PyTorch code for our paper "Gated Multiple Feedback Network for Image Super-Resolution" (BMVC2019)

Related tags

Deep LearningGMFN
Overview

Gated Multiple Feedback Network for Image Super-Resolution

This repository contains the PyTorch implementation for the proposed GMFN [arXiv].

The framework of our proposed GMFN. The colored arrows among different time steps denote the multiple feedback connections. The high-level information carried by them helps low-level features become more representative.

Demo

Clone SRFBN as the backbone and satisfy its requirements.

Test

  1. Copy ./networks/gmfn_arch.py into SRFBN_CVPR19/networks/

  2. Download the pre-trained models from Google driver or Baidu Netdisk, unzip and place them into SRFBN_CVPR19/models.

  3. Copy ./options/test/ to SRFBN_CVPR19/options/test/.

  4. Run commands cd SRFBN_CVPR19 and one of followings for evaluation on Set5:

python test.py -opt options/test/test_GMFN_x2.json
python test.py -opt options/test/test_GMFN_x3.json
python test.py -opt options/test/test_GMFN_x4.json
  1. Finally, PSNR/SSIM values for Set5 are shown on your screen, you can find the reconstruction images in ./results.

To test GMFN on other standard SR benchmarks or your own images, please refer to the instruction in SRFBN.

Train

  1. Prepare the training set according to this (1-3).
  2. Modify ./options/train/train_GMFN.json by following the instructions in ./options/train/README.md.
  3. Run commands:
cd SRFBN_CVPR19
python train.py -opt options/train/train_GNFN.json

  1. You can monitor the training process in ./experiments.

  2. Finally, you can follow the test pipeline to evaluate the model trained by yourself.

Performance

Quantitative Results

Quantitative evaluation under scale factors x2, x3 and x4. The best performance is shown in bold and the second best performance is underlined.

More Qualitative Results (x4)

Acknowledgment

If you find our work useful in your research or publications, please consider citing:

@inproceedings{li2019gmfn,
    author = {Li, Qilei and Li, Zhen and Lu, Lu and Jeon, Gwanggil and Liu, Kai and Yang, Xiaomin},
    title = {Gated Multiple Feedback Network for Image Super-Resolution},
    booktitle = {The British Machine Vision Conference (BMVC)},
    year = {2019}
}

@inproceedings{li2019srfbn,
    author = {Li, Zhen and Yang, Jinglei and Liu, Zheng and Yang, Xiaomin and Jeon, Gwanggil and Wu, Wei},
    title = {Feedback Network for Image Super-Resolution},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year= {2019}
}
You might also like...
Pytorch implementation of our paper under review — Lottery Jackpots Exist in Pre-trained Models

Lottery Jackpots Exist in Pre-trained Models (Paper Link) Requirements Python = 3.7.4 Pytorch = 1.6.1 Torchvision = 0.4.1 Reproduce the Experiment

Yuxin Zhang 27 Jun 28, 2022
The repository offers the official implementation of our paper in PyTorch.

Cloth Interactive Transformer (CIT) Cloth Interactive Transformer for Virtual Try-On Bin Ren1, Hao Tang1, Fanyang Meng2, Runwei Ding3, Ling Shao4, Phi

Bingoren 49 Dec 1, 2022
PyTorch implementations for our SIGGRAPH 2021 paper: Editable Free-viewpoint Video Using a Layered Neural Representation.
PyTorch implementations for our SIGGRAPH 2021 paper: Editable Free-viewpoint Video Using a Layered Neural Representation.

st-nerf We provide PyTorch implementations for our paper: Editable Free-viewpoint Video Using a Layered Neural Representation SIGGRAPH 2021 Jiakai Zha

Diplodocus 258 Jan 2, 2023
PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation
PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation

StructDepth PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimat

SJTU-ViSYS 112 Nov 28, 2022
Pytorch implementation for our ICCV 2021 paper
Pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering".

TRAnsformer Routing Networks (TRAR) This is an official implementation for ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visu

Ren Tianhe 49 Nov 10, 2022
This is the official pytorch implementation for our ICCV 2021 paper
This is the official pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering" on VQA Task

🌈 ERASOR (RA-L'21 with ICRA Option) Official page of "ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point C

Hyungtae Lim 225 Dec 29, 2022
PyTorch implementation of our ICCV 2021 paper, Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents.
PyTorch implementation of our ICCV 2021 paper, Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents.

