How to perform model ensembling in PyTorch?

In PyTorch, model ensembling can typically be achieved through the following methods:

1. Voting Ensemble: Combining predictions from multiple models and selecting the result with the most votes as the final prediction. This can involve a simple majority vote or a weighted voting strategy where models are assigned different weights based on their performance.
2. Averaging Ensemble: Combining the predictions of multiple models by averaging them to obtain the final prediction result. This ensemble method can either take a simple average of the predictions from different models or a weighted average based on the performance of each model.
3. Stacking Ensemble: Utilizing predictions from multiple diverse models as input, a meta-model is employed to make the final prediction. The meta-model can range from a simple linear model to more complex models like neural networks.
4. Bagging ensemble: by randomly sampling the training data, training multiple models, and then averaging or voting on the predictions of multiple models to obtain the final prediction result. This method can reduce the variance of the model and improve the stability of the model.

One option:
When implementing model integration, you can define each model using PyTorch’s nn.Module class, and then combine different integration methods to fuse models. You can train multiple models, save their parameters, and load these parameters during testing to integrate the models. Alternatively, you can use advanced frameworks like PyTorch Lightning to simplify the implementation process of model integration.