Im trying to convert a image classification model built using tensorflow 2.x that uses Efficientnet b0 pretraining. However I keep receiving the same error. I also receive error if I try to convert fresh model from open model zoo in DL workbench
Error Log below
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: None
- Path for generated IR: /opt/intel/openvino_2022.1.0.643/tools/workbench/.
- IR output name: saved_model
- Log level: ERROR
- Batch: Not specified, inherited from the model
- Input layers: Not specified, inherited from the model
- Output layers: Not specified, inherited from the model
- Input shapes: Not specified, inherited from the model
- Source layout: Not specified
- Target layout: Not specified
- Layout: Not specified
- Mean values: Not specified
- Scale values: Not specified
- Scale factor: Not specified
- Precision of IR: FP32
- Enable fusing: True
- User transformations: Not specified
- Reverse input channels: False
- Enable IR generation for fixed input shape: False
- Use the transformations config file: None
Advanced parameters:
- Force the usage of legacy Frontend of Model Optimizer for model conversion into IR: False
- Force the usage of new Frontend of Model Optimizer for model conversion into IR: False
TensorFlow specific parameters:
- Input model in text protobuf format: False
- Path to model dump for TensorBoard: None
- List of shared libraries with TensorFlow custom layers implementation: None
- Update the configuration file with input/output node names: None
- Use configuration file used to generate the model with Object Detection API: None
- Use the config file: None
OpenVINO runtime found in: /home/workbench/.workbench/environments/3/lib/python3.8/site-packages/openvino
OpenVINO runtime version: 2022.1.0-7019-cdb9bec7210-releases/2022/1
Model Optimizer version: 2022.1.0-7019-cdb9bec7210-releases/2022/1
[ ERROR ] -------------------------------------------------
[ ERROR ] ----------------- INTERNAL ERROR ----------------
[ ERROR ] Unexpected exception happened.
[ ERROR ] Please contact Model Optimizer developers and forward the following information:
[ ERROR ] Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
1. Downgrade the protobuf package to 3.20.x or lower.
2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).
More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates
[ ERROR ] Traceback (most recent call last):
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/openvino/tools/mo/main.py", line 533, in main
ret_code = driver(argv)
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/openvino/tools/mo/main.py", line 489, in driver
graph, ngraph_function = prepare_ir(argv)
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/openvino/tools/mo/main.py", line 374, in prepare_ir
argv = arguments_post_parsing(argv)
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/openvino/tools/mo/main.py", line 259, in arguments_post_parsing
ret_code = check_requirements(framework=argv.framework)
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/openvino/tools/mo/utils/versions_checker.py", line 247, in check_requirements
env_setup = get_environment_setup(framework)
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/openvino/tools/mo/utils/versions_checker.py", line 225, in get_environment_setup
exec("import tensorflow")
File "<string>", line 1, in <module>
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/tensorflow/__init__.py", line 41, in <module>
from tensorflow.python.tools import module_util as _module_util
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/tensorflow/python/__init__.py", line 40, in <module>
from tensorflow.python.eager import context
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/tensorflow/python/eager/context.py", line 32, in <module>
from tensorflow.core.framework import function_pb2
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/tensorflow/core/framework/function_pb2.py", line 16, in <module>
from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/tensorflow/core/framework/attr_value_pb2.py", line 16, in <module>
from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/tensorflow/core/framework/tensor_pb2.py", line 16, in <module>
from tensorflow.core.framework import resource_handle_pb2 as tensorflow_dot_core_dot_framework_dot_resource__handle__pb2
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/tensorflow/core/framework/resource_handle_pb2.py", line 16, in <module>
from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/tensorflow/core/framework/tensor_shape_pb2.py", line 36, in <module>
_descriptor.FieldDescriptor(
File "/home/workbench/.workbench/environments/3/lib/python3.8/site-packages/google/protobuf/descriptor.py", line 560, in __new__
_message.Message._CheckCalledFromGeneratedFile()
TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
1. Downgrade the protobuf package to 3.20.x or lower.
2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).
More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates
[ ERROR ] ---------------- END OF BUG REPORT --------------
[ ERROR ] -------------------------------------------------
