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epropnp_det_v1b_220312.py
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epropnp_det_v1b_220312.py
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"""
Copyright (C) 2010-2022 Alibaba Group Holding Limited.
"""
model = dict(
type='EProPnPDet',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, False, True, True),
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')),
neck=dict(
type='FPN',
in_channels=[512, 1024, 2048],
out_channels=256,
num_outs=5,
add_extra_convs='on_output',
relu_before_extra_convs=True),
bbox_head=dict(
type='DeformPnPHead',
num_classes=10,
in_channels=256,
strides=(8, 16, 32, 64, 128),
output_stride=8,
dense_lvl_range=(0, 3),
det_lvl_range=(0, 5),
dense_channels=256,
embed_dims=256,
num_heads=8,
num_points=16,
detector=dict(
type='FCOSEmbHead',
feat_channels=256,
stacked_convs=2,
emb_channels=256,
strides=[8, 16, 32, 64, 128],
regress_ranges=((-1, 48), (48, 96), (96, 192), (192, 384),
(384, 1e8)),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_rp=dict( # reference point loss
type='SmoothL1LossMod', beta=1.0, loss_weight=1.0),
loss_centerness=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0)),
attention_sampler=dict(
type='DeformableAttentionSampler',
embed_dims=256,
num_heads=8,
num_points=16,
stride=8),
center_target=dict(
type='VolumeCenter',
output_stride=8,
render_stride=4,
min_box_size=4.0),
dim_coder=dict(
type='MultiClassLogDimCoder',
target_means=[
(4.62, 1.73, 1.96),
(6.94, 2.84, 2.52),
(12.56, 3.89, 2.94),
(11.22, 3.50, 2.95),
(6.68, 3.21, 2.85),
(1.70, 1.29, 0.61),
(2.11, 1.46, 0.78),
(0.73, 1.77, 0.67),
(0.41, 1.08, 0.41),
(0.50, 0.99, 2.52)],
target_stds=[
(0.46, 0.24, 0.16),
(2.11, 0.84, 0.45),
(4.50, 0.77, 0.54),
(2.06, 0.49, 0.33),
(3.23, 0.93, 1.07),
(0.26, 0.35, 0.16),
(0.33, 0.29, 0.17),
(0.19, 0.19, 0.14),
(0.14, 0.27, 0.13),
(0.17, 0.15, 0.62)]),
positional_encoding=dict(
type='SinePositionalEncodingMod',
num_feats=128,
normalize=True,
offset=-0.5),
pnp=dict(
type='EProPnP4DoF',
mc_samples=512,
num_iter=4,
normalize=True,
solver=dict(
type='LMSolver',
num_iter=10,
normalize=True,
init_solver=dict(
type='RSLMSolver',
num_points=16,
num_proposals=64,
num_iter=3))),
camera=dict(type='PerspectiveCamera'),
cost_fun=dict(
type='AdaptiveHuberPnPCost',
relative_delta=0.5),
loss_pose=dict( # main pose loss
type='MonteCarloPoseLoss',
loss_weight=0.15,
momentum=0.01),
loss_proj=dict( # auxiliary reprojection-based loss
type='MVDGaussianMixtureNLLLoss',
loss_weight=0.5),
loss_dim=dict( # dimension (size) loss
type='SmoothL1LossMod',
loss_weight=1.0),
loss_regr=dict( # auxiliary coordinate regression loss
type='SmoothL1LossMod',
beta=0.05,
loss_weight=0.25),
loss_score=dict( # 3D score loss
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
loss_reg_pos=dict( # derivative regularization loss for position
type='SmoothL1LossMod',
beta=1.0,
loss_weight=0.05),
loss_reg_orient=dict( # derivative regularization loss for orientation
type='CosineAngleLoss',
loss_weight=0.05),
loss_velo=dict(
type='SmoothL1LossMod',
loss_weight=0.05),
loss_attr=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=0.5),
pred_velo=True,
pred_attr=True,
num_attrs=9,
score_type='te'),
train_cfg=dict(
num_obj_samples_per_img=48,
roi_shape=(14, 14)), # RoI shape for the reprojection-based auxiliary loss
test_cfg=dict(
override_cfg={'pnp.solver.num_iter': 5},
mc_scoring_ratio=0.0, # 1.0 for Monte Carlo scoring
nms_iou2d=dict(type='nms', iou_threshold=0.8),
nms_ioubev_thr=0.25))
dataset_type = 'NuScenes3DDataset'
data_root = 'data/nuscenes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile3D',
with_img_dense_x2d=True),
dict(type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_bbox_3d=True,
with_coord_3d=True,
with_truncation=True,
with_attr=True,
with_velo=True),
dict(type='RandomFlip3D', flip_ratio=0.5),
dict(type='Crop3D', crop_box=(0, 228, 1600, 900), trunc_ignore_thres=0.8),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad3D', size_divisor=32),
dict(type='DefaultFormatBundle3D'),
dict(type='Collect',
keys=['img', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels',
'gt_bboxes_3d', 'img_dense_x2d', 'img_dense_x2d_mask', 'cam_intrinsic',
'gt_attr', 'gt_velo', 'gt_x3d', 'gt_x2d']),
]
test_pipeline = [
dict(type='LoadImageFromFile3D',
with_img_dense_x2d=True),
dict(type='MultiScaleFlipAug',
scale_factor=1.0,
flip=False,
transforms=[
dict(type='RandomFlip3D', flip_ratio=0.5),
dict(type='Crop3D', crop_box=(0, 228, 1600, 900)),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad3D', size_divisor=32),
dict(type='DefaultFormatBundle3D'),
dict(type='Collect', keys=[
'img', 'img_dense_x2d', 'img_dense_x2d_mask', 'cam_intrinsic']),
])
]
data = dict(
samples_per_gpu=6,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file='nuscenes_annotations_train.pkl',
pipeline=train_pipeline,
data_root=data_root,
trunc_ignore_thres=0.8,
min_visibility=2,
filter_empty_gt=True),
val=dict(
type=dataset_type,
samples_per_gpu=6,
ann_file='nuscenes_annotations_val.pkl',
pipeline=test_pipeline,
data_root=data_root,
filter_empty_gt=False),
test=dict(
type=dataset_type,
samples_per_gpu=6,
ann_file='nuscenes_annotations_test.pkl',
pipeline=test_pipeline,
data_root=data_root,
filter_empty_gt=False))
evaluation = dict(
interval=1,
metric='NDS')
# optimizer
optimizer = dict(
type='AdamW',
lr=1e-4,
weight_decay=0.0001,
paramwise_cfg=dict(
custom_keys={
'sampling_offsets': dict(lr_mult=0.1)
}))
optimizer_config = dict(
type='OptimizerHookMod',
grad_clip=dict(max_norm=5.0, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[10, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=20,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
custom_hooks = [dict(type='EmptyCacheHook')]