PROCESSOR-SDK-J722S: Issue about convert onnx file to TIDL format

ken zhoung

Prodigy 180 points

Part Number: PROCESSOR-SDK-J722S

Tool/software:

Hi team,

I am trying to run inference on a custom model following the steps below but encountered an issue:

Flow: ONNX file --> convert to TIDL format --> run inference
- I can successfully run inference using models from the Model Zoo.
- However, when I use edgeai-tidl-tools/tidl_tools/tidl_model_import.out to convert my ONNX model to TIDL format and then attempt to run inference, I encounter an error stating that param.yaml does not exist.

My convert model (on the left) differs from the example model in the Model Zoo (on the right).

Questions:

Is it possible to generate config.yaml, dataset.yaml, and param.yaml using tidl_model_import.out? If so, how can I configure them correctly?
Could you please provide an example of converting an ONNX file to TIDL format and running inference?
I also attempted the Custom Model Evaluation method.(github.com/.../custom_model_evaluation.md(github.com/.../custom_model_evaluation.md
- However, the log shows the error: EP Error Unknown Provider Type: TIDLCompilationProvider when using ['TIDLCompilationProvider', 'CPUExecutionProvider'].
- Am I missing any settings? My code is below:

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import torch
import onnx
import onnxruntime as rt
import subprocess
import os
# 定義參數
onnx_model_path = "model.onnx"
simplified_onnx_model_path = "model_simplified.onnx"
tidl_artifacts_folder = "./model-artifacts-dir/"
tidl_tools_path = "./app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/tidl_tools/"
# Step 1: 導出 ONNX 模型 (以 PyTorch 為例)
def export_pytorch_model_to_onnx(model, input_shape):
    dummy_input = torch.randn(*input_shape)
    torch.onnx.export(model, dummy_input, onnx_model_path, opset_version=11)
    print(f"ONNX model exported to {onnx_model_path}")
# Step 2: 檢查模型有效性並推斷形狀
def check_and_infer_shape(onnx_model_path):
    model = onnx.load(onnx_model_path)
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import torch
import onnx
import onnxruntime as rt
import subprocess
import os

# 定義參數
onnx_model_path = "model.onnx"
simplified_onnx_model_path = "model_simplified.onnx"
tidl_artifacts_folder = "./model-artifacts-dir/"
tidl_tools_path = "./app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/tidl_tools/"

# Step 1: 導出 ONNX 模型 (以 PyTorch 為例)
def export_pytorch_model_to_onnx(model, input_shape):
    dummy_input = torch.randn(*input_shape)
    torch.onnx.export(model, dummy_input, onnx_model_path, opset_version=11)
    print(f"ONNX model exported to {onnx_model_path}")

# Step 2: 檢查模型有效性並推斷形狀
def check_and_infer_shape(onnx_model_path):
    model = onnx.load(onnx_model_path)
    inferred_model = onnx.shape_inference.infer_shapes(model)
    onnx.save(inferred_model, "inferred_model.onnx")
    print("Shape inference complete and saved to inferred_model.onnx")

# Step 3: 簡化 ONNX 模型
def simplify_onnx_model(onnx_model_path, simplified_model_path):
    subprocess.run(["python3", "-m", "onnxsim", onnx_model_path, simplified_model_path])
    print(f"Simplified ONNX model saved to {simplified_model_path}")

# Step 4: 設定 ONNX Runtime TIDL 編譯會話
def setup_tidl_session(simplified_model_path):
    # 設置編譯參數
    options = {
        "artifacts_folder": tidl_artifacts_folder,
        "tidl_tools_path": tidl_tools_path
    }
    so = rt.SessionOptions()
    ep_list = ['TIDLCompilationProvider', 'CPUExecutionProvider']
    
    # 設置編譯會話，使用 TIDLCompilationProvider 進行編譯
    sess = rt.InferenceSession(simplified_model_path, providers=ep_list, provider_options=[options, {}], sess_options=so)
    
    print("TIDL model compilation session created.")
    return sess

# Step 5: 驗證模型的輸出
def validate_model_output(sess, input_data):
    # 確保 TIDL 編譯模型可以產生正確輸出
    input_name = sess.get_inputs()[0].name
    output_name = sess.get_outputs()[0].name
    
    output = sess.run([output_name], {input_name: input_data})
    print(f"Model output: {output}")

# 主流程
if __name__ == "__main__":
    # 假設這是 PyTorch 模型和輸入維度
    model = torch.nn.Sequential(
        torch.nn.Conv2d(3, 16, 3, stride=2, padding=1),
        torch.nn.ReLU(),
        torch.nn.Flatten(),
        torch.nn.Linear(16 * 112 * 112, 10)
    )
    input_shape = (1, 3, 224, 224)

    # 導出、檢查、簡化、編譯和驗證模型
    export_pytorch_model_to_onnx(model, input_shape)
    check_and_infer_shape(onnx_model_path)
    simplify_onnx_model(onnx_model_path, simplified_onnx_model_path)
    session = setup_tidl_session(simplified_onnx_model_path)
    
    # 使用隨機數據進行驗證
    input_data = torch.randn(*input_shape).numpy()
    validate_model_output(session, input_data)
    print("Available providers:", rt.get_available_providers())

Thanks for your kindly help.

5 months ago

0 Wen Li 5 months ago

TI__Expert 5301 points

Hi Ken; we will look into this. First we will do exact what you have done, to find out if we will have the same problem. Could you please provide the software version#; the command for each step, and your Linux environment? So we can replicate them on our side.

Thanks and regards

Wen Li

0 ken zhoung 5 months ago in reply to Wen Li

Prodigy 180 points

My TIDL version is 10.00.04.00, and Ubuntu version is 22.04

My setup TIDL sop below:

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apt-get update
apt-get upgrade
apt install sudo
sudo apt-get install libyaml-cpp-dev libglib2.0-dev
apt install git
apt install wget
Pip3 install dlr
pip3 install flatbuffers==2.0
Sudo apt install cmake
sudo apt install libopencv-dev
git clone https://github.com/TexasInstruments/edgeai-tidl-tools.git
cd edgeai-tidl-tools/
git checkout 110634e30a121b6efdfba8faf75c347e21caa49e
export SOC=am67a
source ./setup.sh
export SOC=<Your SOC name>
export TIDL_TOOLS_PATH=$(pwd)/tidl_tools
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TIDL_TOOLS_PATH
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apt-get update
apt-get upgrade
apt install sudo
sudo apt-get install libyaml-cpp-dev libglib2.0-dev
apt install git
apt install wget
Pip3 install dlr
pip3 install flatbuffers==2.0
Sudo apt install cmake
sudo apt install libopencv-dev


git clone https://github.com/TexasInstruments/edgeai-tidl-tools.git
cd edgeai-tidl-tools/
git checkout 110634e30a121b6efdfba8faf75c347e21caa49e
export SOC=am67a
source ./setup.sh

export SOC=<Your SOC name>
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 ..

I try two way to convert onnx to TIDL format:

[1]

- cd <local path>/tidl_tools

- ./tidl_model_import.out ../ken_test_model/test.txt

- test.txt :

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#### lenet
modelType          = 2
numParamBits       = 8
numFeatureBits     = 8
quantizationStyle  = 3
#quantizationStyle  = 2
inputNetFile       = "/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet.onnx"
 
outputNetFile      = "/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet_res/lenet.bin"
outputParamsFile   = "/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet_res/lenet_"
inDataNorm  = 0
inDataFormat = 1
inWidth  = 28
inHeight = 28 
inNumChannels = 1
numFrames = 1
inData  =   "/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/0003.jpg"
# 0 = JPEG/PNG/BMP; 1 = binary; 2 = list
inFileFormat = 0
perfSimConfig = /app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/tidl_tools/device_config.cfg
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#### lenet

modelType          = 2
numParamBits       = 8
numFeatureBits     = 8
quantizationStyle  = 3
#quantizationStyle  = 2
inputNetFile       = "/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet.onnx"
 
outputNetFile      = "/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet_res/lenet.bin"
outputParamsFile   = "/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet_res/lenet_"
inDataNorm  = 0
inDataFormat = 1
inWidth  = 28
inHeight = 28 
inNumChannels = 1
numFrames = 1
inData  =   "/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/0003.jpg"
# 0 = JPEG/PNG/BMP; 1 = binary; 2 = list
inFileFormat = 0
perfSimConfig = /app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/tidl_tools/device_config.cfg
inElementType = 0
metaArchType = 4
postProcType = 3

