[参考译文] TDA4VM：SIGBUS、使用 python edgeai-tidl-tools 的 SIGSEV with RTOS SDK 8.6

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请注意，本文内容源自机器翻译，可能存在语法或其它翻译错误，仅供参考。如需获取准确内容，请参阅链接中的英语原文或自行翻译。

https://e2e.ti.com/support/processors-group/processors/f/processors-forum/1208475/tda4vm-sigbus-sigsev-when-using-python-edgeai-tidl-tools-with-rtos-sdk-8-6

器件型号：TDA4VM

您好！

我正在使用 edgeai-tidl-tools 使用 ONNX 文件为 TDA4编译 TIDL 伪影。自从迁移到 RTOS SDK 8.6 (以及 edgeai 8.6)之后、我在尝试编译时会抛出 SIGBUS 和 SIGSEV。

下面是我的脚本的简短版本：

import onnxruntime as rt
import onnx

# ...

onnx.shape_inference.infer_shapes_path(onnx_model_path, onnx_model_path)
compile_options = {
    'tidl_tools_path': os.environ['TIDL_TOOLS_PATH'],
    'artifacts_folder': artifacts_folder,
    'tensor_bits': 16,
    'accuracy_level': 0,
    'advanced_options:calibration_frames': 1,
    'advanced_options:calibration_iterations': 1,
    'advanced_options:quantization_scale_type': 1,
    'debug_level': 1,
    "platform": "J7"
}
delegate_options = {}
delegate_options.update(compile_options)

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

# ...

output = sess.run(None, {...})

具体而言、当我调用 sess.run (...)时、python 脚本会停止并显示"Bus error (core pose)"。下面是使用 gdb python3运行脚本时的回溯：

Thread 1 "python3" received signal SIGBUS, Bus error.
0x00007ffff7b70ded in ?? () from /lib/x86_64-linux-gnu/libc.so.6
(gdb) backtrace
#0 0x00007ffff7b70ded in ?? () from /lib/x86_64-linux-gnu/libc.so.6
#1 0x00007fff40f09f06 in vxCreateUserDataObject () from /devel/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#2 0x00007fff40f0214c in TIDLRT_create () from /devel/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#3 0x00007fff4cc65cab in TIDL_subgraphRtCreate () from /devel/edgeai-tidl-tools/tidl_tools/tidl_model_import_onnx.so
#4 0x00007fff4cc43dc6 in TIDL_computeImportFunc () from /devel/edgeai-tidl-tools/tidl_tools/tidl_model_import_onnx.so

如果我注释掉 sess.run (...)、则脚本会停止并显示"Segmentation fault (core disposed)"。下面是它的回溯：

Thread 1 "python3" received signal SIGSEGV, Segmentation fault.
0x00007fff40f1707c in ownReleaseReferenceInt () from /devel/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
(gdb) backtrace
#0 0x00007fff40f1707c in ownReleaseReferenceInt () from /devel/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#1 0x00007fff40f02ba7 in TIDLRT_delete () from /devel/edgeai-tidl-tools/tidl_tools/libvx_tidl_rt.so
#2 0x00007fff4cc660bf in TIDL_subgraphRtDelete () from /devel/edgeai-tidl-tools/tidl_tools/tidl_model_import_onnx.so

我已经测试删除"TIDLCompilationProvider"仅保留"CPUExecutionProvider"、然后我可以编译我的工件并在我的 TDA4上使用它们。我认为这证明 ONNX 或我的脚本没有问题。

是否发现了该问题？它看起来像是我身边的恶劣环境、还是类似的东西？

以下是运行 edgeai-tidl-tools/setup.sh 后的我的环境变量：

# echo $LD_LIBRARY_PATH
:/devel/edgeai-tidl-tools/tidl_tools:/devel/edgeai-tidl-tools/tidl_tools/osrt_deps
# echo $ARM64_GCC_PATH
/devel/edgeai-tidl-tools/gcc-arm-9.2-2019.12-x86_64-aarch64-none-linux-gnu
# echo $TIDL_TOOLS_PATH
/devel/edgeai-tidl-tools/tidl_tools
# echo $SOC
am68pa

