OV 2022 Converting SavedModel with Model Optimizer

[Afiq]

I am trying to converting my SavedModel using Model Optimizer, but I encounter this error.

 OV  version- 2022.1.0.643

(plastic) itxotic@itxotic:/media/itxotic/HDD/emoji-project/Bare Electrodes/DL$ mo --saved_model_dir model 
Model Optimizer arguments:
Common parameters:
	- Path to the Input Model: 	None
	- Path for generated IR: 	/media/itxotic/HDD/emoji-project/Bare Electrodes/DL/.
	- 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: 	/opt/intel/openvino_2022/python/python3.8/openvino
OpenVINO runtime version: 	2022.1.0-7019-cdb9bec7210-releases/2022/1
Model Optimizer version: 	2022.1.0-7019-cdb9bec7210-releases/2022/1
[ WARNING ]  Failed to parse a tensor with Unicode characters. Note that Inference Engine does not support string literals, so the string constant should be eliminated from the graph.
[ WARNING ]  Failed to parse a tensor with Unicode characters. Note that Inference Engine does not support string literals, so the string constant should be eliminated from the graph.
[ WARNING ]  The model contains input(s) with partially defined shapes: name="serving_default_input_1" shape="[-1, 224, 224, 3]". Starting from the 2022.1 release the Model Optimizer can generate an IR with partially defined input shapes ("-1" dimension in the TensorFlow model or dimension with string value in the ONNX model). Some of the OpenVINO plugins require model input shapes to be static, so you should call "reshape" method in the Inference Engine and specify static input shapes. For optimal performance, it is still recommended to update input shapes with fixed ones using "--input" or "--input_shape" command-line parameters.
[ ERROR ]  Cannot infer shapes or values for node "StatefulPartitionedCall".
[ ERROR ]  Error converting shape to a TensorShape: Failed to convert 'masked_array(data=[--, 224, 224, 3],
             mask=[ True, False, False, False],
       fill_value=-1000000007)' to a shape: 'masked'could not be converted to a dimension. A shape should either be single dimension (e.g. 10), or an iterable of dimensions (e.g. [1, 10, None])..
[ ERROR ]  
[ ERROR ]  It can happen due to bug in custom shape infer function <function tf_native_tf_node_infer at 0x7ff1ef133310>.
[ ERROR ]  Or because the node inputs have incorrect values/shapes.
[ ERROR ]  Or because input shapes are incorrect (embedded to the model or passed via --input_shape).
[ ERROR ]  Run Model Optimizer with --log_level=DEBUG for more information.
[ ERROR ]  Exception occurred during running replacer "REPLACEMENT_ID" (<class 'openvino.tools.mo.middle.PartialInfer.PartialInfer'>): Stopped shape/value propagation at "StatefulPartitionedCall" node. 
 For more information please refer to Model Optimizer FAQ, question #38. (https://docs.openvino.ai/latest/openvino_docs_MO_DG_prepare_model_Model_Optimizer_FAQ.html?question=38#question-38)

[Zulkifli_Intel]

Hello Afiq,

Thank you for reaching out to us.

We do provide documentation on how to convert a TensorFlow 2 model in SavedModel Format that you can refer to.

Please share with us your model, the MO command used to convert the model, and the link to the original model for replication purposes. 

Sincerely,

Zulkifli

[Afiq]

Link to the Saved_model : https://1drv.ms/u/s!Ajnxd5Cr74oWkcsNlGQ4es0p5sxeYQ?e=A5A6LW

MO command used :

mo --saved_model_dir model 

[Zulkifli_Intel]

Hello Afiq,

We faced the same errors when converting your model to IR.

Can you share with us the link to the original model or the source of your model?

Sincerely,

Zulkifli

[Afiq]

model=tf.keras.applications.EfficientNetB0(
    include_top=True,
    weights=None,
    pooling=max,
    classes=2,
    classifier_activation="softmax"
)

i got the pretrained model by running this line of code

[Zulkifli_Intel · Accepted solution]

Hello Afiq,

Sorry for the delay in response.

OpenVINO does not yet support this model/topology, our development team is working on it. See the release notes for new features and improvements in OpenVINO.

For your reference, Intel provides pre-trained EfficientNet models from the Open Model Zoo that may be of interest to you.

• efficientnet-b0
• efficientnet-v2-b0
• efficientnet-v2-s

Sincerely,

Zulkifli 

[Afiq]

I am using efficientnet b0, shouldn't it be able to support it?

[Zulkifli_Intel]

Hello Afiq,

It's not that the entire model topology is not supported, but it is more related to layers in the model that are not yet compatible with Model Optimizer architecture and our development team is working to enable this. However, we are unable to comment on any timeline of when it will be enabled.

Sincerely,

Zulkifli 

[Zulkifli_Intel]

Hello Afiq,

This thread will no longer be monitored since we have provided a solution. If you need any additional information from Intel, please submit a new question

Sincerely,

Zulkifli