---

name: repo-maintenance description: "Maintain the torchsummary checkout safely, including package layout, public exports, dependency caveats, focused unittest commands, and safe edit boundaries." disable-model-invocation: true metadata: disco-role: operating license: MIT

repo-maintenance

Use this sub-skill when the task is to maintain, edit, test, package, or refresh the tiny torchsummary source checkout. It preserves the repository facts needed to make safe changes without reopening external project pages or depending on original checkout paths.

For user-level model summary calls, input_size usage, multiple-input workflows, return values, or interpreting the printed table, route to ../model-summary-usage/SKILL.md instead.

Route map

• Source layout, package metadata, dependency declaration gaps, focused tests, and safe edit boundaries: read references/testing-and-maintenance.md.
• Maintenance failures, import confusion, CPU/CUDA device issues, dtype pitfalls, and modern PyTorch count comparisons: read references/troubleshooting.md.
• Cross-skill install/import provenance and shared package troubleshooting: see the root skill references ../../references/repo-provenance.md and ../../references/troubleshooting.md when those root files are present in the generated bundle.

Essential facts

• Distribution/import module: torchsummary.
• Source package version: 1.5.1 from setup.py.
• Public exports from torchsummary/__init__.py: summary and summary_string.
• Installed/source signatures verified for both public functions:
  • summary(model, input_size, batch_size=-1, device=torch.device("cuda:0"), dtypes=None)
  • summary_string(model, input_size, batch_size=-1, device=torch.device("cuda:0"), dtypes=None)
• setup.py declares packages=["torchsummary"] and no install_requires, but runtime source imports both torch and numpy.
• The implementation creates random synthetic inputs and runs a real forward pass through the supplied torch.nn.Module; tests are small and safe, but require working torch and numpy imports.
• The README begins with a deprecation/migration note recommending the newer torchinfo project. Preserve that note when editing docs, and prefer migration advice rather than adding broad new features to this legacy package.

Quick maintainer loop

From the repository root of the checkout being edited:

python - <<'PY'
import inspect
import torchsummary
print(torchsummary.__file__)
print(inspect.signature(torchsummary.summary))
print(inspect.signature(torchsummary.summary_string))
PY

python -m unittest torchsummary.tests.unit_tests.torchsummary_test

If the full unit module fails, run the focused test IDs in references/testing-and-maintenance.md, fix the smallest behavior boundary that explains the failure, then rerun the focused test and the full unit module.

Do not change casually

Treat the following as compatibility boundaries unless tests, docs, and generated skill references are updated together:

• Public function names, exports, signatures, defaults, return values, and print behavior.
• input_size semantics: tuple means one input; list of tuples means multiple inputs.
• Tuple/list handling for model outputs in the summary table.
• Per-input dtypes order and length expectations.
• Device behavior, especially the default cuda:0 signature and explicit device="cpu" usage on CPU-only hosts.
• Printed table columns and totals: Layer (type), Output Shape, Param #, total/trainable/non-trainable params, and size estimates.