mindspore.dataset.vision.VerticalFlip

class mindspore.dataset.vision.VerticalFlip[source]

Flip the input image vertically.

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

Raises: RuntimeError – If given tensor shape is not <H, W> or <…, H, W, C>.

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.VerticalFlip()]
>>> 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
(100, 100, 3) uint8
>>>
>>> # Use the transform in eager mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8)
>>> output = vision.VerticalFlip()(data)
>>> print(output.shape, output.dtype)
(100, 100, 3) uint8

Tutorial Examples:

Illustration of vision transforms

device(device_target='CPU')[source]

Set the device for the current operator execution.

When the device is Ascend, input type supports uint8 and float32, input channel supports 1 and 3. The input data has a height limit of [4, 8192] and a width limit of [6, 4096].

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"])
>>> vertical_flip_op = vision.VerticalFlip().device("Ascend")
>>> transforms_list = [vertical_flip_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
(100, 100, 3) uint8
>>>
>>> # Use the transform in eager mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8)
>>> output = vision.VerticalFlip().device("Ascend")(data)
>>> print(output.shape, output.dtype)
(100, 100, 3) uint8

Tutorial Examples:

Illustration of vision transforms