pytorch中的随机溶剂（5）

给我买咖啡☕

*备忘录：

我的帖子说明了关于大小参数的randomresizedcrop（）。

我的帖子解释了有关比例参数的randomresizedcrop（）。

我的帖子解释了关于比率参数的randomresizedcrop（）。

我的帖子解释了随机rastizedcrop（）关于尺寸参数，比例= [0，0]和比率= [1，1]。

我的帖子解释了与比例参数有关的随机rastresizedcrop（），scale = [0，0]。

我的帖子解释了牛津iiitpet（）。

randomresizedcrop（）可以裁剪图像的随机部分，然后将其调整为给定尺寸，如下所示：

from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import RandomResizedCrop from torchvision.transforms.functional import InterpolationMode  origin_data = OxfordIIITPet(     root="data",     transform=None )  s1000sc0_0r1_1origin_data = OxfordIIITPet( # `s` is size and `sc` is scale.     root="data",                           # `r` is ratio.     transform=RandomResizedCrop(size=1000, scale=[0, 0], ratio=[1, 1]) )  s1000sc0_1r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0, 1], ratio=[1, 1]) )  s1000sc0_05r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0, 0.5], ratio=[1, 1]) )  s1000sc05_1r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.5, 1], ratio=[1, 1]) )  s1000sc0001_0001r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.001, 0.001],                                 ratio=[1, 1]) )  s1000sc001_001r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.01, 0.01], ratio=[1, 1]) )  s1000sc01_01r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.1, 0.1], ratio=[1, 1]) )  s1000sc02_02r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.2, 0.2], ratio=[1, 1]) )  s1000sc03_03r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.3, 0.3], ratio=[1, 1]) )  s1000sc04_04r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.4, 0.4], ratio=[1, 1]) )  s1000sc05_05r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.5, 0.5], ratio=[1, 1]) )  s1000sc06_06r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.6, 0.6], ratio=[1, 1]) )  s1000sc07_07r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.7, 0.7], ratio=[1, 1]) )  s1000sc08_08r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.8, 0.8], ratio=[1, 1]) )  s1000sc09_09r1_1_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[0.9, 0.9], ratio=[1, 1]) )  s1000sc1_1r1_1origin_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[1, 1], ratio=[1, 1]) )  s1000sc10_10r1_1origin_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[10, 10], ratio=[1, 1]) )  s1000sc100_100r1_1origin_data = OxfordIIITPet(     root="data",     transform=RandomResizedCrop(size=1000, scale=[100, 100], ratio=[1, 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.tight_layout()     plt.show()  show_images1(data=origin_data, main_title="origin_data") print() show_images1(data=s1000sc0_0r1_1origin_data,              main_title="s1000sc0_0r1_1origin_data") show_images1(data=s1000sc0_1r1_1_data, main_title="s1000sc0_1r1_1_data") show_images1(data=s1000sc0_05r1_1_data, main_title="s1000sc0_05r1_1_data") show_images1(data=s1000sc05_1r1_1_data, main_title="s1000sc05_1r1_1_data") print() show_images1(data=s1000sc0_0r1_1origin_data,              main_title="s1000sc0_0r1_1origin_data") show_images1(data=s1000sc0001_0001r1_1_data,               main_title="s1000sc0001_0001r1_1_data") show_images1(data=s1000sc001_001r1_1_data,               main_title="s1000sc001_001r1_1_data") show_images1(data=s1000sc01_01r1_1_data, main_title="s1000sc01_01r1_1_data") show_images1(data=s1000sc02_02r1_1_data, main_title="s1000sc02_02r1_1_data") show_images1(data=s1000sc03_03r1_1_data, main_title="s1000sc03_03r1_1_data") show_images1(data=s1000sc04_04r1_1_data, main_title="s1000sc04_04r1_1_data") show_images1(data=s1000sc05_05r1_1_data, main_title="s1000sc05_05r1_1_data") show_images1(data=s1000sc06_06r1_1_data, main_title="s1000sc06_06r1_1_data") show_images1(data=s1000sc07_07r1_1_data, main_title="s1000sc07_07r1_1_data") show_images1(data=s1000sc08_08r1_1_data, main_title="s1000sc08_08r1_1_data") show_images1(data=s1000sc09_09r1_1_data, main_title="s1000sc09_09r1_1_data") show_images1(data=s1000sc1_1r1_1origin_data,               main_title="s1000sc1_1r1_1origin_data") show_images1(data=s1000sc10_10r1_1origin_data,              main_title="s1000sc10_10r1_1origin_data") show_images1(data=s1000sc100_100r1_1origin_data,              main_title="s1000sc100_100r1_1origin_data")  # ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓  def show_images2(data, main_title=None, s=None, sc=(0.08, 1.0),                  r=(0.75, 1.3333333333333333),                  ip=InterpolationMode.BILINEAR, a=True):     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)         if s:             rrc = RandomResizedCrop(size=s, scale=sc, # Here                                     ratio=r, interpolation=ip,                                     antialias=a)             plt.imshow(X=rrc(im)) # Here         else:             plt.imshow(X=im)     plt.tight_layout()     plt.show()  show_images2(data=origin_data, main_title="origin_data") print() show_images2(data=origin_data, main_title="s1000sc0_0r1_1origin_data",              s=1000, sc=[0, 0], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc0_1r1_1_data", s=1000,               sc=[0, 1], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc0_05r1_1_data", s=1000,              sc=[0, 0.5], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc05_1r1_1_data", s=1000,              sc=[0.5, 1], r=[1, 1]) print() show_images2(data=origin_data, main_title="s1000sc0_0r1_1origin_data",               s=1000, sc=[0, 0], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc0001_0001r1_1_data",               s=1000, sc=[0.001, 0.001], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc001_001r1_1_data",               s=1000, sc=[0.01, 0.01], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc01_01r1_1_data",               s=1000, sc=[0.1, 0.1], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc02_02r1_1_data",               s=1000, sc=[0.2, 0.2], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc03_03r1_1_data",               s=1000, sc=[0.3, 0.3], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc04_04r1_1_data",               s=1000, sc=[0.4, 0.4], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc05_05r1_1_data",               s=1000, sc=[0.5, 0.5], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc06_06r1_1_data",               s=1000, sc=[0.6, 0.6], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc07_07r1_1_data",               s=1000, sc=[0.7, 0.7], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc08_08r1_1_data",               s=1000, sc=[0.8, 0.8], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc09_09r1_1_data",               s=1000, sc=[0.9, 0.9], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc1_1r1_1origin_data",               s=1000, sc=[1, 1], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc10_10r1_1origin_data",              s=1000, sc=[10, 10], r=[1, 1]) show_images2(data=origin_data, main_title="s1000sc100_100r1_1origin_data",              s=1000, sc=[100, 100], r=[1, 1])

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