def convert_elementwise_mul(
    params, w_name, scope_name, inputs, layers, weights, names
):
    """
    Convert elementwise multiplication.

    Args:
        params: dictionary with layer parameters
        w_name: name prefix in state_dict
        scope_name: pytorch scope name
        inputs: pytorch node inputs
        layers: dictionary with keras tensors
        weights: pytorch state_dict
        names: use short names for keras layers
    """
    print('Converting elementwise_mul ...')
    model0 = layers[inputs[0]]
    model1 = layers[inputs[1]]

    if names == 'short':
        tf_name = 'M' + random_string(7)
    elif names == 'keep':
        tf_name = w_name
    else:
        tf_name = w_name + str(random.random())

    def target_layer(x):
        layer = tf.multiply(
            x[0],
            x[1]
        )
        return layer

    lambda_layer = keras.layers.Lambda(target_layer, name=tf_name)
    layers[scope_name] = lambda_layer([layers[inputs[0]], layers[inputs[1]]])
def convert_elementwise_mul(
    params, w_name, scope_name, inputs, layers, weights, names
):
    """
    Convert elementwise multiplication.

    Args:
        params: dictionary with layer parameters
        w_name: name prefix in state_dict
        scope_name: pytorch scope name
        inputs: pytorch node inputs
        layers: dictionary with keras tensors
        weights: pytorch state_dict
        names: use short names for keras layers
    """
    print('Converting elementwise_mul ...')
    model0 = layers[inputs[0]]
    model1 = layers[inputs[1]]

    if names == 'short':
        tf_name = 'M' + random_string(7)
    elif names == 'keep':
        tf_name = w_name
    else:
        tf_name = w_name + str(random.random())

    def target_layer(x):
        layer = tf.multiply(
            x[0],
            x[1]
        )
        return layer

    lambda_layer = keras.layers.Lambda(target_layer, name=tf_name)
    layers[scope_name] = lambda_layer([layers[inputs[0]], layers[inputs[1]]])
def convert_elementwise_mul ( params , w_name , scope_name , inputs , layers , weights , names ) : print ( 'Converting elementwise_mul ...' ) model0 = layers [ inputs [ 0 ] ] model1 = layers [ inputs [ 1 ] ] if names == 'short' : tf_name = 'M' + random_string ( 7 ) elif names == 'keep' : tf_name = w_name else : tf_name = w_name + str ( random . random ( ) ) def target_layer ( x ) : layer = tf . multiply ( x [ 0 ] , x [ 1 ] ) return layer lambda_layer = keras . layers . Lambda ( target_layer , name = tf_name ) layers [ scope_name ] = lambda_layer ( [ layers [ inputs [ 0 ] ] , layers [ inputs [ 1 ] ] ] )
Convert elementwise multiplication.

    Args:
        params: dictionary with layer parameters
        w_name: name prefix in state_dict
        scope_name: pytorch scope name
        inputs: pytorch node inputs
        layers: dictionary with keras tensors
        weights: pytorch state_dict
        names: use short names for keras layers
Convert elementwise multiplication .