autocast

UniVAD/models/GroundingDINO/.gitignore

@@ -0,0 +1,146 @@
+# IDE
+.idea/
+.vscode/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# vscode
+.vscode/
+output/
+outputs/
+subs/
+logs/
+
+grounding/config/configs
+grounding/version.py
+
+vis/
+tmp/

UniVAD/models/GroundingDINO/Dockerfile

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+FROM pytorch/pytorch:2.1.2-cuda12.1-cudnn8-runtime
+ARG DEBIAN_FRONTEND=noninteractive
+
+ENV CUDA_HOME=/usr/local/cuda \
+     TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6+PTX" \
+     SETUPTOOLS_USE_DISTUTILS=stdlib
+
+RUN conda update conda -y
+
+# Install libraries in the brand new image. 
+RUN apt-get -y update && apt-get install -y --no-install-recommends \
+         wget \
+         build-essential \
+         git \
+         python3-opencv \
+         ca-certificates && \
+    rm -rf /var/lib/apt/lists/*
+
+# Set the working directory for all the subsequent Dockerfile instructions.
+WORKDIR /opt/program
+
+RUN git clone https://github.com/IDEA-Research/GroundingDINO.git
+
+RUN mkdir weights ; cd weights ; wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth ; cd ..
+
+RUN conda install -c "nvidia/label/cuda-12.1.1" cuda -y
+ENV CUDA_HOME=$CONDA_PREFIX
+
+ENV PATH=/usr/local/cuda/bin:$PATH
+
+RUN cd GroundingDINO/ && python -m pip install .
+
+COPY docker_test.py docker_test.py
+
+CMD [ "python", "docker_test.py" ]

UniVAD/models/GroundingDINO/LICENSE

@@ -0,0 +1,201 @@
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UniVAD/models/GroundingDINO/README.md

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+<div align="center">
+  <img src="./.asset/grounding_dino_logo.png" width="30%">
+</div>
+
+# :sauropod: Grounding DINO 
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-mscoco)](https://paperswithcode.com/sota/zero-shot-object-detection-on-mscoco?p=grounding-dino-marrying-dino-with-grounded) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-odinw)](https://paperswithcode.com/sota/zero-shot-object-detection-on-odinw?p=grounding-dino-marrying-dino-with-grounded) \
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/object-detection-on-coco-minival)](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=grounding-dino-marrying-dino-with-grounded) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/object-detection-on-coco)](https://paperswithcode.com/sota/object-detection-on-coco?p=grounding-dino-marrying-dino-with-grounded)
+
+
+**[IDEA-CVR, IDEA-Research](https://github.com/IDEA-Research)** 
+
+[Shilong Liu](http://www.lsl.zone/), [Zhaoyang Zeng](https://scholar.google.com/citations?user=U_cvvUwAAAAJ&hl=zh-CN&oi=ao), [Tianhe Ren](https://rentainhe.github.io/), [Feng Li](https://scholar.google.com/citations?user=ybRe9GcAAAAJ&hl=zh-CN), [Hao Zhang](https://scholar.google.com/citations?user=B8hPxMQAAAAJ&hl=zh-CN), [Jie Yang](https://github.com/yangjie-cv), [Chunyuan Li](https://scholar.google.com/citations?user=Zd7WmXUAAAAJ&hl=zh-CN&oi=ao), [Jianwei Yang](https://jwyang.github.io/), [Hang Su](https://scholar.google.com/citations?hl=en&user=dxN1_X0AAAAJ&view_op=list_works&sortby=pubdate), [Jun Zhu](https://scholar.google.com/citations?hl=en&user=axsP38wAAAAJ), [Lei Zhang](https://www.leizhang.org/)<sup>:email:</sup>.
+
+
+[[`Paper`](https://arxiv.org/abs/2303.05499)] [[`Demo`](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)] [[`BibTex`](#black_nib-citation)]
+
+
+PyTorch implementation and pretrained models for Grounding DINO. For details, see the paper **[Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://arxiv.org/abs/2303.05499)**.
+
+- 🔥 **[Grounding DINO 1.5](https://github.com/IDEA-Research/Grounding-DINO-1.5-API)** is released now, which is IDEA Research's **Most Capable** Open-World Object Detection Model!
