BaseSession: Perf-op

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -39,15 +39,13 @@ namespace Tensorflow
             _graph.as_default();
             _target = UTF8Encoding.UTF8.GetBytes(target);
 
-            SessionOptions newOpts = null;
-            if (opts == null)
-                newOpts = new SessionOptions();
+            SessionOptions newOpts = opts ?? new SessionOptions();
 
             var status = new Status();
 
             _handle = c_api.TF_NewSession(_graph, opts ?? newOpts, status);
 
-            // dispose newOpts
+            // dispose opts only if not provided externally.
             if (opts == null)
                 newOpts.Dispose();
 
@@ -102,25 +100,17 @@ namespace Tensorflow
         private NDArray[] _run(object fetches, FeedItem[] feed_dict = null)
         {
             var feed_dict_tensor = new Dictionary<object, object>();
-            var feed_map = new Dictionary<object, object>();
-
-            Func<FeedItem, IEnumerable<(object, object)>> feed_fn = (item) =>
-            {
-                return new (object, object)[] { (item.Key, item.Value) };
-            };
+            //var feed_map = new Dictionary<object, object>();
 
             // Validate and process feed_dict.
             if (feed_dict != null)
             {
-                foreach (var feed in feed_dict)
+                foreach (var subfeed in feed_dict)
                 {
-                    foreach (var (subfeed, subfeed_val) in feed_fn(feed))
-                    {
-                        var subfeed_t = _graph.as_graph_element(subfeed, allow_tensor: true, allow_operation: false);
-                        //var subfeed_dtype = subfeed_t.dtype.as_numpy_datatype(); // subfeed_dtype was never used
-                        feed_dict_tensor[subfeed_t] = subfeed_val;
-                        feed_map[subfeed_t.name] = (subfeed_t, subfeed_val);
-                    }
+                    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
+                    feed_dict_tensor[subfeed_t] = subfeed.Value;
+                    //feed_map[subfeed_t.name] = (subfeed_t, subfeed.Value);
                 }
             }
 
@@ -157,86 +147,71 @@ namespace Tensorflow
         /// </returns>
         private NDArray[] _do_run(List<Operation> target_list, List<Tensor> fetch_list, Dictionary<object, object> feed_dict)
         {
-            var feeds = feed_dict.Select(x =>
+
+            var feeds = new KeyValuePair<TF_Output, Tensor>[feed_dict.Count];
+            var ignoreDispose = new bool[feed_dict.Count];
+            int i = 0;
+            foreach (var x in feed_dict)
             {
                 if (x.Key is Tensor tensor)
                 {
                     switch (x.Value)
                     {
+                        case Tensor v: ignoreDispose[i] = true; feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), v); break;
+                        case NDArray v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v, tensor.dtype)); break;
 #if _REGEN
-        %types=["sbyte", "byte", "short", "ushort", "int", "uint", "long", "ulong", "float", "double", "Complex"]
-        %foreach types%
-                        case #1 v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case #1[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-        %
+                    %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;
+                    %
 #else
-                        case sbyte v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case sbyte[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case byte v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case byte[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case short v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case short[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case ushort v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case ushort[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case int v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case int[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case uint v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case uint[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case long v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case long[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case ulong v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case ulong[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case float v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case float[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case double v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case double[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case Complex v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case Complex[] v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
+                        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;
 #endif
-                        case bool v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor((byte)(v?1:0), TF_DataType.TF_BOOL));
-                        case string v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case IntPtr v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v));
-                        case Tensor v:
-                            return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), v);
-                        case NDArray v:
-                                return new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v, tensor.dtype));
+                        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)); break;
+                        case IntPtr v: feeds[i++] = new KeyValuePair<TF_Output, Tensor>(tensor._as_tf_output(), new Tensor(v)); break;
                         default:
-                            throw new NotImplementedException($"feed_dict data type {(x.Value?.GetType().Name ?? "<null>")}");
+                            throw new NotImplementedException($"feed_dict data type {x.Value?.GetType().Name ?? "<null>"}");
                     }
                 }
-                throw new NotImplementedException("_do_run.feed_dict");
-            }).ToArray();
-            var fetches = fetch_list.Select(x => x._as_tf_output()).ToArray();
-            var targets = target_list;
+            }
 
-            return _call_tf_sessionrun(feeds, fetches, target_list);
+            var fetches = fetch_list.Select(x => x._as_tf_output()).ToArray();
+            //var targets = target_list;
+            try
+            {
+                return _call_tf_sessionrun(feeds, fetches, target_list);
+            } finally
+            {
+                for (var idx = 0; idx < feeds.Length; idx++)
+                {
+                    if (ignoreDispose[idx])
+                        continue;
+                    feeds[idx].Value.Dispose();
+                }
+            }
         }
 
         private unsafe NDArray[] _call_tf_sessionrun(KeyValuePair<TF_Output, Tensor>[] feed_dict, TF_Output[] fetch_list, List<Operation> target_list)
@@ -268,9 +243,6 @@ namespace Tensorflow
             for (int i = 0; i < fetch_list.Length; i++)
                 result[i] = fetchValue(output_values[i]);
 
-            for (int i = 0; i < feed_dict.Length; i++)
-                feed_dict[i].Value.Dispose();
-
             return result;
         }
 
@@ -280,7 +252,7 @@ namespace Tensorflow
             NDArray nd = null;
             Type type = tensor.dtype.as_numpy_dtype();
             var ndims = tensor.shape;
-            var offset = c_api.TF_TensorData(output);
+            var offset = (byte*) c_api.TF_TensorData(output);
 
             if(ndims.Length == 0)
             {