ModuleNotFoundError: No module named 'models.visual_model.clip'

[Issac304]

there no have this module in your code?

[Issac304]

[MiracleOct]

there no have this module in your code?

I meet the same situation when running train.sh ... """ ModuleNotFoundError: No module named 'models.visual_model.clip' Traceback (most recent call last): File "train.py", line 16, in """ It seems that clip.py is lost in visual_model directory, can you please provide it?

[ghntd]

This is an unwanted code. We will fix it later

[william1111111111]

Please accept our apologies for the delay. We have now uploaded the most recent version of the code along with an updated README. Additionally, we have completed debugging on our local server, and the system is ready for standard training procedures. @MiracleOct @Issac304

[MiracleOct]

Please accept our apologies for the delay. We have now uploaded the most recent version of the code along with an updated README. Additionally, we have completed debugging on our local server, and the system is ready for standard training procedures. @MiracleOct @Issac304

Thanks a lot! I try to run the newest code immediately when you upload, unfortunately, there is still some problem in my Linux system(some errors about BERT). It seems that the requirement.txt is incomplete, and I also don't sure about the version of python. T_T By the way, I'm also a student in Guangxi University. My tutor let me read this article, and I'm eagar to run this program successfully and to acquire relevant knowledge. If you still in school, can I consult you in school when you are convenient?

[william1111111111]

This is the file tree in the pythonProject which is not shown in the github. --bert ----bert-base-uncased ------bert_config.json ------pytorch_model.bin --checkpoints ----detr-r101.pth --data(refcoco dataset put here) --ln_data ----other ------images --------train2014 ----------***(photos here)

As for BERT, it can be downloaded from the Transformers library (something like from_pretrained('bert-base-uncased')). For the data, you can download it via the instructions provided at https://github.com/djiajunustc/TransVG/blob/main/docs/GETTING_STARTED.md. For code learning, our project is based on TransVG https://github.com/djiajunustc/TransVG/tree/main and maybe you can also reproduce their code. If you have any questions, you can leave a message below this project, and I will get back to you as soon as I see @MiracleOct

For the environment, I used 'pip list' to show the packages, but note that it may include many packages that are not being used.

Package Version

---

accelerate 0.25.0 addict 2.4.0 asttokens 2.4.1 backcall 0.2.0 boto3 1.34.4 botocore 1.34.4 certifi 2023.11.17 charset-normalizer 3.3.2 contourpy 1.1.1 cycler 0.12.1 Cython 3.0.7 decorator 5.1.1 executing 2.0.1 filelock 3.13.1 fonttools 4.47.0 fsspec 2023.12.2 grad-cam 1.5.0 h5py 3.10.0 huggingface-hub 0.20.1 idna 3.6 imageio 2.33.1 importlib-metadata 7.0.1 importlib-resources 6.1.1 ipython 8.12.3 jedi 0.19.1 Jinja2 3.1.2 jmespath 1.0.1 joblib 1.3.2 kiwisolver 1.4.5 lazy_loader 0.3 loralib 0.1.1 markdown-it-py 3.0.0 MarkupSafe 2.1.3 matplotlib 3.7.4 matplotlib-inline 0.1.6 mdurl 0.1.2 mmcv 2.2.0 mmengine 0.10.3 mpmath 1.3.0 networkx 3.1 ninja 1.11.1.1 numpy 1.24.4 nvidia-cublas-cu12 12.1.3.1 nvidia-cuda-cupti-cu12 12.1.105 nvidia-cuda-nvrtc-cu12 12.1.105 nvidia-cuda-runtime-cu12 12.1.105 nvidia-cudnn-cu12 8.9.2.26 nvidia-cufft-cu12 11.0.2.54 nvidia-curand-cu12 10.3.2.106 nvidia-cusolver-cu12 11.4.5.107 nvidia-cusparse-cu12 12.1.0.106 nvidia-nccl-cu12 2.18.1 nvidia-nvjitlink-cu12 12.3.101 nvidia-nvtx-cu12 12.1.105 opencv-python 4.8.1.78 opencv-python-headless 4.5.5.64 packaging 23.2 parso 0.8.3 peft 0.7.1 pexpect 4.9.0 pickleshare 0.7.5 Pillow 10.1.0 pip 23.3.1 platformdirs 4.1.0 prompt-toolkit 3.0.43 psutil 5.9.7 ptyprocess 0.7.0 pure-eval 0.2.2 pycocotools 2.0.7 Pygments 2.17.2 pyparsing 3.1.1 python-dateutil 2.8.2 pytorch-pretrained-bert 0.6.2 PyWavelets 1.4.1 PyYAML 6.0.1 regex 2023.10.3 requests 2.31.0 rich 13.7.0 s3transfer 0.9.0 safetensors 0.4.1 scikit-image 0.21.0 scikit-learn 1.3.2 scipy 1.10.1 setuptools 68.2.2 six 1.16.0 stack-data 0.6.3 sympy 1.12 termcolor 2.4.0 threadpoolctl 3.3.0 tifffile 2023.7.10 timm 0.4.9 tokenizers 0.15.0 tomli 2.0.1 torch 1.13.0+cu117 torchaudio 0.13.0+cu117 torchvision 0.14.0+cu117 tqdm 4.64.0 traitlets 5.14.1 transformers 4.36.2 triton 2.1.0 ttach 0.0.3 typing_extensions 4.9.0 urllib3 1.26.18 wcwidth 0.2.12 wheel 0.41.2 yacs 0.1.8 yapf 0.40.2 zipp 3.17.0

[MiracleOct]

Thank you so much! I'm weak foundation so it's a little difficult for me to run the project smoothly, so maybe some of my questions are naive. Thanks to your patient reply, I will retry next!

[Issac304]

thank you so much！