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TDA4VM: Custom model compilation using edgeai tidl tools

Part Number: TDA4VM

Hello TI Forum

I am using yolov8 to perform object detection on TDA4VM, I am using edgeai tidl tools to obtain artifacts that can be ported using edgeai gst apps. But when I try to compile the model there are some issues:

Model dictionary:

{'yolov8' :  {'artifacts_folder': '/home/mugu/edgeai-tidl-tools/model/',
                                     'model_folder': '/home/mugu/edgeai-tidl-tools/model/',
                                     'model_path': model_file_name,
                                     'session_name': config['session_name']} ,
                      'task_type' : model_type,
                      'target_device': 'pc',
                      'postprocess':{'data_layout' : layout },
                      'preprocess' :{'data_layout' : layout ,
                                    'mean':config['mean'],
                                    'scale':config['scale'],
                                    'resize':640,
                                    'crop':640
                                     }


Contents in artifacts folder:

yolov8.zip

Issue:

  • Hi,

    Due to limited bandwidth am unable to look into this thread at the moment.

    I will try to circle back in 1 week.

    Thank you for your patience.

  • HI Pratik

    I could understand the issue from you side, kindly take your time and respond us back as soon as possible.
    Thanks in advance

  • Thanks for understanding. 

  • Hello Pratik
    Any updates on this. I have uploaded the bin and some files produced during compilation in the zip, you can also find the model inside.

    yolo.zip
    thanks

  • Hi Ramaseshan,

    I thought that the model has 3 operators that are not supported by TIDL. See the figure below:

    -- Joy

  • Hello Joy Yang

    I had put them on deny list and tried out the compilation, but I am facing a exception issue which I have mentioned in the starting, can you take a glance, and kindly try to look at our model, if there is an issue

    Thanks in advance

  • Also I could find more number of subgraph created, can you guide as to solve that too, is there any tool to replace layers graphically? 

    Thank you

  • Hello
    We have replaced unsupported ops of tidl with supported tidl layers, but still the compilation stops and crashes.
    I have included the model link and logs for your reference.
    Thanks!

    mugu@mugu-ASUS:~/edgeai-tidl-tools$ python3 compile_model.py /home/mugu/edgeai-tidl-tools/last.onnx /home/mugu/edgeai-tidl-tools/test_data /home/mugu/edgeai-tidl-tools/model-artifacts/
    Available execution providers :  ['TIDLExecutionProvider', 'TIDLCompilationProvider', 'CPUExecutionProvider']
    tidl_tools_path                                 = /home/mugu/edgeai-tidl-tools/tidl_tools 
    artifacts_folder                                = /home/mugu/edgeai-tidl-tools/model-artifacts/tidl_output 
    tidl_tensor_bits                                = 8 
    debug_level                                     = 3 
    num_tidl_subgraphs                              = 16 
    tidl_denylist                                   = 
    tidl_denylist_layer_name                        = 
    tidl_denylist_layer_type                         = 
    tidl_allowlist_layer_name                        = 
    model_type                                      =  
    tidl_calibration_accuracy_level                 = 7 
    tidl_calibration_options:num_frames_calibration = 21 
    tidl_calibration_options:bias_calibration_iterations = 5 
    mixed_precision_factor = -1.000000 
    model_group_id = 0 
    power_of_2_quantization                         = 2 
    ONNX QDQ Enabled                                = 0 
    enable_high_resolution_optimization             = 0 
    pre_batchnorm_fold                              = 1 
    add_data_convert_ops                          = 0 
    output_feature_16bit_names_list                 =  
    m_params_16bit_names_list                       =  
    reserved_compile_constraints_flag               = 1601 
    ti_internal_reserved_1                          = 
    
    
     ****** WARNING : Network not identified as Object Detection network : (1) Ignore if network is not Object Detection network (2) If network is Object Detection network, please specify "model_type":"OD" as part of OSRT compilation options******
    
