Hi,
I'm using R3, but when I try to use --input_shape in Facenet conversion, like
python mo_tf.py --input_model 20180408-102900.pb --input_shape "[1,96,96,3]" --output_dir . --freeze_placeholder_with_value "phase_train->False"
Model Optimizer reports error:
[ ERROR ] Shape [ 1 -1 -1 1792] is not fully defined for output 0 of "InceptionResnetV1/Logits/AvgPool_1a_8x8/AvgPool". Use --input_shape with positive integers to override model input shapes. [ ERROR ] Cannot infer shapes or values for node "InceptionResnetV1/Logits/AvgPool_1a_8x8/AvgPool". [ ERROR ] Not all output shapes were inferred or fully defined for node "InceptionResnetV1/Logits/AvgPool_1a_8x8/AvgPool". For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #40. [ ERROR ] [ ERROR ] It can happen due to bug in custom shape infer function <function tf_pool_ext.<locals>.<lambda> at 0x00000295EFE1A730>. [ 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 ] Stopped shape/value propagation at "InceptionResnetV1/Logits/AvgPool_1a_8x8/AvgPool" node. For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #38.
Do you have any suggestion?
Thank you very much!
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Hi Ives,
I don't know where you got the input shape size, I used tensorflow "summarize_graph.py" but I got "unknown size"
I remove it and mo seems work:
$ python3 ~/deployment_tools/model_optimizer/mo_tf.py --input_model 20180408-102900.pb --output_dir . --freeze_placeholder_with_value "phase_train->False" Model Optimizer arguments: Common parameters: - Path to the Input Model: ~/Downloads/tmp/20180408-102900/20180408-102900.pb - Path for generated IR: ~/Downloads/tmp/20180408-102900/. - IR output name: 20180408-102900 - 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 - 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: 1.2.185.5335e231 ~/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: ~/Downloads/tmp/20180408-102900/./20180408-102900.xml [ SUCCESS ] BIN file: ~/Downloads/tmp/20180408-102900/./20180408-102900.bin [ SUCCESS ] Total execution time: 17.95 seconds.
Let me know if this solves your problem
Mark
Hi Mark,
Thanks for your suggestion firstly.
I know that Facenet conversion will be fine if I don't use the --input_shape, and the result input shape is 1x3x160x160.
But because the document ($(INTEL_CVSDK_DIR)/deployment_tools/documentation/docs/FaceNetTF.html
) says that
--input_shape is applicable with or without
--input
therefore I just want to try using --input_shape to change input shape after conversion.
Hi Sir,
I also got an error if I add input shape to the command.
Because the facenet's input shape is [1,160,160, 3], but after converting the input shape is [1,3,160,160] and generate different result.
I use following command and got errors:
------------------------------------------------
Please help to check this.
And another question, is it possible to generate different result with converted model that uses different input dimension?
Thanks
David
Hi David,
About
is it possible to generate different result with converted model that uses different input dimension?
You may use --model_name and assign different name to converted models which have different input dimensions.
Hi Ives and David,
You both are right, there are actually 2 problem here:
- The input size could not be too small than the original input size, as I tried, the minimum size is (1,139, 139, 3)
- A bug that document in the release note "known issue"#37, this issue was fixed in next release
To work around, you can do following:
- Copy lines 829-830 from <INSTALL_DIR>/deployment_tools/model_optimizer/mo/front/extractor.py and insert it before line 796 line, so it becomes:
...... for port_and_shape_info in user_defined_inputs[node_id]: if 'added' in port_and_shape_info and port_and_shape_info['added']: continue shape = port_and_shape_info['shape'] if 'shape' in port_and_shape_info else None ...... - Run the following command:
python3 ~/intel/computer_vision_sdk_2018.3.343/deployment_tools/model_optimizer/mo.py --input_model 20180408-102900.pb --output_dir . --input_shape "(1,139,139,3)" --freeze_placeholder_with_value "phase_train->False"
Let me know if this solves the problem.
Mark
Hi Mark,
I modified the extractor.py and converted the model again with adding --input_shape or --reverse_input_channels. The model can be generated.
But I still have some questions.
1. The model input order is still [1,3,160,160] after using --input_shape "[1,160,160,3]", Does the order not change even using the input_shape parameter?
2. I converted two version model( 20180402-114759, 20170512-110547) to test, but the output matrices are not the same(or the image are not the same) on the same image.
The following is my sample codes with python:
......
facenet_res_2017 = facenet_2017_exec_net.infer( {'batch_join/fifo_queue': reshaped} )
embedding_2017 = facenet_res_2017['normalize']
# original_2017 is the pre-saved data using facenet model
dist_2017 = np.sqrt( np.sum( np.square( np.subtract( embedding_2017, original_2017 ) ) ) )
print( dist_2017 )
The dist_2017 is larger than 1 on the same image when I compare with the original facenet model.
I think the output matrix will be the same( or the dist will be small) even using the converted model.
And almost all my test face images(5~6 persons) are recognized to the same face( dist < 0.7 ).
Is there any idea about this?
Thanks.
David
Hi David,
Sorry for the late response,
For your first question, do you mean the input order in the generated xml file? I think the order doesn't have to be exact the same as we specified in the command line, the question is, does this order cause the problem?
For your second question, I think this is a different topic, could you open a different post? Let me know the post number so I can follow up.
Also please add the reproducing steps and data you used for this question so I can reproduce it.
Mark
