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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。
# 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
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:
此致、
Takuma.
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。
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?
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.
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 23, Conv, 3, 1, 395, 397 24, Relu, 1, 1, 397, 398 25, Conv, 3, 1, 398, 400 26, Conv, 3, 1, 400, 402 27, Relu, 1, 1, 402, 403 28, Conv, 3, 1, 403, 405 29, Relu, 1, 1, 405, 406 30, Conv, 3, 1, 406, 408 31, Add, 2, 1, 400, 409 32, Conv, 3, 1, 409, 411 33, Relu, 1, 1, 411, 412 34, Conv, 3, 1, 412, 414 35, Relu, 1, 1, 414, 415 36, Conv, 3, 1, 415, 417 37, Add, 2, 1, 409, 418 38, Conv, 3, 1, 418, 420 39, Relu, 1, 1, 420, 421 40, Conv, 3, 1, 421, 423 41, Relu, 1, 1, 423, 424 42, Conv, 3, 1, 424, 426 43, Conv, 3, 1, 426, 428 44, Relu, 1, 1, 428, 429 45, Conv, 3, 1, 429, 431 46, Relu, 1, 1, 431, 432 47, Conv, 3, 1, 432, 434 48, Add, 2, 1, 426, 435 49, Conv, 3, 1, 435, 437 50, Relu, 1, 1, 437, 438 51, Conv, 3, 1, 438, 440 52, Relu, 1, 1, 440, 441 53, Conv, 3, 1, 441, 443 54, Add, 2, 1, 435, 444 55, Conv, 3, 1, 444, 446 56, Relu, 1, 1, 446, 447 57, Conv, 3, 1, 447, 449 58, Relu, 1, 1, 449, 450 59, Conv, 3, 1, 450, 452 60, Add, 2, 1, 444, 453 61, Conv, 3, 1, 453, 455 62, Relu, 1, 1, 455, 456 63, Conv, 3, 1, 456, 458 64, Relu, 1, 1, 458, 459 65, Conv, 3, 1, 459, 461 66, Conv, 3, 1, 461, 463 67, Relu, 1, 1, 463, 464 68, Conv, 3, 1, 464, 466 69, Relu, 1, 1, 466, 467 70, Conv, 3, 1, 467, 469 71, Add, 2, 1, 461, 470 72, Conv, 3, 1, 470, 472 73, Relu, 1, 1, 472, 473 74, Conv, 3, 1, 473, 475 75, Relu, 1, 1, 475, 476 76, Conv, 3, 1, 476, 478 77, Add, 2, 1, 470, 479 78, Conv, 3, 1, 479, 481 79, Relu, 1, 1, 481, 482 80, Conv, 3, 1, 482, 484 81, Relu, 1, 1, 484, 485 82, Conv, 3, 1, 485, 487 83, Conv, 3, 1, 487, 489 84, Relu, 1, 1, 489, 490 85, Conv, 3, 1, 490, 492 86, Relu, 1, 1, 492, 493 87, Conv, 3, 1, 493, 495 88, Add, 2, 1, 487, 496 89, Conv, 3, 1, 496, 498 90, Relu, 1, 1, 498, 499 91, Conv, 3, 1, 499, 501 92, Relu, 1, 1, 501, 502 93, Conv, 3, 1, 502, 504 94, Add, 2, 1, 496, 505 95, Conv, 3, 1, 505, 507 96, Relu, 1, 1, 507, 508 97, Conv, 3, 1, 508, 510 98, Relu, 1, 1, 510, 511 99, Conv, 3, 1, 511, 513 100, Conv, 3, 1, 513, 518 101, Relu, 1, 1, 518, 519 102, Conv, 3, 1, 513, 521 103, Relu, 1, 1, 521, 522 104, Conv, 3, 1, 522, 524 105, Relu, 1, 1, 524, 525 106, Conv, 3, 1, 513, 527 107, Relu, 1, 1, 527, 528 108, Conv, 3, 1, 528, 530 109, Relu, 1, 1, 530, 531 110, Conv, 3, 1, 513, 533 111, Relu, 1, 1, 533, 534 112, Conv, 3, 1, 534, 536 113, Relu, 1, 1, 536, 537 114, Concat, 4, 1, 519, 538 115, Conv, 3, 1, 538, 540 116, Relu, 1, 1, 540, 541 117, Resize, 3, 1, 541, 551 118, Concat, 2, 1, 551, 552 119, Conv, 3, 1, 552, 554 120, Conv, 2, 1, 554, 555 121, 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 10 1.00000 -3.23967 2.83533 6 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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root@mypc:/opt/edgeai-tidl-tools/examples/osrt_python/ort#
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.