From cb4ce83ff1eff145085dab94537ec5006817aa53 Mon Sep 17 00:00:00 2001
From: idealbuq <iryna.dealbuquerquesilva@cea.fr>
Date: Tue, 1 Oct 2024 09:51:45 +0000
Subject: [PATCH] Added exception for non recognized file format for writing
trainable parameters in external file
---
aidge_core/show_graphview.py | 58 ++++++++++++++++++++----------------
1 file changed, 33 insertions(+), 25 deletions(-)
diff --git a/aidge_core/show_graphview.py b/aidge_core/show_graphview.py
index 94669110a..dfc0acc45 100644
--- a/aidge_core/show_graphview.py
+++ b/aidge_core/show_graphview.py
@@ -104,14 +104,14 @@ def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_e
# Check if my node is a metaop
attributes_dict = {}
if isinstance(node.get_operator(), aidge_core.MetaOperator_Op):
- attributes_dict['micro_graph'] = []
- for micro_node in node.get_operator().get_micro_graph().get_nodes():
- 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)
+ attributes_dict['micro_graph'] = []
+ for micro_node in node.get_operator().get_micro_graph().get_nodes():
+ 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)
else:
node_attr_dict = _retrieve_operator_attrs(node)
@@ -120,26 +120,34 @@ def _create_dict(ordered_nodes : list[aidge_core.Node], write_trainable_params_e
node_dict['attributes'] = attributes_dict
if node.type() == 'Producer':
- if write_trainable_params_ext and params_file_format=='npz':
- np.savez_compressed(os.path.join(path_trainable_params, node.name()), **{node.name() : node.get_operator().get_output(0)})
- node_dict['tensor_data'] = os.path.join(path_trainable_params, node.name() + '.npz')
-
- elif write_trainable_params_ext and params_file_format=='json':
- tensor = np.array(node.get_operator().get_output(0))
- tensor_dict = {
- node.name() :
- {
- 'dims' : tensor.shape,
- 'data_type' : str(tensor.dtype),
- 'tensor_data' : tensor.tolist()
- }
- }
+ if write_trainable_params_ext:
+
+ params_file_format.casefold()
+
+ if params_file_format=='npz':
+ np.savez_compressed(os.path.join(path_trainable_params, node.name()), **{node.name() : node.get_operator().get_output(0)})
+ node_dict['tensor_data'] = os.path.join(path_trainable_params, node.name() + '.npz')
+
+ elif params_file_format=='json':
+ tensor = np.array(node.get_operator().get_output(0))
+ tensor_dict = {
+ node.name() :
+ {
+ 'dims' : tensor.shape,
+ 'data_type' : str(tensor.dtype),
+ 'tensor_data' : tensor.tolist()
+ }
+ }
- with open(os.path.join(path_trainable_params, node.name() + '.json'), 'w') as fp:
- json.dump(tensor_dict, fp, indent=4)
+ with open(os.path.join(path_trainable_params, node.name() + '.json'), 'w') as fp:
+ json.dump(tensor_dict, fp, indent=4)
- node_dict['tensor_data'] = os.path.join(path_trainable_params, node.name() + '.json')
+ node_dict['tensor_data'] = os.path.join(path_trainable_params, node.name() + '.json')
+ 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()
--
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