@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_sum_test.js

/**
 * @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 TensorValue3x4() {
    return tf.tensor2d([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]]);
}
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('sparseSegmentSum', ALL_ENVS, () => {
    it('two rows one segment', async () => {
        const result = tf.sparse.sparseSegmentSum(TensorValue3x4(), [0, 1], [0, 0]);
        expectArraysClose(await result.data(), [[0, 0, 0, 0]]);
    });
    it('two rows two segments', async () => {
        const result = tf.sparse.sparseSegmentSum(TensorValue3x4(), [0, 1], [0, 1]);
        expectArraysClose(await result.data(), [[1, 2, 3, 4], [-1, -2, -3, -4]]);
    });
    it('all rows one segment', async () => {
        const result = tf.sparse.sparseSegmentSum(TensorValue3x4(), [0, 1, 2], [0, 0, 1]);
        expectArraysClose(await result.data(), [[0, 0, 0, 0], [5, 6, 7, 8]]);
    });
    it('0 dimensional input invalid', async () => {
        expect(() => tf.sparse.sparseSegmentSum(tf.scalar(1), [], []))
            .toThrowError(/should be at least 1 dimensional/);
    });
    it('1 dimensional input', async () => {
        const result = tf.sparse.sparseSegmentSum(TensorValue10(), [8, 3, 0, 9], [0, 1, 2, 2]);
        expectArraysClose(await result.data(), [9, 4, 11]);
    });
    it('3 dimensional input', async () => {
        const result = tf.sparse.sparseSegmentSum(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]], [[74, 76, 78, 80], [82, 84, 86, 88]]
        ]);
    });
    it('segment ids hole', async () => {
        const result = tf.sparse.sparseSegmentSum(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], [51, 54, 57, 60]]);
    });
    it('segment ids > zero', async () => {
        const result = tf.sparse.sparseSegmentSum(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], [51, 54, 57, 60]]);
    });
    it('baseline valid', async () => {
        // Baseline for the *invalid* tests below.
        const result = tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [0, 1, 2, 2]);
        expectArraysClose(await result.data(), [[33, 34, 35, 36], [13, 14, 15, 16], [38, 40, 42, 44]]);
    });
    it('does not have memory leak.', async () => {
        const beforeDataIds = tf.engine().backend.numDataIds();
        const data = TensorValue3x4();
        const indices = tf.tensor1d([0, 1], 'int32');
        const segmentIds = tf.tensor1d([0, 0], 'int32');
        const result = tf.sparse.sparseSegmentSum(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('indices invalid 1', async () => {
        expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, -1, 0, 9], [
            0, 1, 2, 2
        ])).toThrowError(/indices\[1\] == -1 out of range \[0, 10\)/);
    });
    it('indices invalid 2', async () => {
        expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 10], [
            0, 1, 2, 2
        ])).toThrowError(/indices\[3\] == 10 out of range \[0, 10\)/);
    });
    it('segments invalid 2', async () => {
        expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [
            0, 1, 0, 1
        ])).toThrowError('segment ids are not increasing');
    });
    it('segments invalid 3', async () => {
        expect(() => tf.sparse.sparseSegmentSum(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('segments invalid 4', async () => {
        expect(() => tf.sparse.sparseSegmentSum(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('segments invalid 6', async () => {
        expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [
            0, 0, 0, -1
        ])).toThrowError('segment ids must be >= 0');
    });
    it('segments invalid 7', async () => {
        expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [
            0, 0, 0, -2
        ])).toThrowError('segment ids must be >= 0');
    });
    it('indices invalid rank', async () => {
        expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [[8, 3, 0, 9]], [
            0, 0, 0, -2
        ])).toThrowError(/should be Tensor1D/);
    });
    it('segments invalid rank', async () => {
        expect(() => tf.sparse.sparseSegmentSum(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 TensorValue3x4() {\n  return tf.tensor2d([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]]);\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('sparseSegmentSum', ALL_ENVS, () => {\n  it('two rows one segment', async () => {\n    const result = tf.sparse.sparseSegmentSum(TensorValue3x4(), [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 = tf.sparse.sparseSegmentSum(TensorValue3x4(), [0, 1], [0, 1]);\n    expectArraysClose(await result.data(), [[1, 2, 3, 4], [-1, -2, -3, -4]]);\n  });\n\n  it('all rows one segment', async () => {\n    const result =\n        tf.sparse.sparseSegmentSum(TensorValue3x4(), [0, 1, 2], [0, 0, 1]);\n    expectArraysClose(await result.data(), [[0, 0, 0, 0], [5, 6, 7, 8]]);\n  });\n\n  it('0 dimensional input invalid', async () => {\n    expect(() => tf.sparse.sparseSegmentSum(tf.scalar(1), [], []))\n        .toThrowError(/should be at least 1 dimensional/);\n  });\n\n  it('1 dimensional input', async () => {\n    const result =\n        tf.sparse.sparseSegmentSum(TensorValue10(), [8, 3, 0, 9], [0, 1, 2, 2]);\n    expectArraysClose(await result.data(), [9, 4, 11]);\n  });\n\n  it('3 dimensional input', async () => {\n    const result = tf.sparse.sparseSegmentSum(\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]], [[74, 76, 78, 80], [82, 84, 86, 88]]\n    ]);\n  });\n\n  it('segment ids hole', async () => {\n    const result = tf.sparse.sparseSegmentSum(\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], [51, 54, 57, 60]]);\n  });\n\n  it('segment ids > zero', async () => {\n    const result = tf.sparse.sparseSegmentSum(\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], [51, 54, 57, 60]]);\n  });\n\n  it('baseline valid', async () => {\n    // Baseline for the *invalid* tests below.\n    const result = tf.sparse.sparseSegmentSum(\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], [38, 40, 42, 44]]);\n  });\n\n  it('does not have memory leak.', async () => {\n    const beforeDataIds = tf.engine().backend.numDataIds();\n\n    const data = TensorValue3x4();\n    const indices = tf.tensor1d([0, 1], 'int32');\n    const segmentIds = tf.tensor1d([0, 0], 'int32');\n    const result = tf.sparse.sparseSegmentSum(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('indices invalid 1', async () => {\n    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, -1, 0, 9], [\n      0, 1, 2, 2\n    ])).toThrowError(/indices\\[1\\] == -1 out of range \\[0, 10\\)/);\n  });\n\n  it('indices invalid 2', async () => {\n    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 10], [\n      0, 1, 2, 2\n    ])).toThrowError(/indices\\[3\\] == 10 out of range \\[0, 10\\)/);\n  });\n\n  it('segments invalid 2', async () => {\n    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [\n      0, 1, 0, 1\n    ])).toThrowError('segment ids are not increasing');\n  });\n\n  it('segments invalid 3', async () => {\n    expect(\n        () => tf.sparse.sparseSegmentSum(\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('segments invalid 4', async () => {\n    expect(\n        () => tf.sparse.sparseSegmentSum(\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('segments invalid 6', async () => {\n    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [\n      0, 0, 0, -1\n    ])).toThrowError('segment ids must be >= 0');\n  });\n\n  it('segments invalid 7', async () => {\n    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [\n      0, 0, 0, -2\n    ])).toThrowError('segment ids must be >= 0');\n  });\n\n  it('indices invalid rank', async () => {\n    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [[8, 3, 0, 9]], [\n      0, 0, 0, -2\n    ])).toThrowError(/should be Tensor1D/);\n  });\n\n  it('segments invalid rank', async () => {\n    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [\n      [0, 0, 0, -2]\n    ])).toThrowError(/should be Tensor1D/);\n  });\n});\n"]}