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model = dict(
type="TadTR", # Done
projection=dict(
type="ConvSingleProj",
in_channels=2048,
out_channels=256,
num_convs=1,
conv_cfg=dict(kernel_size=1, padding=0),
norm_cfg=dict(type="GN", num_groups=32),
act_cfg=None,
),
transformer=dict(
type="TadTRTransformer",
num_proposals=40,
num_classes=20,
with_act_reg=True,
roi_size=16,
roi_extend_ratio=0.25,
aux_loss=True,
position_embedding=dict(
type="PositionEmbeddingSine",
num_pos_feats=256,
temperature=10000,
offset=-0.5,
normalize=True,
),
encoder=dict(
type="DeformableDETREncoder",
embed_dim=256,
num_heads=8,
num_points=4,
attn_dropout=0.1,
ffn_dim=1024,
ffn_dropout=0.1,
num_layers=4,
num_feature_levels=1,
post_norm=False,
),
decoder=dict(
type="DeformableDETRDecoder",
embed_dim=256,
num_heads=8,
num_points=4,
attn_dropout=0.1,
ffn_dim=1024,
ffn_dropout=0.1,
num_layers=4,
num_feature_levels=1,
return_intermediate=True,
),
loss=dict(
type="TadTRSetCriterion",
num_classes=20,
matcher=dict(
type="HungarianMatcher",
cost_class=6.0,
cost_bbox=5.0,
cost_giou=2.0,
cost_class_type="focal_loss_cost", # ce_cost, focal_loss_cost
iou_type="iou",
use_multi_class=False,
),
loss_class_type="focal_loss", # ce_loss, focal_loss
weight_dict=dict(
loss_class=2.0,
loss_bbox=5.0,
loss_iou=2.0,
loss_actionness=4.0,
),
use_multi_class=False,
),
),
)
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