super_resolution

Super Resolution

This code is intended for training Super Resolution (SR) algorithms in Pytorch.

Models

Typologies are available for training at this point:

1. Single image super resolution network based on SRResNet architecture ("Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network") but with reduced number of channels and depthwise convolution in the decoder
2. Attention-Based single image super resolution network with reduced number of channels and changes in the network architecture
3. Tiny model to upsampling scanned text images

Results

The PSNR values were calculated with Y channel from YCrCb image.

Model	Set5, PSNRx3, dB	Set5, PSNRx4, dB
SmallModel	33.15	31.16

Setup

Prerequisites

• Ubuntu* 16.04 or higher
• OpenVINO™ 2019 R1 or higher
• Python* 3

Installation

1. Create virtual environment:

   virtualenv venv -p python3 --prompt="(sr)"

2. Activate virtual environment and setup OpenVINO™ variables:

   . venv/bin/activate
   . /opt/intel/openvino/bin/setupvars.sh

   TIP: Good practice is adding . /opt/intel/openvino/bin/setupvars.sh to the end of the venv/bin/activate.

   echo ". /opt/intel/openvino/bin/setupvars.sh" >> venv/bin/activate
   

3. Install the module

   pip3 install -e .

Train and Evaluate

Prepare Dataset

Create two directories for train and test images. Train images may have any resolution higher than path_size. Validation images should have the resolution like the path_size.

./data
├── train
│   ├── 000000.png
│   ...
└── val
    ├── 000000.png
    ...

Training

Use the tools/train.py script to start the training process:

python3 tools/train.py --config configs/smallmodel_scale4.yaml

To start from a pretrained checkpoint, set init_checkpoint in config.

Testing

Use the tools/test.py script to evaluate the trained model.

python3 tools/test.py --test_data_path PATH_TO_TEST_DATA \
    --models_path PATH_TO_MODELS_PATH \
    --exp_name EXPERIMENT_NAME

Export to OpenVINO™

python3 tools/export.py --models_path PATH_TO_MODELS_PATH \
    --exp_name EXPERIMENT_NAME \
    --input_size 200 200 \
    --data_type FP32

Demo

For the Latest Checkpoint

python3 tools/infer.py --model PATH_TO_CHECKPOINT \
    --scale 4 \
    image_path

For Intermediate Representation (IR)

python3 tools/infer_ie.py --model <PATH_TO_IR_XML> \
    image_path

C++ demo

Know Issues

A network cannot be reshaped after conversation to the IR. Set the input_size when running tools/export.py.