File size: 2,289 Bytes
34d1f8b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
_base_ = ['./tr3d.py', 'mmdet3d::_base_/datasets/scannet-3d.py']
custom_imports = dict(imports=['projects.TR3D.tr3d'])
model = dict(
bbox_head=dict(
label2level=[0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0]))
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='LoadAnnotations3D'),
dict(type='GlobalAlignment', rotation_axis=2),
# We do not sample 100k points for ScanNet, as very few scenes have
# significantly more then 100k points. So we sample 33 to 100% of them.
dict(type='TR3DPointSample', num_points=0.33),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.02, 0.02],
scale_ratio_range=[0.9, 1.1],
translation_std=[0.1, 0.1, 0.1],
shift_height=False),
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
# We do not sample 100k points for ScanNet, as very few scenes have
# significantly more then 100k points. So it doesn't affect
# inference time and we can accept all points.
# dict(type='PointSample', num_points=100000),
dict(type='NormalizePointsColor', color_mean=None),
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='RepeatDataset',
times=15,
dataset=dict(pipeline=train_pipeline, filter_empty_gt=False)))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
|