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 onnx_lib_version_satisfied():
"""Check onnx libs version whether is satisfied."""
onnx = import_module("onnx")
ort = import_module("onnxruntime")
optimizer = import_module("onnxoptimizer.version")
if not lib_version_satisfied(getattr(ort, "__version__"),
ONNXRUNTIME_MIN_VER):
log_console.warning(
"onnxruntime's version should be greater than %s, however current version is %s.",
ONNXRUNTIME_MIN_VER, ort.__version__)
if not lib_version_satisfied(getattr(onnx, "__version__"), ONNX_MIN_VER) \
or not lib_version_satisfied(getattr(optimizer, "version"), ONNXOPTIMIZER_MIN_VER):
return False
return True
def _f(graph_path: str, input_nodes: dict, output_nodes: List[str],
output_folder: str, report_folder: str):
not_integral_error = RuntimeIntegrityError(
f"TensorFlow, "
f"{get_third_part_lib_validation_error_info(['tf2onnx', 'onnx', 'onnxruntime', 'onnxoptimizer'])} "
f"are required when using graph based scripts converter for TensorFlow conversion."
)
# Check whether tensorflow is installed.
if not _check_tf_installation() or not onnx_satisfied():
_print_error(not_integral_error)
sys.exit(0)
if not any([
check_common_dependency_integrity("tensorflow"),
check_common_dependency_integrity("tensorflow-gpu")
]):
_print_error(not_integral_error)
sys.exit(0)
tf2onnx = import_module("tf2onnx")
if not lib_version_satisfied(getattr(tf2onnx, "__version__"), TF2ONNX_MIN_VER) \
or not onnx_lib_version_satisfied():
_print_error(not_integral_error)
sys.exit(0)
func(graph_path=graph_path,
input_nodes=input_nodes,
output_nodes=output_nodes,
output_folder=output_folder,
report_folder=report_folder)
def torch_version_satisfied(output_queue):
"""Check Torch version whether is satisfied."""
satisfied = False
pattern = r"\d+\.\d+\.\d+"
torch_version = re.findall(pattern,
getattr(import_module('torch'), "__version__"))
if torch_version:
satisfied = lib_version_satisfied(torch_version[0], TORCH_MIN_VER)
output_queue.put(satisfied)
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)