谢谢。

弗雷德

1 年多前

0 admin 1 年多前

TI__Guru**** 663810 points

请注意，本文内容源自机器翻译，可能存在语法或其它翻译错误，仅供参考。如需获取准确内容，请参阅链接中的英语原文或自行翻译。

尊敬的 Fred：

对拖延答复表示歉意。您能否确认以下有关正在使用的设置的信息：

使用该模型时、错误是否仅出现在 SDK 的8.6版中、以及使用与先前的 SDK (如8.4版)相同的模型时不会显示任何错误？
模型和构件是否已使用8.6 edgeai-tidl-tools 重新编译、或按原样从之前的 SDK 中复制？我做了一个快速实验、其中我将模型+构件从8.4 SDK 复制到8.6 SDK 中、导致了错误、因此使用8.4 SDK 编译的模型似乎与8.6不兼容。

此致、

Takuma.

0 admin 1 年多前

TI__Guru**** 663810 points

请注意，本文内容源自机器翻译，可能存在语法或其它翻译错误，仅供参考。如需获取准确内容，请参阅链接中的英语原文或自行翻译。

Takuma、您好、感谢您的回答。

输入 onnx 文件是来自 TIDL 外部手电筒工具的量化模型输出。

也就是说、将同一个模型与 SDK 8.4搭配使用是可行的、而将其与8.6搭配使用则会产生异常。

模型和工件没有从8.4复制到8.6。我正在尝试生成这些伪影。

0 admin 1 年多前

TI__Guru**** 663810 points

请注意，本文内容源自机器翻译，可能存在语法或其它翻译错误，仅供参考。如需获取准确内容，请参阅链接中的英语原文或自行翻译。

你好、Takuma。

这些错误似乎来自我的 Docker 环境不良。我再也不知道了。

但是、如果我设置'debug_level=3'、我会得到一个 SEGFAULT。您能否在您身边重现问题？

0 admin 1 年多前

TI__Guru**** 663810 points

请注意，本文内容源自机器翻译，可能存在语法或其它翻译错误，仅供参考。如需获取准确内容，请参阅链接中的英语原文或自行翻译。

尊敬的 Fred：

很遗憾、在 DEBUG_LEVEL 设置为3的情况下、我无法重现错误。我所做的实验是使用 edgeai-tidl-tools 的8.6版本标签设置 Docker 容器、并在编译选项中运行示例"python3 onnxrt_ep.py -c"、并将 debug_level 设置为3。

为了澄清、在 Docker 容器内的 PC 上运行编译时是否发生了 SEGFAULT、以及您是否能够共享导致错误的日志(还是与初始 POST 中共享的日志相同)？

此致、

Takuma.

0 admin 1 年多前

TI__Guru**** 663810 points

请注意，本文内容源自机器翻译，可能存在语法或其它翻译错误，仅供参考。如需获取准确内容，请参阅链接中的英语原文或自行翻译。

您好、Takuma、

我成功地使用您提供的示例脚本和使用 edgeai-tidl-tools 8.6的 Docker 重现了问题。

您需要对脚本应用以下补丁：

diff --git a/examples/osrt_python/ort/onnxrt_ep.py b/examples/osrt_python/ort/onnxrt_ep.py
index 03eb9f1..9ad065d 100644
--- a/examples/osrt_python/ort/onnxrt_ep.py
+++ b/examples/osrt_python/ort/onnxrt_ep.py
@@ -20,7 +20,8 @@ from model_configs import *
 
 required_options = {
 "tidl_tools_path":tidl_tools_path,
-"artifacts_folder":artifacts_folder
+"artifacts_folder":artifacts_folder,
+"debug_level":3
 }
 
 parser = argparse.ArgumentParser()
@@ -144,8 +145,8 @@ def run_model(model, mIdx):
         test_images = seg_test_images
     
     delegate_options = {}
-    delegate_options.update(required_options)
     delegate_options.update(optional_options)   
+    delegate_options.update(required_options)
 
     # stripping off the ss-ort- from model namne
     delegate_options['artifacts_folder'] = delegate_options['artifacts_folder'] + '/' + model + '/' #+ 'tempDir/'

需要调用 Delegate_options.update (可选_options) 那样 Delegate_options.update (required_options)。否则、DEBUG_LEVEL 会写回0。

日志中没有什么有用的东西、但它们在这里。我无法放入日志的全部内容,因为它太长了,但运行 deeplabv3lite_mobilenetv2.onnx 似乎失败了。

Running_Model :  ss-ort-deeplabv3lite_mobilenetv2  


Running shape inference on model ../../../models/public/deeplabv3lite_mobilenetv2.onnx 

tidl_tools_path                                 = /opt/edgeai-tidl-tools/tidl_tools 
artifacts_folder                                = ../../../model-artifacts//ss-ort-deeplabv3lite_mobilenetv2/ 
tidl_tensor_bits                                = 8 
debug_level                                     = 3 
num_tidl_subgraphs                              = 16 
tidl_denylist                                   = 
tidl_denylist_layer_name                        = 
tidl_denylist_layer_type                         = 
tidl_allowlist_layer_name                        = 
model_type                                      =  
tidl_calibration_accuracy_level                 = 7 
tidl_calibration_options:num_frames_calibration = 2 
tidl_calibration_options:bias_calibration_iterations = 5 
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                          = 3 
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 ---            Cast --  
Supported TIDL layer type ---             Add --  
Supported TIDL layer type ---             Mul --  
Supported TIDL layer type ---            Conv -- encoder.features.0.0 
Supported TIDL layer type ---            Relu -- 369 
Supported TIDL layer type ---            Conv -- encoder.features.1.conv.0.0 
Supported TIDL layer type ---            Relu -- 372 
Supported TIDL layer type ---            Conv -- encoder.features.1.conv.1 
Supported TIDL layer type ---            Conv -- encoder.features.2.conv.0.0 
Supported TIDL layer type ---            Relu -- 377 
Supported TIDL layer type ---            Conv -- encoder.features.2.conv.1.0 
Supported TIDL layer type ---            Relu -- 380 
Supported TIDL layer type ---            Conv -- encoder.features.2.conv.2 
Supported TIDL layer type ---            Conv -- encoder.features.3.conv.0.0 
Supported TIDL layer type ---            Relu -- 385 
Supported TIDL layer type ---            Conv -- encoder.features.3.conv.1.0 
Supported TIDL layer type ---            Relu -- 388 
Supported TIDL layer type ---            Conv -- encoder.features.3.conv.2 
Supported TIDL layer type ---             Add -- 391 
Supported TIDL layer type ---            Conv -- encoder.features.4.conv.0.0 
Supported TIDL layer type ---            Relu -- 394 
Supported TIDL layer type ---            Conv -- encoder.features.4.conv.1.0 
Supported TIDL layer type ---            Relu -- 397 
Supported TIDL layer type ---            Conv -- encoder.features.4.conv.2 
Supported TIDL layer type ---            Conv -- encoder.features.5.conv.0.0 
Supported TIDL layer type ---            Relu -- 402 
Supported TIDL layer type ---            Conv -- encoder.features.5.conv.1.0 
Supported TIDL layer type ---            Relu -- 405 
Supported TIDL layer type ---            Conv -- encoder.features.5.conv.2 
Supported TIDL layer type ---             Add -- 408 
Supported TIDL layer type ---            Conv -- encoder.features.6.conv.0.0 
Supported TIDL layer type ---            Relu -- 411 
Supported TIDL layer type ---            Conv -- encoder.features.6.conv.1.0 
Supported TIDL layer type ---            Relu -- 414 
Supported TIDL layer type ---            Conv -- encoder.features.6.conv.2 
Supported TIDL layer type ---             Add -- 417 
Supported TIDL layer type ---            Conv -- encoder.features.7.conv.0.0 
Supported TIDL layer type ---            Relu -- 420 
Supported TIDL layer type ---            Conv -- encoder.features.7.conv.1.0 
Supported TIDL layer type ---            Relu -- 423 
Supported TIDL layer type ---            Conv -- encoder.features.7.conv.2 
Supported TIDL layer type ---            Conv -- encoder.features.8.conv.0.0 
Supported TIDL layer type ---            Relu -- 428 
Supported TIDL layer type ---            Conv -- encoder.features.8.conv.1.0 
Supported TIDL layer type ---            Relu -- 431 
Supported TIDL layer type ---            Conv -- encoder.features.8.conv.2 
Supported TIDL layer type ---             Add -- 434 
Supported TIDL layer type ---            Conv -- encoder.features.9.conv.0.0 
Supported TIDL layer type ---            Relu -- 437 
Supported TIDL layer type ---            Conv -- encoder.features.9.conv.1.0 
Supported TIDL layer type ---            Relu -- 440 
