UpSamplingParam

Instance Constructors

new UpSamplingParam(data: Array[NDArray], scale: Integer, sample_type: String, num_args: Integer)

data
Array of tensors to upsample. For bilinear upsampling, there should be 2 inputs - 1 data and 1 weight.
scale
Up sampling scale
sample_type
upsampling method
num_args
Number of inputs to be upsampled. For nearest neighbor upsampling, this can be 1-N; the size of output will be(scale*h_0,scale*w_0) and all other inputs will be upsampled to thesame size. For bilinear upsampling this must be 2; 1 input and 1 weight.

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

Attributes
protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def getData(): Array[NDArray]
def getMulti_input_mode(): String
def getNum_args(): Integer
def getNum_filter(): Integer
def getOut(): mxnet.NDArray
def getSample_type(): String
def getScale(): Integer
def getWorkspace(): Long
def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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def setMulti_input_mode(multi_input_mode: String): UpSamplingParam

multi_input_mode
How to handle multiple input. concat means concatenate upsampled images along the channel dimension. sum means add all images together, only available for nearest neighbor upsampling.
def setNum_filter(num_filter: Integer): UpSamplingParam

num_filter
Input filter. Only used by bilinear sample_type.Since bilinear upsampling uses deconvolution, num_filters is set to the number of channels.
def setOut(out: NDArray): UpSamplingParam
def setWorkspace(workspace: Long): UpSamplingParam

workspace
Tmp workspace for deconvolution (MB)
final def synchronized[T0](arg0: ⇒ T0): T0

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def toString(): String

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final def wait(): Unit

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

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final def wait(arg0: Long): Unit

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@throws( ... )

Related Doc: package javaapi

class UpSamplingParam extends AnyRef

Instance Constructors

new UpSamplingParam(data: Array[NDArray], scale: Integer, sample_type: String, num_args: Integer)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getData(): Array[NDArray]

def getMulti_input_mode(): String

def getNum_args(): Integer

def getNum_filter(): Integer

def getOut(): mxnet.NDArray

def getSample_type(): String

def getScale(): Integer

def getWorkspace(): Long

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def setMulti_input_mode(multi_input_mode: String): UpSamplingParam

def setNum_filter(num_filter: Integer): UpSamplingParam

def setOut(out: NDArray): UpSamplingParam

def setWorkspace(workspace: Long): UpSamplingParam

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

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