org.deeplearning4j.nn.layers.mkldnn.MKLDNNSubsamplingHelper Maven / Gradle / Ivy

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package org.deeplearning4j.nn.layers.mkldnn;

import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.PoolingType;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.util.ConvolutionUtils;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.OpContext;
import org.nd4j.linalg.api.ops.impl.layers.convolution.AvgPooling2D;
import org.nd4j.linalg.api.ops.impl.layers.convolution.MaxPooling2D;
import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2D;
import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2DDerivative;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling2DConfig;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.primitives.Pair;
import org.nd4j.common.util.ArrayUtil;

import java.util.Collections;
import java.util.Map;

public class MKLDNNSubsamplingHelper implements SubsamplingHelper {

    protected OpContext context;

    public MKLDNNSubsamplingHelper(DataType dataType){

    }

    @Override
    public boolean checkSupported() {
        return BaseMKLDNNHelper.mklDnnEnabled();
    }

    @Override
    public Pair backpropGradient(INDArray input, INDArray epsilon, int[] kernel, int[] strides, int[] pad,
                                                     PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation,
                                                     CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
        if(poolingType == PoolingType.SUM || poolingType == PoolingType.PNORM)
            return null;

        INDArray gradAtInput = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, input.dataType(), input.shape());

        int hIdx = 2;
        int wIdx = 3;
        if(format == CNN2DFormat.NHWC){
            hIdx = 1;
            wIdx = 2;
        }

        if (convolutionMode == ConvolutionMode.Same) {
            pad = ConvolutionUtils.getSameModeTopLeftPadding(new int[]{(int)epsilon.size(hIdx), (int)epsilon.size(wIdx)}, new int[] {(int)input.size(hIdx), (int)input.size(wIdx)}, kernel, strides, dilation);
        }

        Pooling2DConfig conf = Pooling2DConfig.builder()
                .paddingMode(ConvolutionMode.mapToMode(convolutionMode))
                .kH(kernel[0]).kW(kernel[1])
                .sH(strides[0]).sW(strides[1])
                .dH(dilation[0]).dW(dilation[1])
                .pH(pad[0]).pW(pad[1])
                .isNHWC(format == CNN2DFormat.NHWC)
                .build();

        switch (poolingType){
            case MAX:
                conf.setType(Pooling2D.Pooling2DType.MAX);
                break;
            case AVG:
                conf.setType(Pooling2D.Pooling2DType.AVG);
                break;
        }

        Pooling2DDerivative d = new Pooling2DDerivative(input, epsilon, gradAtInput, conf);

        Nd4j.exec(d);
        return new Pair(new DefaultGradient(), gradAtInput);
    }

    @Override
    public INDArray activate(INDArray input, boolean training, int[] kernel, int[] strides, int[] pad, PoolingType poolingType,
                             ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {

        int hIdx = 2;
        int wIdx = 3;
        if(format == CNN2DFormat.NHWC){
            hIdx = 1;
            wIdx = 2;
        }

        int[] outSize;
        if (convolutionMode == ConvolutionMode.Same) {
            outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
            pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int)input.size(hIdx), (int)input.size(wIdx)}, kernel, strides, dilation);
        } else {
            outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation, format); //Also performs validation
        }

        long[] outShape = format == CNN2DFormat.NCHW ? new long[]{input.size(0), input.size(1), outSize[0], outSize[1]} :
                new long[]{input.size(0), outSize[0], outSize[1], input.size(3)};
        INDArray output = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), outShape);

        if(context == null){
            context = Nd4j.getExecutioner().buildContext();
            context.setIArguments(
                    kernel[0], kernel[1],
                    strides[0], strides[1],
                    pad[0], pad[1],
                    dilation[0], dilation[1],
                    ArrayUtil.fromBoolean(convolutionMode == ConvolutionMode.Same),
                    0,  //Extra - not used?
                    format == CNN2DFormat.NCHW ? 0 : 1); //0 = NCHW, 1=NHWC
        }

        DynamicCustomOp op;
        switch (poolingType){
            case MAX:
                op = new MaxPooling2D();
                break;
            case AVG:
                op = new AvgPooling2D();
                break;
            case SUM:
            case PNORM:
            default:
                return null;
        }

        context.purge();
        context.setInputArray(0, input);
        context.setOutputArray(0, output);

        Nd4j.exec(op, context);

        return output;
    }

    @Override
    public Map helperMemoryUse() {
        return Collections.emptyMap();
    }
}