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name: modeling-inference description: "Choose and wire MONAI networks, losses, metrics, inferers, postprocessing, visualization, and export-oriented model utilities for inference and evaluation primitives." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MONAI Modeling and Inference

Use this sub-skill when a task needs MONAI model primitives: network selection, tensor shape conventions, segmentation losses, validation metrics, sliding-window inference, postprocessing, visualization, or model export caveats.

Route Here For

• Building channel-first MONAI networks from monai.networks, especially segmentation architectures such as UNet and transformer-style variants such as SwinUNETR.
• Choosing loss functions from monai.losses, including activation and one-hot handling for binary, multi-class, and multi-label segmentation.
• Computing metrics from monai.metrics, especially Dice and surface-distance style metrics that expect postprocessed predictions and reset/aggregate discipline.
• Running inference with monai.inferers.sliding_window_inference or SlidingWindowInferer for volumes too large for one forward pass.
• Adding prediction postprocessing with dictionary transforms such as Activationsd and AsDiscreted.
• Understanding visualization helpers and export-oriented caveats without relying on optional export packages being installed.

Route Elsewhere

• Use ../data-transforms/SKILL.md for data loading, preprocessing, metadata, MetaTensor, caching, decollation, and inverse transforms.
• Use ../training-evaluation/SKILL.md for Ignite trainer/evaluator loops, handlers, checkpointing, AMP, and experiment logging.
• Use ../bundle-config/SKILL.md for MONAI Bundle configuration, Bundle CLI run/verify/export commands, and packaged bundle workflows.
• Use ../apps-auto3dseg/SKILL.md for Auto3DSeg and high-level application orchestration.

Required References

• Read references/workflows.md when choosing a network/loss/metric/inferer recipe or wiring postprocessing, visualization, and export-aware inference flow.
• Read references/api-reference.md when you need concise signatures, tensor shape contracts, or API-specific usage notes for core modeling primitives.
• Read references/troubleshooting.md when debugging channel/shape mismatches, activation or one-hot mistakes, metric lifecycle issues, sliding-window OOM/artifacts, optional export dependencies, or CPU/CUDA limitations.
• Run scripts/modeling_smoke.py --help to inspect the bundled validation script; run scripts/modeling_smoke.py to perform a tiny CPU-only smoke check of MONAI network, loss/metric, and sliding-window primitives.

Quick Defaults

• Assume tensors are channel-first: image/logits shape is usually (batch, channels, spatial...); 3D segmentation commonly uses (B, C, H, W, D).
• For multi-class segmentation logits, start with DiceCELoss(to_onehot_y=True, softmax=True) during training and postprocess validation logits with Activationsd(softmax=True) followed by AsDiscreted(argmax=True, to_onehot=num_classes).
• For binary segmentation logits, start with DiceLoss(sigmoid=True) or DiceCELoss(sigmoid=True) and postprocess with sigmoid plus thresholding.
• For sliding-window inference, keep the predictor in eval mode, choose roi_size in spatial dimensions only, reduce sw_batch_size before reducing ROI, and use device="cpu"/sw_device="cuda" only when you intentionally want stitched output on CPU.
• For metrics, pass postprocessed predictions and labels with compatible one-hot/channel layout, call the metric per batch, then aggregate() and reset() for each validation epoch.