return tf.tidy(function () {
var batchSize = boxPredictions.shape[0];
var boxes = decodeBoxesLayer(tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf.reshape(boxPredictions, [-1, 4]));
boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
var scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
var scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
scores = tf.reshape(scores, [batchSize, scores.shape[1]]);
var boxesByBatch = tf.unstack(boxes);
var scoresByBatch = tf.unstack(scores);
return {
boxes: boxesByBatch,
scores: scoresByBatch
};
});
function decodeBoxesLayer(x0, x1) {
var _a = getCenterCoordinatesAndSizesLayer(x0), sizes = _a.sizes, centers = _a.centers;
var vec = tf.unstack(tf.transpose(x1, [1, 0]));
var div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));
var add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);
var div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));
var add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);
return tf.transpose(tf.stack([
tf.sub(add0_out, div0_out),
tf.sub(add1_out, div1_out),
tf.add(add0_out, div0_out),
tf.add(add1_out, div1_out)
]), [1, 0]);
}
function getCenterCoordinatesAndSizesLayer(x) {
var vec = tf.unstack(tf.transpose(x, [1, 0]));
var sizes = [
tf.sub(vec[2], vec[0]),
tf.sub(vec[3], vec[1])
];
var centers = [
tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))
];
return {
sizes: sizes,
centers: centers
};
}