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_base_ = [ |
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'./datasets/hsi_detection4x.py', './_base_/default_runtime.py' |
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] |
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in_channels = 30 |
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model = dict( |
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type='DABDETR', |
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num_queries=300, |
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with_random_refpoints=False, |
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num_patterns=0, |
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data_preprocessor=dict( |
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type='HSIDetDataPreprocessor', |
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pad_size_divisor=1), |
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backbone=dict( |
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type='ResNet', |
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depth=50, |
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num_stages=4, |
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out_indices=(3, ), |
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frozen_stages=-1, |
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norm_cfg=dict(type='BN', requires_grad=False), |
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in_channels=in_channels, |
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norm_eval=True, |
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style='pytorch', |
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), |
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neck=dict( |
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type='ChannelMapper', |
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in_channels=[2048], |
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kernel_size=1, |
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out_channels=256, |
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act_cfg=None, |
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norm_cfg=None, |
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num_outs=1), |
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encoder=dict( |
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num_layers=6, |
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layer_cfg=dict( |
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self_attn_cfg=dict( |
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embed_dims=256, num_heads=8, dropout=0., batch_first=True), |
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ffn_cfg=dict( |
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embed_dims=256, |
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feedforward_channels=2048, |
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num_fcs=2, |
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ffn_drop=0., |
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act_cfg=dict(type='PReLU')))), |
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decoder=dict( |
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num_layers=6, |
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query_dim=4, |
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query_scale_type='cond_elewise', |
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with_modulated_hw_attn=True, |
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layer_cfg=dict( |
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self_attn_cfg=dict( |
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embed_dims=256, |
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num_heads=8, |
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attn_drop=0., |
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proj_drop=0., |
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cross_attn=False), |
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cross_attn_cfg=dict( |
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embed_dims=256, |
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num_heads=8, |
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attn_drop=0., |
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proj_drop=0., |
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cross_attn=True), |
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ffn_cfg=dict( |
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embed_dims=256, |
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feedforward_channels=2048, |
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num_fcs=2, |
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ffn_drop=0., |
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act_cfg=dict(type='PReLU'))), |
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return_intermediate=True), |
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positional_encoding=dict(num_feats=128, temperature=20, normalize=True), |
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bbox_head=dict( |
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type='DABDETRHead', |
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num_classes=16, |
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embed_dims=256, |
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loss_cls=dict( |
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type='FocalLoss', |
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use_sigmoid=True, |
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gamma=2.0, |
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alpha=0.25, |
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loss_weight=1.0), |
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loss_bbox=dict(type='L1Loss', loss_weight=5.0), |
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loss_iou=dict(type='GIoULoss', loss_weight=2.0)), |
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train_cfg=dict( |
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assigner=dict( |
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type='HungarianAssigner', |
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match_costs=[ |
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dict(type='FocalLossCost', weight=2., eps=1e-8), |
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dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), |
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dict(type='IoUCost', iou_mode='giou', weight=2.0) |
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])), |
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test_cfg=dict(max_per_img=300)) |
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optim_wrapper = dict( |
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type='OptimWrapper', |
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optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001), |
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clip_grad=dict(max_norm=0.1, norm_type=2), |
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paramwise_cfg=dict( |
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custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)})) |
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max_epochs = 100 |
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train_cfg = dict( |
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type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=20) |
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|
val_cfg = dict(type='ValLoop') |
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|
test_cfg = dict(type='TestLoop') |
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|
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param_scheduler = [ |
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dict( |
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type='MultiStepLR', |
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begin=0, |
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end=max_epochs, |
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by_epoch=True, |
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milestones=[90], |
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gamma=0.1) |
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] |
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auto_scale_lr = dict(base_batch_size=4, enable=False) |
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|
