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92 changes: 92 additions & 0 deletions
92
...e/examples/src/main/scala/org/apache/mxnetexamples/infer/predictor/PredictorExample.scala
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| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You 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. | ||
| */ | ||
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| package org.apache.mxnetexamples.infer.predictor | ||
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| import java.io.File | ||
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| import scala.io | ||
| import org.apache.mxnet._ | ||
| import org.apache.mxnet.infer.Predictor | ||
| import org.apache.mxnetexamples.benchmark.CLIParserBase | ||
| import org.kohsuke.args4j.{CmdLineParser, Option} | ||
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| import scala.collection.JavaConverters._ | ||
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| object PredictorExample { | ||
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| def loadModel(modelPathPrefix : String, inputDesc : IndexedSeq[DataDesc], | ||
| context : Context, epoch : Int): Predictor = { | ||
| new Predictor(modelPathPrefix, inputDesc, context, Some(epoch)) | ||
| } | ||
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| def doInference(predictor : Predictor, imageND : NDArray): IndexedSeq[NDArray] = { | ||
| predictor.predictWithNDArray(IndexedSeq(imageND)) | ||
| } | ||
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| def preProcess(imagePath: String, h: Int, w: Int) : NDArray = { | ||
| var img = Image.imRead(imagePath) | ||
| img = Image.imResize(img, h, w) | ||
| // HWC -> CHW | ||
| img = NDArray.api.transpose(img, Some(Shape(2, 0, 1))) | ||
| img = NDArray.api.expand_dims(img, 0) | ||
| img.asType(DType.Float32) | ||
| } | ||
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| def postProcess(modelPathPrefix : String, result : Array[Float]) : String = { | ||
| val dirPath = modelPathPrefix.substring(0, 1 + modelPathPrefix.lastIndexOf(File.separator)) | ||
| val d = new File(dirPath) | ||
| require(d.exists && d.isDirectory, s"directory: $dirPath not found") | ||
| val f = io.Source.fromFile(dirPath + "synset.txt") | ||
| val s = f.getLines().toIndexedSeq | ||
| val maxIdx = result.zipWithIndex.maxBy(_._1)._2 | ||
| printf(s"Predict Result ${s(maxIdx)} with prob ${result(maxIdx)}\n") | ||
| s(maxIdx) | ||
| } | ||
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| def main(args : Array[String]): Unit = { | ||
| val inst = new CLIParser | ||
| val parser: CmdLineParser = new CmdLineParser(inst) | ||
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| parser.parseArgument(args.toList.asJava) | ||
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| var context = Context.cpu() | ||
| if (System.getenv().containsKey("SCALA_TEST_ON_GPU") && | ||
| System.getenv("SCALA_TEST_ON_GPU").toInt == 1) { | ||
| context = Context.gpu() | ||
| } | ||
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| val imgWidth = 224 | ||
| val imgHeight = 224 | ||
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| val inputDesc = IndexedSeq(new DataDesc("data", Shape(1, 3, imgHeight, imgWidth), | ||
| DType.Float32, Layout.NCHW)) | ||
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| val predictor = loadModel(inst.modelPathPrefix, inputDesc, context, 0) | ||
| val img = preProcess(inst.inputImagePath, imgHeight, imgWidth) | ||
| val result = doInference(predictor, img)(0).toArray | ||
| postProcess(inst.modelPathPrefix, result) | ||
| } | ||
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| } | ||
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| class CLIParser extends CLIParserBase{ | ||
| @Option(name = "--model-path-prefix", usage = "the input model directory") | ||
| val modelPathPrefix: String = "/resnet-152/resnet-152" | ||
| @Option(name = "--input-image", usage = "the input image") | ||
| val inputImagePath: String = "/images/kitten.jpg" | ||
| } |
