diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..72a21f7c52dc9e126c325ffd29ff20f9ca3912f6 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
+DragGAN.gif filter=lfs diff=lfs merge=lfs -text
+stylegan_human/img/demo_V5_thumbnails-min.png filter=lfs diff=lfs merge=lfs -text
+stylegan_human/img/preview_samples1.png filter=lfs diff=lfs merge=lfs -text
+stylegan_human/img/test/test.jpg filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..8b626aa3dcff33d6dd97d22a247b4367aa294b99
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,157 @@
+# Created by .ignore support plugin (hsz.mobi)
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
+### VirtualEnv template
+# Virtualenv
+# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
+.Python
+[Bb]in
+[Ii]nclude
+[Ll]ib
+[Ll]ib64
+[Ll]ocal
+[Ss]cripts
+!scripts/
+pyvenv.cfg
+.venv
+pip-selfcheck.json
+### JetBrains template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and Webstorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff:
+.idea/workspace.xml
+.idea/tasks.xml
+.idea/dictionaries
+.idea/vcs.xml
+.idea/jsLibraryMappings.xml
+
+# Sensitive or high-churn files:
+.idea/dataSources.ids
+.idea/dataSources.xml
+.idea/dataSources.local.xml
+.idea/sqlDataSources.xml
+.idea/dynamic.xml
+.idea/uiDesigner.xml
+
+# Gradle:
+.idea/gradle.xml
+.idea/libraries
+
+# Mongo Explorer plugin:
+.idea/mongoSettings.xml
+
+.idea/
+
+## File-based project format:
+*.iws
+
+## Plugin-specific files:
+
+# IntelliJ
+/out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Mac related
+.DS_Store
+
+checkpoints
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..2c6ad403e81f3c3bc8521a5615f8bcd8cf879c57
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,28 @@
+FROM nvcr.io/nvidia/pytorch:23.05-py3
+
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONUNBUFFERED 1
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ make \
+ pkgconf \
+ xz-utils \
+ xorg-dev \
+ libgl1-mesa-dev \
+ libglu1-mesa-dev \
+ libxrandr-dev \
+ libxinerama-dev \
+ libxcursor-dev \
+ libxi-dev \
+ libxxf86vm-dev \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN pip install --no-cache-dir --upgrade pip
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+WORKDIR /workspace
+
+RUN (printf '#!/bin/bash\nexec \"$@\"\n' >> /entry.sh) && chmod a+x /entry.sh
+ENTRYPOINT ["/entry.sh"]
diff --git a/DragGAN.gif b/DragGAN.gif
new file mode 100644
index 0000000000000000000000000000000000000000..d27f0290b6542438f21db5c70bd14c808e148a21
--- /dev/null
+++ b/DragGAN.gif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2eab11d4dd1f11c2efacfcde385899b0164e241a7823eb050ab2e021f337225a
+size 21641074
diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6b5ee9bf994cc9441cb659c3527160b4ee5bcb33
--- /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. Subject to the terms and conditions of this
+ License, each Licensor grants to you a perpetual, worldwide,
+ non-exclusive, royalty-free, copyright license to reproduce,
+ prepare derivative works of, publicly display, publicly perform,
+ sublicense and distribute its Work and any resulting derivative
+ works in any form.
+
+3. Limitations
+
+ 3.1 Redistribution. You may reproduce or distribute the Work only
+ if (a) you do so under this License, (b) you include a complete
+ copy of this License with your distribution, and (c) you retain
+ without modification any copyright, patent, trademark, or
+ attribution notices that are present in the Work.
+
+ 3.2 Derivative Works. 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
index 63d89cb6390311c7cf6ef81a020b907d61c898cc..d701440b3c899733ea43d93a355fbfcc74e3fe76 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,144 @@
---
-title: ShuangzizuoDragGAN
-emoji: 🌖
-colorFrom: purple
-colorTo: yellow
+title: shuangzizuoDragGAN
+app_file: visualizer_drag_gradio.py
sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
+sdk_version: 3.35.2
---
+
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
+
+ Xingang Pan
+ ·
+ Ayush Tewari
+ ·
+ Thomas Leimkühler
+ ·
+ Lingjie Liu
+ ·
+ Abhimitra Meka
+ ·
+ Christian Theobalt
+
+ SIGGRAPH 2023 Conference Proceedings
+
+
+
+
+ 
+ 
+
+ 
+
+
+ 
+ 
+
+
+
+## Web Demos
+
+[](https://openxlab.org.cn/apps/detail/XingangPan/DragGAN)
+
+
+ 
+
+
+## Requirements
+
+If you have CUDA graphic card, please follow the requirements of [NVlabs/stylegan3](https://github.com/NVlabs/stylegan3#requirements).
+
+The usual installation steps involve the following commands, they should set up the correct CUDA version and all the python packages
+
+```
+conda env create -f environment.yml
+conda activate stylegan3
+```
+
+Then install the additional requirements
+
+```
+pip install -r requirements.txt
+```
+
+Otherwise (for GPU acceleration on MacOS with Silicon Mac M1/M2, or just CPU) try the following:
+
+```sh
+cat environment.yml | \
+ grep -v -E 'nvidia|cuda' > environment-no-nvidia.yml && \
+ conda env create -f environment-no-nvidia.yml
+conda activate stylegan3
+
+# On MacOS
+export PYTORCH_ENABLE_MPS_FALLBACK=1
+```
+
+## Run Gradio visualizer in Docker
+
+Provided docker image is based on NGC PyTorch repository. To quickly try out visualizer in Docker, run the following:
+
+```sh
+# before you build the docker container, make sure you have cloned this repo, and downloaded the pretrained model by `python scripts/download_model.py`.
+docker build . -t draggan:latest
+docker run -p 7860:7860 -v "$PWD":/workspace/src -it draggan:latest bash
+# (Use GPU)if you want to utilize your Nvidia gpu to accelerate in docker, please add command tag `--gpus all`, like:
+# docker run --gpus all -p 7860:7860 -v "$PWD":/workspace/src -it draggan:latest bash
+
+cd src && python visualizer_drag_gradio.py --listen
+```
+Now you can open a shared link from Gradio (printed in the terminal console).
+Beware the Docker image takes about 25GB of disk space!
+
+## Download pre-trained StyleGAN2 weights
+
+To download pre-trained weights, simply run:
+
+```
+python scripts/download_model.py
+```
+If you want to try StyleGAN-Human and the Landscapes HQ (LHQ) dataset, please download weights from these links: [StyleGAN-Human](https://drive.google.com/file/d/1dlFEHbu-WzQWJl7nBBZYcTyo000H9hVm/view?usp=sharing), [LHQ](https://drive.google.com/file/d/16twEf0T9QINAEoMsWefoWiyhcTd-aiWc/view?usp=sharing), and put them under `./checkpoints`.
+
+Feel free to try other pretrained StyleGAN.
+
+## Run DragGAN GUI
+
+To start the DragGAN GUI, simply run:
+```sh
+sh scripts/gui.sh
+```
+If you are using windows, you can run:
+```
+.\scripts\gui.bat
+```
+
+This GUI supports editing GAN-generated images. To edit a real image, you need to first perform GAN inversion using tools like [PTI](https://github.com/danielroich/PTI). Then load the new latent code and model weights to the GUI.
+
+You can run DragGAN Gradio demo as well, this is universal for both windows and linux:
+```sh
+python visualizer_drag_gradio.py
+```
+
+## Acknowledgement
+
+This code is developed based on [StyleGAN3](https://github.com/NVlabs/stylegan3). Part of the code is borrowed from [StyleGAN-Human](https://github.com/stylegan-human/StyleGAN-Human).
+
+(cheers to the community as well)
+## License
+
+The code related to the DragGAN algorithm is licensed under [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/).
+However, most of this project are available under a separate license terms: all codes used or modified from [StyleGAN3](https://github.com/NVlabs/stylegan3) is under the [Nvidia Source Code License](https://github.com/NVlabs/stylegan3/blob/main/LICENSE.txt).
+
+Any form of use and derivative of this code must preserve the watermarking functionality showing "AI Generated".
+
+## BibTeX
+
+```bibtex
+@inproceedings{pan2023draggan,
+ title={Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold},
+ author={Pan, Xingang and Tewari, Ayush, and Leimk{\"u}hler, Thomas and Liu, Lingjie and Meka, Abhimitra and Theobalt, Christian},
+ booktitle = {ACM SIGGRAPH 2023 Conference Proceedings},
+ year={2023}
+}
+```
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+++ b/checkpoints/stylegan2-afhqcat-512x512.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 363939583
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+version https://git-lfs.github.com/spec/v1
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+version https://git-lfs.github.com/spec/v1
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+version https://git-lfs.github.com/spec/v1
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+version https://git-lfs.github.com/spec/v1
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+version https://git-lfs.github.com/spec/v1
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+size 357336721
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+version https://git-lfs.github.com/spec/v1
+oid sha256:0a01ff8344521171b1a2eff1e9a51c1acbc48221bdc2594919187f66a3942bcc
+size 363965313
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--- /dev/null
+++ b/checkpoints/stylegan_human_v2_512.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7af33f638e3b3aba7bf99456eec3e9d4a022d8a7dd67683a3605a7dd37665a3b
+size 352745981
diff --git a/dnnlib/__init__.py b/dnnlib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7423bffe245d0ff3f32e8658aa67daae454e64e
--- /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/__pycache__/__init__.cpython-39.pyc b/dnnlib/__pycache__/__init__.cpython-39.pyc
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diff --git a/dnnlib/util.py b/dnnlib/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bbdf3bd8fe1c138cd969d37dcc52190b45c4c16
--- /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/environment.yml b/environment.yml
new file mode 100644
index 0000000000000000000000000000000000000000..289404fe30a0d471500c6fb21b91f090828460f1
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,27 @@
+name: stylegan3
+channels:
+ - pytorch
+ - nvidia
+dependencies:
+ - python >= 3.8
+ - pip
+ - numpy>=1.25
+ - click>=8.0
+ - pillow=9.4.0
+ #- scipy=1.11.0
+ - pytorch>=2.0.1
+ - torchvision>=0.15.2
+ #- 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==2.0.0
+ - glfw==2.6.1
+ - gradio==3.35.2
+ - pyopengl==3.1.5
+ - imageio-ffmpeg==0.4.3
+ # pyspng is currently broken on MacOS (see https://github.com/nurpax/pyspng/pull/6 for instance)
+ - pyspng-seunglab
diff --git a/gen_images.py b/gen_images.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2a85f2d155137bcf5ca9c06c4f46dda70d80ca5
--- /dev/null
+++ b/gen_images.py
@@ -0,0 +1,150 @@
+# 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(','):
+ m = range_re.match(p)
+ if m:
+ 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' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
+ dtype = torch.float32 if device.type == 'mps' else torch.float64
+ with dnnlib.util.open_url(network_pkl) as f:
+ G = legacy.load_network_pkl(f)['G_ema'].to(device, dtype=dtype) # type: ignore
+ # import pickle
+ # G = legacy.load_network_pkl(f)
+ # output = open('checkpoints/stylegan2-car-config-f-pt.pkl', 'wb')
+ # pickle.dump(G, output)
+
+ 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, dtype=dtype)
+
+ # 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/gradio_utils/__init__.py b/gradio_utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a54920c53b4373690fd0ca59ee59159d33d1f92
--- /dev/null
+++ b/gradio_utils/__init__.py
@@ -0,0 +1,9 @@
+from .utils import (ImageMask, draw_mask_on_image, draw_points_on_image,
+ get_latest_points_pair, get_valid_mask,
+ on_change_single_global_state)
+
+__all__ = [
+ 'draw_mask_on_image', 'draw_points_on_image',
+ 'on_change_single_global_state', 'get_latest_points_pair',
+ 'get_valid_mask', 'ImageMask'
+]
diff --git a/gradio_utils/__pycache__/__init__.cpython-39.pyc b/gradio_utils/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6159b39a7880559ddf081a01228f52e2244555e2
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diff --git a/gradio_utils/__pycache__/utils.cpython-39.pyc b/gradio_utils/__pycache__/utils.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..80c21c4e549727440752ad1d7fbfd885c98f48e8
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diff --git a/gradio_utils/utils.py b/gradio_utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4e760e1515f3f69b11d11426ac3e8fa51f1a99c
--- /dev/null
+++ b/gradio_utils/utils.py
@@ -0,0 +1,154 @@
+import gradio as gr
+import numpy as np
+from PIL import Image, ImageDraw
+
+
+class ImageMask(gr.components.Image):
+ """
+ Sets: source="canvas", tool="sketch"
+ """
+
+ is_template = True
+
+ def __init__(self, **kwargs):
+ super().__init__(source="upload",
+ tool="sketch",
+ interactive=False,
+ **kwargs)
+
+ def preprocess(self, x):
+ if x is None:
+ return x
+ if self.tool == "sketch" and self.source in ["upload", "webcam"
+ ] and type(x) != dict:
+ decode_image = gr.processing_utils.decode_base64_to_image(x)
+ width, height = decode_image.size
+ mask = np.ones((height, width, 4), dtype=np.uint8)
+ mask[..., -1] = 255
+ mask = self.postprocess(mask)
+ x = {'image': x, 'mask': mask}
+ return super().preprocess(x)
+
+
+def get_valid_mask(mask: np.ndarray):
+ """Convert mask from gr.Image(0 to 255, RGBA) to binary mask.
+ """
+ if mask.ndim == 3:
+ mask_pil = Image.fromarray(mask).convert('L')
+ mask = np.array(mask_pil)
+ if mask.max() == 255:
+ mask = mask / 255
+ return mask
+
+
+def draw_points_on_image(image,
+ points,
+ curr_point=None,
+ highlight_all=True,
+ radius_scale=0.01):
+ overlay_rgba = Image.new("RGBA", image.size, 0)
+ overlay_draw = ImageDraw.Draw(overlay_rgba)
+ for point_key, point in points.items():
+ if ((curr_point is not None and curr_point == point_key)
+ or highlight_all):
+ p_color = (255, 0, 0)
+ t_color = (0, 0, 255)
+
+ else:
+ p_color = (255, 0, 0, 35)
+ t_color = (0, 0, 255, 35)
+
+ rad_draw = int(image.size[0] * radius_scale)
+
+ p_start = point.get("start_temp", point["start"])
+ p_target = point["target"]
+
+ if p_start is not None and p_target is not None:
+ p_draw = int(p_start[0]), int(p_start[1])
+ t_draw = int(p_target[0]), int(p_target[1])
+
+ overlay_draw.line(
+ (p_draw[0], p_draw[1], t_draw[0], t_draw[1]),
+ fill=(255, 255, 0),
+ width=2,
+ )
+
+ if p_start is not None:
+ p_draw = int(p_start[0]), int(p_start[1])
+ overlay_draw.ellipse(
+ (
+ p_draw[0] - rad_draw,
+ p_draw[1] - rad_draw,
+ p_draw[0] + rad_draw,
+ p_draw[1] + rad_draw,
+ ),
+ fill=p_color,
+ )
+
+ if curr_point is not None and curr_point == point_key:
+ # overlay_draw.text(p_draw, "p", font=font, align="center", fill=(0, 0, 0))
+ overlay_draw.text(p_draw, "p", align="center", fill=(0, 0, 0))
+
+ if p_target is not None:
+ t_draw = int(p_target[0]), int(p_target[1])
+ overlay_draw.ellipse(
+ (
+ t_draw[0] - rad_draw,
+ t_draw[1] - rad_draw,
+ t_draw[0] + rad_draw,
+ t_draw[1] + rad_draw,
+ ),
+ fill=t_color,
+ )
+
+ if curr_point is not None and curr_point == point_key:
+ # overlay_draw.text(t_draw, "t", font=font, align="center", fill=(0, 0, 0))
+ overlay_draw.text(t_draw, "t", align="center", fill=(0, 0, 0))
+
+ return Image.alpha_composite(image.convert("RGBA"),
+ overlay_rgba).convert("RGB")
+
+
+def draw_mask_on_image(image, mask):
+ im_mask = np.uint8(mask * 255)
+ im_mask_rgba = np.concatenate(
+ (
+ np.tile(im_mask[..., None], [1, 1, 3]),
+ 45 * np.ones(
+ (im_mask.shape[0], im_mask.shape[1], 1), dtype=np.uint8),
+ ),
+ axis=-1,
+ )
+ im_mask_rgba = Image.fromarray(im_mask_rgba).convert("RGBA")
+
+ return Image.alpha_composite(image.convert("RGBA"),
+ im_mask_rgba).convert("RGB")
+
+
+def on_change_single_global_state(keys,
+ value,
+ global_state,
+ map_transform=None):
+ if map_transform is not None:
+ value = map_transform(value)
+
+ curr_state = global_state
+ if isinstance(keys, str):
+ last_key = keys
+
+ else:
+ for k in keys[:-1]:
+ curr_state = curr_state[k]
+
+ last_key = keys[-1]
+
+ curr_state[last_key] = value
+ return global_state
+
+
+def get_latest_points_pair(points_dict):
+ if not points_dict:
+ return None
+ point_idx = list(points_dict.keys())
+ latest_point_idx = max(point_idx)
+ return latest_point_idx
diff --git a/gui_utils/__init__.py b/gui_utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..939e7c6c8f94c4ea1141885c3c3295fe083b06aa
--- /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/__pycache__/__init__.cpython-39.pyc b/gui_utils/__pycache__/__init__.cpython-39.pyc
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diff --git a/gui_utils/__pycache__/gl_utils.cpython-39.pyc b/gui_utils/__pycache__/gl_utils.cpython-39.pyc
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diff --git a/gui_utils/__pycache__/glfw_window.cpython-39.pyc b/gui_utils/__pycache__/glfw_window.cpython-39.pyc
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diff --git a/gui_utils/__pycache__/imgui_utils.cpython-39.pyc b/gui_utils/__pycache__/imgui_utils.cpython-39.pyc
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diff --git a/gui_utils/__pycache__/text_utils.cpython-39.pyc b/gui_utils/__pycache__/text_utils.cpython-39.pyc
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diff --git a/gui_utils/gl_utils.py b/gui_utils/gl_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd8841c328d33a0f3c4376eb07dd9845ad660ebe
--- /dev/null
+++ b/gui_utils/gl_utils.py
@@ -0,0 +1,416 @@
+# 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 math
+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_arrow(x1, y1, x2, y2, l=10, width=1.0):
+ # Compute the length and angle of the arrow
+ dx = x2 - x1
+ dy = y2 - y1
+ length = math.sqrt(dx**2 + dy**2)
+ if length < l:
+ return
+ angle = math.atan2(dy, dx)
+
+ # Save the current modelview matrix
+ gl.glPushMatrix()
+
+ # Translate and rotate the coordinate system
+ gl.glTranslatef(x1, y1, 0.0)
+ gl.glRotatef(angle * 180.0 / math.pi, 0.0, 0.0, 1.0)
+
+ # Set the line width
+ gl.glLineWidth(width)
+ # gl.glColor3f(0.75, 0.75, 0.75)
+
+ # Begin drawing lines
+ gl.glBegin(gl.GL_LINES)
+
+ # Draw the shaft of the arrow
+ gl.glVertex2f(0.0, 0.0)
+ gl.glVertex2f(length, 0.0)
+
+ # Draw the head of the arrow
+ gl.glVertex2f(length, 0.0)
+ gl.glVertex2f(length - 2 * l, l)
+ gl.glVertex2f(length, 0.0)
+ gl.glVertex2f(length - 2 * l, -l)
+
+ # End drawing lines
+ gl.glEnd()
+
+ # Restore the modelview matrix
+ gl.glPopMatrix()
+
+#----------------------------------------------------------------------------
+
+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 0000000000000000000000000000000000000000..83264eb89a855ec5038cf255994ee2b4b3ddb5ee
--- /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 0000000000000000000000000000000000000000..5924abe272e45855044929bb2a8c886029f4f261
--- /dev/null
+++ b/gui_utils/imgui_utils.py
@@ -0,0 +1,191 @@
+# 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
+
+#----------------------------------------------------------------------------
+
+def click_hidden_window(label, x, y, width, height, img_w, img_h, 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))
+ clicked, down = False, False
+ img_x, img_y = 0, 0
+ if imgui.is_mouse_down():
+ posx, posy = imgui.get_mouse_pos()
+ if posx >= x and posx < x + width and posy >= y and posy < y + height:
+ if imgui.is_mouse_clicked():
+ clicked = True
+ down = True
+ img_x = round((posx - x) / (width - 1) * (img_w - 1))
+ img_y = round((posy - y) / (height - 1) * (img_h - 1))
+ imgui.end()
+ imgui.pop_style_color(2)
+ return clicked, down, img_x, img_y
+
+#----------------------------------------------------------------------------
diff --git a/gui_utils/imgui_window.py b/gui_utils/imgui_window.py
new file mode 100644
index 0000000000000000000000000000000000000000..30d539a1382def526050c83978d1118348ac77ad
--- /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 0000000000000000000000000000000000000000..35e5e4a16dc62c4be80df5432208bce5d386bf16
--- /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 0000000000000000000000000000000000000000..8cf53cb9396a639261bbcadb4e264e39415c1a56
--- /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/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..556e1278ffb3c04f94396cdff8264836910498c2
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,13 @@
+torch>=2.0.0
+scipy==1.11.0
+Ninja==1.10.2
+gradio>=3.35.2
+imageio-ffmpeg>=0.4.3
+huggingface_hub
+hf_transfer
+pyopengl
+imgui
+glfw==2.6.1
+pillow>=9.4.0
+torchvision>=0.15.2
+imageio>=2.9.0
diff --git a/scripts/download_model.py b/scripts/download_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b951b055cc95e7827c09b9a6572839c51ea53d1d
--- /dev/null
+++ b/scripts/download_model.py
@@ -0,0 +1,78 @@
+import os
+import sys
+import json
+import requests
+from tqdm import tqdm
+
+def download_file(url: str, filename: str, download_dir: str):
+ """Download a file if it does not already exist."""
+
+ try:
+ filepath = os.path.join(download_dir, filename)
+ content_length = int(requests.head(url).headers.get("content-length", 0))
+
+ # If file already exists and size matches, skip download
+ if os.path.isfile(filepath) and os.path.getsize(filepath) == content_length:
+ print(f"{filepath} already exists. Skipping download.")
+ return
+ if os.path.isfile(filepath) and os.path.getsize(filepath) != content_length:
+ print(f"{filepath} already exists but size does not match. Redownloading.")
+ else:
+ print(f"Downloading {filename} from {url}")
+
+ # Start download, stream=True allows for progress tracking
+ response = requests.get(url, stream=True)
+
+ # Check if request was successful
+ response.raise_for_status()
+
+ # Create progress bar
+ total_size = int(response.headers.get('content-length', 0))
+ progress_bar = tqdm(
+ total=total_size,
+ unit='iB',
+ unit_scale=True,
+ ncols=70,
+ file=sys.stdout
+ )
+
+ # Write response content to file
+ with open(filepath, 'wb') as f:
+ for data in response.iter_content(chunk_size=1024):
+ f.write(data)
+ progress_bar.update(len(data)) # Update progress bar
+
+ # Close progress bar
+ progress_bar.close()
+
+ # Error handling for incomplete downloads
+ if total_size != 0 and progress_bar.n != total_size:
+ print("ERROR, something went wrong while downloading")
+ raise Exception()
+
+
+ except Exception as e:
+ print(f"An error occurred: {e}")
+
+def main():
+ """Main function to download files from URLs in a config file."""
+
+ # Get JSON config file path
+ script_dir = os.path.dirname(os.path.realpath(__file__))
+ config_file_path = os.path.join(script_dir, "download_models.json")
+
+ # Set download directory
+ download_dir = "checkpoints"
+ os.makedirs(download_dir, exist_ok=True)
+
+ # Load URL and filenames from JSON
+ with open(config_file_path, "r") as f:
+ config = json.load(f)
+
+ # Download each file specified in config
+ for url, filename in config.items():
+ download_file(url, filename, download_dir)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/download_models.json b/scripts/download_models.json
new file mode 100644
index 0000000000000000000000000000000000000000..637d553ee6484ef674d6808ce556345a747bfb42
--- /dev/null
+++ b/scripts/download_models.json
@@ -0,0 +1,10 @@
+{
+ "https://storage.googleapis.com/self-distilled-stylegan/lions_512_pytorch.pkl": "stylegan2_lions_512_pytorch.pkl",
+ "https://storage.googleapis.com/self-distilled-stylegan/dogs_1024_pytorch.pkl": "stylegan2_dogs_1024_pytorch.pkl",
+ "https://storage.googleapis.com/self-distilled-stylegan/horses_256_pytorch.pkl": "stylegan2_horses_256_pytorch.pkl",
+ "https://storage.googleapis.com/self-distilled-stylegan/elephants_512_pytorch.pkl": "stylegan2_elephants_512_pytorch.pkl",
+ "https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-ffhq-512x512.pkl": "stylegan2-ffhq-512x512.pkl",
+ "https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/stylegan2-afhqcat-512x512.pkl": "stylegan2-afhqcat-512x512.pkl",
+ "http://d36zk2xti64re0.cloudfront.net/stylegan2/networks/stylegan2-car-config-f.pkl": "stylegan2-car-config-f.pkl",
+ "http://d36zk2xti64re0.cloudfront.net/stylegan2/networks/stylegan2-cat-config-f.pkl": "stylegan2-cat-config-f.pkl"
+}
diff --git a/scripts/gui.bat b/scripts/gui.bat
new file mode 100644
index 0000000000000000000000000000000000000000..6b657a7f69de8796ae179f0d20fbc607511d30ad
--- /dev/null
+++ b/scripts/gui.bat
@@ -0,0 +1,12 @@
+@echo off
+python visualizer_drag.py ^
+ checkpoints/stylegan2_lions_512_pytorch.pkl ^
+ checkpoints/stylegan2-ffhq-512x512.pkl ^
+ checkpoints/stylegan2-afhqcat-512x512.pkl ^
+ checkpoints/stylegan2-car-config-f.pkl ^
+ checkpoints/stylegan2_dogs_1024_pytorch.pkl ^
+ checkpoints/stylegan2_horses_256_pytorch.pkl ^
+ checkpoints/stylegan2-cat-config-f.pkl ^
+ checkpoints/stylegan2_elephants_512_pytorch.pkl ^
+ checkpoints/stylegan_human_v2_512.pkl ^
+ checkpoints/stylegan2-lhq-256x256.pkl
diff --git a/scripts/gui.sh b/scripts/gui.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5eb68e3b7d2e51b8781fa2e638c7005f0c994246
--- /dev/null
+++ b/scripts/gui.sh
@@ -0,0 +1,11 @@
+python visualizer_drag.py \
+ checkpoints/stylegan2_lions_512_pytorch.pkl \
+ checkpoints/stylegan2-ffhq-512x512.pkl \
+ checkpoints/stylegan2-afhqcat-512x512.pkl \
+ checkpoints/stylegan2-car-config-f.pkl \
+ checkpoints/stylegan2_dogs_1024_pytorch.pkl \
+ checkpoints/stylegan2_horses_256_pytorch.pkl \
+ checkpoints/stylegan2-cat-config-f.pkl \
+ checkpoints/stylegan2_elephants_512_pytorch.pkl \
+ checkpoints/stylegan_human_v2_512.pkl \
+ checkpoints/stylegan2-lhq-256x256.pkl
diff --git a/stylegan_human/.gitignore b/stylegan_human/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..64fba689a018ec191c2c808a69e2f2a7ad724916
--- /dev/null
+++ b/stylegan_human/.gitignore
@@ -0,0 +1,10 @@
+.DS_Store
+__pycache__
+*.pt
+*.pth
+*.pdparams
+*.pdiparams
+*.pdmodel
+*.pkl
+*.info
+*.yaml
\ No newline at end of file
diff --git a/stylegan_human/PP_HumanSeg/deploy/infer.py b/stylegan_human/PP_HumanSeg/deploy/infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..df4dd04762fec4a78c423075fc5f45d892171af9
--- /dev/null
+++ b/stylegan_human/PP_HumanSeg/deploy/infer.py
@@ -0,0 +1,180 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import codecs
+import os
+import time
+
+import yaml
+import numpy as np
+import cv2
+import paddle
+import paddleseg.transforms as T
+from paddle.inference import create_predictor, PrecisionType
+from paddle.inference import Config as PredictConfig
+from paddleseg.core.infer import reverse_transform
+from paddleseg.cvlibs import manager
+from paddleseg.utils import TimeAverager
+
+from ..scripts.optic_flow_process import optic_flow_process
+
+
+class DeployConfig:
+ def __init__(self, path):
+ with codecs.open(path, 'r', 'utf-8') as file:
+ self.dic = yaml.load(file, Loader=yaml.FullLoader)
+
+ self._transforms = self._load_transforms(self.dic['Deploy'][
+ 'transforms'])
+ self._dir = os.path.dirname(path)
+
+ @property
+ def transforms(self):
+ return self._transforms
+
+ @property
+ def model(self):
+ return os.path.join(self._dir, self.dic['Deploy']['model'])
+
+ @property
+ def params(self):
+ return os.path.join(self._dir, self.dic['Deploy']['params'])
+
+ def _load_transforms(self, t_list):
+ com = manager.TRANSFORMS
+ transforms = []
+ for t in t_list:
+ ctype = t.pop('type')
+ transforms.append(com[ctype](**t))
+
+ return transforms
+
+
+class Predictor:
+ def __init__(self, args):
+ self.cfg = DeployConfig(args.cfg)
+ self.args = args
+ self.compose = T.Compose(self.cfg.transforms)
+ resize_h, resize_w = args.input_shape
+
+ self.disflow = cv2.DISOpticalFlow_create(
+ cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)
+ self.prev_gray = np.zeros((resize_h, resize_w), np.uint8)
+ self.prev_cfd = np.zeros((resize_h, resize_w), np.float32)
+ self.is_init = True
+
+ pred_cfg = PredictConfig(self.cfg.model, self.cfg.params)
+ pred_cfg.disable_glog_info()
+ if self.args.use_gpu:
+ pred_cfg.enable_use_gpu(100, 0)
+
+ self.predictor = create_predictor(pred_cfg)
+ if self.args.test_speed:
+ self.cost_averager = TimeAverager()
+
+ def preprocess(self, img):
+ ori_shapes = []
+ processed_imgs = []
+ processed_img = self.compose(img)[0]
+ processed_imgs.append(processed_img)
+ ori_shapes.append(img.shape)
+ return processed_imgs, ori_shapes
+
+ def run(self, img, bg):
+ input_names = self.predictor.get_input_names()
+ input_handle = self.predictor.get_input_handle(input_names[0])
+ processed_imgs, ori_shapes = self.preprocess(img)
+ data = np.array(processed_imgs)
+ input_handle.reshape(data.shape)
+ input_handle.copy_from_cpu(data)
+ if self.args.test_speed:
+ start = time.time()
+
+ self.predictor.run()
+
+ if self.args.test_speed:
+ self.cost_averager.record(time.time() - start)
+ output_names = self.predictor.get_output_names()
+ output_handle = self.predictor.get_output_handle(output_names[0])
+ output = output_handle.copy_to_cpu()
+ return self.postprocess(output, img, ori_shapes[0], bg)
+
+
+ def postprocess(self, pred, img, ori_shape, bg):
+ if not os.path.exists(self.args.save_dir):
+ os.makedirs(self.args.save_dir)
+ resize_w = pred.shape[-1]
+ resize_h = pred.shape[-2]
+ if self.args.soft_predict:
+ if self.args.use_optic_flow:
+ score_map = pred[:, 1, :, :].squeeze(0)
+ score_map = 255 * score_map
+ cur_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
+ optflow_map = optic_flow_process(cur_gray, score_map, self.prev_gray, self.prev_cfd, \
+ self.disflow, self.is_init)
+ self.prev_gray = cur_gray.copy()
+ self.prev_cfd = optflow_map.copy()
+ self.is_init = False
+
+ score_map = np.repeat(optflow_map[:, :, np.newaxis], 3, axis=2)
+ score_map = np.transpose(score_map, [2, 0, 1])[np.newaxis, ...]
+ score_map = reverse_transform(
+ paddle.to_tensor(score_map),
+ ori_shape,
+ self.cfg.transforms,
+ mode='bilinear')
+ alpha = np.transpose(score_map.numpy().squeeze(0),
+ [1, 2, 0]) / 255
+ else:
+ score_map = pred[:, 1, :, :]
+ score_map = score_map[np.newaxis, ...]
+ score_map = reverse_transform(
+ paddle.to_tensor(score_map),
+ ori_shape,
+ self.cfg.transforms,
+ mode='bilinear')
+ alpha = np.transpose(score_map.numpy().squeeze(0), [1, 2, 0])
+
+ else:
+ if pred.ndim == 3:
+ pred = pred[:, np.newaxis, ...]
+ result = reverse_transform(
+ paddle.to_tensor(
+ pred, dtype='float32'),
+ ori_shape,
+ self.cfg.transforms,
+ mode='bilinear')
+
+ result = np.array(result)
+ if self.args.add_argmax:
+ result = np.argmax(result, axis=1)
+ else:
+ result = result.squeeze(1)
+ alpha = np.transpose(result, [1, 2, 0])
+
+ # background replace
+ h, w, _ = img.shape
+ if bg is None:
+ bg = np.ones_like(img)*255
+ else:
+ bg = cv2.resize(bg, (w, h))
+ if bg.ndim == 2:
+ bg = bg[..., np.newaxis]
+
+ comb = (alpha * img + (1 - alpha) * bg).astype(np.uint8)
+ return comb, alpha, bg, img
diff --git a/stylegan_human/PP_HumanSeg/export_model/download_export_model.py b/stylegan_human/PP_HumanSeg/export_model/download_export_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea4fc64c53441d068fe56dd24db153cf5cb92d45
--- /dev/null
+++ b/stylegan_human/PP_HumanSeg/export_model/download_export_model.py
@@ -0,0 +1,44 @@
+# coding: utf8
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import os
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TEST_PATH = os.path.join(LOCAL_PATH, "../../../", "test")
+sys.path.append(TEST_PATH)
+
+from paddleseg.utils.download import download_file_and_uncompress
+
+model_urls = {
+ "pphumanseg_lite_portrait_398x224_with_softmax":
+ "https://paddleseg.bj.bcebos.com/dygraph/ppseg/ppseg_lite_portrait_398x224_with_softmax.tar.gz",
+ "deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax":
+ "https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax.zip",
+ "fcn_hrnetw18_small_v1_humanseg_192x192_with_softmax":
+ "https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/fcn_hrnetw18_small_v1_humanseg_192x192_with_softmax.zip",
+ "pphumanseg_lite_generic_humanseg_192x192_with_softmax":
+ "https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/pphumanseg_lite_generic_192x192_with_softmax.zip",
+}
+
+if __name__ == "__main__":
+ for model_name, url in model_urls.items():
+ download_file_and_uncompress(
+ url=url,
+ savepath=LOCAL_PATH,
+ extrapath=LOCAL_PATH,
+ extraname=model_name)
+
+ print("Export model download success!")
diff --git a/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py b/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..69e3f0910e5c553cc59a3067ac02881a720a474f
--- /dev/null
+++ b/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py
@@ -0,0 +1,44 @@
+# coding: utf8
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import os
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TEST_PATH = os.path.join(LOCAL_PATH, "../../../", "test")
+sys.path.append(TEST_PATH)
+
+from paddleseg.utils.download import download_file_and_uncompress
+
+model_urls = {
+ "pphumanseg_lite_portrait_398x224":
+ "https://paddleseg.bj.bcebos.com/dygraph/ppseg/ppseg_lite_portrait_398x224.tar.gz",
+ "deeplabv3p_resnet50_os8_humanseg_512x512_100k":
+ "https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/deeplabv3p_resnet50_os8_humanseg_512x512_100k.zip",
+ "fcn_hrnetw18_small_v1_humanseg_192x192":
+ "https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/fcn_hrnetw18_small_v1_humanseg_192x192.zip",
+ "pphumanseg_lite_generic_human_192x192":
+ "https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/pphumanseg_lite_generic_192x192.zip",
+}
+
+if __name__ == "__main__":
+ for model_name, url in model_urls.items():
+ download_file_and_uncompress(
+ url=url,
+ savepath=LOCAL_PATH,
+ extrapath=LOCAL_PATH,
+ extraname=model_name)
+
+ print("Pretrained model download success!")
diff --git a/stylegan_human/README.md b/stylegan_human/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..0442c284c6ce0e9e7a1d6d7f487debab8ccd1a1b
--- /dev/null
+++ b/stylegan_human/README.md
@@ -0,0 +1,229 @@
+# StyleGAN-Human: A Data-Centric Odyssey of Human Generation
+
+
+
+
+>
+>
+> **Abstract:** *Unconditional human image generation is an important task in vision and graphics, which enables various applications in the creative industry. Existing studies in this field mainly focus on "network engineering" such as designing new components and objective functions. This work takes a data-centric perspective and investigates multiple critical aspects in "data engineering", which we believe would complement the current practice. To facilitate a comprehensive study, we collect and annotate a large-scale human image dataset with over 230K samples capturing diverse poses and textures. Equipped with this large dataset, we rigorously investigate three essential factors in data engineering for StyleGAN-based human generation, namely data size, data distribution, and data alignment. Extensive experiments reveal several valuable observations w.r.t. these aspects: 1) Large-scale data, more than 40K images, are needed to train a high-fidelity unconditional human generation model with vanilla StyleGAN. 2) A balanced training set helps improve the generation quality with rare face poses compared to the long-tailed counterpart, whereas simply balancing the clothing texture distribution does not effectively bring an improvement. 3) Human GAN models with body centers for alignment outperform models trained using face centers or pelvis points as alignment anchors. In addition, a model zoo and human editing applications are demonstrated to facilitate future research in the community.*
+**Keyword:** Human Image Generation, Data-Centric, StyleGAN
+
+[Jianglin Fu](mailto:fujianglin@sensetime.com), [Shikai Li](mailto:lishikai@sensetime.com), [Yuming Jiang](https://yumingj.github.io/), [Kwan-Yee Lin](https://kwanyeelin.github.io/), [Chen Qian](https://scholar.google.com/citations?user=AerkT0YAAAAJ&hl=zh-CN), [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/), [Wayne Wu](https://wywu.github.io/), and [Ziwei Liu](https://liuziwei7.github.io/)
+**[[Demo Video]](https://youtu.be/nIrb9hwsdcI)** | **[[Project Page]](https://stylegan-human.github.io/)** | **[[Paper]](https://arxiv.org/pdf/2204.11823.pdf)**
+
+## Updates
+- [20/07/2022] [SHHQ-1.0](./docs/Dataset.md) dataset with 40K images is released! :sparkles:
+- [15/06/2022] Data alignment and real-image inversion scripts are released.
+- [26/04/2022] Technical report released!
+- [22/04/2022] Technical report will be released before May.
+- [21/04/2022] The codebase and project page are created.
+
+## Data Download
+The first version SHHQ-1.0, with 40K images is released. To download and use the dataset set, please read the instructions in [Dataset.md](./docs/Dataset.md)
+
+（We are currently facing large incoming applications, and we need to carefully verify all the applicants, please be patient, and we will reply to you as soon as possible.）
+
+## Model Zoo
+
+| Structure | 1024x512 | Metric | Scores | 512x256 | Metric | Scores |
+| --------- |:----------:| :----------:| :----------:| :-----: | :-----: | :-----: |
+| StyleGAN1 |[stylegan_human_v1_1024.pkl](https://drive.google.com/file/d/1h-R-IV-INGdPEzj4P9ml6JTEvihuNgLX/view?usp=sharing)| fid50k | 3.79 | to be released | - | - |
+| StyleGAN2 |[stylegan_human_v2_1024.pkl](https://drive.google.com/file/d/1FlAb1rYa0r_--Zj_ML8e6shmaF28hQb5/view?usp=sharing)| fid50k_full | 1.57 |[stylegan_human_v2_512.pkl](https://drive.google.com/file/d/1dlFEHbu-WzQWJl7nBBZYcTyo000H9hVm/view?usp=sharing) | fid50k_full | 1.97 |
+| StyleGAN3 |to be released | - | - | [stylegan_human_v3_512.pkl](https://drive.google.com/file/d/1_274jk_N6WSCkKWeu7hjHycqGvbuOFf5/view?usp=sharing) | fid50k_full | 2.54 |
+
+
+
+## Web Demo
+
+Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo for generation: [](https://huggingface.co/spaces/hysts/StyleGAN-Human) and interpolation [](https://huggingface.co/spaces/hysts/StyleGAN-Human-Interpolation)
+
+
+
+
+
+We prepare a Colab demo to allow you to synthesize images with the provided models, as well as visualize the performance of style-mixing, interpolation, and attributes editing.
+The notebook will guide you to install the necessary environment and download pretrained models. The output images can be found in `./StyleGAN-Human/outputs/`.
+Hope you enjoy!
+
+## Usage
+
+### System requirements
+* The original code bases are [stylegan (tensorflow)](https://github.com/NVlabs/stylegan), [stylegan2-ada (pytorch)](https://github.com/NVlabs/stylegan2-ada-pytorch), [stylegan3 (pytorch)](https://github.com/NVlabs/stylegan3), released by NVidia
+
+* We tested in Python 3.8.5 and PyTorch 1.9.1 with CUDA 11.1. (See https://pytorch.org for PyTorch install instructions.)
+
+### Installation
+To work with this project on your own machine, you need to install the environmnet as follows:
+
+```
+conda env create -f environment.yml
+conda activate stylehuman
+# [Optional: tensorflow 1.x is required for StyleGAN1. ]
+pip install nvidia-pyindex
+pip install nvidia-tensorflow[horovod]
+pip install nvidia-tensorboard==1.15
+```
+Extra notes:
+1. In case having some conflicts when calling CUDA version, please try to empty the LD_LIBRARY_PATH. For example:
+```
+LD_LIBRARY_PATH=; python generate.py --outdir=out/stylegan_human_v2_1024 --trunc=1 --seeds=1,3,5,7
+--network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
+```
+
+
+2. We found the following troubleshooting links might be helpful: [1.](https://github.com/NVlabs/stylegan3), [2.](https://github.com/NVlabs/stylegan3/blob/main/docs/troubleshooting.md)
+
+### Train
+The training scripts are based on the original [stylegan1](https://github.com/NVlabs/stylegan), [stylegan2-ada](https://github.com/NVlabs/stylegan2-ada-pytorch), and [stylegan3](https://github.com/NVlabs/stylegan3) with minor changes. Here we only provide the scripts with modifications for SG2 and SG3. You can replace the old files with the provided scripts to train. (assume SHHQ-1.0 is placed under data/)
+
+#### Train Stylegan2-ada-pytorch with SHHQ-1.0
+```
+python train.py --outdir=training_results/sg2/ --data=data/SHHQ-1.0/ \
+ --gpus=8 --aug=noaug --mirror=1 --snap=250 --cfg=shhq --square=False
+```
+#### Train Stylegan3 with SHHQ-1.0
+```
+python train.py --outdir=training_results/sg3/ --cfg=stylegan3-r --gpus=8 --batch=32 --gamma=12.4 \
+ --mirror=1 --aug=noaug --data=data/SHHQ-1.0/ --square=False --snap=250
+```
+
+### Pretrained models
+Please put the downloaded pretrained models [from above link](#Model-Zoo) under the folder 'pretrained_models'.
+
+
+### Generate full-body human images using our pretrained model
+```
+# Generate human full-body images without truncation
+python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=1 --seeds=1,3,5,7 --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
+
+# Generate human full-body images with truncation
+python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=0.8 --seeds=0-10 --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
+
+# Generate human full-body images using stylegan V1
+python generate.py --outdir=outputs/generate/stylegan_human_v1_1024 --network=pretrained_models/stylegan_human_v1_1024.pkl --version 1 --seeds=1,3,5
+
+# Generate human full-body images using stylegan V3
+python generate.py --outdir=outputs/generate/stylegan_human_v3_512 --network=pretrained_models/stylegan_human_v3_512.pkl --version 3 --seeds=1,3,5
+```
+
+
+#### Note: The following demos are generated based on models related to StyleGAN V2 (stylegan_human_v2_512.pkl and stylegan_human_v2_1024.pkl). If you want to see results for V1 or V3, you need to change the loading method of the corresponding models.
+
+
+### Interpolation
+```
+python interpolation.py --network=pretrained_models/stylegan_human_v2_1024.pkl --seeds=85,100 --outdir=outputs/inter_gifs
+```
+
+### Style-mixing **image** using stylegan2
+```
+python style_mixing.py --network=pretrained_models/stylegan_human_v2_1024.pkl --rows=85,100,75,458,1500 \\
+ --cols=55,821,1789,293 --styles=0-3 --outdir=outputs/stylemixing
+```
+
+### Style-mixing **video** using stylegan2
+```
+python stylemixing_video.py --network=pretrained_models/stylegan_human_v2_1024.pkl --row-seed=3859 \\
+ --col-seeds=3098,31759,3791 --col-styles=8-12 --trunc=0.8 --outdir=outputs/stylemixing_video
+```
+
+### Aligned raw images
+For alignment, we use [openpose-pytorch](https://github.com/Hzzone/pytorch-openpose) for body-keypoints detection and [PaddlePaddle](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.5/contrib/PP-HumanSeg) for human segmentation.
+Before running the alignment script, few models need to be installed:
+1. download [body_pose_model.pth](https://drive.google.com/drive/folders/1JsvI4M4ZTg98fmnCZLFM-3TeovnCRElG?usp=sharing) and place it into openpose/model/.
+2. download and extract [deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax](https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax.zip) into PP_HumanSeg/export_model/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax.
+3. download and extract [deeplabv3p_resnet50_os8_humanseg_512x512_100k](https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/deeplabv3p_resnet50_os8_humanseg_512x512_100k.zip) into PP_HumanSeg/pretrained_model/deeplabv3p_resnet50_os8_humanseg_512x512_100k.
+4. install paddlepaddel: ``` pip install paddleseg ```
+
+Then you can start alignment:
+```
+python alignment.py --image-folder img/test/ --output-folder aligned_image/
+```
+
+### Invert real image with [PTI](https://github.com/danielroich/PTI)
+Before inversion, please download our PTI weights: [e4e_w+.pt](https://drive.google.com/file/d/1NUfSJqLhsrU7c9PwAtlZ9xtrxhzS_6tu/view?usp=sharing) into /pti/.
+
+Few parameters you can change:
+- /pti/pti_configs/hyperparameters.py:
+ - first_inv_type = 'w+' -> Use pretrained e4e encoder
+ - first_inv_type = 'w' -> Use projection and optimization
+- /pti/pti_configs/paths_config.py:
+ - input_data_path: path of real images
+ - e4e: path of e4e_w+.pt
+ - stylegan2_ada_shhq: pretrained stylegan2-ada model for SHHQ
+
+```
+python run_pti.py
+```
+Note: we used the test image under 'aligned_image/' (the output of alignment.py), the inverted latent code and fine-tuned generator will be saved in 'outputs/pti/'
+
+
+### Editing with InterfaceGAN, StyleSpace, and Sefa
+```
+python edit.py --network pretrained_models/stylegan_human_v2_1024.pkl --attr_name upper_length \\
+ --seeds 61531,61570,61571,61610 --outdir outputs/edit_results
+```
+
+### Editing using inverted latent code
+```
+python edit.py ---network outputs/pti/checkpoints/model_test.pkl --attr_name upper_length \\
+ --outdir outputs/edit_results --real True --real_w_path outputs/pti/embeddings/test/PTI/test/0.pt --real_img_path aligned_image/test.png
+```
+
+Note:
+1. ''upper_length'' and ''bottom_length'' of ''attr_name'' are available for demo.
+2. Layers to control and editing strength are set in edit/edit_config.py.
+
+
+### Demo for [InsetGAN](https://arxiv.org/abs/2203.07293)
+
+We implement a quick demo using the key idea from InsetGAN: combining the face generated by FFHQ with the human-body generated by our pretrained model, optimizing both face and body latent codes to get a coherent full-body image.
+Before running the script, you need to download the [FFHQ face model]( https://docs.google.com/uc?export=download&confirm=t&id=125OG7SMkXI-Kf2aqiwLLHyCvSW-gZk3M), or you can use your own face model, as well as [pretrained face landmark](https://docs.google.com/uc?export=download&confirm=&id=1A82DnJBJzt8wI2J8ZrCK5fgHcQ2-tcWM) and [pretrained CNN face detection model for dlib](https://docs.google.com/uc?export=download&confirm=&id=1MduBgju5KFNrQfDLoQXJ_1_h5MnctCIG)
+```
+python insetgan.py --body_network=pretrained_models/stylegan_human_v2_1024.pkl --face_network=pretrained_models/ffhq.pkl \\
+ --body_seed=82 --face_seed=43 --trunc=0.6 --outdir=outputs/insetgan/ --video 1
+```
+
+## Results
+
+### Editing with inverted real image
+(from left to right: real image | inverted image | InterFaceGAN result | StyleSpace result | SeFa result)
+
+https://user-images.githubusercontent.com/98547009/173773800-bb7fe54a-84d3-4b30-9864-a6b7b311f8ff.mp4
+
+
+### For more demo, please visit our [**web page**](https://stylegan-human.github.io/) .
+
+
+## TODO List
+
+- [ ] Release 1024x512 version of StyleGAN-Human based on StyleGAN3
+- [ ] Release 512x256 version of StyleGAN-Human based on StyleGAN1
+- [ ] Extension of downstream application (InsetGAN): Add face inversion interface to support fusing user face image and stylegen-human body image
+- [x] Add Inversion Script into the provided editing pipeline
+- [ ] Release Dataset
+
+
+## Related Works
+* (SIGGRAPH 2022) **Text2Human: Text-Driven Controllable Human Image Generation**, Yuming Jiang et al. [[Paper](https://arxiv.org/pdf/2205.15996.pdf)], [[Code](https://github.com/yumingj/Text2Human)], [[Project Page](https://yumingj.github.io/projects/Text2Human.html)], [[Dataset](https://github.com/yumingj/DeepFashion-MultiModal)]
+* (ICCV 2021) **Talk-to-Edit: Fine-Grained Facial Editing via Dialog**, Yuming Jiang et al. [[Paper](https://arxiv.org/abs/2109.04425)], [[Code](https://github.com/yumingj/Talk-to-Edit)], [[Project Page](https://www.mmlab-ntu.com/project/talkedit/)], [[Dataset](https://mmlab.ie.cuhk.edu.hk/projects/CelebA/CelebA_Dialog.html)]
+* (Technical Report 2022) **Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis**, Wei Cheng et al. [[Paper](https://arxiv.org/pdf/2204.11798.pdf)], [[Code](https://github.com/generalizable-neural-performer/gnr)], [[Project Page](https://generalizable-neural-performer.github.io/)], [[Dataset](https://generalizable-neural-performer.github.io/genebody.html)]
+
+## Citation
+
+If you find this work useful for your research, please consider citing our paper:
+
+```bibtex
+@article{fu2022styleganhuman,
+ title={StyleGAN-Human: A Data-Centric Odyssey of Human Generation},
+ author={Fu, Jianglin and Li, Shikai and Jiang, Yuming and Lin, Kwan-Yee and Qian, Chen and Loy, Chen-Change and Wu, Wayne and Liu, Ziwei},
+ journal = {arXiv preprint},
+ volume = {arXiv:2204.11823},
+ year = {2022}
+```
+
+## Acknowlegement
+Part of the code is borrowed from [stylegan (tensorflow)](https://github.com/NVlabs/stylegan), [stylegan2-ada (pytorch)](https://github.com/NVlabs/stylegan2-ada-pytorch), [stylegan3 (pytorch)](https://github.com/NVlabs/stylegan3).
diff --git a/stylegan_human/__init__.py b/stylegan_human/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stylegan_human/alignment.py b/stylegan_human/alignment.py
new file mode 100644
index 0000000000000000000000000000000000000000..012336ee3485599e3c4a314d718dc444e4c93864
--- /dev/null
+++ b/stylegan_human/alignment.py
@@ -0,0 +1,223 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+
+import os
+import argparse
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+from torchvision.transforms import transforms
+from utils.ImagesDataset import ImagesDataset
+
+import cv2
+import time
+import copy
+import imutils
+
+# for openpose body keypoint detector : # (src:https://github.com/Hzzone/pytorch-openpose)
+from openpose.src import util
+from openpose.src.body import Body
+
+# for paddlepaddle human segmentation : #(src: https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.5/contrib/PP-HumanSeg/)
+from PP_HumanSeg.deploy.infer import Predictor as PP_HumenSeg_Predictor
+
+import math
+def angle_between_points(p0,p1,p2):
+ if p0[1]==-1 or p1[1]==-1 or p2[1]==-1:
+ return -1
+ a = (p1[0]-p0[0])**2 + (p1[1]-p0[1])**2
+ b = (p1[0]-p2[0])**2 + (p1[1]-p2[1])**2
+ c = (p2[0]-p0[0])**2 + (p2[1]-p0[1])**2
+ if a * b == 0:
+ return -1
+ return math.acos((a+b-c) / math.sqrt(4*a*b)) * 180 / math.pi
+
+
+def crop_img_with_padding(img, keypoints, rect):
+ person_xmin,person_xmax, ymin, ymax= rect
+ img_h,img_w,_ = img.shape ## find body center using keypoints
+ middle_shoulder_x = keypoints[1][0]
+ middle_hip_x = (keypoints[8][0] + keypoints[11][0]) // 2
+ mid_x = (middle_hip_x + middle_shoulder_x) // 2
+ mid_y = (ymin + ymax) // 2
+ ## find which side (l or r) is further than center x, use the further side
+ if abs(mid_x-person_xmin) > abs(person_xmax-mid_x): #left further
+ xmin = person_xmin
+ xmax = mid_x + (mid_x-person_xmin)
+ else:
+ ############### may be negtive
+ ### in this case, the script won't output any image, leave the case like this
+ ### since we don't want to pad human body
+ xmin = mid_x - (person_xmax-mid_x)
+ xmax = person_xmax
+
+ w = xmax - xmin
+ h = ymax - ymin
+ ## pad rectangle to w:h = 1:2 ## calculate desired border length
+ if h / w >= 2: #pad horizontally
+ target_w = h // 2
+ xmin_prime = int(mid_x - target_w / 2)
+ xmax_prime = int(mid_x + target_w / 2)
+ if xmin_prime < 0:
+ pad_left = abs(xmin_prime)# - xmin
+ xmin = 0
+ else:
+ pad_left = 0
+ xmin = xmin_prime
+ if xmax_prime > img_w:
+ pad_right = xmax_prime - img_w
+ xmax = img_w
+ else:
+ pad_right = 0
+ xmax = xmax_prime
+
+ cropped_img = img[int(ymin):int(ymax), int(xmin):int(xmax)]
+ im_pad = cv2.copyMakeBorder(cropped_img, 0, 0, int(pad_left), int(pad_right), cv2.BORDER_REPLICATE)
+ else: #pad vertically
+ target_h = w * 2
+ ymin_prime = mid_y - (target_h / 2)
+ ymax_prime = mid_y + (target_h / 2)
+ if ymin_prime < 0:
+ pad_up = abs(ymin_prime)# - ymin
+ ymin = 0
+ else:
+ pad_up = 0
+ ymin = ymin_prime
+ if ymax_prime > img_h:
+ pad_down = ymax_prime - img_h
+ ymax = img_h
+ else:
+ pad_down = 0
+ ymax = ymax_prime
+ print(ymin,ymax, xmin,xmax, img.shape)
+
+ cropped_img = img[int(ymin):int(ymax), int(xmin):int(xmax)]
+ im_pad = cv2.copyMakeBorder(cropped_img, int(pad_up), int(pad_down), 0,
+ 0, cv2.BORDER_REPLICATE)
+ result = cv2.resize(im_pad,(512,1024),interpolation = cv2.INTER_AREA)
+ return result
+
+
+def run(args):
+ os.makedirs(args.output_folder, exist_ok=True)
+ dataset = ImagesDataset(args.image_folder, transforms.Compose([transforms.ToTensor()]))
+ dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
+
+ body_estimation = Body('openpose/model/body_pose_model.pth')
+
+ total = len(dataloader)
+ print('Num of dataloader : ', total)
+ os.makedirs(f'{args.output_folder}', exist_ok=True)
+ # os.makedirs(f'{args.output_folder}/middle_result', exist_ok=True)
+
+ ## initialzide HumenSeg
+ human_seg_args = {}
+ human_seg_args['cfg'] = 'PP_HumanSeg/export_model/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax/deploy.yaml'
+ human_seg_args['input_shape'] = [1024,512]
+ human_seg_args['save_dir'] = args.output_folder
+ human_seg_args['soft_predict'] = False
+ human_seg_args['use_gpu'] = True
+ human_seg_args['test_speed'] = False
+ human_seg_args['use_optic_flow'] = False
+ human_seg_args['add_argmax'] = True
+ human_seg_args= argparse.Namespace(**human_seg_args)
+ human_seg = PP_HumenSeg_Predictor(human_seg_args)
+
+ from tqdm import tqdm
+ for fname, image in tqdm(dataloader):
+ # try:
+ ## tensor to numpy image
+ fname = fname[0]
+ print(f'Processing \'{fname}\'.')
