CODEWITHSUNDEEP
pythonpytorchtorchvision
GreedyGroot
GreedyGroot

Reputation: 11

Count number of trainable parameters from model start to given layer

I have a number of pytorch models and would like to print the number of trainable parameters it took to get to specific points in the feed forward function.

I know how get the number of all trainable parameters, but is there a way to get the parameters of only part of the model. I could define new models that only have layers to a given point but doing so manually would be time consuming to do for many models.

Upvotes: 0

Views: 39

Answers (1)

Aryan Raj
Aryan Raj

Reputation: 292

To count parameters at specific points in a PyTorch model, we can take advantage of how PyTorch organizes models as a hierarchy of named modules. Think of it like a tree structure where each layer or operation is a node in the tree. Process When you want to count parameters up to a certain point, we can:

Walk through the model layer by layer in order Keep a running sum of trainable parameters (those with requires_grad=True) Stop when we reach our target layer

Implementation Details The key insight is that PyTorch provides a way to iterate through model layers in order using named_modules(), which gives us both the name and the actual layer object. Each layer object knows which parameters it contains. For example, if you have a simple CNN, you might see layers named like:

features.0.conv, features.1.relu, features.2.pool, classifier.0, classifier.1.

You can pick any of these layer names as your stopping point. The counting function will walk through the model until it hits that name, adding up trainable parameters along the way. Advantages This is much more convenient than manually creating truncated models because:

You only need to write the counting logic once It works with any model architecture You can quickly check multiple points in the model It doesn't require modifying your model definition

Upvotes: 0

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