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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.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.Shape
import scala.reflect.ClassTag
/**
* UpSampling layer for 3D inputs.
* Repeats the 1st, 2nd and 3rd dimensions of the data by size(0), size(1) and size(2) respectively.
* 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).
*
* @param size Int array of length 3. UpSampling factors for dim1, dim2 and dim3.
* Default is (2, 2, 2).
* @param dimOrdering Format of the input data. Please use "CHANNEL_FIRST" (dimOrdering='th').
* @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class UpSampling3D[T: ClassTag](
val size: Array[Int] = Array(2, 2, 2),
val dimOrdering: String = "CHANNEL_FIRST",
val inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) {
require(dimOrdering.toLowerCase() == "channel_first",
s"UpSampling3D currently only supports format CHANNEL_FIRST, but got format $dimOrdering")
require(size.length == 3,
s"UpSampling3D: upsampling sizes should be of length 3, but got ${size.length}")
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val layer = com.intel.analytics.bigdl.nn.UpSampling3D(size)
layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object UpSampling3D {
def apply[@specialized(Float, Double) T: ClassTag](
size: (Int, Int, Int) = (2, 2, 2),
dimOrdering: String = "th",
inputShape: Shape = null)(implicit ev: TensorNumeric[T]): UpSampling3D[T] = {
new UpSampling3D[T](Array(size._1, size._2, size._3),
KerasUtils.toBigDLFormat5D(dimOrdering), inputShape)
}
}
