init
This commit is contained in:
esenke
414
finetune/configs/germany.py
Normal file
414
finetune/configs/germany.py
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@@ -0,0 +1,414 @@
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crop_size = (
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24,
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24,
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)
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data_preprocessor = dict(
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bgr_to_rgb=False,
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mean=[
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2482.0061841829206,
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2456.642580060208,
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2667.8229979675334,
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2744.9377076257624,
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3620.1499158373827,
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4063.9126981046647,
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3922.2406108776354,
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4264.908986788407,
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2453.0070206816135,
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1774.0019119673998,
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],
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pad_val=0,
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seg_pad_val=255,
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size=(
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24,
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24,
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),
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std=[
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2392.1256366526068,
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2100.1364646122875,
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2262.6154840764625,
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2353.899770400333,
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2089.598452203458,
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2057.1247114077073,
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2013.2108514271458,
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2041.0248949410561,
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1380.4643757742374,
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1243.547946113518,
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],
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ts_size=30,
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type='RSTsSegDataPreProcessor')
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data_root = 'rs_datasets/'
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dataset_type = 'GermanyCropDataset'
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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=1,
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save_best='mIoU',
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type='CheckpointHook'),
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logger=dict(interval=20, 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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find_unused_parameters = True
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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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2482.0061841829206,
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2456.642580060208,
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2667.8229979675334,
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2744.9377076257624,
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3620.1499158373827,
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4063.9126981046647,
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3922.2406108776354,
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4264.908986788407,
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2453.0070206816135,
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1774.0019119673998,
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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=1024,
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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=18,
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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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-1,
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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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24,
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24,
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),
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in_channels=10,
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init_cfg=dict(
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checkpoint=
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'pretrain/skysensepp_mmcvt_s2.pth',
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type='Pretrained'),
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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=False,
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mean=[
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2482.0061841829206,
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2456.642580060208,
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2667.8229979675334,
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2744.9377076257624,
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3620.1499158373827,
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4063.9126981046647,
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3922.2406108776354,
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4264.908986788407,
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2453.0070206816135,
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1774.0019119673998,
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],
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pad_val=0,
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seg_pad_val=255,
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size=(
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24,
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24,
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),
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std=[
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2392.1256366526068,
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2100.1364646122875,
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2262.6154840764625,
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2353.899770400333,
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2089.598452203458,
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2057.1247114077073,
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2013.2108514271458,
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2041.0248949410561,
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1380.4643757742374,
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1243.547946113518,
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],
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ts_size=30,
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type='RSTsSegDataPreProcessor'),
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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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1024,
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1024,
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1024,
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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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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=18,
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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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attn_drop_rate=0.0,
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drop_path_rate=0.3,
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drop_rate=0.0,
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embed_dims=1024,
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in_channels=[
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768,
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768,
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768,
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768,
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],
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in_channels_ml=[
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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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init_cfg=dict(
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checkpoint=
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'pretrain/skysensepp_mmcvt_fusion.pth',
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type='Pretrained'),
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input_dims=1024,
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mlp_ratio=4,
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num_heads=16,
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num_layers=24,
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out_channels=768,
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out_channels_ml=1024,
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output_cls_token=True,
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qkv_bias=True,
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scales=[
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4,
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2,
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1,
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0.5,
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],
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scales_ml=[
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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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ts_size=30,
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type='FusionMultiLevelNeck',
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with_cls_token=True),
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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=0.0001, 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=2000, start_factor=1e-06,
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type='LinearLR'),
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dict(
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begin=2000,
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by_epoch=False,
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end=20000,
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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=20240311)
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resume = False
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static_graph = True
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std = [
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2392.1256366526068,
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2100.1364646122875,
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2262.6154840764625,
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2353.899770400333,
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2089.598452203458,
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2057.1247114077073,
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2013.2108514271458,
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2041.0248949410561,
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1380.4643757742374,
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1243.547946113518,
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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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'rs_datasets/germany_crop/germany_crop_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(data_key='image', ts_size=30, type='LoadTsImageFromNpz'),
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dict(
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data_key='image',
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reduce_zero_label=True,
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type='LoadAnnotationsNpz'),
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dict(type='PackSegInputs'),
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],
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type='GermanyCropDataset'),
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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', ts_size=30, type='LoadTsImageFromNpz'),
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dict(data_key='image', reduce_zero_label=True, type='LoadAnnotationsNpz'),
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dict(type='PackSegInputs'),
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]
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train_cfg = dict(
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dynamic_intervals=[
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(
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0,
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1000,
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),
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(
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4000,
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2000,
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),
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(
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8000,
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4000,
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),
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],
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max_iters=20000,
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type='IterBasedTrainLoop',
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val_interval=2000)
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train_dataloader = dict(
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batch_size=2,
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dataset=dict(
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ann_file=
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'rs_datasets/germany_crop/germany_crop_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(data_key='image', ts_size=30, type='LoadTsImageFromNpz'),
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dict(
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data_key='image',
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reduce_zero_label=True,
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type='LoadAnnotationsNpz'),
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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='GermanyCropDataset'),
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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(data_key='image', ts_size=30, type='LoadTsImageFromNpz'),
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dict(data_key='image', reduce_zero_label=True, type='LoadAnnotationsNpz'),
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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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'rs_datasets/germany_crop/germany_crop_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(data_key='image', ts_size=30, type='LoadTsImageFromNpz'),
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dict(
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data_key='image',
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reduce_zero_label=True,
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type='LoadAnnotationsNpz'),
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dict(type='PackSegInputs'),
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],
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type='GermanyCropDataset'),
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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_germany'
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Reference in New Issue
Block a user