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edsr's Introduction

EDSR

Super-resolution based on EDSR

Test Enviroment

python 3.5
    - Numpy >= 1.14.0
    - Tensorflow >= 1.3.0
    - horovod

Repository Architecture

- Data Pipeline
    - TFrecords
        build_tfrecords.py
        tfrecords_reader.py
        build_train_tfrecords.sh
      data_generator.py
- Training Pipeline
    multi_trainer.py
    config_training.py
- Testing Pipeline
    tester.py
    config_testing.py
- EDSR
    models/EDSR.py
    models/ops.py
- Utils
    config_helpers.py

Usage

  1. Copy or build TFrecords for training data
  • Copy ready-made TFrecords for NTIRE2017 and NTIRE2018
  • Or, make TFrecords from .png files (See build_train_tfrecords.sh)
  1. Train the model

Modify config_training.py as you want. Then, run *_trainer*.py

  1. Test the model

Copy .png files from nas/dataset/NTIRE*/.

Modify config_testing.py as you want. Then, run *_tester.py

Multi GPU Training Example

CUDA_VISIBLE_DEVICES=0,1,2,3 \
mpirun -np 4 \
    -H localhost:4 \
    -bind-to none -map-by slot \
    -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH \
    python3 multi_trainer.py

edsr's People

Contributors

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Watchers

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