RandAugment in PyTorch (2)

from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import RandAugment from torchvision.transforms.functional import InterpolationMode origin_data = OxfordIIITPet( root="data", transform=None ) no0_data = OxfordIIITPet( # `no` is num_ops.  root="data", transform=RandAugment(num_ops=0) ) no1_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=1) ) no2_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=2) ) no5_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=5) ) no10_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=10) ) no25_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=25) ) no50_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=50) ) no100_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=100) ) no500_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=500) ) no1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=1000) ) no0m30_data = OxfordIIITPet( # `m` is magnitude.  root="data", transform=RandAugment(num_ops=0, magnitude=30) ) no1m30_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=1, magnitude=30) ) no2m30_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=2, magnitude=30) ) no5m30_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=5, magnitude=30) ) no10m30_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=10, magnitude=30) ) no25m30_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=25, magnitude=30) ) no0nmb1000_data = OxfordIIITPet( # `nmb` is num_magnitude_bins.  root="data", transform=RandAugment(num_ops=0, num_magnitude_bins=1000) ) no1nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=1, num_magnitude_bins=1000) ) no2nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=2, num_magnitude_bins=1000) ) no5nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=5, num_magnitude_bins=1000) ) no10nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=10, num_magnitude_bins=1000) ) no25nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=25, num_magnitude_bins=1000) ) no50nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=50, num_magnitude_bins=1000) ) no100nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=100, num_magnitude_bins=1000) ) no500nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=500, num_magnitude_bins=1000) ) no1000nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=1000, num_magnitude_bins=1000) ) no0m999nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=0, magnitude=999, num_magnitude_bins=1000) ) no1m999nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=1, magnitude=999, num_magnitude_bins=1000) ) no2m999nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=2, magnitude=999, num_magnitude_bins=1000) ) no5m999nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=5, magnitude=999, num_magnitude_bins=1000) ) no10m999nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=10, magnitude=999, num_magnitude_bins=1000) ) no25m999nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=25, magnitude=999, num_magnitude_bins=1000) ) no50m999nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=50, magnitude=999, num_magnitude_bins=1000) ) no100m999nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=100, magnitude=999, num_magnitude_bins=1000) ) no500m999nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=500, magnitude=999, num_magnitude_bins=1000) ) no1000m999nmb1000_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=1000, magnitude=999, num_magnitude_bins=1000) ) no25fgray_data = OxfordIIITPet( # `f` is fill.  root="data", transform=RandAugment(num_ops=25, fill=150) # transform=RandAugment(num_ops=25, fill=[150]) ) no25fpurple_data = OxfordIIITPet( root="data", transform=RandAugment(num_ops=25, 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=no0_data, main_title="no0_data") show_images1(data=no1_data, main_title="no1_data") show_images1(data=no2_data, main_title="no2_data") show_images1(data=no5_data, main_title="no5_data") show_images1(data=no10_data, main_title="no10_data") show_images1(data=no25_data, main_title="no25_data") show_images1(data=no50_data, main_title="no50_data") show_images1(data=no100_data, main_title="no100_data") show_images1(data=no500_data, main_title="no500_data") show_images1(data=no1000_data, main_title="no1000_data") print() show_images1(data=no0m30_data, main_title="no0m30_data") show_images1(data=no1m30_data, main_title="no1m30_data") show_images1(data=no2m30_data, main_title="no2m30_data") show_images1(data=no5m30_data, main_title="no5m30_data") show_images1(data=no10m30_data, main_title="no10m30_data") show_images1(data=no25m30_data, main_title="no25m30_data") print() show_images1(data=no0nmb1000_data, main_title="no0nmb1000_data") show_images1(data=no1nmb1000_data, main_title="no1nmb1000_data") show_images1(data=no2nmb1000_data, main_title="no2nmb1000_data") show_images1(data=no5nmb1000_data, main_title="no5nmb1000_data") show_images1(data=no10nmb1000_data, main_title="no10nmb1000_data") show_images1(data=no25nmb1000_data, main_title="no25nmb1000_data") show_images1(data=no50nmb1000_data, main_title="no50nmb1000_data") show_images1(data=no100nmb1000_data, main_title="no100nmb1000_data") show_images1(data=no500nmb1000_data, main_title="no500nmb1000_data") show_images1(data=no1000nmb1000_data, main_title="no1000nmb1000_data") print() show_images1(data=no0m999nmb1000_data, main_title="no0m999nmb1000_data") show_images1(data=no1m999nmb1000_data, main_title="no1m999nmb1000_data") show_images1(data=no2m999nmb1000_data, main_title="no2m999nmb1000_data") show_images1(data=no5m999nmb1000_data, main_title="no5m999nmb1000_data") show_images1(data=no10m999nmb1000_data, main_title="no10m999nmb1000_data") show_images1(data=no25m999nmb1000_data, main_title="no25m999nmb1000_data") show_images1(data=no50m999nmb1000_data, main_title="no50m999nmb1000_data") show_images1(data=no100m999nmb1000_data, main_title="no100m999nmb1000_data") show_images1(data=no500m999nmb1000_data, main_title="no500m999nmb1000_data") show_images1(data=no1000m999nmb1000_data, main_title="no1000m999nmb1000_data") print() show_images1(data=no25fgray_data, main_title="no25fgray_data") show_images1(data=no25fpurple_data, main_title="no25fpurple_data") # ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓ def show_images2(data, main_title=None, no=2, m=9, nmb=31, ip=InterpolationMode.NEAREST, 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) ra = RandAugment(num_ops=no, magnitude=m, num_magnitude_bins=nmb, interpolation=ip, fill=f) plt.imshow(X=ra(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="no0_data", no=0) show_images2(data=origin_data, main_title="no1_data", no=1) show_images2(data=origin_data, main_title="no2_data", no=2) show_images2(data=origin_data, main_title="no5_data", no=5) show_images2(data=origin_data, main_title="no10_data", no=10) show_images2(data=origin_data, main_title="no25_data", no=25) show_images2(data=origin_data, main_title="no50_data", no=50) show_images2(data=origin_data, main_title="no100_data", no=100) show_images2(data=origin_data, main_title="no500_data", no=500) show_images2(data=origin_data, main_title="no1000_data", no=1000) print() show_images2(data=origin_data, main_title="no0m30_data", no=0, m=30) show_images2(data=origin_data, main_title="no1m30_data", no=1, m=30) show_images2(data=origin_data, main_title="no2m30_data", no=2, m=30) show_images2(data=origin_data, main_title="no5m30_data", no=5, m=30) show_images2(data=origin_data, main_title="no10m30_data", no=10, m=30) show_images2(data=origin_data, main_title="no25m30_data", no=25, m=30) print() show_images2(data=origin_data, main_title="no0nmb1000_data", no=0, nmb=1000) show_images2(data=origin_data, main_title="no1nmb1000_data", no=1, nmb=1000) show_images2(data=origin_data, main_title="no2nmb1000_data", no=2, nmb=1000) show_images2(data=origin_data, main_title="no5nmb1000_data", no=5, nmb=1000) show_images2(data=origin_data, main_title="no10nmb1000_data", no=10, nmb=1000) show_images2(data=origin_data, main_title="no25nmb1000_data", no=25, nmb=1000) show_images2(data=origin_data, main_title="no50nmb1000_data", no=50, nmb=1000) show_images2(data=origin_data, main_title="no100nmb1000_data", no=100, nmb=1000) show_images2(data=origin_data, main_title="no500nmb1000_data", no=500, nmb=1000) show_images2(data=origin_data, main_title="no1000nmb1000_data", no=1000, nmb=1000) print() show_images2(data=origin_data, main_title="no0m999nmb1000_data", no=0, m=999, nmb=1000) show_images2(data=origin_data, main_title="no1m999nmb1000_data", no=1, m=999, nmb=1000) show_images2(data=origin_data, main_title="no2m999nmb1000_data", no=2, m=999, nmb=1000) show_images2(data=origin_data, main_title="no5m999nmb1000_data", no=5, m=999, nmb=1000) show_images2(data=origin_data, main_title="no10m999nmb1000_data", no=10, m=999, nmb=1000) show_images2(data=origin_data, main_title="no25m999nmb1000_data", no=25, m=999, nmb=1000) show_images2(data=origin_data, main_title="no50m999nmb1000_data", no=50, m=999, nmb=1000) show_images2(data=origin_data, main_title="no100m999nmb1000_data", no=100, m=999, nmb=1000) show_images2(data=origin_data, main_title="no500m999nmb1000_data", no=500, m=999, nmb=1000) show_images2(data=origin_data, main_title="no1000m999nmb1000_data", no=1000, m=999, nmb=1000) print() show_images2(data=origin_data, main_title="no25fgray_data", no=25, f=150) show_images2(data=origin_data, main_title="no25fpurple_data", no=25, f=[160, 32, 240])