com.intel.analytics.bigdl.utils.tf.loaders.DepthwiseConv2dNativeBackpropInput.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.ops.{DepthwiseConv2D, DepthwiseConv2DBackpropInput}
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 DepthwiseConv2dNativeBackpropInput 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 (pW, pH) =
if (getString(attributes, "padding") == "SAME") {
(-1, -1)
} else {
(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 "NHWC" =>
require(strideList(3) == 1, s"not support strides on depth")
val strideW = strideList(1)
val strideH = strideList(2)
DepthwiseConv2DBackpropInput[T](strideW, strideH, pW, pH, DataFormat.NHWC)
case "NCHW" =>
require(strideList(1) == 1, s"not support strides on depth")
val strideW = strideList(2)
val strideH = strideList(3)
DepthwiseConv2DBackpropInput[T](strideW, strideH, pW, pH, DataFormat.NCHW)
case _ =>
throw new IllegalArgumentException(s"not supported data format: $format")
}
conv.asInstanceOf[AbstractModule[Activity, Activity, T]]
}
}