ElasticTransform in PyTorch

Buy Me a Coffee☕

*Memos:

My post explains OxfordIIITPet().

ElasticTransform() can do random morphological transformation for an image as shown below:

*Memos:

The 1st argument for initialization is alpha(Optional-Default:50.0-Type:int, float or…


This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)

Buy Me a Coffee

*Memos:

ElasticTransform() can do random morphological transformation for an image as shown below:

*Memos:

  • The 1st argument for initialization is alpha(Optional-Default:50.0-Type:int, float or tuple/list(int or float)): *Memos:
    • It can do morphological transformation.
    • It's the magnitude of displacements [number, number].
    • It must be 0 <= number.
    • A tuple/list must be the 1D with 1 or 2 elements.
    • A single value(int, float or tuple/list(int or float)) means [alpha, alpha].
  • The 2nd argument for initialization is sigma(Optional-Default:0.5-Type:int, float or tuple/list(int or float)): *Memos:
    • It's the smoothness of displacements [number, number].
    • It must be 0 < number.
    • A tuple/list must be the 1D with 1 or 2 elements.
    • A single value(int, float or tuple/list(int or float)) means [sigma, sigma].
  • The 3rd argument for initialization is interpolation(Optional-Default:InterpolationMode.BILINEAR-Type:InterpolationMode).
  • The 4th 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 doing morphological transformation for 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 ElasticTransform
from torchvision.transforms.functional import InterpolationMode

elastictf = ElasticTransform()
elastictf = ElasticTransform(alpha=50,
                             sigma=5,
                             interpolation=InterpolationMode.BILINEAR,
                             fill=0)
elastictf
# ElasticTransform(alpha=[50.0, 50.0],
#                  sigma=[5.0, 5.0],
#                  interpolation=InterpolationMode.BILINEAR,
#                  fill=0)

elastictf.alpha
# [50.0, 50.0]

elastictf.sigma
# [5.0, 5.0]

elastictf.interpolation
# <InterpolationMode.BILINEAR: 'bilinear'>

elastictf.fill
# 0

origin_data = OxfordIIITPet(
    root="data",
    transform=None
    # transform=ElasticTransform(alpha=0, sigma=0)
)

a50_data = OxfordIIITPet( # `a` is alpha.
    root="data",
    transform=ElasticTransform(alpha=50)
    # transform=ElasticTransform(alpha=[50, 50])
)

a100_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=100)
)

a200_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=200)
)

a500_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=500)
)

a1000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=1000)
)

a5000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000)
)

a10000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=10000)
)

a50000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=50000)
)

a100000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=100000)
)

a1000000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=1000000)
)

a10000000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=1000000)
)

a50_0_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[50, 0])
)

a100_0_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[100, 0])
)

a200_0_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[200, 0])
)

a500_0_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[500, 0])
)

a1000_0_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[1000, 0])
)

a5000_0_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[5000, 0])
)

a10000_0_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[10000, 0])
)

a50000_0_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[50000, 0])
)

a100000_0_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[100000, 0])
)

a1000000_0_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[1000000, 0])
)

a10000000_0_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[10000000, 0])
)

a0_50_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[0, 50])
)

a0_100_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[0, 100])
)

a0_200_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[0, 200])
)

a0_500_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[0, 500])
)

a0_1000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[0, 1000])
)

a0_5000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[0, 5000])
)

a0_10000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[0, 10000])
)

a0_50000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[0, 50000])
)

a0_100000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[0, 100000])
)

a0_1000000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[0, 1000000])
)

a0_10000000_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=[0, 10000000])
)

a5000s01_data = OxfordIIITPet( # `s` is sigma.
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=0.1)
    # transform=ElasticTransform(alpha=5000, sigma=[0.1, 0.1])
)

a5000s1_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=1)
)

a5000s5_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=5)
)

a5000s10_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=10)
)

a5000s20_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=20)
)

a5000s40_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=40)
)

a5000s40_01_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[40, 0.1])
)

a5000s40_1_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[40, 1])
)

a5000s40_5_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[40, 5])
)

a5000s40_10_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[40, 10])
)

a5000s40_20_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[40, 20])
)

a5000s40_40_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[40, 40])
)

a5000s01_40_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[0.1, 40])
)

a5000s1_40_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[1, 40])
)

a5000s5_40_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[5, 40])
)

a5000s10_40_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[10, 40])
)

a5000s20_40_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[20, 40])
)

a5000s40_40_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=[40, 40])
)

a5000fgray_data = OxfordIIITPet( # `f` is fill.
    root="data",
    transform=ElasticTransform(alpha=5000, fill=150)
)

a10000fgray_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=10000, fill=150)
)

a5000fpurple_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, fill=[160, 32, 240])
)

a10000fpurple_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=10000, 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=a50_data, main_title="a50_data")
show_images1(data=a100_data, main_title="a100_data")
show_images1(data=a200_data, main_title="a200_data")
show_images1(data=a500_data, main_title="a500_data")
show_images1(data=a1000_data, main_title="a1000_data")
show_images1(data=a5000_data, main_title="a5000_data")
show_images1(data=a10000_data, main_title="a10000_data")
show_images1(data=a50000_data, main_title="a50000_data")
show_images1(data=a100000_data, main_title="a100000_data")
show_images1(data=a1000000_data, main_title="a1000000_data")
show_images1(data=a10000000_data, main_title="a10000000_data")
print()
show_images1(data=origin_data, main_title="origin_data")
show_images1(data=a50_0_data, main_title="a50_0_data")
show_images1(data=a100_0_data, main_title="a100_0_data")
show_images1(data=a200_0_data, main_title="a200_0_data")
show_images1(data=a500_0_data, main_title="a500_0_data")
show_images1(data=a1000_0_data, main_title="a1000_0_data")
show_images1(data=a5000_0_data, main_title="a5000_0_data")
show_images1(data=a10000_0_data, main_title="a10000_0_data")
show_images1(data=a50000_0_data, main_title="a50000_0_data")
show_images1(data=a100000_0_data, main_title="a100000_0_data")
show_images1(data=a1000000_0_data, main_title="a1000000_0_data")
show_images1(data=a10000000_0_data, main_title="a10000000_0_data")
print()
show_images1(data=origin_data, main_title="origin_data")
show_images1(data=a0_50_data, main_title="a0_50_data")
show_images1(data=a0_100_data, main_title="a0_100_data")
show_images1(data=a0_200_data, main_title="a0_200_data")
show_images1(data=a0_500_data, main_title="a0_500_data")
show_images1(data=a0_1000_data, main_title="a0_1000_data")
show_images1(data=a0_5000_data, main_title="a0_5000_data")
show_images1(data=a0_10000_data, main_title="a0_10000_data")
show_images1(data=a0_50000_data, main_title="a0_50000_data")
show_images1(data=a0_100000_data, main_title="a0_100000_data")
show_images1(data=a0_1000000_data, main_title="a0_1000000_data")
show_images1(data=a0_10000000_data, main_title="a0_10000000_data")
print()
show_images1(data=a5000s01_data, main_title="a5000s01_data")
show_images1(data=a5000s1_data, main_title="a5000s1_data")
show_images1(data=a5000s5_data, main_title="a5000s5_data")
show_images1(data=a5000s10_data, main_title="a5000s10_data")
show_images1(data=a5000s20_data, main_title="a5000s20_data")
show_images1(data=a5000s40_data, main_title="a5000s40_data")
print()
show_images1(data=a5000s40_01_data, main_title="a5000s40_01_data")
show_images1(data=a5000s40_1_data, main_title="a5000s40_1_data")
show_images1(data=a5000s40_5_data, main_title="a5000s40_5_data")
show_images1(data=a5000s40_10_data, main_title="a5000s40_10_data")
show_images1(data=a5000s40_20_data, main_title="a5000s40_20_data")
show_images1(data=a5000s40_40_data, main_title="a5000s40_40_data")
print()
show_images1(data=a5000s01_40_data, main_title="a5000s01_40_data")
show_images1(data=a5000s1_40_data, main_title="a5000s1_40_data")
show_images1(data=a5000s5_40_data, main_title="a5000s5_40_data")
show_images1(data=a5000s10_40_data, main_title="a5000s10_40_data")
show_images1(data=a5000s20_40_data, main_title="a5000s20_40_data")
show_images1(data=a5000s40_40_data, main_title="a5000s40_40_data")
print()
show_images1(data=a5000fgray_data, main_title="a5000fgray_data")
show_images1(data=a10000fgray_data, main_title="a10000fgray_data")
show_images1(data=a5000fpurple_data, main_title="a5000fpurple_data")
show_images1(data=a10000fpurple_data, main_title="a10000fpurple_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, a=50, s=5, 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)
        et = ElasticTransform(alpha=a, sigma=s, fill=f) # Here
        plt.imshow(X=et(im)) # Here
        plt.xticks(ticks=[])
        plt.yticks(ticks=[])
    plt.tight_layout()
    plt.show()

