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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 sparseTensorValue5x6() {
| const ind = tf.tensor2d([[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]], [6, 2], 'int32');
| const val = [0, 10, 13, 14, 32, 33];
| const shape = [5, 6];
| return { ind, val, shape };
| }
| function sparseTensorValue2x3x4() {
| const ind = tf.tensor2d([
| [0, 0, 1], [0, 1, 0], [0, 1, 2], [1, 0, 3], [1, 1, 1], [1, 1, 3],
| [1, 2, 2]
| ], [7, 3], 'int32');
| const val = [1, 10, 12, 103, 111, 113, 122];
| const shape = [2, 3, 4];
| return { ind, val, shape };
| }
| describeWithFlags('sparseReshape', ALL_ENVS, () => {
| it('preserve static shape info', async () => {
| const sparseTensor = sparseTensorValue5x6();
| const result = tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [1, 5, 2, 3]);
| expectArraysClose(await result.outputShape.data(), [1, 5, 2, 3]);
| });
| it('preserve shape info with inferred dim', async () => {
| const sparseTensor = sparseTensorValue2x3x4();
| const result = tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [2, -1]);
| expectArraysClose(await result.outputShape.data(), [2, 3 * 4]);
| });
| it('does not have memory leak.', async () => {
| const beforeDataIds = tf.engine().backend.numDataIds();
| const sparseTensor = sparseTensorValue5x6();
| const indices = sparseTensor.ind;
| const shape = tf.tensor1d(sparseTensor.shape, 'int32');
| const newShape = tf.tensor1d([1, 5, 2, 3], 'int32');
| const result = tf.sparse.sparseReshape(indices, shape, newShape);
| await result.outputIndices.data();
| await result.outputShape.data();
| const afterResDataIds = tf.engine().backend.numDataIds();
| expect(afterResDataIds).toEqual(beforeDataIds + 5);
| indices.dispose();
| shape.dispose();
| newShape.dispose();
| result.outputIndices.dispose();
| result.outputShape.dispose();
| const afterDisposeDataIds = tf.engine().backend.numDataIds();
| expect(afterDisposeDataIds).toEqual(beforeDataIds);
| });
| it('throw error if more than one inferred dim', async () => {
| const sparseTensor = sparseTensorValue2x3x4();
| expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [
| -1, 2, -1
| ])).toThrowError(/only one output dimension may be -1/);
| });
| it('throw error if impossible new shape', async () => {
| const sparseTensor = sparseTensorValue2x3x4();
| expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [
| -1, 7
| ])).toThrowError(/multiple of 7/);
| });
| it('throw error if negative output dim', async () => {
| const sparseTensor = sparseTensorValue2x3x4();
| expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [
| 1, -7
| ])).toThrowError('size 1 must be non-negative, not -7');
| });
| it('throw error if negative output dim', async () => {
| const sparseTensor = sparseTensorValue2x3x4();
| expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [
| -1, 0
| ])).toThrowError(/unless all specified input sizes are non-zero/);
| });
| it('same shape', async () => {
| const sparseTensor = sparseTensorValue5x6();
| const result = tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [5, 6]);
| expectArraysClose(await result.outputIndices.data(), await sparseTensor.ind.data());
| expectArraysClose(await result.outputShape.data(), sparseTensor.shape);
| });
| it('same shape with inferred dim', async () => {
| const sparseTensor = sparseTensorValue5x6();
| const result = tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [-1, 6]);
| expectArraysClose(await result.outputIndices.data(), await sparseTensor.ind.data());
| expectArraysClose(await result.outputShape.data(), sparseTensor.shape);
| });
| it('new shape with same rank', async () => {
| const sparseTensor = sparseTensorValue5x6();
| const result = tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [3, 10]);
| expectArraysClose(await result.outputIndices.data(), [[0, 0], [0, 6], [0, 9], [1, 0], [2, 0], [2, 1]]);
| expectArraysClose(await result.outputShape.data(), [3, 10]);
| });
| it('new shape with same rank with inferred dim', async () => {
| const sparseTensor = sparseTensorValue5x6();
| const result = tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [3, -1]);
| expectArraysClose(await result.outputIndices.data(), [[0, 0], [0, 6], [0, 9], [1, 0], [2, 0], [2, 1]]);