PyTorch implementation of our ICCV 2021 paper, Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents.

Saim Wani 4 May 8, 2022
PyTorch implementation for our NeurIPS 2021 Spotlight paper
PyTorch implementation for our NeurIPS 2021 Spotlight paper "Long Short-Term Transformer for Online Action Detection".

Long Short-Term Transformer for Online Action Detection Introduction This is a PyTorch implementation for our NeurIPS 2021 Spotlight paper "Long Short

null 77 Dec 16, 2022
Official PyTorch implemention of our paper "Learning to Rectify for Robust Learning with Noisy Labels".

WarPI The official PyTorch implemention of our paper "Learning to Rectify for Robust Learning with Noisy Labels". Run python main.py --corruption_type

Haoliang Sun 3 Sep 3, 2022
Comments
  • Approximately how many epoches will reach the results in the paper (4x SR result)

    Approximately how many epoches will reach the results in the paper (4x SR result)

    Hi, liqilei After I have run about 700 epoches, the reult on val set is 32.41(highest result). I want to know if my training process seems to be problematic? How long did you reach 32.47 of SRFBN when you were training? How long does it take to reach 32.70? Thank you.

    opened by Senwang98 7
  • train error size not match

    train error size not match

    CUDA_VISIBLE_DEVICES=0 python train.py -opt options/train/train_GMFN.json I use celeba dataset train

    ===> Training Epoch: [1/1000]... Learning Rate: 0.000200 Epoch: [1/1000]: 0%| | 0/251718 [00:00<?, ?it/s] Traceback (most recent call last): File "train.py", line 131, in main() File "train.py", line 69, in main iter_loss = solver.train_step() File "/exp_sr/SRFBN/solvers/SRSolver.py", line 104, in train_step loss_steps = [self.criterion_pix(sr, split_HR) for sr in outputs] File "/exp_sr/SRFBN/solvers/SRSolver.py", line 104, in loss_steps = [self.criterion_pix(sr, split_HR) for sr in outputs] File "/toolscnn/env_pyt0.4_py3.5_awsrn/lib/python3.5/site-packages/torch/nn/modules/module.py", line 477, in call result = self.forward(*input, **kwargs) File "/toolscnn/env_pyt0.4_py3.5_awsrn/lib/python3.5/site-packages/torch/nn/modules/loss.py", line 87, in forward return F.l1_loss(input, target, reduction=self.reduction) File "/toolscnn/env_pyt0.4_py3.5_awsrn/lib/python3.5/site-packages/torch/nn/functional.py", line 1702, in l1_loss input, target, reduction) File "/toolscnn/env_pyt0.4_py3.5_awsrn/lib/python3.5/site-packages/torch/nn/functional.py", line 1674, in _pointwise_loss return lambd_optimized(input, target, reduction) RuntimeError: input and target shapes do not match: input [16 x 3 x 192 x 192], target [16 x 3 x 48 x 48] at /pytorch/aten/src/THCUNN/generic/AbsCriterion.cu:12

    opened by yja1 3
  • Not an Issue

    Not an Issue

    Hey @Paper99,

    Thanks for sharing your code! I wonder if it is possible to help with visualizing featuer-maps as you did in your paper figure 4.

    Thanks

    opened by Auth0rM0rgan 1
  • My training result with scale = 2

    My training result with scale = 2

    Hi, After I have trained the DIV2k, I get the final result(use best_ckp.pth to test):

    set5:38.16/0.9610
set14:33.91/0.9203
urban100:32.81/0.9349
B100:32.30/0.9011
manga109:39.01/0.9776

    It seems much lower than that in your paper.

    opened by Senwang98 6
Owner
Qilei Li
Qilei Li
GitHub Repository
pytorch implementation of ABC : Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning

ABC:Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning, NeurIPS 2021 pytorch implementation of ABC : Auxiliary Balanced Class

Hyuck Lee 25 Dec 22, 2022
AugLiChem - The augmentation library for chemical systems.

