@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2020 Google Inc. 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('gather', ALL_ENVS, (env) => {
it('1D (gather), scalar indices', async () => {
const t = tf.tensor1d([1, 2, 3]);
const t2 = tf.gather(t, tf.scalar(1, 'int32'), 0);
expect(t2.shape).toEqual([]);
expectArraysClose(await t2.data(), [2]);
});
it('1D (gather), 1D indices', async () => {
const t = tf.tensor1d([1, 2, 3]);
const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
expect(t2.shape).toEqual([4]);
expectArraysClose(await t2.data(), [1, 3, 1, 2]);
});
it('1D (gather), 2D indices', async () => {
const t = tf.tensor1d([1, 2, 3]);
const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [1, 4], 'int32'), 0);
expect(t2.shape).toEqual([1, 4]);
expectArraysClose(await t2.data(), [1, 3, 1, 2]);
});
it('2D (gather), scalar indices', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
let t2 = tf.gather(t, tf.scalar(1, 'int32'), 0);
expect(t2.shape).toEqual([2]);
expectArraysClose(await t2.data(), [2, 22]);
t2 = tf.gather(t, tf.scalar(1, 'int32'), 1);
expect(t2.shape).toEqual([2]);
expectArraysClose(await t2.data(), [11, 22]);
});
it('2D (gather), 1D indices', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
let t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 0);
expect(t2.shape).toEqual([4, 2]);
expectArraysClose(await t2.data(), [2, 22, 1, 11, 1, 11, 2, 22]);
t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 1);
expect(t2.shape).toEqual([2, 4]);
expectArraysClose(await t2.data(), [11, 1, 1, 11, 22, 2, 2, 22]);
});
it('2D (gather), 2D indices', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
let t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 0);
expect(t2.shape).toEqual([2, 2, 2]);
expectArraysClose(await t2.data(), [2, 22, 1, 11, 1, 11, 2, 22]);
t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1);
expect(t2.shape).toEqual([2, 2, 2]);
expectArraysClose(await t2.data(), [11, 1, 1, 11, 22, 2, 2, 22]);
});
it('2D (gather), 2D indices, non-zero batchDims', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1, 1);
expect(t2.shape).toEqual([2, 2]);
expectArraysClose(await t2.data(), [11, 1, 2, 22]);
});
it('2D (gather), 2D indices, negative batchDims', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1, -1);
expect(t2.shape).toEqual([2, 2]);
expectArraysClose(await t2.data(), [11, 1, 2, 22]);
});
it('3D (gather), 1D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
const t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 2);
expect(t2.shape).toEqual([2, 2, 4]);
expectArraysClose(await t2.data(), [2, 1, 1, 2, 4, 3, 3, 4, 6, 5, 5, 6, 8, 7, 7, 8]);
});
it('3D (gather), 2D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 2);
expect(t2.shape).toEqual([2, 2, 2, 2]);
expectArraysClose(await t2.data(), [2, 1, 1, 2, 4, 3, 3, 4, 6, 5, 5, 6, 8, 7, 7, 8]);
});
it('3D (gather), 2D indices, non-zero batchDims', async () => {
const t = tf.tensor3d([1, 2, 3, 4], [1, 2, 2]);
const t2 = tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 1);
expect(t2.shape).toEqual([1, 2, 3]);
expectArraysClose(await t2.data(), [2, 1, 2, 4, 3, 4]);
});
it('throws when batch dims greater than axis', () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
expect(() => tf.gather(t, tf.tensor3d([1, 0, 1], [1, 1, 3], 'int32'), 2, 3))
.toThrowError(/must be less than or equal to axis/);
});
it('throws when batch dims greater than indices rank', () => {
const t = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2]);
expect(() => tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 3))
.toThrowError(/Expect batchDims in the range of /);
});
it('throws when batch dims do not match', () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
expect(() => tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 1))
.toThrowError(/should be equal to indices.shape/);
});
