@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_test.js

/**
 * @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 * as tf from '../../index';
import { ALL_ENVS, describeWithFlags } from '../../jasmine_util';
import { expectArraysClose, expectArraysEqual } from '../../test_util';
describeWithFlags('nonMaxSuppression', ALL_ENVS, () => {
    describe('NonMaxSuppression Basic', () => {
        it('select from three clusters', async () => {
            const boxes = tf.tensor2d([
                0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
                0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
            ], [6, 4]);
            const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
            const maxOutputSize = 3;
            const iouThreshold = 0.5;
            const scoreThreshold = 0;
            const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
            expect(indices.shape).toEqual([3]);
            expectArraysEqual(await indices.data(), [3, 0, 5]);
        });
        it('select from three clusters flipped coordinates', async () => {
            const boxes = tf.tensor2d([
                1, 1, 0, 0, 0, 0.1, 1, 1.1, 0, .9, 1, -0.1,
                0, 10, 1, 11, 1, 10.1, 0, 11.1, 1, 101, 0, 100
            ], [6, 4]);
            const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
            const maxOutputSize = 3;
            const iouThreshold = 0.5;
            const scoreThreshold = 0;
            const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
            expect(indices.shape).toEqual([3]);
            expectArraysEqual(await indices.data(), [3, 0, 5]);
        });
        it('select at most two boxes from three clusters', async () => {
            const boxes = tf.tensor2d([
                0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
                0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
            ], [6, 4]);
            const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
            const maxOutputSize = 2;
            const iouThreshold = 0.5;
            const scoreThreshold = 0;
            const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
            expect(indices.shape).toEqual([2]);
            expectArraysEqual(await indices.data(), [3, 0]);
        });
        it('select at most thirty boxes from three clusters', async () => {
            const boxes = tf.tensor2d([
                0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
                0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
            ], [6, 4]);
            const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
            const maxOutputSize = 30;
            const iouThreshold = 0.5;
            const scoreThreshold = 0;
            const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
            expect(indices.shape).toEqual([3]);
            expectArraysEqual(await indices.data(), [3, 0, 5]);
        });
        it('select single box', async () => {
            const boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
            const scores = tf.tensor1d([0.9]);
            const maxOutputSize = 3;
            const iouThreshold = 0.5;
            const scoreThreshold = 0;
            const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
            expect(indices.shape).toEqual([1]);
            expectArraysEqual(await indices.data(), [0]);
        });
        it('select from ten identical boxes', async () => {
            const numBoxes = 10;
            const corners = new Array(numBoxes)
                .fill(0)
                .map(_ => [0, 0, 1, 1])
                .reduce((arr, curr) => arr.concat(curr));
            const boxes = tf.tensor2d(corners, [numBoxes, 4]);
            const scores = tf.tensor1d(Array(numBoxes).fill(0.9));
            const maxOutputSize = 3;
            const iouThreshold = 0.5;
            const scoreThreshold = 0;
            const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
            expect(indices.shape).toEqual([1]);
            expectArraysEqual(await indices.data(), [0]);
        });
        it('inconsistent box and score shapes', () => {
            const boxes = tf.tensor2d([
                0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
                0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
            ], [6, 4]);
            const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5]);
            const maxOutputSize = 30;
            const iouThreshold = 0.5;
            const scoreThreshold = 0;
            expect(() => tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold))
                .toThrowError(/scores has incompatible shape with boxes/);
        });
        it('invalid iou threshold', () => {
            const boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
            const scores = tf.tensor1d([0.9]);
            const maxOutputSize = 3;
            const iouThreshold = 1.2;
            const scoreThreshold = 0;
            expect(() => tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold))
                .toThrowError(/iouThreshold must be in \[0, 1\]/);
        });
        it('empty input', async () => {
            const boxes = tf.tensor2d([], [0, 4]);
            const scores = tf.tensor1d([]);
            const maxOutputSize = 3;
            const iouThreshold = 0.5;
            const scoreThreshold = 0;
            const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
            expect(indices.shape).toEqual([0]);
            expectArraysEqual(await indices.data(), []);
        });
        it('accepts a tensor-like object', async () => {
            const boxes = [[0, 0, 1, 1], [0, 1, 1, 2]];
            const scores = [1, 2];
            const indices = tf.image.nonMaxSuppression(boxes, scores, 10);
            expect(indices.shape).toEqual([2]);
            expect(indices.dtype).toEqual('int32');
            expectArraysEqual(await indices.data(), [1, 0]);
        });
        it('throws when boxes is int32', async () => {
            const boxes = tf.tensor2d([[0, 0, 1, 1], [0, 1, 1, 2]], [2, 4], 'int32');
            const scores = [1, 2];
            expect(() => tf.image.nonMaxSuppression(boxes, scores, 10))
                .toThrowError(/Argument 'boxes' passed to 'nonMaxSuppression' must be float32/);
        });
        it('throws when scores is int32', async () => {
            const boxes = [[0, 0, 1, 1], [0, 1, 1, 2]];
            const scores = tf.tensor1d([1, 2], 'int32');
            const errRegex = /Argument 'scores' passed to 'nonMaxSuppression' must be float32/;
            expect(() => tf.image.nonMaxSuppression(boxes, scores, 10))
                .toThrowError(errRegex);
        });
        it('works when inputs are not explicitly initialized on the CPU', async () => {
            // This test ensures that asynchronous backends work with NMS, which
            // requires inputs to reside on the CPU.
            const boxes = tf.tensor2d([
                0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
                0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
            ], [6, 4]);
            const a = tf.tensor1d([0, 1, -2, -4, 4, -4]);
            const b = tf.tensor1d([0.15, 0.2, 0.25, 0.5, 0.7, 1.2]);
            const scores = a.div(b);
            const maxOutputSize = 2;
            const iouThreshold = 0.5;
            const scoreThreshold = 0;
            await scores.data();
            const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
            expect(indices.shape).toEqual([2]);
            expectArraysEqual(await indices.data(), [4, 1]);
        });
    });
    describe('NonMaxSuppressionWithScore', () => {
        it('select from three clusters with SoftNMS', async () => {
            const boxes = tf.tensor2d([
                0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
                0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
            ], [6, 4]);
            const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
            const maxOutputSize = 6;
            const iouThreshold = 1.0;
            const scoreThreshold = 0;
            const softNmsSigma = 0.5;
            const { selectedIndices, selectedScores } = tf.image.nonMaxSuppressionWithScore(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
            expectArraysEqual(await selectedIndices.data(), [3, 0, 1, 5, 4, 2]);
            expectArraysClose(await selectedScores.data(), [0.95, 0.9, 0.384, 0.3, 0.256, 0.197]);
        });
    });
    describe('NonMaxSuppressionPadded', () => {
        it('select from three clusters with pad five.', async () => {
            const boxes = tf.tensor2d([
                0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
                0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
            ], [6, 4]);
            const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
            const maxOutputSize = 5;
            const iouThreshold = 0.5;
            const scoreThreshold = 0;
            const before = tf.memory().numTensors;
            const { selectedIndices, validOutputs } = tf.image.nonMaxSuppressionPadded(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, true);
            const after = tf.memory().numTensors;
            expectArraysEqual(await selectedIndices.data(), [3, 0, 5, 0, 0]);
            expectArraysEqual(await validOutputs.data(), 3);
            expect(after).toEqual(before + 2);
        });
        it('select from three clusters with pad five and score threshold.', async () => {
            const boxes = tf.tensor2d([
                0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
                0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
            ], [6, 4]);
            const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
            const maxOutputSize = 6;
            const iouThreshold = 0.5;
            const scoreThreshold = 0.4;
            const before = tf.memory().numTensors;
            const { selectedIndices, validOutputs } = tf.image.nonMaxSuppressionPadded(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, true);
            const after = tf.memory().numTensors;
            expectArraysEqual(await selectedIndices.data(), [3, 0, 0, 0, 0, 0]);
            expectArraysEqual(await validOutputs.data(), 2);
            expect(after).toEqual(before + 2);
        });
        it('select from three clusters with no padding when pad option is false.', async () => {
            const boxes = tf.tensor2d([
                0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
                0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
            ], [6, 4]);
            const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
            const maxOutputSize = 5;
            const iouThreshold = 0.5;
            const scoreThreshold = 0.0;
            const { selectedIndices, validOutputs } = tf.image.nonMaxSuppressionPadded(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, false);
            expectArraysEqual(await selectedIndices.data(), [3, 0, 5]);
            expectArraysEqual(await validOutputs.data(), 3);
        });
    });
});
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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('nonMaxSuppression', ALL_ENVS, () => {\n  describe('NonMaxSuppression Basic', () => {\n    it('select from three clusters', async () => {\n      const boxes = tf.tensor2d(\n          [\n            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,\n            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101\n          ],\n          [6, 4]);\n      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);\n      const maxOutputSize = 3;\n      const iouThreshold = 0.5;\n      const scoreThreshold = 0;\n      const indices = tf.image.nonMaxSuppression(\n          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);\n\n      expect(indices.shape).toEqual([3]);\n      expectArraysEqual(await indices.data(), [3, 0, 5]);\n    });\n\n    it('select from three clusters flipped coordinates', async () => {\n      const boxes = tf.tensor2d(\n          [\n            1, 1,  0, 0,  0, 0.1,  1, 1.1,  0, .9,  1, -0.1,\n            0, 10, 1, 11, 1, 10.1, 0, 11.1, 1, 101, 0, 100\n          ],\n          [6, 4]);\n      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);\n      const maxOutputSize = 3;\n      const iouThreshold = 0.5;\n      const scoreThreshold = 0;\n      const indices = tf.image.nonMaxSuppression(\n          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);\n\n      expect(indices.shape).toEqual([3]);\n      expectArraysEqual(await indices.data(), [3, 0, 5]);\n    });\n\n    it('select at most two boxes from three clusters', async () => {\n      const boxes = tf.tensor2d(\n          [\n            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,\n            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101\n          ],\n          [6, 4]);\n      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);\n      const maxOutputSize = 2;\n      const iouThreshold = 0.5;\n      const scoreThreshold = 0;\n      const indices = tf.image.nonMaxSuppression(\n          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);\n\n      expect(indices.shape).toEqual([2]);\n      expectArraysEqual(await indices.data(), [3, 0]);\n    });\n\n    it('select at most thirty boxes from three clusters', async () => {\n      const boxes = tf.tensor2d(\n          [\n            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,\n            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101\n          ],\n          [6, 4]);\n      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);\n      const maxOutputSize = 30;\n      const iouThreshold = 0.5;\n      const scoreThreshold = 0;\n      const indices = tf.image.nonMaxSuppression(\n          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);\n\n      expect(indices.shape).toEqual([3]);\n      expectArraysEqual(await indices.data(), [3, 0, 5]);\n    });\n\n    it('select single box', async () => {\n      const boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);\n      const scores = tf.tensor1d([0.9]);\n      const maxOutputSize = 3;\n      const iouThreshold = 0.5;\n      const scoreThreshold = 0;\n      const indices = tf.image.nonMaxSuppression(\n          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);\n\n      expect(indices.shape).toEqual([1]);\n      expectArraysEqual(await indices.data(), [0]);\n    });\n\n    it('select from ten identical boxes', async () => {\n      const numBoxes = 10;\n      const corners = new Array(numBoxes)\n                          .fill(0)\n                          .map(_ => [0, 0, 1, 1])\n                          .reduce((arr, curr) => arr.concat(curr));\n      const boxes = tf.tensor2d(corners, [numBoxes, 4]);\n      const scores = tf.tensor1d(Array(numBoxes).fill(0.9));\n      const maxOutputSize = 3;\n      const iouThreshold = 0.5;\n      const scoreThreshold = 0;\n      const indices = tf.image.nonMaxSuppression(\n          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);\n\n      expect(indices.shape).toEqual([1]);\n      expectArraysEqual(await indices.data(), [0]);\n    });\n\n    it('inconsistent box and score shapes', () => {\n      const boxes = tf.tensor2d(\n          [\n            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,\n            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101\n          ],\n          [6, 4]);\n      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5]);\n      const maxOutputSize = 30;\n      const iouThreshold = 0.5;\n      const scoreThreshold = 0;\n      expect(\n          () => tf.image.nonMaxSuppression(\n              boxes, scores, maxOutputSize, iouThreshold, scoreThreshold))\n          .toThrowError(/scores has incompatible shape with boxes/);\n    });\n\n    it('invalid iou threshold', () => {\n      const boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);\n      const scores = tf.tensor1d([0.9]);\n      const maxOutputSize = 3;\n      const iouThreshold = 1.2;\n      const scoreThreshold = 0;\n      expect(\n          () => tf.image.nonMaxSuppression(\n              boxes, scores, maxOutputSize, iouThreshold, scoreThreshold))\n          .toThrowError(/iouThreshold must be in \\[0, 1\\]/);\n    });\n\n    it('empty input', async () => {\n      const boxes = tf.tensor2d([], [0, 4]);\n      const scores = tf.tensor1d([]);\n      const maxOutputSize = 3;\n      const iouThreshold = 0.5;\n      const scoreThreshold = 0;\n      const indices = tf.image.nonMaxSuppression(\n          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);\n\n      expect(indices.shape).toEqual([0]);\n      expectArraysEqual(await indices.data(), []);\n    });\n\n    it('accepts a tensor-like object', async () => {\n      const boxes = [[0, 0, 1, 1], [0, 1, 1, 2]];\n      const scores = [1, 2];\n      const indices = tf.image.nonMaxSuppression(boxes, scores, 10);\n      expect(indices.shape).toEqual([2]);\n      expect(indices.dtype).toEqual('int32');\n      expectArraysEqual(await indices.data(), [1, 0]);\n    });\n\n    it('throws when boxes is int32', async () => {\n      const boxes = tf.tensor2d([[0, 0, 1, 1], [0, 1, 1, 2]], [2, 4], 'int32');\n      const scores = [1, 2];\n      expect(() => tf.image.nonMaxSuppression(boxes, scores, 10))\n          .toThrowError(\n              /Argument 'boxes' passed to 'nonMaxSuppression' must be float32/);\n    });\n\n    it('throws when scores is int32', async () => {\n      const boxes = [[0, 0, 1, 1], [0, 1, 1, 2]];\n      const scores = tf.tensor1d([1, 2], 'int32');\n      const errRegex =\n          /Argument 'scores' passed to 'nonMaxSuppression' must be float32/;\n      expect(() => tf.image.nonMaxSuppression(boxes, scores, 10))\n          .toThrowError(errRegex);\n    });\n\n    it('works when inputs are not explicitly initialized on the CPU',\n       async () => {\n         // This test ensures that asynchronous backends work with NMS, which\n         // requires inputs to reside on the CPU.\n         const boxes = tf.tensor2d(\n             [\n               0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,\n               0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101\n             ],\n             [6, 4]);\n         const a = tf.tensor1d([0, 1, -2, -4, 4, -4]);\n         const b = tf.tensor1d([0.15, 0.2, 0.25, 0.5, 0.7, 1.2]);\n         const scores = a.div(b);\n         const maxOutputSize = 2;\n         const iouThreshold = 0.5;\n         const scoreThreshold = 0;\n         await scores.data();\n         const indices = tf.image.nonMaxSuppression(\n             boxes, scores as tf.Tensor1D, maxOutputSize, iouThreshold,\n             scoreThreshold);\n\n         expect(indices.shape).toEqual([2]);\n         expectArraysEqual(await indices.data(), [4, 1]);\n       });\n  });\n\n  describe('NonMaxSuppressionWithScore', () => {\n    it('select from three clusters with SoftNMS', async () => {\n      const boxes = tf.tensor2d(\n          [\n            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,\n            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101\n          ],\n          [6, 4]);\n      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);\n      const maxOutputSize = 6;\n      const iouThreshold = 1.0;\n      const scoreThreshold = 0;\n      const softNmsSigma = 0.5;\n\n      const {selectedIndices, selectedScores} =\n          tf.image.nonMaxSuppressionWithScore(\n              boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,\n              softNmsSigma);\n\n      expectArraysEqual(await selectedIndices.data(), [3, 0, 1, 5, 4, 2]);\n\n      expectArraysClose(\n          await selectedScores.data(), [0.95, 0.9, 0.384, 0.3, 0.256, 0.197]);\n    });\n  });\n\n  describe('NonMaxSuppressionPadded', () => {\n    it('select from three clusters with pad five.', async () => {\n      const boxes = tf.tensor2d(\n          [\n            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,\n            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101\n          ],\n          [6, 4]);\n      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);\n      const maxOutputSize = 5;\n      const iouThreshold = 0.5;\n      const scoreThreshold = 0;\n\n      const before = tf.memory().numTensors;\n      const {selectedIndices, validOutputs} = tf.image.nonMaxSuppressionPadded(\n          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, true);\n      const after = tf.memory().numTensors;\n\n      expectArraysEqual(await selectedIndices.data(), [3, 0, 5, 0, 0]);\n      expectArraysEqual(await validOutputs.data(), 3);\n      expect(after).toEqual(before + 2);\n    });\n\n    it('select from three clusters with pad five and score threshold.',\n       async () => {\n         const boxes = tf.tensor2d(\n             [\n               0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,\n               0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101\n             ],\n             [6, 4]);\n         const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);\n         const maxOutputSize = 6;\n         const iouThreshold = 0.5;\n         const scoreThreshold = 0.4;\n\n         const before = tf.memory().numTensors;\n         const {selectedIndices, validOutputs} =\n             tf.image.nonMaxSuppressionPadded(\n                 boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,\n                 true);\n         const after = tf.memory().numTensors;\n\n         expectArraysEqual(await selectedIndices.data(), [3, 0, 0, 0, 0, 0]);\n         expectArraysEqual(await validOutputs.data(), 2);\n         expect(after).toEqual(before + 2);\n       });\n\n    it('select from three clusters with no padding when pad option is false.',\n       async () => {\n         const boxes = tf.tensor2d(\n             [\n               0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,\n               0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101\n             ],\n             [6, 4]);\n         const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);\n         const maxOutputSize = 5;\n         const iouThreshold = 0.5;\n         const scoreThreshold = 0.0;\n\n         const {selectedIndices, validOutputs} =\n             tf.image.nonMaxSuppressionPadded(\n                 boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,\n                 false);\n\n         expectArraysEqual(await selectedIndices.data(), [3, 0, 5]);\n         expectArraysEqual(await validOutputs.data(), 3);\n       });\n  });\n});\n"]}