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 sigmoid(x) {
return tfc.sigmoid(x);
}