From d89e0853720b4c4c974558f48198812b901d0b3a Mon Sep 17 00:00:00 2001 From: Na Li Date: Thu, 16 Apr 2020 15:22:48 -0700 Subject: [PATCH] [core]Modularize notEqual. --- tfjs-core/src/kernel_names.ts | 3 + tfjs-core/src/ops/compare.ts | 28 -- tfjs-core/src/ops/compare_ops_test.ts | 273 ---------------- tfjs-core/src/ops/not_equal.ts | 61 ++++ tfjs-core/src/ops/not_equal_test.ts | 292 ++++++++++++++++++ tfjs-core/src/ops/ops.ts | 1 + tfjs-core/src/public/chained_ops/not_equal.ts | 31 ++ .../chained_ops/register_all_chained_ops.ts | 1 + .../register_all_chained_ops_test.ts | 4 +- tfjs-core/src/tensor.ts | 6 - tfjs-core/src/tests.ts | 1 + 11 files changed, 392 insertions(+), 309 deletions(-) create mode 100644 tfjs-core/src/ops/not_equal.ts create mode 100644 tfjs-core/src/ops/not_equal_test.ts create mode 100644 tfjs-core/src/public/chained_ops/not_equal.ts diff --git a/tfjs-core/src/kernel_names.ts b/tfjs-core/src/kernel_names.ts index 4abbdc1abca..b54d4ca91d7 100644 --- a/tfjs-core/src/kernel_names.ts +++ b/tfjs-core/src/kernel_names.ts @@ -39,6 +39,9 @@ export interface FusedBatchNormAttrs { varianceEpsilon: number; } +export const NotEqual = 'NotEqual'; +export type NotEqualInputs = BinaryInputs; + export const SquaredDifference = 'SquaredDifference'; export type SquaredDifferenceInputs = BinaryInputs; diff --git a/tfjs-core/src/ops/compare.ts b/tfjs-core/src/ops/compare.ts index 521d269a8ef..974609234ed 100644 --- a/tfjs-core/src/ops/compare.ts +++ b/tfjs-core/src/ops/compare.ts @@ -25,33 +25,6 @@ import {assertAndGetBroadcastShape} from './broadcast_util'; import {op} from './operation'; import {zerosLike} from './tensor_ops'; -/** - * Returns the truth value of (a != b) element-wise. Supports broadcasting. - * - * We also expose `tf.notEqualStrict` which has the same signature as this op - * and asserts that `a` and `b` are the same shape (does not broadcast). - * - * ```js - * const a = tf.tensor1d([1, 2, 3]); - * const b = tf.tensor1d([0, 2, 3]); - * - * a.notEqual(b).print(); - * ``` - * @param a The first input tensor. - * @param b The second input tensor. Must have the same dtype as `a`. - */ -/** @doc {heading: 'Operations', subheading: 'Logical'} */ -function notEqual_( - a: Tensor|TensorLike, b: Tensor|TensorLike): T { - let $a = convertToTensor(a, 'a', 'notEqual'); - let $b = convertToTensor(b, 'b', 'notEqual'); - [$a, $b] = makeTypesMatch($a, $b); - assertAndGetBroadcastShape($a.shape, $b.shape); - return ENGINE.runKernelFunc( - backend => backend.notEqual($a, $b), {a: $a, b: $b}, - null /* grad */, 'NotEqual') as T; -} - /** * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same * shape. @@ -273,5 +246,4 @@ export const less = op({less_}); export const lessEqual = op({lessEqual_}); export const lessEqualStrict = op({lessEqualStrict_}); export const lessStrict = op({lessStrict_}); -export const notEqual = op({notEqual_}); export const notEqualStrict = op({notEqualStrict_}); diff --git a/tfjs-core/src/ops/compare_ops_test.ts b/tfjs-core/src/ops/compare_ops_test.ts index 45ef5e895c7..35678c90e4b 100644 --- a/tfjs-core/src/ops/compare_ops_test.ts +++ b/tfjs-core/src/ops/compare_ops_test.ts @@ -499,279 +499,6 @@ describeWithFlags('equalStrict', ALL_ENVS, () => { }); }); -describeWithFlags('notEqual', ALL_ENVS, () => { - it('Tensor1D - int32', async () => { - let a = tf.tensor1d([1, 4, 5], 'int32'); - let b = tf.tensor1d([2, 3, 5], 'int32'); - - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]); - - a = tf.tensor1d([2, 2, 2], 'int32'); - b = tf.tensor1d([2, 2, 2], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0]); - - a = tf.tensor1d([0, 0], 'int32'); - b = tf.tensor1d([3, 3], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1]); - }); - it('Tensor1D - float32', async () => { - let a = tf.tensor1d([1.1, 4.1, 5.1], 'float32'); - let b = tf.tensor1d([2.2, 3.2, 5.1], 'float32'); - - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]); - - a = tf.tensor1d([2.31, 2.31, 2.31], 'float32'); - b = tf.tensor1d([2.31, 2.31, 2.31], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0]); - - a = tf.tensor1d([0.45, 0.123], 'float32'); - b = tf.tensor1d([3.123, 3.321], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1]); - }); - - it('upcasts when dtypes dont match', async () => { - const a = [1.1, 4.1, 5]; - const b = [2.2, 3.2, 5]; - - let res = - tf.notEqual(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32')); - expect(res.dtype).toBe('bool'); - expect(res.shape).toEqual([3]); - expectArraysClose(await res.data(), [1, 1, 0]); - - res = tf.notEqual(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool')); - expect(res.dtype).toBe('bool'); - expect(res.shape).toEqual([3]); - expectArraysClose(await res.data(), [0, 1, 1]); - }); - - it('TensorLike', async () => { - const a = [1.1, 4.1, 5.1]; - const b = [2.2, 3.2, 5.1]; - - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]); - }); - it('TensorLike Chained', async () => { - const a = tf.tensor1d([1.1, 4.1, 5.1], 'float32'); - const b = [2.2, 3.2, 5.1]; - - expectArraysClose(await a.notEqual(b).data(), [1, 1, 0]); - }); - it('mismatched Tensor1D shapes - int32', () => { - const a = tf.tensor1d([1, 2], 'int32'); - const b = tf.tensor1d([1, 2, 3], 'int32'); - const f = () => { - tf.notEqual(a, b); - }; - expect(f).toThrowError(); - }); - it('mismatched Tensor1D shapes - float32', () => { - const a = tf.tensor1d([1.1, 2.1], 'float32'); - const b = tf.tensor1d([1.1, 2.1, 3.1], 'float32'); - const f = () => { - tf.notEqual(a, b); - }; - expect(f).toThrowError(); - }); - it('NaNs in Tensor1D - float32', async () => { - const a = tf.tensor1d([1.1, NaN, 2.1], 'float32'); - const b = tf.tensor1d([2.1, 3.1, NaN], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1]); - }); - it('works with NaNs', async () => { - const a = tf.tensor1d([2, 5, NaN]); - const b = tf.tensor1d([4, 5, -1]); - - const res = tf.notEqual(a, b); - expect(res.dtype).toBe('bool'); - expectArraysEqual(await res.data(), [1, 0, 1]); - }); - it('scalar and 1D broadcast', async () => { - const a = tf.scalar(2); - const b = tf.tensor1d([1, 2, 3, 4, 5, 2]); - const res = tf.notEqual(a, b); - expect(res.dtype).toBe('bool'); - expect(res.shape).toEqual([6]); - expectArraysEqual(await res.data(), [1, 0, 1, 1, 1, 0]); - }); - - // Tensor2D: - it('Tensor2D - int32', async () => { - let a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32'); - let b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1, 1, 1]); - - a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32'); - b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]); - }); - it('Tensor2D - float32', async () => { - let a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32'); - let b = - tf.tensor2d([[2.1, 4.1, 5.1], [7.1, 10.1, 11.1]], [2, 3], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 0, 0, 1, 1, 1]); - - a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32'); - b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]); - }); - it('broadcasting Tensor2D shapes - int32', async () => { - const a = tf.tensor2d([[3], [7]], [2, 1], 'int32'); - const b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 0, 1, 0, 1, 1]); - }); - it('broadcasting Tensor2D shapes - float32', async () => { - const a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32'); - const b = - tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 0, 1, 0, 1, 1]); - }); - it('NaNs in Tensor2D - float32', async () => { - const a = tf.tensor2d([[1.1, NaN], [1.1, NaN]], [2, 2], 'float32'); - const b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0, 1]); - }); - it('2D and scalar broadcast', async () => { - const a = tf.tensor2d([1, 2, 3, 2, 5, 6], [2, 3]); - const b = tf.scalar(2); - const res = tf.notEqual(a, b); - expect(res.dtype).toBe('bool'); - expect(res.shape).toEqual([2, 3]); - expectArraysEqual(await res.data(), [1, 0, 1, 0, 1, 1]); - }); - it('2D and 2D broadcast each with 1 dim', async () => { - const a = tf.tensor2d([1, 2, 5], [1, 