This is a utility tool for OpenVINO IR model file. This tool has following functions:

Update on 02-APR-2022 : Add IRv11 and OpenVINO 2022.1 API2.0 Support.

1. Display IR model summary information ( ir-summary.py )
   Displays IR version, Input/Output blob names and shapes

2. Weight data extraction ( ir_weight_extractor.py )
   Extract weight data from an IR model and ganerates a pickle file

3. Extract the feature map (intermediate buffer between layers) from an IR model ( ir_featuremap_extractor.py )
   Run an IR model and extract the feature map data of all layers and generates a pickle file

4. Compare the feature map data files ( compare_feature_maps.py )
   Compare two feature map files and check the difference. This tool might be helpful for finding a problem point in accuracy problem between different type of OpenVINO plugins.

5. Calculate individual output blob size of an OpenVINO IR model ( ir_output_blob_size_calculator.py )
   The tool calculates the output blob size of layers in an IR model and summarize it. You can use it to identify the memory hungry layers in the model.

6. Separate the header (XML), weight (.bin), and graph (.xml) from a compiled model for CPU ( exported_model_disassembler.py )
   The tool will split the compiled model into 3 files for further analysis or study.

OpenVINO モデル uses ユーㄇリィーㄨープププグラムです. Write down the function of "holding".

1. IR information representation（ ir-summary.py )
   IR バ ー ジ ョ ン, input/output blob name, shape
2. IR モデルみデーーきし（ ir_weight_extractor.py )
   Create a new map from a new map, Python pickle map
3. Special tower area（ ir_featuremap_extractor.py )
   IR モデルを Aimed line しながら Middle バッファから Special 徴マッびデ゘を Field きしケッファイ, Python pickle ァイ To make a new type of horse
4. Comparison of special tower（ compare_feature_maps.py )
   2 つ徴マップデーを Comparison しを Difference を Expression. Different OpenVINO.
5. IR（ ir_output_blob_sie_calculator.py )
   There are many possibilities to use
6. CPU uses XML,,,,,,,,,,,,,,,,,, す（ exported_model_disassembler.py )
   コンパイょみモデルがどなったかををべたりするにかしれせん.

All tool are Python scripts. Just run them.

1. ir-summary.py
   Options:
   -m , --model : Input IR model path
   -d , --dir : Input IR model directory

2. ir_weight_extractor.py
model_wgt.pickle file will be generated at the same directory as the input model when the input model file name is model.xml .
   Options:
   -m , --model : Input IR model path
   Output pickle file format:
   Dictionary { blobName0 : [ precStr0, shape0, weightBuf0 ], blobName1 : [ precStr1, shape1, weightBuf1 ], ... } precStr='FP32', 'FP16', 'I32', ...

3. ir_featuremap_extractor.py
model_featmap.pickle file will be generated at the same directory as the script file when the input model file name is model.xml .
   IMPORTANT NOTICE:
   This program reads image.jpg and supply it to the 1st input blob as default. In case your model requires special input data (multiple input blobs, non-image input, etc), you need to modify prepareInputs() function to meet the requirements.
   Options:
   -m , --model : Input IR model path
   -i , --input : Input image file path (default= image.jpg )
   Output pickle file format:
   Dictionary { blobName0 : [ precStr0, shape0, featMap0 ], blobName1 : [ precStr1, shape1, featMap1 ], ... } precStr='FP32', 'FP16', 'I32', ...

4. compare_feature_maps.py
   This program takes 2 extracted feature map files generated by ir_featuremap_extractor.py and compare them.
   You may find some hint to resolve accuracy problem of DL models, hopefully :-)
   Options:
   -r , --reference_feature : input feature map pickle file (.pickle)
   -f , --feature : input feature map pickle file to compare with the reference feature map data (.pickle)
   -e , --error : error tolerance (%). default=10
   -v , --verbose : display error values
   -t , --top : # of error values to display (per layer, with -v option)

5. ir_output_blob_size_calculator.py
   This program calculates the output blob size of layers in an OpenVINO IR model. The program reads .xml file, parse it and calculate the output blob size. This tool will give you certain level of idea which layer in the model is memory hungry and how much memory might be required to run the model.
   Options:
   -m : input IR model file name (.xml)

6. exported_model_disassembler.py
   This program breaks an compiled model file for CPU into 3 files. The compiled model can be created with model.export_model() API and the generated model data consists with multiple part, XML header, binary weight data (.bin portion), and XML graph (.xml portion).
   This program will generate foo_hdr.xml , foo_.bin , and foo_.xml from input file foo.model .
   The program takes only the file name as the command line parameter. No flags are available.

 python exported_model_disassembler.py compiled.model

Note: The code to visualize the data is not included in this project.
Weight data for convolution

Feature map generated by a convolution layer

 import  pickle
 with  open ( 'foo.pickle' , 'rb' ) as  f : data  =  pickle . load ( f ) # the pickled data will be loaded to `data`

 a  =  data [ 'conv1' ]

 python ir-summary.py -m .. \. . \p ublic \g ooglenet-v1 \F P16 \g ooglenet-v1.xml Model: .. \. . \p ublic \g ooglenet-v1 \F P16 \g ooglenet-v1 IR version:10 Input Blob(s): BlobName:data, Shape:[1, 3, 224, 224], Precision:FP32 Output Blob(s): BlobName:prob, Shape:[1, 1000], Precision:FP32

