pytorch implementation for Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802

Last update: Jan 09, 2023

Overview

PyTorch SRResNet

Implementation of Paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"(https://arxiv.org/abs/1609.04802) in PyTorch

Usage

Training

usage: main_srresnet.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS]
                        [--lr LR] [--step STEP] [--cuda] [--resume RESUME]
                        [--start-epoch START_EPOCH] [--threads THREADS]
                        [--pretrained PRETRAINED] [--vgg_loss] [--gpus GPUS]

optional arguments:
  -h, --help            show this help message and exit
  --batchSize BATCHSIZE
                        training batch size
  --nEpochs NEPOCHS     number of epochs to train for
  --lr LR               Learning Rate. Default=1e-4
  --step STEP           Sets the learning rate to the initial LR decayed by
                        momentum every n epochs, Default: n=500
  --cuda                Use cuda?
  --resume RESUME       Path to checkpoint (default: none)
  --start-epoch START_EPOCH
                        Manual epoch number (useful on restarts)
  --threads THREADS     Number of threads for data loader to use, Default: 1
  --pretrained PRETRAINED
                        path to pretrained model (default: none)
  --vgg_loss            Use content loss?
  --gpus GPUS           gpu ids (default: 0)

An example of training usage is shown as follows:

python main_srresnet.py --cuda --vgg_loss --gpus 0

demo

usage: demo.py [-h] [--cuda] [--model MODEL] [--image IMAGE]
               [--dataset DATASET] [--scale SCALE] [--gpus GPUS]

optional arguments:
  -h, --help         show this help message and exit
  --cuda             use cuda?
  --model MODEL      model path
  --image IMAGE      image name
  --dataset DATASET  dataset name
  --scale SCALE      scale factor, Default: 4
  --gpus GPUS        gpu ids (default: 0)

We convert Set5 test set images to mat format using Matlab, for simple image reading An example of usage is shown as follows:

python demo.py --model model/model_srresnet.pth --dataset Set5 --image butterfly_GT --scale 4 --cuda

Eval

usage: eval.py [-h] [--cuda] [--model MODEL] [--dataset DATASET]
               [--scale SCALE] [--gpus GPUS]

optional arguments:
  -h, --help         show this help message and exit
  --cuda             use cuda?
  --model MODEL      model path
  --dataset DATASET  dataset name, Default: Set5
  --scale SCALE      scale factor, Default: 4
  --gpus GPUS        gpu ids (default: 0)

We convert Set5 test set images to mat format using Matlab. Since PSNR is evaluated on only Y channel, we import matlab in python, and use rgb2ycbcr function for converting rgb image to ycbcr image. You will have to setup the matlab python interface so as to import matlab library. An example of usage is shown as follows:

python eval.py --model model/model_srresnet.pth --dataset Set5 --cuda

Prepare Training dataset

Please refer Code for Data Generation for creating training files.
Data augmentations including flipping, rotation, downsizing are adopted.

Performance

We provide a pretrained model trained on 291 images with data augmentation
Instance Normalization is applied instead of Batch Normalization for better performance
So far performance in PSNR is not as good as paper, any suggestion is welcome

Dataset	SRResNet Paper	SRResNet PyTorch
Set5	32.05	31.80
Set14	28.49	28.25
BSD100	27.58	27.51

Result

From left to right are ground truth, bicubic and SRResNet

pytorch implementation for Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802

Related tags

Overview

PyTorch SRResNet

Usage

Training

demo

Eval

Prepare Training dataset

Performance

Result

Owner

Jiu XU

UniLM AI - Large-scale Self-supervised Pre-training across Tasks, Languages, and Modalities

Keqing Chatbot With Python

Employee-Managment - Company employee registration software in the face recognition system

Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations

Codes for CyGen, the novel generative modeling framework proposed in "On the Generative Utility of Cyclic Conditionals" (NeurIPS-21)

Official implementation of AAAI-21 paper "Label Confusion Learning to Enhance Text Classification Models"

Semi-supervised learning for object detection

City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces

FocusFace: Multi-task Contrastive Learning for Masked Face Recognition

SingleVC performs any-to-one VC, which is an important component of MediumVC project.

Demos of essentia classifiers hosted on replicate.ai

Static Features Classifier - A static features classifier for Point-Could clusters using an Attention-RNN model

Raptor-Multi-Tool - Raptor Multi Tool With Python

Rocket-recycling with Reinforcement Learning

MM1 and MMC Queue Simulation using python - Results and parameters in excel and csv files

The official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness.

Crawl & visualize ICLR papers and reviews

Continual learning with sketched Jacobian approximations

This git repo contains the implementation of my ML project on Heart Disease Prediction

InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy