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I'm experiencing a segmentation fault trying to run the segnet demo with Jetpack 6.1 on the Orin Nano.
Steps to replicate:
Download and write the JP 6.1 image from https://developer.nvidia.com/downloads/embedded/l4t/r36_release_v4.0/jp61-orin-nano-sd-card-image.zip to micro SD card.
Boot new image. Perform initial user setup, config, etc.
Follow jetson-inference instructions here: https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-repo-2.md. Install PyTorch when prompted.
cd data/images in this repo.
cd data/images
segnet.py airplane_0.jpg airplane_0_output.jpg. I've also ran segnet with the same segmentation fault.
segnet.py airplane_0.jpg airplane_0_output.jpg
segnet
Encounter segmentation fault. See below for log.
segNet -- loading segmentation network model from: -- prototxt: -- model: networks/FCN-ResNet18-Pascal-VOC-320x320/fcn_resnet18.onnx -- labels: networks/FCN-ResNet18-Pascal-VOC-320x320/classes.txt -- colors: networks/FCN-ResNet18-Pascal-VOC-320x320/colors.txt -- input_blob 'input_0' -- output_blob 'output_0' -- batch_size 1 [TRT] TensorRT version 10.3.0 [TRT] loading NVIDIA plugins... [TRT] Registered plugin creator - ::BatchedNMSDynamic_TRT version 1 [TRT] Registered plugin creator - ::BatchedNMS_TRT version 1 [TRT] Registered plugin creator - ::BatchTilePlugin_TRT version 1 [TRT] Registered plugin creator - ::Clip_TRT version 1 [TRT] Registered plugin creator - ::CoordConvAC version 1 [TRT] Registered plugin creator - ::CropAndResizeDynamic version 1 [TRT] Registered plugin creator - ::CropAndResize version 1 [TRT] Registered plugin creator - ::DecodeBbox3DPlugin version 1 [TRT] Registered plugin creator - ::DetectionLayer_TRT version 1 [TRT] Registered plugin creator - ::EfficientNMS_Explicit_TF_TRT version 1 [TRT] Registered plugin creator - ::EfficientNMS_Implicit_TF_TRT version 1 [TRT] Registered plugin creator - ::EfficientNMS_ONNX_TRT version 1 [TRT] Registered plugin creator - ::EfficientNMS_TRT version 1 [TRT] Could not register plugin creator - ::FlattenConcat_TRT version 1 [TRT] Registered plugin creator - ::GenerateDetection_TRT version 1 [TRT] Registered plugin creator - ::GridAnchor_TRT version 1 [TRT] Registered plugin creator - ::GridAnchorRect_TRT version 1 [TRT] Registered plugin creator - ::InstanceNormalization_TRT version 1 [TRT] Registered plugin creator - ::InstanceNormalization_TRT version 2 [TRT] Registered plugin creator - ::InstanceNormalization_TRT version 3 [TRT] Registered plugin creator - ::LReLU_TRT version 1 [TRT] Registered plugin creator - ::ModulatedDeformConv2d version 1 [TRT] Registered plugin creator - ::MultilevelCropAndResize_TRT version 1 [TRT] Registered plugin creator - ::MultilevelProposeROI_TRT version 1 [TRT] Registered plugin creator - ::MultiscaleDeformableAttnPlugin_TRT version 1 [TRT] Registered plugin creator - ::NMSDynamic_TRT version 1 [TRT] Registered plugin creator - ::NMS_TRT version 1 [TRT] Registered plugin creator - ::Normalize_TRT version 1 [TRT] Registered plugin creator - ::PillarScatterPlugin version 1 [TRT] Registered plugin creator - ::PriorBox_TRT version 1 [TRT] Registered plugin creator - ::ProposalDynamic version 1 [TRT] Registered plugin creator - ::ProposalLayer_TRT version 1 [TRT] Registered plugin creator - ::Proposal version 1 [TRT] Registered plugin creator - ::PyramidROIAlign_TRT version 1 [TRT] Registered plugin creator - ::Region_TRT version 1 [TRT] Registered plugin creator - ::Reorg_TRT version 2 [TRT] Registered plugin creator - ::Reorg_TRT version 1 [TRT] Registered plugin creator - ::ResizeNearest_TRT version 1 [TRT] Registered plugin creator - ::ROIAlign_TRT version 1 [TRT] Registered plugin creator - ::ROIAlign_TRT version 2 [TRT] Registered plugin creator - ::RPROI_TRT version 1 [TRT] Registered plugin creator - ::ScatterElements version 2 [TRT] Registered plugin creator - ::ScatterElements version 1 [TRT] Registered