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ttsr-rec_x4_c64b16_g1_200k_CUFED.py
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exp_name = 'ttsr-rec_x4_c64b16_g1_200k_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'),
pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))
# model training and testing settings
train_cfg = None
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(type='Adam', lr=1.e-4)
# learning policy
total_iters = 200000
lr_config = dict(policy='Step', by_epoch=False, step=[100000], gamma=0.5)
checkpoint_config = dict(interval=5000, 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