Model Optimizer arguments:
- 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: 188.8.131.52d067a0
/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.
Dear Purohit, Rushikesh S,
Did you ever resolve this ? I would like to help you with this issue but I don't have access to Dropbox. If you wish to transfer huge files (such as models) to me you can do so via Syncplicity - that is the easiest way.
You say that the model is "pre-trained". I assume this to mean that the customer did not custom train it. Is this the case ? Is it one of these models listed model optimizer supported tensorflow list ?
Thanks and I'm sorry it took so long to address this,