Supported TIDL layer type ---            Conv -- encoder.features.9.conv.2 
Supported TIDL layer type ---             Add -- 443 
Supported TIDL layer type ---            Conv -- encoder.features.10.conv.0.0 
Supported TIDL layer type ---            Relu -- 446 
Supported TIDL layer type ---            Conv -- encoder.features.10.conv.1.0 
Supported TIDL layer type ---            Relu -- 449 
Supported TIDL layer type ---            Conv -- encoder.features.10.conv.2 
Supported TIDL layer type ---             Add -- 452 
Supported TIDL layer type ---            Conv -- encoder.features.11.conv.0.0 
Supported TIDL layer type ---            Relu -- 455 
Supported TIDL layer type ---            Conv -- encoder.features.11.conv.1.0 
Supported TIDL layer type ---            Relu -- 458 
Supported TIDL layer type ---            Conv -- encoder.features.11.conv.2 
Supported TIDL layer type ---            Conv -- encoder.features.12.conv.0.0 
Supported TIDL layer type ---            Relu -- 463 
Supported TIDL layer type ---            Conv -- encoder.features.12.conv.1.0 
Supported TIDL layer type ---            Relu -- 466 
Supported TIDL layer type ---            Conv -- encoder.features.12.conv.2 
Supported TIDL layer type ---             Add -- 469 
Supported TIDL layer type ---            Conv -- encoder.features.13.conv.0.0 
Supported TIDL layer type ---            Relu -- 472 
Supported TIDL layer type ---            Conv -- encoder.features.13.conv.1.0 
Supported TIDL layer type ---            Relu -- 475 
Supported TIDL layer type ---            Conv -- encoder.features.13.conv.2 
Supported TIDL layer type ---             Add -- 478 
Supported TIDL layer type ---            Conv -- encoder.features.14.conv.0.0 
Supported TIDL layer type ---            Relu -- 481 
Supported TIDL layer type ---            Conv -- encoder.features.14.conv.1.0 
Supported TIDL layer type ---            Relu -- 484 
Supported TIDL layer type ---            Conv -- encoder.features.14.conv.2 
Supported TIDL layer type ---            Conv -- encoder.features.15.conv.0.0 
Supported TIDL layer type ---            Relu -- 489 
Supported TIDL layer type ---            Conv -- encoder.features.15.conv.1.0 
Supported TIDL layer type ---            Relu -- 492 
Supported TIDL layer type ---            Conv -- encoder.features.15.conv.2 
Supported TIDL layer type ---             Add -- 495 
Supported TIDL layer type ---            Conv -- encoder.features.16.conv.0.0 
Supported TIDL layer type ---            Relu -- 498 
Supported TIDL layer type ---            Conv -- encoder.features.16.conv.1.0 
Supported TIDL layer type ---            Relu -- 501 
Supported TIDL layer type ---            Conv -- encoder.features.16.conv.2 
Supported TIDL layer type ---             Add -- 504 
Supported TIDL layer type ---            Conv -- encoder.features.17.conv.0.0 
Supported TIDL layer type ---            Relu -- 507 
Supported TIDL layer type ---            Conv -- encoder.features.17.conv.1.0 
Supported TIDL layer type ---            Relu -- 510 
Supported TIDL layer type ---            Conv -- encoder.features.17.conv.2 
Supported TIDL layer type ---            Conv -- decoders.0.aspp.aspp_bra3.0.0 
Supported TIDL layer type ---            Relu -- 533 
Supported TIDL layer type ---            Conv -- decoders.0.aspp.aspp_bra3.1.0 
Supported TIDL layer type ---            Relu -- 536 
Supported TIDL layer type ---            Conv -- decoders.0.aspp.aspp_bra2.0.0 
Supported TIDL layer type ---            Relu -- 527 
Supported TIDL layer type ---            Conv -- decoders.0.aspp.aspp_bra2.1.0 
Supported TIDL layer type ---            Relu -- 530 
Supported TIDL layer type ---            Conv -- decoders.0.aspp.aspp_bra1.0.0 
Supported TIDL layer type ---            Relu -- 521 
Supported TIDL layer type ---            Conv -- decoders.0.aspp.aspp_bra1.1.0 
Supported TIDL layer type ---            Relu -- 524 
Supported TIDL layer type ---            Conv -- decoders.0.aspp.conv1x1.0 
Supported TIDL layer type ---            Relu -- 518 
Supported TIDL layer type ---          Concat -- 537 
Supported TIDL layer type ---            Conv -- decoders.0.aspp.aspp_out.0 
Supported TIDL layer type ---            Relu -- 540 
Supported TIDL layer type ---          Resize -- 571 
Supported TIDL layer type ---            Conv -- decoders.0.shortcut.0 
Supported TIDL layer type ---            Relu -- 515 
Supported TIDL layer type ---          Concat -- 516 
Supported TIDL layer type ---            Conv -- decoders.0.pred.0.0 
Supported TIDL layer type ---            Conv -- decoders.0.pred.1.0 
Supported TIDL layer type ---          Resize -- 576 
Supported TIDL layer type ---          ArgMax -- 565 
Supported TIDL layer type ---            Cast --  

