diff --git a/aidge_export_cpp/export.py b/aidge_export_cpp/export.py
index 7effde97a608c5630cf94870f301e3a56fb0a1a2..caf4d8a30cb97fd4d88f40ecc9fbd1cff52c01b3 100644
--- a/aidge_export_cpp/export.py
+++ b/aidge_export_cpp/export.py
@@ -3,13 +3,14 @@ import shutil
import numpy as np
from pathlib import Path
from typing import List, Union
+import json
import aidge_core
from aidge_core.mem_info import generate_optimized_memory_info
-from aidge_core.export_utils import scheduler_export, generate_main_cpp, aidge2c, generate_file
+from aidge_core.export_utils import scheduler_export, generate_main_cpp
-from aidge_export_cpp import ExportLibCpp, ROOT
-from aidge_export_cpp.export_utils import read_log_file
+from aidge_export_cpp import ExportLibCpp
+from aidge_export_cpp.export_utils import export_aidge_ifmaps
def export(export_folder_name: str,
@@ -59,6 +60,25 @@ def export(export_folder_name: str,
aidge_core.adapt_fc_params_format(graphview)
graphview.forward_dims(dims=[inputs_tensor.dims()] if inputs_tensor is not None else [])
+ # --------------------------------------------------------------
+ # AIDGE CMP
+ # --------------------------------------------------------------
+
+ """
+ If the --aidge_cmp option is enabled, the feature maps generated by aidge with the
+ backend cpu will be exported in the generated export. It will be used as reference
+ to verify that the results with the optimized kernels are correct for the exported
+ model.
+ This option has to be passed to each node in order to be used within the Export Nodes.
+ (JConv, JPad, ...) that you can find in the "operators" folder.
+ """
+
+ if aidge_cmp:
+ for node in graphview.get_nodes():
+ node.attributes().aidge_cmp = True
+
+ graphview.save("exported_model")
+
scheduler.reset_scheduling()
scheduler.generate_scheduling()
@@ -85,25 +105,5 @@ def export(export_folder_name: str,
generate_main_cpp(export_folder_name, graphview, labels=labels, inputs_tensor=inputs_tensor)
# Generate log files (aidge_cmp option)
- """
- If the aidge_cmp option has been enabled, the generated log_outputs will
- be copied into the generated export in order to be used as reference.
- """
if aidge_cmp:
- ranked_nodes = graphview.get_ranked_nodes_name("{0}[{1}#{3}]")
- os.makedirs(export_folder_name / "data" / "aidge_outputs")
- os.makedirs(export_folder_name / "data" / "export_outputs")
- for node in graphview.get_nodes():
- if node.type() != "Producer":
- file_path = 'log_outputs/' + ranked_nodes[node] + '/output_0.log'
- data_t = aidge2c(node.get_operator().get_output(0).dtype())
- name = node.name() + '_output_0_aidge'
- dims = node.get_operator().get_output(0).dims()
- values = read_log_file(file_path)
-
- generate_file(export_folder_name / "data" / "aidge_outputs" / (node.name() + ".hpp"),
- ROOT / "templates" / "data" / "aidge_tensor.jinja",
- data_t=data_t,
- name=name,
- dims=dims,
- values=values)
+ export_aidge_ifmaps(export_folder_name)
diff --git a/aidge_export_cpp/export_utils.py b/aidge_export_cpp/export_utils.py
index e22524fb9058dfb4c8b023d0df8fbe11e2ff791b..ea82167e463b4d2324fd412ecd5681b7a74478a4 100644
--- a/aidge_export_cpp/export_utils.py
+++ b/aidge_export_cpp/export_utils.py
@@ -1,13 +1,17 @@
import os
+import json
+import numpy as np
from collections import OrderedDict
import aidge_core
-from aidge_core.export_utils import get_node_from_metaop
+from aidge_core.export_utils import get_node_from_metaop, aidge2c, generate_file
+
+from aidge_export_cpp import ROOT
def cpp_fuse_to_metaops(graph_view: aidge_core.GraphView):
"""
Fuse nodes into metaops adapted for the CPP Export
- TODO: These recipes should be into aidge_core
+ TODO: These recipes should be in aidge_core
:param graph_view: An instance of :py:class:`aidge_core.GraphView`, providing access to nodes and
ordered input/output data within the computational graph.
