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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
/**
* Cropping layer for 3D data (e.g. spatial or spatio-temporal).
* 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 dim1Crop Int array of length 2. Kernel dim1 of the three cropping dimensions.
* Default is (1, 1).
* @param dim2Crop Int array of length 2. Kernel dim2 of the three cropping dimensions.
* Default is (1, 1).
* @param dim3Crop Int array of length 2. Kernel dim3 of the three cropping dimensions.
* Default is (1, 1).
* @param dimOrdering Format of input data. Either 'CHANNEL_FIRST' (dimOrdering='th') or
* 'CHANNEL_LAST' (dimOrdering='tf'). Default is 'CHANNEL_FIRST'.
* @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class Cropping3D[T: ClassTag](
val dim1Crop: Array[Int] = Array(1, 1),
val dim2Crop: Array[Int] = Array(1, 1),
val dim3Crop: Array[Int] = Array(1, 1),
val dimOrdering: String = "CHANNEL_FIRST",
val inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) {
require(dim1Crop.length == 2,
s"Cropping3D: kernel dim1 cropping values should be of length 2, " +
s"but got length ${dim1Crop.length}")
require(dim2Crop.length == 2,
s"Cropping3D: kernel dim2 cropping values should be of length 2, " +
s"but got length ${dim2Crop.length}")
require(dim3Crop.length == 2,
s"Cropping3D: kernel dim3 cropping values should be of length 2, " +
s"but got length ${dim3Crop.length}")
require(dimOrdering.toLowerCase() == "channel_first" ||
dimOrdering.toLowerCase() == "channel_last",
s"Cropping3D only supports format channel_first or channel_last, but got format $dimOrdering")
private val format = dimOrdering.toLowerCase() match {
case "channel_first" => com.intel.analytics.bigdl.nn.Cropping3D.CHANNEL_FIRST
case "channel_last" => com.intel.analytics.bigdl.nn.Cropping3D.CHANNEL_LAST
}
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val layer = com.intel.analytics.bigdl.nn.Cropping3D(
dim1Crop = dim1Crop,
dim2Crop = dim2Crop,
dim3Crop = dim3Crop,
format = format)
layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object Cropping3D {
def apply[@specialized(Float, Double) T: ClassTag](
cropping: ((Int, Int), (Int, Int), (Int, Int)) = ((1, 1), (1, 1), (1, 1)),
dimOrdering: String = "th",
inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Cropping3D[T] = {
val dim1Crop = Array(cropping._1._1, cropping._1._2)
val dim2Crop = Array(cropping._2._1, cropping._2._2)
val dim3Crop = Array(cropping._3._1, cropping._3._2)
new Cropping3D[T](dim1Crop, dim2Crop, dim3Crop,
KerasUtils.toBigDLFormat5D(dimOrdering), inputShape)
}
}
