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realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py
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realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py
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_base_ = '../_base_/default_runtime.py'
experiment_name = 'realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds'
work_dir = f'./work_dirs/{experiment_name}'
save_dir = './work_dirs/'
scale = 4
# model settings
model = dict(
type='RealBasicVSR',
generator=dict(
type='RealBasicVSRNet',
mid_channels=64,
num_propagation_blocks=20,
num_cleaning_blocks=20,
dynamic_refine_thres=255, # change to 1.5 for test
spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'
'basicvsr/spynet_20210409-c6c1bd09.pth',
is_fix_cleaning=False,
is_sequential_cleaning=False),
pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),
cleaning_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),
is_use_sharpened_gt_in_pixel=True,
is_use_ema=True,
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='gt', channel_order='rgb'),
dict(type='SetValues', dictionary=dict(scale=scale)),
dict(type='FixedCrop', keys=['gt'], crop_size=(256, 256)),
dict(type='Flip', keys=['gt'], flip_ratio=0.5, direction='horizontal'),
dict(type='Flip', keys=['gt'], flip_ratio=0.5, direction='vertical'),
dict(type='RandomTransposeHW', keys=['gt'], transpose_ratio=0.5),
dict(type='MirrorSequence', keys=['gt']),
dict(
type='UnsharpMasking',
keys=['gt'],
kernel_size=51,
sigma=0,
weight=0.5,
threshold=10),
dict(type='CopyValues', src_keys=['gt_unsharp'], dst_keys=['img']),
dict(
type='RandomBlur',
params=dict(
kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],
kernel_list=[
'iso', 'aniso', 'generalized_iso', 'generalized_aniso',
'plateau_iso', 'plateau_aniso', 'sinc'
],
kernel_prob=[0.405, 0.225, 0.108, 0.027, 0.108, 0.027, 0.1],
sigma_x=[0.2, 3],
sigma_y=[0.2, 3],
rotate_angle=[-3.1416, 3.1416],
beta_gaussian=[0.5, 4],
beta_plateau=[1, 2],
sigma_x_step=0.02,
sigma_y_step=0.02,
rotate_angle_step=0.31416,
beta_gaussian_step=0.05,
beta_plateau_step=0.1,
omega_step=0.0628),
keys=['img'],
),
dict(
type='RandomResize',
params=dict(
resize_mode_prob=[0.2, 0.7, 0.1], # up, down, keep
resize_scale=[0.15, 1.5],
resize_opt=['bilinear', 'area', 'bicubic'],
resize_prob=[1 / 3.0, 1 / 3.0, 1 / 3.0],
resize_step=0.015,
is_size_even=True),
keys=['img'],
),
dict(
type='RandomNoise',
params=dict(
noise_type=['gaussian', 'poisson'],
noise_prob=[0.5, 0.5],
gaussian_sigma=[1, 30],
gaussian_gray_noise_prob=0.4,
poisson_scale=[0.05, 3],
poisson_gray_noise_prob=0.4,
gaussian_sigma_step=0.1,
poisson_scale_step=0.005),
keys=['img'],
),
dict(
type='RandomJPEGCompression',
params=dict(quality=[30, 95], quality_step=3),
keys=['img'],
),
dict(
type='RandomVideoCompression',
params=dict(
codec=['libx264', 'h264', 'mpeg4'],
codec_prob=[1 / 3., 1 / 3., 1 / 3.],
bitrate=[1e4, 1e5]),
keys=['img'],
),
dict(
type='RandomBlur',
params=dict(
prob=0.8,
kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],
kernel_list=[
'iso', 'aniso', 'generalized_iso', 'generalized_aniso',
'plateau_iso', 'plateau_aniso', 'sinc'
],
kernel_prob=[0.405, 0.225, 0.108, 0.027, 0.108, 0.027, 0.1],
sigma_x=[0.2, 1.5],
sigma_y=[0.2, 1.5],
rotate_angle=[-3.1416, 3.1416],
beta_gaussian=[0.5, 4],
beta_plateau=[1, 2],
sigma_x_step=0.02,
sigma_y_step=0.02,
rotate_angle_step=0.31416,
beta_gaussian_step=0.05,
beta_plateau_step=0.1,
omega_step=0.0628),
keys=['img'],
),
dict(
type='RandomResize',
params=dict(
resize_mode_prob=[0.3, 0.4, 0.3], # up, down, keep
resize_scale=[0.3, 1.2],
resize_opt=['bilinear', 'area', 'bicubic'],
resize_prob=[1 / 3., 1 / 3., 1 / 3.],
resize_step=0.03,
is_size_even=True),
keys=['img'],
),
dict(
type='RandomNoise',
params=dict(
