open3d.ml.torch.dataloaders.TorchDataloader

class open3d.ml.torch.dataloaders.TorchDataloader(dataset=None, preprocess=None, transform=None, sampler=None, use_cache=True, steps_per_epoch=None, **kwargs)

This class allows you to load datasets for a PyTorch framework.

Example: This example loads the SemanticKITTI dataset using the Torch dataloader:

import torch

from torch.utils.data import Dataset, DataLoader

train_split = TorchDataloader(dataset=dataset.get_split(‘training’))

__init__(dataset=None, preprocess=None, transform=None, sampler=None, use_cache=True, steps_per_epoch=None, **kwargs)

Initialize

Parameters

• dataset – The 3D ML dataset class. You can use the base dataset, sample datasets , or a custom dataset.

• preprocess – The model’s preprocess method.

• transform – The model’s transform method.

• use_cache – Indicates if preprocessed data should be cached.

• steps_per_epoch – The number of steps per epoch that indicates the bactches of samples to train. If it is None, then the step number will be the number of samples in the data.

Returns

The corresponding class.

Return type

class