format

Author: vilktor370

gallery/transforms/plot_transform_tensor_image.py

@@ -7,7 +7,7 @@
     Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_tv_tensors.ipynb>`_
     or :ref:`go to the end <sphx_glr_download_auto_examples_transforms_plot_custom_tv_tensors.py>` to download the full example code.
 
-In thie example, we explain the basic usgae of :func:`~torchvision.transforms.functional.to_tensor`, :func:`~torchvision.transforms.functional.pil_to_tensor` and :func:`~torchvision.transforms.functional.to_pil_image`. 
+In thie example, we explain the basic usgae of :func:`~torchvision.transforms.functional.to_tensor`, :func:`~torchvision.transforms.functional.pil_to_tensor` and :func:`~torchvision.transforms.functional.to_pil_image`.
 And the difference between :func:`~torchvision.transforms.functional.to_tensor` and :func:`~torchvision.transforms.functional.pil_to_tensor`.
 """
 
@@ -17,13 +17,13 @@
 # In this cell, we illustrate the different representation of a PIL image and tensor
 import PIL.Image as Image
 from torchvision.transforms.functional import to_tensor
-from helpers import plot # use your favorite visualization library
+from helpers import plot  # use your favorite visualization library
 
-img_pil = Image.open('../assets/person1.jpg')
+img_pil = Image.open("../assets/person1.jpg")
 width, height = img_pil.size
 # There is no straight forward way to get channel information
 # Please read https://pillow.readthedocs.io/en/stable/handbook/concepts.html for more detail
-num_channels = 3 # hardcoded since it's a color image.
+num_channels = 3  # hardcoded since it's a color image.
 print("PIL image: width x height x num_channels:", width, height, num_channels)
 
 img_tensor = to_tensor(img_pil)
@@ -37,7 +37,7 @@
 # In this cell, we explain the difference between :func:`~torchvision.transforms.functional.pil_to_tensor` vs. :func:`~torchvision.transforms.functional.to_tensor`
 from torchvision.transforms.functional import pil_to_tensor
 
-img_pil = Image.open('../assets/person1.jpg')
+img_pil = Image.open("../assets/person1.jpg")
 img_to_tensor = to_tensor(img_pil)
 num_channels, height, width = img_to_tensor.shape
 print("Tensor image(to_tensor): num_channels x height x width:", num_channels, height, width)
@@ -52,15 +52,16 @@
 # The shape is the same but **data type** is different! The **tensor value** is also different!
 
 # %%
-print(img_to_tensor) # tensor that is returned by calling to_tensor()
-print(img_pil_to_tensor) # tensor that is returned by calling pil_to_tensor()
+print(img_to_tensor)  # tensor that is returned by calling to_tensor()
+print(img_pil_to_tensor)  # tensor that is returned by calling pil_to_tensor()
 
 # %%
 # Notice :func:`~torchvision.transforms.functional.to_tensor` automatically scale the image, but :func:`~torchvision.transforms.functional.pil_to_tensor` does not. To rescale the image back,
 
 import torch
+
 img_pil_to_tensor_2 = (img_to_tensor * 255).to(torch.uint8)
-print((img_pil_to_tensor_2 == img_pil_to_tensor).all().item()) # check if two tensors are same
+print((img_pil_to_tensor_2 == img_pil_to_tensor).all().item())  # check if two tensors are same
 
 # %%
 # **TLDR** it's recommended to use :func:`~torchvision.transforms.functional.pil_to_tensor` for visualization tasks since most visualization library
@@ -74,7 +75,7 @@
 # In this cell, we explain an example usage of :func:`~torchvision.transforms.functional.to_pil_image`
 from torchvision.transforms.functional import to_pil_image
 
-img_pil = Image.open('../assets/person1.jpg')
+img_pil = Image.open("../assets/person1.jpg")
 
 # convert to tensor using to_tensor() and pil_to_tensor()
 img_to_tensor = to_tensor(img_pil)
@@ -88,4 +89,3 @@
 
 # %%
 # Both tensor can be converted back to PIL image.
-