Tool/software:
Hi Expert,
I use Ubuntu 22.04 and Python 3.10.14 built with pyenv virtual environment to use edgeai-tidl-tools. I followed the steps in "Setup on X86_PC".git clone https://github.com/TexasInstruments/edgeai-tidl-tools.git cd edgeai-tidl-tools export SOC=am67a source ./setup.sh cd edgeai-tidl-tools export SOC=am67a export TIDL_TOOLS_PATH=$(pwd)/tidl_tools export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TIDL_TOOLS_PATH export ARM64_GCC_PATH=$(pwd)/gcc-arm-9.2-2019.12-x86_64-aarch64-none-linux-gnu mkdir build && cd build cmake ../examples && make -j && cd .. source ./scripts/run_python_examples.sh
While executing source ./scripts/run_python_examples.sh, I encountered AttributeError: 'InferenceSession' object has no attribute 'get_TI_benchmark_data'.ace428@ace428-System-Product-Name:~/Desktop/edgeai-tidl-tools$ source ./scripts/run_python_examples.sh X64 Architecture Running 4 Models - ['cl-tfl-mobilenet_v1_1.0_224', 'ss-tfl-deeplabv3_mnv2_ade20k_float', 'od-tfl-ssd_mobilenet_v2_300_float', 'od-tfl-ssdlite_mobiledet_dsp_320x320_coco'] Running_Model : cl-tfl-mobilenet_v1_1.0_224 Running_Model : ss-tfl-deeplabv3_mnv2_ade20k_float Running_Model : od-tfl-ssd_mobilenet_v2_300_float Running_Model : od-tfl-ssdlite_mobiledet_dsp_320x320_coco ========================= [Model Compilation Started] ========================= Model compilation will perform the following stages: 1. Parsing 2. Graph Optimization 3. Quantization & Calibration 4. Memory Planning ============================== [Version Summary] ============================== ------------------------------------------------------------------------------- | TIDL Tools Version | 10_00_04_00 | ------------------------------------------------------------------------------- | C7x Firmware Version | 10_00_02_00 | ------------------------------------------------------------------------------- ============================== [Parsing Started] ============================== [TIDL Import] [PARSER] 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 ========================= [Model Compilation Started] ========================= Model compilation will perform the following stages: 1. Parsing 2. Graph Optimization 3. Quantization & Calibration 4. Memory Planning ============================== [Version Summary] ============================== ========================= [Model Compilation Started] ========================= Model compilation will perform the following stages: 1. Parsing 2. Graph Optimization 3. Quantization & Calibration 4. Memory Planning ============================== [Version Summary] ============================== ------------------------------------------------------------------------------- | TIDL Tools Version | 10_00_04_00 | ------------------------------------------------------------------------------- | C7x Firmware Version | 10_00_02_00 | ------------------------------------------------------------------------------- ============================== [Parsing Started] ============================== [TIDL Import] [PARSER] 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 ------------------------------------------------------------------------------- | TIDL Tools Version | 10_00_04_00 | ------------------------------------------------------------------------------- | C7x Firmware Version | 10_00_02_00 | ------------------------------------------------------------------------------- ============================== [Parsing Started] ============================== TIDL Meta pipeLine (proto) file : ../../../models/public/ssdlite_mobiledet_dsp_320x320_coco_20200519.prototxt ========================= [Model Compilation Started] ========================= Model compilation will perform the following stages: 1. Parsing 2. Graph Optimization 3. Quantization & Calibration 4. Memory Planning ============================== [Version