Changed to prepare onnx

src/engines/onnx_tensorrt_compat.py

@@ -0,0 +1,111 @@
+import ast
+import io
+
+import onnx
+from onnx import helper, numpy_helper
+
+
+_REDUCE_OPS_WITH_AXES_INPUT = {
+    "ReduceL1",
+    "ReduceL2",
+    "ReduceLogSum",
+    "ReduceLogSumExp",
+    "ReduceMax",
+    "ReduceMean",
+    "ReduceMin",
+    "ReduceProd",
+    "ReduceSum",
+    "ReduceSumSquare",
+}
+
+
+def _metadata(model):
+    return {p.key: p.value for p in model.metadata_props}
+
+
+def _input_size(model):
+    try:
+        imgsz = _metadata(model).get("imgsz")
+        parsed = ast.literal_eval(imgsz)
+        if isinstance(parsed, (list, tuple)) and len(parsed) == 2:
+            h, w = int(parsed[0]), int(parsed[1])
+            if h > 0 and w > 0:
+                return h, w
+    except Exception:
+        pass
+    return 1280, 1280
+
+
+def _constant_values(graph):
+    values = {init.name: numpy_helper.to_array(init) for init in graph.initializer}
+    for node in graph.node:
+        if node.op_type != "Constant" or not node.output:
+            continue
+        for attr in node.attribute:
+            if attr.name == "value":
+                values[node.output[0]] = numpy_helper.to_array(attr.t)
+                break
+    return values
+
+
+def _as_int_list(value):
+    if value is None:
+        return None
+    if getattr(value, "shape", ()) == ():
+        return [int(value)]
+    return [int(v) for v in value.reshape(-1).tolist()]
+
+
+def _set_static_input_shape(model, batch=1):
+    h, w = _input_size(model)
+    for graph_input in model.graph.input:
+        tensor_type = graph_input.type.tensor_type
+        if tensor_type.elem_type != onnx.TensorProto.FLOAT:
+            continue
+        dims = tensor_type.shape.dim
+        if len(dims) != 4:
+            continue
+        for dim, value in zip(dims, (batch, 3, h, w)):
+            dim.dim_value = value
+        return True
+    return False
+
+
+def _rewrite_reduce_axes_inputs(model):
+    constants = _constant_values(model.graph)
+    changed = False
+    for node in model.graph.node:
+        if node.op_type not in _REDUCE_OPS_WITH_AXES_INPUT or len(node.input) < 2:
+            continue
+        axes = _as_int_list(constants.get(node.input[1]))
+        if axes is None:
+            continue
+        kept_attrs = [attr for attr in node.attribute if attr.name != "axes"]
+        del node.attribute[:]
+        node.attribute.extend(kept_attrs)
+        node.attribute.extend([helper.make_attribute("axes", axes)])
+        del node.input[1:]
+        changed = True
+    return changed
+
+
+def _cap_default_opset(model, max_opset=17):
+    for opset in model.opset_import:
+        if opset.domain in ("", "ai.onnx") and opset.version > max_opset:
+            opset.version = max_opset
+            return True
+    return False
+
+
+def prepare_for_tensorrt(model_bytes):
+    model = onnx.load_model_from_string(model_bytes)
+    changed = False
+    changed = _set_static_input_shape(model) or changed
+    changed = _rewrite_reduce_axes_inputs(model) or changed
+    changed = _cap_default_opset(model) or changed
+    if not changed:
+        return model_bytes
+
+    buffer = io.BytesIO()
+    onnx.save_model(model, buffer)
+    return buffer.getvalue()