/******************************************************************************* * 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.api.ParamInitializer; import org.deeplearning4j.nn.conf.ConvolutionMode; 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.nn.params.EmptyParamInitializer; import org.deeplearning4j.optimize.api.TrainingListener; import org.deeplearning4j.util.Convolution3DUtils; import org.deeplearning4j.util.ConvolutionUtils; 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 org.nd4j.linalg.exception.ND4JArraySizeException; import org.nd4j.linalg.learning.regularization.Regularization; import java.util.Collection; import java.util.List; import java.util.Map; /** * 3D subsampling / pooling layer for convolutional neural networks * <p> * Supports max and average pooling modes * * @author Max Pumperla */ @Data @NoArgsConstructor @ToString(callSuper = true) @EqualsAndHashCode(callSuper = true) public class Subsampling3DLayer extends NoParamLayer { protected ConvolutionMode convolutionMode = ConvolutionMode.Truncate; protected org.deeplearning4j.nn.conf.layers.PoolingType poolingType; protected int[] kernelSize; protected int[] stride; protected int[] padding; protected int[] dilation; protected boolean cudnnAllowFallback = true; protected Convolution3D.DataFormat dataFormat = Convolution3D.DataFormat.NCDHW; //Default for 1.0.0-beta3 and earlier (before config added) public enum PoolingType { MAX, AVG; public org.deeplearning4j.nn.conf.layers.PoolingType toPoolingType() { switch (this) { case MAX: return org.deeplearning4j.nn.conf.layers.PoolingType.MAX; case AVG: return org.deeplearning4j.nn.conf.layers.PoolingType.AVG; } throw new UnsupportedOperationException("Unknown/not supported pooling type: " + this); } } protected Subsampling3DLayer(Builder builder) { super(builder); this.poolingType = builder.poolingType; if (builder.kernelSize.length != 3) { throw new IllegalArgumentException("Kernel size must be length 3"); } this.kernelSize = builder.kernelSize; if (builder.stride.length != 3) { throw new IllegalArgumentException("Invalid stride, must be length 3"); } this.stride = builder.stride; this.padding = builder.padding; this.dilation = builder.dilation; this.convolutionMode = builder.convolutionMode; this.cudnnAllowFallback = builder.cudnnAllowFallback; this.dataFormat = builder.dataFormat; } @Override public Subsampling3DLayer clone() { Subsampling3DLayer clone = (Subsampling3DLayer) super.clone(); if (clone.kernelSize != null) { clone.kernelSize = clone.kernelSize.clone(); } if (clone.stride != null) { clone.stride = clone.stride.clone(); } if (clone.padding != null) { clone.padding = clone.padding.clone(); } if (clone.dilation != null) { clone.dilation = clone.dilation.clone(); } return 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.subsampling.Subsampling3DLayer ret = new org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer(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 ParamInitializer initializer() { return EmptyParamInitializer.getInstance(); } @Override public InputType getOutputType(int layerIndex, InputType inputType) { if (inputType == null || inputType.getType() != InputType.Type.CNN3D) { throw new IllegalStateException("Invalid input for Subsampling 3D layer (layer name=\"" + getLayerName() + "\"): Expected CNN input, got " + inputType); } long inChannels = ((InputType.InputTypeConvolutional3D) inputType).getChannels(); if (inChannels > Integer.MAX_VALUE) throw new ND4JArraySizeException(); return InputTypeUtil.getOutputTypeCnn3DLayers(inputType, dataFormat, kernelSize, stride, padding, new int[] {1, 1, 1}, // no dilation convolutionMode, (int) inChannels, layerIndex, getLayerName(), Subsampling3DLayer.class); } @Override public void setNIn(InputType inputType, boolean override) { //No op: subsampling layer doesn't have nIn value } @Override public InputPreProcessor getPreProcessorForInputType(InputType inputType) { if (inputType == null) { throw new IllegalStateException("Invalid input for Subsampling 3D layer (layer name=\"" + getLayerName() + "\"): input is null"); } return InputTypeUtil.getPreProcessorForInputTypeCnn3DLayers(inputType, getLayerName()); } @Override public List<Regularization> getRegularizationByParam(String paramName) { //Not applicable return null; } @Override public boolean isPretrainParam(String paramName) { throw new UnsupportedOperationException("SubsamplingLayer does not contain parameters"); } @Override public LayerMemoryReport getMemoryReport(InputType inputType) { InputType.InputTypeConvolutional3D c = (InputType.InputTypeConvolutional3D) inputType; InputType.InputTypeConvolutional3D outputType = (InputType.InputTypeConvolutional3D) getOutputType(-1, inputType); val actElementsPerEx = outputType.arrayElementsPerExample(); //During forward pass: im2col array + reduce. Reduce is counted as activations, so only im2col is working mem val im2colSizePerEx = c.getChannels() * outputType.getHeight() * outputType.getWidth() * outputType.getDepth() * kernelSize[0] * kernelSize[1]; //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, Subsampling3DLayer.