/// <summary>
/// Parse the shape information of a tensor.
/// </summary>
/// <param name="tensorShapeProto">ONNX's tensor shape.</param>
public static IEnumerable <int> GetTensorDims(Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TensorShapeProto tensorShapeProto)
{
if (tensorShapeProto == null)
{
// Scalar has null dimensionality.
return(null);
}
List <int> dims = new List <int>();
foreach (var d in tensorShapeProto.Dim)
{
var dimValue = GetDimValue(d);
dims.Add(dimValue);
}
// In ONNX, the first dimension refers to the batch size. If that is set to -1, it means OnnxRuntime can do inferencing in batches on
// multiple rows at once. In ML.NET, a vector is considered to be of known size if the dimensions are all greater than zero
// Leaving the batch size at -1 causes all Onnx vectors to be considered to be of unknown size. Therefore, if the first dimension is -1,
// we need to fix up the shape. But GetDimValue above converts any dimension < 0 to be 0. We need that behavior for dimensions other than
// the first dimension. So we check only the first dimension here and fix it up. (The '<=' comparison below is there to make sure that
// this holds even if the behavior of GetDimValue changes).
if ((dims.Count > 0) && (dims[0] <= 0))
{
dims[0] = 1;
}
return(dims);
}
/// <summary>
/// Parse the shape information of a tensor.
/// </summary>
/// <param name="tensorShapeProto">ONNX's tensor shape.</param>
public static IEnumerable <int> GetTensorDims(Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TensorShapeProto tensorShapeProto)
{
if (tensorShapeProto == null)
{
// Scalar has null dimensionality.
return(null);
}
List <int> dims = new List <int>();
foreach (var d in tensorShapeProto.Dim)
{
var dimValue = GetDimValue(d);
dims.Add(dimValue);
}
return(dims);
}
