OpenVino 2018R5 Model Optimizer error while converting the pre trained model

[SUGUMAR__DINESHKUMAR]

Hi 

I have a pretrained model developed using tensor frame work. When I tried to use the model optimizer to convert the model for FPGA inference. I am facing the below error. Please help to review the error message and let me know your feedback to resolve this issue. 

Model Optimizer arguments:

Common parameters:

               - Path to the Input Model:                /home/macnica/Desktop/bkav_pretrained_model/frozen_inference_graph.pb

               - Path for generated IR:                /opt/intel/computer_vision_sdk_2018.5.455/deployment_tools/model_optimizer/.

               - IR output name:            frozen_inference_graph

               - 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:   [1,1,1,3]

               - Mean values:   Not specified

               - Scale values:    Not specified

               - Scale factor:     Not specified

               - Precision of IR:               FP32

               - Enable fusing: True

               - Enable grouped convolutions fusing:     True

               - Move mean values to preprocess section:          False

               - Reverse input channels:             True

TensorFlow specific parameters:

               - Input model in text protobuf format:    False

               - Offload unsupported operations:           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

               - Operations to offload: None

               - Patterns to offload:      None

               - Use the config file:        None

Model Optimizer version:             1.5.12.49d067a0

/opt/intel/computer_vision_sdk_2018.5.455/deployment_tools/model_optimizer/mo/ops/slice.py:111: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.

  value = value[slice_idx]

[ ERROR ]  Shape is not defined for output 0 of "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Slice_1".

[ ERROR ]  Cannot infer shapes or values for node "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Slice_1".

[ ERROR ]  Not all output shapes were inferred or fully defined for node "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Slice_1".

 For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #40.

[ ERROR ] 

[ ERROR ]  It can happen due to bug in custom shape infer function <function Slice.infer at 0x7f20449d92f0>.

[ 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 ]  Stopped shape/value propagation at "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Slice_1" node.

 For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #38.

thanks and regards

Dineshkumar 

[Shubha_R_Intel]

Dear SUGUMAR, DINESHKUMAR,

You are using an extremely old version of OpenVino. We are now on 2019R1.1 . Please upgrade and try again,

Thanks,

Shubha

[SUGUMAR__DINESHKUMAR]

Dear Shubha

thanks for your suggestion. 

I have tried using Openvino2019R1 and got the same error. Attached is the error message. please help to review and let me know your feedback. 

thanks and regards

Dineshkumar S

[SUGUMAR__DINESHKUMAR]

Attached is the error message

[Manisha_B_]

Hi,

I am also taking the same approach I am also getting the same error while converting the pre-trained model in ssd_mobilenet_v2_coco_2018_03_29. How to deal with it

C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer>python mo_tf.py --input_model "C:\tensorflow2\models\research\object_detection\inference_graph\frozen_inference_graph.pb" --tensorflow_use_custom_operations_config "C:\Users\manisha\Desktop\ssd\ssd_v2_support.json" --tensorflow_object_detection_api_pipeline_config "C:\tensorflow2\models\research\object_detection\inference_graph\pipeline.config" --batch 1
Model Optimizer arguments:
Common parameters:
        - Path to the Input Model:      C:\tensorflow2\models\research\object_detection\inference_graph\frozen_inference_graph.pb
        - Path for generated IR:        C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\.
        - IR output name:       frozen_inference_graph
        - Log level:    ERROR
        - Batch:        1
        - Input layers:         Not specified, inherited from the model
        - Output layers:        Not specified, inherited from the model
        - Input shapes:         Not specified, inherited from the model
        - Mean values:  Not specified
        - Scale values:         Not specified
        - Scale factor:         Not specified
        - Precision of IR:      FP32
        - Enable fusing:        True
        - Enable grouped convolutions fusing:   True
        - Move mean values to preprocess section:       False
        - Reverse input channels:       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:  C:\tensorflow2\models\research\object_detection\inference_graph\pipeline.config
        - Operations to offload:        None
        - Patterns to offload:  None
        - Use the config file:  C:\Users\manisha\Desktop\ssd\ssd_v2_support.json
Model Optimizer version:        2019.1.1-83-g28dfbfd
[ WARNING ]
Detected not satisfied dependencies:
        tensorflow: installed: 1.12.0, required: 1.12
        test-generator: installed: 0.1.2, required: 0.1.1

Please install required versions of components or use install_prerequisites script
C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\install_prerequisites\install_prerequisites_tf.bat
Note that install_prerequisites scripts may install additional components.
The Preprocessor block has been removed. Only nodes performing mean value subtraction and scaling (if applicable) are kept.
[ ERROR ]  Cannot infer shapes or values for node "Postprocessor/Cast".
[ ERROR ]  0
[ ERROR ]
[ ERROR ]  It can happen due to bug in custom shape infer function <function Cast.infer at 0x00000169975F5950>.
[ 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 'extensions.middle.PartialInfer.PartialInfer'>): Stopped shape/value propagation at "Postprocessor/Cast" node.
 For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #38.

Can anybody please help

[Shubha_R_Intel]

Dear Manisha B.,

Please install required versions of components or use install_prerequisites script

in your output is not normal. Please install all prerequisites first under deployment_tools\model_optimizer\install_prerequisites.

Thanks,

Shubha

[Dar__Nadeem]

Hi Shubha,

I am getting same error, pleaase see the screen capture below. I have tesed my setup with the demo script ".\demo_security_barrier_camera.bat -d MYRIAD" and it works fine. I have tried three diffent models with my training data but all fail with the same error. Could you please check the error below and help? rregards, Nadeem

[Shubha_R_Intel]

Dear SUGUMAR, DINESHKUMAR

 in line 57 of ssd_v2_support.json you will see this:

 "Postprocessor/ToFloat"

Please replace it with "Postprocessor/Cast"

and try again. Your issue doesn't have anything to do with FPGA actually. The Tensorflow Object Detection API models have changed and now the shipped ssd_v2_support.json doesn't match.

Let me know if this works for you.

Thanks,

Shubha

[Stangier__Lorenz]

Hello, I have the exact same issue. Unfortunately, for me your proposed file change did not work. But it changed the error from the original error to:

[ ERROR ]  Exception occurred during running replacer "ObjectDetectionAPISSDPostprocessorReplacement" (<class 'extensions.front.tf.ObjectDetectionAPI.ObjectDetectionAPISSDPostprocessorReplacement'>): The matched sub-graph contains network input node "image_tensor".
 For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #75.

It would be great if you could help me.

Kind regards,
Lorenz

[Shubha_R_Intel]

Dear Stangier, Lorenz,

What version of Tensorflow are you using ? Can you kindly upgrade to 1.13 and try again ?

Thanks,

Shubha

[Dar__Nadeem]

Great, the proposed fix ( "Postprocessor/ToFloat"  replacing with "Postprocessor/Cast") works for me.

Many thanks Shubha for the support.

[Shubha_R_Intel]

Dear Dar, Nadeem,

So glad it worked for you ! Thanks for reporting back your success and sharing with the OpenVino community.

Shubha