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...
加载图像并进行预测: img = mmcv.imread('https://github.com/open-mmlab/mmdetection/blob/master/demo/demo.jpg') result = inference_detector(model, img) show_result_pyplot(model, img, result) 通过上述步骤,我们就可以在Colab上轻松使用OpenMMLab进行计算机视觉研究和开发。在Colab的云端环境中,由于可以使用...
labels[name].append(label) metas[name] = dict(id=img_id, filename=filename, width=width, height=height) for i, name in enumerate(image_names): ann = dict( bboxes=np.array(bboxes[name]).astype(np.float32), labels=np.array(labels[name]).astype(np.int64) ) meta = metas[name] ...
整个文件夹被定义为mmdeploy SDK model。换言之,mmdeploy SDK model既包括推理引擎,也包括推理 meta 信息。 以上述模型转换后的end2end.onnx为例,你可以使用如下代码进行推理: frommmdeploy.apis.utilsimportbuild_task_processorfrommmdeploy.utilsimportget_input_shape,load_configimporttorchdeploy_cfg='configs/m...
( META INFORMATION img_shape: (1091, 750) scale: (750, 1101) ori_shape: (512, 352, 3) img: array([[[243, 244, 242], [243, 244, 242], [244, 245, 242], ..., [236, 234, 234], [231, 230, 233], [229, 229, 233]], [[243, 243, 241], [243, 244, 241], [244,...
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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 ...
input_modality = dict(use_lidar=True, use_camera=False) metainfo = dict(classes=['Pedestrian', 'Cyclist', 'Car']) backend_args = None db_sampler = dict( data_root='data/kitti/', info_path='data/kitti/kitti_dbinfos_train.pkl', rate=1.0, prepare=dict( filter_by_difficulty=[-1], ...
(data_root=data_root,metainfo=metainfo,data_prefix=dict(img='train/'),ann_file='train.json'))val_dataloader=dict(dataset=dict(data_root=data_root,metainfo=metainfo,data_prefix=dict(img='val/'),ann_file='val.json'))test_dataloader=val_dataloaderval_evaluator=dict(ann_file=data_root+'...
( type=dataset_type, classes=classes, img_prefix=data_root + 'val/', ann_file=data_root + 'instancesonly_filtered_val.json', ), test=dict( type=dataset_type, classes=classes, img_prefix=data_root + 'val/', ann_file=data_root + 'instancesonly_filtered_val.json', ), ) model = ...