Add more support for mxnet_to_coreml

tools/coreml/converter/_layers.py

@@ -220,6 +220,39 @@ def convert_activation(net, node, module, builder):
                            output_name = output_name)
 
 
+def convert_leakyrelu(net, node, module, builder):
+    """Convert an leakyrelu layer from mxnet to coreml.
+
+    Parameters
+    ----------
+    network: net
+        A mxnet network object.
+
+    layer: node
+        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']
+    inputs = node['inputs']
+    args, _ = module.get_params()
+    mx_non_linearity = _get_attrs(node)['act_type']
+    if mx_non_linearity == 'prelu':
+        non_linearity = 'PRELU'
+        params = args[_get_node_name(net, inputs[1][0])].asnumpy()
+    else:
+        raise TypeError('Unknown activation type %s' % mx_non_linearity)
+    builder.add_activation(name = name,
+                           non_linearity = non_linearity,
+                           input_name = input_name,
+                           output_name = output_name,
+                           params = params)
+
 def convert_elementwise_add(net, node, module, builder):
     """Convert an elementwise add layer from mxnet to coreml.
 
@@ -335,6 +368,7 @@ def convert_convolution(net, node, module, builder):
     border_mode = "valid"
 
     n_filters = int(param['num_filter'])
+    n_groups = int(param['num_group'])
 
     W = args[_get_node_name(net, inputs[1][0])].asnumpy()
     if has_bias:
@@ -361,7 +395,7 @@ def convert_convolution(net, node, module, builder):
         stride_height=stride_height,
         stride_width=stride_width,
         border_mode=border_mode,
-        groups=1,
+        groups=n_groups,
         W=W,
         b=Wb,
         has_bias=has_bias,

tools/coreml/converter/_mxnet_converter.py

@@ -38,11 +38,14 @@
     'elemwise_add'   : _layers.convert_elementwise_add,
     'Reshape'        : _layers.convert_reshape,
     'Deconvolution'  : _layers.convert_deconvolution,
+    'LeakyReLU'      : _layers.convert_leakyrelu,
 }
 
 _MXNET_SKIP_LAYERS = [
     '_MulScalar',
     'Dropout',
+    '_minus_scalar',
+    '_mul_scalar',
 ]
 
 def _mxnet_remove_batch(input_data):