Preliminary subgraphs created = 1 
Final number of subgraphs created are : 1, - Offloaded Nodes - 124, Total Nodes - 124 
INFORMATION -- [TIDL_ResizeLayer]  Any resize ratio which is power of 2 and greater than 4 will be placed by combination of 4x4 resize layer and 2x2 resize layer. For example a 8x8 resize will be replaced by 4x4 resize followed by 2x2 resize.  
INFORMATION -- [TIDL_ResizeLayer]  Any resize ratio which is power of 2 and greater than 4 will be placed by combination of 4x4 resize layer and 2x2 resize layer. For example a 8x8 resize will be replaced by 4x4 resize followed by 2x2 resize.  
Running runtimes graphviz - /opt/edgeai-tidl-tools/tidl_tools/tidl_graphVisualiser_runtimes.out ../../../model-artifacts//ss-ort-deeplabv3lite_mobilenetv2//allowedNode.txt ../../../model-artifacts//ss-ort-deeplabv3lite_mobilenetv2//tempDir/graphvizInfo.txt ../../../model-artifacts//ss-ort-deeplabv3lite_mobilenetv2//tempDir/runtimes_visualization.svg 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_0_0
  0,            Cast, 1, 1, input.1Net_IN, TIDL_cast_in
  1,             Add, 2, 1, TIDL_cast_in, TIDL_Scale_In
  2,             Mul, 2, 1, TIDL_Scale_In, input.1
  3,            Conv, 3, 1, input.1, 369
  4,            Relu, 1, 1, 369, 370
  5,            Conv, 3, 1, 370, 372
  6,            Relu, 1, 1, 372, 373
  7,            Conv, 3, 1, 373, 375
  8,            Conv, 3, 1, 375, 377
  9,            Relu, 1, 1, 377, 378
 10,            Conv, 3, 1, 378, 380
 11,            Relu, 1, 1, 380, 381
 12,            Conv, 3, 1, 381, 383
 13,            Conv, 3, 1, 383, 385
 14,            Relu, 1, 1, 385, 386
 15,            Conv, 3, 1, 386, 388
 16,            Relu, 1, 1, 388, 389
 17,            Conv, 3, 1, 389, 391
 18,             Add, 2, 1, 383, 392
 19,            Conv, 3, 1, 392, 515
 20,            Relu, 1, 1, 515, 516
 21,            Conv, 3, 1, 392, 394
 22,            Relu, 1, 1, 394, 395
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 71,             Add, 2, 1, 461, 470
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100,            Conv, 3, 1, 513, 518
101,            Relu, 1, 1, 518, 519
102,            Conv, 3, 1, 513, 521
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121,          Resize, 3, 1, 555, 565
122,          ArgMax, 1, 1, 565, 566
123,            Cast, 1, 1, 566, 566TIDL_cast_out