Summary] ============================== Number of OD backbone nodes = 112 Size of odBackboneNodeIds = 112 [TIDL Import] WARNING: Kernel with non-power of 2 in [] is not optimal ------------------------------------------------------------------------------- | TIDL Tools Version | 10_00_04_00 | ------------------------------------------------------------------------------- | C7x Firmware Version | 10_00_02_00 | ------------------------------------------------------------------------------- ============================== [Parsing Started] ============================== Total Nodes = 81 Number of OD backbone nodes = 89 Size of odBackboneNodeIds = 89 ------------------------------------------------------------------------------- | Core | No. of Nodes | Number of Subgraphs | ------------------------------------------------------------------------------- | C7x | 81 | 1 | | CPU | 0 | x | ------------------------------------------------------------------------------- ============================= [Parsing Completed] ============================= Total Nodes = 34 ------------------------------------------------------------------------------- | Core | No. of Nodes | Number of Subgraphs | ------------------------------------------------------------------------------- | C7x | 34 | 1 | | CPU | 0 | x | ------------------------------------------------------------------------------- ============================= [Parsing Completed] ============================= Total Nodes = 129 ------------------------------------------------------------------------------- | Core | No. of Nodes | Number of Subgraphs | ------------------------------------------------------------------------------- | C7x | 129 | 1 | | CPU | 0 | x | ------------------------------------------------------------------------------- ============================= [Parsing Completed] ============================= =================== [Optimization for subgraph_201 started] =================== Total Nodes = 107 ------------------------------------------------------------------------------- | Core | No. of Nodes | Number of Subgraphs | ------------------------------------------------------------------------------- | C7x | 107 | 1 | | CPU | 0 | x | ------------------------------------------------------------------------------- ============================= [Parsing Completed] ============================= TF Meta pipeLine (proto) file : ../../../models/public/ssdlite_mobiledet_dsp_320x320_coco_20200519.prototxt num_classes : 91 y_scale : 10.000000 x_scale : 10.000000 w_scale : 5.000000 h_scale : 5.000000 num_keypoints : 5.000000 score_threshold : 0.600000 iou_threshold : 0.450000 max_detections_per_class : 200 max_total_detections : 100 scales, height_stride, width_stride, height_offset, width_offset 0.2000000, -1.0000000, -1.0000000, -1.0000000, -1.0000000 0.3500000, -1.0000000, -1.0000000, -1.0000000, -1.0000000 0.5000000, -1.0000000, -1.0000000, -1.0000000, -1.0000000 0.6500000, -1.0000000, -1.0000000, -1.0000000, -1.0000000 0.8000000, -1.0000000, -1.0000000, -1.0000000, -1.0000000 0.9500000, -1.0000000, -1.0000000, -1.0000000, -1.0000000 aspect_ratios 1.0000000 2.0000000 0.5000000 3.0000000 0.3333000 ==================== [Optimization for subgraph_86 started] ==================== =================== [Optimization for subgraph_321 started] =================== =================== [Optimization for subgraph_264 started] =================== [TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal [TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal [TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal [TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal [TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal [TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal [TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal [TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal [TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal [TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal ----------------------------- Optimization Summary ----------------------------- --------------------------------------------------------------------------------- | Layer | Nodes before optimization | Nodes after optimization | --------------------------------------------------------------------------------- | TIDL_SoftMaxLayer | 1 | 1 | | TIDL_SqueezeLayer | 1 | 0 | | TIDL_ConvolutionLayer | 28 | 27 | | TIDL_EltWiseLayer | 2 | 0 | | TIDL_InnerProductLayer | 0 | 1 | | TIDL_CastLayer | 1 | 0 | | TIDL_PoolingLayer | 1 | 1 | --------------------------------------------------------------------------------- =================== [Optimization for subgraph_86 completed] =================== ----------------------------- Optimization Summary ----------------------------- -------------------------------------------------------------------------------- | Layer | Nodes before optimization | Nodes after optimization | -------------------------------------------------------------------------------- | TIDL_ArgMaxLayer | 1 | 1 | | TIDL_ConcatLayer | 2 | 2 | | TIDL_ResizeLayer | 3 | 5 | | TIDL_ConvolutionLayer | 60 | 60 | | TIDL_EltWiseLayer | 12 | 10 | | TIDL_CastLayer | 2 | 0 | | TIDL_PoolingLayer | 1 | 1 | -------------------------------------------------------------------------------- ================== [Optimization for subgraph_201 completed] ================== ----------------------------- Optimization Summary ----------------------------- ------------------------------------------------------------------------------------- | Layer | Nodes before optimization | Nodes after optimization | ------------------------------------------------------------------------------------- | TIDL_OdOutputReformatLayer | 0 | 4 | | TIDL_ConvolutionLayer | 94 | 94 | | TIDL_EltWiseLayer | 17 | 15 | | TIDL_DetectionOutputLayer | 0 | 1 | | TIDL_CastLayer | 1 | 0 | ------------------------------------------------------------------------------------- ================== [Optimization for subgraph_321 completed] ================== ----------------------------- Optimization Summary ----------------------------- ------------------------------------------------------------------------------------- | Layer | Nodes before optimization | Nodes after optimization | ------------------------------------------------------------------------------------- | TIDL_OdOutputReformatLayer | 0 | 4 | | TIDL_ConvolutionLayer | 76 | 76 | | TIDL_EltWiseLayer | 12 | 10 | | TIDL_DetectionOutputLayer | 0 | 1 | | TIDL_CastLayer | 1 | 0 | ------------------------------------------------------------------------------------- ================== [Optimization for subgraph_264 completed] ================== The soft limit is 10240 The hard limit is 10240 MEM: Init ... !!! MEM: Init ... Done !!! 0.0s: VX_ZONE_INIT:Enabled 0.7s: VX_ZONE_ERROR:Enabled 0.8s: VX_ZONE_WARNING:Enabled 0.1219s: VX_ZONE_INIT:[tivxInit:190] Initialization Done !!! The soft limit is 10240 The hard limit is 10240 MEM: Init ... !!! MEM: Init ... Done !!! 0.0s: VX_ZONE_INIT:Enabled 0.5s: VX_ZONE_ERROR:Enabled 0.7s: VX_ZONE_WARNING:Enabled 0.1157s: VX_ZONE_INIT:[tivxInit:190] Initialization Done !!! The soft limit is 10240 The hard limit is 10240 MEM: Init ... !!! MEM: Init ... Done !!! 0.0s: VX_ZONE_INIT:Enabled 0.5s: VX_ZONE_ERROR:Enabled 0.6s: VX_ZONE_WARNING:Enabled 0.1096s: VX_ZONE_INIT:[tivxInit:190] Initialization Done !!! The soft limit is 10240 The hard limit is 10240 MEM: Init ... !!! MEM: Init ... Done !!! 