[Feature] Add config file of FLAVR

configs/video_interpolators/flavr/flavr_8xb4_vimeo7frames.py

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+exp_name = 'flavr_8xb4_vimeo7frames'
+
+# model settings
+model = dict(
+    type='BasicInterpolator',
+    generator=dict(type='FLAVRNet', input_frames=4, output_frames=1),
+    pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),
+    selected_metrics=['PSNR', 'SSIM', 'MAE'])
+# model training and testing settings
+train_cfg = None
+test_cfg = dict(metrics=['PSNR', 'SSIM', 'MAE'], crop_border=0)
+
+# dataset settings
+train_dataset_type = 'VFIVimeo90K7FramesDataset'
+val_dataset_type = 'VFIVimeo90K7FramesDataset'
+
+train_pipeline = [
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='inputs',
+        channel_order='rgb',
+        backend='pillow'),
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='target',
+        channel_order='rgb',
+        backend='pillow'),
+    dict(type='FixedCrop', keys=['inputs', 'target'], crop_size=(256, 256)),
+    dict(
+        type='Flip',
+        keys=['inputs', 'target'],
+        flip_ratio=0.5,
+        direction='horizontal'),
+    dict(
+        type='Flip',
+        keys=['inputs', 'target'],
+        flip_ratio=0.5,
+        direction='vertical'),
+    dict(
+        type='ColorJitter',
+        keys=['inputs', 'target'],
+        channel_order='rgb',
+        brightness=0.05,
+        contrast=0.05,
+        saturation=0.05,
+        hue=0.05),
+    dict(type='TemporalReverse', keys=['inputs'], reverse_ratio=0.5),
+    dict(type='RescaleToZeroOne', keys=['inputs', 'target']),
+    dict(type='FramesToTensor', keys=['inputs', 'target']),
+    dict(
+        type='Collect',
+        keys=['inputs', 'target'],
+        meta_keys=['inputs_path', 'target_path', 'key'])
+]
+
+valid_pipeline = [
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='inputs',
+        channel_order='rgb',
+        backend='pillow'),
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='target',
+        channel_order='rgb',
+        backend='pillow'),
+    dict(type='RescaleToZeroOne', keys=['inputs', 'target']),
+    dict(type='FramesToTensor', keys=['inputs', 'target']),
+    dict(
+        type='Collect',
+        keys=['inputs', 'target'],
+        meta_keys=['inputs_path', 'target_path', 'key'])
+]
+
+demo_pipeline = [
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='inputs',
+        channel_order='rgb',
+        backend='pillow'),
+    dict(type='RescaleToZeroOne', keys=['inputs']),
+    dict(type='FramesToTensor', keys=['inputs']),
+    dict(type='Collect', keys=['inputs'], meta_keys=['inputs_path', 'key'])
+]
+
+root_dir = 'data/vimeo90k'
+data = dict(
+    workers_per_gpu=16,
+    train_dataloader=dict(samples_per_gpu=4),  # 8 gpu
+    val_dataloader=dict(samples_per_gpu=1),
+    test_dataloader=dict(samples_per_gpu=1),
+
+    # train
+    train=dict(
+        type=train_dataset_type,
+        folder=f'{root_dir}/GT',
+        ann_file=f'{root_dir}/sep_trainlist.txt',
+        pipeline=train_pipeline,
+        input_frames=[1, 3, 5, 7],
+        target_frames=[4],
+        test_mode=False),
+    # val
+    val=dict(
+        type=train_dataset_type,
+        folder=f'{root_dir}/GT',
+        ann_file=f'{root_dir}/sep_testlist.txt',
+        pipeline=valid_pipeline,
+        input_frames=[1, 3, 5, 7],
+        target_frames=[4],
+        test_mode=True),
+    # test
+    test=dict(
+        type=train_dataset_type,
+        folder=f'{root_dir}/GT',
+        ann_file=f'{root_dir}/sep_testlist.txt',
+        pipeline=valid_pipeline,
+        input_frames=[1, 3, 5, 7],
+        target_frames=[4],
+        test_mode=True),
+)
+
+# optimizer
+optimizers = dict(generator=dict(type='Adam', lr=2e-4, betas=(0.9, 0.99)))
+
+# learning policy
+total_iters = 1000000  # >=200*64612/64
+lr_config = dict(
+    policy='Reduce',
+    by_epoch=False,
+    mode='max',
+    val_metric='PSNR',
+    epoch_base_valid=True,  # Support epoch base valid in iter base runner.
+    factor=0.5,
+    patience=10,
+    cooldown=20,
+    verbose=True)
+
+checkpoint_config = dict(interval=2020, save_optimizer=True, by_epoch=False)
+
+evaluation = dict(interval=2020, save_image=False, gpu_collect=True)
+log_config = dict(
+    interval=100,
+    hooks=[
+        dict(type='TextLoggerHook', by_epoch=False),
+        dict(
+            type='TensorboardLoggerHook',
+            log_dir=f'work_dirs/{exp_name}/tb_log/',
+            interval=100,
+            ignore_last=False,
+            reset_flag=False,
+            by_epoch=False),
+    ])
+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)]