EMNIST in PyTorch

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*My post explains EMNIST.

EMNIST() can use EMNIST 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 split(Required-Type:str). *"byclass", "bymerge", "balanced", "letters", "digits" or "mnist" can be set to it.
• There is train argument(Optional-Default:False-Type:float): *Memos:
  • For split="byclass" and split="byclass", if it's True, train data(697,932 images) is used while if it's False, test data(116,323 images) is used.
  • For split="balanced", if it's True, train data(112,800 images) is used while if it's False, test data(188,00 images) is used.
  • For split="letters", if it's True, train data(124,800 images) is used while if it's False, test data(20,800 images) is used.
  • For split="digits", if it's True, train data(240,000 images) is used while if it's False, test data(40,000 images) is used.
  • For split="mnist", if it's True, train data(60,000 images) is used while if it's False, test data(10,000 images) is used.
• There is transform argument(Optional-Default:None-Type:callable).
• There is target_transform argument(Optional-Default:None-Type:callable).
• There is download argument(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/EMNIST/raw/.
• There is the bug which the images are flipped and rotated 90 degrees anticlockwise by default so they should be transformed.

from torchvision.datasets import EMNIST

train_data = EMNIST(
    root="data",
    split="byclass"
)

train_data = EMNIST(
    root="data",
    split="byclass",
    train=True,
    transform=None,
    target_transform=None,
    download=False
)

test_data = EMNIST(
    root="data",
    split="byclass",
    train=False
)

len(train_data), len(test_data)
# 697932 116323

train_data
# Dataset EMNIST
#     Number of datapoints: 697932
#     Root location: data
#     Split: Train

train_data.root
# 'data'

train_data.split
# 'byclass'

train_data.train
# True

print(train_data.transform)
# None

print(train_data.target_transform)
# None

train_data.download
# <bound method EMNIST.download of Dataset EMNIST
#     Number of datapoints: 697932
#     Root location: data
#     Split: Train>

train_data[0]
# (<PIL.Image.Image image mode=L size=28x28>, 35)

train_data[1]
# (<PIL.Image.Image image mode=L size=28x28>, 36)

train_data[2]
# (<PIL.Image.Image image mode=L size=28x28>, 6)

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

train_data[4]
# (<PIL.Image.Image image mode=L size=28x28>, 22)

train_data.classes
# ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
#  'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',
#  'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z',
#  'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm',
#  'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']

from torchvision.datasets import EMNIST

train_data = EMNIST(
    root="data",
    split="byclass",
    train=True
)

test_data = EMNIST(
    root="data",
    split="byclass",
    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)

from torchvision.datasets import EMNIST
from torchvision.transforms import v2

train_data = EMNIST(
    root="data",
    split="byclass",
    train=True,
    transform=v2.Compose([
        v2.RandomHorizontalFlip(p=1.0),
        v2.RandomRotation(degrees=(90, 90))
    ])
)

test_data = EMNIST(
    root="data",
    split="byclass",
    train=False,
    transform=v2.Compose([
        v2.RandomHorizontalFlip(p=1.0),
        v2.RandomRotation(degrees=(90, 90))
    ])
)

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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