Posted on Mar 16 • Edited on May 16

RandAugment in PyTorch (1)

#python #pytorch #randaugment #v2

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*Memos:

My post explains RandAugment() about num_ops and fill argument.
My post explains RandAugment() about magnitude and fill argument.
My post explains RandAugment() about num_magnitude_bins and fill argument.
My post explains TrivialAugmentWide().
My post explains AutoAugment().
My post explains AugMix() about no arguments and full argument.
My post explains OxfordIIITPet().

RandAugment() can randomly augment an image as shown below. *It's about no arguments and fill argument:

*Memos:

The 1st argument for initialization is num_ops(Optional-Default:2-Type:int). *It must be 0 <= x.
The 2nd argument for initialization is magnitude(Optional-Default:9-Type:int or tuple/list(int or float)): *Memos:
- It must be 0 <= x and 0 < num_magnitude_bins.
The 3rd argument for initialization is num_magnitude_bins(Optional-Default:31-Type:int). *It must be 1 <= x.
The 4th argument for initialization is interpolation(Optional-Default:InterpolationMode.NEAREST-Type:InterpolationMode): *Memos:
- NEAREST, NEAREST_EXACT, BILINEAR and BICUBIC modes can be used.
- My post explains InterpolationMode with and without anti-aliasing.
The 5th argument for initialization is fill(Optional-Default:None-Type:int, float or tuple/list(int or float)): *Memos:
- It can change the background of an image. *The background can be seen when 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 Image or tensor(int/float/bool)): *Memos:
- A tensor must be 3D or more D.
- Don't use img=.
v2 is recommended to use according to V1 or V2? Which one should I use?.

from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import RandAugment from torchvision.transforms.functional import InterpolationMode ra = RandAugment() ra = RandAugment(num_ops=2, magnitude=9, num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None) ra # RandAugment(interpolation=InterpolationMode.NEAREST, # num_ops=2, magnitude=9, num_magnitude_bins=31)  ra.num_ops # 2  ra.magnitude # 9  ra.num_magnitude_bins # 31  ra.interpolation # <InterpolationMode.NEAREST: 'nearest'>  print(ra.fill) # None  origin_data = OxfordIIITPet( root="data", transform=None ) noargs_data = OxfordIIITPet( # `noargs` is no arguments.  root="data", transform=RandAugment() ) fgray_data = OxfordIIITPet( # `f` is fill.  root="data", transform=RandAugment(fill=150) # transform=RandAugment(fill=[150]) ) fpurple_data = OxfordIIITPet( root="data", transform=RandAugment(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=noargs_data, main_title="noargs_data") show_images1(data=noargs_data, main_title="noargs_data") show_images1(data=noargs_data, main_title="noargs_data") show_images1(data=noargs_data, main_title="noargs_data") show_images1(data=noargs_data, main_title="noargs_data") show_images1(data=noargs_data, main_title="noargs_data") show_images1(data=noargs_data, main_title="noargs_data") show_images1(data=noargs_data, main_title="noargs_data") show_images1(data=noargs_data, main_title="noargs_data") show_images1(data=noargs_data, main_title="noargs_data") print() show_images1(data=fgray_data, main_title="fgray_data") show_images1(data=fpurple_data, main_title="fpurple_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="noargs_data") show_images2(data=origin_data, main_title="noargs_data") show_images2(data=origin_data, main_title="noargs_data") show_images2(data=origin_data, main_title="noargs_data") show_images2(data=origin_data, main_title="noargs_data") show_images2(data=origin_data, main_title="noargs_data") show_images2(data=origin_data, main_title="noargs_data") show_images2(data=origin_data, main_title="noargs_data") show_images2(data=origin_data, main_title="noargs_data") show_images2(data=origin_data, main_title="noargs_data") print() show_images2(data=origin_data, main_title="fgray_data", f=150) show_images2(data=origin_data, main_title="fpurple_data", f=[160, 32, 240])

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RandAugment in PyTorch (1)

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