+
+ image = (image.permute(0, 2, 3, 1) * 255).clamp(0, 255)
+ image = image.squeeze(0).numpy() # --> tensor to numpy, (H,W,C)
+ # avoid super high res img
+ if image.shape[0] >= 2000: # height ### for shein image
+ ratio = image.shape[0]/1200 #height
+ dim = (int(image.shape[1]/ratio),1200)#(width, height)
+ image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
+ image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+ ## create segmentation
+ # mybg = cv2.imread('mybg.png')
+ comb, segmentation, bg, ori_img = human_seg.run(image,None) #mybg)
+ # cv2.imwrite('comb.png',comb) # [0,255]
+ # cv2.imwrite('alpha.png',segmentation*255) # segmentation [0,1] --> [0.255]
+ # cv2.imwrite('bg.png',bg) #[0,255]
+ # cv2.imwrite('ori_img.png',ori_img) # [0,255]
+
+ masks_np = (segmentation* 255)# .byte().cpu().numpy() #1024,512,1
+ mask0_np = masks_np[:,:,0].astype(np.uint8)#[0, :, :]
+ contours = cv2.findContours(mask0_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+ cnts = imutils.grab_contours(contours)
+ c = max(cnts, key=cv2.contourArea)
+ extTop = tuple(c[c[:, :, 1].argmin()][0])
+ extBot = tuple(c[c[:, :, 1].argmax()][0])
+ extBot = list(extBot)
+ extTop = list(extTop)
+ pad_range = int((extBot[1]-extTop[1])*0.05)
+ if (int(extTop[1])<=5 and int(extTop[1])>0) and (comb.shape[0]>int(extBot[1]) and int(extBot[1])>=comb.shape[0]-5): #seg mask already reaches to the edge
+ #pad with pure white, top 100 px, bottom 100 px
+ comb= cv2.copyMakeBorder(comb,pad_range+5,pad_range+5,0,0,cv2.BORDER_CONSTANT,value=[255,255,255])
+ elif int(extTop[1])<=0 or int(extBot[1])>=comb.shape[0]:
+ print('PAD: body out of boundary', fname) #should not happened
+ return {}
+ else:
+ comb = cv2.copyMakeBorder(comb, pad_range+5, pad_range+5, 0, 0, cv2.BORDER_REPLICATE) #105 instead of 100: give some extra space
+ extBot[1] = extBot[1] + pad_range+5
+ extTop[1] = extTop[1] + pad_range+5
+
+ extLeft = tuple(c[c[:, :, 0].argmin()][0])
+ extRight = tuple(c[c[:, :, 0].argmax()][0])
+ extLeft = list(extLeft)
+ extRight = list(extRight)
+ person_ymin = int(extTop[1])-pad_range # 100
+ person_ymax = int(extBot[1])+pad_range # 100 #height
+ if person_ymin<0 or person_ymax>comb.shape[0]: # out of range
+ return {}
+ person_xmin = int(extLeft[0])
+ person_xmax = int(extRight[0])
+ rect = [person_xmin,person_xmax,person_ymin, person_ymax]
+ # recimg = copy.deepcopy(comb)
+ # cv2.rectangle(recimg,(person_xmin,person_ymin),(person_xmax,person_ymax),(0,255,0),2)
+ # cv2.imwrite(f'{args.output_folder}/middle_result/{fname}_rec.png',recimg)
+
+ ## detect keypoints
+ keypoints, subset = body_estimation(comb)
+ # print(keypoints, subset, len(subset))
+ if len(subset) != 1 or (len(subset)==1 and subset[0][-1]<15):
+ print(f'Processing \'{fname}\'. Please import image contains one person only. Also can check segmentation mask. ')
+ continue
+
+ # canvas = copy.deepcopy(comb)
+ # canvas = util.draw_bodypose(canvas, keypoints, subset, show_number=True)
+ # cv2.imwrite(f'{args.output_folder}/middle_result/{fname}_keypoints.png',canvas)
+
+ comb = crop_img_with_padding(comb, keypoints, rect)
+
+
+ cv2.imwrite(f'{args.output_folder}/{fname}.png', comb)
+ print(f' -- Finished processing \'{fname}\'. --')
+ # except:
+ # print(f'Processing \'{fname}\'. Not satisfied the alignment strategy.')
+
+
+if __name__ == '__main__':
+ torch.backends.cudnn.benchmark = True
+ torch.backends.cudnn.deterministic = False
+
+ t1 = time.time()
+ arg_formatter = argparse.ArgumentDefaultsHelpFormatter
+ description = 'StyleGAN-Human data process'
+ parser = argparse.ArgumentParser(formatter_class=arg_formatter,
+ description=description)
+ parser.add_argument('--image-folder', type=str, dest='image_folder')
+ parser.add_argument('--output-folder', dest='output_folder', default='results', type=str)
+ # parser.add_argument('--cfg', dest='cfg for segmentation', default='PP_HumanSeg/export_model/ppseg_lite_portrait_398x224_with_softmax/deploy.yaml', type=str)
+
+ print('parsing arguments')
+ cmd_args = parser.parse_args()
+ run(cmd_args)
+
+ print('total time elapsed: ', str(time.time() - t1))
\ No newline at end of file
diff --git a/stylegan_human/bg_white.py b/stylegan_human/bg_white.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9bd13169baf5e000599b5b45d22e6d76726518c
--- /dev/null
+++ b/stylegan_human/bg_white.py
@@ -0,0 +1,57 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+import os
+import click
+import cv2
+import numpy as np
+
+def bg_white(seg, raw, blur_level=3, gaussian=81):
+ seg = cv2.blur(seg, (blur_level, blur_level))
+
+ empty = np.ones_like(seg)
+ seg_bg = (empty - seg) * 255
+ seg_bg = cv2.GaussianBlur(seg_bg,(gaussian,gaussian),0)
+
+ background_mask = cv2.cvtColor(255 - cv2.cvtColor(seg, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR)
+ masked_fg = (raw * (1 / 255)) * (seg * (1 / 255))
+ masked_bg = (seg_bg * (1 / 255)) * (background_mask * (1 / 255))
+
+ frame = np.uint8(cv2.add(masked_bg,masked_fg)*255)
+
+ return frame
+
+
+"""
+To turn background into white.
+
+Examples:
+
+\b
+python bg_white.py --raw_img_dir=./SHHQ-1.0/no_segment/ --raw_seg_dir=./SHHQ-1.0/segments/ \\
+ --outdir=./SHHQ-1.0/bg_white/
+"""
+
+@click.command()
+@click.pass_context
+@click.option('--raw_img_dir', default="./SHHQ-1.0/no_segment/", help='folder of raw image', required=True)
+@click.option('--raw_seg_dir', default='./SHHQ-1.0/segments/', help='folder of segmentation masks', required=True)
+@click.option('--outdir', help='Where to save the output images', default= "./SHHQ-1.0/bg_white/" , type=str, required=True, metavar='DIR')
+
+def main(
+ ctx: click.Context,
+ raw_img_dir: str,
+ raw_seg_dir: str,
+ outdir: str):
+ os.makedirs(outdir, exist_ok=True)
+ files = os.listdir(raw_img_dir)
+ for file in files:
+ print(file)
+ raw = cv2.imread(os.path.join(raw_img_dir, file))
+ seg = cv2.imread(os.path.join(raw_seg_dir, file))
+ assert raw is not None
+ assert seg is not None
+ white_frame = bg_white(seg, raw)
+ cv2.imwrite(os.path.join(outdir,file), white_frame)
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/stylegan_human/dnnlib/__init__.py b/stylegan_human/dnnlib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef2c9a6a3f95f9fe55baccad83c9e94842c42453
--- /dev/null
+++ b/stylegan_human/dnnlib/__init__.py
@@ -0,0 +1,11 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2021, NVIDIA CORPORATION. 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/stylegan_human/dnnlib/tflib/__init__.py b/stylegan_human/dnnlib/tflib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7013e8cf7ed660e50bb984226c95052792979b12
--- /dev/null
+++ b/stylegan_human/dnnlib/tflib/__init__.py
@@ -0,0 +1,20 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License-NC.
+# To view a copy of this license, visit
+# https://nvlabs.github.io/stylegan2/license.html
+
+from . import autosummary
+from . import network
+from . import optimizer
+from . import tfutil
+from . import custom_ops
+
+from .tfutil import *
+from .network import Network
+
+from .optimizer import Optimizer
+
+from .custom_ops import get_plugin
diff --git a/stylegan_human/dnnlib/tflib/autosummary.py b/stylegan_human/dnnlib/tflib/autosummary.py
new file mode 100644
index 0000000000000000000000000000000000000000..ede0f23dc3106112d241c70a8d4c17b2fa2af50d
--- /dev/null
+++ b/stylegan_human/dnnlib/tflib/autosummary.py
@@ -0,0 +1,193 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License-NC.
+# To view a copy of this license, visit
+# https://nvlabs.github.io/stylegan2/license.html
+
+"""Helper for adding automatically tracked values to Tensorboard.
+
+Autosummary creates an identity op that internally keeps track of the input
+values and automatically shows up in TensorBoard. The reported value
+represents an average over input components. The average is accumulated
+constantly over time and flushed when save_summaries() is called.
+
+Notes:
+- The output tensor must be used as an input for something else in the
+ graph. Otherwise, the autosummary op will not get executed, and the average
+ value will not get accumulated.
+- It is perfectly fine to include autosummaries with the same name in
+ several places throughout the graph, even if they are executed concurrently.
+- It is ok to also pass in a python scalar or numpy array. In this case, it
+ is added to the average immediately.
+"""
+
+from collections import OrderedDict
+import numpy as np
+import tensorflow as tf
+from tensorboard import summary as summary_lib
+from tensorboard.plugins.custom_scalar import layout_pb2
+
+from . import tfutil
+from .tfutil import TfExpression
+from .tfutil import TfExpressionEx
+
+# Enable "Custom scalars" tab in TensorBoard for advanced formatting.
+# Disabled by default to reduce tfevents file size.
+enable_custom_scalars = False
+
+_dtype = tf.float64
+_vars = OrderedDict() # name => [var, ...]
+_immediate = OrderedDict() # name => update_op, update_value
+_finalized = False
+_merge_op = None
+
+
+def _create_var(name: str, value_expr: TfExpression) -> TfExpression:
+ """Internal helper for creating autosummary accumulators."""
+ assert not _finalized
+ name_id = name.replace("/", "_")
+ v = tf.cast(value_expr, _dtype)
+
+ if v.shape.is_fully_defined():
+ size = np.prod(v.shape.as_list())
+ size_expr = tf.constant(size, dtype=_dtype)
+ else:
+ size = None
+ size_expr = tf.reduce_prod(tf.cast(tf.shape(v), _dtype))
+
+ if size == 1:
+ if v.shape.ndims != 0:
+ v = tf.reshape(v, [])
+ v = [size_expr, v, tf.square(v)]
+ else:
+ v = [size_expr, tf.reduce_sum(v), tf.reduce_sum(tf.square(v))]
+ v = tf.cond(tf.is_finite(v[1]), lambda: tf.stack(v), lambda: tf.zeros(3, dtype=_dtype))
+
+ with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.control_dependencies(None):
+ var = tf.Variable(tf.zeros(3, dtype=_dtype), trainable=False) # [sum(1), sum(x), sum(x**2)]
+ update_op = tf.cond(tf.is_variable_initialized(var), lambda: tf.assign_add(var, v), lambda: tf.assign(var, v))
+
+ if name in _vars:
+ _vars[name].append(var)
+ else:
+ _vars[name] = [var]
+ return update_op
+
+
+def autosummary(name: str, value: TfExpressionEx, passthru: TfExpressionEx = None, condition: TfExpressionEx = True) -> TfExpressionEx:
+ """Create a new autosummary.
+
+ Args:
+ name: Name to use in TensorBoard
+ value: TensorFlow expression or python value to track
+ passthru: Optionally return this TF node without modifications but tack an autosummary update side-effect to this node.
+
+ Example use of the passthru mechanism:
+
+ n = autosummary('l2loss', loss, passthru=n)
+
+ This is a shorthand for the following code:
+
+ with tf.control_dependencies([autosummary('l2loss', loss)]):
+ n = tf.identity(n)
+ """
+ tfutil.assert_tf_initialized()
+ name_id = name.replace("/", "_")
+
+ if tfutil.is_tf_expression(value):
+ with tf.name_scope("summary_" + name_id), tf.device(value.device):
+ condition = tf.convert_to_tensor(condition, name='condition')
+ update_op = tf.cond(condition, lambda: tf.group(_create_var(name, value)), tf.no_op)
+ with tf.control_dependencies([update_op]):
+ return tf.identity(value if passthru is None else passthru)
+
+ else: # python scalar or numpy array
+ assert not tfutil.is_tf_expression(passthru)
+ assert not tfutil.is_tf_expression(condition)
+ if condition:
+ if name not in _immediate:
+ with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.device(None), tf.control_dependencies(None):
+ update_value = tf.placeholder(_dtype)
+ update_op = _create_var(name, update_value)
+ _immediate[name] = update_op, update_value
+ update_op, update_value = _immediate[name]
+ tfutil.run(update_op, {update_value: value})
+ return value if passthru is None else passthru
+
+
+def finalize_autosummaries() -> None:
+ """Create the necessary ops to include autosummaries in TensorBoard report.
+ Note: This should be done only once per graph.
+ """
+ global _finalized
+ tfutil.assert_tf_initialized()
+
+ if _finalized:
+ return None
+
+ _finalized = True
+ tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list])
+
+ # Create summary ops.
+ with tf.device(None), tf.control_dependencies(None):
+ for name, vars_list in _vars.items():
+ name_id = name.replace("/", "_")
+ with tfutil.absolute_name_scope("Autosummary/" + name_id):
+ moments = tf.add_n(vars_list)
+ moments /= moments[0]
+ with tf.control_dependencies([moments]): # read before resetting
+ reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list]
+ with tf.name_scope(None), tf.control_dependencies(reset_ops): # reset before reporting
+ mean = moments[1]
+ std = tf.sqrt(moments[2] - tf.square(moments[1]))
+ tf.summary.scalar(name, mean)
+ if enable_custom_scalars:
+ tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std)
+ tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std)
+
+ # Setup layout for custom scalars.
+ layout = None
+ if enable_custom_scalars:
+ cat_dict = OrderedDict()
+ for series_name in sorted(_vars.keys()):
+ p = series_name.split("/")
+ cat = p[0] if len(p) >= 2 else ""
+ chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1]
+ if cat not in cat_dict:
+ cat_dict[cat] = OrderedDict()
+ if chart not in cat_dict[cat]:
+ cat_dict[cat][chart] = []
+ cat_dict[cat][chart].append(series_name)
+ categories = []
+ for cat_name, chart_dict in cat_dict.items():
+ charts = []
+ for chart_name, series_names in chart_dict.items():
+ series = []
+ for series_name in series_names:
+ series.append(layout_pb2.MarginChartContent.Series(
+ value=series_name,
+ lower="xCustomScalars/" + series_name + "/margin_lo",
+ upper="xCustomScalars/" + series_name + "/margin_hi"))
+ margin = layout_pb2.MarginChartContent(series=series)
+ charts.append(layout_pb2.Chart(title=chart_name, margin=margin))
+ categories.append(layout_pb2.Category(title=cat_name, chart=charts))
+ layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories))
+ return layout
+
+def save_summaries(file_writer, global_step=None):
+ """Call FileWriter.add_summary() with all summaries in the default graph,
+ automatically finalizing and merging them on the first call.
+ """
+ global _merge_op
+ tfutil.assert_tf_initialized()
+
+ if _merge_op is None:
+ layout = finalize_autosummaries()
+ if layout is not None:
+ file_writer.add_summary(layout)
+ with tf.device(None), tf.control_dependencies(None):
+ _merge_op = tf.summary.merge_all()
+
+ file_writer.add_summary(_merge_op.eval(), global_step)
diff --git a/stylegan_human/dnnlib/tflib/custom_ops.py b/stylegan_human/dnnlib/tflib/custom_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..a09ac5dc2a5de80d22a5593ed7725551737d59af
--- /dev/null
+++ b/stylegan_human/dnnlib/tflib/custom_ops.py
@@ -0,0 +1,171 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License-NC.
+# To view a copy of this license, visit
+# https://nvlabs.github.io/stylegan2/license.html
+
+"""TensorFlow custom ops builder.
+"""
+
+import os
+import re
+import uuid
+import hashlib
+import tempfile
+import shutil
+import tensorflow as tf
+from tensorflow.python.client import device_lib # pylint: disable=no-name-in-module
+
+#----------------------------------------------------------------------------
+# Global options.
+
+cuda_cache_path = os.path.join(os.path.dirname(__file__), '_cudacache')
+cuda_cache_version_tag = 'v1'
+do_not_hash_included_headers = False # Speed up compilation by assuming that headers included by the CUDA code never change. Unsafe!
+verbose = True # Print status messages to stdout.
+
+compiler_bindir_search_path = [
+ 'C:/Program Files (x86)/Microsoft Visual Studio/2017/Community/VC/Tools/MSVC/14.14.26428/bin/Hostx64/x64',
+ 'C:/Program Files (x86)/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.23.28105/bin/Hostx64/x64',
+ 'C:/Program Files (x86)/Microsoft Visual Studio 14.0/vc/bin',
+]
+
+#----------------------------------------------------------------------------
+# Internal helper funcs.
+
+def _find_compiler_bindir():
+ for compiler_path in compiler_bindir_search_path:
+ if os.path.isdir(compiler_path):
+ return compiler_path
+ return None
+
+def _get_compute_cap(device):
+ caps_str = device.physical_device_desc
+ m = re.search('compute capability: (\\d+).(\\d+)', caps_str)
+ major = m.group(1)
+ minor = m.group(2)
+ return (major, minor)
+
+def _get_cuda_gpu_arch_string():
+ gpus = [x for x in device_lib.list_local_devices() if x.device_type == 'GPU']
+ if len(gpus) == 0:
+ raise RuntimeError('No GPU devices found')
+ (major, minor) = _get_compute_cap(gpus[0])
+ return 'sm_%s%s' % (major, minor)
+
+def _run_cmd(cmd):
+ with os.popen(cmd) as pipe:
+ output = pipe.read()
+ status = pipe.close()
+ if status is not None:
+ raise RuntimeError('NVCC returned an error. See below for full command line and output log:\n\n%s\n\n%s' % (cmd, output))
+
+def _prepare_nvcc_cli(opts):
+ cmd = 'nvcc ' + opts.strip()
+ cmd += ' --disable-warnings'
+ cmd += ' --include-path "%s"' % tf.sysconfig.get_include()
+ cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'protobuf_archive', 'src')
+ cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'com_google_absl')
+ cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'eigen_archive')
+
+ compiler_bindir = _find_compiler_bindir()
+ if compiler_bindir is None:
+ # Require that _find_compiler_bindir succeeds on Windows. Allow
+ # nvcc to use whatever is the default on Linux.
+ if os.name == 'nt':
+ raise RuntimeError('Could not find MSVC/GCC/CLANG installation on this computer. Check compiler_bindir_search_path list in "%s".' % __file__)
+ else:
+ cmd += ' --compiler-bindir "%s"' % compiler_bindir
+ cmd += ' 2>&1'
+ return cmd
+
+#----------------------------------------------------------------------------
+# Main entry point.
+
+_plugin_cache = dict()
+
+def get_plugin(cuda_file):
+ cuda_file_base = os.path.basename(cuda_file)
+ cuda_file_name, cuda_file_ext = os.path.splitext(cuda_file_base)
+
+ # Already in cache?
+ if cuda_file in _plugin_cache:
+ return _plugin_cache[cuda_file]
+
+ # Setup plugin.
+ if verbose:
+ print('Setting up TensorFlow plugin "%s": ' % cuda_file_base, end='', flush=True)
+ try:
+ # Hash CUDA source.
+ md5 = hashlib.md5()
+ with open(cuda_file, 'rb') as f:
+ md5.update(f.read())
+ md5.update(b'\n')
+
+ # Hash headers included by the CUDA code by running it through the preprocessor.
+ if not do_not_hash_included_headers:
+ if verbose:
+ print('Preprocessing... ', end='', flush=True)
+ with tempfile.TemporaryDirectory() as tmp_dir:
+ tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + cuda_file_ext)
+ _run_cmd(_prepare_nvcc_cli('"%s" --preprocess -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir)))
+ with open(tmp_file, 'rb') as f:
+ bad_file_str = ('"' + cuda_file.replace('\\', '/') + '"').encode('utf-8') # __FILE__ in error check macros
+ good_file_str = ('"' + cuda_file_base + '"').encode('utf-8')
+ for ln in f:
+ if not ln.startswith(b'# ') and not ln.startswith(b'#line '): # ignore line number pragmas
+ ln = ln.replace(bad_file_str, good_file_str)
+ md5.update(ln)
+ md5.update(b'\n')
+
+ # Select compiler options.
+ compile_opts = ''
+ if os.name == 'nt':
+ compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.lib')
+ elif os.name == 'posix':
+ compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.so')
+ compile_opts += ' --compiler-options \'-fPIC -D_GLIBCXX_USE_CXX11_ABI=0\''
+ else:
+ assert False # not Windows or Linux, w00t?
+ compile_opts += ' --gpu-architecture=%s' % _get_cuda_gpu_arch_string()
+ compile_opts += ' --use_fast_math'
+ nvcc_cmd = _prepare_nvcc_cli(compile_opts)
+
+ # Hash build configuration.
+ md5.update(('nvcc_cmd: ' + nvcc_cmd).encode('utf-8') + b'\n')
+ md5.update(('tf.VERSION: ' + tf.VERSION).encode('utf-8') + b'\n')
+ md5.update(('cuda_cache_version_tag: ' + cuda_cache_version_tag).encode('utf-8') + b'\n')
+
+ # Compile if not already compiled.
+ bin_file_ext = '.dll' if os.name == 'nt' else '.so'
+ bin_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + md5.hexdigest() + bin_file_ext)
+ if not os.path.isfile(bin_file):
+ if verbose:
+ print('Compiling... ', end='', flush=True)
+ with tempfile.TemporaryDirectory() as tmp_dir:
+ tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + bin_file_ext)
+ _run_cmd(nvcc_cmd + ' "%s" --shared -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir))
+ os.makedirs(cuda_cache_path, exist_ok=True)
+ intermediate_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + uuid.uuid4().hex + '_tmp' + bin_file_ext)
+ shutil.copyfile(tmp_file, intermediate_file)
+ os.rename(intermediate_file, bin_file) # atomic
+
+ # Load.
+ if verbose:
+ print('Loading... ', end='', flush=True)
+ plugin = tf.load_op_library(bin_file)
+
+ # Add to cache.
+ _plugin_cache[cuda_file] = plugin
+ if verbose:
+ print('Done.', flush=True)
+ return plugin
+
+ except:
+ if verbose:
+ print('Failed!', flush=True)
+ raise
+
+#----------------------------------------------------------------------------
diff --git a/stylegan_human/dnnlib/tflib/network.py b/stylegan_human/dnnlib/tflib/network.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfa73dc5ff2051689d16159871d2bc7e31294502
--- /dev/null
+++ b/stylegan_human/dnnlib/tflib/network.py
@@ -0,0 +1,592 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License-NC.
+# To view a copy of this license, visit
+# https://nvlabs.github.io/stylegan2/license.html
+
+"""Helper for managing networks."""
+
+import types
+import inspect
+import re
+import uuid
+import sys
+import numpy as np
+import tensorflow as tf
+
+from collections import OrderedDict
+from typing import Any, List, Tuple, Union
+
+from . import tfutil
+from .. import util
+
+from .tfutil import TfExpression, TfExpressionEx
+
+_import_handlers = [] # Custom import handlers for dealing with legacy data in pickle import.
+_import_module_src = dict() # Source code for temporary modules created during pickle import.
+
+
+def import_handler(handler_func):
+ """Function decorator for declaring custom import handlers."""
+ _import_handlers.append(handler_func)
+ return handler_func
+
+
+class Network:
+ """Generic network abstraction.
+
+ Acts as a convenience wrapper for a parameterized network construction
+ function, providing several utility methods and convenient access to
+ the inputs/outputs/weights.
+
+ Network objects can be safely pickled and unpickled for long-term
+ archival purposes. The pickling works reliably as long as the underlying
+ network construction function is defined in a standalone Python module
+ that has no side effects or application-specific imports.
+
+ Args:
+ name: Network name. Used to select TensorFlow name and variable scopes.
+ func_name: Fully qualified name of the underlying network construction function, or a top-level function object.
+ static_kwargs: Keyword arguments to be passed in to the network construction function.
+
+ Attributes:
+ name: User-specified name, defaults to build func name if None.
+ scope: Unique TensorFlow scope containing template graph and variables, derived from the user-specified name.
+ static_kwargs: Arguments passed to the user-supplied build func.
+ components: Container for sub-networks. Passed to the build func, and retained between calls.
+ num_inputs: Number of input tensors.
+ num_outputs: Number of output tensors.
+ input_shapes: Input tensor shapes (NC or NCHW), including minibatch dimension.
+ output_shapes: Output tensor shapes (NC or NCHW), including minibatch dimension.
+ input_shape: Short-hand for input_shapes[0].
+ output_shape: Short-hand for output_shapes[0].
+ input_templates: Input placeholders in the template graph.
+ output_templates: Output tensors in the template graph.
+ input_names: Name string for each input.
+ output_names: Name string for each output.
+ own_vars: Variables defined by this network (local_name => var), excluding sub-networks.
+ vars: All variables (local_name => var).
+ trainables: All trainable variables (local_name => var).
+ var_global_to_local: Mapping from variable global names to local names.
+ """
+
+ def __init__(self, name: str = None, func_name: Any = None, **static_kwargs):
+ tfutil.assert_tf_initialized()
+ assert isinstance(name, str) or name is None
+ assert func_name is not None
+ assert isinstance(func_name, str) or util.is_top_level_function(func_name)
+ assert util.is_pickleable(static_kwargs)
+
+ self._init_fields()
+ self.name = name
+ self.static_kwargs = util.EasyDict(static_kwargs)
+
+ # Locate the user-specified network build function.
+ if util.is_top_level_function(func_name):
+ func_name = util.get_top_level_function_name(func_name)
+ module, self._build_func_name = util.get_module_from_obj_name(func_name)
+ self._build_func = util.get_obj_from_module(module, self._build_func_name)
+ assert callable(self._build_func)
+
+ # Dig up source code for the module containing the build function.
+ self._build_module_src = _import_module_src.get(module, None)
+ if self._build_module_src is None:
+ self._build_module_src = inspect.getsource(module)
+
+ # Init TensorFlow graph.
+ self._init_graph()
+ self.reset_own_vars()
+
+ def _init_fields(self) -> None:
+ self.name = None
+ self.scope = None
+ self.static_kwargs = util.EasyDict()
+ self.components = util.EasyDict()
+ self.num_inputs = 0
+ self.num_outputs = 0
+ self.input_shapes = [[]]
+ self.output_shapes = [[]]
+ self.input_shape = []
+ self.output_shape = []
+ self.input_templates = []
+ self.output_templates = []
+ self.input_names = []
+ self.output_names = []
+ self.own_vars = OrderedDict()
+ self.vars = OrderedDict()
+ self.trainables = OrderedDict()
+ self.var_global_to_local = OrderedDict()
+
+ self._build_func = None # User-supplied build function that constructs the network.
+ self._build_func_name = None # Name of the build function.
+ self._build_module_src = None # Full source code of the module containing the build function.
+ self._run_cache = dict() # Cached graph data for Network.run().
+
+ def _init_graph(self) -> None:
+ # Collect inputs.
+ self.input_names = []
+
+ for param in inspect.signature(self._build_func).parameters.values():
+ if param.kind == param.POSITIONAL_OR_KEYWORD and param.default is param.empty:
+ self.input_names.append(param.name)
+
+ self.num_inputs = len(self.input_names)
+ assert self.num_inputs >= 1
+
+ # Choose name and scope.
+ if self.name is None:
+ self.name = self._build_func_name
+ assert re.match("^[A-Za-z0-9_.\\-]*$", self.name)
+ with tf.name_scope(None):
+ self.scope = tf.get_default_graph().unique_name(self.name, mark_as_used=True)
+
+ # Finalize build func kwargs.
+ build_kwargs = dict(self.static_kwargs)
+ build_kwargs["is_template_graph"] = True
+ build_kwargs["components"] = self.components
+
+ # Build template graph.
+ with tfutil.absolute_variable_scope(self.scope, reuse=False), tfutil.absolute_name_scope(self.scope): # ignore surrounding scopes
+ assert tf.get_variable_scope().name == self.scope
+ assert tf.get_default_graph().get_name_scope() == self.scope
+ with tf.control_dependencies(None): # ignore surrounding control dependencies
+ self.input_templates = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
+ out_expr = self._build_func(*self.input_templates, **build_kwargs)
+
+ # Collect outputs.
+ assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple)
+ self.output_templates = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr)
+ self.num_outputs = len(self.output_templates)
+ assert self.num_outputs >= 1
+ assert all(tfutil.is_tf_expression(t) for t in self.output_templates)
+
+ # Perform sanity checks.
+ if any(t.shape.ndims is None for t in self.input_templates):
+ raise ValueError("Network input shapes not defined. Please call x.set_shape() for each input.")
+ if any(t.shape.ndims is None for t in self.output_templates):
+ raise ValueError("Network output shapes not defined. Please call x.set_shape() where applicable.")
+ if any(not isinstance(comp, Network) for comp in self.components.values()):
+ raise ValueError("Components of a Network must be Networks themselves.")
+ if len(self.components) != len(set(comp.name for comp in self.components.values())):
+ raise ValueError("Components of a Network must have unique names.")
+
+ # List inputs and outputs.
+ self.input_shapes = [t.shape.as_list() for t in self.input_templates]
+ self.output_shapes = [t.shape.as_list() for t in self.output_templates]
+ self.input_shape = self.input_shapes[0]
+ self.output_shape = self.output_shapes[0]
+ self.output_names = [t.name.split("/")[-1].split(":")[0] for t in self.output_templates]
+
+ # List variables.
+ self.own_vars = OrderedDict((var.name[len(self.scope) + 1:].split(":")[0], var) for var in tf.global_variables(self.scope + "/"))
+ self.vars = OrderedDict(self.own_vars)
+ self.vars.update((comp.name + "/" + name, var) for comp in self.components.values() for name, var in comp.vars.items())
+ self.trainables = OrderedDict((name, var) for name, var in self.vars.items() if var.trainable)
+ self.var_global_to_local = OrderedDict((var.name.split(":")[0], name) for name, var in self.vars.items())
+
+ def reset_own_vars(self) -> None:
+ """Re-initialize all variables of this network, excluding sub-networks."""
+ tfutil.run([var.initializer for var in self.own_vars.values()])
+
+ def reset_vars(self) -> None:
+ """Re-initialize all variables of this network, including sub-networks."""
+ tfutil.run([var.initializer for var in self.vars.values()])
+
+ def reset_trainables(self) -> None:
+ """Re-initialize all trainable variables of this network, including sub-networks."""
+ tfutil.run([var.initializer for var in self.trainables.values()])
+
+ def get_output_for(self, *in_expr: TfExpression, return_as_list: bool = False, **dynamic_kwargs) -> Union[TfExpression, List[TfExpression]]:
+ """Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s)."""
+ assert len(in_expr) == self.num_inputs
+ assert not all(expr is None for expr in in_expr)
+
+ # Finalize build func kwargs.
+ build_kwargs = dict(self.static_kwargs)
+ build_kwargs.update(dynamic_kwargs)
+ build_kwargs["is_template_graph"] = False
+ build_kwargs["components"] = self.components
+
+ # Build TensorFlow graph to evaluate the network.
+ with tfutil.absolute_variable_scope(self.scope, reuse=True), tf.name_scope(self.name):
+ assert tf.get_variable_scope().name == self.scope
+ valid_inputs = [expr for expr in in_expr if expr is not None]
+ final_inputs = []
+ for expr, name, shape in zip(in_expr, self.input_names, self.input_shapes):
+ if expr is not None:
+ expr = tf.identity(expr, name=name)
+ else:
+ expr = tf.zeros([tf.shape(valid_inputs[0])[0]] + shape[1:], name=name)
+ final_inputs.append(expr)
+ out_expr = self._build_func(*final_inputs, **build_kwargs)
+
+ # Propagate input shapes back to the user-specified expressions.
+ for expr, final in zip(in_expr, final_inputs):
+ if isinstance(expr, tf.Tensor):
+ expr.set_shape(final.shape)
+
+ # Express outputs in the desired format.
+ assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple)
+ if return_as_list:
+ out_expr = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr)
+ return out_expr
+
+ def get_var_local_name(self, var_or_global_name: Union[TfExpression, str]) -> str:
+ """Get the local name of a given variable, without any surrounding name scopes."""
+ assert tfutil.is_tf_expression(var_or_global_name) or isinstance(var_or_global_name, str)
+ global_name = var_or_global_name if isinstance(var_or_global_name, str) else var_or_global_name.name
+ return self.var_global_to_local[global_name]
+
+ def find_var(self, var_or_local_name: Union[TfExpression, str]) -> TfExpression:
+ """Find variable by local or global name."""
+ assert tfutil.is_tf_expression(var_or_local_name) or isinstance(var_or_local_name, str)
+ return self.vars[var_or_local_name] if isinstance(var_or_local_name, str) else var_or_local_name
+
+ def get_var(self, var_or_local_name: Union[TfExpression, str]) -> np.ndarray:
+ """Get the value of a given variable as NumPy array.
+ Note: This method is very inefficient -- prefer to use tflib.run(list_of_vars) whenever possible."""
+ return self.find_var(var_or_local_name).eval()
+
+ def set_var(self, var_or_local_name: Union[TfExpression, str], new_value: Union[int, float, np.ndarray]) -> None:
+ """Set the value of a given variable based on the given NumPy array.
+ Note: This method is very inefficient -- prefer to use tflib.set_vars() whenever possible."""
+ tfutil.set_vars({self.find_var(var_or_local_name): new_value})
+
+ def __getstate__(self) -> dict:
+ """Pickle export."""
+ state = dict()
+ state["version"] = 4
+ state["name"] = self.name
+ state["static_kwargs"] = dict(self.static_kwargs)
+ state["components"] = dict(self.components)
+ state["build_module_src"] = self._build_module_src
+ state["build_func_name"] = self._build_func_name
+ state["variables"] = list(zip(self.own_vars.keys(), tfutil.run(list(self.own_vars.values()))))
+ return state
+
+ def __setstate__(self, state: dict) -> None:
+ """Pickle import."""
+ # pylint: disable=attribute-defined-outside-init
+ tfutil.assert_tf_initialized()
+ self._init_fields()
+
+ # Execute custom import handlers.
+ for handler in _import_handlers:
+ state = handler(state)
+
+ # Set basic fields.
+ assert state["version"] in [2, 3, 4]
+ self.name = state["name"]
+ self.static_kwargs = util.EasyDict(state["static_kwargs"])
+ self.components = util.EasyDict(state.get("components", {}))
+ self._build_module_src = state["build_module_src"]
+ self._build_func_name = state["build_func_name"]
+
+ # Create temporary module from the imported source code.
+ module_name = "_tflib_network_import_" + uuid.uuid4().hex
+ module = types.ModuleType(module_name)
+ sys.modules[module_name] = module
+ _import_module_src[module] = self._build_module_src
+ exec(self._build_module_src, module.__dict__) # pylint: disable=exec-used
+
+ # Locate network build function in the temporary module.
+ self._build_func = util.get_obj_from_module(module, self._build_func_name)
+ assert callable(self._build_func)
+
+ # Init TensorFlow graph.
+ self._init_graph()
+ self.reset_own_vars()
+ tfutil.set_vars({self.find_var(name): value for name, value in state["variables"]})
+
+ def clone(self, name: str = None, **new_static_kwargs) -> "Network":
+ """Create a clone of this network with its own copy of the variables."""
+ # pylint: disable=protected-access
+ net = object.__new__(Network)
+ net._init_fields()
+ net.name = name if name is not None else self.name
+ net.static_kwargs = util.EasyDict(self.static_kwargs)
+ net.static_kwargs.update(new_static_kwargs)
+ net._build_module_src = self._build_module_src
+ net._build_func_name = self._build_func_name
+ net._build_func = self._build_func
+ net._init_graph()
+ net.copy_vars_from(self)
+ return net
+
+ def copy_own_vars_from(self, src_net: "Network") -> None:
+ """Copy the values of all variables from the given network, excluding sub-networks."""
+ names = [name for name in self.own_vars.keys() if name in src_net.own_vars]
+ tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
+
+ def copy_vars_from(self, src_net: "Network") -> None:
+ """Copy the values of all variables from the given network, including sub-networks."""
+ names = [name for name in self.vars.keys() if name in src_net.vars]
+ tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
+
+ def copy_trainables_from(self, src_net: "Network") -> None:
+ """Copy the values of all trainable variables from the given network, including sub-networks."""
+ names = [name for name in self.trainables.keys() if name in src_net.trainables]
+ tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
+
+ def convert(self, new_func_name: str, new_name: str = None, **new_static_kwargs) -> "Network":
+ """Create new network with the given parameters, and copy all variables from this network."""
+ if new_name is None:
+ new_name = self.name
+ static_kwargs = dict(self.static_kwargs)
+ static_kwargs.update(new_static_kwargs)
+ net = Network(name=new_name, func_name=new_func_name, **static_kwargs)
+ net.copy_vars_from(self)
+ return net
+
+ def setup_as_moving_average_of(self, src_net: "Network", beta: TfExpressionEx = 0.99, beta_nontrainable: TfExpressionEx = 0.0) -> tf.Operation:
+ """Construct a TensorFlow op that updates the variables of this network
+ to be slightly closer to those of the given network."""
+ with tfutil.absolute_name_scope(self.scope + "/_MovingAvg"):
+ ops = []
+ for name, var in self.vars.items():
+ if name in src_net.vars:
+ cur_beta = beta if name in self.trainables else beta_nontrainable
+ new_value = tfutil.lerp(src_net.vars[name], var, cur_beta)
+ ops.append(var.assign(new_value))
+ return tf.group(*ops)
+
+ def run(self,
+ *in_arrays: Tuple[Union[np.ndarray, None], ...],
+ input_transform: dict = None,
+ output_transform: dict = None,
+ return_as_list: bool = False,
+ print_progress: bool = False,
+ minibatch_size: int = None,
+ num_gpus: int = 1,
+ assume_frozen: bool = False,
+ **dynamic_kwargs) -> Union[np.ndarray, Tuple[np.ndarray, ...], List[np.ndarray]]:
+ """Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s).
+
+ Args:
+ input_transform: A dict specifying a custom transformation to be applied to the input tensor(s) before evaluating the network.
+ The dict must contain a 'func' field that points to a top-level function. The function is called with the input
+ TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs.
+ output_transform: A dict specifying a custom transformation to be applied to the output tensor(s) after evaluating the network.
+ The dict must contain a 'func' field that points to a top-level function. The function is called with the output
+ TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs.
+ return_as_list: True = return a list of NumPy arrays, False = return a single NumPy array, or a tuple if there are multiple outputs.
+ print_progress: Print progress to the console? Useful for very large input arrays.
+ minibatch_size: Maximum minibatch size to use, None = disable batching.
+ num_gpus: Number of GPUs to use.
+ assume_frozen: Improve multi-GPU performance by assuming that the trainable parameters will remain changed between calls.
+ dynamic_kwargs: Additional keyword arguments to be passed into the network build function.
+ """
+ assert len(in_arrays) == self.num_inputs
+ assert not all(arr is None for arr in in_arrays)
+ assert input_transform is None or util.is_top_level_function(input_transform["func"])
+ assert output_transform is None or util.is_top_level_function(output_transform["func"])
+ output_transform, dynamic_kwargs = _handle_legacy_output_transforms(output_transform, dynamic_kwargs)
+ num_items = in_arrays[0].shape[0]
+ if minibatch_size is None:
+ minibatch_size = num_items
+
+ # Construct unique hash key from all arguments that affect the TensorFlow graph.
+ key = dict(input_transform=input_transform, output_transform=output_transform, num_gpus=num_gpus, assume_frozen=assume_frozen, dynamic_kwargs=dynamic_kwargs)
+ def unwind_key(obj):
+ if isinstance(obj, dict):
+ return [(key, unwind_key(value)) for key, value in sorted(obj.items())]
+ if callable(obj):
+ return util.get_top_level_function_name(obj)
+ return obj
+ key = repr(unwind_key(key))
+
+ # Build graph.
+ if key not in self._run_cache:
+ with tfutil.absolute_name_scope(self.scope + "/_Run"), tf.control_dependencies(None):
+ with tf.device("/cpu:0"):
+ in_expr = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
+ in_split = list(zip(*[tf.split(x, num_gpus) for x in in_expr]))
+
+ out_split = []
+ for gpu in range(num_gpus):
+ with tf.device("/gpu:%d" % gpu):
+ net_gpu = self.clone() if assume_frozen else self
+ in_gpu = in_split[gpu]
+
+ if input_transform is not None:
+ in_kwargs = dict(input_transform)
+ in_gpu = in_kwargs.pop("func")(*in_gpu, **in_kwargs)
+ in_gpu = [in_gpu] if tfutil.is_tf_expression(in_gpu) else list(in_gpu)
+
+ assert len(in_gpu) == self.num_inputs
+ out_gpu = net_gpu.get_output_for(*in_gpu, return_as_list=True, **dynamic_kwargs)
+
+ if output_transform is not None:
+ out_kwargs = dict(output_transform)
+ out_gpu = out_kwargs.pop("func")(*out_gpu, **out_kwargs)
+ out_gpu = [out_gpu] if tfutil.is_tf_expression(out_gpu) else list(out_gpu)
+
+ assert len(out_gpu) == self.num_outputs
+ out_split.append(out_gpu)
+
+ with tf.device("/cpu:0"):
+ out_expr = [tf.concat(outputs, axis=0) for outputs in zip(*out_split)]
+ self._run_cache[key] = in_expr, out_expr
+
+ # Run minibatches.
+ in_expr, out_expr = self._run_cache[key]
+ out_arrays = [np.empty([num_items] + expr.shape.as_list()[1:], expr.dtype.name) for expr in out_expr]
+
+ for mb_begin in range(0, num_items, minibatch_size):
+ if print_progress:
+ print("\r%d / %d" % (mb_begin, num_items), end="")
+
+ mb_end = min(mb_begin + minibatch_size, num_items)
+ mb_num = mb_end - mb_begin
+ mb_in = [src[mb_begin : mb_end] if src is not None else np.zeros([mb_num] + shape[1:]) for src, shape in zip(in_arrays, self.input_shapes)]
+ mb_out = tf.get_default_session().run(out_expr, dict(zip(in_expr, mb_in)))
+
+ for dst, src in zip(out_arrays, mb_out):
+ dst[mb_begin: mb_end] = src
+
+ # Done.
+ if print_progress:
+ print("\r%d / %d" % (num_items, num_items))
+
+ if not return_as_list:
+ out_arrays = out_arrays[0] if len(out_arrays) == 1 else tuple(out_arrays)
+ return out_arrays
+
+ def list_ops(self) -> List[TfExpression]:
+ include_prefix = self.scope + "/"
+ exclude_prefix = include_prefix + "_"
+ ops = tf.get_default_graph().get_operations()
+ ops = [op for op in ops if op.name.startswith(include_prefix)]
+ ops = [op for op in ops if not op.name.startswith(exclude_prefix)]
+ return ops
+
+ def list_layers(self) -> List[Tuple[str, TfExpression, List[TfExpression]]]:
+ """Returns a list of (layer_name, output_expr, trainable_vars) tuples corresponding to
+ individual layers of the network. Mainly intended to be used for reporting."""
+ layers = []
+
+ def recurse(scope, parent_ops, parent_vars, level):
+ # Ignore specific patterns.
+ if any(p in scope for p in ["/Shape", "/strided_slice", "/Cast", "/concat", "/Assign"]):
+ return
+
+ # Filter ops and vars by scope.
+ global_prefix = scope + "/"
+ local_prefix = global_prefix[len(self.scope) + 1:]
+ cur_ops = [op for op in parent_ops if op.name.startswith(global_prefix) or op.name == global_prefix[:-1]]
+ cur_vars = [(name, var) for name, var in parent_vars if name.startswith(local_prefix) or name == local_prefix[:-1]]
+ if not cur_ops and not cur_vars:
+ return
+
+ # Filter out all ops related to variables.
+ for var in [op for op in cur_ops if op.type.startswith("Variable")]:
+ var_prefix = var.name + "/"
+ cur_ops = [op for op in cur_ops if not op.name.startswith(var_prefix)]
+
+ # Scope does not contain ops as immediate children => recurse deeper.
+ contains_direct_ops = any("/" not in op.name[len(global_prefix):] and op.type not in ["Identity", "Cast", "Transpose"] for op in cur_ops)
+ if (level == 0 or not contains_direct_ops) and (len(cur_ops) + len(cur_vars)) > 1:
+ visited = set()
+ for rel_name in [op.name[len(global_prefix):] for op in cur_ops] + [name[len(local_prefix):] for name, _var in cur_vars]:
+ token = rel_name.split("/")[0]
+ if token not in visited:
+ recurse(global_prefix + token, cur_ops, cur_vars, level + 1)
+ visited.add(token)
+ return
+
+ # Report layer.
+ layer_name = scope[len(self.scope) + 1:]
+ layer_output = cur_ops[-1].outputs[0] if cur_ops else cur_vars[-1][1]
+ layer_trainables = [var for _name, var in cur_vars if var.trainable]
+ layers.append((layer_name, layer_output, layer_trainables))
+
+ recurse(self.scope, self.list_ops(), list(self.vars.items()), 0)
+ return layers
+
+ def print_layers(self, title: str = None, hide_layers_with_no_params: bool = False) -> None:
+ """Print a summary table of the network structure."""
+ rows = [[title if title is not None else self.name, "Params", "OutputShape", "WeightShape"]]
+ rows += [["---"] * 4]
+ total_params = 0
+
+ for layer_name, layer_output, layer_trainables in self.list_layers():
+ num_params = sum(int(np.prod(var.shape.as_list())) for var in layer_trainables)
+ weights = [var for var in layer_trainables if var.name.endswith("/weight:0")]
+ weights.sort(key=lambda x: len(x.name))
+ if len(weights) == 0 and len(layer_trainables) == 1:
+ weights = layer_trainables
+ total_params += num_params
+
+ if not hide_layers_with_no_params or num_params != 0:
+ num_params_str = str(num_params) if num_params > 0 else "-"
+ output_shape_str = str(layer_output.shape)
+ weight_shape_str = str(weights[0].shape) if len(weights) >= 1 else "-"
+ rows += [[layer_name, num_params_str, output_shape_str, weight_shape_str]]
+
+ rows += [["---"] * 4]
+ rows += [["Total", str(total_params), "", ""]]
+
+ 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()
+
+ def setup_weight_histograms(self, title: str = None) -> None:
+ """Construct summary ops to include histograms of all trainable parameters in TensorBoard."""
+ if title is None:
+ title = self.name
+
+ with tf.name_scope(None), tf.device(None), tf.control_dependencies(None):
+ for local_name, var in self.trainables.items():
+ if "/" in local_name:
+ p = local_name.split("/")
+ name = title + "_" + p[-1] + "/" + "_".join(p[:-1])
+ else:
+ name = title + "_toplevel/" + local_name
+
+ tf.summary.histogram(name, var)
+
+#----------------------------------------------------------------------------
+# Backwards-compatible emulation of legacy output transformation in Network.run().
+
+_print_legacy_warning = True
+
+def _handle_legacy_output_transforms(output_transform, dynamic_kwargs):
+ global _print_legacy_warning
+ legacy_kwargs = ["out_mul", "out_add", "out_shrink", "out_dtype"]
+ if not any(kwarg in dynamic_kwargs for kwarg in legacy_kwargs):
+ return output_transform, dynamic_kwargs
+
+ if _print_legacy_warning:
+ _print_legacy_warning = False
+ print()
+ print("WARNING: Old-style output transformations in Network.run() are deprecated.")
+ print("Consider using 'output_transform=dict(func=tflib.convert_images_to_uint8)'")
+ print("instead of 'out_mul=127.5, out_add=127.5, out_dtype=np.uint8'.")
+ print()
+ assert output_transform is None
+
+ new_kwargs = dict(dynamic_kwargs)
+ new_transform = {kwarg: new_kwargs.pop(kwarg) for kwarg in legacy_kwargs if kwarg in dynamic_kwargs}
+ new_transform["func"] = _legacy_output_transform_func
+ return new_transform, new_kwargs
+
+def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_shrink=1, out_dtype=None):
+ if out_mul != 1.0:
+ expr = [x * out_mul for x in expr]
+
+ if out_add != 0.0:
+ expr = [x + out_add for x in expr]
+
+ if out_shrink > 1:
+ ksize = [1, 1, out_shrink, out_shrink]
+ expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") for x in expr]
+
+ if out_dtype is not None:
+ if tf.as_dtype(out_dtype).is_integer:
+ expr = [tf.round(x) for x in expr]
+ expr = [tf.saturate_cast(x, out_dtype) for x in expr]
+ return expr
diff --git a/stylegan_human/dnnlib/tflib/ops/__init__.py b/stylegan_human/dnnlib/tflib/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ee9b7dd51bb69639724580f167b2eac39666266
--- /dev/null
+++ b/stylegan_human/dnnlib/tflib/ops/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License-NC.
+# To view a copy of this license, visit
+# https://nvlabs.github.io/stylegan2/license.html
+
+# empty
diff --git a/stylegan_human/dnnlib/tflib/ops/fused_bias_act.cu b/stylegan_human/dnnlib/tflib/ops/fused_bias_act.cu
new file mode 100644
index 0000000000000000000000000000000000000000..d1d0bd6bc46ec3fc94c80f1f7e05c66a96a9072e
--- /dev/null
+++ b/stylegan_human/dnnlib/tflib/ops/fused_bias_act.cu
@@ -0,0 +1,190 @@
+// Copyright (c) SenseTime Research. All rights reserved.
