This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)
*Memos:
-
My post explains RandomAffine() about
degrees
,translate
,fill
andcenter
argument. -
My post explains RandomAffine() about
scale
argument. -
My post explains RandomAffine() about
shear
argument (1). - My post explains OxfordIIITPet().
RandomAffine() can do random rotation or random affine transformation for an image as shown below:
from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandomAffine
origin_data = OxfordIIITPet(
root="data",
transform=None
)
shear0_0_0_0origin_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 0, 0])
)
shear0_0_10_10_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 10, 10])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -170, -170])
)
shear0_0_20_20_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 20, 20])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -160, -160])
)
shear0_0_30_30_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 30, 30])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -150, -150])
)
shear0_0_40_40_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 40, 40])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -140, -140])
)
shear0_0_50_50_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 50, 50])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -130, -130])
)
shear0_0_60_60_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 60, 60])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -120, -120])
)
shear0_0_70_70_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 70, 70])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -110, -110])
)
shear0_0_80_80_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 80, 80])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -100, -100])
)
shear0_0_90_90_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 90, 90])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -90, -90])
)
shear0_0n10n10_data = OxfordIIITPet( # `n` is negative.
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -10, -10])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 170, 170])
)
shear0_0n20n20_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -20, -20])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 160, 160])
)
shear0_0n30n30_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -30, -30])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 150, 150])
)
shear0_0n40n40_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -40, -40])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 140, 140])
)
shear0_0n50n50_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -50, -50])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 130, 130])
)
shear0_0n60n60_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -60, -60])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 120, 120])
)
shear0_0n70n70_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -70, -70])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 110, 110])
)
shear0_0n80n80_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -80, -80])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 100, 100])
)
shear0_0n90n90_data = OxfordIIITPet(
root="data",
transform=RandomAffine(degrees=[0, 0], shear=[0, 0, -90, -90])
# transform=RandomAffine(degrees=[0, 0], shear=[0, 0, 90, 90])
)
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=shear0_0_0_0origin_data,
main_title="shear0_0_0_0origin_data")
show_images1(data=shear0_0_10_10_data, main_title="shear0_0_10_10_data")
show_images1(data=shear0_0_20_20_data, main_title="shear0_0_20_20_data")
show_images1(data=shear0_0_30_30_data, main_title="shear0_0_30_30_data")
show_images1(data=shear0_0_40_40_data, main_title="shear0_0_40_40_data")
show_images1(data=shear0_0_50_50_data, main_title="shear0_0_50_50_data")
show_images1(data=shear0_0_60_60_data, main_title="shear0_0_60_60_data")
show_images1(data=shear0_0_70_70_data, main_title="shear0_0_70_70_data")
show_images1(data=shear0_0_80_80_data, main_title="shear0_0_80_80_data")
show_images1(data=shear0_0_90_90_data, main_title="shear0_0_90_90_data")
print()
show_images1(data=shear0_0_0_0origin_data,
