RandomAffine in PyTorch (2)

Buy Me a Coffee☕ *Memos: My post explains RandomAffine() about degrees, translate, fill and center argument. My post explains RandomAffine() about shear argument. 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 from torchvision.transforms.functional import InterpolationMode origin_data = OxfordIIITPet( root="data", transform=None ) scale1_1origin_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[1, 1]) ) scale01_5_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.1, 5]) ) scale01_1_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.1, 1]) ) scale1_5_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[1, 5]) ) scale09_09_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.9, 0.9]) ) scale08_08_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.8, 0.8]) ) scale07_07_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.7, 0.7]) ) scale06_06_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.6, 0.6]) ) scale05_05_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.5, 0.5]) ) scale04_04_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.4, 0.4]) ) scale03_03_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.3, 0.3]) ) scale02_02_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.2, 0.2]) ) scale01_01_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.1, 0.1]) ) scale001_001_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.01, 0.01]) ) scale0001_0001_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[0.001, 0.001]) ) scale2_2_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[2, 2]) ) scale3_3_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[3, 3]) ) scale4_4_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[4, 4]) ) scale5_5_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[5, 5]) ) scale7_7_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[7, 7]) ) scale10_10_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[10, 10]) ) scale15_15_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[15, 15]) ) scale25_25_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[25, 25]) ) scale50_50_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[50, 50]) ) scale100_100_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[100, 100]) ) scale300_300_data = OxfordIIITPet( root="data", transform=RandomAffine(degrees=[0, 0], scale=[300, 300]) ) 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=scale1_1origin_data, main_title="scale1_1origin_data") show_images1(data=scale01_5_data, main_title="scale01_5_data") show_images1(data=scale01_1_data, main_title="scale01_1_data") show_images1(data=scale1_5_data, main_title="scale1_5_data") print() show_images1(data=scale1_1origin_data, main_title="scale1_1origin_data") show_images1(data=scale09_09_data, main_title="scale09_09_data") show_images1(data=scale08_08_data, main_title="scale08_08_data") show_images1(data=scale07_07_data, main_title="scale07_07_data") show_images1(data=scale06_06_data, main_title="scale06_06_data") show_images1(data=scale05_05_data, main_title="scale05_05_data") show_images1(data=scale04_04_data, main_title="scale04_04_data") show_images1(data=scale03_03_data, main_title="scale03_03_data") show_images1(data=scale02_02_data, main_title="scale02_02_data") show_images1(data=scale01_01_data, main_title="scale01_01_data") show_images1(data=scale001_001_data, main_title="scale001_001_data") show_images1(data=scale0001_0001_data, main_title="scale0001_0001_data") print() show_images1(data=scale1_1origin_data

Feb 21, 2025 - 23:25

*Memos:

My post explains RandomAffine() about degrees, translate, fill and center argument.
My post explains RandomAffine() about shear argument.
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
from torchvision.transforms.functional import InterpolationMode

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

scale1_1origin_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[1, 1])
)

scale01_5_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.1, 5])
)

scale01_1_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.1, 1])
)

scale1_5_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[1, 5])
)

scale09_09_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.9, 0.9])
)

scale08_08_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.8, 0.8])
)

scale07_07_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.7, 0.7])
)

scale06_06_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.6, 0.6])
)

scale05_05_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.5, 0.5])
)

scale04_04_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.4, 0.4])
)

scale03_03_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.3, 0.3])
)

scale02_02_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.2, 0.2])
)

scale01_01_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.1, 0.1])
)

scale001_001_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.01, 0.01])
)

scale0001_0001_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[0.001, 0.001])
)

scale2_2_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[2, 2])
)

scale3_3_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[3, 3])
)

scale4_4_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[4, 4])
)

scale5_5_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[5, 5])
)

scale7_7_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[7, 7])
)

scale10_10_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[10, 10])
)

scale15_15_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[15, 15])
)

scale25_25_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[25, 25])
)

scale50_50_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[50, 50])
)

scale100_100_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[100, 100])
)

scale300_300_data = OxfordIIITPet(
    root="data",
    transform=RandomAffine(degrees=[0, 0], scale=[300, 300])
)

