com.intel.analytics.bigdl.utils.tf.loaders.StridedSlice.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.utils.tf.loaders
import java.nio.ByteOrder
import com.intel.analytics.bigdl.Module
import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
import com.intel.analytics.bigdl.nn.tf.StrideSlice
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.Node
import com.intel.analytics.bigdl.utils.tf.Context
import org.tensorflow.framework.{DataType, NodeDef}
import scala.reflect.ClassTag
class StridedSlice extends TensorflowOpsLoader {
import Utils._
override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder,
context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
val t = getType(nodeDef, "T")
if (t == DataType.DT_INT32) {
return new StridedSliceLoadTF[T, Int]()
}
if (t == DataType.DT_FLOAT) {
return new StridedSliceLoadTF[T, Float]()
}
if (t == DataType.DT_DOUBLE) {
return new StridedSliceLoadTF[T, Double]()
}
throw new UnsupportedOperationException(s"Not support load StridedSlice with type ${t}")
}
}
class StridedSliceLoadTF[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T],
ev2: TensorNumeric[D]) extends Adapter[T](Array(2, 3, 4)) {
import StridedSlice._
override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = {
val start = oneDTensorToArray(tensorArrays(0).asInstanceOf[Tensor[Int]])
val end = oneDTensorToArray(tensorArrays(1).asInstanceOf[Tensor[Int]])
val stride = oneDTensorToArray(tensorArrays(2).asInstanceOf[Tensor[Int]])
val specs = (start zip end zip stride).zipWithIndex
.map(elem => (elem._2 + 1, elem._1._1._1 + 1, elem._1._1._2 + 1, elem._1._2))
StrideSlice[T, D](specs)
}
override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = {
(Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
Array[TensorNumeric[_]](ev, ev2))
}
}
object StridedSlice {
def oneDTensorToArray(tensor: Tensor[Int]): Array[Int] = {
require(tensor.nDimension() == 1, "1D tensor required")
val result = new Array[Int](tensor.nElement())
var i = 0
while(i < tensor.nElement()) {
result(i) = tensor.valueAt(i + 1)
i += 1
}
result
}
}