@tensorflow/tfjs-core/dist/io/io_utils.js

Hardware-accelerated JavaScript library for machine intelligence

"use strict";
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
    return new (P || (P = Promise))(function (resolve, reject) {
        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
        step((generator = generator.apply(thisArg, _arguments || [])).next());
    });
};
var __generator = (this && this.__generator) || function (thisArg, body) {
    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
    function verb(n) { return function (v) { return step([n, v]); }; }
    function step(op) {
        if (f) throw new TypeError("Generator is already executing.");
        while (_) try {
            if (f = 1, y && (t = y[op[0] & 2 ? "return" : op[0] ? "throw" : "next"]) && !(t = t.call(y, op[1])).done) return t;
            if (y = 0, t) op = [0, t.value];
            switch (op[0]) {
                case 0: case 1: t = op; break;
                case 4: _.label++; return { value: op[1], done: false };
                case 5: _.label++; y = op[1]; op = [0]; continue;
                case 7: op = _.ops.pop(); _.trys.pop(); continue;
                default:
                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
                    if (t[2]) _.ops.pop();
                    _.trys.pop(); continue;
            }
            op = body.call(thisArg, _);
        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
    }
};
Object.defineProperty(exports, "__esModule", { value: true });
var array_ops_1 = require("../ops/array_ops");
var util_1 = require("../util");
var types_1 = require("./types");
function encodeWeights(tensors) {
    return __awaiter(this, void 0, void 0, function () {
        var specs, dataPromises, name_1, t, tensorValues;
        return __generator(this, function (_a) {
            switch (_a.label) {
                case 0:
                    specs = [];
                    dataPromises = [];
                    for (name_1 in tensors) {
                        t = tensors[name_1];
                        if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool') {
                            throw new Error("Unsupported dtype in weight '" + name_1 + "': " + t.dtype);
                        }
                        specs.push({ name: name_1, shape: t.shape, dtype: t.dtype });
                        dataPromises.push(t.data());
                    }
                    return [4, Promise.all(dataPromises)];
                case 1:
                    tensorValues = _a.sent();
                    return [2, { data: concatenateTypedArrays(tensorValues), specs: specs }];
            }
        });
    });
}
exports.encodeWeights = encodeWeights;
function decodeWeights(buffer, specs) {
    var out = {};
    var offset = 0;
    for (var _i = 0, specs_1 = specs; _i < specs_1.length; _i++) {
        var spec = specs_1[_i];
        var name_2 = spec.name;
        var dtype = spec.dtype;
        var shape = spec.shape;
        if (spec.quantization != null) {
            throw new Error("decodeWeights does not support quantization yet, but encountered " +
                ("weight '" + name_2 + " with quantization.'"));
        }
        var size = util_1.sizeFromShape(shape);
        var value = void 0;
        if (dtype === 'float32') {
            value = array_ops_1.ArrayOps.tensor(new Float32Array(buffer, offset, size), shape, 'float32');
        }
        else if (dtype === 'int32') {
            value =
                array_ops_1.ArrayOps.tensor(new Int32Array(buffer, offset, size), shape, 'int32');
        }
        else if (dtype === 'bool') {
            value =
                array_ops_1.ArrayOps.tensor(new Uint8Array(buffer, offset, size), shape, 'bool');
        }
        else {
            throw new Error("Unsupported dtype in weight '" + name_2 + "': " + dtype);
        }
        out[name_2] = value;
        offset += size * types_1.DTYPE_VALUE_SIZE_MAP[dtype];
    }
    return out;
}
exports.decodeWeights = decodeWeights;
function concatenateTypedArrays(xs) {
    if (xs === null) {
        throw new Error("Invalid input value: " + JSON.stringify(xs));
    }
    var totalByteLength = 0;
    xs.forEach(function (x) {
        if (x instanceof Float32Array || x instanceof Int32Array) {
            totalByteLength += x.length * 4;
        }
        else if (x instanceof Uint8Array) {
            totalByteLength += x.length;
        }
        else {
            throw new Error("Unsupported TypedArray subtype: " + x.constructor.name);
        }
    });
    var y = new Uint8Array(totalByteLength);
    var offset = 0;
    xs.forEach(function (x) {
        y.set(new Uint8Array(x.buffer), offset);
        if (x instanceof Float32Array || x instanceof Int32Array) {
            offset += x.length * 4;
        }
        else {
            offset += x.length;
        }
    });
    return y.buffer;
}
exports.concatenateTypedArrays = concatenateTypedArrays;