Reformat and clean up some codes.

onnxconverter_common/container.py

@@ -39,47 +39,6 @@ def output_names(self):
         raise NotImplementedError()
 
 
-class SparkmlModelContainer(RawModelContainer):
-
-    def __init__(self, sparkml_model):
-        super(SparkmlModelContainer, self).__init__(sparkml_model)
-        # Sparkml models have no input and output specified, so we create them and store them in this container.
-        self._inputs = []
-        self._outputs = []
-
-    @property
-    def input_names(self):
-        return [variable.raw_name for variable in self._inputs]
-
-    @property
-    def output_names(self):
-        return [variable.raw_name for variable in self._outputs]
-
-    def add_input(self, variable):
-        # The order of adding variables matters. The final model's input names are sequentially added as this list
-        if variable not in self._inputs:
-            self._inputs.append(variable)
-
-    def add_output(self, variable):
-        # The order of adding variables matters. The final model's output names are sequentially added as this list
-        if variable not in self._outputs:
-            self._outputs.append(variable)
-
-
-class CoremlModelContainer(RawModelContainer):
-
-    def __init__(self, coreml_model):
-        super(CoremlModelContainer, self).__init__(coreml_model)
-
-    @property
-    def input_names(self):
-        return [str(var.name) for var in self.raw_model.description.input]
-
-    @property
-    def output_names(self):
-        return [str(var.name) for var in self.raw_model.description.output]
-
-
 class CommonSklearnModelContainer(RawModelContainer):
 
     def __init__(self, sklearn_model):
@@ -107,48 +66,6 @@ def add_output(self, variable):
             self._outputs.append(variable)
 
 
-class SklearnModelContainer(CommonSklearnModelContainer):
-    pass
-
-
-class LibSvmModelContainer(CommonSklearnModelContainer):
-    pass
-
-
-class LightGbmModelContainer(CommonSklearnModelContainer):
-    pass
-
-
-class XGBoostModelContainer(CommonSklearnModelContainer):
-    pass
-
-
-class KerasModelContainer(RawModelContainer):
-
-    def __init__(self, keras_model):
-        super(KerasModelContainer, self).__init__(keras_model)
-        self._input_raw_names = list()
-        self._output_raw_names = list()
-
-    def add_input_name(self, name):
-        # The order of adding strings matters. The final model's input names are sequentially added as this list
-        if name not in self._input_raw_names:
-            self._input_raw_names.append(name)
-
-    def add_output_name(self, name):
-        # The order of adding strings matters. The final model's output names are sequentially added as this list
-        if name not in self._output_raw_names:
-            self._output_raw_names.append(name)
-
-    @property
-    def input_names(self):
-        return [name for name in self._input_raw_names]
-
-    @property
-    def output_names(self):
-        return [name for name in self._output_raw_names]
-
-
 class ModelComponentContainer(ModelContainer):
     '''
     In the conversion phase, this class is used to collect all materials required to build an ONNX GraphProto, which is

onnxconverter_common/metadata_props.py

@@ -75,7 +75,8 @@ def set_denotation(onnx_model, input_name, denotation, target_opset, dimension_d
             if dimension_denotation:
                 dimensions = graph_input.type.tensor_type.shape.dim
                 if len(dimension_denotation) != len(dimensions):
-                    raise RuntimeError('Wrong number of dimensions: input "{}" has {} dimensions'.format(input_name, len(dimensions)))
+                    raise RuntimeError(
+                        'Wrong number of dimensions: input "{}" has {} dimensions'.format(input_name, len(dimensions)))
                 for dimension, channel_denotation in zip(dimensions, dimension_denotation):
                     dimension.denotation = channel_denotation
             return onnx_model

onnxconverter_common/onnx_ops.py

@@ -561,7 +561,8 @@ def apply_reshape(scope, input_name, output_name, container, operator_name=None,
             desired_shape_name = desired_shape
         else:
             desired_shape_name = scope.get_unique_variable_name('shape_tensor')
-            container.add_initializer(desired_shape_name, onnx_proto.TensorProto.INT64, [len(desired_shape)], desired_shape)
+            container.add_initializer(desired_shape_name, onnx_proto.TensorProto.INT64, [len(desired_shape)],
+                                      desired_shape)
 
         # Create ONNX Reshape operator
         if isinstance(input_name, list):
@@ -571,7 +572,8 @@ def apply_reshape(scope, input_name, output_name, container, operator_name=None,
         container.add_node('Reshape', input_name, output_name, op_version=5, name=name)
 
 
-def apply_resize(scope, input_name, output_name, container, operator_name=None, mode='nearest', coordinate_transformation_mode='asymmetric', scales=None):
+def apply_resize(scope, input_name, output_name, container, operator_name=None, mode='nearest',
+                 coordinate_transformation_mode='asymmetric', scales=None):
     '''
     :param mode: "nearest" or "linear"
     :param scales: a float tensor for scaling (upsampling or downsampling) all input dimensions
@@ -806,7 +808,8 @@ def apply_transpose(scope, input_name, output_name, container, operator_name=Non
     container.add_node('Transpose', input_name, output_name, name=name, perm=perm)
 
 
-def apply_upsample(scope, input_name, output_name, container, operator_name=None, mode='nearest', coordinate_transformation_mode='asymmetric', scales=None):
+def apply_upsample(scope, input_name, output_name, container, operator_name=None, mode='nearest',
+                   coordinate_transformation_mode='asymmetric', scales=None):
     '''
     :param mode: nearest or linear
     :param scales: an integer list of scaling-up rate of all input dimensions
@@ -838,4 +841,5 @@ def apply_upsample(scope, input_name, output_name, container, operator_name=None
     else:
         # Upsample op is deprecated in ONNX opset 10
         # We implement Upsample through Resize instead
-        apply_resize(scope, input_name, output_name, container, operator_name, mode, coordinate_transformation_mode, scales)
+        apply_resize(scope, input_name, output_name, container, operator_name, mode, coordinate_transformation_mode,
+                     scales)