Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.DataFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.layers.convolution.Convolution3DLayer;
import org.deeplearning4j.nn.params.Convolution3DParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.deeplearning4j.util.Convolution3DUtils;
import org.deeplearning4j.util.ConvolutionUtils;
import org.deeplearning4j.util.ValidationUtils;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.shade.jackson.annotation.JsonProperty;
import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class Convolution3D extends ConvolutionLayer {
/**
* An optional dataFormat: "NDHWC" or "NCDHW". Defaults to "NCDHW". The data format of the input and output
* data. For "NCDHW" (also known as 'channels first' format), the data storage order is: [batchSize,
* inputChannels, inputDepth, inputHeight, inputWidth]. For "NDHWC" ('channels last' format), the data is stored
* in the order of: [batchSize, inputDepth, inputHeight, inputWidth, inputChannels].
*/
public enum DataFormat implements org.deeplearning4j.nn.conf.DataFormat {
NCDHW, NDHWC
}
@JsonProperty("mode")
protected ConvolutionMode mode = ConvolutionMode.Same; // in libnd4j: 0 - same mode, 1 - valid mode
@JsonProperty("dataFormat")
protected DataFormat dataFormat = DataFormat.NCDHW; // in libnd4j: 1 - NCDHW, 0 - NDHWC
/**
* 3-dimensional convolutional layer configuration nIn in the input layer is the number of channels nOut is the
* number of filters to be used in the net or in other words the depth The builder specifies the filter/kernel size,
* the stride and padding The pooling layer takes the kernel size
*/
public Convolution3D(Builder builder) {
super(builder);
this.dataFormat = builder.dataFormat;
this.convolutionMode = builder.convolutionMode;
}
public boolean hasBias() {
return hasBias;
}
@Override
public Convolution3D clone() {
Convolution3D clone = (Convolution3D) 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 Layer instantiate(NeuralNetConfiguration conf, Collection iterationListeners,
int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
LayerValidation.assertNInNOutSet("Convolution3D", getLayerName(), layerIndex, getNIn(), getNOut());
Convolution3DLayer ret = new Convolution3DLayer(conf, networkDataType);
ret.setListeners(iterationListeners);
ret.setIndex(layerIndex);
ret.setParamsViewArray(layerParamsView);
Map paramTable = initializer().init(conf, layerParamsView, initializeParams);
ret.setParamTable(paramTable);
ret.setConf(conf);
return ret;
}
@Override
public ParamInitializer initializer() {
return Convolution3DParamInitializer.getInstance();
}
@Override
public InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null || inputType.getType() != InputType.Type.CNN3D) {
throw new IllegalStateException("Invalid input for Convolution3D layer (layer name=\"" + getLayerName()
+ "\"): Expected CNN3D input, got " + inputType);
}
return InputTypeUtil.getOutputTypeCnn3DLayers(inputType, dataFormat, kernelSize, stride, padding, dilation, convolutionMode,
nOut, layerIndex, getLayerName(), Convolution3DLayer.class);
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
if (inputType == null) {
throw new IllegalStateException("Invalid input for Convolution3D layer (layer name=\"" + getLayerName()
+ "\"): input is null");
}
return InputTypeUtil.getPreProcessorForInputTypeCnn3DLayers(inputType, getLayerName());
}
@Override
public void setNIn(InputType inputType, boolean override) {
if (inputType == null || inputType.getType() != InputType.Type.CNN3D) {
throw new IllegalStateException("Invalid input for Convolution 3D layer (layer name=\"" + getLayerName()
+ "\"): Expected CNN3D input, got " + inputType);
}
if (nIn <= 0 || override) {
InputType.InputTypeConvolutional3D c = (InputType.InputTypeConvolutional3D) inputType;
this.nIn = c.getChannels();
}
}
@AllArgsConstructor
@Getter
@Setter
public static class Builder extends BaseConvBuilder {
/**
* The data format for input and output activations. NCDHW: activations (in/out) should have shape
* [minibatch, channels, depth, height, width] NDHWC: activations (in/out) should have shape [minibatch,
* depth, height, width, channels]
*/
private DataFormat dataFormat = DataFormat.NCDHW;
public Builder() {
super(new int[] {2, 2, 2}, new int[] {1, 1, 1}, new int[] {0, 0, 0}, new int[] {1, 1, 1}, 3);
}
@Override
protected boolean allowCausal() {
//Causal convolution - allowed for 1D only
return false;
}
public Builder(int[] kernelSize, int[] stride, int[] padding, int[] dilation) {
super(kernelSize, stride, padding, dilation, 3);
}
public Builder(int[] kernelSize, int[] stride, int[] padding) {
this(kernelSize, stride, padding, new int[] {1, 1, 1});
}
public Builder(int[] kernelSize, int[] stride) {
this(kernelSize, stride, new int[] {0, 0, 0});
}
public Builder(int... kernelSize) {
this(kernelSize, new int[] {1, 1, 1});
}
/**
* Set kernel size for 3D convolutions in (depth, height, width) order
*
* @param kernelSize kernel size
* @return 3D convolution layer builder
*/
public Builder kernelSize(int... kernelSize) {
this.setKernelSize(kernelSize);
return this;
}
/**
* Set stride size for 3D convolutions in (depth, height, width) order
*
* @param stride kernel size
* @return 3D convolution layer builder
*/
public Builder stride(int... stride) {
this.setStride(stride);
return this;
}
/**
* Set padding size for 3D convolutions in (depth, height, width) order
*
* @param padding kernel size
* @return 3D convolution layer builder
*/
public Builder padding(int... padding) {
this.setPadding(padding);
return this;
}
/**
* Set dilation size for 3D convolutions in (depth, height, width) order
*
* @param dilation kernel size
* @return 3D convolution layer builder
*/
public Builder dilation(int... dilation) {
this.setDilation(dilation);
return this;
}
public Builder convolutionMode(ConvolutionMode mode) {
this.setConvolutionMode(mode);
return this;
}
/**
* The data format for input and output activations. NCDHW: activations (in/out) should have shape
* [minibatch, channels, depth, height, width] NDHWC: activations (in/out) should have shape [minibatch,
* depth, height, width, channels]
*
* @param dataFormat Data format to use for activations
*/
public Builder dataFormat(DataFormat dataFormat) {
this.setDataFormat(dataFormat);
return this;
}
/**
* Set kernel size for 3D convolutions in (depth, height, width) order
*
* @param kernelSize kernel size
*/
@Override
public void setKernelSize(int... kernelSize) {
this.kernelSize = ValidationUtils.validate3NonNegative(kernelSize, "kernelSize");
}
/**
* Set stride size for 3D convolutions in (depth, height, width) order
*
* @param stride kernel size
*/
@Override
public void setStride(int... stride) {
this.stride = ValidationUtils.validate3NonNegative(stride, "stride");
}
/**
* Set padding size for 3D convolutions in (depth, height, width) order
*
* @param padding kernel size
*/
@Override
public void setPadding(int... padding) {
this.padding = ValidationUtils.validate3NonNegative(padding, "padding");
}
/**
* Set dilation size for 3D convolutions in (depth, height, width) order
*
* @param dilation kernel size
*/
@Override
public void setDilation(int... dilation) {
this.dilation = ValidationUtils.validate3NonNegative(dilation, "dilation");
}
@Override
@SuppressWarnings("unchecked")
public Convolution3D build() {
ConvolutionUtils.validateConvolutionModePadding(convolutionMode, padding);
Convolution3DUtils.validateCnn3DKernelStridePadding(kernelSize, stride, padding);
return new Convolution3D(this);
}
}
}
