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@@ -15,3 +15,7 @@ src/groundingdino/.asset/hero_figure.png filter=lfs diff=lfs merge=lfs -text
 tutorial/img/click_segment.jpg filter=lfs diff=lfs merge=lfs -text
 tutorial/img/input_video.jpg filter=lfs diff=lfs merge=lfs -text
 tutorial/img/start_tracking.jpg filter=lfs diff=lfs merge=lfs -text
+GroundingDINO/.asset/GD_GLIGEN.png filter=lfs diff=lfs merge=lfs -text
+GroundingDINO/.asset/GD_SD.png filter=lfs diff=lfs merge=lfs -text
+GroundingDINO/.asset/hero_figure.png filter=lfs diff=lfs merge=lfs -text
+GroundingDINO/weights/groundingdino_swint_ogc.pth filter=lfs diff=lfs merge=lfs -text

GroundingDINO/.gitignore

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+# 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
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+
+# 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
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+
+# Rope project settings
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+
+# 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/

GroundingDINO/LICENSE

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

@@ -0,0 +1,367 @@
+<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)**.
+
+## :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 Performancce.** 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}
+}
+```
+
+
+
+

GroundingDINO/demo/create_coco_dataset.py

@@ -0,0 +1,83 @@
+import typer
+from groundingdino.util.inference import load_model, load_image, predict
+from tqdm import tqdm
+import torchvision
+import torch
+import fiftyone as fo
+
+
+def main(
+        image_directory: str = 'test_grounding_dino',
+        text_prompt: str = 'bus, car',
+        box_threshold: float = 0.15, 
+        text_threshold: float = 0.10,
+        export_dataset: bool = False,
+        view_dataset: bool = False,
+        export_annotated_images: bool = True,
+        weights_path : str = "groundingdino_swint_ogc.pth",
+        config_path: str = "../../GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py",
+        subsample: int = None,
+    ):
+
+    model = load_model(config_path, weights_path)
+    
+    dataset = fo.Dataset.from_images_dir(image_directory)
+
+    samples = []
+
+    if subsample is not None: 
+        
+        if subsample < len(dataset):
+            dataset = dataset.take(subsample).clone()
+    
+    for sample in tqdm(dataset):
+
+        image_source, image = load_image(sample.filepath)
+
+        boxes, logits, phrases = predict(
+            model=model, 
+            image=image, 
+            caption=text_prompt, 
+            box_threshold=box_threshold, 
+            text_threshold=text_threshold,
+        )
+
+        detections = [] 
+
+        for box, logit, phrase in zip(boxes, logits, phrases):
+
+            rel_box = torchvision.ops.box_convert(box, 'cxcywh', 'xywh')
+
+            detections.append(
+                fo.Detection(
+                    label=phrase, 
+                    bounding_box=rel_box,
+                    confidence=logit,
+            ))
+
+        # Store detections in a field name of your choice
+        sample["detections"] = fo.Detections(detections=detections)
+        sample.save()
+
+    # loads the voxel fiftyone UI ready for viewing the dataset.
+    if view_dataset:
+        session = fo.launch_app(dataset)
+        session.wait()
+        
+    # exports COCO dataset ready for training
+    if export_dataset:
+        dataset.export(
+            'coco_dataset',
+            dataset_type=fo.types.COCODetectionDataset,
+        )
+        
+    # saves bounding boxes plotted on the input images to disk
+    if export_annotated_images:
+        dataset.draw_labels(
+            'images_with_bounding_boxes',
+            label_fields=['detections']
+        )
+
+
+if __name__ == '__main__':
+    typer.run(main)

GroundingDINO/demo/gradio_app.py

@@ -0,0 +1,125 @@
+import argparse
+from functools import partial
+import cv2
+import requests
+import os
+from io import BytesIO
+from PIL import Image
+import numpy as np
+from pathlib import Path
+
+
+import warnings
+
+import torch
+
+# prepare the environment
+os.system("python setup.py build develop --user")
+os.system("pip install packaging==21.3")
+os.system("pip install gradio")
+
+
+warnings.filterwarnings("ignore")
+
+import gradio as gr
+
+from groundingdino.models import build_model
+from groundingdino.util.slconfig import SLConfig
+from groundingdino.util.utils import clean_state_dict
+from groundingdino.util.inference import annotate, load_image, predict
+import groundingdino.datasets.transforms as T
+
+from huggingface_hub import hf_hub_download
+
+
+
+# Use this command for evaluate the Grounding DINO model
+config_file = "groundingdino/config/GroundingDINO_SwinT_OGC.py"
+ckpt_repo_id = "ShilongLiu/GroundingDINO"
+ckpt_filenmae = "groundingdino_swint_ogc.pth"
+
+
+def load_model_hf(model_config_path, repo_id, filename, device='cpu'):
+    args = SLConfig.fromfile(model_config_path) 
+    model = build_model(args)
+    args.device = device
+
+    cache_file = hf_hub_download(repo_id=repo_id, filename=filename)
+    checkpoint = torch.load(cache_file, map_location='cpu')
+    log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False)
+    print("Model loaded from {} \n => {}".format(cache_file, log))
+    _ = model.eval()
+    return model    
+
+def image_transform_grounding(init_image):
+    transform = T.Compose([
+        T.RandomResize([800], max_size=1333),
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    image, _ = transform(init_image, None) # 3, h, w
+    return init_image, image
+
+def image_transform_grounding_for_vis(init_image):
+    transform = T.Compose([
+        T.RandomResize([800], max_size=1333),
+    ])
+    image, _ = transform(init_image, None) # 3, h, w
+    return image
+
+model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae)
+
+def run_grounding(input_image, grounding_caption, box_threshold, text_threshold):
+    init_image = input_image.convert("RGB")
+    original_size = init_image.size
+
+    _, image_tensor = image_transform_grounding(init_image)
+    image_pil: Image = image_transform_grounding_for_vis(init_image)
+
+    # run grounidng
+    boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold, device='cpu')
+    annotated_frame = annotate(image_source=np.asarray(image_pil), boxes=boxes, logits=logits, phrases=phrases)
+    image_with_box = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB))
+
+
+    return image_with_box
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser("Grounding DINO demo", add_help=True)
+    parser.add_argument("--debug", action="store_true", help="using debug mode")
+    parser.add_argument("--share", action="store_true", help="share the app")
+    args = parser.parse_args()
+
+    block = gr.Blocks().queue()
+    with block:
+        gr.Markdown("# [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)")
+        gr.Markdown("### Open-World Detection with Grounding DINO")
+
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(source='upload', type="pil")
+                grounding_caption = gr.Textbox(label="Detection Prompt")
+                run_button = gr.Button(label="Run")
+                with gr.Accordion("Advanced options", open=False):
+                    box_threshold = gr.Slider(
+                        label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
+                    )
+                    text_threshold = gr.Slider(
+                        label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
+                    )
+
+            with gr.Column():
+                gallery = gr.outputs.Image(
+                    type="pil",
+                    # label="grounding results"
+                ).style(full_width=True, full_height=True)
+                # gallery = gr.Gallery(label="Generated images", show_label=False).style(
+                #         grid=[1], height="auto", container=True, full_width=True, full_height=True)
+
+        run_button.click(fn=run_grounding, inputs=[
+                        input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery])
+
+
+    block.launch(server_name='0.0.0.0', server_port=7579, debug=args.debug, share=args.share)
+

GroundingDINO/demo/inference_on_a_image.py

@@ -0,0 +1,214 @@
+import argparse
+import os
+import sys
+
+import numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageFont
+
+import groundingdino.datasets.transforms as T
+from groundingdino.models import build_model
+from groundingdino.util import box_ops
+from groundingdino.util.slconfig import SLConfig
+from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
+from groundingdino.util.vl_utils import create_positive_map_from_span
+
+
+def plot_boxes_to_image(image_pil, tgt):
+    H, W = tgt["size"]
+    boxes = tgt["boxes"]
+    labels = tgt["labels"]
+    assert len(boxes) == len(labels), "boxes and labels must have same length"
+
+    draw = ImageDraw.Draw(image_pil)
+    mask = Image.new("L", image_pil.size, 0)
+    mask_draw = ImageDraw.Draw(mask)
+
+    # draw boxes and masks
+    for box, label in zip(boxes, labels):
+        # from 0..1 to 0..W, 0..H
+        box = box * torch.Tensor([W, H, W, H])
+        # from xywh to xyxy
+        box[:2] -= box[2:] / 2
+        box[2:] += box[:2]
+        # random color
+        color = tuple(np.random.randint(0, 255, size=3).tolist())
+        # draw
+        x0, y0, x1, y1 = box
+        x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)
+
+        draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
+        # draw.text((x0, y0), str(label), fill=color)
+
+        font = ImageFont.load_default()
+        if hasattr(font, "getbbox"):
+            bbox = draw.textbbox((x0, y0), str(label), font)
+        else:
+            w, h = draw.textsize(str(label), font)
+            bbox = (x0, y0, w + x0, y0 + h)
+        # bbox = draw.textbbox((x0, y0), str(label))
+        draw.rectangle(bbox, fill=color)
+        draw.text((x0, y0), str(label), fill="white")
+
+        mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)
+
+    return image_pil, mask
+
+
+def load_image(image_path):
+    # load image
+    image_pil = Image.open(image_path).convert("RGB")  # load image
+
+    transform = T.Compose(
+        [
+            T.RandomResize([800], max_size=1333),
+            T.ToTensor(),
+            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]
+    )
+    image, _ = transform(image_pil, None)  # 3, h, w
+    return image_pil, image
+
+
+def load_model(model_config_path, model_checkpoint_path, cpu_only=False):
+    args = SLConfig.fromfile(model_config_path)
+    args.device = "cuda" if not cpu_only else "cpu"
+    model = build_model(args)
+    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
+    load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
+    print(load_res)
+    _ = model.eval()
+    return model
+
+
+def get_grounding_output(model, image, caption, box_threshold, text_threshold=None, with_logits=True, cpu_only=False, token_spans=None):
+    assert text_threshold is not None or token_spans is not None, "text_threshould and token_spans should not be None at the same time!"
+    caption = caption.lower()
+    caption = caption.strip()
+    if not caption.endswith("."):
+        caption = caption + "."
+    device = "cuda" if not cpu_only else "cpu"
+    model = model.to(device)
+    image = image.to(device)
+    with torch.no_grad():
+        outputs = model(image[None], captions=[caption])
+    logits = outputs["pred_logits"].sigmoid()[0]  # (nq, 256)
+    boxes = outputs["pred_boxes"][0]  # (nq, 4)
+
+    # filter output
+    if token_spans is None:
+        logits_filt = logits.cpu().clone()
+        boxes_filt = boxes.cpu().clone()
+        filt_mask = logits_filt.max(dim=1)[0] > box_threshold
+        logits_filt = logits_filt[filt_mask]  # num_filt, 256
+        boxes_filt = boxes_filt[filt_mask]  # num_filt, 4
+
+        # get phrase
+        tokenlizer = model.tokenizer
+        tokenized = tokenlizer(caption)
+        # build pred
+        pred_phrases = []
+        for logit, box in zip(logits_filt, boxes_filt):
+            pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)
+            if with_logits:
+                pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
+            else:
+                pred_phrases.append(pred_phrase)
+    else:
+        # given-phrase mode
+        positive_maps = create_positive_map_from_span(
+            model.tokenizer(text_prompt),
+            token_span=token_spans
+        ).to(image.device) # n_phrase, 256
+
+        logits_for_phrases = positive_maps @ logits.T # n_phrase, nq
+        all_logits = []
+        all_phrases = []
+        all_boxes = []
+        for (token_span, logit_phr) in zip(token_spans, logits_for_phrases):
+            # get phrase
+            phrase = ' '.join([caption[_s:_e] for (_s, _e) in token_span])
+            # get mask
+            filt_mask = logit_phr > box_threshold
+            # filt box
+            all_boxes.append(boxes[filt_mask])
+            # filt logits
+            all_logits.append(logit_phr[filt_mask])
+            if with_logits:
+                logit_phr_num = logit_phr[filt_mask]
+                all_phrases.extend([phrase + f"({str(logit.item())[:4]})" for logit in logit_phr_num])
+            else:
+                all_phrases.extend([phrase for _ in range(len(filt_mask))])
+        boxes_filt = torch.cat(all_boxes, dim=0).cpu()
+        pred_phrases = all_phrases
+
+
+    return boxes_filt, pred_phrases
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser("Grounding DINO example", add_help=True)
+    parser.add_argument("--config_file", "-c", type=str, required=True, help="path to config file")
+    parser.add_argument(
+        "--checkpoint_path", "-p", type=str, required=True, help="path to checkpoint file"
+    )
+    parser.add_argument("--image_path", "-i", type=str, required=True, help="path to image file")
+    parser.add_argument("--text_prompt", "-t", type=str, required=True, help="text prompt")
+    parser.add_argument(
+        "--output_dir", "-o", type=str, default="outputs", required=True, help="output directory"
+    )
+
+    parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold")
+    parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold")
+    parser.add_argument("--token_spans", type=str, default=None, help=
+                        "The positions of start and end positions of phrases of interest. \
+                        For example, a caption is 'a cat and a dog', \
+                        if you would like to detect 'cat', the token_spans should be '[[[2, 5]], ]', since 'a cat and a dog'[2:5] is 'cat'. \
+                        if you would like to detect 'a cat', the token_spans should be '[[[0, 1], [2, 5]], ]', since 'a cat and a dog'[0:1] is 'a', and 'a cat and a dog'[2:5] is 'cat'. \
+                        ")
+
+    parser.add_argument("--cpu-only", action="store_true", help="running on cpu only!, default=False")
+    args = parser.parse_args()
+
+    # cfg
+    config_file = args.config_file  # change the path of the model config file
+    checkpoint_path = args.checkpoint_path  # change the path of the model
+    image_path = args.image_path
+    text_prompt = args.text_prompt
+    output_dir = args.output_dir
+    box_threshold = args.box_threshold
+    text_threshold = args.text_threshold
+    token_spans = args.token_spans
+
+    # make dir
+    os.makedirs(output_dir, exist_ok=True)
+    # load image
+    image_pil, image = load_image(image_path)
+    # load model
+    model = load_model(config_file, checkpoint_path, cpu_only=args.cpu_only)
+
+    # visualize raw image
+    image_pil.save(os.path.join(output_dir, "raw_image.jpg"))
+
+    # set the text_threshold to None if token_spans is set.
+    if token_spans is not None:
+        text_threshold = None
+        print("Using token_spans. Set the text_threshold to None.")
+
+
+    # run model
+    boxes_filt, pred_phrases = get_grounding_output(
+        model, image, text_prompt, box_threshold, text_threshold, cpu_only=args.cpu_only, token_spans=eval(f"{token_spans}")
+    )
+
+    # visualize pred
+    size = image_pil.size
+    pred_dict = {
+        "boxes": boxes_filt,
+        "size": [size[1], size[0]],  # H,W
+        "labels": pred_phrases,
+    }
+    # import ipdb; ipdb.set_trace()
+    image_with_box = plot_boxes_to_image(image_pil, pred_dict)[0]
+    image_with_box.save(os.path.join(output_dir, "pred.jpg"))

GroundingDINO/demo/test_ap_on_coco.py

@@ -0,0 +1,233 @@
+import argparse
+import os
+import sys
+import time
+
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, DistributedSampler
+
+from groundingdino.models import build_model
+import groundingdino.datasets.transforms as T
+from groundingdino.util import box_ops, get_tokenlizer
+from groundingdino.util.misc import clean_state_dict, collate_fn
+from groundingdino.util.slconfig import SLConfig
+
+# from torchvision.datasets import CocoDetection
+import torchvision
+
+from groundingdino.util.vl_utils import build_captions_and_token_span, create_positive_map_from_span
+from groundingdino.datasets.cocogrounding_eval import CocoGroundingEvaluator
+
+
+def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda"):
+    args = SLConfig.fromfile(model_config_path)
+    args.device = device
+    model = build_model(args)
+    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
+    model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
+    model.eval()
+    return model
+
+
+class CocoDetection(torchvision.datasets.CocoDetection):
+    def __init__(self, img_folder, ann_file, transforms):
+        super().__init__(img_folder, ann_file)
+        self._transforms = transforms
+
+    def __getitem__(self, idx):
+        img, target = super().__getitem__(idx)  # target: list
+
+        # import ipdb; ipdb.set_trace()
+
+        w, h = img.size
+        boxes = [obj["bbox"] for obj in target]
+        boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4)
+        boxes[:, 2:] += boxes[:, :2]  # xywh -> xyxy
+        boxes[:, 0::2].clamp_(min=0, max=w)
+        boxes[:, 1::2].clamp_(min=0, max=h)
+        # filt invalid boxes/masks/keypoints
+        keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
+        boxes = boxes[keep]
+
+        target_new = {}
+        image_id = self.ids[idx]
+        target_new["image_id"] = image_id
+        target_new["boxes"] = boxes
+        target_new["orig_size"] = torch.as_tensor([int(h), int(w)])
+
+        if self._transforms is not None:
+            img, target = self._transforms(img, target_new)
+
+        return img, target
+
+
+class PostProcessCocoGrounding(nn.Module):
+    """ This module converts the model's output into the format expected by the coco api"""
+
+    def __init__(self, num_select=300, coco_api=None, tokenlizer=None) -> None:
+        super().__init__()
+        self.num_select = num_select
+
+        assert coco_api is not None
+        category_dict = coco_api.dataset['categories']
+        cat_list = [item['name'] for item in category_dict]
+        captions, cat2tokenspan = build_captions_and_token_span(cat_list, True)
+        tokenspanlist = [cat2tokenspan[cat] for cat in cat_list]
+        positive_map = create_positive_map_from_span(
+            tokenlizer(captions), tokenspanlist)  # 80, 256. normed
+
+        id_map = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10, 10: 11, 11: 13, 12: 14, 13: 15, 14: 16, 15: 17, 16: 18, 17: 19, 18: 20, 19: 21, 20: 22, 21: 23, 22: 24, 23: 25, 24: 27, 25: 28, 26: 31, 27: 32, 28: 33, 29: 34, 30: 35, 31: 36, 32: 37, 33: 38, 34: 39, 35: 40, 36: 41, 37: 42, 38: 43, 39: 44, 40: 46,
+                  41: 47, 42: 48, 43: 49, 44: 50, 45: 51, 46: 52, 47: 53, 48: 54, 49: 55, 50: 56, 51: 57, 52: 58, 53: 59, 54: 60, 55: 61, 56: 62, 57: 63, 58: 64, 59: 65, 60: 67, 61: 70, 62: 72, 63: 73, 64: 74, 65: 75, 66: 76, 67: 77, 68: 78, 69: 79, 70: 80, 71: 81, 72: 82, 73: 84, 74: 85, 75: 86, 76: 87, 77: 88, 78: 89, 79: 90}
+
+        # build a mapping from label_id to pos_map
+        new_pos_map = torch.zeros((91, 256))
+        for k, v in id_map.items():
+            new_pos_map[v] = positive_map[k]
+        self.positive_map = new_pos_map
+
+    @torch.no_grad()
+    def forward(self, outputs, target_sizes, not_to_xyxy=False):
+        """ Perform the computation
+        Parameters:
+            outputs: raw outputs of the model
+            target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch
+                          For evaluation, this must be the original image size (before any data augmentation)
+                          For visualization, this should be the image size after data augment, but before padding
+        """
+        num_select = self.num_select
+        out_logits, out_bbox = outputs['pred_logits'], outputs['pred_boxes']
+
+        # pos map to logit
+        prob_to_token = out_logits.sigmoid()  # bs, 100, 256
+        pos_maps = self.positive_map.to(prob_to_token.device)
+        # (bs, 100, 256) @ (91, 256).T -> (bs, 100, 91)
