BasicSR入门教程

BasicSR入门教程

1.安装环境

由于安装好的其他环境已经有了pytorch，那么新建环境时直接拷贝该环境就好

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

克隆项目

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

安装依赖包

cd BasicSR
pip install -r requirements.txt

在BasicSR的根目录下安装BasicSR

python setup.py develop

验证BasicSR是否安装成功

import basicsr

通过本地clone安装成功的时候，此时使用pip list 命令查看BasicSR 路径

pip list

2.准备数据集

常用的图像超分数据集如下：

name	数据集	数据描述	下载
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下载地址：https://data.vision.ee.ethz.ch/cvl/DIV2K/

Set5下载地址：https://drive.google.com/drive/folders/1B3DJGQKB6eNdwuQIhdskA64qUuVKLZ9u

Set14下载地址：https://drive.google.com/drive/folders/1B3DJGQKB6eNdwuQIhdskA64qUuVKLZ9u

因为DIV2K 数据集是2K 分辨率的(比如: 2048×1080), 而我们在训练的时候往往并不要那么大(常见的是128×128 或者192×192 的训练patch). 因此我们可以先把2K 的图片裁剪成有overlap 的480×480 的子图像块. 然后再由dataloader 从这个480×480 的子图像块中随机crop 出128×128 或者192×192 的训练patch。运行脚本extract_subimages.py。

cd BasicSR
python scripts/data_preparation/extract_subimages.py

若需要使用LMDB，则需要制作LMDB，数据准备运行脚本：

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

数据集的目录结构如下

3.修改配置文件

创建新的训练配置文件options/train/SRResNet_SRGAN/my_train_MSRResNet_x4.yml，内容如下

# 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

可以开始训练

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

训练完成后，结果会保存在results文件夹下的001_MSRResNet_x4_f64b16_DIV2K_10k_B16G1_wandb_myfirst文件夹中

创建新的测试配置文件options/test/SRResNet_SRGAN/my_test_MSRResNet_x4.yml，内容如下

# ----------- 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

测试完成后，结果会保存在results文件夹下的001_MSRResNet_x4_f64b16_DIV2K_10k_B16G1_wandb_myfirst文件夹中

4.tensorboard可视化训练过程

在用于训练的yml配置文件中设置tensorboard开启

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

在命令行输入以下命令，就可以在服务器的浏览器中查看：

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

tensorboard 在本机可以方便使用，但使用服务器时需要设置一下。

在Windows系统装一个Xshell,在文件->属性->ssh->隧道->添加,类型local，源主机填127.0.0.1（意思是本机），端口设置一个，比如12345，目标主机为服务器，目标端口一般是5500，如果5500被占了可以改为其他端口。

在本地浏览器中输入127.0.0.1:12345即可

最后感谢小伙伴们的学习噢~