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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.example.loadmodel
import java.nio.file.Paths
import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier
import com.intel.analytics.bigdl.nn.Module
import com.intel.analytics.bigdl.optim.{Top1Accuracy, Top5Accuracy, Validator}
import com.intel.analytics.bigdl.utils.Engine
import org.apache.log4j.Logger
import org.apache.spark.SparkContext
import scopt.OptionParser
import scala.language.existentials
/**
* ModelValidator provides an integrated example to load models,
* and test over imagenet validation dataset
* (running as a local Java program, or a standard Spark program).
*/
object ModelValidator {
val logger = Logger.getLogger(getClass)
/**
* This is a trait meaning the model type.
* There are three sorts of model type, which
* are torch model [[TorchModel]], caffe model
* [[CaffeModel]] and BigDL model [[BigDlModel]].
*/
sealed trait ModelType
case object TorchModel extends ModelType
case object CaffeModel extends ModelType
case object BigDlModel extends ModelType
case class TestLocalParams(
folder: String = "./",
modelType: ModelType = null,
modelName: String = "",
caffeDefPath: Option[String] = None,
modelPath: String = "",
batchSize: Int = 32,
meanFile: Option[String] = None
)
val testLocalParser = new OptionParser[TestLocalParams]("BigDL Load Model Example") {
head("BigDL Load Model Example")
opt[String]('f', "folder")
.text("where you put your local image files")
.action((x, c) => c.copy(folder = x))
opt[String]('m', "modelName")
.text("the model name you want to test")
.required()
.action((x, c) => c.copy(modelName = x.toLowerCase()))
opt[String]('t', "modelType")
.text("torch, caffe or bigdl")
.required()
.action((x, c) =>
x.toLowerCase() match {
case "torch" => c.copy(modelType = TorchModel)
case "caffe" => c.copy(modelType = CaffeModel)
case "bigdl" => c.copy(modelType = BigDlModel)
case _ =>
throw new IllegalArgumentException("only torch, caffe or bigdl supported")
}
)
opt[String]("caffeDefPath")
.text("caffe define path")
.action((x, c) => c.copy(caffeDefPath = Some(x)))
opt[String]("modelPath")
.text("model path")
.action((x, c) => c.copy(modelPath = x))
opt[Int]('b', "batchSize")
.text("batch size")
.action((x, c) => c.copy(batchSize = x))
opt[String]("meanFile")
.text("mean file")
.action((x, c) => c.copy(meanFile = Some(x)))
}
def main(args: Array[String]): Unit = {
testLocalParser.parse(args, TestLocalParams()).foreach(param => {
val conf = Engine.createSparkConf()
conf.setAppName("BigDL Image Classifier Example")
val sc = new SparkContext(conf)
Engine.init
val valPath = param.folder
val (model, validateDataSet) = param.modelType match {
case CaffeModel =>
param.modelName match {
case "alexnet" =>
(Module.loadCaffeModel[Float](
param.caffeDefPath.get, param.modelPath),
AlexNetPreprocessor.rdd(valPath, param.batchSize, param.meanFile.get, sc))
case "inception" =>
(Module.loadCaffeModel[Float](
param.caffeDefPath.get, param.modelPath),
InceptionPreprocessor.rdd(valPath, param.batchSize, sc))
}
case TorchModel =>
param.modelName match {
case "resnet" =>
(Module.loadTorch[Float](param.modelPath),
ResNetPreprocessor.rdd(valPath, param.batchSize, sc))
}
case _ => throw new IllegalArgumentException(s"${ param.modelType } is not" +
s"supported in this example, please use alexnet/inception/resnet")
}
println(model)
val result = model.evaluate(
validateDataSet,
Array(new Top1Accuracy[Float](), new Top5Accuracy[Float]()),
Some(param.batchSize))
result.foreach(r => {
logger.info(s"${ r._2 } is ${ r._1 }")
})
sc.stop()
})
}
}
