com.intel.analytics.bigdl.nn.tf.ArrayOps.scala Maven / Gradle / Ivy
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/*
* Copyright 2016 The BigDL Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.intel.analytics.bigdl.nn.tf
import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
import com.intel.analytics.bigdl.nn.ops.{ModuleToOperation, Operation}
import com.intel.analytics.bigdl.tensor.{Storage, Tensor}
import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric}
import com.intel.analytics.bigdl.utils.{T, Table}
import scala.collection.mutable.ArrayBuffer
import scala.reflect.ClassTag
/**
* Some operation may not have input
*/
private[bigdl] trait WithoutInput
private[bigdl] class Const[T: ClassTag, B: ClassTag](val value: Tensor[B])
(implicit ev: TensorNumeric[T])
extends Operation[Activity, Tensor[B], T] with WithoutInput {
override def updateOutput(input: Activity): Tensor[B] = {
output = value
output
}
override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = {
(Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[B]),
Array[TensorNumeric[_]](ev))
}
}
private[bigdl] object Const {
def apply[T: ClassTag, B: ClassTag](value: Tensor[B])
(implicit ev: TensorNumeric[T]): Const[T, B] = {
new Const[T, B](value)
}
}
/**
* This operation computes the inverse of an index permutation. It takes a 1-D integer tensor x,
* which represents the indices of a zero-based array, and swaps each value with its index position.
* In other words, for an output tensor y and an input tensor x, this operation computes the
* following:
* y[x[i]] = i for i in [0, 1, ..., len(x) - 1]
* The values must include 0. There can be no duplicate values or negative values.
*
* @tparam T Parameter numeric type. Only support float/double now
*/
private[bigdl] class InvertPermutation[T: ClassTag]()(implicit ev: TensorNumeric[T])
extends Operation[Tensor[Int], Tensor[Int], T] {
output = Tensor[Int]()
override def updateOutput(input: Tensor[Int]): Tensor[Int] = {
require(input.dim() == 1, "InvertPermutation only accept 1D tensor as input")
output.resizeAs(input)
var i = 0
while(i < input.size(1)) {
output.setValue(input.valueAt(i + 1) + 1, i)
i += 1
}
output
}
}
/**
* Calculate the positions of the input shapes in a concatenation operation. It takes several
* tensors as input. The first tensor must be a scalar, which indicts on which dimension do the
* concatenation. The offset of the dimension is start from zero.
*
* The left tensors must be 1D, as they represent the shapes of tensors. And they must be same
* except the concat dimension.
*
* Her's an example, say, we want to concatenate 3 tensors in the 2nd dimension, the input shape
* tensors should be
* [2, 2, 5, 7]
* [2, 3, 5, 7]
* [2, 4, 5, 7]
*
* The output should be
* [0, 0, 0, 0]
* [0, 2, 0, 0]
* [0, 5, 0, 0]
* @tparam T Parameter numeric type. Only support float/double now
*/
private[bigdl] class ConcatOffset[T: ClassTag]()(implicit ev: TensorNumeric[T])
extends Operation[Table, Table, T] {
output = T()
override def updateOutput(input: Table): Table = {
val concatDim = input[Tensor[Int]](1)
require(concatDim.isScalar, "ConcatOffset: concat dim must be a scalar")
val cdim = concatDim.value()
val n = input.length() - 1
var i = 1
var offset = 0
while(i <= n) {
val shape = input[Tensor[Int]](i + 1)
require(shape.nDimension() == 1, "ConcatOffset: shape must be 1D tensor")
if (!output.contains(i)) {
output(i) = Tensor[Int]()
}
val outputOffset = output[Tensor[Int]](i)
outputOffset.resizeAs(shape).zero()
outputOffset.setValue(cdim + 1, offset)
val dimSize = shape.valueAt(cdim + 1)
