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michalharakalmichalharakal
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Merge pull request #30 from skainet-dev/feature/MNIST
maxpool2d and kdoc
2 parents 1c8b362 + 9c45ef7 commit eb6c089

10 files changed

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‎KDOC_PREPROCESSING.md‎

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# KDoc preprocessing
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This project follows the same approach as [Kotlin/DataFrame](https://github.com/Kotlin/dataframe/blob/master/KDOC_PREPROCESSING.md) for preprocessing KDoc comments before generating documentation with Dokka.
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The Dokka plugin is applied to all modules. Run `./gradlew dokkaHtml` to generate HTML documentation under each module's `build/dokka` directory.

‎build.gradle.kts‎

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import org.jetbrains.dokka.gradle.DokkaMultiModuleTask
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plugins {
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alias(libs.plugins.androidLibrary) apply false
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alias(libs.plugins.kotlinMultiplatform) apply false
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alias(libs.plugins.jetbrainsKotlinJvm) apply false
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alias(libs.plugins.binaryCompatibility) apply false
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alias(libs.plugins.dokka) apply false
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alias(libs.plugins.modulegraph.souza) apply true
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}
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apply(plugin = "org.jetbrains.dokka")
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allprojects {
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group = "sk.ai.net"
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version = "0.0.6-SNAPSHOT"
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readmePath.set("./Modules.md")
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heading = "### Module Graph"
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}
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tasks.register<org.jetbrains.dokka.gradle.DokkaMultiModuleTask>("dokkaHtmlMultiModule") {
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outputDirectory.set(buildDir.resolve("dokka"))
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}

‎core/build.gradle.kts‎

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@@ -8,6 +8,7 @@ plugins {
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alias(libs.plugins.kotlinMultiplatform)
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alias(libs.plugins.androidLibrary)
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alias(libs.plugins.binaryCompatibility)
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alias(libs.plugins.dokka)
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alias(libs.plugins.vanniktech.mavenPublish)
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}
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‎core/src/commonMain/kotlin/sk/ai/net/dsl/NetworkBuilder.kt‎

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@@ -7,6 +7,7 @@ import sk.ai.net.nn.Flatten
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import sk.ai.net.nn.Input
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import sk.ai.net.nn.Linear
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import sk.ai.net.nn.Conv2d
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import sk.ai.net.nn.MaxPool2d
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import sk.ai.net.nn.Module
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import sk.ai.net.nn.topology.MLP
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fun conv2d(id: String = "", content: CONV2D.() -> Unit = {})
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fun maxPool2d(id: String = "", content: MAXPOOL2D.() -> Unit = {})
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fun dense(outputDimension: Int, id: String = "", content: DENSE.() -> Unit = {})
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}
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var padding: Int
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}
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@NetworkDsl
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interface MAXPOOL2D : NetworkDslItem {
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var kernelSize: Int
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var stride: Int
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}
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private fun getDefaultName(id: String, s: String, size: Int): String {
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if (id.isNotEmpty()) return id
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)
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}
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class MaxPool2dImpl(
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override var kernelSize: Int = 2,
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override var stride: Int = 2,
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private val id: String
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) : MAXPOOL2D {
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fun create(): Module = MaxPool2d(
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kernelSize = kernelSize,
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stride = stride,
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name = id
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)
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}
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private class NeuralNetworkDslImpl : NeuralNetworkDsl {
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val modules = mutableListOf<Module>()
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modules += impl.create()
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}
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override fun maxPool2d(id: String, content: MAXPOOL2D.() -> Unit) {
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val impl = MaxPool2dImpl(
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id = getDefaultName(id, "maxPool2d", modules.size)
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)
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impl.content()
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modules += impl.create()
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}
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override fun dense(outputDimension: Int, id: String, content: DENSE.() -> Unit) {
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val inputDimension = lastDimension
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lastDimension = outputDimension

‎core/src/commonMain/kotlin/sk/ai/net/nn/MaxPool2d.kt‎

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package sk.ai.net.nn
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import sk.ai.net.Shape
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import sk.ai.net.Tensor
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import sk.ai.net.impl.DoublesTensor
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/**
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* 2D max pooling layer.
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* Works with tensors of shape (N, C, H, W) or (C, H, W).
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*/
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class MaxPool2d(
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val kernelSize: Int,
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val stride: Int = kernelSize,
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override val name: String = "MaxPool2d"
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) : Module() {
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override val modules: List<Module>
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get() = emptyList()
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override fun forward(input: Tensor): Tensor = maxPool2d(input)
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private fun maxPool2d(input: Tensor): Tensor {
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val tensor = input as DoublesTensor
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val shape = tensor.shape
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require(shape.rank == 3 || shape.rank == 4) {
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"MaxPool2d expected 3D or 4D input tensor, but got shape $shape"
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}
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val batchSize: Int
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val channels: Int
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val height: Int
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val width: Int
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if (shape.rank == 4) {
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batchSize = shape[0]
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channels = shape[1]
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height = shape[2]
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width = shape[3]
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} else {
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batchSize = 1
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channels = shape[0]
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height = shape[1]
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width = shape[2]
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}
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val outH = (height - kernelSize) / stride + 1
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val outW = (width - kernelSize) / stride + 1
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val outElements = DoubleArray(batchSize * channels * outH * outW)
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var idx = 0
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for (n in 0 until batchSize) {
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for (c in 0 until channels) {
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for (i in 0 until outH) {
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for (j in 0 until outW) {
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var maxVal = Double.NEGATIVE_INFINITY
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for (ki in 0 until kernelSize) {
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for (kj in 0 until kernelSize) {
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val h = i * stride + ki
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val w = j * stride + kj
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val value = if (shape.rank == 4) {
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tensor[n, c, h, w]
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} else {
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tensor[c, h, w]
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}
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if (value > maxVal) maxVal = value
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}
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}
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outElements[idx++] = maxVal
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}
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}
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}
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}
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val outShape = Shape(batchSize, channels, outH, outW)
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return DoublesTensor(outShape, outElements)
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}
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}

