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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.models.resnet
import com.intel.analytics.bigdl._
import com.intel.analytics.bigdl.models.inception.{ImageNet2012, ImageNet2012Val}
import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType}
import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule
import com.intel.analytics.bigdl.nn.mkldnn.ResNet.DatasetType.ImageNet
import com.intel.analytics.bigdl.nn.{BatchNormalization, Container, CrossEntropyCriterion, Module}
import com.intel.analytics.bigdl.optim._
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric._
import com.intel.analytics.bigdl.utils._
import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary}
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkContext
object TrainImageNet {
LoggerFilter.redirectSparkInfoLogs()
Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO)
val logger = Logger.getLogger(getClass)
import Utils._
def imageNetDecay(epoch: Int): Double = {
if (epoch >= 80) {
3
} else if (epoch >= 60) {
2
} else if (epoch >= 30) {
1
} else {
0.0
}
}
def main(args: Array[String]): Unit = {
trainParser.parse(args, new TrainParams()).map(param => {
val conf = Engine.createSparkConf().setAppName("Train ResNet on ImageNet2012")
.set("spark.rpc.message.maxSize", "200")
val sc = new SparkContext(conf)
Engine.init
val batchSize = param.batchSize
val (imageSize, dataSetType, maxEpoch, dataSet) =
(224, DatasetType.ImageNet, param.nepochs, ImageNetDataSet)
val trainDataSet = dataSet.trainDataSet(param.folder + "/train", sc, imageSize, batchSize)
val validateSet = dataSet.valDataSet(param.folder + "/val", sc, imageSize, batchSize)
val shortcut: ShortcutType = ShortcutType.B
val model = if (param.modelSnapshot.isDefined) {
Module.load[Float](param.modelSnapshot.get)
} else {
Engine.getEngineType() match {
case MklBlas =>
val curModel =
ResNet(classNum = param.classes, T("shortcutType" -> shortcut, "depth" -> param.depth,
"optnet" -> param.optnet, "dataSet" -> dataSetType))
if (param.optnet) {
ResNet.shareGradInput(curModel)
}
ResNet.modelInit(curModel)
/* Here we set parallism specificall for BatchNormalization and its Sub Layers, this is
very useful especially when you want to leverage more computing resources like you want
to use as many cores as possible but you cannot set batch size too big for each core due
to the memory limitation, so you can set batch size per core smaller, but the smaller
batch size will increase the instability of convergence, the synchronization among BN
layers basically do the parameters synchronization among cores and thus will avoid the
instability while improves the performance a lot. */
val parallisim = Engine.coreNumber
setParallism(curModel, parallisim)
curModel
case MklDnn =>
nn.mkldnn.ResNet.graph(param.batchSize / Engine.nodeNumber(), param.classes,
T("depth" -> 50, "dataSet" -> ImageNet))
}
}
println(model)
val optimMethod = if (param.stateSnapshot.isDefined) {
val optim = OptimMethod.load[Float](param.stateSnapshot.get).asInstanceOf[SGD[Float]]
val baseLr = param.learningRate
val iterationsPerEpoch = math.ceil(1281167 / param.batchSize).toInt
val warmUpIteration = iterationsPerEpoch * param.warmupEpoch
val maxLr = param.maxLr
val delta = (maxLr - baseLr) / warmUpIteration
optim.learningRateSchedule = SGD.EpochDecayWithWarmUp(warmUpIteration, delta, imageNetDecay)
optim
} else {
val baseLr = param.learningRate
val iterationsPerEpoch = math.ceil(1281167 / param.batchSize).toInt
val warmUpIteration = iterationsPerEpoch * param.warmupEpoch
val maxLr = param.maxLr
val delta = (maxLr - baseLr) / warmUpIteration
logger.info(s"warmUpIteraion: $warmUpIteration, startLr: ${param.learningRate}, " +
s"maxLr: $maxLr, " +
s"delta: $delta, nesterov: ${param.nesterov}")
new SGD[Float](learningRate = param.learningRate, learningRateDecay = 0.0,
weightDecay = param.weightDecay, momentum = param.momentum, dampening = param.dampening,
nesterov = param.nesterov,
learningRateSchedule = SGD.EpochDecayWithWarmUp(warmUpIteration, delta, imageNetDecay))
}
val optimizer = Optimizer(
model = model,
dataset = trainDataSet,
criterion = new CrossEntropyCriterion[Float]()
)
if (param.checkpoint.isDefined) {
optimizer.setCheckpoint(param.checkpoint.get, Trigger.everyEpoch)
}
val logdir = "resnet-imagenet"
val appName = s"${sc.applicationId}"
val trainSummary = TrainSummary(logdir, appName)
trainSummary.setSummaryTrigger("LearningRate", Trigger.severalIteration(1))
trainSummary.setSummaryTrigger("Parameters", Trigger.severalIteration(10))
val validationSummary = ValidationSummary(logdir, appName)
optimizer
.setOptimMethod(optimMethod)
.setValidation(Trigger.everyEpoch,
validateSet, Array(new Top1Accuracy[Float], new Top5Accuracy[Float]))
.setEndWhen(Trigger.maxEpoch(maxEpoch))
.optimize()
sc.stop()
})
}
private def setParallism(model: AbstractModule[_, _, Float], parallism: Int): Unit = {
if (model.isInstanceOf[BatchNormalization[Float]]) {
model.asInstanceOf[BatchNormalization[Float]].setParallism(parallism)
}
if(model.isInstanceOf[Container[_, _, Float]]) {
model.asInstanceOf[Container[_, _, Float]].
modules.foreach(sub => setParallism(sub, parallism))
}
}
}
