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[Feature] Add config of ttsr-gan #398

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Jun 30, 2021
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[Feature] Add config of ttsr-gan #398

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Jun 28, 2021
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[Feature] Add config of ttsr-gan
Jun 28, 2021
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7 changes: 4 additions & 3 deletions configs/restorers/ttsr/README.md
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Expand Up @@ -20,6 +20,7 @@ Evaluated on RGB channels, `scale` pixels in each border are cropped before eval

The metrics are `PSNR / SSIM`.

| Method | scale | CUFED | Download |
| :---------------------------------------------------------------------------------------------: | :---: | :--------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [ttsr-rec_x4_c64b16_g1_200k_CUFED](/configs/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED.py) | x4 | 25.2433 / 0.7491 | [model](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.pth?versionId=CAEQKxiBgIDht5ONzRciIDdjZTQ1NmFmYzhjNjQ5NGFhNjkyNzU1N2UxMjMyZWE4) \| [log](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.log.json?versionId=CAEQKxiCgMCnuJONzRciIDUzNmVkNGNmNTlkMDQzMmFhZDAzYzQ5NmUzNTI5YmYz) |
| Method | scale | CUFED | Download |
| :---------------------------------------------------------------------------------------------: | :---: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [ttsr-rec_x4_c64b16_g1_200k_CUFED](/configs/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED.py) | x4 | 25.2433 / 0.7491 | [model](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.log.json) |
| [ttsr-gan_x4_c64b16_g1_500k_CUFED](/configs/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED.py) | x4 | 24.6075 / 0.7234 | [model](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.log.json) |
274 changes: 274 additions & 0 deletions configs/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED.py
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@@ -0,0 +1,274 @@
exp_name = 'ttsr-gan_x4_c64b16_g1_500k_CUFED'
scale = 4

# model settings
model = dict(
type='TTSR',
generator=dict(
type='TTSRNet',
in_channels=3,
out_channels=3,
mid_channels=64,
num_blocks=(16, 16, 8, 4)),
extractor=dict(type='LTE'),
transformer=dict(type='SearchTransformer'),
discriminator=dict(type='TTSRDiscriminator', in_size=160),
pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),
perceptual_loss=dict(
type='PerceptualLoss',
layer_weights={'29': 1.0},
vgg_type='vgg19',
perceptual_weight=1e-2,
style_weight=0,
criterion='mse'),
transferal_perceptual_loss=dict(
type='TransferalPerceptualLoss',
loss_weight=1e-2,
use_attention=False,
criterion='mse'),
gan_loss=dict(
type='GANLoss',
gan_type='vanilla',
loss_weight=1e-3,
real_label_val=1.0,
fake_label_val=0))
# model training and testing settings
train_cfg = dict(fix_iter=25000, disc_steps=2)
test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale)

