com.intel.analytics.bigdl.nn.keras.Convolution3D.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}
import com.intel.analytics.bigdl.nn.{InitializationMethod, VolumetricConvolution, 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
/**
* Applies convolution operator for filtering windows of three-dimensional inputs.
* You can also use Conv3D as an alias of this layer.
* Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th').
* The input of this layer should be 5D.
*
* When you use this layer as the first layer of a model, you need to provide the argument
* inputShape (a Single Shape, does not include the batch dimension),
* e.g. inputShape=Shape(3, 10, 128, 128) 10 frames of 128x128 RGB pictures.
*
* @param nbFilter Number of convolution filters to use.
* @param kernelDim1 Length of the first dimension in the convolution kernel.
* @param kernelDim2 Length of the second dimension in the convolution kernel.
* @param kernelDim3 Length of the third dimension 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 3. Factor by which to subsample output.
* Also called strides elsewhere. Default is (1, 1, 1).
* @param dimOrdering Format of the input data. Please use "CHANNEL_FIRST" (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 Numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class Convolution3D[T: ClassTag](
val nbFilter: Int,
val kernelDim1: Int,
val kernelDim2: Int,
val kernelDim3: 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, 1),
val dimOrdering: String = "CHANNEL_FIRST",
val 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.toLowerCase() == "channel_first", s"Pooling3D currently only supports " +
s"format CHANNEL_FIRST, but got format $dimOrdering")
require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " +
s"Convolution3D: $borderMode")
require(subsample.length == 3,
s"For Convolution3D, subsample should be of length 3 but got length ${subsample.length}")
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val input = inputShape.toSingle().toArray
val pads = KerasUtils.getPadsFromBorderMode3D(borderMode)
val layer = VolumetricConvolution(
nInputPlane = input(1),
nOutputPlane = nbFilter,
kT = kernelDim1,
kW = kernelDim3,
kH = kernelDim2,
dT = subsample(0),
dW = subsample(2),
dH = subsample(1),
padT = pads._1,
padW = pads._3,
padH = pads._2,
withBias = bias,
wRegularizer = wRegularizer,
bRegularizer = bRegularizer)
layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros)
KerasLayer.fuse(layer, activation,
inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object Convolution3D {
def apply[@specialized(Float, Double) T: ClassTag](
nbFilter: Int,
kernelDim1: Int,
kernelDim2: Int,
kernelDim3: Int,
init: String = "glorot_uniform",
activation: String = null,
borderMode: String = "valid",
subsample: (Int, Int, Int) = (1, 1, 1),
dimOrdering: String = "th",
wRegularizer: Regularizer[T] = null,
bRegularizer: Regularizer[T] = null,
bias: Boolean = true,
inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Convolution3D[T] = {
new Convolution3D[T](nbFilter, kernelDim1, kernelDim2, kernelDim3,
KerasUtils.getInitMethod(init), KerasUtils.getKerasActivation(activation),
borderMode, Array(subsample._1, subsample._2, subsample._3),
KerasUtils.toBigDLFormat5D(dimOrdering),
wRegularizer, bRegularizer, bias, inputShape)
}
}