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 = (368, 768) sintel_train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='ColorJitter', asymmetric_prob=0.2, 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=(368, 768), min_scale=-0.2, max_scale=0.6, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), dict(type='Validation', max_flow=1000.0), 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' ]) ] sintel_test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), 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', 'filename1', 'filename2', 'ori_filename1', 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', 'scale_factor', 'pad_shape', 'pad' ]) ] sintel_clean_train = dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='ColorJitter', asymmetric_prob=0.2, 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=(368, 768), min_scale=-0.2, max_scale=0.6, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), dict(type='Validation', max_flow=1000.0), 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' ]) ], data_root='data/Sintel', test_mode=False, pass_style='clean') sintel_final_train = dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='ColorJitter', asymmetric_prob=0.2, 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=(368, 768), min_scale=-0.2, max_scale=0.6, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), dict(type='Validation', max_flow=1000.0), 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' ]) ], data_root='data/Sintel', test_mode=False, pass_style='final') sintel_clean_train_x100 = dict( type='RepeatDataset', times=100, dataset=dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='ColorJitter', asymmetric_prob=0.2, 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=(368, 768), min_scale=-0.2, max_scale=0.6, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), dict(type='Validation', max_flow=1000.0), 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' ]) ], data_root='data/Sintel', test_mode=False, pass_style='clean')) sintel_final_train_x100 = dict( type='RepeatDataset', times=100, dataset=dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='ColorJitter', asymmetric_prob=0.2, 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=(368, 768), min_scale=-0.2, max_scale=0.6, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), dict(type='Validation', max_flow=1000.0), 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' ]) ], data_root='data/Sintel', test_mode=False, pass_style='final')) sintel_clean_test = dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), 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', 'filename1', 'filename2', 'ori_filename1', 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', 'scale_factor', 'pad_shape', 'pad' ]) ], data_root='data/Sintel', test_mode=True, pass_style='clean') sintel_final_test = dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), 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', 'filename1', 'filename2', 'ori_filename1', 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', 'scale_factor', 'pad_shape', 'pad' ]) ], data_root='data/Sintel', test_mode=True, pass_style='final') 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=(368, 768), min_scale=-0.3, max_scale=0.5, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), 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=(368, 768), min_scale=-0.3, max_scale=0.5, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), 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_train_x200 = dict( type='RepeatDataset', times=200, dataset=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=(368, 768), min_scale=-0.3, max_scale=0.5, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), 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)) flyingthing3d_train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='ColorJitter', asymmetric_prob=0.2, 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=(368, 768), min_scale=-0.4, max_scale=0.8, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), dict(type='Validation', max_flow=1000.0), 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' ]) ] flyingthings3d_clean_train = dict( type='FlyingThings3D', data_root='data/flyingthings3d', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='ColorJitter', asymmetric_prob=0.2, 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=(368, 768), min_scale=-0.4, max_scale=0.8, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), dict(type='Validation', max_flow=1000.0), 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' ]) ], pass_style='clean', scene='left') hd1k_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=(368, 768), min_scale=-0.5, max_scale=0.2, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), 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' ]) ] hd1k_train = dict( type='HD1K', 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=(368, 768), min_scale=-0.5, max_scale=0.2, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), 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' ]) ], data_root='data/hd1k', test_mode=False) hd1k_train_x5 = dict( type='RepeatDataset', times=5, dataset=dict( type='HD1K', 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=(368, 768), min_scale=-0.5, max_scale=0.2, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), 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' ]) ], data_root='data/hd1k', test_mode=False)) 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=5, shuffle=False), train=[ dict( type='RepeatDataset', times=100, dataset=dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='ColorJitter', asymmetric_prob=0.2, 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=(368, 768), min_scale=-0.2, max_scale=0.6, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), dict(type='Validation', max_flow=1000.0), 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' ]) ], data_root='data/Sintel', test_mode=False, pass_style='clean')), dict( type='RepeatDataset', times=100, dataset=dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='ColorJitter', asymmetric_prob=0.2, 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=(368, 768), min_scale=-0.2, max_scale=0.6, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), dict(type='Validation', max_flow=1000.0), 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' ]) ], data_root='data/Sintel', test_mode=False, pass_style='final')), dict( type='RepeatDataset', times=200, dataset=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=(368, 768), min_scale=-0.3, max_scale=0.5, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), 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)), dict( type='FlyingThings3D', data_root='data/flyingthings3d', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='ColorJitter', asymmetric_prob=0.2, 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=(368, 768), min_scale=-0.4, max_scale=0.8, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), dict(type='Validation', max_flow=1000.0), 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' ]) ], pass_style='clean', scene='left'), dict( type='RepeatDataset', times=5, dataset=dict( type='HD1K', 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=(368, 768), min_scale=-0.5, max_scale=0.2, max_stretch=0.2), dict(type='RandomCrop', crop_size=(368, 768)), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='RandomFlip', prob=0.1, direction='vertical'), 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' ]) ], data_root='data/hd1k', test_mode=False)) ], val=dict( type='ConcatDataset', datasets=[ dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), 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', 'filename1', 'filename2', 'ori_filename1', 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', 'scale_factor', 'pad_shape', 'pad' ]) ], data_root='data/Sintel', test_mode=True, pass_style='clean'), dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), 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', 'filename1', 'filename2', 'ori_filename1', 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', 'scale_factor', 'pad_shape', 'pad' ]) ], data_root='data/Sintel', test_mode=True, pass_style='final') ], separate_eval=True), test=dict( type='ConcatDataset', datasets=[ dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), 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', 'filename1', 'filename2', 'ori_filename1', 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', 'scale_factor', 'pad_shape', 'pad' ]) ], data_root='data/Sintel', test_mode=True, pass_style='clean'), dict( type='Sintel', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), 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', 'filename1', 'filename2', 'ori_filename1', 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', 'scale_factor', 'pad_shape', 'pad' ]) ], data_root='data/Sintel', test_mode=True, pass_style='final') ], separate_eval=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/1228_raft_syncbn_things3d/iter_90000.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=100100, pct_start=0.05, anneal_strategy='linear') runner = dict(type='IterBasedRunner', max_iters=100000) checkpoint_config = dict(by_epoch=False, interval=10000) evaluation = dict(interval=10000, metric='EPE') work_dir = 'work_dir/raft/1229_raft_8x2_mix_sync' gpu_ids = range(0, 1)