Error in conversion of Inception Model from Keras to CNTK #19

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1. kitstar commented on Dec 5, 2017

   

   Cheng(Kit) CHEN (kitstar)

   on Dec 5, 2017

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   Hi coolrishi2005 ,

   The input layer of keras InceptionV3 doesn't contain shapes info, which is placed in model config batch_input_shape. So it can't be parsed correctly.

   To fix it, please change the code

   BackEndModel = applications.InceptionV3(include_top=False, weights='imagenet')

   to

   BackEndModel = applications.InceptionV3(input_shape=(299, 299, 3), include_top=False, weights='imagenet')

   And I tested it with follow scripts

   $ python -m mmdnn.conversion._script.convertToIR -f keras -d ./kit_imagenet -n incpetionTopModel.json -w incpetionTopModel.h5
   $ python -m mmdnn.conversion._script.IRToCode --dstModelFormat cntk --IRModelPath kit_imagenet.pb --dstModelPath kit_imagenet.py --IRWeightPath kit_imagenet.npy
   $ python -m mmdnn.conversion.examples.cntk.imagenet_test -n kit_imagenet.py -w kit_imagenet.npy --dump cntkmodel.dnn
   
   CNTK model file is saved as [cntkmodel.dnn], generated by [kit_imagenet.py] and [kit_imagenet.npy].

   Hope it can help you.
2. coolrishi2005 commented on Dec 6, 2017

   

   coolrishi2005

   on Dec 6, 2017

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   Hi kitstar,

   Thanks for the help. The model has been converted to *.dnn, but with the following warning:

   (C:\Program Files\Anaconda3\envs\py35) D:\mmdnn\conversion\cntk>python -m mmdnn.conversion.examples.cntk.imagenet_test
   -n cntkInceptionTopModel.py -w IRInceptionTopModel.npy --dump cntkInceptionTopMo
   del.dnn
   Selected CPU as the process wide default device.
   C:\Program Files\Anaconda3\envs\py35\lib\site-packages\cntk\core.py:82: RuntimeW
   arning: data is not C contiguous; rearrange your data/computation to avoid costl
   y data conversions
   RuntimeWarning)
   CNTK model file is saved as [cntkInceptionTopModel.dnn], generated by [cntkIncep
   tionTopModel.py] and [IRInceptionTopModel.npy].

   Is it fine?
3. kitstar commented on Dec 6, 2017

   

   Cheng(Kit) CHEN (kitstar)

   on Dec 6, 2017

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   Supposed to be fine.
4. coolrishi2005 commented on Dec 6, 2017

   

   coolrishi2005

   on Dec 6, 2017

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   Great!! Thanks.
5. Cheng(Kit) CHEN (kitstar)

   added

   question

   on Dec 6, 2017
6. Cheng(Kit) CHEN (kitstar)

   closed this as completedon Dec 6, 2017
7. coolrishi2005 commented on Dec 6, 2017

   

   coolrishi2005

   on Dec 6, 2017

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   Hi kitstar,

   I have saved the created and saved the Inception model with following parameters:
   BackEndModel = applications.InceptionV3(input_shape=(150, 150, 3), include_top=False, weights='imagenet')

   I am trying to load the above saved model (cntkInceptionTopModel.dnn) using cntk CPP Evaluation Example provided by Microsoft (https://github.com/Microsoft/CNTK/blob/release/2.3/Examples/Evaluation/CNTKLibraryCPPEvalCPUOnlyExamples/CNTKLibraryCPPEvalCPUOnlyExamples.cpp)

   Now, for evaluation, I am providing binary RGB data as input (which is of size 150x150x3, i.e. 67500 bytes data). Following is the code for the same:

   void EvaluationSingleSampleUsingDense(const wchar_t* modelFile, const DeviceDescriptor& device)
   {
   printf("\n===== Evaluate single sample using dense format.\n");

   // Load the model.
   // The model is trained by <CNTK>/Examples/Image/Classification/ResNet/Python/TrainResNet_CIFAR10.py
   // Please see README.md in <CNTK>/Examples/Image/Classification/ResNet about how to train the model.
   FunctionPtr modelFunc = Function::Load(modelFile, device);
   
   // Get input variable. The model has only one single input.
   Variable inputVar = modelFunc->Arguments()[0];
   
   // The model has only one output.
   // If the model has more than one output, use modelFunc->Outputs to get the list of output variables.
   Variable outputVar = modelFunc->Output();
   NDShape outputShape = outputVar.Shape();
   std::vector<size_t> outputShapeDim = outputShape.Dimensions();
   
   // Prepare input data.
   // For evaluating an image, you first need to perform some image preprocessing to make sure that the input image has the correct size and layout
   // that match the model inputs.
   // Please note that the model used by this example expects the CHW image layout.
   // inputVar.Shape[0] is image width, inputVar.Shape[1] is image height, and inputVar.Shape[2] is channels.
   // For simplicity and avoiding external dependencies, we skip the preprocessing step here, and just use some artificially created data as input.
   NDShape inputShape = inputVar.Shape();
   std::vector<size_t> inputShapeDim = inputShape.Dimensions();
   
   unsigned char inputDataChar[67500];
   std:string filepath = "D:\\Rishi\\CNTK Evaluation\\TestCNTK\\TestCNTK\\videoRGBData.rgb";
   std::basic_ifstream<unsigned char> infile(filepath, std::ios::binary | std::ios::in);
   infile.read(inputDataChar, 67500);
   infile.close();
   
   std::vector<float> inputData(inputVar.Shape().TotalSize());
   /*for (size_t i = 0; i < inputData.size(); ++i)
   {
   	inputData[i] = static_cast<float>(i % 255);
   }*/
   
   for (int i = 0; i < 67500; i++)
   	inputData[i] = (float)inputDataChar[i];
   
   // Create input value and input data map
   cout << inputVar.Shape();
   ValuePtr inputVal = Value::CreateBatch(inputVar.Shape(), inputData, device);
   std::unordered_map<Variable, ValuePtr> inputDataMap = { { inputVar, inputVal } };
   
   // Create output data map. Using null as Value to indicate using system allocated memory.
   // Alternatively, create a Value object and add it to the data map.
   std::unordered_map<Variable, ValuePtr> outputDataMap = { { outputVar, nullptr } };
   
   // Start evaluation on the device
   modelFunc->Evaluate(inputDataMap, outputDataMap, device);
   
   // Get evaluate result as dense output
   ValuePtr outputVal = outputDataMap[outputVar];
   std::vector<std::vector<float>> outputData;
   outputVal->CopyVariableValueTo(outputVar, outputData);
   
   PrintOutput<float>(outputVar.Shape().TotalSize(), outputData);
   

   }

   But, during the call of modelFunc->Evaluate(inputDataMap, outputDataMap, device), I am getting following runtime error:
   Unhandled exception at 0x00007FFE1527FEDE (Cntk.Core-2.3d.dll) in CNTKLibraryCPPEvalCPUOnlyExamples.exe: 0xC00000FD: Stack overflow (parameters: 0x0000000000000001, 0x000000474F603FD0). occurred

   Can you please help?
8. kitstar commented on Dec 6, 2017

   

   Cheng(Kit) CHEN (kitstar)

   on Dec 6, 2017

   Last edited by kitstar

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   Hi,
   Does the converted model with original input shape (299, 299, 3) work?
   If it works, maybe resizing your image in your application is a workaround.
9. coolrishi2005 commented on Dec 6, 2017

   

   coolrishi2005

   on Dec 6, 2017

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   Hey, no issue. I am working on it.
   Thanks :)