‎core/src/commonTest/kotlin/sk/ai/net/ShapeTest.kt‎

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import kotlin.test.Test
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import kotlin.test.assertEquals
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import kotlin.test.assertFalse
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import kotlin.test.assertContentEquals
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class ShapeTest {
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@Test
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fun `test scalar`() {
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val shape = Shape(0)
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assertEquals(shape, Shape(1, 2))
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fun volumeIsProductOfDimensions() {
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val shape = Shape(2, 3, 4)
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assertEquals(24, shape.volume)
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}
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}
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@Test
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fun rankIsNumberOfDimensions() {
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val shape = Shape(2, 3)
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assertEquals(2, shape.rank)
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}
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@Test
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fun equalityChecksAllDimensions() {
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val shape1 = Shape(2, 3)
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val shape2 = Shape(2, 3)
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val shape3 = Shape(3, 2)
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assertEquals(shape1, shape2)
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assertFalse(shape1 == shape3)
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}
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@Test
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fun constructorCopiesDimensions() {
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val dims = intArrayOf(2, 3)
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val shape = Shape(*dims)
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dims[0] = 5
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assertContentEquals(intArrayOf(2, 3), shape.dimensions)
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}
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@Test
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fun toStringContainsSizeInformation() {
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val shape = Shape(2, 3)
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assertEquals("Shape: Dimensions = [2 x 3], Size (Volume) = 6", shape.toString())
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}
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}

‎core/src/commonTest/kotlin/sk/ai/net/nn/MaxPool2dTest.kt‎

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package sk.ai.net.nn
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import sk.ai.net.Shape
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import sk.ai.net.dsl.network
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import sk.ai.net.impl.DoublesTensor
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import kotlin.test.Test
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import kotlin.test.assertContentEquals
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import kotlin.test.assertEquals
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import kotlin.test.assertTrue
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class MaxPool2dTest {
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@Test
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fun max_pool2d_basic() {
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val input = DoublesTensor(
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Shape(1, 1, 4, 4),
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doubleArrayOf(
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1.0, 2.0, 3.0, 4.0,
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5.0, 6.0, 7.0, 8.0,
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9.0, 10.0, 11.0, 12.0,
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13.0, 14.0, 15.0, 16.0
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)
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)
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val pool = MaxPool2d(kernelSize = 2, stride = 2)
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val result = pool.forward(input) as DoublesTensor
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assertEquals(Shape(1, 1, 2, 2), result.shape)
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assertContentEquals(doubleArrayOf(6.0, 8.0, 14.0, 16.0), result.elements)
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}
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@Test
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fun dsl_support() {
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val module = network {
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input(1)
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maxPool2d {
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kernelSize = 2
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stride = 2
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}
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}
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val mlp = module as sk.ai.net.nn.topology.MLP
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assertTrue(mlp.modules[1] is MaxPool2d)
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val mp = mlp.modules[1] as MaxPool2d
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assertEquals(2, mp.kernelSize)
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assertEquals(2, mp.stride)
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}
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}

‎gguf/build.gradle.kts‎

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plugins {
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alias(libs.plugins.kotlinMultiplatform)
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alias(libs.plugins.androidLibrary)
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alias(libs.plugins.dokka)
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alias(libs.plugins.vanniktech.mavenPublish)
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}
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‎gradle/libs.versions.toml‎

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binaryCompatibility = { id = "org.jetbrains.kotlinx.binary-compatibility-validator", version.ref = "binaryCompatibility" }
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modulegraph-souza = { id = "dev.iurysouza.modulegraph", version.ref = "moduleGraphSouza" }
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vanniktech-mavenPublish = { id = "com.vanniktech.maven.publish", version = "0.32.0" }
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dokka = { id = "org.jetbrains.dokka", version = "1.9.20" }

‎io/build.gradle.kts‎

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alias(libs.plugins.kotlinMultiplatform)
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alias(libs.plugins.androidLibrary)
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alias(libs.plugins.kotlinSerialization)
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alias(libs.plugins.dokka)
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alias(libs.plugins.vanniktech.mavenPublish)
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}

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