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oneformer3d_1xb4_scannet.py
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oneformer3d_1xb4_scannet.py
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_base_ = [
'mmdet3d::_base_/default_runtime.py',
'mmdet3d::_base_/datasets/scannet-seg.py'
]
custom_imports = dict(imports=['oneformer3d'])
# model settings
num_channels = 32
num_instance_classes = 18
num_semantic_classes = 20
model = dict(
type='ScanNetOneFormer3D',
data_preprocessor=dict(type='Det3DDataPreprocessor_'),
in_channels=6,
num_channels=num_channels,
voxel_size=0.02,
num_classes=num_instance_classes,
min_spatial_shape=128,
query_thr=0.5,
backbone=dict(
type='SpConvUNet',
num_planes=[num_channels * (i + 1) for i in range(5)],
return_blocks=True),
decoder=dict(
type='ScanNetQueryDecoder',
num_layers=6,
num_instance_queries=0,
num_semantic_queries=0,
num_instance_classes=num_instance_classes,
num_semantic_classes=num_semantic_classes,
num_semantic_linears=1,
in_channels=32,
d_model=256,
num_heads=8,
hidden_dim=1024,
dropout=0.0,
activation_fn='gelu',
iter_pred=True,
attn_mask=True,
fix_attention=True,
objectness_flag=False),
criterion=dict(
type='ScanNetUnifiedCriterion',
num_semantic_classes=num_semantic_classes,
sem_criterion=dict(
type='ScanNetSemanticCriterion',
ignore_index=num_semantic_classes,
loss_weight=0.2),
inst_criterion=dict(
type='InstanceCriterion',
matcher=dict(
type='SparseMatcher',
costs=[
dict(type='QueryClassificationCost', weight=0.5),
dict(type='MaskBCECost', weight=1.0),
dict(type='MaskDiceCost', weight=1.0)],
topk=1),
loss_weight=[0.5, 1.0, 1.0, 0.5],
num_classes=num_instance_classes,
non_object_weight=0.1,
fix_dice_loss_weight=True,
iter_matcher=True,
fix_mean_loss=True)),
train_cfg=dict(),
test_cfg=dict(
topk_insts=600,
inst_score_thr=0.0,
pan_score_thr=0.5,
npoint_thr=100,
obj_normalization=True,
sp_score_thr=0.4,
nms=True,
matrix_nms_kernel='linear',
stuff_classes=[0, 1]))
# dataset settings
dataset_type = 'ScanNetSegDataset_'
data_prefix = dict(
pts='points',
pts_instance_mask='instance_mask',
pts_semantic_mask='semantic_mask',
sp_pts_mask='super_points')
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_',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=True,
with_seg_3d=True,
with_sp_mask_3d=True),
dict(type='PointSegClassMapping'),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-3.14, 3.14],
scale_ratio_range=[0.8, 1.2],
translation_std=[0.1, 0.1, 0.1],
shift_height=False),
dict(
type='NormalizePointsColor_',
color_mean=[127.5, 127.5, 127.5]),
dict(
type='AddSuperPointAnnotations',
num_classes=num_semantic_classes,
stuff_classes=[0, 1],
merge_non_stuff_cls=False),
dict(
type='ElasticTransfrom',
gran=[6, 20],
mag=[40, 160],
voxel_size=0.02,
p=0.5),
dict(
type='Pack3DDetInputs_',
keys=[
'points', 'gt_labels_3d', 'pts_semantic_mask', 'pts_instance_mask',
'sp_pts_mask', 'gt_sp_masks', 'elastic_coords'
])
]
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='LoadAnnotations3D_',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=True,
with_seg_3d=True,
with_sp_mask_3d=True),
dict(type='PointSegClassMapping'),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='NormalizePointsColor_',
color_mean=[127.5, 127.5, 127.5]),
dict(
type='AddSuperPointAnnotations',
num_classes=num_semantic_classes,
stuff_classes=[0, 1],
merge_non_stuff_cls=False),
]),
dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask'])
]
# run settings
train_dataloader = dict(
batch_size=4,
num_workers=6,
dataset=dict(
type=dataset_type,
ann_file='scannet_oneformer3d_infos_train.pkl',
data_prefix=data_prefix,
pipeline=train_pipeline,
ignore_index=num_semantic_classes,
scene_idxs=None,
test_mode=False))
val_dataloader = dict(
dataset=dict(
type=dataset_type,
ann_file='scannet_oneformer3d_infos_val.pkl',
data_prefix=data_prefix,
pipeline=test_pipeline,
ignore_index=num_semantic_classes,
test_mode=True))
test_dataloader = val_dataloader
class_names = [
'wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink',
'bathtub', 'otherfurniture']
class_names += ['unlabeled']
label2cat = {i: name for i, name in enumerate(class_names)}
metric_meta = dict(
label2cat=label2cat,
ignore_index=[num_semantic_classes],
classes=class_names,
dataset_name='ScanNet')
sem_mapping = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39]
inst_mapping = sem_mapping[2:]
val_evaluator = dict(
type='UnifiedSegMetric',
stuff_class_inds=[0, 1],
thing_class_inds=list(range(2, num_semantic_classes)),
min_num_points=1,
id_offset=2**16,
sem_mapping=sem_mapping,
inst_mapping=inst_mapping,
metric_meta=metric_meta)
test_evaluator = val_evaluator
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.05),
clip_grad=dict(max_norm=10, norm_type=2))
param_scheduler = dict(type='PolyLR', begin=0, end=512, power=0.9)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
default_hooks = dict(
checkpoint=dict(interval=1, max_keep_ckpts=16))
load_from = 'work_dirs/tmp/sstnet_scannet.pth'
train_cfg = dict(
type='EpochBasedTrainLoop',
max_epochs=512,
dynamic_intervals=[(1, 16), (512 - 16, 1)])
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')