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tr3d-ff_sunrgbd-3d-10class.py
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tr3d-ff_sunrgbd-3d-10class.py
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voxel_size = .01
n_points = 100000
model = dict(
type='TR3DFF3DDetector',
img_backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe'),
img_neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
backbone=dict(
type='MinkFFResNet',
in_channels=3,
max_channels=128,
depth=34,
norm='batch'),
neck=dict(
type='TR3DNeck',
in_channels=(64, 128, 128, 128),
out_channels=128),
head=dict(
type='TR3DHead',
in_channels=128,
n_reg_outs=8,
n_classes=10,
voxel_size=voxel_size,
assigner=dict(
type='TR3DAssigner',
top_pts_threshold=6,
label2level=[1, 1, 1, 0, 0, 1, 0, 0, 1, 0]),
bbox_loss=dict(type='RotatedIoU3DLoss', mode='diou', reduction='none')),
voxel_size=voxel_size,
train_cfg=dict(),
test_cfg=dict(nms_pre=1000, iou_thr=.5, score_thr=.3))
# score_thr=.01
optimizer = dict(type='AdamW', lr=.001, weight_decay=.0001)
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
lr_config = dict(policy='step', warmup=None, step=[8, 11])
# runner = dict(type='EpochBasedRunner', max_epochs=12)
runner = dict(type='EpochBasedRunner', max_epochs=12)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
checkpoint_config = dict(interval=1, max_keep_ckpts=2)
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
# dict(type='MMDetWandbHook',
# init_kwargs={'project': 'tr3d-ff'},
# interval=4,
# log_checkpoint=True,
# log_checkpoint_metadata=True)
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = None
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
resume_from = None
workflow = [('train', 1)]
dataset_type = 'SUNRGBDDataset'
data_root = 'data/sunrgbd/'
class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
'night_stand', 'bookshelf', 'bathtub')
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
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='LoadImageFromFile'),
dict(type='LoadAnnotations3D'),
dict(
type='Resize',
img_scale=[(1333, 480), (1333, 504), (1333, 528), (1333, 552),
(1333, 576), (1333, 600)],
multiscale_mode='value',
keep_ratio=True),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='PointSample', num_points=n_points),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=.5,
flip_ratio_bev_vertical=.0),
dict(
type='GlobalRotScaleTrans',
rot_range=[-.523599, .523599],
scale_ratio_range=[.85, 1.15],
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', 'img', 'gt_bboxes_3d', 'gt_labels_3d'])
]
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='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 600),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='Resize', multiscale_mode='value', keep_ratio=True),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='PointSample', num_points=n_points),
# dict(type='NormalizePointsColor', color_mean=None),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points', 'img'])
])
]
data = dict(
samples_per_gpu=16,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=5,
dataset=dict(
type=dataset_type,
modality=dict(use_camera=True, use_lidar=True),
data_root=data_root,
ann_file=data_root + 'sunrgbd_infos_train.pkl',
pipeline=train_pipeline,
filter_empty_gt=False,
classes=class_names,
box_type_3d='Depth')),
val=dict(
type=dataset_type,
modality=dict(use_camera=True, use_lidar=True),
data_root=data_root,
ann_file=data_root + 'sunrgbd_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
test_mode=True,
box_type_3d='Depth'),
test=dict(
type=dataset_type,
modality=dict(use_camera=True, use_lidar=True),
data_root=data_root,
ann_file=data_root + 'sunrgbd_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
test_mode=True,
box_type_3d='Depth'))