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| /**
| * @license
| * Copyright 2021 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';
| function TensorValue3x4Float() {
| return tf.tensor2d([[1, 2, 3, 4], [-1, -2, -3, -4], [6, 7, 8, 9]]);
| }
| function TensorValue3x4Integer() {
| return tf.tensor2d([[1, 2, 3, 4], [-1, -2, -3, -4], [6, 7, 8, 9]], [3, 4], 'int32');
| }
| function TensorValue10() {
| return tf.tensor1d(Array.from(Array(10), (_, i) => i + 1));
| }
| function TensorValue10x4() {
| return tf.tensor2d(Array.from(Array(40), (_, i) => i + 1), [10, 4]);
| }
| function TensorValue10x2x4() {
| return tf.tensor3d(Array.from(Array(80), (_, i) => i + 1), [10, 2, 4]);
| }
| describeWithFlags('sparseSegmentMean', ALL_ENVS, () => {
| it('two rows one segment', async () => {
| const result = tf.sparse.sparseSegmentMean(TensorValue3x4Float(), [0, 1], [0, 0]);
| expectArraysClose(await result.data(), [[0, 0, 0, 0]]);
| });
| it('two rows two segments', async () => {
| const result = tf.sparse.sparseSegmentMean(TensorValue3x4Float(), [0, 1], [0, 1]);
| expectArraysClose(await result.data(), [[1, 2, 3, 4], [-1, -2, -3, -4]]);
| });
| it('all rows two segments', async () => {
| const result = tf.sparse.sparseSegmentMean(TensorValue3x4Float(), [0, 1, 2], [0, 1, 1]);
| expectArraysClose(await result.data(), [[1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]]);
| });
| it('integer data', async () => {
| const result = tf.sparse.sparseSegmentMean(TensorValue3x4Integer(), [0, 1, 2], [0, 1, 1]);
| expectArraysClose(await result.data(), [[1, 2, 3, 4], [2, 2, 2, 2]]);
| });
| it('0 dimensional input invalid', async () => {
| expect(() => tf.sparse.sparseSegmentMean(tf.scalar(1), [], []))
| .toThrowError(/should be at least 1 dimensional/);
| });
| it('1 dimensional input', async () => {
| const result = tf.sparse.sparseSegmentMean(TensorValue10(), [8, 3, 0, 9], [0, 1, 2, 2]);
| expectArraysClose(await result.data(), [9, 4, 5.5]);
| });
| it('3 dimensional input', async () => {
| const result = tf.sparse.sparseSegmentMean(TensorValue10x2x4(), [8, 3, 0, 9], [0, 1, 2, 2]);
| expectArraysClose(await result.data(), [
| [[65, 66, 67, 68], [69, 70, 71, 72]],
| [[25, 26, 27, 28], [29, 30, 31, 32]], [[37, 38, 39, 40], [41, 42, 43, 44]]
| ]);
| });
| it('segment ids hole', async () => {
| const result = tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [0, 3, 3, 3]);
| expectArraysClose(await result.data(), [[33, 34, 35, 36], [0, 0, 0, 0], [0, 0, 0, 0], [17, 18, 19, 20]]);
| });
| it('segment ids > zero', async () => {
| const result = tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [2, 3, 3, 3]);
| expectArraysClose(await result.data(), [[0, 0, 0, 0], [0, 0, 0, 0], [33, 34, 35, 36], [17, 18, 19, 20]]);
| });
| it('baseline valid', async () => {
| // Baseline for the *invalid* tests below.
