*Memos:
-
My post explains RandAugment() about
num_opsargument. - My post explains OxfordIIITPet().
RandAugment() can randomly augment an image as the alternative of AutoAugment() as shown below:
from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandAugment
from torchvision.transforms.functional import InterpolationMode
origin_data = OxfordIIITPet(
root="data",
transform=None
)
m0_data = OxfordIIITPet( # `m` is magnitude.
root="data",
transform=RandAugment(magnitude=0)
)
m1_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=1)
)
m2_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=2)
)
m5_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=5)
)
m10_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=10)
)
m25_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=25)
)
no1000m0_data = OxfordIIITPet( # `no` is num_ops.
root="data",
transform=RandAugment(num_ops=1000, magnitude=0)
)
no1000m1_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=1)
)
no1000m2_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=2)
)
no1000m5_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=5)
)
no1000m10_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=10)
)
no1000m25_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=25)
)
m0nmb1000_data = OxfordIIITPet( # `nmb` is num_magnitude_bins.
root="data",
transform=RandAugment(magnitude=0, num_magnitude_bins=1000)
)
m1nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=1, num_magnitude_bins=1000)
)
m2nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=2, num_magnitude_bins=1000)
)
m5nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=5, num_magnitude_bins=1000)
)
m10nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=10, num_magnitude_bins=1000)
)
m25nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=25, num_magnitude_bins=1000)
)
m50nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=50, num_magnitude_bins=1000)
)
m100nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=100, num_magnitude_bins=1000)
)
m500nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=500, num_magnitude_bins=1000)
)
m999nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(magnitude=999, num_magnitude_bins=1000)
)
no1000m0nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=0, num_magnitude_bins=1000)
)
no1000m1nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=1, num_magnitude_bins=1000)
)
no1000m2nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=2, num_magnitude_bins=1000)
)
no1000m5nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=5, num_magnitude_bins=1000)
)
no1000m10nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=10, num_magnitude_bins=1000)
)
no1000m25nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=25, num_magnitude_bins=1000)
)
no1000m50nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=50, num_magnitude_bins=1000)
)
no1000m100nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=100, num_magnitude_bins=1000)
)
no1000m500nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=500, num_magnitude_bins=1000)
)
no1000m999nmb1000_data = OxfordIIITPet(
root="data",
transform=RandAugment(num_ops=1000, magnitude=999, num_magnitude_bins=1000)
)
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=m0_data, main_title="m0_data")
show_images1(data=m1_data, main_title="m1_data")
show_images1(data=m2_data, main_title="m2_data")
show_images1(data=m5_data, main_title="m5_data")
show_images1(data=m10_data, main_title="m10_data")
show_images1(data=m25_data, main_title="m25_data")
print()
show_images1(data=no1000m0_data, main_title="no1000m0_data")
show_images1(data=no1000m1_data, main_title="no1000m1_data")
show_images1(data=no1000m2_data, main_title="no1000m2_data")
show_images1(data=no1000m5_data, main_title="no1000m5_data")
show_images1(data=no1000m10_data, main_title="no1000m10_data")
show_images1(data=no1000m25_data, main_title="no1000m25_data")
print()
show_images1(data=m0nmb1000_data, main_title="m0nmb1000_data")
show_images1(data=m1nmb1000_data, main_title="m1nmb1000_data")
show_images1(data=m2nmb1000_data, main_title="m2nmb1000_data")