+
+// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+//
+// This work is made available under the Nvidia Source Code License-NC.
+// To view a copy of this license, visit
+// https://nvlabs.github.io/stylegan2/license.html
+
+#define EIGEN_USE_GPU
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include
+
+using namespace tensorflow;
+using namespace tensorflow::shape_inference;
+
+#define OP_CHECK_CUDA_ERROR(CTX, CUDA_CALL) do { cudaError_t err = CUDA_CALL; OP_REQUIRES(CTX, err == cudaSuccess, errors::Internal(cudaGetErrorName(err))); } while (false)
+
+//------------------------------------------------------------------------
+// CUDA kernel.
+
+template
+struct FusedBiasActKernelParams
+{
+ const T* x; // [sizeX]
+ const T* b; // [sizeB] or NULL
+ const T* ref; // [sizeX] or NULL
+ T* y; // [sizeX]
+
+ int grad;
+ int axis;
+ int act;
+ float alpha;
+ float gain;
+
+ int sizeX;
+ int sizeB;
+ int stepB;
+ int loopX;
+};
+
+template
+static __global__ void FusedBiasActKernel(const FusedBiasActKernelParams p)
+{
+ const float expRange = 80.0f;
+ const float halfExpRange = 40.0f;
+ const float seluScale = 1.0507009873554804934193349852946f;
+ const float seluAlpha = 1.6732632423543772848170429916717f;
+
+ // 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 and apply bias.
+ float x = (float)p.x[xi];
+ if (p.b)
+ x += (float)p.b[(xi / p.stepB) % p.sizeB];
+ float ref = (p.ref) ? (float)p.ref[xi] : 0.0f;
+ if (p.gain != 0.0f & p.act != 9)
+ ref /= p.gain;
+
+ // Evaluate activation func.
+ float y;
+ switch (p.act * 10 + p.grad)
+ {
+ // linear
+ default:
+ case 10: y = x; break;
+ case 11: y = x; break;
+ case 12: y = 0.0f; break;
+
+ // relu
+ case 20: y = (x > 0.0f) ? x : 0.0f; break;
+ case 21: y = (ref > 0.0f) ? x : 0.0f; break;
+ case 22: y = 0.0f; break;
+
+ // lrelu
+ case 30: y = (x > 0.0f) ? x : x * p.alpha; break;
+ case 31: y = (ref > 0.0f) ? x : x * p.alpha; break;
+ case 32: y = 0.0f; break;
+
+ // tanh
+ case 40: { float c = expf(x); float d = 1.0f / c; y = (x < -expRange) ? -1.0f : (x > expRange) ? 1.0f : (c - d) / (c + d); } break;
+ case 41: y = x * (1.0f - ref * ref); break;
+ case 42: y = x * (1.0f - ref * ref) * (-2.0f * ref); break;
+
+ // sigmoid
+ case 50: y = (x < -expRange) ? 0.0f : 1.0f / (expf(-x) + 1.0f); break;
+ case 51: y = x * ref * (1.0f - ref); break;
+ case 52: y = x * ref * (1.0f - ref) * (1.0f - 2.0f * ref); break;
+
+ // elu
+ case 60: y = (x >= 0.0f) ? x : expf(x) - 1.0f; break;
+ case 61: y = (ref >= 0.0f) ? x : x * (ref + 1.0f); break;
+ case 62: y = (ref >= 0.0f) ? 0.0f : x * (ref + 1.0f); break;
+
+ // selu
+ case 70: y = (x >= 0.0f) ? seluScale * x : (seluScale * seluAlpha) * (expf(x) - 1.0f); break;
+ case 71: y = (ref >= 0.0f) ? x * seluScale : x * (ref + seluScale * seluAlpha); break;
+ case 72: y = (ref >= 0.0f) ? 0.0f : x * (ref + seluScale * seluAlpha); break;
+
+ // softplus
+ case 80: y = (x > expRange) ? x : logf(expf(x) + 1.0f); break;
+ case 81: y = x * (1.0f - expf(-ref)); break;
+ case 82: { float c = expf(-ref); y = x * c * (1.0f - c); } break;
+
+ // swish
+ case 90: y = (x < -expRange) ? 0.0f : x / (expf(-x) + 1.0f); break;
+ case 91: { float c = expf(ref); float d = c + 1.0f; y = (ref > halfExpRange) ? x : x * c * (ref + d) / (d * d); } break;
+ case 92: { float c = expf(ref); float d = c + 1.0f; y = (ref > halfExpRange) ? 0.0f : x * c * (ref * (2.0f - d) + 2.0f * d) / (d * d * d); } break;
+ }
+
+ // Apply gain and store.
+ p.y[xi] = (T)(y * p.gain);
+ }
+}
+
+//------------------------------------------------------------------------
+// TensorFlow op.
+
+template
+struct FusedBiasActOp : public OpKernel
+{
+ FusedBiasActKernelParams m_attribs;
+
+ FusedBiasActOp(OpKernelConstruction* ctx) : OpKernel(ctx)
+ {
+ memset(&m_attribs, 0, sizeof(m_attribs));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("grad", &m_attribs.grad));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("axis", &m_attribs.axis));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("act", &m_attribs.act));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("alpha", &m_attribs.alpha));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("gain", &m_attribs.gain));
+ OP_REQUIRES(ctx, m_attribs.grad >= 0, errors::InvalidArgument("grad must be non-negative"));
+ OP_REQUIRES(ctx, m_attribs.axis >= 0, errors::InvalidArgument("axis must be non-negative"));
+ OP_REQUIRES(ctx, m_attribs.act >= 0, errors::InvalidArgument("act must be non-negative"));
+ }
+
+ void Compute(OpKernelContext* ctx)
+ {
+ FusedBiasActKernelParams p = m_attribs;
+ cudaStream_t stream = ctx->eigen_device().stream();
+
+ const Tensor& x = ctx->input(0); // [...]
+ const Tensor& b = ctx->input(1); // [sizeB] or [0]
+ const Tensor& ref = ctx->input(2); // x.shape or [0]
+ p.x = x.flat().data();
+ p.b = (b.NumElements()) ? b.flat().data() : NULL;
+ p.ref = (ref.NumElements()) ? ref.flat().data() : NULL;
+ OP_REQUIRES(ctx, b.NumElements() == 0 || m_attribs.axis < x.dims(), errors::InvalidArgument("axis out of bounds"));
+ OP_REQUIRES(ctx, b.dims() == 1, errors::InvalidArgument("b must have rank 1"));
+ OP_REQUIRES(ctx, b.NumElements() == 0 || b.NumElements() == x.dim_size(m_attribs.axis), errors::InvalidArgument("b has wrong number of elements"));
+ OP_REQUIRES(ctx, ref.NumElements() == ((p.grad == 0) ? 0 : x.NumElements()), errors::InvalidArgument("ref has wrong number of elements"));
+ OP_REQUIRES(ctx, x.NumElements() <= kint32max, errors::InvalidArgument("x is too large"));
+
+ p.sizeX = (int)x.NumElements();
+ p.sizeB = (int)b.NumElements();
+ p.stepB = 1;
+ for (int i = m_attribs.axis + 1; i < x.dims(); i++)
+ p.stepB *= (int)x.dim_size(i);
+
+ Tensor* y = NULL; // x.shape
+ OP_REQUIRES_OK(ctx, ctx->allocate_output(0, x.shape(), &y));
+ p.y = y->flat().data();
+
+ p.loopX = 4;
+ int blockSize = 4 * 32;
+ int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
+ void* args[] = {&p};
+ OP_CHECK_CUDA_ERROR(ctx, cudaLaunchKernel((void*)FusedBiasActKernel, gridSize, blockSize, args, 0, stream));
+ }
+};
+
+REGISTER_OP("FusedBiasAct")
+ .Input ("x: T")
+ .Input ("b: T")
+ .Input ("ref: T")
+ .Output ("y: T")
+ .Attr ("T: {float, half}")
+ .Attr ("grad: int = 0")
+ .Attr ("axis: int = 1")
+ .Attr ("act: int = 0")
+ .Attr ("alpha: float = 0.0")
+ .Attr ("gain: float = 1.0");
+REGISTER_KERNEL_BUILDER(Name("FusedBiasAct").Device(DEVICE_GPU).TypeConstraint("T"), FusedBiasActOp);
+REGISTER_KERNEL_BUILDER(Name("FusedBiasAct").Device(DEVICE_GPU).TypeConstraint("T"), FusedBiasActOp);
+
+//------------------------------------------------------------------------
diff --git a/stylegan_human/dnnlib/tflib/ops/fused_bias_act.py b/stylegan_human/dnnlib/tflib/ops/fused_bias_act.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b0dfd08d475f4d6759fd4bbdc133aef85f3bb24
--- /dev/null
+++ b/stylegan_human/dnnlib/tflib/ops/fused_bias_act.py
@@ -0,0 +1,198 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License-NC.
+# To view a copy of this license, visit
+# https://nvlabs.github.io/stylegan2/license.html
+
+"""Custom TensorFlow ops for efficient bias and activation."""
+
+import os
+import numpy as np
+import tensorflow as tf
+from .. import custom_ops
+from ...util import EasyDict
+
+def _get_plugin():
+ return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu')
+
+#----------------------------------------------------------------------------
+
+activation_funcs = {
+ 'linear': EasyDict(func=lambda x, **_: x, def_alpha=None, def_gain=1.0, cuda_idx=1, ref='y', zero_2nd_grad=True),
+ 'relu': EasyDict(func=lambda x, **_: tf.nn.relu(x), def_alpha=None, def_gain=np.sqrt(2), cuda_idx=2, ref='y', zero_2nd_grad=True),
+ 'lrelu': EasyDict(func=lambda x, alpha, **_: tf.nn.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', zero_2nd_grad=True),
+ 'tanh': EasyDict(func=lambda x, **_: tf.nn.tanh(x), def_alpha=None, def_gain=1.0, cuda_idx=4, ref='y', zero_2nd_grad=False),
+ 'sigmoid': EasyDict(func=lambda x, **_: tf.nn.sigmoid(x), def_alpha=None, def_gain=1.0, cuda_idx=5, ref='y', zero_2nd_grad=False),
+ 'elu': EasyDict(func=lambda x, **_: tf.nn.elu(x), def_alpha=None, def_gain=1.0, cuda_idx=6, ref='y', zero_2nd_grad=False),
+ 'selu': EasyDict(func=lambda x, **_: tf.nn.selu(x), def_alpha=None, def_gain=1.0, cuda_idx=7, ref='y', zero_2nd_grad=False),
+ 'softplus': EasyDict(func=lambda x, **_: tf.nn.softplus(x), def_alpha=None, def_gain=1.0, cuda_idx=8, ref='y', zero_2nd_grad=False),
+ 'swish': EasyDict(func=lambda x, **_: tf.nn.sigmoid(x) * x, def_alpha=None, def_gain=np.sqrt(2), cuda_idx=9, ref='x', zero_2nd_grad=False),
+}
+
+#----------------------------------------------------------------------------
+
+def fused_bias_act(x, b=None, axis=1, act='linear', alpha=None, gain=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 TensorFlow ops. It supports first and second order gradients,
+ but not third order gradients.
+
+ Args:
+ x: Input activation tensor. Can have any shape, but if `b` is defined, the
+ dimension corresponding to `axis`, as well as the rank, must be known.
+ 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 `axis`.
+ axis: The dimension in `x` corresponding to the elements of `b`.
+ The value of `axis` 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 `1.0`.
+ impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
+
+ Returns:
+ Tensor of the same shape and datatype as `x`.
+ """
+
+ impl_dict = {
+ 'ref': _fused_bias_act_ref,
+ 'cuda': _fused_bias_act_cuda,
+ }
+ return impl_dict[impl](x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain)
+
+#----------------------------------------------------------------------------
+
+def _fused_bias_act_ref(x, b, axis, act, alpha, gain):
+ """Slow reference implementation of `fused_bias_act()` using standard TensorFlow ops."""
+
+ # Validate arguments.
+ x = tf.convert_to_tensor(x)
+ b = tf.convert_to_tensor(b) if b is not None else tf.constant([], dtype=x.dtype)
+ act_spec = activation_funcs[act]
+ assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis])
+ assert b.shape[0] == 0 or 0 <= axis < x.shape.rank
+ if alpha is None:
+ alpha = act_spec.def_alpha
+ if gain is None:
+ gain = act_spec.def_gain
+
+ # Add bias.
+ if b.shape[0] != 0:
+ x += tf.reshape(b, [-1 if i == axis else 1 for i in range(x.shape.rank)])
+
+ # Evaluate activation function.
+ x = act_spec.func(x, alpha=alpha)
+
+ # Scale by gain.
+ if gain != 1:
+ x *= gain
+ return x
+
+#----------------------------------------------------------------------------
+
+def _fused_bias_act_cuda(x, b, axis, act, alpha, gain):
+ """Fast CUDA implementation of `fused_bias_act()` using custom ops."""
+
+ # Validate arguments.
+ x = tf.convert_to_tensor(x)
+ empty_tensor = tf.constant([], dtype=x.dtype)
+ b = tf.convert_to_tensor(b) if b is not None else empty_tensor
+ act_spec = activation_funcs[act]
+ assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis])
+ assert b.shape[0] == 0 or 0 <= axis < x.shape.rank
+ if alpha is None:
+ alpha = act_spec.def_alpha
+ if gain is None:
+ gain = act_spec.def_gain
+
+ # Special cases.
+ if act == 'linear' and b is None and gain == 1.0:
+ return x
+ if act_spec.cuda_idx is None:
+ return _fused_bias_act_ref(x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain)
+
+ # CUDA kernel.
+ cuda_kernel = _get_plugin().fused_bias_act
+ cuda_kwargs = dict(axis=axis, act=act_spec.cuda_idx, alpha=alpha, gain=gain)
+
+ # Forward pass: y = func(x, b).
+ def func_y(x, b):
+ y = cuda_kernel(x=x, b=b, ref=empty_tensor, grad=0, **cuda_kwargs)
+ y.set_shape(x.shape)
+ return y
+
+ # Backward pass: dx, db = grad(dy, x, y)
+ def grad_dx(dy, x, y):
+ ref = {'x': x, 'y': y}[act_spec.ref]
+ dx = cuda_kernel(x=dy, b=empty_tensor, ref=ref, grad=1, **cuda_kwargs)
+ dx.set_shape(x.shape)
+ return dx
+ def grad_db(dx):
+ if b.shape[0] == 0:
+ return empty_tensor
+ db = dx
+ if axis < x.shape.rank - 1:
+ db = tf.reduce_sum(db, list(range(axis + 1, x.shape.rank)))
+ if axis > 0:
+ db = tf.reduce_sum(db, list(range(axis)))
+ db.set_shape(b.shape)
+ return db
+
+ # Second order gradients: d_dy, d_x = grad2(d_dx, d_db, x, y)
+ def grad2_d_dy(d_dx, d_db, x, y):
+ ref = {'x': x, 'y': y}[act_spec.ref]
+ d_dy = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=1, **cuda_kwargs)
+ d_dy.set_shape(x.shape)
+ return d_dy
+ def grad2_d_x(d_dx, d_db, x, y):
+ ref = {'x': x, 'y': y}[act_spec.ref]
+ d_x = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=2, **cuda_kwargs)
+ d_x.set_shape(x.shape)
+ return d_x
+
+ # Fast version for piecewise-linear activation funcs.
+ @tf.custom_gradient
+ def func_zero_2nd_grad(x, b):
+ y = func_y(x, b)
+ @tf.custom_gradient
+ def grad(dy):
+ dx = grad_dx(dy, x, y)
+ db = grad_db(dx)
+ def grad2(d_dx, d_db):
+ d_dy = grad2_d_dy(d_dx, d_db, x, y)
+ return d_dy
+ return (dx, db), grad2
+ return y, grad
+
+ # Slow version for general activation funcs.
+ @tf.custom_gradient
+ def func_nonzero_2nd_grad(x, b):
+ y = func_y(x, b)
+ def grad_wrap(dy):
+ @tf.custom_gradient
+ def grad_impl(dy, x):
+ dx = grad_dx(dy, x, y)
+ db = grad_db(dx)
+ def grad2(d_dx, d_db):
+ d_dy = grad2_d_dy(d_dx, d_db, x, y)
+ d_x = grad2_d_x(d_dx, d_db, x, y)
+ return d_dy, d_x
+ return (dx, db), grad2
+ return grad_impl(dy, x)
+ return y, grad_wrap
+
+ # Which version to use?
+ if act_spec.zero_2nd_grad:
+ return func_zero_2nd_grad(x, b)
+ return func_nonzero_2nd_grad(x, b)
+
+#----------------------------------------------------------------------------
diff --git a/stylegan_human/dnnlib/tflib/ops/upfirdn_2d.cu b/stylegan_human/dnnlib/tflib/ops/upfirdn_2d.cu
new file mode 100644
index 0000000000000000000000000000000000000000..d2a2cea5c7f533a98819f2d40c1f56e488fd35aa
--- /dev/null
+++ b/stylegan_human/dnnlib/tflib/ops/upfirdn_2d.cu
@@ -0,0 +1,328 @@
+// Copyright (c) SenseTime Research. All rights reserved.
+
+// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+//
+// This work is made available under the Nvidia Source Code License-NC.
+// To view a copy of this license, visit
+// https://nvlabs.github.io/stylegan2/license.html
+
+#define EIGEN_USE_GPU
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include
+
+using namespace tensorflow;
+using namespace tensorflow::shape_inference;
+
+//------------------------------------------------------------------------
+// Helpers.
+
+#define OP_CHECK_CUDA_ERROR(CTX, CUDA_CALL) do { cudaError_t err = CUDA_CALL; OP_REQUIRES(CTX, err == cudaSuccess, errors::Internal(cudaGetErrorName(err))); } while (false)
+
+static __host__ __device__ __forceinline__ int floorDiv(int a, int b)
+{
+ int c = a / b;
+ if (c * b > a)
+ c--;
+ return c;
+}
+
+//------------------------------------------------------------------------
+// CUDA kernel params.
+
+template
+struct UpFirDn2DKernelParams
+{
+ const T* x; // [majorDim, inH, inW, minorDim]
+ const T* k; // [kernelH, kernelW]
+ T* y; // [majorDim, outH, outW, minorDim]
+
+ int upx;
+ int upy;
+ int downx;
+ int downy;
+ int padx0;
+ int padx1;
+ int pady0;
+ int pady1;
+
+ int majorDim;
+ int inH;
+ int inW;
+ int minorDim;
+ int kernelH;
+ int kernelW;
+ int outH;
+ int outW;
+ int loopMajor;
+ int loopX;
+};
+
+//------------------------------------------------------------------------
+// General CUDA implementation for large filter kernels.
+
+template
+static __global__ void UpFirDn2DKernel_large(const UpFirDn2DKernelParams p)
+{
+ // Calculate thread index.
+ int minorIdx = blockIdx.x * blockDim.x + threadIdx.x;
+ int outY = minorIdx / p.minorDim;
+ minorIdx -= outY * p.minorDim;
+ int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y;
+ int majorIdxBase = blockIdx.z * p.loopMajor;
+ if (outXBase >= p.outW || outY >= p.outH || majorIdxBase >= p.majorDim)
+ return;
+
+ // Setup Y receptive field.
+ int midY = outY * p.downy + p.upy - 1 - p.pady0;
+ int inY = min(max(floorDiv(midY, p.upy), 0), p.inH);
+ int h = min(max(floorDiv(midY + p.kernelH, p.upy), 0), p.inH) - inY;
+ int kernelY = midY + p.kernelH - (inY + 1) * p.upy;
+
+ // Loop over majorDim and outX.
+ for (int loopMajor = 0, majorIdx = majorIdxBase; loopMajor < p.loopMajor && majorIdx < p.majorDim; loopMajor++, majorIdx++)
+ for (int loopX = 0, outX = outXBase; loopX < p.loopX && outX < p.outW; loopX++, outX += blockDim.y)
+ {
+ // Setup X receptive field.
+ int midX = outX * p.downx + p.upx - 1 - p.padx0;
+ int inX = min(max(floorDiv(midX, p.upx), 0), p.inW);
+ int w = min(max(floorDiv(midX + p.kernelW, p.upx), 0), p.inW) - inX;
+ int kernelX = midX + p.kernelW - (inX + 1) * p.upx;
+
+ // Initialize pointers.
+ const T* xp = &p.x[((majorIdx * p.inH + inY) * p.inW + inX) * p.minorDim + minorIdx];
+ const T* kp = &p.k[kernelY * p.kernelW + kernelX];
+ int xpx = p.minorDim;
+ int kpx = -p.upx;
+ int xpy = p.inW * p.minorDim;
+ int kpy = -p.upy * p.kernelW;
+
+ // Inner loop.
+ float v = 0.0f;
+ for (int y = 0; y < h; y++)
+ {
+ for (int x = 0; x < w; x++)
+ {
+ v += (float)(*xp) * (float)(*kp);
+ xp += xpx;
+ kp += kpx;
+ }
+ xp += xpy - w * xpx;
+ kp += kpy - w * kpx;
+ }
+
+ // Store result.
+ p.y[((majorIdx * p.outH + outY) * p.outW + outX) * p.minorDim + minorIdx] = (T)v;
+ }
+}
+
+//------------------------------------------------------------------------
+// Specialized CUDA implementation for small filter kernels.
+
+template
+static __global__ void UpFirDn2DKernel_small(const UpFirDn2DKernelParams p)
+{
+ //assert(kernelW % upx == 0);
+ //assert(kernelH % upy == 0);
+ const int tileInW = ((tileOutW - 1) * downx + kernelW - 1) / upx + 1;
+ const int tileInH = ((tileOutH - 1) * downy + kernelH - 1) / upy + 1;
+ __shared__ volatile float sk[kernelH][kernelW];
+ __shared__ volatile float sx[tileInH][tileInW];
+
+ // Calculate tile index.
+ int minorIdx = blockIdx.x;
+ int tileOutY = minorIdx / p.minorDim;
+ minorIdx -= tileOutY * p.minorDim;
+ tileOutY *= tileOutH;
+ int tileOutXBase = blockIdx.y * p.loopX * tileOutW;
+ int majorIdxBase = blockIdx.z * p.loopMajor;
+ if (tileOutXBase >= p.outW | tileOutY >= p.outH | majorIdxBase >= p.majorDim)
+ return;
+
+ // Load filter kernel (flipped).
+ for (int tapIdx = threadIdx.x; tapIdx < kernelH * kernelW; tapIdx += blockDim.x)
+ {
+ int ky = tapIdx / kernelW;
+ int kx = tapIdx - ky * kernelW;
+ float v = 0.0f;
+ if (kx < p.kernelW & ky < p.kernelH)
+ v = (float)p.k[(p.kernelH - 1 - ky) * p.kernelW + (p.kernelW - 1 - kx)];
+ sk[ky][kx] = v;
+ }
+
+ // Loop over majorDim and outX.
+ for (int loopMajor = 0, majorIdx = majorIdxBase; loopMajor < p.loopMajor & majorIdx < p.majorDim; loopMajor++, majorIdx++)
+ for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outW; loopX++, tileOutX += tileOutW)
+ {
+ // Load input pixels.
+ int tileMidX = tileOutX * downx + upx - 1 - p.padx0;
+ int tileMidY = tileOutY * downy + upy - 1 - p.pady0;
+ int tileInX = floorDiv(tileMidX, upx);
+ int tileInY = floorDiv(tileMidY, upy);
+ __syncthreads();
+ for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW; inIdx += blockDim.x)
+ {
+ int relInY = inIdx / tileInW;
+ int relInX = inIdx - relInY * tileInW;
+ int inX = relInX + tileInX;
+ int inY = relInY + tileInY;
+ float v = 0.0f;
+ if (inX >= 0 & inY >= 0 & inX < p.inW & inY < p.inH)
+ v = (float)p.x[((majorIdx * p.inH + inY) * p.inW + inX) * p.minorDim + minorIdx];
+ sx[relInY][relInX] = v;
+ }
+
+ // Loop over output pixels.
+ __syncthreads();
+ for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW; outIdx += blockDim.x)
+ {
+ int relOutY = outIdx / tileOutW;
+ int relOutX = outIdx - relOutY * tileOutW;
+ int outX = relOutX + tileOutX;
+ int outY = relOutY + tileOutY;
+
+ // Setup receptive field.
+ int midX = tileMidX + relOutX * downx;
+ int midY = tileMidY + relOutY * downy;
+ int inX = floorDiv(midX, upx);
+ int inY = floorDiv(midY, upy);
+ int relInX = inX - tileInX;
+ int relInY = inY - tileInY;
+ int kernelX = (inX + 1) * upx - midX - 1; // flipped
+ int kernelY = (inY + 1) * upy - midY - 1; // flipped
+
+ // Inner loop.
+ float v = 0.0f;
+ #pragma unroll
+ for (int y = 0; y < kernelH / upy; y++)
+ #pragma unroll
+ for (int x = 0; x < kernelW / upx; x++)
+ v += sx[relInY + y][relInX + x] * sk[kernelY + y * upy][kernelX + x * upx];
+
+ // Store result.
+ if (outX < p.outW & outY < p.outH)
+ p.y[((majorIdx * p.outH + outY) * p.outW + outX) * p.minorDim + minorIdx] = (T)v;
+ }
+ }
+}
+
+//------------------------------------------------------------------------
+// TensorFlow op.
+
+template
+struct UpFirDn2DOp : public OpKernel
+{
+ UpFirDn2DKernelParams m_attribs;
+
+ UpFirDn2DOp(OpKernelConstruction* ctx) : OpKernel(ctx)
+ {
+ memset(&m_attribs, 0, sizeof(m_attribs));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("upx", &m_attribs.upx));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("upy", &m_attribs.upy));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("downx", &m_attribs.downx));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("downy", &m_attribs.downy));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("padx0", &m_attribs.padx0));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("padx1", &m_attribs.padx1));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("pady0", &m_attribs.pady0));
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("pady1", &m_attribs.pady1));
+ OP_REQUIRES(ctx, m_attribs.upx >= 1 && m_attribs.upy >= 1, errors::InvalidArgument("upx and upy must be at least 1x1"));
+ OP_REQUIRES(ctx, m_attribs.downx >= 1 && m_attribs.downy >= 1, errors::InvalidArgument("downx and downy must be at least 1x1"));
+ }
+
+ void Compute(OpKernelContext* ctx)
+ {
+ UpFirDn2DKernelParams p = m_attribs;
+ cudaStream_t stream = ctx->eigen_device().stream();
+
+ const Tensor& x = ctx->input(0); // [majorDim, inH, inW, minorDim]
+ const Tensor& k = ctx->input(1); // [kernelH, kernelW]
+ p.x = x.flat().data();
+ p.k = k.flat().data();
+ OP_REQUIRES(ctx, x.dims() == 4, errors::InvalidArgument("input must have rank 4"));
+ OP_REQUIRES(ctx, k.dims() == 2, errors::InvalidArgument("kernel must have rank 2"));
+ OP_REQUIRES(ctx, x.NumElements() <= kint32max, errors::InvalidArgument("input too large"));
+ OP_REQUIRES(ctx, k.NumElements() <= kint32max, errors::InvalidArgument("kernel too large"));
+
+ p.majorDim = (int)x.dim_size(0);
+ p.inH = (int)x.dim_size(1);
+ p.inW = (int)x.dim_size(2);
+ p.minorDim = (int)x.dim_size(3);
+ p.kernelH = (int)k.dim_size(0);
+ p.kernelW = (int)k.dim_size(1);
+ OP_REQUIRES(ctx, p.kernelW >= 1 && p.kernelH >= 1, errors::InvalidArgument("kernel must be at least 1x1"));
+
+ p.outW = (p.inW * p.upx + p.padx0 + p.padx1 - p.kernelW + p.downx) / p.downx;
+ p.outH = (p.inH * p.upy + p.pady0 + p.pady1 - p.kernelH + p.downy) / p.downy;
+ OP_REQUIRES(ctx, p.outW >= 1 && p.outH >= 1, errors::InvalidArgument("output must be at least 1x1"));
+
+ Tensor* y = NULL; // [majorDim, outH, outW, minorDim]
+ TensorShape ys;
+ ys.AddDim(p.majorDim);
+ ys.AddDim(p.outH);
+ ys.AddDim(p.outW);
+ ys.AddDim(p.minorDim);
+ OP_REQUIRES_OK(ctx, ctx->allocate_output(0, ys, &y));
+ p.y = y->flat().data();
+ OP_REQUIRES(ctx, y->NumElements() <= kint32max, errors::InvalidArgument("output too large"));
+
+ // Choose CUDA kernel to use.
+ void* cudaKernel = (void*)UpFirDn2DKernel_large;
+ int tileOutW = -1;
+ int tileOutH = -1;
+ if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 7 && p.kernelH <= 7) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; }
+ if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 6 && p.kernelH <= 6) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; }
+ if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 5 && p.kernelH <= 5) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; }
+ if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 4 && p.kernelH <= 4) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; }
+ if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 3 && p.kernelH <= 3) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; }
+ if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 8 && p.kernelH <= 8) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; }
+ if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 6 && p.kernelH <= 6) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; }
+ if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 4 && p.kernelH <= 4) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; }
+ if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 2 && p.kernelH <= 2) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; }
+ if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 8 && p.kernelH <= 8) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 8; }
+ if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 6 && p.kernelH <= 6) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 8; }
+ if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 4 && p.kernelH <= 4) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 8; }
+ if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 2 && p.kernelH <= 2) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 8; }
+
+ // Choose launch params.
+ dim3 blockSize;
+ dim3 gridSize;
+ if (tileOutW > 0 && tileOutH > 0) // small
+ {
+ p.loopMajor = (p.majorDim - 1) / 16384 + 1;
+ p.loopX = 1;
+ blockSize = dim3(32 * 8, 1, 1);
+ gridSize = dim3(((p.outH - 1) / tileOutH + 1) * p.minorDim, (p.outW - 1) / (p.loopX * tileOutW) + 1, (p.majorDim - 1) / p.loopMajor + 1);
+ }
+ else // large
+ {
+ p.loopMajor = (p.majorDim - 1) / 16384 + 1;
+ p.loopX = 4;
+ blockSize = dim3(4, 32, 1);
+ gridSize = dim3((p.outH * p.minorDim - 1) / blockSize.x + 1, (p.outW - 1) / (p.loopX * blockSize.y) + 1, (p.majorDim - 1) / p.loopMajor + 1);
+ }
+
+ // Launch CUDA kernel.
+ void* args[] = {&p};
+ OP_CHECK_CUDA_ERROR(ctx, cudaLaunchKernel(cudaKernel, gridSize, blockSize, args, 0, stream));
+ }
+};
+
+REGISTER_OP("UpFirDn2D")
+ .Input ("x: T")
+ .Input ("k: T")
+ .Output ("y: T")
+ .Attr ("T: {float, half}")
+ .Attr ("upx: int = 1")
+ .Attr ("upy: int = 1")
+ .Attr ("downx: int = 1")
+ .Attr ("downy: int = 1")
+ .Attr ("padx0: int = 0")
+ .Attr ("padx1: int = 0")
+ .Attr ("pady0: int = 0")
+ .Attr ("pady1: int = 0");
+REGISTER_KERNEL_BUILDER(Name("UpFirDn2D").Device(DEVICE_GPU).TypeConstraint("T"), UpFirDn2DOp);
+REGISTER_KERNEL_BUILDER(Name("UpFirDn2D").Device(DEVICE_GPU).TypeConstraint("T"), UpFirDn2DOp);
+
+//------------------------------------------------------------------------
diff --git a/stylegan_human/dnnlib/tflib/ops/upfirdn_2d.py b/stylegan_human/dnnlib/tflib/ops/upfirdn_2d.py
new file mode 100644
index 0000000000000000000000000000000000000000..22e4b14fd5436e42336d3dd82f6135876076c518
--- /dev/null
+++ b/stylegan_human/dnnlib/tflib/ops/upfirdn_2d.py
@@ -0,0 +1,366 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License-NC.
+# To view a copy of this license, visit
+# https://nvlabs.github.io/stylegan2/license.html
+
+"""Custom TensorFlow ops for efficient resampling of 2D images."""
+
+import os
+import numpy as np
+import tensorflow as tf
+from .. import custom_ops
+
+def _get_plugin():
+ return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu')
+
+#----------------------------------------------------------------------------
+
+def upfirdn_2d(x, k, upx=1, upy=1, downx=1, downy=1, padx0=0, padx1=0, pady0=0, pady1=0, impl='cuda'):
+ r"""Pad, upsample, FIR filter, and downsample a batch of 2D images.
+
+ Accepts a batch of 2D images of the shape `[majorDim, inH, inW, minorDim]`
+ and performs the following operations for each image, batched across
+ `majorDim` and `minorDim`:
+
+ 1. Pad the image with zeros by the specified number of pixels on each side
+ (`padx0`, `padx1`, `pady0`, `pady1`). Specifying a negative value
+ corresponds to cropping the image.
+
+ 2. Upsample the image by inserting the zeros after each pixel (`upx`, `upy`).
+
+ 3. Convolve the image with the specified 2D FIR filter (`k`), shrinking the
+ image so that the footprint of all output pixels lies within the input image.
+
+ 4. Downsample the image by throwing away pixels (`downx`, `downy`).
+
+ 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 TensorFlow ops. It supports gradients of arbitrary order.
+
+ Args:
+ x: Input tensor of the shape `[majorDim, inH, inW, minorDim]`.
+ k: 2D FIR filter of the shape `[firH, firW]`.
+ upx: Integer upsampling factor along the X-axis (default: 1).
+ upy: Integer upsampling factor along the Y-axis (default: 1).
+ downx: Integer downsampling factor along the X-axis (default: 1).
+ downy: Integer downsampling factor along the Y-axis (default: 1).
+ padx0: Number of pixels to pad on the left side (default: 0).
+ padx1: Number of pixels to pad on the right side (default: 0).
+ pady0: Number of pixels to pad on the top side (default: 0).
+ pady1: Number of pixels to pad on the bottom side (default: 0).
+ impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
+
+ Returns:
+ Tensor of the shape `[majorDim, outH, outW, minorDim]`, and same datatype as `x`.
+ """
+
+ impl_dict = {
+ 'ref': _upfirdn_2d_ref,
+ 'cuda': _upfirdn_2d_cuda,
+ }
+ return impl_dict[impl](x=x, k=k, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1)
+
+#----------------------------------------------------------------------------
+
+def _upfirdn_2d_ref(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1):
+ """Slow reference implementation of `upfirdn_2d()` using standard TensorFlow ops."""
+
+ x = tf.convert_to_tensor(x)
+ k = np.asarray(k, dtype=np.float32)
+ assert x.shape.rank == 4
+ inH = x.shape[1].value
+ inW = x.shape[2].value
+ minorDim = _shape(x, 3)
+ kernelH, kernelW = k.shape
+ assert inW >= 1 and inH >= 1
+ assert kernelW >= 1 and kernelH >= 1
+ assert isinstance(upx, int) and isinstance(upy, int)
+ assert isinstance(downx, int) and isinstance(downy, int)
+ assert isinstance(padx0, int) and isinstance(padx1, int)
+ assert isinstance(pady0, int) and isinstance(pady1, int)
+
+ # Upsample (insert zeros).
+ x = tf.reshape(x, [-1, inH, 1, inW, 1, minorDim])
+ x = tf.pad(x, [[0, 0], [0, 0], [0, upy - 1], [0, 0], [0, upx - 1], [0, 0]])
+ x = tf.reshape(x, [-1, inH * upy, inW * upx, minorDim])
+
+ # Pad (crop if negative).
+ x = tf.pad(x, [[0, 0], [max(pady0, 0), max(pady1, 0)], [max(padx0, 0), max(padx1, 0)], [0, 0]])
+ x = x[:, max(-pady0, 0) : x.shape[1].value - max(-pady1, 0), max(-padx0, 0) : x.shape[2].value - max(-padx1, 0), :]
+
+ # Convolve with filter.
+ x = tf.transpose(x, [0, 3, 1, 2])
+ x = tf.reshape(x, [-1, 1, inH * upy + pady0 + pady1, inW * upx + padx0 + padx1])
+ w = tf.constant(k[::-1, ::-1, np.newaxis, np.newaxis], dtype=x.dtype)
+ x = tf.nn.conv2d(x, w, strides=[1,1,1,1], padding='VALID', data_format='NCHW')
+ x = tf.reshape(x, [-1, minorDim, inH * upy + pady0 + pady1 - kernelH + 1, inW * upx + padx0 + padx1 - kernelW + 1])
+ x = tf.transpose(x, [0, 2, 3, 1])
+
+ # Downsample (throw away pixels).
+ return x[:, ::downy, ::downx, :]
+
+#----------------------------------------------------------------------------
+
+def _upfirdn_2d_cuda(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1):
+ """Fast CUDA implementation of `upfirdn_2d()` using custom ops."""
+
+ x = tf.convert_to_tensor(x)
+ k = np.asarray(k, dtype=np.float32)
+ majorDim, inH, inW, minorDim = x.shape.as_list()
+ kernelH, kernelW = k.shape
+ assert inW >= 1 and inH >= 1
+ assert kernelW >= 1 and kernelH >= 1
+ assert isinstance(upx, int) and isinstance(upy, int)
+ assert isinstance(downx, int) and isinstance(downy, int)
+ assert isinstance(padx0, int) and isinstance(padx1, int)
+ assert isinstance(pady0, int) and isinstance(pady1, int)
+
+ outW = (inW * upx + padx0 + padx1 - kernelW) // downx + 1
+ outH = (inH * upy + pady0 + pady1 - kernelH) // downy + 1
+ assert outW >= 1 and outH >= 1
+
+ kc = tf.constant(k, dtype=x.dtype)
+ gkc = tf.constant(k[::-1, ::-1], dtype=x.dtype)
+ gpadx0 = kernelW - padx0 - 1
+ gpady0 = kernelH - pady0 - 1
+ gpadx1 = inW * upx - outW * downx + padx0 - upx + 1
+ gpady1 = inH * upy - outH * downy + pady0 - upy + 1
+
+ @tf.custom_gradient
+ def func(x):
+ y = _get_plugin().up_fir_dn2d(x=x, k=kc, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1)
+ y.set_shape([majorDim, outH, outW, minorDim])
+ @tf.custom_gradient
+ def grad(dy):
+ dx = _get_plugin().up_fir_dn2d(x=dy, k=gkc, upx=downx, upy=downy, downx=upx, downy=upy, padx0=gpadx0, padx1=gpadx1, pady0=gpady0, pady1=gpady1)
+ dx.set_shape([majorDim, inH, inW, minorDim])
+ return dx, func
+ return y, grad
+ return func(x)
+
+#----------------------------------------------------------------------------
+
+def filter_2d(x, k, gain=1, data_format='NCHW', impl='cuda'):
+ r"""Filter a batch of 2D images with the given FIR filter.
+
+ Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
+ and filters each image with the given filter. The filter is normalized so that
+ if the input pixels are constant, they will be scaled by the specified `gain`.
+ Pixels outside the image are assumed to be zero.
+
+ Args:
+ x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
+ k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
+ gain: Scaling factor for signal magnitude (default: 1.0).
+ data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
+ impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
+
+ Returns:
+ Tensor of the same shape and datatype as `x`.
+ """
+
+ k = _setup_kernel(k) * gain
+ p = k.shape[0] - 1
+ return _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)
+
+#----------------------------------------------------------------------------
+
+def upsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
+ r"""Upsample a batch of 2D images with the given filter.
+
+ Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
+ and upsamples each image with the given filter. The filter is normalized so that
+ if the input pixels are constant, they will be scaled by the specified `gain`.
+ Pixels outside the image are assumed to be zero, and the filter is padded with
+ zeros so that its shape is a multiple of the upsampling factor.
+
+ Args:
+ x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
+ k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
+ The default is `[1] * factor`, which corresponds to nearest-neighbor
+ upsampling.
+ factor: Integer upsampling factor (default: 2).
+ gain: Scaling factor for signal magnitude (default: 1.0).
+ data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
+ impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
+
+ Returns:
+ Tensor of the shape `[N, C, H * factor, W * factor]` or
+ `[N, H * factor, W * factor, C]`, and same datatype as `x`.
+ """
+
+ assert isinstance(factor, int) and factor >= 1
+ if k is None:
+ k = [1] * factor
+ k = _setup_kernel(k) * (gain * (factor ** 2))
+ p = k.shape[0] - factor
+ return _simple_upfirdn_2d(x, k, up=factor, pad0=(p+1)//2+factor-1, pad1=p//2, data_format=data_format, impl=impl)
+
+#----------------------------------------------------------------------------
+
+def downsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
+ r"""Downsample a batch of 2D images with the given filter.
+
+ Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
+ and downsamples each image with the given filter. The filter is normalized so that
+ if the input pixels are constant, they will be scaled by the specified `gain`.
+ Pixels outside the image are assumed to be zero, and the filter is padded with
+ zeros so that its shape is a multiple of the downsampling factor.
+
+ Args:
+ x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
+ k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
+ The default is `[1] * factor`, which corresponds to average pooling.
+ factor: Integer downsampling factor (default: 2).
+ gain: Scaling factor for signal magnitude (default: 1.0).
+ data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
+ impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
+
+ Returns:
+ Tensor of the shape `[N, C, H // factor, W // factor]` or
+ `[N, H // factor, W // factor, C]`, and same datatype as `x`.
+ """
+
+ assert isinstance(factor, int) and factor >= 1
+ if k is None:
+ k = [1] * factor
+ k = _setup_kernel(k) * gain
+ p = k.shape[0] - factor
+ return _simple_upfirdn_2d(x, k, down=factor, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)
+
+#----------------------------------------------------------------------------
+
+def upsample_conv_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
+ r"""Fused `upsample_2d()` followed by `tf.nn.conv2d()`.
+
+ Padding is performed only once at the beginning, not between the operations.
+ The fused op is considerably more efficient than performing the same calculation
+ using standard TensorFlow ops. It supports gradients of arbitrary order.
+
+ Args:
+ x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
+ w: Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
+ Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
+ k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
+ The default is `[1] * factor`, which corresponds to nearest-neighbor
+ upsampling.
+ factor: Integer upsampling factor (default: 2).
+ gain: Scaling factor for signal magnitude (default: 1.0).
+ data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
+ impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
+
+ Returns:
+ Tensor of the shape `[N, C, H * factor, W * factor]` or
+ `[N, H * factor, W * factor, C]`, and same datatype as `x`.
+ """
+
+ assert isinstance(factor, int) and factor >= 1
+
+ # Check weight shape.
+ w = tf.convert_to_tensor(w)
+ assert w.shape.rank == 4
+ convH = w.shape[0].value
+ convW = w.shape[1].value
+ inC = _shape(w, 2)
+ outC = _shape(w, 3)
+ assert convW == convH
+
+ # Setup filter kernel.
+ if k is None:
+ k = [1] * factor
+ k = _setup_kernel(k) * (gain * (factor ** 2))
+ p = (k.shape[0] - factor) - (convW - 1)
+
+ # Determine data dimensions.
+ if data_format == 'NCHW':
+ stride = [1, 1, factor, factor]
+ output_shape = [_shape(x, 0), outC, (_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW]
+ num_groups = _shape(x, 1) // inC
+ else:
+ stride = [1, factor, factor, 1]
+ output_shape = [_shape(x, 0), (_shape(x, 1) - 1) * factor + convH, (_shape(x, 2) - 1) * factor + convW, outC]
+ num_groups = _shape(x, 3) // inC
+
+ # Transpose weights.
+ w = tf.reshape(w, [convH, convW, inC, num_groups, -1])
+ w = tf.transpose(w[::-1, ::-1], [0, 1, 4, 3, 2])
+ w = tf.reshape(w, [convH, convW, -1, num_groups * inC])
+
+ # Execute.
+ x = tf.nn.conv2d_transpose(x, w, output_shape=output_shape, strides=stride, padding='VALID', data_format=data_format)
+ return _simple_upfirdn_2d(x, k, pad0=(p+1)//2+factor-1, pad1=p//2+1, data_format=data_format, impl=impl)
+
+#----------------------------------------------------------------------------
+
+def conv_downsample_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
+ r"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`.
+
+ Padding is performed only once at the beginning, not between the operations.
+ The fused op is considerably more efficient than performing the same calculation
+ using standard TensorFlow ops. It supports gradients of arbitrary order.
+
+ Args:
+ x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
+ w: Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
+ Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
+ k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
+ The default is `[1] * factor`, which corresponds to average pooling.
+ factor: Integer downsampling factor (default: 2).
+ gain: Scaling factor for signal magnitude (default: 1.0).
+ data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
+ impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
+
+ Returns:
+ Tensor of the shape `[N, C, H // factor, W // factor]` or
+ `[N, H // factor, W // factor, C]`, and same datatype as `x`.
+ """
+
+ assert isinstance(factor, int) and factor >= 1
+ w = tf.convert_to_tensor(w)
+ convH, convW, _inC, _outC = w.shape.as_list()
+ assert convW == convH
+ if k is None:
+ k = [1] * factor
+ k = _setup_kernel(k) * gain
+ p = (k.shape[0] - factor) + (convW - 1)
+ if data_format == 'NCHW':
+ s = [1, 1, factor, factor]
+ else:
+ s = [1, factor, factor, 1]
+ x = _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)
+ return tf.nn.conv2d(x, w, strides=s, padding='VALID', data_format=data_format)
+
+#----------------------------------------------------------------------------
+# Internal helper funcs.
+
+def _shape(tf_expr, dim_idx):
+ if tf_expr.shape.rank is not None:
+ dim = tf_expr.shape[dim_idx].value
+ if dim is not None:
+ return dim
+ return tf.shape(tf_expr)[dim_idx]
+
+def _setup_kernel(k):
+ k = np.asarray(k, dtype=np.float32)
+ if k.ndim == 1:
+ k = np.outer(k, k)
+ k /= np.sum(k)
+ assert k.ndim == 2
+ assert k.shape[0] == k.shape[1]
+ return k
+
+def _simple_upfirdn_2d(x, k, up=1, down=1, pad0=0, pad1=0, data_format='NCHW', impl='cuda'):
+ assert data_format in ['NCHW', 'NHWC']
+ assert x.shape.rank == 4
+ y = x
+ if data_format == 'NCHW':
+ y = tf.reshape(y, [-1, _shape(y, 2), _shape(y, 3), 1])
+ y = upfirdn_2d(y, k, upx=up, upy=up, downx=down, downy=down, padx0=pad0, padx1=pad1, pady0=pad0, pady1=pad1, impl=impl)
+ if data_format == 'NCHW':
+ y = tf.reshape(y, [-1, _shape(x, 1), _shape(y, 1), _shape(y, 2)])
+ return y
+
+#----------------------------------------------------------------------------
diff --git a/stylegan_human/dnnlib/tflib/optimizer.py b/stylegan_human/dnnlib/tflib/optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..93d5dcc6172209985308784c9b9e590759612a0b
--- /dev/null
+++ b/stylegan_human/dnnlib/tflib/optimizer.py
@@ -0,0 +1,338 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License-NC.
+# To view a copy of this license, visit
+# https://nvlabs.github.io/stylegan2/license.html
+
+"""Helper wrapper for a Tensorflow optimizer."""
+
+import numpy as np
+import tensorflow as tf
+
+from collections import OrderedDict
+from typing import List, Union
+
+from . import autosummary
+from . import tfutil
+from .. import util
+
+from .tfutil import TfExpression, TfExpressionEx
+
+try:
+ # TensorFlow 1.13
+ from tensorflow.python.ops import nccl_ops
+except:
+ # Older TensorFlow versions
+ import tensorflow.contrib.nccl as nccl_ops
+
+class Optimizer:
+ """A Wrapper for tf.train.Optimizer.
+
+ Automatically takes care of:
+ - Gradient averaging for multi-GPU training.
+ - Gradient accumulation for arbitrarily large minibatches.
+ - Dynamic loss scaling and typecasts for FP16 training.
+ - Ignoring corrupted gradients that contain NaNs/Infs.
+ - Reporting statistics.
+ - Well-chosen default settings.
+ """
+
+ def __init__(self,
+ name: str = "Train", # Name string that will appear in TensorFlow graph.
+ tf_optimizer: str = "tf.train.AdamOptimizer", # Underlying optimizer class.
+ learning_rate: TfExpressionEx = 0.001, # Learning rate. Can vary over time.
+ minibatch_multiplier: TfExpressionEx = None, # Treat N consecutive minibatches as one by accumulating gradients.
+ share: "Optimizer" = None, # Share internal state with a previously created optimizer?
+ use_loss_scaling: bool = False, # Enable dynamic loss scaling for robust mixed-precision training?
+ loss_scaling_init: float = 64.0, # Log2 of initial loss scaling factor.
+ loss_scaling_inc: float = 0.0005, # Log2 of per-minibatch loss scaling increment when there is no overflow.
+ loss_scaling_dec: float = 1.0, # Log2 of per-minibatch loss scaling decrement when there is an overflow.
+ report_mem_usage: bool = False, # Report fine-grained memory usage statistics in TensorBoard?
+ **kwargs):
+
+ # Public fields.
+ self.name = name
+ self.learning_rate = learning_rate
+ self.minibatch_multiplier = minibatch_multiplier
+ self.id = self.name.replace("/", ".")
+ self.scope = tf.get_default_graph().unique_name(self.id)
+ self.optimizer_class = util.get_obj_by_name(tf_optimizer)
+ self.optimizer_kwargs = dict(kwargs)
+ self.use_loss_scaling = use_loss_scaling
+ self.loss_scaling_init = loss_scaling_init
+ self.loss_scaling_inc = loss_scaling_inc
+ self.loss_scaling_dec = loss_scaling_dec
+
+ # Private fields.
+ self._updates_applied = False
+ self._devices = OrderedDict() # device_name => EasyDict()
+ self._shared_optimizers = OrderedDict() # device_name => optimizer_class
+ self._gradient_shapes = None # [shape, ...]
+ self._report_mem_usage = report_mem_usage
+
+ # Validate arguments.
+ assert callable(self.optimizer_class)
+
+ # Share internal state if requested.
+ if share is not None:
+ assert isinstance(share, Optimizer)
+ assert self.optimizer_class is share.optimizer_class
+ assert self.learning_rate is share.learning_rate
+ assert self.optimizer_kwargs == share.optimizer_kwargs
+ self._shared_optimizers = share._shared_optimizers # pylint: disable=protected-access
+
+ def _get_device(self, device_name: str):
+ """Get internal state for the given TensorFlow device."""
+ tfutil.assert_tf_initialized()
+ if device_name in self._devices:
+ return self._devices[device_name]
+
+ # Initialize fields.
+ device = util.EasyDict()
+ device.name = device_name
+ device.optimizer = None # Underlying optimizer: optimizer_class
+ device.loss_scaling_var = None # Log2 of loss scaling: tf.Variable
+ device.grad_raw = OrderedDict() # Raw gradients: var => [grad, ...]
+ device.grad_clean = OrderedDict() # Clean gradients: var => grad
+ device.grad_acc_vars = OrderedDict() # Accumulation sums: var => tf.Variable
+ device.grad_acc_count = None # Accumulation counter: tf.Variable
+ device.grad_acc = OrderedDict() # Accumulated gradients: var => grad
+
+ # Setup TensorFlow objects.
+ with tfutil.absolute_name_scope(self.scope + "/Devices"), tf.device(device_name), tf.control_dependencies(None):
+ if device_name not in self._shared_optimizers:
+ optimizer_name = self.scope.replace("/", "_") + "_opt%d" % len(self._shared_optimizers)
+ self._shared_optimizers[device_name] = self.optimizer_class(name=optimizer_name, learning_rate=self.learning_rate, **self.optimizer_kwargs)
+ device.optimizer = self._shared_optimizers[device_name]
+ if self.use_loss_scaling:
+ device.loss_scaling_var = tf.Variable(np.float32(self.loss_scaling_init), trainable=False, name="loss_scaling_var")
+
+ # Register device.
+ self._devices[device_name] = device
+ return device
+
+ def register_gradients(self, loss: TfExpression, trainable_vars: Union[List, dict]) -> None:
+ """Register the gradients of the given loss function with respect to the given variables.
+ Intended to be called once per GPU."""
+ tfutil.assert_tf_initialized()
+ assert not self._updates_applied
+ device = self._get_device(loss.device)
+
+ # Validate trainables.
+ if isinstance(trainable_vars, dict):
+ trainable_vars = list(trainable_vars.values()) # allow passing in Network.trainables as vars
+ assert isinstance(trainable_vars, list) and len(trainable_vars) >= 1
+ assert all(tfutil.is_tf_expression(expr) for expr in trainable_vars + [loss])
+ assert all(var.device == device.name for var in trainable_vars)
+
+ # Validate shapes.
+ if self._gradient_shapes is None:
+ self._gradient_shapes = [var.shape.as_list() for var in trainable_vars]
+ assert len(trainable_vars) == len(self._gradient_shapes)
+ assert all(var.shape.as_list() == var_shape for var, var_shape in zip(trainable_vars, self._gradient_shapes))
+
+ # Report memory usage if requested.
+ deps = []
+ if self._report_mem_usage:
+ self._report_mem_usage = False
+ try:
+ with tf.name_scope(self.id + '_mem'), tf.device(device.name), tf.control_dependencies([loss]):
+ deps.append(autosummary.autosummary(self.id + "/mem_usage_gb", tf.contrib.memory_stats.BytesInUse() / 2**30))
+ except tf.errors.NotFoundError:
+ pass
+
+ # Compute gradients.
