diff --git a/aidge_export_arm_cortexm/export.py b/aidge_export_arm_cortexm/export.py
index 110e9431924ae6ad45a9d6fa852a1201e4779428..d1f6595e89eb327ddb67b39a21d708c9078ea3ab 100644
--- a/aidge_export_arm_cortexm/export.py
+++ b/aidge_export_arm_cortexm/export.py
@@ -9,11 +9,16 @@ from aidge_core.mem_info import compute_default_mem_info, generate_optimized_mem
from aidge_core.export_utils import scheduler_export
+BOARD_PATH : str = ROOT / "boards"
+
+BOARDS_MAP: dict[str, Path] = {
+ "stm32h7" : BOARD_PATH / "stm32" / "H7",
+}
+
def export(export_folder_name,
graphview,
scheduler = None,
board:str ="stm32h7",
- library:str = "aidge",
mem_wrapping = False):
scheduler_export(
@@ -21,137 +26,30 @@ def export(export_folder_name,
export_folder_name,
ExportLibAidgeARM,
memory_manager=generate_optimized_memory_info,
- memory_manager_args={"stats_folder": f"{export_folder_name}/stats", "wrapping":False }
+ memory_manager_args={"stats_folder": f"{export_folder_name}/stats", "wrapping": mem_wrapping }
)
- # Create export directory
- export_folder = Path().absolute() / export_folder_name
- os.makedirs(str(export_folder), exist_ok=True)
- # Create dnn directory
- dnn_folder = export_folder / "dnn"
+ gen_board_files(export_folder_name, board)
+
+
+def supported_boards() -> list[str]:
+ return BOARDS_MAP.keys()
+
+def gen_board_files(path:str, board:str)->None:
+ if board not in supported_boards():
+ raise ValueError(f"Board {board} is not supported, supported board are:\n\t-{'\n\t-'.join(supported_boards())}")
+
+ if isinstance(path, str): path = Path(path)
+ # Create dnn directory is not exist
+ dnn_folder = path / "dnn"
os.makedirs(str(dnn_folder), exist_ok=True)
# Determine which board the user wants
# to select correct config
- board_path = ROOT / "boards" / "stm32" / "H7"
+
# Copy all static files in the export
- shutil.copytree(board_path, str(export_folder), dirs_exist_ok=True)
+ 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)
-
- # # Create statistics directory
- # stats_folder = export_folder / "statistics"
- # os.makedirs(str(stats_folder), exist_ok=True)
-
- # # Sort layers according to a scheduler
- # if not isinstance(scheduler, aidge_core.Scheduler):
- # # No scheduler provided by the user, use the default one
- # list_forward_nodes = topological_sort(graphview)
- # mem_size, mem_info = compute_default_mem_info(list_forward_nodes)
- # else:
- # list_forward_nodes = scheduler.get_static_scheduling()
- # mem_size, mem_info = generate_optimized_memory_info(stats_folder, scheduler, mem_wrapping)
-
- # Set some lists of elements for generating forward file
- # list_actions = []
- # list_configs = []
-
- # # Export layer configurations
- # for node in list_forward_nodes:
- # if node.type() == "Producer":
- # # We do not treat Producer here but i the nodes which use them
- # continue
-
- # if node.type() in supported_operators():
- # op = OPERATORS_REGISTRY[node.type()](node, board, library)
- # # Export the configuration
- # list_configs = op.export(dnn_folder, list_configs)
-
- # # Add forward kernel
- # list_actions = op.forward(list_actions)
- # else:
- # print(f"Warning: {node.type()} is not supported in the export.\nPlease add the implementation.")
-
- # Generate the memory file
- # generate_file(
- # str(dnn_folder / "memory" / "mem_info.h"),
- # str(ROOT / "templates" / "memory" / "mem_info.jinja"),
- # mem_size = mem_size,
- # mem_info_legends = MEMORY_INFO_TEMPLATE,
- # mem_info = mem_info,
- # mem_alignment = 1 # Fixed memory alignement so far, feel free to adapt it
- # )
- # list_configs.append("memory/mem_info.h")
-
- # Get entry nodes
- # It supposes the entry nodes are producers with constant=false
- # Store the datatype & name
- # list_inputs_name = []
- # first_element_added = False
- # for node in graphview.get_nodes():
- # if node.type() == "Producer":
- # if not first_element_added:
- # export_type = aidge2c(node.get_operator().get_output(0).dtype())
- # list_inputs_name.append((export_type, node.name()))
- # first_element_added = True
- # if not node.get_operator().attr.constant:
- # export_type = aidge2c(node.get_operator().get_output(0).dtype())
- # list_inputs_name.append((export_type, node.name()))
-
- # Get output nodes
- # Store the datatype & name, like entry nodes
-
- # list_outputs_name = []
- # for node in graphview.get_nodes():
- # if len(node.get_children()) == 0:
- # if node.get_operator().attr.has_attr('dtype'):
- # # Temporary fix because impossible to set DataType of a generic operator
- # export_type = aidge2c(node.get_operator().attr.dtype)
- # else:
- # export_type = aidge2c(node.get_operator().get_output(0).dtype())
-
- # list_outputs_name.append((export_type, node.name()))
-
- # if library == "n2d2":
- # forward_file = "forward.cpp"
- # else:
- # forward_file = "forward.c"
-
- # # Generate forward file
- # generate_file(
- # str(dnn_folder / "src" / forward_file),
- # str(ROOT / "templates" / "network" / "network_forward.jinja"),
- # headers=set(list_configs),
- # actions=list_actions,
- # inputs= list_inputs_name,
- # outputs=list_outputs_name
- # )
-
- # # Generate dnn internal API
- # if library == "aidge":
- # # For Aidge, parse all kernels source code and retrieve function prototypes
- # generate_file(
- # str(dnn_folder / "include" / "network_functions.h"),
- # str(ROOT / "templates" / "network" / "network_prototypes.jinja"),
- # libraries=[],
- # functions=get_functions_from_c_folder(str(dnn_folder / "src" / "kernels")),
- # )
- # elif library == "n2d2":
- # # For N2D2, parse all the files in include/kernel/ and retrieve the names of the files
- # generate_file(
- # str(dnn_folder / "include" / "network_functions.h"),
- # str(ROOT / "templates" / "network" / "network_prototypes.jinja"),
- # libraries=[],
- # files=[str(Path("kernels") / x) for x in get_filenames_from_folder(str(dnn_folder / "include" / "kernels"), r'^.*\.hpp$')],
- # )
-
- # # Generate dnn API
- # generate_file(
- # str(dnn_folder / "include" / "dnn.h"),
- # str(ROOT / "templates" / "network" / "dnn_header.jinja"),
- # libraries=["stdint.h"],
- # functions=get_functions_from_c_file(str(dnn_folder / "src" / forward_file)),
- # )
-
diff --git a/aidge_export_arm_cortexm/operators.py b/aidge_export_arm_cortexm/operators.py
index b4e6aa85cd8709dd0944c4eaeb2b25560ab8b945..69281a8dd1e17d296ece79bea186c103b6232f36 100644
--- a/aidge_export_arm_cortexm/operators.py
+++ b/aidge_export_arm_cortexm/operators.py
@@ -197,7 +197,6 @@ class Scaling():
class ReLU_ARMCortexM(ExportNodeCpp):
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
- self.attributes["activation_type"] = "RELU"
self.config_template = str(ROOT / "_Aidge_Arm" / "templates" / "configuration" / "relu.jinja")