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When giving this command, sudo python3 /opt/intel/openvino/deployment_tools/model_optimizer/mo_tf.py --input_model pbmodels/irim.pb --output_dir lrmodels/tiny-YoloV3/FP16/ --data_type FP16 --batch 1 --tensorflow_use_custom_operations_config yolo_v3_tiny_changed.json
The following is the error I'm getting.
OpenVINO R1 2019, Ubuntu 18.04, TensorFlow 1.12, CUDA 10.1
Model Optimizer arguments: Common parameters: - Path to the Input Model: /home/nadeem/OpenVINO-YoloV3-master/pbmodels/irim.pb - Path for generated IR: /home/nadeem/OpenVINO-YoloV3-master/lrmodels/tiny-YoloV3/FP16/ - IR output name: irim - 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: FP16 - 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: None - Operations to offload: None - Patterns to offload: None - Use the config file: /home/nadeem/OpenVINO-YoloV3-master/yolo_v3_tiny_changed.json Model Optimizer version: 2019.1.0-341-gc9b66a2 /usr/lib/python3/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. from ._conv import register_converters as _register_converters [ ERROR ] Cannot infer shapes or values for node "detector/yolo-v3-tiny/Conv/LeakyRelu". [ ERROR ] Op type not registered 'LeakyRelu' in binary running on Nadeem-XPS. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed. [ ERROR ] [ ERROR ] It can happen due to bug in custom shape infer function <function tf_native_tf_node_infer at 0x7fc876610048>. [ 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 "detector/yolo-v3-tiny/Conv/LeakyRelu" node. For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #38.
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Dear Mohammed, Nadeem,
Is this a custom-trained model or a pre-trained one ?
If you carefully followed These instructions for a pre-trained model it should work. Custom trained should also work but who knows - there may be a bug in custom trained models.
Please report back here regarding your status.
Thanks,
Shubha
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Hi Shubha,
Yes, it is a custom trained model for just one class. I'm able to import the same using caffe but not tensorflow.
Best,
Nadeem
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Dear Mohammed, Nadeem
If it's custom trained then it's probably an MO bug. Can you kindly attach your custom-trained tiny.pb file as a *.zip ? Please allow me to reproduce this issue.
Thanks kindly,
Shubha
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