Parse Caffe prototxt into symbol string

def _parse_proto(prototxt_fname):
    """Parse Caffe prototxt into symbol string
    """
    proto = caffe_parser.read_prototxt(prototxt_fname)

    # process data layer
    input_name, input_dim, layers = _get_input(proto)
    # only support single input, so always use `data` as the input data
    mapping = {input_name: 'data'}
    need_flatten = {input_name: False}
    symbol_string = "import mxnet as mx\ndata = mx.symbol.Variable(name='data')\n"

    flatten_count = 0
    output_name = ""
    prev_name = None
    _output_name = {}

    # convert reset layers one by one
    for i, layer in enumerate(layers):
        type_string = ''
        param_string = ''
        skip_layer = False
        name = re.sub('[-/]', '_', layer.name)
        for k in range(len(layer.bottom)):
            if layer.bottom[k] in _output_name:
                _output_name[layer.bottom[k]]['count'] = _output_name[layer.bottom[k]]['count']+1
            else:
                _output_name[layer.bottom[k]] = {'count':0}
        for k in range(len(layer.top)):
            if layer.top[k] in _output_name:
                _output_name[layer.top[k]]['count'] = _output_name[layer.top[k]]['count']+1
            else:
                _output_name[layer.top[k]] = {'count':0, 'name':name}
        if layer.type == 'Convolution' or layer.type == 4:
            type_string = 'mx.symbol.Convolution'
            param_string = _convert_conv_param(layer.convolution_param)
            need_flatten[name] = True
        if layer.type == 'Deconvolution' or layer.type == 39:
            type_string = 'mx.symbol.Deconvolution'
            param_string = _convert_conv_param(layer.convolution_param)
            need_flatten[name] = True
        if layer.type == 'Pooling' or layer.type == 17:
            type_string = 'mx.symbol.Pooling'
            param_string = _convert_pooling_param(layer.pooling_param)
            need_flatten[name] = True
        if layer.type == 'ReLU' or layer.type == 18:
            type_string = 'mx.symbol.Activation'
            param_string = "act_type='relu'"
            param = layer.relu_param
            if hasattr(param, 'negative_slope'):
                if param.negative_slope > 0:
                    type_string = 'mx.symbol.LeakyReLU'
                    param_string = "act_type='leaky', slope=%f" % param.negative_slope
            need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
        if layer.type == 'TanH' or layer.type == 23:
            type_string = 'mx.symbol.Activation'
            param_string = "act_type='tanh'"
            need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
        if layer.type == 'Sigmoid' or layer.type == 19:
            type_string = 'mx.symbol.Activation'
            param_string = "act_type='sigmoid'"
            need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
        if layer.type == 'LRN' or layer.type == 15:
            type_string = 'mx.symbol.LRN'
            param = layer.lrn_param
            param_string = "alpha=%f, beta=%f, knorm=%f, nsize=%d" % (
                param.alpha, param.beta, param.k, param.local_size)
            need_flatten[name] = True
        if layer.type == 'InnerProduct' or layer.type == 14:
            type_string = 'mx.symbol.FullyConnected'
            param = layer.inner_product_param
            param_string = "num_hidden=%d, no_bias=%s" % (
                param.num_output, not param.bias_term)
            need_flatten[name] = False
        if layer.type == 'Dropout' or layer.type == 6:
            type_string = 'mx.symbol.Dropout'
            param = layer.dropout_param
            param_string = "p=%f" % param.dropout_ratio
            need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
        if layer.type == 'Softmax' or layer.type == 20:
            type_string = 'mx.symbol.SoftmaxOutput'
        if layer.type == 'Flatten' or layer.type == 8:
            type_string = 'mx.symbol.Flatten'
            need_flatten[name] = False
        if layer.type == 'Split' or layer.type == 22:
            type_string = 'split'  # will process later
        if layer.type == 'Concat' or layer.type == 3:
            type_string = 'mx.symbol.Concat'
            need_flatten[name] = True
        if layer.type == 'Crop':
            type_string = 'mx.symbol.Crop'
            need_flatten[name] = True
            param_string = 'center_crop=True'
        if layer.type == 'BatchNorm':
            type_string = 'mx.symbol.BatchNorm'
            param = layer.batch_norm_param
            # CuDNN requires eps to be greater than 1e-05
            # We compensate for this change in convert_model
            epsilon = param.eps
            if (epsilon <= 1e-05):
                epsilon = 1e-04
            # if next layer is scale, don't fix gamma
            fix_gamma = layers[i+1].type != 'Scale'
            param_string = 'use_global_stats=%s, fix_gamma=%s, eps=%f' % (
                param.use_global_stats, fix_gamma, epsilon)
            need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
        if layer.type == 'Scale':
            assert layers[i-1].type == 'BatchNorm'
            need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
            skip_layer = True
            prev_name = re.sub('[-/]', '_', layers[i-1].name)
        if layer.type == 'PReLU':
            type_string = 'mx.symbol.LeakyReLU'
            param = layer.prelu_param
            param_string = "act_type='prelu', slope=%f" % param.filler.value
            need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
        if layer.type == 'Eltwise':
            type_string = 'mx.symbol.broadcast_add'
            param = layer.eltwise_param
            param_string = ""
            need_flatten[name] = False
        if layer.type == 'Reshape':
            type_string = 'mx.symbol.Reshape'
            need_flatten[name] = False
            param = layer.reshape_param
            param_string = "shape=(%s)" % (','.join(param.shape.dim),)
        if layer.type == 'AbsVal':
            type_string = 'mx.symbol.abs'
            need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]

        if skip_layer:
            assert len(layer.bottom) == 1
            symbol_string += "%s = %s\n" % (name, prev_name)
        elif type_string == '':
            raise ValueError('Unknown layer %s!' % layer.type)
        elif type_string != 'split':
            bottom = layer.bottom
            if param_string != "":
                param_string = ", " + param_string
            if len(bottom) == 1:
                if need_flatten[mapping[bottom[0]]] and type_string == 'mx.symbol.FullyConnected':
                    flatten_name = "flatten_%d" % flatten_count
                    symbol_string += "%s=mx.symbol.Flatten(name='%s', data=%s)\n" % (
                        flatten_name, flatten_name, mapping[bottom[0]])
                    flatten_count += 1
                    need_flatten[flatten_name] = False
                    bottom[0] = flatten_name
                    mapping[bottom[0]] = bottom[0]
                symbol_string += "%s = %s(name='%s', data=%s %s)\n" % (
                    name, type_string, name, mapping[bottom[0]], param_string)
            else:
                if layer.type == 'Eltwise' and param.operation == 1 and len(param.coeff) > 0:
                    symbol_string += "%s = " % name
                    symbol_string += " + ".join(["%s * %s" % (
                        mapping[bottom[i]], param.coeff[i]) for i in range(len(param.coeff))])
                    symbol_string += "\n"
                else:
                    symbol_string += "%s = %s(name='%s', *[%s] %s)\n" % (
                        name, type_string, name, ','.join(
                            [mapping[x] for x in bottom]), param_string)
        for j in range(len(layer.top)):
            mapping[layer.top[j]] = name
        output_name = name
    output_name = []
    for i in _output_name:
        if 'name' in _output_name[i] and _output_name[i]['count'] == 0:
            output_name.append(_output_name[i]['name'])

    return symbol_string, output_name, input_dim