mmdetection3d 源码学习 mvxnet（多模态融合）
配置文件
dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py
模型
# model settings
voxel_size = [0.05, 0.05, 0.1]
point_cloud_range = [0, -40, -3, 70.4, 40, 1]
##模型 图像：主干 ResNet，neck FPN；点云：voxel编码，主干second（稀疏编码），neck secondFPN；融合：点融合
model = dict(
type='DynamicMVXFasterRCNN',
img_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='caffe'),
img_neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
pts_voxel_layer=dict(
max_num_points=-1,
point_cloud_range=point_cloud_range,
voxel_size=voxel_size,
max_voxels=(-1, -1),
),
pts_voxel_encoder=dict(
type='DynamicVFE',
in_channels=4,
feat_channels=[64, 64],
with_distance=False,
voxel_size=voxel_size,
with_cluster_center=True,
with_voxel_center=True,
point_cloud_range=point_cloud_range,
fusion_layer=dict(
type='PointFusion',
img_channels=256,
pts_channels=64,
mid_channels=128,
out_channels=128,
img_levels=[0, 1, 2, 3, 4],
align_corners=False,
activate_out=True,
fuse_out=False)),
pts_middle_encoder=dict(
type='SparseEncoder',
in_channels=128,
sparse_shape=[41, 1600, 1408],
order=('conv', 'norm', 'act')),
pts_backbone=dict(
type='SECOND',
in_channels=256,
layer_nums=[5, 5],
layer_strides=[1, 2],
out_channels=[128, 256]),
pts_neck=dict(
type='SECONDFPN',
in_channels=[128, 256],
upsample_strides=[1, 2],
out_channels=[256, 256]),
pts_bbox_head=dict(
type='Anchor3DHead',
num_classes=3,
in_channels=512,
feat_channels=512,
use_direction_classifier=True,
anchor_generator=dict(
type='Anchor3DRangeGenerator',
ranges=[
[0, -40.0, -0.6, 70.4, 40.0, -0.6],
[0, -40.0, -0.6, 70.4, 40.0, -0.6],
[0, -40.0, -1.78, 70.4, 40.0, -1.78],
],
sizes=[[0.6, 0.8, 1.73], [0.6, 1.76, 1.73], [1.6, 3.9, 1.56]],
rotations=[0, 1.57],
reshape_out=False),
assigner_per_size=True,
diff_rad_by_sin=True,
assign_per_class=True,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
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=2.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
# model training and testing settings
train_cfg=dict(
pts=dict(
assigner=[
dict( # for Pedestrian
type='MaxIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.35,
neg_iou_thr=0.2,
min_pos_iou=0.2,
ignore_iof_thr=-1),
dict( # for Cyclist
type='MaxIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.35,
neg_iou_thr=0.2,
min_pos_iou=0.2,
ignore_iof_thr=-1),
dict( # for Car
type='MaxIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
min_pos_iou=0.45,
ignore_iof_thr=-1),
],
allowed_border=0,
pos_weight=-1,
debug=False)),
test_cfg=dict(
pts=dict(
use_rotate_nms=True,
nms_across_levels=False,
nms_thr=0.01,
score_thr=0.1,
min_bbox_size=0,
nms_pre=100,
max_num=50)))
数据集
# dataset settingskitti数据集，三类样本
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
input_modality = dict(use_lidar=True, use_camera=True)
训练pipeline
train_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='Resize',
img_scale=[(640, 192), (2560, 768)],
multiscale_mode='range',
keep_ratio=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.95, 1.05],
translation_std=[0.2, 0.2, 0.2]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d']),
]
测试pipeline
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1280, 384),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='Resize', multiscale_mode='value', keep_ratio=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points', 'img'])
])
]
数据
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=train_pipeline,
modality=input_modality,
classes=class_names,
test_mode=False)),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True))
# Training settings
optimizer = dict(type='AdamW', lr=0.003, betas=(0.95, 0.99), weight_decay=0.01)
# max_norm=10 is better for SECOND
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=1000,
warmup_ratio=1.0 / 10,
min_lr_ratio=1e-5)
momentum_config = None
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
evaluation = dict(interval=1)
# runtime settings
total_epochs = 40
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = None
# You may need to download the model first is the network is unstable
预训练数据
load_from = 'https://download.openmmlab.com/mmdetection3d/pretrain_models/mvx_faster_rcnn_detectron2-caffe_20e_coco-pretrain_gt-sample_kitti-3-class_moderate-79.3_20200207-a4a6a3c7.pth' # noqa
resume_from = None
workflow = [('train', 1)]
检测器
mvx_two_stage.py
导入
#导入包
import mmcv
import torch
from mmcv.parallel import DataContainer as DC
from mmcv.runner import force_fp32
from os import path as osp
from torch import nn as nn
from torch.nn import functional as F
from mmdet3d.core import (Box3DMode, Coord3DMode, bbox3d2result,
merge_aug_bboxes_3d, show_result)
from mmdet3d.ops import Voxelization
from mmdet.core import multi_apply
from mmdet.models import DETECTORS
from .. import builder
from .base import Base3DDetector
定义MVXTwoStageDetector类
@DETECTORS.register_module()
class MVXTwoStageDetector(Base3DDetector):
"""Base class of Multi-modality VoxelNet."""
