Create EagerTensor from NDArray.

Oceania2018 · Oceania2018 · commit dcfaa77490ae · 2020-03-14T08:56:35.000-05:00
diff --git a/src/TensorFlowNET.Core/APIs/tf.linalg.cs b/src/TensorFlowNET.Core/APIs/tf.linalg.cs
@@ -22,7 +22,7 @@ public Tensor diag(Tensor diagonal, string name = null)
             => gen_array_ops.diag(diagonal, name: name);
 
         public Tensor matmul(Tensor a, Tensor b) 
-            => gen_math_ops.mat_mul(a, b);
+            => math_ops.matmul(a, b);
 
         public Tensor batch_matmul(Tensor x, Tensor y)
             => gen_math_ops.batch_mat_mul(x, y);
diff --git a/src/TensorFlowNET.Core/Eager/EagerTensor.cs b/src/TensorFlowNET.Core/Eager/EagerTensor.cs
@@ -1,4 +1,5 @@
-﻿using System;
+﻿using NumSharp;
+using System;
 using System.Collections.Generic;
 using System.Text;
 
@@ -18,6 +19,10 @@ public EagerTensor(int value, string device_name) : base(value)
         {
         }
 
+        public EagerTensor(NDArray value, string device_name) : base(value)
+        {
+        }
+
         public override string ToString()
         {
             switch (rank)
diff --git a/src/TensorFlowNET.Core/Operations/gen_math_ops.cs b/src/TensorFlowNET.Core/Operations/gen_math_ops.cs
@@ -638,6 +638,14 @@ public static Tensor floor_div(Tensor x, Tensor y, string name = null)
         /// <returns></returns>
         public static Tensor mat_mul(Tensor a, Tensor b, bool transpose_a = false, bool transpose_b = false, string name = null)
         {
+            if (tf.context.executing_eagerly())
+            {
+                var _result = wrap_tfe_src.TFE_Py_FastPathExecute(tf.context, tf.context.device_name, 
+                    "MatMul", name, null,
+                    a, b, "transpose_a", transpose_a, "transpose_b", transpose_b);
+                return _result;
+            }
+
             var _op = _op_def_lib._apply_op_helper("MatMul", name, args: new { a, b, transpose_a, transpose_b });
 
             return _op.output;
@@ -738,17 +746,37 @@ public static Tensor _sum<Tx, Ty>(Tx input, Ty axis = default, bool keep_dims =
         {
             if (tf.context.executing_eagerly())
             {
-                var _result = wrap_tfe_src.TFE_Py_FastPathExecute(tf.context, tf.context.device_name, 
-                    "Sum", name, null, 
-                    input, axis, "keep_dims", keep_dims);
-                return _result;
+                try
+                {
+                    var _result = wrap_tfe_src.TFE_Py_FastPathExecute(tf.context, tf.context.device_name,
+                        "Sum", name, null,
+                        input, axis, "keep_dims", keep_dims);
+                    return _result;
+                }
+                catch (Exception)
+                {
+                    return _sum_eager_fallback(input as Tensor[], axis as Tensor, 
+                        keep_dims: keep_dims, name: name, ctx: tf.context);
+                }
             }
 
             var _op = _op_def_lib._apply_op_helper("Sum", name, args: new { input, reduction_indices = axis, keep_dims });
 
             return _op.outputs[0];
         }
 
+        private static Tensor _sum_eager_fallback(Tensor[] inputs, Tensor axis, bool keep_dims = false, string name = null, Context ctx = null)
+        {
+            var (_attr_T, input) = _execute.args_to_matching_eager(inputs, ctx);
+            var (_attr_Tidx, axis1) = _execute.args_to_matching_eager(new[] { axis }, ctx, TF_DataType.TF_INT32);
+            var _inputs_flat = new Tensor[] { input, axis1 };
+
+            var _attrs = new object[] { "keep_dims", keep_dims, "T", _attr_T, "Tidx", _attr_Tidx };
+
+            var _result = _execute.execute(ctx, "Sum", _inputs_flat, _attrs, name: name);
+            return _result;
+        }
+
         /// <summary>
         /// Creates a sequence of numbers.
         /// </summary>
diff --git a/src/TensorFlowNET.Core/Tensors/Tensor.Value.cs b/src/TensorFlowNET.Core/Tensors/Tensor.Value.cs
@@ -163,6 +163,8 @@ public NDArray numpy()
                         return StringData();
                     case TF_DataType.TF_INT32:
                         return ToArray<int>();
+                    case TF_DataType.TF_FLOAT:
+                        return ToArray<float>();
                     default:
                         return BufferToArray();
                 }
diff --git a/src/TensorFlowNET.Core/Tensors/constant_op.cs b/src/TensorFlowNET.Core/Tensors/constant_op.cs
@@ -14,6 +14,7 @@ You may obtain a copy of the License at
    limitations under the License.
 ******************************************************************************/
 
+using NumSharp;
 using System;
 using System.Collections.Generic;
 using Tensorflow.Eager;
@@ -84,6 +85,8 @@ private static EagerTensor convert_to_eager_tensor(object value, Context ctx, TF
         {
             switch (value)
             {
+                case NDArray nd:
+                    return new EagerTensor(nd, ctx.device_name);
                 case string str:
                     return new EagerTensor(str, ctx.device_name);
                 case int int32:

Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,5 @@`
`1`		`-using System;`
	`1`	`+using NumSharp;`
	`2`	`+using System;`
`2`	`3`	`using System.Collections.Generic;`
`3`	`4`	`using System.Text;`
`4`	`5`
`@@ -18,6 +19,10 @@ public EagerTensor(int value, string device_name) : base(value)`
`18`	`19`	`{`
`19`	`20`	`}`
`20`	`21`
	`22`	`+ public EagerTensor(NDArray value, string device_name) : base(value)`
	`23`	`+ {`
	`24`	`+ }`
	`25`	`+`
`21`	`26`	`public override string ToString()`
`22`	`27`	`{`
`23`	`28`	`switch (rank)`
Original file line number	Diff line number	Diff line change
`@@ -163,6 +163,8 @@ public NDArray numpy()`
`163`	`163`	`return StringData();`
`164`	`164`	`case TF_DataType.TF_INT32:`
`165`	`165`	`return ToArray<int>();`
	`166`	`+ case TF_DataType.TF_FLOAT:`
	`167`	`+ return ToArray<float>();`
`166`	`168`	`default:`
`167`	`169`	`return BufferToArray();`
`168`	`170`	`}`