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Commitba0f98e

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Config file for best model thus far (MMSegmentation row 84).
1 parent37f7d11 commitba0f98e

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backbone_norm_cfg=dict(requires_grad=True,type='LN')
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checkpoint_file='pretrain/upernet_swinB_pretrain_ImageNet22K_ade20k_row4.pth'
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data_preprocessor=dict(
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bgr_to_rgb=True,
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mean=[
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124.95,
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124.95,
8+
124.95,
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],
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pad_val=0,
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seg_pad_val=255,
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size_divisor=32,
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std=[
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24.735,
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24.735,
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24.735,
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],
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type='SegDataPreProcessor')
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dataset_type='HeadCTDataset'
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default_hooks=dict(
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checkpoint=dict(by_epoch=False,interval=5000,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(interval=1,type='HeadCTVisualizationHook'))
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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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img_ratios= [
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0.5,
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0.75,
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1.0,
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1.25,
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1.5,
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1.75,
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]
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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=2,
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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=7,
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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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depths=[
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2,
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2,
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18,
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2,
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],
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drop_path_rate=0.3,
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drop_rate=0.0,
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embed_dims=128,
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init_cfg=dict(
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checkpoint=
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'pretrain/upernet_swinB_pretrain_ImageNet22K_ade20k_row4.pth',
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type='Pretrained'),
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mlp_ratio=4,
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norm_cfg=dict(requires_grad=True,type='LN'),
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num_heads=[
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4,
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8,
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16,
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32,
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],
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out_indices=(
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0,
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1,
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2,
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3,
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),
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patch_norm=True,
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patch_size=4,
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pretrain_img_size=224,
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qk_scale=None,
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qkv_bias=True,
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strides=(
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4,
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2,
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2,
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2,
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),
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type='SwinTransformer',
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use_abs_pos_embed=False,
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window_size=7),
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data_preprocessor=dict(
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bgr_to_rgb=True,
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mean=[
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124.95,
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124.95,
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124.95,
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],
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pad_val=0,
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seg_pad_val=255,
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size_divisor=32,
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std=[
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24.735,
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24.735,
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24.735,
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],
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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=[
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128,
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256,
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512,
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1024,
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],
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in_index=[
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0,
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1,
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2,
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3,
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],
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loss_decode=dict(
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class_weight=[
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1.0,
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1.0,
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2.0,
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1.0,
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1.0,
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3.0,
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1.0,
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],
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loss_weight=1.0,
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type='CrossEntropyLoss',
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use_sigmoid=False),
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norm_cfg=dict(requires_grad=True,type='SyncBN'),
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num_classes=7,
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pool_scales=(
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1,
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2,
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3,
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6,
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),
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sampler=dict(min_kept=20000,thresh=0.5,type='OHEMPixelSampler'),
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type='UPerHead'),
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pretrained=None,
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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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norm_cfg=dict(requires_grad=True,type='SyncBN')
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num_classes=7
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optim_wrapper=dict(
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optimizer=dict(
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betas=(
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0.9,
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0.999,
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),lr=0.00018,type='AdamW',weight_decay=0.01),
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paramwise_cfg=dict(
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custom_keys=dict(
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absolute_pos_embed=dict(decay_mult=0.0),
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head=dict(lr_mult=5.0),
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norm=dict(decay_mult=0.0),
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relative_position_bias_table=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=1500,start_factor=1e-06,
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type='LinearLR'),
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dict(
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begin=1500,
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by_epoch=False,
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end=160000,
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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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ratio_range= (
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0.6,
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1.66,
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)
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reduce_zero_label=False
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resume=True
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test_cfg=dict(type='TestLoop')
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test_crop_size= (
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512,
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512,
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)
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test_data_root='data/track'
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test_dataloader=dict(
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batch_size=4,
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dataset=dict(
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data_prefix=dict(
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img_path='images/validation',
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seg_map_path='annotations/validation'),
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data_root='data/track',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(keep_ratio=True,scale=(
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512,
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512,
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),type='Resize'),
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dict(reduce_zero_label=False,type='LoadAnnotations'),
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dict(type='PackSegInputs'),
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],
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type='HeadCTDataset'),
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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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'mDice',
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'mIoU',
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],type='IoUROCMetric')
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test_pipeline= [
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dict(type='LoadImageFromFile'),
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dict(keep_ratio=True,scale=(
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512,
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512,
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),type='Resize'),
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dict(reduce_zero_label=False,type='LoadAnnotations'),
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dict(type='PackSegInputs'),
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]
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total_steps=160000
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train_cfg=dict(
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max_iters=160000,type='IterBasedTrainLoop',val_interval=32000)
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train_crop_size= (
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256,
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256,
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)
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train_data_root='data/gotham'
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train_dataloader=dict(
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batch_size=15,
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dataset=dict(
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data_prefix=dict(
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img_path='images/training',seg_map_path='annotations/training'),
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data_root='data/gotham',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(reduce_zero_label=False,type='LoadAnnotations'),
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dict(
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keep_ratio=True,
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ratio_range=(
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0.6,
255+
1.66,
256+
),
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scale=(
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512,
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512,
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),
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type='RandomResize'),
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dict(
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cat_max_ratio=0.99,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='PhotoMetricDistortion'),
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dict(type='PackSegInputs'),
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],
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type='HeadCTDataset'),
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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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train_img_path='images/training'
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train_pipeline= [
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dict(type='LoadImageFromFile'),
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dict(reduce_zero_label=False,type='LoadAnnotations'),
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dict(
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keep_ratio=True,
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ratio_range=(
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0.6,
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1.66,
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),
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scale=(
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512,
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512,
288+
),
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type='RandomResize'),
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dict(cat_max_ratio=0.99,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='PhotoMetricDistortion'),
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dict(type='PackSegInputs'),
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]
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train_seg_map_path='annotations/training'
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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=0.5,type='Resize'),
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dict(keep_ratio=True,scale_factor=0.75,type='Resize'),
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dict(keep_ratio=True,scale_factor=1.0,type='Resize'),
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dict(keep_ratio=True,scale_factor=1.25,type='Resize'),
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dict(keep_ratio=True,scale_factor=1.5,type='Resize'),
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dict(keep_ratio=True,scale_factor=1.75,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=4,
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dataset=dict(
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data_prefix=dict(
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img_path='images/validation',
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seg_map_path='annotations/validation'),
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data_root='data/track',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(keep_ratio=True,scale=(
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512,
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512,
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),type='Resize'),
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dict(reduce_zero_label=False,type='LoadAnnotations'),
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dict(type='PackSegInputs'),
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],
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type='HeadCTDataset'),
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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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'mDice',
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'mIoU',
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],type='IoUROCMetric')
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val_img_path='images/validation'
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val_interval=32000
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val_seg_map_path='annotations/validation'
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vis_backends= [
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dict(type='LocalVisBackend'),
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]
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vis_interval=1
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visualizer=dict(
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name='visualizer',
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type='HeadCTVisualizer',
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vis_backends=[
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dict(type='LocalVisBackend'),
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])
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warmup_steps=1500
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work_dir='./work_dirs/20240223_4gpu_b60_p256_iter160k'

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