/// <summary>
        /// This is a factory method that creates a disposable instance of FixedBufferOnnxValue
        /// on top of a buffer. Internally, it will pin managed buffer and will create
        /// an OrtValue containing a tensor that will not own the memory.
        /// Such instance of FixedBufferOnnxValue can be used both as input and output in InferenceSession.Run()
        /// overload. As compared to CreateFromTensor(), this allows you to pass in buffers with custom data types
        /// that are blittable as defined in https://docs.microsoft.com/en-us/dotnet/framework/interop/blittable-and-non-blittable-types
        /// I.e. those that have the same binary representation as the original type. This includes all existing types
        /// but may also allow using custom types for Float16 and BFloat16 providing they have the same layout and size.
        /// The resulting instance must be disposed of to release pinned memory and deallocate native OrtValue
        /// See example below.
        /// </summary>
        /// <typeparam name="T">Blittable data type, compatible with supported types</typeparam>
        /// <param name="memoryInfo">memoryInfo. For managed buffers simply use OrtMemoryInfo.DefaultInstance</param>
        /// <param name="memory"></param>
        /// <param name="elementType">TensorElementType</param>
        /// <param name="shape">shape of the tensor to be created</param>
        /// <param name="bytesSize">size of the allocation in bytes</param>
        /// <returns>a disposable instance of FixedBufferOnnxValue</returns>
        /// <example>
        /// Here is an example of using a 3rd party library class for processing float16/bfloat16.
        /// Currently, to pass tensor data and create a tensor one must copy data to Float16/BFloat16 structures
        /// so DenseTensor can recognize it.
        ///
        /// If you are using a library that has a class Half and it is blittable, that is its managed in memory representation
        /// matches native one and its size is 16-bits, you can use the following conceptual example
        /// to feed/fetch data for inference using Half array. This allows you to avoid copying data from your Half[] to Float16[]
        ///
        /// \code{.cs}
        /// unsafe { Debug.Assert(sizeof(ushort) == sizeof(Half)); }
        /// Half[] input = new Half[] { 5646, 12345 };
        /// var input_shape = new long[] {input.Length};
        /// Half[] output = new Half[40]; // Whatever the expected len/shape is must match
        /// var output_shape = new long[] {output.Length};
        ///
        /// var memInfo = OrtMemoryInfo.DefaultInstance; // CPU
        ///
        /// using(var fixedBufferInput = FixedBufferOnnxvalue.CreateFromMemory<Half>(memInfo,
        ///                         input, TensorElementType.Float16, input_shape, input.Length * sizeof(ushort))
        /// using(var fixedBufferOutput = FixedBufferOnnxvalue.CreateFromMemory<Half>(memInfo,
        ///                               output, TensorElementType.Float16, output_shape, output.Length * sizeof(ushort))
        /// {
        ///    FixedBufferOnnxvalue[] inputValues = new FixedBufferOnnxvalue[]{fixedBufferInput};
        ///    FixedBufferOnnxvalue[] outputValues = new FixedBufferOnnxvalue[]{fixedBufferOutput};
        ///    session.Run(inputNames, inputValues, outputNames, outputValues);
        ///   // Output is now in output[]
        /// }
        /// \endcode
        /// </example>
        public static FixedBufferOnnxValue CreateFromMemory <T>(OrtMemoryInfo memoryInfo, Memory <T> memory,
                                                                TensorElementType elementType, long[] shape, long bytesSize)
        {
            if (elementType == TensorElementType.String)
            {
                throw new ArgumentException("String data type is not supported");
            }

            var memHandle = memory.Pin();

            try
            {
                IntPtr memPtr;
                unsafe
                {
                    memPtr = (IntPtr)memHandle.Pointer;
                }
                var ortValue = OrtValue.CreateTensorValueWithData(memoryInfo,
                                                                  elementType,
                                                                  shape,
                                                                  memPtr, bytesSize);
                return(new FixedBufferOnnxValue(memHandle, ortValue, OnnxValueType.ONNX_TYPE_TENSOR, elementType));
            }
            catch (Exception)
            {
                memHandle.Dispose();
                throw;
            }
        }
Пример #2
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 /// <summary>
 /// Bind model output to an OrtValue as Tensor with a given type and shape. An instance of OrtMemoryAllocaiton
 /// owns the memory and should be alive for the time of execution.The size of the allocation can not be less than required
 /// by the Tensor of the given size.
 /// </summary>
 /// <param name="name">of the output</param>
 /// <param name="elementType">tensor element type</param>
 /// <param name="shape">tensor shape</param>
 /// <param name="allocation">allocated memory</param>
 public void BindOutput(string name, Tensors.TensorElementType elementType, long[] shape, OrtMemoryAllocation allocation)
 {
     using (var ortValue = OrtValue.CreateTensorValueWithData(allocation.Info,
                                                              elementType,
                                                              shape,
                                                              allocation.Pointer, allocation.Size))
         BindInputOrOutput(name, ortValue.Handle, false);
 }
Пример #3
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 /// <summary>
 /// Bind external allocation as input or output.
 /// The allocation is owned by the user code.
 /// </summary>
 /// <param name="name">name </param>
 /// <param name="allocation">non ort allocated memory</param>
 /// <param name="isInput">whether this is an input or output</param>
 private void BindExternalAllocation(string name, OrtExternalAllocation allocation, bool isInput)
 {
     using (var ortValue = OrtValue.CreateTensorValueWithData(allocation.Info,
                                                              allocation.ElementType,
                                                              allocation.Shape,
                                                              allocation.Pointer,
                                                              allocation.Size))
         BindInputOrOutput(name, ortValue.Handle, isInput);
 }