首頁 後端開發 Python教學 pytorch中的隨機克羅普(1)

pytorch中的隨機克羅普(1)

Jan 30, 2025 pm 12:12 PM

給我買咖啡☕

*備忘錄:

  • 我的帖子解釋了牛津iiitpet()。

Randomcrop()可以隨機裁剪圖像,如下所示:

*備忘錄:

    >初始化的第一個參數是大小(必需類型:int或tuple/list/list(int)或size()): *備忘錄:
    • 是[高度,寬度]。
    • 必須是1< = x。
    • 元組/列表必須是具有1或2個元素的1D。
    • 單個值(int或tuple/list(int))是指[size,size]。
    • >初始化的第二個參數是填充(可選默認:非類型:int或tuple/list(int)): *備忘錄:
    是[左上,右,底部],可以從[左右,底部]或[左右右下]轉換。 一個元組/列表必須是1D,具有1、2或4個元素。
  • 單個值(int或tuple/list(int))是指[填充,填充,填充,填充]。
    • > double值(元組/列表(int))表示[填充[0],填充[1],填充[0],填充[1]。
    • 初始化的第三個參數是pad_if_needed(可選默認:false-type:bool):
    • 如果是錯誤的,並且大小小於原始圖像或填充圖像的填充圖像,則出現錯誤。
    • >
    • 如果它的真實且大小小於原始圖像或填充圖像的填充圖像,則沒有錯誤,則該圖像被隨機填充以變為尺寸。
  • 初始化的第四個參數是填充(可選默認:0型:int,float或tuple/tuple/list(int或float)): *備忘錄:
    • >它可以更改圖像的背景。 *當圖像被正面填充時,可以看到背景。
    • 元組/列表必須是具有1或3個元素的1D。
  • 初始化的第五個參數是padding_mode(可選默認:'constant'-type:str)。 *可以將其設置為 *'常數','edge',“反射”或“對稱”。
  • >
      第一個參數是img(必需類型:pil圖像或張量(int)): *備忘錄:
    • 張量必須為2D或3D。
    • 不使用img =。
  • 建議根據V1或V2使用V2?我應該使用哪一個?

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from torchvision.datasets import OxfordIIITPet

from torchvision.transforms.v2 import RandomCrop

 

randomcrop = RandomCrop(size=100)

randomcrop = RandomCrop(size=100,

                        padding=None,

                        pad_if_needed=False,

                        fill=0,

                        padding_mode='constant')

randomcrop

# RandomCrop(size=(100, 100),

#            pad_if_needed=False,

#            fill=0,

#            padding_mode=constant)

 

randomcrop.size

# (100, 100)

 

print(randomcrop.padding)

# None

 

randomcrop.pad_if_needed

# False

 

randomcrop.fill

# 0

 

randomcrop.padding_mode

# 'constant'

 

origin_data = OxfordIIITPet(

    root="data",

    transform=None

)

 

s300_data = OxfordIIITPet( # `s` is size.

    root="data",

    transform=RandomCrop(size=300)

    # transform=RandomCrop(size=[300, 300])

)

 

s200_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=200)

)

 

s100_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=100)

)

 

s50_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=50)

)

 

s10_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=10)

)

 

s1_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=1)

)

 

s200_300_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[200, 300])

)

 

s300_200_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[300, 200])

)

 

s300p100_data = OxfordIIITPet( # `p` is padding.

    root="data",

    transform=RandomCrop(size=300, padding=100)

    # transform=RandomCrop(size=300, padding=[100, 100])

    # transform=RandomCrop(size=300, padding=[100, 100, 100, 100])

)

 

s300p200_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=300, padding=200)

)

 

s700_594p100origin_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[700, 594], padding=100)

)

 

s300p100_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=300, padding=100)

)

 

s600_594p100_50origin_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[600, 594], padding=[100, 50])

)

 

s300p100_50_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=300, padding=[100, 50])

)

 

s650_494p25_50_75_100origin_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[650, 494], padding=[25, 50, 75, 100])

)

 

s300p25_50_75_100_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=300, padding=[25, 50, 75, 100])

)

