/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://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. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ package org.deeplearning4j.nn.conf.layers; import lombok.*; import org.deeplearning4j.nn.conf.InputPreProcessor; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.inputs.InputType; import org.deeplearning4j.nn.conf.memory.LayerMemoryReport; import org.deeplearning4j.nn.conf.memory.MemoryReport; import org.deeplearning4j.optimize.api.TrainingListener; import org.deeplearning4j.util.ValidationUtils; import org.nd4j.common.base.Preconditions; import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import java.util.Collection; import java.util.Map; /** * Upsampling 3D layer<br> Repeats each value (all channel values for each x/y/z location) by size[0], size[1] and * size[2]<br> If input has shape {@code [minibatch, channels, depth, height, width]} then output has shape {@code * [minibatch, channels, size[0] * depth, size[1] * height, size[2] * width]} * * @author Max Pumperla */ @Data @NoArgsConstructor @ToString(callSuper = true) @EqualsAndHashCode(callSuper = true) public class Upsampling3D extends BaseUpsamplingLayer { protected int[] size; protected Convolution3D.DataFormat dataFormat = Convolution3D.DataFormat.NCDHW; //Default to NCDHW for 1.0.0-beta4 and earlier, when no config existed (NCDHW only) protected Upsampling3D(Builder builder) { super(builder); this.size = builder.size; this.dataFormat = builder.dataFormat; } @Override public Upsampling3D clone() { return (Upsampling3D) super.clone(); } @Override public org.deeplearning4j.nn.api.Layer instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> iterationListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) { org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D ret = new org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D(conf, networkDataType); ret.setListeners(iterationListeners); ret.setIndex(layerIndex); ret.setParamsViewArray(layerParamsView); Map<String, INDArray> paramTable = initializer().init(conf, layerParamsView, initializeParams); ret.setParamTable(paramTable); ret.setConf(conf); return ret; } @Override public InputType getOutputType(int layerIndex, InputType inputType) { if (inputType == null || inputType.getType() != InputType.Type.CNN3D) { throw new IllegalStateException("Invalid input for Upsampling 3D layer (layer name=\"" + getLayerName() + "\"): Expected CNN3D input, got " + inputType); } InputType.InputTypeConvolutional3D i = (InputType.InputTypeConvolutional3D) inputType; long inHeight = (int) i.getHeight(); long inWidth = (int) i.getWidth(); long inDepth = (int) i.getDepth(); long inChannels = (int) i.getChannels(); return InputType.convolutional3D(size[0] * inDepth, size[1] * inHeight, size[2] * inWidth, inChannels); } @Override public InputPreProcessor getPreProcessorForInputType(InputType inputType) { if (inputType == null) { throw new IllegalStateException("Invalid input for Upsampling 3D layer (layer name=\"" + getLayerName() + "\"): input is null"); } return InputTypeUtil.getPreProcessorForInputTypeCnn3DLayers(inputType, getLayerName()); } @Override public LayerMemoryReport getMemoryReport(InputType inputType) { InputType.InputTypeConvolutional3D c = (InputType.InputTypeConvolutional3D) inputType; InputType.InputTypeConvolutional3D outputType = (InputType.InputTypeConvolutional3D) getOutputType(-1, inputType); // During forward pass: im2col array + reduce. Reduce is counted as activations, so only im2col is working mem val im2colSizePerEx = c.getChannels() & outputType.getDepth() * outputType.getHeight() * outputType.getWidth() * size[0] * size[1] * size[2]; // Current implementation does NOT cache im2col etc... which means: it's recalculated on each backward pass long trainingWorkingSizePerEx = im2colSizePerEx; if (getIDropout() != null) { //Dup on the input before dropout, but only for training trainingWorkingSizePerEx += inputType.arrayElementsPerExample(); } return new LayerMemoryReport.Builder(layerName, Upsampling3D.class, inputType, outputType).standardMemory(0, 0) //No params .workingMemory(0, im2colSizePerEx, 0, trainingWorkingSizePerEx) .cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching .build(); } @NoArgsConstructor public static class Builder extends UpsamplingBuilder<Builder> { protected Convolution3D.DataFormat dataFormat = Convolution3D.DataFormat.NCDHW; /** * @param size Upsampling layer size (most common value: 2) */ public Builder(int size) { super(new int[] {size, size, size}); } /** * @param dataFormat Data format - see {@link Convolution3D.DataFormat} for more details * @param size Upsampling layer size (most common value: 2) */ public Builder(@NonNull Convolution3D.DataFormat dataFormat, int size){ super(new int[]{size, size, size}); this.dataFormat = dataFormat; } /** * Sets the DataFormat. See {@link Convolution3D.DataFormat} for more details */ public Builder dataFormat(@NonNull Convolution3D.DataFormat dataFormat){ this.dataFormat = dataFormat; return this; } /** * Upsampling size as int, so same upsampling size is used for depth, width and height * * @param size upsampling size in height, width and depth dimensions */ public Builder size(int size) { this.setSize(new int[] {size, size, size}); return this; } /** * Upsampling size as int, so same upsampling size is used for depth, width and height * * @param size upsampling size in height, width and depth dimensions */ public Builder size(int[] size) { Preconditions.checkArgument(size.length == 3); this.setSize(size); return this; } @Override @SuppressWarnings("unchecked") public Upsampling3D build() { return new Upsampling3D(this); } @Override public void setSize(int... size) { this.size = ValidationUtils.validate3NonNegative(size, "size"); } } }