commit 8fb5177d77556d19bf3ccfa03621aa4cbdf29dc9 Author: Janne Hellsten Date: Thu Oct 7 12:55:26 2021 +0300 initial commit diff --git a/.github/bug_report.md b/.github/bug_report.md new file mode 100644 index 0000000..de29664 --- /dev/null +++ b/.github/bug_report.md @@ -0,0 +1,35 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. In '...' directory, run command '...' +2. See error (copy&paste full log, including exceptions and **stacktraces**). + +Please copy&paste text instead of screenshots for better searchability. + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. Linux Ubuntu 20.04, Windows 10] + - PyTorch version (e.g., pytorch 1.9.0) + - CUDA toolkit version (e.g., CUDA 11.4) + - NVIDIA driver version + - GPU [e.g., Titan V, RTX 3090] + - Docker: did you use Docker? If yes, specify docker image URL (e.g., nvcr.io/nvidia/pytorch:21.08-py3) + +**Additional context** +Add any other context about the problem here. diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..6944e76 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,19 @@ +# 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"] diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..6b5ee9b --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,97 @@ +Copyright (c) 2021, NVIDIA Corporation & affiliates. All rights reserved. + + +NVIDIA Source Code License for StyleGAN3 + + +======================================================================= + +1. Definitions + +"Licensor" means any person or entity that distributes its Work. + +"Software" means the original work of authorship made available under +this License. + +"Work" means the Software and any additions to or derivative works of +the Software that are made available under this License. + +The terms "reproduce," "reproduction," "derivative works," and +"distribution" have the meaning as provided under U.S. copyright law; +provided, however, that for the purposes of this License, derivative +works shall not include works that remain separable from, or merely +link (or bind by name) to the interfaces of, the Work. + +Works, including the Software, are "made available" under this License +by including in or with the Work either (a) a copyright notice +referencing the applicability of this License to the Work, or (b) a +copy of this License. + +2. License Grants + + 2.1 Copyright Grant. 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You may specify that additional or different + terms apply to the use, reproduction, and distribution of your + derivative works of the Work ("Your Terms") only if (a) Your Terms + provide that the use limitation in Section 3.3 applies to your + derivative works, and (b) you identify the specific derivative + works that are subject to Your Terms. Notwithstanding Your Terms, + this License (including the redistribution requirements in Section + 3.1) will continue to apply to the Work itself. + + 3.3 Use Limitation. The Work and any derivative works thereof only + may be used or intended for use non-commercially. Notwithstanding + the foregoing, NVIDIA and its affiliates may use the Work and any + derivative works commercially. As used herein, "non-commercially" + means for research or evaluation purposes only. + + 3.4 Patent Claims. If you bring or threaten to bring a patent claim + against any Licensor (including any claim, cross-claim or + counterclaim in a lawsuit) to enforce any patents that you allege + are infringed by any Work, then your rights under this License from + such Licensor (including the grant in Section 2.1) will terminate + immediately. + + 3.5 Trademarks. This License does not grant any rights to use any + Licensor’s or its affiliates’ names, logos, or trademarks, except + as necessary to reproduce the notices described in this License. + + 3.6 Termination. If you violate any term of this License, then your + rights under this License (including the grant in Section 2.1) will + terminate immediately. + +4. Disclaimer of Warranty. + +THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR +NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER +THIS LICENSE. + +5. Limitation of Liability. + +EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL +THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE +SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, +INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF +OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK +(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, +LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER +COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF +THE POSSIBILITY OF SUCH DAMAGES. + +======================================================================= diff --git a/README.md b/README.md new file mode 100644 index 0000000..fcce585 --- /dev/null +++ b/README.md @@ -0,0 +1,296 @@ +## Alias-Free Generative Adversarial Networks (StyleGAN3)
Official PyTorch implementation of the NeurIPS 2021 paper + +![Teaser image](./docs/stylegan3-teaser-1920x1006.png) + +**Alias-Free Generative Adversarial Networks**
+Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila
+https://nvlabs.github.io/stylegan3
+ +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)
+For press and other inquiries, please contact Hector Marinez at [hmarinez@nvidia.com](mailto:hmarinez@nvidia.com)
+ +## 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.) + > Access individual networks via `https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/`, where `` is one of:
+ > `stylegan3-t-ffhq-1024x1024.pkl`, `stylegan3-t-ffhqu-1024x1024.pkl`, `stylegan3-t-ffhqu-256x256.pkl`
+ > `stylegan3-r-ffhq-1024x1024.pkl`, `stylegan3-r-ffhqu-1024x1024.pkl`, `stylegan3-r-ffhqu-256x256.pkl`
+ > `stylegan3-t-metfaces-1024x1024.pkl`, `stylegan3-t-metfacesu-1024x1024.pkl`
+ > `stylegan3-r-metfaces-1024x1024.pkl`, `stylegan3-r-metfacesu-1024x1024.pkl`
+ > `stylegan3-t-afhqv2-512x512.pkl`
+ > `stylegan3-r-afhqv2-512x512.pkl`
+- [StyleGAN2 pre-trained models](https://ngc.nvidia.com/catalog/models/nvidia:research:stylegan2) compatible with this codebase + > Access individual networks via `https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/`, where `` is one of:
+ > `stylegan2-ffhq-1024x1024.pkl`, `stylegan2-ffhq-512x512.pkl`, `stylegan2-ffhq-256x256.pkl`
+ > `stylegan2-ffhqu-1024x1024.pkl`, `stylegan2-ffhqu-256x256.pkl`
+ > `stylegan2-metfaces-1024x1024.pkl`, `stylegan2-metfacesu-1024x1024.pkl`
+ > `stylegan2-afhqv2-512x512.pkl`
+ > `stylegan2-afhqcat-512x512.pkl`, `stylegan2-afhqdog-512x512.pkl`, `stylegan2-afhqwild-512x512.pkl`
+ > `stylegan2-brecahad-512x512.pkl`, `stylegan2-cifar10-32x32.pkl`
+ > `stylegan2-celebahq-256x256.pkl`, `stylegan2-lsundog-256x256.pkl`
+ +## Requirements + +* Linux and Windows are supported, but we recommend Linux for performance and compatibility reasons. +* 1–8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using 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\\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 +``` + +Visualizer screenshot + +## 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 — 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 & recommendations, along with the expected training speed & 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-.pkl`) and random image grids (`fakes.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`: Fréchet inception distance[1] against the full dataset. +* `kid50k_full`: Kernel inception distance[2] against the full dataset. +* `pr50k3_full`: Precision and recall[3] againt the full dataset. +* `ppl2_wend`: Perceptual path length[4] in W, endpoints, full image. +* `eqt50k_int`: Equivariance[5] w.r.t. integer translation (EQ-T). +* `eqt50k_frac`: Equivariance w.r.t. fractional translation (EQ-Tfrac). +* `eqr50k`: Equivariance w.r.t. rotation (EQ-R). + +Legacy metrics: +* `fid50k`: Fréchet 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[6] 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), Bińkowski et al. 2018 +3. [Improved Precision and Recall Metric for Assessing Generative Models](https://arxiv.org/abs/1904.06991), Kynkäänniemi 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 +``` + +Average spectra screenshot + +## License + +Copyright © 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 Kynkäänniemi, and Timo Viitanen for reviewing early drafts and helpful suggestions. Frédo 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. diff --git a/avg_spectra.py b/avg_spectra.py new file mode 100644 index 0000000..afaef87 --- /dev/null +++ b/avg_spectra.py @@ -0,0 +1,276 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/calc_metrics.py b/calc_metrics.py new file mode 100644 index 0000000..74a398a --- /dev/null +++ b/calc_metrics.py @@ -0,0 +1,188 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/dataset_tool.py b/dataset_tool.py new file mode 100644 index 0000000..e9382fb --- /dev/null +++ b/dataset_tool.py @@ -0,0 +1,455 @@ +# 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 diff --git a/dnnlib/__init__.py b/dnnlib/__init__.py new file mode 100644 index 0000000..e0a0067 --- /dev/null +++ b/dnnlib/__init__.py @@ -0,0 +1,9 @@ +# 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 diff --git a/dnnlib/util.py b/dnnlib/util.py new file mode 100644 index 0000000..191b52f --- /dev/null +++ b/dnnlib/util.py @@ -0,0 +1,491 @@ +# 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) diff --git a/docs/avg_spectra_screen0.png b/docs/avg_spectra_screen0.png new file mode 100644 index 0000000..94339f8 Binary files /dev/null and b/docs/avg_spectra_screen0.png differ diff --git a/docs/avg_spectra_screen0_half.png b/docs/avg_spectra_screen0_half.png new file mode 100644 index 0000000..0768901 Binary files /dev/null and b/docs/avg_spectra_screen0_half.png differ diff --git a/docs/configs.md b/docs/configs.md new file mode 100644 index 0000000..7469c87 --- /dev/null +++ b/docs/configs.md @@ -0,0 +1,201 @@ +# 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: + +| Config

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :--------------------------- | :--------------: | :--------------: | :------------: | :-- +| StyleGAN3‑T | 18.47 | 12.29 | 4.3 | `--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=8.2 --mirror=1` + +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 ±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 ≈ 485,000 seconds ≈ 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 R1 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 → 512x512) means that `--gamma` should be multiplied by 4 (e.g., 2 → 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., 1–2), 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 + +| Config

| GPUs

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :--------------------------- | :----------: | :--------------: | :--------------: | :------------: | :-- +| StyleGAN3‑T | 1 | 73.68 | 27.20 | 7.2 | `--cfg=stylegan3-t --gpus=1 --batch=32 --gamma=0.5 --batch-gpu=16 --snap=10` +| StyleGAN3‑T | 2 | 37.30 | 13.74 | 7.1 | `--cfg=stylegan3-t --gpus=2 --batch=32 --gamma=0.5 --snap=20` +| StyleGAN3‑T | 4 | 20.66 | 7.52 | 4.1 | `--cfg=stylegan3-t --gpus=4 --batch=32 --gamma=0.5` +| StyleGAN3‑T | 8 | 11.31 | 4.40 | 2.6 | `--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=0.5` +| StyleGAN3‑R | 1 | 58.44 | 34.23 | 8.3 | `--cfg=stylegan3-r --gpus=1 --batch=32 --gamma=0.5 --batch-gpu=16 --snap=10` +| StyleGAN3‑R | 2 | 29.92 | 17.29 | 8.2 | `--cfg=stylegan3-r --gpus=2 --batch=32 --gamma=0.5 --snap=20` +| StyleGAN3‑R | 4 | 15.49 | 9.53 | 4.5 | `--cfg=stylegan3-r --gpus=4 --batch=32 --gamma=0.5` +| StyleGAN3‑R | 8 | 8.43 | 5.69 | 2.7 | `--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=0.5` + +#### 256x256 resolution + +| Config

| GPUs

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :--------------------------- | :----------: | :--------------: | :--------------: | :------------: | :-- +| StyleGAN3‑T | 1 | 89.15 | 49.81 | 9.5 | `--cfg=stylegan3-t --gpus=1 --batch=32 --gamma=2 --batch-gpu=16 --snap=10` +| StyleGAN3‑T | 2 | 45.45 | 25.05 | 9.3 | `--cfg=stylegan3-t --gpus=2 --batch=32 --gamma=2 --snap=20` +| StyleGAN3‑T | 4 | 23.94 | 13.26 | 5.2 | `--cfg=stylegan3-t --gpus=4 --batch=32 --gamma=2` +| StyleGAN3‑T | 8 | 13.04 | 7.32 | 3.1 | `--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=2` +| StyleGAN3‑R | 1 | 87.37 | 56.73 | 6.7 | `--cfg=stylegan3-r --gpus=1 --batch=32 --gamma=2 --batch-gpu=8 --snap=10` +| StyleGAN3‑R | 2 | 44.12 | 28.60 | 6.7 | `--cfg=stylegan3-r --gpus=2 --batch=32 --gamma=2 --batch-gpu=8 --snap=20` +| StyleGAN3‑R | 4 | 22.42 | 14.39 | 6.6 | `--cfg=stylegan3-r --gpus=4 --batch=32 --gamma=2` +| StyleGAN3‑R | 8 | 11.88 | 8.03 | 3.7 | `--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=2` + +#### 512x512 resolution + +| Config