| const result = tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [0, 1, 2, 2]);
| expectArraysClose(await result.data(), [[33, 34, 35, 36], [13, 14, 15, 16], [19, 20, 21, 22]]);
| });
| it('does not have memory leak.', async () => {
| const beforeDataIds = tf.engine().backend.numDataIds();
| const data = TensorValue3x4Float();
| const indices = tf.tensor1d([0, 1], 'int32');
| const segmentIds = tf.tensor1d([0, 0], 'int32');
| const result = tf.sparse.sparseSegmentMean(data, indices, segmentIds);
| await result.data();
| const afterResDataIds = tf.engine().backend.numDataIds();
| expect(afterResDataIds).toEqual(beforeDataIds + 4);
| data.dispose();
| indices.dispose();
| segmentIds.dispose();
| result.dispose();
| const afterDisposeDataIds = tf.engine().backend.numDataIds();
| expect(afterDisposeDataIds).toEqual(beforeDataIds);
| });
| it('throw error if indices < 0', async () => {
| expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, -1, 0, 9], [
| 0, 1, 2, 2
| ])).toThrowError(/indices\[1\] == -1 out of range \[0, 10\)/);
| });
| it('throw error if indices >= max rows', async () => {
| expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 10], [
| 0, 1, 2, 2
| ])).toThrowError(/indices\[3\] == 10 out of range \[0, 10\)/);
| });
| it('throw error if segment ids are not increasing', async () => {
| expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [
| 0, 1, 0, 1
| ])).toThrowError('segment ids are not increasing');
| });
| it('throw error if segment id is out of range', async () => {
| expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [0, 1, 2, 0]))
| .toThrowError('Segment id 1 out of range [0, 1), possibly because segmentIds ' +
| 'input is not sorted.');
| });
| it('throw error if segment id is out of range and negative', async () => {
| expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [-1, 0, 1, 1]))
| .toThrowError('Segment id -1 out of range [0, 2), possibly because segmentIds ' +
| 'input is not sorted.');
| });
| it('throw error if segment id is negative', async () => {
| expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [
| 0, 0, 0, -1
| ])).toThrowError('segment ids must be >= 0');
| });
| it('throw error if segment id is negative and there is a hole', async () => {
| expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [
| 0, 0, 0, -2
| ])).toThrowError('segment ids must be >= 0');
| });
| it('throw error if indices has invalid rank', async () => {
| expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [[8, 3, 0, 9]], [0, 0, 0, -2]))
| .toThrowError(/should be Tensor1D/);
| });
| it('throw error if segments has invalid rank', async () => {
| expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [
| [0, 0, 0, -2]
| ])).toThrowError(/should be Tensor1D/);
| });
| });
| //# 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* @license\n * Copyright 2021 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\nfunction TensorValue3x4Float() {\n  return tf.tensor2d([[1, 2, 3, 4], [-1, -2, -3, -4], [6, 7, 8, 9]]);\n}\n\nfunction TensorValue3x4Integer() {\n  return tf.tensor2d(\n      [[1, 2, 3, 4], [-1, -2, -3, -4], [6, 7, 8, 9]], [3, 4], 'int32');\n}\n\nfunction TensorValue10() {\n  return tf.tensor1d(Array.from(Array(10), (_, i) => i + 1));\n}\n\nfunction TensorValue10x4() {\n  return tf.tensor2d(Array.from(Array(40), (_, i) => i + 1), [10, 4]);\n}\n\nfunction TensorValue10x2x4() {\n  return tf.tensor3d(Array.from(Array(80), (_, i) => i + 1), [10, 2, 4]);\n}\n\ndescribeWithFlags('sparseSegmentMean', ALL_ENVS, () => {\n  it('two rows one segment', async () => {\n    const result =\n        tf.sparse.sparseSegmentMean(TensorValue3x4Float(), [0, 1], [0, 0]);\n    expectArraysClose(await result.data(), [[0, 0, 0, 0]]);\n  });\n\n  it('two rows two segments', async () => {\n    const result =\n        tf.sparse.sparseSegmentMean(TensorValue3x4Float(), [0, 1], [0, 1]);\n    expectArraysClose(await result.data(), [[1, 2, 3, 4], [-1, -2, -3, -4]]);\n  });\n\n  it('all rows two segments', async () => {\n    const result = tf.sparse.sparseSegmentMean(\n        TensorValue3x4Float(), [0, 1, 2], [0, 1, 1]);\n    expectArraysClose(\n        await result.data(), [[1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]]);\n  });\n\n  it('integer data', async () => {\n    const result = tf.sparse.sparseSegmentMean(\n        TensorValue3x4Integer(), [0, 1, 2], [0, 1, 1]);\n    expectArraysClose(await result.data(), [[1, 2, 3, 4], [2, 2, 2, 2]]);\n  });\n\n  it('0 dimensional input invalid', async () => {\n    expect(() => tf.sparse.sparseSegmentMean(tf.scalar(1), [], []))\n        .toThrowError(/should be at least 1 dimensional/);\n  });\n\n  it('1 dimensional input', async () => {\n    const result = tf.sparse.sparseSegmentMean(\n        TensorValue10(), [8, 3, 0, 9], [0, 1, 2, 2]);\n    expectArraysClose(await result.data(), [9, 4, 5.5]);\n  });\n\n  it('3 dimensional input', async () => {\n    const result = tf.sparse.sparseSegmentMean(\n        TensorValue10x2x4(), [8, 3, 0, 9], [0, 1, 2, 2]);\n    expectArraysClose(await result.data(), [\n      [[65, 66, 67, 68], [69, 70, 71, 72]],\n      [[25, 26, 27, 28], [29, 30, 31, 32]], [[37, 38, 39, 40], [41, 42, 43, 44]]\n    ]);\n  });\n\n  it('segment ids hole', async () => {\n    const result = tf.sparse.sparseSegmentMean(\n        TensorValue10x4(), [8, 3, 0, 9], [0, 3, 3, 3]);\n    expectArraysClose(\n        await result.data(),\n        [[33, 34, 35, 36], [0, 0, 0, 0], [0, 0, 0, 0], [17, 18, 19, 20]]);\n  });\n\n  it('segment ids > zero', async () => {\n    const result = tf.sparse.sparseSegmentMean(\n        TensorValue10x4(), [8, 3, 0, 9], [2, 3, 3, 3]);\n    expectArraysClose(\n        await result.data(),\n        [[0, 0, 0, 0], [0, 0, 0, 0], [33, 34, 35, 36], [17, 18, 19, 20]]);\n  });\n\n  it('baseline valid', async () => {\n    // Baseline for the *invalid* tests below.\n    const result = tf.sparse.sparseSegmentMean(\n        TensorValue10x4(), [8, 3, 0, 9], [0, 1, 2, 2]);\n    expectArraysClose(\n        await result.data(),\n        [[33, 34, 35, 36], [13, 14, 15, 16], [19, 20, 21, 22]]);\n  });\n\n  it('does not have memory leak.', async () => {\n    const beforeDataIds = tf.engine().backend.numDataIds();\n\n    const data = TensorValue3x4Float();\n    const indices = tf.tensor1d([0, 1], 'int32');\n    const segmentIds = tf.tensor1d([0, 0], 'int32');\n    const result = tf.sparse.sparseSegmentMean(data, indices, segmentIds);\n\n    await result.data();\n\n    const afterResDataIds = tf.engine().backend.numDataIds();\n    expect(afterResDataIds).toEqual(beforeDataIds + 4);\n\n    data.dispose();\n    indices.dispose();\n    segmentIds.dispose();\n    result.dispose();\n\n    const afterDisposeDataIds = tf.engine().backend.numDataIds();\n    expect(afterDisposeDataIds).toEqual(beforeDataIds);\n  });\n\n  it('throw error if indices < 0', async () => {\n    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, -1, 0, 9], [\n      0, 1, 2, 2\n    ])).toThrowError(/indices\\[1\\] == -1 out of range \\[0, 10\\)/);\n  });\n\n  it('throw error if indices >= max rows', async () => {\n    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 10], [\n      0, 1, 2, 2\n    ])).toThrowError(/indices\\[3\\] == 10 out of range \\[0, 10\\)/);\n  });\n\n  it('throw error if segment ids are not increasing', async () => {\n    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [\n      0, 1, 0, 1\n    ])).toThrowError('segment ids are not increasing');\n  });\n\n  it('throw error if segment id is out of range', async () => {\n    expect(\n        () => tf.sparse.sparseSegmentMean(\n            TensorValue10x4(), [8, 3, 0, 9], [0, 1, 2, 0]))\n        .toThrowError(\n            'Segment id 1 out of range [0, 1), possibly because segmentIds ' +\n            'input is not sorted.');\n  });\n\n  it('throw error if segment id is out of range and negative', async () => {\n    expect(\n        () => tf.sparse.sparseSegmentMean(\n            TensorValue10x4(), [8, 3, 0, 9], [-1, 0, 1, 1]))\n        .toThrowError(\n            'Segment id -1 out of range [0, 2), possibly because segmentIds ' +\n            'input is not sorted.');\n  });\n\n  it('throw error if segment id is negative', async () => {\n    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [\n      0, 0, 0, -1\n    ])).toThrowError('segment ids must be >= 0');\n  });\n\n  it('throw error if segment id is negative and there is a hole', async () => {\n    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [\n      0, 0, 0, -2\n    ])).toThrowError('segment ids must be >= 0');\n  });\n\n  it('throw error if indices has invalid rank', async () => {\n    expect(\n        () => tf.sparse.sparseSegmentMean(\n            TensorValue10x4(), [[8, 3, 0, 9]], [0, 0, 0, -2]))\n        .toThrowError(/should be Tensor1D/);\n  });\n\n  it('throw error if segments has invalid rank', async () => {\n    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [\n      [0, 0, 0, -2]\n    ])).toThrowError(/should be Tensor1D/);\n  });\n});\n"]}
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