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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.nn.{CrossEntropyCriterion, Module}
import com.intel.analytics.bigdl._
import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType}
import com.intel.analytics.bigdl.optim._
import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, OptimizerV1, OptimizerV2, T, Table}
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkContext
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric._
object TrainCIFAR10 {
LoggerFilter.redirectSparkInfoLogs()
import Utils._
def cifar10Decay(epoch: Int): Double =
if (epoch >= 122) 2.0 else if (epoch >= 81) 1.0 else 0.0
def main(args: Array[String]): Unit = {
trainParser.parse(args, new TrainParams()).map(param => {
val conf = Engine.createSparkConf().setAppName("Train ResNet on Cifar10")
// Will throw exception without this config when has only one executor
.set("spark.rpc.message.maxSize", "200")
val sc = new SparkContext(conf)
Engine.init
val batchSize = param.batchSize
val (imageSize, lrSchedule, maxEpoch, dataSet) =
(32, DatasetType.CIFAR10, param.nepochs, Cifar10DataSet)
val trainDataSet = dataSet.trainDataSet(param.folder, sc, imageSize, batchSize)
val validateSet = dataSet.valDataSet(param.folder, sc, imageSize, batchSize)
val shortcut: ShortcutType = param.shortcutType match {
case "A" => ShortcutType.A
case "B" => ShortcutType.B
case _ => ShortcutType.C
}
val model = if (param.modelSnapshot.isDefined) {
Module.load[Float](param.modelSnapshot.get)
} else {
val curModel = if (param.graphModel) {
ResNet.graph(param.classes,
T("shortcutType" -> shortcut, "depth" -> param.depth, "optnet" -> param.optnet))
} else {
ResNet(param.classes,
T("shortcutType" -> shortcut, "depth" -> param.depth, "optnet" -> param.optnet))
}
if (param.optnet) {
ResNet.shareGradInput(curModel)
}
ResNet.modelInit(curModel)
curModel
}
if (param.optimizerVersion.isDefined) {
param.optimizerVersion.get.toLowerCase match {
case "optimizerv1" => Engine.setOptimizerVersion(OptimizerV1)
case "optimizerv2" => Engine.setOptimizerVersion(OptimizerV2)
}
}
val optimMethod = if (param.stateSnapshot.isDefined) {
OptimMethod.load[Float](param.stateSnapshot.get)
} else {
new SGD[Float](learningRate = param.learningRate, learningRateDecay = 0.0,
weightDecay = param.weightDecay, momentum = param.momentum, dampening = param.dampening,
nesterov = param.nesterov, learningRateSchedule = SGD.EpochDecay(cifar10Decay))
}
val optimizer = Optimizer(
model = model,
dataset = trainDataSet,
criterion = new CrossEntropyCriterion[Float]()
)
if (param.checkpoint.isDefined) {
optimizer.setCheckpoint(param.checkpoint.get, Trigger.everyEpoch)
}
optimizer
.setOptimMethod(optimMethod)
.setValidation(Trigger.everyEpoch,
validateSet, Array(new Top1Accuracy[Float]))
.setEndWhen(Trigger.maxEpoch(maxEpoch))
.optimize()
sc.stop()
})
}
}
