@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
;
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 };
}
};
var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("../index");
var ops_1 = require("../ops/ops");
var test_util_1 = require("../test_util");
var io_utils_1 = require("./io_utils");
describe('concatenateTypedArrays', function () {
it('Single float arrays', function () {
var x = new Float32Array([1.1, 2.2, 3.3]);
var buffer = io_utils_1.concatenateTypedArrays([x]);
expect(buffer.byteLength).toEqual(12);
expect(new Float32Array(buffer, 0, 3)).toEqual(x);
});
it('Float arrays', function () {
var x = new Float32Array([1.1, 2.2, 3.3]);
var y = new Float32Array([-1.1, -2.2, -3.3]);
var buffer = io_utils_1.concatenateTypedArrays([x, y]);
expect(buffer.byteLength).toEqual(24);
expect(new Float32Array(buffer, 0, 3)).toEqual(x);
expect(new Float32Array(buffer, 12, 3)).toEqual(y);
});
it('Single int32 arrays', function () {
var x = new Int32Array([11, 22, 33]);
var buffer = io_utils_1.concatenateTypedArrays([x]);
expect(buffer.byteLength).toEqual(12);
expect(new Int32Array(buffer, 0, 3)).toEqual(x);
});
it('Int32 arrays', function () {
var x = new Int32Array([11, 22, 33]);
var y = new Int32Array([-11, -22, -33]);
var buffer = io_utils_1.concatenateTypedArrays([x, y]);
expect(buffer.byteLength).toEqual(24);
expect(new Int32Array(buffer, 0, 3)).toEqual(x);
expect(new Int32Array(buffer, 12, 3)).toEqual(y);
});
it('Single uint8 arrays', function () {
var x = new Uint8Array([11, 22, 33]);
var buffer = io_utils_1.concatenateTypedArrays([x]);
expect(buffer.byteLength).toEqual(3);
expect(new Uint8Array(buffer, 0, 3)).toEqual(x);
});
it('Uint8 arrays', function () {
var x = new Uint8Array([11, 22, 33]);
var y = new Uint8Array([111, 122, 133]);
var buffer = io_utils_1.concatenateTypedArrays([x, y]);
expect(buffer.byteLength).toEqual(6);
expect(new Uint8Array(buffer, 0, 3)).toEqual(x);
expect(new Uint8Array(buffer, 3, 3)).toEqual(y);
});
it('Mixed Uint8, Int32 and Float32 arrays', function () {
var x = new Uint8Array([0, 1, 1, 0]);
var y = new Int32Array([10, 20, 30, 40]);
var z = new Float32Array([-1.1, -2.2, -3.3, -4.4]);
var buffer = io_utils_1.concatenateTypedArrays([x, y, z]);
expect(buffer.byteLength).toEqual(1 * 4 + 4 * 4 + 4 * 4);
expect(new Uint8Array(buffer, 0, 4)).toEqual(x);
expect(new Int32Array(buffer, 4, 4)).toEqual(y);
expect(new Float32Array(buffer, 20, 4)).toEqual(z);
});
it('null and undefined inputs', function () {
expect(function () { return io_utils_1.concatenateTypedArrays(null); }).toThrow();
expect(function () { return io_utils_1.concatenateTypedArrays(undefined); }).toThrow();
});
it('empty input array', function () {
expect(io_utils_1.concatenateTypedArrays([]).byteLength).toEqual(0);
});
it('Unsupported dtype', function () {
var x = new Int16Array([0, 1, 1, 0]);
expect(function () { return io_utils_1.concatenateTypedArrays([x]); })
.toThrowError(/Unsupported TypedArray subtype: Int16Array/);
});
});
describe('encodeWeights', function () {
it('Float32 tensors', function (done) { return __awaiter(_this, void 0, void 0, function () {
var tensors;
return __generator(this, function (_a) {
tensors = {
x1: ops_1.tensor2d([[10, 20], [30, 40]]),
x2: ops_1.scalar(42),
x3: ops_1.tensor1d([-1.3, -3.7, 1.3, 3.7]),
};
tf.io.encodeWeights(tensors)
.then(function (dataAndSpecs) {
var data = dataAndSpecs.data;
var specs = dataAndSpecs.specs;
expect(data.byteLength).toEqual(4 * (4 + 1 + 4));
expect(new Float32Array(data, 0, 4)).toEqual(new Float32Array([
10, 20, 30, 40
]));
expect(new Float32Array(data, 16, 1)).toEqual(new Float32Array([42]));
expect(new Float32Array(data, 20, 4)).toEqual(new Float32Array([
-1.3, -3.7, 1.3, 3.7
]));
expect(specs).toEqual([
{
name: 'x1',
dtype: 'float32',
shape: [2, 2],
},
{
name: 'x2',
dtype: 'float32',
shape: [],
},
{
name: 'x3',
dtype: 'float32',
shape: [4],
}
]);
done();
})
.catch(function (err) {
console.error(err.stack);
});
return [2];
});
}); });
it('Int32 tensors', function (done) { return __awaiter(_this, void 0, void 0, function () {
var tensors;
return __generator(this, function (_a) {
