com.intel.analytics.bigdl.models.vgg.Train.scala Maven / Gradle / Ivy

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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.vgg

import java.text.SimpleDateFormat
import java.util.Date

import com.intel.analytics.bigdl._
import com.intel.analytics.bigdl.dataset.DataSet
import com.intel.analytics.bigdl.dataset.image._
import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Module}
import com.intel.analytics.bigdl.optim._
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric._
import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, T, Table}
import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary}
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkContext

object Train {
  LoggerFilter.redirectSparkInfoLogs()


  import Utils._

  def main(args: Array[String]): Unit = {
    trainParser.parse(args, new TrainParams()).map(param => {
      val conf = Engine.createSparkConf().setAppName("Train Vgg 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 trainDataSet = DataSet.array(Utils.loadTrain(param.folder), sc) ->
        BytesToBGRImg() -> BGRImgNormalizer(trainMean, trainStd) ->
        BGRImgToBatch(param.batchSize)

      val model = if (param.modelSnapshot.isDefined) {
        Module.load[Float](param.modelSnapshot.get)
      } else {
        if (param.graphModel) VggForCifar10.graph(classNum = 10) else VggForCifar10(classNum = 10)
      }

      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 = 0.9, dampening = 0.0, nesterov = false,
          learningRateSchedule = SGD.EpochStep(25, 0.5))
      }

      val optimizer = Optimizer(
        model = model,
        dataset = trainDataSet,
        criterion = new ClassNLLCriterion[Float]()
      )

      val validateSet = DataSet.array(Utils.loadTest(param.folder), sc) ->
        BytesToBGRImg() -> BGRImgNormalizer(testMean, testStd) ->
        BGRImgToBatch(param.batchSize)

      if (param.checkpoint.isDefined) {
        optimizer.setCheckpoint(param.checkpoint.get, Trigger.everyEpoch)
      }

      if (param.overWriteCheckpoint) {
        optimizer.overWriteCheckpoint()
      }

      if (param.summaryPath.isDefined) {
        val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
        val timeStamp = sdf.format(new Date())
        val trainSummry = new TrainSummary(param.summaryPath.get,
          s"vgg-on-cifar10-train-$timeStamp")
        optimizer.setTrainSummary(trainSummry)
        val validationSummary = new ValidationSummary(param.summaryPath.get,
          s"vgg-on-cifar10-val-$timeStamp")
        optimizer.setValidationSummary(validationSummary)
      }

      optimizer
        .setValidation(Trigger.everyEpoch, validateSet, Array(new Top1Accuracy[Float]))
        .setOptimMethod(optimMethod)
        .setEndWhen(Trigger.maxEpoch(param.maxEpoch))
        .optimize()

      sc.stop()
    })
  }
}