@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose } from '../test_util';
describeWithFlags('slice2d', ALL_ENVS, () => {
it('slicing a 1x1 from a 1x1 returns a 1x1', () => {
const a = tf.tensor2d([0], [1, 1]);
const b = tf.slice2d(a, [0, 0], [1, 1]);
expect(b.shape).toEqual([1, 1]);
});
it('returns a tensor of slice size', () => {
const a = tf.zeros([100, 100]);
const b = tf.slice2d(a, [0, 0], [12, 34]);
expect(b.shape).toEqual([12, 34]);
});
it('returns the upper-left submatrix when begin is [0, 0]', async () => {
const a = tf.randomUniform([10, 10], -1, 1);
const b = tf.slice2d(a, [0, 0], [2, 2]);
const aValues = await a.data();
expectArraysClose(await b.data(), [aValues[0], aValues[1], aValues[10], aValues[11]]);
});
it('returns the rectangle specified', async () => {
const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [4, 3]);
const b = tf.slice2d(a, [1, 1], [3, 2]);
expectArraysClose(await b.data(), [5, 6, 8, 9, 11, 12]);
});
it('throws when requesting out of bounds slice', () => {
const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [4, 3]);
expect(() => tf.slice2d(a, [1, 1], [10, 10])).toThrowError();
});
it('grad', async () => {
const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
const dy = tf.tensor2d([[20], [50]]);
const da = tf.grad((x) => tf.slice2d(a, [0, 1], [2, 1]))(a, dy);
expect(da.shape).toEqual([2, 3]);
expectArraysClose(await da.data(), [0, 20, 0, 0, 50, 0]);
});
it('accepts a tensor-like object', () => {
const a = [[0]]; // 1x1
const b = tf.slice2d(a, [0, 0], [1, 1]);
expect(b.shape).toEqual([1, 1]);
});
it('slice an already sliced tensor, first was not continous', async () => {
const a = [
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
]; // 3x4.
const b = tf.slice(a, [0, 1]);
const c = tf.slice(b, [1, 1], [1, 1]);
expect(c.shape).toEqual([1, 1]);
expectArraysClose(await c.data(), [7]);
});
it('slice an already sliced tensor, first was continous', async () => {
const a = [
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
]; // 3x4.
const b = tf.slice(a, [1, 0]);
const c = tf.slice(b, [1, 0]);
expect(c.shape).toEqual([1, 4]);
expectArraysClose(await c.data(), [9, 10, 11, 12]);
});
it('slice an already sliced tensor and do async read', async () => {
const a = [
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
]; // 3x4.
const b = tf.slice(a, [0, 1]);
const c = tf.slice(b, [1, 1], [1, 1]);
expect(c.shape).toEqual([1, 1]);
expectArraysClose(await c.data(), new Float32Array([7]));
});
it('square a sliced texture, followed by non-sliced texture of same shape', async () => {
// Make a 2x3 tensor, upload to gpu and reshape to 3x2.
const input = tf.tensor([[1, 2, 3], [4, 5, 6]]).abs().as2D(3, 2);
const slicedInput = tf.slice(input, [0, 0], [3, 2]);
// First square program takes the sliced input.
const a = slicedInput.square();
expectArraysClose(await a.data(), [1, 4, 9, 16, 25, 36]);
// Second square program takes the non-sliced input.
const b = tf.square(input);
expectArraysClose(await b.data(), [1, 4, 9, 16, 25, 36]);
});
it('square a non-sliced texture, followed by a sliced texture of same shape', async () => {
// Make a 2x3 tensor, upload to gpu and reshape to 3x2.
const input = tf.tensor([[1, 2, 3], [4, 5, 6]]).abs().as2D(3, 2);
// Make a sliced version of the same tensor with the same shape.
const slicedInput = tf.slice(input, [0, 0], [3, 2]);
// First square program takes the non-sliced input.
const a = input.square();
expectArraysClose(await a.data(), [1, 4, 9, 16, 25, 36]);
// Second square program takes the sliced input.
const b = tf.square(slicedInput);
expectArraysClose(await b.data(), [1, 4, 9, 16, 25, 36]);
});
it('slice a tensor and do async read', async () => {
const a = [
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
]; // 3x4.
const b = tf.slice(a, [0, 1], [3, 2]);
expect(b.shape).toEqual([3, 2]);
const vals = await b.data();
expectArraysClose(vals, new Float32Array([2, 3, 6, 7, 10, 11]));
});
it('flatten a sliced tensor that was continuous in memory', async () => {
const a = [
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
]; // 3x4.
const b = tf.slice(a, [1, 0]).flatten();
expect(b.shape).toEqual([8]);
expectArraysClose(await b.data(), [5, 6, 7, 8, 9, 10, 11, 12]);
});
it('slice a tensor that was not continuous in memory', async () => {
const a = [
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
]; // 3x4.
const b = tf.slice(a, [0, 1]);
expect(b.shape).toEqual([3, 3]);
expectArraysClose(await b.data(), [2, 3, 4, 6, 7, 8, 10, 11, 12]);
});
it('flatten a sliced tensor that was not continuous in memory', async () => {
const a = [
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
]; // 3x4.
const b = tf.slice(a, [0, 1]).flatten();
expect(b.shape).toEqual([9]);
expectArraysClose(await b.data(), [2, 3, 4, 6, 7, 8, 10, 11, 12]);
});
it('flatten a sliced tensor not continuous in memory and run program', async () => {
const a = [
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
]; // 3x4.
const b = tf.slice(a, [0, 1]).flatten();
const c = tf.square(b);
expectArraysClose(await c.data(), [4, 9, 16, 36, 49, 64, 100, 121, 144]);
});
it('reshape a sliced 1d into a 2d tensor', async () => {
const a = [1, 2, 3, 4, 5];
const b = tf.slice(a, 1).as2D(2, 2);
expect(b.shape).toEqual([2, 2]);
expectArraysClose(await b.data(), [2, 3, 4, 5]);
});
it('reshape a sliced 1d into a 2d tensor and run program', async () => {
const a = [1, 2, 3, 4, 5];
const b = tf.slice(a, 1).as2D(2, 2).square();
expect(b.shape).toEqual([2, 2]);
expectArraysClose(await b.data(), [4, 9, 16, 25]);
});
it('broadcast the original with the sliced tensor', async () => {
const a = [[1, 2], [3, 4]];
const b = tf.slice(a, [0, 1]);
const c = tf.add(a, b);
expect(c.shape).toEqual([2, 2]);
expectArraysClose(await c.data(), [3, 4, 7, 8]);
});
it('zero-sized slice out of a non-zero sized tensor', async () => {
const a = tf.zeros([4, 2]);
const res = tf.slice(a, [0, 0], [0, 2]);
expect(res.shape).toEqual([0, 2]);
expectArraysClose(await res.data(), []);
});
it('zero-sized slice out of a zero-sized tensor', async () => {
const a = tf.zeros([0, 4]);
const res = tf.slice(a, [0, 1], [0, 3]);
expect(res.shape).toEqual([0, 3]);
expectArraysClose(await res.data(), []);
});
});
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