Community
cancel
Showing results for 
Search instead for 
Did you mean: 
ggeol
Beginner
743 Views

OpenCV using Movidius NCS2 gives error when using Darknet neural network

Jump to solution

I am trying to perform OCR using the Intel Movidius Neural Compute Stick 2. The OCR network that I am using is based on YOLO, and therefore has the graph structure saved as a  `.cfg` file and the weights as a `.weights` file. I use OpenCV which was installed with Openvino on a Raspberry Pi 3b+. My code is given below:

import cv2   # load network ocr_net = cv2.dnn.readNetFromDarknet('ocr-net.cfg', 'ocr-net.weights') ocr_net.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD) ocr_net.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)   # perform inference im = cv2.imread('img_path.jpg') blob = cv2.dnn.blobFromImage(im, swapRB=False) ocr_net.setInput(blob) result = ocr_net.forward()

On running this code, I get the following error:

Traceback (most recent call last): File "test.py", line 12, in <module> result = ocr_net.forward() cv2.error: OpenCV(4.1.2-openvino) /home/jenkins/workspace/OpenCV/OpenVINO/build/opencv/modules/dnn/src/op_inf_engine.cpp:704: error: (-215:Assertion failed) Failed to initialize Inference Engine backend: Device with "CPU" name is not registered in the InferenceEngine in function 'initPlugin'

If I change the inference target to `cv2.dnn.DNN_TARGET_CPU` and the inference backend to `cv2.dnn.DNN_BACKEND_OPENCV`, the model works fine and is able to correctly perform OCR. My openvino/openCV setup is also correctly installed since I can correctly run other Tensorflow/Caffe models using openCV (using `readNetFromTensorflow()`/`readNetFromCaffe()`).

 

The OCR network that I am attempting to use can be found here: cfg , weights .

Any help is appreciated!

 

Tags (3)
0 Kudos
1 Solution
MAURICIOAL_R_Intel
Moderator
409 Views

Hello ggeol,

 

In this case, we only have a guide for converting yolo models from DarkNet. Since your model is not the standard yolo model, it may not be supported, and that is why we recommended opening a new issue as you did. You can try to modify the .json file to match his model but it may or may not work.

 

Thank you.

View solution in original post

3 Replies
MAURICIOAL_R_Intel
Moderator
409 Views

Hello ggeol,

 

This issue may be specific to OpenCV and our recommendation for you is to open a new GitHub issue in the following link: https://github.com/opencv/opencv/issues.

 

In addition, you can try to read your model in IR format instead of darknet (.cfg & .weights) as shown in the setup guide: https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_raspbian.html#run-i...

 

To convert the model from darknet to IR format, please follow these instructions: https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_...

 

If you have more questions, please feel free to ask us, and we will help you further. 

Thank you. 

 

Mauricio R. 

Intel Customer Support Technician 

A Contingent Worker at Intel 

ggeol
Beginner
409 Views

Hi @Mauricio.R_Intel​ ,

Thanks for your reply. I have opened up a issue on OpenCV as suggested.

 

I have tried the steps given to convert the model to the IR graph. I am able to get the tensorflow graph, but when i try to convert the tensorflow graph to IR i get the following error message:

Model Optimizer arguments: Common parameters: - Path to the Input Model: path\to\ocr-net.pb - Path for generated IR: C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer\. - IR output name: ocr-net - 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: None - Operations to offload: None - Patterns to offload: None - Use the config file: C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer\extensions\front\tf\yolo_v1_tiny.json Model Optimizer version: 2019.3.0-375-g332562022 OMP: Info #212: KMP_AFFINITY: decoding x2APIC ids. OMP: Info #210: KMP_AFFINITY: Affinity capable, using global cpuid leaf 11 info OMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0-7 OMP: Info #156: KMP_AFFINITY: 8 available OS procs OMP: Info #157: KMP_AFFINITY: Uniform topology OMP: Info #179: KMP_AFFINITY: 1 packages x 4 cores/pkg x 2 threads/core (4 total cores) OMP: Info #214: KMP_AFFINITY: OS proc to physical thread map: OMP: Info #171: KMP_AFFINITY: OS proc 0 maps to package 0 core 0 thread 0 OMP: Info #171: KMP_AFFINITY: OS proc 1 maps to package 0 core 0 thread 1 OMP: Info #171: KMP_AFFINITY: OS proc 2 maps to package 0 core 1 thread 0 OMP: Info #171: KMP_AFFINITY: OS proc 3 maps to package 0 core 1 thread 1 OMP: Info #171: KMP_AFFINITY: OS proc 4 maps to package 0 core 2 thread 0 OMP: Info #171: KMP_AFFINITY: OS proc 5 maps to package 0 core 2 thread 1 OMP: Info #171: KMP_AFFINITY: OS proc 6 maps to package 0 core 3 thread 0 OMP: Info #171: KMP_AFFINITY: OS proc 7 maps to package 0 core 3 thread 1 OMP: Info #250: KMP_AFFINITY: pid 12488 tid 2984 thread 0 bound to OS proc set 0 OMP: Info #250: KMP_AFFINITY: pid 12488 tid 2664 thread 1 bound to OS proc set 2 OMP: Info #250: KMP_AFFINITY: pid 12488 tid 16768 thread 2 bound to OS proc set 4 OMP: Info #250: KMP_AFFINITY: pid 12488 tid 5540 thread 3 bound to OS proc set 6 [ ERROR ] Cannot infer shapes or values for node "output/YoloRegion". [ ERROR ] 'mask' [ ERROR ] [ ERROR ] It can happen due to bug in custom shape infer function <function RegionYoloOp.regionyolo_infer at 0x000001E299EBAEA0>. [ 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 ] 'mask' Stopped shape/value propagation at "output/YoloRegion" node. For more information please refer to Model Optimizer FAQ (https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Model_Optimizer_FAQ.html), question #38. Exception occurred during running replacer "REPLACEMENT_ID" (<class 'extensions.middle.PartialInfer.PartialInfer'>): 'mask' Stopped shape/value propagation at "output/YoloRegion" node. For more information please refer to Model Optimizer FAQ (https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Model_Optimizer_FAQ.html), question #38.

I believe this error comes form the fact that this is not exactly the YOLO model. How to I convert a custom darknet model to the IR format? If it helps, when I directly try to use the `.pb` file with OpenCV, I get the following error:

File "test.py", line 12 ocr_net.forward() cv2.error: OpenCV(4.1.0) C:\projects\opencv-python\opencv\modules\dnn\src\dnn.cpp:524: error: (-2:Unspecified error) Can't create layer "init" of type "NoOp" in function 'cv::dnn::dnn4_v20190122::LayerData::getLayerInstance'

 

MAURICIOAL_R_Intel
Moderator
410 Views

Hello ggeol,

 

In this case, we only have a guide for converting yolo models from DarkNet. Since your model is not the standard yolo model, it may not be supported, and that is why we recommended opening a new issue as you did. You can try to modify the .json file to match his model but it may or may not work.

 

Thank you.

View solution in original post

Reply