+        prob_to_label = prob_to_token @ pos_maps.T
+
+        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+        #     import ipdb; ipdb.set_trace()
+
+        assert len(out_logits) == len(target_sizes)
+        assert target_sizes.shape[1] == 2
+
+        prob = prob_to_label
+        topk_values, topk_indexes = torch.topk(
+            prob.view(out_logits.shape[0], -1), num_select, dim=1)
+        scores = topk_values
+        topk_boxes = topk_indexes // prob.shape[2]
+        labels = topk_indexes % prob.shape[2]
+
+        if not_to_xyxy:
+            boxes = out_bbox
+        else:
+            boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)
+
+        boxes = torch.gather(
+            boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
+
+        # and from relative [0, 1] to absolute [0, height] coordinates
+        img_h, img_w = target_sizes.unbind(1)
+        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
+        boxes = boxes * scale_fct[:, None, :]
+
+        results = [{'scores': s, 'labels': l, 'boxes': b}
+                   for s, l, b in zip(scores, labels, boxes)]
+
+        return results
+
+
+def main(args):
+    # config
+    cfg = SLConfig.fromfile(args.config_file)
+
+    # build model
+    model = load_model(args.config_file, args.checkpoint_path)
+    model = model.to(args.device)
+    model = model.eval()
+
+    # build dataloader
+    transform = T.Compose(
+        [
+            T.RandomResize([800], max_size=1333),
+            T.ToTensor(),
+            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]
+    )
+    dataset = CocoDetection(
+        args.image_dir, args.anno_path, transforms=transform)
+    data_loader = DataLoader(
+        dataset, batch_size=1, shuffle=False, num_workers=args.num_workers, collate_fn=collate_fn)
+
+    # build post processor
+    tokenlizer = get_tokenlizer.get_tokenlizer(cfg.text_encoder_type)
+    postprocessor = PostProcessCocoGrounding(
+        coco_api=dataset.coco, tokenlizer=tokenlizer)
+
+    # build evaluator
+    evaluator = CocoGroundingEvaluator(
+        dataset.coco, iou_types=("bbox",), useCats=True)
+
+    # build captions
+    category_dict = dataset.coco.dataset['categories']
+    cat_list = [item['name'] for item in category_dict]
+    caption = " . ".join(cat_list) + ' .'
+    print("Input text prompt:", caption)
+
+    # run inference
+    start = time.time()
+    for i, (images, targets) in enumerate(data_loader):
+        # get images and captions
+        images = images.tensors.to(args.device)
+        bs = images.shape[0]
+        input_captions = [caption] * bs
+
+        # feed to the model
+        outputs = model(images, captions=input_captions)
+
+        orig_target_sizes = torch.stack(
+            [t["orig_size"] for t in targets], dim=0).to(images.device)
+        results = postprocessor(outputs, orig_target_sizes)
+        cocogrounding_res = {
+            target["image_id"]: output for target, output in zip(targets, results)}
+        evaluator.update(cocogrounding_res)
+
+        if (i+1) % 30 == 0:
+            used_time = time.time() - start
+            eta = len(data_loader) / (i+1e-5) * used_time - used_time
+            print(
+                f"processed {i}/{len(data_loader)} images. time: {used_time:.2f}s, ETA: {eta:.2f}s")
+
+    evaluator.synchronize_between_processes()
+    evaluator.accumulate()
+    evaluator.summarize()
+
+    print("Final results:", evaluator.coco_eval["bbox"].stats.tolist())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        "Grounding DINO eval on COCO", add_help=True)
+    # load model
+    parser.add_argument("--config_file", "-c", type=str,
+                        required=True, help="path to config file")
+    parser.add_argument(
+        "--checkpoint_path", "-p", type=str, required=True, help="path to checkpoint file"
+    )
+    parser.add_argument("--device", type=str, default="cuda",
+                        help="running device (default: cuda)")
+
+    # post processing
+    parser.add_argument("--num_select", type=int, default=300,
+                        help="number of topk to select")
+
+    # coco info
+    parser.add_argument("--anno_path", type=str,
+                        required=True, help="coco root")
+    parser.add_argument("--image_dir", type=str,
+                        required=True, help="coco image dir")
+    parser.add_argument("--num_workers", type=int, default=4,
+                        help="number of workers for dataloader")
+    args = parser.parse_args()
+
+    main(args)