 

s300_194pn100origin_data = OxfordIIITPet( # `n` is negative.

    root="data",

    transform=RandomCrop(size=[300, 194], padding=-100)

)

 

s150pn100_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=150, padding=-100)

)

 

s300_294pn50n100origin_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[300, 294], padding=[-50, -100])

)

 

s150pn50n100_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=150, padding=[-50, -100])

)

 

s350_294pn25n50n75n100origin_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[350, 294], padding=[-25, -50, -75, -100])

)

 

s150pn25n50n75n100_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=150, padding=[-25, -50, -75, -100])

)

 

s600_444p25_50origin_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[600, 444], padding=[25, 50])

)

 

s200p25_50_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=200, padding=[25, 50])

)

 

s400_344pn25n50origin_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[400, 344], padding=[-25, -50])

)

 

s200pn25n50_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=200, padding=[-25, -50])

)

 

s400_444p25n50origin_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[400, 444], padding=[25, -50])

)

 

s200p25n50_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=200, padding=[25, -50])

)

 

s600_344pn25_50origin_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[600, 344], padding=[-25, 50])

)

 

s200pn25_50_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=200, padding=[-25, 50])

)

 

s700_594p100fgrayorigin_data = OxfordIIITPet( # `f` is fill.

    root="data",

    transform=RandomCrop(size=[700, 594], padding=100, fill=150)

    # transform=RandomCrop(size=[700, 594], padding=100, fill=[150])

)

 

s300p100fgray_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=300, padding=100, fill=150)

)

 

s700_594p100fpurpleorigin_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[700, 594], padding=100, fill=[160, 32, 240])

)

 

s300p100fpurple_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=300, padding=100, fill=[160, 32, 240])

)

 

s700_594p100pmconstorigin_data = OxfordIIITPet( # `pm` is padding_mode.

    root="data",                                # `const` is constant.

    transform=RandomCrop(size=[700, 594], padding=100, padding_mode='constant')

)

 

s300p100pmconst_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=300, padding=100, padding_mode='constant')

)

 

s700_594p100pmedgeorigin_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=[700, 594], padding=100, padding_mode='edge')

)

 

s300p100pmedge_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=300, padding=100, padding_mode='edge')

)

 

s700_594p100pmrefleorigin_data = OxfordIIITPet( # `refle` is reflect.

    root="data",

    transform=RandomCrop(size=[700, 594], padding=100, padding_mode='reflect')

)

 

s300p100pmrefle_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=300, padding=100, padding_mode='reflect')

)

 

s700_594p100pmsymmeorigin_data = OxfordIIITPet( # `symme` is symmetric.

    root="data",

    transform=RandomCrop(size=[700, 594], padding=100,

                         padding_mode='symmetric')

)

 

s300p100pmsymme_data = OxfordIIITPet(

    root="data",

    transform=RandomCrop(size=300, padding=100, padding_mode='symmetric')

)

 

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 in range(1, 6):

        plt.subplot(1, 5, i)

        plt.imshow(X=data[0][0])

    plt.tight_layout()

    plt.show()

 

plt.figure(figsize=(7, 9))

plt.title(label="s500_394origin_data", fontsize=14)

plt.imshow(X=origin_data[0][0])

show_images1(data=origin_data, main_title="s500_394origin_data")

show_images1(data=s300_data, main_title="s300_data")

show_images1(data=s200_data, main_title="s200_data")

show_images1(data=s100_data, main_title="s100_data")

show_images1(data=s50_data, main_title="s50_data")

show_images1(data=s10_data, main_title="s10_data")

show_images1(data=s1_data, main_title="s1_data")

show_images1(data=s200_300_data, main_title="s200_300_data")

show_images1(data=s300_200_data, main_title="s300_200_data")

print()

show_images1(data=s700_594p100origin_data,

             main_title="s700_594p100origin_data")

show_images1(data=s300p100_data, main_title="s300p100_data")

print()

show_images1(data=s600_594p100_50origin_data,

             main_title="s600_594p100_50origin_data")

show_images1(data=s300p100_50_data, main_title="s300p100_50_data")

print()

