/**
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* @license
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* Copyright 2018 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import {nonMaxSuppressionV3, nonMaxSuppressionV5} from '../backends/non_max_suppression_impl';
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import {ENGINE, ForwardFunc} from '../engine';
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import {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';
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import {NamedTensorMap} from '../tensor_types';
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import {convertToTensor} from '../tensor_util_env';
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import {TensorLike} from '../types';
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import * as util from '../util';
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import {op} from './operation';
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/**
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* Bilinear resize a batch of 3D images to a new shape.
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*
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* @param images The images, of rank 4 or rank 3, of shape
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* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
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* @param size The new shape `[newHeight, newWidth]` to resize the
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* images to. Each channel is resized individually.
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* @param alignCorners Defaults to False. If true, rescale
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* input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
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* corners of images and resized images. If false, rescale by
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* `new_height / height`. Treat similarly the width dimension.
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*/
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/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
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function resizeBilinear_<T extends Tensor3D|Tensor4D>(
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images: T|TensorLike, size: [number, number], alignCorners = false): T {
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const $images = convertToTensor(images, 'images', 'resizeBilinear');
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util.assert(
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$images.rank === 3 || $images.rank === 4,
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() => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +
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`rank ${$images.rank}.`);
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util.assert(
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size.length === 2,
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() => `Error in resizeBilinear: new shape must 2D, but got shape ` +
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`${size}.`);
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let batchImages = $images as Tensor4D;
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let reshapedTo4D = false;
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if ($images.rank === 3) {
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reshapedTo4D = true;
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batchImages =
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$images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
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}
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const [newHeight, newWidth] = size;
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const forward: ForwardFunc<Tensor4D> = (backend, save) => {
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save([batchImages]);
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return backend.resizeBilinear(
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batchImages, newHeight, newWidth, alignCorners);
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};
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const backward = (dy: Tensor4D, saved: Tensor[]) => {
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return {
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x: () => ENGINE.runKernelFunc(
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backend => backend.resizeBilinearBackprop(
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dy, saved[0] as Tensor4D, alignCorners),
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{})
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};
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};
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const res = ENGINE.runKernelFunc(
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forward, {x: batchImages}, backward, 'ResizeBilinear',
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{alignCorners, newHeight, newWidth});
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if (reshapedTo4D) {
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return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;
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}
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return res as T;
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}
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/**
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* NearestNeighbor resize a batch of 3D images to a new shape.
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*
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* @param images The images, of rank 4 or rank 3, of shape
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* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
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* @param size The new shape `[newHeight, newWidth]` to resize the
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* images to. Each channel is resized individually.
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* @param alignCorners Defaults to False. If true, rescale
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* input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
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* corners of images and resized images. If false, rescale by
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* `new_height / height`. Treat similarly the width dimension.
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*/
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/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
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function resizeNearestNeighbor_<T extends Tensor3D|Tensor4D>(
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images: T|TensorLike, size: [number, number], alignCorners = false): T {
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const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');
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util.assert(
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$images.rank === 3 || $images.rank === 4,
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() => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +
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`rank ${$images.rank}.`);
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util.assert(
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size.length === 2,
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() =>
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`Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +
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`${size}.`);
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util.assert(
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$images.dtype === 'float32' || $images.dtype === 'int32',
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() => '`images` must have `int32` or `float32` as dtype');
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let batchImages = $images as Tensor4D;
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let reshapedTo4D = false;
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if ($images.rank === 3) {
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reshapedTo4D = true;
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batchImages =
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$images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
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}
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const [newHeight, newWidth] = size;
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const forward: ForwardFunc<Tensor4D> = (backend, save) => {
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save([batchImages]);
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return backend.resizeNearestNeighbor(
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batchImages, newHeight, newWidth, alignCorners);
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};
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const backward = (dy: Tensor4D, saved: Tensor[]) => {
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return {
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batchImages: () => ENGINE.runKernelFunc(
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backend => backend.resizeNearestNeighborBackprop(
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dy, saved[0] as Tensor4D, alignCorners),
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{})
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};
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};
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const res = ENGINE.runKernelFunc(forward, {batchImages}, backward);
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if (reshapedTo4D) {
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return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;
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}
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return res as T;
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}
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/**
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* Performs non maximum suppression of bounding boxes based on
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* iou (intersection over union).
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*
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* @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
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* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
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* the bounding box.
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* @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
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* @param maxOutputSize The maximum number of boxes to be selected.
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* @param iouThreshold A float representing the threshold for deciding whether
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* boxes overlap too much with respect to IOU. Must be between [0, 1].
