AugMix in PyTorch (6)

#python #pytorch #augmix #v2

*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 argument (3).
My post explains AugMix() about mixture_width argument (1).
My post explains AugMix() about mixture_width argument (3).

AugMix() can randomly do AugMix to an image as shown below. *It's about mixture_width argument (2):

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

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

s10mw0_data = OxfordIIITPet( # `s` is severity and `mw` is mixture_width.
    root="data",
    transform=AugMix(severity=10, mixture_width=0)
)

s10mw1_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=1)
)

s10mw2_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=2)
)

s10mw5_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=5)
)

s10mw10_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=10)
)

s10mw25_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=25)
)

s10mw50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=10, mixture_width=50)
)

mw0cd50_data = OxfordIIITPet( # `cd` is chain_depth.
    root="data",                    
    transform=AugMix(mixture_width=0, chain_depth=50)
)

mw1cd50_data = OxfordIIITPet(
    root="data",                    
    transform=AugMix(mixture_width=1, chain_depth=50)
)

mw2cd50_data = OxfordIIITPet(
    root="data",                    
    transform=AugMix(mixture_width=2, chain_depth=50)
)

mw5cd50_data = OxfordIIITPet(
    root="data",                    
    transform=AugMix(mixture_width=5, chain_depth=50)
)

mw10cd50_data = OxfordIIITPet(
    root="data",                    
    transform=AugMix(mixture_width=10, chain_depth=50)
)

mw25cd50_data = OxfordIIITPet(
    root="data",                    
    transform=AugMix(mixture_width=25, chain_depth=50)
)

mw50cd50_data = OxfordIIITPet(
    root="data",                    
    transform=AugMix(mixture_width=50, chain_depth=50)
)

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=s10mw0_data, main_title="s10mw0_data")
show_images1(data=s10mw1_data, main_title="s10mw1_data")
show_images1(data=s10mw2_data, main_title="s10mw2_data")
show_images1(data=s10mw5_data, main_title="s10mw5_data")
show_images1(data=s10mw10_data, main_title="s10mw10_data")
show_images1(data=s10mw25_data, main_title="s10mw25_data")
show_images1(data=s10mw50_data, main_title="s10mw50_data")
print()
show_images1(data=mw0cd50_data, main_title="mw0cd50_data")
show_images1(data=mw1cd50_data, main_title="mw1cd50_data")
show_images1(data=mw2cd50_data, main_title="mw2cd50_data")
show_images1(data=mw5cd50_data, main_title="mw5cd50_data")
show_images1(data=mw10cd50_data, main_title="mw10cd50_data")
show_images1(data=mw25cd50_data, main_title="mw25cd50_data")
show_images1(data=mw50cd50_data, main_title="mw50cd50_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="s10mw0_data", s=10, mw=0)
show_images2(data=origin_data, main_title="s10mw1_data", s=10, mw=1)
show_images2(data=origin_data, main_title="s10mw2_data", s=10, mw=2)
show_images2(data=origin_data, main_title="s10mw5_data", s=10, mw=5)
show_images2(data=origin_data, main_title="s10mw10_data", s=10, mw=10)
show_images2(data=origin_data, main_title="s10mw25_data", s=10, mw=25)
show_images2(data=origin_data, main_title="s10mw50_data", s=10, mw=50)
print()
show_images2(data=origin_data, main_title="mw0cd50_data", mw=0, cd=50)
show_images2(data=origin_data, main_title="mw1cd50_data", mw=1, cd=50)
show_images2(data=origin_data, main_title="mw2cd50_data", mw=2, cd=50)
show_images2(data=origin_data, main_title="mw5cd50_data", mw=5, cd=50)
show_images2(data=origin_data, main_title="mw10cd50_data", mw=10, cd=50)
show_images2(data=origin_data, main_title="mw25cd50_data", mw=25, cd=50)
show_images2(data=origin_data, main_title="mw50cd50_data", mw=50, cd=50)