show_images1(data=s650_494p25_50_75_100origin_data,

             main_title="s650_494p25_50_75_100origin_data")

show_images1(data=s300p25_50_75_100_data,

             main_title="s300p25_50_75_100_data")

print()

show_images1(data=s300_194pn100origin_data,

             main_title="s300_194pn100origin_data")

show_images1(data=s150pn100_data,

             main_title="s150pn100_data")

print()

show_images1(data=s300_294pn50n100origin_data,

             main_title="s300_294pn50n100origin_data")

show_images1(data=s150pn50n100_data,

             main_title="s150pn50n100_data")

print()

show_images1(data=s350_294pn25n50n75n100origin_data,

             main_title="s350_294pn25n50n75n100origin_data")

show_images1(data=s150pn25n50n75n100_data,

             main_title="s150pn25n50n75n100_data")

print()

show_images1(data=s600_444p25_50origin_data,

             main_title="s600_444p25_50origin_data")

show_images1(data=s200p25_50_data,

             main_title="s200p25_50_data")

print()

show_images1(data=s400_344pn25n50origin_data,

             main_title="s400_344pn25n50origin_data")

show_images1(data=s200pn25n50_data,

             main_title="s200pn25n50_data")

print()

show_images1(data=s400_444p25n50origin_data,

             main_title="s400_444p25n50origin_data")

show_images1(data=s200p25n50_data,

             main_title="s200p25n50_data")

print()

show_images1(data=s600_344pn25_50origin_data,

             main_title="s600_344pn25_50origin_data")

show_images1(data=s200pn25_50_data,

             main_title="s200pn25_50_data")

print()

show_images1(data=s700_594p100fgrayorigin_data,

             main_title="s700_594p100fgrayorigin_data")

show_images1(data=s300p100fgray_data, main_title="s300p100fgray_data")

print()

show_images1(data=s700_594p100fpurpleorigin_data,

             main_title="s700_594p100fpurpleorigin_data")

show_images1(data=s300p100fpurple_data, main_title="s300p100fpurple_data")

print()

show_images1(data=s700_594p100pmconstorigin_data,

             main_title="s700_594p100pmconstorigin_data")

show_images1(data=s300p100pmconst_data, main_title="s300p100pmconst_data")

print()

show_images1(data=s700_594p100pmedgeorigin_data,

             main_title="s700_594p100pmedgeorigin_data")

show_images1(data=s300p100pmedge_data, main_title="s300p100pmedge_data")

print()

show_images1(data=s700_594p100pmrefleorigin_data,

             main_title="s700_594p100pmrefleorigin_data")

show_images1(data=s300p100pmrefle_data, main_title="s300p100pmrefle_data")

print()

show_images1(data=s700_594p100pmsymmeorigin_data,

             main_title="s700_594p100pmsymmeorigin_data")

show_images1(data=s300p100pmsymme_data, main_title="s300p100pmsymme_data")

 

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓

def show_images2(data, main_title=None, s=None, p=None,

                 pin=False, f=0, pm='constant'):

    plt.figure(figsize=(10, 5))

    plt.suptitle(t=main_title, y=0.8, fontsize=14)

    temp_s = s

    im = data[0][0]

    for i in range(1, 6):

        plt.subplot(1, 5, i)

        if not temp_s:

            s = [im.size[1], im.size[0]]

        rc = RandomCrop(size=s, padding=p, # Here

                        pad_if_needed=pin, fill=f, padding_mode=pm)

        plt.imshow(X=rc(im)) # Here

    plt.tight_layout()

    plt.show()

