What is the torchsummary library in PyTorch used for?

The torchsummary library in PyTorch is used to generate a summary of the model structure, including information about the input shape, number of parameters, and output shape of each layer. It provides a simple and convenient way to view and analyze the structure of PyTorch models, helping users better understand the model for debugging and optimization.

In specific terms, the torchsummary library is mainly used for the following aspects:

1. The torchsummary can display the hierarchical structure of a PyTorch model, including the type of each layer, input and output shapes, as well as the number of parameters. This helps users understand the composition and architecture of the model.
2. Counting the number of parameters: With torchsummary, users can quickly understand the number of parameters in each layer of the model, including trainable parameters and non-trainable parameters, helping to evaluate the complexity and scale of the model.
3. Calculate total number of parameters: torchsummary is also able to calculate the total number of parameters in the entire model, which is particularly useful for evaluating memory consumption and model size.

In summary, torchsummary offers an intuitive and convenient way to view and analyze the structure and parameter information of PyTorch models, making it a useful tool during the model design and debugging process.