@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
201 lines • 47.4 kB
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('spaceToBatchND', ALL_ENVS, () => {
it('tensor4d, input shape=[1, 2, 2, 1], blockShape=[2, 2]', async () => {
const t = tf.tensor4d([[[[1], [2]], [[3], [4]]]], [1, 2, 2, 1]);
const blockShape = [2, 2];
const paddings = [[0, 0], [0, 0]];
const res = tf.spaceToBatchND(t, blockShape, paddings);
expect(res.shape).toEqual([4, 1, 1, 1]);
expectArraysClose(await res.data(), [1, 2, 3, 4]);
});
it('tensor4d, input shape=[1, 2, 2, 3], blockShape=[2, 2]', async () => {
const t = tf.tensor4d([[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]], [1, 2, 2, 3]);
const blockShape = [2, 2];
const paddings = [[0, 0], [0, 0]];
const res = tf.spaceToBatchND(t, blockShape, paddings);
expect(res.shape).toEqual([4, 1, 1, 3]);
expectArraysClose(await res.data(), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
});
it('tensor4d, input shape=[1, 4, 4, 1], blockShape=[2, 2]', async () => {
const t = tf.tensor4d([[
[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]],
[[13], [14], [15], [16]]
]], [1, 4, 4, 1]);
const blockShape = [2, 2];
const paddings = [[0, 0], [0, 0]];
const res = tf.spaceToBatchND(t, blockShape, paddings);
expect(res.shape).toEqual([4, 2, 2, 1]);
expectArraysClose(await res.data(), [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16]);
});
it('tensor4d, input shape=[2, 6, 6, 1], blockShape=[2, 2]', async () => {
const t = tf.tensor4d([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
], [2, 6, 6, 1]);
const blockShape = [2, 2];
const paddings = [[0, 0], [0, 0]];
const res = tf.spaceToBatchND(t, blockShape, paddings);
expect(res.shape).toEqual([8, 3, 3, 1]);
expectArraysClose(await res.data(), [
1, 3, 5, 13, 15, 17, 25, 27, 29, 37, 39, 41, 49, 51, 53, 61, 63, 65,
2, 4, 6, 14, 16, 18, 26, 28, 30, 38, 40, 42, 50, 52, 54, 62, 64, 66,
7, 9, 11, 19, 21, 23, 31, 33, 35, 43, 45, 47, 55, 57, 59, 67, 69, 71,
8, 10, 12, 20, 22, 24, 32, 34, 36, 44, 46, 48, 56, 58, 60, 68, 70, 72
]);
});
it('tensor4d, input shape=[2, 2, 4, 1], blockShape=[2, 2]', async () => {
const t = tf.tensor4d([
[[[1], [2], [3], [4]], [[5], [6], [7], [8]]],
[[[9], [10], [11], [12]], [[13], [14], [15], [16]]]
], [2, 2, 4, 1]);
const blockShape = [2, 2];
const paddings = [[0, 0], [2, 0]];
const res = tf.spaceToBatchND(t, blockShape, paddings);
expect(res.shape).toEqual([8, 1, 3, 1]);
expectArraysClose(await res.data(), [
0, 1, 3, 0, 9, 11, 0, 2, 4, 0, 10, 12,
0, 5, 7, 0, 13, 15, 0, 6, 8, 0, 14, 16
]);
});
it('tensor2d, blockShape [2]', async () => {
const t = tf.tensor2d([1, 3, 2, 4], [1, 4]);
const blockShape = [2];
const paddings = [[0, 0]];
const res = tf.spaceToBatchND(t, blockShape, paddings);
expect(res.shape).toEqual([2, 2]);
expectArraysClose(await res.data(), [1, 2, 3, 4]);
});
it('throws when blockShape equal to input rank', () => {
const t = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
const blockShape = [2, 2, 2, 2];
const paddings = [[0, 0], [0, 0], [0, 0], [0, 0]];
expect(() => tf.spaceToBatchND(t, blockShape, paddings))
.toThrowError('input rank 4 should be > than [blockShape] 4');
});
it('throws when paddings row dimension not equal to blockshape', () => {
const t = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
const blockShape = [2, 2];
const paddings = [[0, 0]];
expect(() => tf.spaceToBatchND(t, blockShape, paddings))
.toThrowError('paddings.shape[0] 1 must be equal to [blockShape] 2');
});
it('throws when input tensor spatial dimension not divisible by blockshapes', () => {
const t = tf.tensor4d([1, 2, 3, 4, 5, 6], [1, 2, 3, 1]);
const blockShape = [2, 2];
const paddings = [[0, 0], [0, 0]];
expect(() => tf.spaceToBatchND(t, blockShape, paddings))
.toThrowError('input spatial dimensions 2,3,1 with paddings 0,0,0,0 must be ' +
'divisible by blockShapes 2,2');
});
it('accepts a tensor-like object', async () => {
const t = [[[[1], [2]], [[3], [4]]]];
const blockShape = [2, 2];
const paddings = [[0, 0], [0, 0]];
const res = tf.spaceToBatchND(t, blockShape, paddings);
expect(res.shape).toEqual([4, 1, 1, 1]);
expectArraysClose(await res.data(), [1, 2, 3, 4]);
});
});
describeWithFlags('batchToSpaceND X spaceToBatchND', ALL_ENVS, () => {
it('tensor4d, input shape=[4, 1, 1, 1], blockShape=[2, 2]', async () => {
const t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
const blockShape = [2, 2];
const crops = [[0, 0], [0, 0]];
const paddings = [[0, 0], [0, 0]];
const b2s = tf.batchToSpaceND(t, blockShape, crops);
expect(b2s.shape).toEqual([1, 2, 2, 1]);
expectArraysClose(await b2s.data(), [1, 2, 3, 4]);
const s2b = tf.spaceToBatchND(b2s, blockShape, paddings);
expect(s2b.shape).toEqual([4, 1, 1, 1]);
expectArraysClose(await s2b.data(), [1, 2, 3, 4]);
});
it('tensor4d, input shape=[2, 6, 6, 1], blockShape=[2, 2]', async () => {
const t = tf.tensor4d([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
], [2, 6, 6, 1]);
const blockShape = [2, 2];
const crops = [[0, 0], [0, 0]];
const paddings = [[0, 0], [0, 0]];
const s2b = tf.spaceToBatchND(t, blockShape, paddings);
expect(s2b.shape).toEqual([8, 3, 3, 1]);
expectArraysClose(await s2b.data(), [
1, 3, 5, 13, 15, 17, 25, 27, 29, 37, 39, 41, 49, 51, 53, 61, 63, 65,
2, 4, 6, 14, 16, 18, 26, 28, 30, 38, 40, 42, 50, 52, 54, 62, 64, 66,
7, 9, 11, 19, 21, 23, 31, 33, 35, 43, 45, 47, 55, 57, 59, 67, 69, 71,
8, 10, 12, 20, 22, 24, 32, 34, 36, 44, 46, 48, 56, 58, 60, 68, 70, 72
]);
const b2s = tf.batchToSpaceND(s2b, blockShape, crops);
expect(b2s.shape).toEqual([2, 6, 6, 1]);
expectArraysClose(await b2s.data(), [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
]);
});
it('gradients, input shape=[4, 2, 2], block shape=[2]', async () => {
const t = tf.tensor([-61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96, 44, -55, -64, -88, -94], [4, 2, 2]);
const blockShape = [2];
const paddings = [[0, 2]];
const dy = tf.tensor([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32
], [8, 2, 2]);
const gradient = tf.grad(t => tf.spaceToBatchND(t, blockShape, paddings))(t, dy);
expect(gradient.shape).toEqual([4, 2, 2]);
expectArraysClose(await gradient.data(), [1, 2, 17, 18, 5, 6, 21, 22, 9, 10, 25, 26, 13, 14, 29, 30]);
});
it('gradient with clones input=[4, 2, 2], block shape=[2]', async () => {
const t = tf.tensor([-61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96, 44, -55, -64, -88, -94], [4, 2, 2]);
const blockShape = [2];
const paddings = [[0, 2]];
const dy = tf.tensor([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32
], [8, 2, 2]);
const gradient = tf.grad(t => tf.spaceToBatchND(t.clone(), blockShape, paddings).clone())(t, dy);
expect(gradient.shape).toEqual([4, 2, 2]);
expectArraysClose(await gradient.data(), [1, 2, 17, 18, 5, 6, 21, 22, 9, 10, 25, 26, 13, 14, 29, 30]);
});
it('gradients, input shape=[2, 2, 4, 1], block shape=[2, 2]', async () => {
const t = tf.tensor4d([
[[[1], [2], [3], [4]], [[5], [6], [7], [8]]],
[[[9], [10], [11], [12]], [[13], [14], [15], [16]]]
], [2, 2, 4, 1]);
const blockShape = [2, 2];
const paddings = [[0, 0], [2, 0]];
const dy = tf.tensor([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
], [8, 1, 3, 1]);
const gradient = tf.grad(t => tf.spaceToBatchND(t, blockShape, paddings))(t, dy);
expect(gradient.shape).toEqual([2, 2, 4, 1]);
expectArraysClose(await gradient.data(), [2, 8, 3, 9, 14, 20, 15, 21, 5, 11, 6, 12, 17, 23, 18, 24]);
});
});
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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('spaceToBatchND', ALL_ENVS, () => {\n  it('tensor4d, input shape=[1, 2, 2, 1], blockShape=[2, 2]', async () => {\n    const t = tf.tensor4d([[[[1], [2]], [[3], [4]]]], [1, 2, 2, 1]);\n    const blockShape = [2, 2];\n    const paddings = [[0, 0], [0, 0]];\n\n    const res = tf.spaceToBatchND(t, blockShape, paddings);\n    expect(res.shape).toEqual([4, 1, 1, 1]);\n    expectArraysClose(await res.data(), [1, 2, 3, 4]);\n  });\n\n  it('tensor4d, input shape=[1, 2, 2, 3], blockShape=[2, 2]', async () => {\n    const t = tf.tensor4d(\n        [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]], [1, 2, 2, 3]);\n    const blockShape = [2, 2];\n    const paddings = [[0, 0], [0, 0]];\n\n    const res = tf.spaceToBatchND(t, blockShape, paddings);\n    expect(res.shape).toEqual([4, 1, 1, 3]);\n    expectArraysClose(\n        await res.data(), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);\n  });\n\n  it('tensor4d, input shape=[1, 4, 4, 1], blockShape=[2, 2]', async () => {\n    const t = tf.tensor4d(\n        [[\n          [[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]],\n          [[13], [14], [15], [16]]\n        ]],\n        [1, 4, 4, 1]);\n    const blockShape = [2, 2];\n    const paddings = [[0, 0], [0, 0]];\n\n    const res = tf.spaceToBatchND(t, blockShape, paddings);\n    expect(res.shape).toEqual([4, 2, 2, 1]);\n    expectArraysClose(\n        await res.data(),\n        [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16]);\n  });\n\n  it('tensor4d, input shape=[2, 6, 6, 1], blockShape=[2, 2]', async () => {\n    const t = tf.tensor4d(\n        [\n          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,\n          16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,\n          31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,\n          46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,\n          61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72\n        ],\n        [2, 6, 6, 1]);\n    const blockShape = [2, 2];\n    const paddings = [[0, 0], [0, 0]];\n\n    const res = tf.spaceToBatchND(t, blockShape, paddings);\n    expect(res.shape).toEqual([8, 3, 3, 1]);\n    expectArraysClose(await res.data(), [\n      1, 3,  5,  13, 15, 17, 25, 27, 29, 37, 39, 41, 49, 51, 53, 61, 63, 65,\n      2, 4,  6,  14, 16, 18, 26, 28, 30, 38, 40, 42, 50, 52, 54, 62, 64, 66,\n      7, 9,  11, 19, 21, 23, 31, 33, 35, 43, 45, 47, 55, 57, 59, 67, 69, 71,\n      8, 10, 12, 20, 22, 24, 32, 34, 36, 44, 46, 48, 56, 58, 60, 68, 70, 72\n    ]);\n  });\n\n  it('tensor4d, input shape=[2, 2, 4, 1], blockShape=[2, 2]', async () => {\n    const t = tf.tensor4d(\n        [\n          [[[1], [2], [3], [4]], [[5], [6], [7], [8]]],\n          [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]\n        ],\n        [2, 2, 4, 1]);\n    const blockShape = [2, 2];\n    const paddings = [[0, 0], [2, 0]];\n\n    const res = tf.spaceToBatchND(t, blockShape, paddings);\n    expect(res.shape).toEqual([8, 1, 3, 1]);\n    expectArraysClose(await res.data(), [\n      0, 1, 3, 0, 9,  11, 0, 2, 4, 0, 10, 12,\n      0, 5, 7, 0, 13, 15, 0, 6, 8, 0, 14, 16\n    ]);\n  });\n\n  it('tensor2d, blockShape [2]', async () => {\n    const t = tf.tensor2d([1, 3, 2, 4], [1, 4]);\n    const blockShape = [2];\n    const paddings = [[0, 0]];\n\n    const res = tf.spaceToBatchND(t, blockShape, paddings);\n    expect(res.shape).toEqual([2, 2]);\n    expectArraysClose(await res.data(), [1, 2, 3, 4]);\n  });\n\n  it('throws when blockShape equal to input rank', () => {\n    const t = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n    const blockShape = [2, 2, 2, 2];\n    const paddings = [[0, 0], [0, 0], [0, 0], [0, 0]];\n\n    expect(() => tf.spaceToBatchND(t, blockShape, paddings))\n        .toThrowError('input rank 4 should be > than [blockShape] 4');\n  });\n\n  it('throws when paddings row dimension not equal to blockshape', () => {\n    const t = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n    const blockShape = [2, 2];\n    const paddings = [[0, 0]];\n\n    expect(() => tf.spaceToBatchND(t, blockShape, paddings))\n        .toThrowError('paddings.shape[0] 1 must be equal to [blockShape] 2');\n  });\n\n  it('throws when input tensor spatial dimension not divisible by blockshapes',\n     () => {\n       const t = tf.tensor4d([1, 2, 3, 4, 5, 6], [1, 2, 3, 1]);\n       const blockShape = [2, 2];\n       const paddings = [[0, 0], [0, 0]];\n\n       expect(() => tf.spaceToBatchND(t, blockShape, paddings))\n           .toThrowError(\n               'input spatial dimensions 2,3,1 with paddings 0,0,0,0 must be ' +\n               'divisible by blockShapes 2,2');\n     });\n\n  it('accepts a tensor-like object', async () => {\n    const t = [[[[1], [2]], [[3], [4]]]];\n    const blockShape = [2, 2];\n    const paddings = [[0, 0], [0, 0]];\n\n    const res = tf.spaceToBatchND(t, blockShape, paddings);\n    expect(res.shape).toEqual([4, 1, 1, 1]);\n    expectArraysClose(await res.data(), [1, 2, 3, 4]);\n  });\n});\n\ndescribeWithFlags('batchToSpaceND X spaceToBatchND', ALL_ENVS, () => {\n  it('tensor4d, input shape=[4, 1, 1, 1], blockShape=[2, 2]', async () => {\n    const t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n    const blockShape = [2, 2];\n    const crops = [[0, 0], [0, 0]];\n    const paddings = [[0, 0], [0, 0]];\n\n    const b2s = tf.batchToSpaceND(t, blockShape, crops);\n    expect(b2s.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await b2s.data(), [1, 2, 3, 4]);\n\n    const s2b = tf.spaceToBatchND(b2s, blockShape, paddings);\n    expect(s2b.shape).toEqual([4, 1, 1, 1]);\n    expectArraysClose(await s2b.data(), [1, 2, 3, 4]);\n  });\n\n  it('tensor4d, input shape=[2, 6, 6, 1], blockShape=[2, 2]', async () => {\n    const t = tf.tensor4d(\n        [\n          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,\n          16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,\n          31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,\n          46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,\n          61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72\n        ],\n        [2, 6, 