Differences with torchvision.transforms.ToTensor

torchvision.transforms.ToTensor

class torchvision.transforms.ToTensor

For more information, see torchvision.transforms.ToTensor.

mindspore.dataset.vision.ToTensor

class mindspore.dataset.vision.ToTensor(
    output_type=np.float32
    )

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

Differences

PyTorch: Convert the PIL Image or Numpy array to tensor. The input Numpy array is usually in the format of <H, W, C> and the value is in the range of [0, 255], and the output is <C, H, W > Torch Tensor with format and value in [0.0, 1.0].

MindSpore: The input is an image of PIL type or a Numpy array with a value in the range of [0, 255] in the format of <H, W, C>, and the output is in the range of [0.0, 1.0] with <C, H, W> Format Numpy array; it is equivalent to two operations of channel conversion and pixel value normalization on the original input image.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameters 1	-	output_type	Specify the data type of output numpy array

Code Exampleimport numpy as np
from PIL import Image
from download import download
from torchvision import transforms
import mindspore.dataset.vision as vision

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

# In MindSpore, ToTensor convert PIL Image into numpy array.
to_tensor = vision.ToTensor()
img_data = to_tensor(img)
print("img_data shape:", img_data.shape)
print("img_data type:", type(img_data))
# Out:
# img_data shape: (3, 292, 471)
# img_data type: <class 'numpy.ndarray'>

# In torch, ToTensor transforms the input to tensor.
image_transform = transforms.Compose([transforms.ToTensor()])
img_data = image_transform(img)
print("img_data shape:", img_data.shape)
print("img_data type:", type(img_data))
# Out:
# img_data shape: torch.Size([3, 292, 471])
# img_data type: <class 'torch.Tensor'>