diff --git a/aidge_core/show_graphview.py b/aidge_core/show_graphview.py
index 633298f10dbfdafe40022f88f741f82d2d35c681..4f6a2960348c44dc7b8a0b957f777ddac5a8562a 100644
--- a/aidge_core/show_graphview.py
+++ b/aidge_core/show_graphview.py
@@ -79,29 +79,32 @@ 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 = []
- for parent in parents:
+ input_idx = 0
+ for parent in node.get_parents():
if parent is not None:
- for output_idx in range(parent.get_operator().nb_outputs()):
- for input_idx in range(node.get_operator().nb_inputs()):
- if parent.get_operator().get_output(output_idx).dims() == node.get_operator().get_input(input_idx).dims():
+ for children in parent.outputs():
+ for child in children:
+ if child[0] == node and child[1] == input_idx:
parents_inputs.append((parent.name(), input_idx))
-
+
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.sort(key=lambda x: x[1])
+ if input_idx not in [item[1] for item in parents_inputs]:
+ parents_inputs.append((None, input_idx))
+
+ input_idx += 1
node_dict['parents'] = parents_inputs
children_outputs = []
- for child in node.get_children():
- for input_idx in range(child.get_operator().nb_inputs()):
- for output_idx in range(node.get_operator().nb_outputs()):
- if child.get_operator().get_input(input_idx).dims() == node.get_operator().get_output(output_idx).dims():
- children_outputs.append((child.name(), output_idx))
+ output_idx = 0
+ for children in node.get_ordered_children():
+ for child in children:
+ if child is not None:
+ for parent in child.inputs():
+ if parent[0] == node and parent[1] == output_idx:
+ children_outputs.append((child.name(), output_idx))
+ output_idx += 1
node_dict['children'] = children_outputs
# Check if my node is a metaop
@@ -129,7 +132,7 @@ def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_e
if params_file_format=='npz':
np.savez_compressed(Path(path_trainable_params, node.name()), **{node.name() : node.get_operator().get_output(0)})
- node_dict['tensor_data'] = Path(path_trainable_params, node.name() + '.npz')
+ node_dict['tensor_data'] = str(Path(path_trainable_params, node.name() + '.npz'))
elif params_file_format=='json':
tensor = np.array(node.get_operator().get_output(0))
@@ -145,13 +148,13 @@ def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_e
with open(Path(path_trainable_params, node.name() + '.json'), 'w') as fp:
json.dump(tensor_dict, fp, indent=4)
- node_dict['tensor_data'] = Path(path_trainable_params, node.name() + '.json')
+ node_dict['tensor_data'] = str(Path(path_trainable_params, node.name() + '.json'))
else:
raise Exception("File format to write trainable parameters not recognized.")
- elif write_trainable_params_embed:
+ if write_trainable_params_embed:
node_dict['tensor_data'] = np.array(node.get_operator().get_output(0)).tolist()
else:
@@ -195,17 +198,21 @@ def gview_to_json(gview : aidge_core.GraphView, json_path : Path, write_trainabl
:type params_file_format: str, optional
"""
- if json_path.is_dir():
- json_path = (json_path.parent).joinpath('model.json')
+ if not json_path.suffix:
+ if not json_path.is_dir():
+ json_path.mkdir(parents=True, exist_ok=True)
+ json_path = json_path.joinpath('model.json')
- elif not json_path.is_dir():
- if json_path.suffix == '.json':
- pass
- else:
- raise Exception('If ``json_path`` contains a filename it must be of JSON format.')
+ else:
+ if json_path.suffix != '.json':
+ raise Exception('If ``json_path`` contains a filename, it must be of JSON format.')
