Tensorflow 2.x based implementation of EDSR, WDSR and SRGAN for single image super-resolution

Single Image Super-Resolution with EDSR, WDSR and SRGAN

A Tensorflow 2.x based implementation of

• Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR), winner of the NTIRE 2017 super-resolution challenge.
• Wide Activation for Efficient and Accurate Image Super-Resolution (WDSR), winner of the NTIRE 2018 super-resolution challenge (realistic tracks).
• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGAN).

This is a complete re-write of the old Keras/Tensorflow 1.x based implementation available here. Some parts are still work in progress but you can already train models as described in the papers via a high-level training API. Furthermore, you can also fine-tune EDSR and WDSR models in an SRGAN context. Training and usage examples are given in the notebooks

• example-edsr.ipynb
• example-wdsr.ipynb
• example-srgan.ipynb

A DIV2K data provider automatically downloads DIV2K training and validation images of given scale (2, 3, 4 or 8) and downgrade operator (“bicubic”, “unknown”, “mild” or “difficult”).

Important: if you want to evaluate the pre-trained models with a dataset other than DIV2K please read this comment (and replies) first.

Environment setup

Create a new conda environment with

conda env create -f environment.yml

and activate it with

conda activate sisr

Introduction

You can find an introduction to single-image super-resolution in this article. It also demonstrates how EDSR and WDSR models can be fine-tuned with SRGAN (see also this section).

Getting started

Examples in this section require following pre-trained weights for running (see also example notebooks):

Pre-trained weights

• weights-edsr-16-x4.tar.gz
  • EDSR x4 baseline as described in the EDSR paper: 16 residual blocks, 64 filters, 1.52M parameters.
  • PSNR on DIV2K validation set = 28.89 dB (images 801 - 900, 6 + 4 pixel border included).
• weights-wdsr-b-32-x4.tar.gz
  • WDSR B x4 custom model: 32 residual blocks, 32 filters, expansion factor 6, 0.62M parameters.
  • PSNR on DIV2K validation set = 28.91 dB (images 801 - 900, 6 + 4 pixel border included).
• weights-srgan.tar.gz
  • SRGAN as described in the SRGAN paper: 1.55M parameters, trained with VGG54 content loss.

After download, extract them in the root folder of the project with

tar xvfz weights-<...>.tar.gz

EDSR

from model import resolve_single
from model.edsr import edsr

from utils import load_image, plot_sample

model = edsr(scale=4, num_res_blocks=16)
model.load_weights('weights/edsr-16-x4/weights.h5')

lr = load_image('demo/0851x4-crop.png')
sr = resolve_single(model, lr)

plot_sample(lr, sr)

WDSR

from model.wdsr import wdsr_b

model = wdsr_b(scale=4, num_res_blocks=32)
model.load_weights('weights/wdsr-b-32-x4/weights.h5')

lr = load_image('demo/0829x4-crop.png')
sr = resolve_single(model, lr)

plot_sample(lr, sr)

Weight normalization in WDSR models is implemented with the new WeightNormalization layer wrapper of Tensorflow Addons. In its latest version, this wrapper seems to corrupt weights when running model.predict(...). A workaround is to set model.run_eagerly = True or compile the model with model.compile(loss='mae') in advance. This issue doesn’t arise when calling the model directly with model(...) though. To be further investigated …

SRGAN

from model.srgan import generator

model = generator()
model.load_weights('weights/srgan/gan_generator.h5')

lr = load_image('demo/0869x4-crop.png')
sr = resolve_single(model, lr)

plot_sample(lr, sr)

DIV2K dataset

For training and validation on DIV2K images, applications should use the provided DIV2K data loader. It automatically downloads DIV2K images to .div2k directory and converts them to a different format for faster loading.

Training dataset

from data import DIV2K

train_loader = DIV2K(scale=4,             # 2, 3, 4 or 8
                     downgrade='bicubic', # 'bicubic', 'unknown', 'mild' or 'difficult' 
                     subset='train')      # Training dataset are images 001 - 800
                     
# Create a tf.data.Dataset          
train_ds = train_loader.dataset(batch_size=16,         # batch size as described in the EDSR and WDSR papers
                                random_transform=True, # random crop, flip, rotate as described in the EDSR paper
                                repeat_count=None)     # repeat iterating over training images indefinitely

# Iterate over LR/HR image pairs                                
for lr, hr in train_ds:
    # .... 

Crop size in HR images is 96x96.

