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
.- 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 mindspore.dataset as ds >>> import mindspore.dataset.vision as vision >>> from mindspore.dataset.vision import Inter >>> >>> decode_op = vision.Decode() >>> resize_op = vision.Resize([100, 75], Inter.BICUBIC) >>> transforms_list = [decode_op, resize_op] >>> image_folder_dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory") >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, ... input_columns=["image"])
- Tutorial Examples:
- device(device_target='CPU')[source]
Set the device for the current operator execution.
- Parameters
device_target (str, optional) – The operator will be executed on this device. Currently supports
CPU
. Default:CPU
.- Raises
TypeError – If device_target is not of type str.
ValueError – If device_target is not
CPU
.
- Supported Platforms:
CPU
Examples
>>> import mindspore.dataset as ds >>> import mindspore.dataset.vision as vision >>> from mindspore.dataset.vision import Inter >>> >>> decode_op = vision.Decode() >>> resize_op = vision.Resize([100, 75], Inter.BICUBIC).device("Ascend") >>> transforms_list = [decode_op, resize_op] >>> image_folder_dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory") >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, ... input_columns=["image"])
- Tutorial Examples: