SK-AM62A-LP: Custom model compilation failed on 'Model Analyzer' platform

Anaïs Gastineau

Part Number: SK-AM62A-LP
Other Parts Discussed in Thread: TDA4VM

Hello,

I'm trying to use the jupyter notebook to compile custom model on the 'Model Analyzer' platform. However, each time I launch the compilation, the kernel crash. I tried onnx files from Pytorch and Tensorflow woth a ResNet18, ResNet34, ResNet50 and EfficientNet. I also tried with a tflite model but I have the same issue.

I juste changed, the variables 'output_path' and 'onnx_model_path'. I also check the input size of the tensor to make it compatible. I uploaded my file in 'notebooks/models/public/onnx' for a onnx files and in 'notebooks/models/public/tflite' for tflite files.

Do you know why it crashes ?

Thanks,

over 2 years ago

0 Qutaiba Saleh over 2 years ago

TI__Expert 5130 points

Hello,

What kind of errors do you see? Can you share the error printout?

Best regards,

Qutaiba

0 Anaïs Gastineau over 2 years ago in reply to Qutaiba Saleh

Prodigy 80 points

hello,
thanks for your answer. You can find attached a screen shot of the jupyter notebook with the error. I'm sorry it's in French but it's written "Kernel restarting. The core seems to be crashed. It will restart automatically."

Thanks,

Anaïs

0 Qutaiba Saleh over 2 years ago in reply to Anaïs Gastineau

TI__Expert 5130 points

Hello Anaïs,

Thank you for sharing the screenshot. Would you please provide more information about:

Were you able to run the original example for custom model compilation (i.e resnet18_opset9.onnx)? Does the kernel crash when you run this?
Can you run the cells in the notebook one at a time and see on which cell does the kernel crash?
Try running the debug notebook "evm-console-log" to look at the console log from the system.

Bests,

Qutaiba

0 Anaïs Gastineau over 2 years ago in reply to Qutaiba Saleh

Prodigy 80 points

Hello,
Thanks for your answer. Yes, I succeeded to run the original example with resnet18_opset9.onnx. The cell that is causing the problem is the cell 7:

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_for_onnx_resent18v2(calib_images[num])}))[0]

I will try running the dbug notebook.

Anaïs

0 Anaïs Gastineau over 2 years ago in reply to Anaïs Gastineau

Prodigy 80 points

After running the debug notebbok I have the following error:

Starting TIIE
Exception: failed to remove id = -1, not allocated

Stack trace:
  [bt] (0) ./ti_inference_server(StackTrace[abi:cxx11](unsigned long, unsigned long)+0x130) [0x40a800]
  [bt] (1) ./plugins/libonnxruntime_plugin.so(idr<onnxruntime::InferenceSession*>::remove(int)+0xc0) [0xffffbab05b58]
  [bt] (2) ./plugins/libonnxruntime_plugin.so(onnx_destroy_session_req::handle()+0x44) [0xffffbab05ea4]
  [bt] (3) ./ti_inference_server(main+0x304) [0x408704]
  [bt] (4) /lib/libc.so.6(__libc_start_main+0xe4) [0xffffbbc0afac]
  [bt] (5) ./ti_inference_server() [0x408e58]

Exception: failed to remove id = -1, not allocated

Stack trace:
  [bt] (0) ./ti_inference_server(StackTrace[abi:cxx11](unsigned long, unsigned long)+0x130) [0x40a800]
  [bt] (1) ./plugins/libonnxruntime_plugin.so(idr<onnxruntime::InferenceSession*>::remove(int)+0xc0) [0xffffbab05b58]
  [bt] (2) ./plugins/libonnxruntime_plugin.so(onnx_destroy_session_req::handle()+0x44) [0xffffbab05ea4]
  [bt] (3) ./ti_inference_server(main+0x304) [0x408704]
  [bt] (4) /lib/libc.so.6(__libc_start_main+0xe4) [0xffffbbc0afac]
  [bt] (5) ./ti_inference_server() [0x408e58]

Exception: failed to remove id = -1, not allocated

Stack trace:
  [bt] (0) ./ti_inference_server(StackTrace[abi:cxx11](unsigned long, unsigned long)+0x130) [0x40a800]
  [bt] (1) ./plugins/libonnxruntime_plugin.so(idr<onnxruntime::InferenceSession*>::remove(int)+0xc0) [0xffffbab05b58]
  [bt] (2) ./plugins/libonnxruntime_plugin.so(onnx_destroy_session_req::handle()+0x44) [0xffffbab05ea4]
  [bt] (3) ./ti_inference_server(main+0x304) [0x408704]
  [bt] (4) /lib/libc.so.6(__libc_start_main+0xe4) [0xffffbbc0afac]
  [bt] (5) ./ti_inference_server() [0x408e58]

Exception: failed to remove id = -1, not allocated

Stack trace:
  [bt] (0) ./ti_inference_server(StackTrace[abi:cxx11](unsigned long, unsigned long)+0x130) [0x40a800]
  [bt] (1) ./plugins/libonnxruntime_plugin.so(idr<onnxruntime::InferenceSession*>::remove(int)+0xc0) [0xffffbab05b58]
  [bt] (2) ./plugins/libonnxruntime_plugin.so(onnx_destroy_session_req::handle()+0x44) [0xffffbab05ea4]
  [bt] (3) ./ti_inference_server(main+0x304) [0x408704]
  [bt] (4) /lib/libc.so.6(__libc_start_main+0xe4) [0xffffbbc0afac]
  [bt] (5) ./ti_inference_server() [0x408e58]

Exception: failed to remove id = -1, not allocated

Stack trace:
  [bt] (0) ./ti_inference_server(StackTrace[abi:cxx11](unsigned long, unsigned long)+0x130) [0x40a800]
  [bt] (1) ./plugins/libonnxruntime_plugin.so(idr<onnxruntime::InferenceSession*>::remove(int)+0xc0) [0xffffbab05b58]
  [bt] (2) ./plugins/libonnxruntime_plugin.so(onnx_destroy_session_req::handle()+0x44) [0xffffbab05ea4]
  [bt] (3) ./ti_inference_server(main+0x304) [0x408704]
  [bt] (4) /lib/libc.so.6(__libc_start_main+0xe4) [0xffffbbc0afac]
  [bt] (5) ./ti_inference_server() [0x408e58]

0 Paula Carrillo over 2 years ago in reply to Anaïs Gastineau

TI__Mastermind 40580 points

Hi Anaïs, could you also please share files from "notebooks/logs"?

thank you,

Paula

0 Anaïs Gastineau over 1 year ago in reply to Paula Carrillo

Prodigy 80 points

Hello Paula,

In the folder "notebooks/logs", I have 2 files for onnx model and 2 files for tflite model. You can find attached the 4 files.

I hope it will help you.

Best regards,

Anaïs

Fullscreen 8688.custon-model-tfl_out.log Download

tidl_tools_path                                 = /home/root/notebooks/tidl_tools 
artifacts_folder                                = custom-artifacts/tflite/resnet50_res 
tidl_tensor_bits                                = 8 
debug_level                                     = 3 
num_tidl_subgraphs                              = 16 
tidl_denylist                                   = 1   25   
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 = 4 
tidl_calibration_options:bias_calibration_iterations = 3 
mixed_precision_factor = -1.000000 
model_group_id = 0 
power_of_2_quantization                         = 2 
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 --- 26 Tflite layer type --- 34 layer output name--- resnet50/conv1_pad/Pad 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv1_relu/Relu;resnet50/conv1_bn/FusedBatchNormV3;resnet50/conv1_conv/BiasAdd/ReadVariableOp;resnet50/conv1_conv/BiasAdd;resnet50/conv2_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv1_conv/Conv2D 
Supported TIDL layer type --- 26 Tflite layer type --- 34 layer output name--- resnet50/pool1_pad/Pad 
Supported TIDL layer type --- 2 Tflite layer type --- 17 layer output name--- resnet50/pool1_pool/MaxPool 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv2_block1_0_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv2_block1_0_conv/BiasAdd;resnet50/conv2_block1_0_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv2_block1_1_relu/Relu;resnet50/conv2_block1_1_bn/FusedBatchNormV3;resnet50/conv2_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv2_block1_1_conv/BiasAdd;resnet50/conv2_block1_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv2_block1_2_relu/Relu;resnet50/conv2_block1_2_bn/FusedBatchNormV3;resnet50/conv2_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv2_block1_2_conv/BiasAdd;resnet50/conv2_block1_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv2_block1_3_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv2_block1_3_conv/BiasAdd;resnet50/conv2_block1_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv2_block1_out/Relu;resnet50/conv2_block1_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv2_block2_1_relu/Relu;resnet50/conv2_block2_1_bn/FusedBatchNormV3;resnet50/conv2_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv2_block2_1_conv/BiasAdd;resnet50/conv2_block2_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv2_block2_2_relu/Relu;resnet50/conv2_block2_2_bn/FusedBatchNormV3;resnet50/conv2_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv2_block2_2_conv/BiasAdd;resnet50/conv2_block2_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv2_block2_3_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv2_block2_3_conv/BiasAdd;resnet50/conv2_block2_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv2_block2_out/Relu;resnet50/conv2_block2_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv2_block3_1_relu/Relu;resnet50/conv2_block3_1_bn/FusedBatchNormV3;resnet50/conv2_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv2_block3_1_conv/BiasAdd;resnet50/conv2_block3_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv2_block3_2_relu/Relu;resnet50/conv2_block3_2_bn/FusedBatchNormV3;resnet50/conv2_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv2_block3_2_conv/BiasAdd;resnet50/conv2_block3_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv2_block3_3_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv2_block3_3_conv/BiasAdd;resnet50/conv2_block3_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv2_block3_out/Relu;resnet50/conv2_block3_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block1_0_bn/FusedBatchNormV3;resnet50/conv3_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block1_0_conv/BiasAdd;resnet50/conv3_block1_0_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block1_1_relu/Relu;resnet50/conv3_block1_1_bn/FusedBatchNormV3;resnet50/conv3_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block1_1_conv/BiasAdd;resnet50/conv3_block1_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block1_2_relu/Relu;resnet50/conv3_block1_2_bn/FusedBatchNormV3;resnet50/conv3_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block1_2_conv/BiasAdd;resnet50/conv3_block1_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block1_3_bn/FusedBatchNormV3;resnet50/conv3_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block1_3_conv/BiasAdd;resnet50/conv3_block1_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv3_block1_out/Relu;resnet50/conv3_block1_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block2_1_relu/Relu;resnet50/conv3_block2_1_bn/FusedBatchNormV3;resnet50/conv3_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block2_1_conv/BiasAdd;resnet50/conv3_block2_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block2_2_relu/Relu;resnet50/conv3_block2_2_bn/FusedBatchNormV3;resnet50/conv3_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block2_2_conv/BiasAdd;resnet50/conv3_block2_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block2_3_bn/FusedBatchNormV3;resnet50/conv3_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block2_3_conv/BiasAdd;resnet50/conv3_block2_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv3_block2_out/Relu;resnet50/conv3_block2_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block3_1_relu/Relu;resnet50/conv3_block3_1_bn/FusedBatchNormV3;resnet50/conv3_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block3_1_conv/BiasAdd;resnet50/conv3_block3_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block3_2_relu/Relu;resnet50/conv3_block3_2_bn/FusedBatchNormV3;resnet50/conv3_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block3_2_conv/BiasAdd;resnet50/conv3_block3_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block3_3_bn/FusedBatchNormV3;resnet50/conv3_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block3_3_conv/BiasAdd;resnet50/conv3_block3_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv3_block3_out/Relu;resnet50/conv3_block3_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block4_1_relu/Relu;resnet50/conv3_block4_1_bn/FusedBatchNormV3;resnet50/conv3_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block4_1_conv/BiasAdd;resnet50/conv3_block4_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block4_2_relu/Relu;resnet50/conv3_block4_2_bn/FusedBatchNormV3;resnet50/conv3_block1_1_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block4_2_conv/BiasAdd;resnet50/conv3_block4_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv3_block4_3_bn/FusedBatchNormV3;resnet50/conv3_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv3_block4_3_conv/BiasAdd;resnet50/conv3_block4_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv3_block4_out/Relu;resnet50/conv3_block4_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block1_0_bn/FusedBatchNormV3;resnet50/conv4_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block1_0_conv/BiasAdd;resnet50/conv4_block1_0_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block1_1_relu/Relu;resnet50/conv4_block1_1_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block1_1_conv/BiasAdd;resnet50/conv4_block1_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block1_2_relu/Relu;resnet50/conv4_block1_2_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block1_2_conv/BiasAdd;resnet50/conv4_block1_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block1_3_bn/FusedBatchNormV3;resnet50/conv4_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block1_3_conv/BiasAdd;resnet50/conv4_block1_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv4_block1_out/Relu;resnet50/conv4_block1_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block2_1_relu/Relu;resnet50/conv4_block2_1_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block2_1_conv/BiasAdd;resnet50/conv4_block2_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block2_2_relu/Relu;resnet50/conv4_block2_2_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block2_2_conv/BiasAdd;resnet50/conv4_block2_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block2_3_bn/FusedBatchNormV3;resnet50/conv4_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block2_3_conv/BiasAdd;resnet50/conv4_block2_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv4_block2_out/Relu;resnet50/conv4_block2_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block3_1_relu/Relu;resnet50/conv4_block3_1_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block3_1_conv/BiasAdd;resnet50/conv4_block3_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block3_2_relu/Relu;resnet50/conv4_block3_2_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block3_2_conv/BiasAdd;resnet50/conv4_block3_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block3_3_bn/FusedBatchNormV3;resnet50/conv4_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block3_3_conv/BiasAdd;resnet50/conv4_block3_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv4_block3_out/Relu;resnet50/conv4_block3_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block4_1_relu/Relu;resnet50/conv4_block4_1_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block4_1_conv/BiasAdd;resnet50/conv4_block4_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block4_2_relu/Relu;resnet50/conv4_block4_2_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block4_2_conv/BiasAdd;resnet50/conv4_block4_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block4_3_bn/FusedBatchNormV3;resnet50/conv4_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block4_3_conv/BiasAdd;resnet50/conv4_block4_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv4_block4_out/Relu;resnet50/conv4_block4_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block5_1_relu/Relu;resnet50/conv4_block5_1_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block5_1_conv/BiasAdd;resnet50/conv4_block5_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block5_2_relu/Relu;resnet50/conv4_block5_2_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block5_2_conv/BiasAdd;resnet50/conv4_block5_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block5_3_bn/FusedBatchNormV3;resnet50/conv4_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block5_3_conv/BiasAdd;resnet50/conv4_block5_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv4_block5_out/Relu;resnet50/conv4_block5_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block6_1_relu/Relu;resnet50/conv4_block6_1_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block6_1_conv/BiasAdd;resnet50/conv4_block6_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block6_2_relu/Relu;resnet50/conv4_block6_2_bn/FusedBatchNormV3;resnet50/conv2_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block6_2_conv/BiasAdd;resnet50/conv4_block6_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv4_block6_3_bn/FusedBatchNormV3;resnet50/conv4_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv4_block6_3_conv/BiasAdd;resnet50/conv4_block6_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv4_block6_out/Relu;resnet50/conv4_block6_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv5_block1_0_bn/FusedBatchNormV3;resnet50/conv5_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv5_block1_0_conv/BiasAdd;resnet50/conv5_block1_0_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv5_block1_1_relu/Relu;resnet50/conv5_block1_1_bn/FusedBatchNormV3;resnet50/conv3_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv5_block1_1_conv/BiasAdd;resnet50/conv5_block1_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv5_block1_2_relu/Relu;resnet50/conv5_block1_2_bn/FusedBatchNormV3;resnet50/conv3_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv5_block1_2_conv/BiasAdd;resnet50/conv5_block1_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv5_block1_3_bn/FusedBatchNormV3;resnet50/conv5_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv5_block1_3_conv/BiasAdd;resnet50/conv5_block1_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv5_block1_out/Relu;resnet50/conv5_block1_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv5_block2_1_relu/Relu;resnet50/conv5_block2_1_bn/FusedBatchNormV3;resnet50/conv3_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv5_block2_1_conv/BiasAdd;resnet50/conv5_block2_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv5_block2_2_relu/Relu;resnet50/conv5_block2_2_bn/FusedBatchNormV3;resnet50/conv3_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv5_block2_2_conv/BiasAdd;resnet50/conv5_block2_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv5_block2_3_bn/FusedBatchNormV3;resnet50/conv5_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv5_block2_3_conv/BiasAdd;resnet50/conv5_block2_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv5_block2_out/Relu;resnet50/conv5_block2_add/add 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv5_block3_1_relu/Relu;resnet50/conv5_block3_1_bn/FusedBatchNormV3;resnet50/conv3_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv5_block3_1_conv/BiasAdd;resnet50/conv5_block3_1_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv5_block3_2_relu/Relu;resnet50/conv5_block3_2_bn/FusedBatchNormV3;resnet50/conv3_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv5_block3_2_conv/BiasAdd;resnet50/conv5_block3_2_conv/Conv2D 
Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- resnet50/conv5_block3_3_bn/FusedBatchNormV3;resnet50/conv5_block1_0_conv/BiasAdd/ReadVariableOp;resnet50/conv5_block3_3_conv/BiasAdd;resnet50/conv5_block3_3_conv/Conv2D 
Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- resnet50/conv5_block3_out/Relu;resnet50/conv5_block3_add/add 
Supported TIDL layer type --- 2 Tflite layer type --- 40 layer output name--- resnet50/avg_pool/Mean 
Supported TIDL layer type --- 6 Tflite layer type --- 9 layer output name--- resnet50/predictions/MatMul;resnet50/predictions/BiasAdd 
Layer 'StatefulPartitionedCall:0' added to unsupported nodes as specified in deny list 

 Preliminary number of subgraphs:1 , 74 nodes delegated out of 75 nodes 
 
Node in deny list...delegated to ARM --- tflite layer code - 25, tensor name - StatefulPartitionedCall:0  
In TIDL_tfliteRtImportInit subgraph_id=185
Layer 0, subgraph id 185, name=resnet50/predictions/MatMul;resnet50/predictions/BiasAdd
Layer 1, subgraph id 185, name=serving_default_input_2:0
In TIDL_tfliteRtImportNode  TIDL Layer type 26   Tflite builtin code type 34 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 26   Tflite builtin code type 34 
In TIDL_tfliteRtImportNode  TIDL Layer type 2   Tflite builtin code type 17 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 1   Tflite builtin code type 3 
In TIDL_tfliteRtImportNode  TIDL Layer type 5   Tflite builtin code type 0 
In TIDL_tfliteRtImportNode  TIDL Layer type 2   Tflite builtin code type 40 
In TIDL_tfliteRtImportNode  TIDL Layer type 6   Tflite builtin code type 9 
In TIDL_runtimesOptimizeNet: LayerIndex = 76, dataIndex = 75 
WARNING : Pad layer won't be merged in the succeeding layer, it will be treated as a stand alone layer

 ************** Frame index 1 : Running float import ************* 
In TIDL_runtimesPostProcessNet 
****************************************************
**                ALL MODEL CHECK PASSED          **
****************************************************