plugin creator - ::ScatterND version 1 [TRT] Registered plugin creator - ::SpecialSlice_TRT version 1 [TRT] Registered plugin creator - ::Split version 1 [TRT] Registered plugin creator - ::VoxelGeneratorPlugin version 1 [TRT] completed loading NVIDIA plugins. [TRT] detected model format - ONNX (extension '.onnx') [TRT] desired precision specified for GPU: FASTEST [TRT] requested fasted precision for device GPU without providing valid calibrator, disabling INT8 [TRT] [MemUsageChange] Init CUDA: CPU +13, GPU +0, now: CPU 41, GPU 2406 (MiB) [TRT] Trying to load shared library libnvinfer_builder_resource.so.10.3.0 [TRT] Loaded shared library libnvinfer_builder_resource.so.10.3.0 [TRT] [MemUsageChange] Init builder kernel library: CPU +927, GPU +687, now: CPU 1011, GPU 3116 (MiB) [TRT] CUDA lazy loading is enabled. [TRT] native precisions detected for GPU: FP32, FP16, INT8 [TRT] selecting fastest native precision for GPU: FP16 [TRT] found engine cache file /usr/local/bin/networks/FCN-ResNet18-Pascal-VOC-320x320/fcn_resnet18.onnx.1.1.100300.GPU.FP16.engine [TRT] found model checksum /usr/local/bin/networks/FCN-ResNet18-Pascal-VOC-320x320/fcn_resnet18.onnx.sha256sum [TRT] echo "$(cat /usr/local/bin/networks/FCN-ResNet18-Pascal-VOC-320x320/fcn_resnet18.onnx.sha256sum) /usr/local/bin/networks/FCN-ResNet18-Pascal-VOC-320x320/fcn_resnet18.onnx" | sha256sum --check --status [TRT] model matched checksum /usr/local/bin/networks/FCN-ResNet18-Pascal-VOC-320x320/fcn_resnet18.onnx.sha256sum [TRT] loading network plan from engine cache... /usr/local/bin/networks/FCN-ResNet18-Pascal-VOC-320x320/fcn_resnet18.onnx.1.1.100300.GPU.FP16.engine [TRT] device GPU, loaded /usr/local/bin/networks/FCN-ResNet18-Pascal-VOC-320x320/fcn_resnet18.onnx [TRT] Loaded engine size: 22 MiB [TRT] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors. [TRT] Deserialization required 13320 microseconds. [TRT] Total per-runner device persistent memory is 0 [TRT] Total per-runner host persistent memory is 128224 [TRT] Allocated device scratch memory of size 4915200 [TRT] - Runner scratch: 4915200 bytes [TRT] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +5, now: CPU 0, GPU 27 (MiB) [TRT] CUDA lazy loading is enabled. [TRT] [TRT] CUDA engine context initialized on device GPU: [TRT] -- layers 25 [TRT] -- maxBatchSize 1 [TRT] -- deviceMemory 4915200 [TRT] -- bindings 2 [TRT] binding 0 -- index 0 -- name 'input_0' -- type Row major linear FP32 format (kLINEAR) -- in/out INPUT -- device DEVICE -- # dims 4 -- dim #0 1 -- dim #1 3 -- dim #2 320 -- dim #3 320 [TRT] binding 1 -- index 1 -- name 'output_0' -- type Row major linear FP32 format (kLINEAR) -- in/out OUTPUT -- device DEVICE -- # dims 4 -- dim #0 1 -- dim #1 21 -- dim #2 10 -- dim #3 10 [TRT] [TRT] binding to input 0 input_0 binding index: 0 [TRT] binding to input 0 input_0 dims (b=1 c=3 h=320 w=320) size=1228800 [TRT] binding to output 0 output_0 binding index: 1 [TRT] binding to output 0 output_0 dims (b=1 c=1 h=21 w=10) size=8400 Segmentation fault (core dumped)
The text was updated successfully, but these errors were encountered:
same issue.. moved to using Jetpack 6.0 and that worked for now.
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I'm experiencing a segmentation fault trying to run the segnet demo with Jetpack 6.1 on the Orin Nano.
Steps to replicate:
Download and write the JP 6.1 image from https://developer.nvidia.com/downloads/embedded/l4t/r36_release_v4.0/jp61-orin-nano-sd-card-image.zip to micro SD card.
Boot new image. Perform initial user setup, config, etc.
Follow jetson-inference instructions here: https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-repo-2.md. Install PyTorch when prompted.
cd data/images
in this repo.segnet.py airplane_0.jpg airplane_0_output.jpg
. I've also ransegnet
with the same segmentation fault.Encounter segmentation fault. See below for log.
The text was updated successfully, but these errors were encountered: