diff --git a/aidge_core/show_graphview.py b/aidge_core/show_graphview.py
index ddf0fc4b4659a727c7879738ef5e3eb40186cac1..633298f10dbfdafe40022f88f741f82d2d35c681 100644
--- a/aidge_core/show_graphview.py
+++ b/aidge_core/show_graphview.py
@@ -4,24 +4,24 @@ import builtins
import aidge_core
import numpy as np
from pathlib import Path
-
+
def _retrieve_operator_attrs(node : aidge_core.Node) -> dict[str, int, float, bool, None]:
"""
Returns the dictionary containing the attributes of a given Node.
- :param graph: A Node in the list of ordered nodes.
+ :param graph: A Node in the list of ordered nodes.
:type graph: aidge_core.Node
:return: A dictionary with the Node's attributes.
:rtype: dict[str, int, float, bool, None]
- """
+ """
if node.get_operator().attr is not None:
node_attr_dict = node.get_operator().attr.dict()
for key,value in node_attr_dict.items():
if not type(value).__name__ in dir(builtins):
node_attr_dict[key] = value.name
-
+
else:
node_attr_dict = {}
@@ -29,49 +29,49 @@ def _retrieve_operator_attrs(node : aidge_core.Node) -> dict[str, int, float, bo
def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_embed : bool, write_trainable_params_ext : bool, path_trainable_params : Path, params_file_format : str) -> dict[str, int, float, bool, None]:
"""
- Creates a dictionary to store the information of a given ordered GraphView.
+ Creates a dictionary to store the information of a given ordered GraphView.
:param ordered_nodes: A list with the GraphView's ordered nodes.
:type graph: list
- :param write_trainable_params_embed: Whether or not to write the eventual trainable parameters of the Nodes in the same file as the dict (embed).
+ :param write_trainable_params_embed: Whether or not to write the eventual trainable parameters of the Nodes in the same file as the dict (embed).
:type write_trainable_params_embed: bool
- :param write_trainable_params_ext: Whether or not to write the eventual trainable parameters of the Nodes in an external file.
+ :param write_trainable_params_ext: Whether or not to write the eventual trainable parameters of the Nodes in an external file.
:type write_trainable_params_ext: bool
:param path_trainable_params: Path of the external file used to store the Nodes' trainable parameters.
:type path_trainable_params: Path
:param params_file_format: Format of the external file used to store the Nodes' trainable parameters. Options: ``npz`` or ``json``. Default : ``json``. Requires ``write_trainable_params_ext``.
:type params_file_format: str
-
+
:return: A dictionary with the GraphView description.
:rtype: dict[str, int, float, bool, None]
- """
+ """
graphview_dict = {'graph': []}
for node in ordered_nodes:
-
+
if node is not None:
- node_dict = {'name' : node.name(),
+ node_dict = {'name' : node.name(),
'optype' : node.get_operator().type(),
'nb_inputs' : node.get_operator().nb_inputs(),
'nb_outputs' : node.get_operator().nb_outputs()}
-
+
inputs = []
for input_idx in range(node.get_operator().nb_inputs()):
input_dict = {'dims' : node.get_operator().get_input(input_idx).dims(),
'data_type' : str(node.get_operator().get_input(input_idx).dtype()),
- 'data_format' : str(node.get_operator().get_input(input_idx).dformat())}
- inputs.append(input_dict)
-
+ 'data_format' : str(node.get_operator().get_input(input_idx).dformat())}
+ inputs.append(input_dict)
+
node_dict['inputs'] = inputs
outputs = []
for output_idx in range(node.get_operator().nb_outputs()):
output_dict = {'dims' : node.get_operator().get_output(output_idx).dims(),
'data_type' : str(node.get_operator().get_output(output_idx).dtype()),
- 'data_format' : str(node.get_operator().get_output(output_idx).dformat())}
- outputs.append(output_dict)
-
+ 'data_format' : str(node.get_operator().get_output(output_idx).dformat())}