************ in TIDL_subgraphRtCreate ************ 
 The soft limit is 2048
The hard limit is 2048
MEM: Init ... !!!
MEM: Init ... Done !!!
 0.0s:  VX_ZONE_INIT:Enabled
 0.6s:  VX_ZONE_ERROR:Enabled
 0.6s:  VX_ZONE_WARNING:Enabled
 0.1271s:  VX_ZONE_INIT:[tivxInit:184] Initialization Done !!!
************ TIDL_subgraphRtCreate done ************ 
 tidl_tfLiteRtImport_delegate.cpp Invoke 472 
*******   In TIDL_subgraphRtInvoke  ******** 
   0         1.00000        -1.00000         0.99219 6
   1         1.00000         0.00000         6.37599 6
   2         1.00000         0.00000         6.37599 6
   3         1.00000         0.00000         6.37599 6
   4         1.00000        -2.36153         2.48477 6
   5         1.00000         0.00000         3.99792 6
   6         1.00000         0.00000         6.31873 6
   7         1.00000        -8.45618         7.06603 6
   8         1.00000         0.00000         7.64503 6
   9         1.00000         0.00000         3.40655 6
  10         1.00000         0.00000         4.25327 6
  11         1.00000       -10.84118         6.13870 6
  12         1.00000         0.00000         7.45148 6
  13         1.00000         0.00000         4.30986 6
  14         1.00000         0.00000         4.29252 6
  15         1.00000        -3.44873         4.61394 6
  16         1.00000         0.00000         7.15418 6
  17         1.00000        -5.63600         5.39006 6
  18         1.00000         0.00000         7.08078 6
  19         1.00000         0.00000         6.08823 6
  20         1.00000       -10.65282        12.67574 6
  21         1.00000         0.00000        12.67058 6
  22         1.00000         0.00000         8.68013 6
  23         1.00000         0.00000         7.52764 6
  24         1.00000        -6.67832        14.90568 6
  25         1.00000         0.00000        16.02365 6
  26         1.00000         0.00000        18.04773 6
  27         1.00000         0.00000        47.49588 6
  28         1.00000       -20.42931        15.08198 6
  29         1.00000         0.00000        16.22721 6
  30         1.00000         0.00000        23.08875 6
  31         1.00000         0.00000        11.33198 6
  32         1.00000       -10.65950         9.54050 6
  33         1.00000         0.00000        16.12005 6
  34         1.00000       -13.39652        11.90828 6
  35         1.00000         0.00000        13.79491 6
  36         1.00000         0.00000        11.43195 6
  37         1.00000       -13.45553        13.55506 6
  38         1.00000         0.00000        16.30051 6
  39         1.00000         0.00000        14.62940 6
  40         1.00000         0.00000        27.53911 6
  41         1.00000       -16.66064        12.34311 6
  42         1.00000         0.00000        12.34311 6
  43         1.00000         0.00000        14.61796 6
  44         1.00000         0.00000        25.30610 6
  45         1.00000       -10.09836        14.43837 6
  46         1.00000         0.00000        18.67059 6
  47         1.00000         0.00000        16.86055 6
  48         1.00000         0.00000        32.71607 6
  49         1.00000       -20.93223        16.70530 6
  50         1.00000         0.00000        22.58823 6
  51         1.00000         0.00000        14.44002 6
  52         1.00000         0.00000        25.82981 6
  53         1.00000       -19.82811        11.18865 6
  54         1.00000         0.00000        24.35683 6
  55         1.00000         0.00000        13.57607 6
  56         1.00000         0.00000        43.97537 6
  57         1.00000       -13.89548        25.74662 6
  58         1.00000         0.00000        25.74662 6
  59         1.00000       -10.17096        12.49467 6
  60         1.00000         0.00000         4.67871 6
  61         1.00000         0.00000         2.10772 6
  62         1.00000        -6.67874         8.00488 6
  63         1.00000         0.00000        16.32167 6
  64         1.00000         0.00000         2.69738 6
  65         1.00000         0.00000         2.45562 6
  66         1.00000        -9.25019         7.17859 6
  67         1.00000         0.00000        18.64527 6
  68         1.00000         0.00000         2.18053 6
  69         1.00000         0.00000         2.45341 6
  70         1.00000       -10.50753         9.73151 6
  71         1.00000         0.00000        22.13766 6
  72         1.00000         0.00000        12.21559 6
  73         1.00000        -4.23888         7.05661 6
 Layer,   Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger,    paddingWait,LayerWithoutPad,LayerHandleCopy,   BackupCycles,  RestoreCycles,

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Fullscreen custon-model-tfl_err.log Download

INFO: Created TensorFlow Lite XNNPACK delegate for CPU.

