| 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 = ( | |
| 256, | |
| 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.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.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=( | |
| 256, | |
| 512, | |
| ), | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| test_cfg=dict(size_divisor=32), | |
| type='SegDataPreProcessor') | |
| data_root = '/dataset/cityscapes/' | |
| dataset_type = 'CityscapesDataset' | |
| default_hooks = dict( | |
| checkpoint=dict( | |
| by_epoch=False, interval=5000, 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' | |
| depths = [ | |
| 2, | |
| 2, | |
| 6, | |
| 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, | |
| ] | |
| launcher = 'pytorch' | |
| load_from = 'work_dirs/mask2former-swint-8xb2-512x1024-90k/mask2former-swint-8xb2-512x1024-90k_ckpt.pth' | |
| log_level = 'INFO' | |
| log_processor = dict(by_epoch=False) | |
| model = dict( | |
| backbone=dict( | |
| attn_drop_rate=0.0, | |
| depths=[ | |
| 2, | |
| 2, | |
| 6, | |
| 2, | |
| ], | |
| drop_path_rate=0.3, | |
| drop_rate=0.0, | |
| embed_dims=96, | |
| frozen_stages=-1, | |
| init_cfg=dict( | |
| checkpoint= | |
| 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', | |
| type='Pretrained'), | |
| mlp_ratio=4, | |
| num_heads=[ | |
| 3, | |
| 6, | |
| 12, | |
| 24, | |
| ], | |
| out_indices=( | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| ), | |
| patch_norm=True, | |
| qk_scale=None, | |
| qkv_bias=True, | |
| type='SwinTransformer', | |
| window_size=7, | |
| 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=( | |
| 256, | |
| 512, | |
| ), | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| test_cfg=dict(size_divisor=32), | |
| type='SegDataPreProcessor'), | |
| decode_head=dict( | |
| align_corners=False, | |
| enforce_decoder_input_project=False, | |
| feat_channels=256, | |
| in_channels=[ | |
| 96, | |
| 192, | |
| 384, | |
| 768, | |
| ], | |
| loss_cls=dict( | |
| class_weight=[ | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 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=19, | |
| 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') | |
| num_classes = 19 | |
| 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.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.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=90000, | |
| eta_min=0, | |
| power=0.9, | |
| type='PolyLR'), | |
| ] | |
| pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' | |
| resume = False | |
| test_cfg = dict(type='TestLoop') | |
| test_dataloader = dict( | |
| batch_size=1, | |
| dataset=dict( | |
| data_prefix=dict( | |
| img_path='leftImg8bit/val', seg_map_path='gtFine/val'), | |
| data_root='/dataset/cityscapes/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 2048, | |
| 1024, | |
| ), type='Resize'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='PackSegInputs'), | |
| ], | |
| type='CityscapesDataset'), | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(shuffle=False, type='DefaultSampler')) | |
| test_evaluator = dict( | |
| iou_metrics=[ | |
| 'mIoU', | |
| ], 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=90000, type='IterBasedTrainLoop', val_interval=5000) | |
| train_dataloader = dict( | |
| batch_size=2, | |
| dataset=dict( | |
| data_prefix=dict( | |
| img_path='leftImg8bit/train', seg_map_path='gtFine/train'), | |
| data_root='/dataset/cityscapes/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations'), | |
| dict( | |
| max_size=4096, | |
| resize_type='ResizeShortestEdge', | |
| scales=[ | |
| 512, | |
| 614, | |
| 716, | |
| 819, | |
| 921, | |
| 1024, | |
| 1126, | |
| 1228, | |
| 1331, | |
| 1433, | |
| 1536, | |
| 1638, | |
| 1740, | |
| 1843, | |
| 1945, | |
| 2048, | |
| ], | |
| type='RandomChoiceResize'), | |
| dict( | |
| cat_max_ratio=0.75, crop_size=( | |
| 256, | |
| 512, | |
| ), type='RandomCrop'), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='PackSegInputs'), | |
| ], | |
| type='CityscapesDataset'), | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(shuffle=True, type='InfiniteSampler')) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations'), | |
| dict( | |
| max_size=4096, | |
| resize_type='ResizeShortestEdge', | |
| scales=[ | |
| 512, | |
| 614, | |
| 716, | |
| 819, | |
| 921, | |
| 1024, | |
| 1126, | |
| 1228, | |
| 1331, | |
| 1433, | |
| 1536, | |
| 1638, | |
| 1740, | |
| 1843, | |
| 1945, | |
| 2048, | |
| ], | |
| type='RandomChoiceResize'), | |
| dict(cat_max_ratio=0.75, crop_size=( | |
| 256, | |
| 512, | |
| ), type='RandomCrop'), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='PackSegInputs'), | |
| ] | |
| tta_model = dict(type='SegTTAModel') | |
| tta_pipeline = [ | |
| dict(backend_args=None, 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='leftImg8bit/val', seg_map_path='gtFine/val'), | |
| data_root='/dataset/cityscapes/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 2048, | |
| 1024, | |
| ), type='Resize'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='PackSegInputs'), | |
| ], | |
| type='CityscapesDataset'), | |
| 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'), | |
| ]) | |
| work_dir = './work_dirs/mask2former-swint-8xb2-512x1024-90k' | |