Validation dataset

from data import DIV2K

valid_loader = DIV2K(scale=4,             # 2, 3, 4 or 8
                     downgrade='bicubic', # 'bicubic', 'unknown', 'mild' or 'difficult' 
                     subset='valid')      # Validation dataset are images 801 - 900
                     
# Create a tf.data.Dataset          
valid_ds = valid_loader.dataset(batch_size=1,           # use batch size of 1 as DIV2K images have different size
                                random_transform=False, # use DIV2K images in original size 
                                repeat_count=1)         # 1 epoch
                                
# Iterate over LR/HR image pairs                                
for lr, hr in valid_ds:
    # ....                                 

Training

The following training examples use the training and validation datasets described earlier. The high-level training API is designed around steps (= minibatch updates) rather than epochs to better match the descriptions in the papers.

EDSR

from model.edsr import edsr
from train import EdsrTrainer

# Create a training context for an EDSR x4 model with 16 
# residual blocks.
trainer = EdsrTrainer(model=edsr(scale=4, num_res_blocks=16), 
                      checkpoint_dir=f'.ckpt/edsr-16-x4')
                      
# Train EDSR model for 300,000 steps and evaluate model
# every 1000 steps on the first 10 images of the DIV2K
# validation set. Save a checkpoint only if evaluation
# PSNR has improved.
trainer.train(train_ds,
              valid_ds.take(10),
              steps=300000, 
              evaluate_every=1000, 
              save_best_only=True)
              
# Restore from checkpoint with highest PSNR.
trainer.restore()

# Evaluate model on full validation set.
psnr = trainer.evaluate(valid_ds)
print(f'PSNR = {psnr.numpy():3f}')

# Save weights to separate location.
trainer.model.save_weights('weights/edsr-16-x4/weights.h5')                                    

Interrupting training and restarting it again resumes from the latest saved checkpoint. The trained Keras model can be accessed with trainer.model.

WDSR

from model.wdsr import wdsr_b
from train import WdsrTrainer

# Create a training context for a WDSR B x4 model with 32 
# residual blocks.
trainer = WdsrTrainer(model=wdsr_b(scale=4, num_res_blocks=32), 
                      checkpoint_dir=f'.ckpt/wdsr-b-8-x4')

# Train WDSR B model for 300,000 steps and evaluate model
# every 1000 steps on the first 10 images of the DIV2K
# validation set. Save a checkpoint only if evaluation
# PSNR has improved.
trainer.train(train_ds,
              valid_ds.take(10),
              steps=300000, 
              evaluate_every=1000, 
              save_best_only=True)

# Restore from checkpoint with highest PSNR.
trainer.restore()

# Evaluate model on full validation set.
psnr = trainer.evaluate(valid_ds)
print(f'PSNR = {psnr.numpy():3f}')

# Save weights to separate location.
trainer.model.save_weights('weights/wdsr-b-32-x4/weights.h5')

SRGAN

Generator pre-training

from model.srgan import generator
from train import SrganGeneratorTrainer

# Create a training context for the generator (SRResNet) alone.
pre_trainer = SrganGeneratorTrainer(model=generator(), checkpoint_dir=f'.ckpt/pre_generator')

# Pre-train the generator with 1,000,000 steps (100,000 works fine too). 
pre_trainer.train(train_ds, valid_ds.take(10), steps=1000000, evaluate_every=1000)

# Save weights of pre-trained generator (needed for fine-tuning with GAN).
pre_trainer.model.save_weights('weights/srgan/pre_generator.h5')

Generator fine-tuning (GAN)

from model.srgan import generator, discriminator
from train import SrganTrainer

# Create a new generator and init it with pre-trained weights.
gan_generator = generator()
gan_generator.load_weights('weights/srgan/pre_generator.h5')

# Create a training context for the GAN (generator + discriminator).
gan_trainer = SrganTrainer(generator=gan_generator, discriminator=discriminator())

# Train the GAN with 200,000 steps.
gan_trainer.train(train_ds, steps=200000)

# Save weights of generator and discriminator.
gan_trainer.generator.save_weights('weights/srgan/gan_generator.h5')
gan_trainer.discriminator.save_weights('weights/srgan/gan_discriminator.h5')

SRGAN for fine-tuning EDSR and WDSR models

It is also possible to fine-tune EDSR and WDSR x4 models with SRGAN. They can be used as drop-in replacement for the original SRGAN generator. More details in this article.

# Create EDSR generator and init with pre-trained weights
generator = edsr(scale=4, num_res_blocks=16)
generator.load_weights('weights/edsr-16-x4/weights.h5')

# Fine-tune EDSR model via SRGAN training.
gan_trainer = SrganTrainer(generator=generator, discriminator=discriminator())
gan_trainer.train(train_ds, steps=200000)

# Create WDSR B generator and init with pre-trained weights
generator = wdsr_b(scale=4, num_res_blocks=32)
generator.load_weights('weights/wdsr-b-16-32/weights.h5')

# Fine-tune WDSR B  model via SRGAN training.
gan_trainer = SrganTrainer(generator=generator, discriminator=discriminator())
gan_trainer.train(train_ds, steps=200000)