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 the torch tensor with format of <C, H, W > in the range of [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 a Numpy array in the range of [0.0, 1.0] with the format of <C, H, W>; 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)
# img_data shape: (3, 292, 471)
print("img_data type:", type(img_data))
# 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)
# img_data shape: torch.Size([3, 292, 471])
print("img_data type:", type(img_data))
# img_data type: <class 'torch.Tensor'>