CodingCat commented on a change in pull request #9678: [First cut] Scala Inference APIs URL: https://github.com/apache/incubator-mxnet/pull/9678#discussion_r166182216
########## File path: scala-package/infer/src/main/scala/ml/dmlc/mxnet/infer/PredictBase.scala ########## @@ -0,0 +1,200 @@ +/* + * 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. + */ + +package ml.dmlc.mxnet.infer + +import ml.dmlc.mxnet.io.NDArrayIter +import ml.dmlc.mxnet.{DataDesc, NDArray, Shape} +import ml.dmlc.mxnet.module.Module + +import scala.collection.mutable.ListBuffer +import org.slf4j.LoggerFactory + +/** + * Base Trait for MXNNet Predictor classes. + */ +trait PredictBase { + + /** + * This method will take input as IndexedSeq one dimensional arrays and creates + * NDArray needed for inference. The array will be reshaped based on the input descriptors. + * @param input: A IndexedSequence of Java one-dimensional array, An IndexedSequence is + * is needed when the model has more than one input/output + * @return IndexedSequence array of outputs. + */ + def predict(input: IndexedSeq[Array[Float]]): IndexedSeq[Array[Float]] + + /** + * Predict using NDArray as input. This method is useful when the input is a batch of data + * or when multiple operations on the input/output have to performed. + * Note: User is responsible for managing allocation/deallocation of NDArrays. + * @param input: IndexedSequence NDArrays. + * @return output of Predictions as NDArrays. + */ + def predictWithNDArray(input: IndexedSeq[NDArray]): IndexedSeq[NDArray] + +} + +/** + * Implementation of predict routines. + * + * @param modelPathPrefix PathPrefix from where to load the model. + * Example: file://model-dir/resnet-152(containing resnet-152-symbol.json, + * resnet-152-XXXX.params and optionally synset.txt). + * Supports model loading from various sources like local disk, + * hdfs, https and s3. file://, hdfs://, https://, s3:// + * @param inputDescriptors Descriptors defining the input node names, shape, + * layout and Type parameters + * @param outputDescriptors Descriptors defining the output node names, shape, + * layout and Type parameters + */ +class Predictor(modelPathPrefix: String, + protected val inputDescriptors: IndexedSeq[DataDesc], + protected var outputDescriptors: + Option[IndexedSeq[DataDesc]] = None) extends PredictBase { + + private val logger = LoggerFactory.getLogger(classOf[Predictor]) + + protected var batchIndex = inputDescriptors(0).layout.indexOf('N') + protected var batchSize = if (batchIndex != -1 ) inputDescriptors(0).shape(batchIndex) else 1 + + protected var iDescriptors = inputDescriptors + + inputDescriptors.foreach((f: DataDesc) => require(f.layout.indexOf('N') == batchIndex, + "batch size should be in the same index for all inputs")) + + + if (batchIndex != -1) { + inputDescriptors.foreach((f: DataDesc) => require(f.shape(batchIndex) == batchSize, + "batch size should be same for all inputs")) + } else { + // TODO: this is assuming that the input needs a batch + iDescriptors = inputDescriptors.map((f : DataDesc) => new DataDesc(f.name, + Shape(1 +: f.shape.toVector), f.dtype, 'N' +: f.layout) ) + batchIndex = 1 + } + + protected val mxNetHandler = MXNetHandler() + + protected val mod = loadModule() + + /** + * This method will take input as IndexedSeq one dimensional arrays and creates + * NDArray needed for inference. The array will be reshaped based on the input descriptors. + * + * @param input : A IndexedSequence of Java one-dimensional array, An IndexedSequence is + * is needed when the model has more than one input/output + * @return IndexedSequence array of outputs. + */ + override def predict(input: IndexedSeq[Array[Float]]): IndexedSeq[Array[Float]] = { + + require(input.length == inputDescriptors.length, "number of inputs provided: %d" + + " do not match number of inputs in inputDescriptors: %d".format(input.length, + inputDescriptors.length)) + + for((i, d) <- input.zip(inputDescriptors)) { + require (i.length == d.shape.product/batchSize, "number of elements:" + + " %d in the input does not match the shape:%s".format( i.length, d.shape.toString())) + } + + var inputND: ListBuffer[NDArray] = ListBuffer.empty[NDArray] + + for((i, d) <- input.zip(inputDescriptors)) { + val shape = d.shape.toVector.patch(from = batchIndex, patch = Vector(1), replaced = 1) + + inputND += mxNetHandler.execute(NDArray.array(i, Shape(shape))) + + } + + // rebind with batchsize 1 + if (batchSize != 1) { + val desc = iDescriptors.map((f : DataDesc) => new DataDesc(f.name, + Shape(f.shape.toVector.patch(batchIndex, Vector(1), 1)), f.dtype, f.layout) ) + mxNetHandler.execute(mod.bind(desc, outputDescriptors, forceRebind = true, + forTraining = false)) + } + + val resultND = mxNetHandler.execute(mod.predict(new NDArrayIter(inputND.toIndexedSeq))) + + val result = resultND.map((f : NDArray) => f.toArray) + + mxNetHandler.execute(inputND.foreach((f: NDArray) => f.dispose)) + mxNetHandler.execute(resultND.foreach((f: NDArray) => f.dispose)) + + // rebind to batchSize + if (batchSize != 1) { + mxNetHandler.execute(mod.bind(inputDescriptors, forTraining = false, forceRebind = true)) + } + Review comment: extra empty line ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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