ElasticTransform in PyTorch (1)

#python #pytorch #elastictransform #v2

*Memos:

My post explains ElasticTransform() about alpha and fill argument.
My post explains ElasticTransform() about sigma and fill argument.
My post explains ElasticTransform() about alpha, sigma and fill argument.
My post explains OxfordIIITPet().

ElasticTransform() can do random morphological transformation for an image as shown below. *It's about no arguments, alpha argument with sigma=0.1 and sigma argument with alpha=0:

*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].
- 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:5.0-Type:int, float or tuple/list(int or float)): *Memos:
- It's the smoothness of displacements [number, number].
- It must be 0 < number. *A sigle value of number <= 0 works which may be error.
- 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.
- If all values are x <= 0, it's black.
- If all values are 255 <= x, it's white.
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

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

et.alpha
# [50.0, 50.0]

et.sigma
# [5.0, 5.0]

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

et.fill
# 0

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

noargs_data = OxfordIIITPet( # `noargs` is no arguments.
    root="data",
    transform=ElasticTransform()
)

a0s01_data = OxfordIIITPet( # `a` is alpha and `s` is sigma.
    root="data",
    transform=ElasticTransform(alpha=0, sigma=0.1)
)

a50s01_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=50, sigma=0.1)
)

a100s01_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=100, sigma=0.1)
)

a200s01_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=200, sigma=0.1)
)

a500s01_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=500, sigma=0.1)
)

a1000s01_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=1000, sigma=0.1)
)

a5000s01_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=0.1)
)

a10000s01_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=10000, sigma=0.1)
)

a0s1_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=0, sigma=1)
)

a0s5_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=0, sigma=5)
)

a0s10_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=0, sigma=10)
)

a0s20_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=0, sigma=20)
)

a0s40_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=0, sigma=40)
)

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=noargs_data, main_title="noargs_data")
show_images1(data=noargs_data, main_title="noargs_data")
show_images1(data=noargs_data, main_title="noargs_data")
show_images1(data=noargs_data, main_title="noargs_data")
show_images1(data=noargs_data, main_title="noargs_data")
show_images1(data=noargs_data, main_title="noargs_data")
show_images1(data=noargs_data, main_title="noargs_data")
show_images1(data=noargs_data, main_title="noargs_data")
show_images1(data=noargs_data, main_title="noargs_data")
show_images1(data=noargs_data, main_title="noargs_data")
print()
show_images1(data=a0s01_data, main_title="a0s01_data")
show_images1(data=a50s01_data, main_title="a50s01_data")
show_images1(data=a100s01_data, main_title="a100s01_data")
show_images1(data=a200s01_data, main_title="a200s01_data")
show_images1(data=a500s01_data, main_title="a500s01_data")
show_images1(data=a1000s01_data, main_title="a1000s01_data")
show_images1(data=a5000s01_data, main_title="a5000s01_data")
show_images1(data=a10000s01_data, main_title="a10000s01_data")
print()
show_images1(data=a0s01_data, main_title="a0s01_data")
show_images1(data=a0s1_data, main_title="a0s1_data")
show_images1(data=a0s5_data, main_title="a0s5_data")
show_images1(data=a0s10_data, main_title="a0s10_data")
show_images1(data=a0s20_data, main_title="a0s20_data")
show_images1(data=a0s40_data, main_title="a0s40_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, a=50, s=5, 
                 ip=InterpolationMode.BILINEAR, f=0):
    plt.figure(figsize=[10, 5])
    plt.suptitle(t=main_title, y=0.8, fontsize=14)
    if main_title != "origin_data":
        for i, (im, _) in zip(range(1, 6), data):
            plt.subplot(1, 5, i)
            et = ElasticTransform(alpha=a, sigma=s,
                                  interpolation=ip, fill=f)
            plt.imshow(X=et(im))
            plt.xticks(ticks=[])
            plt.yticks(ticks=[])
    else:
        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_images2(data=origin_data, main_title="origin_data")
print()
show_images2(data=origin_data, main_title="noargs_data")
show_images2(data=origin_data, main_title="noargs_data")
show_images2(data=origin_data, main_title="noargs_data")
show_images2(data=origin_data, main_title="noargs_data")
show_images2(data=origin_data, main_title="noargs_data")
show_images2(data=origin_data, main_title="noargs_data")
show_images2(data=origin_data, main_title="noargs_data")
show_images2(data=origin_data, main_title="noargs_data")
show_images2(data=origin_data, main_title="noargs_data")
show_images2(data=origin_data, main_title="noargs_data")
print()
show_images2(data=origin_data, main_title="a0s01_data", a=0, s=0.1)
show_images2(data=origin_data, main_title="a50s01_data", a=50, s=0.1)
show_images2(data=origin_data, main_title="a100s01_data", a=100, s=0.1)
show_images2(data=origin_data, main_title="a200s01_data", a=200, s=0.1)
show_images2(data=origin_data, main_title="a500s01_data", a=500, s=0.1)
show_images2(data=origin_data, main_title="a1000s01_data", a=1000, s=0.1)
show_images2(data=origin_data, main_title="a5000s01_data", a=5000, s=0.1)
show_images2(data=origin_data, main_title="a10000s01_data", a=10000, s=0.1)
print()
show_images2(data=origin_data, main_title="a0s01_data", a=0, s=0.1)
show_images2(data=origin_data, main_title="a0s1_data", a=0, s=1)
show_images2(data=origin_data, main_title="a0s5_data", a=0, s=5)
show_images2(data=origin_data, main_title="a0s10_data", a=0, s=10)
show_images2(data=origin_data, main_title="a0s20_data", a=0, s=20)
show_images2(data=origin_data, main_title="a0s40_data", a=0, s=40)