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train_vsrTransformer_x4_REDS.yml
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train_vsrTransformer_x4_REDS.yml
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# general settings
name: vsrTransformer_x4_reds
model_type: EDVRModel
scale: 4
num_gpu: 8 # set num_gpu: 0 for cpu mode
manual_seed: 10
crop: true
# network structures
network_g:
type: vsrTransformer
image_ch: 3
num_feat: 64
num_frame: 5
num_extract_block: 5
num_reconstruct_block: 30
depth: 5
heads: 1
patch_size: 8
spynet_pretrained: '/cluster/work/cvl/videosr/pretrained_models/spynet/spynet.pth'
# path
path:
pretrain_network_g: experiments/pretrained_models/vsrTransformer_reds_x4/vsrTransformer_reds_x4_warmup.pth
strict_load_g: false #true #
resume_state: ~
root: /cluster/work/cvl/videosr/experiments/
# dataset and data loader settings
datasets:
train:
name: REDS
type: REDSDataset
dataroot_gt: /cluster/work/cvl/videosr/REDS/train_sharp_with_val.lmdb
dataroot_lq: /cluster/work/cvl/videosr/REDS/train_sharp_bicubic_with_val.lmdb
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: lmdb
all_gt: True # False #
num_frame: 5
gt_size: 256
interval_list: [1]
random_reverse: true
use_flip: true
use_rot: true
# data loader
use_shuffle: true
num_worker_per_gpu: 3
batch_size_per_gpu: 4
dataset_enlarge_ratio: 200
prefetch_mode: ~
val:
name: REDS4
type: VideoTestDataset
dataroot_gt: ./train_sharp
dataroot_lq: ./train_sharp_bicubic/X4/
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
# training settings
train:
optim_g:
type: Adam
lr: !!float 2e-4
weight_decay: 0
betas: [0.9, 0.99]
scheduler:
type: CosineAnnealingRestartLR
periods: [300000, 300000, 300000, 300000]
restart_weights: [1, 0.5, 0.5, 0.5]
eta_min: !!float 1e-7
reset_periods: true
total_iter: 1200000
warmup_iter: -1 # no warm up
# 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