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87 changes: 87 additions & 0 deletions
87
...mples/src/test/scala/org/apache/mxnetexamples/infer/predictor/PredictorExampleSuite.scala
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| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You 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. | ||
| */ | ||
|
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| package org.apache.mxnetexamples.infer.predictor | ||
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| import java.io.File | ||
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| import org.apache.mxnet._ | ||
| import org.apache.mxnetexamples.Util | ||
| import org.scalatest.{BeforeAndAfterAll, FunSuite} | ||
| import org.slf4j.LoggerFactory | ||
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| class PredictorExampleSuite extends FunSuite with BeforeAndAfterAll { | ||
| private val logger = LoggerFactory.getLogger(classOf[PredictorExampleSuite]) | ||
| private var modelDirPrefix = "" | ||
| private var inputImagePath = "" | ||
| private var context = Context.cpu() | ||
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| override def beforeAll(): Unit = { | ||
| logger.info("Downloading resnet-18 model") | ||
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| val tempDirPath = System.getProperty("java.io.tmpdir") | ||
| logger.info("tempDirPath: %s".format(tempDirPath)) | ||
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| val baseUrl = "https://s3.us-east-2.amazonaws.com/scala-infer-models" | ||
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| Util.downloadUrl(baseUrl + "/resnet-18/resnet-18-symbol.json", | ||
| tempDirPath + "/resnet18/resnet-18-symbol.json") | ||
| Util.downloadUrl(baseUrl + "/resnet-18/resnet-18-0000.params", | ||
| tempDirPath + "/resnet18/resnet-18-0000.params") | ||
| Util.downloadUrl(baseUrl + "/resnet-18/synset.txt", | ||
| tempDirPath + "/resnet18/synset.txt") | ||
| Util.downloadUrl("https://s3.amazonaws.com/model-server/inputs/Pug-Cookie.jpg", | ||
| tempDirPath + "/inputImages/resnet18/Pug-Cookie.jpg") | ||
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| modelDirPrefix = tempDirPath + File.separator + "resnet18/resnet-18" | ||
| inputImagePath = tempDirPath + File.separator + | ||
| "inputImages/resnet18/Pug-Cookie.jpg" | ||
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| if (System.getenv().containsKey("SCALA_TEST_ON_GPU") && | ||
| System.getenv("SCALA_TEST_ON_GPU").toInt == 1) { | ||
| context = Context.gpu() | ||
| } | ||
| val props = System.getProperties | ||
| props.setProperty("mxnet.disableShapeCheck", "true") | ||
| } | ||
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| override def afterAll(): Unit = { | ||
| val props = System.getProperties | ||
| props.setProperty("mxnet.disableShapeCheck", "false") | ||
| } | ||
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| test("test Predictor With Fixed Shape and random shape") { | ||
| val inputDesc = IndexedSeq(new DataDesc("data", Shape(1, 3, 224, 224), | ||
| DType.Float32, Layout.NCHW)) | ||
| val predictor = PredictorExample.loadModel(modelDirPrefix, inputDesc, context, 0) | ||
| // fix size | ||
| var img = PredictorExample.preProcess(inputImagePath, 224, 224) | ||
| var result = PredictorExample.doInference(predictor, img)(0) | ||
| var top1 = PredictorExample.postProcess(modelDirPrefix, result.toArray) | ||
| assert(top1 === "n02110958 pug, pug-dog") | ||
| // random size 512 | ||
| img = PredictorExample.preProcess(inputImagePath, 512, 512) | ||
| result = PredictorExample.doInference(predictor, img)(0) | ||
| top1 = PredictorExample.postProcess(modelDirPrefix, result.toArray) | ||
| assert(top1 === "n02110958 pug, pug-dog") | ||
| // original size | ||
| img = PredictorExample.preProcess(inputImagePath, 1024, 576) | ||
| result = PredictorExample.doInference(predictor, img)(0) | ||
| top1 = PredictorExample.postProcess(modelDirPrefix, result.toArray) | ||
| assert(top1 === "n02110958 pug, pug-dog") | ||
| } | ||
| } |
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