diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml deleted file mode 100644 index e5c7119..0000000 --- a/.github/workflows/publish.yml +++ /dev/null @@ -1,52 +0,0 @@ -name: Publish to PyPI - -on: - release: - types: [published] - -jobs: - build: - name: Build distribution - runs-on: ubuntu-latest - if: startsWith(github.event.release.tag_name, 'py.') - - steps: - - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - - name: Install build tools - run: pip install build - - - name: Build package - run: python -m build - - - name: Upload build artifacts - uses: actions/upload-artifact@v4 - with: - name: dist - path: dist/ - - publish: - name: Publish to PyPI - needs: build - runs-on: ubuntu-latest - environment: - name: pypi - url: https://pypi.org/project/garmentiq/ - - permissions: - id-token: write # required for trusted publishing (no API token needed) - - steps: - - name: Download build artifacts - uses: actions/download-artifact@v4 - with: - name: dist - path: dist/ - - - name: Publish to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..e52bb61 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,34 @@ +# Use official Python base image +FROM python:3.11.12 + +# Set working directory +WORKDIR /app + +# Install system dependencies +RUN apt-get update && apt-get install -y \ + git \ + wget \ + libgl1 \ + libglib2.0-0 \ + curl \ + && rm -rf /var/lib/apt/lists/* + +# Copy requirements and install Python dependencies +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +# Install JupyterLab and ipywidgets to suppress tqdm warnings +RUN pip install --no-cache-dir jupyterlab==4.4.1 ipywidgets + +# Suppress matplotlib.font_manager INFO logs using a hidden startup script +RUN mkdir -p /etc/python_startup && \ + echo "import logging; logging.basicConfig(level=logging.WARNING)" > /etc/python_startup/startup.py + +# Run the startup script for all Python sessions +ENV PYTHONSTARTUP=/etc/python_startup/startup.py + +# Expose JupyterLab port +EXPOSE 8888 + +# Set default command to start JupyterLab +CMD ["jupyter", "lab", "--ip=0.0.0.0", "--port=8888", "--allow-root", "--NotebookApp.token=''"] diff --git a/LICENSE b/LICENSE index c35f900..a612ad9 100644 --- a/LICENSE +++ b/LICENSE @@ -1,21 +1,373 @@ -MIT License - -Copyright (c) 2026 Li Yuan, Xinrui Zhai, and Fangzhou Ma - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. +Mozilla Public License Version 2.0 +================================== + +1. Definitions +-------------- + +1.1. "Contributor" + means each individual or legal entity that creates, contributes to + the creation of, or owns Covered Software. + +1.2. "Contributor Version" + means the combination of the Contributions of others (if any) used + by a Contributor and that particular Contributor's Contribution. + +1.3. "Contribution" + means Covered Software of a particular Contributor. + +1.4. "Covered Software" + means Source Code Form to which the initial Contributor has attached + the notice in Exhibit A, the Executable Form of such Source Code + Form, and Modifications of such Source Code Form, in each case + including portions thereof. + +1.5. "Incompatible With Secondary Licenses" + means + + (a) that the initial Contributor has attached the notice described + in Exhibit B to the Covered Software; or + + (b) that the Covered Software was made available under the terms of + version 1.1 or earlier of the License, but not also under the + terms of a Secondary License. + +1.6. "Executable Form" + means any form of the work other than Source Code Form. + +1.7. "Larger Work" + means a work that combines Covered Software with other material, in + a separate file or files, that is not Covered Software. + +1.8. "License" + means this document. + +1.9. "Licensable" + means having the right to grant, to the maximum extent possible, + whether at the time of the initial grant or subsequently, any and + all of the rights conveyed by this License. + +1.10. "Modifications" + means any of the following: + + (a) any file in Source Code Form that results from an addition to, + deletion from, or modification of the contents of Covered + Software; or + + (b) any new file in Source Code Form that contains any Covered + Software. + +1.11. "Patent Claims" of a Contributor + means any patent claim(s), including without limitation, method, + process, and apparatus claims, in any patent Licensable by such + Contributor that would be infringed, but for the grant of the + License, by the making, using, selling, offering for sale, having + made, import, or transfer of either its Contributions or its + Contributor Version. + +1.12. "Secondary License" + means either the GNU General Public License, Version 2.0, the GNU + Lesser General Public License, Version 2.1, the GNU Affero General + Public License, Version 3.0, or any later versions of those + licenses. + +1.13. "Source Code Form" + means the form of the work preferred for making modifications. + +1.14. "You" (or "Your") + means an individual or a legal entity exercising rights under this + License. For legal entities, "You" includes any entity that + controls, is controlled by, or is under common control with You. For + purposes of this definition, "control" means (a) the power, direct + or indirect, to cause the direction or management of such entity, + whether by contract or otherwise, or (b) ownership of more than + fifty percent (50%) of the outstanding shares or beneficial + ownership of such entity. + +2. License Grants and Conditions +-------------------------------- + +2.1. Grants + +Each Contributor hereby grants You a world-wide, royalty-free, +non-exclusive license: + +(a) under intellectual property rights (other than patent or trademark) + Licensable by such Contributor to use, reproduce, make available, + modify, display, perform, distribute, and otherwise exploit its + Contributions, either on an unmodified basis, with Modifications, or + as part of a Larger Work; and + +(b) under Patent Claims of such Contributor to make, use, sell, offer + for sale, have made, import, and otherwise transfer either its + Contributions or its Contributor Version. + +2.2. Effective Date + +The licenses granted in Section 2.1 with respect to any Contribution +become effective for each Contribution on the date the Contributor first +distributes such Contribution. + +2.3. Limitations on Grant Scope + +The licenses granted in this Section 2 are the only rights granted under +this License. No additional rights or licenses will be implied from the +distribution or licensing of Covered Software under this License. +Notwithstanding Section 2.1(b) above, no patent license is granted by a +Contributor: + +(a) for any code that a Contributor has removed from Covered Software; + or + +(b) for infringements caused by: (i) Your and any other third party's + modifications of Covered Software, or (ii) the combination of its + Contributions with other software (except as part of its Contributor + Version); or + +(c) under Patent Claims infringed by Covered Software in the absence of + its Contributions. + +This License does not grant any rights in the trademarks, service marks, +or logos of any Contributor (except as may be necessary to comply with +the notice requirements in Section 3.4). + +2.4. Subsequent Licenses + +No Contributor makes additional grants as a result of Your choice to +distribute the Covered Software under a subsequent version of this +License (see Section 10.2) or under the terms of a Secondary License (if +permitted under the terms of Section 3.3). + +2.5. Representation + +Each Contributor represents that the Contributor believes its +Contributions are its original creation(s) or it has sufficient rights +to grant the rights to its Contributions conveyed by this License. + +2.6. Fair Use + +This License is not intended to limit any rights You have under +applicable copyright doctrines of fair use, fair dealing, or other +equivalents. + +2.7. Conditions + +Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted +in Section 2.1. + +3. Responsibilities +------------------- + +3.1. Distribution of Source Form + +All distribution of Covered Software in Source Code Form, including any +Modifications that You create or to which You contribute, must be under +the terms of this License. You must inform recipients that the Source +Code Form of the Covered Software is governed by the terms of this +License, and how they can obtain a copy of this License. You may not +attempt to alter or restrict the recipients' rights in the Source Code +Form. + +3.2. Distribution of Executable Form + +If You distribute Covered Software in Executable Form then: + +(a) such Covered Software must also be made available in Source Code + Form, as described in Section 3.1, and You must inform recipients of + the Executable Form how they can obtain a copy of such Source Code + Form by reasonable means in a timely manner, at a charge no more + than the cost of distribution to the recipient; and + +(b) You may distribute such Executable Form under the terms of this + License, or sublicense it under different terms, provided that the + license for the Executable Form does not attempt to limit or alter + the recipients' rights in the Source Code Form under this License. + +3.3. Distribution of a Larger Work + +You may create and distribute a Larger Work under terms of Your choice, +provided that You also comply with the requirements of this License for +the Covered Software. If the Larger Work is a combination of Covered +Software with a work governed by one or more Secondary Licenses, and the +Covered Software is not Incompatible With Secondary Licenses, this +License permits You to additionally distribute such Covered Software +under the terms of such Secondary License(s), so that the recipient of +the Larger Work may, at their option, further distribute the Covered +Software under the terms of either this License or such Secondary +License(s). + +3.4. Notices + +You may not remove or alter the substance of any license notices +(including copyright notices, patent notices, disclaimers of warranty, +or limitations of liability) contained within the Source Code Form of +the Covered Software, except that You may alter any license notices to +the extent required to remedy known factual inaccuracies. + +3.5. Application of Additional Terms + +You may choose to offer, and to charge a fee for, warranty, support, +indemnity or liability obligations to one or more recipients of Covered +Software. However, You may do so only on Your own behalf, and not on +behalf of any Contributor. You must make it absolutely clear that any +such warranty, support, indemnity, or liability obligation is offered by +You alone, and You hereby agree to indemnify every Contributor for any +liability incurred by such Contributor as a result of warranty, support, +indemnity or liability terms You offer. You may include additional +disclaimers of warranty and limitations of liability specific to any +jurisdiction. + +4. Inability to Comply Due to Statute or Regulation +--------------------------------------------------- + +If it is impossible for You to comply with any of the terms of this +License with respect to some or all of the Covered Software due to +statute, judicial order, or regulation then You must: (a) comply with +the terms of this License to the maximum extent possible; and (b) +describe the limitations and the code they affect. Such description must +be placed in a text file included with all distributions of the Covered +Software under this License. Except to the extent prohibited by statute +or regulation, such description must be sufficiently detailed for a +recipient of ordinary skill to be able to understand it. + +5. Termination +-------------- + +5.1. The rights granted under this License will terminate automatically +if You fail to comply with any of its terms. However, if You become +compliant, then the rights granted under this License from a particular +Contributor are reinstated (a) provisionally, unless and until such +Contributor explicitly and finally terminates Your grants, and (b) on an +ongoing basis, if such Contributor fails to notify You of the +non-compliance by some reasonable means prior to 60 days after You have +come back into compliance. Moreover, Your grants from a particular +Contributor are reinstated on an ongoing basis if such Contributor +notifies You of the non-compliance by some reasonable means, this is the +first time You have received notice of non-compliance with this License +from such Contributor, and You become compliant prior to 30 days after +Your receipt of the notice. + +5.2. If You initiate litigation against any entity by asserting a patent +infringement claim (excluding declaratory judgment actions, +counter-claims, and cross-claims) alleging that a Contributor Version +directly or indirectly infringes any patent, then the rights granted to +You by any and all Contributors for the Covered Software under Section +2.1 of this License shall terminate. + +5.3. In the event of termination under Sections 5.1 or 5.2 above, all +end user license agreements (excluding distributors and resellers) which +have been validly granted by You or Your distributors under this License +prior to termination shall survive termination. + +************************************************************************ +* * +* 6. Disclaimer of Warranty * +* ------------------------- * +* * +* Covered Software is provided under this License on an "as is" * +* basis, without warranty of any kind, either expressed, implied, or * +* statutory, including, without limitation, warranties that the * +* Covered Software is free of defects, merchantable, fit for a * +* particular purpose or non-infringing. The entire risk as to the * +* quality and performance of the Covered Software is with You. * +* Should any Covered Software prove defective in any respect, You * +* (not any Contributor) assume the cost of any necessary servicing, * +* repair, or correction. This disclaimer of warranty constitutes an * +* essential part of this License. No use of any Covered Software is * +* authorized under this License except under this disclaimer. * +* * +************************************************************************ + +************************************************************************ +* * +* 7. Limitation of Liability * +* -------------------------- * +* * +* Under no circumstances and under no legal theory, whether tort * +* (including negligence), contract, or otherwise, shall any * +* Contributor, or anyone who distributes Covered Software as * +* permitted above, be liable to You for any direct, indirect, * +* special, incidental, or consequential damages of any character * +* including, without limitation, damages for lost profits, loss of * +* goodwill, work stoppage, computer failure or malfunction, or any * +* and all other commercial damages or losses, even if such party * +* shall have been informed of the possibility of such damages. This * +* limitation of liability shall not apply to liability for death or * +* personal injury resulting from such party's negligence to the * +* extent applicable law prohibits such limitation. Some * +* jurisdictions do not allow the exclusion or limitation of * +* incidental or consequential damages, so this exclusion and * +* limitation may not apply to You. * +* * +************************************************************************ + +8. Litigation +------------- + +Any litigation relating to this License may be brought only in the +courts of a jurisdiction where the defendant maintains its principal +place of business and such litigation shall be governed by laws of that +jurisdiction, without reference to its conflict-of-law provisions. +Nothing in this Section shall prevent a party's ability to bring +cross-claims or counter-claims. + +9. Miscellaneous +---------------- + +This License represents the complete agreement concerning the subject +matter hereof. If any provision of this License is held to be +unenforceable, such provision shall be reformed only to the extent +necessary to make it enforceable. Any law or regulation which provides +that the language of a contract shall be construed against the drafter +shall not be used to construe this License against a Contributor. + +10. Versions of the License +--------------------------- + +10.1. New Versions + +Mozilla Foundation is the license steward. Except as provided in Section +10.3, no one other than the license steward has the right to modify or +publish new versions of this License. Each version will be given a +distinguishing version number. + +10.2. Effect of New Versions + +You may distribute the Covered Software under the terms of the version +of the License under which You originally received the Covered Software, +or under the terms of any subsequent version published by the license +steward. + +10.3. Modified Versions + +If you create software not governed by this License, and you want to +create a new license for such software, you may create and use a +modified version of this License if you rename the license and remove +any references to the name of the license steward (except to note that +such modified license differs from this License). + +10.4. Distributing Source Code Form that is Incompatible With Secondary +Licenses + +If You choose to distribute Source Code Form that is Incompatible With +Secondary Licenses under the terms of this version of the License, the +notice described in Exhibit B of this License must be attached. + +Exhibit A - Source Code Form License Notice +------------------------------------------- + + This Source Code Form is subject to the terms of the Mozilla Public + License, v. 2.0. If a copy of the MPL was not distributed with this + file, You can obtain one at http://mozilla.org/MPL/2.0/. + +If it is not possible or desirable to put the notice in a particular +file, then You may include the notice in a location (such as a LICENSE +file in a relevant directory) where a recipient would be likely to look +for such a notice. + +You may add additional accurate notices of copyright ownership. + +Exhibit B - "Incompatible With Secondary Licenses" Notice +--------------------------------------------------------- + + This Source Code Form is "Incompatible With Secondary Licenses", as + defined by the Mozilla Public License, v. 2.0. diff --git a/README.md b/README.md index 50cc504..66a81fc 100644 --- a/README.md +++ b/README.md @@ -1,169 +1,134 @@ -# GarmentIQ: Automated Garment Measurement for Fashion Retail +# GarmentIQ MagicBox (Version 1.8) -[🌐 Official website](https://garmentiq.ly.gd.edu.kg/) | [📖 Documentation](https://garmentiq.ly.gd.edu.kg/documentation/) | [⚙️ Web pipeline](https://garmentiq.ly.gd.edu.kg/application/) | [🪄 MagicBox](https://garmentiq.ly.gd.edu.kg/documentation/magicbox/) | [📄 Paper](https://archive.gd.edu.kg/abs/20250525121523/) +*Last update: 09/12/2025* -**Free & Open Source. Precise and flexible garment measurements from images - no tape measures, no delays, just fashion - forward automation.** +*Navigation*: -> 🎉 **Update (09/11/2026):** GarmentIQ now supports **SAM 1, SAM 2, and SAM 3**, plus **natural-language (text) prompts** for [segmentation](#tutorial-notebooks), a new **[matting](#tutorial-notebooks) module** (ViTMatte and Matting Anything) for soft alpha edges, and an explicit `device` argument (`cpu`, `cuda`, `mps`) on every model function! Cheers! 🥂 - -GarmentIQ +1. [How to install GarmentIQ MagicBox?](#how-to-install-garmentiq-magicbox) +2. [How to stop / uninstall GarmentIQ MagicBox?](#how-to-stop--uninstall-garmentiq-magicbox) +3. [How to run code in GarmentIQ MagicBox?](#how-to-run-code-in-garmentiq-magicbox) --- -**Content**: - -1. [What Are the Key Features of GarmentIQ?](#what-are-the-key-features-of-garmentiq) -2. [Overview of GarmentIQ Python Package](#overview-of-garmentiq-python-package) -3. [Tutorials](#tutorials) - - [Installation](#installation) - - [Tutorial notebooks](#tutorial-notebooks) -4. [Advanced Usage](#advanced-usage) -5. [Trained Models for Classification](#trained-models-for-classification) -6. [Issues & Feedback](#issues--feedback) -7. [License](#license) -8. [Acknowledgements](#acknowledgements) +![image](https://github.com/user-attachments/assets/bd40b7b6-941c-429f-a160-0dd0eae0d295) ---- +GarmentIQ MagicBox is a streamlined, Docker-based JupyterLab environment designed to simplify the use of the GarmentIQ Python API. By packaging the development tools and dependencies into a Docker container, MagicBox ensures a consistent and reproducible environment for all users—eliminating the need for complex local setup or manual dependency management. Whether you're analyzing garment data, prototyping models, or running production-grade scripts, MagicBox offers a plug-and-play solution that gets you up and running quickly and reliably. -## What Are the Key Features of GarmentIQ? +## How to install GarmentIQ MagicBox? -GarmentIQ uses computer vision and models like DeiT, BiRefNet, SAM, and HRNet to classify garments, remove backgrounds, and detect key features with precision. It turns expert know-how into an intuitive measurement system - no intensive coding required. Fully modular and customizable, it adapts to your workflows while delivering fast, accurate results out of the box. +Before installing GarmentIQ MagicBox, ensure that Docker is installed and running on your system. For optimal performance, it is recommended to have at least 16 GB of RAM, an NVIDIA GPU with CUDA support and a minimum of 4 GB of GPU memory, and at least 20 GB space available on disk. -| Feature | Web Demo | Video guide | -|---------|----------|----------| -| **Tailor (the whole pipeline)** | [Try web demo](https://garmentiq.ly.gd.edu.kg/application/) | [Watch video guide](https://garmentiq.ly.gd.edu.kg/application/guide.mp4) | -| **Garment measurement instruction generation** | [Try web demo](https://garmentiq.ly.gd.edu.kg/application/demo/instruction-generation/) | [Watch video guide](https://garmentiq.ly.gd.edu.kg/application/demo/instruction-generation/guide.mp4) | -| **Garment image classification** | [Try web demo](https://garmentiq.ly.gd.edu.kg/application/demo/image-classification/) | [Watch video guide](https://garmentiq.ly.gd.edu.kg/application/demo/image-classification/guide.mp4) | -| **Garment image segmentation** | [Try web demo](https://garmentiq.ly.gd.edu.kg/application/demo/image-segmentation/) | [Watch video guide](https://garmentiq.ly.gd.edu.kg/application/demo/image-segmentation/guide.mp4) | -| **Garment landmark detection** | [Try web demo](https://garmentiq.ly.gd.edu.kg/application/demo/landmark-detection/) | [Watch video guide](https://garmentiq.ly.gd.edu.kg/application/demo/landmark-detection/guide.mp4) | -| **Garment landmark adjustment** | [Try web demo](https://garmentiq.ly.gd.edu.kg/application/demo/landmark-adjustment/) | [Watch video guide](https://garmentiq.ly.gd.edu.kg/application/demo/landmark-adjustment/guide.mp4) | +### Step 1: Download GarmentIQ MagicBox configuration files -## Overview of GarmentIQ Python Package +Choosing a preferred directory. -The `garmentiq` package provides an automated solution for garment measurement from images, utilizing computer vision techniques for classification, segmentation, and landmark extraction. +- For Windows, run the following command in Windows command prompt. -- `tailor`: This module acts as the central agent for the entire pipeline, orchestrating the different stages of garment measurement from classification to landmark derivation. It integrates the functionalities of other modules to provide a smooth end-to-end process. + ```bash + powershell -Command "Invoke-WebRequest -Uri 'https://github.com/lygitdata/GarmentIQ/archive/refs/heads/magicbox.zip' -OutFile 'magicbox.zip'; Expand-Archive -Path 'magicbox.zip' -DestinationPath .; Rename-Item 'GarmentIQ-magicbox' 'garmentiq_magicbox'; Remove-Item 'magicbox.zip'" + ``` -- `classification`: This module is responsible for identifying the type of garment in an image. Its key functions include: `fine_tune_pytorch_nn`, `load_data`, `load_model`, `predict`, `test_pytorch_nn`, `train_pytorch_nn`, and `train_test_split` +- For Linux / MacOS, run the following command in terminal. Make sure you have already installed `curl` and `unzip`. -- `segmentation`: This module focuses on isolating garment features from the background for improved measurement accuracy. Its key functions include: `change_background_color`, `extract`, `load_model`, and `process_and_save_images`. + ```bash + curl -L -o magicbox.zip https://github.com/lygitdata/GarmentIQ/archive/refs/heads/magicbox.zip && unzip magicbox.zip && mv GarmentIQ-magicbox garmentiq_magicbox && rm magicbox.zip + ``` -- `landmark`: This module handles the detection, derivation, and refinement of key points on garments. Its key functions include: `derive`, `detect`, and `refine`. +### Step 2: Switch the directory -- `matting`: This module refines a hard segmentation mask into a soft alpha matte, so edges and semi-transparent detail composite naturally. Its key functions include: `generate_trimap`, `load_model`, `matte`, and `composite`. +Run the following command. -- `grounding`: This module turns a natural-language phrase into bounding boxes, which is what gives SAM 1 and SAM 2 text-prompted segmentation. Its key functions include: `load_grounding_model`, `load_grounding_processor`, and `ground_text_to_boxes`. +```bash +cd garmentiq_magicbox +``` -### Common conventions +### Step 3: Build the Docker image -Every model-backed module follows the same two-step shape, so moving between them requires no -relearning: +Run the following command to build the Docker image. Make sure you have already installed Docker. This process is time consuming. Make sure your have a stable internet connection. -```python -model = giq..load_model(...) # 1. load weights onto a device -result = giq..(model=model, image_path=..., device=...) # 2. run on an image +```bash +docker build --no-cache -t garmentiq_magicbox . ``` -where `` is `predict` for classification, `extract` for segmentation, `matte` for matting, -and `detect` for landmarks. Across all of them: +### Step 4: Run the Docker container -- **`device` is opt-in and identical everywhere.** Every `load_model` and every inference - function takes `device`, defaulting to `"cpu"`. Pass `"cuda"` or `"mps"` explicitly to use an - accelerator; nothing is auto-detected. -- **Accelerator memory is released automatically** after each inference call. -- **Configurations are bundled.** BiRefNet, every SAM variant and ViTMatte load their configs - and processors from inside the package, so only weights need downloading. -- **Arguments are keyword-style.** The examples throughout this README pass every argument by - name, which is the supported way to call these functions. -- **A mismatched checkpoint warns.** Weights are loaded leniently so partial checkpoints keep - working, but if a checkpoint does not match the model class GarmentIQ raises a - `RuntimeWarning` instead of silently returning a randomly initialised model. +- For Windows, run one of the following commands in Windows command prompt. -- Instruction Schemas: The `instruction/` folder contains 9 predefined measurement schemas in `.json` format, which are utilized by the `garment_classes.py` file `garment_classes` dictionary to define different garment types and their predefined measurement properties. Users can also define their own custom measurement instructions by creating new dictionaries formatted similarly to the existing garment classes. + - (Recommended) Run with GPU in addition to CPU. -## Tutorials + ```bash + docker run -d --name magicbox_container -p 8888:8888 -p 5000:5000 -p 5001:5001 -p 5002:5002 --gpus all -v "%cd%\working:/app/working" garmentiq_magicbox + ``` -Each tutorial is a self-contained Colab notebook covering one part of the `garmentiq` Python API, from a single module up to the whole pipeline. Open one to read the explanation and run the code. For the full API reference, see our [documentation](https://garmentiq.ly.gd.edu.kg/documentation/). + - Run with CPU only. -GarmentIQ Example + ```bash + docker run -d --name magicbox_container -p 8888:8888 -p 5000:5000 -p 5001:5001 -p 5002:5002 -v "%cd%\working:/app/working" garmentiq_magicbox + ``` -> ⚠️ Note: If you encounter errors while running these notebooks in Colab, they are likely due to Python package version conflicts specific to the Colab environment. We recommend installing [MagicBox](https://garmentiq.ly.gd.edu.kg/documentation/magicbox/) on your local machine, where you can find and run these examples under `working/examples/`. +- For Linux / MacOS, run the following command in terminal. -### Installation + - (Recommended) Run with GPU in addition to CPU. -Please install from PyPI using the following command. + ```bash + docker run -d --name magicbox_container -p 8888:8888 -p 5000:5000 -p 5001:5001 -p 5002:5002 --gpus all -v "$(pwd)/working:/app/working" garmentiq_magicbox + ``` -```bash -pip install garmentiq -q -``` - -### Tutorial notebooks - -| Tutorial | What it covers | Open in Colab | -|---|---|---| -| **Tailor (the whole pipeline)** | Run every stage end to end on a folder of images, then read the masks, annotated images, and measurements it writes out. | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lygitdata/GarmentIQ/blob/main/test/tutorial_tailor.ipynb) | -| **Classification** | Identify the garment type, which decides the landmarks and measurement instructions used by every later stage. | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lygitdata/GarmentIQ/blob/main/test/tutorial_classification.ipynb) | -| **Segmentation** | Separate the garment from its background with BiRefNet or SAM 1, 2, and 3, using point, box, label, and text prompts. | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lygitdata/GarmentIQ/blob/main/test/tutorial_segmentation.ipynb) | -| **Grounding** | Turn a natural-language phrase into bounding boxes, which is what gives SAM 1 and SAM 2 text-prompted segmentation. | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lygitdata/GarmentIQ/blob/main/test/tutorial_grounding.ipynb) | -| **Matting** | Refine a hard mask into a soft alpha matte with ViTMatte or Matting Anything, so edges composite naturally. | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lygitdata/GarmentIQ/blob/main/test/tutorial_matting.ipynb) | -| **Landmark detection** | Locate the key points a measurement runs between, such as shoulders, sleeve ends, and hems. | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lygitdata/GarmentIQ/blob/main/test/tutorial_landmark_detection.ipynb) | -| **Landmark refinement and derivation** | Snap detected landmarks onto the true garment edge, and compute extra points the model does not predict at all. | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lygitdata/GarmentIQ/blob/main/test/tutorial_landmark_refinement_and_derivation.ipynb) | + - Run with CPU only. -## Advanced Usage + ```bash + docker run -d --name magicbox_container -p 8888:8888 -p 5000:5000 -p 5001:5001 -p 5002:5002 -v "$(pwd)/working:/app/working" garmentiq_magicbox + ``` -These notebooks go beyond everyday use: defining your own measurements, and training or fine-tuning the classification model on your own data. +### Step 5: Start GarmentIQ MagicBox -> ⚠️ Note: If you encounter errors while running these notebooks in Colab, they are likely due to Python package version conflicts specific to the Colab environment. We recommend installing [MagicBox](https://garmentiq.ly.gd.edu.kg/documentation/magicbox/) on your local machine, where you can find and run these examples under `working/examples/`. +Open your browser, type http://127.0.0.1:8888 to access the Jupyter Lab interface of GarmentIQ MagicBox. -| Notebook | What it covers | Open in Colab | -|---|---|---| -| **Custom measurement instruction** | Define your own landmarks and measurements, then register them in a garment class dictionary. | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lygitdata/GarmentIQ/blob/main/test/adv_usage_custom_measurement_instruction.ipynb) | -| **Classification model training & evaluation** | Train the built-in CNN3 or a model you wrote yourself from scratch, then compare them on a held-out test set. | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lygitdata/GarmentIQ/blob/main/test/adv_usage_classification_model_training_evaluation.ipynb) | -| **Classification model fine-tuning** | Adapt the pretrained tinyViT classifier to your own catalog by freezing the backbone and retraining the head. | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lygitdata/GarmentIQ/blob/main/test/adv_usage_classification_model_fine_tuning.ipynb) | +## How to stop / uninstall GarmentIQ MagicBox? -## Trained Models for Classification +### Stop and Remove the Docker Container -We release the following models trained as part of this project. Models having `_inditex_finetuned` in their names means that they were finetuned on a small set of garment data from Inditex - Zara. +- To stop the Docker container, run the following command. -| Model | Test Accuracy | Test F1 Score | Fine-tune Accuracy | Fine-tune F1 Score | Link | -|---------|----------|----------|----------|----------|----------| -| `cnn_3.pt` | 0.9458 | 0.9459 | / | / | [See the model](https://huggingface.co/lygitdata/garmentiq/blob/main/cnn_3.pt) | -| `cnn_4.pt` | 0.9533 | 0.9533 | / | / | [See the model](https://huggingface.co/lygitdata/garmentiq/blob/main/cnn_4.pt) | -| `tiny_vit.pt` | 0.9576 | 0.9576 | / | / | [See the model](https://huggingface.co/lygitdata/garmentiq/blob/main/tiny_vit.pt) | -| `cnn_3_inditex_finetuned.pt` | 0.9074 | 0.9068 | 0.9197 | 0.9216 | [See the model](https://huggingface.co/lygitdata/garmentiq/blob/main/cnn_3_inditex_finetuned.pt) | -| `cnn_4_inditex_finetuned.pt` | 0.9132 | 0.9137 | 0.9592 | 0.9585 | [See the model](https://huggingface.co/lygitdata/garmentiq/blob/main/cnn_4_inditex_finetuned.pt) | -| `tiny_vit_inditex_finetuned.pt` | 0.9484 | 0.9483 | 0.9916 | 0.9917 | [See the model](https://huggingface.co/lygitdata/garmentiq/blob/main/tiny_vit_inditex_finetuned.pt) | + ```bash + docker stop magicbox_container + ``` -## Issues & Feedback +- To remove the Docker container, run the following command. + + ```bash + docker rm magicbox_container + ``` -Found a bug or have a feature request? Please open an issue on our [GitHub Issues page](https://github.com/lygitdata/GarmentIQ/issues). +### Uninstall the Docker Image -## License +To remove the Docker image, run the following command. -GarmentIQ's Python API code is licensed under the MIT License. +```bash +docker rmi garmentiq_magicbox +``` -## Acknowledgements +## How to Run Code in GarmentIQ MagicBox -We sincerely thank Adrian Gonzalez-Sieira and Laura Rodriguez-Barreiro from INDITEX for their invaluable suggestions and continuous support throughout this research. We are also grateful to everyone at ETH Zurich and the ETH AI Center for their coordination and collaborative efforts. +To run code in GarmentIQ MagicBox, follow the steps below: -We gratefully acknowledge the use and adaptation of the following open-source resources: +### 1. Prepare Your Local Directory +- Place all your configuration files for GarmentIQ MagicBox in your local directory. +- The contents inside the `working/` folder will be synchronized with this directory, so ensure it reflects the latest state. -- https://github.com/facebookresearch/deit -- https://github.com/ZhengPeng7/BiRefNet -- https://github.com/facebookresearch/segment-anything -- https://github.com/facebookresearch/sam2 -- https://github.com/facebookresearch/sam3 -- https://github.com/SHI-Labs/Matting-Anything -- https://github.com/hustvl/ViTMatte -- https://github.com/svip-lab/HRNet-for-Fashion-Landmark-Estimation.PyTorch -- https://github.com/switchablenorms/DeepFashion2 -- https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-dataset -- https://www.kaggle.com/datasets/lygitdata/garmentiq-classification-set-nordstrom-and-myntra -- https://www.kaggle.com/datasets/lygitdata/zara-clothes-image-data -- All Python packages listed in `requirements.txt` +### 2. Check Dependencies +- All necessary dependencies are already included in GarmentIQ MagicBox. +- You don't need to manually install them, making it easy to get started right away. +- In case if you need additional dipendencies, modify the `requirements.txt` in the configuration files in your local machine, then rebuild the Docker image and container, or simply using `!pip install`. ---- +### 3. Create a New Notebook or Script +- You can create a new Jupyter notebook or a Python script to write your code. +- Ensure your notebook or script is placed inside the `working/` folder to enable synchronization. -© 2025 - 2026 GarmentIQ +### 4. Explore Example Notebooks +- If you're new to GarmentIQ MagicBox or want to quickly see how things work, check out the example notebooks under the `working/examples/` folder. +- These examples will help you understand how to use the system. -"GarmentIQ" is a registered trademark. Unauthorized use, reproduction, or distribution is strictly prohibited and may result in legal action. +### 5. Run Your Code +- Once your notebook or script is ready, simply run it. +- Any outputs or changes made within the `working/` directory will be reflected in your local directory. diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..8b317a0 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,11 @@ +version: '3.8' + +services: + garmentiq: + build: . + ports: + - "8888:8888" + volumes: + - ./working:/app/working + environment: + - PYTHONPATH=/app:/app/working \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index 768e420..0000000 --- a/pyproject.toml +++ /dev/null @@ -1,112 +0,0 @@ -[build-system] -requires = ["setuptools>=77", "wheel"] -build-backend = "setuptools.build_meta" - -[project] -name = "garmentiq" -dynamic = ["version"] -authors = [ - { name="Li Yuan", email="lyuan@gd.edu.kg" }, - { name="Xinrui Zhai", email="zxr86272731@gmail.com" }, - { name="Fangzhou Ma", email="fangzhou.ma029@gmail.com" }, -] -maintainers = [ - { name="Li Yuan", email="lyuan@gd.edu.kg" }, - { name="Xinrui Zhai", email="zxr86272731@gmail.com" }, - { name="Fangzhou Ma", email="fangzhou.ma029@gmail.com" }, -] -description = "Automated Garment Measurement for Fashion Retail" -readme = "README.md" -requires-python = ">=3.11" -license = "MIT" -license-files = ["LICENSE"] -keywords = [ - "garment measurement", - "fashion", - "apparel", - "retail", - "computer vision", - "deep learning", - "segmentation", - "landmark detection", - "image classification", - "pytorch", - "sam", - "birefnet", - "hrnet", - "deit", -] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Science/Research", - "Intended Audience :: Developers", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Scientific/Engineering :: Image Processing", - "Topic :: Scientific/Engineering :: Image Recognition", - "Topic :: Multimedia :: Graphics", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", - "Operating System :: OS Independent", - "Natural Language :: English", -] -dependencies = [ - "requests>=2.30.0", - "tqdm>=4.67.1", - "pandas>=2.2.2", - "numpy>=2.0.0,<3.0.0", - "torch>=2.7.0", - "torchvision>=0.22.0", - "scikit-learn>=1.6.1", - "Pillow>=11.1.0", - "matplotlib>=3.10.0", - "transformers>=5.0.0", - "kornia>=0.8.0", - "timm>=1.0.15", - "einops>=0.8.1", - "safetensors>=0.7.0", - "shapely>=2.1.1", - "opencv-python>=4.11.0.86" -] - -[project.optional-dependencies] -dev = [ - "build>=1.2.0", - "twine>=5.0.0", - "black>=24.0.0", - "pytest>=8.0.0", - "nbmake>=1.5.0", -] -notebook = [ - "jupyter>=1.0.0", - "ipykernel>=6.29.0", -] - -[project.urls] -Homepage = "https://garmentiq.ly.gd.edu.kg/" -Documentation = "https://garmentiq.ly.gd.edu.kg/documentation/" -Repository = "https://github.com/lygitdata/GarmentIQ" -Issues = "https://github.com/lygitdata/GarmentIQ/issues" -"Web Pipeline" = "https://garmentiq.ly.gd.edu.kg/application/" -Paper = "https://archive.gd.edu.kg/abs/20250525121523/" -Changelog = "https://github.com/lygitdata/GarmentIQ/releases" - -[tool.setuptools.dynamic] -version = { attr = "garmentiq.__version__" } - -[tool.setuptools.packages.find] -where = ["src"] - -[tool.setuptools.package-data] -# Bundled model configurations, plus the SAM 3 tokenizer vocabulary. `merges.txt` is -# not JSON, so it needs its own pattern or the shipped tokenizer would be incomplete. -"*" = ["**/*.json", "**/*.txt"] - -[tool.black] -line-length = 88 -target-version = ["py311"] - -[tool.pytest.ini_options] -testpaths = ["test"] -addopts = "--nbmake" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..b7da4d3 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +flask==3.0.2 +flask-cors==4.0.0 +gunicorn==21.2.0 +garmentiq diff --git a/src/garmentiq/__init__.py b/src/garmentiq/__init__.py deleted file mode 100644 index 4391f82..0000000 --- a/src/garmentiq/__init__.py +++ /dev/null @@ -1,43 +0,0 @@ -# garmentiq/__init__.py -"""Automated garment measurement from images. - -GarmentIQ turns a photograph of a garment into a set of tape-measure-style -measurements. The pipeline runs in four stages: **classification** identifies the -garment type, **segmentation** separates it from the background, **landmark** -detection locates its key points, and the measurements are computed between those -points using a per-garment instruction schema. - -Each stage is a module that can be used on its own, or the whole sequence can be run -by the `tailor` agent. Two optional modules refine the result: `matting` turns a hard -mask into a soft alpha matte, and `grounding` turns a text phrase into boxes so SAM 1 -and SAM 2 can be prompted with natural language. - -Every model-backed module follows the same two-step shape:: - - model = giq..load_model(...) - result = giq..(model=model, image_path=..., device="cpu") - -Hardware acceleration is opt-in everywhere: `device` defaults to `"cpu"` and is never -auto-detected. Pass `"cuda"` or `"mps"` to use an accelerator. -""" -__version__ = "0.0.4.11" - -from .tailor import tailor -from . import utils -from . import classification -from . import segmentation -from . import landmark -from . import matting - - -def __getattr__(name): - """Expose optional subpackages without importing their heavy dependencies eagerly.""" - if name == "grounding": - import importlib - - # import_module avoids re-entering this __getattr__ (which `from . import - # grounding` would do, recursing infinitely); caching stops repeat lookups. - module = importlib.import_module(f"{__name__}.grounding") - globals()["grounding"] = module - return module - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/src/garmentiq/classification/__init__.py b/src/garmentiq/classification/__init__.py deleted file mode 100644 index 8592a0d..0000000 --- a/src/garmentiq/classification/__init__.py +++ /dev/null @@ -1,32 +0,0 @@ -# garmentiq/classification/__init__.py -"""Garment type classification. - -Identifies which category a garment image belongs to, such as a short sleeve top, a -vest dress, or a skirt. This is the first pipeline stage, because the garment type -decides which landmarks and which measurement instructions apply downstream. - -Beyond inference, this module covers the full model lifecycle: splitting a dataset, -caching it in memory, training a model from scratch, fine-tuning a pretrained one, and -evaluating the result. -""" -from .train_test_split import train_test_split -from .load_data import load_data -from .load_model import load_model -from .train_pytorch_nn import train_pytorch_nn -from .fine_tune_pytorch_nn import fine_tune_pytorch_nn -from .test_pytorch_nn import test_pytorch_nn -from .predict import predict -from .utils import ( - CachedDataset, - seed_worker, - train_epoch, - validate_epoch, - save_best_model, - validate_train_param, - validate_test_param, -) -from .model_definition import ( - CNN3, - CNN4, - tinyViT, -) diff --git a/src/garmentiq/classification/fine_tune_pytorch_nn.py b/src/garmentiq/classification/fine_tune_pytorch_nn.py deleted file mode 100644 index 8f7d370..0000000 --- a/src/garmentiq/classification/fine_tune_pytorch_nn.py +++ /dev/null @@ -1,166 +0,0 @@ -"""Fine-tuning a pretrained classification model on new data. - -Freezes the backbone and retrains only the layers matching `unfreeze_patterns`, so a -model can be adapted to a new catalogue with far less data than full training. -""" -import torch -import torch.nn as nn -from torch.utils.data import DataLoader -from typing import Callable, Type -from tqdm.auto import tqdm -import os -from sklearn.model_selection import StratifiedKFold -from garmentiq.utils.device import empty_cache -from garmentiq.classification.utils import ( - CachedDataset, - seed_worker, - train_epoch, - validate_epoch, - save_best_model, - validate_train_param, - validate_test_param, -) - -def fine_tune_pytorch_nn( - model_class: Type[torch.nn.Module], - model_args: dict, - dataset_class: Callable, - dataset_args: dict, - param: dict, -): - """ - Fine-tunes a pretrained PyTorch model using k-fold cross-validation, early stopping, and checkpointing. - - This function loads pretrained weights, optionally freezes specified layers, and trains the model on a new dataset - while preserving original learned features. It performs stratified k-fold CV, monitors validation loss, and saves - the best performing model. - - Args: - model_class (Type[torch.nn.Module]): Class of the PyTorch model (inherits from `torch.nn.Module`). - model_args (dict): Arguments for model initialization. - dataset_class (Callable): Callable that returns a Dataset given indices and cached tensors. - dataset_args (dict): Dict containing: - - 'metadata_df': DataFrame for stratification - - 'raw_labels': Labels array for KFold - - 'cached_images': Tensor of images - - 'cached_labels': Tensor of labels - param (dict): Training configuration dict. Must include: - - 'pretrained_path' (str): Path to pretrained weights (.pt) - - 'freeze_layers' (bool): Whether to freeze base layers - - 'optimizer_class', 'optimizer_args' - - optional: 'device', 'n_fold', 'n_epoch', 'patience', - 'batch_size', 'model_save_dir', 'seed', - 'seed_worker', 'max_workers', 'pin_memory', - 'persistent_workers', 'best_model_name' - - The optional 'device' entry accepts either a string or a `torch.device`, e.g. `"cpu"`, - `"cuda"`, `"cuda:0"`, or `"mps"`. Hardware acceleration is opt-in; pass it explicitly - to use a GPU or Apple Silicon. Default is `"cpu"`. - - Raises: - ValueError: If required keys are missing, or if the requested 'device' is invalid or - unavailable on this machine. - Returns: None - """ - # Validate parameters - validate_train_param(param) - os.makedirs(param.get("model_save_dir", "./models"), exist_ok=True) - overall_best_loss = float("inf") - best_model_path = os.path.join(param["model_save_dir"], param["best_model_name"]) - - # Stratified KFold - kfold = StratifiedKFold( - n_splits=param.get("n_fold", 5), shuffle=True, random_state=param.get("seed", 88) - ) - - for fold, (train_idx, val_idx) in enumerate( - kfold.split(dataset_args["metadata_df"], dataset_args["raw_labels"]) - ): - print(f"\nFold {fold+1}/{param.get('n_fold',5)}") - - # Prepare data loaders - train_dataset = dataset_class( - train_idx, dataset_args["cached_images"], dataset_args["cached_labels"] - ) - val_dataset = dataset_class( - val_idx, dataset_args["cached_images"], dataset_args["cached_labels"] - ) - - g = torch.Generator() - g.manual_seed(param.get("seed", 88)) - - train_loader = DataLoader( - train_dataset, - batch_size=param.get("batch_size", 64), - shuffle=True, - num_workers=param.get("max_workers", 1), - worker_init_fn=param.get("seed_worker", seed_worker), - generator=g, - pin_memory=param.get("pin_memory", True), - persistent_workers=param.get("persistent_workers", False), - ) - val_loader = DataLoader( - val_dataset, - batch_size=param.get("batch_size", 64), - shuffle=False, - num_workers=param.get("max_workers", 1), - worker_init_fn=param.get("seed_worker", seed_worker), - generator=g, - pin_memory=param.get("pin_memory", True), - persistent_workers=param.get("persistent_workers", False), - ) - - # Initialize model and load pretrained weights - device = param["device"] - model = model_class(**model_args).to(device) - - # Load pretrained weights - state_dict = torch.load(param["pretrained_path"], map_location=device) - cleaned = {k.replace("module.", ""): v for k, v in state_dict.items()} - model.load_state_dict(cleaned, strict=False) - - # Freeze base layers if requested - if param.get("freeze_layers", False): - for name, p in model.named_parameters(): - if not any(x in name for x in param.get("unfreeze_patterns", [])): - p.requires_grad = False - - # DataParallel if multiple GPUs - if device.type == "cuda" and torch.cuda.device_count() > 1: - model = nn.DataParallel(model) - - optimizer = param["optimizer_class"]( - filter(lambda p: p.requires_grad, model.parameters()), - **param["optimizer_args"] - ) - empty_cache(device) - - best_fold_loss = float("inf") - patience_counter = 0 - epoch_pbar = tqdm(range(param.get("n_epoch", 100)), desc="Epoch", leave=False) - - # Training loop - for epoch in epoch_pbar: - train_loss = train_epoch(model, train_loader, optimizer, param) - val_loss, val_f1, val_acc = validate_epoch(model, val_loader, param) - - best_fold_loss, patience_counter, overall_best_loss = save_best_model( - model, val_loss, best_fold_loss, patience_counter, - overall_best_loss, param, fold, best_model_path - ) - - epoch_pbar.set_postfix({ - 'train_loss': f"{train_loss:.4f}", - 'val_loss': f"{val_loss:.4f}", - 'val_acc': f"{val_acc:.4f}", - 'val_f1': f"{val_f1:.4f}", - 'patience': patience_counter, - }) - - print(f"Fold {fold+1} | Epoch {epoch+1} | Val Loss: {val_loss:.4f} | Acc: {val_acc:.4f} | F1: {val_f1:.4f}") - if patience_counter >= param.get("patience", 5): - print(f"Early stopping at epoch {epoch+1}") - break - - empty_cache(param["device"]) - print(f"\nFine-tuning completed. Best model saved at: {best_model_path}") diff --git a/src/garmentiq/classification/load_data.py b/src/garmentiq/classification/load_data.py deleted file mode 100644 index e264571..0000000 --- a/src/garmentiq/classification/load_data.py +++ /dev/null @@ -1,77 +0,0 @@ -"""Loading and caching image data for training and evaluation.""" -from torchvision import transforms -import os -from PIL import Image -import torch -from garmentiq.classification.utils import ( - CachedDataset, - seed_worker, - train_epoch, - validate_epoch, - save_best_model, - validate_train_param, - validate_test_param, -) -from tqdm.auto import tqdm - - -def load_data( - df, - img_dir, - label_column, - resize_dim=(120, 184), - normalize_mean=[0.8047, 0.7808, 0.7769], - normalize_std=[0.2957, 0.3077, 0.3081], -): - """ - Loads and preprocesses image data into memory from a DataFrame of filenames and labels. - - This function reads images from the specified directory, applies resizing, normalization, - and tensor conversion, and encodes labels from a specified column. It returns tensors for - images and labels, along with the transform pipeline used. - - Args: - df (pandas.DataFrame): A pandas DataFrame containing at least a 'filename' column and a label column. - img_dir (str): Path to the directory containing image files. - label_column (str): Name of the column in `df` containing class labels. - resize_dim (tuple[int, int]): Tuple indicating the dimensions (height, width) to resize each image to. - Defaults to (120, 184). - normalize_mean (list[float]): Mean values for normalization (per channel). - Defaults to `[0.8047, 0.7808, 0.7769]`. - normalize_std (list[float]): Standard deviation values for normalization (per channel). - Defaults to `[0.2957, 0.3077, 0.3081]`. - - Returns: - tuple[torch.Tensor, torch.Tensor, torchvision.transforms.Compose]: A tuple containing: - - cached_images (torch.Tensor): Tensor containing all preprocessed images. - - cached_labels (torch.Tensor): Tensor containing all encoded labels. - - transform (torchvision.transforms.Compose): The transformation pipeline used. - """ - transform = transforms.Compose( - [ - transforms.Resize(resize_dim), - transforms.ToTensor(), - transforms.Normalize(mean=normalize_mean, std=normalize_std), - ] - ) - - classes = sorted(df[label_column].unique()) - class_to_idx = {c: i for i, c in enumerate(classes)} - - cached_images = [] - cached_labels = [] - - for _, row in tqdm(df.iterrows(), total=len(df), desc="Loading data into memory"): - img_path = os.path.join(img_dir, row["filename"]) - image = Image.open(img_path).convert("RGB") - image = transform(image) - - label = class_to_idx[row[label_column]] - - cached_images.append(image) - cached_labels.append(label) - - cached_images = torch.stack(cached_images) - cached_labels = torch.tensor(cached_labels) - - return cached_images, cached_labels, transform diff --git a/src/garmentiq/classification/load_model.py b/src/garmentiq/classification/load_model.py deleted file mode 100644 index e7abddf..0000000 --- a/src/garmentiq/classification/load_model.py +++ /dev/null @@ -1,47 +0,0 @@ -"""Loading a trained classification model.""" -import torch -import torch.nn as nn -import torch.nn.functional as F -from typing import Type, List, Union -from garmentiq.utils.device import resolve_device -from garmentiq.utils.checkpoint import load_state_dict_checked - - -def load_model( - model_path: str, - model_class: Type[nn.Module], - model_args: dict, - device: Union[str, torch.device] = "cpu", -): - """ - Loads a PyTorch model from a checkpoint and prepares it for inference. - - This function initializes a model from the provided `model_class`, loads its weights from - the given file path, moves it to the requested device, and sets it to evaluation mode. - - Args: - model_path (str): Path to the saved model checkpoint (.pth or .pt file). - model_class (Type[nn.Module]): The class definition of the model to be instantiated. - This must be a subclass of `torch.nn.Module`. - model_args (dict): A dictionary of arguments used to initialize the model class. - device (Union[str, torch.device], optional): The device to load the model onto, e.g. - `"cpu"`, `"cuda"`, `"cuda:0"`, or `"mps"`. - Hardware acceleration is opt-in; pass it - explicitly to use a GPU or Apple Silicon. - Default is `"cpu"`. - - Raises: - ValueError: If the requested `device` is invalid or unavailable on this machine. - - Returns: - torch.nn.Module: The loaded and ready-to-use model, placed on `device`. - """ - device = resolve_device(device) - - model = model_class(**model_args).to(device) - state_dict = torch.load(model_path, map_location=device, weights_only=True) - new_state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} - load_state_dict_checked(model, new_state_dict, model_path) - model.eval() - - return model diff --git a/src/garmentiq/classification/model_definition.py b/src/garmentiq/classification/model_definition.py deleted file mode 100644 index 6c865ae..0000000 --- a/src/garmentiq/classification/model_definition.py +++ /dev/null @@ -1,231 +0,0 @@ -"""Classification model architectures. - -Three architectures are provided: `CNN3` and `CNN4`, two convolutional networks of -increasing depth, and `tinyViT`, a DeiT-tiny vision transformer. Any `torch.nn.Module` -can be used instead; these are simply the ones GarmentIQ ships trained weights for. -""" -import torch.nn as nn -import timm - - -class CNN3(nn.Module): - """ - CNN3 is a convolutional neural network designed for image classification with moderate depth. - - It consists of three convolutional blocks followed by fully connected layers. - - Args: - num_classes (int): Number of output classes for classification. - - Attributes: - features (nn.Sequential): Convolutional feature extractor. - classifier (nn.Sequential): Fully connected classifier. - """ - - def __init__(self, num_classes): - """ - Initializes the CNN3 model architecture. - - Args: - num_classes (int): Number of target classes. - """ - super(CNN3, self).__init__() - self.features = nn.Sequential( - # Block 1 - nn.Conv2d(3, 64, kernel_size=3, padding=1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True), - nn.Conv2d(64, 64, kernel_size=3, padding=1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True), - nn.MaxPool2d(2, 2), - nn.Dropout(0.25), - # Block 2 - nn.Conv2d(64, 128, kernel_size=3, padding=1), - nn.BatchNorm2d(128), - nn.ReLU(inplace=True), - nn.Conv2d(128, 128, kernel_size=3, padding=1), - nn.BatchNorm2d(128), - nn.ReLU(inplace=True), - nn.MaxPool2d(2, 2), - nn.Dropout(0.25), - # Block 3 - nn.Conv2d(128, 256, kernel_size=3, padding=1), - nn.BatchNorm2d(256), - nn.ReLU(inplace=True), - nn.Conv2d(256, 256, kernel_size=3, padding=1), - nn.BatchNorm2d(256), - nn.ReLU(inplace=True), - nn.AdaptiveAvgPool2d((4, 6)), - ) - - self.classifier = nn.Sequential( - nn.Linear(256 * 4 * 6, 512), - nn.BatchNorm1d(512), - nn.ReLU(inplace=True), - nn.Dropout(0.5), - nn.Linear(512, 256), - nn.BatchNorm1d(256), - nn.ReLU(inplace=True), - nn.Dropout(0.3), - nn.Linear(256, num_classes), - ) - - def forward(self, x): - """ - Defines the forward pass of the CNN3 model. - - Args: - x (torch.Tensor): Input tensor of shape (batch_size, 3, H, W). - - Returns: - torch.Tensor: Output logits tensor of shape (batch_size, num_classes). - """ - x = self.features(x) - x = x.view(x.size(0), -1) - x = self.classifier(x) - return x - - -class CNN4(nn.Module): - """ - CNN4 is a deeper convolutional neural network with four convolutional blocks. - - This architecture is designed for improved feature extraction by adding an extra conv block - and adaptive pooling before classification layers. - - Args: - num_classes (int): Number of output classes for classification. - - Attributes: - features (nn.Sequential): Convolutional feature extractor with four blocks. - classifier (nn.Sequential): Fully connected classifier. - """ - - def __init__(self, num_classes): - """ - Initializes the CNN4 model architecture. - - Args: - num_classes (int): Number of target classes. - """ - super(CNN4, self).__init__() - self.features = nn.Sequential( - # Block 1 - nn.Conv2d(3, 64, kernel_size=3, padding=1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True), - nn.Conv2d(64, 64, kernel_size=3, padding=1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True), - nn.MaxPool2d(2, 2), - nn.Dropout(0.25), - # Block 2 - nn.Conv2d(64, 128, kernel_size=3, padding=1), - nn.BatchNorm2d(128), - nn.ReLU(inplace=True), - nn.Conv2d(128, 128, kernel_size=3, padding=1), - nn.BatchNorm2d(128), - nn.ReLU(inplace=True), - nn.MaxPool2d(2, 2), - nn.Dropout(0.25), - # Block 3 - nn.Conv2d(128, 256, kernel_size=3, padding=1), - nn.BatchNorm2d(256), - nn.ReLU(inplace=True), - nn.Conv2d(256, 256, kernel_size=3, padding=1), - nn.BatchNorm2d(256), - nn.ReLU(inplace=True), - nn.MaxPool2d(2, 2), - nn.Dropout(0.25), - # Block 4 (Additional block for more depth) - nn.Conv2d(256, 512, kernel_size=3, padding=1), - nn.BatchNorm2d(512), - nn.ReLU(inplace=True), - nn.Conv2d(512, 512, kernel_size=3, padding=1), - nn.BatchNorm2d(512), - nn.ReLU(inplace=True), - # Replace MaxPool with adaptive pooling to fix feature map size - nn.AdaptiveAvgPool2d((4, 6)), - ) - - self.classifier = nn.Sequential( - nn.Linear(512 * 4 * 6, 1024), - nn.BatchNorm1d(1024), - nn.ReLU(inplace=True), - nn.Dropout(0.5), - nn.Linear(1024, 512), - nn.BatchNorm1d(512), - nn.ReLU(inplace=True), - nn.Dropout(0.3), - nn.Linear(512, num_classes), - ) - - def forward(self, x): - """ - Defines the forward pass of the CNN4 model. - - Args: - x (torch.Tensor): Input tensor of shape (batch_size, 3, H, W). - - Returns: - torch.Tensor: Output logits tensor of shape (batch_size, num_classes). - """ - x = self.features(x) - x = x.view(x.size(0), -1) - x = self.classifier(x) - return x - - -class tinyViT(nn.Module): - """ - tinyViT is a vision transformer model based on the DeiT tiny architecture, pretrained on ImageNet. - - It replaces the original classification head with a linear layer matching the number of classes. - - Args: - num_classes (int): Number of output classes. - img_size (int): Input image size (assumed square). - patch_size (int): Size of the image patches used by the transformer. - - Attributes: - backbone (nn.Module): The underlying vision transformer model with modified head. - """ - - def __init__( - self, - num_classes: int, - img_size: int, - patch_size: int, - pretrained: bool = False, - ): - """ - Initializes the tinyViT model with a pretrained DeiT backbone. - - Args: - num_classes (int): Number of target classes. - img_size (int): Input image size (height and width). - patch_size (int): Patch size for the transformer. - pretrained (bool): Whether to use the pretrained model, deault to `False`. - """ - super(tinyViT, self).__init__() - self.backbone = timm.create_model( - "deit_tiny_patch16_224", - pretrained=pretrained, - img_size=img_size, - patch_size=patch_size, - ) - in_features = self.backbone.head.in_features - self.backbone.head = nn.Linear(in_features, num_classes) - - def forward(self, x): - """ - Defines the forward pass of the tinyViT model. - - Args: - x (torch.Tensor): Input tensor of shape (batch_size, 3, img_size, img_size). - - Returns: - torch.Tensor: Output logits tensor of shape (batch_size, num_classes). - """ - return self.backbone(x) diff --git a/src/garmentiq/classification/predict.py b/src/garmentiq/classification/predict.py deleted file mode 100644 index 1271205..0000000 --- a/src/garmentiq/classification/predict.py +++ /dev/null @@ -1,102 +0,0 @@ -"""Single-image garment classification.""" -import torch -import torch.nn as nn -import torch.nn.functional as F -from PIL import Image -from torchvision import transforms -from typing import Type, List, Union -from garmentiq.utils.device import resolve_device, empty_cache - - -def predict( - model: Type[nn.Module], - image_path: str, - classes: List[str], - resize_dim=(120, 184), - normalize_mean=[0.8047, 0.7808, 0.7769], - normalize_std=[0.2957, 0.3077, 0.3081], - device: Union[str, torch.device] = "cpu", - verbose=False, -): - """ - Loads a trained PyTorch model and makes a prediction on a single image. - - This function processes a single image from disk, applies resizing and normalization, - feeds it through a loaded model, and returns the predicted class along with the - class probabilities. The model is expected to output logits over a fixed number of classes. - - The model and the preprocessed image tensor are placed on `device`, so the same value should - be passed here as was used when loading the model. Any accelerator memory cached during - inference is released before returning. - - Args: - model (Type[nn.Module]): The loaded PyTorch model instance ready for inference. - image_path (str): Path to the input image file (.jpg, .jpeg, .png). - classes (List[str]): List of class names corresponding to model outputs. Will be sorted internally. - resize_dim (tuple[int, int]): Tuple indicating the dimensions to resize the image to. Default is (120, 184). - normalize_mean (list[float]): List of mean values for normalization. Default is [0.8047, 0.7808, 0.7769]. - normalize_std (list[float]): List of standard deviation values for normalization. Default is [0.2957, 0.3077, 0.3081]. - device (Union[str, torch.device], optional): The device to run inference on, e.g. `"cpu"`, - `"cuda"`, `"cuda:0"`, or `"mps"`. Hardware - acceleration is opt-in; pass it explicitly to - use a GPU or Apple Silicon. Defaults to `"cpu"`. - verbose (bool): If True, prints the predicted label and class probabilities. - - Raises: - ValueError: If the image file does not have a supported extension (.jpg, .jpeg, .png), - or if the requested `device` is invalid or unavailable on this machine. - FileNotFoundError: If the model checkpoint file is not found or cannot be loaded. - - Returns: - tuple[str, List[float]]: A tuple containing: - - predicted label (str): The class label with the highest predicted probability. - - prob_list (List[float]): The list of class probabilities in the same order as the sorted class list. - """ - device = resolve_device(device) - - # Validate image extension - if not any( - image_path.lower().endswith(ext) for ext in [".jpg", ".jpeg", ".png", ".JPG"] - ): - raise ValueError("Image file must end with .jpg, .jpeg, .png, or .JPG") - - # Sort the classes list to have a consistent order - sorted_classes = sorted(classes) - - # Define the preprocessing transformation. - transform = transforms.Compose( - [ - transforms.Resize(resize_dim), - transforms.ToTensor(), - transforms.Normalize(mean=normalize_mean, std=normalize_std), - ] - ) - - # Load and preprocess the image - image = Image.open(image_path).convert("RGB") - model = model.to(device) - image_tensor = transform(image).unsqueeze(0).to(device) # add batch dimension - - # Forward pass - with torch.no_grad(): - outputs = model(image_tensor) - # Compute probabilities using softmax - probabilities = ( - F.softmax(outputs, dim=1).cpu().numpy()[0] - ) # shape: (num_classes,) - - # Determine the predicted index and label - pred_index = int(probabilities.argmax()) - pred_label = sorted_classes[pred_index] - - # Optionally, you might want to return probabilities as a list of floats: - prob_list = probabilities.tolist() - - if verbose: - print(f"Prediction: {pred_label}") - print(f"Probabilities: {prob_list}") - - del image_tensor, outputs - empty_cache(device) - - return pred_label, prob_list diff --git a/src/garmentiq/classification/test_pytorch_nn.py b/src/garmentiq/classification/test_pytorch_nn.py deleted file mode 100644 index 8485f7e..0000000 --- a/src/garmentiq/classification/test_pytorch_nn.py +++ /dev/null @@ -1,112 +0,0 @@ -"""Evaluating a classification model on a held-out dataset.""" -import torch -import torch.nn as nn -from torch.utils.data import DataLoader, TensorDataset -from typing import Callable, Type -from tqdm.auto import tqdm -from sklearn.metrics import f1_score, accuracy_score, classification_report -from garmentiq.utils.device import empty_cache -from garmentiq.classification.utils import ( - CachedDataset, - seed_worker, - train_epoch, - validate_epoch, - save_best_model, - validate_train_param, - validate_test_param, -) - - -def test_pytorch_nn( - model_path: str, - model_class: Type[torch.nn.Module], - model_args: dict, - dataset_class: Callable, - dataset_args: dict, - param: dict, -): - """ - Evaluates a trained PyTorch model on a test dataset. - - Loads the model from disk, prepares the test dataset, and computes loss, accuracy, - F1 score, and prints a full classification report. - - Args: - model_path (str): Path to the saved model checkpoint file. - model_class (Type[torch.nn.Module]): The class of the PyTorch model to instantiate. - Must inherit from `torch.nn.Module`. - model_args (dict): Dictionary of arguments used to initialize the model. - dataset_class (Callable): A callable class or function that returns a `torch.utils.data.Dataset`-compatible dataset. - (Note: Not directly used, but included for consistency with training pipeline.) - dataset_args (dict): Dictionary with dataset components: - - `cached_images` (torch.Tensor): Preprocessed test image tensors. - - `cached_labels` (torch.Tensor): Corresponding test labels. - - `raw_labels` (pandas.Series or array-like): Original labels for report generation. - param (dict): Dictionary of optional configuration parameters. - Optional Keys: - - `device` (Union[str, torch.device]): Device for computation, e.g. `"cpu"`, - `"cuda"`, `"cuda:0"`, or `"mps"`. Hardware acceleration is opt-in; - pass it explicitly to use a GPU or Apple Silicon. Default is `"cpu"`. - - `batch_size` (int): Batch size used for testing. Default is 64. - - Raises: - FileNotFoundError: If the model checkpoint cannot be loaded. - ValueError: If the requested `device` is invalid or unavailable on this machine. - TypeError: If any parameter is of an incorrect type. - - Returns: - None — prints test loss, accuracy, F1 score, and a classification report. - """ - validate_test_param(param) - model = model_class(**model_args).to(param["device"]) - state_dict = torch.load(model_path, map_location=param["device"], weights_only=True) - new_state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} - model.load_state_dict(new_state_dict, strict=False) - model.eval() - - test_dataset = TensorDataset( - dataset_args["cached_images"], dataset_args["cached_labels"] - ) - test_loader = DataLoader( - test_dataset, batch_size=param["batch_size"], shuffle=False - ) - - # Evaluation - all_preds = [] - all_labels = [] - total_loss = 0.0 - - with torch.no_grad(): - for images, labels in tqdm(test_loader, desc="Evaluating"): - images = images.to(param["device"]) - labels = labels.to(param["device"]) - - outputs = model(images) - criterion = nn.CrossEntropyLoss() - loss = criterion(outputs, labels) - - total_loss += loss.item() * images.size(0) - _, preds = torch.max(outputs, 1) - - all_preds.extend(preds.cpu().numpy()) - all_labels.extend(labels.cpu().numpy()) - - # Calculate metrics - test_loss = total_loss / len(test_loader.dataset) - test_acc = accuracy_score(all_labels, all_preds) - test_f1 = f1_score(all_labels, all_preds, average="weighted") - - del model - empty_cache(param["device"]) - - print(f"Test Loss: {test_loss:.4f}") - print(f"Test Accuracy: {test_acc:.4f}") - print(f"Test F1 Score: {test_f1:.4f}") - print("\nClassification Report:") - print( - classification_report( - all_labels, - all_preds, - target_names=sorted(dataset_args["raw_labels"].unique()), - ) - ) diff --git a/src/garmentiq/classification/train_pytorch_nn.py b/src/garmentiq/classification/train_pytorch_nn.py deleted file mode 100644 index c11150c..0000000 --- a/src/garmentiq/classification/train_pytorch_nn.py +++ /dev/null @@ -1,173 +0,0 @@ -"""Cross-validated training of a classification model from scratch.""" -import torch -import torch.nn as nn -from torch.utils.data import DataLoader, Dataset -from typing import Callable, Type -from tqdm.auto import tqdm -import os -from sklearn.model_selection import StratifiedKFold -from garmentiq.utils.device import empty_cache -from garmentiq.classification.utils import ( - CachedDataset, - seed_worker, - train_epoch, - validate_epoch, - save_best_model, - validate_train_param, - validate_test_param, -) - - -def train_pytorch_nn( - model_class: Type[torch.nn.Module], - model_args: dict, - dataset_class: Callable, - dataset_args: dict, - param: dict, -): - """ - Trains a PyTorch neural network using k-fold cross-validation with early stopping and model checkpointing. - - This function performs training and validation across multiple folds using stratified sampling. - It manages model instantiation, training loops, early stopping, and saves the best model based on validation loss. - - Args: - model_class (Type[torch.nn.Module]): The class of the PyTorch model to instantiate. - Must inherit from `torch.nn.Module`. - model_args (dict): Dictionary of arguments used to initialize `model_class`. - dataset_class (Callable): A callable class or function that returns a `torch.utils.data.Dataset`-compatible dataset. - dataset_args (dict): Dictionary with dataset components: - - 'metadata_df' (pandas.DataFrame): Metadata with labels, used for stratification. - - 'raw_labels' (array-like): Raw class labels used by StratifiedKFold. - - 'cached_images' (torch.Tensor): Preprocessed image tensor. - - 'cached_labels' (torch.Tensor): Corresponding labels. - param (dict): Dictionary of training hyperparameters and configuration values. - Required Keys: - - `optimizer_class` (type): PyTorch optimizer class (e.g., `torch.optim.Adam`). - - `optimizer_args` (dict): Arguments passed to the optimizer. - Optional Keys (with defaults and types): - - `device` (Union[str, torch.device]): Training device, e.g. `"cpu"`, - `"cuda"`, `"cuda:0"`, or `"mps"`. Hardware acceleration is opt-in; - pass it explicitly to use a GPU or Apple Silicon. Default: `"cpu"`. - - `n_fold` (int): Number of stratified folds for cross-validation. Default: 5. - - `n_epoch` (int): Number of training epochs per fold. Default: 100. - - `patience` (int): Epochs to wait before early stopping. Default: 5. - - `batch_size` (int): Batch size for training and validation. Default: 64. - - `model_save_dir` (str): Directory to save model checkpoints. Default: `"./models"`. - - `seed` (int): Random seed for reproducibility. Default: 88. - - `seed_worker` (Callable): Function to seed workers in the DataLoader. Default: `seed_worker`. - - `max_workers` (int): Number of subprocesses for data loading. Default: `os.cpu_count()`. - - `best_model_name` (str): Filename for saving the best model. Default: `"best_model.pt"`. - - Raises: - ValueError: If any required key is missing from `param`. - TypeError: If any parameter is of the wrong type. - FileNotFoundError: If the model directory cannot be created or accessed. - - Returns: - None - """ - # Validate and catch parameters - validate_train_param(param) - - # Prepare save directories - os.makedirs(param["model_save_dir"], exist_ok=True) - overall_best_loss = float("inf") - best_model_path = os.path.join(param["model_save_dir"], param["best_model_name"]) - - kfold = StratifiedKFold( - n_splits=param["n_fold"], shuffle=True, random_state=param["seed"] - ) - - # Loop through each fold - for fold, (train_idx, val_idx) in enumerate( - kfold.split(dataset_args["metadata_df"], dataset_args["raw_labels"]) - ): - print(f"\nFold {fold + 1}/{param['n_fold']}") - - # Prepare datasets and dataloaders - train_dataset = dataset_class( - train_idx, dataset_args["cached_images"], dataset_args["cached_labels"] - ) - val_dataset = dataset_class( - val_idx, dataset_args["cached_images"], dataset_args["cached_labels"] - ) - - g = torch.Generator() - g.manual_seed(param["seed"]) - - train_loader = DataLoader( - train_dataset, - batch_size=param["batch_size"], - shuffle=True, - num_workers=param["max_workers"], - worker_init_fn=param["seed_worker"], - generator=g, - pin_memory=param["pin_memory"], - persistent_workers=param["persistent_workers"], - ) - val_loader = DataLoader( - val_dataset, - batch_size=param["batch_size"], - shuffle=False, - num_workers=param["max_workers"], - worker_init_fn=param["seed_worker"], - generator=g, - pin_memory=param["pin_memory"], - persistent_workers=param["persistent_workers"], - ) - - # Initialize model and optimizer - model = model_class(**model_args).to(param["device"]) - if param["device"].type == "cuda" and torch.cuda.device_count() > 1: - model = torch.nn.DataParallel(model) - optimizer = param["optimizer_class"]( - model.parameters(), **param["optimizer_args"] - ) - empty_cache(param["device"]) - - best_fold_loss = float("inf") - patience_counter = 0 - epoch_pbar = tqdm(range(param["n_epoch"]), desc="Total Progress", leave=False) - - # Training and Validation Loop - for epoch in epoch_pbar: - # Training phase - epoch_loss = train_epoch(model, train_loader, optimizer, param) - # Validation phase - val_loss, f1, acc = validate_epoch(model, val_loader, param) - - # Save the best model and check for early stopping - best_fold_loss, patience_counter, overall_best_loss = save_best_model( - model, - val_loss, - best_fold_loss, - patience_counter, - overall_best_loss, - param, - fold, - best_model_path, - ) - # Early stopping - epoch_pbar.set_postfix( - { - "train_loss": f"{epoch_loss:.4f}", - "val_loss": f"{val_loss:.4f}", - "val_acc": f"{acc:.4f}", - "val_f1": f"{f1:.4f}", - "patience": patience_counter, - } - ) - - print( - f"Fold {fold+1} | Epoch {epoch+1} | Val Loss: {val_loss:.4f} | F1: {f1:.4f} | Acc: {acc:.4f}" - ) - - if patience_counter >= param["patience"]: - print(f"Early stopping at epoch {epoch+1} (fold {fold + 1})") - break - - del model - empty_cache(param["device"]) - - print(f"\nTraining completed. Best model saved at: {best_model_path}") diff --git a/src/garmentiq/classification/train_test_split.py b/src/garmentiq/classification/train_test_split.py deleted file mode 100644 index 72cb8c0..0000000 --- a/src/garmentiq/classification/train_test_split.py +++ /dev/null @@ -1,169 +0,0 @@ -"""Unpacking a dataset archive and splitting it into train and test sets.""" -import os -import pandas as pd -import shutil -import random -from typing import Optional -from garmentiq.utils.check_unzipped_dir import check_unzipped_dir -from garmentiq.utils.unzip import unzip -from garmentiq.utils.check_filenames_metadata import check_filenames_metadata - - -def train_test_split( - output_dir: str, - train_zip_dir: str, - metadata_csv: str, - label_column: str, - test_zip_dir: Optional[str] = None, - test_size: float = 0.2, - seed: int = 88, - verbose: bool = False, -): - """ - Prepares training and testing datasets from zipped image data and associated metadata. - - This function supports two operation modes: - - 1. **Two-Zip Mode**: If both `train_zip_dir` and `test_zip_dir` are provided, each dataset is unzipped, - validated against the metadata, and returned as-is. - 2. **Split Mode**: If only `train_zip_dir` is provided, the function splits the training data - into new training and test sets based on `test_size`. - - It ensures: - - The unzipped directories contain the expected structure (`images/` and metadata CSV). - - Image filenames match those specified in the metadata. - - Output datasets are organized into `train/` and `test/` folders under `output_dir`. - - Args: - output_dir (str): Directory where the processed datasets will be saved. - train_zip_dir (str): Path to the ZIP file containing training data (with `images/` and metadata CSV). - metadata_csv (str): Filename of the metadata CSV inside each ZIP archive (e.g., 'metadata.csv'). - label_column (str): Name of the column in the metadata CSV to use for class distribution summaries. - test_zip_dir (Optional[str]): Optional path to the ZIP file containing testing data. - If not provided, the function performs a split. - test_size (float): Proportion of data to use for testing if splitting from training data. - Ignored if `test_zip_dir` is provided. - seed (int): Random seed used for reproducible splitting of training data. - verbose (bool): Whether to print class distribution summaries after processing. - - Raises: - FileNotFoundError: If any expected files or images are missing. - ValueError: If metadata is missing required columns or if filenames and metadata don't align. - - Returns: - dict: A dictionary containing: - - 'train_images': Path to the directory with training images. - - 'train_metadata': DataFrame of training metadata. - - 'test_images': Path to the directory with testing images. - - 'test_metadata': DataFrame of testing metadata. - """ - os.makedirs(output_dir, exist_ok=True) - - # Unzip train data - train_out = os.path.join(output_dir, "train") - unzip(train_zip_dir, train_out) - print("\n") - check_unzipped_dir(train_out) - - # If a test zip is provided, unzip and process it - if test_zip_dir: - test_out = os.path.join(output_dir, "test") - unzip(test_zip_dir, test_out) - check_unzipped_dir(test_out) - - # Load train metadata and check filenames - train_metadata_path = os.path.join(train_out, metadata_csv) - df_train = pd.read_csv(train_metadata_path) - if "filename" not in df_train.columns: - raise ValueError("Train metadata must contain a 'filename' column.") - check_filenames_metadata( - output_dir, os.path.join(train_out, "images"), df_train - ) - - # Load test metadata and check filenames - test_metadata_path = os.path.join(test_out, metadata_csv) - df_test = pd.read_csv(test_metadata_path) - if "filename" not in df_test.columns: - raise ValueError("Test metadata must contain a 'filename' column.") - check_filenames_metadata(output_dir, os.path.join(test_out, "images"), df_test) - - # Summary information - if verbose: - print(f"\n\nTrain set summary (sample size: {len(df_train)}):\n") - print(f"{df_train[label_column].value_counts()}\n") - - print(f"Test set summary (sample size: {len(df_test)}):\n") - print(f"{df_test[label_column].value_counts()}\n") - - return { - "train_images": f"{train_out}/images", - "train_metadata": pd.read_csv(f"{train_out}/metadata.csv"), - "test_images": f"{test_out}/images", - "test_metadata": pd.read_csv(f"{test_out}/metadata.csv"), - } - - # If no test zip is provided, split from train data - print("Splitting train data into train/test sets...") - - # Load train metadata - metadata_path = os.path.join(train_out, metadata_csv) - df = pd.read_csv(metadata_path) - - if "filename" not in df.columns: - raise ValueError("metadata.csv must contain a 'filename' column.") - - # Select test split - random.seed(seed) - filenames = df["filename"].tolist() - test_filenames = set(random.sample(filenames, int(len(filenames) * test_size))) - - # Prepare test folder - test_out = os.path.join(output_dir, "test") - test_images_dir = os.path.join(test_out, "images") - os.makedirs(test_images_dir, exist_ok=True) - - train_images_dir = os.path.join(train_out, "images") - - # Move test files from train to test folder - for fname in test_filenames: - src = os.path.join(train_images_dir, fname) - dst = os.path.join(test_images_dir, fname) - if not os.path.exists(src): - raise FileNotFoundError(f"File listed in metadata not found: {fname}") - shutil.move(src, dst) - - # Save updated CSVs - df_test = df[df["filename"].isin(test_filenames)] - df_train = df[~df["filename"].isin(test_filenames)] - - df_test.to_csv(os.path.join(test_out, metadata_csv), index=False) - df_train.to_csv(metadata_path, index=False) - - # Check if output images match the metadata records - check_filenames_metadata(output_dir, os.path.join(train_out, "images"), df_train) - check_filenames_metadata(output_dir, os.path.join(test_out, "images"), df_test) - - # Summary information - if verbose: - print(f"\n\nTrain set summary (sample size: {len(df_train)}):\n") - print(f"{df_train[label_column].value_counts()}\n") - - print(f"Test set summary (sample size: {len(df_test)}):\n") - print(f"{df_test[label_column].value_counts()}\n") - - return { - "train_images": f"{train_out}/images", - "train_metadata": pd.read_csv(f"{train_out}/metadata.csv"), - "test_images": f"{test_out}/images", - "test_metadata": pd.read_csv(f"{test_out}/metadata.csv"), - } - - # If any mismatch found, remove the output directory and raise an error - try: - check_filenames_metadata( - output_dir, os.path.join(train_out, "images"), df_train - ) - check_filenames_metadata(output_dir, os.path.join(test_out, "images"), df_test) - except ValueError as e: - shutil.rmtree(output_dir) # Clean up the output directory in case of error - raise e diff --git a/src/garmentiq/classification/utils.py b/src/garmentiq/classification/utils.py deleted file mode 100644 index 77a1945..0000000 --- a/src/garmentiq/classification/utils.py +++ /dev/null @@ -1,376 +0,0 @@ -"""Training helpers for the classification module. - -Holds the in-memory dataset wrapper, the per-epoch train and validation loops, -checkpoint saving, and the parameter validators that apply defaults to the `param` -dictionary, including normalising `device`. -""" -import torch -import torch.nn as nn -import torch.optim as optim -from torch.utils.data import DataLoader, Dataset -from typing import Callable -from tqdm.auto import tqdm -import os -from sklearn.metrics import f1_score, accuracy_score -import random -import numpy as np -from garmentiq.utils.device import resolve_device - - -class CachedDataset(Dataset): - """ - A PyTorch Dataset that wraps pre-loaded data (images and labels) in memory. - - This dataset is designed to be used when images and labels have already been - loaded and preprocessed into PyTorch tensors or NumPy arrays, avoiding - repeated disk I/O during training/validation. - - Attributes: - indices (list or numpy.ndarray): A list or array of indices that map - to specific items in `cached_images` and `cached_labels`. This - allows for flexible subsetting (e.g., for train/validation splits). - cached_images (torch.Tensor): A tensor containing the pre-loaded images. - cached_labels (torch.Tensor): A tensor containing the pre-loaded labels. - """ - - def __init__(self, indices, cached_images, cached_labels): - """ - Initializes the CachedDataset. - - Args: - indices (list or numpy.ndarray): Indices to select from the cached data. - cached_images (torch.Tensor): Pre-loaded image tensor. - cached_labels (torch.Tensor): Pre-loaded label tensor. - """ - self.indices = indices - self.cached_images = cached_images - self.cached_labels = cached_labels - - def __len__(self): - """ - Returns the number of samples in the dataset. - - Returns: - int: The number of samples. - """ - return len(self.indices) - - def __getitem__(self, idx): - """ - Retrieves a sample from the dataset at the given index. - - Args: - idx (int): The index of the sample to retrieve. - - Returns: - tuple: A tuple containing the image and its corresponding label. - """ - actual_idx = self.indices[idx] - return self.cached_images[actual_idx], self.cached_labels[actual_idx] - - -def seed_worker(worker_id, SEED=88): - """ - Seeds the random number generators for a DataLoader worker. - - This function is intended to be passed as `worker_init_fn` to a PyTorch - DataLoader to ensure reproducibility across different worker processes. - It seeds Python's `random` module, NumPy, and PyTorch for each worker. - - Args: - worker_id (int): The ID of the current worker process. - SEED (int, optional): The base seed value. The worker's seed will be - `SEED + worker_id`. Defaults to 88. - """ - worker_seed = SEED + worker_id - random.seed(worker_seed) - np.random.seed(worker_seed) - torch.manual_seed(worker_seed) - - -def train_epoch(model, train_loader, optimizer, param): - """ - Performs a single training epoch for a PyTorch model. - - Sets the model to training mode, iterates through the `train_loader`, - performs forward and backward passes, and updates model weights using - the provided optimizer. A progress bar is displayed, showing the batch loss. - - Args: - model (torch.nn.Module): The PyTorch model to train. - train_loader (torch.utils.data.DataLoader): DataLoader for the training data. - optimizer (torch.optim.Optimizer): The optimizer used for updating model weights. - param (dict): A dictionary containing training parameters, including: - - "device" (torch.device): The device to use for training, e.g. `"cpu"`, - `"cuda"`, `"cuda:0"`, or `"mps"`. Normalised by `validate_train_param`. - - Returns: - float: The average loss for the epoch. - """ - model.train() - running_loss = 0.0 - train_pbar = tqdm(train_loader, desc=f"Training", leave=False) - - for images, labels in train_pbar: - images, labels = images.to(param["device"], non_blocking=True), labels.to( - param["device"], non_blocking=True - ) - optimizer.zero_grad() - outputs = model(images) - - # Calculate the loss - criterion = nn.CrossEntropyLoss() - loss = criterion(outputs, labels) - loss.backward() - optimizer.step() - - running_loss += loss.item() * images.size(0) - train_pbar.set_postfix({"batch_loss": f"{loss.item():.4f}"}) - - epoch_loss = running_loss / len(train_loader.dataset) - return epoch_loss - - -def validate_epoch(model, val_loader, param): - """ - Performs a single validation epoch for a PyTorch model. - - Sets the model to evaluation mode, iterates through the `val_loader` - without gradient calculations, and computes the validation loss, F1 score, - and accuracy. A progress bar is displayed, showing the batch validation loss. - - Args: - model (torch.nn.Module): The PyTorch model to validate. - val_loader (torch.utils.data.DataLoader): DataLoader for the validation data. - param (dict): A dictionary containing training parameters, including: - - "device" (torch.device): The device to use for validation, e.g. `"cpu"`, - `"cuda"`, `"cuda:0"`, or `"mps"`. Normalised by `validate_train_param`. - - Returns: - tuple: A tuple containing: - - val_loss (float): The average validation loss for the epoch. - - f1 (float): The weighted average F1 score on the validation set. - - acc (float): The accuracy score on the validation set. - """ - model.eval() - val_loss = 0.0 - all_preds = [] - all_labels = [] - val_pbar = tqdm(val_loader, desc=f"Validation", leave=False) - - with torch.no_grad(): - for images, labels in val_pbar: - images, labels = images.to(param["device"]), labels.to(param["device"]) - outputs = model(images) - criterion = nn.CrossEntropyLoss() - loss = criterion(outputs, labels) - val_loss += loss.item() * images.size(0) - - # Collect predictions and true labels for F1 score calculation - _, preds = torch.max(outputs, 1) - all_preds.extend(preds.cpu().numpy()) # Move to CPU and convert to numpy - all_labels.extend(labels.cpu().numpy()) # Move to CPU and convert to numpy - - val_pbar.set_postfix({"val_batch_loss": f"{loss.item():.4f}"}) - - # Calculate the average validation loss - val_loss /= len(val_loader.dataset) - - # Calculate F1 Score (if needed) - f1 = f1_score(all_labels, all_preds, average="weighted") - acc = accuracy_score(all_labels, all_preds) - - return val_loss, f1, acc - - -def save_best_model( - model, - val_loss, - best_fold_loss, - patience_counter, - overall_best_loss, - param, - fold, - best_model_path, -): - """ - Saves the best model checkpoints based on validation loss and manages early stopping. - - This function updates the `best_fold_loss` and `patience_counter` for the current - cross-validation fold. It also saves the model's state dictionary if it's the best - performing model for the current fold or the overall best model across all folds. - - Args: - model (torch.nn.Module): The current PyTorch model being trained. - val_loss (float): The validation loss from the current epoch. - best_fold_loss (float): The best validation loss recorded so far for the current fold. - patience_counter (int): The number of epochs since the last improvement for the current fold. - overall_best_loss (float): The best validation loss recorded so far across all folds. - param (dict): A dictionary containing training parameters, including: - - "model_save_dir" (str): Directory where model checkpoints will be saved. - fold (int): The current fold number (0-indexed). - best_model_path (str): The full path where the overall best model will be saved. - - Returns: - tuple: A tuple containing: - - best_fold_loss (float): The updated best validation loss for the current fold. - - patience_counter (int): The updated patience counter for the current fold. - - overall_best_loss (float): The updated overall best validation loss. - """ - # Save the best model for this fold - if val_loss < best_fold_loss: - best_fold_loss = val_loss - patience_counter = 0 - torch.save( - model.state_dict(), - os.path.join(param["model_save_dir"], f"fold_{fold + 1}_best.pt"), - ) - else: - patience_counter += 1 - - # Save the overall best model - if val_loss < overall_best_loss: - overall_best_loss = val_loss - torch.save(model.state_dict(), best_model_path) - - return best_fold_loss, patience_counter, overall_best_loss - - -def validate_train_param(param: dict): - """ - Validates the parameter dictionary for training configuration. - - This function checks for the presence and correct types of required - parameters for training, and applies default values for optional parameters - if they are not provided. The `device` entry is normalised in place into a - `torch.device`, so callers may supply either a string or a `torch.device`. - - Args: - param (dict): The dictionary of training parameters to validate. - Optional Keys: - - `device` (Union[str, torch.device]): The device to train on, e.g. - `"cpu"`, `"cuda"`, `"cuda:0"`, or `"mps"`. Hardware acceleration is - opt-in; pass it explicitly to use a GPU or Apple Silicon. - Default is `"cpu"`. - - Raises: - ValueError: If a required parameter is missing, or if the requested `device` is invalid - or unavailable on this machine. - TypeError: If a parameter has an incorrect type. - """ - # --- Required fields and types - required_keys = {"optimizer_class": type, "optimizer_args": dict} - - # --- Normalise the requested device (accepts str or torch.device, defaults to CPU) - param["device"] = resolve_device(param.get("device", "cpu")) - - # --- Optional fields with default values and expected types - optional_keys = { - "n_fold": (int, 5), - "n_epoch": (int, 100), - "patience": (int, 5), - "batch_size": (int, 64), - "model_save_dir": (str, "./models"), - "seed": (int, 88), - "seed_worker": (Callable, seed_worker), - "max_workers": (int, 0), - "best_model_name": (str, "best_model.pt"), - "pin_memory": (bool, False), - "persistent_workers": (bool, False), - } - - # --- Validate required keys - for key, expected_types in required_keys.items(): - if key not in param: - raise ValueError(f"Missing required param key: '{key}'") - if not isinstance(param[key], expected_types): - raise TypeError( - f"param['{key}'] must be of type {expected_types}, got {type(param[key])}" - ) - - # --- Apply defaults and type-check optional keys - for key, (expected_type, default) in optional_keys.items(): - if key not in param: - param[key] = default - elif expected_type is not None and not isinstance(param[key], expected_type): - raise TypeError( - f"param['{key}'] must be of type {expected_type}, got {type(param[key])}" - ) - - -def validate_test_param(param: dict): - """ - Validates the parameter dictionary for testing configuration. - - This function checks for the presence and correct types of optional - parameters for testing, and applies default values if they are not provided. - The `device` entry is normalised in place into a `torch.device`, so callers - may supply either a string or a `torch.device`. - - Args: - param (dict): The dictionary of testing parameters to validate. - Optional Keys: - - `device` (Union[str, torch.device]): The device to evaluate on, e.g. - `"cpu"`, `"cuda"`, `"cuda:0"`, or `"mps"`. Hardware acceleration is - opt-in; pass it explicitly to use a GPU or Apple Silicon. - Default is `"cpu"`. - - Raises: - ValueError: If the requested `device` is invalid or unavailable on this machine. - TypeError: If a parameter has an incorrect type. - """ - # --- Normalise the requested device (accepts str or torch.device, defaults to CPU) - param["device"] = resolve_device(param.get("device", "cpu")) - - # --- Optional fields with default values and expected types - optional_keys = { - "batch_size": (int, 64), - } - - # --- Apply defaults and type-check optional keys - for key, (expected_type, default) in optional_keys.items(): - if key not in param: - param[key] = default - elif expected_type is not None and not isinstance(param[key], expected_type): - raise TypeError( - f"param['{key}'] must be of type {expected_type}, got {type(param[key])}" - ) - - -def validate_pred_param(param: dict): - """ - Validates the parameter dictionary for prediction configuration. - - This function checks for the presence and correct types of optional - parameters for prediction, and applies default values if they are not provided. - The `device` entry is normalised in place into a `torch.device`, so callers - may supply either a string or a `torch.device`. - - Args: - param (dict): The dictionary of prediction parameters to validate. - Optional Keys: - - `device` (Union[str, torch.device]): The device to predict on, e.g. - `"cpu"`, `"cuda"`, `"cuda:0"`, or `"mps"`. Hardware acceleration is - opt-in; pass it explicitly to use a GPU or Apple Silicon. - Default is `"cpu"`. - - Raises: - ValueError: If the requested `device` is invalid or unavailable on this machine. - TypeError: If a parameter has an incorrect type. - """ - # --- Normalise the requested device (accepts str or torch.device, defaults to CPU) - param["device"] = resolve_device(param.get("device", "cpu")) - - # --- Optional fields with default values and expected types - optional_keys = { - "batch_size": (int, 64), - } - - # --- Apply defaults and type-check optional keys - for key, (expected_type, default) in optional_keys.items(): - if key not in param: - param[key] = default - elif expected_type is not None and not isinstance(param[key], expected_type): - raise TypeError( - f"param['{key}'] must be of type {expected_type}, got {type(param[key])}" - ) diff --git a/src/garmentiq/garment_classes.py b/src/garmentiq/garment_classes.py deleted file mode 100644 index a13406f..0000000 --- a/src/garmentiq/garment_classes.py +++ /dev/null @@ -1,83 +0,0 @@ -"""The predefined garment classes and their measurement instructions. - -`garment_classes` maps each supported garment to the number of landmarks the detection -model predicts for it, the slice of the model output those landmarks occupy, and the -path to the JSON instruction defining its measurements. Instruction files are bundled -with the package and resolved to absolute paths at import time. - -Pass a modified copy as `class_dict` to use your own measurement instructions. -""" -from importlib.resources import files - -package_name = __name__.split(".")[0] - - -def _instruction_path(filename: str) -> str: - """Resolve a bundled instruction JSON to an absolute filesystem path.""" - return str(files(package_name) / "instruction" / filename) - - -garment_classes = { - "long sleeve dress": { - "num_predefined_points": 37, - "index_range": (219, 256), - "instruction": _instruction_path("long sleeve dress.json") - }, - "long sleeve top": { - "num_predefined_points": 33, - "index_range": (25, 58), - "instruction": _instruction_path("long sleeve top.json") - }, - "short sleeve dress": { - "num_predefined_points": 29, - "index_range": (190, 219), - "instruction": _instruction_path("short sleeve dress.json") - }, - "short sleeve top": { - "num_predefined_points": 25, - "index_range": (0, 25), - "instruction": _instruction_path("short sleeve top.json") - }, - "shorts": { - "num_predefined_points": 10, - "index_range": (158, 168), - "instruction": _instruction_path("shorts.json") - }, - "skirt": { - "num_predefined_points": 8, - "index_range": (182, 190), - "instruction": _instruction_path("skirt.json") - }, - "trousers": { - "num_predefined_points": 14, - "index_range": (168, 182), - "instruction": _instruction_path("trousers.json") - }, - "vest": { - "num_predefined_points": 15, - "index_range": (128, 143), - "instruction": _instruction_path("vest.json") - }, - "vest dress": { - "num_predefined_points": 19, - "index_range": (256, 275), - "instruction": _instruction_path("vest dress.json") - } -} -"""dict: A comprehensive dictionary defining various garment classes and their properties for GarmentIQ. - -Each key in this dictionary represents a specific garment type (e.g., "long sleeve dress"). -The value associated with each garment type is a dictionary containing: - - "num_predefined_points" (int): The total number of predefined (detected) - keypoints for this garment class. - - "index_range" (tuple): A tuple (start_index, end_index) specifying the - slice of the model's output that corresponds to the predefined keypoints - for this garment. These indices are 0-based and exclusive of the end_index. - - "instruction" (str): The file path to a JSON schema that details the - specific landmarks (predefined and derivable) and measurements for this - garment type. These paths are resolved using `importlib.resources.files` - to ensure they are correctly located within the installed package. - -This dictionary serves as a central registry for the GarmentIQ system to -understand and process different types of clothing. -""" diff --git a/src/garmentiq/grounding/__init__.py b/src/garmentiq/grounding/__init__.py deleted file mode 100644 index b6fbd52..0000000 --- a/src/garmentiq/grounding/__init__.py +++ /dev/null @@ -1,46 +0,0 @@ -# garmentiq/grounding/__init__.py -"""Text-to-region grounding. - -Grounding models turn a natural-language phrase into bounding boxes. This is what -gives SAM 1 and SAM 2 text-prompted segmentation, since neither contains a text -encoder. The boxes are model-agnostic, so this module is deliberately independent -of the segmentation package. - -The model classes are re-exported from `transformers` and documented upstream, so they -are not repeated here. `GroundingDinoForObjectDetection`, `GroundingDinoConfig`, and -`GroundingDinoProcessor` can all be imported from this module directly. -""" -from .grounding_dino import ( - load_grounding_config, - load_grounding_processor, - load_grounding_model, - ground_text_to_boxes, -) - -_LAZY = { - "GroundingDinoForObjectDetection", - "GroundingDinoConfig", - "GroundingDinoProcessor", -} - - -def __getattr__(name): - """Defer transformers imports until a grounding class is actually requested.""" - if name in _LAZY: - from . import grounding_dino - - return getattr(grounding_dino, name) - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") - - -__all__ = [ - "load_grounding_config", - "load_grounding_processor", - "load_grounding_model", - "ground_text_to_boxes", -] - - -def __dir__(): - """List the lazily resolved model classes alongside the eager exports.""" - return sorted(set(__all__) | _LAZY) diff --git a/src/garmentiq/grounding/grounding_dino.py b/src/garmentiq/grounding/grounding_dino.py deleted file mode 100644 index 121338e..0000000 --- a/src/garmentiq/grounding/grounding_dino.py +++ /dev/null @@ -1,254 +0,0 @@ -"""The Grounding DINO text-to-region detector. - -Loads the model fully offline from a local directory and converts a natural-language -phrase into bounding boxes. A dedicated loader is used because Grounding DINO ties -several decoder heads to one shared set of weights, which the generic loader would -leave randomly initialised. -""" -import os -import json -from typing import TYPE_CHECKING, Optional, Union - -import torch -from PIL import Image - -from garmentiq.utils.device import resolve_device, inputs_to_device - -if TYPE_CHECKING: # pragma: no cover - import only for type checkers - from transformers import ( - GroundingDinoForObjectDetection, - GroundingDinoConfig, - GroundingDinoProcessor, - ) - -# Transformers classes are resolved lazily so that importing GarmentIQ never -# depends on a specific transformers release. The helpful error is raised only -# when a grounding feature is actually used. -_LAZY_CLASSES = { - "GroundingDinoForObjectDetection": "Text-prompted grounding", - "GroundingDinoConfig": "Text-prompted grounding", - "GroundingDinoProcessor": "Text-prompted grounding", -} - - -def _resolve(name: str): - """Fetches a transformers class on demand with an actionable error message.""" - import transformers - - try: - return getattr(transformers, name) - except AttributeError as e: - raise ImportError( - f"'{name}' is not available in transformers " - f"{getattr(transformers, '__version__', 'unknown')}. " - f"{_LAZY_CLASSES.get(name, 'This feature')} requires a newer transformers " - f"release; upgrade with `pip install -U transformers`." - ) from e - - -def __getattr__(name): - """Module-level lazy attribute access (PEP 562).""" - if name in _LAZY_CLASSES: - return _resolve(name) - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") - - -def load_grounding_config(model_dir: str): - """ - Reads and loads a local Grounding DINO configuration for offline use. - - Grounding DINO turns a natural-language phrase into bounding boxes, which SAM 1 and - SAM 2 accept natively as prompts. Its configuration and tokenizer files are not - bundled with GarmentIQ because they belong to a separate upstream model, so download - the model once and point `model_dir` at the resulting directory. - - Args: - model_dir (str): Path to a local directory containing the Grounding DINO - `config.json`, e.g. a download of `IDEA-Research/grounding-dino-tiny`. - - Raises: - FileNotFoundError: If `model_dir` is not a directory or does not contain `config.json`. - ImportError: If the installed transformers release does not provide Grounding DINO. - - Returns: - GroundingDinoConfig: The loaded configuration object, ready to be passed into - `GroundingDinoForObjectDetection`. - """ - if not os.path.isdir(model_dir): - raise FileNotFoundError( - f"Provided model_dir '{model_dir}' is not an existing directory." - ) - - config_path = os.path.join(model_dir, "config.json") - if not os.path.exists(config_path): - raise FileNotFoundError(f"Offline config missing at {config_path}.") - - with open(config_path, "r") as f: - config_dict = json.load(f) - - return _resolve("GroundingDinoConfig").from_dict(config_dict) - - -def load_grounding_processor(model_dir: str): - """ - Loads a local Grounding DINO processor for offline use. - - The processor pairs an image processor with a text tokenizer, so the whole local - directory is required rather than a single JSON file. - - Args: - model_dir (str): Path to a local directory containing the Grounding DINO processor - and tokenizer files. - - Raises: - FileNotFoundError: If `model_dir` is not an existing directory. - ImportError: If the installed transformers release does not provide Grounding DINO. - - Returns: - GroundingDinoProcessor: The instantiated processor ready for image and text inputs. - """ - if not os.path.isdir(model_dir): - raise FileNotFoundError( - f"Provided model_dir '{model_dir}' is not an existing directory." - ) - - return _resolve("GroundingDinoProcessor").from_pretrained(model_dir) - - -def load_grounding_model( - model_dir: str, - device: Union[str, torch.device] = "cpu", -): - """ - Loads a Grounding DINO model from a local directory, fully offline. - - Grounding DINO ties several decoder heads to a shared set of weights, so its checkpoint - stores only one copy of them. Constructing the model from a config and calling - `load_state_dict` therefore leaves those tied tensors randomly initialised, which - silently degrades grounding quality. `from_pretrained` performs the tying correctly, - so it is used here rather than the generic loader used for other GarmentIQ models. - - Args: - model_dir (str): Path to a local directory holding the Grounding DINO `config.json`, - weights, and tokenizer files, e.g. a download of - `IDEA-Research/grounding-dino-tiny`. - device (Union[str, torch.device], optional): The device to load the model onto, e.g. - `"cpu"`, `"cuda"`, or `"mps"`. - Default is `"cpu"`. - - Raises: - FileNotFoundError: If `model_dir` is not an existing directory. - ValueError: If the requested `device` is invalid or unavailable on this machine. - ImportError: If the installed transformers release does not provide Grounding DINO. - - Returns: - GroundingDinoForObjectDetection: The loaded model in evaluation mode, on `device`. - """ - if not os.path.isdir(model_dir): - raise FileNotFoundError( - f"Provided model_dir '{model_dir}' is not an existing directory." - ) - - device = resolve_device(device) - model = _resolve("GroundingDinoForObjectDetection").from_pretrained(model_dir) - model = model.to(device) - model.eval() - return model - - -def ground_text_to_boxes( - model, - processor, - image: Image.Image, - text: str, - box_threshold: float = 0.3, - text_threshold: float = 0.3, - max_boxes: Optional[int] = None, - device: Union[str, torch.device] = "cpu", -): - """ - Converts a natural-language phrase into bounding boxes for an image. - - This is the bridge that gives SAM 1 and SAM 2 text-prompted segmentation. Neither model - contains a text encoder, so the phrase is first grounded into boxes by Grounding DINO, - and those boxes are then used as ordinary geometric prompts for SAM. - - Grounding DINO expects lowercase phrases terminated by a period; the text is normalised - to that convention automatically. - - Args: - model (GroundingDinoForObjectDetection): The loaded Grounding DINO model. - processor (GroundingDinoProcessor): The matching processor with its tokenizer. - image (PIL.Image.Image): The image to ground the phrase against. - text (str): The natural-language prompt, e.g. `"a shirt"` or `"trousers"`. - box_threshold (float, optional): Minimum box confidence to keep a detection. - Default is 0.3. - text_threshold (float, optional): Minimum text-matching score to keep a detection. - Default is 0.3. - max_boxes (int, optional): If given, keep at most this many highest-scoring boxes. - Default is None, meaning keep all boxes above threshold. - device (Union[str, torch.device], optional): The device to run grounding on, e.g. - `"cpu"`, `"cuda"`, or `"mps"`. - Default is `"cpu"`. - - Raises: - ValueError: If `text` is empty, if the requested `device` is unavailable, or if no - region of the image matches the phrase at the given thresholds. - - Returns: - list[list[float]]: Bounding boxes in `[x_min, y_min, x_max, y_max]` pixel coordinates, - ordered by descending confidence. - """ - if not text or not text.strip(): - raise ValueError("A non-empty text prompt is required for grounding.") - - device = resolve_device(device) - model = model.to(device) - - # Grounding DINO is trained on lowercase phrases ending with a period. - caption = text.strip().lower() - if not caption.endswith("."): - caption = caption + "." - - inputs = inputs_to_device( - processor(images=image, text=caption, return_tensors="pt"), device - ) - - with torch.no_grad(): - outputs = model(**inputs) - - results = processor.post_process_grounded_object_detection( - outputs, - inputs["input_ids"], - threshold=box_threshold, - text_threshold=text_threshold, - target_sizes=[(image.height, image.width)], - )[0] - - boxes = results["boxes"].detach().cpu() - scores = results["scores"].detach().cpu() - - if boxes.numel() == 0: - raise ValueError( - f"Grounding model found no region matching text prompt {text!r} at " - f"box_threshold={box_threshold} and text_threshold={text_threshold}. " - f"Try lowering the thresholds or rephrasing the prompt." - ) - - order = torch.argsort(scores, descending=True) - boxes = boxes[order] - if max_boxes is not None: - boxes = boxes[:max_boxes] - - return [[float(v) for v in box] for box in boxes] - - -__all__ = [ - "GroundingDinoForObjectDetection", - "GroundingDinoConfig", - "GroundingDinoProcessor", - "load_grounding_config", - "load_grounding_processor", - "load_grounding_model", - "ground_text_to_boxes", -] diff --git a/src/garmentiq/instruction/long sleeve dress.json b/src/garmentiq/instruction/long sleeve dress.json deleted file mode 100644 index 810a7a3..0000000 --- a/src/garmentiq/instruction/long sleeve dress.json +++ /dev/null @@ -1,162 +0,0 @@ -{ - "long sleeve dress": { - "landmarks": { - "2": { - "predefined": true, - "description": "neck_left_outer", - "x": 80, - "y": 18 - }, - "16": { - "predefined": true, - "description": "armpit_left", - "x": 60, - "y": 65 - }, - "18": { - "predefined": true, - "description": "side_seam_left_upper_mid", - "x": 62, - "y": 110 - }, - "19": { - "predefined": true, - "description": "waist_left", - "x": 60, - "y": 155 - }, - "25": { - "predefined": true, - "description": "waist_right", - "x": 140, - "y": 155 - }, - "26": { - "predefined": true, - "description": "side_seam_right_upper_mid", - "x": 138, - "y": 110 - }, - "28": { - "predefined": true, - "description": "armpit_right", - "x": 140, - "y": 65 - }, - "33": { - "predefined": true, - "description": "cuff_right_outer", - "x": 171, - "y": 148 - }, - "37": { - "predefined": true, - "description": "shoulder_right_top", - "x": 151, - "y": 32 - }, - "38": { - "predefined": false, - "description": "custom_landmark", - "x": 39.11378860473633, - "y": 54.05463409423828, - "derivation": { - "function": "derive_keypoint_coord", - "p1_id": "16", - "p2_id": "7", - "p3_id": "9", - "p4_id": "7", - "p5_id": "9", - "direction": "perpendicular" - }, - "neighbors": { - "8": { - "predefined": true, - "description": "sleeve_outer_left_upper", - "x": 37, - "y": 63 - }, - "16": { - "predefined": true, - "description": "armpit_left", - "x": 60, - "y": 65 - } - } - }, - "39": { - "predefined": false, - "description": "custom_landmark", - "x": 78.73814392089844, - "y": 244.92807006835938, - "derivation": { - "function": "derive_keypoint_coord", - "p1_id": "2", - "p2_id": "4", - "p3_id": "22", - "p4_id": "21", - "p5_id": "23", - "direction": "parallel" - }, - "neighbors": { - "21": { - "predefined": true, - "description": "hem_left", - "x": 51, - "y": 241 - }, - "22": { - "predefined": true, - "description": "hem_center", - "x": 100, - "y": 246 - } - } - } - }, - "measurements": { - "full length": { - "landmarks": { - "start": "2", - "end": "39" - }, - "description": "39: intersection of vertical line from 2 to bottom contour" - }, - "chest": { - "landmarks": { - "start": "16", - "end": "28" - }, - "description": "/" - }, - "sleeve width": { - "landmarks": { - "start": "16", - "end": "38" - }, - "description": "38: intersection of perpendicular line from 16 to sleeve contour" - }, - "waist": { - "landmarks": { - "start": "18", - "end": "26" - }, - "description": "/" - }, - "hips": { - "landmarks": { - "start": "19", - "end": "25" - }, - "description": "/" - }, - "sleeve length": { - "landmarks": { - "start": "33", - "end": "37" - }, - "description": "/" - } - } - } -} \ No newline at end of file diff --git a/src/garmentiq/instruction/long sleeve top.json b/src/garmentiq/instruction/long sleeve top.json deleted file mode 100644 index 84d54ed..0000000 --- a/src/garmentiq/instruction/long sleeve top.json +++ /dev/null @@ -1,137 +0,0 @@ -{ - "long sleeve top": { - "landmarks": { - "2": { - "predefined": true, - "description": "neck_left_outer", - "x": 80, - "y": 18 - }, - "7": { - "predefined": true, - "description": "shoulder_left_top", - "x": 52, - "y": 30 - }, - "16": { - "predefined": true, - "description": "armpit_left", - "x": 60, - "y": 65 - }, - "24": { - "predefined": true, - "description": "armpit_right", - "x": 140, - "y": 65 - }, - "29": { - "predefined": true, - "description": "cuff_right_outer", - "x": 171, - "y": 148 - }, - "33": { - "predefined": true, - "description": "shoulder_right_top", - "x": 151, - "y": 32 - }, - "34": { - "predefined": false, - "description": "custom_landmark", - "x": 79.2851333618164, - "y": 163.4697265625, - "derivation": { - "function": "derive_keypoint_coord", - "p1_id": "2", - "p2_id": "4", - "p3_id": "20", - "p4_id": "19", - "p5_id": "21", - "direction": "parallel" - }, - "neighbors": { - "19": { - "predefined": true, - "description": "hem_left", - "x": 55, - "y": 160 - }, - "20": { - "predefined": true, - "description": "hem_center", - "x": 100, - "y": 165 - } - } - }, - "35": { - "predefined": false, - "description": "custom_landmark", - "x": 38.667747497558594, - "y": 55.427490234375, - "derivation": { - "function": "derive_keypoint_coord", - "p1_id": "16", - "p2_id": "7", - "p3_id": "9", - "p4_id": "7", - "p5_id": "9", - "direction": "perpendicular" - }, - "neighbors": { - "8": { - "predefined": true, - "description": "sleeve_outer_left_upper", - "x": 37, - "y": 63 - }, - "16": { - "predefined": true, - "description": "armpit_left", - "x": 60, - "y": 65 - } - } - } - }, - "measurements": { - "front length": { - "landmarks": { - "start": "2", - "end": "34" - }, - "description": "34: intersection of vertical line from 2 to bottom contour" - }, - "back width": { - "landmarks": { - "start": "7", - "end": "33" - }, - "description": "/" - }, - "chest": { - "landmarks": { - "start": "16", - "end": "24" - }, - "description": "/" - }, - "sleeve width": { - "landmarks": { - "start": "16", - "end": "35" - }, - "description": "35: intersection of perpendicular line from 16 to sleeve contour" - }, - "sleeve length": { - "landmarks": { - "start": "29", - "end": "33" - }, - "description": "/" - } - } - } -} \ No newline at end of file diff --git a/src/garmentiq/instruction/short sleeve dress.json b/src/garmentiq/instruction/short sleeve dress.json deleted file mode 100644 index 46db0fa..0000000 --- a/src/garmentiq/instruction/short sleeve dress.json +++ /dev/null @@ -1,162 +0,0 @@ -{ - "short sleeve dress": { - "landmarks": { - "2": { - "predefined": true, - "description": "neck_left_outer", - "x": 83, - "y": 15 - }, - "12": { - "predefined": true, - "description": "armpit_left", - "x": 60, - "y": 65 - }, - "14": { - "predefined": true, - "description": "side_seam_left_upper_mid", - "x": 62, - "y": 88 - }, - "15": { - "predefined": true, - "description": "waist_left", - "x": 62, - "y": 121 - }, - "21": { - "predefined": true, - "description": "waist_right", - "x": 137, - "y": 121 - }, - "22": { - "predefined": true, - "description": "side_seam_right_upper_mid", - "x": 137, - "y": 88 - }, - "24": { - "predefined": true, - "description": "armpit_right", - "x": 140, - "y": 65 - }, - "27": { - "predefined": true, - "description": "cuff_right_outer", - "x": 170, - "y": 50 - }, - "29": { - "predefined": true, - "description": "shoulder_right_top", - "x": 143, - "y": 25 - }, - "30": { - "predefined": false, - "description": "custom_landmark", - "x": 82.60393524169922, - "y": 246.6676788330078, - "derivation": { - "function": "derive_keypoint_coord", - "p1_id": "2", - "p2_id": "4", - "p3_id": "18", - "p4_id": "17", - "p5_id": "19", - "direction": "parallel" - }, - "neighbors": { - "17": { - "predefined": true, - "description": "hem_left", - "x": 47, - "y": 242 - }, - "18": { - "predefined": true, - "description": "hem_center", - "x": 