Use aidge2c to convert datatype instead of aidge_datatype2ctype.

aidge_export_arm_cortexm/operators.py

@@ -9,8 +9,10 @@ from typing import Tuple, List, Union, Dict
 import aidge_core
 from aidge_core import ExportNode
 from aidge_core.export_utils.code_generation import *
+from aidge_core.export_utils.data_conversion import aidge2c
+
 from aidge_export_arm_cortexm.utils import ROOT, operator_register
-from aidge_export_arm_cortexm.utils.converter import numpy_dtype2ctype, aidge_datatype2dataformat, aidge_datatype2ctype
+from aidge_export_arm_cortexm.utils.converter import numpy_dtype2ctype, aidge_datatype2dataformat, aidge2c
 from aidge_export_arm_cortexm.utils.generation import *
 
 ##############################################
@@ -210,7 +212,7 @@ class ReLU_ARMCortexM(ExportNode):
         self.board = board
         self.library = library
         self.dataformat = aidge_datatype2dataformat(node.get_operator().get_output(0).dtype())
-        self.datatype = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
+        self.datatype = aidge2c(node.get_operator().get_output(0).dtype())
 
     def export(self, export_folder:Path, list_configs:list):
 
@@ -263,7 +265,7 @@ class Conv_ARMCortexM(ExportNode):
         self.board = board
         self.library = library
         self.dataformat = aidge_datatype2dataformat(node.get_operator().get_output(0).dtype())
-        self.datatype = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
+        self.datatype = aidge2c(node.get_operator().get_output(0).dtype())
         self.scaling = Scaling()("no_scaling")
         self.activation = "Linear"
 
@@ -386,7 +388,7 @@ class PaddedConv_ARMCortexM(Conv_ARMCortexM):
         self.board = board
         self.library = library
         self.dataformat = aidge_datatype2dataformat(node.get_operator().get_output(0).dtype())
-        self.datatype = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
+        self.datatype = aidge2c(node.get_operator().get_output(0).dtype())
         self.scaling = Scaling()("no_scaling")
         self.activation = "Linear"
 
@@ -420,13 +422,13 @@ class ConvReluScaling_ARMCortexM(Conv_ARMCortexM):
         self.activation = "Rectifier"
 
         # Should do this line but there is a bug while changing the datatype of generic operator...
-        # self.datatype = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
+        # self.datatype = aidge2c(node.get_operator().get_output(0).dtype())
         # Do this instead
         if self.operator.get_attr("quantizedNbBits") == 8:
             if self.operator.get_attr("isOutputUnsigned"):
-                self.datatype = aidge_datatype2ctype(aidge_core.DataType.UInt8)
+                self.datatype = aidge2c(aidge_core.DataType.UInt8)
             else:
-                self.datatype = aidge_datatype2ctype(aidge_core.DataType.Int8)
+                self.datatype = aidge2c(aidge_core.DataType.Int8)
 
         # Impose Single Shift (perhaps change it to have a more modular system)
         self.scaling = Scaling(self.operator.get_attr("scalingFactor"),
@@ -440,7 +442,7 @@ class Pooling_ARMCortexM(ExportNode):
         self.board = board
         self.library = library
         self.dataformat = aidge_datatype2dataformat(node.get_operator().get_output(0).dtype())
-        self.datatype = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
+        self.datatype = aidge2c(node.get_operator().get_output(0).dtype())
         self.pool_type = "None"
         self.activation = "Linear"
 
@@ -564,7 +566,7 @@ class FC_ARMCortexM(ExportNode):
         self.board = board
         self.library = library
         self.dataformat = aidge_datatype2dataformat(node.get_operator().get_output(0).dtype())
-        self.datatype = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
+        self.datatype = aidge2c(node.get_operator().get_output(0).dtype())
         self.scaling = Scaling()("no_scaling")
         self.activation = "Linear"
 
@@ -675,13 +677,13 @@ class FCScaling_ARMCortexM(FC_ARMCortexM):
         super(FC_ARMCortexM, self).__init__(node, board, library)
 
         # Should do this line but there is a bug while changing the datatype of generic operator...
-        # self.datatype = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
+        # self.datatype = aidge2c(node.get_operator().get_output(0).dtype())
         # Do this instead
         if self.operator.get_attr("quantizedNbBits") == 8:
             if self.operator.get_attr("isOutputUnsigned"):
-                self.datatype = aidge_datatype2ctype(aidge_core.DataType.UInt8)
+                self.datatype = aidge2c(aidge_core.DataType.UInt8)
             else:
-                self.datatype = aidge_datatype2ctype(aidge_core.DataType.Int8)
+                self.datatype = aidge2c(aidge_core.DataType.Int8)
 
         # Impose Single Shift (perhaps change it to have a more modular system)
         self.scaling = Scaling(self.operator.get_attr("scalingFactor"),

aidge_export_arm_cortexm/utils/converter.py

@@ -19,7 +19,7 @@ def numpy_dtype2ctype(dtype):
     # Add more dtype mappings as needed
     else:
         raise ValueError(f"Unsupported {dtype} dtype")
-    
+
 
 def aidge_datatype2ctype(datatype):
     if datatype == aidge_core.DataType.Int8:
@@ -37,7 +37,7 @@ def aidge_datatype2ctype(datatype):
     # Add more dtype mappings as needed
     else:
         raise ValueError(f"Unsupported {datatype} aidge datatype")
-    
+
 
 def aidge_datatype2dataformat(datatype):
     if datatype == aidge_core.DataType.Int8: