*Memos:
My post explains OxfordIIITPet().
AutoAugment() can randomly augment an image with AutoAugmentPolicy() as shown below:
*Memos:
- The 1st argument for initialization is
policy(Optional-Default:AutoAugmentPolicy.IMAGENET-Type:AutoAugmentPolicy). *AutoAugmentPolicy.IMAGENET,AutoAugmentPolicy.CIFAR10orAutoAugmentPolicy.SVHNcan be set to it. - The 2nd argument for initialization is
interpolation(Optional-Default:InterpolationMode.NEAREST-Type:InterpolationMode). *If the input is a tensor, onlyInterpolationMode.NEAREST,InterpolationMode.BILINEARcan be set to it. - The 3rd argument for initialization is
fill(Optional-Default:0-Type:int,floatortuple/list(intorfloat)):
*Memos:
- It can change the background of an image.
- A tuple/list must be the 1D with 1 or 3 elements.
- The 1st argument is
img(Required-Type:PIL Imageortensor(int)):
*Memos:
- A tensor must be 2D or 3D.
- Don't use
img=.
v2is recommended to use according to V1 or V2? Which one should I use?.
from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import AutoAugment
from torchvision.transforms.v2 import AutoAugmentPolicy
from torchvision.transforms.functional import InterpolationMode
aa = AutoAugment()
aa = AutoAugment(policy=AutoAugmentPolicy.IMAGENET,
interpolation = InterpolationMode.NEAREST,
fill=None)
aa
# AutoAugment(interpolation=InterpolationMode.NEAREST,
# policy=AutoAugmentPolicy.IMAGENET)
aa.policy
# <AutoAugmentPolicy.IMAGENET: 'imagenet'>
aa.interpolation
# <InterpolationMode.NEAREST: 'nearest'>
print(aa.fill)
# None
origin_data = OxfordIIITPet(
root="data",
transform=None
)
pIMAGENET_data = OxfordIIITPet( # `p` is policy.
root="data",
transform=AutoAugment(policy=AutoAugmentPolicy.IMAGENET)
)
pCIFAR10_data = OxfordIIITPet(
root="data",
transform=AutoAugment(policy=AutoAugmentPolicy.CIFAR10)
)
pSVHN_data = OxfordIIITPet(
root="data",
transform=AutoAugment(policy=AutoAugmentPolicy.SVHN)
)
pIMAGENETf150_data = OxfordIIITPet(
root="data",
transform=AutoAugment(policy=AutoAugmentPolicy.IMAGENET, fill=150)
)
pIMAGENETf160_32_240_data = OxfordIIITPet(
root="data",
transform=AutoAugment(policy=AutoAugmentPolicy.IMAGENET,
fill=[160, 32, 240])
)
pCIFAR10f150_data = OxfordIIITPet(
root="data",
transform=AutoAugment(policy=AutoAugmentPolicy.CIFAR10, fill=150)
)
pCIFAR10f160_32_240_data = OxfordIIITPet(
root="data",
transform=AutoAugment(policy=AutoAugmentPolicy.CIFAR10,
fill=[160, 32, 240])
)
pSVHNf150_data = OxfordIIITPet(
root="data",
transform=AutoAugment(policy=AutoAugmentPolicy.SVHN, fill=150)
)
pSVHNf160_32_240_data = OxfordIIITPet(
root="data",
transform=AutoAugment(policy=AutoAugmentPolicy.SVHN,
fill=[160, 32, 240])
)
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=pIMAGENET_data, main_title="pIMAGENET_data")
show_images1(data=pIMAGENET_data, main_title="pIMAGENET_data")
show_images1(data=pIMAGENET_data, main_title="pIMAGENET_data")
print()
show_images1(data=pCIFAR10_data, main_title="pCIFAR10_data")
show_images1(data=pCIFAR10_data, main_title="pCIFAR10_data")
show_images1(data=pCIFAR10_data, main_title="pCIFAR10_data")
print()
show_images1(data=pSVHN_data, main_title="pSVHN_data")
show_images1(data=pSVHN_data, main_title="pSVHN_data")
show_images1(data=pSVHN_data, main_title="pSVHN_data")
print()
show_images1(data=pIMAGENETf150_data, main_title="pIMAGENETf150_data")
show_images1(data=pIMAGENETf160_32_240_data,
main_title="pIMAGENETf160_32_240_data")
print()
show_images1(data=pCIFAR10f150_data, main_title="pCIFAR10f150_data")
show_images1(data=pCIFAR10f160_32_240_data,
main_title="pCIFAR10f160_32_240_data")
print()
show_images1(data=pSVHNf150_data, main_title="pSVHNf150_data")
show_images1(data=pSVHNf160_32_240_data,
main_title="pSVHNf160_32_240_data")
# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, p=None,
ip=InterpolationMode.NEAREST,
f=None):
plt.figure(figsize=[10, 5])
plt.suptitle(t=main_title, y=0.8, fontsize=14)
if p != None:
for i, (im, _) in zip(range(1, 6), data):
plt.subplot(1, 5, i)
aa = AutoAugment(policy=p, interpolation=ip, fill=f)
plt.imshow(X=aa(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="pIMAGENET_data",
p=AutoAugmentPolicy.IMAGENET)
show_images2(data=origin_data, main_title="pIMAGENET_data",
p=AutoAugmentPolicy.IMAGENET)
show_images2(data=origin_data, main_title="pIMAGENET_data",
p=AutoAugmentPolicy.IMAGENET)
print()
show_images2(data=origin_data, main_title="pCIFAR10_data",
p=AutoAugmentPolicy.CIFAR10)
show_images2(data=origin_data, main_title="pCIFAR10_data",
p=AutoAugmentPolicy.CIFAR10)
show_images2(data=origin_data, main_title="pCIFAR10_data",
p=AutoAugmentPolicy.CIFAR10)
print()
show_images2(data=origin_data, main_title="pSVHN_data",
p=AutoAugmentPolicy.SVHN)
show_images2(data=origin_data, main_title="pSVHN_data",
p=AutoAugmentPolicy.SVHN)
show_images2(data=origin_data, main_title="pSVHN_data",
p=AutoAugmentPolicy.SVHN)
print()
show_images2(data=origin_data, main_title="pIMAGENETf150_data",
p=AutoAugmentPolicy.IMAGENET, f=150)
show_images2(data=origin_data, main_title="pIMAGENETf160_32_240_data",
p=AutoAugmentPolicy.IMAGENET, f=[160, 32, 240])
print()
show_images2(data=origin_data, main_title="pCIFAR10f150_data",
p=AutoAugmentPolicy.CIFAR10, f=150)
show_images2(data=origin_data, main_title="pCIFAR10f160_32_240_data",
p=AutoAugmentPolicy.CIFAR10, f=[160, 32, 240])
print()
show_images2(data=origin_data, main_title="pSVHNf150_data",
p=AutoAugmentPolicy.SVHN, f=150)
show_images2(data=origin_data, main_title="pSVHNf160_32_240_data",
p=AutoAugmentPolicy.SVHN, f=[160, 32, 240])
