_like(y)
c = f.shape[0] // 2
m[:, :, c:-c, c:-c] = 1
return y, m
#----------------------------------------------------------------------------
# Compute the selected equivariance metrics for the given generator.
def compute_equivariance_metrics(opts, num_samples, batch_size, translate_max=0.125, rotate_max=1, compute_eqt_int=False, compute_eqt_frac=False, compute_eqr=False):
assert compute_eqt_int or compute_eqt_frac or compute_eqr
# Setup generator and labels.
G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device)
I = torch.eye(3, device=opts.device)
M = getattr(getattr(getattr(G, 'synthesis', None), 'input', None), 'transform', None)
if M is None:
raise ValueError('Cannot compute equivariance metrics; the given generator does not support user-specified image transformations')
c_iter = metric_utils.iterate_random_labels(opts=opts, batch_size=batch_size)
# Sampling loop.
sums = None
progress = opts.progress.sub(tag='eq sampling', num_items=num_samples)
for batch_start in range(0, num_samples, batch_size * opts.num_gpus):
progress.update(batch_start)
s = []
# Randomize noise buffers, if any.
for name, buf in G.named_buffers():
if name.endswith('.noise_const'):
buf.copy_(torch.randn_like(buf))
# Run mapping network.
z = torch.randn([batch_size, G.z_dim], device=opts.device)
c = next(c_iter)
ws = G.mapping(z=z, c=c)
# Generate reference image.
M[:] = I
orig = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs)
# Integer translation (EQ-T).
if compute_eqt_int:
t = (torch.rand(2, device=opts.device) * 2 - 1) * translate_max
t = (t * G.img_resolution).round() / G.img_resolution
M[:] = I
M[:2, 2] = -t
img = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs)
ref, mask = apply_integer_translation(orig, t[0], t[1])
s += [(ref - img).square() * mask, mask]
# Fractional translation (EQ-T_frac).
if compute_eqt_frac:
t = (torch.rand(2, device=opts.device) * 2 - 1) * translate_max
M[:] = I
M[:2, 2] = -t
img = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs)
ref, mask = apply_fractional_translation(orig, t[0], t[1])
s += [(ref - img).square() * mask, mask]
# Rotation (EQ-R).
if compute_eqr:
angle = (torch.rand([], device=opts.device) * 2 - 1) * (rotate_max * np.pi)
M[:] = rotation_matrix(-angle)
img = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs)
ref, ref_mask = apply_fractional_rotation(orig, angle)
pseudo, pseudo_mask = apply_fractional_pseudo_rotation(img, angle)
mask = ref_mask * pseudo_mask
s += [(ref - pseudo).square() * mask, mask]
# Accumulate results.
s = torch.stack([x.to(torch.float64).sum() for x in s])
sums = sums + s if sums is not None else s
progress.update(num_samples)
# Compute PSNRs.
if opts.num_gpus > 1:
torch.distributed.all_reduce(sums)
sums = sums.cpu()
mses = sums[0::2] / sums[1::2]
psnrs = np.log10(2) * 20 - mses.log10() * 10
psnrs = tuple(psnrs.numpy())
return psnrs[0] if len(psnrs) == 1 else psnrs
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Frechet Inception Distance (FID) from the paper
"GANs trained by a two time-scale update rule converge to a local Nash
equilibrium". Matches the original implementation by Heusel et al. at
https://github.com/bioinf-jku/TTUR/blob/master/fid.py"""
import numpy as np
import scipy.linalg
from . import metric_utils
#----------------------------------------------------------------------------
def compute_fid(opts, max_real, num_gen):
# Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl'
detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer.
mu_real, sigma_real = metric_utils.compute_feature_stats_for_dataset(
opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
rel_lo=0, rel_hi=0, capture_mean_cov=True, max_items=max_real).get_mean_cov()
mu_gen, sigma_gen = metric_utils.compute_feature_stats_for_generator(
opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
rel_lo=0, rel_hi=1, capture_mean_cov=True, max_items=num_gen).get_mean_cov()
if opts.rank != 0:
return float('nan')
m = np.square(mu_gen - mu_real).sum()
s, _ = scipy.linalg.sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member
fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2))
return float(fid)
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Inception Score (IS) from the paper "Improved techniques for training
GANs". Matches the original implementation by Salimans et al. at
https://github.com/openai/improved-gan/blob/master/inception_score/model.py"""
import numpy as np
from . import metric_utils
#----------------------------------------------------------------------------
def compute_is(opts, num_gen, num_splits):
# Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl'
detector_kwargs = dict(no_output_bias=True) # Match the original implementation by not applying bias in the softmax layer.
gen_probs = metric_utils.compute_feature_stats_for_generator(
opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
capture_all=True, max_items=num_gen).get_all()
if opts.rank != 0:
return float('nan'), float('nan')
scores = []
for i in range(num_splits):
part = gen_probs[i * num_gen // num_splits : (i + 1) * num_gen // num_splits]
kl = part * (np.log(part) - np.log(np.mean(part, axis=0, keepdims=True)))
kl = np.mean(np.sum(kl, axis=1))
scores.append(np.exp(kl))
return float(np.mean(scores)), float(np.std(scores))
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Kernel Inception Distance (KID) from the paper "Demystifying MMD
GANs". Matches the original implementation by Binkowski et al. at
https://github.com/mbinkowski/MMD-GAN/blob/master/gan/compute_scores.py"""
import numpy as np
from . import metric_utils
#----------------------------------------------------------------------------
def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size):
# Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl'
detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer.
real_features = metric_utils.compute_feature_stats_for_dataset(
opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all()
gen_features = metric_utils.compute_feature_stats_for_generator(
opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all()
if opts.rank != 0:
return float('nan')
n = real_features.shape[1]
m = min(min(real_features.shape[0], gen_features.shape[0]), max_subset_size)
t = 0
for _subset_idx in range(num_subsets):
x = gen_features[np.random.choice(gen_features.shape[0], m, replace=False)]
y = real_features[np.random.choice(real_features.shape[0], m, replace=False)]
a = (x @ x.T / n + 1) ** 3 + (y @ y.T / n + 1) ** 3
b = (x @ y.T / n + 1) ** 3
t += (a.sum() - np.diag(a).sum()) / (m - 1) - b.sum() * 2 / m
kid = t / num_subsets / m
return float(kid)
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Main API for computing and reporting quality metrics."""
import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
#----------------------------------------------------------------------------
_metric_dict = dict() # name => fn
def register_metric(fn):
assert callable(fn)
_metric_dict[fn.__name__] = fn
return fn
def is_valid_metric(metric):
return metric in _metric_dict
def list_valid_metrics():
return list(_metric_dict.keys())
#----------------------------------------------------------------------------
def calc_metric(metric, **kwargs): # See metric_utils.MetricOptions for the full list of arguments.
assert is_valid_metric(metric)
opts = metric_utils.MetricOptions(**kwargs)
# Calculate.
start_time = time.time()
results = _metric_dict[metric](opts)
total_time = time.time() - start_time
# Broadcast results.
for key, value in list(results.items()):
if opts.num_gpus > 1:
value = torch.as_tensor(value, dtype=torch.float64, device=opts.device)
torch.distributed.broadcast(tensor=value, src=0)
value = float(value.cpu())
results[key] = value
# Decorate with metadata.
return dnnlib.EasyDict(
results = dnnlib.EasyDict(results),
metric = metric,
total_time = total_time,
total_time_str = dnnlib.util.format_time(total_time),
num_gpus = opts.num_gpus,
)
#----------------------------------------------------------------------------
def report_metric(result_dict, run_dir=None, snapshot_pkl=None):
metric = result_dict['metric']
assert is_valid_metric(metric)
if run_dir is not None and snapshot_pkl is not None:
snapshot_pkl = os.path.relpath(snapshot_pkl, run_dir)
jsonl_line = json.dumps(dict(result_dict, snapshot_pkl=snapshot_pkl, timestamp=time.time()))
print(jsonl_line)
if run_dir is not None and os.path.isdir(run_dir):
with open(os.path.join(run_dir, f'metric-{metric}.jsonl'), 'at') as f:
f.write(jsonl_line + '\n')
#----------------------------------------------------------------------------
# Recommended metrics.
@register_metric
def fid50k_full(opts):
opts.dataset_kwargs.update(max_size=None, xflip=False)
fid = frechet_inception_distance.compute_fid(opts, max_real=None, num_gen=50000)
return dict(fid50k_full=fid)
@register_metric
def kid50k_full(opts):
opts.dataset_kwargs.update(max_size=None, xflip=False)
kid = kernel_inception_distance.compute_kid(opts, max_real=1000000, num_gen=50000, num_subsets=100, max_subset_size=1000)
return dict(kid50k_full=kid)
@register_metric
def pr50k3_full(opts):
opts.dataset_kwargs.update(max_size=None, xflip=False)
precision, recall = precision_recall.compute_pr(opts, max_real=200000, num_gen=50000, nhood_size=3, row_batch_size=10000, col_batch_size=10000)
return dict(pr50k3_full_precision=precision, pr50k3_full_recall=recall)
@register_metric
def ppl2_wend(opts):
ppl = perceptual_path_length.compute_ppl(opts, num_samples=50000, epsilon=1e-4, space='w', sampling='end', crop=False, batch_size=2)
return dict(ppl2_wend=ppl)
@register_metric
def eqt50k_int(opts):
opts.G_kwargs.update(force_fp32=True)
psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqt_int=True)
return dict(eqt50k_int=psnr)
@register_metric
def eqt50k_frac(opts):
opts.G_kwargs.update(force_fp32=True)
psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqt_frac=True)
return dict(eqt50k_frac=psnr)
@register_metric
def eqr50k(opts):
opts.G_kwargs.update(force_fp32=True)
psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqr=True)
return dict(eqr50k=psnr)
#----------------------------------------------------------------------------
# Legacy metrics.
@register_metric
def fid50k(opts):
opts.dataset_kwargs.update(max_size=None)
fid = frechet_inception_distance.compute_fid(opts, max_real=50000, num_gen=50000)
return dict(fid50k=fid)
@register_metric
def kid50k(opts):
opts.dataset_kwargs.update(max_size=None)
kid = kernel_inception_distance.compute_kid(opts, max_real=50000, num_gen=50000, num_subsets=100, max_subset_size=1000)
return dict(kid50k=kid)
@register_metric
def pr50k3(opts):
opts.dataset_kwargs.update(max_size=None)
precision, recall = precision_recall.compute_pr(opts, max_real=50000, num_gen=50000, nhood_size=3, row_batch_size=10000, col_batch_size=10000)
return dict(pr50k3_precision=precision, pr50k3_recall=recall)
@register_metric
def is50k(opts):
opts.dataset_kwargs.update(max_size=None, xflip=False)
mean, std = inception_score.compute_is(opts, num_gen=50000, num_splits=10)
return dict(is50k_mean=mean, is50k_std=std)
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Miscellaneous utilities used internally by the quality metrics."""
import os
import time
import hashlib
import pickle
import copy
import uuid
import numpy as np
import torch
import dnnlib
#----------------------------------------------------------------------------
class MetricOptions:
def __init__(self, G=None, G_kwargs={}, dataset_kwargs={}, num_gpus=1, rank=0, device=None, progress=None, cache=True):
assert 0 <= rank < num_gpus
self.G = G
self.G_kwargs = dnnlib.EasyDict(G_kwargs)
self.dataset_kwargs = dnnlib.EasyDict(dataset_kwargs)
self.num_gpus = num_gpus
self.rank = rank
self.device = device if device is not None else torch.device('cuda', rank)
self.progress = progress.sub() if progress is not None and rank == 0 else ProgressMonitor()
self.cache = cache
#----------------------------------------------------------------------------
_feature_detector_cache = dict()
def get_feature_detector_name(url):
return os.path.splitext(url.split('/')[-1])[0]
def get_feature_detector(url, device=torch.device('cpu'), num_gpus=1, rank=0, verbose=False):
assert 0 <= rank < num_gpus
key = (url, device)
if key not in _feature_detector_cache:
is_leader = (rank == 0)
if not is_leader and num_gpus > 1:
torch.distributed.barrier() # leader goes first
with dnnlib.util.open_url(url, verbose=(verbose and is_leader)) as f:
_feature_detector_cache[key] = pickle.load(f).to(device)
if is_leader and num_gpus > 1:
torch.distributed.barrier() # others follow
return _feature_detector_cache[key]
#----------------------------------------------------------------------------
def iterate_random_labels(opts, batch_size):
if opts.G.c_dim == 0:
c = torch.zeros([batch_size, opts.G.c_dim], device=opts.device)
while True:
yield c
else:
dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs)
while True:
c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(batch_size)]
c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device)
yield c
#----------------------------------------------------------------------------
class FeatureStats:
def __init__(self, capture_all=False, capture_mean_cov=False, max_items=None):
self.capture_all = capture_all
self.capture_mean_cov = capture_mean_cov
self.max_items = max_items
self.num_items = 0
self.num_features = None
self.all_features = None
self.raw_mean = None
self.raw_cov = None
def set_num_features(self, num_features):
if self.num_features is not None:
assert num_features == self.num_features
else:
self.num_features = num_features
self.all_features = []
self.raw_mean = np.zeros([num_features], dtype=np.float64)
self.raw_cov = np.zeros([num_features, num_features], dtype=np.float64)
def is_full(self):
return (self.max_items is not None) and (self.num_items >= self.max_items)
def append(self, x):
x = np.asarray(x, dtype=np.float32)
assert x.ndim == 2
if (self.max_items is not None) and (self.num_items + x.shape[0] > self.max_items):
if self.num_items >= self.max_items:
return
x = x[:self.max_items - self.num_items]
self.set_num_features(x.shape[1])
self.num_items += x.shape[0]
if self.capture_all:
self.all_features.append(x)
if self.capture_mean_cov:
x64 = x.astype(np.float64)
self.raw_mean += x64.sum(axis=0)
self.raw_cov += x64.T @ x64
def append_torch(self, x, num_gpus=1, rank=0):
assert isinstance(x, torch.Tensor) and x.ndim == 2
assert 0 <= rank < num_gpus
if num_gpus > 1:
ys = []
for src in range(num_gpus):
y = x.clone()
torch.distributed.broadcast(y, src=src)
ys.append(y)
x = torch.stack(ys, dim=1).flatten(0, 1) # interleave samples
self.append(x.cpu().numpy())
def get_all(self):
assert self.capture_all
return np.concatenate(self.all_features, axis=0)
def get_all_torch(self):
return torch.from_numpy(self.get_all())
def get_mean_cov(self):
assert self.capture_mean_cov
mean = self.raw_mean / self.num_items
cov = self.raw_cov / self.num_items
cov = cov - np.outer(mean, mean)
return mean, cov
def save(self, pkl_file):
with open(pkl_file, 'wb') as f:
pickle.dump(self.__dict__, f)
@staticmethod
def load(pkl_file):
with open(pkl_file, 'rb') as f:
s = dnnlib.EasyDict(pickle.load(f))
obj = FeatureStats(capture_all=s.capture_all, max_items=s.max_items)
obj.__dict__.update(s)
return obj
#----------------------------------------------------------------------------
class ProgressMonitor:
def __init__(self, tag=None, num_items=None, flush_interval=1000, verbose=False, progress_fn=None, pfn_lo=0, pfn_hi=1000, pfn_total=1000):
self.tag = tag
self.num_items = num_items
self.verbose = verbose
self.flush_interval = flush_interval
self.progress_fn = progress_fn
self.pfn_lo = pfn_lo
self.pfn_hi = pfn_hi
self.pfn_total = pfn_total
self.start_time = time.time()
self.batch_time = self.start_time
self.batch_items = 0
if self.progress_fn is not None:
self.progress_fn(self.pfn_lo, self.pfn_total)
def update(self, cur_items):
assert (self.num_items is None) or (cur_items <= self.num_items)
if (cur_items < self.batch_items + self.flush_interval) and (self.num_items is None or cur_items < self.num_items):
return
cur_time = time.time()
total_time = cur_time - self.start_time
time_per_item = (cur_time - self.batch_time) / max(cur_items - self.batch_items, 1)
if (self.verbose) and (self.tag is not None):
print(f'{self.tag:<19s} items {cur_items:<7d} time {dnnlib.util.format_time(total_time):<12s} ms/item {time_per_item*1e3:.2f}')
self.batch_time = cur_time
self.batch_items = cur_items
if (self.progress_fn is not None) and (self.num_items is not None):
self.progress_fn(self.pfn_lo + (self.pfn_hi - self.pfn_lo) * (cur_items / self.num_items), self.pfn_total)
def sub(self, tag=None, num_items=None, flush_interval=1000, rel_lo=0, rel_hi=1):
return ProgressMonitor(
tag = tag,
num_items = num_items,
flush_interval = flush_interval,
verbose = self.verbose,
progress_fn = self.progress_fn,
pfn_lo = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_lo,
pfn_hi = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_hi,
pfn_total = self.pfn_total,
)
#----------------------------------------------------------------------------
def compute_feature_stats_for_dataset(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, data_loader_kwargs=None, max_items=None, **stats_kwargs):
dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs)
if data_loader_kwargs is None:
data_loader_kwargs = dict(pin_memory=True, num_workers=3, prefetch_factor=2)
# Try to lookup from cache.
cache_file = None
if opts.cache:
# Choose cache file name.
args = dict(dataset_kwargs=opts.dataset_kwargs, detector_url=detector_url, detector_kwargs=detector_kwargs, stats_kwargs=stats_kwargs)
md5 = hashlib.md5(repr(sorted(args.items())).encode('utf-8'))
cache_tag = f'{dataset.name}-{get_feature_detector_name(detector_url)}-{md5.hexdigest()}'
cache_file = dnnlib.make_cache_dir_path('gan-metrics', cache_tag + '.pkl')
# Check if the file exists (all processes must agree).
flag = os.path.isfile(cache_file) if opts.rank == 0 else False
if opts.num_gpus > 1:
flag = torch.as_tensor(flag, dtype=torch.float32, device=opts.device)
torch.distributed.broadcast(tensor=flag, src=0)
flag = (float(flag.cpu()) != 0)
# Load.
if flag:
return FeatureStats.load(cache_file)
# Initialize.
num_items = len(dataset)
if max_items is not None:
num_items = min(num_items, max_items)
stats = FeatureStats(max_items=num_items, **stats_kwargs)
progress = opts.progress.sub(tag='dataset features', num_items=num_items, rel_lo=rel_lo, rel_hi=rel_hi)
detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose)
# Main loop.
item_subset = [(i * opts.num_gpus + opts.rank) % num_items for i in range((num_items - 1) // opts.num_gpus + 1)]
for images, _labels in torch.utils.data.DataLoader(dataset=dataset, sampler=item_subset, batch_size=batch_size, **data_loader_kwargs):
if images.shape[1] == 1:
images = images.repeat([1, 3, 1, 1])
features = detector(images.to(opts.device), **detector_kwargs)
stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank)
progress.update(stats.num_items)
# Save to cache.
if cache_file is not None and opts.rank == 0:
os.makedirs(os.path.dirname(cache_file), exist_ok=True)
temp_file = cache_file + '.' + uuid.uuid4().hex
stats.save(temp_file)
os.replace(temp_file, cache_file) # atomic
return stats
#----------------------------------------------------------------------------
def compute_feature_stats_for_generator(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, batch_gen=None, **stats_kwargs):
if batch_gen is None:
batch_gen = min(batch_size, 4)
assert batch_size % batch_gen == 0
# Setup generator and labels.
G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device)
c_iter = iterate_random_labels(opts=opts, batch_size=batch_gen)
# Initialize.
stats = FeatureStats(**stats_kwargs)
assert stats.max_items is not None
progress = opts.progress.sub(tag='generator features', num_items=stats.max_items, rel_lo=rel_lo, rel_hi=rel_hi)
detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose)
# Main loop.
while not stats.is_full():
images = []
for _i in range(batch_size // batch_gen):
z = torch.randn([batch_gen, G.z_dim], device=opts.device)
img = G(z=z, c=next(c_iter), **opts.G_kwargs)
img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)
images.append(img)
images = torch.cat(images)
if images.shape[1] == 1:
images = images.repeat([1, 3, 1, 1])
features = detector(images, **detector_kwargs)
stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank)
progress.update(stats.num_items)
return stats
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Perceptual Path Length (PPL) from the paper "A Style-Based Generator
Architecture for Generative Adversarial Networks". Matches the original
implementation by Karras et al. at
https://github.com/NVlabs/stylegan/blob/master/metrics/perceptual_path_length.py"""
import copy
import numpy as np
import torch
from . import metric_utils
#----------------------------------------------------------------------------
# Spherical interpolation of a batch of vectors.
def slerp(a, b, t):
a = a / a.norm(dim=-1, keepdim=True)
b = b / b.norm(dim=-1, keepdim=True)
d = (a * b).sum(dim=-1, keepdim=True)
p = t * torch.acos(d)
c = b - d * a
c = c / c.norm(dim=-1, keepdim=True)
d = a * torch.cos(p) + c * torch.sin(p)
d = d / d.norm(dim=-1, keepdim=True)
return d
#----------------------------------------------------------------------------
class PPLSampler(torch.nn.Module):
def __init__(self, G, G_kwargs, epsilon, space, sampling, crop, vgg16):
assert space in ['z', 'w']
assert sampling in ['full', 'end']
super().__init__()
self.G = copy.deepcopy(G)
self.G_kwargs = G_kwargs
self.epsilon = epsilon
self.space = space
self.sampling = sampling
self.crop = crop
self.vgg16 = copy.deepcopy(vgg16)
def forward(self, c):
# Generate random latents and interpolation t-values.
t = torch.rand([c.shape[0]], device=c.device) * (1 if self.sampling == 'full' else 0)
z0, z1 = torch.randn([c.shape[0] * 2, self.G.z_dim], device=c.device).chunk(2)
# Interpolate in W or Z.
if self.space == 'w':
w0, w1 = self.G.mapping(z=torch.cat([z0,z1]), c=torch.cat([c,c])).chunk(2)
wt0 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2))
wt1 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2) + self.epsilon)
else: # space == 'z'
zt0 = slerp(z0, z1, t.unsqueeze(1))
zt1 = slerp(z0, z1, t.unsqueeze(1) + self.epsilon)
wt0, wt1 = self.G.mapping(z=torch.cat([zt0,zt1]), c=torch.cat([c,c])).chunk(2)
# Randomize noise buffers.
for name, buf in self.G.named_buffers():
if name.endswith('.noise_const'):
buf.copy_(torch.randn_like(buf))
# Generate images.
img = self.G.synthesis(ws=torch.cat([wt0,wt1]), noise_mode='const', force_fp32=True, **self.G_kwargs)
# Center crop.
if self.crop:
assert img.shape[2] == img.shape[3]
c = img.shape[2] // 8
img = img[:, :, c*3 : c*7, c*2 : c*6]
# Downsample to 256x256.
factor = self.G.img_resolution // 256
if factor > 1:
img = img.reshape([-1, img.shape[1], img.shape[2] // factor, factor, img.shape[3] // factor, factor]).mean([3, 5])
# Scale dynamic range from [-1,1] to [0,255].
img = (img + 1) * (255 / 2)
if self.G.img_channels == 1:
img = img.repeat([1, 3, 1, 1])
# Evaluate differential LPIPS.
lpips_t0, lpips_t1 = self.vgg16(img, resize_images=False, return_lpips=True).chunk(2)
dist = (lpips_t0 - lpips_t1).square().sum(1) / self.epsilon ** 2
return dist
#----------------------------------------------------------------------------
def compute_ppl(opts, num_samples, epsilon, space, sampling, crop, batch_size):
vgg16_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/vgg16.pkl'
vgg16 = metric_utils.get_feature_detector(vgg16_url, num_gpus=opts.num_gpus, rank=opts.rank, verbose=opts.progress.verbose)
# Setup sampler and labels.
sampler = PPLSampler(G=opts.G, G_kwargs=opts.G_kwargs, epsilon=epsilon, space=space, sampling=sampling, crop=crop, vgg16=vgg16)
sampler.eval().requires_grad_(False).to(opts.device)
c_iter = metric_utils.iterate_random_labels(opts=opts, batch_size=batch_size)
# Sampling loop.
dist = []
progress = opts.progress.sub(tag='ppl sampling', num_items=num_samples)
for batch_start in range(0, num_samples, batch_size * opts.num_gpus):
progress.update(batch_start)
x = sampler(next(c_iter))
for src in range(opts.num_gpus):
y = x.clone()
if opts.num_gpus > 1:
torch.distributed.broadcast(y, src=src)
dist.append(y)
progress.update(num_samples)
# Compute PPL.
if opts.rank != 0:
return float('nan')
dist = torch.cat(dist)[:num_samples].cpu().numpy()
lo = np.percentile(dist, 1, interpolation='lower')
hi = np.percentile(dist, 99, interpolation='higher')
ppl = np.extract(np.logical_and(dist >= lo, dist <= hi), dist).mean()
return float(ppl)
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Precision/Recall (PR) from the paper "Improved Precision and Recall
Metric for Assessing Generative Models". Matches the original implementation
by Kynkaanniemi et al. at
https://github.com/kynkaat/improved-precision-and-recall-metric/blob/master/precision_recall.py"""
import torch
from . import metric_utils
#----------------------------------------------------------------------------
def compute_distances(row_features, col_features, num_gpus, rank, col_batch_size):
assert 0 <= rank < num_gpus
num_cols = col_features.shape[0]
num_batches = ((num_cols - 1) // col_batch_size // num_gpus + 1) * num_gpus
col_batches = torch.nn.functional.pad(col_features, [0, 0, 0, -num_cols % num_batches]).chunk(num_batches)
dist_batches = []
for col_batch in col_batches[rank :: num_gpus]:
dist_batch = torch.cdist(row_features.unsqueeze(0), col_batch.unsqueeze(0))[0]
for src in range(num_gpus):
dist_broadcast = dist_batch.clone()
if num_gpus > 1:
torch.distributed.broadcast(dist_broadcast, src=src)
dist_batches.append(dist_broadcast.cpu() if rank == 0 else None)
return torch.cat(dist_batches, dim=1)[:, :num_cols] if rank == 0 else None
#----------------------------------------------------------------------------
def compute_pr(opts, max_real, num_gen, nhood_size, row_batch_size, col_batch_size):
detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/vgg16.pkl'
detector_kwargs = dict(return_features=True)
real_features = metric_utils.compute_feature_stats_for_dataset(
opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all_torch().to(torch.float16).to(opts.device)
gen_features = metric_utils.compute_feature_stats_for_generator(
opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all_torch().to(torch.float16).to(opts.device)
results = dict()
for name, manifold, probes in [('precision', real_features, gen_features), ('recall', gen_features, real_features)]:
kth = []
for manifold_batch in manifold.split(row_batch_size):
dist = compute_distances(row_features=manifold_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size)
kth.append(dist.to(torch.float32).kthvalue(nhood_size + 1).values.to(torch.float16) if opts.rank == 0 else None)
kth = torch.cat(kth) if opts.rank == 0 else None
pred = []
for probes_batch in probes.split(row_batch_size):
dist = compute_distances(row_features=probes_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size)
pred.append((dist <= kth).any(dim=1) if opts.rank == 0 else None)
results[name] = float(torch.cat(pred).to(torch.float32).mean() if opts.rank == 0 else 'nan')
return results['precision'], results['recall']
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
# empty

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import glob
import hashlib
import importlib
import os
import re
import shutil
import uuid
import torch
import torch.utils.cpp_extension
from torch.utils.file_baton import FileBaton
#----------------------------------------------------------------------------
# Global options.
verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full'
#----------------------------------------------------------------------------
# Internal helper funcs.
def _find_compiler_bindir():
patterns = [
'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
]
for pattern in patterns:
matches = sorted(glob.glob(pattern))
if len(matches):
return matches[-1]
return None
#----------------------------------------------------------------------------
def _get_mangled_gpu_name():
name = torch.cuda.get_device_name().lower()
out = []
for c in name:
if re.match('[a-z0-9_-]+', c):
out.append(c)
else:
out.append('-')
return ''.join(out)
#----------------------------------------------------------------------------
# Main entry point for compiling and loading C++/CUDA plugins.
_cached_plugins = dict()
def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwargs):
assert verbosity in ['none', 'brief', 'full']
if headers is None:
headers = []
if source_dir is not None:
sources = [os.path.join(source_dir, fname) for fname in sources]
headers = [os.path.join(source_dir, fname) for fname in headers]
# Already cached?
if module_name in _cached_plugins:
return _cached_plugins[module_name]
# Print status.
if verbosity == 'full':
print(f'Setting up PyTorch plugin "{module_name}"...')
elif verbosity == 'brief':
print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
verbose_build = (verbosity == 'full')
# Compile and load.
try: # pylint: disable=too-many-nested-blocks
# Make sure we can find the necessary compiler binaries.
if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
compiler_bindir = _find_compiler_bindir()
if compiler_bindir is None:
raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
os.environ['PATH'] += ';' + compiler_bindir
# Some containers set TORCH_CUDA_ARCH_LIST to a list that can either
# break the build or unnecessarily restrict what's available to nvcc.
# Unset it to let nvcc decide based on what's available on the
# machine.
os.environ['TORCH_CUDA_ARCH_LIST'] = ''
# Incremental build md5sum trickery. Copies all the input source files
# into a cached build directory under a combined md5 digest of the input
# source files. Copying is done only if the combined digest has changed.
# This keeps input file timestamps and filenames the same as in previous
# extension builds, allowing for fast incremental rebuilds.
#
# This optimization is done only in case all the source files reside in
# a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
# environment variable is set (we take this as a signal that the user
# actually cares about this.)
#
# EDIT: We now do it regardless of TORCH_EXTENSIOS_DIR, in order to work
# around the *.cu dependency bug in ninja config.
#
all_source_files = sorted(sources + headers)
all_source_dirs = set(os.path.dirname(fname) for fname in all_source_files)
if len(all_source_dirs) == 1: # and ('TORCH_EXTENSIONS_DIR' in os.environ):
# Compute combined hash digest for all source files.
hash_md5 = hashlib.md5()
for src in all_source_files:
with open(src, 'rb') as f:
hash_md5.update(f.read())
# Select cached build directory name.
source_digest = hash_md5.hexdigest()
build_top_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access
cached_build_dir = os.path.join(build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}')
if not os.path.isdir(cached_build_dir):
tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}'
os.makedirs(tmpdir)
for src in all_source_files:
shutil.copyfile(src, os.path.join(tmpdir, os.path.basename(src)))
try:
os.replace(tmpdir, cached_build_dir) # atomic
except OSError:
# source directory already exists, delete tmpdir and its contents.
shutil.rmtree(tmpdir)
if not os.path.isdir(cached_build_dir): raise
# Compile.
cached_sources = [os.path.join(cached_build_dir, os.path.basename(fname)) for fname in sources]
torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir,
verbose=verbose_build, sources=cached_sources, **build_kwargs)
else:
torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
# Load.
module = importlib.import_module(module_name)
except:
if verbosity == 'brief':
print('Failed!')
raise
# Print status and add to cache dict.
if verbosity == 'full':
print(f'Done setting up PyTorch plugin "{module_name}".')
elif verbosity == 'brief':
print('Done.')
_cached_plugins[module_name] = module
return module
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import re
import contextlib
import numpy as np
import torch
import warnings
import dnnlib
#----------------------------------------------------------------------------
# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
# same constant is used multiple times.
_constant_cache = dict()
def constant(value, shape=None, dtype=None, device=None, memory_format=None):
value = np.asarray(value)
if shape is not None:
shape = tuple(shape)
if dtype is None:
dtype = torch.get_default_dtype()
if device is None:
device = torch.device('cpu')
if memory_format is None:
memory_format = torch.contiguous_format
key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
tensor = _constant_cache.get(key, None)
if tensor is None:
tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
if shape is not None:
tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
tensor = tensor.contiguous(memory_format=memory_format)
_constant_cache[key] = tensor
return tensor
#----------------------------------------------------------------------------
# Replace NaN/Inf with specified numerical values.
try:
nan_to_num = torch.nan_to_num # 1.8.0a0
except AttributeError:
def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
assert isinstance(input, torch.Tensor)
if posinf is None:
posinf = torch.finfo(input.dtype).max
if neginf is None:
neginf = torch.finfo(input.dtype).min
assert nan == 0
return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
#----------------------------------------------------------------------------
# Symbolic assert.
try:
symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
except AttributeError:
symbolic_assert = torch.Assert # 1.7.0
#----------------------------------------------------------------------------
# Context manager to temporarily suppress known warnings in torch.jit.trace().
# Note: Cannot use catch_warnings because of https://bugs.python.org/issue29672
@contextlib.contextmanager
def suppress_tracer_warnings():
flt = ('ignore', None, torch.jit.TracerWarning, None, 0)
warnings.filters.insert(0, flt)
yield
warnings.filters.remove(flt)
#----------------------------------------------------------------------------
# Assert that the shape of a tensor matches the given list of integers.
# None indicates that the size of a dimension is allowed to vary.
# Performs symbolic assertion when used in torch.jit.trace().
def assert_shape(tensor, ref_shape):
if tensor.ndim != len(ref_shape):
raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
if ref_size is None:
pass
elif isinstance(ref_size, torch.Tensor):
with suppress_tracer_warnings(): # as_tensor results are registered as constants
symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
elif isinstance(size, torch.Tensor):
with suppress_tracer_warnings(): # as_tensor results are registered as constants
symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
elif size != ref_size:
raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
#----------------------------------------------------------------------------
# Function decorator that calls torch.autograd.profiler.record_function().
def profiled_function(fn):
def decorator(*args, **kwargs):
with torch.autograd.profiler.record_function(fn.__name__):
return fn(*args, **kwargs)
decorator.__name__ = fn.__name__
return decorator
#----------------------------------------------------------------------------
# Sampler for torch.utils.data.DataLoader that loops over the dataset
# indefinitely, shuffling items as it goes.
class InfiniteSampler(torch.utils.data.Sampler):
def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
assert len(dataset) > 0
assert num_replicas > 0
assert 0 <= rank < num_replicas
assert 0 <= window_size <= 1
super().__init__(dataset)
self.dataset = dataset
self.rank = rank
self.num_replicas = num_replicas
self.shuffle = shuffle
self.seed = seed
self.window_size = window_size
def __iter__(self):
order = np.arange(len(self.dataset))
rnd = None
window = 0
if self.shuffle:
rnd = np.random.RandomState(self.seed)
rnd.shuffle(order)
window = int(np.rint(order.size * self.window_size))
idx = 0
while True:
i = idx % order.size
if idx % self.num_replicas == self.rank:
yield order[i]
if window >= 2:
j = (i - rnd.randint(window)) % order.size
order[i], order[j] = order[j], order[i]
idx += 1
#----------------------------------------------------------------------------
# Utilities for operating with torch.nn.Module parameters and buffers.
def params_and_buffers(module):
assert isinstance(module, torch.nn.Module)
return list(module.parameters()) + list(module.buffers())
def named_params_and_buffers(module):
assert isinstance(module, torch.nn.Module)
return list(module.named_parameters()) + list(module.named_buffers())
def copy_params_and_buffers(src_module, dst_module, require_all=False):
assert isinstance(src_module, torch.nn.Module)
assert isinstance(dst_module, torch.nn.Module)
src_tensors = dict(named_params_and_buffers(src_module))
for name, tensor in named_params_and_buffers(dst_module):
assert (name in src_tensors) or (not require_all)
if name in src_tensors:
tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
#----------------------------------------------------------------------------
# Context manager for easily enabling/disabling DistributedDataParallel
# synchronization.
@contextlib.contextmanager
def ddp_sync(module, sync):
assert isinstance(module, torch.nn.Module)
if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
yield
else:
with module.no_sync():
yield
#----------------------------------------------------------------------------
# Check DistributedDataParallel consistency across processes.
def check_ddp_consistency(module, ignore_regex=None):
assert isinstance(module, torch.nn.Module)
for name, tensor in named_params_and_buffers(module):
fullname = type(module).__name__ + '.' + name
if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
continue
tensor = tensor.detach()
if tensor.is_floating_point():
tensor = nan_to_num(tensor)
other = tensor.clone()
torch.distributed.broadcast(tensor=other, src=0)
assert (tensor == other).all(), fullname
#----------------------------------------------------------------------------
# Print summary table of module hierarchy.
def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
assert isinstance(module, torch.nn.Module)
assert not isinstance(module, torch.jit.ScriptModule)
assert isinstance(inputs, (tuple, list))
# Register hooks.
entries = []
nesting = [0]
def pre_hook(_mod, _inputs):
nesting[0] += 1
def post_hook(mod, _inputs, outputs):
nesting[0] -= 1
if nesting[0] <= max_nesting:
outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
# Run module.
outputs = module(*inputs)
for hook in hooks:
hook.remove()
# Identify unique outputs, parameters, and buffers.
tensors_seen = set()
for e in entries:
e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
# Filter out redundant entries.
if skip_redundant:
entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
# Construct table.
rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
rows += [['---'] * len(rows[0])]
param_total = 0
buffer_total = 0
submodule_names = {mod: name for name, mod in module.named_modules()}
for e in entries:
name = '<top-level>' if e.mod is module else submodule_names[e.mod]
param_size = sum(t.numel() for t in e.unique_params)
buffer_size = sum(t.numel() for t in e.unique_buffers)
output_shapes = [str(list(t.shape)) for t in e.outputs]
output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
rows += [[
name + (':0' if len(e.outputs) >= 2 else ''),
str(param_size) if param_size else '-',
str(buffer_size) if buffer_size else '-',
(output_shapes + ['-'])[0],
(output_dtypes + ['-'])[0],
]]
for idx in range(1, len(e.outputs)):
rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
param_total += param_size
buffer_total += buffer_size
rows += [['---'] * len(rows[0])]
rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
# Print table.
widths = [max(len(cell) for cell in column) for column in zip(*rows)]
print()
for row in rows:
print(' '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
print()
return outputs
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
# empty

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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include "bias_act.h"
//------------------------------------------------------------------------
static bool has_same_layout(torch::Tensor x, torch::Tensor y)
{
if (x.dim() != y.dim())
return false;
for (int64_t i = 0; i < x.dim(); i++)
{
if (x.size(i) != y.size(i))
return false;
if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
return false;
}
return true;
}
//------------------------------------------------------------------------
static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp)
{
// Validate arguments.
TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x");
TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x");
TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x");
TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
TORCH_CHECK(b.dim() == 1, "b must have rank 1");
TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
TORCH_CHECK(grad >= 0, "grad must be non-negative");
// Validate layout.
TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
// Create output tensor.
const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
torch::Tensor y = torch::empty_like(x);
TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
// Initialize CUDA kernel parameters.
bias_act_kernel_params p;
p.x = x.data_ptr();
p.b = (b.numel()) ? b.data_ptr() : NULL;
p.xref = (xref.numel()) ? xref.data_ptr() : NULL;
p.yref = (yref.numel()) ? yref.data_ptr() : NULL;
p.dy = (dy.numel()) ? dy.data_ptr() : NULL;
p.y = y.data_ptr();
p.grad = grad;
p.act = act;
p.alpha = alpha;
p.gain = gain;
p.clamp = clamp;
p.sizeX = (int)x.numel();
p.sizeB = (int)b.numel();
p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
// Choose CUDA kernel.
void* kernel;
AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
{
kernel = choose_bias_act_kernel<scalar_t>(p);
});
TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
// Launch CUDA kernel.
p.loopX = 4;
int blockSize = 4 * 32;
int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
void* args[] = {&p};
AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
return y;
}
//------------------------------------------------------------------------
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
m.def("bias_act", &bias_act);
}
//------------------------------------------------------------------------

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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
#include <c10/util/Half.h>
#include "bias_act.h"
//------------------------------------------------------------------------
// Helpers.
template <class T> struct InternalType;
template <> struct InternalType<double> { typedef double scalar_t; };
template <> struct InternalType<float> { typedef float scalar_t; };
template <> struct InternalType<c10::Half> { typedef float scalar_t; };
//------------------------------------------------------------------------
// CUDA kernel.
template <class T, int A>
__global__ void bias_act_kernel(bias_act_kernel_params p)
{
typedef typename InternalType<T>::scalar_t scalar_t;
int G = p.grad;
scalar_t alpha = (scalar_t)p.alpha;
scalar_t gain = (scalar_t)p.gain;
scalar_t clamp = (scalar_t)p.clamp;
scalar_t one = (scalar_t)1;
scalar_t two = (scalar_t)2;
scalar_t expRange = (scalar_t)80;
scalar_t halfExpRange = (scalar_t)40;
scalar_t seluScale = (scalar_t)1.0507009873554804934193349852946;
scalar_t seluAlpha = (scalar_t)1.6732632423543772848170429916717;
// Loop over elements.
int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x;
for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x)
{
// Load.
scalar_t x = (scalar_t)((const T*)p.x)[xi];
scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0;
scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0;
scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0;
scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one;
scalar_t yy = (gain != 0) ? yref / gain : 0;
scalar_t y = 0;
// Apply bias.
((G == 0) ? x : xref) += b;
// linear
if (A == 1)
{
if (G == 0) y = x;
if (G == 1) y = x;
}
// relu
if (A == 2)
{
if (G == 0) y = (x > 0) ? x : 0;
if (G == 1) y = (yy > 0) ? x : 0;
}
// lrelu
if (A == 3)
{
if (G == 0) y = (x > 0) ? x : x * alpha;
if (G == 1) y = (yy > 0) ? x : x * alpha;
}
// tanh
if (A == 4)
{
if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); }
if (G == 1) y = x * (one - yy * yy);
if (G == 2) y = x * (one - yy * yy) * (-two * yy);
}
// sigmoid
if (A == 5)
{
if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one);
if (G == 1) y = x * yy * (one - yy);
if (G == 2) y = x * yy * (one - yy) * (one - two * yy);
}
// elu
if (A == 6)
{
if (G == 0) y = (x >= 0) ? x : exp(x) - one;
if (G == 1) y = (yy >= 0) ? x : x * (yy + one);
if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one);
}
// selu
if (A == 7)
{
if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one);
if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha);
if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha);
}
// softplus
if (A == 8)
{
if (G == 0) y = (x > expRange) ? x : log(exp(x) + one);
if (G == 1) y = x * (one - exp(-yy));
if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); }
}
// swish
if (A == 9)
{
if (G == 0)
y = (x < -expRange) ? 0 : x / (exp(-x) + one);
else
{
scalar_t c = exp(xref);
scalar_t d = c + one;
if (G == 1)
y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d);
else
y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d);
yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain;
}
}
// Apply gain.
y *= gain * dy;
// Clamp.
if (clamp >= 0)
{
if (G == 0)
y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp;
else
y = (yref > -clamp & yref < clamp) ? y : 0;
}
// Store.
((T*)p.y)[xi] = (T)y;
}
}
//------------------------------------------------------------------------
// CUDA kernel selection.
template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p)
{
if (p.act == 1) return (void*)bias_act_kernel<T, 1>;
if (p.act == 2) return (void*)bias_act_kernel<T, 2>;
if (p.act == 3) return (void*)bias_act_kernel<T, 3>;
if (p.act == 4) return (void*)bias_act_kernel<T, 4>;
if (p.act == 5) return (void*)bias_act_kernel<T, 5>;
if (p.act == 6) return (void*)bias_act_kernel<T, 6>;
if (p.act == 7) return (void*)bias_act_kernel<T, 7>;
if (p.act == 8) return (void*)bias_act_kernel<T, 8>;
if (p.act == 9) return (void*)bias_act_kernel<T, 9>;
return NULL;
}
//------------------------------------------------------------------------
// Template specializations.
template void* choose_bias_act_kernel<double> (const bias_act_kernel_params& p);
template void* choose_bias_act_kernel<float> (const bias_act_kernel_params& p);
template void* choose_bias_act_kernel<c10::Half> (const bias_act_kernel_params& p);
//------------------------------------------------------------------------

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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
//------------------------------------------------------------------------
// CUDA kernel parameters.
struct bias_act_kernel_params
{
const void* x; // [sizeX]
const void* b; // [sizeB] or NULL
const void* xref; // [sizeX] or NULL
const void* yref; // [sizeX] or NULL
const void* dy; // [sizeX] or NULL
void* y; // [sizeX]
int grad;
int act;
float alpha;
float gain;
float clamp;
int sizeX;
int sizeB;
int stepB;
int loopX;
};
//------------------------------------------------------------------------
// CUDA kernel selection.
template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p);
//------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Custom PyTorch ops for efficient bias and activation."""
import os
import numpy as np
import torch
import dnnlib
from .. import custom_ops
from .. import misc
#----------------------------------------------------------------------------
activation_funcs = {
'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False),
'relu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.relu(x), def_alpha=0, def_gain=np.sqrt(2), cuda_idx=2, ref='y', has_2nd_grad=False),
'lrelu': dnnlib.EasyDict(func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', has_2nd_grad=False),
'tanh': dnnlib.EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y', has_2nd_grad=True),
'sigmoid': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y', has_2nd_grad=True),
'elu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.elu(x), def_alpha=0, def_gain=1, cuda_idx=6, ref='y', has_2nd_grad=True),
'selu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.selu(x), def_alpha=0, def_gain=1, cuda_idx=7, ref='y', has_2nd_grad=True),
'softplus': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x), def_alpha=0, def_gain=1, cuda_idx=8, ref='y', has_2nd_grad=True),
'swish': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x', has_2nd_grad=True),
}
#----------------------------------------------------------------------------
_plugin = None
_null_tensor = torch.empty([0])
def _init():
global _plugin
if _plugin is None:
_plugin = custom_ops.get_plugin(
module_name='bias_act_plugin',
sources=['bias_act.cpp', 'bias_act.cu'],
headers=['bias_act.h'],
source_dir=os.path.dirname(__file__),
extra_cuda_cflags=['--use_fast_math'],
)
return True
#----------------------------------------------------------------------------
def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
r"""Fused bias and activation function.
Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
and scales the result by `gain`. Each of the steps is optional. In most cases,
the fused op is considerably more efficient than performing the same calculation
using standard PyTorch ops. It supports first and second order gradients,
but not third order gradients.
Args:
x: Input activation tensor. Can be of any shape.
b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type
as `x`. The shape must be known, and it must match the dimension of `x`
corresponding to `dim`.
dim: The dimension in `x` corresponding to the elements of `b`.
The value of `dim` is ignored if `b` is not specified.
act: Name of the activation function to evaluate, or `"linear"` to disable.
Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
See `activation_funcs` for a full list. `None` is not allowed.
alpha: Shape parameter for the activation function, or `None` to use the default.
gain: Scaling factor for the output tensor, or `None` to use default.
See `activation_funcs` for the default scaling of each activation function.
If unsure, consider specifying 1.
clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
the clamping (default).
impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
Returns:
Tensor of the same shape and datatype as `x`.
"""
assert isinstance(x, torch.Tensor)
assert impl in ['ref', 'cuda']
if impl == 'cuda' and x.device.type == 'cuda' and _init():
return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
#----------------------------------------------------------------------------
@misc.profiled_function
def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
"""Slow reference implementation of `bias_act()` using standard TensorFlow ops.
"""
assert isinstance(x, torch.Tensor)
assert clamp is None or clamp >= 0
spec = activation_funcs[act]
alpha = float(alpha if alpha is not None else spec.def_alpha)
gain = float(gain if gain is not None else spec.def_gain)
clamp = float(clamp if clamp is not None else -1)
# Add bias.
if b is not None:
assert isinstance(b, torch.Tensor) and b.ndim == 1
assert 0 <= dim < x.ndim
assert b.shape[0] == x.shape[dim]
x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
# Evaluate activation function.
alpha = float(alpha)
x = spec.func(x, alpha=alpha)
# Scale by gain.
gain = float(gain)
if gain != 1:
x = x * gain
# Clamp.
if clamp >= 0:
x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
return x
#----------------------------------------------------------------------------
_bias_act_cuda_cache = dict()
def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
"""Fast CUDA implementation of `bias_act()` using custom ops.
"""
# Parse arguments.
assert clamp is None or clamp >= 0
spec = activation_funcs[act]
alpha = float(alpha if alpha is not None else spec.def_alpha)
gain = float(gain if gain is not None else spec.def_gain)
clamp = float(clamp if clamp is not None else -1)
# Lookup from cache.
key = (dim, act, alpha, gain, clamp)
if key in _bias_act_cuda_cache:
return _bias_act_cuda_cache[key]
# Forward op.
class BiasActCuda(torch.autograd.Function):
@staticmethod
def forward(ctx, x, b): # pylint: disable=arguments-differ
ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride(1) == 1 else torch.contiguous_format
x = x.contiguous(memory_format=ctx.memory_format)
b = b.contiguous() if b is not None else _null_tensor
y = x
if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
ctx.save_for_backward(
x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
y if 'y' in spec.ref else _null_tensor)
return y
@staticmethod
def backward(ctx, dy): # pylint: disable=arguments-differ
dy = dy.contiguous(memory_format=ctx.memory_format)
x, b, y = ctx.saved_tensors
dx = None
db = None
if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
dx = dy
if act != 'linear' or gain != 1 or clamp >= 0:
dx = BiasActCudaGrad.apply(dy, x, b, y)
if ctx.needs_input_grad[1]:
db = dx.sum([i for i in range(dx.ndim) if i != dim])
return dx, db
# Backward op.
class BiasActCudaGrad(torch.autograd.Function):
@staticmethod
def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ
ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride(1) == 1 else torch.contiguous_format
dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp)
ctx.save_for_backward(
dy if spec.has_2nd_grad else _null_tensor,
x, b, y)
return dx
@staticmethod
def backward(ctx, d_dx): # pylint: disable=arguments-differ
d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
dy, x, b, y = ctx.saved_tensors
d_dy = None
d_x = None
d_b = None
d_y = None
if ctx.needs_input_grad[0]:
d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
if spec.has_2nd_grad and ctx.needs_input_grad[2]:
d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
return d_dy, d_x, d_b, d_y
# Add to cache.
_bias_act_cuda_cache[key] = BiasActCuda
return BiasActCuda
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Custom replacement for `torch.nn.functional.conv2d` that supports
arbitrarily high order gradients with zero performance penalty."""
import contextlib
import torch
# pylint: disable=redefined-builtin
# pylint: disable=arguments-differ
# pylint: disable=protected-access
#----------------------------------------------------------------------------
enabled = False # Enable the custom op by setting this to true.
weight_gradients_disabled = False # Forcefully disable computation of gradients with respect to the weights.
@contextlib.contextmanager
def no_weight_gradients(disable=True):
global weight_gradients_disabled
old = weight_gradients_disabled
if disable:
weight_gradients_disabled = True
yield
weight_gradients_disabled = old
#----------------------------------------------------------------------------
def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
if _should_use_custom_op(input):
return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
if _should_use_custom_op(input):
return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
#----------------------------------------------------------------------------
def _should_use_custom_op(input):
assert isinstance(input, torch.Tensor)
if (not enabled) or (not torch.backends.cudnn.enabled):
return False
if input.device.type != 'cuda':
return False
return True
def _tuple_of_ints(xs, ndim):
xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
assert len(xs) == ndim
assert all(isinstance(x, int) for x in xs)
return xs
#----------------------------------------------------------------------------
_conv2d_gradfix_cache = dict()
_null_tensor = torch.empty([0])
def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
# Parse arguments.
ndim = 2
weight_shape = tuple(weight_shape)
stride = _tuple_of_ints(stride, ndim)
padding = _tuple_of_ints(padding, ndim)
output_padding = _tuple_of_ints(output_padding, ndim)
dilation = _tuple_of_ints(dilation, ndim)
# Lookup from cache.
key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
if key in _conv2d_gradfix_cache:
return _conv2d_gradfix_cache[key]
# Validate arguments.
assert groups >= 1
assert len(weight_shape) == ndim + 2
assert all(stride[i] >= 1 for i in range(ndim))
assert all(padding[i] >= 0 for i in range(ndim))
assert all(dilation[i] >= 0 for i in range(ndim))
if not transpose:
assert all(output_padding[i] == 0 for i in range(ndim))
else: # transpose
assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
# Helpers.
common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
def calc_output_padding(input_shape, output_shape):
if transpose:
return [0, 0]
return [
input_shape[i + 2]
- (output_shape[i + 2] - 1) * stride[i]
- (1 - 2 * padding[i])
- dilation[i] * (weight_shape[i + 2] - 1)
for i in range(ndim)
]
# Forward & backward.
class Conv2d(torch.autograd.Function):
@staticmethod
def forward(ctx, input, weight, bias):
assert weight.shape == weight_shape
ctx.save_for_backward(
input if weight.requires_grad else _null_tensor,
weight if input.requires_grad else _null_tensor,
)
ctx.input_shape = input.shape
# Simple 1x1 convolution => cuBLAS (only on Volta, not on Ampere).
if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0) and torch.cuda.get_device_capability(input.device) < (8, 0):
a = weight.reshape(groups, weight_shape[0] // groups, weight_shape[1])
b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1)
c = (a.transpose(1, 2) if transpose else a) @ b.permute(1, 2, 0, 3).flatten(2)
c = c.reshape(-1, input.shape[0], *input.shape[2:]).transpose(0, 1)
c = c if bias is None else c + bias.unsqueeze(0).unsqueeze(2).unsqueeze(3)
return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
# General case => cuDNN.
if transpose:
return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
@staticmethod
def backward(ctx, grad_output):
input, weight = ctx.saved_tensors
input_shape = ctx.input_shape
grad_input = None
grad_weight = None
grad_bias = None
if ctx.needs_input_grad[0]:
p = calc_output_padding(input_shape=input_shape, output_shape=grad_output.shape)
op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
grad_input = op.apply(grad_output, weight, None)
assert grad_input.shape == input_shape
if ctx.needs_input_grad[1] and not weight_gradients_disabled:
grad_weight = Conv2dGradWeight.apply(grad_output, input)
assert grad_weight.shape == weight_shape
if ctx.needs_input_grad[2]:
grad_bias = grad_output.sum([0, 2, 3])
return grad_input, grad_weight, grad_bias
# Gradient with respect to the weights.
class Conv2dGradWeight(torch.autograd.Function):
@staticmethod
def forward(ctx, grad_output, input):
ctx.save_for_backward(
grad_output if input.requires_grad else _null_tensor,
input if grad_output.requires_grad else _null_tensor,
)
ctx.grad_output_shape = grad_output.shape
ctx.input_shape = input.shape
# Simple 1x1 convolution => cuBLAS (on both Volta and Ampere).
if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0):
a = grad_output.reshape(grad_output.shape[0], groups, grad_output.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
c = (b @ a.transpose(1, 2) if transpose else a @ b.transpose(1, 2)).reshape(weight_shape)
return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
# General case => cuDNN.
name = 'aten::cudnn_convolution_transpose_backward_weight' if transpose else 'aten::cudnn_convolution_backward_weight'
flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
return torch._C._jit_get_operation(name)(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
@staticmethod
def backward(ctx, grad2_grad_weight):
grad_output, input = ctx.saved_tensors
grad_output_shape = ctx.grad_output_shape
input_shape = ctx.input_shape
grad2_grad_output = None
grad2_input = None
if ctx.needs_input_grad[0]:
grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
assert grad2_grad_output.shape == grad_output_shape
if ctx.needs_input_grad[1]:
p = calc_output_padding(input_shape=input_shape, output_shape=grad_output_shape)
op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
grad2_input = op.apply(grad_output, grad2_grad_weight, None)
assert grad2_input.shape == input_shape
return grad2_grad_output, grad2_input
_conv2d_gradfix_cache[key] = Conv2d
return Conv2d
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""2D convolution with optional up/downsampling."""
import torch
from .. import misc
from . import conv2d_gradfix
from . import upfirdn2d
from .upfirdn2d import _parse_padding
from .upfirdn2d import _get_filter_size
#----------------------------------------------------------------------------
def _get_weight_shape(w):
with misc.suppress_tracer_warnings(): # this value will be treated as a constant
shape = [int(sz) for sz in w.shape]
misc.assert_shape(w, shape)
return shape
#----------------------------------------------------------------------------
def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True):
"""Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
"""
_out_channels, _in_channels_per_group, kh, kw = _get_weight_shape(w)
# Flip weight if requested.
# Note: conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
if not flip_weight and (kw > 1 or kh > 1):
w = w.flip([2, 3])
# Execute using conv2d_gradfix.
op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d
return op(x, w, stride=stride, padding=padding, groups=groups)
#----------------------------------------------------------------------------
@misc.profiled_function
def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False):
r"""2D convolution with optional up/downsampling.
Padding is performed only once at the beginning, not between the operations.
Args:
x: Input tensor of shape
`[batch_size, in_channels, in_height, in_width]`.
w: Weight tensor of shape
`[out_channels, in_channels//groups, kernel_height, kernel_width]`.
f: Low-pass filter for up/downsampling. Must be prepared beforehand by
calling upfirdn2d.setup_filter(). None = identity (default).
up: Integer upsampling factor (default: 1).
down: Integer downsampling factor (default: 1).
padding: Padding with respect to the upsampled image. Can be a single number
or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
(default: 0).
groups: Split input channels into N groups (default: 1).
flip_weight: False = convolution, True = correlation (default: True).
flip_filter: False = convolution, True = correlation (default: False).
Returns:
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
"""
# Validate arguments.
assert isinstance(x, torch.Tensor) and (x.ndim == 4)
assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32)
assert isinstance(up, int) and (up >= 1)
assert isinstance(down, int) and (down >= 1)
assert isinstance(groups, int) and (groups >= 1)
out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
fw, fh = _get_filter_size(f)
px0, px1, py0, py1 = _parse_padding(padding)
# Adjust padding to account for up/downsampling.
if up > 1:
px0 += (fw + up - 1) // 2
px1 += (fw - up) // 2
py0 += (fh + up - 1) // 2
py1 += (fh - up) // 2
if down > 1:
px0 += (fw - down + 1) // 2
px1 += (fw - down) // 2
py0 += (fh - down + 1) // 2
py1 += (fh - down) // 2
# Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
if kw == 1 and kh == 1 and (down > 1 and up == 1):
x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
return x
# Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
if kw == 1 and kh == 1 and (up > 1 and down == 1):
x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
return x
# Fast path: downsampling only => use strided convolution.
if down > 1 and up == 1:
x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight)
return x
# Fast path: upsampling with optional downsampling => use transpose strided convolution.
if up > 1:
if groups == 1:
w = w.transpose(0, 1)
else:
w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
w = w.transpose(1, 2)
w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw)
px0 -= kw - 1
px1 -= kw - up
py0 -= kh - 1
py1 -= kh - up
pxt = max(min(-px0, -px1), 0)
pyt = max(min(-py0, -py1), 0)
x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight))
x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter)
if down > 1:
x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
return x
# Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
if up == 1 and down == 1:
if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight)
# Fallback: Generic reference implementation.
x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
if down > 1:
x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
return x
#----------------------------------------------------------------------------

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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include "filtered_lrelu.h"
//------------------------------------------------------------------------
static std::tuple<torch::Tensor, torch::Tensor, int> filtered_lrelu(
torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si,
int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns)
{
// Set CUDA device.
TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
// Validate arguments.
TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device");
TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32");
TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype");
TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32");
TORCH_CHECK(x.dim() == 4, "x must be rank 4");
TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
TORCH_CHECK(x.numel() > 0, "x is empty");
TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2");
TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large");
TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large");
TORCH_CHECK(fu.numel() > 0, "fu is empty");
TORCH_CHECK(fd.numel() > 0, "fd is empty");
TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x");
TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1");
// Figure out how much shared memory is available on the device.
int maxSharedBytes = 0;
AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index()));
int sharedKB = maxSharedBytes >> 10;
// Populate enough launch parameters to check if a CUDA kernel exists.
filtered_lrelu_kernel_params p;
p.up = up;
p.down = down;
p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter.
p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0);
filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel<float, int32_t, false, false>(p, sharedKB);
if (!test_spec.exec)
{
// No kernel found - return empty tensors and indicate missing kernel with return code of -1.
return std::make_tuple(torch::Tensor(), torch::Tensor(), -1);
}
// Input/output element size.
int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4;
// Input sizes.
int64_t xw = (int)x.size(3);
int64_t xh = (int)x.size(2);
int64_t fut_w = (int)fu.size(-1) - 1;
int64_t fut_h = (int)fu.size(0) - 1;
int64_t fdt_w = (int)fd.size(-1) - 1;
int64_t fdt_h = (int)fd.size(0) - 1;
// Logical size of upsampled buffer.
int64_t cw = xw * up + (px0 + px1) - fut_w;
int64_t ch = xh * up + (py0 + py1) - fut_h;
TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter");
TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large");
// Compute output size and allocate.
int64_t yw = (cw - fdt_w + (down - 1)) / down;
int64_t yh = (ch - fdt_h + (down - 1)) / down;
TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1");
TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large");
torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format());
// Allocate sign tensor.
torch::Tensor so;
torch::Tensor s = si;
bool readSigns = !!s.numel();
int64_t sw_active = 0; // Active width of sign tensor.
if (writeSigns)
{
sw_active = yw * down - (down - 1) + fdt_w; // Active width in elements.
int64_t sh = yh * down - (down - 1) + fdt_h; // Height = active height.
int64_t sw = (sw_active + 15) & ~15; // Width = active width in elements, rounded up to multiple of 16.
TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large");
s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
}
else if (readSigns)
sw_active = s.size(3) << 2;
// Validate sign tensor if in use.
if (readSigns || writeSigns)
{
TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large");
}
// Populate rest of CUDA kernel parameters.
p.x = x.data_ptr();
p.y = y.data_ptr();
p.b = b.data_ptr();
p.s = (readSigns || writeSigns) ? s.data_ptr<unsigned char>() : 0;
p.fu = fu.data_ptr<float>();
p.fd = fd.data_ptr<float>();
p.pad0 = make_int2(px0, py0);
p.gain = gain;
p.slope = slope;
p.clamp = clamp;
p.flip = (flip_filters) ? 1 : 0;
p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
p.yShape = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous.
p.sOfs = make_int2(sx, sy);
p.swLimit = (sw_active + 3) >> 2; // Rounded up to bytes.
// x, y, b strides are in bytes.
p.xStride = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0));
p.yStride = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0));
p.bStride = sz * b.stride(0);
// fu, fd strides are in elements.
p.fuStride = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0);
p.fdStride = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0);
// Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those.
bool index64b = false;
if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true;
if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true;
if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) > INT_MAX) index64b = true;
if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true;
if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) > INT_MAX) index64b = true;
if (s.numel() > INT_MAX) index64b = true;
// Choose CUDA kernel.
filtered_lrelu_kernel_spec spec = { 0 };
AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&]
{
if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation.
{
// Choose kernel based on index type, datatype and sign read/write modes.
if (!index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, true, false>(p, sharedKB);
else if (!index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, false, true >(p, sharedKB);
else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, false, false>(p, sharedKB);
else if ( index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, true, false>(p, sharedKB);
else if ( index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, false, true >(p, sharedKB);
else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, false, false>(p, sharedKB);
}
});
TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists.
// Launch CUDA kernel.
void* args[] = {&p};
int bx = spec.numWarps * 32;
int gx = (p.yShape.x - 1) / spec.tileOut.x + 1;
int gy = (p.yShape.y - 1) / spec.tileOut.y + 1;
int gz = p.yShape.z * p.yShape.w;
// Repeat multiple horizontal tiles in a CTA?
if (spec.xrep)
{
p.tilesXrep = spec.xrep;
p.tilesXdim = gx;
gx = (gx + p.tilesXrep - 1) / p.tilesXrep;
std::swap(gx, gy);
}
else
{
p.tilesXrep = 0;
p.tilesXdim = 0;
}
// Launch filter setup kernel.
AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream()));
// Copy kernels to constant memory.
if ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters<true, false>(at::cuda::getCurrentCUDAStream())));
else if (!writeSigns && readSigns) AT_CUDA_CHECK((copy_filters<false, true >(at::cuda::getCurrentCUDAStream())));
else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters<false, false>(at::cuda::getCurrentCUDAStream())));
// Set cache and shared memory configurations for main kernel.
AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared));
if (spec.dynamicSharedKB) // Need dynamically allocated shared memory?
AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10));
AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte));
// Launch main kernel.
const int maxSubGz = 65535; // CUDA maximum for block z dimension.
for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big.
{
p.blockZofs = zofs;
int subGz = std::min(maxSubGz, gz - zofs);
AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream()));
}
// Done.
return std::make_tuple(y, so, 0);
}
//------------------------------------------------------------------------
static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns)
{
// Set CUDA device.
TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
// Validate arguments.
TORCH_CHECK(x.dim() == 4, "x must be rank 4");
TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
TORCH_CHECK(x.numel() > 0, "x is empty");
TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64");
// Output signs if we don't have sign input.
torch::Tensor so;
torch::Tensor s = si;
bool readSigns = !!s.numel();
if (writeSigns)
{
int64_t sw = x.size(3);
sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing.
s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
}
// Validate sign tensor if in use.
if (readSigns || writeSigns)
{
TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large");
}
// Initialize CUDA kernel parameters.
filtered_lrelu_act_kernel_params p;
p.x = x.data_ptr();
p.s = (readSigns || writeSigns) ? s.data_ptr<unsigned char>() : 0;
p.gain = gain;
p.slope = slope;
p.clamp = clamp;
p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
p.xStride = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0));
p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous.
p.sOfs = make_int2(sx, sy);
// Choose CUDA kernel.
void* func = 0;
AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&]
{
if (writeSigns)
func = choose_filtered_lrelu_act_kernel<scalar_t, true, false>();
else if (readSigns)
func = choose_filtered_lrelu_act_kernel<scalar_t, false, true>();
else
func = choose_filtered_lrelu_act_kernel<scalar_t, false, false>();
});
TORCH_CHECK(func, "internal error - CUDA kernel not found");
// Launch CUDA kernel.
void* args[] = {&p};
int bx = 128; // 4 warps per block.
// Logical size of launch = writeSigns ? p.s : p.x
uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x;
uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y;
uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use.
gx = (gx - 1) / bx + 1;
// Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest.
const uint32_t gmax = 65535;
gy = std::min(gy, gmax);
gz = std::min(gz, gmax);
// Launch.
AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream()));
return so;
}
//------------------------------------------------------------------------
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
m.def("filtered_lrelu", &filtered_lrelu); // The whole thing.
m.def("filtered_lrelu_act_", &filtered_lrelu_act); // Activation and sign tensor handling only. Modifies data tensor in-place.
}
//------------------------------------------------------------------------

