netron/source/tengine.js

// Experimental

var tengine = {};
var base = require('./base');

tengine.ModelFactory = class {

    match(context) {
        return tengine.Reader.open(context.stream);
    }

    open(context, match) {
        return tengine.Metadata.open(context).then((metadata) => {
            return new tengine.Model(metadata, match);
        });
    }
};

tengine.Model = class {

    constructor(metadata, reader) {
        this._version = reader.version;
        this._metadata = [
            { name: 'source', value: reader.source }
        ];
        this._graphs = reader.graphs.map((graph) => new tengine.Graph(metadata, graph));
    }

    get format() {
        return "Tengine v" + this._version;
    }

    get metadata() {
        return this._metadata;
    }

    get graphs() {
        return this._graphs;
    }
};

tengine.Graph = class {

    constructor(metadata, graph) {
        this._name = graph.id.toString();
        this._inputs = [];
        this._outputs = [];
        this._nodes = [];
        const tensors = graph.tensors.map((tensor) => new tengine.Argument(tensor));
        for (const input of graph.inputs) {
            const node = graph.nodes[input];
            this._inputs.push(new tengine.Parameter(node.name, true, node.outputs.map((output) => tensors[output])));
        }
        for (const output of graph.outputs) {
            const node = graph.nodes[output];
            this._outputs.push(new tengine.Parameter(node.name, true, node.outputs.map((output) => tensors[output])));
        }
        for (const node of graph.nodes) {
            switch (node.type) {
                case 'INPUT':
                case 'Const':
                    break;
                default:
                    this._nodes.push(new tengine.Node(metadata, node, tensors));
                    break;
            }
        }
    }

    get name() {
        return this._name;
    }

    get inputs() {
        return this._inputs;
    }

    get outputs() {
        return this._outputs;
    }

    get nodes() {
        return this._nodes;
    }
};

tengine.Parameter = class {

    constructor(name, visible, args) {
        this._name = name;
        this._visible = visible;
        this._arguments = args;
    }

    get name() {
        return this._name;
    }

    get visible() {
        return this._visible;
    }

    get arguments() {
        return this._arguments;
    }
};

tengine.Argument = class {

    constructor(tensor) {
        this._name = tensor.name;
        this._type = new tengine.TensorType(tensor.dataType, new tengine.TensorShape(tensor.dims));
        this._initializer = (tensor.type === 2) ? new tengine.Tensor(this._type, tensor.buffer) : null;
    }

    get name() {
        return this._name;
    }

    get type() {
        if (this._initializer) {
            return this._initializer.type;
        }
        return this._type;
    }

    get quantization() {
        return null;
    }

    get initializer() {
        return this._initializer;
    }
};


tengine.Node = class {

    constructor(metadata, node, tensors) {
        this._name = node.name;
        const type = node.type;
        const version = node.version;
        this._inputs = [];
        this._outputs = [];
        this._attributes = [];
        this._type = metadata.type(type, version) || { name: type };

        for (let i = 0; i < node.params.length; i++) {
            const metadata = (this._type && this._type.attributes && i < this._type.attributes.length) ? this._type.attributes[i] : null;
            const name = metadata ? metadata.name : i.toString();
            this._attributes.push(new tengine.Attribute(metadata, name, node.params[i]));
        }

        const inputs = node.inputs;
        let inputIndex = 0;
        if (this._type && this._type.inputs) {
            for (const inputDef of this._type.inputs) {
                if (inputIndex < inputs.length || inputDef.option != 'optional') {
                    const inputCount = (inputDef.option == 'variadic') ? (inputs.length - inputIndex) : 1;
                    const inputArguments = inputs.slice(inputIndex, inputIndex + inputCount).filter((id) => id != '' || inputDef.option != 'optional').map((id) => tensors[id]);
                    this._inputs.push(new tengine.Parameter(inputDef.name, true, inputArguments));
                    inputIndex += inputCount;
                }
            }
        }
        else {
            this._inputs.push(...inputs.slice(inputIndex).map((id, index) => {
                const inputName = ((inputIndex + index) == 0) ? 'input' : (inputIndex + index).toString();
                return new tengine.Parameter(inputName, true, [ tensors[id] ]);
            }));
        }

