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I have a model trained offline, I converted it to IR (xml and bin, fp32). when I used the calibration_tool to generate INT8 model, met a exception:
my command : ./calibration_tool -t C -i ../../data/ -m ../../xx_tensor.xml
[ INFO ] InferenceEngine: API version ............ 1.4 Build .................. 19154 [ INFO ] Parsing input parameters [ INFO ] Loading plugin API version ............ 1.5 Build .................. lnx_20181004 Description ....... MKLDNNPlugin [ INFO ] Loading network files [ INFO ] Preparing input blobs [ INFO ] Batch size is 32 [ INFO ] Collecting accuracy metric in FP32 mode to get a baseline, collecting activation statistics Progress: [....................] 100.00% done FP32 Accuracy: 0.00% [ INFO ] Verification of network accuracy if all possible layers converted to INT8 Validate int8 accuracy, threshold for activation statistics = 100.00 /opt/intel/computer_vision_sdk/inference_engine/samples/calibration_tool/main.cpp_440 /opt/intel/computer_vision_sdk/inference_engine/samples/calibration_tool/main.cpp_442 /opt/intel/computer_vision_sdk/inference_engine/samples/calibration_tool/calibrator_processors.cpp_217 /opt/intel/computer_vision_sdk/inference_engine/samples/calibration_tool/calibrator_processors.cpp_232 ../../xx_tensor.bin /opt/intel/computer_vision_sdk/inference_engine/samples/calibration_tool/calibrator_processors.cpp_243 /opt/intel/computer_vision_sdk/inference_engine/samples/calibration_tool/calibrator_processors.cpp_249 [ ERROR ] Inference problem: min and max sizes should be equal to channels count
The model (IR) can be excute normally with mkldnn plugin. how can i convert to int8 model by calibration tool?
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Hi Hou. I was able to reproduce your problem however xx_tensor.labels is missing from your zip file. I don't think it has any relevance to the issue however. Please remain patient while I investigate this issue. Thanks for using OpenVino !
Shubha
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Shubha R. (Intel) wrote:Hi Hou. I was able to reproduce your problem however xx_tensor.labels is missing from your zip file. I don't think it has any relevance to the issue however. Please remain patient while I investigate this issue. Thanks for using OpenVino !
Shubha
thanks for you reply, the model is binary classfication. I upload a part of data and label file.
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Dear Hou:
If you look at your IR XML your output dimensions are 2x32 which is not the output size for a traditional classification network. So instead of -t C use instead -t RawC and it will work.
layer id="77" name="ip2" precision="FP32" type="FullyConnected">
<data out-size="2"/>
<input>
<port id="0">
<dim>32</dim>
<dim>256</dim>
</port>
</input>
<output>
<port id="2">
<dim>32</dim>
<dim>2</dim>
</port>
</output>
Thanks for using OpenVIno !
Shubha
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Shubha R. (Intel) wrote:Dear Hou:
If you look at your IR XML your output dimensions are 2x32 which is not the output size for a traditional classification network. So instead of -t C use instead -t RawC and it will work.
layer id="77" name="ip2" precision="FP32" type="FullyConnected">
<data out-size="2"/>
<input>
<port id="0">
<dim>32</dim>
<dim>256</dim>
</port>
</input>
<output>
<port id="2">
<dim>32</dim>
<dim>2</dim>
</port>
</output>
Thanks for using OpenVIno !
Shubha
I excute the command as "./calibration_tool -t RawC -i ../../data/ -m ../../xx_tensor.xml", generate xx_tensor_i8.xml and bin. But I inference with Int8 model, met the same error.
LoadNetwork error min and max sizes should be equal to channels count ..\src\inference_engine\cnn_network_int8_normalizer.cpp:106[NETWORK_NOT_LOADED] ../src/inference_engine/cpp_interfaces/impl/ie_plugin_internal.hpp:132_k:\openclworkspace\openvino_proj\openvino_proj\inferenceengine.cpp_214
32 is just batch size, in my deploy model, the num is 1. I alter it to 32 in order to speed up processing of calibration. I changle it to 1, and excute "./calibration_tool -t C -i ../../data/ -m ../../xx_tensor.xml", the same error is still happened. I attach my model.
______________________________________________________________________________________________________________________________________
I have other question. how to calibrate Metric learning model ? I train model as classification, but I don't need results of the last softmax layer, or other classifcation layers. the number of the classifcation is huge. I can delete the last layer, using "-t RawC" to get statistics ?
Looking forward to your reply. thanks.
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Dear Hou, sorry about that. What do you mean by "Metric learning model" or is that the kind of model you're dealing with here (it's just a name, in other words) ?
May I ask, how are you inferencing ? Are you running one of the OpenVino samples ? Or have you written your own code ?
If you have written your own code can you also include that in the zip file - or you can send me a private message with the attachment (use Send Author A Message link).
Thanks,
Shubha

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