fix issue 5020, allow ML.NET to load tf model with primitive input and output column

src/Microsoft.ML.TensorFlow/TensorflowTransform.cs

@@ -503,20 +503,20 @@ public Mapper(TensorFlowTransformer parent, DataViewSchema inputSchema) :
                         throw Host.Except("Variable length input columns not supported");
 
                     _isInputVector[i] = type is VectorDataViewType;
-                    if (!_isInputVector[i])
-                        throw Host.Except("Non-vector columns are not supported and should be loaded as vector columns of size 1");
-                    vecType = (VectorDataViewType)type;
+                    //if (!_isInputVector[i])
+                    //    throw Host.Except("Non-vector columns are not supported and should be loaded as vector columns of size 1");
                     var expectedType = Tf2MlNetType(_parent.TFInputTypes[i]);
                     if (type.GetItemType() != expectedType)
                         throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", _parent.Inputs[i], expectedType.ToString(), type.ToString());
                     var originalShape = _parent.TFInputShapes[i];
                     var shape = originalShape.dims;
 
-                    var colTypeDims = vecType.Dimensions.Select(dim => (int)dim).ToArray();
                     if (shape == null || (shape.Length == 0))
-                        _fullySpecifiedShapes[i] = new TensorShape(colTypeDims);
+                        _fullySpecifiedShapes[i] = new TensorShape();
                     else
                     {
+                        vecType = (VectorDataViewType)type;
+                        var colTypeDims = vecType.Dimensions.Select(dim => (int)dim).ToArray();
                         // If the column is one dimension we make sure that the total size of the TF shape matches.
                         // Compute the total size of the known dimensions of the shape.
                         int valCount = 1;
@@ -729,11 +729,10 @@ public TensorValueGetter(DataViewRow input, int colIndex, TensorShape tfShape)
             {
                 _srcgetter = input.GetGetter<T>(input.Schema[colIndex]);
                 _tfShape = tfShape;
-                long size = 0;
+                long size = 1;
                 _position = 0;
-                if (tfShape.dims.Length != 0)
+                if (tfShape.dims != null && tfShape.dims.Length != 0)
                 {
-                    size = 1;
                     foreach (var dim in tfShape.dims)
                         size *= dim;
                 }
@@ -744,8 +743,7 @@ public Tensor GetTensor()
             {
                 var scalar = default(T);
                 _srcgetter(ref scalar);
-                var tensor = new Tensor(new[] { scalar });
-                tensor.set_shape(_tfShape);
+                var tensor = TensorFlowUtils.CastDataAndReturnAsTensor(scalar);
                 return tensor;
             }
 
@@ -928,8 +926,8 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema)
                 var input = _options.InputColumns[i];
                 if (!inputSchema.TryFindColumn(input, out var col))
                     throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", input);
-                if (!(col.Kind == SchemaShape.Column.VectorKind.Vector))
-                    throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", input, "vector", col.GetTypeString());
+                //if (!(col.Kind == SchemaShape.Column.VectorKind.Vector))
+                //    throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", input, "vector", col.GetTypeString());
                 var expectedType = Tf2MlNetType(_tfInputTypes[i]);
                 if (col.ItemType != expectedType)
                     throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", input, expectedType.ToString(), col.ItemType.ToString());

src/Microsoft.ML.TensorFlow/TensorflowUtils.cs

@@ -452,6 +452,36 @@ internal static Tensor CastDataAndReturnAsTensor<T>(T[] data, TensorShape tfShap
             return new Tensor(new NDArray(data, tfShape));
         }
 
+        internal static Tensor CastDataAndReturnAsTensor<T>(T data)
+        {
+            if (typeof(T) == typeof(sbyte))
+                return new Tensor((sbyte)(object)data, TF_DataType.TF_INT8);
+            else if (typeof(T) == typeof(long))
+                return new Tensor((long)(object)data, TF_DataType.TF_INT64);
+            else if (typeof(T) == typeof(Int32))
+                return new Tensor((Int32)(object)data, TF_DataType.TF_INT32);
+            else if (typeof(T) == typeof(Int16))
+                return new Tensor((Int16)(object)data, TF_DataType.TF_INT16);
+            else if (typeof(T) == typeof(byte))
+                return new Tensor((byte)(object)data, TF_DataType.TF_UINT8);
+            else if (typeof(T) == typeof(ulong))
+                return new Tensor((ulong)(object)data, TF_DataType.TF_UINT64);
+            else if (typeof(T) == typeof(UInt32))
+                return new Tensor((UInt32)(object)data, TF_DataType.TF_UINT32);
+            else if (typeof(T) == typeof(UInt16))
+                return new Tensor((UInt16)(object)data, TF_DataType.TF_UINT16);
+            else if (typeof(T) == typeof(bool))
+                return new Tensor((bool)(object)data, TF_DataType.TF_BOOL);
+            else if (typeof(T) == typeof(float))
+                return new Tensor((float)(object)data, TF_DataType.TF_FLOAT);
+            else if (typeof(T) == typeof(double))
+                return new Tensor((double)(object)data, TF_DataType.TF_DOUBLE);
+            else if (typeof(T) == typeof(ReadOnlyMemory<char>))
+                return new Tensor(data.ToString());
+
+            throw new ArgumentException($"Unsupported data type of {typeof(T)} to convert to Tensor.");
+        }
+
         /// <summary>
         /// Use the runner class to easily configure inputs, outputs and targets to be passed to the session runner.
         /// </summary>