InferenceModelFactory

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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

    

14. def loadFloatModel(modelPath: String, weightPath: String, blas: Boolean = true): FloatModel

    

15. def loadFloatModel(modelPath: String): FloatModel

    

16. def loadFloatModelForCaffe(modelPath: String, weightPath: String, blas: Boolean = true): FloatModel

    

17. def loadFloatModelForPyTorch(modelBytes: Array[Byte]): FloatModel

    

18. def loadFloatModelForPyTorch(modelPath: String): FloatModel

    

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

    

20. def loadFloatModelForTFSavedModel(modelPath: String, inputs: Array[String], outputs: Array[String], intraOpParallelismThreads: Int = 1, interOpParallelismThreads: Int = 1, usePerSessionThreads: Boolean = true): FloatModel

    

21. def loadFloatModelForTFSavedModelBytes(savedModelBytes: Array[Byte], inputs: Array[String], outputs: Array[String], intraOpParallelismThreads: Int = 1, interOpParallelismThreads: Int = 1, usePerSessionThreads: Boolean = true): FloatModel

    

22. def loadOpenVINOModelForIR(modelBytes: Array[Byte], weightBytes: Array[Byte], deviceType: DeviceTypeEnumVal, batchSize: Int): OpenVINOModel

    

23. def loadOpenVINOModelForIR(modelFilePath: String, weightFilePath: String, deviceType: DeviceTypeEnumVal, batchSize: Int = 0): OpenVINOModel

    

24. def loadOpenVINOModelForTF(savedModelBytes: Array[Byte], inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float, input: String): OpenVINOModel

    

25. def loadOpenVINOModelForTF(savedModelDir: String, inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float, input: String): OpenVINOModel

    

26. def loadOpenVINOModelForTF(modelBytes: Array[Byte], imageClassificationModelType: String, checkpointBytes: Array[Byte], inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float): OpenVINOModel

    

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

    

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

    

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

    

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

    

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

    

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32. final def notify(): Unit

    

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33. final def notifyAll(): Unit

    

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

    

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

    

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

    

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37. def toString(): String

    

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

    

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

    

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

    

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

    

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

    

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43. final def wait(): Unit

    

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

    

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

    

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