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Hi all together,
i just created a "Faster R-CNN ResNet 50" IR with OpenVINO computer_vision_sdk_2018.3.343 as shown in your documentation: ./deployment_tools/documentation/docs/TensorFlowObjectDetectionFasterRCNN.html
The frozen inference graph was downloaded from the link shown in your Tutorial: faster_rcnn_resnet50_lowproposals_coco_2018_01_28.tar.gz
The conversion is done with the command shown in your tutorial too, modified with --data_type FP16. I used FP16 because MYRIAD only supports FP16. The command i used is this:
~/intel/computer_vision_sdk_2018.3.343/deployment_tools/model_optimizer$ ./mo.py --input_model=~/Desktop/faster_rcnn/frozen_inference_graph.pb --output=detection_boxes,detection_scores,num_detections --tensorflow_use_custom_operations_config extensions/front/tf/legacy_faster_rcnn_support.json --output_dir=~/Desktop/faster_rcnn/IR --model_name frozen_inference_graph_FP16 --data_type FP16
The full output of the Conversion is this:
Model Optimizer arguments: Common parameters: - Path to the Input Model: /home/up-board/Desktop/faster_rcnn/frozen_inference_graph.pb - Path for generated IR: /home/up-board/Desktop/faster_rcnn/IR - IR output name: frozen_inference_graph_FP16 - 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 - 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 - 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: None - Operations to offload: None - Patterns to offload: None - Use the config file: /home/up-board/intel/computer_vision_sdk_2018.3.343/deployment_tools/model_optimizer/extensions/front/tf/legacy_faster_rcnn_support.json Model Optimizer version: 1.2.185.5335e231 WARNING: the "PreprocessorReplacement" is a legacy replacer that will be removed in the future release. Please, consider using replacers defined in the "extensions/front/tf/ObjectDetectionAPI.py" The Preprocessor block has been removed. Only nodes performing mean value subtraction and scaling (if applicable) are kept. WARNING: the "TFObjectDetectionAPIFasterRCNNProposalAndROIPooling" is a legacy replacer that will be removed in the future release. Please, consider using replacers defined in the "extensions/front/tf/ObjectDetectionAPI.py" WARNING: the "SecondStagePostprocessorReplacement" is a legacy replacer that will be removed in the future release. Please, consider using replacers defined in the "extensions/front/tf/ObjectDetectionAPI.py" The graph output nodes "num_detections", "detection_boxes", "detection_classes", "detection_scores" have been replaced with a single layer of type "Detection Output". Refer to IR catalogue in the Inference Engine documentation for information about this layer. The "object_detection_sample_ssd" sample can be used to run the generated model. /home/up-board/intel/computer_vision_sdk_2018.3.343/deployment_tools/model_optimizer/mo/front/common/partial_infer/slice.py:90: 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] [ SUCCESS ] Generated IR model. [ SUCCESS ] XML file: /home/up-board/Desktop/faster_rcnn/IR/frozen_inference_graph_FP16.xml [ SUCCESS ] BIN file: /home/up-board/Desktop/faster_rcnn/IR/frozen_inference_graph_FP16.bin [ SUCCESS ] Total execution time: 124.86 seconds.
In a python script i load the network into PlugIn "GPU" and its working well. If i change the PlugIn to "MYRIAD" the following Exception is thrown:
Traceback (most recent call last): File "~/InferenceEngine.py", line 43, in load_network self.exec_network = self.plugin.load(network=self.network) File "ie_api.pyx", line 237, in inference_engine.ie_api.IEPlugin.load File "ie_api.pyx", line 249, in inference_engine.ie_api.IEPlugin.load RuntimeError: [VPU] Reshape input or output reshape_4d_ has invalid batch /teamcity/work/scoring_engine_build/releases_openvino-2018-r3/ie_bridges/python/inference_engine/ie_api_impl.cpp:226
Does someone know whats happening? Is MYRIAD not supporting the models shown in the tutorials?
Greetings,
Timo
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Dear Timo,
indeed Myriad does not support TF Faster RCNN. It is actually indicated in our documentation: https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#inpage-nav-2-1 , but we definitely can make it clearer in our tutorial.
Best,
Severine
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It's now supported, right ?
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Dear De Boer, Ronald,
Yes today Faster R-CNN is supported on MYRIAD. Please see Object Detection Demo sample doc . Please update to the latest release 2019R1.1.
Thanks,
Shubha
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Dear Dutta, Jeet,
Kindly study the faster rcnn C++ sample for guidance on how you should write your code.
Thanks,
Shubha
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