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Add tests for clip and reciprocal
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@EmergentOrder
EmergentOrder committed Apr 12, 2021
1 parent 017c2b7 commit a1934a4
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2 changes: 1 addition & 1 deletion ONNXScala/src/main/scala/ndscala/ONNXScalaOps.scala
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Expand Up @@ -192,7 +192,7 @@ given NDArrayOps[Tensor] with {
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: Tensor[DType, (Tt,Td,S)]) def prelu(slope: Tensor[DType, (Tt,Td,S)])(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): Tensor[DType, (Tt,Td,S)] = onnx.PReluV9("prelu", arr, slope)
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: Tensor[DType, (Tt,Td,S)]) def isNaN()(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): Tensor[Boolean, (Tt,Td,S)] = onnx.IsNaNV9[DType, Boolean, Tt, Td, S]("isNaN", arr)
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: Tensor[DType, (Tt,Td,S)]) def sign()(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): Tensor[DType, (Tt,Td,S)] = onnx.SignV9("sign", arr)
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: Tensor[DType, (Tt,Td,S)]) def clip(min: Float = -3.402823e+38, max: Float = 3.402823e+38)(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): Tensor[DType, (Tt,Td,S)] = onnx.ClipV6("clip", max = max, min = min, arr)
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: Tensor[DType, (Tt,Td,S)]) def clip(min: DType, max: DType)(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): Tensor[DType, (Tt,Td,S)] = onnx.ClipV12("clip", arr, Tensor(Array(min), tt.value, td.value, SNil), Tensor(Array(max), tt.value, td.value, SNil))
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: Tensor[DType, (Tt,Td,S)]) def dropout(seed: Int = 42, ratio: Float)(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): Tensor[DType, (Tt,Td,S)] = onnx.DropoutV12("dropout", seed, arr, Some(Tensor(Array(ratio), tt.value, td.value, 1 #: SNil)))
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Dimension #: Dimension #: SNil] (arr: Tensor[DType, (Tt,Td,S)]) def softmax(axis: Int = 1)(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): Tensor[DType, (Tt,Td,S)] = onnx.SoftmaxV11("softmax", input = arr)
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Dimension #: Dimension #: Dimension #: Dimension #: SNil] (arr: Tensor[DType, (Tt,Td,S)]) def instanceNormalization[Tt1 <: TensorTypeDenotation, Td1 <: TensorShapeDenotation, S1 <: Dimension #: SNil](epsilon: Float = 1e-5, scale: Tensor[DType, (Tt1,Td1,S1)], b: Tensor[DType, (Tt1,Td1,S1)])(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): Tensor[DType, (Tt,Td,S)] = onnx.InstanceNormalizationV6("instanceNormalization", epsilon, arr, scale, b)
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15 changes: 11 additions & 4 deletions ONNXScala/src/test/scala/ndscala/ONNXScalaNDArraySpec.scala
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Expand Up @@ -243,12 +243,19 @@ type TD = "TensorShapeDenotation" ##: TSNil
arr.rank == 2
}

/*

"Tensor" should "clip" in {
val arr: Tensor[Double] = (Array(41.7, 84.5), Mat(1,2))
(arr.clip(50.0, 90.0)) shouldEqual (Array(50.0, 84.5), Mat(1,2))
val arr = Tensor(Array(42.0f, 84.0f),"TensorTypeDenotation", "TensorShapeDenotation" ##: "TensorShapeDenotation" ##: TSNil, 1 #: 2 #: SNil)
val expectedResult = Tensor(Array(50.0f, 80.0f),"TensorTypeDenotation", "TensorShapeDenotation" ##: "TensorShapeDenotation" ##: TSNil, 1 #: 2 #: SNil)
doAssert((arr.clip(50.0f, 80.0f)) ==== expectedResult)
}
*/

"Tensor" should "reciprocal" in {
val arr = Tensor(Array(42.0f, 84.0f),"TensorTypeDenotation", "TensorShapeDenotation" ##: "TensorShapeDenotation" ##: TSNil, 1 #: 2 #: SNil)
val expectedResult = Tensor(Array(0.023809524f, 0.011904762f),"TensorTypeDenotation", "TensorShapeDenotation" ##: "TensorShapeDenotation" ##: TSNil, 1 #: 2 #: SNil)
doAssert((arr.reciprocal()) ==== expectedResult)
}