| GPUs

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :--------------------------- | :----------: | :---------------: | :---------------: | :------------: | :-- +| StyleGAN3‑T | 1 | 137.33 | 90.25 | 7.8 | `--cfg=stylegan3-t --gpus=1 --batch=32 --gamma=8 --batch-gpu=8 --snap=10` +| StyleGAN3‑T | 2 | 69.65 | 45.42 | 7.7 | `--cfg=stylegan3-t --gpus=2 --batch=32 --gamma=8 --batch-gpu=8 --snap=20` +| StyleGAN3‑T | 4 | 34.88 | 22.81 | 7.6 | `--cfg=stylegan3-t --gpus=4 --batch=32 --gamma=8` +| StyleGAN3‑T | 8 | 18.47 | 12.29 | 4.3 | `--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=8` +| StyleGAN3‑R | 1 | 158.91 | 110.13 | 6.0 | `--cfg=stylegan3-r --gpus=1 --batch=32 --gamma=8 --batch-gpu=4 --snap=10` +| StyleGAN3‑R | 2 | 79.96 | 55.18 | 6.0 | `--cfg=stylegan3-r --gpus=2 --batch=32 --gamma=8 --batch-gpu=4 --snap=20` +| StyleGAN3‑R | 4 | 40.86 | 27.99 | 5.9 | `--cfg=stylegan3-r --gpus=4 --batch=32 --gamma=8 --batch-gpu=4` +| StyleGAN3‑R | 8 | 20.44 | 14.04 | 5.9 | `--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=8` + +#### 1024x1024 resolution + +| Config

| GPUs

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :--------------------------- | :----------: | :---------------: | :---------------: | :-------------: | :-- +| StyleGAN3‑T | 1 | 221.85 | 156.91 | 7.0 | `--cfg=stylegan3-t --gpus=1 --batch=32 --gamma=32 --batch-gpu=4 --snap=5` +| StyleGAN3‑T | 2 | 113.44 | 79.16 | 6.8 | `--cfg=stylegan3-t --gpus=2 --batch=32 --gamma=32 --batch-gpu=4 --snap=10` +| StyleGAN3‑T | 4 | 57.04 | 39.62 | 6.7 | `--cfg=stylegan3-t --gpus=4 --batch=32 --gamma=32 --batch-gpu=4 --snap=20` +| StyleGAN3‑T | 8 | 28.71 | 20.01 | 6.6 | `--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=32` +| StyleGAN3‑R | 1 | 263.44 | 184.81 | 10.2 | `--cfg=stylegan3-r --gpus=1 --batch=32 --gamma=32 --batch-gpu=4 --snap=5` +| StyleGAN3‑R | 2 | 134.22 | 92.58 | 10.1 | `--cfg=stylegan3-r --gpus=2 --batch=32 --gamma=32 --batch-gpu=4 --snap=10` +| StyleGAN3‑R | 4 | 67.33 | 46.53 | 10.0 | `--cfg=stylegan3-r --gpus=4 --batch=32 --gamma=32 --batch-gpu=4 --snap=20` +| StyleGAN3‑R | 8 | 34.12 | 23.42 | 9.9 | `--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=32` + +## 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 + +| Config

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :--------------------------- | :--------------: | :--------------: | :------------: | :-- +| StyleGAN2 | 17.55 | 14.57 | 6.2 | `--cfg=stylegan2 --gpus=8 --batch=32 --gamma=10 --mirror=1 --aug=noaug` +| StyleGAN3‑T | 28.71 | 20.01 | 6.6 | `--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=32.8 --mirror=1 --aug=noaug` +| StyleGAN3‑R | 34.12 | 23.42 | 9.9 | `--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=32.8 --mirror=1 --aug=noaug` + +#### MetFaces-U at 1024x1024 resolution + +| Config

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :--------------------------- | :--------------: | :--------------: | :-------------: | :-- +| StyleGAN2 | 18.74 | 11.80 | 7.4 | `--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` +| StyleGAN3‑T | 29.84 | 21.06 | 7.7 | `--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` +| StyleGAN3‑R | 35.10 | 24.32 | 10.9 | `--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` + +#### MetFaces at 1024x1024 resolution + +| Config

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :--------------------------- | :--------------: | :--------------: | :-------------: | :-- +| StyleGAN2 | 18.74 | 11.80 | 7.4 | `--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` +| StyleGAN3‑T | 29.84 | 21.06 | 7.7 | `--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` +| StyleGAN3‑R | 35.10 | 24.32 | 10.9 | `--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` + +#### AFHQv2 at 512x512 resolution + +| Config

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :--------------------------- | :--------------: | :--------------: | :------------: | :-- +| StyleGAN2 | 10.90 | 6.60 | 3.9 | `--cfg=stylegan2 --gpus=8 --batch=32 --gamma=5 --mirror=1` +| StyleGAN3‑T | 18.47 | 12.29 | 4.3 | `--cfg=stylegan3-t --gpus=8 --batch=32 --gamma=8.2 --mirror=1` +| StyleGAN3‑R | 20.44 | 14.04 | 5.9 | `--cfg=stylegan3-r --gpus=8 --batch=32 --gamma=16.4 --mirror=1` + +#### FFHQ-U ablations at 256x256 resolution + +| Config

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :--------------------------- | :-------------: | :-------------: | :------------: | :-- +| StyleGAN2 | 3.61 | 2.19 | 2.7 | `--cfg=stylegan2 --gpus=8 --batch=64 --gamma=1 --mirror=1 --aug=noaug --cbase=16384 --glr=0.0025 --dlr=0.0025 --mbstd-group=8` +| StyleGAN3‑T | 7.40 | 3.74 | 3.5 | `--cfg=stylegan3-t --gpus=8 --batch=64 --gamma=1 --mirror=1 --aug=noaug --cbase=16384 --dlr=0.0025` +| StyleGAN3‑R | 6.71 | 4.81 | 4.2 | `--cfg=stylegan3-r --gpus=8 --batch=64 --gamma=1 --mirror=1 --aug=noaug --cbase=16384 --dlr=0.0025` + +## 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 + +| Res.

| GPUs

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :---------------------- | :----------: | :---------------: | :--------------: | :------------: | :-- +| 128² | 1 | 12.51 | 6.79 | 6.2 | `--cfg=stylegan2 --gpus=1 --batch=32 --gamma=0.1024 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384` +| 128² | 2 | 6.43 | 3.45 | 6.2 | `--cfg=stylegan2 --gpus=2 --batch=64 --gamma=0.0512 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384` +| 128² | 4 | 3.82 | 2.23 | 3.5 | `--cfg=stylegan2 --gpus=4 --batch=64 --gamma=0.0512 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384` +| 256² | 1 | 20.84 | 12.53 | 4.5 | `--cfg=stylegan2 --gpus=1 --batch=16 --gamma=0.8192 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384` +| 256² | 2 | 10.93 | 6.36 | 4.5 | `--cfg=stylegan2 --gpus=2 --batch=32 --gamma=0.4096 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384` +| 256² | 4 | 5.39 | 3.20 | 4.5 | `--cfg=stylegan2 --gpus=4 --batch=64 --gamma=0.2048 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384` +| 256² | 8 | 3.89 | 2.38 | 2.6 | `--cfg=stylegan2 --gpus=8 --batch=64 --gamma=0.2048 --map-depth=2 --glr=0.0025 --dlr=0.0025 --cbase=16384` +| 512² | 1 | 71.59 | 41.06 | 6.8 | `--cfg=stylegan2 --gpus=1 --batch=8 --gamma=6.5536 --map-depth=2 --glr=0.0025 --dlr=0.0025` +| 512² | 2 | 36.79 | 20.83 | 6.8 | `--cfg=stylegan2 --gpus=2 --batch=16 --gamma=3.2768 --map-depth=2 --glr=0.0025 --dlr=0.0025` +| 512² | 4 | 18.12 | 10.45 | 6.7 | `--cfg=stylegan2 --gpus=4 --batch=32 --gamma=1.6384 --map-depth=2 --glr=0.0025 --dlr=0.0025` +| 512² | 8 | 9.09 | 5.24 | 6.8 | `--cfg=stylegan2 --gpus=8 --batch=64 --gamma=0.8192 --map-depth=2 --glr=0.0025 --dlr=0.0025` +| 1024² | 1 | 141.83 | 90.39 | 7.2 | `--cfg=stylegan2 --gpus=1 --batch=4 --gamma=52.4288 --map-depth=2` +| 1024² | 2 | 73.13 | 46.04 | 7.2 | `--cfg=stylegan2 --gpus=2 --batch=8 --gamma=26.2144 --map-depth=2` +| 1024² | 4 | 36.95 | 23.15 | 7.0 | `--cfg=stylegan2 --gpus=4 --batch=16 --gamma=13.1072 --map-depth=2` +| 1024² | 8 | 18.47 | 11.66 | 7.3 | `--cfg=stylegan2 --gpus=8 --batch=32 --gamma=6.5536 --map-depth=2` + +#### Repro configurations + +| Name

| s/kimg
(V100) | s/kimg
(A100) | GPU
mem | Options

+| :---------------------- | :--------------: | :--------------: | :------------: | :-- +| `stylegan2` | 17.55 | 14.57 | 6.2 | `--cfg=stylegan2 --gpus=8 --batch=32 --gamma=10` +| `paper256` | 4.01 | 2.47 | 2.7 | `--cfg=stylegan2 --gpus=8 --batch=64 --gamma=1 --cbase=16384 --glr=0.0025 --dlr=0.0025 --mbstd-group=8` +| `paper512` | 9.11 | 5.28 | 6.7 | `--cfg=stylegan2 --gpus=8 --batch=64 --gamma=0.5 --glr=0.0025 --dlr=0.0025 --mbstd-group=8` +| `paper1024` | 18.56 | 11.75 | 6.9 | `--cfg=stylegan2 --gpus=8 --batch=32 --gamma=2` diff --git a/docs/dataset-tool-help.txt b/docs/dataset-tool-help.txt new file mode 100644 index 0000000..b8fe51d --- /dev/null +++ b/docs/dataset-tool-help.txt @@ -0,0 +1,70 @@ +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. diff --git a/docs/stylegan3-teaser-1920x1006.png b/docs/stylegan3-teaser-1920x1006.png new file mode 100644 index 0000000..9132fba Binary files /dev/null and b/docs/stylegan3-teaser-1920x1006.png differ diff --git a/docs/train-help.txt b/docs/train-help.txt new file mode 100644 index 0000000..5839bf1 --- /dev/null +++ b/docs/train-help.txt @@ -0,0 +1,53 @@ +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. diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md new file mode 100644 index 0000000..b7218b7 --- /dev/null +++ b/docs/troubleshooting.md @@ -0,0 +1,30 @@ +# 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: `$HOME/.cache/torch_extensions` (Linux) or `C:\Users\\AppData\Local\torch_extensions\torch_extensions\Cache` (Windows) and re-run StyleGAN3 python code. +- Run ninja in `$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. diff --git a/docs/visualizer_screen0.png b/docs/visualizer_screen0.png new file mode 100644 index 0000000..3b969b8 Binary files /dev/null and b/docs/visualizer_screen0.png differ diff --git a/docs/visualizer_screen0_half.png b/docs/visualizer_screen0_half.png new file mode 100644 index 0000000..ab76f7f Binary files /dev/null and b/docs/visualizer_screen0_half.png differ diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..578a58a --- /dev/null +++ b/environment.yml @@ -0,0 +1,24 @@ +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 diff --git a/gen_images.py b/gen_images.py new file mode 100644 index 0000000..f8a4b11 --- /dev/null +++ b/gen_images.py @@ -0,0 +1,144 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/gen_video.py b/gen_video.py new file mode 100644 index 0000000..7a4bcc0 --- /dev/null +++ b/gen_video.py @@ -0,0 +1,178 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/gui_utils/__init__.py b/gui_utils/__init__.py new file mode 100644 index 0000000..8dd3488 --- /dev/null +++ b/gui_utils/__init__.py @@ -0,0 +1,9 @@ +# 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 diff --git a/gui_utils/gl_utils.py b/gui_utils/gl_utils.py new file mode 100644 index 0000000..2dd8bd9 --- /dev/null +++ b/gui_utils/gl_utils.py @@ -0,0 +1,374 @@ +# 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') + +#---------------------------------------------------------------------------- diff --git a/gui_utils/glfw_window.py b/gui_utils/glfw_window.py new file mode 100644 index 0000000..94c4a8d --- /dev/null +++ b/gui_utils/glfw_window.py @@ -0,0 +1,229 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/gui_utils/imgui_utils.py b/gui_utils/imgui_utils.py new file mode 100644 index 0000000..e5cb118 --- /dev/null +++ b/gui_utils/imgui_utils.py @@ -0,0 +1,169 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/gui_utils/imgui_window.py b/gui_utils/imgui_window.py new file mode 100644 index 0000000..aaf7caa --- /dev/null +++ b/gui_utils/imgui_window.py @@ -0,0 +1,103 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/gui_utils/text_utils.py b/gui_utils/text_utils.py new file mode 100644 index 0000000..ed0c7a9 --- /dev/null +++ b/gui_utils/text_utils.py @@ -0,0 +1,123 @@ +# 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) + +#---------------------------------------------------------------------------- diff --git a/legacy.py b/legacy.py new file mode 100644 index 0000000..e361fc2 --- /dev/null +++ b/legacy.py @@ -0,0 +1,323 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/metrics/__init__.py b/metrics/__init__.py new file mode 100644 index 0000000..8dd3488 --- /dev/null +++ b/metrics/__init__.py @@ -0,0 +1,9 @@ +# 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 diff --git a/metrics/equivariance.py b/metrics/equivariance.py new file mode 100644 index 0000000..c96ebed --- /dev/null +++ b/metrics/equivariance.py @@ -0,0 +1,267 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/metrics/frechet_inception_distance.py b/metrics/frechet_inception_distance.py new file mode 100644 index 0000000..1bdd6b6 --- /dev/null +++ b/metrics/frechet_inception_distance.py @@ -0,0 +1,41 @@ +# 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) + +#---------------------------------------------------------------------------- diff --git a/metrics/inception_score.py b/metrics/inception_score.py new file mode 100644 index 0000000..b9a7f4a --- /dev/null +++ b/metrics/inception_score.py @@ -0,0 +1,38 @@ +# 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)) + +#---------------------------------------------------------------------------- diff --git a/metrics/kernel_inception_distance.py b/metrics/kernel_inception_distance.py new file mode 100644 index 0000000..e8e0bd1 --- /dev/null +++ b/metrics/kernel_inception_distance.py @@ -0,0 +1,46 @@ +# 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) + +#---------------------------------------------------------------------------- diff --git a/metrics/metric_main.py b/metrics/metric_main.py new file mode 100644 index 0000000..e4f7389 --- /dev/null +++ b/metrics/metric_main.py @@ -0,0 +1,153 @@ +# 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) + +#---------------------------------------------------------------------------- diff --git a/metrics/metric_utils.py b/metrics/metric_utils.py new file mode 100644 index 0000000..44b67ee --- /dev/null +++ b/metrics/metric_utils.py @@ -0,0 +1,279 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/metrics/perceptual_path_length.py b/metrics/perceptual_path_length.py new file mode 100644 index 0000000..7fb7439 --- /dev/null +++ b/metrics/perceptual_path_length.py @@ -0,0 +1,125 @@ +# 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) + +#---------------------------------------------------------------------------- diff --git a/metrics/precision_recall.py b/metrics/precision_recall.py new file mode 100644 index 0000000..17e5b42 --- /dev/null +++ b/metrics/precision_recall.py @@ -0,0 +1,62 @@ +# 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'] + +#---------------------------------------------------------------------------- diff --git a/torch_utils/__init__.py b/torch_utils/__init__.py new file mode 100644 index 0000000..8dd3488 --- /dev/null +++ b/torch_utils/__init__.py @@ -0,0 +1,9 @@ +# 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 diff --git a/torch_utils/custom_ops.py b/torch_utils/custom_ops.py new file mode 100644 index 0000000..dffd4bd --- /dev/null +++ b/torch_utils/custom_ops.py @@ -0,0 +1,157 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/torch_utils/misc.py b/torch_utils/misc.py new file mode 100644 index 0000000..02f97e2 --- /dev/null +++ b/torch_utils/misc.py @@ -0,0 +1,266 @@ +# 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 = '' 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 + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/__init__.py b/torch_utils/ops/__init__.py new file mode 100644 index 0000000..8dd3488 --- /dev/null +++ b/torch_utils/ops/__init__.py @@ -0,0 +1,9 @@ +# 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 diff --git a/torch_utils/ops/bias_act.cpp b/torch_utils/ops/bias_act.cpp new file mode 100644 index 0000000..218bc8f --- /dev/null +++ b/torch_utils/ops/bias_act.cpp @@ -0,0 +1,99 @@ +// 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 +#include +#include +#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(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); +} + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/bias_act.cu b/torch_utils/ops/bias_act.cu new file mode 100644 index 0000000..77979ab --- /dev/null +++ b/torch_utils/ops/bias_act.cu @@ -0,0 +1,173 @@ +// 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 +#include "bias_act.h" + +//------------------------------------------------------------------------ +// Helpers. + +template struct InternalType; +template <> struct InternalType { typedef double scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; + +//------------------------------------------------------------------------ +// CUDA kernel. + +template +__global__ void bias_act_kernel(bias_act_kernel_params p) +{ + typedef typename InternalType::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 void* choose_bias_act_kernel(const bias_act_kernel_params& p) +{ + if (p.act == 1) return (void*)bias_act_kernel; + if (p.act == 2) return (void*)bias_act_kernel; + if (p.act == 3) return (void*)bias_act_kernel; + if (p.act == 4) return (void*)bias_act_kernel; + if (p.act == 5) return (void*)bias_act_kernel; + if (p.act == 6) return (void*)bias_act_kernel; + if (p.act == 7) return (void*)bias_act_kernel; + if (p.act == 8) return (void*)bias_act_kernel; + if (p.act == 9) return (void*)bias_act_kernel; + return NULL; +} + +//------------------------------------------------------------------------ +// Template specializations. + +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/bias_act.h b/torch_utils/ops/bias_act.h new file mode 100644 index 0000000..db23152 --- /dev/null +++ b/torch_utils/ops/bias_act.h @@ -0,0 +1,38 @@ +// 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 void* choose_bias_act_kernel(const bias_act_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/bias_act.py b/torch_utils/ops/bias_act.py new file mode 100644 index 0000000..af13db5 --- /dev/null +++ b/torch_utils/ops/bias_act.py @@ -0,0 +1,209 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/conv2d_gradfix.py b/torch_utils/ops/conv2d_gradfix.py new file mode 100644 index 0000000..8056b5d --- /dev/null +++ b/torch_utils/ops/conv2d_gradfix.py @@ -0,0 +1,198 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/conv2d_resample.py b/torch_utils/ops/conv2d_resample.py new file mode 100644 index 0000000..a6d7240 --- /dev/null +++ b/torch_utils/ops/conv2d_resample.py @@ -0,0 +1,143 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/filtered_lrelu.cpp b/torch_utils/ops/filtered_lrelu.cpp new file mode 100644 index 0000000..6a99e11 --- /dev/null +++ b/torch_utils/ops/filtered_lrelu.cpp @@ -0,0 +1,300 @@ +// 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 +#include +#include +#include "filtered_lrelu.h" + +//------------------------------------------------------------------------ + +static std::tuple 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(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() : 0; + p.fu = fu.data_ptr(); + p.fd = fd.data_ptr(); + 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(p, sharedKB); + else if (!index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); + else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); + else if ( index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); + else if ( index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); + else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(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(at::cuda::getCurrentCUDAStream()))); + else if (!writeSigns && readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream()))); + else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(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() : 