This walkthrough uses TorchVision's Swin-B in tensorViz, a free VS Code extension we build. The recorded capture has 2,964 graph nodes. It follows one route through the model: feature stages → a transformer block → its MLP → a linear operation.

Watch the recorded walkthrough.

Keep an overview you can return to

Begin at the stage level. Identify the sequence of major modules before opening their internals. On Swin-B, the transformer stages contain 2, 2, 18 and 2 blocks. The long stage is exactly where an overview helps: you can find one block without reading the operations inside all 18.

This is a useful habit in source code too. model.children() gives direct child modules. model.named_modules() walks nested modules recursively. Starting with the direct children keeps the first pass manageable; inspect a particular nested module when you have a reason to go deeper. PyTorch's module guide documents the distinction between immediate and recursive traversal.

Registration order alone does not prove runtime data flow. Read forward and use a capture for the inputs you care about when that distinction matters.

Follow one path, keeping the module names

In the recording, the route ends at features.5.0.mlp.0. The path carries useful context: an operation belongs to an MLP, inside one block, inside a feature stage. Keep that context visible while you inspect the details.

In tensorViz, open the relevant stage, then one transformer block. Open its MLP only when you want to inspect that part of the computation. The breadcrumbs give you a route back to the parent. This lets you answer a local question without losing where the module sits in the larger model.

A practical stopping rule: if the current view already answers your question, stop expanding. For a block-boundary question, the block's inputs and outputs may be enough. For a projection question, descend into the MLP and select the linear operation.

One transformer block, with its place in the model kept visible.

Read tensor shapes in the context of the capture

Once you reach the operation, inspect its captured inputs and outputs. Keep the example input beside your notes so a shape is not separated from the run that produced it.

This capture used CPU evaluation, random weights and one float32 image tensor of shape [1, 3, 224, 224]. No pretrained checkpoint was downloaded. The graph helps inspect this execution; it is not a claim about model accuracy, training behavior, every input size or every possible branch.

The walkthrough also uses previously visited scopes with restored camera views. It demonstrates navigation and context, not a benchmark of first-open camera placement or performance.

Use the graph to decide what code to read next

After finding the module, inspect its Python implementation and the operations in forward. The graph gives you a concrete place to start. The source explains the computation, including details that a compact diagram may leave out.

For a different model, the same sequence works as an inspection method:

1. Name the question and the example input.

2. Find the major stage containing the relevant module.

3. Open one block, keeping its parent path visible.

4. Compare the captured input and output shapes.

5. Read the corresponding source, then return to the overview.

Whether a particular model can be captured depends on its implementation and setup. Keep unsupported paths or missing context visible instead of treating a diagram as a complete proof of behavior.

You can explore browser examples before installing anything. For local models, tensorViz is on the VS Code Marketplace.

This topic was informed by a public r/pytorch discussion about graph size. Those comments concerned another tool in 2025; they are not tensorViz testimonials or a comparison with that tool's current behavior.

Disclosure: published by the tensorViz team, with AI assistance. The workflow and capture details were checked against the recorded demonstration and its source manifest. The recording was made with a development build.