tensors = {
x1: ops_1.tensor2d([[10, 20], [30, 40]], [2, 2], 'int32'),
x2: ops_1.scalar(42, 'int32'),
x3: ops_1.tensor1d([-1, -3, -3, -7], 'int32'),
};
tf.io.encodeWeights(tensors)
.then(function (dataAndSpecs) {
var data = dataAndSpecs.data;
var specs = dataAndSpecs.specs;
expect(data.byteLength).toEqual(4 * (4 + 1 + 4));
expect(new Int32Array(data, 0, 4)).toEqual(new Int32Array([
10, 20, 30, 40
]));
expect(new Int32Array(data, 16, 1)).toEqual(new Int32Array([42]));
expect(new Int32Array(data, 20, 4)).toEqual(new Int32Array([
-1, -3, -3, -7
]));
expect(specs).toEqual([
{
name: 'x1',
dtype: 'int32',
shape: [2, 2],
},
{
name: 'x2',
dtype: 'int32',
shape: [],
},
{
name: 'x3',
dtype: 'int32',
shape: [4],
}
]);
done();
})
.catch(function (err) {
console.error(err.stack);
});
return [2];
});
}); });
it('Bool tensors', function (done) { return __awaiter(_this, void 0, void 0, function () {
var tensors;
return __generator(this, function (_a) {
tensors = {
x1: ops_1.tensor2d([[true, false], [false, true]], [2, 2], 'bool'),
x2: ops_1.scalar(false, 'bool'),
x3: ops_1.tensor1d([false, true, true, false], 'bool'),
};
tf.io.encodeWeights(tensors)
.then(function (dataAndSpecs) {
var data = dataAndSpecs.data;
var specs = dataAndSpecs.specs;
expect(data.byteLength).toEqual(4 + 1 + 4);
expect(new Uint8Array(data, 0, 4)).toEqual(new Uint8Array([
1, 0, 0, 1
]));
expect(new Uint8Array(data, 4, 1)).toEqual(new Uint8Array([0]));
expect(new Uint8Array(data, 5, 4)).toEqual(new Uint8Array([
0, 1, 1, 0
]));
expect(specs).toEqual([
{
name: 'x1',
dtype: 'bool',
shape: [2, 2],
},
{
name: 'x2',
dtype: 'bool',
shape: [],
},
{
name: 'x3',
dtype: 'bool',
shape: [4],
}
]);
done();
})
.catch(function (err) {
console.error(err.stack);
});
return [2];
});
}); });
it('Mixed dtype tensors', function (done) { return __awaiter(_this, void 0, void 0, function () {
var tensors;
return __generator(this, function (_a) {
tensors = {
x1: ops_1.tensor2d([[10, 20], [30, 40]], [2, 2], 'int32'),
x2: ops_1.scalar(13.37, 'float32'),
x3: ops_1.tensor1d([true, false, false, true], 'bool'),
};
tf.io.encodeWeights(tensors)
.then(function (dataAndSpecs) {
var data = dataAndSpecs.data;
var specs = dataAndSpecs.specs;
expect(data.byteLength).toEqual(4 * 4 + 4 * 1 + 1 * 4);
expect(new Int32Array(data, 0, 4)).toEqual(new Int32Array([
10, 20, 30, 40
]));
expect(new Float32Array(data, 16, 1))
.toEqual(new Float32Array([13.37]));
expect(new Uint8Array(data, 20, 4)).toEqual(new Uint8Array([
1, 0, 0, 1
]));
expect(specs).toEqual([
{
name: 'x1',
dtype: 'int32',
shape: [2, 2],
},
{
name: 'x2',
dtype: 'float32',
shape: [],
},
{
name: 'x3',
dtype: 'bool',
shape: [4],
}
]);
done();
})
.catch(function (err) {
console.error(err.stack);
});
return [2];
});
}); });
});
describe('decodeWeights', function () {
it('Mixed dtype tensors', function (done) { return __awaiter(_this, void 0, void 0, function () {
var tensors;
return __generator(this, function (_a) {
tensors = {
x1: ops_1.tensor2d([[10, 20], [30, 40]], [2, 2], 'int32'),
x2: ops_1.scalar(13.37, 'float32'),
x3: ops_1.tensor1d([true, false, false, true], 'bool'),
y1: ops_1.tensor2d([-10, -20, -30], [3, 1], 'float32'),
};
tf.io.encodeWeights(tensors)
.then(function (dataAndSpecs) {
var data = dataAndSpecs.data;
var specs = dataAndSpecs.specs;
expect(data.byteLength).toEqual(4 * 4 + 4 * 1 + 1 * 4 + 4 * 3);
var decoded = tf.io.decodeWeights(data, specs);
expect(Object.keys(decoded).length).toEqual(4);
test_util_1.expectArraysEqual(decoded['x1'], tensors['x1']);
test_util_1.expectArraysEqual(decoded['x2'], tensors['x2']);
test_util_1.expectArraysEqual(decoded['x3'], tensors['x3']);
test_util_1.expectArraysEqual(decoded['y1'], tensors['y1']);
done();
})
.catch(function (err) {
console.error(err.stack);
});
return [2];
});
}); });
it('Unsupported dtype raises Error', function () {
var buffer = new ArrayBuffer(4);
var specs = [
{
name: 'x',
dtype: 'int16',
shape: [],
},
{ name: 'y', dtype: 'int16', shape: [] }
];
expect(function () { return tf.io.decodeWeights(buffer, specs); })
.toThrowError(/Unsupported dtype in weight \'x\': int16/);
});
});