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(); + else if (readSigns) + func = choose_filtered_lrelu_act_kernel(); + else + func = choose_filtered_lrelu_act_kernel(); + }); + 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. +} + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/filtered_lrelu.cu b/torch_utils/ops/filtered_lrelu.cu new file mode 100644 index 0000000..d76f1e5 --- /dev/null +++ b/torch_utils/ops/filtered_lrelu.cu @@ -0,0 +1,1284 @@ +// 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 +#include "filtered_lrelu.h" +#include + +//------------------------------------------------------------------------ +// Helpers. + +enum // Filter modes. +{ + MODE_SUSD = 0, // Separable upsampling, separable downsampling. + MODE_FUSD = 1, // Full upsampling, separable downsampling. + MODE_SUFD = 2, // Separable upsampling, full downsampling. + MODE_FUFD = 3, // Full upsampling, full downsampling. +}; + +template struct InternalType; +template <> struct InternalType +{ + typedef double scalar_t; typedef double2 vec2_t; typedef double4 vec4_t; + __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_double2(0, 0); } + __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_double4(0, 0, 0, 0); } + __device__ __forceinline__ static double clamp(double x, double c) { return fmin(fmax(x, -c), c); } +}; +template <> struct InternalType +{ + typedef float scalar_t; typedef float2 vec2_t; typedef float4 vec4_t; + __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_float2(0, 0); } + __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_float4(0, 0, 0, 0); } + __device__ __forceinline__ static float clamp(float x, float c) { return fminf(fmaxf(x, -c), c); } +}; +template <> struct InternalType +{ + typedef float scalar_t; typedef float2 vec2_t; typedef float4 vec4_t; + __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_float2(0, 0); } + __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_float4(0, 0, 0, 0); } + __device__ __forceinline__ static float clamp(float x, float c) { return fminf(fmaxf(x, -c), c); } +}; + +#define MIN(A, B) ((A) < (B) ? (A) : (B)) +#define MAX(A, B) ((A) > (B) ? (A) : (B)) +#define CEIL_DIV(A, B) (((B)==1) ? (A) : \ + ((B)==2) ? ((int)((A)+1) >> 1) : \ + ((B)==4) ? ((int)((A)+3) >> 2) : \ + (((A) + ((A) > 0 ? (B) - 1 : 0)) / (B))) + +// This works only up to blocks of size 256 x 256 and for all N that are powers of two. +template __device__ __forceinline__ void fast_div_mod(int& x, int& y, unsigned int i) +{ + if ((N & (N-1)) && N <= 256) + y = (i * ((1<<24)/N + 1)) >> 24; // Assumes N <= 256, i < N*256. + else + y = i/N; + + x = i - y*N; +} + +// Type cast stride before reading it. +template __device__ __forceinline__ T get_stride(const int64_t& x) +{ + return *reinterpret_cast(&x); +} + +//------------------------------------------------------------------------ +// Filters, setup kernel, copying function. + +#define MAX_FILTER_SIZE 32 + +// Combined up/down filter buffers so that transfer can be done with one copy. +__device__ float g_fbuf[2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE]; // Filters in global memory, written by setup kernel. +__device__ __constant__ float c_fbuf[2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE]; // Filters in constant memory, read by main kernel. + +// Accessors to combined buffers to index up/down filters individually. +#define c_fu (c_fbuf) +#define c_fd (c_fbuf + MAX_FILTER_SIZE * MAX_FILTER_SIZE) +#define g_fu (g_fbuf) +#define g_fd (g_fbuf + MAX_FILTER_SIZE * MAX_FILTER_SIZE) + +// Set up filters into global memory buffer. +static __global__ void setup_filters_kernel(filtered_lrelu_kernel_params p) +{ + for (int idx = threadIdx.x; idx < MAX_FILTER_SIZE * MAX_FILTER_SIZE; idx += blockDim.x) + { + int x, y; + fast_div_mod(x, y, idx); + + int fu_x = p.flip ? x : (p.fuShape.x - 1 - x); + int fu_y = p.flip ? y : (p.fuShape.y - 1 - y); + if (p.fuShape.y > 0) + g_fu[idx] = (x >= p.fuShape.x || y >= p.fuShape.y) ? 0.0f : p.fu[fu_x * p.fuStride.x + fu_y * p.fuStride.y]; + else + g_fu[idx] = (x >= p.fuShape.x || y > 0) ? 0.0f : p.fu[fu_x * p.fuStride.x]; + + int fd_x = p.flip ? x : (p.fdShape.x - 1 - x); + int fd_y = p.flip ? y : (p.fdShape.y - 1 - y); + if (p.fdShape.y > 0) + g_fd[idx] = (x >= p.fdShape.x || y >= p.fdShape.y) ? 0.0f : p.fd[fd_x * p.fdStride.x + fd_y * p.fdStride.y]; + else + g_fd[idx] = (x >= p.fdShape.x || y > 0) ? 0.0f : p.fd[fd_x * p.fdStride.x]; + } +} + +// Host function to copy filters written by setup kernel into constant buffer for main kernel. +template static cudaError_t copy_filters(cudaStream_t stream) +{ + void* src = 0; + cudaError_t err = cudaGetSymbolAddress(&src, g_fbuf); + if (err) return err; + return cudaMemcpyToSymbolAsync(c_fbuf, src, 2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE * sizeof(float), 0, cudaMemcpyDeviceToDevice, stream); +} + +//------------------------------------------------------------------------ +// Coordinate spaces: +// - Relative to input tensor: inX, inY, tileInX, tileInY +// - Relative to input tile: relInX, relInY, tileInW, tileInH +// - Relative to upsampled tile: relUpX, relUpY, tileUpW, tileUpH +// - Relative to output tile: relOutX, relOutY, tileOutW, tileOutH +// - Relative to output tensor: outX, outY, tileOutX, tileOutY +// +// Relationships between coordinate spaces: +// - inX = tileInX + relInX +// - inY = tileInY + relInY +// - relUpX = relInX * up + phaseInX +// - relUpY = relInY * up + phaseInY +// - relUpX = relOutX * down +// - relUpY = relOutY * down +// - outX = tileOutX + relOutX +// - outY = tileOutY + relOutY + +extern __shared__ char s_buf_raw[]; // When sharedKB <= 48, allocate shared memory statically inside the kernel, otherwise use the externally allocated shared memory buffer. + +template +static __global__ void filtered_lrelu_kernel(filtered_lrelu_kernel_params p) +{ + // Check that we don't try to support non-existing filter modes. + static_assert(up == 1 || up == 2 || up == 4, "only up=1, up=2, up=4 scales supported"); + static_assert(down == 1 || down == 2 || down == 4, "only down=1, down=2, down=4 scales supported"); + static_assert(fuSize >= up, "upsampling filter size must be at least upsampling factor"); + static_assert(fdSize >= down, "downsampling filter size must be at least downsampling factor"); + static_assert(fuSize % up == 0, "upsampling filter size must be divisible with upsampling factor"); + static_assert(fdSize % down == 0, "downsampling filter size must be divisible with downsampling factor"); + static_assert(fuSize <= MAX_FILTER_SIZE && fdSize <= MAX_FILTER_SIZE, "filter size greater than MAX_FILTER_SIZE"); + static_assert(up != 1 || (fuSize == 1 && (filterMode == MODE_FUFD || filterMode == MODE_FUSD)), "up=1 supported only for 1x1 full filters"); + static_assert(down != 1 || (fdSize == 1 && (filterMode == MODE_FUFD || filterMode == MODE_SUFD)), "down=1 supported only for 1x1 full filters"); + static_assert(!(up == 4 && (filterMode == MODE_FUFD || filterMode == MODE_FUSD)), "full filters not supported for up=4"); + static_assert(!(down == 4 && (filterMode == MODE_FUFD || filterMode == MODE_SUFD)), "full filters not supported for down=4"); + + // Static definitions. + typedef typename InternalType::scalar_t scalar_t; + typedef typename InternalType::vec2_t vec2_t; + typedef typename InternalType::vec4_t vec4_t; + const int tileUpW = (tileOutW * down + (fdSize - 1) - (down - 1) + 3) & ~3; // Upsampled tile width, rounded up to multiple of 4. + const int tileUpH = tileOutH * down + (fdSize - 1) - (down - 1); // Upsampled tile height. + const int tileInW = CEIL_DIV(tileUpW + (fuSize - 1), up); // Input tile width. + const int tileInH = CEIL_DIV(tileUpH + (fuSize - 1), up); // Input tile height. + const int tileUpH_up = CEIL_DIV(tileUpH, up) * up; // Upsampled tile height rounded up to a multiple of up. + const int tileInH_up = CEIL_DIV(tileUpH_up + (fuSize - 1), up); // For allocations only, to avoid shared memory read overruns with up=2 and up=4. + + // Merge 1x1 downsampling into last upsampling step for upf1 and ups2. + const bool downInline = (down == 1) && ((up == 1 && filterMode == MODE_FUFD) || (up == 2 && filterMode == MODE_SUFD)); + + // Sizes of logical buffers. + const int szIn = tileInH_up * tileInW; + const int szUpX = tileInH_up * tileUpW; + const int szUpXY = downInline ? 0 : (tileUpH * tileUpW); + const int szDownX = tileUpH * tileOutW; + + // Sizes for shared memory arrays. + const int s_buf0_size_base = + (filterMode == MODE_SUSD) ? MAX(szIn, szUpXY) : + (filterMode == MODE_FUSD) ? MAX(szIn, szDownX) : + (filterMode == MODE_SUFD) ? MAX(szIn, szUpXY) : + (filterMode == MODE_FUFD) ? szIn : + -1; + const int s_buf1_size_base = + (filterMode == MODE_SUSD) ? MAX(szUpX, szDownX) : + (filterMode == MODE_FUSD) ? szUpXY : + (filterMode == MODE_SUFD) ? szUpX : + (filterMode == MODE_FUFD) ? szUpXY : + -1; + + // Ensure U128 alignment. + const int s_buf0_size = (s_buf0_size_base + 3) & ~3; + const int s_buf1_size = (s_buf1_size_base + 3) & ~3; + + // Check at compile time that we don't use too much shared memory. + static_assert((s_buf0_size + s_buf1_size) * sizeof(scalar_t) <= (sharedKB << 10), "shared memory overflow"); + + // Declare shared memory arrays. + scalar_t* s_buf0; + scalar_t* s_buf1; + if (sharedKB <= 48) + { + // Allocate shared memory arrays here. + __shared__ scalar_t s_buf0_st[(sharedKB > 48) ? (1<<24) : (s_buf0_size + s_buf1_size)]; // Prevent launching if this isn't optimized away when unused. + s_buf0 = s_buf0_st; + s_buf1 = s_buf0 + s_buf0_size; + } + else + { + // Use the dynamically allocated shared memory array. + s_buf0 = (scalar_t*)s_buf_raw; + s_buf1 = s_buf0 + s_buf0_size; + } + + // Pointers to the buffers. + scalar_t* s_tileIn; // Input tile: [relInX * tileInH + relInY] + scalar_t* s_tileUpX; // After horizontal upsampling: [relInY * tileUpW + relUpX] + scalar_t* s_tileUpXY; // After upsampling: [relUpY * tileUpW + relUpX] + scalar_t* s_tileDownX; // After horizontal downsampling: [relUpY * tileOutW + relOutX] + if (filterMode == MODE_SUSD) + { + s_tileIn = s_buf0; + s_tileUpX = s_buf1; + s_tileUpXY = s_buf0; + s_tileDownX = s_buf1; + } + else if (filterMode == MODE_FUSD) + { + s_tileIn = s_buf0; + s_tileUpXY = s_buf1; + s_tileDownX = s_buf0; + } + else if (filterMode == MODE_SUFD) + { + s_tileIn = s_buf0; + s_tileUpX = s_buf1; + s_tileUpXY = s_buf0; + } + else if (filterMode == MODE_FUFD) + { + s_tileIn = s_buf0; + s_tileUpXY = s_buf1; + } + + // Allow large grids in z direction via per-launch offset. + int channelIdx = blockIdx.z + p.blockZofs; + int batchIdx = channelIdx / p.yShape.z; + channelIdx -= batchIdx * p.yShape.z; + + // Offset to output feature map. In bytes. + index_t mapOfsOut = channelIdx * get_stride(p.yStride.z) + batchIdx * get_stride(p.yStride.w); + + // Sign shift amount. + uint32_t signXo = ((threadIdx.x + p.sOfs.x) << 1) & 6; + + // Inner tile loop. + #pragma unroll 1 + for (int tileIdx = 0; !enableXrep || (tileIdx < MIN(p.tilesXrep, p.tilesXdim - p.tilesXrep * blockIdx.y)); tileIdx++) + { + // Locate output tile. + int tileX = enableXrep ? blockIdx.y * p.tilesXrep + tileIdx : blockIdx.x; + int tileOutX = tileX * tileOutW; + int tileOutY = (enableXrep ? blockIdx.x : blockIdx.y) * tileOutH; + + // Locate input tile. + int tmpX = tileOutX * down - p.pad0.x; + int tmpY = tileOutY * down - p.pad0.y; + int tileInX = CEIL_DIV(tmpX, up); + int tileInY = CEIL_DIV(tmpY, up); + const int phaseInX = tileInX * up - tmpX; + const int phaseInY = tileInY * up - tmpY; + + // Extra sync if input and output buffers are the same and we are not on first tile. + if (enableXrep && tileIdx > 0 && (filterMode == MODE_FUSD || (filterMode == MODE_SUFD && !downInline) || (filterMode == MODE_FUFD && downInline))) + __syncthreads(); + + // Load input tile & apply bias. Unrolled. + scalar_t b = (scalar_t)*(const T*)((const char*)p.b + (channelIdx * get_stride(p.bStride))); + index_t mapOfsIn = channelIdx * get_stride(p.xStride.z) + batchIdx * get_stride(p.xStride.w); + int idx = threadIdx.x; + const int loopCountIN = CEIL_DIV(tileInW * tileInH, threadsPerBlock); + #pragma unroll + for (int loop = 0; loop < loopCountIN; loop++) + { + int relInX, relInY; + fast_div_mod(relInX, relInY, idx); + int inX = tileInX + relInX; + int inY = tileInY + relInY; + scalar_t v = 0; + + if ((uint32_t)inX < p.xShape.x && (uint32_t)inY < p.xShape.y) + v = (scalar_t)*((const T*)((const char*)p.x + (inX * get_stride(p.xStride.x) + inY * get_stride(p.xStride.y) + mapOfsIn))) + b; + + bool skip = (loop == loopCountIN-1) && (idx >= tileInW * tileInH); + if (!skip) + s_tileIn[idx] = v; + + idx += threadsPerBlock; + } + + if (filterMode == MODE_SUSD || filterMode == MODE_SUFD) // Separable upsampling filter. + { + // Horizontal upsampling. + __syncthreads(); + if (up == 4) + { + for (int idx = threadIdx.x*up; idx < tileUpW * tileInH; idx += blockDim.x*up) + { + int relUpX0, relInY; + fast_div_mod(relUpX0, relInY, idx); + int relInX0 = relUpX0 / up; + int src0 = relInX0 + tileInW * relInY; + int dst = relInY * tileUpW + relUpX0; + vec4_t v = InternalType::zero_vec4(); + scalar_t a = s_tileIn[src0]; + if (phaseInX == 0) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + v.y += a * (scalar_t)c_fu[step * up + 3]; + v.z += a * (scalar_t)c_fu[step * up + 2]; + v.w += a * (scalar_t)c_fu[step * up + 1]; + } + } + else if (phaseInX == 1) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 1]; + v.y += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + v.z += a * (scalar_t)c_fu[step * up + 3]; + v.w += a * (scalar_t)c_fu[step * up + 2]; + } + } + else if (phaseInX == 2) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 2]; + v.y += a * (scalar_t)c_fu[step * up + 1]; + v.z += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + v.w += a * (scalar_t)c_fu[step * up + 3]; + } + } + else // (phaseInX == 3) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 3]; + v.y += a * (scalar_t)c_fu[step * up + 2]; + v.z += a * (scalar_t)c_fu[step * up + 1]; + v.w += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + } + } + s_tileUpX[dst+0] = v.x; + s_tileUpX[dst+1] = v.y; + s_tileUpX[dst+2] = v.z; + s_tileUpX[dst+3] = v.w; + } + } + else if (up == 2) + { + bool p0 = (phaseInX == 0); + for (int idx = threadIdx.x*up; idx < tileUpW * tileInH; idx += blockDim.x*up) + { + int relUpX0, relInY; + fast_div_mod(relUpX0, relInY, idx); + int relInX0 = relUpX0 / up; + int src0 = relInX0 + tileInW * relInY; + int dst = relInY * tileUpW + relUpX0; + vec2_t v = InternalType::zero_vec2(); + scalar_t a = s_tileIn[src0]; + if (p0) // (phaseInX == 0) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + v.y += a * (scalar_t)c_fu[step * up + 1]; + } + } + else // (phaseInX == 1) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 1]; + v.y += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + } + } + s_tileUpX[dst+0] = v.x; + s_tileUpX[dst+1] = v.y; + } + } + + // Vertical upsampling & nonlinearity. + + __syncthreads(); + int groupMask = 15 << ((threadIdx.x & 31) & ~3); + int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH : 0; // Skip already written signs. + int sShapeMaxY = MIN(p.sShape.y, tileOutY * down + tileUpH); // Avoid out-of-tile sign writes. + if (up == 4) + { + minY -= 3; // Adjust according to block height. + for (int idx = threadIdx.x; idx < tileUpW * tileUpH_up / up; idx += blockDim.x) + { + int relUpX, relInY0; + fast_div_mod(relUpX, relInY0, idx); + int relUpY0 = relInY0 * up; + int src0 = relInY0 * tileUpW + relUpX; + int dst = relUpY0 * tileUpW + relUpX; + vec4_t v = InternalType::zero_vec4(); + + scalar_t a = s_tileUpX[src0]; + if (phaseInY == 0) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + v.y += a * (scalar_t)c_fu[step * up + 3]; + v.z += a * (scalar_t)c_fu[step * up + 2]; + v.w += a * (scalar_t)c_fu[step * up + 1]; + } + } + else if (phaseInY == 1) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 1]; + v.y += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + v.z += a * (scalar_t)c_fu[step * up + 3]; + v.w += a * (scalar_t)c_fu[step * up + 2]; + } + } + else if (phaseInY == 2) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 2]; + v.y += a * (scalar_t)c_fu[step * up + 1]; + v.z += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + v.w += a * (scalar_t)c_fu[step * up + 3]; + } + } + else // (phaseInY == 3) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 3]; + v.y += a * (scalar_t)c_fu[step * up + 2]; + v.z += a * (scalar_t)c_fu[step * up + 1]; + v.w += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + } + } + + int x = tileOutX * down + relUpX; + int y = tileOutY * down + relUpY0; + int signX = x + p.sOfs.x; + int signY = y + p.sOfs.y; + int signZ = blockIdx.z + p.blockZofs; + int signXb = signX >> 2; + index_t si0 = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); + index_t si1 = si0 + p.sShape.x; + index_t si2 = si0 + p.sShape.x * 2; + index_t si3 = si0 + p.sShape.x * 3; + + v.x *= (scalar_t)((float)up * (float)up * p.gain); + v.y *= (scalar_t)((float)up * (float)up * p.gain); + v.z *= (scalar_t)((float)up * (float)up * p.gain); + v.w *= (scalar_t)((float)up * (float)up * p.gain); + + if (signWrite) + { + if (!enableWriteSkip) + { + // Determine and write signs. + int sx = __float_as_uint(v.x) >> 31 << 0; + int sy = __float_as_uint(v.y) >> 31 << 8; + int sz = __float_as_uint(v.z) >> 31 << 16; + int sw = __float_as_uint(v.w) >> 31 << 24; + if (sx) v.x *= p.slope; + if (sy) v.y *= p.slope; + if (sz) v.z *= p.slope; + if (sw) v.w *= p.slope; + if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } + if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } + if (fabsf(v.z) > p.clamp) { sz = 2 << 16; v.z = InternalType::clamp(v.z, p.clamp); } + if (fabsf(v.w) > p.clamp) { sw = 2 << 24; v.w = InternalType::clamp(v.w, p.clamp); } + + if ((uint32_t)signXb < p.swLimit && signY >= minY) + { + // Combine signs. + uint32_t s = sx + sy + sw + sz; + s <<= (signX & 3) << 1; + s |= __shfl_xor_sync(groupMask, s, 1); + s |= __shfl_xor_sync(groupMask, s, 2); + + // Write signs. + if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } + if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } + if ((uint32_t)(signY + 2) < sShapeMaxY) { p.s[si2] = (unsigned char)(s >> 16); } + if ((uint32_t)(signY + 3) < sShapeMaxY) { p.s[si3] = (unsigned char)(s >> 24); } + } + } + else + { + // Determine and write signs. + if ((uint32_t)signXb < p.swLimit && signY >= minY) + { + int sx = __float_as_uint(v.x) >> 31 << 0; + int sy = __float_as_uint(v.y) >> 31 << 8; + int sz = __float_as_uint(v.z) >> 31 << 16; + int sw = __float_as_uint(v.w) >> 31 << 24; + if (sx) v.x *= p.slope; + if (sy) v.y *= p.slope; + if (sz) v.z *= p.slope; + if (sw) v.w *= p.slope; + if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } + if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } + if (fabsf(v.z) > p.clamp) { sz = 2 << 16; v.z = InternalType::clamp(v.z, p.clamp); } + if (fabsf(v.w) > p.clamp) { sw = 2 << 24; v.w = InternalType::clamp(v.w, p.clamp); } + + // Combine signs. + uint32_t s = sx + sy + sw + sz; + s <<= (signX & 3) << 1; + s |= __shfl_xor_sync(groupMask, s, 1); + s |= __shfl_xor_sync(groupMask, s, 2); + + // Write signs. + if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } + if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } + if ((uint32_t)(signY + 2) < sShapeMaxY) { p.s[si2] = (unsigned char)(s >> 16); } + if ((uint32_t)(signY + 3) < sShapeMaxY) { p.s[si3] = (unsigned char)(s >> 24); } + } + else + { + // Just compute the values. + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); + if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); + } + } + } + else if (signRead) // Read signs and apply. + { + if ((uint32_t)signXb < p.swLimit) + { + int ss = (signX & 3) << 1; + if ((uint32_t)(signY + 0) < p.sShape.y) { int s = p.s[si0] >> ss; if (s & 1) v.x *= p.slope; if (s & 2) v.x = 0.f; } + if ((uint32_t)(signY + 1) < p.sShape.y) { int s = p.s[si1] >> ss; if (s & 1) v.y *= p.slope; if (s & 2) v.y = 0.f; } + if ((uint32_t)(signY + 2) < p.sShape.y) { int s = p.s[si2] >> ss; if (s & 1) v.z *= p.slope; if (s & 2) v.z = 0.f; } + if ((uint32_t)(signY + 3) < p.sShape.y) { int s = p.s[si3] >> ss; if (s & 1) v.w *= p.slope; if (s & 2) v.w = 0.f; } + } + } + else // Forward pass with no sign write. + { + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); + if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); + } + + s_tileUpXY[dst + 0 * tileUpW] = v.x; + if (relUpY0 + 1 < tileUpH) s_tileUpXY[dst + 1 * tileUpW] = v.y; + if (relUpY0 + 2 < tileUpH) s_tileUpXY[dst + 2 * tileUpW] = v.z; + if (relUpY0 + 3 < tileUpH) s_tileUpXY[dst + 3 * tileUpW] = v.w; + } + } + else if (up == 2) + { + minY -= 1; // Adjust according to block height. + for (int idx = threadIdx.x; idx < tileUpW * tileUpH_up / up; idx += blockDim.x) + { + int relUpX, relInY0; + fast_div_mod(relUpX, relInY0, idx); + int relUpY0 = relInY0 * up; + int src0 = relInY0 * tileUpW + relUpX; + int dst = relUpY0 * tileUpW + relUpX; + vec2_t v = InternalType::zero_vec2(); + + scalar_t a = s_tileUpX[src0]; + if (phaseInY == 0) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + v.y += a * (scalar_t)c_fu[step * up + 1]; + } + } + else // (phaseInY == 1) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 1]; + v.y += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + } + } + + int x = tileOutX * down + relUpX; + int y = tileOutY * down + relUpY0; + int signX = x + p.sOfs.x; + int signY = y + p.sOfs.y; + int signZ = blockIdx.z + p.blockZofs; + int signXb = signX >> 2; + index_t si0 = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); + index_t si1 = si0 + p.sShape.x; + + v.x *= (scalar_t)((float)up * (float)up * p.gain); + v.y *= (scalar_t)((float)up * (float)up * p.gain); + + if (signWrite) + { + if (!enableWriteSkip) + { + // Determine and write signs. + int sx = __float_as_uint(v.x) >> 31 << 0; + int sy = __float_as_uint(v.y) >> 31 << 8; + if (sx) v.x *= p.slope; + if (sy) v.y *= p.slope; + if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } + if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } + + if ((uint32_t)signXb < p.swLimit && signY >= minY) + { + // Combine signs. + int s = sx + sy; + s <<= signXo; + s |= __shfl_xor_sync(groupMask, s, 1); + s |= __shfl_xor_sync(groupMask, s, 2); + + // Write signs. + if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } + if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } + } + } + else + { + // Determine and write signs. + if ((uint32_t)signXb < p.swLimit && signY >= minY) + { + int sx = __float_as_uint(v.x) >> 31 << 0; + int sy = __float_as_uint(v.y) >> 31 << 8; + if (sx) v.x *= p.slope; + if (sy) v.y *= p.slope; + if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } + if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } + + // Combine signs. + int s = sx + sy; + s <<= signXo; + s |= __shfl_xor_sync(groupMask, s, 1); + s |= __shfl_xor_sync(groupMask, s, 2); + + // Write signs. + if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } + if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } + } + else + { + // Just compute the values. + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + } + } + } + else if (signRead) // Read signs and apply. + { + if ((uint32_t)signXb < p.swLimit) + { + if ((uint32_t)(signY + 0) < p.sShape.y) { int s = p.s[si0] >> signXo; if (s & 1) v.x *= p.slope; if (s & 2) v.x = 0.f; } + if ((uint32_t)(signY + 1) < p.sShape.y) { int s = p.s[si1] >> signXo; if (s & 1) v.y *= p.slope; if (s & 2) v.y = 0.f; } + } + } + else // Forward pass with