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Unverified Commit 318499ac authored by hjin2902's avatar hjin2902 Committed by GitHub
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[Feature] Group-Free-3D head (#539) 



* group-free-3d head

* GroupFree3DNet->VoteNet

* modify docstring

* bugfix: calculate pts_instance_label, decoder self/cross posembed init

* support GroupFree3DNet, modify scannnet train config

* support point cloud input features dim = 0

* add groupfree3dnet test case

* bugfix: softmax in decode boxes

* support multi-stage predictions

* modify GroupFree3DMultiheadAttention input parameters

* refactor: support sunrgbd-based train

* refactor: support sunrgbd-based train

* fix parts of bug

* modify multi-stage prediction

* fixbug: conv_channels

* bugfix: permute

* bugfix: permute

* bugfix: expand

* fix MAX_NUM_OBJ=64

* 4 gpu training, score_thr = 0.0

* modify config, repeattime=1

* bigfix: expand

* modify: GroupFree3DMHA, build_positional_encoding

* modify: GroupFree3DMHA, build_positional_encoding

* bugfix: torch.nn

* bugfix: mean loss

* residual -> identity

* fix name: DropOut -> Dropout

* delete sunrgbd-based congfig

* Fix: trailing whitespace

* suffix -> prefix

* bugfix: groupfree3d config
Co-authored-by: default avatarjinhui <PJLAB\jinhui@shai14001019l.pjlab.org>
parent e63e0473
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configs/_base_/models/groupfree3d.py 0 → 100644
View file @ 318499ac
model = dict(
type='GroupFree3DNet',
backbone=dict(
type='PointNet2SASSG',
in_channels=3,
num_points=(2048, 1024, 512, 256),
radius=(0.2, 0.4, 0.8, 1.2),
num_samples=(64, 32, 16, 16),
sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
(128, 128, 256)),
fp_channels=((256, 256), (256, 288)),
norm_cfg=dict(type='BN2d'),
sa_cfg=dict(
type='PointSAModule',
pool_mod='max',
use_xyz=True,
normalize_xyz=True)),
bbox_head=dict(
type='GroupFree3DHead',
in_channels=288,
num_decoder_layers=6,
num_proposal=256,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=dict(
type='GroupFree3DMHA',
embed_dims=288,
num_heads=8,
attn_drop=0.1,
dropout_layer=dict(type='Dropout', drop_prob=0.1)),
ffn_cfgs=dict(
embed_dims=288,
feedforward_channels=2048,
ffn_drop=0.1,
act_cfg=dict(type='ReLU', inplace=True)),
operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn',
'norm')),
pred_layer_cfg=dict(
in_channels=288, shared_conv_channels=(288, 288), bias=True),
sampling_objectness_loss=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=8.0),
objectness_loss=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
center_loss=dict(
type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
dir_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
dir_res_loss=dict(
type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
size_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
size_res_loss=dict(
type='SmoothL1Loss', beta=1.0, reduction='sum', loss_weight=10.0),
semantic_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0)),
# model training and testing settings
train_cfg=dict(sample_mod='kps'),
test_cfg=dict(
sample_mod='kps',
nms_thr=0.25,
score_thr=0.0,
per_class_proposal=True,
prediction_stages='last'))
configs/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L12-O256.py 0 → 100644
View file @ 318499ac
_base_ = [
'../_base_/datasets/scannet-3d-18class.py',
'../_base_/models/groupfree3d.py', '../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
# model settings
model = dict(
bbox_head=dict(
num_classes=18,
num_decoder_layers=12,
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='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=8.0),
objectness_loss=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
center_loss=dict(
type='SmoothL1Loss', beta=0.04, reduction='sum', loss_weight=10.0),
dir_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
dir_res_loss=dict(
type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
size_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
size_res_loss=dict(
type='SmoothL1Loss',
beta=1.0 / 9.0,
reduction='sum',
loss_weight=10.0 / 9.0),
semantic_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0)),
test_cfg=dict(
sample_mod='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')
