SogCLR PyTorch Implementation

in

Now, if you’ve ever tried implementing this algorithm in TensorFlow or Keras, you know how frustrating it can be to deal with all those ***** tensor shapes and data augmentation techniques. But don’t be scared! With SogCLR PyTorch, we’re going to make things a lot easier for ourselves.

First off, let’s take a look at the code:

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor, Normalize
from torch.optim.swa_utils import AveragedModel

class SogCLR(nn.Module):
    def __init__(self):
        super().__init__()
        self.net = nn.Sequential(
            # Your favorite pre-trained model here!
        )
        
        for param in self.net.parameters():
            param.requires_grad = False # Set all parameters to not require gradients, as we will only be updating specific parameters during training
    
    def forward(self, x):
        return self.net(x)
    
    def get_params(self):
        # Get the parameters that we want to update during training
        for name, param in self.named_parameters(): # Use named_parameters() to get the name and parameter of each layer in the network
            if 'fc' not in name: # Exclude fully connected layers from being updated
                yield param
    
def train(model, dataloader, optimizer, epochs=100):
    for e in range(epochs):
        # Loop over the dataset
        for batch_idx, (data, target) in enumerate(dataloader):
            # Get the input and target tensors
            data = data.to('cuda') # Move data to GPU for faster processing
            target = target.to('cuda') # Move target to GPU for faster processing
    
            # Set up the optimizer with SWA averaging
            avg_model = AveragedModel(model, average_step=10) # Create an averaged model using SWA (Stochastic Weight Averaging) with a step size of 10
            for param in model.parameters():
                param.detach() # Detach parameters from the computation graph to prevent them from being updated during training
    
            # Calculate the loss and update the parameters using SGD
            output = model(data) # Pass data through the model to get predictions
            loss = nn.CrossEntropyLoss()(output, target) # Calculate the loss using Cross Entropy Loss function
            optimizer.zero_grad() # Clear the gradients of all optimized parameters
            loss.backward() # Backpropagate the loss to calculate gradients for each parameter
            optimizer.step() # Update the parameters using the calculated gradients
    
            # Update the averaged model every 10 epochs
            if batch_idx % 10 == 0:
                avg_model.update(model) # Update the averaged model with the current model's parameters
    
    return avg_model # Return the averaged model after training

Now, let’s break it down:

– First off, we import the necessary libraries and datasets (CIFAR10 in this case).
– We define our SogCLR model by wrapping a pre-trained model with an `nn.Sequential()`.
– Next, we loop over the dataset using a DataLoader to load batches of data into memory.
– For each batch, we set up the optimizer and calculate the loss using CrossEntropyLoss().
– We then update the parameters using SGD (Stochastic Gradient Descent) and detach them from their gradients for better performance.
– Finally, we update our averaged model every 10 epochs by calling `avg_model.update(model)`.

And that’s it! With this implementation, you can easily train your SogCLR models on any dataset using PyTorch and achieve state-of-the-art results without all the hassle of dealing with tensor shapes and data augmentation techniques.

So give it a try who knows, maybe you’ll be the next AI wizard to revolutionize the field!

SICORPS