add _init_from_proto to create variable.

src/TensorFlowNET.Core/Framework/meta_graph.py.cs

@@ -1,4 +1,5 @@
-using System;
+using Google.Protobuf;
+using System;
 using System.Collections.Generic;
 using System.IO;
 using System.Linq;
@@ -59,7 +60,7 @@ namespace Tensorflow
                 return_elements: return_elements);
 
             // Restores all the other collections.
-            var variable_objects = new Dictionary<string, RefVariable>();
+            var variable_objects = new Dictionary<ByteString, RefVariable>();
             foreach(var col in meta_graph_def.CollectionDef.OrderBy(x => x.Key))
             {
                 // Don't add unbound_inputs to the new graph.
@@ -81,8 +82,15 @@ namespace Tensorflow
                         {
                             foreach (var value in col.Value.BytesList.Value)
                             {
-                                var proto = VariableDef.Parser.ParseFrom(value);
-                                throw new NotImplementedException("import_scoped_meta_graph_with_return_elements");
+                                RefVariable variable = null;
+                                if (!variable_objects.ContainsKey(value))
+                                {
+                                    var proto = VariableDef.Parser.ParseFrom(value);
+                                    variable = new RefVariable(variable_def: proto, import_scope: scope_to_prepend_to_names);
+                                    variable_objects[value] = variable;
+                                }
+                                
+                                graph.add_to_collection(col.Key, variable);
                             }
                         }
                         else

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -23,18 +23,68 @@ namespace Tensorflow
 
         public string name => _variable.name;
 
-        public RefVariable(object initial_value,
+        public RefVariable(object initial_value = null,
             bool trainable = true,
             List<string> collections = null,
             bool validate_shape = true,
             string caching_device = "",
             string name = "",
-            TF_DataType dtype = TF_DataType.DtInvalid) : 
-            base(initial_value, trainable, collections, validate_shape, caching_device, name, dtype)
+            VariableDef variable_def = null,
+            TF_DataType dtype = TF_DataType.DtInvalid,
+            string import_scope = "") : base(initial_value,
+                    trainable,
+                    collections,
+                    validate_shape,
+                    caching_device,
+                    name,
+                    dtype)
         {
             _in_graph_mode = true;
 
-            _init_from_args(initial_value, trainable, collections, validate_shape, caching_device, name, dtype);
+            if (variable_def != null)
+            {
+                if (initial_value != null)
+                    throw new ValueError("variable_def and initial_value are mutually exclusive.");
+                _init_from_proto(variable_def, import_scope: import_scope);
+            }
+            else
+            {
+                _init_from_args(initial_value, trainable, collections, validate_shape, caching_device, name, dtype);
+            }
+        }
+
+        private void _init_from_proto(VariableDef variable_def, string import_scope = "")
+        {
+            var g = ops.get_default_graph();
+
+            _variable = g.as_graph_element(
+                ops.prepend_name_scope(variable_def.VariableName,
+                                import_scope: import_scope)) as Tensor;
+
+            _initializer_op = g.as_graph_element(
+                ops.prepend_name_scope(variable_def.InitializerName,
+                               import_scope: import_scope)) as Operation;
+
+            // Tests whether initial_value_name exists first for backwards compatibility.
+            if (!string.IsNullOrEmpty(variable_def.InitialValueName))
+                _initial_value = g.as_graph_element(
+                    ops.prepend_name_scope(variable_def.InitialValueName,
+                                 import_scope: import_scope)) as Tensor;
+            else
+                _initial_value = null;
+
+            _trainable = variable_def.Trainable;
+            _snapshot = g.as_graph_element(
+                ops.prepend_name_scope(variable_def.SnapshotName,
+                               import_scope: import_scope)) as Tensor;
+
+            if (variable_def.SaveSliceInfoDef != null)
+                throw new NotImplementedException("save_slice_info_def");
+            else
+                ;// _save_slice_info = null;
+
+            //_caching_device = null;
+            //_constraint = null;
         }
 
         private void _init_from_args(object initial_value,

src/TensorFlowNET.Core/Variables/VariableV1.cs

@@ -16,7 +16,7 @@ namespace Tensorflow
     /// </summary>
     public class VariableV1
     {
-        public VariableV1(object initial_value,
+        public VariableV1(object initial_value = null,
             bool trainable = true,
             List<string> collections = null,
             bool validate_shape = true,