no sign write. + { + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + } + + if (!downInline) + { + // Write into temporary buffer. + s_tileUpXY[dst] = v.x; + if (relUpY0 < tileUpH - 1) + s_tileUpXY[dst + tileUpW] = v.y; + } + else + { + // Write directly into output buffer. + if ((uint32_t)x < p.yShape.x) + { + int ymax = MIN(p.yShape.y, tileUpH + tileOutY * down); + index_t ofs = x * get_stride(p.yStride.x) + y * get_stride(p.yStride.y) + mapOfsOut; + if ((uint32_t)y + 0 < p.yShape.y) *((T*)((char*)p.y + ofs)) = (T)(v.x * (scalar_t)c_fd[0]); + if ((uint32_t)y + 1 < ymax) *((T*)((char*)p.y + ofs + get_stride(p.yStride.y))) = (T)(v.y * (scalar_t)c_fd[0]); + } + } + } + } + } + else if (filterMode == MODE_FUSD || filterMode == MODE_FUFD) + { + // Full upsampling filter. + + if (up == 2) + { + // 2 x 2-wide. + __syncthreads(); + int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH + p.sOfs.y : 0; // Skip already written signs. + for (int idx = threadIdx.x * 4; idx < tileUpW * tileUpH; idx += blockDim.x * 4) + { + int relUpX0, relUpY0; + fast_div_mod(relUpX0, relUpY0, idx); + int relInX0 = CEIL_DIV(relUpX0 - phaseInX, up); + int relInY0 = CEIL_DIV(relUpY0 - phaseInY, up); + int src0 = relInX0 + tileInW * relInY0; + int tap0y = (relInY0 * up + phaseInY - relUpY0); + + #define X_LOOP(TAPY, PX) \ + for (int sx = 0; sx < fuSize / up; sx++) \ + { \ + v.x += a * (scalar_t)c_fu[(sx * up + (((PX) - 0) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; \ + v.z += b * (scalar_t)c_fu[(sx * up + (((PX) - 0) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; if ((PX) == 0) { a = b; b = s_tileIn[src0 + 2 + sx + sy * tileInW]; } \ + v.y += a * (scalar_t)c_fu[(sx * up + (((PX) - 1) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; \ + v.w += b * (scalar_t)c_fu[(sx * up + (((PX) - 1) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; if ((PX) == 1) { a = b; b = s_tileIn[src0 + 2 + sx + sy * tileInW]; } \ + } + + vec4_t v = InternalType::zero_vec4(); + if (tap0y == 0 && phaseInX == 0) + #pragma unroll + for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; + #pragma unroll + X_LOOP(0, 0) } + if (tap0y == 0 && phaseInX == 1) + #pragma unroll + for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; + #pragma unroll + X_LOOP(0, 1) } + if (tap0y == 1 && phaseInX == 0) + #pragma unroll + for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; + #pragma unroll + X_LOOP(1, 0) } + if (tap0y == 1 && phaseInX == 1) + #pragma unroll + for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; + #pragma unroll + X_LOOP(1, 1) } + + #undef X_LOOP + + int x = tileOutX * down + relUpX0; + int y = tileOutY * down + relUpY0; + int signX = x + p.sOfs.x; + int signY = y + p.sOfs.y; + int signZ = blockIdx.z + p.blockZofs; + int signXb = signX >> 2; + index_t si = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); + + v.x *= (scalar_t)((float)up * (float)up * p.gain); + v.y *= (scalar_t)((float)up * (float)up * p.gain); + v.z *= (scalar_t)((float)up * (float)up * p.gain); + v.w *= (scalar_t)((float)up * (float)up * p.gain); + + if (signWrite) + { + if (!enableWriteSkip) + { + // Determine and write signs. + int sx = __float_as_uint(v.x) >> 31; + int sy = __float_as_uint(v.y) >> 31; + int sz = __float_as_uint(v.z) >> 31; + int sw = __float_as_uint(v.w) >> 31; + if (sx) v.x *= p.slope; if (fabsf(v.x) > p.clamp) { sx = 2; v.x = InternalType::clamp(v.x, p.clamp); } + if (sy) v.y *= p.slope; if (fabsf(v.y) > p.clamp) { sy = 2; v.y = InternalType::clamp(v.y, p.clamp); } + if (sz) v.z *= p.slope; if (fabsf(v.z) > p.clamp) { sz = 2; v.z = InternalType::clamp(v.z, p.clamp); } + if (sw) v.w *= p.slope; if (fabsf(v.w) > p.clamp) { sw = 2; v.w = InternalType::clamp(v.w, p.clamp); } + + if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) + { + p.s[si] = sx + (sy << 2) + (sz << 4) + (sw << 6); + } + } + else + { + // Determine and write signs. + if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) + { + int sx = __float_as_uint(v.x) >> 31; + int sy = __float_as_uint(v.y) >> 31; + int sz = __float_as_uint(v.z) >> 31; + int sw = __float_as_uint(v.w) >> 31; + if (sx) v.x *= p.slope; if (fabsf(v.x) > p.clamp) { sx = 2; v.x = InternalType::clamp(v.x, p.clamp); } + if (sy) v.y *= p.slope; if (fabsf(v.y) > p.clamp) { sy = 2; v.y = InternalType::clamp(v.y, p.clamp); } + if (sz) v.z *= p.slope; if (fabsf(v.z) > p.clamp) { sz = 2; v.z = InternalType::clamp(v.z, p.clamp); } + if (sw) v.w *= p.slope; if (fabsf(v.w) > p.clamp) { sw = 2; v.w = InternalType::clamp(v.w, p.clamp); } + + p.s[si] = sx + (sy << 2) + (sz << 4) + (sw << 6); + } + else + { + // Just compute the values. + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); + if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); + } + } + } + else if (signRead) // Read sign and apply. + { + if ((uint32_t)signY < p.sShape.y) + { + int s = 0; + if ((uint32_t)signXb < p.swLimit) s = p.s[si]; + if ((uint32_t)signXb + 1 < p.swLimit) s |= p.s[si + 1] << 8; + s >>= (signX & 3) << 1; + if (s & 0x01) v.x *= p.slope; if (s & 0x02) v.x = 0.f; + if (s & 0x04) v.y *= p.slope; if (s & 0x08) v.y = 0.f; + if (s & 0x10) v.z *= p.slope; if (s & 0x20) v.z = 0.f; + if (s & 0x40) v.w *= p.slope; if (s & 0x80) v.w = 0.f; + } + } + else // Forward pass with no sign write. + { + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); + if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); + } + + s_tileUpXY[idx + 0] = v.x; + s_tileUpXY[idx + 1] = v.y; + s_tileUpXY[idx + 2] = v.z; + s_tileUpXY[idx + 3] = v.w; + } + } + else if (up == 1) + { + __syncthreads(); + uint32_t groupMask = 15 << ((threadIdx.x & 31) & ~3); + int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH : 0; // Skip already written signs. + for (int idx = threadIdx.x; idx < tileUpW * tileUpH; idx += blockDim.x) + { + int relUpX0, relUpY0; + fast_div_mod(relUpX0, relUpY0, idx); + scalar_t v = s_tileIn[idx] * (scalar_t)c_fu[0]; // 1x1 filter. + + int x = tileOutX * down + relUpX0; + int y = tileOutY * down + relUpY0; + int signX = x + p.sOfs.x; + int signY = y + p.sOfs.y; + int signZ = blockIdx.z + p.blockZofs; + int signXb = signX >> 2; + index_t si = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); + v *= (scalar_t)((float)up * (float)up * p.gain); + + if (signWrite) + { + if (!enableWriteSkip) + { + // Determine and write sign. + uint32_t s = 0; + uint32_t signXbit = (1u << signXo); + if (v < 0.f) + { + s = signXbit; + v *= p.slope; + } + if (fabsf(v) > p.clamp) + { + s = signXbit * 2; + v = InternalType::clamp(v, p.clamp); + } + if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) + { + s += __shfl_xor_sync(groupMask, s, 1); // Coalesce. + s += __shfl_xor_sync(groupMask, s, 2); // Coalesce. + p.s[si] = s; // Write. + } + } + else + { + // Determine and write sign. + if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) + { + uint32_t s = 0; + uint32_t signXbit = (1u << signXo); + if (v < 0.f) + { + s = signXbit; + v *= p.slope; + } + if (fabsf(v) > p.clamp) + { + s = signXbit * 2; + v = InternalType::clamp(v, p.clamp); + } + s += __shfl_xor_sync(groupMask, s, 1); // Coalesce. + s += __shfl_xor_sync(groupMask, s, 2); // Coalesce. + p.s[si] = s; // Write. + } + else + { + // Just compute the value. + if (v < 0.f) v *= p.slope; + v = InternalType::clamp(v, p.clamp); + } + } + } + else if (signRead) + { + // Read sign and apply if within sign tensor bounds. + if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y) + { + int s = p.s[si]; + s >>= signXo; + if (s & 1) v *= p.slope; + if (s & 2) v = 0.f; + } + } + else // Forward pass with no sign write. + { + if (v < 0.f) v *= p.slope; + v = InternalType::clamp(v, p.clamp); + } + + if (!downInline) // Write into temporary buffer. + s_tileUpXY[idx] = v; + else if ((uint32_t)x < p.yShape.x && (uint32_t)y < p.yShape.y) // Write directly into output buffer + *((T*)((char*)p.y + (x * get_stride(p.yStride.x) + y * get_stride(p.yStride.y) + mapOfsOut))) = (T)(v * (scalar_t)c_fd[0]); + } + } + } + + // Downsampling. + if (filterMode == MODE_SUSD || filterMode == MODE_FUSD) + { + // Horizontal downsampling. + __syncthreads(); + if (down == 4 && tileOutW % 4 == 0) + { + // Calculate 4 pixels at a time. + for (int idx = threadIdx.x * 4; idx < tileOutW * tileUpH; idx += blockDim.x * 4) + { + int relOutX0, relUpY; + fast_div_mod(relOutX0, relUpY, idx); + int relUpX0 = relOutX0 * down; + int src0 = relUpY * tileUpW + relUpX0; + vec4_t v = InternalType::zero_vec4(); + #pragma unroll + for (int step = 0; step < fdSize; step++) + { + v.x += s_tileUpXY[src0 + 0 + step] * (scalar_t)c_fd[step]; + v.y += s_tileUpXY[src0 + 4 + step] * (scalar_t)c_fd[step]; + v.z += s_tileUpXY[src0 + 8 + step] * (scalar_t)c_fd[step]; + v.w += s_tileUpXY[src0 + 12 + step] * (scalar_t)c_fd[step]; + } + s_tileDownX[idx+0] = v.x; + s_tileDownX[idx+1] = v.y; + s_tileDownX[idx+2] = v.z; + s_tileDownX[idx+3] = v.w; + } + } + else if ((down == 2 || down == 4) && (tileOutW % 2 == 0)) + { + // Calculate 2 pixels at a time. + for (int idx = threadIdx.x * 2; idx < tileOutW * tileUpH; idx += blockDim.x * 2) + { + int relOutX0, relUpY; + fast_div_mod(relOutX0, relUpY, idx); + int relUpX0 = relOutX0 * down; + int src0 = relUpY * tileUpW + relUpX0; + vec2_t v = InternalType::zero_vec2(); + #pragma unroll + for (int step = 0; step < fdSize; step++) + { + v.x += s_tileUpXY[src0 + 0 + step] * (scalar_t)c_fd[step]; + v.y += s_tileUpXY[src0 + down + step] * (scalar_t)c_fd[step]; + } + s_tileDownX[idx+0] = v.x; + s_tileDownX[idx+1] = v.y; + } + } + else + { + // Calculate 1 pixel at a time. + for (int idx = threadIdx.x; idx < tileOutW * tileUpH; idx += blockDim.x) + { + int relOutX0, relUpY; + fast_div_mod(relOutX0, relUpY, idx); + int relUpX0 = relOutX0 * down; + int src = relUpY * tileUpW + relUpX0; + scalar_t v = 0.f; + #pragma unroll + for (int step = 0; step < fdSize; step++) + v += s_tileUpXY[src + step] * (scalar_t)c_fd[step]; + s_tileDownX[idx] = v; + } + } + + // Vertical downsampling & store output tile. + __syncthreads(); + for (int idx = threadIdx.x; idx < tileOutW * tileOutH; idx += blockDim.x) + { + int relOutX, relOutY0; + fast_div_mod(relOutX, relOutY0, idx); + int relUpY0 = relOutY0 * down; + int src0 = relUpY0 * tileOutW + relOutX; + scalar_t v = 0; + #pragma unroll + for (int step = 0; step < fdSize; step++) + v += s_tileDownX[src0 + step * tileOutW] * (scalar_t)c_fd[step]; + + int outX = tileOutX + relOutX; + int outY = tileOutY + relOutY0; + + if (outX < p.yShape.x & outY < p.yShape.y) + *((T*)((char*)p.y + (outX * get_stride(p.yStride.x) + outY * get_stride(p.yStride.y) + mapOfsOut))) = (T)v; + } + } + else if (filterMode == MODE_SUFD || filterMode == MODE_FUFD) + { + // Full downsampling filter. + if (down == 2) + { + // 2-wide. + __syncthreads(); + for (int idx = threadIdx.x * 2; idx < tileOutW * tileOutH; idx += blockDim.x * 2) + { + int relOutX0, relOutY0; + fast_div_mod(relOutX0, relOutY0, idx); + int relUpX0 = relOutX0 * down; + int relUpY0 = relOutY0 * down; + int src0 = relUpY0 * tileUpW + relUpX0; + vec2_t v = InternalType::zero_vec2(); + #pragma unroll + for (int sy = 0; sy < fdSize; sy++) + #pragma unroll + for (int sx = 0; sx < fdSize; sx++) + { + v.x += s_tileUpXY[src0 + 0 + sx + sy * tileUpW] * (scalar_t)c_fd[sx + sy * MAX_FILTER_SIZE]; + v.y += s_tileUpXY[src0 + 2 + sx + sy * tileUpW] * (scalar_t)c_fd[sx + sy * MAX_FILTER_SIZE]; + } + + int outX = tileOutX + relOutX0; + int outY = tileOutY + relOutY0; + if ((uint32_t)outY < p.yShape.y) + { + index_t ofs = outX * get_stride(p.yStride.x) + outY * get_stride(p.yStride.y) + mapOfsOut; + if (outX + 0 < p.yShape.x) *((T*)((char*)p.y + ofs)) = (T)v.x; + if (outX + 1 < p.yShape.x) *((T*)((char*)p.y + ofs + get_stride(p.yStride.x))) = (T)v.y; + } + } + } + else if (down == 1 && !downInline) + { + // Thread per pixel. + __syncthreads(); + for (int idx = threadIdx.x; idx < tileOutW * tileOutH; idx += blockDim.x) + { + int relOutX0, relOutY0; + fast_div_mod(relOutX0, relOutY0, idx); + scalar_t v = s_tileUpXY[idx] * (scalar_t)c_fd[0]; // 1x1 filter. + + int outX = tileOutX + relOutX0; + int outY = tileOutY + relOutY0; + if ((uint32_t)outX < p.yShape.x && (uint32_t)outY < p.yShape.y) + *((T*)((char*)p.y + (outX * get_stride(p.yStride.x) + outY * get_stride(p.yStride.y) + mapOfsOut))) = (T)v; + } + } + } + + if (!enableXrep) + break; + } +} + +//------------------------------------------------------------------------ +// Compute activation function and signs for upsampled data tensor, modifying data tensor in-place. Used for accelerating the generic variant. +// Sign tensor is known to be contiguous, and p.x and p.s have the same z, w dimensions. 