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 @Getter @Setter public static class Builder extends BaseSubsamplingBuilder<Builder> { /** * The data format for input and output activations.<br> NCDHW: activations (in/out) should have shape * [minibatch, channels, depth, height, width]<br> NDHWC: activations (in/out) should have shape [minibatch, * depth, height, width, channels]<br> */ protected Convolution3D.DataFormat dataFormat = Convolution3D.DataFormat.NCDHW; public Builder(PoolingType poolingType, int[] kernelSize, int[] stride) { super(poolingType, kernelSize, stride); } public Builder(PoolingType poolingType, int[] kernelSize) { super(poolingType, kernelSize); } public Builder(PoolingType poolingType, int[] kernelSize, int[] stride, int[] padding) { super(poolingType, kernelSize, stride, padding); } public Builder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType, int[] kernelSize) { super(poolingType, kernelSize); } public Builder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType, int[] kernelSize, int[] stride, int[] padding) { super(poolingType, kernelSize, stride, padding); } public Builder(int[] kernelSize, int[] stride, int[] padding) { super(kernelSize, stride, padding); } public Builder(int[] kernelSize, int[] stride) { super(kernelSize, stride); } public Builder(int... kernelSize) { super(kernelSize); } public Builder(PoolingType poolingType) { super(poolingType); } public Builder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType) { super(poolingType); } /** * Kernel size * * @param kernelSize kernel size in height and width dimensions */ public Builder kernelSize(int... kernelSize) { this.setKernelSize(kernelSize); return this; } /** * Stride * * @param stride stride in height and width dimensions */ public Builder stride(int... stride) { this.setStride(stride); return this; } /** * Padding * * @param padding padding in the height and width dimensions */ public Builder padding(int... padding) { this.setPadding(padding); return this; } /** * The data format for input and output activations.<br> NCDHW: activations (in/out) should have shape * [minibatch, channels, depth, height, width]<br> NDHWC: activations (in/out) should have shape [minibatch, * depth, height, width, channels]<br> * * @param dataFormat Data format to use for activations */ public Builder dataFormat(Convolution3D.DataFormat dataFormat) { this.setDataFormat(dataFormat); return this; } @Override @SuppressWarnings("unchecked") public Subsampling3DLayer build() { ConvolutionUtils.validateConvolutionModePadding(convolutionMode, padding); Convolution3DUtils.validateCnn3DKernelStridePadding(kernelSize, stride, padding); return new Subsampling3DLayer(this); } @Override public void setKernelSize(int... kernelSize) { this.kernelSize = ValidationUtils.validate3NonNegative(kernelSize, "kernelSize"); } /** * Stride * * @param stride stride in height and width dimensions */ @Override public void setStride(int... stride) { this.stride = ValidationUtils.validate3NonNegative(stride, "stride"); } /** * Padding * * @param padding padding in the height and width dimensions */ @Override public void setPadding(int... padding) { this.padding = ValidationUtils.validate3NonNegative(padding, "padding"); } /** * Dilation * * @param dilation padding in the height and width dimensions */ @Override public void setDilation(int... dilation) { this.dilation = ValidationUtils.validate3NonNegative(dilation, "dilation"); } } @Getter @Setter @NoArgsConstructor protected static abstract class BaseSubsamplingBuilder<T extends BaseSubsamplingBuilder<T>> extends Layer.Builder<T> { protected org.deeplearning4j.nn.conf.layers.PoolingType poolingType = org.deeplearning4j.nn.conf.layers.PoolingType.MAX; protected int[] kernelSize = new int[] {1, 1, 1}; protected int[] stride = new int[] {2, 2, 