*Memos:
- My post explains Compose().
- My post explains RandomInvert().
- My post explains CenterCrop().
- My post explains Pad().
- My post explains OxfordIIITPet().
RandomApply() can randomly apply zero or more transformations to an image with a given probability as shown below:
*Memos:
- The 1st argument for initialization is
transforms(Required-Type:tuple/list/torch.nn.Module(transform)): *Memos: - The transforms are applied from the 1st index in order.
- It must be at least one transformation except when
p=0. - The 2nd argument for initialization is
p(Optional-Default:0.5-Type:intorfloat): *Memos:- It's the probability of whether an image is posterized or not.
- It must be
0 <= x <= 1.
- The 1st argument is
img(Required-Type:PIL Imageortensor). *Don't useimg=. -
v2is recommended to use according to V1 or V2? Which one should I use?.
from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import RandomApply from torchvision.transforms.v2 import RandomInvert from torchvision.transforms.v2 import RandomVerticalFlip from torchvision.transforms.v2 import CenterCrop from torchvision.transforms.v2 import Pad rp = RandomApply(transforms=[RandomInvert(p=1), RandomVerticalFlip(p=1), CenterCrop(size=200), Pad(padding=20)]) rp = RandomApply(transforms=[RandomInvert(p=1), RandomVerticalFlip(p=1), CenterCrop(size=200), Pad(padding=20)], p=0.5) rp # RandomApply(RandomInvert(p=1) # RandomVerticalFlip(p=1) # CenterCrop(size=(200, 200)) # Pad(padding=20, fill=0, padding_mode=constant)) rp.transforms # [RandomInvert(p=1), # RandomVerticalFlip(p=1), # CenterCrop(size=(200, 200)), # Pad(padding=20, fill=0, padding_mode=constant)] rp.p # 0.5 origin_data = OxfordIIITPet( root="data", transform=None ) # `ri` is RandomInvert() and `rv` is RandomVerticalFlip(). # `cc` is CenterCrop() and `pad` is Pad(). ri_rv_cc_pad_p1_data = OxfordIIITPet( root="data", transform=RandomApply(transforms=[RandomInvert(p=1), RandomVerticalFlip(p=1), CenterCrop(size=200), Pad(padding=20)], p=1) # transform=RandomApply(transforms=ModuleList( # [RandomInvert(p=1), # RandomVerticalFlip(p=1), # CenterCrop(size=200), # Pad(padding=20)]), p=1) ) ri_rv_pad_cc_p1_data = OxfordIIITPet( root="data", transform=RandomApply(transforms=[RandomInvert(p=1), RandomVerticalFlip(p=1), Pad(padding=20), CenterCrop(size=200)], p=1) ) ri_rv_cc_pad_p0_data = OxfordIIITPet( root="data", transform=RandomApply(transforms=[RandomInvert(p=1), RandomVerticalFlip(p=1), CenterCrop(size=200), Pad(padding=20)], p=0) ) ri_rv_cc_pad_p05_data = OxfordIIITPet( root="data", transform=RandomApply(transforms=[RandomInvert(p=1), RandomVerticalFlip(p=1), CenterCrop(size=200), Pad(padding=20)], p=0.5) ) 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=ri_rv_cc_pad_p1_data, main_title="ri_rv_cc_pad_p1_data") show_images1(data=ri_rv_pad_cc_p1_data, main_title="ri_rv_pad_cc_p1_data") print() show_images1(data=ri_rv_cc_pad_p0_data, main_title="ri_rv_cc_pad_p0_data") show_images1(data=ri_rv_cc_pad_p0_data, main_title="ri_rv_cc_pad_p0_data") show_images1(data=ri_rv_cc_pad_p0_data, main_title="ri_rv_cc_pad_p0_data") print() show_images1(data=ri_rv_cc_pad_p05_data, main_title="ri_rv_cc_pad_p05_data") show_images1(data=ri_rv_cc_pad_p05_data, main_title="ri_rv_cc_pad_p05_data") show_images1(data=ri_rv_cc_pad_p05_data, main_title="ri_rv_cc_pad_p05_data") print() show_images1(data=ri_rv_cc_pad_p1_data, main_title="ri_rv_cc_pad_p1_data") show_images1(data=ri_rv_cc_pad_p1_data, main_title="ri_rv_cc_pad_p1_data") show_images1(data=ri_rv_cc_pad_p1_data, main_title="ri_rv_cc_pad_p1_data") # ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓ def show_images2(data, main_title=None, t=None, p=0.5): plt.figure(figsize=[10, 5]) plt.suptitle(t=main_title, y=0.8, fontsize=14) if main_title != "origin_data": for i, (im, _) in zip(range(1, 6), data): plt.subplot(1, 5, i) rs = RandomApply(transforms=t, p=p) plt.imshow(X=rs(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="ri_rv_cc_pad_p1_data", t=[RandomInvert(p=1), RandomVerticalFlip(p=1), CenterCrop(size=200), Pad(padding=20)], p=1) show_images2(data=origin_data, main_title="ri_rv_pad_cc_p1_data", t=[RandomInvert(p=1), RandomVerticalFlip(p=1), Pad(padding=20), CenterCrop(size=200)], p=1) print() show_images2(data=origin_data, main_title="ri_rv_cc_pad_p0_data", t=[RandomInvert(p=1), RandomVerticalFlip(p=0), CenterCrop(size=200), Pad(padding=20)], p=0) show_images2(data=origin_data, main_title="ri_rv_cc_pad_p0_data", t=[RandomInvert(p=1), RandomVerticalFlip(p=0), CenterCrop(size=200), Pad(padding=20)], p=0) show_images2(data=origin_data, main_title="ri_rv_cc_pad_p0_data", t=[RandomInvert(p=1), RandomVerticalFlip(p=0), CenterCrop(size=200), Pad(padding=20)], p=0) print() show_images2(data=origin_data, main_title="ri_rv_cc_pad_p05_data", t=[RandomInvert(p=1), RandomVerticalFlip(p=1), CenterCrop(size=200), Pad(padding=20)], p=0.5) show_images2(data=origin_data, main_title="ri_rv_cc_pad_p05_data", t=[RandomInvert(p=1), RandomVerticalFlip(p=1), CenterCrop(size=200), Pad(padding=20)], p=0.5) show_images2(data=origin_data, main_title="ri_rv_cc_pad_p05_data", t=[RandomInvert(p=1), RandomVerticalFlip(p=1), CenterCrop(size=200), Pad(padding=20)], p=0.5) print() show_images2(data=origin_data, main_title="ri_rv_cc_pad_p1_data", t=[RandomInvert(p=1), RandomVerticalFlip(p=1), CenterCrop(size=200), Pad(padding=20)], p=1) show_images2(data=origin_data, main_title="ri_rv_cc_pad_p1_data", t=[RandomInvert(p=1), RandomVerticalFlip(p=1), CenterCrop(size=200), Pad(padding=20)], p=1) 
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