offset += dimSize
i += 1
}
output
}
}
private[bigdl] class Fill[T: ClassTag]() (implicit ev: TensorNumeric[T])
extends AbstractModule[Table, Tensor[_], T] {
override def updateOutput(input: Table): Tensor[_] = {
val shapeTensor = input[Tensor[Int]](1)
val value = input[Tensor[_]](2)
if (shapeTensor.isEmpty) {
if (value.getType() != output.getType()) {
output = value.emptyInstance()
}
output.resizeAs(value).asInstanceOf[Tensor[NumericWildCard]]
.copy(value.asInstanceOf[Tensor[NumericWildCard]])
} else {
require(shapeTensor.nDimension() == 1, "shape tensor is not a vector")
val shape = new Array[Int](shapeTensor.nElement())
var i = 0
while (i < shapeTensor.nElement()) {
shape(i) = shapeTensor.valueAt(i + 1)
i = i + 1
}
require(value.isScalar, "value tensor is not a scalar")
if (value.getType() != output.getType()) {
output = value.emptyInstance().resize(shape)
} else {
output.resize(shape)
}
output.forceFill(value.value())
}
output
}
override def updateGradInput(input: Table, gradOutput: Tensor[_]): Table = {
if (gradInput.contains(1)) {
gradInput[Tensor[_]](1).resize(input[Tensor[_]](1).size()).zero()
} else {
val inputTensor = input[Tensor[_]](1)
gradInput(1) = inputTensor.emptyInstance().resize(inputTensor.size())
}
if (gradInput.contains(2)) {
gradInput[Tensor[_]](2).resize(input[Tensor[_]](2).size()).zero()
} else {
val inputTensor = input[Tensor[_]](2)
gradInput(2) = inputTensor.emptyInstance().resize(inputTensor.size())
}
gradInput
}
}
private[bigdl] object Fill {
def apply[T: ClassTag]()
(implicit ev: TensorNumeric[T]) : Fill[T] = {
new Fill[T]()
}
}
/**
* Given shapes of two tensors, computes the reduction indices for the
* gradient computation.
*
* @tparam T Numeric type. Only support float/double now
*/
private[bigdl] class BroadcastGradientArgs[T: ClassTag]()
(implicit ev: TensorNumeric[T]) extends Operation[Table, Table, T] {
override def updateOutput(input: Table): Table = {
val input1 = input[Tensor[Int]](1)
val input2 = input[Tensor[Int]](2)
val output1 = Tensor[Int]()
val output2 = Tensor[Int]()
output.insert(output1).insert(output2)
// Reverse the shape of x and y for convenience.
// After the reverse, 0-th is the inner-most dimension.
val rx =
if (input1.storage() == null) Array[Int]().toBuffer
else input1.storage().array().reverse.toBuffer
val ry =
if (input2.storage() == null) Array[Int]().toBuffer
else input2.storage().array().reverse.toBuffer
if (rx.length < ry.length) {
while (rx.length < ry.length) {
rx.append(1)
}
} else {
while (rx.length > ry.length) {
ry.append(1)
}
}
val xReducedIndexBuffer = new ArrayBuffer[Int]()
val yReducedIndexBuffer = new ArrayBuffer[Int]()
val n = rx.length
var i = 0
while (i < n) {
val xi = rx(i)
val yi = ry(i)
if (xi == yi) {
if (xi == 1) {
xReducedIndexBuffer.append(n - 1 - i)
yReducedIndexBuffer.append(n - 1 - i)
}
} else if (xi == 1) {
xReducedIndexBuffer.append(n - 1 - i)
} else if (yi == 1) {
yReducedIndexBuffer.append(n - 1 - i)
} else {
return output
}
i += 1
}
if (xReducedIndexBuffer.isEmpty) {
input(1) = Tensor[Int]()
} else {
output1.resize(Array(xReducedIndexBuffer.length))
.set(Tensor[Int](Storage(xReducedIndexBuffer.reverse.toArray)))
}
if (yReducedIndexBuffer.isEmpty) {
input(2) = Tensor[Int]()
} else {
output2.resize(Array(yReducedIndexBuffer.length))
.set(Tensor[Int](Storage(yReducedIndexBuffer.reverse.toArray)))
}
output
}
}
private[bigdl] object BroadcastGradientArgs {
def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T]
= ModuleToOperation[T](new BroadcastGradientArgs())
}
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