[Feature] Config of ttsr.

configs/restorers/ttsr/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', 'lq_up', 'ref', 'ref_downup'],
+        mean=[127.5, 127.5, 127.5],
+        std=[127.5, 127.5, 127.5]),
+    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', 'lq_up', 'ref', 'ref_downup'],
+        mean=[127.5, 127.5, 127.5],
+        std=[127.5, 127.5, 127.5]),
+    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'])
+]
+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', 'lq_up', 'ref', 'ref_downup'],
+        mean=[127.5, 127.5, 127.5],
+        std=[127.5, 127.5, 127.5]),
+    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