Demo Additional Training Methods

In this article, we dive into advanced training techniques in PyTorch. We explore methods such as transfer learning, creating learning rate schedulers, and sharing models with the PyTorch community. With step-by-step examples, you'll learn how to modify pre-trained models, fine-tune parameters, and efficiently manage training routines.

Below is an outline of the topics covered:

• Using the TorchVision library to load pre-trained models
• Modifying model output layers for custom tasks
• Utilizing the PyTorch Hub to list, download, and share models
• Creating and using learning rate schedulers
• Freezing model layers and fine-tuning only the final layer

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Loading and Using Pre-trained Models with TorchVision

PyTorch offers a wide array of pre-trained models through the TorchVision library. You can leverage these models directly for fine-tuning or use them as feature extractors. The examples below demonstrate how to list available models, load the VGG19 model, and implement both the legacy pretrained=True approach and the modern API using the weights argument.

# Import PyTorch vision models
from torchvision import models

# List available models and print the count
print(models.list_models())
print(f"Number available: {len(models.list_models())}")

# Load a pre-trained VGG19 model (old API)
model = models.vgg19(pretrained=True)

# Load with weights argument (new API)
model = models.vgg19(weights=models.VGG19_Weights.DEFAULT)

# Display the model parameters
print(model.state_dict())

As demonstrated, the models module downloads the required weights to your local directory. The updated API offers the flexibility of choosing specific pre-trained weights for your models.

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Modifying the Model Output

Pre-trained models are typically configured for 1,000 classes. When working on a custom task with a different number of classes, you must adjust the output layer accordingly. The following example shows how to modify the final classifier layer of the VGG19 model to output 20 classes:

# Display the classifier layers
print(model.classifier)

import torch.nn as nn

# Modify the output layer (for 20 classes)
model.classifier[6] = nn.Linear(4096, 20)

# Verify the updated classifier
print(model.classifier)

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Using the PyTorch Hub to Share and Load Models

The PyTorch Hub provides a seamless way to list, download, and share models from GitHub repositories. It also supports loading pre-trained weights effortlessly. For instance, the following snippet demonstrates how to list available models from the PyTorch Vision repository on GitHub:

from torch import hub

# List models from a specific version of torchvision (v0.10.0)
hub.list('pytorch/vision:v0.10.0')

Similarly, to explore models like YOLOv5 from Ultralytics:

# List available YOLOv5 models
hub.list('ultralytics/yolov5')

To load a model from the hub, you can use the code below:

from torch import hub

# Load the YOLOv5s model
model = hub.load('ultralytics/yolov5', 'yolov5s')

# Provide a batch of images for inference
imgs = ['cat1.jpg', 'zidane.jpg']
results = model(imgs)

# Print the inference results
results.print()

Additionally, here’s how to load a ResNet-50 model with pre-trained weights from the PyTorch Vision GitHub repository:

# Load model weights and the model using the hub
weights = hub.load("pytorch/vision", "get_model_weights", name="resnet50")
model = hub.load("pytorch/vision", "resnet50", weights=weights.DEFAULT)

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Sharing Your Own Model via PyTorch Hub

You can easily share your custom models using a GitHub repository by including a hub configuration file (typically named hub-conf.py). This file defines dependencies, model URLs, and entry points. Below is an excerpt from a typical hub-conf.py that specifies a simple neural network model called FakeNet:

# Define dependencies
dependencies = ['torch']
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.hub import load_state_dict_from_url

# Dictionary mapping model names to URLs containing the state dict
models_url = {
    'fake_model': 'https://github.com/kodekloudhub/PyTorch/raw/refs/heads/main/section_3/demos/030-105-additional-training-methods/model_state_dict'
}

# Define the model class
class FakeNet(nn.Module):
    def __init__(self):
        super(FakeNet, self).__init__()
        self.fc1 = nn.Linear(10, 50)
        self.batch_norm = nn.BatchNorm1d(50)
        self.fc2 = nn.Linear(50, 1)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.batch_norm(x)
        x = self.fc2(x)
        return x

# Entry point for the fake_model
def fake_model(pretrained=False, **kwargs):
    """
    FakeNet model
    pretrained (bool): Load pretrained weights if True.
    """
    model = FakeNet(**kwargs)
    if pretrained:
        model.load_state_dict(load_state_dict_from_url(models_url['fake_model'], progress=True))
    return model

Once hosted on GitHub, users can load your model with the following code:

import torch

# Load the fake_model with pretrained parameters
model = torch.hub.load('kodekloudhub/PyTorch', 'fake_model', pretrained=True)
print(model.state_dict())

