Fix export path.

aidge_export_arm_cortexm/__init__.py

@@ -3,7 +3,12 @@
 Use this module to generate CPP exports for ARM CortexM boards.
 This module has to be used with the Aidge suite
 """
+from pathlib import Path
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]
 
 from .export import *
 from .export_registry import ExportLibAidgeARM, ExportLibCMSISNN
 from .operators import *
+

aidge_export_arm_cortexm/export.py

 import os
 import shutil
 from pathlib import Path
-from aidge_export_arm_cortexm.utils import (ROOT, AVAILABLE_BOARDS, has_board)
+from aidge_export_arm_cortexm import ROOT
 from aidge_export_arm_cortexm.export_registry import ExportLibAidgeARM
 # from aidge_export_arm_cortexm.utils.converter import numpy_dtype2ctype
 
@@ -50,7 +50,3 @@ def gen_board_files(path:str, board:str)->None:
 
     # Copy all static files in the export
     shutil.copytree(BOARDS_MAP[board], str(path), dirs_exist_ok=True)
-    # For N2D2 library, copy static folder to export/include
-    dnn_include_folder = dnn_folder / "include"
-    os.makedirs(str(dnn_include_folder), exist_ok=True)
-    shutil.copytree(str(ROOT / "_N2D2" / "static"), str(dnn_include_folder), dirs_exist_ok=True)

aidge_export_arm_cortexm/operators.py

@@ -7,14 +7,30 @@ from typing import Tuple, List
 import aidge_core
 from aidge_core.export_utils import ExportNode, ExportNodeCpp
 from aidge_core.export_utils.code_generation import *
-from aidge_export_arm_cortexm.utils import ROOT
-from aidge_export_arm_cortexm.utils.converter import numpy_dtype2ctype
-from aidge_export_arm_cortexm.utils.generation import *
+from aidge_export_arm_cortexm import ROOT
 from aidge_export_arm_cortexm.export_registry import ExportLibAidgeARM
 
 ##############################################
 ############## Export functions ##############
 ##############################################
+# Note: to remove
+def numpy_dtype2ctype(dtype):
+    if dtype == np.int8:
+        return "int8_t"
+    elif dtype == np.int16:
+        return "int16_t"
+    elif dtype == np.int32:
+        return "int32_t"
+    elif dtype == np.int64:
+        return "int64_t"
+    elif dtype == np.float32:
+        return "float"
+    elif dtype == np.float64:
+        return "double"
+    # Add more dtype mappings as needed
+    else:
+        raise ValueError(f"Unsupported {dtype} dtype")
+
 
 def export_params(name:str,
                   array: np.ndarray,