- log below: (but can't fnd param.yaml / dataset.yaml, can't run inference)

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root@914dec19bac9:/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/tidl_tools# ./tidl_model_import.out ../ken_test_model/test.txt
========================= [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             |
-------------------------------------------------------------------------------
No Meta Arch layer to parse
ONNX model (Proto) file      : /app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet.onnx  
TIDL network file            : /app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet_res/lenet.bin  
TIDL IO info file            : /app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet_res/lenet_  
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root@914dec19bac9:/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/tidl_tools# ./tidl_model_import.out ../ken_test_model/test.txt
========================= [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             |
-------------------------------------------------------------------------------

No Meta Arch layer to parse
ONNX model (Proto) file      : /app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet.onnx  
TIDL network file            : /app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet_res/lenet.bin  
TIDL IO info file            : /app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/ken_test_model/lenet_res/lenet_  
Current ONNX OpSet version   : 9  
============================ [Optimization started] ============================

----------------------------- Optimization Summary -----------------------------
---------------------------------------------------------------------------------
|          Layer         | Nodes before optimization | Nodes after optimization |
---------------------------------------------------------------------------------
| TIDL_CropLayer         |                         0 |                        2 |
| TIDL_ConvolutionLayer  |                         2 |                        2 |
| TIDL_ReLULayer         |                         1 |                        0 |
| TIDL_InnerProductLayer |                         2 |                        2 |
| TIDL_SoftMaxLayer      |                         1 |                        1 |
| TIDL_PoolingLayer      |                         2 |                        2 |
---------------------------------------------------------------------------------

=========================== [Optimization completed] ===========================

Couldn't open tidlStatsTool file: ../../test/PC_dsp_test_dl_algo.out
------------------ Fixed-point Calibration Iteration [1 / 1]: ------------------
Couldn't open tidlStatsTool file: ../../test/PC_dsp_test_dl_algo.out
==================== [Quantization & Calibration Completed] ====================

[TIDL Import]  WARNING: Couldn't open perfSimTool file: ../../utils/perfsim/ti_cnnperfsim.out. Skipping Performance Simulation.
Rerunning network compiler...
[TIDL Import]  WARNING: Couldn't open perfSimTool file: ../../utils/perfsim/ti_cnnperfsim.out. Skipping Performance Simulation.
[TIDL Import]  WARNING: Couldn't open graphVizTool file: ../../utils/tidlModelGraphviz/out/tidl_graphVisualiser.out . Skipping Graph Visualization.
[TIDL Import]  WARNING: Couldn't open graphVizTool file: ../../utils/tidlModelGraphviz/out/tidl_graphVisualiser.out . Skipping Graph Visualization.
[TIDL Import] [PARSER] WARNING: 
********************************************************************
*     Network compiler returned with error or didn't executed      *
*      This model can only be used on PC/Host emulation mode       *
*            It is not expected to work on target/EVM              *
********************************************************************

======================== Subgraph Compiled Successfully ========================

[2]

- cd <local path>/examples/jupyter_notebooks

- source ./launch_notebook.sh

- python3 tidl_model_convert_onnx.py

- tidl_model_convert_onnx.py below:

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import os
import tqdm
import cv2
import numpy as np
import onnxruntime as rt
import shutil
from scripts.utils import imagenet_class_to_name, download_model
import matplotlib.pyplot as plt
from pathlib import Path
from IPython.display import Markdown as md
from scripts.utils import loggerWritter
from scripts.utils import get_svg_path
import onnx
def preprocess(image_path):
    
    # read the image using openCV
    img = cv2.imread(image_path)
    
    # convert to RGB
    img = img[:,:,::-1]
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import os
import tqdm
import cv2
import numpy as np
import onnxruntime as rt
import shutil
from scripts.utils import imagenet_class_to_name, download_model
import matplotlib.pyplot as plt
from pathlib import Path
from IPython.display import Markdown as md
from scripts.utils import loggerWritter
from scripts.utils import get_svg_path
import onnx

def preprocess(image_path):
    
