Merge pull request #1257 from novikov-alexander/alnovi/generic-cast

fix: More generic array cast

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -67,7 +67,7 @@ namespace Tensorflow
 
             T[] ExpandArrayToSize<T>(IList<T> src)
             {
-                if(src.Count == 0)
+                if (src.Count == 0)
                 {
                     return new T[0];
                 }
@@ -77,7 +77,7 @@ namespace Tensorflow
                 var first_elem = src[0];
                 var last_elem = src[src.Count - 1];
                 T[] res = new T[num_elements];
-                for(long i = 0; i < num_elements; i++)
+                for (long i = 0; i < num_elements; i++)
                 {
                     if (i < pre) res[i] = first_elem;
                     else if (i >= num_elements - after) res[i] = last_elem;
@@ -121,7 +121,7 @@ namespace Tensorflow
                     $"https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes.");
             }
 
-            if(values.size == 0)
+            if (values.size == 0)
             {
                 return np.zeros(shape, tensor_dtype);
             }
@@ -135,23 +135,47 @@ namespace Tensorflow
             TF_DataType.TF_QINT32
         };
 
-        private static TOut[,] ConvertArray2D<TIn, TOut>(TIn[,] inputArray, Func<TIn, TOut> converter)
+        private static Array ConvertArray<TOut>(Array inputArray, Func<object, TOut> converter)
         {
-            var rows = inputArray.GetLength(0);
-            var cols = inputArray.GetLength(1);
-            var outputArray = new TOut[rows, cols];
+            if (inputArray == null)
+                throw new ArgumentNullException(nameof(inputArray));
 
-            for (var i = 0; i < rows; i++)
+            var elementType = typeof(TOut);
+            var lengths = new int[inputArray.Rank];
+            for (var i = 0; i < inputArray.Rank; i++)
             {
-                for (var j = 0; j < cols; j++)
-                {
-                    outputArray[i, j] = converter(inputArray[i, j]);
-                }
+                lengths[i] = inputArray.GetLength(i);
             }
 
+            var outputArray = Array.CreateInstance(elementType, lengths);
+
+            FillArray(inputArray, outputArray, converter, new int[inputArray.Rank], 0);
+
             return outputArray;
         }
 
+        private static void FillArray<TIn, TOut>(Array inputArray, Array outputArray, Func<TIn, TOut> converter, int[] indices, int dimension)
+        {
+            if (dimension == inputArray.Rank - 1)
+            {
+                for (int i = 0; i < inputArray.GetLength(dimension); i++)
+                {
+                    indices[dimension] = i;
+                    var inputValue = (TIn)inputArray.GetValue(indices);
+                    var convertedValue = converter(inputValue);
+                    outputArray.SetValue(convertedValue, indices);
+                }
+            }
+            else
+            {
+                for (int i = 0; i < inputArray.GetLength(dimension); i++)
+                {
+                    indices[dimension] = i;
+                    FillArray(inputArray, outputArray, converter, indices, dimension + 1);
+                }
+            }
+        }
+
         /// <summary>
         /// Create a TensorProto, invoked in graph mode
         /// </summary>
@@ -171,24 +195,30 @@ namespace Tensorflow
             var origin_dtype = values.GetDataType();
             if (dtype == TF_DataType.DtInvalid)
                 dtype = origin_dtype;
-            else if(origin_dtype != dtype)
+            else if (origin_dtype != dtype)
             {
                 var new_system_dtype = dtype.as_system_dtype();
-                
-                values = values switch
+
+                if (dtype != TF_DataType.TF_STRING && dtype != TF_DataType.TF_VARIANT && dtype != TF_DataType.TF_RESOURCE)
+                {
+                    if (values is Array arrayValues)
+                    {
+                        values = dtype switch
+                        {
+                            TF_DataType.TF_INT32 => ConvertArray(arrayValues, Convert.ToInt32),
+                            TF_DataType.TF_FLOAT => ConvertArray(arrayValues, Convert.ToSingle),
+                            TF_DataType.TF_DOUBLE => ConvertArray(arrayValues, Convert.ToDouble),
+                            _ => values,
+                        };
+                    } else
+                    {
+                        values = Convert.ChangeType(values, new_system_dtype);
+                    }
+                    
+                } else
                 {
-                    long[] longValues when dtype == TF_DataType.TF_INT32 => longValues.Select(x => (int)x).ToArray(),
-                    long[] longValues => values,
-                    float[] floatValues when dtype == TF_DataType.TF_DOUBLE => floatValues.Select(x => (double)x).ToArray(),
-                    float[] floatValues => values,
-                    float[,] float2DValues when dtype == TF_DataType.TF_DOUBLE => ConvertArray2D(float2DValues, Convert.ToDouble),
-                    float[,] float2DValues => values,
-                    double[] doubleValues when dtype == TF_DataType.TF_FLOAT => doubleValues.Select(x => (float)x).ToArray(),
-                    double[] doubleValues => values,
-                    double[,] double2DValues when dtype == TF_DataType.TF_FLOAT => ConvertArray2D(double2DValues, Convert.ToSingle),
-                    double[,] double2DValues => values,
-                    _ => Convert.ChangeType(values, new_system_dtype),
-                };
+
+                }
                 dtype = values.GetDataType();
             }
 
@@ -306,7 +336,7 @@ namespace Tensorflow
 
             if (tensor is EagerTensor eagerTensor)
             {
-                if(tensor.dtype == tf.int64)
+                if (tensor.dtype == tf.int64)
                     return new Shape(tensor.ToArray<long>());
                 else
                     return new Shape(tensor.ToArray<int>());
@@ -481,7 +511,7 @@ would not be rank 1.", tensor.op.get_attr("axis")));
                 var d_ = new int[value.size];
                 foreach (var (index, d) in enumerate(value.ToArray<int>()))
                     d_[index] = d >= 0 ? d : -1;
-                
+
                 ret = ret.merge_with(new Shape(d_));
             }
             return ret;