yzhliu commented on a change in pull request #9678: [MXNET-50] Scala Inference APIs URL: https://github.com/apache/incubator-mxnet/pull/9678#discussion_r174998313
########## File path: scala-package/infer/src/main/scala/ml/dmlc/mxnet/infer/Predictor.scala ########## @@ -0,0 +1,188 @@ +/* + * 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 MXNet Predictor classes. + */ +private[mxnet] 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 Scala 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, + * @param inputDescriptors Descriptors defining the input node names, shape, + * layout and Type parameters. + * <p>Note: If the input Descriptors is missing batchSize('N' in layout), + * a batchSize of 1 is assumed for the model. + * </p> + */ +class Predictor(modelPathPrefix: String, protected val inputDescriptors: IndexedSeq[DataDesc]) + 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 { + // Note: this is assuming that the input needs a batch + logger.warn("InputDescriptor does not have batchSize, using 1 as the default batchSize") + 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 Scala 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" + + " does 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, 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(_.dispose)) + mxNetHandler.execute(resultND.foreach(_.dispose)) + + // rebind to batchSize + if (batchSize != 1) { + mxNetHandler.execute(mod.bind(inputDescriptors, forTraining = false, forceRebind = true)) + } + + result + } + + /** + * 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 inputBatch : IndexedSequence NDArrays. + * @return output of Predictions as NDArrays. + */ + override def predictWithNDArray(inputBatch: IndexedSeq[NDArray]): IndexedSeq[NDArray] = { + + require(inputBatch.length == inputDescriptors.length, "number of inputs provided: %d" + + " do not match number of inputs in inputDescriptors: %d".format(inputBatch.length, + inputDescriptors.length)) + + // Shape validation, remove this when backend throws better error messages. + for((i, d) <- inputBatch.zip(iDescriptors)) { + require(inputBatch(0).shape(batchIndex) == i.shape(batchIndex), + "All inputs should be of same batch size") + require(i.shape.drop(batchIndex + 1) == d.shape.drop(batchIndex + 1), + "Input Data Shape: %s should match the inputDescriptor shape: %s except batchSize".format( + i.shape.toString, d.shape.toString)) + } + + val inputBatchSize = inputBatch(0).shape(batchIndex) + + // rebind with the new batchSize + if (batchSize != inputBatchSize) { + val desc = iDescriptors.map((f : DataDesc) => new DataDesc(f.name, + Shape(f.shape.toVector.patch(batchIndex, Vector(inputBatchSize), 1)), f.dtype, f.layout) ) + mxNetHandler.execute(mod.bind(desc, forceRebind = true, + forTraining = false)) + } + + val resultND = mxNetHandler.execute(mod.predict(new NDArrayIter(inputBatch))) + + if (batchSize != inputBatchSize) { + mxNetHandler.execute(mod.bind(iDescriptors, forceRebind = true, + forTraining = false)) + } + resultND + } + + def loadModule(): Module = { Review comment: this function should be `private`, right? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
