ElasticTransform in PyTorch (4)

from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import ElasticTransform from torchvision.transforms.functional import InterpolationMode origin_data = OxfordIIITPet( root="data", transform=None ) a0s01_data = OxfordIIITPet( # `a` is alpha and `s` is sigma.  root="data", transform=ElasticTransform(alpha=0, sigma=0.1) # transform=ElasticTransform(alpha=[0, 0], sigma=[0.1, 0.1]) ) a0s1_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=0, sigma=1) ) a0s10_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=0, sigma=10) ) a0s40_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=0, sigma=40) ) a10s01_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=10, sigma=0.1) # transform=ElasticTransform(alpha=-10, sigma=0.1) ) a10s1_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=10, sigma=1) ) a10s10_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=10, sigma=10) ) a10s40_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=10, sigma=40) ) a100s01_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=100, sigma=0.1) # transform=ElasticTransform(alpha=-100, sigma=0.1) ) a100s1_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=100, sigma=1) ) a100s10_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=100, sigma=10) ) a100s40_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=100, sigma=40) ) a1000s01_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=1000, sigma=0.1) # transform=ElasticTransform(alpha=-1000, sigma=0.1) ) a1000s1_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=1000, sigma=1) ) a1000s10_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=1000, sigma=10) ) a1000s40_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=1000, sigma=40) ) a10000s01_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=10000, sigma=0.1) # transform=ElasticTransform(alpha=-10000, sigma=0.1) ) a10000s1_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=10000, sigma=1) ) a10000s10_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=10000, sigma=10) ) a10000s40_data = OxfordIIITPet( root="data", transform=ElasticTransform(alpha=10000, 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=a0s01_data, main_title="a0s01_data") show_images1(data=a0s1_data, main_title="a0s1_data") show_images1(data=a0s10_data, main_title="a0s10_data") show_images1(data=a0s40_data, main_title="a0s40_data") print() show_images1(data=a10s01_data, main_title="a10s01_data") show_images1(data=a10s1_data, main_title="a10s1_data") show_images1(data=a10s10_data, main_title="a10s10_data") show_images1(data=a10s40_data, main_title="a10s40_data") print() show_images1(data=a100s01_data, main_title="a100s01_data") show_images1(data=a100s1_data, main_title="a100s1_data") show_images1(data=a100s10_data, main_title="a100s10_data") show_images1(data=a100s40_data, main_title="a100s40_data") print() show_images1(data=a1000s01_data, main_title="a1000s01_data") show_images1(data=a1000s1_data, main_title="a1000s1_data") show_images1(data=a1000s10_data, main_title="a1000s10_data") show_images1(data=a1000s40_data, main_title="a1000s40_data") print() show_images1(data=a10000s01_data, main_title="a10000s01_data") show_images1(data=a10000s1_data, main_title="a10000s1_data") show_images1(data=a10000s10_data, main_title="a10000s10_data") show_images1(data=a10000s40_data, main_title="a10000s40_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="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="a0s10_data", a=0, s=10) show_images2(data=origin_data, main_title="a0s40_data", a=0, s=40) print() show_images2(data=origin_data, main_title="a10s01_data", a=10, s=0.1) show_images2(data=origin_data, main_title="a10s1_data", a=10, s=1) show_images2(data=origin_data, main_title="a10s10_data", a=10, s=10) show_images2(data=origin_data, main_title="a10s40_data", a=10, s=40) print() show_images2(data=origin_data, main_title="a100s01_data", a=100, s=0.1) show_images2(data=origin_data, main_title="a100s1_data", a=100, s=1) show_images2(data=origin_data, main_title="a100s10_data", a=100, s=10) show_images2(data=origin_data, main_title="a100s40_data", a=100, s=40) print() show_images2(data=origin_data, main_title="a1000s01_data", a=1000, s=0.1) show_images2(data=origin_data, main_title="a1000s1_data", a=1000, s=1) show_images2(data=origin_data, main_title="a1000s10_data", a=1000, s=10) show_images2(data=origin_data, main_title="a1000s40_data", a=1000, s=40) print() show_images2(data=origin_data, main_title="a10000s01_data", a=10000, s=0.1) show_images2(data=origin_data, main_title="a10000s1_data", a=10000, s=1) show_images2(data=origin_data, main_title="a10000s10_data", a=10000, s=10) show_images2(data=origin_data, main_title="a10000s40_data", a=10000, s=40)