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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.{InitializationMethod, SpatialFullConvolution, Xavier, Zeros}
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
/**
* Transposed convolution operator for filtering windows of 2-D inputs.
* The need for transposed convolutions generally arises from the desire to use a transformation
* going in the opposite direction of a normal convolution, i.e., from something that has
* the shape of the output of some convolution to something that has the shape of its input
* while maintaining a connectivity pattern that is compatible with said convolution.
* Data format currently supported for this layer is DataFormat.NCHW (dimOrdering='th').
* Border mode currently supported for this layer is 'valid'.
* You can also use Deconv2D as an alias of this layer.
* 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).
* e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures.
*
* @param nbFilter Number of transposed convolution filters to use.
* @param nbRow Number of rows in the transposed convolution kernel.
* @param nbCol Number of columns in the transposed convolution kernel.
* @param init Initialization method for the weights of the layer. Default is Xavier.
* You can also pass in corresponding string representations such as 'glorot_uniform'
* or 'normal', etc. for simple init methods in the factory method.
* @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 subsample Int array of length 2. 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. Please use DataFormat.NCHW (dimOrdering='th').
* @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 Deconvolution2D[T: ClassTag](
val nbFilter: Int,
val nbRow: Int,
val nbCol: Int,
val init: InitializationMethod = Xavier,
val activation: KerasLayer[Tensor[T], Tensor[T], T] = null,
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(dimOrdering == DataFormat.NCHW, s"Deconvolution2D currently only supports " +
s"format NCHW, but got format $dimOrdering")
require(subsample.length == 2,
s"For Deconvolution2D, 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 layer = SpatialFullConvolution(
nInputPlane = input(1),
nOutputPlane = nbFilter,
kW = nbCol,
kH = nbRow,
dW = subsample(1),
dH = subsample(0),
noBias = !bias,
wRegularizer = wRegularizer,
bRegularizer = bRegularizer)
layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros)
KerasLayer.fuse(layer, activation,
inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object Deconvolution2D {
def apply[@specialized(Float, Double) T: ClassTag](
nbFilter: Int,
nbRow: Int,
nbCol: Int,
init: String = "glorot_uniform",
activation: String = null,
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]): Deconvolution2D[T] = {
new Deconvolution2D[T](nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init),
KerasUtils.getKerasActivation(activation), Array(subsample._1, subsample._2),
KerasUtils.toBigDLFormat(dimOrdering), wRegularizer,
bRegularizer, bias, inputShape)
}
}
