KMNIST in PyTorch

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*My post explains KMNIST.

KMNIST() can use KMNIST dataset as shown below:

*Memos:

• The 1st argument is root(Required-Type:str or pathlib.Path). *An absolute or relative path is possible.
• The 2nd argument is train(Optional-Default:True-Type:bool). *If it's True, train data(60,000 images) is used while if it's False, test data(10,000 images) is used.
• The 3rd argument is transform(Optional-Default:None-Type:callable).
• The 4th argument is target_transform(Optional-Default:None-Type:callable).
• The 5th argument is download(Optional-Default:False-Type:bool): *Memos:
  • If it's True, the dataset is downloaded from the internet and extracted(unzipped) to root.
  • If it's True and the dataset is already downloaded, it's extracted.
  • If it's True and the dataset is already downloaded and extracted, nothing happens.
  • It should be False if the dataset is already downloaded and extracted because it's faster.
  • You can manually download and extract the dataset from here to e.g. data/KMNIST/raw/.

from torchvision.datasets import KMNIST

train_data = KMNIST(
    root="data"
)

train_data = KMNIST(
    root="data",
    train=True,
    transform=None,
    target_transform=None,
    download=False
)

test_data = KMNIST(
    root="data",
    train=False
)

len(train_data), len(test_data)
# (60000, 10000)

train_data
# Dataset KMNIST
#     Number of datapoints: 60000
#     Root location: data
#     Split: Train

train_data.root
# 'data'

train_data.train
# True

print(train_data.transform)
# None

print(train_data.target_transform)
# None

train_data.download
# <bound method MNIST.download of Dataset KMNIST
#     Number of datapoints: 60000
#     Root location: data
#     Split: Train>

train_data[0]
# (<PIL.Image.Image image mode=L size=28x28>, 8)

train_data[1]
# (<PIL.Image.Image image mode=L size=28x28>, 7)

train_data[2]
# (<PIL.Image.Image image mode=L size=28x28>, 0)

train_data[3]
# (<PIL.Image.Image image mode=L size=28x28>, 1)

train_data[4]
# (<PIL.Image.Image image mode=L size=28x28>, 4)

train_data.classes
# ['o', 'ki', 'su', 'tsu', 'na', 'ha', 'ma', 'ya', 're', 'wo']

from torchvision.datasets import KMNIST

train_data = KMNIST(
    root="data",
    train=True
)

test_data = KMNIST(
    root="data",
    train=False
)

import matplotlib.pyplot as plt

def show_images(data):
    plt.figure(figsize=(12, 2))
    col = 5
    for i, (image, label) in enumerate(data, 1):
        plt.subplot(1, col, i)
        plt.title(label)
        plt.imshow(image)
        if i == col:
            break
    plt.show()

show_images(data=train_data)
show_images(data=test_data)

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