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groupfree3d_head-L6-O256_4xb8_scannet-seg.py
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_base_ = [
'../_base_/datasets/scannet-3d.py', '../_base_/models/groupfree3d.py',
'../_base_/schedules/schedule-3x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
bbox_head=dict(
num_classes=18,
size_cls_agnostic=False,
bbox_coder=dict(
type='GroupFree3DBBoxCoder',
num_sizes=18,
num_dir_bins=1,
with_rot=False,
size_cls_agnostic=False,
mean_sizes=[[0.76966727, 0.8116021, 0.92573744],
[1.876858, 1.8425595, 1.1931566],
[0.61328, 0.6148609, 0.7182701],
[1.3955007, 1.5121545, 0.83443564],
[0.97949594, 1.0675149, 0.6329687],
[0.531663, 0.5955577, 1.7500148],
[0.9624706, 0.72462326, 1.1481868],
[0.83221924, 1.0490936, 1.6875663],
[0.21132214, 0.4206159, 0.5372846],
[1.4440073, 1.8970833, 0.26985747],
[1.0294262, 1.4040797, 0.87554324],
[1.3766412, 0.65521795, 1.6813129],
[0.6650819, 0.71111923, 1.298853],
[0.41999173, 0.37906948, 1.7513971],
[0.59359556, 0.5912492, 0.73919016],
[0.50867593, 0.50656086, 0.30136237],
[1.1511526, 1.0546296, 0.49706793],
[0.47535285, 0.49249494, 0.5802117]]),
sampling_objectness_loss=dict(
type='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=8.0),
objectness_loss=dict(
type='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
center_loss=dict(
type='mmdet.SmoothL1Loss',
beta=0.04,
reduction='sum',
loss_weight=10.0),
dir_class_loss=dict(
type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0),
dir_res_loss=dict(
type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=10.0),
size_class_loss=dict(
type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0),
size_res_loss=dict(
type='mmdet.SmoothL1Loss',
beta=1.0 / 9.0,
reduction='sum',
loss_weight=10.0 / 9.0),
semantic_loss=dict(
type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0)),
test_cfg=dict(
sample_mode='kps',
nms_thr=0.25,
score_thr=0.0,
per_class_proposal=True,
prediction_stages='last_three'))
# dataset settings
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')
metainfo = dict(classes=class_names)
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True,
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='PointSegClassMapping'),
dict(type='PointSample', num_points=50000),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[1.0, 1.0]),
dict(
type='Pack3DDetInputs',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
'pts_instance_mask'
])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointSample', num_points=50000),
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=8,
num_workers=4,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=5,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='scannet_infos_train.pkl',
pipeline=train_pipeline,
filter_empty_gt=False,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Depth',
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='scannet_infos_val.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='scannet_infos_val.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth',
backend_args=backend_args))
val_evaluator = dict(type='IndoorMetric')
test_evaluator = val_evaluator
# optimizer
lr = 0.006
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=lr, weight_decay=0.0005),
clip_grad=dict(max_norm=0.1, norm_type=2),
paramwise_cfg=dict(
custom_keys={
'bbox_head.decoder_layers': dict(lr_mult=0.1, decay_mult=1.0),
'bbox_head.decoder_self_posembeds': dict(
lr_mult=0.1, decay_mult=1.0),
'bbox_head.decoder_cross_posembeds': dict(
lr_mult=0.1, decay_mult=1.0),
'bbox_head.decoder_query_proj': dict(lr_mult=0.1, decay_mult=1.0),
'bbox_head.decoder_key_proj': dict(lr_mult=0.1, decay_mult=1.0)
}))
# learning rate
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=80,
by_epoch=True,
milestones=[56, 68],
gamma=0.1)
]
# training schedule for 1x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=80, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=10))
randomness = dict(seed=4)