Can't use any model with jetson-inference

antomineleford · October 29, 2024, 2:12pm

I installed jetpack 6.1 on my jetson orin nano and trying to use jetson inference.

I got this error:

./imagenet.py images/orange_0.jpg images/test/output_0.jpg

imageNet -- loading classification network model from:
         -- prototxt     networks/Googlenet/googlenet.prototxt
         -- model        networks/Googlenet/bvlc_googlenet.caffemodel
         -- class_labels networks/ilsvrc12_synset_words.txt
         -- input_blob   'data'
         -- output_blob  'prob'
         -- 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 - caffe  (extension '.caffemodel')
[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 3970 (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 +1103, now: CPU 1011, GPU 5129 (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]    could not find engine cache networks/Googlenet/bvlc_googlenet.caffemodel.1.1.100300.GPU.FP16.engine
[TRT]    cache file invalid, profiling network model on device GPU
[TRT]    [MemUsageChange] Init CUDA: CPU +0, GPU +0, now: CPU 85, GPU 5089 (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 +926, GPU +108, now: CPU 1011, GPU 5198 (MiB)
[TRT]    CUDA lazy loading is enabled.
[TRT]    device GPU, loading networks/Googlenet/googlenet.prototxt networks/Googlenet/bvlc_googlenet.caffemodel
[TRT]    TensorRT 10.3 does not support legacy caffe models
[TRT]    device GPU, failed to load networks/Googlenet/bvlc_googlenet.caffemodel
[TRT]    failed to load networks/Googlenet/bvlc_googlenet.caffemodel
[TRT]    imageNet -- failed to initialize.
Traceback (most recent call last):
  File "imagenet.py", line 49, in <module>
    net = imageNet(args.network, sys.argv)
Exception: jetson.inference -- imageNet failed to load network

AastaLLL · October 30, 2024, 4:48am

Hi,

TensorRT 10 already removed the Caffe model support.
So please use the ONNX-based model instead.

Based on the description below, please try resnet18-tagging-voc for the classification.

github.com

dusty-nv/jetson-inference/blob/master/data/networks/models.json

{
    "classification": {
        "alexnet": {
            "url": "https://nvidia.box.com/shared/static/2y1x8v1q7g6w5d1355dqaxd3snx6bpze.gz",
            "tar": "Alexnet.tar.gz",
            "dir": "Alexnet",
            "model": "bvlc_alexnet.caffemodel",
            "prototxt": "alexnet.prototxt",
            "labels": "networks/ilsvrc12_synset_words.txt",
            "description": "Alexnet trained on 1000-class ILSVRC12"
        },
        
        "googlenet": {
            "url": "https://nvidia.box.com/shared/static/u28j5jm4hnf1ex94dnhsuyu8p799l5d5.gz",
            "tar": "Googlenet.tar.gz",
            "dir": "Googlenet",
            "model": "bvlc_googlenet.caffemodel",
            "prototxt": "googlenet.prototxt",
            "labels": "networks/ilsvrc12_synset_words.txt",
            "description": "Googlenet trained 1000-class ILSVRC12"

This file has been truncated. show original

Thanks.

antomineleford · October 30, 2024, 6:38am

Thanks for the answer, now I’m facing another problem - I get a segmentation error

./imagenet.py --network=resnet18_tagging_voc images/coral.jpg images/test/output_coral.jpg

...

[TRT]    binding to input 0 input_0  binding index:  0
[TRT]    binding to input 0 input_0  dims (b=1 c=3 h=224 w=224) size=602112
[TRT]    binding to output 0 output_0  binding index:  1
[TRT]    binding to output 0 output_0  dims (b=1 c=1 h=20 w=0) size=80
Segmentation fault (core dumped)

AastaLLL · November 4, 2024, 5:59am

Hi,

resnet18-tagging-voc is a multi-label classification model.
Please run it in a similar command like below:

$ imagenet.py --model=resnet18-tagging-voc --topK=0 --threshold=0.25 "images/object_*.jpg" images/test/tagging_%i.jpg

For more info, please check the below tutorial:

github.com

dusty-nv/jetson-inference/blob/master/docs/imagenet-tagging.md

<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
<p align="right"><sup><a href="imagenet-camera-2.md">Back</a> | <a href="detectnet-console-2.md">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
<br/>
<sup>Image Classification</sup></p>

# Multi-Label Classification for Image Tagging

Multi-label classification models are able to recognize multiple object classes simultaneously for performing tasks like image tagging.  The multi-label DNNs are almost identical in topology to ordinary single-class models, except they use a sigmoid activation layer as opposed to softmax.  There's a pre-trained `resnet18-tagging-voc` multi-label model available that was trained on the Pascal VOC dataset:  

<img src=https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/imagenet_tagging.jpg>

To enable image tagging, you'll want to run imagenet/imagenet.py with `--topK=0` and a `--threshold` of your choosing: 

``` bash
# C++
$ imagenet --model=resnet18-tagging-voc --topK=0 --threshold=0.25 "images/object_*.jpg" images/test/tagging_%i.jpg

# Python
$ imagenet.py --model=resnet18-tagging-voc --topK=0 --threshold=0.25 "images/object_*.jpg" images/test/tagging_%i.jpg
```

This file has been truncated. show original

Thanks.

system · December 3, 2024, 1:19pm

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Can't use any model with jetson-inference

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