 

plt.figure(figsize=(7, 9))

plt.title(label="s500_394origin_data", fontsize=14)

plt.imshow(X=origin_data[0][0])

show_images2(data=origin_data, main_title="s500_394origin_data")

show_images2(data=origin_data, main_title="s300_data", s=300)

show_images2(data=origin_data, main_title="s200_data", s=200)

show_images2(data=origin_data, main_title="s100_data", s=100)

show_images2(data=origin_data, main_title="s50_data", s=50)

show_images2(data=origin_data, main_title="s10_data", s=10)

show_images2(data=origin_data, main_title="s1_data", s=1)

show_images2(data=origin_data, main_title="s200_300_data", s=[200, 300])

show_images2(data=origin_data, main_title="s300_200_data", s=[300, 200])

print()

show_images2(data=origin_data, main_title="s700_594p100origin_data",

             s=[700, 594], p=100)

show_images2(data=origin_data, main_title="s300p100_data", s=300, p=100)

print()

show_images2(data=origin_data, main_title="s600_594p100_50origin_data",

             s=[600, 594], p=[100, 50])

show_images2(data=origin_data, main_title="s300p100_50_data", s=300,

             p=[100, 50])

print()

show_images2(data=origin_data, main_title="s650_494p25_50_75_100origin_data",

             s=[650, 494], p=[25, 50, 75, 100])

show_images2(data=origin_data, main_title="s300p25_50_75_100_data", s=300,

             p=[25, 50, 75, 100])

print()

show_images2(data=origin_data, main_title="s300_194pn100origin_data",

             s=[300, 194], p=-100)

show_images2(data=origin_data, main_title="s150pn100_data", s=150, p=-100)

print()

show_images2(data=origin_data, main_title="s300_294pn50n100origin_data",

             s=[300, 294], p=[-50, -100])

show_images2(data=origin_data, main_title="s150pn50n100_data", s=150,

             p=[-50, -100])

print()

show_images2(data=origin_data, main_title="s350_294pn25n50n75n100origin_data",

             s=[350, 294], p=[-25, -50, -75, -100])

show_images2(data=origin_data, main_title="s150pn25n50n75n100_data", s=150,

             p=[-25, -50, -75, -100])

print()

show_images2(data=origin_data, main_title="s600_444p25_50origin_data",

             s=[600, 444], p=[25, 50])

show_images2(data=origin_data, main_title="s200p25_50_data", s=200,

             p=[25, 50])

print()

show_images2(data=origin_data, main_title="s400_344pn25n50origin_data",

             s=[400, 344], p=[-25, -50])

show_images2(data=origin_data, main_title="s200pn25n50_data", s=200,

             p=[-25, -50])

print()

show_images2(data=origin_data, main_title="s400_444p25n50origin_data",

             s=[400, 444], p=[25, -50])

show_images2(data=origin_data, main_title="s200p25n50_data", s=200,

             p=[25, -50])

print()

show_images2(data=origin_data, main_title="s600_344pn25_50origin_data",

             s=[600, 344], p=[-25, 50])

show_images2(data=origin_data, main_title="s200pn25_50_data", s=200,

             p=[-25, 50])

print()

show_images2(data=origin_data, main_title="s700_594p100fgrayorigin_data",

             s=[700, 594], p=100, f=150)

show_images2(data=origin_data, main_title="s300p100fgray_data", s=300,

             p=100, f=150)

print()

show_images2(data=origin_data, main_title="s700_594p100fpurpleorigin_data",

             s=[700, 594], p=100, f=[160, 32, 240])

show_images2(data=origin_data, main_title="s300p100fpurple_data", s=300,

             p=100, f=[160, 32, 240])

print()

show_images2(data=origin_data, main_title="s700_594p100pmconstorigin_data",

             s=[700, 594], p=100, pm='constant')

show_images2(data=origin_data, main_title="s300p100pmconst_data", s=300,

             p=100, pm='constant')

print()

show_images2(data=origin_data, main_title="s700_594p100pmedgeorigin_data",

             s=[700, 594], p=100, pm='edge')

show_images2(data=origin_data, main_title="s300p100pmedge_data", s=300,

             p=100, pm='edge')

print()

show_images2(data=origin_data, main_title="s700_594p100pmrefleorigin_data",

             s=[700, 594], p=100, pm='reflect')

show_images2(data=origin_data, main_title="s300p100pmrefle_data", s=300,

             p=100, pm='reflect')

print()

show_images2(data=origin_data, main_title="s700_594p100pmsymmeorigin_data",

             s=[700, 594], p=100, pm='symmetric')

show_images2(data=origin_data, main_title="s300p100pmsymme_data", s=300,

             p=100, pm='symmetric')

登入後複製

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