Fix operator.py with new merge.

aidge_export_cpp/operators.py

@@ -126,7 +126,7 @@ class ReLUCPP(ExportNode):
         list_actions.append(generate_str(
             str(ROOT / "templates" / "kernel_forward" / "activation_forward.jinja"),
             name=self.name,
-            input_name=self.inputs[0].name() if self.inputs[0] else self.name + "_input",
+            input_name=f"{self.name}_input" if self.inputs[0] is None else self.inputs[0].name(),
             output_name=self.name
         ))
         return list_actions
@@ -191,10 +191,10 @@ class ConvCPP(ExportNode):
         list_actions.append(generate_str(
             str(ROOT / "templates" / "kernel_forward" / "convolution_forward.jinja"),
             name=self.name,
-            input_name=self.inputs[0].name() if self.inputs[0] else self.name + "_input",
+            input_name=f"{self.name}_input_0" if self.inputs[0] is None else self.inputs[0].name(),
             output_name=self.name,
-            weights_name=self.inputs[1].name(),
-            biases_name=self.inputs[2].name()
+            weights_name=f"{self.name}_input_1" if self.inputs[1] is None else self.inputs[1].name(),
+            biases_name=f"{self.name}_input_2" if self.inputs[2] is None else self.inputs[2].name()
         ))
         return list_actions
 
@@ -251,8 +251,8 @@ class AddCPP(ExportNode):
         list_actions.append(generate_str(
             str(ROOT / "templates" / "kernel_forward" / "elemwise_forward.jinja"),
             name=self.name,
-            inputs1_name=self.parents[0].name() if self.parents[0] else self.name + "_input1",
-            inputs2_name=self.parents[1].name() if self.parents[1] else self.name + "_input2",
+            inputs1_name=self.inputs[0].name() if self.inputs[0] else self.name + "_input1",
+            inputs2_name=self.inputs[1].name() if self.inputs[1] else self.name + "_input2",
             output_name=self.name
         ))
         return list_actions
@@ -284,15 +284,15 @@ class SubCPP(ExportNode):
         list_actions.append(generate_str(
             str(ROOT / "templates" / "kernel_forward" / "elemwise_forward.jinja"),
             name=self.name,
-            inputs1_name=self.inputs[0].name() if self.inputs[0] else self.name + "_input1",
-            inputs2_name=self.inputs[1].name() if self.inputs[1] else self.name + "_input2",
+            inputs1_name=f"{self.name}_input_0" if self.inputs[0] is None else self.inputs[0].name(),
+            inputs2_name=f"{self.name}_input_1" if self.inputs[1] is None else self.inputs[1].name(),
             output_name=self.name
         ))
         return list_actions
 
 
 @operator_register("PaddedMaxPooling")
-class MaxPoolCPP(ExportNode):
+class PaddedMaxPoolCPP(ExportNode):
     def __init__(self, node):
         super().__init__(node)
         for n in self.operator.get_micro_graph().get_nodes():
@@ -342,9 +342,7 @@ class MaxPoolCPP(ExportNode):
         list_actions.append(generate_str(
             str(ROOT / "templates" / "kernel_forward" / "pooling_forward.jinja"),
             name=self.name,
-            input_name=f"{self.name}_0" if self.inputs[0] is None else self.inputs[0].name(),
-            inputs1_name=self.inputs[0].name() if self.inputs[0] else self.name + "_input1",
-            inputs2_name=self.inputs[1].name() if self.inputs[1] else self.name + "_input2",
+            input_name=f"{self.name}_input_0" if self.inputs[0] is None else self.inputs[0].name(),
             output_name=self.name
         ))
         return list_actions
@@ -457,9 +455,9 @@ class FcCPP(ExportNode):
         list_actions.append(generate_str(
             str(ROOT / "templates" / "kernel_forward" / "fullyconnected_forward.jinja"),
             name=self.name,
-            inputs_name= self.inputs[0].name() if (self.inputs[0] is not None) else self.name + '_input',
+            inputs_name=f"{self.name}_input" if self.inputs[0] is None else self.inputs[0].name(),
             weights_name=self.inputs[1].name(),
-            biases_name=self.inputs[2].name(),
+            biases_name=self.inputs[2].name(), # TODO we should check if bias
             outputs_name=self.name
         ))
         return list_actions