Examples with BroadcastCopyOp org.nd4j.linalg.api.ops.impl.broadcast.BroadcastCopyOp used on opensource projects

Example 1 with BroadcastCopyOp

use of org.nd4j.linalg.api.ops.impl.broadcast.BroadcastCopyOp in project deeplearning4j by deeplearning4j.

the class MaskedReductionUtil method maskedPoolingEpsilonCnn.

public static INDArray maskedPoolingEpsilonCnn(PoolingType poolingType, INDArray input, INDArray mask, INDArray epsilon2d, boolean alongHeight, int pnorm) {
    // [minibatch, depth, h=1, w=X] or [minibatch, depth, h=X, w=1] data
    // with a mask array of shape [minibatch, X]
    //If masking along height: broadcast dimensions are [0,2]
    //If masking along width: broadcast dimensions are [0,3]
    int[] dimensions = (alongHeight ? CNN_DIM_MASK_H : CNN_DIM_MASK_W);
    switch(poolingType) {
        case MAX:
            //TODO This is ugly - replace it with something better... Need something like a Broadcast CAS op
            INDArray negInfMask = Transforms.not(mask);
            BooleanIndexing.replaceWhere(negInfMask, Double.NEGATIVE_INFINITY, Conditions.equals(1.0));
            INDArray withInf = Nd4j.createUninitialized(input.shape());
            Nd4j.getExecutioner().exec(new BroadcastAddOp(input, negInfMask, withInf, dimensions));
            //At this point: all the masked out steps have value -inf, hence can't be the output of the MAX op
            INDArray isMax = Nd4j.getExecutioner().execAndReturn(new IsMax(withInf, 2, 3));
            return Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(isMax, epsilon2d, isMax, 0, 1));
        case AVG:
        case SUM:
            //if out = sum(in,dims) then dL/dIn = dL/dOut -> duplicate to each step and mask
            //if out = avg(in,dims) then dL/dIn = 1/N * dL/dOut
            //With masking: N differs for different time series
            INDArray out = Nd4j.createUninitialized(input.shape(), 'f');
            //Broadcast copy op, then divide and mask to 0 as appropriate
            Nd4j.getExecutioner().exec(new BroadcastCopyOp(out, epsilon2d, out, 0, 1));
            Nd4j.getExecutioner().exec(new BroadcastMulOp(out, mask, out, dimensions));
            if (poolingType == PoolingType.SUM) {
                return out;
            }
            //Note that with CNNs, current design is restricted to [minibatch, depth, 1, W] ot [minibatch, depth, H, 1]
            //[minibatchSize,tsLength] -> [minibatchSize,1]
            INDArray nEachTimeSeries = mask.sum(1);
            Nd4j.getExecutioner().exec(new BroadcastDivOp(out, nEachTimeSeries, out, 0));
            return out;
        case PNORM:
            //Similar to average and sum pooling: there's no N term here, so we can just set the masked values to 0
            INDArray masked2 = Nd4j.createUninitialized(input.shape());
            Nd4j.getExecutioner().exec(new BroadcastMulOp(input, mask, masked2, dimensions));
            INDArray abs = Transforms.abs(masked2, true);
            Transforms.pow(abs, pnorm, false);
            INDArray pNorm = Transforms.pow(abs.sum(2, 3), 1.0 / pnorm);
            INDArray numerator;
            if (pnorm == 2) {
                numerator = input.dup();
            } else {
                INDArray absp2 = Transforms.pow(Transforms.abs(input, true), pnorm - 2, false);
                numerator = input.mul(absp2);
            }
            INDArray denom = Transforms.pow(pNorm, pnorm - 1, false);
            denom.rdivi(epsilon2d);
            Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(numerator, denom, numerator, 0, 1));
            //Apply mask
            Nd4j.getExecutioner().exec(new BroadcastMulOp(numerator, mask, numerator, dimensions));
            return numerator;
        case NONE:
            throw new UnsupportedOperationException("NONE pooling type not supported");
        default:
            throw new UnsupportedOperationException("Unknown or not supported pooling type: " + poolingType);
    }
}

Example 2 with BroadcastCopyOp

use of org.nd4j.linalg.api.ops.impl.broadcast.BroadcastCopyOp in project deeplearning4j by deeplearning4j.

the class GlobalPoolingLayer method epsilonHelperFullArray.