+ with tf.name_scope(self.id + "_grad"), tf.device(device.name), tf.control_dependencies(deps):
+ loss = self.apply_loss_scaling(tf.cast(loss, tf.float32))
+ gate = tf.train.Optimizer.GATE_NONE # disable gating to reduce memory usage
+ grad_list = device.optimizer.compute_gradients(loss=loss, var_list=trainable_vars, gate_gradients=gate)
+
+ # Register gradients.
+ for grad, var in grad_list:
+ if var not in device.grad_raw:
+ device.grad_raw[var] = []
+ device.grad_raw[var].append(grad)
+
+ def apply_updates(self, allow_no_op: bool = False) -> tf.Operation:
+ """Construct training op to update the registered variables based on their gradients."""
+ tfutil.assert_tf_initialized()
+ assert not self._updates_applied
+ self._updates_applied = True
+ all_ops = []
+
+ # Check for no-op.
+ if allow_no_op and len(self._devices) == 0:
+ with tfutil.absolute_name_scope(self.scope):
+ return tf.no_op(name='TrainingOp')
+
+ # Clean up gradients.
+ for device_idx, device in enumerate(self._devices.values()):
+ with tfutil.absolute_name_scope(self.scope + "/Clean%d" % device_idx), tf.device(device.name):
+ for var, grad in device.grad_raw.items():
+
+ # Filter out disconnected gradients and convert to float32.
+ grad = [g for g in grad if g is not None]
+ grad = [tf.cast(g, tf.float32) for g in grad]
+
+ # Sum within the device.
+ if len(grad) == 0:
+ grad = tf.zeros(var.shape) # No gradients => zero.
+ elif len(grad) == 1:
+ grad = grad[0] # Single gradient => use as is.
+ else:
+ grad = tf.add_n(grad) # Multiple gradients => sum.
+
+ # Scale as needed.
+ scale = 1.0 / len(device.grad_raw[var]) / len(self._devices)
+ scale = tf.constant(scale, dtype=tf.float32, name="scale")
+ if self.minibatch_multiplier is not None:
+ scale /= tf.cast(self.minibatch_multiplier, tf.float32)
+ scale = self.undo_loss_scaling(scale)
+ device.grad_clean[var] = grad * scale
+
+ # Sum gradients across devices.
+ if len(self._devices) > 1:
+ with tfutil.absolute_name_scope(self.scope + "/Broadcast"), tf.device(None):
+ for all_vars in zip(*[device.grad_clean.keys() for device in self._devices.values()]):
+ if len(all_vars) > 0 and all(dim > 0 for dim in all_vars[0].shape.as_list()): # NCCL does not support zero-sized tensors.
+ all_grads = [device.grad_clean[var] for device, var in zip(self._devices.values(), all_vars)]
+ all_grads = nccl_ops.all_sum(all_grads)
+ for device, var, grad in zip(self._devices.values(), all_vars, all_grads):
+ device.grad_clean[var] = grad
+
+ # Apply updates separately on each device.
+ for device_idx, device in enumerate(self._devices.values()):
+ with tfutil.absolute_name_scope(self.scope + "/Apply%d" % device_idx), tf.device(device.name):
+ # pylint: disable=cell-var-from-loop
+
+ # Accumulate gradients over time.
+ if self.minibatch_multiplier is None:
+ acc_ok = tf.constant(True, name='acc_ok')
+ device.grad_acc = OrderedDict(device.grad_clean)
+ else:
+ # Create variables.
+ with tf.control_dependencies(None):
+ for var in device.grad_clean.keys():
+ device.grad_acc_vars[var] = tf.Variable(tf.zeros(var.shape), trainable=False, name="grad_acc_var")
+ device.grad_acc_count = tf.Variable(tf.zeros([]), trainable=False, name="grad_acc_count")
+
+ # Track counter.
+ count_cur = device.grad_acc_count + 1.0
+ count_inc_op = lambda: tf.assign(device.grad_acc_count, count_cur)
+ count_reset_op = lambda: tf.assign(device.grad_acc_count, tf.zeros([]))
+ acc_ok = (count_cur >= tf.cast(self.minibatch_multiplier, tf.float32))
+ all_ops.append(tf.cond(acc_ok, count_reset_op, count_inc_op))
+
+ # Track gradients.
+ for var, grad in device.grad_clean.items():
+ acc_var = device.grad_acc_vars[var]
+ acc_cur = acc_var + grad
+ device.grad_acc[var] = acc_cur
+ with tf.control_dependencies([acc_cur]):
+ acc_inc_op = lambda: tf.assign(acc_var, acc_cur)
+ acc_reset_op = lambda: tf.assign(acc_var, tf.zeros(var.shape))
+ all_ops.append(tf.cond(acc_ok, acc_reset_op, acc_inc_op))
+
+ # No overflow => apply gradients.
+ all_ok = tf.reduce_all(tf.stack([acc_ok] + [tf.reduce_all(tf.is_finite(g)) for g in device.grad_acc.values()]))
+ apply_op = lambda: device.optimizer.apply_gradients([(tf.cast(grad, var.dtype), var) for var, grad in device.grad_acc.items()])
+ all_ops.append(tf.cond(all_ok, apply_op, tf.no_op))
+
+ # Adjust loss scaling.
+ if self.use_loss_scaling:
+ ls_inc_op = lambda: tf.assign_add(device.loss_scaling_var, self.loss_scaling_inc)
+ ls_dec_op = lambda: tf.assign_sub(device.loss_scaling_var, self.loss_scaling_dec)
+ ls_update_op = lambda: tf.group(tf.cond(all_ok, ls_inc_op, ls_dec_op))
+ all_ops.append(tf.cond(acc_ok, ls_update_op, tf.no_op))
+
+ # Last device => report statistics.
+ if device_idx == len(self._devices) - 1:
+ all_ops.append(autosummary.autosummary(self.id + "/learning_rate", self.learning_rate))
+ all_ops.append(autosummary.autosummary(self.id + "/overflow_frequency", tf.where(all_ok, 0, 1), condition=acc_ok))
+ if self.use_loss_scaling:
+ all_ops.append(autosummary.autosummary(self.id + "/loss_scaling_log2", device.loss_scaling_var))
+
+ # Initialize variables.
+ self.reset_optimizer_state()
+ if self.use_loss_scaling:
+ tfutil.init_uninitialized_vars([device.loss_scaling_var for device in self._devices.values()])
+ if self.minibatch_multiplier is not None:
+ tfutil.run([var.initializer for device in self._devices.values() for var in list(device.grad_acc_vars.values()) + [device.grad_acc_count]])
+
+ # Group everything into a single op.
+ with tfutil.absolute_name_scope(self.scope):
+ return tf.group(*all_ops, name="TrainingOp")
+
+ def reset_optimizer_state(self) -> None:
+ """Reset internal state of the underlying optimizer."""
+ tfutil.assert_tf_initialized()
+ tfutil.run([var.initializer for device in self._devices.values() for var in device.optimizer.variables()])
+
+ def get_loss_scaling_var(self, device: str) -> Union[tf.Variable, None]:
+ """Get or create variable representing log2 of the current dynamic loss scaling factor."""
+ return self._get_device(device).loss_scaling_var
+
+ def apply_loss_scaling(self, value: TfExpression) -> TfExpression:
+ """Apply dynamic loss scaling for the given expression."""
+ assert tfutil.is_tf_expression(value)
+ if not self.use_loss_scaling:
+ return value
+ return value * tfutil.exp2(self.get_loss_scaling_var(value.device))
+
+ def undo_loss_scaling(self, value: TfExpression) -> TfExpression:
+ """Undo the effect of dynamic loss scaling for the given expression."""
+ assert tfutil.is_tf_expression(value)
+ if not self.use_loss_scaling:
+ return value
+ return value * tfutil.exp2(-self.get_loss_scaling_var(value.device)) # pylint: disable=invalid-unary-operand-type
+
+
+class SimpleAdam:
+ """Simplified version of tf.train.AdamOptimizer that behaves identically when used with dnnlib.tflib.Optimizer."""
+
+ def __init__(self, name="Adam", learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8):
+ self.name = name
+ self.learning_rate = learning_rate
+ self.beta1 = beta1
+ self.beta2 = beta2
+ self.epsilon = epsilon
+ self.all_state_vars = []
+
+ def variables(self):
+ return self.all_state_vars
+
+ def compute_gradients(self, loss, var_list, gate_gradients=tf.train.Optimizer.GATE_NONE):
+ assert gate_gradients == tf.train.Optimizer.GATE_NONE
+ return list(zip(tf.gradients(loss, var_list), var_list))
+
+ def apply_gradients(self, grads_and_vars):
+ with tf.name_scope(self.name):
+ state_vars = []
+ update_ops = []
+
+ # Adjust learning rate to deal with startup bias.
+ with tf.control_dependencies(None):
+ b1pow_var = tf.Variable(dtype=tf.float32, initial_value=1, trainable=False)
+ b2pow_var = tf.Variable(dtype=tf.float32, initial_value=1, trainable=False)
+ state_vars += [b1pow_var, b2pow_var]
+ b1pow_new = b1pow_var * self.beta1
+ b2pow_new = b2pow_var * self.beta2
+ update_ops += [tf.assign(b1pow_var, b1pow_new), tf.assign(b2pow_var, b2pow_new)]
+ lr_new = self.learning_rate * tf.sqrt(1 - b2pow_new) / (1 - b1pow_new)
+
+ # Construct ops to update each variable.
+ for grad, var in grads_and_vars:
+ with tf.control_dependencies(None):
+ m_var = tf.Variable(dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False)
+ v_var = tf.Variable(dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False)
+ state_vars += [m_var, v_var]
+ m_new = self.beta1 * m_var + (1 - self.beta1) * grad
+ v_new = self.beta2 * v_var + (1 - self.beta2) * tf.square(grad)
+ var_delta = lr_new * m_new / (tf.sqrt(v_new) + self.epsilon)
+ update_ops += [tf.assign(m_var, m_new), tf.assign(v_var, v_new), tf.assign_sub(var, var_delta)]
+
+ # Group everything together.
+ self.all_state_vars += state_vars
+ return tf.group(*update_ops)
diff --git a/stylegan_human/dnnlib/tflib/tfutil.py b/stylegan_human/dnnlib/tflib/tfutil.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b04c59e41a1b1548bc798379ceb551a488ed2a6
--- /dev/null
+++ b/stylegan_human/dnnlib/tflib/tfutil.py
@@ -0,0 +1,254 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License-NC.
+# To view a copy of this license, visit
+# https://nvlabs.github.io/stylegan2/license.html
+
+"""Miscellaneous helper utils for Tensorflow."""
+
+import os
+import numpy as np
+import tensorflow as tf
+
+# Silence deprecation warnings from TensorFlow 1.13 onwards
+import logging
+logging.getLogger('tensorflow').setLevel(logging.ERROR)
+import tensorflow.contrib # requires TensorFlow 1.x!
+tf.contrib = tensorflow.contrib
+
+from typing import Any, Iterable, List, Union
+
+TfExpression = Union[tf.Tensor, tf.Variable, tf.Operation]
+"""A type that represents a valid Tensorflow expression."""
+
+TfExpressionEx = Union[TfExpression, int, float, np.ndarray]
+"""A type that can be converted to a valid Tensorflow expression."""
+
+
+def run(*args, **kwargs) -> Any:
+ """Run the specified ops in the default session."""
+ assert_tf_initialized()
+ return tf.get_default_session().run(*args, **kwargs)
+
+
+def is_tf_expression(x: Any) -> bool:
+ """Check whether the input is a valid Tensorflow expression, i.e., Tensorflow Tensor, Variable, or Operation."""
+ return isinstance(x, (tf.Tensor, tf.Variable, tf.Operation))
+
+
+def shape_to_list(shape: Iterable[tf.Dimension]) -> List[Union[int, None]]:
+ """Convert a Tensorflow shape to a list of ints. Retained for backwards compatibility -- use TensorShape.as_list() in new code."""
+ return [dim.value for dim in shape]
+
+
+def flatten(x: TfExpressionEx) -> TfExpression:
+ """Shortcut function for flattening a tensor."""
+ with tf.name_scope("Flatten"):
+ return tf.reshape(x, [-1])
+
+
+def log2(x: TfExpressionEx) -> TfExpression:
+ """Logarithm in base 2."""
+ with tf.name_scope("Log2"):
+ return tf.log(x) * np.float32(1.0 / np.log(2.0))
+
+
+def exp2(x: TfExpressionEx) -> TfExpression:
+ """Exponent in base 2."""
+ with tf.name_scope("Exp2"):
+ return tf.exp(x * np.float32(np.log(2.0)))
+
+
+def lerp(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpressionEx:
+ """Linear interpolation."""
+ with tf.name_scope("Lerp"):
+ return a + (b - a) * t
+
+
+def lerp_clip(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpression:
+ """Linear interpolation with clip."""
+ with tf.name_scope("LerpClip"):
+ return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
+
+
+def absolute_name_scope(scope: str) -> tf.name_scope:
+ """Forcefully enter the specified name scope, ignoring any surrounding scopes."""
+ return tf.name_scope(scope + "/")
+
+
+def absolute_variable_scope(scope: str, **kwargs) -> tf.variable_scope:
+ """Forcefully enter the specified variable scope, ignoring any surrounding scopes."""
+ return tf.variable_scope(tf.VariableScope(name=scope, **kwargs), auxiliary_name_scope=False)
+
+
+def _sanitize_tf_config(config_dict: dict = None) -> dict:
+ # Defaults.
+ cfg = dict()
+ cfg["rnd.np_random_seed"] = None # Random seed for NumPy. None = keep as is.
+ cfg["rnd.tf_random_seed"] = "auto" # Random seed for TensorFlow. 'auto' = derive from NumPy random state. None = keep as is.
+ cfg["env.TF_CPP_MIN_LOG_LEVEL"] = "1" # 0 = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info.
+ cfg["graph_options.place_pruned_graph"] = True # False = Check that all ops are available on the designated device. True = Skip the check for ops that are not used.
+ cfg["gpu_options.allow_growth"] = True # False = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed.
+
+ # Remove defaults for environment variables that are already set.
+ for key in list(cfg):
+ fields = key.split(".")
+ if fields[0] == "env":
+ assert len(fields) == 2
+ if fields[1] in os.environ:
+ del cfg[key]
+
+ # User overrides.
+ if config_dict is not None:
+ cfg.update(config_dict)
+ return cfg
+
+
+def init_tf(config_dict: dict = None) -> None:
+ """Initialize TensorFlow session using good default settings."""
+ # Skip if already initialized.
+ if tf.get_default_session() is not None:
+ return
+
+ # Setup config dict and random seeds.
+ cfg = _sanitize_tf_config(config_dict)
+ np_random_seed = cfg["rnd.np_random_seed"]
+ if np_random_seed is not None:
+ np.random.seed(np_random_seed)
+ tf_random_seed = cfg["rnd.tf_random_seed"]
+ if tf_random_seed == "auto":
+ tf_random_seed = np.random.randint(1 << 31)
+ if tf_random_seed is not None:
+ tf.set_random_seed(tf_random_seed)
+
+ # Setup environment variables.
+ for key, value in cfg.items():
+ fields = key.split(".")
+ if fields[0] == "env":
+ assert len(fields) == 2
+ os.environ[fields[1]] = str(value)
+
+ # Create default TensorFlow session.
+ create_session(cfg, force_as_default=True)
+
+
+def assert_tf_initialized():
+ """Check that TensorFlow session has been initialized."""
+ if tf.get_default_session() is None:
+ raise RuntimeError("No default TensorFlow session found. Please call dnnlib.tflib.init_tf().")
+
+
+def create_session(config_dict: dict = None, force_as_default: bool = False) -> tf.Session:
+ """Create tf.Session based on config dict."""
+ # Setup TensorFlow config proto.
+ cfg = _sanitize_tf_config(config_dict)
+ config_proto = tf.ConfigProto()
+ for key, value in cfg.items():
+ fields = key.split(".")
+ if fields[0] not in ["rnd", "env"]:
+ obj = config_proto
+ for field in fields[:-1]:
+ obj = getattr(obj, field)
+ setattr(obj, fields[-1], value)
+
+ # Create session.
+ session = tf.Session(config=config_proto)
+ if force_as_default:
+ # pylint: disable=protected-access
+ session._default_session = session.as_default()
+ session._default_session.enforce_nesting = False
+ session._default_session.__enter__()
+ return session
+
+
+def init_uninitialized_vars(target_vars: List[tf.Variable] = None) -> None:
+ """Initialize all tf.Variables that have not already been initialized.
+
+ Equivalent to the following, but more efficient and does not bloat the tf graph:
+ tf.variables_initializer(tf.report_uninitialized_variables()).run()
+ """
+ assert_tf_initialized()
+ if target_vars is None:
+ target_vars = tf.global_variables()
+
+ test_vars = []
+ test_ops = []
+
+ with tf.control_dependencies(None): # ignore surrounding control_dependencies
+ for var in target_vars:
+ assert is_tf_expression(var)
+
+ try:
+ tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/IsVariableInitialized:0"))
+ except KeyError:
+ # Op does not exist => variable may be uninitialized.
+ test_vars.append(var)
+
+ with absolute_name_scope(var.name.split(":")[0]):
+ test_ops.append(tf.is_variable_initialized(var))
+
+ init_vars = [var for var, inited in zip(test_vars, run(test_ops)) if not inited]
+ run([var.initializer for var in init_vars])
+
+
+def set_vars(var_to_value_dict: dict) -> None:
+ """Set the values of given tf.Variables.
+
+ Equivalent to the following, but more efficient and does not bloat the tf graph:
+ tflib.run([tf.assign(var, value) for var, value in var_to_value_dict.items()]
+ """
+ assert_tf_initialized()
+ ops = []
+ feed_dict = {}
+
+ for var, value in var_to_value_dict.items():
+ assert is_tf_expression(var)
+
+ try:
+ setter = tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/setter:0")) # look for existing op
+ except KeyError:
+ with absolute_name_scope(var.name.split(":")[0]):
+ with tf.control_dependencies(None): # ignore surrounding control_dependencies
+ setter = tf.assign(var, tf.placeholder(var.dtype, var.shape, "new_value"), name="setter") # create new setter
+
+ ops.append(setter)
+ feed_dict[setter.op.inputs[1]] = value
+
+ run(ops, feed_dict)
+
+
+def create_var_with_large_initial_value(initial_value: np.ndarray, *args, **kwargs):
+ """Create tf.Variable with large initial value without bloating the tf graph."""
+ assert_tf_initialized()
+ assert isinstance(initial_value, np.ndarray)
+ zeros = tf.zeros(initial_value.shape, initial_value.dtype)
+ var = tf.Variable(zeros, *args, **kwargs)
+ set_vars({var: initial_value})
+ return var
+
+
+def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False):
+ """Convert a minibatch of images from uint8 to float32 with configurable dynamic range.
+ Can be used as an input transformation for Network.run().
+ """
+ images = tf.cast(images, tf.float32)
+ if nhwc_to_nchw:
+ images = tf.transpose(images, [0, 3, 1, 2])
+ return images * ((drange[1] - drange[0]) / 255) + drange[0]
+
+
+def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False, shrink=1):
+ """Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
+ Can be used as an output transformation for Network.run().
+ """
+ images = tf.cast(images, tf.float32)
+ if shrink > 1:
+ ksize = [1, 1, shrink, shrink]
+ images = tf.nn.avg_pool(images, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW")
+ if nchw_to_nhwc:
+ images = tf.transpose(images, [0, 2, 3, 1])
+ scale = 255 / (drange[1] - drange[0])
+ images = images * scale + (0.5 - drange[0] * scale)
+ return tf.saturate_cast(images, tf.uint8)
diff --git a/stylegan_human/dnnlib/util.py b/stylegan_human/dnnlib/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2bf7a73d546895ac6eb73d9c56db2a04b096f3e
--- /dev/null
+++ b/stylegan_human/dnnlib/util.py
@@ -0,0 +1,479 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+# Copyright (c) 2021, NVIDIA CORPORATION. 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 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
+ # print('func_name: ', func_name) #'training.dataset.ImageFolderDataset'
+ 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/stylegan_human/docs/Dataset.md b/stylegan_human/docs/Dataset.md
new file mode 100644
index 0000000000000000000000000000000000000000..ef6c56cedab89f3ab09306826240b075af244899
--- /dev/null
+++ b/stylegan_human/docs/Dataset.md
@@ -0,0 +1,74 @@
+# SHHQ Dataset
+
+
+## Overview
+SHHQ is a dataset with high-quality full-body human images in a resolution of 1024 × 512.
+Since we need to follow a rigorous legal review in our institute, we can not release all of the data at once.
+
+For now, SHHQ-1.0 with 40K images is released! More data will be released in the later versions.
+
+
+## Data Sources
+Images are collected in two main ways:
+1) From the Internet.
+We developed a crawler tool with an official API, mainly downloading images from Flickr, Pixabay and Pexels. So you need to meet all the following licenses when using the dataset: CC0, [Pixabay License](https://pixabay.com/service/license/), and [Pexels Licenses](https://www.pexels.com/license/).
+2) From the data providers.
+We purchased images from databases of individual photographers, modeling agencies and other suppliers.
+Images were reviewed by our legal team prior to purchase to ensure permission for use in research.
+
+### Note:
+The composition of SHHQ-1.0:
+
+1) Images obtained from the above sources.
+2) Processed 9991 DeepFashion [[1]](#1) images (retain only full body images).
+3) 1940 African images from the InFashAI [[2]](#2) dataset to increase data diversity.
+
+## Data License
+We are aware of privacy concerns and seriously treat the license and privacy issues. All released data will be ensured under the license of CC0 and free for research use. Also, persons in the dataset are anonymised without additional private or sensitive metadata.
+
+## Agreement
+The SHHQ is available for non-commercial research purposes only.
+
+You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit any portion of the images and any portion of the derived data for commercial purposes.
+
+You agree NOT to further copy, publish or distribute any portion of SHHQ to any third party for any purpose. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.
+
+Shanghai AI Lab reserves the right to terminate your access to the SHHQ at any time.
+
+## Dataset Preview
+For those interested in our dataset, we provide a preview version with 100 images randomly sampled from SHHQ-1.0: [SHHQ-1.0_samples](https://drive.google.com/file/d/1tnNFfmFtzRbYL3qEnNXQ_ShaN9YV5tI5/view?usp=sharing).
+
+In SHHQ-1.0, we provide aligned raw images along with machine-calculated segmentation masks. Later we are planning to release manually annotated human-parsing version of these 40,000 images. Please stay tuned.
+
+> We also provide script [bg_white.py](../bg_white.py) to whiten the background of the raw image using its segmentation mask.
+
+If you want to access the full SHHQ-1.0, please read the following instructions.
+
+## Model trained using SHHQ-1.0
+
+| Structure | 1024x512 | Metric | Scores | 512x256 | Metric | Scores |
+| --------- |:----------:| :----------:| :----------:| :-----: | :-----: | :-----: |
+| StyleGAN1 | to be released | - | - | to be released | - | - |
+| StyleGAN2 | [SHHQ-1.0_sg2_1024.pkl](https://drive.google.com/file/d/1PuvE72xpc69Zq4y58dohuKbG9dFnnjEX/view?usp=sharing) | fid50k_full | 3.56 | [SHHQ-1.0_sg2_512.pkl](https://drive.google.com/file/d/170t2FRWxR8_TG3_y0nVtDBogLPOClnyf/view?usp=sharing) | fid50k_full | 3.68 |
+| StyleGAN3 | to be released | - | - |to be released | - | - |
+
+
+## Download Instructions
+Please download the SHHQ Dataset Release Agreement from [link](./SHHQ_Dataset_Release_Agreement.pdf).
+Read it carefully, complete and sign it appropriately.
+
+Please send the completed form to Jianglin Fu (arlenefu@outlook.com) and Shikai Li (lishikai@pjlab.org.cn), and cc to Wayne Wu (wuwenyan0503@gmail.com) using institutional email address. The email Subject Title is "SHHQ Dataset Release Agreement". We will verify your request and contact you with the dataset link and password to unzip the image data.
+
+Note:
+
+1. We are currently facing large incoming applications, and we need to carefully verify all the applicants, please be patient, and we will reply to you as soon as possible.
+
+2. The signature in the agreement should be hand-written.
+
+## References
+[1]
+Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou. DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations. CVPR (2016)
+
+[2]
+Hacheme, Gilles and Sayouti, Noureini. Neural fashion image captioning: Accounting for data diversity. arXiv preprint arXiv:2106.12154 (2021)
+
diff --git a/stylegan_human/docs/SHHQ_Dataset_Release_Agreement.pdf b/stylegan_human/docs/SHHQ_Dataset_Release_Agreement.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..208033c0eaf2b4c094140d7f4cb9fee940636e7f
Binary files /dev/null and b/stylegan_human/docs/SHHQ_Dataset_Release_Agreement.pdf differ
diff --git a/stylegan_human/edit.py b/stylegan_human/edit.py
new file mode 100644
index 0000000000000000000000000000000000000000..e91e625a21c914e42430b7b8779bd5335d76e037
--- /dev/null
+++ b/stylegan_human/edit.py
@@ -0,0 +1,194 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+import os
+import sys
+import torch
+import numpy as np
+sys.path.append(".")
+from torch_utils.models import Generator
+import click
+import cv2
+from typing import List, Optional
+import subprocess
+import legacy
+from edit.edit_helper import conv_warper, decoder, encoder_ifg, encoder_ss, encoder_sefa
+
+
+"""
+Edit generated images with different SOTA methods.
+ Notes:
+ 1. We provide some latent directions in the folder, you can play around with them.
+ 2. ''upper_length'' and ''bottom_length'' of ''attr_name'' are available for demo.
+ 3. Layers to control and editing strength are set in edit/edit_config.py.
+
+Examples:
+
+\b
+# Editing with InterfaceGAN, StyleSpace, and Sefa
+python edit.py --network pretrained_models/stylegan_human_v2_1024.pkl --attr_name upper_length \\
+ --seeds 61531,61570,61571,61610 --outdir outputs/edit_results
+
+
+# Editing using inverted latent code
+python edit.py ---network outputs/pti/checkpoints/model_test.pkl --attr_name upper_length \\
+ --outdir outputs/edit_results --real True --real_w_path outputs/pti/embeddings/test/PTI/test/0.pt --real_img_path aligned_image/test.png
+
+"""
+
+
+
+@click.command()
+@click.pass_context
+@click.option('--network', 'ckpt_path', help='Network pickle filename', required=True)
+@click.option('--attr_name', help='choose one of the attr: upper_length or bottom_length', type=str, required=True)
+@click.option('--trunc', 'truncation', type=float, help='Truncation psi', default=0.8, show_default=True)
+@click.option('--gen_video', type=bool, default=True, help='If want to generate video')
+@click.option('--combine', type=bool, default=True, help='If want to combine different editing results in the same frame')
+@click.option('--seeds', type=legacy.num_range, help='List of random seeds')
+@click.option('--outdir', help='Where to save the output images', type=str, required=True, default='outputs/editing', metavar='DIR')
+@click.option('--real', type=bool, help='True for editing real image', default=False)
+@click.option('--real_w_path', help='Path of latent code for real image')
+@click.option('--real_img_path', help='Path of real image, this just concat real image with inverted and edited results together')
+
+
+
+def main(
+ ctx: click.Context,
+ ckpt_path: str,
+ attr_name: str,
+ truncation: float,
+ gen_video: bool,
+ combine: bool,
+ seeds: Optional[List[int]],
+ outdir: str,
+ real: str,
+ real_w_path: str,
+ real_img_path: str
+):
+ ## convert pkl to pth
+ # if not os.path.exists(ckpt_path.replace('.pkl','.pth')):
+ legacy.convert(ckpt_path, ckpt_path.replace('.pkl','.pth'), G_only=real)
+ ckpt_path = ckpt_path.replace('.pkl','.pth')
+ print("start...", flush=True)
+ config = {"latent" : 512, "n_mlp" : 8, "channel_multiplier": 2}
+ generator = Generator(
+ size = 1024,
+ style_dim=config["latent"],
+ n_mlp=config["n_mlp"],
+ channel_multiplier=config["channel_multiplier"]
+ )
+
+ generator.load_state_dict(torch.load(ckpt_path)['g_ema'])
+ generator.eval().cuda()
+
+ with torch.no_grad():
+ mean_path = os.path.join('edit','mean_latent.pkl')
+ if not os.path.exists(mean_path):
+ mean_n = 3000
+ mean_latent = generator.mean_latent(mean_n).detach()
+ legacy.save_obj(mean_latent, mean_path)
+ else:
+ mean_latent = legacy.load_pkl(mean_path).cuda()
+ finals = []
+
+ ## -- selected sample seeds -- ##
+ # seeds = [60948,60965,61174,61210,61511,61598,61610] #bottom -> long
+ # [60941,61064,61103,61313,61531,61570,61571] # bottom -> short
+ # [60941,60965,61064,61103,6117461210,61531,61570,61571,61610] # upper --> long
+ # [60948,61313,61511,61598] # upper --> short
+ if real: seeds = [0]
+
+ for t in seeds:
+ if real: # now assume process single real image only
+ if real_img_path:
+ real_image = cv2.imread(real_img_path)
+ real_image = cv2.cvtColor(real_image, cv2.COLOR_BGR2RGB)
+ import torchvision.transforms as transforms
+ transform = transforms.Compose( # normalize to (-1, 1)
+ [transforms.ToTensor(),
+ transforms.Normalize(mean=(.5,.5,.5), std=(.5,.5,.5))]
+ )
+ real_image = transform(real_image).unsqueeze(0).cuda()
+
+ test_input = torch.load(real_w_path)
+ output, _ = generator(test_input, False, truncation=1,input_is_latent=True, real=True)
+
+ else: # generate image from random seeds
+ test_input = torch.from_numpy(np.random.RandomState(t).randn(1, 512)).float().cuda() # torch.Size([1, 512])
+ output, _ = generator([test_input], False, truncation=truncation, truncation_latent=mean_latent, real=real)
+
+ # interfacegan
+ style_space, latent, noise = encoder_ifg(generator, test_input, attr_name, truncation, mean_latent,real=real)
+ image1 = decoder(generator, style_space, latent, noise)
+ # stylespace
+ style_space, latent, noise = encoder_ss(generator, test_input, attr_name, truncation, mean_latent,real=real)
+ image2 = decoder(generator, style_space, latent, noise)
+ # sefa
+ latent, noise = encoder_sefa(generator, test_input, attr_name, truncation, mean_latent,real=real)
+ image3, _ = generator([latent], noise=noise, input_is_latent=True)
+ if real_img_path:
+ final = torch.cat((real_image, output, image1, image2, image3), 3)
+ else:
+ final = torch.cat((output, image1, image2, image3), 3)
+
+ # legacy.visual(output, f'{outdir}/{attr_name}_{t:05d}_raw.jpg')
+ # legacy.visual(image1, f'{outdir}/{attr_name}_{t:05d}_ifg.jpg')
+ # legacy.visual(image2, f'{outdir}/{attr_name}_{t:05d}_ss.jpg')
+ # legacy.visual(image3, f'{outdir}/{attr_name}_{t:05d}_sefa.jpg')
+
+ if gen_video:
+ total_step = 90
+ if real:
+ video_ifg_path = f"{outdir}/video/ifg_{attr_name}_{real_w_path.split('/')[-2]}/"
+ video_ss_path = f"{outdir}/video/ss_{attr_name}_{real_w_path.split('/')[-2]}/"
+ video_sefa_path = f"{outdir}/video/ss_{attr_name}_{real_w_path.split('/')[-2]}/"
+ else:
+ video_ifg_path = f"{outdir}/video/ifg_{attr_name}_{t:05d}/"
+ video_ss_path = f"{outdir}/video/ss_{attr_name}_{t:05d}/"
+ video_sefa_path = f"{outdir}/video/ss_{attr_name}_{t:05d}/"
+ video_comb_path = f"{outdir}/video/tmp"
+
+ if combine:
+ if not os.path.exists(video_comb_path):
+ os.makedirs(video_comb_path)
+ else:
+ if not os.path.exists(video_ifg_path):
+ os.makedirs(video_ifg_path)
+ if not os.path.exists(video_ss_path):
+ os.makedirs(video_ss_path)
+ if not os.path.exists(video_sefa_path):
+ os.makedirs(video_sefa_path)
+ for i in range(total_step):
+ style_space, latent, noise = encoder_ifg(generator, test_input, attr_name, truncation, mean_latent, step=i, total=total_step,real=real)
+ image1 = decoder(generator, style_space, latent, noise)
+ style_space, latent, noise = encoder_ss(generator, test_input, attr_name, truncation, mean_latent, step=i, total=total_step,real=real)
+ image2 = decoder(generator, style_space, latent, noise)
+ latent, noise = encoder_sefa(generator, test_input, attr_name, truncation, mean_latent, step=i, total=total_step,real=real)
+ image3, _ = generator([latent], noise=noise, input_is_latent=True)
+ if combine:
+ if real_img_path:
+ comb_img = torch.cat((real_image, output, image1, image2, image3), 3)
+ else:
+ comb_img = torch.cat((output, image1, image2, image3), 3)
+ legacy.visual(comb_img, os.path.join(video_comb_path, f'{i:05d}.jpg'))
+ else:
+ legacy.visual(image1, os.path.join(video_ifg_path, f'{i:05d}.jpg'))
+ legacy.visual(image2, os.path.join(video_ss_path, f'{i:05d}.jpg'))
+ if combine:
+ cmd=f"ffmpeg -hide_banner -loglevel error -y -r 30 -i {video_comb_path}/%05d.jpg -vcodec libx264 -pix_fmt yuv420p {video_ifg_path.replace('ifg_', '')[:-1] + '.mp4'}"
+ subprocess.call(cmd, shell=True)
+ else:
+ cmd=f"ffmpeg -hide_banner -loglevel error -y -r 30 -i {video_ifg_path}/%05d.jpg -vcodec libx264 -pix_fmt yuv420p {video_ifg_path[:-1] + '.mp4'}"
+ subprocess.call(cmd, shell=True)
+ cmd=f"ffmpeg -hide_banner -loglevel error -y -r 30 -i {video_ss_path}/%05d.jpg -vcodec libx264 -pix_fmt yuv420p {video_ss_path[:-1] + '.mp4'}"
+ subprocess.call(cmd, shell=True)
+
+ # interfacegan, stylespace, sefa
+ finals.append(final)
+
+ final = torch.cat(finals, 2)
+ legacy.visual(final, os.path.join(outdir,'final.jpg'))
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/stylegan_human/edit/__init__.py b/stylegan_human/edit/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..864cbcfc7b3ac4df80cedd74c3f6cde9685434fb
--- /dev/null
+++ b/stylegan_human/edit/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# empty
\ No newline at end of file
diff --git a/stylegan_human/edit/edit_config.py b/stylegan_human/edit/edit_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5c6c5fc234c696fb161874f972ca2b83ec9896a
--- /dev/null
+++ b/stylegan_human/edit/edit_config.py
@@ -0,0 +1,16 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+attr_dict = dict(
+ interface_gan={ # strength
+ 'upper_length': [-1], # strength: negative for shorter, positive for longer
+ 'bottom_length': [1]
+ },
+ stylespace={ # layer, strength, threshold
+ 'upper_length': [5, -5, 0.0028], # strength: negative for shorter, positive for longer
+ 'bottom_length': [3, 5, 0.003]
+ },
+ sefa={ # layer, strength
+ 'upper_length': [[4, 5, 6, 7], 5], #-5 # strength: negative for longer, positive for shorter
+ 'bottom_length': [[4, 5, 6, 7], 5]
+ }
+)
\ No newline at end of file
diff --git a/stylegan_human/edit/edit_helper.py b/stylegan_human/edit/edit_helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..137a3dbd7cf1f48b21545673c422668b4d20765d
--- /dev/null
+++ b/stylegan_human/edit/edit_helper.py
@@ -0,0 +1,215 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+from legacy import save_obj, load_pkl
+import torch
+from torch.nn import functional as F
+import pandas as pd
+from .edit_config import attr_dict
+import os
+
+def conv_warper(layer, input, style, noise):
+ # the conv should change
+ conv = layer.conv
+ batch, in_channel, height, width = input.shape
+
+ style = style.view(batch, 1, in_channel, 1, 1)
+ weight = conv.scale * conv.weight * style
+
+ if conv.demodulate:
+ demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
+ weight = weight * demod.view(batch, conv.out_channel, 1, 1, 1)
+
+ weight = weight.view(
+ batch * conv.out_channel, in_channel, conv.kernel_size, conv.kernel_size
+ )
+
+ if conv.upsample:
+ input = input.view(1, batch * in_channel, height, width)
+ weight = weight.view(
+ batch, conv.out_channel, in_channel, conv.kernel_size, conv.kernel_size
+ )
+ weight = weight.transpose(1, 2).reshape(
+ batch * in_channel, conv.out_channel, conv.kernel_size, conv.kernel_size
+ )
+ out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, conv.out_channel, height, width)
+ out = conv.blur(out)
+
+ elif conv.downsample:
+ input = conv.blur(input)
+ _, _, height, width = input.shape
+ input = input.view(1, batch * in_channel, height, width)
+ out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, conv.out_channel, height, width)
+
+ else:
+ input = input.view(1, batch * in_channel, height, width)
+ out = F.conv2d(input, weight, padding=conv.padding, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, conv.out_channel, height, width)
+
+ out = layer.noise(out, noise=noise)
+ out = layer.activate(out)
+
+ return out
+
+def decoder(G, style_space, latent, noise):
+ # an decoder warper for G
+ out = G.input(latent)
+ out = conv_warper(G.conv1, out, style_space[0], noise[0])
+ skip = G.to_rgb1(out, latent[:, 1])
+
+ i = 1
+ for conv1, conv2, noise1, noise2, to_rgb in zip(
+ G.convs[::2], G.convs[1::2], noise[1::2], noise[2::2], G.to_rgbs
+ ):
+ out = conv_warper(conv1, out, style_space[i], noise=noise1)
+ out = conv_warper(conv2, out, style_space[i+1], noise=noise2)
+ skip = to_rgb(out, latent[:, i + 2], skip)
+ i += 2
+ image = skip
+
+ return image
+
+def encoder_ifg(G, noise, attr_name, truncation=1, truncation_latent=None,
+ latent_dir='latent_direction/ss/',
+ step=0, total=0, real=False):
+ if not real:
+ styles = [noise]
+ styles = [G.style(s) for s in styles]
+ style_space = []
+
+ if truncation<1:
+ if not real:
+ style_t = []
+ for style in styles:
+ style_t.append(truncation_latent + truncation * (style - truncation_latent))
+ styles = style_t
+ else: # styles are latent (tensor: 1,18,512), for real PTI output
+ truncation_latent = truncation_latent.repeat(18,1).unsqueeze(0) # (1,512) --> (1,18,512)
+ styles = torch.add(truncation_latent,torch.mul(torch.sub(noise,truncation_latent),truncation))
+
+
+ noise = [getattr(G.noises, 'noise_{}'.format(i)) for i in range(G.num_layers)]
+ if not real:
+ inject_index = G.n_latent
+ latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+ else: latent=styles
+
+ style_space.append(G.conv1.conv.modulation(latent[:, 0]))
+ i = 1
+ for conv1, conv2, noise1, noise2, to_rgb in zip(
+ G.convs[::2], G.convs[1::2], noise[1::2], noise[2::2], G.to_rgbs
+ ):
+ style_space.append(conv1.conv.modulation(latent[:, i]))
+ style_space.append(conv2.conv.modulation(latent[:, i+1]))
+ i += 2
+
+ # get layer, strength by dict
+ strength = attr_dict['interface_gan'][attr_name][0]
+
+ if step != 0 and total != 0:
+ strength = step / total * strength
+ for i in range(15):
+ style_vect = load_pkl(os.path.join(latent_dir, '{}/style_vect_mean_{}.pkl'.format(attr_name, i)))
+ style_vect = torch.from_numpy(style_vect).to(latent.device).float()
+ style_space[i] += style_vect * strength
+
+ return style_space, latent, noise
+
+def encoder_ss(G, noise, attr_name, truncation=1, truncation_latent=None,
+ statics_dir="latent_direction/ss_statics",
+ latent_dir="latent_direction/ss/",
+ step=0, total=0,real=False):
+ if not real:
+ styles = [noise]
+ styles = [G.style(s) for s in styles]
+ style_space = []
+
+ if truncation<1:
+ if not real:
+ style_t = []
+ for style in styles:
+ style_t.append(
+ truncation_latent + truncation * (style - truncation_latent)
+ )
+ styles = style_t
+ else: # styles are latent (tensor: 1,18,512), for real PTI output
+ truncation_latent = truncation_latent.repeat(18,1).unsqueeze(0) # (1,512) --> (1,18,512)
+ styles = torch.add(truncation_latent,torch.mul(torch.sub(noise,truncation_latent),truncation))
+
+ noise = [getattr(G.noises, 'noise_{}'.format(i)) for i in range(G.num_layers)]
+
+ if not real:
+ inject_index = G.n_latent
+ latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+ else: latent = styles
+
+ style_space.append(G.conv1.conv.modulation(latent[:, 0]))
+ i = 1
+ for conv1, conv2, noise1, noise2, to_rgb in zip(
+ G.convs[::2], G.convs[1::2], noise[1::2], noise[2::2], G.to_rgbs
+ ):
+ style_space.append(conv1.conv.modulation(latent[:, i]))
+ style_space.append(conv2.conv.modulation(latent[:, i+1]))
+ i += 2
+ # get threshold, layer, strength by dict
+ layer, strength, threshold = attr_dict['stylespace'][attr_name]
+
+ statis_dir = os.path.join(statics_dir, "{}_statis/{}".format(attr_name, layer))
+ statis_csv_path = os.path.join(statis_dir, "statis.csv")
+ statis_df = pd.read_csv(statis_csv_path)
+ statis_df = statis_df.sort_values(by='channel', ascending=True)
+ ch_mask = statis_df['strength'].values
+ ch_mask = torch.from_numpy(ch_mask).to(latent.device).float()
+ ch_mask = (ch_mask.abs()>threshold).float()
+ style_vect = load_pkl(os.path.join(latent_dir, '{}/style_vect_mean_{}.pkl'.format(attr_name, layer)))
+ style_vect = torch.from_numpy(style_vect).to(latent.device).float()
+
+ style_vect = style_vect * ch_mask
+
+ if step != 0 and total != 0:
+ strength = step / total * strength
+
+ style_space[layer] += style_vect * strength
+
+ return style_space, latent, noise
+
+def encoder_sefa(G, noise, attr_name, truncation=1, truncation_latent=None,
+ latent_dir='latent_direction/sefa/',
+ step=0, total=0, real=False):
+ if not real:
+ styles = [noise]
+ styles = [G.style(s) for s in styles]
+
+ if truncation<1:
+ if not real:
+ style_t = []
+ for style in styles:
+ style_t.append(
+ truncation_latent + truncation * (style - truncation_latent)
+ )
+ styles = style_t
+ else:
+ truncation_latent = truncation_latent.repeat(18,1).unsqueeze(0) # (1,512) --> (1,18,512)
+ styles = torch.add(truncation_latent,torch.mul(torch.sub(noise,truncation_latent),truncation))
+
+
+ noise = [getattr(G.noises, 'noise_{}'.format(i)) for i in range(G.num_layers)]
+ if not real:
+ inject_index = G.n_latent
+ latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+ else: latent = styles
+
+ layer, strength = attr_dict['sefa'][attr_name]
+
+ sefa_vect = torch.load(os.path.join(latent_dir, '{}.pt'.format(attr_name))).to(latent.device).float()
+ if step != 0 and total != 0:
+ strength = step / total * strength
+ for l in layer:
+ latent[:, l, :] += (sefa_vect * strength * 2)
+
+
+ return latent, noise
diff --git a/stylegan_human/environment.yml b/stylegan_human/environment.yml
new file mode 100644
index 0000000000000000000000000000000000000000..589a963c7987e712193cfe02c79f7b3edc4d7439
--- /dev/null
+++ b/stylegan_human/environment.yml
@@ -0,0 +1,30 @@
+name: stylehuman
+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
+ - lpips==0.1.4
+ - pyspng
+ - dlib
+ - opencv-python
+ - pandas
+ - moviepy
+ - imutils
\ No newline at end of file
diff --git a/stylegan_human/generate.py b/stylegan_human/generate.py
new file mode 100644
index 0000000000000000000000000000000000000000..e364ebaea3628f1d325ce1a32a5f5c8f66fac21d
--- /dev/null
+++ b/stylegan_human/generate.py
@@ -0,0 +1,120 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+# This work is made available under the Nvidia Source Code License-NC.
+# To view a copy of this license, visit
+# https://nvlabs.github.io/stylegan2/license.html
+
+
+## this script is for generating images from pre-trained network based on StyleGAN1 (TensorFlow) and StyleGAN2-ada (PyTorch) ##
+
+import os
+import click
+import dnnlib
+import numpy as np
+import PIL.Image
+import legacy
+from typing import List, Optional
+
+"""
+Generate images using pretrained network pickle.
+Examples:
+
+\b
+# Generate human full-body images without truncation
+python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=1 --seeds=1,3,5,7 \\
+ --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
+
+\b
+# Generate human full-body images with truncation
+python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=0.8 --seeds=0-100\\
+ --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
+
+# \b
+# Generate human full-body images using stylegan V1
+# python generate.py --outdir=outputs/generate/stylegan_human_v1_1024 \\
+# --network=pretrained_models/stylegan_human_v1_1024.pkl --version 1
+"""
+
+
+@click.command()
+@click.pass_context
+@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
+@click.option('--seeds', type=legacy.num_range, help='List of random seeds')
+@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
+@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
+@click.option('--outdir', help='Where to save the output images', default= 'outputs/generate/' , type=str, required=True, metavar='DIR')
+@click.option('--version', help="stylegan version, 1, 2 or 3", type=int, default=2)
+def generate_images(
+ ctx: click.Context,
+ network_pkl: str,
+ seeds: Optional[List[int]],
+ truncation_psi: float,
+ noise_mode: str,
+ outdir: str,
+ version: int
+):
+
+ print('Loading networks from "%s"...' % network_pkl)
+ if version == 1:
+ import dnnlib.tflib as tflib
+ tflib.init_tf()
+ G, D, Gs = legacy.load_pkl(network_pkl)
+
+ else:
+ import torch
+ device = torch.device('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
+ dtype = torch.float32 if device.type == 'mps' else torch.float64
+ with dnnlib.util.open_url(network_pkl) as f:
+ G = legacy.load_network_pkl(f)['G_ema'].to(device, dtype=dtype) # type: ignore
+ os.makedirs(outdir, exist_ok=True)
+
+
+ if seeds is None:
+ ctx.fail('--seeds option is required.')
+
+
+ # Generate images.
+ target_z = np.array([])
+ target_w = np.array([])
+ latent_out = outdir.replace('/images/','')
+ for seed_idx, seed in enumerate(seeds):
+ if seed % 5000 == 0:
+ print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
+
+ if version == 1: ## stylegan v1
+ z = np.random.RandomState(seed).randn(1, Gs.input_shape[1])
+ # Generate image.
+ fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
+ if noise_mode == 'const': randomize_noise=False
+ else: randomize_noise = True
+ images = Gs.run(z, None, truncation_psi=truncation_psi, randomize_noise=randomize_noise, output_transform=fmt)
+ PIL.Image.fromarray(images[0], 'RGB').save(f'{outdir}/seed{seed:04d}.png')
+
+ else: ## stylegan v2/v3
+ label = torch.zeros([1, G.c_dim], device=device)
+ z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device, dtype=dtype)
+ if target_z.size==0:
+ target_z= z.cpu()
+ else:
+ target_z=np.append(target_z, z.cpu(), axis=0)
+
+ w = G.mapping(z, label,truncation_psi=truncation_psi)
+ img = G.synthesis(w, noise_mode=noise_mode,force_fp32 = True)
+ if target_w.size==0:
+ target_w= w.cpu()
+ else:
+ target_w=np.append(target_w, w.cpu(), axis=0)
+
+ 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')
+ # print(target_z)
+ # print(target_z.shape,target_w.shape)
+
+
+#----------------------------------------------------------------------------
+
+if __name__ == "__main__":
+ generate_images()
+
+#----------------------------------------------------------------------------
\ No newline at end of file
diff --git a/stylegan_human/img/demo_V5_thumbnails-min.png b/stylegan_human/img/demo_V5_thumbnails-min.png
new file mode 100644
index 0000000000000000000000000000000000000000..788c8e0b7aca123b8040072f20615a991f151adb
--- /dev/null
+++ b/stylegan_human/img/demo_V5_thumbnails-min.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09cedc7ed7cdcf9430a4edc05f5ec0481332a479fbdef7ae897df0199b7c2a76
+size 2407115
diff --git a/stylegan_human/img/preview_samples1.png b/stylegan_human/img/preview_samples1.png
new file mode 100644
index 0000000000000000000000000000000000000000..3e6587615f830f273a5c965d42287f61a9c223fd
--- /dev/null
+++ b/stylegan_human/img/preview_samples1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b35184b1be70dfe999b8ca44d71d18c7701c5fa7eefea05d24498fdff9c1ee90
+size 7639686
diff --git a/stylegan_human/img/test/test.jpg b/stylegan_human/img/test/test.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cb26680adb1cd439a2513786f033d25fe4149665
--- /dev/null
+++ b/stylegan_human/img/test/test.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38b7a628097686a5d3701673a22ace8a760bd2dd22a6b1e14167193e24549172
+size 3925669
diff --git a/stylegan_human/insetgan.py b/stylegan_human/insetgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..06c29decec875b8f128014237eda3fd2f8094bc9
--- /dev/null
+++ b/stylegan_human/insetgan.py
@@ -0,0 +1,398 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+from lpips import LPIPS
+import numpy as np
+from torch_utils.models import Generator as bodyGAN
+from torch_utils.models_face import Generator as FaceGAN
+import dlib
+from utils.face_alignment import align_face_for_insetgan
+from utils.util import visual,tensor_to_numpy, numpy_to_tensor
+import legacy
+import os
+import click
+
+
+class InsetGAN(torch.nn.Module):
+ def __init__(self, stylebody_ckpt, styleface_ckpt):
+ super().__init__()
+
+ ## convert pkl to pth
+ if not os.path.exists(stylebody_ckpt.replace('.pkl','.pth')):
+ legacy.convert(stylebody_ckpt, stylebody_ckpt.replace('.pkl','.pth'))
+ stylebody_ckpt = stylebody_ckpt.replace('.pkl','.pth')
+
+ if not os.path.exists(styleface_ckpt.replace('.pkl','.pth')):
+ legacy.convert(styleface_ckpt, styleface_ckpt.replace('.pkl','.pth'))
+ styleface_ckpt = styleface_ckpt.replace('.pkl','.pth')
+
+ # dual generator
+ config = {"latent" : 512, "n_mlp" : 8, "channel_multiplier": 2}
+ self.body_generator = bodyGAN(
+ size = 1024,
+ style_dim=config["latent"],
+ n_mlp=config["n_mlp"],
+ channel_multiplier=config["channel_multiplier"]
+ )
+ self.body_generator.load_state_dict(torch.load(stylebody_ckpt)['g_ema'])
+ self.body_generator.eval().requires_grad_(False).cuda()
+
+ self.face_generator = FaceGAN(
+ size = 1024,
+ style_dim=config["latent"],
+ n_mlp=config["n_mlp"],
+ channel_multiplier=config["channel_multiplier"]
+ )
+ self.face_generator.load_state_dict(torch.load(styleface_ckpt)['g_ema'])
+ self.face_generator.eval().requires_grad_(False).cuda()
+ # crop function
+ self.dlib_predictor = dlib.shape_predictor('./pretrained_models/shape_predictor_68_face_landmarks.dat')
+ self.dlib_cnn_face_detector = dlib.cnn_face_detection_model_v1("pretrained_models/mmod_human_face_detector.dat")
+
+ # criterion
+ self.lpips_loss = LPIPS(net='alex').cuda().eval()
+ self.l1_loss = torch.nn.L1Loss(reduction='mean')
+
+ def loss_coarse(self, A_face, B, p1=500, p2=0.05):
+ A_face = F.interpolate(A_face, size=(64, 64), mode='area')
+ B = F.interpolate(B, size=(64, 64), mode='area')
+ loss_l1 = p1 * self.l1_loss(A_face, B)
+ loss_lpips = p2 * self.lpips_loss(A_face, B)
+ return loss_l1 + loss_lpips
+
+ @staticmethod
+ def get_border_mask(A, x, spec):
+ mask = torch.zeros_like(A)
+ mask[:, :, :x, ] = 1
+ mask[:, :, -x:, ] = 1
+ mask[:, :, :, :x ] = 1
+ mask[:, :, :, -x:] = 1
+ return mask
+
+ @staticmethod
+ def get_body_mask(A, crop, padding=4):
+ mask = torch.ones_like(A)
+ mask[:, :, crop[1]-padding:crop[3]+padding, crop[0]-padding:crop[2]+padding] = 0
+ return mask
+
+ def loss_border(self, A_face, B, p1=10000, p2=2, spec=None):
+ mask = self.get_border_mask(A_face, 8, spec)
+ loss_l1 = p1 * self.l1_loss(A_face*mask, B*mask)
+ loss_lpips = p2 * self.lpips_loss(A_face*mask, B*mask)
+ return loss_l1 + loss_lpips
+
+ def loss_body(self, A, B, crop, p1=9000, p2=0.1):
+ padding = int((crop[3] - crop[1]) / 20)
+ mask = self.get_body_mask(A, crop, padding)
+ loss_l1 = p1 * self.l1_loss(A*mask, B*mask)
+ loss_lpips = p2 * self.lpips_loss(A*mask, B*mask)
+ return loss_l1+loss_lpips
+
+ def loss_face(self, A, B, crop, p1=5000, p2=1.75):
+ mask = 1 - self.get_body_mask(A, crop)
+ loss_l1 = p1 * self.l1_loss(A*mask, B*mask)
+ loss_lpips = p2 * self.lpips_loss(A*mask, B*mask)
+ return loss_l1+loss_lpips
+
+ def loss_reg(self, w, w_mean, p1, w_plus_delta=None, p2=None):
+ return p1 * torch.mean(((w - w_mean) ** 2)) + p2 * torch.mean(w_plus_delta ** 2)
+
+ # FFHQ type
+ def detect_face_dlib(self, img):
+ # tensor to numpy array rgb uint8
+ img = tensor_to_numpy(img)
+ aligned_image, crop, rect = align_face_for_insetgan(img=img,
+ detector=self.dlib_cnn_face_detector,
+ predictor=self.dlib_predictor,
+ output_size=256)
+
+ aligned_image = np.array(aligned_image)
+ aligned_image = numpy_to_tensor(aligned_image)
+ return aligned_image, crop, rect
+
+ # joint optimization
+ def dual_optimizer(self,
+ face_w,
+ body_w,
+ joint_steps=500,
+ face_initial_learning_rate=0.02,
+ body_initial_learning_rate=0.05,
+ lr_rampdown_length=0.25,
+ lr_rampup_length=0.05,
+ seed=None,
+ output_path=None,
+ video=0):
+ '''
+ Given a face_w, optimize a body_w with suitable body pose & shape for face_w
+ '''
+ def visual_(path, synth_body, synth_face, body_crop, step, both=False, init_body_with_face=None):
+ tmp = synth_body.clone().detach()
+ tmp[:, :, body_crop[1]:body_crop[3], body_crop[0]:body_crop[2]] = synth_face
+ if both:
+ tmp = torch.cat([synth_body, tmp], dim=3)
+ save_path = os.path.join(path, f"{step:04d}.jpg")
+ visual(tmp, save_path)
+
+ def forward(face_w_opt,
+ body_w_opt,
+ face_w_delta,
+ body_w_delta,
+ body_crop,
+ update_crop=False
+ ):
+ if face_w_opt.shape[1] != 18:
+ face_ws = (face_w_opt).repeat([1, 18, 1])
+ else:
+ face_ws = face_w_opt.clone()
+ face_ws = face_ws + face_w_delta
+ synth_face, _ = self.face_generator([face_ws], input_is_latent=True, randomize_noise=False)
+
+ body_ws = (body_w_opt).repeat([1, 18, 1])
+ body_ws = body_ws + body_w_delta
+ synth_body, _ = self.body_generator([body_ws], input_is_latent=True, randomize_noise=False)
+
+ if update_crop:
+ old_r = (body_crop[3]-body_crop[1]) // 2, (body_crop[2]-body_crop[0]) // 2
+ _, body_crop, _ = self.detect_face_dlib(synth_body)
+ center = (body_crop[1] + body_crop[3]) // 2, (body_crop[0] + body_crop[2]) // 2
+ body_crop = (center[1] - old_r[1], center[0] - old_r[0], center[1] + old_r[1], center[0] + old_r[0])
+
+ synth_body_face = synth_body[:, :, body_crop[1]:body_crop[3], body_crop[0]:body_crop[2]]
+
+ if synth_face.shape[2] > body_crop[3]-body_crop[1]:
+ synth_face_resize = F.interpolate(synth_face, size=(body_crop[3]-body_crop[1], body_crop[2]-body_crop[0]), mode='area')
+
+ return synth_body, synth_body_face, synth_face, synth_face_resize, body_crop
+
+ def update_lr(init_lr, step, num_steps, lr_rampdown_length, lr_rampup_length):
+ t = step / num_steps
+ lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
+ lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
+ lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
+ lr = init_lr * lr_ramp
+ return lr
+
+ # update output_path
+ output_path = os.path.join(output_path, seed)
+ os.makedirs(output_path, exist_ok=True)
+
+ # define optimized params
+ body_w_mean = self.body_generator.mean_latent(10000).detach()
+ face_w_opt = face_w.clone().detach().requires_grad_(True)
+ body_w_opt = body_w.clone().detach().requires_grad_(True)
+ face_w_delta = torch.zeros_like(face_w.repeat([1, 18, 1])).requires_grad_(True)
+ body_w_delta = torch.zeros_like(body_w.repeat([1, 18, 1])).requires_grad_(True)
+ # generate ref face & body
+ ref_body, _ = self.body_generator([body_w.repeat([1, 18, 1])], input_is_latent=True, randomize_noise=False)
+ # for inversion
+ ref_face, _ = self.face_generator([face_w.repeat([1, 18, 1])], input_is_latent=True, randomize_noise=False)
+ # get initilized crop
+ _, body_crop, _ = self.detect_face_dlib(ref_body)
+ _, _, face_crop = self.detect_face_dlib(ref_face) # NOTE: this is face rect only. no FFHQ type.
