@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
361 lines • 21.4 kB
JavaScript
"use strict";
/**
* @license
* Copyright 2018 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.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
var non_max_suppression_impl_1 = require("../backends/non_max_suppression_impl");
var engine_1 = require("../engine");
var tensor_util_env_1 = require("../tensor_util_env");
var util = require("../util");
var operation_1 = require("./operation");
/**
* Bilinear resize a batch of 3D images to a new shape.
*
* @param images The images, of rank 4 or rank 3, of shape
* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
* @param size The new shape `[newHeight, newWidth]` to resize the
* images to. Each channel is resized individually.
* @param alignCorners Defaults to False. If true, rescale
* input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
* corners of images and resized images. If false, rescale by
* `new_height / height`. Treat similarly the width dimension.
*/
/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
function resizeBilinear_(images, size, alignCorners) {
if (alignCorners === void 0) { alignCorners = false; }
var $images = tensor_util_env_1.convertToTensor(images, 'images', 'resizeBilinear');
util.assert($images.rank === 3 || $images.rank === 4, function () { return "Error in resizeBilinear: x must be rank 3 or 4, but got " +
("rank " + $images.rank + "."); });
util.assert(size.length === 2, function () { return "Error in resizeBilinear: new shape must 2D, but got shape " +
(size + "."); });
var batchImages = $images;
var reshapedTo4D = false;
if ($images.rank === 3) {
reshapedTo4D = true;
batchImages =
$images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
}
var newHeight = size[0], newWidth = size[1];
var forward = function (backend, save) {
save([batchImages]);
return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);
};
var backward = function (dy, saved) {
return {
x: function () { return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.resizeBilinearBackprop(dy, saved[0], alignCorners); }, {}); }
};
};
var res = engine_1.ENGINE.runKernelFunc(forward, { x: batchImages }, backward, 'ResizeBilinear', { alignCorners: alignCorners, newHeight: newHeight, newWidth: newWidth });
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
}
return res;
}
/**
* NearestNeighbor resize a batch of 3D images to a new shape.
*
* @param images The images, of rank 4 or rank 3, of shape
* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
* @param size The new shape `[newHeight, newWidth]` to resize the
* images to. Each channel is resized individually.
* @param alignCorners Defaults to False. If true, rescale
* input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
* corners of images and resized images. If false, rescale by
* `new_height / height`. Treat similarly the width dimension.
*/
/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
function resizeNearestNeighbor_(images, size, alignCorners) {
if (alignCorners === void 0) { alignCorners = false; }
var $images = tensor_util_env_1.convertToTensor(images, 'images', 'resizeNearestNeighbor');
util.assert($images.rank === 3 || $images.rank === 4, function () { return "Error in resizeNearestNeighbor: x must be rank 3 or 4, but got " +
("rank " + $images.rank + "."); });
util.assert(size.length === 2, function () {
return "Error in resizeNearestNeighbor: new shape must 2D, but got shape " +
(size + ".");
});
util.assert($images.dtype === 'float32' || $images.dtype === 'int32', function () { return '`images` must have `int32` or `float32` as dtype'; });
var batchImages = $images;
var reshapedTo4D = false;
if ($images.rank === 3) {
reshapedTo4D = true;
batchImages =
$images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
}
var newHeight = size[0], newWidth = size[1];
var forward = function (backend, save) {
save([batchImages]);
return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);
};
var backward = function (dy, saved) {
return {
batchImages: function () { return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.resizeNearestNeighborBackprop(dy, saved[0], alignCorners); }, {}); }
};
};
var res = engine_1.ENGINE.runKernelFunc(forward, { batchImages: batchImages }, backward);
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
}
return res;
}
/**
* Performs non maximum suppression of bounding boxes based on
* iou (intersection over union).
*
* @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
* the bounding box.
* @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
* @param maxOutputSize The maximum number of boxes to be selected.
* @param iouThreshold A float representing the threshold for deciding whether
* boxes overlap too much with respect to IOU. Must be between [0, 1].
* Defaults to 0.5 (50% box overlap).
* @param scoreThreshold A threshold for deciding when to remove boxes based
* on score. Defaults to -inf, which means any score is accepted.
* @return A 1D tensor with the selected box indices.
*/
/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
if (iouThreshold === void 0) { iouThreshold = 0.5; }
if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
var $boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
var $scores = tensor_util_env_1.convertToTensor(scores, 'scores', 'nonMaxSuppression');
var inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
maxOutputSize = inputs.maxOutputSize;
iouThreshold = inputs.iouThreshold;
scoreThreshold = inputs.scoreThreshold;
var attrs = { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold };
return engine_1.ENGINE.runKernelFunc(function (b) { return b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); }, { boxes: $boxes, scores: $scores }, null /* grad */, 'NonMaxSuppressionV3', attrs);
}
/** This is the async version of `nonMaxSuppression` */
function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
if (iouThreshold === void 0) { iouThreshold = 0.5; }
if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
return __awaiter(this, void 0, void 0, function () {
var $boxes, $scores, inputs, boxesAndScores, boxesVals, scoresVals, res;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
$boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
$scores = tensor_util_env_1.convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
maxOutputSize = inputs.maxOutputSize;
iouThreshold = inputs.iouThreshold;
scoreThreshold = inputs.scoreThreshold;
return [4 /*yield*/, Promise.all([$boxes.data(), $scores.data()])];
case 1:
boxesAndScores = _a.sent();
boxesVals = boxesAndScores[0];
scoresVals = boxesAndScores[1];
res = non_max_suppression_impl_1.nonMaxSuppressionV3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
if ($boxes !== boxes) {
$boxes.dispose();
}
if ($scores !== scores) {
$scores.dispose();
}
return [2 /*return*/, res];
}
});
});
}
/**
* Performs non maximum suppression of bounding boxes based on
* iou (intersection over union).
*
* This op also supports a Soft-NMS mode (c.f.
* Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
* of other overlapping boxes, therefore favoring different regions of the image
* with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`
* parameter to be larger than 0.
*
* @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
* the bounding box.
* @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
* @param maxOutputSize The maximum number of boxes to be selected.
* @param iouThreshold A float representing the threshold for deciding whether
* boxes overlap too much with respect to IOU. Must be between [0, 1].
* Defaults to 0.5 (50% box overlap).
* @param scoreThreshold A threshold for deciding when to remove boxes based
* on score. Defaults to -inf, which means any score is accepted.
* @param softNmsSigma A float representing the sigma parameter for Soft NMS.
* When sigma is 0, it falls back to nonMaxSuppression.
* @return A map with the following properties:
* - selectedIndices: A 1D tensor with the selected box indices.
* - selectedScores: A 1D tensor with the corresponding scores for each
* selected box.
*/
/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
if (iouThreshold === void 0) { iouThreshold = 0.5; }
if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
if (softNmsSigma === void 0) { softNmsSigma = 0.0; }
var $boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
var $scores = tensor_util_env_1.convertToTensor(scores, 'scores', 'nonMaxSuppression');
var inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
maxOutputSize = inputs.maxOutputSize;
iouThreshold = inputs.iouThreshold;
scoreThreshold = inputs.scoreThreshold;
softNmsSigma = inputs.softNmsSigma;
var attrs = { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold, softNmsSigma: softNmsSigma };
var result = engine_1.ENGINE.runKernel('NonMaxSuppressionV5', { boxes: $boxes, scores: $scores }, attrs);
return { selectedIndices: result[0], selectedScores: result[1] };
}
/** This is the async version of `nonMaxSuppressionWithScore` */
function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
if (iouThreshold === void 0) { iouThreshold = 0.5; }
if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
if (softNmsSigma === void 0) { softNmsSigma = 0.0; }
return __awaiter(this, void 0, void 0, function () {
var $boxes, $scores, inputs, boxesAndScores, boxesVals, scoresVals, res;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
$boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
$scores = tensor_util_env_1.convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
maxOutputSize = inputs.maxOutputSize;
iouThreshold = inputs.iouThreshold;
scoreThreshold = inputs.scoreThreshold;
softNmsSigma = inputs.softNmsSigma;
return [4 /*yield*/, Promise.all([$boxes.data(), $scores.data()])];
case 1:
boxesAndScores = _a.sent();
boxesVals = boxesAndScores[0];
scoresVals = boxesAndScores[1];
res = non_max_suppression_impl_1.nonMaxSuppressionV5(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
if ($boxes !== boxes) {
$boxes.dispose();
}
if ($scores !== scores) {
$scores.dispose();
}
return [2 /*return*/, res];
}
});
});
}
function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
if (iouThreshold == null) {
iouThreshold = 0.5;
}
if (scoreThreshold == null) {
scoreThreshold = Number.NEGATIVE_INFINITY;
}
if (softNmsSigma == null) {
softNmsSigma = 0.0;
}
var numBoxes = boxes.shape[0];
maxOutputSize = Math.min(maxOutputSize, numBoxes);
util.assert(0 <= iouThreshold && iouThreshold <= 1, function () { return "iouThreshold must be in [0, 1], but was '" + iouThreshold + "'"; });
util.assert(boxes.rank === 2, function () { return "boxes must be a 2D tensor, but was of rank '" + boxes.rank + "'"; });
util.assert(boxes.shape[1] === 4, function () {
return "boxes must have 4 columns, but 2nd dimension was " + boxes.shape[1];
});
util.assert(scores.rank === 1, function () { return 'scores must be a 1D tensor'; });
util.assert(scores.shape[0] === numBoxes, function () { return "scores has incompatible shape with boxes. Expected " + numBoxes + ", " +
("but was " + scores.shape[0]); });
util.assert(0 <= softNmsSigma && softNmsSigma <= 1, function () { return "softNmsSigma must be in [0, 1], but was '" + softNmsSigma + "'"; });
return { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold, softNmsSigma: softNmsSigma };
}
/**
* Extracts crops from the input image tensor and resizes them using bilinear
* sampling or nearest neighbor sampling (possibly with aspect ratio change)
* to a common output size specified by crop_size.
*
* @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,
* where imageHeight and imageWidth must be positive, specifying the
* batch of images from which to take crops
* @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is
* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized
* coordinates of the box in the boxInd[i]'th image in the batch
* @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range
* `[0, batch)` that specifies the image that the `i`-th box refers to.
* @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`
* specifying the size to which all crops are resized to.
* @param method Optional string from `'bilinear' | 'nearest'`,
* defaults to bilinear, which specifies the sampling method for resizing
* @param extrapolationValue A threshold for deciding when to remove boxes based
* on score. Defaults to 0.
* @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`
*/
/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
function cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {
var $image = tensor_util_env_1.convertToTensor(image, 'image', 'cropAndResize');
var $boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');
var $boxInd = tensor_util_env_1.convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');
method = method || 'bilinear';
extrapolationValue = extrapolationValue || 0;
var numBoxes = $boxes.shape[0];
util.assert($image.rank === 4, function () { return 'Error in cropAndResize: image must be rank 4,' +
("but got rank " + $image.rank + "."); });
util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, function () { return "Error in cropAndResize: boxes must be have size [" + numBoxes + ",4] " +
("but had shape " + $boxes.shape + "."); });
util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, function () { return "Error in cropAndResize: boxInd must be have size [" + numBoxes + "] " +
("but had shape " + $boxes.shape + "."); });
util.assert(cropSize.length === 2, function () { return "Error in cropAndResize: cropSize must be of length 2, but got " +
("length " + cropSize.length + "."); });
util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, function () { return "cropSize must be atleast [1,1], but was " + cropSize; });
util.assert(method === 'bilinear' || method === 'nearest', function () { return "method must be bilinear or nearest, but was " + method; });
var forward = function (backend, save) {
return backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);
};
var res = engine_1.ENGINE.runKernelFunc(forward, { images: $image, boxes: $boxes, boxInd: $boxInd }, null /* der */, 'CropAndResize', { method: method, extrapolationValue: extrapolationValue, cropSize: cropSize });
return res;
}
exports.resizeBilinear = operation_1.op({ resizeBilinear_: resizeBilinear_ });
exports.resizeNearestNeighbor = operation_1.op({ resizeNearestNeighbor_: resizeNearestNeighbor_ });
exports.nonMaxSuppression = operation_1.op({ nonMaxSuppression_: nonMaxSuppression_ });
exports.nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
exports.nonMaxSuppressionWithScore = operation_1.op({ nonMaxSuppressionWithScore_: nonMaxSuppressionWithScore_ });
exports.nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
exports.cropAndResize = operation_1.op({ cropAndResize_: cropAndResize_ });
//# sourceMappingURL=image_ops.js.map