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/*
* 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.vgg
import java.io.ByteArrayOutputStream
import java.nio.ByteBuffer
import java.nio.file.{Files, Path, Paths}
import com.intel.analytics.bigdl.dataset.ByteRecord
import com.intel.analytics.bigdl.utils.File
import scopt.OptionParser
import scala.collection.mutable.ArrayBuffer
object Utils {
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)
case class TrainParams(
folder: String = "./",
checkpoint: Option[String] = None,
modelSnapshot: Option[String] = None,
stateSnapshot: Option[String] = None,
summaryPath: Option[String] = None,
batchSize: Int = 112,
maxEpoch: Int = 90,
overWriteCheckpoint: Boolean = false,
learningRate: Double = 0.01,
weightDecay: Double = 0.0005,
graphModel: Boolean = false
)
val trainParser = new OptionParser[TrainParams]("BigDL Vgg on Cifar10 Example") {
opt[String]('f', "folder")
.text("where you put the Cifar10 data")
.action((x, c) => c.copy(folder = x))
opt[String]("model")
.text("model snapshot location")
.action((x, c) => c.copy(modelSnapshot = Some(x)))
opt[String]("state")
.text("state snapshot location")
.action((x, c) => c.copy(stateSnapshot = Some(x)))
opt[String]("checkpoint")
.text("where to cache the model and state")
.action((x, c) => c.copy(checkpoint = Some(x)))
opt[String]("summary")
.text("where to store the training summary")
.action((x, c) => c.copy(summaryPath = Some(x)))
opt[Int]('e', "maxEpoch")
.text("epoch numbers")
.action((x, c) => c.copy(maxEpoch = x))
opt[Int]('b', "batchSize")
.text("batch size")
.action((x, c) => c.copy(batchSize = x))
opt[Unit]("overWrite")
.text("overwrite checkpoint files")
.action( (_, c) => c.copy(overWriteCheckpoint = true) )
opt[Double]("weightDecay")
.text("weight decay")
.action((x, c) => c.copy(weightDecay = x))
opt[Double]('l', "learningRate")
.text("inital learning rate")
.action((x, c) => c.copy(learningRate = x))
opt[Unit]('g', "graphModel")
.text("use graph model")
.action((x, c) => c.copy(graphModel = true))
}
case class TestParams(
folder: String = "./",
model: String = "",
batchSize: Int = 112
)
val testParser = new OptionParser[TestParams]("BigDL Vgg on Cifar10 Test Example") {
opt[String]('f', "folder")
.text("where you put the Cifar10 data")
.action((x, c) => c.copy(folder = x))
opt[String]("model")
.text("model snapshot location")
.action((x, c) => c.copy(model = x))
.required()
opt[Int]('b', "batchSize")
.text("batch size")
.action((x, c) => c.copy(batchSize = x))
}
private[bigdl] def loadTrain(dataFile: String): Array[ByteRecord] = {
val allFiles = Array(
dataFile + "/data_batch_1.bin",
dataFile + "/data_batch_2.bin",
dataFile + "/data_batch_3.bin",
dataFile + "/data_batch_4.bin",
dataFile + "/data_batch_5.bin"
)
val result = new ArrayBuffer[ByteRecord]()
allFiles.foreach(load(_, result))
result.toArray
}
private[bigdl] def loadTest(dataFile: String): Array[ByteRecord] = {
val result = new ArrayBuffer[ByteRecord]()
val testFile = dataFile + "/test_batch.bin"
load(testFile, result)
result.toArray
}
/**
* load cifar data.
* read cifar from hdfs if data folder starts with "hdfs:", otherwise form local file.
* @param featureFile
* @param result
*/
private[bigdl] def load(featureFile: String, result : ArrayBuffer[ByteRecord]): Unit = {
val rowNum = 32
val colNum = 32
val imageOffset = rowNum * colNum * 3 + 1
val channelOffset = rowNum * colNum
val bufferOffset = 8
val featureBuffer = if (featureFile.startsWith(File.hdfsPrefix)) {
ByteBuffer.wrap(File.readHdfsByte(featureFile))
} else {
ByteBuffer.wrap(Files.readAllBytes(Paths.get(featureFile)))
}
val featureArray = featureBuffer.array()
val featureCount = featureArray.length / (rowNum * colNum * 3 + 1)
var i = 0
while (i < featureCount) {
val img = new Array[Byte]((rowNum * colNum * 3 + bufferOffset))
val byteBuffer = ByteBuffer.wrap(img)
byteBuffer.putInt(rowNum)
byteBuffer.putInt(colNum)
val label = featureArray(i * imageOffset).toFloat
var y = 0
val start = i * imageOffset + 1
while (y < rowNum) {
var x = 0
while (x < colNum) {
img((x + y * colNum) * 3 + 2 + bufferOffset) =
featureArray(start + x + y * colNum)
img((x + y * colNum) * 3 + 1 + bufferOffset) =
featureArray(start + x + y * colNum + channelOffset)
img((x + y * colNum) * 3 + bufferOffset) =
featureArray(start + x + y * colNum + 2 * channelOffset)
x += 1
}
y += 1
}
result.append(ByteRecord(img, label + 1.0f))
i += 1
}
}
}
