Examples/tensorflow/quantization/cle_bc.py
def aimet_cross_layer_equalization(sess: tf.Session, start_op_names: List[str], output_op_names: List[str]) -> tf.Session:
    """
    Applies CLE on the model which consists of:
        Batch Norm Folding
        Cross Layer Scaling
        High Bias Fold
    Also converts any ReLU6 into ReLU.

    :param sess: The sess with graph to be evaluated
    :param start_op_names: The list of input op names of the sess.graph
    :param output_op_names: The list of output op names of the sess.graph
    :return: The sess with graph after CLE applied
    """

    # Applying Cross Layer Equalization
    cle_applied_sess = aimet_cle.equalize_model(sess,
                                                start_op_names=start_op_names,
                                                output_op_names=output_op_names)

    return cle_applied_sess

    Applies CLE on the model which consists of:
        Batch Norm Folding
        Cross Layer Scaling
        High Bias Fold
    Also converts any ReLU6 into ReLU.

    :param sess: The sess with graph to be evaluated
    :param start_op_names: The list of input op names of the sess.graph
    :param output_op_names: The list of output op names of the sess.graph
    :return: The sess with graph after CLE applied
    
Applies CLE on the model which consists of:
Batch Norm Folding
Cross Layer Scaling
High Bias Fold
Also converts any ReLU6 into ReLU.
Applies CLE on the model which consists of : Batch Norm Folding Cross Layer Scaling High Bias Fold Also converts any ReLU6 into ReLU .
def aimet_cross_layer_equalization(sess: tf.Session, start_op_names: List[str], output_op_names: List[str]) -> tf.Session:
    cle_applied_sess = aimet_cle.equalize_model(sess,
                                                start_op_names=start_op_names,
                                                output_op_names=output_op_names)
    return cle_applied_sess
def aimet_cross_layer_equalization ( sess : tf . Session , start_op_names : List [ str ] , output_op_names : List [ str ] ) -> tf . Session : cle_applied_sess = aimet_cle . equalize_model ( sess , start_op_names = start_op_names , output_op_names = output_op_names ) return cle_applied_sess
Applies CLE on the model which consists of:
Batch Norm Folding
Cross Layer Scaling
High Bias Fold
Also converts any ReLU6 into ReLU.
Applies CLE on the model which consists of : Batch Norm Folding Cross Layer Scaling High Bias Fold Also converts any ReLU6 into ReLU .
"""
    Applies CLE on the model which consists of:
        Batch Norm Folding
        Cross Layer Scaling
        High Bias Fold
    Also converts any ReLU6 into ReLU.

    :param sess: The sess with graph to be evaluated
    :param start_op_names: The list of input op names of the sess.graph
    :param output_op_names: The list of output op names of the sess.graph
    :return: The sess with graph after CLE applied
    """ # Applying Cross Layer Equalization
The sess with graph after CLE applied
The sess with graph after CLE applied
sess
tf.Session
The sess with graph to be evaluated
The sess with graph to be evaluated
start_op_names
List[str]
The list of input op names of the sess.graph
The list of input op names of the sess . graph
output_op_names
List[str]
The list of output op names of the sess.graph
The list of output op names of the sess . graph