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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
import com.intel.analytics.bigdl.nn.abstractnn.TensorModule
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.tensor.{Storage, Tensor}
import scala.reflect.ClassTag
/**
* This module adds pad units of padding to dimension dim of the input. If pad is negative,
* padding is added to the left, otherwise, it is added to the right of the dimension.
*
* The input to this layer is expected to be a tensor, or a batch of tensors;
* when using mini-batch, a batch of sample tensors will be passed to the layer and
* the user need to specify the number of dimensions of each sample tensor in the
* batch using nInputDims.
*
* @param dim the dimension to be applied padding operation
* @param pad num of the pad units
* @param nInputDim specify the number of dimensions that this module will receive
* If it is more than the dimension of input tensors, the first dimension
* would be considered as batch size
* @param value padding value
*/
@SerialVersionUID(- 3401298839313169602L)
class Padding[T: ClassTag](
val dim: Int,
val pad: Int,
val nInputDim: Int,
val value: Double = 0.0,
val nIndex: Int = 1)(implicit ev: TensorNumeric[T]) extends TensorModule[T] {
var outputSize = Storage[Int]()
override def updateOutput(input: Tensor[T]): Tensor[T] = {
outputSize.resize(input.dim()).copy(Storage(input.size()))
val dim = if (input.dim() != nInputDim) this.dim + 1 else this.dim
outputSize(dim - 1) += math.abs(this.pad)
output.resize(outputSize.array()).fill(ev.fromType(value))
val index = if (this.pad > 0) input.size(dim) - nIndex + 2 else nIndex
val pad = if (this.pad > 0) this.pad else -this.pad
if (index == 1) {
output.narrow(dim, 1 + pad, input.size(dim)).copy(input)
} else if (index == (input.size(dim) + 1)) {
output.narrow(dim, 1, input.size(dim)).copy(input)
} else {
output.narrow(dim, 1, index - 1).copy(input.narrow(dim, 1, index - 1))
output.narrow(dim, index + pad, input.size(dim) - (index - 1)).
copy(input.narrow(dim, index, input.size(dim) - (index - 1)))
}
output
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
gradInput.resizeAs(input)
val dim = if (input.dim() != nInputDim) this.dim + 1 else this.dim
val index = if (this.pad > 0) input.size(dim) - nIndex + 2 else nIndex
val pad = if (this.pad > 0) this.pad else -this.pad
if (index == 1) {
gradInput.copy(gradOutput.narrow(dim, 1 + pad, input.size(dim)))
} else if (index == input.size(dim) + 1) {
gradInput.copy(gradOutput.narrow(dim, 1, input.size(dim)))
} else {
gradInput.narrow(dim, 1, index - 1).
copy(gradOutput.narrow(dim, 1, index - 1))
gradInput.narrow(dim, index, input.size(dim) - (index - 1)).copy(
gradOutput.narrow(dim, index + pad, input.size(dim) - (index - 1)))
}
gradInput
}
override def toString(): String = {
s"${getPrintName}($dim, $pad, $nInputDim, $value, $nIndex)"
}
override def canEqual(other: Any): Boolean = other.isInstanceOf[Padding[T]]
override def equals(other: Any): Boolean = other match {
case that: Padding[T] =>
super.equals(that) &&
(that canEqual this) &&
dim == that.dim &&
pad == that.pad &&
nInputDim == that.nInputDim &&
value == that.value &&
nIndex == that.nIndex
case _ => false
}
override def hashCode(): Int = {
def getHashCode(a: Any): Int = if (a == null) 0 else a.hashCode()
val state = Seq(super.hashCode(), dim, pad, nInputDim, value, nIndex)
state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b)
}
}
object Padding{
def apply[T: ClassTag](
dim: Int,
pad: Int,
nInputDim: Int,
value: Double = 0.0,
nIndex: Int = 1)(implicit ev: TensorNumeric[T]) : Padding[T] =
new Padding[T](dim, pad, nInputDim, value, nIndex)
}
