init
This commit is contained in:
esenke
303
finetune/configs/atlantic.py
Normal file
303
finetune/configs/atlantic.py
Normal file
@@ -0,0 +1,303 @@
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crop_size = (
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256,
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256,
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)
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data_preprocessor = dict(
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bgr_to_rgb=True,
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mean=[
|
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537.9411629981602,
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615.7886221108977,
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343.4481583821405,
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3010.641650390625,
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],
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pad_val=0,
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seg_pad_val=255,
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size=crop_size,
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std=[
|
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367.4598430230881,
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254.2473100510193,
|
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187.5437562223154,
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921.0792775874182,
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],
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type='SegDataPreProcessor')
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data_root = 'rs_datasets/'
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dataset_type = 'AtlanticDataset'
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default_hooks = dict(
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checkpoint=dict(
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by_epoch=False, interval=1000, save_best='mIoU',max_keep_ckpts=1,
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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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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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mean = [
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537.9411629981602,
|
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615.7886221108977,
|
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343.4481583821405,
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3010.641650390625,
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]
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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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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=crop_size,
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in_channels=4,
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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=(
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5,
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11,
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17,
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23,
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),
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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=[
|
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537.9411629981602,
|
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615.7886221108977,
|
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343.4481583821405,
|
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3010.641650390625,
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],
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pad_val=0,
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seg_pad_val=255,
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size=crop_size,
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std=[
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367.4598430230881,
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254.2473100510193,
|
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187.5437562223154,
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921.0792775874182,
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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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512,
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512,
