Improve coverage

model_compression_toolkit/core/common/graph/memory_graph/cut.py

@@ -64,10 +64,10 @@ def __eq__(self, other) -> bool:
         """
         if isinstance(other, Cut):
             return self.mem_elements == other.mem_elements
-        return False
+        return False  # pragma: no cover
 
     def __hash__(self):
         return hash((frozenset(self.op_order), frozenset(self.op_record), self.mem_elements))
 
     def __repr__(self):
-        return f"<Cut: Nodes={[e.node_name for e in self.mem_elements.elements]}, size={self.memory_size()}>"
+        return f"<Cut: Nodes={[e.node_name for e in self.mem_elements.elements]}, size={self.memory_size()}>"  # pragma: no cover

model_compression_toolkit/core/keras/back2framework/keras_model_builder.py

@@ -49,38 +49,6 @@
 BATCH_INPUT_SHAPE = 'batch_input_shape'
 
 
-def get_node_name_from_layer(layer: Layer) -> str:
-    """
-    Get a node's name from the layer it was built from. For TensorFlowOpLayer
-    we remove the prefix "tf_op_layer".
-
-    Args:
-        layer: Keras Layer to get its corresponding node's name.
-
-    Returns:
-        Name of the node that was built from the passed layer.
-    """
-
-    name = layer.name
-    if isinstance(layer, TensorFlowOpLayer):  # remove TF op layer prefix
-        name = '_'.join(name.split('_')[3:])
-    return name
-
-
-def is_layer_fake_quant(layer: Layer) -> bool:
-    """
-    Check whether a Keras layer is a fake quantization layer or not.
-    Args:
-        layer: Layer to check if it's a fake quantization layer or not.
-
-    Returns:
-        Whether a Keras layer is a fake quantization layer or not.
-    """
-    # in tf2.3 fake quant node is implemented as TensorFlowOpLayer, while in tf2.4 as TFOpLambda
-    return (isinstance(layer, TensorFlowOpLayer) and layer.node_def.op == FQ_NODE_OP_V2_3) or (
-            isinstance(layer, TFOpLambda) and layer.symbol == FQ_NODE_OP_V2_4)
-
-
 class KerasModelBuilder(BaseModelBuilder):
     """
     Builder for Keras models.
@@ -291,7 +259,7 @@ def _run_operation(self,
                                     arg = n.weights.get(pos)
                                     if arg is None:
                                         if len(input_tensors) == 0:
-                                            Logger.critical(f"Couldn't find a weight or input tensor matching operator's "
+                                            Logger.critical(f"Couldn't find a weight or input tensor matching operator's "  # pragma: no cover
                                                             f"argument name '{k}' in location {pos} for node {n.name}.")
                                         arg = input_tensors.pop(0)
                                     op_call_kwargs.update({k: arg})

model_compression_toolkit/core/keras/graph_substitutions/substitutions/conv_funcs_to_layer.py

@@ -141,7 +141,7 @@ def substitute(self,
         strides = self._parse_tf_stride_dilation(conv_func_node, STRIDES)
         if strides is None:
             # Non-standard strides -> skip substitution.
-            return graph
+            return graph  # pragma: no cover
         conv_fw_attr[STRIDES] = strides
 
         padding = conv_func_node.op_call_kwargs.get(PADDING) or 'VALID'
@@ -153,7 +153,7 @@ def substitute(self,
         dilations = self._parse_tf_stride_dilation(conv_func_node, DILATIONS)
         if dilations is None:
             # Non-standard dilations -> skip substitution.
-            return graph
+            return graph  # pragma: no cover
         conv_fw_attr[DILATION_RATE] = dilations
 
         if b is None: