mindspore.dataset.vision.HorizontalFlip
- class mindspore.dataset.vision.HorizontalFlip[source]
Flip the input image horizontally.
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.HorizontalFlip()] >>> 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.array([[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]], dtype=np.uint8).reshape((2, 2, 3)) >>> output = vision.HorizontalFlip()(data) >>> print(output.shape, output.dtype) (2, 2, 3) uint8
- Tutorial Examples:
- 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
andAscend
. 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"]) >>> horizontal_flip_op = vision.HorizontalFlip().device("Ascend") >>> transforms_list = [horizontal_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.HorizontalFlip().device("Ascend")(data) >>> print(output.shape, output.dtype) (100, 100, 3) uint8
- Tutorial Examples: