def convert(cls, op_name: str, params: Dict, weights: Dict = None):
"""
Convert third party operation's param into MindSpore operation.
Args:
op_name (str): Operation name in ONNX.
params (dict): Params in onnx.
weights (dict): Weights in onnx.
Returns:
Tuple[str, dict, dict], operation name and params and settings.
"""
global TABLE
module_name = TABLE.get(op_name)
if not module_name:
return None, dict(), None, dict()
pos = module_name.rfind(".")
try:
converter = getattr(importlib.import_module(module_name[:pos]),
module_name[pos + 1:])
op_name_converter = getattr(converter, GET_OP_NAME)
params_converter = getattr(converter, GET_OP_PARAMS)
weights_converter = getattr(converter, GET_OP_WEIGHTS)
template_generator = getattr(converter, GET_OP_TEMPLATE)
except (ModuleNotFoundError,) as e:
# If mapper can not be found, then skip it.
err_msg = f"Converting {op_name} failed, see {str(e)}"
log.error(err_msg)
return None, None, None, None
try:
converter_name = op_name_converter(params=params, weights=weights, op_name=op_name)
converted_params = params_converter(params=params, weights=weights)
if "input_shape" in converted_params:
converted_params.pop("input_shape")
if "output_shape" in converted_params:
converted_params.pop("output_shape")
# set to converted_weights to enable weight migration
converted_weights = weights_converter(weights=weights) if weights else dict()
code_template, exchange_msg, outputs_list, outputs_mapping = template_generator(
operation=converter_name,
converted_params=converted_params,
raw_params=params,
weights=weights,
trainable_params=converted_weights
)
except (AttributeError, KeyError, ValueError, TypeError, IndexError) as e:
err_msg = f"Converting {op_name} failed, see {str(e)}"
log.error(err_msg)
code_template, exchange_msg, outputs_list, outputs_mapping = template_generator(
operation=op_name,
params=params,
weights=weights
)
return code_template, exchange_msg, outputs_list, outputs_mapping
def create(cls, graph):
"""
Factory method of hierarchical tree.
Args:
graph: Graph obj.
Returns:
HierarchicalTree, tree.
"""
tree = HierarchicalTree()
node_scope_name = dict()
for _, node_name in enumerate(graph.nodes_in_topological_order):
node_inst = graph.get_node(node_name)
node_input = graph.get_input_shape(node_name)
node_output = graph.get_output_shape(node_name)
if not node_input:
err_msg = f"This model is not supported now. " \
f"Cannot find {node_name}'s input shape."
error = NodeInputMissing(err_msg)
log.error(str(error))
raise error
if isinstance(node_inst, OnnxGraphNode):
node_name_with_scope = _tf_model_node_name_reformat(node_inst, node_name)
node_scope_name[node_name] = node_name_with_scope
node_name = node_name_with_scope
node_inst.add_input_and_output_shape(node_input, node_output)
tree.insert(node_inst, node_name)
if node_scope_name:
return tree, node_scope_name
return tree
def _f(graph_path: str, sample_shape: tuple,
output_folder: str, report_folder: str = None):
# Check whether pytorch is installed.
if not find_spec("torch") or not find_spec("onnx") or not find_spec("onnxruntime"):
error = RuntimeIntegrityError(f"PyTorch, onnx(>={ONNX_MIN_VER}) and "
f"onnxruntime(>={ONNXRUNTIME_MIN_VER}) "
f"are required when using graph based "
f"scripts converter, and PyTorch version must "
f"be consisted with model generation runtime.")
log.error(error)
log_console.error("\n")
log_console.error(str(error))
log_console.error("\n")
sys.exit(0)
onnx = import_module("onnx")
ort = import_module("onnxruntime")
if not lib_version_satisfied(getattr(onnx, "__version__"), ONNX_MIN_VER) \
or not lib_version_satisfied(getattr(ort, "__version__"), ONNXRUNTIME_MIN_VER):
error = RuntimeIntegrityError(
f"onnx(>={ONNX_MIN_VER}) and "
f"onnxruntime(>={ONNXRUNTIME_MIN_VER}) are required when using graph "
f"based scripts converter for Pytorch conversion."
)
log.error(error)
log_console.error("\n")
log_console.error(str(error))
log_console.error("\n")
sys.exit(0)
func(graph_path=graph_path, sample_shape=sample_shape,
output_folder=output_folder, report_folder=report_folder)
def initialize(self):
"""Initialize the OnnxDataLoader."""
# check init conditions met
if not self._check_initialization():
err = ModuleNotFoundError("Unable to Find ONNX Model")
log.error(str(err))
log.exception(err)
# 1. parse all nodes
self._parse_nodes()
# 2. parse value info (incl. node output shape)
if self._is_infer_shape:
try:
self._infer_model()
self._parse_value_info()
self._parse_node_output_shape()
except Exception as e:
log.error(str(e))
log.exception(e)
raise e
# 3. parse all tensors
self._parse_tensors()
# 4. Optimize graph to eliminate some nodes.
self._find_nodes_to_be_eliminated()
# 5. build nodes connections
self.build_nodes_connection()
# 6. Run onnx model to fetch actual value of eliminated nodes.
self._fetch_eliminated_nodes_value()
def parse(cls, model_path: str, **kwargs):
"""
Parser pytorch graph.
Args:
model_path (str): Model file path.
Returns:
object, torch model.
"""
if not os.path.exists(model_path):
error = FileNotFoundError("`model_path` must be assigned with "
"an existed file path.")
log.error(str(error))
raise error
try:
onnx_model_sim = cls._convert_pytorch_graph_to_onnx(
model_path, kwargs['input_nodes'], opset_version=11)
return onnx_model_sim
except ModuleNotFoundError:
error_msg = "Cannot find model scripts in system path, " \
"set `--project_path` to the path of model scripts folder correctly."
error = ModuleNotFoundError(error_msg)
raise error
def main_graph_base_converter(file_config):
"""
The entrance for converter, script files will be converted.
Args:
file_config (dict): The config of file which to convert.
"""
graph_path = file_config['model_file']
frame_type = get_framework_type(graph_path)
if frame_type == FrameworkType.PYTORCH.value:
graph_based_converter_pytorch_to_ms(
graph_path=graph_path,
sample_shape=file_config['shape'],
output_folder=file_config['outfile_dir'],
report_folder=file_config['report_dir'])
elif frame_type == FrameworkType.TENSORFLOW.value:
check_params = ['input_nodes', 'output_nodes']
check_params_exist(check_params, file_config)
graph_based_converter_tf_to_ms(
graph_path=graph_path,
sample_shape=file_config['shape'],
input_nodes=file_config['input_nodes'],
output_nodes=file_config['output_nodes'],
output_folder=file_config['outfile_dir'],
report_folder=file_config['report_dir'])
else:
error_msg = "Get UNSUPPORTED model."
error = UnknownModel(error_msg)
log.error(str(error))
raise error
def _trace_torch_graph(self, input_shape):
"""
Trace torch computational graph.
Args:
input_shape (tuple): Shape.
Returns:
object, pytorch graph.
"""
import torch
from torch.onnx import OperatorExportTypes
from .torch_utils import OverloadTorchModuleTemporarily
from .torch_utils import create_autograd_variable
from .torch_utils import onnx_tracer
batched_sample = create_autograd_variable(torch.rand(*input_shape))
try:
# Assign execution mode to eval.
self.model.eval()
with OverloadTorchModuleTemporarily() as _:
# In pytorch higher version, trace function has a known.
graph = onnx_tracer(self.model, batched_sample,
OperatorExportTypes.ONNX)
return graph
except RuntimeError as error:
log.error(str(error))
log.exception(error)
raise error
def _run(in_files, model_file, shape, input_nodes, output_nodes, out_dir,
report, project_path):
"""
Run converter command.
Args:
in_files (str): The file path or directory to convert.
model_file(str): The pytorch .pth to convert on graph based schema.
shape(list): The input tensor shape of module_file.
input_nodes(str): The input node(s) name of Tensorflow model, split by ','.
output_nodes(str): The output node(s) name of Tensorflow model, split by ','.
out_dir (str): The output directory to save converted file.
report (str): The report file path.
project_path(str): Pytorch scripts project path.
"""
if in_files:
files_config = {
'root_path': in_files,
'in_files': [],
'outfile_dir': out_dir,
'report_dir': report if report else out_dir
}
if os.path.isfile(in_files):
files_config['root_path'] = os.path.dirname(in_files)
files_config['in_files'] = [in_files]
else:
for root_dir, _, files in os.walk(in_files):
for file in files:
files_config['in_files'].append(
os.path.join(root_dir, file))
main(files_config)
log_console.info("\n")
log_console.info("MindConverter: conversion is completed.")
log_console.info("\n")
elif model_file:
file_config = {
'model_file': model_file,
'shape': shape if shape else [],
'input_nodes': input_nodes,
'output_nodes': output_nodes,
'outfile_dir': out_dir,
'report_dir': report if report else out_dir
}
if project_path:
paths = sys.path
if project_path not in paths:
sys.path.append(project_path)
main_graph_base_converter(file_config)
log_console.info("\n")
log_console.info("MindConverter: conversion is completed.")
log_console.info("\n")
else:
error_msg = "`--in_file` and `--model_file` should be set at least one."
error = FileNotFoundError(error_msg)
log.error(str(error))
log.exception(error)
raise error
def parse(cls, model_path: str, **kwargs):
"""
Parser pytorch graph.
Args:
model_path (str): Model file path.
Returns:
object, torch model.
"""
torch = import_module("torch")
if not os.path.exists(model_path):
error = FileNotFoundError("`model_path` must be assigned with "
"an existed file path.")
log.error(str(error))
raise error
try:
if torch.cuda.is_available():
model = torch.load(f=model_path)
else:
model = torch.load(f=model_path, map_location="cpu")
except ModuleNotFoundError:
error_msg = "Cannot find model scripts in system path, " \
"set `--project_path` to the path of model scripts folder correctly."
error = ModuleNotFoundError(error_msg)
raise error
return model
def save_source_files(self, out_folder: str, mapper: Mapper,
model_name: str,
report_folder: str = None,
scope_name_map: dict = None) -> NoReturn:
"""
Save source codes to target folder.
Args:
report_folder (str): Report folder.
mapper (Mapper): Mapper of third party framework and mindspore.
model_name(str): Name of Converted model.
out_folder (str): Output folder.
scope_name_map(str): Scope name map of tensorflow.
"""
if scope_name_map:
self._scope_name_map = scope_name_map
try:
self._adjust_structure()
code_fragments = self._generate_codes(mapper)
except (NodeInputTypeNotSupportError, ScriptGenerationError, ReportGenerationError) as e:
log.error("Error occur when generating codes.")
raise e
save_code_file_and_report(model_name, code_fragments, out_folder, report_folder)
def parse(cls, model_path: str, **kwargs):
"""
Parse TF Computational Graph File (.pb)
Args:
model_path (str): Model file path.
Returns:
object, ONNX model.
"""
onnx_utils = import_module(
"mindinsight.mindconverter.graph_based_converter.third_party_graph.onnx_utils")
convert_tf_graph_to_onnx = getattr(onnx_utils, "convert_tf_graph_to_onnx")
tf_input_nodes = kwargs.get('input_nodes')
tf_output_nodes = kwargs.get('output_nodes')
if not os.path.exists(model_path):
error = FileNotFoundError("`model_path` must be assigned with "
"an existed file path.")