# List available models in the repo and get help about the fake_model
print(torch.hub.list('kodekloudhub/PyTorch'))
torch.hub.help('kodekloudhub/PyTorch', 'fake_model')

To load the model without pre-trained weights and modify it for a custom task:

import torch

model = torch.hub.load('kodekloudhub/PyTorch', 'fake_model', pretrained=False)
print(model)

# Modify the network output to have two outputs instead of one
import torch.nn as nn
model.fc2 = nn.Linear(50, 2)
print(model)

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Learning Rate Schedulers

Learning rate schedulers are pivotal in adjusting the learning rate during training, ensuring efficient convergence and preventing overshooting. PyTorch optimizers support several schedulers. Here are some examples:

import torch.optim as optim

# Define an optimizer for the model parameters
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Create a StepLR scheduler: reduce the learning rate by 0.1 every 5 epochs
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)

# Create an ExponentialLR scheduler: decay the learning rate by multiplying with 0.1 every epoch
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.1, last_epoch=-1)

# Create a ReduceLROnPlateau scheduler: reduce LR by 0.1 if no improvement for 2 epochs
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=2)

# Print the scheduler state
print(scheduler.state_dict())

Each scheduler has a specific use case. For example, StepLR decreases the learning rate at fixed intervals, while ReduceLROnPlateau monitors a validation metric and adjusts the learning rate when performance stalls.

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Transfer Learning and Fine-tuning the Final Layer

Transfer learning allows you to leverage pre-trained models and fine-tune just the final layers for a specific task. This section illustrates how to adapt the VGG19 model for a 10-class problem using the CIFAR10 dataset. We begin by preparing the dataset and updating the model's output layer.

Prepare the Dataset and Update the Model

Using image transformations and the CIFAR10 dataset (which includes classes like plane, car, bird, cat, deer, dog, frog, horse, ship, and truck), we update the classifier for our custom task:

import torch
import torchvision.transforms.v2 as v2
from torchvision import datasets

transform = v2.Compose([
    v2.RandomHorizontalFlip(),
    v2.ToImage(),
    v2.ToDtype(torch.float32, scale=True),
    v2.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  # Important normalization
])

# CIFAR10 dataset
trainset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=1)

import torch.nn as nn
from torchvision import models

# Load VGG19 with default weights
model = models.vgg19(weights=models.VGG19_Weights.DEFAULT)

# Update the final classifier layer for 10 classes
model.classifier[-1] = nn.Linear(4096, 10)

Freezing and Unfreezing Layers

For effective feature extraction, freeze all layers except the final classifier. This ensures that during training, only the last layer's parameters are updated.

# Freeze all layers
for param in model.parameters():
    param.requires_grad = False

# Unfreeze only the final classifier layer
for param in model.classifier[-1].parameters():
    param.requires_grad = True

# Verify which layers are trainable
for name, param in model.named_parameters():
    print(f"Layer: {name}, requires_grad: {param.requires_grad}")

Training Loop with a Learning Rate Scheduler

The training loop below defines a loss function, optimizer (limited to the final classifier), and a learning rate scheduler. It also saves checkpoints periodically, including the state of the optimizer and scheduler.

import torch.optim as optim

# Define loss, optimizer, and scheduler
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.classifier[-1].parameters(), lr=0.001, momentum=0.9)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1)

N_EPOCHS = 10
for epoch in range(N_EPOCHS):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        
        # Zero the gradients
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
    
    # Print the average loss for this epoch
    print(f"Epoch: {epoch} Loss: {running_loss / len(trainloader):.4f}")

    # Step the scheduler at the end of the epoch
    scheduler.step()

    # Save a checkpoint every 2 epochs
    if epoch % 2 == 0:
        torch.save({
            'epoch': epoch,
            'model_state_dict': model.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
            'scheduler_state_dict': scheduler.state_dict(),
            'loss': loss,
        }, f'training_checkpoint_{epoch}.tar')

# Save the final checkpoint after the last epoch
torch.save({
    'epoch': N_EPOCHS,
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict(),
    'scheduler_state_dict': scheduler.state_dict(),
    'loss': loss,
}, 'training_checkpoint_final.tar')

This training approach demonstrates effective feature extraction by fine-tuning only the final layer over 10 epochs while preserving the state of training through periodic checkpointing.

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Conclusion

In this article, we explored several advanced training techniques in PyTorch. We covered the process of loading and modifying pre-trained models, using PyTorch Hub for model sharing, setting up various learning rate schedulers, and implementing transfer learning by fine-tuning the model's final layer. These methodologies not only boost model performance but also streamline the training process. Happy coding!