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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
#include <cuda_runtime.h>
//------------------------------------------------------------------------
// CUDA kernel parameters.
struct filtered_lrelu_kernel_params
{
// These parameters decide which kernel to use.
int up; // upsampling ratio (1, 2, 4)
int down; // downsampling ratio (1, 2, 4)
int2 fuShape; // [size, 1] | [size, size]
int2 fdShape; // [size, 1] | [size, size]
int _dummy; // Alignment.
// Rest of the parameters.
const void* x; // Input tensor.
void* y; // Output tensor.
const void* b; // Bias tensor.
unsigned char* s; // Sign tensor in/out. NULL if unused.
const float* fu; // Upsampling filter.
const float* fd; // Downsampling filter.
int2 pad0; // Left/top padding.
float gain; // Additional gain factor.
float slope; // Leaky ReLU slope on negative side.
float clamp; // Clamp after nonlinearity.
int flip; // Filter kernel flip for gradient computation.
int tilesXdim; // Original number of horizontal output tiles.
int tilesXrep; // Number of horizontal tiles per CTA.
int blockZofs; // Block z offset to support large minibatch, channel dimensions.
int4 xShape; // [width, height, channel, batch]
int4 yShape; // [width, height, channel, batch]
int2 sShape; // [width, height] - width is in bytes. Contiguous. Zeros if unused.
int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor.
int swLimit; // Active width of sign tensor in bytes.
longlong4 xStride; // Strides of all tensors except signs, same component order as shapes.
longlong4 yStride; //
int64_t bStride; //
longlong3 fuStride; //
longlong3 fdStride; //
};
struct filtered_lrelu_act_kernel_params
{
void* x; // Input/output, modified in-place.
unsigned char* s; // Sign tensor in/out. NULL if unused.
float gain; // Additional gain factor.
float slope; // Leaky ReLU slope on negative side.
float clamp; // Clamp after nonlinearity.
int4 xShape; // [width, height, channel, batch]
longlong4 xStride; // Input/output tensor strides, same order as in shape.
int2 sShape; // [width, height] - width is in elements. Contiguous. Zeros if unused.
int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor.
};
//------------------------------------------------------------------------
// CUDA kernel specialization.
struct filtered_lrelu_kernel_spec
{
void* setup; // Function for filter kernel setup.
void* exec; // Function for main operation.
int2 tileOut; // Width/height of launch tile.
int numWarps; // Number of warps per thread block, determines launch block size.
int xrep; // For processing multiple horizontal tiles per thread block.
int dynamicSharedKB; // How much dynamic shared memory the exec kernel wants.
};
//------------------------------------------------------------------------
// CUDA kernel selection.
template <class T, class index_t, bool signWrite, bool signRead> filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB);
template <class T, bool signWrite, bool signRead> void* choose_filtered_lrelu_act_kernel(void);
template <bool signWrite, bool signRead> cudaError_t copy_filters(cudaStream_t stream);
//------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import os
import numpy as np
import torch
import warnings
from .. import custom_ops
from .. import misc
from . import upfirdn2d
from . import bias_act
#----------------------------------------------------------------------------
_plugin = None
def _init():
global _plugin
if _plugin is None:
_plugin = custom_ops.get_plugin(
module_name='filtered_lrelu_plugin',
sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'],
headers=['filtered_lrelu.h', 'filtered_lrelu.cu'],
source_dir=os.path.dirname(__file__),
extra_cuda_cflags=['--use_fast_math'],
)
return True
def _get_filter_size(f):
if f is None:
return 1, 1
assert isinstance(f, torch.Tensor)
assert 1 <= f.ndim <= 2
return f.shape[-1], f.shape[0] # width, height
def _parse_padding(padding):
if isinstance(padding, int):
padding = [padding, padding]
assert isinstance(padding, (list, tuple))
assert all(isinstance(x, (int, np.integer)) for x in padding)
padding = [int(x) for x in padding]
if len(padding) == 2:
px, py = padding
padding = [px, px, py, py]
px0, px1, py0, py1 = padding
return px0, px1, py0, py1
#----------------------------------------------------------------------------
def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False, impl='cuda'):
r"""Filtered leaky ReLU for a batch of 2D images.
Performs the following sequence of operations for each channel:
1. Add channel-specific bias if provided (`b`).
2. Upsample the image by inserting N-1 zeros after each pixel (`up`).
3. Pad the image with the specified number of zeros on each side (`padding`).
Negative padding corresponds to cropping the image.
4. Convolve the image with the specified upsampling FIR filter (`fu`), shrinking it
so that the footprint of all output pixels lies within the input image.
5. Multiply each value by the provided gain factor (`gain`).
6. Apply leaky ReLU activation function to each value.
7. Clamp each value between -clamp and +clamp, if `clamp` parameter is provided.
8. Convolve the image with the specified downsampling FIR filter (`fd`), shrinking
it so that the footprint of all output pixels lies within the input image.
9. Downsample the image by keeping every Nth pixel (`down`).
The fused op is considerably more efficient than performing the same calculation
using standard PyTorch ops. It supports gradients of arbitrary order.
Args:
x: Float32/float16/float64 input tensor of the shape
`[batch_size, num_channels, in_height, in_width]`.
fu: Float32 upsampling FIR filter of the shape
`[filter_height, filter_width]` (non-separable),
`[filter_taps]` (separable), or
`None` (identity).
fd: Float32 downsampling FIR filter of the shape
`[filter_height, filter_width]` (non-separable),
`[filter_taps]` (separable), or
`None` (identity).
b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type
as `x`. The length of vector must must match the channel dimension of `x`.
up: Integer upsampling factor (default: 1).
down: Integer downsampling factor. (default: 1).
padding: Padding with respect to the upsampled image. Can be a single number
or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
(default: 0).
gain: Overall scaling factor for signal magnitude (default: sqrt(2)).
slope: Slope on the negative side of leaky ReLU (default: 0.2).
clamp: Maximum magnitude for leaky ReLU output (default: None).
flip_filter: False = convolution, True = correlation (default: False).
impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
Returns:
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
"""
assert isinstance(x, torch.Tensor)
assert impl in ['ref', 'cuda']
if impl == 'cuda' and x.device.type == 'cuda' and _init():
return _filtered_lrelu_cuda(up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter).apply(x, fu, fd, b, None, 0, 0)
return _filtered_lrelu_ref(x, fu=fu, fd=fd, b=b, up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter)
#----------------------------------------------------------------------------
@misc.profiled_function
def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False):
"""Slow and memory-inefficient reference implementation of `filtered_lrelu()` using
existing `upfirdn2n()` and `bias_act()` ops.
"""
assert isinstance(x, torch.Tensor) and x.ndim == 4
fu_w, fu_h = _get_filter_size(fu)
fd_w, fd_h = _get_filter_size(fd)
if b is not None:
assert isinstance(b, torch.Tensor) and b.dtype == x.dtype
misc.assert_shape(b, [x.shape[1]])
assert isinstance(up, int) and up >= 1
assert isinstance(down, int) and down >= 1
px0, px1, py0, py1 = _parse_padding(padding)
assert gain == float(gain) and gain > 0
assert slope == float(slope) and slope >= 0
assert clamp is None or (clamp == float(clamp) and clamp >= 0)
# Calculate output size.
batch_size, channels, in_h, in_w = x.shape
in_dtype = x.dtype
out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - (fd_w - 1) + (down - 1)) // down
out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - (fd_h - 1) + (down - 1)) // down
# Compute using existing ops.
x = bias_act.bias_act(x=x, b=b) # Apply bias.
x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample.
x = bias_act.bias_act(x=x, act='lrelu', alpha=slope, gain=gain, clamp=clamp) # Bias, leaky ReLU, clamp.
x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter) # Downsample.
# Check output shape & dtype.
misc.assert_shape(x, [batch_size, channels, out_h, out_w])
assert x.dtype == in_dtype
return x
#----------------------------------------------------------------------------
_filtered_lrelu_cuda_cache = dict()
def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False):
"""Fast CUDA implementation of `filtered_lrelu()` using custom ops.
"""
assert isinstance(up, int) and up >= 1
assert isinstance(down, int) and down >= 1
px0, px1, py0, py1 = _parse_padding(padding)
assert gain == float(gain) and gain > 0
gain = float(gain)
assert slope == float(slope) and slope >= 0
slope = float(slope)
assert clamp is None or (clamp == float(clamp) and clamp >= 0)
clamp = float(clamp if clamp is not None else 'inf')
# Lookup from cache.
key = (up, down, px0, px1, py0, py1, gain, slope, clamp, flip_filter)
if key in _filtered_lrelu_cuda_cache:
return _filtered_lrelu_cuda_cache[key]
# Forward op.
class FilteredLReluCuda(torch.autograd.Function):
@staticmethod
def forward(ctx, x, fu, fd, b, si, sx, sy): # pylint: disable=arguments-differ
assert isinstance(x, torch.Tensor) and x.ndim == 4
# Replace empty up/downsample kernels with full 1x1 kernels (faster than separable).
if fu is None:
fu = torch.ones([1, 1], dtype=torch.float32, device=x.device)
if fd is None:
fd = torch.ones([1, 1], dtype=torch.float32, device=x.device)
assert 1 <= fu.ndim <= 2
assert 1 <= fd.ndim <= 2
# Replace separable 1x1 kernels with full 1x1 kernels when scale factor is 1.
if up == 1 and fu.ndim == 1 and fu.shape[0] == 1:
fu = fu.square()[None]
if down == 1 and fd.ndim == 1 and fd.shape[0] == 1:
fd = fd.square()[None]
# Missing sign input tensor.
if si is None:
si = torch.empty([0])
# Missing bias tensor.
if b is None:
b = torch.zeros([x.shape[1]], dtype=x.dtype, device=x.device)
# Construct internal sign tensor only if gradients are needed.
write_signs = (si.numel() == 0) and (x.requires_grad or b.requires_grad)
# Warn if input storage strides are not in decreasing order due to e.g. channels-last layout.
strides = [x.stride(i) for i in range(x.ndim) if x.size(i) > 1]
if any(a < b for a, b in zip(strides[:-1], strides[1:])):
warnings.warn("low-performance memory layout detected in filtered_lrelu input", RuntimeWarning)
# Call C++/Cuda plugin if datatype is supported.
if x.dtype in [torch.float16, torch.float32]:
if torch.cuda.current_stream(x.device) != torch.cuda.default_stream(x.device):
warnings.warn("filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning)
y, so, return_code = _plugin.filtered_lrelu(x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs)
else:
return_code = -1
# No Cuda kernel found? Fall back to generic implementation. Still more memory efficient than the reference implementation because
# only the bit-packed sign tensor is retained for gradient computation.
if return_code < 0:
warnings.warn("filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning)
y = x.add(b.unsqueeze(-1).unsqueeze(-1)) # Add bias.
y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample.
so = _plugin.filtered_lrelu_act_(y, si, sx, sy, gain, slope, clamp, write_signs) # Activation function and sign handling. Modifies y in-place.
y = upfirdn2d.upfirdn2d(x=y, f=fd, down=down, flip_filter=flip_filter) # Downsample.
# Prepare for gradient computation.
ctx.save_for_backward(fu, fd, (si if si.numel() else so))
ctx.x_shape = x.shape
ctx.y_shape = y.shape
ctx.s_ofs = sx, sy
return y
@staticmethod
def backward(ctx, dy): # pylint: disable=arguments-differ
fu, fd, si = ctx.saved_tensors
_, _, xh, xw = ctx.x_shape
_, _, yh, yw = ctx.y_shape
sx, sy = ctx.s_ofs
dx = None # 0
dfu = None; assert not ctx.needs_input_grad[1]
dfd = None; assert not ctx.needs_input_grad[2]
db = None # 3
dsi = None; assert not ctx.needs_input_grad[4]
dsx = None; assert not ctx.needs_input_grad[5]
dsy = None; assert not ctx.needs_input_grad[6]
if ctx.needs_input_grad[0] or ctx.needs_input_grad[3]:
pp = [
(fu.shape[-1] - 1) + (fd.shape[-1] - 1) - px0,
xw * up - yw * down + px0 - (up - 1),
(fu.shape[0] - 1) + (fd.shape[0] - 1) - py0,
xh * up - yh * down + py0 - (up - 1),
]
gg = gain * (up ** 2) / (down ** 2)
ff = (not flip_filter)
sx = sx - (fu.shape[-1] - 1) + px0
sy = sy - (fu.shape[0] - 1) + py0
dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope, clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy)
if ctx.needs_input_grad[3]:
db = dx.sum([0, 2, 3])
return dx, dfu, dfd, db, dsi, dsx, dsy
# Add to cache.
_filtered_lrelu_cuda_cache[key] = FilteredLReluCuda
return FilteredLReluCuda
#----------------------------------------------------------------------------

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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
#include "filtered_lrelu.cu"
// Template/kernel specializations for no signs mode (no gradients required).
// Full op, 32-bit indexing.
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int32_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float, int32_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
// Full op, 64-bit indexing.
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int64_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float, int64_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
// Activation/signs only for generic variant. 64-bit indexing.
template void* choose_filtered_lrelu_act_kernel<c10::Half, false, false>(void);
template void* choose_filtered_lrelu_act_kernel<float, false, false>(void);
template void* choose_filtered_lrelu_act_kernel<double, false, false>(void);
// Copy filters to constant memory.
template cudaError_t copy_filters<false, false>(cudaStream_t stream);

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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
#include "filtered_lrelu.cu"
// Template/kernel specializations for sign read mode.
// Full op, 32-bit indexing.
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int32_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float, int32_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
// Full op, 64-bit indexing.
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int64_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float, int64_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
// Activation/signs only for generic variant. 64-bit indexing.
template void* choose_filtered_lrelu_act_kernel<c10::Half, false, true>(void);
template void* choose_filtered_lrelu_act_kernel<float, false, true>(void);
template void* choose_filtered_lrelu_act_kernel<double, false, true>(void);
// Copy filters to constant memory.
template cudaError_t copy_filters<false, true>(cudaStream_t stream);

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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
#include "filtered_lrelu.cu"
// Template/kernel specializations for sign write mode.
// Full op, 32-bit indexing.
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int32_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float, int32_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
// Full op, 64-bit indexing.
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int64_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float, int64_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
// Activation/signs only for generic variant. 64-bit indexing.
template void* choose_filtered_lrelu_act_kernel<c10::Half, true, false>(void);
template void* choose_filtered_lrelu_act_kernel<float, true, false>(void);
template void* choose_filtered_lrelu_act_kernel<double, true, false>(void);
// Copy filters to constant memory.
template cudaError_t copy_filters<true, false>(cudaStream_t stream);

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`."""
import torch
#----------------------------------------------------------------------------
def fma(a, b, c): # => a * b + c
return _FusedMultiplyAdd.apply(a, b, c)
#----------------------------------------------------------------------------
class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c
@staticmethod
def forward(ctx, a, b, c): # pylint: disable=arguments-differ
out = torch.addcmul(c, a, b)
ctx.save_for_backward(a, b)
ctx.c_shape = c.shape
return out
@staticmethod
def backward(ctx, dout): # pylint: disable=arguments-differ
a, b = ctx.saved_tensors
c_shape = ctx.c_shape
da = None
db = None
dc = None
if ctx.needs_input_grad[0]:
da = _unbroadcast(dout * b, a.shape)
if ctx.needs_input_grad[1]:
db = _unbroadcast(dout * a, b.shape)
if ctx.needs_input_grad[2]:
dc = _unbroadcast(dout, c_shape)
return da, db, dc
#----------------------------------------------------------------------------
def _unbroadcast(x, shape):
extra_dims = x.ndim - len(shape)
assert extra_dims >= 0
dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)]
if len(dim):
x = x.sum(dim=dim, keepdim=True)
if extra_dims:
x = x.reshape(-1, *x.shape[extra_dims+1:])
assert x.shape == shape
return x
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Custom replacement for `torch.nn.functional.grid_sample` that
supports arbitrarily high order gradients between the input and output.
Only works on 2D images and assumes
`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
import torch
# pylint: disable=redefined-builtin
# pylint: disable=arguments-differ
# pylint: disable=protected-access
#----------------------------------------------------------------------------
enabled = False # Enable the custom op by setting this to true.
#----------------------------------------------------------------------------
def grid_sample(input, grid):
if _should_use_custom_op():
return _GridSample2dForward.apply(input, grid)
return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
#----------------------------------------------------------------------------
def _should_use_custom_op():
return enabled
#----------------------------------------------------------------------------
class _GridSample2dForward(torch.autograd.Function):
@staticmethod
def forward(ctx, input, grid):
assert input.ndim == 4
assert grid.ndim == 4
output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
ctx.save_for_backward(input, grid)
return output
@staticmethod
def backward(ctx, grad_output):
input, grid = ctx.saved_tensors
grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
return grad_input, grad_grid
#----------------------------------------------------------------------------
class _GridSample2dBackward(torch.autograd.Function):
@staticmethod
def forward(ctx, grad_output, input, grid):
op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
ctx.save_for_backward(grid)
return grad_input, grad_grid
@staticmethod
def backward(ctx, grad2_grad_input, grad2_grad_grid):
_ = grad2_grad_grid # unused
grid, = ctx.saved_tensors
grad2_grad_output = None
grad2_input = None
grad2_grid = None
if ctx.needs_input_grad[0]:
grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid)
assert not ctx.needs_input_grad[2]
return grad2_grad_output, grad2_input, grad2_grid
#----------------------------------------------------------------------------

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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include "upfirdn2d.h"
//------------------------------------------------------------------------
static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
{
// Validate arguments.
TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
TORCH_CHECK(x.numel() > 0, "x has zero size");
TORCH_CHECK(f.numel() > 0, "f has zero size");
TORCH_CHECK(x.dim() == 4, "x must be rank 4");
TORCH_CHECK(f.dim() == 2, "f must be rank 2");
TORCH_CHECK((x.size(0)-1)*x.stride(0) + (x.size(1)-1)*x.stride(1) + (x.size(2)-1)*x.stride(2) + (x.size(3)-1)*x.stride(3) <= INT_MAX, "x memory footprint is too large");
TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
// Create output tensor.
const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
TORCH_CHECK((y.size(0)-1)*y.stride(0) + (y.size(1)-1)*y.stride(1) + (y.size(2)-1)*y.stride(2) + (y.size(3)-1)*y.stride(3) <= INT_MAX, "output memory footprint is too large");
// Initialize CUDA kernel parameters.
upfirdn2d_kernel_params p;
p.x = x.data_ptr();
p.f = f.data_ptr<float>();
p.y = y.data_ptr();
p.up = make_int2(upx, upy);
p.down = make_int2(downx, downy);
p.pad0 = make_int2(padx0, pady0);
p.flip = (flip) ? 1 : 0;
p.gain = gain;
p.inSize = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
p.inStride = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
p.filterSize = make_int2((int)f.size(1), (int)f.size(0));
p.filterStride = make_int2((int)f.stride(1), (int)f.stride(0));
p.outSize = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
p.outStride = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
p.sizeMajor = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
p.sizeMinor = (p.inStride.z == 1) ? p.inSize.z : 1;
// Choose CUDA kernel.
upfirdn2d_kernel_spec spec;
AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
{
spec = choose_upfirdn2d_kernel<scalar_t>(p);
});
// Set looping options.
p.loopMajor = (p.sizeMajor - 1) / 16384 + 1;
p.loopMinor = spec.loopMinor;
p.loopX = spec.loopX;
p.launchMinor = (p.sizeMinor - 1) / p.loopMinor + 1;
p.launchMajor = (p.sizeMajor - 1) / p.loopMajor + 1;
// Compute grid size.
dim3 blockSize, gridSize;
if (spec.tileOutW < 0) // large
{
blockSize = dim3(4, 32, 1);
gridSize = dim3(
((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
(p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
p.launchMajor);
}
else // small
{
blockSize = dim3(256, 1, 1);
gridSize = dim3(
((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
(p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
p.launchMajor);
}
// Launch CUDA kernel.
void* args[] = {&p};
AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
return y;
}
//------------------------------------------------------------------------
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
m.def("upfirdn2d", &upfirdn2d);
}
//------------------------------------------------------------------------

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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
#include <c10/util/Half.h>
#include "upfirdn2d.h"
//------------------------------------------------------------------------
// Helpers.
template <class T> struct InternalType;
template <> struct InternalType<double> { typedef double scalar_t; };
template <> struct InternalType<float> { typedef float scalar_t; };
template <> struct InternalType<c10::Half> { typedef float scalar_t; };
static __device__ __forceinline__ int floor_div(int a, int b)
{
int t = 1 - a / b;
return (a + t * b) / b - t;
}
//------------------------------------------------------------------------
// Generic CUDA implementation for large filters.
template <class T> static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p)
{
typedef typename InternalType<T>::scalar_t scalar_t;
// Calculate thread index.
int minorBase = blockIdx.x * blockDim.x + threadIdx.x;
int outY = minorBase / p.launchMinor;
minorBase -= outY * p.launchMinor;
int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y;
int majorBase = blockIdx.z * p.loopMajor;
if (outXBase >= p.outSize.x | outY >= p.outSize.y | majorBase >= p.sizeMajor)
return;
// Setup Y receptive field.
int midY = outY * p.down.y + p.up.y - 1 - p.pad0.y;
int inY = min(max(floor_div(midY, p.up.y), 0), p.inSize.y);
int h = min(max(floor_div(midY + p.filterSize.y, p.up.y), 0), p.inSize.y) - inY;
int filterY = midY + p.filterSize.y - (inY + 1) * p.up.y;
if (p.flip)
filterY = p.filterSize.y - 1 - filterY;
// Loop over major, minor, and X.
for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
for (int minorIdx = 0, minor = minorBase; minorIdx < p.loopMinor & minor < p.sizeMinor; minorIdx++, minor += p.launchMinor)
{
int nc = major * p.sizeMinor + minor;
int n = nc / p.inSize.z;
int c = nc - n * p.inSize.z;
for (int loopX = 0, outX = outXBase; loopX < p.loopX & outX < p.outSize.x; loopX++, outX += blockDim.y)
{
// Setup X receptive field.
int midX = outX * p.down.x + p.up.x - 1 - p.pad0.x;
int inX = min(max(floor_div(midX, p.up.x), 0), p.inSize.x);
int w = min(max(floor_div(midX + p.filterSize.x, p.up.x), 0), p.inSize.x) - inX;
int filterX = midX + p.filterSize.x - (inX + 1) * p.up.x;
if (p.flip)
filterX = p.filterSize.x - 1 - filterX;
// Initialize pointers.
const T* xp = &((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
const float* fp = &p.f[filterX * p.filterStride.x + filterY * p.filterStride.y];
int filterStepX = ((p.flip) ? p.up.x : -p.up.x) * p.filterStride.x;
int filterStepY = ((p.flip) ? p.up.y : -p.up.y) * p.filterStride.y;
// Inner loop.
scalar_t v = 0;
for (int y = 0; y < h; y++)
{
for (int x = 0; x < w; x++)
{
v += (scalar_t)(*xp) * (scalar_t)(*fp);
xp += p.inStride.x;
fp += filterStepX;
}
xp += p.inStride.y - w * p.inStride.x;
fp += filterStepY - w * filterStepX;
}
// Store result.
v *= p.gain;
((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
}
}
}
//------------------------------------------------------------------------
// Specialized CUDA implementation for small filters.
template <class T, int upx, int upy, int downx, int downy, int filterW, int filterH, int tileOutW, int tileOutH, int loopMinor>
static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p)
{
typedef typename InternalType<T>::scalar_t scalar_t;
const int tileInW = ((tileOutW - 1) * downx + filterW - 1) / upx + 1;
const int tileInH = ((tileOutH - 1) * downy + filterH - 1) / upy + 1;
__shared__ volatile scalar_t sf[filterH][filterW];
__shared__ volatile scalar_t sx[tileInH][tileInW][loopMinor];
// Calculate tile index.
int minorBase = blockIdx.x;
int tileOutY = minorBase / p.launchMinor;
minorBase -= tileOutY * p.launchMinor;
minorBase *= loopMinor;
tileOutY *= tileOutH;
int tileOutXBase = blockIdx.y * p.loopX * tileOutW;
int majorBase = blockIdx.z * p.loopMajor;
if (tileOutXBase >= p.outSize.x | tileOutY >= p.outSize.y | majorBase >= p.sizeMajor)
return;
// Load filter (flipped).
for (int tapIdx = threadIdx.x; tapIdx < filterH * filterW; tapIdx += blockDim.x)
{
int fy = tapIdx / filterW;
int fx = tapIdx - fy * filterW;
scalar_t v = 0;
if (fx < p.filterSize.x & fy < p.filterSize.y)
{
int ffx = (p.flip) ? fx : p.filterSize.x - 1 - fx;
int ffy = (p.flip) ? fy : p.filterSize.y - 1 - fy;
v = (scalar_t)p.f[ffx * p.filterStride.x + ffy * p.filterStride.y];
}
sf[fy][fx] = v;
}
// Loop over major and X.
for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
{
int baseNC = major * p.sizeMinor + minorBase;
int n = baseNC / p.inSize.z;
int baseC = baseNC - n * p.inSize.z;
for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outSize.x; loopX++, tileOutX += tileOutW)
{
// Load input pixels.
int tileMidX = tileOutX * downx + upx - 1 - p.pad0.x;
int tileMidY = tileOutY * downy + upy - 1 - p.pad0.y;
int tileInX = floor_div(tileMidX, upx);
int tileInY = floor_div(tileMidY, upy);
__syncthreads();
for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW * loopMinor; inIdx += blockDim.x)
{
int relC = inIdx;
int relInX = relC / loopMinor;
int relInY = relInX / tileInW;
relC -= relInX * loopMinor;
relInX -= relInY * tileInW;
int c = baseC + relC;
int inX = tileInX + relInX;
int inY = tileInY + relInY;
scalar_t v = 0;
if (inX >= 0 & inY >= 0 & inX < p.inSize.x & inY < p.inSize.y & c < p.inSize.z)
v = (scalar_t)((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
sx[relInY][relInX][relC] = v;
}
// Loop over output pixels.
__syncthreads();
for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW * loopMinor; outIdx += blockDim.x)
{
int relC = outIdx;
int relOutX = relC / loopMinor;
int relOutY = relOutX / tileOutW;
relC -= relOutX * loopMinor;
relOutX -= relOutY * tileOutW;
int c = baseC + relC;
int outX = tileOutX + relOutX;
int outY = tileOutY + relOutY;
// Setup receptive field.
int midX = tileMidX + relOutX * downx;
int midY = tileMidY + relOutY * downy;
int inX = floor_div(midX, upx);
int inY = floor_div(midY, upy);
int relInX = inX - tileInX;
int relInY = inY - tileInY;
int filterX = (inX + 1) * upx - midX - 1; // flipped
int filterY = (inY + 1) * upy - midY - 1; // flipped
// Inner loop.
if (outX < p.outSize.x & outY < p.outSize.y & c < p.outSize.z)
{
scalar_t v = 0;
#pragma unroll
for (int y = 0; y < filterH / upy; y++)
#pragma unroll
for (int x = 0; x < filterW / upx; x++)
v += sx[relInY + y][relInX + x][relC] * sf[filterY + y * upy][filterX + x * upx];
v *= p.gain;
((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
}
}
}
}
}
//------------------------------------------------------------------------
// CUDA kernel selection.
template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p)
{
int s = p.inStride.z, fx = p.filterSize.x, fy = p.filterSize.y;
upfirdn2d_kernel_spec spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,1, 4}; // contiguous
if (s == 1) spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,4, 1}; // channels_last
// No up/downsampling.
if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1)
{
// contiguous
if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,24, 64,32,1>, 64,32,1, 1};
if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,16, 64,32,1>, 64,32,1, 1};
if (s != 1 && fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7, 64,16,1>, 64,16,1, 1};
if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 6,6, 64,16,1>, 64,16,1, 1};
if (s != 1 && fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 5,5, 64,16,1>, 64,16,1, 1};
if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4, 64,16,1>, 64,16,1, 1};
if (s != 1 && fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 3,3, 64,16,1>, 64,16,1, 1};
if (s != 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1, 128,8,1>, 128,8,1, 1};
if (s != 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1, 128,8,1>, 128,8,1, 1};
if (s != 1 && fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1, 128,8,1>, 128,8,1, 1};
if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24, 32,32,1>, 32,32,1, 1};
if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16, 32,32,1>, 32,32,1, 1};
if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8, 32,32,1>, 32,32,1, 1};
// channels_last
if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,24, 32,32,1>, 32,32,1, 1};
if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,16, 32,32,1>, 32,32,1, 1};
if (s == 1 && fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7, 16,16,8>, 16,16,8, 1};
if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 6,6, 16,16,8>, 16,16,8, 1};
if (s == 1 && fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 5,5, 16,16,8>, 16,16,8, 1};
if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4, 16,16,8>, 16,16,8, 1};
if (s == 1 && fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 3,3, 16,16,8>, 16,16,8, 1};
if (s == 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1, 128,1,16>, 128,1,16, 1};
if (s == 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1, 128,1,16>, 128,1,16, 1};
if (s == 1 && fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1, 128,1,16>, 128,1,16, 1};
if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24, 1,128,16>, 1,128,16, 1};
if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16, 1,128,16>, 1,128,16, 1};
if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8, 1,128,16>, 1,128,16, 1};
}
// 2x upsampling.
if (p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1)
{
// contiguous
if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 24,24, 64,32,1>, 64,32,1, 1};
if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 16,16, 64,32,1>, 64,32,1, 1};
if (s != 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8, 64,16,1>, 64,16,1, 1};
if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6, 64,16,1>, 64,16,1, 1};
if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4, 64,16,1>, 64,16,1, 1};
if (s != 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2, 64,16,1>, 64,16,1, 1};
// channels_last
if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 24,24, 32,32,1>, 32,32,1, 1};
if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 16,16, 32,32,1>, 32,32,1, 1};
if (s == 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8, 16,16,8>, 16,16,8, 1};
if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6, 16,16,8>, 16,16,8, 1};
if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4, 16,16,8>, 16,16,8, 1};
if (s == 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2, 16,16,8>, 16,16,8, 1};
}
if (p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1)
{
// contiguous
if (s != 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,8,1>, 128,8,1, 1};
if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,8,1>, 128,8,1, 1};
if (s != 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1, 128,8,1>, 128,8,1, 1};
// channels_last
if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,1,16>, 128,1,16, 1};
if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,1,16>, 128,1,16, 1};
if (s == 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1, 128,1,16>, 128,1,16, 1};
}
if (p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1)
{
// contiguous
if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 32,32,1>, 32,32,1, 1};
if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 32,32,1>, 32,32,1, 1};
if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8, 32,32,1>, 32,32,1, 1};
// channels_last
if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 1,128,16>, 1,128,16, 1};
if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 1,128,16>, 1,128,16, 1};
if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8, 1,128,16>, 1,128,16, 1};
}
// 2x downsampling.
if (p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2)
{
// contiguous
if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 24,24, 32,16,1>, 32,16,1, 1};
if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 16,16, 32,16,1>, 32,16,1, 1};
if (s != 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8, 32,8,1>, 32,8,1, 1};
if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6, 32,8,1>, 32,8,1, 1};
if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4, 32,8,1>, 32,8,1, 1};
if (s != 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2, 32,8,1>, 32,8,1, 1};
// channels_last
if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 24,24, 16,16,1>, 16,16,1, 1};
if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 16,16, 16,16,1>, 16,16,1, 1};
if (s == 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8, 8,8,8>, 8,8,8, 1};
if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6, 8,8,8>, 8,8,8, 1};
if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4, 8,8,8>, 8,8,8, 1};
if (s == 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2, 8,8,8>, 8,8,8, 1};
}
if (p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1)
{
// contiguous
if (s != 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,8,1>, 64,8,1, 1};
if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,8,1>, 64,8,1, 1};
if (s != 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1, 64,8,1>, 64,8,1, 1};
// channels_last
if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,1,8>, 64,1,8, 1};
if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,1,8>, 64,1,8, 1};
if (s == 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1, 64,1,8>, 64,1,8, 1};
}
if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2)
{
// contiguous
if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 32,16,1>, 32,16,1, 1};
if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 32,16,1>, 32,16,1, 1};
if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8, 32,16,1>, 32,16,1, 1};
// channels_last
if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 1,64,8>, 1,64,8, 1};
if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 1,64,8>, 1,64,8, 1};
if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8, 1,64,8>, 1,64,8, 1};
}
// 4x upsampling.
if (p.up.x == 4 && p.up.y == 4 && p.down.x == 1 && p.down.y == 1)
{
// contiguous
if (s != 1 && fx <= 48 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 48,48, 64,32,1>, 64,32,1, 1};
if (s != 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 32,32, 64,32,1>, 64,32,1, 1};
// channels_last
if (s == 1 && fx <= 48 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 48,48, 32,32,1>, 32,32,1, 1};
if (s == 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 32,32, 32,32,1>, 32,32,1, 1};
}
if (p.up.x == 4 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1)
{
// contiguous
if (s != 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 48,1, 128,8,1>, 128,8,1, 1};
if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 32,1, 128,8,1>, 128,8,1, 1};
// channels_last
if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 48,1, 128,1,16>, 128,1,16, 1};
if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 32,1, 128,1,16>, 128,1,16, 1};
}
if (p.up.x == 1 && p.up.y == 4 && p.down.x == 1 && p.down.y == 1)
{
// contiguous
if (s != 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,48, 32,32,1>, 32,32,1, 1};
if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,32, 32,32,1>, 32,32,1, 1};
// channels_last
if (s == 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,48, 1,128,16>, 1,128,16, 1};
if (s == 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,32, 1,128,16>, 1,128,16, 1};
}
// 4x downsampling (inefficient).
if (p.up.x == 1 && p.up.y == 1 && p.down.x == 4 && p.down.y == 1)
{
// contiguous
if (s != 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 48,1, 32,8,1>, 32,8,1, 1};
if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 32,1, 32,8,1>, 32,8,1, 1};
// channels_last
if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 48,1, 32,1,8>, 32,1,8, 1};
if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 32,1, 32,1,8>, 32,1,8, 1};
}
if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 4)
{
// contiguous
if (s != 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,48, 32,8,1>, 32,8,1, 1};
if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,32, 32,8,1>, 32,8,1, 1};
// channels_last
if (s == 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,48, 1,32,8>, 1,32,8, 1};
if (s == 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,32, 1,32,8>, 1,32,8, 1};
}
return spec;
}
//------------------------------------------------------------------------
// Template specializations.
template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<double> (const upfirdn2d_kernel_params& p);
template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<float> (const upfirdn2d_kernel_params& p);
template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<c10::Half>(const upfirdn2d_kernel_params& p);
//------------------------------------------------------------------------

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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
#include <cuda_runtime.h>
//------------------------------------------------------------------------
// CUDA kernel parameters.
struct upfirdn2d_kernel_params
{
const void* x;
const float* f;
void* y;
int2 up;
int2 down;
int2 pad0;
int flip;
float gain;
int4 inSize; // [width, height, channel, batch]
int4 inStride;
int2 filterSize; // [width, height]
int2 filterStride;
int4 outSize; // [width, height, channel, batch]
int4 outStride;
int sizeMinor;
int sizeMajor;
int loopMinor;
int loopMajor;
int loopX;
int launchMinor;
int launchMajor;
};
//------------------------------------------------------------------------
// CUDA kernel specialization.
struct upfirdn2d_kernel_spec
{
void* kernel;
int tileOutW;
int tileOutH;
int loopMinor;
int loopX;
};
//------------------------------------------------------------------------
// CUDA kernel selection.
template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
//------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Custom PyTorch ops for efficient resampling of 2D images."""
import os
import numpy as np
import torch
from .. import custom_ops
from .. import misc
from . import conv2d_gradfix
#----------------------------------------------------------------------------
_plugin = None
def _init():
global _plugin
if _plugin is None:
_plugin = custom_ops.get_plugin(
module_name='upfirdn2d_plugin',
sources=['upfirdn2d.cpp', 'upfirdn2d.cu'],
headers=['upfirdn2d.h'],
source_dir=os.path.dirname(__file__),
extra_cuda_cflags=['--use_fast_math'],
)
return True
def _parse_scaling(scaling):
if isinstance(scaling, int):
scaling = [scaling, scaling]
assert isinstance(scaling, (list, tuple))
assert all(isinstance(x, int) for x in scaling)
sx, sy = scaling
assert sx >= 1 and sy >= 1
return sx, sy
def _parse_padding(padding):
if isinstance(padding, int):
padding = [padding, padding]
assert isinstance(padding, (list, tuple))
assert all(isinstance(x, int) for x in padding)
if len(padding) == 2:
padx, pady = padding
padding = [padx, padx, pady, pady]
padx0, padx1, pady0, pady1 = padding
return padx0, padx1, pady0, pady1
def _get_filter_size(f):
if f is None:
return 1, 1
assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
fw = f.shape[-1]
fh = f.shape[0]
with misc.suppress_tracer_warnings():
fw = int(fw)
fh = int(fh)
misc.assert_shape(f, [fh, fw][:f.ndim])
assert fw >= 1 and fh >= 1
return fw, fh
#----------------------------------------------------------------------------
def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
Args:
f: Torch tensor, numpy array, or python list of the shape
`[filter_height, filter_width]` (non-separable),
`[filter_taps]` (separable),
`[]` (impulse), or
`None` (identity).
device: Result device (default: cpu).
normalize: Normalize the filter so that it retains the magnitude
for constant input signal (DC)? (default: True).
flip_filter: Flip the filter? (default: False).
gain: Overall scaling factor for signal magnitude (default: 1).
separable: Return a separable filter? (default: select automatically).
Returns:
Float32 tensor of the shape
`[filter_height, filter_width]` (non-separable) or
`[filter_taps]` (separable).
"""
# Validate.
if f is None:
f = 1
f = torch.as_tensor(f, dtype=torch.float32)
assert f.ndim in [0, 1, 2]
assert f.numel() > 0
if f.ndim == 0:
f = f[np.newaxis]
# Separable?
if separable is None:
separable = (f.ndim == 1 and f.numel() >= 8)
if f.ndim == 1 and not separable:
f = f.ger(f)
assert f.ndim == (1 if separable else 2)
# Apply normalize, flip, gain, and device.
if normalize:
f /= f.sum()
if flip_filter:
f = f.flip(list(range(f.ndim)))
f = f * (gain ** (f.ndim / 2))
f = f.to(device=device)
return f
#----------------------------------------------------------------------------
def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
r"""Pad, upsample, filter, and downsample a batch of 2D images.
Performs the following sequence of operations for each channel:
1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
2. Pad the image with the specified number of zeros on each side (`padding`).
Negative padding corresponds to cropping the image.
3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
so that the footprint of all output pixels lies within the input image.
4. Downsample the image by keeping every Nth pixel (`down`).
This sequence of operations bears close resemblance to scipy.signal.upfirdn().
The fused op is considerably more efficient than performing the same calculation
using standard PyTorch ops. It supports gradients of arbitrary order.
Args:
x: Float32/float64/float16 input tensor of the shape
`[batch_size, num_channels, in_height, in_width]`.
f: Float32 FIR filter of the shape
`[filter_height, filter_width]` (non-separable),
`[filter_taps]` (separable), or
`None` (identity).
up: Integer upsampling factor. Can be a single int or a list/tuple
`[x, y]` (default: 1).
down: Integer downsampling factor. Can be a single int or a list/tuple
`[x, y]` (default: 1).
padding: Padding with respect to the upsampled image. Can be a single number
or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
(default: 0).
flip_filter: False = convolution, True = correlation (default: False).
gain: Overall scaling factor for signal magnitude (default: 1).
impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
Returns:
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
"""
assert isinstance(x, torch.Tensor)
assert impl in ['ref', 'cuda']
if impl == 'cuda' and x.device.type == 'cuda' and _init():
return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
#----------------------------------------------------------------------------
@misc.profiled_function
def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
"""Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
"""
# Validate arguments.
assert isinstance(x, torch.Tensor) and x.ndim == 4
if f is None:
f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
assert f.dtype == torch.float32 and not f.requires_grad
batch_size, num_channels, in_height, in_width = x.shape
upx, upy = _parse_scaling(up)
downx, downy = _parse_scaling(down)
padx0, padx1, pady0, pady1 = _parse_padding(padding)
# Check that upsampled buffer is not smaller than the filter.
upW = in_width * upx + padx0 + padx1
upH = in_height * upy + pady0 + pady1
assert upW >= f.shape[-1] and upH >= f.shape[0]
# Upsample by inserting zeros.
x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
# Pad or crop.
x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)]
# Setup filter.
f = f * (gain ** (f.ndim / 2))
f = f.to(x.dtype)
if not flip_filter:
f = f.flip(list(range(f.ndim)))
# Convolve with the filter.
f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
if f.ndim == 4:
x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
else:
x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
# Downsample by throwing away pixels.
x = x[:, :, ::downy, ::downx]
return x
#----------------------------------------------------------------------------
_upfirdn2d_cuda_cache = dict()
def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
"""Fast CUDA implementation of `upfirdn2d()` using custom ops.
"""
# Parse arguments.
upx, upy = _parse_scaling(up)
downx, downy = _parse_scaling(down)
padx0, padx1, pady0, pady1 = _parse_padding(padding)
# Lookup from cache.
key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
if key in _upfirdn2d_cuda_cache:
return _upfirdn2d_cuda_cache[key]
# Forward op.
class Upfirdn2dCuda(torch.autograd.Function):
@staticmethod
def forward(ctx, x, f): # pylint: disable=arguments-differ
assert isinstance(x, torch.Tensor) and x.ndim == 4
if f is None:
f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
if f.ndim == 1 and f.shape[0] == 1:
f = f.square().unsqueeze(0) # Convert separable-1 into full-1x1.
assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
y = x
if f.ndim == 2:
y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
else:
y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, 1.0)
y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, gain)
ctx.save_for_backward(f)
ctx.x_shape = x.shape
return y
@staticmethod
def backward(ctx, dy): # pylint: disable=arguments-differ
f, = ctx.saved_tensors
_, _, ih, iw = ctx.x_shape
_, _, oh, ow = dy.shape
fw, fh = _get_filter_size(f)
p = [
fw - padx0 - 1,
iw * upx - ow * downx + padx0 - upx + 1,
fh - pady0 - 1,
ih * upy - oh * downy + pady0 - upy + 1,
]
dx = None
df = None
if ctx.needs_input_grad[0]:
dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f)
assert not ctx.needs_input_grad[1]
return dx, df
# Add to cache.
_upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
return Upfirdn2dCuda
#----------------------------------------------------------------------------
def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
r"""Filter a batch of 2D images using the given 2D FIR filter.
By default, the result is padded so that its shape matches the input.
User-specified padding is applied on top of that, with negative values
indicating cropping. Pixels outside the image are assumed to be zero.
Args:
x: Float32/float64/float16 input tensor of the shape
`[batch_size, num_channels, in_height, in_width]`.
f: Float32 FIR filter of the shape
`[filter_height, filter_width]` (non-separable),
`[filter_taps]` (separable), or
`None` (identity).
padding: Padding with respect to the output. Can be a single number or a
list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
(default: 0).
flip_filter: False = convolution, True = correlation (default: False).
gain: Overall scaling factor for signal magnitude (default: 1).
impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
Returns:
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
"""
padx0, padx1, pady0, pady1 = _parse_padding(padding)
fw, fh = _get_filter_size(f)
p = [
padx0 + fw // 2,
padx1 + (fw - 1) // 2,
pady0 + fh // 2,
pady1 + (fh - 1) // 2,
]
return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
#----------------------------------------------------------------------------
def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
r"""Upsample a batch of 2D images using the given 2D FIR filter.
By default, the result is padded so that its shape is a multiple of the input.
User-specified padding is applied on top of that, with negative values
indicating cropping. Pixels outside the image are assumed to be zero.
Args:
x: Float32/float64/float16 input tensor of the shape
`[batch_size, num_channels, in_height, in_width]`.
f: Float32 FIR filter of the shape
`[filter_height, filter_width]` (non-separable),
`[filter_taps]` (separable), or
`None` (identity).
up: Integer upsampling factor. Can be a single int or a list/tuple
`[x, y]` (default: 1).
padding: Padding with respect to the output. Can be a single number or a
list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
(default: 0).
flip_filter: False = convolution, True = correlation (default: False).
gain: Overall scaling factor for signal magnitude (default: 1).
impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
Returns:
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
"""
upx, upy = _parse_scaling(up)
padx0, padx1, pady0, pady1 = _parse_padding(padding)
fw, fh = _get_filter_size(f)
p = [
padx0 + (fw + upx - 1) // 2,
padx1 + (fw - upx) // 2,
pady0 + (fh + upy - 1) // 2,
pady1 + (fh - upy) // 2,
]
return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl)
#----------------------------------------------------------------------------
def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
r"""Downsample a batch of 2D images using the given 2D FIR filter.
By default, the result is padded so that its shape is a fraction of the input.
User-specified padding is applied on top of that, with negative values
indicating cropping. Pixels outside the image are assumed to be zero.
Args:
x: Float32/float64/float16 input tensor of the shape
`[batch_size, num_channels, in_height, in_width]`.
f: Float32 FIR filter of the shape
`[filter_height, filter_width]` (non-separable),
`[filter_taps]` (separable), or
`None` (identity).
down: Integer downsampling factor. Can be a single int or a list/tuple
`[x, y]` (default: 1).
padding: Padding with respect to the input. Can be a single number or a
list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
(default: 0).
flip_filter: False = convolution, True = correlation (default: False).
gain: Overall scaling factor for signal magnitude (default: 1).
impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
Returns:
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
"""
downx, downy = _parse_scaling(down)
padx0, padx1, pady0, pady1 = _parse_padding(padding)
fw, fh = _get_filter_size(f)
p = [
padx0 + (fw - downx + 1) // 2,
padx1 + (fw - downx) // 2,
pady0 + (fh - downy + 1) // 2,
pady1 + (fh - downy) // 2,
]
return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Facilities for pickling Python code alongside other data.
The pickled code is automatically imported into a separate Python module
during unpickling. This way, any previously exported pickles will remain
usable even if the original code is no longer available, or if the current
version of the code is not consistent with what was originally pickled."""
import sys
import pickle
import io
import inspect
import copy
import uuid
import types
import dnnlib
#----------------------------------------------------------------------------
_version = 6 # internal version number
_decorators = set() # {decorator_class, ...}
_import_hooks = [] # [hook_function, ...]
_module_to_src_dict = dict() # {module: src, ...}
_src_to_module_dict = dict() # {src: module, ...}
#----------------------------------------------------------------------------
def persistent_class(orig_class):
r"""Class decorator that extends a given class to save its source code
when pickled.
Example:
from torch_utils import persistence
@persistence.persistent_class
class MyNetwork(torch.nn.Module):
def __init__(self, num_inputs, num_outputs):
super().__init__()
self.fc = MyLayer(num_inputs, num_outputs)
...
@persistence.persistent_class
class MyLayer(torch.nn.Module):
...
When pickled, any instance of `MyNetwork` and `MyLayer` will save its
source code alongside other internal state (e.g., parameters, buffers,
and submodules). This way, any previously exported pickle will remain
usable even if the class definitions have been modified or are no
longer available.
The decorator saves the source code of the entire Python module
containing the decorated class. It does *not* save the source code of
any imported modules. Thus, the imported modules must be available
during unpickling, also including `torch_utils.persistence` itself.
It is ok to call functions defined in the same module from the
decorated class. However, if the decorated class depends on other
classes defined in the same module, they must be decorated as well.
This is illustrated in the above example in the case of `MyLayer`.
It is also possible to employ the decorator just-in-time before
calling the constructor. For example:
cls = MyLayer
if want_to_make_it_persistent:
cls = persistence.persistent_class(cls)
layer = cls(num_inputs, num_outputs)
As an additional feature, the decorator also keeps track of the
arguments that were used to construct each instance of the decorated
class. The arguments can be queried via `obj.init_args` and
`obj.init_kwargs`, and they are automatically pickled alongside other
object state. A typical use case is to first unpickle a previous
instance of a persistent class, and then upgrade it to use the latest
version of the source code:
with open('old_pickle.pkl', 'rb') as f:
old_net = pickle.load(f)
new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs)
misc.copy_params_and_buffers(old_net, new_net, require_all=True)
"""
assert isinstance(orig_class, type)
if is_persistent(orig_class):
return orig_class
assert orig_class.__module__ in sys.modules
orig_module = sys.modules[orig_class.__module__]
orig_module_src = _module_to_src(orig_module)
class Decorator(orig_class):
_orig_module_src = orig_module_src
_orig_class_name = orig_class.__name__
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._init_args = copy.deepcopy(args)
self._init_kwargs = copy.deepcopy(kwargs)
assert orig_class.__name__ in orig_module.__dict__
_check_pickleable(self.__reduce__())
@property
def init_args(self):
return copy.deepcopy(self._init_args)
@property
def init_kwargs(self):
return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs))
def __reduce__(self):
fields = list(super().__reduce__())
fields += [None] * max(3 - len(fields), 0)
if fields[0] is not _reconstruct_persistent_obj:
meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2])
fields[0] = _reconstruct_persistent_obj # reconstruct func
fields[1] = (meta,) # reconstruct args
fields[2] = None # state dict
return tuple(fields)
Decorator.__name__ = orig_class.__name__
_decorators.add(Decorator)
return Decorator
#----------------------------------------------------------------------------
def is_persistent(obj):
r"""Test whether the given object or class is persistent, i.e.,
whether it will save its source code when pickled.
"""
try:
if obj in _decorators:
return True
except TypeError:
pass
return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck
#----------------------------------------------------------------------------
def import_hook(hook):
r"""Register an import hook that is called whenever a persistent object
is being unpickled. A typical use case is to patch the pickled source
code to avoid errors and inconsistencies when the API of some imported
module has changed.
The hook should have the following signature:
hook(meta) -> modified meta
`meta` is an instance of `dnnlib.EasyDict` with the following fields:
type: Type of the persistent object, e.g. `'class'`.
version: Internal version number of `torch_utils.persistence`.
module_src Original source code of the Python module.
class_name: Class name in the original Python module.
state: Internal state of the object.
Example:
@persistence.import_hook
def wreck_my_network(meta):
if meta.class_name == 'MyNetwork':
print('MyNetwork is being imported. I will wreck it!')
meta.module_src = meta.module_src.replace("True", "False")
return meta
"""
assert callable(hook)
_import_hooks.append(hook)
#----------------------------------------------------------------------------
def _reconstruct_persistent_obj(meta):
r"""Hook that is called internally by the `pickle` module to unpickle
a persistent object.
"""
meta = dnnlib.EasyDict(meta)
meta.state = dnnlib.EasyDict(meta.state)
for hook in _import_hooks:
meta = hook(meta)
assert meta is not None
assert meta.version == _version
module = _src_to_module(meta.module_src)
assert meta.type == 'class'
orig_class = module.__dict__[meta.class_name]
decorator_class = persistent_class(orig_class)
obj = decorator_class.__new__(decorator_class)
setstate = getattr(obj, '__setstate__', None)
if callable(setstate):
setstate(meta.state) # pylint: disable=not-callable
else:
obj.__dict__.update(meta.state)
return obj
#----------------------------------------------------------------------------
def _module_to_src(module):
r"""Query the source code of a given Python module.
"""
src = _module_to_src_dict.get(module, None)
if src is None:
src = inspect.getsource(module)
_module_to_src_dict[module] = src
_src_to_module_dict[src] = module
return src
def _src_to_module(src):
r"""Get or create a Python module for the given source code.
"""
module = _src_to_module_dict.get(src, None)
if module is None:
module_name = "_imported_module_" + uuid.uuid4().hex
module = types.ModuleType(module_name)
sys.modules[module_name] = module
_module_to_src_dict[module] = src
_src_to_module_dict[src] = module
exec(src, module.__dict__) # pylint: disable=exec-used
return module
#----------------------------------------------------------------------------
def _check_pickleable(obj):
r"""Check that the given object is pickleable, raising an exception if
it is not. This function is expected to be considerably more efficient
than actually pickling the object.
"""
def recurse(obj):
if isinstance(obj, (list, tuple, set)):
return [recurse(x) for x in obj]
if isinstance(obj, dict):
return [[recurse(x), recurse(y)] for x, y in obj.items()]
if isinstance(obj, (str, int, float, bool, bytes, bytearray)):
return None # Python primitive types are pickleable.
if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor', 'torch.nn.parameter.Parameter']:
return None # NumPy arrays and PyTorch tensors are pickleable.
if is_persistent(obj):
return None # Persistent objects are pickleable, by virtue of the constructor check.
return obj
with io.BytesIO() as f:
pickle.dump(recurse(obj), f)
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Facilities for reporting and collecting training statistics across
multiple processes and devices. The interface is designed to minimize
synchronization overhead as well as the amount of boilerplate in user
code."""
import re
import numpy as np
import torch
import dnnlib
from . import misc
#----------------------------------------------------------------------------
_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares]
_reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction.
_counter_dtype = torch.float64 # Data type to use for the internal counters.
_rank = 0 # Rank of the current process.
_sync_device = None # Device to use for multiprocess communication. None = single-process.
_sync_called = False # Has _sync() been called yet?
_counters = dict() # Running counters on each device, updated by report(): name => device => torch.Tensor
_cumulative = dict() # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
#----------------------------------------------------------------------------
def init_multiprocessing(rank, sync_device):
r"""Initializes `torch_utils.training_stats` for collecting statistics
across multiple processes.
This function must be called after
`torch.distributed.init_process_group()` and before `Collector.update()`.
The call is not necessary if multi-process collection is not needed.
Args:
rank: Rank of the current process.
sync_device: PyTorch device to use for inter-process
communication, or None to disable multi-process
collection. Typically `torch.device('cuda', rank)`.
"""
global _rank, _sync_device
assert not _sync_called
_rank = rank
_sync_device = sync_device
#----------------------------------------------------------------------------
@misc.profiled_function
def report(name, value):
r"""Broadcasts the given set of scalars to all interested instances of
`Collector`, across device and process boundaries.
This function is expected to be extremely cheap and can be safely
called from anywhere in the training loop, loss function, or inside a
`torch.nn.Module`.
Warning: The current implementation expects the set of unique names to
be consistent across processes. Please make sure that `report()` is
called at least once for each unique name by each process, and in the
same order. If a given process has no scalars to broadcast, it can do
`report(name, [])` (empty list).
Args:
name: Arbitrary string specifying the name of the statistic.
Averages are accumulated separately for each unique name.
value: Arbitrary set of scalars. Can be a list, tuple,
NumPy array, PyTorch tensor, or Python scalar.
Returns:
The same `value` that was passed in.
"""
if name not in _counters:
_counters[name] = dict()
elems = torch.as_tensor(value)
if elems.numel() == 0:
return value
elems = elems.detach().flatten().to(_reduce_dtype)
moments = torch.stack([
torch.ones_like(elems).sum(),
elems.sum(),
elems.square().sum(),
])
assert moments.ndim == 1 and moments.shape[0] == _num_moments
moments = moments.to(_counter_dtype)
device = moments.device
if device not in _counters[name]:
_counters[name][device] = torch.zeros_like(moments)
_counters[name][device].add_(moments)
return value
#----------------------------------------------------------------------------
def report0(name, value):
r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
but ignores any scalars provided by the other processes.
See `report()` for further details.
"""
report(name, value if _rank == 0 else [])
return value
#----------------------------------------------------------------------------
class Collector:
r"""Collects the scalars broadcasted by `report()` and `report0()` and
computes their long-term averages (mean and standard deviation) over
user-defined periods of time.
The averages are first collected into internal counters that are not
directly visible to the user. They are then copied to the user-visible
state as a result of calling `update()` and can then be queried using
`mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
internal counters for the next round, so that the user-visible state
effectively reflects averages collected between the last two calls to
`update()`.
Args:
regex: Regular expression defining which statistics to
collect. The default is to collect everything.
keep_previous: Whether to retain the previous averages if no
scalars were collected on a given round
(default: True).
"""
def __init__(self, regex='.*', keep_previous=True):
self._regex = re.compile(regex)
self._keep_previous = keep_previous
self._cumulative = dict()
self._moments = dict()
self.update()
self._moments.clear()
def names(self):
r"""Returns the names of all statistics broadcasted so far that
match the regular expression specified at construction time.
"""
return [name for name in _counters if self._regex.fullmatch(name)]
def update(self):
r"""Copies current values of the internal counters to the
user-visible state and resets them for the next round.
If `keep_previous=True` was specified at construction time, the
operation is skipped for statistics that have received no scalars
since the last update, retaining their previous averages.
This method performs a number of GPU-to-CPU transfers and one
`torch.distributed.all_reduce()`. It is intended to be called
periodically in the main training loop, typically once every
N training steps.
"""
if not self._keep_previous:
self._moments.clear()
for name, cumulative in _sync(self.names()):
if name not in self._cumulative:
self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
delta = cumulative - self._cumulative[name]
self._cumulative[name].copy_(cumulative)
if float(delta[0]) != 0:
self._moments[name] = delta
def _get_delta(self, name):
r"""Returns the raw moments that were accumulated for the given
statistic between the last two calls to `update()`, or zero if
no scalars were collected.
"""
assert self._regex.fullmatch(name)
if name not in self._moments:
self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
return self._moments[name]
def num(self, name):
r"""Returns the number of scalars that were accumulated for the given
statistic between the last two calls to `update()`, or zero if
no scalars were collected.
"""
delta = self._get_delta(name)
return int(delta[0])
def mean(self, name):
r"""Returns the mean of the scalars that were accumulated for the
given statistic between the last two calls to `update()`, or NaN if
no scalars were collected.
"""
delta = self._get_delta(name)
if int(delta[0]) == 0:
return float('nan')
return float(delta[1] / delta[0])
def std(self, name):
r"""Returns the standard deviation of the scalars that were
accumulated for the given statistic between the last two calls to
`update()`, or NaN if no scalars were collected.
"""
delta = self._get_delta(name)
if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
return float('nan')
if int(delta[0]) == 1:
return float(0)
mean = float(delta[1] / delta[0])
raw_var = float(delta[2] / delta[0])
return np.sqrt(max(raw_var - np.square(mean), 0))
def as_dict(self):
r"""Returns the averages accumulated between the last two calls to
`update()` as an `dnnlib.EasyDict`. The contents are as follows:
dnnlib.EasyDict(
NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
...
)
"""
stats = dnnlib.EasyDict()
for name in self.names():
stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name))
return stats
def __getitem__(self, name):
r"""Convenience getter.
`collector[name]` is a synonym for `collector.mean(name)`.
"""
return self.mean(name)
#----------------------------------------------------------------------------
def _sync(names):
r"""Synchronize the global cumulative counters across devices and
processes. Called internally by `Collector.update()`.
"""
if len(names) == 0:
return []
global _sync_called
_sync_called = True
# Collect deltas within current rank.
deltas = []
device = _sync_device if _sync_device is not None else torch.device('cpu')
for name in names:
delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device)
for counter in _counters[name].values():
delta.add_(counter.to(device))
counter.copy_(torch.zeros_like(counter))
deltas.append(delta)
deltas = torch.stack(deltas)
# Sum deltas across ranks.
if _sync_device is not None:
torch.distributed.all_reduce(deltas)
# Update cumulative values.
deltas = deltas.cpu()
for idx, name in enumerate(names):
if name not in _cumulative:
_cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
_cumulative[name].add_(deltas[idx])
# Return name-value pairs.
return [(name, _cumulative[name]) for name in names]
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Train a GAN using the techniques described in the paper
"Alias-Free Generative Adversarial Networks"."""
import os
import click
import re
import json
import tempfile
import torch
import dnnlib
from training import training_loop
from metrics import metric_main
from torch_utils import training_stats
from torch_utils import custom_ops
#----------------------------------------------------------------------------
def subprocess_fn(rank, c, temp_dir):
dnnlib.util.Logger(file_name=os.path.join(c.run_dir, 'log.txt'), file_mode='a', should_flush=True)
# Init torch.distributed.
if c.num_gpus > 1:
init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
if os.name == 'nt':
init_method = 'file:///' + init_file.replace('\\', '/')
torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=c.num_gpus)
else:
init_method = f'file://{init_file}'
torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=c.num_gpus)
# Init torch_utils.
sync_device = torch.device('cuda', rank) if c.num_gpus > 1 else None
training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
if rank != 0:
custom_ops.verbosity = 'none'
# Execute training loop.
training_loop.training_loop(rank=rank, **c)
#----------------------------------------------------------------------------
def launch_training(c, desc, outdir, dry_run):
dnnlib.util.Logger(should_flush=True)
# Pick output directory.
prev_run_dirs = []
if os.path.isdir(outdir):
prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))]
prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs]
prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]
cur_run_id = max(prev_run_ids, default=-1) + 1
c.run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{desc}')
assert not os.path.exists(c.run_dir)
# Print options.
print()
print('Training options:')
print(json.dumps(c, indent=2))
print()
print(f'Output directory: {c.run_dir}')
print(f'Number of GPUs: {c.num_gpus}')
print(f'Batch size: {c.batch_size} images')
print(f'Training duration: {c.total_kimg} kimg')
print(f'Dataset path: {c.training_set_kwargs.path}')
print(f'Dataset size: {c.training_set_kwargs.max_size} images')
print(f'Dataset resolution: {c.training_set_kwargs.resolution}')
print(f'Dataset labels: {c.training_set_kwargs.use_labels}')
print(f'Dataset x-flips: {c.training_set_kwargs.xflip}')
print()
# Dry run?
if dry_run:
print('Dry run; exiting.')
return
# Create output directory.
print('Creating output directory...')
os.makedirs(c.run_dir)
with open(os.path.join(c.run_dir, 'training_options.json'), 'wt') as f:
json.dump(c, f, indent=2)
# Launch processes.
print('Launching processes...')
torch.multiprocessing.set_start_method('spawn')
with tempfile.TemporaryDirectory() as temp_dir:
if c.num_gpus == 1:
subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
else:
torch.multiprocessing.spawn(fn=subprocess_fn, args=(c, temp_dir), nprocs=c.num_gpus)
#----------------------------------------------------------------------------
def init_dataset_kwargs(data):
try:
dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False)
dataset_obj = dnnlib.util.construct_class_by_name(**dataset_kwargs) # Subclass of training.dataset.Dataset.
dataset_kwargs.resolution = dataset_obj.resolution # Be explicit about resolution.
dataset_kwargs.use_labels = dataset_obj.has_labels # Be explicit about labels.
dataset_kwargs.max_size = len(dataset_obj) # Be explicit about dataset size.
return dataset_kwargs, dataset_obj.name
except IOError as err:
raise click.ClickException(f'--data: {err}')
#----------------------------------------------------------------------------
def parse_comma_separated_list(s):
if isinstance(s, list):
return s
if s is None or s.lower() == 'none' or s == '':
return []
return s.split(',')
#----------------------------------------------------------------------------
@click.command()
# Required.
@click.option('--outdir', help='Where to save the results', metavar='DIR', required=True)
@click.option('--cfg', help='Base configuration', type=click.Choice(['stylegan3-t', 'stylegan3-r', 'stylegan2']), required=True)
@click.option('--data', help='Training data', metavar='[ZIP|DIR]', type=str, required=True)
@click.option('--gpus', help='Number of GPUs to use', metavar='INT', type=click.IntRange(min=1), required=True)
@click.option('--batch', help='Total batch size', metavar='INT', type=click.IntRange(min=1), required=True)
@click.option('--gamma', help='R1 regularization weight', metavar='FLOAT', type=click.FloatRange(min=0), required=True)
# Optional features.
@click.option('--cond', help='Train conditional model', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--mirror', help='Enable dataset x-flips', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--aug', help='Augmentation mode', type=click.Choice(['noaug', 'ada', 'fixed']), default='ada', show_default=True)
@click.option('--resume', help='Resume from given network pickle', metavar='[PATH|URL]', type=str)
@click.option('--freezed', help='Freeze first layers of D', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True)
# Misc hyperparameters.
@click.option('--p', help='Probability for --aug=fixed', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.2, show_default=True)
@click.option('--target', help='Target value for --aug=ada', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.6, show_default=True)
@click.option('--batch-gpu', help='Limit batch size per GPU', metavar='INT', type=click.IntRange(min=1))
@click.option('--cbase', help='Capacity multiplier', metavar='INT', type=click.IntRange(min=1), default=32768, show_default=True)
@click.option('--cmax', help='Max. feature maps', metavar='INT', type=click.IntRange(min=1), default=512, show_default=True)
@click.option('--glr', help='G learning rate [default: varies]', metavar='FLOAT', type=click.FloatRange(min=0))
@click.option('--dlr', help='D learning rate', metavar='FLOAT', type=click.FloatRange(min=0), default=0.002, show_default=True)
@click.option('--map-depth', help='Mapping network depth [default: varies]', metavar='INT', type=click.IntRange(min=1))
@click.option('--mbstd-group', help='Minibatch std group size', metavar='INT', type=click.IntRange(min=1), default=4, show_default=True)
# Misc settings.
@click.option('--desc', help='String to include in result dir name', metavar='STR', type=str)
@click.option('--metrics', help='Quality metrics', metavar='[NAME|A,B,C|none]', type=parse_comma_separated_list, default='fid50k_full', show_default=True)
@click.option('--kimg', help='Total training duration', metavar='KIMG', type=click.IntRange(min=1), default=25000, show_default=True)
@click.option('--tick', help='How often to print progress', metavar='KIMG', type=click.IntRange(min=1), default=4, show_default=True)
@click.option('--snap', help='How often to save snapshots', metavar='TICKS', type=click.IntRange(min=1), default=50, show_default=True)
@click.option('--seed', help='Random seed', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True)
@click.option('--fp32', help='Disable mixed-precision', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--nobench', help='Disable cuDNN benchmarking', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--workers', help='DataLoader worker processes', metavar='INT', type=click.IntRange(min=1), default=3, show_default=True)
@click.option('-n','--dry-run', help='Print training options and exit', is_flag=True)
def main(**kwargs):
"""Train a GAN using the techniques described in the paper
"Alias-Free Generative Adversarial Networks".
Examples:
\b
# Train StyleGAN3-T for AFHQv2 using 8 GPUs.
python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/afhqv2-512x512.zip \\
--gpus=8 --batch=32 --gamma=8.2 --mirror=1
\b
# Fine-tune StyleGAN3-R for MetFaces-U using 1 GPU, starting from the pre-trained FFHQ-U pickle.
python train.py --outdir=~/training-runs --cfg=stylegan3-r --data=~/datasets/metfacesu-1024x1024.zip \\
--gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=5 \\
--resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl
\b
# Train StyleGAN2 for FFHQ at 1024x1024 resolution using 8 GPUs.
python train.py --outdir=~/training-runs --cfg=stylegan2 --data=~/datasets/ffhq-1024x1024.zip \\
--gpus=8 --batch=32 --gamma=10 --mirror=1 --aug=noaug
"""
# Initialize config.
opts = dnnlib.EasyDict(kwargs) # Command line arguments.
c = dnnlib.EasyDict() # Main config dict.
c.G_kwargs = dnnlib.EasyDict(class_name=None, z_dim=512, w_dim=512, mapping_kwargs=dnnlib.EasyDict())
c.D_kwargs = dnnlib.EasyDict(class_name='training.networks_stylegan2.Discriminator', block_kwargs=dnnlib.EasyDict(), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict())
c.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8)
c.D_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8)
c.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.StyleGAN2Loss')
c.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, prefetch_factor=2)
# Training set.
c.training_set_kwargs, dataset_name = init_dataset_kwargs(data=opts.data)
if opts.cond and not c.training_set_kwargs.use_labels:
raise click.ClickException('--cond=True requires labels specified in dataset.json')
c.training_set_kwargs.use_labels = opts.cond
c.training_set_kwargs.xflip = opts.mirror
# Hyperparameters & settings.
c.num_gpus = opts.gpus
c.batch_size = opts.batch
c.batch_gpu = opts.batch_gpu or opts.batch // opts.gpus
c.G_kwargs.channel_base = c.D_kwargs.channel_base = opts.cbase
c.G_kwargs.channel_max = c.D_kwargs.channel_max = opts.cmax
c.G_kwargs.mapping_kwargs.num_layers = (8 if opts.cfg == 'stylegan2' else 2) if opts.map_depth is None else opts.map_depth
c.D_kwargs.block_kwargs.freeze_layers = opts.freezed
c.D_kwargs.epilogue_kwargs.mbstd_group_size = opts.mbstd_group
c.loss_kwargs.r1_gamma = opts.gamma
c.G_opt_kwargs.lr = (0.002 if opts.cfg == 'stylegan2' else 0.0025) if opts.glr is None else opts.glr
c.D_opt_kwargs.lr = opts.dlr
c.metrics = opts.metrics
c.total_kimg = opts.kimg
c.kimg_per_tick = opts.tick
c.image_snapshot_ticks = c.network_snapshot_ticks = opts.snap
c.random_seed = c.training_set_kwargs.random_seed = opts.seed
c.data_loader_kwargs.num_workers = opts.workers
# Sanity checks.
if c.batch_size % c.num_gpus != 0:
raise click.ClickException('--batch must be a multiple of --gpus')
if c.batch_size % (c.num_gpus * c.batch_gpu) != 0:
raise click.ClickException('--batch must be a multiple of --gpus times --batch-gpu')
if c.batch_gpu < c.D_kwargs.epilogue_kwargs.mbstd_group_size:
raise click.ClickException('--batch-gpu cannot be smaller than --mbstd')
if any(not metric_main.is_valid_metric(metric) for metric in c.metrics):
raise click.ClickException('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics()))
# Base configuration.
c.ema_kimg = c.batch_size * 10 / 32
if opts.cfg == 'stylegan2':
c.G_kwargs.class_name = 'training.networks_stylegan2.Generator'
c.loss_kwargs.style_mixing_prob = 0.9 # Enable style mixing regularization.
c.loss_kwargs.pl_weight = 2 # Enable path length regularization.
c.G_reg_interval = 4 # Enable lazy regularization for G.
c.G_kwargs.fused_modconv_default = 'inference_only' # Speed up training by using regular convolutions instead of grouped convolutions.
c.loss_kwargs.pl_no_weight_grad = True # Speed up path length regularization by skipping gradient computation wrt. conv2d weights.
else:
c.G_kwargs.class_name = 'training.networks_stylegan3.Generator'
c.G_kwargs.magnitude_ema_beta = 0.5 ** (c.batch_size / (20 * 1e3))
if opts.cfg == 'stylegan3-r':
c.G_kwargs.conv_kernel = 1 # Use 1x1 convolutions.
c.G_kwargs.channel_base *= 2 # Double the number of feature maps.
c.G_kwargs.channel_max *= 2
c.G_kwargs.use_radial_filters = True # Use radially symmetric downsampling filters.
c.loss_kwargs.blur_init_sigma = 10 # Blur the images seen by the discriminator.
c.loss_kwargs.blur_fade_kimg = c.batch_size * 200 / 32 # Fade out the blur during the first N kimg.
# Augmentation.
if opts.aug != 'noaug':
c.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1)
if opts.aug == 'ada':
c.ada_target = opts.target
if opts.aug == 'fixed':
c.augment_p = opts.p
# Resume.
if opts.resume is not None:
c.resume_pkl = opts.resume
c.ada_kimg = 100 # Make ADA react faster at the beginning.
c.ema_rampup = None # Disable EMA rampup.
c.loss_kwargs.blur_init_sigma = 0 # Disable blur rampup.
# Performance-related toggles.
if opts.fp32:
c.G_kwargs.num_fp16_res = c.D_kwargs.num_fp16_res = 0
c.G_kwargs.conv_clamp = c.D_kwargs.conv_clamp = None
if opts.nobench:
c.cudnn_benchmark = False
# Description string.
desc = f'{opts.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}'
if opts.desc is not None:
desc += f'-{opts.desc}'
# Launch.
launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
#----------------------------------------------------------------------------
if __name__ == "__main__":
main() # pylint: disable=no-value-for-parameter
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
# empty

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Augmentation pipeline from the paper
"Training Generative Adversarial Networks with Limited Data".
Matches the original implementation by Karras et al. at
https://github.com/NVlabs/stylegan2-ada/blob/main/training/augment.py"""
import numpy as np
import scipy.signal
import torch
from torch_utils import persistence
from torch_utils import misc
from torch_utils.ops import upfirdn2d
from torch_utils.ops import grid_sample_gradfix
from torch_utils.ops import conv2d_gradfix
#----------------------------------------------------------------------------
# Coefficients of various wavelet decomposition low-pass filters.
wavelets = {
'haar': [0.7071067811865476, 0.7071067811865476],
'db1': [0.7071067811865476, 0.7071067811865476],
'db2': [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
'db3': [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
'db4': [-0.010597401784997278, 0.032883011666982945, 0.030841381835986965, -0.18703481171888114, -0.02798376941698385, 0.6308807679295904, 0.7148465705525415, 0.23037781330885523],
'db5': [0.003335725285001549, -0.012580751999015526, -0.006241490213011705, 0.07757149384006515, -0.03224486958502952, -0.24229488706619015, 0.13842814590110342, 0.7243085284385744, 0.6038292697974729, 0.160102397974125],
'db6': [-0.00107730108499558, 0.004777257511010651, 0.0005538422009938016, -0.031582039318031156, 0.02752286553001629, 0.09750160558707936, -0.12976686756709563, -0.22626469396516913, 0.3152503517092432, 0.7511339080215775, 0.4946238903983854, 0.11154074335008017],
'db7': [0.0003537138000010399, -0.0018016407039998328, 0.00042957797300470274, 0.012550998556013784, -0.01657454163101562, -0.03802993693503463, 0.0806126091510659, 0.07130921926705004, -0.22403618499416572, -0.14390600392910627, 0.4697822874053586, 0.7291320908465551, 0.39653931948230575, 0.07785205408506236],
'db8': [-0.00011747678400228192, 0.0006754494059985568, -0.0003917403729959771, -0.00487035299301066, 0.008746094047015655, 0.013981027917015516, -0.04408825393106472, -0.01736930100202211, 0.128747426620186, 0.00047248457399797254, -0.2840155429624281, -0.015829105256023893, 0.5853546836548691, 0.6756307362980128, 0.3128715909144659, 0.05441584224308161],
'sym2': [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
'sym3': [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
'sym4': [-0.07576571478927333, -0.02963552764599851, 0.49761866763201545, 0.8037387518059161, 0.29785779560527736, -0.09921954357684722, -0.012603967262037833, 0.0322231006040427],
'sym5': [0.027333068345077982, 0.029519490925774643, -0.039134249302383094, 0.1993975339773936, 0.7234076904024206, 0.6339789634582119, 0.01660210576452232, -0.17532808990845047, -0.021101834024758855, 0.019538882735286728],
'sym6': [0.015404109327027373, 0.0034907120842174702, -0.11799011114819057, -0.048311742585633, 0.4910559419267466, 0.787641141030194, 0.3379294217276218, -0.07263752278646252, -0.021060292512300564, 0.04472490177066578, 0.0017677118642428036, -0.007800708325034148],
'sym7': [0.002681814568257878, -0.0010473848886829163, -0.01263630340325193, 0.03051551316596357, 0.0678926935013727, -0.049552834937127255, 0.017441255086855827, 0.5361019170917628, 0.767764317003164, 0.2886296317515146, -0.14004724044296152, -0.10780823770381774, 0.004010244871533663, 0.010268176708511255],
'sym8': [-0.0033824159510061256, -0.0005421323317911481, 0.03169508781149298, 0.007607487324917605, -0.1432942383508097, -0.061273359067658524, 0.4813596512583722, 0.7771857517005235, 0.3644418948353314, -0.05194583810770904, -0.027219029917056003, 0.049137179673607506, 0.003808752013890615, -0.01495225833704823, -0.0003029205147213668, 0.0018899503327594609],
}
#----------------------------------------------------------------------------
# Helpers for constructing transformation matrices.
def matrix(*rows, device=None):
assert all(len(row) == len(rows[0]) for row in rows)
elems = [x for row in rows for x in row]
ref = [x for x in elems if isinstance(x, torch.Tensor)]
if len(ref) == 0:
return misc.constant(np.asarray(rows), device=device)
assert device is None or device == ref[0].device
elems = [x if isinstance(x, torch.Tensor) else misc.constant(x, shape=ref[0].shape, device=ref[0].device) for x in elems]
return torch.stack(elems, dim=-1).reshape(ref[0].shape + (len(rows), -1))
def translate2d(tx, ty, **kwargs):
return matrix(
[1, 0, tx],
[0, 1, ty],
[0, 0, 1],
**kwargs)
def translate3d(tx, ty, tz, **kwargs):
return matrix(
[1, 0, 0, tx],
[0, 1, 0, ty],
[0, 0, 1, tz],
[0, 0, 0, 1],
**kwargs)
def scale2d(sx, sy, **kwargs):
return matrix(
[sx, 0, 0],
[0, sy, 0],
[0, 0, 1],
**kwargs)
def scale3d(sx, sy, sz, **kwargs):
return matrix(
[sx, 0, 0, 0],
[0, sy, 0, 0],
[0, 0, sz, 0],
[0, 0, 0, 1],
**kwargs)
def rotate2d(theta, **kwargs):
return matrix(
[torch.cos(theta), torch.sin(-theta), 0],
[torch.sin(theta), torch.cos(theta), 0],
[0, 0, 1],
**kwargs)
def rotate3d(v, theta, **kwargs):
vx = v[..., 0]; vy = v[..., 1]; vz = v[..., 2]
s = torch.sin(theta); c = torch.cos(theta); cc = 1 - c
return matrix(
[vx*vx*cc+c, vx*vy*cc-vz*s, vx*vz*cc+vy*s, 0],
[vy*vx*cc+vz*s, vy*vy*cc+c, vy*vz*cc-vx*s, 0],
[vz*vx*cc-vy*s, vz*vy*cc+vx*s, vz*vz*cc+c, 0],
[0, 0, 0, 1],
**kwargs)
def translate2d_inv(tx, ty, **kwargs):
return translate2d(-tx, -ty, **kwargs)
def scale2d_inv(sx, sy, **kwargs):
return scale2d(1 / sx, 1 / sy, **kwargs)
def rotate2d_inv(theta, **kwargs):
return rotate2d(-theta, **kwargs)
#----------------------------------------------------------------------------
# Versatile image augmentation pipeline from the paper
# "Training Generative Adversarial Networks with Limited Data".
#
# All augmentations are disabled by default; individual augmentations can
# be enabled by setting their probability multipliers to 1.
@persistence.persistent_class
class AugmentPipe(torch.nn.Module):
def __init__(self,
xflip=0, rotate90=0, xint=0, xint_max=0.125,
scale=0, rotate=0, aniso=0, xfrac=0, scale_std=0.2, rotate_max=1, aniso_std=0.2, xfrac_std=0.125,
brightness=0, contrast=0, lumaflip=0, hue=0, saturation=0, brightness_std=0.2, contrast_std=0.5, hue_max=1, saturation_std=1,
imgfilter=0, imgfilter_bands=[1,1,1,1], imgfilter_std=1,
noise=0, cutout=0, noise_std=0.1, cutout_size=0.5,
):
super().__init__()
self.register_buffer('p', torch.ones([])) # Overall multiplier for augmentation probability.
# Pixel blitting.
self.xflip = float(xflip) # Probability multiplier for x-flip.
self.rotate90 = float(rotate90) # Probability multiplier for 90 degree rotations.
self.xint = float(xint) # Probability multiplier for integer translation.
self.xint_max = float(xint_max) # Range of integer translation, relative to image dimensions.
# General geometric transformations.
self.scale = float(scale) # Probability multiplier for isotropic scaling.
self.rotate = float(rotate) # Probability multiplier for arbitrary rotation.
self.aniso = float(aniso) # Probability multiplier for anisotropic scaling.
self.xfrac = float(xfrac) # Probability multiplier for fractional translation.
self.scale_std = float(scale_std) # Log2 standard deviation of isotropic scaling.
self.rotate_max = float(rotate_max) # Range of arbitrary rotation, 1 = full circle.
self.aniso_std = float(aniso_std) # Log2 standard deviation of anisotropic scaling.
self.xfrac_std = float(xfrac_std) # Standard deviation of frational translation, relative to image dimensions.
# Color transformations.
self.brightness = float(brightness) # Probability multiplier for brightness.
self.contrast = float(contrast) # Probability multiplier for contrast.
self.lumaflip = float(lumaflip) # Probability multiplier for luma flip.
self.hue = float(hue) # Probability multiplier for hue rotation.
self.saturation = float(saturation) # Probability multiplier for saturation.
self.brightness_std = float(brightness_std) # Standard deviation of brightness.
self.contrast_std = float(contrast_std) # Log2 standard deviation of contrast.
self.hue_max = float(hue_max) # Range of hue rotation, 1 = full circle.
self.saturation_std = float(saturation_std) # Log2 standard deviation of saturation.
# Image-space filtering.
self.imgfilter = float(imgfilter) # Probability multiplier for image-space filtering.
self.imgfilter_bands = list(imgfilter_bands) # Probability multipliers for individual frequency bands.
self.imgfilter_std = float(imgfilter_std) # Log2 standard deviation of image-space filter amplification.
# Image-space corruptions.
self.noise = float(noise) # Probability multiplier for additive RGB noise.
self.cutout = float(cutout) # Probability multiplier for cutout.
self.noise_std = float(noise_std) # Standard deviation of additive RGB noise.
self.cutout_size = float(cutout_size) # Size of the cutout rectangle, relative to image dimensions.
# Setup orthogonal lowpass filter for geometric augmentations.
self.register_buffer('Hz_geom', upfirdn2d.setup_filter(wavelets['sym6']))
# Construct filter bank for image-space filtering.
Hz_lo = np.asarray(wavelets['sym2']) # H(z)
Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z)
Hz_lo2 = np.convolve(Hz_lo, Hz_lo[::-1]) / 2 # H(z) * H(z^-1) / 2
Hz_hi2 = np.convolve(Hz_hi, Hz_hi[::-1]) / 2 # H(-z) * H(-z^-1) / 2
Hz_fbank = np.eye(4, 1) # Bandpass(H(z), b_i)
for i in range(1, Hz_fbank.shape[0]):
Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape(Hz_fbank.shape[0], -1)[:, :-1]
Hz_fbank = scipy.signal.convolve(Hz_fbank, [Hz_lo2])
Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) // 2 : (Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2
self.register_buffer('Hz_fbank', torch.as_tensor(Hz_fbank, dtype=torch.float32))
def forward(self, images, debug_percentile=None):
assert isinstance(images, torch.Tensor) and images.ndim == 4
batch_size, num_channels, height, width = images.shape
device = images.device
if debug_percentile is not None:
debug_percentile = torch.as_tensor(debug_percentile, dtype=torch.float32, device=device)
# -------------------------------------
# Select parameters for pixel blitting.
# -------------------------------------
# Initialize inverse homogeneous 2D transform: G_inv @ pixel_out ==> pixel_in
I_3 = torch.eye(3, device=device)
G_inv = I_3
# Apply x-flip with probability (xflip * strength).
if self.xflip > 0:
i = torch.floor(torch.rand([batch_size], device=device) * 2)
i = torch.where(torch.rand([batch_size], device=device) < self.xflip * self.p, i, torch.zeros_like(i))
if debug_percentile is not None:
i = torch.full_like(i, torch.floor(debug_percentile * 2))
G_inv = G_inv @ scale2d_inv(1 - 2 * i, 1)
# Apply 90 degree rotations with probability (rotate90 * strength).
if self.rotate90 > 0:
i = torch.floor(torch.rand([batch_size], device=device) * 4)
i = torch.where(torch.rand([batch_size], device=device) < self.rotate90 * self.p, i, torch.zeros_like(i))
if debug_percentile is not None:
i = torch.full_like(i, torch.floor(debug_percentile * 4))
G_inv = G_inv @ rotate2d_inv(-np.pi / 2 * i)
# Apply integer translation with probability (xint * strength).
if self.xint > 0:
t = (torch.rand([batch_size, 2], device=device) * 2 - 1) * self.xint_max
t = torch.where(torch.rand([batch_size, 1], device=device) < self.xint * self.p, t, torch.zeros_like(t))
if debug_percentile is not None:
t = torch.full_like(t, (debug_percentile * 2 - 1) * self.xint_max)
G_inv = G_inv @ translate2d_inv(torch.round(t[:,0] * width), torch.round(t[:,1] * height))
# --------------------------------------------------------
# Select parameters for general geometric transformations.
# --------------------------------------------------------
# Apply isotropic scaling with probability (scale * strength).
if self.scale > 0:
s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std)
s = torch.where(torch.rand([batch_size], device=device) < self.scale * self.p, s, torch.ones_like(s))
if debug_percentile is not None:
s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.scale_std))
G_inv = G_inv @ scale2d_inv(s, s)
# Apply pre-rotation with probability p_rot.
p_rot = 1 - torch.sqrt((1 - self.rotate * self.p).clamp(0, 1)) # P(pre OR post) = p
if self.rotate > 0:
theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
if debug_percentile is not None:
theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max)
G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling.
# Apply anisotropic scaling with probability (aniso * strength).
if self.aniso > 0:
s = torch.exp2(torch.randn([batch_size], device=device) * self.aniso_std)
s = torch.where(torch.rand([batch_size], device=device) < self.aniso * self.p, s, torch.ones_like(s))
if debug_percentile is not None:
s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.aniso_std))
G_inv = G_inv @ scale2d_inv(s, 1 / s)
# Apply post-rotation with probability p_rot.
if self.rotate > 0:
theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
if debug_percentile is not None:
theta = torch.zeros_like(theta)
G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling.
# Apply fractional translation with probability (xfrac * strength).
if self.xfrac > 0:
t = torch.randn([batch_size, 2], device=device) * self.xfrac_std
t = torch.where(torch.rand([batch_size, 1], device=device) < self.xfrac * self.p, t, torch.zeros_like(t))
if debug_percentile is not None:
t = torch.full_like(t, torch.erfinv(debug_percentile * 2 - 1) * self.xfrac_std)
G_inv = G_inv @ translate2d_inv(t[:,0] * width, t[:,1] * height)
# ----------------------------------
# Execute geometric transformations.
# ----------------------------------
# Execute if the transform is not identity.
if G_inv is not I_3:
# Calculate padding.
cx = (width - 1) / 2
cy = (height - 1) / 2
cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], [-cx, cy, 1], device=device) # [idx, xyz]
cp = G_inv @ cp.t() # [batch, xyz, idx]
Hz_pad = self.Hz_geom.shape[0] // 4
margin = cp[:, :2, :].permute(1, 0, 2).flatten(1) # [xy, batch * idx]
margin = torch.cat([-margin, margin]).max(dim=1).values # [x0, y0, x1, y1]
margin = margin + misc.constant([Hz_pad * 2 - cx, Hz_pad * 2 - cy] * 2, device=device)
margin = margin.max(misc.constant([0, 0] * 2, device=device))
margin = margin.min(misc.constant([width-1, height-1] * 2, device=device))
mx0, my0, mx1, my1 = margin.ceil().to(torch.int32)
# Pad image and adjust origin.
images = torch.nn.functional.pad(input=images, pad=[mx0,mx1,my0,my1], mode='reflect')
G_inv = translate2d((mx0 - mx1) / 2, (my0 - my1) / 2) @ G_inv
# Upsample.
images = upfirdn2d.upsample2d(x=images, f=self.Hz_geom, up=2)
G_inv = scale2d(2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device)
G_inv = translate2d(-0.5, -0.5, device=device) @ G_inv @ translate2d_inv(-0.5, -0.5, device=device)
# Execute transformation.
shape = [batch_size, num_channels, (height + Hz_pad * 2) * 2, (width + Hz_pad * 2) * 2]
G_inv = scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) @ G_inv @ scale2d_inv(2 / shape[3], 2 / shape[2], device=device)
grid = torch.nn.functional.affine_grid(theta=G_inv[:,:2,:], size=shape, align_corners=False)
images = grid_sample_gradfix.grid_sample(images, grid)
# Downsample and crop.
images = upfirdn2d.downsample2d(x=images, f=self.Hz_geom, down=2, padding=-Hz_pad*2, flip_filter=True)
# --------------------------------------------
# Select parameters for color transformations.
# --------------------------------------------
# Initialize homogeneous 3D transformation matrix: C @ color_in ==> color_out
I_4 = torch.eye(4, device=device)
C = I_4
# Apply brightness with probability (brightness * strength).
if self.brightness > 0:
b = torch.randn([batch_size], device=device) * self.brightness_std
b = torch.where(torch.rand([batch_size], device=device) < self.brightness * self.p, b, torch.zeros_like(b))
if debug_percentile is not None:
b = torch.full_like(b, torch.erfinv(debug_percentile * 2 - 1) * self.brightness_std)
C = translate3d(b, b, b) @ C
# Apply contrast with probability (contrast * strength).
if self.contrast > 0:
c = torch.exp2(torch.randn([batch_size], device=device) * self.contrast_std)
c = torch.where(torch.rand([batch_size], device=device) < self.contrast * self.p, c, torch.ones_like(c))
if debug_percentile is not None:
c = torch.full_like(c, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.contrast_std))
C = scale3d(c, c, c) @ C
# Apply luma flip with probability (lumaflip * strength).
v = misc.constant(np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) # Luma axis.
if self.lumaflip > 0:
i = torch.floor(torch.rand([batch_size, 1, 1], device=device) * 2)
i = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.lumaflip * self.p, i, torch.zeros_like(i))
if debug_percentile is not None:
i = torch.full_like(i, torch.floor(debug_percentile * 2))
C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection.
# Apply hue rotation with probability (hue * strength).
if self.hue > 0 and num_channels > 1:
theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.hue_max
theta = torch.where(torch.rand([batch_size], device=device) < self.hue * self.p, theta, torch.zeros_like(theta))
if debug_percentile is not None:
theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max)
C = rotate3d(v, theta) @ C # Rotate around v.
# Apply saturation with probability (saturation * strength).
if self.saturation > 0 and num_channels > 1:
s = torch.exp2(torch.randn([batch_size, 1, 1], device=device) * self.saturation_std)
s = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.saturation * self.p, s, torch.ones_like(s))
if debug_percentile is not None:
s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.saturation_std))
C = (v.ger(v) + (I_4 - v.ger(v)) * s) @ C
# ------------------------------
# Execute color transformations.
# ------------------------------
# Execute if the transform is not identity.
if C is not I_4:
images = images.reshape([batch_size, num_channels, height * width])
if num_channels == 3:
images = C[:, :3, :3] @ images + C[:, :3, 3:]
elif num_channels == 1:
C = C[:, :3, :].mean(dim=1, keepdims=True)
images = images * C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:]
else:
raise ValueError('Image must be RGB (3 channels) or L (1 channel)')
images = images.reshape([batch_size, num_channels, height, width])
# ----------------------
# Image-space filtering.
# ----------------------
if self.imgfilter > 0:
num_bands = self.Hz_fbank.shape[0]
assert len(self.imgfilter_bands) == num_bands
expected_power = misc.constant(np.array([10, 1, 1, 1]) / 13, device=device) # Expected power spectrum (1/f).
# Apply amplification for each band with probability (imgfilter * strength * band_strength).
g = torch.ones([batch_size, num_bands], device=device) # Global gain vector (identity).
for i, band_strength in enumerate(self.imgfilter_bands):
t_i = torch.exp2(torch.randn([batch_size], device=device) * self.imgfilter_std)
t_i = torch.where(torch.rand([batch_size], device=device) < self.imgfilter * self.p * band_strength, t_i, torch.ones_like(t_i))
if debug_percentile is not None:
t_i = torch.full_like(t_i, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.imgfilter_std)) if band_strength > 0 else torch.ones_like(t_i)
t = torch.ones([batch_size, num_bands], device=device) # Temporary gain vector.
t[:, i] = t_i # Replace i'th element.
t = t / (expected_power * t.square()).sum(dim=-1, keepdims=True).sqrt() # Normalize power.
g = g * t # Accumulate into global gain.
# Construct combined amplification filter.
Hz_prime = g @ self.Hz_fbank # [batch, tap]
Hz_prime = Hz_prime.unsqueeze(1).repeat([1, num_channels, 1]) # [batch, channels, tap]
Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1, -1]) # [batch * channels, 1, tap]
# Apply filter.
p = self.Hz_fbank.shape[1] // 2
images = images.reshape([1, batch_size * num_channels, height, width])
images = torch.nn.functional.pad(input=images, pad=[p,p,p,p], mode='reflect')
images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(2), groups=batch_size*num_channels)
images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(3), groups=batch_size*num_channels)
images = images.reshape([batch_size, num_channels, height, width])
# ------------------------
# Image-space corruptions.
# ------------------------
# Apply additive RGB noise with probability (noise * strength).
if self.noise > 0:
sigma = torch.randn([batch_size, 1, 1, 1], device=device).abs() * self.noise_std
sigma = torch.where(torch.rand([batch_size, 1, 1, 1], device=device) < self.noise * self.p, sigma, torch.zeros_like(sigma))
if debug_percentile is not None:
sigma = torch.full_like(sigma, torch.erfinv(debug_percentile) * self.noise_std)
images = images + torch.randn([batch_size, num_channels, height, width], device=device) * sigma
# Apply cutout with probability (cutout * strength).
if self.cutout > 0:
size = torch.full([batch_size, 2, 1, 1, 1], self.cutout_size, device=device)
size = torch.where(torch.rand([batch_size, 1, 1, 1, 1], device=device) < self.cutout * self.p, size, torch.zeros_like(size))
center = torch.rand([batch_size, 2, 1, 1, 1], device=device)
if debug_percentile is not None:
size = torch.full_like(size, self.cutout_size)
center = torch.full_like(center, debug_percentile)
coord_x = torch.arange(width, device=device).reshape([1, 1, 1, -1])
coord_y = torch.arange(height, device=device).reshape([1, 1, -1, 1])
mask_x = (((coord_x + 0.5) / width - center[:, 0]).abs() >= size[:, 0] / 2)
mask_y = (((coord_y + 0.5) / height - center[:, 1]).abs() >= size[:, 1] / 2)
mask = torch.logical_or(mask_x, mask_y).to(torch.float32)
images = images * mask
return images
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Streaming images and labels from datasets created with dataset_tool.py."""
import os
import numpy as np
import zipfile
import PIL.Image
import json
import torch
import dnnlib
try:
import pyspng
except ImportError:
pyspng = None
#----------------------------------------------------------------------------
class Dataset(torch.utils.data.Dataset):
def __init__(self,
name, # Name of the dataset.
raw_shape, # Shape of the raw image data (NCHW).
max_size = None, # Artificially limit the size of the dataset. None = no limit. Applied before xflip.
use_labels = False, # Enable conditioning labels? False = label dimension is zero.
xflip = False, # Artificially double the size of the dataset via x-flips. Applied after max_size.
random_seed = 0, # Random seed to use when applying max_size.
):
self._name = name
self._raw_shape = list(raw_shape)
self._use_labels = use_labels
self._raw_labels = None
self._label_shape = None
# Apply max_size.
self._raw_idx = np.arange(self._raw_shape[0], dtype=np.int64)
if (max_size is not None) and (self._raw_idx.size > max_size):
np.random.RandomState(random_seed).shuffle(self._raw_idx)
self._raw_idx = np.sort(self._raw_idx[:max_size])
# Apply xflip.
self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8)
if xflip:
self._raw_idx = np.tile(self._raw_idx, 2)
self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)])
def _get_raw_labels(self):
if self._raw_labels is None:
self._raw_labels = self._load_raw_labels() if self._use_labels else None
if self._raw_labels is None:
self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32)
assert isinstance(self._raw_labels, np.ndarray)
assert self._raw_labels.shape[0] == self._raw_shape[0]
assert self._raw_labels.dtype in [np.float32, np.int64]
if self._raw_labels.dtype == np.int64:
assert self._raw_labels.ndim == 1
assert np.all(self._raw_labels >= 0)
return self._raw_labels
def close(self): # to be overridden by subclass
pass
def _load_raw_image(self, raw_idx): # to be overridden by subclass
raise NotImplementedError
def _load_raw_labels(self): # to be overridden by subclass
raise NotImplementedError
def __getstate__(self):
return dict(self.__dict__, _raw_labels=None)
def __del__(self):
try:
self.close()
except:
pass
def __len__(self):
return self._raw_idx.size
def __getitem__(self, idx):
image = self._load_raw_image(self._raw_idx[idx])
assert isinstance(image, np.ndarray)
assert list(image.shape) == self.image_shape
assert image.dtype == np.uint8
if self._xflip[idx]:
assert image.ndim == 3 # CHW
image = image[:, :, ::-1]
return image.copy(), self.get_label(idx)
def get_label(self, idx):
label = self._get_raw_labels()[self._raw_idx[idx]]
if label.dtype == np.int64:
onehot = np.zeros(self.label_shape, dtype=np.float32)
onehot[label] = 1
label = onehot
return label.copy()
def get_details(self, idx):
d = dnnlib.EasyDict()
d.raw_idx = int(self._raw_idx[idx])
d.xflip = (int(self._xflip[idx]) != 0)
d.raw_label = self._get_raw_labels()[d.raw_idx].copy()
return d
@property
def name(self):
return self._name
@property
def image_shape(self):
return list(self._raw_shape[1:])
@property
def num_channels(self):
assert len(self.image_shape) == 3 # CHW
return self.image_shape[0]
@property
def resolution(self):
assert len(self.image_shape) == 3 # CHW
assert self.image_shape[1] == self.image_shape[2]
return self.image_shape[1]
@property
def label_shape(self):
if self._label_shape is None:
raw_labels = self._get_raw_labels()
if raw_labels.dtype == np.int64:
self._label_shape = [int(np.max(raw_labels)) + 1]
else:
self._label_shape = raw_labels.shape[1:]
return list(self._label_shape)
@property
def label_dim(self):
assert len(self.label_shape) == 1
return self.label_shape[0]
@property
def has_labels(self):
return any(x != 0 for x in self.label_shape)
@property
def has_onehot_labels(self):
return self._get_raw_labels().dtype == np.int64
#----------------------------------------------------------------------------
class ImageFolderDataset(Dataset):
def __init__(self,
path, # Path to directory or zip.
resolution = None, # Ensure specific resolution, None = highest available.
**super_kwargs, # Additional arguments for the Dataset base class.
):
self._path = path
self._zipfile = None
if os.path.isdir(self._path):
self._type = 'dir'
self._all_fnames = {os.path.relpath(os.path.join(root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files}
elif self._file_ext(self._path) == '.zip':
self._type = 'zip'
self._all_fnames = set(self._get_zipfile().namelist())
else:
raise IOError('Path must point to a directory or zip')
PIL.Image.init()
self._image_fnames = sorted(fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION)
if len(self._image_fnames) == 0:
raise IOError('No image files found in the specified path')
name = os.path.splitext(os.path.basename(self._path))[0]
raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape)
if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution):
raise IOError('Image files do not match the specified resolution')
super().__init__(name=name, raw_shape=raw_shape, **super_kwargs)
@staticmethod
def _file_ext(fname):
return os.path.splitext(fname)[1].lower()
def _get_zipfile(self):
assert self._type == 'zip'
if self._zipfile is None:
self._zipfile = zipfile.ZipFile(self._path)
return self._zipfile
def _open_file(self, fname):
if self._type == 'dir':
return open(os.path.join(self._path, fname), 'rb')
if self._type == 'zip':
return self._get_zipfile().open(fname, 'r')
return None
def close(self):
try:
if self._zipfile is not None:
self._zipfile.close()
finally:
self._zipfile = None
def __getstate__(self):
return dict(super().__getstate__(), _zipfile=None)
def _load_raw_image(self, raw_idx):
fname = self._image_fnames[raw_idx]
with self._open_file(fname) as f:
if pyspng is not None and self._file_ext(fname) == '.png':
image = pyspng.load(f.read())
else:
image = np.array(PIL.Image.open(f))
if image.ndim == 2:
image = image[:, :, np.newaxis] # HW => HWC
image = image.transpose(2, 0, 1) # HWC => CHW
return image
def _load_raw_labels(self):
fname = 'dataset.json'
if fname not in self._all_fnames:
return None
with self._open_file(fname) as f:
labels = json.load(f)['labels']
if labels is None:
return None
labels = dict(labels)
labels = [labels[fname.replace('\\', '/')] for fname in self._image_fnames]
labels = np.array(labels)
labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim])
return labels
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Loss functions."""
import numpy as np
import torch
from torch_utils import training_stats
from torch_utils.ops import conv2d_gradfix
from torch_utils.ops import upfirdn2d
#----------------------------------------------------------------------------
class Loss:
def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): # to be overridden by subclass
raise NotImplementedError()
#----------------------------------------------------------------------------
class StyleGAN2Loss(Loss):
def __init__(self, device, G, D, augment_pipe=None, r1_gamma=10, style_mixing_prob=0, pl_weight=0, pl_batch_shrink=2, pl_decay=0.01, pl_no_weight_grad=False, blur_init_sigma=0, blur_fade_kimg=0):
super().__init__()
self.device = device
self.G = G
self.D = D
self.augment_pipe = augment_pipe
self.r1_gamma = r1_gamma
self.style_mixing_prob = style_mixing_prob
self.pl_weight = pl_weight
self.pl_batch_shrink = pl_batch_shrink
self.pl_decay = pl_decay
self.pl_no_weight_grad = pl_no_weight_grad
self.pl_mean = torch.zeros([], device=device)
self.blur_init_sigma = blur_init_sigma
self.blur_fade_kimg = blur_fade_kimg
def run_G(self, z, c, update_emas=False):
ws = self.G.mapping(z, c, update_emas=update_emas)
if self.style_mixing_prob > 0:
with torch.autograd.profiler.record_function('style_mixing'):
cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1])
cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1]))
ws[:, cutoff:] = self.G.mapping(torch.randn_like(z), c, update_emas=False)[:, cutoff:]
img = self.G.synthesis(ws, update_emas=update_emas)
return img, ws
def run_D(self, img, c, blur_sigma=0, update_emas=False):
blur_size = np.floor(blur_sigma * 3)
if blur_size > 0:
with torch.autograd.profiler.record_function('blur'):
f = torch.arange(-blur_size, blur_size + 1, device=img.device).div(blur_sigma).square().neg().exp2()
img = upfirdn2d.filter2d(img, f / f.sum())
if self.augment_pipe is not None:
img = self.augment_pipe(img)
logits = self.D(img, c, update_emas=update_emas)
return logits
def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg):
assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth']
if self.pl_weight == 0:
phase = {'Greg': 'none', 'Gboth': 'Gmain'}.get(phase, phase)
if self.r1_gamma == 0:
phase = {'Dreg': 'none', 'Dboth': 'Dmain'}.get(phase, phase)
blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * self.blur_init_sigma if self.blur_fade_kimg > 0 else 0
# Gmain: Maximize logits for generated images.
if phase in ['Gmain', 'Gboth']:
with torch.autograd.profiler.record_function('Gmain_forward'):
gen_img, _gen_ws = self.run_G(gen_z, gen_c)
gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma)
training_stats.report('Loss/scores/fake', gen_logits)
training_stats.report('Loss/signs/fake', gen_logits.sign())
loss_Gmain = torch.nn.functional.softplus(-gen_logits) # -log(sigmoid(gen_logits))
training_stats.report('Loss/G/loss', loss_Gmain)
with torch.autograd.profiler.record_function('Gmain_backward'):
loss_Gmain.mean().mul(gain).backward()
# Gpl: Apply path length regularization.
if phase in ['Greg', 'Gboth']:
with torch.autograd.profiler.record_function('Gpl_forward'):
batch_size = gen_z.shape[0] // self.pl_batch_shrink
gen_img, gen_ws = self.run_G(gen_z[:batch_size], gen_c[:batch_size])
pl_noise = torch.randn_like(gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3])
with torch.autograd.profiler.record_function('pl_grads'), conv2d_gradfix.no_weight_gradients(self.pl_no_weight_grad):
pl_grads = torch.autograd.grad(outputs=[(gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0]
pl_lengths = pl_grads.square().sum(2).mean(1).sqrt()
pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay)
self.pl_mean.copy_(pl_mean.detach())
pl_penalty = (pl_lengths - pl_mean).square()
training_stats.report('Loss/pl_penalty', pl_penalty)
loss_Gpl = pl_penalty * self.pl_weight
training_stats.report('Loss/G/reg', loss_Gpl)
with torch.autograd.profiler.record_function('Gpl_backward'):
loss_Gpl.mean().mul(gain).backward()
# Dmain: Minimize logits for generated images.
loss_Dgen = 0
if phase in ['Dmain', 'Dboth']:
with torch.autograd.profiler.record_function('Dgen_forward'):
gen_img, _gen_ws = self.run_G(gen_z, gen_c, update_emas=True)
gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True)
training_stats.report('Loss/scores/fake', gen_logits)
training_stats.report('Loss/signs/fake', gen_logits.sign())
loss_Dgen = torch.nn.functional.softplus(gen_logits) # -log(1 - sigmoid(gen_logits))
with torch.autograd.profiler.record_function('Dgen_backward'):
loss_Dgen.mean().mul(gain).backward()
# Dmain: Maximize logits for real images.
# Dr1: Apply R1 regularization.
if phase in ['Dmain', 'Dreg', 'Dboth']:
name = 'Dreal' if phase == 'Dmain' else 'Dr1' if phase == 'Dreg' else 'Dreal_Dr1'
with torch.autograd.profiler.record_function(name + '_forward'):
real_img_tmp = real_img.detach().requires_grad_(phase in ['Dreg', 'Dboth'])
real_logits = self.run_D(real_img_tmp, real_c, blur_sigma=blur_sigma)
training_stats.report('Loss/scores/real', real_logits)
training_stats.report('Loss/signs/real', real_logits.sign())
loss_Dreal = 0
if phase in ['Dmain', 'Dboth']:
loss_Dreal = torch.nn.functional.softplus(-real_logits) # -log(sigmoid(real_logits))
training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal)
loss_Dr1 = 0
if phase in ['Dreg', 'Dboth']:
with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients():
r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[real_img_tmp], create_graph=True, only_inputs=True)[0]
r1_penalty = r1_grads.square().sum([1,2,3])
loss_Dr1 = r1_penalty * (self.r1_gamma / 2)
training_stats.report('Loss/r1_penalty', r1_penalty)
training_stats.report('Loss/D/reg', loss_Dr1)
with torch.autograd.profiler.record_function(name + '_backward'):
(loss_Dreal + loss_Dr1).mean().mul(gain).backward()
#----------------------------------------------------------------------------

