| auto_scale_lr = dict(base_batch_size=16, enable=False) |
| backbone_embed_multi = dict(decay_mult=0.0, lr_mult=0.1) |
| backbone_norm_multi = dict(decay_mult=0.0, lr_mult=0.1) |
| crop_size = ( |
| 512, |
| 512, |
| ) |
| custom_keys = dict({ |
| 'absolute_pos_embed': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone': |
| dict(decay_mult=1.0, lr_mult=0.1), |
| 'backbone.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.patch_embed.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.0.blocks.0.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.0.blocks.1.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.0.downsample.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.1.blocks.0.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.1.blocks.1.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.1.downsample.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.0.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.1.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.10.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.11.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.12.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.13.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.14.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.15.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.16.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.17.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.2.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.3.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.4.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.5.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.6.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.7.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.8.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.9.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.downsample.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.3.blocks.0.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.3.blocks.1.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'level_embed': |
| dict(decay_mult=0.0, lr_mult=1.0), |
| 'query_embed': |
| dict(decay_mult=0.0, lr_mult=1.0), |
| 'query_feat': |
| dict(decay_mult=0.0, lr_mult=1.0), |
| 'relative_position_bias_table': |
| dict(decay_mult=0.0, lr_mult=0.1) |
| }) |
| data_preprocessor = dict( |
| bgr_to_rgb=True, |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| pad_val=0, |
| seg_pad_val=255, |
| size=( |
| 640, |
| 640, |
| ), |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| type='SegDataPreProcessor') |
| data_root = 'CVRPDataset/' |
| dataset_type = 'CVRPDataset' |
| default_hooks = dict( |
| checkpoint=dict( |
| by_epoch=False, |
| interval=2500, |
| max_keep_ckpts=1, |
| save_best='mIoU', |
| type='CheckpointHook'), |
| logger=dict(interval=100, 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' |
| depths = [ |
| 2, |
| 2, |
| 18, |
| 2, |
| ] |
| embed_multi = dict(decay_mult=0.0, lr_mult=1.0) |
| env_cfg = dict( |
| cudnn_benchmark=True, |
| dist_cfg=dict(backend='nccl'), |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) |
| img_ratios = [ |
| 0.5, |
| 0.75, |
| 1.0, |
| 1.25, |
| 1.5, |
| 1.75, |
| ] |
| load_from = None |
| log_level = 'INFO' |
| log_processor = dict(by_epoch=False) |
| model = dict( |
| backbone=dict( |
| attn_drop_rate=0.0, |
| depths=[ |
| 2, |
| 2, |
| 18, |
| 2, |
| ], |
| drop_path_rate=0.3, |
| drop_rate=0.0, |
| embed_dims=192, |
| frozen_stages=-1, |
| init_cfg=dict( |
| checkpoint= |
| 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window12_384_22k_20220412-6580f57d.pth', |
| type='Pretrained'), |
| mlp_ratio=4, |
| num_heads=[ |
| 6, |
| 12, |
| 24, |
| 48, |
| ], |
| out_indices=( |
| 0, |
| 1, |
| 2, |
| 3, |
| ), |
| patch_norm=True, |
| pretrain_img_size=384, |
| qk_scale=None, |
| qkv_bias=True, |
| type='SwinTransformer', |
| window_size=12, |
| with_cp=False), |
| data_preprocessor=dict( |
| bgr_to_rgb=True, |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| pad_val=0, |
| seg_pad_val=255, |
| size=( |
| 512, |
| 512, |
| ), |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| type='SegDataPreProcessor'), |
| decode_head=dict( |
| align_corners=False, |
| enforce_decoder_input_project=False, |
| feat_channels=256, |
| in_channels=[ |
| 192, |
| 384, |
| 768, |
| 1536, |
| ], |
| loss_cls=dict( |
| class_weight=[ |