    Supported TIDL layer type ---            Conv -- /model.0/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.0/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.2/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.2/cv1/act/Relu 
    Supported TIDL layer type ---           Split -- /model.2/Split 
    Supported TIDL layer type ---            Conv -- /model.2/m.0/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.2/m.0/cv1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.2/m.0/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.2/m.0/cv2/act/Relu 
    Supported TIDL layer type ---             Add -- /model.2/m.0/Add 
    Supported TIDL layer type ---          Concat -- /model.2/Concat 
    Supported TIDL layer type ---            Conv -- /model.2/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.2/cv2/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.3/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.3/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.4/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.4/cv1/act/Relu 
    Supported TIDL layer type ---           Split -- /model.4/Split 
    Supported TIDL layer type ---            Conv -- /model.4/m.0/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.4/m.0/cv1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.4/m.0/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.4/m.0/cv2/act/Relu 
    Supported TIDL layer type ---             Add -- /model.4/m.0/Add 
    Supported TIDL layer type ---            Conv -- /model.4/m.1/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.4/m.1/cv1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.4/m.1/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.4/m.1/cv2/act/Relu 
    Supported TIDL layer type ---             Add -- /model.4/m.1/Add 
    Supported TIDL layer type ---          Concat -- /model.4/Concat 
    Supported TIDL layer type ---            Conv -- /model.4/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.4/cv2/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.5/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.5/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.6/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.6/cv1/act/Relu 
    Supported TIDL layer type ---           Split -- /model.6/Split 
    Supported TIDL layer type ---            Conv -- /model.6/m.0/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.6/m.0/cv1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.6/m.0/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.6/m.0/cv2/act/Relu 
    Supported TIDL layer type ---             Add -- /model.6/m.0/Add 
    Supported TIDL layer type ---            Conv -- /model.6/m.1/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.6/m.1/cv1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.6/m.1/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.6/m.1/cv2/act/Relu 
    Supported TIDL layer type ---             Add -- /model.6/m.1/Add 
    Supported TIDL layer type ---          Concat -- /model.6/Concat 
    Supported TIDL layer type ---            Conv -- /model.6/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.6/cv2/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.7/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.7/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.8/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.8/cv1/act/Relu 
    Supported TIDL layer type ---           Split -- /model.8/Split 
    Supported TIDL layer type ---            Conv -- /model.8/m.0/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.8/m.0/cv1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.8/m.0/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.8/m.0/cv2/act/Relu 
    Supported TIDL layer type ---             Add -- /model.8/m.0/Add 
    Supported TIDL layer type ---          Concat -- /model.8/Concat 
    Supported TIDL layer type ---            Conv -- /model.8/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.8/cv2/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.9/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.9/cv1/act/Relu 
    Supported TIDL layer type ---         MaxPool -- /model.9/m/MaxPool 
    Supported TIDL layer type ---         MaxPool -- /model.9/m_1/MaxPool 
    Supported TIDL layer type ---         MaxPool -- /model.9/m_2/MaxPool 
    Supported TIDL layer type ---          Concat -- /model.9/Concat 
    Supported TIDL layer type ---            Conv -- /model.9/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.9/cv2/act/Relu 
    Supported TIDL layer type ---          Resize -- /model.10/Resize 
    Supported TIDL layer type ---          Concat -- /model.11/Concat 
    Supported TIDL layer type ---            Conv -- /model.12/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.12/cv1/act/Relu 
    Supported TIDL layer type ---           Split -- /model.12/Split 
    Supported TIDL layer type ---            Conv -- /model.12/m.0/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.12/m.0/cv1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.12/m.0/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.12/m.0/cv2/act/Relu 
    Supported TIDL layer type ---          Concat -- /model.12/Concat 
    Supported TIDL layer type ---            Conv -- /model.12/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.12/cv2/act/Relu 
    Supported TIDL layer type ---          Resize -- /model.13/Resize 
    Supported TIDL layer type ---          Concat -- /model.14/Concat 
    Supported TIDL layer type ---            Conv -- /model.15/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.15/cv1/act/Relu 
    Supported TIDL layer type ---           Split -- /model.15/Split 
    Supported TIDL layer type ---            Conv -- /model.15/m.0/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.15/m.0/cv1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.15/m.0/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.15/m.0/cv2/act/Relu 
    Supported TIDL layer type ---          Concat -- /model.15/Concat 
    Supported TIDL layer type ---            Conv -- /model.15/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.15/cv2/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.16/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.16/act/Relu 
    Supported TIDL layer type ---          Concat -- /model.17/Concat 
    Supported TIDL layer type ---            Conv -- /model.18/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.18/cv1/act/Relu 
    Supported TIDL layer type ---           Split -- /model.18/Split 
    Supported TIDL layer type ---            Conv -- /model.18/m.0/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.18/m.0/cv1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.18/m.0/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.18/m.0/cv2/act/Relu 
    Supported TIDL layer type ---          Concat -- /model.18/Concat 
    Supported TIDL layer type ---            Conv -- /model.18/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.18/cv2/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.19/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.19/act/Relu 
    Supported TIDL layer type ---          Concat -- /model.20/Concat 
    Supported TIDL layer type ---            Conv -- /model.21/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.21/cv1/act/Relu 
    Supported TIDL layer type ---           Split -- /model.21/Split 
    Supported TIDL layer type ---            Conv -- /model.21/m.0/cv1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.21/m.0/cv1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.21/m.0/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.21/m.0/cv2/act/Relu 
    Supported TIDL layer type ---          Concat -- /model.21/Concat 
    Supported TIDL layer type ---            Conv -- /model.21/cv2/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.21/cv2/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv3.2/cv3.2.0/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv3.2/cv3.2.0/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv3.2/cv3.2.1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv3.2/cv3.2.1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv3.2/cv3.2.2/Conv 
    Supported TIDL layer type ---            Conv -- /model.22/cv2.2/cv2.2.0/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv2.2/cv2.2.0/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv2.2/cv2.2.1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv2.2/cv2.2.1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv2.2/cv2.2.2/Conv 
    Supported TIDL layer type ---          Concat -- /model.22/Concat_2 
    Supported TIDL layer type ---         Reshape -- /model.22/Reshape_2 
    Supported TIDL layer type ---            Conv -- /model.22/cv3.1/cv3.1.0/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv3.1/cv3.1.0/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv3.1/cv3.1.1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv3.1/cv3.1.1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv3.1/cv3.1.2/Conv 
    Supported TIDL layer type ---            Conv -- /model.22/cv2.1/cv2.1.0/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv2.1/cv2.1.0/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv2.1/cv2.1.1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv2.1/cv2.1.1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv2.1/cv2.1.2/Conv 
    Supported TIDL layer type ---          Concat -- /model.22/Concat_1 
    Supported TIDL layer type ---         Reshape -- /model.22/Reshape_1 
    Supported TIDL layer type ---            Conv -- /model.22/cv3.0/cv3.0.0/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv3.0/cv3.0.0/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv3.0/cv3.0.1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv3.0/cv3.0.1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv3.0/cv3.0.2/Conv 
    Supported TIDL layer type ---            Conv -- /model.22/cv2.0/cv2.0.0/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv2.0/cv2.0.0/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv2.0/cv2.0.1/conv/Conv 
    Supported TIDL layer type ---            Relu -- /model.22/cv2.0/cv2.0.1/act/Relu 
    Supported TIDL layer type ---            Conv -- /model.22/cv2.0/cv2.0.2/Conv 
    Supported TIDL layer type ---          Concat -- /model.22/Concat 
    Supported TIDL layer type ---         Reshape -- /model.22/Reshape 
    Supported TIDL layer type ---          Concat -- /model.22/Concat_3 
    Supported TIDL layer type ---           Split -- /model.22/Split 
    Supported TIDL layer type ---         Sigmoid -- /model.22/Sigmoid 
    Supported TIDL layer type ---         Reshape -- /model.22/dfl/Reshape 
    Supported TIDL layer type ---       Transpose -- /model.22/dfl/Transpose 
    Supported TIDL layer type ---         Softmax -- /model.22/dfl/Softmax 
    Supported TIDL layer type ---       Transpose -- /model.22/dfl/Transpose_1 
    Supported TIDL layer type ---            Conv -- /model.22/dfl/conv/Conv 
    Supported TIDL layer type ---         Reshape -- /model.22/dfl/Reshape_1 
    Supported TIDL layer type ---           Slice -- /model.22/Slice_1 
    Supported TIDL layer type ---             Add -- /model.22/Add_1 
    Supported TIDL layer type ---           Slice -- /model.22/Slice 
    Supported TIDL layer type ---             Sub -- /model.22/Sub 
    Supported TIDL layer type ---             Sub -- /model.22/Sub_1 
    Supported TIDL layer type ---             Add -- /model.22/Add_2 
    Supported TIDL layer type ---             Div -- /model.22/Div_1 
    Supported TIDL layer type ---          Concat -- /model.22/Concat_4 
    Supported TIDL layer type ---             Mul -- /model.22/Mul_2 
    Supported TIDL layer type ---          Concat -- /model.22/Concat_5 
    
