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com.intel.analytics.bigdl.models.resnet.DataSet.scala Maven / Gradle / Ivy
/*
* 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.models.resnet
import com.intel.analytics.bigdl.DataSet
import com.intel.analytics.bigdl.dataset._
import com.intel.analytics.bigdl.dataset.image._
import org.apache.spark.SparkContext
/**
* define some resnet datasets: trainDataSet and valDataSet.
*/
trait ResNetDataSet {
def trainDataSet(path: String, batchSize: Int, size: Int)
: DataSet[MiniBatch[Float]]
def valDataSet(path: String, batchSize: Int, size: Int)
: DataSet[MiniBatch[Float]]
def valDataSet(path: String, sc: SparkContext, imageSize: Int, batchSize: Int)
: DataSet[MiniBatch[Float]]
def trainDataSet(path: String, sc: SparkContext, imageSize: Int, batchSize: Int)
: DataSet[MiniBatch[Float]]
}
object Cifar10DataSet extends ResNetDataSet {
val trainMean = (0.4913996898739353, 0.4821584196221302, 0.44653092422369434)
val trainStd = (0.24703223517429462, 0.2434851308749409, 0.26158784442034005)
val testMean = (0.4942142913295297, 0.4851314002725445, 0.45040910258647154)
val testStd = (0.2466525177466614, 0.2428922662655766, 0.26159238066790275)
override def trainDataSet(path: String, batchSize: Int, size: Int)
: DataSet[MiniBatch[Float]] = {
DataSet.array(Utils.loadTrain(path))
.transform(BytesToBGRImg())
.transform(BGRImgNormalizer(trainMean, trainStd))
.transform(HFlip(0.5))
.transform(BGRImgRdmCropper(cropWidth = 32, cropHeight = 32, padding = 4))
.transform(BGRImgToBatch(batchSize))
}
override def valDataSet(path: String, batchSize: Int, size: Int)
: DataSet[MiniBatch[Float]] = {
DataSet.array(Utils.loadTest(path))
.transform(BytesToBGRImg())
.transform(BGRImgNormalizer(testMean, testStd))
.transform(BGRImgToBatch(batchSize))
}
override def valDataSet(path: String, sc: SparkContext, imageSize: Int, batchSize: Int)
: DataSet[MiniBatch[Float]] = {
DataSet.array(Utils.loadTest(path), sc)
.transform(BytesToBGRImg())
.transform(BGRImgNormalizer(trainMean, trainStd))
.transform(BGRImgToBatch(batchSize))
}
override def trainDataSet(path: String, sc: SparkContext, imageSize: Int, batchSize: Int)
: DataSet[MiniBatch[Float]] = {
DataSet.array(Utils.loadTrain(path), sc)
.transform(BytesToBGRImg())
.transform(BGRImgNormalizer(testMean, testStd))
.transform(HFlip(0.5))
.transform(BGRImgRdmCropper(cropWidth = 32, cropHeight = 32, padding = 4))
.transform(BGRImgToBatch(batchSize))
}
}