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_base_ = ['./multiview-dfm_r101-dcn_16xb2_waymoD5-3d-3class.py']
model = dict(
bbox_head=dict(
_delete_=True,
type='CenterHead',
in_channels=256,
tasks=[
dict(num_class=1, class_names=['Pedestrian']),
dict(num_class=1, class_names=['Cyclist']),
dict(num_class=1, class_names=['Car']),
],
common_heads=dict(reg=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2)),
share_conv_channel=64,
bbox_coder=dict(
type='CenterPointBBoxCoder',
post_center_range=[-35.0, -75.0, -2, 75.0, 75.0, 4],
pc_range=[-35.0, -75.0, -2, 75.0, 75.0, 4],
max_num=2000,
score_threshold=0,
out_size_factor=1,
voxel_size=(.50, .50),
code_size=7),
separate_head=dict(
type='SeparateHead', init_bias=-2.19, final_kernel=3),
loss_cls=dict(type='mmdet.GaussianFocalLoss', reduction='mean'),
loss_bbox=dict(
type='mmdet.L1Loss', reduction='mean', loss_weight=0.25),
norm_bbox=True),
train_cfg=dict(
_delete_=True,
grid_size=[220, 300, 1],
voxel_size=(0.5, 0.5, 6),
out_size_factor=1,
dense_reg=1,
gaussian_overlap=0.1,
max_objs=500,
min_radius=2,
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
point_cloud_range=[-35.0, -75.0, -2, 75.0, 75.0, 4]),
test_cfg=dict(
_delete_=True,
post_center_limit_range=[-35.0, -75.0, -2, 75.0, 75.0, 4],
max_per_img=4096,
max_pool_nms=False,
min_radius=[0.5, 2, 6],
score_threshold=0,
out_size_factor=1,
voxel_size=(0.5, 0.5),
nms_type='circle',
pre_max_size=2000,
post_max_size=200,
nms_thr=0.2))
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