DEV Community

Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

Posted on • Edited on

transform vs target_transform vs transforms in PyTorch (1)

#python #pytorch #transform #target

Buy Me a Coffee☕

*Memos:

There are the differences between transform, target_transform and transforms as shown below. *It's about origin and transform:
*Memos:

  • transform is for the function which must have one parameter for transform. *Resize(), RandomRotation(), RandomHorizontalFlip(), etc also can be used.
  • target_transform is for the function which must have one parameter for target(label).
  • transforms is for the function which must have two parameters for both transform and target(label). *Resize(), RandomRotation(), RandomHorizontalFlip(), etc also can be used.
  • Both transform and target_transform can be used at the same time.
  • transforms cannot be used with transform and/or target_transform at the same time.
  • According to my experiments, target_transform and transforms are useless.

<origin> 

from torchvision.datasets import OxfordIIITPet origin_data = OxfordIIITPet( root="data" ) origin_data[0] # (<PIL.Image.Image image mode=RGB size=394x500>, 0)  origin_data[50] # (<PIL.Image.Image image mode=RGB size=500x333>, 1)  origin_data[100] # (<PIL.Image.Image image mode=RGB size=333x500>, 2)
Enter fullscreen mode Exit fullscreen mode

<transform> 

from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import Resize tfresize100_50_data = OxfordIIITPet( root="data", transform=Resize(size=[100, 50]) ) tfresize100_50_data[0] # (<PIL.Image.Image image mode=RGB size=50x100>, 0)  tfresize100_50_data[50] # (<PIL.Image.Image image mode=RGB size=50x100>, 1)  tfresize100_50_data[100] # (<PIL.Image.Image image mode=RGB size=50x100>, 2)
Enter fullscreen mode Exit fullscreen mode 
from torchvision.datasets import OxfordIIITPet def tf_func(transform): return transform tf_func_data = OxfordIIITPet( root="data", transform=tf_func # transform=lambda transform: transform ) tf_func_data[0] # (<PIL.Image.Image image mode=RGB size=394x500>, 0)  tf_func_data[50] # (<PIL.Image.Image image mode=RGB size=500x333>, 1)  tf_func_data[100] # (<PIL.Image.Image image mode=RGB size=333x500>, 2)
Enter fullscreen mode Exit fullscreen mode 
from torchvision.datasets import OxfordIIITPet def tf_func(transform): return [0, 1, 2] tf_func_data = OxfordIIITPet( root="data", transform=tf_func # transform=lambda transform: [0, 1, 2] ) tf_func_data[0] # ([0, 1, 2], 0)  tf_func_data[50] # ([0, 1, 2], 1)  tf_func_data[100] # ([0, 1, 2], 2)
Enter fullscreen mode Exit fullscreen mode 
from torchvision.datasets import OxfordIIITPet def tf_func(): return [0, 1, 2] tf_func_data = OxfordIIITPet( root="data", transform=tf_func # transform=lambda: [0, 1, 2] ) tf_func_data[0] # TypeError: tf_func() takes 0 positional arguments but 1 was given
Enter fullscreen mode Exit fullscreen mode 
from torchvision.datasets import OxfordIIITPet def tf_func(transform, target): return [0, 1, 2] tf_func_data = OxfordIIITPet( root="data", transform=tf_func # transform=lambda transform, target: [0, 1, 2] ) tf_func_data[0] # TypeError: tf_func() missing 1 required positional argument: 'target'
Enter fullscreen mode Exit fullscreen mode

Top comments (0)

Some comments may only be visible to logged-in visitors. Sign in to view all comments.