org.tensorflow.framework.NodeDef#getInputCount

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

6 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    if(nodeDef.getInputCount() == 2) {
        val targetNode = TFGraphMapper.getInstance().getNodeWithNameFromGraph(graph,nodeDef.getInput(1));
        val mapper = TFGraphMapper.getInstance();
        val secondInputAsScalar = mapper.getNDArrayFromTensor("value",targetNode,graph);
        //must be scalar
        if(secondInputAsScalar.length() == 1) {
            addTArgument(secondInputAsScalar.getDouble(0));
        }
        else {
            throw new ND4JIllegalStateException("Second input to node " + nodeDef + " should be scalar!");
        }
    }

}

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

5 votes

protected boolean hasReductionIndices(NodeDef nodeDef) {
    for(int i = 0; i < nodeDef.getInputCount(); i++) {
        if(nodeDef.getInput(i).contains("reduction_indices")) {
            return true;
        }
    }

    return false;
}

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

5 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
    //permute dimensions are not specified as second input
    if (nodeDef.getInputCount() < 2)
        return;
    NodeDef permuteDimsNode = null;
    for (int i = 0; i < graph.getNodeCount(); i++) {
        if (graph.getNode(i).getName().equals(nodeDef.getInput(1))) {
            permuteDimsNode = graph.getNode(i);
        }

    }

    INDArray permuteArrayOp = TFGraphMapper.getNDArrayFromTensor(permuteDimsNode);
    if (permuteArrayOp != null) {
        this.permuteDims = permuteArrayOp.data().asInt();
    }

    //handle once properly mapped
    if (arg().getShape() == null || arg().getVariableType() == VariableType.PLACEHOLDER || arg().getArr() == null) {
        return;
    }

    INDArray arr = sameDiff.getArrForVarName(arg().name());

    if(permuteArrayOp != null){
        addInputArgument(arr, permuteArrayOp);
    } else {
        addInputArgument(arr);
    }

    if (arr != null && permuteDims == null) {
        this.permuteDims = ArrayUtil.reverseCopy(ArrayUtil.range(0, arr.rank()));
    }

    if (permuteDims != null && permuteDims.length < arg().getShape().length)
        throw new ND4JIllegalStateException("Illegal permute found. Not all dimensions specified");
}

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

5 votes

protected boolean hasReductionIndices(NodeDef nodeDef) {
    for(int i = 0; i < nodeDef.getInputCount(); i++) {
        if(nodeDef.getInput(i).contains("reduction_indices")) {
            return true;
        }
    }

    return false;
}

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

4 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    int concatDimension = -1;
    String input = null;
    for(int i = 0; i < nodeDef.getInputCount(); i++) {
        if(nodeDef.getInput(i).contains("/concat_dim")) {
            input = nodeDef.getInput(i);
            break;
        }
    }

    //older versions may specify a concat_dim, usually it's the last argument
    if(input == null) {
        input = nodeDef.getInput(nodeDef.getInputCount() - 1);
    }

    val variable = initWith.getVariable(input);
    // concat dimension is only possible
    if (variable != null && variable.getArr() == null) {
        sameDiff.addPropertyToResolve(this, input);

    } else if (variable != null) {
        val arr = variable.getArr();
        if (arr.length() == 1) {
            concatDimension = arr.getInt(0);
        }

        this.concatDimension = concatDimension;
        addIArgument(this.concatDimension);
        log.debug("Concat dimension: {}", concatDimension);

    }

    //don't pass both iArg and last axis down to libnd4j
    if(inputArguments().length == nodeDef.getInputCount()) {
        val inputArgs = inputArguments();
        removeInputArgument(inputArgs[inputArguments().length - 1]);
    }

    sameDiff.removeArgFromFunction(input,this);
}

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

4 votes

@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
    super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
    //permute dimensions re not specified as second input
    if (nodeDef.getInputCount() < 2)
        return;
    NodeDef permuteDimsNode = null;
    for (int i = 0; i < graph.getNodeCount(); i++) {
        if (graph.getNode(i).getName().equals(nodeDef.getInput(1))) {
            permuteDimsNode = graph.getNode(i);
        }

    }

    val permuteArrayOp = TFGraphMapper.getInstance().getNDArrayFromTensor("value", permuteDimsNode, graph);
    if (permuteArrayOp != null) {
        this.permuteDims = permuteArrayOp.data().asInt();
        for (int i = 0; i < permuteDims.length; i++) {
            addIArgument(permuteDims[i]);
        }
    }

    //handle once properly mapped
    if (arg().getShape() == null) {
        return;
    }


    INDArray arr = sameDiff.getArrForVarName(arg().getVarName());
    if (arr == null) {
        val arrVar = sameDiff.getVariable(arg().getVarName());
        arr = arrVar.getWeightInitScheme().create(arrVar.getShape());
        sameDiff.putArrayForVarName(arg().getVarName(), arr);
    }

    addInputArgument(arr);

    if (arr != null && permuteDims == null) {
        this.permuteDims = ArrayUtil.reverseCopy(ArrayUtil.range(0, arr.rank()));
    }

    if (permuteDims != null && permuteDims.length < arg().getShape().length)
        throw new ND4JIllegalStateException("Illegal permute found. Not all dimensions specified");


}

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

4 votes

private void initSessionAndStatusIfNeeded(org.tensorflow.framework.GraphDef graphDef1) {
    //infer the inputs and outputSchema for the graph
    Set<String> seenAsInput = new LinkedHashSet<>();
    for(int i = 0; i < graphDef1.getNodeCount(); i++) {
        NodeDef node = graphDef1.getNode(i);
        for(int input = 0; input < node.getInputCount(); input++) {
            seenAsInput.add(node.getInput(input));
        }
    }

    if(outputOrder == null) {
        outputOrder = new ArrayList<>();
        log.trace("Attempting to automatically resolve tensorflow output names..");
        //find the nodes that were not inputs to any  nodes: these are the outputSchema
        for(int i = 0; i < graphDef1.getNodeCount(); i++) {
            if(!seenAsInput.contains(graphDef1.getNode(i).getName()) && !graphDef1.getNode(i).getOp().equals("Placeholder")) {
                outputOrder.add(graphDef1.getNode(i).getName());
            }
        }

        //multiple names: purge any generated names from the output
        if(outputOrder.size() > 1) {
            Set<String> remove = new HashSet<>();
            for (String name : outputOrder) {
                if(name.contains("/")) {
                    remove.add(name);
                }
            }

            outputOrder.removeAll(remove);
        }
    }


    //setup and configure the session, factoring
    //in the ConfigObject as needed
    if(session == null) {
        initOptionsIfNeeded();
        session = TF_NewSession(graph, options, status);
        if (TF_GetCode(status) != TF_OK) {
            throw new IllegalStateException("ERROR: Unable to open session " + TF_Message(status).getString());
        }

    }

}

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