def graph_based_converter_tf_to_ms(graph_path: str,
input_nodes: dict,
output_nodes: List[str],
output_folder: str,
report_folder: str = None):
"""
Tensorflow to MindSpore based on Graph.
Args:
graph_path (str): Graph file path.
input_nodes (dict): Input node(s) of the model.
output_nodes (list[str]): Output node(s) of the model.
output_folder (str): Output folder.
report_folder (str): Report output folder path.
"""
# Close unnecessary log.
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
graph_obj = GraphFactory.init(graph_path,
input_nodes=input_nodes,
output_nodes=output_nodes)
generator_inst = batch_add_nodes(graph_obj, ONNXToMindSporeMapper)
model_name = _extract_model_name(graph_path)
code_fragments = generator_inst.generate()
save_code_file_and_report(model_name, code_fragments, output_folder,
report_folder)
# Release global context.
GlobalContext.release()
def graph_based_converter_tf_to_ms(graph_path: str,
input_nodes: dict, output_nodes: List[str],
output_folder: str, report_folder: str = None,
query_result_folder: str = None):
"""
Tensorflow to MindSpore based on Graph.
Args:
graph_path (str): Graph file path.
input_nodes (dict): Input node(s) of the model.
output_nodes (list[str]): Output node(s) of the model.
output_folder (str): Output folder.
report_folder (str): Report output folder path.
query_result_folder (str): Save the optimized graph and its topological order to disk.
"""
# Close unnecessary log.
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
graph_obj = GraphFactory.init(graph_path, input_nodes=input_nodes, output_nodes=output_nodes)
if query_result_folder:
save_intermediate_graph(graph_obj.dataloader, query_result_folder)
GlobalContext.release()
return
graph_obj.build()
generator_inst = batch_add_nodes(graph_obj, ONNXToMindSporeMapper)
model_name = _extract_model_name(graph_path)
log_console.info("Code saving begins.")
code_fragments = generator_inst.generate()
save_code_file_and_report(model_name, code_fragments, output_folder, report_folder)
log_console.info("Code saving is finished.")
# Release global context.
GlobalContext.release()
def graph_based_converter_pytorch_to_ms(graph_path: str,
input_nodes: dict,
output_nodes: List[str],
output_folder: str,
report_folder: str = None):
"""
PyTorch to MindSpore based on Graph.
Args:
graph_path (str): Graph file path.
input_nodes (dict): Input node(s) of the model.
output_nodes (list[str]): Output node(s) of the model.
output_folder (str): Output folder.
report_folder (str): Report output folder path.
"""
graph_obj = GraphFactory.init(graph_path,
input_nodes=input_nodes,
output_nodes=output_nodes)
generator_inst = batch_add_nodes(graph_obj, ONNXToMindSporeMapper)
model_name = _extract_model_name(graph_path)
code_fragments = generator_inst.generate()
save_code_file_and_report(model_name, code_fragments, output_folder,
report_folder)
# Release global context.
GlobalContext.release()
def convert_according_to_user_selections(graph_obj, output_folder: str, report_folder: str = None,
user_operations: Mapping[str, Dict] = None):
"""
ONNX to MindSpore based on Graph.
Args:
graph_obj (OnnxGraph): Onnx graph object.
output_folder (str): Output folder.
report_folder (str): Report output folder path.
user_operations (dict): Record user's operations.
"""
graph_obj.generate_scope_name(user_operations)
graph_obj.build()
generator_inst = batch_add_nodes(graph_obj, ONNXToMindSporeMapper)
model_name = _extract_model_name(graph_obj.model_path)
log_console.info("Code saving begins.")
code_fragments = generator_inst.generate()
save_code_file_and_report(model_name, code_fragments, output_folder, report_folder)
log_console.info("Code saving is finished.")
# Release global context.
GlobalContext.release()