it('bool (gather), 1D indices', async () => {
const t = tf.tensor1d([true, false, true], 'bool');
const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
expect(t2.shape).toEqual([4]);
expect(t2.dtype).toBe('bool');
expect(await t2.data()).toEqual(new Uint8Array([1, 1, 1, 0]));
});
it('bool (gather), 2D indices', async () => {
const t = tf.tensor1d([true, false, true], 'bool');
const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32'), 0);
expect(t2.shape).toEqual([2, 2]);
expect(t2.dtype).toBe('bool');
expect(await t2.data()).toEqual(new Uint8Array([1, 1, 1, 0]));
});
it('int32 (gather), 1D indices', async () => {
const t = tf.tensor1d([1, 2, 5], 'int32');
const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
expect(t2.shape).toEqual([4]);
expect(t2.dtype).toBe('int32');
expect(await t2.data()).toEqual(new Int32Array([1, 5, 1, 2]));
});
it('int32 (gather), 2D indices', async () => {
const t = tf.tensor1d([1, 2, 5], 'int32');
const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32'), 0);
expect(t2.shape).toEqual([2, 2]);
expect(t2.dtype).toBe('int32');
expect(await t2.data()).toEqual(new Int32Array([1, 5, 1, 2]));
});
it('propagates NaNs', async () => {
const t = tf.tensor1d([1, 2, NaN]);
const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
expect(t2.shape).toEqual([4]);
expectArraysClose(await t2.data(), [1, NaN, 1, 2]);
});
it('chaining, axis=1', () => {
const x = tf.zeros([2, 4, 6]);
// [0, 2, 4]
const indices = tf.range(0, 6, 2, 'int32');
const axis = 2;
expect(x.gather(indices, axis).shape).toEqual([2, 4, 3]);
});
it('indices not int32 throws error', () => {
const x = tf.zeros([2, 4, 6]);
// [0, 2, 4]
const indices = tf.range(0, 6, 2);
const axis = 2;
expect(() => x.gather(indices, axis)).toThrowError();
});
it('throws when passed x as a non-tensor', () => {
expect(() => tf.gather({}, tf.tensor1d([1])))
.toThrowError(/Argument 'x' passed to 'gather' must be a Tensor/);
});
it('throws when passed indices as a non-tensor', () => {
// tslint:disable-next-line:no-any
expect(() => tf.gather(tf.tensor1d([1]), {}))
.toThrowError(/Argument 'indices' passed to 'gather' must be a Tensor/);
});
it('throws when index is out of bound', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
expect(() => tf.gather(t, tf.tensor1d([100], 'int32')))
.toThrowError(/GatherV2: the index value 100 is not in \[0, 1\]/);
expect(() => tf.gather(t, tf.tensor1d([-1], 'int32')))
.toThrowError(/GatherV2: the index value -1 is not in \[0, 1\]/);
});
it('accepts a tensor-like object', async () => {
const res = tf.gather([1, 2, 3], [0, 2, 0, 1], 0);
expect(res.shape).toEqual([4]);
expectArraysClose(await res.data(), [1, 3, 1, 2]);
});
it('gradient 1D (gather), 1D indices', async () => {
const t = tf.tensor1d([1, 2, 3]);
const indices = tf.tensor1d([0, 2, 0, 1], 'int32');
const dy = tf.tensor([3, 4, 5, 6]);
const gradients = tf.grad(t => tf.gather(t, indices))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [8, 6, 4]);
});
it('gradient with clones', () => {
const t = tf.tensor1d([1, 2, 3]);
const indices = tf.tensor1d([0, 2, 0, 1], 'int32');
const gradF = tf.grad(t => tf.gather(t.clone(), indices.clone()).clone());
const dt = gradF(t);
expect(dt.shape).toEqual(t.shape);
});
it('gradient 1D (gather), 2D indices', async () => {
const t = tf.tensor1d([1, 2, 3]);
const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32');
const dy = tf.tensor2d([3, 4, 5, 6], [2, 2]);
const gradients = tf.grad(t => tf.gather(t, indices))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [8, 6, 4]);
});
it('gradient 2D (gather) axis=0 shape=[2, 2] 1D indices', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [4, 2]);
const axis = 0;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [12, 14, 12, 14]);
});
it('gradient 2D (gather) axis=0 shape=[2, 2] 2D indices', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 2, 2]);
