BasicSR Getting Started Tutorial

BasicSR Getting Started Tutorial

1. Installation environment

Since other installed environments already have pytorch, it is good to directly copy the environment when creating a new environment

//复制环境
conda create --name my-basicsr --clone mmediting

clone project

git clone https://github.com/XPixelGroup/BasicSR.git

Install dependencies

cd BasicSR
pip install -r requirements.txt

Install BasicSR in the root directory of BasicSR

python setup.py develop

Verify that BasicSR is successfully installed

import basicsr

When the local clone installation is successful, use the pip list command to view the BasicSR path

pip list

2. Prepare the dataset

Commonly used image super-resolution datasets are as follows:

name	data set	data description	download
2K Resolution	DIV2K	proposed in NTIRE17 (800 train and 100 validation)	official website
Classical SR Testing	Set5	Set5 test dataset	Google Drive / Baidu Drive
Classical SR Testing	Set14	Set14 test dataset	Google Drive / Baidu Drive

DIV2K download link: https://data.vision.ee.ethz.ch/cvl/DIV2K/

Set5 download link: https://drive.google.com/drive/folders/1B3DJGQKB6eNdwuQIhdskA64qUuVKLZ9u

Set14 download link: https://drive.google.com/drive/folders/1B3DJGQKB6eNdwuQIhdskA64qUuVKLZ9u

Because the DIV2K data set is 2K resolution (for example: 2048×1080), and we often do not need to be so large when training (commonly 128×128 or 192×192 training patch). Therefore, we can first put the 2K The image is cropped into a 480×480 sub-image block with overlap. Then the dataloader randomly crops a 128×128 or 192×192 training patch from the 480×480 sub-image block. Run the script extract_subimages.py.

cd BasicSR
python scripts/data_preparation/extract_subimages.py

If you need to use LMDB, you need to make LMDB, and the data is ready to run the script:

python scripts/data_preparation/create_lmdb.py --dataset div2k

The directory structure of the dataset is as follows

3. Modify the configuration file

Create a new training configuration file options/train/SRResNet_SRGAN/my_train_MSRResNet_x4.ymlwith the following content

# Modified SRResNet w/o BN from:
# Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

# ----------- Commands for running
# ----------- Single GPU with auto_resume
# PYTHONPATH="./:${PYTHONPATH}"  CUDA_VISIBLE_DEVICES=0 python basicsr/train.py -opt options/train/SRResNet_SRGAN/train_MSRResNet_x4.yml --auto_resume

# general settings
name: 001_MSRResNet_x4_f64b16_DIV2K_10k_B16G1_wandb_myfirst
model_type: SRModel
scale: 4
num_gpu: 1  # set num_gpu: 0 for cpu mode
manual_seed: 0

# dataset and data loader settings
datasets:
  train:
    name: DIV2K
    type: PairedImageDataset
    # dataroot_gt: datasets/DF2K/DIV2K_train_HR_sub
    # dataroot_lq: datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub
    # meta_info_file: basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt
    # dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub
    # dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X4_sub
    # meta_info_file: basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt
    # (for lmdb)
    dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub.lmdb
    dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X4_sub.lmdb
    filename_tmpl: '{}'
    io_backend:
      # type: disk
      # (for lmdb)
      type: lmdb

    gt_size: 128
    use_hflip: true
    use_rot: true

    # data loader
    num_worker_per_gpu: 6
    batch_size_per_gpu: 16
    dataset_enlarge_ratio: 100
    prefetch_mode: ~

  val:
    name: Set5
    type: PairedImageDataset
    dataroot_gt: datasets/Set5/GTmod12
    dataroot_lq: datasets/Set5/LRbicx4
    io_backend:
      type: disk

  val_2:
    name: Set14
    type: PairedImageDataset
    dataroot_gt: datasets/Set14/GTmod12
    dataroot_lq: datasets/Set14/LRbicx4
    io_backend:
      type: disk

# network structures
network_g:
  type: MSRResNet
  num_in_ch: 3
  num_out_ch: 3
  num_feat: 64
  num_block: 16
  upscale: 4

# path
path:
  pretrain_network_g: ~
  param_key_g: params
  strict_load_g: true
  resume_state: ~

# training settings
train:
  ema_decay: 0.999
  optim_g:
    type: Adam
    lr: !!float 2e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: CosineAnnealingRestartLR
    periods: [250000, 250000, 250000, 250000]
    restart_weights: [1, 1, 1, 1]
    eta_min: !!float 1e-7