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training/networks_stylegan2.py Normal file
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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Network architectures from the paper
"Analyzing and Improving the Image Quality of StyleGAN".
Matches the original implementation of configs E-F by Karras et al. at
https://github.com/NVlabs/stylegan2/blob/master/training/networks_stylegan2.py"""
import numpy as np
import torch
from torch_utils import misc
from torch_utils import persistence
from torch_utils.ops import conv2d_resample
from torch_utils.ops import upfirdn2d
from torch_utils.ops import bias_act
from torch_utils.ops import fma
#----------------------------------------------------------------------------
@misc.profiled_function
def normalize_2nd_moment(x, dim=1, eps=1e-8):
return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
#----------------------------------------------------------------------------
@misc.profiled_function
def modulated_conv2d(
x, # Input tensor of shape [batch_size, in_channels, in_height, in_width].
weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
styles, # Modulation coefficients of shape [batch_size, in_channels].
noise = None, # Optional noise tensor to add to the output activations.
up = 1, # Integer upsampling factor.
down = 1, # Integer downsampling factor.
padding = 0, # Padding with respect to the upsampled image.
resample_filter = None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
demodulate = True, # Apply weight demodulation?
flip_weight = True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
fused_modconv = True, # Perform modulation, convolution, and demodulation as a single fused operation?
):
batch_size = x.shape[0]
out_channels, in_channels, kh, kw = weight.shape
misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk]
misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
misc.assert_shape(styles, [batch_size, in_channels]) # [NI]
# Pre-normalize inputs to avoid FP16 overflow.
if x.dtype == torch.float16 and demodulate:
weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk
styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I
# Calculate per-sample weights and demodulation coefficients.
w = None
dcoefs = None
if demodulate or fused_modconv:
w = weight.unsqueeze(0) # [NOIkk]
w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk]
if demodulate:
dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO]
if demodulate and fused_modconv:
w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk]
# Execute by scaling the activations before and after the convolution.
if not fused_modconv:
x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight)
if demodulate and noise is not None:
x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype))
elif demodulate:
x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
elif noise is not None:
x = x.add_(noise.to(x.dtype))
return x
# Execute as one fused op using grouped convolution.
with misc.suppress_tracer_warnings(): # this value will be treated as a constant
batch_size = int(batch_size)
misc.assert_shape(x, [batch_size, in_channels, None, None])
x = x.reshape(1, -1, *x.shape[2:])
w = w.reshape(-1, in_channels, kh, kw)
x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight)
x = x.reshape(batch_size, -1, *x.shape[2:])
if noise is not None:
x = x.add_(noise)
return x
#----------------------------------------------------------------------------
@persistence.persistent_class
class FullyConnectedLayer(torch.nn.Module):
def __init__(self,
in_features, # Number of input features.
out_features, # Number of output features.
bias = True, # Apply additive bias before the activation function?
activation = 'linear', # Activation function: 'relu', 'lrelu', etc.
lr_multiplier = 1, # Learning rate multiplier.
bias_init = 0, # Initial value for the additive bias.
):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.activation = activation
self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier)
self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None
self.weight_gain = lr_multiplier / np.sqrt(in_features)
self.bias_gain = lr_multiplier
def forward(self, x):
w = self.weight.to(x.dtype) * self.weight_gain
b = self.bias
if b is not None:
b = b.to(x.dtype)
if self.bias_gain != 1:
b = b * self.bias_gain
if self.activation == 'linear' and b is not None:
x = torch.addmm(b.unsqueeze(0), x, w.t())
else:
x = x.matmul(w.t())
x = bias_act.bias_act(x, b, act=self.activation)
return x
def extra_repr(self):
return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
#----------------------------------------------------------------------------
@persistence.persistent_class
class Conv2dLayer(torch.nn.Module):
def __init__(self,
in_channels, # Number of input channels.
out_channels, # Number of output channels.
kernel_size, # Width and height of the convolution kernel.
bias = True, # Apply additive bias before the activation function?
activation = 'linear', # Activation function: 'relu', 'lrelu', etc.
up = 1, # Integer upsampling factor.
down = 1, # Integer downsampling factor.
resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations.
conv_clamp = None, # Clamp the output to +-X, None = disable clamping.
channels_last = False, # Expect the input to have memory_format=channels_last?
trainable = True, # Update the weights of this layer during training?
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.activation = activation
self.up = up
self.down = down
self.conv_clamp = conv_clamp
self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
self.padding = kernel_size // 2
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
self.act_gain = bias_act.activation_funcs[activation].def_gain
memory_format = torch.channels_last if channels_last else torch.contiguous_format
weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)
bias = torch.zeros([out_channels]) if bias else None
if trainable:
self.weight = torch.nn.Parameter(weight)
self.bias = torch.nn.Parameter(bias) if bias is not None else None
else:
self.register_buffer('weight', weight)
if bias is not None:
self.register_buffer('bias', bias)
else:
self.bias = None
def forward(self, x, gain=1):
w = self.weight * self.weight_gain
b = self.bias.to(x.dtype) if self.bias is not None else None
flip_weight = (self.up == 1) # slightly faster
x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight)
act_gain = self.act_gain * gain
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp)
return x
def extra_repr(self):
return ' '.join([
f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},',
f'up={self.up}, down={self.down}'])
#----------------------------------------------------------------------------
@persistence.persistent_class
class MappingNetwork(torch.nn.Module):
def __init__(self,
z_dim, # Input latent (Z) dimensionality, 0 = no latent.
c_dim, # Conditioning label (C) dimensionality, 0 = no label.
w_dim, # Intermediate latent (W) dimensionality.
num_ws, # Number of intermediate latents to output, None = do not broadcast.
num_layers = 8, # Number of mapping layers.
embed_features = None, # Label embedding dimensionality, None = same as w_dim.
layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim.
activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc.
lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers.
w_avg_beta = 0.998, # Decay for tracking the moving average of W during training, None = do not track.
):
super().__init__()
self.z_dim = z_dim
self.c_dim = c_dim
self.w_dim = w_dim
self.num_ws = num_ws
self.num_layers = num_layers
self.w_avg_beta = w_avg_beta
if embed_features is None:
embed_features = w_dim
if c_dim == 0:
embed_features = 0
if layer_features is None:
layer_features = w_dim
features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim]
if c_dim > 0:
self.embed = FullyConnectedLayer(c_dim, embed_features)
for idx in range(num_layers):
in_features = features_list[idx]
out_features = features_list[idx + 1]
layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
setattr(self, f'fc{idx}', layer)
if num_ws is not None and w_avg_beta is not None:
self.register_buffer('w_avg', torch.zeros([w_dim]))
def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
# Embed, normalize, and concat inputs.
x = None
with torch.autograd.profiler.record_function('input'):
if self.z_dim > 0:
misc.assert_shape(z, [None, self.z_dim])
x = normalize_2nd_moment(z.to(torch.float32))
if self.c_dim > 0:
misc.assert_shape(c, [None, self.c_dim])
y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
x = torch.cat([x, y], dim=1) if x is not None else y
# Main layers.
for idx in range(self.num_layers):
layer = getattr(self, f'fc{idx}')
x = layer(x)
# Update moving average of W.
if update_emas and self.w_avg_beta is not None:
with torch.autograd.profiler.record_function('update_w_avg'):
self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
# Broadcast.
if self.num_ws is not None:
with torch.autograd.profiler.record_function('broadcast'):
x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
# Apply truncation.
if truncation_psi != 1:
with torch.autograd.profiler.record_function('truncate'):
assert self.w_avg_beta is not None
if self.num_ws is None or truncation_cutoff is None:
x = self.w_avg.lerp(x, truncation_psi)
else:
x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)
return x
def extra_repr(self):
return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
#----------------------------------------------------------------------------
@persistence.persistent_class
class SynthesisLayer(torch.nn.Module):
def __init__(self,
in_channels, # Number of input channels.
out_channels, # Number of output channels.
w_dim, # Intermediate latent (W) dimensionality.
resolution, # Resolution of this layer.
kernel_size = 3, # Convolution kernel size.
up = 1, # Integer upsampling factor.
use_noise = True, # Enable noise input?
activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc.
resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations.
conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping.
channels_last = False, # Use channels_last format for the weights?
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.w_dim = w_dim
self.resolution = resolution
self.up = up
self.use_noise = use_noise
self.activation = activation
self.conv_clamp = conv_clamp
self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
self.padding = kernel_size // 2
self.act_gain = bias_act.activation_funcs[activation].def_gain
self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
memory_format = torch.channels_last if channels_last else torch.contiguous_format
self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
if use_noise:
self.register_buffer('noise_const', torch.randn([resolution, resolution]))
self.noise_strength = torch.nn.Parameter(torch.zeros([]))
self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
assert noise_mode in ['random', 'const', 'none']
in_resolution = self.resolution // self.up
misc.assert_shape(x, [None, self.in_channels, in_resolution, in_resolution])
styles = self.affine(w)
noise = None
if self.use_noise and noise_mode == 'random':
noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength
if self.use_noise and noise_mode == 'const':
noise = self.noise_const * self.noise_strength
flip_weight = (self.up == 1) # slightly faster
x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)
act_gain = self.act_gain * gain
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
return x
def extra_repr(self):
return ' '.join([
f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},',
f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}'])
#----------------------------------------------------------------------------
@persistence.persistent_class
class ToRGBLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.w_dim = w_dim
self.conv_clamp = conv_clamp
self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
memory_format = torch.channels_last if channels_last else torch.contiguous_format
self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
def forward(self, x, w, fused_modconv=True):
styles = self.affine(w) * self.weight_gain
x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv)
x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
return x
def extra_repr(self):
return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}'
#----------------------------------------------------------------------------
@persistence.persistent_class
class SynthesisBlock(torch.nn.Module):
def __init__(self,
in_channels, # Number of input channels, 0 = first block.
out_channels, # Number of output channels.
w_dim, # Intermediate latent (W) dimensionality.
resolution, # Resolution of this block.
img_channels, # Number of output color channels.
is_last, # Is this the last block?
architecture = 'skip', # Architecture: 'orig', 'skip', 'resnet'.
resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations.
conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping.
use_fp16 = False, # Use FP16 for this block?
fp16_channels_last = False, # Use channels-last memory format with FP16?
fused_modconv_default = True, # Default value of fused_modconv. 'inference_only' = True for inference, False for training.
**layer_kwargs, # Arguments for SynthesisLayer.
):
assert architecture in ['orig', 'skip', 'resnet']
super().__init__()
self.in_channels = in_channels
self.w_dim = w_dim
self.resolution = resolution
self.img_channels = img_channels
self.is_last = is_last
self.architecture = architecture
self.use_fp16 = use_fp16
self.channels_last = (use_fp16 and fp16_channels_last)
self.fused_modconv_default = fused_modconv_default
self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
self.num_conv = 0
self.num_torgb = 0
if in_channels == 0:
self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution]))
if in_channels != 0:
self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2,
resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
self.num_conv += 1
self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution,
conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
self.num_conv += 1
if is_last or architecture == 'skip':
self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim,
conv_clamp=conv_clamp, channels_last=self.channels_last)
self.num_torgb += 1
if in_channels != 0 and architecture == 'resnet':
self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
resample_filter=resample_filter, channels_last=self.channels_last)
def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs):
_ = update_emas # unused
misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim])
w_iter = iter(ws.unbind(dim=1))
if ws.device.type != 'cuda':
force_fp32 = True
dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
if fused_modconv is None:
fused_modconv = self.fused_modconv_default
if fused_modconv == 'inference_only':
fused_modconv = (not self.training)
# Input.
if self.in_channels == 0:
x = self.const.to(dtype=dtype, memory_format=memory_format)
x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
else:
misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2])
x = x.to(dtype=dtype, memory_format=memory_format)
# Main layers.
if self.in_channels == 0:
x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
elif self.architecture == 'resnet':
y = self.skip(x, gain=np.sqrt(0.5))
x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs)
x = y.add_(x)
else:
x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
# ToRGB.
if img is not None:
misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
img = upfirdn2d.upsample2d(img, self.resample_filter)
if self.is_last or self.architecture == 'skip':
y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format)
img = img.add_(y) if img is not None else y
assert x.dtype == dtype
assert img is None or img.dtype == torch.float32
return x, img
def extra_repr(self):
return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
#----------------------------------------------------------------------------
@persistence.persistent_class
class SynthesisNetwork(torch.nn.Module):
def __init__(self,
w_dim, # Intermediate latent (W) dimensionality.
img_resolution, # Output image resolution.
img_channels, # Number of color channels.
channel_base = 32768, # Overall multiplier for the number of channels.
channel_max = 512, # Maximum number of channels in any layer.
num_fp16_res = 4, # Use FP16 for the N highest resolutions.
**block_kwargs, # Arguments for SynthesisBlock.
):
assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0
super().__init__()
self.w_dim = w_dim
self.img_resolution = img_resolution
self.img_resolution_log2 = int(np.log2(img_resolution))
self.img_channels = img_channels
self.num_fp16_res = num_fp16_res
self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)]
channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions}
fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
self.num_ws = 0
for res in self.block_resolutions:
in_channels = channels_dict[res // 2] if res > 4 else 0
out_channels = channels_dict[res]
use_fp16 = (res >= fp16_resolution)
is_last = (res == self.img_resolution)
block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res,
img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs)
self.num_ws += block.num_conv
if is_last:
self.num_ws += block.num_torgb
setattr(self, f'b{res}', block)
def forward(self, ws, **block_kwargs):
block_ws = []
with torch.autograd.profiler.record_function('split_ws'):
misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
ws = ws.to(torch.float32)
w_idx = 0
for res in self.block_resolutions:
block = getattr(self, f'b{res}')
block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
w_idx += block.num_conv
x = img = None
for res, cur_ws in zip(self.block_resolutions, block_ws):
block = getattr(self, f'b{res}')
x, img = block(x, img, cur_ws, **block_kwargs)
return img
def extra_repr(self):
return ' '.join([
f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
f'num_fp16_res={self.num_fp16_res:d}'])
#----------------------------------------------------------------------------
@persistence.persistent_class
class Generator(torch.nn.Module):
def __init__(self,
z_dim, # Input latent (Z) dimensionality.
c_dim, # Conditioning label (C) dimensionality.
w_dim, # Intermediate latent (W) dimensionality.
img_resolution, # Output resolution.
img_channels, # Number of output color channels.
mapping_kwargs = {}, # Arguments for MappingNetwork.
**synthesis_kwargs, # Arguments for SynthesisNetwork.
):
super().__init__()
self.z_dim = z_dim
self.c_dim = c_dim
self.w_dim = w_dim
self.img_resolution = img_resolution
self.img_channels = img_channels
self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
self.num_ws = self.synthesis.num_ws
self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
return img
#----------------------------------------------------------------------------
@persistence.persistent_class
class DiscriminatorBlock(torch.nn.Module):
def __init__(self,
in_channels, # Number of input channels, 0 = first block.
tmp_channels, # Number of intermediate channels.
out_channels, # Number of output channels.
resolution, # Resolution of this block.
img_channels, # Number of input color channels.
first_layer_idx, # Index of the first layer.
architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc.
resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations.
conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping.
use_fp16 = False, # Use FP16 for this block?
fp16_channels_last = False, # Use channels-last memory format with FP16?
freeze_layers = 0, # Freeze-D: Number of layers to freeze.
):
assert in_channels in [0, tmp_channels]
assert architecture in ['orig', 'skip', 'resnet']
super().__init__()
self.in_channels = in_channels
self.resolution = resolution
self.img_channels = img_channels
self.first_layer_idx = first_layer_idx
self.architecture = architecture
self.use_fp16 = use_fp16
self.channels_last = (use_fp16 and fp16_channels_last)
self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
self.num_layers = 0
def trainable_gen():
while True:
layer_idx = self.first_layer_idx + self.num_layers
trainable = (layer_idx >= freeze_layers)
self.num_layers += 1
yield trainable
trainable_iter = trainable_gen()
if in_channels == 0 or architecture == 'skip':
self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation,
trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation,
trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2,
trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last)
if architecture == 'resnet':
self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2,
trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last)
def forward(self, x, img, force_fp32=False):
if (x if x is not None else img).device.type != 'cuda':
force_fp32 = True
dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
# Input.
if x is not None:
misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution])
x = x.to(dtype=dtype, memory_format=memory_format)
# FromRGB.
if self.in_channels == 0 or self.architecture == 'skip':
misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution])
img = img.to(dtype=dtype, memory_format=memory_format)
y = self.fromrgb(img)
x = x + y if x is not None else y
img = upfirdn2d.downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None
# Main layers.
if self.architecture == 'resnet':
y = self.skip(x, gain=np.sqrt(0.5))
x = self.conv0(x)
x = self.conv1(x, gain=np.sqrt(0.5))
x = y.add_(x)
else:
x = self.conv0(x)
x = self.conv1(x)
assert x.dtype == dtype
return x, img
def extra_repr(self):
return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
#----------------------------------------------------------------------------
@persistence.persistent_class
class MinibatchStdLayer(torch.nn.Module):
def __init__(self, group_size, num_channels=1):
super().__init__()
self.group_size = group_size
self.num_channels = num_channels
def forward(self, x):
N, C, H, W = x.shape
with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants
G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) if self.group_size is not None else N
F = self.num_channels
c = C // F
y = x.reshape(G, -1, F, c, H, W) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group.
y = y.square().mean(dim=0) # [nFcHW] Calc variance over group.
y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group.
y = y.mean(dim=[2,3,4]) # [nF] Take average over channels and pixels.
y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions.
y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels.
x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels.
return x
def extra_repr(self):
return f'group_size={self.group_size}, num_channels={self.num_channels:d}'
#----------------------------------------------------------------------------
@persistence.persistent_class
class DiscriminatorEpilogue(torch.nn.Module):
def __init__(self,
in_channels, # Number of input channels.
cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label.
resolution, # Resolution of this block.
img_channels, # Number of input color channels.
architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
mbstd_group_size = 4, # Group size for the minibatch standard deviation layer, None = entire minibatch.
mbstd_num_channels = 1, # Number of features for the minibatch standard deviation layer, 0 = disable.
activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc.
conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping.
):
assert architecture in ['orig', 'skip', 'resnet']
super().__init__()
self.in_channels = in_channels
self.cmap_dim = cmap_dim
self.resolution = resolution
self.img_channels = img_channels
self.architecture = architecture
if architecture == 'skip':
self.fromrgb = Conv2dLayer(img_channels, in_channels, kernel_size=1, activation=activation)
self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None
self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp)
self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation)
self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim)
def forward(self, x, img, cmap, force_fp32=False):
misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) # [NCHW]
_ = force_fp32 # unused
dtype = torch.float32
memory_format = torch.contiguous_format
# FromRGB.
x = x.to(dtype=dtype, memory_format=memory_format)
if self.architecture == 'skip':
misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution])
img = img.to(dtype=dtype, memory_format=memory_format)
x = x + self.fromrgb(img)
# Main layers.
if self.mbstd is not None:
x = self.mbstd(x)
x = self.conv(x)
x = self.fc(x.flatten(1))
x = self.out(x)
# Conditioning.
if self.cmap_dim > 0:
misc.assert_shape(cmap, [None, self.cmap_dim])
x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
assert x.dtype == dtype
return x
def extra_repr(self):
return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
#----------------------------------------------------------------------------
@persistence.persistent_class
class Discriminator(torch.nn.Module):
def __init__(self,
c_dim, # Conditioning label (C) dimensionality.
img_resolution, # Input resolution.
img_channels, # Number of input color channels.
architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
channel_base = 32768, # Overall multiplier for the number of channels.
channel_max = 512, # Maximum number of channels in any layer.
num_fp16_res = 4, # Use FP16 for the N highest resolutions.
conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping.
cmap_dim = None, # Dimensionality of mapped conditioning label, None = default.
block_kwargs = {}, # Arguments for DiscriminatorBlock.
mapping_kwargs = {}, # Arguments for MappingNetwork.
epilogue_kwargs = {}, # Arguments for DiscriminatorEpilogue.
):
super().__init__()
self.c_dim = c_dim
self.img_resolution = img_resolution
self.img_resolution_log2 = int(np.log2(img_resolution))
self.img_channels = img_channels
self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)]
channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]}
fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
if cmap_dim is None:
cmap_dim = channels_dict[4]
if c_dim == 0:
cmap_dim = 0
common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp)
cur_layer_idx = 0
for res in self.block_resolutions:
in_channels = channels_dict[res] if res < img_resolution else 0
tmp_channels = channels_dict[res]
out_channels = channels_dict[res // 2]
use_fp16 = (res >= fp16_resolution)
block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs)
setattr(self, f'b{res}', block)
cur_layer_idx += block.num_layers
if c_dim > 0:
self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs)
def forward(self, img, c, update_emas=False, **block_kwargs):
_ = update_emas # unused
x = None
for res in self.block_resolutions:
block = getattr(self, f'b{res}')
x, img = block(x, img, **block_kwargs)
cmap = None
if self.c_dim > 0:
cmap = self.mapping(None, c)
x = self.b4(x, img, cmap)
return x
def extra_repr(self):
return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Generator architecture from the paper
"Alias-Free Generative Adversarial Networks"."""
import numpy as np
import scipy.signal
import scipy.optimize
import torch
from torch_utils import misc
from torch_utils import persistence
from torch_utils.ops import conv2d_gradfix
from torch_utils.ops import filtered_lrelu
from torch_utils.ops import bias_act
#----------------------------------------------------------------------------
@misc.profiled_function
def modulated_conv2d(
x, # Input tensor: [batch_size, in_channels, in_height, in_width]
w, # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width]
s, # Style tensor: [batch_size, in_channels]
demodulate = True, # Apply weight demodulation?
padding = 0, # Padding: int or [padH, padW]
input_gain = None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels]
):
with misc.suppress_tracer_warnings(): # this value will be treated as a constant
batch_size = int(x.shape[0])
out_channels, in_channels, kh, kw = w.shape
misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk]
misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
misc.assert_shape(s, [batch_size, in_channels]) # [NI]
# Pre-normalize inputs.
if demodulate:
w = w * w.square().mean([1,2,3], keepdim=True).rsqrt()
s = s * s.square().mean().rsqrt()
# Modulate weights.
w = w.unsqueeze(0) # [NOIkk]
w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
# Demodulate weights.
if demodulate:
dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO]
w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk]
# Apply input scaling.
if input_gain is not None:
input_gain = input_gain.expand(batch_size, in_channels) # [NI]
w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
# Execute as one fused op using grouped convolution.
x = x.reshape(1, -1, *x.shape[2:])
w = w.reshape(-1, in_channels, kh, kw)
x = conv2d_gradfix.conv2d(input=x, weight=w.to(x.dtype), padding=padding, groups=batch_size)
x = x.reshape(batch_size, -1, *x.shape[2:])
return x
#----------------------------------------------------------------------------
@persistence.persistent_class
class FullyConnectedLayer(torch.nn.Module):
def __init__(self,
in_features, # Number of input features.
out_features, # Number of output features.
activation = 'linear', # Activation function: 'relu', 'lrelu', etc.
bias = True, # Apply additive bias before the activation function?
lr_multiplier = 1, # Learning rate multiplier.
weight_init = 1, # Initial standard deviation of the weight tensor.
bias_init = 0, # Initial value of the additive bias.
):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.activation = activation
self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) * (weight_init / lr_multiplier))
bias_init = np.broadcast_to(np.asarray(bias_init, dtype=np.float32), [out_features])
self.bias = torch.nn.Parameter(torch.from_numpy(bias_init / lr_multiplier)) if bias else None
self.weight_gain = lr_multiplier / np.sqrt(in_features)
self.bias_gain = lr_multiplier
def forward(self, x):
w = self.weight.to(x.dtype) * self.weight_gain
b = self.bias
if b is not None:
b = b.to(x.dtype)
if self.bias_gain != 1:
b = b * self.bias_gain
if self.activation == 'linear' and b is not None:
x = torch.addmm(b.unsqueeze(0), x, w.t())
else:
x = x.matmul(w.t())
x = bias_act.bias_act(x, b, act=self.activation)
return x
def extra_repr(self):
return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
#----------------------------------------------------------------------------
@persistence.persistent_class
class MappingNetwork(torch.nn.Module):
def __init__(self,
z_dim, # Input latent (Z) dimensionality.
c_dim, # Conditioning label (C) dimensionality, 0 = no labels.
w_dim, # Intermediate latent (W) dimensionality.
num_ws, # Number of intermediate latents to output.
num_layers = 2, # Number of mapping layers.
lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers.
w_avg_beta = 0.998, # Decay for tracking the moving average of W during training.
):
super().__init__()
self.z_dim = z_dim
self.c_dim = c_dim
self.w_dim = w_dim
self.num_ws = num_ws
self.num_layers = num_layers
self.w_avg_beta = w_avg_beta
# Construct layers.
self.embed = FullyConnectedLayer(self.c_dim, self.w_dim) if self.c_dim > 0 else None
features = [self.z_dim + (self.w_dim if self.c_dim > 0 else 0)] + [self.w_dim] * self.num_layers
for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]):
layer = FullyConnectedLayer(in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier)
setattr(self, f'fc{idx}', layer)
self.register_buffer('w_avg', torch.zeros([w_dim]))
def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
misc.assert_shape(z, [None, self.z_dim])
if truncation_cutoff is None:
truncation_cutoff = self.num_ws
# Embed, normalize, and concatenate inputs.
x = z.to(torch.float32)
x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt()
if self.c_dim > 0:
misc.assert_shape(c, [None, self.c_dim])
y = self.embed(c.to(torch.float32))
y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt()
x = torch.cat([x, y], dim=1) if x is not None else y
# Execute layers.
for idx in range(self.num_layers):
x = getattr(self, f'fc{idx}')(x)
# Update moving average of W.
if update_emas:
self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
# Broadcast and apply truncation.
x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
if truncation_psi != 1:
x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)
return x
def extra_repr(self):
return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
#----------------------------------------------------------------------------
@persistence.persistent_class
class SynthesisInput(torch.nn.Module):
def __init__(self,
w_dim, # Intermediate latent (W) dimensionality.
channels, # Number of output channels.
size, # Output spatial size: int or [width, height].
sampling_rate, # Output sampling rate.
bandwidth, # Output bandwidth.
):
super().__init__()
self.w_dim = w_dim
self.channels = channels
self.size = np.broadcast_to(np.asarray(size), [2])
self.sampling_rate = sampling_rate
self.bandwidth = bandwidth
# Draw random frequencies from uniform 2D disc.
freqs = torch.randn([self.channels, 2])
radii = freqs.square().sum(dim=1, keepdim=True).sqrt()
freqs /= radii * radii.square().exp().pow(0.25)
freqs *= bandwidth
phases = torch.rand([self.channels]) - 0.5
# Setup parameters and buffers.
self.weight = torch.nn.Parameter(torch.randn([self.channels, self.channels]))
self.affine = FullyConnectedLayer(w_dim, 4, weight_init=0, bias_init=[1,0,0,0])
self.register_buffer('transform', torch.eye(3, 3)) # User-specified inverse transform wrt. resulting image.
self.register_buffer('freqs', freqs)
self.register_buffer('phases', phases)
def forward(self, w):
# Introduce batch dimension.
transforms = self.transform.unsqueeze(0) # [batch, row, col]
freqs = self.freqs.unsqueeze(0) # [batch, channel, xy]
phases = self.phases.unsqueeze(0) # [batch, channel]
# Apply learned transformation.
t = self.affine(w) # t = (r_c, r_s, t_x, t_y)
t = t / t[:, :2].norm(dim=1, keepdim=True) # t' = (r'_c, r'_s, t'_x, t'_y)
m_r = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse rotation wrt. resulting image.
m_r[:, 0, 0] = t[:, 0] # r'_c
m_r[:, 0, 1] = -t[:, 1] # r'_s
m_r[:, 1, 0] = t[:, 1] # r'_s
m_r[:, 1, 1] = t[:, 0] # r'_c
m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse translation wrt. resulting image.
m_t[:, 0, 2] = -t[:, 2] # t'_x
m_t[:, 1, 2] = -t[:, 3] # t'_y
transforms = m_r @ m_t @ transforms # First rotate resulting image, then translate, and finally apply user-specified transform.
# Transform frequencies.
phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2)
freqs = freqs @ transforms[:, :2, :2]
# Dampen out-of-band frequencies that may occur due to the user-specified transform.
amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1)
# Construct sampling grid.
theta = torch.eye(2, 3, device=w.device)
theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate
theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate
grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]], align_corners=False)
# Compute Fourier features.
x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel]
x = x + phases.unsqueeze(1).unsqueeze(2)
x = torch.sin(x * (np.pi * 2))
x = x * amplitudes.unsqueeze(1).unsqueeze(2)
# Apply trainable mapping.
weight = self.weight / np.sqrt(self.channels)
x = x @ weight.t()
# Ensure correct shape.
x = x.permute(0, 3, 1, 2) # [batch, channel, height, width]
misc.assert_shape(x, [w.shape[0], self.channels, int(self.size[1]), int(self.size[0])])
return x
def extra_repr(self):
return '\n'.join([
f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},',
f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}'])
#----------------------------------------------------------------------------
@persistence.persistent_class
class SynthesisLayer(torch.nn.Module):
def __init__(self,
w_dim, # Intermediate latent (W) dimensionality.
is_torgb, # Is this the final ToRGB layer?
is_critically_sampled, # Does this layer use critical sampling?
use_fp16, # Does this layer use FP16?
# Input & output specifications.
in_channels, # Number of input channels.
out_channels, # Number of output channels.
in_size, # Input spatial size: int or [width, height].
out_size, # Output spatial size: int or [width, height].
in_sampling_rate, # Input sampling rate (s).
out_sampling_rate, # Output sampling rate (s).
in_cutoff, # Input cutoff frequency (f_c).
out_cutoff, # Output cutoff frequency (f_c).
in_half_width, # Input transition band half-width (f_h).
out_half_width, # Output Transition band half-width (f_h).
# Hyperparameters.
conv_kernel = 3, # Convolution kernel size. Ignored for final the ToRGB layer.
filter_size = 6, # Low-pass filter size relative to the lower resolution when up/downsampling.
lrelu_upsampling = 2, # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer.
use_radial_filters = False, # Use radially symmetric downsampling filter? Ignored for critically sampled layers.
conv_clamp = 256, # Clamp the output to [-X, +X], None = disable clamping.
magnitude_ema_beta = 0.999, # Decay rate for the moving average of input magnitudes.
):
super().__init__()
self.w_dim = w_dim
self.is_torgb = is_torgb
self.is_critically_sampled = is_critically_sampled
self.use_fp16 = use_fp16
self.in_channels = in_channels
self.out_channels = out_channels
self.in_size = np.broadcast_to(np.asarray(in_size), [2])
self.out_size = np.broadcast_to(np.asarray(out_size), [2])
self.in_sampling_rate = in_sampling_rate
self.out_sampling_rate = out_sampling_rate
self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling)
self.in_cutoff = in_cutoff
self.out_cutoff = out_cutoff
self.in_half_width = in_half_width
self.out_half_width = out_half_width
self.conv_kernel = 1 if is_torgb else conv_kernel
self.conv_clamp = conv_clamp
self.magnitude_ema_beta = magnitude_ema_beta
# Setup parameters and buffers.
self.affine = FullyConnectedLayer(self.w_dim, self.in_channels, bias_init=1)
self.weight = torch.nn.Parameter(torch.randn([self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel]))
self.bias = torch.nn.Parameter(torch.zeros([self.out_channels]))
self.register_buffer('magnitude_ema', torch.ones([]))
# Design upsampling filter.
self.up_factor = int(np.rint(self.tmp_sampling_rate / self.in_sampling_rate))
assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate
self.up_taps = filter_size * self.up_factor if self.up_factor > 1 and not self.is_torgb else 1
self.register_buffer('up_filter', self.design_lowpass_filter(
numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate))
# Design downsampling filter.
self.down_factor = int(np.rint(self.tmp_sampling_rate / self.out_sampling_rate))
assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate
self.down_taps = filter_size * self.down_factor if self.down_factor > 1 and not self.is_torgb else 1
self.down_radial = use_radial_filters and not self.is_critically_sampled
self.register_buffer('down_filter', self.design_lowpass_filter(
numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial))
# Compute padding.
pad_total = (self.out_size - 1) * self.down_factor + 1 # Desired output size before downsampling.
pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor # Input size after upsampling.
pad_total += self.up_taps + self.down_taps - 2 # Size reduction caused by the filters.
pad_lo = (pad_total + self.up_factor) // 2 # Shift sample locations according to the symmetric interpretation (Appendix C.3).
pad_hi = pad_total - pad_lo
self.padding = [int(pad_lo[0]), int(pad_hi[0]), int(pad_lo[1]), int(pad_hi[1])]
def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False):
assert noise_mode in ['random', 'const', 'none'] # unused
misc.assert_shape(x, [None, self.in_channels, int(self.in_size[1]), int(self.in_size[0])])
misc.assert_shape(w, [x.shape[0], self.w_dim])
# Track input magnitude.
if update_emas:
with torch.autograd.profiler.record_function('update_magnitude_ema'):
magnitude_cur = x.detach().to(torch.float32).square().mean()
self.magnitude_ema.copy_(magnitude_cur.lerp(self.magnitude_ema, self.magnitude_ema_beta))
input_gain = self.magnitude_ema.rsqrt()
# Execute affine layer.
styles = self.affine(w)
if self.is_torgb:
weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel ** 2))
styles = styles * weight_gain
# Execute modulated conv2d.
dtype = torch.float16 if (self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32
x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles,
padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain)
# Execute bias, filtered leaky ReLU, and clamping.
gain = 1 if self.is_torgb else np.sqrt(2)
slope = 1 if self.is_torgb else 0.2
x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype),
up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp)
# Ensure correct shape and dtype.
misc.assert_shape(x, [None, self.out_channels, int(self.out_size[1]), int(self.out_size[0])])
assert x.dtype == dtype
return x
@staticmethod
def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False):
assert numtaps >= 1
# Identity filter.
if numtaps == 1:
return None
# Separable Kaiser low-pass filter.
if not radial:
f = scipy.signal.firwin(numtaps=numtaps, cutoff=cutoff, width=width, fs=fs)
return torch.as_tensor(f, dtype=torch.float32)
# Radially symmetric jinc-based filter.
x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs
r = np.hypot(*np.meshgrid(x, x))
f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r)
beta = scipy.signal.kaiser_beta(scipy.signal.kaiser_atten(numtaps, width / (fs / 2)))
w = np.kaiser(numtaps, beta)
f *= np.outer(w, w)
f /= np.sum(f)
return torch.as_tensor(f, dtype=torch.float32)
def extra_repr(self):
return '\n'.join([
f'w_dim={self.w_dim:d}, is_torgb={self.is_torgb},',
f'is_critically_sampled={self.is_critically_sampled}, use_fp16={self.use_fp16},',
f'in_sampling_rate={self.in_sampling_rate:g}, out_sampling_rate={self.out_sampling_rate:g},',
f'in_cutoff={self.in_cutoff:g}, out_cutoff={self.out_cutoff:g},',
f'in_half_width={self.in_half_width:g}, out_half_width={self.out_half_width:g},',
f'in_size={list(self.in_size)}, out_size={list(self.out_size)},',
f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}'])
#----------------------------------------------------------------------------
@persistence.persistent_class
class SynthesisNetwork(torch.nn.Module):
def __init__(self,
w_dim, # Intermediate latent (W) dimensionality.
img_resolution, # Output image resolution.
img_channels, # Number of color channels.
channel_base = 32768, # Overall multiplier for the number of channels.
channel_max = 512, # Maximum number of channels in any layer.
num_layers = 14, # Total number of layers, excluding Fourier features and ToRGB.
num_critical = 2, # Number of critically sampled layers at the end.
first_cutoff = 2, # Cutoff frequency of the first layer (f_{c,0}).
first_stopband = 2**2.1, # Minimum stopband of the first layer (f_{t,0}).
last_stopband_rel = 2**0.3, # Minimum stopband of the last layer, expressed relative to the cutoff.
margin_size = 10, # Number of additional pixels outside the image.
output_scale = 0.25, # Scale factor for the output image.
num_fp16_res = 4, # Use FP16 for the N highest resolutions.
**layer_kwargs, # Arguments for SynthesisLayer.
):
super().__init__()
self.w_dim = w_dim
self.num_ws = num_layers + 2
self.img_resolution = img_resolution
self.img_channels = img_channels
self.num_layers = num_layers
self.num_critical = num_critical
self.margin_size = margin_size
self.output_scale = output_scale
self.num_fp16_res = num_fp16_res
# Geometric progression of layer cutoffs and min. stopbands.
last_cutoff = self.img_resolution / 2 # f_{c,N}
last_stopband = last_cutoff * last_stopband_rel # f_{t,N}
exponents = np.minimum(np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1)
cutoffs = first_cutoff * (last_cutoff / first_cutoff) ** exponents # f_c[i]
stopbands = first_stopband * (last_stopband / first_stopband) ** exponents # f_t[i]
# Compute remaining layer parameters.
sampling_rates = np.exp2(np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i]
half_widths = np.maximum(stopbands, sampling_rates / 2) - cutoffs # f_h[i]
sizes = sampling_rates + self.margin_size * 2
sizes[-2:] = self.img_resolution
channels = np.rint(np.minimum((channel_base / 2) / cutoffs, channel_max))
channels[-1] = self.img_channels
# Construct layers.
self.input = SynthesisInput(
w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]),
sampling_rate=sampling_rates[0], bandwidth=cutoffs[0])
self.layer_names = []
for idx in range(self.num_layers + 1):
prev = max(idx - 1, 0)
is_torgb = (idx == self.num_layers)
is_critically_sampled = (idx >= self.num_layers - self.num_critical)
use_fp16 = (sampling_rates[idx] * (2 ** self.num_fp16_res) > self.img_resolution)
layer = SynthesisLayer(
w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16,
in_channels=int(channels[prev]), out_channels= int(channels[idx]),
in_size=int(sizes[prev]), out_size=int(sizes[idx]),
in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]),
in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx],
in_half_width=half_widths[prev], out_half_width=half_widths[idx],
**layer_kwargs)
name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}'
setattr(self, name, layer)
self.layer_names.append(name)
def forward(self, ws, **layer_kwargs):
misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
ws = ws.to(torch.float32).unbind(dim=1)
# Execute layers.
x = self.input(ws[0])
for name, w in zip(self.layer_names, ws[1:]):
x = getattr(self, name)(x, w, **layer_kwargs)
if self.output_scale != 1:
x = x * self.output_scale
# Ensure correct shape and dtype.
misc.assert_shape(x, [None, self.img_channels, self.img_resolution, self.img_resolution])
x = x.to(torch.float32)
return x
def extra_repr(self):
return '\n'.join([
f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},',
f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}'])
#----------------------------------------------------------------------------
@persistence.persistent_class
class Generator(torch.nn.Module):
def __init__(self,
z_dim, # Input latent (Z) dimensionality.
c_dim, # Conditioning label (C) dimensionality.
w_dim, # Intermediate latent (W) dimensionality.
img_resolution, # Output resolution.
img_channels, # Number of output color channels.
mapping_kwargs = {}, # Arguments for MappingNetwork.
**synthesis_kwargs, # Arguments for SynthesisNetwork.
):
super().__init__()
self.z_dim = z_dim
self.c_dim = c_dim
self.w_dim = w_dim
self.img_resolution = img_resolution
self.img_channels = img_channels
self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
self.num_ws = self.synthesis.num_ws
self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
return img
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Main training loop."""
import os
import time
import copy
import json
import pickle
import psutil
import PIL.Image
import numpy as np
import torch
import dnnlib
from torch_utils import misc
from torch_utils import training_stats
from torch_utils.ops import conv2d_gradfix
from torch_utils.ops import grid_sample_gradfix
import legacy
from metrics import metric_main
#----------------------------------------------------------------------------
def setup_snapshot_image_grid(training_set, random_seed=0):
rnd = np.random.RandomState(random_seed)
gw = np.clip(7680 // training_set.image_shape[2], 7, 32)
gh = np.clip(4320 // training_set.image_shape[1], 4, 32)
# No labels => show random subset of training samples.
if not training_set.has_labels:
all_indices = list(range(len(training_set)))
rnd.shuffle(all_indices)
grid_indices = [all_indices[i % len(all_indices)] for i in range(gw * gh)]
else:
# Group training samples by label.
label_groups = dict() # label => [idx, ...]
for idx in range(len(training_set)):
label = tuple(training_set.get_details(idx).raw_label.flat[::-1])
if label not in label_groups:
label_groups[label] = []
label_groups[label].append(idx)
# Reorder.
label_order = sorted(label_groups.keys())
for label in label_order:
rnd.shuffle(label_groups[label])
# Organize into grid.
grid_indices = []
for y in range(gh):
label = label_order[y % len(label_order)]
indices = label_groups[label]
grid_indices += [indices[x % len(indices)] for x in range(gw)]
label_groups[label] = [indices[(i + gw) % len(indices)] for i in range(len(indices))]
# Load data.
images, labels = zip(*[training_set[i] for i in grid_indices])
return (gw, gh), np.stack(images), np.stack(labels)
#----------------------------------------------------------------------------
def save_image_grid(img, fname, drange, grid_size):
lo, hi = drange
img = np.asarray(img, dtype=np.float32)
img = (img - lo) * (255 / (hi - lo))
img = np.rint(img).clip(0, 255).astype(np.uint8)
gw, gh = grid_size
_N, C, H, W = img.shape
img = img.reshape([gh, gw, C, H, W])
img = img.transpose(0, 3, 1, 4, 2)
img = img.reshape([gh * H, gw * W, C])
assert C in [1, 3]
if C == 1:
PIL.Image.fromarray(img[:, :, 0], 'L').save(fname)
if C == 3:
PIL.Image.fromarray(img, 'RGB').save(fname)
#----------------------------------------------------------------------------
def training_loop(
run_dir = '.', # Output directory.
training_set_kwargs = {}, # Options for training set.
data_loader_kwargs = {}, # Options for torch.utils.data.DataLoader.
G_kwargs = {}, # Options for generator network.
D_kwargs = {}, # Options for discriminator network.
G_opt_kwargs = {}, # Options for generator optimizer.
D_opt_kwargs = {}, # Options for discriminator optimizer.
augment_kwargs = None, # Options for augmentation pipeline. None = disable.
loss_kwargs = {}, # Options for loss function.
metrics = [], # Metrics to evaluate during training.
random_seed = 0, # Global random seed.
num_gpus = 1, # Number of GPUs participating in the training.
rank = 0, # Rank of the current process in [0, num_gpus[.
batch_size = 4, # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus.
batch_gpu = 4, # Number of samples processed at a time by one GPU.
ema_kimg = 10, # Half-life of the exponential moving average (EMA) of generator weights.
ema_rampup = 0.05, # EMA ramp-up coefficient. None = no rampup.
G_reg_interval = None, # How often to perform regularization for G? None = disable lazy regularization.
D_reg_interval = 16, # How often to perform regularization for D? None = disable lazy regularization.
augment_p = 0, # Initial value of augmentation probability.
ada_target = None, # ADA target value. None = fixed p.
ada_interval = 4, # How often to perform ADA adjustment?
ada_kimg = 500, # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit.
total_kimg = 25000, # Total length of the training, measured in thousands of real images.
kimg_per_tick = 4, # Progress snapshot interval.
image_snapshot_ticks = 50, # How often to save image snapshots? None = disable.
network_snapshot_ticks = 50, # How often to save network snapshots? None = disable.
resume_pkl = None, # Network pickle to resume training from.
resume_kimg = 0, # First kimg to report when resuming training.
cudnn_benchmark = True, # Enable torch.backends.cudnn.benchmark?
abort_fn = None, # Callback function for determining whether to abort training. Must return consistent results across ranks.
progress_fn = None, # Callback function for updating training progress. Called for all ranks.
):
# Initialize.
start_time = time.time()
device = torch.device('cuda', rank)
np.random.seed(random_seed * num_gpus + rank)
torch.manual_seed(random_seed * num_gpus + rank)
torch.backends.cudnn.benchmark = cudnn_benchmark # Improves training speed.
torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy.
torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy.
conv2d_gradfix.enabled = True # Improves training speed.
grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe.
# Load training set.
if rank == 0:
print('Loading training set...')
training_set = dnnlib.util.construct_class_by_name(**training_set_kwargs) # subclass of training.dataset.Dataset
training_set_sampler = misc.InfiniteSampler(dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed)
training_set_iterator = iter(torch.utils.data.DataLoader(dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs))
if rank == 0:
print()
print('Num images: ', len(training_set))
print('Image shape:', training_set.image_shape)
print('Label shape:', training_set.label_shape)
print()
# Construct networks.
if rank == 0:
print('Constructing networks...')
common_kwargs = dict(c_dim=training_set.label_dim, img_resolution=training_set.resolution, img_channels=training_set.num_channels)
G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
G_ema = copy.deepcopy(G).eval()
# Resume from existing pickle.
if (resume_pkl is not None) and (rank == 0):
print(f'Resuming from "{resume_pkl}"')
with dnnlib.util.open_url(resume_pkl) as f:
resume_data = legacy.load_network_pkl(f)
for name, module in [('G', G), ('D', D), ('G_ema', G_ema)]:
misc.copy_params_and_buffers(resume_data[name], module, require_all=False)
# Print network summary tables.
if rank == 0:
z = torch.empty([batch_gpu, G.z_dim], device=device)
c = torch.empty([batch_gpu, G.c_dim], device=device)
img = misc.print_module_summary(G, [z, c])
misc.print_module_summary(D, [img, c])
# Setup augmentation.
if rank == 0:
print('Setting up augmentation...')
augment_pipe = None
ada_stats = None
if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None):
augment_pipe = dnnlib.util.construct_class_by_name(**augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
augment_pipe.p.copy_(torch.as_tensor(augment_p))
if ada_target is not None:
ada_stats = training_stats.Collector(regex='Loss/signs/real')
# Distribute across GPUs.
if rank == 0:
print(f'Distributing across {num_gpus} GPUs...')
for module in [G, D, G_ema, augment_pipe]:
if module is not None:
for param in misc.params_and_buffers(module):
if param.numel() > 0 and num_gpus > 1:
torch.distributed.broadcast(param, src=0)
# Setup training phases.
if rank == 0:
print('Setting up training phases...')
loss = dnnlib.util.construct_class_by_name(device=device, G=G, D=D, augment_pipe=augment_pipe, **loss_kwargs) # subclass of training.loss.Loss
phases = []
for name, module, opt_kwargs, reg_interval in [('G', G, G_opt_kwargs, G_reg_interval), ('D', D, D_opt_kwargs, D_reg_interval)]:
if reg_interval is None:
opt = dnnlib.util.construct_class_by_name(params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer
phases += [dnnlib.EasyDict(name=name+'both', module=module, opt=opt, interval=1)]
else: # Lazy regularization.
mb_ratio = reg_interval / (reg_interval + 1)
opt_kwargs = dnnlib.EasyDict(opt_kwargs)
opt_kwargs.lr = opt_kwargs.lr * mb_ratio
opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas]
opt = dnnlib.util.construct_class_by_name(module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer
phases += [dnnlib.EasyDict(name=name+'main', module=module, opt=opt, interval=1)]
phases += [dnnlib.EasyDict(name=name+'reg', module=module, opt=opt, interval=reg_interval)]
for phase in phases:
phase.start_event = None
phase.end_event = None
if rank == 0:
phase.start_event = torch.cuda.Event(enable_timing=True)
phase.end_event = torch.cuda.Event(enable_timing=True)
# Export sample images.
grid_size = None
grid_z = None
grid_c = None
if rank == 0:
print('Exporting sample images...')
grid_size, images, labels = setup_snapshot_image_grid(training_set=training_set)
save_image_grid(images, os.path.join(run_dir, 'reals.png'), drange=[0,255], grid_size=grid_size)
grid_z = torch.randn([labels.shape[0], G.z_dim], device=device).split(batch_gpu)
grid_c = torch.from_numpy(labels).to(device).split(batch_gpu)
images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy()
save_image_grid(images, os.path.join(run_dir, 'fakes_init.png'), drange=[-1,1], grid_size=grid_size)
# Initialize logs.
if rank == 0:
print('Initializing logs...')
stats_collector = training_stats.Collector(regex='.*')
stats_metrics = dict()
stats_jsonl = None
stats_tfevents = None
if rank == 0:
stats_jsonl = open(os.path.join(run_dir, 'stats.jsonl'), 'wt')
try:
import torch.utils.tensorboard as tensorboard
stats_tfevents = tensorboard.SummaryWriter(run_dir)
except ImportError as err:
print('Skipping tfevents export:', err)
# Train.
if rank == 0:
print(f'Training for {total_kimg} kimg...')
print()
cur_nimg = resume_kimg * 1000
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
maintenance_time = tick_start_time - start_time
batch_idx = 0
if progress_fn is not None:
progress_fn(0, total_kimg)
while True:
# Fetch training data.
with torch.autograd.profiler.record_function('data_fetch'):
phase_real_img, phase_real_c = next(training_set_iterator)
phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu)
phase_real_c = phase_real_c.to(device).split(batch_gpu)
all_gen_z = torch.randn([len(phases) * batch_size, G.z_dim], device=device)
all_gen_z = [phase_gen_z.split(batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)]
all_gen_c = [training_set.get_label(np.random.randint(len(training_set))) for _ in range(len(phases) * batch_size)]
all_gen_c = torch.from_numpy(np.stack(all_gen_c)).pin_memory().to(device)
all_gen_c = [phase_gen_c.split(batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)]
# Execute training phases.
for phase, phase_gen_z, phase_gen_c in zip(phases, all_gen_z, all_gen_c):
if batch_idx % phase.interval != 0:
continue
if phase.start_event is not None:
phase.start_event.record(torch.cuda.current_stream(device))
# Accumulate gradients.
phase.opt.zero_grad(set_to_none=True)
phase.module.requires_grad_(True)
for real_img, real_c, gen_z, gen_c in zip(phase_real_img, phase_real_c, phase_gen_z, phase_gen_c):
loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg)
phase.module.requires_grad_(False)
# Update weights.
with torch.autograd.profiler.record_function(phase.name + '_opt'):
params = [param for param in phase.module.parameters() if param.numel() > 0 and param.grad is not None]
if len(params) > 0:
flat = torch.cat([param.grad.flatten() for param in params])
if num_gpus > 1:
torch.distributed.all_reduce(flat)
flat /= num_gpus
misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat)
grads = flat.split([param.numel() for param in params])
for param, grad in zip(params, grads):
param.grad = grad.reshape(param.shape)
phase.opt.step()
# Phase done.
if phase.end_event is not None:
phase.end_event.record(torch.cuda.current_stream(device))
# Update G_ema.
with torch.autograd.profiler.record_function('Gema'):
ema_nimg = ema_kimg * 1000
if ema_rampup is not None:
ema_nimg = min(ema_nimg, cur_nimg * ema_rampup)
ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8))
for p_ema, p in zip(G_ema.parameters(), G.parameters()):
p_ema.copy_(p.lerp(p_ema, ema_beta))
for b_ema, b in zip(G_ema.buffers(), G.buffers()):
b_ema.copy_(b)
# Update state.
cur_nimg += batch_size
batch_idx += 1
# Execute ADA heuristic.
if (ada_stats is not None) and (batch_idx % ada_interval == 0):
ada_stats.update()
adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * (batch_size * ada_interval) / (ada_kimg * 1000)
augment_pipe.p.copy_((augment_pipe.p + adjust).max(misc.constant(0, device=device)))
# Perform maintenance tasks once per tick.
done = (cur_nimg >= total_kimg * 1000)
if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000):
continue
# Print status line, accumulating the same information in training_stats.
tick_end_time = time.time()
fields = []
fields += [f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"]
fields += [f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"]
fields += [f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"]
fields += [f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"]
fields += [f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"]
fields += [f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"]
fields += [f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"]
fields += [f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"]
fields += [f"reserved {training_stats.report0('Resources/peak_gpu_mem_reserved_gb', torch.cuda.max_memory_reserved(device) / 2**30):<6.2f}"]
torch.cuda.reset_peak_memory_stats()
fields += [f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"]
training_stats.report0('Timing/total_hours', (tick_end_time - start_time) / (60 * 60))
training_stats.report0('Timing/total_days', (tick_end_time - start_time) / (24 * 60 * 60))
if rank == 0:
print(' '.join(fields))
# Check for abort.
if (not done) and (abort_fn is not None) and abort_fn():
done = True
if rank == 0:
print()
print('Aborting...')
# Save image snapshot.
if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0):
images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy()
save_image_grid(images, os.path.join(run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1,1], grid_size=grid_size)
# Save network snapshot.
snapshot_pkl = None
snapshot_data = None
if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0):
snapshot_data = dict(training_set_kwargs=dict(training_set_kwargs))
for name, module in [('G', G), ('D', D), ('G_ema', G_ema), ('augment_pipe', augment_pipe)]:
if module is not None:
if num_gpus > 1:
misc.check_ddp_consistency(module, ignore_regex=r'.*\.[^.]+_(avg|ema)')
module = copy.deepcopy(module).eval().requires_grad_(False).cpu()
snapshot_data[name] = module
del module # conserve memory
snapshot_pkl = os.path.join(run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl')
if rank == 0:
with open(snapshot_pkl, 'wb') as f:
pickle.dump(snapshot_data, f)
# Evaluate metrics.
if (snapshot_data is not None) and (len(metrics) > 0):
if rank == 0:
print('Evaluating metrics...')
for metric in metrics:
result_dict = metric_main.calc_metric(metric=metric, G=snapshot_data['G_ema'],
dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device)
if rank == 0:
metric_main.report_metric(result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl)
stats_metrics.update(result_dict.results)
del snapshot_data # conserve memory
# Collect statistics.
for phase in phases:
value = []
if (phase.start_event is not None) and (phase.end_event is not None):
phase.end_event.synchronize()
value = phase.start_event.elapsed_time(phase.end_event)
training_stats.report0('Timing/' + phase.name, value)
stats_collector.update()
stats_dict = stats_collector.as_dict()
# Update logs.
timestamp = time.time()
if stats_jsonl is not None:
fields = dict(stats_dict, timestamp=timestamp)
stats_jsonl.write(json.dumps(fields) + '\n')
stats_jsonl.flush()
if stats_tfevents is not None:
global_step = int(cur_nimg / 1e3)
walltime = timestamp - start_time
for name, value in stats_dict.items():
stats_tfevents.add_scalar(name, value.mean, global_step=global_step, walltime=walltime)
for name, value in stats_metrics.items():
stats_tfevents.add_scalar(f'Metrics/{name}', value, global_step=global_step, walltime=walltime)
stats_tfevents.flush()
if progress_fn is not None:
progress_fn(cur_nimg // 1000, total_kimg)
# Update state.
cur_tick += 1
tick_start_nimg = cur_nimg
tick_start_time = time.time()
maintenance_time = tick_start_time - tick_end_time
if done:
break
# Done.
if rank == 0:
print()
print('Exiting...')
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import click
import os
import multiprocessing
import numpy as np
import imgui
import dnnlib
from gui_utils import imgui_window
from gui_utils import imgui_utils
from gui_utils import gl_utils
from gui_utils import text_utils
from viz import renderer
from viz import pickle_widget
from viz import latent_widget
from viz import stylemix_widget
from viz import trunc_noise_widget
from viz import performance_widget
from viz import capture_widget
from viz import layer_widget
from viz import equivariance_widget
#----------------------------------------------------------------------------
class Visualizer(imgui_window.ImguiWindow):
def __init__(self, capture_dir=None):
super().__init__(title='GAN Visualizer', window_width=3840, window_height=2160)
# Internals.
self._last_error_print = None
self._async_renderer = AsyncRenderer()
self._defer_rendering = 0
self._tex_img = None
self._tex_obj = None
# Widget interface.
self.args = dnnlib.EasyDict()
self.result = dnnlib.EasyDict()
self.pane_w = 0
self.label_w = 0
self.button_w = 0
# Widgets.
self.pickle_widget = pickle_widget.PickleWidget(self)
self.latent_widget = latent_widget.LatentWidget(self)
self.stylemix_widget = stylemix_widget.StyleMixingWidget(self)
self.trunc_noise_widget = trunc_noise_widget.TruncationNoiseWidget(self)
self.perf_widget = performance_widget.PerformanceWidget(self)
self.capture_widget = capture_widget.CaptureWidget(self)
self.layer_widget = layer_widget.LayerWidget(self)
self.eq_widget = equivariance_widget.EquivarianceWidget(self)
if capture_dir is not None:
self.capture_widget.path = capture_dir
# Initialize window.
self.set_position(0, 0)
self._adjust_font_size()
self.skip_frame() # Layout may change after first frame.
def close(self):
super().close()
if self._async_renderer is not None:
self._async_renderer.close()
self._async_renderer = None
def add_recent_pickle(self, pkl, ignore_errors=False):
self.pickle_widget.add_recent(pkl, ignore_errors=ignore_errors)
def load_pickle(self, pkl, ignore_errors=False):
self.pickle_widget.load(pkl, ignore_errors=ignore_errors)
def print_error(self, error):
error = str(error)
if error != self._last_error_print:
print('\n' + error + '\n')
self._last_error_print = error
def defer_rendering(self, num_frames=1):
self._defer_rendering = max(self._defer_rendering, num_frames)
def clear_result(self):
self._async_renderer.clear_result()
def set_async(self, is_async):
if is_async != self._async_renderer.is_async:
self._async_renderer.set_async(is_async)
self.clear_result()
if 'image' in self.result:
self.result.message = 'Switching rendering process...'
self.defer_rendering()
def _adjust_font_size(self):
old = self.font_size
self.set_font_size(min(self.content_width / 120, self.content_height / 60))
if self.font_size != old:
self.skip_frame() # Layout changed.
def draw_frame(self):
self.begin_frame()
self.args = dnnlib.EasyDict()
self.pane_w = self.font_size * 45
self.button_w = self.font_size * 5
self.label_w = round(self.font_size * 4.5)
# Detect mouse dragging in the result area.
dragging, dx, dy = imgui_utils.drag_hidden_window('##result_area', x=self.pane_w, y=0, width=self.content_width-self.pane_w, height=self.content_height)
if dragging:
self.latent_widget.drag(dx, dy)
# Begin control pane.
imgui.set_next_window_position(0, 0)
imgui.set_next_window_size(self.pane_w, self.content_height)
imgui.begin('##control_pane', closable=False, flags=(imgui.WINDOW_NO_TITLE_BAR | imgui.WINDOW_NO_RESIZE | imgui.WINDOW_NO_MOVE))
# Widgets.
expanded, _visible = imgui_utils.collapsing_header('Network & latent', default=True)
self.pickle_widget(expanded)
self.latent_widget(expanded)
self.stylemix_widget(expanded)
self.trunc_noise_widget(expanded)
expanded, _visible = imgui_utils.collapsing_header('Performance & capture', default=True)
self.perf_widget(expanded)
self.capture_widget(expanded)
expanded, _visible = imgui_utils.collapsing_header('Layers & channels', default=True)
self.layer_widget(expanded)
with imgui_utils.grayed_out(not self.result.get('has_input_transform', False)):
expanded, _visible = imgui_utils.collapsing_header('Equivariance', default=True)
self.eq_widget(expanded)
# Render.
if self.is_skipping_frames():
pass
elif self._defer_rendering > 0:
self._defer_rendering -= 1
elif self.args.pkl is not None:
self._async_renderer.set_args(**self.args)
result = self._async_renderer.get_result()
if result is not None:
self.result = result
# Display.
max_w = self.content_width - self.pane_w
max_h = self.content_height
pos = np.array([self.pane_w + max_w / 2, max_h / 2])
if 'image' in self.result:
if self._tex_img is not self.result.image:
self._tex_img = self.result.image
if self._tex_obj is None or not self._tex_obj.is_compatible(image=self._tex_img):
self._tex_obj = gl_utils.Texture(image=self._tex_img, bilinear=False, mipmap=False)
else:
self._tex_obj.update(self._tex_img)
zoom = min(max_w / self._tex_obj.width, max_h / self._tex_obj.height)
zoom = np.floor(zoom) if zoom >= 1 else zoom
self._tex_obj.draw(pos=pos, zoom=zoom, align=0.5, rint=True)
if 'error' in self.result:
self.print_error(self.result.error)
if 'message' not in self.result:
self.result.message = str(self.result.error)
if 'message' in self.result:
tex = text_utils.get_texture(self.result.message, size=self.font_size, max_width=max_w, max_height=max_h, outline=2)
tex.draw(pos=pos, align=0.5, rint=True, color=1)
# End frame.
self._adjust_font_size()
imgui.end()
self.end_frame()
#----------------------------------------------------------------------------
class AsyncRenderer:
def __init__(self):
self._closed = False
self._is_async = False
self._cur_args = None
self._cur_result = None
self._cur_stamp = 0
self._renderer_obj = None
self._args_queue = None
self._result_queue = None
self._process = None
def close(self):
self._closed = True
self._renderer_obj = None
if self._process is not None:
self._process.terminate()
self._process = None
self._args_queue = None
self._result_queue = None
@property
def is_async(self):
return self._is_async
def set_async(self, is_async):
self._is_async = is_async
def set_args(self, **args):
assert not self._closed
if args != self._cur_args:
if self._is_async:
self._set_args_async(**args)
else:
self._set_args_sync(**args)
self._cur_args = args
def _set_args_async(self, **args):
if self._process is None:
self._args_queue = multiprocessing.Queue()
self._result_queue = multiprocessing.Queue()
try:
multiprocessing.set_start_method('spawn')
except RuntimeError:
pass
self._process = multiprocessing.Process(target=self._process_fn, args=(self._args_queue, self._result_queue), daemon=True)
self._process.start()
self._args_queue.put([args, self._cur_stamp])
def _set_args_sync(self, **args):
if self._renderer_obj is None:
self._renderer_obj = renderer.Renderer()
self._cur_result = self._renderer_obj.render(**args)
def get_result(self):
assert not self._closed
if self._result_queue is not None:
while self._result_queue.qsize() > 0:
result, stamp = self._result_queue.get()
if stamp == self._cur_stamp:
self._cur_result = result
return self._cur_result
def clear_result(self):
assert not self._closed
self._cur_args = None
self._cur_result = None
self._cur_stamp += 1
@staticmethod
def _process_fn(args_queue, result_queue):
renderer_obj = renderer.Renderer()
cur_args = None
cur_stamp = None
while True:
args, stamp = args_queue.get()
while args_queue.qsize() > 0:
args, stamp = args_queue.get()
if args != cur_args or stamp != cur_stamp:
result = renderer_obj.render(**args)
if 'error' in result:
result.error = renderer.CapturedException(result.error)
result_queue.put([result, stamp])
cur_args = args
cur_stamp = stamp
#----------------------------------------------------------------------------
@click.command()
@click.argument('pkls', metavar='PATH', nargs=-1)
@click.option('--capture-dir', help='Where to save screenshot captures', metavar='PATH', default=None)
@click.option('--browse-dir', help='Specify model path for the \'Browse...\' button', metavar='PATH')
def main(
pkls,
capture_dir,
browse_dir
):
"""Interactive model visualizer.
Optional PATH argument can be used specify which .pkl file to load.
"""
viz = Visualizer(capture_dir=capture_dir)
if browse_dir is not None:
viz.pickle_widget.search_dirs = [browse_dir]
# List pickles.
if len(pkls) > 0:
for pkl in pkls:
viz.add_recent_pickle(pkl)
viz.load_pickle(pkls[0])
else:
pretrained = [
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhq-1024x1024.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-256x256.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-metfaces-1024x1024.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-metfacesu-1024x1024.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-afhqv2-512x512.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-ffhq-1024x1024.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-ffhqu-1024x1024.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-ffhqu-256x256.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfaces-1024x1024.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfacesu-1024x1024.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-afhqcat-512x512.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-afhqdog-512x512.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-afhqv2-512x512.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-afhqwild-512x512.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-brecahad-512x512.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-celebahq-256x256.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-cifar10-32x32.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-ffhq-1024x1024.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-ffhq-256x256.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-ffhq-512x512.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-ffhqu-1024x1024.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-ffhqu-256x256.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-lsundog-256x256.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-metfaces-1024x1024.pkl',
'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-metfacesu-1024x1024.pkl'
]
# Populate recent pickles list with pretrained model URLs.
for url in pretrained:
viz.add_recent_pickle(url)
# Run.
while not viz.should_close():
viz.draw_frame()
viz.close()
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
# empty