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512,
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512,
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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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loss_weight=1.0, 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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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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type='UPerHead'),
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neck=dict(
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in_channels=[
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1024,
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1024,
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1024,
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1024,
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],
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out_channels=512,
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scales=[
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1,
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1,
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1,
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1,
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],
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type='MultiLevelNeck'),
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pretrained="pretrain/skysensepp_release_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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norm_cfg = dict(requires_grad=True, type='SyncBN')
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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=6e-05, type='AdamW', weight_decay=0.01),
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paramwise_cfg=dict(
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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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optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
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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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std = [
|
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367.4598430230881,
|
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254.2473100510193,
|
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187.5437562223154,
|
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921.0792775874182,
|
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]
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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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'deforestation_atlantic/deforestation_atlantic_test.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(type='LoadSingleRSImageFromFile'),
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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='AtlanticDataset'),
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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(type='LoadSingleRSImageFromFile'),
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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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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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'deforestation_atlantic/deforestation_atlantic_train.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(type='LoadSingleRSImageFromFile'),
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dict(reduce_zero_label=False, type='LoadAnnotations'),
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dict(cat_max_ratio=0.75, crop_size=crop_size, 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='AtlanticDataset'),
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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_pipeline = [
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dict(type='LoadSingleRSImageFromFile'),
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dict(reduce_zero_label=False, type='LoadAnnotations'),
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dict(cat_max_ratio=0.75, crop_size=crop_size, 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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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=1,
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dataset=dict(
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ann_file=
