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[Feature] Configuration file of GLEAN for blind face image restoration #530

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Sep 16, 2021
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[Feature] Configuration file of GLEAN for blind face image restoration #530

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255f0a1
config and readme
ckkelvinchan Sep 12, 2021
66526d3
rename
ckkelvinchan Sep 12, 2021
ebf72fa
add demo_pipeline
ckkelvinchan Sep 12, 2021
81308aa
Merge branch 'master' of https://github.com/open-mmlab/mmediting into…
ckkelvinchan Sep 16, 2021
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11 changes: 6 additions & 5 deletions configs/restorers/glean/README.md
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For the meta info used in training and test, please refer to [here](https://github.com/ckkelvinchan/GLEAN). The results are evaluated on RGB channels.

| Method | PSNR | Download |
| :----------------------------------------------------------: | :---: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [glean_cat_8x](/configs/restorers/glean/glean_cat_8x.py) | 23.98 | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614_145540.log.json) |
| [glean_ffhq_16x](/configs/restorers/glean/glean_ffhq_16x.py) | 26.91 | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527-61a3afad.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527_194536.log.json) |
| [glean_cat_16x](/configs/restorers/glean/glean_cat_16x.py) | 20.88 | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527-68912543.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527_103708.log.json) |
| Method | PSNR | Download |
|--------------------------------------------------------------------------------------------------------------------|-------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [glean_cat_8x](/configs/restorers/glean/glean_cat_8x.py) | 23.98 | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614_145540.log.json) |
| [glean_ffhq_16x](/configs/restorers/glean/glean_ffhq_16x.py) | 26.91 | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527-61a3afad.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527_194536.log.json) |
| [glean_cat_16x](/configs/restorers/glean/glean_cat_16x.py) | 20.88 | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527-68912543.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527_103708.log.json) |
| [glean_in128out1024_4x2_300k_ffhq_celebahq](/configs/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq.py) | 27.94 | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812-acbcb04f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812_100549.log.json) |
213 changes: 213 additions & 0 deletions configs/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq.py
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exp_name = 'glean_in128out1024_4x2_300k_ffhq_celebahq'

scale = 8
# model settings
model = dict(
type='GLEAN',
generator=dict(
type='GLEANStyleGANv2',
in_size=128,
out_size=1024,
style_channels=512,
pretrained=dict(
ckpt_path='http://download.openmmlab.com/mmgen/stylegan2/'
'official_weights/stylegan2-ffhq-config-f-official_20210327'
'_171224-bce9310c.pth',
prefix='generator_ema')),
discriminator=dict(
type='StyleGAN2Discriminator',
in_size=1024,
pretrained=dict(
ckpt_path='http://download.openmmlab.com/mmgen/stylegan2/'
'official_weights/stylegan2-ffhq-config-f-official_20210327'
'_171224-bce9310c.pth',
prefix='discriminator')),
pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean'),
perceptual_loss=dict(
type='PerceptualLoss',
layer_weights={'21': 1.0},
vgg_type='vgg16',
perceptual_weight=1e-2,
style_weight=0,
norm_img=True,
criterion='mse',
pretrained='torchvision://vgg16'),
gan_loss=dict(
type='GANLoss',
gan_type='vanilla',
loss_weight=1e-2,
real_label_val=1.0,
fake_label_val=0),
pretrained=None,
)

