diff options
| author | CoprDistGit <infra@openeuler.org> | 2023-05-05 15:03:29 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-05-05 15:03:29 +0000 |
| commit | a34efbe1bab57a98df08845a03c2d5bd12fe22bd (patch) | |
| tree | 56377bb13583c40db8b0d6ecef57bdb13ba875cf | |
| parent | ae5facb1d3ab4b407e26cace3ca0a6f421951ebc (diff) | |
automatic import of python-kubric-nightlyopeneuler20.03
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-kubric-nightly.spec | 349 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 351 insertions, 0 deletions
@@ -0,0 +1 @@ +/kubric-nightly-2023.5.5.tar.gz diff --git a/python-kubric-nightly.spec b/python-kubric-nightly.spec new file mode 100644 index 0000000..8639430 --- /dev/null +++ b/python-kubric-nightly.spec @@ -0,0 +1,349 @@ +%global _empty_manifest_terminate_build 0 +Name: python-kubric-nightly +Version: 2023.5.5 +Release: 1 +Summary: A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation, depth maps, and optical flow. +License: Apache 2.0 +URL: https://github.com/google-research/kubric +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ab/21/dd6ef91b9ac33da89c980bab1286ebd9debd8a308d7464b103dbee47cb40/kubric-nightly-2023.5.5.tar.gz +BuildArch: noarch + +Requires: python3-apache-beam[gcp] +Requires: python3-bidict +Requires: python3-dataclasses +Requires: python3-etils[epath_no_tf] +Requires: python3-cloudml-hypertune +Requires: python3-google-cloud-storage +Requires: python3-imageio +Requires: python3-munch +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-pypng +Requires: python3-pyquaternion +Requires: python3-Levenshtein +Requires: python3-scikit-learn +Requires: python3-singledispatchmethod +Requires: python3-tensorflow +Requires: python3-tensorflow-datasets +Requires: python3-traitlets +Requires: python3-trimesh + +%description +# Kubric + +[](https://github.com/google-research/kubric/actions/workflows/blender.yml) +[](https://github.com/google-research/kubric/actions/workflows/kubruntu.yml) +[](https://github.com/google-research/kubric/actions/workflows/test.yml) +[](https://codecov.io/github/google-research/kubric) +[](https://kubric.readthedocs.io/en/latest/) + +A data generation pipeline for creating semi-realistic synthetic multi-object +videos with rich annotations such as instance segmentation masks, depth maps, +and optical flow. + + + + +## Motivation and design +We need better data for training and evaluating machine learning systems, especially in the collntext of unsupervised multi-object video understanding. +Current systems succeed on [toy datasets](https://github.com/deepmind/multi_object_datasets), but fail on real-world data. +Progress could be greatly accelerated if we had the ability to create suitable datasets of varying complexity on demand. +Kubric is mainly built on-top of pybullet (for physics simulation) and Blender (for rendering); however, the code is kept modular to potentially support different rendering backends. + +## Getting started +For instructions, please refer to [https://kubric.readthedocs.io](https://kubric.readthedocs.io) + +Assuming you have docker installed, to generate the data above simply execute: +``` +git clone https://github.com/google-research/kubric.git +cd kubric +docker pull kubricdockerhub/kubruntu +docker run --rm --interactive \ + --user $(id -u):$(id -g) \ + --volume "$(pwd):/kubric" \ + kubricdockerhub/kubruntu \ + /usr/bin/python3 examples/helloworld.py +ls output +``` + +Kubric employs **Blender 2.93** (see [here](https://github.com/google-research/kubric/blob/01a08d274234f32f2adc4f7d5666b39490f953ad/docker/Blender.Dockerfile#L48)), so if you want to inspect the generated `*.blend` scene file for interactive inspection (i.e. without needing to render the scene), please make sure you have installed the correct Blender version. + +## Requirements +- A pipeline for conveniently generating video data. +- Physics simulation for automatically generating physical interactions between multiple objects. +- Good control over the complexity of the generated data, so that we can evaluate individual aspects such as variability of objects and textures. +- Realism: Ideally, the ability to span the entire complexity range from CLEVR all the way to real-world video such