我正在尝试编写 Tensorflow RecordWriter 类的纯 Java/Scala 实现,以便将 Spark DataFrame 转换为 TFRecords文件。根据文档,在 TFRecords 中,每条记录的格式如下:
uint64 length
uint32 masked_crc32_of_length
byte data[length]
uint32 masked_crc32_of_data
和CRC掩码
masked_crc = ((crc >> 15) | (crc << 17)) + 0xa282ead8ul
目前,我使用以下代码使用 guava 实现计算 CRC:
import com.google.common.hash.Hashing
object CRC32 {
val kMaskDelta = 0xa282ead8
def hash(in: Array[Byte]): Int = {
val hashing = Hashing.crc32c()
hashing.hashBytes(in).asInt()
}
def mask(crc: Int): Int ={
((crc >> 15) | (crc << 17)) + kMaskDelta
}
}
我的其余代码是:
数据编码部分是用下面的一段代码完成的:
object LittleEndianEncoding {
def encodeLong(in: Long): Array[Byte] = {
val baos = new ByteArrayOutputStream()
val out = new LittleEndianDataOutputStream(baos)
out.writeLong(in)
baos.toByteArray
}
def encodeInt(in: Int): Array[Byte] = {
val baos = new ByteArrayOutputStream()
val out = new LittleEndianDataOutputStream(baos)
out.writeInt(in)
baos.toByteArray
}
}
记录是用protocol buffer生成的:
import com.google.protobuf.ByteString
import org.tensorflow.example._
import collection.JavaConversions._
import collection.mutable._
object TFRecord {
def int64Feature(in: Long): Feature = {
val valueBuilder = Int64List.newBuilder()
valueBuilder.addValue(in)
Feature.newBuilder()
.setInt64List(valueBuilder.build())
.build()
}
def floatFeature(in: Float): Feature = {
val valueBuilder = FloatList.newBuilder()
valueBuilder.addValue(in)
Feature.newBuilder()
.setFloatList(valueBuilder.build())
.build()
}
def floatVectorFeature(in: Array[Float]): Feature = {
val valueBuilder = FloatList.newBuilder()
in.foreach(valueBuilder.addValue)
Feature.newBuilder()
.setFloatList(valueBuilder.build())
.build()
}
def bytesFeature(in: Array[Byte]): Feature = {
val valueBuilder = BytesList.newBuilder()
valueBuilder.addValue(ByteString.copyFrom(in))
Feature.newBuilder()
.setBytesList(valueBuilder.build())
.build()
}
def makeFeatures(features: HashMap[String, Feature]): Features = {
Features.newBuilder().putAllFeature(features).build()
}
def makeExample(features: Features): Example = {
Example.newBuilder().setFeatures(features).build()
}
}
下面是一个示例,说明我如何结合使用这些东西来生成我的 TFRecords 文件:
val label = TFRecord.int64Feature(1)
val feature = TFRecord.floatVectorFeature(Array[Float](1, 2, 3, 4))
val features = TFRecord.makeFeatures(HashMap[String, Feature] ("feature"->feature, "label"-> label))
val ex = TFRecord.makeExample(features)
val exSerialized = ex.toByteArray()
val length = LittleEndianEncoding.encodeLong(exSerialized.length)
val crcLength = LittleEndianEncoding.encodeInt(CRC32.mask(CRC32.hash(length)))
val crcEx = LittleEndianEncoding.encodeInt(CRC32.mask(CRC32.hash(exSerialized)))
val out = new FileOutputStream(new File("test.tfrecords"))
out.write(length)
out.write(crcLength)
out.write(exSerialized)
out.write(crcEx)
out.close()
当我尝试读取我通过 TFRecordReader 进入 Tensorflow 的文件时,我收到以下错误:
W tensorflow/core/common_runtime/executor.cc:1076] 0x24cc430 Compute status: Data loss: corrupted record at 0
我怀疑CRC掩码计算不正确或字节序 java和c++生成的文件不一样。
最佳答案
FWIW,Tensorflow 团队提供了用于读取/写入 TFRecords 的实用程序代码,可以是 found in the ecosystem repo
关于java - 用于编写 Tensorflow TFRecords 数据文件的纯 Java/Scala 代码,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/34711264/