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name: layers-and-components description: "Use timm reusable layers, classifier heads, pooling, normalization, attention/drop modules, patch embeddings, and feature-extraction wrappers when building or validating custom models." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Layers and Components

Use this sub-skill when a task asks an agent to assemble custom timm-style model pieces, validate tensor shapes through reusable layers, replace classifier heads or pooling, configure normalization/activation/drop modules, use PatchEmbed, or wrap a model for feature extraction.

Fast Path

1. Import from the current public layer surface: from timm.layers import PatchEmbed, ClassifierHead, SelectAdaptivePool2d, DropPath.
2. Validate tensor layout before wiring components: most convolutional layers consume NCHW; PatchEmbed(flatten=True) returns NLC; PatchEmbed(output_fmt='NHWC') returns channel-last spatial output.
3. Use ClassifierHead(in_features, num_classes, pool_type='avg') for NCHW feature maps and set num_classes=0 when the caller wants pre-classifier features.
4. Use SelectAdaptivePool2d(pool_type='avg'|'max'|'avgmax'|'catavgmax', flatten=True) when a custom head needs global pooling; remember catavgmax doubles channels.
5. Use features_only=True, out_indices, output_stride, and model.feature_info for normal backbone feature maps; use forward_intermediates() only for families that expose it.
6. Run python sub-skills/layers-and-components/scripts/layer_component_probe.py from the skill root, or copy it into a project, for a no-download component shape smoke check.

Decision Guide

• Need import names, constructor patterns, output shapes, and layout notes: read references/layers-api.md.
• Need recipes for custom blocks, patch embeddings, classifier heads, and feature wrappers: read references/custom-model-components.md.
• Need to debug shape, dtype, train/eval, feature extraction, FX tracing, or moved-import issues: read references/troubleshooting.md.
• Need architecture discovery, create_model, pretrained configs, or model registry behavior: route to ../model-library/.
• Need data transforms or loaders around model inputs: route to ../data-pipelines/.
• Need training loops, losses, schedulers, or stochastic-depth schedules in a full run: route to ../training-workflows/.

Guardrails

• Prefer timm.layers imports; timm.models.layers is a deprecated compatibility path.
• Do not assume all feature wrappers work with arbitrary custom models; provide feature_info with num_chs, reduction, and module when wrapping your own network.
• Do not mix NCHW, NHWC, and NLC silently; choose the component's input_fmt/output_fmt explicitly when leaving the default layout.
• Do not test components with pretrained=True; reusable layer validation should work on random tensors without downloads.