| checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window7_224_22k_20220308-d5bdebaf.pth' | |
| conv_kernel_size = 1 | |
| crop_size = ( | |
| 512, | |
| 512, | |
| ) | |
| 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') | |
| data_root = 'PanicleDataset/' | |
| dataset_type = 'TzyDataset' | |
| 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' | |
| 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( | |
| auxiliary_head=dict( | |
| align_corners=False, | |
| channels=256, | |
| concat_input=False, | |
| dropout_ratio=0.1, | |
| in_channels=768, | |
| in_index=2, | |
| loss_decode=dict( | |
| loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), | |
| norm_cfg=dict(requires_grad=True, type='SyncBN'), | |
| num_classes=2, | |
| num_convs=1, | |
| type='FCNHead'), | |
| backbone=dict( | |
| attn_drop_rate=0.0, | |
| depths=[ | |
| 2, | |
| 2, | |
| 18, | |
| 2, | |
| ], | |
| drop_path_rate=0.3, | |
| drop_rate=0.0, | |
| embed_dims=192, | |
| mlp_ratio=4, | |
| num_heads=[ | |
| 6, | |
| 12, | |
| 24, | |
| 48, | |
| ], | |
| out_indices=( | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| ), | |
| patch_norm=True, | |
| qk_scale=None, | |
| qkv_bias=True, | |
| type='SwinTransformer', | |
| use_abs_pos_embed=False, | |
| window_size=7), | |
| 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( | |
| kernel_generate_head=dict( | |
| align_corners=False, | |
| channels=512, | |
| dropout_ratio=0.1, | |
| in_channels=[ | |
| 192, | |
| 384, | |
| 768, | |
| 1536, | |
| ], | |
| 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=2, | |
| pool_scales=( | |
| 1, | |
| 2, | |
| 3, | |
| 6, | |
| ), | |
| type='UPerHead'), | |
| kernel_update_head=[ | |
| dict( | |
| conv_kernel_size=1, | |
| dropout=0.0, | |
| feat_transform_cfg=dict( | |
| act_cfg=None, conv_cfg=dict(type='Conv2d')), | |
| feedforward_channels=2048, | |
| ffn_act_cfg=dict(inplace=True, type='ReLU'), | |
| in_channels=512, | |
| kernel_updator_cfg=dict( | |
| act_cfg=dict(inplace=True, type='ReLU'), | |
| feat_channels=256, | |
| in_channels=256, | |
| norm_cfg=dict(type='LN'), | |
| out_channels=256, | |
| type='KernelUpdator'), | |
| num_classes=150, | |
| num_ffn_fcs=2, | |
| num_heads=8, | |
| num_mask_fcs=1, | |
| out_channels=512, | |
| type='KernelUpdateHead', | |
| with_ffn=True), | |
| dict( | |
| conv_kernel_size=1, | |
| dropout=0.0, | |
| feat_transform_cfg=dict( | |
| act_cfg=None, conv_cfg=dict(type='Conv2d')), | |
| feedforward_channels=2048, | |
| ffn_act_cfg=dict(inplace=True, type='ReLU'), | |
| in_channels=512, | |
| kernel_updator_cfg=dict( | |
| act_cfg=dict(inplace=True, type='ReLU'), | |
| feat_channels=256, | |
| in_channels=256, | |
| norm_cfg=dict(type='LN'), | |
| out_channels=256, | |
| type='KernelUpdator'), | |
| num_classes=150, | |
| num_ffn_fcs=2, | |
| num_heads=8, | |
| num_mask_fcs=1, | |
| out_channels=512, | |
| type='KernelUpdateHead', | |
| with_ffn=True), | |
| dict( | |
| conv_kernel_size=1, | |
| dropout=0.0, | |
| feat_transform_cfg=dict( | |
| act_cfg=None, conv_cfg=dict(type='Conv2d')), | |
| feedforward_channels=2048, | |
| ffn_act_cfg=dict(inplace=True, type='ReLU'), | |
| in_channels=512, | |
| kernel_updator_cfg=dict( | |
| act_cfg=dict(inplace=True, type='ReLU'), | |
| feat_channels=256, | |
| in_channels=256, | |
| norm_cfg=dict(type='LN'), | |
| out_channels=256, | |
| type='KernelUpdator'), | |
| num_classes=150, | |
| num_ffn_fcs=2, | |
| num_heads=8, | |
| num_mask_fcs=1, | |
| out_channels=512, | |
| type='KernelUpdateHead', | |
| with_ffn=True), | |
| ], | |
| num_stages=3, | |
| type='IterativeDecodeHead'), | |
| pretrained= | |
| 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window7_224_22k_20220308-d5bdebaf.pth', | |
| test_cfg=dict(mode='whole'), | |
| train_cfg=dict(), | |
| type='EncoderDecoder') | |
| norm_cfg = dict(requires_grad=True, type='BN') | |
| num_stages = 3 | |
| optim_wrapper = dict( | |
| clip_grad=dict(max_norm=1, norm_type=2), | |
| optimizer=dict( | |
| betas=( | |
| 0.9, | |
| 0.999, | |
| ), lr=6e-05, type='AdamW', weight_decay=0.0005), | |
| paramwise_cfg=dict( | |
| custom_keys=dict( | |
| absolute_pos_embed=dict(decay_mult=0.0), | |
| norm=dict(decay_mult=0.0), | |
| relative_position_bias_table=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=1000, start_factor=0.001, | |
| type='LinearLR'), | |
| dict( | |
| begin=1000, | |
| by_epoch=False, | |
| end=80000, | |
| milestones=[ | |
| 60000, | |
| 72000, | |
| ], | |
| type='MultiStepLR'), | |
| ] | |
| 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='PanicleDataset/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 2048, | |
| 1024, | |
| ), type='Resize'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='PackSegInputs'), | |
| ], | |
| type='TzyDataset'), | |
| 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='PanicleDataset/', | |
| 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='TzyDataset'), | |
| 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='PanicleDataset/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 2048, | |
| 1024, | |
| ), type='Resize'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='PackSegInputs'), | |
| ], | |
| type='TzyDataset'), | |
| 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/TzyDataset-KNet-0721' | |