0.0s: VX_ZONE_INIT:Enabled 0.5s: VX_ZONE_ERROR:Enabled 0.6s: VX_ZONE_WARNING:Enabled 0.1121s: VX_ZONE_INIT:[tivxInit:190] Initialization Done !!! ************ Frame index 1 : Running float inference **************** ************ Frame index 2 : Running fixed point mode for calibration **************** -------- Running Calibration in Float Mode to Collect Tensor Statistics -------- [======================================> ] [=============================================================================] 100 % ************ Frame index 1 : Running float inference **************** ------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------ ************ Frame index 1 : Running float inference **************** [======================================> ] [=============================================================================] 100 % ************ Frame index 2 : Running fixed point mode for calibration **************** ------------------ Fixed-point Calibration Iteration [2 / 5]: ------------------ -------- Running Calibration in Float Mode to Collect Tensor Statistics -------- [======================================> ] [======================================> ] [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [3 / 5]: ------------------ ************ Frame index 2 : Running fixed point mode for calibration **************** [======================================> ] 50 % -------- Running Calibration in Float Mode to Collect Tensor Statistics -------- [======================================> ] [=============================================================================] [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [4 / 5]: ------------------ [======================================> ] [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------ ------------------ Fixed-point Calibration Iteration [5 / 5]: ------------------ [======================================> ] [=============================================================================] [======================================> ] [=============================================================================] 100 % ==================== [Quantization & Calibration Completed] ==================== ========================== [Memory Planning Started] ========================== ------------------------- Network Compiler Traces ------------------------------ Successful Memory Allocation Successful Workload Creation ========================= [Memory Planning Completed] ========================= ======================== Subgraph Compiled Successfully ======================== Final number of subgraphs:1 , 34 nodes delegated to accelerator Completed_Model : 1, Name : cl-tfl-mobilenet_v1_1.0_224 , Total time : 2420.16, Offload Time : 0.00 , DDR RW MBs : 18446744073709.55, Output File : py_out_cl-tfl-mobilenet_v1_1.0_224_ADE_val_00001801.jpg MEM: Deinit ... !!! MEM: Alloc's: 26 alloc's of 87828789 bytes MEM: Free's : 26 free's of 87828789 bytes MEM: Open's : 0 allocs of 0 bytes MEM: Deinit ... Done !!! [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------ [======================================> ] 50 % ------------------ Fixed-point Calibration Iteration [2 / 5]: ------------------ [======================================> ] [=============================================================================] [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [3 / 5]: ------------------ ------------------ Fixed-point Calibration Iteration [2 / 5]: ------------------ [======================================> ] [======================================> ] [=============================================================================] [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [4 / 5]: ------------------ ************ Frame index 1 : Running float inference **************** [======================================> ] 50 % ------------------ Fixed-point Calibration Iteration [3 / 5]: ------------------ [======================================> ] [=============================================================================] [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [5 / 5]: ------------------ [======================================> ] 50 % ------------------ Fixed-point Calibration Iteration [4 / 5]: ------------------ [======================================> ] [=============================================================================] 100 % [=============================================================================] ==================== [Quantization & Calibration Completed] ==================== ========================== [Memory Planning Started] ========================== ------------------------- Network Compiler Traces ------------------------------ Successful Memory Allocation Successful Workload Creation ========================= [Memory Planning Completed] ========================= ======================== Subgraph Compiled Successfully ======================== Final number of subgraphs:1 , 107 nodes delegated to accelerator Completed_Model : 3, Name : od-tfl-ssd_mobilenet_v2_300_float , Total time : 6771.57, Offload Time : 0.00 , DDR RW MBs : 18446744073709.55, Output File : py_out_od-tfl-ssd_mobilenet_v2_300_float_ADE_val_00001801.jpg MEM: Deinit ... !!! MEM: Alloc's: 29 alloc's of 232691165 bytes MEM: Free's : 29 free's of 232691165 bytes MEM: Open's : 0 allocs of 0 bytes MEM: Deinit ... Done !!! ------------------ Fixed-point Calibration Iteration [5 / 5]: ------------------ [======================================> ] [=============================================================================] 100 % ==================== [Quantization & Calibration Completed] ==================== ========================== [Memory Planning Started] ========================== ------------------------- Network Compiler Traces ------------------------------ Successful Memory Allocation Successful Workload Creation ========================= [Memory Planning Completed] ========================= ======================== Subgraph Compiled Successfully ======================== Final number of subgraphs:1 , 129 nodes delegated to accelerator Completed_Model : 4, Name : od-tfl-ssdlite_mobiledet_dsp_320x320_coco , Total time : 8438.49, Offload Time : 0.00 , DDR RW MBs : 18446744073709.55, Output File : py_out_od-tfl-ssdlite_mobiledet_dsp_320x320_coco_ADE_val_00001801.jpg MEM: Deinit ... !!! MEM: Alloc's: 29 alloc's of 139198365 bytes MEM: Free's : 29 free's of 139198365 bytes MEM: Open's : 0 allocs of 0 bytes MEM: Deinit ... Done !!! ************ Frame index 2 : Running fixed point mode for calibration **************** -------- Running Calibration in Float Mode to Collect Tensor Statistics -------- [======================================> ] [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------ [======================================> ] [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [2 / 5]: ------------------ [======================================> ] [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [3 / 5]: ------------------ [======================================> ] [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [4 / 5]: ------------------ [======================================> ] [=============================================================================] 100 % ------------------ Fixed-point Calibration Iteration [5 / 5]: ------------------ [======================================> ] [=============================================================================] 100 % ==================== [Quantization & Calibration Completed] ==================== ========================== [Memory Planning Started] ========================== ------------------------- Network Compiler Traces ------------------------------ Successful Memory Allocation Successful Workload Creation ========================= [Memory Planning Completed] ========================= ======================== Subgraph