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='PointSegClassMapping',
valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
36, 39)),
dict(type='IndoorPointSample', 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='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
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]),
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='IndoorPointSample', num_points=50000),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=1,
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,
# 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')),
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'))
# optimizer
lr = 0.006
optimizer = dict(
lr=lr,
weight_decay=0.0005,
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)
}))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
lr_config = dict(policy='step', warmup=None, step=[280, 340])
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=400)
# yapf:disable
log_config = dict(
interval=30,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
configs/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L6-O256.py 0 → 100644
View file @ 318499ac
_base_ = [
'../_base_/datasets/scannet-3d-18class.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='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=8.0),
objectness_loss=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
center_loss=dict(
type='SmoothL1Loss', beta=0.04, reduction='sum', loss_weight=10.0),
dir_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
dir_res_loss=dict(
type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
size_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
size_res_loss=dict(
type='SmoothL1Loss',
beta=1.0 / 9.0,
reduction='sum',
loss_weight=10.0 / 9.0),
semantic_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0)),
test_cfg=dict(
sample_mod='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')
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='PointSegClassMapping',
valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
36, 39)),
dict(type='IndoorPointSample', 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='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
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]),
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='IndoorPointSample', num_points=50000),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=1,
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,
# 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')),
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'))
# optimizer
lr = 0.006
optimizer = dict(
lr=lr,
weight_decay=0.0005,
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)
}))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
lr_config = dict(policy='step', warmup=None, step=[280, 340])
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=400)
# yapf:disable
log_config = dict(
interval=30,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
configs/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256.py 0 → 100644
View file @ 318499ac
_base_ = [
'../_base_/datasets/scannet-3d-18class.py',
'../_base_/models/groupfree3d.py', '../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
type='PointNet2SASSG',
in_channels=3,
num_points=(2048, 1024, 512, 256),
radius=(0.2, 0.4, 0.8, 1.2),
num_samples=(64, 32, 16, 16),
sa_channels=((128, 128, 256), (256, 256, 512), (256, 256, 512),
(256, 256, 512)),
fp_channels=((512, 512), (512, 288)),
norm_cfg=dict(type='BN2d'),
sa_cfg=dict(
type='PointSAModule',
pool_mod='max',
use_xyz=True,
normalize_xyz=True)),
bbox_head=dict(
num_classes=18,
num_decoder_layers=12,
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='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=8.0),
objectness_loss=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
center_loss=dict(
type='SmoothL1Loss', beta=0.04, reduction='sum', loss_weight=10.0),
dir_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
dir_res_loss=dict(
type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
size_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
size_res_loss=dict(
type='SmoothL1Loss',
beta=1.0 / 9.0,
reduction='sum',
loss_weight=10.0 / 9.0),
semantic_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0)),