show_images2(data=origin_data, main_title="origin_data", a=0, s=0)
show_images2(data=origin_data, main_title="a50_data", a=50)
show_images2(data=origin_data, main_title="a100_data", a=100)
show_images2(data=origin_data, main_title="a200_data", a=200)
show_images2(data=origin_data, main_title="a500_data", a=500)
show_images2(data=origin_data, main_title="a1000_data", a=1000)
show_images2(data=origin_data, main_title="a5000_data", a=5000)
show_images2(data=origin_data, main_title="a10000_data", a=10000)
show_images2(data=origin_data, main_title="a50000_data", a=50000)
show_images2(data=origin_data, main_title="a100000_data", a=100000)
show_images2(data=origin_data, main_title="a1000000_data", a=1000000)
show_images2(data=origin_data, main_title="a10000000_data", a=10000000)
print()
show_images2(data=origin_data, main_title="origin_data", a=0, s=0)
show_images2(data=origin_data, main_title="a50_0_data", a=[50, 0])
show_images2(data=origin_data, main_title="a100_0_data", a=[100, 0])
show_images2(data=origin_data, main_title="a200_0_data", a=[200, 0])
show_images2(data=origin_data, main_title="a500_0_data", a=[500, 0])
show_images2(data=origin_data, main_title="a1000_0_data", a=[1000, 0])
show_images2(data=origin_data, main_title="a5000_0_data", a=[5000, 0])
show_images2(data=origin_data, main_title="a10000_0_data", a=[10000, 0])
show_images2(data=origin_data, main_title="a50000_0_data", a=[50000, 0])
show_images2(data=origin_data, main_title="a100000_0_data", a=[100000, 0])
show_images2(data=origin_data, main_title="a1000000_0_data", a=[1000000, 0])
show_images2(data=origin_data, main_title="a10000000_0_data", a=[10000000, 0])
print()
show_images2(data=origin_data, main_title="origin_data", a=0, s=0)
show_images2(data=origin_data, main_title="a0_50_data", a=[0, 50])
show_images2(data=origin_data, main_title="a0_100_data", a=[0, 100])
show_images2(data=origin_data, main_title="a0_200_data", a=[0, 200])
show_images2(data=origin_data, main_title="a0_500_data", a=[0, 500])
show_images2(data=origin_data, main_title="a0_1000_data", a=[0, 1000])
show_images2(data=origin_data, main_title="a0_5000_data", a=[0, 5000])
show_images2(data=origin_data, main_title="a0_10000_data", a=[0, 10000])
show_images2(data=origin_data, main_title="a0_50000_data", a=[0, 50000])
show_images2(data=origin_data, main_title="a0_100000_data", a=[0, 100000])
show_images2(data=origin_data, main_title="a0_1000000_data", a=[0, 1000000])
show_images2(data=origin_data, main_title="a0_10000000_data", a=[0, 10000000])
print()
show_images2(data=origin_data, main_title="a5000s01_data", a=5000, s=0.1)
show_images2(data=origin_data, main_title="a5000s1_data", a=5000, s=1)
show_images2(data=origin_data, main_title="a5000s5_data", a=5000, s=5)
show_images2(data=origin_data, main_title="a5000s10_data", a=5000, s=10)
show_images2(data=origin_data, main_title="a5000s20_data", a=5000, s=20)
show_images2(data=origin_data, main_title="a5000s40_data", a=5000, s=40)
print()
show_images2(data=origin_data, main_title="a5000s40_01_data", a=5000,
             s=[40, 0.1])
show_images2(data=origin_data, main_title="a5000s40_1_data", a=5000,
             s=[40, 1])
show_images2(data=origin_data, main_title="a5000s40_5_data", a=5000,
             s=[40, 5])
show_images2(data=origin_data, main_title="a5000s40_10_data", a=5000,
             s=[40, 10])
show_images2(data=origin_data, main_title="a5000s40_20_data", a=5000,
             s=[40, 20])
show_images2(data=origin_data, main_title="a5000s40_40_data", a=5000,
             s=[40, 40])
print()
show_images2(data=origin_data, main_title="a5000s01_40_data", a=5000,
             s=[0.1, 40])
show_images2(data=origin_data, main_title="a5000s1_40_data", a=5000,
             s=[1, 40])
show_images2(data=origin_data, main_title="a5000s5_40_data", a=5000,
             s=[5, 40])
show_images2(data=origin_data, main_title="a5000s10_40_data", a=5000,
             s=[10, 40])
show_images2(data=origin_data, main_title="a5000s20_40_data", a=5000,
             s=[20, 40])
show_images2(data=origin_data, main_title="a5000s40_40_data", a=5000,
             s=[40, 40])
print()
show_images2(data=origin_data, main_title="a5000fgray_data", a=5000, f=150)
show_images2(data=origin_data, main_title="a10000fgray_data", a=10000, f=150)
show_images2(data=origin_data, main_title="a5000fpurple_data", a=5000,
             f=[160, 32, 240])
show_images2(data=origin_data, main_title="a10000fpurple_data", a=10000,
             f=[160, 32, 240])

