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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 */\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose, expectArraysEqual} from '../test_util';\n\ndescribeWithFlags('equal', ALL_ENVS, () => {\n it('Tensor1D - int32', async () => {\n let a = tf.tensor1d([1, 4, 5], 'int32');\n let b = tf.tensor1d([2, 3, 5], 'int32');\n\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1]);\n\n a = tf.tensor1d([2, 2, 2], 'int32');\n b = tf.tensor1d([2, 2, 2], 'int32');\n expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1]);\n\n a = tf.tensor1d([0, 0], 'int32');\n b = tf.tensor1d([3, 3], 'int32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0]);\n });\n it('Tensor1D - float32', async () => {\n let a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');\n let b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');\n\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1]);\n\n a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');\n b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1]);\n\n a = tf.tensor1d([0.45, 0.123], 'float32');\n b = tf.tensor1d([3.123, 3.321], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0]);\n });\n\n it('upcasts when dtypes dont match', async () => {\n const a = [1.1, 4.1, 5];\n const b = [2.2, 3.2, 5];\n\n let res =\n tf.equal(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));\n expect(res.dtype).toBe('bool');\n expect(res.shape).toEqual([3]);\n expectArraysClose(await res.data(), [0, 0, 1]);\n\n res = tf.equal(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));\n expect(res.dtype).toBe('bool');\n expect(res.shape).toEqual([3]);\n expectArraysClose(await res.data(), [1, 0, 0]);\n });\n\n it('TensorLike', async () => {\n const a = [1.1, 4.1, 5.1];\n const b = [2.2, 3.2, 5.1];\n\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1]);\n });\n it('TensorLike chained', async () => {\n const a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');\n const b = [2.2, 3.2, 5.1];\n\n expectArraysClose(await a.equal(b).data(), [0, 0, 1]);\n });\n\n it('mismatched Tensor1D shapes - int32', () => {\n const a = tf.tensor1d([1, 2], 'int32');\n const b = tf.tensor1d([1, 2, 3], 'int32');\n const f = () => {\n tf.equal(a, b);\n };\n expect(f).toThrowError();\n });\n it('mismatched Tensor1D shapes - float32', () => {\n const a = tf.tensor1d([1.1, 2.1], 'float32');\n const b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');\n const f = () => {\n tf.equal(a, b);\n };\n expect(f).toThrowError();\n });\n it('NaNs in Tensor1D - float32', async () => {\n const a = tf.tensor1d([1.1, NaN, 2.1], 'float32');\n const b = tf.tensor1d([2.1, 3.1, NaN], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0]);\n });\n it('scalar and 1D broadcast', async () => {\n const a = tf.scalar(2);\n const b = tf.tensor1d([1, 2, 3, 4, 5, 2]);\n const res = tf.equal(a, b);\n expect(res.dtype).toBe('bool');\n expect(res.shape).toEqual([6]);\n expectArraysEqual(await res.data(), [0, 1, 0, 0, 0, 1]);\n });\n\n // Tensor2D:\n it('Tensor2D - int32', async () => {\n let a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');\n let b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 0, 0, 0]);\n\n a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');\n b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');\n expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1]);\n });\n it('Tensor2D - float32', async () => {\n let a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');\n let b =\n tf.tensor2d([[2.1, 4.1, 5.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 1, 1, 0, 0, 0]);\n\n a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');\n b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1]);\n });\n it('broadcasting Tensor2D shapes - int32', async () => {\n const a = tf.tensor2d([[3], [7]], [2, 1], 'int32');\n const b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 1, 0, 1, 0, 0]);\n });\n it('broadcasting Tensor2D shapes - float32', async () => {\n const a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');\n const b =\n tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 1, 0, 1, 0, 0]);\n });\n it('NaNs in Tensor2D - float32', async () => {\n const a = tf.tensor2d([[1.1, NaN], [1.1, NaN]], [2, 2], 'float32');\n const b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1, 0]);\n });\n it('broadcasting Tensor2D shapes each with 1 dim', async () => {\n const a = tf.tensor2d([1, 2, 5], [1, 3]);\n const b = tf.tensor2d([5, 1], [2, 1]);\n const res = tf.equal(a, b);\n expect(res.dtype).toBe('bool');\n expect(res.shape).toEqual([2, 3]);\n expectArraysEqual(await res.data(), [0, 0, 1, 1, 0, 0]);\n });\n it('2D and scalar broadcast', async () => {\n const a = tf.tensor2d([1, 2, 3, 2, 5, 6], [2, 3]);\n const b = tf.scalar(2);\n const res = tf.equal(a, b);\n expect(res.dtype).toBe('bool');\n expect(res.shape).toEqual([2, 3]);\n expectArraysEqual(await res.data(), [0, 