pytorch2keras/operation_layers.py
def convert_sum(
    params, w_name, scope_name, inputs, layers, weights, names
):
    """
    Convert sum.

    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 Sum ...')

    def target_layer(x):
        import keras.backend as K
        return K.sum(x)

    lambda_layer = keras.layers.Lambda(target_layer)
    layers[scope_name] = lambda_layer(layers[inputs[0]])
def convert_sum(
    params, w_name, scope_name, inputs, layers, weights, names
):
    """
    Convert sum.

    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 Sum ...')

    def target_layer(x):
        import keras.backend as K
        return K.sum(x)

    lambda_layer = keras.layers.Lambda(target_layer)
    layers[scope_name] = lambda_layer(layers[inputs[0]])
def convert_sum ( params , w_name , scope_name , inputs , layers , weights , names ) : print ( 'Converting Sum ...' ) def target_layer ( x ) : import keras . backend as K return K . sum ( x ) lambda_layer = keras . layers . Lambda ( target_layer ) layers [ scope_name ] = lambda_layer ( layers [ inputs [ 0 ] ] )
Convert sum.

    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 sum .