load_dataset#
- load_dataset(train: bool = True, batch_size: int = 32, num_workers: int = 0, shuffle: bool = False, pin_memory: bool = True, object_type: Literal['VisionData', 'DataLoader'] = 'DataLoader', n_samples: Optional[int] = None, device: Union[str, device] = 'cpu') Union[DataLoader, VisionData][source]#
Get the COCO128 dataset and return a dataloader.
- Parameters
- trainbool, default: True
if True train dataset, otherwise test dataset
- batch_sizeint, default: 32
Batch size for the dataloader.
- num_workersint, default: 0
Number of workers for the dataloader.
- shufflebool, default: False
Whether to shuffle the dataset.
- pin_memorybool, default: True
If
True, the data loader will copy Tensors into CUDA pinned memory before returning them.- object_typeLiteral[‘Dataset’, ‘DataLoader’], default: ‘DataLoader’
type of the return value. If ‘Dataset’,
deepchecks.vision.VisionDatawill be returned, otherwisetorch.utils.data.DataLoader- n_samplesint, optional
Only relevant for loading a VisionData. Number of samples to load. Return the first n_samples if shuffle is False otherwise selects n_samples at random. If None, returns all samples.
- devicet.Union[str, torch.device], default‘cpu’
device to use in tensor calculations
- Returns
- Union[DataLoader, VisionData]
A DataLoader or VisionData instance representing COCO128 dataset