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imvoxelnet_scannet_fast.py
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imvoxelnet_scannet_fast.py
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model = dict(
type='ImVoxelNet',
pretrained='torchvision://resnet50',
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='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=4),
neck_3d=dict(
type='FastIndoorImVoxelNeck',
in_channels=256,
out_channels=128,
n_blocks=[1, 1, 1]),
bbox_head=dict(
type='ScanNetImVoxelHeadV2',
loss_bbox=dict(type='AxisAlignedIoULoss', loss_weight=1.0),
n_classes=18,
n_channels=128,
n_reg_outs=6,
n_scales=3,
limit=27,
centerness_topk=18),
voxel_size=(.16, .16, .16),
n_voxels=(40, 40, 16))
train_cfg = dict()
test_cfg = dict(
nms_pre=1000,
iou_thr=.25,
score_thr=.01)
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
dataset_type = 'ScanNetMultiViewDataset'
data_root = 'data/scannet/'
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'bookshelf', 'picture', 'counter', 'desk', 'curtain',
'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin')
train_pipeline = [
dict(type='LoadAnnotations3D'),
dict(
type='MultiViewPipeline',
n_images=20,
transforms=[
dict(type='LoadImageFromFile'),
dict(type='Resize', img_scale=(640, 480), keep_ratio=True),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=(480, 640))
]),
dict(type='RandomShiftOrigin', std=(.7, .7, .0)),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='MultiViewPipeline',
n_images=50,
transforms=[
dict(type='LoadImageFromFile'),
dict(type='Resize', img_scale=(640, 480), keep_ratio=True),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=(480, 640))
]),
dict(type='DefaultFormatBundle3D', class_names=class_names, with_label=False),
dict(type='Collect3D', keys=['img'])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root 'scannet_infos_train.pkl',
pipeline=train_pipeline,
classes=class_names,
filter_empty_gt=True,
box_type_3d='Depth')),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root 'scannet_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
test_mode=True,
box_type_3d='Depth'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root 'scannet_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
test_mode=True,
box_type_3d='Depth')
)
optimizer = dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0001,
paramwise_cfg=dict(
custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
optimizer_config = dict(grad_clip=dict(max_norm=35., norm_type=2))
lr_config = dict(policy='step', step=[8, 11])
total_epochs = 12
checkpoint_config = dict(interval=1, max_keep_ckpts=1)
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
evaluation = dict(interval=1)
dist_params = dict(backend='nccl')
find_unused_parameters = True # todo: fix number of FPN outputs
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]