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'deforestation_atlantic/deforestation_atlantic_val.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(type='LoadSingleRSImageFromFile'),
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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='AtlanticDataset'),
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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 = 'save/atlantic_skysensepp'
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284
finetune/configs/c2smsflood.py
Normal file
284
finetune/configs/c2smsflood.py
Normal file
@@ -0,0 +1,284 @@
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dataset_type = 'C2SFloodDataset'
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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=2000,
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max_keep_ckpts=2,
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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=4,
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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=(
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5,
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11,
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),
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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=(
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256,
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256,
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),
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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=(
|
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5,
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11,
|
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17,
|
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23,
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),
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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=[
|
||||
1381.26,
|
||||
1302.05,
|
||||
1179.27,
|
||||
1393.56,
|
||||
2164.76,
|
||||
2561.75,
|
||||
2377.94,
|
||||
2791.13,
|
||||
1998.09,
|
||||
1196.08,
|
||||
],
|
||||
pad_val=0,
|
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seg_pad_val=255,
|
||||
size=(
|
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256,
|
||||
256,
|
||||
),
|
||||
std=[
|
||||
653.25,
|
||||
659.61,
|
||||
779.58,
|
||||
720.45,
|
||||
871.09,
|
||||
1035.57,
|
||||
965.36,
|
||||
1141.71,
|
||||
1019.73,
|
||||
825.01,
|
||||
],
|
||||
type='SegDataPreProcessor'),
|
||||
decode_head=dict(
|
||||
align_corners=False,
|
||||
channels=512,
|
||||
dropout_ratio=0.1,
|
||||
in_channels=[
|
||||
512,
|
||||
512,
|
||||
512,
|
||||
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=4,
|
||||
pool_scales=(
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
6,
|
||||
),
|
||||
type='UPerHead'),
|
||||
neck=dict(
|
||||
in_channels=[
|
||||
1024,
|
||||
1024,
|
||||
1024,
|
||||
1024,
|
||||
],
|
||||
out_channels=512,
|
||||
scales=[
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
],
|
||||
type='MultiLevelNeck'),
|
||||
pretrained=
|
||||
'pretrain/skysensepp_mmcvt_s2.pth',
|
||||
test_cfg=dict(mode='whole'),
|
||||
train_cfg=dict(),
|
||||
type='EncoderDecoder')
|
||||
norm_cfg = None
|
||||
optim_wrapper = dict(
|
||||
constructor='LearningRateDecayOptimizerConstructor',
|
||||
optimizer=dict(
|
||||
betas=(
|
||||
0.9,
|
||||
0.999,
|
||||
), lr=0.0005, type='AdamW', weight_decay=0.015),
|
||||
paramwise_cfg=dict(
|
||||
custom_keys=dict(
|
||||
cls_token=dict(decay_mult=0.0),
|
||||
norm=dict(decay_mult=0.0),
|
||||
pos_embed=dict(decay_mult=0.0)),
|
||||
decay_rate=0.84,
|
||||
decay_type='layer_wise',
|
||||
num_layers=24),
|
||||
type='OptimWrapper')
|
||||
param_scheduler = [
|
||||
dict(
|
||||
begin=0, by_epoch=False, end=1000, start_factor=1e-06,
|
||||
type='LinearLR'),
|
||||
dict(
|
||||
begin=1000,
|
||||
by_epoch=False,
|
||||
end=10000,
|
||||
eta_min=0.0,
|
||||
power=1.0,
|
||||
type='PolyLR'),
|
||||
]
|
||||
randomness = dict(seed=1)
|
||||
resume = False
|
||||
test_cfg = dict(type='TestLoop')
|
||||
test_dataloader = dict(
|
||||
batch_size=1,
|
||||
dataset=dict(
|
||||
ann_file=
|
||||
'rs_datasets/c2smsfloods/c2s_s2_val_mm.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(type='LoadImageFromNpz'),
|
||||
dict(reduce_zero_label=False, type='LoadAnnotationsNpz'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='C2SFloodDataset'),
|
||||
num_workers=4,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=False, type='DefaultSampler'))