| expectArraysClose(await result.outputShape.data(), [3, 10]);
| });
| it('up rank', async () => {
| const sparseTensor = sparseTensorValue5x6();
| const result = tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [2, 3, 5]);
| expectArraysClose(await result.outputIndices.data(), [[0, 0, 0], [0, 1, 1], [0, 1, 4], [0, 2, 0], [1, 1, 0], [1, 1, 1]]);
| expectArraysClose(await result.outputShape.data(), [2, 3, 5]);
| });
| it('up rank with inferred dim', async () => {
| const sparseTensor = sparseTensorValue5x6();
| const result = tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [2, -1, 5]);
| expectArraysClose(await result.outputIndices.data(), [[0, 0, 0], [0, 1, 1], [0, 1, 4], [0, 2, 0], [1, 1, 0], [1, 1, 1]]);
| expectArraysClose(await result.outputShape.data(), [2, 3, 5]);
| });
| it('down rank', async () => {
| const sparseTensor = sparseTensorValue2x3x4();
| const result = tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [6, 4]);
| expectArraysClose(await result.outputIndices.data(), [[0, 1], [1, 0], [1, 2], [3, 3], [4, 1], [4, 3], [5, 2]]);
| expectArraysClose(await result.outputShape.data(), [6, 4]);
| });
| it('down rank with inferred dim', async () => {
| const sparseTensor = sparseTensorValue2x3x4();
| const result = tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [6, -1]);
| expectArraysClose(await result.outputIndices.data(), [[0, 1], [1, 0], [1, 2], [3, 3], [4, 1], [4, 3], [5, 2]]);
| expectArraysClose(await result.outputShape.data(), [6, 4]);
| });
| it('throw error if mismatch size', async () => {
| const sparseTensor = sparseTensorValue5x6();
| expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [
| 4, 7
| ])).toThrowError(/Input to reshape is a tensor with 30 dense values/);
| });
| });
| //# 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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 sparseTensorValue5x6() {\n  const ind = tf.tensor2d(\n      [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]], [6, 2], 'int32');\n  const val = [0, 10, 13, 14, 32, 33];\n  const shape = [5, 6];\n  return {ind, val, shape};\n}\n\nfunction sparseTensorValue2x3x4() {\n  const ind = tf.tensor2d(\n      [\n        [0, 0, 1], [0, 1, 0], [0, 1, 2], [1, 0, 3], [1, 1, 1], [1, 1, 3],\n        [1, 2, 2]\n      ],\n      [7, 3], 'int32');\n  const val = [1, 10, 12, 103, 111, 113, 122];\n  const shape = [2, 3, 4];\n  return {ind, val, shape};\n}\ndescribeWithFlags('sparseReshape', ALL_ENVS, () => {\n  it('preserve static shape info', async () => {\n    const sparseTensor = sparseTensorValue5x6();\n    const result = tf.sparse.sparseReshape(\n        sparseTensor.ind, sparseTensor.shape, [1, 5, 2, 3]);\n    expectArraysClose(await result.outputShape.data(), [1, 5, 2, 3]);\n  });\n\n  it('preserve shape info with inferred dim', async () => {\n    const sparseTensor = sparseTensorValue2x3x4();\n    const result =\n        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [2, -1]);\n    expectArraysClose(await result.outputShape.data(), [2, 3 * 4]);\n  });\n\n  it('does not have memory leak.', async () => {\n    const beforeDataIds = tf.engine().backend.numDataIds();\n\n    const sparseTensor = sparseTensorValue5x6();\n    const indices = sparseTensor.ind;\n    const shape = tf.tensor1d(sparseTensor.shape, 'int32');\n    const newShape = tf.tensor1d([1, 5, 2, 3], 'int32');\n    const result = tf.sparse.sparseReshape(indices, shape, newShape);\n\n    await result.outputIndices.data();\n    await result.outputShape.data();\n\n    const afterResDataIds = tf.engine().backend.numDataIds();\n    expect(afterResDataIds).toEqual(beforeDataIds + 5);\n\n    indices.dispose();\n    shape.dispose();\n    newShape.dispose();\n    result.outputIndices.dispose();\n    result.outputShape.dispose();\n\n    const afterDisposeDataIds = tf.engine().backend.numDataIds();\n    expect(afterDisposeDataIds).toEqual(beforeDataIds);\n  });\n\n  it('throw error if more than one inferred dim', async () => {\n    const sparseTensor = sparseTensorValue2x3x4();\n    expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [\n      -1, 2, -1\n    ])).toThrowError(/only one output dimension may be -1/);\n  });\n\n  it('throw error if impossible new shape', async () => {\n    const sparseTensor = sparseTensorValue2x3x4();\n    expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [\n      -1, 7\n    ])).toThrowError(/multiple of 7/);\n  });\n  it('throw error if negative output dim', async () => {\n    const sparseTensor = sparseTensorValue2x3x4();\n    expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [\n      1, -7\n    ])).toThrowError('size 1 must be non-negative, not -7');\n  });\n  it('throw error if negative output dim', async () => {\n    const sparseTensor = sparseTensorValue2x3x4();\n    expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [\n      -1, 0\n    ])).toThrowError(/unless all specified input sizes are non-zero/);\n  });\n  it('same shape', async () => {\n    const sparseTensor = sparseTensorValue5x6();\n    const result =\n        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [5, 6]);\n    expectArraysClose(\n        await result.outputIndices.data(), await sparseTensor.ind.data());\n    expectArraysClose(await result.outputShape.data(), sparseTensor.shape);\n  });\n\n  it('same shape with inferred dim', async () => {\n    const sparseTensor = sparseTensorValue5x6();\n    const result =\n        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [-1, 6]);\n    expectArraysClose(\n        await result.outputIndices.data(), await sparseTensor.ind.data());\n    expectArraysClose(await result.outputShape.data(), sparseTensor.shape);\n  });\n\n  it('new shape with same rank', async () => {\n    const sparseTensor = sparseTensorValue5x6();\n    const result =\n        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [3, 10]);\n    expectArraysClose(\n        await result.outputIndices.data(),\n        [[0, 0], [0, 6], [0, 9], [1, 0], [2, 0], [2, 1]]);\n    expectArraysClose(await result.outputShape.data(), [3, 10]);\n  });\n\n  it('new shape with same rank with inferred dim', async () => {\n    const sparseTensor = sparseTensorValue5x6();\n    const result =\n        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [3, -1]);\n    expectArraysClose(\n        await result.outputIndices.data(),\n        [[0, 0], [0, 6], [0, 9], [1, 0], [2, 0], [2, 1]]);\n    expectArraysClose(await result.outputShape.data(), [3, 10]);\n  });\n  it('up rank', async () => {\n    const sparseTensor = sparseTensorValue5x6();\n    const result = tf.sparse.sparseReshape(\n        sparseTensor.ind, sparseTensor.shape, [2, 3, 5]);\n    expectArraysClose(\n        await result.outputIndices.data(),\n        [[0, 0, 0], [0, 1, 1], [0, 1, 4], [0, 2, 0], [1, 1, 0], [1, 1, 1]]);\n    expectArraysClose(await result.outputShape.data(), [2, 3, 5]);\n  });\n  it('up rank with inferred dim', async () => {\n    const sparseTensor = sparseTensorValue5x6();\n    const result = tf.sparse.sparseReshape(\n        sparseTensor.ind, sparseTensor.shape, [2, -1, 5]);\n    expectArraysClose(\n        await result.outputIndices.data(),\n        [[0, 0, 0], [0, 1, 1], [0, 1, 4], [0, 2, 0], [1, 1, 0], [1, 1, 1]]);\n    expectArraysClose(await result.outputShape.data(), [2, 3, 5]);\n  });\n\n  it('down rank', async () => {\n    const sparseTensor = sparseTensorValue2x3x4();\n    const result =\n        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [6, 4]);\n    expectArraysClose(\n        await result.outputIndices.data(),\n        [[0, 1], [1, 0], [1, 2], [3, 3], [4, 1], [4, 3], [5, 2]]);\n    expectArraysClose(await result.outputShape.data(), [6, 4]);\n  });\n\n  it('down rank with inferred dim', async () => {\n    const sparseTensor = sparseTensorValue2x3x4();\n    const result =\n        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [6, -1]);\n    expectArraysClose(\n        await result.outputIndices.data(),\n        [[0, 1], [1, 0], [1, 2], [3, 3], [4, 1], [4, 3], [5, 2]]);\n    expectArraysClose(await result.outputShape.data(), [6, 4]);\n  });\n\n  it('throw error if mismatch size', async () => {\n    const sparseTensor = sparseTensorValue5x6();\n    expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [\n      4, 7\n    ])).toThrowError(/Input to reshape is a tensor with 30 dense values/);\n  });\n});\n"]}
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