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=scale1_1origin_data, main_title="scale1_1origin_data")
show_images1(data=scale01_5_data, main_title="scale01_5_data")
show_images1(data=scale01_1_data, main_title="scale01_1_data")
show_images1(data=scale1_5_data, main_title="scale1_5_data")
print()
show_images1(data=scale1_1origin_data, main_title="scale1_1origin_data")
show_images1(data=scale09_09_data, main_title="scale09_09_data")
show_images1(data=scale08_08_data, main_title="scale08_08_data")
show_images1(data=scale07_07_data, main_title="scale07_07_data")
show_images1(data=scale06_06_data, main_title="scale06_06_data")
show_images1(data=scale05_05_data, main_title="scale05_05_data")
show_images1(data=scale04_04_data, main_title="scale04_04_data")
show_images1(data=scale03_03_data, main_title="scale03_03_data")
show_images1(data=scale02_02_data, main_title="scale02_02_data")
show_images1(data=scale01_01_data, main_title="scale01_01_data")
show_images1(data=scale001_001_data, main_title="scale001_001_data")
show_images1(data=scale0001_0001_data, main_title="scale0001_0001_data")
print()
show_images1(data=scale1_1origin_data, main_title="scale1_1origin_data")
show_images1(data=scale2_2_data, main_title="scale2_2_data")
show_images1(data=scale3_3_data, main_title="scale3_3_data")
show_images1(data=scale4_4_data, main_title="scale4_4_data")
show_images1(data=scale5_5_data, main_title="scale5_5_data")
show_images1(data=scale7_7_data, main_title="scale7_7_data")
show_images1(data=scale10_10_data, main_title="scale10_10_data")
show_images1(data=scale15_15_data, main_title="scale15_15_data")
show_images1(data=scale25_25_data, main_title="scale25_25_data")
show_images1(data=scale50_50_data, main_title="scale50_50_data")
show_images1(data=scale100_100_data, main_title="scale100_100_data")
show_images1(data=scale300_300_data, main_title="scale300_300_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,
                 ip=InterpolationMode.NEAREST, 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, interpolation=ip, 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="scale1_1origin_data", d=[0, 0], 
             sc=[1, 1])
show_images2(data=origin_data, main_title="scale09_09_data", d=[0, 0],
             sc=[0.9, 0.9])
show_images2(data=origin_data, main_title="scale08_08_data", d=[0, 0],
             sc=[0.8, 0.8])
show_images2(data=origin_data, main_title="scale07_07_data", d=[0, 0],
             sc=[0.7, 0.7])
show_images2(data=origin_data, main_title="scale06_06_data", d=[0, 0],
             sc=[0.6, 0.6])
show_images2(data=origin_data, main_title="scale05_05_data", d=[0, 0],
             sc=[0.5, 0.5])
show_images2(data=origin_data, main_title="scale04_04_data", d=[0, 0],
             sc=[0.4, 0.4])
show_images2(data=origin_data, main_title="scale03_03_data", d=[0, 0],
             sc=[0.3, 0.3])
show_images2(data=origin_data, main_title="scale02_02_data", d=[0, 0],
             sc=[0.2, 0.2])
show_images2(data=origin_data, main_title="scale01_01_data", d=[0, 0],
             sc=[0.1, 0.1])
show_images2(data=origin_data, main_title="scale001_001_data", d=[0, 0],
             sc=[0.01, 0.01])
show_images2(data=origin_data, main_title="scale0001_0001_data", d=[0, 0], 
             sc=[0.001, 0.001])
print()
show_images2(data=origin_data, main_title="scale1_1origin_data", d=[0, 0], 
             sc=[1, 1])
show_images2(data=origin_data, main_title="scale2_2_data", d=[0, 0],
             sc=[2, 2])
show_images2(data=origin_data, main_title="scale3_3_data", d=[0, 0],
             sc=[3, 3])
show_images2(data=origin_data, main_title="scale4_4_data", d=[0, 0],
             sc=[4, 4])
show_images2(data=origin_data, main_title="scale5_5_data", d=[0, 0],
             sc=[5, 5])
show_images2(data=origin_data, main_title="scale7_7_data", d=[0, 0],
             sc=[7, 7])
show_images2(data=origin_data, main_title="scale10_10_data", d=[0, 0],
             sc=[10, 10])
show_images2(data=origin_data, main_title="scale15_15_data", d=[0, 0],
             sc=[15, 15])
show_images2(data=origin_data, main_title="scale25_25_data", d=[0, 0],
             sc=[25, 25])
show_images2(data=origin_data, main_title="scale50_50_data", d=[0, 0],
             sc=[50, 50])
show_images2(data=origin_data, main_title="scale100_100_data", d=[0, 0],
             sc=[100, 100])
show_images2(data=origin_data, main_title="scale300_300_data", d=[0, 0],
             sc=[300, 300])