|
||||
test_evaluator = dict(
|
||||
iou_metrics=[
|
||||
'mIoU',
|
||||
'mFscore'
|
||||
], type='IoUMetric')
|
||||
test_pipeline = [
|
||||
dict(type='LoadImageFromNpz'),
|
||||
dict(reduce_zero_label=False, type='LoadAnnotationsNpz'),
|
||||
dict(type='PackSegInputs'),
|
||||
]
|
||||
train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=500)
|
||||
train_dataloader = dict(
|
||||
batch_size=2,
|
||||
dataset=dict(
|
||||
ann_file=
|
||||
'rs_datasets/c2smsfloods/c2s_s2_train_mm.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(type='LoadImageFromNpz'),
|
||||
dict(reduce_zero_label=False, type='LoadAnnotationsNpz'),
|
||||
dict(
|
||||
cat_max_ratio=0.75, crop_size=(
|
||||
256,
|
||||
256,
|
||||
), type='RandomCrop'),
|
||||
dict(prob=0.5, type='RandomFlip'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='C2SFloodDataset'),
|
||||
num_workers=4,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=True, type='InfiniteSampler'))
|
||||
tta_model = dict(type='SegTTAModel')
|
||||
tta_pipeline = [
|
||||
dict(backend_args=None, type='LoadImageFromFile'),
|
||||
dict(
|
||||
transforms=[
|
||||
[
|
||||
dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
|
||||
dict(keep_ratio=True, scale_factor=1.2, type='Resize'),
|
||||
dict(keep_ratio=True, scale_factor=1.4, type='Resize'),
|
||||
dict(keep_ratio=True, scale_factor=1.6, type='Resize'),
|
||||
dict(keep_ratio=True, scale_factor=1.8, type='Resize'),
|
||||
dict(keep_ratio=True, scale_factor=2.0, 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(
|
||||
ann_file=
|
||||
'rs_datasets/c2smsfloods/c2s_s2_val_mm.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(type='LoadImageFromNpz'),
|
||||
dict(reduce_zero_label=False, type='LoadAnnotationsNpz'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='C2SFloodDataset'),
|
||||
num_workers=4,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=False, type='DefaultSampler'))
|
||||
val_evaluator = dict(
|
||||
iou_metrics=[
|
||||
'mIoU',
|
||||
], type='IoUMetric')
|
||||
vis_backends = [
|
||||
dict(type='LocalVisBackend'),
|
||||
]
|
||||
visualizer = dict(
|
||||
name='visualizer',
|
||||
type='SegLocalVisualizer',
|
||||
vis_backends=[
|
||||
dict(type='LocalVisBackend'),
|
||||
])
|
||||
227
finetune/configs/cabuar.py
Normal file
227
finetune/configs/cabuar.py
Normal file
@@ -0,0 +1,227 @@
|
||||
default_hooks = dict(
|
||||
checkpoint=dict(
|
||||
by_epoch=False,
|
||||
interval=1000,
|
||||
max_keep_ckpts=1,
|
||||
save_best='mIoU',
|
||||
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(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))
|
||||
launcher = 'pytorch'
|
||||
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=512,
|
||||
in_index=3,
|
||||
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(
|
||||
act_cfg=dict(type='GELU'),
|
||||
attn_drop_rate=0.0,
|
||||
downscale_indices=(5, 11,),
|
||||
drop_path_rate=0.0,
|
||||
drop_rate=0.1,
|
||||
embed_dims=1024,
|
||||
img_size=(256, 256,),
|
||||
in_channels=10,
|
||||
interpolate_mode='bilinear',
|
||||
mlp_ratio=4,
|
||||
norm_cfg=dict(eps=1e-06, requires_grad=True, type='LN'),
|
||||
norm_eval=False,
|
||||
num_heads=16,
|
||||
num_layers=24,
|
||||
out_indices=(5, 11, 17, 23,),
|
||||
patch_size=4,
|
||||
qkv_bias=True,
|
||||
type='VisionTransformer',
|
||||
with_cls_token=False),
|
||||
data_preprocessor=dict(
|
||||
bgr_to_rgb=True,
|
||||
mean=[4.50021132, 6.09891466, 7.50766315, 9.54643074, 12.82568112, 14.29062133, 15.24644993, 15.73945708, 16.60374872, 12.31011599,],
|
||||
pad_val=0,
|
||||
seg_pad_val=255,
|
||||
size=(256, 256,),
|
||||
std=[2.6094148, 2.49566825, 1.37103968, 2.6094148, 2.49566825, 1.37103968, 2.6094148, 2.49566825, 1.37103968, 1.37103968, ],
|
||||
type='SegDataPreProcessor'),
|
||||
decode_head=dict(
|
||||
align_corners=False,
|
||||
channels=512,
|
||||
dropout_ratio=0.1,
|
||||
in_channels=[512, 512, 512, 512,],
|
||||
in_index=[0, 1, 2, 3,],
|
||||
loss_decode=dict(
|
||||
loss_weight=1.0, type='CrossEntropyLoss', balance=True, max_scale=4.0, use_sigmoid=False),
|
||||
norm_cfg=dict(requires_grad=True, type='SyncBN'),
|
||||
num_classes=2,
|
||||
pool_scales=(1, 2, 3, 6,),
|
||||
type='UPerHead'),
|
||||
neck=dict(
|
||||
in_channels=[1024, 1024, 1024, 1024,],
|
||||
out_channels=512,
|
||||
scales=[1, 1, 1, 1,],
|
||||
type='MultiLevelNeck'),
|
||||
pretrained='pretrain/skysensepp_mmcvt_s2.pth',
|
||||
test_cfg=dict(mode='whole'),
|
||||
train_cfg=dict(),
|
||||
type='EncoderDecoder')
|
||||
optim_wrapper = dict(
|
||||
optimizer=dict(betas=(0.9, 0.999,), lr=None, type='AdamW', weight_decay=None,),