        const outputs = node.outputs;
        let outputIndex = 0;
        if (this._type && this._type.outputs) {
            for (const outputDef of this._type.outputs) {
                if (outputIndex < outputs.length || outputDef.option != 'optional') {
                    const outputCount = (outputDef.option == 'variadic') ? (outputs.length - outputIndex) : 1;
                    const outputArguments = outputs.slice(outputIndex, outputIndex + outputCount).map((id) => tensors[id]);
                    this._outputs.push(new tengine.Parameter(outputDef.name, true, outputArguments));
                    outputIndex += outputCount;
                }
            }
        }
        else {
            this._outputs.push(...outputs.slice(outputIndex).map((id, index) => {
                const outputName = ((outputIndex + index) == 0) ? 'output' : (outputIndex + index).toString();
                return new tengine.Parameter(outputName, true, [ tensors[id] ]);
            }));
        }
    }

    get type() {
        return this._type;
    }

    get name() {
        return this._name;
    }

    get attributes() {
        return this._attributes;
    }

    get inputs() {
        return this._inputs;
    }

    get outputs() {
        return this._outputs;
    }
};

tengine.Attribute = class {

    constructor(schema, key, value) {
        this._type = '';
        this._name = key;
        this._value = value;
        if (schema) {
            this._name = schema.name;
            if (schema.type) {
                this._type = schema.type;
            }
            if (Object.prototype.hasOwnProperty.call(schema, 'visible') && !schema.visible) {
                this._visible = false;
            }
            else if (Object.prototype.hasOwnProperty.call(schema, 'default')) {
                if (this._value == schema.default || (this._value && this._value.toString() == schema.default.toString())) {
                    this._visible = false;
                }
            }
        }
    }

    get type() {
        return this._type;
    }

    get name() {
        return this._name;
    }

    get value() {
        return this._value;
    }

    get visible() {
        return this._visible == false ? false : true;
    }
};

tengine.Tensor = class {

    constructor(type, data) {
        this._type = type;
        this._data = data;
    }

    get type() {
        return this._type;
    }

    get values() {
        return this._data;
    }

};

tengine.TensorType = class {

    constructor(dataType, shape) {
        switch (dataType) {
            case 0: this._dataType = 'float32'; break;
            case 1: this._dataType = 'float16'; break;
            case 2: this._dataType = 'int8'; break;
            case 3: this._dataType = 'uint8'; break;
            case 4: this._dataType = 'int32'; break;
            case 5: this._dataType = 'int16'; break;
            default: throw new tengine.Error("Unsupported data type'" + dataType + "'.");
        }
        this._shape = shape;
    }

    get dataType() {
        return this._dataType;
    }

    get shape() {
        return this._shape;
    }

    toString() {
        return this._dataType + this._shape.toString();
    }
};

tengine.TensorShape = class {

    constructor(dimensions) {
        this._dimensions = dimensions;
    }

    get dimensions() {
        return this._dimensions;
    }

    toString() {
        return this._dimensions ? ('[' + this._dimensions.map((dimension) => dimension ? dimension.toString() : '?').join(',') + ']') : '';
    }
};

tengine.Metadata = class {

    static open(context) {
        if (tengine.Metadata._metadata) {
            return Promise.resolve(tengine.Metadata._metadata);
        }
        return context.request('tengine-metadata.json', 'utf-8', null).then((data) => {
            tengine.Metadata._metadata = new tengine.Metadata(data);
            return tengine.Metadata._metadata;
        }).catch(() => {
            tengine.Metadata._metadata = new tengine.Metadata(null);
            return tengine.Metadata._metadata;
        });
    }

    constructor(data) {
        this._map = new Map();
        if (data) {
            const metadata = JSON.parse(data);
            for (const item of metadata) {
                if (item.name) {
                    const version = item.version || 0;
                    const name = item.name + ':' + version.toString();
                    this._map.set(name, item);
                }
            }
        }
    }

    type(name, version) {
        let current = version;
        while (current > 0) {
            if (this._map.has(name + ':' + current.toString())) {
                break;
            }
            current--;
        }
        if (current >= 0) {
            const schema = this._map.get(name + ':' + current.toString());
            if (current !== version) {
                this._map.set(name + ':' + version.toString(), schema);
            }
            return schema;
        }
        return null;
    }
};

tengine.Reader = class {

    static open(stream) {
        if (stream && stream.length > 4) {
            const buffer = stream.peek(2);
            if (buffer[0] < 4 && buffer[1] === 0) {
                return new tengine.Reader(stream);
            }
        }
        return null;
    }

    constructor(stream) {
        this._stream = stream;
        // https://github.com/OAID/Tengine/wiki/The-format-of-tmfile
        // https://github.com/OAID/Tengine/blob/tengine-lite/source/serializer/tmfile/tm2_format.h
    }