64-bit indexing is always used. + +template +static __global__ void filtered_lrelu_act_kernel(filtered_lrelu_act_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + + // Indexing. + int32_t x = threadIdx.x + blockIdx.x * blockDim.x; + int32_t ymax = signWrite ? p.sShape.y : p.xShape.y; + int32_t qmax = p.xShape.z * p.xShape.w; // Combined minibatch*channel maximum index. + + // Loop to accommodate oversized tensors. + for (int32_t q = blockIdx.z; q < qmax; q += gridDim.z) + for (int32_t y = blockIdx.y; y < ymax; y += gridDim.y) + { + // Extract z and w (channel, minibatch index). + int32_t w = q / p.xShape.z; + int32_t z = q - w * p.xShape.z; + + // Choose behavior based on sign read/write mode. + if (signWrite) + { + // Process value if in p.x. + uint32_t s = 0; + if (x < p.xShape.x && y < p.xShape.y) + { + int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w; + T* pv = ((T*)p.x) + ix; + scalar_t v = (scalar_t)(*pv); + + // Gain, LReLU, clamp. + v *= p.gain; + if (v < 0.f) + { + v *= p.slope; + s = 1; // Sign. + } + if (fabsf(v) > p.clamp) + { + v = InternalType::clamp(v, p.clamp); + s = 2; // Clamp. + } + + *pv = (T)v; // Write value. + } + + // Coalesce into threads 0 and 16 of warp. + uint32_t m = (threadIdx.x & 16) ? 0xffff0000u : 0x0000ffffu; + s <<= ((threadIdx.x & 15) << 1); // Shift into place. + s |= __shfl_xor_sync(m, s, 1); // Distribute. + s |= __shfl_xor_sync(m, s, 2); + s |= __shfl_xor_sync(m, s, 4); + s |= __shfl_xor_sync(m, s, 8); + + // Write signs if leader and in p.s. + if (!(threadIdx.x & 15) && x < p.sShape.x) // y is always in. + { + uint64_t is = x + p.sShape.x * (y + (int64_t)p.sShape.y * q); // Contiguous. + ((uint32_t*)p.s)[is >> 4] = s; + } + } + else if (signRead) + { + // Process value if in p.x. + if (x < p.xShape.x) // y is always in. + { + int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w; + T* pv = ((T*)p.x) + ix; + scalar_t v = (scalar_t)(*pv); + v *= p.gain; + + // Apply sign buffer offset. + uint32_t sx = x + p.sOfs.x; + uint32_t sy = y + p.sOfs.y; + + // Read and apply signs if we land inside valid region of sign buffer. + if (sx < p.sShape.x && sy < p.sShape.y) + { + uint64_t is = (sx >> 2) + (p.sShape.x >> 2) * (sy + (uint64_t)p.sShape.y * q); // Contiguous. + unsigned char s = p.s[is]; + s >>= (sx & 3) << 1; // Shift into place. + if (s & 1) // Sign? + v *= p.slope; + if (s & 2) // Clamp? + v = 0.f; + } + + *pv = (T)v; // Write value. + } + } + else + { + // Forward pass with no sign write. Process value if in p.x. + if (x < p.xShape.x) // y is always in. + { + int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w; + T* pv = ((T*)p.x) + ix; + scalar_t v = (scalar_t)(*pv); + v *= p.gain; + if (v < 0.f) + v *= p.slope; + if (fabsf(v) > p.clamp) + v = InternalType::clamp(v, p.clamp); + *pv = (T)v; // Write value. + } + } + } +} + +template void* choose_filtered_lrelu_act_kernel(void) +{ + return (void*)filtered_lrelu_act_kernel; +} + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB) +{ + filtered_lrelu_kernel_spec s = { 0 }; + + // Return the first matching kernel. +#define CASE(SH, U, FU, D, FD, MODE, TW, TH, W, XR, WS) \ + if (sharedKB >= SH) \ + if ((p.fuShape.y == 0 && (MODE == MODE_SUSD || MODE == MODE_SUFD)) || (p.fuShape.y > 0 && (MODE == MODE_FUSD || MODE == MODE_FUFD))) \ + if ((p.fdShape.y == 0 && (MODE == MODE_SUSD || MODE == MODE_FUSD)) || (p.fdShape.y > 0 && (MODE == MODE_SUFD || MODE == MODE_FUFD))) \ + if (p.up == U && p.fuShape.x <= FU && p.fuShape.y <= FU && p.down == D && p.fdShape.x <= FD && p.fdShape.y <= FD) \ + { \ + static_assert((D*TW % 4) == 0, "down * tileWidth must be divisible by 4"); \ + static_assert(FU % U == 0, "upscaling filter size must be multiple of upscaling factor"); \ + static_assert(FD % D == 0, "downscaling filter size must be multiple of downscaling factor"); \ + s.setup = (void*)setup_filters_kernel; \ + s.exec = (void*)filtered_lrelu_kernel; \ + s.tileOut = make_int2(TW, TH); \ + s.numWarps = W; \ + s.xrep = XR; \ + s.dynamicSharedKB = (SH == 48) ? 0 : SH; \ + return s; \ + } + + // Launch parameters for various kernel specializations. + // Small filters must be listed before large filters, otherwise the kernel for larger filter will always match first. + // Kernels that use more shared memory must be listed before those that use less, for the same reason. + + CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/1,1, /*mode*/MODE_FUFD, /*tw,th,warps,xrep,wskip*/64, 178, 32, 0, 0) // 1t-upf1-downf1 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/1,1, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/152, 95, 16, 0, 0) // 4t-ups2-downf1 + CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/2,8, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/56, 22, 16, 0, 0) // 4t-upf1-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/2,8, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/56, 29, 16, 11, 0) // 4t-ups2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/2,8, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/60, 28, 16, 0, 0) // 4t-upf2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/2,8, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/56, 28, 16, 0, 0) // 4t-ups2-downf2 + CASE(/*sharedKB*/48, /*up,fu*/4,16, /*down,fd*/2,8, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/56, 31, 16, 11, 0) // 4t-ups4-downs2 + CASE(/*sharedKB*/48, /*up,fu*/4,16, /*down,fd*/2,8, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/56, 36, 16, 0, 0) // 4t-ups4-downf2 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/4,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16, 22, 16, 12, 0) // 4t-ups2-downs4 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/4,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/29, 15, 16, 0, 0) // 4t-upf2-downs4 + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/1,1, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/96, 150, 28, 0, 0) // 6t-ups2-downf1 + CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/2,12, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/32, 35, 24, 0, 0) // 6t-upf1-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 46, 16, 10, 0) // 6t-ups2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/58, 28, 24, 8, 0) // 6t-upf2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/52, 28, 16, 0, 0) // 6t-ups2-downf2 + CASE(/*sharedKB*/48, /*up,fu*/4,24, /*down,fd*/2,12, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 51, 16, 5, 0) // 6t-ups4-downs2 + CASE(/*sharedKB*/48, /*up,fu*/4,24, /*down,fd*/2,12, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32, 56, 16, 6, 0) // 6t-ups4-downf2 + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16, 18, 16, 12, 0) // 6t-ups2-downs4 + CASE(/*sharedKB*/96, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/27, 31, 32, 6, 0) // 6t-upf2-downs4 96kB + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/27, 13, 24, 0, 0) // 6t-upf2-downs4 + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/1,1, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/148, 89, 24, 0, 0) // 8t-ups2-downf1 + CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/2,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/32, 31, 16, 5, 0) // 8t-upf1-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 41, 16, 9, 0) // 8t-ups2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/56, 26, 24, 0, 0) // 8t-upf2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32, 40, 16, 0, 0) // 8t-ups2-downf2 + CASE(/*sharedKB*/48, /*up,fu*/4,32, /*down,fd*/2,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 46, 24, 5, 0) // 8t-ups4-downs2 + CASE(/*sharedKB*/48, /*up,fu*/4,32, /*down,fd*/2,16, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32, 50, 16, 0, 0) // 8t-ups4-downf2 + CASE(/*sharedKB*/96, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/24, 24, 32, 12, 1) // 8t-ups2-downs4 96kB + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16, 13, 16, 10, 1) // 8t-ups2-downs4 + CASE(/*sharedKB*/96, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/25, 28, 28, 4, 0) // 8t-upf2-downs4 96kB + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/25, 10, 24, 0, 0) // 8t-upf2-downs4 + + #undef CASE + return s; // No kernel found. +} + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/filtered_lrelu.h b/torch_utils/ops/filtered_lrelu.h new file mode 100644 index 0000000..2060f5d --- /dev/null +++ b/torch_utils/ops/filtered_lrelu.h @@ -0,0 +1,90 @@ +// 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 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 filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); +template void* choose_filtered_lrelu_act_kernel(void); +template cudaError_t copy_filters(cudaStream_t stream); + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/filtered_lrelu.py b/torch_utils/ops/filtered_lrelu.py new file mode 100644 index 0000000..48d619a --- /dev/null +++ b/torch_utils/ops/filtered_lrelu.py @@ -0,0 +1,274 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/filtered_lrelu_ns.cu b/torch_utils/ops/filtered_lrelu_ns.cu new file mode 100644 index 0000000..55ef841 --- /dev/null +++ b/torch_utils/ops/filtered_lrelu_ns.cu @@ -0,0 +1,27 @@ +// 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(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Full op, 64-bit indexing. +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Activation/signs only for generic variant. 64-bit indexing. +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); + +// Copy filters to constant memory. +template cudaError_t copy_filters(cudaStream_t stream); diff --git a/torch_utils/ops/filtered_lrelu_rd.cu b/torch_utils/ops/filtered_lrelu_rd.cu new file mode 100644 index 0000000..f41832c --- /dev/null +++ b/torch_utils/ops/filtered_lrelu_rd.cu @@ -0,0 +1,27 @@ +// 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(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Full op, 64-bit indexing. +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Activation/signs only for generic variant. 64-bit indexing. +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); + +// Copy filters to constant memory. +template cudaError_t copy_filters(cudaStream_t stream); diff --git a/torch_utils/ops/filtered_lrelu_wr.cu b/torch_utils/ops/filtered_lrelu_wr.cu new file mode 100644 index 0000000..0265d18 --- /dev/null +++ b/torch_utils/ops/filtered_lrelu_wr.cu @@ -0,0 +1,27 @@ +// 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(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Full op, 64-bit indexing. +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Activation/signs only for generic variant. 