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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.{InitializationMethod, SpatialSeparableConvolution, Xavier}
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 com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat}
import scala.reflect.ClassTag
/**
* Applies separable convolution operator for 2D inputs.
* Separable convolutions consist in first performing a depthwise spatial convolution (which acts
* on each input channel separately) followed by a pointwise convolution which mixes together the
* resulting output channels. The depthMultiplier argument controls how many output channels are
* generated per input channel in the depthwise step.
* You can also use SeparableConv2D 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 convolution filters to use.
* @param nbRow Number of rows in the convolution kernel.
* @param nbCol Number of columns in the 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 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 depthMultiplier How many output channel to use per input channel
* for the depthwise convolution step. Integer. Default is 1.
* @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or
* DataFormat.NHWC (dimOrdering='tf'). Default is NCHW.
* @param depthwiseRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization),
* applied to the depthwise weights matrices. Default is null.
* @param pointwiseRegularizer An instance of [[Regularizer]], applied to the pointwise 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 SeparableConvolution2D[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 borderMode: String = "valid",
val subsample: Array[Int] = Array(1, 1),
val depthMultiplier: Int = 1,
val dimOrdering: DataFormat = DataFormat.NCHW,
var depthwiseRegularizer: Regularizer[T] = null,
var pointwiseRegularizer: 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"SeparableConvolution2D: $borderMode")
require(subsample.length == 2, s"For SeparableConvolution2D, " +
s"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 stackSize = if (dimOrdering == DataFormat.NCHW) input(1) else input(3)
val pad = KerasUtils.getPadsFromBorderMode(borderMode)
val layer = SpatialSeparableConvolution(
nInputChannel = stackSize,
nOutputChannel = nbFilter,
depthMultiplier = depthMultiplier,
kW = nbCol,
kH = nbRow,
sW = subsample(1),
sH = subsample(0),
pW = pad._2,
pH = pad._1,
hasBias = bias,
dataFormat = dimOrdering,
wRegularizer = depthwiseRegularizer,
bRegularizer = bRegularizer,
pRegularizer = pointwiseRegularizer)
KerasLayer.fuse(layer, activation,
inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object SeparableConvolution2D {
def apply[@specialized(Float, Double) T: ClassTag](
nbFilter: Int,
nbRow: Int,
nbCol: Int,
init: String = "glorot_uniform",
activation: String = null,
borderMode: String = "valid",
subsample: (Int, Int) = (1, 1),
depthMultiplier: Int = 1,
dimOrdering: String = "th",
depthwiseRegularizer: Regularizer[T] = null,
pointwiseRegularizer: Regularizer[T] = null,
bRegularizer: Regularizer[T] = null,
bias: Boolean = true,
inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : SeparableConvolution2D[T] = {
new SeparableConvolution2D[T](nbFilter, nbRow, nbCol,
KerasUtils.getInitMethod(init), KerasUtils.getKerasActivation(activation),
borderMode, Array(subsample._1, subsample._2), depthMultiplier,
KerasUtils.toBigDLFormat(dimOrdering), depthwiseRegularizer,
pointwiseRegularizer, bRegularizer, bias, inputShape)
}
}
