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_base_ = [
'../_base_/datasets/waymoD3-fov-mono3d-3class.py',
'../_base_/models/pgd.py', '../_base_/schedules/mmdet-schedule-1x.py',
'../_base_/default_runtime.py'
]
# load_from = '../Depth-from-Motion/checkpoints/pgd_init.pth'
# model settings
model = dict(
backbone=dict(
type='mmdet.ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'),
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, False, True, True)),
neck=dict(num_outs=3),
bbox_head=dict(
num_classes=3,
bbox_code_size=7,
pred_attrs=False,
pred_velo=False,
pred_bbox2d=True,
use_onlyreg_proj=True,
strides=(8, 16, 32),
regress_ranges=((-1, 128), (128, 256), (256, 1e8)),
group_reg_dims=(2, 1, 3, 1, 16,
4), # offset, depth, size, rot, kpts, bbox2d
reg_branch=(
(256, ), # offset
(256, ), # depth
(256, ), # size
(256, ), # rot
(256, ), # kpts
(256, ) # bbox2d
),
centerness_branch=(256, ),
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=1.0),
loss_centerness=dict(
type='mmdet.CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
use_depth_classifier=True,
depth_branch=(256, ),
depth_range=(0, 50),
depth_unit=10,
division='uniform',
depth_bins=6,
pred_keypoints=True,
weight_dim=1,
loss_depth=dict(
type='UncertainSmoothL1Loss', alpha=1.0, beta=3.0,
loss_weight=1.0),
loss_bbox2d=dict(
type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.0),
loss_consistency=dict(type='mmdet.GIoULoss', loss_weight=0.0),
bbox_coder=dict(
type='PGDBBoxCoder',
base_depths=((41.01, 18.44), ),
base_dims=(
(0.91, 1.74, 0.84), # Pedestrian
(1.81, 1.77, 0.84), # Cyclist
(4.73, 1.77, 2.08)), # Car
code_size=7)),
# set weight 1.0 for base 7 dims (offset, depth, size, rot)
# 0.2 for 16-dim keypoint offsets and 1.0 for 4-dim 2D distance targets
train_cfg=dict(code_weight=[
1.0, 1.0, 0.2, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 1.0, 1.0, 1.0, 1.0
]),
test_cfg=dict(nms_pre=100, nms_thr=0.05, score_thr=0.001, max_per_img=20))
# optimizer
optim_wrapper = dict(
optimizer=dict(
type='SGD',
lr=0.008,
),
paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.),
clip_grad=dict(max_norm=35, norm_type=2))
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0 / 3,
by_epoch=False,
begin=0,
end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
by_epoch=True,
milestones=[16, 22],
gamma=0.1)
]
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=24)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
auto_scale_lr = dict(enable=False, base_batch_size=48)
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