log.error(str(error))
raise error
input_nodes = ",".join(tf_input_nodes.keys())
output_nodes = ",".join(tf_output_nodes)
invalid_inputs = TFGraphParser.invalid_nodes_name(input_nodes)
invalid_outputs = TFGraphParser.invalid_nodes_name(output_nodes)
if invalid_inputs:
raise ModelLoadingError(f"Invalid Input Node Name Found: {', '.join(invalid_inputs)}")
if invalid_outputs:
raise ModelLoadingError(f"Invalid Output Node Name Found: {', '.join(invalid_outputs)}")
model = convert_tf_graph_to_onnx(model_path,
model_inputs=input_nodes,
model_outputs=output_nodes)
return model
def initialize(self):
"""Initialize the OnnxDataLoader."""
# Parse ONNX Graph level info
self._parse_graph()
# 1. parse all tensors
self._parse_tensors()
# 2. parse all nodes, note that parse tensors must be done as nodes require tensor info
# to process the node weight sharing.
self._parse_nodes()
# 3. parse value info (incl. node output shape)
if self._is_infer_shape:
try:
self._infer_model()
self._parse_value_info()
self._parse_node_output_shape()
except Exception as e:
log.error(str(e))
log.exception(e)
raise e
# 4. Optimize graph to eliminate some nodes.
self._find_nodes_to_be_eliminated()
# 5. build nodes connections
self.build_nodes_connection()
# 6. Run onnx model to fetch actual value of eliminated nodes.
self._fetch_eliminated_nodes_value()
def load_metadata(**kwargs):
"""
Load graph metadata.
"""
err_msg = "class `PyTorchGraph` has not implemented " \
"`load_metadata()`."
log.error(err_msg)
raise NotImplementedError(err_msg)
def _f(arch, mapper):
try:
output = func(arch, mapper=mapper)
except cls.raise_from() as e:
error = cls(msg=msg)
log.error(msg)
log.exception(e)
raise error from e
return output
def _f(arch, model_path, **kwargs):
try:
output = func(arch, model_path=model_path, **kwargs)
except cls.raise_from() as e:
error = cls(msg=msg)
log.error(msg)
log.exception(e)
raise error from e
return output
def __new__(cls, *args, **kwargs):
"""Control the create action of graph."""
model_param = args[0] if args else kwargs.get(
cls._REQUIRED_PARAM_OF_MODEL)
if not model_param:
error = ValueError(f"`{cls._REQUIRED_PARAM_OF_MODEL}` "
f"can not be None.")
log.error(str(error))
log.exception(error)
raise error
return super(BaseGraph, cls).__new__(cls)
def check_params_exist(params: list, config):
"""Check params exist."""
miss_param_list = ''
for param in params:
if not config.get(param) or not config[param]:
miss_param_list = ', '.join(
(miss_param_list, param)) if miss_param_list else param
if miss_param_list:
error = ParamMissError(miss_param_list)
log.error(str(error))
raise error
def _run(in_files, model_file, shape, input_nodes, output_nodes, out_dir,
report):
"""
Run converter command.
Args:
in_files (str): The file path or directory to convert.
model_file (str): The model to convert on graph based schema.
shape (Sequence[tuple]): The input tensor shape of the model.
input_nodes (Sequence[str]): The input node(s) name of model.
output_nodes (Sequence[str]): The output node(s) name of model.
out_dir (str): The output directory to save converted file.
report (str): The report file path.
"""
if in_files:
files_config = {
'root_path': in_files,
'in_files': [],
'outfile_dir': out_dir,
'report_dir': report if report else out_dir
}
if os.path.isfile(in_files):
files_config['root_path'] = os.path.dirname(in_files)
files_config['in_files'] = [in_files]
else:
for root_dir, _, files in os.walk(in_files):
for file in files:
files_config['in_files'].append(
os.path.join(root_dir, file))
main(files_config)
log_console.info("MindConverter: conversion is completed.")
elif model_file:
file_config = {
'model_file': model_file,
'shape': shape if shape else [],
'input_nodes': input_nodes,
'output_nodes': output_nodes,
'outfile_dir': out_dir,
'report_dir': report if report else out_dir
}
main_graph_base_converter(file_config)
log_console.info("MindConverter: conversion is completed.")
else:
error_msg = "`--in_file` and `--model_file` should be set at least one."
error = FileNotFoundError(error_msg)
log.error(str(error))
log_console.error(str(error))
sys.exit(-1)
def convert(cls, op_name: str, params: Dict, weights: Dict = None):
"""
Convert third party operation's param into MindSpore operation.
