"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("@tensorflow/tfjs-core");
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var common_1 = require("../common");
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var fullyConnectedLayer_1 = require("../common/fullyConnectedLayer");
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var prelu_1 = require("./prelu");
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var sharedLayers_1 = require("./sharedLayers");
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function ONet(x, params) {
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return tf.tidy(function () {
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var out = sharedLayers_1.sharedLayer(x, params);
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out = tf.maxPool(out, [2, 2], [2, 2], 'same');
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out = common_1.convLayer(out, params.conv4, 'valid');
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out = prelu_1.prelu(out, params.prelu4_alpha);
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var vectorized = tf.reshape(out, [out.shape[0], params.fc1.weights.shape[0]]);
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var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(vectorized, params.fc1);
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var prelu5 = prelu_1.prelu(fc1, params.prelu5_alpha);
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var fc2_1 = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_1);
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var max = tf.expandDims(tf.max(fc2_1, 1), 1);
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var prob = tf.softmax(tf.sub(fc2_1, max), 1);
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var regions = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_2);
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var points = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_3);
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var scores = tf.unstack(prob, 1)[1];
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return { scores: scores, regions: regions, points: points };
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});
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}
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exports.ONet = ONet;
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//# sourceMappingURL=ONet.js.map
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