rf-detr
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roboflow
Unable to export model to onnx format
Search before asking
- [x] I have searched the RF-DETR issues and found no similar bug report.
Bug
from rfdetr import RFDETRBase
best_checkpoint = "/home/RF-DETR/output/checkpoint_best_ema.pth"
model = RFDETRBase(resolution=672, num_classes=1, pretrain_weights=best_checkpoint)
model.export()
When I execute the code, I don't encounter any errors, but the execution never completes, even after waiting for several hours. Instead, I only receive the following warnings:
Note: I face no issues when using model.predict()
Loading pretrain weights num_classes mismatch: pretrain weights has 0 classes, but your model has 1 classes reinitializing detection head with 0 classes Exporting model to ONNX format UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1729647429097/work/aten/src/ATen/native/TensorShape.cpp:3595.) PyTorch inference output shapes - Boxes: torch.Size([1, 3900, 4]), Labels: torch.Size([1, 3900, 1]) TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect. TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results). TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect. TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect. TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results). TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
Environment
RF-DETR 1.0.8 OS. Ubuntu 22.04 python 3.11.11
Minimal Reproducible Example
from rfdetr import RFDETRBase best_checkpoint = "/home/RF-DETR/output/checkpoint_best_ema.pth" model = RFDETRBase(resolution=672, num_classes=1, pretrain_weights=best_checkpoint) model.export()
Additional
No response
Are you willing to submit a PR?
- [ ] Yes, I'd like to help by submitting a PR!
Nothing comes to mind! Can you do a pip freeze and provide versions?
Sure @isaacrob-roboflow
absl-py==2.2.1 accelerate==1.4.0 albucore==0.0.23 albumentations==2.0.5 annotated-types==0.7.0 anyio==4.8.0 anywidget==0.9.18 argon2-cffi==23.1.0 argon2-cffi-bindings==21.2.0 arrow==1.3.0 asttokens==3.0.0 async-lru==2.0.4 attrs==25.2.0 babel==2.17.0 bbox_visualizer==0.2.0 beautifulsoup4==4.13.3 bleach==6.2.0 Brotli @ file:///croot/brotli-split_1736182456865/work certifi @ file:///croot/certifi_1738623731865/work/certifi cffi==1.17.1 charset-normalizer @ file:///croot/charset-normalizer_1721748349566/work click==8.1.8 coloredlogs==15.0.1 comm==0.2.2 contourpy==1.3.1 cycler==0.12.1 Cython==3.0.12 dataclasses==0.6 debugpy==1.8.13 decorator==5.2.1 defusedxml==0.7.1 docker-pycreds==0.4.0 einops==0.8.1 executing==2.2.0 fairscale==0.4.13 fastjsonschema==2.21.1 filelock @ file:///croot/filelock_1700591183607/work filetype==1.2.0 flatbuffers==25.2.10 fonttools==4.56.0 fqdn==1.5.1 fsspec==2025.3.0 ftfy==6.3.1 gitdb==4.0.12 GitPython==3.1.44 gmpy2 @ file:///croot/gmpy2_1738085463648/work grpcio==1.71.0 h11==0.14.0 httpcore==1.0.7 httpx==0.28.1 huggingface-hub==0.29.2 humanfriendly==10.0 idna @ file:///croot/idna_1714398848350/work ipykernel @ file:///croot/ipykernel_1737660677549/work ipython==9.0.2 ipython_pygments_lexers==1.1.1 ipywidgets==8.1.5 isoduration==20.11.0 jedi==0.19.2 Jinja2 @ file:///croot/jinja2_1737760107953/work joblib==1.4.2 json5==0.10.0 jsonpointer==3.0.