mindspore.dataset.vision.Resize
- class mindspore.dataset.vision.Resize(size, interpolation=Inter.LINEAR)[source]
Resize the input image to the given size with a given interpolation mode
Inter
.Supports Ascend hardware acceleration and can be enabled through the .device("Ascend") method.
- Parameters
size (Union[int, Sequence[int]]) – The output size of the resized image. The size value(s) must be positive. If size is an integer, the smaller edge of the image will be resized to this value with the same image aspect ratio. If size is a sequence of length 2, it should be (height, width).
interpolation (Inter, optional) – Image interpolation method defined by
Inter
. Default:Inter.LINEAR
.
- Raises
TypeError – If size is not of type int or Sequence[int].
ValueError – If size is not positive.
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 >>> from mindspore.dataset.vision import Inter >>> >>> # 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"]) >>> resize_op = vision.Resize([100, 75], Inter.BICUBIC) >>> transforms_list = [resize_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, 75, 3) uint8 >>> >>> # Use the transform in eager mode >>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8) >>> output = vision.Resize([5, 5], Inter.BICUBIC)(data) >>> print(output.shape, output.dtype) (5, 5, 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
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 >>> from mindspore.dataset.vision import Inter >>> >>> # Use the transform in dataset pipeline mode >>> resize_op = vision.Resize([100, 75], Inter.BICUBIC).device("Ascend") >>> transforms_list = [resize_op] >>> data = np.random.randint(0, 255, size=(1, 100, 100, 3)).astype(np.uint8) >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"]) >>> 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, 75, 3) uint8 >>> >>> # Use the transform in eager mode >>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8) >>> output = vision.Resize([25, 25], Inter.BICUBIC).device("Ascend")(data) >>> print(output.shape, output.dtype) (25, 25, 3) uint8
- Tutorial Examples: