com.intel.analytics.bigdl.nn.keras.LocallyConnected2D.scala Maven / Gradle / Ivy

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/*
 * 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.keras

import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat}
import com.intel.analytics.bigdl.nn.{Container => TContainer, LocallyConnected2D => TLocallyConnected2D}
import com.intel.analytics.bigdl.optim.Regularizer
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.Shape

import scala.reflect.ClassTag

/**
 * Locally-connected layer for 2D inputs that works similarly to the SpatialConvolution layer,
 * except that weights are unshared, that is, a different set of filters
 * is applied at each different patch of the input.
 * The input of this layer should be 4D.
 *
 * When using this layer as the first layer in a model, you need to provide the argument
 * inputShape (a Single Shape, does not include the batch dimension).
 *
 * @param nbFilter Number of convolution filters to use.
 * @param nbRow Number of rows in the convolution kernel.
 * @param nbCol Number of columns in the convolution kernel.
 * @param activation Activation function to use. Default is null.
 *                   You can also pass in corresponding string representations such as 'relu'
 *                   or 'sigmoid', etc. for simple activations in the factory method.
 * @param borderMode Either 'valid' or 'same'. Default is 'valid'.
 * @param subsample Int array of length 2 corresponding to the step of the convolution in the height
 *                  and width dimension. Also called strides elsewhere. Default is (1, 1).
 * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or
 *                    DataFormat.NHWC (dimOrdering='tf'). Default is NCHW.
 * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization),
 *                     applied to the input weights matrices. Default is null.
 * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null.
 * @param bias Whether to include a bias (i.e. make the layer affine rather than linear).
 *             Default is true.
 * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now.
 */
class LocallyConnected2D[T: ClassTag](
   val nbFilter: Int,
   val nbRow: Int,
   val nbCol: Int,
   val activation: KerasLayer[Tensor[T], Tensor[T], T] = null,
   val borderMode: String = "valid",
   val subsample: Array[Int] = Array(1, 1),
   val dimOrdering: DataFormat = DataFormat.NCHW,
   var wRegularizer: Regularizer[T] = null,
   var bRegularizer: Regularizer[T] = null,
   val bias: Boolean = true,
   val inputShape: Shape = null)(implicit ev: TensorNumeric[T])
  extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) {

  require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " +
    s"LocallyConnected2D: $borderMode")
  require(subsample.length == 2,
    s"For LocallyConnected2D, subsample should be of length 2 but got length ${subsample.length}")

  override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
    val input = inputShape.toSingle().toArray
    val stack = if (dimOrdering == DataFormat.NCHW) (input(1), input(3), input(2))
      else (input(3), input(2), input(1))
    val pad = KerasUtils.getPadsFromBorderMode(borderMode)
    val layer = com.intel.analytics.bigdl.nn.LocallyConnected2D(
      nInputPlane = stack._1,
      inputWidth = stack._2,
      inputHeight = stack._3,
      nOutputPlane = nbFilter,
      kernelW = nbCol,
      kernelH = nbRow,
      strideW = subsample(1),
      strideH = subsample(0),
      padW = pad._2,
      padH = pad._1,
      wRegularizer = wRegularizer,
      bRegularizer = bRegularizer,
      withBias = bias,
      format = dimOrdering)
    KerasLayer.fuse(layer, activation,
      inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
  }
}

object LocallyConnected2D {
  def apply[@specialized(Float, Double) T: ClassTag](
    nbFilter: Int,
    nbRow: Int,
    nbCol: Int,
    activation: String = null,
    borderMode: String = "valid",
    subsample: (Int, Int) = (1, 1),
    dimOrdering: String = "th",
    wRegularizer: Regularizer[T] = null,
    bRegularizer: Regularizer[T] = null,
    bias: Boolean = true,
    inputShape: Shape = null)(implicit ev: TensorNumeric[T]): LocallyConnected2D[T] = {
    new LocallyConnected2D[T](nbFilter, nbRow, nbCol,
      KerasUtils.getKerasActivation(activation), borderMode, Array(subsample._1, subsample._2),
      KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, bias, inputShape)
  }
}