Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* 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.TensorCriterion
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import scala.reflect.ClassTag
/**
* Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss)
* between input x and output y (which is a target class index).
*
* @param p
* @param weights
* @param margin
* @param sizeAverage
*/
@SerialVersionUID(- 5690966547738588572L)
class MultiMarginCriterion[@specialized(Float, Double) T: ClassTag](val p: Int = 1,
val weights: Tensor[T] = null, margin: Double = 1.0, val sizeAverage: Boolean = true)
(implicit ev: TensorNumeric[T]) extends TensorCriterion[T] {
require(p == 1 || p == 2, s"MultiMarginCriterion: only p=1 and p=2 supported, but get p $p")
if (null != weights) {
require(weights.dim() == 1, s"MultiMarginCriterion: weights input should be 1-D Tensor, " +
s"but get weights dim ${weights.dim()}")
}
override def updateOutput(input: Tensor[T], target: Tensor[T]): T = {
require(input.nDimension() == 1 || input.nDimension() == 2,
"MultiMarginCriterion: " +
ErrorInfo.constrainInputAsVectorOrBatch +
s"input dimension ${input.nDimension()}")
val (nframe, dim) = if (input.nDimension() == 1) {
(1, input.size(1))
} else {
require(target.nDimension() == 1 && target.size(1) == input.size(1),
"MultiMarginCriterion: " + ErrorInfo.constrainInputSizeSameAsTarget)
(input.size(1), input.size(2))
}
require(ev.isGreaterEq(target.min(), ev.fromType(0)) &&
ev.isGreaterEq(ev.fromType(dim), target.max()), "MultiMarginCriterion: " +
s"target out of range, target min should be greater than or equal to zero, but get " +
s"${target.min()}, target max should be less than or equal to $dim, but get ${target.max()}")
val _target = target.contiguous()
val _input = input.contiguous()
val _weights = if (null != weights) weights.contiguous() else null
val input_data = _input.storage().array()
val target_data = _target.storage().array()
val weights_data = if (null != _weights) _weights.storage().array() else null
val input_offset = _input.storageOffset() - 1
val target_offset = _target.storageOffset() - 1
val weights_offset = if (null != _weights) _weights.storageOffset() - 1 else 0
var sum: T = ev.fromType(0)
var t = 0
var n = 0
while (t < nframe) {
val target_idx = ev.toType[Int](target_data(t + target_offset)) - 1
val input_target = input_data(n + target_idx + input_offset)
var d = 0
while (d < dim) {
val z = ev.plus(ev.minus(ev.fromType(margin), input_target),
input_data(n + d + input_offset))
if ((d != target_idx) && (ev.isGreater(z, ev.fromType(0)))) {
var h = if (p == 1) z else ev.times(z, z)
if (null != weights_data) h = ev.times(h, weights_data(target_idx + weights_offset))
sum = ev.plus(sum, h)
}
d += 1
}
t += 1
n += dim
}
sum = ev.divide(sum, ev.fromType(dim))
if (sizeAverage) sum = ev.divide(sum, ev.fromType(nframe))
sum
}
override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = {
require(input.nDimension() == 1 || input.nDimension() == 2,
"MultiMarginCriterion: " +
ErrorInfo.constrainInputAsVectorOrBatch +
s"input dimension ${input.nDimension()}")
val (nframe, dim) = if (input.nDimension() == 1) {
(1, input.size(1))
} else {
require(target.nDimension() == 1 && target.size(1) == input.size(1),
"MultiMarginCriterion: " +
ErrorInfo.constrainInputSizeSameAsTarget +
s"target dimension ${target.nDimension()}, " +
s"target size[1] ${target.size(1)}, " +
s"input size[1] ${input.size(1)}")
(input.size(1), input.size(2))
}
val g = ev.fromType(if (sizeAverage) 1.0/(nframe*dim) else 1.0/(dim))
val _target = target.contiguous()
val _input = input.contiguous()
val _weights = if (null != weights) weights.contiguous() else null
val input_data = _input.storage().array()
val target_data = _target.storage().array()
val weights_data = if (null != _weights) _weights.storage().array() else null
val input_offset = _input.storageOffset() - 1
val target_offset = _target.storageOffset() - 1
val weights_offset = if (null != _weights) _weights.storageOffset() - 1 else 0
gradInput.resizeAs(input).zero()
val gradInput_data = gradInput.storage().array()
var t = 0
var n = 0
while (t < nframe) {
val target_idx = ev.toType[Int](target_data(t + target_offset)) - 1
val input_target = input_data(n + target_idx + input_offset)
var gradInput_target = ev.fromType(0)
var d = 0
while (d < dim) {
val z = ev.plus(ev.minus(ev.fromType(margin), input_target), input_data(n + d))
if (d != target_idx) {
if (ev.isGreater(z, ev.fromType(0))) {
var h = if (p == 1) g else ev.times(ev.fromType(2), ev.times(g, z))
if (null != weights_data) h = ev.times(h, weights_data(target_idx + weights_offset))
gradInput_target = ev.minus(gradInput_target, h)
gradInput_data(n + d) = h
} else {
gradInput_data(n + d) = ev.fromType(0)
}
}
d += 1
}
gradInput_data(n + target_idx) = gradInput_target
n += dim
t += 1
}
gradInput
}
override def toString(): String = {
s"nn.MultiMarginCriterion($sizeAverage, $weights, $margin)"
}
override def canEqual(other: Any): Boolean = other.isInstanceOf[MultiMarginCriterion[T]]
override def equals(other: Any): Boolean = other match {
case that: MultiMarginCriterion[T] =>
super.equals(that) &&
(that canEqual this) &&
p == that.p &&
weights == that.weights &&
sizeAverage == that.sizeAverage
case _ => false
}
override def hashCode(): Int = {
def getHashCode(a: Any): Int = if (a == null) 0 else a.hashCode()
val state = Seq(super.hashCode(), p, weights, sizeAverage)
state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b)
}
}
object MultiMarginCriterion {
def apply[@specialized(Float, Double) T: ClassTag](
p: Int = 1,
weights: Tensor[T] = null,
margin: Double = 1.0,
sizeAverage: Boolean = true)(implicit ev: TensorNumeric[T]) : MultiMarginCriterion[T] = {
new MultiMarginCriterion[T](p, weights, margin, sizeAverage)
}
}