|
||||
constructor='LearningRateDecayOptimizerConstructor',
|
||||
paramwise_cfg=dict(
|
||||
num_layers=24,
|
||||
decay_rate=None,
|
||||
decay_type='layer_wise',
|
||||
custom_keys=dict(
|
||||
cls_token=dict(decay_mult=0.0),
|
||||
norm=dict(decay_mult=0.0),
|
||||
pos_embed=dict(decay_mult=0.0))),
|
||||
type='OptimWrapper')
|
||||
param_scheduler = [
|
||||
dict(
|
||||
begin=0, by_epoch=False, end=1000, start_factor=1e-06,
|
||||
type='LinearLR'),
|
||||
dict(
|
||||
begin=1000,
|
||||
by_epoch=False,
|
||||
end=10000,
|
||||
eta_min=0.0,
|
||||
power=1.0,
|
||||
type='PolyLR'),
|
||||
]
|
||||
randomness = dict(seed=20240315)
|
||||
resume = False
|
||||
test_cfg = dict(type='TestLoop')
|
||||
test_dataloader = dict(
|
||||
batch_size=1,
|
||||
dataset=dict(
|
||||
ann_file=
|
||||
'rs_datasets/cabuar/cabura_val_fold_4.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(data_key='image', type='LoadImageFromNpz'),
|
||||
dict(
|
||||
data_key='image',
|
||||
reduce_zero_label=False,
|
||||
type='LoadAnnotationsNpz'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='CABURADataset'),
|
||||
num_workers=4,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=False, type='DefaultSampler'))
|
||||
test_evaluator = dict(
|
||||
iou_metrics=[
|
||||
'mIoU',
|
||||
'mFscore',
|
||||
], type='IoUMetric')
|
||||
test_pipeline = [
|
||||
dict(data_key='image', type='LoadImageFromNpz'),
|
||||
dict(data_key='image', reduce_zero_label=False, type='LoadAnnotationsNpz'),
|
||||
dict(type='PackSegInputs'),
|
||||
]
|
||||
train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=500)
|
||||
train_dataloader = dict(
|
||||
batch_size=1,
|
||||
dataset=dict(
|
||||
ann_file=
|
||||
'rs_datasets/cabuar/cabura_train_fold0_3.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(data_key='image', type='LoadImageFromNpz'),
|
||||
dict(
|
||||
data_key='image',
|
||||
reduce_zero_label=False,
|
||||
type='LoadAnnotationsNpz'),
|
||||
dict(
|
||||
cat_max_ratio=0.75, crop_size=(
|
||||
256,
|
||||
256,
|
||||
), type='RandomCrop'),
|
||||
dict(prob=0.5, type='RandomFlip'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='CABURADataset'),
|
||||
num_workers=4,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=True, type='InfiniteSampler'))
|
||||
tta_model = dict(type='SegTTAModel')
|
||||
tta_pipeline = [
|
||||
dict(backend_args=None, type='LoadImageFromFile'),
|
||||
dict(
|
||||
transforms=[
|
||||
[
|
||||
dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
|
||||
dict(keep_ratio=True, scale_factor=1.2, type='Resize'),
|
||||
dict(keep_ratio=True, scale_factor=1.4, type='Resize'),
|
||||
dict(keep_ratio=True, scale_factor=1.6, type='Resize'),
|
||||
dict(keep_ratio=True, scale_factor=1.8, type='Resize'),
|
||||
dict(keep_ratio=True, scale_factor=2.0, 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(
|
||||
ann_file=
|
||||
'rs_datasets/cabuar/cabura_val_fold_4.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(data_key='image', type='LoadImageFromNpz'),
|
||||
dict(
|
||||
data_key='image',
|
||||
reduce_zero_label=False,
|
||||
type='LoadAnnotationsNpz'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='CABURADataset'),
|
||||
num_workers=4,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=False, type='DefaultSampler'))
|
||||
val_evaluator = dict(
|
||||
iou_metrics=[
|
||||
'mIoU',
|
||||
'mFscore',
|
||||
], type='IoUMetric')
|
||||
vis_backends = [
|
||||
dict(type='LocalVisBackend'),
|
||||
]
|
||||
visualizer = dict(
|
||||
name='visualizer',
|
||||
type='SegLocalVisualizer',
|
||||
vis_backends=[
|
||||
dict(type='LocalVisBackend'),
|
||||
])
|
||||
work_dir = 'work_dirs/ft_cabura_test'
|
||||
414
finetune/configs/germany.py
Normal file
414
finetune/configs/germany.py
Normal file
@@ -0,0 +1,414 @@
|
||||
crop_size = (
|
||||
24,
|
||||
24,
|
||||
)
|
||||
data_preprocessor = dict(
|
||||
bgr_to_rgb=False,
|
||||
mean=[
|
||||
2482.0061841829206,
|
||||
2456.642580060208,
|
||||
2667.8229979675334,
|
||||
2744.9377076257624,
|
||||
3620.1499158373827,
|
||||
4063.9126981046647,
|
||||
3922.2406108776354,
|
||||
4264.908986788407,
|
||||
2453.0070206816135,
|
||||
1774.0019119673998,
|
||||
],
|
||||
pad_val=0,
|
||||
seg_pad_val=255,
|
||||
size=(
|
||||
24,
|
||||
24,
|
||||
),
|
||||
std=[
|
||||
2392.1256366526068,
|
||||
2100.1364646122875,
|
||||
2262.6154840764625,
|
||||
2353.899770400333,
|
||||
2089.598452203458,
|
||||
2057.1247114077073,
|
||||
2013.2108514271458,
|
||||
2041.0248949410561,
|
||||
1380.4643757742374,
|
||||
1243.547946113518,
|
||||
],
|
||||
ts_size=30,
|
||||
type='RSTsSegDataPreProcessor')
