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

Last update: Nov 03, 2022

Related tags

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

Copy ./networks/gmfn_arch.py into SRFBN_CVPR19/networks/
Download the pre-trained models from Google driver or Baidu Netdisk, unzip and place them into SRFBN_CVPR19/models.
Copy ./options/test/ to SRFBN_CVPR19/options/test/.
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

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

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

cd SRFBN_CVPR19
python train.py -opt options/train/train_GNFN.json

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

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

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

49 Dec 1, 2022

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

258 Jan 2, 2023

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

112 Nov 28, 2022

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

49 Nov 10, 2022

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

225 Dec 29, 2022

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

4 May 8, 2022

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

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

3 Sep 3, 2022

Comments

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

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

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
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

Releases(v1.0)

v1.0(Aug 26, 2020)

Source code(tar.gz)
Source code(zip)
GMFN_BMVC19_Models.zip(104.38 MB)

Owner

Qilei Li

GitHub Repository

Reading list for research topics in Masked Image Modeling

awesome-MIM Reading list for research topics in Masked Image Modeling(MIM). We list the most popular methods for MIM, if I missed something, please su

231 Dec 07, 2022

LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT

LightHuBERT LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT | Github | Huggingface | SUPER

46 Dec 29, 2022

Code for Mining the Benefits of Two-stage and One-stage HOI Detection

Status: Archive (code is provided as-is, no updates expected) PPO-EWMA [Paper] This is code for training agents using PPO-EWMA and PPG-EWMA, introduce

33 Dec 15, 2022

Repo for Photon-Starved Scene Inference using Single Photon Cameras, ICCV 2021

Photon-Starved Scene Inference using Single Photon Cameras ICCV 2021 Arxiv Project Video Bhavya Goyal, Mohit Gupta University of Wisconsin-Madison Abs

5 Nov 15, 2022

Privacy-Preserving Machine Learning (PPML) Tutorial Presented at PyConDE 2022

PPML: Machine Learning on Data you cannot see Repository for the tutorial on Privacy-Preserving Machine Learning (PPML) presented at PyConDE 2022 Abst

10 Aug 16, 2022

A project for developing transformer-based models for clinical relation extraction

Clinical Relation Extration with Transformers Aim This package is developed for researchers easily to use state-of-the-art transformers models for ext

101 Dec 19, 2022

Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices

Face-Mesh Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices. It employs machine learning

9 Dec 21, 2022

Python Environment for Bayesian Learning

Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl in

103 Jul 14, 2022

Source code for our paper "Do Not Trust Prediction Scores for Membership Inference Attacks"

Do Not Trust Prediction Scores for Membership Inference Attacks Abstract: Membership inference attacks (MIAs) aim to determine whether a specific samp

[email protected]"> 3 Oct 25, 2022

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection Acknowledgement We implement our model, BtcDet, based on [OpenPcdet 0.3.0]. Insta

163 Dec 19, 2022

Text-to-Music Retrieval using Pre-defined/Data-driven Emotion Embeddings

Text2Music Emotion Embedding Text-to-Music Retrieval using Pre-defined/Data-driven Emotion Embeddings Reference Emotion Embedding Spaces for Matching

50 Dec 05, 2022

A PyTorch Implementation of Single Shot Scale-invariant Face Detector.

S³FD: Single Shot Scale-invariant Face Detector A PyTorch Implementation of Single Shot Scale-invariant Face Detector. Eval python wider_eval_pytorch.

235 Jan 07, 2023

The PyTorch implementation for paper "Neural Texture Extraction and Distribution for Controllable Person Image Synthesis" (CVPR2022 Oral)

ArXiv | Get Start Neural-Texture-Extraction-Distribution The PyTorch implementation for our paper "Neural Texture Extraction and Distribution for Cont

111 Dec 10, 2022