+ # create optimizer
+ face_optimizer = torch.optim.Adam([face_w_opt, face_w_delta], betas=(0.9, 0.999), lr=face_initial_learning_rate)
+ body_optimizer = torch.optim.Adam([body_w_opt, body_w_delta], betas=(0.9, 0.999), lr=body_initial_learning_rate)
+
+ global_step = 0
+ # Stage1: remove background of face image
+ face_steps = 25
+ pbar = tqdm(range(face_steps))
+ for step in pbar:
+ face_lr = update_lr(face_initial_learning_rate / 2, step, face_steps, lr_rampdown_length, lr_rampup_length)
+ for param_group in face_optimizer.param_groups:
+ param_group['lr'] =face_lr
+ synth_body, synth_body_face, synth_face_raw, synth_face, body_crop = forward(face_w_opt,
+ body_w_opt,
+ face_w_delta,
+ body_w_delta,
+ body_crop)
+ loss_face = self.loss_face(synth_face_raw, ref_face, face_crop, 5000, 1.75)
+ loss_coarse = self.loss_coarse(synth_face, synth_body_face, 50, 0.05)
+ loss_border = self.loss_border(synth_face, synth_body_face, 1000, 0.1)
+ loss = loss_coarse + loss_border + loss_face
+ face_optimizer.zero_grad()
+ loss.backward()
+ face_optimizer.step()
+ # visualization
+ if video:
+ visual_(output_path, synth_body, synth_face, body_crop, global_step)
+ pbar.set_description(
+ (
+ f"face: {step:.4f}, lr: {face_lr}, loss: {loss.item():.2f}, loss_coarse: {loss_coarse.item():.2f};"
+ f"loss_border: {loss_border.item():.2f}, loss_face: {loss_face.item():.2f};"
+ )
+ )
+ global_step += 1
+
+ # Stage2: find a suitable body
+ body_steps = 150
+ pbar = tqdm(range(body_steps))
+ for step in pbar:
+ body_lr = update_lr(body_initial_learning_rate, step, body_steps, lr_rampdown_length, lr_rampup_length)
+ update_crop = True if (step % 50 == 0) else False
+ # update_crop = False
+ for param_group in body_optimizer.param_groups:
+ param_group['lr'] =body_lr
+ synth_body, synth_body_face, synth_face_raw, synth_face, body_crop = forward(face_w_opt,
+ body_w_opt,
+ face_w_delta,
+ body_w_delta,
+ body_crop,
+ update_crop=update_crop)
+ loss_coarse = self.loss_coarse(synth_face, synth_body_face, 500, 0.05)
+ loss_border = self.loss_border(synth_face, synth_body_face, 2500, 0)
+ loss_body = self.loss_body(synth_body, ref_body, body_crop, 9000, 0.1)
+ loss_reg = self.loss_reg(body_w_opt, body_w_mean, 15000, body_w_delta, 0)
+ loss = loss_coarse + loss_border + loss_body + loss_reg
+ body_optimizer.zero_grad()
+ loss.backward()
+ body_optimizer.step()
+
+ # visualization
+ if video:
+ visual_(output_path, synth_body, synth_face, body_crop, global_step)
+ pbar.set_description(
+ (
+ f"body: {step:.4f}, lr: {body_lr}, loss: {loss.item():.2f}, loss_coarse: {loss_coarse.item():.2f};"
+ f"loss_border: {loss_border.item():.2f}, loss_body: {loss_body.item():.2f}, loss_reg: {loss_reg:.2f}"
+ )
+ )
+ global_step += 1
+
+ # Stage3: joint optimization
+ interval = 50
+ joint_face_steps = joint_steps // 2
+ joint_body_steps = joint_steps // 2
+ face_step = 0
+ body_step = 0
+ pbar = tqdm(range(joint_steps))
+ flag = -1
+ for step in pbar:
+ if step % interval == 0: flag += 1
+ text_flag = 'optimize_face' if flag % 2 == 0 else 'optimize_body'
+ synth_body, synth_body_face, synth_face_raw, synth_face, body_crop = forward(face_w_opt,
+ body_w_opt,
+ face_w_delta,
+ body_w_delta,
+ body_crop)
+ if text_flag == 'optimize_face':
+ face_lr = update_lr(face_initial_learning_rate, face_step, joint_face_steps, lr_rampdown_length, lr_rampup_length)
+ for param_group in face_optimizer.param_groups:
+ param_group['lr'] =face_lr
+ loss_face = self.loss_face(synth_face_raw, ref_face, face_crop, 5000, 1.75)
+ loss_coarse = self.loss_coarse(synth_face, synth_body_face, 500, 0.05)
+ loss_border = self.loss_border(synth_face, synth_body_face, 25000, 0)
+ loss = loss_coarse + loss_border + loss_face
+ face_optimizer.zero_grad()
+ loss.backward()
+ face_optimizer.step()
+ pbar.set_description(
+ (
+ f"face: {step}, lr: {face_lr:.4f}, loss: {loss.item():.2f}, loss_coarse: {loss_coarse.item():.2f};"
+ f"loss_border: {loss_border.item():.2f}, loss_face: {loss_face.item():.2f};"
+ )
+ )
+ face_step += 1
+ else:
+ body_lr = update_lr(body_initial_learning_rate, body_step, joint_body_steps, lr_rampdown_length, lr_rampup_length)
+ for param_group in body_optimizer.param_groups:
+ param_group['lr'] =body_lr
+ loss_coarse = self.loss_coarse(synth_face, synth_body_face, 500, 0.05)
+ loss_border = self.loss_border(synth_face, synth_body_face, 2500, 0)
+ loss_body = self.loss_body(synth_body, ref_body, body_crop, 9000, 0.1)
+ loss_reg = self.loss_reg(body_w_opt, body_w_mean, 25000, body_w_delta, 0)
+ loss = loss_coarse + loss_border + loss_body + loss_reg
+ body_optimizer.zero_grad()
+ loss.backward()
+ body_optimizer.step()
+ pbar.set_description(
+ (
+ f"body: {step}, lr: {body_lr:.4f}, loss: {loss.item():.2f}, loss_coarse: {loss_coarse.item():.2f};"
+ f"loss_border: {loss_border.item():.2f}, loss_body: {loss_body.item():.2f}, loss_reg: {loss_reg:.2f}"
+ )
+ )
+ body_step += 1
+ if video:
+ visual_(output_path, synth_body, synth_face, body_crop, global_step)
+ global_step += 1
+ return face_w_opt.repeat([1, 18, 1])+face_w_delta, body_w_opt.repeat([1, 18, 1])+body_w_delta, body_crop
+
+
+
+
+"""
+Jointly combine and optimize generated faces and bodies .
+Examples:
+
+\b
+# Combine the generate human full-body image from the provided StyleGAN-Human pre-trained model
+# and the generated face image from FFHQ model, optimize both latent codes to produce the coherent face-body image
+python insetgan.py --body_network=pretrained_models/stylegan_human_v2_1024.pkl --face_network=pretrained_models/ffhq.pkl \\
+ --body_seed=82 --face_seed=43 --trunc=0.6 --outdir=outputs/insetgan/ --video 1
+"""
+
+@click.command()
+@click.pass_context
+@click.option('--face_network', default="./pretrained_models/ffhq.pkl", help='Network pickle filename', required=True)
+@click.option('--body_network', default='./pretrained_models/stylegan2_1024.pkl', help='Network pickle filename', required=True)
+@click.option('--face_seed', type=int, default=82, help='selected random seed')
+@click.option('--body_seed', type=int, default=43, help='selected random seed')
+@click.option('--joint_steps', type=int, default=500, help='num steps for joint optimization')
+@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=0.6, show_default=True)
+@click.option('--outdir', help='Where to save the output images', default= "outputs/insetgan/" , type=str, required=True, metavar='DIR')
+@click.option('--video', help="set to 1 if want to save video", type=int, default=0)
+def main(
+ ctx: click.Context,
+ face_network: str,
+ body_network: str,
+ face_seed: int,
+ body_seed: int,
+ joint_steps: int,
+ truncation_psi: float,
+ outdir: str,
+ video: int):
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+ insgan = InsetGAN(body_network, face_network)
+ os.makedirs(outdir, exist_ok=True)
+ face_z = np.random.RandomState(face_seed).randn(1, 512).astype(np.float32)
+ face_mean = insgan.face_generator.mean_latent(3000)
+ face_w = insgan.face_generator.get_latent(torch.from_numpy(face_z).to(device)) # [N, L, C]
+ face_w = truncation_psi * face_w + (1-truncation_psi) * face_mean
+ face_img, _ = insgan.face_generator([face_w], input_is_latent=True)
+
+ body_z = np.random.RandomState(body_seed).randn(1, 512).astype(np.float32)
+ body_mean = insgan.body_generator.mean_latent(3000)
+ body_w = insgan.body_generator.get_latent(torch.from_numpy(body_z).to(device)) # [N, L, C]
+ body_w = truncation_psi * body_w + (1-truncation_psi) * body_mean
+ body_img, _ = insgan.body_generator([body_w], input_is_latent=True)
+
+ _, body_crop, _ = insgan.detect_face_dlib(body_img)
+ face_img = F.interpolate(face_img, size=(body_crop[3]-body_crop[1], body_crop[2]-body_crop[0]), mode='area')
+ cp_body = body_img.clone()
+ cp_body[:, :, body_crop[1]:body_crop[3], body_crop[0]:body_crop[2]] = face_img
+
+ optim_face_w, optim_body_w, crop = insgan.dual_optimizer(
+ face_w,
+ body_w,
+ joint_steps=joint_steps,
+ seed=f'{face_seed:04d}_{body_seed:04d}',
+ output_path=outdir,
+ video=video
+ )
+
+ if video:
+ ffmpeg_cmd = f"ffmpeg -hide_banner -loglevel error -i ./{outdir}/{face_seed:04d}_{body_seed:04d}/%04d.jpg -c:v libx264 -vf fps=30 -pix_fmt yuv420p ./{outdir}/{face_seed:04d}_{body_seed:04d}.mp4"
+ os.system(ffmpeg_cmd)
+ new_face_img, _ = insgan.face_generator([optim_face_w], input_is_latent=True)
+ new_shape = crop[3] - crop[1], crop[2] - crop[0]
+ new_face_img_crop = F.interpolate(new_face_img, size=new_shape, mode='area')
+ seamless_body, _ = insgan.body_generator([optim_body_w], input_is_latent=True)
+ seamless_body[:, :, crop[1]:crop[3], crop[0]:crop[2]] = new_face_img_crop
+ temp = torch.cat([cp_body, seamless_body], dim=3)
+ visual(temp, f"{outdir}/{face_seed:04d}_{body_seed:04d}.png")
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/stylegan_human/interpolation.py b/stylegan_human/interpolation.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bbc856aa997c5d6c3c7ceea598ce618e38ff3da
--- /dev/null
+++ b/stylegan_human/interpolation.py
@@ -0,0 +1,147 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+## interpolate between two z code
+## score all middle latent code
+# https://www.aiuai.cn/aifarm1929.html
+
+import os
+import re
+from typing import List
+from tqdm import tqdm
+import click
+import dnnlib
+import numpy as np
+import PIL.Image
+import torch
+import click
+import legacy
+import random
+from typing import List, Optional
+
+
+def lerp(code1, code2, alpha):
+ return code1 * alpha + code2 * (1 - alpha)
+
+# Taken and adapted from wikipedia's slerp article
+# https://en.wikipedia.org/wiki/Slerp
+def slerp(code1, code2, alpha, DOT_THRESHOLD=0.9995): # Spherical linear interpolation
+ code1_copy = np.copy(code1)
+ code2_copy = np.copy(code2)
+
+ code1 = code1 / np.linalg.norm(code1)
+ code2 = code2 / np.linalg.norm(code2)
+ dot = np.sum(code1 * code2)
+ if np.abs(dot) > DOT_THRESHOLD:
+ return lerp(code1_copy, code2_copy, alpha)
+
+ # Calculate initial angle between v0 and v1
+ theta_0 = np.arccos(dot)
+ sin_theta_0 = np.sin(theta_0)
+ # Angle at timestep t
+ theta_t = theta_0 * alpha
+ sin_theta_t = np.sin(theta_t)
+
+ s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+ s1 = sin_theta_t / sin_theta_0
+ code3 = s0 * code1_copy + s1 * code2_copy
+ return code3
+
+def generate_image_from_z(G, z, noise_mode, truncation_psi, device):
+ label = torch.zeros([1, G.c_dim], device=device)
+ w = G.mapping(z, label,truncation_psi=truncation_psi)
+ img = G.synthesis(w, noise_mode=noise_mode,force_fp32 = True)
+ img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
+ img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
+ return img
+
+
+def get_concat_h(im1, im2):
+ dst = PIL.Image.new('RGB', (im1.width + im2.width, im1.height))
+ dst.paste(im1, (0, 0))
+ dst.paste(im2, (im1.width, 0))
+ return dst
+
+
+def make_latent_interp_animation(G, code1, code2, img1, img2, num_interps, noise_mode, save_mid_image, truncation_psi,device, outdir,fps):
+ step_size = 1.0/num_interps
+
+ all_imgs = []
+ amounts = np.arange(0, 1, step_size)
+ for seed_idx, alpha in enumerate(tqdm(amounts)):
+ interpolated_latent_code = lerp(code1, code2, alpha)
+ image = generate_image_from_z(G,interpolated_latent_code, noise_mode, truncation_psi, device)
+ interp_latent_image = image.resize((512, 1024))
+ if not os.path.exists(os.path.join(outdir,'img')): os.makedirs(os.path.join(outdir,'img'), exist_ok=True)
+ if save_mid_image:
+ interp_latent_image.save(f'{outdir}/img/seed{seed_idx:04d}.png')
+
+ frame = get_concat_h(img2, interp_latent_image)
+ frame = get_concat_h(frame, img1)
+ all_imgs.append(frame)
+
+ save_name = os.path.join(outdir,'latent_space_traversal.gif')
+ all_imgs[0].save(save_name, save_all=True, append_images=all_imgs[1:], duration=1000/fps, loop=0)
+
+
+"""
+Create interpolated images between two given seeds using pretrained network pickle.
+
+Examples:
+
+\b
+python interpolation.py --network=pretrained_models/stylegan_human_v2_1024.pkl --seeds=85,100 --outdir=outputs/inter_gifs
+
+"""
+
+@click.command()
+@click.pass_context
+@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
+@click.option('--seeds', type=legacy.num_range, help='List of 2 random seeds, e.g. 1,2')
+@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=0.8, show_default=True)
+@click.option('--noise-mode', 'noise_mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
+@click.option('--outdir', default= 'outputs/inter_gifs', help='Where to save the output images', type=str, required=True, metavar='DIR')
+@click.option('--save_mid_image', default=True, type=bool, help='select True if you want to save all interpolated images')
+@click.option('--fps', default= 15, help='FPS for GIF', type=int)
+@click.option('--num_interps', default= 100, help='Number of interpolation images', type=int)
+def main(
+ ctx: click.Context,
+ network_pkl: str,
+ seeds: Optional[List[int]],
+ truncation_psi: float,
+ noise_mode: str,
+ outdir: str,
+ save_mid_image: bool,
+ fps:int,
+ num_interps:int
+):
+
+
+ device = torch.device('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
+ dtype = torch.float32 if device.type == 'mps' else torch.float64
+ with dnnlib.util.open_url(network_pkl) as f:
+ G = legacy.load_network_pkl(f)['G_ema'].to(device, dtype=dtype) # type: ignore
+
+ outdir = os.path.join(outdir)
+ if not os.path.exists(outdir):
+ os.makedirs(outdir,exist_ok=True)
+ os.makedirs(os.path.join(outdir,'img'),exist_ok=True)
+
+ if len(seeds) > 2:
+ print("Receiving more than two seeds, only use the first two.")
+ seeds = seeds[0:2]
+ elif len(seeds) == 1:
+ print('Require two seeds, randomly generate two now.')
+ seeds = [seeds[0],random.randint(0,10000)]
+
+ z1 = torch.from_numpy(np.random.RandomState(seeds[0]).randn(1, G.z_dim)).to(device, dtype=dtype)
+ z2 = torch.from_numpy(np.random.RandomState(seeds[1]).randn(1, G.z_dim)).to(device, dtype=dtype)
+ img1 = generate_image_from_z(G, z1, noise_mode, truncation_psi, device)
+ img2 = generate_image_from_z(G, z2, noise_mode, truncation_psi, device)
+ img1.save(f'{outdir}/seed{seeds[0]:04d}.png')
+ img2.save(f'{outdir}/seed{seeds[1]:04d}.png')
+
+ make_latent_interp_animation(G, z1, z2, img1, img2, num_interps, noise_mode, save_mid_image, truncation_psi, device, outdir, fps)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/stylegan_human/latent_direction/ss_statics/bottom_length_statis/3/statis.csv b/stylegan_human/latent_direction/ss_statics/bottom_length_statis/3/statis.csv
new file mode 100644
index 0000000000000000000000000000000000000000..433fd3dd093e89c504abada24392b87013af7dce
--- /dev/null
+++ b/stylegan_human/latent_direction/ss_statics/bottom_length_statis/3/statis.csv
@@ -0,0 +1,513 @@
+,channel,strength
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diff --git a/stylegan_human/latent_direction/ss_statics/bottom_length_statis/4/statis.csv b/stylegan_human/latent_direction/ss_statics/bottom_length_statis/4/statis.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cc7cfcccc962fc6c34447f89d7611f94d934799d
--- /dev/null
+++ b/stylegan_human/latent_direction/ss_statics/bottom_length_statis/4/statis.csv
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diff --git a/stylegan_human/latent_direction/ss_statics/bottom_length_statis/5/statis.csv b/stylegan_human/latent_direction/ss_statics/bottom_length_statis/5/statis.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e4312735542bb2da898565efd87de0ce7f8b0b59
--- /dev/null
+++ b/stylegan_human/latent_direction/ss_statics/bottom_length_statis/5/statis.csv
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diff --git a/stylegan_human/latent_direction/ss_statics/upper_length_statis/5/statis.csv b/stylegan_human/latent_direction/ss_statics/upper_length_statis/5/statis.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3e8a63780b913995863f7f86e7b3e9f8292574d9
--- /dev/null
+++ b/stylegan_human/latent_direction/ss_statics/upper_length_statis/5/statis.csv
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diff --git a/stylegan_human/legacy.py b/stylegan_human/legacy.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef0e838df5426e5f01ba3b917244de553678ed0f
--- /dev/null
+++ b/stylegan_human/legacy.py
@@ -0,0 +1,200 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2021, NVIDIA CORPORATION. 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 pickle
+import dnnlib
+import re
+from typing import List, Optional
+import torch
+import copy
+import numpy as np
+from torch_utils import misc
+
+
+#----------------------------------------------------------------------------
+## loading torch pkl
+def load_network_pkl(f, force_fp16=False, G_only=False):
+ data = _LegacyUnpickler(f).load()
+ if G_only:
+ f = open('ori_model_Gonly.txt','a+')
+ else: f = open('ori_model.txt','a+')
+ for key in data.keys():
+ f.write(str(data[key]))
+ f.close()
+
+ ## We comment out this part, if you want to convert TF pickle, you can use the original script from StyleGAN2-ada-pytorch
+ # # 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_ema'], torch.nn.Module)
+ if not G_only:
+ assert isinstance(data['D'], torch.nn.Module)
+ assert isinstance(data['G'], 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:
+ if G_only:
+ convert_list = ['G_ema'] #'G'
+ else: convert_list = ['G', 'D', 'G_ema']
+ for key in convert_list:
+ old = data[key]
+ kwargs = copy.deepcopy(old.init_kwargs)
+ if key.startswith('G'):
+ kwargs.synthesis_kwargs = dnnlib.EasyDict(kwargs.get('synthesis_kwargs', {}))
+ kwargs.synthesis_kwargs.num_fp16_res = 4
+ kwargs.synthesis_kwargs.conv_clamp = 256
+ if key.startswith('D'):
+ kwargs.num_fp16_res = 4
+ 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 num_range(s: str) -> List[int]:
+ '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.'''
+
+ range_re = re.compile(r'^(\d+)-(\d+)$')
+ m = range_re.match(s)
+ if m:
+ return list(range(int(m.group(1)), int(m.group(2))+1))
+ vals = s.split(',')
+ return [int(x) for x in vals]
+
+
+
+#----------------------------------------------------------------------------
+#### loading tf pkl
+def load_pkl(file_or_url):
+ with open(file_or_url, 'rb') as file:
+ return pickle.load(file, encoding='latin1')
+
+#----------------------------------------------------------------------------
+
+### For editing
+def visual(output, out_path):
+ import torch
+ import cv2
+ import numpy as np
+ output = (output + 1)/2
+ output = torch.clamp(output, 0, 1)
+ if output.shape[1] == 1:
+ output = torch.cat([output, output, output], 1)
+ output = output[0].detach().cpu().permute(1,2,0).numpy()
+ output = (output*255).astype(np.uint8)
+ output = output[:,:,::-1]
+ cv2.imwrite(out_path, output)
+
+def save_obj(obj, path):
+ with open(path, 'wb+') as f:
+ pickle.dump(obj, f, protocol=4)
+
+#----------------------------------------------------------------------------
+
+## Converting pkl to pth, change dict info inside pickle
+
+def convert_to_rgb(state_ros, state_nv, ros_name, nv_name):
+ state_ros[f"{ros_name}.conv.weight"] = state_nv[f"{nv_name}.torgb.weight"].unsqueeze(0)
+ state_ros[f"{ros_name}.bias"] = state_nv[f"{nv_name}.torgb.bias"].unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+ state_ros[f"{ros_name}.conv.modulation.weight"] = state_nv[f"{nv_name}.torgb.affine.weight"]
+ state_ros[f"{ros_name}.conv.modulation.bias"] = state_nv[f"{nv_name}.torgb.affine.bias"]
+
+
+def convert_conv(state_ros, state_nv, ros_name, nv_name):
+ state_ros[f"{ros_name}.conv.weight"] = state_nv[f"{nv_name}.weight"].unsqueeze(0)
+ state_ros[f"{ros_name}.activate.bias"] = state_nv[f"{nv_name}.bias"]
+ state_ros[f"{ros_name}.conv.modulation.weight"] = state_nv[f"{nv_name}.affine.weight"]
+ state_ros[f"{ros_name}.conv.modulation.bias"] = state_nv[f"{nv_name}.affine.bias"]
+ state_ros[f"{ros_name}.noise.weight"] = state_nv[f"{nv_name}.noise_strength"].unsqueeze(0)
+
+
+def convert_blur_kernel(state_ros, state_nv, level):
+ """Not quite sure why there is a factor of 4 here"""
+ # They are all the same
+ state_ros[f"convs.{2*level}.conv.blur.kernel"] = 4*state_nv["synthesis.b4.resample_filter"]
+ state_ros[f"to_rgbs.{level}.upsample.kernel"] = 4*state_nv["synthesis.b4.resample_filter"]
+
+
+def determine_config(state_nv):
+ mapping_names = [name for name in state_nv.keys() if "mapping.fc" in name]
+ sythesis_names = [name for name in state_nv.keys() if "synthesis.b" in name]
+
+ n_mapping = max([int(re.findall("(\d+)", n)[0]) for n in mapping_names]) + 1
+ resolution = max([int(re.findall("(\d+)", n)[0]) for n in sythesis_names])
+ n_layers = np.log(resolution/2)/np.log(2)
+
+ return n_mapping, n_layers
+
+
+def convert(network_pkl, output_file, G_only=False):
+ with dnnlib.util.open_url(network_pkl) as f:
+ G_nvidia = load_network_pkl(f,G_only=G_only)['G_ema']
+
+ state_nv = G_nvidia.state_dict()
+ n_mapping, n_layers = determine_config(state_nv)
+
+ state_ros = {}
+
+ for i in range(n_mapping):
+ state_ros[f"style.{i+1}.weight"] = state_nv[f"mapping.fc{i}.weight"]
+ state_ros[f"style.{i+1}.bias"] = state_nv[f"mapping.fc{i}.bias"]
+
+ for i in range(int(n_layers)):
+ if i > 0:
+ for conv_level in range(2):
+ convert_conv(state_ros, state_nv, f"convs.{2*i-2+conv_level}", f"synthesis.b{4*(2**i)}.conv{conv_level}")
+ state_ros[f"noises.noise_{2*i-1+conv_level}"] = state_nv[f"synthesis.b{4*(2**i)}.conv{conv_level}.noise_const"].unsqueeze(0).unsqueeze(0)
+
+ convert_to_rgb(state_ros, state_nv, f"to_rgbs.{i-1}", f"synthesis.b{4*(2**i)}")
+ convert_blur_kernel(state_ros, state_nv, i-1)
+
+ else:
+ state_ros[f"input.input"] = state_nv[f"synthesis.b{4*(2**i)}.const"].unsqueeze(0)
+ convert_conv(state_ros, state_nv, "conv1", f"synthesis.b{4*(2**i)}.conv1")
+ state_ros[f"noises.noise_{2*i}"] = state_nv[f"synthesis.b{4*(2**i)}.conv1.noise_const"].unsqueeze(0).unsqueeze(0)
+ convert_to_rgb(state_ros, state_nv, "to_rgb1", f"synthesis.b{4*(2**i)}")
+
+ # https://github.com/yuval-alaluf/restyle-encoder/issues/1#issuecomment-828354736
+ latent_avg = state_nv['mapping.w_avg']
+ state_dict = {"g_ema": state_ros, "latent_avg": latent_avg}
+ # if G_only:
+ # f = open('converted_model_Gonly.txt','a+')
+ # else:
+ # f = open('converted_model.txt','a+')
+ # for key in state_dict['g_ema'].keys():
+ # f.write(str(key)+': '+str(state_dict['g_ema'][key].shape)+'\n')
+ # f.close()
+ torch.save(state_dict, output_file)
+
diff --git a/stylegan_human/openpose/model/.gitkeep b/stylegan_human/openpose/model/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stylegan_human/openpose/src/__init__.py b/stylegan_human/openpose/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stylegan_human/openpose/src/body.py b/stylegan_human/openpose/src/body.py
new file mode 100644
index 0000000000000000000000000000000000000000..13dc2fb8215f2ad63ee6b54a6641800fddcc20a4
--- /dev/null
+++ b/stylegan_human/openpose/src/body.py
@@ -0,0 +1,217 @@
+import cv2
+import numpy as np
+import math
+import time
+from scipy.ndimage.filters import gaussian_filter
+import matplotlib.pyplot as plt
+import matplotlib
+import torch
+from torchvision import transforms
+
+from openpose.src import util
+from openpose.src.model import bodypose_model
+
+class Body(object):
+ def __init__(self, model_path):
+ self.model = bodypose_model()
+ if torch.cuda.is_available():
+ self.model = self.model.cuda()
+ model_dict = util.transfer(self.model, torch.load(model_path))
+ self.model.load_state_dict(model_dict)
+ self.model.eval()
+
+ def __call__(self, oriImg):
+ # scale_search = [0.5, 1.0, 1.5, 2.0]
+ scale_search = [0.5]
+ boxsize = 368
+ stride = 8
+ padValue = 128
+ thre1 = 0.1
+ thre2 = 0.05
+ multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
+ heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
+ paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
+
+ for m in range(len(multiplier)):
+ scale = multiplier[m]
+ imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
+ imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
+ im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
+ im = np.ascontiguousarray(im)
+
+ data = torch.from_numpy(im).float()
+ if torch.cuda.is_available():
+ data = data.cuda()
+ # data = data.permute([2, 0, 1]).unsqueeze(0).float()
+ with torch.no_grad():
+ Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data)
+ Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
+ Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
+
+ # extract outputs, resize, and remove padding
+ # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0)) # output 1 is heatmaps
+ heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0)) # output 1 is heatmaps
+ heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
+ heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+ heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
+
+ # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0)) # output 0 is PAFs
+ paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0)) # output 0 is PAFs
+ paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
+ paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+ paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
+
+ heatmap_avg += heatmap_avg + heatmap / len(multiplier)
+ paf_avg += + paf / len(multiplier)
+ all_peaks = []
+ peak_counter = 0
+
+ for part in range(18):
+ map_ori = heatmap_avg[:, :, part]
+ one_heatmap = gaussian_filter(map_ori, sigma=3)
+
+ map_left = np.zeros(one_heatmap.shape)
+ map_left[1:, :] = one_heatmap[:-1, :]
+ map_right = np.zeros(one_heatmap.shape)
+ map_right[:-1, :] = one_heatmap[1:, :]
+ map_up = np.zeros(one_heatmap.shape)
+ map_up[:, 1:] = one_heatmap[:, :-1]
+ map_down = np.zeros(one_heatmap.shape)
+ map_down[:, :-1] = one_heatmap[:, 1:]
+
+ peaks_binary = np.logical_and.reduce(
+ (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1))
+ peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse
+ peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
+ peak_id = range(peak_counter, peak_counter + len(peaks))
+ peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))]
+
+ all_peaks.append(peaks_with_score_and_id)
+ peak_counter += len(peaks)
+
+ # find connection in the specified sequence, center 29 is in the position 15
+ limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
+ [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
+ [1, 16], [16, 18], [3, 17], [6, 18]]
+ # the middle joints heatmap correpondence
+ mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \
+ [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \
+ [55, 56], [37, 38], [45, 46]]
+
+ connection_all = []
+ special_k = []
+ mid_num = 10
+
+ for k in range(len(mapIdx)):
+ score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
+ candA = all_peaks[limbSeq[k][0] - 1]
+ candB = all_peaks[limbSeq[k][1] - 1]
+ nA = len(candA)
+ nB = len(candB)
+ indexA, indexB = limbSeq[k]
+ if (nA != 0 and nB != 0):
+ connection_candidate = []
+ for i in range(nA):
+ for j in range(nB):
+ vec = np.subtract(candB[j][:2], candA[i][:2])
+ norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
+ norm = max(0.001, norm)
+ vec = np.divide(vec, norm)
+
+ startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
+ np.linspace(candA[i][1], candB[j][1], num=mid_num)))
+
+ vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
+ for I in range(len(startend))])
+ vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
+ for I in range(len(startend))])
+
+ score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
+ score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
+ 0.5 * oriImg.shape[0] / norm - 1, 0)
+ criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts)
+ criterion2 = score_with_dist_prior > 0
+ if criterion1 and criterion2:
+ connection_candidate.append(
+ [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]])
+
+ connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
+ connection = np.zeros((0, 5))
+ for c in range(len(connection_candidate)):
+ i, j, s = connection_candidate[c][0:3]
+ if (i not in connection[:, 3] and j not in connection[:, 4]):
+ connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
+ if (len(connection) >= min(nA, nB)):
+ break
+
+ connection_all.append(connection)
+ else:
+ special_k.append(k)
+ connection_all.append([])
+
+ # last number in each row is the total parts number of that person
+ # the second last number in each row is the score of the overall configuration
+ subset = -1 * np.ones((0, 20))
+ candidate = np.array([item for sublist in all_peaks for item in sublist])
+
+ for k in range(len(mapIdx)):
+ if k not in special_k:
+ partAs = connection_all[k][:, 0]
+ partBs = connection_all[k][:, 1]
+ indexA, indexB = np.array(limbSeq[k]) - 1
+
+ for i in range(len(connection_all[k])): # = 1:size(temp,1)
+ found = 0
+ subset_idx = [-1, -1]
+ for j in range(len(subset)): # 1:size(subset,1):
+ if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
+ subset_idx[found] = j
+ found += 1
+
+ if found == 1:
+ j = subset_idx[0]
+ if subset[j][indexB] != partBs[i]:
+ subset[j][indexB] = partBs[i]
+ subset[j][-1] += 1
+ subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
+ elif found == 2: # if found 2 and disjoint, merge them
+ j1, j2 = subset_idx
+ membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
+ if len(np.nonzero(membership == 2)[0]) == 0: # merge
+ subset[j1][:-2] += (subset[j2][:-2] + 1)
+ subset[j1][-2:] += subset[j2][-2:]
+ subset[j1][-2] += connection_all[k][i][2]
+ subset = np.delete(subset, j2, 0)
+ else: # as like found == 1
+ subset[j1][indexB] = partBs[i]
+ subset[j1][-1] += 1
+ subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
+
+ # if find no partA in the subset, create a new subset
+ elif not found and k < 17:
+ row = -1 * np.ones(20)
+ row[indexA] = partAs[i]
+ row[indexB] = partBs[i]
+ row[-1] = 2
+ row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2]
+ subset = np.vstack([subset, row])
+ # delete some rows of subset which has few parts occur
+ deleteIdx = []
+ for i in range(len(subset)):
+ if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
+ deleteIdx.append(i)
+ subset = np.delete(subset, deleteIdx, axis=0)
+
+ # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
+ # candidate: x, y, score, id
+ return candidate, subset
+
+if __name__ == "__main__":
+ body_estimation = Body('../model/body_pose_model.pth')
+
+ test_image = '../images/ski.jpg'
+ oriImg = cv2.imread(test_image) # B,G,R order
+ candidate, subset = body_estimation(oriImg)
+ canvas = util.draw_bodypose(oriImg, candidate, subset)
+ plt.imshow(canvas[:, :, [2, 1, 0]])
+ plt.show()
diff --git a/stylegan_human/openpose/src/model.py b/stylegan_human/openpose/src/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..5dfc80de827a17beccb9b0f3f7588545be78c9de
--- /dev/null
+++ b/stylegan_human/openpose/src/model.py
@@ -0,0 +1,219 @@
+import torch
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+def make_layers(block, no_relu_layers):
+ layers = []
+ for layer_name, v in block.items():
+ if 'pool' in layer_name:
+ layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
+ padding=v[2])
+ layers.append((layer_name, layer))
+ else:
+ conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],
+ kernel_size=v[2], stride=v[3],
+ padding=v[4])
+ layers.append((layer_name, conv2d))
+ if layer_name not in no_relu_layers:
+ layers.append(('relu_'+layer_name, nn.ReLU(inplace=True)))
+
+ return nn.Sequential(OrderedDict(layers))
+
+class bodypose_model(nn.Module):
+ def __init__(self):
+ super(bodypose_model, self).__init__()
+
+ # these layers have no relu layer
+ no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\
+ 'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\
+ 'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\
+ 'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
+ blocks = {}
+ block0 = OrderedDict([
+ ('conv1_1', [3, 64, 3, 1, 1]),
+ ('conv1_2', [64, 64, 3, 1, 1]),
+ ('pool1_stage1', [2, 2, 0]),
+ ('conv2_1', [64, 128, 3, 1, 1]),
+ ('conv2_2', [128, 128, 3, 1, 1]),
+ ('pool2_stage1', [2, 2, 0]),
+ ('conv3_1', [128, 256, 3, 1, 1]),
+ ('conv3_2', [256, 256, 3, 1, 1]),
+ ('conv3_3', [256, 256, 3, 1, 1]),
+ ('conv3_4', [256, 256, 3, 1, 1]),
+ ('pool3_stage1', [2, 2, 0]),
+ ('conv4_1', [256, 512, 3, 1, 1]),
+ ('conv4_2', [512, 512, 3, 1, 1]),
+ ('conv4_3_CPM', [512, 256, 3, 1, 1]),
+ ('conv4_4_CPM', [256, 128, 3, 1, 1])
+ ])
+
+
+ # Stage 1
+ block1_1 = OrderedDict([
+ ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]),
+ ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
+ ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]),
+ ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
+ ('conv5_5_CPM_L1', [512, 38, 1, 1, 0])
+ ])
+
+ block1_2 = OrderedDict([
+ ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]),
+ ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
+ ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]),
+ ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
+ ('conv5_5_CPM_L2', [512, 19, 1, 1, 0])
+ ])
+ blocks['block1_1'] = block1_1
+ blocks['block1_2'] = block1_2
+
+ self.model0 = make_layers(block0, no_relu_layers)
+
+ # Stages 2 - 6
+ for i in range(2, 7):
+ blocks['block%d_1' % i] = OrderedDict([
+ ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
+ ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+ ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+ ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+ ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+ ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
+ ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])
+ ])
+
+ blocks['block%d_2' % i] = OrderedDict([
+ ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
+ ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+ ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+ ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+ ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+ ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
+ ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])
+ ])
+
+ for k in blocks.keys():
+ blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+ self.model1_1 = blocks['block1_1']
+ self.model2_1 = blocks['block2_1']
+ self.model3_1 = blocks['block3_1']
+ self.model4_1 = blocks['block4_1']
+ self.model5_1 = blocks['block5_1']
+ self.model6_1 = blocks['block6_1']
+
+ self.model1_2 = blocks['block1_2']
+ self.model2_2 = blocks['block2_2']
+ self.model3_2 = blocks['block3_2']
+ self.model4_2 = blocks['block4_2']
+ self.model5_2 = blocks['block5_2']
+ self.model6_2 = blocks['block6_2']
+
+
+ def forward(self, x):
+
+ out1 = self.model0(x)
+
+ out1_1 = self.model1_1(out1)
+ out1_2 = self.model1_2(out1)
+ out2 = torch.cat([out1_1, out1_2, out1], 1)
+
+ out2_1 = self.model2_1(out2)
+ out2_2 = self.model2_2(out2)
+ out3 = torch.cat([out2_1, out2_2, out1], 1)
+
+ out3_1 = self.model3_1(out3)
+ out3_2 = self.model3_2(out3)
+ out4 = torch.cat([out3_1, out3_2, out1], 1)
+
+ out4_1 = self.model4_1(out4)
+ out4_2 = self.model4_2(out4)
+ out5 = torch.cat([out4_1, out4_2, out1], 1)
+
+ out5_1 = self.model5_1(out5)
+ out5_2 = self.model5_2(out5)
+ out6 = torch.cat([out5_1, out5_2, out1], 1)
+
+ out6_1 = self.model6_1(out6)
+ out6_2 = self.model6_2(out6)
+
+ return out6_1, out6_2
+
+class handpose_model(nn.Module):
+ def __init__(self):
+ super(handpose_model, self).__init__()
+
+ # these layers have no relu layer
+ no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\
+ 'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6']
+ # stage 1
+ block1_0 = OrderedDict([
+ ('conv1_1', [3, 64, 3, 1, 1]),
+ ('conv1_2', [64, 64, 3, 1, 1]),
+ ('pool1_stage1', [2, 2, 0]),
+ ('conv2_1', [64, 128, 3, 1, 1]),
+ ('conv2_2', [128, 128, 3, 1, 1]),
+ ('pool2_stage1', [2, 2, 0]),
+ ('conv3_1', [128, 256, 3, 1, 1]),
+ ('conv3_2', [256, 256, 3, 1, 1]),
+ ('conv3_3', [256, 256, 3, 1, 1]),
+ ('conv3_4', [256, 256, 3, 1, 1]),
+ ('pool3_stage1', [2, 2, 0]),
+ ('conv4_1', [256, 512, 3, 1, 1]),
+ ('conv4_2', [512, 512, 3, 1, 1]),
+ ('conv4_3', [512, 512, 3, 1, 1]),
+ ('conv4_4', [512, 512, 3, 1, 1]),
+ ('conv5_1', [512, 512, 3, 1, 1]),
+ ('conv5_2', [512, 512, 3, 1, 1]),
+ ('conv5_3_CPM', [512, 128, 3, 1, 1])
+ ])
+
+ block1_1 = OrderedDict([
+ ('conv6_1_CPM', [128, 512, 1, 1, 0]),
+ ('conv6_2_CPM', [512, 22, 1, 1, 0])
+ ])
+
+ blocks = {}
+ blocks['block1_0'] = block1_0
+ blocks['block1_1'] = block1_1
+
+ # stage 2-6
+ for i in range(2, 7):
+ blocks['block%d' % i] = OrderedDict([
+ ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]),
+ ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]),
+ ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]),
+ ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]),
+ ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]),
+ ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]),
+ ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0])
+ ])
+
+ for k in blocks.keys():
+ blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+ self.model1_0 = blocks['block1_0']
+ self.model1_1 = blocks['block1_1']
+ self.model2 = blocks['block2']
+ self.model3 = blocks['block3']
+ self.model4 = blocks['block4']
+ self.model5 = blocks['block5']
+ self.model6 = blocks['block6']
+
+ def forward(self, x):
+ out1_0 = self.model1_0(x)
+ out1_1 = self.model1_1(out1_0)
+ concat_stage2 = torch.cat([out1_1, out1_0], 1)
+ out_stage2 = self.model2(concat_stage2)
+ concat_stage3 = torch.cat([out_stage2, out1_0], 1)
+ out_stage3 = self.model3(concat_stage3)
+ concat_stage4 = torch.cat([out_stage3, out1_0], 1)
+ out_stage4 = self.model4(concat_stage4)
+ concat_stage5 = torch.cat([out_stage4, out1_0], 1)
+ out_stage5 = self.model5(concat_stage5)
+ concat_stage6 = torch.cat([out_stage5, out1_0], 1)
+ out_stage6 = self.model6(concat_stage6)
+ return out_stage6
+
+
diff --git a/stylegan_human/openpose/src/util.py b/stylegan_human/openpose/src/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..59819ee503d478e7ec2f4fcd3a967b04a3a1ea9f
--- /dev/null
+++ b/stylegan_human/openpose/src/util.py
@@ -0,0 +1,95 @@
+import numpy as np
+import math
+import cv2
+import matplotlib
+from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+from matplotlib.figure import Figure
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+
+
+def padRightDownCorner(img, stride, padValue):
+ h = img.shape[0]
+ w = img.shape[1]
+
+ pad = 4 * [None]
+ pad[0] = 0 # up
+ pad[1] = 0 # left
+ pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
+ pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
+
+ img_padded = img
+ pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1))
+ img_padded = np.concatenate((pad_up, img_padded), axis=0)
+ pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1))
+ img_padded = np.concatenate((pad_left, img_padded), axis=1)
+ pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1))
+ img_padded = np.concatenate((img_padded, pad_down), axis=0)
+ pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1))
+ img_padded = np.concatenate((img_padded, pad_right), axis=1)
+
+ return img_padded, pad
+
+# transfer caffe model to pytorch which will match the layer name
+def transfer(model, model_weights):
+ transfered_model_weights = {}
+ for weights_name in model.state_dict().keys():
+ transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])]
+ return transfered_model_weights
+
+# draw the body keypoint and lims
+def draw_bodypose(canvas, candidate, subset,show_number=False):
+ stickwidth = 4
+ limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
+ [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
+ [1, 16], [16, 18], [3, 17], [6, 18]]
+
+ colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
+ [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
+ [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
+ for i in range(18):
+ for n in range(len(subset)):
+ index = int(subset[n][i])
+ if index == -1:
+ continue
+ x, y = candidate[index][0:2]
+ cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
+ if show_number:
+ cv2.putText(canvas, f'{index}', (int(x), int(y)),cv2.FONT_HERSHEY_SIMPLEX, 0.6,
+ (255,255,0), 1, cv2.LINE_AA)
+ ## calc and print average
+ for i in range(17):
+ for n in range(len(subset)):
+ index = subset[n][np.array(limbSeq[i]) - 1]
+ if -1 in index:
+ continue
+ cur_canvas = canvas.copy()
+ Y = candidate[index.astype(int), 0]
+ X = candidate[index.astype(int), 1]
+ mX = np.mean(X)
+ mY = np.mean(Y)
+ length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+ angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+ polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+ cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
+ canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
+
+ return canvas
+
+# get max index of 2d array
+def npmax(array):
+ arrayindex = array.argmax(1)
+ arrayvalue = array.max(1)
+ i = arrayvalue.argmax()
+ j = arrayindex[i]
+ return i, j
+
+# get max index of 2d array
+def npmax_with_score(array):
+ arrayindex = array.argmax(1)
+ arrayvalue = array.max(1)
+ i = arrayvalue.argmax()
+ j = arrayindex[i]
+ score =array[i][j]
+ return i, j,score
diff --git a/stylegan_human/pti/pti_configs/__init__.py b/stylegan_human/pti/pti_configs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stylegan_human/pti/pti_configs/global_config.py b/stylegan_human/pti/pti_configs/global_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce793517213d2325a98b061314755c2edf69fbd9
--- /dev/null
+++ b/stylegan_human/pti/pti_configs/global_config.py
@@ -0,0 +1,12 @@
+## Device
+cuda_visible_devices = '0'
+device = 'cuda:0'
+
+## Logs
+training_step = 1
+image_rec_result_log_snapshot = 100
+pivotal_training_steps = 0
+model_snapshot_interval = 400
+
+## Run name to be updated during PTI
+run_name = 'exp'
diff --git a/stylegan_human/pti/pti_configs/hyperparameters.py b/stylegan_human/pti/pti_configs/hyperparameters.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1014875cc3d46871056cf17fdc8c778ac6139de
--- /dev/null
+++ b/stylegan_human/pti/pti_configs/hyperparameters.py
@@ -0,0 +1,28 @@
+## Architechture
+lpips_type = 'alex'
+first_inv_type = 'w+'#'w+'
+optim_type = 'adam'
+
+## Locality regularization
+latent_ball_num_of_samples = 1
+locality_regularization_interval = 1
+use_locality_regularization = False
+regulizer_l2_lambda = 0.1
+regulizer_lpips_lambda = 0.1
+regulizer_alpha = 30
+
+## Loss
+pt_l2_lambda = 1
+pt_lpips_lambda = 1
+
+## Steps
+LPIPS_value_threshold = 0.04
+max_pti_steps = 350
+first_inv_steps = 450
+max_images_to_invert = 30
+
+## Optimization
+pti_learning_rate = 5e-4
+first_inv_lr = 8e-3
+train_batch_size = 1
+use_last_w_pivots = False
diff --git a/stylegan_human/pti/pti_configs/paths_config.py b/stylegan_human/pti/pti_configs/paths_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4b6fce7bfcf285fb09ae4ddf9432fa6aa562ea5
--- /dev/null
+++ b/stylegan_human/pti/pti_configs/paths_config.py
@@ -0,0 +1,24 @@
+import os
+
+## Pretrained models paths
+e4e = './pti/e4e_w+.pt'
+stylegan2_ada_shhq = './pretrained_models/stylegan_human_v2_1024.pkl'
+ir_se50 = '' #'./model_ir_se50.pth'
+
+## Dirs for output files
+checkpoints_dir = './outputs/pti/checkpoints/'
+embedding_base_dir = './outputs/pti/embeddings'
+experiments_output_dir = './outputs/pti/'
+
+## Input info
+### Input dir, where the images reside
+input_data_path = 'aligned_image/'
+### Inversion identifier, used to keeping track of the inversion results. Both the latent code and the generator
+input_data_id = 'test'
+
+## Keywords
+pti_results_keyword = 'PTI'
+e4e_results_keyword = 'e4e'
+sg2_results_keyword = 'SG2'
+sg2_plus_results_keyword = 'SG2_Plus'
+multi_id_model_type = 'multi_id'
diff --git a/stylegan_human/pti/pti_models/__init__.py b/stylegan_human/pti/pti_models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stylegan_human/pti/pti_models/e4e/__init__.py b/stylegan_human/pti/pti_models/e4e/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stylegan_human/pti/pti_models/e4e/encoders/__init__.py b/stylegan_human/pti/pti_models/e4e/encoders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stylegan_human/pti/pti_models/e4e/encoders/helpers.py b/stylegan_human/pti/pti_models/e4e/encoders/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4a58b34ea5ca6912fe53c63dede0a8696f5c024
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/encoders/helpers.py
@@ -0,0 +1,140 @@
+from collections import namedtuple
+import torch
+import torch.nn.functional as F
+from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
+
+"""
+ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
+"""
+
+
+class Flatten(Module):
+ def forward(self, input):
+ return input.view(input.size(0), -1)
+
+
+def l2_norm(input, axis=1):
+ norm = torch.norm(input, 2, axis, True)
+ output = torch.div(input, norm)
+ return output
+
+
+class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
+ """ A named tuple describing a ResNet block. """
+
+
+def get_block(in_channel, depth, num_units, stride=2):
+ return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
+
+
+def get_blocks(num_layers):
+ if num_layers == 50:
+ blocks = [
+ get_block(in_channel=64, depth=64, num_units=3),
+ get_block(in_channel=64, depth=128, num_units=4),
+ get_block(in_channel=128, depth=256, num_units=14),
+ get_block(in_channel=256, depth=512, num_units=3)
+ ]
+ elif num_layers == 100:
+ blocks = [
+ get_block(in_channel=64, depth=64, num_units=3),
+ get_block(in_channel=64, depth=128, num_units=13),
+ get_block(in_channel=128, depth=256, num_units=30),
+ get_block(in_channel=256, depth=512, num_units=3)
+ ]
+ elif num_layers == 152:
+ blocks = [
+ get_block(in_channel=64, depth=64, num_units=3),
+ get_block(in_channel=64, depth=128, num_units=8),
+ get_block(in_channel=128, depth=256, num_units=36),
+ get_block(in_channel=256, depth=512, num_units=3)
+ ]
+ else:
+ raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers))
+ return blocks
+
+
+class SEModule(Module):
+ def __init__(self, channels, reduction):
+ super(SEModule, self).__init__()
+ self.avg_pool = AdaptiveAvgPool2d(1)
+ self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
+ self.relu = ReLU(inplace=True)
+ self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
+ self.sigmoid = Sigmoid()
+
+ def forward(self, x):
+ module_input = x
+ x = self.avg_pool(x)
+ x = self.fc1(x)
+ x = self.relu(x)
+ x = self.fc2(x)
+ x = self.sigmoid(x)
+ return module_input * x
+
+
+class bottleneck_IR(Module):
+ def __init__(self, in_channel, depth, stride):
+ super(bottleneck_IR, self).__init__()
+ if in_channel == depth:
+ self.shortcut_layer = MaxPool2d(1, stride)
+ else:
+ self.shortcut_layer = Sequential(
+ Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+ BatchNorm2d(depth)
+ )
+ self.res_layer = Sequential(
+ BatchNorm2d(in_channel),
+ Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
+ Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth)
+ )
+
+ def forward(self, x):
+ shortcut = self.shortcut_layer(x)
+ res = self.res_layer(x)
+ return res + shortcut
+
+
+class bottleneck_IR_SE(Module):
+ def __init__(self, in_channel, depth, stride):
+ super(bottleneck_IR_SE, self).__init__()
+ if in_channel == depth:
+ self.shortcut_layer = MaxPool2d(1, stride)
+ else:
+ self.shortcut_layer = Sequential(
+ Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+ BatchNorm2d(depth)
+ )
+ self.res_layer = Sequential(
+ BatchNorm2d(in_channel),
+ Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
+ PReLU(depth),
+ Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
+ BatchNorm2d(depth),
+ SEModule(depth, 16)
+ )
+
+ def forward(self, x):
+ shortcut = self.shortcut_layer(x)
+ res = self.res_layer(x)
+ return res + shortcut
+
+
+def _upsample_add(x, y):
+ """Upsample and add two feature maps.
+ Args:
+ x: (Variable) top feature map to be upsampled.
+ y: (Variable) lateral feature map.
+ Returns:
+ (Variable) added feature map.
+ Note in PyTorch, when input size is odd, the upsampled feature map
+ with `F.upsample(..., scale_factor=2, mode='nearest')`
+ maybe not equal to the lateral feature map size.
+ e.g.
+ original input size: [N,_,15,15] ->
+ conv2d feature map size: [N,_,8,8] ->
+ upsampled feature map size: [N,_,16,16]
+ So we choose bilinear upsample which supports arbitrary output sizes.
+ """
+ _, _, H, W = y.size()
+ return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y
diff --git a/stylegan_human/pti/pti_models/e4e/encoders/model_irse.py b/stylegan_human/pti/pti_models/e4e/encoders/model_irse.py
new file mode 100644
index 0000000000000000000000000000000000000000..976ce2c61104efdc6b0015d895830346dd01bc10
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/encoders/model_irse.py
@@ -0,0 +1,84 @@
+from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
+from encoder4editing.models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
+
+"""
+Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
+"""
+
+
+class Backbone(Module):
+ def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
+ super(Backbone, self).__init__()
+ assert input_size in [112, 224], "input_size should be 112 or 224"
+ assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
+ assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
+ blocks = get_blocks(num_layers)
+ if mode == 'ir':
+ unit_module = bottleneck_IR
+ elif mode == 'ir_se':
+ unit_module = bottleneck_IR_SE
+ self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
+ BatchNorm2d(64),
+ PReLU(64))
+ if input_size == 112:
+ self.output_layer = Sequential(BatchNorm2d(512),
+ Dropout(drop_ratio),
+ Flatten(),
+ Linear(512 * 7 * 7, 512),
+ BatchNorm1d(512, affine=affine))
+ else:
+ self.output_layer = Sequential(BatchNorm2d(512),
+ Dropout(drop_ratio),
+ Flatten(),
+ Linear(512 * 14 * 14, 512),
+ BatchNorm1d(512, affine=affine))
+
+ modules = []
+ for block in blocks:
+ for bottleneck in block:
+ modules.append(unit_module(bottleneck.in_channel,
+ bottleneck.depth,
+ bottleneck.stride))
+ self.body = Sequential(*modules)
+
+ def forward(self, x):
+ x = self.input_layer(x)
+ x = self.body(x)
+ x = self.output_layer(x)
+ return l2_norm(x)
+
+
+def IR_50(input_size):
+ """Constructs a ir-50 model."""
+ model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
+ return model
+
+
+def IR_101(input_size):
+ """Constructs a ir-101 model."""
+ model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
+ return model
+
+
+def IR_152(input_size):
+ """Constructs a ir-152 model."""
+ model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
+ return model
+
+
+def IR_SE_50(input_size):
+ """Constructs a ir_se-50 model."""
+ model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
+ return model
+
+
+def IR_SE_101(input_size):
+ """Constructs a ir_se-101 model."""
+ model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
+ return model
+
+
+def IR_SE_152(input_size):
+ """Constructs a ir_se-152 model."""
+ model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
+ return model
diff --git a/stylegan_human/pti/pti_models/e4e/encoders/psp_encoders.py b/stylegan_human/pti/pti_models/e4e/encoders/psp_encoders.py
new file mode 100644
index 0000000000000000000000000000000000000000..85da8a41461e20170cc3f3afaff3f25be9f6b2d1
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/encoders/psp_encoders.py
@@ -0,0 +1,200 @@
+from enum import Enum
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module
+
+from pti.pti_models.e4e.encoders.helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE, _upsample_add
+from pti.pti_models.e4e.stylegan2.model import EqualLinear
+
+
+class ProgressiveStage(Enum):
+ WTraining = 0
+ Delta1Training = 1
+ Delta2Training = 2
+ Delta3Training = 3
+ Delta4Training = 4
+ Delta5Training = 5
+ Delta6Training = 6
+ Delta7Training = 7
+ Delta8Training = 8
+ Delta9Training = 9
+ Delta10Training = 10
+ Delta11Training = 11
+ Delta12Training = 12
+ Delta13Training = 13
+ Delta14Training = 14
+ Delta15Training = 15
+ Delta16Training = 16
+ Delta17Training = 17
+ Inference = 18
+
+
+class GradualStyleBlock(Module):
+ def __init__(self, in_c, out_c, spatial):
+ super(GradualStyleBlock, self).__init__()
+ self.out_c = out_c
+ self.spatial = spatial
+ num_pools = int(np.log2(spatial))
+ modules = []
+ modules += [Conv2d(in_c, out_c, kernel_size=3, stride=2, padding=1),
+ nn.LeakyReLU()]
+ for i in range(num_pools - 1):
+ modules += [
+ Conv2d(out_c, out_c, kernel_size=3, stride=2, padding=1),
+ nn.LeakyReLU()
+ ]
+ self.convs = nn.Sequential(*modules)
+ self.linear = EqualLinear(out_c, out_c, lr_mul=1)
+
+ def forward(self, x):
+ x = self.convs(x)
+ x = x.view(-1, self.out_c)
+ x = self.linear(x)
+ return x
+
+
+class GradualStyleEncoder(Module):
+ def __init__(self, num_layers, mode='ir', opts=None):
+ super(GradualStyleEncoder, self).__init__()
+ assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
+ assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
+ blocks = get_blocks(num_layers)
+ if mode == 'ir':
+ unit_module = bottleneck_IR
+ elif mode == 'ir_se':
+ unit_module = bottleneck_IR_SE
+ self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
+ BatchNorm2d(64),
+ PReLU(64))
+ modules = []
+ for block in blocks:
+ for bottleneck in block:
+ modules.append(unit_module(bottleneck.in_channel,
+ bottleneck.depth,
+ bottleneck.stride))
+ self.body = Sequential(*modules)
+
+ self.styles = nn.ModuleList()
+ log_size = int(math.log(opts.stylegan_size, 2))
+ self.style_count = 2 * log_size - 2
+ self.coarse_ind = 3
+ self.middle_ind = 7
+ for i in range(self.style_count):
+ if i < self.coarse_ind:
+ style = GradualStyleBlock(512, 512, 16)
+ elif i < self.middle_ind:
+ style = GradualStyleBlock(512, 512, 32)
+ else:
+ style = GradualStyleBlock(512, 512, 64)
+ self.styles.append(style)
+ self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0)
+ self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0)
+
+ def forward(self, x):
+ x = self.input_layer(x)
+
+ latents = []
+ modulelist = list(self.body._modules.values())
+ for i, l in enumerate(modulelist):
+ x = l(x)
+ if i == 6:
+ c1 = x
+ elif i == 20:
+ c2 = x
+ elif i == 23:
+ c3 = x
+
+ for j in range(self.coarse_ind):
+ latents.append(self.styles[j](c3))
+
+ p2 = _upsample_add(c3, self.latlayer1(c2))
+ for j in range(self.coarse_ind, self.middle_ind):
+ latents.append(self.styles[j](p2))
+
+ p1 = _upsample_add(p2, self.latlayer2(c1))
+ for j in range(self.middle_ind, self.style_count):
+ latents.append(self.styles[j](p1))
+
+ out = torch.stack(latents, dim=1)
+ return out
+
+
+class Encoder4Editing(Module):
+ def __init__(self, num_layers, mode='ir', opts=None):
+ super(Encoder4Editing, self).__init__()
+ assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
+ assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
+ blocks = get_blocks(num_layers)
+ if mode == 'ir':
+ unit_module = bottleneck_IR
+ elif mode == 'ir_se':
+ unit_module = bottleneck_IR_SE
+ self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
+ BatchNorm2d(64),
+ PReLU(64))
+ modules = []
+ for block in blocks:
+ for bottleneck in block:
+ modules.append(unit_module(bottleneck.in_channel,
+ bottleneck.depth,
+ bottleneck.stride))
+ self.body = Sequential(*modules)
+
+ self.styles = nn.ModuleList()
+ log_size = int(math.log(opts.stylegan_size, 2))
+ self.style_count = 2 * log_size - 2
+ self.coarse_ind = 3
+ self.middle_ind = 7
+
+ for i in range(self.style_count):
+ if i < self.coarse_ind:
+ style = GradualStyleBlock(512, 512, 16)
+ elif i < self.middle_ind:
+ style = GradualStyleBlock(512, 512, 32)
+ else:
+ style = GradualStyleBlock(512, 512, 64)
+ self.styles.append(style)
+
+ self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0)
+ self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0)
+
+ self.progressive_stage = ProgressiveStage.Inference
+
+ def get_deltas_starting_dimensions(self):
+ ''' Get a list of the initial dimension of every delta from which it is applied '''
+ return list(range(self.style_count)) # Each dimension has a delta applied to it
+
+ def set_progressive_stage(self, new_stage: ProgressiveStage):
+ self.progressive_stage = new_stage
+ print('Changed progressive stage to: ', new_stage)
+
+ def forward(self, x):
+ x = self.input_layer(x)
+
+ modulelist = list(self.body._modules.values())
+ for i, l in enumerate(modulelist):
+ x = l(x)
+ if i == 6:
+ c1 = x
+ elif i == 20:
+ c2 = x
+ elif i == 23:
+ c3 = x
+
+ # Infer main W and duplicate it
+ w0 = self.styles[0](c3)
+ w = w0.repeat(self.style_count, 1, 1).permute(1, 0, 2)
+ stage = self.progressive_stage.value
+ features = c3
+ for i in range(1, min(stage + 1, self.style_count)): # Infer additional deltas
+ if i == self.coarse_ind:
+ p2 = _upsample_add(c3, self.latlayer1(c2)) # FPN's middle features
+ features = p2
+ elif i == self.middle_ind:
+ p1 = _upsample_add(p2, self.latlayer2(c1)) # FPN's fine features
+ features = p1
+ delta_i = self.styles[i](features)
+ w[:, i] += delta_i
+ return w
diff --git a/stylegan_human/pti/pti_models/e4e/latent_codes_pool.py b/stylegan_human/pti/pti_models/e4e/latent_codes_pool.py
new file mode 100644
index 0000000000000000000000000000000000000000..0281d4b5e80f8eb26e824fa35b4f908dcb6634e6
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/latent_codes_pool.py
@@ -0,0 +1,55 @@
+import random
+import torch
+
+
+class LatentCodesPool:
+ """This class implements latent codes buffer that stores previously generated w latent codes.
+ This buffer enables us to update discriminators using a history of generated w's
+ rather than the ones produced by the latest encoder.
+ """
+
+ def __init__(self, pool_size):
+ """Initialize the ImagePool class
+ Parameters:
+ pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created
+ """
+ self.pool_size = pool_size
+ if self.pool_size > 0: # create an empty pool
+ self.num_ws = 0
+ self.ws = []
+
+ def query(self, ws):
+ """Return w's from the pool.
+ Parameters:
+ ws: the latest generated w's from the generator
+ Returns w's from the buffer.
+ By 50/100, the buffer will return input w's.
+ By 50/100, the buffer will return w's previously stored in the buffer,
+ and insert the current w's to the buffer.
+ """
+ if self.pool_size == 0: # if the buffer size is 0, do nothing
+ return ws
+ return_ws = []
+ for w in ws: # ws.shape: (batch, 512) or (batch, n_latent, 512)
+ # w = torch.unsqueeze(image.data, 0)
+ if w.ndim == 2:
+ i = random.randint(0, len(w) - 1) # apply a random latent index as a candidate
+ w = w[i]
+ self.handle_w(w, return_ws)
+ return_ws = torch.stack(return_ws, 0) # collect all the images and return
+ return return_ws
+
+ def handle_w(self, w, return_ws):
+ if self.num_ws < self.pool_size: # if the buffer is not full; keep inserting current codes to the buffer
+ self.num_ws = self.num_ws + 1
+ self.ws.append(w)
+ return_ws.append(w)
+ else:
+ p = random.uniform(0, 1)
+ if p > 0.5: # by 50% chance, the buffer will return a previously stored latent code, and insert the current code into the buffer
+ random_id = random.randint(0, self.pool_size - 1) # randint is inclusive
+ tmp = self.ws[random_id].clone()
+ self.ws[random_id] = w
+ return_ws.append(tmp)
+ else: # by another 50% chance, the buffer will return the current image
+ return_ws.append(w)
diff --git a/stylegan_human/pti/pti_models/e4e/psp.py b/stylegan_human/pti/pti_models/e4e/psp.py
new file mode 100644
index 0000000000000000000000000000000000000000..da7f66099059792f6857a7a0c295be35563a9587
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/psp.py
@@ -0,0 +1,99 @@
+import matplotlib
+from pti.pti_configs import paths_config
+matplotlib.use('Agg')
+import torch
+from torch import nn
+from pti.pti_models.e4e.encoders import psp_encoders
+from pti.pti_models.e4e.stylegan2.model import Generator
+
+
+def get_keys(d, name):
+ if 'state_dict' in d:
+ d = d['state_dict']
+ d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name}
+ return d_filt
+
+
+class pSp(nn.Module):
+
+ def __init__(self, opts):
+ super(pSp, self).__init__()
+ self.opts = opts
+ # Define architecture
+ self.encoder = self.set_encoder()
+ self.decoder = Generator(opts.stylegan_size, 512, 8, channel_multiplier=2)
+ self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256 // 2))
+ # Load weights if needed
+ self.load_weights()
+
+ def set_encoder(self):
+ if self.opts.encoder_type == 'GradualStyleEncoder':
+ encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts)
+ elif self.opts.encoder_type == 'Encoder4Editing':
+ encoder = psp_encoders.Encoder4Editing(50, 'ir_se', self.opts)
+ elif self.opts.encoder_type == 'SingleStyleCodeEncoder':
+ encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoW(50, 'ir_se', self.opts)
+ else:
+ raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type))
+ return encoder
+
+ def load_weights(self):
+ if self.opts.checkpoint_path is not None:
+ print('Loading e4e over the pSp framework from checkpoint: {}'.format(self.opts.checkpoint_path))
+ ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu')
+ self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=True)
+ self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=True)
+ self.__load_latent_avg(ckpt)
+ else:
+ print('Loading encoders weights from irse50!')
+ encoder_ckpt = torch.load(model_paths['ir_se50'])
+ self.encoder.load_state_dict(encoder_ckpt, strict=False)
+ print('Loading decoder weights from pretrained!')
+ ckpt = torch.load(self.opts.stylegan_weights)
+ self.decoder.load_state_dict(ckpt['g_ema'], strict=False)
+ self.__load_latent_avg(ckpt, repeat=self.encoder.style_count)
+
+ def forward(self, x, resize=True, latent_mask=None, input_code=False, randomize_noise=True,
+ inject_latent=None, return_latents=False, alpha=None):
+ if input_code:
+ codes = x
+ else:
+ codes = self.encoder(x)
+ # normalize with respect to the center of an average face
+ if self.opts.start_from_latent_avg:
+ if codes.ndim == 2:
+ codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :]
+ else:
+ codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)
+
+ if latent_mask is not None:
+ for i in latent_mask:
+ if inject_latent is not None:
+ if alpha is not None:
+ codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i]
+ else:
+ codes[:, i] = inject_latent[:, i]
+ else:
+ codes[:, i] = 0
+
+ input_is_latent = not input_code
+ images, result_latent = self.decoder([codes],
+ input_is_latent=input_is_latent,
+ randomize_noise=randomize_noise,
+ return_latents=return_latents)
+
+ if resize:
+ images = self.face_pool(images)
+
+ if return_latents:
+ return images, result_latent
+ else:
+ return images
+
+ def __load_latent_avg(self, ckpt, repeat=None):
+ if 'latent_avg' in ckpt:
+ self.latent_avg = ckpt['latent_avg'].to(self.opts.device)
+ if repeat is not None:
+ self.latent_avg = self.latent_avg.repeat(repeat, 1)
+ else:
+ self.latent_avg = None
diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/__init__.py b/stylegan_human/pti/pti_models/e4e/stylegan2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/model.py b/stylegan_human/pti/pti_models/e4e/stylegan2/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8c8f16d6baf48d95082abba9aa72d30f2bc4377
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/stylegan2/model.py
@@ -0,0 +1,680 @@
+import math
+import random
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .op.fused_act import FusedLeakyReLU, fused_leaky_relu
+from .op.upfirdn2d import upfirdn2d
+
+
+class PixelNorm(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, input):
+ return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
+
+
+def make_kernel(k):
+ k = torch.tensor(k, dtype=torch.float32)
+
+ if k.ndim == 1:
+ k = k[None, :] * k[:, None]
+
+ k /= k.sum()
+
+ return k
+
+
+class Upsample(nn.Module):
+ def __init__(self, kernel, factor=2):
+ super().__init__()
+
+ self.factor = factor
+ kernel = make_kernel(kernel) * (factor ** 2)
+ self.register_buffer('kernel', kernel)
+
+ p = kernel.shape[0] - factor
+
+ pad0 = (p + 1) // 2 + factor - 1
+ pad1 = p // 2
+
+ self.pad = (pad0, pad1)
+
+ def forward(self, input):
+ out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
+
+ return out
+
+
+class Downsample(nn.Module):
+ def __init__(self, kernel, factor=2):
+ super().__init__()
+
+ self.factor = factor
+ kernel = make_kernel(kernel)
+ self.register_buffer('kernel', kernel)
+
+ p = kernel.shape[0] - factor
+
+ pad0 = (p + 1) // 2
+ pad1 = p // 2
+
+ self.pad = (pad0, pad1)
+
+ def forward(self, input):
+ out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
+
+ return out
+
+
+class Blur(nn.Module):
+ def __init__(self, kernel, pad, upsample_factor=1):
+ super().__init__()
+
+ kernel = make_kernel(kernel)
+
+ if upsample_factor > 1:
+ kernel = kernel * (upsample_factor ** 2)
+
+ self.register_buffer('kernel', kernel)
+
+ self.pad = pad
+
+ def forward(self, input):
+ out = upfirdn2d(input, self.kernel, pad=self.pad)
+
+ return out
+
+
+class EqualConv2d(nn.Module):
+ def __init__(
+ self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
+ ):
+ super().__init__()
+
+ self.weight = nn.Parameter(
+ torch.randn(out_channel, in_channel, kernel_size, kernel_size)
+ )
+ self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
+
+ self.stride = stride
+ self.padding = padding
+
+ if bias:
+ self.bias = nn.Parameter(torch.zeros(out_channel))
+
+ else:
+ self.bias = None
+
+ def forward(self, input):
+ out = F.conv2d(
+ input,
+ self.weight * self.scale,
+ bias=self.bias,
+ stride=self.stride,
+ padding=self.padding,
+ )
+
+ return out
+
+ def __repr__(self):
+ return (
+ f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
+ f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
+ )
+
+
+class EqualLinear(nn.Module):
+ def __init__(
+ self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
+ ):
+ super().__init__()
+
+ self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
+
+ if bias:
+ self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
+
+ else:
+ self.bias = None
+
+ self.activation = activation
+
+ self.scale = (1 / math.sqrt(in_dim)) * lr_mul
+ self.lr_mul = lr_mul
+
+ def forward(self, input):
+ if self.activation:
+ out = F.linear(input, self.weight * self.scale)
+ out = fused_leaky_relu(out, self.bias * self.lr_mul)
+
+ else:
+ out = F.linear(
+ input, self.weight * self.scale, bias=self.bias * self.lr_mul
+ )
+
+ return out
+
+ def __repr__(self):
+ return (
+ f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
+ )
+
+
+class ScaledLeakyReLU(nn.Module):
+ def __init__(self, negative_slope=0.2):
+ super().__init__()
+
+ self.negative_slope = negative_slope
+
+ def forward(self, input):
+ out = F.leaky_relu(input, negative_slope=self.negative_slope)
+
+ return out * math.sqrt(2)
+
+
+class ModulatedConv2d(nn.Module):
+ def __init__(
+ self,
+ in_channel,
+ out_channel,
+ kernel_size,
+ style_dim,
+ demodulate=True,
+ upsample=False,
+ downsample=False,
+ blur_kernel=[1, 3, 3, 1],
+ ):
+ super().__init__()
+
+ self.eps = 1e-8
+ self.kernel_size = kernel_size
+ self.in_channel = in_channel
+ self.out_channel = out_channel
+ self.upsample = upsample
+ self.downsample = downsample
+
+ if upsample:
+ factor = 2
+ p = (len(blur_kernel) - factor) - (kernel_size - 1)
+ pad0 = (p + 1) // 2 + factor - 1
+ pad1 = p // 2 + 1
+
+ self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
+
+ if downsample:
+ factor = 2
+ p = (len(blur_kernel) - factor) + (kernel_size - 1)
+ pad0 = (p + 1) // 2
+ pad1 = p // 2
+
+ self.blur = Blur(blur_kernel, pad=(pad0, pad1))
+
+ fan_in = in_channel * kernel_size ** 2
+ self.scale = 1 / math.sqrt(fan_in)
+ self.padding = kernel_size // 2
+
+ self.weight = nn.Parameter(
+ torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
+ )
+
+ self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
+
+ self.demodulate = demodulate
+
+ def __repr__(self):
+ return (
+ f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
+ f'upsample={self.upsample}, downsample={self.downsample})'
+ )
+
+ def forward(self, input, style):
+ batch, in_channel, height, width = input.shape
+
+ style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
+ weight = self.scale * self.weight * style
+
+ if self.demodulate:
+ demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
+ weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
+
+ weight = weight.view(
+ batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
+ )
+
+ if self.upsample:
+ input = input.view(1, batch * in_channel, height, width)
+ weight = weight.view(
+ batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
+ )
+ weight = weight.transpose(1, 2).reshape(
+ batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
+ )
+ out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, self.out_channel, height, width)
+ out = self.blur(out)
+
+ elif self.downsample:
+ input = self.blur(input)
+ _, _, height, width = input.shape
+ input = input.view(1, batch * in_channel, height, width)
+ out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, self.out_channel, height, width)
+
+ else:
+ input = input.view(1, batch * in_channel, height, width)
+ out = F.conv2d(input, weight, padding=self.padding, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, self.out_channel, height, width)
+
+ return out
+
+
+class NoiseInjection(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ self.weight = nn.Parameter(torch.zeros(1))
+
+ def forward(self, image, noise=None):
+ if noise is None:
+ batch, _, height, width = image.shape
+ noise = image.new_empty(batch, 1, height, width).normal_()
+
+ return image + self.weight * noise
+
+
+class ConstantInput(nn.Module):
+ def __init__(self, channel, size=4):
+ super().__init__()
+
+ self.input = nn.Parameter(torch.randn(1, channel, size, size // 2))
+
+ def forward(self, input):
+ batch = input.shape[0]
+ out = self.input.repeat(batch, 1, 1, 1)
+
+ return out
+
+
+class StyledConv(nn.Module):
+ def __init__(
+ self,
+ in_channel,
+ out_channel,
+ kernel_size,
+ style_dim,
+ upsample=False,
+ blur_kernel=[1, 3, 3, 1],
+ demodulate=True,
+ ):
+ super().__init__()
+
+ self.conv = ModulatedConv2d(
+ in_channel,
+ out_channel,
+ kernel_size,
+ style_dim,
+ upsample=upsample,
+ blur_kernel=blur_kernel,
+ demodulate=demodulate,
+ )
+
+ self.noise = NoiseInjection()
+ # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
+ # self.activate = ScaledLeakyReLU(0.2)
+ self.activate = FusedLeakyReLU(out_channel)
+
+ def forward(self, input, style, noise=None):
+ out = self.conv(input, style)
+ out = self.noise(out, noise=noise)
+ # out = out + self.bias
+ out = self.activate(out)
+
+ return out
+
+
+class ToRGB(nn.Module):
+ def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
+ super().__init__()
+
+ if upsample:
+ self.upsample = Upsample(blur_kernel)
+
+ self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
+ self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
+
+ def forward(self, input, style, skip=None):
+ out = self.conv(input, style)
+ out = out + self.bias
+
+ if skip is not None:
+ skip = self.upsample(skip)
+
+ out = out + skip
+
+ return out
+
+
+class Generator(nn.Module):
+ def __init__(
+ self,
+ size,
+ style_dim,
+ n_mlp,
+ channel_multiplier=2,
+ blur_kernel=[1, 3, 3, 1],
+ lr_mlp=0.01,
+ ):
+ super().__init__()
+
+ self.size = size
+
+ self.style_dim = style_dim
+
+ layers = [PixelNorm()]
+
+ for i in range(n_mlp):
+ layers.append(
+ EqualLinear(
+ style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu'
+ )
+ )
+
+ self.style = nn.Sequential(*layers)
+
+ self.channels = {
+ 4: 512,
+ 8: 512,
+ 16: 512,
+ 32: 512,
+ 64: 256 * channel_multiplier,
+ 128: 128 * channel_multiplier,
+ 256: 64 * channel_multiplier,
+ 512: 32 * channel_multiplier,
+ 1024: 16 * channel_multiplier,
+ }
+
+ self.input = ConstantInput(self.channels[4])
+ self.conv1 = StyledConv(
+ self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
+ )
+ self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
+
+ self.log_size = int(math.log(size, 2))
+ self.num_layers = (self.log_size - 2) * 2 + 1
+
+ self.convs = nn.ModuleList()
+ self.upsamples = nn.ModuleList()
+ self.to_rgbs = nn.ModuleList()
+ self.noises = nn.Module()
+
+ in_channel = self.channels[4]
+
+ for layer_idx in range(self.num_layers):
+ res = (layer_idx + 5) // 2
+ shape = [1, 1, 2 ** res, 2 ** res // 2]
+ self.noises.register_buffer(
+ "noise_{}".format(layer_idx), torch.randn(*shape)
+ )
+
+ for i in range(3, self.log_size + 1):
+ out_channel = self.channels[2 ** i]
+
+ self.convs.append(
+ StyledConv(
+ in_channel,
+ out_channel,
+ 3,
+ style_dim,
+ upsample=True,
+ blur_kernel=blur_kernel,
+ )
+ )
+
+ self.convs.append(
+ StyledConv(
+ out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
+ )
+ )
+
+ self.to_rgbs.append(ToRGB(out_channel, style_dim))
+
+ in_channel = out_channel
+
+ self.n_latent = self.log_size * 2 - 2
+
+ def make_noise(self):
+ device = self.input.input.device
+
+ noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2 // 2, device=device)]
+
+ for i in range(3, self.log_size + 1):
+ for _ in range(2):
+ noises.append(torch.randn(1, 1, 2 ** i, 2 ** i // 2, device=device))
+
+ return noises
+
+ def mean_latent(self, n_latent):
+ latent_in = torch.randn(
+ n_latent, self.style_dim, device=self.input.input.device
+ )
+ latent = self.style(latent_in).mean(0, keepdim=True)
+
+ return latent
+
+ def get_latent(self, input):
+ return self.style(input)
+
+ def forward(
+ self,
+ styles,
+ return_latents=False,
+ return_features=False,
+ inject_index=None,
+ truncation=1,
+ truncation_latent=None,
+ input_is_latent=False,
+ noise=None,
+ randomize_noise=True,
+ ):
+ if not input_is_latent:
+ styles = [self.style(s) for s in styles]
+
+ if noise is None:
+ if randomize_noise:
+ noise = [None] * self.num_layers
+ else:
+ noise = [
+ getattr(self.noises, f'noise_{i}') for i in range(self.num_layers)
+ ]
+
+ if truncation < 1:
+ style_t = []
+
+ for style in styles:
+ style_t.append(
+ truncation_latent + truncation * (style - truncation_latent)
+ )
+
+ styles = style_t
+
+ if len(styles) < 2:
+ inject_index = self.n_latent
+ if styles[0].ndim < 3:
+ latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+ else:
+ latent = styles[0]
+
+ else:
+ if inject_index is None:
+ inject_index = random.randint(1, self.n_latent - 1)
+
+ # latent = styles[0].unsqueeze(0)
+ # if latent.shape[1] == 1:
+ # latent = latent.repeat(1, inject_index, 1)
+ # else:
+ # latent = latent[:, :inject_index, :]
+ latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+ latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
+ # latent = styles[0][:, :inject_index, :]
+ # latent2 = styles[1][:, inject_index:, :]
+ latent = torch.cat([latent, latent2], 1)
+ out = self.input(latent)
+ out = self.conv1(out, latent[:, 0], noise=noise[0])
+
+ skip = self.to_rgb1(out, latent[:, 1])
+
+ i = 1
+ for conv1, conv2, noise1, noise2, to_rgb in zip(
+ self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
+ ):
+ out = conv1(out, latent[:, i], noise=noise1)
+ out = conv2(out, latent[:, i + 1], noise=noise2)
+ skip = to_rgb(out, latent[:, i + 2], skip)
+
+ i += 2
+
+ image = skip
+
+ if return_latents:
+ return image, latent
+ elif return_features:
+ return image, out
+ else:
+ return image, None
+
+
+class ConvLayer(nn.Sequential):
+ def __init__(
+ self,
+ in_channel,
+ out_channel,
+ kernel_size,
+ downsample=False,
+ blur_kernel=[1, 3, 3, 1],
+ bias=True,
+ activate=True,
+ ):
+ layers = []
+
+ if downsample:
+ factor = 2
+ p = (len(blur_kernel) - factor) + (kernel_size - 1)
+ pad0 = (p + 1) // 2
+ pad1 = p // 2
+
+ layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
+
+ stride = 2
+ self.padding = 0
+
+ else:
+ stride = 1
+ self.padding = kernel_size // 2
+
+ layers.append(
+ EqualConv2d(
+ in_channel,
+ out_channel,
+ kernel_size,
+ padding=self.padding,
+ stride=stride,
+ bias=bias and not activate,
+ )
+ )
+
+ if activate:
+ if bias:
+ layers.append(FusedLeakyReLU(out_channel))
+
+ else:
+ layers.append(ScaledLeakyReLU(0.2))
+
+ super().__init__(*layers)
+
+
+class ResBlock(nn.Module):
+ def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
+ super().__init__()
+
+ self.conv1 = ConvLayer(in_channel, in_channel, 3)
+ self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
+
+ self.skip = ConvLayer(
+ in_channel, out_channel, 1, downsample=True, activate=False, bias=False
+ )
+
+ def forward(self, input):
+ out = self.conv1(input)
+ out = self.conv2(out)
+
+ skip = self.skip(input)
+ out = (out + skip) / math.sqrt(2)
+
+ return out
+
+
+class Discriminator(nn.Module):
+ def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
+ super().__init__()
+
+ channels = {
+ 4: 512,
+ 8: 512,
+ 16: 512,
+ 32: 512,
+ 64: 256 * channel_multiplier,
+ 128: 128 * channel_multiplier,
+ 256: 64 * channel_multiplier,
+ 512: 32 * channel_multiplier,
+ 1024: 16 * channel_multiplier,
+ }
+
+ convs = [ConvLayer(3, channels[size], 1)]
+
+ log_size = int(math.log(size, 2))
+
+ in_channel = channels[size]
+
+ for i in range(log_size, 2, -1):
+ out_channel = channels[2 ** (i - 1)]
+
+ convs.append(ResBlock(in_channel, out_channel, blur_kernel))
+
+ in_channel = out_channel
+
+ self.convs = nn.Sequential(*convs)
+
+ self.stddev_group = 4
+ self.stddev_feat = 1
+
+ self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
+ self.final_linear = nn.Sequential(
+ EqualLinear(channels[4] * 4 * 4 // 2, channels[4], activation='fused_lrelu'),
+ EqualLinear(channels[4], 1),
+ )
+
+ def forward(self, input):
+ out = self.convs(input)
+
+ batch, channel, height, width = out.shape
+ group = min(batch, self.stddev_group)
+ stddev = out.view(
+ group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
+ )
+ stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
+ stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
+ stddev = stddev.repeat(group, 1, height, width)
+ out = torch.cat([out, stddev], 1)
+
+ out = self.final_conv(out)
+
+ out = out.view(batch, -1)
+ out = self.final_linear(out)
+
+ return out
diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/op/__init__.py b/stylegan_human/pti/pti_models/e4e/stylegan2/op/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0918d92285955855be89f00096b888ee5597ce3
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/stylegan2/op/__init__.py
@@ -0,0 +1,2 @@
+from .fused_act import FusedLeakyReLU, fused_leaky_relu
+from .upfirdn2d import upfirdn2d
diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_act.py b/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_act.py
new file mode 100644
index 0000000000000000000000000000000000000000..90949545ba955dabf2e17d8cf5e524d5cb190a63
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_act.py
@@ -0,0 +1,34 @@
+import os
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.autograd import Function
+
+
+module_path = os.path.dirname(__file__)
+
+
+
+class FusedLeakyReLU(nn.Module):
+ def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
+ super().__init__()
+
+ self.bias = nn.Parameter(torch.zeros(channel))
+ self.negative_slope = negative_slope
+ self.scale = scale
+
+ def forward(self, input):
+ return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
+
+
+def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
+ rest_dim = [1] * (input.ndim - bias.ndim - 1)
+ input = input.cuda()
+ return (
+ F.leaky_relu(
+ input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
+ )
+ * scale
+ )
+
diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_bias_act.cpp b/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_bias_act.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..02be898f970bcc8ea297867fcaa4e71b24b3d949
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_bias_act.cpp
@@ -0,0 +1,21 @@
+#include
+
+
+torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
+ int act, int grad, float alpha, float scale);
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+
+torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
+ int act, int grad, float alpha, float scale) {
+ CHECK_CUDA(input);
+ CHECK_CUDA(bias);
+
+ return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
+}
\ No newline at end of file
diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_bias_act_kernel.cu b/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_bias_act_kernel.cu
new file mode 100644
index 0000000000000000000000000000000000000000..c9fa56fea7ede7072dc8925cfb0148f136eb85b8
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_bias_act_kernel.cu
@@ -0,0 +1,99 @@
+// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+//
+// This work is made available under the Nvidia Source Code License-NC.
+// To view a copy of this license, visit
+// https://nvlabs.github.io/stylegan2/license.html
+
+#include
+
+#include
+#include
+#include
+#include
+
+#include
+#include
+
+
+template
+static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref,
+ int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) {
+ int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x;
+
+ scalar_t zero = 0.0;
+
+ for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) {
+ scalar_t x = p_x[xi];
+
+ if (use_bias) {
+ x += p_b[(xi / step_b) % size_b];
+ }
+
+ scalar_t ref = use_ref ? p_ref[xi] : zero;
+
+ scalar_t y;
+
+ switch (act * 10 + grad) {
+ default:
+ case 10: y = x; break;
+ case 11: y = x; break;
+ case 12: y = 0.0; break;
+
+ case 30: y = (x > 0.0) ? x : x * alpha; break;
+ case 31: y = (ref > 0.0) ? x : x * alpha; break;
+ case 32: y = 0.0; break;
+ }
+
+ out[xi] = y * scale;
+ }
+}
+
+
+torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
+ int act, int grad, float alpha, float scale) {
+ int curDevice = -1;
+ cudaGetDevice(&curDevice);
+ cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);
+
+ auto x = input.contiguous();
+ auto b = bias.contiguous();
+ auto ref = refer.contiguous();
+
+ int use_bias = b.numel() ? 1 : 0;
+ int use_ref = ref.numel() ? 1 : 0;
+
+ int size_x = x.numel();
+ int size_b = b.numel();
+ int step_b = 1;
+
+ for (int i = 1 + 1; i < x.dim(); i++) {
+ step_b *= x.size(i);
+ }
+
+ int loop_x = 4;
+ int block_size = 4 * 32;
+ int grid_size = (size_x - 1) / (loop_x * block_size) + 1;
+
+ auto y = torch::empty_like(x);
+
+ AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] {
+ fused_bias_act_kernel<<>>(
+ y.data_ptr(),
+ x.data_ptr(),
+ b.data_ptr(),
+ ref.data_ptr(),
+ act,
+ grad,
+ alpha,
+ scale,
+ loop_x,
+ size_x,
+ step_b,
+ size_b,
+ use_bias,
+ use_ref
+ );
+ });
+
+ return y;
+}
\ No newline at end of file
diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.cpp b/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..d2e633dc896433c205e18bc3e455539192ff968e
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.cpp
@@ -0,0 +1,23 @@
+#include
+
+
+torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
+ int up_x, int up_y, int down_x, int down_y,
+ int pad_x0, int pad_x1, int pad_y0, int pad_y1);
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+
+torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
+ int up_x, int up_y, int down_x, int down_y,
+ int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
+ CHECK_CUDA(input);
+ CHECK_CUDA(kernel);
+
+ return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
+}
\ No newline at end of file
diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.py b/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.py
new file mode 100644
index 0000000000000000000000000000000000000000..02fc25af780868d9b883631eb6b03a25c225d745
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.py
@@ -0,0 +1,60 @@
+import os
+
+import torch
+from torch.nn import functional as F
+
+
+module_path = os.path.dirname(__file__)
+
+
+
+def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
+ out = upfirdn2d_native(
+ input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
+ )
+
+ return out
+
+
+def upfirdn2d_native(
+ input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
+):
+ _, channel, in_h, in_w = input.shape
+ input = input.reshape(-1, in_h, in_w, 1)
+
+ _, in_h, in_w, minor = input.shape
+ kernel_h, kernel_w = kernel.shape
+
+ out = input.view(-1, in_h, 1, in_w, 1, minor)
+ out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
+ out = out.view(-1, in_h * up_y, in_w * up_x, minor)
+
+ out = F.pad(
+ out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
+ )
+ out = out[
+ :,
+ max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
+ max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
+ :,
+ ]
+
+ out = out.permute(0, 3, 1, 2)
+ out = out.reshape(
+ [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
+ )
+ w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
+ out = F.conv2d(out, w)
+ out = out.reshape(
+ -1,
+ minor,
+ in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
+ in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
+ )
+ out = out.permute(0, 2, 3, 1)
+ out = out[:, ::down_y, ::down_x, :]
+
+ out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
+ out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
+
+ return out.view(-1, channel, out_h, out_w)
\ No newline at end of file
diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d_kernel.cu b/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d_kernel.cu
new file mode 100644
index 0000000000000000000000000000000000000000..2a710aa6adc3d43ac93136a1814e3c39970e1c7e
--- /dev/null
+++ b/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d_kernel.cu
@@ -0,0 +1,272 @@
+// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+//
+// This work is made available under the Nvidia Source Code License-NC.
+// To view a copy of this license, visit
+// https://nvlabs.github.io/stylegan2/license.html
+
+#include
+
+#include
+#include
+#include
+#include
+
+#include
+#include
+
+
+static __host__ __device__ __forceinline__ int floor_div(int a, int b) {
+ int c = a / b;
+
+ if (c * b > a) {
+ c--;
+ }
+
+ return c;
+}
+
+
+struct UpFirDn2DKernelParams {
+ int up_x;
+ int up_y;
+ int down_x;
+ int down_y;
+ int pad_x0;
+ int pad_x1;
+ int pad_y0;
+ int pad_y1;
+
+ int major_dim;
+ int in_h;
+ int in_w;
+ int minor_dim;
+ int kernel_h;
+ int kernel_w;
+ int out_h;
+ int out_w;
+ int loop_major;
+ int loop_x;
+};
+
+
+template
+__global__ void upfirdn2d_kernel(scalar_t* out, const scalar_t* input, const scalar_t* kernel, const UpFirDn2DKernelParams p) {
+ const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1;
+ const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1;
+
+ __shared__ volatile float sk[kernel_h][kernel_w];
+ __shared__ volatile float sx[tile_in_h][tile_in_w];
+
+ int minor_idx = blockIdx.x;
+ int tile_out_y = minor_idx / p.minor_dim;
+ minor_idx -= tile_out_y * p.minor_dim;
+ tile_out_y *= tile_out_h;
+ int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w;
+ int major_idx_base = blockIdx.z * p.loop_major;
+
+ if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | major_idx_base >= p.major_dim) {
+ return;
+ }
+
+ for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; tap_idx += blockDim.x) {
+ int ky = tap_idx / kernel_w;
+ int kx = tap_idx - ky * kernel_w;
+ scalar_t v = 0.0;
+
+ if (kx < p.kernel_w & ky < p.kernel_h) {
+ v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)];
+ }
+
+ sk[ky][kx] = v;
+ }
+
+ for (int loop_major = 0, major_idx = major_idx_base; loop_major < p.loop_major & major_idx < p.major_dim; loop_major++, major_idx++) {
+ for (int loop_x = 0, tile_out_x = tile_out_x_base; loop_x < p.loop_x & tile_out_x < p.out_w; loop_x++, tile_out_x += tile_out_w) {
+ int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0;
+ int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0;
+ int tile_in_x = floor_div(tile_mid_x, up_x);
+ int tile_in_y = floor_div(tile_mid_y, up_y);
+
+ __syncthreads();
+
+ for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; in_idx += blockDim.x) {
+ int rel_in_y = in_idx / tile_in_w;
+ int rel_in_x = in_idx - rel_in_y * tile_in_w;
+ int in_x = rel_in_x + tile_in_x;
+ int in_y = rel_in_y + tile_in_y;
+
+ scalar_t v = 0.0;
+
+ if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) {
+ v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + minor_idx];
+ }
+
+ sx[rel_in_y][rel_in_x] = v;
+ }
+
+ __syncthreads();
+ for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; out_idx += blockDim.x) {
+ int rel_out_y = out_idx / tile_out_w;
+ int rel_out_x = out_idx - rel_out_y * tile_out_w;
+ int out_x = rel_out_x + tile_out_x;
+ int out_y = rel_out_y + tile_out_y;
+
+ int mid_x = tile_mid_x + rel_out_x * down_x;
+ int mid_y = tile_mid_y + rel_out_y * down_y;
+ int in_x = floor_div(mid_x, up_x);
+ int in_y = floor_div(mid_y, up_y);
+ int rel_in_x = in_x - tile_in_x;
+ int rel_in_y = in_y - tile_in_y;
+ int kernel_x = (in_x + 1) * up_x - mid_x - 1;
+ int kernel_y = (in_y + 1) * up_y - mid_y - 1;
+
+ scalar_t v = 0.0;
+
+ #pragma unroll
+ for (int y = 0; y < kernel_h / up_y; y++)
+ #pragma unroll
+ for (int x = 0; x < kernel_w / up_x; x++)
+ v += sx[rel_in_y + y][rel_in_x + x] * sk[kernel_y + y * up_y][kernel_x + x * up_x];
+
+ if (out_x < p.out_w & out_y < p.out_h) {
+ out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + minor_idx] = v;
+ }
+ }
+ }
+ }
+}
+
+
+torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
+ int up_x, int up_y, int down_x, int down_y,
+ int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
+ int curDevice = -1;
+ cudaGetDevice(&curDevice);
+ cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);
+
+ UpFirDn2DKernelParams p;
+
+ auto x = input.contiguous();
+ auto k = kernel.contiguous();
+
+ p.major_dim = x.size(0);
+ p.in_h = x.size(1);
+ p.in_w = x.size(2);
+ p.minor_dim = x.size(3);
+ p.kernel_h = k.size(0);
+ p.kernel_w = k.size(1);
+ p.up_x = up_x;
+ p.up_y = up_y;
+ p.down_x = down_x;
+ p.down_y = down_y;
+ p.pad_x0 = pad_x0;
+ p.pad_x1 = pad_x1;
+ p.pad_y0 = pad_y0;
+ p.pad_y1 = pad_y1;
+
+ p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / p.down_y;
+ p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / p.down_x;
+
+ auto out = at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options());
+
+ int mode = -1;
+
+ int tile_out_h;
+ int tile_out_w;
+
+ if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) {
+ mode = 1;
+ tile_out_h = 16;
+ tile_out_w = 64;
+ }
+
+ if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 3 && p.kernel_w <= 3) {
+ mode = 2;
+ tile_out_h = 16;
+ tile_out_w = 64;
+ }
+
+ if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) {
+ mode = 3;
+ tile_out_h = 16;
+ tile_out_w = 64;
+ }
+
+ if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 2 && p.kernel_w <= 2) {
+ mode = 4;
+ tile_out_h = 16;
+ tile_out_w = 64;
+ }
+
+ if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 4 && p.kernel_w <= 4) {
+ mode = 5;
+ tile_out_h = 8;
+ tile_out_w = 32;
+ }
+
+ if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 2 && p.kernel_w <= 2) {
+ mode = 6;
+ tile_out_h = 8;
+ tile_out_w = 32;
+ }
+
+ dim3 block_size;
+ dim3 grid_size;
+
+ if (tile_out_h > 0 && tile_out_w) {
+ p.loop_major = (p.major_dim - 1) / 16384 + 1;
+ p.loop_x = 1;
+ block_size = dim3(32 * 8, 1, 1);
+ grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim,
+ (p.out_w - 1) / (p.loop_x * tile_out_w) + 1,
+ (p.major_dim - 1) / p.loop_major + 1);
+ }
+
+ AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] {
+ switch (mode) {
+ case 1:
+ upfirdn2d_kernel<<>>(
+ out.data_ptr(), x.data_ptr(), k.data_ptr(), p
+ );
+
+ break;
+
+ case 2:
+ upfirdn2d_kernel<<>>(
+ out.data_ptr(), x.data_ptr(), k.data_ptr(), p
+ );
+
+ break;
+
+ case 3:
+ upfirdn2d_kernel<<>>(
+ out.data_ptr(), x.data_ptr(), k.data_ptr(), p
+ );
+
+ break;
+
+ case 4:
+ upfirdn2d_kernel<<>>(
+ out.data_ptr(), x.data_ptr(), k.data_ptr(), p
+ );
+
+ break;
+
+ case 5:
+ upfirdn2d_kernel<<>>(
+ out.data_ptr(), x.data_ptr(), k.data_ptr(), p
+ );
+
+ break;
+
+ case 6:
+ upfirdn2d_kernel<<>>(
+ out.data_ptr(), x.data_ptr(), k.data_ptr(), p
+ );
+
+ break;
+ }
+ });
+
+ return out;
+}
\ No newline at end of file
diff --git a/stylegan_human/pti/training/__init__.py b/stylegan_human/pti/training/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stylegan_human/pti/training/coaches/__init__.py b/stylegan_human/pti/training/coaches/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stylegan_human/pti/training/coaches/base_coach.py b/stylegan_human/pti/training/coaches/base_coach.py
new file mode 100644
index 0000000000000000000000000000000000000000..b429bd3cf186fc32bbf9491a39a889eebfe2110d
--- /dev/null
+++ b/stylegan_human/pti/training/coaches/base_coach.py
@@ -0,0 +1,150 @@
+import abc
+import os
+import pickle
+from argparse import Namespace
+import wandb
+import os.path
+from .localitly_regulizer import Space_Regulizer, l2_loss
+import torch
+from torchvision import transforms
+from lpips import LPIPS
+from pti.training.projectors import w_projector
+from pti.pti_configs import global_config, paths_config, hyperparameters
+from pti.pti_models.e4e.psp import pSp
+from utils.log_utils import log_image_from_w
+from utils.models_utils import toogle_grad, load_old_G
+
+
+class BaseCoach:
+ def __init__(self, data_loader, use_wandb):
+
+ self.use_wandb = use_wandb
+ self.data_loader = data_loader
+ self.w_pivots = {}
+ self.image_counter = 0
+
+ if hyperparameters.first_inv_type == 'w+':
+ self.initilize_e4e()
+
+ self.e4e_image_transform = transforms.Compose([
+ transforms.ToPILImage(),
+ transforms.Resize((256, 128)),
+ transforms.ToTensor(),
+ transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
+
+ # Initialize loss
+ self.lpips_loss = LPIPS(net=hyperparameters.lpips_type).to(global_config.device).eval()
+
+ self.restart_training()
+
+ # Initialize checkpoint dir
+ self.checkpoint_dir = paths_config.checkpoints_dir
+ os.makedirs(self.checkpoint_dir, exist_ok=True)
+
+ def restart_training(self):
+
+ # Initialize networks
+ self.G = load_old_G()
+ toogle_grad(self.G, True)
+
+ self.original_G = load_old_G()
+
+ self.space_regulizer = Space_Regulizer(self.original_G, self.lpips_loss)
+ self.optimizer = self.configure_optimizers()
+
+ def get_inversion(self, w_path_dir, image_name, image):
+ embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}'
+ os.makedirs(embedding_dir, exist_ok=True)
+
+ w_pivot = None
+
+ if hyperparameters.use_last_w_pivots:
+ w_pivot = self.load_inversions(w_path_dir, image_name)
+
+ if not hyperparameters.use_last_w_pivots or w_pivot is None:
+ w_pivot = self.calc_inversions(image, image_name)
+ torch.save(w_pivot, f'{embedding_dir}/0.pt')
+
+ w_pivot = w_pivot.to(global_config.device)
+ return w_pivot
+
+ def load_inversions(self, w_path_dir, image_name):
+ if image_name in self.w_pivots:
+ return self.w_pivots[image_name]
+
+ if hyperparameters.first_inv_type == 'w+':
+ w_potential_path = f'{w_path_dir}/{paths_config.e4e_results_keyword}/{image_name}/0.pt'
+ else:
+ w_potential_path = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}/0.pt'
+ if not os.path.isfile(w_potential_path):
+ return None
+ w = torch.load(w_potential_path).to(global_config.device)
+ self.w_pivots[image_name] = w
+ return w
+
+ def calc_inversions(self, image, image_name):
+ if hyperparameters.first_inv_type == 'w+':
+ w = self.get_e4e_inversion(image)
+
+ else:
+ id_image = torch.squeeze((image.to(global_config.device) + 1) / 2) * 255
+ w = w_projector.project(self.G, id_image, device=torch.device(global_config.device), w_avg_samples=600,
+ num_steps=hyperparameters.first_inv_steps, w_name=image_name,
+ use_wandb=self.use_wandb)
+
+ return w
+
+ @abc.abstractmethod
+ def train(self):
+ pass
+
+ def configure_optimizers(self):
+ optimizer = torch.optim.Adam(self.G.parameters(), lr=hyperparameters.pti_learning_rate)
+
+ return optimizer
+
+ def calc_loss(self, generated_images, real_images, log_name, new_G, use_ball_holder, w_batch):
+ loss = 0.0
+
+ if hyperparameters.pt_l2_lambda > 0:
+ l2_loss_val = l2_loss(generated_images, real_images)
+ if self.use_wandb:
+ wandb.log({f'MSE_loss_val_{log_name}': l2_loss_val.detach().cpu()}, step=global_config.training_step)
+ loss += l2_loss_val * hyperparameters.pt_l2_lambda
+ if hyperparameters.pt_lpips_lambda > 0:
+ loss_lpips = self.lpips_loss(generated_images, real_images)
+ loss_lpips = torch.squeeze(loss_lpips)
+ if self.use_wandb:
+ wandb.log({f'LPIPS_loss_val_{log_name}': loss_lpips.detach().cpu()}, step=global_config.training_step)
+ loss += loss_lpips * hyperparameters.pt_lpips_lambda
+
+ if use_ball_holder and hyperparameters.use_locality_regularization:
+ ball_holder_loss_val = self.space_regulizer.space_regulizer_loss(new_G, w_batch, use_wandb=self.use_wandb)
+ loss += ball_holder_loss_val
+
+ return loss, l2_loss_val, loss_lpips
+
+ def forward(self, w):
+ generated_images = self.G.synthesis(w, noise_mode='const', force_fp32=True)
+
+ return generated_images
+
+ def initilize_e4e(self):
+ ckpt = torch.load(paths_config.e4e, map_location='cpu')
+ opts = ckpt['opts']
+ opts['batch_size'] = hyperparameters.train_batch_size
+ opts['checkpoint_path'] = paths_config.e4e
+ opts = Namespace(**opts)
+ self.e4e_inversion_net = pSp(opts)
+ self.e4e_inversion_net.eval()
+ self.e4e_inversion_net = self.e4e_inversion_net.to(global_config.device)
+ toogle_grad(self.e4e_inversion_net, False)
+
+ def get_e4e_inversion(self, image):
+ image = (image + 1) / 2
+ new_image = self.e4e_image_transform(image[0]).to(global_config.device)
+ _, w = self.e4e_inversion_net(new_image.unsqueeze(0), randomize_noise=False, return_latents=True, resize=False,
+ input_code=False)
+ if self.use_wandb:
+ log_image_from_w(w, self.G, 'First e4e inversion')
+ return w
diff --git a/stylegan_human/pti/training/coaches/localitly_regulizer.py b/stylegan_human/pti/training/coaches/localitly_regulizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a4edc3694dd4134d9caa6af0184909451032cc6
--- /dev/null
+++ b/stylegan_human/pti/training/coaches/localitly_regulizer.py
@@ -0,0 +1,63 @@
+import torch
+import numpy as np
+import wandb
+from pti.pti_configs import hyperparameters, global_config
+l2_criterion = torch.nn.MSELoss(reduction='mean')
+
+
+def l2_loss(real_images, generated_images):
+ loss = l2_criterion(real_images, generated_images)
+ return loss
+
+
+class Space_Regulizer:
+ def __init__(self, original_G, lpips_net):
+ self.original_G = original_G
+ self.morphing_regulizer_alpha = hyperparameters.regulizer_alpha
+ self.lpips_loss = lpips_net
+
+ def get_morphed_w_code(self, new_w_code, fixed_w):
+ interpolation_direction = new_w_code - fixed_w
+ interpolation_direction_norm = torch.norm(interpolation_direction, p=2)
+ direction_to_move = hyperparameters.regulizer_alpha * interpolation_direction / interpolation_direction_norm
+ result_w = fixed_w + direction_to_move
+ self.morphing_regulizer_alpha * fixed_w + (1 - self.morphing_regulizer_alpha) * new_w_code
+
+ return result_w
+
+ def get_image_from_ws(self, w_codes, G):
+ return torch.cat([G.synthesis(w_code, noise_mode='none', force_fp32=True) for w_code in w_codes])
+
+ def ball_holder_loss_lazy(self, new_G, num_of_sampled_latents, w_batch, use_wandb=False):
+ loss = 0.0
+
+ z_samples = np.random.randn(num_of_sampled_latents, self.original_G.z_dim)
+ w_samples = self.original_G.mapping(torch.from_numpy(z_samples).to(global_config.device), None,
+ truncation_psi=0.5)
+ territory_indicator_ws = [self.get_morphed_w_code(w_code.unsqueeze(0), w_batch) for w_code in w_samples]
+
+ for w_code in territory_indicator_ws:
+ new_img = new_G.synthesis(w_code, noise_mode='none', force_fp32=True)
+ with torch.no_grad():
+ old_img = self.original_G.synthesis(w_code, noise_mode='none', force_fp32=True)
+
+ if hyperparameters.regulizer_l2_lambda > 0:
+ l2_loss_val = l2_loss.l2_loss(old_img, new_img)
+ if use_wandb:
+ wandb.log({f'space_regulizer_l2_loss_val': l2_loss_val.detach().cpu()},
+ step=global_config.training_step)
+ loss += l2_loss_val * hyperparameters.regulizer_l2_lambda
+
+ if hyperparameters.regulizer_lpips_lambda > 0:
+ loss_lpips = self.lpips_loss(old_img, new_img)
+ loss_lpips = torch.mean(torch.squeeze(loss_lpips))
+ if use_wandb:
+ wandb.log({f'space_regulizer_lpips_loss_val': loss_lpips.detach().cpu()},
+ step=global_config.training_step)
+ loss += loss_lpips * hyperparameters.regulizer_lpips_lambda
+
+ return loss / len(territory_indicator_ws)
+
+ def space_regulizer_loss(self, new_G, w_batch, use_wandb):
+ ret_val = self.ball_holder_loss_lazy(new_G, hyperparameters.latent_ball_num_of_samples, w_batch, use_wandb)
+ return ret_val
diff --git a/stylegan_human/pti/training/coaches/multi_id_coach.py b/stylegan_human/pti/training/coaches/multi_id_coach.py
new file mode 100644
index 0000000000000000000000000000000000000000..97a9cd796fef1c2989a57d6b821e269d274f2d24
--- /dev/null
+++ b/stylegan_human/pti/training/coaches/multi_id_coach.py
@@ -0,0 +1,79 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+import os
+
+import torch
+from tqdm import tqdm
+
+from pti.pti_configs import paths_config, hyperparameters, global_config
+from pti.training.coaches.base_coach import BaseCoach
+from utils.log_utils import log_images_from_w
+
+
+class MultiIDCoach(BaseCoach):
+
+ def __init__(self, data_loader, use_wandb):
+ super().__init__(data_loader, use_wandb)
+
+ def train(self):
+ self.G.synthesis.train()
+ self.G.mapping.train()
+
+ w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}'
+ os.makedirs(w_path_dir, exist_ok=True)
+ os.makedirs(f'{w_path_dir}/{paths_config.pti_results_keyword}', exist_ok=True)
+
+ use_ball_holder = True
+ w_pivots = []
+ images = []
+
+ for fname, image in self.data_loader:
+ if self.image_counter >= hyperparameters.max_images_to_invert:
+ break
+
+ image_name = fname[0]
+ if hyperparameters.first_inv_type == 'w+':
+ embedding_dir = f'{w_path_dir}/{paths_config.e4e_results_keyword}/{image_name}'
+ else:
+ embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}'
+ os.makedirs(embedding_dir, exist_ok=True)
+
+ w_pivot = self.get_inversion(w_path_dir, image_name, image)
+ w_pivots.append(w_pivot)
+ images.append((image_name, image))
+ self.image_counter += 1
+
+ for i in tqdm(range(hyperparameters.max_pti_steps)):
+ self.image_counter = 0
+
+ for data, w_pivot in zip(images, w_pivots):
+ image_name, image = data
+
+ if self.image_counter >= hyperparameters.max_images_to_invert:
+ break
+
+ real_images_batch = image.to(global_config.device)
+
+ generated_images = self.forward(w_pivot)
+ loss, l2_loss_val, loss_lpips = self.calc_loss(generated_images, real_images_batch, image_name,
+ self.G, use_ball_holder, w_pivot)
+
+ self.optimizer.zero_grad()
+ loss.backward()
+ self.optimizer.step()
+
+ use_ball_holder = global_config.training_step % hyperparameters.locality_regularization_interval == 0
+
+ global_config.training_step += 1
+ self.image_counter += 1
+
+ if self.use_wandb:
+ log_images_from_w(w_pivots, self.G, [image[0] for image in images])
+
+ # torch.save(self.G,
+ # f'{paths_config.checkpoints_dir}/model_{global_config.run_name}_multi_id.pt')
+ snapshot_data = dict()
+ snapshot_data['G_ema'] = self.G
+ import pickle
+ with open(f'{paths_config.checkpoints_dir}/model_{global_config.run_name}_multi_id.pkl', 'wb') as f:
+ pickle.dump(snapshot_data, f)
diff --git a/stylegan_human/pti/training/coaches/single_id_coach.py b/stylegan_human/pti/training/coaches/single_id_coach.py
new file mode 100644
index 0000000000000000000000000000000000000000..7521a6eed000de76c14504f293efd2b6789eb5f1
--- /dev/null
+++ b/stylegan_human/pti/training/coaches/single_id_coach.py
@@ -0,0 +1,89 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+import os
+import torch
+from tqdm import tqdm
+from pti.pti_configs import paths_config, hyperparameters, global_config
+from pti.training.coaches.base_coach import BaseCoach
+from utils.log_utils import log_images_from_w
+from torchvision.utils import save_image
+
+class SingleIDCoach(BaseCoach):
+
+ def __init__(self, data_loader, use_wandb):
+ super().__init__(data_loader, use_wandb)
+
+ def train(self):
+
+ w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}'
+ os.makedirs(w_path_dir, exist_ok=True)
+ os.makedirs(f'{w_path_dir}/{paths_config.pti_results_keyword}', exist_ok=True)
+
+ use_ball_holder = True
+
+ for fname, image in tqdm(self.data_loader):
+ image_name = fname[0]
+
+ self.restart_training()
+
+ if self.image_counter >= hyperparameters.max_images_to_invert:
+ break
+
+ embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}'
+ os.makedirs(embedding_dir, exist_ok=True)
+
+ w_pivot = None
+
+ if hyperparameters.use_last_w_pivots:
+ w_pivot = self.load_inversions(w_path_dir, image_name)
+# Copyright (c) SenseTime Research. All rights reserved.