Args:
op_name (str): Operation name in ONNX.
params (dict): Params in onnx.
weights (dict): Weights in onnx.
Returns:
Tuple[str, dict, dict], operation name and params and settings.
"""
global TABLE
module_name = TABLE.get(op_name)
if not module_name:
return None, dict(), None, dict()
pos = module_name.rfind(".")
try:
converter = getattr(importlib.import_module(module_name[:pos]),
module_name[pos + 1:])
op_name_converter = getattr(converter, GET_OP_NAME)
params_converter = getattr(converter, GET_OP_PARAMS)
weights_converter = getattr(converter, GET_OP_WEIGHTS)
settings_converter = getattr(converter, GET_OP_SETTINGS)
except (ModuleNotFoundError, ) as e:
# If mapper can not be found, then skip it.
err_msg = f"Converting {op_name} failed, see {str(e)}"
log.error(err_msg)
return None, dict(), None, dict()
try:
converter_name = op_name_converter(params=params,
weights=weights,
op_name=op_name)
converted_params = params_converter(params=params, weights=weights)
converted_weights = weights_converter(
weights=weights) if weights else dict()
converted_params.update(converted_weights)
converted_settings = settings_converter(params=params,
weights=weights)
except (AttributeError, KeyError, ValueError, TypeError,
IndexError) as e:
err_msg = f"Converting {op_name} failed, see {str(e)}"
log.error(err_msg)
return None, dict(), None, dict()
return converter_name, converted_params, converted_settings, converted_weights
def _f(*args, **kwargs):
try:
output = func(*args, **kwargs)
except cls.raise_from() as e:
error = cls(msg=msg)
error_code = e.error_code() if isinstance(e, MindConverterException) else None
error.root_exception_error_code = error_code
log.error(msg)
log.exception(e)
raise error
except Exception as e:
log.error(msg)
log.exception(e)
raise e
return output
def save_intermediate_graph(dataloader, output_folder):
"""
Save intermediate graph and topological order into output_folder.
Args:
dataloader (OnnxDataLoader): Dataloader inst.
output_folder (str): Output folder path.
"""
node_topo_order = []
placeholder_width = 30
for node_name, node in dataloader.nodes_dict.items():
row = f"{node.op_type.ljust(placeholder_width)} {node_name}\n"
node_topo_order.append(row)
# Import onnx lib.
onnx = import_module("onnx")
out_folder = os.path.realpath(output_folder)
if not os.path.exists(out_folder):
os.makedirs(out_folder, RWX_MODE_FOR_OWNER)
graph_file = os.path.join(out_folder, "graph.onnx")
topological_order_file = os.path.join(out_folder, "topological_order.txt")
if os.path.exists(topological_order_file):
err_msg = f"{os.path.basename(topological_order_file)} already exists."
log.error(err_msg)
raise FileExistsError(err_msg)
if os.path.exists(graph_file):
err_msg = f"{os.path.basename(graph_file)} already exists."
log.error(err_msg)
raise FileExistsError(err_msg)
# Write topological order to disk.
with os.fdopen(os.open(topological_order_file, WRITE_FLAGS, stat.S_IRUSR),
"w") as topo_file:
topo_file.writelines(node_topo_order)
try:
# Save graph to disk.
onnx.save_model(dataloader.inferred_model, graph_file)
os.chmod(graph_file, RW_MODE_FOR_OWNER)
except (IOError, OSError, FileExistsError) as e:
if os.path.exists(topological_order_file):
os.remove(topological_order_file)
if os.path.exists(graph_file):
os.remove(graph_file)
raise e
def get_framework_type(model_path):
"""Get framework type."""
