org.tensorflow.framework.NodeDef#containsAttr

Source File: ScatterNdUpdate.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);

    if (nodeDef.containsAttr("use_locking")) {
        if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
            bArguments.add(true);
        } else {
            bArguments.add(false);
        }
    } else
        bArguments.add(false);
}

Source File: ScatterNdAdd.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);

    if (nodeDef.containsAttr("use_locking")) {
        if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
            bArguments.add(true);
        } else {
            bArguments.add(false);
        }
    } else
        bArguments.add(false);
}

Source File: ScatterAdd.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);

    if (nodeDef.containsAttr("use_locking")) {
        if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
            bArguments.add(true);
        } else {
            bArguments.add(false);
        }
    } else
        bArguments.add(false);
}

Source File: ScatterNdSub.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);

    if (nodeDef.containsAttr("use_locking")) {
        if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
            bArguments.add(true);
        } else {
            bArguments.add(false);
        }
    } else
        bArguments.add(false);
}

Source File: ScatterNd.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);

    if (nodeDef.containsAttr("use_locking")) {
        if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
            bArguments.add(true);
        } else {
            bArguments.add(false);
        }
    } else
        bArguments.add(false);
}

Source File: ScatterDiv.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);

    if (nodeDef.containsAttr("use_locking")) {
        if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
            bArguments.add(true);
        } else {
            bArguments.add(false);
        }
    } else
        bArguments.add(false);
}

Source File: ScatterUpdate.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    if (nodeDef.containsAttr("use_locking")) {
        if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
            bArguments.add(true);
        } else {
            bArguments.add(false);
        }
    } else
        bArguments.add(false);
}

Source File: ScatterMul.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);

    if (nodeDef.containsAttr("use_locking")) {
        if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
            bArguments.add(true);
        } else {
            bArguments.add(false);
        }
    } else
        bArguments.add(false);
}

Source File: ScatterMax.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);

    if (nodeDef.containsAttr("use_locking")) {
        if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
            bArguments.add(true);
        } else {
            bArguments.add(false);
        }
    } else
        bArguments.add(false);
}

Source File: ScatterSub.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);

    if (nodeDef.containsAttr("use_locking")) {
        if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
            bArguments.add(true);
        } else {
            bArguments.add(false);
        }
    } else
        bArguments.add(false);
}

Source File: ScatterMin.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);

    if (nodeDef.containsAttr("use_locking")) {
        if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
            bArguments.add(true);
        } else {
            bArguments.add(false);
        }
    } else
        bArguments.add(false);
}

Source File: AvgPooling2D.java From deeplearning4j with Apache License 2.0

4 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    val aStrides = nodeDef.getAttrOrThrow("strides");
    val tfStrides = aStrides.getList().getIList();

    val aKernels = nodeDef.getAttrOrThrow("ksize");
    val tfKernels = aKernels.getList().getIList();

    int sH = 0;
    int sW = 0;

    int pH = 0;
    int pW = 0;

    int kH = 0;
    int kW = 0;

    val aPadding = nodeDef.getAttrOrThrow("padding");
    val padding = aPadding.getList().getIList();

    val paddingMode = aPadding.getS().toStringUtf8().replaceAll("\"", "");

    boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");

    String data_format = "nhwc";
    if (nodeDef.containsAttr("data_format")) {
        val attr = nodeDef.getAttrOrThrow("data_format");

        data_format = attr.getS().toStringUtf8().toLowerCase();
    }

    if (data_format.equalsIgnoreCase("nhwc")) {
        sH = tfStrides.get(1).intValue();
        sW = tfStrides.get(2).intValue();

        kH = tfKernels.get(1).intValue();
        kW = tfKernels.get(2).intValue();

        pH = padding.size() > 0 ? padding.get(1).intValue() : 0;
        pW = padding.size() > 0 ? padding.get(2).intValue() : 0;
    } else {
        sH = tfStrides.get(2).intValue();
        sW = tfStrides.get(3).intValue();

        kH = tfKernels.get(2).intValue();
        kW = tfKernels.get(3).intValue();

        pH = padding.size() > 0 ? padding.get(2).intValue() : 0;
        pW = padding.size() > 0 ? padding.get(3).intValue() : 0;
    }

    Pooling2DConfig pooling2DConfig = Pooling2DConfig.builder()
            .sH(sH)
            .sW(sW)
            .type(Pooling2D.Pooling2DType.AVG)
            .isSameMode(isSameMode)
            .kH(kH)
            .kW(kW)
            .pH(pH)
            .pW(pW)
            .isNHWC(data_format.equalsIgnoreCase("nhwc"))
            .extra(0.0) // averaging only for non-padded values
            .build();
    this.config = pooling2DConfig;
    addArgs();
    //log.debug("Pooling: k: [{},{}]; s: [{}, {}], padding: {}", kH, kW, sH, sW, aPadding);
}