| 1.0, |
| 1.0, |
| 0.1, |
| ], |
| loss_weight=2.0, |
| reduction='mean', |
| type='mmdet.CrossEntropyLoss', |
| use_sigmoid=False), |
| loss_dice=dict( |
| activate=True, |
| eps=1.0, |
| loss_weight=5.0, |
| naive_dice=True, |
| reduction='mean', |
| type='mmdet.DiceLoss', |
| use_sigmoid=True), |
| loss_mask=dict( |
| loss_weight=5.0, |
| reduction='mean', |
| type='mmdet.CrossEntropyLoss', |
| use_sigmoid=True), |
| num_classes=2, |
| num_queries=100, |
| num_transformer_feat_level=3, |
| out_channels=256, |
| pixel_decoder=dict( |
| act_cfg=dict(type='ReLU'), |
| encoder=dict( |
| init_cfg=None, |
| layer_cfg=dict( |
| ffn_cfg=dict( |
| act_cfg=dict(inplace=True, type='ReLU'), |
| embed_dims=256, |
| feedforward_channels=1024, |
| ffn_drop=0.0, |
| num_fcs=2), |
| self_attn_cfg=dict( |
| batch_first=True, |
| dropout=0.0, |
| embed_dims=256, |
| im2col_step=64, |
| init_cfg=None, |
| norm_cfg=None, |
| num_heads=8, |
| num_levels=3, |
| num_points=4)), |
| num_layers=6), |
| init_cfg=None, |
| norm_cfg=dict(num_groups=32, type='GN'), |
| num_outs=3, |
| positional_encoding=dict(normalize=True, num_feats=128), |
| type='mmdet.MSDeformAttnPixelDecoder'), |
| positional_encoding=dict(normalize=True, num_feats=128), |
| strides=[ |
| 4, |
| 8, |
| 16, |
| 32, |
| ], |
| train_cfg=dict( |
| assigner=dict( |
| match_costs=[ |
| dict(type='mmdet.ClassificationCost', weight=2.0), |
| dict( |
| type='mmdet.CrossEntropyLossCost', |
| use_sigmoid=True, |
| weight=5.0), |
| dict( |
| eps=1.0, |
| pred_act=True, |
| type='mmdet.DiceCost', |
| weight=5.0), |
| ], |
| type='mmdet.HungarianAssigner'), |
| importance_sample_ratio=0.75, |
| num_points=12544, |
| oversample_ratio=3.0, |
| sampler=dict(type='mmdet.MaskPseudoSampler')), |
| transformer_decoder=dict( |
| init_cfg=None, |
| layer_cfg=dict( |
| cross_attn_cfg=dict( |
| attn_drop=0.0, |
| batch_first=True, |
| dropout_layer=None, |
| embed_dims=256, |
| num_heads=8, |
| proj_drop=0.0), |
| ffn_cfg=dict( |
| act_cfg=dict(inplace=True, type='ReLU'), |
| add_identity=True, |
| dropout_layer=None, |
| embed_dims=256, |
| feedforward_channels=2048, |
| ffn_drop=0.0, |
| num_fcs=2), |
| self_attn_cfg=dict( |
| attn_drop=0.0, |
| batch_first=True, |
| dropout_layer=None, |
| embed_dims=256, |
| num_heads=8, |
| proj_drop=0.0)), |
| num_layers=9, |
| return_intermediate=True), |
| type='Mask2FormerHead'), |
| test_cfg=dict(mode='whole'), |
| train_cfg=dict(), |
| type='EncoderDecoder') |
| norm_cfg = dict(requires_grad=True, type='BN') |
| num_classes = 150 |
| optim_wrapper = dict( |
| clip_grad=dict(max_norm=0.01, norm_type=2), |
| optimizer=dict( |
| betas=( |
| 0.9, |
| 0.999, |
| ), |
| eps=1e-08, |
| lr=0.0001, |
| type='AdamW', |
| weight_decay=0.05), |
| paramwise_cfg=dict( |
| custom_keys=dict({ |
| 'absolute_pos_embed': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone': |
| dict(decay_mult=1.0, lr_mult=0.1), |
| 'backbone.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.patch_embed.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.0.blocks.0.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.0.blocks.1.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.0.downsample.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.1.blocks.0.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.1.blocks.1.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.1.downsample.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.0.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.1.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.10.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.11.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.12.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.13.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.14.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.15.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.16.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.17.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.2.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.3.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.4.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.5.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.6.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.7.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.8.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.blocks.9.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.2.downsample.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.3.blocks.0.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'backbone.stages.3.blocks.1.norm': |
