kfoldcv.py

import os
import torch
from torch import nn
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader, ConcatDataset
from torchvision import transforms
from sklearn.model_selection import KFold

def reset_weights(m):
  '''
    Try resetting model weights to avoid
    weight leakage.
  '''
  for layer in m.children():
   if hasattr(layer, 'reset_parameters'):
    print(f'Reset trainable parameters of layer = {layer}')
    layer.reset_parameters()

class SimpleConvNet(nn.Module):
  '''
    Simple Convolutional Neural Network
  '''
  def __init__(self):
    super().__init__()
    self.layers = nn.Sequential(
      nn.Conv2d(1, 10, kernel_size=3),
      nn.ReLU(),
      nn.Flatten(),
      nn.Linear(26 * 26 * 10, 50),
      nn.ReLU(),
      nn.Linear(50, 20),
      nn.ReLU(),
      nn.Linear(20, 10)
    )


  def forward(self, x):
    '''Forward pass'''
    return self.layers(x)
  
  
if __name__ == '__main__':
  
  # Configuration options
  k_folds = 5
  num_epochs = 1
  loss_function = nn.CrossEntropyLoss()
  
  # For fold results
  results = {}
  
  # Set fixed random number seed
  torch.manual_seed(42)
  
  # Prepare MNIST dataset by concatenating Train/Test part; we split later.
  dataset_train_part = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor(), train=True)
  dataset_test_part = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor(), train=False)
  dataset = ConcatDataset([dataset_train_part, dataset_test_part])
  
  # Define the K-fold Cross Validator
  kfold = KFold(n_splits=k_folds, shuffle=True)
    
  # Start print
  print('--------------------------------')

  # K-fold Cross Validation model evaluation
  for fold, (train_ids, test_ids) in enumerate(kfold.split(dataset)):
    
    # Print
    print(f'FOLD {fold}')
    print('--------------------------------')
    
    # Sample elements randomly from a given list of ids, no replacement.
    train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids)
    test_subsampler = torch.utils.data.SubsetRandomSampler(test_ids)
    
    # Define data loaders for training and testing data in this fold
    trainloader = torch.utils.data.DataLoader(
                      dataset, 
                      batch_size=10, sampler=train_subsampler)
    testloader = torch.utils.data.DataLoader(
                      dataset,
                      batch_size=10, sampler=test_subsampler)
    
    # Init the neural network
    network = SimpleConvNet()
    network.apply(reset_weights)
    
    # Initialize optimizer
    optimizer = torch.optim.Adam(network.parameters(), lr=1e-4)
    
    # Run the training loop for defined number of epochs
    for epoch in range(0, num_epochs):

      # Print epoch
      print(f'Starting epoch {epoch+1}')

      # Set current loss value
      current_loss = 0.0

      # Iterate over the DataLoader for training data
      for i, data in enumerate(trainloader, 0):
        
        # Get inputs
        inputs, targets = data
        
        # Zero the gradients
        optimizer.zero_grad()
        
        # Perform forward pass
        outputs = network(inputs)
        
        # Compute loss
        loss = loss_function(outputs, targets)
        
        # Perform backward pass
        loss.backward()
        
        # Perform optimization
        optimizer.step()
        
        # Print statistics
        current_loss += loss.item()
        if i % 500 == 499:
            print('Loss after mini-batch %5d: %.3f' %
                  (i + 1, current_loss / 500))
            current_loss = 0.0
            
    # Process is complete.
    print('Training process has finished. Saving trained model.')

    # Print about testing
    print('Starting testing')
    
    # Saving the model
    save_path = f'./model-fold-{fold}.pth'
    torch.save(network.state_dict(), save_path)

    # Evaluationfor this fold
    correct, total = 0, 0
    with torch.no_grad():

      # Iterate over the test data and generate predictions
      for i, data in enumerate(testloader, 0):

        # Get inputs
        inputs, targets = data

        # Generate outputs
        outputs = network(inputs)

        # Set total and correct
        _, predicted = torch.max(outputs.data, 1)
        total += targets.size(0)
        correct += (predicted == targets).sum().item()

      # Print accuracy
      print('Accuracy for fold %d: %d %%' % (fold, 100.0 * correct / total))
      print('--------------------------------')
      results[fold] = 100.0 * (correct / total)
    
  # Print fold results
  print(f'K-FOLD CROSS VALIDATION RESULTS FOR {k_folds} FOLDS')
  print('--------------------------------')
  sum = 0.0
  for key, value in results.items():
    print(f'Fold {key}: {value} %')
    sum += value
  print(f'Average: {sum/len(results.items())} %')