24 lines
No EOL
1.2 KiB
JavaScript
24 lines
No EOL
1.2 KiB
JavaScript
import * as tf from '@tensorflow/tfjs-core';
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import { TfjsImageRecognitionBase } from 'tfjs-image-recognition-base';
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import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
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import { prelu } from './prelu';
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import { sharedLayer } from './sharedLayers';
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export function ONet(x, params) {
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return tf.tidy(function () {
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var out = sharedLayer(x, params);
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out = tf.maxPool(out, [2, 2], [2, 2], 'same');
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out = TfjsImageRecognitionBase.convLayer(out, params.conv4, 'valid');
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out = 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(vectorized, params.fc1);
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var prelu5 = prelu(fc1, params.prelu5_alpha);
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var fc2_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(prelu5, params.fc2_2);
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var points = 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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//# sourceMappingURL=ONet.js.map
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