com.intel.analytics.bigdl.utils.tf.loaders.Pad.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.{Padding, Sequential}
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.tf.{Context, TFUtils}
import org.tensorflow.framework.NodeDef
import scala.collection.mutable.ArrayBuffer
import scala.reflect.ClassTag
class Pad extends TensorflowOpsLoader {
import Utils._
override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder,
context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
new PadLoadTF[T]()
}
}
class PadLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(2)) {
override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = {
val paddings = tensorArrays(0).asInstanceOf[Tensor[Int]]
val pad = ArrayBuffer[Int]()
val padding = Sequential[T]()
for(dim <- 1 to paddings.size(1)) {
if (paddings.valueAt(dim, 1) != 0 || paddings.valueAt(dim, 2) != 0 ) {
if (paddings(Array(dim, 1)) != 0) {
padding.add(Padding[T](dim, -paddings.valueAt(dim, 1), 4))
}
if (paddings(Array(dim, 2)) != 0) {
padding.add(Padding[T](dim, paddings.valueAt(dim, 2), 4))
}
}
}
padding
}
}