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model = dict(
type='PSGTr',
backbone=dict(type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 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')),
bbox_head=dict(
type='PSGFormerHead',
num_classes=80,
num_relations=117,
in_channels=2048,
transformer=dict(
type='DualTransformer',
encoder=dict(type='DetrTransformerEncoder',
num_layers=6,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=[
dict(type='MultiheadAttention',
embed_dims=256,
num_heads=8,
dropout=0.1)
],
feedforward_channels=2048,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'ffn',
'norm'))),
decoder1=dict(type='DetrTransformerDecoder',
return_intermediate=True,
num_layers=6,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=dict(type='MultiheadAttention',
embed_dims=256,
num_heads=8,
dropout=0.1),
feedforward_channels=2048,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm',
'cross_attn', 'norm', 'ffn',
'norm'))),
decoder2=dict(type='DetrTransformerDecoder',
return_intermediate=True,
num_layers=6,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=dict(type='MultiheadAttention',
embed_dims=256,
num_heads=8,
dropout=0.1),
feedforward_channels=2048,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm',
'cross_attn', 'norm', 'ffn',
'norm'))),
),
positional_encoding=dict(type='SinePositionalEncoding',
num_feats=128,
normalize=True),
rel_loss_cls=dict(type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=2.0,
class_weight=1.0),
sub_id_loss=dict(type='MultilabelCrossEntropy', loss_weight=2.0),
obj_id_loss=dict(type='MultilabelCrossEntropy', loss_weight=2.0),
loss_cls=dict(type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=4.0,
class_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=3.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0),
focal_loss=dict(type='BCEFocalLoss', loss_weight=1.0),
dice_loss=dict(type='psgtrDiceLoss', loss_weight=1.0)),
# training and testing settings
train_cfg=dict(id_assigner=dict(type='IdMatcher',
sub_id_cost=dict(type='ClassificationCost',
weight=1.),
obj_id_cost=dict(type='ClassificationCost',
weight=1.),
r_cls_cost=dict(type='ClassificationCost',
weight=1.)),
bbox_assigner=dict(type='HungarianAssigner',
cls_cost=dict(type='ClassificationCost',
weight=4.0),
reg_cost=dict(type='BBoxL1Cost',
weight=3.0),
iou_cost=dict(type='IoUCost',
iou_mode='giou',
weight=2.0))),
test_cfg=dict(max_per_img=100))
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