test_cfg=dict(
sample_mod='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')
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='PointSegClassMapping',
valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
36, 39)),
dict(type='IndoorPointSample', 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='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
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]),
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='IndoorPointSample', num_points=50000),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=1,
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,
# 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')),
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'))
# optimizer
lr = 0.006
optimizer = dict(
lr=lr,
weight_decay=0.0005,
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)
}))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
lr_config = dict(policy='step', warmup=None, step=[280, 340])
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=400)
# yapf:disable
log_config = dict(
interval=30,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
configs/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512.py 0 → 100644
View file @ 318499ac
_base_ = [
'../_base_/datasets/scannet-3d-18class.py',
'../_base_/models/groupfree3d.py', '../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
type='PointNet2SASSG',
in_channels=3,
num_points=(2048, 1024, 512, 256),
radius=(0.2, 0.4, 0.8, 1.2),
num_samples=(64, 32, 16, 16),
sa_channels=((128, 128, 256), (256, 256, 512), (256, 256, 512),
(256, 256, 512)),
fp_channels=((512, 512), (512, 288)),
norm_cfg=dict(type='BN2d'),
sa_cfg=dict(
type='PointSAModule',
pool_mod='max',
use_xyz=True,
normalize_xyz=True)),
bbox_head=dict(
num_classes=18,
num_decoder_layers=12,
num_proposal=512,
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='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=8.0),
objectness_loss=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
center_loss=dict(
type='SmoothL1Loss', beta=0.04, reduction='sum', loss_weight=10.0),
dir_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
dir_res_loss=dict(
type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
size_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
size_res_loss=dict(
type='SmoothL1Loss',
beta=1.0 / 9.0,
reduction='sum',
loss_weight=10.0 / 9.0),
semantic_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0)),
test_cfg=dict(
sample_mod='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')
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='PointSegClassMapping',
valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
36, 39)),
dict(type='IndoorPointSample', 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='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
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]),
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='IndoorPointSample', num_points=50000),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=1,
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,
# 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')),
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'))
# optimizer
lr = 0.006
optimizer = dict(
lr=lr,
weight_decay=0.0005,
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)
}))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
lr_config = dict(policy='step', warmup=None, step=[280, 340])
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=400)
# yapf:disable
log_config = dict(
interval=30,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
mmdet3d/core/bbox/coders/__init__.py
View file @ 318499ac
......@@ -2,9 +2,10 @@ from mmdet.core.bbox import build_bbox_coder
from .anchor_free_bbox_coder import AnchorFreeBBoxCoder
from .centerpoint_bbox_coders import CenterPointBBoxCoder
from .delta_xyzwhlr_bbox_coder import DeltaXYZWLHRBBoxCoder
from .groupfree3d_bbox_coder import GroupFree3DBBoxCoder
from .partial_bin_based_bbox_coder import PartialBinBasedBBoxCoder
__all__ = [
'build_bbox_coder', 'DeltaXYZWLHRBBoxCoder', 'PartialBinBasedBBoxCoder',
'CenterPointBBoxCoder', 'AnchorFreeBBoxCoder'
'CenterPointBBoxCoder', 'AnchorFreeBBoxCoder', 'GroupFree3DBBoxCoder'
