Programming

RandomApply in PyTorch

Buy Me a Coffee☕ 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 or torch.nn.Module of transformations). The transformations are applied from the 1st index in order. The 2nd argument for initialization is p(Optional-Default:0.5-Type:int or float): Memos: It's the probability of whether an image is posterized or not. It must be 0

Apr 17, 2025 - 05:23

Buy Me a Coffee☕

*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 or torch.nn.Module of transformations). *The transformations are applied from the 1st index in order.
The 2nd argument for initialization is p(Optional-Default:0.5-Type:int or float): *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 Image or tensor(int)): *Memos:
- A tensor must be 2D or 3D.
- Don't use img=.
v2 is 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)

RandomApply in PyTorch

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