# dataset settings
train_dataset_type = 'SRFolderRefDataset'
val_dataset_type = 'SRFolderRefDataset'
test_dataset_type = 'SRFolderRefDataset'
train_pipeline = [
dict(
type='LoadImageFromFile',
io_backend='disk',
key='gt',
flag='color',
channel_order='rgb',
backend='pillow'),
dict(
type='LoadImageFromFile',
io_backend='disk',
key='ref',
flag='color',
channel_order='rgb',
backend='pillow'),
dict(type='CropLike', target_key='ref', reference_key='gt'),
dict(
type='Resize',
scale=1 / scale,
keep_ratio=True,
keys=['gt', 'ref'],
output_keys=['lq', 'ref_down'],
interpolation='bicubic',
backend='pillow'),
dict(
type='Resize',
scale=float(scale),
keep_ratio=True,
keys=['lq', 'ref_down'],
output_keys=['lq_up', 'ref_downup'],
interpolation='bicubic',
backend='pillow'),
dict(
type='Normalize',
keys=['lq', 'gt'],
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5]),
dict(
type='Normalize',
keys=['lq_up', 'ref', 'ref_downup'],
mean=[0., 0., 0.],
std=[255., 255., 255.]),
dict(
type='Flip',
keys=['lq', 'gt', 'lq_up'],
flip_ratio=0.5,
direction='horizontal'),
dict(
type='Flip',
keys=['lq', 'gt', 'lq_up'],
flip_ratio=0.5,
direction='vertical'),
dict(
type='RandomTransposeHW',
keys=['lq', 'gt', 'lq_up'],
transpose_ratio=0.5),
dict(
type='Flip',
keys=['ref', 'ref_downup'],
flip_ratio=0.5,
direction='horizontal'),
dict(
type='Flip',
keys=['ref', 'ref_downup'],
flip_ratio=0.5,
direction='vertical'),
dict(
type='RandomTransposeHW',
keys=['ref', 'ref_downup'],
transpose_ratio=0.5),
dict(
type='ImageToTensor', keys=['lq', 'gt', 'lq_up', 'ref', 'ref_downup']),
dict(
type='Collect',
keys=['lq', 'gt', 'lq_up', 'ref', 'ref_downup'],
meta_keys=['gt_path', 'ref_path'])
]
valid_pipeline = [
dict(
type='LoadImageFromFile',
io_backend='disk',
key='gt',
flag='color',
channel_order='rgb',
backend='pillow'),
dict(
type='LoadImageFromFile',
io_backend='disk',
key='ref',
flag='color',
channel_order='rgb',
backend='pillow'),
dict(type='CropLike', target_key='ref', reference_key='gt'),
dict(
type='Resize',
scale=1 / scale,
keep_ratio=True,
keys=['gt', 'ref'],
output_keys=['lq', 'ref_down'],
interpolation='bicubic',
backend='pillow'),
dict(
type='Resize',
scale=float(scale),
keep_ratio=True,
keys=['lq', 'ref_down'],
output_keys=['lq_up', 'ref_downup'],
interpolation='bicubic',
backend='pillow'),
dict(
type='Normalize',
keys=['lq', 'gt'],
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5]),
dict(
type='Normalize',
keys=['lq_up', 'ref', 'ref_downup'],
mean=[0., 0., 0.],
std=[255., 255., 255.]),
dict(
type='ImageToTensor', keys=['lq', 'gt', 'lq_up', 'ref', 'ref_downup']),
dict(
type='Collect',
keys=['lq', 'gt', 'lq_up', 'ref', 'ref_downup'],
meta_keys=['gt_path', 'ref_path'])
]
test_pipeline = [
dict(
type='LoadImageFromFile',
io_backend='disk',
key='lq',
flag='color',
channel_order='rgb',
backend='pillow'),
dict(
type='LoadImageFromFile',
io_backend='disk',
key='ref',
flag='color',
channel_order='rgb',
backend='pillow'),
dict(
type='Resize',
scale=1 / scale,
keep_ratio=True,
keys=['ref'],
output_keys=['ref_down'],
interpolation='bicubic',
backend='pillow'),
dict(
type='Resize',
scale=float(scale),
keep_ratio=True,
keys=['lq', 'ref_down'],
output_keys=['lq_up', 'ref_downup'],
interpolation='bicubic',
backend='pillow'),
dict(
type='Normalize',
keys=['lq'],
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5]),
dict(
type='Normalize',
keys=['lq_up', 'ref', 'ref_downup'],
mean=[0., 0., 0.],
std=[255., 255., 255.]),
dict(type='ImageToTensor', keys=['lq', 'lq_up', 'ref', 'ref_downup']),
dict(
type='Collect',
keys=['lq', 'lq_up', 'ref', 'ref_downup'],
meta_keys=['lq_path', 'ref_path'])
]

data = dict(
workers_per_gpu=9,
train_dataloader=dict(samples_per_gpu=9, drop_last=True),
val_dataloader=dict(samples_per_gpu=1),
test_dataloader=dict(samples_per_gpu=1),
train=dict(
type='RepeatDataset',
times=52,
dataset=dict(
type=train_dataset_type,
gt_folder='data/CUFED/train/input/',
ref_folder='data/CUFED/train/ref/',
pipeline=train_pipeline,
scale=scale)),
val=dict(
type=val_dataset_type,
gt_folder='data/CUFED/valid/input_format/',
ref_folder='data/CUFED/valid/ref1_format/',
pipeline=valid_pipeline,
scale=scale),
test=dict(
type=test_dataset_type,
gt_folder='data/CUFED/valid/input_format/',
ref_folder='data/CUFED/valid/ref1_format/',
pipeline=valid_pipeline,
scale=scale))

# optimizer
optimizers = dict(
generator=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999)),
discriminator=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999)))

# learning policy
total_iters = 500000
lr_config = dict(
policy='Step',
by_epoch=False,
step=[100000, 200000, 300000, 400000],
gamma=0.5)

checkpoint_config = dict(interval=100, save_optimizer=True, by_epoch=False)
evaluation = dict(interval=5000, save_image=True, 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')
log_level = 'INFO'
work_dir = f'./work_dirs/{exp_name}'
load_from = None
resume_from = None
workflow = [('train', 1)]
find_unused_parameters = True
21 changes: 20 additions & 1 deletion tests/test_models/test_restorers/test_ttsr.py
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Expand Up @@ -72,7 +72,26 @@ def test_ttsr():
num_blocks=(16, 16, 8, 4)),
extractor=dict(type='LTE'),
transformer=dict(type='SearchTransformer'),
pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))
discriminator=dict(type='TTSRDiscriminator', in_size=64),
pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),
perceptual_loss=dict(
type='PerceptualLoss',
layer_weights={'29': 1.0},
vgg_type='vgg19',
perceptual_weight=1e-2,
style_weight=0.001,
criterion='mse'),
transferal_perceptual_loss=dict(
type='TransferalPerceptualLoss',
loss_weight=1e-2,
use_attention=False,
criterion='mse'),
gan_loss=dict(
type='GANLoss',
gan_type='vanilla',
loss_weight=1e-3,
real_label_val=1.0,
fake_label_val=0))

scale = 4
train_cfg = None
Expand Down