89 lines
No EOL
4.3 KiB
JavaScript
89 lines
No EOL
4.3 KiB
JavaScript
import * as tslib_1 from "tslib";
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import * as tf from '@tensorflow/tfjs-core';
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import { NeuralNetwork, normalize, toNetInput } from 'tfjs-image-recognition-base';
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import { convDown } from './convLayer';
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import { extractParams } from './extractParams';
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import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
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import { residual, residualDown } from './residualLayer';
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var FaceRecognitionNet = /** @class */ (function (_super) {
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tslib_1.__extends(FaceRecognitionNet, _super);
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function FaceRecognitionNet() {
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return _super.call(this, 'FaceRecognitionNet') || this;
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}
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FaceRecognitionNet.prototype.forwardInput = function (input) {
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var params = this.params;
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if (!params) {
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throw new Error('FaceRecognitionNet - load model before inference');
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}
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return tf.tidy(function () {
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var batchTensor = input.toBatchTensor(150, true).toFloat();
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var meanRgb = [122.782, 117.001, 104.298];
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var normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256));
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var out = convDown(normalized, params.conv32_down);
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out = tf.maxPool(out, 3, 2, 'valid');
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out = residual(out, params.conv32_1);
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out = residual(out, params.conv32_2);
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out = residual(out, params.conv32_3);
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out = residualDown(out, params.conv64_down);
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out = residual(out, params.conv64_1);
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out = residual(out, params.conv64_2);
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out = residual(out, params.conv64_3);
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out = residualDown(out, params.conv128_down);
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out = residual(out, params.conv128_1);
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out = residual(out, params.conv128_2);
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out = residualDown(out, params.conv256_down);
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out = residual(out, params.conv256_1);
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out = residual(out, params.conv256_2);
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out = residualDown(out, params.conv256_down_out);
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var globalAvg = out.mean([1, 2]);
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var fullyConnected = tf.matMul(globalAvg, params.fc);
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return fullyConnected;
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});
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};
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FaceRecognitionNet.prototype.forward = function (input) {
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return tslib_1.__awaiter(this, void 0, void 0, function () {
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var _a;
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return tslib_1.__generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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_a = this.forwardInput;
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return [4 /*yield*/, toNetInput(input)];
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case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
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}
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});
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});
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};
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FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
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return tslib_1.__awaiter(this, void 0, void 0, function () {
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var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
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var _this = this;
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return tslib_1.__generator(this, function (_a) {
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switch (_a.label) {
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case 0: return [4 /*yield*/, toNetInput(input)];
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case 1:
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netInput = _a.sent();
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faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
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return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];
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case 2:
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faceDescriptorsForBatch = _a.sent();
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faceDescriptorTensors.forEach(function (t) { return t.dispose(); });
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return [2 /*return*/, netInput.isBatchInput
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? faceDescriptorsForBatch
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: faceDescriptorsForBatch[0]];
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}
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});
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});
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};
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FaceRecognitionNet.prototype.getDefaultModelName = function () {
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return 'face_recognition_model';
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};
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FaceRecognitionNet.prototype.extractParamsFromWeigthMap = function (weightMap) {
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return extractParamsFromWeigthMap(weightMap);
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};
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FaceRecognitionNet.prototype.extractParams = function (weights) {
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return extractParams(weights);
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};
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return FaceRecognitionNet;
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}(NeuralNetwork));
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export { FaceRecognitionNet };
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//# sourceMappingURL=FaceRecognitionNet.js.map
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