@@ -152,25 +156,6 @@ def set_nodes_datatypes(graph_view: aidge_core.GraphView):
-def read_log_file(file_path: str):
- """ Read log file
- Used to read the aidge generated log files containing the intermediate
- tensors of the exported model.
-
- :param file_path: Path to the file to read.
- :type file_path: str
- """
- # Check if the file exists
- if not os.path.isfile(file_path):
- print(f"File not found: {file_path}")
- return None
-
- with open(file_path, 'r') as file:
- content = file.read()
- return content
-
-
-
def exclude_unwanted_producers(model):
""" Exclude some producers not needed for the export
@@ -226,3 +211,48 @@ def normalize(array):
array = (array - array.min()) / (array.max() - array.min())
return 2 * array - 1
+
+
+def generate_aidge_ifmaps(model):
+
+ json_nodes = []
+ for node in model.get_nodes():
+ if node.type() != "Producer":
+
+ output = node.get_operator().get_output(0)
+ data = {
+ "name": node.name(),
+ "dims": output.dims(),
+ "dtype": aidge2c(output.dtype()),
+ "values": np.array(output).tolist()
+ }
+ json_nodes.append(data)
+
+ # Write the entire list to the JSON file after the loop
+ with open('aidge_output.json', 'w') as file:
+ json.dump(json_nodes, file, indent=2, separators=(",", ": "))
+
+
+
+def export_aidge_ifmaps(export_folder_name):
+ os.makedirs(export_folder_name / "data" / "aidge_outputs")
+ os.makedirs(export_folder_name / "data" / "export_outputs")
+
+ # Load the JSON data from the file
+ with open('aidge_output.json', 'r') as file:
+ json_nodes = json.load(file)
+
+ # Access the data
+ for node in json_nodes:
+ name = node["name"]
+ dims = node["dims"]
+ dtype = node["dtype"]
+ values = node["values"]
+
+ generate_file(export_folder_name / "data" / "aidge_outputs" / (name + ".hpp"),
+ ROOT / "templates" / "data" / "aidge_tensor.jinja",
+ data_t=dtype,
+ name=name + "_output_0_aidge",
+ dims=dims,
+ values=values)
+
diff --git a/examples/export_LeNet/lenet.py b/examples/export_LeNet/lenet.py
index 1d7960b0837cae7fe46aa23037b4712935579811..d80a3077ef51268c27af21788322969065a14307 100644
--- a/examples/export_LeNet/lenet.py
+++ b/examples/export_LeNet/lenet.py
@@ -18,11 +18,7 @@ import aidge_backend_cpu
import aidge_quantization
import aidge_export_cpp
-from aidge_export_cpp.export_utils import (
- cpp_fuse_to_metaops,
- set_nodes_names,
- set_nodes_datatypes,
- exclude_unwanted_producers)
+from aidge_export_cpp.export_utils import *
from aidge_core.export_utils import remove_optional_inputs, get_node_from_metaop
@@ -442,9 +438,8 @@ Once the tensors have been casted, the log_outputs() function can be
called to store their values into log files.
"""
-if os.path.isdir("log_outputs"):
- shutil.rmtree("log_outputs")
-model.log_outputs("log_outputs")
+if AIDGE_CMP:
+ generate_aidge_ifmaps(model)
# --------------------------------------------------------------
# TEST MODE
@@ -461,29 +456,10 @@ changing the inputs of the layer, to isolate the source of the issue.
for node in model.get_nodes():
node.attributes().dev_mode = DEV_MODE
-# --------------------------------------------------------------
-# AIDGE CMP
-# --------------------------------------------------------------
-
-"""
-If the --aidge_cmp option is enabled, the feature maps generated by aidge with the
-backend cpu will be exported in the generated export. It will be used as reference
-to verify that the results with the optimized kernels are correct for the exported
-model.
-This option has to be passed to each node in order to be used within the Export Nodes.
-(JConv, JPad, ...) that you can find in the "operators" folder.
-"""
-
-if AIDGE_CMP:
- for node in model.get_nodes():
- node.attributes().aidge_cmp = True
-
# --------------------------------------------------------------
# EXPORT THE MODEL
# --------------------------------------------------------------
-model.save("exported_model")
-
aidge_export_cpp.export(EXPORT_FOLDER,
model,
scheduler,