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ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py
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ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py
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_base_ = [
'../_base_/models/pointpillars_hv_fpn_lyft.py',
'../_base_/datasets/lyft-3d.py',
'../_base_/schedules/schedule-2x.py',
'../_base_/default_runtime.py',
]
point_cloud_range = [-100, -100, -5, 100, 100, 3]
# Note that the order of class names should be consistent with
# the following anchors' order
class_names = [
'bicycle', 'motorcycle', 'pedestrian', 'animal', 'car',
'emergency_vehicle', 'bus', 'other_vehicle', 'truck'
]
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range)
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=2, num_workers=4, dataset=dict(pipeline=train_pipeline))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
# model settings
model = dict(
data_preprocessor=dict(
voxel_layer=dict(point_cloud_range=[-100, -100, -5, 100, 100, 3])),
pts_voxel_encoder=dict(
feat_channels=[32, 64],
point_cloud_range=[-100, -100, -5, 100, 100, 3]),
pts_middle_encoder=dict(output_shape=[800, 800]),
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(
_delete_=True,
type='ShapeAwareHead',
num_classes=9,
in_channels=384,
feat_channels=384,
use_direction_classifier=True,
anchor_generator=dict(
type='AlignedAnchor3DRangeGeneratorPerCls',
ranges=[[-100, -100, -1.0709302, 100, 100, -1.0709302],
[-100, -100, -1.3220503, 100, 100, -1.3220503],
[-100, -100, -0.9122268, 100, 100, -0.9122268],
[-100, -100, -1.8012227, 100, 100, -1.8012227],
[-100, -100, -1.0715024, 100, 100, -1.0715024],
[-100, -100, -0.8871424, 100, 100, -0.8871424],
[-100, -100, -0.3519405, 100, 100, -0.3519405],
[-100, -100, -0.6276341, 100, 100, -0.6276341],
[-100, -100, -0.3033737, 100, 100, -0.3033737]],
sizes=[
[1.76, 0.63, 1.44], # bicycle
[2.35, 0.96, 1.59], # motorcycle
[0.80, 0.76, 1.76], # pedestrian
[0.73, 0.35, 0.50], # animal
[4.75, 1.92, 1.71], # car
[6.52, 2.42, 2.34], # emergency vehicle
[12.70, 2.92, 3.42], # bus
[8.17, 2.75, 3.20], # other vehicle
[10.24, 2.84, 3.44] # truck
],
custom_values=[],
rotations=[0, 1.57],
reshape_out=False),
tasks=[
dict(
num_class=2,
class_names=['bicycle', 'motorcycle'],
shared_conv_channels=(64, 64),
shared_conv_strides=(1, 1),
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01)),
dict(
num_class=2,
class_names=['pedestrian', 'animal'],
shared_conv_channels=(64, 64),
shared_conv_strides=(1, 1),
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01)),
dict(
num_class=2,
class_names=['car', 'emergency_vehicle'],
shared_conv_channels=(64, 64, 64),
shared_conv_strides=(2, 1, 1),
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01)),
dict(
num_class=3,
class_names=['bus', 'other_vehicle', 'truck'],
shared_conv_channels=(64, 64, 64),
shared_conv_strides=(2, 1, 1),
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01))
],
assign_per_class=True,
diff_rad_by_sin=True,
dir_offset=-0.7854, # -pi/4
dir_limit_offset=0,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=7),
loss_cls=dict(
type='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(
type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
loss_dir=dict(
type='mmdet.CrossEntropyLoss', use_sigmoid=False,
loss_weight=0.2)),
# model training and testing settings
train_cfg=dict(
_delete_=True,
pts=dict(
assigner=[
dict( # bicycle
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.55,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
dict( # motorcycle
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.55,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
dict( # pedestrian
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.55,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
dict( # animal
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.55,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
dict( # car
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
min_pos_iou=0.45,
ignore_iof_thr=-1),
dict( # emergency vehicle
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.55,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
dict( # bus
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
min_pos_iou=0.45,
ignore_iof_thr=-1),
dict( # other vehicle
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.55,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
dict( # truck
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
min_pos_iou=0.45,
ignore_iof_thr=-1)
],
allowed_border=0,
code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
pos_weight=-1,
debug=False)))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (16 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=32)