+
+ elif not hyperparameters.use_last_w_pivots or w_pivot is None:
+ w_pivot = self.calc_inversions(image, image_name)
+
+ # w_pivot = w_pivot.detach().clone().to(global_config.device)
+ w_pivot = w_pivot.to(global_config.device)
+
+ torch.save(w_pivot, f'{embedding_dir}/0.pt')
+ log_images_counter = 0
+ real_images_batch = image.to(global_config.device)
+
+ for i in range(hyperparameters.max_pti_steps):
+
+ generated_images = self.forward(w_pivot)
+ loss, l2_loss_val, loss_lpips = self.calc_loss(generated_images, real_images_batch, image_name,
+ self.G, use_ball_holder, w_pivot)
+ if i == 0:
+ tmp1 = torch.clone(generated_images)
+ if i % 10 == 0:
+ print("pti loss: ", i, loss.data, loss_lpips.data)
+ self.optimizer.zero_grad()
+
+ if loss_lpips <= hyperparameters.LPIPS_value_threshold:
+ break
+
+ loss.backward()
+ self.optimizer.step()
+
+ use_ball_holder = global_config.training_step % hyperparameters.locality_regularization_interval == 0
+
+ if self.use_wandb and log_images_counter % global_config.image_rec_result_log_snapshot == 0:
+ log_images_from_w([w_pivot], self.G, [image_name])
+
+ global_config.training_step += 1
+ log_images_counter += 1
+
+ # save output image
+ tmp = torch.cat([real_images_batch, tmp1, generated_images], axis= 3)
+ save_image(tmp, f"{paths_config.experiments_output_dir}/{image_name}.png", normalize=True)
+
+
+ self.image_counter += 1
+
+ # torch.save(self.G,
+ # f'{paths_config.checkpoints_dir}/model_{image_name}.pt') #'.pt'
+ snapshot_data = dict()
+ snapshot_data['G_ema'] = self.G
+ import pickle
+ with open(f'{paths_config.checkpoints_dir}/model_{image_name}.pkl', 'wb') as f:
+ pickle.dump(snapshot_data, f)
diff --git a/stylegan_human/pti/training/projectors/__init__.py b/stylegan_human/pti/training/projectors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stylegan_human/pti/training/projectors/w_plus_projector.py b/stylegan_human/pti/training/projectors/w_plus_projector.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d4abaf0ef32378504191559d7c95f3e58a63ffa
--- /dev/null
+++ b/stylegan_human/pti/training/projectors/w_plus_projector.py
@@ -0,0 +1,147 @@
+# Copyright (c) 2021, NVIDIA CORPORATION. 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.
+
+"""Project given image to the latent space of pretrained network pickle."""
+
+import copy
+import wandb
+import numpy as np
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+from configs import global_config, hyperparameters
+import dnnlib
+from utils.log_utils import log_image_from_w
+
+
+def project(
+ G,
+ target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
+ *,
+ num_steps=1000,
+ w_avg_samples=10000,
+ initial_learning_rate=0.01,
+ initial_noise_factor=0.05,
+ lr_rampdown_length=0.25,
+ lr_rampup_length=0.05,
+ noise_ramp_length=0.75,
+ regularize_noise_weight=1e5,
+ verbose=False,
+ device: torch.device,
+ use_wandb=False,
+ initial_w=None,
+ image_log_step=global_config.image_rec_result_log_snapshot,
+ w_name: str
+):
+ print('inside training/projectors/w_plus_projector')
+ print(target.shape, G.img_channels, G.img_resolution * 2 , G.img_resolution)
+ assert target.shape == (G.img_channels, G.img_resolution * 2, G.img_resolution)
+
+ def logprint(*args):
+ if verbose:
+ print(*args)
+
+ G = copy.deepcopy(G).eval().requires_grad_(False).to(device).float() # type: ignore
+
+ # Compute w stats.
+ logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...')
+ z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
+ w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None) # [N, L, C]
+ w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32) # [N, 1, C]
+ w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C]
+ w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device)
+ w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5
+
+ start_w = initial_w if initial_w is not None else w_avg
+
+ # Setup noise inputs.
+ noise_bufs = {name: buf for (name, buf) in G.synthesis.named_buffers() if 'noise_const' in name}
+
+ # Load VGG16 feature detector.
+ url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
+ with dnnlib.util.open_url(url) as f:
+ vgg16 = torch.jit.load(f).eval().to(device)
+
+ # Features for target image.
+ target_images = target.unsqueeze(0).to(device).to(torch.float32)
+ if target_images.shape[2] > 256:
+ target_images = F.interpolate(target_images, size=(256, 256), mode='area')
+ target_features = vgg16(target_images, resize_images=False, return_lpips=True)
+
+ start_w = np.repeat(start_w, G.mapping.num_ws, axis=1)
+ w_opt = torch.tensor(start_w, dtype=torch.float32, device=device,
+ requires_grad=True) # pylint: disable=not-callable
+
+ optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999),
+ lr=hyperparameters.first_inv_lr)
+
+ # Init noise.
+ for buf in noise_bufs.values():
+ buf[:] = torch.randn_like(buf)
+ buf.requires_grad = True
+
+ for step in tqdm(range(num_steps)):
+
+ # Learning rate schedule.
+ t = step / num_steps
+ w_noise_scale = w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2
+ lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
+ lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
+ lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
+ lr = initial_learning_rate * lr_ramp
+ for param_group in optimizer.param_groups:
+ param_group['lr'] = lr
+
+ # Synth images from opt_w.
+ w_noise = torch.randn_like(w_opt) * w_noise_scale
+ ws = (w_opt + w_noise)
+
+ synth_images = G.synthesis(ws, noise_mode='const', force_fp32=True)
+
+ # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
+ synth_images = (synth_images + 1) * (255 / 2)
+ if synth_images.shape[2] > 256:
+ synth_images = F.interpolate(synth_images, size=(256, 256), mode='area')
+
+ # Features for synth images.
+ synth_features = vgg16(synth_images, resize_images=False, return_lpips=True)
+ dist = (target_features - synth_features).square().sum()
+
+ # Noise regularization.
+ reg_loss = 0.0
+ for v in noise_bufs.values():
+ noise = v[None, None, :, :] # must be [1,1,H,W] for F.avg_pool2d()
+ while True:
+ reg_loss += (noise * torch.roll(noise, shifts=1, dims=3)).mean() ** 2
+ reg_loss += (noise * torch.roll(noise, shifts=1, dims=2)).mean() ** 2
+ if noise.shape[2] <= 8:
+ break
+ noise = F.avg_pool2d(noise, kernel_size=2)
+ loss = dist + reg_loss * regularize_noise_weight
+
+ if step % image_log_step == 0:
+ with torch.no_grad():
+ if use_wandb:
+ global_config.training_step += 1
+ wandb.log({f'first projection _{w_name}': loss.detach().cpu()}, step=global_config.training_step)
+ log_image_from_w(w_opt, G, w_name)
+
+ # Step
+ optimizer.zero_grad(set_to_none=True)
+ loss.backward()
+ optimizer.step()
+ logprint(f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}')
+
+ # Normalize noise.
+ with torch.no_grad():
+ for buf in noise_bufs.values():
+ buf -= buf.mean()
+ buf *= buf.square().mean().rsqrt()
+
+ del G
+ return w_opt
diff --git a/stylegan_human/pti/training/projectors/w_projector.py b/stylegan_human/pti/training/projectors/w_projector.py
new file mode 100644
index 0000000000000000000000000000000000000000..36d93c1d61e5c6fe91e0ccdb68cd1e953a0a3807
--- /dev/null
+++ b/stylegan_human/pti/training/projectors/w_projector.py
@@ -0,0 +1,144 @@
+# Copyright (c) 2021, NVIDIA CORPORATION. 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.
+
+"""Project given image to the latent space of pretrained network pickle."""
+
+import copy
+import wandb
+import numpy as np
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+from pti.pti_configs import global_config, hyperparameters
+from utils import log_utils
+import dnnlib
+
+
+def project(
+ G,
+ target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
+ *,
+ num_steps=1000,
+ w_avg_samples=10000,
+ initial_learning_rate=0.01,
+ initial_noise_factor=0.05,
+ lr_rampdown_length=0.25,
+ lr_rampup_length=0.05,
+ noise_ramp_length=0.75,
+ regularize_noise_weight=1e5,
+ verbose=False,
+ device: torch.device,
+ use_wandb=False,
+ initial_w=None,
+ image_log_step=global_config.image_rec_result_log_snapshot,
+ w_name: str
+):
+ print(target.shape,G.img_channels, G.img_resolution, G.img_resolution//2)
+ assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution // 2)
+
+ def logprint(*args):
+ if verbose:
+ print(*args)
+
+ G = copy.deepcopy(G).eval().requires_grad_(False).to(device).float() # type: ignore
+
+ # Compute w stats.
+ logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...')
+ z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
+ w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None) # [N, L, C]
+ w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32) # [N, 1, C]
+ w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C]
+ w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device)
+ w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5
+
+ start_w = initial_w if initial_w is not None else w_avg
+
+ # Setup noise inputs.
+ noise_bufs = {name: buf for (name, buf) in G.synthesis.named_buffers() if 'noise_const' in name}
+
+ # Load VGG16 feature detector.
+ url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
+ with dnnlib.util.open_url(url) as f:
+ vgg16 = torch.jit.load(f).eval().to(device)
+
+ # Features for target image.
+ target_images = target.unsqueeze(0).to(device).to(torch.float32)
+ if target_images.shape[2] > 256:
+ target_images = F.interpolate(target_images, size=(256, 256), mode='area')
+ target_features = vgg16(target_images, resize_images=False, return_lpips=True)
+
+ w_opt = torch.tensor(start_w, dtype=torch.float32, device=device,
+ requires_grad=True) # pylint: disable=not-callable
+ optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999),
+ lr=hyperparameters.first_inv_lr)
+
+ # Init noise.
+ for buf in noise_bufs.values():
+ buf[:] = torch.randn_like(buf)
+ buf.requires_grad = True
+
+ for step in range(num_steps):
+
+ # Learning rate schedule.
+ t = step / num_steps
+ w_noise_scale = w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2
+ lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
+ lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
+ lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
+ lr = initial_learning_rate * lr_ramp
+ for param_group in optimizer.param_groups:
+ param_group['lr'] = lr
+
+ # Synth images from opt_w.
+ w_noise = torch.randn_like(w_opt) * w_noise_scale
+ ws = (w_opt + w_noise).repeat([1, G.mapping.num_ws, 1])
+ synth_images = G.synthesis(ws, noise_mode='const', force_fp32=True)
+
+ # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
+ synth_images = (synth_images + 1) * (255 / 2)
+ if synth_images.shape[2] > 256:
+ synth_images = F.interpolate(synth_images, size=(256, 256), mode='area')
+
+ # Features for synth images.
+ synth_features = vgg16(synth_images, resize_images=False, return_lpips=True)
+ dist = (target_features - synth_features).square().sum()
+
+ # Noise regularization.
+ reg_loss = 0.0
+ for v in noise_bufs.values():
+ noise = v[None, None, :, :] # must be [1,1,H,W] for F.avg_pool2d()
+ while True:
+ reg_loss += (noise * torch.roll(noise, shifts=1, dims=3)).mean() ** 2
+ reg_loss += (noise * torch.roll(noise, shifts=1, dims=2)).mean() ** 2
+ if noise.shape[2] <= 8:
+ break
+ noise = F.avg_pool2d(noise, kernel_size=2)
+ loss = dist + reg_loss * regularize_noise_weight
+ if step % 10 == 0:
+ print("project loss", step, loss.data)
+ if step % image_log_step == 0:
+ with torch.no_grad():
+ if use_wandb:
+ global_config.training_step += 1
+ wandb.log({f'first projection _{w_name}': loss.detach().cpu()}, step=global_config.training_step)
+ log_utils.log_image_from_w(w_opt.repeat([1, G.mapping.num_ws, 1]), G, w_name)
+
+ # Step
+ optimizer.zero_grad(set_to_none=True)
+ loss.backward()
+ optimizer.step()
+ logprint(f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}')
+
+ # Normalize noise.
+ with torch.no_grad():
+ for buf in noise_bufs.values():
+ buf -= buf.mean()
+ buf *= buf.square().mean().rsqrt()
+
+ del G
+ return w_opt.repeat([1, 18, 1])
diff --git a/stylegan_human/run_pti.py b/stylegan_human/run_pti.py
new file mode 100644
index 0000000000000000000000000000000000000000..c335c5b156554ea1069af0c153ec6edbea70d931
--- /dev/null
+++ b/stylegan_human/run_pti.py
@@ -0,0 +1,52 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+from random import choice
+from string import ascii_uppercase
+from torch.utils.data import DataLoader
+from torchvision.transforms import transforms
+import os
+from pti.pti_configs import global_config, paths_config
+import wandb
+
+from pti.training.coaches.multi_id_coach import MultiIDCoach
+from pti.training.coaches.single_id_coach import SingleIDCoach
+from utils.ImagesDataset import ImagesDataset
+
+
+def run_PTI(run_name='', use_wandb=False, use_multi_id_training=False):
+ os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
+ os.environ['CUDA_VISIBLE_DEVICES'] = global_config.cuda_visible_devices
+
+ if run_name == '':
+ global_config.run_name = ''.join(choice(ascii_uppercase) for i in range(12))
+ else:
+ global_config.run_name = run_name
+
+ if use_wandb:
+ run = wandb.init(project=paths_config.pti_results_keyword, reinit=True, name=global_config.run_name)
+ global_config.pivotal_training_steps = 1
+ global_config.training_step = 1
+
+ embedding_dir_path = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{paths_config.pti_results_keyword}'
+ # print('embedding_dir_path: ', embedding_dir_path) #./embeddings/barcelona/PTI
+ os.makedirs(embedding_dir_path, exist_ok=True)
+
+ dataset = ImagesDataset(paths_config.input_data_path, transforms.Compose([
+ transforms.Resize((1024, 512)),
+ transforms.ToTensor(),
+ transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]))
+
+ dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
+
+ if use_multi_id_training:
+ coach = MultiIDCoach(dataloader, use_wandb)
+ else:
+ coach = SingleIDCoach(dataloader, use_wandb)
+
+ coach.train()
+
+ return global_config.run_name
+
+
+if __name__ == '__main__':
+ run_PTI(run_name='', use_wandb=False, use_multi_id_training=False)
diff --git a/stylegan_human/style_mixing.py b/stylegan_human/style_mixing.py
new file mode 100644
index 0000000000000000000000000000000000000000..36410015e0637131450efd17b92a2877ed00b8e1
--- /dev/null
+++ b/stylegan_human/style_mixing.py
@@ -0,0 +1,108 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2021, NVIDIA CORPORATION. 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
+from typing import List
+import legacy
+
+import click
+import dnnlib
+import numpy as np
+import PIL.Image
+import torch
+
+"""
+Style mixing using pretrained network pickle.
+
+Examples:
+
+\b
+python style_mixing.py --network=pretrained_models/stylegan_human_v2_1024.pkl --rows=85,100,75,458,1500 \\
+ --cols=55,821,1789,293 --styles=0-3 --outdir=outputs/stylemixing
+"""
+
+@click.command()
+@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
+@click.option('--rows', 'row_seeds', type=legacy.num_range, help='Random seeds to use for image rows', required=True)
+@click.option('--cols', 'col_seeds', type=legacy.num_range, help='Random seeds to use for image columns', required=True)
+@click.option('--styles', 'col_styles', type=legacy.num_range, help='Style layer range', default='0-6', show_default=True)
+@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=0.8, show_default=True)
+@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
+@click.option('--outdir', type=str, required=True, default='outputs/stylemixing')
+def generate_style_mix(
+ network_pkl: str,
+ row_seeds: List[int],
+ col_seeds: List[int],
+ col_styles: List[int],
+ truncation_psi: float,
+ noise_mode: str,
+ outdir: str
+):
+
+ print('Loading networks from "%s"...' % network_pkl)
+ device = torch.device('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
+ dtype = torch.float32 if device.type == 'mps' else torch.float64
+ with dnnlib.util.open_url(network_pkl) as f:
+ G = legacy.load_network_pkl(f)['G_ema'].to(device, dtype=dtype)
+
+ os.makedirs(outdir, exist_ok=True)
+
+ print('Generating W vectors...')
+ all_seeds = list(set(row_seeds + col_seeds))
+ all_z = np.stack([np.random.RandomState(seed).randn(G.z_dim) for seed in all_seeds])
+ all_w = G.mapping(torch.from_numpy(all_z).to(device, dtype=dtype), None)
+ w_avg = G.mapping.w_avg
+ all_w = w_avg + (all_w - w_avg) * truncation_psi
+ w_dict = {seed: w for seed, w in zip(all_seeds, list(all_w))}
+
+ print('Generating images...')
+ all_images = G.synthesis(all_w, noise_mode=noise_mode)
+ all_images = (all_images.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).cpu().numpy()
+ image_dict = {(seed, seed): image for seed, image in zip(all_seeds, list(all_images))}
+
+ print('Generating style-mixed images...')
+ for row_seed in row_seeds:
+ for col_seed in col_seeds:
+ w = w_dict[row_seed].clone()
+ w[col_styles] = w_dict[col_seed][col_styles]
+ image = G.synthesis(w[np.newaxis], noise_mode=noise_mode)
+ image = (image.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
+ image_dict[(row_seed, col_seed)] = image[0].cpu().numpy()
+
+ os.makedirs(outdir, exist_ok=True)
+ # print('Saving images...')
+ # for (row_seed, col_seed), image in image_dict.items():
+ # PIL.Image.fromarray(image, 'RGB').save(f'{outdir}/{row_seed}-{col_seed}.png')
+
+ print('Saving image grid...')
+ W = G.img_resolution // 2
+ H = G.img_resolution
+ canvas = PIL.Image.new('RGB', (W * (len(col_seeds) + 1), H * (len(row_seeds) + 1)), 'black')
+ for row_idx, row_seed in enumerate([0] + row_seeds):
+ for col_idx, col_seed in enumerate([0] + col_seeds):
+ if row_idx == 0 and col_idx == 0:
+ continue
+ key = (row_seed, col_seed)
+ if row_idx == 0:
+ key = (col_seed, col_seed)
+ if col_idx == 0:
+ key = (row_seed, row_seed)
+ canvas.paste(PIL.Image.fromarray(image_dict[key], 'RGB'), (W * col_idx, H * row_idx))
+ canvas.save(f'{outdir}/grid.png')
+
+
+#----------------------------------------------------------------------------
+
+if __name__ == "__main__":
+ generate_style_mix() # pylint: disable=no-value-for-parameter
+
+#----------------------------------------------------------------------------
diff --git a/stylegan_human/stylemixing_video.py b/stylegan_human/stylemixing_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e8d9dc5c786419939f4d6cbd4b2342830cf711a
--- /dev/null
+++ b/stylegan_human/stylemixing_video.py
@@ -0,0 +1,154 @@
+
+# Copyright (c) SenseTime Research. All rights reserved.
+
+"""Here we demo style-mixing results using StyleGAN2 pretrained model.
+ Script reference: https://github.com/PDillis/stylegan2-fun """
+
+
+import argparse
+import legacy
+
+import scipy
+import numpy as np
+import PIL.Image
+
+import dnnlib
+import dnnlib.tflib as tflib
+from typing import List
+import re
+import sys
+import os
+import click
+import torch
+
+os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = "hide"
+import moviepy.editor
+
+
+"""
+Generate style mixing video.
+Examples:
+
+\b
+python stylemixing_video.py --network=pretrained_models/stylegan_human_v2_1024.pkl --row-seed=3859 \\
+ --col-seeds=3098,31759,3791 --col-styles=8-12 --trunc=0.8 --outdir=outputs/stylemixing_video
+"""
+
+@click.command()
+@click.option('--network', 'network_pkl', help='Path to network pickle filename', required=True)
+@click.option('--row-seed', 'src_seed', type=legacy.num_range, help='Random seed to use for image source row', required=True)
+@click.option('--col-seeds', 'dst_seeds', type=legacy.num_range, help='Random seeds to use for image columns (style)', required=True)
+@click.option('--col-styles', 'col_styles', type=legacy.num_range, help='Style layer range (default: %(default)s)', default='0-6')
+@click.option('--only-stylemix', 'only_stylemix', help='Add flag to only show the style mxied images in the video',default=False)
+@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi (default: %(default)s)', default=1)
+@click.option('--duration-sec', 'duration_sec', type=float, help='Duration of video (default: %(default)s)', default=10)
+@click.option('--fps', 'mp4_fps', type=int, help='FPS of generated video (default: %(default)s)', default=10)
+@click.option('--indent-range', 'indent_range', type=int, default=30)
+@click.option('--outdir', help='Root directory for run results (default: %(default)s)', default='outputs/stylemixing_video', metavar='DIR')
+
+def style_mixing_video(network_pkl: str,
+ src_seed: List[int], # Seed of the source image style (row)
+ dst_seeds: List[int], # Seeds of the destination image styles (columns)
+ col_styles: List[int], # Styles to transfer from first row to first column
+ truncation_psi=float,
+ only_stylemix=bool, # True if user wishes to show only thre style transferred result
+ duration_sec=float,
+ smoothing_sec=1.0,
+ mp4_fps=int,
+ mp4_codec="libx264",
+ mp4_bitrate="16M",
+ minibatch_size=8,
+ noise_mode='const',
+ indent_range=int,
+ outdir=str):
+ # Calculate the number of frames:
+ print('col_seeds: ', dst_seeds)
+ num_frames = int(np.rint(duration_sec * mp4_fps))
+ print('Loading networks from "%s"...' % network_pkl)
+ device = torch.device('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
+ dtype = torch.float32 if device.type == 'mps' else torch.float64
+ with dnnlib.util.open_url(network_pkl) as f:
+ Gs = legacy.load_network_pkl(f)['G_ema'].to(device, dtype=dtype)
+
+ print(Gs.num_ws, Gs.w_dim, Gs.img_resolution)
+ max_style = int(2 * np.log2(Gs.img_resolution)) - 3
+ assert max(col_styles) <= max_style, f"Maximum col-style allowed: {max_style}"
+
+ # Left col latents
+ print('Generating Source W vectors...')
+ src_shape = [num_frames] + [Gs.z_dim]
+ src_z = np.random.RandomState(*src_seed).randn(*src_shape).astype(np.float32) # [frames, src, component]
+ src_z = scipy.ndimage.gaussian_filter(src_z, [smoothing_sec * mp4_fps] + [0] * (2- 1), mode="wrap")
+ src_z /= np.sqrt(np.mean(np.square(src_z)))
+ # Map into the detangled latent space W and do truncation trick
+ src_w = Gs.mapping(torch.from_numpy(src_z).to(device, dtype=dtype), None)
+ w_avg = Gs.mapping.w_avg
+ src_w = w_avg + (src_w - w_avg) * truncation_psi
+
+ # Top row latents (fixed reference)
+ print('Generating Destination W vectors...')
+ dst_z = np.stack([np.random.RandomState(seed).randn(Gs.z_dim) for seed in dst_seeds])
+ dst_w = Gs.mapping(torch.from_numpy(dst_z).to(device, dtype=dtype), None)
+ dst_w = w_avg + (dst_w - w_avg) * truncation_psi
+ # Get the width and height of each image:
+ H = Gs.img_resolution # 1024
+ W = Gs.img_resolution//2 # 512
+
+ # Generate ALL the source images:
+ src_images = Gs.synthesis(src_w, noise_mode=noise_mode)
+ src_images = (src_images.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
+
+ # Generate the column images:
+ dst_images = Gs.synthesis(dst_w, noise_mode=noise_mode)
+ dst_images = (dst_images.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
+
+
+ print('Generating full video (including source and destination images)')
+ # Generate our canvas where we will paste all the generated images:
+ canvas = PIL.Image.new("RGB", ((W-indent_range) * (len(dst_seeds) + 1), H * (len(src_seed) + 1)), "white") # W, H
+
+ for col, dst_image in enumerate(list(dst_images)): #dst_image:[3,1024,512]
+ canvas.paste(PIL.Image.fromarray(dst_image.cpu().numpy(), "RGB"), ((col + 1) * (W-indent_range), 0)) #H
+ # Aux functions: Frame generation func for moviepy.
+ def make_frame(t):
+ # Get the frame number according to time t:
+ frame_idx = int(np.clip(np.round(t * mp4_fps), 0, num_frames - 1))
+ # We wish the image belonging to the frame at time t:
+ src_image = src_images[frame_idx] # always in the same place
+ canvas.paste(PIL.Image.fromarray(src_image.cpu().numpy(), "RGB"), (0-indent_range, H)) # Paste it to the lower left
+
+ # Now, for each of the column images:
+ for col, dst_image in enumerate(list(dst_images)):
+ # Select the pertinent latent w column:
+ w_col = np.stack([dst_w[col].cpu()]) # [18, 512] -> [1, 18, 512]
+ w_col = torch.from_numpy(w_col).to(device, dtype=dtype)
+ # Replace the values defined by col_styles:
+ w_col[:, col_styles] = src_w[frame_idx, col_styles]#.cpu()
+ # Generate these synthesized images:
+ col_images = Gs.synthesis(w_col, noise_mode=noise_mode)
+ col_images = (col_images.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
+ # Paste them in their respective spot:
+ for row, image in enumerate(list(col_images)):
+ canvas.paste(
+ PIL.Image.fromarray(image.cpu().numpy(), "RGB"),
+ ((col + 1) * (W - indent_range), (row + 1) * H),
+ )
+ return np.array(canvas)
+
+ # Generate video using make_frame:
+ print('Generating style-mixed video...')
+ videoclip = moviepy.editor.VideoClip(make_frame, duration=duration_sec)
+ grid_size = [len(dst_seeds), len(src_seed)]
+ mp4 = "{}x{}-style-mixing_{}_{}.mp4".format(*grid_size,min(col_styles),max(col_styles))
+ if not os.path.exists(outdir): os.makedirs(outdir)
+ videoclip.write_videofile(os.path.join(outdir,mp4),
+ fps=mp4_fps,
+ codec=mp4_codec,
+ bitrate=mp4_bitrate)
+
+
+if __name__ == "__main__":
+ style_mixing_video()
+
+
+
diff --git a/stylegan_human/torch_utils/__init__.py b/stylegan_human/torch_utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0b0f4efcbe1e3cd4199eeecb043d5afe1548307
--- /dev/null
+++ b/stylegan_human/torch_utils/__init__.py
@@ -0,0 +1,11 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2021, NVIDIA CORPORATION. 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/stylegan_human/torch_utils/custom_ops.py b/stylegan_human/torch_utils/custom_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..fda77a69777a69bd3eda96713c29f66fe3b016b9
--- /dev/null
+++ b/stylegan_human/torch_utils/custom_ops.py
@@ -0,0 +1,238 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2021, NVIDIA CORPORATION. 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 glob
+import torch
+import torch.utils.cpp_extension
+import importlib
+import hashlib
+import shutil
+from pathlib import Path
+import re
+import uuid
+
+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, **build_kwargs):
+ assert verbosity in ['none', 'brief', 'full']
+
+ # 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)
+
+ 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
+
+ # Compile and load.
+ verbose_build = (verbosity == 'full')
+
+ # 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.)
+ source_dirs_set = set(os.path.dirname(source) for source in sources)
+ if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ):
+ all_source_files = sorted(list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file()))
+
+ # Compute a combined hash digest for all source files in the same
+ # custom op directory (usually .cu, .cpp, .py and .h files).
+ hash_md5 = hashlib.md5()
+ for src in all_source_files:
+ with open(src, 'rb') as f:
+ hash_md5.update(f.read())
+ build_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access
+ digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest())
+
+ if not os.path.isdir(digest_build_dir):
+ os.makedirs(digest_build_dir, exist_ok=True)
+ baton = FileBaton(os.path.join(digest_build_dir, 'lock'))
+ if baton.try_acquire():
+ try:
+ for src in all_source_files:
+ shutil.copyfile(src, os.path.join(digest_build_dir, os.path.basename(src)))
+ finally:
+ baton.release()
+ else:
+ # Someone else is copying source files under the digest dir,
+ # wait until done and continue.
+ baton.wait()
+ digest_sources = [os.path.join(digest_build_dir, os.path.basename(x)) for x in sources]
+ torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir,
+ verbose=verbose_build, sources=digest_sources, **build_kwargs)
+ else:
+ torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
+ module = importlib.import_module(module_name)
+
+ except:
+ if verbosity == 'brief':
+ print('Failed!')
+ raise
+
+ # Print status and add to cache.
+ if verbosity == 'full':
+ print(f'Done setting up PyTorch plugin "{module_name}".')
+ elif verbosity == 'brief':
+ print('Done.')
+ _cached_plugins[module_name] = module
+ return module
+
+#----------------------------------------------------------------------------
+def get_plugin_v3(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
\ No newline at end of file
diff --git a/stylegan_human/torch_utils/misc.py b/stylegan_human/torch_utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd512ab8b61ece35d81ec35f43948a843efbbce1
--- /dev/null
+++ b/stylegan_human/torch_utils/misc.py
@@ -0,0 +1,264 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2021, NVIDIA CORPORATION. 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 suppress known warnings in torch.jit.trace().
+
+class suppress_tracer_warnings(warnings.catch_warnings):
+ def __enter__(self):
+ super().__enter__()
+ warnings.simplefilter('ignore', category=torch.jit.TracerWarning)
+ return self
+
+#----------------------------------------------------------------------------
+# 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 = {name: tensor for name, tensor in 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()
+ other = tensor.clone()
+ torch.distributed.broadcast(tensor=other, src=0)
+ assert (nan_to_num(tensor) == nan_to_num(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(e.outputs[0].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/stylegan_human/torch_utils/models.py b/stylegan_human/torch_utils/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..762550239ba6f1e09f4887bf1b27fd421745a589
--- /dev/null
+++ b/stylegan_human/torch_utils/models.py
@@ -0,0 +1,756 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# https://github.com/rosinality/stylegan2-pytorch/blob/master/model.py
+
+import math
+import random
+import functools
+import operator
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+import torch.nn.init as init
+from torch.autograd import Function
+
+from .op_edit import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
+
+
+class PixelNorm(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, input):
+ return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
+
+
+def make_kernel(k):
+ k = torch.tensor(k, dtype=torch.float32)
+ if k.ndim == 1:
+ k = k[None, :] * k[:, None]
+ k /= k.sum()
+ return k
+
+
+class Upsample(nn.Module):
+ def __init__(self, kernel, factor=2):
+ super().__init__()
+
+ self.factor = factor
+ kernel = make_kernel(kernel) * (factor ** 2)
+ self.register_buffer("kernel", kernel)
+
+ p = kernel.shape[0] - factor
+
+ pad0 = (p + 1) // 2 + factor - 1
+ pad1 = p // 2
+
+ self.pad = (pad0, pad1)
+
+ def forward(self, input):
+ out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
+ return out
+
+
+class Downsample(nn.Module):
+ def __init__(self, kernel, factor=2):
+ super().__init__()
+
+ self.factor = factor
+ kernel = make_kernel(kernel)
+ self.register_buffer("kernel", kernel)
+
+ p = kernel.shape[0] - factor
+
+ pad0 = (p + 1) // 2
+ pad1 = p // 2
+
+ self.pad = (pad0, pad1)
+
+ def forward(self, input):
+ out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
+ return out
+
+
+class Blur(nn.Module):
+ def __init__(self, kernel, pad, upsample_factor=1):
+ super().__init__()
+
+ kernel = make_kernel(kernel)
+
+ if upsample_factor > 1:
+ kernel = kernel * (upsample_factor ** 2)
+
+ self.register_buffer("kernel", kernel)
+
+ self.pad = pad
+
+ def forward(self, input):
+ out = upfirdn2d(input, self.kernel, pad=self.pad)
+ return out
+
+
+class EqualConv2d(nn.Module):
+ def __init__(
+ self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
+ ):
+ super().__init__()
+
+ self.weight = nn.Parameter(
+ torch.randn(out_channel, in_channel, kernel_size, kernel_size)
+ )
+ self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
+
+ self.stride = stride
+ self.padding = padding
+
+ if bias:
+ self.bias = nn.Parameter(torch.zeros(out_channel))
+
+ else:
+ self.bias = None
+
+ def forward(self, input):
+ out = F.conv2d(
+ input,
+ self.weight * self.scale,
+ bias=self.bias,
+ stride=self.stride,
+ padding=self.padding,
+ )
+ return out
+
+ def __repr__(self):
+ return (
+ f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},"
+ f" {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})"
+ )
+
+
+class EqualLinear(nn.Module):
+ def __init__(
+ self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
+ ):
+ super().__init__()
+
+ self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
+
+ if bias:
+ self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
+ else:
+ self.bias = None
+
+ self.activation = activation
+
+ self.scale = (1 / math.sqrt(in_dim)) * lr_mul
+ self.lr_mul = lr_mul
+
+ def forward(self, input):
+ if self.activation:
+ out = F.linear(input, self.weight * self.scale)
+ out = fused_leaky_relu(out, self.bias * self.lr_mul)
+ else:
+ out = F.linear(
+ input, self.weight * self.scale, bias=self.bias * self.lr_mul
+ )
+ return out
+
+ def __repr__(self):
+ return (
+ f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})"
+ )
+
+
+class ScaledLeakyReLU(nn.Module):
+ def __init__(self, negative_slope=0.2):
+ super().__init__()
+ self.negative_slope = negative_slope
+
+ def forward(self, input):
+ out = F.leaky_relu(input, negative_slope=self.negative_slope)
+ return out * math.sqrt(2)
+
+
+class ModulatedConv2d(nn.Module):
+ def __init__(
+ self,
+ in_channel,
+ out_channel,
+ kernel_size,
+ style_dim,
+ demodulate=True,
+ upsample=False,
+ downsample=False,
+ blur_kernel=[1, 3, 3, 1],
+ ):
+ super().__init__()
+
+ self.eps = 1e-8
+ self.kernel_size = kernel_size
+ self.in_channel = in_channel
+ self.out_channel = out_channel
+ self.upsample = upsample
+ self.downsample = downsample
+
+ if upsample:
+ factor = 2
+ p = (len(blur_kernel) - factor) - (kernel_size - 1)
+ pad0 = (p + 1) // 2 + factor - 1
+ pad1 = p // 2 + 1
+ self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
+
+ if downsample:
+ factor = 2
+ p = (len(blur_kernel) - factor) + (kernel_size - 1)
+ pad0 = (p + 1) // 2
+ pad1 = p // 2
+ self.blur = Blur(blur_kernel, pad=(pad0, pad1))
+
+ fan_in = in_channel * kernel_size ** 2
+ self.scale = 1 / math.sqrt(fan_in)
+ self.padding = kernel_size // 2
+ self.weight = nn.Parameter(
+ torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
+ )
+ self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
+ self.demodulate = demodulate
+
+ def __repr__(self):
+ return (
+ f"{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, "
+ f"upsample={self.upsample}, downsample={self.downsample})"
+ )
+
+ def forward(self, input, style):
+ batch, in_channel, height, width = input.shape
+
+ style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
+ weight = self.scale * self.weight * style
+
+ if self.demodulate:
+ demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
+ weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
+
+ weight = weight.view(
+ batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
+ )
+
+ if self.upsample:
+ input = input.view(1, batch * in_channel, height, width)
+ weight = weight.view(
+ batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
+ )
+ weight = weight.transpose(1, 2).reshape(
+ batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
+ )
+ out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, self.out_channel, height, width)
+ out = self.blur(out)
+
+ elif self.downsample:
+ input = self.blur(input)
+ _, _, height, width = input.shape
+ input = input.view(1, batch * in_channel, height, width)
+ out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, self.out_channel, height, width)
+
+ else:
+ input = input.view(1, batch * in_channel, height, width)
+ out = F.conv2d(input, weight, padding=self.padding, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, self.out_channel, height, width)
+
+ return out
+
+
+class NoiseInjection(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.weight = nn.Parameter(torch.zeros(1))
+
+ def forward(self, image, noise=None):
+ if noise is None:
+ batch, _, height, width = image.shape
+ noise = image.new_empty(batch, 1, height, width).normal_()
+ return image + self.weight * noise
+
+
+class ConstantInput(nn.Module):
+ def __init__(self, channel, size=4):
+ super().__init__()
+ self.input = nn.Parameter(torch.randn(1, channel, size, size // 2))
+
+ def forward(self, input):
+ batch = input.shape[0]
+ out = self.input.repeat(batch, 1, 1, 1)
+ return out
+
+
+class StyledConv(nn.Module):
+ def __init__(
+ self,
+ in_channel,
+ out_channel,
+ kernel_size,
+ style_dim,
+ upsample=False,
+ blur_kernel=[1, 3, 3, 1],
+ demodulate=True,
+ ):
+ super().__init__()
+ self.conv = ModulatedConv2d(
+ in_channel,
+ out_channel,
+ kernel_size,
+ style_dim,
+ upsample=upsample,
+ blur_kernel=blur_kernel,
+ demodulate=demodulate,
+ )
+ self.noise = NoiseInjection()
+ self.activate = FusedLeakyReLU(out_channel)
+
+ def forward(self, input, style, noise=None):
+ out = self.conv(input, style)
+ out = self.noise(out, noise=noise)
+ out = self.activate(out)
+ return out
+
+
+class ToRGB(nn.Module):
+ def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
+ super().__init__()
+ if upsample:
+ self.upsample = Upsample(blur_kernel)
+
+ self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
+ self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
+
+ def forward(self, input, style, skip=None):
+ out = self.conv(input, style)
+ out = out + self.bias
+
+ if skip is not None:
+ skip = self.upsample(skip)
+ out = out + skip
+
+ return out
+
+
+class Generator(nn.Module):
+ def __init__(
+ self,
+ size,
+ style_dim,
+ n_mlp,
+ channel_multiplier=1,
+ blur_kernel=[1, 3, 3, 1],
+ lr_mlp=0.01,
+ small=False,
+ small_isaac=False,
+ ):
+ super().__init__()
+
+ self.size = size
+
+ if small and size > 64:
+ raise ValueError("small only works for sizes <= 64")
+
+ self.style_dim = style_dim
+ layers = [PixelNorm()]
+
+ for i in range(n_mlp):
+ layers.append(
+ EqualLinear(
+ style_dim, style_dim, lr_mul=lr_mlp, activation="fused_lrelu"
+ )
+ )
+
+ self.style = nn.Sequential(*layers)
+
+ if small:
+ self.channels = {
+ 4: 64 * channel_multiplier,
+ 8: 64 * channel_multiplier,
+ 16: 64 * channel_multiplier,
+ 32: 64 * channel_multiplier,
+ 64: 64 * channel_multiplier,
+ }
+ elif small_isaac:
+ self.channels = {4: 256, 8: 256, 16: 256, 32: 256, 64: 128, 128: 128}
+ else:
+ self.channels = {
+ 4: 512,
+ 8: 512,
+ 16: 512,
+ 32: 512,
+ 64: 256 * channel_multiplier,
+ 128: 128 * channel_multiplier,
+ 256: 64 * channel_multiplier,
+ 512: 32 * channel_multiplier,
+ 1024: 16 * channel_multiplier,
+ }
+
+ self.input = ConstantInput(self.channels[4])
+ self.conv1 = StyledConv(
+ self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
+ )
+ self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
+
+ self.log_size = int(math.log(size, 2))
+ self.num_layers = (self.log_size - 2) * 2 + 1
+
+ self.convs = nn.ModuleList()
+ self.upsamples = nn.ModuleList()
+ self.to_rgbs = nn.ModuleList()
+ self.noises = nn.Module()
+
+ in_channel = self.channels[4]
+
+ for layer_idx in range(self.num_layers):
+ res = (layer_idx + 5) // 2
+ shape = [1, 1, 2 ** res, 2 ** res // 2]
+ self.noises.register_buffer(
+ "noise_{}".format(layer_idx), torch.randn(*shape)
+ )
+
+ for i in range(3, self.log_size + 1):
+ out_channel = self.channels[2 ** i]
+
+ self.convs.append(
+ StyledConv(
+ in_channel,
+ out_channel,
+ 3,
+ style_dim,
+ upsample=True,
+ blur_kernel=blur_kernel,
+ )
+ )
+
+ self.convs.append(
+ StyledConv(
+ out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
+ )
+ )
+
+ self.to_rgbs.append(ToRGB(out_channel, style_dim))
+ in_channel = out_channel
+
+ self.n_latent = self.log_size * 2 - 2
+
+ def make_noise(self):
+ device = self.input.input.device
+
+ noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2 // 2, device=device)]
+
+ for i in range(3, self.log_size + 1):
+ for _ in range(2):
+ noises.append(torch.randn(1, 1, 2 ** i, 2 ** i // 2, device=device))
+
+ return noises
+
+ def mean_latent(self, n_latent):
+ latent_in = torch.randn(
+ n_latent, self.style_dim, device=self.input.input.device
+ )
+ latent = self.style(latent_in).mean(0, keepdim=True)
+
+ return latent
+
+ def get_latent(self, input):
+ return self.style(input)
+
+ def forward(
+ self,
+ styles,
+ return_latents=False,
+ return_features=False,
+ inject_index=None,
+ truncation=1,
+ truncation_latent=None,
+ input_is_latent=False,
+ noise=None,
+ randomize_noise=True,
+ real=False,
+ ):
+ if not input_is_latent:
+ styles = [self.style(s) for s in styles]
+ if noise is None:
+ if randomize_noise:
+ noise = [None] * self.num_layers
+ else:
+ noise = [
+ getattr(self.noises, "noise_{}".format(i))
+ for i in range(self.num_layers)
+ ]
+
+ if truncation < 1:
+ # print('truncation_latent: ', truncation_latent.shape)
+ if not real: #if type(styles) == list:
+ style_t = []
+ for style in styles:
+ style_t.append(
+ truncation_latent + truncation * (style - truncation_latent)
+ ) # (-1.1162e-03-(-1.0914e-01))*0.8+(-1.0914e-01)
+ styles = style_t
+ else: # styles are latent (tensor: 1,18,512), for real PTI output
+ truncation_latent = truncation_latent.repeat(18,1).unsqueeze(0) # (1,512) --> (1,18,512)
+ styles = torch.add(truncation_latent,torch.mul(torch.sub(styles,truncation_latent),truncation))
+ # print('now styles after truncation : ', styles)
+ #if type(styles) == list and len(styles) < 2: # this if for input as list of [(1,512)]
+ if not real:
+ if len(styles) < 2:
+ inject_index = self.n_latent
+ if styles[0].ndim < 3:
+ latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+ else:
+ latent = styles[0]
+ elif type(styles) == list:
+ if inject_index is None:
+ inject_index = 4
+
+ latent = styles[0].unsqueeze(0)
+ if latent.shape[1] == 1:
+ latent = latent.repeat(1, inject_index, 1)
+ else:
+ latent = latent[:, :inject_index, :]
+ latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
+ latent = torch.cat([latent, latent2], 1)
+ else: # input is tensor of size with torch.Size([1, 18, 512]), for real PTI output
+ latent = styles
+
+ # print(f'processed latent: {latent.shape}')
+
+ features = {}
+ out = self.input(latent)
+ features["out_0"] = out
+ out = self.conv1(out, latent[:, 0], noise=noise[0])
+ features["conv1_0"] = out
+
+ skip = self.to_rgb1(out, latent[:, 1])
+ features["skip_0"] = skip
+ i = 1
+ for conv1, conv2, noise1, noise2, to_rgb in zip(
+ self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
+ ):
+ out = conv1(out, latent[:, i], noise=noise1)
+ features["conv1_{}".format(i)] = out
+ out = conv2(out, latent[:, i + 1], noise=noise2)
+ features["conv2_{}".format(i)] = out
+ skip = to_rgb(out, latent[:, i + 2], skip)
+ features["skip_{}".format(i)] = skip
+
+ i += 2
+
+ image = skip
+
+ if return_latents:
+ return image, latent
+ elif return_features:
+ return image, features
+ else:
+ return image, None
+
+
+class ConvLayer(nn.Sequential):
+ def __init__(
+ self,
+ in_channel,
+ out_channel,
+ kernel_size,
+ downsample=False,
+ blur_kernel=[1, 3, 3, 1],
+ bias=True,
+ activate=True,
+ ):
+ layers = []
+
+ if downsample:
+ factor = 2
+ p = (len(blur_kernel) - factor) + (kernel_size - 1)
+ pad0 = (p + 1) // 2
+ pad1 = p // 2
+
+ layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
+
+ stride = 2
+ self.padding = 0
+
+ else:
+ stride = 1
+ self.padding = kernel_size // 2
+
+ layers.append(
+ EqualConv2d(
+ in_channel,
+ out_channel,
+ kernel_size,
+ padding=self.padding,
+ stride=stride,
+ bias=bias and not activate,
+ )
+ )
+
+ if activate:
+ if bias:
+ layers.append(FusedLeakyReLU(out_channel))
+ else:
+ layers.append(ScaledLeakyReLU(0.2))
+
+ super().__init__(*layers)
+
+
+class ResBlock(nn.Module):
+ def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
+ super().__init__()
+
+ self.conv1 = ConvLayer(in_channel, in_channel, 3)
+ self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
+
+ self.skip = ConvLayer(
+ in_channel, out_channel, 1, downsample=True, activate=False, bias=False
+ )
+
+ def forward(self, input):
+ out = self.conv1(input)
+ out = self.conv2(out)
+
+ skip = self.skip(input)
+ out = (out + skip) / math.sqrt(2)
+
+ return out
+
+
+class StyleDiscriminator(nn.Module):
+ def __init__(
+ self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], small=False
+ ):
+ super().__init__()
+
+ if small:
+ channels = {4: 64, 8: 64, 16: 64, 32: 64, 64: 64}
+
+ else:
+ channels = {
+ 4: 512,
+ 8: 512,
+ 16: 512,
+ 32: 512,
+ 64: 256 * channel_multiplier,
+ 128: 128 * channel_multiplier,
+ 256: 64 * channel_multiplier,
+ 512: 32 * channel_multiplier,
+ 1024: 16 * channel_multiplier,
+ }
+
+ convs = [ConvLayer(3, channels[size], 1)]
+
+ log_size = int(math.log(size, 2))
+ in_channel = channels[size]
+
+ for i in range(log_size, 2, -1):
+ out_channel = channels[2 ** (i - 1)]
+
+ convs.append(ResBlock(in_channel, out_channel, blur_kernel))
+
+ in_channel = out_channel
+
+ self.convs = nn.Sequential(*convs)
+
+ self.stddev_group = 4
+ self.stddev_feat = 1
+
+ self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
+ self.final_linear = nn.Sequential(
+ EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"),
+ EqualLinear(channels[4], 1),
+ )
+
+
+ def forward(self, input):
+ h = input
+ h_list = []
+
+ for index, blocklist in enumerate(self.convs):
+ h = blocklist(h)
+ h_list.append(h)
+
+ out = h
+ batch, channel, height, width = out.shape
+ group = min(batch, self.stddev_group)
+ stddev = out.view(
+ group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
+ )
+ stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
+ stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
+ stddev = stddev.repeat(group, 1, height, width)
+ out = torch.cat([out, stddev], 1)
+
+ out = self.final_conv(out)
+ h_list.append(out)
+
+ out = out.view(batch, -1)
+ out = self.final_linear(out)
+
+ return out, h_list
+
+
+class StyleEncoder(nn.Module):
+ def __init__(self, size, w_dim=512):
+ super().__init__()
+
+ channels = {
+ 4: 512,
+ 8: 512,
+ 16: 512,
+ 32: 512,
+ 64: 256,
+ 128: 128,
+ 256: 64,
+ 512: 32,
+ 1024: 16
+ }
+
+ self.w_dim = w_dim
+ log_size = int(math.log(size, 2))
+ convs = [ConvLayer(3, channels[size], 1)]
+
+ in_channel = channels[size]
+ for i in range(log_size, 2, -1):
+ out_channel = channels[2 ** (i - 1)]
+ convs.append(ResBlock(in_channel, out_channel))
+ in_channel = out_channel
+
+ convs.append(EqualConv2d(in_channel,2*self.w_dim, 4, padding=0, bias=False))
+
+ self.convs = nn.Sequential(*convs)
+
+ def forward(self, input):
+ out = self.convs(input)
+ # return out.view(len(input), self.n_latents, self.w_dim)
+ reshaped = out.view(len(input), 2*self.w_dim)
+ return reshaped[:,:self.w_dim], reshaped[:,self.w_dim:]
+
+def kaiming_init(m):
+ if isinstance(m, (nn.Linear, nn.Conv2d)):
+ init.kaiming_normal_(m.weight)
+ if m.bias is not None:
+ m.bias.data.fill_(0)
+ elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
+ m.weight.data.fill_(1)
+ if m.bias is not None:
+ m.bias.data.fill_(0)
+
+
+def normal_init(m):
+ if isinstance(m, (nn.Linear, nn.Conv2d)):
+ init.normal_(m.weight, 0, 0.02)
+ if m.bias is not None:
+ m.bias.data.fill_(0)
+ elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
+ m.weight.data.fill_(1)
+ if m.bias is not None:
+ m.bias.data.fill_(0)
\ No newline at end of file
diff --git a/stylegan_human/torch_utils/models_face.py b/stylegan_human/torch_utils/models_face.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce3f5d2f3c41206c18a9dba973c8e5999ddf47fd
--- /dev/null
+++ b/stylegan_human/torch_utils/models_face.py
@@ -0,0 +1,809 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+import math
+import random
+import functools
+import operator
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+import torch.nn.init as init
+from torch.autograd import Function
+
+from .op_edit import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
+
+
+class PixelNorm(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, input):
+ return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
+
+
+def make_kernel(k):
+ k = torch.tensor(k, dtype=torch.float32)
+
+ if k.ndim == 1:
+ k = k[None, :] * k[:, None]
+
+ k /= k.sum()
+
+ return k
+
+
+class Upsample(nn.Module):
+ def __init__(self, kernel, factor=2):
+ super().__init__()
+
+ self.factor = factor
+ kernel = make_kernel(kernel) * (factor ** 2)
+ self.register_buffer("kernel", kernel)
+
+ p = kernel.shape[0] - factor
+
+ pad0 = (p + 1) // 2 + factor - 1
+ pad1 = p // 2
+
+ self.pad = (pad0, pad1)
+
+ def forward(self, input):
+ out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
+
+ return out
+
+
+class Downsample(nn.Module):
+ def __init__(self, kernel, factor=2):
+ super().__init__()
+
+ self.factor = factor
+ kernel = make_kernel(kernel)
+ self.register_buffer("kernel", kernel)
+
+ p = kernel.shape[0] - factor
+
+ pad0 = (p + 1) // 2
+ pad1 = p // 2
+
+ self.pad = (pad0, pad1)
+
+ def forward(self, input):
+ out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
+
+ return out
+
+
+class Blur(nn.Module):
+ def __init__(self, kernel, pad, upsample_factor=1):
+ super().__init__()
+
+ kernel = make_kernel(kernel)
+
+ if upsample_factor > 1:
+ kernel = kernel * (upsample_factor ** 2)
+
+ self.register_buffer("kernel", kernel)
+
+ self.pad = pad
+
+ def forward(self, input):
+ out = upfirdn2d(input, self.kernel, pad=self.pad)
+
+ return out
+
+
+class EqualConv2d(nn.Module):
+ def __init__(
+ self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
+ ):
+ super().__init__()
+
+ self.weight = nn.Parameter(
+ torch.randn(out_channel, in_channel, kernel_size, kernel_size)
+ )
+ self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
+
+ self.stride = stride
+ self.padding = padding
+
+ if bias:
+ self.bias = nn.Parameter(torch.zeros(out_channel))
+
+ else:
+ self.bias = None
+
+ def forward(self, input):
+ out = F.conv2d(
+ input,
+ self.weight * self.scale,
+ bias=self.bias,
+ stride=self.stride,
+ padding=self.padding,
+ )
+
+ return out
+
+ def __repr__(self):
+ return (
+ f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},"
+ f" {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})"
+ )
+
+
+class EqualLinear(nn.Module):
+ def __init__(
+ self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
+ ):
+ super().__init__()
+
+ self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
+
+ if bias:
+ self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
+
+ else:
+ self.bias = None
+
+ self.activation = activation
+
+ self.scale = (1 / math.sqrt(in_dim)) * lr_mul
+ self.lr_mul = lr_mul
+
+ def forward(self, input):
+ if self.activation:
+ out = F.linear(input, self.weight * self.scale)
+ out = fused_leaky_relu(out, self.bias * self.lr_mul)
+
+ else:
+ out = F.linear(
+ input, self.weight * self.scale, bias=self.bias * self.lr_mul
+ )
+
+ return out
+
+ def __repr__(self):
+ return (
+ f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})"
+ )
+
+
+class ScaledLeakyReLU(nn.Module):
+ def __init__(self, negative_slope=0.2):
+ super().__init__()
+
+ self.negative_slope = negative_slope
+
+ def forward(self, input):
+ out = F.leaky_relu(input, negative_slope=self.negative_slope)
+
+ return out * math.sqrt(2)
+
+
+class ModulatedConv2d(nn.Module):
+ def __init__(
+ self,
+ in_channel,
+ out_channel,
+ kernel_size,
+ style_dim,
+ demodulate=True,
+ upsample=False,
+ downsample=False,
+ blur_kernel=[1, 3, 3, 1],
+ ):
+ super().__init__()
+
+ self.eps = 1e-8
+ self.kernel_size = kernel_size
+ self.in_channel = in_channel
+ self.out_channel = out_channel
+ self.upsample = upsample
+ self.downsample = downsample
+
+ if upsample:
+ factor = 2
+ p = (len(blur_kernel) - factor) - (kernel_size - 1)
+ pad0 = (p + 1) // 2 + factor - 1
+ pad1 = p // 2 + 1
+
+ self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
+
+ if downsample:
+ factor = 2
+ p = (len(blur_kernel) - factor) + (kernel_size - 1)
+ pad0 = (p + 1) // 2
+ pad1 = p // 2
+
+ self.blur = Blur(blur_kernel, pad=(pad0, pad1))
+
+ fan_in = in_channel * kernel_size ** 2
+ self.scale = 1 / math.sqrt(fan_in)
+ self.padding = kernel_size // 2
+
+ self.weight = nn.Parameter(
+ torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
+ )
+
+ self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
+
+ self.demodulate = demodulate
+
+ def __repr__(self):
+ return (
+ f"{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, "
+ f"upsample={self.upsample}, downsample={self.downsample})"
+ )
+
+ def forward(self, input, style):
+ batch, in_channel, height, width = input.shape
+
+ style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
+ weight = self.scale * self.weight * style
+
+ if self.demodulate:
+ demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
+ weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
+
+ weight = weight.view(
+ batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
+ )
+
+ if self.upsample:
+ input = input.view(1, batch * in_channel, height, width)
+ weight = weight.view(
+ batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
+ )
+ weight = weight.transpose(1, 2).reshape(
+ batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
+ )
+ out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, self.out_channel, height, width)
+ out = self.blur(out)
+
+ elif self.downsample:
+ input = self.blur(input)
+ _, _, height, width = input.shape
+ input = input.view(1, batch * in_channel, height, width)
+ out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, self.out_channel, height, width)
+
+ else:
+ input = input.view(1, batch * in_channel, height, width)
+ out = F.conv2d(input, weight, padding=self.padding, groups=batch)
+ _, _, height, width = out.shape
+ out = out.view(batch, self.out_channel, height, width)
+
+ return out
+
+
+class NoiseInjection(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ self.weight = nn.Parameter(torch.zeros(1))
+
+ def forward(self, image, noise=None):
+ if noise is None:
+ batch, _, height, width = image.shape
+ noise = image.new_empty(batch, 1, height, width).normal_()
+
+ return image + self.weight * noise
+
+
+class ConstantInput(nn.Module):
+ def __init__(self, channel, size=4):
+ super().__init__()
+
+ self.input = nn.Parameter(torch.randn(1, channel, size, size))
+
+ def forward(self, input):
+ batch = input.shape[0]
+ out = self.input.repeat(batch, 1, 1, 1)
+
+ return out
+
+
+class StyledConv(nn.Module):
+ def __init__(
+ self,
+ in_channel,
+ out_channel,
+ kernel_size,
+ style_dim,
+ upsample=False,
+ blur_kernel=[1, 3, 3, 1],
+ demodulate=True,
+ ):
+ super().__init__()
+
+ self.conv = ModulatedConv2d(
+ in_channel,
+ out_channel,
+ kernel_size,
+ style_dim,
+ upsample=upsample,
+ blur_kernel=blur_kernel,
+ demodulate=demodulate,
+ )
+
+ self.noise = NoiseInjection()
+ # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
+ # self.activate = ScaledLeakyReLU(0.2)
+ self.activate = FusedLeakyReLU(out_channel)
+
+ def forward(self, input, style, noise=None):
+ out = self.conv(input, style)
+ out = self.noise(out, noise=noise)
+ # out = out + self.bias
+ out = self.activate(out)
+
+ return out
+
+
+class ToRGB(nn.Module):
+ def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
+ super().__init__()
+
+ if upsample:
+ self.upsample = Upsample(blur_kernel)
+
+ self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
+ self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
+
+ def forward(self, input, style, skip=None):
+ out = self.conv(input, style)
+ out = out + self.bias
+
+ if skip is not None:
+ skip = self.upsample(skip)
+
+ out = out + skip
+
+ return out
+
+
+class Generator(nn.Module):
+ def __init__(
+ self,
+ size,
+ style_dim,
+ n_mlp,
+ channel_multiplier=1,
+ blur_kernel=[1, 3, 3, 1],
+ lr_mlp=0.01,
+ small=False,
+ small_isaac=False,
+ ):
+ super().__init__()
+
+ self.size = size
+
+ if small and size > 64:
+ raise ValueError("small only works for sizes <= 64")
+
+ self.style_dim = style_dim
+
+ layers = [PixelNorm()]
+
+ for i in range(n_mlp):
+ layers.append(
+ EqualLinear(
+ style_dim, style_dim, lr_mul=lr_mlp, activation="fused_lrelu"
+ )
+ )
+
+ self.style = nn.Sequential(*layers)
+
+ if small:
+ self.channels = {
+ 4: 64 * channel_multiplier,
+ 8: 64 * channel_multiplier,
+ 16: 64 * channel_multiplier,
+ 32: 64 * channel_multiplier,
+ 64: 64 * channel_multiplier,
+ }
+ elif small_isaac:
+ self.channels = {4: 256, 8: 256, 16: 256, 32: 256, 64: 128, 128: 128}
+ else:
+ self.channels = {
+ 4: 512,
+ 8: 512,
+ 16: 512,
+ 32: 512,
+ 64: 256 * channel_multiplier,
+ 128: 128 * channel_multiplier,
+ 256: 64 * channel_multiplier,
+ 512: 32 * channel_multiplier,
+ 1024: 16 * channel_multiplier,
+ }
+
+ self.input = ConstantInput(self.channels[4])
+ self.conv1 = StyledConv(
+ self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
+ )
+ self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
+
+ self.log_size = int(math.log(size, 2))
+ self.num_layers = (self.log_size - 2) * 2 + 1
+
+ self.convs = nn.ModuleList()
+ self.upsamples = nn.ModuleList()
+ self.to_rgbs = nn.ModuleList()
+ self.noises = nn.Module()
+
+ in_channel = self.channels[4]
+
+ for layer_idx in range(self.num_layers):
+ res = (layer_idx + 5) // 2
+ shape = [1, 1, 2 ** res, 2 ** res]
+ self.noises.register_buffer(
+ "noise_{}".format(layer_idx), torch.randn(*shape)
+ )
+
+ for i in range(3, self.log_size + 1):
+ out_channel = self.channels[2 ** i]
+
+ self.convs.append(
+ StyledConv(
+ in_channel,
+ out_channel,
+ 3,
+ style_dim,
+ upsample=True,
+ blur_kernel=blur_kernel,
+ )
+ )
+
+ self.convs.append(
+ StyledConv(
+ out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
+ )
+ )
+
+ self.to_rgbs.append(ToRGB(out_channel, style_dim))
+
+ in_channel = out_channel
+
+ self.n_latent = self.log_size * 2 - 2
+
+ def make_noise(self):
+ device = self.input.input.device
+
+ noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
+
+ for i in range(3, self.log_size + 1):
+ for _ in range(2):
+ noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
+
+ return noises
+
+ def mean_latent(self, n_latent):
+ latent_in = torch.randn(
+ n_latent, self.style_dim, device=self.input.input.device
+ )
+ latent = self.style(latent_in).mean(0, keepdim=True)
+
+ return latent
+
+ def get_latent(self, input):
+ return self.style(input)
+
+ def forward(
+ self,
+ styles,
+ return_latents=False,
+ return_features=False,
+ inject_index=None,
+ truncation=1,
+ truncation_latent=None,
+ input_is_latent=False,
+ noise=None,
+ randomize_noise=True,
+ ):
+ if not input_is_latent:
+ # print("haha")
+ styles = [self.style(s) for s in styles]
+ if noise is None:
+ if randomize_noise:
+ noise = [None] * self.num_layers
+ else:
+ noise = [
+ getattr(self.noises, "noise_{}".format(i))
+ for i in range(self.num_layers)
+ ]
+
+ if truncation < 1:
+ style_t = []
+
+ for style in styles:
+ style_t.append(
+ truncation_latent + truncation * (style - truncation_latent)
+ )
+
+ styles = style_t
+ # print(styles)
+ if len(styles) < 2:
+ inject_index = self.n_latent
+
+ if styles[0].ndim < 3:
+ latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+ # print("a")
+ else:
+ # print(len(styles))
+ latent = styles[0]
+ # print("b", latent.shape)
+
+ else:
+ # print("c")
+ if inject_index is None:
+ inject_index = 4
+
+ latent = styles[0].unsqueeze(0)
+ if latent.shape[1] == 1:
+ latent = latent.repeat(1, inject_index, 1)
+ else:
+ latent = latent[:, :inject_index, :]
+ latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
+
+ latent = torch.cat([latent, latent2], 1)
+
+ features = {}
+ out = self.input(latent)
+ features["out_0"] = out
+ out = self.conv1(out, latent[:, 0], noise=noise[0])
+ features["conv1_0"] = out
+
+ skip = self.to_rgb1(out, latent[:, 1])
+ features["skip_0"] = skip
+ i = 1
+ for conv1, conv2, noise1, noise2, to_rgb in zip(
+ self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
+ ):
+ out = conv1(out, latent[:, i], noise=noise1)
+ features["conv1_{}".format(i)] = out
+ out = conv2(out, latent[:, i + 1], noise=noise2)
+ features["conv2_{}".format(i)] = out
+ skip = to_rgb(out, latent[:, i + 2], skip)
+ features["skip_{}".format(i)] = skip
+
+ i += 2
+
+ image = skip
+
+ if return_latents:
+ return image, latent
+ elif return_features:
+ return image, features
+ else:
+ return image, None
+
+
+class ConvLayer(nn.Sequential):
+ def __init__(
+ self,
+ in_channel,
+ out_channel,
+ kernel_size,
+ downsample=False,
+ blur_kernel=[1, 3, 3, 1],
+ bias=True,
+ activate=True,
+ ):
+ layers = []
+
+ if downsample:
+ factor = 2
+ p = (len(blur_kernel) - factor) + (kernel_size - 1)
+ pad0 = (p + 1) // 2
+ pad1 = p // 2
+
+ layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
+
+ stride = 2
+ self.padding = 0
+
+ else:
+ stride = 1
+ self.padding = kernel_size // 2
+
+ layers.append(
+ EqualConv2d(
+ in_channel,
+ out_channel,
+ kernel_size,
+ padding=self.padding,
+ stride=stride,
+ bias=bias and not activate,
+ )
+ )
+
+ if activate:
+ if bias:
+ layers.append(FusedLeakyReLU(out_channel))
+
+ else:
+ layers.append(ScaledLeakyReLU(0.2))
+
+ super().__init__(*layers)
+
+
+class ResBlock(nn.Module):
+ def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
+ super().__init__()
+
+ self.conv1 = ConvLayer(in_channel, in_channel, 3)
+ self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
+
+ self.skip = ConvLayer(
+ in_channel, out_channel, 1, downsample=True, activate=False, bias=False
+ )
+
+ def forward(self, input):
+ out = self.conv1(input)
+ out = self.conv2(out)
+
+ skip = self.skip(input)
+ out = (out + skip) / math.sqrt(2)
+
+ return out
+
+
+class StyleDiscriminator(nn.Module):
+ def __init__(
+ self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], small=False
+ ):
+ super().__init__()
+
+ if small:
+ channels = {4: 64, 8: 64, 16: 64, 32: 64, 64: 64}
+
+ else:
+ channels = {
+ 4: 512,
+ 8: 512,
+ 16: 512,
+ 32: 512,
+ 64: 256 * channel_multiplier,
+ 128: 128 * channel_multiplier,
+ 256: 64 * channel_multiplier,
+ 512: 32 * channel_multiplier,
+ 1024: 16 * channel_multiplier,
+ }
+
+ convs = [ConvLayer(3, channels[size], 1)]
+
+ log_size = int(math.log(size, 2))
+
+ in_channel = channels[size]
+
+ for i in range(log_size, 2, -1):
+ out_channel = channels[2 ** (i - 1)]
+
+ convs.append(ResBlock(in_channel, out_channel, blur_kernel))
+
+ in_channel = out_channel
+
+ self.convs = nn.Sequential(*convs)
+
+ self.stddev_group = 4
+ self.stddev_feat = 1
+
+ self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
+ self.final_linear = nn.Sequential(
+ EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"),
+ EqualLinear(channels[4], 1),
+ )
+
+# def forward(self, input):
+# out = self.convs(input)
+
+# batch, channel, height, width = out.shape
+# group = min(batch, self.stddev_group)
+# stddev = out.view(
+# group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
+# )
+# stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
+# stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
+# stddev = stddev.repeat(group, 1, height, width)
+# out = torch.cat([out, stddev], 1)
+
+# out = self.final_conv(out)
+
+# out = out.view(batch, -1)
+# out = self.final_linear(out)
+
+# return out
+
+ def forward(self, input):
+ h = input
+ h_list = []
+
+ for index, blocklist in enumerate(self.convs):
+ h = blocklist(h)
+ h_list.append(h)
+
+ out = h
+ batch, channel, height, width = out.shape
+ group = min(batch, self.stddev_group)
+ stddev = out.view(
+ group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
+ )
+ stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
+ stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
+ stddev = stddev.repeat(group, 1, height, width)
+ out = torch.cat([out, stddev], 1)
+
+ out = self.final_conv(out)
+ h_list.append(out)
+
+ out = out.view(batch, -1)
+ out = self.final_linear(out)
+
+ return out, h_list
+
+
+class StyleEncoder(nn.Module):
+ def __init__(self, size, w_dim=512):
+ super().__init__()
+
+ channels = {
+ 4: 512,
+ 8: 512,
+ 16: 512,
+ 32: 512,
+ 64: 256,
+ 128: 128,
+ 256: 64,
+ 512: 32,
+ 1024: 16
+ }
+
+ self.w_dim = w_dim
+ log_size = int(math.log(size, 2))
+
+ # self.n_latents = log_size*2 - 2
+
+ convs = [ConvLayer(3, channels[size], 1)]
+
+ in_channel = channels[size]
+ for i in range(log_size, 2, -1):
+ out_channel = channels[2 ** (i - 1)]
+ convs.append(ResBlock(in_channel, out_channel))
+ in_channel = out_channel
+
+ # convs.append(EqualConv2d(in_channel, self.n_latents*self.w_dim, 4, padding=0, bias=False))
+ convs.append(EqualConv2d(in_channel,2*self.w_dim, 4, padding=0, bias=False))
+
+
+ self.convs = nn.Sequential(*convs)
+
+ def forward(self, input):
+ out = self.convs(input)
+ # return out.view(len(input), self.n_latents, self.w_dim)
+ reshaped = out.view(len(input), 2*self.w_dim)
+ return reshaped[:,:self.w_dim], reshaped[:,self.w_dim:]
+
+def kaiming_init(m):
+ if isinstance(m, (nn.Linear, nn.Conv2d)):
+ init.kaiming_normal_(m.weight)
+ if m.bias is not None:
+ m.bias.data.fill_(0)
+ elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
+ m.weight.data.fill_(1)
+ if m.bias is not None:
+ m.bias.data.fill_(0)
+
+
+def normal_init(m):
+ if isinstance(m, (nn.Linear, nn.Conv2d)):
+ init.normal_(m.weight, 0, 0.02)
+ if m.bias is not None:
+ m.bias.data.fill_(0)
+ elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
+ m.weight.data.fill_(1)
+ if m.bias is not None:
+ m.bias.data.fill_(0)
\ No newline at end of file
diff --git a/stylegan_human/torch_utils/op_edit/__init__.py b/stylegan_human/torch_utils/op_edit/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2a7efe79d871852affd9de7b46f726a7942f218
--- /dev/null
+++ b/stylegan_human/torch_utils/op_edit/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+from .fused_act import FusedLeakyReLU, fused_leaky_relu
+from .upfirdn2d import upfirdn2d
diff --git a/stylegan_human/torch_utils/op_edit/fused_act.py b/stylegan_human/torch_utils/op_edit/fused_act.py
new file mode 100644
index 0000000000000000000000000000000000000000..138f090bc67b94b363c346cbf405990f1bbdff68
--- /dev/null
+++ b/stylegan_human/torch_utils/op_edit/fused_act.py
@@ -0,0 +1,99 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+import os
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.autograd import Function
+from torch.utils.cpp_extension import load
+
+
+module_path = os.path.dirname(__file__)
+fused = load(
+ "fused",
+ sources=[
+ os.path.join(module_path, "fused_bias_act.cpp"),
+ os.path.join(module_path, "fused_bias_act_kernel.cu"),
+ ],
+)
+
+
+class FusedLeakyReLUFunctionBackward(Function):
+ @staticmethod
+ def forward(ctx, grad_output, out, negative_slope, scale):
+ ctx.save_for_backward(out)
+ ctx.negative_slope = negative_slope
+ ctx.scale = scale
+
+ empty = grad_output.new_empty(0)
+
+ grad_input = fused.fused_bias_act(
+ grad_output, empty, out, 3, 1, negative_slope, scale
+ )
+
+ dim = [0]
+
+ if grad_input.ndim > 2:
+ dim += list(range(2, grad_input.ndim))
+
+ grad_bias = grad_input.sum(dim).detach()
+
+ return grad_input, grad_bias
+
+ @staticmethod
+ def backward(ctx, gradgrad_input, gradgrad_bias):
+ (out,) = ctx.saved_tensors
+ gradgrad_out = fused.fused_bias_act(
+ gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
+ )
+
+ return gradgrad_out, None, None, None
+
+
+class FusedLeakyReLUFunction(Function):
+ @staticmethod
+ def forward(ctx, input, bias, negative_slope, scale):
+ empty = input.new_empty(0)
+ out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
+ ctx.save_for_backward(out)
+ ctx.negative_slope = negative_slope
+ ctx.scale = scale
+
+ return out
+
+ @staticmethod
+ def backward(ctx, grad_output):
+ (out,) = ctx.saved_tensors
+
+ grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
+ grad_output, out, ctx.negative_slope, ctx.scale
+ )
+
+ return grad_input, grad_bias, None, None
+
+
+class FusedLeakyReLU(nn.Module):
+ def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
+ super().__init__()
+
+ self.bias = nn.Parameter(torch.zeros(channel))
+ self.negative_slope = negative_slope
+ self.scale = scale
+
+ def forward(self, input):
+ return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
+
+
+def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
+ if input.device.type == "cpu":
+ rest_dim = [1] * (input.ndim - bias.ndim - 1)
+ return (
+ F.leaky_relu(
+ input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
+ )
+ * scale
+ )
+
+ else:
+ return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/stylegan_human/torch_utils/op_edit/fused_bias_act.cpp b/stylegan_human/torch_utils/op_edit/fused_bias_act.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..a79a3d65b8fb56393c954630ae8ce5a5c8a8bb7d
--- /dev/null
+++ b/stylegan_human/torch_utils/op_edit/fused_bias_act.cpp
@@ -0,0 +1,23 @@
+// Copyright (c) SenseTime Research. All rights reserved.
+
+#include
+
+
+torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
+ int act, int grad, float alpha, float scale);
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+
+torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
+ int act, int grad, float alpha, float scale) {
+ CHECK_CUDA(input);
+ CHECK_CUDA(bias);
+
+ return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
+}
\ No newline at end of file
diff --git a/stylegan_human/torch_utils/op_edit/fused_bias_act_kernel.cu b/stylegan_human/torch_utils/op_edit/fused_bias_act_kernel.cu
new file mode 100644
index 0000000000000000000000000000000000000000..2d72170bfbd766d7f6ccaf9bdd866833a5dad14f
--- /dev/null
+++ b/stylegan_human/torch_utils/op_edit/fused_bias_act_kernel.cu
@@ -0,0 +1,101 @@
+// Copyright (c) SenseTime Research. All rights reserved.
+
+// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+//
+// This work is made available under the Nvidia Source Code License-NC.
+// To view a copy of this license, visit
+// https://nvlabs.github.io/stylegan2/license.html
+
+#include
+
+#include
+#include
+#include
+#include
+
+#include
+#include
+
+
+template
+static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref,
+ int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) {
+ int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x;
+
+ scalar_t zero = 0.0;
+
+ for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) {
+ scalar_t x = p_x[xi];
+
+ if (use_bias) {
+ x += p_b[(xi / step_b) % size_b];
+ }
+
+ scalar_t ref = use_ref ? p_ref[xi] : zero;
+
+ scalar_t y;
+
+ switch (act * 10 + grad) {
+ default:
+ case 10: y = x; break;
+ case 11: y = x; break;
+ case 12: y = 0.0; break;
+
+ case 30: y = (x > 0.0) ? x : x * alpha; break;
+ case 31: y = (ref > 0.0) ? x : x * alpha; break;
+ case 32: y = 0.0; break;
+ }
+
+ out[xi] = y * scale;
+ }
+}
+
+
+torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
+ int act, int grad, float alpha, float scale) {
+ int curDevice = -1;
+ cudaGetDevice(&curDevice);
+ cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);
+
+ auto x = input.contiguous();
+ auto b = bias.contiguous();
+ auto ref = refer.contiguous();
+
+ int use_bias = b.numel() ? 1 : 0;
+ int use_ref = ref.numel() ? 1 : 0;
+
+ int size_x = x.numel();
+ int size_b = b.numel();
+ int step_b = 1;
+
+ for (int i = 1 + 1; i < x.dim(); i++) {
+ step_b *= x.size(i);
+ }
+
+ int loop_x = 4;
+ int block_size = 4 * 32;
+ int grid_size = (size_x - 1) / (loop_x * block_size) + 1;
+
+ auto y = torch::empty_like(x);
+
+ AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] {
+ fused_bias_act_kernel<<>>(
+ y.data_ptr(),
+ x.data_ptr(),
+ b.data_ptr(),
+ ref.data_ptr(),
+ act,
+ grad,
+ alpha,
+ scale,
+ loop_x,
+ size_x,
+ step_b,
+ size_b,
+ use_bias,
+ use_ref
+ );
+ });
+
+ return y;
+}
\ No newline at end of file
diff --git a/stylegan_human/torch_utils/op_edit/upfirdn2d.cpp b/stylegan_human/torch_utils/op_edit/upfirdn2d.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..ec39812ba6f50386a0b7f5bf95545265ec419930
--- /dev/null
+++ b/stylegan_human/torch_utils/op_edit/upfirdn2d.cpp
@@ -0,0 +1,25 @@
+// Copyright (c) SenseTime Research. All rights reserved.
+
+#include
+
+
+torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
+ int up_x, int up_y, int down_x, int down_y,
+ int pad_x0, int pad_x1, int pad_y0, int pad_y1);
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+
+torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
+ int up_x, int up_y, int down_x, int down_y,
+ int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
+ CHECK_CUDA(input);
+ CHECK_CUDA(kernel);
+
+ return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
+}
\ No newline at end of file
diff --git a/stylegan_human/torch_utils/op_edit/upfirdn2d.py b/stylegan_human/torch_utils/op_edit/upfirdn2d.py
new file mode 100644
index 0000000000000000000000000000000000000000..874c09c5e98bee1ace64408aa31ec547dfe695a4
--- /dev/null
+++ b/stylegan_human/torch_utils/op_edit/upfirdn2d.py
@@ -0,0 +1,202 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+import os
+
+import torch
+from torch.nn import functional as F
+from torch.autograd import Function
+from torch.utils.cpp_extension import load
+
+
+module_path = os.path.dirname(__file__)
+upfirdn2d_op = load(
+ "upfirdn2d",
+ sources=[
+ os.path.join(module_path, "upfirdn2d.cpp"),
+ os.path.join(module_path, "upfirdn2d_kernel.cu"),
+ ],
+)
+
+
+class UpFirDn2dBackward(Function):
+ @staticmethod
+ def forward(
+ ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
+ ):
+
+ up_x, up_y = up
+ down_x, down_y = down
+ g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
+
+ grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
+
+ grad_input = upfirdn2d_op.upfirdn2d(
+ grad_output,
+ grad_kernel,
+ down_x,
+ down_y,
+ up_x,
+ up_y,
+ g_pad_x0,
+ g_pad_x1,
+ g_pad_y0,
+ g_pad_y1,
+ )
+ grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
+
+ ctx.save_for_backward(kernel)
+
+ pad_x0, pad_x1, pad_y0, pad_y1 = pad
+
+ ctx.up_x = up_x
+ ctx.up_y = up_y
+ ctx.down_x = down_x
+ ctx.down_y = down_y
+ ctx.pad_x0 = pad_x0
+ ctx.pad_x1 = pad_x1
+ ctx.pad_y0 = pad_y0
+ ctx.pad_y1 = pad_y1
+ ctx.in_size = in_size
+ ctx.out_size = out_size
+
+ return grad_input
+
+ @staticmethod
+ def backward(ctx, gradgrad_input):
+ (kernel,) = ctx.saved_tensors
+
+ gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
+
+ gradgrad_out = upfirdn2d_op.upfirdn2d(
+ gradgrad_input,
+ kernel,
+ ctx.up_x,
+ ctx.up_y,
+ ctx.down_x,
+ ctx.down_y,
+ ctx.pad_x0,
+ ctx.pad_x1,
+ ctx.pad_y0,
+ ctx.pad_y1,
+ )
+ # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
+ gradgrad_out = gradgrad_out.view(
+ ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
+ )
+
+ return gradgrad_out, None, None, None, None, None, None, None, None
+
+
+class UpFirDn2d(Function):
+ @staticmethod
+ def forward(ctx, input, kernel, up, down, pad):
+ up_x, up_y = up
+ down_x, down_y = down
+ pad_x0, pad_x1, pad_y0, pad_y1 = pad
+
+ kernel_h, kernel_w = kernel.shape
+ batch, channel, in_h, in_w = input.shape
+ ctx.in_size = input.shape
+
+ input = input.reshape(-1, in_h, in_w, 1)
+
+ ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
+
+ out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
+ out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
+ ctx.out_size = (out_h, out_w)
+
+ ctx.up = (up_x, up_y)
+ ctx.down = (down_x, down_y)
+ ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
+
+ g_pad_x0 = kernel_w - pad_x0 - 1
+ g_pad_y0 = kernel_h - pad_y0 - 1
+ g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
+ g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
+
+ ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
+
+ out = upfirdn2d_op.upfirdn2d(
+ input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
+ )
+ # out = out.view(major, out_h, out_w, minor)
+ out = out.view(-1, channel, out_h, out_w)
+
+ return out
+
+ @staticmethod
+ def backward(ctx, grad_output):
+ kernel, grad_kernel = ctx.saved_tensors
+
+ grad_input = UpFirDn2dBackward.apply(
+ grad_output,
+ kernel,
+ grad_kernel,
+ ctx.up,
+ ctx.down,
+ ctx.pad,
+ ctx.g_pad,
+ ctx.in_size,
+ ctx.out_size,
+ )
+
+ return grad_input, None, None, None, None
+
+
+def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
+ if input.device.type == "cpu":
+ out = upfirdn2d_native(
+ input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
+ )
+
+ else:
+ out = UpFirDn2d.apply(
+ input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])
+ )
+
+ return out
+
+
+def upfirdn2d_native(
+ input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
+):
+ _, channel, in_h, in_w = input.shape
+ input = input.reshape(-1, in_h, in_w, 1)
+
+ _, in_h, in_w, minor = input.shape
+ kernel_h, kernel_w = kernel.shape
+
+ out = input.view(-1, in_h, 1, in_w, 1, minor)
+ out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
+ out = out.view(-1, in_h * up_y, in_w * up_x, minor)
+
+ out = F.pad(
+ out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
+ )
+ out = out[
+ :,
+ max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
+ max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
+ :,
+ ]
+
+ out = out.permute(0, 3, 1, 2)
+ out = out.reshape(
+ [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
+ )
+ w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
+ out = F.conv2d(out, w)
+ out = out.reshape(
+ -1,
+ minor,
+ in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
+ in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
+ )
+ out = out.permute(0, 2, 3, 1)
+ out = out[:, ::down_y, ::down_x, :]
+
+ out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
+ out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
+
+ return out.view(-1, channel, out_h, out_w)
diff --git a/stylegan_human/torch_utils/op_edit/upfirdn2d_kernel.cu b/stylegan_human/torch_utils/op_edit/upfirdn2d_kernel.cu
new file mode 100644
index 0000000000000000000000000000000000000000..f82f113bd489e86b4e5ee6bc40c9c3d75e30aead
--- /dev/null
+++ b/stylegan_human/torch_utils/op_edit/upfirdn2d_kernel.cu
@@ -0,0 +1,371 @@
+// Copyright (c) SenseTime Research. All rights reserved.
+
+// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+//
+// This work is made available under the Nvidia Source Code License-NC.
+// To view a copy of this license, visit
+// https://nvlabs.github.io/stylegan2/license.html
+
+#include
+
+#include
+#include
+#include
+#include
+
+#include
+#include
+
+static __host__ __device__ __forceinline__ int floor_div(int a, int b) {
+ int c = a / b;
+
+ if (c * b > a) {
+ c--;
+ }
+
+ return c;
+}
+
+struct UpFirDn2DKernelParams {
+ int up_x;
+ int up_y;
+ int down_x;
+ int down_y;
+ int pad_x0;
+ int pad_x1;
+ int pad_y0;
+ int pad_y1;
+
+ int major_dim;
+ int in_h;
+ int in_w;
+ int minor_dim;
+ int kernel_h;
+ int kernel_w;
+ int out_h;
+ int out_w;
+ int loop_major;
+ int loop_x;
+};
+
+template
+__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input,
+ const scalar_t *kernel,
+ const UpFirDn2DKernelParams p) {
+ int minor_idx = blockIdx.x * blockDim.x + threadIdx.x;
+ int out_y = minor_idx / p.minor_dim;
+ minor_idx -= out_y * p.minor_dim;
+ int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y;
+ int major_idx_base = blockIdx.z * p.loop_major;
+
+ if (out_x_base >= p.out_w || out_y >= p.out_h ||
+ major_idx_base >= p.major_dim) {
+ return;
+ }
+
+ int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0;
+ int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h);
+ int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y;
+ int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y;
+
+ for (int loop_major = 0, major_idx = major_idx_base;
+ loop_major < p.loop_major && major_idx < p.major_dim;
+ loop_major++, major_idx++) {
+ for (int loop_x = 0, out_x = out_x_base;
+ loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) {
+ int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0;
+ int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w);
+ int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x;
+ int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x;
+
+ const scalar_t *x_p =
+ &input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim +
+ minor_idx];
+ const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x];
+ int x_px = p.minor_dim;
+ int k_px = -p.up_x;
+ int x_py = p.in_w * p.minor_dim;
+ int k_py = -p.up_y * p.kernel_w;
+
+ scalar_t v = 0.0f;
+
+ for (int y = 0; y < h; y++) {
+ for (int x = 0; x < w; x++) {
+ v += static_cast(*x_p) * static_cast(*k_p);
+ x_p += x_px;
+ k_p += k_px;
+ }
+
+ x_p += x_py - w * x_px;
+ k_p += k_py - w * k_px;
+ }
+
+ out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim +
+ minor_idx] = v;
+ }
+ }
+}
+
+template
+__global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input,
+ const scalar_t *kernel,
+ const UpFirDn2DKernelParams p) {
+ const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1;
+ const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1;
+
+ __shared__ volatile float sk[kernel_h][kernel_w];
+ __shared__ volatile float sx[tile_in_h][tile_in_w];
+
+ int minor_idx = blockIdx.x;
+ int tile_out_y = minor_idx / p.minor_dim;
+ minor_idx -= tile_out_y * p.minor_dim;
+ tile_out_y *= tile_out_h;
+ int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w;
+ int major_idx_base = blockIdx.z * p.loop_major;
+
+ if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h |
+ major_idx_base >= p.major_dim) {
+ return;
+ }
+
+ for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w;
+ tap_idx += blockDim.x) {
+ int ky = tap_idx / kernel_w;
+ int kx = tap_idx - ky * kernel_w;
+ scalar_t v = 0.0;
+
+ if (kx < p.kernel_w & ky < p.kernel_h) {
+ v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)];
+ }
+
+ sk[ky][kx] = v;
+ }
+
+ for (int loop_major = 0, major_idx = major_idx_base;
+ loop_major < p.loop_major & major_idx < p.major_dim;
+ loop_major++, major_idx++) {
+ for (int loop_x = 0, tile_out_x = tile_out_x_base;
+ loop_x < p.loop_x & tile_out_x < p.out_w;
+ loop_x++, tile_out_x += tile_out_w) {
+ int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0;
+ int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0;
+ int tile_in_x = floor_div(tile_mid_x, up_x);
+ int tile_in_y = floor_div(tile_mid_y, up_y);
+
+ __syncthreads();
+
+ for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w;
+ in_idx += blockDim.x) {
+ int rel_in_y = in_idx / tile_in_w;
+ int rel_in_x = in_idx - rel_in_y * tile_in_w;
+ int in_x = rel_in_x + tile_in_x;
+ int in_y = rel_in_y + tile_in_y;
+
+ scalar_t v = 0.0;
+
+ if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) {
+ v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) *
+ p.minor_dim +
+ minor_idx];
+ }
+
+ sx[rel_in_y][rel_in_x] = v;
+ }
+
+ __syncthreads();
+ for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w;
+ out_idx += blockDim.x) {
+ int rel_out_y = out_idx / tile_out_w;
+ int rel_out_x = out_idx - rel_out_y * tile_out_w;
+ int out_x = rel_out_x + tile_out_x;
+ int out_y = rel_out_y + tile_out_y;
+
+ int mid_x = tile_mid_x + rel_out_x * down_x;
+ int mid_y = tile_mid_y + rel_out_y * down_y;
+ int in_x = floor_div(mid_x, up_x);
+ int in_y = floor_div(mid_y, up_y);
+ int rel_in_x = in_x - tile_in_x;
+ int rel_in_y = in_y - tile_in_y;
+ int kernel_x = (in_x + 1) * up_x - mid_x - 1;
+ int kernel_y = (in_y + 1) * up_y - mid_y - 1;
+
+ scalar_t v = 0.0;
+
+#pragma unroll
+ for (int y = 0; y < kernel_h / up_y; y++)
+#pragma unroll
+ for (int x = 0; x < kernel_w / up_x; x++)
+ v += sx[rel_in_y + y][rel_in_x + x] *
+ sk[kernel_y + y * up_y][kernel_x + x * up_x];
+
+ if (out_x < p.out_w & out_y < p.out_h) {
+ out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim +
+ minor_idx] = v;
+ }
+ }
+ }
+ }
+}
+
+torch::Tensor upfirdn2d_op(const torch::Tensor &input,
+ const torch::Tensor &kernel, int up_x, int up_y,
+ int down_x, int down_y, int pad_x0, int pad_x1,
+ int pad_y0, int pad_y1) {
+ int curDevice = -1;
+ cudaGetDevice(&curDevice);
+ cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);
+
+ UpFirDn2DKernelParams p;
+
+ auto x = input.contiguous();
+ auto k = kernel.contiguous();
+
+ p.major_dim = x.size(0);
+ p.in_h = x.size(1);
+ p.in_w = x.size(2);
+ p.minor_dim = x.size(3);
+ p.kernel_h = k.size(0);
+ p.kernel_w = k.size(1);
+ p.up_x = up_x;
+ p.up_y = up_y;
+ p.down_x = down_x;
+ p.down_y = down_y;
+ p.pad_x0 = pad_x0;
+ p.pad_x1 = pad_x1;
+ p.pad_y0 = pad_y0;
+ p.pad_y1 = pad_y1;
+
+ p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) /
+ p.down_y;
+ p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) /
+ p.down_x;
+
+ auto out =
+ at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options());
+
+ int mode = -1;
+
+ int tile_out_h = -1;
+ int tile_out_w = -1;
+
+ if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 &&
+ p.kernel_h <= 4 && p.kernel_w <= 4) {
+ mode = 1;
+ tile_out_h = 16;
+ tile_out_w = 64;
+ }
+
+ if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 &&
+ p.kernel_h <= 3 && p.kernel_w <= 3) {
+ mode = 2;
+ tile_out_h = 16;
+ tile_out_w = 64;
+ }
+
+ if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 &&
+ p.kernel_h <= 4 && p.kernel_w <= 4) {
+ mode = 3;
+ tile_out_h = 16;
+ tile_out_w = 64;
+ }
+
+ if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 &&
+ p.kernel_h <= 2 && p.kernel_w <= 2) {
+ mode = 4;
+ tile_out_h = 16;
+ tile_out_w = 64;
+ }
+
+ if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 &&
+ p.kernel_h <= 4 && p.kernel_w <= 4) {
+ mode = 5;
+ tile_out_h = 8;
+ tile_out_w = 32;
+ }
+
+ if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 &&
+ p.kernel_h <= 2 && p.kernel_w <= 2) {
+ mode = 6;
+ tile_out_h = 8;
+ tile_out_w = 32;
+ }
+
+ dim3 block_size;
+ dim3 grid_size;
+
+ if (tile_out_h > 0 && tile_out_w > 0) {
+ p.loop_major = (p.major_dim - 1) / 16384 + 1;
+ p.loop_x = 1;
+ block_size = dim3(32 * 8, 1, 1);
+ grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim,
+ (p.out_w - 1) / (p.loop_x * tile_out_w) + 1,
+ (p.major_dim - 1) / p.loop_major + 1);
+ } else {
+ p.loop_major = (p.major_dim - 1) / 16384 + 1;
+ p.loop_x = 4;
+ block_size = dim3(4, 32, 1);
+ grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1,
+ (p.out_w - 1) / (p.loop_x * block_size.y) + 1,
+ (p.major_dim - 1) / p.loop_major + 1);
+ }
+
+ AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] {
+ switch (mode) {
+ case 1:
+ upfirdn2d_kernel
+ <<>>(out.data_ptr(),
+ x.data_ptr(),
+ k.data_ptr(), p);
+
+ break;
+
+ case 2:
+ upfirdn2d_kernel
+ <<>>(out.data_ptr(),
+ x.data_ptr(),
+ k.data_ptr(), p);
+
+ break;
+
+ case 3:
+ upfirdn2d_kernel
+ <<>>(out.data_ptr(),
+ x.data_ptr(),
+ k.data_ptr(), p);
+
+ break;
+
+ case 4:
+ upfirdn2d_kernel
+ <<>>(out.data_ptr(),
+ x.data_ptr(),
+ k.data_ptr(), p);
+
+ break;
+
+ case 5:
+ upfirdn2d_kernel
+ <<>>(out.data_ptr(),
+ x.data_ptr(),
+ k.data_ptr(), p);
+
+ break;
+
+ case 6:
+ upfirdn2d_kernel
+ <<>>(out.data_ptr(),
+ x.data_ptr(),
+ k.data_ptr(), p);
+
+ break;
+
+ default:
+ upfirdn2d_kernel_large<<>>(
+ out.data_ptr(), x.data_ptr(),
+ k.data_ptr(), p);
+ }
+ });
+
+ return out;
+}
\ No newline at end of file
diff --git a/stylegan_human/torch_utils/ops/__init__.py b/stylegan_human/torch_utils/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c46c314cf2ff24fff74d7308dd8cc50767dd870
--- /dev/null
+++ b/stylegan_human/torch_utils/ops/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+#empty
\ No newline at end of file
diff --git a/stylegan_human/torch_utils/ops/bias_act.cpp b/stylegan_human/torch_utils/ops/bias_act.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..aef47317a3ae018de6ea620060337bcf44b2d649
--- /dev/null
+++ b/stylegan_human/torch_utils/ops/bias_act.cpp
@@ -0,0 +1,101 @@
+// Copyright (c) SenseTime Research. All rights reserved.
+
+// Copyright (c) 2021, NVIDIA CORPORATION. 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/stylegan_human/torch_utils/ops/bias_act.cu b/stylegan_human/torch_utils/ops/bias_act.cu
new file mode 100644
index 0000000000000000000000000000000000000000..f0fc48475dbceb3476e5a41954a9711d5ade07e1
--- /dev/null
+++ b/stylegan_human/torch_utils/ops/bias_act.cu
@@ -0,0 +1,175 @@
+// Copyright (c) SenseTime Research. All rights reserved.
+
+// Copyright (c) 2021, NVIDIA CORPORATION. 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/stylegan_human/torch_utils/ops/bias_act.h b/stylegan_human/torch_utils/ops/bias_act.h
new file mode 100644
index 0000000000000000000000000000000000000000..d0246aa06c3dcd5919111fdc914136014b9044b5
--- /dev/null
+++ b/stylegan_human/torch_utils/ops/bias_act.h
@@ -0,0 +1,40 @@
+// Copyright (c) SenseTime Research. All rights reserved.
+
+// Copyright (c) 2021, NVIDIA CORPORATION. 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/stylegan_human/torch_utils/ops/bias_act.py b/stylegan_human/torch_utils/ops/bias_act.py
new file mode 100644
index 0000000000000000000000000000000000000000..8041208be7680ddeceb1a87a9db9faae7101e7bf
--- /dev/null
+++ b/stylegan_human/torch_utils/ops/bias_act.py
@@ -0,0 +1,214 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2021, NVIDIA CORPORATION. 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 warnings
+import numpy as np
+import torch
+import dnnlib
+import traceback
+
+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),
+}
+
+#----------------------------------------------------------------------------
+
+_inited = False
+_plugin = None
+_null_tensor = torch.empty([0])
+
+def _init():
+ global _inited, _plugin
+ if not _inited:
+ _inited = True
+ sources = ['bias_act.cpp', 'bias_act.cu']
+ sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
+ try:
+ _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
+ except:
+ warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
+ return _plugin is not None
+
+#----------------------------------------------------------------------------
+
+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/stylegan_human/torch_utils/ops/conv2d_gradfix.py b/stylegan_human/torch_utils/ops/conv2d_gradfix.py
new file mode 100644
index 0000000000000000000000000000000000000000..093036b728336d6f2f593aaea187054a8af8d523
--- /dev/null
+++ b/stylegan_human/torch_utils/ops/conv2d_gradfix.py
@@ -0,0 +1,172 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2021, NVIDIA CORPORATION. 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 warnings
+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():
+ global weight_gradients_disabled
+ old = weight_gradients_disabled
+ 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
+ if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
+ return True
+ warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().')
+ return False
+
+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()
+
+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
+ if not transpose:
+ output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
+ else: # transpose
+ output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
+ ctx.save_for_backward(input, weight)
+ return output
+
+ @staticmethod
+ def backward(ctx, grad_output):
+ input, weight = ctx.saved_tensors
+ 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)
+ grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).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):
+ op = torch._C._jit_get_operation('aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight')
+ flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
+ grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
+ assert grad_weight.shape == weight_shape
+ ctx.save_for_backward(grad_output, input)
+ return grad_weight
+
+ @staticmethod
+ def backward(ctx, grad2_grad_weight):
+ grad_output, input = ctx.saved_tensors
+ 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)
+ grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).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/stylegan_human/torch_utils/ops/conv2d_resample.py b/stylegan_human/torch_utils/ops/conv2d_resample.py
new file mode 100644
index 0000000000000000000000000000000000000000..44a0883f731156af72ec19829ef0bfb8026682be
--- /dev/null
+++ b/stylegan_human/torch_utils/ops/conv2d_resample.py
@@ -0,0 +1,158 @@
+# Copyright (c) SenseTime Research. All rights reserved.
+
+# Copyright (c) 2021, NVIDIA CORPORATION. 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.
+ if not flip_weight: # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
+ w = w.flip([2, 3])
+
+ # Workaround performance pitfall in cuDNN 8.0.5, triggered when using
+ # 1x1 kernel + memory_format=channels_last + less than 64 channels.
+ if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose:
+ if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64:
+ if out_channels <= 4 and groups == 1:
+ in_shape = x.shape
+ x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1])
+ x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]])
+ else:
+ x = x.to(memory_format=torch.contiguous_format)
+ w = w.to(memory_format=torch.contiguous_format)
+ x = conv2d_gradfix.conv2d(x, w, groups=groups)
+ return x.to(memory_format=torch.channels_last)
+
+ # Otherwise => 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/stylegan_human/torch_utils/ops/filtered_lrelu.cpp b/stylegan_human/torch_utils/ops/filtered_lrelu.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..4e253d1f3ffe84e54e667bf61a45dfe66264a73c
--- /dev/null
+++ b/stylegan_human/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.
+}
+
+//------------------------------------------------------------------------
\ No newline at end of file
diff --git a/stylegan_human/torch_utils/ops/filtered_lrelu.cu b/stylegan_human/torch_utils/ops/filtered_lrelu.cu
new file mode 100644
index 0000000000000000000000000000000000000000..50bad61678bec055dade2b337f12ea395aeb382e
--- /dev/null
+++ b/stylegan_human/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