tlt.datasets.pytorch_dataset.PyTorchDataset¶

class tlt.datasets.pytorch_dataset.PyTorchDataset(dataset_dir, dataset_name='', dataset_catalog='')[source]¶

Base class to represent a PyTorch Dataset

__init__(dataset_dir, dataset_name='', dataset_catalog='')[source]¶: Class constructor

Methods

`__init__`(dataset_dir[, dataset_name, ...])	Class constructor
`get_batch`([subset])	Get a single batch of images and labels from the dataset.
`get_inc_dataloaders`()
`preprocess`([image_size, batch_size, add_aug])	Preprocess the dataset to resize, normalize, and batch the images.
`shuffle_split`([train_pct, val_pct, ...])	Randomly split the dataset into train, validation, and test subsets with a pseudo-random seed option.

Attributes

`data_loader`	A data loader object corresponding to the dataset
`dataset`	The framework dataset object
`dataset_catalog`	The string name of the dataset catalog (or None)
`dataset_dir`	Host directory containing the dataset files
`dataset_name`	Name of the dataset
`test_loader`	A data loader object corresponding to the test subset
`test_subset`	A subset of the dataset held out for final testing/evaluation
`train_loader`	A data loader object corresponding to the training subset
`train_subset`	A subset of the dataset used for training
`validation_loader`	A data loader object corresponding to the validation subset
`validation_subset`	A subset of the dataset used for validation/evaluation