controlnet

BasicSR/options/train/BasicVSR/train_BasicVSR_REDS.yml

+# general settings
+name: BasicVSR_REDS
+model_type: VideoRecurrentModel
+scale: 4
+num_gpu: auto  # official: 8 GPUs
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: REDS
+    type: REDSRecurrentDataset
+    dataroot_gt: datasets/REDS/train_sharp
+    dataroot_lq: datasets/REDS/train_sharp_bicubic/X4
+    meta_info_file: basicsr/data/meta_info/meta_info_REDS_GT.txt
+    val_partition: REDS4  # set to 'official' when use the official validation partition
+    test_mode: False
+    io_backend:
+      type: disk
+
+    num_frame: 15
+    gt_size: 256
+    interval_list: [1]
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+
+    # data loader
+    num_worker_per_gpu: 6
+    batch_size_per_gpu: 4
+    dataset_enlarge_ratio: 200
+    prefetch_mode: ~
+
+  val:
+    name: REDS4
+    type: VideoRecurrentTestDataset
+    dataroot_gt: datasets/REDS4/GT
+    dataroot_lq: datasets/REDS4/sharp_bicubic
+
+    cache_data: true
+    io_backend:
+      type: disk
+
+    num_frame: -1  # not needed
+
+# network structures
+network_g:
+  type: BasicVSR
+  num_feat: 64
+  num_block: 30
+  spynet_path: experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth
+
+# path
+path:
+  pretrain_network_g: ~
+  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: [300000]
+    restart_weights: [1]
+    eta_min: !!float 1e-7
+
+  total_iter: 300000
+  warmup_iter: -1  # no warm up
+  fix_flow: 5000
+  flow_lr_mul: 0.125
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    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: 0
+      test_y_channel: false
+
+# 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
+
+find_unused_parameters: true

BasicSR/options/train/BasicVSR/train_BasicVSR_Vimeo90K_BDx4.yml

+# general settings
+name: BasicVSR_Vimeo90K_BDx4
+model_type: VideoRecurrentModel
+scale: 4
+num_gpu: 2  # set num_gpu: 0 for cpu mode
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: Vimeo90K
+    type: Vimeo90KRecurrentDataset
+    dataroot_gt: datasets/vimeo90k/vimeo_septuplet/sequences
+    dataroot_lq: datasets/vimeo90k/BDx4
+    meta_info_file: basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt
+    io_backend:
+      type: disk
+
+    num_frame: -1
+    gt_size: 256
+    interval_list: [1]
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+    flip_sequence: true
+
+    # data loader
+    num_worker_per_gpu: 6
+    batch_size_per_gpu: 4
+    dataset_enlarge_ratio: 200
+    prefetch_mode: ~
+
+  val:
+    name: Vid4
+    type: VideoRecurrentTestDataset
+    dataroot_gt: datasets/Vid4/GT
+    dataroot_lq: datasets/Vid4/BDx4
+
+    cache_data: True
+    io_backend:
+      type: disk
+
+    num_frame: -1  # not needed
+
+# network structures
+network_g:
+  type: BasicVSR
+  num_feat: 64
+  num_block: 30
+  spynet_path: experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth
+
+# path
+path:
+  pretrain_network_g: ~
+  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: [300000]
+    restart_weights: [1]
+    eta_min: !!float 1e-7
+
+  total_iter: 300000
+  warmup_iter: -1  # no warm up
+  fix_flow: 5000
+  flow_lr_mul: 0.125
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    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: 0
+      test_y_channel: true
+
+# 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
+
+find_unused_parameters: true

BasicSR/options/train/BasicVSR/train_BasicVSR_Vimeo90K_BIx4.yml

+# general settings
+name: BasicVSR_Vimeo90K_BIx4
+model_type: VideoRecurrentModel
+scale: 4
+num_gpu: 2  # set num_gpu: 0 for cpu mode
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: Vimeo90K
+    type: Vimeo90KRecurrentDataset
+    dataroot_gt: datasets/vimeo90k/vimeo_septuplet/sequences
+    dataroot_lq: datasets/vimeo90k/BIx4
+    meta_info_file: basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt
+    io_backend:
+      type: disk
+
+    num_frame: -1
+    gt_size: 256
+    interval_list: [1]
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+    flip_sequence: true
+
+    # data loader
+    num_worker_per_gpu: 6
+    batch_size_per_gpu: 4
+    dataset_enlarge_ratio: 200
+    prefetch_mode: ~
+
+  val:
+    name: Vid4
+    type: VideoRecurrentTestDataset
+    dataroot_gt: datasets/Vid4/GT
+    dataroot_lq: datasets/Vid4/BIx4
+
+    cache_data: True
+    io_backend:
+      type: disk
+
+    num_frame: -1  # not needed
+
+# network structures
+network_g:
+  type: BasicVSR
+  num_feat: 64
+  num_block: 30
+  spynet_path: experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth
+
+# path
+path:
+  pretrain_network_g: ~
+  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: [300000]
+    restart_weights: [1]
+    eta_min: !!float 1e-7
+
+  total_iter: 300000
+  warmup_iter: -1  # no warm up
+  fix_flow: 5000
+  flow_lr_mul: 0.125
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    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: 0
+      test_y_channel: true
+
+# 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
+
+find_unused_parameters: true