]
mmdet3d/core/bbox/coders/groupfree3d_bbox_coder.py 0 → 100644
View file @ 318499ac
import numpy as np
import torch
from mmdet.core.bbox.builder import BBOX_CODERS
from .partial_bin_based_bbox_coder import PartialBinBasedBBoxCoder
@BBOX_CODERS.register_module()
class GroupFree3DBBoxCoder(PartialBinBasedBBoxCoder):
"""Modified partial bin based bbox coder for GroupFree3D.
Args:
num_dir_bins (int): Number of bins to encode direction angle.
num_sizes (int): Number of size clusters.
mean_sizes (list[list[int]]): Mean size of bboxes in each class.
with_rot (bool): Whether the bbox is with rotation. Defaults to True.
size_cls_agnostic (bool): Whether the predicted size is class-agnostic.
Defaults to True.
"""
def __init__(self,
num_dir_bins,
num_sizes,
mean_sizes,
with_rot=True,
size_cls_agnostic=True):
super(GroupFree3DBBoxCoder, self).__init__(
num_dir_bins=num_dir_bins,
num_sizes=num_sizes,
mean_sizes=mean_sizes,
with_rot=with_rot)
self.size_cls_agnostic = size_cls_agnostic
def encode(self, gt_bboxes_3d, gt_labels_3d):
"""Encode ground truth to prediction targets.
Args:
gt_bboxes_3d (BaseInstance3DBoxes): Ground truth bboxes \
with shape (n, 7).
gt_labels_3d (torch.Tensor): Ground truth classes.
Returns:
tuple: Targets of center, size and direction.
"""
# generate center target
center_target = gt_bboxes_3d.gravity_center
# generate bbox size target
size_target = gt_bboxes_3d.dims
size_class_target = gt_labels_3d
size_res_target = gt_bboxes_3d.dims - gt_bboxes_3d.tensor.new_tensor(
self.mean_sizes)[size_class_target]
# generate dir target
box_num = gt_labels_3d.shape[0]
if self.with_rot:
(dir_class_target,
dir_res_target) = self.angle2class(gt_bboxes_3d.yaw)
else:
dir_class_target = gt_labels_3d.new_zeros(box_num)
dir_res_target = gt_bboxes_3d.tensor.new_zeros(box_num)
return (center_target, size_target, size_class_target, size_res_target,
dir_class_target, dir_res_target)
def decode(self, bbox_out, prefix=''):
"""Decode predicted parts to bbox3d.
Args:
bbox_out (dict): Predictions from model, should contain keys below.
- center: predicted bottom center of bboxes.
- dir_class: predicted bbox direction class.
- dir_res: predicted bbox direction residual.
- size_class: predicted bbox size class.
- size_res: predicted bbox size residual.
- size: predicted class-agnostic bbox size
prefix (str): Decode predictions with specific prefix.
Defaults to ''.
Returns:
torch.Tensor: Decoded bbox3d with shape (batch, n, 7).
"""
center = bbox_out[f'{prefix}center']
batch_size, num_proposal = center.shape[:2]
# decode heading angle
if self.with_rot:
dir_class = torch.argmax(bbox_out[f'{prefix}dir_class'], -1)
dir_res = torch.gather(bbox_out[f'{prefix}dir_res'], 2,
dir_class.unsqueeze(-1))
dir_res.squeeze_(2)
dir_angle = self.class2angle(dir_class, dir_res).reshape(
batch_size, num_proposal, 1)
else:
dir_angle = center.new_zeros(batch_size, num_proposal, 1)
# decode bbox size
if self.size_cls_agnostic:
bbox_size = bbox_out[f'{prefix}size'].reshape(
batch_size, num_proposal, 3)
else:
size_class = torch.argmax(
bbox_out[f'{prefix}size_class'], -1, keepdim=True)
size_res = torch.gather(
bbox_out[f'{prefix}size_res'], 2,
size_class.unsqueeze(-1).repeat(1, 1, 1, 3))
mean_sizes = center.new_tensor(self.mean_sizes)
size_base = torch.index_select(mean_sizes, 0,
size_class.reshape(-1))
bbox_size = size_base.reshape(batch_size, num_proposal,
-1) + size_res.squeeze(2)
bbox3d = torch.cat([center, bbox_size, dir_angle], dim=-1)
return bbox3d
def split_pred(self, cls_preds, reg_preds, base_xyz, prefix=''):
"""Split predicted features to specific parts.
Args:
cls_preds (torch.Tensor): Class predicted features to split.
reg_preds (torch.Tensor): Regression predicted features to split.
base_xyz (torch.Tensor): Coordinates of points.
prefix (str): Decode predictions with specific prefix.
Defaults to ''.
Returns:
dict[str, torch.Tensor]: Split results.
"""
results = {}
start, end = 0, 0
cls_preds_trans = cls_preds.transpose(2, 1)
reg_preds_trans = reg_preds.transpose(2, 1)
# decode center
end += 3
# (batch_size, num_proposal, 3)
results[f'{prefix}center_residual'] = \
reg_preds_trans[..., start:end].contiguous()
results[f'{prefix}center'] = base_xyz + \
reg_preds_trans[..., start:end].contiguous()
start = end