Fullscreen 3312.custon-model-onnx_out.log Download

Internal Error: do_recv() expected MSG_ID 5005, got 0!

Stack trace:
  [bt] (0) /usr/lib/libti_inference_client.so(StackTrace[abi:cxx11](unsigned long, unsigned long)+0x1ed) [0x7f0ba22ee05d]
  [bt] (1) /usr/lib/libti_inference_client.so(send_once(unsigned int, void*, int, progressbar*, progressbar*)+0x838) [0x7f0ba22edac8]
  [bt] (2) /usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_pybind11_state.so(+0x8074c) [0x7f0ba258074c]
  [bt] (3) /usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_pybind11_state.so(+0x80aa9) [0x7f0ba2580aa9]
  [bt] (4) /usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_pybind11_state.so(+0x880ef) [0x7f0ba25880ef]
  [bt] (5) /usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_pybind11_state.so(+0xa5a67) [0x7f0ba25a5a67]
  [bt] (6) /usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_pybind11_state.so(+0x74433) [0x7f0ba2574433]
  [bt] (7) /usr/bin/python3(_PyCFunction_FastCallDict+0x35c) [0x566b0c]
  [bt] (8) /usr/bin/python3() [0x594741]
  [bt] (9) /usr/bin/python3() [0x549ea5]
  [bt] (10) /usr/bin/python3() [0x5513f1]
  [bt] (11) /usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_pybind11_state.so(+0x767a7) [0x7f0ba25767a7]
  [bt] (12) /usr/bin/python3(_PyObject_FastCallKeywords+0x19c) [0x5a9b9c]
  [bt] (13) /usr/bin/python3() [0x50a2c3]
  [bt] (14) /usr/bin/python3(_PyEval_EvalFrameDefault+0x444) [0x50bcb4]
  [bt] (15) /usr/bin/python3() [0x509459]

Fullscreen 3312.custon-model-onnx_err.log Download

terminate called after throwing an instance of 'std::exception'
  what():  std::exception

0 Anaïs Gastineau over 1 year ago in reply to Anaïs Gastineau

Prodigy 80 points

I also forgot to mention that these files are created for an onnx and a tflite models when the kernel crashed for both models.

Best regards,

Anaïs

0 Paula Carrillo over 1 year ago in reply to Anaïs Gastineau

TI__Mastermind 40580 points

Hi Anaïs, from TFLite log I don't see any obvious error. Are any files created inside custom-artifacts/tflite folder?

From onnxruntime log, message "Internal Error: do_recv() expected MSG_ID 5005, got 0!" points to a communication issue between container (your workspace) and reserved EVM (board assigned in our HW farm).

I am wondering if you got this error after maybe running TFLite? could we try to do 2 fresh runs? Ex: log in, get a board, run tflite demo, save logs, log out and repeat for onnx? Are you getting same logs?

Also, how do you create the models?. Current Model Analyzer is a bit behind SDK so it still uses ONNXRT 1.0.7 while latest PSDK9.1 uses ONNXRT 1.14. For TFLite we are currently using 2.8 for both cloud and PSDK. I believe, next PSDK9.2 will update TFLite to 2.12. Anyhow, something to keep in mind..

thank you,

Paula

0 Anaïs Gastineau over 1 year ago in reply to Paula Carrillo

Prodigy 80 points

Hello Paula,

Thanks for your answer. I followed your instructions. First, I log in, then start a session for tflite, save results, log out and repeat the procedure with onnx. In both cases, the kernel crashes.

For the onnx session:

the kernel crashes immediately at the cell "so = rt.SessionOptions() [...]"
the folder "custom-artifacts/onnx/mymodel" is empty
Log files are the same as previously
All files : onnx_session.zip

For the tflite session:

the kernel crashes after 1 or 2 minutes at the cell "so = rt.SessionOptions() [...]"
the folder "custom-artifacts/tflite/mymodel" is not empty
Log files are the same as previously
All files : tflite_session.zip

Also, to create my model, I used model from keras for tensorflow and model from torchvision for pytorch, then I convert pytorch models in onnx. I also try by creating a custom model composed of 5 convolutional layers and 1 linear in pytorch.