+- 🔥 **[Grounding DINO](https://arxiv.org/abs/2303.05499)** and **[Grounded SAM](https://arxiv.org/abs/2401.14159)** are now supported in Huggingface. For more convenient use, you can refer to [this documentation](https://huggingface.co/docs/transformers/model_doc/grounding-dino)
+
+## :sun_with_face: Helpful Tutorial
+
+- :grapes: [[Read our arXiv Paper](https://arxiv.org/abs/2303.05499)]
+- :apple:  [[Watch our simple introduction video on YouTube](https://youtu.be/wxWDt5UiwY8)]
+- :blossom:   &nbsp;[[Try the Colab Demo](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb)]
+- :sunflower: [[Try our Official Huggingface Demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)]
+- :maple_leaf: [[Watch the Step by Step Tutorial about GroundingDINO by Roboflow AI](https://youtu.be/cMa77r3YrDk)]
+- :mushroom: [[GroundingDINO: Automated Dataset Annotation and Evaluation by Roboflow AI](https://youtu.be/C4NqaRBz_Kw)]
+- :hibiscus: [[Accelerate Image Annotation with SAM and GroundingDINO by Roboflow AI](https://youtu.be/oEQYStnF2l8)]
+- :white_flower: [[Autodistill: Train YOLOv8 with ZERO Annotations based on Grounding-DINO and Grounded-SAM by Roboflow AI](https://github.com/autodistill/autodistill)]
+
+<!-- Grounding DINO Methods | 
+[![arXiv](https://img.shields.io/badge/arXiv-2303.05499-b31b1b.svg)](https://arxiv.org/abs/2303.05499) 
+[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/wxWDt5UiwY8) -->
+
+<!-- Grounding DINO Demos |
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb) -->
+<!-- [![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/cMa77r3YrDk)
+[![HuggingFace space](https://img.shields.io/badge/🤗-HuggingFace%20Space-cyan.svg)](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)
+[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/oEQYStnF2l8)
+[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/C4NqaRBz_Kw) -->
+
+## :sparkles: Highlight Projects
+
+- [Semantic-SAM: a universal image segmentation model to enable segment and recognize anything at any desired granularity.](https://github.com/UX-Decoder/Semantic-SAM), 
+- [DetGPT: Detect What You Need via Reasoning](https://github.com/OptimalScale/DetGPT)
+- [Grounded-SAM: Marrying Grounding DINO with Segment Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything)
+- [Grounding DINO with Stable Diffusion](demo/image_editing_with_groundingdino_stablediffusion.ipynb)
+- [Grounding DINO with GLIGEN for Controllable Image Editing](demo/image_editing_with_groundingdino_gligen.ipynb)
+- [OpenSeeD: A Simple and Strong Openset Segmentation Model](https://github.com/IDEA-Research/OpenSeeD)
+- [SEEM: Segment Everything Everywhere All at Once](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)
+- [X-GPT: Conversational Visual Agent supported by X-Decoder](https://github.com/microsoft/X-Decoder/tree/xgpt)
+- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://github.com/gligen/GLIGEN)
+- [LLaVA: Large Language and Vision Assistant](https://github.com/haotian-liu/LLaVA)
+
+<!-- Extensions | [Grounding DINO with Segment Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything); [Grounding DINO with Stable Diffusion](demo/image_editing_with_groundingdino_stablediffusion.ipynb); [Grounding DINO with GLIGEN](demo/image_editing_with_groundingdino_gligen.ipynb)  -->
+
+
+
+<!-- Official PyTorch implementation of [Grounding DINO](https://arxiv.org/abs/2303.05499), a stronger open-set object detector. Code is available now! -->
+
+
+## :bulb: Highlight
+
+- **Open-Set Detection.** Detect **everything** with language!
+- **High Performance.** COCO zero-shot **52.5 AP** (training without COCO data!). COCO fine-tune **63.0 AP**.
+- **Flexible.** Collaboration with Stable Diffusion for Image Editting.
+
+
+
+
+## :fire: News
+- **`2023/07/18`**: We release [Semantic-SAM](https://github.com/UX-Decoder/Semantic-SAM), a universal image segmentation model to enable segment and recognize anything at any desired granularity. **Code** and **checkpoint** are available!
+- **`2023/06/17`**: We provide an example to evaluate Grounding DINO on COCO zero-shot performance.
+- **`2023/04/15`**: Refer to [CV in the Wild Readings](https://github.com/Computer-Vision-in-the-Wild/CVinW_Readings) for those who are interested in open-set recognition!
+- **`2023/04/08`**: We release [demos](demo/image_editing_with_groundingdino_gligen.ipynb) to combine [Grounding DINO](https://arxiv.org/abs/2303.05499) with [GLIGEN](https://github.com/gligen/GLIGEN)  for more controllable image editings.
+- **`2023/04/08`**: We release [demos](demo/image_editing_with_groundingdino_stablediffusion.ipynb) to combine [Grounding DINO](https://arxiv.org/abs/2303.05499) with [Stable Diffusion](https://github.com/Stability-AI/StableDiffusion) for image editings.
+- **`2023/04/06`**: We build a new demo by marrying GroundingDINO with [Segment-Anything](https://github.com/facebookresearch/segment-anything) named **[Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything)** aims to support segmentation in GroundingDINO.