# decode direction
end += self.num_dir_bins
results[f'{prefix}dir_class'] = \
reg_preds_trans[..., start:end].contiguous()
start = end
end += self.num_dir_bins
dir_res_norm = reg_preds_trans[..., start:end].contiguous()
start = end
results[f'{prefix}dir_res_norm'] = dir_res_norm
results[f'{prefix}dir_res'] = dir_res_norm * (
np.pi / self.num_dir_bins)
# decode size
if self.size_cls_agnostic:
end += 3
results[f'{prefix}size'] = \
reg_preds_trans[..., start:end].contiguous()
else:
end += self.num_sizes
results[f'{prefix}size_class'] = reg_preds_trans[
..., start:end].contiguous()
start = end
end += self.num_sizes * 3
size_res_norm = reg_preds_trans[..., start:end]
batch_size, num_proposal = reg_preds_trans.shape[:2]
size_res_norm = size_res_norm.view(
[batch_size, num_proposal, self.num_sizes, 3])
start = end
results[f'{prefix}size_res_norm'] = size_res_norm.contiguous()
mean_sizes = reg_preds.new_tensor(self.mean_sizes)
results[f'{prefix}size_res'] = (
size_res_norm * mean_sizes.unsqueeze(0).unsqueeze(0))
# decode objectness score
# Group-Free-3D objectness output shape (batch, proposal, 1)
results[f'{prefix}obj_scores'] = cls_preds_trans[..., :1].contiguous()
# decode semantic score
results[f'{prefix}sem_scores'] = cls_preds_trans[..., 1:].contiguous()
return results
mmdet3d/models/dense_heads/__init__.py
View file @ 318499ac
......@@ -5,6 +5,7 @@ from .base_mono3d_dense_head import BaseMono3DDenseHead
from .centerpoint_head import CenterHead
from .fcos_mono3d_head import FCOSMono3DHead
from .free_anchor3d_head import FreeAnchor3DHead
from .groupfree3d_head import GroupFree3DHead
from .parta2_rpn_head import PartA2RPNHead
from .shape_aware_head import ShapeAwareHead
from .ssd_3d_head import SSD3DHead
......@@ -13,5 +14,6 @@ from .vote_head import VoteHead
__all__ = [
'Anchor3DHead', 'FreeAnchor3DHead', 'PartA2RPNHead', 'VoteHead',
'SSD3DHead', 'BaseConvBboxHead', 'CenterHead', 'ShapeAwareHead',
'BaseMono3DDenseHead', 'AnchorFreeMono3DHead', 'FCOSMono3DHead'
'BaseMono3DDenseHead', 'AnchorFreeMono3DHead', 'FCOSMono3DHead',
'GroupFree3DHead'
]
mmdet3d/models/dense_heads/groupfree3d_head.py 0 → 100644
View file @ 318499ac
This diff is collapsed.
mmdet3d/models/detectors/__init__.py
View file @ 318499ac
......@@ -2,6 +2,7 @@ from .base import Base3DDetector
from .centerpoint import CenterPoint
from .dynamic_voxelnet import DynamicVoxelNet
from .fcos_mono3d import FCOSMono3D
from .groupfree3dnet import GroupFree3DNet
from .h3dnet import H3DNet
from .imvotenet import ImVoteNet
from .imvoxelnet import ImVoxelNet
......@@ -17,5 +18,5 @@ __all__ = [
'Base3DDetector', 'VoxelNet', 'DynamicVoxelNet', 'MVXTwoStageDetector',
'DynamicMVXFasterRCNN', 'MVXFasterRCNN', 'PartA2', 'VoteNet', 'H3DNet',
'CenterPoint', 'SSD3DNet', 'ImVoteNet', 'SingleStageMono3DDetector',
'FCOSMono3D', 'ImVoxelNet'
'FCOSMono3D', 'ImVoxelNet', 'GroupFree3DNet'
]
mmdet3d/models/detectors/groupfree3dnet.py 0 → 100644
View file @ 318499ac
import torch
from mmdet3d.core import bbox3d2result, merge_aug_bboxes_3d
from mmdet.models import DETECTORS
from .single_stage import SingleStage3DDetector
@DETECTORS.register_module()
class GroupFree3DNet(SingleStage3DDetector):
"""`Group-Free 3D <https://arxiv.org/abs/2104.00678>`_."""
def __init__(self,
backbone,
bbox_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None):
super(GroupFree3DNet, self).__init__(
backbone=backbone,
bbox_head=bbox_head,
train_cfg=train_cfg,
test_cfg=test_cfg,
pretrained=pretrained)
def forward_train(self,
points,
img_metas,
gt_bboxes_3d,
gt_labels_3d,
pts_semantic_mask=None,
pts_instance_mask=None,
gt_bboxes_ignore=None):
"""Forward of training.
Args:
points (list[torch.Tensor]): Points of each batch.
img_metas (list): Image metas.
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): gt bboxes of each batch.
gt_labels_3d (list[torch.Tensor]): gt class labels of each batch.
pts_semantic_mask (None | list[torch.Tensor]): point-wise semantic
label of each batch.
pts_instance_mask (None | list[torch.Tensor]): point-wise instance
label of each batch.
gt_bboxes_ignore (None | list[torch.Tensor]): Specify
which bounding.
Returns:
dict[str: torch.Tensor]: Losses.
"""
# TODO: refactor votenet series to reduce redundant codes.
points_cat = torch.stack(points)
