PyTorch LapSRN
Implementation of CVPR2017 Paper: "Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution"(http://vllab.ucmerced.edu/wlai24/LapSRN/) in PyTorch
Usage
Training
usage: main.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS] [--lr LR]
[--step STEP] [--cuda] [--resume RESUME]
[--start-epoch START_EPOCH] [--threads THREADS]
[--momentum MOMENTUM] [--weight-decay WEIGHT_DECAY]
[--pretrained PRETRAINED]
PyTorch LapSRN
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=10
--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
--momentum MOMENTUM Momentum, Default: 0.9
--weight-decay WEIGHT_DECAY, --wd WEIGHT_DECAY
weight decay, Default: 1e-4
--pretrained PRETRAINED
path to pretrained model (default: none)
An example of training usage is shown as follows:
python main_lapsrn.py --cuda
Evaluation
usage: eval.py [-h] [--cuda] [--model MODEL] [--dataset DATASET]
[--scale SCALE]
PyTorch LapSRN Eval
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
Demo
usage: demo.py [-h] [--cuda] [--model MODEL] [--image IMAGE] [--scale SCALE]
PyTorch LapSRN Demo
optional arguments:
-h, --help show this help message and exit
--cuda use cuda?
--model MODEL model path
--image IMAGE image name
--scale SCALE scale factor, Default: 4
We convert Set5 test set images to mat format using Matlab, for best PSNR performance, please use Matlab
Prepare Training dataset
- We provide a simple hdf5 format training sample in data folder with 'data', 'label_x2', and 'label_x4' keys, the training data is generated with Matlab Bicubic Interplotation, please refer Code for Data Generation for creating training files.
Performance
- We provide a pretrained LapSRN x4 model trained on T91 and BSDS200 images from SR_training_datasets with data augmentation as mentioned in the paper
- No bias is used in this implementation, and another difference from paper is that Adam optimizer with 1e-4 learning is applied instead of SGD
- Performance in PSNR on Set5, Set14, and BSD100
| DataSet/Method | LapSRN Paper | LapSRN PyTorch |
|---|---|---|
| Set5 | 31.54 | 31.65 |
| Set14 | 28.19 | 28.27 |
| BSD100 | 27.32 | 27.36 |
ToDos
- LapSRN x8
- LapGAN Evaluation
Citation
If you find the code and datasets useful in your research, please cite:
@inproceedings{LapSRN,
author = {Lai, Wei-Sheng and Huang, Jia-Bin and Ahuja, Narendra and Yang, Ming-Hsuan},
title = {Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution},
booktitle = {IEEE Conferene on Computer Vision and Pattern Recognition},
year = {2017}
}