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Adds a formatter that renders `nn.Module` instances as rich, collapsible HTML trees. The formatter uses PyTorch's subpackage structure to map known modules to four display categories (Weight, Activation, Normalization, Regularization). Custom modules that follow PyTorch conventions work automatically, and unknown/containers default to "Other". Keyword-heavy layers (Conv2d with 8+ args) get an expandable row that shows the full args truncated with CSS ellipsis, expandable to a line-by-line view. Positional args (like channel dimensions) stay always-visible next to the type pill for quick scanning. Frozen layers are visually dimmed and show "(frozen)" next to their param count, while partially-frozen containers show the trainable subset. A hover legend in the footer explains the category color coding using mini swatch replicas of the actual type pills.
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for more information, see https://pre-commit.ci
Collaborator
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😍 Checked in darkmode too: 
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Contributor
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Nevermind, found it! 
Maybe the dtype of the layer too? Maybe what device (cpu/gpu) the element is on? The general feeling is that it is really sweet :) |
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Adds a formatter that renders
nn.Moduleinstances as rich, collapsible HTML trees.The formatter uses PyTorch's subpackage structure to map known modules to four display categories (Weight, Activation, Normalization, Regularization). Custom modules that follow PyTorch conventions work automatically, and unknown/containers default to "Other".
Keyword-heavy layers (Conv2d with 8+ args) get an expandable row that shows the full args truncated with CSS ellipsis, expandable to a line-by-line view. Positional args (like channel dimensions) stay always-visible next to the type pill for quick scanning. Frozen layers are visually dimmed and show "(frozen)" next to their param count, while partially-frozen containers show the trainable subset.
A hover legend in the footer explains the category color coding using mini swatch replicas of the actual type pills.
Hovering legend: