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_base_ = [
'./datasets/hsi_detection4x.py', './_base_/default_runtime.py'
]
in_channels = 30
model = dict(
type='DINO',
num_queries=900, # num_matching_queries 900
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
type='HSIDetDataPreprocessor',
pad_size_divisor=1),
backbone=dict(
type='ResNet',
in_channels=in_channels,
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')
),
neck=dict(
type='ChannelMapper',
in_channels=[512, 1024, 2048],
kernel_size=1,
out_channels=256,
act_cfg=None,
norm_cfg=dict(type='GN', num_groups=32),
num_outs=4),
encoder=dict(
num_layers=6,
layer_cfg=dict(
self_attn_cfg=dict(embed_dims=256, num_levels=4,
dropout=0.0), # 0.1 for DeformDETR
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048, # 1024 for DeformDETR
ffn_drop=0.0))), # 0.1 for DeformDETR
decoder=dict(
num_layers=6,
return_intermediate=True,
layer_cfg=dict(
self_attn_cfg=dict(embed_dims=256, num_heads=8,
dropout=0.0), # 0.1 for DeformDETR
cross_attn_cfg=dict(embed_dims=256, num_levels=4,
dropout=0.0), # 0.1 for DeformDETR
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048, # 1024 for DeformDETR
ffn_drop=0.0)), # 0.1 for DeformDETR
post_norm_cfg=None),
positional_encoding=dict(
num_feats=128,
normalize=True,
offset=0.0, # -0.5 for DeformDETR
temperature=20), # 10000 for DeformDETR
bbox_head=dict(
type='DINOHead',
num_classes=8,
sync_cls_avg_factor=True,
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0), # 2.0 in DeformDETR
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
dn_cfg=dict( # TODO: Move to model.train_cfg ?
label_noise_scale=0.5,
box_noise_scale=1.0, # 0.4 for DN-DETR
group_cfg=dict(dynamic=True, num_groups=None,
num_dn_queries=100)), # TODO: half num_dn_queries
# training and testing settings
train_cfg=dict(
assigner=dict(
type='HungarianAssigner',
match_costs=[
dict(type='FocalLossCost', weight=2.0),
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)) # 100 for DeformDETR
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(
type='AdamW',
lr=0.0001, # 0.0002 for DeformDETR
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)})
) # custom_keys contains sampling_offsets and reference_points in DeformDETR # noqa
# learning policy
max_epochs = 12
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=12)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[11],
gamma=0.1)
]
train_dataloader = dict(
batch_size=4,)
test_dataloader = dict(
batch_size=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) |