docs/Training.md

:computer: How to Train/Finetune Real-ESRGAN

• Train Real-ESRGAN
  • Overview
  • Dataset Preparation
  • Train Real-ESRNet
  • Train Real-ESRGAN
• Finetune Real-ESRGAN on your own dataset
  • Generate degraded images on the fly
  • Use paired training data

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Train Real-ESRGAN

Overview

The training has been divided into two stages. These two stages have the same data synthesis process and training pipeline, except for the loss functions. Specifically,

1. We first train Real-ESRNet with L1 loss from the pre-trained model ESRGAN.
2. We then use the trained Real-ESRNet model as an initialization of the generator, and train the Real-ESRGAN with a combination of L1 loss, perceptual loss and GAN loss.

Dataset Preparation

We use DF2K (DIV2K and Flickr2K) + OST datasets for our training. Only HR images are required.
You can download from :

1. DIV2K: http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip
2. Flickr2K: https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar
3. OST: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip

Here are steps for data preparation.

Step 1: [Optional] Generate multi-scale images

For the DF2K dataset, we use a multi-scale strategy, i.e., we downsample HR images to obtain several Ground-Truth images with different scales.
You can use the scripts/generate_multiscale_DF2K.py script to generate multi-scale images.
Note that this step can be omitted if you just want to have a fast try.

python scripts/generate_multiscale_DF2K.py --input datasets/DF2K/DF2K_HR --output datasets/DF2K/DF2K_multiscale

Step 2: [Optional] Crop to sub-images

We then crop DF2K images into sub-images for faster IO and processing.
This step is optional if your IO is enough or your disk space is limited.

You can use the scripts/extract_subimages.py script. Here is the example:

 python scripts/extract_subimages.py --input datasets/DF2K/DF2K_multiscale --output datasets/DF2K/DF2K_multiscale_sub --crop_size 400 --step 200

Step 3: Prepare a txt for meta information

You need to prepare a txt file containing the image paths. The following are some examples in meta_info_DF2Kmultiscale+OST_sub.txt (As different users may have different sub-images partitions, this file is not suitable for your purpose and you need to prepare your own txt file):

DF2K_HR_sub/000001_s001.png
DF2K_HR_sub/000001_s002.png
DF2K_HR_sub/000001_s003.png
...

You can use the scripts/generate_meta_info.py script to generate the txt file.
You can merge several folders into one meta_info txt. Here is the example:

 python scripts/generate_meta_info.py --input datasets/DF2K/DF2K_HR datasets/DF2K/DF2K_multiscale --root datasets/DF2K datasets/DF2K --meta_info datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt

Train Real-ESRNet

1. Download pre-trained model ESRGAN into experiments/pretrained_models.

   wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth -P experiments/pretrained_models
   

2. Modify the content in the option file options/train_realesrnet_x4plus.yml accordingly:

   train:
       name: DF2K+OST
       type: RealESRGANDataset
       dataroot_gt: datasets/DF2K  # modify to the root path of your folder
       meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt  # modify to your own generate meta info txt
       io_backend:
           type: disk
   

3. If you want to perform validation during training, uncomment those lines and modify accordingly:

     # Uncomment these for validation
     # val:
     #   name: validation
     #   type: PairedImageDataset
     #   dataroot_gt: path_to_gt
     #   dataroot_lq: path_to_lq
     #   io_backend:
     #     type: disk
   
   ...
   
     # Uncomment these for validation
     # validation settings
     # val:
     #   val_freq: !!float 5e3
     #   save_img: True
   
     #   metrics:
     #     psnr: # metric name, can be arbitrary
     #       type: calculate_psnr
     #       crop_border: 4
     #       test_y_channel: false
   

4. Before the formal training, you may run in the --debug mode to see whether everything is OK. We use four GPUs for training:

   CUDA_VISIBLE_DEVICES=0,1,2,3 \
   python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --debug
   

   Train with a single GPU in the debug mode:

   python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --debug
   

5. The formal training. We use four GPUs for training. We use the --auto_resume argument to automatically resume the training if necessary.

