com.intel.analytics.zoo.pipeline.inference.FloatInferenceModel.scala Maven / Gradle / Ivy

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/*
 * Copyright 2018 Analytics Zoo 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.zoo.pipeline.inference

import com.intel.analytics.bigdl.optim.LocalPredictor
import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule
import com.intel.analytics.bigdl.nn.abstractnn.Activity
import com.intel.analytics.bigdl.tensor.Tensor

import scala.collection.JavaConverters._
import java.util.{List => JList}
import java.lang.{Float => JFloat}
import java.lang.{Integer => JInt}

case class FloatInferenceModel(
  model: AbstractModule[Activity, Activity, Float],
  predictor: LocalPredictor[Float]) extends InferenceSupportive {

  def predict(input: JList[JFloat], shape: JList[JInt]): JList[JFloat] = {
    timing("model predict") {
      val sample = transferInputToSample(input, shape.asScala.toArray.map(_.asInstanceOf[Int]))
      val result = predictor.predict(Array(sample))
      require(result.length == 1, "only one input, should get only one prediction")
      result(0).asInstanceOf[Tensor[Float]].toArray().toList.asJava.asInstanceOf[JList[JFloat]]
    }
  }
}