@@ -28,16 +28,15 @@ static void UseApi()
2828 using ( var session = new InferenceSession ( modelPath ) )
2929 {
3030 var inputMeta = session . InputMetadata ;
31+ var container = new List < NamedOnnxValue > ( ) ;
3132
32- // User should be able to detect input name/type/shape from the metadata.
33- // Currently InputMetadata implementation is inclomplete, so assuming Tensor<flot> of predefined dimension.
34-
35- var shape0 = new int [ ] { 1 , 3 , 224 , 224 } ;
36- float [ ] inputData0 = LoadInputsFloat ( ) ;
37- var tensor = new DenseTensor < float > ( inputData0 , shape0 ) ;
33+ float [ ] inputData = LoadTensorFromFile ( @"bench.in" ) ; // this is the data for only one input tensor for this model
3834
39- var container = new List < NamedOnnxValue > ( ) ;
40- container . Add ( new NamedOnnxValue ( "data_0" , tensor ) ) ;
35+ foreach ( var name in inputMeta . Keys )
36+ {
37+ var tensor = new DenseTensor < float > ( inputData , inputMeta [ name ] . Dimensions ) ;
38+ container . Add ( new NamedOnnxValue ( name , tensor ) ) ;
39+ }
4140
4241 // Run the inference
4342 var results = session . Run ( container ) ; // results is an IReadOnlyList<NamedOnnxValue> container
@@ -49,40 +48,27 @@ static void UseApi()
4948 Console . WriteLine ( r . AsTensor < float > ( ) . GetArrayString ( ) ) ;
5049 }
5150
52- // Just try some GC collect
53- results = null ;
54- container = null ;
55-
56- GC . Collect ( ) ;
57- GC . WaitForPendingFinalizers ( ) ;
5851 }
5952 }
6053
61- static int [ ] LoadInputsInt32 ( )
54+ static float [ ] LoadTensorFromFile ( string filename )
6255 {
63- return null ;
64- }
65-
66- static float [ ] LoadInputsFloat ( )
67- {
68- // input: data_0 = float32[1,3,224,224] for squeezenet model
69- // output: softmaxout_1 = float32[1,1000,1,1]
70- uint size = 1 * 3 * 224 * 224 ;
71- float [ ] tensor = new float [ size ] ;
56+ var tensorData = new List < float > ( ) ;
7257
7358 // read data from file
74- using ( var inputFile = new System . IO . StreamReader ( @"bench.in" ) )
59+ using ( var inputFile = new System . IO . StreamReader ( filename ) )
7560 {
7661 inputFile . ReadLine ( ) ; //skip the input name
7762 string [ ] dataStr = inputFile . ReadLine ( ) . Split ( new char [ ] { ',' , '[' , ']' } , StringSplitOptions . RemoveEmptyEntries ) ;
7863 for ( int i = 0 ; i < dataStr . Length ; i ++ )
7964 {
80- tensor [ i ] = Single . Parse ( dataStr [ i ] ) ;
65+ tensorData . Add ( Single . Parse ( dataStr [ i ] ) ) ;
8166 }
8267 }
8368
84- return tensor ;
69+ return tensorData . ToArray ( ) ;
8570 }
8671
72+
8773 }
8874}
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