as YouTube8. This is clearly not feasible, but we would like to get as close as possible. +- Access to rich ground truth information about the objects in a scene for the purpose of evaluation (eg. object segmentations and properties) +- Control the train/test split to evaluate compositionality and systematic generalization (for example on held-out combinations of features or objects) + + +## Challenges and datasets +Generally, we store datasets for the challenges in this [Google Cloud Bucket](https://console.cloud.google.com/storage/browser/kubric-public). +More specifically, these challenges are *dataset contributions* of the Kubric CVPR'22 paper: +* [MOVi: Multi-Object Video](challenges/movi) +* [Texture-Structure in NeRF](challenges/texture_structure_nerf) +* [Optical Flow](challenges/optical_flow) +* [Pre-training Visual Representations](challenges/pretraining_visual) +* [Robust NeRF](challenges/robust_nerf) +* [Multi-View Object Matting](challenges/multiview_matting) +* [Complex BRDFs](challenges/complex_brdf) +* [Single View Reconstruction](challenges/single_view_reconstruction) +* [Video Based Reconstruction](challenges/video_based_reconstruction) +* [Point Tracking](challenges/point_tracking) + +Pointers to additional datasets/workers: +* [ToyBox (from Neural Semantic Fields)](https://nesf3d.github.io) +* [MultiShapeNet (from Scene Representation Transformer)](https://srt-paper.github.io) +* [SyntheticTrio(from Controllable Neural Radiance Fields)](https://github.com/kacperkan/conerf-kubric-dataset#readme) + +## Bibtex +``` +@article{greff2021kubric, + title = {Kubric: a scalable dataset generator}, + author = {Klaus Greff and Francois Belletti and Lucas Beyer and Carl Doersch and + Yilun Du and Daniel Duckworth and David J Fleet and Dan Gnanapragasam and + Florian Golemo and Charles Herrmann and Thomas Kipf and Abhijit Kundu and + Dmitry Lagun and Issam Laradji and Hsueh-Ti (Derek) Liu and Henning Meyer and + Yishu Miao and Derek Nowrouzezahrai and Cengiz Oztireli and Etienne Pot and + Noha Radwan and Daniel Rebain and Sara Sabour and Mehdi S. M. Sajjadi and Matan Sela and + Vincent Sitzmann and Austin Stone and Deqing Sun and Suhani Vora and Ziyu Wang and + Tianhao Wu and Kwang Moo Yi and Fangcheng Zhong and Andrea Tagliasacchi}, + booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2022}, +} +``` + +## Disclaimer +This is not an official Google Product + + +%package -n python3-kubric-nightly +Summary: A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation, depth maps, and optical flow. +Provides: python-kubric-nightly +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-kubric-nightly +# Kubric + +[](https://github.com/google-research/kubric/actions/workflows/blender.yml) +[](https://github.com/google-research/kubric/actions/workflows/kubruntu.yml) +[](https://github.com/google-research/kubric/actions/workflows/test.yml) +[](https://codecov.io/github/google-research/kubric) +[](https://kubric.readthedocs.io/en/latest/) + +A data generation pipeline for creating semi-realistic synthetic multi-object +videos with rich annotations such as instance segmentation masks, depth maps, +and optical flow. + + + + +## Motivation and design +We need better data for training and evaluating machine learning systems, especially in the collntext of unsupervised multi-object video understanding. +Current systems succeed on [toy datasets](https://github.com/deepmind/multi_object_datasets), but fail on real-world data. +Progress could be greatly accelerated if we had the ability to create suitable datasets of varying complexity on demand. +Kubric is mainly built on-top of pybullet (for physics simulation) and Blender (for rendering); however, the code is kept modular to potentially support different rendering backends. + +## Getting started +For instructions, please refer to [https://kubric.readthedocs.io](https://kubric.readthedocs.io) + +Assuming you have docker installed, to generate the data above simply execute: +``` +git clone https://github.com/google-research/kubric.git +cd kubric +docker pull kubricdockerhub/kubruntu +docker run --rm --interactive \ + --user $(id -u):$(id -g) \ + --volume "$(pwd):/kubric" \ + kubricdockerhub/kubruntu \ + /usr/bin/python3 