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name: cam-generation description: "Routes core pytorch-grad-cam heatmap generation, target layer selection, smoothing, guided backprop, and safe smoke-test workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

CAM Generation

Use this sub-skill when a user wants to create class activation maps for a PyTorch image/classification-style model, overlay heatmaps on images, choose class targets, apply smoothing, combine CAM with guided backpropagation, or run a safe no-download smoke test.

First read/run

• Read references/cam-workflows.md for the core GradCAM pattern, target layer choices, smoothing, guided backprop, batch behavior, and output validation.
• Read references/troubleshooting.md when a CAM is blank, slow, wrong-shaped, on the wrong device, or leaking hooks.
• Run scripts/tiny_cam_smoke.py to verify the installed package with a tiny in-memory CNN and no external weights.

Core workflow

1. Put the model in eval() mode and choose one or more spatial target layers.

2. Prepare an input tensor shaped like the model expects, usually B x C x H x W.

3. Choose targets. Use targets=None for each batch member's top class, or a list such as [ClassifierOutputTarget(class_id) for _ in range(batch_size)].

4. Construct the CAM object once, preferably in a with block:

   from pytorch_grad_cam import GradCAM
   from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
   
   target_layers = [model.layer4[-1]]
   targets = [ClassifierOutputTarget(281)]
   with GradCAM(model=model, target_layers=target_layers) as cam:
       grayscale_cam = cam(input_tensor=input_tensor, targets=targets)
   

5. Overlay a single CAM using show_cam_on_image, ensuring the base image is np.float32 in [0, 1].

Common decisions

• Use aug_smooth=True for test-time augmentation smoothing; expect slower runtime.
• Use eigen_smooth=True to denoise by projecting activations * weights.
• Set cam.batch_size for ScoreCAM and AblationCAM because they perform many forward passes.
• Use a list of target layers when uncertain; outputs are averaged across selected layers.
• Route ViT/Swin/CLIP, detection, segmentation, and embeddings to model-task-adaptation because they need reshape transforms or custom target callables.
• Route method tradeoffs and exact signatures to methods-and-api.
• Route ROAD/ARCC/DFF and explanation scoring to metrics-and-evaluation.