100, - "y": 248 - } - } - }, - "31": { - "predefined": false, - "description": "custom_landmark", - "x": 47.32859420776367, - "y": 34.049163818359375, - "derivation": { - "function": "derive_keypoint_coord", - "p1_id": "12", - "p2_id": "7", - "p3_id": "9", - "p4_id": "7", - "p5_id": "9", - "direction": "perpendicular" - }, - "neighbors": { - "7": { - "predefined": true, - "description": "shoulder_left_top", - "x": 58, - "y": 25 - }, - "8": { - "predefined": true, - "description": "sleeve_left_outer_mid", - "x": 40, - "y": 40 - } - } - } - }, - "measurements": { - "full length": { - "landmarks": { - "start": "2", - "end": "30" - }, - "description": "30: intersection of vertical line from 2 to bottom contour" - }, - "chest": { - "landmarks": { - "start": "12", - "end": "24" - }, - "description": "/" - }, - "sleeve width": { - "landmarks": { - "start": "12", - "end": "31" - }, - "description": "31: intersection of perpendicular line from 12 to sleeve contour" - }, - "waist": { - "landmarks": { - "start": "14", - "end": "22" - }, - "description": "/" - }, - "hips": { - "landmarks": { - "start": "15", - "end": "21" - }, - "description": "/" - }, - "sleeve length": { - "landmarks": { - "start": "27", - "end": "29" - }, - "description": "/" - } - } - } -} \ No newline at end of file diff --git a/src/garmentiq/instruction/short sleeve top.json b/src/garmentiq/instruction/short sleeve top.json deleted file mode 100644 index d5ad228..0000000 --- a/src/garmentiq/instruction/short sleeve top.json +++ /dev/null @@ -1,137 +0,0 @@ -{ - "short sleeve top": { - "landmarks": { - "2": { - "predefined": true, - "description": "neck_left_outer", - "x": 83, - "y": 15 - }, - "7": { - "predefined": true, - "description": "shoulder_left_top", - "x": 58, - "y": 25 - }, - "12": { - "predefined": true, - "description": "armpit_left", - "x": 60, - "y": 65 - }, - "20": { - "predefined": true, - "description": "armpit_right", - "x": 140, - "y": 65 - }, - "23": { - "predefined": true, - "description": "cuff_right_outer", - "x": 170, - "y": 50 - }, - "25": { - "predefined": true, - "description": "shoulder_right_top", - "x": 143, - "y": 25 - }, - "26": { - "predefined": false, - "description": "custom_landmark", - "x": 82.12834930419922, - "y": 164.28207397460938, - "derivation": { - "function": "derive_keypoint_coord", - "p1_id": "2", - "p2_id": "4", - "p3_id": "16", - "p4_id": "15", - "p5_id": "17", - "direction": "parallel" - }, - "neighbors": { - "15": { - "predefined": true, - "description": "hem_left", - "x": 56, - "y": 160 - }, - "16": { - "predefined": true, - "description": "hem_center", - "x": 100, - "y": 165 - } - } - }, - "27": { - "predefined": false, - "description": "custom_landmark", - "x": 46.79122543334961, - "y": 33.494102478027344, - "derivation": { - "function": "derive_keypoint_coord", - "p1_id": "12", - "p2_id": "7", - "p3_id": "9", - "p4_id": "7", - "p5_id": "9", - "direction": "perpendicular" - }, - "neighbors": { - "7": { - "predefined": true, - "description": "shoulder_left_top", - "x": 58, - "y": 25 - }, - "8": { - "predefined": true, - "description": "sleeve_left_outer_mid", - "x": 40, - "y": 40 - } - } - } - }, - "measurements": { - "front length": { - "landmarks": { - "start": "2", - "end": "26" - }, - "description": "26: intersection of vertical line from 2 to bottom contour" - }, - "back width": { - "landmarks": { - "start": "7", - "end": "25" - }, - "description": "/" - }, - "chest": { - "landmarks": { - "start": "12", - "end": "20" - }, - "description": "/" - }, - "sleeve width": { - "landmarks": { - "start": "12", - "end": "27" - }, - "description": "27: intersection of perpendicular line from 12 to sleeve contour" - }, - "sleeve length": { - "landmarks": { - "start": "23", - "end": "25" - }, - "description": "/" - } - } - } -} \ No newline at end of file diff --git a/src/garmentiq/instruction/shorts.json b/src/garmentiq/instruction/shorts.json deleted file mode 100644 index 0ce5795..0000000 --- a/src/garmentiq/instruction/shorts.json +++ /dev/null @@ -1,78 +0,0 @@ -{ - "shorts": { - "landmarks": { - "1": { - "predefined": true, - "description": "waist_left", - "x": 50, - "y": 35 - }, - "2": { - "predefined": true, - "description": "waist_center", - "x": 94, - "y": 39 - }, - "3": { - "predefined": true, - "description": "waist_right", - "x": 138, - "y": 35 - }, - "4": { - "predefined": true, - "description": "side_seam_left_mid", - "x": 48, - "y": 77 - }, - "5": { - "predefined": true, - "description": "hem_left_outer", - "x": 46, - "y": 119 - }, - "7": { - "predefined": true, - "description": "crotch_apex", - "x": 94, - "y": 80 - }, - "10": { - "predefined": true, - "description": "side_seam_right_mid", - "x": 140, - "y": 77 - } - }, - "measurements": { - "waist": { - "landmarks": { - "start": "1", - "end": "3" - }, - "description": "/" - }, - "full length": { - "landmarks": { - "start": "1", - "end": "5" - }, - "description": "/" - }, - "front rise": { - "landmarks": { - "start": "2", - "end": "7" - }, - "description": "/" - }, - "hips": { - "landmarks": { - "start": "4", - "end": "10" - }, - "description": "/" - } - } - } -} \ No newline at end of file diff --git a/src/garmentiq/instruction/skirt.json b/src/garmentiq/instruction/skirt.json deleted file mode 100644 index c40a0c0..0000000 --- a/src/garmentiq/instruction/skirt.json +++ /dev/null @@ -1,65 +0,0 @@ -{ - "skirt": { - "landmarks": { - "1": { - "predefined": true, - "description": "waist_left", - "x": 60, - "y": 40 - }, - "2": { - "predefined": true, - "description": "waist_center", - "x": 100, - "y": 45 - }, - "3": { - "predefined": true, - "description": "waist_right", - "x": 140, - "y": 40 - }, - "4": { - "predefined": true, - "description": "side_seam_left", - "x": 50, - "y": 80 - }, - "6": { - "predefined": true, - "description": "hem_center", - "x": 100, - "y": 135 - }, - "8": { - "predefined": true, - "description": "side_seam_right", - "x": 150, - "y": 80 - } - }, - "measurements": { - "waist": { - "landmarks": { - "start": "1", - "end": "3" - }, - "description": "/" - }, - "full length": { - "landmarks": { - "start": "2", - "end": "6" - }, - "description": "/" - }, - "hips": { - "landmarks": { - "start": "4", - "end": "8" - }, - "description": "/" - } - } - } -} \ No newline at end of file diff --git a/src/garmentiq/instruction/trousers.json b/src/garmentiq/instruction/trousers.json deleted file mode 100644 index d61281d..0000000 --- a/src/garmentiq/instruction/trousers.json +++ /dev/null @@ -1,78 +0,0 @@ -{ - "trousers": { - "landmarks": { - "1": { - "predefined": true, - "description": "waist_left", - "x": 50, - "y": 35 - }, - "2": { - "predefined": true, - "description": "waist_center", - "x": 94, - "y": 39 - }, - "3": { - "predefined": true, - "description": "waist_right", - "x": 138, - "y": 35 - }, - "4": { - "predefined": true, - "description": "side_seam_left_upper", - "x": 48, - "y": 80 - }, - "6": { - "predefined": true, - "description": "hem_left_outer", - "x": 42, - "y": 238 - }, - "9": { - "predefined": true, - "description": "crotch_apex", - "x": 94, - "y": 85 - }, - "14": { - "predefined": true, - "description": "outer_right_seam_upper", - "x": 140, - "y": 80 - } - }, - "measurements": { - "waist": { - "landmarks": { - "start": "1", - "end": "3" - }, - "description": "/" - }, - "full length": { - "landmarks": { - "start": "1", - "end": "6" - }, - "description": "/" - }, - "front rise": { - "landmarks": { - "start": "2", - "end": "9" - }, - "description": "/" - }, - "hips": { - "landmarks": { - "start": "4", - "end": "14" - }, - "description": "/" - } - } - } -} \ No newline at end of file diff --git a/src/garmentiq/instruction/vest dress.json b/src/garmentiq/instruction/vest dress.json deleted file mode 100644 index fef65e2..0000000 --- a/src/garmentiq/instruction/vest dress.json +++ /dev/null @@ -1,107 +0,0 @@ -{ - "vest dress": { - "landmarks": { - "2": { - "predefined": true, - "description": "neck_left_outer", - "x": 83, - "y": 15 - }, - "8": { - "predefined": true, - "description": "side_seam_left_upper", - "x": 59, - "y": 70 - }, - "9": { - "predefined": true, - "description": "waist_left", - "x": 57.5, - "y": 100 - }, - "10": { - "predefined": true, - "description": "hip_left", - "x": 56, - "y": 122 - }, - "16": { - "predefined": true, - "description": "hip_right", - "x": 145, - "y": 122 - }, - "17": { - "predefined": true, - "description": "wast_right", - "x": 143, - "y": 100 - }, - "18": { - "predefined": true, - "description": "side_seam_right_upper", - "x": 141, - "y": 70 - }, - "20": { - "predefined": false, - "description": "custom_landmark", - "x": 79.22137451171875, - "y": 245.21800231933594, - "derivation": { - "function": "derive_keypoint_coord", - "p1_id": "2", - "p2_id": "4", - "p3_id": "13", - "p4_id": "12", - "p5_id": "14", - "direction": "parallel" - }, - "neighbors": { - "12": { - "predefined": true, - "description": "hem_left", - "x": 43, - "y": 240 - }, - "13": { - "predefined": true, - "description": "hem_center", - "x": 99, - "y": 246 - } - } - } - }, - "measurements": { - "full length": { - "landmarks": { - "start": "2", - "end": "20" - }, - "description": "20: intersection of vertical line from 2 to bottom contour" - }, - "chest": { - "landmarks": { - "start": "8", - "end": "18" - }, - "description": "/" - }, - "waist": { - "landmarks": { - "start": "9", - "end": "17" - }, - "description": "/" - }, - "hips": { - "landmarks": { - "start": "10", - "end": "16" - }, - "description": "/" - } - } - } -} \ No newline at end of file diff --git a/src/garmentiq/instruction/vest.json b/src/garmentiq/instruction/vest.json deleted file mode 100644 index 3a95d4a..0000000 --- a/src/garmentiq/instruction/vest.json +++ /dev/null @@ -1,88 +0,0 @@ -{ - "vest": { - "landmarks": { - "2": { - "predefined": true, - "description": "neck_left_outer", - "x": 83, - "y": 15 - }, - "7": { - "predefined": true, - "description": "shoulder_left_top", - "x": 58, - "y": 25 - }, - "8": { - "predefined": true, - "description": "side_seam_left_upper", - "x": 59, - "y": 70 - }, - "14": { - "predefined": true, - "description": "side_seam_right_upper", - "x": 141, - "y": 70 - }, - "15": { - "predefined": true, - "description": "shoulder_right", - "x": 143, - "y": 25 - }, - "16": { - "predefined": false, - "description": "custom_landmark", - "x": 81.31600189208984, - "y": 163.8759002685547, - "derivation": { - "function": "derive_keypoint_coord", - "p1_id": "2", - "p2_id": "4", - "p3_id": "11", - "p4_id": "10", - "p5_id": "12", - "direction": "parallel" - }, - "neighbors": { - "10": { - "predefined": true, - "description": "hem_left", - "x": 55, - "y": 160 - }, - "11": { - "predefined": true, - "description": "hem_center", - "x": 99, - "y": 165 - } - } - } - }, - "measurements": { - "front length": { - "landmarks": { - "start": "2", - "end": "16" - }, - "description": "16: intersection of vertical line from 2 to bottom contour" - }, - "back width": { - "landmarks": { - "start": "7", - "end": "15" - }, - "description": "/" - }, - "chest": { - "landmarks": { - "start": "8", - "end": "14" - }, - "description": "/" - } - } - } -} \ No newline at end of file diff --git a/src/garmentiq/landmark/__init__.py b/src/garmentiq/landmark/__init__.py deleted file mode 100644 index d58e4a1..0000000 --- a/src/garmentiq/landmark/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -# garmentiq/landmark/__init__.py -"""Garment landmark detection, refinement, and derivation. - -Landmarks are the key points a measurement runs between, such as shoulders, sleeve -ends, and hems. This module produces them in up to three steps: - -- `detect` predicts the landmarks the pose model was trained on. -- `refine` snaps those predictions onto the true garment boundary using a - segmentation mask, correcting points that sit slightly off the edge. -- `derive` computes additional landmarks that the model does not predict at all, - using geometric rules evaluated against the mask. - -Refinement and derivation both require a segmentation mask. -""" -from .detect import detect -from .detection import * -from .derive import derive -from .derivation import * -from .refine import refine -from .refinement import * -from .plot import plot -from .utils import ( - find_instruction_landmark_index, - fill_instruction_landmark_coordinate, -) diff --git a/src/garmentiq/landmark/derivation/__init__.py b/src/garmentiq/landmark/derivation/__init__.py deleted file mode 100644 index 615a65f..0000000 --- a/src/garmentiq/landmark/derivation/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# garmentiq/landmark/derivation/__init__.py -"""Derivation of landmarks the detection model does not predict. - -Some measurement points are defined geometrically rather than learned, and are marked -`predefined: False` in a garment class definition. They are computed by intersecting a -constructed line with the segmentation mask, following the rules in a derivation -dictionary. -""" -from .derive_keypoint_coord import derive_keypoint_coord -from .prepare_args import prepare_args -from .process import process \ No newline at end of file diff --git a/src/garmentiq/landmark/derivation/derivation_dict.py b/src/garmentiq/landmark/derivation/derivation_dict.py deleted file mode 100644 index c73d02d..0000000 --- a/src/garmentiq/landmark/derivation/derivation_dict.py +++ /dev/null @@ -1,33 +0,0 @@ -"""The registry of available derivation functions. - -Maps each derivation method name to its callable and the arguments it expects. Pass a -modified copy to `landmark.derive` to add your own derivation rules. -""" -derivation_dict = { - "derive_keypoint_coord": { - "p1_id": None, - "p2_id": None, - "p3_id": None, - "p4_id": None, - "p5_id": None, - "direction": ["parallel", "perpendicular"], - } -} -"""dict: A dictionary defining the schemas for various landmark derivation functions. - -Each key in this dictionary represents a derivation function, and its value is -another dictionary specifying the expected parameters for that function. -`None` as a parameter value indicates that the parameter must be provided -when calling the function, and it is typically an integer ID corresponding to a -predefined landmark. - -Example structure for "derive_keypoint_coord": - "derive_keypoint_coord": { - "p1_id": None, # Required: Integer ID of the first point. - "p2_id": None, # Required: Integer ID of the second point. - "p3_id": None, # Required: Integer ID of the third point. - "p4_id": None, # Required: Integer ID of the fourth point. - "p5_id": None, # Required: Integer ID of the fifth point. - "direction": ["parallel", "perpendicular"], # Required: How to derive line 1's direction. - } -""" \ No newline at end of file diff --git a/src/garmentiq/landmark/derivation/derive_keypoint_coord.py b/src/garmentiq/landmark/derivation/derive_keypoint_coord.py deleted file mode 100644 index ab2c404..0000000 --- a/src/garmentiq/landmark/derivation/derive_keypoint_coord.py +++ /dev/null @@ -1,110 +0,0 @@ -"""Deriving a landmark coordinate from surrounding landmarks and a mask.""" -from typing import Tuple, Optional -import numpy as np -from .line_intersect import _find_line_line_intersection -from .mask_intersect import _get_mask_boundary, _find_line_mask_intersections -from .utils import _calculate_line1_vector, _find_closest_point - - -def derive_keypoint_coord( - p1_id: int, - p2_id: int, - p3_id: int, - p4_id: int, - p5_id: int, - direction: str, - landmark_coords: np.array, - np_mask: np.array, - line_length_factor: float = 10000.0, -) -> Optional[Tuple[float, float]]: - """ - Derives the coordinate of a new keypoint based on intersections of lines defined by - existing predefined keypoints and the segmentation mask boundary. - - This function implements a geometric method to derive a new landmark: - 1. It uses `p1_id` as a point on the first line. - 2. The direction of the first line is derived from `p2_id`, `p3_id`, and `direction` - (parallel or perpendicular to the line formed by p2 and p3). - 3. The second line is defined by `p4_id` and `p5_id`. - 4. An initial intersection point is found between Line 1 and Line 2. - 5. The boundary of the `np_mask` is extracted. - 6. Intersections between Line 1 and the `np_mask` boundary are found. - 7. The final derived point is the mask intersection point closest to the line-line intersection, - or the line-line intersection itself if no mask intersections are found or an error occurs. - - Args: - p1_id (int): ID of the first keypoint used to define Line 1. - p2_id (int): ID of the second keypoint used to define Line 1's direction. - p3_id (int): ID of the third keypoint used to define Line 1's direction. - p4_id (int): ID of the fourth keypoint used to define Line 2. - p5_id (int): ID of the fifth keypoint used to define Line 2. - direction (str): Specifies the direction of Line 1 relative to (p2, p3). - Can be "parallel" or "perpendicular". - landmark_coords (np.array): NumPy array of all detected landmark coordinates. - Shape: (1, N, 2) where N is the total number of landmarks. - np_mask (np.array): NumPy array of the segmentation mask. - line_length_factor (float, optional): Factor to extend lines for intersection calculations. - Defaults to 10000.0. - - Returns: - Optional[Tuple[float, float]]: The (x, y) coordinates of the derived keypoint, or None if - derivation fails (e.g., parallel lines, no valid mask boundary). - """ - p1_coord = landmark_coords[:, p1_id - 1, :].reshape(2) - p2_coord = landmark_coords[:, p2_id - 1, :].reshape(2) - p3_coord = landmark_coords[:, p3_id - 1, :].reshape(2) - p4_coord = landmark_coords[:, p4_id - 1, :].reshape(2) - p5_coord = landmark_coords[:, p5_id - 1, :].reshape(2) - - # 3. Define Line 1 Vector (v1) - v1 = _calculate_line1_vector(p2_coord, p3_coord, direction) - if v1 is None: - return None # Error or zero vector detected - - # 4. Define Line 2 Vector (v2) - v2 = (p5_coord[0] - p4_coord[0], p5_coord[1] - p4_coord[1]) - if np.isclose(v2[0], 0) and np.isclose(v2[1], 0): - print( - f"Warning: Direction vector for Line 2 is zero (p4_id={p4_id} and p5_id={p5_id} likely coincide)." - ) - return None # Cannot define Line 2 - - # 5. Calculate Line-Line Intersection - line_intersection_point = _find_line_line_intersection(p1_coord, v1, p4_coord, v2) - if line_intersection_point is None: - print( - "Info: Line 1 and Line 2 are parallel or collinear. No unique intersection." - ) - return None - - # 6. Load Mask Boundary - mask_boundary_geom = _get_mask_boundary(np_mask) - if mask_boundary_geom is None or mask_boundary_geom.is_empty: - print( - "Warning: No valid mask boundary found or mask is empty. Returning line-line intersection." - ) - return line_intersection_point - - # 7. Find Intersection(s) between Line 1 and Mask Boundary - mask_intersection_points = _find_line_mask_intersections( - p1_coord, v1, mask_boundary_geom, line_length_factor - ) - - # 8. Determine Final Point - if mask_intersection_points is None: - # An error occurred during intersection calculation - print( - "Warning: Error finding mask intersections. Returning line-line intersection as fallback." - ) - return line_intersection_point - elif not mask_intersection_points: - # No intersection found between line and mask boundary - # print("Info: Line 1 does not intersect the mask boundary. Returning line-line intersection.") - return line_intersection_point - else: - # Found intersection(s), find the one closest to the line-line intersection - closest_mask_point = _find_closest_point( - mask_intersection_points, line_intersection_point - ) - # _find_closest_point should always return a point if the list is not empty - return closest_mask_point diff --git a/src/garmentiq/landmark/derivation/line_intersect.py b/src/garmentiq/landmark/derivation/line_intersect.py deleted file mode 100644 index 1737b6c..0000000 --- a/src/garmentiq/landmark/derivation/line_intersect.py +++ /dev/null @@ -1,40 +0,0 @@ -"""Intersection of two lines in the image plane.""" -import numpy as np -from typing import Tuple, Optional - - -def _find_line_line_intersection( - p1: Tuple[float, float], - v1: Tuple[float, float], - p2: Tuple[float, float], - v2: Tuple[float, float], -) -> Optional[Tuple[float, float]]: - """ - Calculates the intersection point of two lines, each defined by a point and a direction vector. - - Args: - p1 (Tuple[float, float]): A point (x1, y1) on the first line. - v1 (Tuple[float, float]): The direction vector (dx1, dy1) of the first line. - p2 (Tuple[float, float]): A point (x2, y2) on the second line. - v2 (Tuple[float, float]): The direction vector (dx2, dy2) of the second line. - - Returns: - Optional[Tuple[float, float]]: The (x, y) coordinates of the intersection point, - or None if the lines are parallel or collinear (no unique intersection). - """ - x1, y1 = p1 - dx1, dy1 = v1 - x2, y2 = p2 - dx2, dy2 = v2 - - denominator = dx2 * dy1 - dy2 * dx1 - if np.isclose(denominator, 0): # Lines are parallel or collinear - return None - - qp_x = x1 - x2 - qp_y = y1 - y2 - t = (qp_x * dy1 - qp_y * dx1) / denominator - ix = x2 + t * dx2 - iy = y2 + t * dy2 - - return ix, iy diff --git a/src/garmentiq/landmark/derivation/mask_intersect.py b/src/garmentiq/landmark/derivation/mask_intersect.py deleted file mode 100644 index e8034de..0000000 --- a/src/garmentiq/landmark/derivation/mask_intersect.py +++ /dev/null @@ -1,136 +0,0 @@ -"""Intersection of a line with the garment mask contour.""" -import numpy as np -import cv2 -from typing import Optional, List, Tuple, Any -from shapely.geometry import Point, LineString, Polygon, MultiPoint, MultiLineString -from shapely.ops import unary_union - - -def _get_mask_boundary(mask: np.ndarray): - """ - Processes a binary or grayscale mask array and returns the primary boundary as Shapely geometry. - - This function finds contours in the mask, converts them into Shapely Polygon boundaries - or LineStrings, and then unions them into a single (potentially Multi-) geometry. - - Args: - mask (np.ndarray): The input binary or grayscale mask array. - - Returns: - Optional[Any]: A Shapely geometry (Polygon.boundary, LineString, MultiPoint, or MultiLineString) - representing the mask boundary, or None if the mask is invalid or no contours are found. - """ - try: - if mask is None or not isinstance(mask, np.ndarray): - print("Error: Provided mask is not a valid NumPy array.") - return None - - # Ensure binary mask (values 0 or 1) - binary_mask = (mask > 0).astype(np.uint8) - - contours, _ = cv2.findContours( - binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE - ) - - if not contours: - print("Warning: No contours found in the mask array.") - return None - - geometries = [] - for contour in contours: - points = [tuple(p[0]) for p in contour] - if len(points) >= 3: - geometries.append(Polygon(points).boundary) - elif len(points) == 2: - geometries.append(LineString(points)) - - if not geometries: - print("Warning: No valid boundary geometries found in the mask.") - return None - - return unary_union(geometries) - - except Exception as e: - print(f"Error processing mask array: {str(e)}") - return None - - -def _find_line_mask_intersections( - line_point: Tuple[float, float], - line_vector: Tuple[float, float], - mask_boundary: Any, # Shapely Geometry - line_length_factor: float, -) -> Optional[List[Tuple[float, float]]]: - """ - Finds intersection points between a line (defined by a point and vector) and a Shapely mask boundary. - - Constructs a long Shapely LineString from the input line definition and - computes its intersection with the provided `mask_boundary` geometry. - - Args: - line_point (Tuple[float, float]): A point (x, y) on the line. - line_vector (Tuple[float, float]): The direction vector (dx, dy) of the line. - mask_boundary (Any): A Shapely Geometry object representing the mask boundary (e.g., from `_get_mask_boundary`). - line_length_factor (float): A factor to extend the line segment for intersection testing, - ensuring it crosses the entire mask if needed. - - Returns: - Optional[List[Tuple[float, float]]]: A list of (x, y) tuples for all unique intersection points, - an empty list if no intersections, or None if an error occurs. - """ - try: - # Create a long Shapely line representing the mathematical line - norm_v = np.linalg.norm(line_vector) - if np.isclose(norm_v, 0): - print("Error: Line vector is zero during Shapely line creation.") - return None # Cannot create line - - unit_v = (line_vector[0] / norm_v, line_vector[1] / norm_v) - - pt_a = ( - line_point[0] - line_length_factor * unit_v[0], - line_point[1] - line_length_factor * unit_v[1], - ) - pt_b = ( - line_point[0] + line_length_factor * unit_v[0], - line_point[1] + line_length_factor * unit_v[1], - ) - shapely_line = LineString([pt_a, pt_b]) - - # Calculate intersection - intersection = mask_boundary.intersection(shapely_line) - - # Process intersection results - if intersection.is_empty: - return [] # Return empty list for no intersection - - intersection_points = [] - geoms_to_process = [] - - if isinstance(intersection, Point): - geoms_to_process.append(intersection) - elif isinstance(intersection, (MultiPoint, LineString, MultiLineString)): - # Use .geoms for MultiPoint/MultiLineString, .coords for LineString - if hasattr(intersection, "geoms"): - geoms_to_process.extend(list(intersection.geoms)) - elif hasattr(intersection, "coords"): # LineString (boundary coincidence) - # Extract points from the LineString coords - coords = list(intersection.coords) - for coord in coords: - intersection_points.append(coord) # Add individual vertices - # Avoid processing LineString further as Point below - - # Extract coordinates from Point geometries - for geom in geoms_to_process: - if isinstance(geom, Point): - intersection_points.append((geom.x, geom.y)) - - # Remove duplicates if necessary (e.g., from LineString endpoints) - # Using list(dict.fromkeys(intersection_points)) preserves order unlike set - unique_intersection_points = list(dict.fromkeys(intersection_points)) - - return unique_intersection_points - - except Exception as e: - print(f"Error during Shapely intersection calculation: {str(e)}") - return None # Indicate an error occurred diff --git a/src/garmentiq/landmark/derivation/prepare_args.py b/src/garmentiq/landmark/derivation/prepare_args.py deleted file mode 100644 index eb3d070..0000000 --- a/src/garmentiq/landmark/derivation/prepare_args.py +++ /dev/null @@ -1,87 +0,0 @@ -"""Assembling the arguments a derivation function needs.""" -import numpy as np -from .derivation_dict import derivation_dict - - -def prepare_args( - entry: dict, derivation_dict: dict = derivation_dict, **extra_args -) -> dict: - """ - Prepares arguments for a specific derivation function based on an entry from the - landmark derivation configuration and extra arguments. - - This function ensures that all required arguments for a given derivation function - are collected and validated against its schema defined in `derivation_dict`. - It also adds any necessary `extra_args` (like `landmark_coords` or `np_mask`). - - Args: - entry (dict): A dictionary representing a single landmark's derivation entry, - which must include a 'function' key and its specific parameters. - derivation_dict (dict): The global dictionary defining schemas for all derivation functions. - Defaults to `derivation_dict`. - **extra_args: Additional keyword arguments that might be required by the derivation function, - such as `landmark_coords` (NumPy array of landmark coordinates) - and `np_mask` (NumPy array of the segmentation mask). - - Raises: - ValueError: If the 'function' key is missing in `entry`, or if the function name is unknown. - TypeError: If an unsupported schema format is encountered for a key. - ValueError: If a required `extra_arg` is missing for a specific function (e.g., `landmark_coords`). - - Returns: - dict: A dictionary where the key is the function name and its value is a dictionary - of arguments ready to be passed to that derivation function. - """ - function_name = entry.get("function") - if function_name is None: - raise ValueError("Entry must include a 'function' key.") - if function_name not in derivation_dict: - raise ValueError(f"Unknown function: {function_name}") - - function_schema = derivation_dict[function_name] - args = {} - - for key, value in entry.items(): - if key == "function": - continue - - expected_type = function_schema.get(key) - - if expected_type is None: - # Should be cast to int - args[key] = int(value) - elif isinstance(expected_type, list): - # Should be one of the listed options - if value not in expected_type: - raise ValueError( - f"Invalid value '{value}' for {key}; expected one of {expected_type}" - ) - args[key] = value - else: - raise TypeError( - f"Unsupported schema format for key '{key}' in function '{function_name}'" - ) - - # Add function-specific extra arguments - if function_name == "derive_keypoint_coord": - if "landmark_coords" not in extra_args: - raise ValueError( - "'landmark_coords' is required for 'derive_keypoint_coord'" - ) - elif not isinstance(extra_args["landmark_coords"], np.ndarray): - raise ValueError("'landmark_coords' must be a 'np.ndarray'") - - if "np_mask" not in extra_args: - raise ValueError("'np_mask' is required for 'derive_keypoint_coord'") - elif not isinstance(extra_args["np_mask"], np.ndarray): - raise ValueError("'np_mask' must be a 'np.ndarray'") - args["landmark_coords"] = extra_args["landmark_coords"] - args["np_mask"] = extra_args["np_mask"] - return {"derive_keypoint_coord": args} - # Add more if conditions if there are more derivation functions in the future - # elif function_name == "another_function_1": - # if "arg_3" not in extra_args: - # raise ValueError("'arg_3' is required for 'another_function_1'") - # else: - # args['mask_path'] = extra_args['mask_path'] - # return args diff --git a/src/garmentiq/landmark/derivation/process.py b/src/garmentiq/landmark/derivation/process.py deleted file mode 100644 index fad71df..0000000 --- a/src/garmentiq/landmark/derivation/process.py +++ /dev/null @@ -1,32 +0,0 @@ -"""Dispatching a derivation request to its registered function.""" -from .derive_keypoint_coord import derive_keypoint_coord - - -def process(**args): - """ - Dispatches to the appropriate derivation function based on the 'function' name - provided in the arguments. - - This function acts as a router, taking a dictionary of arguments which - includes the name of the derivation function to call (under a key - that matches a function name in its internal dispatch table). - - Args: - **args: Keyword arguments where one key's value is a dictionary - containing the arguments for a specific derivation function, - and that key's name corresponds to the derivation function. - Example: `{'derive_keypoint_coord': {'p1_id': 1, ...}}` - - Raises: - ValueError: If the function name specified in `args` is unknown. - - Returns: - Any: The result returned by the dispatched derivation function. - """ - dispatch = { - "derive_keypoint_coord": derive_keypoint_coord, - } - for func_name, func_args in args.items(): - if func_name not in dispatch: - raise ValueError(f"Unknown function: {func_name}") - return dispatch[func_name](**func_args) diff --git a/src/garmentiq/landmark/derivation/utils.py b/src/garmentiq/landmark/derivation/utils.py deleted file mode 100644 index 054357f..0000000 --- a/src/garmentiq/landmark/derivation/utils.py +++ /dev/null @@ -1,106 +0,0 @@ -"""Geometric helpers used by the derivation functions.""" -import numpy as np -from typing import Tuple, Optional, List - - -def _calculate_line1_vector( - p2_coord: Tuple[float, float], p3_coord: Tuple[float, float], direction: str -) -> Optional[Tuple[float, float]]: - """ - Calculates the direction vector for Line 1 based on p2, p3, and direction. - - Args: - p2_coord (Tuple[float, float]): The (x, y) coordinates of the second point. - p3_coord (Tuple[float, float]): The (x, y) coordinates of the third point. - direction (str): The desired direction of Line 1 relative to the vector - from `p2_coord` to `p3_coord`. Must be "parallel" or "perpendicular". - - Returns: - Optional[Tuple[float, float]]: The calculated direction vector (dx, dy) - as a tuple of floats, or `None` if the direction is invalid or the - vector (p3-p2) is a zero vector. - """ - ref_dx = p3_coord[0] - p2_coord[0] - ref_dy = p3_coord[1] - p2_coord[1] - - if direction == "parallel": - v1 = (ref_dx, ref_dy) - elif direction == "perpendicular": - v1 = (-ref_dy, ref_dx) - else: - print( - f"Error: Invalid direction '{direction}'. Use 'parallel' or 'perpendicular'." - ) - return None - - # Check for zero vector - if np.isclose(v1[0], 0) and np.isclose(v1[1], 0): - print( - f"Warning: Direction vector for Line 1 is zero (p2 and p3 likely coincide)." - ) - # Decide if this should be a fatal error or handled downstream - # Returning None signals an issue. - return None - - return v1 - - -def _find_closest_point( - points_list: List[Tuple[float, float]], target_point: Tuple[float, float] -) -> Optional[Tuple[float, float]]: - """ - Finds the point in `points_list` that is closest (Euclidean distance) to `target_point`. - - Args: - points_list (List[Tuple[float, float]]): A list of 2D points (x, y) to search within. - target_point (Tuple[float, float]): The reference point (x, y) to find the closest point to. - - Returns: - Optional[Tuple[float, float]]: The (x, y) coordinates of the closest point from - `points_list` as a tuple of floats, or `None` if `points_list` is empty. - """ - if not points_list: - return None - - points_np = np.array(points_list) - target_np = np.array(target_point) - - distances = np.linalg.norm(points_np - target_np, axis=1) - closest_index = np.argmin(distances) - - return tuple(points_np[closest_index]) - - -def parse_derivation_args(deriv_dict, json_path, mask_path): - """ - Parses a derivation dictionary to extract arguments for a derivation function. - - This function is a helper for preparing arguments required by specific derivation - functions (e.g., `derive_keypoint_coord`). It extracts parameters and adds fixed - inputs like `json_path` and `mask_path`. - - Args: - deriv_dict (dict): A dictionary containing derivation parameters for a specific landmark. - json_path (str): Path to the JSON file related to the image. - mask_path (str): Path to the mask file related to the image. - - Returns: - dict: A dictionary of parsed arguments ready to be passed to a derivation function. - """ - args = {} - for k, v in deriv_dict.items(): - if k == "function": - continue - # p*_id should be ints, everything else leave as‐is - if k.endswith("_id"): - try: - args[k] = int(v) - except ValueError: - # in case someone uses numbers not strictly digits - args[k] = int(float(v)) - else: - args[k] = v - args["json_path"] = json_path - args["mask_path"] = mask_path - return args - return args diff --git a/src/garmentiq/landmark/derive.py b/src/garmentiq/landmark/derive.py deleted file mode 100644 index 96e3d89..0000000 --- a/src/garmentiq/landmark/derive.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Deriving landmarks that the detection model does not predict.""" -import copy -from garmentiq.landmark.derivation import ( - prepare_args, - process, -) - - -def derive( - class_name: str, detection_dict: dict, derivation_dict: dict, **extra_args -) -> tuple: - """ - Derives non-predefined landmark coordinates based on predefined landmarks and a mask. - - This function identifies landmarks marked for derivation within the `detection_dict`, - prepares arguments for the derivation function using `prepare_args`, - processes the derivation using `process`, and updates the `detection_dict` - with the newly derived coordinates. - - Args: - class_name (str): The name of the garment class. - detection_dict (dict): The dictionary containing detected landmarks, including predefined ones. - derivation_dict (dict): The dictionary defining derivation rules and available functions. - **extra_args: Additional keyword arguments required by the derivation functions, - such as `landmark_coords` (NumPy array of landmark coordinates) - and `np_mask` (NumPy array of the segmentation mask). - - Returns: - tuple: A tuple containing: - - derived_coords (dict): A dictionary mapping the derived landmark IDs to their new (x, y) coordinates. - - detection_dict (dict): The original `detection_dict` updated with the derived landmark coordinates. - """ - non_predefined_landmark = { - k: detection_dict[class_name]["landmarks"][k]["derivation"] - for k, v in detection_dict[class_name]["landmarks"].items() - if v.get("predefined") is False - } - derived_coords = {} - detection_dict_copy = copy.deepcopy(detection_dict) - for k, v in non_predefined_landmark.items(): - args = prepare_args(non_predefined_landmark[k], derivation_dict, **extra_args) - derived_coord = tuple(float(x) for x in process(**args)) - derived_coords[k] = derived_coord - detection_dict_copy[class_name]["landmarks"][k]["x"] = derived_coord[0] - detection_dict_copy[class_name]["landmarks"][k]["y"] = derived_coord[1] - return derived_coords, detection_dict_copy diff --git a/src/garmentiq/landmark/detect.py b/src/garmentiq/landmark/detect.py deleted file mode 100644 index 6d9bbd4..0000000 --- a/src/garmentiq/landmark/detect.py +++ /dev/null @@ -1,145 +0,0 @@ -"""Detecting the predefined landmarks of a garment.""" -import json -import os -from typing import Type, Union -import torch -import requests -import numpy as np -from garmentiq.utils import validate_garment_class_dict -from garmentiq.utils.device import resolve_device, empty_cache -from garmentiq.landmark.utils import ( - find_instruction_landmark_index, - fill_instruction_landmark_coordinate, -) -from garmentiq.landmark.detection.utils import ( - input_image_transform, - get_final_preds, - transform_preds, -) - - -def detect( - class_name: str, - class_dict: dict, - image_path: Union[str, np.ndarray], - model: Type[torch.nn.Module], - scale_std: float = 200.0, - resize_dim: list[int, int] = [288, 384], - normalize_mean: list[float, float, float] = [0.485, 0.456, 0.406], - normalize_std: list[float, float, float] = [0.229, 0.224, 0.225], - device: Union[str, torch.device] = "cpu", -): - """ - Detects predefined landmarks on a garment image using a specified model and class instructions. - - This function validates the input class dictionary and class name, loads the appropriate - instruction schema (from local file or URL), preprocesses the image, runs it through - the landmark detection model, and then transforms the detected heatmap predictions - into image coordinates. The detected coordinates are then filled into the instruction data. - - The model and the preprocessed input tensor are placed on `device`, so the same value should - be passed here as was used when loading the model. Any accelerator memory cached during - inference is released before returning. - - Args: - class_name (str): The name of the garment class (e.g., "vest dress", "trousers"). - class_dict (dict): A dictionary mapping class names to their properties, including - `num_predefined_points`, `index_range`, and `instruction` file path. - image_path (Union[str, np.ndarray]): The path to the image file or a NumPy array of the image. - model (Type[torch.nn.Module]): The loaded PyTorch landmark detection model. - scale_std (float, optional): Standard scale for image transformation during preprocessing. Defaults to 200.0. - resize_dim (list[int, int], optional): Target dimensions [width, height] for the transformed image. - Defaults to [288, 384]. - normalize_mean (list[float, float, float], optional): Mean values for image normalization (RGB channels). - Defaults to [0.485, 0.456, 0.406]. - normalize_std (list[float, float, float], optional): Standard deviation values for image normalization (RGB channels). - Defaults to [0.229, 0.224, 0.225]. - device (Union[str, torch.device], optional): The device to run inference on, e.g. `"cpu"`, - `"cuda"`, `"cuda:0"`, or `"mps"`. Hardware - acceleration is opt-in; pass it explicitly to - use a GPU or Apple Silicon. Defaults to `"cpu"`. - - Raises: - ValueError: If `class_dict` is invalid, `class_name` is not found in `class_dict`, or the - requested `device` is invalid or unavailable on this machine. - FileNotFoundError: If the instruction file is not found. - ValueError: If loading instruction JSON from URL fails or `class_name` is not found in instruction file. - - Returns: - tuple: - - preds_all (np.array): All predicted landmark coordinates (including non-predefined). - - maxvals (np.array): Confidence scores for the predefined landmark predictions. - - instruction_data (dict): The instruction dictionary updated with detected landmark coordinates and confidences. - """ - device = resolve_device(device) - - if not validate_garment_class_dict(class_dict): - raise ValueError( - "Provided class_dict is not in the expected garment_classes format." - ) - - if class_name not in class_dict: - raise ValueError( - f"Invalid class '{class_name}'. Must be one of: {list(class_dict.keys())}" - ) - - class_element = class_dict[class_name] - - instruction_path = class_element["instruction"] - - if instruction_path.startswith("http://") or instruction_path.startswith( - "https://" - ): - try: - response = requests.get(instruction_path) - response.raise_for_status() - instruction_data = response.json() - except Exception as e: - raise ValueError( - f"Failed to load instruction JSON from URL: {instruction_path}\nError: {e}" - ) - else: - if not os.path.exists(instruction_path): - raise FileNotFoundError(f"Instruction file not found: {instruction_path}") - with open(instruction_path, "r") as f: - instruction_data = json.load(f) - - if class_name not in instruction_data: - raise ValueError(f"Class '{class_name}' not found in instruction file.") - - (input_tensor, image_np, center, scale,) = input_image_transform( - image_path, scale_std, resize_dim, normalize_mean, normalize_std - ) - - model = model.to(device) - input_tensor = input_tensor.to(device) - - with torch.no_grad(): - np_output_heatmap = model(input_tensor).detach().cpu().numpy() - - empty_cache(device) - - preds_heatmap, maxvals = get_final_preds( - np_output_heatmap[ - :, class_element["index_range"][0] : class_element["index_range"][1], :, : - ] - ) - - predefined_index = find_instruction_landmark_index( - instruction_data[class_name]["landmarks"], predefined=True - ) - preds_all = np.stack([transform_preds(p, center, scale) for p in preds_heatmap]) - preds = preds_all[:, predefined_index, :] - - instruction_data[class_name]["landmarks"] = fill_instruction_landmark_coordinate( - instruction_landmarks=instruction_data[class_name]["landmarks"], - index=predefined_index, - fill_in_value=preds, - ) - - for idx in predefined_index: - instruction_data[class_name]["landmarks"][str(idx + 1)]["conf"] = float( - maxvals[0, idx, 0] - ) - - return preds_all, maxvals, instruction_data diff --git a/src/garmentiq/landmark/detection/__init__.py b/src/garmentiq/landmark/detection/__init__.py deleted file mode 100644 index 5dcf25b..0000000 --- a/src/garmentiq/landmark/detection/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -# garmentiq/landmark/detection/__init__.py -"""Landmark detection model and its pre- and post-processing. - -Holds the HRNet-based pose model used to predict garment landmarks, its loader, and -the coordinate transforms that map between the original image and the model's input -resolution, including affine cropping and heatmap decoding. -""" -from .load_model import load_model -from .model_definition import PoseHighResolutionNet -from .utils import ( - get_max_preds, - get_final_preds, - flip_back, - fliplr_joints, - transform_preds, - get_affine_transform, - affine_transform, - get_3rd_point, - get_dir, - crop, - input_image_transform, -) diff --git a/src/garmentiq/landmark/detection/load_model.py b/src/garmentiq/landmark/detection/load_model.py deleted file mode 100644 index 2a3968d..0000000 --- a/src/garmentiq/landmark/detection/load_model.py +++ /dev/null @@ -1,51 +0,0 @@ -"""Loading the HRNet landmark detection model onto a device.""" -import torch -from typing import Callable, Type, Union -from garmentiq.utils.device import resolve_device -from garmentiq.utils.checkpoint import load_state_dict_checked - - -def load_model( - model_path: str, - model_class: Type[torch.nn.Module], - device: Union[str, torch.device] = "cpu", -): - """ - Load a PyTorch model from a checkpoint and prepare it for inference. - - This function initializes a model from the provided `model_class`, loads its weights from - the given file path, moves it to the requested device, and sets it to evaluation mode. - When multiple CUDA GPUs are available and a CUDA device is requested, the model is - additionally wrapped with `DataParallel` for multi-GPU inference. - - Args: - model_path (str): Path to the saved model checkpoint (.pth or .pt file). - model_class (Type[torch.nn.Module]): The class definition of the model to be instantiated. - This must be a subclass of `torch.nn.Module`. - device (Union[str, torch.device], optional): The device to load the model onto, e.g. - `"cpu"`, `"cuda"`, `"cuda:0"`, or `"mps"`. - Hardware acceleration is opt-in; pass it - explicitly to use a GPU or Apple Silicon. - Default is `"cpu"`. - - Raises: - ValueError: If the requested `device` is invalid or unavailable on this machine. - RuntimeError: If the model checkpoint cannot be loaded. - - Returns: - torch.nn.Module: The loaded and ready-to-use model, placed on `device`. - """ - device = resolve_device(device) - - model = model_class - load_state_dict_checked( - model, torch.load(model_path, map_location=device), model_path - ) - model = model.to(device) - - if device.type == "cuda" and torch.cuda.device_count() > 1: - model = torch.nn.DataParallel(model) - - model.eval() - - return model diff --git a/src/garmentiq/landmark/detection/model_definition.py b/src/garmentiq/landmark/detection/model_definition.py deleted file mode 100644 index 732b56c..0000000 --- a/src/garmentiq/landmark/detection/model_definition.py +++ /dev/null @@ -1,788 +0,0 @@ -"""The HRNet pose model used for landmark detection. - -`PoseHighResolutionNet` maintains high-resolution feature maps throughout the network -rather than recovering resolution at the end, which is what makes it accurate for -precise keypoint localisation. -""" -import os -import logging -import torch -import torch.nn as nn -import torch.nn.functional as F - - -BN_MOMENTUM = 0.1 -logger = logging.getLogger(__name__) - - -def conv3x3(in_planes, out_planes, stride=1): - """ - Creates a 3x3 convolutional layer with padding. - - Args: - in_planes (int): Number of input channels. - out_planes (int): Number of output channels. - stride (int, optional): Stride of the convolution. Defaults to 1. - - Returns: - nn.Conv2d: 3x3 convolution layer with specified parameters. - """ - return nn.Conv2d( - in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False - ) - - -class BasicBlock(nn.Module): - """ - Basic residual block with two 3x3 convolutional layers. - - Attributes: - expansion (int): Expansion factor for output channels (always 1 for BasicBlock). - conv1 (nn.Conv2d): First convolutional layer. - bn1 (nn.BatchNorm2d): Batch normalization after first conv. - conv2 (nn.Conv2d): Second convolutional layer. - bn2 (nn.BatchNorm2d): Batch normalization after second conv. - downsample (nn.Module or None): Optional downsampling layer for residual connection. - stride (int): Stride of the first convolution. - """ - - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - """ - Initializes BasicBlock. - - Args: - inplanes (int): Number of input channels. - planes (int): Number of output channels. - stride (int, optional): Stride of the first convolution. Defaults to 1. - downsample (nn.Module or None, optional): Downsampling layer for residual. Defaults to None. - """ - super(BasicBlock, self).__init__() - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) - self.conv2 = conv3x3(planes, planes) - self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - """ - Forward pass of the BasicBlock. - - Args: - x (torch.Tensor): Input tensor. - - Returns: - torch.Tensor: Output tensor after residual addition and activation. - """ - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = F.relu(out, inplace=True) - - out = self.conv2(out) - out = self.bn2(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = F.relu(out, inplace=True) - - return out - - -class Bottleneck(nn.Module): - """ - Bottleneck residual block with 1x1, 3x3, and 1x1 convolutions. - - Attributes: - expansion (int): Expansion factor for output channels (usually 4 for Bottleneck). - conv1 (nn.Conv2d): 1x1 convolution reducing channels. - bn1 (nn.BatchNorm2d): Batch normalization after conv1. - conv2 (nn.Conv2d): 3x3 convolution. - bn2 (nn.BatchNorm2d): Batch normalization after conv2. - conv3 (nn.Conv2d): 1x1 convolution expanding channels. - bn3 (nn.BatchNorm2d): Batch normalization after conv3. - downsample (nn.Module or None): Optional downsampling layer for residual connection. - stride (int): Stride for the 3x3 convolution. - """ - - expansion = 4 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - """ - Initializes Bottleneck block. - - Args: - inplanes (int): Number of input channels. - planes (int): Number of output channels before expansion. - stride (int, optional): Stride for the 3x3 convolution. Defaults to 1. - downsample (nn.Module or None, optional): Downsampling layer for residual. Defaults to None. - """ - super(Bottleneck, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) - self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) - self.conv2 = nn.Conv2d( - planes, planes, kernel_size=3, stride=stride, padding=1, bias=False - ) - self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) - self.conv3 = nn.Conv2d( - planes, planes * self.expansion, kernel_size=1, bias=False - ) - self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - """ - Forward pass of the Bottleneck block. - - Args: - x (torch.Tensor): Input tensor. - - Returns: - torch.Tensor: Output tensor after residual addition and activation. - """ - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = F.relu(out, inplace=True) - - out = self.conv2(out) - out = self.bn2(out) - out = F.relu(out, inplace=True) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = F.relu(out, inplace=True) - - return out - - -class HighResolutionModule(nn.Module): - """ - HighResolutionModule maintains high-resolution representations through multi-branch architecture and fusion. - - This module consists of several parallel branches with residual blocks, and fuse layers to combine - features from different resolutions. - - Attributes: - num_branches (int): Number of parallel branches. - blocks (nn.Module): Residual block class (BasicBlock or Bottleneck). - num_blocks (list[int]): Number of residual blocks per branch. - num_inchannels (list[int]): Number of input channels for each branch. - num_channels (list[int]): Number of channels per branch before expansion. - fuse_method (str): Method to fuse multi-branch outputs ('SUM' supported). - multi_scale_output (bool): Whether to output multi-scale features. - branches (nn.ModuleList): The parallel branches. - fuse_layers (nn.ModuleList or None): Layers that fuse features from branches. - """ - - def __init__( - self, - num_branches, - blocks, - num_blocks, - num_inchannels, - num_channels, - fuse_method, - multi_scale_output=True, - ): - """ - Initializes HighResolutionModule. - - Args: - num_branches (int): Number of parallel branches. - blocks (nn.Module): Residual block class (BasicBlock or Bottleneck). - num_blocks (list[int]): Number of residual blocks per branch. - num_inchannels (list[int]): Number of input channels for each branch. - num_channels (list[int]): Number of channels per branch before expansion. - fuse_method (str): Method to fuse multi-branch outputs. - multi_scale_output (bool, optional): Output multi-scale features or not. Defaults to True. - - Raises: - ValueError: If lengths of inputs do not match num_branches. - """ - super(HighResolutionModule, self).__init__() - self._check_branches( - num_branches, blocks, num_blocks, num_inchannels, num_channels - ) - - self.num_inchannels = num_inchannels - self.fuse_method = fuse_method - self.num_branches = num_branches - - self.multi_scale_output = multi_scale_output - - self.branches = self._make_branches( - num_branches, blocks, num_blocks, num_channels - ) - self.fuse_layers = self._make_fuse_layers() - - def _check_branches( - self, num_branches, blocks, num_blocks, num_inchannels, num_channels - ): - """ - Validates that lengths of num_blocks, num_inchannels, and num_channels match num_branches. - - Args: - num_branches (int): Number of branches. - blocks (nn.Module): Block type. - num_blocks (list[int]): Number of blocks per branch. - num_inchannels (list[int]): Number of input channels per branch. - num_channels (list[int]): Number of channels per branch. - - Raises: - ValueError: If any length mismatch occurs. - """ - if num_branches != len(num_blocks): - error_msg = "NUM_BRANCHES({}) <> NUM_BLOCKS({})".format( - num_branches, len(num_blocks) - ) - logger.error(error_msg) - raise ValueError(error_msg) - - if num_branches != len(num_channels): - error_msg = "NUM_BRANCHES({}) <> NUM_CHANNELS({})".format( - num_branches, len(num_channels) - ) - logger.error(error_msg) - raise ValueError(error_msg) - - if num_branches != len(num_inchannels): - error_msg = "NUM_BRANCHES({}) <> NUM_INCHANNELS({})".format( - num_branches, len(num_inchannels) - ) - logger.error(error_msg) - raise ValueError(error_msg) - - def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1): - """ - Constructs one branch of the module consisting of sequential residual blocks. - - Args: - branch_index (int): Index of the branch. - block (nn.Module): Residual block class. - num_blocks (int): Number of residual blocks in this branch. - num_channels (list[int]): Number of channels per branch. - stride (int, optional): Stride of the first block. Defaults to 1. - - Returns: - nn.Sequential: Sequential container of residual blocks. - """ - downsample = None - if ( - stride != 1 - or self.num_inchannels[branch_index] - != num_channels[branch_index] * block.expansion - ): - downsample = nn.Sequential( - nn.Conv2d( - self.num_inchannels[branch_index], - num_channels[branch_index] * block.expansion, - kernel_size=1, - stride=stride, - bias=False, - ), - nn.BatchNorm2d( - num_channels[branch_index] * block.expansion, momentum=BN_MOMENTUM - ), - ) - - layers = [] - layers.append( - block( - self.num_inchannels[branch_index], - num_channels[branch_index], - stride, - downsample, - ) - ) - self.num_inchannels[branch_index] = num_channels[branch_index] * block.expansion - for i in range(1, num_blocks[branch_index]): - layers.append( - block(self.num_inchannels[branch_index], num_channels[branch_index]) - ) - - return nn.Sequential(*layers) - - def _make_branches(self, num_branches, block, num_blocks, num_channels): - """ - Constructs all branches for the module. - - Args: - num_branches (int): Number of branches. - block (nn.Module): Residual block class. - num_blocks (list[int]): Number of blocks per branch. - num_channels (list[int]): Number of channels per branch. - - Returns: - nn.ModuleList: List of branch modules. - """ - branches = [] - - for i in range(num_branches): - branches.append(self._make_one_branch(i, block, num_blocks, num_channels)) - - return nn.ModuleList(branches) - - def _make_fuse_layers(self): - """ - Constructs layers to fuse multi-resolution branch outputs. - - Returns: - nn.ModuleList or None: Fuse layers or None if single branch. - """ - if self.num_branches == 1: - return None - - num_branches = self.num_branches - num_inchannels = self.num_inchannels - fuse_layers = [] - for i in range(num_branches if self.multi_scale_output else 1): - fuse_layer = [] - for j in range(num_branches): - if j > i: - fuse_layer.append( - nn.Sequential( - nn.Conv2d( - num_inchannels[j], - num_inchannels[i], - 1, - 1, - 0, - bias=False, - ), - nn.BatchNorm2d(num_inchannels[i]), - nn.Upsample(scale_factor=2 ** (j - i), mode="nearest"), - ) - ) - elif j == i: - fuse_layer.append(None) - else: - conv3x3s = [] - for k in range(i - j): - if k == i - j - 1: - num_outchannels_conv3x3 = num_inchannels[i] - conv3x3s.append( - nn.Sequential( - nn.Conv2d( - num_inchannels[j], - num_outchannels_conv3x3, - 3, - 2, - 1, - bias=False, - ), - nn.BatchNorm2d(num_outchannels_conv3x3), - ) - ) - else: - num_outchannels_conv3x3 = num_inchannels[j] - conv3x3s.append( - nn.Sequential( - nn.Conv2d( - num_inchannels[j], - num_outchannels_conv3x3, - 3, - 2, - 1, - bias=False, - ), - nn.BatchNorm2d(num_outchannels_conv3x3), - nn.ReLU(True), - ) - ) - fuse_layer.append(nn.Sequential(*conv3x3s)) - fuse_layers.append(nn.ModuleList(fuse_layer)) - - return nn.ModuleList(fuse_layers) - - def get_num_inchannels(self): - """ - Returns the number of input channels for each branch after block expansion. - - Returns: - list[int]: Number of input channels per branch. - """ - return self.num_inchannels - - def forward(self, x): - """ - Forward pass through the HighResolutionModule. - - Args: - x (list[torch.Tensor]): List of input tensors for each branch. - - Returns: - list[torch.Tensor]: List of output tensors after multi-branch fusion. - """ - if self.num_branches == 1: - return [self.branches[0](x[0])] - - for i in range(self.num_branches): - x[i] = self.branches[i](x[i]) - - x_fuse = [] - - for i in range(len(self.fuse_layers)): - y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) - for j in range(1, self.num_branches): - if i == j: - y = y + x[j] - else: - y = y + self.fuse_layers[i][j](x[j]) - x_fuse.append(F.relu(y, inplace=True)) - - return x_fuse - - -blocks_dict = {"BASIC": BasicBlock, "BOTTLENECK": Bottleneck} - - -class PoseHighResolutionNet(nn.Module): - """ - High-Resolution Network (HRNet) tailored for garment landmark detection. - - The network maintains high-resolution representations through multiple stages and branches, - fusing multi-scale features and finally predicting heatmaps or coordinates for landmarks. - - Attributes: - inplanes (int): Initial number of input channels. - conv1 (nn.Conv2d): Initial 3x3 convolution. - bn1 (nn.BatchNorm2d): Batch normalization after conv1. - conv2 (nn.Conv2d): Second 3x3 convolution. - bn2 (nn.BatchNorm2d): Batch normalization after conv2. - relu (nn.ReLU): ReLU activation. - stage1_cfg (dict): Configuration for stage1. - stage2_cfg (dict): Configuration for stage2. - stage3_cfg (dict): Configuration for stage3. - stage4_cfg (dict): Configuration for stage4. - transition1 (nn.ModuleList): Transition layers between stages. - stage2 (HighResolutionModule): Stage 2 module. - transition2 (nn.ModuleList): Transition layers between stages. - stage3 (HighResolutionModule): Stage 3 module. - transition3 (nn.ModuleList): Transition layers between stages. - stage4 (HighResolutionModule): Stage 4 module. - final_layer (nn.Conv2d): Final convolution layer to output predictions. - target_type (str): Output format type ('gaussian' or 'coordinate'). - """ - - def __init__(self, **kwargs): - """ - Initializes PoseHighResolutionNet with default HRNet configurations. - - Args: - target_type (str, optional): Type of target output. Either "gaussian" or "coordinate". Defaults to "gaussian". - """ - self.inplanes = 64 - # Hardcoded values from YAML MODEL.EXTRA - extra = { - "PRETRAINED_LAYERS": [ - "conv1", - "bn1", - "conv2", - "bn2", - "layer1", - "transition1", - "stage2", - "transition2", - "stage3", - "transition3", - "stage4", - ], - "FINAL_CONV_KERNEL": 1, - "STAGE2": { - "NUM_MODULES": 1, - "NUM_BRANCHES": 2, - "BLOCK": "BASIC", - "NUM_BLOCKS": [4, 4], - "NUM_CHANNELS": [48, 96], - "FUSE_METHOD": "SUM", - }, - "STAGE3": { - "NUM_MODULES": 4, - "NUM_BRANCHES": 3, - "BLOCK": "BASIC", - "NUM_BLOCKS": [4, 4, 4], - "NUM_CHANNELS": [48, 96, 192], - "FUSE_METHOD": "SUM", - }, - "STAGE4": { - "NUM_MODULES": 3, - "NUM_BRANCHES": 4, - "BLOCK": "BASIC", - "NUM_BLOCKS": [4, 4, 4, 4], - "NUM_CHANNELS": [48, 96, 192, 384], - "FUSE_METHOD": "SUM", - }, - } - - self.model_name = "pose_hrnet" - self.target_type = "gaussian" - - super(PoseHighResolutionNet, self).__init__() - - # stem net - self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) - self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) - self.layer1 = self._make_layer(Bottleneck, 64, 4) - - # Stage2 - self.stage2_cfg = extra["STAGE2"] - num_channels = self.stage2_cfg["NUM_CHANNELS"] - block = blocks_dict[self.stage2_cfg["BLOCK"]] - num_channels = [ - num_channels[i] * block.expansion for i in range(len(num_channels)) - ] - self.transition1 = self._make_transition_layer([256], num_channels) - self.stage2, pre_stage_channels = self._make_stage( - self.stage2_cfg, num_channels - ) - - # Stage3 - self.stage3_cfg = extra["STAGE3"] - num_channels = self.stage3_cfg["NUM_CHANNELS"] - block = blocks_dict[self.stage3_cfg["BLOCK"]] - num_channels = [ - num_channels[i] * block.expansion for i in range(len(num_channels)) - ] - self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels) - self.stage3, pre_stage_channels = self._make_stage( - self.stage3_cfg, num_channels - ) - - # Stage4 - self.stage4_cfg = extra["STAGE4"] - num_channels = self.stage4_cfg["NUM_CHANNELS"] - block = blocks_dict[self.stage4_cfg["BLOCK"]] - num_channels = [ - num_channels[i] * block.expansion for i in range(len(num_channels)) - ] - self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels) - self.stage4, pre_stage_channels = self._make_stage( - self.stage4_cfg, num_channels, multi_scale_output=False - ) - - # Final layer - self.final_layer = nn.Conv2d( - in_channels=pre_stage_channels[0], - out_channels=294, # from MODEL.NUM_JOINTS - kernel_size=extra["FINAL_CONV_KERNEL"], - stride=1, - padding=1 if extra["FINAL_CONV_KERNEL"] == 3 else 0, - ) - - self.pretrained_layers = extra["PRETRAINED_LAYERS"] - - def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer): - """ - Creates transition layers to match the number of channels between stages. - - Args: - num_channels_pre_layer (list[int]): Channels from previous stage. - num_channels_cur_layer (list[int]): Channels for current stage. - - Returns: - nn.ModuleList: List of transition layers. - """ - num_branches_cur = len(num_channels_cur_layer) - num_branches_pre = len(num_channels_pre_layer) - - transition_layers = [] - for i in range(num_branches_cur): - if i < num_branches_pre: - if num_channels_cur_layer[i] != num_channels_pre_layer[i]: - transition_layers.append( - nn.Sequential( - nn.Conv2d( - num_channels_pre_layer[i], - num_channels_cur_layer[i], - 3, - 1, - 1, - bias=False, - ), - nn.BatchNorm2d(num_channels_cur_layer[i]), - nn.ReLU(inplace=True), - ) - ) - else: - transition_layers.append(None) - else: - conv3x3s = [] - for j in range(i + 1 - num_branches_pre): - inchannels = num_channels_pre_layer[-1] - outchannels = ( - num_channels_cur_layer[i] - if j == i - num_branches_pre - else inchannels - ) - conv3x3s.append( - nn.Sequential( - nn.Conv2d(inchannels, outchannels, 3, 2, 1, bias=False), - nn.BatchNorm2d(outchannels), - nn.ReLU(inplace=True), - ) - ) - transition_layers.append(nn.Sequential(*conv3x3s)) - - return nn.ModuleList(transition_layers) - - def _make_layer(self, block, planes, blocks, stride=1): - """ - Creates a layer composed of sequential residual blocks. - - Args: - block (nn.Module): Residual block type (BasicBlock or Bottleneck). - planes (int): Number of output channels. - blocks (int): Number of blocks in this layer. - stride (int, optional): Stride for the first block. Defaults to 1. - - Returns: - nn.Sequential: Sequential container of residual blocks. - """ - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d( - self.inplanes, - planes * block.expansion, - kernel_size=1, - stride=stride, - bias=False, - ), - nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), - ) - - layers = [] - layers.append(block(self.inplanes, planes, stride, downsample)) - self.inplanes = planes * block.expansion - for i in range(1, blocks): - layers.append(block(self.inplanes, planes)) - - return nn.Sequential(*layers) - - def _make_stage(self, layer_config, num_inchannels, multi_scale_output=True): - """ - Constructs a stage consisting of one or more HighResolutionModules. - - Args: - layer_config (dict): Configuration dictionary for the stage. - num_inchannels (list[int]): Number of input channels for each branch. - multi_scale_output (bool, optional): Output multi-scale features or not. Defaults to True. - - Returns: - tuple: - nn.Sequential: Stage module. - list[int]: Number of output channels for each branch. - """ - num_modules = layer_config["NUM_MODULES"] - num_branches = layer_config["NUM_BRANCHES"] - num_blocks = layer_config["NUM_BLOCKS"] - num_channels = layer_config["NUM_CHANNELS"] - block = blocks_dict[layer_config["BLOCK"]] - fuse_method = layer_config["FUSE_METHOD"] - - modules = [] - for i in range(num_modules): - # multi_scale_output is only used last module - if not multi_scale_output and i == num_modules - 1: - reset_multi_scale_output = False - else: - reset_multi_scale_output = True - - modules.append( - HighResolutionModule( - num_branches, - block, - num_blocks, - num_inchannels, - num_channels, - fuse_method, - reset_multi_scale_output, - ) - ) - num_inchannels = modules[-1].get_num_inchannels() - - return nn.Sequential(*modules), num_inchannels - - def forward(self, x): - """ - Forward pass of the PoseHighResolutionNet. - - Args: - x (torch.Tensor): Input tensor of shape (batch_size, 3, H, W). - - Returns: - torch.Tensor: Output heatmaps or coordinates for landmarks. - """ - x = self.conv1(x) - x = self.bn1(x) - x = F.relu(x, inplace=True) - x = self.conv2(x) - x = self.bn2(x) - x = F.relu(x, inplace=True) - x = self.layer1(x) - - x_list = [] - for i in range(self.stage2_cfg["NUM_BRANCHES"]): - if self.transition1[i] is not None: - x_list.append(self.transition1[i](x)) - else: - x_list.append(x) - y_list = self.stage2(x_list) - - x_list = [] - for i in range(self.stage3_cfg["NUM_BRANCHES"]): - if self.transition2[i] is not None: - x_list.append(self.transition2[i](y_list[-1])) - else: - x_list.append(y_list[i]) - y_list = self.stage3(x_list) - - x_list = [] - for i in range(self.stage4_cfg["NUM_BRANCHES"]): - if self.transition3[i] is not None: - x_list.append(self.transition3[i](y_list[-1])) - else: - x_list.append(y_list[i]) - y_list = self.stage4(x_list) - - if self.model_name == "pose_hrnet" or "pose_metric_gcn": - x = self.final_layer(y_list[0]) - else: - x = y_list[0] - - if self.target_type == "gaussian": - return x - - elif self.target_type == "coordinate": - B, C, H, W = x.shape - - """B - cal x,y seperately""" - h = F.softmax(x.view(B, C, H * W) * 1, dim=2) - h = h.view(B, C, H, W) - hx = h.sum(dim=2) # (B, C, W) - px = (hx * (torch.arange(W, device=h.device).float().view(1, 1, W))).sum( - 2, keepdim=True - ) - hy = h.sum(dim=3) # (B, C, H) - py = (hy * (torch.arange(H, device=h.device).float().view(1, 1, H))).sum( - 2, keepdim=True - ) - x = torch.cat([px, py], dim=2) - return h, x - else: - raise NotImplementedError(f"{self.target_type} is unknown.") diff --git a/src/garmentiq/landmark/detection/utils.py b/src/garmentiq/landmark/detection/utils.py deleted file mode 100644 index 3896126..0000000 --- a/src/garmentiq/landmark/detection/utils.py +++ /dev/null @@ -1,362 +0,0 @@ -"""Pre- and post-processing for the landmark detection model. - -Covers the affine transforms that crop and scale an image to the model input size, and -the decoding that turns predicted heatmaps back into image-space coordinates with -confidence scores. -""" -import math -import numpy as np -import cv2 -from PIL import Image -import torchvision.transforms as transforms -from typing import Union - - -def get_max_preds(batch_heatmaps): - """ - get predictions from score maps - heatmaps: numpy.ndarray([batch_size, num_joints, height, width]) - - Args: - batch_heatmaps (numpy.ndarray): Heatmaps generated by the model. - Shape: [batch_size, num_joints, height, width]. - - Returns: - tuple: - - preds (numpy.ndarray): Predicted coordinates. - - maxvals (numpy.ndarray): Maximum values (confidence scores) for each prediction. - """ - assert isinstance( - batch_heatmaps, np.ndarray - ), "batch_heatmaps should be numpy.ndarray" - assert batch_heatmaps.ndim == 4, "batch_images should be 4-ndim" - - batch_size = batch_heatmaps.shape[0] - num_joints = batch_heatmaps.shape[1] - width = batch_heatmaps.shape[3] - heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1)) - idx = np.argmax(heatmaps_reshaped, 2) - maxvals = np.amax(heatmaps_reshaped, 2) - - maxvals = maxvals.reshape((batch_size, num_joints, 1)) - idx = idx.reshape((batch_size, num_joints, 1)) - - preds = np.tile(idx, (1, 1, 2)).astype(np.float32) - - preds[:, :, 0] = (preds[:, :, 0]) % width - preds[:, :, 1] = np.floor((preds[:, :, 1]) / width) - - pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2)) - pred_mask = pred_mask.astype(np.float32) - - preds *= pred_mask - return preds, maxvals - - -def get_final_preds(output, height=96, width=72): - """ - Transforms raw heatmap outputs into final landmark coordinates. - - Applies post-processing (e.g., quarter offset for sub-pixel accuracy) - to refine the landmark predictions from heatmaps. - - Args: - output (numpy.ndarray): Raw heatmap output from the model. - height (int): Height of the heatmap. Defaults to 96. - width (int): Width of the heatmap. Defaults to 72. - - Returns: - tuple: - - coords (numpy.ndarray): Final predicted coordinates. - - maxvals (numpy.ndarray): Confidence scores for the predictions. - """ - heatmap_height = height - heatmap_width = width - - batch_heatmaps = output - coords, maxvals = get_max_preds(batch_heatmaps) - # post-processing - for n in range(coords.shape[0]): - for p in range(coords.shape[1]): - hm = batch_heatmaps[n][p] - px = int(math.floor(coords[n][p][0] + 0.5)) - py = int(math.floor(coords[n][p][1] + 0.5)) - if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1: - diff = np.array( - [hm[py][px + 1] - hm[py][px - 1], hm[py + 1][px] - hm[py - 1][px]] - ) - coords[n][p] += np.sign(diff) * 0.25 - - return coords, maxvals - - -def flip_back(output_flipped, matched_parts, heatmap_wid): - """ - Flips the output (coordinates or heatmaps) horizontally for test-time augmentation. - - Args: - output_flipped (numpy.ndarray): The output (heatmaps or coordinates) that has been flipped. - matched_parts (list): A list of tuples indicating which joint pairs are left-right symmetric. - heatmap_wid (int): The width of the heatmap (used for coordinate flipping). - - Returns: - numpy.ndarray: The flipped output with joints correctly reordered. - """ - if output_flipped.ndim == 4: - output_flipped = output_flipped[:, :, :, ::-1] - for pair in matched_parts: - tmp = output_flipped[:, pair[0], :, :].copy() - output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :] - output_flipped[:, pair[1], :, :] = tmp - elif output_flipped.ndim == 3: - output_flipped[:, :, 0] = heatmap_wid - output_flipped[:, :, 0] - for pair in matched_parts: - tmp = output_flipped[:, pair[0], :].copy() - output_flipped[:, pair[0], :] = output_flipped[:, pair[1], :] - output_flipped[:, pair[1], :] = tmp - else: - raise NotImplementedError( - "output_flipped should be [batch_size, num_joints, height, width], " - "or [batch_size, num_joints, coord_dim" - ) - - return output_flipped - - -def fliplr_joints(joints, joints_vis, width, matched_parts): - """ - Flips joint coordinates horizontally and reorders them based on matched parts. - - Args: - joints (numpy.ndarray): Array of joint coordinates. - joints_vis (numpy.ndarray): Array indicating visibility of joints. - width (int): Width of the image or feature map. - matched_parts (list): A list of tuples indicating which joint pairs are left-right symmetric. - - Returns: - tuple: - - joints (numpy.ndarray): Flipped and reordered joint coordinates. - - joints_vis (numpy.ndarray): Corresponding joint visibility. - """ - # Flip horizontal - joints[:, 0] = width - joints[:, 0] - 1 - - # Change left-right parts - for pair in matched_parts: - joints[pair[0], :], joints[pair[1], :] = ( - joints[pair[1], :], - joints[pair[0], :].copy(), - ) - joints_vis[pair[0], :], joints_vis[pair[1], :] = ( - joints_vis[pair[1], :], - joints_vis[pair[0], :].copy(), - ) - - return joints * joints_vis, joints_vis - - -def transform_preds(coords, center, scale, output_size: list[int, int] = [72, 96]): - """ - Transforms predicted coordinates from heatmap space back to original image space. - - Args: - coords (numpy.ndarray): Predicted coordinates in heatmap space. - center (numpy.ndarray): Center of the original image (or cropped region). - scale (numpy.ndarray): Scale factor applied during preprocessing. - output_size (list[int, int], optional): The size of the output image after transformation. Defaults to [72, 96]. - - Returns: - numpy.ndarray: Transformed coordinates in original image space. - """ - target_coords = np.zeros(coords.shape) - trans = get_affine_transform(center, scale, 0, output_size, inv=1) - for p in range(coords.shape[0]): - target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) - return target_coords - - -def get_affine_transform( - center, scale, rot, output_size, shift=np.array([0, 0], dtype=np.float32), inv=0 -): - """ - Calculates the 2x3 affine transformation matrix for image cropping and resizing. - - Args: - center (numpy.ndarray): Center of the original image or region of interest. - scale (numpy.ndarray): Scale factor for the transformation. - rot (float): Rotation angle in degrees. - output_size (list): Target output size [width, height]. - shift (numpy.ndarray, optional): Shift applied to the center. Defaults to [0, 0]. - inv (int, optional): If 1, returns the inverse transformation matrix. Defaults to 0. - - Returns: - numpy.ndarray: The 2x3 affine transformation matrix. - """ - if not isinstance(scale, np.ndarray) and not isinstance(scale, list): - print(scale) - scale = np.array([scale, scale]) - - scale_tmp = scale * 200.0 - src_w = scale_tmp[0] - dst_w = output_size[0] - dst_h = output_size[1] - - rot_rad = np.pi * rot / 180 - src_dir = get_dir([0, src_w * -0.5], rot_rad) - dst_dir = np.array([0, dst_w * -0.5], np.float32) - - src = np.zeros((3, 2), dtype=np.float32) - dst = np.zeros((3, 2), dtype=np.float32) - src[0, :] = center + scale_tmp * shift - src[1, :] = center + src_dir + scale_tmp * shift - dst[0, :] = [dst_w * 0.5, dst_h * 0.5] - dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir - - src[2:, :] = get_3rd_point(src[0, :], src[1, :]) - dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) - - if inv: - trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) - else: - trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) - - return trans - - -def affine_transform(pt, t): - """ - Applies an affine transformation matrix to a 2D point. - - Args: - pt (tuple or list): The 2D point (x, y) to transform. - t (numpy.ndarray): The 2x3 affine transformation matrix. - - Returns: - numpy.ndarray: The transformed 2D point. - """ - new_pt = np.array([pt[0], pt[1], 1.0]).T - new_pt = np.dot(t, new_pt) - return new_pt[:2] - - -def get_3rd_point(a, b): - """ - Calculates a third point to form a right-angled triangle with two given points. - Used for creating a 3-point basis for affine transformations. - - Args: - a (numpy.ndarray): First point. - b (numpy.ndarray): Second point. - - Returns: - numpy.ndarray: The calculated third point. - """ - direct = a - b - return b + np.array([-direct[1], direct[0]], dtype=np.float32) - - -def get_dir(src_point, rot_rad): - """ - Calculates the direction vector after rotation. - - Args: - src_point (list): Source point [x, y]. - rot_rad (float): Rotation angle in radians. - - Returns: - list: The rotated direction vector. - """ - sn, cs = np.sin(rot_rad), np.cos(rot_rad) - - src_result = [0, 0] - src_result[0] = src_point[0] * cs - src_point[1] * sn - src_result[1] = src_point[0] * sn + src_point[1] * cs - - return src_result - - -def crop(img, center, scale, output_size, rot=0): - """ - Crops and resizes an image using an affine transformation. - - Args: - img (numpy.ndarray): The input image. - center (numpy.ndarray): The center of the crop region. - scale (numpy.ndarray): The scale factor for the crop. - output_size (tuple): The target output size (width, height). - rot (int, optional): Rotation angle in degrees. Defaults to 0. - - Returns: - numpy.ndarray: The cropped and transformed image. - """ - trans = get_affine_transform(center, scale, rot, output_size) - - dst_img = cv2.warpAffine( - img, trans, (int(output_size[0]), int(output_size[1])), flags=cv2.INTER_LINEAR - ) - - return dst_img - - -def input_image_transform( - img_input: Union[str, np.ndarray], - scale_std: float = 200.0, - resize_dim: list[int] = [288, 384], - normalize_mean: list[float] = [0.485, 0.456, 0.406], - normalize_std: list[float] = [0.229, 0.224, 0.225], -): - """ - Preprocesses an input image for landmark detection. - - This function takes an image (either path or NumPy array), applies an affine - transformation (scaling, centering), resizes it, converts it to a PyTorch tensor, - and normalizes it. - - Args: - img_input (Union[str, np.ndarray]): Path to the image file or a NumPy array of the image. - scale_std (float, optional): Standard scale for image transformation. Defaults to 200.0. - resize_dim (list[int], optional): Target dimensions [width, height] for the transformed image. - Defaults to [288, 384]. - normalize_mean (list[float], optional): Mean values for image normalization (RGB channels). - Defaults to [0.485, 0.456, 0.406]. - normalize_std (list[float], optional): Standard deviation values for image normalization (RGB channels). - Defaults to [0.229, 0.224, 0.225]. - - Raises: - ValueError: If `img_input` is neither a file path nor a NumPy array. - - Returns: - tuple: - - input_tensor (torch.Tensor): The preprocessed image as a PyTorch tensor, ready for model input. - - image_np (numpy.ndarray): The original image as a NumPy array (RGB). - - center (numpy.ndarray): The center of the original image used for transformation. - - scale (numpy.ndarray): The scale factor used for transformation. - """ - if isinstance(img_input, str): - img = Image.open(img_input).convert("RGB") - elif isinstance(img_input, np.ndarray): - img = Image.fromarray(img_input.astype(np.uint8)) - else: - raise ValueError("img_input must be a file path or a NumPy array.") - - image_np = np.array(img) - - h, w = image_np.shape[:2] - center = np.array([w / 2, h / 2], dtype=np.float32) - scale = np.array([w / scale_std, h / scale_std], dtype=np.float32) - image_size = np.array(resize_dim) - rotation = 0 - - trans = get_affine_transform(center, scale, rotation, image_size) - warped_image = cv2.warpAffine( - image_np, - trans, - (int(image_size[0]), int(image_size[1])), - flags=cv2.INTER_LINEAR, - ) - - to_tensor = transforms.ToTensor() - normalize = transforms.Normalize(normalize_mean, normalize_std) - input_tensor = normalize(to_tensor(warped_image)).unsqueeze(0) - - return input_tensor, image_np, center, scale diff --git a/src/garmentiq/landmark/plot.py b/src/garmentiq/landmark/plot.py deleted file mode 100644 index 264f2a4..0000000 --- a/src/garmentiq/landmark/plot.