Compiled Successfully ======================== Final number of subgraphs:1 , 81 nodes delegated to accelerator Completed_Model : 2, Name : ss-tfl-deeplabv3_mnv2_ade20k_float , Total time : 38532.05, Offload Time : 0.00 , DDR RW MBs : 18446744073709.55, Output File : py_out_ss-tfl-deeplabv3_mnv2_ade20k_float_ADE_val_00001801.jpg MEM: Deinit ... !!! MEM: Alloc's: 26 alloc's of 408724625 bytes MEM: Free's : 26 free's of 408724625 bytes MEM: Open's : 0 allocs of 0 bytes MEM: Deinit ... Done !!! run python3 tflrt_delegate.py Running 4 Models - ['cl-tfl-mobilenet_v1_1.0_224', 'ss-tfl-deeplabv3_mnv2_ade20k_float', 'od-tfl-ssd_mobilenet_v2_300_float', 'od-tfl-ssdlite_mobiledet_dsp_320x320_coco'] Running_Model : cl-tfl-mobilenet_v1_1.0_224 Running_Model : ss-tfl-deeplabv3_mnv2_ade20k_float Running_Model : od-tfl-ssd_mobilenet_v2_300_float Running_Model : od-tfl-ssdlite_mobiledet_dsp_320x320_coco Number of subgraphs:1 , 34 nodes delegated out of 34 nodes Number of subgraphs:1 , 81 nodes delegated out of 81 nodes Number of subgraphs:1 , 107 nodes delegated out of 107 nodes The soft limit is 10240 The hard limit is 10240 MEM: Init ... !!! Number of subgraphs:1 , 129 nodes delegated out of 129 nodes MEM: Init ... Done !!! The soft limit is 10240 The hard limit is 10240 MEM: Init ... !!! 0.0s: VX_ZONE_INIT:Enabled 0.6s: VX_ZONE_ERROR:Enabled 0.7s: VX_ZONE_WARNING:Enabled MEM: Init ... Done !!! 0.0s: VX_ZONE_INIT:Enabled 0.5s: VX_ZONE_ERROR:Enabled 0.7s: VX_ZONE_WARNING:Enabled 0.2013s: VX_ZONE_INIT:[tivxInit:190] Initialization Done !!! 0.2451s: VX_ZONE_INIT:[tivxInit:190] Initialization Done !!! The soft limit is 10240 The hard limit is 10240 MEM: Init ... !!! MEM: Init ... Done !!! 0.0s: VX_ZONE_INIT:Enabled 0.5s: VX_ZONE_ERROR:Enabled 0.7s: VX_ZONE_WARNING:Enabled 0.1102s: VX_ZONE_INIT:[tivxInit:190] Initialization Done !!! The soft limit is 10240 The hard limit is 10240 MEM: Init ... !!! MEM: Init ... Done !!! 0.0s: VX_ZONE_INIT:Enabled 0.7s: VX_ZONE_ERROR:Enabled 0.9s: VX_ZONE_WARNING:Enabled 0.1510s: VX_ZONE_INIT:[tivxInit:190] Initialization Done !!! , 0 0.691726 warplane, military plane ,, 1 0.181373 missile ,, 2 0.109571 projectile, missile ,, 3 0.006352 cannon ,, 4 0.005370 aircraft carrier, carrier, flattop, attack aircraft carrier , Saving image to ../../../output_images/ Completed_Model : 1, Name : cl-tfl-mobilenet_v1_1.0_224 , Total time : 252.48, Offload Time : 252.47 , DDR RW MBs : 18446744073709.55, Output File : py_out_cl-tfl-mobilenet_v1_1.0_224_airshow.jpg MEM: Deinit ... !!! MEM: Alloc's: 26 alloc's of 27127485 bytes MEM: Free's : 26 free's of 27127485 bytes MEM: Open's : 0 allocs of 0 bytes MEM: Deinit ... Done !!! Saving image to ../../../output_images/ Completed_Model : 3, Name : od-tfl-ssd_mobilenet_v2_300_float , Total time : 930.89, Offload Time : 930.88 , DDR RW MBs : 18446744073709.55, Output File : py_out_od-tfl-ssd_mobilenet_v2_300_float_ADE_val_00001801.jpg MEM: Deinit ... !!! MEM: Alloc's: 29 alloc's of 86511875 bytes MEM: Free's : 29 free's of 86511875 bytes MEM: Open's : 0 allocs of 0 bytes MEM: Deinit ... Done !!! Saving image to ../../../output_images/ Completed_Model : 4, Name : od-tfl-ssdlite_mobiledet_dsp_320x320_coco , Total time : 1429.23, Offload Time : 1429.22 , DDR RW MBs : 18446744073709.55, Output File : py_out_od-tfl-ssdlite_mobiledet_dsp_320x320_coco_ADE_val_00001801.jpg MEM: Deinit ... !!! MEM: Alloc's: 29 alloc's of 38501115 bytes MEM: Free's : 29 free's of 38501115 bytes MEM: Open's : 0 allocs of 0 bytes MEM: Deinit ... Done !!! Saving image to ../../../output_images/ Completed_Model : 2, Name : ss-tfl-deeplabv3_mnv2_ade20k_float , Total time : 4264.49, Offload Time : 4264.48 , DDR RW MBs : 18446744073709.55, Output File : py_out_ss-tfl-deeplabv3_mnv2_ade20k_float_ADE_val_00001801.jpg MEM: Deinit ... !!! MEM: Alloc's: 26 alloc's of 85712597 