show_images1(data=m5nmb1000_data, main_title="m5nmb1000_data")
show_images1(data=m10nmb1000_data, main_title="m10nmb1000_data")
show_images1(data=m25nmb1000_data, main_title="m25nmb1000_data")
show_images1(data=m50nmb1000_data, main_title="m50nmb1000_data")
show_images1(data=m100nmb1000_data, main_title="m100nmb1000_data")
show_images1(data=m500nmb1000_data, main_title="m500nmb1000_data")
show_images1(data=m999nmb1000_data, main_title="m999nmb1000_data")
print()
show_images1(data=no1000m0nmb1000_data, main_title="no1000m0nmb1000_data")
show_images1(data=no1000m1nmb1000_data, main_title="no1000m1nmb1000_data")
show_images1(data=no1000m2nmb1000_data, main_title="no1000m2nmb1000_data")
show_images1(data=no1000m5nmb1000_data, main_title="no1000m5nmb1000_data")
show_images1(data=no1000m10nmb1000_data, main_title="no1000m10nmb1000_data")
show_images1(data=no1000m25nmb1000_data, main_title="no1000m25nmb1000_data")
show_images1(data=no1000m50nmb1000_data, main_title="no1000m50nmb1000_data")
show_images1(data=no1000m100nmb1000_data, main_title="no1000m100nmb1000_data")
show_images1(data=no1000m500nmb1000_data, main_title="no1000m500nmb1000_data")
show_images1(data=no1000m999nmb1000_data, main_title="no1000m999nmb1000_data")
# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, no=2, m=9, nmb=31,
ip=InterpolationMode.NEAREST, 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)
ra = RandAugment(num_ops=no, magnitude=m,
num_magnitude_bins=nmb,
interpolation=ip, fill=f)
plt.imshow(X=ra(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="m0_data", m=0)
show_images2(data=origin_data, main_title="m1_data", m=1)
show_images2(data=origin_data, main_title="m2_data", m=2)
show_images2(data=origin_data, main_title="m5_data", m=5)
show_images2(data=origin_data, main_title="m10_data", m=10)
show_images2(data=origin_data, main_title="m25_data", m=25)
print()
show_images2(data=origin_data, main_title="no1000m0_data", no=1000, m=0)
show_images2(data=origin_data, main_title="no1000m1_data", no=1000, m=1)
show_images2(data=origin_data, main_title="no1000m2_data", no=1000, m=2)
show_images2(data=origin_data, main_title="no1000m5_data", no=1000, m=5)
show_images2(data=origin_data, main_title="no1000m10_data", no=1000, m=10)
show_images2(data=origin_data, main_title="no1000m25_data", no=1000, m=25)
print()
show_images2(data=origin_data, main_title="m0nmb1000_data", m=0, nmb=1000)
show_images2(data=origin_data, main_title="m1nmb1000_data", m=1, nmb=1000)
show_images2(data=origin_data, main_title="m2nmb1000_data", m=2, nmb=1000)
show_images2(data=origin_data, main_title="m5nmb1000_data", m=5, nmb=1000)
show_images2(data=origin_data, main_title="m10nmb1000_data", m=10, nmb=1000)
show_images2(data=origin_data, main_title="m25nmb1000_data", m=25, nmb=1000)
show_images2(data=origin_data, main_title="m50nmb1000_data", m=50, nmb=1000)
show_images2(data=origin_data, main_title="m100nmb1000_data", m=100, nmb=1000)
show_images2(data=origin_data, main_title="m500nmb1000_data", m=500, nmb=1000)
show_images2(data=origin_data, main_title="m999nmb1000_data", m=999, nmb=1000)
print()
show_images2(data=origin_data, main_title="no1000m0nmb1000_data", no=1000, m=0,
nmb=1000)
show_images2(data=origin_data, main_title="no1000m1nmb1000_data", no=1000, m=1,
nmb=1000)
show_images2(data=origin_data, main_title="no1000m2nmb1000_data", no=1000, m=2,
nmb=1000)
show_images2(data=origin_data, main_title="no1000m5nmb1000_data", no=1000, m=5,
nmb=1000)
show_images2(data=origin_data, main_title="no1000m10nmb1000_data", no=1000,
m=10, nmb=1000)
show_images2(data=origin_data, main_title="no1000m25nmb1000_data", no=1000,
m=25, nmb=1000)
show_images2(data=origin_data, main_title="no1000m50nmb1000_data", no=1000,
m=50, nmb=1000)
show_images2(data=origin_data, main_title="no1000m100nmb1000_data", no=1000,
m=100, nmb=1000)
show_images2(data=origin_data, main_title="no1000m500nmb1000_data", no=1000,
m=500, nmb=1000)
show_images2(data=origin_data, main_title="no1000m999nmb1000_data", no=1000,
m=999, nmb=1000)
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