Pad in PyTorch
Buy Me a Coffee☕ *Memos: My post explains OxfordIIITPet(). Pad() can add padding to zero or more images as shown below: *Memos: The 1st argument for initialization is padding(Required-Type:int or tuple/list(int)): *Memos: It can do add padding. A tuple/list must be the 1D with 2 or 4 elements. The 2nd argument for initialization is fill(Optional-Default:0-Type:int, float or tuple/list(int or float)): *Memos: It can change the background of images. *The background can be seen when adding padding for images. A tuple/list must be the 1D with 3 elements. The 3rd argument for initialization is padding_mode(Optional-Default:'constant'-Type:str). *'constant', 'edge', 'reflect' or 'symmetric' can be set to it. There is the 1st argument(Required-Type:PIL Image or tensor(int)). *It must be a 3D or more D tensor. v2 is recommended to use according to V1 or V2? Which one should I use?. from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import Pad pad = Pad(padding=100) pad = Pad(padding=100, fill=0, padding_mode='constant') pad # Pad(padding=100, fill=0, padding_mode=constant) pad.padding # 100 pad.fill # 0 pad.padding_mode # 'constant' origin_data = OxfordIIITPet( root="data", transform=None # transform=Pad(padding=0) ) p50_data = OxfordIIITPet( root="data", transform=Pad(padding=50) ) p100_data = OxfordIIITPet( root="data", transform=Pad(padding=100) ) p150_data = OxfordIIITPet( root="data", transform=Pad(padding=150) ) m50_data = OxfordIIITPet( root="data", transform=Pad(padding=-50) ) m100_data = OxfordIIITPet( root="data", transform=Pad(padding=-100) ) m150_data = OxfordIIITPet( root="data", transform=Pad(padding=-150) ) p100p50_data = OxfordIIITPet( root="data", transform=Pad(padding=[100, 50]) ) m100m50_data = OxfordIIITPet( root="data", transform=Pad(padding=[-100, -50]) ) p100m50_data = OxfordIIITPet( root="data", transform=Pad(padding=[100, -50]) ) p25p50p75p100_data = OxfordIIITPet( root="data", transform=Pad(padding=[25, 50, 75, 100]) ) m25m50m75m100_data = OxfordIIITPet( root="data", transform=Pad(padding=[-25, -50, -75, -100]) ) p25m50p75m100_data = OxfordIIITPet( root="data", transform=Pad(padding=[25, -50, 75, -100]) ) p100fillgray_data = OxfordIIITPet( root="data", transform=Pad(padding=100, fill=150) ) p100fillpurple_data = OxfordIIITPet( root="data", transform=Pad(padding=100, fill=[160, 32, 240]) ) p100edge_data = OxfordIIITPet( root="data", transform=Pad(padding=100, padding_mode="edge") ) p100reflect_data = OxfordIIITPet( root="data", transform=Pad(padding=100, padding_mode="reflect") ) p100symmetric_data = OxfordIIITPet( root="data", transform=Pad(padding=100, padding_mode="symmetric") ) 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') show_images1(data=p50_data, main_title='p50_data') show_images1(data=p100_data, main_title='p100_data') show_images1(data=p150_data, main_title='p150_data') print() show_images1(data=origin_data, main_title='origin_data') show_images1(data=m50_data, main_title='m50_data') show_images1(data=m100_data, main_title='m100_data') show_images1(data=m150_data, main_title='m150_data') print() show_images1(data=origin_data, main_title='origin_data') show_images1(data=p100p50_data, main_title='p100p50_data') show_images1(data=m100m50_data, main_title='m100m50_data') show_images1(data=p100m50_data, main_title='p100m50_data') print() show_images1(data=origin_data, main_title='origin_data') show_images1(data=p25p50p75p100_data, main_title='p25p50p75p100_data') show_images1(data=m25m50m75m100_data, main_title='m25m50m75m100_data') show_images1(data=p25m50p75m100_data, main_title='p25m50p75m100_data') print() show_images1(data=p100fillgray_data, main_title='p100fillgray_data') show_images1(data=p100fillpurple_data, main_title='p100fillpurple_data') print() show_images1(data=p100edge_data, main_title='p100edge_data') show_images1(data=p100reflect_data, main_title='p100reflect_data') show_images1(data=p100symmetric_data, main_title='p100symmetric_data') # ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓ def show_images2(data, main_title=None, p=0, f=0, pm='constant'): 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) pad = Pad(padding=p, fill=f, padding_mode=pm) # Here plt.imshow(X=pad(im)) # Here plt.xticks(ticks=[]) plt.yticks(ticks=[])
*Memos:
- My post explains OxfordIIITPet().
Pad() can add padding to zero or more images as shown below:
*Memos:
- The 1st argument for initialization is
padding(Required-Type:intortuple/list(int)): *Memos:- It can do add padding.
- A tuple/list must be the 1D with 2 or 4 elements.
- The 2nd argument for initialization is
fill(Optional-Default:0-Type:int,floatortuple/list(intorfloat)): *Memos:- It can change the background of images. *The background can be seen when adding padding for images.
- A tuple/list must be the 1D with 3 elements.
