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Java example source code file (CudnnSubsamplingHelper.java)

This example Java source code file (CudnnSubsamplingHelper.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

cudacontext, cudnn_pooling_average_count_include_padding, cudnn_pooling_max, cudnn_propagate_nan, cudnncontext, deallocator, floatpointer, indarray, none, override, pair, pointer, poolingtype, runtimeexception

The CudnnSubsamplingHelper.java Java example source code

/*
 *
 *  * Copyright 2016 Skymind,Inc.
 *  *
 *  *    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 org.deeplearning4j.nn.layers.convolution.subsampling;

import org.bytedeco.javacpp.FloatPointer;
import org.bytedeco.javacpp.Pointer;
import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.BaseLayer;
import org.deeplearning4j.util.Dropout;
import org.nd4j.jita.allocator.Allocator;
import org.nd4j.jita.allocator.impl.AtomicAllocator;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.IsMax;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.convolution.Convolution;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.jcublas.context.CudaContext;
import org.nd4j.linalg.util.ArrayUtil;

import static org.bytedeco.javacpp.cuda.*;
import static org.bytedeco.javacpp.cudnn.*;

/**
 * cuDNN-based helper for the subsampling layer.
 *
 * @author saudet
 */
public class CudnnSubsamplingHelper implements SubsamplingHelper {

    static void checkCuda(int error) {
        if (error != cudaSuccess) {
            throw new RuntimeException("CUDA error = " + error);
        }
    }

    static void checkCudnn(int status) {
        if (status != CUDNN_STATUS_SUCCESS) {
            throw new RuntimeException("cuDNN status = " + status);
        }
    }

    static class CudnnContext extends cudnnContext {

        static class Deallocator extends CudnnContext implements Pointer.Deallocator {
            Deallocator(CudnnContext c) { super(c); }
            @Override public void deallocate() { destroyHandles(); }
        }

        cudnnTensorStruct srcTensorDesc = new cudnnTensorStruct(),
                          dstTensorDesc = new cudnnTensorStruct(),
                          deltaTensorDesc = new cudnnTensorStruct();
        cudnnPoolingStruct poolingDesc = new cudnnPoolingStruct();

        CudnnContext() {
            createHandles();
            deallocator(new Deallocator(this));
        }

        CudnnContext(CudnnContext c) {
            super(c);
            srcTensorDesc = new cudnnTensorStruct(c.srcTensorDesc);
            dstTensorDesc = new cudnnTensorStruct(c.dstTensorDesc);
            deltaTensorDesc = new cudnnTensorStruct(c.deltaTensorDesc);
            poolingDesc = new cudnnPoolingStruct(c.poolingDesc);
        }

        void createHandles() {
            checkCudnn(cudnnCreate(this));
            checkCudnn(cudnnCreateTensorDescriptor(srcTensorDesc));
            checkCudnn(cudnnCreateTensorDescriptor(dstTensorDesc));
            checkCudnn(cudnnCreateTensorDescriptor(deltaTensorDesc));
            checkCudnn(cudnnCreatePoolingDescriptor(poolingDesc));
        }

        void destroyHandles() {
            checkCudnn(cudnnDestroyPoolingDescriptor(poolingDesc));
            checkCudnn(cudnnDestroyTensorDescriptor(srcTensorDesc));
            checkCudnn(cudnnDestroyTensorDescriptor(dstTensorDesc));
            checkCudnn(cudnnDestroyTensorDescriptor(deltaTensorDesc));
            checkCudnn(cudnnDestroy(this));
        }
    }

    CudnnContext cudnnContext = new CudnnContext();
    int dataType = Nd4j.dataType() == DataBuffer.Type.DOUBLE ? CUDNN_DATA_DOUBLE : CUDNN_DATA_FLOAT;
    int tensorFormat = CUDNN_TENSOR_NCHW;
    FloatPointer alpha = new FloatPointer(1.0f);
    FloatPointer beta  = new FloatPointer(0.0f);

    @Override
    public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilon,
            int[] kernel, int[] strides, int[] pad, PoolingType poolingType) {
        int miniBatch = input.size(0);
        int depth = input.size(1);
        int inH = input.size(2);
        int inW = input.size(3);

        int outH = Convolution.outSize(inH, kernel[0], strides[0], pad[0],false);
        int outW = Convolution.outSize(inW, kernel[1], strides[1], pad[1], false);

        //subsampling doesn't have weights and thus gradients are not calculated for this layer
        //only scale and reshape epsilon
        Gradient retGradient = new DefaultGradient();

