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tr3d_s3dis-3d-5class.py
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tr3d_s3dis-3d-5class.py
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voxel_size = .01
n_points = 100000
model = dict(
type='MinkSingleStage3DDetector',
voxel_size=voxel_size,
backbone=dict(type='MinkResNet', in_channels=3, max_channels=128, depth=34, norm='batch'),
neck=dict(
type='TR3DNeck',
in_channels=(64, 128, 128, 128),
out_channels=128),
head=dict(
type='TR3DHead',
in_channels=128,
n_reg_outs=6,
n_classes=5,
voxel_size=voxel_size,
assigner=dict(
type='TR3DAssigner',
top_pts_threshold=6,
label2level=[1, 0, 1, 1, 0]),
bbox_loss=dict(type='AxisAlignedIoULoss', mode='diou', reduction='none')),
train_cfg=dict(),
test_cfg=dict(nms_pre=1000, iou_thr=.5, score_thr=.01))
optimizer = dict(type='AdamW', lr=.001, weight_decay=.0001)
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
lr_config = dict(policy='step', warmup=None, step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
checkpoint_config = dict(interval=1, max_keep_ckpts=1)
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = None
load_from = None
resume_from = None
workflow = [('train', 1)]
dataset_type = 'S3DISDataset'
data_root = './data/s3dis/'
class_names = ('table', 'chair', 'sofa', 'bookcase', 'board')
train_area = [1, 2, 3, 4, 6]
test_area = 5
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='PointSample', num_points=n_points),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=.5,
flip_ratio_bev_vertical=.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[.95, 1.05],
translation_std=[.1, .1, .1],
shift_height=False),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
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='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='PointSample', num_points=n_points),
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
samples_per_gpu=16,
workers_per_gpu=16,
train=dict(
type='RepeatDataset',
times=13,
dataset=dict(
type='ConcatDataset',
datasets=[
dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + f's3dis_infos_Area_{i}.pkl',
pipeline=train_pipeline,
filter_empty_gt=False,
classes=class_names,
box_type_3d='Depth') for i in train_area
],
separate_eval=False)),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + f's3dis_infos_Area_{test_area}.pkl',
pipeline=test_pipeline,
classes=class_names,
test_mode=True,
box_type_3d='Depth'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + f's3dis_infos_Area_{test_area}.pkl',
pipeline=test_pipeline,
classes=class_names,
test_mode=True,
box_type_3d='Depth'))