BaseSession: Added transparent dtype conversion to feed_dict

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -107,7 +107,7 @@ namespace Tensorflow
                 foreach (var subfeed in feed_dict)
                 {
                     var subfeed_t = _graph.as_graph_element(subfeed.Key, allow_tensor: true, allow_operation: false);
-                    //var subfeed_dtype = subfeed_t.dtype.as_numpy_datatype(); // subfeed_dtype was never used
+                    //var target_dtype = subfeed_t.dtype.as_numpy_typecode(); // subfeed_dtype was never used
                     feed_dict_tensor[subfeed_t] = subfeed.Value;
                     //feed_map[subfeed_t.name] = (subfeed_t, subfeed.Value);
                 }
@@ -150,58 +150,64 @@ namespace Tensorflow
             int i = 0;
             foreach (var x in feed_dict)
             {
-                if (x.Key is Tensor tensor)
+                if (x.Key is Tensor key)
                 {
                     switch (x.Value)
                     {
                         case Tensor v:
-                            feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), v);
+                            if (v.dtype != key.dtype)
+                                throw new ValueError($"Tensor {v} does not match the expected dtype {key.dtype}, actual dtype: {v.dtype}");
+                            feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), v);
                             break;
                         case NDArray v:
-                            feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v, tensor.dtype));
+                            feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype));
                             break;
                         case IntPtr v:
-                            feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
+                            var tensor = new Tensor(v);
+                            if (tensor.dtype != key.dtype)
+                                throw new ValueError($"Tensor {v} does not match the expected dtype {key.dtype}, actual dtype: {tensor.dtype}");
+
+                            feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), tensor);
                             break;
 #if _REGEN
                         // @formatter:off — disable formatter after this line
-                    %types = ["sbyte", "byte", "short", "ushort", "int", "uint", "long", "ulong", "float", "double", "Complex"]
-                    %foreach types%
-                        case #1 v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case #1[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                    %
+                        %types = ["bool", "sbyte", "byte", "short", "ushort", "int", "uint", "long", "ulong", "float", "double", "Complex"]
+                        %foreach types%
+                        case #1 v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case #1[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        %
                         // @formatter:on — enable formatter after this line
 #else
                         // @formatter:off — disable formatter after this line
-                        case sbyte v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case sbyte[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case byte v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case byte[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case short v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case short[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case ushort v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case ushort[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case int v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case int[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case uint v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case uint[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case long v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case long[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case ulong v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case ulong[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case float v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case float[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case double v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case double[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case Complex v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
-                        case Complex[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
+                        case bool v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case bool[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case sbyte v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case sbyte[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case byte v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case byte[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case short v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case short[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case ushort v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case ushort[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case int v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case int[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case uint v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case uint[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case long v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case long[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case ulong v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case ulong[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case float v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case float[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case double v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case double[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case Complex v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
+                        case Complex[] v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype)); break;
                         // @formatter:on — enable formatter after this line
 #endif
-                        case bool v:
-                            feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor((byte) (v ? 1 : 0), TF_DataType.TF_BOOL));
-                            break;
+
                         case string v:
-                            feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
+                            feeds[i++] = new KeyValuePair<TF_Output, Tensor>(key._as_tf_output(), TensorConverter.ToTensor(v, key.dtype));
                             break;
                         default:
                             throw new NotImplementedException($"feed_dict data type {x.Value?.GetType().Name ?? "<null>"}");
@@ -214,6 +220,7 @@ namespace Tensorflow
             return _call_tf_sessionrun(feeds, fetches, target_list);
         }
 
+
         private unsafe NDArray[] _call_tf_sessionrun(KeyValuePair<TF_Output, Tensor>[] feed_dict, TF_Output[] fetch_list, List<Operation> target_list)
         {
             // Ensure any changes to the graph are reflected in the runtime.