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在pytorch中进行杂乱无章

来源：dev.to 2025-02-19 10:22:02 0浏览收藏

哈喽！今天心血来潮给大家带来了《在pytorch中进行杂乱无章》，想必大家应该对文章都不陌生吧，那么阅读本文就都不会很困难，以下内容主要涉及到，若是你正在学习文章，千万别错过这篇文章~希望能帮助到你！

给我买咖啡☕

*备忘录：

我的帖子解释了牛津iiitpet（）。

> randomposterize（）可以随机将带有给定概率的图像随机寄电，如下所示：

*备忘录：

初始化的第一个参数是位（必需类型：int）： *备忘录：
- >是每个频道要保留的位数。
- 它必须是x < = 8。
- >
第一个参数是img（必需类型：pil图像或张量（int））： *备忘录：
张量必须为2d或3d。

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandomPosterize

randomposterize = RandomPosterize(bits=1)
randomposterize = RandomPosterize(bits=1, p=0.5)

randomposterize 
# RandomPosterize(p=0.5, bits=1)

randomposterize.bits
# 1

randomposterize.p
# 0.5

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

b8p1origin_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=8, p=1)
)

b7p1_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=7, p=1)
)

b6p1_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=6, p=1)
)

b5p1_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=5, p=1)
)

b4p1_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=4, p=1)
)

b3p1_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=3, p=1)
)

b2p1_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=2, p=1)
)

b1p1_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=1, p=1)
)

b0p1_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=0, p=1)
)

bn1p1_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=-1, p=1)
)

bn10p1_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=-10, p=1)
)

bn100p1_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=-100, p=1)
)

b1p0_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=1, p=0)
)

b1p05_data = OxfordIIITPet(
    root="data",
    transform=RandomPosterize(bits=1, p=0.5)
    # transform=RandomPosterize(bits=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.xticks(ticks=[])
        plt.yticks(ticks=[])
    plt.tight_layout()
    plt.show()

show_images1(data=origin_data, main_title="origin_data")
print()
show_images1(data=b8p1origin_data, main_title="b8p1origin_data")
show_images1(data=b7p1_data, main_title="b7p1_data")
show_images1(data=b6p1_data, main_title="b6p1_data")
show_images1(data=b5p1_data, main_title="b5p1_data")
show_images1(data=b4p1_data, main_title="b4p1_data")
show_images1(data=b3p1_data, main_title="b3p1_data")
show_images1(data=b2p1_data, main_title="b2p1_data")
show_images1(data=b1p1_data, main_title="b1p1_data")
show_images1(data=b0p1_data, main_title="b0p1_data")
show_images1(data=bn1p1_data, main_title="bn1p1_data")
show_images1(data=bn10p1_data, main_title="bn10p1_data")
show_images1(data=bn100p1_data, main_title="bn100p1_data")
print()
show_images1(data=b1p0_data, main_title="b1p0_data")
show_images1(data=b1p0_data, main_title="b1p0_data")
show_images1(data=b1p0_data, main_title="b1p0_data")
print()
show_images1(data=b1p05_data, main_title="b1p05_data")
show_images1(data=b1p05_data, main_title="b1p05_data")
show_images1(data=b1p05_data, main_title="b1p05_data")
print()
show_images1(data=b1p1_data, main_title="b1p1_data")
show_images1(data=b1p1_data, main_title="b1p1_data")
show_images1(data=b1p1_data, main_title="b1p1_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, b=None, prob=0):
    plt.figure(figsize=[10, 5])
    plt.suptitle(t=main_title, y=0.8, fontsize=14)
    if b != None:
        for i, (im, _) in zip(range(1, 6), data):
            plt.subplot(1, 5, i)
            rp = RandomPosterize(bits=b, p=prob)
            plt.imshow(X=rp(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="b8p1origin_data", b=8, prob=1)
show_images2(data=origin_data, main_title="b7p1_data", b=7, prob=1)
show_images2(data=origin_data, main_title="b6p1_data", b=6, prob=1)
show_images2(data=origin_data, main_title="b5p1_data", b=5, prob=1)
show_images2(data=origin_data, main_title="b4p1_data", b=4, prob=1)
show_images2(data=origin_data, main_title="b3p1_data", b=3, prob=1)
show_images2(data=origin_data, main_title="b2p1_data", b=2, prob=1)
show_images2(data=origin_data, main_title="b1p1_data", b=1, prob=1)
show_images2(data=origin_data, main_title="b0p1_data", b=0, prob=1)
show_images2(data=origin_data, main_title="bn1p1_data", b=-1, prob=1)
show_images2(data=origin_data, main_title="bn10p1_data", b=-10, prob=1)
show_images2(data=origin_data, main_title="bn100p1_data", b=-100, prob=1)
print()
show_images2(data=origin_data, main_title="b1p0_data", b=1, prob=0)
show_images2(data=origin_data, main_title="b1p0_data", b=1, prob=0)
show_images2(data=origin_data, main_title="b1p0_data", b=1, prob=0)
print()
show_images2(data=origin_data, main_title="b1p05_data", b=1, prob=0.5)
show_images2(data=origin_data, main_title="b1p05_data", b=1, prob=0.5)
show_images2(data=origin_data, main_title="b1p05_data", b=1, prob=0.5)
print()
show_images2(data=origin_data, main_title="b1p1_data", b=1, prob=1)
show_images2(data=origin_data, main_title="b1p1_data", b=1, prob=1)
show_images2(data=origin_data, main_title="b1p1_data", b=1, prob=1)

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