GaussianBlur in PyTorch (2)

from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import GaussianBlur origin_data = OxfordIIITPet( root="data", transform=None ) ks1s01_data = OxfordIIITPet( # `ks` is kernel_size.  root="data", transform=GaussianBlur(kernel_size=1, sigma=0.1) ) ks1s1_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=1, sigma=1) ) ks1s5_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=1, sigma=5) ) ks1s10_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=1, sigma=10) ) ks1s15_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=1, sigma=15) ) ks1s25_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=1, sigma=25) ) ks1s50_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=1, sigma=50) ) ks1s01_50_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=1, sigma=[0.1, 50]) ) ks1s01_10_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=1, sigma=[0.1, 10]) ) ks1s10_50_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=1, sigma=[10, 50]) ) ks101s01_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=101, sigma=0.1) ) ks101s1_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=101, sigma=1) ) ks101s5_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=101, sigma=5) ) ks101s10_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=101, sigma=10) ) ks101s15_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=101, sigma=15) ) ks101s25_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=101, sigma=25) ) ks101s50_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=101, sigma=50) ) ks101s01_50_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=101, sigma=[0.1, 50]) ) ks101s01_10_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=101, sigma=[0.1, 10]) ) ks101s10_50_data = OxfordIIITPet( root="data", transform=GaussianBlur(kernel_size=101, sigma=[10, 50]) ) 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=ks1s01_data, main_title="ks1s01_data") show_images1(data=ks1s1_data, main_title="ks1s1_data") show_images1(data=ks1s5_data, main_title="ks1s5_data") show_images1(data=ks1s10_data, main_title="ks1s10_data") show_images1(data=ks1s15_data, main_title="ks1s15_data") show_images1(data=ks1s25_data, main_title="ks1s25_data") show_images1(data=ks1s50_data, main_title="ks1s50_data") show_images1(data=ks1s01_50_data, main_title="ks1s01_50_data") show_images1(data=ks1s01_10_data, main_title="ks1s01_10_data") show_images1(data=ks1s10_50_data, main_title="ks1s10_50_data") print() show_images1(data=origin_data, main_title="origin_data") show_images1(data=ks101s01_data, main_title="ks101s01_data") show_images1(data=ks101s1_data, main_title="ks101s1_data") show_images1(data=ks101s5_data, main_title="ks101s5_data") show_images1(data=ks101s10_data, main_title="ks101s10_data") show_images1(data=ks101s15_data, main_title="ks101s15_data") show_images1(data=ks101s25_data, main_title="ks101s25_data") show_images1(data=ks101s50_data, main_title="ks101s50_data") show_images1(data=ks101s01_50_data, main_title="ks101s01_50_data") show_images1(data=ks101s01_10_data, main_title="ks101s01_10_data") show_images1(data=ks101s10_50_data, main_title="ks101s10_50_data") # ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓ def show_images2(data, main_title=None, ks=None, s=(0.1, 2)): 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) gb = GaussianBlur(kernel_size=ks, sigma=s) plt.imshow(X=gb(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") show_images2(data=origin_data, main_title="ks1s01_data", ks=1, s=0.1) show_images2(data=origin_data, main_title="ks1s1_data", ks=1, s=1) show_images2(data=origin_data, main_title="ks1s5_data", ks=1, s=5) show_images2(data=origin_data, main_title="ks1s10_data", ks=1, s=10) show_images2(data=origin_data, main_title="ks1s15_data", ks=1, s=15) show_images2(data=origin_data, main_title="ks1s25_data", ks=1, s=25) show_images2(data=origin_data, main_title="ks1s50_data", ks=1, s=50) show_images2(data=origin_data, main_title="ks1s01_50_data", ks=1, s=[0.1, 50]) show_images2(data=origin_data, main_title="ks1s01_10_data", ks=1, s=[0.1, 10]) show_images2(data=origin_data, main_title="ks1s10_50_data", ks=1, s=[10, 50]) print() show_images2(data=origin_data, main_title="origin_data") show_images2(data=origin_data, main_title="ks101s01_data", ks=101, s=0.1) show_images2(data=origin_data, main_title="ks101s1_data", ks=101, s=1) show_images2(data=origin_data, main_title="ks101s5_data", ks=101, s=5) show_images2(data=origin_data, main_title="ks101s10_data", ks=101, s=10) show_images2(data=origin_data, main_title="ks101s15_data", ks=101, s=15) show_images2(data=origin_data, main_title="ks101s25_data", ks=101, s=25) show_images2(data=origin_data, main_title="ks101s50_data", ks=101, s=50) show_images2(data=origin_data, main_title="ks101s01_50_data", ks=101, s=[0.1, 50]) show_images2(data=origin_data, main_title="ks101s01_10_data", ks=101, s=[0.1, 10]) show_images2(data=origin_data, main_title="ks101s10_50_data", ks=101, s=[10, 50])