BasicSR/options/train/BasicVSR/train_IconVSR_REDS.yml

+# general settings
+name: IconVSR_REDS
+model_type: VideoRecurrentModel
+scale: 4
+num_gpu: 2  # set num_gpu: 0 for cpu mode
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: REDS
+    type: REDSRecurrentDataset
+    dataroot_gt: datasets/REDS/train_sharp
+    dataroot_lq: datasets/REDS/train_sharp_bicubic/X4
+    meta_info_file: basicsr/data/meta_info/meta_info_REDS_GT.txt
+    val_partition: REDS4  # set to 'official' when use the official validation partition
+    test_mode: False
+    io_backend:
+      type: disk
+
+    num_frame: 15
+    gt_size: 256
+    interval_list: [1]
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+
+    # data loader
+    num_worker_per_gpu: 6
+    batch_size_per_gpu: 4
+    dataset_enlarge_ratio: 200
+    prefetch_mode: ~
+
+  val:
+    name: REDS4
+    type: VideoRecurrentTestDataset
+    dataroot_gt: datasets/REDS4/GT
+    dataroot_lq: datasets/REDS4/sharp_bicubic
+
+    cache_data: True
+    io_backend:
+      type: disk
+
+    num_frame: -1  # not needed
+
+# network structures
+network_g:
+  type: IconVSR
+  num_feat: 64
+  num_block: 30
+  keyframe_stride: 5
+  temporal_padding: 2
+  spynet_path: experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth
+  edvr_path: experiments/pretrained_models/edvr_reds_pretrained_new.pth
+
+# path
+path:
+  pretrain_network_g: ~
+  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: [300000]
+    restart_weights: [1]
+    eta_min: !!float 1e-7
+
+  total_iter: 300000
+  warmup_iter: -1  # no warm up
+  fix_flow: 5000
+  flow_lr_mul: 0.125
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    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: 0
+      test_y_channel: false
+
+# 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
+
+find_unused_parameters: true

BasicSR/options/train/BasicVSR/train_IconVSR_Vimeo90K_BDx4.yml

+# general settings
+name: IconVSR_Vimeo90K_BDx4
+model_type: VideoRecurrentModel
+scale: 4
+num_gpu: 2  # set num_gpu: 0 for cpu mode
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: Vimeo90K
+    type: Vimeo90KRecurrentDataset
+    dataroot_gt: datasets/vimeo90k/vimeo_septuplet/sequences
+    dataroot_lq: datasets/vimeo90k/BDx4
+    meta_info_file: basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt
+    io_backend:
+      type: disk
+
+    num_frame: -1
+    gt_size: 256
+    interval_list: [1]
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+    flip_sequence: true
+
+    # data loader
+    num_worker_per_gpu: 6
+    batch_size_per_gpu: 4
+    dataset_enlarge_ratio: 200
+    prefetch_mode: ~
+
+  val:
+    name: Vid4
+    type: VideoRecurrentTestDataset
+    dataroot_gt: datasets/Vid4/GT
+    dataroot_lq: datasets/Vid4/BDx4
+
+    cache_data: True
+    io_backend:
+      type: disk
+
+    num_frame: -1  # not needed
+
+# network structures
+network_g:
+  type: IconVSR
+  num_feat: 64
+  num_block: 30
+  keyframe_stride: 5
+  temporal_padding: 3
+  spynet_path: experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth
+  edvr_path: experiments/pretrained_models/edvr_vimeo90k_pretrained.pth
+
+# path
+path:
+  pretrain_network_g: ~
+  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: [300000]
+    restart_weights: [1]
+    eta_min: !!float 1e-7
+
+  total_iter: 300000
+  warmup_iter: -1  # no warm up
+  fix_flow: 5000
+  flow_lr_mul: 0.125
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    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: 0
+      test_y_channel: true
+
+# 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
+
+find_unused_parameters: true

