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name: model-task-adaptation description: "Routes pytorch-grad-cam adaptation for ViT, Swin, CLIP, object detection, semantic segmentation, embeddings, custom targets, and reshape transforms." disable-model-invocation: true metadata: disco-role: operating license: MIT

Model and Task Adaptation

Use this sub-skill when the model output or intermediate activation is not an ordinary CNN feature map plus classification logits. It covers transformer token reshaping, CLIP prompt scoring, HuggingFace/timm optional dependencies, object detection and segmentation targets, embeddings/similarity outputs, and custom callables.

Read first

• references/target-and-reshape-reference.md for target callable and reshape contracts.
• references/transformer-and-clip-workflows.md for ViT, SwinT, CLIP, and optional dependency patterns.
• references/detection-segmentation-embeddings.md for Faster R-CNN, semantic segmentation, embedding outputs, and DFF routing.
• references/troubleshooting.md for shape, dtype, device, and missing optional dependency failures.
• Run scripts/validate_reshape_transform.py before integrating a new token-to-spatial transform.

Adaptation contract

1. Identify the tensor or structured output whose scalar should be explained.
2. Write a target callable that reduces that output to one scalar per batch item.
3. Identify the activation shape at the chosen target layer.
4. If it is not already spatial B x C x H x W, pass a reshape_transform that removes non-spatial tokens and transposes to channel-first layout.
5. Verify output shapes and device/dtype behavior with synthetic tensors before downloading external models or running expensive examples.

Do not select a final transformer block blindly: if the classifier reads only a class token, patch gradients at the final block can be zero. Use the nearest pre-head spatially meaningful layer and validate it.