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

GroundingDINO/groundingdino.egg-info/PKG-INFO

@@ -0,0 +1,209 @@
+Metadata-Version: 2.1
+Name: groundingdino
+Version: 0.1.0
+Summary: open-set object detector
+Home-page: https://github.com/IDEA-Research/GroundingDINO
+Author: International Digital Economy Academy, Shilong Liu
+License:                                  Apache License
+                                   Version 2.0, January 2004
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+        
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+License-File: LICENSE

GroundingDINO/groundingdino.egg-info/SOURCES.txt

@@ -0,0 +1,42 @@
+LICENSE
+README.md
+setup.py
+groundingdino/__init__.py
+groundingdino/version.py
+groundingdino.egg-info/PKG-INFO
+groundingdino.egg-info/SOURCES.txt
+groundingdino.egg-info/dependency_links.txt
+groundingdino.egg-info/requires.txt
+groundingdino.egg-info/top_level.txt
+groundingdino/config/GroundingDINO_SwinB_cfg.py
+groundingdino/config/GroundingDINO_SwinT_OGC.py
+groundingdino/config/__init__.py
+groundingdino/datasets/__init__.py
+groundingdino/datasets/cocogrounding_eval.py
+groundingdino/datasets/transforms.py
+groundingdino/models/__init__.py
+groundingdino/models/registry.py
+groundingdino/models/GroundingDINO/__init__.py
+groundingdino/models/GroundingDINO/bertwarper.py
+groundingdino/models/GroundingDINO/fuse_modules.py
+groundingdino/models/GroundingDINO/groundingdino.py
+groundingdino/models/GroundingDINO/ms_deform_attn.py
+groundingdino/models/GroundingDINO/transformer.py
+groundingdino/models/GroundingDINO/transformer_vanilla.py
+groundingdino/models/GroundingDINO/utils.py
+groundingdino/models/GroundingDINO/backbone/__init__.py
+groundingdino/models/GroundingDINO/backbone/backbone.py
+groundingdino/models/GroundingDINO/backbone/position_encoding.py
+groundingdino/models/GroundingDINO/backbone/swin_transformer.py
+groundingdino/util/__init__.py
+groundingdino/util/box_ops.py
+groundingdino/util/get_tokenlizer.py
+groundingdino/util/inference.py
+groundingdino/util/logger.py
+groundingdino/util/misc.py
+groundingdino/util/slconfig.py
+groundingdino/util/slio.py
+groundingdino/util/time_counter.py
+groundingdino/util/utils.py
+groundingdino/util/visualizer.py
+groundingdino/util/vl_utils.py

GroundingDINO/groundingdino.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

GroundingDINO/groundingdino.egg-info/requires.txt

@@ -0,0 +1,10 @@
+torch
+torchvision
+transformers
+addict
+yapf
+timm
+numpy
+opencv-python
+supervision==0.6.0
+pycocotools

GroundingDINO/groundingdino.egg-info/top_level.txt

@@ -0,0 +1 @@
+groundingdino

GroundingDINO/groundingdino/config/GroundingDINO_SwinB_cfg.py

@@ -0,0 +1,43 @@
+batch_size = 1
+modelname = "groundingdino"
+backbone = "swin_B_384_22k"
+position_embedding = "sine"
+pe_temperatureH = 20
+pe_temperatureW = 20
+return_interm_indices = [1, 2, 3]
+backbone_freeze_keywords = None
+enc_layers = 6
+dec_layers = 6
+pre_norm = False
+dim_feedforward = 2048
+hidden_dim = 256
+dropout = 0.0
+nheads = 8
+num_queries = 900
+query_dim = 4
+num_patterns = 0
+num_feature_levels = 4
+enc_n_points = 4
+dec_n_points = 4
+two_stage_type = "standard"
+two_stage_bbox_embed_share = False
+two_stage_class_embed_share = False
+transformer_activation = "relu"
+dec_pred_bbox_embed_share = True
+dn_box_noise_scale = 1.0
+dn_label_noise_ratio = 0.5
+dn_label_coef = 1.0
+dn_bbox_coef = 1.0
+embed_init_tgt = True
+dn_labelbook_size = 2000
+max_text_len = 256
+text_encoder_type = "bert-base-uncased"
+use_text_enhancer = True
+use_fusion_layer = True
+use_checkpoint = True
+use_transformer_ckpt = True
+use_text_cross_attention = True
+text_dropout = 0.0
+fusion_dropout = 0.0
+fusion_droppath = 0.1
+sub_sentence_present = True

GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py

@@ -0,0 +1,43 @@
+batch_size = 1
+modelname = "groundingdino"
+backbone = "swin_T_224_1k"
+position_embedding = "sine"
+pe_temperatureH = 20
+pe_temperatureW = 20
+return_interm_indices = [1, 2, 3]
+backbone_freeze_keywords = None
+enc_layers = 6
+dec_layers = 6
+pre_norm = False
+dim_feedforward = 2048
+hidden_dim = 256
+dropout = 0.0
+nheads = 8
+num_queries = 900
+query_dim = 4
+num_patterns = 0
+num_feature_levels = 4
+enc_n_points = 4
+dec_n_points = 4
+two_stage_type = "standard"
+two_stage_bbox_embed_share = False
+two_stage_class_embed_share = False
+transformer_activation = "relu"
+dec_pred_bbox_embed_share = True
+dn_box_noise_scale = 1.0
+dn_label_noise_ratio = 0.5
+dn_label_coef = 1.0
+dn_bbox_coef = 1.0
+embed_init_tgt = True
+dn_labelbook_size = 2000
+max_text_len = 256
+text_encoder_type = "bert-base-uncased"
+use_text_enhancer = True
+use_fusion_layer = True
+use_checkpoint = True
+use_transformer_ckpt = True
+use_text_cross_attention = True
+text_dropout = 0.0
+fusion_dropout = 0.0
+fusion_droppath = 0.1
+sub_sentence_present = True

