main.py

import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt

import os
import pandas as pd
from torchvision.io import read_image

# ================================================================ #
#                          Loading a Dataset                       #
# ================================================================ #

train_data = datasets.FashionMNIST(
    root='data',
    train=True,
    download=True,
    transform=ToTensor()
)

test_data = datasets.FashionMNIST(
    root='data',
    train=False,
    download=True,
    transform=ToTensor()
)

# ================================================================ #
#                       Visualize the Dataset                      #
# ================================================================ #

labels_map = {
    0: "T-Shirt",
    1: "Trouser",
    2: "Pullover",
    3: "Dress",
    4: "Coat",
    5: "Sandal",
    6: "Shirt",
    7: "Sneaker",
    8: "Bag",
    9: "Ankle Boot",
}
figure = plt.figure(figsize=(8, 8))
cols, rows = 3, 3
for i in range(1, cols * rows + 1):
    random_idx = torch.randint(len(train_data), size=(1,)).item()
    img, label = train_data[random_idx]
    figure.add_subplot(rows, cols, i)
    plt.title(labels_map[label])
    plt.axis('off')
    plt.imshow(img.squeeze(), cmap='gray')
plt.show()

# ================================================================ #
#                         Custom Dataset                           #
# ================================================================ #
''' A custom Dataset class must implement three functions: __init__, __len__, and __getitem__ '''


class CustomDataset(Dataset):
    def __init__(self, annotation_file, img_dir, transform=None, target_transform=None):
        self.img_labels = pd.read_csv(annotation_file)
        self.img_dir = img_dir
        self.transform = transform
        self.target_transform = target_transform

    # The __len__ function returns the number of samples in our dataset.
    def __len__(self):
        return len(self.img_labels)

    # The __getitem__ function loads and returns a sample from the dataset at the given index idx
    def __getitem__(self, idx):
        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
        image = read_image(img_path)
        label = self.img_labels.iloc[idx, 1]
        if self.transform:
            image = self.transform(image)
        if self.target_transform:
            label = self.target_transform(label)
        sample = {"image": image, "label": label}
        return sample


# ================================================================ #
#                            DataLoaders                           #
# ================================================================ #
train_loader = DataLoader(
    dataset=train_data,
    batch_size=64,
    shuffle=True
)

test_loader = DataLoader(
    dataset=test_data,
    batch_size=64,
    shuffle=False
)

# ================================================================ #
#                      Iterate through DataLoaders                 #
# ================================================================ #

# Display image and label
train_features, train_labels = next(iter(train_loader))
print(f'Feature batch shape: {train_features.size()}')
print(f'Lables batch shape: {train_labels.size()}')
img = train_features[0].squeeze()
label = train_labels[0]
plt.imshow(img, cmap='gray')
plt.show()
print(f'Label: {label}')