mindspore.dataset.transforms.PadEnd
- class mindspore.dataset.transforms.PadEnd(pad_shape, pad_value=None)[source]
Pad input tensor according to pad_shape, input tensor needs to have same rank.
- Parameters
pad_shape (list(int)) – List of integers representing the shape needed. Dimensions that set to
None
will not be padded (i.e., original dim will be used). Shorter dimensions will truncate the values.pad_value (Union[str, bytes, int, float, bool], optional) – Value used to pad. Default:
None
. Default to0
in case of tensors of Numbers, or empty string in case of tensors of strings.
- Raises
TypeError – If pad_shape is not of type list.
TypeError – If pad_value is not of type str, float, bool, int or bytes.
TypeError – If elements of pad_shape is not of type int.
ValueError – If elements of pad_shape is not of positive.
- Supported Platforms:
CPU
Examples
>>> import mindspore.dataset as ds >>> import mindspore.dataset.transforms as transforms >>> >>> # Use the transform in dataset pipeline mode >>> # Data before >>> # | col | >>> # +---------+ >>> # | [1,2,3] | >>> # +---------| >>> data = [[1, 2, 3]] >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["col"]) >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms.PadEnd(pad_shape=[4], pad_value=10)) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["col"].shape, item["col"].dtype) ... break (4,) int64 >>> # Data after >>> # | col | >>> # +------------+ >>> # | [1,2,3,10] | >>> # +------------| >>> >>> # Use the transform in eager mode >>> data = [1, 2, 3] >>> output = transforms.PadEnd(pad_shape=[4], pad_value=10)(data) >>> print(output.shape, output.dtype) (4,) int64