This is an unofficial implementation of "Deep Recursive Residual Network for Super Resolution (DRRN)", CVPR 2017 in Pytorch. [Paper]
You can get the official Caffe implementation here.
usage: main.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS] [--lr LR]
[--step STEP] [--cuda] [--resume RESUME]
[--start-epoch START_EPOCH] [--clip CLIP] [--threads THREADS]
[--momentum MOMENTUM] [--weight-decay WEIGHT_DECAY]
[--pretrained PRETRAINED]
optional arguments:
-h, --help Show this help message and exit
--batchSize Training batch size
--nEpochs Number of epochs to train for
--lr Learning rate. Default=0.1
--step Learning rate decay, Default: n=5 epochs
--cuda Use cuda?
--resume Path to checkpoint
--clip Clipping Gradients. Default=0.01
--threads Number of threads for data loader to use Default=1
--momentum Momentum, Default: 0.9
--weight-decay Weight decay, Default: 1e-4
--pretrained Path to the pretrained model, used for weight initialization (default: none)
usage: eval.py [-h] [--cuda] [--model MODEL] [--dataset DATASET]
[--scale SCALE]
PyTorch DRRN Evaluation
optional arguments:
-h, --help show this help message and exit
--cuda use cuda?
--model MODEL model path
--dataset DATASET dataset name, Default: Set5
An example of training usage is shown as follows:
python eval.py --cuda
- the training data is generated with Matlab Bicubic Interpolation, please refer Code for Data Generation for creating training files.
- We provide a rough pre-trained DRRN_B1U25 model trained on 291 images with data augmentation. The model can achieve a better performance with a smart optimization strategy. For the DRRN_B1U9 implementation, you can manually modify the number of recursive blocks here.
- The same adjustable gradient clipping's implementation as original paper.
- No bias is used in this implementation.
- No batch normalization is used in this implementation.
- Performance in PSNR on Set5
| Scale | DRRN_B1U25 Paper | DRRN_B1U25 PyTorch |
|---|---|---|
| x2 | 37.74 | 37.69 |
| x3 | 34.03 | 34.02 |
| x4 | 31.68 | 31.70 |