examples/helloworld.py +ls output +``` + +Kubric employs **Blender 2.93** (see [here](https://github.com/google-research/kubric/blob/01a08d274234f32f2adc4f7d5666b39490f953ad/docker/Blender.Dockerfile#L48)), so if you want to inspect the generated `*.blend` scene file for interactive inspection (i.e. without needing to render the scene), please make sure you have installed the correct Blender version. + +## Requirements +- A pipeline for conveniently generating video data. +- Physics simulation for automatically generating physical interactions between multiple objects. +- Good control over the complexity of the generated data, so that we can evaluate individual aspects such as variability of objects and textures. +- Realism: Ideally, the ability to span the entire complexity range from CLEVR all the way to real-world video such as YouTube8. This is clearly not feasible, but we would like to get as close as possible. +- Access to rich ground truth information about the objects in a scene for the purpose of evaluation (eg. object segmentations and properties) +- Control the train/test split to evaluate compositionality and systematic generalization (for example on held-out combinations of features or objects) + + +## Challenges and datasets +Generally, we store datasets for the challenges in this [Google Cloud Bucket](https://console.cloud.google.com/storage/browser/kubric-public). +More specifically, these challenges are *dataset contributions* of the Kubric CVPR'22 paper: +* [MOVi: Multi-Object Video](challenges/movi) +* [Texture-Structure in NeRF](challenges/texture_structure_nerf) +* [Optical Flow](challenges/optical_flow) +* [Pre-training Visual Representations](challenges/pretraining_visual) +* [Robust NeRF](challenges/robust_nerf) +* [Multi-View Object Matting](challenges/multiview_matting) +* [Complex BRDFs](challenges/complex_brdf) +* [Single View Reconstruction](challenges/single_view_reconstruction) +* [Video Based Reconstruction](challenges/video_based_reconstruction) +* [Point Tracking](challenges/point_tracking) + +Pointers to additional datasets/workers: +* [ToyBox (from Neural Semantic Fields)](https://nesf3d.github.io) +* [MultiShapeNet (from Scene Representation Transformer)](https://srt-paper.github.io) +* [SyntheticTrio(from Controllable Neural Radiance Fields)](https://github.com/kacperkan/conerf-kubric-dataset#readme) + +## Bibtex +``` +@article{greff2021kubric, + title = {Kubric: a scalable dataset generator}, + author = {Klaus Greff and Francois Belletti and Lucas Beyer and Carl Doersch and + Yilun Du and Daniel Duckworth and David J Fleet and Dan Gnanapragasam and + Florian Golemo and Charles Herrmann and Thomas Kipf and Abhijit Kundu and + Dmitry Lagun and Issam Laradji and Hsueh-Ti (Derek) Liu and Henning Meyer and + Yishu Miao and Derek Nowrouzezahrai and Cengiz Oztireli and Etienne Pot and + Noha Radwan and Daniel Rebain and Sara Sabour and Mehdi S. M. Sajjadi and Matan Sela and + Vincent Sitzmann and Austin Stone and Deqing Sun and Suhani Vora and Ziyu Wang and + Tianhao Wu and Kwang Moo Yi and Fangcheng Zhong and Andrea Tagliasacchi}, + booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2022}, +} +``` + +## Disclaimer +This is not an official Google Product + + +%package help +Summary: Development documents and examples for kubric-nightly +Provides: python3-kubric-nightly-doc +%description help +# Kubric + +[](https://github.com/google-research/kubric/actions/workflows/blender.yml) +[](https://github.com/google-research/kubric/actions/workflows/kubruntu.yml) +[](https://github.com/google-research/kubric/actions/workflows/test.yml) +[](https://codecov.io/github/google-research/kubric) +[](https://kubric.readthedocs.io/en/latest/) + +A data generation pipeline for creating semi-realistic synthetic multi-object +videos with rich annotations such as instance segmentation masks, depth maps, +and optical flow. + + + + +## Motivation and design +We need better data for training and evaluating machine learning systems, especially in the collntext of unsupervised multi-object video understanding. +Current systems succeed on [toy datasets](https://github.com/deepmind/multi_object_datasets), but fail on real-world data. +Progress could be greatly accelerated if we