com.intel.analytics.bigdl.nn.Tile.scala Maven / Gradle / Ivy
/*
* 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
import com.intel.analytics.bigdl.nn.abstractnn.TensorModule
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import scala.reflect.ClassTag
/**
* Tile repeats input `nFeatures` times along its `dim` dimension
*
*
* @param dim dimension to be replicated.
* @param copies specify the number of copies.
*/
@SerialVersionUID( - 7341965298635163982L)
class Tile[T: ClassTag](
val dim : Int = 1,
val copies : Int = 2)
(implicit ev: TensorNumeric[T]) extends TensorModule[T] {
require(dim > 0, "Can only replicate across positive integer dimensions.")
require(copies >= 2, "copies should be at least 2")
override def updateOutput(input: Tensor[T]): Tensor[T] = {
require(dim <= input.dim() + 1,
s"Not enough input dimensions to replicate along dimension $dim.")
val sizes = new Array[Int](input.size().length)
var index = 0
input.size().foreach(size => {
index += 1
sizes(index - 1) = if (index == dim) copies else 1
})
output = input.repeatTensor(sizes)
output
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
gradInput.resizeAs(output).zero()
val size = new Array[Int](input.dim() + 1)
var i = 0
while (i < input.dim()) {
size(i) = input.size(i)
i += 1
}
size(dim - 1) = size(dim - 1) * copies
gradInput.view(size).sum(gradOutput, dim)
gradInput
}
override def toString(): String = {
s"${getPrintName}($dim,$copies)"
}
}
object Tile {
def apply[@specialized(Float, Double) T: ClassTag](
dim : Int = 1,
copies : Int = 2)(implicit ev: TensorNumeric[T]) : Tile[T] = {
new Tile[T](dim, copies)
}
}