+ outputs.append(output_dict)
+
node_dict['outputs'] = outputs
parents = node.get_parents()
@@ -79,8 +79,8 @@ def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_e
if parents[0] is None: parents.append(parents.pop(0))
else:
pass
-
- parents_inputs = []
+
+ parents_inputs = []
for parent in parents:
if parent is not None:
for output_idx in range(parent.get_operator().nb_outputs()):
@@ -91,7 +91,7 @@ def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_e
elif parent is None:
for input_idx in list(range(node.get_operator().nb_inputs())):
if input_idx not in [item[1] for item in parents_inputs]:
- parents_inputs.append((None, input_idx))
+ parents_inputs.append((None, input_idx))
parents_inputs.sort(key=lambda x: x[1])
node_dict['parents'] = parents_inputs
@@ -103,15 +103,15 @@ def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_e
if child.get_operator().get_input(input_idx).dims() == node.get_operator().get_output(output_idx).dims():
children_outputs.append((child.name(), output_idx))
node_dict['children'] = children_outputs
-
+
# Check if my node is a metaop
attributes_dict = {}
- if isinstance(node.get_operator(), aidge_core.MetaOperator_Op):
+ if isinstance(node.get_operator(), aidge_core.MetaOperatorOp):
attributes_dict['micro_graph'] = []
for micro_node in node.get_operator().get_micro_graph().get_nodes():
- micro_node_dict = {'name' : micro_node.name(),
+ micro_node_dict = {'name' : micro_node.name(),
'optype' : micro_node.type()}
-
+
micro_node_attr_dict = _retrieve_operator_attrs(micro_node)
micro_node_dict['attributes'] = micro_node_attr_dict
attributes_dict['micro_graph'].append(micro_node_dict)
@@ -124,7 +124,7 @@ def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_e
if node.type() == 'Producer':
if write_trainable_params_ext:
-
+
params_file_format.casefold()
if params_file_format=='npz':
@@ -134,14 +134,14 @@ def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_e
elif params_file_format=='json':
tensor = np.array(node.get_operator().get_output(0))
tensor_dict = {
- node.name() :
+ node.name() :
{
'dims' : tensor.shape,
'data_type' : str(tensor.dtype),
'tensor_data' : tensor.tolist()
- }
+ }
}
-
+
with open(Path(path_trainable_params, node.name() + '.json'), 'w') as fp:
json.dump(tensor_dict, fp, indent=4)
@@ -150,10 +150,10 @@ def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_e
else:
raise Exception("File format to write trainable parameters not recognized.")
-
+
elif write_trainable_params_embed:
node_dict['tensor_data'] = np.array(node.get_operator().get_output(0)).tolist()
-
+
else:
pass
@@ -161,13 +161,13 @@ def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_e
else: # node is None
pass
-
+
return graphview_dict
def _write_dict_json(graphview_dict : dict[str, int, float, bool, None], json_path : str) -> None:
"""
Writes dictionary containing GraphView description to a JSON file.
-
+
:param graphview_dict: A dictionary with the GraphView description.
:type graphview_dict: dict[str, int, float, bool, None]
:param json_path: Path to write JSON file.
@@ -178,18 +178,18 @@ def _write_dict_json(graphview_dict : dict[str, int, float, bool, None], json_pa
json.dump(graphview_dict, fp, indent=4)
return None
-
-def gview_to_json(gview : aidge_core.GraphView, json_path : Path, write_trainable_params_embed : bool = False, write_trainable_params_ext : bool = False, params_file_format : str = 'json') -> None:
+
+def gview_to_json(gview : aidge_core.GraphView, json_path : Path, write_trainable_params_embed : bool = False, write_trainable_params_ext : bool = False, params_file_format : str = 'json') -> None:
"""
Generates the description for a GraphView in the JSON format.
-
+
:param graph: A GraphView of Aidge.
:type graph: aidge_core.GraphView
:param json_path: Path to write JSON file.