had the ability to create suitable datasets of varying complexity on demand. +Kubric is mainly built on-top of pybullet (for physics simulation) and Blender (for rendering); however, the code is kept modular to potentially support different rendering backends. + +## Getting started +For instructions, please refer to [https://kubric.readthedocs.io](https://kubric.readthedocs.io) + +Assuming you have docker installed, to generate the data above simply execute: +``` +git clone https://github.com/google-research/kubric.git +cd kubric +docker pull kubricdockerhub/kubruntu +docker run --rm --interactive \ + --user $(id -u):$(id -g) \ + --volume "$(pwd):/kubric" \ + kubricdockerhub/kubruntu \ + /usr/bin/python3 examples/helloworld.py +ls output +``` + +Kubric employs **Blender 2.93** (see [here](https://github.com/google-research/kubric/blob/01a08d274234f32f2adc4f7d5666b39490f953ad/docker/Blender.Dockerfile#L48)), so if you want to inspect the generated `*.blend` scene file for interactive inspection (i.e. without needing to render the scene), please make sure you have installed the correct Blender version. + +## Requirements +- A pipeline for conveniently generating video data. +- Physics simulation for automatically generating physical interactions between multiple objects. +- Good control over the complexity of the generated data, so that we can evaluate individual aspects such as variability of objects and textures. +- Realism: Ideally, the ability to span the entire complexity range from CLEVR all the way to real-world video such as YouTube8. This is clearly not feasible, but we would like to get as close as possible. +- Access to rich ground truth information about the objects in a scene for the purpose of evaluation (eg. object segmentations and properties) +- Control the train/test split to evaluate compositionality and systematic generalization (for example on held-out combinations of features or objects) + + +## Challenges and datasets +Generally, we store datasets for the challenges in this [Google Cloud Bucket](https://console.cloud.google.com/storage/browser/kubric-public). +More specifically, these challenges are *dataset contributions* of the Kubric CVPR'22 paper: +* [MOVi: Multi-Object Video](challenges/movi) +* [Texture-Structure in NeRF](challenges/texture_structure_nerf) +* [Optical Flow](challenges/optical_flow) +* [Pre-training Visual Representations](challenges/pretraining_visual) +* [Robust NeRF](challenges/robust_nerf) +* [Multi-View Object Matting](challenges/multiview_matting) +* [Complex BRDFs](challenges/complex_brdf) +* [Single View Reconstruction](challenges/single_view_reconstruction) +* [Video Based Reconstruction](challenges/video_based_reconstruction) +* [Point Tracking](challenges/point_tracking) + +Pointers to additional datasets/workers: +* [ToyBox (from Neural Semantic Fields)](https://nesf3d.github.io) +* [MultiShapeNet (from Scene Representation Transformer)](https://srt-paper.github.io) +* [SyntheticTrio(from Controllable Neural Radiance Fields)](https://github.com/kacperkan/conerf-kubric-dataset#readme) + +## Bibtex +``` +@article{greff2021kubric, + title = {Kubric: a scalable dataset generator}, + author = {Klaus Greff and Francois Belletti and Lucas Beyer and Carl Doersch and + Yilun Du and Daniel Duckworth and David J Fleet and Dan Gnanapragasam and + Florian Golemo and Charles Herrmann and Thomas Kipf and Abhijit Kundu and + Dmitry Lagun and Issam Laradji and Hsueh-Ti (Derek) Liu and Henning Meyer and + Yishu Miao and Derek Nowrouzezahrai and Cengiz Oztireli and Etienne Pot and + Noha Radwan and Daniel Rebain and Sara Sabour and Mehdi S. M. Sajjadi and Matan Sela and + Vincent Sitzmann and Austin Stone and Deqing Sun and Suhani Vora and Ziyu Wang and + Tianhao Wu and Kwang Moo Yi and Fangcheng Zhong and Andrea Tagliasacchi}, + booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2022}, +} +``` + +## Disclaimer +This is not an official Google Product + + +%prep +%autosetup -n kubric-nightly-2023.5.5 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-kubric-nightly -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2023.5.5-1 +- Package Spec generated @@ -0,0 +1 @@ +01c16d78066ceed3222a0eaafa4d4d94 kubric-nightly-2023.5.5.tar.gz |