com.intel.analytics.bigdl.utils.tf.loaders.Conv3DBackpropInputV2.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, DataFormat}
import com.intel.analytics.bigdl.nn.tf.{Conv3DBackpropInputV2 => Conv3DBackpropInputV2Ops}
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.tf.Context
import org.tensorflow.framework.NodeDef
import scala.reflect.ClassTag
class Conv3DBackpropInputV2 extends TensorflowOpsLoader {
import Utils._
override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder,
context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
val attributes = nodeDef.getAttrMap
val (pT, pW, pH) =
if (getString(attributes, "padding") == "SAME") {
(-1, -1, -1)
} else {
(0, 0, 0)
}
val strideList = getIntList(attributes, "strides")
require(strideList.head == 1, s"not support strides on batch")
val format = getString(attributes, "data_format")
val conv = format match {
case "NDHWC" =>
require(strideList(4) == 1, s"not support strides on depth")
val dT = strideList(1)
val dW = strideList(2)
val dH = strideList(3)
Conv3DBackpropInputV2Ops[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC)
case "NCDHW" =>
require(strideList(1) == 1, s"not support strides on depth")
val dT = strideList(2)
val dW = strideList(3)
val dH = strideList(4)
Conv3DBackpropInputV2Ops[T](dT, dW, dH, pT, pW, pH, DataFormat.NCHW)
case _ =>
throw new IllegalArgumentException(s"not supported data format: $format")
}
conv.asInstanceOf[AbstractModule[Activity, Activity, T]]
}
}