|
||||
data_root = 'rs_datasets/'
|
||||
dataset_type = 'GermanyCropDataset'
|
||||
default_hooks = dict(
|
||||
checkpoint=dict(
|
||||
by_epoch=False,
|
||||
interval=2000,
|
||||
max_keep_ckpts=1,
|
||||
save_best='mIoU',
|
||||
type='CheckpointHook'),
|
||||
logger=dict(interval=20, 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))
|
||||
find_unused_parameters = True
|
||||
img_ratios = [
|
||||
0.5,
|
||||
0.75,
|
||||
1.0,
|
||||
1.25,
|
||||
1.5,
|
||||
1.75,
|
||||
]
|
||||
launcher = 'pytorch'
|
||||
load_from = None
|
||||
log_level = 'INFO'
|
||||
log_processor = dict(by_epoch=False)
|
||||
mean = [
|
||||
2482.0061841829206,
|
||||
2456.642580060208,
|
||||
2667.8229979675334,
|
||||
2744.9377076257624,
|
||||
3620.1499158373827,
|
||||
4063.9126981046647,
|
||||
3922.2406108776354,
|
||||
4264.908986788407,
|
||||
2453.0070206816135,
|
||||
1774.0019119673998,
|
||||
]
|
||||
model = dict(
|
||||
auxiliary_head=dict(
|
||||
align_corners=False,
|
||||
channels=256,
|
||||
concat_input=False,
|
||||
dropout_ratio=0.1,
|
||||
in_channels=1024,
|
||||
in_index=3,
|
||||
loss_decode=dict(
|
||||
loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False),
|
||||
norm_cfg=dict(requires_grad=True, type='SyncBN'),
|
||||
num_classes=18,
|
||||
num_convs=1,
|
||||
type='FCNHead'),
|
||||
backbone=dict(
|
||||
act_cfg=dict(type='GELU'),
|
||||
attn_drop_rate=0.0,
|
||||
downscale_indices=[
|
||||
-1,
|
||||
],
|
||||
drop_path_rate=0.0,
|
||||
drop_rate=0.1,
|
||||
embed_dims=1024,
|
||||
img_size=(
|
||||
24,
|
||||
24,
|
||||
),
|
||||
in_channels=10,
|
||||
init_cfg=dict(
|
||||
checkpoint=
|
||||
'pretrain/skysensepp_mmcvt_s2.pth',
|
||||
type='Pretrained'),
|
||||
interpolate_mode='bilinear',
|
||||
mlp_ratio=4,
|
||||
norm_cfg=dict(eps=1e-06, requires_grad=True, type='LN'),
|
||||
norm_eval=False,
|
||||
num_heads=16,
|
||||
num_layers=24,
|
||||
out_indices=(
|
||||
5,
|
||||
11,
|
||||
17,
|
||||
23,
|
||||
),
|
||||
patch_size=4,
|
||||
qkv_bias=True,
|
||||
type='VisionTransformer',
|
||||
with_cls_token=False),
|
||||
data_preprocessor=dict(
|
||||
bgr_to_rgb=False,
|
||||
mean=[
|
||||
2482.0061841829206,
|
||||
2456.642580060208,
|
||||
2667.8229979675334,
|
||||
2744.9377076257624,
|
||||
3620.1499158373827,
|
||||
4063.9126981046647,
|
||||
3922.2406108776354,
|
||||
4264.908986788407,
|
||||
2453.0070206816135,
|
||||
1774.0019119673998,
|
||||
],
|
||||
pad_val=0,
|
||||
seg_pad_val=255,
|
||||
size=(
|
||||
24,
|
||||
24,
|
||||
),
|
||||
std=[
|
||||
2392.1256366526068,
|
||||
2100.1364646122875,
|
||||
2262.6154840764625,
|
||||
2353.899770400333,
|
||||
2089.598452203458,
|
||||
2057.1247114077073,
|
||||
2013.2108514271458,
|
||||
2041.0248949410561,
|
||||
1380.4643757742374,
|
||||
1243.547946113518,
|
||||
],
|
||||
ts_size=30,
|
||||
type='RSTsSegDataPreProcessor'),
|
||||
decode_head=dict(
|
||||
align_corners=False,
|
||||
channels=512,
|
||||
dropout_ratio=0.1,
|
||||
in_channels=[
|
||||
1024,
|
||||
1024,
|
||||
1024,
|
||||
1024,
|
||||
],
|
||||
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=18,
|
||||
pool_scales=(
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
6,
|
||||
),
|
||||
type='UPerHead'),
|
||||
neck=dict(
|
||||
attn_drop_rate=0.0,
|
||||
drop_path_rate=0.3,
|
||||
drop_rate=0.0,
|
||||
embed_dims=1024,
|
||||
in_channels=[
|
||||
768,
|
||||
768,
|
||||
768,
|
||||
768,
|
||||
],
|
||||
in_channels_ml=[
|
||||
1024,
|
||||
1024,
|
||||
1024,
|
||||
1024,
|
||||
],
|
||||
init_cfg=dict(
|
||||
checkpoint=
|
||||
'pretrain/skysensepp_mmcvt_fusion.pth',
|
||||
type='Pretrained'),
|
||||
input_dims=1024,
|
||||
mlp_ratio=4,
|
||||
num_heads=16,
|
||||
num_layers=24,
|
||||
out_channels=768,
|
||||
out_channels_ml=1024,
|
||||
output_cls_token=True,
|
||||
qkv_bias=True,
|
||||
scales=[
|
||||
4,
|
||||
2,
|
||||
1,
|
||||
0.5,
|
||||
],
|
||||
scales_ml=[
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
],
|
||||
ts_size=30,
|
||||
type='FusionMultiLevelNeck',
|
||||
with_cls_token=True),
|
||||
test_cfg=dict(mode='whole'),
|
||||
train_cfg=dict(),
|
||||
type='EncoderDecoder')
|
||||
norm_cfg = dict(requires_grad=True, type='SyncBN')
|
||||
optim_wrapper = dict(
|
||||
optimizer=dict(
|
||||
betas=(
|
||||
0.9,
|
||||
0.999,
|
||||
), lr=0.0001, type='AdamW', weight_decay=0.01),
|
||||
paramwise_cfg=dict(
|
||||
custom_keys=dict(
|
||||
cls_token=dict(decay_mult=0.0),
|
||||
norm=dict(decay_mult=0.0),
|
||||
pos_embed=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=2000, start_factor=1e-06,
|
||||
type='LinearLR'),
|
||||
dict(
|
||||
begin=2000,
|
||||
by_epoch=False,
