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 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。不使用img =。建议根据v1或v2使用v2？我应该使用哪一个？
from torchvision.datasets import OxfordIIITPetfrom torchvision.transforms.v2 import RandomPosterizerandomposterize = RandomPosterize(bits=1)randomposterize = RandomPosterize(bits=1, p=0.5)randomposterize # RandomPosterize(p=0.5, bits=1)randomposterize.bits# 1randomposterize.p# 0.5origin_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 pltdef 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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