BasicSR/options/train/BasicVSR/train_IconVSR_Vimeo90K_BIx4.yml

+# general settings
+name: IconVSR_Vimeo90K_BIx4
+model_type: VideoRecurrentModel
+scale: 4
+num_gpu: 2  # set num_gpu: 0 for cpu mode
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: Vimeo90K
+    type: Vimeo90KRecurrentDataset
+    dataroot_gt: datasets/vimeo90k/vimeo_septuplet/sequences
+    dataroot_lq: datasets/vimeo90k/BIx4
+    meta_info_file: basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt
+    io_backend:
+      type: disk
+
+    num_frame: -1
+    gt_size: 256
+    interval_list: [1]
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+    flip_sequence: true
+
+    # data loader
+    num_worker_per_gpu: 6
+    batch_size_per_gpu: 4
+    dataset_enlarge_ratio: 200
+    prefetch_mode: ~
+
+  val:
+    name: Vid4
+    type: VideoRecurrentTestDataset
+    dataroot_gt: datasets/Vid4/GT
+    dataroot_lq: datasets/Vid4/BIx4
+
+    cache_data: True
+    io_backend:
+      type: disk
+
+    num_frame: -1  # not needed
+
+# network structures
+network_g:
+  type: IconVSR
+  num_feat: 64
+  num_block: 30
+  keyframe_stride: 5
+  temporal_padding: 3
+  spynet_path: experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth
+  edvr_path: experiments/pretrained_models/edvr_vimeo90k_pretrained.pth
+
+# path
+path:
+  pretrain_network_g: ~
+  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: [300000]
+    restart_weights: [1]
+    eta_min: !!float 1e-7
+
+  total_iter: 300000
+  warmup_iter: -1  # no warm up
+  fix_flow: 5000
+  flow_lr_mul: 0.125
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    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: 0
+      test_y_channel: true
+
+# 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
+
+find_unused_parameters: true

BasicSR/options/train/BasicVSRPP/train_BasicVSRPP_REDS.yml

+# general settings
+name: train_BasicVSRPP_REDS
+model_type: VideoRecurrentModel
+scale: 4
+num_gpu: 8  # official: 8 GPUs
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: REDS
+    type: REDSRecurrentDataset
+    dataroot_gt: datasets/REDS/train_sharp
+    dataroot_lq: datasets/REDS/train_sharp_bicubic
+    meta_info_file: datasets/REDS/meta_info/meta_info_REDS_GT.txt
+    val_partition: REDS4  # set to 'official' when use the official validation partition
+    test_mode: False
+    io_backend:
+      type: disk
+
+    num_frame: 30
+    gt_size: 256
+    interval_list: [1]
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+
+    # data loader
+    num_worker_per_gpu: 6
+    batch_size_per_gpu: 1
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  val:
+    name: REDS4
+    type: VideoRecurrentTestDataset
+    dataroot_gt: datasets/REDS4/GT
+    dataroot_lq: datasets/REDS4/sharp_bicubic
+
+    cache_data: true
+    io_backend:
+      type: disk
+
+    num_frame: -1  # not needed
+
+# network structures
+network_g:
+  type: BasicVSRPlusPlus
+  mid_channels: 64
+  num_blocks: 7
+  is_low_res_input: true
+  spynet_path: experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: CosineAnnealingRestartLR
+    periods: [600000]
+    restart_weights: [1]
+    eta_min: !!float 1e-7
+
+  total_iter: 600000
+  warmup_iter: -1  # no warm up
+  fix_flow: 5000
+  flow_lr_mul: 0.25
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    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: 0
+      test_y_channel: false
+
+# 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
+
+find_unused_parameters: true