private INDArray epsilonHelperFullArray(INDArray inputArray, INDArray epsilon, int[] poolDim) {
    //Broadcast: occurs on the remaining dimensions, after the pool dimensions have been removed.
    //TODO find a more efficient way to do this
    int[] broadcastDims = new int[inputArray.rank() - poolDim.length];
    int count = 0;
    for (int i = 0; i < inputArray.rank(); i++) {
        if (ArrayUtils.contains(poolDim, i))
            continue;
        broadcastDims[count++] = i;
    }
    switch(poolingType) {
        case MAX:
            INDArray isMax = Nd4j.getExecutioner().execAndReturn(new IsMax(inputArray.dup(), poolDim));
            return Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(isMax, epsilon, isMax, broadcastDims));
        case AVG:
            //if out = avg(in,dims) then dL/dIn = 1/N * dL/dOut
            int n = 1;
            for (int d : poolDim) {
                n *= inputArray.size(d);
            }
            INDArray ret = Nd4j.create(inputArray.shape());
            Nd4j.getExecutioner().exec(new BroadcastCopyOp(ret, epsilon, ret, broadcastDims));
            ret.divi(n);
            return ret;
        case SUM:
            INDArray retSum = Nd4j.create(inputArray.shape());
            Nd4j.getExecutioner().exec(new BroadcastCopyOp(retSum, epsilon, retSum, broadcastDims));
            return retSum;
        case PNORM:
            int pnorm = layerConf().getPnorm();
            //First: do forward pass to get pNorm array
            INDArray abs = Transforms.abs(inputArray, true);
            Transforms.pow(abs, pnorm, false);
            INDArray pNorm = Transforms.pow(abs.sum(poolDim), 1.0 / pnorm);
            //dL/dIn = dL/dOut * dOut/dIn
            //dOut/dIn = in .* |in|^(p-2) /  ||in||_p^(p-1), where ||in||_p is the output p-norm
            INDArray numerator;
            if (pnorm == 2) {
                numerator = inputArray.dup();
            } else {
                INDArray absp2 = Transforms.pow(Transforms.abs(inputArray, true), pnorm - 2, false);
                numerator = inputArray.mul(absp2);
            }
            INDArray denom = Transforms.pow(pNorm, pnorm - 1, false);
            denom.rdivi(epsilon);
            Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(numerator, denom, numerator, broadcastDims));
            return numerator;
        default:
            throw new RuntimeException("Unknown or not supported pooling type: " + poolingType);
    }
}

Example 3 with BroadcastCopyOp

use of org.nd4j.linalg.api.ops.impl.broadcast.BroadcastCopyOp in project deeplearning4j by deeplearning4j.

the class MaskedReductionUtil method maskedPoolingEpsilonTimeSeries.

public static INDArray maskedPoolingEpsilonTimeSeries(PoolingType poolingType, INDArray input, INDArray mask, INDArray epsilon2d, int pnorm) {
    if (input.rank() != 3) {
        throw new IllegalArgumentException("Expect rank 3 input activation array: got " + input.rank());
    }
    if (mask.rank() != 2) {
        throw new IllegalArgumentException("Expect rank 2 array for mask: got " + mask.rank());
    }
    if (epsilon2d.rank() != 2) {
        throw new IllegalArgumentException("Expected rank 2 array for errors: got " + epsilon2d.rank());
    }
    switch(poolingType) {
        case MAX:
            //TODO This is ugly - replace it with something better... Need something like a Broadcast CAS op
            INDArray negInfMask = Transforms.not(mask);
            BooleanIndexing.replaceWhere(negInfMask, Double.NEGATIVE_INFINITY, Conditions.equals(1.0));
            INDArray withInf = Nd4j.createUninitialized(input.shape());
            Nd4j.getExecutioner().exec(new BroadcastAddOp(input, negInfMask, withInf, 0, 2));
            //At this point: all the masked out steps have value -inf, hence can't be the output of the MAX op
            INDArray isMax = Nd4j.getExecutioner().execAndReturn(new IsMax(withInf, 2));
            return Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(isMax, epsilon2d, isMax, 0, 1));
        case AVG:
        case SUM:
            //if out = sum(in,dims) then dL/dIn = dL/dOut -> duplicate to each step and mask
            //if out = avg(in,dims) then dL/dIn = 1/N * dL/dOut
            //With masking: N differs for different time series
            INDArray out = Nd4j.createUninitialized(input.shape(), 'f');
            //Broadcast copy op, then divide and mask to 0 as appropriate
            Nd4j.getExecutioner().exec(new BroadcastCopyOp(out, epsilon2d, out, 0, 1));
            Nd4j.getExecutioner().exec(new BroadcastMulOp(out, mask, out, 0, 2));
            if (poolingType == PoolingType.SUM) {
                return out;
            }
            //[minibatchSize,tsLength] -> [minibatchSize,1]
            INDArray nEachTimeSeries = mask.sum(1);
            Nd4j.getExecutioner().exec(new BroadcastDivOp(out, nEachTimeSeries, out, 0));
            return out;
        case PNORM:
            //Similar to average and sum pooling: there's no N term here, so we can just set the masked values to 0
            INDArray masked2 = Nd4j.createUninitialized(input.shape());
            Nd4j.getExecutioner().exec(new BroadcastMulOp(input, mask, masked2, 0, 2));
            INDArray abs = Transforms.abs(masked2, true);
            Transforms.pow(abs, pnorm, false);
            INDArray pNorm = Transforms.pow(abs.sum(2), 1.0 / pnorm);
            INDArray numerator;
            if (pnorm == 2) {
                numerator = input.dup();
            } else {
                INDArray absp2 = Transforms.pow(Transforms.abs(input, true), pnorm - 2, false);
                numerator = input.mul(absp2);
            }
            INDArray denom = Transforms.pow(pNorm, pnorm - 1, false);
            denom.rdivi(epsilon2d);
            Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(numerator, denom, numerator, 0, 1));
            //Apply mask
            Nd4j.getExecutioner().exec(new BroadcastMulOp(numerator, mask, numerator, 0, 2));
            return numerator;
        case NONE:
            throw new UnsupportedOperationException("NONE pooling type not supported");
        default:
            throw new UnsupportedOperationException("Unknown or not supported pooling type: " + poolingType);
    }
}