+- **`2023/03/28`**: A YouTube [video](https://youtu.be/cMa77r3YrDk) about Grounding DINO and basic object detection prompt engineering. [[SkalskiP](https://github.com/SkalskiP)]
+- **`2023/03/28`**: Add a [demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo) on Hugging Face Space!
+- **`2023/03/27`**: Support CPU-only mode. Now the model can run on machines without GPUs.
+- **`2023/03/25`**: A [demo](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb) for Grounding DINO is available at Colab. [[SkalskiP](https://github.com/SkalskiP)]
+- **`2023/03/22`**: Code is available Now!
+
+<details open>
+<summary><font size="4">
+Description
+</font></summary>
+ <a href="https://arxiv.org/abs/2303.05499">Paper</a> introduction.
+<img src=".asset/hero_figure.png" alt="ODinW" width="100%">
+Marrying <a href="https://github.com/IDEA-Research/GroundingDINO">Grounding DINO</a> and <a href="https://github.com/gligen/GLIGEN">GLIGEN</a>
+<img src="https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/GD_GLIGEN.png" alt="gd_gligen" width="100%">
+</details>
+
+## :star: Explanations/Tips for Grounding DINO Inputs and Outputs
+- Grounding DINO accepts an `(image, text)` pair as inputs.
+- It outputs `900` (by default) object boxes. Each box has similarity scores across all input words. (as shown in Figures below.)
+- We defaultly choose the boxes whose highest similarities are higher than a `box_threshold`.
+- We extract the words whose similarities are higher than the `text_threshold` as predicted labels.
+- If you want to obtain objects of specific phrases, like the `dogs` in the sentence `two dogs with a stick.`, you can select the boxes with highest text similarities with `dogs` as final outputs. 
+- Note that each word can be split to **more than one** tokens with different tokenlizers. The number of words in a sentence may not equal to the number of text tokens.
+- We suggest separating different category names with `.` for Grounding DINO.
+![model_explain1](.asset/model_explan1.PNG)
+![model_explain2](.asset/model_explan2.PNG)
+
+## :label: TODO 
+
+- [x] Release inference code and demo.
+- [x] Release checkpoints.
+- [x] Grounding DINO with Stable Diffusion and GLIGEN demos.
+- [ ] Release training codes.
+
+## :hammer_and_wrench: Install 
+
+**Note:**
+
+0. If you have a CUDA environment, please make sure the environment variable `CUDA_HOME` is set. It will be compiled under CPU-only mode if no CUDA available.
+
+Please make sure following the installation steps strictly, otherwise the program may produce: 
+```bash
+NameError: name '_C' is not defined
+```
+
+If this happened, please reinstalled the groundingDINO by reclone the git and do all the installation steps again.
+ 
+#### how to check cuda:
+```bash
+echo $CUDA_HOME
+```
+If it print nothing, then it means you haven't set up the path/
+
+Run this so the environment variable will be set under current shell. 
+```bash
+export CUDA_HOME=/path/to/cuda-11.3
+```
+
+Notice the version of cuda should be aligned with your CUDA runtime, for there might exists multiple cuda at the same time. 
+
+If you want to set the CUDA_HOME permanently, store it using:
+
+```bash
+echo 'export CUDA_HOME=/path/to/cuda' >> ~/.bashrc
+```
+after that, source the bashrc file and check CUDA_HOME:
+```bash
+source ~/.bashrc
+echo $CUDA_HOME
+```
+
+In this example, /path/to/cuda-11.3 should be replaced with the path where your CUDA toolkit is installed. You can find this by typing **which nvcc** in your terminal:
+
+For instance, 
+if the output is /usr/local/cuda/bin/nvcc, then:
+```bash
+export CUDA_HOME=/usr/local/cuda
+```
+**Installation:**
+
+1.Clone the GroundingDINO repository from GitHub.
+
+```bash
+git clone https://github.com/IDEA-Research/GroundingDINO.git
+```
+
+2. Change the current directory to the GroundingDINO folder.
+
+```bash
+cd GroundingDINO/
+```
+
+3. Install the required dependencies in the current directory.
+
+```bash
+pip install -e .
+```
+
+4. Download pre-trained model weights.
+
+```bash
+mkdir weights
+cd weights
+wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth
+cd ..