GroundingDINO/groundingdino/datasets/cocogrounding_eval.py

@@ -0,0 +1,269 @@
+# ------------------------------------------------------------------------
+# Grounding DINO. Midified by Shilong Liu.
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+COCO evaluator that works in distributed mode.
+
+Mostly copy-paste from https://github.com/pytorch/vision/blob/edfd5a7/references/detection/coco_eval.py
+The difference is that there is less copy-pasting from pycocotools
+in the end of the file, as python3 can suppress prints with contextlib
+"""
+import contextlib
+import copy
+import os
+
+import numpy as np
+import pycocotools.mask as mask_util
+import torch
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+from groundingdino.util.misc import all_gather
+
+
+class CocoGroundingEvaluator(object):
+    def __init__(self, coco_gt, iou_types, useCats=True):
+        assert isinstance(iou_types, (list, tuple))
+        coco_gt = copy.deepcopy(coco_gt)
+        self.coco_gt = coco_gt
+
+        self.iou_types = iou_types
+        self.coco_eval = {}
+        for iou_type in iou_types:
+            self.coco_eval[iou_type] = COCOeval(coco_gt, iouType=iou_type)
+            self.coco_eval[iou_type].useCats = useCats
+
+        self.img_ids = []
+        self.eval_imgs = {k: [] for k in iou_types}
+        self.useCats = useCats
+
+    def update(self, predictions):
+        img_ids = list(np.unique(list(predictions.keys())))
+        self.img_ids.extend(img_ids)
+
+        for iou_type in self.iou_types:
+            results = self.prepare(predictions, iou_type)
+
+            # suppress pycocotools prints
+            with open(os.devnull, "w") as devnull:
+                with contextlib.redirect_stdout(devnull):
+                    coco_dt = COCO.loadRes(self.coco_gt, results) if results else COCO()
+
+            coco_eval = self.coco_eval[iou_type]
+
+            coco_eval.cocoDt = coco_dt
+            coco_eval.params.imgIds = list(img_ids)
+            coco_eval.params.useCats = self.useCats
+            img_ids, eval_imgs = evaluate(coco_eval)
+
+            self.eval_imgs[iou_type].append(eval_imgs)
+
+    def synchronize_between_processes(self):
+        for iou_type in self.iou_types:
+            self.eval_imgs[iou_type] = np.concatenate(self.eval_imgs[iou_type], 2)
+            create_common_coco_eval(self.coco_eval[iou_type], self.img_ids, self.eval_imgs[iou_type])
+
+    def accumulate(self):
+        for coco_eval in self.coco_eval.values():
+            coco_eval.accumulate()
+
+    def summarize(self):
+        for iou_type, coco_eval in self.coco_eval.items():
+            print("IoU metric: {}".format(iou_type))
+            coco_eval.summarize()
+
+    def prepare(self, predictions, iou_type):
+        if iou_type == "bbox":
+            return self.prepare_for_coco_detection(predictions)
+        elif iou_type == "segm":
+            return self.prepare_for_coco_segmentation(predictions)
+        elif iou_type == "keypoints":
+            return self.prepare_for_coco_keypoint(predictions)
+        else:
+            raise ValueError("Unknown iou type {}".format(iou_type))
+
+    def prepare_for_coco_detection(self, predictions):
+        coco_results = []
+        for original_id, prediction in predictions.items():
+            if len(prediction) == 0:
+                continue
+
+            boxes = prediction["boxes"]
+            boxes = convert_to_xywh(boxes).tolist()
+            scores = prediction["scores"].tolist()
+            labels = prediction["labels"].tolist()
+
+            coco_results.extend(
+                [
+                    {
+                        "image_id": original_id,
+                        "category_id": labels[k],
+                        "bbox": box,
+                        "score": scores[k],
+                    }
+                    for k, box in enumerate(boxes)
+                ]
+            )
+        return coco_results
+
+    def prepare_for_coco_segmentation(self, predictions):
+        coco_results = []
+        for original_id, prediction in predictions.items():
+            if len(prediction) == 0:
+                continue
+
+            scores = prediction["scores"]
+            labels = prediction["labels"]
+            masks = prediction["masks"]
+
+            masks = masks > 0.5
+
+            scores = prediction["scores"].tolist()
+            labels = prediction["labels"].tolist()
+
+            rles = [
+                mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F"))[0] 
+                for mask in masks
+            ]
+            for rle in rles:
