com.intel.analytics.bigdl.models.vgg.Test.scala Maven / Gradle / Ivy
/*
* 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 com.intel.analytics.bigdl.dataset.DataSet
import com.intel.analytics.bigdl.dataset.image._
import com.intel.analytics.bigdl.models.lenet.Utils._
import com.intel.analytics.bigdl.nn.Module
import com.intel.analytics.bigdl.optim.{Top1Accuracy, Validator}
import com.intel.analytics.bigdl.utils.Engine
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkContext
object Test {
Logger.getLogger("org").setLevel(Level.ERROR)
Logger.getLogger("akka").setLevel(Level.ERROR)
Logger.getLogger("breeze").setLevel(Level.ERROR)
import Utils._
def main(args: Array[String]) {
testParser.parse(args, new TestParams()).foreach { param =>
val conf = Engine.createSparkConf().setAppName("Test Vgg on Cifar10")
.set("spark.akka.frameSize", 64.toString)
val sc = new SparkContext(conf)
Engine.init
val partitionNum = Engine.nodeNumber() * Engine.coreNumber()
val rddData = sc.parallelize(Utils.loadTest(param.folder), partitionNum)
val transformer = BytesToBGRImg() -> BGRImgNormalizer(testMean, testStd) -> BGRImgToSample()
val evaluationSet = transformer(rddData)
val model = Module.load[Float](param.model)
val result = model.evaluate(evaluationSet,
Array(new Top1Accuracy[Float]), Some(param.batchSize))
result.foreach(r => println(s"${r._2} is ${r._1}"))
sc.stop()
}
}
}