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pgd_r101_fpn_gn-head_dcn_3x16_2x_waymoD3-mv3d.py
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pgd_r101_fpn_gn-head_dcn_3x16_2x_waymoD3-mv3d.py
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_base_ = [
'../_base_/datasets/waymoD3-mv3d.py', '../_base_/models/pgd.py',
'../_base_/schedules/mmdet_schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'),
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, False, True, True)),
neck=dict(num_outs=3),
bbox_head=dict(
num_classes=3,
bbox_code_size=7,
pred_attrs=False,
pred_velo=False,
pred_bbox2d=True,
use_onlyreg_proj=True,
strides=(8, 16, 32),
regress_ranges=((-1, 128), (128, 256), (256, 1e8)),
group_reg_dims=(2, 1, 3, 1, 16,
4), # offset, depth, size, rot, kpts, bbox2d
reg_branch=(
(256, ), # offset
(256, ), # depth
(256, ), # size
(256, ), # rot
(256, ), # kpts
(256, ) # bbox2d
),
centerness_branch=(256, ),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
use_depth_classifier=True,
depth_branch=(256, ),
depth_range=(0, 50),
depth_unit=10,
division='uniform',
depth_bins=6,
pred_keypoints=True,
weight_dim=1,
loss_depth=dict(
type='UncertainSmoothL1Loss', alpha=1.0, beta=3.0,
loss_weight=1.0),
loss_bbox2d=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.0),
loss_consistency=dict(type='GIoULoss', loss_weight=0.0),
bbox_coder=dict(
type='PGDBBoxCoder',
base_depths=((41.01, 18.44), ),
base_dims=((0.91, 1.74, 0.84), (1.81, 1.77, 0.84), (4.73, 1.77,
2.08)),
code_size=7)),
# set weight 1.0 for base 7 dims (offset, depth, size, rot)
# 0.2 for 16-dim keypoint offsets and 1.0 for 4-dim 2D distance targets
train_cfg=dict(code_weight=[
1.0, 1.0, 0.2, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 1.0, 1.0, 1.0, 1.0
]),
test_cfg=dict(nms_pre=100, nms_thr=0.05, score_thr=0.001, max_per_img=20))
class_names = ['Pedestrian', 'Cyclist', 'Car']
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
file_client_args = dict(
backend='petrel',
path_mapping=dict({
'./data/waymo/':
's3://openmmlab/datasets/detection3d/waymo/',
'data/waymo/':
's3://openmmlab/datasets/detection3d/waymo/'
}))
train_pipeline = [
dict(type='LoadImageFromFileMono3D', file_client_args=file_client_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True,
with_bbox_depth=True),
dict(type='Resize3D', img_scale=(1248, 832), ratio_range=(0.95, 1.05)),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
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', 'gt_labels', 'gt_bboxes_3d', 'gt_labels_3d',
'centers2d', 'depths'
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D', file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug',
scale_factor=0.65,
flip=False,
transforms=[
dict(type='Resize3D', img_scale=(1248, 832), keep_ratio=True),
dict(type='RandomFlip3D'),
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=3,
workers_per_gpu=3,
train=dict(pipeline=train_pipeline, classes=class_names, cam_sync=True),
val=dict(pipeline=test_pipeline, classes=class_names, cam_sync=True),
test=dict(pipeline=test_pipeline, classes=class_names, cam_sync=True))
# optimizer
optimizer = dict(
lr=0.008, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[16, 22])
total_epochs = 24
runner = dict(type='EpochBasedRunner', max_epochs=24)
evaluation = dict(interval=24)
checkpoint_config = dict(interval=1, max_keep_ckpts=1)
dist_params = dict(backend='nccl', port=29510)