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Modularize equal. #3189

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453047a
Resolve merge conflict.
lina128 May 4, 2020
2efc642
Merge branch 'master' into temp2
lina128 May 4, 2020
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3 changes: 3 additions & 0 deletions tfjs-core/src/kernel_names.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,9 @@ export interface ConcatAttrs {
export const Div = 'Div';
export type DivInputs = BinaryInputs;

export const Equal = 'Equal';
export type EqualInputs = BinaryInputs;

export const FusedBatchNorm = 'FusedBatchNorm';
export type FusedBatchNormInputs =
Pick<NamedTensorInfoMap, 'x'|'scale'|'offset'|'mean'|'variance'>;
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28 changes: 0 additions & 28 deletions tfjs-core/src/ops/compare.ts
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Expand Up @@ -56,33 +56,6 @@ function lessStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
return $a.less($b);
}

/**
* Returns the truth value of (a == b) element-wise. Supports broadcasting.
*
* We also expose `tf.equalStrict` 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([2, 2, 2]);
*
* a.equal(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 equal_<T extends Tensor>(
a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'equal');
let $b = convertToTensor(b, 'b', 'equal');
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);

return ENGINE.runKernelFunc(backend => backend.equal($a, $b), {$a, $b}) as T;
}

function equalStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
const $a = convertToTensor(a, 'a', 'equalStrict');
const $b = convertToTensor(b, 'b', 'equalStrict');
Expand Down Expand Up @@ -179,7 +152,6 @@ function greaterEqualStrict_<T extends Tensor>(
return $a.greaterEqual($b);
}

export const equal = op({equal_});
export const equalStrict = op({equalStrict_});
export const greaterEqual = op({greaterEqual_});
export const greaterEqualStrict = op({greaterEqualStrict_});
Expand Down
268 changes: 1 addition & 267 deletions tfjs-core/src/ops/compare_ops_test.ts
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Expand Up @@ -17,273 +17,7 @@

import * as tf from '../index';
import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
import {expectArraysClose, expectArraysEqual} from '../test_util';

describeWithFlags('equal', 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.equal(a, b).data(), [0, 0, 1]);

a = tf.tensor1d([2, 2, 2], 'int32');
b = tf.tensor1d([2, 2, 2], 'int32');
expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1]);

a = tf.tensor1d([0, 0], 'int32');
b = tf.tensor1d([3, 3], 'int32');
expectArraysClose(await tf.equal(a, b).data(), [0, 0]);
});
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.equal(a, b).data(), [0, 0, 1]);

a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1]);

a = tf.tensor1d([0.45, 0.123], 'float32');
b = tf.tensor1d([3.123, 3.321], 'float32');
expectArraysClose(await tf.equal(a, b).data(), [0, 0]);
});

it('upcasts when dtypes dont match', async () => {
const a = [1.1, 4.1, 5];
const b = [2.2, 3.2, 5];

let res =
tf.equal(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
expect(res.dtype).toBe('bool');
expect(res.shape).toEqual([3]);
expectArraysClose(await res.data(), [0, 0, 1]);

res = tf.equal(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
expect(res.dtype).toBe('bool');
expect(res.shape).toEqual([3]);
expectArraysClose(await res.data(), [1, 0, 0]);
});

it('TensorLike', async () => {
const a = [1.1, 4.1, 5.1];
const b = [2.2, 3.2, 5.1];

expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1]);
});
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.equal(b).data(), [0, 0, 1]);
});

it('mismatched Tensor1D shapes - int32', () => {
const a = tf.tensor1d([1, 2], 'int32');
const b = tf.tensor1d([1, 2, 3], 'int32');
const f = () => {
tf.equal(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.equal(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.equal(a, b).data(), [0, 0, 0]);
});
it('scalar and 1D broadcast', async () => {
const a = tf.scalar(2);
const b = tf.tensor1d([1, 2, 3, 4, 5, 2]);
const res = tf.equal(a, b);
expect(res.dtype).toBe('bool');
expect(res.shape).toEqual([6]);
expectArraysEqual(await res.data(), [0, 1, 0, 0, 0, 1]);
});

// 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.equal(a, b).data(), [0, 0, 0, 0, 0, 0]);

a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1]);
});
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.equal(a, b).data(), [0, 1, 1, 0, 0, 0]);

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.equal(a, b).data(), [1, 1, 1, 1]);
});
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.equal(a, b).data(), [0, 1, 0, 1, 0, 0]);
});
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.equal(a, b).data(), [0, 1, 0, 1, 0, 0]);
});
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.equal(a, b).data(), [0, 0, 1, 0]);
});
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.equal(a, b);
expect(res.dtype).toBe('bool');
expect(res.shape).toEqual([2, 3]);
expectArraysEqual(await res.data(), [0, 0, 1, 1, 0, 0]);
});
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.equal(a, b);
expect(res.dtype).toBe('bool');
expect(res.shape).toEqual([2, 3]);
expectArraysEqual(await res.data(), [0, 1, 0, 1, 0, 0]);
});

// 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.equal(a, b).data(), [0, 0, 0, 0, 0, 1]);

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.equal(a, b).data(), [1, 1, 1, 1, 1, 1]);
});
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.equal(a, b).data(), [0, 0, 0, 0, 0, 1]);

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.equal(a, b).data(), [1, 1, 1, 1, 1, 1]);
});
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.equal(a, b).data(), [1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0]);
});
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.equal(a, b).data(), [1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0]);
});
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.equal(a, b).data(), [0, 0, 1, 0, 1, 0]);
});
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.equal(a, b);
expect(res.dtype).toBe('bool');
expect(res.shape).toEqual([2, 3, 1]);
expectArraysEqual(await res.data(), [0, 0, 0, 0, 0, 1]);
});

// 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.equal(a, b).data(), [0, 0, 0, 1]);

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.equal(a, b).data(), [1, 1, 1, 1]);

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.equal(a, b).data(), [0, 0, 0, 0]);
});
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.equal(a, b).data(), [0, 0, 0, 1]);

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.equal(a, b).data(), [1, 1, 1, 1]);

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.equal(a, b).data(), [0, 0, 0, 0]);
});
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.equal(a, b).data(), [1, 0, 0, 0, 1, 0, 0, 0]);
});
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.equal(a, b).data(), [1, 0, 0, 0, 1, 0, 0, 0]);
});
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.equal(a, b).data(), [0, 0, 1, 0]);
});

it('throws when passed a as a non-tensor', () => {
expect(() => tf.equal({} as tf.Tensor, tf.scalar(1)))
.toThrowError(/Argument 'a' passed to 'equal' must be a Tensor/);
});
it('throws when passed b as a non-tensor', () => {
expect(() => tf.equal(tf.scalar(1), {} as tf.Tensor))
.toThrowError(/Argument 'b' passed to 'equal' must be a Tensor/);
});

it('accepts a tensor-like object', async () => {
const a = [1, 4, 5];
const b = [2, 3, 5];
expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1]);
});
});
import {expectArraysClose} from '../test_util';

describeWithFlags('equalStrict', ALL_ENVS, () => {
it('Tensor1D - int32', async () => {
Expand Down
61 changes: 61 additions & 0 deletions tfjs-core/src/ops/equal.ts
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@@ -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 {Equal, EqualInputs} 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.equalStrict` 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([2, 2, 2]);
*
* a.equal(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 equal_<T extends Tensor>(
a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'equal');
let $b = convertToTensor(b, 'b', 'equal');
[$a, $b] = makeTypesMatch($a, $b);

assertAndGetBroadcastShape($a.shape, $b.shape);

const forward: ForwardFunc<Tensor> = backend => backend.equal($a, $b);

const inputs: EqualInputs = {a: $a, b: $b};

return ENGINE.runKernelFunc(
forward, inputs as {} as NamedTensorMap, null, Equal) as T;
}

export const equal = op({equal_});
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