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import os
import pandas as pd
import torch
from torch.utils.data import Dataset
from torchvision.io import read_image
class DRDataset(Dataset):
def __init__(self, csv_path: str, transform=None):
self.csv_path = csv_path
self.transform = transform
self.image_paths, self.labels = self.load_csv_data()
def load_csv_data(self):
# Check if CSV file exists
if not os.path.isfile(self.csv_path):
raise FileNotFoundError(f"CSV file '{self.csv_path}' not found.")
# Load data from CSV file
data = pd.read_csv(self.csv_path)
# Check if 'image_path' and 'label' columns exist
if "image_path" not in data.columns or "label" not in data.columns:
raise ValueError("CSV file must contain 'image_path' and 'label' columns.")
# Extract image paths and labels
image_paths = data["image_path"].tolist()
labels = data["label"].tolist()
# Check if any image paths are invalid
invalid_image_paths = [
img_path for img_path in image_paths if not os.path.isfile(img_path)
]
if invalid_image_paths:
raise FileNotFoundError(f"Invalid image paths found: {invalid_image_paths}")
# Convert labels to LongTensor
labels = torch.LongTensor(labels)
return image_paths, labels
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
image_path = self.image_paths[idx]
label = self.labels[idx]
# Load image
try:
image = read_image(image_path)
except Exception as e:
raise IOError(f"Error loading image at path '{image_path}': {e}")
# Apply transformations if provided
if self.transform:
try:
image = self.transform(image)
except Exception as e:
raise RuntimeError(
f"Error applying transformations to image at path '{image_path}': {e}"
)
return image, label
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