Source File: MaxPooling2D.java From nd4j with Apache License 2.0

4 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    val aStrides = nodeDef.getAttrOrThrow("strides");
    val tfStrides = aStrides.getList().getIList();

    val aKernels = nodeDef.getAttrOrThrow("ksize");
    val tfKernels = aKernels.getList().getIList();

    int sY = 0;
    int sX = 0;

    int ph = 0;
    int pw = 0;

    int kY = 0;
    int kX = 0;

    val aPadding = nodeDef.getAttrOrThrow("padding");
    val padding = aPadding.getList().getIList();

    val paddingMode = aPadding.getS().toStringUtf8().replaceAll("\"", "");

    boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");

    String data_format = "nhwc";
    if (nodeDef.containsAttr("data_format")) {
        val attr = nodeDef.getAttrOrThrow("data_format");

        data_format = attr.getS().toStringUtf8().toLowerCase();
    }

    if (data_format.equalsIgnoreCase("nhwc")) {
        sY = tfStrides.get(1).intValue();
        sX = tfStrides.get(2).intValue();

        kY = tfKernels.get(1).intValue();
        kX = tfKernels.get(2).intValue();

        ph = padding.size() > 0 ? padding.get(1).intValue() : 0;
        pw = padding.size() > 0 ? padding.get(2).intValue() : 0;
    } else {
        sY = tfStrides.get(2).intValue();
        sX = tfStrides.get(3).intValue();

        kY = tfKernels.get(2).intValue();
        kX = tfKernels.get(3).intValue();

        ph = padding.size() > 0 ? padding.get(2).intValue() : 0;
        pw = padding.size() > 0 ? padding.get(3).intValue() : 0;
    }

    Pooling2DConfig pooling2DConfig = Pooling2DConfig.builder()
            .sy(sY)
            .sx(sX)
            .type(Pooling2D.Pooling2DType.MAX)
            .isSameMode(isSameMode)
            .kh(kY)
            .kw(kX)
            .ph(ph)
            .pw(pw)
            .virtualWidth(1)
            .virtualHeight(1)
            .isNHWC(data_format.equalsIgnoreCase("nhwc"))
            .extra(1.0) // averaging only for non-padded values
            .build();
    this.config = pooling2DConfig;
    addArgs();
    log.debug("Pooling: k: [{},{}]; s: [{}, {}], padding: {}", kY, kX, sY, sX, aPadding);


}

Source File: Pooling3D.java From deeplearning4j with Apache License 2.0

4 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    val aStrides = nodeDef.getAttrOrThrow("strides");
    List<Long> tfStrides = aStrides.getList().getIList();
    val aKernels = nodeDef.getAttrOrThrow("ksize");
    List<Long> tfKernels = aKernels.getList().getIList();
    val aPadding = nodeDef.getAttrOrThrow("padding");
    List<Long> tfPadding = aPadding.getList().getIList();

    String paddingMode = aPadding.getS().toStringUtf8().replaceAll("\"", "");

    boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");

    String data_format = "ndhwc";
    if (nodeDef.containsAttr("data_format")) {
        val attr = nodeDef.getAttrOrThrow("data_format");

        data_format = attr.getS().toStringUtf8().toLowerCase();
    }

    //Order: depth, height, width
    //TF doesn't have dilation, it seems?
    int[] strides = new int[3];
    int[] padding = new int[3];
    int[] kernel = new int[3];
    for( int i=0; i<3; i++ ) {
        //TF values here have 5 values: minibatch and Channels at positions 0 and 4, which are almost always 1
        strides[i] = tfStrides.get(i+1).intValue();
        if(tfPadding != null && tfPadding.size() > 0) {
            //Empty for SAME mode
            padding[i] = tfPadding.get(i + 1).intValue();
        }
        kernel[i] = tfKernels.get(i+1).intValue();
    }

    Pooling3DType type;
    String name = nodeDef.getOp().toLowerCase();
    if(name.startsWith("max")){
        type = Pooling3DType.MAX;
    } else if(name.startsWith("av")){
        type = Pooling3DType.AVG;
    } else {
        throw new IllegalStateException("Unknown or not supported pooling type: " + name);
    }

