59f713480fdd443bcec294459ffe8149ba49d140,keras_retinanet/models/retinanet.py,,retinanet_bbox,#Any#Any#Any#Any#,203

Before Change

    anchors        = model.outputs[0]
    regression     = model.outputs[1]
    classification = model.outputs[2]
    if len(model.outputs) > 3:
        other = keras.layers.Concatenate(axis=2, name="other")(model.outputs[2:])
    else:
        other = None

    // apply predicted regression to anchors
    boxes      = keras_retinanet.layers.RegressBoxes(name="boxes")([anchors, regression])
    detections = keras.layers.Concatenate(axis=2)([boxes, classification] + ([other] if other is not None else []))

    // additionally apply non maximum suppression

After Change

        detections = keras_retinanet.layers.NonMaximumSuppression(name="nms")([boxes, classification, detections])

    // construct the model
    return keras.models.Model(inputs=inputs, outputs=[*model.outputs[1:], detections], name=name)

Instances