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Hi,
I'm trying to convert mask-rcnn model with below command:
>> python3 mo_tf.py --input_model ~/frozen_inference_graph.pb --output=detection_boxes,detection_scores,num_detections,Reshape_16 --tensorflow_object_detection_api_pipeline_config ~/pipeline.config
An error is generated as:
ERROR: Node Preprocessor/map/while/ResizeToRange/unstack has more than one outputs. Provide output port explicit
Detailed info. :
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: /opt/intel/computer_vision_sdk_2018.3.343/deployment_tools/model_optimizer/mask_rcnn_resnet101_atrous_coco_2018_01_28/frozen_inference_graph.pb
- Path for generated IR: /opt/intel/computer_vision_sdk_2018.3.343/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: detection_boxes,detection_scores,num_detections,Reshape_16
- 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
- Offload unsupported operations: False
- Path to model dump for TensorBoard: None
- Update the configuration file with input/output node names: None
- Use configuration file used to generate the model with Object Detection API: /opt/intel/computer_vision_sdk_2018.3.343/deployment_tools/model_optimizer/mask_rcnn_resnet101_atrous_coco_2018_01_28/pipeline.config
- Operations to offload: None
- Patterns to offload: None
- Use the config file: None
Model Optimizer version: 1.2.185.5335e231
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Hi Qianying,
the correct MO command line for mask_rcnn is this one:
python3 mo_tf.py --input_model ~/frozen_inference_graph.pb --tensorflow_use_custom_operations_config extensions/front/tf/mask_rcnn_support.json --tensorflow_object_detection_api_pipeline_config ~/pipeline.config --reverse_input_channels
In our latest release, we have changed the way to handle the models from the TF Object Detection API, I invite you to read the documentation about it: computer_vision_sdk_2018.3.343/deployment_tools/documentation/docs/TensorFlowObjectDetectionAPIModels.html
Best,
Severine
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Hi Severine,
I used this command, python3 mo_tf.py
-
-
input_model ~
/
frozen_inference_graph.pb
-
-
tensorflow_use_custom_operations_config extensions
/
front
/
tf
/
mask_rcnn_support.json
-
-
tensorflow_object_detection_api_pipeline_config ~
/
pipeline.config
-
-
reverse_input_channels
But during the inference stage, I'm getting this error "[ ERROR ] Error reading network: Incorrect crop data! Offset(2) + result size of output(100) should be less then input size(7) for axis(3) ". The error is same with both CPU & GPU plugins on Skylake.
I used resnet-101 based mask rcnn from TF model zoo (mask_rcnn_resnet101_atrous_coco in https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
My OpenVino version is 2018.3.338. Please suggest how to resolve this issue.
Thanks,
Palanivel
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