| dict(decay_mult=0.0, lr_mult=0.1), |
| 'level_embed': |
| dict(decay_mult=0.0, lr_mult=1.0), |
| 'query_embed': |
| dict(decay_mult=0.0, lr_mult=1.0), |
| 'query_feat': |
| dict(decay_mult=0.0, lr_mult=1.0), |
| 'relative_position_bias_table': |
| dict(decay_mult=0.0, lr_mult=0.1) |
| }), |
| norm_decay_mult=0.0), |
| type='OptimWrapper') |
| optimizer = dict( |
| betas=( |
| 0.9, |
| 0.999, |
| ), |
| eps=1e-08, |
| lr=0.0001, |
| type='AdamW', |
| weight_decay=0.05) |
| param_scheduler = [ |
| dict( |
| begin=0, |
| by_epoch=False, |
| end=160000, |
| eta_min=0, |
| power=0.9, |
| type='PolyLR'), |
| ] |
| pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window12_384_22k_20220412-6580f57d.pth' |
| randomness = dict(seed=0) |
| resume = False |
| test_cfg = dict(type='TestLoop') |
| test_dataloader = dict( |
| batch_size=1, |
| dataset=dict( |
| data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'), |
| data_root='CVRPDataset/', |
| pipeline=[ |
| dict(type='LoadImageFromFile'), |
| dict(keep_ratio=True, scale=( |
| 2048, |
| 1024, |
| ), type='Resize'), |
| dict(type='LoadAnnotations'), |
| dict(type='PackSegInputs'), |
| ], |
| type='CVRPDataset'), |
| num_workers=4, |
| persistent_workers=True, |
| sampler=dict(shuffle=False, type='DefaultSampler')) |
| test_evaluator = dict( |
| iou_metrics=[ |
| 'mIoU', |
| 'mDice', |
| 'mFscore', |
| ], type='IoUMetric') |
| test_pipeline = [ |
| dict(type='LoadImageFromFile'), |
| dict(keep_ratio=True, scale=( |
| 2048, |
| 1024, |
| ), type='Resize'), |
| dict(type='LoadAnnotations'), |
| dict(type='PackSegInputs'), |
| ] |
| train_cfg = dict(max_iters=20000, type='IterBasedTrainLoop', val_interval=500) |
| train_dataloader = dict( |
| batch_size=2, |
| dataset=dict( |
| data_prefix=dict( |
| img_path='img_dir/train', seg_map_path='ann_dir/train'), |
| data_root='CVRPDataset/', |
| pipeline=[ |
| dict(type='LoadImageFromFile'), |
| dict(type='LoadAnnotations'), |
| dict( |
| keep_ratio=True, |
| ratio_range=( |
| 0.5, |
| 2.0, |
| ), |
| scale=( |
| 2048, |
| 1024, |
| ), |
| type='RandomResize'), |
| dict( |
| cat_max_ratio=0.75, crop_size=( |
| 512, |
| 512, |
| ), type='RandomCrop'), |
| dict(prob=0.5, type='RandomFlip'), |
| dict(type='PhotoMetricDistortion'), |
| dict(type='PackSegInputs'), |
| ], |
| type='CVRPDataset'), |
| num_workers=2, |
| persistent_workers=True, |
| sampler=dict(shuffle=True, type='InfiniteSampler')) |
| train_pipeline = [ |
| dict(type='LoadImageFromFile'), |
| dict(type='LoadAnnotations'), |
| dict( |
| keep_ratio=True, |
| ratio_range=( |
| 0.5, |
| 2.0, |
| ), |
| scale=( |
| 2048, |
| 1024, |
| ), |
| type='RandomResize'), |
| dict(cat_max_ratio=0.75, crop_size=( |
| 512, |
| 512, |
| ), type='RandomCrop'), |
| dict(prob=0.5, type='RandomFlip'), |
| dict(type='PhotoMetricDistortion'), |
| dict(type='PackSegInputs'), |
| ] |
| tta_model = dict(type='SegTTAModel') |
| tta_pipeline = [ |
| dict(file_client_args=dict(backend='disk'), type='LoadImageFromFile'), |
| dict( |
| transforms=[ |
| [ |
| dict(keep_ratio=True, scale_factor=0.5, type='Resize'), |
| dict(keep_ratio=True, scale_factor=0.75, type='Resize'), |
| dict(keep_ratio=True, scale_factor=1.0, type='Resize'), |
| dict(keep_ratio=True, scale_factor=1.25, type='Resize'), |
| dict(keep_ratio=True, scale_factor=1.5, type='Resize'), |
| dict(keep_ratio=True, scale_factor=1.75, 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( |
| data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'), |
| data_root='CVRPDataset/', |
| pipeline=[ |
| dict(type='LoadImageFromFile'), |
| dict(keep_ratio=True, scale=( |
| 2048, |
| 1024, |
| ), type='Resize'), |
| dict(type='LoadAnnotations'), |
| dict(type='PackSegInputs'), |
| ], |
| type='CVRPDataset'), |
| num_workers=4, |
| persistent_workers=True, |
| sampler=dict(shuffle=False, type='DefaultSampler')) |
| val_evaluator = dict( |
| iou_metrics=[ |
| 'mIoU', |
| 'mDice', |
| 'mFscore', |
| ], type='IoUMetric') |
| vis_backends = [ |
| dict(type='LocalVisBackend'), |
| ] |
| visualizer = dict( |
| name='visualizer', |
| type='SegLocalVisualizer', |
| vis_backends=[ |
| dict(type='LocalVisBackend'), |
| ]) |
| work_dir = './work_dirs/CVRP_mask2former' |
|
|
