ModuleUtil

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. def addConvRelu[T](prevNodes: ModuleNode[T], p: (Int, Int, Int, Int, Int), name: String, prefix: String = "conv", nGroup: Int = 1, propogateBack: Boolean = true)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): ModuleNode[T]

   

   Create Convolution layer followed by Relu activation

   Create Convolution layer followed by Relu activation

   prevNodes

   previous node for convolution layer node

   p

   convolution size info. Should be (input plane number, output plane number, kernel size, stride size, pad size). We'are assuming kernel width = kernel height, stride width = stride height, pad width = pad height

   name

   layer name

   prefix

   prefix for the layer name

   nGroup

   kernel group number

   propogateBack

   whether to propagate gradient back

   returns

   Relu node

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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7. final def eq(arg0: AnyRef): Boolean

   

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8. def equals(arg0: Any): Boolean

   

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9. def finalize(): Unit

   

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10. final def getClass(): Class[_]

    

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11. def hashCode(): Int

    

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12. final def isInstanceOf[T0]: Boolean

    

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13. def loadModelWeights(srcModel: Module[Float], targetModel: Module[Float], matchAll: Boolean = true): ModuleUtil.this.type

    

    Load model weights and bias from source model to target model

    Load model weights and bias from source model to target model

    srcModel

    source model

    targetModel

    target model

    matchAll

    whether to match all layers' weights and bias, if not, only load existing source weights and bias

14. final def ne(arg0: AnyRef): Boolean

    

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15. final def notify(): Unit

    

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16. final def notifyAll(): Unit

    

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17. def selectResults[T](start: Int, dim: Int, nInputDims: Int, numComponents: Int, name: String)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Module[T]

    

    select results (confs || locs || priorboxes), use JoinTable to concat them into one tensor

    select results (confs || locs || priorboxes), use JoinTable to concat them into one tensor

    start

    start index of the result

    dim

    dimension to join

    nInputDims

    specify the number of dimensions for the input

    name

    result layer name

18. val shareFinput: Tensor[Float]

    

19. def shareMemory(model: Module[Float], isShareOutput: Boolean = false): Unit

    

    share the storage of SpatialConvolution fInput note that this sharing only works for Inference only

    share the storage of SpatialConvolution fInput note that this sharing only works for Inference only

    model

    model to share

20. def stopGradient[T](model: Graph[T])(implicit arg0: ClassTag[T]): Unit

    

    Stop the input gradient of layers whose name ended with priorbox in a model, their input gradient are not computed.

    Stop the input gradient of layers whose name ended with priorbox in a model, their input gradient are not computed.

    model

    the graph model

21. final def synchronized[T0](arg0: ⇒ T0): T0

    

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22. def toString(): String

    

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23. final def wait(): Unit

    

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24. final def wait(arg0: Long, arg1: Int): Unit

    

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25. final def wait(arg0: Long): Unit

    

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