Resize in PyTorch

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*Memos:

• My post explains OxfordIIITPet().

Resize() can resize zero or more images as shown below:

*Memos:

• The 1st argument for initialization is size(Required-Type:int, tuple/list(int) or size()): *Memos:
  • It's [height, width].
  • It must be 1 <= x.
  • None can be explicitly set to it only if max_size isn't None.
  • A tuple/list must be the 1D with 1 or 2 elements.
  • A single value(int or tuple/list(int)) is applied to a smaller image's width or height edge, then the other larger width or height edge is also resized: *Memos:
  • If an image's width is smaller than its height, it's [size * height / width, size].
  • If an image width is larger than its height, it's [size, size * width / height].
  • If an image width is equal to its height, it's [size, size].
• The 2nd argument for initialization is interpolation(Optional-Default:InterpolationMode.BILINEAR-Type:InterpolationMode).
• The 3rd argument for initialization is max_size(Optional-Default:None-Type:int): *Memos:
  • It's only supported if size is a single value(int or tuple/list(int)).
  • After size is applied if a larger image's width or height edge exceeds it, it's applied to a larger image's width or height edge to limit the image size, then the other smaller image's width or height edge also becomes smaller than before.
• The 4th argument for initialization is antialias(Optional-Default:True-Type:bool). *Even if setting False to it, it's always True if interpolation is InterpolationMode.BILINEAR or InterpolationMode.BICUBIC.
• The 1st argument is img(Required-Type:PIL Image or tensor(int, float, complex or bool)): *Memos:
  • A tensor must be the 3D or more D of one or more elements.
  • Don't use img=.
• 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 Resize
from torchvision.transforms.functional import InterpolationMode

resize = Resize(size=100)
resize = Resize(size=100,
                interpolation=InterpolationMode.BILINEAR,
                max_size=None,
                antialias=True)
resize
# Resize(size=[100],
#        interpolation=InterpolationMode.BILINEAR,
#        antialias=True)

resize.size
# [100]

resize.interpolation
# <InterpolationMode.BILINEAR: 'bilinear'>

print(resize.max_size)
# None

resize.antialias
# True

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

s1000_data = OxfordIIITPet(
    root="data",
    transform=Resize(size=1000)
    # transform=Resize(size=[1000])
    # transform=Resize(size=[1000, 1000])
)

s100_data = OxfordIIITPet(
    root="data",
    transform=Resize(size=100)
)

s50_data = OxfordIIITPet(
    root="data",
    transform=Resize(size=50)
)

s10_data = OxfordIIITPet(
    root="data",
    transform=Resize(size=10)
)

s100_180_data = OxfordIIITPet(
    root="data",
    transform=Resize(size=[100, 180])
)

s180_100_data = OxfordIIITPet(
    root="data",
    transform=Resize(size=[180, 100])
)

s100ms110_data = OxfordIIITPet(
    root="data",
    transform=Resize(size=100, max_size=110)
)

sNonems110_data = OxfordIIITPet(
    root="data",
    transform=Resize(size=None, max_size=110)
)

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.tight_layout()
    plt.show()

show_images1(data=origin_data, main_title="origin_data")
show_images1(data=s1000_data, main_title="s1000_data")
show_images1(data=s100_data, main_title="s100_data")
show_images1(data=s50_data, main_title="s50_data")
show_images1(data=s10_data, main_title="s10_data")
print()
show_images1(data=origin_data, main_title="origin_data")
show_images1(data=s100_180_data, main_title="s100_180_data")
show_images1(data=s180_100_data, main_title="s180_100_data")
print()
show_images1(data=s100_data, main_title="s100_data")
show_images1(data=s100ms110_data, main_title="s100ms110_data")
show_images1(data=sNonems110_data, main_title="sNonems110_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, s=None, ms=None):
    plt.figure(figsize=(10, 5))
    plt.suptitle(t=main_title, y=0.8, fontsize=14)
    temp_s = s
    for i, (im, _) in zip(range(1, 6), data):
        plt.subplot(1, 5, i)
        if not temp_s and not ms:
            s = [im.size[1], im.size[0]]
        resize = Resize(size=s, max_size=ms) # Here
        plt.imshow(X=resize(im)) # Here
    plt.tight_layout()
    plt.show()

show_images2(data=origin_data, main_title="origin_data")
show_images2(data=origin_data, main_title="s1000_data", s=1000)
show_images2(data=origin_data, main_title="s100_data", s=100)
show_images2(data=origin_data, main_title="s100_data", s=50)
show_images2(data=origin_data, main_title="s10_data", s=10)
print()
show_images2(data=origin_data, main_title="origin_data")
show_images2(data=origin_data, main_title="s100_180_data", s=[100, 180])
show_images2(data=origin_data, main_title="s180_100_data", s=[180, 100])
print()
show_images2(data=origin_data, main_title="s100_data", s=100)
show_images2(data=origin_data, main_title="s100ms110_data", s=100, ms=110)
show_images2(data=origin_data, main_title="sNonems110_data", ms=110)

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