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48 changes: 48 additions & 0 deletions
48
api/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/pooling/GlobalAvgPool1D.kt
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| /* | ||
| * Copyright 2020 JetBrains s.r.o. and Kotlin Deep Learning project contributors. All Rights Reserved. | ||
| * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE.txt file. | ||
| */ | ||
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| package org.jetbrains.kotlinx.dl.api.core.layer.pooling | ||
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| import org.jetbrains.kotlinx.dl.api.core.KGraph | ||
| import org.jetbrains.kotlinx.dl.api.core.layer.Layer | ||
| import org.jetbrains.kotlinx.dl.api.core.util.TF | ||
| import org.tensorflow.Operand | ||
| import org.tensorflow.Shape | ||
| import org.tensorflow.op.Ops | ||
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| /** | ||
| * Global average pooling operation for temporal data. | ||
| * NOTE: Works with tensors which must have rank 3 (batch, steps, features). | ||
| * Input shape: 3D tensor with shape `(batch_size, steps, features)`. | ||
| * Output shape: 2D tensor with shape `(batch_size, features)`. | ||
| * @property [name] Custom layer name. | ||
| * @constructor Creates [GlobalAvgPool1D] object. | ||
| */ | ||
| public class GlobalAvgPool1D( | ||
| name: String = "" | ||
| ) : Layer(name) { | ||
| override fun build(tf: Ops, kGraph: KGraph, inputShape: Shape) { } | ||
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| override fun computeOutputShape(inputShape: Shape): Shape { | ||
| return Shape.make(inputShape.size(0), inputShape.size(2)) | ||
| } | ||
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| override fun forward(tf: Ops, input: Operand<Float>, isTraining: Operand<Boolean>, numberOfLosses: Operand<Float>?): Operand<Float> { | ||
| // TODO support for different dataFormat("channel_last", "channel_first") | ||
| var stepAxis = 1 | ||
| // TODO support for masking | ||
| return TF.mean(tf, input, tf.constant(stepAxis)) | ||
| } | ||
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| override val weights: Map<String, Array<*>> get() = emptyMap() | ||
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| override val hasActivation: Boolean get() = false | ||
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| override val paramCount: Int get() = 0 | ||
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| override fun toString(): String { | ||
| return "GlobalAvgPool1D(name=$name)" | ||
| } | ||
| } |
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13
api/src/test/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/GlobalAvgPooling1DTest.kt
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| package org.jetbrains.kotlinx.dl.api.core.layer | ||
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| import org.jetbrains.kotlinx.dl.api.core.layer.pooling.GlobalAvgPool1D | ||
| import org.junit.jupiter.api.Test | ||
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| internal class GlobalAvgPooling1DTest : PoolLayerTest() { | ||
| @Test | ||
| fun globalAvgPool1DTest(){ | ||
| val input = Array(2, { Array(3, { FloatArray(4) { 0f } } ) } ) | ||
| val expected = Array(2, {FloatArray(4) { 0f } }) | ||
| assertGlobalAvgPool1DEquals(GlobalAvgPool1D(),input, expected ) | ||
| } | ||
| } |
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api/src/test/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/PoolLayerTest.kt
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| package org.jetbrains.kotlinx.dl.api.core.layer | ||
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| import org.jetbrains.kotlinx.dl.api.core.KGraph | ||
| import org.jetbrains.kotlinx.dl.api.core.activation.EPS | ||
| import org.jetbrains.kotlinx.dl.api.core.shape.* | ||
| import org.junit.jupiter.api.Assertions.assertArrayEquals | ||
| import org.junit.jupiter.api.Assertions.assertEquals | ||
| import org.tensorflow.EagerSession | ||
| import org.tensorflow.Graph | ||
| import org.tensorflow.* | ||
| import org.tensorflow.op.Ops | ||
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| open class PoolLayerTest { | ||
| protected fun assertGlobalAvgPool1DEquals( | ||
| layer: Layer, | ||
| input:Array<Array<FloatArray>>, | ||
| expected: Array<FloatArray>, | ||
| ) { | ||
| val actual = Array(expected.size) {FloatArray(expected[0].size) { 0.toFloat() } } | ||
| assertPoolingLayer(layer,input, expected,actual,::assertGlobalAvgPool1DEquals) | ||
| } | ||
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| private fun assertPoolingLayer( | ||
| layer: Layer, | ||
| input:Array<Array<FloatArray>>, | ||
| expected: Array<FloatArray>, | ||
| actual:Array<FloatArray>, | ||
| assertEqual: (Array<FloatArray>, Array<FloatArray>)->Unit, | ||
| ){ | ||
| val inputSize = input.size | ||
| val inputShape = Shape.make(inputSize.toLong()) | ||
| EagerSession.create().use { | ||
| val tf = Ops.create(it) | ||
| val inputOp = tf.constant(input) | ||
| layer.build(tf, KGraph(Graph().toGraphDef()), inputShape) | ||
| val isTraining = tf.constant(true) | ||
| val numberOfLosses = tf.constant(1.0f) | ||
| val output = layer.forward(tf, inputOp, isTraining, numberOfLosses).asOutput().tensor() | ||
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| val expectedShape = Shape.make( | ||
| expected.size.toLong(), | ||
| expected[0].size.toLong() | ||
| ) | ||
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| val actualShape = shapeFromDims(*output.shape()) | ||
| output.copyTo(actual) | ||
| assertEquals(expectedShape, actualShape) | ||
| assertEqual(expected,actual) | ||
| } | ||
| } | ||
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| private fun assertGlobalAvgPool1DEquals( | ||
| expected: Array<FloatArray>, | ||
| actual: Array<FloatArray> | ||
| ) { | ||
| val expectedTensor = expected | ||
| val actualTensor = actual | ||
| val msg = "Expected ${expectedTensor.contentDeepToString()} " + | ||
| "to equal ${actualTensor.contentDeepToString()}" | ||
| for (i in expectedTensor.indices) { | ||
| assertArrayEquals(expectedTensor[i], actualTensor[i], EPS, msg) | ||
| } | ||
| } | ||
| } |