    _read() {
        if (this._stream) {
            const types = new Map();
            const register = (index, version, name, params) => {
                types.set(index.toString() + ':' + version.toString(), { name: name, params: params });
            };
            const operator = (index, version) => {
                let current = version;
                while (current >= 0) {
                    if (types.has(index.toString() + ':' + current.toString())) {
                        break;
                    }
                    current--;
                }
                if (current >= 0) {
                    const schema = types.get(index.toString() + ':' + current.toString());
                    if (current !== version) {
                        types.set(index.toString() + ':' + version.toString(), schema);
                    }
                    return schema;
                }
                return null;
            };
            /* eslint-disable space-in-parens */
            register( 0, 0, 'Accuracy', []);
            register( 1, 0, 'BatchNormalization', [ 'f', 'f', 'i' ]);
            register( 2, 0, 'BilinearResize', [ 'f', 'f', 'i' ]);
            register( 3, 0, 'Concat', [ 'i' ]);
            register( 4, 0, 'Const', []);
            register( 5, 0, 'Convolution', [ 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i' ]);
            register( 6, 0, 'Deconvolution', [ 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i' ]);
            register( 7, 0, 'DetectionOutput', [ 'i', 'i', 'i', 'f', 'f' ]);
            register( 8, 0, 'DropOut', []);
            register( 9, 0, 'Eltwise', [ 'i', 'i' ]);
            register(10, 0, 'Flatten', [ 'i' ]);
            register(11, 0, 'FullyConnected', [ 'i' ]);
            register(12, 0, 'INPUT', []);
            register(13, 0, 'LRN', [ 'i', 'f', 'f', 'i', 'f' ]);
            register(14, 0, 'Normalize', [ 'i', 'i' ]);
            register(15, 0, 'Permute', [ 'i', 'i', 'i', 'i', 'i' ]);
            register(16, 0, 'Pooling', [ 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i' ]);
            register(17, 0, 'Prelu', []);
            register(18, 0, 'PriorBox', [ 'f[]', 'f[]', 'f[]', 'f[]', 'i', 'i', 'i', 'i', 'i', 'f', 'f', 'f', 'i', 'i' ]);
            register(19, 0, 'Region', [ 'i', 'i', 'i', 'i', 'f', 'f', 'f[]' ]);
            register(20, 0, 'ReLU', [ 'f' ]);
            register(21, 0, 'ReLU6', []);
            register(22, 0, 'Reorg', [ 'i' ]);
            register(23, 0, 'Reshape', [ 'i', 'i', 'i', 'i', 'i', 'i' ]);
            // register(23, 0, 'Reshape', [ 'i', 'i', 'i[]' ]);
            register(24, 0, 'RoiPooling', [ 'i', 'i', 'f' ]);
            register(25, 0, 'RPN', [ 'f[]', 'f[]', 'i', 'i', 'i', 'i', 'i', 'f', 'anchors' ]);
            register(26, 0, 'Scale', [ 'i', 'i', 'i' ]);
            register(27, 0, 'Slice', [ 'i', 'i[]', 'i[]', 'i[]', 'i', 'i', 'i', 'i', 'i' ]);
            register(28, 0, 'SoftMax', [ 'i' ]);
            register(29, 0, 'Split', [ 'i', 'i', 'boolean', 'boolean', 'i[]' ]);
            register(30, 0, 'DetectionPostProcess', [ 'i', 'i', 'f', 'f', 'i', 'f[]' ]);
            register(31, 0, 'Gemm', [ 'f', 'f', 'i', 'i' ]);
            register(32, 0, 'Generic', [ 'i', 'i', 'string' ]);
            register(33, 0, 'Logistic', []);
            register(34, 0, 'LSTM', [ 'f', 'f', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i' ]);
            register(35, 0, 'RNN', [ 'f', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i' ]);
            register(36, 0, 'TanH', []);
            register(37, 0, 'Sigmoid', []);
