netron/source/rknn.js

var rknn = rknn || {};
var json = json || require('./json');

rknn.ModelFactory = class {

    match(context) {
        return rknn.Reader.open(context);
    }

    open(context, match) {
        return rknn.Metadata.open(context).then((metadata) => {
            const reader = match;
            return new rknn.Model(metadata, reader.model, reader.weights);
        });
    }
};

rknn.Model = class {

    constructor(metadata, model, weights) {
        this._version = model.version;
        this._producer = model.ori_network_platform || model.network_platform || '';
        this._runtime = model.target_platform ? model.target_platform.join(',') : '';
        this._graphs = [ new rknn.Graph(metadata, model, weights) ];
    }

    get format() {
        return 'RKNN v' + this._version;
    }

    get producer() {
        return this._producer;
    }

    get runtime() {
        return this._runtime;
    }

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

rknn.Graph = class {

    constructor(metadata, model, weights) {
        this._name = model.name || '';
        this._inputs = [];
        this._outputs = [];
        this._nodes = [];

        const args = new Map();
        for (const const_tensor of model.const_tensor) {
            const name = 'const_tensor:' + const_tensor.tensor_id.toString();
            const shape = new rknn.TensorShape(const_tensor.size);
            const type = new rknn.TensorType(const_tensor.dtype, shape);
            const tensor = new rknn.Tensor(type, const_tensor.offset, weights);
            const argument = new rknn.Argument(name, type, tensor);
            args.set(name, argument);
        }
        for (const virtual_tensor of model.virtual_tensor) {
            const name = virtual_tensor.node_id.toString() + ':' + virtual_tensor.output_port.toString();
            const argument = new rknn.Argument(name, null, null);
            args.set(name, argument);
        }
        for (const norm_tensor of model.norm_tensor) {
            const name = 'norm_tensor:' + norm_tensor.tensor_id.toString();
            const shape = new rknn.TensorShape(norm_tensor.size);
            const type = new rknn.TensorType(norm_tensor.dtype, shape);
            const argument = new rknn.Argument(name, type, null);
            args.set(name, argument);
        }
        const arg = (name) => {
            if (!args.has(name)) {
                const argument = new rknn.Argument(name, null, null);
                args.set(name, argument);
            }
            return args.get(name);
        };

        for (const node of model.nodes) {
            node.input = [];
            node.output = [];
        }
        for (const connection of model.connection) {
            switch (connection.left) {
                case 'input':
                    model.nodes[connection.node_id].input.push(connection);
                    if (connection.right_node) {
                        model.nodes[connection.right_node.node_id].output[connection.right_node.tensor_id] = connection;
                    }
                    break;
                case 'output':
                    model.nodes[connection.node_id].output.push(connection);
                    break;
                default:
                    throw new rknn.Error("Unsupported left connection '" + connection.left + "'.");
            }
        }

        for (const graph of model.graph) {
            const key = graph.right + ':' + graph.right_tensor_id.toString();
            const argument = arg(key);
            const name = graph.left + (graph.left_tensor_id === 0 ? '' : graph.left_tensor_id.toString());
            const parameter = new rknn.Parameter(name, [ argument ]);
            switch (graph.left) {
                case 'input':
                    this._inputs.push(parameter);
                    break;
                case 'output':
                    this._outputs.push(parameter);
                    break;
                default:
                    throw new rknn.Error("Unsupported left graph connection '" + graph.left + "'.");
            }
        }

        this._nodes = model.nodes.map((node) => new rknn.Node(metadata, node, arg));
    }

    get name() {
        return this._name;
    }

    get inputs() {
        return this._inputs;
    }

    get outputs() {
        return this._outputs;
    }

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

rknn.Parameter = class {

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

    get name() {
        return this._name;
    }

    get visible() {
        return true;
    }

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

rknn.Argument = class {

    constructor(name, type, initializer) {
        if (typeof name !== 'string') {
            throw new rknn.Error("Invalid argument identifier '" + JSON.stringify(name) + "'.");
        }
        this._name = name;
        this._type = type || null;
        this._initializer = initializer || null;
    }

    get name() {
        return this._name;
    }

    get type() {
        return this._type;
    }

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

rknn.Node = class {

    constructor(metadata, node, arg) {
        this._name = node.name || '';
        this._type = Object.assign({}, metadata.type(node.op) || { name: node.op });
        for (const prefix of [ 'VSI_NN_OP_', 'RKNN_OP_' ]) {
            this._type.name = this._type.name.startsWith(prefix) ? this._type.name.substring(prefix.length) : this._type.name;
        }
        this._inputs = [];
        this._outputs = [];
        this._attributes = [];
        node.input = node.input || [];
        for (let i = 0; i < node.input.length; ) {
            const input = this._type && this._type.inputs && i < this._type.inputs.length ? this._type.inputs[i] : { name: i === 0 ? 'input' : i.toString() };
            const count = input.list ? node.input.length - i : 1;
            const list = node.input.slice(i, i + count).map((input) => {
                if (input.right_tensor) {
                    return arg(input.right_tensor.type + ':' + input.right_tensor.tensor_id.toString());
                }
                if (input.right_node) {
                    return arg(input.right_node.node_id.toString() + ':' + input.right_node.tensor_id.toString());
                }
                throw new rknn.Error('Invalid input argument.');
            });
            this._inputs.push(new rknn.Parameter(input.name, list));
            i += count;
        }
        node.output = node.output || [];
        for (let i = 0; i < node.output.length; ) {
            const output = this._metadata && this._metadata.outputs && i < this._metadata.outputs.length ? this._metadata.outputs[i] : { name: i === 0 ? 'output' : i.toString() };
            const count = output.list ? node.output.length - i : 1;
            const list = node.output.slice(i, i + count).map((output) => {
                if (output.right_tensor) {
                    return arg(output.right_tensor.type + ':' + output.right_tensor.tensor_id.toString());
                }
                if (output.right_node) {
                    return arg(output.right_node.node_id.toString() + ':' + output.right_node.tensor_id.toString());
                }
                throw new rknn.Error('Invalid output argument.');
            });
            this._outputs.push(new rknn.Parameter(output.name, list));
            i += count;
        }
        if (node.nn) {
            const nn = node.nn;
            for (const key of Object.keys(nn)) {
                const params = nn[key];
                for (const name of Object.keys(params)) {
                    const value = params[name];
                    this._attributes.push(new rknn.Attribute(name, value));
                }
            }
        }
    }

    get name() {
        return this._name;
    }

    get type() {
        return this._type;
    }

    get inputs() {
        return this._inputs;
    }

    get outputs() {
        return this._outputs;
    }

    get attributes() {
        return this._attributes;
    }
};

rknn.Attribute = class {

    constructor(name, value) {
        this._name = name;
        this._value = value;
    }

    get name() {
        return this._name;
    }

    get value() {
        return this._value;
    }
};

rknn.Tensor = class {

    constructor(type, offset, weights) {
        this._type = type;
        let size = 0;
        switch (this._type.dataType) {
            case 'uint8': size = 1; break;
            case 'int8': size = 1; break;
            case 'int16': size = 2; break;
            case 'int32': size = 4; break;
            case 'int64': size = 8; break;
            case 'float16': size = 2; break;
            case 'float32': size = 4; break;
            case 'float64': size = 8; break;
            default: throw new rknn.Error("Unsupported tensor data type '" + this._type.dataType + "'.");
        }
        const shape = type.shape.dimensions;
        size = size * shape.reduce((a, b) => a * b, 1);
        if (size > 0) {
            this._data = weights.slice(offset, offset + size);
        }
    }

    get type() {
        return this._type;
    }

    get state() {
        return this._context().state || null;
    }

    get value() {
        const context = this._context();
        if (context.state) {
            return null;
        }
        context.limit = Number.MAX_SAFE_INTEGER;
        return this._decode(context, 0);
    }

    toString() {
        const context = this._context();
        if (context.state) {
            return '';
        }
        context.limit = 10000;
        const value = this._decode(context, 0);
        return JSON.stringify(value, '', '    ');
    }

    _context() {
        const context = {};
        if (!this._type.dataType) {
            context.state = 'Tensor data type is not implemented.';
            return context;
        }
        if (!this._data) {
            context.state = 'Tensor data is empty.';
            return context;
        }
        context.index = 0;
        context.count = 0;
        context.shape = this._type.shape.dimensions;
        context.dataType = this._type.dataType;
        context.view = new DataView(this._data.buffer, this._data.byteOffset, this._data.byteLength);
        return context;
    }

    _decode(context, dimension) {
        const shape = context.shape.length !== 0 ? context.shape : [ 1 ];
        const results = [];
        const size = shape[dimension];
        if (dimension == shape.length - 1) {
            for (let i = 0; i < size; i++) {
                if (context.count > context.limit) {
                    results.push('...');
                    return results;
                }
                switch (context.dataType) {
                    case 'float16':
                        results.push(context.view.getFloat16(context.index, true));
                        context.index += 2;
                        context.count++;
                        break;
                    case 'float32':
                        results.push(context.view.getFloat32(context.index, true));
                        context.index += 4;
                        context.count++;
                        break;
                    case 'float64':
                        results.push(context.view.getFloat64(context.index, true));
                        context.index += 8;
                        context.count++;
                        break;
                    case 'uint8':
                        results.push(context.view.getUint8(context.index, true));
                        context.index++;
                        context.count++;
                        break;
                    case 'int8':
                        results.push(context.view.getInt8(context.index, true));
                        context.index += 1;
                        context.count++;
                        break;
                    case 'int16':
                        results.push(context.view.getInt16(context.index, true));
                        context.index += 2;
                        context.count++;
                        break;
                    case 'int32':
                        results.push(context.view.getInt32(context.index, true));
                        context.index += 4;
                        context.count++;
                        break;
                    case 'int64':
                        results.push(context.view.getInt64(context.index, true));
                        context.index += 8;
                        context.count++;
                        break;
                    default:
                        throw new rknn.Error("Unsupported tensor data type '" + context.dataType + "'.");
                }
            }
        }
        else {
            for (let j = 0; j < size; j++) {
                if (context.count > context.limit) {
                    results.push('...');
                    return results;
                }
                results.push(this._decode(context, dimension + 1));
            }
        }
        if (context.shape.length == 0) {
            return results[0];
        }
        return results;
    }
};

rknn.TensorType = class {

    constructor(dataType, shape) {
        const type = dataType.vx_type.startsWith('VSI_NN_TYPE_') ? dataType.vx_type.split('_').pop().toLowerCase() : dataType.vx_type;
        switch (type) {
            case 'uint8':
            case 'int8':
            case 'int16':
            case 'int32':
            case 'int64':
            case 'float16':
            case 'float32':
            case 'float64':
            case 'vdata':
                this._dataType = type;
                break;
            default:
                if (dataType.vx_type !== '') {
                    throw new rknn.Error("Invalid data type '" + JSON.stringify(dataType) + "'.");
                }
                this._dataType = '?';
                break;
        }
        this._shape = shape;
    }

    get dataType() {
        return this._dataType;
    }

    get shape() {
        return this._shape;
    }

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

rknn.TensorShape = class {

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

    get dimensions() {
        return this._dimensions;
    }

    toString() {
        if (!this._dimensions || this._dimensions.length == 0) {
            return '';
        }
        return '[' + this._dimensions.join(',') + ']';
    }
};

rknn.Reader = class {

    static open(context) {
        const stream = context.stream;
        if (stream.length >= 8) {
            const buffer = stream.read(8);
            const decoder = new TextDecoder();
            const signature = decoder.decode(buffer);
            if (signature === 'RKNN\0\0\0\0' || signature === 'CYPTRKNN') {
                return new rknn.Reader(stream, signature);
            }
        }
        return null;
    }

    constructor(stream, signature) {
        this._stream = stream;
        this._signature = signature;
    }

    get version() {
        this._decode();
        return this._version;
    }

    get weights() {
        this._decode();
        return this._weights;
    }

    get model() {
        this._decode();
        return this._model;
    }

    _decode() {
        if (this._stream) {
            if (this._signature === 'CYPTRKNN') {
                throw new rknn.Error('Invalid file content. File contains undocumented encrypted RKNN data.');
            }
            this._version = this._uint64();
            const weights_size = this._uint64();
            if (this._version > 1) {
                this._stream.read(40);
            }
            this._weights = this._stream.read(weights_size);
            const model_size = this._uint64();
            const model_buffer = this._stream.read(model_size);
            const reader = json.TextReader.open(model_buffer);
            this._model = reader.read();
            delete this._stream;
        }
    }

    _uint64() {
        const buffer = this._stream.read(8);
        const view = new DataView(buffer.buffer, buffer.byteOffset, buffer.byteLength);
        return view.getUint64(0, true).toNumber();
    }

    _read() {
        const size = this._uint64();
        return this._stream.read(size);
    }
};

rknn.Metadata = class {

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

    constructor(data) {
        this._types = new Map();
        this._attributes = new Map();
        if (data) {
            const items = JSON.parse(data);
            for (const item of items) {
                this._types.set(item.name, item);
            }
        }
    }

    type(name) {
        if (!this._types.has(name)) {
            this._types.set(name, { name: name });
        }
        return this._types.get(name);
    }

    attribute(type, name) {
        const key = type + ':' + name;
        if (!this._attributes.has(key)) {
            this._attributes.set(key, null);
            const metadata = this.type(type);
            if (metadata && Array.isArray(metadata.attributes)) {
                for (const attribute of metadata.attributes) {
                    this._attributes.set(type + ':' + attribute.name, attribute);
                }
            }
        }
        return this._attributes.get(key);
    }
};

rknn.Error = class extends Error {

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

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