+```
+
+## :arrow_forward: Demo
+Check your GPU ID (only if you're using a GPU)
+
+```bash
+nvidia-smi
+```
+Replace `{GPU ID}`, `image_you_want_to_detect.jpg`, and `"dir you want to save the output"` with appropriate values in the following command
+```bash
+CUDA_VISIBLE_DEVICES={GPU ID} python demo/inference_on_a_image.py \
+-c groundingdino/config/GroundingDINO_SwinT_OGC.py \
+-p weights/groundingdino_swint_ogc.pth \
+-i image_you_want_to_detect.jpg \
+-o "dir you want to save the output" \
+-t "chair"
+ [--cpu-only] # open it for cpu mode
+```
+
+If you would like to specify the phrases to detect, here is a demo:
+```bash
+CUDA_VISIBLE_DEVICES={GPU ID} python demo/inference_on_a_image.py \
+-c groundingdino/config/GroundingDINO_SwinT_OGC.py \
+-p ./groundingdino_swint_ogc.pth \
+-i .asset/cat_dog.jpeg \
+-o logs/1111 \
+-t "There is a cat and a dog in the image ." \
+--token_spans "[[[9, 10], [11, 14]], [[19, 20], [21, 24]]]"
+ [--cpu-only] # open it for cpu mode
+```
+The token_spans specify the start and end positions of a phrases. For example, the first phrase is `[[9, 10], [11, 14]]`. `"There is a cat and a dog in the image ."[9:10] = 'a'`, `"There is a cat and a dog in the image ."[11:14] = 'cat'`. Hence it refers to the phrase `a cat` . Similarly, the `[[19, 20], [21, 24]]` refers to the phrase `a dog`.
+
+See the `demo/inference_on_a_image.py` for more details.
+
+**Running with Python:**
+
+```python
+from groundingdino.util.inference import load_model, load_image, predict, annotate
+import cv2
+
+model = load_model("groundingdino/config/GroundingDINO_SwinT_OGC.py", "weights/groundingdino_swint_ogc.pth")
+IMAGE_PATH = "weights/dog-3.jpeg"
+TEXT_PROMPT = "chair . person . dog ."
+BOX_TRESHOLD = 0.35
+TEXT_TRESHOLD = 0.25
+
+image_source, image = load_image(IMAGE_PATH)
+
+boxes, logits, phrases = predict(
+    model=model,
+    image=image,
+    caption=TEXT_PROMPT,
+    box_threshold=BOX_TRESHOLD,
+    text_threshold=TEXT_TRESHOLD
+)
+
+annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)
+cv2.imwrite("annotated_image.jpg", annotated_frame)
+```
+**Web UI**
+
+We also provide a demo code to integrate Grounding DINO with Gradio Web UI. See the file `demo/gradio_app.py` for more details.
+
+**Notebooks**
+
+- We release [demos](demo/image_editing_with_groundingdino_gligen.ipynb) to combine [Grounding DINO](https://arxiv.org/abs/2303.05499) with [GLIGEN](https://github.com/gligen/GLIGEN)  for more controllable image editings.
+- We release [demos](demo/image_editing_with_groundingdino_stablediffusion.ipynb) to combine [Grounding DINO](https://arxiv.org/abs/2303.05499) with [Stable Diffusion](https://github.com/Stability-AI/StableDiffusion) for image editings.
+
+## COCO Zero-shot Evaluations
+
+We provide an example to evaluate Grounding DINO zero-shot performance on COCO. The results should be **48.5**.
+
+```bash
+CUDA_VISIBLE_DEVICES=0 \
+python demo/test_ap_on_coco.py \
+ -c groundingdino/config/GroundingDINO_SwinT_OGC.py \
+ -p weights/groundingdino_swint_ogc.pth \
+ --anno_path /path/to/annoataions/ie/instances_val2017.json \
+ --image_dir /path/to/imagedir/ie/val2017
+```
+
+
+## :luggage: Checkpoints
+
+<!-- insert a table -->
+<table>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>name</th>
+      <th>backbone</th>
+      <th>Data</th>
+      <th>box AP on COCO</th>
+      <th>Checkpoint</th>
+      <th>Config</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>1</th>
+      <td>GroundingDINO-T</td>
+      <td>Swin-T</td>
+      <td>O365,GoldG,Cap4M</td>
+      <td>48.4 (zero-shot) / 57.2 (fine-tune)</td>
+      <td><a href="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth">GitHub link</a> | <a href="https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth">HF link</a></td>
+      <td><a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinT_OGC.py">link</a></td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>GroundingDINO-B</td>
+      <td>Swin-B</td>
+      <td>COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO</td>
+      <td>56.7 </td>
+      <td><a href="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha2/groundingdino_swinb_cogcoor.pth">GitHub link</a>  | <a href="https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swinb_cogcoor.pth">HF link</a> 
+      <td><a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinB_cfg.py">link</a></td>
+    </tr>
+  </tbody>
+</table>
+
+## :medal_military: Results
+
+<details open>
+<summary><font size="4">
+COCO Object Detection Results
+</font></summary>
+<img src=".asset/COCO.png" alt="COCO" width="100%">
+</details>
+
+<details open>
+<summary><font size="4">
+ODinW Object Detection Results
+</font></summary>
+<img src=".asset/ODinW.png" alt="ODinW" width="100%">
+</details>
+
+<details open>
+<summary><font size="4">
+Marrying Grounding DINO with <a href="https://github.com/Stability-AI/StableDiffusion">Stable Diffusion</a> for Image Editing
+</font></summary>
+See our example <a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/demo/image_editing_with_groundingdino_stablediffusion.ipynb">notebook</a> for more details.
