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_base_ = [
'./datasets/hsi_detection.py', '../_base_/default_runtime.py'
]
# fp16 = dict(loss_scale=512.)
# pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
num_levels = 2
in_channels = 30
embed_dims =256 # embed_dims256
model = dict(
type='DINO',
num_queries=900, # num_matching_queries 900
with_box_refine=True,
as_two_stage=True,
num_feature_levels=num_levels,
candidate_bboxes_size = 0.01,
scale_gt_bboxes_size = 0,# new parament for point detection
data_preprocessor=dict(
type='HSIDetDataPreprocessor'),
backbone=dict(
type='No_backbone_ST',
in_channels=in_channels,
embed_dims=embed_dims,
patch_size=(1,),
# Please only add indices that would be used
# in FPN, otherwise some parameter will not be used
num_levels =num_levels
),
encoder=dict(
num_layers=6,
layer_cfg=dict(
self_attn_cfg=dict(embed_dims=embed_dims, num_levels=num_levels,num_points=4, #local_attn_type ='initial_version',
dropout=0.0), # 0.1 for DeformDETR
ffn_cfg=dict(
embed_dims=embed_dims,
feedforward_channels=embed_dims*8, # 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=embed_dims, num_heads=8,
dropout=0.0), # 0.1 for DeformDETR
cross_attn_cfg=dict(embed_dims=embed_dims, num_levels=num_levels, num_points=4, #local_attn_type = 'initial_version',
dropout=0.0), # 0.1 for DeformDETR
ffn_cfg=dict(
embed_dims=embed_dims,
feedforward_channels=embed_dims*8, # 1024 for DeformDETR 2048 for dino
ffn_drop=0.0)), # 0.1 for DeformDETR norm_cfg=dict(type='LN')
post_norm_cfg=None),
positional_encoding=dict(
num_feats=embed_dims//2,
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,
pre_bboxes_round=True, # new parament for point detection
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, # 0.5
box_noise_scale=1, # 0.4 for DN-DETR 1
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=4,)
#
# # 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)
# auto_scale_lr = dict(base_batch_size=4)
auto_scale_lr = dict(base_batch_size=4) |