    Preliminary subgraphs created = 1 
    Final number of subgraphs created are : 1, - Offloaded Nodes - 177, Total Nodes - 177 
    INFORMATION -- [TIDL_ResizeLayer]  Any resize ratio which is power of 2 and greater than 4 will be placed by combination of 4x4 resize layer and 2x2 resize layer. For example a 8x8 resize will be replaced by 4x4 resize followed by 2x2 resize.  
    INFORMATION -- [TIDL_ResizeLayer]  Any resize ratio which is power of 2 and greater than 4 will be placed by combination of 4x4 resize layer and 2x2 resize layer. For example a 8x8 resize will be replaced by 4x4 resize followed by 2x2 resize.  
    Running runtimes graphviz - /home/mugu/edgeai-tidl-tools/tidl_tools/tidl_graphVisualiser_runtimes.out /home/mugu/edgeai-tidl-tools/model-artifacts/tidl_output/allowedNode.txt /home/mugu/edgeai-tidl-tools/model-artifacts/tidl_output/tempDir/graphvizInfo.txt /home/mugu/edgeai-tidl-tools/model-artifacts/tidl_output/tempDir/runtimes_visualization.svg 
    *** In TIDL_createStateImportFunc *** 
    Compute on node : TIDLExecutionProvider_TIDL_0_0
      0,            Conv, 3, 1, images, /model.0/conv/Conv_output_0
      1,            Relu, 1, 1, /model.0/conv/Conv_output_0, /model.0/act/Relu_output_0
      2,            Conv, 3, 1, /model.0/act/Relu_output_0, /model.1/conv/Conv_output_0
      3,            Relu, 1, 1, /model.1/conv/Conv_output_0, /model.1/act/Relu_output_0
      4,            Conv, 3, 1, /model.1/act/Relu_output_0, /model.2/cv1/conv/Conv_output_0
      5,            Relu, 1, 1, /model.2/cv1/conv/Conv_output_0, /model.2/cv1/act/Relu_output_0
      6,           Split, 1, 2, /model.2/cv1/act/Relu_output_0, /model.2/Split_output_0
      7,            Conv, 3, 1, /model.2/Split_output_1, /model.2/m.0/cv1/conv/Conv_output_0
      8,            Relu, 1, 1, /model.2/m.0/cv1/conv/Conv_output_0, /model.2/m.0/cv1/act/Relu_output_0
      9,            Conv, 3, 1, /model.2/m.0/cv1/act/Relu_output_0, /model.2/m.0/cv2/conv/Conv_output_0
     10,            Relu, 1, 1, /model.2/m.0/cv2/conv/Conv_output_0, /model.2/m.0/cv2/act/Relu_output_0
     11,             Add, 2, 1, /model.2/Split_output_1, /model.2/m.0/Add_output_0
     12,          Concat, 3, 1, /model.2/Split_output_0, /model.2/Concat_output_0
     13,            Conv, 3, 1, /model.2/Concat_output_0, /model.2/cv2/conv/Conv_output_0
     14,            Relu, 1, 1, /model.2/cv2/conv/Conv_output_0, /model.2/cv2/act/Relu_output_0
     15,            Conv, 3, 1, /model.2/cv2/act/Relu_output_0, /model.3/conv/Conv_output_0
     16,            Relu, 1, 1, /model.3/conv/Conv_output_0, /model.3/act/Relu_output_0
     17,            Conv, 3, 1, /model.3/act/Relu_output_0, /model.4/cv1/conv/Conv_output_0
     18,            Relu, 1, 1, /model.4/cv1/conv/Conv_output_0, /model.4/cv1/act/Relu_output_0
     19,           Split, 1, 2, /model.4/cv1/act/Relu_output_0, /model.4/Split_output_0
     20,            Conv, 3, 1, /model.4/Split_output_1, /model.4/m.0/cv1/conv/Conv_output_0
     21,            Relu, 1, 1, /model.4/m.0/cv1/conv/Conv_output_0, /model.4/m.0/cv1/act/Relu_output_0
     22,            Conv, 3, 1, /model.4/m.0/cv1/act/Relu_output_0, /model.4/m.0/cv2/conv/Conv_output_0
     23,            Relu, 1, 1, /model.4/m.0/cv2/conv/Conv_output_0, /model.4/m.0/cv2/act/Relu_output_0
     24,             Add, 2, 1, /model.4/Split_output_1, /model.4/m.0/Add_output_0
     25,            Conv, 3, 1, /model.4/m.0/Add_output_0, /model.4/m.1/cv1/conv/Conv_output_0
     26,            Relu, 1, 1, /model.4/m.1/cv1/conv/Conv_output_0, /model.4/m.1/cv1/act/Relu_output_0
     27,            Conv, 3, 1, /model.4/m.1/cv1/act/Relu_output_0, /model.4/m.1/cv2/conv/Conv_output_0
     28,            Relu, 1, 1, /model.4/m.1/cv2/conv/Conv_output_0, /model.4/m.1/cv2/act/Relu_output_0
     29,             Add, 2, 1, /model.4/m.0/Add_output_0, /model.4/m.1/Add_output_0
     30,          Concat, 4, 1, /model.4/Split_output_0, /model.4/Concat_output_0
     31,            Conv, 3, 1, /model.4/Concat_output_0, /model.4/cv2/conv/Conv_output_0
     32,            Relu, 1, 1, /model.4/cv2/conv/Conv_output_0, /model.4/cv2/act/Relu_output_0
     33,            Conv, 3, 1, /model.4/cv2/act/Relu_output_0, /model.5/conv/Conv_output_0
     34,            Relu, 1, 1, /model.5/conv/Conv_output_0, /model.5/act/Relu_output_0
     35,            Conv, 3, 1, /model.5/act/Relu_output_0, /model.6/cv1/conv/Conv_output_0
     36,            Relu, 1, 1, /model.6/cv1/conv/Conv_output_0, /model.6/cv1/act/Relu_output_0
     37,           Split, 1, 2, /model.6/cv1/act/Relu_output_0, /model.6/Split_output_0
     38,            Conv, 3, 1, /model.6/Split_output_1, /model.6/m.0/cv1/conv/Conv_output_0
     39,            Relu, 1, 1, /model.6/m.0/cv1/conv/Conv_output_0, /model.6/m.0/cv1/act/Relu_output_0
     40,            Conv, 3, 1, /model.6/m.0/cv1/act/Relu_output_0, /model.6/m.0/cv2/conv/Conv_output_0
     41,            Relu, 1, 1, /model.6/m.0/cv2/conv/Conv_output_0, /model.6/m.0/cv2/act/Relu_output_0
     42,             Add, 2, 1, /model.6/Split_output_1, /model.6/m.0/Add_output_0
     43,            Conv, 3, 1, /model.6/m.0/Add_output_0, /model.6/m.1/cv1/conv/Conv_output_0
     44,            Relu, 1, 1, /model.6/m.1/cv1/conv/Conv_output_0, /model.6/m.1/cv1/act/Relu_output_0
     45,            Conv, 3, 1, /model.6/m.1/cv1/act/Relu_output_0, /model.6/m.1/cv2/conv/Conv_output_0
     46,            Relu, 1, 1, /model.6/m.1/cv2/conv/Conv_output_0, /model.6/m.1/cv2/act/Relu_output_0
     47,             Add, 2, 1, /model.6/m.0/Add_output_0, /model.6/m.1/Add_output_0
     48,          Concat, 4, 1, /model.6/Split_output_0, /model.6/Concat_output_0
     49,            Conv, 3, 1, /model.6/Concat_output_0, /model.6/cv2/conv/Conv_output_0
     50,            Relu, 1, 1, /model.6/cv2/conv/Conv_output_0, /model.6/cv2/act/Relu_output_0
     51,            Conv, 3, 1, /model.6/cv2/act/Relu_output_0, /model.7/conv/Conv_output_0
     52,            Relu, 1, 1, /model.7/conv/Conv_output_0, /model.7/act/Relu_output_0
     53,            Conv, 3, 1, /model.7/act/Relu_output_0, /model.8/cv1/conv/Conv_output_0
     54,            Relu, 1, 1, /model.8/cv1/conv/Conv_output_0, /model.8/cv1/act/Relu_output_0
     55,           Split, 1, 2, /model.8/cv1/act/Relu_output_0, /model.8/Split_output_0
     56,            Conv, 3, 1, /model.8/Split_output_1, /model.8/m.0/cv1/conv/Conv_output_0
     57,            Relu, 1, 1, /model.8/m.0/cv1/conv/Conv_output_0, /model.8/m.0/cv1/act/Relu_output_0
     58,            Conv, 3, 1, /model.8/m.0/cv1/act/Relu_output_0, /model.8/m.0/cv2/conv/Conv_output_0
     59,            Relu, 1, 1, /model.8/m.0/cv2/conv/Conv_output_0, /model.8/m.0/cv2/act/Relu_output_0
     60,             Add, 2, 1, /model.8/Split_output_1, /model.8/m.0/Add_output_0
     61,          Concat, 3, 1, /model.8/Split_output_0, /model.8/Concat_output_0
     62,            Conv, 3, 1, /model.8/Concat_output_0, /model.8/cv2/conv/Conv_output_0
     63,            Relu, 1, 1, /model.8/cv2/conv/Conv_output_0, /model.8/cv2/act/Relu_output_0
     64,            Conv, 3, 1, /model.8/cv2/act/Relu_output_0, /model.9/cv1/conv/Conv_output_0
     65,            Relu, 1, 1, /model.9/cv1/conv/Conv_output_0, /model.9/cv1/act/Relu_output_0
     66,         MaxPool, 1, 1, /model.9/cv1/act/Relu_output_0, /model.9/m/MaxPool_output_0
     67,         MaxPool, 1, 1, /model.9/m/MaxPool_output_0, /model.9/m_1/MaxPool_output_0
     68,         MaxPool, 1, 1, /model.9/m_1/MaxPool_output_0, /model.9/m_2/MaxPool_output_0
     69,          Concat, 4, 1, /model.9/cv1/act/Relu_output_0, /model.9/Concat_output_0
     70,            Conv, 3, 1, /model.9/Concat_output_0, /model.9/cv2/conv/Conv_output_0
     71,            Relu, 1, 1, /model.9/cv2/conv/Conv_output_0, /model.9/cv2/act/Relu_output_0
     72,          Resize, 3, 1, /model.9/cv2/act/Relu_output_0, /model.10/Resize_output_0
     73,          Concat, 2, 1, /model.10/Resize_output_0, /model.11/Concat_output_0
     74,            Conv, 3, 1, /model.11/Concat_output_0, /model.12/cv1/conv/Conv_output_0
     75,            Relu, 1, 1, /model.12/cv1/conv/Conv_output_0, /model.12/cv1/act/Relu_output_0
     76,           Split, 1, 2, /model.12/cv1/act/Relu_output_0, /model.12/Split_output_0
     77,            Conv, 3, 1, /model.12/Split_output_1, /model.12/m.0/cv1/conv/Conv_output_0
     78,            Relu, 1, 1, /model.12/m.0/cv1/conv/Conv_output_0, /model.12/m.0/cv1/act/Relu_output_0
     79,            Conv, 3, 1, /model.12/m.0/cv1/act/Relu_output_0, /model.12/m.0/cv2/conv/Conv_output_0
     80,            Relu, 1, 1, /model.12/m.0/cv2/conv/Conv_output_0, /model.12/m.0/cv2/act/Relu_output_0
     81,          Concat, 3, 1, /model.12/Split_output_0, /model.12/Concat_output_0
     82,            Conv, 3, 1, /model.12/Concat_output_0, /model.12/cv2/conv/Conv_output_0
     83,            Relu, 1, 1, /model.12/cv2/conv/Conv_output_0, /model.12/cv2/act/Relu_output_0
     84,          Resize, 3, 1, /model.12/cv2/act/Relu_output_0, /model.13/Resize_output_0
     85,          Concat, 2, 1, /model.13/Resize_output_0, /model.14/Concat_output_0
     86,            Conv, 3, 1, /model.14/Concat_output_0, /model.15/cv1/conv/Conv_output_0
     87,            Relu, 1, 1, /model.15/cv1/conv/Conv_output_0, /model.15/cv1/act/Relu_output_0
     88,           Split, 1, 2, /model.15/cv1/act/Relu_output_0, /model.15/Split_output_0
     89,            Conv, 3, 1, /model.15/Split_output_1, /model.15/m.0/cv1/conv/Conv_output_0
     90,            Relu, 1, 1, /model.15/m.0/cv1/conv/Conv_output_0, /model.15/m.0/cv1/act/Relu_output_0
     91,            Conv, 3, 1, /model.15/m.0/cv1/act/Relu_output_0, /model.15/m.0/cv2/conv/Conv_output_0
     92,            Relu, 1, 1, /model.15/m.0/cv2/conv/Conv_output_0, /model.15/m.0/cv2/act/Relu_output_0
     93,          Concat, 3, 1, /model.15/Split_output_0, /model.15/Concat_output_0
     94,            Conv, 3, 1, /model.15/Concat_output_0, /model.15/cv2/conv/Conv_output_0
     95,            Relu, 1, 1, /model.15/cv2/conv/Conv_output_0, /model.15/cv2/act/Relu_output_0
     96,            Conv, 3, 1, /model.15/cv2/act/Relu_output_0, /model.22/cv2.0/cv2.0.0/conv/Conv_output_0
     97,            Relu, 1, 1, /model.22/cv2.0/cv2.0.0/conv/Conv_output_0, /model.22/cv2.0/cv2.0.0/act/Relu_output_0
     98,            Conv, 3, 1, /model.22/cv2.0/cv2.0.0/act/Relu_output_0, /model.22/cv2.0/cv2.0.1/conv/Conv_output_0
     99,            Relu, 1, 1, /model.22/cv2.0/cv2.0.1/conv/Conv_output_0, /model.22/cv2.0/cv2.0.1/act/Relu_output_0
    100,            Conv, 3, 1, /model.22/cv2.0/cv2.0.1/act/Relu_output_0, /model.22/cv2.0/cv2.0.2/Conv_output_0
    101,            Conv, 3, 1, /model.15/cv2/act/Relu_output_0, /model.22/cv3.0/cv3.0.0/conv/Conv_output_0
    102,            Relu, 1, 1, /model.22/cv3.0/cv3.0.0/conv/Conv_output_0, /model.22/cv3.0/cv3.0.0/act/Relu_output_0
    103,            Conv, 3, 1, /model.22/cv3.0/cv3.0.0/act/Relu_output_0, /model.22/cv3.0/cv3.0.1/conv/Conv_output_0
    104,            Relu, 1, 1, /model.22/cv3.0/cv3.0.1/conv/Conv_output_0, /model.22/cv3.0/cv3.0.1/act/Relu_output_0
    105,            Conv, 3, 1, /model.22/cv3.0/cv3.0.1/act/Relu_output_0, /model.22/cv3.0/cv3.0.2/Conv_output_0
    106,          Concat, 2, 1, /model.22/cv2.0/cv2.0.2/Conv_output_0, /model.22/Concat_output_0
    107,         Reshape, 2, 1, /model.22/Concat_output_0, /model.22/Reshape_output_0
    108,            Conv, 3, 1, /model.15/cv2/act/Relu_output_0, /model.16/conv/Conv_output_0
    109,            Relu, 1, 1, /model.16/conv/Conv_output_0, /model.16/act/Relu_output_0
    110,          Concat, 2, 1, /model.16/act/Relu_output_0, /model.17/Concat_output_0
    111,            Conv, 3, 1, /model.17/Concat_output_0, /model.18/cv1/conv/Conv_output_0
    112,            Relu, 1, 1, /model.18/cv1/conv/Conv_output_0, /model.18/cv1/act/Relu_output_0
    113,           Split, 1, 2, /model.18/cv1/act/Relu_output_0, /model.18/Split_output_0
    114,            Conv, 3, 1, /model.18/Split_output_1, /model.18/m.0/cv1/conv/Conv_output_0
    115,            Relu, 1, 1, /model.18/m.0/cv1/conv/Conv_output_0, /model.18/m.0/cv1/act/Relu_output_0
    116,            Conv, 3, 1, /model.18/m.0/cv1/act/Relu_output_0, /model.18/m.0/cv2/conv/Conv_output_0
    117,            Relu, 1, 1, /model.18/m.0/cv2/conv/Conv_output_0, /model.18/m.0/cv2/act/Relu_output_0
    118,          Concat, 3, 1, /model.18/Split_output_0, /model.18/Concat_output_0
    119,            Conv, 3, 1, /model.18/Concat_output_0, /model.18/cv2/conv/Conv_output_0
    120,            Relu, 1, 1, /model.18/cv2/conv/Conv_output_0, /model.18/cv2/act/Relu_output_0
    121,            Conv, 3, 1, /model.18/cv2/act/Relu_output_0, /model.22/cv2.1/cv2.1.0/conv/Conv_output_0
    122,            Relu, 1, 1, /model.22/cv2.1/cv2.1.0/conv/Conv_output_0, /model.22/cv2.1/cv2.1.0/act/Relu_output_0
    123,            Conv, 3, 1, /model.22/cv2.1/cv2.1.0/act/Relu_output_0, /model.22/cv2.1/cv2.1.1/conv/Conv_output_0
    124,            Relu, 1, 1, /model.22/cv2.1/cv2.1.1/conv/Conv_output_0, /model.22/cv2.1/cv2.1.1/act/Relu_output_0
    125,            Conv, 3, 1, /model.22/cv2.1/cv2.1.1/act/Relu_output_0, /model.22/cv2.1/cv2.1.2/Conv_output_0
    126,            Conv, 3, 1, /model.18/cv2/act/Relu_output_0, /model.22/cv3.1/cv3.1.0/conv/Conv_output_0
    127,            Relu, 1, 1, /model.22/cv3.1/cv3.1.0/conv/Conv_output_0, /model.22/cv3.1/cv3.1.0/act/Relu_output_0
    128,            Conv, 3, 1, /model.22/cv3.1/cv3.1.0/act/Relu_output_0, /model.22/cv3.1/cv3.1.1/conv/Conv_output_0
    129,            Relu, 1, 1, /model.22/cv3.1/cv3.1.1/conv/Conv_output_0, /model.22/cv3.1/cv3.1.1/act/Relu_output_0
