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_base_ = ['../../../configs/_base_/default_runtime.py']
custom_imports = dict(imports=['projects.NeRF-Det.nerfdet'])
prior_generator = dict(
type='AlignedAnchor3DRangeGenerator',
ranges=[[-3.2, -3.2, -1.28, 3.2, 3.2, 1.28]],
rotations=[.0])
model = dict(
type='NerfDet',
data_preprocessor=dict(
type='NeRFDetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=10),
backbone=dict(
type='mmdet.ResNet',
depth=101,
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://resnet101'),
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='NerfDetHead',
bbox_loss=dict(type='AxisAlignedIoULoss', loss_weight=1.0),
n_classes=18,
n_levels=3,
n_channels=128,
n_reg_outs=6,
pts_assign_threshold=27,
pts_center_threshold=18,
prior_generator=prior_generator),
prior_generator=prior_generator,
voxel_size=[.16, .16, .2],
n_voxels=[40, 40, 16],
aabb=([-2.7, -2.7, -0.78], [3.7, 3.7, 1.78]),
near_far_range=[0.2, 8.0],
N_samples=64,
N_rand=2048,
nerf_mode='image',
depth_supervise=True,
use_nerf_mask=True,
nerf_sample_view=20,
squeeze_scale=4,
nerf_density=True,
train_cfg=dict(),
test_cfg=dict(nms_pre=1000, iou_thr=.25, score_thr=.01))
dataset_type = 'MultiViewScanNetDataset'
data_root = 'data/scannet/'
class_names = [
'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf',
'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain',
'toilet', 'sink', 'bathtub', 'garbagebin'
]
metainfo = dict(CLASSES=class_names)
file_client_args = dict(backend='disk')
input_modality = dict(
use_camera=True,
use_depth=True,
use_lidar=False,
use_neuralrecon_depth=False,
use_ray=True)
backend_args = None
train_collect_keys = [
'img', 'gt_bboxes_3d', 'gt_labels_3d', 'depth', 'lightpos', 'nerf_sizes',
'raydirs', 'gt_images', 'gt_depths', 'denorm_images'
]
test_collect_keys = [
'img',
'depth',
'lightpos',
'nerf_sizes',
'raydirs',
'gt_images',
'gt_depths',
'denorm_images',
]
train_pipeline = [
dict(type='LoadAnnotations3D'),
dict(
type='MultiViewPipeline',
n_images=48,
transforms=[
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(type='Resize', scale=(320, 240), keep_ratio=True),
],
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
margin=10,
depth_range=[0.5, 5.5],
loading='random',
nerf_target_views=10),
dict(type='RandomShiftOrigin', std=(.7, .7, .0)),
dict(type='PackNeRFDetInputs', keys=train_collect_keys)
]
test_pipeline = [
dict(type='LoadAnnotations3D'),
dict(
type='MultiViewPipeline',
n_images=101,
transforms=[
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(type='Resize', scale=(320, 240), keep_ratio=True),
],
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
margin=10,
depth_range=[0.5, 5.5],
loading='random',
nerf_target_views=1),
dict(type='PackNeRFDetInputs', keys=test_collect_keys)
]
train_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=6,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='scannet_infos_train_new.pkl',
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
filter_empty_gt=True,
box_type_3d='Depth',
metainfo=metainfo)))
val_dataloader = dict(
batch_size=1,
num_workers=5,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='scannet_infos_val_new.pkl',
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
filter_empty_gt=True,
box_type_3d='Depth',
metainfo=metainfo))
test_dataloader = val_dataloader
val_evaluator = dict(type='IndoorMetric')
test_evaluator = val_evaluator
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
test_cfg = dict()
val_cfg = dict()
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.0002, 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', interval=1, max_keep_ckpts=12))
# runtime
find_unused_parameters = True # only 1 of 4 FPN outputs is used
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