RandomInvert in PyTorch

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*Memos:

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

RandomInvert() can randomly invert an image as shown below:


*Memos:

• The 1st argument for initialization is p(Optional-Default:0.5-Type:int or float):
  *Memos:
  • It's the probability of whether an image is inverted or not.
  • It must be 0 .
• The 1st argument is img(Required-Type:PIL Image or tensor(int)):
  *Memos:
  • A tensor must be 2D or 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 RandomInvert

randominvert = RandomInvert()
randominvert = RandomInvert(p=0.5)

randominvert
# RandomInvert(p=0.5)

randominvert.p
# 0.5

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

p0_data = OxfordIIITPet(
root="data",
transform=RandomInvert(p=0)
)

p05_data = OxfordIIITPet(
root="data",
transform=RandomInvert(p=0.5)
)

p1_data = OxfordIIITPet(
root="data",
transform=RandomInvert(p=1)
)

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")
print()
show_images1(data=p0_data, main_title="p0_data")
show_images1(data=p0_data, main_title="p0_data")
show_images1(data=p0_data, main_title="p0_data")
print()
show_images1(data=p05_data, main_title="p05_data")
show_images1(data=p05_data, main_title="p05_data")
show_images1(data=p05_data, main_title="p05_data")
print()
show_images1(data=p1_data, main_title="p1_data")
show_images1(data=p1_data, main_title="p1_data")
show_images1(data=p1_data, main_title="p1_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, prob=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)
ri = RandomInvert(p=prob)
plt.imshow(X=ri(im))
plt.xticks(ticks=[])
plt.yticks(ticks=[])
plt.tight_layout()
plt.show()

show_images2(data=origin_data, main_title="origin_data")
print()
show_images2(data=origin_data, main_title="p0_data", prob=0)
show_images2(data=origin_data, main_title="p0_data", prob=0)
show_images2(data=origin_data, main_title="p0_data", prob=0)
print()
show_images2(data=origin_data, main_title="p05_data", prob=0.5)
show_images2(data=origin_data, main_title="p05_data", prob=0.5)
show_images2(data=origin_data, main_title="p05_data", prob=0.5)
print()
show_images2(data=origin_data, main_title="p1_data", prob=1)
show_images2(data=origin_data, main_title="p1_data", prob=1)
show_images2(data=origin_data, main_title="p1_data", prob=1)
















































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