x = self.extract_feat(points_cat)
bbox_preds = self.bbox_head(x, self.train_cfg.sample_mod)
loss_inputs = (points, gt_bboxes_3d, gt_labels_3d, pts_semantic_mask,
pts_instance_mask, img_metas)
losses = self.bbox_head.loss(
bbox_preds, *loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
return losses
def simple_test(self, points, img_metas, imgs=None, rescale=False):
"""Forward of testing.
Args:
points (list[torch.Tensor]): Points of each sample.
img_metas (list): Image metas.
rescale (bool): Whether to rescale results.
Returns:
list: Predicted 3d boxes.
"""
points_cat = torch.stack(points)
x = self.extract_feat(points_cat)
bbox_preds = self.bbox_head(x, self.test_cfg.sample_mod)
bbox_list = self.bbox_head.get_bboxes(
points_cat, bbox_preds, img_metas, rescale=rescale)
bbox_results = [
bbox3d2result(bboxes, scores, labels)
for bboxes, scores, labels in bbox_list
]
return bbox_results
def aug_test(self, points, img_metas, imgs=None, rescale=False):
"""Test with augmentation."""
points_cat = [torch.stack(pts) for pts in points]
feats = self.extract_feats(points_cat, img_metas)
# only support aug_test for one sample
aug_bboxes = []
for x, pts_cat, img_meta in zip(feats, points_cat, img_metas):
bbox_preds = self.bbox_head(x, self.test_cfg.sample_mod)
bbox_list = self.bbox_head.get_bboxes(
pts_cat, bbox_preds, img_meta, rescale=rescale)
bbox_list = [
dict(boxes_3d=bboxes, scores_3d=scores, labels_3d=labels)
for bboxes, scores, labels in bbox_list
]
aug_bboxes.append(bbox_list[0])
# after merging, bboxes will be rescaled to the original image size
merged_bboxes = merge_aug_bboxes_3d(aug_bboxes, img_metas,
self.bbox_head.test_cfg)
return [merged_bboxes]
mmdet3d/models/model_utils/__init__.py
View file @ 318499ac
from .transformer import GroupFree3DMHA
from .vote_module import VoteModule
__all__ = ['VoteModule']
__all__ = ['VoteModule', 'GroupFree3DMHA']
mmdet3d/models/model_utils/transformer.py 0 → 100644
View file @ 318499ac
from mmcv.cnn.bricks.registry import ATTENTION
from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING, MultiheadAttention
from torch import nn as nn
@ATTENTION.register_module()
class GroupFree3DMHA(MultiheadAttention):
"""A warpper for torch.nn.MultiheadAttention for GroupFree3D.
This module implements MultiheadAttention with identity connection,
and positional encoding used in DETR is also passed as input.
Args:
embed_dims (int): The embedding dimension.
num_heads (int): Parallel attention heads. Same as
`nn.MultiheadAttention`.
attn_drop (float): A Dropout layer on attn_output_weights. Default 0.0.
proj_drop (float): A Dropout layer. Default 0.0.
dropout_layer (obj:`ConfigDict`): The dropout_layer used
when adding the shortcut.
init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
Default: None.
batch_first (bool): Key, Query and Value are shape of
(batch, n, embed_dim)
or (n, batch, embed_dim). Default to False.
"""
def __init__(self,
embed_dims,
num_heads,
attn_drop=0.,
proj_drop=0.,
dropout_layer=dict(type='DropOut', drop_prob=0.),
init_cfg=None,
batch_first=False,
**kwargs):
super().__init__(embed_dims, num_heads, attn_drop, proj_drop,
dropout_layer, init_cfg, batch_first, **kwargs)
def forward(self,
query,
key,
value,
identity,
query_pos=None,
key_pos=None,
attn_mask=None,
key_padding_mask=None,
**kwargs):
"""Forward function for `GroupFree3DMHA`.
**kwargs allow passing a more general data flow when combining
with other operations in `transformerlayer`.
Args:
query (Tensor): The input query with shape [num_queries, bs,
embed_dims]. Same in `nn.MultiheadAttention.forward`.
key (Tensor): The key tensor with shape [num_keys, bs,
embed_dims]. Same in `nn.MultiheadAttention.forward`.
If None, the ``query`` will be used. Defaults to None.
value (Tensor): The value tensor with same shape as `key`.
Same in `nn.MultiheadAttention.forward`. Defaults to None.
If None, the `key` will be used.
identity (Tensor): This tensor, with the same shape as x,
will be used for the identity link.
If None, `x` will be used. Defaults to None.
query_pos (Tensor): The positional encoding for query, with
the same shape as `x`. If not None, it will
be added to `x` before forward function. Defaults to None.
key_pos (Tensor): The positional encoding for `key`, with the
