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esenke
227
finetune/configs/cabuar.py
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227
finetune/configs/cabuar.py
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default_hooks = dict(
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checkpoint=dict(
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by_epoch=False,
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interval=1000,
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max_keep_ckpts=1,
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save_best='mIoU',
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type='CheckpointHook'),
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logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'),
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param_scheduler=dict(type='ParamSchedulerHook'),
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sampler_seed=dict(type='DistSamplerSeedHook'),
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timer=dict(type='IterTimerHook'),
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visualization=dict(type='SegVisualizationHook'))
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default_scope = 'mmseg'
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env_cfg = dict(
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cudnn_benchmark=True,
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dist_cfg=dict(backend='nccl'),
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mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
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launcher = 'pytorch'
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load_from = None
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log_level = 'INFO'
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log_processor = dict(by_epoch=False)
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model = dict(
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auxiliary_head=dict(
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align_corners=False,
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channels=256,
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concat_input=False,
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dropout_ratio=0.1,
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in_channels=512,
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in_index=3,
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loss_decode=dict(
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loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False),
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norm_cfg=dict(requires_grad=True, type='SyncBN'),
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num_classes=2,
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num_convs=1,
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type='FCNHead'),
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backbone=dict(
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act_cfg=dict(type='GELU'),
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attn_drop_rate=0.0,
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downscale_indices=(5, 11,),
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drop_path_rate=0.0,
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drop_rate=0.1,
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embed_dims=1024,
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img_size=(256, 256,),
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in_channels=10,
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interpolate_mode='bilinear',
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mlp_ratio=4,
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norm_cfg=dict(eps=1e-06, requires_grad=True, type='LN'),
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norm_eval=False,
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num_heads=16,
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num_layers=24,
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out_indices=(5, 11, 17, 23,),
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patch_size=4,
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qkv_bias=True,
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type='VisionTransformer',
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with_cls_token=False),
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data_preprocessor=dict(
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bgr_to_rgb=True,
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mean=[4.50021132, 6.09891466, 7.50766315, 9.54643074, 12.82568112, 14.29062133, 15.24644993, 15.73945708, 16.60374872, 12.31011599,],
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pad_val=0,
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seg_pad_val=255,
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size=(256, 256,),
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std=[2.6094148, 2.49566825, 1.37103968, 2.6094148, 2.49566825, 1.37103968, 2.6094148, 2.49566825, 1.37103968, 1.37103968, ],
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type='SegDataPreProcessor'),
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decode_head=dict(
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align_corners=False,
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channels=512,
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dropout_ratio=0.1,
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in_channels=[512, 512, 512, 512,],
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in_index=[0, 1, 2, 3,],
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loss_decode=dict(
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loss_weight=1.0, type='CrossEntropyLoss', balance=True, max_scale=4.0, use_sigmoid=False),
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norm_cfg=dict(requires_grad=True, type='SyncBN'),
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num_classes=2,
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pool_scales=(1, 2, 3, 6,),
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type='UPerHead'),
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neck=dict(
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in_channels=[1024, 1024, 1024, 1024,],
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out_channels=512,
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scales=[1, 1, 1, 1,],
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type='MultiLevelNeck'),
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pretrained='pretrain/skysensepp_mmcvt_s2.pth',
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test_cfg=dict(mode='whole'),
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train_cfg=dict(),
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type='EncoderDecoder')
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optim_wrapper = dict(
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optimizer=dict(betas=(0.9, 0.999,), lr=None, type='AdamW', weight_decay=None,),
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constructor='LearningRateDecayOptimizerConstructor',
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paramwise_cfg=dict(
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num_layers=24,
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decay_rate=None,
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decay_type='layer_wise',
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custom_keys=dict(
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cls_token=dict(decay_mult=0.0),
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norm=dict(decay_mult=0.0),
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pos_embed=dict(decay_mult=0.0))),
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type='OptimWrapper')
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param_scheduler = [
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dict(
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begin=0, by_epoch=False, end=1000, start_factor=1e-06,
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type='LinearLR'),
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dict(
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begin=1000,
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by_epoch=False,
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end=10000,
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eta_min=0.0,
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power=1.0,
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type='PolyLR'),
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]
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randomness = dict(seed=20240315)
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resume = False
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test_cfg = dict(type='TestLoop')
