Change RunInAutoMode to ExecuteOp

Author: Oceania2018

src/TensorFlowNET.Core/Contexts/Context.AutoMode.cs

@@ -30,49 +30,19 @@ namespace Tensorflow.Contexts
     public sealed partial class Context
     {
         // [DebuggerStepThrough]
-        public T RunInAutoMode<T>(Func<T> graphAction, Func<T> eagerAction, params object[] args)
-        {
-            if (tf.Context.has_graph_arg(args))
-            {
-                if (executing_eagerly())
-                {
-                    graph_mode();
-                    var result = graphAction();
-                    restore_mode();
-                    return result;
-                }
-                else
-                {
-                    return graphAction();
-                }
-            }
-            else
-            {
-                if (tf.Context.executing_eagerly())
-                {
-                    return eagerAction();
-                }
-                else
-                {
-                    return graphAction();
-                }
-            }
-        }
-        
-        // [DebuggerStepThrough]
-        public Tensors RunInAutoMode2(string OpType, string Name, AutoModeArgs args)
+        public Tensors ExecuteOp(string OpType, string Name, AutoModeArgs args)
         {
             var inputArgs = ConvertToDict(args.OpInputArgs);
             var attrDict = ConvertToDict(args.OpAttrs);
 
-            Func<Tensor> graphAction = () =>
+            Func<Tensors> graphAction = () =>
             {
                 foreach (var attr in attrDict)
                     inputArgs[attr.Key] = attr.Value;
-                return tf.OpDefLib._apply_op_helper(OpType, Name, inputArgs).output;
+                return tf.OpDefLib._apply_op_helper(OpType, Name, inputArgs).outputs;
             };
 
-            Func<Tensor> eagerAction = () =>
+            Func<Tensors> eagerAction = () =>
             {
                 var attrs = new object[attrDict.Count() * 2];
                 int i = 0;
@@ -87,7 +57,7 @@ public Tensors RunInAutoMode2(string OpType, string Name, AutoModeArgs args)
                     OpType, Name,
                     null,
                     inputArgs.Values.ToArray(), 
-                    attrs).FirstOrDefault();
+                    attrs);
             };
 
             if (tf.Context.has_graph_arg(inputArgs.Values))

src/TensorFlowNET.Core/Operations/NnOps/gen_nn_ops.cs

@@ -269,29 +269,24 @@ public static Tensor[] fused_batch_norm_grad(FusedBatchNormParams @params)
         }
 
         public static Tensor[] fused_batch_norm_grad_v3(FusedBatchNormParams @params)
-            => tf.Context.RunInAutoMode(()
-                => tf.OpDefLib._apply_op_helper("FusedBatchNormGradV3", name: @params.Name,
-                    args: new 
-                    {
-                        y_backprop = @params.YBackprop,
-                        x = @params.X,
-                        scale = @params.Scale,
-                        reserve_space_1 = @params.ReserveSpace1,
-                        reserve_space_2 = @params.ReserveSpace2,
-                        reserve_space_3 = @params.ReserveSpace3,
-                        epsilon = @params.Epsilon,
-                        data_format = @params.DataFormat,
-                        is_training = @params.IsTraining
-                    }).outputs, ()
-                => tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
-                    "FusedBatchNormGradV3", @params.Name,
-                    null,
-                    @params.YBackprop, @params.X, @params.Scale,
-                    @params.ReserveSpace1, @params.ReserveSpace2, @params.ReserveSpace3,
-                    "epsilon", @params.Epsilon, 
-                    "data_format", @params.DataFormat, 
-                    "is_training", @params.IsTraining),
-                @params.YBackprop);
+            => tf.Context.ExecuteOp("FusedBatchNormGradV3", @params.Name, new AutoModeArgs
+            {
+                OpInputArgs = new 
+                {
+                    y_backprop = @params.YBackprop,
+                    x = @params.X,
+                    scale = @params.Scale,
+                    reserve_space_1 = @params.ReserveSpace1,
+                    reserve_space_2 = @params.ReserveSpace2,
+                    reserve_space_3 = @params.ReserveSpace3
+                },
+                OpAttrs = new 
+                {
+                    epsilon = @params.Epsilon,
+                    data_format = @params.DataFormat,
+                    is_training = @params.IsTraining
+                }
+            });
 
         public static Tensor[] fused_batch_norm(Tensor x,
                 Tensor scale,
@@ -388,14 +383,10 @@ public static Tensor local_response_normalization(Tensor input, int depth_radius
         }
 
         public static Tensor log_softmax(Tensor logits, string name = null)
-            => tf.Context.RunInAutoMode(()
-                => tf.OpDefLib._apply_op_helper("LogSoftmax", name: name,
-                    args: new { logits }).output, ()
-                => tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
-                    "LogSoftmax", name,
-                    null,
-                    logits).FirstOrDefault(),
-                logits);
+            => tf.Context.ExecuteOp("LogSoftmax", name, new AutoModeArgs
+            {
+                OpInputArgs = new { logits }
+            });
 
         /// <summary>
         /// Says whether the targets are in the top `K` predictions.
@@ -418,19 +409,11 @@ public static Tensor in_top_kv2(Tensor predictions, Tensor targets, int k, strin
         }
 
         public static Tensor leaky_relu(Tensor features, float alpha = 0.2f, string name = null)
-            => tf.Context.RunInAutoMode(()
-                => tf.OpDefLib._apply_op_helper("LeakyRelu", name: name,
-                    args: new
-                    {
-                        features,
-                        alpha
-                    }).output, ()
-                => tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
-                    "LeakyRelu", name,
-                    null,
-                    features,
-                    "alpha", alpha).FirstOrDefault(),
-                features);
+            => tf.Context.ExecuteOp("LeakyRelu", name, new AutoModeArgs
+            {
+                OpInputArgs = new { features },
+                OpAttrs = new { alpha }
+            });
 
         public static Tensor max_pool(Tensor input,
             int[] ksize,

src/TensorFlowNET.Core/Operations/array_ops.cs

@@ -737,7 +737,7 @@ public static Tensor strided_slice(Tensor input_, Tensor begin, Tensor end,
         public static Tensor strided_slice_grad(Tensor shape, Tensor begin, Tensor end, Tensor strides, Tensor dy,
             long begin_mask = 0, long end_mask = 0, long ellipsis_mask = 0, long new_axis_mask = 0,
             long shrink_axis_mask = 0, string name = null)
-            => tf.Context.RunInAutoMode2("StridedSliceGrad", name, new AutoModeArgs
+            => tf.Context.ExecuteOp("StridedSliceGrad", name, new AutoModeArgs
             {
                 OpInputArgs = new
                 {
@@ -960,7 +960,7 @@ public static Tensor slice<Tb, Ts>(Tensor input, Tb begin, Ts size, string name
             => gen_array_ops.slice(input, begin, size, name: name);
 
         public static Tensor slice(Tensor input, Tensor begin, Tensor size, string name = null)
-            => tf.Context.RunInAutoMode2("Slice", name, new AutoModeArgs
+            => tf.Context.ExecuteOp("Slice", name, new AutoModeArgs
             {
                 OpInputArgs = new { input, begin, size },
                 GetGradientAttrs = (op) => new