+                rle["counts"] = rle["counts"].decode("utf-8")
+
+            coco_results.extend(
+                [
+                    {
+                        "image_id": original_id,
+                        "category_id": labels[k],
+                        "segmentation": rle,
+                        "score": scores[k],
+                    }
+                    for k, rle in enumerate(rles)
+                ]
+            )
+        return coco_results
+
+    def prepare_for_coco_keypoint(self, predictions):
+        coco_results = []
+        for original_id, prediction in predictions.items():
+            if len(prediction) == 0:
+                continue
+
+            boxes = prediction["boxes"]
+            boxes = convert_to_xywh(boxes).tolist()
+            scores = prediction["scores"].tolist()
+            labels = prediction["labels"].tolist()
+            keypoints = prediction["keypoints"]
+            keypoints = keypoints.flatten(start_dim=1).tolist()
+
+            coco_results.extend(
+                [
+                    {
+                        "image_id": original_id,
+                        "category_id": labels[k],
+                        "keypoints": keypoint,
+                        "score": scores[k],
+                    }
+                    for k, keypoint in enumerate(keypoints)
+                ]
+            )
+        return coco_results
+
+
+def convert_to_xywh(boxes):
+    xmin, ymin, xmax, ymax = boxes.unbind(1)
+    return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1)
+
+
+def merge(img_ids, eval_imgs):
+    all_img_ids = all_gather(img_ids)
+    all_eval_imgs = all_gather(eval_imgs)
+
+    merged_img_ids = []
+    for p in all_img_ids:
+        merged_img_ids.extend(p)
+
+    merged_eval_imgs = []
+    for p in all_eval_imgs:
+        merged_eval_imgs.append(p)
+
+    merged_img_ids = np.array(merged_img_ids)
+    merged_eval_imgs = np.concatenate(merged_eval_imgs, 2)
+
+    # keep only unique (and in sorted order) images
+    merged_img_ids, idx = np.unique(merged_img_ids, return_index=True)
+    merged_eval_imgs = merged_eval_imgs[..., idx]
+
+    return merged_img_ids, merged_eval_imgs
+
+
+def create_common_coco_eval(coco_eval, img_ids, eval_imgs):
+    img_ids, eval_imgs = merge(img_ids, eval_imgs)
+    img_ids = list(img_ids)
+    eval_imgs = list(eval_imgs.flatten())
+
+    coco_eval.evalImgs = eval_imgs
+    coco_eval.params.imgIds = img_ids
+    coco_eval._paramsEval = copy.deepcopy(coco_eval.params)
+
+
+#################################################################
+# From pycocotools, just removed the prints and fixed
+# a Python3 bug about unicode not defined
+#################################################################
+
+
+def evaluate(self):
+    """
+    Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
+    :return: None
+    """
+    # tic = time.time()
+    # print('Running per image evaluation...')
+    p = self.params
+    # add backward compatibility if useSegm is specified in params
+    if p.useSegm is not None:
+        p.iouType = "segm" if p.useSegm == 1 else "bbox"
+        print("useSegm (deprecated) is not None. Running {} evaluation".format(p.iouType))
+    # print('Evaluate annotation type *{}*'.format(p.iouType))
+    p.imgIds = list(np.unique(p.imgIds))
+    if p.useCats:
+        p.catIds = list(np.unique(p.catIds))
+    p.maxDets = sorted(p.maxDets)
+    self.params = p
+
+    self._prepare()
+    # loop through images, area range, max detection number
+    catIds = p.catIds if p.useCats else [-1]
+
+    if p.iouType == "segm" or p.iouType == "bbox":
+        computeIoU = self.computeIoU
+    elif p.iouType == "keypoints":
+        computeIoU = self.computeOks
+    self.ious = {
+        (imgId, catId): computeIoU(imgId, catId) 
+        for imgId in p.imgIds 
+        for catId in catIds}
+
+    evaluateImg = self.evaluateImg
+    maxDet = p.maxDets[-1]
+    evalImgs = [
+        evaluateImg(imgId, catId, areaRng, maxDet) 
+        for catId in catIds 
+        for areaRng in p.areaRng 
+        for imgId in p.imgIds
+    ]
+    # this is NOT in the pycocotools code, but could be done outside
+    evalImgs = np.asarray(evalImgs).reshape(len(catIds), len(p.areaRng), len(p.imgIds))
+    self._paramsEval = copy.deepcopy(self.params)
+    # toc = time.time()
+    # print('DONE (t={:0.2f}s).'.format(toc-tic))
+    return p.imgIds, evalImgs
+
+
+#################################################################
+# end of straight copy from pycocotools, just removing the prints
+#################################################################