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test_dataloader = dict(
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batch_size=1,
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dataset=dict(
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ann_file=
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'rs_datasets/cabuar/cabura_val_fold_4.json',
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data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
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data_root='rs_datasets/',
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pipeline=[
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dict(data_key='image', type='LoadImageFromNpz'),
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dict(
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data_key='image',
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reduce_zero_label=False,
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type='LoadAnnotationsNpz'),
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dict(type='PackSegInputs'),
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],
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type='CABURADataset'),
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num_workers=4,
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persistent_workers=True,
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sampler=dict(shuffle=False, type='DefaultSampler'))
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test_evaluator = dict(
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iou_metrics=[
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'mIoU',
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'mFscore',
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], type='IoUMetric')
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test_pipeline = [
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dict(data_key='image', type='LoadImageFromNpz'),
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dict(data_key='image', reduce_zero_label=False, type='LoadAnnotationsNpz'),
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dict(type='PackSegInputs'),
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]
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train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=500)
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train_dataloader = dict(
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batch_size=1,
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dataset=dict(
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ann_file=
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'rs_datasets/cabuar/cabura_train_fold0_3.json',
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data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
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data_root='rs_datasets/',
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pipeline=[
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dict(data_key='image', type='LoadImageFromNpz'),
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dict(
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data_key='image',
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reduce_zero_label=False,
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type='LoadAnnotationsNpz'),
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dict(
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cat_max_ratio=0.75, crop_size=(
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256,
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256,
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), type='RandomCrop'),
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dict(prob=0.5, type='RandomFlip'),
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dict(type='PackSegInputs'),
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],
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type='CABURADataset'),
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num_workers=4,
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persistent_workers=True,
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sampler=dict(shuffle=True, type='InfiniteSampler'))
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tta_model = dict(type='SegTTAModel')
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tta_pipeline = [
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dict(backend_args=None, type='LoadImageFromFile'),
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dict(
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transforms=[
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[
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dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
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dict(keep_ratio=True, scale_factor=1.2, type='Resize'),
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dict(keep_ratio=True, scale_factor=1.4, type='Resize'),
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dict(keep_ratio=True, scale_factor=1.6, type='Resize'),
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dict(keep_ratio=True, scale_factor=1.8, type='Resize'),
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dict(keep_ratio=True, scale_factor=2.0, type='Resize'),
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],
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[
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dict(direction='horizontal', prob=0.0, type='RandomFlip'),
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dict(direction='horizontal', prob=1.0, type='RandomFlip'),
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],
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[
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dict(type='LoadAnnotations'),
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],
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[
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dict(type='PackSegInputs'),
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],
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],
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type='TestTimeAug'),
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]
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val_cfg = dict(type='ValLoop')
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val_dataloader = dict(
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batch_size=1,
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dataset=dict(
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ann_file=
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'rs_datasets/cabuar/cabura_val_fold_4.json',
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data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
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data_root='rs_datasets/',
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pipeline=[
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dict(data_key='image', type='LoadImageFromNpz'),
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dict(
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data_key='image',
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reduce_zero_label=False,
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type='LoadAnnotationsNpz'),
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dict(type='PackSegInputs'),
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],
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type='CABURADataset'),
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num_workers=4,
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persistent_workers=True,
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sampler=dict(shuffle=False, type='DefaultSampler'))
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val_evaluator = dict(
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iou_metrics=[
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'mIoU',
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'mFscore',
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], type='IoUMetric')
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vis_backends = [
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dict(type='LocalVisBackend'),
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]
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visualizer = dict(
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name='visualizer',
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type='SegLocalVisualizer',
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vis_backends=[
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dict(type='LocalVisBackend'),
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])
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work_dir = 'work_dirs/ft_cabura_test'
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