PyTorch PointCloud¶

import torch

import torch_pointcloud as tp
from torch_pointcloud.datasets import ModelNetNormalResampled
from torch_pointcloud.utils.data import collate

# Load pretrained checkpoint.
model, info = tp.create_model(
    "pointnet2-ssg.modelnet40.xu-yan",
    task="classification",
    pretrained=True,
    return_info=True,
)
model = model.eval()

# Get associated transform pipeline.
transform = info["transform"]

# Load a preprocessed sample and collate in packed format
dataset = ModelNetNormalResampled(root="data", variant="40", train=False, download=True, transform=transform)
data = collate([dataset[0]])

# Run inference
with torch.no_grad():
    logits = model(None, data["pos"], data["batch"])

print(f"Prediction: {logits.argmax().item()}")
# Prediction: 0