same shape as `key`. Defaults to None. If not None, it will
be added to `key` before forward function. If None, and
`query_pos` has the same shape as `key`, then `query_pos`
will be used for `key_pos`. Defaults to None.
attn_mask (Tensor): ByteTensor mask with shape [num_queries,
num_keys]. Same in `nn.MultiheadAttention.forward`.
Defaults to None.
key_padding_mask (Tensor): ByteTensor with shape [bs, num_keys].
Same in `nn.MultiheadAttention.forward`. Defaults to None.
Returns:
Tensor: forwarded results with shape [num_queries, bs, embed_dims].
"""
if hasattr(self, 'operation_name'):
if self.operation_name == 'self_attn':
value = value + query_pos
elif self.operation_name == 'cross_attn':
value = value + key_pos
else:
raise NotImplementedError(
f'{self.__class__.name} '
f"can't be used as {self.operation_name}")
else:
value = value + query_pos
return super(GroupFree3DMHA, self).forward(
query=query,
key=key,
value=value,
identity=identity,
query_pos=query_pos,
key_pos=key_pos,
attn_mask=attn_mask,
key_padding_mask=key_padding_mask,
**kwargs)
@POSITIONAL_ENCODING.register_module()
class ConvBNPositionalEncoding(nn.Module):
"""Absolute position embedding with Conv learning.
Args:
input_channel (int): input features dim.
num_pos_feats (int): output position features dim.
Defaults to 288 to be consistent with seed features dim.
"""
def __init__(self, input_channel, num_pos_feats=288):
super().__init__()
self.position_embedding_head = nn.Sequential(
nn.Conv1d(input_channel, num_pos_feats, kernel_size=1),
nn.BatchNorm1d(num_pos_feats), nn.ReLU(inplace=True),
nn.Conv1d(num_pos_feats, num_pos_feats, kernel_size=1))
def forward(self, xyz):
"""Forward pass.
Args:
xyz (Tensor)： (B, N, 3) the coordinates to embed.
Returns:
Tensor: (B, num_pos_feats, N) the embeded position features.
"""
xyz = xyz.permute(0, 2, 1)
position_embedding = self.position_embedding_head(xyz)
return position_embedding
tests/test_models/test_detectors.py
View file @ 318499ac
......@@ -379,6 +379,59 @@ def test_fcos3d():
assert attrs_3d.shape[0] >= 0
def test_groupfree3dnet():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
_setup_seed(0)
groupfree3d_cfg = _get_detector_cfg(
'groupfree3d/groupfree3d_8x8_scannet-3d-18class-L6-O256.py')
self = build_detector(groupfree3d_cfg).cuda()
points_0 = torch.rand([50000, 3], device='cuda')
points_1 = torch.rand([50000, 3], device='cuda')
points = [points_0, points_1]
img_meta_0 = dict(box_type_3d=DepthInstance3DBoxes)
img_meta_1 = dict(box_type_3d=DepthInstance3DBoxes)
img_metas = [img_meta_0, img_meta_1]
gt_bbox_0 = DepthInstance3DBoxes(torch.rand([10, 7], device='cuda'))
gt_bbox_1 = DepthInstance3DBoxes(torch.rand([10, 7], device='cuda'))
gt_bboxes = [gt_bbox_0, gt_bbox_1]
gt_labels_0 = torch.randint(0, 18, [10], device='cuda')
gt_labels_1 = torch.randint(0, 18, [10], device='cuda')
gt_labels = [gt_labels_0, gt_labels_1]
pts_instance_mask_1 = torch.randint(0, 10, [50000], device='cuda')
pts_instance_mask_2 = torch.randint(0, 10, [50000], device='cuda')
pts_instance_mask = [pts_instance_mask_1, pts_instance_mask_2]
pts_semantic_mask_1 = torch.randint(0, 19, [50000], device='cuda')
pts_semantic_mask_2 = torch.randint(0, 19, [50000], device='cuda')
pts_semantic_mask = [pts_semantic_mask_1, pts_semantic_mask_2]
# test forward_train
losses = self.forward_train(points, img_metas, gt_bboxes, gt_labels,
pts_semantic_mask, pts_instance_mask)
assert losses['sampling_objectness_loss'] >= 0
assert losses['s5.objectness_loss'] >= 0
assert losses['s5.semantic_loss'] >= 0
assert losses['s5.center_loss'] >= 0
assert losses['s5.dir_class_loss'] >= 0
assert losses['s5.dir_res_loss'] >= 0
assert losses['s5.size_class_loss'] >= 0
assert losses['s5.size_res_loss'] >= 0
# test simple_test
with torch.no_grad():
results = self.simple_test(points, img_metas)
boxes_3d = results[0]['boxes_3d']
scores_3d = results[0]['scores_3d']
labels_3d = results[0]['labels_3d']
assert boxes_3d.tensor.shape[0] >= 0
assert boxes_3d.tensor.shape[1] == 7
assert scores_3d.shape[0] >= 0
assert labels_3d.shape[0] >= 0
def test_imvoxelnet():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
......
tests/test_models/test_heads/test_heads.py
View file @ 318499ac
......@@ -1114,3 +1114,110 @@ def test_fcos_mono3d_head():
assert results[0][1].shape == torch.Size([200])
