Predict With a Trained Surrogate

physiotwin4d-infer-physicsnemo loads a model directory written by physiotwin4d-train-physicsnemo and predicts a subject’s per-vertex targets at any stage — including stages that were never acquired, which is the reason to train a surrogate. It is the command-line form of WorkflowInferPhysicsNeMo and WorkflowInferMovement.

Requires the same optional extra as training.

Manifest Mode

Predict every phase in a manifest and score against its stored targets:

physiotwin4d-infer-physicsnemo \
    --model-dir output/mgn_run \
    --manifest manifests/Case1Pack_manifest.json \
    --output output/mgn_run/eval

Add --displacement when the targets are displacements from the subject’s reference mesh: the command then writes reference + prediction meshes, a reference surface colored by per-point RMSE, and error statistics in millimetres, instead of the raw target arrays.

Pass --stages 0.15 0.35 to predict arbitrary stages instead of the manifest’s phases; no ground truth exists for those, so no statistics are written.

Single-Subject Mode

No manifest — just the subject’s PCA coefficients:

physiotwin4d-infer-physicsnemo \
    --model-dir output/mgn_run \
    --shape-parameters Case1Pack_ssm_pca_coefficients.json \
    --stage 0.7 \
    --reference-mesh Case1Pack_ssm_surface.vtp \
    --output output/prediction

Omit --reference-mesh to displace the mesh reconstructed from the PCA coefficients alone, which needs no per-subject geometry but stays in the model’s own frame. Supply --ground-truth to score the prediction against a known surface.

Deformation Fields

With --reference-image, the command rasterizes the predicted displacements and the reference-surface normals onto that image’s voxel grid:

physiotwin4d-infer-physicsnemo \
    --model-dir output/mgn_run \
    --shape-parameters coefficients.json \
    --stage 0.5 \
    --reference-mesh patient_surface.vtp \
    --reference-image patient_ct.mha \
    --output output/fields

This writes deformation_field.mha and surface_normal_field.mha — apply them to volumes and labelmaps with TransformTools.

Options

--model-dir PATH

Required. The trained model directory.

--network {mgn,mlp,auto}

Auto-detected from the checkpoint present in --model-dir by default.

--epoch N

Load a periodic epoch checkpoint instead of the final weights.

--manifest JSON, --stages [FLOAT ...], --displacement

Manifest mode, as above.

--shape-parameters JSON, --stage FLOAT, --reference-mesh PATH, --ground-truth PATH, --reference-image PATH

Single-subject mode, as above.

--output PATH

Output directory; defaults to a subdirectory of the model directory.

See Also