Implementation:Onnx Onnx Helper Make Tensor Value Info
| Knowledge Sources | |
|---|---|
| Domains | Model_Construction, Intermediate_Representation |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
Concrete tool for creating tensor type and shape specifications provided by the ONNX helper module.
Description
The make_tensor_value_info function constructs a ValueInfoProto protobuf message that describes a tensor's name, element data type, and shape. It is the primary entry point for declaring graph inputs and outputs when building ONNX models programmatically. Internally, it delegates to make_tensor_type_proto to populate the type field with a TypeProto containing tensor type and shape information.
Usage
Import this function when you need to define input or output tensor specifications for an ONNX graph. It is always the first step in programmatic model construction, called before make_graph to create the input/output declarations that the graph requires.
Code Reference
Source Location
- Repository: onnx
- File: onnx/helper.py
- Lines: 829-844
Signature
def make_tensor_value_info(
name: str,
elem_type: int,
shape: Sequence[str | int | None] | None,
doc_string: str = "",
shape_denotation: list[str] | None = None,
) -> ValueInfoProto:
"""Makes a ValueInfoProto based on the data type and shape.
Args:
name: Tensor name identifier (unique within the graph).
elem_type: TensorProto data type constant (e.g., TensorProto.FLOAT).
shape: Dimension sizes. int for fixed, str for symbolic, None for unknown.
Pass None for a completely unranked tensor.
doc_string: Optional documentation string.
shape_denotation: Optional semantic labels for each dimension.
"""
Import
from onnx import helper, TensorProto
I/O Contract
Inputs
| Name | Type | Required | Description |
|---|---|---|---|
| name | str | Yes | Unique tensor name identifier within the graph |
| elem_type | int | Yes | TensorProto data type constant (e.g., TensorProto.FLOAT = 1) |
| shape | Sequence[str, int, None] or None | Yes | Dimension sizes; int=fixed, str=symbolic, None=unknown dimension |
| doc_string | str | No | Optional documentation string (default: "") |
| shape_denotation | list[str] or None | No | Optional semantic labels per dimension |
Outputs
| Name | Type | Description |
|---|---|---|
| return | ValueInfoProto | Protobuf message describing the tensor's name, type, and shape |
Usage Examples
Basic Float Tensor
from onnx import helper, TensorProto
# Define a 2D float tensor input with fixed shape
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4])
# Define an output with symbolic batch dimension
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, ["batch", 10])
Dynamic and Unknown Dimensions
from onnx import helper, TensorProto
# Symbolic batch dimension, fixed feature dimension
input_spec = helper.make_tensor_value_info("input", TensorProto.FLOAT, ["N", 784])
# Completely unknown rank (unranked tensor)
unknown_spec = helper.make_tensor_value_info("dynamic", TensorProto.FLOAT, None)
# Mixed: some dimensions unknown
mixed_spec = helper.make_tensor_value_info("mixed", TensorProto.INT64, [None, 256, None])