第十三个:Collect3D,这一步其实很简单,就是用于pipeline的最后一步,其根据我们初始化时候给它指定的meta_keys,为了我们具体的任务搞出来所需要的整顿好,把dict中我们已经存下的这么多东西进行自定义的收集,放入img_metas这个key中,其是一个包含了我们所收集的内容的DataContainer。 """Collect data from the loader...
use_camera=False),test_mode=False,metainfo=dict(class_names=class_names),box_type_3d='LiDAR')...
METAINFO = { 'classes': ('Pedestrian', 'Cyclist', 'Car') } def parse_ann_info(self, info): """Process the `instances` in data info to `ann_info`. Args: info (dict): Data information of single data sample. Returns: dict: Annotation information consists of the following keys: - g...
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def__init__(self,model,optimizer=None,work_dir=None,logger=None,meta=None,max_iters=None,max_epochs=None):………self.model=modelself.optimizer=optimizerself.logger=loggerself.meta=meta# create work_dirifisinstance(work_dir,str):self.work_dir=osp.abspath(work_dir)mmcv.mkdir_or_exist(self...
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create_meg_info首先解读数据集，整理成 python 的字典格式，其包括两个值：data_list和meta_info。然后...
dict(type='Pack3DDetInputs', keys=['points']) ], modality=dict(use_lidar=True, use_camera=False), test_mode=True, metainfo=dict(classes=['Pedestrian', 'Cyclist', 'Car']), box_type_3d='LiDAR', backend_args=None)) test_dataloader = dict( ...
训练的时候,首先进入parse_data_info函数,然后调用parse_ann_info函数。其中的参数info是 读取的 pkl 格式。其中,METAINFO 根据自定义数据集的实际类别情况修改。下面 gt_bboxes_3d 也根据实际情况自行编写: gt_bboxes_3d=LiDARInstance3DBoxes(ann_info['gt_bboxes_3d'],box_dim=ann_info['gt_bboxes_3d...
The metainfo has been changed to lowercase since#9469. On the custom dataset, use lowercase letters for keys in the dictionary instead. For example, in the jupyter notebook demo, change cfg.metainfo={'CLASSES': ('balloon', ),'PALETTE': [ (220,20,60), ] } ...