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com.intel.analytics.zoo.pipeline.inference

InferenceModelFactory

Related Doc: package inference

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object InferenceModelFactory extends InferenceSupportive

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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. final def asInstanceOf[T0]: T0

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  5. def clearWeightBias(model: Module[Float]): Unit

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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 loadCalibratedOpenVINOModelForTF(modelPath: String, modelType: String, checkpointPath: String, inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float, networkType: String, validationFilePath: String, subset: Int, opencvLibPath: String): OpenVINOModel

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  14. def loadFloatModel(modelPath: String, weightPath: String): FloatModel

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  15. def loadFloatModel(modelPath: String): FloatModel

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  16. def loadFloatModelForCaffe(modelPath: String, weightPath: String): FloatModel

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  17. def loadFloatModelForTF(modelPath: String, intraOpParallelismThreads: Int = 1, interOpParallelismThreads: Int = 1, usePerSessionThreads: Boolean = true): FloatModel

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  18. def loadOpenVINOModelForIR(modelFilePath: String, weightFilePath: String, deviceType: DeviceTypeEnumVal): OpenVINOModel

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  19. def loadOpenVINOModelForIRInt8(modelFilePath: String, weightFilePath: String, deviceType: DeviceTypeEnumVal, batchSize: Int): OpenVINOModel

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  20. def loadOpenVINOModelForTF(modelPath: String, imageClassificationModelType: String, checkpointPath: String, inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float): OpenVINOModel

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  21. def loadOpenVINOModelForTF(modelPath: String, modelType: String, pipelineConfigPath: String, extensionsConfigPath: String): OpenVINOModel

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  22. def makeMetaModel(original: AbstractModule[Activity, Activity, Float]): AbstractModule[Activity, Activity, Float]

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  23. def makeUpModel(clonedModel: Module[Float], weightBias: Array[Tensor[Float]]): AbstractModule[Activity, Activity, Float]

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

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  25. final def notify(): Unit

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  26. final def notifyAll(): Unit

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  27. def releaseWeightBias(model: Module[Float]): Unit

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

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  29. def timing[T](name: String)(f: ⇒ T): T

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  30. def toString(): String

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  31. def transferBatchTableToJListOfJListOfJTensor(batchTable: Table, batchSize: Int): List[List[JTensor]]

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  32. def transferBatchTensorToJListOfJListOfJTensor(batchTensor: Tensor[Float], batchSize: Int): List[List[JTensor]]

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  33. def transferListOfActivityToActivityOfBatch(inputs: List[List[JTensor]], batchSize: Int): Activity

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  34. def transferTensorToJTensor(input: Tensor[Float]): JTensor

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  35. def transferTensorsToTensorOfBatch(tensors: Array[JTensor]): Tensor[Float]

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  36. final def wait(): Unit

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

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

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