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� Conflicts: � api/src/main/kotlin/org/jetbrains/kotlinx/dl/api/inference/keras/ModelSaver.kt
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api/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/convolutional/AbstractConv.kt
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| /* | ||
| * Copyright 2021 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.convolutional | ||
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| import org.jetbrains.kotlinx.dl.api.core.KGraph | ||
| import org.jetbrains.kotlinx.dl.api.core.activation.Activations | ||
| import org.jetbrains.kotlinx.dl.api.core.initializer.Initializer | ||
| import org.jetbrains.kotlinx.dl.api.core.layer.Layer | ||
| import org.jetbrains.kotlinx.dl.api.core.regularizer.Regularizer | ||
| import org.jetbrains.kotlinx.dl.api.core.shape.* | ||
| import org.jetbrains.kotlinx.dl.api.core.util.getDType | ||
| import org.tensorflow.Operand | ||
| import org.tensorflow.Shape | ||
| import org.tensorflow.op.Ops | ||
| import org.tensorflow.op.core.Variable | ||
| import java.lang.IllegalArgumentException | ||
| import kotlin.math.roundToInt | ||
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| public abstract class AbstractConv( | ||
| protected val filtersInternal: Long, | ||
| protected val kernelSizeInternal: LongArray, | ||
| protected val stridesInternal: LongArray, | ||
| protected val dilationsInternal: LongArray, | ||
| protected val activationInternal: Activations, | ||
| protected val kernelInitializerInternal: Initializer, | ||
| protected val biasInitializerInternal: Initializer, | ||
| protected val kernelRegularizerInternal: Regularizer?, | ||
| protected val biasRegularizerInternal: Regularizer?, | ||
| protected val activityRegularizerInternal: Regularizer?, | ||
| protected val paddingInternal: ConvPadding, | ||
| protected val useBiasInternal: Boolean, | ||
| protected val kernelVariableName: String, | ||
| protected val biasVariableName: String, | ||
| name: String | ||
| ) : Layer(name) { | ||
| // weight tensors | ||
| protected lateinit var kernel: Variable<Float> | ||
| protected var bias: Variable<Float>? = null | ||
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| // weight tensor shapes | ||
| protected lateinit var kernelShape: Shape | ||
| protected lateinit var biasShape: Shape | ||
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| override fun build(tf: Ops, kGraph: KGraph, inputShape: Shape) { | ||
| // Amount of channels should be the last value in the inputShape | ||
| val numberOfChannels = inputShape.size(inputShape.numDimensions() - 1) | ||
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| // Compute shapes of kernel and bias matrices | ||
| computeMatricesShapes(numberOfChannels) | ||
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| // should be calculated before addWeight because it's used in calculation, | ||
| // need to rewrite addWeight to avoid strange behaviour calculate fanIn, fanOut | ||
| val inputDepth = getInputDepth(numberOfChannels) // number of input channels | ||
| val outputDepth = getOutputDepth(numberOfChannels) // number of output channels | ||
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| fanIn = (inputDepth * multiply(*kernelSizeInternal)).toInt() | ||
| fanOut = ((outputDepth * multiply(*kernelSizeInternal)).toDouble() / | ||
| multiply(*stridesInternal).toDouble()).roundToInt() | ||
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| val (kernelVariableName, biasVariableName) = defineVariableNames() | ||
| createConvVariables(tf, kernelVariableName, biasVariableName, kGraph) | ||
| } | ||
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| override fun computeOutputShape(inputShape: Shape): Shape { | ||
| val shape = defineOutputShape(inputShape) | ||
| outputShape = TensorShape(shape) | ||
| return shape | ||
| } | ||
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| override fun forward( | ||
| tf: Ops, | ||
| input: Operand<Float>, | ||
| isTraining: Operand<Boolean>, | ||
| numberOfLosses: Operand<Float>? | ||
| ): Operand<Float> { | ||
| var output = convImplementation(tf, input) | ||
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| if (useBiasInternal) { | ||
| output = tf.nn.biasAdd(output, bias) | ||
| } | ||
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| return Activations.convert(activationInternal).apply(tf, output, name) | ||
| } | ||
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| /** Returns the shape of kernel weights. */ | ||
| public val kernelShapeArray: LongArray get() = TensorShape(kernelShape).dims() | ||
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| /** Returns the shape of bias weights. */ | ||
| public val biasShapeArray: LongArray get() = TensorShape(biasShape).dims() | ||
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| override var weights: Map<String, Array<*>> | ||
| get() = extractConvWeights() | ||
| set(value) = assignWeights(value) | ||
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| override val hasActivation: Boolean get() = true | ||
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| override val paramCount: Int | ||
| get() = (kernelShape.numElements() + biasShape.numElements()).toInt() | ||
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| private fun extractConvWeights(): Map<String, Array<*>> = extractWeights(defineVariableNames().toList()) | ||
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| private fun defineVariableNames(): Pair<String, String> = if (name.isNotEmpty()) { | ||
| Pair(kernelVarName(name), biasVarName(name)) | ||
| } else { | ||
| Pair(kernelVariableName, biasVariableName) | ||
| } | ||
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| private fun createConvVariables( | ||
| tf: Ops, | ||
| kernelVariableName: String, | ||
| biasVariableName: String, | ||
| kGraph: KGraph | ||
| ) { | ||
| kernel = tf.withName(kernelVariableName).variable(kernelShape, getDType()) | ||
| if (useBiasInternal) bias = tf.withName(biasVariableName).variable(biasShape, getDType()) | ||
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| kernel = addWeight(tf, kGraph, kernelVariableName, kernel, kernelInitializerInternal, kernelRegularizerInternal) | ||
| if (useBiasInternal) bias = addWeight(tf, kGraph, biasVariableName, bias!!, biasInitializerInternal, biasRegularizerInternal) | ||
| } | ||
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| protected open fun getInputDepth(numberOfChannels: Long): Long = numberOfChannels | ||
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| protected open fun getOutputDepth(numberOfChannels: Long): Long = filtersInternal | ||
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| protected open fun computeMatricesShapes(numberOfChannels: Long) { | ||
| kernelShape = shapeFromDims(*kernelSizeInternal, numberOfChannels, filtersInternal) | ||
| biasShape = Shape.make(filtersInternal) | ||
| } | ||
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| protected abstract fun kernelVarName(name: String): String | ||
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| protected abstract fun biasVarName(name: String): String | ||
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| protected abstract fun convImplementation(tf: Ops, input: Operand<Float>): Operand<Float> | ||
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| protected abstract fun defineOutputShape(inputShape: Shape): Shape | ||
| } | ||
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| private fun multiply(vararg values: Long) = values.fold(1L, Long::times) |
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