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...
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。然后再使用mmengine.dump保存成 pkl 文件,它的定义以及实现如下: # meg_converter.pyimportosfromosimportpathasospimportmmenginefrompyquaternionimportQuaternionimportjsonclass_names=["小汽车","汽车","货车","工程...
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...
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.work_dir)elifwork_dirisNone:self.work_dir=Noneelse:raiseTypeError('"work_dir"...
keys=['points', 'gt_labels_3d', 'gt_bboxes_3d']) ], modality=dict(use_lidar=True, use_camera=False), test_mode=False, metainfo=dict(classes=['Pedestrian', 'Cyclist', 'Car']), box_type_3d='LiDAR', backend_args=None))) ...
det_inferencer.py inference.py configs datasets engine evaluation models structures testing utils visualization __init__.py registry.py version.py projects requirements resources tests tools .gitignore .owners.yml .pre-commit-config-zh-cn.yaml ...
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...
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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...
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), ] } to cfg.metainfo={'classes': ('balloon', ),'palette': [ (220,20,60), ] ...