main_title="shear0_0_0_0origin_data")
show_images1(data=shear0_0n10n10_data, main_title="shear0_0n10n10_data")
show_images1(data=shear0_0n20n20_data, main_title="shear0_0n20n20_data")
show_images1(data=shear0_0n30n30_data, main_title="shear0_0n30n30_data")
show_images1(data=shear0_0n40n40_data, main_title="shear0_0n40n40_data")
show_images1(data=shear0_0n50n50_data, main_title="shear0_0n50n50_data")
show_images1(data=shear0_0n60n60_data, main_title="shear0_0n60n60_data")
show_images1(data=shear0_0n70n70_data, main_title="shear0_0n70n70_data")
show_images1(data=shear0_0n80n80_data, main_title="shear0_0n80n80_data")
show_images1(data=shear0_0n90n90_data, main_title="shear0_0n90n90_data")
# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, d=0, t=None,
sc=None, sh=None, f=0, c=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)
ra = RandomAffine(degrees=d, translate=t, scale=sc,
shear=sh, center=c, fill=f)
plt.imshow(X=ra(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="shear0_0_0_0origin_data",
d=[0, 0], sh=[0, 0, 0, 0])
show_images2(data=origin_data, main_title="shear0_0_10_10_data",
d=[0, 0], sh=[0, 0, 10, 10])
show_images2(data=origin_data, main_title="shear0_0_20_20_data",
d=[0, 0], sh=[0, 0, 20, 20])
show_images2(data=origin_data, main_title="shear0_0_30_30_data",
d=[0, 0], sh=[0, 0, 30, 30])
show_images2(data=origin_data, main_title="shear0_0_40_40_data",
d=[0, 0], sh=[0, 0, 40, 40])
show_images2(data=origin_data, main_title="shear0_0_50_50_data",
d=[0, 0], sh=[0, 0, 50, 50])
show_images2(data=origin_data, main_title="shear0_0_60_60_data",
d=[0, 0], sh=[0, 0, 60, 60])
show_images2(data=origin_data, main_title="shear0_0_70_70_data",
d=[0, 0], sh=[0, 0, 70, 70])
show_images2(data=origin_data, main_title="shear0_0_80_80_data",
d=[0, 0], sh=[0, 0, 80, 80])
show_images2(data=origin_data, main_title="shear0_0_90_90_data",
d=[0, 0], sh=[0, 0, 90, 90])
print()
show_images2(data=origin_data, main_title="shear0_0_0_0origin_data",
d=[0, 0], sh=[0, 0, 0, 0])
show_images2(data=origin_data, main_title="shear0_0n10n10_data",
d=[0, 0], sh=[0, 0, -10, -10])
show_images2(data=origin_data, main_title="shear0_0n20n20_data",
d=[0, 0], sh=[0, 0, -20, -20])
show_images2(data=origin_data, main_title="shear0_0n30n30_data",
d=[0, 0], sh=[0, 0, -30, -30])
show_images2(data=origin_data, main_title="shear0_0n40n40_data",
d=[0, 0], sh=[0, 0, -40, -40])
show_images2(data=origin_data, main_title="shear0_0n50n50_data",
d=[0, 0], sh=[0, 0, -50, -50])
show_images2(data=origin_data, main_title="shear0_0n60n60_data",
d=[0, 0], sh=[0, 0, -60, -60])
show_images2(data=origin_data, main_title="shear0_0n70n70_data",
d=[0, 0], sh=[0, 0, -70, -70])
show_images2(data=origin_data, main_title="shear0_0n80n80_data",
d=[0, 0], sh=[0, 0, -80, -80])
show_images2(data=origin_data, main_title="shear0_0n90n90_data",
d=[0, 0], sh=[0, 0, -90, -90])
This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)
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Super Kai (Kazuya Ito) | Sciencx (2025-02-22T00:35:25+00:00) RandomAffine in PyTorch (4). Retrieved from https://www.scien.cx/2025/02/22/randomaffine-in-pytorch-4/
" » RandomAffine in PyTorch (4)." Super Kai (Kazuya Ito) | Sciencx - Saturday February 22, 2025, https://www.scien.cx/2025/02/22/randomaffine-in-pytorch-4/
HARVARDSuper Kai (Kazuya Ito) | Sciencx Saturday February 22, 2025 » RandomAffine in PyTorch (4)., viewed ,<https://www.scien.cx/2025/02/22/randomaffine-in-pytorch-4/>
VANCOUVERSuper Kai (Kazuya Ito) | Sciencx - » RandomAffine in PyTorch (4). [Internet]. [Accessed ]. Available from: https://www.scien.cx/2025/02/22/randomaffine-in-pytorch-4/
CHICAGO" » RandomAffine in PyTorch (4)." Super Kai (Kazuya Ito) | Sciencx - Accessed . https://www.scien.cx/2025/02/22/randomaffine-in-pytorch-4/
IEEE" » RandomAffine in PyTorch (4)." Super Kai (Kazuya Ito) | Sciencx [Online]. Available: https://www.scien.cx/2025/02/22/randomaffine-in-pytorch-4/. [Accessed: ]
rf:citation » RandomAffine in PyTorch (4) | Super Kai (Kazuya Ito) | Sciencx | https://www.scien.cx/2025/02/22/randomaffine-in-pytorch-4/ |
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