d52eb0bf3d6df25c70933321a841dac157806479,luminoth/models/ssd/ssd.py,SSD,_build,#SSD#Any#Any#Any#,69

Before Change

            //       them in image scale from the start instead of scaling
            //       them to their feature map size.
            feat_map_shape = feat_map.get_shape().as_list()[1:3]
            adjusted_bboxes = adjust_bboxes(
                self.anchors[feat_map_name],
                tf.cast(feat_map_shape[0], tf.float32),
                tf.cast(feat_map_shape[1], tf.float32),
                tf.cast(tf.shape(image)[1], tf.float32),
                tf.cast(tf.shape(image)[2], tf.float32)
            )
            // Clip anchors to the image.
            adjusted_bboxes = clip_boxes(
                adjusted_bboxes, tf.cast(tf.shape(image)[1:3], tf.int32))
            all_anchors_list.append(adjusted_bboxes)

After Change

            all_anchors_list.append(clipped_bboxes)
        all_anchors = np.concatenate(all_anchors_list, axis=0)
        // They were using float64, is all this precision necesary?
        all_anchors = tf.convert_to_tensor(all_anchors, dtype=tf.float64)

        prediction_dict = {}
        if gt_boxes is not None:
            // Generate targets

Instances