Graph: fixed bug with dependencies of nested with calls

src/TensorFlowNET.Core/Graphs/Graph.Control.cs

@@ -30,7 +30,7 @@ namespace Tensorflow
         /// <returns>A list of control inputs for the op to be created.</returns>
         private ITensorOrOperation[] _control_dependencies_for_inputs(ITensorOrOperation[] input_ops)
         {
-            var ret = new ITensorOrOperation[0];
+            var ret = new List<ITensorOrOperation>();
 
             foreach(var controller in _control_dependencies_stack)
             {
@@ -48,10 +48,10 @@ namespace Tensorflow
                 }
 
                 if (!dominated)
-                    ret = controller.control_inputs.Where(x => !input_ops.Contains(x)).ToArray();
+                    ret.AddRange( controller.control_inputs.Where(x => !input_ops.Contains(x)));
             }
 
-            return ret;
+            return ret.ToArray();
         }
 
         /// <summary>

test/TensorFlowNET.UnitTest/ControlDependenciesTest.cs

@@ -65,8 +65,8 @@ namespace TensorFlowNET.UnitTest
                 }
                 else
                 {
-                    var graph = tf.Graph();
-                    with<Graph>(graph.as_default(), g =>
+                    var graph = tf.Graph().as_default();
+                    with<Graph>(graph, g =>
                     {
                         a = constant_op.constant(1.0);
                         b = future();
@@ -122,7 +122,7 @@ def _apply_op(g, *args, **kwargs):
         [TestMethod]
         public void TestBasicWithConversion()
         {
-            var g = ops.get_default_graph();
+            var g = tf.Graph().as_default();
             // Note: _apply_op can be replaced by g.create_op
             var a = g.create_op("FloatOutput", new Tensor[] { }, new[] { TF_DataType.TF_FLOAT });
             // TODO: ConvertibleObj, see original source below
@@ -142,20 +142,20 @@ def _apply_op(g, *args, **kwargs):
     self.assertEqual(c.op.control_inputs, [a.op])
              */
         }
-
-        [Ignore("Fails with message: Op type not registered 'FloatOutput' in binary running on ...")]
-        [TestMethod]
+        
+        //[Ignore]
+        [TestMethod()]
         public void TestNested()
         {
             var g = ops.get_default_graph();
-            var a_1 = g.create_op("FloatOutput", new Tensor[] { }, new[] { TF_DataType.TF_FLOAT });
-            var a_2 = g.create_op("FloatOutput", new Tensor[] { }, new[] { TF_DataType.TF_FLOAT });
-            var a_3 = g.create_op("FloatOutput", new Tensor[] { }, new[] { TF_DataType.TF_FLOAT });
-            var a_4 = g.create_op("FloatOutput", new Tensor[] { }, new[] { TF_DataType.TF_FLOAT });
+            var a_1 = constant_op.constant(1.0);
+            var a_2 = constant_op.constant(3.0);
+            var a_3 = constant_op.constant(4.0);
+            var a_4 = constant_op.constant(5.0);
             Operation b_1 = null, b_2 = null;
             with(g.control_dependencies(new ITensorOrOperation[] { a_1, a_2, a_3, a_4 }), ctrl =>
             {
-                b_1 = g.create_op("FloatOutput", new Tensor[] { }, new[] { TF_DataType.TF_FLOAT });
+                b_1 = constant_op.constant(6.0);
             });
             with(g.control_dependencies(new ITensorOrOperation[] { a_1 }), ctrl1 =>
             {
@@ -165,34 +165,13 @@ def _apply_op(g, *args, **kwargs):
                     {
                         with(g.control_dependencies(new ITensorOrOperation[] { a_4 }), ctrl4 =>
                         {
-                            b_2 = g.create_op("FloatOutput", new Tensor[] { }, new[] { TF_DataType.TF_FLOAT });
+                            b_2 = constant_op.constant(7.0);
                         });
                     });
                 });
             });
             AssertItemsEqual(new[] {a_1.op, a_2.op, a_3.op, a_4.op}, b_1.op.control_inputs);
             AssertItemsEqual(b_1.op.control_inputs, b_2.op.control_inputs);
-            /*
-def testNested(self):
-g = ops.Graph()
-a_1 = _apply_op(g, "FloatOutput", [], [dtypes.float32])
-a_2 = _apply_op(g, "FloatOutput", [], [dtypes.float32])
-a_3 = _apply_op(g, "FloatOutput", [], [dtypes.float32])
-a_4 = _apply_op(g, "FloatOutput", [], [dtypes.float32])
-
-with g.control_dependencies([a_1, a_2, a_3, a_4]):
-  b_1 = _apply_op(g, "FloatOutput", [], [dtypes.float32])
-
-with g.control_dependencies([a_1]):
-  with g.control_dependencies([a_2]):
-    with g.control_dependencies([a_3]):
-      with g.control_dependencies([a_4]):
-        b_2 = _apply_op(g, "FloatOutput", [], [dtypes.float32])
-
-self.assertItemsEqual([a_1.op, a_2.op, a_3.op, a_4.op],
-                      b_1.op.control_inputs)
-self.assertItemsEqual(b_1.op.control_inputs, b_2.op.control_inputs)
-         */
         }