- The 3rd argument for initialization is
padding_mode(Optional-Default:'constant'-Type:str). *'constant','edge','reflect'or'symmetric'can be set to it. - There is the 1st argument(Required-Type:
PIL Imageortensor(int)). *It must be a 3D or more D tensor. -
v2is recommended to use according to V1 or V2? Which one should I use?.
from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import Pad
pad = Pad(padding=100)
pad = Pad(padding=100, fill=0, padding_mode='constant')
pad
# Pad(padding=100, fill=0, padding_mode=constant)
pad.padding
# 100
pad.fill
# 0
pad.padding_mode
# 'constant'
origin_data = OxfordIIITPet(
root="data",
transform=None
# transform=Pad(padding=0)
)
p50_data = OxfordIIITPet(
root="data",
transform=Pad(padding=50)
)
p100_data = OxfordIIITPet(
root="data",
transform=Pad(padding=100)
)
p150_data = OxfordIIITPet(
root="data",
transform=Pad(padding=150)
)
m50_data = OxfordIIITPet(
root="data",
transform=Pad(padding=-50)
)
m100_data = OxfordIIITPet(
root="data",
transform=Pad(padding=-100)
)
m150_data = OxfordIIITPet(
root="data",
transform=Pad(padding=-150)
)
p100p50_data = OxfordIIITPet(
root="data",
transform=Pad(padding=[100, 50])
)
m100m50_data = OxfordIIITPet(
root="data",
transform=Pad(padding=[-100, -50])
)
p100m50_data = OxfordIIITPet(
root="data",
transform=Pad(padding=[100, -50])
)
p25p50p75p100_data = OxfordIIITPet(
root="data",
transform=Pad(padding=[25, 50, 75, 100])
)
m25m50m75m100_data = OxfordIIITPet(
root="data",
transform=Pad(padding=[-25, -50, -75, -100])
)
p25m50p75m100_data = OxfordIIITPet(
root="data",
transform=Pad(padding=[25, -50, 75, -100])
)
p100fillgray_data = OxfordIIITPet(
root="data",
transform=Pad(padding=100, fill=150)
)
p100fillpurple_data = OxfordIIITPet(
root="data",
transform=Pad(padding=100, fill=[160, 32, 240])
)
p100edge_data = OxfordIIITPet(
root="data",
transform=Pad(padding=100, padding_mode="edge")
)
p100reflect_data = OxfordIIITPet(
root="data",
transform=Pad(padding=100, padding_mode="reflect")
)
p100symmetric_data = OxfordIIITPet(
root="data",
transform=Pad(padding=100, padding_mode="symmetric")
)
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')
show_images1(data=p50_data, main_title='p50_data')
show_images1(data=p100_data, main_title='p100_data')
show_images1(data=p150_data, main_title='p150_data')
print()
show_images1(data=origin_data, main_title='origin_data')
show_images1(data=m50_data, main_title='m50_data')
show_images1(data=m100_data, main_title='m100_data')
show_images1(data=m150_data, main_title='m150_data')
print()
show_images1(data=origin_data, main_title='origin_data')
show_images1(data=p100p50_data, main_title='p100p50_data')
show_images1(data=m100m50_data, main_title='m100m50_data')
show_images1(data=p100m50_data, main_title='p100m50_data')
print()
show_images1(data=origin_data, main_title='origin_data')
show_images1(data=p25p50p75p100_data, main_title='p25p50p75p100_data')
show_images1(data=m25m50m75m100_data, main_title='m25m50m75m100_data')
show_images1(data=p25m50p75m100_data, main_title='p25m50p75m100_data')
print()
show_images1(data=p100fillgray_data, main_title='p100fillgray_data')
show_images1(data=p100fillpurple_data, main_title='p100fillpurple_data')
print()
show_images1(data=p100edge_data, main_title='p100edge_data')
show_images1(data=p100reflect_data, main_title='p100reflect_data')
show_images1(data=p100symmetric_data, main_title='p100symmetric_data')
# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, p=0, f=0, pm='constant'):
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)
pad = Pad(padding=p, fill=f, padding_mode=pm) # Here
plt.imshow(X=pad(im)) # Here
plt.xticks(ticks=[])
plt.yticks(ticks=[])
plt.tight_layout()
plt.show()
show_images2(data=origin_data, main_title='origin_data')
show_images2(data=origin_data, main_title='p50_data', p=50)
show_images2(data=origin_data, main_title='p100_data', p=100)
show_images2(data=origin_data, main_title='p150_data', p=150)
print()
show_images2(data=origin_data, main_title='origin_data')
show_images2(data=origin_data, main_title='m50_data', p=-50)
show_images2(data=origin_data, main_title='m100_data', p=-100)
show_images2(data=origin_data, main_title='m150_data', p=-150)
print()
show_images2(data=origin_data, main_title='origin_data')
show_images2(data=origin_data, main_title='p100p50_data', p=[100, 50])
show_images2(data=origin_data, main_title='m100m50_data', p=[-100, -50])
show_images2(data=origin_data, main_title='p100m50_data', p=[100, -50])
print()
show_images2(data=origin_data, main_title='origin_data')
show_images2(data=origin_data, main_title='p25p50p75p100_data',
p=[25, 50, 75, 100])
show_images2(data=origin_data, main_title='m25m50m75m100_data',
p=[-25, -50, -75, -100])
show_images2(data=origin_data, main_title='p25m50p75m100_data',
p=[25, -50, 75, -100])
print()
show_images2(data=origin_data, main_title='p100fillgray_data', p=100,
f=[150])
show_images2(data=origin_data, main_title='p100fillpurple_data', p=100,
f=[160, 32, 240])
print()
show_images2(data=origin_data, main_title='p100edge_data', p=100,
pm='edge')
show_images2(data=origin_data, main_title='p100reflect_data', p=100,
pm='reflect')
show_images2(data=origin_data, main_title='p100symmetric_data', p=100,
pm='symmetric')