def __init__(self,
pts_voxel_layer=None,
pts_voxel_encoder=None,
pts_middle_encoder=None,
pts_fusion_layer=None,
img_backbone=None,
pts_backbone=None,
img_neck=None,
pts_neck=None,
pts_bbox_head=None,
img_roi_head=None,
img_rpn_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None):
super(MVXTwoStageDetector, self).__init__()
if pts_voxel_layer:
self.pts_voxel_layer = Voxelization(**pts_voxel_layer)
if pts_voxel_encoder:
self.pts_voxel_encoder = builder.build_voxel_encoder(
pts_voxel_encoder)
if pts_middle_encoder:
self.pts_middle_encoder = builder.build_middle_encoder(
pts_middle_encoder)
if pts_backbone:
self.pts_backbone = builder.build_backbone(pts_backbone)
if pts_fusion_layer:
self.pts_fusion_layer = builder.build_fusion_layer(
pts_fusion_layer)
if pts_neck is not None:
self.pts_neck = builder.build_neck(pts_neck)
if pts_bbox_head:
pts_train_cfg = train_cfg.pts if train_cfg else None
pts_bbox_head.update(train_cfg=pts_train_cfg)
pts_test_cfg = test_cfg.pts if test_cfg else None
pts_bbox_head.update(test_cfg=pts_test_cfg)
self.pts_bbox_head = builder.build_head(pts_bbox_head)
if img_backbone:
self.img_backbone = builder.build_backbone(img_backbone)
if img_neck is not None:
self.img_neck = builder.build_neck(img_neck)
if img_rpn_head is not None:
self.img_rpn_head = builder.build_head(img_rpn_head)
if img_roi_head is not None:
self.img_roi_head = builder.build_head(img_roi_head)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.init_weights(pretrained=pretrained)
def init_weights(self, pretrained=None):
"""Initialize model weights."""
super(MVXTwoStageDetector, self).init_weights(pretrained)
if pretrained is None:
img_pretrained = None
pts_pretrained = None
elif isinstance(pretrained, dict):
img_pretrained = pretrained.get('img', None)
pts_pretrained = pretrained.get('pts', None)
else:
raise ValueError(
f'pretrained should be a dict, got {type(pretrained)}')
if self.with_img_backbone:
self.img_backbone.init_weights(pretrained=img_pretrained)
if self.with_pts_backbone:
self.pts_backbone.init_weights(pretrained=pts_pretrained)
if self.with_img_neck:
if isinstance(self.img_neck, nn.Sequential):
for m in self.img_neck:
m.init_weights()
else:
self.img_neck.init_weights()
if self.with_img_roi_head:
self.img_roi_head.init_weights(img_pretrained)
if self.with_img_rpn:
self.img_rpn_head.init_weights()
if self.with_pts_bbox:
self.pts_bbox_head.init_weights()
@property##图像分支共享head？
def with_img_shared_head(self):
"""bool: Whether the detector has a shared head in image branch."""
return hasattr(self,
'img_shared_head') and self.img_shared_head is not None
@property##3D box head？
def with_pts_bbox(self):
"""bool: Whether the detector has a 3D box head."""
return hasattr(self,
'pts_bbox_head') and self.pts_bbox_head is not None
@property##2D box head？
def with_img_bbox(self):
"""bool: Whether the detector has a 2D image box head."""
return hasattr(self,
'img_bbox_head') and self.img_bbox_head is not None
@property##2D 主干？
def with_img_backbone(self):
"""bool: Whether the detector has a 2D image backbone."""
return hasattr(self, 'img_backbone') and self.img_backbone is not None
@property##3D主干？
def with_pts_backbone(self):
"""bool: Whether the detector has a 3D backbone."""
return hasattr(self, 'pts_backbone') and self.pts_backbone is not None
@property##融合层？
def with_fusion(self):
"""bool: Whether the detector has a fusion layer."""