    Pooling3DConfig conf = Pooling3DConfig.builder()
            .sD(strides[0]).sH(strides[1]).sW(strides[2])
            .pD(padding[0]).pH(padding[1]).pW(padding[2])
            .kD(kernel[0]).kH(kernel[1]).kW(kernel[2])
            .type(type)
            .isSameMode(isSameMode)
            .isNCDHW(data_format.equalsIgnoreCase("ncdhw"))
            .build();
    this.config = conf;
    addArgs();
}

Source File: MaxPoolWithArgmax.java From deeplearning4j with Apache License 2.0

4 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    val aStrides = nodeDef.getAttrOrThrow("strides");
    val tfStrides = aStrides.getList().getIList();

    val aKernels = nodeDef.getAttrOrThrow("ksize");
    val tfKernels = aKernels.getList().getIList();

    int sH = 0;
    int sW = 0;

    int pH = 0;
    int pW = 0;

    int kH = 0;
    int kW = 0;

    val aPadding = nodeDef.getAttrOrThrow("padding");
    val padding = aPadding.getList().getIList();

    val paddingMode = aPadding.getS().toStringUtf8().replaceAll("\"", "");

    boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");

    String data_format = "nhwc";
    if (nodeDef.containsAttr("data_format")) {
        val attr = nodeDef.getAttrOrThrow("data_format");

        data_format = attr.getS().toStringUtf8().toLowerCase();
    }

    if (data_format.equalsIgnoreCase("nhwc")) {
        sH = tfStrides.get(1).intValue();
        sW = tfStrides.get(2).intValue();

        kH = tfKernels.get(1).intValue();
        kW = tfKernels.get(2).intValue();

        pH = padding.size() > 0 ? padding.get(1).intValue() : 0;
        pW = padding.size() > 0 ? padding.get(2).intValue() : 0;
    } else {
        sH = tfStrides.get(2).intValue();
        sW = tfStrides.get(3).intValue();

        kH = tfKernels.get(2).intValue();
        kW = tfKernels.get(3).intValue();

        pH = padding.size() > 0 ? padding.get(2).intValue() : 0;
        pW = padding.size() > 0 ? padding.get(3).intValue() : 0;
    }

    Pooling2DConfig pooling2DConfig = Pooling2DConfig.builder()
            .sH(sH)
            .sW(sW)
            .type(Pooling2D.Pooling2DType.MAX)
            .isSameMode(isSameMode)
            .kH(kH)
            .kW(kW)
            .pH(pH)
            .pW(pW)
            .isNHWC(data_format.equalsIgnoreCase("nhwc"))
            .extra(1.0) // averaging only for non-padded values
            .build();
    this.config = pooling2DConfig;
    addArgs();
    if(attributesForNode.containsKey("argmax")) {
        outputType = TFGraphMapper.convertType(attributesForNode.get("argmax").getType());
    } else {
        outputType = DataType.LONG;
    }
}

Source File: DeConv2D.java From deeplearning4j with Apache License 2.0

4 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    val aStrides = nodeDef.getAttrOrThrow("strides");
    val tfStrides = aStrides.getList().getIList();
    long sH = 1;
    long sW = 1;
    long kH = 1;
    long kW = 1;

    val aPadding = nodeDef.getAttrOrDefault("padding", null);

    val paddingMode = aPadding.getS().toStringUtf8();

    val args = args();
    INDArray arr = sameDiff.getVariable(args[1].name()).getArr();
    if (arr == null) {
        arr = TFGraphMapper.getNDArrayFromTensor(nodeDef);
        // TODO: arguable. it might be easier to permute weights once
        //arr = (arr.permute(3, 2, 0, 1).dup('c'));
        val varForOp = initWith.getVariable(args[1].name());
        if (arr != null)
            initWith.associateArrayWithVariable(arr, varForOp);


    }

    String dataFormat = "nhwc";
    if (nodeDef.containsAttr("data_format")) {
        val attr = nodeDef.getAttrOrThrow("data_format");
        dataFormat = attr.getS().toStringUtf8().toLowerCase();
    }

    if (dataFormat.equalsIgnoreCase(DeConv2DConfig.NCHW)) {
        sH = tfStrides.get(2).longValue();
        sW = tfStrides.get(3).longValue();

        kH = arr.size(2);
        kW = arr.size(3);
    } else {
        sH = tfStrides.get(1).longValue();
        sW = tfStrides.get(2).longValue();

        kH = arr.size(0);
        kW = arr.size(1);
    }


    boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");
    DeConv2DConfig conv2DConfig = DeConv2DConfig.builder()
            .kH(kH)
            .kW(kW)
            .sH(sW)
            .sW(sH)
            .isSameMode(isSameMode)
            .dataFormat(dataFormat.equalsIgnoreCase(DeConv2DConfig.NHWC) ? DeConv2DConfig.NHWC : DeConv2DConfig.NCHW)
            .build();
    this.config = conv2DConfig;

    addArgs();