:type json_path: Path
- :param write_trainable_params_embed: Whether or not to write the eventual trainable parameters of the Nodes in the same file as the dict (embed).
+ :param write_trainable_params_embed: Whether or not to write the eventual trainable parameters of the Nodes in the same file as the dict (embed).
:type write_trainable_params_embed: bool, optional
- :param write_trainable_params_ext: Whether or not to write the eventual trainable parameters of the Nodes in an external file.
+ :param write_trainable_params_ext: Whether or not to write the eventual trainable parameters of the Nodes in an external file.
:type write_trainable_params_ext: bool, optional
:param params_file_format: Format of the external file used to store the Nodes' trainable parameters. Options: ``npz`` or ``json``. Default : ``json``. Requires ``write_trainable_params_ext``.
:type params_file_format: str, optional
@@ -201,7 +201,7 @@ def gview_to_json(gview : aidge_core.GraphView, json_path : Path, write_trainabl
elif not json_path.is_dir():
if json_path.suffix == '.json':
pass
- else:
+ else:
raise Exception('If ``json_path`` contains a filename it must be of JSON format.')
if write_trainable_params_ext:
@@ -212,14 +212,14 @@ def gview_to_json(gview : aidge_core.GraphView, json_path : Path, write_trainabl
if isinstance(gview, aidge_core.GraphView):
# Sort GraphView in topological order
ordered_nodes = gview.get_ordered_nodes()
-
- # Create dict from GraphView
+
+ # Create dict from GraphView
graphview_dict = _create_dict(ordered_nodes, write_trainable_params_embed, write_trainable_params_ext, path_trainable_params, params_file_format)
-
+
# Write dict to JSON
_write_dict_json(graphview_dict, json_path)
else:
raise Exception("Graph must be an instance of aidge_core.GraphView.")
-
+
return None
\ No newline at end of file
diff --git a/python_binding/operator/pybind_MetaOperatorDefs.cpp b/python_binding/operator/pybind_MetaOperatorDefs.cpp
index 5f6cc12fc1e84ee245fdb7babf025b5af4316315..5f173068af0f1140830d458979ec924c38ade078 100644
--- a/python_binding/operator/pybind_MetaOperatorDefs.cpp
+++ b/python_binding/operator/pybind_MetaOperatorDefs.cpp
@@ -194,7 +194,7 @@ void init_MetaOperatorDefs(py::module &m) {
// declare_PaddedMaxPoolingOp<3>(m);
declare_LSTMOp(m);
- py::class_<MetaOperator_Op, std::shared_ptr<MetaOperator_Op>, OperatorTensor>(m, "MetaOperator_Op", py::multiple_inheritance())
+ py::class_<MetaOperator_Op, std::shared_ptr<MetaOperator_Op>, OperatorTensor>(m, "MetaOperatorOp", py::multiple_inheritance())
.def(py::init<const char *, const std::shared_ptr<GraphView>&, const std::vector<InputCategory>&>(),
py::arg("type"),
py::arg("graph"),
diff --git a/unit_tests/graphRegex/Test_GraphRegex.cpp b/unit_tests/graphRegex/Test_GraphRegex.cpp
index 68ac509e79e347106a9a132249f125ebe6e39f6a..79e471d44a49dfb52fd5eb4aa1ed2dc4ab8dc0bb 100644
--- a/unit_tests/graphRegex/Test_GraphRegex.cpp
+++ b/unit_tests/graphRegex/Test_GraphRegex.cpp
@@ -153,9 +153,9 @@ TEST_CASE("GraphRegexUser") {
// generate the original GraphView
auto matmul0 = MatMul("matmul0");
- auto add0 = Add(2, "add0");
+ auto add0 = Add("add0");
auto matmul1 = MatMul("matmul1");
- auto add1 = Add(2, "add1");
+ auto add1 = Add("add1");
auto b0 = Producer({5}, "B0");
auto w0 = Producer({5, 5}, "W0");