BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

Overview

BasicVSR

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

Ported from https://github.com/xinntao/BasicSR

Dependencies

  • NumPy
  • PyTorch, preferably with CUDA. Note that torchvision and torchaudio are not required and hence can be omitted from the command.
  • VapourSynth

Installation

pip install --upgrade vsbasicvsr

Usage

from vsbasicvsr import BasicVSR

ret = BasicVSR(clip)

See __init__.py for the description of the parameters.

You might also like...
VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).
VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).

VSR-Transformer By Jiezhang Cao, Yawei Li, Kai Zhang, Luc Van Gool This paper proposes a new Transformer for video super-resolution (called VSR-Transf

EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation

EFENet EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation Code is a bit messy now. I woud clean up soon. For training the EF

MoCoPnet - Deformable 3D Convolution for Video Super-Resolution
MoCoPnet - Deformable 3D Convolution for Video Super-Resolution

Deformable 3D Convolution for Video Super-Resolution Pytorch implementation of l

Simple, but essential Bayesian optimization package

BayesO: A Bayesian optimization framework in Python Simple, but essential Bayesian optimization package. http://bayeso.org Online documentation Instal

An essential implementation of BYOL in PyTorch + PyTorch Lightning
An essential implementation of BYOL in PyTorch + PyTorch Lightning

Essential BYOL A simple and complete implementation of Bootstrap your own latent: A new approach to self-supervised Learning in PyTorch + PyTorch Ligh

A 2D Visual Localization Framework based on Essential Matrices [ICRA2020]
A 2D Visual Localization Framework based on Essential Matrices [ICRA2020]

A 2D Visual Localization Framework based on Essential Matrices This repository provides implementation of our paper accepted at ICRA: To Learn or Not

Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.
Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.

Deep Image Search - AI-Based Image Search Engine Deep Image Search is an AI-based image search engine that includes deep transfer learning features Ex

Official Pytorch implementation of
Official Pytorch implementation of "Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video", CVPR 2021

TCMR: Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video Qualtitative result Paper teaser video Introduction This r

MAU: A Motion-Aware Unit for Video Prediction and Beyond, NeurIPS2021

MAU (NeurIPS2021) Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xinguang Xiang, Wen GAo. Official PyTorch Code for "MAU: A Motion-Aware

Comments
  • Tile doesn't seem to work.

    Tile doesn't seem to work.

    Using:

    # Imports
    import vapoursynth as vs
    # getting Vapoursynth core
    core = vs.core
    # Loading Plugins
    core.std.LoadPlugin(path="I:/Hybrid/64bit/vsfilters/Support/fmtconv.dll")
    core.std.LoadPlugin(path="I:/Hybrid/64bit/vsfilters/SourceFilter/vsrawsource/vsrawsource.dll")
    # source: 'C:\Users\Selur\Desktop\stefan_sif.y4m'
    # current color space: YUV420P8, bit depth: 8, resolution: 352x240, fps: 29.97, color matrix: 470bg, yuv luminance scale: full, scanorder: progressive
    # Loading C:\Users\Selur\Desktop\stefan_sif.y4m using RawsSource
    clip = core.raws.Source("C:/Users/Selur/Desktop/stefan_sif.y4m")
    # making sure input color matrix is set as 470bg
    clip = core.resize.Bicubic(clip, matrix_in_s="470bg",range_s="full")
    # making sure frame rate is set to 29.970
    clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
    # Setting color range to PC (full) range.
    clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=0)
    # adjusting color space from YUV420P8 to RGBS for vsBasicVSR
    clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="full")
    # resizing using BasicVSR
    from vsbasicvsr import BasicVSR
    clip = BasicVSR(clip=clip, radius=15, device_type="cuda", tile=2)
    # adjusting resizing to hit target resolution 
    clip = core.fmtc.resample(clip=clip, w=1280, h=874, kernel="lanczos", interlaced=False, interlacedd=False)
    # adjusting output color from: RGB48 to YUV420P10 for x265Model
    clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P10, matrix_s="470bg", range_s="full")
    # set output frame rate to 29.970fps
    clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
    # Output
    clip.set_output()
    

    gives me

    Input and output sizes should be greater than 0, but got input (H: 0, W: 0) output (H: 0, W: 0)
    

    without the tile=2, it works.

    opened by Selur 5
Releases(v1.2.0)
Owner
Holy Wu
Holy Wu
UnpNet - Rethinking 3-D LiDAR Point Cloud Segmentation(IEEE TNNLS)

UnpNet Citation Please cite the following paper if you use this repository in your reseach. @article {PMID:34914599, Title = {Rethinking 3-D LiDAR Po

Shijie Li 4 Jul 15, 2022
PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision.

PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{CV2018, author = {Donny You ( Donny You 40 Sep 14, 2022

Experiments on continual learning from a stream of pretrained models.

Ex-model CL Ex-model continual learning is a setting where a stream of experts (i.e. model's parameters) is available and a CL model learns from them

Antonio Carta 6 Dec 04, 2022
tensorflow implementation of 'YOLO : Real-Time Object Detection'

YOLO_tensorflow (Version 0.3, Last updated :2017.02.21) 1.Introduction This is tensorflow implementation of the YOLO:Real-Time Object Detection It can

Jinyoung Choi 1.7k Nov 21, 2022
PyTorch implementation for "HyperSPNs: Compact and Expressive Probabilistic Circuits", NeurIPS 2021

HyperSPN This repository contains code for the paper: HyperSPNs: Compact and Expressive Probabilistic Circuits "HyperSPNs: Compact and Expressive Prob

8 Nov 08, 2022
CTC segmentation python package

CTC segmentation CTC segmentation can be used to find utterances alignments within large audio files. This repository contains the ctc-segmentation py

Ludwig Kürzinger 217 Jan 04, 2023
[ICCV 2021] Group-aware Contrastive Regression for Action Quality Assessment

CoRe Created by Xumin Yu*, Yongming Rao*, Wenliang Zhao, Jiwen Lu, Jie Zhou This is the PyTorch implementation for ICCV paper Group-aware Contrastive

Xumin Yu 31 Dec 24, 2022
A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).

Splitter ⠀⠀ A PyTorch implementation of Splitter: Learning Node Representations that Capture Multiple Social Contexts (WWW 2019). Abstract Recent inte

Benedek Rozemberczki 201 Nov 09, 2022
Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))

Aerial Imagery dataset for fire detection: classification and segmentation using Unmanned Aerial Vehicle (UAV) Title FLAME (Fire Luminosity Airborne-b

79 Jan 06, 2023
Joint deep network for feature line detection and description

SOLD² - Self-supervised Occlusion-aware Line Description and Detection This repository contains the implementation of the paper: SOLD² : Self-supervis

Computer Vision and Geometry Lab 427 Dec 27, 2022
Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo"

dblmahmc Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo" Requirements: https://github.com

1 Dec 17, 2021
This repo contains the pytorch implementation for Dynamic Concept Learner (accepted by ICLR 2021).

DCL-PyTorch Pytorch implementation for the Dynamic Concept Learner (DCL). More details can be found at the project page. Framework Grounding Physical

Zhenfang Chen 31 Jan 06, 2023
Implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

SemCo The official pytorch implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

42 Nov 14, 2022
Molecular AutoEncoder in PyTorch

MolEncoder Molecular AutoEncoder in PyTorch Install $ git clone https://github.com/cxhernandez/molencoder.git && cd molencoder $ python setup.py insta

Carlos Hernández 80 Dec 05, 2022
StyleSwin: Transformer-based GAN for High-resolution Image Generation

StyleSwin This repo is the official implementation of "StyleSwin: Transformer-based GAN for High-resolution Image Generation". By Bowen Zhang, Shuyang

Microsoft 349 Dec 28, 2022
PyTorch implementation of DUL (Data Uncertainty Learning in Face Recognition, CVPR2020)

PyTorch implementation of DUL (Data Uncertainty Learning in Face Recognition, CVPR2020)

Mouxiao Huang 20 Nov 15, 2022
Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

Densely Connected Convolutional Networks (DenseNets) This repository contains the code for DenseNet introduced in the following paper Densely Connecte

Zhuang Liu 4.5k Jan 03, 2023
RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?

RaftMLP RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality? By Yuki Tatsunami and Masato Taki (Rikkyo University) [arxiv]

Okojo 20 Aug 31, 2022
StyleGAN-Human: A Data-Centric Odyssey of Human Generation

StyleGAN-Human: A Data-Centric Odyssey of Human Generation Abstract: Unconditional human image generation is an important task in vision and graphics,

stylegan-human 762 Jan 08, 2023
Context-Sensitive Misspelling Correction of Clinical Text via Conditional Independence, CHIL 2022

cim-misspelling Pytorch implementation of Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence, CHIL 2022. This model (

Juyong Kim 11 Dec 19, 2022