+<img src=".asset/GD_SD.png" alt="GD_SD" width="100%">
+</details>
+
+
+<details open>
+<summary><font size="4">
+Marrying Grounding DINO with <a href="https://github.com/gligen/GLIGEN">GLIGEN</a> for more Detailed Image Editing.
+</font></summary>
+See our example <a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/demo/image_editing_with_groundingdino_gligen.ipynb">notebook</a> for more details.
+<img src=".asset/GD_GLIGEN.png" alt="GD_GLIGEN" width="100%">
+</details>
+
+## :sauropod: Model: Grounding DINO
+
+Includes: a text backbone, an image backbone, a feature enhancer, a language-guided query selection, and a cross-modality decoder.
+
+![arch](.asset/arch.png)
+
+
+## :hearts: Acknowledgement
+
+Our model is related to [DINO](https://github.com/IDEA-Research/DINO) and [GLIP](https://github.com/microsoft/GLIP). Thanks for their great work!
+
+We also thank great previous work including DETR, Deformable DETR, SMCA, Conditional DETR, Anchor DETR, Dynamic DETR, DAB-DETR, DN-DETR, etc. More related work are available at [Awesome Detection Transformer](https://github.com/IDEACVR/awesome-detection-transformer). A new toolbox [detrex](https://github.com/IDEA-Research/detrex) is available as well.
+
+Thanks [Stable Diffusion](https://github.com/Stability-AI/StableDiffusion) and [GLIGEN](https://github.com/gligen/GLIGEN) for their awesome models.
+
+
+## :black_nib: Citation
+
+If you find our work helpful for your research, please consider citing the following BibTeX entry.   
+
+```bibtex
+@article{liu2023grounding,
+  title={Grounding dino: Marrying dino with grounded pre-training for open-set object detection},
+  author={Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and Li, Chunyuan and Yang, Jianwei and Su, Hang and Zhu, Jun and others},
+  journal={arXiv preprint arXiv:2303.05499},
+  year={2023}
+}
+```
+
+
+
+

UniVAD/models/GroundingDINO/environment.yaml

@@ -0,0 +1,248 @@
+name: dino
+channels:
+  - pytorch
+  - nvidia
+  - conda-forge
+  - defaults
+dependencies:
+  - addict=2.4.0=pyhd8ed1ab_2
+  - aiohttp=3.8.5=py39ha55989b_0
+  - aiosignal=1.3.1=pyhd8ed1ab_0
+  - asttokens=2.0.5=pyhd3eb1b0_0
+  - async-timeout=4.0.3=pyhd8ed1ab_0
+  - attrs=23.1.0=pyh71513ae_1
+  - aws-c-auth=0.7.0=h6f3c987_2
+  - aws-c-cal=0.6.0=h6ba3258_0
+  - aws-c-common=0.8.23=hcfcfb64_0
+  - aws-c-compression=0.2.17=h420beca_1
+  - aws-c-event-stream=0.3.1=had47b81_1
+  - aws-c-http=0.7.11=h72ba615_0
+  - aws-c-io=0.13.28=ha35c040_0
+  - aws-c-mqtt=0.8.14=h4941efa_2
+  - aws-c-s3=0.3.13=he04eaa7_2
+  - aws-c-sdkutils=0.1.11=h420beca_1
+  - aws-checksums=0.1.16=h420beca_1
+  - aws-crt-cpp=0.20.3=h247a981_4
+  - aws-sdk-cpp=1.10.57=h1a0519f_17
+  - backcall=0.2.0=pyhd3eb1b0_0
+  - blas=2.118=mkl
+  - blas-devel=3.9.0=18_win64_mkl
+  - brotli=1.0.9=hcfcfb64_9
+  - brotli-bin=1.0.9=hcfcfb64_9
+  - brotli-python=1.0.9=py39h99910a6_9
+  - bzip2=1.0.8=h8ffe710_4
+  - c-ares=1.19.1=hcfcfb64_0
+  - ca-certificates=2023.08.22=haa95532_0
+  - certifi=2023.7.22=py39haa95532_0
+  - charset-normalizer=3.2.0=pyhd8ed1ab_0