   CUDA_VISIBLE_DEVICES=0,1,2,3 \
   python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --auto_resume
   

   Train with a single GPU:

   python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --auto_resume
   

Train Real-ESRGAN

1. After the training of Real-ESRNet, you now have the file experiments/train_RealESRNetx4plus_1000k_B12G4_fromESRGAN/model/net_g_1000000.pth. If you need to specify the pre-trained path to other files, modify the pretrain_network_g value in the option file train_realesrgan_x4plus.yml.

2. Modify the option file train_realesrgan_x4plus.yml accordingly. Most modifications are similar to those listed above.

3. Before the formal training, you may run in the --debug mode to see whether everything is OK. We use four GPUs for training:

   CUDA_VISIBLE_DEVICES=0,1,2,3 \
   python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --debug
   

   Train with a single GPU in the debug mode:

   python realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --debug
   

4. The formal training. We use four GPUs for training. We use the --auto_resume argument to automatically resume the training if necessary.

   CUDA_VISIBLE_DEVICES=0,1,2,3 \
   python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --auto_resume
   

   Train with a single GPU:

   python realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --auto_resume
   

Finetune Real-ESRGAN on your own dataset

You can finetune Real-ESRGAN on your own dataset. Typically, the fine-tuning process can be divided into two cases:

1. Generate degraded images on the fly
2. Use your own paired data

Generate degraded images on the fly

Only high-resolution images are required. The low-quality images are generated with the degradation process described in Real-ESRGAN during training.

1. Prepare dataset

See this section for more details.

2. Download pre-trained models

Download pre-trained models into experiments/pretrained_models.

• RealESRGAN_x4plus.pth:

  wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
  

• RealESRGAN_x4plus_netD.pth:

  wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
  

3. Finetune

Modify options/finetune_realesrgan_x4plus.yml accordingly, especially the datasets part:

train:
    name: DF2K+OST
    type: RealESRGANDataset
    dataroot_gt: datasets/DF2K  # modify to the root path of your folder
    meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt  # modify to your own generate meta info txt
    io_backend:
        type: disk

We use four GPUs for training. We use the --auto_resume argument to automatically resume the training if necessary.

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --launcher pytorch --auto_resume

Finetune with a single GPU:

python realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --auto_resume

Use your own paired data

You can also finetune RealESRGAN with your own paired data. It is more similar to fine-tuning ESRGAN.

1. Prepare dataset

Assume that you already have two folders:

• gt folder (Ground-truth, high-resolution images): datasets/DF2K/DIV2K_train_HR_sub
• lq folder (Low quality, low-resolution images): datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub

Then, you can prepare the meta_info txt file using the script scripts/generate_meta_info_pairdata.py:

python scripts/generate_meta_info_pairdata.py --input datasets/DF2K/DIV2K_train_HR_sub datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub --meta_info datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt

2. Download pre-trained models

Download pre-trained models into experiments/pretrained_models.

• RealESRGAN_x4plus.pth

  wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
  

• RealESRGAN_x4plus_netD.pth

  wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
  

3. Finetune

Modify options/finetune_realesrgan_x4plus_pairdata.yml accordingly, especially the datasets part:

train:
    name: DIV2K
    type: RealESRGANPairedDataset
    dataroot_gt: datasets/DF2K  # modify to the root path of your folder
    dataroot_lq: datasets/DF2K  # modify to the root path of your folder
    meta_info: datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt  # modify to your own generate meta info txt
    io_backend:
        type: disk

We use four GPUs for training. We use the --auto_resume argument to automatically resume the training if necessary.

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus_pairdata.yml --launcher pytorch --auto_resume

Finetune with a single GPU:

python realesrgan/train.py -opt options/finetune_realesrgan_x4plus_pairdata.yml --auto_resume