64-bit indexing. +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); + +// Copy filters to constant memory. +template cudaError_t copy_filters(cudaStream_t stream); diff --git a/torch_utils/ops/fma.py b/torch_utils/ops/fma.py new file mode 100644 index 0000000..8e9b3e9 --- /dev/null +++ b/torch_utils/ops/fma.py @@ -0,0 +1,60 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/grid_sample_gradfix.py b/torch_utils/ops/grid_sample_gradfix.py new file mode 100644 index 0000000..269ffe8 --- /dev/null +++ b/torch_utils/ops/grid_sample_gradfix.py @@ -0,0 +1,77 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/upfirdn2d.cpp b/torch_utils/ops/upfirdn2d.cpp new file mode 100644 index 0000000..8262e0f --- /dev/null +++ b/torch_utils/ops/upfirdn2d.cpp @@ -0,0 +1,107 @@ +// 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 +#include +#include +#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(); + 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(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); +} + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/upfirdn2d.cu b/torch_utils/ops/upfirdn2d.cu new file mode 100644 index 0000000..84d45ba --- /dev/null +++ b/torch_utils/ops/upfirdn2d.cu @@ -0,0 +1,384 @@ +// 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 +#include "upfirdn2d.h" + +//------------------------------------------------------------------------ +// Helpers. + +template struct InternalType; +template <> struct InternalType { typedef double scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; +template <> struct InternalType { 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 static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p) +{ + typedef typename InternalType::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 +static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p) +{ + typedef typename InternalType::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 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, -1,-1,1, 4}; // contiguous + if (s == 1) spec = {(void*)upfirdn2d_kernel_large, -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, 64,32,1, 1}; + if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; + if (s != 1 && fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (s != 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (s != 1 && fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + // channels_last + if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s == 1 && fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 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, 64,32,1, 1}; + if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; + if (s != 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + // channels_last + if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s == 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 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, 128,8,1, 1}; + if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (s != 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + // channels_last + if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 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, 32,32,1, 1}; + if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + // channels_last + if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 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, 32,16,1, 1}; + if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (s != 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (s != 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + // channels_last + if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 16,16,1, 1}; + if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 16,16,1, 1}; + if (s == 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (s == 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 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, 64,8,1, 1}; + if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (s != 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + // channels_last + if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (s == 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 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, 32,16,1, 1}; + if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + // channels_last + if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 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, 64,32,1, 1}; + if (s != 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; + // channels_last + if (s == 1 && fx <= 48 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s == 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 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, 128,8,1, 1}; + if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + // channels_last + if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 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, 32,32,1, 1}; + if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + // channels_last + if (s == 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (s == 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 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, 32,8,1, 1}; + if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + // channels_last + if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 32,1,8, 1}; + if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 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, 32,8,1, 1}; + if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + // channels_last + if (s == 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 1,32,8, 1}; + if (s == 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 1,32,8, 1}; + } + return spec; +} + +//------------------------------------------------------------------------ +// Template specializations. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/upfirdn2d.h b/torch_utils/ops/upfirdn2d.h new file mode 100644 index 0000000..eb7db43 --- /dev/null +++ b/torch_utils/ops/upfirdn2d.h @@ -0,0 +1,59 @@ +// 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 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 upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/upfirdn2d.py b/torch_utils/ops/upfirdn2d.py new file mode 100644 index 0000000..504d3d5 --- /dev/null +++ b/torch_utils/ops/upfirdn2d.py @@ -0,0 +1,389 @@ +# 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) + +#---------------------------------------------------------------------------- diff --git a/torch_utils/persistence.py b/torch_utils/persistence.py new file mode 100644 index 0000000..a61fa03 --- /dev/null +++ b/torch_utils/persistence.py @@ -0,0 +1,251 @@ +# 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) + +#---------------------------------------------------------------------------- diff --git a/torch_utils/training_stats.py b/torch_utils/training_stats.py new file mode 100644 index 0000000..64e7835 --- /dev/null +++ b/torch_utils/training_stats.py @@ -0,0 +1,268 @@ +# 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] + +#---------------------------------------------------------------------------- diff --git a/train.py b/train.py new file mode 100644 index 0000000..7f055b3 --- /dev/null +++ b/train.py @@ -0,0 +1,288 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/training/__init__.py b/training/__init__.py new file mode 100644 index 0000000..8dd3488 --- /dev/null +++ b/training/__init__.py @@ -0,0 +1,9 @@ +# 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 diff --git a/training/augment.py b/training/augment.py new file mode 100644 index 0000000..c5a4b02 --- /dev/null +++ b/training/augment.py @@ -0,0 +1,436 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/training/dataset.py b/training/dataset.py new file mode 100644 index 0000000..1d87ba7 --- /dev/null +++ b/training/dataset.py @@ -0,0 +1,238 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/training/loss.py b/training/loss.py new file mode 100644 index 0000000..920d059 --- /dev/null +++ b/training/loss.py @@ -0,0 +1,140 @@ +# 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() + +#---------------------------------------------------------------------------- diff --git a/training/networks_stylegan2.py b/training/networks_stylegan2.py new file mode 100644 index 0000000..8ab3106 --- /dev/null +++ b/training/networks_stylegan2.py @@ -0,0 +1,794 @@ +# 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}' + +#---------------------------------------------------------------------------- diff --git a/training/networks_stylegan3.py b/training/networks_stylegan3.py new file mode 100644 index 0000000..a6a2b4d --- /dev/null +++ b/training/networks_stylegan3.py @@ -0,0 +1,515 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/training/training_loop.py b/training/training_loop.py new file mode 100644 index 0000000..656839c --- /dev/null +++ b/training/training_loop.py @@ -0,0 +1,426 @@ +# 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...') + +#---------------------------------------------------------------------------- diff --git a/visualizer.py b/visualizer.py new file mode 100644 index 0000000..4168447 --- /dev/null +++ b/visualizer.py @@ -0,0 +1,334 @@ +# 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() + +#---------------------------------------------------------------------------- diff --git a/viz/__init__.py b/viz/__init__.py new file mode 100644 index 0000000..8dd3488 --- /dev/null +++ b/viz/__init__.py @@ -0,0 +1,9 @@ +# 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 diff --git a/viz/capture_widget.py b/viz/capture_widget.py new file mode 100644 index 0000000..311ae88 --- /dev/null +++ b/viz/capture_widget.py @@ -0,0 +1,87 @@ +# 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) + +#---------------------------------------------------------------------------- diff --git a/viz/equivariance_widget.py b/viz/equivariance_widget.py new file mode 100644 index 0000000..49ef74f --- /dev/null +++ b/viz/equivariance_widget.py @@ -0,0 +1,115 @@ +# 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) + +#---------------------------------------------------------------------------- diff --git a/viz/latent_widget.py b/viz/latent_widget.py new file mode 100644 index 0000000..ca6fa62 --- /dev/null +++ b/viz/latent_widget.py @@ -0,0 +1,78 @@ +# 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]) + +#---------------------------------------------------------------------------- diff --git a/viz/layer_widget.py b/viz/layer_widget.py new file mode 100644 index 0000000..52758a9 --- /dev/null +++ b/viz/layer_widget.py @@ -0,0 +1,183 @@ +# 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) + +#---------------------------------------------------------------------------- diff --git a/viz/performance_widget.py b/viz/performance_widget.py new file mode 100644 index 0000000..67e874a --- /dev/null +++ b/viz/performance_widget.py @@ -0,0 +1,73 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/viz/pickle_widget.py b/viz/pickle_widget.py new file mode 100644 index 0000000..5e5f953 --- /dev/null +++ b/viz/pickle_widget.py @@ -0,0 +1,170 @@ +# 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=' | | | | /.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 + +#---------------------------------------------------------------------------- diff --git a/viz/renderer.py b/viz/renderer.py new file mode 100644 index 0000000..dc89412 --- /dev/null +++ b/viz/renderer.py @@ -0,0 +1,377 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/viz/stylemix_widget.py b/viz/stylemix_widget.py new file mode 100644 index 0000000..b997855 --- /dev/null +++ b/viz/stylemix_widget.py @@ -0,0 +1,66 @@ +# 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 + +#---------------------------------------------------------------------------- diff --git a/viz/trunc_noise_widget.py b/viz/trunc_noise_widget.py new file mode 100644 index 0000000..4597b28 --- /dev/null +++ b/viz/trunc_noise_widget.py @@ -0,0 +1,75 @@ +# 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') + +#----------------------------------------------------------------------------