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pytorch中的随机溶剂（5）

来源：dev.to 2025-02-16 22:31:37 0浏览收藏

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*备忘录：

我的帖子说明了关于大小参数的randomresizedcrop（）。
我的帖子解释了关于比率参数的randomresizedcrop（）。
我的帖子解释了随机rastizedcrop（）关于尺寸参数，比例= [0，0]和比率= [1，1]。
我的帖子解释了牛津iiitpet（）。

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandomResizedCrop
from torchvision.transforms.functional import InterpolationMode

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

s1000sc0_0r1_1origin_data = OxfordIIITPet( # `s` is size and `sc` is scale.
    root="data",                           # `r` is ratio.
    transform=RandomResizedCrop(size=1000, scale=[0, 0], ratio=[1, 1])
)

s1000sc0_1r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0, 1], ratio=[1, 1])
)

s1000sc0_05r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0, 0.5], ratio=[1, 1])
)

s1000sc05_1r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.5, 1], ratio=[1, 1])
)

s1000sc0001_0001r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.001, 0.001],
                                ratio=[1, 1])
)

s1000sc001_001r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.01, 0.01], ratio=[1, 1])
)

s1000sc01_01r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.1, 0.1], ratio=[1, 1])
)

s1000sc02_02r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.2, 0.2], ratio=[1, 1])
)

s1000sc03_03r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.3, 0.3], ratio=[1, 1])
)

s1000sc04_04r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.4, 0.4], ratio=[1, 1])
)

s1000sc05_05r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.5, 0.5], ratio=[1, 1])
)

s1000sc06_06r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.6, 0.6], ratio=[1, 1])
)

s1000sc07_07r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.7, 0.7], ratio=[1, 1])
)

s1000sc08_08r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.8, 0.8], ratio=[1, 1])
)

s1000sc09_09r1_1_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[0.9, 0.9], ratio=[1, 1])
)

s1000sc1_1r1_1origin_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[1, 1], ratio=[1, 1])
)

s1000sc10_10r1_1origin_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[10, 10], ratio=[1, 1])
)

s1000sc100_100r1_1origin_data = OxfordIIITPet(
    root="data",
    transform=RandomResizedCrop(size=1000, scale=[100, 100], ratio=[1, 1])
)

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")
print()
show_images1(data=s1000sc0_0r1_1origin_data,
             main_title="s1000sc0_0r1_1origin_data")
show_images1(data=s1000sc0_1r1_1_data, main_title="s1000sc0_1r1_1_data")
show_images1(data=s1000sc0_05r1_1_data, main_title="s1000sc0_05r1_1_data")
show_images1(data=s1000sc05_1r1_1_data, main_title="s1000sc05_1r1_1_data")
print()
show_images1(data=s1000sc0_0r1_1origin_data,
             main_title="s1000sc0_0r1_1origin_data")
show_images1(data=s1000sc0001_0001r1_1_data, 
             main_title="s1000sc0001_0001r1_1_data")
show_images1(data=s1000sc001_001r1_1_data, 
             main_title="s1000sc001_001r1_1_data")
show_images1(data=s1000sc01_01r1_1_data, main_title="s1000sc01_01r1_1_data")
show_images1(data=s1000sc02_02r1_1_data, main_title="s1000sc02_02r1_1_data")
show_images1(data=s1000sc03_03r1_1_data, main_title="s1000sc03_03r1_1_data")
show_images1(data=s1000sc04_04r1_1_data, main_title="s1000sc04_04r1_1_data")
show_images1(data=s1000sc05_05r1_1_data, main_title="s1000sc05_05r1_1_data")
show_images1(data=s1000sc06_06r1_1_data, main_title="s1000sc06_06r1_1_data")
show_images1(data=s1000sc07_07r1_1_data, main_title="s1000sc07_07r1_1_data")
show_images1(data=s1000sc08_08r1_1_data, main_title="s1000sc08_08r1_1_data")
show_images1(data=s1000sc09_09r1_1_data, main_title="s1000sc09_09r1_1_data")
show_images1(data=s1000sc1_1r1_1origin_data, 
             main_title="s1000sc1_1r1_1origin_data")
show_images1(data=s1000sc10_10r1_1origin_data,
             main_title="s1000sc10_10r1_1origin_data")
show_images1(data=s1000sc100_100r1_1origin_data,
             main_title="s1000sc100_100r1_1origin_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓ 
def show_images2(data, main_title=None, s=None, sc=(0.08, 1.0),
                 r=(0.75, 1.3333333333333333),
                 ip=InterpolationMode.BILINEAR, a=True):
    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)
        if s:
            rrc = RandomResizedCrop(size=s, scale=sc, # Here
                                    ratio=r, interpolation=ip,
                                    antialias=a)
            plt.imshow(X=rrc(im)) # Here
        else:
            plt.imshow(X=im)
    plt.tight_layout()
    plt.show()

show_images2(data=origin_data, main_title="origin_data")
print()
show_images2(data=origin_data, main_title="s1000sc0_0r1_1origin_data",
             s=1000, sc=[0, 0], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc0_1r1_1_data", s=1000, 
             sc=[0, 1], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc0_05r1_1_data", s=1000,
             sc=[0, 0.5], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc05_1r1_1_data", s=1000,
             sc=[0.5, 1], r=[1, 1])
print()
show_images2(data=origin_data, main_title="s1000sc0_0r1_1origin_data", 
             s=1000, sc=[0, 0], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc0001_0001r1_1_data", 
             s=1000, sc=[0.001, 0.001], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc001_001r1_1_data", 
             s=1000, sc=[0.01, 0.01], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc01_01r1_1_data", 
             s=1000, sc=[0.1, 0.1], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc02_02r1_1_data", 
             s=1000, sc=[0.2, 0.2], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc03_03r1_1_data", 
             s=1000, sc=[0.3, 0.3], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc04_04r1_1_data", 
             s=1000, sc=[0.4, 0.4], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc05_05r1_1_data", 
             s=1000, sc=[0.5, 0.5], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc06_06r1_1_data", 
             s=1000, sc=[0.6, 0.6], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc07_07r1_1_data", 
             s=1000, sc=[0.7, 0.7], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc08_08r1_1_data", 
             s=1000, sc=[0.8, 0.8], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc09_09r1_1_data", 
             s=1000, sc=[0.9, 0.9], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc1_1r1_1origin_data", 
             s=1000, sc=[1, 1], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc10_10r1_1origin_data",
             s=1000, sc=[10, 10], r=[1, 1])
show_images2(data=origin_data, main_title="s1000sc100_100r1_1origin_data",
             s=1000, sc=[100, 100], r=[1, 1])

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