        //Epsilons in shape: [miniBatch, depth, outH, outW]
        //Epsilons out shape: [miniBatch, depth, inH, inW]

        int poolingMode;
        switch(poolingType) {
            case AVG:
                poolingMode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
                break;
            case MAX:
                poolingMode = CUDNN_POOLING_MAX;
                break;
            case NONE:
                return new Pair<>(retGradient, epsilon);
            default:
                return null;
        }

        INDArray z = activate(input, true, kernel, strides, pad, poolingType);

        if (!Shape.strideDescendingCAscendingF(epsilon)) {
            // apparently not supported by cuDNN
            epsilon = epsilon.dup();
        }

        int[] srcStride = input.stride();
        int[] deltaStride = epsilon.stride();
        checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.srcTensorDesc, dataType, miniBatch, depth, inH, inW,
                srcStride[0], srcStride[1], srcStride[2], srcStride[3]));
        checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.deltaTensorDesc, dataType, miniBatch, depth, outH, outW,
                deltaStride[0], deltaStride[1], deltaStride[2], deltaStride[3]));
        checkCudnn(cudnnSetPooling2dDescriptor(cudnnContext.poolingDesc, poolingMode, CUDNN_PROPAGATE_NAN,
                kernel[0], kernel[1], pad[0], pad[1], strides[0], strides[1]));

        INDArray outEpsilon = Nd4j.create(new int[]{miniBatch,depth,inH,inW},'c');
        int[] dstStride = outEpsilon.stride();
        checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.dstTensorDesc, dataType, miniBatch, depth, inH, inW,
                dstStride[0], dstStride[1], dstStride[2], dstStride[3]));

        Allocator allocator = AtomicAllocator.getInstance();
        CudaContext context = allocator.getFlowController().prepareAction(input, epsilon, z, outEpsilon);
        Pointer srcData = allocator.getPointer(input, context);
        Pointer epsData = allocator.getPointer(epsilon, context);
        Pointer zData = allocator.getPointer(z, context);
        Pointer dstData = allocator.getPointer(outEpsilon, context);

        checkCudnn(cudnnPoolingBackward(cudnnContext, cudnnContext.poolingDesc, alpha, cudnnContext.deltaTensorDesc, zData,
                cudnnContext.deltaTensorDesc, epsData, cudnnContext.srcTensorDesc, srcData, beta, cudnnContext.dstTensorDesc, dstData));

        allocator.registerAction(context, input, epsilon, z, outEpsilon);

        return new Pair<>(retGradient,outEpsilon);
    }


    @Override
    public INDArray activate(INDArray input, boolean training,
            int[] kernel, int[] strides, int[] pad, PoolingType poolingType) {
        int miniBatch = input.size(0);
        int inDepth = input.size(1);
        int inH = input.size(2);
        int inW = input.size(3);

        int outH = Convolution.outSize(inH, kernel[0], strides[0], pad[0],false);
        int outW = Convolution.outSize(inW, kernel[1], strides[1], pad[1], false);

        int poolingMode;
        switch(poolingType) {
            case AVG:
                poolingMode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
                break;
            case MAX:
                poolingMode = CUDNN_POOLING_MAX;
                break;
            case NONE:
                return input;
            default:
                return null;
        }

        int[] srcStride = input.stride();
        checkCudnn(cudnnSetPooling2dDescriptor(cudnnContext.poolingDesc, poolingMode, CUDNN_PROPAGATE_NAN,
                kernel[0], kernel[1], pad[0], pad[1], strides[0], strides[1]));
        checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.srcTensorDesc, dataType, miniBatch, inDepth, inH, inW,
                srcStride[0], srcStride[1], srcStride[2], srcStride[3]));

        INDArray reduced = Nd4j.createUninitialized(new int[]{miniBatch,inDepth,outH,outW},'c');
        int[] dstStride = reduced.stride();
        checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.dstTensorDesc, dataType, miniBatch, inDepth, outH, outW,
                dstStride[0], dstStride[1], dstStride[2], dstStride[3]));

        Allocator allocator = AtomicAllocator.getInstance();
        CudaContext context = allocator.getFlowController().prepareAction(input, reduced);
        Pointer srcData = allocator.getPointer(input, context);
        Pointer dstData = allocator.getPointer(reduced, context);

        checkCudnn(cudnnPoolingForward(cudnnContext, cudnnContext.poolingDesc,
                alpha, cudnnContext.srcTensorDesc, srcData, beta, cudnnContext.dstTensorDesc, dstData));

        allocator.registerAction(context, input, reduced);

        return reduced;
    }

}

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