| 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=1024, | |
| in_index=2, | |
| loss_decode=dict( | |
| loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), | |
| norm_cfg=dict(requires_grad=True, type='BN'), | |
| num_classes=2, | |
| num_convs=1, | |
| type='FCNHead'), | |
| backbone=dict( | |
| contract_dilation=True, | |
| depth=101, | |
| dilations=( | |
| 1, | |
| 1, | |
| 2, | |
| 4, | |
| ), | |
| norm_cfg=dict(requires_grad=True, type='BN'), | |
| norm_eval=False, | |
| num_stages=4, | |
| out_indices=( | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| ), | |
| strides=( | |
| 1, | |
| 2, | |
| 1, | |
| 1, | |
| ), | |
| style='pytorch', | |
| type='ResNetV1c'), | |
| 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, | |
| c1_channels=48, | |
| c1_in_channels=256, | |
| channels=512, | |
| dilations=( | |
| 1, | |
| 12, | |
| 24, | |
| 36, | |
| ), | |
| dropout_ratio=0.1, | |
| in_channels=2048, | |
| in_index=3, | |
| loss_decode=dict( | |
| loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), | |
| norm_cfg=dict(requires_grad=True, type='BN'), | |
| num_classes=2, | |
| type='DepthwiseSeparableASPPHead'), | |
| pretrained='open-mmlab://resnet101_v1c', | |
| test_cfg=dict(mode='whole'), | |
| train_cfg=dict(), | |
| type='EncoderDecoder') | |
| norm_cfg = dict(requires_grad=True, type='BN') | |
| optim_wrapper = dict( | |
| clip_grad=None, | |
| optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), | |
| 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=160000, | |
| eta_min=0.0001, | |
| power=0.9, | |
| type='PolyLR'), | |
| ] | |
| 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=4, | |
| 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-DeepLabV3plus-0725' | |