pytorch的兰德姆布森

给我买咖啡☕

*备忘录：

我的帖子解释了randomrotation（）。

我的帖子解释了randomaffine（）。
我的帖子解释了牛津iiitpet（）。

> randomperspective（）可以对图像进行随机的透视转换，如下所示：

*备忘录：

初始化的第一个参数是distortion_scale（可选默认：0.5型：int或float）： *备忘录：
- 它可以进行透视转换。
- 必须为0

>初始化的第二个参数为p（可选默认：0.5-type：int或float）： *备忘录：

- 必须为0
初始化的第四个参数是填充（可选默认：0型：int，float或tuple/tuple/list（int或float））： *备忘录：
>它可以更改图像的背景。 *在为图像进行透视转换时，可以看到背景。

元组/列表必须是具有1或3个元素的1d。
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 OxfordIIITPet from torchvision.transforms.v2 import RandomPerspective from torchvision.transforms.functional import InterpolationMode  randompers = RandomPerspective() randompers = RandomPerspective(distortion_scale=0.5,                                p=0.5,                                interpolation=InterpolationMode.BILINEAR,                                fill=0) randompers # RandomPerspective(p=0.5, #                   distortion_scale=0.5, #                   interpolation=InterpolationMode.BILINEAR, #                   fill=0)  randompers.distortion_scale # 0.5  randompers.p # 0.5  randompers.interpolation # <InterpolationMode.BILINEAR: 'bilinear'>  randompers.fill # 0  origin_data = OxfordIIITPet(     root="data",     transform=None )  ds0p1origin_data = OxfordIIITPet( # `ds` is distortion_scale.     root="data",     transform=RandomPerspective(distortion_scale=0, p=1) )  ds01p1_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(distortion_scale=0.1, p=1) )  ds02p1_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(distortion_scale=0.2, p=1) )  ds03p1_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(distortion_scale=0.3, p=1) )  ds04p1_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(distortion_scale=0.4, p=1) )  ds05p1_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(distortion_scale=0.5, p=1) )  ds06p1_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(distortion_scale=0.6, p=1) )  ds07p1_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(distortion_scale=0.7, p=1) )  ds08p1_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(distortion_scale=0.8, p=1) )  ds09p1_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(distortion_scale=0.9, p=1) )  ds1p1_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(distortion_scale=1, p=1) )  p0_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(p=0) )  p05_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(p=0.5) )  p1_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(p=1) )  p1fgray_data = OxfordIIITPet( # `f` is fill.     root="data",     transform=RandomPerspective(p=1, fill=150) )  p1fpurple_data = OxfordIIITPet(     root="data",     transform=RandomPerspective(p=1, fill=[160, 32, 240]) )  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=ds0p1origin_data, main_title="ds0p1origin_data") show_images1(data=ds01p1_data, main_title="ds01p1_data") show_images1(data=ds02p1_data, main_title="ds02p1_data") show_images1(data=ds03p1_data, main_title="ds03p1_data") show_images1(data=ds04p1_data, main_title="ds04p1_data") show_images1(data=ds05p1_data, main_title="ds05p1_data") show_images1(data=ds06p1_data, main_title="ds06p1_data") show_images1(data=ds07p1_data, main_title="ds07p1_data") show_images1(data=ds08p1_data, main_title="ds08p1_data") show_images1(data=ds09p1_data, main_title="ds09p1_data") show_images1(data=ds1p1_data, main_title="ds1p1_data") print() show_images1(data=p0_data, main_title="p0_data") show_images1(data=p0_data, main_title="p0_data") show_images1(data=p0_data, main_title="p0_data") print() show_images1(data=p05_data, main_title="p05_data") show_images1(data=p05_data, main_title="p05_data") show_images1(data=p05_data, main_title="p05_data") print() show_images1(data=p1_data, main_title="p1_data") show_images1(data=p1_data, main_title="p1_data") show_images1(data=p1_data, main_title="p1_data") print() show_images1(data=p1fgray_data, main_title="p1fgray_data") show_images1(data=p1fpurple_data, main_title="p1fpurple_data")  # ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓ def show_images2(data, main_title=None, ds=0.5, prob=0.5,                  ip=InterpolationMode.BILINEAR, f=0):     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)         rp = RandomPerspective(distortion_scale=ds, p=prob, # Here                                interpolation=ip, fill=f)         plt.imshow(X=rp(im)) # Here         plt.xticks(ticks=[])         plt.yticks(ticks=[])     plt.tight_layout()     plt.show()  show_images2(data=origin_data, main_title="origin_data", ds=0) print() show_images2(data=origin_data, main_title="ds0p1origin_data", ds=0,              prob=1) show_images2(data=origin_data, main_title="ds01p1_data", ds=0.1, prob=1) show_images2(data=origin_data, main_title="ds02p1_data", ds=0.2, prob=1) show_images2(data=origin_data, main_title="ds03p1_data", ds=0.3, prob=1) show_images2(data=origin_data, main_title="ds04p1_data", ds=0.4, prob=1) show_images2(data=origin_data, main_title="ds05p1_data", ds=0.5, prob=1) show_images2(data=origin_data, main_title="ds06p1_data", ds=0.6, prob=1) show_images2(data=origin_data, main_title="ds07p1_data", ds=0.7, prob=1) show_images2(data=origin_data, main_title="ds08p1_data", ds=0.8, prob=1) show_images2(data=origin_data, main_title="ds09p1_data", ds=0.9, prob=1) show_images2(data=origin_data, main_title="ds1p1_data", ds=1, prob=1) print() show_images2(data=origin_data, main_title="p0_data", prob=0) show_images2(data=origin_data, main_title="p0_data", prob=0) show_images2(data=origin_data, main_title="p0_data", prob=0) print() show_images2(data=origin_data, main_title="p05_data", prob=0.5) show_images2(data=origin_data, main_title="p05_data", prob=0.5) show_images2(data=origin_data, main_title="p05_data", prob=0.5) print() show_images2(data=origin_data, main_title="p1_data", prob=1) show_images2(data=origin_data, main_title="p1_data", prob=1) show_images2(data=origin_data, main_title="p1_data", prob=1) print() show_images2(data=origin_data, main_title="p1fgray_data", prob=1, f=150) show_images2(data=origin_data, main_title="p1fpurple_data", prob=1,              f=[160, 32, 240])

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