ExampleLoadingOfDataset

DatasetGenerator.py

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+### Example loading of the dataset
+
+import torch
+from torch.utils.data import Dataset
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+import matplotlib.pyplot as plt
+import zipfile
+import os
+import pandas as pd
+from torchvision.io import read_image
+
+class CustomImageDataset(Dataset):
+    def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):
+        self.img_labels = pd.read_csv(annotations_file)
+        self.img_dir = img_dir
+        self.transform = transform
+        self.target_transform = target_transform
+
+    def __len__(self):
+        return len(self.img_labels)
+
+    def __getitem__(self, idx):
+        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, -1])
+        image = read_image(img_path)
+        label = self.img_labels.iloc[idx, 2]
+        if self.transform:
+            image = self.transform(image)
+        if self.target_transform:
+            label = self.target_transform(label)
+        return image, label
+
+with zipfile.ZipFile("150_Dataset(1).zip", 'r') as zip_ref:
+    zip_ref.extractall(".")
+
+train_dataset = CustomImageDataset(annotations_file="./images/train/train.csv",
+                                   img_dir="./images/train")
+                                 
+train_dataloader = DataLoader(train_dataset, batch_size=12, shuffle=True)
+
+train_features, train_labels = next(iter(train_dataloader))
+print(f"Feature batch shape: {train_features.size()}")
+print(f"Labels batch shape: {len(train_labels)}")
+img = train_features[0].squeeze()
+label = train_labels[0]
+plt.imshow(img, cmap="gray")
+plt.show()
+print(f"Label: {label}")