Input tensor name -  input.1Net_IN 
Output tensor name - 566TIDL_cast_out 
In TIDL_onnxRtImportInit subgraph_name=566TIDL_cast_out
Layer 0, subgraph id 566TIDL_cast_out, name=566TIDL_cast_out
Layer 1, subgraph id 566TIDL_cast_out, name=input.1Net_IN
In TIDL_runtimesOptimizeNet: LayerIndex = 126, dataIndex = 125 
Warning : Requested Output Data Convert Layer is not Added to the network, It is currently not Optimal

 ************** Frame index 1 : Running float import ************* 
In TIDL_runtimesPostProcessNet 
INFORMATION: [TIDL_ResizeLayer] 571 Any resize ratio which is power of 2 and greater than 4 will be placed by combination of 4x4 resize layer and 2x2 resize layer. For example a 8x8 resize will be replaced by 4x4 resize followed by 2x2 resize.
INFORMATION: [TIDL_ResizeLayer] 576 Any resize ratio which is power of 2 and greater than 4 will be placed by combination of 4x4 resize layer and 2x2 resize layer. For example a 8x8 resize will be replaced by 4x4 resize followed by 2x2 resize.
****************************************************
**          2 WARNINGS          0 ERRORS          **
****************************************************
************ in TIDL_subgraphRtCreate ************ 
 The soft limit is 2048
The hard limit is 2048
MEM: Init ... !!!
MEM: Init ... Done !!!
 56.768821s:  VX_ZONE_INIT:Enabled
 56.768824s:  VX_ZONE_ERROR:Enabled
 56.768826s:  VX_ZONE_WARNING:Enabled
 56.769548s:  VX_ZONE_INIT:[tivxInit:184] Initialization Done !!!
************ TIDL_subgraphRtCreate done ************ 
 *******   In TIDL_subgraphRtInvoke  ******** 
   0         1.00000        13.00000       255.00000 6
   1         1.00000        13.00000       255.00000 6
   2         1.00000         0.00000         1.70939 6
   3         1.00000         0.00000         6.63638 6
   4         1.00000        -6.26595         4.70380 6
   5         1.00000         0.00000         4.82374 6
   6         1.00000         0.00000         3.33056 6
   7         1.00000        -2.43808         3.36087 6
   8         1.00000         0.00000         1.01476 6
   9         1.00000         0.00000         1.82469 6
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  11         1.00000        -4.95676         4.71945 6
  12         1.00000         0.00000         1.35142 6
  13         1.00000         0.00000         1.91816 6
  14         1.00000         0.00000         2.74106 6
  15         1.00000        -2.92971         2.87725 6
  16         1.00000         0.00000         0.92410 6
  17         1.00000         0.00000         1.58639 6
  18         1.00000        -2.13769         2.07801 6
  19         1.00000        -3.14827         3.24599 6
  20         1.00000         0.00000         1.27750 6
  21         1.00000         0.00000         1.47342 6
  22         1.00000        -2.48598         2.16254 6
  23         1.00000        -4.13893         3.83608 6
  24         1.00000         0.00000         1.87032 6
  25         1.00000         0.00000         3.24930 6
  26         1.00000        -2.22035         2.01417 6
  27         1.00000         0.00000         0.79210 6
  28         1.00000         0.00000         1.27267 6
  29         1.00000        -2.13012         1.73467 6
  30         1.00000        -2.59007         2.31123 6
  31         1.00000         0.00000         0.69329 6
  32         1.00000         0.00000         1.40499 6