    130,            Conv, 3, 1, /model.22/cv3.1/cv3.1.1/act/Relu_output_0, /model.22/cv3.1/cv3.1.2/Conv_output_0
    131,          Concat, 2, 1, /model.22/cv2.1/cv2.1.2/Conv_output_0, /model.22/Concat_1_output_0
    132,         Reshape, 2, 1, /model.22/Concat_1_output_0, /model.22/Reshape_1_output_0
    133,            Conv, 3, 1, /model.18/cv2/act/Relu_output_0, /model.19/conv/Conv_output_0
    134,            Relu, 1, 1, /model.19/conv/Conv_output_0, /model.19/act/Relu_output_0
    135,          Concat, 2, 1, /model.19/act/Relu_output_0, /model.20/Concat_output_0
    136,            Conv, 3, 1, /model.20/Concat_output_0, /model.21/cv1/conv/Conv_output_0
    137,            Relu, 1, 1, /model.21/cv1/conv/Conv_output_0, /model.21/cv1/act/Relu_output_0
    138,           Split, 1, 2, /model.21/cv1/act/Relu_output_0, /model.21/Split_output_0
    139,            Conv, 3, 1, /model.21/Split_output_1, /model.21/m.0/cv1/conv/Conv_output_0
    140,            Relu, 1, 1, /model.21/m.0/cv1/conv/Conv_output_0, /model.21/m.0/cv1/act/Relu_output_0
    141,            Conv, 3, 1, /model.21/m.0/cv1/act/Relu_output_0, /model.21/m.0/cv2/conv/Conv_output_0
    142,            Relu, 1, 1, /model.21/m.0/cv2/conv/Conv_output_0, /model.21/m.0/cv2/act/Relu_output_0
    143,          Concat, 3, 1, /model.21/Split_output_0, /model.21/Concat_output_0
    144,            Conv, 3, 1, /model.21/Concat_output_0, /model.21/cv2/conv/Conv_output_0
    145,            Relu, 1, 1, /model.21/cv2/conv/Conv_output_0, /model.21/cv2/act/Relu_output_0
    146,            Conv, 3, 1, /model.21/cv2/act/Relu_output_0, /model.22/cv2.2/cv2.2.0/conv/Conv_output_0
    147,            Relu, 1, 1, /model.22/cv2.2/cv2.2.0/conv/Conv_output_0, /model.22/cv2.2/cv2.2.0/act/Relu_output_0
    148,            Conv, 3, 1, /model.22/cv2.2/cv2.2.0/act/Relu_output_0, /model.22/cv2.2/cv2.2.1/conv/Conv_output_0
    149,            Relu, 1, 1, /model.22/cv2.2/cv2.2.1/conv/Conv_output_0, /model.22/cv2.2/cv2.2.1/act/Relu_output_0
    150,            Conv, 3, 1, /model.22/cv2.2/cv2.2.1/act/Relu_output_0, /model.22/cv2.2/cv2.2.2/Conv_output_0
    151,            Conv, 3, 1, /model.21/cv2/act/Relu_output_0, /model.22/cv3.2/cv3.2.0/conv/Conv_output_0
    152,            Relu, 1, 1, /model.22/cv3.2/cv3.2.0/conv/Conv_output_0, /model.22/cv3.2/cv3.2.0/act/Relu_output_0
    153,            Conv, 3, 1, /model.22/cv3.2/cv3.2.0/act/Relu_output_0, /model.22/cv3.2/cv3.2.1/conv/Conv_output_0
    154,            Relu, 1, 1, /model.22/cv3.2/cv3.2.1/conv/Conv_output_0, /model.22/cv3.2/cv3.2.1/act/Relu_output_0
    155,            Conv, 3, 1, /model.22/cv3.2/cv3.2.1/act/Relu_output_0, /model.22/cv3.2/cv3.2.2/Conv_output_0
    156,          Concat, 2, 1, /model.22/cv2.2/cv2.2.2/Conv_output_0, /model.22/Concat_2_output_0
    157,         Reshape, 2, 1, /model.22/Concat_2_output_0, /model.22/Reshape_2_output_0
    158,          Concat, 3, 1, /model.22/Reshape_output_0, /model.22/Concat_3_output_0
    159,           Split, 1, 2, /model.22/Concat_3_output_0, /model.22/Split_output_0
    160,         Reshape, 2, 1, /model.22/Split_output_0, /model.22/dfl/Reshape_output_0
    161,       Transpose, 1, 1, /model.22/dfl/Reshape_output_0, /model.22/dfl/Transpose_output_0
    162,         Softmax, 1, 1, /model.22/dfl/Transpose_output_0, /model.22/dfl/Softmax_output_0
    163,       Transpose, 1, 1, /model.22/dfl/Softmax_output_0, /model.22/dfl/Transpose_1_output_0
    164,            Conv, 2, 1, /model.22/dfl/Transpose_1_output_0, /model.22/dfl/conv/Conv_output_0
    165,         Reshape, 2, 1, /model.22/dfl/conv/Conv_output_0, /model.22/dfl/Reshape_1_output_0
    166,           Slice, 4, 1, /model.22/dfl/Reshape_1_output_0, /model.22/Slice_output_0
    167,             Sub, 2, 1, /model.22/Constant_9_output_0, /model.22/Sub_output_0
    168,           Slice, 4, 1, /model.22/dfl/Reshape_1_output_0, /model.22/Slice_1_output_0
    169,             Add, 2, 1, /model.22/Constant_9_output_0, /model.22/Add_1_output_0
    170,             Add, 2, 1, /model.22/Sub_output_0, /model.22/Add_2_output_0
    171,             Div, 2, 1, /model.22/Add_2_output_0, /model.22/Div_1_output_0
    172,             Sub, 2, 1, /model.22/Add_1_output_0, /model.22/Sub_1_output_0
    173,          Concat, 2, 1, /model.22/Div_1_output_0, /model.22/Concat_4_output_0
    174,             Mul, 2, 1, /model.22/Concat_4_output_0, /model.22/Mul_2_output_0
    175,         Sigmoid, 1, 1, /model.22/Split_output_1, /model.22/Sigmoid_output_0
    176,          Concat, 2, 1, /model.22/Mul_2_output_0, output0
    
    Input tensor name -  images 
    Output tensor name - output0 
    Input shape: [1, 3, 640, 640]
    Input "images": tensor(float)
     Graph Domain TO version : 11In TIDL_onnxRtImportInit subgraph_name=output0
    Layer 0, subgraph id output0, name=output0
    Layer 1, subgraph id output0, name=images
    In TIDL_runtimesOptimizeNet: LayerIndex = 179, dataIndex = 187 
    WARNING: Batch Norm Layer /model.22/Sub's coeff cannot be found(or not match) in coef file, Random bias will be generated! Only for evaluation usage! Results are all random!
    WARNING: Batch Norm Layer /model.22/Div_1's coeff cannot be found(or not match) in coef file, Random bias will be generated! Only for evaluation usage! Results are all random!
    Error: Layer 126, /model.22/Sub_1:/model.22/Sub_1_output_0 is missing inputs in the network and cannot be topologically sorted
      Input 0: /model.22/Add_1_output_0, dataId=124
      Input 1: /model.22/Sub_output_0, dataId=254
    
     ************** Frame index 1 : Running float import ************* 
    In TIDL_runtimesPostProcessNet 
    In TIDL_runtimesPostProcessNet 1
    In TIDL_runtimesPostProcessNet 2
    In TIDL_runtimesPostProcessNet 3
    INFORMATION: [TIDL_ResizeLayer] /model.10/Resize Any resize ratio which is power of 2 and greater than 4 will be placed by combination of 4x4 resize layer and 2x2 resize layer. For example a 8x8 resize will be replaced by 4x4 resize followed by 2x2 resize.
    INFORMATION: [TIDL_ResizeLayer] /model.13/Resize Any resize ratio which is power of 2 and greater than 4 will be placed by combination of 4x4 resize layer and 2x2 resize layer. For example a 8x8 resize will be replaced by 4x4 resize followed by 2x2 resize.
    ****************************************************
    **          2 WARNINGS          0 ERRORS          **
    ****************************************************
    In TIDL_runtimesPostProcessNet 4
    ************ in TIDL_subgraphRtCreate ************ 
     The soft limit is 2048
    The hard limit is 2048
    MEM: Init ... !!!
    MEM: Init ... Done !!!
     0.0s:  VX_ZONE_INIT:Enabled
     0.4s:  VX_ZONE_ERROR:Enabled
     0.5s:  VX_ZONE_WARNING:Enabled
     0.1382s:  VX_ZONE_INIT:[tivxInit:185] Initialization Done !!!
    ************ TIDL_subgraphRtCreate done ************ 
     Warning : Couldn't find corresponding ioBuf tensor for onnx tensor with matching name 
    2024-03-22 12:05:15.362450533 [W:onnxruntime:, execution_frame.cc:835 VerifyOutputSizes] Expected shape from model of {1,5,8400} does not match actual shape of {} for output output0
    *******   In TIDL_subgraphRtInvoke  ******** 
    Segmentation fault (core dumped)
    
