/
imvoxelnet_total_sunrgbd.py
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/
imvoxelnet_total_sunrgbd.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'),
head_2d=dict(
type='LayoutHead',
n_channels=2048,
linear_size=256,
dropout=.0,
loss_angle=dict(type='SmoothL1Loss', loss_weight=100.),
loss_layout=dict(type='IoU3DLoss', loss_weight=1.)),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=64,
num_outs=4),
neck_3d=dict(
type='ImVoxelNeck',
channels=[64, 128, 256, 512],
out_channels=64,
down_layers=[1, 2, 3, 4],
up_layers=[3, 2, 1],
conditional=False),
bbox_head=dict(
type='SunRgbdImVoxelHead',
n_classes=33,
n_channels=64,
n_convs=0,
n_reg_outs=7),
n_voxels=(80, 80, 32),
voxel_size=(.08, .08, .08))
train_cfg = dict()
test_cfg = dict(
nms_pre=1000,
nms_thr=.15,
use_rotate_nms=True,
score_thr=.05)
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
dataset_type = 'SunRgbdTotalMultiViewDataset'
data_root = 'data/sunrgbd/'
class_names = [
'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter',
'blinds', 'desk', 'shelves', 'curtain', 'dresser', 'pillow', 'mirror', 'clothes', 'books',
'fridge', 'tv', 'paper', 'towel', 'shower_curtain', 'box', 'whiteboard', 'person', 'night_stand', 'toilet',
'sink', 'lamp', 'bathtub', 'bag'
]
train_pipeline = [
dict(type='LoadAnnotations3D'),
dict(
type='MultiViewPipeline',
n_images=1,
transforms=[
dict(type='SunRgbdTotalLoadImageFromFile'),
dict(type='Resize', img_scale=[(512, 384), (768, 576)], multiscale_mode='range', keep_ratio=True),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32)]),
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=1,
transforms=[
dict(type='LoadImageFromFile'),
dict(type='Resize', img_scale=(640, 480), keep_ratio=True),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32)]),
dict(type='DefaultFormatBundle3D', class_names=class_names, with_label=False),
dict(type='Collect3D', keys=['img'])]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=1,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'sunrgbd_total_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 + 'sunrgbd_total_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 + 'sunrgbd_total_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)]