RandomVerticalFlip in PyTorch

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*Memos:

• My post explains RandomHorizontalFlip().
• My post explains OxfordIIITPet().

RandomVerticalFlip() can flip zero or more images vertically as shown below:

*Memos:

• The 1st argument for initialization is p(Optional-Default:0.5-Type:float). *It's the probability which each image is flipped. *It's the probability which each image is flipped.
• The 1st argument is img(Required-Type:PIL Image or tensor, tuple or list of int): *Memos:
  • It must be 2D.
  • 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 RandomVerticalFlip

RandomVerticalFlip()
# RandomVerticalFlip(p=0.5)

RandomVerticalFlip().p
# 0.5

origin_data = OxfordIIITPet(
    root="data",
    transform=None
)

trans100_data = OxfordIIITPet(
    root="data",
    transform=RandomVerticalFlip(p=1.0)
)

trans50_data = OxfordIIITPet(
    root="data",
    transform=RandomVerticalFlip(p=0.5)
)

import matplotlib.pyplot as plt

def show_images(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_images(data=origin_data, main_title="origin_data")
show_images(data=trans100_data, main_title="trans100_data")
show_images(data=trans50_data, main_title="trans50_data")

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandomVerticalFlip

my_data = OxfordIIITPet(
    root="data",
    transform=None
)

import matplotlib.pyplot as plt

def show_images(data, main_title=None, prob=0.0):
    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)
        rvf = RandomVerticalFlip(p=prob)
        plt.imshow(X=rvf(im))
        plt.xticks(ticks=[])
        plt.yticks(ticks=[])
    plt.tight_layout()
    plt.show()

show_images(data=my_data, main_title="origin_data")
show_images(data=my_data, main_title="trans100_data", prob=1.0)
show_images(data=my_data, main_title="trans50_data", prob=0.5)

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