"Tensor" should "unary subtract" in {
val arr = Tensor(Array(42, 84),"TensorTypeDenotation", "TensorShapeDenotation" ##: TSNil, 1 #: 2 #: SNil)
doAssert((-arr) ==== Tensor(Array(-42, -84),"TensorTypeDenotation", "TensorShapeDenotation" ##: TSNil, 1 #: 2 #: SNil))
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4 changes: 1 addition & 3 deletions core/src/main/scala/ndscala/NDArrayOps.scala
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Expand Up @@ -71,8 +71,6 @@ trait NDArrayOps[SomeNDArray[_ <: AllSupported, _ <: Axes]] {
// extension[DType <: Supported : ClassTag : IsSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: SomeNDArray[DType, (Tt,Td,S)]) def unsqueeze[Tt1 <: TensorTypeDenotation, Axes <: Indices](using tt: ValueOf[Tt1], td: TensorShapeDenotationOf[KeepOrReduceDimDenotations[Td,Axes,false]], s: ShapeOf[KeepOrReduceDims[S,Axes,false]], i: IndicesOf[Axes]): SomeNDArray[DType, Tuple3[Tt1,KeepOrReduceDimDenotations[Td,Axes,false],KeepOrReduceDims[S,Axes,false]]]
extension[DType <: Supported : ClassTag : IsSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: SomeNDArray[DType, (Tt,Td,S)]) def rank: Int


// def clip[DType : ClassTag: Numeric: IsFloatSupported](arr: SomeNDArray[DType], min: DType, max: DType): SomeNDArray[DType]
extension[DType <: NumericSupported : ClassTag: Numeric : IsNumericSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: SomeNDArray[DType, (Tt,Td,S)]) def unary_- (using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): SomeNDArray[DType, (Tt,Td,S)]
extension[DType <: NumericSupported : ClassTag : Numeric : IsNumericSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: SomeNDArray[DType, (Tt,Td,S)]) def abs()(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): SomeNDArray[DType, (Tt,Td,S)]
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: SomeNDArray[DType, (Tt,Td,S)]) def ceil()(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): SomeNDArray[DType, (Tt,Td,S)]
Expand Down Expand Up @@ -118,7 +116,7 @@ trait NDArrayOps[SomeNDArray[_ <: AllSupported, _ <: Axes]] {
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: SomeNDArray[DType, (Tt,Td,S)]) def prelu(slope: SomeNDArray[DType, (Tt,Td,S)])(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): SomeNDArray[DType, (Tt,Td,S)]
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: SomeNDArray[DType, (Tt,Td,S)]) def isNaN()(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): SomeNDArray[Boolean, (Tt,Td,S)]
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: SomeNDArray[DType, (Tt,Td,S)]) def sign()(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): SomeNDArray[DType, (Tt,Td,S)]
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: SomeNDArray[DType, (Tt,Td,S)]) def clip(min: Float = -3.402823e+38, max: Float = 3.402823e+38)(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): SomeNDArray[DType, (Tt,Td,S)]
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Shape] (arr: SomeNDArray[DType, (Tt,Td,S)]) def clip(min: DType, max: DType)(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): SomeNDArray[DType, (Tt,Td,S)]
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Dimension #: Dimension #: Dimension #: Dimension #: SNil] (arr: SomeNDArray[DType, (Tt,Td,S)]) def instanceNormalization[Tt1 <: TensorTypeDenotation, Td1 <: TensorShapeDenotation, S1 <: Dimension #: SNil](epsilon: Float = 1e-5, scale: SomeNDArray[DType, (Tt1,Td1,S1)], b: SomeNDArray[DType, (Tt1,Td1,S1)])(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): SomeNDArray[DType, (Tt,Td,S)]
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Dimension #: Dimension #: Dimension #: Dimension #: SNil] (arr: SomeNDArray[DType, (Tt,Td,S)]) def lrn(alpha: Float = 0.0001, beta: Float = 0.75, bias: Float = 1.0, size: Int)(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): SomeNDArray[DType, (Tt,Td,S)]
extension[DType <: FloatSupported : ClassTag: Numeric : IsFloatSupported, Tt <: TensorTypeDenotation, Td <: TensorShapeDenotation, S <: Dimension #: Dimension #: SNil] (arr: SomeNDArray[DType, (Tt,Td,S)]) def softmax(axis: Int = 1)(using tt: ValueOf[Tt], td: TensorShapeDenotationOf[Td], s: ShapeOf[S]): SomeNDArray[DType, (Tt,Td,S)]
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