GroundingDINO/groundingdino/datasets/transforms.py

@@ -0,0 +1,311 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Transforms and data augmentation for both image + bbox.
+"""
+import os
+import random
+
+import PIL
+import torch
+import torchvision.transforms as T
+import torchvision.transforms.functional as F
+
+from groundingdino.util.box_ops import box_xyxy_to_cxcywh
+from groundingdino.util.misc import interpolate
+
+
+def crop(image, target, region):
+    cropped_image = F.crop(image, *region)
+
+    target = target.copy()
+    i, j, h, w = region
+
+    # should we do something wrt the original size?
+    target["size"] = torch.tensor([h, w])
+
+    fields = ["labels", "area", "iscrowd", "positive_map"]
+
+    if "boxes" in target:
+        boxes = target["boxes"]
+        max_size = torch.as_tensor([w, h], dtype=torch.float32)
+        cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
+        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
+        cropped_boxes = cropped_boxes.clamp(min=0)
+        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
+        target["boxes"] = cropped_boxes.reshape(-1, 4)
+        target["area"] = area
+        fields.append("boxes")
+
+    if "masks" in target:
+        # FIXME should we update the area here if there are no boxes?
+        target["masks"] = target["masks"][:, i : i + h, j : j + w]
+        fields.append("masks")
+
+    # remove elements for which the boxes or masks that have zero area
+    if "boxes" in target or "masks" in target:
+        # favor boxes selection when defining which elements to keep
+        # this is compatible with previous implementation
+        if "boxes" in target:
+            cropped_boxes = target["boxes"].reshape(-1, 2, 2)
+            keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
+        else:
+            keep = target["masks"].flatten(1).any(1)
+
+        for field in fields:
+            if field in target:
+                target[field] = target[field][keep]
+
+    if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO":
+        # for debug and visualization only.
+        if "strings_positive" in target:
+            target["strings_positive"] = [
+                _i for _i, _j in zip(target["strings_positive"], keep) if _j
+            ]
+
+    return cropped_image, target
+
+
+def hflip(image, target):
+    flipped_image = F.hflip(image)
+
+    w, h = image.size
+
+    target = target.copy()
+    if "boxes" in target:
+        boxes = target["boxes"]
+        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
+            [w, 0, w, 0]
+        )
+        target["boxes"] = boxes
+
+    if "masks" in target:
+        target["masks"] = target["masks"].flip(-1)
+
+    return flipped_image, target
+
+
+def resize(image, target, size, max_size=None):
+    # size can be min_size (scalar) or (w, h) tuple
+
+    def get_size_with_aspect_ratio(image_size, size, max_size=None):
+        w, h = image_size
+        if max_size is not None:
+            min_original_size = float(min((w, h)))
+            max_original_size = float(max((w, h)))
+            if max_original_size / min_original_size * size > max_size:
+                size = int(round(max_size * min_original_size / max_original_size))
+
+        if (w <= h and w == size) or (h <= w and h == size):
+            return (h, w)
+
+        if w < h:
+            ow = size
+            oh = int(size * h / w)
+        else:
+            oh = size
+            ow = int(size * w / h)
+
+        return (oh, ow)
+
+    def get_size(image_size, size, max_size=None):
+        if isinstance(size, (list, tuple)):
+            return size[::-1]
+        else:
+            return get_size_with_aspect_ratio(image_size, size, max_size)
+
+    size = get_size(image.size, size, max_size)
+    rescaled_image = F.resize(image, size)
+
+    if target is None:
+        return rescaled_image, None
+
+    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
+    ratio_width, ratio_height = ratios
+
+    target = target.copy()
+    if "boxes" in target:
+        boxes = target["boxes"]
+        scaled_boxes = boxes * torch.as_tensor(
+            [ratio_width, ratio_height, ratio_width, ratio_height]
+        )
+        target["boxes"] = scaled_boxes
+
+    if "area" in target:
+        area = target["area"]
+        scaled_area = area * (ratio_width * ratio_height)
+        target["area"] = scaled_area
+
+    h, w = size
+    target["size"] = torch.tensor([h, w])
+
+    if "masks" in target:
+        target["masks"] = (
+            interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
+        )
+
+    return rescaled_image, target
+
+
+def pad(image, target, padding):
+    # assumes that we only pad on the bottom right corners
+    padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
+    if target is None:
+        return padded_image, None
+    target = target.copy()
+    # should we do something wrt the original size?
+    target["size"] = torch.tensor(padded_image.size[::-1])
+    if "masks" in target:
+        target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
+    return padded_image, target
+
+
+class ResizeDebug(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        return resize(img, target, self.size)
+
+
+class RandomCrop(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        region = T.RandomCrop.get_params(img, self.size)
+        return crop(img, target, region)
+
+
+class RandomSizeCrop(object):
+    def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
+        # respect_boxes:    True to keep all boxes
+        #                   False to tolerence box filter
+        self.min_size = min_size
+        self.max_size = max_size
+        self.respect_boxes = respect_boxes
+
+    def __call__(self, img: PIL.Image.Image, target: dict):
+        init_boxes = len(target["boxes"])
+        max_patience = 10
+        for i in range(max_patience):
+            w = random.randint(self.min_size, min(img.width, self.max_size))
+            h = random.randint(self.min_size, min(img.height, self.max_size))
+            region = T.RandomCrop.get_params(img, [h, w])
+            result_img, result_target = crop(img, target, region)
+            if (
+                not self.respect_boxes
+                or len(result_target["boxes"]) == init_boxes
+                or i == max_patience - 1
+            ):
+                return result_img, result_target
+        return result_img, result_target
+
+
+class CenterCrop(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        image_width, image_height = img.size
+        crop_height, crop_width = self.size
+        crop_top = int(round((image_height - crop_height) / 2.0))
+        crop_left = int(round((image_width - crop_width) / 2.0))
+        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
+
+
+class RandomHorizontalFlip(object):
+    def __init__(self, p=0.5):
+        self.p = p
+
+    def __call__(self, img, target):
+        if random.random() < self.p:
+            return hflip(img, target)
+        return img, target
+
+
+class RandomResize(object):
+    def __init__(self, sizes, max_size=None):
+        assert isinstance(sizes, (list, tuple))
+        self.sizes = sizes
+        self.max_size = max_size
+
+    def __call__(self, img, target=None):
+        size = random.choice(self.sizes)
+        return resize(img, target, size, self.max_size)
+
+
+class RandomPad(object):
+    def __init__(self, max_pad):
+        self.max_pad = max_pad
+
+    def __call__(self, img, target):
+        pad_x = random.randint(0, self.max_pad)
+        pad_y = random.randint(0, self.max_pad)
+        return pad(img, target, (pad_x, pad_y))
+
+
+class RandomSelect(object):
+    """
+    Randomly selects between transforms1 and transforms2,
+    with probability p for transforms1 and (1 - p) for transforms2
+    """
+
+    def __init__(self, transforms1, transforms2, p=0.5):
+        self.transforms1 = transforms1
+        self.transforms2 = transforms2
+        self.p = p
+
+    def __call__(self, img, target):
+        if random.random() < self.p:
+            return self.transforms1(img, target)
+        return self.transforms2(img, target)
+
+
+class ToTensor(object):
+    def __call__(self, img, target):
+        return F.to_tensor(img), target
+
+
+class RandomErasing(object):
+    def __init__(self, *args, **kwargs):
+        self.eraser = T.RandomErasing(*args, **kwargs)
+
+    def __call__(self, img, target):
+        return self.eraser(img), target
+
+
+class Normalize(object):
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, image, target=None):
+        image = F.normalize(image, mean=self.mean, std=self.std)
+        if target is None:
+            return image, None
+        target = target.copy()
+        h, w = image.shape[-2:]
+        if "boxes" in target:
+            boxes = target["boxes"]
+            boxes = box_xyxy_to_cxcywh(boxes)
+            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
+            target["boxes"] = boxes
+        return image, target
+
+
+class Compose(object):
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, image, target):
+        for t in self.transforms:
+            image, target = t(image, target)
+        return image, target
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + "("
+        for t in self.transforms:
+            format_string += "\n"
+            format_string += "    {0}".format(t)
+        format_string += "\n)"
+        return format_string

GroundingDINO/groundingdino/models/GroundingDINO/__init__.py

@@ -0,0 +1,15 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+from .groundingdino import build_groundingdino