|
_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]], |
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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 |
|
|
|
|
|
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) |
|
] |
|
|
|
|
|
default_hooks = dict(checkpoint=dict(type='CheckpointHook', max_keep_ckpts=1)) |
|
|
|
|
|
find_unused_parameters = True |
|
|
