pytorch2keras/operation_layers.py
def convert_clip(params, w_name, scope_name, inputs, layers, weights, names):
    """
    Convert clip operation.

    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 clip ...')

    if params['min'] == 0:
        print("using ReLU({0})".format(params['max']))
        layer = keras.layers.ReLU(max_value=params['max'])
    else:
        def target_layer(x, vmin=params['min'], vmax=params['max']):
            import tensorflow as tf
            return tf.clip_by_value(x, vmin, vmax)
        layer = keras.layers.Lambda(target_layer)

    layers[scope_name] = layer(layers[inputs[0]])
def convert_clip(params, w_name, scope_name, inputs, layers, weights, names):
    """
    Convert clip operation.

    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 clip ...')

    if params['min'] == 0:
        print("using ReLU({0})".format(params['max']))
        layer = keras.layers.ReLU(max_value=params['max'])
    else:
        def target_layer(x, vmin=params['min'], vmax=params['max']):
            import tensorflow as tf
            return tf.clip_by_value(x, vmin, vmax)
        layer = keras.layers.Lambda(target_layer)

    layers[scope_name] = layer(layers[inputs[0]])
def convert_clip ( params , w_name , scope_name , inputs , layers , weights , names ) : print ( 'Converting clip ...' ) if params [ 'min' ] == 0 : print ( "using ReLU({0})" . format ( params [ 'max' ] ) ) layer = keras . layers . ReLU ( max_value = params [ 'max' ] ) else : def target_layer ( x , vmin = params [ 'min' ] , vmax = params [ 'max' ] ) : import tensorflow as tf return tf . clip_by_value ( x , vmin , vmax ) layer = keras . layers . Lambda ( target_layer ) layers [ scope_name ] = layer ( layers [ inputs [ 0 ] ] )
Convert clip operation.

    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 clip operation .