BasicSR/options/train/ECBSR/train_ECBSR_x2_m4c16_prelu.yml

+# general settings
+name: 101_train_ECBSR_x2_m4c16_prelu
+model_type: SRModel
+scale: 2
+num_gpu: 1  # set num_gpu: 0 for cpu mode
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: DIV2K
+    type: PairedImageDataset
+    # It is strongly recommended to use lmdb for faster IO speed, especially for small networks
+    dataroot_gt: datasets/DF2K/DIV2K_train_HR_sub
+    dataroot_lq: datasets/DF2K/DIV2K_train_LR_bicubic_X2_sub
+    meta_info_file: basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+
+    gt_size: 128
+    use_hflip: true
+    use_rot: true
+    color: y
+
+    # data loader
+    num_worker_per_gpu: 12
+    batch_size_per_gpu: 32
+    dataset_enlarge_ratio: 10
+    prefetch_mode: ~
+
+  # we use multiple validation datasets. The SR benchmark datasets can be download from: https://cv.snu.ac.kr/research/EDSR/benchmark.tar
+  val:
+    name: Set5
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/Set5/HR
+    dataroot_lq: datasets/benchmark/Set5/LR_bicubic/X2
+    filename_tmpl: '{}x2'
+    color: y
+    io_backend:
+      type: disk
+
+  val_2:
+    name: Set14
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/Set14/HR
+    dataroot_lq: datasets/benchmark/Set14/LR_bicubic/X2
+    filename_tmpl: '{}x2'
+    color: y
+    io_backend:
+      type: disk
+
+  val_3:
+    name: B100
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/B100/HR
+    dataroot_lq: datasets/benchmark/B100/LR_bicubic/X2
+    filename_tmpl: '{}x2'
+    color: y
+    io_backend:
+      type: disk
+
+  val_4:
+    name: Urban100
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/Urban100/HR
+    dataroot_lq: datasets/benchmark/Urban100/LR_bicubic/X2
+    filename_tmpl: '{}x2'
+    color: y
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: ECBSR
+  num_in_ch: 1
+  num_out_ch: 1
+  num_block: 4
+  num_channel: 16
+  with_idt: False
+  act_type: prelu
+  scale: 2
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0
+  optim_g:
+    type: Adam
+    lr: !!float 5e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [1600000]
+    gamma: 1
+
+  total_iter: 1600000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1.0
+    reduction: mean
+
+# validation settings
+val:
+  val_freq: !!float 1600  # the same as the original setting. # TODO: Can be larger
+  save_img: false
+  pbar: False
+
+  metrics:
+    psnr:
+      type: calculate_psnr
+      crop_border: 2
+      test_y_channel: true
+      better: higher  # the higher, the better. Default: higher
+    ssim:
+      type: calculate_ssim
+      crop_border: 2
+      test_y_channel: true
+      better: higher  # the higher, the better. Default: higher
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 1600
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

BasicSR/options/train/ECBSR/train_ECBSR_x4_m4c16_prelu.yml

+# general settings
+name: 100_train_ECBSR_x4_m4c16_prelu
+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
+    # It is strongly recommended to use lmdb for faster IO speed, especially for small networks
+    dataroot_gt: datasets/DF2K/DIV2K_train_HR_sub.lmdb
+    dataroot_lq: datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub.lmdb
+    meta_info_file: basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt
+    filename_tmpl: '{}'
+    io_backend:
+      type: lmdb
+
+    gt_size: 256
+    use_hflip: true
+    use_rot: true
+    color: y
+
+    # data loader
+    num_worker_per_gpu: 12
+    batch_size_per_gpu: 32
+    dataset_enlarge_ratio: 10
+    prefetch_mode: ~
+
+  # we use multiple validation datasets. The SR benchmark datasets can be download from: https://cv.snu.ac.kr/research/EDSR/benchmark.tar
+  val:
+    name: Set5
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/Set5/HR
+    dataroot_lq: datasets/benchmark/Set5/LR_bicubic/X4
+    filename_tmpl: '{}x4'
+    color: y
+    io_backend:
+      type: disk
+
+  val_2:
+    name: Set14
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/Set14/HR
+    dataroot_lq: datasets/benchmark/Set14/LR_bicubic/X4
+    filename_tmpl: '{}x4'
+    color: y
+    io_backend:
+      type: disk
+
+  val_3:
+    name: B100
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/B100/HR
+    dataroot_lq: datasets/benchmark/B100/LR_bicubic/X4
+    filename_tmpl: '{}x4'
+    color: y
+    io_backend:
+      type: disk
+
+  val_4:
+    name: Urban100
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/Urban100/HR
+    dataroot_lq: datasets/benchmark/Urban100/LR_bicubic/X4
+    filename_tmpl: '{}x4'
+    color: y
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: ECBSR
+  num_in_ch: 1
+  num_out_ch: 1
+  num_block: 4
+  num_channel: 16
+  with_idt: False
+  act_type: prelu
+  scale: 4
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0
+  optim_g:
+    type: Adam
+    lr: !!float 5e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [1600000]
+    gamma: 1
+
+  total_iter: 1600000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1.0
+    reduction: mean
+
+# validation settings
+val:
+  val_freq: !!float 1600  # the same as the original setting. # TODO: Can be larger
+  save_img: false
+  pbar: False
+
+  metrics:
+    psnr:
+      type: calculate_psnr
+      crop_border: 4
+      test_y_channel: true
+      better: higher  # the higher, the better. Default: higher
+    ssim:
+      type: calculate_ssim
+      crop_border: 4
+      test_y_channel: true
+      better: higher  # the higher, the better. Default: higher
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 1600
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