  # total_iter: 1000000
  total_iter: 10000
  warmup_iter: -1  # no warm up

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean

# validation settings
val:
  val_freq: !!float 5e3
  save_img: false

  metrics:
    psnr: # metric name, can be arbitrary
      type: calculate_psnr
      crop_border: 4
      test_y_channel: false
      better: higher  # the higher, the better. Default: higher
    niqe:
      type: calculate_niqe
      crop_border: 4
      better: lower  # the lower, the better

# logging settings
logger:
  print_freq: 100
  save_checkpoint_freq: !!float 5e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500

can start training

python basicsr/train.py -opt options/train/SRResNet_SRGAN/my_train_MSRResNet_x4.yml

001_MSRResNet_x4_f64b16_DIV2K_10k_B16G1_wandb_myfirstAfter the training is complete, the results will be saved in a folder under the results folder

Create a new test configuration file options/test/SRResNet_SRGAN/my_test_MSRResNet_x4.ymlwith the following content

# ----------- Commands for running
# ----------- Single GPU
# PYTHONPATH="./:${PYTHONPATH}"  CUDA_VISIBLE_DEVICES=0 python basicsr/test.py -opt options/test/SRResNet_SRGAN/test_MSRResNet_x4.yml

# general settings
name: 001_MSRResNet_x4_f64b16_DIV2K_10k_B16G1_wandb_myfirst
model_type: SRModel
scale: 4
num_gpu: 1  # set num_gpu: 0 for cpu mode
manual_seed: 0

# test dataset settings
datasets:
  test_1:  # the 1st test dataset
    name: Set5
    type: PairedImageDataset
    dataroot_gt: datasets/Set5/GTmod12
    dataroot_lq: datasets/Set5/LRbicx4
    io_backend:
      type: disk
  test_2:  # the 2nd test dataset
    name: Set14
    type: PairedImageDataset
    dataroot_gt: datasets/Set14/GTmod12
    dataroot_lq: datasets/Set14/LRbicx4
    io_backend:
      type: disk
  test_3: # the 3rd test dataset
    name: DIV2K100
    type: PairedImageDataset
    dataroot_gt: datasets/DIV2K/DIV2K_valid_HR
    dataroot_lq: datasets/DIV2K/DIV2K_valid_LR_bicubic/X4
    filename_tmpl: '{}x4'
    io_backend:
      type: disk

# network structures
network_g:
  type: MSRResNet
  num_in_ch: 3
  num_out_ch: 3
  num_feat: 64
  num_block: 16
  upscale: 4

# path
path:
  pretrain_network_g: experiments/001_MSRResNet_x4_f64b16_DIV2K_10k_B16G1_wandb_myfirst/models/net_g_10000.pth
  param_key_g: params
  strict_load_g: true

# validation settings
val:
  save_img: true
  suffix: ~  # add suffix to saved images, if None, use exp name

  metrics:
    psnr: # metric name, can be arbitrary
      type: calculate_psnr
      crop_border: 4
      test_y_channel: false
      better: higher  # the higher, the better. Default: higher
    ssim:
      type: calculate_ssim
      crop_border: 4
      test_y_channel: false
      better: higher

001_MSRResNet_x4_f64b16_DIV2K_10k_B16G1_wandb_myfirstAfter the test is completed, the results are saved in a folder under the results folder

4. Tensorboard visualizes the training process

Set tensorboard to open in the yml configuration file for training

# logging settings
logger:
  print_freq: 100
  save_checkpoint_freq: !!float 5e3
  use_tb_logger: true # 设置为true
  wandb:
    project: ~
    resume_id: ~

Enter the following command on the command line to view it in the browser of the server :

tensorboard --logdir tb_logger --port 5500 --bind_all

Tensorboard can be used easily on this machine , but it needs to be set up when using the server.

Install an Xshell in the Windows system , go to File->Properties->ssh->Tunnel->Add, type local, fill in 127.0.0.1 for the source host (meaning this machine), set a port, such as 12345, and set the target host as a server. The target port is generally 5500, if 5500 is occupied, it can be changed to other ports.

127.0.0.1:12345Just type in your local browser

Finally, thank you for your study~