return hasattr(self,
'pts_fusion_layer') and self.fusion_layer is not None
@property##图像neck？
def with_img_neck(self):
"""bool: Whether the detector has a neck in image branch."""
return hasattr(self, 'img_neck') and self.img_neck is not None
@property
def with_pts_neck(self):##3Dneck？
"""bool: Whether the detector has a neck in 3D detector branch."""
return hasattr(self, 'pts_neck') and self.pts_neck is not None
@property
def with_img_rpn(self):##2D RPN？
"""bool: Whether the detector has a 2D RPN in image detector branch."""
return hasattr(self, 'img_rpn_head') and self.img_rpn_head is not None
@property
def with_img_roi_head(self):##图像ROI head？
"""bool: Whether the detector has a RoI Head in image branch."""
return hasattr(self, 'img_roi_head') and self.img_roi_head is not None
@property
def with_voxel_encoder(self):##体素编码？
"""bool: Whether the detector has a voxel encoder."""
return hasattr(self,
'voxel_encoder') and self.voxel_encoder is not None
@property##中间层编码？
def with_middle_encoder(self):
"""bool: Whether the detector has a middle encoder."""
return hasattr(self,
'middle_encoder') and self.middle_encoder is not None
##图像特征提取
def extract_img_feat(self, img, img_metas):
"""Extract features of images."""
if self.with_img_backbone and img is not None:
input_shape = img.shape[-2:]
# update real input shape of each single img
for img_meta in img_metas:
img_meta.update(input_shape=input_shape)
if img.dim() == 5 and img.size(0) == 1:
img.squeeze_()
elif img.dim() == 5 and img.size(0) > 1:
B, N, C, H, W = img.size()
img = img.view(B * N, C, H, W)
img_feats = self.img_backbone(img)
else:
return None
if self.with_img_neck:
img_feats = self.img_neck(img_feats)
return img_feats
##点云特征提取
def extract_pts_feat(self, pts, img_feats, img_metas):
"""Extract features of points."""
if not self.with_pts_bbox:
return None
voxels, num_points, coors = self.voxelize(pts)
voxel_features = self.pts_voxel_encoder(voxels, num_points, coors,
img_feats, img_metas)
batch_size = coors[-1, 0] + 1
x = self.pts_middle_encoder(voxel_features, coors, batch_size)
x = self.pts_backbone(x)
if self.with_pts_neck:
x = self.pts_neck(x)
return x
##图像与点云特征提取
def extract_feat(self, points, img, img_metas):
"""Extract features from images and points."""
img_feats = self.extract_img_feat(img, img_metas)
pts_feats = self.extract_pts_feat(points, img_feats, img_metas)
return (img_feats, pts_feats)
@torch.no_grad()
@force_fp32()
def voxelize(self, points):##点的体素化
"""Apply dynamic voxelization to points.
Args:
points (list[torch.Tensor]): Points of each sample.
Returns:
tuple[torch.Tensor]: Concatenated points, number of points
per voxel, and coordinates.
"""
voxels, coors, num_points = [], [], []
for res in points:
res_voxels, res_coors, res_num_points = self.pts_voxel_layer(res)
voxels.append(res_voxels)
coors.append(res_coors)
num_points.append(res_num_points)
voxels = torch.cat(voxels, dim=0)
num_points = torch.cat(num_points, dim=0)
coors_batch = []
for i, coor in enumerate(coors):
coor_pad = F.pad(coor, (1, 0), mode='constant', value=i)
coors_batch.append(coor_pad)
coors_batch = torch.cat(coors_batch, dim=0)
return voxels, num_points, coors_batch
##前向训练
def forward_train(self,
points=None,
img_metas=None,
gt_bboxes_3d=None,
gt_labels_3d=None,
gt_labels=None,
gt_bboxes=None,
img=None,
proposals=None,
gt_bboxes_ignore=None):
"""Forward training function.
Args:
points (list[torch.Tensor], optional): Points of each sample.
Defaults to None.
img_metas (list[dict], optional): Meta information of each sample.
Defaults to None.
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`], optional):
Ground truth 3D boxes. Defaults to None.
gt_labels_3d (list[torch.Tensor], optional): Ground truth labels
of 3D boxes. Defaults to None.
gt_labels (list[torch.Tensor], optional): Ground truth labels
of 2D boxes in images. Defaults to None.
gt_bboxes (list[torch.Tensor], optional): Ground truth 2D boxes in
images. Defaults to None.
img (torch.Tensor optional): Images of each sample with shape
(N, C, H, W). Defaults to None.
proposals ([list[torch.Tensor], optional): Predicted proposals
used for training Fast RCNN. Defaults to None.
gt_bboxes_ignore (list[torch.Tensor], optional): Ground truth
2D boxes in images to be ignored. Defaults to None.
