*Memos:
- My post explains RandAugment() about
num_opsandfillargument. - My post explains AutoAugment().
- My post explains AugMix() about no arguments and
fullargument. - My post explains OxfordIIITPet().
TrivialAugmentWide() can randomly trivial-augment an image as shown below:
*Memos:
- The 1st argument for initialization is
num_magnitude_bins(Optional-Default:31-Type:int). *It must be1 <= x. - The 2nd argument for initialization is
interpolation(Optional-Default:InterpolationMode.NEAREST-Type:InterpolationMode): *Memos:-
NEAREST,NEAREST_EXACT,BILINEARandBICUBICmodes can be used. - My post explains InterpolationMode with and without anti-aliasing.
-
- The 3rd argument for initialization is
fill(Optional-Default:None-Type:int,floatortuple/list(intorfloat)): *Memos:- It can change the background of an image. *The background can be seen when trivial-augmenting an image.
- A tuple/list must be the 1D with 1 or 3 elements.
- If all values are
x <= 0, it's black. - If all values are
255 <= x, it's white.
- The 1st argument is
img(Required-Type:PIL Imageortensor(int/float/complex/bool)): *Memos:- A tensor must be 2D or more D.
- Don't use
img=.
-
v2is recommended to use according to V1 or V2? Which one should I use?.
from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import TrivialAugmentWide from torchvision.transforms.functional import InterpolationMode taw = TrivialAugmentWide() taw = TrivialAugmentWide(num_magnitude_bins = 31, interpolation = InterpolationMode.NEAREST, fill= None) taw # TrivialAugmentWide(interpolation=InterpolationMode.NEAREST, # num_magnitude_bins=31) taw.num_magnitude_bins # 31 taw.interpolation # <InterpolationMode.NEAREST: 'nearest'> print(taw.fill) # None origin_data = OxfordIIITPet( root="data", transform=None ) nmb1_data = OxfordIIITPet( # `nmb` is num_magnitude_bins. root="data", transform=TrivialAugmentWide(num_magnitude_bins=1) ) nmb2_data = OxfordIIITPet( root="data", transform=TrivialAugmentWide(num_magnitude_bins=2) ) nmb5_data = OxfordIIITPet( root="data", transform=TrivialAugmentWide(num_magnitude_bins=5) ) nmb10_data = OxfordIIITPet( root="data", transform=TrivialAugmentWide(num_magnitude_bins=10) ) nmb25_data = OxfordIIITPet( root="data", transform=TrivialAugmentWide(num_magnitude_bins=25) ) nmb50_data = OxfordIIITPet( root="data", transform=TrivialAugmentWide(num_magnitude_bins=50) ) nmb100_data = OxfordIIITPet( root="data", transform=TrivialAugmentWide(num_magnitude_bins=100) ) nmb500_data = OxfordIIITPet( root="data", transform=TrivialAugmentWide(num_magnitude_bins=500) ) nmb1000_data = OxfordIIITPet( root="data", transform=TrivialAugmentWide(num_magnitude_bins=1000) ) nmb10fgray_data = OxfordIIITPet( # `f` is fill. root="data", transform=TrivialAugmentWide(num_magnitude_bins=10, fill=150) # transform=TrivialAugmentWide(num_magnitude_bins=10, fill=[150]) ) nmb10fpurple_data = OxfordIIITPet( root="data", transform=TrivialAugmentWide(num_magnitude_bins=10, 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=nmb1_data, main_title="nmb1_data") show_images1(data=nmb2_data, main_title="nmb2_data") show_images1(data=nmb5_data, main_title="nmb5_data") show_images1(data=nmb10_data, main_title="nmb10_data") show_images1(data=nmb25_data, main_title="nmb25_data") show_images1(data=nmb50_data, main_title="nmb50_data") show_images1(data=nmb100_data, main_title="nmb100_data") show_images1(data=nmb500_data, main_title="nmb500_data") show_images1(data=nmb1000_data, main_title="nmb1000_data") print() show_images1(data=nmb10fgray_data, main_title="nmb10fgray_data") show_images1(data=nmb10fpurple_data, main_title="nmb10fpurple_data") # ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓ def show_images2(data, main_title=None, 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 = TrivialAugmentWide(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="nmb1_data", nmb=1) show_images2(data=origin_data, main_title="nmb2_data", nmb=2) show_images2(data=origin_data, main_title="nmb5_data", nmb=5) show_images2(data=origin_data, main_title="nmb10_data", nmb=10) show_images2(data=origin_data, main_title="nmb25_data", nmb=25) show_images2(data=origin_data, main_title="nmb50_data", nmb=50) show_images2(data=origin_data, main_title="nmb100_data", nmb=100) show_images2(data=origin_data, main_title="nmb500_data", nmb=500) show_images2(data=origin_data, main_title="nmb1000_data", nmb=1000) print() show_images2(data=origin_data, main_title="nmb10fgray_data", nmb=10, f=150) show_images2(data=origin_data, main_title="nmb10fpurple_data", nmb=10, f=[160, 32, 240]) 
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