@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
78 lines • 3.96 kB
JavaScript
var __decorate = (this && this.__decorate) || function (decorators, target, key, desc) {
var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;
if (typeof Reflect === "object" && typeof Reflect.decorate === "function") r = Reflect.decorate(decorators, target, key, desc);
else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;
return c > 3 && r && Object.defineProperty(target, key, r), r;
};
import { doc } from '../doc';
import { ENV } from '../environment';
import * as util from '../util';
import { operation } from './operation';
var ImageOps = (function () {
function ImageOps() {
}
ImageOps.resizeBilinear = function (images, size, alignCorners) {
if (alignCorners === void 0) { alignCorners = false; }
util.assertArgumentsAreTensors({ 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 ENV.engine.runKernel(function (backend) {
return backend.resizeBilinearBackprop(dy, batchImages, alignCorners);
}, {}); }
};
};
var res = ENV.engine.runKernel(forward, { batchImages: batchImages }, backward);
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
}
return res;
};
ImageOps.resizeNearestNeighbor = function (images, size, alignCorners) {
if (alignCorners === void 0) { alignCorners = false; }
util.assertArgumentsAreTensors({ 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 res = ENV.engine.runKernel(function (backend) { return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners); }, { batchImages: batchImages });
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
}
return res;
};
__decorate([
doc({ heading: 'Operations', subheading: 'Images', namespace: 'image' }),
operation
], ImageOps, "resizeBilinear", null);
__decorate([
doc({ heading: 'Operations', subheading: 'Images', namespace: 'image' }),
operation
], ImageOps, "resizeNearestNeighbor", null);
return ImageOps;
}());
export { ImageOps };
//# sourceMappingURL=image_ops.js.map