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：

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。

建议根据v1或v2使用v2？我应该使用哪一个？

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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