1, 0, 1, 0, 0]);\n });\n\n // Tensor3D:\n it('Tensor3D - int32', async () => {\n let a =\n tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');\n let b =\n tf.tensor3d([[[2], [3], [6]], [[7], [10], [12]]], [2, 3, 1], 'int32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 0, 0, 1]);\n\n a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');\n b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');\n expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1, 1, 1]);\n });\n it('Tensor3D - float32', async () => {\n let a = tf.tensor3d(\n [[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');\n let b = tf.tensor3d(\n [[[2.1], [3.1], [6.1]], [[7.1], [10.1], [12.1]]], [2, 3, 1], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 0, 0, 1]);\n\n a = tf.tensor3d(\n [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');\n b = tf.tensor3d(\n [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1, 1, 1]);\n });\n it('broadcasting Tensor3D shapes - int32', async () => {\n const a = tf.tensor3d(\n [[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2],\n 'int32');\n const b =\n tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');\n expectArraysClose(\n await tf.equal(a, b).data(), [1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0]);\n });\n it('broadcasting Tensor3D shapes - float32', async () => {\n const a = tf.tensor3d(\n [\n [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],\n [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]\n ],\n [2, 3, 2], 'float32');\n const b = tf.tensor3d(\n [[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');\n expectArraysClose(\n await tf.equal(a, b).data(), [1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0]);\n });\n it('NaNs in Tensor3D - float32', async () => {\n const a = tf.tensor3d(\n [[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');\n const b = tf.tensor3d(\n [[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1, 0, 1, 0]);\n });\n it('3D and scalar', async () => {\n const a = tf.tensor3d([1, 2, 3, 4, 5, -1], [2, 3, 1]);\n const b = tf.scalar(-1);\n const res = tf.equal(a, b);\n expect(res.dtype).toBe('bool');\n expect(res.shape).toEqual([2, 3, 1]);\n expectArraysEqual(await res.data(), [0, 0, 0, 0, 0, 1]);\n });\n\n // Tensor4D:\n it('Tensor4D - int32', async () => {\n let a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');\n let b = tf.tensor4d([2, 3, 6, 8], [2, 2, 1, 1], 'int32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 1]);\n\n a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');\n b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');\n expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1]);\n\n a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');\n b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 0]);\n });\n it('Tensor4D - float32', async () => {\n let a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');\n let b = tf.tensor4d([2.1, 3.1, 6.1, 8.1], [2, 2, 1, 1], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 1]);\n\n a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');\n b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1]);\n\n a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');\n b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 0]);\n });\n it('broadcasting Tensor4D shapes - int32', async () => {\n const a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');\n const b = tf.tensor4d(\n [[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');\n expectArraysClose(await tf.equal(a, b).data(), [1, 0, 0, 0, 1, 0, 0, 0]);\n });\n it('broadcasting Tensor4D shapes - float32', async () => {\n const a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');\n const b = tf.tensor4d(\n [[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]],\n [2, 2, 1, 2], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [1, 0, 0, 0, 1, 0, 0, 0]);\n });\n it('NaNs in Tensor4D - float32', async () => {\n const a = tf.tensor4d([1.1, NaN, 1.1, 0.1], [2, 2, 1, 1], 'float32');\n const b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1, 0]);\n });\n\n it('throws when passed a as a non-tensor', () => {\n expect(() => tf.equal({} as tf.Tensor, tf.scalar(1)))\n .toThrowError(/Argument 'a' passed to 'equal' must be a Tensor/);\n });\n it('throws when passed b as a non-tensor', () => {\n expect(() => tf.equal(tf.scalar(1), {} as tf.Tensor))\n .toThrowError(/Argument 'b' passed to 'equal' must be a Tensor/);\n });\n\n it('accepts a tensor-like object', async () => {\n const a = [1, 4, 5];\n const b = [2, 3, 5];\n expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1]);\n });\n\n it('should support string comparison', async () => {\n const tensorA = tf.tensor('aa', [], 'string');\n const tensorB = tf.tensor(['aa', 'ab', 'aaa'], [3], 'string');\n const result = await tf.equal(tensorA, tensorB);\n\n expectArraysEqual(result.shape, [3]);\n expectArraysEqual(await result.data(), [1, 0, 0]);\n });\n});\n"]} |