noise_type=['gaussian', 'poisson'],
noise_prob=[0.5, 0.5],
gaussian_sigma=[1, 25],
gaussian_gray_noise_prob=0.4,
poisson_scale=[0.05, 2.5],
poisson_gray_noise_prob=0.4,
gaussian_sigma_step=0.1,
poisson_scale_step=0.005),
keys=['img'],
),
dict(
type='RandomJPEGCompression',
params=dict(quality=[30, 95], quality_step=3),
keys=['img'],
),
dict(
type='DegradationsWithShuffle',
degradations=[
dict(
type='RandomVideoCompression',
params=dict(
codec=['libx264', 'h264', 'mpeg4'],
codec_prob=[1 / 3., 1 / 3., 1 / 3.],
bitrate=[1e4, 1e5]),
keys=['img'],
),
[
dict(
type='RandomResize',
params=dict(
target_size=(64, 64),
resize_opt=['bilinear', 'area', 'bicubic'],
resize_prob=[1 / 3., 1 / 3., 1 / 3.]),
),
dict(
type='RandomBlur',
params=dict(
prob=0.8,
kernel_size=[7, 9, 11, 13, 15, 17, 19, 21],
kernel_list=['sinc'],
kernel_prob=[1],
omega=[3.1416 / 3, 3.1416],
omega_step=0.0628),
),
]
],
keys=['img'],
),
dict(type='Clip', keys=['img']),
dict(type='PackInputs')
]
val_pipeline = [
dict(
type='GenerateSegmentIndices',
interval_list=[1],
filename_tmpl='{:04d}.png'),
dict(type='LoadImageFromFile', key='img', channel_order='rgb'),
dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),
dict(type='PackInputs')
]
test_pipeline = [
dict(
type='GenerateSegmentIndices',
interval_list=[1],
filename_tmpl='{:08d}.png'),
dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),
dict(type='LoadImageFromFile', key='img', channel_order='rgb'),
dict(type='PackInputs')
]
demo_pipeline = [
dict(type='GenerateSegmentIndices', interval_list=[1]),
dict(type='LoadImageFromFile', key='img', channel_order='rgb'),
dict(type='PackInputs')
]
data_root = 'data'
train_dataloader = dict(
num_workers=10,
batch_size=2,
persistent_workers=False,
sampler=dict(type='InfiniteSampler', shuffle=True),
dataset=dict(
type='BasicFramesDataset',
metainfo=dict(dataset_type='reds', task_name='vsr'),
data_root=f'{data_root}/REDS',
data_prefix=dict(img='train_sharp_sub', gt='train_sharp_sub'),
depth=1,
num_input_frames=15,
pipeline=train_pipeline))
val_dataloader = dict(
num_workers=1,
batch_size=1,
persistent_workers=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='BasicFramesDataset',
metainfo=dict(dataset_type='udm10', task_name='vsr'),
data_root=f'{data_root}/UDM10',
data_prefix=dict(img='BIx4', gt='GT'),
pipeline=val_pipeline))
test_dataloader = dict(
num_workers=1,
batch_size=1,
persistent_workers=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='BasicFramesDataset',
metainfo=dict(dataset_type='video_lq', task_name='vsr'),
data_root=f'{data_root}/VideoLQ',
data_prefix=dict(img='', gt=''),
pipeline=test_pipeline))
val_evaluator = dict(
type='Evaluator', metrics=[
dict(type='PSNR'),
dict(type='SSIM'),
])
test_evaluator = dict(
type='Evaluator',
metrics=[dict(type='NIQE', input_order='CHW', convert_to='Y')])
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=300_000, val_interval=5000)
val_cfg = dict(type='MultiValLoop')
test_cfg = dict(type='MultiTestLoop')
# optimizer
optim_wrapper = dict(
constructor='MultiOptimWrapperConstructor',
generator=dict(
type='OptimWrapper',
optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))))
# NO learning policy
default_hooks = dict(
checkpoint=dict(
type='CheckpointHook',
interval=5000,
save_optimizer=True,
out_dir=save_dir,
max_keep_ckpts=10,
save_best='PSNR',
rule='greater',
by_epoch=False),
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=100),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
)
custom_hooks = [
dict(type='BasicVisualizationHook', interval=5),
dict(
type='ExponentialMovingAverageHook',
module_keys=('generator_ema'),
interval=1,
interp_cfg=dict(momentum=0.001),
)
]
model_wrapper_cfg = dict(
type='MMSeparateDistributedDataParallel',
broadcast_buffers=False,
find_unused_parameters=False)