Power Up Your Deep Learning: Mastering Custom Dataset Splitting with PyTorch

Custom Dataset Class:

• You'll define a custom class inheriting from torch.utils.data.Dataset.
• This class will handle loading your data (text, images, etc.) and returning them along with their labels.
• Implement methods like __getitem__ to retrieve a specific data point and label at a given index, and __len__ to return the total number of data points.

Splitting the Data:

• Use torch.utils.data.sampler.SubsetRandomSampler to create splits.
• This allows defining ratios for training and testing data.
• It shuffles the data indices for randomness before splitting.

Here's an example code snippet:

import torch

class CustomDatasetFromCSV(torch.utils.data.Dataset):
  # ... your custom class logic for loading data and labels ...

dataset = CustomDatasetFromCSV("my_data.csv")

# Define the split ratio (e.g., 80% for training, 20% for testing)
train_ratio = 0.8
dataset_size = len(dataset)
indices = list(range(dataset_size))

# Shuffle the indices for randomness
if shuffle_dataset:
  torch.random.manual_seed(random_seed)  # For reproducibility
  torch.random.shuffle(indices)

# Split based on the ratio
split = int(train_ratio * dataset_size)
train_indices, test_indices = indices[:split], indices[split:]

# Create samplers for training and testing data
train_sampler = SubsetRandomSampler(train_indices)
test_sampler = SubsetRandomSampler(test_indices)

# Create data loaders for training and testing
train_loader = torch.utils.data.DataLoader(dataset, sampler=train_sampler, batch_size=batch_size)
test_loader = torch.utils.data.DataLoader(dataset, sampler=test_sampler, batch_size=batch_size)

Explanation:

1. We define a CustomDatasetFromCSV class (replace with your data format) to handle your specific data loading.
2. dataset holds your entire dataset.
3. train_ratio defines the split between training and testing data.
4. dataset_size gets the total number of data points.
5. We create a list of indices corresponding to each data point.
6. Shuffling with a seed ensures the same split across runs if needed.
7. split calculates the index to separate training and testing data based on the ratio.
8. train_indices and test_indices hold indices for each set.
9. train_sampler and test_sampler are created using SubsetRandomSampler for training and testing data respectively.
10. Finally, train_loader and test_loader are created using DataLoader to manage loading data in batches during training and testing.

This approach allows you to efficiently split your custom dataset for training and testing your deep learning model in PyTorch.

---

import torch
from torchvision import transforms

class ImageLabelDataset(torch.utils.data.Dataset):
  def __init__(self, image_dir, label_path, transform=None):
    self.image_dir = image_dir
    self.labels = self._get_labels(label_path)
    self.transform = transform

  def __len__(self):
    return len(self.labels)

  def __getitem__(self, idx):
    image_path = self.image_dir + "/" + str(idx) + ".jpg"  # Modify based on your image format
    image = cv2.imread(image_path)  # Assuming you use OpenCV for image loading
    label = self.labels[idx]

    if self.transform:
      image = self.transform(image)

    return image, label

  def _get_labels(self, label_path):
    # Implement logic to read labels from the provided path (e.g., CSV, text file)
    # This is specific to your label format
    with open(label_path, 'r') as f:
      labels = [int(line.strip()) for line in f]
    return labels

# Example Usage
image_dir = "path/to/images"
label_path = "path/to/labels.txt"

# Define data transformations (optional)
transform = transforms.Compose([
  transforms.ToTensor(),  # Convert image to PyTorch tensor
  transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # Normalize pixel values
])

# Create the dataset
dataset = ImageLabelDataset(image_dir, label_path, transform=transform)

# Define split ratio
train_ratio = 0.8
dataset_size = len(dataset)
indices = list(range(dataset_size))

# Shuffle indices
torch.random.manual_seed(42)  # Set a seed for reproducibility (optional)
torch.random.shuffle(indices)

# Split data based on ratio
split = int(train_ratio * dataset_size)
train_indices, test_indices = indices[:split], indices[split:]

# Create samplers
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(train_indices)
test_sampler = torch.utils.data.sampler.SubsetRandomSampler(test_indices)

# Create data loaders (batch size can be adjusted)
batch_size = 32
train_loader = torch.utils.data.DataLoader(dataset, sampler=train_sampler, batch_size=batch_size)
test_loader = torch.utils.data.DataLoader(dataset, sampler=test_sampler, batch_size=batch_size)

# Accessing data in a training loop (example)
for images, labels in train_loader:
  # Your training logic here
  # images will be a batch of images (tensor)
  # labels will be a batch of labels (tensor)
  # ...

Remember to replace placeholders like image_dir, label_path, and image loading method (cv2.imread) with your specific data structure. This example demonstrates loading images, applying transformations, and splitting the data. You'll need to adapt the _get_labels function to handle your label format (CSV, text file, etc.).

---

torch.utils.data.random_split:

This function directly splits a dataset into non-overlapping subsets with provided ratios. It's simpler than using SubsetRandomSampler but offers less control over shuffling.

train_size = int(0.8 * len(dataset))
train_data, test_data = torch.utils.data.random_split(dataset, [train_size, len(dataset) - train_size])

# Create data loaders for training and testing
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size)

Stratified Split (For Imbalanced Datasets):

If your dataset has imbalanced classes, consider stratified splitting. This ensures each split (training and testing) maintains the same class distribution as the original dataset. Libraries like scikit-learn offer functions like StratifiedKFold for this purpose.

from sklearn.model_selection import StratifiedKFold

# Get labels from your dataset
labels = ...  # Extract labels from your custom dataset

# Define number of folds (e.g., 5 for KFold)
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

# Use KFold to iterate through splits for cross-validation (example)
for train_index, test_index in kfold.split(X=dataset, y=labels):
  # Create training and testing datasets based on the indices
  train_data = Subset(dataset, train_index)
  test_data = Subset(dataset, test_index)

  # Create data loaders for training and testing
  train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size)
  test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size)

  # Train and evaluate your model here
  # ...

Custom Splitting Logic:

For specific splitting needs, you can implement your own logic. This could involve splitting based on certain criteria within your data or using more advanced techniques.

Remember to choose the method that best suits your dataset characteristics and splitting requirements.

python deep-learning pytorch