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 jupyter-events==0.12.0 jupyter-lsp==2.2.5 jupyter_bbox_widget==0.6.0 jupyter_client @ file:///croot/jupyter_client_1737570961872/work jupyter_core @ file:///croot/jupyter_core_1718818295206/work jupyter_server==2.15.0 jupyter_server_terminals==0.5.3 jupyterlab==4.3.5 jupyterlab_pygments==0.3.0 jupyterlab_server==2.27.3 jupyterlab_widgets==3.0.13 kiwisolver==1.4.8 lightning-utilities==0.14.0 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe @ file:///croot/markupsafe_1738584038848/work matplotlib==3.10.1 matplotlib-inline==0.1.7 mdurl==0.1.2 mistune==3.1.2 mkl-service==2.4.0 mkl_fft @ file:///io/mkl313/mkl_fft_1730824109137/work mkl_random @ file:///io/mkl313/mkl_random_1730823916628/work mpmath @ file:///croot/mpmath_1690848262763/work nbclient==0.10.2 nbconvert==7.16.6 nbformat==5.10.4 nest-asyncio==1.6.0 networkx @ file:///croot/networkx_1737039604450/work ninja==1.11.1.4 notebook==7.3.2 notebook_shim==0.2.4 numpy @ file:///croot/numpy_and_numpy_base_1725470312869/work/dist/numpy-2.0.1-cp311-cp311-linux_x86_64.whl#sha256=372b9067cf9c96d8662c6f20ef49eeda43f0f8d2f074820224c954d8d8fae7bc onnx==1.16.1 onnx-graphsurgeon==0.5.7 onnxruntime==1.21.0 onnxruntime-gpu==1.21.0 onnxsim==0.4.36 open_clip_torch==2.31.0 opencv-python==4.11.0.86 opencv-python-headless==4.10.0.84 overrides==7.7.0 packaging==24.2 pandas==2.2.3 pandocfilters==1.5.1 parso==0.8.4 peft==0.15.1 pexpect==4.9.0 pillow @ file:///croot/pillow_1738010226202/work platformdirs==4.3.6 polygraphy==0.49.20 pprintpp==0.4.0 prometheus_client==0.21.1 prompt_toolkit==3.0.50 protobuf==5.29.3 psutil==7.0.0 psygnal==0.12.0 ptyprocess==0.7.0 pure_eval==0.2.3 pycocotools==2.0.8 pycparser==2.22 pydantic==2.10.6 pydantic_core==2.27.2 Pygments==2.19.1 pylabel==0.1.55 pyparsing==3.2.1 PySocks @ file:///work/ci_py311/pysocks_1676822712504/work python-dateutil==2.9.0.post0 python-dotenv==1.0.1 python-json-logger==3.3.0 pytz==2025.2 PyYAML @ file:///croot/pyyaml_1728657952215/work pyzmq==26.3.0 referencing==0.36.2 regex==2024.11.6 requests @ file:///croot/requests_1730999120400/work requests-toolbelt==1.0.0 rf100vl==1.0.0 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 rfdetr==1.0.8 rich==14.0.0 roboflow==1.1.56 rpds-py==0.23.1 safetensors==0.5.3 scikit-learn==1.6.1 scipy==1.15.2 Send2Trash==1.8.3 sentry-sdk==2.22.0 setproctitle==1.3.5 simsimd==6.2.1 six==1.17.0 smmap==5.0.2 sniffio==1.3.1 soupsieve==2.6 stack-data==0.6.3 stringzilla==3.12.3 supervision==0.25.1 sympy==1.13.1 tensorboard==2.19.0 tensorboard-data-server==0.7.2 terminado==0.18.1 threadpoolctl==3.6.0 timm==1.0.15 tinycss2==1.4.0 tokenizers==0.21.0 torch==2.5.1 torchaudio==2.5.1 torchmetrics==1.6.2 torchvision==0.20.1 tornado==6.4.2 tqdm==4.67.1 traitlets==5.14.3 transformers==4.49.0 triton==3.1.0 types-python-dateutil==2.9.0.20241206 typing_extensions @ file:///croot/typing_extensions_1734714854207/work tzdata==2025.2 uri-template==1.3.0 urllib3 @ file:///croot/urllib3_1737133630106/work wandb==0.19.8 wcwidth==0.2.13 webcolors==24.11.1 webencodings==0.5.1 websocket-client==1.8.0 Werkzeug==3.1.3 widgetsnbextension==4.0.13
interesting, no obvious issues. would you be comfortable sharing your checkpoint? or a minimal reproduction checkpoint that shows the issue without violating your model / data privacy
I was able to successfully convert my checkpoint to an ONNX model using Google Colab, but I'm still encountering issues when attempting the same conversion on my local machine (Tried it on multiple machines). Regarding the checkpoint, I’m unable to export even the base RFDETR model to ONNX format, so the issue doesn’t seem to be related to my checkpoint.
interesting, no obvious issues. would you be comfortable sharing your checkpoint? or a minimal reproduction checkpoint that shows the issue without violating your model / data privacy很有趣，没有明显的问题。你愿意分享你的检查站吗？或者一个最小的再现检查点，在不违反模型/数据隐私的情况下显示问题
我也遇到了相同的问题，转换一直持续，没有报错
@SkalskiP it worked in colab which is non windows. @PranavDV-VCTI are all the machines on which it has failed windows?
