AugMix in PyTorch (15)

Buy Me a Coffee☕ 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 (2). My post explains AugMix() about mixture_width argument (3). My post explains AugMix() about chain_depth argument (1). My post explains AugMix() about chain_depth argument (2). My post explains AugMix() about chain_depth argument (3). My post explains AugMix() about alpha argument (1). My post explains AugMix() about alpha argument (2). My post explains AugMix() about alpha argument (3). My post explains AugMix() about severity argument with mixture_width=0, chain_depth=0 and alpha=0.0 and mixture_width argument with severity=1, chain_depth=0 and alpha=0.0. AugMix() can randomly do AugMix to an image as shown below. It's about chain_depth argument with severity=1, mixture_width=0 and alpha=0.0 and alpha argument with severity=1, mixture_width=0 and chain_depth=0: from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import AugMix from torchvision.transforms.functional import InterpolationMode origin_data = OxfordIIITPet( root="data", transform=None ) s1mw0cd0a0_data = OxfordIIITPet( # `s` is severity and `mw` is mixture_width. root="data", # `cd` is chain_depth and `a` is alpha. transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=0.0) ) s1mw0cd1a0_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=1, alpha=0.0) ) s1mw0cd2a0_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=2, alpha=0.0) ) s1mw0cd5a0_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=5, alpha=0.0) ) s1mw0cd10a0_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=10, alpha=0.0) ) s1mw0cd25a0_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=25, alpha=0.0) ) s1mw0cd50a0_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=50, alpha=0.0) ) s1mw0cd0a1_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=1.0) ) s1mw0cd0a2_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=2.0) ) s1mw0cd0a5_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=5.0) ) s1mw0cd0a10_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=10.0) ) s1mw0cd0a25_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=25.0) ) s1mw0cd0a50_data = OxfordIIITPet( root="data", transform=AugMix(severity=1, mixture_width=0, chain_depth=0, 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=s1mw0cd0a0_data, main_title="s1mw0cd0a0_data") show_images1(data=s1mw0cd1a0_data, main_title="s1mw0cd1a0_data") show_images1(data=s1mw0cd2a0_data, main_title="s1mw0cd2a0_data") show_images1(data=s1mw0cd5a0_data, main_title="s1mw0cd5a0_data") show_images1(data=s1mw0cd10a0_data, main_title="s1mw0cd10a0_data") show_images1(data=s1mw0cd25a0_data, main_title="s1mw0cd25a0_data") show_images1(data=s1mw0cd50a0_data, main_title="s1mw0cd50a0_data") print() show_images1(data=s1mw0cd0a0_data, main_title="s1mw0cd0a0_data") show_images1(data=s1mw0cd0a1_data, main_title="s1mw0cd0a1_data") show_images1(data=s1mw0cd0a2_data, main_title="s1mw0cd0a2_data") show_images1(data=s1mw0cd0a5_data, main_title="s1mw0cd0a5_data") show_images1(data=s1mw0cd0a10_data, main_title="s1mw0cd0a10_data") show_images1(data=s1mw0cd0a25_data, main_title="s1mw0cd0a25_data") show_images1(data=s1mw0cd0a50_data, main_title="s1mw0cd0a50_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, chai

Apr 13, 2025 - 07:05

*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 (2).
My post explains AugMix() about mixture_width argument (3).
My post explains AugMix() about chain_depth argument (1).
My post explains AugMix() about chain_depth argument (2).
My post explains AugMix() about chain_depth argument (3).
My post explains AugMix() about alpha argument (1).
My post explains AugMix() about alpha argument (2).
My post explains AugMix() about alpha argument (3).
My post explains AugMix() about severity argument with mixture_width=0, chain_depth=0 and alpha=0.0 and mixture_width argument with severity=1, chain_depth=0 and alpha=0.0.

AugMix() can randomly do AugMix to an image as shown below. *It's about chain_depth argument with severity=1, mixture_width=0 and alpha=0.0 and alpha argument with severity=1, mixture_width=0 and chain_depth=0:

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

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

s1mw0cd0a0_data = OxfordIIITPet( # `s` is severity and `mw` is mixture_width.
    root="data",                 # `cd` is chain_depth and `a` is alpha. 
    transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=0.0)
)

s1mw0cd1a0_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=1, alpha=0.0)
)

s1mw0cd2a0_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=2, alpha=0.0)
)

s1mw0cd5a0_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=5, alpha=0.0)
)

s1mw0cd10a0_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=10, alpha=0.0)
)

s1mw0cd25a0_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=25, alpha=0.0)
)

s1mw0cd50a0_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=50, alpha=0.0)
)

s1mw0cd0a1_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=1.0)
)

s1mw0cd0a2_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=2.0)
)

s1mw0cd0a5_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=5.0)
)

s1mw0cd0a10_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=10.0)
)

s1mw0cd0a25_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=0, alpha=25.0)
)

s1mw0cd0a50_data = OxfordIIITPet(
    root="data",
    transform=AugMix(severity=1, mixture_width=0, chain_depth=0, 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=s1mw0cd0a0_data, main_title="s1mw0cd0a0_data")
show_images1(data=s1mw0cd1a0_data, main_title="s1mw0cd1a0_data")
show_images1(data=s1mw0cd2a0_data, main_title="s1mw0cd2a0_data")
show_images1(data=s1mw0cd5a0_data, main_title="s1mw0cd5a0_data")
show_images1(data=s1mw0cd10a0_data, main_title="s1mw0cd10a0_data")
show_images1(data=s1mw0cd25a0_data, main_title="s1mw0cd25a0_data")
show_images1(data=s1mw0cd50a0_data, main_title="s1mw0cd50a0_data")
print()
show_images1(data=s1mw0cd0a0_data, main_title="s1mw0cd0a0_data")
show_images1(data=s1mw0cd0a1_data, main_title="s1mw0cd0a1_data")
show_images1(data=s1mw0cd0a2_data, main_title="s1mw0cd0a2_data")
show_images1(data=s1mw0cd0a5_data, main_title="s1mw0cd0a5_data")
show_images1(data=s1mw0cd0a10_data, main_title="s1mw0cd0a10_data")
show_images1(data=s1mw0cd0a25_data, main_title="s1mw0cd0a25_data")
show_images1(data=s1mw0cd0a50_data, main_title="s1mw0cd0a50_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="s1mw0cd0a0_data", s=1, mw=0, cd=0,
             a=0.0)
show_images2(data=origin_data, main_title="s1mw0cd1a0_data", s=1, mw=0, cd=1,
             a=0.0)
show_images2(data=origin_data, main_title="s1mw0cd2a0_data", s=1, mw=0, cd=2,
             a=0.0)
show_images2(data=origin_data, main_title="s1mw0cd5a0_data", s=1, mw=0, cd=5,
             a=0.0)
show_images2(data=origin_data, main_title="s1mw0cd10a0_data", s=1, mw=0, cd=10,
             a=0.0)
show_images2(data=origin_data, main_title="s1mw0cd25a0_data", s=1, mw=0, cd=25,
             a=0.0)
show_images2(data=origin_data, main_title="s1mw0cd50a0_data", s=1, mw=0, cd=50,
             a=0.0)
print()
show_images2(data=origin_data, main_title="s1mw0cd0a0_data", s=1, mw=0, cd=0,
             a=0.0)
show_images2(data=origin_data, main_title="s1mw0cd0a1_data", s=1, mw=0, cd=0,
             a=1.0)
show_images2(data=origin_data, main_title="s1mw0cd0a2_data", s=1, mw=0, cd=0,
             a=2.0)
show_images2(data=origin_data, main_title="s1mw0cd0a5_data", s=1, mw=0, cd=0,
             a=5.0)
show_images2(data=origin_data, main_title="s1mw0cd0a10_data", s=1, mw=0, cd=0,
             a=10.0)
show_images2(data=origin_data, main_title="s1mw0cd0a25_data", s=1, mw=0, cd=0,
             a=25.0)
show_images2(data=origin_data, main_title="s1mw0cd0a50_data", s=1, mw=0, cd=0,
             a=50.0)