            register(38, 0, 'Squeeze', [ 'i', 'i', 'i', 'i' ]);
            register(39, 0, 'FusedbnScaleRelu', []);
            register(40, 0, 'Pad', [ 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'f' ]);
            register(41, 0, 'StridedSlice', [ 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i' ]);
            register(42, 0, 'ArgMax', [ 'i' ]);
            register(43, 0, 'ArgMin', [ 'i' ]);
            register(44, 0, 'TopKV2', [ 'i', 'i' ]);
            register(45, 0, 'Reduction', [ 'i', 'i', 'i', 'i', 'i', 'i' ]);
            register(46, 0, 'Max', []);
            register(47, 0, 'Min', []);
            register(48, 0, 'GRU', [ 'f', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i' ]);
            register(49, 0, 'Addn', 'i');
            register(50, 0, 'SwapAxis', [ 'i', 'i' ]);
            register(51, 0, 'Upsample', [ 'f' ]);
            register(52, 0, 'SpaceToBatchND', [ 'i', 'i', 'i', 'i', 'i', 'i' ]);
            register(53, 0, 'BatchToSpaceND', [ 'i', 'i', 'i', 'i', 'i', 'i' ]);
            register(54, 0, 'Resize', [ 'f', 'f', 'i' ]);
            register(55, 0, 'ShuffleChannel', [ 'i' ]);
            register(56, 0, 'Crop', [ 'i', 'i', 'i', 'i', 'i', 'i', 'boolean', 'i', 'i' ]);
            register(57, 0, 'ROIAlign', [ 'i', 'i', 'f' ]);
            register(58, 0, 'Psroipooling', [ 'i', 'i', 'f', 'i' ]);
            register(59, 0, 'Unary', [ 'i' ]);
            register(60, 0, 'Expanddims', [ 'i' ]);
            register(61, 0, 'Bias', [ 'i' ]);
            register(62, 0, 'Noop', []);
            register(63, 0, 'Threshold', [ 'f' ]);
            register(64, 0, 'Hardsigmoid', [ 'f', 'f' ]);
            register(65, 0, 'Embed', [ 'f', 'f', 'f', 'f' ]);
            register(66, 0, 'InstanceNorm', [ 'f' ]);
            register(67, 0, 'MVN', [ 'i', 'i', 'f' ]);
            register(68, 0, 'Absval', []);
            register(69, 0, 'Cast', [ 'i', 'i' ]);
            register(70, 0, 'HardSwish', [ 'f', 'f' ]);
            register(71, 0, 'Interp', [ 'i', 'f', 'f', 'i', 'i' ]);
            register(72, 0, 'SELU', [ 'f', 'f' ]);
            register(73, 0, 'ELU', [ 'f' ]);
            register(74, 0, 'BroadMul', []);
            register(75, 0, 'Logical', [ 'i' ]);
            register(76, 0, 'Gather', [ 'i', 'i' ]);
            register(77, 0, 'Transpose', [ 'i[]' ]);
            register(78, 0, 'Comparison', [ 'i' ]);
            register(79, 0, 'SpaceToDepth', [ 'i' ]);
            register(80, 0, 'DepthToSpace', [ 'i' ]);
            register(81, 0, 'Reverse', []);
            register(82, 0, 'SparseToDense', [ 'i','i','i' ]);
            register(83, 0, 'Ceil', []);
            register(84, 0, 'SquaredDifference', []);
            register(85, 0, 'Round', []);
            register(86, 0, 'ZerosLike', []);
            register(87, 0, 'Clip', [ 'f','f' ]);
            register(88, 0, 'Unsqueeze', [ 'i[]' ]);
            register(89, 0, 'ReduceL2', [ 'i','i' ]);
            register(90, 0, 'Mean', []);
            register(91, 0, 'MatMul', []);
            register(92, 0, 'Expand', ['i[]']);
            register(93, 0, 'Scatter', ['i','boolean']);
            register(94, 0, 'Shape', []);
            register(95, 0, 'Where', []);
            register(96, 0, 'Tile', ['i','i']);
            register(97, 0, 'Mish', []);
            register(98, 0, 'L2Pool', []);
            register(99, 0, 'LogSoftmax', []);
            register(100, 0, 'ReLU1', []);
            register(101, 0, 'L2Normalization', []);
            register(102, 0, 'PackModel', ['i','i']);
            register(103, 0, 'Num', []);
            /* eslint-enable space-in-parens */

            const buffer = this._stream.peek();
            const reader = new tengine.BinaryReader(buffer);
            this._majorVersion = reader.uint16();
            this._minorVersion = reader.uint16();
            if (this._majorVersion !== 2) {
                throw new tengine.Error("Unsupported format version 'v" + this._majorVersion.toString() + "." + this._minorVersion.toString() + "'.");
            }
            this._compileVersion = reader.uint16();
            reader.skip(2); // struct align
            reader.seek(reader.uint32()); // root table
            this._originalFormat = reader.int32();
            this._subFormat = reader.int32();
            this._graphs = [];
            const subgraphOffsets = reader.uint32s();
            for (const subgraphOffset of subgraphOffsets) {
                reader.seek(subgraphOffset);

                const subgraph = {};
                subgraph.id = reader.int32();
                subgraph.graphLayout = reader.int32();
                /*
                if (graphLayout == 0) {
                    return "NCHW";
                }
                if (graphLayout == 1) {
                    return "NHWC";
                }
                */
                subgraph.originalLayout = reader.int32();
                subgraph.inputs = reader.uint32s();
                subgraph.outputs = reader.uint32s();
                const nodeOffsets = reader.uint32s();
                const tensorOffsets = reader.uint32s();
                const bufferOffsets = reader.uint32s();
                subgraph.name = reader.string();
                subgraph.nodes = [];
                subgraph.tensors = [];
                this._graphs.push(subgraph);

                // nodes
                for (const nodeOffset of nodeOffsets) {
                    reader.seek(nodeOffset);
                    const node = {};
                    node.id = reader.int32();
                    node.inputs = reader.uint32s();
                    node.outputs = reader.uint32s();
                    const typeOffset = reader.int32();
                    node.name = reader.string();
                    const attributeOffsets = reader.uint32s();
                    node.dynamicShape = reader.boolean();

                    reader.seek(typeOffset);
                    node.version = reader.int32();
                    const index = reader.int32();
                    const paramsOffset = reader.uint32();

                    const schema = operator(index, node.version);
                    node.type = schema ? schema.name : index.toString();
                    const paramTypes = schema ? schema.params : [];

                    node.params = [];
                    if (paramsOffset) {
                        reader.seek(paramsOffset);
                        for (const paramType of paramTypes) {
                            if (paramType !== 'boolean') {
                                reader.align(4);
                            }
                            switch (paramType) {
                                case 'i':
                                    node.params.push(reader.int32());
                                    break;
                                case 'f':
                                    node.params.push(reader.float32());
                                    break;
                                case 'i[]':
                                    node.params.push(reader.int32s());
                                    break;
                                case 'f[]':
                                    node.params.push(reader.float32s());
                                    break;
                                case 'boolean':
                                    node.params.push(reader.boolean());
                                    break;
                                case 'string':
                                    node.params.push(reader.string());
                                    break;
                                case 'anchors':
                                    node.params.push(reader.anchors(4));
                                    break;
                                default:
                                    throw new tengine.Error("Unsupported param type '" + paramType + "' in '" + node.type + "'.");
                            }
                        }
                    }

                    if (node.type === 'Slice') {
                        node.params[6] = (this._originalFormat == 5) ? node.params[6] : 0;
                    }

                    node.attributes = attributeOffsets.map((attributeOffset) => {
                        reader.seek(attributeOffset);
                        const name = reader.string();
                        const value = reader.string();
                        const type = reader.int32();
                        return { name: name, value: value, type: type };
                    });

                    subgraph.nodes.push(node);
                }

                // buffers
                const buffers = bufferOffsets.map((bufferOffset) => {
                    reader.seek(bufferOffset);
                    const size = reader.uint32();
                    const offset = reader.int32();
                    if (offset !== 0) {
                        reader.seek(offset);
                        return reader.read(size);
                    }
                    return null;
                });