}

Source File: MaxPooling2D.java From deeplearning4j with Apache License 2.0

4 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    val aStrides = nodeDef.getAttrOrThrow("strides");
    val tfStrides = aStrides.getList().getIList();

    val aKernels = nodeDef.getAttrOrThrow("ksize");
    val tfKernels = aKernels.getList().getIList();

    int sH = 0;
    int sW = 0;

    int pH = 0;
    int pW = 0;

    int kH = 0;
    int kW = 0;

    val aPadding = nodeDef.getAttrOrThrow("padding");
    val padding = aPadding.getList().getIList();

    val paddingMode = aPadding.getS().toStringUtf8().replaceAll("\"", "");

    boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");

    String data_format = "nhwc";
    if (nodeDef.containsAttr("data_format")) {
        val attr = nodeDef.getAttrOrThrow("data_format");

        data_format = attr.getS().toStringUtf8().toLowerCase();
    }

    if (data_format.equalsIgnoreCase("nhwc")) {
        sH = tfStrides.get(1).intValue();
        sW = tfStrides.get(2).intValue();

        kH = tfKernels.get(1).intValue();
        kW = tfKernels.get(2).intValue();

        pH = padding.size() > 0 ? padding.get(1).intValue() : 0;
        pW = padding.size() > 0 ? padding.get(2).intValue() : 0;
    } else {
        sH = tfStrides.get(2).intValue();
        sW = tfStrides.get(3).intValue();

        kH = tfKernels.get(2).intValue();
        kW = tfKernels.get(3).intValue();

        pH = padding.size() > 0 ? padding.get(2).intValue() : 0;
        pW = padding.size() > 0 ? padding.get(3).intValue() : 0;
    }

    Pooling2DConfig pooling2DConfig = Pooling2DConfig.builder()
            .sH(sH)
            .sW(sW)
            .type(Pooling2D.Pooling2DType.MAX)
            .isSameMode(isSameMode)
            .kH(kH)
            .kW(kW)
            .pH(pH)
            .pW(pW)
            .isNHWC(data_format.equalsIgnoreCase("nhwc"))
            .extra(1.0) // averaging only for non-padded values
            .build();
    this.config = pooling2DConfig;
    addArgs();
}

Source File: DeConv3DTF.java From deeplearning4j with Apache License 2.0

4 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {

    val aStrides = nodeDef.getAttrOrThrow("strides");
    val aDilations = nodeDef.getAttrOrDefault("dilations", null);
    val tfStrides = aStrides.getList().getIList();
    val tfDilation = aDilations == null ? null : aDilations.getList().getIList();
    int sD, sH, sW, dD, dH, dW;

    val aPadding = nodeDef.getAttrOrDefault("padding", null);
    String paddingMode = aPadding.getS().toStringUtf8();

    String dataFormat = DeConv3DConfig.NDHWC;
    if (nodeDef.containsAttr("data_format")) {
        val attr = nodeDef.getAttrOrThrow("data_format");
        dataFormat = attr.getS().toStringUtf8().toLowerCase();
    }

    if (dataFormat.equalsIgnoreCase(DeConv3DConfig.NCDHW)) {
        sD = tfStrides.get(2).intValue();
        sH = tfStrides.get(3).intValue();
        sW = tfStrides.get(4).intValue();


        dD = tfDilation == null ? 1 : tfDilation.get(2).intValue();
        dH = tfDilation == null ? 1 : tfDilation.get(3).intValue();
        dW = tfDilation == null ? 1 : tfDilation.get(4).intValue();
    } else {
        sD = tfStrides.get(1).intValue();
        sH = tfStrides.get(2).intValue();
        sW = tfStrides.get(3).intValue();

        dD = tfDilation == null ? 1 : tfDilation.get(1).intValue();
        dH = tfDilation == null ? 1 : tfDilation.get(2).intValue();
        dW = tfDilation == null ? 1 : tfDilation.get(3).intValue();
    }


    boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");
    DeConv3DConfig conv3DConfig = DeConv3DConfig.builder()
            .kD(-1)
            .kH(-1)
            .kW(-1)
            .sD(sD)
            .sH(sW)
            .sW(sH)
            .dD(dD)
            .dH(dH)
            .dW(dW)
            .isSameMode(isSameMode)
            .dataFormat(dataFormat.equalsIgnoreCase(DeConv3DConfig.NCDHW) ? DeConv3DConfig.NCDHW : DeConv3DConfig.NDHWC)
            .build();
    this.config = conv3DConfig;

