mindspore.dataset.vision.Pad

class mindspore.dataset.vision.Pad(padding, fill_value=0, padding_mode=Border.CONSTANT)[source]

Pad the image according to padding parameters.

Supports Ascend hardware acceleration and can be enabled through the .device("Ascend") method.

Parameters

  • padding (Union[int, Sequence[int, int], Sequence[int, int, int, int]]) –

    The number of pixels to pad each border of the image.

    • If a single number is provided, it pads all borders with this value.

    • If a tuple or lists of 2 values are provided, it pads the (left and right) with the first value and (top and bottom) with the second value.

    • If 4 values are provided as a list or tuple, it pads the left, top, right and bottom respectively.

    The pad values must be non-negative.

  • fill_value (Union[int, tuple[int]], optional) –

    The pixel intensity of the borders, only valid for padding_mode Border.CONSTANT.

    • If it is a 3-tuple, it is used to fill R, G, B channels respectively.

    • If it is an integer, it is used for all RGB channels.

    The fill_value values must be in range [0, 255]. Default: 0.

  • padding_mode (Border, optional) –

    The method of padding. Default: Border.CONSTANT. Can be Border.CONSTANT, Border.EDGE, Border.REFLECT, Border.SYMMETRIC.

    • Border.CONSTANT , means it fills the border with constant values.

    • Border.EDGE , means it pads with the last value on the edge.

    • Border.REFLECT , means it reflects the values on the edge omitting the last value of edge.

    • Border.SYMMETRIC , means it reflects the values on the edge repeating the last value of edge.

Raises
Supported Platforms:

CPU Ascend

Examples

>>> import numpy as np
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>>
>>> # Use the transform in dataset pipeline mode
>>> data = np.random.randint(0, 255, size=(1, 100, 100, 3)).astype(np.uint8)
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"])
>>> transforms_list = [vision.Pad([100, 100, 100, 100])]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list, input_columns=["image"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["image"].shape, item["image"].dtype)
...     break
(300, 300, 3) uint8
>>>
>>> # Use the transform in eager mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8)
>>> output = vision.Pad([100, 100, 100, 100])(data)
>>> print(output.shape, output.dtype)
(300, 300, 3) uint8
Tutorial Examples:
device(device_target='CPU')[source]

Set the device for the current operator execution.

When the device is Ascend, input/output shape should be limited from [4, 6] to [32768, 32768].

Parameters

device_target (str, optional) – The operator will be executed on this device. Currently supports "CPU" and "Ascend" . Default: "CPU" .

Raises

  • TypeError – If device_target is not of type str.

  • ValueError – If device_target is not within the valid set of ["CPU", "Ascend"].

Supported Platforms:

CPU Ascend

Examples

>>> import numpy as np
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>>
>>> # Use the transform in dataset pipeline mode
>>> data = np.random.randint(0, 255, size=(1, 100, 100, 3)).astype(np.uint8)
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"])
>>> pad_op = vision.Pad([100, 100, 100, 100]).device("Ascend")
>>> transforms_list = [pad_op]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list, input_columns=["image"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["image"].shape, item["image"].dtype)
...     break
(300, 300, 3) uint8
>>>
>>> # Use the transform in eager mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8)
>>> output = vision.Pad([100, 100, 100, 100]).device("Ascend")(data)
>>> print(output.shape, output.dtype)
(300, 300, 3) uint8
Tutorial Examples: