Tensor memory release

[GlockPL]

Hi, I'm writing a DLL that is loaded into certain software which creates drag and drop blocks that than can be used as additional functionality in software. I wrote block that load model and a block that performs prediction. Everything works fine to a point. When a try to close the software it crashes which tells me that memory is not released properly. Problem starts to show already after I load vector into tensor and the perform transpose. Is there a way to force the tensor to reset or free up it’s memory?

[ljn917]

Is it something related to #131 ?

[GlockPL]

Ok, so I tried wrapping tensor in unique_pointer but the moment I want to perform some action on it I get this compile error: Error C2280 'std::unique_ptr<cppflow::tensor,std::default_delete<cppflow::tensor>>::unique_ptr(const std::unique_ptr<cppflow::tensor,std::default_delete<cppflow::tensor>> &)': attempting to reference a deleted function (compiling source file TFHSISegment.cpp) CppFlow D:\libs\cppflow\include\cppflow\tensor.h 149

I also tried using cppflow::tensor* input = new cppflow::tensor; and the perform operations like *input = cppflow::transpose(*input, { 1,2,0 }); ending with delete input; but there's still some memory that is not released.

[ljn917]

I cannot give further suggestions without the actual code. Please refer to the code in the examples directory.

[GlockPL]

Ok so here is my code, part of the DLL that is loaded to the software It works fine, it just blocks the software from shuting down, like something is stuck.

    atl::Array<avl::Image> inImageArray;
    atl::Array<avl::Pixel> inColors;
    ReadInput(L"inImageArray", inImageArray);
    ReadInput(L"inColors", inColors);
    

    int width = inImageArray[0].Width();
    int height = inImageArray[0].Height();
    int pitch = inImageArray[0].Pitch();        
    int channels = inImageArray.Size();
    int bitwidth = inImageArray[0].PixelSize();
    int no_pixels = width * height * channels;
    int temp_shift = static_cast<int>(pitch / bitwidth);

    std::vector<uint16_t> v;
    std::vector<int64_t> shape{ channels , height, width };
    //This copies image to 1d vector
    for (int c = 0; c < channels; c++) {
        const uint16_t* tmp_data = inImageArray[c].Ptr<uint16_t>(avl::Location(0, 0));
        for (int y = 0; y < height; y++) {
            std::copy_n(tmp_data, width, std::back_inserter(v));                
            tmp_data += temp_shift;
        }
    }
            
   
    //I'm creating input tensor with the vector and the shape of the data
     cppflow::tensor input = cppflow::tensor(v, shape);
   //I have to perform few operations in order to get proper input data for model
    input = cppflow::transpose(input, { 1,2,0 });
    input = cppflow::expand_dims(input, 0);
    input = cppflow::expand_dims(input, 4);
    input = cppflow::cast(input, TF_UINT16, TF_FLOAT);

    //Loading singleton model
    cppflow::model model = CppFlow::ModelState::getInstance().getModel();

    cppflow::tensor output = model({ {"serving_default_hsi:0", input} }, { "StatefulPartitionedCall:0" })[0];        
    output = cppflow::arg_max(output, 3);
    output = cppflow::squeeze(output, {0});


    outImage = avl::Image(width, height, avl::PlainType::UInt8, 3, atl::NIL);
    
    auto values = output.get_data<int64_t>();
    //This creates color image from data from the network
    for (int w = 0; w < width; ++w) {
        for (int h = 0; h < height; ++h) {
            int data = values[width * h + w];
            uint8_t* pixel = outImage.Ptr<uint8_t>(w, h);
            *pixel = (uint8_t)inColors[data].X();
            *(pixel + 1) = (uint8_t)inColors[data].Y();
            *(pixel + 2) = (uint8_t)inColors[data].Z();
        }
    }       
    
    
    
    v.clear();
    WriteOutput(L"outImage", outImage);
    return avs::INVOKE_NORMAL;

[ljn917]

As #131 mentioned, loading models in a singleton in a dynamic library causes initialization order issues. The problem is that model deconstructor uses the global status (context::get_status()), and the status is released before the model, leading to null pointer dereference.

At the moment, there are two possible solutions (untested): 1) call ~~get_global_context()~~ get_status() before loading any model, or 2) change the model class to allocate a per model status in its constructor.

[ljn917]

#132 was an attempt to fix this, but memory allocation in deconstructor is not a good practice in general.

[GlockPL]

I don't think this is a model problem, yet. If you reduce the code just to this:

atl::Array<avl::Image> inImageArray;
atl::Array<avl::Pixel> inColors;
ReadInput(L"inImageArray", inImageArray);
ReadInput(L"inColors", inColors);


int width = inImageArray[0].Width();
int height = inImageArray[0].Height();
int pitch = inImageArray[0].Pitch();        
int channels = inImageArray.Size();
int bitwidth = inImageArray[0].PixelSize();
int no_pixels = width * height * channels;
int temp_shift = static_cast<int>(pitch / bitwidth);

std::vector<uint16_t> v;
std::vector<int64_t> shape{ channels , height, width };
//This copies image to 1d vector
for (int c = 0; c < channels; c++) {
    const uint16_t* tmp_data = inImageArray[c].Ptr<uint16_t>(avl::Location(0, 0));
    for (int y = 0; y < height; y++) {
        std::copy_n(tmp_data, width, std::back_inserter(v));                
        tmp_data += temp_shift;
    }
}            

//I'm creating input tensor with the vector and the shape of the data
 cppflow::tensor input = cppflow::tensor(v, shape);
//I have to perform few operations in order to get proper input data for model
input = cppflow::transpose(input, { 1,2,0 });

WriteOutput(L"outImage", outImage);
return avs::INVOKE_NORMAL;

I already get the software freezing. So there maybe similiar problem in tensor somewhere.

[ljn917]

I did not see any issue from the code you posted. I suggest isolating a minimum runnable example that reproduces the problems. As mentioned before, I also recommend testing the examples in this repo.

[GlockPL]

I tested load model example and it also freezes during exit of the software

[ljn917]

I cannot reproduce the issue. Are you running the example produced by https://github.com/serizba/cppflow/blob/master/examples/load_model/main.cpp?

[GlockPL]

I copied the example into minimal code needed to run it as dll inside Adaptive Vision Software.

[ljn917]

I think you have the exact problem reported in #131. Try #132.

[GlockPL]

I don't think this is the issue. Because the software freezes already after first operation on tensor. Besides that I changed session deleter according to #131 and it still doesn't work

[ljn917]

I am afraid I cannot provide more help unless I see the full code. You could also attach a debugger and see where it freezes.

On Wed, Sep 29, 2021, 06:17 GlockPL @.***> wrote:

I don't think this is the issue. Because the software freezes already after first operation on tensor. Besides that I changed session deleter according to #131 https://github.com/serizba/cppflow/issues/131 and it still doesn't work

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[serizba]

@GlockPL

Can you check if the last changes fixed your error?

[serizba]

Error should be fixed with #201

Closing due to inactivity.