const axis = 0;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [12, 14, 12, 14]);
});
it('gradient 2D (gather) axis=0 shape=[4, 1] 1D indices', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
const dy = tf.tensor([23, 7, 19, 13], [4, 1]);
const axis = 0;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [26, 36, 0, 0]);
});
it('gradient 2D (gather) axis=0 shape=[4, 1] 2D indices', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
const dy = tf.tensor([23, 7, 19, 13], [2, 2, 1]);
const axis = 0;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [26, 36, 0, 0]);
});
it('gradient 2D (gather) axis=1 shape=[4, 2] 1D indices batchDims 1', async () => {
const t = tf.variable(tf.tensor([[0, 1],
[1, 2],
[2, 3],
[3, 4]]));
const indices = tf.tensor([0, 1, 0, 1], [4, 1], 'int32');
const dy = tf.tensor([1, 1, 1, 1], [4, 1]);
const axis = 1;
const gradients = tf.grad(t => tf.gather(t, indices, axis, 1))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [1, 0, 0, 1, 1, 0, 0, 1]);
});
it('gradient 2D (gather) axis=1 shape=[2, 2] 1D indices', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 4]);
const axis = 1;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [9, 9, 17, 17]);
});
it('gradient 2D (gather) axis=1 shape=[2, 2] 2D indices', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 2, 2]);
const axis = 1;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [9, 9, 17, 17]);
});
it('gradient 2D (gather) axis=1 shape=[4, 1] 1D indices', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
const indices = tf.tensor1d([0, 0, 0, 0], 'int32');
const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [4, 4]);
const axis = 1;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [18, 34, 50, 66]);
});
it('gradient 2D (gather) axis=1 shape=[4, 1] 2D indices', async () => {
const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
const indices = tf.tensor2d([0, 0, 0, 0], [2, 2], 'int32');
const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [4, 2, 2]);
const axis = 1;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [18, 34, 50, 66]);
});
it('gradient 3D (gather) axis=0 shape=[2, 3, 2] 1D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
const dy = tf.tensor([
2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,
4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25
], [4, 3, 2]);
const axis = 0;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [5, 33, 12.01, -7, 30, 32, 4, 18, 10, 38, 30, 25.7]);
});
it('gradient 3D (gather) axis=0 shape=[2, 3, 2] 2D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
const dy = tf.tensor([
2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,
4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25
], [2, 2, 3, 2]);
const axis = 0;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [5, 33, 12.01, -7, 30, 32, 4, 18, 10, 38, 30, 25.7]);
});
it('gradient 3D (gather) axis=0 shape=[1, 4, 4]', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
const indices = tf.tensor1d([0, 0], 'int32');
const dy = tf.tensor([
2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7,
18, 19, 2, 21, 6, 23, 24, 25, 101, 31, 34, 54, 1, 0, -3, -4
], [2, 4, 4]);
const axis = 0;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [20, 16, 6, 36, 12, 23.7, 25, 43, 101.01, 31, 46, 67, 5, 15, 9, -11]);
});
it('gradient 3D (gather) axis=0 shape=[1, 4, 4] 1D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
const indices = tf.tensor1d([0, 0], 'int32');
const dy = tf.tensor([
2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7,
18, 19, 2, 21, 6, 23, 24, 25, 101, 31, 34, 54, 1, 0, -3, -4
], [2, 4, 4]);
const axis = 0;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [20, 16, 6, 36, 12, 23.7, 25, 43, 101.01, 31, 46, 67, 5, 15, 9, -11]);
});
it('gradient 3D (gather) axis=1 shape=[2, 3, 2] 2D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
const indices = tf.tensor2d([1, 2, 2, 1], [2, 2], 'int32');
const dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7], [2, 2, 2, 2]);
const axis = 1;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [0, 0, 3, 15, 10, 15.7, 0, 0, 12.01, -7, 16, 28]);
});
it('gradient 3D (gather) axis=1 shape=[1, 4, 4] 1D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
const indices = tf.tensor1d([1, 2, 2, 1], 'int32');
const dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7], [1, 4, 4]);
const axis = 1;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [0, 0, 0, 0, 6, 12, 16, 8, 6.01, .7, 13, 31, 0, 0, 0, 0]);
});
it('gradient 3D (gather) axis=1 shape=[1, 4, 4] 2D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
const indices = tf.tensor2d([1, 2, 2, 1], [2, 2], 'int32');
const dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7], [1, 2, 2, 4]);
const axis = 1;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [0, 0, 0, 0, 6, 12, 16, 8, 6.01, .7, 13, 31, 0, 0, 0, 0]);
});
it('gradient 3D (gather) axis=2 shape=[2, 3, 2] 1D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
const indices = tf.tensor1d([1, 0, 1, 0], 'int32');
const dy = tf.tensor([
2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,
4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25
], [2, 3, 4]);
const axis = 2;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [12, 6, 18.7, 7, 13, 12.01, 8, 16, 40, 20, 48, 30]);
});
it('gradient 3D (gather) axis=2 shape=[2, 3, 2] 2D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
const indices = tf.tensor2d([1, 0, 1, 0], [2, 2], 'int32');
const dy = tf.tensor([
2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,
4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25
], [2, 3, 2, 2]);
const axis = 2;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [12, 6, 18.7, 7, 13, 12.01, 8, 16, 40, 20, 48, 30]);
});
it('gradient 3D (gather) axis=2 shape=[4, 1, 4] 1D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 1, 4]);
const indices = tf.tensor1d([1, 3, 1], 'int32');
const dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 4, 15], [4, 1, 3]);
const axis = 2;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [0, 6, 0, -3, 0, 15.7, 0, 6, 0, 1.01, 0, 18, 0, 15, 0, 4]);
});
it('gradient 3D (gather) axis=2 shape=[4, 1, 4] 2D indices', async () => {
const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 1, 4]);
const indices = tf.tensor2d([1, 3, 1], [1, 3], 'int32');
const dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 4, 15], [4, 1, 1, 3]);
const axis = 2;
const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
expect(gradients.shape).toEqual(t.shape);
expectArraysClose(await gradients.data(), [0, 6, 0, -3, 0, 15.7, 0, 6, 0, 1.01, 0, 18, 0, 15, 0, 4]);
});
it('ensure no memory leak', async () => {
const numTensorsBefore = tf.memory().numTensors;
const numDataIdBefore = tf.engine().backend.numDataIds();
const t = tf.tensor1d([1, 2, 3]);
const t1 = tf.scalar(1, 'int32');
const t2 = tf.gather(t, t1, 0);
expect(t2.shape).toEqual([]);
expectArraysClose(await t2.data(), [2]);
t.dispose();
t1.dispose();
t2.dispose();
const numTensorsAfter = tf.memory().numTensors;
const numDataIdAfter = tf.engine().backend.numDataIds();
expect(numTensorsAfter).toBe(numTensorsBefore);
expect(numDataIdAfter).toBe(numDataIdBefore);
});
it('fills with zero when index is out of bound', async () => {
if (env.backendName === 'webgl' || env.backendName === 'webgpu') {
const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
const tInt = tf.tensor2d([1, 11, 2, 22], [2, 2], 'int32');
const index = tf.tensor1d([0, 1, 100, -1, 2, -4], 'int32');
const res = tf.gather(t, index);
const resInt = tf.gather(tInt, index);
const expected = [1, 11, 2, 22, 0, 0, 0, 0, 0, 0, 0, 0];
expectArraysClose(await res.data(), expected);
expectArraysClose(await resInt.data(), expected);
}
});
});
//# 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