DEV Community

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

Posted on • Edited on

RandomRotation in PyTorch

Buy Me a Coffee

*Memos:

RandomRotation() can rotate an image randomly as shown below:

*Memos:

  • The 1st argument for initialization is degrees(Required-Type:int, float or tuple/list(int or float)): *Memos:
    • It can do rotation.
    • It's the range of the degrees [min, max] so it must be min <= max.
    • A tuple/list must be the 1D with 2 elements.
    • A single value must be 0 <= x.
    • A single value means [-degrees, +degrees].
  • The 2nd argument for initialization is interpolation(Optional-Default:InterpolationMode.NEAREST-Type:InterpolationMode).
  • The 3rd argument for initialization is expand(Optional-Default:False-Type:bool).
  • The 4th argument for initialization is center(Optional-Default:None-Type:tuple/list(int or float)): *Memos:
    • It can change the center position of an image.
    • It must be the 1D with 2 elements.
  • The 5th argument for initialization is fill(Optional-Default:0-Type:int, float or tuple/list(int or float)): *Memos:
    • It can change the background of an image. *The background can be seen when rotating an image.
    • A tuple/list must be the 1D with 1 or 3 elements.
  • The 1st argument is img(Required-Type:PIL Image or tensor(int)): *Memos:
    • A tensor must be 3D.
    • Don't use img=.
  • v2 is recommended to use according to V1 or V2? Which one should I use?.
from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandomRotation
from torchvision.transforms.functional import InterpolationMode

randomrotation = RandomRotation(degrees=90)
randomrotation = RandomRotation(degrees=[-90, 90], 
                                interpolation=InterpolationMode.NEAREST,
                                expand=False,
                                center=None,
                                fill=0)
randomrotation
# RandomRotation(degrees=[-90.0, 90.0],
#                interpolation=InterpolationMode.NEAREST,
#                expand=False,
#                fill=0)

randomrotation.degrees
# [-90.0, 90.0]

randomrotation.interpolation
# <InterpolationMode.NEAREST: 'nearest'>

randomrotation.expand
# False

print(randomrotation.center)
# None

randomrotation.fill
# 0

origin_data = OxfordIIITPet(
    root="data",
    transform=None
    # transform=RandomRotation(degrees=[0, 0])
)

d90_data = OxfordIIITPet( # `d` is degrees.
    root="data",
    transform=RandomRotation(degrees=90)
    # transform=RandomRotation(degrees=[-90, 90])
)

d90_90_data = OxfordIIITPet(
    root="data",
    transform=RandomRotation(degrees=[90, 90])
)

dn90n90expand_data = OxfordIIITPet( # `n` is negative.
    root="data",
    transform=RandomRotation(degrees=[-90, -90], expand=True)
)

d180_180c270_200_data = OxfordIIITPet( # `c` is center.
    root="data",
    transform=RandomRotation(degrees=[180, 180], center=[270, 200])
)

dn45n45fgray_data = OxfordIIITPet( # `f` is fill.
    root="data",
    transform=RandomRotation(degrees=[-45, -45], fill=150)
)

d135_135fpurple_data = OxfordIIITPet(
    root="data",
    transform=RandomRotation(degrees=[135, 135], fill=[160, 32, 240])
)

import matplotlib.pyplot as plt

def show_images1(data, main_title=None):
    plt.figure(figsize=(10, 5))
    plt.suptitle(t=main_title, y=0.8, fontsize=14)
    for i, (im, _) in zip(range(1, 6), data):
        plt.subplot(1, 5, i)
        plt.imshow(X=im)
        plt.xticks(ticks=[])
        plt.yticks(ticks=[])
    plt.tight_layout()
    plt.show()

show_images1(data=origin_data, main_title="origin_data")
show_images1(data=d90_data , main_title="d90_data ")
show_images1(data=d90_90_data, main_title="d90_90_data")
show_images1(data=dn90n90expand_data, main_title="dn90n90expand_data")
show_images1(data=d180_180c270_200_data, main_title="d180_180c270_200_data")
show_images1(data=dn45n45fgray_data, main_title="dn45n45fgray_data")
show_images1(data=d135_135fpurple_data, main_title="d135_135fpurple_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, d=0, e=False, c=None, f=0):
    plt.figure(figsize=(10, 5))
    plt.suptitle(t=main_title, y=0.8, fontsize=14)
    for i, (im, _) in zip(range(1, 6), data):
        plt.subplot(1, 5, i)
        rr = RandomRotation(degrees=d, expand=e, center=c, fill=f) # Here
        plt.imshow(X=rr(im)) # Here
        plt.xticks(ticks=[])
        plt.yticks(ticks=[])
    plt.tight_layout()
    plt.show()

show_images2(data=origin_data, main_title="origin_data")
show_images2(data=origin_data, main_title="d90_data", d=90)
show_images2(data=origin_data, main_title="d90_90_data", d=[90, 90])
show_images2(data=origin_data, main_title="dn90n90expand_data", d=[-90, -90], 
             e=True)
show_images2(data=origin_data, main_title="d180_180c270_200_data",
             d=[180, 180], c=[270, 200])
show_images2(data=origin_data, main_title="dn45n45fgray_data",
             d=[-45, -45], f=150)
show_images2(data=origin_data, main_title="d135_135fpurple_data",
             d=[135, 135], f=[160, 32, 240])
Enter fullscreen mode Exit fullscreen mode

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Postmark Image

Speedy emails, satisfied customers

Are delayed transactional emails costing you user satisfaction? Postmark delivers your emails almost instantly, keeping your customers happy and connected.

Sign up

Top comments (0)

👋 Kindness is contagious

Dive into an ocean of knowledge with this thought-provoking post, revered deeply within the supportive DEV Community. Developers of all levels are welcome to join and enhance our collective intelligence.

Saying a simple "thank you" can brighten someone's day. Share your gratitude in the comments below!

On DEV, sharing ideas eases our path and fortifies our community connections. Found this helpful? Sending a quick thanks to the author can be profoundly valued.

Okay