6, 1]);\n    const blockShape = [2, 2];\n    const crops = [[0, 0], [0, 0]];\n    const paddings = [[0, 0], [0, 0]];\n\n    const s2b = tf.spaceToBatchND(t, blockShape, paddings);\n    expect(s2b.shape).toEqual([8, 3, 3, 1]);\n    expectArraysClose(await s2b.data(), [\n      1, 3,  5,  13, 15, 17, 25, 27, 29, 37, 39, 41, 49, 51, 53, 61, 63, 65,\n      2, 4,  6,  14, 16, 18, 26, 28, 30, 38, 40, 42, 50, 52, 54, 62, 64, 66,\n      7, 9,  11, 19, 21, 23, 31, 33, 35, 43, 45, 47, 55, 57, 59, 67, 69, 71,\n      8, 10, 12, 20, 22, 24, 32, 34, 36, 44, 46, 48, 56, 58, 60, 68, 70, 72\n    ]);\n\n    const b2s = tf.batchToSpaceND(s2b, blockShape, crops);\n    expect(b2s.shape).toEqual([2, 6, 6, 1]);\n    expectArraysClose(await b2s.data(), [\n      1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18,\n      19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n      37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n      55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72\n    ]);\n  });\n\n  it('gradients,  input shape=[4, 2, 2], block shape=[2]', async () => {\n    const t = tf.tensor(\n        [-61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96, 44, -55, -64, -88, -94],\n        [4, 2, 2]);\n    const blockShape = [2];\n    const paddings = [[0, 2]];\n    const dy = tf.tensor(\n        [\n          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,\n          17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32\n        ],\n        [8, 2, 2]);\n\n    const gradient =\n        tf.grad(t => tf.spaceToBatchND(t, blockShape, paddings))(t, dy);\n    expect(gradient.shape).toEqual([4, 2, 2]);\n    expectArraysClose(\n        await gradient.data(),\n        [1, 2, 17, 18, 5, 6, 21, 22, 9, 10, 25, 26, 13, 14, 29, 30]);\n  });\n\n  it('gradient with clones input=[4, 2, 2], block shape=[2]', async () => {\n    const t = tf.tensor(\n        [-61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96, 44, -55, -64, -88, -94],\n        [4, 2, 2]);\n    const blockShape = [2];\n    const paddings = [[0, 2]];\n    const dy = tf.tensor(\n        [\n          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,\n          17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32\n        ],\n        [8, 2, 2]);\n\n    const gradient = tf.grad(\n        t => tf.spaceToBatchND(t.clone(), blockShape, paddings).clone())(t, dy);\n    expect(gradient.shape).toEqual([4, 2, 2]);\n    expectArraysClose(\n        await gradient.data(),\n        [1, 2, 17, 18, 5, 6, 21, 22, 9, 10, 25, 26, 13, 14, 29, 30]);\n  });\n\n  it('gradients, input shape=[2, 2, 4, 1], block shape=[2, 2]', async () => {\n    const t = tf.tensor4d(\n        [\n          [[[1], [2], [3], [4]], [[5], [6], [7], [8]]],\n          [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]\n        ],\n        [2, 2, 4, 1]);\n    const blockShape = [2, 2];\n    const paddings = [[0, 0], [2, 0]];\n    const dy = tf.tensor(\n        [\n          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12,\n          13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24\n        ],\n        [8, 1, 3, 1]);\n\n    const gradient =\n        tf.grad(t => tf.spaceToBatchND(t, blockShape, paddings))(t, dy);\n    expect(gradient.shape).toEqual([2, 2, 4, 1]);\n    expectArraysClose(\n        await gradient.data(),\n        [2, 8, 3, 9, 14, 20, 15, 21, 5, 11, 6, 12, 17, 23, 18, 24]);\n  });\n});\n"]}