    addArgs();
}

Source File: AvgPooling2D.java From nd4j with Apache License 2.0

4 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    val aStrides = nodeDef.getAttrOrThrow("strides");
    val tfStrides = aStrides.getList().getIList();

    val aKernels = nodeDef.getAttrOrThrow("ksize");
    val tfKernels = aKernels.getList().getIList();

    int sY = 0;
    int sX = 0;

    int ph = 0;
    int pw = 0;

    int kY = 0;
    int kX = 0;

    val aPadding = nodeDef.getAttrOrThrow("padding");
    val padding = aPadding.getList().getIList();

    val paddingMode = aPadding.getS().toStringUtf8().replaceAll("\"","");

    boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");

    String data_format = "nhwc";
    if (nodeDef.containsAttr("data_format")) {
        val attr = nodeDef.getAttrOrThrow("data_format");

        data_format = attr.getS().toStringUtf8().toLowerCase();
    }

    if (data_format.equalsIgnoreCase("nhwc")) {
        sY = tfStrides.get(1).intValue();
        sX = tfStrides.get(2).intValue();

        kY = tfKernels.get(1).intValue();
        kX = tfKernels.get(2).intValue();

        ph = padding.size() > 0 ? padding.get(1).intValue() : 0;
        pw = padding.size() > 0 ? padding.get(2).intValue() : 0;
    } else {
        sY = tfStrides.get(2).intValue();
        sX = tfStrides.get(3).intValue();

        kY = tfKernels.get(2).intValue();
        kX = tfKernels.get(3).intValue();

        ph = padding.size() > 0 ? padding.get(2).intValue() : 0;
        pw = padding.size() > 0 ? padding.get(3).intValue() : 0;
    }

    Pooling2DConfig pooling2DConfig = Pooling2DConfig.builder()
            .sy(sY)
            .sx(sX)
            .type(Pooling2D.Pooling2DType.AVG)
            .isSameMode(isSameMode)
            .kh(kY)
            .kw(kX)
            .ph(ph)
            .pw(pw)
            .virtualWidth(1)
            .virtualHeight(1)
            .isNHWC(data_format.equalsIgnoreCase("nhwc"))
            .extra(0.0) // averaging only for non-padded values
            .build();
    this.config = pooling2DConfig;
    addArgs();
    log.debug("Pooling: k: [{},{}]; s: [{}, {}], padding: {}", kY, kX, sY, sX, aPadding);


}

Source File: DeConv2D.java From nd4j with Apache License 2.0

4 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    val aStrides = nodeDef.getAttrOrThrow("strides");
    val tfStrides = aStrides.getList().getIList();
    int sY = 1;
    int sX = 1;
    int kY = 1;
    int kX = 1;

    val aPadding = nodeDef.getAttrOrDefault("padding", null);

    val paddingMode = aPadding.getS().toStringUtf8();

    val args = args();
    INDArray arr = sameDiff.getVariable(args[1].getVarName()).getArr();
    if (arr == null) {
        arr = TFGraphMapper.getInstance().getNDArrayFromTensor(nodeDef.getInput(0), nodeDef, graph);
        // TODO: arguable. it might be easier to permute weights once
        //arr = (arr.permute(3, 2, 0, 1).dup('c'));
        val varForOp = initWith.getVariable(args[1].getVarName());
        if (arr != null)
            initWith.associateArrayWithVariable(arr, varForOp);


    }

    String dataFormat = "nhwc";
    if (nodeDef.containsAttr("data_format")) {
        val attr = nodeDef.getAttrOrThrow("data_format");
        dataFormat = attr.getS().toStringUtf8().toLowerCase();
    }

    // FIXME: int cast


    if (dataFormat.equalsIgnoreCase("nchw")) {
        sY = tfStrides.get(2).intValue();
        sX = tfStrides.get(3).intValue();

        kY = (int) arr.size(2);
        kX = (int) arr.size(3);
    } else {
        sY = tfStrides.get(1).intValue();
        sX = tfStrides.get(2).intValue();

        kY = (int) arr.size(0);
        kX = (int) arr.size(1);
    }


    boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");
    DeConv2DConfig conv2DConfig = DeConv2DConfig.builder()
            .kY(kY)
            .kX(kX)
            .sX(sX)
            .sY(sY)
            .isSameMode(isSameMode)
            //c++ check checks for nchw
            .isNHWC(dataFormat.equalsIgnoreCase("nhwc"))
            .build();
    this.config = conv2DConfig;

    addArgs();


}

Java Code Examples for org.tensorflow.framework.NodeDef#containsAttr()