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M-Optimus prediction server-side model. Loads the exported M-Optimus spatial transcriptomics model. Accepts optional bulk RNA counts alongside the tile image. In the two-stage pipeline, bulk RNA preprocessing and gene count validation happen during prepare_sample (Stage 1), so a malformed bulk_rna field fails only the individual request without affecting the batch.

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MOptimusModel

M-Optimus prediction model (224x224, MPP 0.5). Produces per-tile gene expression predictions. Optionally incorporates bulk RNA counts when provided in the request.
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Filename of the .pt2 checkpoint.
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Torch device string.
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Directory containing the checkpoint.
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Directory containing gene set CSVs.

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input_gene_names

Ordered list of input (bulk RNA) Ensembl gene IDs.

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output_gene_names

Ordered list of predicted output Ensembl gene IDs.

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get_metadata

Returns base metadata plus input and output gene sets. Returns: Dictionary with model info, gene sets, and I/O specs.

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prepare_sample

Deserializes tile image and validates bulk RNA per-sample. Bulk RNA preprocessing and gene count validation happen here so that a single request with malformed RNA data fails with a per-request error (HTTP 400) and never enters the batch.
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Raw model request.
Returns: A PreparedSample with tile tensor and optional bulk RNA tensor in extra_tensors. Raises:
  • BioptimusValueError - Tagged with ErrorCode.RNA_SCHEMA_MISMATCH if bulk RNA preprocessing fails or the gene count does not match the model panel. Subclasses ValueError, so it still maps to an HTTP 400 at the API boundary.
  • ValueError - If the tile image is invalid (raised by the base class).

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forward

Runs M-Optimus inference with optional bulk RNA (legacy path).
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List of tile requests with optional bulk RNA.
Returns: Model output tensor of shape (N, num_output_genes).