2}; protected int[] padding = new int[] {0, 0, 0}; @Setter(AccessLevel.NONE) protected int[] dilation = new int[] {1, 1, 1}; /** * Set the convolution mode for the Convolution layer. See {@link ConvolutionMode} for more details * */ protected ConvolutionMode convolutionMode = ConvolutionMode.Same; /** * When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? * If set to false, an exception in CuDNN will be propagated back to the user. If false, the built-in * (non-CuDNN) implementation for ConvolutionLayer will be used */ protected boolean cudnnAllowFallback = true; public void setDilation(int... dilation) { Preconditions.checkArgument(dilation.length == 1 || dilation.length == 3, "Must have 1 or 3 dilation values - got %s", dilation); if (dilation.length == 1) { dilation(dilation[0], dilation[0], dilation[0]); } else { dilation(dilation[0], dilation[1], dilation[2]); } } protected BaseSubsamplingBuilder(PoolingType poolingType, int[] kernelSize, int[] stride) { this.setPoolingType(poolingType.toPoolingType()); this.setKernelSize(kernelSize); this.setStride(stride); } protected BaseSubsamplingBuilder(PoolingType poolingType, int[] kernelSize) { this.setPoolingType(poolingType.toPoolingType()); this.setKernelSize(kernelSize); } protected BaseSubsamplingBuilder(PoolingType poolingType, int[] kernelSize, int[] stride, int[] padding) { this.setPoolingType(poolingType.toPoolingType()); this.setKernelSize(kernelSize); this.setStride(stride); this.setPadding(padding); } protected BaseSubsamplingBuilder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType, int[] kernelSize) { this.setPoolingType(poolingType); this.setKernelSize(kernelSize); } protected BaseSubsamplingBuilder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType, int[] kernelSize, int[] stride, int[] padding) { this.setPoolingType(poolingType); this.setKernelSize(kernelSize); this.setStride(stride); this.setPadding(padding); } protected BaseSubsamplingBuilder(int[] kernelSize, int[] stride, int[] padding) { this.setKernelSize(kernelSize); this.setStride(stride); this.setPadding(padding); } protected BaseSubsamplingBuilder(int[] kernelSize, int[] stride) { this.setKernelSize(kernelSize); this.setStride(stride); } protected BaseSubsamplingBuilder(int... kernelSize) { this.setKernelSize(kernelSize); } protected BaseSubsamplingBuilder(PoolingType poolingType) { this.setPoolingType(poolingType.toPoolingType()); } protected BaseSubsamplingBuilder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType) { this.setPoolingType(poolingType); } protected void setConvolutionMode(ConvolutionMode convolutionMode){ Preconditions.checkState(convolutionMode != ConvolutionMode.Causal, "Causal convolution mode can only be used with 1D" + " convolutional neural network layers"); this.convolutionMode = convolutionMode; } /** * Set the convolution mode for the Convolution layer. See {@link ConvolutionMode} for more details * * @param convolutionMode Convolution mode for layer */ public T convolutionMode(ConvolutionMode convolutionMode) { this.setConvolutionMode(convolutionMode); return (T) this; } public T poolingType(PoolingType poolingType) { this.setPoolingType(poolingType.toPoolingType()); return (T) this; } public T poolingType(org.deeplearning4j.nn.conf.layers.PoolingType poolingType){ this.setPoolingType(poolingType); return (T) this; } public T dilation(int dDepth, int dHeight, int dWidth) { this.setDilation(new int[] {dDepth, dHeight, dWidth}); return (T) this; } /** * When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? * If set to false, an exception in CuDNN will be propagated back to the user. If true, the built-in * (non-CuDNN) implementation for ConvolutionLayer will be used * * @deprecated Use {@link #helperAllowFallback(boolean)} * * @param allowFallback Whether fallback to non-CuDNN implementation should be used */ @Deprecated public T cudnnAllowFallback(boolean allowFallback) { this.setCudnnAllowFallback(allowFallback); return (T) this; } /** * When using CuDNN or MKLDNN and an error is encountered, should fallback to the non-helper implementation be allowed? * If set to false, an exception in the helper will be propagated back to the user. If true, the built-in * (non-MKL/CuDNN) implementation for Subsampling3DLayer will be used * * @param allowFallback Whether fallback to non-CuDNN implementation should be used */ public T helperAllowFallback(boolean allowFallback) { this.cudnnAllowFallback = allowFallback; return (T) this; } } }