Building inference for 3D point cloud using Pointcept V3 Point Transformer
import torch from torch.utils.data import Dataset, DataLoader import numpy as np import open3d as o3d from pointcept.models.point_transformer_v3.point_transformer_v3m1_base import PointTransformerV3 from pointcept.engines.defaults import ( default_argument_parser, default_config_parser, default_setup, ) from pointcept.engines.train import TRAINERS from pointcept.engines.test import TESTERS def load_model(): args = { ‘save_path’: ‘semsegV3_waymo_20_epochs_2’, ‘weight’: ‘semsegV3_waymo_20_epochs_2/model/model_best.pth’ } cfg = default_config_parser(‘PointTransformerV3.1/configs/waymo/semseg-pt-v3m1-0-base.py’,args) cfg = default_setup(cfg) print(cfg) model = TESTERS.build(dict(type=cfg.test.type, […]