Returns:
dict: Losses of different branches.
"""
img_feats, pts_feats = self.extract_feat(
points, img=img, img_metas=img_metas)
losses = dict()
if pts_feats:
losses_pts = self.forward_pts_train(pts_feats, gt_bboxes_3d,
gt_labels_3d, img_metas,
gt_bboxes_ignore)
losses.update(losses_pts)
if img_feats:
losses_img = self.forward_img_train(
img_feats,
img_metas=img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
gt_bboxes_ignore=gt_bboxes_ignore,
proposals=proposals)
losses.update(losses_img)
return losses
##点云前向训练
def forward_pts_train(self,
pts_feats,
gt_bboxes_3d,
gt_labels_3d,
img_metas,
gt_bboxes_ignore=None):
"""Forward function for point cloud branch.
Args:
pts_feats (list[torch.Tensor]): Features of point cloud branch
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
boxes for each sample.
gt_labels_3d (list[torch.Tensor]): Ground truth labels for
boxes of each sampole
img_metas (list[dict]): Meta information of samples.
gt_bboxes_ignore (list[torch.Tensor], optional): Ground truth
boxes to be ignored. Defaults to None.
Returns:
dict: Losses of each branch.
"""
outs = self.pts_bbox_head(pts_feats)
loss_inputs = outs + (gt_bboxes_3d, gt_labels_3d, img_metas)
losses = self.pts_bbox_head.loss(
*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
return losses
##图像前向训练
def forward_img_train(self,
x,
img_metas,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
proposals=None,
**kwargs):
"""Forward function for image branch.
This function works similar to the forward function of Faster R-CNN.
Args:
x (list[torch.Tensor]): Image features of shape (B, C, H, W)
of multiple levels.
img_metas (list[dict]): Meta information of images.
gt_bboxes (list[torch.Tensor]): Ground truth boxes of each image
sample.
gt_labels (list[torch.Tensor]): Ground truth labels of boxes.
gt_bboxes_ignore (list[torch.Tensor], optional): Ground truth
boxes to be ignored. Defaults to None.
proposals (list[torch.Tensor], optional): Proposals of each sample.
Defaults to None.
Returns:
dict: Losses of each branch.
"""
losses = dict()
# RPN forward and loss
if self.with_img_rpn:
rpn_outs = self.img_rpn_head(x)
rpn_loss_inputs = rpn_outs + (gt_bboxes, img_metas,
self.train_cfg.img_rpn)
rpn_losses = self.img_rpn_head.loss(
*rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
losses.update(rpn_losses)
proposal_cfg = self.train_cfg.get('img_rpn_proposal',
self.test_cfg.img_rpn)
proposal_inputs = rpn_outs + (img_metas, proposal_cfg)
proposal_list = self.img_rpn_head.get_bboxes(*proposal_inputs)
else:
proposal_list = proposals
# bbox head forward and loss
if self.with_img_bbox:
# bbox head forward and loss
img_roi_losses = self.img_roi_head.forward_train(
x, img_metas, proposal_list, gt_bboxes, gt_labels,
gt_bboxes_ignore, **kwargs)
losses.update(img_roi_losses)
return losses
##在无数据增强的前提下测试图像
def simple_test_img(self, x, img_metas, proposals=None, rescale=False):
"""Test without augmentation."""
if proposals is None:
proposal_list = self.simple_test_rpn(x, img_metas,
self.test_cfg.img_rpn)
else:
proposal_list = proposals
return self.img_roi_head.simple_test(
x, proposal_list, img_metas, rescale=rescale)
##在无数据增强的前提下测试rpn
def simple_test_rpn(self, x, img_metas, rpn_test_cfg):
"""RPN test function."""
rpn_outs = self.img_rpn_head(x)
proposal_inputs = rpn_outs + (img_metas, rpn_test_cfg)
proposal_list = self.img_rpn_head.get_bboxes(*proposal_inputs)
return proposal_list
##在无数据增强的前提下点云
def simple_test_pts(self, x, img_metas, rescale=False):
"""Test function of point cloud branch."""
outs = self.pts_bbox_head(x)
bbox_list = self.pts_bbox_head.get_bboxes(
*outs, img_metas, rescale=rescale)
bbox_results = [
bbox3d2result(bboxes, scores, labels)
for bboxes, scores, labels in bbox_list
]
return bbox_results
##在无数据增强的前提下测试
def simple_test(self, points, img_metas, img=None, rescale=False):
"""Test function without augmentaiton."""