BasicSR/options/train/ECBSR/train_ECBSR_x4_m4c16_prelu_RGB.yml

+# general settings
+name: 100_train_ECBSR_x4_m4c16_prelu_RGB
+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
+    # It is strongly recommended to use lmdb for faster IO speed, especially for small networks
+    dataroot_gt: datasets/DF2K/DIV2K_train_HR_sub.lmdb
+    dataroot_lq: datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub.lmdb
+    meta_info_file: basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt
+    filename_tmpl: '{}'
+    io_backend:
+      type: lmdb
+
+    gt_size: 256
+    use_hflip: true
+    use_rot: true
+
+    # data loader
+    num_worker_per_gpu: 12
+    batch_size_per_gpu: 32
+    dataset_enlarge_ratio: 10
+    prefetch_mode: ~
+
+  # we use multiple validation datasets. The SR benchmark datasets can be download from: https://cv.snu.ac.kr/research/EDSR/benchmark.tar
+  val:
+    name: Set5
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/Set5/HR
+    dataroot_lq: datasets/benchmark/Set5/LR_bicubic/X4
+    filename_tmpl: '{}x4'
+    io_backend:
+      type: disk
+
+  val_2:
+    name: Set14
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/Set14/HR
+    dataroot_lq: datasets/benchmark/Set14/LR_bicubic/X4
+    filename_tmpl: '{}x4'
+    io_backend:
+      type: disk
+
+  val_3:
+    name: B100
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/B100/HR
+    dataroot_lq: datasets/benchmark/B100/LR_bicubic/X4
+    filename_tmpl: '{}x4'
+    io_backend:
+      type: disk
+
+  val_4:
+    name: Urban100
+    type: PairedImageDataset
+    dataroot_gt: datasets/benchmark/Urban100/HR
+    dataroot_lq: datasets/benchmark/Urban100/LR_bicubic/X4
+    filename_tmpl: '{}x4'
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: ECBSR
+  num_in_ch: 3
+  num_out_ch: 3
+  num_block: 4
+  num_channel: 16
+  with_idt: False
+  act_type: prelu
+  scale: 4
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0
+  optim_g:
+    type: Adam
+    lr: !!float 5e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [1600000]
+    gamma: 1
+
+  total_iter: 1600000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1.0
+    reduction: mean
+
+# validation settings
+val:
+  val_freq: !!float 1600  # the same as the original setting. # TODO: Can be larger
+  save_img: false
+  pbar: False
+
+  metrics:
+    psnr:
+      type: calculate_psnr
+      crop_border: 4
+      test_y_channel: true
+      better: higher  # the higher, the better. Default: higher
+    ssim:
+      type: calculate_ssim
+      crop_border: 4
+      test_y_channel: true
+      better: higher  # the higher, the better. Default: higher
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 1600
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

BasicSR/options/train/EDSR/train_EDSR_Lx2.yml

+# general settings
+name: 204_EDSR_Lx2_f256b32_DIV2K_300k_B16G1_wandb
+model_type: SRModel
+scale: 2
+num_gpu: 1  # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: DIV2K
+    type: PairedImageDataset
+    dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub
+    dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic/X2_sub
+    # (for lmdb)
+    # dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub.lmdb
+    # dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X2_sub.lmdb
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+      # (for lmdb)
+      # type: lmdb
+
+    gt_size: 96
+    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/LRbicx2
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: EDSR
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 256
+  num_block: 32
+  upscale: 2
+  res_scale: 0.1
+  img_range: 255.
+  rgb_mean: [0.4488, 0.4371, 0.4040]
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [200000]
+    gamma: 0.5
+
+  total_iter: 300000
+  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: 2
+      test_y_channel: false
+
+# 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

BasicSR/options/train/EDSR/train_EDSR_Lx3.yml

+# general settings
+name: 205_EDSR_Lx3_f256b32_DIV2K_300k_B16G1_204pretrain_wandb
+model_type: SRModel
+scale: 3
+num_gpu: 1  # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: DIV2K
+    type: PairedImageDataset
+    dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub
+    dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic/X3_sub
+    # (for lmdb)
+    # dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub.lmdb
+    # dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X3_sub.lmdb
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+      # (for lmdb)
+      # type: lmdb
+
+    gt_size: 144
+    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/LRbicx3
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: EDSR
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 256
+  num_block: 32
+  upscale: 3
+  res_scale: 0.1
+  img_range: 255.
+  rgb_mean: [0.4488, 0.4371, 0.4040]
+
+# path
+path:
+  pretrain_network_g: experiments/204_EDSR_Lx2_f256b32_DIV2K_300k_B16G1_wandb/models/net_g_300000.pth
+  strict_load_g: false
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [200000]
+    gamma: 0.5
+
+  total_iter: 300000
+  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: 3
+      test_y_channel: false
+
+# 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

