AugMix in PyTorch (4)

from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import AugMix from torchvision.transforms.functional import InterpolationMode origin_data = OxfordIIITPet( root="data", transform=None ) mw0_data = OxfordIIITPet( # `mw` is mixture_width.  root="data", transform=AugMix(mixture_width=0) ) mw1_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=1) ) mw2_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=2) ) mw5_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=5) ) mw10_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=10) ) mw25_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=25) ) mw50_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=50) ) s10mw0_data = OxfordIIITPet( # `s` is severity.  root="data", transform=AugMix(severity=10, mixture_width=0) ) s10mw1_data = OxfordIIITPet( root="data", transform=AugMix(severity=10, mixture_width=1) ) s10mw2_data = OxfordIIITPet( root="data", transform=AugMix(severity=10, mixture_width=2) ) s10mw5_data = OxfordIIITPet( root="data", transform=AugMix(severity=10, mixture_width=5) ) s10mw10_data = OxfordIIITPet( root="data", transform=AugMix(severity=10, mixture_width=10) ) s10mw25_data = OxfordIIITPet( root="data", transform=AugMix(severity=10, mixture_width=25) ) s10mw50_data = OxfordIIITPet( root="data", transform=AugMix(severity=10, mixture_width=50) ) mw0cd50_data = OxfordIIITPet( # `cd` is chain_depth.  root="data", transform=AugMix(mixture_width=0, chain_depth=50) ) mw1cd50_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=1, chain_depth=50) ) mw2cd50_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=2, chain_depth=50) ) mw5cd50_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=5, chain_depth=50) ) mw10cd50_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=10, chain_depth=50) ) mw25cd50_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=25, chain_depth=50) ) mw50cd50_data = OxfordIIITPet( root="data", transform=AugMix(mixture_width=50, chain_depth=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") print() show_images1(data=mw0_data, main_title="mw0_data") show_images1(data=mw1_data, main_title="mw1_data") show_images1(data=mw2_data, main_title="mw2_data") show_images1(data=mw5_data, main_title="mw5_data") show_images1(data=mw10_data, main_title="mw10_data") show_images1(data=mw25_data, main_title="mw25_data") show_images1(data=mw50_data, main_title="mw50_data") print() show_images1(data=s10mw0_data, main_title="s10mw0_data") show_images1(data=s10mw1_data, main_title="s10mw1_data") show_images1(data=s10mw2_data, main_title="s10mw2_data") show_images1(data=s10mw5_data, main_title="s10mw5_data") show_images1(data=s10mw10_data, main_title="s10mw10_data") show_images1(data=s10mw25_data, main_title="s10mw25_data") show_images1(data=s10mw50_data, main_title="s10mw50_data") print() show_images1(data=mw0cd50_data, main_title="mw0cd50_data") show_images1(data=mw1cd50_data, main_title="mw1cd50_data") show_images1(data=mw2cd50_data, main_title="mw2cd50_data") show_images1(data=mw5cd50_data, main_title="mw5cd50_data") show_images1(data=mw10cd50_data, main_title="mw10cd50_data") show_images1(data=mw25cd50_data, main_title="mw25cd50_data") show_images1(data=mw50cd50_data, main_title="mw50cd50_data") # ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓ def show_images2(data, main_title=None, s=3, mw=3, cd=-1, a=1.0, ao=True, ip=InterpolationMode.BILINEAR, f=None): 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) am = AugMix(severity=s, mixture_width=mw, chain_depth=cd, alpha=a, all_ops=ao, interpolation=ip, fill=f) plt.imshow(X=am(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="mw0_data", mw=0) show_images2(data=origin_data, main_title="mw1_data", mw=1) show_images2(data=origin_data, main_title="mw2_data", mw=2) show_images2(data=origin_data, main_title="mw5_data", mw=5) show_images2(data=origin_data, main_title="mw10_data", mw=10) show_images2(data=origin_data, main_title="mw25_data", mw=25) show_images2(data=origin_data, main_title="mw50_data", mw=50) print() show_images2(data=origin_data, main_title="s10mw0_data", s=10, mw=0) show_images2(data=origin_data, main_title="s10mw1_data", s=10, mw=1) show_images2(data=origin_data, main_title="s10mw2_data", s=10, mw=2) show_images2(data=origin_data, main_title="s10mw5_data", s=10, mw=5) show_images2(data=origin_data, main_title="s10mw10_data", s=10, mw=10) show_images2(data=origin_data, main_title="s10mw25_data", s=10, mw=25) show_images2(data=origin_data, main_title="s10mw50_data", s=10, mw=50) print() show_images2(data=origin_data, main_title="mw0cd50_data", mw=0, cd=50) show_images2(data=origin_data, main_title="mw1cd50_data", mw=1, cd=50) show_images2(data=origin_data, main_title="mw2cd50_data", mw=2, cd=50) show_images2(data=origin_data, main_title="mw5cd50_data", mw=5, cd=50) show_images2(data=origin_data, main_title="mw10cd50_data", mw=10, cd=50) show_images2(data=origin_data, main_title="mw25cd50_data", mw=25, cd=50) show_images2(data=origin_data, main_title="mw50cd50_data", mw=50, cd=50)