+ if not json_path.parent.is_dir():
+ json_path.parent.mkdir(parents=True, exist_ok=True)
if write_trainable_params_ext:
path_trainable_params = (json_path.parent).joinpath(json_path.stem + '_trainable_params/')
+ path_trainable_params.mkdir(parents=True, exist_ok=True)
+
else:
path_trainable_params = Path()
diff --git a/include/aidge/backend/cpu/data/TensorImpl.hpp b/include/aidge/backend/cpu/data/TensorImpl.hpp
index fd2a0b3f42d5888a68edb18caf046cea71dec0f3..9390fe5860b5d3523886856d9b2a40752d338af5 100644
--- a/include/aidge/backend/cpu/data/TensorImpl.hpp
+++ b/include/aidge/backend/cpu/data/TensorImpl.hpp
@@ -50,10 +50,10 @@ public:
void zeros() override final;
void copy(const void *src, NbElts_t length, NbElts_t offset = 0) override final {
+ AIDGE_ASSERT(offset + length <= mNbElts, "TensorImpl_cpu<{}>::copy(): copy offset ({}) + length ({}) is above capacity ({})", typeid(T).name(), offset, length, mNbElts);
const T* srcT = static_cast<const T *>(src);
T* dstT = static_cast<T *>(rawPtr(offset));
- AIDGE_ASSERT(length <= mData.size() || length <= mNbElts, "TensorImpl_cpu<{}>::copy(): copy length ({}) is above capacity ({})", typeid(T).name(), length, mNbElts);
AIDGE_ASSERT(dstT < srcT || dstT >= srcT + length, "TensorImpl_cpu<{}>::copy(): overlapping copy is not supported", typeid(T).name());
std::copy(srcT, srcT + length, dstT);
}
@@ -72,7 +72,7 @@ public:
void copyToHost(void *dst, NbElts_t length, NbElts_t offset = 0) const override final {
const T* src = static_cast<const T*>(rawPtr(offset));
- AIDGE_ASSERT(length <= mData.size() || length <= mNbElts, "TensorImpl_cpu<{}>::copyToHost(): copy length ({}) is above capacity ({})", typeid(T).name(), length, mNbElts);
+ AIDGE_ASSERT(offset + length <= mData.size(), "TensorImpl_cpu<{}>::copy(): copy offset ({}) + length ({}) is above capacity ({})", typeid(T).name(), offset, length, mData.size());
std::copy(src, src + length, static_cast<T *>(dst));
}
diff --git a/python_binding/graph/pybind_Node.cpp b/python_binding/graph/pybind_Node.cpp
index 35f6327444a874d8f5c2e94da6520244e095263a..69a28960b57e6ba2ac8a699bf45ff09961fa4135 100644
--- a/python_binding/graph/pybind_Node.cpp
+++ b/python_binding/graph/pybind_Node.cpp
@@ -176,6 +176,11 @@ void init_Node(py::module& m) {
Get children.
)mydelimiter")
+ .def("get_ordered_children", &Node::getOrderedChildren,
+ R"mydelimiter(
+ Get ordered children.
+ )mydelimiter")
+
.def("__call__",
[](Node &self, pybind11::args args) {
std::vector<Connector> connectors;
diff --git a/src/backend/cpu/data/TensorImpl.cpp b/src/backend/cpu/data/TensorImpl.cpp
index ed3c96f80c1b8bafd70425451d6618428d1888f0..506287a0c520915e6426f1f0b64d9c562c754d33 100644
--- a/src/backend/cpu/data/TensorImpl.cpp
+++ b/src/backend/cpu/data/TensorImpl.cpp
@@ -47,8 +47,8 @@ void Aidge::TensorImpl_cpu<T>::copyCast(const void *src, const Aidge::DataType s
return;
}
+ AIDGE_ASSERT(offset + length <= mNbElts, "TensorImpl_cpu<{}>::copyCast(): copy offset ({}) + length ({}) is above capacity ({})", typeid(T).name(), offset, length, mNbElts);
T* dstT = static_cast<T *>(rawPtr(offset));
- AIDGE_ASSERT(length <= mData.size() || length <= mNbElts, "TensorImpl_cpu<{}>::copyCast(): copy length ({}) is above capacity ({})", typeid(T).name(), length, mNbElts);
switch (srcDt)
{
case DataType::Float64:
diff --git a/unit_tests/backend/Test_TensorImpl.cpp b/unit_tests/backend/Test_TensorImpl.cpp
index 43e25092a0f502698bbff7b0142969154f2cb0b0..ceb6772d01d4ee84524896fead96abcb445f84ff 100644
--- a/unit_tests/backend/Test_TensorImpl.cpp
+++ b/unit_tests/backend/Test_TensorImpl.cpp
@@ -47,6 +47,7 @@ TEST_CASE("Tensor fill", "[TensorImpl][fill]") {
concatenatedTensor->getImpl()->copy(myTensor1->getImpl()->rawPtr(), 5, 0);
concatenatedTensor->getImpl()->copy(myTensor2->getImpl()->rawPtr(), 5, 5);
concatenatedTensor->getImpl()->copy(myTensor3->getImpl()->rawPtr(), 5, 10);
+ REQUIRE_THROWS(concatenatedTensor->getImpl()->copy(myTensor3->getImpl()->rawPtr(), 5, 11));
// concatenatedTensor->print();
std::shared_ptr<Tensor> expectedTensor= std::make_shared<Tensor>(Array2D<int, 3, 5>{