---

name: model-summary-usage description: "Use torchsummary.summary and torchsummary.summary_string to inspect PyTorch nn.Module shapes, parameter counts, devices, dtypes, and memory estimates." disable-model-invocation: true metadata: disco-role: operating license: MIT

model-summary-usage

Use this sub-skill when a task needs to call torchsummary.summary or torchsummary.summary_string for a PyTorch nn.Module without reopening the source repository.

Read this when

• You need a Keras-style printed summary table for a PyTorch model.
• You need programmatic total/trainable parameter counts from summary_string.
• The model has one input, multiple inputs, or per-input dtype requirements.
• A summary run is failing because of CPU/CUDA placement, dtype, input-size, or shape issues.
• You need to decide whether this lightweight package is enough or whether to use torchinfo for a newer or more advanced model-inspection task.

Do not use this for

• Editing package source, changing tests, packaging metadata, or release work; route those tasks to repo-maintenance.
• General PyTorch debugging unrelated to this package's summary calls.
• Precise memory profiling or complex input/output model introspection; prefer torchinfo or a PyTorch profiler workflow for those cases.

Start here

1. Confirm the runtime has torchsummary, torch, and numpy available.

2. Import only the public API:

   from torchsummary import summary, summary_string
   

3. For CPU-safe usage, pass the device explicitly and move the model yourself:

   import torch
   
   device = torch.device("cpu")
   model = model.to(device)
   summary(model, input_size=(channels, height, width), device=device)
   

4. Use the bundled smoke helper from this sub-skill directory when you need a quick verification of the installed package:

   python scripts/smoke_summary.py --help
   python scripts/smoke_summary.py --case all --device cpu
   

   From the root generated skill directory, use:

   python sub-skills/model-summary-usage/scripts/smoke_summary.py --case all --device cpu
   

References

• API reference: exact signatures, return values, input_size, batch_size, device, dtype, hook, and memory-estimate semantics.
• Workflows: single-input, multiple-input, dtype, device, summary_string, and output-interpretation recipes.
• Troubleshooting: workflow-specific failures and fixes.
• Root shared troubleshooting: package install/import and cross-cutting backend issues shared with other sub-skills.

Key facts to preserve

• Distribution/package name: torchsummary; version evidenced for this skill: 1.5.1.
• Public exports: summary and summary_string from torchsummary.
• summary(...) prints the formatted table and returns the parameter-info tuple produced by summary_string(...).
• summary_string(...) returns (summary_str, (total_params, trainable_params)).
• input_size excludes the batch dimension. A tuple means one input; a list of tuples means multiple inputs.
• The default device is CUDA (cuda:0), so CPU-only calls should pass device="cpu" or torch.device("cpu").