*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 (2). -
My post explains AugMix() about
alpha
argument (3). -
My post explains AugMix() about
severity
argument withmixture_width=0
,chain_depth=0
andalpha=0.0
andmixture_width
argument withseverity=1
,chain_depth=0
andalpha=0.0
. -
My post explains AugMix() about
chain_depth
argument withseverity=1
,mixture_width=0
andalpha=0.0
andalpha
argument withseverity=1
,mixture_width=0
andchain_depth=0
.
AugMix() can randomly do AugMix to an image as shown below. *It's about alpha
argument (1):
from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import AugMix
from torchvision.transforms.functional import InterpolationMode
origin_data = OxfordIIITPet(
root="data",
transform=None
)
a0_data = OxfordIIITPet( # `a` is alpha.
root="data",
transform=AugMix(alpha=0.0)
)
a1_data = OxfordIIITPet(
root="data",
transform=AugMix(alpha=1.0)
)
a2_data = OxfordIIITPet(
root="data",
transform=AugMix(alpha=2.0)
)
a5_data = OxfordIIITPet(
root="data",
transform=AugMix(alpha=5.0)
)
a10_data = OxfordIIITPet(
root="data",
transform=AugMix(alpha=10.0)
)
a25_data = OxfordIIITPet(
root="data",
transform=AugMix(alpha=25.0)
)
a50_data = OxfordIIITPet(
root="data",
transform=AugMix(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=a0_data, main_title="a0_data")
show_images1(data=a1_data, main_title="a1_data")
show_images1(data=a2_data, main_title="a2_data")
show_images1(data=a5_data, main_title="a5_data")
show_images1(data=a10_data, main_title="a10_data")
show_images1(data=a25_data, main_title="a25_data")
show_images1(data=a50_data, main_title="a50_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="a0_data", a=0.0)
show_images2(data=origin_data, main_title="a1_data", a=1.0)
show_images2(data=origin_data, main_title="a2_data", a=2.0)
show_images2(data=origin_data, main_title="a5_data", a=5.0)
show_images2(data=origin_data, main_title="a10_data", a=10.0)
show_images2(data=origin_data, main_title="a25_data", a=25.0)
show_images2(data=origin_data, main_title="a50_data", a=50.0)
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