VRT acheives 32.56 PSNR on REDS4 dataset, is it correct? #65
Description
Using the configuration file below:
_base_ = [
'../_base_/default_runtime.py',
'../_base_/datasets/basicvsr_test_config.py'
]
experiment_name = 'vrt-pp_c64n7_8xb1-600k_reds4'
work_dir = f'./work_dirs/{experiment_name}'
save_dir = './work_dirs'
scale = 4
# model settings
model = dict(
type='NaiveVSR',
generator=dict(
type='VRTNet',
spynet_path='cache/spynet_sintel_final-3d2a1287.pth'),
pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'),
train_cfg=dict(fix_iter=10000),
data_preprocessor=dict(
type='DataPreprocessor',
mean=[0., 0., 0.],
std=[255., 255., 255.],
))
train_pipeline = [
dict(type='GenerateSegmentIndices', interval_list=[1]),
dict(type='LoadImageFromFile', key='img', channel_order='rgb'),
dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),
dict(type='SetValues', dictionary=dict(scale=scale)),
dict(type='PairedRandomCrop', gt_patch_size=256),
dict(
type='Flip',
keys=['img', 'gt'],
flip_ratio=0.5,
direction='horizontal'),
dict(
type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),
dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),
dict(type='PackInputs')
]
val_pipeline = [
dict(type='GenerateSegmentIndices', interval_list=[1]),
dict(type='LoadImageFromFile', key='img', channel_order='rgb'),
dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),
dict(type='SetValues', dictionary=dict(scale=scale)),
dict(type='PairedRandomCrop', gt_patch_size=256),
dict(type='PackInputs')
]
data_root = '/workspace/mmagic/datasets/REDS'
train_dataloader = dict(
num_workers=15,
batch_size=1,
persistent_workers=False,
sampler=dict(type='InfiniteSampler', shuffle=True),
dataset=dict(
type='BasicFramesDataset',
metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),
data_root=data_root,
data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),
ann_file='meta_info_reds4_train.txt',
depth=1,
num_input_frames=6,
pipeline=train_pipeline))
val_dataloader = dict(
num_workers=6,
batch_size=1,
persistent_workers=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='BasicFramesDataset',
metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),
data_root=data_root,
data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),
ann_file='meta_info_reds4_val.txt',
depth=1,
num_input_frames=6,
fixed_seq_len=50,
pipeline=val_pipeline))
val_evaluator = dict(
type='Evaluator', metrics=[
dict(type='PSNR'),
dict(type='SSIM'),
])
default_hooks = dict(checkpoint=dict(out_dir=save_dir))
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=300_000, val_interval=100)
val_cfg = dict(type='MultiValLoop')
# optimizer
optim_wrapper = dict(
constructor='DefaultOptimWrapperConstructor',
type='OptimWrapper',
optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.99)),
paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)}))
# learning policy
param_scheduler = dict(
type='CosineRestartLR',
by_epoch=False,
periods=[300000],
restart_weights=[1],
eta_min=1e-7)