在pytorch中进行杂乱无章

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
初始化的第一个参数是p（可选默认：0.5-type：int或float）： *备忘录：
• 这是图像是否被后代的概率。
  • >
  必须为0

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)

---

汇成企业网站管理系统4.6

汇成企业网站管理系统是以asp+access进行开发的企业建站系统，能为中小企业免费建立属于自己的网站。拥有企业网站常用的模块功能：企业简介模块、联系我们模块、新闻(文章)模块、产品模块、图片模块、招聘模块、在线留言、反馈系统、在线交流、友情链接、网站地图、栏目管理、网站碎片、管理员与权限管理等等，所有模块的分类均支持无限级别的分类，可拓展性非常强大。其中包括万能的栏目管理系统、网站碎片管理系统，

下载

---

---

---