@@ -58,9 +58,14 @@ val imageTensDefaultDenotations = Tensor(data,shape)
5858Note that ONNX Tensor content is in row-major order.
5959
6060``` scala
61- val out = squeezenet.fullModel[Float , " ImageNetClassification" ," Batch" ##: " Class" ##: TSNil ,1 #: 1000 #: SNil ](Tuple (imageTens))
61+ val out = squeezenet.fullModel[Float ,
62+ " ImageNetClassification" ,
63+ " Batch" ##: " Class" ##: TSNil ,
64+ 1 #: 1000 #: SNil ](Tuple (imageTens))
6265// val out:
63- // org.emergentorder.onnx.Tensors.Tensor[Float, ("ImageNetClassification", "Batch" ##: "Class" ##:
66+ // org.emergentorder.onnx.Tensors.Tensor[Float,
67+ // ("ImageNetClassification",
68+ // "Batch" ##: "Class" ##:
6469// org.emergentorder.compiletime.TSNil
6570// , 1 #: 1000 #: io.kjaer.compiletime.SNil)] = (Array(0.8230729,
6671// ...
@@ -96,7 +101,9 @@ val onnxBackend = new ORTOperatorBackendAll()
96101
97102val longTens = Tensor (Array .fill(1 * 3 * 224 * 224 ){- 42l },tensorDenotation,tensorShapeDenotation,shape)
98103// longTens:
99- // org.emergentorder.onnx.Tensors.Tensor[Float, ("Image", "Batch" ##: "Channel" ##: "Height" ##: "Width" ##:
104+ // org.emergentorder.onnx.Tensors.Tensor[Float,
105+ // ("Image",
106+ // "Batch" ##: "Channel" ##: "Height" ##: "Width" ##:
100107// org.emergentorder.compiletime.TSNil
101108// , 1 #: 1000 #: io.kjaer.compiletime.SNil)] = (
102109// Array(
@@ -106,7 +113,9 @@ val longTens = Tensor(Array.fill(1*3*224*224){-42l},tensorDenotation,tensorShape
106113
107114onnxBackend.AbsV6 (" abs" , longTens)
108115// res2:
109- // org.emergentorder.onnx.Tensors.Tensor[Float, ("Image", "Batch" ##: "Channel" ##: "Height" ##: "Width" ##:
116+ // org.emergentorder.onnx.Tensors.Tensor[Float,
117+ // ("Image",
118+ // "Batch" ##: "Channel" ##: "Height" ##: "Width" ##:
110119// org.emergentorder.compiletime.TSNil
111120// , 1 #: 1000 #: io.kjaer.compiletime.SNil)] = (
112121// Array(
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