ElasticTransform in PyTorch (2)

#python #pytorch #elastictransform #v2
*Memos:
My post explains ElasticTransform() about alpha and fill argument.
My post explains OxfordIIITPet().
ElasticTransform() can do random morphological transformation for an image as shown below:
from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import ElasticTransform
from torchvision.transforms.functional import InterpolationMode

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

a5000s01_data = OxfordIIITPet( # `a` is alpha and `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])
)

a5000s5fgray_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=5, fill=150)
)

a5000s10fgray_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=10, fill=150)
)

a5000s5fpurple_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=5, fill=[160, 32, 240])
)

a5000s10fpurple_data = OxfordIIITPet(
    root="data",
    transform=ElasticTransform(alpha=5000, sigma=10, 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")
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=0, s=0, 
                 ip=InterpolationMode.BILINEAR, 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,
                              interpolation=ip, fill=f)
        plt.imshow(X=et(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="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])
print()
show_images2(data=origin_data, main_title="a5000s5fgray_data", a=5000, 
             s=5, f=150)
show_images2(data=origin_data, main_title="a5000s10fgray_data", a=5000, 
             s=10, f=150)
print()
show_images2(data=origin_data, main_title="a5000s5fpurple_data", a=5000, 
             s=5, f=[160, 32, 240])
show_images2(data=origin_data, main_title="a5000s10fpurple_data", a=5000, 
             s=10, f=[160, 32, 240])