com.intel.analytics.bigdl.nn.DenseToSparse.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.nn
import com.intel.analytics.bigdl.nn.abstractnn.{TensorCriterion, TensorModule}
import com.intel.analytics.bigdl.tensor.{DenseType, SparseType, Tensor}
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import scala.reflect.ClassTag
/**
* Convert DenseTensor to SparseTensor.
* @param propagateBack whether propagate gradient back, default value is true
* @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]]
*/
class DenseToSparse[T: ClassTag](val propagateBack: Boolean = true // propagate gradient back
)(implicit ev: TensorNumeric[T]) extends TensorModule[T] {
override def updateOutput(input: Tensor[T]): Tensor[T] = {
require(input.getTensorType == DenseType, "DenseToSparse: input should be a DenseTensor," +
s"but got ${input.getTensorType}")
output = Tensor.sparse(input)
output
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
if (propagateBack) {
this.gradInput.resizeAs(input)
Tensor.dense(gradOutput, gradInput)
}
this.gradInput
}
override def toString(): String = s"DenseToSparse()"
}
object DenseToSparse {
def apply[@specialized(Float, Double) T: ClassTag]
(propagateBack: Boolean = true)(implicit ev: TensorNumeric[T]) : DenseToSparse[T] = {
new DenseToSparse(propagateBack)
}
}