Pytorch implementation of "ARM: Any-Time Super-Resolution Method"

Last update: Nov 24, 2022

Related tags

Overview

ARM-Net

Dependencies

Python 3.6
Pytorch 1.7

Results

Train

Data preprocessing

cd data_scripts
python extract_subimages_test.py
python data_augmentation.py
python generate_mod_LR_bic.py
python extract_subimages_train.py

Modify the configuration file (options/*.yml)

# train_us_fsrcnn.yml
is_train: True

# train_us_carn.yml
is_train: True

# train_us_srresnet.yml
is_train: True

Run training scripts

python main.py -opt options/train_us_fsrcnn.yml
python main.py -opt options/train_us_carn.yml
python main.py -opt options/train_us_srresnet.yml

Test

Modify the configuration file (options/*.yml)

# train_us_fsrcnn.yml
path:
  pretrain_model_G: ckpt/arm-fsrcnn.pth
  resume_state: ckpt/arm-fsrcnn.state
is_train: False
is_test: True

# train_us_carn.yml
path:
  pretrain_model_G: ckpt/arm-carn.pth
  resume_state: ckpt/arm-carn.state
is_train: False
is_test: True

# train_us_srresnet.yml
path:
  pretrain_model_G: ckpt/arm-srresnet.pth
  resume_state: ckpt/arm-srresnet.state
is_train: False
is_test: True

Run test scripts

python main.py -opt options/train_us_fsrcnn.yml
python main.py -opt options/train_us_carn.yml
python main.py -opt options/train_us_srresnet.yml

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

This repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch. Some of the code here will be included in upstream Pytorch eventually. The intention of Apex is to make up-to-date utilities available to users as quickly as possible.

6.9k Jan 3, 2023

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. List of Algorithms

119 Nov 24, 2022

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

1.4k Jan 1, 2023

Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

Amazon Forest Computer Vision Satellite Image tagging code using PyTorch / Keras Here is a sample of images we had to work with Source: https://www.ka

360 Dec 10, 2022

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. Feel free to make a pu

9.2k Jan 2, 2023

Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

Amazon Forest Computer Vision Satellite Image tagging code using PyTorch / Keras Here is a sample of images we had to work with Source: https://www.ka

359 Jan 5, 2023

A bunch of random PyTorch models using PyTorch's C++ frontend

PyTorch Deep Learning Models using the C++ frontend Gettting started Clone the repo 1. https://github.com/mrdvince/pytorchcpp 2. cd fashionmnist or

0 Jul 13, 2021

PyTorch Autoencoders - Implementing a Variational Autoencoder (VAE) Series in Pytorch.

PyTorch Autoencoders Implementing a Variational Autoencoder (VAE) Series in Pytorch. Inspired by this repository Model List check model paper conferen

8 Nov 21, 2022

PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

PyTorch-LIT PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices. With

157 Dec 11, 2022

Comments

What does ARM-L/ARM-M/ARM-S mean?
It is a interesting work. I have some questions about your paper.

In your work. When inference, my understanding is to build edge table at first, then use this table when inference. But How to decide which width network a patch choose? The others methods in Figure 7 is clear. But I can't know how ARM-FSRCNN choose width. It is confuse me (

In Table 1&2, ARM-L/ARM-M/ARM-S means: fixed width no matter what the input patch is, right?

About ARM-L/ARM-M/ARM-S, If just fix width. May be you should compare with ClassSR use your width choose policy insted of fixing width?

By the way. In table 1 (row: Module FSRCNN, column: FLOPS) is 0%, may be error. :)

Many questions from me. Thanks!
opened by wangqiim 2
Where is the defination of "ProgressBar()" in "utils.util" which is imported in extract_subimages_train.py ?

Where is the defination of "ProgressBar()" in "utils.util" ? The "ProgressBar" is imported in extract_subimages_train.py.

opened by huang-hz 1
New Super-Resolution Benchmarks
Hello,

MSU Graphics & Media Lab Video Group has recently launched two new Super-Resolution Benchmarks.

Video Upscalers Benchmark: Quality Enhancement determines the best upscaling methods for increasing video resolution and improving visual quality.

Super-Resolution for Video Compression benchmark aims to test Super-Resolution methods on compressed videos and select the best model for each video codec standard.

If you are interested in participating, you can add your algorithm following the submission steps:

Submit for Video Upscalers Benchmark: Quality Enhancement

Submit for Super-Resolution for Video Compression benchmark

We would be grateful for your feedback on our work!
opened by EvgeneyBogatyrev 0
How to get Fig.4. (a) 0.85 ?

In Fig.4.(a), I find different models will cause different PSNR lookup results. How to get Spearman correlation (0.85) coefficient picture (Fig.4.(a)). What model do you use to generate psnr values. (complex model will cause more pricise PSNR value)

opened by wangqiim 0

Pytorch implementation of "ARM: Any-Time Super-Resolution Method"

Related tags

Overview

ARM-Net

Dependencies

Results

Train

Data preprocessing

Modify the configuration file (options/*.yml)

Run training scripts

Test

Modify the configuration file (options/*.yml)

Run test scripts

You might also like...

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

A bunch of random PyTorch models using PyTorch's C++ frontend

PyTorch Autoencoders - Implementing a Variational Autoencoder (VAE) Series in Pytorch.

PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

Comments

What does ARM-L/ARM-M/ARM-S mean?

Where is the defination of "ProgressBar()" in "utils.util" which is imported in extract_subimages_train.py ?

New Super-Resolution Benchmarks

How to get Fig.4. (a) 0.85 ?

Releases(v1.0.0)

v1.0.0(Sep 28, 2022)

Owner

Bohong Chen

🌈 PyTorch Implementation for EMNLP'21 Findings "Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer"

PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.

TCNN Temporal convolutional neural network for real-time speech enhancement in the time domain

MvtecAD unsupervised Anomaly Detection

Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.

Apache Flink

AI Summer's complete catalog of articles

An architecture that makes any doodle realistic, in any specified style, using VQGAN, CLIP and some basic embedding arithmetics.

SCNet: Learning Semantic Correspondence

The official implementation of VAENAR-TTS, a VAE based non-autoregressive TTS model.

Securetar - A streaming wrapper around python tarfile and allow secure handling files and support encryption

Official PyTorch implementation of our AAAI22 paper: TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework via Self-Supervised Multi-Task Learning. Code will be available soon.

给yolov5加个gui界面，使用pyqt5，yolov5是5.0版本

MiniSom is a minimalistic implementation of the Self Organizing Maps

constructing maps of intellectual influence from publication data

MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images (ISBI 2021, MELBA 2021)

Second-order Attention Network for Single Image Super-resolution (CVPR-2019)

Code for "Learning Graph Cellular Automata"

PyTorch code for training MM-DistillNet for multimodal knowledge distillation