Extending test to use model and demonstrate memory leak (#858)

ADH-LukeBollam · web-flow · commit 3feff99550b4 · 2021-10-09T07:19:12.000-05:00
* adding saved model cleanup benchmark

* less loops so it doesnt waste so much time

* testing with a running model

* actually calling the model

* stopping test from running immediately
diff --git a/src/TensorFlowNet.Benchmarks/Leak/SavedModelCleanup.cs b/src/TensorFlowNet.Benchmarks/Leak/SavedModelCleanup.cs
@@ -6,6 +6,7 @@
 using System.Reflection;
 using System.Text;
 using System.Threading.Tasks;
+using Tensorflow.NumPy;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Benchmark.Leak
@@ -23,9 +24,15 @@ public void Run()
 
 			for (var i = 0; i < 1024; i++)
             {
-				using var sess = Session.LoadFromSavedModel(ClassifierModelPath);
-				// destory graph
-				using var g = sess.graph;
+				using (var sess = Session.LoadFromSavedModel(ClassifierModelPath)) {
+					using (var g = sess.graph.as_default()) {
+						var inputOp = g.OperationByName("inference_input");
+						var outputOp = g.OperationByName("StatefulPartitionedCall");
+
+						var inp = np.zeros(new Shape(new int[] { 1, 2, 96 }), TF_DataType.TF_FLOAT);
+						sess.run(outputOp.outputs[0], new FeedItem(inputOp.outputs[0], inp));
+					}
+				}
 			}
 		}
 	}

Original file line number	Diff line number	Diff line change
`@@ -6,6 +6,7 @@`
`6`	`6`	`using System.Reflection;`
`7`	`7`	`using System.Text;`
`8`	`8`	`using System.Threading.Tasks;`
	`9`	`+using Tensorflow.NumPy;`
`9`	`10`	`using static Tensorflow.Binding;`
`10`	`11`
`11`	`12`	`namespace Tensorflow.Benchmark.Leak`
`@@ -23,9 +24,15 @@ public void Run()`
`23`	`24`
`24`	`25`	`for (var i = 0; i < 1024; i++)`
`25`	`26`	`{`
`26`		`- using var sess = Session.LoadFromSavedModel(ClassifierModelPath);`
`27`		`- // destory graph`
`28`		`- using var g = sess.graph;`
	`27`	`+ using (var sess = Session.LoadFromSavedModel(ClassifierModelPath)) {`
	`28`	`+ using (var g = sess.graph.as_default()) {`
	`29`	`+ var inputOp = g.OperationByName("inference_input");`
	`30`	`+ var outputOp = g.OperationByName("StatefulPartitionedCall");`
	`31`	`+`
	`32`	`+ var inp = np.zeros(new Shape(new int[] { 1, 2, 96 }), TF_DataType.TF_FLOAT);`
	`33`	`+ sess.run(outputOp.outputs[0], new FeedItem(inputOp.outputs[0], inp));`
	`34`	`+ }`
	`35`	`+ }`
`29`	`36`	`}`
`30`	`37`	`}`
`31`	`38`	`}`