    # read the image using openCV
    img = cv2.imread(image_path)
    
    # convert to RGB
    img = img[:,:,::-1]
    
    # Most of the onnx models are trained using
    # 224x224 images. The general rule of thumb
    # is to scale the input image while preserving
    # the original aspect ratio so that the
    # short edge is 256 pixels, and then
    # center-crop the scaled image to 224x224
    orig_height, orig_width, _ = img.shape
    short_edge = min(img.shape[:2])
    new_height = (orig_height * 256) // short_edge
    new_width = (orig_width * 256) // short_edge
    img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)

    startx = new_width//2 - (224//2)
    starty = new_height//2 - (224//2)
    img = img[starty:starty+224,startx:startx+224]
    
    # apply scaling and mean subtraction.
    # if your model is built with an input
    # normalization layer, then you might
    # need to skip this
    img = img.astype('float32')
    for mean, scale, ch in zip([128, 128, 128], [0.0078125, 0.0078125, 0.0078125], range(img.shape[2])):
            img[:,:,ch] = ((img.astype('float32')[:,:,ch] - mean) * scale)
    img = np.expand_dims(img,axis=0)
    img = np.transpose(img, (0, 3, 1, 2))
    
    return img
calib_images = [
'sample-images/elephant.bmp',
'sample-images/bus.bmp',
'sample-images/bicycle.bmp',
'sample-images/zebra.bmp',
]
output_dir = 'custom-artifacts/onnx/resnet18_opset9.onnx'
onnx_model_path = 'models/public/onnx/resnet18_opset9.onnx'
download_model(onnx_model_path)
onnx.shape_inference.infer_shapes_path(onnx_model_path, onnx_model_path)
#compilation options - knobs to tweak 
num_bits =8
accuracy =1

log_dir = Path("logs").mkdir(parents=True, exist_ok=True)

# stdout and stderr saved to a *.log file.  
#with loggerWritter("logs/custon-model-onnx"):

# model compilation options
compile_options = {
    'tidl_tools_path' : os.environ['TIDL_TOOLS_PATH'],
    'artifacts_folder' : output_dir,
    'tensor_bits' : num_bits,
    'accuracy_level' : accuracy,
    'advanced_options:calibration_frames' : len(calib_images), 
    'advanced_options:calibration_iterations' : 3, # used if accuracy_level = 1
    'advanced_options:add_data_convert_ops' : 1,
    'debug_level' : 1,
    #'deny_list' : "MaxPool" #Comma separated string of operator types as defined by ONNX runtime, ex "MaxPool, Concat"
}
# create the output dir if not present
# clear the directory
os.makedirs(output_dir, exist_ok=True)
for root, dirs, files in os.walk(output_dir, topdown=False):
    [os.remove(os.path.join(root, f)) for f in files]
    [os.rmdir(os.path.join(root, d)) for d in dirs]

so = rt.SessionOptions()
EP_list = ['TIDLCompilationProvider','CPUExecutionProvider']
sess = rt.InferenceSession(onnx_model_path ,providers=EP_list, provider_options=[compile_options, {}], sess_options=so)

input_details = sess.get_inputs()
for num in tqdm.trange(len(calib_images)):
    output = list(sess.run(None, {input_details[0].name : preprocess(calib_images[num])}))[0]

##optional
#subgraph_link =get_svg_path(output_dir) 
#for sg in subgraph_link:
#    hl_text = os.path.join(*Path(sg).parts[4:])
#    sg_rel = os.path.join('../', sg)
#    display(md("[{}]({})".format(hl_text,sg_rel)))
#
#EP_list = ['TIDLExecutionProvider','CPUExecutionProvider']
#print("[ken debug] 5 ")
#sess = rt.InferenceSession(onnx_model_path ,providers=EP_list, provider_options=[compile_options, {}], sess_options=so)
##Running inference several times to get an stable performance output
#for i in range(5):
#    output = list(sess.run(None, {input_details[0].name : preprocess('sample-images/elephant.bmp')}))
#
#for idx, cls in enumerate(output[0].squeeze().argsort()[-5:][::-1]):
#    print('[%d] %s' % (idx, '/'.join(imagenet_class_to_name(cls))))
#    
#from scripts.utils import plot_TI_performance_data, plot_TI_DDRBW_data, get_benchmark_output
#stats = sess.get_TI_benchmark_data()
#fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10,5))
#plot_TI_performance_data(stats, axis=ax)
#plt.show()
#print("[ken debug] 6 ")
#tt, st, rb, wb = get_benchmark_output(stats)
#print(f'Statistics : \n Inferences Per Second   : {1000.0/tt :7.2f} fps')
#print(f' Inference Time Per Image : {tt :7.2f} ms  \n DDR BW Per Image        : {rb+ wb : 7.2f} MB')

