@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
132 lines • 6.11 kB
JavaScript
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var environment_1 = require("../environment");
var tf = require("../index");
var jasmine_util_1 = require("../jasmine_util");
var test_util_1 = require("../test_util");
jasmine_util_1.describeWithFlags('pad1d', test_util_1.ALL_ENVS, function () {
it('Should pad 1D arrays', function () {
var a = tf.tensor1d([1, 2, 3, 4, 5, 6], 'int32');
var b = tf.pad1d(a, [2, 3]);
test_util_1.expectArraysClose(b, [0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0]);
});
it('Should not pad 1D arrays with 0s', function () {
var a = tf.tensor1d([1, 2, 3, 4], 'int32');
var b = tf.pad1d(a, [0, 0]);
test_util_1.expectArraysClose(b, [1, 2, 3, 4]);
});
it('Should handle padding with custom value', function () {
var a = tf.tensor1d([1, 2, 3, 4], 'int32');
var b = tf.pad1d(a, [2, 3], 9);
test_util_1.expectArraysClose(b, [9, 9, 1, 2, 3, 4, 9, 9, 9]);
a = tf.tensor1d([1, 2, 3, 4]);
b = tf.pad1d(a, [2, 1], 1.1);
test_util_1.expectArraysClose(b, [1.1, 1.1, 1, 2, 3, 4, 1.1]);
a = tf.tensor1d([1, 2, 3, 4]);
b = tf.pad1d(a, [2, 1], 1);
test_util_1.expectArraysClose(b, [1, 1, 1, 2, 3, 4, 1]);
});
it('Should handle NaNs with 1D arrays', function () {
var a = tf.tensor1d([1, NaN, 2, NaN]);
var b = tf.pad1d(a, [1, 1]);
test_util_1.expectArraysClose(b, [0, 1, NaN, 2, NaN, 0]);
});
it('Should handle invalid paddings', function () {
var a = tf.tensor1d([1, 2, 3, 4], 'int32');
var f = function () {
tf.pad1d(a, [2, 2, 2]);
};
expect(f).toThrowError();
});
it('grad', function () {
var a = tf.tensor1d([1, 2, 3]);
var dy = tf.tensor1d([10, 20, 30, 40, 50, 60]);
var da = tf.grad(function (a) { return tf.pad1d(a, [2, 1]); })(a, dy);
expect(da.shape).toEqual([3]);
test_util_1.expectArraysClose(da, [30, 40, 50]);
});
});
jasmine_util_1.describeWithFlags('pad2d', test_util_1.ALL_ENVS, function () {
it('Should pad 2D arrays', function () {
var a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
var b = tf.pad2d(a, [[1, 1], [1, 1]]);
test_util_1.expectArraysClose(b, [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]);
a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
b = tf.pad2d(a, [[2, 2], [1, 1]]);
test_util_1.expectArraysClose(b, [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0,
0, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]);
});
it('Should not pad 2D arrays with 0s', function () {
var a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
var b = tf.pad2d(a, [[0, 0], [0, 0]]);
test_util_1.expectArraysClose(b, [1, 2, 3, 4, 5, 6]);
});
it('Should handle padding with custom value', function () {
var a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
var b = tf.pad2d(a, [[1, 1], [1, 1]], 10);
test_util_1.expectArraysClose(b, [
10, 10, 10, 10, 10, 10, 1, 2, 3, 10,
10, 4, 5, 6, 10, 10, 10, 10, 10, 10
]);
a = tf.tensor2d([[1], [1]], [2, 1]);
b = tf.pad2d(a, [[1, 1], [1, 1]], -2.1);
test_util_1.expectArraysClose(b, [-2.1, -2.1, -2.1, -2.1, 1, -2.1, -2.1, 1, -2.1, -2.1, -2.1, -2.1]);
a = tf.tensor2d([[1], [1]], [2, 1]);
b = tf.pad2d(a, [[1, 1], [1, 1]], -2);
test_util_1.expectArraysClose(b, [-2, -2, -2, -2, 1, -2, -2, 1, -2, -2, -2, -2]);
});
it('Should handle NaNs with 2D arrays', function () {
var a = tf.tensor2d([[1, NaN], [1, NaN]], [2, 2]);
var b = tf.pad2d(a, [[1, 1], [1, 1]]);
test_util_1.expectArraysClose(b, [0, 0, 0, 0, 0, 1, NaN, 0, 0, 1, NaN, 0, 0, 0, 0, 0]);
});
it('Should handle invalid paddings', function () {
var a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
var f = function () {
tf.pad2d(a, [[2, 2, 2], [1, 1, 1]]);
};
expect(f).toThrowError();
});
it('grad', function () {
var a = tf.tensor2d([[1, 2], [3, 4]]);
var dy = tf.tensor2d([[0, 0, 0], [10, 20, 0], [30, 40, 0]], [3, 3]);
var da = tf.grad(function (a) { return tf.pad2d(a, [[1, 0], [0, 1]]); })(a, dy);
expect(da.shape).toEqual([2, 2]);
test_util_1.expectArraysClose(da, [10, 20, 30, 40]);
});
});
jasmine_util_1.describeWithFlags('pad4d', test_util_1.ALL_ENVS, function () {
it('Should pad 4D arrays', function () {
var a = tf.tensor4d([[[[9]]]], [1, 1, 1, 1], 'int32');
var b = tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
var expected = tf.tensor4d([[[[0], [0], [0]], [[0], [9], [0]], [[0], [0], [0]]]], [1, 3, 3, 1], 'int32');
test_util_1.expectArraysClose(b, expected);
});
it('does not leak memory', function () {
var a = tf.tensor4d([[[[9]]]], [1, 1, 1, 1], 'int32');
tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
var numTensors = environment_1.Environment.memory().numTensors;
tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
expect(environment_1.Environment.memory().numTensors).toEqual(numTensors + 1);
});
});
jasmine_util_1.describeWithFlags('pad', test_util_1.ALL_ENVS, function () {
it('Pad tensor2d', function () {
var a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
var b = tf.pad(a, [[1, 1], [1, 1]]);
test_util_1.expectArraysClose(b, [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]);
a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
b = tf.pad(a, [[2, 2], [1, 1]]);
test_util_1.expectArraysClose(b, [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0,
0, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]);
});
it('throws when passed a non-tensor', function () {
expect(function () { return tf.pad({}, [[0, 0]]); })
.toThrowError(/Argument 'x' passed to 'pad' must be a Tensor/);
});
});
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