Model optimizer error(FusedBatchNormV3)

alexa__Kim · ‎10-23-2019

Hi, I ask the question because I wanted to solve the error I experienced.

I want to use 'SSD lite Mobilenet V2' in Raspberry Pi 3 B+ and NCS(not 2, it is NCS1).

So I installed OpenVINO 2019_R3 on my Pi(Raspbian stretch) and Laptop(Linux, not all programs, just Model optimizer).

When I optimize SSD lite mobilenet v2(trained zoo model), it was fine.

So, i trained my model in Google Colab using Tensorflow object detection api.

But when I optimize my own SSD lite model, here is log.

sudo python3 mo_tf.py --input_model frozen_inference_graph.pb --tensorflow_use_custom_operations_config ssd_support_api_v1.14.json --tensorflow_object_detection_api_pipeline_config pipeline.config --reverse_input_channels --data_type FP16 --keep_shape_ops --disable_fusing

[ WARNING ] Use of deprecated cli option --disable_fusing detected. Option use in the following releases will be fatal. Please use --finegrain_fusing cli option instead
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: /opt/intel/openvino_2019.3.334/deployment_tools/model_optimizer/frozen_inference_graph.pb
- Path for generated IR: /opt/intel/openvino_2019.3.334/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: 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: False
- Enable grouped convolutions fusing: True
- Move mean values to preprocess section: False
- Reverse input channels: True
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: /opt/intel/openvino_2019.3.334/deployment_tools/model_optimizer/pipeline.config
- Operations to offload: None
- Patterns to offload: None
- Use the config file: /opt/intel/openvino_2019.3.334/deployment_tools/model_optimizer/ssd_support_api_v1.14.json
Model Optimizer version: 2019.3.0-375-g332562022
The Preprocessor block has been removed. Only nodes performing mean value subtraction and scaling (if applicable) are kept.
[ ERROR ] List of operations that cannot be converted to Inference Engine IR:
[ ERROR ] FusedBatchNormV3 (76)
[ ERROR ] FeatureExtractor/MobilenetV2/Conv/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_1/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_1/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_2/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_2/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_3/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_3/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_4/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_4/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_5/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_5/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_6/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_6/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_7/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_7/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_8/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_8/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_9/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_9/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_10/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_10/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_11/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_11/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_12/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_12/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_13/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_0/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_13/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_14/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_14/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_15/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_15/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_16/expand/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/expanded_conv_16/project/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/Conv_1/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_1/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_2/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_3/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_4/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_5/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_0/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_1/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_2/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_3/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_4/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] BoxPredictor_5/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ] Part of the nodes was not converted to IR. Stopped.
For more information please refer to Model Optimizer FAQ (https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Model_Optimizer_FAQ.html), question #24.

I attach pipeline.config(.txt) file. I guess there are problems in using 'FusedBatchNormV3'. Tensorflow 1.15.0-rc0 is installed in laptop and colab.

Sahira_Intel · ‎11-01-2019

Hi Kim,

FusedBatchNormV3 is not a supported layer in OpenVINO and therefore cannot be converted to IR. Another OpenVINO community member ran into this same issue and wrote a blog post about his workaround. Please let me know if this is helpful.

Sincerely,

Sahira