3]); - const b = tf.tensor2d([5, 1], [2, 1]); - const res = tf.notEqual(a, b); - expect(res.dtype).toBe('bool'); - expect(res.shape).toEqual([2, 3]); - expectArraysEqual(await res.data(), [1, 1, 0, 0, 1, 1]); - }); - - // Tensor3D: - it('Tensor3D - int32', async () => { - let a = - tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32'); - let b = - tf.tensor3d([[[2], [3], [6]], [[7], [10], [12]]], [2, 3, 1], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1, 1, 0]); - - a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32'); - b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0, 0, 0]); - }); - it('Tensor3D - float32', async () => { - let a = tf.tensor3d( - [[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32'); - let b = tf.tensor3d( - [[[2.1], [3.1], [6.1]], [[7.1], [10.1], [12.1]]], [2, 3, 1], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1, 1, 0]); - - a = tf.tensor3d( - [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32'); - b = tf.tensor3d( - [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0, 0, 0]); - }); - it('broadcasting Tensor3D shapes - int32', async () => { - const a = tf.tensor3d( - [[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2], - 'int32'); - const b = - tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32'); - expectArraysClose( - await tf.notEqual(a, b).data(), [0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1]); - }); - it('broadcasting Tensor3D shapes - float32', async () => { - const a = tf.tensor3d( - [ - [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]], - [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]] - ], - [2, 3, 2], 'float32'); - const b = tf.tensor3d( - [[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32'); - expectArraysClose( - await tf.notEqual(a, b).data(), [0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1]); - }); - it('NaNs in Tensor3D - float32', async () => { - const a = tf.tensor3d( - [[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32'); - const b = tf.tensor3d( - [[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0, 1, 0, 1]); - }); - it('3D and scalar', async () => { - const a = tf.tensor3d([1, 2, 3, 4, 5, -1], [2, 3, 1]); - const b = tf.scalar(-1); - const res = tf.notEqual(a, b); - expect(res.dtype).toBe('bool'); - expect(res.shape).toEqual([2, 3, 1]); - expectArraysEqual(await res.data(), [1, 1, 1, 1, 1, 0]); - }); - - // Tensor4D: - it('Tensor4D - int32', async () => { - let a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32'); - let b = tf.tensor4d([2, 3, 6, 8], [2, 2, 1, 1], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 0]); - - a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32'); - b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]); - - a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32'); - b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1]); - }); - it('Tensor4D - float32', async () => { - let a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32'); - let b = tf.tensor4d([2.1, 3.1, 6.1, 8.1], [2, 2, 1, 1], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 0]); - - a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32'); - b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]); - - a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32'); - b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1]); - }); - it('broadcasting Tensor4D shapes - int32', async () => { - const a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32'); - const b = tf.tensor4d( - [[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [0, 1, 1, 1, 0, 1, 1, 1]); - }); - it('broadcasting Tensor4D shapes - float32', async () => { - const a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32'); - const b = tf.tensor4d( - [[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], - [2, 2, 1, 2], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [0, 1, 1, 1, 0, 1, 1, 1]); - }); - it('NaNs in Tensor4D - float32', async () => { - const a = tf.tensor4d([1.1, NaN, 1.1, 0.1], [2, 2, 1, 1], 'float32'); - const b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0, 1]); - }); - - it('throws when passed a as a non-tensor', () => { - expect(() => tf.notEqual({} as tf.Tensor, tf.scalar(1))) - .toThrowError(/Argument 'a' passed to 'notEqual' must be a Tensor/); - }); - it('throws when passed b as a non-tensor', () => { - expect(() => tf.notEqual(tf.scalar(1), {} as tf.Tensor)) - .toThrowError(/Argument 'b' passed to 'notEqual' must be a Tensor/); - }); - - it('accepts a tensor-like object', async () => { - const a = tf.tensor1d([1, 4, 5], 'int32'); - const b = tf.tensor1d([2, 3, 5], 'int32'); - expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]); - }); -}); - describeWithFlags('notEqualStrict', ALL_ENVS, () => { it('Tensor1D - int32', async () => { let a = tf.tensor1d([1, 4, 5], 'int32'); diff --git a/tfjs-core/src/ops/not_equal.ts b/tfjs-core/src/ops/not_equal.ts new file mode 100644 index 00000000000..f210da73492 --- /dev/null +++ b/tfjs-core/src/ops/not_equal.ts @@ -0,0 +1,61 @@ +/** + * @license + * Copyright 2020 Google Inc. 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 {ENGINE, ForwardFunc} from '../engine'; +import {NotEqual, NotEqualInputs} from '../kernel_names'; +import {Tensor} from '../tensor'; +import {NamedTensorMap} from '../tensor_types'; +import {makeTypesMatch} from '../tensor_util'; +import {convertToTensor} from '../tensor_util_env'; +import {TensorLike} from '../types'; + +import {assertAndGetBroadcastShape} from './broadcast_util'; +import {op} from './operation'; + +/** + * Returns the truth value of (a != b) element-wise. Supports broadcasting. + * + * We also expose `tf.notEqualStrict` which has the same signature as this op + * and asserts that `a` and `b` are the same shape (does not broadcast). + * + * ```js + * const a = tf.tensor1d([1, 2, 3]); + * const b = tf.tensor1d([0, 2, 3]); + * + * a.notEqual(b).print(); + * ``` + * @param a The first input tensor. + * @param b The second input tensor. Must have the same dtype as `a`. + */ +/** @doc {heading: 'Operations', subheading: 'Logical'} */ +function notEqual_( + a: Tensor|TensorLike, b: Tensor|TensorLike): T { + let $a = convertToTensor(a, 'a', 'notEqual'); + let $b = convertToTensor(b, 'b', 'notEqual'); + [$a, $b] = makeTypesMatch($a, $b); + + assertAndGetBroadcastShape($a.shape, $b.shape); + + const forward: ForwardFunc = (backend) => backend.notEqual($a, $b); + + const inputs: NotEqualInputs = {a: $a, b: $b}; + + return ENGINE.runKernelFunc( + forward, inputs as {} as NamedTensorMap, null /* grad */, + NotEqual) as T; +} + +export const notEqual = op({notEqual_}); diff --git a/tfjs-core/src/ops/not_equal_test.ts b/tfjs-core/src/ops/not_equal_test.ts new file mode 100644 index 00000000000..94504519c8b --- /dev/null +++ b/tfjs-core/src/ops/not_equal_test.ts @@ -0,0 +1,292 @@ +/** + * @license + * Copyright 2020 Google Inc. 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, expectArraysEqual} from '../test_util'; + +describeWithFlags('notEqual', ALL_ENVS, () => { + it('Tensor1D - int32', async () => { + let a = tf.tensor1d([1, 4, 5], 'int32'); + let b = tf.tensor1d([2, 3, 5], 'int32'); + + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]); + + a = tf.tensor1d([2, 2, 2], 'int32'); + b = tf.tensor1d([2, 2, 2], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0]); + + a = tf.tensor1d([0, 0], 'int32'); + b = tf.tensor1d([3, 3], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1]); + }); + it('Tensor1D - float32', async () => { + let a = tf.tensor1d([1.1, 4.1, 5.1], 'float32'); + let b = tf.tensor1d([2.2, 3.2, 5.1], 'float32'); + + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]); + + a = tf.tensor1d([2.31, 2.31, 2.31], 'float32'); + b = tf.tensor1d([2.31, 2.31, 2.31], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0]); + + a = tf.tensor1d([0.45, 0.123], 'float32'); + b = tf.tensor1d([3.123, 3.321], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1]); + }); + + it('upcasts