+  - click=8.1.7=win_pyh7428d3b_0
+  - colorama=0.4.6=pyhd8ed1ab_0
+  - comm=0.1.2=py39haa95532_0
+  - contourpy=1.1.1=py39h1f6ef14_1
+  - cuda-cccl=12.2.140=0
+  - cuda-cudart=11.8.89=0
+  - cuda-cudart-dev=11.8.89=0
+  - cuda-cupti=11.8.87=0
+  - cuda-libraries=11.8.0=0
+  - cuda-libraries-dev=11.8.0=0
+  - cuda-nvrtc=11.8.89=0
+  - cuda-nvrtc-dev=11.8.89=0
+  - cuda-nvtx=11.8.86=0
+  - cuda-profiler-api=12.2.140=0
+  - cuda-runtime=11.8.0=0
+  - cycler=0.11.0=pyhd8ed1ab_0
+  - cython=3.0.0=py39h2bbff1b_0
+  - dataclasses=0.8=pyhc8e2a94_3
+  - datasets=2.14.5=pyhd8ed1ab_0
+  - debugpy=1.6.7=py39hd77b12b_0
+  - decorator=5.1.1=pyhd3eb1b0_0
+  - dill=0.3.7=pyhd8ed1ab_0
+  - exceptiongroup=1.0.4=py39haa95532_0
+  - executing=0.8.3=pyhd3eb1b0_0
+  - filelock=3.12.4=pyhd8ed1ab_0
+  - fonttools=4.42.1=py39ha55989b_0
+  - freeglut=3.2.2=h63175ca_2
+  - freetype=2.12.1=hdaf720e_2
+  - frozenlist=1.4.0=py39ha55989b_1
+  - fsspec=2023.6.0=pyh1a96a4e_0
+  - gettext=0.21.1=h5728263_0
+  - glib=2.78.0=h12be248_0
+  - glib-tools=2.78.0=h12be248_0
+  - gst-plugins-base=1.22.6=h001b923_1
+  - gstreamer=1.22.6=hb4038d2_1
+  - huggingface_hub=0.17.3=pyhd8ed1ab_0
+  - icu=70.1=h0e60522_0
+  - idna=3.4=pyhd8ed1ab_0
+  - importlib-metadata=6.8.0=pyha770c72_0
+  - importlib-resources=6.1.0=pyhd8ed1ab_0
+  - importlib_metadata=6.8.0=hd8ed1ab_0
+  - importlib_resources=6.1.0=pyhd8ed1ab_0
+  - intel-openmp=2023.2.0=h57928b3_49503
+  - ipykernel=6.25.0=py39h9909e9c_0
+  - ipython=8.15.0=py39haa95532_0
+  - jasper=2.0.33=hc2e4405_1
+  - jedi=0.18.1=py39haa95532_1
+  - jinja2=3.1.2=pyhd8ed1ab_1
+  - joblib=1.3.2=pyhd8ed1ab_0
+  - jpeg=9e=hcfcfb64_3
+  - jupyter_client=8.1.0=py39haa95532_0
+  - jupyter_core=5.3.0=py39haa95532_0
+  - kiwisolver=1.4.5=py39h1f6ef14_1
+  - krb5=1.20.1=heb0366b_0
+  - lcms2=2.14=h90d422f_0
+  - lerc=4.0.0=h63175ca_0
+  - libabseil=20230125.3=cxx17_h63175ca_0
+  - libarrow=12.0.1=h12e5d06_5_cpu
+  - libblas=3.9.0=18_win64_mkl
+  - libbrotlicommon=1.0.9=hcfcfb64_9
+  - libbrotlidec=1.0.9=hcfcfb64_9
+  - libbrotlienc=1.0.9=hcfcfb64_9
+  - libcblas=3.9.0=18_win64_mkl
+  - libclang=15.0.7=default_h77d9078_3
+  - libclang13=15.0.7=default_h77d9078_3
+  - libcrc32c=1.1.2=h0e60522_0
+  - libcublas=11.11.3.6=0
+  - libcublas-dev=11.11.3.6=0
+  - libcufft=10.9.0.58=0
+  - libcufft-dev=10.9.0.58=0
+  - libcurand=10.3.3.141=0
+  - libcurand-dev=10.3.3.141=0
+  - libcurl=8.1.2=h68f0423_0
+  - libcusolver=11.4.1.48=0
+  - libcusolver-dev=11.4.1.48=0
+  - libcusparse=11.7.5.86=0
+  - libcusparse-dev=11.7.5.86=0
+  - libdeflate=1.14=hcfcfb64_0
+  - libevent=2.1.12=h3671451_1
+  - libffi=3.4.2=h8ffe710_5
+  - libglib=2.78.0=he8f3873_0
+  - libgoogle-cloud=2.12.0=h00b2bdc_1
+  - libgrpc=1.54.3=ha177ca7_0
+  - libhwloc=2.9.3=default_haede6df_1009
+  - libiconv=1.17=h8ffe710_0
+  - liblapack=3.9.0=18_win64_mkl
+  - liblapacke=3.9.0=18_win64_mkl
+  - libnpp=11.8.0.86=0
+  - libnpp-dev=11.8.0.86=0
+  - libnvjpeg=11.9.0.86=0
+  - libnvjpeg-dev=11.9.0.86=0
+  - libogg=1.3.4=h8ffe710_1
+  - libopencv=4.5.3=py39h488c12c_8
