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AM69A: Not able to run custom ONXX model for object detection on on Eagle AI studio

Akhilesh Gangwar
Intellectual 310 points
Part Number: AM69A

Hi,

I have been trying to run custom ONXX model on eagle ai studio for object detection. Below steps I followed-

Model analyzer -> selected AM69A - 32 TOPS -> selected custom model onnx runtime

Then I downloaded the pre trained model from this link https://github.com/onnx/models/tree/main/vision/object_detection_segmentation/mask-rcnn (Mask R-CNN R-50-FPN) and tried to run.

Each time I am trying to compile the model, the kernel is getting dead and in some cases, it is giving bad malloc error. This happens when I am using custom onnx model only. So far I am not able to compile and run any single onnx custom model. I have 3 models. 

Mask RCNN, yolact (resnet50) and yolact (resnet18).

Even I tried the simple onxx code to check the model -

import onnx
import os


# Preprocessing: load the ONNX model
#model_path = os.path.join("resources", "single_relu.onnx")
onnx_model = onnx.load('path to uploaded model')

print("The model is:\n{}".format(onnx_model))

# Check the model
try:
    onnx.checker.check_model(onnx_model)
except onnx.checker.ValidationError as e:
    print("The model is invalid: %s" % e)
else:
    print("The model is valid!")

Here also the kernel keeps getting disconnected and dead. I tried 1 more custom model onnx based but got the same issue. I have to do log out and login to make it work again. Restarting EVM or kernel is not fixing this issue.

Please let me know why is it happening.

Thanks

  • 0
    TI__Mastermind 38820 points

    Hi Akhilesh, 

    I tried to compile "mask_rcnn_R_50_FPN_1" and I saw in notebooks/logs error message "Could not find const or initializer of layer 2170 !!!" (attached log for your reference)

    I am not familiar with this model, but maybe this could give you a hint.

    One thing we recommend is to run models in ARM only, to confirm they run OK. I tried to run it, I used vseg-onnx notebook as a baseline, as I saw in github this is a segmentation model, but you could use either as only postprocessing and visualization would change. Anyhow, it didn't work for me. Attached notebook used. Could you try to run it on ARM only? to discard any possible mistake from my side. Let me know your results.

    Fullscreen custon-model-onnx_out_cat.log Download 
    tidl_tools_path                                 = /home/root/notebooks/tidl_tools 
artifacts_folder                                = custom-artifacts/onnx/mask_rcnn_R_50_FPN_1x.onnx 
tidl_tensor_bits                                = 8 
debug_level                                     = 1 
num_tidl_subgraphs                              = 16 
tidl_denylist                                   = 
tidl_denylist_layer_name                        = 
tidl_denylist_layer_type                         = 
tidl_allowlist_layer_name                        = 
model_type                                      =  
tidl_calibration_accuracy_level                 = 7 
tidl_calibration_options:num_frames_calibration = 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 : 'meta_layers_names_list' is not provided - running OD post processing in ARM mode 
 
Number of OD backbone nodes = 0 
Size of odBackboneNodeIds = 0 
Could not find const or initializer of layer 2170 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2178 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2170 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2178 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2170 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2178 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2170 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2178 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2170 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2178 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2170 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2178 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2170 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2178 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2170 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2178 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2170 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2178 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2170 !!!
Only float and INT64 tensor is supported 
Could not find const or initializer of layer 2178 !!!
Only float and INT64 tensor is supported 
Layer 0 -- layer name -- 0 
 Input dims size = 3     dims --- 3   0   0   
Layer 0 --- op type -  Unsqueeze,   Number of input dims 3  !=  4 .. not supported by TIDL 
Layer 1 -- layer name -- 2 
 Input dims size = 4     dims --- 1   3   0   0   
Supported TIDL layer type ---            Conv -- 2 
Layer 2 -- layer name -- 7 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Relu -- 7 
Layer 3 -- layer name -- 8 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---         MaxPool -- 8 
Layer 4 -- layer name -- 30 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Conv -- 30 
Layer 5 -- layer name -- 10 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Conv -- 10 
Layer 6 -- layer name -- 15 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Relu -- 15 
Layer 7 -- layer name -- 17 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Conv -- 17 
Layer 8 -- layer name -- 22 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Relu -- 22 
Layer 9 -- layer name -- 24 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Conv -- 24 
Layer 10 -- layer name -- 35 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---             Add -- 35 
Layer 11 -- layer name -- 36 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 36 
Layer 12 -- layer name -- 38 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 38 
Layer 13 -- layer name -- 43 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Relu -- 43 
Layer 14 -- layer name -- 45 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Conv -- 45 
Layer 15 -- layer name -- 50 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Relu -- 50 
Layer 16 -- layer name -- 52 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Conv -- 52 
Layer 17 -- layer name -- 57 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---             Add -- 57 
Layer 18 -- layer name -- 58 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 58 
Layer 19 -- layer name -- 60 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 60 
Layer 20 -- layer name -- 65 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Relu -- 65 
Layer 21 -- layer name -- 67 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Conv -- 67 
Layer 22 -- layer name -- 72 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Relu -- 72 
Layer 23 -- layer name -- 74 
 Input dims size = 4     dims --- 1   64   0   0   
Supported TIDL layer type ---            Conv -- 74 
Layer 24 -- layer name -- 79 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---             Add -- 79 
Layer 25 -- layer name -- 80 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 80 
Layer 26 -- layer name -- 102 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 102 
Layer 27 -- layer name -- 82 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 82 
Layer 28 -- layer name -- 87 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Relu -- 87 
Layer 29 -- layer name -- 89 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Conv -- 89 
Layer 30 -- layer name -- 94 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Relu -- 94 
Layer 31 -- layer name -- 96 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Conv -- 96 
Layer 32 -- layer name -- 107 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---             Add -- 107 
Layer 33 -- layer name -- 108 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---            Relu -- 108 
Layer 34 -- layer name -- 110 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---            Conv -- 110 
Layer 35 -- layer name -- 115 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Relu -- 115 
Layer 36 -- layer name -- 117 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Conv -- 117 
Layer 37 -- layer name -- 122 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Relu -- 122 
Layer 38 -- layer name -- 124 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Conv -- 124 
Layer 39 -- layer name -- 129 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---             Add -- 129 
Layer 40 -- layer name -- 130 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---            Relu -- 130 
Layer 41 -- layer name -- 132 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---            Conv -- 132 
Layer 42 -- layer name -- 137 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Relu -- 137 
Layer 43 -- layer name -- 139 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Conv -- 139 
Layer 44 -- layer name -- 144 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Relu -- 144 
Layer 45 -- layer name -- 146 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Conv -- 146 
Layer 46 -- layer name -- 151 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---             Add -- 151 
Layer 47 -- layer name -- 152 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---            Relu -- 152 
Layer 48 -- layer name -- 154 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---            Conv -- 154 
Layer 49 -- layer name -- 159 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Relu -- 159 
Layer 50 -- layer name -- 161 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Conv -- 161 
Layer 51 -- layer name -- 166 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Relu -- 166 
Layer 52 -- layer name -- 168 
 Input dims size = 4     dims --- 1   128   0   0   
Supported TIDL layer type ---            Conv -- 168 
Layer 53 -- layer name -- 173 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---             Add -- 173 
Layer 54 -- layer name -- 174 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---            Relu -- 174 
Layer 55 -- layer name -- 196 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---            Conv -- 196 
Layer 56 -- layer name -- 176 
 Input dims size = 4     dims --- 1   512   0   0   
Supported TIDL layer type ---            Conv -- 176 
Layer 57 -- layer name -- 181 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 181 
Layer 58 -- layer name -- 183 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 183 
Layer 59 -- layer name -- 188 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 188 
Layer 60 -- layer name -- 190 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 190 
Layer 61 -- layer name -- 201 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---             Add -- 201 
Layer 62 -- layer name -- 202 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---            Relu -- 202 
Layer 63 -- layer name -- 204 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---            Conv -- 204 
Layer 64 -- layer name -- 209 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 209 
Layer 65 -- layer name -- 211 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 211 
Layer 66 -- layer name -- 216 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 216 
Layer 67 -- layer name -- 218 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 218 
Layer 68 -- layer name -- 223 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---             Add -- 223 
Layer 69 -- layer name -- 224 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---            Relu -- 224 
Layer 70 -- layer name -- 226 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---            Conv -- 226 
Layer 71 -- layer name -- 231 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 231 
Layer 72 -- layer name -- 233 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 233 
Layer 73 -- layer name -- 238 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 238 
Layer 74 -- layer name -- 240 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 240 
Layer 75 -- layer name -- 245 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---             Add -- 245 
Layer 76 -- layer name -- 246 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---            Relu -- 246 
Layer 77 -- layer name -- 248 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---            Conv -- 248 
Layer 78 -- layer name -- 253 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 253 
Layer 79 -- layer name -- 255 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 255 
Layer 80 -- layer name -- 260 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 260 
Layer 81 -- layer name -- 262 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 262 
Layer 82 -- layer name -- 267 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---             Add -- 267 
Layer 83 -- layer name -- 268 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---            Relu -- 268 
Layer 84 -- layer name -- 270 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---            Conv -- 270 
Layer 85 -- layer name -- 275 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 275 
Layer 86 -- layer name -- 277 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 277 
Layer 87 -- layer name -- 282 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 282 
Layer 88 -- layer name -- 284 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 284 
Layer 89 -- layer name -- 289 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---             Add -- 289 
Layer 90 -- layer name -- 290 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---            Relu -- 290 
Layer 91 -- layer name -- 292 
 Input dims size = 4     dims --- 1   1024   0   0   
Supported TIDL layer type ---            Conv -- 292 
Layer 92 -- layer name -- 297 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 297 
Layer 93 -- layer name -- 299 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Conv -- 299 
Layer 94 -- layer name -- 304 
 Input dims size = 4     dims --- 1   256   0   0   
Supported TIDL layer type ---            Relu -- 304 
L

    https://e2e.ti.com/cfs-file/__key/communityserver-discussions-components-files/791/vseg_2D00_onnx_2D00_cat.ipynb

    Thank you,

    Paula

  • 0 in reply to Paula Carrillo
    Intellectual 310 points

    Hi,

    Thanks for reply. I check the same and found the same issue. Let's ignore this model.

    I tried to run one more model which is a custom model with resnet18 backbone. This model also I am not able to compile. This is an instance segmentation model, yolact github.com/.../yolact

    I am attaching the logs. It says unsupported TIDL layers for some of the ops. Does this mean TI framework has not supported these operations yet? like transpose, padding?

    tidl_tools_path                                 = /home/root/notebooks/tidl_tools 
artifacts_folder                                = custom-artifacts/yolact_resnet18_54_400000.onnx 
tidl_tensor_bits                                = 8 
debug_level                                     = 1 
num_tidl_subgraphs                              = 16 
tidl_denylist                                   = MaxPool   
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 ---            Conv -- Conv_16 
Supported TIDL layer type ---            Relu -- Relu_17 
Op type 'MaxPool'  added to unsupported nodes as specified in deny list 
Supported TIDL layer type ---            Conv -- Conv_19 
Supported TIDL layer type ---            Relu -- Relu_20 
Supported TIDL layer type ---            Conv -- Conv_21 
Supported TIDL layer type ---             Add -- Add_22 
Supported TIDL layer type ---            Relu -- Relu_23 
Supported TIDL layer type ---            Conv -- Conv_24 
Supported TIDL layer type ---            Relu -- Relu_25 
Supported TIDL layer type ---            Conv -- Conv_26 
Supported TIDL layer type ---             Add -- Add_27 
Supported TIDL layer type ---            Relu -- Relu_28 
Supported TIDL layer type ---            Conv -- Conv_32 
Supported TIDL layer type ---            Conv -- Conv_29 
Supported TIDL layer type ---            Relu -- Relu_30 
Supported TIDL layer type ---            Conv -- Conv_31 
Supported TIDL layer type ---             Add -- Add_33 
Supported TIDL layer type ---            Relu -- Relu_34 
Supported TIDL layer type ---            Conv -- Conv_35 
Supported TIDL layer type ---            Relu -- Relu_36 
Supported TIDL layer type ---            Conv -- Conv_37 
Supported TIDL layer type ---             Add -- Add_38 
Supported TIDL layer type ---            Relu -- Relu_39 
Supported TIDL layer type ---            Conv -- Conv_43 
Supported TIDL layer type ---            Conv -- Conv_40 
Supported TIDL layer type ---            Relu -- Relu_41 
Supported TIDL layer type ---            Conv -- Conv_42 
Supported TIDL layer type ---             Add -- Add_44 
Supported TIDL layer type ---            Relu -- Relu_45 
Supported TIDL layer type ---            Conv -- Conv_46 
Supported TIDL layer type ---            Relu -- Relu_47 
Supported TIDL layer type ---            Conv -- Conv_48 
Supported TIDL layer type ---             Add -- Add_49 
Supported TIDL layer type ---            Relu -- Relu_50 
Supported TIDL layer type ---            Conv -- Conv_54 
Supported TIDL layer type ---            Conv -- Conv_51 
Supported TIDL layer type ---            Relu -- Relu_52 
Supported TIDL layer type ---            Conv -- Conv_53 
Supported TIDL layer type ---             Add -- Add_55 
Supported TIDL layer type ---            Relu -- Relu_56 
Supported TIDL layer type ---            Conv -- Conv_57 
Supported TIDL layer type ---            Relu -- Relu_58 
Supported TIDL layer type ---            Conv -- Conv_59 
Supported TIDL layer type ---             Add -- Add_60 
Supported TIDL layer type ---            Relu -- Relu_61 
Supported TIDL layer type ---            Conv -- Conv_62 
Supported TIDL layer type ---            Conv -- Conv_75 
Supported TIDL layer type ---            Relu -- Relu_76 
Supported TIDL layer type ---            Conv -- Conv_81 
Supported TIDL layer type ---            Conv -- Conv_82 
Supported TIDL layer type ---            Conv -- Conv_175 
Supported TIDL layer type ---            Relu -- Relu_176 
Supported TIDL layer type ---            Conv -- Conv_180 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_182 
Supported TIDL layer type ---            Conv -- Conv_164 
Supported TIDL layer type ---            Relu -- Relu_165 
Supported TIDL layer type ---            Conv -- Conv_169 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_171 
Supported TIDL layer type ---            Conv -- Conv_153 
Supported TIDL layer type ---            Relu -- Relu_154 
Supported TIDL layer type ---            Conv -- Conv_158 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_160 
Supported TIDL layer type ---            Conv -- Conv_67 
Error : Unsupported Opset for Resize OP 
Resize layer delegated to ARM -- 'Resize_63' 
Unsupported (TIDL check) TIDL layer type ---          Resize 
ERROR : Opset 13 pad is not supported 
Unsupported (TIDL check) TIDL layer type ---             Pad 
Unsupported (TIDL check) TIDL layer type ---     AveragePool 
Supported TIDL layer type ---             Add -- Add_68 
Supported TIDL layer type ---            Conv -- Conv_77 
Supported TIDL layer type ---            Relu -- Relu_78 
Supported TIDL layer type ---            Conv -- Conv_124 
Supported TIDL layer type ---            Relu -- Relu_125 
Supported TIDL layer type ---            Conv -- Conv_135 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_143 
Supported TIDL layer type ---            Conv -- Conv_73 
Error : Unsupported Opset for Resize OP 
Resize layer delegated to ARM -- 'Resize_69' 
Unsupported (TIDL check) TIDL layer type ---          Resize 
ERROR : Opset 13 pad is not supported 
Unsupported (TIDL check) TIDL layer type ---             Pad 
Unsupported (TIDL check) TIDL layer type ---     AveragePool 
Supported TIDL layer type ---             Add -- Add_74 
Supported TIDL layer type ---            Conv -- Conv_79 
Supported TIDL layer type ---            Relu -- Relu_80 
Supported TIDL layer type ---            Conv -- Conv_95 
Supported TIDL layer type ---            Relu -- Relu_96 
Supported TIDL layer type ---            Conv -- Conv_106 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_114 
Supported TIDL layer type ---          Concat -- Concat_187 
Supported TIDL layer type ---         Softmax -- Softmax_190 
Supported TIDL layer type ---            Conv -- Conv_183 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_185 
Supported TIDL layer type ---            Conv -- Conv_172 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_174 
Supported TIDL layer type ---            Conv -- Conv_161 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_163 
Supported TIDL layer type ---            Conv -- Conv_144 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_152 
Supported TIDL layer type ---            Conv -- Conv_115 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_123 
Supported TIDL layer type ---          Concat -- Concat_188 
Supported TIDL layer type ---            T

    Let me know about unsupported TIDL layers.

    Any recommendations on instance segmentation model? Because I checked the TI model zoo, it does not have any instance seg models.

    Thanks

    Akhilesh Gangwar

  • 0 in reply to Akhilesh Gangwar
    Intellectual 310 points

    Also, I am not able to run yolov5s and yolov5l as well. Kernel is getting dead. This is the device log-

    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) [0x7f5d7fca505d]
  [bt] (1) /usr/lib/libti_inference_client.so(send_once(unsigned int, void*, int, progressbar*, progressbar*)+0x838) [0x7f5d7fca4ac8]
  [bt] (2) /usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_pybind11_state.so(+0x8074c) [0x7f5d7ff3774c]
  [bt] (3) /usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_pybind11_state.so(+0x80aa9) [0x7f5d7ff37aa9]
  [bt] (4) /usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_pybind11_state.so(+0x880ef) [0x7f5d7ff3f0ef]
  [bt] (5) /usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_pybind11_state.so(+0xa5a67) [0x7f5d7ff5ca67]
  [bt] (6) /usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_pybind11_state.so(+0x74433) [0x7f5d7ff2b433]
  [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) [0x7f5d7ff2d7a7]
  [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]

    Thanks

    Akhilesh

  • 0 in reply to Akhilesh Gangwar
    TI__Mastermind 38820 points

    Hi Akhilesh, 

    From logs I see "Maxpool" is in "deny_list" could you please delete this from "compile_options". It is there just as an example.

    Also, one option to avoid creation of a lot of subgraphs, which are probably causing the crash, is to use TIDL's meta-architecture in order to replace postprocessing nodes with an OD layer supported by TI. 

    edgeai-tidl-tools/docs/tidl_fsg_od_meta_arch.md at master · TexasInstruments/edgeai-tidl-tools · GitHub

    Training video, with some additional details: Process This: Efficient human pose estimation using the YOLO-Pose model and TI processors | Video | TI.com Video is for YoloP

    The main idea is to replace unsupported layers by an OD layer and, be able to offload most (or all) of the model to TIDL.

    In Model Analyzer notebooks/prebuilt-models/8bits you can untar and check OD models to get prototex examples

    Thank you,

    Paula

  • 0 in reply to Paula Carrillo
    Intellectual 310 points

    Hi Paula,

    I am able to run yolov5 provided by TI model zoo as a custom model. I am trying one instance segmentation model and getting some undefined behavior.

    Sometimes I am able to compile the model and can see the input and output details. Other times I am getting error while compiling the model itself. 

    error -

    ---------------------------------------------------------------------------
RuntimeException                          Traceback (most recent call last)
<ipython-input-16-5cc78ed11a7d> in <module>
----> 1 sess = rt.InferenceSession(onnx_model_path ,providers=EP_list, provider_options=[compile_options, {}], sess_options=so)

/usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_inference_collection.py in __init__(self, path_or_bytes, sess_options, providers, provider_options)
    281 
    282         try:
--> 283             self._create_inference_session(providers, provider_options)
    284         except RuntimeError:
    285             if self._enable_fallback:

/usr/local/lib/python3.6/dist-packages/onnxruntime/capi/onnxruntime_inference_collection.py in _create_inference_session(self, providers, provider_options)
    313 
    314         # initialize the C++ InferenceSession
--> 315         sess.initialize_session(providers, provider_options)
    316 
    317         self._sess = sess

RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Exception during initialization: basic_string::_M_create

    I checked the logs. There are some unsupported operators as well. 

    5, 256
 87,          Concat, 4, 1, 250, 257
 88,            Conv, 3, 1, 257, 258
 89,            Relu, 1, 1, 258, 259
 90,            Conv, 3, 1, 259, 260
 91,            Relu, 1, 1, 260, 261
 92,            Conv, 3, 1, 261, 262
 93,            Relu, 1, 1, 262, 263
 94,            Conv, 3, 1, 263, 264
 95,            Relu, 1, 1, 264, 265
 96,            Conv, 3, 1, 259, 266
 97,            Relu, 1, 1, 266, 267
 98,          Concat, 2, 1, 265, 268
 99,            Conv, 3, 1, 268, 269
100,            Relu, 1, 1, 269, 270
101,            Conv, 3, 1, 270, 271
102,            Relu, 1, 1, 271, 272
103,          Resize, 3, 1, 272, 277
104,          Concat, 2, 1, 277, 278
105,            Conv, 3, 1, 278, 279
106,            Relu, 1, 1, 279, 280
107,            Conv, 3, 1, 280, 281
108,            Relu, 1, 1, 281, 282
109,            Conv, 3, 1, 282, 283
110,            Relu, 1, 1, 283, 284
111,            Conv, 3, 1, 278, 285
112,            Relu, 1, 1, 285, 286
113,          Concat, 2, 1, 284, 287
114,            Conv, 3, 1, 287, 288
115,            Relu, 1, 1, 288, 289
116,            Conv, 3, 1, 289, 290
117,            Relu, 1, 1, 290, 291
118,          Resize, 3, 1, 291, 296
119,          Concat, 2, 1, 296, 297
120,            Conv, 3, 1, 297, 298
121,            Relu, 1, 1, 298, 299
122,            Conv, 3, 1, 299, 300
123,            Relu, 1, 1, 300, 301
124,            Conv, 3, 1, 301, 302
125,            Relu, 1, 1, 302, 303
126,            Conv, 3, 1, 297, 304
127,            Relu, 1, 1, 304, 305
128,          Concat, 2, 1, 303, 306
129,            Conv, 3, 1, 306, 307
130,            Relu, 1, 1, 307, 308
131,            Conv, 3, 1, 308, 309
132,            Relu, 1, 1, 309, 310
133,          Resize, 3, 1, 310, 315
134,          Concat, 2, 1, 315, 316
135,            Conv, 3, 1, 316, 317
136,            Relu, 1, 1, 317, 318
137,            Conv, 3, 1, 318, 319
138,            Relu, 1, 1, 319, 320
139,            Conv, 3, 1, 320, 321
140,            Relu, 1, 1, 321, 322
141,            Conv, 3, 1, 316, 323
142,            Relu, 1, 1, 323, 324
143,          Concat, 2, 1, 322, 325
144,            Conv, 3, 1, 325, 326
145,            Relu, 1, 1, 326, 327
146,            Conv, 3, 1, 327, 370
147,            Conv, 3, 1, 327, 328
148,            Relu, 1, 1, 328, 329
149,          Concat, 2, 1, 329, 330
150,            Conv, 3, 1, 330, 331
151,            Relu, 1, 1, 331, 332
152,            Conv, 3, 1, 332, 333
153,            Relu, 1, 1, 333, 334
154,            Conv, 3, 1, 334, 335
155,            Relu, 1, 1, 335, 336
156,            Conv, 3, 1, 330, 337
157,            Relu, 1, 1, 337, 338
158,          Concat, 2, 1, 336, 339
159,            Conv, 3, 1, 339, 340
160,            Relu, 1, 1, 340, 341
161,            Conv, 3, 1, 341, 680
162,            Conv, 3, 1, 341, 342
163,            Relu, 1, 1, 342, 343
164,          Concat, 2, 1, 343, 344
165,            Conv, 3, 1, 344, 345
166,            Relu, 1, 1, 345, 346
167,            Conv, 3, 1, 346, 347
168,            Relu, 1, 1, 347, 348
169,            Conv, 3, 1, 348, 349
170,            Relu, 1, 1, 349, 350
171,            Conv, 3, 1, 344, 351
172,            Relu, 1, 1, 351, 352
173,          Concat, 2, 1, 350, 353
174,            Conv, 3, 1, 353, 354
175,            Relu, 1, 1, 354, 355
176,            Conv, 3, 1, 355, 990
177,            Conv, 3, 1, 355, 356
178,            Relu, 1, 1, 356, 357
179,          Concat, 2, 1, 357, 358
180,            Conv, 3, 1, 358, 359
181,            Relu, 1, 1, 359, 360
182,            Conv, 3, 1, 360, 361
183,            Relu, 1, 1, 361, 362
184,            Conv, 3, 1, 362, 363
185,            Relu, 1, 1, 363, 364
186,            Conv, 3, 1, 358, 365
187,            Relu, 1, 1, 365, 366
188,          Concat, 2, 1, 364, 367
189,            Conv, 3, 1, 367, 368
190,            Relu, 1, 1, 368, 369
191,            Conv, 3, 1, 369, 1300

Input tensor name -  images 
Output tensor name - 1300 
Output tensor name - 990 
Output tensor name - 680 
Output tensor name - 370 
tidl_tools_path                                 = /home/root/notebooks/tidl_tools 
artifacts_folder                                = custom-artifacts/onnx/yolact_resnet18_54_400000.onnx 
tidl_tensor_bits                                = 8 
debug_level                                     = 1 
num_tidl_subgraphs                              = 16 
tidl_denylist                                   = 
tidl_denylist_layer_name                        = 
tidl_denylist_layer_type                         = 
tidl_allowlist_layer_name                        = 
model_type                                      =  
tidl_calibration_accuracy_level                 = 7 
tidl_calibration_options:num_frames_calibration = 5 
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 ---            Conv -- Conv_16 
Supported TIDL layer type ---            Relu -- Relu_17 
Supported TIDL layer type ---         MaxPool -- MaxPool_18 
Supported TIDL layer type ---            Conv -- Conv_19 
Supported TIDL layer type ---            Relu -- Relu_20 
Supported TIDL layer type ---            Conv -- Conv_21 
Supported TIDL layer type ---             Add -- Add_22 
Supported TIDL layer type ---            Relu -- Relu_23 
Supported TIDL layer type ---            Conv -- Conv_24 
Supported TIDL layer type ---            Relu -- Relu_25 
Supported TIDL layer type ---            Conv -- Conv_26 
Supported TIDL layer type ---             Add -- Add_27 
Supported TIDL layer type ---            Relu -- Relu_28 
Supported TIDL layer type ---            Conv -- Conv_32 
Supported TIDL layer type ---            Conv -- Conv_29 
Supported TIDL layer type ---            Relu -- Relu_30 
Supported TIDL layer type ---            Conv -- Conv_31 
Supported TIDL layer type ---             Add -- Add_33 
Supported TIDL layer type ---            Relu -- Relu_34 
Supported TIDL layer type ---            Conv -- Conv_35 
Supported TIDL layer type ---            Relu -- Relu_36 
Supported TIDL layer type ---            Conv -- Conv_37 
Supported TIDL layer type ---             Add -- Add_38 
Supported TIDL layer type ---            Relu -- Relu_39 
Supported TIDL layer type ---            Conv -- Conv_43 
Supported TIDL layer type ---            Conv -- Conv_40 
Supported TIDL layer type ---            Relu -- Relu_41 
Supported TIDL layer type ---            Conv -- Conv_42 
Supported TIDL layer type ---             Add -- Add_44 
Supported TIDL layer type ---            Relu -- Relu_45 
Supported TIDL layer type ---            Conv -- Conv_46 
Supported TIDL layer type ---            Relu -- Relu_47 
Supported TIDL layer type ---            Conv -- Conv_48 
Supported TIDL layer type ---             Add -- Add_49 
Supported TIDL layer type ---            Relu -- Relu_50 
Supported TIDL layer type ---            Conv -- Conv_54 
Supported TIDL layer type ---            Conv -- Conv_51 
Supported TIDL layer type ---            Relu -- Relu_52 
Supported TIDL layer type ---            Conv -- Conv_53 
Supported TIDL layer type ---             Add -- Add_55 
Supported TIDL layer type ---            Relu -- Relu_56 
Supported TIDL layer type ---            Conv -- Conv_57 
Supported TIDL layer type ---            Relu -- Relu_58 
Supported TIDL layer type ---            Conv -- Conv_59 
Supported TIDL layer type ---             Add -- Add_60 
Supported TIDL layer type ---            Relu -- Relu_61 
Supported TIDL layer type ---            Conv -- Conv_62 
Supported TIDL layer type ---            Conv -- Conv_75 
Supported TIDL layer type ---            Relu -- Relu_76 
Supported TIDL layer type ---            Conv -- Conv_81 
Supported TIDL layer type ---            Conv -- Conv_82 
Supported TIDL layer type ---            Conv -- Conv_175 
Supported TIDL layer type ---            Relu -- Relu_176 
Supported TIDL layer type ---            Conv -- Conv_180 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_182 
Supported TIDL layer type ---            Conv -- Conv_164 
Supported TIDL layer type ---            Relu -- Relu_165 
Supported TIDL layer type ---            Conv -- Conv_169 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_171 
Supported TIDL layer type ---            Conv -- Conv_153 
Supported TIDL layer type ---            Relu -- Relu_154 
Supported TIDL layer type ---            Conv -- Conv_158 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_160 
Supported TIDL layer type ---            Conv -- Conv_67 
Error : Unsupported Opset for Resize OP 
Resize layer delegated to ARM -- 'Resize_63' 
Unsupported (TIDL check) TIDL layer type ---          Resize 
ERROR : Opset 13 pad is not supported 
Unsupported (TIDL check) TIDL layer type ---             Pad 
Unsupported (TIDL check) TIDL layer type ---     AveragePool 
Supported TIDL layer type ---             Add -- Add_68 
Supported TIDL layer type ---            Conv -- Conv_77 
Supported TIDL layer type ---            Relu -- Relu_78 
Supported TIDL layer type ---            Conv -- Conv_124 
Supported TIDL layer type ---            Relu -- Relu_125 
Supported TIDL layer type ---            Conv -- Conv_135 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_143 
Supported TIDL layer type ---            Conv -- Conv_73 
Error : Unsupported Opset for Resize OP 
Resize layer delegated to ARM -- 'Resize_69' 
Unsupported (TIDL check) TIDL layer type ---          Resize 
ERROR : Opset 13 pad is not supported 
Unsupported (TIDL check) TIDL layer type ---             Pad 
Unsupported (TIDL check) TIDL layer type ---     AveragePool 
Supported TIDL layer type ---             Add -- Add_74 
Supported TIDL layer type ---            Conv -- Conv_79 
Supported TIDL layer type ---            Relu -- Relu_80 
Supported TIDL layer type ---            Conv -- Conv_95 
Supported TIDL layer type ---            Relu -- Relu_96 
Supported TIDL layer type ---            Conv -- Conv_106 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_114 
Supported TIDL layer type ---          Concat -- Concat_187 
Supported TIDL layer type ---         Softmax -- Softmax_190 
Supported TIDL layer type ---            Conv -- Conv_183 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_185 
Supported TIDL layer type ---            Conv -- Conv_172 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_174 
Supported TIDL layer type ---            Conv -- Conv_161 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_163 
Supported TIDL layer type ---            Conv -- Conv_144 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_152 
Supported TIDL layer type ---            Conv -- Conv_115 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_123 
Supported TIDL layer type ---          Concat -- Concat_188 
Unsupported (TIDL check) TIDL layer type ---            Tanh 
Supported TIDL layer type ---            Conv -- Conv_177 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_179 
Supported TIDL layer type ---            Conv -- Conv_166 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_168 
Supported TIDL layer type ---            Conv -- Conv_155 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_157 
Supported TIDL layer type ---            Conv -- Conv_126 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_134 
Supported TIDL layer type ---            Conv -- Conv_97 
Unsupported (TIDL check) TIDL layer type ---       Transpose 
Supported TIDL layer type ---         Reshape -- Reshape_105 
Supported TIDL layer type ---          Concat -- Concat_186 
Supported TIDL layer type ---            Conv -- Conv_83 
Supported TIDL layer type ---            Relu -- Relu_84 
Supported TIDL layer type ---            Conv -- Conv_85 
Supported TIDL layer type ---            Relu -- Relu_86 
Supported TIDL layer type ---            Conv -- Conv_87 
Supported TIDL layer type ---            Relu -- Relu_88 
Error : Unsupported Opset for Resize OP 
Resize layer delegated to ARM -- 'Resize_89' 
Unsupported (TIDL check) TIDL layer type ---          Resize 
Supported TIDL layer type ---            Conv -- Conv_90 
Supported TIDL layer type ---            Relu -- Relu_91 
Supported TIDL layer type ---            Conv -- Conv_92 
Supported TIDL layer type ---            Relu -- Relu_93 
Unsupported (TIDL check) TIDL layer type ---       Transpose 

Preliminary subgraphs created = 20 

*** WARNING : Number of subgraphs generated > max_num_subgraphs provided in options - additional subgraphs are delegated to ARM *** 
Final number of subgraphs created are : 16, - Offloaded Nodes - 99, Total Nodes - 137 
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- TIDL_PoolingLayer '': kernel size 2x2 with stride 1x1 not supported  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- TIDL_PoolingLayer '': kernel size 2x2 with stride 1x1 not supported  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TanhLayer]  should be removed in import process. This activation type is not supported now !!!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
TIDL ALLOWLISTING LAYER CHECK -- [TIDL_TransposeLayer]  should be removed in import process. If not, this model will not work!  
Running runtimes graphviz - /home/root/notebooks/tidl_tools/tidl_graphVisualiser_runtimes.out custom-artifacts/onnx/yolact_resnet18_54_400000.onnx/allowedNode.txt custom-artifacts/onnx/yolact_resnet18_54_400000.onnx/tempDir/graphvizInfo.txt custom-artifacts/onnx/yolact_resnet18_54_400000.onnx/tempDir/runtimes_visualization.svg 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_0_0
  0,            Conv, 3, 1, input.1, input.4
  1,            Relu, 1, 1, input.4, onnx::MaxPool_159
  2,         MaxPool, 1, 1, onnx::MaxPool_159, input.8
  3,            Conv, 3, 1, input.8, input.16
  4,            Relu, 1, 1, input.16, onnx::Conv_163
  5,            Conv, 3, 1, onnx::Conv_163, onnx::Add_496
  6,             Add, 2, 1, onnx::Add_496, onnx::Relu_166
  7,            Relu, 1, 1, onnx::Relu_166, input.24
  8,            Conv, 3, 1, input.24, input.32
  9,            Relu, 1, 1, input.32, onnx::Conv_170
 10,            Conv, 3, 1, onnx::Conv_170, onnx::Add_502
 11,             Add, 2, 1, onnx::Add_502, onnx::Relu_173
 12,            Relu, 1, 1, onnx::Relu_173, input.40
 13,            Conv, 3, 1, input.40, input.48
 14,            Relu, 1, 1, input.48, onnx::Conv_177
 15,            Conv, 3, 1, onnx::Conv_177, onnx::Add_508
 16,            Conv, 3, 1, input.40, onnx::Add_511
 17,             Add, 2, 1, onnx::Add_508, onnx::Relu_182
 18,            Relu, 1, 1, onnx::Relu_182, input.60
 19,            Conv, 3, 1, input.60, input.68
 20,            Relu, 1, 1, input.68, onnx::Conv_186
 21,            Conv, 3, 1, onnx::Conv_186, onnx::Add_517
 22,             Add, 2, 1, onnx::Add_517, onnx::Relu_189
 23,            Relu, 1, 1, onnx::Relu_189, input.76
 24,            Conv, 3, 1, input.76, input.84
 25,            Relu, 1, 1, input.84, onnx::Conv_193
 26,            Conv, 3, 1, onnx::Conv_193, onnx::Add_523
 27,            Conv, 3, 1, input.76, onnx::Add_526
 28,             Add, 2, 1, onnx::Add_523, onnx::Relu_198
 29,            Relu, 1, 1, onnx::Relu_198, input.96
 30,            Conv, 3, 1, input.96, input.104
 31,            Relu, 1, 1, input.104, onnx::Conv_202
 32,            Conv, 3, 1, onnx::Conv_202, onnx::Add_532
 33,             Add, 2, 1, onnx::Add_532, onnx::Relu_205
 34,            Relu, 1, 1, onnx::Relu_205, input.112
 35,            Conv, 3, 1, input.112, input.120
 36,            Relu, 1, 1, input.120, onnx::Conv_209
 37,            Conv, 3, 1, onnx::Conv_209, onnx::Add_538
 38,            Conv, 3, 1, input.112, onnx::Add_541
 39,             Add, 2, 1, onnx::Add_538, onnx::Relu_214
 40,            Relu, 1, 1, onnx::Relu_214, input.132
 41,            Conv, 3, 1, input.132, input.140
 42,            Relu, 1, 1, input.140, onnx::Conv_218
 43,            Conv, 3, 1, onnx::Conv_218, onnx::Add_547
 44,             Add, 2, 1, onnx::Add_547, onnx::Relu_221
 45,            Relu, 1, 1, onnx::Relu_221, input.148
 46,            Conv, 3, 1, input.148, x
 47,            Conv, 3, 1, x, onnx::Relu_244
 48,            Relu, 1, 1, onnx::Relu_244, onnx::Conv_245
 49,            Conv, 3, 1, onnx::Conv_245, input.156
 50,            Conv, 3, 1, input.156, input.160
 51,            Conv, 3, 1, input.160, input.200
 52,            Relu, 1, 1, input.200, onnx::Conv_445
 53,            Conv, 3, 1, onnx::Conv_445, onnx::Transpose_446
 54,            Conv, 3, 1, onnx::Conv_445, onnx::Transpose_459

Input tensor name -  input.1 
Output tensor name - input.76 
Output tensor name - input.112 
Output tensor name - x 
Output tensor name - onnx::Conv_245 
Output tensor name - input.156 
Output tensor name - onnx::Conv_445 
Output tensor name - onnx::Transpose_459 
Output tensor name - onnx::Transpose_446 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_1_1
  0,            Conv, 3, 1, input.156, input.196
  1,            Relu, 1, 1, input.196, onnx::Conv_404
  2,            Conv, 3, 1, onnx::Conv_404, onnx::Transpose_418
  3,         Reshape, 2, 1, onnx::Reshape_460, onnx::Concat_471

Input tensor name -  onnx::Reshape_460 

Input tensor name -  input.156 
Output tensor name - onnx::Concat_471 
Output tensor name - onnx::Conv_404 
Output tensor name - onnx::Transpose_418 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_2_2
  0,            Conv, 3, 1, onnx::Conv_245, input.192
  1,            Relu, 1, 1, input.192, onnx::Conv_363
  2,            Conv, 3, 1, onnx::Conv_363, onnx::Transpose_377
  3,         Reshape, 2, 1, onnx::Reshape_419, onnx::Concat_430

Input tensor name -  onnx::Reshape_419 

Input tensor name -  onnx::Conv_245 
Output tensor name - onnx::Concat_430 
Output tensor name - onnx::Conv_363 
Output tensor name - onnx::Transpose_377 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_3_3
  0,            Conv, 3, 1, input.112, onnx::Add_232
  1,         Reshape, 2, 1, onnx::Reshape_378, onnx::Concat_389

Input tensor name -  onnx::Reshape_378 

Input tensor name -  input.112 
Output tensor name - onnx::Concat_389 
Output tensor name - onnx::Add_232 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_4_4
  0,             Add, 2, 1, onnx::Add_231, x.3
  1,            Conv, 3, 1, x.3, onnx::Relu_246
  2,            Relu, 1, 1, onnx::Relu_246, onnx::Conv_247
  3,            Conv, 3, 1, onnx::Conv_247, input.188
  4,            Relu, 1, 1, input.188, onnx::Conv_316
  5,            Conv, 3, 1, onnx::Conv_316, onnx::Transpose_332

Input tensor name -  onnx::Add_231 

Input tensor name -  onnx::Add_232 
Output tensor name - x.3 
Output tensor name - onnx::Conv_316 
Output tensor name - onnx::Transpose_332 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_5_5
  0,            Conv, 3, 1, input.76, onnx::Add_242
  1,         Reshape, 2, 1, onnx::Reshape_333, onnx::Concat_346

Input tensor name -  onnx::Reshape_333 

Input tensor name -  input.76 
Output tensor name - onnx::Concat_346 
Output tensor name - onnx::Add_242 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_6_6
  0,             Add, 2, 1, onnx::Add_241, input.152
  1,            Conv, 3, 1, input.152, onnx::Relu_248
  2,            Relu, 1, 1, onnx::Relu_248, onnx::Conv_249
  3,            Conv, 3, 1, onnx::Conv_249, input.184
  4,            Relu, 1, 1, input.184, onnx::Conv_269
  5,            Conv, 3, 1, onnx::Conv_269, onnx::Transpose_285

Input tensor name -  onnx::Add_241 

Input tensor name -  onnx::Add_242 
Output tensor name - onnx::Conv_249 
Output tensor name - onnx::Conv_269 
Output tensor name - onnx::Transpose_285 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_7_7
  0,            Conv, 3, 1, onnx::Conv_445, onnx::Transpose_472
  1,         Reshape, 2, 1, onnx::Reshape_286, onnx::Concat_299
  2,          Concat, 5, 1, onnx::Concat_299, onnx::Softmax_486
  3,         Softmax, 1, 1, onnx::Softmax_486, 489

Input tensor name -  onnx::Reshape_286 

Input tensor name -  onnx::Concat_346 

Input tensor name -  onnx::Concat_389 

Input tensor name -  onnx::Concat_430 

Input tensor name -  onnx::Concat_471 

Input tensor name -  onnx::Conv_445 
Output tensor name - 489 
Output tensor name - onnx::Transpose_472 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_8_8
  0,            Conv, 3, 1, onnx::Conv_404, onnx::Transpose_431
  1,         Reshape, 2, 1, onnx::Reshape_473, onnx::Concat_484

Input tensor name -  onnx::Reshape_473 

Input tensor name -  onnx::Conv_404 
Output tensor name - onnx::Concat_484 
Output tensor name - onnx::Transpose_431 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_9_9
  0,            Conv, 3, 1, onnx::Conv_363, onnx::Transpose_390
  1,         Reshape, 2, 1, onnx::Reshape_432, onnx::Concat_443

Input tensor name -  onnx::Reshape_432 

Input tensor name -  onn

    The logs actually interrupted and gave the error.

    I have also seen that sometimes the kernel is dead too. 

    Let me know what do you think of above logs and erros.

    Thanks

    Akhilesh

  • 0 in reply to Akhilesh Gangwar
    Intellectual 310 points

    Also, when I am trying on tvm for custom model, the example you people has given is for resnet18. Here also I am seeing some issues.

    For TVM-

    I am getting this error 

    curl_easy_perform: Peer certificate cannot be authenticated with given CA certificates (from logs).

    For ONXX-

    kernel is getting dead and got the same error -

    curl_easy_perform: Peer certificate cannot be authenticated with given CA certificates

    Let me know how are you people able to cop up with these issues. 

    Thanks

  • 0 in reply to Akhilesh Gangwar
    TI__Mastermind 38820 points

    Hi Akhilesh, Curl error seems a communication error, maybe it was a hiccup in the communication between the container's server and the EVM farm. Could you try again and let me know if the error persists? if so, can you share the TVM model or the other model so I can reproduce it?. 

    thank you,

    Paula

  • 0 in reply to Paula Carrillo
    Intellectual 310 points

    Hi Paula,

    Curl error exists for multiple time I tried. It was always there.

    For TVM - 

    I was using onnx model for instance segmentation. I was able to compile the model using tvm but during runtime, I am getting this error - 

    DLRError                                  Traceback (most recent call last)
<ipython-input-16-41f189c6abd0> in <module>
      2 #Running inference several times to get an stable performance output
      3 for i in range(2):
----> 4     res = model.run({input_name : preprocess_for_onnx_resnet18v2('sample-images/elephant.bmp')})

/usr/local/lib/python3.6/dist-packages/dlr/api.py in run(self, input_values)
    117         except Exception as ex:
    118             self.neo_logger.exception("error in running inference {} {}".format(self._impl.__class__.__name__, ex))
--> 119             raise ex
    120 
    121     def get_input_names(self):

/usr/local/lib/python3.6/dist-packages/dlr/api.py in run(self, input_values)
    114         """
    115         try:
--> 116             return self._impl.run(input_values)
    117         except Exception as ex:
    118             self.neo_logger.exception("error in running inference {} {}".format(self._impl.__class__.__name__, ex))

/usr/local/lib/python3.6/dist-packages/dlr/dlr_model.py in run(self, input_values)
    449                              "or a np.ndarray/generic (representing treelite models)")
    450         # run model
--> 451         self._run()
    452         # get output
    453         for i in range(self.num_outputs):

/usr/local/lib/python3.6/dist-packages/dlr/dlr_model.py in _run(self)
    331     def _run(self):
    332         """A light wrapper to call run in the DLR backend."""
--> 333         self._check_call(self._lib.RunDLRModel(byref(self.handle)))
    334         if self.backend == "relayvm":
    335             self._lazy_init_output_shape()

/usr/local/lib/python3.6/dist-packages/dlr/dlr_model.py in _check_call(self, ret)
    158         """
    159         if ret != 0:
--> 160             raise DLRError(self._lib.DLRGetLastError().decode('ascii'))
    161 
    162     def get_input_names(self):

DLRError:

    It took around 35-40 minutes to compile the model. I am attaching the compiled logs too.

    For ONXX - 

    The same instance segmentation model when I am trying to compile gives me this error - 

    RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : 
Exception during initialization: std::bad_alloc

    Please find the model, compiled logs for tvm and notebook in this zip. 

    notebooks.zip

  • 0 in reply to Akhilesh Gangwar
    TI__Mastermind 38820 points

    hi Akhilesh, In the cloud, I was able to reproduce the errors you posted for attached "yolact_resnet18_54_400000.onnx". I tried ARM only and didn't work for me either, I am wondering if you were able to run it in ARM only OK as a sanity check. Please let me know.

    Also, I would recommend you to start testing using edgeai-tidl-tools (https://github.com/TexasInstruments/edgeai-tidl-tools) so you can compile and run inference in host emulation using your local setup instead of the cloud. This could give you more debugging freedom as you will have all the scripts running locally. Also, edgeai-tidl-tools is using latest PSDK9.0, in the cloud we are one release behind for now (soon to be updated).

    thank you,

    Paula

  • 0 in reply to Paula Carrillo
    TI__Mastermind 38820 points

    Hi Akhilesh, I did a quick try using edgeai-tidl-tools. I am getting unsupported Opset error for Resize. We currently support Opset vesions 10 and 11 but those resize layer are 13. Could be possible for you to change them?.

    Those unsupported layers are delegated to ARM, as mentioned in below log, which is creating more subgraphs and probably some other issue gets uncovered. If we can get those layers to run in TIDL we would only have one graph, which is more ideal.

    Running_Model :  yolact_resnet18_54_400000  


    Running shape inference on model ../../../models/public/yolact_resnet18_54_400000.onnx

    Error : Unsupported Opset for Resize OP
    Resize layer delegated to ARM -- 'Resize_63'
    ERROR : Opset 13 pad is not supported
    Error : Unsupported Opset for Resize OP
    Resize layer delegated to ARM -- 'Resize_69'
    ERROR : Opset 13 pad is not supported
    Error : Unsupported Opset for Resize OP
    Resize layer delegated to ARM -- 'Resize_89'

    thank you,

    Paula

  • 0 in reply to Paula Carrillo
    Intellectual 310 points

    " We currently support Opset visions 10 and 11 but those resize layer are 13 "

    Something interesting happened. I did change opset from 13 to 10 and tried to compile the onnx model for tvm. I was successfully able to compile and run the model. I did not visualize the outputs but checked the shapes of the output and those are expected. Then I ran the benchmarking function and checked the FPS and all. 

    I thought just by changing the opset to 10 works but by the next time I tried to recompile and run, I am still able to compile but while running, I am getting error. 

    While executing this line - 

    model = DLRmodel(output_dir, 'cpu')

    I got this error - 

    DLRError                                  Traceback (most recent call last)
<ipython-input-55-a370abb07017> in <module>
      1 # use deployed artifacts from the compiled model
----> 2 model = DLRModel(output_dir, 'cpu')

/usr/local/lib/python3.6/dist-packages/dlr/api.py in __init__(self, model_path, dev_type, dev_id, error_log_file, use_default_dlr)
     90         except Exception as ex:
     91             self.neo_logger.exception("error in DLRModel instantiation {}".format(ex))
---> 92             raise ex
     93 
     94     def run(self, input_values):

/usr/local/lib/python3.6/dist-packages/dlr/api.py in __init__(self, model_path, dev_type, dev_id, error_log_file, use_default_dlr)
     87             if dev_id is None:
     88                 dev_id = 0
---> 89             self._impl = DLRModelImpl(model_path, dev_type, dev_id, error_log_file, use_default_dlr)
     90         except Exception as ex:
     91             self.neo_logger.exception("error in DLRModel instantiation {}".format(ex))

/usr/local/lib/python3.6/dist-packages/dlr/dlr_model.py in __init__(self, model_path, dev_type, dev_id, error_log_file, use_default_dlr)
     80                                         c_char_p(model_path.encode()),
     81                                         c_int(device_table[dev_type]),
---> 82                                         c_int(dev_id)))
     83 
     84         self.backend = self._parse_backend()

/usr/local/lib/python3.6/dist-packages/dlr/dlr_model.py in _check_call(self, ret)
    158         """
    159         if ret != 0:
--> 160             raise DLRError(self._lib.DLRGetLastError().decode('ascii'))
    161 
    162     def get_input_names(self):

DLRError:

    Only 1 time I am able to run. This was happening on cloud.

    I'll setup everything locally and will update you again. 

    I don't understand this behavior of executing everything sometimes and another time getting errors. 

    Thanks

    Akhilesh

  • 0 in reply to Akhilesh Gangwar
    TI__Mastermind 38820 points

    Hi Akhilesh, no sure. if you onnxrt instead of tvm does it works OK?

    thank you,

    Paula

  • 0 in reply to Paula Carrillo
    Intellectual 310 points

    Hi Paula,

    Using onnxrt, I am not even able to compile the model. The kernel is getting dead. Let me setup local environment and will see if the same error comes there as well. 

    Thanks

    Akhilesh

  • 0 in reply to Akhilesh Gangwar
    Intellectual 310 points

    Hi Paula,

    I am trying to setup things locally as you mentioned and following the below link - https://github.com/TexasInstruments/edgeai-tidl-tools

    I have setup the docker and built it. When I am trying to run the ./setup.sh script, I was getting protobuf compiler not found error and then I installed it separately and ran again the script.sh. Everything ran smoothly without any error. 

    Now I am trying to run and compile the examples by running - 

    cmake ../examples && make -j && cd ..

    But I am getting some linking errors. Attaching the error logs. Please let me know. 
    Device - am69a

    Thanks
    Akhilesh

    root@b9da96a9c641:/home/root/build# cmake ../examples && make -j && cd ..
-- The C compiler identification is GNU 11.4.0
-- The CXX compiler identification is GNU 11.4.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Detected processor: x86_64
-- TARGET_DEVICE setting to: am69a
-- TARGET_CPU not specicfied using x86
-- CMAKE_BUILD_TYPE = Release PROJECT_NAME = edgeai_tidl_examples
-- setting TENSORFLOW_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/tflite_2.8_x86_u22/
-- setting DLR_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/dlr_1.10.0_x86_u22/
-- setting OPENCV_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/opencv_4.2.0_x86_u22/
-- Compiling for x86 with am69a config
-- CMAKE_BUILD_TYPE = Release PROJECT_NAME = edgeai_tidl_examples
-- setting TENSORFLOW_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/tflite_2.8_x86_u22/
-- setting DLR_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/dlr_1.10.0_x86_u22/
-- setting OPENCV_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/opencv_4.2.0_x86_u22/
-- Compiling for x86 with am69a config
-- CMAKE_BUILD_TYPE = Release PROJECT_NAME = edgeai_tidl_examples
-- setting TENSORFLOW_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/tflite_2.8_x86_u22/
-- setting DLR_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/dlr_1.10.0_x86_u22/
-- setting OPENCV_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/opencv_4.2.0_x86_u22/
-- Compiling for x86 with am69a config
-- CMAKE_BUILD_TYPE = Release PROJECT_NAME = tfl_main
-- setting TENSORFLOW_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/tflite_2.8_x86_u22/
-- setting DLR_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/dlr_1.10.0_x86_u22/
-- setting OPENCV_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/opencv_4.2.0_x86_u22/
-- Compiling for x86 with am69a config
-- CMAKE_BUILD_TYPE = Release PROJECT_NAME = tfl_priority_scheduling
-- setting TENSORFLOW_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/tflite_2.8_x86_u22/
-- setting DLR_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/dlr_1.10.0_x86_u22/
-- setting OPENCV_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/opencv_4.2.0_x86_u22/
-- Compiling for x86 with am69a config
-- CMAKE_BUILD_TYPE = Release PROJECT_NAME = ort_priority_scheduling
-- setting TENSORFLOW_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/tflite_2.8_x86_u22/
-- setting DLR_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/dlr_1.10.0_x86_u22/
-- setting OPENCV_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/opencv_4.2.0_x86_u22/
-- Compiling for x86 with am69a config
-- CMAKE_BUILD_TYPE = Release PROJECT_NAME = edgeai_tidl_examples
-- setting TENSORFLOW_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/tflite_2.8_x86_u22/
-- setting DLR_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/dlr_1.10.0_x86_u22/
-- setting OPENCV_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/opencv_4.2.0_x86_u22/
-- Compiling for x86 with am69a config
-- CMAKE_BUILD_TYPE = Release PROJECT_NAME = tidlrt_clasification
-- setting TENSORFLOW_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/tflite_2.8_x86_u22/
-- setting DLR_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/dlr_1.10.0_x86_u22/
-- setting OPENCV_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/opencv_4.2.0_x86_u22/
-- Compiling for x86 with am69a config
-- CMAKE_BUILD_TYPE = Release PROJECT_NAME = dlr_main
-- setting TENSORFLOW_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/tflite_2.8_x86_u22/
-- setting DLR_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/dlr_1.10.0_x86_u22/
-- setting OPENCV_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/opencv_4.2.0_x86_u22/
-- Compiling for x86 with am69a config
-- CMAKE_BUILD_TYPE = Release PROJECT_NAME = ort_main
-- setting TENSORFLOW_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/tflite_2.8_x86_u22/
-- setting DLR_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/dlr_1.10.0_x86_u22/
-- setting OPENCV_INSTALL_DIR path:/home/root/tidl_tools/osrt_deps/opencv_4.2.0_x86_u22/
-- Compiling for x86 with am69a config
-- Configuring done
-- Generating done
-- Build files have been written to: /home/root/build
[  7%] Building CXX object osrt_cpp/utils/CMakeFiles/utils.dir/src/utility_functs.cpp.o
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  • 0 in reply to Akhilesh Gangwar
    TI__Mastermind 38820 points

    Hi Akhilesh, I am not too familiar with current c model examples from edgeai-tidl-tools. Could you instead try python examples?

    Location: /edgeai-tidl-tools/examples/osrt_python/ort
    Run: python3 onnxrt_ep.py

    Probably you are working in a docker container. If so, and you exit it, you need to run again ""source ./setup.sh"

    if you container is still running, or using another setup in which you already run "source ./setup.sh" then you only need to do:

    export SOC=am69a
    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

    In order to add your model, please check https://github.com/TexasInstruments/edgeai-tidl-tools/blob/master/examples/osrt_python/model_configs.py

    Something like this needs to be added (please correct it if wrong):

        'yolact_resnet18_54_400000' : {
            'model_path' : os.path.join(models_base_path, 'yolact_resnet18_54_400000.onnx'),
            'mean': [123.675, 116.28, 103.53],
            'scale' : [0.017125, 0.017507, 0.017429],
            'num_images' : numImages,
            'num_classes': 19,
            'session_name' : 'onnxrt' ,
            'model_type': 'seg'
        },

    And then you can use it inside onnxrt_ep.py by changing models list to test. Ex:models = ['yolact_resnet18_54_400000']

    Also, could you share your modified model (opset 10)?

    thank you,

    Paula

  • 0 in reply to Paula Carrillo
    Intellectual 310 points

    Sure. Let me try python examples. Attaching yolact onnxyolact_resnet18_54_400000opset10.zip model with opest10

  • 0 in reply to Akhilesh Gangwar
    Intellectual 310 points

    Hi Paula

    I setup the docker based system and able to compile the same model as well. I ran the code with compile option -d that says Disable offload to TIDL. I am not sure whether I have fully understood this or not. Is it running model on PC or on ARM of emulated hardware?

    When I tried to run without any compile option, this supposed to run on emulated device using TIDL but I got this error -

    python3: src/workload_ref_exec.c:107: int32_t WorkloadRefExec_getIndexFromDataId(int32_t, int32_t*, int32_t): Assertion `arrIdx != WORKLOAD_REF_BUF_NOT_FOUND' failed.
Aborted (core dumped)

    Am I not supposed to run the part that has to be run on device in this emulated env? I thought since we are emulating the hardware itself, I could have able to run. 

    The next part I have doubt over is the operator. I saw in the document that Pooling has been validated for the following kernel sizes: 3x3,2x2,1x1, with a maximum stride of 2.

    I am getting unsupported ops for average pooling with kernel 2 and stride 1. Not sure why.

    Thanks

    Akhilesh

  • 0 in reply to Akhilesh Gangwar
    TI__Mastermind 38820 points

    Hi Akhilesh, I was able to reproduce it. Let me check why we are denying in alllowlist below two layers and come back to you

    69 AveragePool_65 AveragePool outputAdjNodes 1 70 inputAdjNodes 1 68 additionalOutput 0 diagInfo TIDL ALLOWLISTING LAYER CHECK -- TIDL_PoolingLayer '': kernel size 2x2 with stride 1x1 not supported

    81 AveragePool_70 AveragePool outputAdjNodes 1 82 inputAdjNodes 1 80 additionalOutput 0 diagInfo TIDL ALLOWLISTING LAYER CHECK -- TIDL_PoolingLayer '': kernel size 2x2 with stride 1x1 not supported

    thank you,

    Paula

  • 0 in reply to Paula Carrillo
    Intellectual 310 points

    Hi Paula, when I am running the model using TIDL artifacts, I am getting runtime issues.

    This is the logs I got using gdb.

    root@50a41c466951:/home/root/edgeai-tidl-tools/examples/osrt_python/ort# gdb --args python3 onnxrt_ep.py
GNU gdb (Ubuntu 12.1-0ubuntu1~22.04) 12.1
Copyright (C) 2022 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
Type "show copying" and "show warranty" for details.
This GDB was configured as "x86_64-linux-gnu".
Type "show configuration" for configuration details.
For bug reporting instructions, please see:
<https://www.gnu.org/software/gdb/bugs/>.
Find the GDB manual and other documentation resources online at:
    <http://www.gnu.org/software/gdb/documentation/>.

For help, type "help".
Type "apropos word" to search for commands related to "word"...
Reading symbols from python3...
(No debugging symbols found in python3)
(gdb) run
Starting program: /usr/bin/python3 onnxrt_ep.py
warning: Error disabling address space randomization: Operation not permitted
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
[New Thread 0x7f318e3ff640 (LWP 1946)]
[New Thread 0x7f318dbfe640 (LWP 1947)]
[New Thread 0x7f318b3fd640 (LWP 1948)]
[New Thread 0x7f3188bfc640 (LWP 1949)]
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[New Thread 0x7f316dbf2640 (LWP 1959)]
[New Thread 0x7f316d3f1640 (LWP 1960)]
args,  Namespace(compile=False, disable_offload=False, run_model_zoo=False, models=[])
Available execution providers :  ['TIDLExecutionProvider', 'TIDLCompilationProvider', 'CPUExecutionProvider']

Running_Model :  yolact_resnet18_54_400000_opset10_noAvgPool

platform.machine() :  x86_64
model config :  {'model_path': '../../../models/akhilesh/yolact_resnet18_54_400000_opset10_noAvgPool.onnx', 'mean': [123.675, 116.28, 103.53], 'scale': [0.017125, 0.017507, 0.017429], 'num_images': 3, 'num_classes': 81, 'session_name': 'onnxrt', 'model_type': 'seg'}
***** executing model *****
[New Thread 0x7f315c20a640 (LWP 1961)]
[New Thread 0x7f315ba09640 (LWP 1962)]
[New Thread 0x7f315b208640 (LWP 1963)]
[New Thread 0x7f315aa07640 (LWP 1964)]
[New Thread 0x7f315a206640 (LWP 1965)]
[New Thread 0x7f3159a05640 (LWP 1966)]
[New Thread 0x7f3159204640 (LWP 1967)]
libtidl_onnxrt_EP loaded 0x564292e93d30
Final number of subgraphs created are : 3, - Offloaded Nodes - 133, Total Nodes - 135
The soft limit is 2048
The hard limit is 2048
MEM: Init ... !!!
MEM: Init ... Done !!!
 0.0s:  VX_ZONE_INIT:Enabled
 0.5s:  VX_ZONE_ERROR:Enabled
 0.7s:  VX_ZONE_WARNING:Enabled
[New Thread 0x7f31589f2640 (LWP 1968)]
[New Thread 0x7f313a95e640 (LWP 1969)]
[New Thread 0x7f313a15d640 (LWP 1970)]
[New Thread 0x7f313995c640 (LWP 1971)]
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[New Thread 0x7f31007ad640 (LWP 2042)]
 0.5446s:  VX_ZONE_INIT:[tivxInit:185] Initialization Done !!!
created session for model.
height, width, channel, batch, floating_model:  550 550 3 1 True
running session for the model
input image shape :  (1, 3, 550, 550)
python3: src/workload_ref_exec.c:107: int32_t WorkloadRefExec_getIndexFromDataId(int32_t, int32_t*, int32_t): Assertion `arrIdx != WORKLOAD_REF_BUF_NOT_FOUND' failed.

Thread 24 "python3" received signal SIGABRT, Aborted.
[Switching to Thread 0x7f31589f2640 (LWP 1968)]
__pthread_kill_implementation (no_tid=0, signo=6, threadid=139849916950080) at ./nptl/pthread_kill.c:44
44      ./nptl/pthread_kill.c: No such file or directory.
(gdb) bt
#0  __pthread_kill_implementation (no_tid=0, signo=6, threadid=139849916950080) at ./nptl/pthread_kill.c:44
#1  __pthread_kill_internal (signo=6, threadid=139849916950080) at ./nptl/pthread_kill.c:78
#2  __GI___pthread_kill (threadid=139849916950080, signo=signo@entry=6) at ./nptl/pthread_kill.c:89
#3  0x00007f31929a8476 in __GI_raise (sig=sig@entry=6) at ../sysdeps/posix/raise.c:26
#4  0x00007f319298e7f3 in __GI_abort () at ./stdlib/abort.c:79
#5  0x00007f319298e71b in __assert_fail_base (fmt=0x7f3192b43150 "%s%s%s:%u: %s%sAssertion `%s' failed.\n%n",
    assertion=0x7f312c909280 "arrIdx != WORKLOAD_REF_BUF_NOT_FOUND",
    file=0x7f312c9092fd "src/workload_ref_exec.c", line=107, function=<optimized out>) at ./assert/assert.c:92
#6  0x00007f319299fe96 in __GI___assert_fail (assertion=0x7f312c909280 "arrIdx != WORKLOAD_REF_BUF_NOT_FOUND",
    file=0x7f312c9092fd "src/workload_ref_exec.c", line=107,
    function=0x7f312c909238 "int32_t WorkloadRefExec_getIndexFromDataId(int32_t, int32_t*, int32_t)")
    at ./assert/assert.c:101
#7  0x00007f312bf530ef in WorkloadRefExec_getPtrsFromWorkload(TIDL_NetworkCommonParams*, sWorkloadUnit_t*, sTIDL_AlgLayer_t*, sTIDL_Layer_t*, void**, void**) () from /home/root/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#8  0x00007f312bf53c67 in WorkloadRefExec_Process(TIDL_Obj*, TIDL_NetworkCommonParams*, sWorkloadUnit_t*, sTIDL_AlgLayer_t*, sTIDL_Layer_t*, void**, void**, int, int, int*) ()
   from /home/root/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#9  0x00007f312bef3046 in TIDL_process(IVISION_Obj*, IVISION_BufDescList*, IVISION_BufDescList*, IVISION_InArgs*, IVISION_OutArgs*) () from /home/root/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#10 0x00007f312bee2f0a in tivxKernelTIDLProcess () from /home/root/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#11 0x00007f312becfa71 in ownTargetKernelExecute () from /home/root/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#12 0x00007f312bece23f in ownTargetNodeDescNodeExecuteTargetKernel ()
   from /home/root/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#13 0x00007f312bece7cf in ownTargetNodeDescNodeExecute ()
   from /home/root/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#14 0x00007f312becec0c in ownTargetTaskMain () from /home/root/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#15 0x00007f312bedef5c in tivxTaskMain () from /home/root/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#16 0x00007f31929faac3 in start_thread (arg=<optimized out>) at ./nptl/pthread_create.c:442
#17 0x00007f3192a8bbf4 in clone () at ../sysdeps/unix/sysv/linux/x86_64/clone.S:100
(gdb)

    Did you look into 3 subgraphs and why average pooling is not in support list?

    I even tried to change average pooling to maxpooling and got the same issue. I also can not see any other operator that is not supported. 

    Thanks

    Akhilesh

  • 0 in reply to Akhilesh Gangwar
    TI__Mastermind 38820 points

    Hi Akhilesh, I got the same "WORKLOAD_REF_BUF_NOT_FOUND" error during runtime. I requested help from a colleague w.r.t average pooling not being supported. He will reply back to you. Please wait for few days as there are some Holidays.

    thank you,

    Paula

  • 0 in reply to Paula Carrillo
    Intellectual 310 points

    Sure Paula.

    I also checked if I was putting average pooling or max pooling in the deny list of compile option. That's not the case. We don't have anything in the deny list. I am using the compile options given in common_utils.py .

    Thanks

    Akhilesh

  • 0 in reply to Akhilesh Gangwar
    Intellectual 310 points

    Hi Paula, any update on this?

    Thanks

    Akhilesh