Differences with torchvision.transforms.RandomAffine

torchvision.transforms.RandomAffine

class torchvision.transforms.RandomAffine(degrees, translate=None, scale=None, shear=None, interpolation=<InterpolationMode.NEAREST: 'nearest'>, fill=0, fillcolor=None, resample=None)

For more information, see torchvision.transforms.RandomAffine.

mindspore.dataset.vision.RandomAffine

class mindspore.dataset.vision.RandomAffine(degrees, translate=None, scale=None, shear=None, resample=Inter.NEAREST, fill_value=0)

For more information, see mindspore.dataset.vision.RandomAffine.

Differences

PyTorch: Apply random affine transformation to a tensor image. The rotation center position can be specified.

MindSpore: Apply random affine transformation to the input image. The rotation center is in the center of the image.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameter	Parameter1	degrees	degrees	-
	Parameter2	translate	translate	-
	Parameter3	scale	scale	-
	Parameter4	shear	shear	-
	Parameter5	interpolation	resample	-
	Parameter6	fill	fill_value	-
	Parameter7	fillcolor	-	Deprecated in PyTorch, same with fill
	Parameter8	resample	-	Deprecated in PyTorch, same with interpolation

Code Example

from download import download
from PIL import Image

url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/flamingos.jpg"
download(url, './flamingos.jpg', replace=True)
orig_img = Image.open('flamingos.jpg')

# PyTorch
import torchvision.transforms as T

affine_transfomer = T.RandomAffine(degrees=(30, 70), translate=(0.1, 0.3), fill=0)
img_torch = affine_transfomer(orig_img)

# MindSpore
import mindspore.dataset.vision as vision
import mindspore.dataset.transforms as transforms

affine_transfomer = vision.RandomAffine(degrees=(30, 70), translate=(0.1, 0.3), fill_value=0)
img_ms = affine_transfomer(orig_img)