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Super Resolution Training Toolbox Pytorch

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

Models

Two 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 decoder.
  2. Attention-Based single image super resolution network (https://arxiv.org/pdf/1807.06779.pdf) with reduced number of channels and changes in network architecture.

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 newer
  • OpenVINO 2019 R1 or newer
  • Python 3

Installation

  1. Create virtual environment
virtualenv venv -p python3 --prompt="(sr)"
  1. Activate virtual environment and setup OpenVINO variables
. venv/bin/activate
. /opt/intel/openvino/bin/setupvars.sh

NOTE 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
  1. Install the module
pip3 install -e .

Train and evaluation

Prepare dataset

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

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

Training

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

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

To start from pretrained checkpoint set init_checkpoint in config. Checkpoints can be downloaded here.

Testing

Use 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

  1. Network can't be reshaped after conversation to IR. You should set input_size when run tools/export.py.