dataset_type = 'C2SFloodDataset' default_hooks = dict( checkpoint=dict( by_epoch=False, interval=2000, max_keep_ckpts=2, save_best='mIoU', type='CheckpointHook'), logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), sampler_seed=dict(type='DistSamplerSeedHook'), timer=dict(type='IterTimerHook'), visualization=dict(type='SegVisualizationHook')) default_scope = 'mmseg' env_cfg = dict( cudnn_benchmark=True, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) launcher = 'pytorch' load_from = None log_level = 'INFO' log_processor = dict(by_epoch=False) model = dict( auxiliary_head=dict( align_corners=False, channels=256, concat_input=False, dropout_ratio=0.1, in_channels=512, in_index=3, loss_decode=dict( loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), norm_cfg=dict(requires_grad=True, type='SyncBN'), num_classes=4, num_convs=1, type='FCNHead'), backbone=dict( act_cfg=dict(type='GELU'), attn_drop_rate=0.0, downscale_indices=( 5, 11, ), drop_path_rate=0.0, drop_rate=0.1, embed_dims=1024, img_size=( 256, 256, ), in_channels=10, interpolate_mode='bilinear', mlp_ratio=4, norm_cfg=dict(eps=1e-06, requires_grad=True, type='LN'), norm_eval=False, num_heads=16, num_layers=24, out_indices=( 5, 11, 17, 23, ), patch_size=4, qkv_bias=True, type='VisionTransformer', with_cls_token=False), data_preprocessor=dict( bgr_to_rgb=True, mean=[ 1381.26, 1302.05, 1179.27, 1393.56, 2164.76, 2561.75, 2377.94, 2791.13, 1998.09, 1196.08, ], pad_val=0, seg_pad_val=255, size=( 256, 256, ), std=[ 653.25, 659.61, 779.58, 720.45, 871.09, 1035.57, 965.36, 1141.71, 1019.73, 825.01, ], type='SegDataPreProcessor'), decode_head=dict( align_corners=False, channels=512, dropout_ratio=0.1, in_channels=[ 512, 512, 512, 512, ], in_index=[ 0, 1, 2, 3, ], loss_decode=dict( loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), norm_cfg=dict(requires_grad=True, type='SyncBN'), num_classes=4, pool_scales=( 1, 2, 3, 6, ), type='UPerHead'), neck=dict( in_channels=[ 1024, 1024, 1024, 1024, ], out_channels=512, scales=[ 1, 1, 1, 1, ], type='MultiLevelNeck'), pretrained= 'pretrain/skysensepp_mmcvt_s2.pth', test_cfg=dict(mode='whole'), train_cfg=dict(), type='EncoderDecoder') norm_cfg = None optim_wrapper = dict( constructor='LearningRateDecayOptimizerConstructor', optimizer=dict( betas=( 0.9, 0.999, ), lr=0.0005, type='AdamW', weight_decay=0.015), paramwise_cfg=dict( custom_keys=dict( cls_token=dict(decay_mult=0.0), norm=dict(decay_mult=0.0), pos_embed=dict(decay_mult=0.0)), decay_rate=0.84, decay_type='layer_wise', num_layers=24), type='OptimWrapper') param_scheduler = [ dict( begin=0, by_epoch=False, end=1000, start_factor=1e-06, type='LinearLR'), dict( begin=1000, by_epoch=False, end=10000, eta_min=0.0, power=1.0, type='PolyLR'), ] randomness = dict(seed=1) resume = False test_cfg = dict(type='TestLoop') test_dataloader = dict( batch_size=1, dataset=dict( ann_file= 'rs_datasets/c2smsfloods/c2s_s2_val_mm.json', data_prefix=dict(img_path='images', seg_map_path='idx_labels'), data_root='rs_datasets/', pipeline=[ dict(type='LoadImageFromNpz'), dict(reduce_zero_label=False, type='LoadAnnotationsNpz'), dict(type='PackSegInputs'), ], type='C2SFloodDataset'), num_workers=4, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) test_evaluator = dict( iou_metrics=[ 'mIoU', 'mFscore' ], type='IoUMetric') test_pipeline = [ dict(type='LoadImageFromNpz'), dict(reduce_zero_label=False, type='LoadAnnotationsNpz'), dict(type='PackSegInputs'), ] train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=500) train_dataloader = dict( batch_size=2, dataset=dict( ann_file= 'rs_datasets/c2smsfloods/c2s_s2_train_mm.json', data_prefix=dict(img_path='images', seg_map_path='idx_labels'), data_root='rs_datasets/', pipeline=[ dict(type='LoadImageFromNpz'), dict(reduce_zero_label=False, type='LoadAnnotationsNpz'), dict( cat_max_ratio=0.75, crop_size=( 256, 256, ), type='RandomCrop'), dict(prob=0.5, type='RandomFlip'), dict(type='PackSegInputs'), ], type='C2SFloodDataset'), num_workers=4, persistent_workers=True, sampler=dict(shuffle=True, type='InfiniteSampler')) tta_model = dict(type='SegTTAModel') tta_pipeline = [ dict(backend_args=None, type='LoadImageFromFile'), dict( transforms=[ [ dict(keep_ratio=True, scale_factor=1.0, type='Resize'), dict(keep_ratio=True, scale_factor=1.2, type='Resize'), dict(keep_ratio=True, scale_factor=1.4, type='Resize'), dict(keep_ratio=True, scale_factor=1.6, type='Resize'), dict(keep_ratio=True, scale_factor=1.8, type='Resize'), dict(keep_ratio=True, scale_factor=2.0, type='Resize'), ], [ dict(direction='horizontal', prob=0.0, type='RandomFlip'), dict(direction='horizontal', prob=1.0, type='RandomFlip'), ], [ dict(type='LoadAnnotations'), ], [ dict(type='PackSegInputs'), ], ], type='TestTimeAug'), ] val_cfg = dict(type='ValLoop') val_dataloader = dict( batch_size=1, dataset=dict( ann_file= 'rs_datasets/c2smsfloods/c2s_s2_val_mm.json', data_prefix=dict(img_path='images', seg_map_path='idx_labels'), data_root='rs_datasets/', pipeline=[ dict(type='LoadImageFromNpz'), dict(reduce_zero_label=False, type='LoadAnnotationsNpz'), dict(type='PackSegInputs'), ], type='C2SFloodDataset'), num_workers=4, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) val_evaluator = dict( iou_metrics=[ 'mIoU', ], type='IoUMetric') vis_backends = [ dict(type='LocalVisBackend'), ] visualizer = dict( name='visualizer', type='SegLocalVisualizer', vis_backends=[ dict(type='LocalVisBackend'), ])