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import os
import re
import numpy as np
import imgui
import PIL.Image
from gui_utils import imgui_utils
from . import renderer
#----------------------------------------------------------------------------
class CaptureWidget:
def __init__(self, viz):
self.viz = viz
self.path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '_screenshots'))
self.dump_image = False
self.dump_gui = False
self.defer_frames = 0
self.disabled_time = 0
def dump_png(self, image):
viz = self.viz
try:
_height, _width, channels = image.shape
assert channels in [1, 3]
assert image.dtype == np.uint8
os.makedirs(self.path, exist_ok=True)
file_id = 0
for entry in os.scandir(self.path):
if entry.is_file():
match = re.fullmatch(r'(\d+).*', entry.name)
if match:
file_id = max(file_id, int(match.group(1)) + 1)
if channels == 1:
pil_image = PIL.Image.fromarray(image[:, :, 0], 'L')
else:
pil_image = PIL.Image.fromarray(image, 'RGB')
pil_image.save(os.path.join(self.path, f'{file_id:05d}.png'))
except:
viz.result.error = renderer.CapturedException()
@imgui_utils.scoped_by_object_id
def __call__(self, show=True):
viz = self.viz
if show:
with imgui_utils.grayed_out(self.disabled_time != 0):
imgui.text('Capture')
imgui.same_line(viz.label_w)
_changed, self.path = imgui_utils.input_text('##path', self.path, 1024,
flags=(imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE),
width=(-1 - viz.button_w * 2 - viz.spacing * 2),
help_text='PATH')
if imgui.is_item_hovered() and not imgui.is_item_active() and self.path != '':
imgui.set_tooltip(self.path)
imgui.same_line()
if imgui_utils.button('Save image', width=viz.button_w, enabled=(self.disabled_time == 0 and 'image' in viz.result)):
self.dump_image = True
self.defer_frames = 2
self.disabled_time = 0.5
imgui.same_line()
if imgui_utils.button('Save GUI', width=-1, enabled=(self.disabled_time == 0)):
self.dump_gui = True
self.defer_frames = 2
self.disabled_time = 0.5
self.disabled_time = max(self.disabled_time - viz.frame_delta, 0)
if self.defer_frames > 0:
self.defer_frames -= 1
elif self.dump_image:
if 'image' in viz.result:
self.dump_png(viz.result.image)
self.dump_image = False
elif self.dump_gui:
viz.capture_next_frame()
self.dump_gui = False
captured_frame = viz.pop_captured_frame()
if captured_frame is not None:
self.dump_png(captured_frame)
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import numpy as np
import imgui
import dnnlib
from gui_utils import imgui_utils
#----------------------------------------------------------------------------
class EquivarianceWidget:
def __init__(self, viz):
self.viz = viz
self.xlate = dnnlib.EasyDict(x=0, y=0, anim=False, round=False, speed=1e-2)
self.xlate_def = dnnlib.EasyDict(self.xlate)
self.rotate = dnnlib.EasyDict(val=0, anim=False, speed=5e-3)
self.rotate_def = dnnlib.EasyDict(self.rotate)
self.opts = dnnlib.EasyDict(untransform=False)
self.opts_def = dnnlib.EasyDict(self.opts)
@imgui_utils.scoped_by_object_id
def __call__(self, show=True):
viz = self.viz
if show:
imgui.text('Translate')
imgui.same_line(viz.label_w)
with imgui_utils.item_width(viz.font_size * 8):
_changed, (self.xlate.x, self.xlate.y) = imgui.input_float2('##xlate', self.xlate.x, self.xlate.y, format='%.4f')
imgui.same_line(viz.label_w + viz.font_size * 8 + viz.spacing)
_clicked, dragging, dx, dy = imgui_utils.drag_button('Drag fast##xlate', width=viz.button_w)
if dragging:
self.xlate.x += dx / viz.font_size * 2e-2
self.xlate.y += dy / viz.font_size * 2e-2
imgui.same_line()
_clicked, dragging, dx, dy = imgui_utils.drag_button('Drag slow##xlate', width=viz.button_w)
if dragging:
self.xlate.x += dx / viz.font_size * 4e-4
self.xlate.y += dy / viz.font_size * 4e-4
imgui.same_line()
_clicked, self.xlate.anim = imgui.checkbox('Anim##xlate', self.xlate.anim)
imgui.same_line()
_clicked, self.xlate.round = imgui.checkbox('Round##xlate', self.xlate.round)
imgui.same_line()
with imgui_utils.item_width(-1 - viz.button_w - viz.spacing), imgui_utils.grayed_out(not self.xlate.anim):
changed, speed = imgui.slider_float('##xlate_speed', self.xlate.speed, 0, 0.5, format='Speed %.5f', power=5)
if changed:
self.xlate.speed = speed
imgui.same_line()
if imgui_utils.button('Reset##xlate', width=-1, enabled=(self.xlate != self.xlate_def)):
self.xlate = dnnlib.EasyDict(self.xlate_def)
if show:
imgui.text('Rotate')
imgui.same_line(viz.label_w)
with imgui_utils.item_width(viz.font_size * 8):
_changed, self.rotate.val = imgui.input_float('##rotate', self.rotate.val, format='%.4f')
imgui.same_line(viz.label_w + viz.font_size * 8 + viz.spacing)
_clicked, dragging, dx, _dy = imgui_utils.drag_button('Drag fast##rotate', width=viz.button_w)
if dragging:
self.rotate.val += dx / viz.font_size * 2e-2
imgui.same_line()
_clicked, dragging, dx, _dy = imgui_utils.drag_button('Drag slow##rotate', width=viz.button_w)
if dragging:
self.rotate.val += dx / viz.font_size * 4e-4
imgui.same_line()
_clicked, self.rotate.anim = imgui.checkbox('Anim##rotate', self.rotate.anim)
imgui.same_line()
with imgui_utils.item_width(-1 - viz.button_w - viz.spacing), imgui_utils.grayed_out(not self.rotate.anim):
changed, speed = imgui.slider_float('##rotate_speed', self.rotate.speed, -1, 1, format='Speed %.4f', power=3)
if changed:
self.rotate.speed = speed
imgui.same_line()
if imgui_utils.button('Reset##rotate', width=-1, enabled=(self.rotate != self.rotate_def)):
self.rotate = dnnlib.EasyDict(self.rotate_def)
if show:
imgui.set_cursor_pos_x(imgui.get_content_region_max()[0] - 1 - viz.button_w*1 - viz.font_size*16)
_clicked, self.opts.untransform = imgui.checkbox('Untransform', self.opts.untransform)
imgui.same_line(imgui.get_content_region_max()[0] - 1 - viz.button_w)
if imgui_utils.button('Reset##opts', width=-1, enabled=(self.opts != self.opts_def)):
self.opts = dnnlib.EasyDict(self.opts_def)
if self.xlate.anim:
c = np.array([self.xlate.x, self.xlate.y], dtype=np.float64)
t = c.copy()
if np.max(np.abs(t)) < 1e-4:
t += 1
t *= 0.1 / np.hypot(*t)
t += c[::-1] * [1, -1]
d = t - c
d *= (viz.frame_delta * self.xlate.speed) / np.hypot(*d)
self.xlate.x += d[0]
self.xlate.y += d[1]
if self.rotate.anim:
self.rotate.val += viz.frame_delta * self.rotate.speed
pos = np.array([self.xlate.x, self.xlate.y], dtype=np.float64)
if self.xlate.round and 'img_resolution' in viz.result:
pos = np.rint(pos * viz.result.img_resolution) / viz.result.img_resolution
angle = self.rotate.val * np.pi * 2
viz.args.input_transform = [
[np.cos(angle), np.sin(angle), pos[0]],
[-np.sin(angle), np.cos(angle), pos[1]],
[0, 0, 1]]
viz.args.update(untransform=self.opts.untransform)
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import numpy as np
import imgui
import dnnlib
from gui_utils import imgui_utils
#----------------------------------------------------------------------------
class LatentWidget:
def __init__(self, viz):
self.viz = viz
self.latent = dnnlib.EasyDict(x=0, y=0, anim=False, speed=0.25)
self.latent_def = dnnlib.EasyDict(self.latent)
self.step_y = 100
def drag(self, dx, dy):
viz = self.viz
self.latent.x += dx / viz.font_size * 4e-2
self.latent.y += dy / viz.font_size * 4e-2
@imgui_utils.scoped_by_object_id
def __call__(self, show=True):
viz = self.viz
if show:
imgui.text('Latent')
imgui.same_line(viz.label_w)
seed = round(self.latent.x) + round(self.latent.y) * self.step_y
with imgui_utils.item_width(viz.font_size * 8):
changed, seed = imgui.input_int('##seed', seed)
if changed:
self.latent.x = seed
self.latent.y = 0
imgui.same_line(viz.label_w + viz.font_size * 8 + viz.spacing)
frac_x = self.latent.x - round(self.latent.x)
frac_y = self.latent.y - round(self.latent.y)
with imgui_utils.item_width(viz.font_size * 5):
changed, (new_frac_x, new_frac_y) = imgui.input_float2('##frac', frac_x, frac_y, format='%+.2f', flags=imgui.INPUT_TEXT_ENTER_RETURNS_TRUE)
if changed:
self.latent.x += new_frac_x - frac_x
self.latent.y += new_frac_y - frac_y
imgui.same_line(viz.label_w + viz.font_size * 13 + viz.spacing * 2)
_clicked, dragging, dx, dy = imgui_utils.drag_button('Drag', width=viz.button_w)
if dragging:
self.drag(dx, dy)
imgui.same_line(viz.label_w + viz.font_size * 13 + viz.button_w + viz.spacing * 3)
_clicked, self.latent.anim = imgui.checkbox('Anim', self.latent.anim)
imgui.same_line(round(viz.font_size * 27.7))
with imgui_utils.item_width(-1 - viz.button_w * 2 - viz.spacing * 2), imgui_utils.grayed_out(not self.latent.anim):
changed, speed = imgui.slider_float('##speed', self.latent.speed, -5, 5, format='Speed %.3f', power=3)
if changed:
self.latent.speed = speed
imgui.same_line()
snapped = dnnlib.EasyDict(self.latent, x=round(self.latent.x), y=round(self.latent.y))
if imgui_utils.button('Snap', width=viz.button_w, enabled=(self.latent != snapped)):
self.latent = snapped
imgui.same_line()
if imgui_utils.button('Reset', width=-1, enabled=(self.latent != self.latent_def)):
self.latent = dnnlib.EasyDict(self.latent_def)
if self.latent.anim:
self.latent.x += viz.frame_delta * self.latent.speed
viz.args.w0_seeds = [] # [[seed, weight], ...]
for ofs_x, ofs_y in [[0, 0], [1, 0], [0, 1], [1, 1]]:
seed_x = np.floor(self.latent.x) + ofs_x
seed_y = np.floor(self.latent.y) + ofs_y
seed = (int(seed_x) + int(seed_y) * self.step_y) & ((1 << 32) - 1)
weight = (1 - abs(self.latent.x - seed_x)) * (1 - abs(self.latent.y - seed_y))
if weight > 0:
viz.args.w0_seeds.append([seed, weight])
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import imgui
from gui_utils import imgui_utils
#----------------------------------------------------------------------------
class LayerWidget:
def __init__(self, viz):
self.viz = viz
self.prev_layers = None
self.cur_layer = None
self.sel_channels = 3
self.base_channel = 0
self.img_scale_db = 0
self.img_normalize = False
self.fft_show = False
self.fft_all = True
self.fft_range_db = 50
self.fft_beta = 8
self.refocus = False
@imgui_utils.scoped_by_object_id
def __call__(self, show=True):
viz = self.viz
layers = viz.result.get('layers', [])
if self.prev_layers != layers:
self.prev_layers = layers
self.refocus = True
layer = ([layer for layer in layers if layer.name == self.cur_layer] + [None])[0]
if layer is None and len(layers) > 0:
layer = layers[-1]
self.cur_layer = layer.name
num_channels = layer.shape[1] if layer is not None else 0
base_channel_max = max(num_channels - self.sel_channels, 0)
if show:
bg_color = [0.16, 0.29, 0.48, 0.2]
dim_color = list(imgui.get_style().colors[imgui.COLOR_TEXT])
dim_color[-1] *= 0.5
# Begin list.
width = viz.font_size * 28
height = imgui.get_text_line_height_with_spacing() * 12 + viz.spacing
imgui.push_style_var(imgui.STYLE_FRAME_PADDING, [0, 0])
imgui.push_style_color(imgui.COLOR_CHILD_BACKGROUND, *bg_color)
imgui.push_style_color(imgui.COLOR_HEADER, 0, 0, 0, 0)
imgui.push_style_color(imgui.COLOR_HEADER_HOVERED, 0.16, 0.29, 0.48, 0.5)
imgui.push_style_color(imgui.COLOR_HEADER_ACTIVE, 0.16, 0.29, 0.48, 0.9)
imgui.begin_child('##list', width=width, height=height, border=True, flags=imgui.WINDOW_ALWAYS_VERTICAL_SCROLLBAR)
# List items.
for layer in layers:
selected = (self.cur_layer == layer.name)
_opened, selected = imgui.selectable(f'##{layer.name}_selectable', selected)
imgui.same_line(viz.spacing)
_clicked, selected = imgui.checkbox(f'{layer.name}##radio', selected)
if selected:
self.cur_layer = layer.name
if self.refocus:
imgui.set_scroll_here()
viz.skip_frame() # Focus will change on next frame.
self.refocus = False
imgui.same_line(width - viz.font_size * 13)
imgui.text_colored('x'.join(str(x) for x in layer.shape[2:]), *dim_color)
imgui.same_line(width - viz.font_size * 8)
imgui.text_colored(str(layer.shape[1]), *dim_color)
imgui.same_line(width - viz.font_size * 5)
imgui.text_colored(layer.dtype, *dim_color)
# End list.
if len(layers) == 0:
imgui.text_colored('No layers found', *dim_color)
imgui.end_child()
imgui.pop_style_color(4)
imgui.pop_style_var(1)
# Begin options.
imgui.same_line()
imgui.begin_child('##options', width=-1, height=height, border=False)
# RGB & normalize.
rgb = (self.sel_channels == 3)
_clicked, rgb = imgui.checkbox('RGB', rgb)
self.sel_channels = 3 if rgb else 1
imgui.same_line(viz.font_size * 4)
_clicked, self.img_normalize = imgui.checkbox('Normalize', self.img_normalize)
imgui.same_line(imgui.get_content_region_max()[0] - 1 - viz.button_w)
if imgui_utils.button('Reset##img_flags', width=-1, enabled=(self.sel_channels != 3 or self.img_normalize)):
self.sel_channels = 3
self.img_normalize = False
# Image scale.
with imgui_utils.item_width(-1 - viz.button_w - viz.spacing):
_changed, self.img_scale_db = imgui.slider_float('##scale', self.img_scale_db, min_value=-40, max_value=40, format='Scale %+.1f dB')
imgui.same_line()
if imgui_utils.button('Reset##scale', width=-1, enabled=(self.img_scale_db != 0)):
self.img_scale_db = 0
# Base channel.
self.base_channel = min(max(self.base_channel, 0), base_channel_max)
narrow_w = imgui.get_text_line_height_with_spacing()
with imgui_utils.grayed_out(base_channel_max == 0):
with imgui_utils.item_width(-1 - viz.button_w - narrow_w * 2 - viz.spacing * 3):
_changed, self.base_channel = imgui.drag_int('##channel', self.base_channel, change_speed=0.05, min_value=0, max_value=base_channel_max, format=f'Channel %d/{num_channels}')
imgui.same_line()
if imgui_utils.button('-##channel', width=narrow_w):
self.base_channel -= 1
imgui.same_line()
if imgui_utils.button('+##channel', width=narrow_w):
self.base_channel += 1
imgui.same_line()
self.base_channel = min(max(self.base_channel, 0), base_channel_max)
if imgui_utils.button('Reset##channel', width=-1, enabled=(self.base_channel != 0 and base_channel_max > 0)):
self.base_channel = 0
# Stats.
stats = viz.result.get('stats', None)
stats = [f'{stats[idx]:g}' if stats is not None else 'N/A' for idx in range(6)]
rows = [
['Statistic', 'All channels', 'Selected'],
['Mean', stats[0], stats[1]],
['Std', stats[2], stats[3]],
['Max', stats[4], stats[5]],
]
height = imgui.get_text_line_height_with_spacing() * len(rows) + viz.spacing
imgui.push_style_color(imgui.COLOR_CHILD_BACKGROUND, *bg_color)
imgui.begin_child('##stats', width=-1, height=height, border=True)
for y, cols in enumerate(rows):
for x, col in enumerate(cols):
if x != 0:
imgui.same_line(viz.font_size * (4 + (x - 1) * 6))
if x == 0 or y == 0:
imgui.text_colored(col, *dim_color)
else:
imgui.text(col)
imgui.end_child()
imgui.pop_style_color(1)
# FFT & all.
_clicked, self.fft_show = imgui.checkbox('FFT', self.fft_show)
imgui.same_line(viz.font_size * 4)
with imgui_utils.grayed_out(not self.fft_show or base_channel_max == 0):
_clicked, self.fft_all = imgui.checkbox('All channels', self.fft_all)
imgui.same_line(imgui.get_content_region_max()[0] - 1 - viz.button_w)
with imgui_utils.grayed_out(not self.fft_show):
if imgui_utils.button('Reset##fft_flags', width=-1, enabled=(self.fft_show or not self.fft_all)):
self.fft_show = False
self.fft_all = True
# FFT range.
with imgui_utils.grayed_out(not self.fft_show):
with imgui_utils.item_width(-1 - viz.button_w - viz.spacing):
_changed, self.fft_range_db = imgui.slider_float('##fft_range_db', self.fft_range_db, min_value=0.1, max_value=100, format='Range +-%.1f dB')
imgui.same_line()
if imgui_utils.button('Reset##fft_range_db', width=-1, enabled=(self.fft_range_db != 50)):
self.fft_range_db = 50
# FFT beta.
with imgui_utils.grayed_out(not self.fft_show):
with imgui_utils.item_width(-1 - viz.button_w - viz.spacing):
_changed, self.fft_beta = imgui.slider_float('##fft_beta', self.fft_beta, min_value=0, max_value=50, format='Kaiser beta %.2f', power=2.63)
imgui.same_line()
if imgui_utils.button('Reset##fft_beta', width=-1, enabled=(self.fft_beta != 8)):
self.fft_beta = 8
# End options.
imgui.end_child()
self.base_channel = min(max(self.base_channel, 0), base_channel_max)
viz.args.layer_name = self.cur_layer if len(layers) > 0 and self.cur_layer != layers[-1].name else None
viz.args.update(sel_channels=self.sel_channels, base_channel=self.base_channel, img_scale_db=self.img_scale_db, img_normalize=self.img_normalize)
viz.args.fft_show = self.fft_show
if self.fft_show:
viz.args.update(fft_all=self.fft_all, fft_range_db=self.fft_range_db, fft_beta=self.fft_beta)
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import array
import numpy as np
import imgui
from gui_utils import imgui_utils
#----------------------------------------------------------------------------
class PerformanceWidget:
def __init__(self, viz):
self.viz = viz
self.gui_times = [float('nan')] * 60
self.render_times = [float('nan')] * 30
self.fps_limit = 60
self.use_vsync = False
self.is_async = False
self.force_fp32 = False
@imgui_utils.scoped_by_object_id
def __call__(self, show=True):
viz = self.viz
self.gui_times = self.gui_times[1:] + [viz.frame_delta]
if 'render_time' in viz.result:
self.render_times = self.render_times[1:] + [viz.result.render_time]
del viz.result.render_time
if show:
imgui.text('GUI')
imgui.same_line(viz.label_w)
with imgui_utils.item_width(viz.font_size * 8):
imgui.plot_lines('##gui_times', array.array('f', self.gui_times), scale_min=0)
imgui.same_line(viz.label_w + viz.font_size * 9)
t = [x for x in self.gui_times if x > 0]
t = np.mean(t) if len(t) > 0 else 0
imgui.text(f'{t*1e3:.1f} ms' if t > 0 else 'N/A')
imgui.same_line(viz.label_w + viz.font_size * 14)
imgui.text(f'{1/t:.1f} FPS' if t > 0 else 'N/A')
imgui.same_line(viz.label_w + viz.font_size * 18 + viz.spacing * 3)
with imgui_utils.item_width(viz.font_size * 6):
_changed, self.fps_limit = imgui.input_int('FPS limit', self.fps_limit, flags=imgui.INPUT_TEXT_ENTER_RETURNS_TRUE)
self.fps_limit = min(max(self.fps_limit, 5), 1000)
imgui.same_line(imgui.get_content_region_max()[0] - 1 - viz.button_w * 2 - viz.spacing)
_clicked, self.use_vsync = imgui.checkbox('Vertical sync', self.use_vsync)
if show:
imgui.text('Render')
imgui.same_line(viz.label_w)
with imgui_utils.item_width(viz.font_size * 8):
imgui.plot_lines('##render_times', array.array('f', self.render_times), scale_min=0)
imgui.same_line(viz.label_w + viz.font_size * 9)
t = [x for x in self.render_times if x > 0]
t = np.mean(t) if len(t) > 0 else 0
imgui.text(f'{t*1e3:.1f} ms' if t > 0 else 'N/A')
imgui.same_line(viz.label_w + viz.font_size * 14)
imgui.text(f'{1/t:.1f} FPS' if t > 0 else 'N/A')
imgui.same_line(viz.label_w + viz.font_size * 18 + viz.spacing * 3)
_clicked, self.is_async = imgui.checkbox('Separate process', self.is_async)
imgui.same_line(imgui.get_content_region_max()[0] - 1 - viz.button_w * 2 - viz.spacing)
_clicked, self.force_fp32 = imgui.checkbox('Force FP32', self.force_fp32)
viz.set_fps_limit(self.fps_limit)
viz.set_vsync(self.use_vsync)
viz.set_async(self.is_async)
viz.args.force_fp32 = self.force_fp32
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import glob
import os
import re
import dnnlib
import imgui
import numpy as np
from gui_utils import imgui_utils
from . import renderer
#----------------------------------------------------------------------------
def _locate_results(pattern):
return pattern
#----------------------------------------------------------------------------
class PickleWidget:
def __init__(self, viz):
self.viz = viz
self.search_dirs = []
self.cur_pkl = None
self.user_pkl = ''
self.recent_pkls = []
self.browse_cache = dict() # {tuple(path, ...): [dnnlib.EasyDict(), ...], ...}
self.browse_refocus = False
self.load('', ignore_errors=True)
def add_recent(self, pkl, ignore_errors=False):
try:
resolved = self.resolve_pkl(pkl)
if resolved not in self.recent_pkls:
self.recent_pkls.append(resolved)
except:
if not ignore_errors:
raise
def load(self, pkl, ignore_errors=False):
viz = self.viz
viz.clear_result()
viz.skip_frame() # The input field will change on next frame.
try:
resolved = self.resolve_pkl(pkl)
name = resolved.replace('\\', '/').split('/')[-1]
self.cur_pkl = resolved
self.user_pkl = resolved
viz.result.message = f'Loading {name}...'
viz.defer_rendering()
if resolved in self.recent_pkls:
self.recent_pkls.remove(resolved)
self.recent_pkls.insert(0, resolved)
except:
self.cur_pkl = None
self.user_pkl = pkl
if pkl == '':
viz.result = dnnlib.EasyDict(message='No network pickle loaded')
else:
viz.result = dnnlib.EasyDict(error=renderer.CapturedException())
if not ignore_errors:
raise
@imgui_utils.scoped_by_object_id
def __call__(self, show=True):
viz = self.viz
recent_pkls = [pkl for pkl in self.recent_pkls if pkl != self.user_pkl]
if show:
imgui.text('Pickle')
imgui.same_line(viz.label_w)
changed, self.user_pkl = imgui_utils.input_text('##pkl', self.user_pkl, 1024,
flags=(imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE),
width=(-1 - viz.button_w * 2 - viz.spacing * 2),
help_text='<PATH> | <URL> | <RUN_DIR> | <RUN_ID> | <RUN_ID>/<KIMG>.pkl')
if changed:
self.load(self.user_pkl, ignore_errors=True)
if imgui.is_item_hovered() and not imgui.is_item_active() and self.user_pkl != '':
imgui.set_tooltip(self.user_pkl)
imgui.same_line()
if imgui_utils.button('Recent...', width=viz.button_w, enabled=(len(recent_pkls) != 0)):
imgui.open_popup('recent_pkls_popup')
imgui.same_line()
if imgui_utils.button('Browse...', enabled=len(self.search_dirs) > 0, width=-1):
imgui.open_popup('browse_pkls_popup')
self.browse_cache.clear()
self.browse_refocus = True
if imgui.begin_popup('recent_pkls_popup'):
for pkl in recent_pkls:
clicked, _state = imgui.menu_item(pkl)
if clicked:
self.load(pkl, ignore_errors=True)
imgui.end_popup()
if imgui.begin_popup('browse_pkls_popup'):
def recurse(parents):
key = tuple(parents)
items = self.browse_cache.get(key, None)
if items is None:
items = self.list_runs_and_pkls(parents)
self.browse_cache[key] = items
for item in items:
if item.type == 'run' and imgui.begin_menu(item.name):
recurse([item.path])
imgui.end_menu()
if item.type == 'pkl':
clicked, _state = imgui.menu_item(item.name)
if clicked:
self.load(item.path, ignore_errors=True)
if len(items) == 0:
with imgui_utils.grayed_out():
imgui.menu_item('No results found')
recurse(self.search_dirs)
if self.browse_refocus:
imgui.set_scroll_here()
viz.skip_frame() # Focus will change on next frame.
self.browse_refocus = False
imgui.end_popup()
paths = viz.pop_drag_and_drop_paths()
if paths is not None and len(paths) >= 1:
self.load(paths[0], ignore_errors=True)
viz.args.pkl = self.cur_pkl
def list_runs_and_pkls(self, parents):
items = []
run_regex = re.compile(r'\d+-.*')
pkl_regex = re.compile(r'network-snapshot-\d+\.pkl')
for parent in set(parents):
if os.path.isdir(parent):
for entry in os.scandir(parent):
if entry.is_dir() and run_regex.fullmatch(entry.name):
items.append(dnnlib.EasyDict(type='run', name=entry.name, path=os.path.join(parent, entry.name)))
if entry.is_file() and pkl_regex.fullmatch(entry.name):
items.append(dnnlib.EasyDict(type='pkl', name=entry.name, path=os.path.join(parent, entry.name)))
items = sorted(items, key=lambda item: (item.name.replace('_', ' '), item.path))
return items
def resolve_pkl(self, pattern):
assert isinstance(pattern, str)
assert pattern != ''
# URL => return as is.
if dnnlib.util.is_url(pattern):
return pattern
# Short-hand pattern => locate.
path = _locate_results(pattern)
# Run dir => pick the last saved snapshot.
if os.path.isdir(path):
pkl_files = sorted(glob.glob(os.path.join(path, 'network-snapshot-*.pkl')))
if len(pkl_files) == 0:
raise IOError(f'No network pickle found in "{path}"')
path = pkl_files[-1]
# Normalize.
path = os.path.abspath(path)
return path
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import sys
import copy
import traceback
import numpy as np
import torch
import torch.fft
import torch.nn
import matplotlib.cm
import dnnlib
from torch_utils.ops import upfirdn2d
import legacy # pylint: disable=import-error
#----------------------------------------------------------------------------
class CapturedException(Exception):
def __init__(self, msg=None):
if msg is None:
_type, value, _traceback = sys.exc_info()
assert value is not None
if isinstance(value, CapturedException):
msg = str(value)
else:
msg = traceback.format_exc()
assert isinstance(msg, str)
super().__init__(msg)
#----------------------------------------------------------------------------
class CaptureSuccess(Exception):
def __init__(self, out):
super().__init__()
self.out = out
#----------------------------------------------------------------------------
def _sinc(x):
y = (x * np.pi).abs()
z = torch.sin(y) / y.clamp(1e-30, float('inf'))
return torch.where(y < 1e-30, torch.ones_like(x), z)
def _lanczos_window(x, a):
x = x.abs() / a
return torch.where(x < 1, _sinc(x), torch.zeros_like(x))
#----------------------------------------------------------------------------
def _construct_affine_bandlimit_filter(mat, a=3, amax=16, aflt=64, up=4, cutoff_in=1, cutoff_out=1):
assert a <= amax < aflt
mat = torch.as_tensor(mat).to(torch.float32)
# Construct 2D filter taps in input & output coordinate spaces.
taps = ((torch.arange(aflt * up * 2 - 1, device=mat.device) + 1) / up - aflt).roll(1 - aflt * up)
yi, xi = torch.meshgrid(taps, taps)
xo, yo = (torch.stack([xi, yi], dim=2) @ mat[:2, :2].t()).unbind(2)
# Convolution of two oriented 2D sinc filters.
fi = _sinc(xi * cutoff_in) * _sinc(yi * cutoff_in)
fo = _sinc(xo * cutoff_out) * _sinc(yo * cutoff_out)
f = torch.fft.ifftn(torch.fft.fftn(fi) * torch.fft.fftn(fo)).real
# Convolution of two oriented 2D Lanczos windows.
wi = _lanczos_window(xi, a) * _lanczos_window(yi, a)
wo = _lanczos_window(xo, a) * _lanczos_window(yo, a)
w = torch.fft.ifftn(torch.fft.fftn(wi) * torch.fft.fftn(wo)).real
# Construct windowed FIR filter.
f = f * w
# Finalize.
c = (aflt - amax) * up
f = f.roll([aflt * up - 1] * 2, dims=[0,1])[c:-c, c:-c]
f = torch.nn.functional.pad(f, [0, 1, 0, 1]).reshape(amax * 2, up, amax * 2, up)
f = f / f.sum([0,2], keepdim=True) / (up ** 2)
f = f.reshape(amax * 2 * up, amax * 2 * up)[:-1, :-1]
return f
#----------------------------------------------------------------------------
def _apply_affine_transformation(x, mat, up=4, **filter_kwargs):
_N, _C, H, W = x.shape
mat = torch.as_tensor(mat).to(dtype=torch.float32, device=x.device)
# Construct filter.
f = _construct_affine_bandlimit_filter(mat, up=up, **filter_kwargs)
assert f.ndim == 2 and f.shape[0] == f.shape[1] and f.shape[0] % 2 == 1
p = f.shape[0] // 2
# Construct sampling grid.
theta = mat.inverse()
theta[:2, 2] *= 2
theta[0, 2] += 1 / up / W
theta[1, 2] += 1 / up / H
theta[0, :] *= W / (W + p / up * 2)
theta[1, :] *= H / (H + p / up * 2)
theta = theta[:2, :3].unsqueeze(0).repeat([x.shape[0], 1, 1])
g = torch.nn.functional.affine_grid(theta, x.shape, align_corners=False)
# Resample image.
y = upfirdn2d.upsample2d(x=x, f=f, up=up, padding=p)
z = torch.nn.functional.grid_sample(y, g, mode='bilinear', padding_mode='zeros', align_corners=False)
# Form mask.
m = torch.zeros_like(y)
c = p * 2 + 1
m[:, :, c:-c, c:-c] = 1
m = torch.nn.functional.grid_sample(m, g, mode='nearest', padding_mode='zeros', align_corners=False)
return z, m
#----------------------------------------------------------------------------
class Renderer:
def __init__(self):
self._device = torch.device('cuda')
self._pkl_data = dict() # {pkl: dict | CapturedException, ...}
self._networks = dict() # {cache_key: torch.nn.Module, ...}
self._pinned_bufs = dict() # {(shape, dtype): torch.Tensor, ...}
self._cmaps = dict() # {name: torch.Tensor, ...}
self._is_timing = False
self._start_event = torch.cuda.Event(enable_timing=True)
self._end_event = torch.cuda.Event(enable_timing=True)
self._net_layers = dict() # {cache_key: [dnnlib.EasyDict, ...], ...}
def render(self, **args):
self._is_timing = True
self._start_event.record(torch.cuda.current_stream(self._device))
res = dnnlib.EasyDict()
try:
self._render_impl(res, **args)
except:
res.error = CapturedException()
self._end_event.record(torch.cuda.current_stream(self._device))
if 'image' in res:
res.image = self.to_cpu(res.image).numpy()
if 'stats' in res:
res.stats = self.to_cpu(res.stats).numpy()
if 'error' in res:
res.error = str(res.error)
if self._is_timing:
self._end_event.synchronize()
res.render_time = self._start_event.elapsed_time(self._end_event) * 1e-3
self._is_timing = False
return res
def get_network(self, pkl, key, **tweak_kwargs):
data = self._pkl_data.get(pkl, None)
if data is None:
print(f'Loading "{pkl}"... ', end='', flush=True)
try:
with dnnlib.util.open_url(pkl, verbose=False) as f:
data = legacy.load_network_pkl(f)
print('Done.')
except:
data = CapturedException()
print('Failed!')
self._pkl_data[pkl] = data
self._ignore_timing()
if isinstance(data, CapturedException):
raise data
orig_net = data[key]
cache_key = (orig_net, self._device, tuple(sorted(tweak_kwargs.items())))
net = self._networks.get(cache_key, None)
if net is None:
try:
net = copy.deepcopy(orig_net)
net = self._tweak_network(net, **tweak_kwargs)
net.to(self._device)
except:
net = CapturedException()
self._networks[cache_key] = net
self._ignore_timing()
if isinstance(net, CapturedException):
raise net
return net
def _tweak_network(self, net):
# Print diagnostics.
#for name, value in misc.named_params_and_buffers(net):
# if name.endswith('.magnitude_ema'):
# value = value.rsqrt().numpy()
# print(f'{name:<50s}{np.min(value):<16g}{np.max(value):g}')
# if name.endswith('.weight') and value.ndim == 4:
# value = value.square().mean([1,2,3]).sqrt().numpy()
# print(f'{name:<50s}{np.min(value):<16g}{np.max(value):g}')
return net
def _get_pinned_buf(self, ref):
key = (tuple(ref.shape), ref.dtype)
buf = self._pinned_bufs.get(key, None)
if buf is None:
buf = torch.empty(ref.shape, dtype=ref.dtype).pin_memory()
self._pinned_bufs[key] = buf
return buf
def to_device(self, buf):
return self._get_pinned_buf(buf).copy_(buf).to(self._device)
def to_cpu(self, buf):
return self._get_pinned_buf(buf).copy_(buf).clone()
def _ignore_timing(self):
self._is_timing = False
def _apply_cmap(self, x, name='viridis'):
cmap = self._cmaps.get(name, None)
if cmap is None:
cmap = matplotlib.cm.get_cmap(name)
cmap = cmap(np.linspace(0, 1, num=1024), bytes=True)[:, :3]
cmap = self.to_device(torch.from_numpy(cmap))
self._cmaps[name] = cmap
hi = cmap.shape[0] - 1
x = (x * hi + 0.5).clamp(0, hi).to(torch.int64)
x = torch.nn.functional.embedding(x, cmap)
return x
def _render_impl(self, res,
pkl = None,
w0_seeds = [[0, 1]],
stylemix_idx = [],
stylemix_seed = 0,
trunc_psi = 1,
trunc_cutoff = 0,
random_seed = 0,
noise_mode = 'const',
force_fp32 = False,
layer_name = None,
sel_channels = 3,
base_channel = 0,
img_scale_db = 0,
img_normalize = False,
fft_show = False,
fft_all = True,
fft_range_db = 50,
fft_beta = 8,
input_transform = None,
untransform = False,
):
# Dig up network details.
G = self.get_network(pkl, 'G_ema')
res.img_resolution = G.img_resolution
res.num_ws = G.num_ws
res.has_noise = any('noise_const' in name for name, _buf in G.synthesis.named_buffers())
res.has_input_transform = (hasattr(G.synthesis, 'input') and hasattr(G.synthesis.input, 'transform'))
# Set input transform.
if res.has_input_transform:
m = np.eye(3)
try:
if input_transform is not None:
m = np.linalg.inv(np.asarray(input_transform))
except np.linalg.LinAlgError:
res.error = CapturedException()
G.synthesis.input.transform.copy_(torch.from_numpy(m))
# Generate random latents.
all_seeds = [seed for seed, _weight in w0_seeds] + [stylemix_seed]
all_seeds = list(set(all_seeds))
all_zs = np.zeros([len(all_seeds), G.z_dim], dtype=np.float32)
all_cs = np.zeros([len(all_seeds), G.c_dim], dtype=np.float32)
for idx, seed in enumerate(all_seeds):
rnd = np.random.RandomState(seed)
all_zs[idx] = rnd.randn(G.z_dim)
if G.c_dim > 0:
all_cs[idx, rnd.randint(G.c_dim)] = 1
# Run mapping network.
w_avg = G.mapping.w_avg
all_zs = self.to_device(torch.from_numpy(all_zs))
all_cs = self.to_device(torch.from_numpy(all_cs))
all_ws = G.mapping(z=all_zs, c=all_cs, truncation_psi=trunc_psi, truncation_cutoff=trunc_cutoff) - w_avg
all_ws = dict(zip(all_seeds, all_ws))
# Calculate final W.
w = torch.stack([all_ws[seed] * weight for seed, weight in w0_seeds]).sum(dim=0, keepdim=True)
stylemix_idx = [idx for idx in stylemix_idx if 0 <= idx < G.num_ws]
if len(stylemix_idx) > 0:
w[:, stylemix_idx] = all_ws[stylemix_seed][np.newaxis, stylemix_idx]
w += w_avg
# Run synthesis network.
synthesis_kwargs = dnnlib.EasyDict(noise_mode=noise_mode, force_fp32=force_fp32)
torch.manual_seed(random_seed)
out, layers = self.run_synthesis_net(G.synthesis, w, capture_layer=layer_name, **synthesis_kwargs)
# Update layer list.
cache_key = (G.synthesis, tuple(sorted(synthesis_kwargs.items())))
if cache_key not in self._net_layers:
if layer_name is not None:
torch.manual_seed(random_seed)
_out, layers = self.run_synthesis_net(G.synthesis, w, **synthesis_kwargs)
self._net_layers[cache_key] = layers
res.layers = self._net_layers[cache_key]
# Untransform.
if untransform and res.has_input_transform:
out, _mask = _apply_affine_transformation(out.to(torch.float32), G.synthesis.input.transform, amax=6) # Override amax to hit the fast path in upfirdn2d.
# Select channels and compute statistics.
out = out[0].to(torch.float32)
if sel_channels > out.shape[0]:
sel_channels = 1
base_channel = max(min(base_channel, out.shape[0] - sel_channels), 0)
sel = out[base_channel : base_channel + sel_channels]
res.stats = torch.stack([
out.mean(), sel.mean(),
out.std(), sel.std(),
out.norm(float('inf')), sel.norm(float('inf')),
])
# Scale and convert to uint8.
img = sel
if img_normalize:
img = img / img.norm(float('inf'), dim=[1,2], keepdim=True).clip(1e-8, 1e8)
img = img * (10 ** (img_scale_db / 20))
img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8).permute(1, 2, 0)
res.image = img
# FFT.
if fft_show:
sig = out if fft_all else sel
sig = sig.to(torch.float32)
sig = sig - sig.mean(dim=[1,2], keepdim=True)
sig = sig * torch.kaiser_window(sig.shape[1], periodic=False, beta=fft_beta, device=self._device)[None, :, None]
sig = sig * torch.kaiser_window(sig.shape[2], periodic=False, beta=fft_beta, device=self._device)[None, None, :]
fft = torch.fft.fftn(sig, dim=[1,2]).abs().square().sum(dim=0)
fft = fft.roll(shifts=[fft.shape[0] // 2, fft.shape[1] // 2], dims=[0,1])
fft = (fft / fft.mean()).log10() * 10 # dB
fft = self._apply_cmap((fft / fft_range_db + 1) / 2)
res.image = torch.cat([img.expand_as(fft), fft], dim=1)
@staticmethod
def run_synthesis_net(net, *args, capture_layer=None, **kwargs): # => out, layers
submodule_names = {mod: name for name, mod in net.named_modules()}
unique_names = set()
layers = []
def module_hook(module, _inputs, outputs):
outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
outputs = [out for out in outputs if isinstance(out, torch.Tensor) and out.ndim in [4, 5]]
for idx, out in enumerate(outputs):
if out.ndim == 5: # G-CNN => remove group dimension.
out = out.mean(2)
name = submodule_names[module]
if name == '':
name = 'output'
if len(outputs) > 1:
name += f':{idx}'
if name in unique_names:
suffix = 2
while f'{name}_{suffix}' in unique_names:
suffix += 1
name += f'_{suffix}'
unique_names.add(name)
shape = [int(x) for x in out.shape]
dtype = str(out.dtype).split('.')[-1]
layers.append(dnnlib.EasyDict(name=name, shape=shape, dtype=dtype))
if name == capture_layer:
raise CaptureSuccess(out)
hooks = [module.register_forward_hook(module_hook) for module in net.modules()]
try:
out = net(*args, **kwargs)
except CaptureSuccess as e:
out = e.out
for hook in hooks:
hook.remove()
return out, layers
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import imgui
from gui_utils import imgui_utils
#----------------------------------------------------------------------------
class StyleMixingWidget:
def __init__(self, viz):
self.viz = viz
self.seed_def = 1000
self.seed = self.seed_def
self.animate = False
self.enables = []
@imgui_utils.scoped_by_object_id
def __call__(self, show=True):
viz = self.viz
num_ws = viz.result.get('num_ws', 0)
num_enables = viz.result.get('num_ws', 18)
self.enables += [False] * max(num_enables - len(self.enables), 0)
if show:
imgui.text('Stylemix')
imgui.same_line(viz.label_w)
with imgui_utils.item_width(viz.font_size * 8), imgui_utils.grayed_out(num_ws == 0):
_changed, self.seed = imgui.input_int('##seed', self.seed)
imgui.same_line(viz.label_w + viz.font_size * 8 + viz.spacing)
with imgui_utils.grayed_out(num_ws == 0):
_clicked, self.animate = imgui.checkbox('Anim', self.animate)
pos2 = imgui.get_content_region_max()[0] - 1 - viz.button_w
pos1 = pos2 - imgui.get_text_line_height() - viz.spacing
pos0 = viz.label_w + viz.font_size * 12
imgui.push_style_var(imgui.STYLE_FRAME_PADDING, [0, 0])
for idx in range(num_enables):
imgui.same_line(round(pos0 + (pos1 - pos0) * (idx / (num_enables - 1))))
if idx == 0:
imgui.set_cursor_pos_y(imgui.get_cursor_pos_y() + 3)
with imgui_utils.grayed_out(num_ws == 0):
_clicked, self.enables[idx] = imgui.checkbox(f'##{idx}', self.enables[idx])
if imgui.is_item_hovered():
imgui.set_tooltip(f'{idx}')
imgui.pop_style_var(1)
imgui.same_line(pos2)
imgui.set_cursor_pos_y(imgui.get_cursor_pos_y() - 3)
with imgui_utils.grayed_out(num_ws == 0):
if imgui_utils.button('Reset', width=-1, enabled=(self.seed != self.seed_def or self.animate or any(self.enables[:num_enables]))):
self.seed = self.seed_def
self.animate = False
self.enables = [False] * num_enables
if any(self.enables[:num_ws]):
viz.args.stylemix_idx = [idx for idx, enable in enumerate(self.enables) if enable]
viz.args.stylemix_seed = self.seed & ((1 << 32) - 1)
if self.animate:
self.seed += 1
#----------------------------------------------------------------------------