+  - libpng=1.6.39=h19919ed_0
+  - libprotobuf=3.21.12=h12be248_2
+  - libsodium=1.0.18=h62dcd97_0
+  - libsqlite=3.43.0=hcfcfb64_0
+  - libssh2=1.11.0=h7dfc565_0
+  - libthrift=0.18.1=h06f6336_2
+  - libtiff=4.4.0=hc4f729c_5
+  - libutf8proc=2.8.0=h82a8f57_0
+  - libuv=1.44.2=hcfcfb64_1
+  - libvorbis=1.3.7=h0e60522_0
+  - libwebp-base=1.3.2=hcfcfb64_0
+  - libxcb=1.13=hcd874cb_1004
+  - libxml2=2.11.5=hc3477c8_1
+  - libzlib=1.2.13=hcfcfb64_5
+  - lz4-c=1.9.4=hcfcfb64_0
+  - m2w64-gcc-libgfortran=5.3.0=6
+  - m2w64-gcc-libs=5.3.0=7
+  - m2w64-gcc-libs-core=5.3.0=7
+  - m2w64-gmp=6.1.0=2
+  - m2w64-libwinpthread-git=5.0.0.4634.697f757=2
+  - markupsafe=2.1.3=py39ha55989b_1
+  - matplotlib-base=3.8.0=py39hf19769e_1
+  - matplotlib-inline=0.1.6=py39haa95532_0
+  - mkl=2022.1.0=h6a75c08_874
+  - mkl-devel=2022.1.0=h57928b3_875
+  - mkl-include=2022.1.0=h6a75c08_874
+  - mpmath=1.3.0=pyhd8ed1ab_0
+  - msys2-conda-epoch=20160418=1
+  - multidict=6.0.4=py39ha55989b_0
+  - multiprocess=0.70.15=py39ha55989b_1
+  - munkres=1.1.4=pyh9f0ad1d_0
+  - nest-asyncio=1.5.6=py39haa95532_0
+  - networkx=3.1=pyhd8ed1ab_0
+  - numpy=1.26.0=py39hddb5d58_0
+  - opencv=4.5.3=py39hcbf5309_8
+  - openjpeg=2.5.0=hc9384bd_1
+  - openssl=3.1.3=hcfcfb64_0
+  - orc=1.9.0=hada7b9e_1
+  - packaging=23.1=pyhd8ed1ab_0
+  - pandas=2.1.1=py39h32e6231_0
+  - parso=0.8.3=pyhd3eb1b0_0
+  - pcre2=10.40=h17e33f8_0
+  - pickleshare=0.7.5=pyhd3eb1b0_1003
+  - pillow=9.2.0=py39h595c93f_3
+  - pip=23.2.1=pyhd8ed1ab_0
+  - platformdirs=3.10.0=pyhd8ed1ab_0
+  - prompt-toolkit=3.0.36=py39haa95532_0
+  - psutil=5.9.0=py39h2bbff1b_0
+  - pthread-stubs=0.4=hcd874cb_1001
+  - pthreads-win32=2.9.1=hfa6e2cd_3
+  - pure_eval=0.2.2=pyhd3eb1b0_0
+  - py-opencv=4.5.3=py39h00e5391_8
+  - pyarrow=12.0.1=py39hca4e8af_5_cpu
+  - pycocotools=2.0.6=py39hc266a54_1
+  - pygments=2.15.1=py39haa95532_1
+  - pyparsing=3.1.1=pyhd8ed1ab_0
+  - pysocks=1.7.1=pyh0701188_6
+  - python=3.9.18=h4de0772_0_cpython
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python-tzdata=2023.3=pyhd8ed1ab_0
+  - python-xxhash=3.3.0=py39ha55989b_1
+  - python_abi=3.9=4_cp39
+  - pytorch=2.0.1=py3.9_cuda11.8_cudnn8_0
+  - pytorch-cuda=11.8=h24eeafa_5
+  - pytorch-mutex=1.0=cuda
+  - pytz=2023.3.post1=pyhd8ed1ab_0
+  - pywin32=305=py39h2bbff1b_0
+  - pyyaml=6.0.1=py39ha55989b_1
+  - pyzmq=25.1.0=py39hd77b12b_0
+  - qt-main=5.15.8=h720456b_6
+  - re2=2023.03.02=hd4eee63_0
+  - regex=2023.8.8=py39ha55989b_1
+  - requests=2.31.0=pyhd8ed1ab_0
+  - sacremoses=0.0.53=pyhd8ed1ab_0
+  - safetensors=0.3.3=py39hf21820d_1
+  - setuptools=68.2.2=pyhd8ed1ab_0
+  - six=1.16.0=pyh6c4a22f_0
+  - snappy=1.1.10=hfb803bf_0
+  - stack_data=0.2.0=pyhd3eb1b0_0
+  - sympy=1.12=pyh04b8f61_3
+  - tbb=2021.10.0=h91493d7_1
+  - timm=0.9.7=pyhd8ed1ab_0
+  - tk=8.6.13=hcfcfb64_0
+  - tokenizers=0.13.3=py39hca44cb7_0
+  - tomli=2.0.1=pyhd8ed1ab_0
+  - tornado=6.3.2=py39h2bbff1b_0
+  - tqdm=4.66.1=pyhd8ed1ab_0
+  - traitlets=5.7.1=py39haa95532_0
+  - transformers=4.33.2=pyhd8ed1ab_0
+  - typing-extensions=4.8.0=hd8ed1ab_0
