refactor

machine-learning/app/models/session.py

@@ -0,0 +1,34 @@
+from pathlib import Path
+
+import numpy as np
+import onnx
+import onnxruntime as ort
+from numpy.typing import NDArray
+from onnx.shape_inference import infer_shapes
+from onnx.tools.update_model_dims import update_inputs_outputs_dims
+
+
+def ort_has_batch_dim(session: ort.InferenceSession) -> bool:
+    return session.get_inputs()[0].shape[0] == "batch"
+
+
+def ort_squeeze_outputs(session: ort.InferenceSession) -> None:
+    original_run = session.run
+
+    def run(output_names: list[str], input_feed: dict[str, NDArray[np.float32]]) -> list[NDArray[np.float32]]:
+        out: list[NDArray[np.float32]] = original_run(output_names, input_feed)
+        out = [o.squeeze(axis=0) for o in out]
+        return out
+
+    session.run = run
+
+
+def ort_add_batch_dim(input_path: Path, output_path: Path) -> None:
+    proto = onnx.load(input_path)
+    static_input_dims = [shape.dim_value for shape in proto.graph.input[0].type.tensor_type.shape.dim[1:]]
+    static_output_dims = [shape.dim_value for shape in proto.graph.output[0].type.tensor_type.shape.dim[1:]]
+    input_dims = {proto.graph.input[0].name: ["batch"] + static_input_dims}
+    output_dims = {proto.graph.output[0].name: ["batch"] + static_output_dims}
+    updated_proto = update_inputs_outputs_dims(proto, input_dims, output_dims)
+    inferred = infer_shapes(updated_proto)
+    onnx.save(inferred, output_path)