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* Defaults to 0.5 (50% box overlap).
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* @param scoreThreshold A threshold for deciding when to remove boxes based
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* on score. Defaults to -inf, which means any score is accepted.
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* @return A 1D tensor with the selected box indices.
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*/
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/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
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function nonMaxSuppression_(
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boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,
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maxOutputSize: number, iouThreshold = 0.5,
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scoreThreshold = Number.NEGATIVE_INFINITY): Tensor1D {
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const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
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const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');
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const inputs = nonMaxSuppSanityCheck(
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$boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
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maxOutputSize = inputs.maxOutputSize;
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iouThreshold = inputs.iouThreshold;
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scoreThreshold = inputs.scoreThreshold;
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const attrs = {maxOutputSize, iouThreshold, scoreThreshold};
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return ENGINE.runKernelFunc(
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b => b.nonMaxSuppression(
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$boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold),
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{boxes: $boxes, scores: $scores}, null /* grad */, 'NonMaxSuppressionV3',
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attrs);
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}
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/** This is the async version of `nonMaxSuppression` */
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async function nonMaxSuppressionAsync_(
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boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,
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maxOutputSize: number, iouThreshold = 0.5,
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scoreThreshold = Number.NEGATIVE_INFINITY): Promise<Tensor1D> {
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const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
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const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
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const inputs = nonMaxSuppSanityCheck(
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$boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
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maxOutputSize = inputs.maxOutputSize;
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iouThreshold = inputs.iouThreshold;
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scoreThreshold = inputs.scoreThreshold;
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const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
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const boxesVals = boxesAndScores[0];
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const scoresVals = boxesAndScores[1];
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const res = nonMaxSuppressionV3(
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boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
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if ($boxes !== boxes) {
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$boxes.dispose();
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}
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if ($scores !== scores) {
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$scores.dispose();
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}
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return res;
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}
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/**
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* Performs non maximum suppression of bounding boxes based on
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* iou (intersection over union).
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*
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* This op also supports a Soft-NMS mode (c.f.
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* Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
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* of other overlapping boxes, therefore favoring different regions of the image
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* with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`
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* parameter to be larger than 0.
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*
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* @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
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* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
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* the bounding box.
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* @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
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* @param maxOutputSize The maximum number of boxes to be selected.
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* @param iouThreshold A float representing the threshold for deciding whether
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* boxes overlap too much with respect to IOU. Must be between [0, 1].
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* Defaults to 0.5 (50% box overlap).
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* @param scoreThreshold A threshold for deciding when to remove boxes based
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* on score. Defaults to -inf, which means any score is accepted.
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* @param softNmsSigma A float representing the sigma parameter for Soft NMS.
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* When sigma is 0, it falls back to nonMaxSuppression.
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* @return A map with the following properties:
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* - selectedIndices: A 1D tensor with the selected box indices.
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* - selectedScores: A 1D tensor with the corresponding scores for each
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* selected box.
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*/
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/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
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function nonMaxSuppressionWithScore_(
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boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,
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maxOutputSize: number, iouThreshold = 0.5,
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scoreThreshold = Number.NEGATIVE_INFINITY,
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softNmsSigma = 0.0): NamedTensorMap {
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const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
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const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');
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const inputs = nonMaxSuppSanityCheck(
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$boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,
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softNmsSigma);
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maxOutputSize = inputs.maxOutputSize;
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iouThreshold = inputs.iouThreshold;
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scoreThreshold = inputs.scoreThreshold;
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softNmsSigma = inputs.softNmsSigma;
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const attrs = {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
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const result = ENGINE.runKernel(
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'NonMaxSuppressionV5', {boxes: $boxes, scores: $scores},
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attrs) as Tensor[];
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return {selectedIndices: result[0], selectedScores: result[1]};
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}
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/** This is the async version of `nonMaxSuppressionWithScore` */
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async function nonMaxSuppressionWithScoreAsync_(
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boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,
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maxOutputSize: number, iouThreshold = 0.5,
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scoreThreshold = Number.NEGATIVE_INFINITY,
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softNmsSigma = 0.0): Promise<NamedTensorMap> {
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const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
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const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
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const inputs = nonMaxSuppSanityCheck(
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$boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,
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softNmsSigma);
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maxOutputSize = inputs.maxOutputSize;
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iouThreshold = inputs.iouThreshold;
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scoreThreshold = inputs.scoreThreshold;
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softNmsSigma = inputs.softNmsSigma;
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const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
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const boxesVals = boxesAndScores[0];
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const scoresVals = boxesAndScores[1];
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const res = nonMaxSuppressionV5(
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boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold,
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softNmsSigma);
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if ($boxes !== boxes) {
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$boxes.dispose();
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}
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if ($scores !== scores) {
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$scores.dispose();
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}
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return res;
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}
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function nonMaxSuppSanityCheck(
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boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,
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iouThreshold: number, scoreThreshold: number, softNmsSigma?: number): {
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maxOutputSize: number,
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iouThreshold: number,
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scoreThreshold: number,
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softNmsSigma: number
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} {
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if (iouThreshold == null) {
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iouThreshold = 0.5;
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}
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if (scoreThreshold == null) {
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scoreThreshold = Number.NEGATIVE_INFINITY;
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}
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if (softNmsSigma == null) {
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softNmsSigma = 0.0;
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}
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const numBoxes = boxes.shape[0];
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maxOutputSize = Math.min(maxOutputSize, numBoxes);
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util.assert(
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0 <= iouThreshold && iouThreshold <= 1,
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() => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);
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util.assert(
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boxes.rank === 2,
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() => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);
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util.assert(
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boxes.shape[1] === 4,
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() =>
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`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);
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util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');
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util.assert(
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scores.shape[0] === numBoxes,
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() => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +
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`but was ${scores.shape[0]}`);
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util.assert(
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0 <= softNmsSigma && softNmsSigma <= 1,
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() => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);
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return {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
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}
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/**
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* Extracts crops from the input image tensor and resizes them using bilinear
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* sampling or nearest neighbor sampling (possibly with aspect ratio change)
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* to a common output size specified by crop_size.
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*
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* @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,
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* where imageHeight and imageWidth must be positive, specifying the
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* batch of images from which to take crops
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* @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is
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* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized
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* coordinates of the box in the boxInd[i]'th image in the batch
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* @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range
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* `[0, batch)` that specifies the image that the `i`-th box refers to.
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* @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`
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* specifying the size to which all crops are resized to.
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* @param method Optional string from `'bilinear' | 'nearest'`,
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* defaults to bilinear, which specifies the sampling method for resizing
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* @param extrapolationValue A threshold for deciding when to remove boxes based
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* on score. Defaults to 0.
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* @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`
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*/
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/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
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function cropAndResize_(
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image: Tensor4D|TensorLike,
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boxes: Tensor2D|TensorLike,
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boxInd: Tensor1D|TensorLike,
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cropSize: [number, number],
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method?: 'bilinear'|'nearest',
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extrapolationValue?: number,
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): Tensor4D {
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const $image = convertToTensor(image, 'image', 'cropAndResize');
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const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');
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const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');
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method = method || 'bilinear';
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extrapolationValue = extrapolationValue || 0;
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const numBoxes = $boxes.shape[0];
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util.assert(
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$image.rank === 4,
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() => 'Error in cropAndResize: image must be rank 4,' +
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`but got rank ${$image.rank}.`);
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util.assert(
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$boxes.rank === 2 && $boxes.shape[1] === 4,
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() => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +
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`but had shape ${$boxes.shape}.`);
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util.assert(
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$boxInd.rank === 1 && $boxInd.shape[0] === numBoxes,
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() => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +
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`but had shape ${$boxes.shape}.`);
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util.assert(
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cropSize.length === 2,
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() => `Error in cropAndResize: cropSize must be of length 2, but got ` +
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`length ${cropSize.length}.`);
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util.assert(
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cropSize[0] >= 1 && cropSize[1] >= 1,
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() => `cropSize must be atleast [1,1], but was ${cropSize}`);
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util.assert(
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method === 'bilinear' || method === 'nearest',
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() => `method must be bilinear or nearest, but was ${method}`);
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const forward: ForwardFunc<Tensor4D> = (backend, save) =>
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backend.cropAndResize(
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$image, $boxes, $boxInd, cropSize, method, extrapolationValue);
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const res = ENGINE.runKernelFunc(
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forward, {images: $image, boxes: $boxes, boxInd: $boxInd}, null /* der */,
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'CropAndResize', {method, extrapolationValue, cropSize});
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return res;
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}
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export const resizeBilinear = op({resizeBilinear_});
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export const resizeNearestNeighbor = op({resizeNearestNeighbor_});
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export const nonMaxSuppression = op({nonMaxSuppression_});
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export const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
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export const nonMaxSuppressionWithScore = op({nonMaxSuppressionWithScore_});
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export const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
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export const cropAndResize = op({cropAndResize_});
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