model = dict(
    type='RAFT',
    num_levels=4,
    radius=4,
    cxt_channels=128,
    h_channels=128,
    encoder=dict(
        type='RAFTEncoder',
        in_channels=3,
        out_channels=256,
        net_type='Basic',
        norm_cfg=dict(type='IN'),
        init_cfg=[
            dict(
                type='Kaiming',
                layer=['Conv2d'],
                mode='fan_out',
                nonlinearity='relu'),
            dict(type='Constant', layer=['InstanceNorm2d'], val=1, bias=0)
        ]),
    cxt_encoder=dict(
        type='RAFTEncoder',
        in_channels=3,
        out_channels=256,
        net_type='Basic',
        norm_cfg=dict(type='SyncBN'),
        init_cfg=[
            dict(
                type='Kaiming',
                layer=['Conv2d'],
                mode='fan_out',
                nonlinearity='relu'),
            dict(type='Constant', layer=['SyncBatchNorm2d'], val=1, bias=0)
        ]),
    decoder=dict(
        type='RAFTDecoder',
        net_type='Basic',
        num_levels=4,
        radius=4,
        iters=12,
        corr_op_cfg=dict(type='CorrLookup', align_corners=True),
        gru_type='SeqConv',
        flow_loss=dict(type='SequenceLoss', gamma=0.85),
        act_cfg=dict(type='ReLU')),
    freeze_bn=True,
    train_cfg=dict(),
    test_cfg=dict(iters=32))
img_norm_cfg = dict(
    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=False)
crop_size = (288, 960)
kitti_train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', sparse=True),
    dict(
        type='ColorJitter',
        asymmetric_prob=0.0,
        brightness=0.4,
        contrast=0.4,
        saturation=0.4,
        hue=0.1592356687898089),
    dict(type='Erase', prob=0.5, bounds=[50, 100], max_num=3),
    dict(
        type='SpacialTransform',
        spacial_prob=0.8,
        stretch_prob=0.8,
        crop_size=(288, 960),
        min_scale=-0.2,
        max_scale=0.4,
        max_stretch=0.2),
    dict(type='RandomCrop', crop_size=(288, 960)),
    dict(
        type='Normalize',
        mean=[127.5, 127.5, 127.5],
        std=[127.5, 127.5, 127.5],
        to_rgb=False),
    dict(type='DefaultFormatBundle'),
    dict(
        type='Collect',
        keys=['imgs', 'flow_gt', 'valid'],
        meta_keys=[
            'filename1', 'filename2', 'ori_filename1', 'ori_filename2',
            'filename_flow', 'ori_filename_flow', 'ori_shape', 'img_shape',
            'erase_bounds', 'erase_num', 'scale_factor'
        ])
]
kitti_train = dict(
    type='KITTI2015',
    data_root='data/kitti2015',
    pipeline=[
        dict(type='LoadImageFromFile'),
        dict(type='LoadAnnotations', sparse=True),
        dict(
            type='ColorJitter',
            asymmetric_prob=0.0,
            brightness=0.4,
            contrast=0.4,
            saturation=0.4,
            hue=0.1592356687898089),
        dict(type='Erase', prob=0.5, bounds=[50, 100], max_num=3),
        dict(
            type='SpacialTransform',
            spacial_prob=0.8,
            stretch_prob=0.8,
            crop_size=(288, 960),
            min_scale=-0.2,
            max_scale=0.4,
            max_stretch=0.2),
        dict(type='RandomCrop', crop_size=(288, 960)),
        dict(
            type='Normalize',
            mean=[127.5, 127.5, 127.5],
            std=[127.5, 127.5, 127.5],
            to_rgb=False),
        dict(type='DefaultFormatBundle'),
        dict(
            type='Collect',
            keys=['imgs', 'flow_gt', 'valid'],
            meta_keys=[
                'filename1', 'filename2', 'ori_filename1', 'ori_filename2',
                'filename_flow', 'ori_filename_flow', 'ori_shape', 'img_shape',
                'erase_bounds', 'erase_num', 'scale_factor'
            ])
    ],
    test_mode=False)
kitti_test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', sparse=True),
    dict(type='InputPad', exponent=3),
    dict(
        type='Normalize',
        mean=[127.5, 127.5, 127.5],
        std=[127.5, 127.5, 127.5],
        to_rgb=False),
    dict(type='TestFormatBundle'),
    dict(
        type='Collect',
        keys=['imgs'],
        meta_keys=[
            'flow_gt', 'valid', 'filename1', 'filename2', 'ori_filename1',
            'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg',
            'scale_factor', 'pad_shape', 'pad'
        ])
]
kitti2015_val_test = dict(
    type='KITTI2015',
    data_root='data/kitti2015',
    pipeline=[
        dict(type='LoadImageFromFile'),
        dict(type='LoadAnnotations', sparse=True),
        dict(type='InputPad', exponent=3),
        dict(
            type='Normalize',
            mean=[127.5, 127.5, 127.5],
            std=[127.5, 127.5, 127.5],
            to_rgb=False),
        dict(type='TestFormatBundle'),
        dict(
            type='Collect',
            keys=['imgs'],
            meta_keys=[
                'flow_gt', 'valid', 'filename1', 'filename2', 'ori_filename1',
                'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg',
                'scale_factor', 'pad_shape', 'pad'
            ])
    ],
    test_mode=True)
data = dict(
    train_dataloader=dict(
        samples_per_gpu=2,
        workers_per_gpu=5,
        drop_last=True,
        shuffle=True,
        persistent_workers=True),
    val_dataloader=dict(
        samples_per_gpu=1,
        workers_per_gpu=5,
        shuffle=False,
        persistent_workers=True),
    test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=2, shuffle=False),
    train=dict(
        type='KITTI2015',
        data_root='data/kitti2015',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', sparse=True),
            dict(
                type='ColorJitter',
                asymmetric_prob=0.0,
                brightness=0.4,
                contrast=0.4,
                saturation=0.4,
                hue=0.1592356687898089),
            dict(type='Erase', prob=0.5, bounds=[50, 100], max_num=3),
            dict(
                type='SpacialTransform',
                spacial_prob=0.8,
                stretch_prob=0.8,
                crop_size=(288, 960),
                min_scale=-0.2,
                max_scale=0.4,
                max_stretch=0.2),
            dict(type='RandomCrop', crop_size=(288, 960)),
            dict(
                type='Normalize',
                mean=[127.5, 127.5, 127.5],
                std=[127.5, 127.5, 127.5],
                to_rgb=False),
            dict(type='DefaultFormatBundle'),
            dict(
                type='Collect',
                keys=['imgs', 'flow_gt', 'valid'],
                meta_keys=[
                    'filename1', 'filename2', 'ori_filename1', 'ori_filename2',
                    'filename_flow', 'ori_filename_flow', 'ori_shape',
                    'img_shape', 'erase_bounds', 'erase_num', 'scale_factor'
                ])
        ],
        test_mode=False),
    val=dict(
        type='KITTI2015',
        data_root='data/kitti2015',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', sparse=True),
            dict(type='InputPad', exponent=3),
            dict(
                type='Normalize',
                mean=[127.5, 127.5, 127.5],
                std=[127.5, 127.5, 127.5],
                to_rgb=False),
            dict(type='TestFormatBundle'),
            dict(
                type='Collect',
                keys=['imgs'],
                meta_keys=[
                    'flow_gt', 'valid', 'filename1', 'filename2',
                    'ori_filename1', 'ori_filename2', 'ori_shape', 'img_shape',
                    'img_norm_cfg', 'scale_factor', 'pad_shape', 'pad'
                ])
        ],
        test_mode=True),
    test=dict(
        type='KITTI2015',
        data_root='data/kitti2015',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', sparse=True),
            dict(type='InputPad', exponent=3),
            dict(
                type='Normalize',
                mean=[127.5, 127.5, 127.5],
                std=[127.5, 127.5, 127.5],
                to_rgb=False),
            dict(type='TestFormatBundle'),
            dict(
                type='Collect',
                keys=['imgs'],
                meta_keys=[
                    'flow_gt', 'valid', 'filename1', 'filename2',
                    'ori_filename1', 'ori_filename2', 'ori_shape', 'img_shape',
                    'img_norm_cfg', 'scale_factor', 'pad_shape', 'pad'
                ])
        ],
        test_mode=True))
log_config = dict(
    interval=50,
    hooks=[dict(type='TextLoggerHook'),
           dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'work_dir/raft/1229_raft_8x2_mix_sync/latest.pth'
resume_from = None
workflow = [('train', 1)]
optimizer = dict(
    type='AdamW',
    lr=0.000125,
    betas=(0.9, 0.999),
    eps=1e-08,
    weight_decay=1e-05,
    amsgrad=False)
optimizer_config = dict(grad_clip=dict(max_norm=1.0))
lr_config = dict(
    policy='OneCycle',
    max_lr=0.000125,
    total_steps=50100,
    pct_start=0.05,
    anneal_strategy='linear')
runner = dict(type='IterBasedRunner', max_iters=50000)
checkpoint_config = dict(by_epoch=False, interval=5000)
evaluation = dict(interval=5000, metric='EPE')
work_dir = 'work_dir/raft/0104_raft_kitti8x2_50k_syncbn'
gpu_ids = range(0, 1)