bytes MEM: Free's : 26 free's of 85712597 bytes MEM: Open's : 0 allocs of 0 bytes MEM: Deinit ... Done !!! Available execution providers : ['AzureExecutionProvider', 'CPUExecutionProvider'] Running 2 Models - ['cl-ort-resnet18-v1', 'od-ort-ssd-lite_mobilenetv2_fpn'] Running_Model : cl-ort-resnet18-v1 Downloading ../../../models/public/resnet18_opset9.onnx Running_Model : od-ort-ssd-lite_mobilenetv2_fpn Running shape inference on model ../../../models/public/ssd-lite_mobilenetv2_fpn.onnx /home/ace428/.local/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:69: UserWarning: Specified provider 'TIDLCompilationProvider' is not in available provider names.Available providers: 'AzureExecutionProvider, CPUExecutionProvider' warnings.warn( *************** EP Error *************** EP Error Unknown Provider Type: TIDLCompilationProvider when using ['TIDLCompilationProvider', 'CPUExecutionProvider'] Falling back to ['CPUExecutionProvider'] and retrying. **************************************** Process Process-2: Traceback (most recent call last): File "/home/ace428/.pyenv/versions/3.10.14/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/home/ace428/.pyenv/versions/3.10.14/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/ace428/Desktop/edgeai-tidl-tools/examples/osrt_python/ort/onnxrt_ep.py", line 239, in run_model imgs, output, proc_time, sub_graph_time, height, width = infer_image(sess, input_images, config) File "/home/ace428/Desktop/edgeai-tidl-tools/examples/osrt_python/ort/onnxrt_ep.py", line 135, in infer_image copy_time, sub_graphs_proc_time, totaltime = get_benchmark_output(sess) File "/home/ace428/Desktop/edgeai-tidl-tools/examples/osrt_python/ort/onnxrt_ep.py", line 84, in get_benchmark_output benchmark_dict = interpreter.get_TI_benchmark_data() AttributeError: 'InferenceSession' object has no attribute 'get_TI_benchmark_data'
This is my current environment information.Package Version Editable project location ------------------------ --------------- ---------------------------------------------- absl-py 2.1.0 attrs 24.2.0 autocfg 0.0.8 caffe2onnx 1.0.2 certifi 2024.8.30 charset-normalizer 3.3.2 cloudpickle 3.0.0 coloredlogs 15.0.1 contourpy 1.3.0 cycler 0.12.1 dataclasses 0.6 decorator 5.1.1 distro 1.9.0 dlr 1.13.0 exceptiongroup 1.2.2 filelock 3.16.0 flatbuffers 1.12 fonttools 4.53.1 fsspec 2024.9.0 gluoncv 0.10.5.post0 graphviz 0.20.3 grpcio 1.66.1 huggingface-hub 0.24.7 humanfriendly 10.0 idna 3.8 iniconfig 2.0.0 Jinja2 3.1.4 kiwisolver 1.4.7 Markdown 3.7 markdown-it-py 3.0.0 MarkupSafe 2.1.5 matplotlib 3.9.2 mdurl 0.1.2 mpmath 1.3.0 networkx 3.3 numpy 1.23.0 nvidia-cublas-cu12 12.1.3.1 nvidia-cuda-cupti-cu12 12.1.105 nvidia-cuda-nvrtc-cu12 12.1.105 nvidia-cuda-runtime-cu12 12.1.105 nvidia-cudnn-cu12 9.1.0.70 nvidia-cufft-cu12 11.0.2.54 nvidia-curand-cu12 10.3.2.106 nvidia-cusolver-cu12 11.4.5.107 nvidia-cusparse-cu12 12.1.0.106 nvidia-nccl-cu12 2.20.5 nvidia-nvjitlink-cu12 12.6.68 nvidia-nvtx-cu12 12.1.105 onnx 1.13.0 onnx_graphsurgeon 0.3.26 onnxruntime 1.19.0 onnxruntime-tidl 1.14.0+10000005 onnxsim 0.4.35 opencv-python 4.10.0.84 osrt_model_tools 1.2 /home/ace428/Desktop/edgeai-tidl-tools/scripts packaging 24.1 pandas 2.2.2 pillow 10.4.0 pip 24.2 pluggy 1.5.0 portalocker 2.10.1 protobuf 3.20.3 psutil 6.0.0 pybind11 2.13.5 pybind11_global 2.13.5 Pygments 2.18.0 pyparsing 3.1.4 pytest 8.3.3 python-dateutil 2.9.0.post0 pytz 2024.2 PyYAML 6.0.2 requests 2.32.3 rich 13.8.1 safetensors 0.4.5 scipy 1.13.1 seaborn 0.13.2 setuptools 74.1.2 six 1.16.0 sympy 1.13.2 tensorboard 2.17.1 tensorboard-data-server 0.7.2 tflite 2.10.0 tflite-runtime 2.12.0 timm 1.0.9 tomli 2.0.1 torch 2.4.1 torchvision 0.19.1 tornado 6.4.1 tqdm 4.66.5 triton 3.0.0 tvm 0.12.0 typing_extensions 4.12.2 tzdata 2024.1 urllib3 2.2.3 Werkzeug 3.0.4 wheel 0.44.0 yacs 0.1.8
How to resolve this issue?
Thanks
Kathy