AugLiChem Welcome to AugLiChem! The augmentation library for chemical systems. This package supports augmentation for both crystaline and molecular sy

BaratiLab 17 Jan 08, 2023
Header-only library for using Keras models in C++.

frugally-deep Use Keras models in C++ with ease Table of contents Introduction Usage Performance Requirements and Installation FAQ Introduction Would

Tobias Hermann 927 Jan 05, 2023
Official implementation of Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models at NeurIPS 2021

Representer Point Selection via Local Jacobian Expansion for Classifier Explanation of Deep Neural Networks and Ensemble Models This repository is the

Yi(Amy) Sui 2 Dec 01, 2021
ESTDepth: Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks (CVPR 2021)

ESTDepth: Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks (CVPR 2021) Project Page | Video | Paper | Data We present a novel metho

65 Nov 28, 2022
Gym environment for FLIPIT: The Game of "Stealthy Takeover"

gym-flipit Gym environment for FLIPIT: The Game of "Stealthy Takeover" invented by Marten van Dijk, Ari Juels, Alina Oprea, and Ronald L. Rivest. Desi

Lisa Oakley 2 Dec 15, 2021
Morphable Detector for Object Detection on Demand

Morphable Detector for Object Detection on Demand (ICCV 2021) PyTorch implementation of the paper Morphable Detector for Object Detection on Demand. I

9 Feb 23, 2022
A Simple Framwork for CV Pre-training Model (SOCO, VirTex, BEiT)

A Simple Framwork for CV Pre-training Model (SOCO, VirTex, BEiT)

Sense-GVT 14 Jul 07, 2022
A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.

AnimeGAN A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing. Randomly Generated Images The images are

Jie Lei 雷杰 1.2k Jan 03, 2023
Continual learning with sketched Jacobian approximations

Continual learning with sketched Jacobian approximations This repository contains the code for reproducing figures and results in the paper ``Provable

Machine Learning and Information Processing Laboratory 1 Jun 30, 2022
Dynamic vae - Dynamic VAE algorithm is used for anomaly detection of battery data

Dynamic VAE frame Automatic feature extraction can be achieved by probability di

10 Oct 07, 2022
Tensorflow implementation of soft-attention mechanism for video caption generation.

SA-tensorflow Tensorflow implementation of soft-attention mechanism for video caption generation. An example of soft-attention mechanism. The attentio

Paul Chen 153 Nov 14, 2022
the official implementation of the paper "Isometric Multi-Shape Matching" (CVPR 2021)

Isometric Multi-Shape Matching (IsoMuSh) Paper-CVF | Paper-arXiv | Video | Code Citation If you find our work useful in your research, please consider

Maolin Gao 9 Jul 17, 2022
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching（CVPR2021）

CFNet(CVPR 2021) This is the implementation of the paper CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching, CVPR 2021, Zhelun Shen, Yuch

106 Dec 28, 2022
Deep Video Matting via Spatio-Temporal Alignment and Aggregation [CVPR2021]

Deep Video Matting via Spatio-Temporal Alignment and Aggregation [CVPR2021] Paper: https://arxiv.org/abs/2104.11208 Introduction Despite the significa

76 Dec 07, 2022
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation (CVPR 2021)

Self-supervised Augmentation Consistency for Adapting Semantic Segmentation This repository contains the official implementation of our paper: Self-su

Visual Inference Lab @TU Darmstadt 132 Dec 21, 2022
Pytorch implementation of Masked Auto-Encoder

Masked Auto-Encoder (MAE) Pytorch implementation of Masked Auto-Encoder: Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick

Jiyuan 22 Dec 13, 2022
[ICCV 2021] Official Tensorflow Implementation for "Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions"

KPAC: Kernel-Sharing Parallel Atrous Convolutional block This repository contains the official Tensorflow implementation of the following paper: Singl

Hyeongseok Son 50 Dec 29, 2022
“袋鼯麻麻——智能购物平台”能够精准地定位识别每一个商品

“袋鼯麻麻——智能购物平台”能够精准地定位识别每一个商品，并且能够返回完整地购物清单及顾客应付的实际商品总价格，极大地降低零售行业实际运营过程中巨大的人力成本，提升零售行业无人化、自动化、智能化水平。

thomas-yanxin 192 Jan 05, 2023
For auto aligning, cropping, and scaling HR and LR images for training image based neural networks

ImgAlign For auto aligning, cropping, and scaling HR and LR images for training image based neural networks Usage Make sure OpenCV is installed, 'pip

15 Dec 04, 2022