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imvotenet.py
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_base_ = [
'../_base_/datasets/sunrgbd-3d-10class.py',
'../_base_/schedules/schedule_3x.py', '../_base_/default_runtime.py',
'../_base_/models/imvotenet_image.py'
]
class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
'night_stand', 'bookshelf', 'bathtub')
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
model = dict(
pts_backbone=dict(
type='PointNet2SASSG',
in_channels=4,
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, 256)),
norm_cfg=dict(type='BN2d'),
sa_cfg=dict(
type='PointSAModule',
pool_mod='max',
use_xyz=True,
normalize_xyz=True)),
pts_bbox_heads=dict(
common=dict(
type='CAVoteHead',
num_classes=10,
bbox_coder=dict(
type="ClassAgnosticBBoxCoder",
num_dir_bins=12,
with_rot=True,
num_sizes=10,
mean_sizes=[[2.114256, 1.620300, 0.927272],
[0.791118, 1.279516, 0.718182],
[0.923508, 1.867419, 0.845495],
[0.591958, 0.552978, 0.827272],
[0.699104, 0.454178, 0.75625],
[0.69519, 1.346299, 0.736364],
[0.528526, 1.002642, 1.172878],
[0.500618, 0.632163, 0.683424],
[0.404671, 1.071108, 1.688889],
[0.76584, 1.398258, 0.472728]]
),
pred_layer_cfg=dict(
in_channels=128, shared_conv_channels=(128, 128), bias=True),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
objectness_loss=dict(
type='CrossEntropyLoss',
class_weight=[0.2, 0.8],
reduction='sum',
loss_weight=5.0),
center_loss=dict(
type='ChamferDistance',
mode='l2',
reduction='sum',
loss_src_weight=10.0,
loss_dst_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',
reduction='sum',
loss_weight=10.0,
beta=0.15,
),
iou_loss=dict(
type='AxisAlignedIoULoss',
reduction='sum',
loss_weight=12.0 / 3.0,
),
semantic_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
),
joint=dict(
vote_module_cfg=dict(
in_channels=512,
vote_per_seed=1,
gt_per_seed=3,
conv_channels=(512, 256),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
norm_feats=True,
vote_loss=dict(
type='ChamferDistance',
mode='l1',
reduction='none',
loss_dst_weight=10.0)),
vote_aggregation_cfg=dict(
type='PointSAModule',
num_point=256,
radius=0.3,
num_sample=16,
mlp_channels=[512, 128, 128, 128],
use_xyz=True,
normalize_xyz=True)),
pts=dict(
vote_module_cfg=dict(
in_channels=256,
vote_per_seed=1,
gt_per_seed=3,
conv_channels=(256, 256),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
norm_feats=True,
vote_loss=dict(
type='ChamferDistance',
mode='l1',
reduction='none',
loss_dst_weight=10.0)),
vote_aggregation_cfg=dict(
type='PointSAModule',
num_point=256,
radius=0.3,
num_sample=16,
mlp_channels=[256, 128, 128, 128],
use_xyz=True,
normalize_xyz=True)),
img=dict(
vote_module_cfg=dict(
in_channels=256,
vote_per_seed=1,
gt_per_seed=3,
conv_channels=(256, 256),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
norm_feats=True,
vote_loss=dict(
type='ChamferDistance',
mode='l1',
reduction='none',
loss_dst_weight=10.0)),
vote_aggregation_cfg=dict(
type='PointSAModule',
num_point=256,
radius=0.3,
num_sample=16,
mlp_channels=[256, 128, 128, 128],
use_xyz=True,
normalize_xyz=True)),
loss_weights=[0.4, 0.3, 0.3],
),
img_mlp=dict(
in_channel=18,
conv_channels=(256, 256),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
act_cfg=dict(type='ReLU')),
fusion_layer=dict(
type='VoteFusion',
num_classes=len(class_names),
max_imvote_per_pixel=3),
num_sampled_seed=1024,
freeze_img_branch=True,
train_cfg=dict(
pts=dict(
pos_distance_thr=0.3,
neg_distance_thr=0.6,
sample_mod='seed',
)
),
test_cfg=dict(
img_rcnn=dict(score_thr=0.1),
pts=dict(
sample_mod='seed',
nms_thr=0.25,
score_thr=0.05,
per_class_proposal=True))
)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations3D'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 600), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.0),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.523599, 0.523599],
scale_ratio_range=[0.85, 1.15],
shift_height=True),
dict(type='PointSample', num_points=20000),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'points', 'gt_bboxes_3d',
'gt_labels_3d'
])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 600),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.0),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
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,
),
dict(type='PointSample', num_points=20000),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['img', 'points'])
]),
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['img', 'points'])
]
data = dict(
train=dict(dataset=dict(pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
evaluation = dict(pipeline=eval_pipeline)
# may also use your own pre-trained image branch
load_from = 'https://download.openmmlab.com/mmdetection3d/v0.1.0_models/imvotenet/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class_20210323_173222-cad62aeb.pth' # noqa