Error while converting model from tensorflow to IR

[bondarenko__mikhail]

I try to convert onnx model to IR:

python3 mo_tf.py --input_meta_graph /home/www/text_recognition/EAST_classic/east_icdar2015_resnet_v1_50_rbox/model.ckpt-49491.meta
Model Optimizer arguments:
Common parameters:
	- Path to the Input Model: 	None
	- Path for generated IR: 	/opt/intel/computer_vision_sdk_2018.5.445/deployment_tools/model_optimizer/.
	- IR output name: 	model.ckpt-49491
	- 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: 	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
	- 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: 	None
Model Optimizer version: 	1.5.12.49d067a0
[ ERROR ]  Exception occurred during running replacer "None" (<class 'extensions.front.tf.assign_elimination.AssignSubElimination'>): Data flow edge coming out of AssignSub node model_1/resnet_v1_50/block2/unit_3/bottleneck_v1/conv3/BatchNorm/AssignMovingAvg

[jingling__fu]

I have the same problem, do you solved it?

[Shubha_R_Intel]

Dear bondarenko, mikhail

It looks like a Tensorflow model that you're converting (Resnet 50) not Onnx. As long as you pulled the Resnet 50 from one of the supported and validated models, there should be no problem. Please select your Resnet 50 v1 or v2 from the following list.

Model Optimizer Tensorflow Supported Models

Hope it helps. Also please make sure you are on 2019R2.01, the latest and greatest. We should be releasing R3 really soon though - like any day now.

Thanks,

Shubha

[Nguyễn_Văn__Nam]

How does solve the above error?

[Nguyễn_Văn__Nam]

Nguyễn Văn, Nam wrote:

How does cash the above error?