| checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b4_20220624-d588d980.pth' | |
| crop_size = ( | |
| 1024, | |
| 1024, | |
| ) | |
| data_preprocessor = dict( | |
| bgr_to_rgb=True, | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| pad_val=0, | |
| seg_pad_val=0, | |
| size=( | |
| 1024, | |
| 1024, | |
| ), | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| type='SegDataPreProcessor') | |
| data_root = '/root/floor_boundary_segm/' | |
| dataset_type = 'BoundaryDataset' | |
| default_hooks = dict( | |
| checkpoint=dict(by_epoch=False, interval=2000, 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(draw=True, interval=1, 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)) | |
| img_ratios = [ | |
| 0.5, | |
| 0.75, | |
| 1.0, | |
| 1.25, | |
| 1.5, | |
| ] | |
| load_from = '/opt/sdb/disk-sdb/layout_parser/checkpoints/iter_20000.pth' | |
| log_level = 'INFO' | |
| log_processor = dict(by_epoch=False) | |
| model = dict( | |
| backbone=dict( | |
| attn_drop_rate=0.0, | |
| drop_path_rate=0.1, | |
| drop_rate=0.0, | |
| embed_dims=64, | |
| in_channels=3, | |
| init_cfg=dict( | |
| checkpoint= | |
| 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b4_20220624-d588d980.pth', | |
| type='Pretrained'), | |
| mlp_ratio=4, | |
| num_heads=[ | |
| 1, | |
| 2, | |
| 5, | |
| 8, | |
| ], | |
| num_layers=[ | |
| 3, | |
| 8, | |
| 27, | |
| 3, | |
| ], | |
| num_stages=4, | |
| out_indices=( | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| ), | |
| patch_sizes=[ | |
| 7, | |
| 3, | |
| 3, | |
| 3, | |
| ], | |
| qkv_bias=True, | |
| sr_ratios=[ | |
| 8, | |
| 4, | |
| 2, | |
| 1, | |
| ], | |
| type='MixVisionTransformer'), | |
| data_preprocessor=dict( | |
| bgr_to_rgb=True, | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| pad_val=0, | |
| seg_pad_val=0, | |
| size=( | |
| 1024, | |
| 1024, | |
| ), | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| type='SegDataPreProcessor'), | |
| decode_head=dict( | |
| align_corners=False, | |
| channels=256, | |
| dropout_ratio=0.1, | |
| in_channels=[ | |
| 64, | |
| 128, | |
| 320, | |
| 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=5, | |
| type='SegformerHead'), | |
| pretrained=None, | |
| test_cfg=dict(crop_size=( | |
| 1024, | |
| 1024, | |
| ), mode='slide', stride=( | |
| 768, | |
| 768, | |
| )), | |
| train_cfg=dict(), | |
| type='EncoderDecoder') | |
| norm_cfg = dict(requires_grad=True, type='SyncBN') | |
| optim_wrapper = dict( | |
| optimizer=dict( | |
| betas=( | |
| 0.9, | |
| 0.999, | |
| ), lr=6e-05, type='AdamW', weight_decay=0.01), | |
| paramwise_cfg=dict( | |
| custom_keys=dict( | |
| head=dict(lr_mult=10.0), | |
| norm=dict(decay_mult=0.0), | |
| pos_block=dict(decay_mult=0.0))), | |
| type='OptimWrapper') | |
| optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) | |
| param_scheduler = [ | |
| dict( | |
| begin=0, by_epoch=False, end=1500, start_factor=1e-06, | |
| type='LinearLR'), | |
| dict( | |
| begin=1500, | |
| by_epoch=False, | |
| end=160000, | |
| eta_min=0.0, | |
| power=1.0, | |
| type='PolyLR'), | |
| ] | |
| resume = True | |
| 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='/root/floor_boundary_segm/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 1024, | |
| 1024, | |
| ), type='Resize'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='PackSegInputs'), | |
| ], | |
| type='BoundaryDataset'), | |
| 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( | |
| interpolation='bicubic', | |
| keep_ratio=True, | |
| scale=( | |
| 1024, | |
| 1024, | |
| ), | |
| type='Resize'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='PackSegInputs'), | |
| ] | |
| train_cfg = dict(max_iters=20000, type='IterBasedTrainLoop', val_interval=2000) | |
| train_dataloader = dict( | |
| batch_size=1, | |
| dataset=dict( | |
| data_prefix=dict( | |
| img_path='img_dir/train', seg_map_path='ann_dir/train'), | |
| data_root='/root/floor_boundary_segm/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations'), | |
| dict( | |
| keep_ratio=True, | |
| ratio_range=( | |
| 0.5, | |
| 2.0, | |
| ), | |
| scale=( | |
| 1024, | |
| 1024, | |
| ), | |
| type='RandomResize'), | |
| dict( | |
| cat_max_ratio=0.75, | |
| crop_size=( | |
| 1024, | |
| 1024, | |
| ), | |
| type='RandomCrop'), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='PackSegInputs'), | |
| ], | |
| type='BoundaryDataset'), | |
| 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=( | |
| 1024, | |
| 1024, | |
| ), | |
| type='RandomResize'), | |
| dict(cat_max_ratio=0.75, crop_size=( | |
| 1024, | |
| 1024, | |
| ), 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='img_dir/val', seg_map_path='ann_dir/val'), | |
| data_root='/root/floor_boundary_segm/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 1024, | |
| 1024, | |
| ), type='Resize'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='PackSegInputs'), | |
| ], | |
| type='BoundaryDataset'), | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(shuffle=False, type='DefaultSampler')) | |
| val_evaluator = dict( | |
| iou_metrics=[ | |
| 'mIoU', | |
| ], type='IoUMetric') | |
| val_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 1024, | |
| 1024, | |
| ), type='Resize'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='PackSegInputs'), | |
| ] | |
| vis_backends = [ | |
| dict(type='LocalVisBackend'), | |
| ] | |
| visualizer = dict( | |
| name='visualizer', | |
| type='SegLocalVisualizer', | |
| vis_backends=[ | |
| dict(type='LocalVisBackend'), | |
| dict( | |
| init_kwargs=dict( | |
| group='segformer', | |
| name='1024x1024', | |
| project='floorplan parser'), | |
| type='WandbVisBackend'), | |
| ]) | |