open3d.ml.tf.dataloaders.TFDataloader

class open3d.ml.tf.dataloaders.TFDataloader(*args, dataset=None, model=None, use_cache=True, steps_per_epoch=None, preprocess=None, transform=None, get_batch_gen=None, **kwargs)

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

Example:

This example loads the SemanticKITTI dataset using the a point cloud to the visualizer:

import tensorflow as tf
from ..dataloaders import TFDataloader

train_split = TFDataloader(dataset=tf.dataset.get_split('training'),
                model=model,
                use_cache=tf.dataset.cfg.use_cache,
                steps_per_epoch=tf.dataset.cfg.get(
                'steps_per_epoch_train', None))
__init__(*args, dataset=None, model=None, use_cache=True, steps_per_epoch=None, preprocess=None, transform=None, get_batch_gen=None, **kwargs)

Initializes the object, and includes the following steps:

  • Checks if preprocess is available. If yes, then uses the preprocessed data.

  • Checks if cache is used. If not, then uses data from the cache.

Parameters

  • dataset – The 3DML dataset object. You can use the base dataset, sample datasets, or a custom dataset.

  • model – 3DML model object.

  • use_cache – Indicates if preprocessed data should be cached.

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

  • preprocess – The model’s preprocess method.

  • transform – The model’s transform method.

  • get_batch_gen – <NTD>

get_loader(batch_size=1, num_threads=3, transform=True)

This constructs the tensorflow dataloader.

Parameters

  • batch_size – The batch size to be used for data loading.

  • num_threads – The number of parallel threads to be used to data loading.

Returns

The tensorflow dataloader and the number of steps in one epoch.

read_data(index)

Returns the data at the index.

This does one of the following:

  • If cache is available, then gets the data from the cache.

  • If preprocess is available, then gets the preprocessed dataset and then the data.

  • If cache or preprocess is not available, then get the data from the dataset.