    

    Model link: 1145.model.zip

  • Hi, Thanks for continued interest in this issue.

    However due to limited bandwidth, i will circle back the in 1 week.

    Thanks 

  • Hello
    We have some deadlines, kindly look out the issues and reply us soon!
    Thank you

  • Hi,

    Regarding prioritization of this issue can you please connect with local FAE.

    Thanks

  • Hello 

    Where can I get the link or email for Local FAE? If possible kindly share here for reference. 

    Thank you

  • Hi,

    You can connect with your business team regarding same.

    Thanks

  • Hello 

    Is the TI team is available now, can anyone take over this issue kindly? 

    thank you

  • Per the discussion at: https://github.com/TexasInstruments/edgeai-modeloptimization/issues/2

    Attached a sample yolov8 lite model and training script changes.

    Also includes TIDL compilation options used in edgeai-benchmark in the config yaml file corresponding to thsi config: https://github.com/TexasInstruments/edgeai-benchmark/blob/main/configs/detection_v2.py#L169

  • Hello 

    Can you please elaborate the changes you have made in yolov8 for my reference? 

    Thank you

  • Hello 

    One last doubt in my custom yolov8 model I had 3maxpool layers with kernal shape 5. So I replaced it with 3 maxpool layers with kernal shape 3. But in your example I could see 6 maxpool layers with 3 kernal shape. Can you please assist me regarding this. Also kindly look out my custom model zip I attached in this thread up

    thank you

  • kernel_size 3 is what is supported for Maxpool currently in TIDL.

    This was trained and by using the option --model-surgery 2 to the train.py script that I shared and the model optimization tool takes care of the layer changes such as changing activation layers from SiLU to ReLU and breaking down large Maxpool to 3x3.

    Other wise you can compare both the onnx graphs and understand the exact changes.

  • Hello  
    How many 3x3 max pool layers is required to replace one 5x5 maxpool layer?

    Thanks

  • Hi 

    Two 3x3 max pool is required to replace one 5x5 maxpool. See more details at https://github.com/TexasInstruments/edgeai-yolov5.

    --Joy

  • Corrected typo above but posting again to avoid confusion: changing activation layers from SiLU to ReLU and breaking down large Maxpool to 3x3.

  • Hello
    Thanks for the reply!
    One last question:
    1. Can we use artifacts generated from tidl tools to run inference using edgeai gst apps for my custom pretrained model? My model has been successfully compiled using tidl tools.
    2. I have model with number of channel as 1, I try to compile the model using edgeai benchmark, But I ended up by getting shape mismatch error. How can I can add or modify model dict to get number of channels as 1?

            'imageseg-3': dict(
                task_type='segmentation',
                calibration_dataset=imageseg_calib_dataset,
                input_dataset=imageseg_val_dataset,
                preprocess=preproc_transforms.get_transform_jai((128,96), (128,96), backend='cv2', interpolation=cv2.INTER_LINEAR),
                session=sessions.ONNXRTSession(**jai_session_cfg,
                    runtime_options=settings.runtime_options_onnx_np2(),
                    model_path=f'/home/mugu/edgeai-benchmark/model/seat.onnx'),
                postprocess=postproc_transforms.get_transform_segmentation_onnx(),
                model_info=dict(metric_reference={'accuracy_mean_iou%':57.77})
            )

    Thanks in advance

  • >>> Can we use artifacts generated from tidl tools

    What exactly do you mean by this? Did you compile by using edgeai-benchmark? If so generally it will work in edgeai-gst-apps. However, yolov8 has special post processing that is not yet support in edgeai-gist-apps - so you will have to modify the post processing in edgeai-gist-apps to support yolov8.

    The logic is under the flag logits_bbox_to_bbox_ls in edgeai-benchmark postprocess - the logic under that flag is not yet implemented in edgeai-gst-apps:

    https://github.com/TexasInstruments/edgeai-benchmark/blob/main/edgeai_benchmark/postprocess/__init__.py#L67

  • Hello
    Here are the exact query:

    1) Can we use artifacts compiled from edgeai tidl tools to deploy on target using gst apps?
    2) I have successfully compiled my segmentation model using tidl tools, Can I compile the same model  using edgeai benchmark too?I could see there is default preprocessing function processing images in RGB scale i.e, Number of channels=3. But my model has gray scale, i.e number of channels=1.

    Thanks

  • The binaries generated in edgeai-tidl-tools and edgeai-benchmark are same, but currently there are some differences, which you can manually fix:

    (1) The directly structure should be same as what is generated by edgeai-benchmark (i.e. with the model folder and the artifacts folder)

    (2) param.yaml should be there in the format that edgeai-benchmark expects.

    I suspect that these details may be a bit overwhelming for you. Why not use edgeai-benchmark itself to compile? Especially since there is an example YOLOV8 model in edgeai-benchark. 

  • Hello 

    This question is related to another model which is a pretrained segmentation model, this model has number of input channels as 1. I could see default configuration dictionary in edgeai benchmark preprocess images in RGB format. How can I changes to GRAY scaling that is num channel as 1

    Thanks

  • Can you please open another e2e thread for that - mixing these two in same thread might be confusing.