@isaacrob-roboflow I know Colab is non windows...
@PranavDV-VCTI said: "I'm still encountering issues when attempting the same conversion on my local machine (Tried it on multiple machines)".
I'm curious if any of those machines was non-Windows? Mostly because I'm curious if that problem exist only on Windows.
@SkalskiP @isaacrob-roboflow All the machines are running Linux; I haven't tested it on a Windows system yet.
Same problem. A few days ago it worked fine on debian 12, but now the same messages on google colab:
Collecting rfdetr
Using cached rfdetr-1.1.0-py3-none-any.whl.metadata (26 kB)
Requirement already satisfied: wandb in /usr/local/lib/python3.11/dist-packages (0.19.9)
Requirement already satisfied: cython in /usr/local/lib/python3.11/dist-packages (from rfdetr) (3.0.12)
Requirement already satisfied: pycocotools in /usr/local/lib/python3.11/dist-packages (from rfdetr) (2.0.8)
Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.11/dist-packages (from rfdetr) (2.6.0+cu124)
Requirement already satisfied: torchvision>=0.14.0 in /usr/local/lib/python3.11/dist-packages (from rfdetr) (0.21.0+cu124)
Collecting fairscale (from rfdetr)
Using cached fairscale-0.4.13.tar.gz (266 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Installing backend dependencies ... done
Preparing metadata (pyproject.toml) ... done
Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from rfdetr) (1.14.1)
Requirement already satisfied: timm in /usr/local/lib/python3.11/dist-packages (from rfdetr) (1.0.15)
Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from rfdetr) (4.67.1)
Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from rfdetr) (2.0.2)
Requirement already satisfied: accelerate in /usr/local/lib/python3.11/dist-packages (from rfdetr) (1.5.2)
Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (from rfdetr) (4.51.3)
Requirement already satisfied: peft in /usr/local/lib/python3.11/dist-packages (from rfdetr) (0.14.0)
Collecting ninja (from rfdetr)
Using cached ninja-1.11.1.4-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.0 kB)
Requirement already satisfied: einops in /usr/local/lib/python3.11/dist-packages (from rfdetr) (0.8.1)
Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from rfdetr) (2.2.2)
Collecting pylabel (from rfdetr)
Using cached pylabel-0.1.55-py3-none-any.whl.metadata (3.8 kB)
Collecting onnx (from rfdetr)
Using cached onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (16 kB)
Collecting onnxsim (from rfdetr)
Using cached onnxsim-0.4.36-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.3 kB)
Collecting onnx_graphsurgeon (from rfdetr)
Using cached onnx_graphsurgeon-0.5.8-py2.py3-none-any.whl.metadata (8.2 kB)
Collecting polygraphy (from rfdetr)
Using cached polygraphy-0.49.20-py2.py3-none-any.whl.metadata (5.8 kB)
Collecting open_clip_torch (from rfdetr)
Using cached open_clip_torch-2.32.0-py3-none-any.whl.metadata (31 kB)
Collecting rf100vl (from rfdetr)
Using cached rf100vl-1.0.0-py3-none-any.whl.metadata (8.2 kB)
Requirement already satisfied: pydantic in /usr/local/lib/python3.11/dist-packages (from rfdetr) (2.11.3)
Collecting supervision (from rfdetr)
Using cached supervision-0.25.1-py3-none-any.whl.metadata (14 kB)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from rfdetr) (3.10.0)
Requirement already satisfied: click!=8.0.0,>=7.1 in /usr/local/lib/python3.11/dist-packages (from wandb) (8.1.8)
Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (0.4.0)
Requirement already satisfied: gitpython!=3.1.29,>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (3.1.44)
Requirement already satisfied: platformdirs in /usr/local/lib/python3.11/dist-packages (from wandb) (4.3.7)
Requirement already satisfied: protobuf!=4.21.0,!=5.28.0,<6,>=3.19.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (5.29.4)
Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (5.9.5)
Requirement already satisfied: pyyaml in /usr/local/lib/python3.11/dist-packages (from wandb) (6.0.2)
Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (2.32.3)
Requirement already satisfied: sentry-sdk>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (2.26.1)
Requirement already satisfied: setproctitle in /usr/local/lib/python3.11/dist-packages (from wandb) (1.3.5)
Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from wandb) (75.2.0)
Requirement already satisfied: typing-extensions<5,>=4.4 in /usr/local/lib/python3.11/dist-packages (from wandb) (4.13.2)
Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from docker-pycreds>=0.4.0->wandb) (1.17.0)
Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.11/dist-packages (from gitpython!=3.1.29,>=1.0.0->wandb) (4.0.12)
Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic->rfdetr) (0.7.0)
Requirement already satisfied: pydantic-core==2.33.1 in /usr/local/lib/python3.11/dist-packages (from pydantic->rfdetr) (2.33.1)
Requirement already satisfied: typing-inspection>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from pydantic->rfdetr) (0.4.0)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.0.0->wandb) (3.4.1)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.0.0->wandb) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.0.0->wandb) (2.3.0)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.0.0->wandb) (2025.1.31)
Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->rfdetr) (3.18.0)
Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->rfdetr) (3.4.2)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->rfdetr) (3.1.6)
Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->rfdetr) (2025.3.2)
Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=1.13.0->rfdetr)
Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.13.0->rfdetr)
Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.13.0->rfdetr)
Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.13.0->rfdetr)
Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.13.0->rfdetr)
Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.13.0->rfdetr)
Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=1.13.0->rfdetr)
Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=1.13.0->rfdetr)
Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=1.13.0->rfdetr)
Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)
Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->rfdetr) (0.6.2)
Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->rfdetr) (2.21.5)
Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->rfdetr) (12.4.127)
Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch>=1.13.0->rfdetr)
Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->rfdetr) (3.2.0)
Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->rfdetr) (1.13.1)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.13.0->rfdetr) (1.3.0)
Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.11/dist-packages (from torchvision>=0.14.0->rfdetr) (11.1.0)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from accelerate->rfdetr) (24.2)
Requirement already satisfied: huggingface-hub>=0.21.0 in /usr/local/lib/python3.11/dist-packages (from accelerate->rfdetr) (0.30.2)
Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.11/dist-packages (from accelerate->rfdetr) (0.5.3)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->rfdetr) (1.3.2)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->rfdetr) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->rfdetr) (4.57.0)
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Building wheels for collected packages: fairscale
Building wheel for fairscale (pyproject.toml) ... done
Created wheel for fairscale: filename=fairscale-0.4.13-py3-none-any.whl size=332207 sha256=00f7559037c0d8b623364b49e6bcf5540e2bb74da245c52d6c5354b2655ccd42
Stored in directory: /root/.cache/pip/wheels/95/ef/96/5044bde220b2ea299bdc6ec05051e0ef187fad45b341d1c273
Successfully built fairscale
Installing collected packages: filetype, python-dotenv, psygnal, polygraphy, pillow-heif, opencv-python-headless, onnx, nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, ninja, jedi, idna, ftfy, onnx_graphsurgeon, nvidia-cusparse-cu12, nvidia-cudnn-cu12, bbox-visualizer, supervision, onnxsim, nvidia-cusolver-cu12, roboflow, rf100vl, fairscale, open_clip_torch, anywidget, jupyter-bbox-widget, pylabel, rfdetr
Attempting uninstall: opencv-python-headless
Found existing installation: opencv-python-headless 4.11.0.86
Uninstalling opencv-python-headless-4.11.0.86:
Successfully uninstalled opencv-python-headless-4.11.0.86
Attempting uninstall: nvidia-nvjitlink-cu12
Found existing installation: nvidia-nvjitlink-cu12 12.5.82
Uninstalling nvidia-nvjitlink-cu12-12.5.82:
Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82
Attempting uninstall: nvidia-curand-cu12
Found existing installation: nvidia-curand-cu12 10.3.6.82
Uninstalling nvidia-curand-cu12-10.3.6.82:
Successfully uninstalled nvidia-curand-cu12-10.3.6.82
Attempting uninstall: nvidia-cufft-cu12
Found existing installation: nvidia-cufft-cu12 11.2.3.61
Uninstalling nvidia-cufft-cu12-11.2.3.61:
Successfully uninstalled nvidia-cufft-cu12-11.2.3.61
Attempting uninstall: nvidia-cuda-runtime-cu12
Found existing installation: nvidia-cuda-runtime-cu12 12.5.82
Uninstalling nvidia-cuda-runtime-cu12-12.5.82:
Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82
Attempting uninstall: nvidia-cuda-nvrtc-cu12
Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82
Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:
Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82
Attempting uninstall: nvidia-cuda-cupti-cu12
Found existing installation: nvidia-cuda-cupti-cu12 12.5.82
Uninstalling nvidia-cuda-cupti-cu12-12.5.82:
Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82
Attempting uninstall: nvidia-cublas-cu12
Found existing installation: nvidia-cublas-cu12 12.5.3.2
Uninstalling nvidia-cublas-cu12-12.5.3.2:
Successfully uninstalled nvidia-cublas-cu12-12.5.3.2
Attempting uninstall: idna
Found existing installation: idna 3.10
Uninstalling idna-3.10:
Successfully uninstalled idna-3.10
Attempting uninstall: nvidia-cusparse-cu12
Found existing installation: nvidia-cusparse-cu12 12.5.1.3
Uninstalling nvidia-cusparse-cu12-12.5.1.3:
Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3
Attempting uninstall: nvidia-cudnn-cu12
Found existing installation: nvidia-cudnn-cu12 9.3.0.75
Uninstalling nvidia-cudnn-cu12-9.3.0.75:
Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75
Attempting uninstall: nvidia-cusolver-cu12
Found existing installation: nvidia-cusolver-cu12 11.6.3.83
Uninstalling nvidia-cusolver-cu12-11.6.3.83:
Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83
Successfully installed anywidget-0.9.18 bbox-visualizer-0.2.0 fairscale-0.4.13 filetype-1.2.0 ftfy-6.3.1 idna-3.7 jedi-0.19.2 jupyter-bbox-widget-0.6.0 ninja-1.11.1.4 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 onnx-1.17.0 onnx_graphsurgeon-0.5.8 onnxsim-0.4.36 open_clip_torch-2.32.0 opencv-python-headless-4.10.0.84 pillow-heif-0.22.0 polygraphy-0.49.20 psygnal-0.12.0 pylabel-0.1.55 python-dotenv-1.1.0 rf100vl-1.0.0 rfdetr-1.1.0 roboflow-1.1.61 supervision-0.25.1
rf-detr-base.pth: 100%|██████████| 355M/355M [00:05<00:00, 69.0MiB/s]
UserWarning:
The secret `HF_TOKEN` does not exist in your Colab secrets.
To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.
You will be able to reuse this secret in all of your notebooks.
Please note that authentication is recommended but still optional to access public models or datasets.
config.json: 100%
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Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
model.safetensors: 100%
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Loading pretrain weights
Exporting model to ONNX format
Installing onnxruntime by `/usr/bin/python3 -m pip install onnxruntime`, please wait for a moment..
UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:3637.)
PyTorch inference output shapes - Boxes: torch.Size([1, 3900, 4]), Labels: torch.Size([1, 3900, 91])
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
Successfully exported ONNX model: output/inference_model.onnx
Successfully exported ONNX model to: output/inference_model.onnx
ONNX export completed successfully
Code:
%pip install rfdetr wandb
from rfdetr import RFDETRBase
model = RFDETRBase(resolution=560)
model.export()
I experienced the issue you described while working in Jupyter Notebook. Interestingly, the problem does not appear when the code is executed as a standalone script. I'm not certain about the root cause, but I hope this observation is helpful.
I experienced the issue you described while working in Jupyter Notebook. Interestingly, the problem does not appear when the code is executed as a standalone script. I'm not certain about the root cause, but I hope this observation is helpful.
Yep, thats helped for me, thanks
I would like to register that I faced the same issue. In a jupyter notebook on a linux system, I was not able to export the onnx model despite waiting for it for over an hour, with a standalone python script, it exported within 10 seconds.
Also, if you interrupt the kernel twice with a 5 second gap while the model.export() cell is running in a jupyter notebook, it will complete the onnx export. However, the onnx model will be very broken, results won't be correct at all.
I experienced the issue you described while working in Jupyter Notebook. Interestingly, the problem does not appear when the code is executed as a standalone script. I'm not certain about the root cause, but I hope this observation is helpful.
I have a similar problem on Kaggle, but now it's working with this method. @peiseng thank you.
