Native: move samples to backend.native/tests/
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Svyatoslav Scherbina
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headers = tensorflow/c/c_api.h
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/*
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* Copyright 2010-2018 JetBrains s.r.o. Use of this source code is governed by the Apache 2.0 license
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* that can be found in the license/LICENSE.txt file.
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*/
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package sample.tensorflow
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import kotlinx.cinterop.*
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import platform.posix.size_t
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import tensorflow.*
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typealias Status = CPointer<TF_Status>
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typealias Operation = CPointer<TF_Operation>
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typealias Tensor = CPointer<TF_Tensor>
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val Status.isOk: Boolean get() = TF_GetCode(this) == TF_OK
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val Status.errorMessage: String get() = TF_Message(this)!!.toKString()
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fun Status.delete() = TF_DeleteStatus(this)
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fun Status.validate() {
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try {
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if (!isOk) {
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throw Error("Status is not ok: $errorMessage")
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}
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} finally {
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delete()
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}
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}
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inline fun <T> statusValidated(block: (Status) -> T): T {
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val status = TF_NewStatus()!!
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val result = block(status)
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status.validate()
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return result
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}
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fun scalarTensor(value: Int): Tensor {
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val data = nativeHeap.allocArray<IntVar>(1)
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data[0] = value
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return TF_NewTensor(
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TF_INT32,
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/* dims = */ null,
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/* num_dims = */ 0,
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/* data = */ data,
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/* len = */ IntVar.size.convert(),
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/* deallocator = */ staticCFunction { dataToFree, _, _ -> nativeHeap.free(dataToFree!!.reinterpret<IntVar>()) },
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/* deallocator_arg = */ null
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)!!
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}
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val Tensor.scalarIntValue: Int get() {
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if (TF_INT32 != TF_TensorType(this) || IntVar.size.convert<size_t>() != TF_TensorByteSize(this)) {
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throw Error("Tensor is not of type int.")
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}
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if (0 != TF_NumDims(this)) {
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throw Error("Tensor is not scalar.")
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}
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return TF_TensorData(this)!!.reinterpret<IntVar>().pointed.value
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}
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class Graph {
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val tensorflowGraph = TF_NewGraph()!!
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inline fun operation(type: String, name: String, initDescription: (CPointer<TF_OperationDescription>) -> Unit): Operation {
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val description = TF_NewOperation(tensorflowGraph, type, name)!!
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initDescription(description)
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return statusValidated { TF_FinishOperation(description, it)!! }
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}
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fun constant(value: Int, name: String = "scalarIntConstant") = operation("Const", name) { description ->
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statusValidated { TF_SetAttrTensor(description, "value", scalarTensor(value), it) }
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TF_SetAttrType(description, "dtype", TF_INT32)
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}
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fun intInput(name: String = "input") = operation("Placeholder", name) { description ->
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TF_SetAttrType(description, "dtype", TF_INT32)
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}
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fun add(left: Operation, right: Operation, name: String = "add") = memScoped {
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val inputs = allocArray<TF_Output>(2)
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inputs[0].apply { oper = left; index = 0 }
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inputs[1].apply { oper = right; index = 0 }
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operation("AddN", name) { description ->
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TF_AddInputList(description, inputs, 2)
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}
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}
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// TODO set unique operation names
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operator fun Operation.plus(right: Operation) = add(this, right)
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inline fun <T> withSession(block: Session.() -> T): T {
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val session = Session(this)
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try {
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return session.block()
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} finally {
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session.dispose()
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}
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}
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}
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class Session(val graph: Graph) {
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private val inputs = mutableListOf<TF_Output>()
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private val inputValues = mutableListOf<Tensor>()
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private var outputs = mutableListOf<TF_Output>()
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private val outputValues = mutableListOf<Tensor?>()
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private val targets = listOf<Operation>()
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private fun createNewSession(): CPointer<TF_Session> {
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val options = TF_NewSessionOptions()
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val session = statusValidated { TF_NewSession(graph.tensorflowGraph, options, it)!! }
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TF_DeleteSessionOptions(options)
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return session
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}
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private var tensorflowSession: CPointer<TF_Session>? = createNewSession()
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private fun clearInputValues() {
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for (inputValue in inputValues) {
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TF_DeleteTensor(inputValue)
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}
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inputValues.clear()
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}
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private fun clearOutputValues() {
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for (outputValue in outputValues) {
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if (outputValue != null)
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TF_DeleteTensor(outputValue)
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}
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outputValues.clear()
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}
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fun dispose() {
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clearInputValues()
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clearOutputValues()
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clearInputs()
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clearOutputs()
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if (tensorflowSession != null) {
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statusValidated { TF_CloseSession(tensorflowSession, it) }
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statusValidated { TF_DeleteSession(tensorflowSession, it) }
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tensorflowSession = null
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}
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}
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private fun setInputsWithValues(inputsWithValues: List<Pair<Operation, Tensor>>) {
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clearInputValues()
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clearInputs()
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for ((input, inputValue) in inputsWithValues) {
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this.inputs.add(nativeHeap.alloc<TF_Output>().apply { oper = input; index = 0 })
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inputValues.add(inputValue)
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}
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}
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private fun setOutputs(outputs: List<Operation>) {
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clearOutputValues()
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clearOutputs()
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this.outputs = outputs.map { nativeHeap.alloc<TF_Output>().apply { oper = it; index = 0 } }.toMutableList()
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}
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private fun clearOutputs() {
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this.outputs.forEach { nativeHeap.free(it) }
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this.outputs.clear()
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}
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private fun clearInputs() {
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this.inputs.forEach { nativeHeap.free(it) }
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this.inputs.clear()
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}
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operator fun invoke(outputs: List<Operation>, inputsWithValues: List<Pair<Operation, Tensor>> = listOf()): List<Tensor?> {
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setInputsWithValues(inputsWithValues)
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setOutputs(outputs)
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return invoke()
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}
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operator fun invoke(output: Operation, inputsWithValues: List<Pair<Operation, Tensor>> = listOf()) =
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invoke(listOf(output), inputsWithValues).single()!!
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operator fun invoke(): List<Tensor?> {
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if (inputs.size != inputValues.size) {
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throw Error("Call SetInputs() before Run()")
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}
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clearOutputValues()
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val inputsCArray = if (inputs.any()) nativeHeap.allocArray<TF_Output>(inputs.size) else null
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inputs.forEachIndexed { i, input ->
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inputsCArray!![i].apply {
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oper = input.oper
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index = input.index
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}
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}
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val outputsCArray = if (outputs.any()) nativeHeap.allocArray<TF_Output>(outputs.size) else null
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outputs.forEachIndexed { i, output ->
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outputsCArray!![i].apply {
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oper = output.oper
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index = output.index
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}
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}
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memScoped {
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val outputValuesCArray = allocArrayOfPointersTo<TF_Tensor>(outputs.map { null })
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statusValidated {
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TF_SessionRun(tensorflowSession, null,
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inputsCArray, inputValues.toCValues(), inputs.size,
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outputsCArray, outputValuesCArray, outputs.size,
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targets.toCValues(), targets.size,
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null, it)
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}
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for (index in outputs.indices) {
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outputValues.add(outputValuesCArray[index])
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}
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}
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clearInputValues()
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return outputValues
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}
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}
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fun main() {
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println("Hello, TensorFlow ${TF_Version()!!.toKString()}!")
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val result = Graph().run {
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val input = intInput()
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withSession { invoke(input + constant(2), inputsWithValues = listOf(input to scalarTensor(3))).scalarIntValue }
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}
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println("3 + 2 is $result.")
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}
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