For Pytorch:

torch 2.1.2+cu118
onnx 1.15.0
To convert a model pytorch to onnx : torch.onnx.export(...)

For TensorFlow:

tensorflow 2.10.0
tf2onnx 1.16.1
onnxruntime 1.16.0
To convert the model in onnx: tf2onnx.convert.from_keras(...)
to convert in tflite : tf.lite.TFLiteConverter.from_keras_model(...)

It seems I don't have the good version of TF, TFlite and Onnxruntime. Can you tell me which version of pytorch is supported please ?

Thanks,

Anaïs

0 Anaïs Gastineau over 1 year ago in reply to Anaïs Gastineau

Prodigy 80 points

Paula,

I tried to convert resnet50 from keras in tflite with tensorflow 2.8 as you recommended. However, I still have the same error... Also, I can't install onnxruntime 1.0.7 because it's too old. The oldest version I can get is 1.7.

Do you have another idea for my problem ?

Best regards,

Anaïs

0 Paula Carrillo over 1 year ago in reply to Anaïs Gastineau

TI__Mastermind 40580 points

Anaïs, my apologies, I mistype, we are currently using in cloud onnxruntime 1.7 no 1.0.7.. I am suspecting some memory issues with AM62A. Would you mind to try to run same model in another platform? Maybe TDA4VM? Does it work? or same issue?

Also, could you share your model? if not, could you create a toy model (close to your model) which reproduce the issue?

thank you,

Paula

0 Anaïs Gastineau over 1 year ago in reply to Paula Carrillo

Prodigy 80 points

Hello Paula,

Thanks for your answer. I tried on a TDA4VM platform, but I still have the same issue. First I try with the example given in the notebook and it works. Then, I tried with my onnx model from Pytorch. Idem for tflite model. It works on your example but not on mine.

You can find attached a zip folder with my models.

2768.models.zip.

Thanks,

Anaïs

0 Qutaiba Saleh over 1 year ago in reply to Anaïs Gastineau

TI__Expert 5130 points

Hello Anaïs,

We have been investigating the issue you are facing with Model Analyzer. We were able to compile some of the models you have using the Edge AI TIDL Tools running on a local PC. While we complete the investigation with Model Analyzer, you can use the edgeai tidle tool. You can access it using this link https://github.com/TexasInstruments/edgeai-tidl-tools/tree/master. You can update the python scripts in this folder to compile your model https://github.com/TexasInstruments/edgeai-tidl-tools/tree/master/examples/osrt_python.

Best regards,

Qutaiba

0 Anaïs Gastineau over 1 year ago in reply to Qutaiba Saleh

Prodigy 80 points

Hello,

Thanks for your answer, I will try on PC.

Best regards,

Anaïs

+1 Anaïs Gastineau over 1 year ago in reply to Anaïs Gastineau

Prodigy 80 points

Hello,

As you recommended, I tried to compile my models with the github tidl-tools. I succeeded to compile all onnx and tflite model for classification.

However, can you let me know if you success to make it possible on Model Analyzer, please?

Thanks,

Anaïs

0 Qutaiba Saleh over 1 year ago in reply to Anaïs Gastineau

TI__Expert 5130 points

Hello Anaïs,

I am glad to hear that you successfully compiled the model using edgeai-tidl-tools. The Model Analyzer backend server runs an older version of edgdai-tild-tools. It will be updated to the next version. I will let you know when I have a definite date from the respective team.

Please, don't hesitate to open a new thread if you have any other questions.

Best regards,

Qutaiba

0 Paula Carrillo over 1 year ago in reply to Qutaiba Saleh

TI__Mastermind 40580 points

Hi Anaïs, a 5-cents comments. In the meantime, you can also inference using edgeai-tidl-tools host emulation

Thank you,

Paula

0 Anaïs Gastineau over 1 year ago in reply to Paula Carrillo

Prodigy 80 points

Hello,

Thanks for both answer.

Anaïs

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SK-AM62A-LP: Custom model compilation failed on 'Model Analyzer' platform