|
||||
end=20000,
|
||||
eta_min=0.0,
|
||||
power=1.0,
|
||||
type='PolyLR'),
|
||||
]
|
||||
randomness = dict(seed=20240311)
|
||||
resume = False
|
||||
static_graph = True
|
||||
std = [
|
||||
2392.1256366526068,
|
||||
2100.1364646122875,
|
||||
2262.6154840764625,
|
||||
2353.899770400333,
|
||||
2089.598452203458,
|
||||
2057.1247114077073,
|
||||
2013.2108514271458,
|
||||
2041.0248949410561,
|
||||
1380.4643757742374,
|
||||
1243.547946113518,
|
||||
]
|
||||
test_cfg = dict(type='TestLoop')
|
||||
test_dataloader = dict(
|
||||
batch_size=1,
|
||||
dataset=dict(
|
||||
ann_file=
|
||||
'rs_datasets/germany_crop/germany_crop_val.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(data_key='image', ts_size=30, type='LoadTsImageFromNpz'),
|
||||
dict(
|
||||
data_key='image',
|
||||
reduce_zero_label=True,
|
||||
type='LoadAnnotationsNpz'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='GermanyCropDataset'),
|
||||
num_workers=4,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=False, type='DefaultSampler'))
|
||||
test_evaluator = dict(
|
||||
iou_metrics=[
|
||||
'mIoU',
|
||||
'mFscore',
|
||||
], type='IoUMetric')
|
||||
test_pipeline = [
|
||||
dict(data_key='image', ts_size=30, type='LoadTsImageFromNpz'),
|
||||
dict(data_key='image', reduce_zero_label=True, type='LoadAnnotationsNpz'),
|
||||
dict(type='PackSegInputs'),
|
||||
]
|
||||
train_cfg = dict(
|
||||
dynamic_intervals=[
|
||||
(
|
||||
0,
|
||||
1000,
|
||||
),
|
||||
(
|
||||
4000,
|
||||
2000,
|
||||
),
|
||||
(
|
||||
8000,
|
||||
4000,
|
||||
),
|
||||
],
|
||||
max_iters=20000,
|
||||
type='IterBasedTrainLoop',
|
||||
val_interval=2000)
|
||||
train_dataloader = dict(
|
||||
batch_size=2,
|
||||
dataset=dict(
|
||||
ann_file=
|
||||
'rs_datasets/germany_crop/germany_crop_train.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(data_key='image', ts_size=30, type='LoadTsImageFromNpz'),
|
||||
dict(
|
||||
data_key='image',
|
||||
reduce_zero_label=True,
|
||||
type='LoadAnnotationsNpz'),
|
||||
dict(prob=0.5, type='RandomFlip'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='GermanyCropDataset'),
|
||||
num_workers=4,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=True, type='InfiniteSampler'))
|
||||
train_pipeline = [
|
||||
dict(data_key='image', ts_size=30, type='LoadTsImageFromNpz'),
|
||||
dict(data_key='image', reduce_zero_label=True, type='LoadAnnotationsNpz'),
|
||||
dict(prob=0.5, type='RandomFlip'),
|
||||
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(
|
||||
ann_file=
|
||||
'rs_datasets/germany_crop/germany_crop_val.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(data_key='image', ts_size=30, type='LoadTsImageFromNpz'),
|
||||
dict(
|
||||
data_key='image',
|
||||
reduce_zero_label=True,
|
||||
type='LoadAnnotationsNpz'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='GermanyCropDataset'),
|
||||
num_workers=4,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=False, type='DefaultSampler'))
|
||||
val_evaluator = dict(
|
||||
iou_metrics=[
|
||||
'mIoU',
|
||||
'mFscore',
|
||||
], type='IoUMetric')
|
||||
vis_backends = [
|
||||
dict(type='LocalVisBackend'),
|
||||
]
|
||||
visualizer = dict(
|
||||
name='visualizer',
|
||||
type='SegLocalVisualizer',
|
||||
vis_backends=[
|
||||
dict(type='LocalVisBackend'),
|
||||
])
|
||||
work_dir = 'save_germany'
|
||||
297
finetune/configs/sos.py
Normal file
297
finetune/configs/sos.py
Normal file
@@ -0,0 +1,297 @@
|
||||
crop_size = (
|
||||
256,
|
||||
256,
|
||||
)
|
||||
data_preprocessor = dict(
|
||||
bgr_to_rgb=True,
|
||||
mean=[
|
||||
83.37,
|
||||
83.37,
|
||||
83.37,
|
||||
],
|
||||
pad_val=0,
|
||||
seg_pad_val=255,
|
||||
size=(
|
||||
256,
|
||||
256,
|
||||
),
|
||||
std=[
|
||||
40.45,
|
||||
40.45,
|
||||
40.45,
|
||||
],
|
||||
type='SegDataPreProcessor')
|
||||
data_root = 'rs_datasets/'
|
||||
dataset_type = 'SOSDataset'
|
||||
default_hooks = dict(
|
||||
checkpoint=dict(
|
||||
by_epoch=False,
|
||||
interval=1000,
|
||||
max_keep_ckpts=2,
|
||||
save_best='mIoU',
|
||||
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(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,
|
||||
]
|
||||
launcher = 'pytorch'
|
||||
load_from = None
|
||||
log_level = 'INFO'
|
||||
log_processor = dict(by_epoch=False)
|
||||
mean = [
|
||||
83.37,
|
||||
83.37,
|
||||
83.37,
|
||||
]
|
||||
model = dict(
|
||||
backbone=dict(
|
||||
act_cfg=dict(type='GELU'),
|
||||
attn_drop_rate=0.0,
|
||||
downscale_indices=[
|
||||
-1,
|
||||
],
|
||||
drop_path_rate=0.0,
|
||||
drop_rate=0.1,
|
||||
embed_dims=1024,
|
||||
img_size=(
|
||||
256,
|
||||
256,
|