 > python ir-summary.py -d .. \. . \p ublic Model: .. \. . \p ublic \g ooglenet-v1 \F P16 \g ooglenet-v1 IR version:10 Input Blob(s): BlobName:data, Shape:[1, 3, 224, 224], Precision:FP32 Output Blob(s): BlobName:prob, Shape:[1, 1000], Precision:FP32 Model: .. \. . \p ublic \g ooglenet-v1 \F P32 \g ooglenet-v1 IR version:10 Input Blob(s): BlobName:data, Shape:[1, 3, 224, 224], Precision:FP32 Output Blob(s): BlobName:prob, Shape:[1, 1000], Precision:FP32 :

 python ir_weight_extractor.py -m public \g ooglenet-v1 \F P16 \g ooglenet-v1.xml *** OpenVINO IR model weight data extractor size : nodeName six : data_add_/copy_const eighteen thousand eight hundred and sixteen : 175/Output_0/Data__const one hundred and twenty-eight : conv1/7x7_s2/Dims2528/copy_const eight : pool1/norm16956/value6958_const : two hundred and fifty-six : inception_5b/pool_proj/Dims2558/copy_const sixteen : loss3/classifier/flatten_fc_input/Cast_18765_const two million and forty-eight thousand : loss3/classifier/WithoutBiases/1_port_transpose6482_const two thousand : 238/Output_0/Data_/copy_const public \g ooglenet-v1 \F P16 \g ooglenet-v1_wgt.pickle is generated

 python ir_featuremap_checker.py -m googlenet-v1.xml -i car_1.bmp *** OpenVINO feature map extractor @@@ This program takes ' image.jpg ' and supply to the 1st input blob as default. @@@ In case your model requires special data input, you need to modify ' prepareInputs() '  function  to meet the requirements. node# : nodeName zero : data two : Add_ four : conv1/7x7_s2/WithoutBiases six : conv1/7x7_s2 :       : three hundred and eighteen : loss3/classifier/WithoutBiases three hundred and twenty : loss3/classifier three hundred and twenty-one : prob Feature maps are output to ' googlenet-v1_featmap.pickle '

 > python compare_feature_maps.py -f googlenet-v1_featmap_cpu.pickle -r googlenet-v1_featmap_gpu.pickle -e 10 *** OpenVINO feature map comparator Error tolerance : 10% PASS:150528, ERROR:     0, E-Rate:  0.00%, NaN:     0, Inf:     0 - data PASS:150528, ERROR:     0, E-Rate:  0.00%, NaN:     0, Inf:     0 - Add_ PASS:768060, ERROR: 34756, E-Rate:  4.33%, NaN:     0, Inf:     0 - conv1/7x7_s2/WithoutBiases PASS:768917, ERROR: 33899, E-Rate:  4.22%, NaN:     0, Inf:     0 - conv1/7x7_s2 PASS:784953, ERROR: 17863, E-Rate:  2.23%, NaN:     0, Inf:     0 - conv1/relu_7x7 :      :       :        : PASS: 49824, ERROR:   352, E-Rate:  0.70%, NaN:     0, Inf:     0 - inception_5b/output PASS:   998, ERROR:    26, E-Rate:  2.54%, NaN:     0, Inf:     0 - pool5/7x7_s1 PASS:   998, ERROR:    26, E-Rate:  2.54%, NaN:     0, Inf:     0 - loss3/classifier/flatten_fc_input PASS:   977, ERROR:    23, E-Rate:  2.30%, NaN:     0, Inf:     0 - loss3/classifier/WithoutBiases PASS:   974, ERROR:    26, E-Rate:  2.60%, NaN:     0, Inf:     0 - loss3/classifier PASS:   957, ERROR:    43, E-Rate:  4.30%, NaN:     0, Inf:     0 - prob

 > python ir_output_blob_size_calculator.py -m googlenet-v1.xml *** OpenVINO IR model output blob size calculator layer name, layer type, port num, precision, oblob_size,  [dims...] data,Parameter,0,FP16,301056,1,3,224,224 data_add_/copy_const,Const,1,FP16,6,1,3,1,1 Add_,Add,2,FP16,301056,1,3,224,224 231/Output_0/Data__const, Const,1,FP16,18816,64,3,7,7 conv1/7x7_s2/WithoutBiases,Convolution,2,FP16,1605632,1,64,112,112 : loss3/classifier/WithoutBiases,MatMul,2,FP16,2000,1,1000 246/Output_0/Data_/copy_const, Const,1,FP16,2000,1,1000 loss3/classifier,Add,2,FP16,2000,1,1000 prob,SoftMax,1,FP16,2000,1,1000 Total output blob size : 40,878,054B / 38.98MB

• Windows 10
• Intel(r) Distribution of OpenVINO(tm) toolkit 2021.3