RandomRotation in PyTorch

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*Memos:

• My post explains RandomAffine().
• My post explains RandomPerspective().
• My post explains RandomHorizontalFlip().
• My post explains RandomVerticalFlip().
• My post explains OxfordIIITPet().

RandomRotation() can rotate zero or more images 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].
  • A single value must be 0 <= x.
  • A single value is converted to [-degrees, +degrees].
  • A tuple or list must be the 1D with 2 elements. *The 1st element must be less than or equal to the 2nd element.
• 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)). *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 images. *The background can be seen when rotating images.
  • A tuple/list must be the 1D with 3 elements.
• The 1st argument is img(Required-Type:PIL Image or tensor(int, float, complex or bool)): *Memos:
  • It must be the 3D or more D tensor of zero or more elements.
  • 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=90.0)
randomrotation = RandomRotation(degrees=[-90.0, 90.0], 
                                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, 0.0]) )

p90_data = OxfordIIITPet( # `p` is plus.     root="data",
    transform=RandomRotation(degrees=90.0)
    # transform=RandomRotation(degrees=[-90.0, 90.0]) )

p90p90_data = OxfordIIITPet(
    root="data",
    transform=RandomRotation(degrees=[90.0, 90.0])
)

m90m90expand_data = OxfordIIITPet( # `m` is minus.     root="data",
    transform=RandomRotation(degrees=[-90.0, -90.0], expand=True)
)

p180p180offcenter_data = OxfordIIITPet(
    root="data",
    transform=RandomRotation(degrees=[180.0, 180.0], center=[270, 200])
)

m45m45fillgray_data = OxfordIIITPet(
    root="data",
    transform=RandomRotation(degrees=[-45.0, -45.0], fill=150)
)

p135p135fillpurple_data = OxfordIIITPet(
    root="data",
    transform=RandomRotation(degrees=[135.0, 135.0], 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=p90_data, main_title="p90_data")
show_images1(data=p90p90_data, main_title="p90p90_data")
show_images1(data=m90m90expand_data, main_title="m90m90expand_data")
show_images1(data=p180p180offcenter_data, main_title="p180p180offcenter_data")
show_images1(data=m45m45fillgray_data, main_title="m45m45fillgray_data")
show_images1(data=p135p135fillpurple_data,
             main_title="p135p135fillpurple_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="p90_data", d=90)
show_images2(data=origin_data, main_title="p90p90_data", d=[90, 90])
show_images2(data=origin_data, main_title="m90m90expand_data", d=[-90, -90])
show_images2(data=origin_data, main_title="p180p180offcenter_data",
            d=[180, 180], c=[270, 200])
show_images2(data=origin_data, main_title="m45m45fillgray_data",
            d=[-45, -45], f=150)
show_images2(data=origin_data, main_title="p135p135fillpurple_data",
            d=[135, 135], f=[160, 32, 240])

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原文链接：RandomRotation in PyTorch