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pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d.py
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pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d.py
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_base_ = [
'../_base_/models/pointpillars_hv_fpn_nus.py',
'../_base_/datasets/nus-3d.py',
'../_base_/schedules/schedule-2x.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
pts_neck=dict(
_delete_=True,
type='SECONDFPN',
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
in_channels=[64, 128, 256],
upsample_strides=[1, 2, 4],
out_channels=[128, 128, 128]),
pts_bbox_head=dict(
in_channels=384,
feat_channels=384,
anchor_generator=dict(
_delete_=True,
type='AlignedAnchor3DRangeGenerator',
ranges=[
[-49.6, -49.6, -1.80032795, 49.6, 49.6, -1.80032795],
[-49.6, -49.6, -1.74440365, 49.6, 49.6, -1.74440365],
[-49.6, -49.6, -1.68526504, 49.6, 49.6, -1.68526504],
[-49.6, -49.6, -1.67339111, 49.6, 49.6, -1.67339111],
[-49.6, -49.6, -1.61785072, 49.6, 49.6, -1.61785072],
[-49.6, -49.6, -1.80984986, 49.6, 49.6, -1.80984986],
[-49.6, -49.6, -1.763965, 49.6, 49.6, -1.763965],
],
sizes=[
[4.60718145, 1.95017717, 1.72270761], # car
[6.73778078, 2.4560939, 2.73004906], # truck
[12.01320693, 2.87427237, 3.81509561], # trailer
[1.68452161, 0.60058911, 1.27192197], # bicycle
[0.7256437, 0.66344886, 1.75748069], # pedestrian
[0.40359262, 0.39694519, 1.06232151], # traffic_cone
[0.48578221, 2.49008838, 0.98297065], # barrier
],
custom_values=[0, 0],
rotations=[0, 1.57],
reshape_out=True)))
# For nuScenes dataset, we usually evaluate the model at the end of training.
# Since the models are trained by 24 epochs by default, we set evaluation
# interval to be 24. Please change the interval accordingly if you do not
# use a default schedule.
train_cfg = dict(val_interval=24)