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_base_ = [
'./datasets/hsi_detection4x.py', './_base_/default_runtime.py'
]
in_channels = 30
model = dict(
type='DABDETR',
num_queries=300,
with_random_refpoints=False,
num_patterns=0,
data_preprocessor=dict(
type='HSIDetDataPreprocessor',
pad_size_divisor=1),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
frozen_stages=-1,
norm_cfg=dict(type='BN', requires_grad=False),
in_channels=in_channels,
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='ChannelMapper',
in_channels=[2048],
kernel_size=1,
out_channels=256,
act_cfg=None,
norm_cfg=None,
num_outs=1),
encoder=dict(
num_layers=6,
layer_cfg=dict(
self_attn_cfg=dict(
embed_dims=256, num_heads=8, dropout=0., batch_first=True),
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
ffn_drop=0.,
act_cfg=dict(type='PReLU')))),
decoder=dict(
num_layers=6,
query_dim=4,
query_scale_type='cond_elewise',
with_modulated_hw_attn=True,
layer_cfg=dict(
self_attn_cfg=dict(
embed_dims=256,
num_heads=8,
attn_drop=0.,
proj_drop=0.,
cross_attn=False),
cross_attn_cfg=dict(
embed_dims=256,
num_heads=8,
attn_drop=0.,
proj_drop=0.,
cross_attn=True),
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
ffn_drop=0.,
act_cfg=dict(type='PReLU'))),
return_intermediate=True),
positional_encoding=dict(num_feats=128, temperature=20, normalize=True),
bbox_head=dict(
type='DABDETRHead',
num_classes=16,
embed_dims=256,
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='HungarianAssigner',
match_costs=[
dict(type='FocalLossCost', weight=2., eps=1e-8),
dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
dict(type='IoUCost', iou_mode='giou', weight=2.0)
])),
test_cfg=dict(max_per_img=300))
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001),
clip_grad=dict(max_norm=0.1, norm_type=2),
paramwise_cfg=dict(
custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
# learning policy
max_epochs = 100
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=20)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[90],
gamma=0.1)
]
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=4, enable=False)
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