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option_R3_mfqev2_4G.yml
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dataset:
train: # LMDB
type: MFQEv2Dataset
# for lmdb
root: /raid/xql/datasets/MFQEv2_dataset
gt_folder: train_108/raw/
lq_folder: train_108/HM16.5_LDP/QP37/
# for dataset
gt_path: mfqev2_train_gt.lmdb
lq_path: mfqev2_train_lq.lmdb
meta_info_fp: meta_info.txt
gt_size: 128 # ground truth patch size: gt_size * gt_size
use_flip: True
use_rot: True # rotation per 90 degrees
random_reverse: False
# for datasampler
enlarge_ratio: 300 # enlarge dataset by randomly cropping.
# for dataloader
num_worker_per_gpu: 3 # 12 in total. mainly affect IO speed
batch_size_per_gpu: 8 # bs=32, divided by 4 GPUs
val: # Disk IO
type: VideoTestMFQEv2Dataset
#root: /media/x/Database/MFQEv2/
gt_path: test_18/raw/
lq_path: test_18/HM16.5_LDP/QP37/
#meta_info_fp: meta_info.txt
#enlarge_ratio: 1
#use_flip: False
#use_rot: False
#random_reverse: False
test:
type: VideoTestMFQEv2Dataset
gt_path: test_18/raw/
lq_path: test_18/HM16.5_LDP/QP37/
network:
radius: 3 # total num of input frame = 2 * radius + 1
stdf:
in_nc: 1 # 1 for Y
out_nc: 64
nf: 32 # num of feature maps
nb: 3 # num of conv layers
base_ks: 3
deform_ks: 3 # size of the deformable kernel
qenet:
in_nc: 64 # = out_nc of stdf
out_nc: 1 # 1 for Y
nf: 48
nb: 8
base_ks: 3
train:
exp_name: MFQEv2_R3_enlarge300x # default: timestr. None: ~
random_seed: 7
pre-val: True # evaluate criterion before training, e.g., ori PSNR
num_iter: !!float 3e+5
interval_print: !!float 100
interval_val: !!float 5e+3 # also save model
pbar_len: 100
optim:
type: Adam
lr: !!float 1e-4 # init lr of scheduler
betas: [0.9, 0.999]
eps: !!float 1e-08
scheduler:
is_on: False
type: CosineAnnealingRestartLR
periods: [!!float 5e+4, !!float 5e+4, !!float 5e+4, !!float 5e+4, !!float 5e+4, !!float 5e+4] # epoch interval
restart_weights: [1, 0.5, 0.5, 0.5, 0.5, 0.5]
eta_min: !!float 1e-7
loss:
type: CharbonnierLoss
eps: !!float 1e-6
criterion:
type: PSNR
unit: dB
test:
restore_iter: !!float 290000
pbar_len: 100
criterion:
type: PSNR
unit: dB