                // tensors
                subgraph.tensors = tensorOffsets.map((tensorOffset) => {
                    reader.seek(tensorOffset);
                    const tensor = {};
                    tensor.id = reader.int32();
                    tensor.buffer = buffers[reader.int32()];
                    tensor.dims = reader.int32s();
                    tensor.name = reader.string();
                    const quantparamsOffset = reader.int32();
                    tensor.layout = reader.int32();
                    tensor.type = reader.int32(); // ar = 1, const = 2, input = 3, vdep, unknown
                    tensor.dataType = reader.int32();
                    if (quantparamsOffset) {
                        reader.seek(quantparamsOffset);
                        tensor.quantparams = {
                            zeroPoint: reader.int32(),
                            scale: reader.float32(),
                            width: reader.int32()
                        };
                    }
                    return tensor;
                });

                for (const node of subgraph.nodes) {
                    if (node.type === 'Convolution') {
                        switch (subgraph.graphLayout) {
                            case 0: // NCHW
                                node.params[6] = subgraph.tensors[node.inputs[1]].dims[1];
                                break;
                            case 1: // NHWC
                                node.params[6] = subgraph.tensors[node.inputs[1]].dims[3];
                                break;
                            default:
                                throw new tengine.Error("Unsupported 'Convolution' layout '" + subgraph.graphLayout + "'.");
                        }
                    }
                }
            }
            delete this._stream;
        }
    }

    get version() {
        this._read();
        return this._majorVersion + '.' + this._minorVersion;
    }

    get source() {
        this._read();
        switch (this._originalFormat) {
            case 0: return '';
            case 1: return 'Tengine';
            case 2: return 'Caffe';
            case 3: return 'ONNX';
            case 4: return 'MXNet';
            case 5: return 'TensorFlow';
            case 6: return 'TensorFlow Lite';
            case 7: return 'Darknet';
            case 8: return 'DLA v' + this._subFormat;
            case 9: return 'ncnn';
            case 10: return 'MegEngine';
            case 11: return 'OneFlow';
            case 12: return 'Horizon';
            case 13: return 'Bitman';
            default: throw new tengine.Error("Unsupported source '" + this._originalFormat.toString() + "'.");
        }
    }

    get graphs() {
        this._read();
        return this._graphs;
    }
};

tengine.BinaryReader = class extends base.BinaryReader {

    string() {
        const position = this.uint32();
        let content = '';
        if (position) {
            const next = this._position;
            this.seek(position);
            const size = this.uint32();
            this.seek(this.uint32());
            for (let i = 0; i < size - 1; i++) {
                content += String.fromCharCode(this._buffer[this._position++]);
            }
            this.seek(next);
        }
        return content;
    }

    uint32s() {
        const values = [];
        const offset = this.uint32();
        if (offset) {
            const next = this.position;
            this.seek(offset);
            const count = this.uint32();
            for (let i = 0; i < count; i++) {
                values.push(this.uint32());
            }
            this.seek(next);
        }
        return values;
    }

    int32s() {
        const values = [];
        const offset = this.uint32();
        if (offset) {
            const next = this.position;
            this.seek(offset);
            const count = this.uint32();
            for (let i = 0; i < count; i++) {
                values.push(this.int32());
            }
            this.seek(next);
        }
        return values;
    }

    float32s() {
        const values = [];
        const offset = this.uint32();
        if (offset) {
            const next = this.position;
            this.seek(offset);
            const count = this.uint32();
            for (let i = 0; i < count; i++) {
                values.push(this.float32());
            }
            this.seek(next);
        }
        return values;
    }

    anchors(length) {
        const arrays = [];
        const offset = this.uint32();
        if (offset) {
            const next = this._position;
            this.seek(offset);
            const count = this.uint32();
            for (let i = 0; i < count; i++) {
                const array = [];
                for (let j = 0; j < length; j++) {
                    array.push(this.float32());
                }
                arrays.push(array);
            }
            this.seek(next);
        }
        return arrays;
    }
};

tengine.Error = class extends Error {

    constructor(message) {
        super(message);
        this.name = 'Error loading Tengine model.';
    }
};

if (typeof module !== 'undefined' && typeof module.exports === 'object') {
    module.exports.ModelFactory = tengine.ModelFactory;
}