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realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py
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realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py
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_base_ = '../_base_/default_runtime.py'
experiment_name = 'realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost'
work_dir = f'./work_dirs/{experiment_name}'
save_dir = './work_dirs/'
scale = 4
gt_crop_size = 400
# DistributedDataParallel
model_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')
# model settings
model = dict(
type='RealESRGAN',
generator=dict(
type='RRDBNet',
in_channels=3,
out_channels=3,
mid_channels=64,
num_blocks=23,
growth_channels=32,
upscale_factor=scale),
pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),
is_use_sharpened_gt_in_pixel=True,
is_use_ema=True,
train_cfg=dict(),
test_cfg=dict(),
data_preprocessor=dict(
type='DataPreprocessor',
mean=[0., 0., 0.],
std=[255., 255., 255.],
))
train_pipeline = [
dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),
dict(type='SetValues', dictionary=dict(scale=scale)),
dict(
type='Crop',
keys=['gt'],
crop_size=(gt_crop_size, gt_crop_size),
random_crop=True),
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]),
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]),
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),
keys=['img'],
),
dict(
type='RandomJPEGCompression',
params=dict(quality=[30, 95]),
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]),
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 / 3.0, 1 / 3.0, 1 / 3.0]),
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),
keys=['img'],
),
dict(
type='DegradationsWithShuffle',
degradations=[
dict(
type='RandomJPEGCompression',
params=dict(quality=[5, 50]),
),
[
dict(
type='RandomResize',
params=dict(
target_size=(gt_crop_size // scale,
gt_crop_size // scale),
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]),
),
]
],
keys=['img'],
),
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='PairedRandomCrop', gt_patch_size=256),
dict(type='Clip', keys=['img']),
dict(
type='UnsharpMasking',
keys=['gt'],
kernel_size=51,
sigma=0,
weight=0.5,
threshold=10),
dict(type='PackInputs')
]
val_pipeline = [
dict(type='LoadImageFromFile', key='img', channel_order='rgb'),
dict(type='LoadImageFromFile', key='gt', channel_order='rgb'),
dict(type='PackInputs')
]
# dataset settings
dataset_type = 'BasicImageDataset'
train_dataloader = dict(
num_workers=12,
batch_size=12, # gpus 4
persistent_workers=False,
sampler=dict(type='InfiniteSampler', shuffle=True),
dataset=dict(
type=dataset_type,
metainfo=dict(dataset_type='df2k_ost', task_name='real_sr'),
data_root='data/df2k_ost',
data_prefix=dict(gt='GT_sub', img='GT_sub'),
ann_file='meta_info_df2k_ost.txt',
pipeline=train_pipeline))
val_dataloader = dict(
num_workers=4,
persistent_workers=False,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
metainfo=dict(dataset_type='set5', task_name='real_sr'),
data_root='data/Set5',
data_prefix=dict(gt='GTmod12', img='LRbicx4'),
pipeline=val_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(
type='Evaluator', metrics=[
dict(type='PSNR'),
dict(type='SSIM'),
])
test_evaluator = val_evaluator
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=1_000_000, val_interval=2000)
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=2e-4, betas=(0.9, 0.99))))
# NO learning policy
default_hooks = dict(
checkpoint=dict(
type='CheckpointHook',
interval=5000,
save_optimizer=True,
by_epoch=False,
out_dir=save_dir,
),
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=100),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
)
# custom hook
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='ConcatImageVisualizer',
vis_backends=vis_backends,
fn_key='gt_path',
img_keys=['gt_img', 'input', 'pred_img'],
bgr2rgb=False)
custom_hooks = [
dict(type='BasicVisualizationHook', interval=1),
dict(
type='ExponentialMovingAverageHook',
module_keys=('generator_ema'),
interval=1,
interp_cfg=dict(momentum=0.001),
)
]