# model training and testing settings
train_cfg = None
test_cfg = dict(metrics=['PSNR'], crop_border=0)

# dataset settings
train_dataset_type = 'SRFolderDataset'
val_dataset_type = 'SRAnnotationDataset'
train_pipeline = [
dict(
type='LoadImageFromFile',
io_backend='disk',
key='gt',
channel_order='rgb'),
dict(type='RescaleToZeroOne', keys=['gt']),
dict(type='CopyValues', src_keys=['gt'], dst_keys=['lq']),
dict(
type='RandomBlur',
params=dict(
kernel_size=[41],
kernel_list=['iso', 'aniso'],
kernel_prob=[0.5, 0.5],
sigma_x=[0.2, 10],
sigma_y=[0.2, 10],
rotate_angle=[-3.1416, 3.1416],
),
keys=['lq'],
),
dict(
type='RandomResize',
params=dict(
resize_mode_prob=[0, 1, 0], # up, down, keep
resize_scale=[0.03125, 1],
resize_opt=['bilinear', 'area', 'bicubic'],
resize_prob=[1 / 3., 1 / 3., 1 / 3.]),
keys=['lq'],
),
dict(
type='RandomNoise',
params=dict(
noise_type=['gaussian'],
noise_prob=[1],
gaussian_sigma=[0, 50],
gaussian_gray_noise_prob=0),
keys=['lq'],
),
dict(
type='RandomJPEGCompression',
params=dict(quality=[5, 50]),
keys=['lq']),
dict(
type='RandomResize',
params=dict(
target_size=[1024, 1024],
resize_opt=['bilinear', 'area', 'bicubic'],
resize_prob=[1 / 3., 1 / 3., 1 / 3.]),
keys=['lq'],
),
dict(type='Quantize', keys=['lq']),
dict(
type='RandomResize',
params=dict(
target_size=[128, 128], resize_opt=['area'], resize_prob=[1]),
keys=['lq'],
),
dict(
type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,
direction='horizontal'),
dict(
type='Normalize',
keys=['lq', 'gt'],
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5],
),
dict(type='ImageToTensor', keys=['lq', 'gt']),
dict(type='Collect', keys=['lq', 'gt'], meta_keys=['gt_path'])
]

test_pipeline = [
dict(type='LoadImageFromFile', io_backend='disk', key='lq'),
dict(type='LoadImageFromFile', io_backend='disk', key='gt'),
dict(type='RescaleToZeroOne', keys=['lq', 'gt']),
dict(
type='Normalize',
keys=['lq', 'gt'],
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5],
to_rgb=True),
dict(type='ImageToTensor', keys=['lq', 'gt']),
dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'lq_path'])
]

demo_pipeline = [
dict(
type='RandomResize',
params=dict(
target_size=[128, 128], resize_opt=['area'], resize_prob=[1]),
keys=['lq'],
),
dict(type='RescaleToZeroOne', keys=['lq']),
dict(
type='Normalize',
keys=['lq'],
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5],
to_rgb=True),
dict(type='ImageToTensor', keys=['lq']),
dict(type='Collect', keys=['lq'], meta_keys=[])
]

data = dict(
workers_per_gpu=6,
train_dataloader=dict(samples_per_gpu=2, drop_last=True), # 4 gpus
val_dataloader=dict(samples_per_gpu=1, persistent_workers=False),
test_dataloader=dict(samples_per_gpu=1),
train=dict(
type='RepeatDataset',
times=30,
dataset=dict(
type=train_dataset_type,
lq_folder='data/FFHQ_CelebAHQ/GT',
gt_folder='data/FFHQ_CelebAHQ/GT',
pipeline=train_pipeline,
scale=scale)),
val=dict(
type=val_dataset_type,
lq_folder='data/CelebA-HQ/BIx8_down',
gt_folder='data/CelebA-HQ/GT',
ann_file='data/CelebA-HQ/meta_info_CelebAHQ_val100_GT.txt',
pipeline=test_pipeline,
scale=scale),
test=dict(
type=val_dataset_type,
lq_folder='data/CelebA-HQ/BIx8_down',
gt_folder='data/CelebA-HQ/GT',
ann_file='data/CelebA-HQ/meta_info_CelebAHQ_val100_GT.txt',
pipeline=test_pipeline,
scale=scale))

# optimizer
optimizers = dict(
generator=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99)),
discriminator=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99)))

# learning policy
total_iters = 300000
lr_config = dict(
policy='CosineRestart',
by_epoch=False,
periods=[300000],
restart_weights=[1],
min_lr=1e-7)

checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)
evaluation = dict(interval=5000, save_image=False, gpu_collect=True)
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
# dict(type='TensorboardLoggerHook'),
])
visual_config = None

# runtime settings
dist_params = dict(backend='nccl', port=29501)
log_level = 'INFO'
work_dir = f'./work_dirs/{exp_name}'
load_from = None
resume_from = None
workflow = [('train', 1)]
find_unused_parameters = True