assert results[0][2].shape == torch.Size([200])
assert results[0][3].shape == torch.Size([200])
def test_groupfree3d_head():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
_setup_seed(0)
vote_head_cfg = _get_vote_head_cfg(
'groupfree3d/groupfree3d_8x8_scannet-3d-18class-L6-O256.py')
self = build_head(vote_head_cfg).cuda()
fp_xyz = [torch.rand([2, 256, 3], dtype=torch.float32).cuda()]
fp_features = [torch.rand([2, 288, 256], dtype=torch.float32).cuda()]
fp_indices = [torch.randint(0, 128, [2, 256]).cuda()]
input_dict = dict(
fp_xyz=fp_xyz, fp_features=fp_features, fp_indices=fp_indices)
# test forward
ret_dict = self(input_dict, 'kps')
assert ret_dict['seeds_obj_cls_logits'].shape == torch.Size([2, 1, 256])
assert ret_dict['s5.center'].shape == torch.Size([2, 128, 3])
assert ret_dict['s5.dir_class'].shape == torch.Size([2, 128, 1])
assert ret_dict['s5.dir_res'].shape == torch.Size([2, 128, 1])
assert ret_dict['s5.size_class'].shape == torch.Size([2, 128, 18])
assert ret_dict['s5.size_res'].shape == torch.Size([2, 128, 18, 3])
assert ret_dict['s5.obj_scores'].shape == torch.Size([2, 128, 1])
assert ret_dict['s5.sem_scores'].shape == torch.Size([2, 128, 18])
# test losses
points = [torch.rand([50000, 4], device='cuda') for i in range(2)]
gt_bbox1 = torch.rand([10, 7], dtype=torch.float32).cuda()
gt_bbox2 = torch.rand([10, 7], dtype=torch.float32).cuda()
gt_bbox1 = DepthInstance3DBoxes(gt_bbox1)
gt_bbox2 = DepthInstance3DBoxes(gt_bbox2)
gt_bboxes = [gt_bbox1, gt_bbox2]
pts_instance_mask_1 = torch.randint(0, 10, [50000], device='cuda')
pts_instance_mask_2 = torch.randint(0, 10, [50000], device='cuda')
pts_instance_mask = [pts_instance_mask_1, pts_instance_mask_2]
pts_semantic_mask_1 = torch.randint(0, 19, [50000], device='cuda')
pts_semantic_mask_2 = torch.randint(0, 19, [50000], device='cuda')
pts_semantic_mask = [pts_semantic_mask_1, pts_semantic_mask_2]
labels_1 = torch.randint(0, 18, [10], device='cuda')
labels_2 = torch.randint(0, 18, [10], device='cuda')
gt_labels = [labels_1, labels_2]
losses = self.loss(ret_dict, points, gt_bboxes, gt_labels,
pts_semantic_mask, pts_instance_mask)
assert losses['s5.objectness_loss'] >= 0
assert losses['s5.semantic_loss'] >= 0
assert losses['s5.center_loss'] >= 0
assert losses['s5.dir_class_loss'] >= 0
assert losses['s5.dir_res_loss'] >= 0
assert losses['s5.size_class_loss'] >= 0
assert losses['s5.size_res_loss'] >= 0
# test multiclass_nms_single
obj_scores = torch.rand([256], device='cuda')
sem_scores = torch.rand([256, 18], device='cuda')
points = torch.rand([50000, 3], device='cuda')
bbox = torch.rand([256, 7], device='cuda')
input_meta = dict(box_type_3d=DepthInstance3DBoxes)
bbox_selected, score_selected, labels = \
self.multiclass_nms_single(obj_scores,
sem_scores,
bbox,
points,
input_meta)
assert bbox_selected.shape[0] >= 0
assert bbox_selected.shape[1] == 7
assert score_selected.shape[0] >= 0
assert labels.shape[0] >= 0
# test get_boxes
points = torch.rand([1, 50000, 3], device='cuda')
seed_points = torch.rand([1, 1024, 3], device='cuda')
seed_indices = torch.randint(0, 50000, [1, 1024], device='cuda')
obj_scores = torch.rand([1, 256, 1], device='cuda')
center = torch.rand([1, 256, 3], device='cuda')
dir_class = torch.rand([1, 256, 1], device='cuda')
dir_res_norm = torch.rand([1, 256, 1], device='cuda')
dir_res = torch.rand([1, 256, 1], device='cuda')
size_class = torch.rand([1, 256, 18], device='cuda')
size_res = torch.rand([1, 256, 18, 3], device='cuda')
sem_scores = torch.rand([1, 256, 18], device='cuda')
bbox_preds = dict()
bbox_preds['seed_points'] = seed_points
bbox_preds['seed_indices'] = seed_indices
bbox_preds['s5.obj_scores'] = obj_scores
bbox_preds['s5.center'] = center
bbox_preds['s5.dir_class'] = dir_class
bbox_preds['s5.dir_res_norm'] = dir_res_norm
bbox_preds['s5.dir_res'] = dir_res
bbox_preds['s5.size_class'] = size_class
bbox_preds['s5.size_res'] = size_res
bbox_preds['s5.sem_scores'] = sem_scores
self.test_cfg['prediction_stages'] == 'last'
results = self.get_bboxes(points, bbox_preds, [input_meta])
assert results[0][0].tensor.shape[0] >= 0
assert results[0][0].tensor.shape[1] == 7
assert results[0][1].shape[0] >= 0
assert results[0][2].shape[0] >= 0
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