def convert_upsample(net, node, module, builder):
    """Convert a UpSampling layer from mxnet to coreml.

    Parameters
    ----------
    net: network
        A mxnet network object.

    node: layer
        Node to convert.

    module: module
        An module for MXNet

    builder: NeuralNetworkBuilder
        A neural network builder object.
    """
    input_name, output_name = _get_input_output_name(net, node)
    name = node['name']
    param = _get_attr(node)
    inputs = node['inputs']
    args, _ = module.get_params()

    scale = literal_eval(param['scale'])

    #method
    if 'sample_type' in param.keys():
        method = param['sample_type']
        if method == 'nearest':
            mode = 'NN'
        elif method == '':
            mode = 'BILINEAR'

    builder.add_upsample(name, scaling_factor_h=scale, scaling_factor_w=scale,
                         input_name=input_name, output_name=output_name, mode=mode)
def convert_upsample ( net , node , module , builder ) : input_name , output_name = _get_input_output_name ( net , node ) name = node [ 'name' ] param = _get_attr ( node ) inputs = node [ 'inputs' ] args , _ = module . get_params ( ) scale = literal_eval ( param [ 'scale' ] ) if 'sample_type' in param . keys ( ) : method = param [ 'sample_type' ] if method == 'nearest' : mode = 'NN' elif method == '' : mode = 'BILINEAR' builder . add_upsample ( name , scaling_factor_h = scale , scaling_factor_w = scale , input_name = input_name , output_name = output_name , mode = mode )
Convert a UpSampling layer from mxnet to coreml.

    Parameters
    ----------
    net: network
        A mxnet network object.

    node: layer
        Node to convert.

    module: module
        An module for MXNet

    builder: NeuralNetworkBuilder
        A neural network builder object.