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_base_ = [
'../_base_/datasets/kitti-mono3d.py', '../_base_/models/smoke.py',
'../_base_/default_runtime.py'
]
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True,
with_bbox_depth=True),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='RandomShiftScale', shift_scale=(0.2, 0.4), aug_prob=0.3),
dict(type='AffineResize', img_scale=(1280, 384), down_ratio=4),
dict(
type='Pack3DDetInputs',
keys=[
'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
'gt_labels_3d', 'centers_2d', 'depths'
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(type='AffineResize', img_scale=(1280, 384), down_ratio=4),
dict(type='Pack3DDetInputs', keys=['img'])
]
train_dataloader = dict(
batch_size=8, num_workers=4, dataset=dict(pipeline=train_pipeline))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
# training schedule for 6x
max_epochs = 72
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=5)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[50],
gamma=0.1)
]
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='Adam', lr=2.5e-4),
clip_grad=None)
find_unused_parameters = True
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