BasicSR/options/train/EDSR/train_EDSR_Lx4.yml

+# general settings
+name: 206_EDSR_Lx4_f256b32_DIV2K_300k_B16G1_204pretrain_wandb
+model_type: SRModel
+scale: 4
+num_gpu: 1  # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: DIV2K
+    type: PairedImageDataset
+    dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub
+    dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub
+    # (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: 192
+    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
+
+# network structures
+network_g:
+  type: EDSR
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 256
+  num_block: 32
+  upscale: 4
+  res_scale: 0.1
+  img_range: 255.
+  rgb_mean: [0.4488, 0.4371, 0.4040]
+
+# path
+path:
+  pretrain_network_g: experiments/204_EDSR_Lx2_f256b32_DIV2K_300k_B16G1_wandb/models/net_g_300000.pth
+  strict_load_g: false
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [200000]
+    gamma: 0.5
+
+  total_iter: 300000
+  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
+
+# 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

BasicSR/options/train/EDSR/train_EDSR_Mx2.yml

+# general settings
+name: 201_EDSR_Mx2_f64b16_DIV2K_300k_B16G1_wandb
+model_type: SRModel
+scale: 2
+num_gpu: 1  # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: DIV2K
+    type: PairedImageDataset
+    dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub
+    dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic/X2_sub
+    # (for lmdb)
+    # dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub.lmdb
+    # dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X2_sub.lmdb
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+      # (for lmdb)
+      # type: lmdb
+
+    gt_size: 96
+    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/LRbicx2
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: EDSR
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 64
+  num_block: 16
+  upscale: 2
+  res_scale: 1
+  img_range: 255.
+  rgb_mean: [0.4488, 0.4371, 0.4040]
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [200000]
+    gamma: 0.5
+
+  total_iter: 300000
+  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: 2
+      test_y_channel: false
+
+# 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

BasicSR/options/train/EDSR/train_EDSR_Mx3.yml

+# general settings
+name: 202_EDSR_Mx3_f64b16_DIV2K_300k_B16G1_201pretrain_wandb
+model_type: SRModel
+scale: 3
+num_gpu: 1  # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: DIV2K
+    type: PairedImageDataset
+    dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub
+    dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic/X3_sub
+    # (for lmdb)
+    # dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub.lmdb
+    # dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X3_sub.lmdb
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+      # (for lmdb)
+      # type: lmdb
+
+    gt_size: 144
+    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/LRbicx3
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: EDSR
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 64
+  num_block: 16
+  upscale: 3
+  res_scale: 1
+  img_range: 255.
+  rgb_mean: [0.4488, 0.4371, 0.4040]
+
+# path
+path:
+  pretrain_network_g: experiments/201_EDSR_Mx2_f64b16_DIV2K_300k_B16G1_wandb/models/net_g_300000.pth
+  strict_load_g: false
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [200000]
+    gamma: 0.5
+
+  total_iter: 300000
+  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: 3
+      test_y_channel: false
+
+# 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

BasicSR/options/train/EDSR/train_EDSR_Mx4.yml

+# general settings
+name: 203_EDSR_Mx4_f64b16_DIV2K_300k_B16G1_201pretrain_wandb
+model_type: SRModel
+scale: 4
+num_gpu: 1  # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: DIV2K
+    type: PairedImageDataset
+    dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub
+    dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub
+    # (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: 192
+    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
+
+# network structures
+network_g:
+  type: EDSR
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 64
+  num_block: 16
+  upscale: 4
+  res_scale: 1
+  img_range: 255.
+  rgb_mean: [0.4488, 0.4371, 0.4040]
+
+# path
+path:
+  pretrain_network_g: experiments/201_EDSR_Mx2_f64b16_DIV2K_300k_B16G1_wandb/models/net_g_300000.pth
+  strict_load_g: false
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [200000]
+    gamma: 0.5
+
+  total_iter: 300000
+  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
+
+# 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

BasicSR/options/train/EDVR/train_EDVRM_woTSA_GAN_TODO.yml

+# TODO
+# general settings
+name: 107_EDVRwoTSA_CBInit_lr1e-4_400k_REDS_SyncBN
+model_type: EDVRModel
+scale: 4
+num_gpu: 8  # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: REDS
+    type: REDSDataset
+    dataroot_gt: datasets/REDS/train_sharp
+    dataroot_lq: datasets/REDS/train_sharp_bicubic
+    dataroot_flow: ~
+    meta_info_file: basicsr/data/meta_info/meta_info_REDS_GT.txt
+    val_partition: REDS4  # set to 'official' when use the official validation partition
+    io_backend:
+      type: disk
+
+    num_frame: 5
+    gt_size: 256
+    interval_list: [1]
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+
+    # data loader
+    num_worker_per_gpu: 3
+    batch_size_per_gpu: 4
+    dataset_enlarge_ratio: 200
+    prefetch_mode: ~
+
+  val:
+    name: REDS4
+    type: VideoTestDataset
+    dataroot_gt: datasets/REDS/train_sharp
+    dataroot_lq: datasets/REDS/train_sharp_bicubic
+    meta_info_file: basicsr/data/meta_info/meta_info_REDS4_test_GT.txt
+    # change to 'meta_info_REDSofficial4_test_GT' when use the official validation partition
+    io_backend:
+      type: disk
+
+    cache_data: false
+    num_frame: 5
+    padding: reflection_circle
+
+# network structures
+network_g:
+  type: EDVR
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 128
+  num_frame: 5
+  deformable_groups: 8
+  num_extract_block: 5
+  num_reconstruct_block: 40
+  center_frame_idx: ~
+  hr_in: false
+  with_predeblur: false
+  with_tsa: false
+
+network_d:
+  type: VGGStyleDiscriminator
+  num_in_ch: 3
+  num_feat: 64
+  input_size: 256
+
+
+# path
+path:
+  pretrain_network_g: experiments/101_EDVR_M_x4_SR_REDS_woTSA_600k_B4G8_valREDS4_wandb/models/net_g_600000.pth
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+  optim_d:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [50000, 100000, 200000, 300000]
+    gamma: 0.5
+
+  total_iter: 400000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    loss_weight: !!float 1e-2
+    reduction: sum
+  perceptual_opt:
+    type: PerceptualLoss
+    layer_weights:
+      'conv5_4': 1  # before relu
+    vgg_type: vgg19
+    use_input_norm: true
+    range_norm: false
+    perceptual_weight: 1.0
+    style_weight: 0
+    criterion: l1
+  gan_opt:
+    type: GANLoss
+    gan_type: vanilla
+    real_label_val: 1.0
+    fake_label_val: 0.0
+    loss_weight: !!float 5e-3
+
+  net_d_iters: 1
+  net_d_init_iters: 0
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: false
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# 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

BasicSR/options/train/EDVR/train_EDVR_L_x4_SR_REDS.yml

+# general settings
+name: 104_EDVR_L_x4_SR_REDS_600k_B4G8_valREDS4_103pretrain_wandb
+model_type: EDVRModel
+scale: 4
+num_gpu: 8  # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: REDS
+    type: REDSDataset
+    dataroot_gt: datasets/REDS/train_sharp
+    dataroot_lq: datasets/REDS/train_sharp_bicubic
+    dataroot_flow: ~
+    meta_info_file: basicsr/data/meta_info/meta_info_REDS_GT.txt
+    val_partition: REDS4  # set to 'official' when use the official validation partition
+    io_backend:
+      type: disk
+
+    num_frame: 5
+    gt_size: 256
+    interval_list: [1]
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+
+    # data loader
+    num_worker_per_gpu: 3
+    batch_size_per_gpu: 4
+    dataset_enlarge_ratio: 200
+    prefetch_mode: ~
+
+  val:
+    name: REDS4
+    type: VideoTestDataset
+    dataroot_gt: datasets/REDS/train_sharp
+    dataroot_lq: datasets/REDS/train_sharp_bicubic
+    meta_info_file: basicsr/data/meta_info/meta_info_REDS4_test_GT.txt
+    # change to 'meta_info_REDSofficial4_test_GT' when use the official validation partition
+    io_backend:
+      type: disk
+
+    cache_data: false
+    num_frame: 5
+    padding: reflection_circle
+
+# network structures
+network_g:
+  ema_decay: 0.999
+  type: EDVR
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 128
+  num_frame: 5
+  deformable_groups: 8
+  num_extract_block: 5
+  num_reconstruct_block: 40
+  center_frame_idx: ~
+  hr_in: false
+  with_predeblur: false
+  with_tsa: true
+
+# path
+path:
+  pretrain_network_g: experiments/103_EDVR_L_x4_SR_REDS_woTSA_600k_B4G8_valREDS4_wandb/models/net_g_600000.pth
+  strict_load_g: false
+  resume_state: ~
+
+# training settings
+train:
+  optim_g:
+    type: Adam
+    lr: !!float 4e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: CosineAnnealingRestartLR
+    periods: [50000, 100000, 150000, 150000, 150000]
+    restart_weights: [1, 0.5, 0.5, 0.5, 0.5]
+    eta_min: !!float 1e-7
+
+  total_iter: 600000
+  warmup_iter: -1  # no warm up
+  tsa_iter: 50000
+  dcn_lr_mul: 1
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    loss_weight: 1.0
+    reduction: sum
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: false
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# 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
+
+find_unused_parameters: true

BasicSR/options/train/EDVR/train_EDVR_L_x4_SR_REDS_woTSA.yml

+# general settings
+name: 103_EDVR_L_x4_SR_REDS_woTSA_600k_B4G8_valREDS4_wandb
+model_type: EDVRModel
+scale: 4
+num_gpu: 8  # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: REDS
+    type: REDSDataset
+    dataroot_gt: datasets/REDS/train_sharp
+    dataroot_lq: datasets/REDS/train_sharp_bicubic
+    dataroot_flow: ~
+    meta_info_file: basicsr/data/meta_info/meta_info_REDS_GT.txt
+    val_partition: REDS4  # set to 'official' when use the official validation partition
+    io_backend:
+      type: disk
+
+    num_frame: 5
+    gt_size: 256
+    interval_list: [1]
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+
+    # data loader
+    num_worker_per_gpu: 3
+    batch_size_per_gpu: 4
+    dataset_enlarge_ratio: 200
+    prefetch_mode: ~
+
+  val:
+    name: REDS4
+    type: VideoTestDataset
+    dataroot_gt: datasets/REDS/train_sharp
+    dataroot_lq: datasets/REDS/train_sharp_bicubic
+    meta_info_file: basicsr/data/meta_info/meta_info_REDS4_test_GT.txt
+    # change to 'meta_info_REDSofficial4_test_GT' when use the official validation partition
+    io_backend:
+      type: disk
+
+    cache_data: false
+    num_frame: 5
+    padding: reflection_circle
+
+# network structures
+network_g:
+  ema_decay: 0.999
+  type: EDVR
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 128
+  num_frame: 5
+  deformable_groups: 8
+  num_extract_block: 5
+  num_reconstruct_block: 40
+  center_frame_idx: ~
+  hr_in: false
+  with_predeblur: false
+  with_tsa: false
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  optim_g:
+    type: Adam
+    lr: !!float 4e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: CosineAnnealingRestartLR
+    periods: [150000, 150000, 150000, 150000]
+    restart_weights: [1, 0.5, 0.5, 0.5]
+    eta_min: !!float 1e-7
+
+  total_iter: 600000
+  warmup_iter: -1  # no warm up
+  dcn_lr_mul: 1
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    loss_weight: 1.0
+    reduction: sum
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: false
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# 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

BasicSR/options/train/EDVR/train_EDVR_M_x4_SR_REDS.yml

+# general settings
+name: 102_EDVR_M_x4_SR_REDS_600k_B4G8_valREDS4_101pretrain_wandb
+model_type: EDVRModel
+scale: 4
+num_gpu: 8  # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: REDS
+    type: REDSDataset
+    dataroot_gt: datasets/REDS/train_sharp
+    dataroot_lq: datasets/REDS/train_sharp_bicubic
+    dataroot_flow: ~
+    meta_info_file: basicsr/data/meta_info/meta_info_REDS_GT.txt
+    val_partition: REDS4  # set to 'official' when use the official validation partition
+    io_backend:
+      type: disk
+
+    num_frame: 5
+    gt_size: 256
+    interval_list: [1]
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+
+    # data loader
+    num_worker_per_gpu: 3
+    batch_size_per_gpu: 4
+    dataset_enlarge_ratio: 200
+    prefetch_mode: ~
+
+  val:
+    name: REDS4
+    type: VideoTestDataset
+    dataroot_gt: datasets/REDS/train_sharp
+    dataroot_lq: datasets/REDS/train_sharp_bicubic
+    meta_info_file: basicsr/data/meta_info/meta_info_REDS4_test_GT.txt
+    # change to 'meta_info_REDSofficial4_test_GT' when use the official validation partition
+    io_backend:
+      type: disk
+
+    cache_data: false
+    num_frame: 5
+    padding: reflection_circle
+
+# network structures
+network_g:
+  type: EDVR
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 64
+  num_frame: 5
+  deformable_groups: 8
+  num_extract_block: 5
+  num_reconstruct_block: 10
+  center_frame_idx: ~
+  hr_in: false
+  with_predeblur: false
+  with_tsa: true
+
+# path
+path:
+  pretrain_network_g: experiments/101_EDVR_M_x4_SR_REDS_woTSA_600k_B4G8_valREDS4_wandb/models/net_g_600000.pth
+  strict_load_g: false
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 4e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: CosineAnnealingRestartLR
+    periods: [50000, 100000, 150000, 150000, 150000]
+    restart_weights: [1, 1, 1, 1, 1]
+    eta_min: !!float 1e-7
+
+  total_iter: 600000
+  warmup_iter: -1  # no warm up
+  tsa_iter: 50000
+  dcn_lr_mul: 1
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    loss_weight: 1.0
+    reduction: sum
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: false
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# 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
+
+find_unused_parameters: true