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_base_ = ['./nerfdet_res50_2x_low_res_depth.py']
model = dict(depth_supervise=False)
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_depth=False)
backend_args = None
train_collect_keys = [
'img', 'gt_bboxes_3d', 'gt_labels_3d', 'lightpos', 'nerf_sizes', 'raydirs',
'gt_images', 'gt_depths', 'denorm_images'
]
test_collect_keys = [
'img',
'lightpos',
'nerf_sizes',
'raydirs',
'gt_images',
'gt_depths',
'denorm_images',
]
train_pipeline = [
dict(type='LoadAnnotations3D'),
dict(
type='MultiViewPipeline',
n_images=50,
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=1,
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
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