AugMix in PyTorch (7)

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*Memos:

My post explains AugMix() about no arguments and full argument.

My post explains AugMix() about severity argument (1).

My post explains AugMix() about severity argument (2).

My post explains AugMix() about severity argum…


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. *It's about mixture_width argument (3):

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import AugMix
from torchvision.transforms.functional import InterpolationMode

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

mw0a50_data = OxfordIIITPet( # `mw` is mixture_width and `a` is alpha.
    root="data",
    transform=AugMix(mixture_width=0, alpha=50.0)
)

mw1a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(mixture_width=1, alpha=50.0)
)

mw2a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(mixture_width=2, alpha=50.0)
)

mw5a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(mixture_width=5, alpha=50.0)
)

mw10a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(mixture_width=10, alpha=50.0)
)

mw25a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(mixture_width=25, alpha=50.0)
)

mw50a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(mixture_width=50, alpha=50.0)
)

s10mw0cd50a50_data = OxfordIIITPet( # `s` is severity and `cd` is chain_depth.
    root="data",
    transform=AugMix(severity=10, mixture_width=0, chain_depth=50, alpha=50.0)
)

s10mw1cd50a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=1, chain_depth=50, alpha=50.0)
)

s10mw2cd50a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=2, chain_depth=50, alpha=50.0)
)

s10mw5cd50a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=5, chain_depth=50, alpha=50.0)
)

s10mw10cd50a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=10, chain_depth=50, alpha=50.0)
)

s10mw25cd50a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=25, chain_depth=50, alpha=50.0)
)

s10mw50cd50a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=50, chain_depth=50, alpha=50.0)
)

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=mw0a50_data, main_title="mw0a50_data")
show_images1(data=mw1a50_data, main_title="mw1a50_data")
show_images1(data=mw2a50_data, main_title="mw2a50_data")
show_images1(data=mw5a50_data, main_title="mw5a50_data")
show_images1(data=mw10a50_data, main_title="mw10a50_data")
show_images1(data=mw25a50_data, main_title="mw25a50_data")
show_images1(data=mw50a50_data, main_title="mw50a50_data")
print()
show_images1(data=s10mw0cd50a50_data, main_title="s10mw0cd50a50_data")
show_images1(data=s10mw1cd50a50_data, main_title="s10mw1cd50a50_data")
show_images1(data=s10mw2cd50a50_data, main_title="s10mw2cd50a50_data")
show_images1(data=s10mw5cd50a50_data, main_title="s10mw5cd50a50_data")
show_images1(data=s10mw10cd50a50_data, main_title="s10mw10cd50a50_data")
show_images1(data=s10mw25cd50a50_data, main_title="s10mw25cd50a50_data")
show_images1(data=s10mw50cd50a50_data, main_title="s10mw50cd50a50_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="mw0a50_data", mw=0, a=50.0)
show_images2(data=origin_data, main_title="mw1a50_data", mw=1, a=50.0)
show_images2(data=origin_data, main_title="mw2a50_data", mw=2, a=50.0)
show_images2(data=origin_data, main_title="mw5a50_data", mw=5, a=50.0)
show_images2(data=origin_data, main_title="mw10a50_data", mw=10, a=50.0)
show_images2(data=origin_data, main_title="mw25a50_data", mw=25, a=50.0)
show_images2(data=origin_data, main_title="mw50a50_data", mw=50, a=50.0)
print()
show_images2(data=origin_data, main_title="s10mw0cd50a50_data", s=10, mw=0,
             cd=50, a=50.0)
show_images2(data=origin_data, main_title="s10mw1cd50a50_data", s=10, mw=1,
             cd=50, a=50.0)
show_images2(data=origin_data, main_title="s10mw2cd50a50_data", s=10, mw=2,
             cd=50, a=50.0)
show_images2(data=origin_data, main_title="s10mw5cd50a50_data", s=10, mw=5,
             cd=50, a=50.0)
show_images2(data=origin_data, main_title="s10mw10cd50a50_data", s=10, mw=10,
             cd=50, a=50.0)
show_images2(data=origin_data, main_title="s10mw25cd50a50_data", s=10, mw=25,
             cd=50, a=50.0)
show_images2(data=origin_data, main_title="s10mw50cd50a50_data", s=10, mw=50,
             cd=50, a=50.0)

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


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