Convert convolution layer from keras to coreml.

    Parameters
    ----------
    keras_layer: layer
        A keras layer object.

    builder: NeuralNetworkBuilder
        A neural network builder object.

def convert_convolution(builder, layer, input_names, output_names, keras_layer):
    """
    Convert convolution layer from keras to coreml.

    Parameters
    ----------
    keras_layer: layer
        A keras layer object.

    builder: NeuralNetworkBuilder
        A neural network builder object.
    """

    _check_data_format(keras_layer)

    # Get input and output names
    input_name, output_name = (input_names[0], output_names[0])

    has_bias = keras_layer.use_bias
    is_deconv = isinstance(keras_layer,
            _keras.layers.convolutional.Conv2DTranspose)

    # Get the weights from _keras.
    weightList = keras_layer.get_weights()

    # Dimensions and weights
    if is_deconv:
        height, width, n_filters, channels = weightList[0].shape
        W = weightList[0].transpose([0,1,3,2])
        try:
            output_blob_shape = list(filter(None, keras_layer.output_shape))
            output_shape = output_blob_shape[:-1]
        except:
            output_shape = None
    else:
        height, width, channels, n_filters = weightList[0].shape
        W = weightList[0]
        output_shape = None
    b = weightList[1] if has_bias else None

    output_channels = n_filters

    stride_height, stride_width = keras_layer.strides

    # Dilations
    dilations = [1,1]
    if (type(keras_layer.dilation_rate) is list) or (type(keras_layer.dilation_rate) is tuple):
        dilations = [keras_layer.dilation_rate[0], keras_layer.dilation_rate[1]]
    else:
        dilations = [keras_layer.dilation_rate, keras_layer.dilation_rate]
    if is_deconv and not dilations == [1,1]:
        raise ValueError("Unsupported non-unity dilation for Deconvolution layer")

    groups = 1
    kernel_channels = channels
    # depth-wise convolution
    if isinstance(keras_layer, DepthwiseConv2D):
        groups = channels
        kernel_channels = 1
        depth_multiplier = keras_layer.depth_multiplier
        W = _np.reshape(W,(height, width,1,channels * depth_multiplier))
        output_channels = channels * depth_multiplier

    builder.add_convolution(name = layer,
             kernel_channels = kernel_channels,
             output_channels = output_channels,
             height = height,
             width = width,
             stride_height = stride_height,
             stride_width = stride_width,
             border_mode = keras_layer.padding,
             groups = groups,
             W = W,
             b = b,
             has_bias = has_bias,
             is_deconv = is_deconv,
             output_shape = output_shape,
             input_name = input_name,
             output_name = output_name,
             dilation_factors = dilations)