  33         1.00000        -1.67600         1.13149 6
  34         1.00000        -2.67766         2.79643 6
  35         1.00000         0.00000         0.98111 6
  36         1.00000         0.00000         1.68029 6
  37         1.00000        -1.26448         1.36133 6
  38         1.00000        -3.29024         3.32031 6
  39         1.00000         0.00000         1.53051 6
  40         1.00000         0.00000         3.13890 6
  41         1.00000        -1.89363         2.02331 6
  42         1.00000         0.00000         0.89560 6
  43         1.00000         0.00000         1.77731 6
  44         1.00000        -1.41292         1.19110 6
  45         1.00000        -1.90091         2.41924 6
  46         1.00000         0.00000         0.86644 6
  47         1.00000         0.00000         1.44425 6
  48         1.00000        -1.69233         1.44870 6
  49         1.00000        -2.41101         2.40814 6
  50         1.00000         0.00000         1.50448 6
  51         1.00000         0.00000         2.77799 6
  52         1.00000        -1.51747         1.50009 6
  53         1.00000         0.00000         3.22712 6
  54         1.00000         0.00000         1.32348 6
  55         1.00000        -1.28662         1.12000 6
  56         1.00000        -1.57279         1.41987 6
  57         1.00000         0.00000         1.23090 6
  58         1.00000         0.00000         1.38300 6
  59         1.00000        -0.90853         1.93517 6
  60         1.00000        -1.72649         1.73335 6
  61         1.00000         0.00000         0.75028 6
  62         1.00000         0.00000         3.64279 6
  63         1.00000        -1.76884         1.59342 6
  64         1.00000         0.00000         0.69980 6
  65         1.00000         0.00000         1.14739 6
  66         1.00000         0.00000         0.94319 6
  67         1.00000         0.00000         2.97372 6
  68         1.00000         0.00000         1.28226 6
  69         1.00000         0.00000         3.15015 6
  70         1.00000         0.00000         2.12302 6
  71         1.00000         0.00000         2.12302 6
  72         1.00000         0.00000         9.21194 6
  73         1.00000         0.00000         9.21098 6
  74         1.00000         0.00000         9.21098 6
  75         1.00000       -34.97995         7.52468 6
  76         1.00000       -51.73018        16.70612 6
  77         1.00000       -51.72625        16.53502 6
  78         1.00000         0.00000        21.00000 6
 Layer,   Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger,    paddingWait,LayerWithoutPad,LayerHandleCopy,   BackupCycles,  RestoreCycles,

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    78,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,

 Sum of Layer Cycles 0 
Segmentation fault (core dumped)
root@mypc:/opt/edgeai-tidl-tools/examples/osrt_python/ort#

0 admin 1 年多前

TI__Guru**** 663810 points

请注意，本文内容源自机器翻译，可能存在语法或其它翻译错误，仅供参考。如需获取准确内容，请参阅链接中的英语原文或自行翻译。

尊敬的 Fred：

感谢您的耐心。是的、我能够重现错误。我们将在我们这边更深入地研究这一点。

对于原始问题、之前提到您可以解决它、但该后续问题是否会阻碍开发？我主要要求测量优先级。

此致、

Takuma.

0 admin 1 年多前

TI__Guru**** 663810 points

请注意，本文内容源自机器翻译，可能存在语法或其它翻译错误，仅供参考。如需获取准确内容，请参阅链接中的英语原文或自行翻译。

非阻塞、感谢验证。

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[参考译文] TDA4VM：SIGBUS、使用 python edgeai-tidl-tools 的 SIGSEV with RTOS SDK 8.6