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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import imgui
from gui_utils import imgui_utils
#----------------------------------------------------------------------------
class TruncationNoiseWidget:
def __init__(self, viz):
self.viz = viz
self.prev_num_ws = 0
self.trunc_psi = 1
self.trunc_cutoff = 0
self.noise_enable = True
self.noise_seed = 0
self.noise_anim = False
@imgui_utils.scoped_by_object_id
def __call__(self, show=True):
viz = self.viz
num_ws = viz.result.get('num_ws', 0)
has_noise = viz.result.get('has_noise', False)
if num_ws > 0 and num_ws != self.prev_num_ws:
if self.trunc_cutoff > num_ws or self.trunc_cutoff == self.prev_num_ws:
self.trunc_cutoff = num_ws
self.prev_num_ws = num_ws
if show:
imgui.text('Truncate')
imgui.same_line(viz.label_w)
with imgui_utils.item_width(viz.font_size * 10), imgui_utils.grayed_out(num_ws == 0):
_changed, self.trunc_psi = imgui.slider_float('##psi', self.trunc_psi, -1, 2, format='Psi %.2f')
imgui.same_line()
if num_ws == 0:
imgui_utils.button('Cutoff 0', width=(viz.font_size * 8 + viz.spacing), enabled=False)
else:
with imgui_utils.item_width(viz.font_size * 8 + viz.spacing):
changed, new_cutoff = imgui.slider_int('##cutoff', self.trunc_cutoff, 0, num_ws, format='Cutoff %d')
if changed:
self.trunc_cutoff = min(max(new_cutoff, 0), num_ws)
with imgui_utils.grayed_out(not has_noise):
imgui.same_line()
_clicked, self.noise_enable = imgui.checkbox('Noise##enable', self.noise_enable)
imgui.same_line(round(viz.font_size * 27.7))
with imgui_utils.grayed_out(not self.noise_enable):
with imgui_utils.item_width(-1 - viz.button_w - viz.spacing - viz.font_size * 4):
_changed, self.noise_seed = imgui.input_int('##seed', self.noise_seed)
imgui.same_line(spacing=0)
_clicked, self.noise_anim = imgui.checkbox('Anim##noise', self.noise_anim)
is_def_trunc = (self.trunc_psi == 1 and self.trunc_cutoff == num_ws)
is_def_noise = (self.noise_enable and self.noise_seed == 0 and not self.noise_anim)
with imgui_utils.grayed_out(is_def_trunc and not has_noise):
imgui.same_line(imgui.get_content_region_max()[0] - 1 - viz.button_w)
if imgui_utils.button('Reset', width=-1, enabled=(not is_def_trunc or not is_def_noise)):
self.prev_num_ws = num_ws
self.trunc_psi = 1
self.trunc_cutoff = num_ws
self.noise_enable = True
self.noise_seed = 0
self.noise_anim = False
if self.noise_anim:
self.noise_seed += 1
viz.args.update(trunc_psi=self.trunc_psi, trunc_cutoff=self.trunc_cutoff, random_seed=self.noise_seed)
viz.args.noise_mode = ('none' if not self.noise_enable else 'const' if self.noise_seed == 0 else 'random')
#----------------------------------------------------------------------------