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_base_ = [
'../_base_/schedules/mmdet-schedule-1x.py', '../_base_/default_runtime.py'
]
prior_generator = dict(
type='AlignedAnchor3DRangeGenerator',
ranges=[[-3.2, -0.2, -2.28, 3.2, 6.2, 0.28]],
rotations=[.0])
model = dict(
type='ImVoxelNet',
data_preprocessor=dict(
type='Det3DDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='mmdet.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,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
style='pytorch'),
neck=dict(
type='mmdet.FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=4),
neck_3d=dict(
type='IndoorImVoxelNeck',
in_channels=256,
out_channels=128,
n_blocks=[1, 1, 1]),
bbox_head=dict(
type='ImVoxelHead',
n_classes=10,
n_levels=3,
n_channels=128,
n_reg_outs=7,
pts_assign_threshold=27,
pts_center_threshold=18,
prior_generator=prior_generator),
prior_generator=prior_generator,
n_voxels=[40, 40, 16],
coord_type='DEPTH',
train_cfg=dict(),
test_cfg=dict(nms_pre=1000, iou_thr=.25, score_thr=.01))
dataset_type = 'SUNRGBDDataset'
data_root = 'data/sunrgbd/'
class_names = [
'bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
'night_stand', 'bookshelf', 'bathtub'
]
metainfo = dict(CLASSES=class_names)
backend_args = None
train_pipeline = [
dict(type='LoadAnnotations3D', backend_args=backend_args),
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='RandomResize', scale=[(512, 384), (768, 576)], keep_ratio=True),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='Pack3DDetInputs', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(640, 480), keep_ratio=True),
dict(type='Pack3DDetInputs', keys=['img'])
]
train_dataloader = dict(
batch_size=4,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='sunrgbd_infos_train.pkl',
pipeline=train_pipeline,
test_mode=False,
filter_empty_gt=True,
box_type_3d='Depth',
metainfo=metainfo,
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='sunrgbd_infos_val.pkl',
pipeline=test_pipeline,
test_mode=True,
box_type_3d='Depth',
metainfo=metainfo,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='IndoorMetric',
ann_file=data_root + 'sunrgbd_infos_val.pkl',
metric='bbox')
test_evaluator = val_evaluator
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(
_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001),
paramwise_cfg=dict(
custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}),
clip_grad=dict(max_norm=35., norm_type=2))
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
# hooks
default_hooks = dict(checkpoint=dict(type='CheckpointHook', max_keep_ckpts=1))
# runtime
find_unused_parameters = True # only 1 of 4 FPN outputs is used
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