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description

Image description


This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)


Print Share Comment Cite Upload Translate Updates
APA

Super Kai (Kazuya Ito) | Sciencx (2025-02-01T21:46:03+00:00) ElasticTransform in PyTorch. Retrieved from https://www.scien.cx/2025/02/01/elastictransform-in-pytorch/

MLA
" » ElasticTransform in PyTorch." Super Kai (Kazuya Ito) | Sciencx - Saturday February 1, 2025, https://www.scien.cx/2025/02/01/elastictransform-in-pytorch/
HARVARD
Super Kai (Kazuya Ito) | Sciencx Saturday February 1, 2025 » ElasticTransform in PyTorch., viewed ,<https://www.scien.cx/2025/02/01/elastictransform-in-pytorch/>
VANCOUVER
Super Kai (Kazuya Ito) | Sciencx - » ElasticTransform in PyTorch. [Internet]. [Accessed ]. Available from: https://www.scien.cx/2025/02/01/elastictransform-in-pytorch/
CHICAGO
" » ElasticTransform in PyTorch." Super Kai (Kazuya Ito) | Sciencx - Accessed . https://www.scien.cx/2025/02/01/elastictransform-in-pytorch/
IEEE
" » ElasticTransform in PyTorch." Super Kai (Kazuya Ito) | Sciencx [Online]. Available: https://www.scien.cx/2025/02/01/elastictransform-in-pytorch/. [Accessed: ]
rf:citation
» ElasticTransform in PyTorch | Super Kai (Kazuya Ito) | Sciencx | https://www.scien.cx/2025/02/01/elastictransform-in-pytorch/ |

Please log in to upload a file.




There are no updates yet.
Click the Upload button above to add an update.

You must be logged in to translate posts. Please log in or register.