AugMix in PyTorch (1)

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*Memos:

My post explains RandAugment() about num_ops and fill argument.

My post explains AutoAugment().

My post explains TrivialAugmentWide().

My post explains OxfordIIITPet().

AugMix() can randomly do AugMix to an image as sh…


This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)

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*Memos:

AugMix() can randomly do AugMix to an image as shown below:

*Memos:

  • The 1st argument for initialization is severity(Optional-Default:3-Type:int). *It must be 1 <= x <= 10.
  • The 2nd argument for initialization is mixture_width(Optional-Default:3-Type:int).
  • The 3rd argument for initialization is chain_depth(Optional-Default:-1-Type:int). *If it's x <= 0, it's randomly taken from the interval [1, 3].
  • The 4th argument for initialization is alpha(Optional-Default:1.0-Type:float). *It must be 1 <= x.
  • The 5th argument for initialization is all_ops(Optional-Default:True-Type:bool). *It must be 1 <= x.
  • The 6th argument for initialization is interpolation(Optional-Default:InterpolationMode.NEAREST-Type:InterpolationMode). *If the input is a tensor, only InterpolationMode.NEAREST and InterpolationMode.BILINEAR can be set to it.
  • The 7th argument for initialization is fill(Optional-Default:0-Type:int, float or tuple/list(int or float)): *Memos:
    • It can change the background of an image. *The background can be seen when doing AugMix to 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)): *Memos:
    • A tensor must be 2D or 3D.
    • 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 AugMix
from torchvision.transforms.functional import InterpolationMode

am = AugMix()
am = AugMix(severity=3, mixture_width=3, chain_depth=-1, alpha=1.0, 
            all_ops=True, interpolation=InterpolationMode.BILINEAR,
            fill=None)
am
# AugMix(interpolation=InterpolationMode.BILINEAR, severity=3,
#        mixture_width=3, chain_depth=-1, alpha=1.0, all_ops=True)

am.severity
# 3

am.mixture_width
# 3

am.chain_depth
# -1

am.alpha
# 1.0

am.all_ops
# True

am.interpolation
# <InterpolationMode.BILINEAR: 'bilinear'>

print(am.fill)
# None

origin_data = OxfordIIITPet(
    root="data",
    transform=None
)

default_data = OxfordIIITPet(
    root="data",
    transform=AugMix()
)

aoFalse_data = OxfordIIITPet( # `ao` is all_ops.
    root="data",
    transform=AugMix(all_ops=False)
    # transform=AugMix(severity=3, mixture_width=3, chain_depth=-1, 
    #                  alpha=1.0, all_ops=True,
    #                  interpolation=InterpolationMode.BILINEAR,
    #                  fill=None)
)

s10cd25fgray_data = OxfordIIITPet( # `s` is severity and `cd` is chain_depth.
    root="data",                   # `f` is fill.
    transform=AugMix(severity=10, chain_depth=25, fill=150)
)

s10cd25fpurple_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, chain_depth=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=default_data, main_title="default_data")
show_images1(data=default_data, main_title="default_data")
show_images1(data=default_data, main_title="default_data")
show_images1(data=default_data, main_title="default_data")
show_images1(data=default_data, main_title="default_data")
show_images1(data=default_data, main_title="default_data")
show_images1(data=default_data, main_title="default_data")
show_images1(data=default_data, main_title="default_data")
show_images1(data=default_data, main_title="default_data")
show_images1(data=default_data, main_title="default_data")
print()
show_images1(data=aoFalse_data, main_title="aoFalse_data")
show_images1(data=aoFalse_data, main_title="aoFalse_data")
show_images1(data=aoFalse_data, main_title="aoFalse_data")
show_images1(data=aoFalse_data, main_title="aoFalse_data")
show_images1(data=aoFalse_data, main_title="aoFalse_data")
show_images1(data=aoFalse_data, main_title="aoFalse_data")
show_images1(data=aoFalse_data, main_title="aoFalse_data")
show_images1(data=aoFalse_data, main_title="aoFalse_data")
show_images1(data=aoFalse_data, main_title="aoFalse_data")
show_images1(data=aoFalse_data, main_title="aoFalse_data")
print()
show_images1(data=s10cd25fgray_data, main_title="s10cd25fgray_data")
show_images1(data=s10cd25fpurple_data, main_title="s10cd25fpurple_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="default_data")
show_images2(data=origin_data, main_title="default_data")
show_images2(data=origin_data, main_title="default_data")
show_images2(data=origin_data, main_title="default_data")
show_images2(data=origin_data, main_title="default_data")
show_images2(data=origin_data, main_title="default_data")
show_images2(data=origin_data, main_title="default_data")
show_images2(data=origin_data, main_title="default_data")
show_images2(data=origin_data, main_title="default_data")
show_images2(data=origin_data, main_title="default_data")
print()
show_images2(data=origin_data, main_title="aoFalse_data", ao=False)
show_images2(data=origin_data, main_title="aoFalse_data", ao=False)
show_images2(data=origin_data, main_title="aoFalse_data", ao=False)
show_images2(data=origin_data, main_title="aoFalse_data", ao=False)
show_images2(data=origin_data, main_title="aoFalse_data", ao=False)
show_images2(data=origin_data, main_title="aoFalse_data", ao=False)
show_images2(data=origin_data, main_title="aoFalse_data", ao=False)
show_images2(data=origin_data, main_title="aoFalse_data", ao=False)
show_images2(data=origin_data, main_title="aoFalse_data", ao=False)
show_images2(data=origin_data, main_title="aoFalse_data", ao=False)
print()
show_images2(data=origin_data, main_title="s10cd25fgray_data", s=10, cd=25,
             f=150)
show_images2(data=origin_data, main_title="s10cd25fpurple_data", s=10, 
             cd=25, f=[160, 32, 240])

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This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)


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Super Kai (Kazuya Ito) | Sciencx (2025-03-07T23:30:15+00:00) AugMix in PyTorch (1). Retrieved from https://www.scien.cx/2025/03/07/augmix-in-pytorch-1/

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