81 lines
No EOL
4 KiB
JavaScript
81 lines
No EOL
4 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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var tslib_1 = require("tslib");
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var tf = require("@tensorflow/tfjs-core");
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var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
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var depthwiseSeparableConv_1 = require("../common/depthwiseSeparableConv");
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var extractParams_1 = require("./extractParams");
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var extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
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function conv(x, params, stride) {
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return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);
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}
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function reductionBlock(x, params, isActivateInput) {
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if (isActivateInput === void 0) { isActivateInput = true; }
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var out = isActivateInput ? tf.relu(x) : x;
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out = depthwiseSeparableConv_1.depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
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out = depthwiseSeparableConv_1.depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);
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out = tf.maxPool(out, [3, 3], [2, 2], 'same');
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out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));
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return out;
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}
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function mainBlock(x, params) {
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var out = depthwiseSeparableConv_1.depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);
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out = depthwiseSeparableConv_1.depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);
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out = depthwiseSeparableConv_1.depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);
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out = tf.add(out, x);
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return out;
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}
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var TinyXception = /** @class */ (function (_super) {
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tslib_1.__extends(TinyXception, _super);
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function TinyXception(numMainBlocks) {
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var _this = _super.call(this, 'TinyXception') || this;
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_this._numMainBlocks = numMainBlocks;
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return _this;
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}
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TinyXception.prototype.forwardInput = function (input) {
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var _this = this;
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var params = this.params;
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if (!params) {
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throw new Error('TinyXception - load model before inference');
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}
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return tf.tidy(function () {
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var batchTensor = input.toBatchTensor(112, true);
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var meanRgb = [122.782, 117.001, 104.298];
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var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(256));
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var out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
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out = reductionBlock(out, params.entry_flow.reduction_block_0, false);
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out = reductionBlock(out, params.entry_flow.reduction_block_1);
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tfjs_image_recognition_base_1.range(_this._numMainBlocks, 0, 1).forEach(function (idx) {
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out = mainBlock(out, params.middle_flow["main_block_" + idx]);
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});
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out = reductionBlock(out, params.exit_flow.reduction_block);
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out = tf.relu(depthwiseSeparableConv_1.depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
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return out;
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});
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};
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TinyXception.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*/, tfjs_image_recognition_base_1.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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TinyXception.prototype.getDefaultModelName = function () {
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return 'tiny_xception_model';
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};
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TinyXception.prototype.extractParamsFromWeigthMap = function (weightMap) {
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return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(weightMap, this._numMainBlocks);
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};
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TinyXception.prototype.extractParams = function (weights) {
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return extractParams_1.extractParams(weights, this._numMainBlocks);
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};
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return TinyXception;
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}(tfjs_image_recognition_base_1.NeuralNetwork));
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exports.TinyXception = TinyXception;
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//# sourceMappingURL=TinyXception.js.map
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