try:
with open(model_path, 'rb') as f:
if f.read(
BINARY_HEADER_PYTORCH_BITS) == BINARY_HEADER_PYTORCH_FILE:
framework_type = FrameworkType.PYTORCH.value
else:
framework_type = FrameworkType.TENSORFLOW.value
except IOError:
error_msg = "Get UNSUPPORTED model."
error = UnknownModel(error_msg)
log.error(str(error))
raise error
return framework_type
def visit_Call(self, node):
"""Callback function when visit AST tree"""
code = pasta.dump(node)
api_name = pasta.dump(node.func)
# The parent node first call is equal to this node, skip when parent node is replaced.
# This scenario occurs, for example, when out.view(out.size(0), -1) is first converted to
# P.Reshape()(out, (out.size(0). -1)), will skip P.Reshape() in following visiting.
# Access from the penultimate element in reverse order.
for parent_node in self._stack[-2::-1]:
if parent_node in self._new_call_nodes and pasta.dump(
parent_node).startswith(api_name):
return
parent = self._stack[-2]
new_node = None
new_code = code
matched_api_name, match_case = self.match_api(
node.func, self._is_forward_function)
if match_case in [
ApiMatchingEnum.API_INFER, ApiMatchingEnum.API_MATCHED
]:
new_node = self._convert_call(node, matched_api_name)
elif match_case in [
ApiMatchingEnum.API_STANDARD, ApiMatchingEnum.API_FOUND
]:
self._process_log.warning(node.lineno, node.col_offset,
LOG_FMT_NOT_CONVERT % (api_name, ''))
else:
pass
if parent and new_node:
update_line_col = _LineColEditVisitor()
update_line_col.update(new_node, node)
pasta.ast_utils.replace_child(parent, node, new_node)
self._new_call_nodes.append(new_node)
node = new_node
self._stack[-1] = node
try:
self.generic_visit(node)
except Exception:
logger.error('original code:%s, new code:%s',
code,
new_code,
exc_info=True)
raise
def _f(graph_path: str,
sample_shape: tuple,
output_folder: str,
report_folder: str = None):
# Check whether pytorch is installed.
if not find_spec("torch"):
error = ModuleNotFoundError(
"PyTorch is required when using graph based "
"scripts converter, and PyTorch vision must "
"be consisted with model generation runtime.")
log.error(str(error))
log.exception(error)
raise error
func(graph_path=graph_path,
sample_shape=sample_shape,
output_folder=output_folder,
report_folder=report_folder)
def _f(graph_path: str,
sample_shape: tuple,
output_folder: str,
report_folder: str = None,
input_nodes: str = None,
output_nodes: str = None):
# Check whether tensorflow is installed.
if not find_spec("tensorflow") or not find_spec("tf2onnx"):
error = ModuleNotFoundError(
"Tensorflow and tf2onnx are required when using "
"graph based scripts converter.")
log.error(str(error))
raise error
func(graph_path=graph_path,
sample_shape=sample_shape,
output_folder=output_folder,
report_folder=report_folder,
input_nodes=input_nodes,
output_nodes=output_nodes)
def _check_input_shape(input_shape):
"""
Check input shape.
Args:
input_shape (tuple): Input tensor shape.
"""
if not input_shape:
err_msg = "`input_shape` can not be None."
log.error(err_msg)
raise ValueError(err_msg)
for item in input_shape:
if not isinstance(item, int):
err_msg = "Only support model with one input now, " \
"and each shape value in `input_shape` should be int."
log.error(err_msg)
raise ValueError(err_msg)
def get_framework_type(model_path):
"""Get framework type."""
try:
with open(model_path, 'rb') as f:
if f.read(
BINARY_HEADER_PYTORCH_BITS) == BINARY_HEADER_PYTORCH_FILE:
framework_type = FrameworkType.PYTORCH.value
elif os.path.basename(model_path).split(
".")[-1].lower() == TENSORFLOW_MODEL_SUFFIX:
framework_type = FrameworkType.TENSORFLOW.value
else:
framework_type = FrameworkType.UNKNOWN.value
except IOError:
error_msg = "Get UNSUPPORTED model."
error = UnknownModelError(error_msg)
log.error(str(error))
raise error
return framework_type
def normalize_dict_key(d):
"""
Normalize dictionary key.
