@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2018 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, SYNC_BACKEND_ENVS } from '../jasmine_util';
import { encodeStrings, expectArraysClose } from '../test_util';
describeWithFlags('slice ', ALL_ENVS, () => {
describeWithFlags('ergonomics', ALL_ENVS, () => {
it('slices 2x2x2 array into 2x1x1 no size', async () => {
const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
const result = a.slice([0, 1, 1]);
expect(result.shape).toEqual([2, 1, 1]);
expectArraysClose(await result.data(), [4, 8]);
});
it('slices 2x2x2 array into 1x2x2 with scalar begin no size', async () => {
const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
const result = a.slice(1);
expect(result.shape).toEqual([1, 2, 2]);
expectArraysClose(await result.data(), [5, 6, 7, 8]);
});
it('slices 2x2x2 array using 2d size and 2d size', async () => {
const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
const result = a.slice([0, 1]);
expect(result.shape).toEqual([2, 1, 2]);
expectArraysClose(await result.data(), [3, 4, 7, 8]);
});
it('slices 2x2x2 array using negative size', async () => {
const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
const result = a.slice([0, 1], [-1, 1]);
expect(result.shape).toEqual([2, 1, 2]);
expectArraysClose(await result.data(), [3, 4, 7, 8]);
});
it('slices 2x2x2 array using 1d size', async () => {
const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
const result = a.slice(0, 1);
expect(result.shape).toEqual([1, 2, 2]);
expectArraysClose(await result.data(), [1, 2, 3, 4]);
});
it('throws when passed a non-tensor', () => {
expect(() => tf.slice({}, 0, 0))
.toThrowError(/Argument 'x' passed to 'slice' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const a = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]; // 2x2x2
const result = tf.slice(a, [0, 1, 1]);
expect(result.shape).toEqual([2, 1, 1]);
expectArraysClose(await result.data(), [4, 8]);
});
it('should match source tensor dtype', () => {
const a = tf.tensor1d([1, 2, 3, 4, 5], 'int32');
const b = a.asType('float32');
expect(tf.slice(b, 0).dtype).toEqual('float32');
});
it('throws when begin is negative', async () => {
const a = [[1, 2], [3, 4]]; // 2x2
expect(() => tf.slice(a, [-1, 1], [
1, 1
])).toThrowError(/slice\(\) does not support negative begin indexing./);
});
});
describeWithFlags('shallow slicing', ALL_ENVS, () => {
it('shallow slice an input that was cast', async () => {
const a = tf.tensor([[1, 2], [3, 4]], [2, 2], 'int32');
const b = a.toFloat();
const c = b.slice(1, 1);
expect(c.dtype).toBe('float32');
expect(c.shape).toEqual([1, 2]);
expectArraysClose(await c.data(), [3, 4]);
});
it('delayed async read of sliced tensor has no mem leak', async () => {
const a = tf.zeros([10]);
const b = tf.slice(a, 0, 1);
const nBefore = tf.memory().numTensors;
expect(nBefore).toBe(2);
await b.data();
const nAfter = tf.memory().numTensors;
expect(nAfter).toBe(2);
tf.dispose([a, b]);
expect(tf.memory().numTensors).toBe(0);
});
});
describeWithFlags('shallow slicing', SYNC_BACKEND_ENVS, () => {
it('delayed sync read of sliced tensor has no mem leak', () => {
const a = tf.zeros([10]);
const b = tf.slice(a, 0, 1);
const nBefore = tf.memory().numTensors;
expect(nBefore).toBe(2);
b.dataSync();
const nAfter = tf.memory().numTensors;
expect(nAfter).toBe(2);
tf.dispose([a, b]);
expect(tf.memory().numTensors).toBe(0);
});
});
describeWithFlags('slice5d', ALL_ENVS, () => {
it('slices 1x1x1x1x1 into shape 1x1x1x1x1 (effectively a copy)', async () => {
const a = tf.tensor5d([[[[[5]]]]], [1, 1, 1, 1, 1]);
const result = tf.slice(a, [0, 0, 0, 0, 0], [1, 1, 1, 1, 1]);
expect(result.shape).toEqual([1, 1, 1, 1, 1]);
expectArraysClose(await result.data(), [5]);
});
it('slices 2x2x2x2x2 array into 1x2x2x2x2 starting at [1,0,0,0,0]', async () => {
const a = tf.tensor5d([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 11, 22, 33, 44, 55, 66,