when dtypes dont match', async () => { + const a = [1.1, 4.1, 5]; + const b = [2.2, 3.2, 5]; + + let res = + tf.notEqual(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32')); + expect(res.dtype).toBe('bool'); + expect(res.shape).toEqual([3]); + expectArraysClose(await res.data(), [1, 1, 0]); + + res = tf.notEqual(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool')); + expect(res.dtype).toBe('bool'); + expect(res.shape).toEqual([3]); + expectArraysClose(await res.data(), [0, 1, 1]); + }); + + it('TensorLike', async () => { + const a = [1.1, 4.1, 5.1]; + const b = [2.2, 3.2, 5.1]; + + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]); + }); + it('TensorLike Chained', async () => { + const a = tf.tensor1d([1.1, 4.1, 5.1], 'float32'); + const b = [2.2, 3.2, 5.1]; + + expectArraysClose(await a.notEqual(b).data(), [1, 1, 0]); + }); + it('mismatched Tensor1D shapes - int32', () => { + const a = tf.tensor1d([1, 2], 'int32'); + const b = tf.tensor1d([1, 2, 3], 'int32'); + const f = () => { + tf.notEqual(a, b); + }; + expect(f).toThrowError(); + }); + it('mismatched Tensor1D shapes - float32', () => { + const a = tf.tensor1d([1.1, 2.1], 'float32'); + const b = tf.tensor1d([1.1, 2.1, 3.1], 'float32'); + const f = () => { + tf.notEqual(a, b); + }; + expect(f).toThrowError(); + }); + it('NaNs in Tensor1D - float32', async () => { + const a = tf.tensor1d([1.1, NaN, 2.1], 'float32'); + const b = tf.tensor1d([2.1, 3.1, NaN], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1]); + }); + it('works with NaNs', async () => { + const a = tf.tensor1d([2, 5, NaN]); + const b = tf.tensor1d([4, 5, -1]); + + const res = tf.notEqual(a, b); + expect(res.dtype).toBe('bool'); + expectArraysEqual(await res.data(), [1, 0, 1]); + }); + it('scalar and 1D broadcast', async () => { + const a = tf.scalar(2); + const b = tf.tensor1d([1, 2, 3, 4, 5, 2]); + const res = tf.notEqual(a, b); + expect(res.dtype).toBe('bool'); + expect(res.shape).toEqual([6]); + expectArraysEqual(await res.data(), [1, 0, 1, 1, 1, 0]); + }); + + // Tensor2D: + it('Tensor2D - int32', async () => { + let a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32'); + let b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1, 1, 1]); + + a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32'); + b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]); + }); + it('Tensor2D - float32', async () => { + let a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32'); + let b = + tf.tensor2d([[2.1, 4.1, 5.1], [7.1, 10.1, 11.1]], [2, 3], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 0, 0, 1, 1, 1]); + + a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32'); + b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]); + }); + it('broadcasting Tensor2D shapes - int32', async () => { + const a = tf.tensor2d([[3], [7]], [2, 1], 'int32'); + const b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 0, 1, 0, 1, 1]); + }); + it('broadcasting Tensor2D shapes - float32', async () => { + const a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32'); + const b = + tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 0, 1, 0, 1, 1]); + }); + it('NaNs in Tensor2D - float32', async () => { + const a = tf.tensor2d([[1.1, NaN], [1.1, NaN]], [2, 2], 'float32'); + const b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0, 1]); + }); + it('2D and scalar broadcast', async () => { + const a = tf.tensor2d([1, 2, 3, 2, 5, 6], [2, 3]); + const b = tf.scalar(2); + const res = tf.notEqual(a, b); + expect(res.dtype).toBe('bool'); + expect(res.shape).toEqual([2, 3]); + expectArraysEqual(await res.data(), [1, 0, 1, 0, 1, 1]); + }); + it('2D and 2D broadcast each with 1 dim', async () => { + const a = tf.tensor2d([1, 2, 5], [1, 3]); + const b = tf.tensor2d([5, 1], [2, 1]); + const res = tf.notEqual(a, b); + expect(res.dtype).toBe('bool'); + expect(res.shape).toEqual([2, 3]); + expectArraysEqual(await res.data(), [1, 1, 0, 0, 1, 1]); + }); + + // Tensor3D: + it('Tensor3D - int32', async () => { + let a = + tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32'); + let b = + tf.tensor3d([[[2], [3], [6]], [[7], [10], [12]]], [2, 3, 1], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1, 1, 0]); + + a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32'); + b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0, 0, 0]); + }); + it('Tensor3D - float32', async () => { + let a = tf.tensor3d( + [[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32'); + let b = tf.tensor3d( + [[[2.1], [3.1], [6.1]], [[7.1], [10.1], [12.1]]], [2, 3, 1], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1, 1, 0]); + + a = tf.tensor3d( + [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32'); + b = tf.tensor3d( + [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0, 0, 0]); + }); + it('broadcasting Tensor3D shapes - int32', async () => { + const a = tf.tensor3d( + [[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2], + 'int32'); + const b = + tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32'); + expectArraysClose( + await tf.notEqual(a, b).data(), [0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1]); + }); + it('broadcasting Tensor3D shapes - float32', async () => { + const a = tf.tensor3d( + [ + [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]], + [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]] + ], + [2, 3, 2], 'float32'); + const b = tf.tensor3d( + [[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32'); + expectArraysClose( + await tf.notEqual(a, b).data(), [0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1]); + }); + it('NaNs in Tensor3D - float32', async () => { + const a = tf.tensor3d( + [[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32'); + const b = tf.tensor3d( + [[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0, 1, 0, 1]); + }); + it('3D and scalar', async () => { + const a = tf.tensor3d([1, 2, 3, 4, 5, -1], [2, 3, 1]); + const b = tf.scalar(-1); + const res = tf.notEqual(a, b); + expect(res.dtype).toBe('bool'); + expect(res.shape).toEqual([2, 3, 1]); + expectArraysEqual(await res.data(), [1, 1, 1, 1, 1, 0]); + }); + + // Tensor4D: + it('Tensor4D - int32', async () => { + let a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32'); + let b = tf.tensor4d([2, 3, 6, 8], [2, 2, 1, 1], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 0]); + + a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32'); + b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]); + + a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32'); + b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1]); + }); + it('Tensor4D - float32', async () => { + let a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32'); + let b = tf.tensor4d([2.1, 3.1, 6.1, 8.1], [2, 2, 1, 1], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 0]); + + a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32'); + b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]); + + a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32'); + b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1]); + }); + it('broadcasting Tensor4D shapes - int32', async () => { + const a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32'); + const b = tf.tensor4d( + [[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [0, 1, 1, 1, 0, 1, 1, 1]); + }); + it('broadcasting Tensor4D shapes - float32', async () => { + const a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32'); + const b = tf.tensor4d( + [[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], + [2, 2, 1, 2], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [0, 1, 1, 1, 0, 1, 1, 1]); + }); + it('NaNs in Tensor4D - float32', async () => { + const a = tf.tensor4d([1.1, NaN, 1.1, 0.1], [2, 2, 1, 1], 'float32'); + const b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0, 1]); + }); + + it('throws when passed a as a non-tensor', () => { + expect(() => tf.notEqual({} as tf.Tensor, tf.scalar(1))) + .toThrowError(/Argument 'a' passed to 'notEqual' must be a Tensor/); + }); + it('throws when passed b as a non-tensor', () => { + expect(() => tf.notEqual(tf.scalar(1), {} as tf.Tensor)) + .toThrowError(/Argument 'b' passed to 'notEqual' must be a Tensor/); + }); + + it('accepts a tensor-like object', async () => { + const a = tf.tensor1d([1, 4, 5], 'int32'); + const b = tf.tensor1d([2, 3, 5], 'int32'); + expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]); + }); +}); diff --git a/tfjs-core/src/ops/ops.ts b/tfjs-core/src/ops/ops.ts index 8dced31b20c..00237b3052d 100644 --- a/tfjs-core/src/ops/ops.ts +++ b/tfjs-core/src/ops/ops.ts @@ -28,6 +28,7 @@ export {div} from './div'; export {divNoNan} from './div_no_nan'; export {eye} from './eye'; export {multinomial} from './multinomial'; +export {notEqual} from './not_equal'; export {oneHot} from './one_hot'; export {pad} from './pad'; export {pad1d} from './pad1d'; diff --git a/tfjs-core/src/public/chained_ops/not_equal.ts b/tfjs-core/src/public/chained_ops/not_equal.ts new file mode 100644 index 00000000000..08fc8114419 --- /dev/null +++ b/tfjs-core/src/public/chained_ops/not_equal.ts @@ -0,0 +1,31 @@ +/** + * @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 {notEqual} from '../../ops/not_equal'; +import {Tensor} from '../../tensor'; +import {Rank, TensorLike} from '../../types'; + +declare module '../../tensor' { + interface Tensor { + notEqual(b: Tensor|TensorLike): T; + } +} + +Tensor.prototype.notEqual = function(b: Tensor| + TensorLike): T { + this.throwIfDisposed(); + return notEqual(this, b); +}; diff --git a/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts b/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts index 1e46d1a6ca6..6381adc4449 100644 --- a/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts +++ b/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts @@ -20,6 +20,7 @@ import './broadcast_to'; import './div'; import './div_no_nan'; import './one_hot'; +import './not_equal'; import './pad'; import './squared_difference'; import './sub'; diff --git a/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts b/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts index f6b03ee34a1..e9175206785 100644 --- a/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts +++ b/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts @@ -24,8 +24,8 @@ import {ALL_ENVS, describeWithFlags} from '../../jasmine_util'; // flexibility to change in future. const CHAINED_OPS = [ - 'add', 'batchNorm', 'broadcastTo', 'div', 'divNoNan', 'oneHot', 'pad', - 'square', 'sub', 'tile', 'transpose' + 'add', 'batchNorm', 'broadcastTo', 'div', 'divNoNan', 'oneHot', 'notEqual', + 'pad', 'square', 'sub', 'tile', 'transpose' ]; describeWithFlags('chained ops', ALL_ENVS, () => { diff --git a/tfjs-core/src/tensor.ts b/tfjs-core/src/tensor.ts index b184b0eaf49..3759a3e8233 100644 --- a/tfjs-core/src/tensor.ts +++ b/tfjs-core/src/tensor.ts @@ -230,7 +230,6 @@ export interface OpHandler { logicalXor(a: Tensor, b: Tensor|TensorLike): T; where(condition: Tensor|TensorLike, a: T, b: T|TensorLike): T; - notEqual(a: Tensor, b: Tensor|TensorLike): T; notEqualStrict(a: T, b: T|TensorLike): T; less(a: Tensor, b: Tensor|TensorLike): T; lessStrict(a: T, b: T|TensorLike): T; @@ -941,11 +940,6 @@ export class Tensor { } // Compare ops. - - notEqual(x: Tensor|TensorLike): T { - this.throwIfDisposed(); - return opHandler.notEqual(this, x); - } notEqualStrict(this: T, x: T|TensorLike): T { this.throwIfDisposed(); return opHandler.notEqualStrict(this, x); diff --git a/tfjs-core/src/tests.ts b/tfjs-core/src/tests.ts index ec2962598b7..4aa3b5d4c87 100644 --- a/tfjs-core/src/tests.ts +++ b/tfjs-core/src/tests.ts @@ -78,6 +78,7 @@ import './ops/lstm_test'; import './ops/matmul_test'; import './ops/moving_average_test'; import './ops/multinomial_test'; +import './ops/not_equal_test'; import './ops/one_hot_test'; import './ops/operation_test'; import './ops/pad_test';