||||
),
|
||||
in_channels=2,
|
||||
interpolate_mode='bilinear',
|
||||
mlp_ratio=4,
|
||||
norm_cfg=dict(eps=1e-06, requires_grad=True, type='LN'),
|
||||
norm_eval=False,
|
||||
num_heads=16,
|
||||
num_layers=24,
|
||||
out_indices=(
|
||||
5,
|
||||
11,
|
||||
17,
|
||||
23,
|
||||
),
|
||||
patch_size=4,
|
||||
qkv_bias=True,
|
||||
type='VisionTransformer',
|
||||
with_cls_token=False),
|
||||
data_preprocessor=dict(
|
||||
bgr_to_rgb=True,
|
||||
mean=[
|
||||
83.37,
|
||||
83.37,
|
||||
83.37,
|
||||
],
|
||||
pad_val=0,
|
||||
seg_pad_val=255,
|
||||
size=(
|
||||
256,
|
||||
256,
|
||||
),
|
||||
std=[
|
||||
40.45,
|
||||
40.45,
|
||||
40.45,
|
||||
],
|
||||
type='SegDataPreProcessor'),
|
||||
decode_head=dict(
|
||||
align_corners=False,
|
||||
channels=512,
|
||||
dropout_ratio=0.1,
|
||||
in_channels=[
|
||||
1024,
|
||||
1024,
|
||||
1024,
|
||||
1024,
|
||||
],
|
||||
in_index=[
|
||||
0,
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
],
|
||||
loss_decode=dict(
|
||||
loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
|
||||
norm_cfg=dict(requires_grad=True, type='SyncBN'),
|
||||
num_classes=2,
|
||||
pool_scales=(
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
6,
|
||||
),
|
||||
type='UPerHead'),
|
||||
neck=dict(
|
||||
in_channels=[
|
||||
1024,
|
||||
1024,
|
||||
1024,
|
||||
1024,
|
||||
],
|
||||
out_channels=1024,
|
||||
scales=[
|
||||
4,
|
||||
2,
|
||||
1,
|
||||
0.5,
|
||||
],
|
||||
type='MultiLevelNeck'),
|
||||
pretrained=
|
||||
'pretrain/skysensepp_mmcvt_s1.pth',
|
||||
test_cfg=dict(mode='whole'),
|
||||
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(
|
||||
cls_token=dict(decay_mult=0.0),
|
||||
norm=dict(decay_mult=0.0),
|
||||
pos_embed=dict(decay_mult=0.0))),
|
||||
type='OptimWrapper')
|
||||
param_scheduler = [
|
||||
dict(
|
||||
begin=0, by_epoch=False, end=1000, start_factor=1e-06,
|
||||
type='LinearLR'),
|
||||
dict(
|
||||
begin=1000,
|
||||
by_epoch=False,
|
||||
end=10000,
|
||||
eta_min=0.0,
|
||||
power=1.0,
|
||||
type='PolyLR'),
|
||||
]
|
||||
randomness = dict(seed=0)
|
||||
resume = False
|
||||
std = [
|
||||
40.45,
|
||||
40.45,
|
||||
40.45,
|
||||
]
|
||||
test_cfg = dict(type='TestLoop')
|
||||
test_dataloader = dict(
|
||||
batch_size=1,
|
||||
dataset=dict(
|
||||
ann_file=
|
||||
'rs_datasets/sos/sos_test_sentinel.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(type='LoadImageFromFile'),
|
||||
dict(reduce_zero_label=False, type='LoadAnnotations'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='SOSDataset'),
|
||||
num_workers=2,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=False, type='DefaultSampler'))
|
||||
test_evaluator = dict(
|
||||
iou_metrics=[
|
||||
'mIoU',
|
||||
'mFscore',
|
||||
], type='IoUMetric')
|
||||
test_pipeline = [
|
||||
dict(type='LoadImageFromFile'),
|
||||
dict(reduce_zero_label=False, type='LoadAnnotations'),
|
||||
dict(type='PackSegInputs'),
|
||||
]
|
||||
train_cfg = dict(max_iters=0, type='IterBasedTrainLoop', val_interval=500)
|
||||
train_dataloader = dict(
|
||||
batch_size=1,
|
||||
dataset=dict(
|
||||
ann_file=
|
||||
'rs_datasets/sos/sos_train_sentinel.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(type='LoadImageFromFile'),
|
||||
dict(reduce_zero_label=False, type='LoadAnnotations'),
|
||||
dict(prob=0.5, type='RandomFlip'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='SOSDataset'),
|
||||
num_workers=2,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=True, type='InfiniteSampler'))
|
||||
train_pipeline = [
|
||||
dict(type='LoadImageFromFile'),
|
||||
dict(reduce_zero_label=False, type='LoadAnnotations'),
|
||||
dict(prob=0.5, type='RandomFlip'),
|
||||
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(
|
||||
ann_file=
|
||||
'rs_datasets/sos/sos_test_sentinel.json',
|
||||
data_prefix=dict(img_path='images', seg_map_path='idx_labels'),
|
||||
data_root='rs_datasets/',
|
||||
pipeline=[
|
||||
dict(type='LoadImageFromFile'),
|
||||
dict(reduce_zero_label=False, type='LoadAnnotations'),
|
||||
dict(type='PackSegInputs'),
|
||||
],
|
||||
type='SOSDataset'),
|
||||
num_workers=2,
|
||||
persistent_workers=True,
|
||||
sampler=dict(shuffle=False, type='DefaultSampler'))
|
||||
val_evaluator = dict(
|
||||
iou_metrics=[
|
||||
'mIoU',
|
||||
'mFscore',
|
||||
], type='IoUMetric')
|
||||
vis_backends = [
|
||||
dict(type='LocalVisBackend'),
|
||||
]
|
||||
visualizer = dict(
|
||||
name='visualizer',
|
||||
type='SegLocalVisualizer',
|
||||
vis_backends=[
|
||||
dict(type='LocalVisBackend'),
|
||||
])
|
||||
work_dir = 'save_sos/'
|
||||
Reference in New Issue
Block a user