Differences with torchvision.transforms.RandomResizedCrop
torchvision.transforms.RandomResizedCrop
class torchvision.transforms.RandomResizedCrop(size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=InterpolationMode.BILINEAR)
For more information, see torchvision.transforms.RandomResizedCrop.
mindspore.dataset.vision.RandomResizedCrop
class mindspore.dataset.vision.RandomResizedCrop(size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Inter.BILINEAR, max_attempts=10)
For more information, see mindspore.dataset.vision.RandomResizedCrop.
Differences
PyTorch: Crop a random portion of image and resize it to a given size.
MindSpore: Crop a random portion of image and resize it to a given size.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameter |
Parameter1 |
size |
size |
- |
Parameter2 |
scale |
scale |
- |
|
Parameter3 |
ratio |
ratio |
- |
|
Parameter4 |
interpolation |
interpolation |
- |
|
Parameter5 |
- |
max_attempts |
The maximum number of attempts to propose a valid crop_area |
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
op = T.RandomResizedCrop((32, 32), scale=(0.08, 1.0), ratio=(0.75, 0.8))
img_torch =op(orig_img)
print(img_torch.size)
# Out: (32, 32)
# MindSpore
import mindspore.dataset.vision as vision
op = vision.RandomResizedCrop((32, 32), scale=(0.08, 1.0), ratio=(0.75, 0.8))
img_ms = op(orig_img)
print(img_ms.size)
# Out: (32, 32)