@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
192 lines • 11.2 kB
JavaScript
"use strict";
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 environment_1 = require("../environment");
var non_max_suppression_impl_1 = require("../kernels/non_max_suppression_impl");
var tensor_util_env_1 = require("../tensor_util_env");
var util = require("../util");
var operation_1 = require("./operation");
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, "Error in resizeBilinear: x must be rank 3 or 4, but got " +
("rank " + $images.rank + "."));
util.assert(size.length === 2, "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) {
return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);
};
var backward = function (dy, saved) {
return {
batchImages: function () { return environment_1.ENV.engine.runKernel(function (backend) {
return backend.resizeBilinearBackprop(dy, batchImages, alignCorners);
}, {}); }
};
};
var res = environment_1.ENV.engine.runKernel(forward, { batchImages: batchImages }, backward);
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
}
return res;
}
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, "Error in resizeNearestNeighbor: x must be rank 3 or 4, but got " +
("rank " + $images.rank + "."));
util.assert(size.length === 2, "Error in resizeNearestNeighbor: new shape must 2D, but got shape " +
(size + "."));
util.assert($images.dtype === 'float32' || $images.dtype === 'int32', '`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) {
return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);
};
var backward = function (dy, saved) {
return {
batchImages: function () { return environment_1.ENV.engine.runKernel(function (backend) { return backend.resizeNearestNeighborBackprop(dy, batchImages, alignCorners); }, {}); }
};
};
var res = environment_1.ENV.engine.runKernel(forward, { batchImages: batchImages }, backward);
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
}
return res;
}
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, $scores, maxOutputSize, iouThreshold, scoreThreshold);
maxOutputSize = inputs.maxOutputSize;
iouThreshold = inputs.iouThreshold;
scoreThreshold = inputs.scoreThreshold;
return environment_1.ENV.engine.runKernel(function (b) { return b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); }, { $boxes: $boxes });
}
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, 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, $boxes.data()];
case 1:
boxesVals = _a.sent();
return [4, $scores.data()];
case 2:
scoresVals = _a.sent();
res = non_max_suppression_impl_1.nonMaxSuppressionImpl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
if ($boxes !== boxes) {
$boxes.dispose();
}
if ($scores !== scores) {
$scores.dispose();
}
return [2, res];
}
});
});
}
function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
if (iouThreshold == null) {
iouThreshold = 0.5;
}
if (scoreThreshold == null) {
scoreThreshold = Number.NEGATIVE_INFINITY;
}
var numBoxes = boxes.shape[0];
maxOutputSize = Math.min(maxOutputSize, numBoxes);
util.assert(0 <= iouThreshold && iouThreshold <= 1, "iouThreshold must be in [0, 1], but was '" + iouThreshold + "'");
util.assert(boxes.rank === 2, "boxes must be a 2D tensor, but was of rank '" + boxes.rank + "'");
util.assert(boxes.shape[1] === 4, "boxes must have 4 columns, but 2nd dimension was " + boxes.shape[1]);
util.assert(scores.rank === 1, 'scores must be a 1D tensor');
util.assert(scores.shape[0] === numBoxes, "scores has incompatible shape with boxes. Expected " + numBoxes + ", " +
("but was " + scores.shape[0]));
return { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold };
}
function cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {
var $image = tensor_util_env_1.convertToTensor(image, 'image', 'cropAndResize', 'float32');
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, 'Error in cropAndResize: image must be rank 4,' +
("but got rank " + $image.rank + "."));
util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, "Error in cropAndResize: boxes must be have size [" + numBoxes + ",4] " +
("but had shape " + $boxes.shape + "."));
util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, "Error in cropAndResize: boxInd must be have size [" + numBoxes + "] " +
("but had shape " + $boxes.shape + "."));
util.assert(cropSize.length === 2, "Error in cropAndResize: cropSize must be of length 2, but got length " +
(cropSize.length + "."));
util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, "cropSize must be atleast [1,1], but was " + cropSize);
util.assert(method === 'bilinear' || method === 'nearest', "method must be bilinear or nearest, but was " + method);
var forward = function (backend, save) {
return backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);
};
var res = environment_1.ENV.engine.runKernel(forward, { $image: $image, $boxes: $boxes });
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.cropAndResize = cropAndResize_;
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