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td3d_scannet.py
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td3d_scannet.py
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voxel_size = .02
n_points = 100000
model = dict(
type='ColorPointMink',
voxel_size=voxel_size,
backbone=dict(type='MinkResNet', in_channels=64, depth=34, norm='batch', return_stem=True, stride=1),
neck=dict(
type='NgfcTinySegmentationNeck',
in_channels=(64, 128, 256, 512),
out_channels=128,
feat_channels=32,),
decode_head=dict(
type='ColorPointHead',
channels=32,
hidden_dim=128,
dropout_ratio=0.5,
num_classes=20,
pseudo_obj_thr=2.0,
temperature=0.4,
epsilon=0.05,
),
train_cfg=dict(num_rois=2),
test_cfg=dict(
gt_classes=20,
nms_pre=1200,
iou_thr=.4,
score_thr=.1,
binary_score_thr=0.2))
# runtime settings
checkpoint_config = dict(interval=50, max_keep_ckpts=10)
runner = dict(type='EpochBasedRunner', max_epochs=200)
lr_config = dict(policy='CosineAnnealing', warmup=None, min_lr=1e-5)
optimizer = dict(type='AdamW', lr=1e-3, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
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 = 'ScanNetDataset'
data_root = './data/scannet/'
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'bookshelf', 'picture', 'counter', 'desk', 'curtain',
'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin')
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),
dict(type='PointSample', num_points=100000),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-.02, .02],
scale_ratio_range=[.9, 1.1],
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'])
]
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=[
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=4,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=15,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'scannet_infos_train.pkl',
pipeline=train_pipeline,
filter_empty_gt=False,
classes=class_names,
box_type_3d='Depth')),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'scannet_infos_val.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 + 'scannet_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
test_mode=True,
box_type_3d='Depth'))