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Molane__Tshepo
Beginner
109 Views

Problem converting faster rcnn inception v2 model using Model Optimiser

I am currently having trouble converting a custom faster rcnn inception v2 model. The commands I input are

sudo python3 mo_tf.py --input_model ~/Distraction/inference_graph/frozen_inference_graph.pb --reverse_input_channels --output_dir ~/Distraction/inference_graph/ --input image_tensor --output detection_boxes,detection_scores,detection_classes,num_detections --tensorflow_object_detection_api_pipeline ~/Distraction/inference_graph/pipeline.config --tensorflow_use_custom_operations_config /opt/intel/openvino/deployment_tools/model_optimizer/extensions/front/tf/faster_rcnn_support_api_v1.7.json --log_level=DEBUG

 

The error that I get is 

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/opt/intel/openvino_2019.1.144/deployment_tools/model_optimizer/mo/main.py", line 312, in main
    return driver(argv)
  File "/opt/intel/openvino_2019.1.144/deployment_tools/model_optimizer/mo/main.py", line 263, in driver
    is_binary=not argv.input_model_is_text)
  File "/opt/intel/openvino_2019.1.144/deployment_tools/model_optimizer/mo/pipeline/tf.py", line 128, in tf2nx
    class_registration.apply_replacements(graph, class_registration.ClassType.MIDDLE_REPLACER)
  File "/opt/intel/openvino_2019.1.144/deployment_tools/model_optimizer/mo/utils/class_registration.py", line 184, in apply_replacements
    )) from err
mo.utils.error.Error: Exception occurred during running replacer "REPLACEMENT_ID" (<class 'extensions.middle.PartialInfer.PartialInfer'>): Stopped shape/value propagation at "add" node. 
 For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #38. 

 

I've explored the forums on users that have a similar problem and the closest one is this post:

https://software.intel.com/en-us/forums/computer-vision/topic/809407

I'd like to get assistance on the correct .json file to use to successfully convert this model

This is rather urgent, so some help on this will really be appreciated.

 

I've attached files I have used to convert the model

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4 Replies
109 Views

Hi Tshepo,

With Tensorflow v1.13.1 (not version 1.13), use the faster_rcnn_support.json. Also, try providing the input shape argument. Let us know if this fixes the issue.

Molane__Tshepo
Beginner
109 Views

Hemanth Kumar G. (Intel) wrote:

Hi Tshepo,

With Tensorflow v1.13.1 (not version 1.13), use the faster_rcnn_support.json. Also, try providing the input shape argument. Let us know if this fixes the issue.

 

Thank you for the reply Hemanth, Kumar G. 

Results on Input Shape and using faster_rcnn_support.json file:

So the input for my model is of the shape (-1,-1,-1,3). So according the documentation, I should either append the arguments -b 1 to the  above command in the terminal, or, give no shape input at all. I have used the faster_rcnn_support.json as you have suggested, I still get the same error message

109 Views

Hi Tshepo,

[N,C,H,W] - Image data layout. Refers to the representation of batches of images. 

N - Number of images in a batch

C - Number of channels

H - Number of pixels in the vertical dimension

W - Number of pixels in the horizontal dimension

Molane__Tshepo
Beginner
109 Views

Hemanth Kumar G. (Intel) wrote:

Hi Tshepo,

[N,C,H,W] - Image data layout. Refers to the representation of batches of images. 

N - Number of images in a batch

C - Number of channels

H - Number of pixels in the vertical dimension

W - Number of pixels in the horizontal dimension

 

Hey there.

 

So I used the input shape [1, 3, 600, 600]. I still get the same error.