Java Code Examples for org.nd4j.linalg.dataset.api.DataSet#setFeatures()

The following examples show how to use org.nd4j.linalg.dataset.api.DataSet#setFeatures() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: ImageFlatteningDataSetPreProcessor.java    From nd4j with Apache License 2.0 6 votes vote down vote up 
@Override
public void preProcess(DataSet toPreProcess) {
    INDArray input = toPreProcess.getFeatures();
    if (input.rank() == 2)
        return; //No op: should usually never happen in a properly configured data pipeline

    //Assume input is standard rank 4 activations - i.e., CNN image data
    //First: we require input to be in c order. But c order (as declared in array order) isn't enough; also need strides to be correct
    if (input.ordering() != 'c' || !Shape.strideDescendingCAscendingF(input))
        input = input.dup('c');

    val inShape = input.shape(); //[miniBatch,depthOut,outH,outW]
    val outShape = new long[] {inShape[0], inShape[1] * inShape[2] * inShape[3]};

    INDArray reshaped = input.reshape('c', outShape);
    toPreProcess.setFeatures(reshaped);
}
Example 2
Source File: ImageFlatteningDataSetPreProcessor.java    From deeplearning4j with Apache License 2.0 6 votes vote down vote up 
@Override
public void preProcess(DataSet toPreProcess) {
    INDArray input = toPreProcess.getFeatures();
    if (input.rank() == 2)
        return; //No op: should usually never happen in a properly configured data pipeline

    //Assume input is standard rank 4 activations - i.e., CNN image data
    //First: we require input to be in c order. But c order (as declared in array order) isn't enough; also need strides to be correct
    if (input.ordering() != 'c' || !Shape.strideDescendingCAscendingF(input))
        input = input.dup('c');

    val inShape = input.shape(); //[miniBatch,depthOut,outH,outW]
    val outShape = new long[] {inShape[0], inShape[1] * inShape[2] * inShape[3]};

    INDArray reshaped = input.reshape('c', outShape);
    toPreProcess.setFeatures(reshaped);
}
Example 3
Source File: CropAndResizeDataSetPreProcessor.java    From deeplearning4j with Apache License 2.0 6 votes vote down vote up 
/**
 * NOTE: The data format must be NHWC
 */
@Override
public void preProcess(DataSet dataSet) {
    Preconditions.checkNotNull(dataSet, "Encountered null dataSet");

    if(dataSet.isEmpty()) {
        return;
    }

    INDArray input = dataSet.getFeatures();
    INDArray output = Nd4j.create(LongShapeDescriptor.fromShape(resizedShape, input.dataType()), false);

    CustomOp op = DynamicCustomOp.builder("crop_and_resize")
            .addInputs(input, boxes, indices, resize)
            .addIntegerArguments(method)
            .addOutputs(output)
            .build();
    Nd4j.getExecutioner().exec(op);

    dataSet.setFeatures(output);
}
Example 4
Source File: RGBtoGrayscaleDataSetPreProcessor.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Override
public void preProcess(DataSet dataSet) {
    Preconditions.checkNotNull(dataSet, "Encountered null dataSet");

    if(dataSet.isEmpty()) {
        return;
    }

    INDArray originalFeatures = dataSet.getFeatures();
    long[] originalShape = originalFeatures.shape();

    // result shape is NHW
    INDArray result = Nd4j.create(originalShape[0], originalShape[2], originalShape[3]);

    for(long n = 0, numExamples = originalShape[0]; n < numExamples; ++n) {
        // Extract channels
        INDArray itemFeatures = originalFeatures.slice(n, 0); // shape is CHW
        INDArray R = itemFeatures.slice(0, 0);  // shape is HW
        INDArray G = itemFeatures.slice(1, 0);
        INDArray B = itemFeatures.slice(2, 0);

        // Convert
        R.muli(RED_RATIO);
        G.muli(GREEN_RATIO);
        B.muli(BLUE_RATIO);
        R.addi(G).addi(B);

        result.putSlice((int)n, R);
    }

    dataSet.setFeatures(result);
}
Example 5
Source File: PermuteDataSetPreProcessor.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Override
public void preProcess(DataSet dataSet) {
    Preconditions.checkNotNull(dataSet, "Encountered null dataSet");

    if(dataSet.isEmpty()) {
        return;
    }

    INDArray input = dataSet.getFeatures();
    INDArray output;
    switch (permutationType) {
        case NCHWtoNHWC:
            output = input.permute(0, 2, 3, 1);
            break;

        case NHWCtoNCHW:
            output = input.permute(0, 3, 1, 2);
            break;

        case Custom:
            output = input.permute(rearrange);
            break;

        default:
            output = input;
            break;
    }

    dataSet.setFeatures(output);
}