1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
|
# Super-Resolution using an efficient sub-pixel convolutional neural network
ported from [pytorch-examples](https://github.com/pytorch/examples/tree/main/super_resolution)
This example illustrates how to use the efficient sub-pixel convolution layer described in ["Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network" - Shi et al. 2016](https://arxiv.org/abs/1609.05158) for increasing spatial resolution within your network for tasks such as superresolution.
```
usage: main.py [-h] --upscale_factor UPSCALE_FACTOR [--crop_size CROPSIZE] [--batch_size BATCHSIZE]
[--test_batch_size TESTBATCHSIZE] [--n_epochs NEPOCHS] [--lr LR]
[--cuda] [--threads THREADS] [--seed SEED] [--debug]
PyTorch Super Res Example
optional arguments:
-h, --help show this help message and exit
--upscale_factor super resolution upscale factor
--crop_size cropped size of the images for training
--batch_size training batch size
--test_batch_size testing batch size
--n_epochs number of epochs to train for
--lr Learning Rate. Default=0.01
--cuda use cuda
--mps enable GPU on macOS
--threads number of threads for data loader to use Default=4
--seed random seed to use. Default=123
--debug debug mode for testing
```
This example trains a super-resolution network on the [Caltech101 dataset](https://pytorch.org/vision/main/generated/torchvision.datasets.Caltech101.html). A snapshot of the model after every epoch with filename `model_epoch_<epoch_number>.pth`
## Example Usage:
### Train
`python main.py --upscale_factor 3 --crop_size 180 --batch_size 4 --test_batch_size 100 --n_epochs 30 --lr 0.001`
### Super-Resolve
`python super_resolve.py --input_image <in>.jpg --model model_epoch_500.pth --output_filename out.png`
### Super-resolve example on a Cifar-10 image
#### Input Image
#### Output Images
| Output image from Model | Output from bicubic sampling |
|-------------------------------|------------------------------------|
|  | |
|