img_feats, pts_feats = self.extract_feat(
points, img=img, img_metas=img_metas)
bbox_list = [dict() for i in range(len(img_metas))]
if pts_feats and self.with_pts_bbox:
bbox_pts = self.simple_test_pts(
pts_feats, img_metas, rescale=rescale)
for result_dict, pts_bbox in zip(bbox_list, bbox_pts):
result_dict['pts_bbox'] = pts_bbox
if img_feats and self.with_img_bbox:
bbox_img = self.simple_test_img(
img_feats, img_metas, rescale=rescale)
for result_dict, img_bbox in zip(bbox_list, bbox_img):
result_dict['img_bbox'] = img_bbox
return bbox_list
##在数据增强的前提下测试
def aug_test(self, points, img_metas, imgs=None, rescale=False):
"""Test function with augmentaiton."""
img_feats, pts_feats = self.extract_feats(points, img_metas, imgs)
bbox_list = dict()
if pts_feats and self.with_pts_bbox:
bbox_pts = self.aug_test_pts(pts_feats, img_metas, rescale)
bbox_list.update(pts_bbox=bbox_pts)
return [bbox_list]
##提取点云和图像特征
def extract_feats(self, points, img_metas, imgs=None):
"""Extract point and image features of multiple samples."""
if imgs is None:
imgs = [None] * len(img_metas)
img_feats, pts_feats = multi_apply(self.extract_feat, points, imgs,
img_metas)
return img_feats, pts_feats
##在数据增强的前提下测试点云
def aug_test_pts(self, feats, img_metas, rescale=False):
"""Test function of point cloud branch with augmentaiton."""
# only support aug_test for one sample
aug_bboxes = []
for x, img_meta in zip(feats, img_metas):
outs = self.pts_bbox_head(x)
bbox_list = self.pts_bbox_head.get_bboxes(
*outs, img_meta, rescale=rescale)
bbox_list = [
dict(boxes_3d=bboxes, scores_3d=scores, labels_3d=labels)
for bboxes, scores, labels in bbox_list
]
aug_bboxes.append(bbox_list[0])
# after merging, bboxes will be rescaled to the original image size
merged_bboxes = merge_aug_bboxes_3d(aug_bboxes, img_metas,
self.pts_bbox_head.test_cfg)
return merged_bboxes
##显示结果
def show_results(self, data, result, out_dir):
"""Results visualization.
Args:
data (dict): Input points and the information of the sample.
result (dict): Prediction results.
out_dir (str): Output directory of visualization result.
"""
for batch_id in range(len(result)):
if isinstance(data['points'][0], DC):
points = data['points'][0]._data[0][batch_id].numpy()
elif mmcv.is_list_of(data['points'][0], torch.Tensor):
points = data['points'][0][batch_id]
else:
ValueError(f"Unsupported data type {type(data['points'][0])} "
f'for visualization!')
if isinstance(data['img_metas'][0], DC):
pts_filename = data['img_metas'][0]._data[0][batch_id][
'pts_filename']
box_mode_3d = data['img_metas'][0]._data[0][batch_id][
'box_mode_3d']
elif mmcv.is_list_of(data['img_metas'][0], dict):
pts_filename = data['img_metas'][0][batch_id]['pts_filename']
box_mode_3d = data['img_metas'][0][batch_id]['box_mode_3d']
else:
ValueError(
f"Unsupported data type {type(data['img_metas'][0])} "
f'for visualization!')
file_name = osp.split(pts_filename)[-1].split('.')[0]
assert out_dir is not None, 'Expect out_dir, got none.'
inds = result[batch_id]['pts_bbox']['scores_3d'] > 0.1
pred_bboxes = result[batch_id]['pts_bbox']['boxes_3d'][inds]
# for now we convert points and bbox into depth mode
if (box_mode_3d == Box3DMode.CAM) or (box_mode_3d
== Box3DMode.LIDAR):
points = Coord3DMode.convert_point(points, Coord3DMode.LIDAR,
Coord3DMode.DEPTH)
pred_bboxes = Box3DMode.convert(pred_bboxes, box_mode_3d,
Box3DMode.DEPTH)
elif box_mode_3d != Box3DMode.DEPTH:
ValueError(
f'Unsupported box_mode_3d {box_mode_3d} for convertion!')
pred_bboxes = pred_bboxes.tensor.cpu().numpy()
show_result(points, None, pred_bboxes, out_dir, file_name)
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