77, 88, 111, 222, 333, 444, 555, 666, 777, 888
], [2, 2, 2, 2, 2]);
const result = tf.slice(a, [1, 0, 0, 0, 0], [1, 2, 2, 2, 2]);
expect(result.shape).toEqual([1, 2, 2, 2, 2]);
expectArraysClose(await result.data(), [
11, 22, 33, 44, 55, 66, 77, 88, 111, 222, 333, 444, 555, 666, 777,
888
]);
});
it('slices 2x2x2x2x2 array into 2x1x1x1x1 starting at [0,1,1,1,1]', async () => {
const a = tf.tensor5d([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 11, 22, 33, 44, 55, 66,
77, 88, 111, 222, 333, 444, 555, 666, 777, 888
], [2, 2, 2, 2, 2]);
const result = tf.slice(a, [0, 1, 1, 1, 1], [2, 1, 1, 1, 1]);
expect(result.shape).toEqual([2, 1, 1, 1, 1]);
expectArraysClose(await result.data(), [16, 888]);
});
it('accepts a tensor-like object', async () => {
const a = [[[[[5]]]]]; // 1x1x1x1x1
const result = tf.slice(a, [0, 0, 0, 0, 0], [1, 1, 1, 1, 1]);
expect(result.shape).toEqual([1, 1, 1, 1, 1]);
expectArraysClose(await result.data(), [5]);
});
});
describeWithFlags('slice6d', ALL_ENVS, () => {
it('slices 1x1x1x1x1x1 into shape 1x1x1x1x1x1 (effectively a copy)', async () => {
const a = tf.tensor6d([[[[[[5]]]]]], [1, 1, 1, 1, 1, 1]);
const result = tf.slice(a, [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]);
expect(result.shape).toEqual([1, 1, 1, 1, 1, 1]);
expectArraysClose(await result.data(), [5]);
});
it('slices 2x2x2x2x2x2 array into 1x2x2x2x2x2 starting at [1,0,0,0,0,0]', async () => {
const a = tf.tensor6d([
31, 32, 33, 34, 35, 36, 37, 38, 39, 310, 311,
312, 313, 314, 315, 316, 311, 322, 333, 344, 355, 366,
377, 388, 3111, 3222, 3333, 3444, 3555, 3666, 3777, 3888,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 11, 22, 33, 44, 55, 66,
77, 88, 111, 222, 333, 444, 555, 666, 777, 888
], [2, 2, 2, 2, 2, 2]);
const result = tf.slice(a, [1, 0, 0, 0, 0, 0], [1, 2, 2, 2, 2, 2]);
expect(result.shape).toEqual([1, 2, 2, 2, 2, 2]);
expectArraysClose(await result.data(), [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 11, 22, 33, 44, 55, 66,
77, 88, 111, 222, 333, 444, 555, 666, 777, 888
]);
});
it('slices 2x2x2x2x2x2 array into 2x1x1x1x1x1 starting at [0,1,1,1,1,1]', async () => {
const a = tf.tensor6d([
31, 32, 33, 34, 35, 36, 37, 38, 39, 310, 311,
312, 313, 314, 315, 316, 311, 322, 333, 344, 355, 366,
377, 388, 3111, 3222, 3333, 3444, 3555, 3666, 3777, 3888,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 11, 22, 33, 44, 55, 66,
77, 88, 111, 222, 333, 444, 555, 666, 777, 888
], [2, 2, 2, 2, 2, 2]);
const result = tf.slice(a, [0, 1, 1, 1, 1, 1], [2, 1, 1, 1, 1, 1]);
expect(result.shape).toEqual([2, 1, 1, 1, 1, 1]);
expectArraysClose(await result.data(), [3888, 888]);
});
it('accepts a tensor-like object', async () => {
const a = [[[[[[5]]]]]]; // 1x1x1x1x1x1
const result = tf.slice(a, [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]);
expect(result.shape).toEqual([1, 1, 1, 1, 1, 1]);
expectArraysClose(await result.data(), [5]);
});
});
describeWithFlags('accepts string', ALL_ENVS, () => {
it('slices 2x2x2 array into 2x1x1 no size.', async () => {
const a = tf.tensor3d(['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'], [2, 2, 2], 'string');
const result = a.slice([0, 1, 1]);
expect(result.shape).toEqual([2, 1, 1]);
expectArraysClose(await result.data(), ['four', 'eight']);
});
it('slices 2x2x2 array into 1x2x2 with scalar begin no size.', async () => {
const a = tf.tensor3d(['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'], [2, 2, 2]);
const result = a.slice(1);
expect(result.shape).toEqual([1, 2, 2]);
expectArraysClose(await result.data(), ['five', 'six', 'seven', 'eight']);
});
it('slice encoded string.', async () => {
const bytes = encodeStrings([
'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'
]);
const a = tf.tensor3d(bytes, [2, 2, 2], 'string');
const result = a.slice([0, 1, 1]);
expect(result.shape).toEqual([2, 1, 1]);
expectArraysClose(await result.data(), ['four', 'eight']);
});
});
});
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