Note:
The normalization is removing :0 in each node or output name.
Args:
d (dict): Dictionary where keys are node/output names.
Returns:
dict, normalized dictionary.
"""
if not isinstance(d, (dict, OrderedDict)):
error_msg = "Error occurs in normalizing dictionary key.\
Object passed in is not a dictionary."
error = TypeError(error_msg)
log.error(error_msg)
log.exception(error)
raise error
new_d = None
if isinstance(d, dict):
new_d = {}
for key_old in d.keys():
key_new = key_old.split(':')[0]
new_d[key_new] = d.get(key_old)
if isinstance(d, OrderedDict):
new_d = OrderedDict()
for key_old in d.keys():
key_new = key_old.split(':')[0]
new_d[key_new] = d.get(key_old)
if not new_d:
error_msg = "Error occurs in normalizing dictionary key."
error = ValueError(error_msg)
log.error(error_msg)
log.exception(error)
raise error
return new_d
def _f(*args, **kwargs):
try:
res = func(*args, **kwargs)
except cls.raise_from() as e:
error = cls() if not msg else cls(msg=msg)
detail_info = str(e)
if not isinstance(e, MindConverterException):
detail_info = cls.normalize_error_msg(str(e))
log.error(error)
log_console.error("\n")
log_console.error(detail_info)
log_console.error("\n")
log.exception(e)
sys.exit(0)
except ModuleNotFoundError as e:
detail_info = "Error detail: Required package not found, please check the runtime environment."
log_console.error("\n")
log_console.error(str(e))
log_console.error(detail_info)
log_console.error("\n")
log.exception(e)
sys.exit(0)
return res
def _f(graph_path: str, sample_shape: tuple,
output_folder: str, report_folder: str = None,
input_nodes: str = None, output_nodes: str = None):
# Check whether tensorflow is installed.
if not _check_tf_installation() or not find_spec("tf2onnx") \
or not find_spec("onnx") or not find_spec("onnxruntime"):
error = RuntimeIntegrityError(
f"TensorFlow, tf2onnx(>={TF2ONNX_MIN_VER}), onnx(>={ONNX_MIN_VER}) and "
f"onnxruntime(>={ONNXRUNTIME_MIN_VER}) are required when using graph "
f"based scripts converter for TensorFlow conversion."
)
log.error(error)
log_console.error("\n")
log_console.error(str(error))
log_console.error("\n")
sys.exit(0)
onnx, tf2onnx = import_module("onnx"), import_module("tf2onnx")
ort = import_module("onnxruntime")
if not lib_version_satisfied(getattr(onnx, "__version__"), ONNX_MIN_VER) \
or not lib_version_satisfied(getattr(ort, "__version__"), ONNXRUNTIME_MIN_VER) \
or not lib_version_satisfied(getattr(tf2onnx, "__version__"), TF2ONNX_MIN_VER):
error = RuntimeIntegrityError(
f"TensorFlow, tf2onnx(>={TF2ONNX_MIN_VER}), onnx(>={ONNX_MIN_VER}) and "
f"onnxruntime(>={ONNXRUNTIME_MIN_VER}) are required when using graph "
f"based scripts converter for TensorFlow conversion."
)
log.error(error)
log_console.error("\n")
log_console.error(str(error))
log_console.error("\n")
sys.exit(0)
func(graph_path=graph_path, sample_shape=sample_shape,
output_folder=output_folder, report_folder=report_folder,
input_nodes=input_nodes, output_nodes=output_nodes)