- log below (If this successfully, will convert param.yaml and dataset.yaml?, inference code need these two files):

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root@914dec19bac9:/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/examples/jupyter_notebooks# ls
README.md  custom-artifacts         custom-model-tvm-dlr.ipynb  evm-console-log.ipynb             lidar-3dod-onnx.ipynb  prebuilt-models  tidl_model_convert_onnx.py    vcls-hr.ipynb        vcls-tfl-arm.ipynb  vdet-onnx.ipynb  vseg-onnx.ipynb
colab      custom-model-onnx.ipynb  debug_tips.ipynb            human-pose-estimation-onnx.ipynb  logs                   sample-images    tidl_model_convert_tflite.py  vcls-onnx-arm.ipynb  vcls-tfl.ipynb      vdet-tfl.ipynb   vseg-tfl.ipynb
configs    custom-model-tfl.ipynb   docs                        launch_notebook.sh                models                 scripts          vcls-dlr.ipynb                vcls-onnx.ipynb      vdet-dlr.ipynb      vseg-dlr.ipynb
root@914dec19bac9:/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/examples/jupyter_notebooks# python3 tidl_model_convert_onnx.py
/usr/local/lib/python3.10/dist-packages/onnxruntime/capi/onnxruntime_inference_collection.py:115: UserWarning: Specified provider 'TIDLCompilationProvider' is not in available provider names.Available providers: 'AzureExecutionProvider, CPUExecutionProvider'
  warnings.warn(
2024-11-08 01:54:33.885035952 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885066086 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885073018 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885080827 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885086808 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885092786 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885099389 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885105336 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885110886 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885117031 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885123454 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885130440 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885137239 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885143287 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
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root@914dec19bac9:/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/examples/jupyter_notebooks# ls
README.md  custom-artifacts         custom-model-tvm-dlr.ipynb  evm-console-log.ipynb             lidar-3dod-onnx.ipynb  prebuilt-models  tidl_model_convert_onnx.py    vcls-hr.ipynb        vcls-tfl-arm.ipynb  vdet-onnx.ipynb  vseg-onnx.ipynb
colab      custom-model-onnx.ipynb  debug_tips.ipynb            human-pose-estimation-onnx.ipynb  logs                   sample-images    tidl_model_convert_tflite.py  vcls-onnx-arm.ipynb  vcls-tfl.ipynb      vdet-tfl.ipynb   vseg-tfl.ipynb
configs    custom-model-tfl.ipynb   docs                        launch_notebook.sh                models                 scripts          vcls-dlr.ipynb                vcls-onnx.ipynb      vdet-dlr.ipynb      vseg-dlr.ipynb
root@914dec19bac9:/app/ken/tda4/TIDL10.00.06.00/edgeai-tidl-tools/examples/jupyter_notebooks# python3 tidl_model_convert_onnx.py
/usr/local/lib/python3.10/dist-packages/onnxruntime/capi/onnxruntime_inference_collection.py:115: UserWarning: Specified provider 'TIDLCompilationProvider' is not in available provider names.Available providers: 'AzureExecutionProvider, CPUExecutionProvider'
  warnings.warn(
2024-11-08 01:54:33.885035952 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885066086 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885073018 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885080827 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885086808 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885092786 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885099389 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885105336 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885110886 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885117031 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885123454 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885130440 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885137239 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885143287 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885149198 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885155127 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885161162 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885167884 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885173780 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.885180693 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
*************** EP Error ***************
EP Error Unknown Provider Type: TIDLCompilationProvider when using ['TIDLCompilationProvider', 'CPUExecutionProvider']
Falling back to ['CPUExecutionProvider'] and retrying.
****************************************
2024-11-08 01:54:33.927323820 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927345830 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927350568 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927354911 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927359205 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927364325 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927371761 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927375518 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927379046 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927383303 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927388536 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927392920 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927397340 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927401788 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer1.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927406226 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927409846 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer4.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927413522 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927418452 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer3.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927423215 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2024-11-08 01:54:33.927427769 [W:onnxruntime:, graph.cc:4285 CleanUnusedInitializersAndNodeArgs] Removing initializer 'layer2.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 104.86it/s]

Please help me to check,

Thanks for your kindly help.