+  - typing_extensions=4.8.0=pyha770c72_0
+  - tzdata=2023c=h71feb2d_0
+  - ucrt=10.0.22621.0=h57928b3_0
+  - unicodedata2=15.0.0=py39ha55989b_1
+  - urllib3=2.0.5=pyhd8ed1ab_0
+  - vc=14.3=h64f974e_17
+  - vc14_runtime=14.36.32532=hdcecf7f_17
+  - vs2015_runtime=14.36.32532=h05e6639_17
+  - wcwidth=0.2.5=pyhd3eb1b0_0
+  - wheel=0.41.2=pyhd8ed1ab_0
+  - win_inet_pton=1.1.0=pyhd8ed1ab_6
+  - xorg-libxau=1.0.11=hcd874cb_0
+  - xorg-libxdmcp=1.1.3=hcd874cb_0
+  - xxhash=0.8.2=hcfcfb64_0
+  - xz=5.2.6=h8d14728_0
+  - yaml=0.2.5=h8ffe710_2
+  - yapf=0.40.1=pyhd8ed1ab_0
+  - yarl=1.9.2=py39ha55989b_0
+  - zeromq=4.3.4=hd77b12b_0
+  - zipp=3.17.0=pyhd8ed1ab_0
+  - zlib=1.2.13=hcfcfb64_5
+  - zstd=1.5.5=h12be248_0
+  - pip:
+      - opencv-python==4.8.0.76
+      - supervision==0.6.0
+      - torchaudio==2.0.2
+      - torchvision==0.15.2
+prefix: C:\Users\Makoto\miniconda3\envs\dino

UniVAD/models/GroundingDINO/requirements.txt

@@ -0,0 +1,10 @@
+torch
+torchvision
+transformers
+addict
+yapf
+timm
+numpy
+opencv-python
+supervision
+pycocotools

UniVAD/models/GroundingDINO/test.ipynb

@@ -0,0 +1,114 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "final text_encoder_type: bert-base-uncased\n"
+     ]
+    },
+    {
+     "data": {
+      "application/json": {
+       "ascii": false,
+       "bar_format": null,
+       "colour": null,
+       "elapsed": 0.014210224151611328,
+       "initial": 0,
+       "n": 0,
+       "ncols": null,
+       "nrows": null,
+       "postfix": null,
+       "prefix": "Downloading model.safetensors",
+       "rate": null,
+       "total": 440449768,
+       "unit": "B",
+       "unit_divisor": 1000,
+       "unit_scale": true
+      },
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5922f34578364d36afa13de9f01254bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniconda3/lib/python3.8/site-packages/transformers/modeling_utils.py:881: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.\n",
+      "  warnings.warn(\n",
+      "/root/miniconda3/lib/python3.8/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
+      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from groundingdino.util.inference import load_model, load_image, predict, annotate\n",
+    "import cv2\n",
+    "\n",
+    "model = load_model(\"groundingdino/config/GroundingDINO_SwinT_OGC.py\", \"../04-06-segment-anything/weights/groundingdino_swint_ogc.pth\")\n",
+    "IMAGE_PATH = \".asset/cat_dog.jpeg\"\n",
+    "TEXT_PROMPT = \"chair . person . dog .\"\n",
+    "BOX_TRESHOLD = 0.35\n",
+    "TEXT_TRESHOLD = 0.25\n",
+    "\n",
+    "image_source, image = load_image(IMAGE_PATH)\n",
+    "\n",
+    "boxes, logits, phrases = predict(\n",
+    "    model=model,\n",
+    "    image=image,\n",
+    "    caption=TEXT_PROMPT,\n",
+    "    box_threshold=BOX_TRESHOLD,\n",
+    "    text_threshold=TEXT_TRESHOLD\n",
+    ")\n",
+    "\n",
+    "annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)\n",
+    "cv2.imwrite(\"annotated_image.jpg\", annotated_frame)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}