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Displaying an image with landmark coordinates overlaid.""" -import matplotlib.pyplot as plt -import numpy as np -from PIL import Image - - -def plot( - image_path: str, - coordinate: np.ndarray = None, - figsize: tuple = (6, 6), - color: str = "red", -): - """ - Display an image from a file path using matplotlib, with optional overlay of coordinates. - - This function loads an image from the given file path, displays it using matplotlib, - and optionally overlays coordinate points on the image. - - Args: - image_path (str): Path to the image file. The image will be loaded as RGB. - coordinate (np.ndarray, optional): Optional array of coordinates to overlay on the image. - Expected shape: (1, N, 2), where N is the number of points. - figsize (tuple, optional): Size of the displayed figure in inches (width, height). - color (str, optional): Color of the overlay points. Default is 'red'. - - Raises: - ValueError: If image cannot be loaded or coordinate format is invalid. - - Returns: - None - """ - try: - image_np = np.array(Image.open(image_path).convert("RGB")) - except Exception as e: - raise ValueError(f"Unable to load image from path: {image_path}. Error: {e}") - - plt.figure(figsize=figsize) - - if image_np.ndim == 2: - plt.imshow(image_np, cmap="gray") - else: - plt.imshow(image_np) - - if coordinate is not None: - try: - plt.scatter(coordinate[0][:, 0], coordinate[0][:, 1], c=color, s=10) - except Exception as e: - raise ValueError(f"Invalid coordinate format: {coordinate}. Error: {e}") - - plt.axis("off") - plt.subplots_adjust(left=0, right=1, top=1, bottom=0) - plt.show() diff --git a/src/garmentiq/landmark/refine.py b/src/garmentiq/landmark/refine.py deleted file mode 100644 index e7b7fe9..0000000 --- a/src/garmentiq/landmark/refine.py +++ /dev/null @@ -1,70 +0,0 @@ -"""Refining detected landmarks against a segmentation mask.""" -import numpy as np -import cv2 -import copy -from garmentiq.landmark.refinement import refine_landmark_with_blur -from garmentiq.landmark.utils import ( - find_instruction_landmark_index, - fill_instruction_landmark_coordinate, -) - - -def refine( - class_name: str, - detection_np: np.array, - detection_conf: np.array, - detection_dict: dict, - mask: np.array, - window_size: int = 5, - ksize: tuple = (11, 11), - sigmaX: float = 0.0, -): - """ - Refines detected landmarks using a blurred mask and updates the detection dictionary. - - This function applies Gaussian blur to the given mask, then refines landmark coordinates - based on their confidence scores and local intensity structure. Only landmarks with a - confidence score greater than 0 are refined. The refined coordinates are used to update - predefined landmarks in the detection dictionary. - - Args: - class_name (str): The name of the class to access in the detection dictionary. - detection_np (np.array): The initial landmark predictions. Shape: (1, N, 2). - detection_conf (np.array): Confidence scores for each predicted landmark. Shape: (1, N, 1). - detection_dict (dict): Dictionary containing landmark data for each class. - mask (np.array): Grayscale mask image used to guide refinement. - window_size (int, optional): Size of the window used in the refinement algorithm. Defaults to 5. - ksize (tuple, optional): Kernel size for Gaussian blur. Must be odd integers. Defaults to (11, 11). - sigmaX (float, optional): Gaussian kernel standard deviation in the X direction. Defaults to 0.0. - - Returns: - tuple: - - refined_detection_np (np.array): Array of the same shape as `detection_np` with refined coordinates. - - detection_dict (dict): Updated detection dictionary with refined landmark coordinates. - """ - blurred_mask = cv2.GaussianBlur(mask, ksize, sigmaX) - - refined_detection_np = np.zeros(detection_np.shape) - - for i, coord in enumerate(detection_np[0]): - if detection_conf[0, i, 0] > 0: - refined_x, refined_y = refine_landmark_with_blur( - coord[0], coord[1], blurred_mask, window_size - ) - refined_detection_np[0, i] = [refined_x, refined_y] - - predefined_index = find_instruction_landmark_index( - detection_dict[class_name]["landmarks"], predefined=True - ) - - preds = refined_detection_np[:, predefined_index, :] - - detection_dict_copy = copy.deepcopy(detection_dict) - - detection_dict_copy[class_name]["landmarks"] = fill_instruction_landmark_coordinate( - instruction_landmarks=detection_dict_copy[class_name]["landmarks"], - index=predefined_index, - fill_in_value=preds, - ) - - return refined_detection_np, detection_dict_copy diff --git a/src/garmentiq/landmark/refinement/__init__.py b/src/garmentiq/landmark/refinement/__init__.py deleted file mode 100644 index 2f614dd..0000000 --- a/src/garmentiq/landmark/refinement/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# garmentiq/landmark/refinement/__init__.py -"""Refinement of detected landmarks against a segmentation mask. - -The pose model predicts landmarks to within a few pixels, which can leave them just -inside or outside the garment edge. Refinement searches a small window around each -point and moves it onto the mask boundary, after blurring the mask to smooth away -jagged edges. -""" -from .refine_landmark_with_blur import refine_landmark_with_blur diff --git a/src/garmentiq/landmark/refinement/refine_landmark_with_blur.py b/src/garmentiq/landmark/refinement/refine_landmark_with_blur.py deleted file mode 100644 index b840331..0000000 --- a/src/garmentiq/landmark/refinement/refine_landmark_with_blur.py +++ /dev/null @@ -1,36 +0,0 @@ -"""Snapping a landmark onto a blurred mask boundary.""" -import cv2 - - -def refine_landmark_with_blur(x, y, blurred_mask, window_size=5): - """ - Refine the landmark location using a Gaussian-blurred version of the segmentation mask. - The function searches a small window around (x, y) in the blurred mask and returns - the coordinates of the pixel with the maximum value, which is assumed to be a more - reliable location for the landmark. - - Args: - x (float): The initial x-coordinate of the landmark. - y (float): The initial y-coordinate of the landmark. - blurred_mask (np.ndarray): The Gaussian-blurred segmentation mask. - window_size (int, optional): The size of the square window (in pixels) around (x, y) - to search for the maximum value. Defaults to 5. - - Returns: - tuple: A tuple (refined_x, refined_y) representing the refined coordinates. - """ - height, width = blurred_mask.shape - x_min = int(max(0, x - window_size)) - x_max = int(min(width, x + window_size + 1)) - y_min = int(max(0, y - window_size)) - y_max = int(min(height, y + window_size + 1)) - - # Extract the local region from the blurred mask. - local_region = blurred_mask[y_min:y_max, x_min:x_max] - # Find the location of the maximum value within the local region. - _, max_val, _, max_loc = cv2.minMaxLoc(local_region) - - # Compute refined coordinates relative to the entire image. - refined_x = x_min + max_loc[0] - refined_y = y_min + max_loc[1] - return refined_x, refined_y diff --git a/src/garmentiq/landmark/utils.py b/src/garmentiq/landmark/utils.py deleted file mode 100644 index ff594c5..0000000 --- a/src/garmentiq/landmark/utils.py +++ /dev/null @@ -1,54 +0,0 @@ -"""Helpers for mapping landmarks onto a measurement instruction.""" -import numpy as np - - -def find_instruction_landmark_index(instruction_landmarks: dict, predefined: bool): - """ - Finds the indices of predefined or non-predefined landmarks from an instruction landmarks dictionary. - - Args: - instruction_landmarks (dict): A dictionary of landmark information from an instruction schema. - predefined (bool): If True, returns indices of predefined landmarks. - If False, returns indices of non-predefined (derived) landmarks. - - Returns: - list: A list of integer indices (0-based) for the requested type of landmarks. - """ - if predefined: - return [ - int(k) - 1 - for k, v in instruction_landmarks.items() - if v.get("predefined") is True - ] - else: - return [ - int(k) - 1 - for k, v in instruction_landmarks.items() - if v.get("predefined") is False - ] - - -def fill_instruction_landmark_coordinate( - instruction_landmarks: dict, index: list, fill_in_value: np.array -): - """ - Fills the 'x' and 'y' coordinates for specified landmarks in the instruction dictionary - based on provided coordinate values. - - Args: - instruction_landmarks (dict): The dictionary of landmark instructions to be updated. - index (list): A list of integer indices (0-based) corresponding to the landmarks - in `instruction_landmarks` that should be updated. - fill_in_value (np.array): A NumPy array containing the new coordinates. - Expected shape: (1, N, 2), where N is the number of landmarks to fill. - - Returns: - dict: The updated `instruction_landmarks` dictionary with filled coordinates. - """ - for k in instruction_landmarks: - idx = int(k) - 1 - if idx in index: - preds_idx = index.index(idx) - instruction_landmarks[k]["x"] = float(fill_in_value[0, preds_idx, 0]) - instruction_landmarks[k]["y"] = float(fill_in_value[0, preds_idx, 1]) - return instruction_landmarks diff --git a/src/garmentiq/matting/__init__.py b/src/garmentiq/matting/__init__.py deleted file mode 100644 index 2ec1d57..0000000 --- a/src/garmentiq/matting/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# garmentiq/matting/__init__.py -"""Alpha matting. - -Segmentation produces a hard yes/no mask; matting refines it into a continuous alpha -matte so edges and semi-transparent detail composite naturally. Two approaches are -supported: ViTMatte, which needs a trimap, and Matting Anything, which refines a SAM -mask directly. -""" -from .trimap import generate_trimap -from .matte import matte, composite -from .load_model import load_model - -__all__ = [ - "generate_trimap", - "matte", - "composite", - "load_model", -] diff --git a/src/garmentiq/matting/load_model.py b/src/garmentiq/matting/load_model.py deleted file mode 100644 index d0d6532..0000000 --- a/src/garmentiq/matting/load_model.py +++ /dev/null @@ -1,90 +0,0 @@ -"""Loading a matting model onto a device.""" -import inspect -import torch -import torch.nn as nn -from typing import Type, Union -from safetensors.torch import load_file -from garmentiq.utils.device import resolve_device -from garmentiq.utils.checkpoint import load_state_dict_checked - - -def load_model( - model_class: Type[nn.Module], - model_path: str, - model_args: dict = None, - device: Union[str, torch.device] = "cpu", - **kwargs, -): - """ - Loads a matting model from a local checkpoint and prepares it for inference. - - This mirrors `garmentiq.segmentation.load_model`: the model class is instantiated with - safely filtered configuration arguments, weights are read from a local `.pth` or - `.safetensors` file, the model is moved to the requested device and set to evaluation - mode, and common weight prefixes are stripped for compatibility. - - Matting Anything checkpoints are not loaded here because they pair a decoder with a - separate SAM model; use `garmentiq.matting.model_definition.mam.load_mam` for those. - - Args: - model_class (Type[nn.Module]): The uninstantiated model class, typically - `VitMatteForImageMatting`. - model_path (str): Local path to the checkpoint weights, ending in `.pth` or - `.safetensors`. - model_args (dict, optional): Configuration arguments for initializing the model, - e.g. `{"config": load_vitmatte_config(...)}`. - Incompatible arguments are safely ignored. - Default is None. - device (Union[str, torch.device], optional): The device to load the model onto, e.g. - `"cpu"`, `"cuda"`, or `"mps"`. Hardware - acceleration is opt-in. Default is `"cpu"`. - **kwargs: Additional arbitrary keyword arguments. - - Raises: - ValueError: If the requested `device` is invalid or unavailable on this machine. - Exception: If the weights cannot be loaded or the file format is unsupported. - - Returns: - nn.Module: The loaded model in evaluation mode, placed on `device`. - """ - model_args = model_args or {} - - if not isinstance(model_args, dict): - if hasattr(model_args, "to_dict"): - model_args = model_args.to_dict() - elif hasattr(model_args, "__dict__"): - model_args = vars(model_args) - else: - raise TypeError( - "model_args must be a dictionary or a configuration object." - ) - - device = resolve_device(device) - - sig = inspect.signature(model_class.__init__) - valid_params = set(sig.parameters.keys()) - has_kwargs = any( - p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values() - ) - - if not has_kwargs: - filtered_args = {k: v for k, v in model_args.items() if k in valid_params} - else: - filtered_args = model_args - - model = model_class(**filtered_args).to(device) - - if model_path.endswith(".safetensors"): - state_dict = load_file(model_path, device=str(device)) - else: - state_dict = torch.load(model_path, map_location=device, weights_only=True) - - new_state_dict = { - k.removeprefix("module.").removeprefix("model."): v - for k, v in state_dict.items() - } - - load_state_dict_checked(model, new_state_dict, model_path) - model.eval() - - return model diff --git a/src/garmentiq/matting/matte.py b/src/garmentiq/matting/matte.py deleted file mode 100644 index 8317aec..0000000 --- a/src/garmentiq/matting/matte.py +++ /dev/null @@ -1,259 +0,0 @@ -"""Producing an alpha matte and compositing with it. - -Dispatches on the model family: ViTMatte is guided by a trimap, which can be supplied -directly or derived from a segmentation mask, while Matting Anything is guided by a SAM -prompt and needs no trimap at all. -""" -from PIL import Image -import numpy as np -import torch -import warnings -from typing import Optional, Union - -from garmentiq.utils.device import resolve_device, empty_cache, inputs_to_device -from garmentiq.matting.trimap import generate_trimap - -# Above roughly this many pixels the MPS backend has been observed to diverge from CPU -# for ViTMatte, silently producing a degenerate matte. Verified identical up to -# 2048x1536 (3.15 MP) and divergent at 2400x1800 (4.32 MP) on Apple Silicon. -_MPS_PIXEL_WARN_THRESHOLD = 3_500_000 - - -def _detect_matting_family(model): - """ - Identifies which matting approach a loaded model implements. - - Detection walks the class hierarchy by name rather than importing the transformers - classes, so a model is recognised without requiring every backend to be installed. - """ - names = {cls.__name__ for cls in type(model).__mro__} - if "MattingAnything" in names: - return "mam" - if {"VitMatteForImageMatting", "VitMattePreTrainedModel"} & names: - return "vitmatte" - return None - - -def matte( - model, - image_path: Union[str, np.ndarray, Image.Image], - processor=None, - mask: Optional[np.ndarray] = None, - trimap: Optional[np.ndarray] = None, - prompt: Optional[dict] = None, - trimap_args: Optional[dict] = None, - device: Union[str, torch.device] = "cpu", -): - """ - Extracts a soft alpha matte for an image, refining a hard segmentation into soft edges. - - Segmentation answers "which pixels belong to the garment" with a yes/no decision, which - leaves stair-stepped borders and loses semi-transparent detail such as loose fabric, - lace, and stray fibres. Matting instead predicts a continuous alpha value per pixel, so - compositing onto a new background looks natural. - - The two supported approaches need different guidance: - - **ViTMatte** requires a trimap marking definite foreground, definite background, - and the uncertain band between them. Pass `trimap` directly, or pass `mask` and a - trimap will be derived from it via `generate_trimap`. - - **Matting Anything (MAM)** needs no trimap. It prompts a frozen SAM with `prompt` - and refines the resulting coarse mask into an alpha matte. - - Args: - model: A loaded matting model, either `VitMatteForImageMatting` or `MattingAnything`. - image_path (Union[str, numpy.ndarray, PIL.Image.Image]): The image to matte, given as - a file path, RGB array, or - PIL image. - processor (VitMatteImageProcessor, optional): Required for ViTMatte, which uses it to - stack the image and trimap into a - four-channel input. Default is None. - mask (numpy.ndarray, optional): A binary segmentation mask used to derive a trimap - when `trimap` is not supplied. Default is None. - trimap (numpy.ndarray, optional): An explicit trimap with values `0`, `128`, and - `255`. Takes precedence over `mask`. Default is None. - prompt (dict, optional): Prompt for MAM's internal SAM, accepting `"points"`, - `"labels"`, and/or `"boxes"` exactly as - `garmentiq.segmentation.extract` does. Default is None. - trimap_args (dict, optional): Options forwarded to `generate_trimap`, such as - `"erode_size"` and `"dilate_size"`. Default is None. - device (Union[str, torch.device], optional): The device to run inference on, e.g. - `"cpu"`, `"cuda"`, or `"mps"`. Hardware - acceleration is opt-in. Default is `"cpu"`. - - Raises: - ValueError: If the model is not a recognised matting model, if ViTMatte is used - without a processor or without either `trimap` or `mask`, if MAM is used - without a prompt, or if the requested `device` is unavailable. - FileNotFoundError: If `image_path` points to a file that does not exist. - - Returns: - tuple (numpy.ndarray, numpy.ndarray): The original image as an RGB array, and the - alpha matte as a `uint8` array in `[0, 255]` - matching the image's height and width. - """ - device = resolve_device(device) - family = _detect_matting_family(model) - if family is None: - raise ValueError( - "Unrecognised matting model. Expected a ViTMatte model " - "(VitMatteForImageMatting) or a Matting Anything model (MattingAnything)." - ) - - if isinstance(image_path, str): - image = Image.open(image_path).convert("RGB") - elif isinstance(image_path, np.ndarray): - image = Image.fromarray(image_path.astype(np.uint8)).convert("RGB") - else: - image = image_path.convert("RGB") - - image_np = np.array(image) - model = model.to(device) - - if ( - device.type == "mps" - and image_np.shape[0] * image_np.shape[1] > _MPS_PIXEL_WARN_THRESHOLD - ): - warnings.warn( - f"Matting a {image_np.shape[1]}x{image_np.shape[0]} image on the MPS backend. " - f"Above roughly {_MPS_PIXEL_WARN_THRESHOLD / 1e6:.1f} megapixels MPS has been " - f"observed to return a degenerate alpha matte that differs substantially from " - f"CPU. Consider device='cpu' for images this large, or downscale first.", - RuntimeWarning, - stacklevel=2, - ) - - if family == "vitmatte": - if processor is None: - raise ValueError( - "ViTMatte requires a processor. Pass processor=load_vitmatte_processor(...)." - ) - if trimap is None: - if mask is None: - raise ValueError( - "ViTMatte is a trimap-based model, so it needs either an explicit " - "trimap= or a segmentation mask= to derive one from. Run segmentation " - "first, then pass its mask here." - ) - trimap = generate_trimap(mask, **(trimap_args or {})) - - trimap_arr = np.asarray(trimap) - if trimap_arr.shape[:2] != image_np.shape[:2]: - raise ValueError( - f"trimap shape {trimap_arr.shape[:2]} does not match image shape " - f"{image_np.shape[:2]}." - ) - - # The processor rescales trimaps by its own rescale_factor (1/255), so hand it - # the raw 0-255 trimap. Pre-normalising here would rescale twice and collapse - # the trimap to near-zero, making the model see everything as background. - inputs = inputs_to_device( - processor( - images=image, trimaps=trimap_arr.astype(np.uint8), return_tensors="pt" - ), - device, - ) - - with torch.no_grad(): - outputs = model(**inputs) - - alpha = outputs.alphas - # ViTMatte pads to a multiple of its patch size; crop back to the true size. - alpha = alpha[..., : image_np.shape[0], : image_np.shape[1]] - alpha_np = alpha[0, 0].detach().cpu().numpy() - del inputs, outputs - - else: - from garmentiq.matting.model_definition.mam.mam import fuse_alpha - - if not prompt: - raise ValueError( - "Matting Anything prompts a frozen SAM, so it needs a prompt. Pass " - "prompt={'points': [[[x, y]]]} or prompt={'boxes': [[[x0, y0, x1, y1]]]}." - ) - - sam_processor = model.sam_processor - processor_kwargs = {} - if prompt.get("points") is not None: - processor_kwargs["input_points"] = prompt["points"] - if prompt.get("labels") is not None: - processor_kwargs["input_labels"] = prompt["labels"] - if prompt.get("boxes") is not None: - processor_kwargs["input_boxes"] = prompt["boxes"] - - inputs = inputs_to_device( - sam_processor(images=image, return_tensors="pt", **processor_kwargs), - device, - ) - - pixel_values = inputs.pop("pixel_values") - original_sizes = inputs.pop("original_sizes") - reshaped_sizes = inputs.pop("reshaped_input_sizes") - - with torch.no_grad(): - pred, post_mask = model( - pixel_values=pixel_values, - reshaped_size=reshaped_sizes[0].tolist(), - original_size=original_sizes[0].tolist(), - **inputs, - ) - alpha = fuse_alpha( - pred, - reshaped_size=reshaped_sizes[0].tolist(), - original_size=original_sizes[0].tolist(), - post_mask=post_mask, - ) - - alpha_np = alpha[0, 0].detach().cpu().numpy() - del inputs, pred - - alpha_np = np.clip(alpha_np, 0.0, 1.0) - alpha_u8 = (alpha_np * 255).astype(np.uint8) - - empty_cache(device) - return image_np, alpha_u8 - - -def composite( - image_np: np.ndarray, - alpha_np: np.ndarray, - background_color=(255, 255, 255), -): - """ - Composites a matted foreground onto a solid background colour. - - Unlike hard mask replacement, this blends each pixel by its alpha value, so soft edges - stay soft instead of showing a cut-out outline. - - Args: - image_np (numpy.ndarray): The original RGB image, shape `(H, W, 3)`. - alpha_np (numpy.ndarray): The alpha matte, shape `(H, W)`, `uint8` in `[0, 255]` - or float in `[0, 1]`. - background_color (tuple[int, int, int], optional): RGB colour to composite onto. - Default is white. - - Raises: - ValueError: If the image and alpha shapes do not match. - - Returns: - numpy.ndarray: The composited RGB image as `uint8`, shape `(H, W, 3)`. - """ - image_np = np.asarray(image_np) - alpha_np = np.asarray(alpha_np) - - if image_np.shape[:2] != alpha_np.shape[:2]: - raise ValueError( - f"image shape {image_np.shape[:2]} does not match alpha shape " - f"{alpha_np.shape[:2]}." - ) - - alpha = alpha_np.astype(np.float32) - if alpha.max() > 1.0: - alpha = alpha / 255.0 - alpha = alpha[..., None] - - background = np.array(background_color, dtype=np.float32).reshape(1, 1, 3) - out = alpha * image_np.astype(np.float32) + (1.0 - alpha) * background - return np.clip(out, 0, 255).astype(np.uint8) - - -__all__ = ["matte", "composite"] diff --git a/src/garmentiq/matting/model_definition/__init__.py b/src/garmentiq/matting/model_definition/__init__.py deleted file mode 100644 index 9596a30..0000000 --- a/src/garmentiq/matting/model_definition/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# garmentiq/matting/model_definition/__init__.py -"""Matting model architectures and their offline configurations. - -Holds ViTMatte, which is guided by a trimap, and Matting Anything, which refines a SAM -mask directly. ViTMatte configurations and processors are bundled with the package. -""" diff --git a/src/garmentiq/matting/model_definition/mam/__init__.py b/src/garmentiq/matting/model_definition/mam/__init__.py deleted file mode 100644 index 5a75fde..0000000 --- a/src/garmentiq/matting/model_definition/mam/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# garmentiq/matting/model_definition/mam/__init__.py -"""The Matting Anything model. - -Matting Anything refines a SAM mask into an alpha matte. It consumes SAM's own image -embeddings rather than just its mask, so it is structurally tied to SAM and cannot run -from another segmentation model. `load_mam` reads only the matting decoder from the -checkpoint and pairs it with a SAM model you already loaded. -""" -from .m2m import SAM_Decoder_Deep, sam_decoder_deep -from .mam import MattingAnything, load_mam, fuse_alpha - -__all__ = [ - "SAM_Decoder_Deep", - "sam_decoder_deep", - "MattingAnything", - "load_mam", - "fuse_alpha", -] diff --git a/src/garmentiq/matting/model_definition/mam/m2m.py b/src/garmentiq/matting/model_definition/mam/m2m.py deleted file mode 100644 index 6abf080..0000000 --- a/src/garmentiq/matting/model_definition/mam/m2m.py +++ /dev/null @@ -1,362 +0,0 @@ -"""Mask-to-matte (M2M) decoder used by Matting Anything. - -Vendored from Matting Anything (https://github.com/SHI-Labs/Matting-Anything), -Copyright (c) 2023 SHI Labs, MIT License, which in turn adapts MGMatting -(https://github.com/yucornetto/MGMatting). - -The layer names and shapes are kept identical to upstream so that published -Matting Anything checkpoints load without key remapping. -""" - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from garmentiq.matting.model_definition.mam import ops - - -def conv5x5(in_planes, out_planes, stride=1, groups=1, dilation=1): - """5x5 convolution with padding.""" - return nn.Conv2d( - in_planes, - out_planes, - kernel_size=5, - stride=stride, - padding=2, - groups=groups, - bias=False, - dilation=dilation, - ) - - -def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): - """3x3 convolution with padding.""" - return nn.Conv2d( - in_planes, - out_planes, - kernel_size=3, - stride=stride, - padding=dilation, - groups=groups, - bias=False, - dilation=dilation, - ) - - -def conv1x1(in_planes, out_planes, stride=1): - """1x1 convolution.""" - return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) - - -class BasicBlock(nn.Module): - """ - Residual upsampling block used throughout the M2M decoder. - - When `stride > 1` the block upsamples with a transposed convolution, otherwise it - applies a plain convolution. Both branches are spectrally normalised. - """ - - expansion = 1 - - def __init__( - self, - inplanes, - planes, - stride=1, - upsample=None, - norm_layer=None, - large_kernel=False, - ): - super(BasicBlock, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - self.stride = stride - conv = conv5x5 if large_kernel else conv3x3 - if self.stride > 1: - self.conv1 = ops.SpectralNorm( - nn.ConvTranspose2d( - inplanes, inplanes, kernel_size=4, stride=2, padding=1, bias=False - ) - ) - else: - self.conv1 = ops.SpectralNorm(conv(inplanes, inplanes)) - self.bn1 = norm_layer(inplanes) - self.activation = nn.LeakyReLU(0.2, inplace=True) - self.conv2 = ops.SpectralNorm(conv(inplanes, planes)) - self.bn2 = norm_layer(planes) - self.upsample = upsample - - def forward(self, x): - identity = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.activation(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.upsample is not None: - identity = self.upsample(x) - - out += identity - out = self.activation(out) - - return out - - -class SAM_Decoder_Deep(nn.Module): - """ - Progressively upsamples SAM image embeddings into an alpha matte. - - At each scale the decoder re-injects the source image and the coarse guidance mask, - so the matte stays anchored to the original detail rather than drifting from the - low-resolution embedding. Alpha is predicted at three output strides (1, 4 and 8) - which the caller fuses. - - Attributes: - inplanes (int): Channel count of the incoming SAM embedding (256). - midplanes (int): Channel count feeding the final upsampling stage. - """ - - def __init__( - self, - nc, - layers, - block=BasicBlock, - norm_layer=None, - large_kernel=False, - late_downsample=False, - ): - """ - Builds the decoder. - - Args: - nc (int): Channel count of the SAM embedding fed to the decoder (256). - layers (list[int]): Number of residual blocks per decoder stage. - block (nn.Module, optional): Residual block class. Default is `BasicBlock`. - norm_layer (nn.Module, optional): Normalisation layer. Default is `nn.BatchNorm2d`. - large_kernel (bool, optional): Use 5x5 instead of 3x3 convolutions. Default is False. - late_downsample (bool, optional): Widen the final stage. Default is False. - """ - super(SAM_Decoder_Deep, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - self._norm_layer = norm_layer - self.large_kernel = large_kernel - self.kernel_size = 5 if self.large_kernel else 3 - - self.inplanes = 256 - self.late_downsample = late_downsample - self.midplanes = 64 if late_downsample else 32 - - self.conv1 = ops.SpectralNorm( - nn.ConvTranspose2d( - self.midplanes, 32, kernel_size=4, stride=2, padding=1, bias=False - ) - ) - self.bn1 = norm_layer(32) - self.leaky_relu = nn.LeakyReLU(0.2, inplace=True) - - self.upsample = nn.UpsamplingNearest2d(scale_factor=2) - self.tanh = nn.Tanh() - self.layer2 = self._make_layer(block, 128, layers[1], stride=2) - self.layer3 = self._make_layer(block, 64, layers[2], stride=2) - self.layer4 = self._make_layer(block, self.midplanes, layers[3], stride=2) - - self.refine_OS1 = nn.Sequential( - nn.Conv2d( - 32, - 32, - kernel_size=self.kernel_size, - stride=1, - padding=self.kernel_size // 2, - bias=False, - ), - norm_layer(32), - self.leaky_relu, - nn.Conv2d( - 32, - 1, - kernel_size=self.kernel_size, - stride=1, - padding=self.kernel_size // 2, - ), - ) - - self.refine_OS4 = nn.Sequential( - nn.Conv2d( - 64, - 32, - kernel_size=self.kernel_size, - stride=1, - padding=self.kernel_size // 2, - bias=False, - ), - norm_layer(32), - self.leaky_relu, - nn.Conv2d( - 32, - 1, - kernel_size=self.kernel_size, - stride=1, - padding=self.kernel_size // 2, - ), - ) - - self.refine_OS8 = nn.Sequential( - nn.Conv2d( - 128, - 32, - kernel_size=self.kernel_size, - stride=1, - padding=self.kernel_size // 2, - bias=False, - ), - norm_layer(32), - self.leaky_relu, - nn.Conv2d( - 32, - 1, - kernel_size=self.kernel_size, - stride=1, - padding=self.kernel_size // 2, - ), - ) - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - if hasattr(m, "weight_bar"): - nn.init.xavier_uniform_(m.weight_bar) - else: - nn.init.xavier_uniform_(m.weight) - elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - - # Zero-initialise the last BN in each residual branch so the branch starts - # as an identity, per https://arxiv.org/abs/1706.02677 - for m in self.modules(): - if isinstance(m, BasicBlock): - nn.init.constant_(m.bn2.weight, 0) - - def _make_layer(self, block, planes, blocks, stride=1): - if blocks == 0: - return nn.Sequential(nn.Identity()) - norm_layer = self._norm_layer - upsample = None - if stride != 1: - upsample = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor=2), - ops.SpectralNorm(conv1x1(self.inplanes + 4, planes * block.expansion)), - norm_layer(planes * block.expansion), - ) - elif self.inplanes != planes * block.expansion: - upsample = nn.Sequential( - ops.SpectralNorm(conv1x1(self.inplanes + 4, planes * block.expansion)), - norm_layer(planes * block.expansion), - ) - - layers = [ - block( - self.inplanes + 4, - planes, - stride, - upsample, - norm_layer, - self.large_kernel, - ) - ] - self.inplanes = planes * block.expansion - for _ in range(1, blocks): - layers.append( - block( - self.inplanes, - planes, - norm_layer=norm_layer, - large_kernel=self.large_kernel, - ) - ) - - return nn.Sequential(*layers) - - def forward(self, x_os16, img, mask): - """ - Decodes SAM embeddings into alpha predictions at three output strides. - - Args: - x_os16 (torch.Tensor): SAM image embedding, shape `(N, 256, H/16, W/16)`. - img (torch.Tensor): Preprocessed image tensor, shape `(N, 3, H, W)`. - mask (torch.Tensor): Coarse binary guidance mask, shape `(N, 1, h, w)`. - - Returns: - dict: Keys `alpha_os1`, `alpha_os4`, `alpha_os8` (each in `[0, 1]`) and the - guidance `mask` resampled to full resolution. - """ - ret = {} - mask_os16 = F.interpolate( - mask, x_os16.shape[2:], mode="bilinear", align_corners=False - ) - img_os16 = F.interpolate( - img, x_os16.shape[2:], mode="bilinear", align_corners=False - ) - - x = self.layer2(torch.cat((x_os16, img_os16, mask_os16), dim=1)) - - x_os8 = self.refine_OS8(x) - - mask_os8 = F.interpolate( - mask, x.shape[2:], mode="bilinear", align_corners=False - ) - img_os8 = F.interpolate(img, x.shape[2:], mode="bilinear", align_corners=False) - - x = self.layer3(torch.cat((x, img_os8, mask_os8), dim=1)) - - x_os4 = self.refine_OS4(x) - - mask_os4 = F.interpolate( - mask, x.shape[2:], mode="bilinear", align_corners=False - ) - img_os4 = F.interpolate(img, x.shape[2:], mode="bilinear", align_corners=False) - - x = self.layer4(torch.cat((x, img_os4, mask_os4), dim=1)) - x = self.conv1(x) - x = self.bn1(x) - x = self.leaky_relu(x) - - x_os1 = self.refine_OS1(x) - - x_os4 = F.interpolate(x_os4, scale_factor=4.0, mode="bilinear", align_corners=False) - x_os8 = F.interpolate(x_os8, scale_factor=8.0, mode="bilinear", align_corners=False) - - x_os1 = (torch.tanh(x_os1) + 1.0) / 2.0 - x_os4 = (torch.tanh(x_os4) + 1.0) / 2.0 - x_os8 = (torch.tanh(x_os8) + 1.0) / 2.0 - - mask_os1 = F.interpolate( - mask, x_os1.shape[2:], mode="bilinear", align_corners=False - ) - - ret["alpha_os1"] = x_os1 - ret["alpha_os4"] = x_os4 - ret["alpha_os8"] = x_os8 - ret["mask"] = mask_os1 - - return ret - - -def sam_decoder_deep(nc=256, **kwargs): - """ - Builds the M2M decoder configuration used by the published Matting Anything weights. - - Args: - nc (int, optional): SAM embedding channel count. Default is 256. - **kwargs: Extra arguments forwarded to `SAM_Decoder_Deep`. - - Returns: - SAM_Decoder_Deep: The constructed decoder. - """ - return SAM_Decoder_Deep(nc, [2, 3, 3, 2], **kwargs) - - -__all__ = ["SAM_Decoder_Deep", "BasicBlock", "sam_decoder_deep"] diff --git a/src/garmentiq/matting/model_definition/mam/mam.py b/src/garmentiq/matting/model_definition/mam/mam.py deleted file mode 100644 index 686ee80..0000000 --- a/src/garmentiq/matting/model_definition/mam/mam.py +++ /dev/null @@ -1,292 +0,0 @@ -"""Matting Anything (MAM) wired to the Hugging Face SAM implementation. - -MAM refines a coarse SAM mask into a soft alpha matte. Upstream ships its own fork of -segment-anything exposing a custom `forward_m2m`; GarmentIQ reproduces that behaviour on -top of the standard Hugging Face `SamModel` so a single SAM checkpoint serves both the -segmentation and matting modules. - -The decoder architecture and weights come from Matting Anything -(https://github.com/SHI-Labs/Matting-Anything), Copyright (c) 2023 SHI Labs, MIT License. -MAM keeps SAM frozen during training, so any standard SAM checkpoint of the matching -variant is equivalent to the one embedded in the published MAM checkpoint. -""" - -import os -from typing import Optional, Union - -import cv2 -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - -from garmentiq.matting.model_definition.mam.m2m import sam_decoder_deep -from garmentiq.utils.device import resolve_device - -# Elliptical structuring elements indexed by width, matching upstream MAM. -_KERNELS = [None] + [ - cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size, size)) for size in range(1, 30) -] - - -def _unknown_region_from_mask(mask: torch.Tensor, rand_width: int = 30): - """ - Finds the uncertain band just outside a hard guidance mask. - - Everything inside the mask, and everything comfortably outside it, is treated as - confident; only the ring between them is left for the matting decoder to refine. - - Args: - mask (torch.Tensor): Binary mask of shape `(N, 1, H, W)`. - rand_width (int, optional): Erosion width controlling band thickness. Default is 30. - - Returns: - torch.Tensor: Binary weight map of shape `(N, 1, H, W)` on the mask's device. - """ - mask_np = mask.detach().cpu().numpy().astype(np.uint8) - weight = np.ones_like(mask_np, dtype=np.uint8) - - width = max(1, min(rand_width // 2, len(_KERNELS) - 1)) - for i in range(mask_np.shape[0]): - fg = mask_np[i, 0] - bg = cv2.erode(1 - mask_np[i, 0], _KERNELS[width]) - weight[i, 0][fg == 1] = 0 - weight[i, 0][bg == 1] = 0 - - return torch.from_numpy(weight).to(mask.device) - - -def _unknown_region_from_pred(pred: torch.Tensor, rand_width: int = 30): - """ - Finds the uncertain (soft) band of an alpha prediction. - - Progressive fusion replaces only the uncertain band of a coarse prediction with a - finer one, which is how MAM keeps confident interior/exterior regions stable while - sharpening edges. - - Args: - pred (torch.Tensor): Alpha prediction of shape `(N, 1, H, W)` in `[0, 1]`. - rand_width (int, optional): Dilation width controlling band thickness. Default is 30. - - Returns: - torch.Tensor: Binary weight map of shape `(N, 1, H, W)` on the same device as `pred`. - """ - n = pred.shape[0] - pred_np = pred.detach().cpu().numpy() - uncertain = np.ones_like(pred_np, dtype=np.uint8) - uncertain[pred_np < 1.0 / 255.0] = 0 - - width = max(1, min(rand_width // 2, len(_KERNELS) - 1)) - for i in range(n): - uncertain[i, 0] = cv2.dilate(uncertain[i, 0], _KERNELS[width]) - - uncertain[pred_np > 1 - 1.0 / 255.0] = 0 - # Upstream hardcodes .cuda(); keep the weight on the caller's device instead. - return torch.from_numpy(uncertain).to(pred.device) - - -class MattingAnything(nn.Module): - """ - Couples a frozen Hugging Face SAM with the Matting Anything M2M decoder. - - SAM supplies both the image embedding and a coarse mask for whatever prompt is given; - the M2M decoder turns that pair into a soft alpha matte. Unlike ViTMatte, no trimap is - required, because the SAM mask itself acts as the guidance signal. - - Attributes: - sam_model (nn.Module): The frozen SAM model providing embeddings and coarse masks. - sam_processor: The processor matching `sam_model`. - m2m (nn.Module): The mask-to-matte decoder. - """ - - def __init__(self, sam_model, sam_processor, m2m): - """ - Args: - sam_model (nn.Module): A loaded Hugging Face `SamModel`. - sam_processor: The matching `SamProcessor`. - m2m (nn.Module): The `SAM_Decoder_Deep` instance holding MAM weights. - """ - super().__init__() - self.sam_model = sam_model - self.sam_processor = sam_processor - self.m2m = m2m - - def forward(self, pixel_values, reshaped_size=None, original_size=None, **sam_prompt_kwargs): - """ - Produces multi-scale alpha predictions plus the full-resolution SAM mask. - - Args: - pixel_values (torch.Tensor): SAM-preprocessed image, shape `(N, 3, 1024, 1024)`. - reshaped_size (tuple[int, int], optional): Image size after SAM resizing but before - padding, as `(height, width)`. Required to - return a full-resolution mask. - original_size (tuple[int, int], optional): Original image size as `(height, width)`. - **sam_prompt_kwargs: Prompt tensors accepted by `SamModel`, such as - `input_points`, `input_labels`, or `input_boxes`. - - Returns: - tuple[dict, torch.Tensor | None]: The decoder output (`alpha_os1`, `alpha_os4`, - `alpha_os8`, `mask`) and the binarised SAM mask - at original resolution, or None when sizes are - not supplied. - """ - self.sam_model.eval() - with torch.no_grad(): - image_embeddings = self.sam_model.get_image_embeddings(pixel_values) - outputs = self.sam_model( - image_embeddings=image_embeddings, - multimask_output=True, - **sam_prompt_kwargs, - ) - # pred_masks: (batch, point_batch, num_masks, 256, 256) - low_res = outputs.pred_masks[:, 0] - iou = outputs.iou_scores[:, 0] - best = int(torch.argmax(iou[0])) - selected = low_res[:, best : best + 1] - guide_mask = (selected > 0.0).float() - - post_mask = None - if reshaped_size is not None and original_size is not None: - # Mirror SAM's postprocess_masks: upsample to the padded canvas, drop the - # padding, then resize to the true image size. - upscaled = F.interpolate( - selected, - (pixel_values.shape[-2], pixel_values.shape[-1]), - mode="bilinear", - align_corners=False, - ) - rh, rw = int(reshaped_size[0]), int(reshaped_size[1]) - upscaled = upscaled[..., :rh, :rw] - upscaled = F.interpolate( - upscaled, - (int(original_size[0]), int(original_size[1])), - mode="bilinear", - align_corners=False, - ) - post_mask = (upscaled > 0.0).float() - - return self.m2m(image_embeddings, pixel_values, guide_mask), post_mask - - -def load_mam( - checkpoint_path: str, - sam_model, - sam_processor, - device: Union[str, torch.device] = "cpu", -): - """ - Loads Matting Anything decoder weights and pairs them with a SAM model. - - The published MAM checkpoint stores both the frozen SAM (`module.seg_model.*`) and the - matting decoder (`module.m2m.*`). Only the decoder weights are read here; SAM comes from - the model you pass in, so the same SAM checkpoint can be shared with the segmentation - module. Loading is strict, so a mismatched checkpoint fails loudly instead of silently - producing a meaningless matte. - - Args: - checkpoint_path (str): Local path to a MAM checkpoint, e.g. `mam_sam_vitb.pth`. - sam_model (nn.Module): A loaded Hugging Face `SamModel` of the matching variant. - sam_processor: The matching `SamProcessor`. - device (Union[str, torch.device], optional): Device to place the model on, e.g. - `"cpu"`, `"cuda"`, or `"mps"`. - Default is `"cpu"`. - - Raises: - FileNotFoundError: If `checkpoint_path` does not exist. - ValueError: If the checkpoint contains no `module.m2m.*` weights, or if the decoder - weights do not match the expected architecture. - - Returns: - MattingAnything: The assembled model in evaluation mode, placed on `device`. - """ - if not os.path.exists(checkpoint_path): - raise FileNotFoundError(f"MAM checkpoint not found at {checkpoint_path}.") - - device = resolve_device(device) - - checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False) - state_dict = checkpoint.get("state_dict", checkpoint) - - m2m_state = { - k[len("module.m2m.") :]: v - for k, v in state_dict.items() - if k.startswith("module.m2m.") - } - if not m2m_state: - # Fall back to a checkpoint holding only the decoder. - m2m_state = { - k[len("m2m.") :]: v for k, v in state_dict.items() if k.startswith("m2m.") - } - if not m2m_state: - raise ValueError( - f"No Matting Anything decoder weights found in {checkpoint_path!r}. " - f"Expected keys prefixed with 'module.m2m.' or 'm2m.'." - ) - - m2m = sam_decoder_deep(nc=256) - missing, unexpected = m2m.load_state_dict(m2m_state, strict=False) - if missing or unexpected: - raise ValueError( - f"Matting Anything decoder weights do not match the expected architecture. " - f"{len(missing)} missing and {len(unexpected)} unexpected tensors " - f"(first missing: {missing[:3]}, first unexpected: {unexpected[:3]})." - ) - - model = MattingAnything(sam_model=sam_model, sam_processor=sam_processor, m2m=m2m) - model = model.to(device) - model.eval() - return model - - -def fuse_alpha( - pred: dict, - reshaped_size, - original_size, - post_mask: Optional[torch.Tensor] = None, -): - """ - Fuses MAM's three alpha predictions into a single full-resolution matte. - - When the hard SAM mask is supplied, fusion follows upstream's mask-guidance mode: the - matte starts from the confident mask and only the uncertain ring around its border is - replaced by predicted alpha, progressively from the coarsest scale to the finest. This - is what keeps the garment interior fully opaque while edges stay soft. Without the mask - the matte starts from the coarsest prediction instead, which leaves interiors dim. - - Args: - pred (dict): Decoder output containing `alpha_os1`, `alpha_os4`, and `alpha_os8`. - reshaped_size (tuple[int, int]): Image size after SAM resizing but before padding, - as `(height, width)`. - original_size (tuple[int, int]): Original image size as `(height, width)`. - post_mask (torch.Tensor, optional): Binarised SAM mask at original resolution, - shape `(1, 1, H, W)`. Default is None. - - Returns: - torch.Tensor: Fused alpha of shape `(1, 1, height, width)` in `[0, 1]`. - """ - h, w = int(reshaped_size[0]), int(reshaped_size[1]) - os1 = pred["alpha_os1"][..., :h, :w] - os4 = pred["alpha_os4"][..., :h, :w] - os8 = pred["alpha_os8"][..., :h, :w] - - target = (int(original_size[0]), int(original_size[1])) - os1 = F.interpolate(os1, target, mode="bilinear", align_corners=False) - os4 = F.interpolate(os4, target, mode="bilinear", align_corners=False) - os8 = F.interpolate(os8, target, mode="bilinear", align_corners=False) - - if post_mask is not None: - alpha = post_mask.clone() - weight_os8 = _unknown_region_from_mask(post_mask, rand_width=10) - alpha[weight_os8 > 0] = os8[weight_os8 > 0] - else: - alpha = os8.clone() - - weight_os4 = _unknown_region_from_pred(alpha, rand_width=20) - alpha[weight_os4 > 0] = os4[weight_os4 > 0] - weight_os1 = _unknown_region_from_pred(alpha, rand_width=10) - alpha[weight_os1 > 0] = os1[weight_os1 > 0] - - return alpha.clamp(0.0, 1.0) - - -__all__ = ["MattingAnything", "load_mam", "fuse_alpha"] diff --git a/src/garmentiq/matting/model_definition/mam/ops.py b/src/garmentiq/matting/model_definition/mam/ops.py deleted file mode 100644 index 6f09b9f..0000000 --- a/src/garmentiq/matting/model_definition/mam/ops.py +++ /dev/null @@ -1,129 +0,0 @@ -"""Spectral normalisation used by the Matting Anything decoder. - -Vendored from Matting Anything (https://github.com/SHI-Labs/Matting-Anything), -Copyright (c) 2023 SHI Labs, MIT License, which in turn adapts -https://github.com/heykeetae/Self-Attention-GAN. - -The implementation is kept byte-for-byte compatible with the upstream module so -that published Matting Anything checkpoints load without any key remapping. -""" - -import torch -from torch import nn -from torch.nn import Parameter - - -def l2normalize(v, eps=1e-12): - """ - Normalises a vector to unit L2 norm. - - Args: - v (torch.Tensor): The vector to normalise. - eps (float, optional): Small constant guarding against division by zero. - Default is 1e-12. - - Returns: - torch.Tensor: The normalised vector. - """ - return v / (v.norm() + eps) - - -class SpectralNorm(nn.Module): - """ - Wraps a module so its weight matrix is divided by its largest singular value. - - The wrapper stores three buffers (`weight_u`, `weight_v`, `weight_bar`) rather than a - plain `weight`, which is why Matting Anything checkpoints contain those key names. - During evaluation the power iteration is skipped so results stay deterministic. - - Attributes: - module (nn.Module): The wrapped module whose weight is normalised. - name (str): Name of the weight attribute being normalised. - power_iterations (int): Number of power iterations used during training. - """ - - def __init__(self, module, name="weight", power_iterations=1): - """ - Initialises the spectral norm wrapper. - - Args: - module (nn.Module): The module to wrap, typically a convolution. - name (str, optional): The weight attribute to normalise. Default is `"weight"`. - power_iterations (int, optional): Power iterations per training step. Default is 1. - """ - super(SpectralNorm, self).__init__() - self.module = module - self.name = name - self.power_iterations = power_iterations - if not self._made_params(): - self._make_params() - - def _update_u_v(self): - u = getattr(self.module, self.name + "_u") - v = getattr(self.module, self.name + "_v") - w = getattr(self.module, self.name + "_bar") - - height = w.data.shape[0] - for _ in range(self.power_iterations): - v.data = l2normalize( - torch.mv(torch.t(w.view(height, -1).data), u.data) - ) - u.data = l2normalize(torch.mv(w.view(height, -1).data, v.data)) - - sigma = u.dot(w.view(height, -1).mv(v)) - setattr(self.module, self.name, w / sigma.expand_as(w)) - - def _noupdate_u_v(self): - u = getattr(self.module, self.name + "_u") - v = getattr(self.module, self.name + "_v") - w = getattr(self.module, self.name + "_bar") - - height = w.data.shape[0] - sigma = u.dot(w.view(height, -1).mv(v)) - setattr(self.module, self.name, w / sigma.expand_as(w)) - - def _made_params(self): - try: - getattr(self.module, self.name + "_u") - getattr(self.module, self.name + "_v") - getattr(self.module, self.name + "_bar") - return True - except AttributeError: - return False - - def _make_params(self): - w = getattr(self.module, self.name) - - height = w.data.shape[0] - width = w.view(height, -1).data.shape[1] - - u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False) - v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False) - u.data = l2normalize(u.data) - v.data = l2normalize(v.data) - w_bar = Parameter(w.data) - - del self.module._parameters[self.name] - - self.module.register_parameter(self.name + "_u", u) - self.module.register_parameter(self.name + "_v", v) - self.module.register_parameter(self.name + "_bar", w_bar) - - def forward(self, *args): - """ - Applies the wrapped module with a spectrally normalised weight. - - Args: - *args: Positional arguments forwarded to the wrapped module. - - Returns: - torch.Tensor: The wrapped module's output. - """ - if self.module.training: - self._update_u_v() - else: - self._noupdate_u_v() - return self.module.forward(*args) - - -__all__ = ["SpectralNorm", "l2normalize"] diff --git a/src/garmentiq/matting/model_definition/vitmatte/__init__.py b/src/garmentiq/matting/model_definition/vitmatte/__init__.py deleted file mode 100644 index 82c1778..0000000 --- a/src/garmentiq/matting/model_definition/vitmatte/__init__.py +++ /dev/null @@ -1,28 +0,0 @@ -# garmentiq/matting/model_definition/vitmatte/__init__.py -"""The ViTMatte image matting model.""" -from .vitmatte import ( - load_vitmatte_config, - load_vitmatte_processor, - VITMATTE_MODELS, -) - -_LAZY = {"VitMatteForImageMatting", "VitMatteConfig", "VitMatteImageProcessor"} - - -def __getattr__(name): - """Defer transformers imports until a ViTMatte class is actually requested.""" - if name in _LAZY: - from . import vitmatte - - return getattr(vitmatte, name) - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") - - -__all__ = [ - "VitMatteForImageMatting", - "VitMatteConfig", - "VitMatteImageProcessor", - "load_vitmatte_config", - "load_vitmatte_processor", - "VITMATTE_MODELS", -] diff --git a/src/garmentiq/matting/model_definition/vitmatte/vitmatte-base-composition-1k/config.json b/src/garmentiq/matting/model_definition/vitmatte/vitmatte-base-composition-1k/config.json deleted file mode 100644 index a84e915..0000000 --- a/src/garmentiq/matting/model_definition/vitmatte/vitmatte-base-composition-1k/config.json +++ /dev/null @@ -1,51 +0,0 @@ -{ - "_commit_hash": null, - "architectures": [ - "VitMatteForImageMatting" - ], - "backbone_config": { - "image_size": 512, - "model_type": "vitdet", - "num_channels": 4, - "out_features": [ - "stage12" - ], - "out_indices": [ - 12 - ], - "residual_block_indices": [ - 2, - 5, - 8, - 11 - ], - "use_relative_position_embeddings": true, - "window_block_indices": [ - 0, - 1, - 3, - 4, - 6, - 7, - 9, - 10 - ], - "window_size": 14 - }, - "convstream_hidden_sizes": [ - 48, - 96, - 192 - ], - "fusion_hidden_sizes": [ - 256, - 128, - 64, - 32 - ], - "hidden_size": 768, - "initializer_range": 0.02, - "model_type": "vitmatte", - "torch_dtype": "float32", - "transformers_version": null -} diff --git a/src/garmentiq/matting/model_definition/vitmatte/vitmatte-base-composition-1k/preprocessor_config.json b/src/garmentiq/matting/model_definition/vitmatte/vitmatte-base-composition-1k/preprocessor_config.json deleted file mode 100644 index a0f7e44..0000000 --- a/src/garmentiq/matting/model_definition/vitmatte/vitmatte-base-composition-1k/preprocessor_config.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "do_normalize": true, - "do_pad": true, - "do_rescale": true, - "image_mean": [ - 0.5, - 0.5, - 0.5 - ], - "image_processor_type": "VitMatteImageProcessor", - "image_std": [ - 0.5, - 0.5, - 0.5 - ], - "rescale_factor": 0.00392156862745098, - "size_divisibility": 32 -} diff --git a/src/garmentiq/matting/model_definition/vitmatte/vitmatte-small-composition-1k/config.json b/src/garmentiq/matting/model_definition/vitmatte/vitmatte-small-composition-1k/config.json deleted file mode 100644 index 75dd09e..0000000 --- a/src/garmentiq/matting/model_definition/vitmatte/vitmatte-small-composition-1k/config.json +++ /dev/null @@ -1,53 +0,0 @@ -{ - "_commit_hash": null, - "architectures": [ - "VitMatteForImageMatting" - ], - "backbone_config": { - "hidden_size": 384, - "image_size": 512, - "model_type": "vitdet", - "num_attention_heads": 6, - "num_channels": 4, - "out_features": [ - "stage12" - ], - "out_indices": [ - 12 - ], - "residual_block_indices": [ - 2, - 5, - 8, - 11 - ], - "use_relative_position_embeddings": true, - "window_block_indices": [ - 0, - 1, - 3, - 4, - 6, - 7, - 9, - 10 - ], - "window_size": 14 - }, - "convstream_hidden_sizes": [ - 48, - 96, - 192 - ], - "fusion_hidden_sizes": [ - 256, - 128, - 64, - 32 - ], - "hidden_size": 384, - "initializer_range": 0.02, - "model_type": "vitmatte", - "torch_dtype": "float32", - "transformers_version": null -} diff --git a/src/garmentiq/matting/model_definition/vitmatte/vitmatte-small-composition-1k/preprocessor_config.json b/src/garmentiq/matting/model_definition/vitmatte/vitmatte-small-composition-1k/preprocessor_config.json deleted file mode 100644 index a0f7e44..0000000 --- a/src/garmentiq/matting/model_definition/vitmatte/vitmatte-small-composition-1k/preprocessor_config.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "do_normalize": true, - "do_pad": true, - "do_rescale": true, - "image_mean": [ - 0.5, - 0.5, - 0.5 - ], - "image_processor_type": "VitMatteImageProcessor", - "image_std": [ - 0.5, - 0.5, - 0.5 - ], - "rescale_factor": 0.00392156862745098, - "size_divisibility": 32 -} diff --git a/src/garmentiq/matting/model_definition/vitmatte/vitmatte.py b/src/garmentiq/matting/model_definition/vitmatte/vitmatte.py deleted file mode 100644 index 41a5914..0000000 --- a/src/garmentiq/matting/model_definition/vitmatte/vitmatte.py +++ /dev/null @@ -1,154 +0,0 @@ -"""ViTMatte loading and its bundled configurations. - -Configurations and processors for the `small` and `base` variants ship with the -package, so only the weights are read from disk. The processor rescales the trimap -itself, so it must be handed the raw 0-255 trimap rather than a pre-normalised one. -""" -import os -import json -from typing import TYPE_CHECKING, Optional - -if TYPE_CHECKING: # pragma: no cover - import only for type checkers - from transformers import ( - VitMatteForImageMatting, - VitMatteConfig, - VitMatteImageProcessor, - ) - -# ViTMatte variants whose configurations ship with GarmentIQ. -VITMATTE_MODELS = [ - "vitmatte-small-composition-1k", - "vitmatte-base-composition-1k", -] - -_LAZY_CLASSES = { - "VitMatteForImageMatting": "ViTMatte", - "VitMatteConfig": "ViTMatte", - "VitMatteImageProcessor": "ViTMatte", -} - - -def _resolve(name: str): - """Fetches a transformers class on demand with an actionable error message.""" - import transformers - - try: - return getattr(transformers, name) - except AttributeError as e: - raise ImportError( - f"'{name}' is not available in transformers " - f"{getattr(transformers, '__version__', 'unknown')}. " - f"{_LAZY_CLASSES.get(name, 'This feature')} support requires a newer " - f"transformers release; upgrade with `pip install -U transformers`." - ) from e - - -def __getattr__(name): - """Module-level lazy attribute access (PEP 562).""" - if name in _LAZY_CLASSES: - return _resolve(name) - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") - - -def _variant_dir(model_type: str, model_dir: Optional[str]) -> str: - """Resolves the directory holding a variant's offline configuration files.""" - if model_dir is not None: - if not os.path.isdir(model_dir): - raise FileNotFoundError( - f"Provided model_dir '{model_dir}' is not an existing directory." - ) - return model_dir - - if model_type not in VITMATTE_MODELS: - raise ValueError( - f"Invalid model_type '{model_type}'. Choose from: {VITMATTE_MODELS}" - ) - - current_dir = os.path.dirname(os.path.abspath(__file__)) - return os.path.join(current_dir, model_type) - - -def load_vitmatte_config( - model_type: str = "vitmatte-small-composition-1k", - model_dir: Optional[str] = None, -): - """ - Reads and loads the bundled configuration for a specified ViTMatte variant. - - This function facilitates strictly offline initialization by locating the `config.json` - associated with the chosen ViTMatte variant within the local package directory and - converting it into a Hugging Face `VitMatteConfig`, entirely bypassing network requests. - - Args: - model_type (str, optional): The identifier for the desired ViTMatte variant. Must be - one of `["vitmatte-small-composition-1k", - "vitmatte-base-composition-1k"]`. - Default is `"vitmatte-small-composition-1k"`. - model_dir (str, optional): Path to a local directory containing `config.json`, used - to override the bundled configuration. Default is None. - - Raises: - ValueError: If `model_type` is not a supported variant. - FileNotFoundError: If the offline `config.json` cannot be located. - ImportError: If the installed transformers release does not provide ViTMatte. - - Returns: - VitMatteConfig: The loaded configuration, ready for `VitMatteForImageMatting`. - """ - config_path = os.path.join(_variant_dir(model_type, model_dir), "config.json") - - if not os.path.exists(config_path): - raise FileNotFoundError(f"Offline config missing at {config_path}.") - - with open(config_path, "r") as f: - config_dict = json.load(f) - - return _resolve("VitMatteConfig").from_dict(config_dict) - - -def load_vitmatte_processor( - model_type: str = "vitmatte-small-composition-1k", - model_dir: Optional[str] = None, -): - """ - Loads the offline image processor for a specified ViTMatte variant. - - The ViTMatte processor stacks the image and its trimap into a single four-channel - tensor, which is the input the model expects. - - Args: - model_type (str, optional): The identifier for the desired ViTMatte variant. - Default is `"vitmatte-small-composition-1k"`. - model_dir (str, optional): Path to a local directory containing - `preprocessor_config.json`. Default is None. - - Raises: - ValueError: If `model_type` is not a supported variant. - FileNotFoundError: If the offline processor configuration cannot be located. - ImportError: If the installed transformers release does not provide ViTMatte. - - Returns: - VitMatteImageProcessor: The instantiated processor. - """ - processor_path = os.path.join( - _variant_dir(model_type, model_dir), "preprocessor_config.json" - ) - - if not os.path.exists(processor_path): - raise FileNotFoundError(f"Offline processor config missing at {processor_path}.") - - with open(processor_path, "r") as f: - processor_dict = json.load(f) - - processor_dict.pop("image_processor_type", None) - return _resolve("VitMatteImageProcessor")(**processor_dict) - - -__all__ = [ - "VitMatteForImageMatting", - "VitMatteConfig", - "VitMatteImageProcessor", - "load_vitmatte_config", - "load_vitmatte_processor", - "VITMATTE_MODELS", -] diff --git a/src/garmentiq/matting/trimap.py b/src/garmentiq/matting/trimap.py deleted file mode 100644 index ac32e49..0000000 --- a/src/garmentiq/matting/trimap.py +++ /dev/null @@ -1,89 +0,0 @@ -"""Building a trimap from a segmentation mask. - -A trimap marks pixels as definite foreground, definite background, or unknown. The -unknown band is the only region where a matting model may predict soft alpha, so its -width controls how much of the edge can be refined. -""" -import cv2 -import numpy as np - - -def generate_trimap( - mask: np.ndarray, - erode_size: int = 10, - dilate_size: int = 10, - threshold: int = 127, -): - """ - Builds a trimap from a binary segmentation mask. - - Trimap-based matting models such as ViTMatte need to be told which pixels are - definitely foreground, definitely background, and uncertain. This function derives - that from a segmentation mask by eroding it to obtain confident foreground, dilating - it to obtain confident background, and marking the band in between as unknown, which - is where the matting model is free to predict soft alpha values. - - The size of the unknown band matters: too narrow and hair or fabric edges fall outside - it and can never become soft, too wide and the model has to guess over large areas. - - Args: - mask (numpy.ndarray): Segmentation mask as a 2D array. Values may be binary - (`0`/`1`) or 8-bit (`0`-`255`). - erode_size (int, optional): Erosion kernel size controlling how far the confident - foreground is pulled inward. Default is 10. - dilate_size (int, optional): Dilation kernel size controlling how far the unknown - band extends outward. Default is 10. - threshold (int, optional): Cutoff used to binarise an 8-bit mask. Default is 127. - - Raises: - ValueError: If `mask` is not 2D, or if `erode_size` or `dilate_size` is negative. - - Returns: - numpy.ndarray: Trimap as `uint8` with values `0` (background), `128` (unknown), - and `255` (foreground), matching `mask` in shape. - """ - if mask is None or not hasattr(mask, "ndim"): - raise ValueError("mask must be a numpy array.") - - mask = np.asarray(mask) - if mask.ndim == 3 and mask.shape[-1] == 1: - mask = mask[..., 0] - if mask.ndim != 2: - raise ValueError( - f"mask must be a 2D array, got shape {mask.shape}. Pass a single-channel " - f"segmentation mask." - ) - if erode_size < 0 or dilate_size < 0: - raise ValueError( - f"erode_size and dilate_size must be non-negative, got " - f"erode_size={erode_size}, dilate_size={dilate_size}." - ) - - if mask.dtype == bool: - binary = mask.astype(np.uint8) - elif mask.max() <= 1: - binary = (mask > 0).astype(np.uint8) - else: - binary = (mask > threshold).astype(np.uint8) - - foreground = binary - if erode_size > 0: - erode_kernel = cv2.getStructuringElement( - cv2.MORPH_ELLIPSE, (erode_size, erode_size) - ) - foreground = cv2.erode(binary, erode_kernel, iterations=1) - - background = binary - if dilate_size > 0: - dilate_kernel = cv2.getStructuringElement( - cv2.MORPH_ELLIPSE, (dilate_size, dilate_size) - ) - background = cv2.dilate(binary, dilate_kernel, iterations=1) - - trimap = np.full(binary.shape, 128, dtype=np.uint8) - trimap[background == 0] = 0 - trimap[foreground == 1] = 255 - return trimap - - -__all__ = ["generate_trimap"] diff --git a/src/garmentiq/segmentation/__init__.py b/src/garmentiq/segmentation/__init__.py deleted file mode 100644 index 6705d5a..0000000 --- a/src/garmentiq/segmentation/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# garmentiq/segmentation/__init__.py -"""Garment segmentation. - -Separates the garment from its background and produces the mask the later stages rely -on. Landmark refinement and derivation both need this mask, and matting uses it as -guidance. - -Four backends are supported through one interface: BiRefNet, which needs no prompt at -all, and SAM 1, SAM 2, and SAM 3, which are guided by a unified `prompt` dictionary -accepting points, labels, boxes, and text. -""" -from .load_model import load_model -from .extract import extract -from .plot import plot -from .change_background_color import change_background_color -from .process_and_save_images import process_and_save_images diff --git a/src/garmentiq/segmentation/change_background_color.py b/src/garmentiq/segmentation/change_background_color.py deleted file mode 100644 index 8bfca50..0000000 --- a/src/garmentiq/segmentation/change_background_color.py +++ /dev/null @@ -1,32 +0,0 @@ -"""Replacing the background of an image using a segmentation mask.""" -import numpy as np - - -def change_background_color( - image_np: np.ndarray, mask_np: np.ndarray, background_color: tuple[int, int, int] -): - """ - Changes the background color of an image based on a given mask. - - This function modifies the background of an image by replacing the pixels - that correspond to the background (as identified by the mask) with a specified color. - - Args: - image_np (numpy.ndarray): The original image as a numpy array (RGB). - mask_np (numpy.ndarray): A binary mask where background pixels are labeled as 0 and foreground as 1. - background_color (tuple[int, int, int]): A tuple representing the RGB color to replace the background with, - e.g., (255, 255, 255) for white. - - Returns: - numpy.ndarray: A new image with the background color changed. - """ - # Create a mask where the background (black) is 1, and the foreground is 0 - background_mask = mask_np == 0 # Mask values of 0 represent background - - # Create a copy of the original image to modify - modified_image = image_np.copy() - - # Change the background areas to the specified background color - modified_image[background_mask] = background_color - - return modified_image diff --git a/src/garmentiq/segmentation/extract.py b/src/garmentiq/segmentation/extract.py deleted file mode 100644 index 646b6e9..0000000 --- a/src/garmentiq/segmentation/extract.py +++ /dev/null @@ -1,402 +0,0 @@ -"""Running segmentation to obtain a garment mask. - -Dispatches on the model family, so BiRefNet and every SAM generation share one entry -point. SAM prompts arrive in a unified `prompt` dictionary accepting `points`, -`labels`, `boxes`, and `text`; nesting differences between SAM versions are normalised -here, and a text prompt for SAM 1 or SAM 2 is routed through a grounding model first. -""" -from PIL import Image -import torch -from torchvision import transforms -import numpy as np -from typing import Optional, Union -from garmentiq.utils.device import resolve_device, empty_cache, inputs_to_device -from garmentiq.segmentation.model_definition.sam import ( - SAM_CAPABILITIES, - SAM_PROMPT_DEPTHS, -) - -PROMPT_KEYS = ("points", "labels", "boxes", "text") - -# Legacy keyword arguments accepted for backwards compatibility, mapped onto the -# unified prompt dictionary. -LEGACY_PROMPT_KWARGS = { - "input_points": "points", - "input_labels": "labels", - "input_boxes": "boxes", -} - - -def _nesting_depth(value): - """Returns how many list/tuple levels wrap the first scalar in `value`.""" - depth = 0 - current = value - while isinstance(current, (list, tuple)): - depth += 1 - if len(current) == 0: - break - current = current[0] - return depth - - -def _to_depth(value, target: int, name: str): - """ - Reshapes a nested prompt list to the nesting depth a processor expects. - - SAM 1 and SAM 2 disagree on prompt nesting (SAM 2 requires exactly one extra - level and raises otherwise), so prompts are normalised here to let callers use - a single consistent format across every SAM family. - """ - if isinstance(value, torch.Tensor) or isinstance(value, np.ndarray): - return value - - depth = _nesting_depth(value) - while depth < target: - value = [value] - depth += 1 - while depth > target: - if len(value) != 1: - raise ValueError( - f"Prompt '{name}' is nested {depth} levels deep but this SAM family " - f"expects {target}, and it cannot be unwrapped because the outermost " - f"level holds {len(value)} items. Provide '{name}' with {target} levels." - ) - value = value[0] - depth -= 1 - return value - - -def _detect_sam_family(model): - """ - Identifies which SAM family a loaded model belongs to, if any. - - Detection walks the class hierarchy by name rather than importing the transformers - classes, so a model from any SAM generation is recognised without requiring every - generation to exist in the installed transformers release. - """ - names = {cls.__name__ for cls in type(model).__mro__} - # Checked before Sam3Model: the tracker is a separate class that carries the SAM-style - # prompt encoder, so it accepts points where the SAM 3 detector does not. - if {"Sam3TrackerModel", "Sam3TrackerPreTrainedModel"} & names: - return "sam3-tracker" - if {"Sam3Model", "Sam3PreTrainedModel"} & names: - return "sam3" - if {"Sam2Model", "Sam2PreTrainedModel"} & names: - return "sam2" - if {"SamModel", "SamPreTrainedModel"} & names: - return "sam1" - return None - - -def _build_prompt(prompt: Optional[dict], kwargs: dict): - """Merges the `prompt` dict with legacy `input_*` keyword arguments.""" - merged = {} - - if prompt is not None: - if not isinstance(prompt, dict): - raise ValueError( - f"`prompt` must be a dictionary with any of {list(PROMPT_KEYS)}, " - f"got {type(prompt).__name__}." - ) - unknown = set(prompt) - set(PROMPT_KEYS) - if unknown: - raise ValueError( - f"Unknown prompt key(s) {sorted(unknown)}. " - f"Valid keys are {list(PROMPT_KEYS)}." - ) - merged.update({k: v for k, v in prompt.items() if v is not None}) - - for legacy_key, new_key in LEGACY_PROMPT_KWARGS.items(): - if kwargs.get(legacy_key) is not None: - merged.setdefault(new_key, kwargs[legacy_key]) - - return merged - - -def extract( - model: torch.nn.Module, - image_path: str, - processor=None, - prompt: Optional[dict] = None, - device: Union[str, torch.device] = "cpu", - grounding_model=None, - grounding_processor=None, - grounding_args: Optional[dict] = None, - **kwargs, -): - """ - Intelligently extracts an image segmentation mask from a given image using either a standard - PyTorch model or a Processor-based foundation model. - - This function takes an image and processes it based on the model strategy. If a processor is supplied, - it delegates preprocessing (e.g., resizing, scaling, prompt-handling) to the processor. Otherwise, - it applies standard manual transformations based on provided kwargs. It then feeds the input into - the model to generate a segmentation mask. The original image and the mask are returned as numpy arrays. - - For Segment Anything models the prompt is supplied through the unified `prompt` dictionary, - which may carry geometric prompts (`"points"`, `"labels"`, `"boxes"`) and/or a natural-language - prompt (`"text"`). At least one prompt is required, because an unprompted SAM silently returns - a meaningless mask rather than raising. Prompt nesting is normalised per family, so the same - `prompt` works across SAM 1, SAM 2, and SAM 3. - - Text prompts are handled differently per family, because only SAM 3 has a text encoder: - - SAM 3 consumes the text directly. - - SAM 1 and SAM 2 have no text encoder, so `grounding_model` and `grounding_processor` - must be supplied. The phrase is grounded into boxes first, and those boxes are then - used as ordinary SAM prompts. - - The model and its inputs are placed on `device`, so the same value should be passed here as was - used when loading the model. Any accelerator memory cached during inference is released before - returning. - - Args: - model (torch.nn.Module): The pretrained PyTorch model to use for segmentation predictions. - image_path (str): The path to the image file on which to perform segmentation. - processor (Any, optional): The model-specific processor (e.g., from Hugging Face) used for preprocessing - inputs. If None, standard PyTorch manual transformations are applied. - Default is None. - prompt (dict, optional): The unified prompt for Segment Anything models. Recognised keys: - - `"points"` (list): Point coordinates, e.g. `[[[x, y]]]`. - Not supported by SAM 3. - - `"labels"` (list): Point labels, `1` for foreground and `0` for - background, e.g. `[[1]]`. Not supported by SAM 3. - - `"boxes"` (list): Boxes as `[[[x_min, y_min, x_max, y_max]]]`. - - `"text"` (str): Natural-language prompt, e.g. `"a shirt"`. - Default is None. - device (Union[str, torch.device], optional): The device to run inference on, e.g. `"cpu"`, - `"cuda"`, `"cuda:0"`, or `"mps"`. Hardware - acceleration is opt-in; pass it explicitly to - use a GPU or Apple Silicon. Default is `"cpu"`. - grounding_model (GroundingDinoForObjectDetection, optional): Grounding model used to turn a - text prompt into boxes for SAM 1 - and SAM 2. Default is None. - grounding_processor (GroundingDinoProcessor, optional): Processor matching `grounding_model`. - Default is None. - grounding_args (dict, optional): Extra grounding options, accepting `"box_threshold"`, - `"text_threshold"`, and `"max_boxes"`. Default is None. - **kwargs: Additional arbitrary keyword arguments for model-specific configurations. - For standard models (e.g., BiRefNet): `resize_dim`, `normalize_mean`, `normalize_std`. - The legacy `input_points`, `input_labels`, and `input_boxes` arguments are still - accepted and are folded into `prompt`. - - Raises: - FileNotFoundError: If the image file at `image_path` does not exist. - ValueError: If the requested `device` is invalid or unavailable, if no prompt is supplied for - a SAM model, if a prompt type is unsupported by the chosen SAM family, if a text - prompt is given for SAM 1 or SAM 2 without a grounding model, or if the processor - output format is unrecognized. - - Returns: - tuple (numpy.ndarray, numpy.ndarray): The original image converted to a numpy array, - and the extracted segmentation mask as a numpy array. - """ - device = resolve_device(device) - model = model.to(device) - image = Image.open(image_path).convert("RGB") - - # Processor-Based Models (SAM 1 / SAM 2 / SAM 3) - if processor is not None: - family = _detect_sam_family(model) - prompt_dict = _build_prompt(prompt, kwargs) - - if family is not None: - capabilities = SAM_CAPABILITIES[family] - - if not prompt_dict: - options = [] - if capabilities["points"]: - options.append("prompt={'points': [[[x, y]]]}") - if capabilities["boxes"]: - options.append("prompt={'boxes': [[[x0, y0, x1, y1]]]}") - if capabilities["text"]: - options.append("prompt={'text': 'a shirt'}") - else: - options.append( - "prompt={'text': 'a shirt'} together with a grounding model" - ) - raise ValueError( - f"A prompt is required for {family}. Pass one of: {', '.join(options)}. " - f"Without a prompt SAM returns an arbitrary mask instead of failing." - ) - - # `text` is intentionally excluded here: it is always acceptable, either - # natively (SAM 3) or by grounding it into boxes first (SAM 1 / SAM 2). - unsupported = [ - key - for key in prompt_dict - if key != "text" and key in capabilities and not capabilities[key] - ] - if unsupported: - detail = "" - if family == "sam3" and {"points", "labels"} & set(unsupported): - # SAM 3's release does contain a point-promptable model, but it is - # the tracker rather than the detector loaded here. - detail = ( - " SAM 3's image model is an open-vocabulary detector, prompted by " - "description rather than by clicking. For point prompts on a still " - "image load SAM 3's tracker with " - "garmentiq.segmentation.model_definition.sam.load_sam3_tracker(), " - "or use SAM 1 / SAM 2." - ) - raise ValueError( - f"Prompt key(s) {sorted(unsupported)} are not supported by {family}. " - f"{family} supports: " - f"{sorted(k for k, v in capabilities.items() if v)}.{detail}" - ) - - # Resolve a text prompt into boxes for the families without a text encoder. - text = prompt_dict.pop("text", None) - if text is not None and not capabilities["text"]: - if grounding_model is None or grounding_processor is None: - raise ValueError( - f"{family} has no text encoder, so a text prompt requires a grounding " - f"model. Pass grounding_model and grounding_processor (see " - f"garmentiq.grounding), or use SAM 3 which understands text natively." - ) - # Imported here so the grounding stack is only required when used. - from garmentiq.grounding import ground_text_to_boxes - - grounded = ground_text_to_boxes( - model=grounding_model, - processor=grounding_processor, - image=image, - text=text, - device=device, - **(grounding_args or {}), - ) - # Grounded boxes replace any caller-supplied boxes for this prompt. - prompt_dict["boxes"] = [grounded] - text = None - - depths = SAM_PROMPT_DEPTHS[family] - processor_kwargs = {} - if "points" in prompt_dict: - processor_kwargs["input_points"] = _to_depth( - prompt_dict["points"], depths["points"], "points" - ) - if "labels" in prompt_dict: - processor_kwargs["input_labels"] = _to_depth( - prompt_dict["labels"], depths["labels"], "labels" - ) - if "boxes" in prompt_dict: - processor_kwargs["input_boxes"] = _to_depth( - prompt_dict["boxes"], depths["boxes"], "boxes" - ) - if text is not None: - processor_kwargs["text"] = text - - inputs = inputs_to_device( - processor(images=image, return_tensors="pt", **processor_kwargs), - device, - ) - - with torch.no_grad(): - outputs = model(**inputs) - - if family == "sam3": - results = processor.post_process_instance_segmentation( - outputs, target_sizes=[(image.height, image.width)] - )[0] - instance_masks = results["masks"] - if instance_masks.shape[0] == 0: - raise ValueError( - f"SAM 3 found no instance matching the prompt in {image_path!r}. " - f"Try rephrasing the text prompt or lowering the threshold." - ) - # Keep the highest-scoring instance, mirroring the single-mask contract. - best = int(torch.argmax(results["scores"])) - best_mask = instance_masks[best].detach().cpu().numpy() - else: - post_kwargs = {} - if family == "sam1": - post_kwargs["reshaped_input_sizes"] = inputs[ - "reshaped_input_sizes" - ].cpu() - masks = processor.post_process_masks( - outputs.pred_masks.cpu(), - inputs["original_sizes"].cpu(), - **post_kwargs, - ) - if family == "sam3-tracker": - # The tracker returns three candidate masks per prompt with an IoU - # score for each, so pick the highest-scoring one rather than the - # first, which is often the smallest sub-part. - per_prompt_masks = masks[0] - iou = outputs.iou_scores.cpu() - selected = [] - for i in range(per_prompt_masks.shape[0]): - scores = iou[0, i] if iou.ndim == 3 else iou[i] - selected.append(per_prompt_masks[i, int(torch.argmax(scores))]) - per_prompt = torch.stack(selected, dim=0) - else: - # masks[0] has shape (num_prompts, num_masks, H, W). Keep the first - # mask of each prompt and union them, which is identical to the - # previous single-prompt behaviour when only one prompt is given. - per_prompt = masks[0][:, 0] - best_mask = ( - torch.any(per_prompt.bool(), dim=0).detach().cpu().numpy() - ) - - mask_np = (best_mask.astype(np.float32) * 255).astype(np.uint8) - del inputs, outputs - - else: - # Unknown processor-based model: fall back to the generic SAM-like contract. - inputs = inputs_to_device( - processor(image, return_tensors="pt", **kwargs), device - ) - - with torch.no_grad(): - outputs = model(**inputs) - - if hasattr(outputs, "pred_masks"): - masks = processor.image_processor.post_process_masks( - outputs.pred_masks.cpu(), - inputs["original_sizes"].cpu(), - inputs["reshaped_input_sizes"].cpu(), - ) - best_mask = masks[0][0][0].numpy() - mask_np = (best_mask * 255).astype(np.uint8) - else: - raise ValueError("Unrecognized processor output format.") - - del inputs, outputs, masks - - # Standard Models (BiRefNet) - else: - # Extract BiRefNet-specific kwargs with safe defaults - resize_dim = kwargs.get("resize_dim", (1024, 1024)) - normalize_mean = kwargs.get("normalize_mean", [0.485, 0.456, 0.406]) - normalize_std = kwargs.get("normalize_std", [0.229, 0.224, 0.225]) - - transform = transforms.Compose( - [ - transforms.Resize(resize_dim), - transforms.ToTensor(), - transforms.Normalize(normalize_mean, normalize_std), - ] - ) - - input_tensor = transform(image).unsqueeze(0).to(device) - - with torch.no_grad(): - preds = model(input_tensor) - - # BiRefNet returns a tuple/list of tensors; we want the last one - if isinstance(preds, (list, tuple)): - preds = preds[-1] - - preds = preds.sigmoid().cpu() - - pred = preds[0].squeeze() - pred_pil = transforms.ToPILImage()(pred) - mask = pred_pil.resize(image.size) - mask_np = np.array(mask) - - del input_tensor, preds - - # Clean up and Return - image_np = np.array(image) - empty_cache(device) - - return image_np, mask_np diff --git a/src/garmentiq/segmentation/load_model.py b/src/garmentiq/segmentation/load_model.py deleted file mode 100644 index fb9bd44..0000000 --- a/src/garmentiq/segmentation/load_model.py +++ /dev/null @@ -1,103 +0,0 @@ -"""Loading a segmentation model onto a device.""" -import inspect -import torch -import torch.nn as nn -from typing import Type, Union -from safetensors.torch import load_file -from garmentiq.utils.device import resolve_device -from garmentiq.utils.checkpoint import load_state_dict_checked - - -def load_model( - model_class: Type[nn.Module], - model_path: str, - model_args: dict = None, - device: Union[str, torch.device] = "cpu", - **kwargs, -): - """ - Loads a PyTorch model from a local checkpoint and prepares it for inference. - - This function instantiates the provided model class using safely filtered configuration - arguments, loads the weights from a local `.pth` or `.safetensors` file, moves the model - to the requested device, and sets it to evaluation mode. It automatically - strips common weight prefixes (e.g., "module.", "model.") to ensure compatibility. - - Args: - model_class (Type[nn.Module]): The uninstantiated PyTorch model class to be used. - model_path (str): The local file path to the model checkpoint weights, typically - ending in `.pth` or `.safetensors`. - model_args (dict, optional): A dictionary of configuration arguments for initializing - the model. Incompatible arguments are safely ignored. - Default is None. - device (Union[str, torch.device], optional): The device to load the model onto, e.g. - `"cpu"`, `"cuda"`, `"cuda:0"`, or `"mps"`. - Hardware acceleration is opt-in; pass it - explicitly to use a GPU or Apple Silicon. - Default is `"cpu"`. - **kwargs: Additional arbitrary keyword arguments. - - Raises: - ValueError: If the requested `device` is invalid or unavailable on this machine. - Exception: If the model weights cannot be loaded from the specified local path or if - the file format is unsupported. - - Returns: - nn.Module: The loaded and prepared PyTorch model instance, placed on `device`. - """ - model_args = model_args or {} - - # --- THE SMART CONVERTER --- - # If the user passes a Config object instead of a dictionary, safely convert it. - if not isinstance(model_args, dict): - if hasattr(model_args, "to_dict"): - model_args = model_args.to_dict() # Hugging Face standard - elif hasattr(model_args, "__dict__"): - model_args = vars(model_args) # Standard Python objects - else: - raise TypeError("model_args must be a dictionary or a configuration object.") - # --------------------------- - - device = resolve_device(device) - - sig = inspect.signature(model_class.__init__) - valid_params = set(sig.parameters.keys()) - - has_kwargs = any( - p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values() - ) - - if not has_kwargs: - filtered_args = {k: v for k, v in model_args.items() if k in valid_params} - else: - filtered_args = model_args - - # ... [Keep the rest of your loading logic exactly the same] ... - model = model_class(**filtered_args).to(device) - - if model_path.endswith(".safetensors"): - state_dict = load_file(model_path, device=str(device)) - else: - state_dict = torch.load(model_path, map_location=device, weights_only=True) - - # SAM 3 is published as a video checkpoint that nests the image ("detector") model - # alongside tracker weights. Keep only the detector half when that layout is seen, - # otherwise the image model would silently load nothing. - from garmentiq.segmentation.model_definition.sam.sam import SAM3_DETECTOR_PREFIX - - if any(k.startswith(SAM3_DETECTOR_PREFIX) for k in state_dict): - state_dict = { - k[len(SAM3_DETECTOR_PREFIX) :]: v - for k, v in state_dict.items() - if k.startswith(SAM3_DETECTOR_PREFIX) - } - - new_state_dict = { - k.removeprefix("module.").removeprefix("model."): v - for k, v in state_dict.items() - } - - load_state_dict_checked(model, new_state_dict, model_path) - model.eval() - - return model diff --git a/src/garmentiq/segmentation/model_definition/__init__.py b/src/garmentiq/segmentation/model_definition/__init__.py deleted file mode 100644 index a58c877..0000000 --- a/src/garmentiq/segmentation/model_definition/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# This file can be completely empty! -"""Segmentation model architectures and their offline configurations. - -Holds BiRefNet and the Segment Anything family. Configurations, processors, and -tokenizers are bundled with the package, so only model weights need downloading and -every model can be loaded without network access. -""" -# Its presence alone allows Python to traverse into the /birefnet and /sam folders. \ No newline at end of file diff --git a/src/garmentiq/segmentation/model_definition/birefnet/BiRefNet_config.py b/src/garmentiq/segmentation/model_definition/birefnet/BiRefNet_config.py deleted file mode 100644 index 37c8ac5..0000000 --- a/src/garmentiq/segmentation/model_definition/birefnet/BiRefNet_config.py +++ /dev/null @@ -1,11 +0,0 @@ -from transformers import PretrainedConfig - -class BiRefNetConfig(PretrainedConfig): - model_type = "SegformerForSemanticSegmentation" - def __init__( - self, - bb_pretrained=False, - **kwargs - ): - self.bb_pretrained = bb_pretrained - super().__init__(**kwargs) diff --git a/src/garmentiq/segmentation/model_definition/birefnet/__init__.py b/src/garmentiq/segmentation/model_definition/birefnet/__init__.py deleted file mode 100644 index c7d9bb3..0000000 --- a/src/garmentiq/segmentation/model_definition/birefnet/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# garmentiq/segmentation/birefnet/__init__.py -from .birefnet import BiRefNet -from .load_birefnet_config import load_birefnet_config - -__all__ = ["BiRefNet", "load_birefnet_config"] \ No newline at end of file diff --git a/src/garmentiq/segmentation/model_definition/birefnet/birefnet.py b/src/garmentiq/segmentation/model_definition/birefnet/birefnet.py deleted file mode 100644 index 4056955..0000000 --- a/src/garmentiq/segmentation/model_definition/birefnet/birefnet.py +++ /dev/null @@ -1,2249 +0,0 @@ -### config.py - -import os -import math -from transformers import PretrainedConfig - - -class Config(PretrainedConfig): - def __init__(self) -> None: - # PATH settings - self.sys_home_dir = os.path.expanduser('~') # Make up your file system as: SYS_HOME_DIR/codes/dis/BiRefNet, SYS_HOME_DIR/datasets/dis/xx, SYS_HOME_DIR/weights/xx - - # TASK settings - self.task = ['DIS5K', 'COD', 'HRSOD', 'DIS5K+HRSOD+HRS10K', 'P3M-10k'][0] - self.training_set = { - 'DIS5K': ['DIS-TR', 'DIS-TR+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4'][0], - 'COD': 'TR-COD10K+TR-CAMO', - 'HRSOD': ['TR-DUTS', 'TR-HRSOD', 'TR-UHRSD', 'TR-DUTS+TR-HRSOD', 'TR-DUTS+TR-UHRSD', 'TR-HRSOD+TR-UHRSD', 'TR-DUTS+TR-HRSOD+TR-UHRSD'][5], - 'DIS5K+HRSOD+HRS10K': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TE-HRS10K+TE-HRSOD+TE-UHRSD+TR-HRS10K+TR-HRSOD+TR-UHRSD', # leave DIS-VD for evaluation. - 'P3M-10k': 'TR-P3M-10k', - }[self.task] - self.prompt4loc = ['dense', 'sparse'][0] - - # Faster-Training settings - self.load_all = True - self.compile = True # 1. Trigger CPU memory leak in some extend, which is an inherent problem of PyTorch. - # Machines with > 70GB CPU memory can run the whole training on DIS5K with default setting. - # 2. Higher PyTorch version may fix it: https://github.com/pytorch/pytorch/issues/119607. - # 3. But compile in Pytorch > 2.0.1 seems to bring no acceleration for training. - self.precisionHigh = True - - # MODEL settings - self.ms_supervision = True - self.out_ref = self.ms_supervision and True - self.dec_ipt = True - self.dec_ipt_split = True - self.cxt_num = [0, 3][1] # multi-scale skip connections from encoder - self.mul_scl_ipt = ['', 'add', 'cat'][2] - self.dec_att = ['', 'ASPP', 'ASPPDeformable'][2] - self.squeeze_block = ['', 'BasicDecBlk_x1', 'ResBlk_x4', 'ASPP_x3', 'ASPPDeformable_x3'][1] - self.dec_blk = ['BasicDecBlk', 'ResBlk', 'HierarAttDecBlk'][0] - - # TRAINING settings - self.batch_size = 4 - self.IoU_finetune_last_epochs = [ - 0, - { - 'DIS5K': -50, - 'COD': -20, - 'HRSOD': -20, - 'DIS5K+HRSOD+HRS10K': -20, - 'P3M-10k': -20, - }[self.task] - ][1] # choose 0 to skip - self.lr = (1e-4 if 'DIS5K' in self.task else 1e-5) * math.sqrt(self.batch_size / 4) # DIS needs high lr to converge faster. Adapt the lr linearly - self.size = 1024 - self.num_workers = max(4, self.batch_size) # will be decrease to min(it, batch_size) at the initialization of the data_loader - - # Backbone settings - self.bb = [ - 'vgg16', 'vgg16bn', 'resnet50', # 0, 1, 2 - 'swin_v1_t', 'swin_v1_s', # 3, 4 - 'swin_v1_b', 'swin_v1_l', # 5-bs9, 6-bs4 - 'pvt_v2_b0', 'pvt_v2_b1', # 7, 8 - 'pvt_v2_b2', 'pvt_v2_b5', # 9-bs10, 10-bs5 - ][6] - self.lateral_channels_in_collection = { - 'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64], - 'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64], - 'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192], - 'swin_v1_t': [768, 384, 192, 96], 'swin_v1_s': [768, 384, 192, 96], - 'pvt_v2_b0': [256, 160, 64, 32], 'pvt_v2_b1': [512, 320, 128, 64], - }[self.bb] - if self.mul_scl_ipt == 'cat': - self.lateral_channels_in_collection = [channel * 2 for channel in self.lateral_channels_in_collection] - self.cxt = self.lateral_channels_in_collection[1:][::-1][-self.cxt_num:] if self.cxt_num else [] - - # MODEL settings - inactive - self.lat_blk = ['BasicLatBlk'][0] - self.dec_channels_inter = ['fixed', 'adap'][0] - self.refine = ['', 'itself', 'RefUNet', 'Refiner', 'RefinerPVTInChannels4'][0] - self.progressive_ref = self.refine and True - self.ender = self.progressive_ref and False - self.scale = self.progressive_ref and 2 - self.auxiliary_classification = False # Only for DIS5K, where class labels are saved in `dataset.py`. - self.refine_iteration = 1 - self.freeze_bb = False - self.model = [ - 'BiRefNet', - ][0] - if self.dec_blk == 'HierarAttDecBlk': - self.batch_size = 2 ** [0, 1, 2, 3, 4][2] - - # TRAINING settings - inactive - self.preproc_methods = ['flip', 'enhance', 'rotate', 'pepper', 'crop'][:4] - self.optimizer = ['Adam', 'AdamW'][1] - self.lr_decay_epochs = [1e5] # Set to negative N to decay the lr in the last N-th epoch. - self.lr_decay_rate = 0.5 - # Loss - self.lambdas_pix_last = { - # not 0 means opening this loss - # original rate -- 1 : 30 : 1.5 : 0.2, bce x 30 - 'bce': 30 * 1, # high performance - 'iou': 0.5 * 1, # 0 / 255 - 'iou_patch': 0.5 * 0, # 0 / 255, win_size = (64, 64) - 'mse': 150 * 0, # can smooth the saliency map - 'triplet': 3 * 0, - 'reg': 100 * 0, - 'ssim': 10 * 1, # help contours, - 'cnt': 5 * 0, # help contours - 'structure': 5 * 0, # structure loss from codes of MVANet. A little improvement on DIS-TE[1,2,3], a bit more decrease on DIS-TE4. - } - self.lambdas_cls = { - 'ce': 5.0 - } - # Adv - self.lambda_adv_g = 10. * 0 # turn to 0 to avoid adv training - self.lambda_adv_d = 3. * (self.lambda_adv_g > 0) - - # PATH settings - inactive - self.data_root_dir = os.path.join(self.sys_home_dir, 'datasets/dis') - self.weights_root_dir = os.path.join(self.sys_home_dir, 'weights') - self.weights = { - 'pvt_v2_b2': os.path.join(self.weights_root_dir, 'pvt_v2_b2.pth'), - 'pvt_v2_b5': os.path.join(self.weights_root_dir, ['pvt_v2_b5.pth', 'pvt_v2_b5_22k.pth'][0]), - 'swin_v1_b': os.path.join(self.weights_root_dir, ['swin_base_patch4_window12_384_22kto1k.pth', 'swin_base_patch4_window12_384_22k.pth'][0]), - 'swin_v1_l': os.path.join(self.weights_root_dir, ['swin_large_patch4_window12_384_22kto1k.pth', 'swin_large_patch4_window12_384_22k.pth'][0]), - 'swin_v1_t': os.path.join(self.weights_root_dir, ['swin_tiny_patch4_window7_224_22kto1k_finetune.pth'][0]), - 'swin_v1_s': os.path.join(self.weights_root_dir, ['swin_small_patch4_window7_224_22kto1k_finetune.pth'][0]), - 'pvt_v2_b0': os.path.join(self.weights_root_dir, ['pvt_v2_b0.pth'][0]), - 'pvt_v2_b1': os.path.join(self.weights_root_dir, ['pvt_v2_b1.pth'][0]), - } - - # Callbacks - inactive - self.verbose_eval = True - self.only_S_MAE = False - self.use_fp16 = False # Bugs. It may cause nan in training. - self.SDPA_enabled = False # Bugs. Slower and errors occur in multi-GPUs - - # others - self.device = [0, 'cpu'][0] # .to(0) == .to('cuda:0') - - self.batch_size_valid = 1 - self.rand_seed = 7 - # run_sh_file = [f for f in os.listdir('.') if 'train.sh' == f] + [os.path.join('..', f) for f in os.listdir('..') if 'train.sh' == f] - # with open(run_sh_file[0], 'r') as f: - # lines = f.readlines() - # self.save_last = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'val_last=' in l][0].split('val_last=')[-1].split()[0]) - # self.save_step = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'step=' in l][0].split('step=')[-1].split()[0]) - # self.val_step = [0, self.save_step][0] - - def print_task(self) -> None: - # Return task for choosing settings in shell scripts. - print(self.task) - - - -### models/backbones/pvt_v2.py - -import torch -import torch.nn as nn -from functools import partial - -from timm.layers import DropPath, to_2tuple, trunc_normal_ -from timm.models import register_model - -import math - -# from config import Config - -# config = Config() - -class Mlp(nn.Module): - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.dwconv = DWConv(hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - elif isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - fan_out //= m.groups - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - - def forward(self, x, H, W): - x = self.fc1(x) - x = self.dwconv(x, H, W) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -class Attention(nn.Module): - def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1): - super().__init__() - assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." - - self.dim = dim - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 - - self.q = nn.Linear(dim, dim, bias=qkv_bias) - self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias) - self.attn_drop_prob = attn_drop - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - self.sr_ratio = sr_ratio - if sr_ratio > 1: - self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) - self.norm = nn.LayerNorm(dim) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - elif isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - fan_out //= m.groups - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - - def forward(self, x, H, W): - B, N, C = x.shape - q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) - - if self.sr_ratio > 1: - x_ = x.permute(0, 2, 1).reshape(B, C, H, W) - x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1) - x_ = self.norm(x_) - kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - else: - kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - k, v = kv[0], kv[1] - - if config.SDPA_enabled: - x = torch.nn.functional.scaled_dot_product_attention( - q, k, v, - attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False - ).transpose(1, 2).reshape(B, N, C) - else: - attn = (q @ k.transpose(-2, -1)) * self.scale - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B, N, C) - x = self.proj(x) - x = self.proj_drop(x) - - return x - - -class Block(nn.Module): - - def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1): - super().__init__() - self.norm1 = norm_layer(dim) - self.attn = Attention( - dim, - num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, - attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio) - # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - elif isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - fan_out //= m.groups - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - - def forward(self, x, H, W): - x = x + self.drop_path(self.attn(self.norm1(x), H, W)) - x = x + self.drop_path(self.mlp(self.norm2(x), H, W)) - - return x - - -class OverlapPatchEmbed(nn.Module): - """ Image to Patch Embedding - """ - - def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - - self.img_size = img_size - self.patch_size = patch_size - self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1] - self.num_patches = self.H * self.W - self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride, - padding=(patch_size[0] // 2, patch_size[1] // 2)) - self.norm = nn.LayerNorm(embed_dim) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - elif isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - fan_out //= m.groups - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - - def forward(self, x): - x = self.proj(x) - _, _, H, W = x.shape - x = x.flatten(2).transpose(1, 2) - x = self.norm(x) - - return x, H, W - - -class PyramidVisionTransformerImpr(nn.Module): - def __init__(self, img_size=224, patch_size=16, in_channels=3, num_classes=1000, embed_dims=[64, 128, 256, 512], - num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0., - attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, - depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]): - super().__init__() - self.num_classes = num_classes - self.depths = depths - - # patch_embed - self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_channels=in_channels, - embed_dim=embed_dims[0]) - self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_channels=embed_dims[0], - embed_dim=embed_dims[1]) - self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_channels=embed_dims[1], - embed_dim=embed_dims[2]) - self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_channels=embed_dims[2], - embed_dim=embed_dims[3]) - - # transformer encoder - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - cur = 0 - self.block1 = nn.ModuleList([Block( - dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, - sr_ratio=sr_ratios[0]) - for i in range(depths[0])]) - self.norm1 = norm_layer(embed_dims[0]) - - cur += depths[0] - self.block2 = nn.ModuleList([Block( - dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, - sr_ratio=sr_ratios[1]) - for i in range(depths[1])]) - self.norm2 = norm_layer(embed_dims[1]) - - cur += depths[1] - self.block3 = nn.ModuleList([Block( - dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, - sr_ratio=sr_ratios[2]) - for i in range(depths[2])]) - self.norm3 = norm_layer(embed_dims[2]) - - cur += depths[2] - self.block4 = nn.ModuleList([Block( - dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, - sr_ratio=sr_ratios[3]) - for i in range(depths[3])]) - self.norm4 = norm_layer(embed_dims[3]) - - # classification head - # self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity() - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - elif isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - fan_out //= m.groups - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - - def init_weights(self, pretrained=None): - if isinstance(pretrained, str): - logger = 1 - #load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger) - - def reset_drop_path(self, drop_path_rate): - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))] - cur = 0 - for i in range(self.depths[0]): - self.block1[i].drop_path.drop_prob = dpr[cur + i] - - cur += self.depths[0] - for i in range(self.depths[1]): - self.block2[i].drop_path.drop_prob = dpr[cur + i] - - cur += self.depths[1] - for i in range(self.depths[2]): - self.block3[i].drop_path.drop_prob = dpr[cur + i] - - cur += self.depths[2] - for i in range(self.depths[3]): - self.block4[i].drop_path.drop_prob = dpr[cur + i] - - def freeze_patch_emb(self): - self.patch_embed1.requires_grad = False - - @torch.jit.ignore - def no_weight_decay(self): - return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better - - def get_classifier(self): - return self.head - - def reset_classifier(self, num_classes, global_pool=''): - self.num_classes = num_classes - self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() - - def forward_features(self, x): - B = x.shape[0] - outs = [] - - # stage 1 - x, H, W = self.patch_embed1(x) - for i, blk in enumerate(self.block1): - x = blk(x, H, W) - x = self.norm1(x) - x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() - outs.append(x) - - # stage 2 - x, H, W = self.patch_embed2(x) - for i, blk in enumerate(self.block2): - x = blk(x, H, W) - x = self.norm2(x) - x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() - outs.append(x) - - # stage 3 - x, H, W = self.patch_embed3(x) - for i, blk in enumerate(self.block3): - x = blk(x, H, W) - x = self.norm3(x) - x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() - outs.append(x) - - # stage 4 - x, H, W = self.patch_embed4(x) - for i, blk in enumerate(self.block4): - x = blk(x, H, W) - x = self.norm4(x) - x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() - outs.append(x) - - return outs - - # return x.mean(dim=1) - - def forward(self, x): - x = self.forward_features(x) - # x = self.head(x) - - return x - - -class DWConv(nn.Module): - def __init__(self, dim=768): - super(DWConv, self).__init__() - self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) - - def forward(self, x, H, W): - B, N, C = x.shape - x = x.transpose(1, 2).view(B, C, H, W).contiguous() - x = self.dwconv(x) - x = x.flatten(2).transpose(1, 2) - - return x - - -def _conv_filter(state_dict, patch_size=16): - """ convert patch embedding weight from manual patchify + linear proj to conv""" - out_dict = {} - for k, v in state_dict.items(): - if 'patch_embed.proj.weight' in k: - v = v.reshape((v.shape[0], 3, patch_size, patch_size)) - out_dict[k] = v - - return out_dict - - -## @register_model -class pvt_v2_b0(PyramidVisionTransformerImpr): - def __init__(self, **kwargs): - super(pvt_v2_b0, self).__init__( - patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], - qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1) - - - -## @register_model -class pvt_v2_b1(PyramidVisionTransformerImpr): - def __init__(self, **kwargs): - super(pvt_v2_b1, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], - qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1) - -## @register_model -class pvt_v2_b2(PyramidVisionTransformerImpr): - def __init__(self, in_channels=3, **kwargs): - super(pvt_v2_b2, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], - qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1, in_channels=in_channels) - -## @register_model -class pvt_v2_b3(PyramidVisionTransformerImpr): - def __init__(self, **kwargs): - super(pvt_v2_b3, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], - qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1) - -## @register_model -class pvt_v2_b4(PyramidVisionTransformerImpr): - def __init__(self, **kwargs): - super(pvt_v2_b4, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], - qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1) - - -## @register_model -class pvt_v2_b5(PyramidVisionTransformerImpr): - def __init__(self, **kwargs): - super(pvt_v2_b5, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], - qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1) - - - -### models/backbones/swin_v1.py - -# -------------------------------------------------------- -# Swin Transformer -# Copyright (c) 2021 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ze Liu, Yutong Lin, Yixuan Wei -# -------------------------------------------------------- - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as checkpoint -import numpy as np -from timm.layers import DropPath, to_2tuple, trunc_normal_ - -# from config import Config - - -# config = Config() - - -class Mlp(nn.Module): - """ Multilayer perceptron.""" - - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class WindowAttention(nn.Module): - """ Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij')) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop_prob = attn_drop - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ Forward function. - - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - - if config.SDPA_enabled: - x = torch.nn.functional.scaled_dot_product_attention( - q, k, v, - attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False - ).transpose(1, 2).reshape(B_, N, C) - else: - attn = (q @ k.transpose(-2, -1)) - - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 - ) # Wh*Ww, Wh*Ww, nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class SwinTransformerBlock(nn.Module): - """ Swin Transformer Block. - - Args: - dim (int): Number of input channels. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, dim, num_heads, window_size=7, shift_size=0, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., - act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, - qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - self.H = None - self.W = None - - def forward(self, x, mask_matrix): - """ Forward function. - - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - mask_matrix: Attention mask for cyclic shift. - """ - B, L, C = x.shape - H, W = self.H, self.W - assert L == H * W, "input feature has wrong size" - - shortcut = x - x = self.norm1(x) - x = x.view(B, H, W, C) - - # pad feature maps to multiples of window size - pad_l = pad_t = 0 - pad_r = (self.window_size - W % self.window_size) % self.window_size - pad_b = (self.window_size - H % self.window_size) % self.window_size - x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) - _, Hp, Wp, _ = x.shape - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - attn_mask = mask_matrix - else: - shifted_x = x - attn_mask = None - - # partition windows - x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA - attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - - if pad_r > 0 or pad_b > 0: - x = x[:, :H, :W, :].contiguous() - - x = x.view(B, H * W, C) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - -class PatchMerging(nn.Module): - """ Patch Merging Layer - - Args: - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - def __init__(self, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x, H, W): - """ Forward function. - - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - """ - B, L, C = x.shape - assert L == H * W, "input feature has wrong size" - - x = x.view(B, H, W, C) - - # padding - pad_input = (H % 2 == 1) or (W % 2 == 1) - if pad_input: - x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) - - x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C - x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C - x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C - x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C - x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C - x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C - - x = self.norm(x) - x = self.reduction(x) - - return x - - -class BasicLayer(nn.Module): - """ A basic Swin Transformer layer for one stage. - - Args: - dim (int): Number of feature channels - depth (int): Depths of this stage. - num_heads (int): Number of attention head. - window_size (int): Local window size. Default: 7. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, - dim, - depth, - num_heads, - window_size=7, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop=0., - attn_drop=0., - drop_path=0., - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False): - super().__init__() - self.window_size = window_size - self.shift_size = window_size // 2 - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList([ - SwinTransformerBlock( - dim=dim, - num_heads=num_heads, - window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, - norm_layer=norm_layer) - for i in range(depth)]) - - # patch merging layer - if downsample is not None: - self.downsample = downsample(dim=dim, norm_layer=norm_layer) - else: - self.downsample = None - - def forward(self, x, H, W): - """ Forward function. - - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - """ - - # calculate attention mask for SW-MSA - # Turn int to torch.tensor for the compatiability with torch.compile in PyTorch 2.5. - Hp = torch.ceil(torch.tensor(H) / self.window_size).to(torch.int64) * self.window_size - Wp = torch.ceil(torch.tensor(W) / self.window_size).to(torch.int64) * self.window_size - img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 - h_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)).to(x.dtype) - - for blk in self.blocks: - blk.H, blk.W = H, W - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x, attn_mask) - else: - x = blk(x, attn_mask) - if self.downsample is not None: - x_down = self.downsample(x, H, W) - Wh, Ww = (H + 1) // 2, (W + 1) // 2 - return x, H, W, x_down, Wh, Ww - else: - return x, H, W, x, H, W - - -class PatchEmbed(nn.Module): - """ Image to Patch Embedding - - Args: - patch_size (int): Patch token size. Default: 4. - in_channels (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None): - super().__init__() - patch_size = to_2tuple(patch_size) - self.patch_size = patch_size - - self.in_channels = in_channels - self.embed_dim = embed_dim - - self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size) - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - """Forward function.""" - # padding - _, _, H, W = x.size() - if W % self.patch_size[1] != 0: - x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) - if H % self.patch_size[0] != 0: - x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) - - x = self.proj(x) # B C Wh Ww - if self.norm is not None: - Wh, Ww = x.size(2), x.size(3) - x = x.flatten(2).transpose(1, 2) - x = self.norm(x) - x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) - - return x - - -class SwinTransformer(nn.Module): - """ Swin Transformer backbone. - A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - - https://arxiv.org/pdf/2103.14030 - - Args: - pretrain_img_size (int): Input image size for training the pretrained model, - used in absolute postion embedding. Default 224. - patch_size (int | tuple(int)): Patch size. Default: 4. - in_channels (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - depths (tuple[int]): Depths of each Swin Transformer stage. - num_heads (tuple[int]): Number of attention head of each stage. - window_size (int): Window size. Default: 7. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. - drop_rate (float): Dropout rate. - attn_drop_rate (float): Attention dropout rate. Default: 0. - drop_path_rate (float): Stochastic depth rate. Default: 0.2. - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. - patch_norm (bool): If True, add normalization after patch embedding. Default: True. - out_indices (Sequence[int]): Output from which stages. - frozen_stages (int): Stages to be frozen (stop grad and set eval mode). - -1 means not freezing any parameters. - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, - pretrain_img_size=224, - patch_size=4, - in_channels=3, - embed_dim=96, - depths=[2, 2, 6, 2], - num_heads=[3, 6, 12, 24], - window_size=7, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0.2, - norm_layer=nn.LayerNorm, - ape=False, - patch_norm=True, - out_indices=(0, 1, 2, 3), - frozen_stages=-1, - use_checkpoint=False): - super().__init__() - - self.pretrain_img_size = pretrain_img_size - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.out_indices = out_indices - self.frozen_stages = frozen_stages - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - - # absolute position embedding - if self.ape: - pretrain_img_size = to_2tuple(pretrain_img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] - - self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])) - trunc_normal_(self.absolute_pos_embed, std=.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - - # build layers - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = BasicLayer( - dim=int(embed_dim * 2 ** i_layer), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], - norm_layer=norm_layer, - downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, - use_checkpoint=use_checkpoint) - self.layers.append(layer) - - num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] - self.num_features = num_features - - # add a norm layer for each output - for i_layer in out_indices: - layer = norm_layer(num_features[i_layer]) - layer_name = f'norm{i_layer}' - self.add_module(layer_name, layer) - - self._freeze_stages() - - def _freeze_stages(self): - if self.frozen_stages >= 0: - self.patch_embed.eval() - for param in self.patch_embed.parameters(): - param.requires_grad = False - - if self.frozen_stages >= 1 and self.ape: - self.absolute_pos_embed.requires_grad = False - - if self.frozen_stages >= 2: - self.pos_drop.eval() - for i in range(0, self.frozen_stages - 1): - m = self.layers[i] - m.eval() - for param in m.parameters(): - param.requires_grad = False - - - def forward(self, x): - """Forward function.""" - x = self.patch_embed(x) - - Wh, Ww = x.size(2), x.size(3) - if self.ape: - # interpolate the position embedding to the corresponding size - absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic') - x = (x + absolute_pos_embed) # B Wh*Ww C - - outs = []#x.contiguous()] - x = x.flatten(2).transpose(1, 2) - x = self.pos_drop(x) - for i in range(self.num_layers): - layer = self.layers[i] - x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) - - if i in self.out_indices: - norm_layer = getattr(self, f'norm{i}') - x_out = norm_layer(x_out) - - out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() - outs.append(out) - - return tuple(outs) - - def train(self, mode=True): - """Convert the model into training mode while keep layers freezed.""" - super(SwinTransformer, self).train(mode) - self._freeze_stages() - -def swin_v1_t(): - model = SwinTransformer(embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7) - return model - -def swin_v1_s(): - model = SwinTransformer(embed_dim=96, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24], window_size=7) - return model - -def swin_v1_b(): - model = SwinTransformer(embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12) - return model - -def swin_v1_l(): - model = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12) - return model - - - -### models/modules/deform_conv.py - -import torch -import torch.nn as nn -from torchvision.ops import deform_conv2d - - -class DeformableConv2d(nn.Module): - def __init__(self, - in_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1, - bias=False): - - super(DeformableConv2d, self).__init__() - - assert type(kernel_size) == tuple or type(kernel_size) == int - - kernel_size = kernel_size if type(kernel_size) == tuple else (kernel_size, kernel_size) - self.stride = stride if type(stride) == tuple else (stride, stride) - self.padding = padding - - self.offset_conv = nn.Conv2d(in_channels, - 2 * kernel_size[0] * kernel_size[1], - kernel_size=kernel_size, - stride=stride, - padding=self.padding, - bias=True) - - nn.init.constant_(self.offset_conv.weight, 0.) - nn.init.constant_(self.offset_conv.bias, 0.) - - self.modulator_conv = nn.Conv2d(in_channels, - 1 * kernel_size[0] * kernel_size[1], - kernel_size=kernel_size, - stride=stride, - padding=self.padding, - bias=True) - - nn.init.constant_(self.modulator_conv.weight, 0.) - nn.init.constant_(self.modulator_conv.bias, 0.) - - self.regular_conv = nn.Conv2d(in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - padding=self.padding, - bias=bias) - - def forward(self, x): - #h, w = x.shape[2:] - #max_offset = max(h, w)/4. - - offset = self.offset_conv(x)#.clamp(-max_offset, max_offset) - modulator = 2. * torch.sigmoid(self.modulator_conv(x)) - - x = deform_conv2d( - input=x, - offset=offset, - weight=self.regular_conv.weight, - bias=self.regular_conv.bias, - padding=self.padding, - mask=modulator, - stride=self.stride, - ) - return x - - - - -### utils.py - -import torch.nn as nn - - -def build_act_layer(act_layer): - if act_layer == 'ReLU': - return nn.ReLU(inplace=True) - elif act_layer == 'SiLU': - return nn.SiLU(inplace=True) - elif act_layer == 'GELU': - return nn.GELU() - - raise NotImplementedError(f'build_act_layer does not support {act_layer}') - - -def build_norm_layer(dim, - norm_layer, - in_format='channels_last', - out_format='channels_last', - eps=1e-6): - layers = [] - if norm_layer == 'BN': - if in_format == 'channels_last': - layers.append(to_channels_first()) - layers.append(nn.BatchNorm2d(dim)) - if out_format == 'channels_last': - layers.append(to_channels_last()) - elif norm_layer == 'LN': - if in_format == 'channels_first': - layers.append(to_channels_last()) - layers.append(nn.LayerNorm(dim, eps=eps)) - if out_format == 'channels_first': - layers.append(to_channels_first()) - else: - raise NotImplementedError( - f'build_norm_layer does not support {norm_layer}') - return nn.Sequential(*layers) - - -class to_channels_first(nn.Module): - - def __init__(self): - super().__init__() - - def forward(self, x): - return x.permute(0, 3, 1, 2) - - -class to_channels_last(nn.Module): - - def __init__(self): - super().__init__() - - def forward(self, x): - return x.permute(0, 2, 3, 1) - - - -### dataset.py - -_class_labels_TR_sorted = ( - 'Airplane, Ant, Antenna, Archery, Axe, BabyCarriage, Bag, BalanceBeam, Balcony, Balloon, Basket, BasketballHoop, Beatle, Bed, Bee, Bench, Bicycle, ' - 'BicycleFrame, BicycleStand, Boat, Bonsai, BoomLift, Bridge, BunkBed, Butterfly, Button, Cable, CableLift, Cage, Camcorder, Cannon, Canoe, Car, ' - 'CarParkDropArm, Carriage, Cart, Caterpillar, CeilingLamp, Centipede, Chair, Clip, Clock, Clothes, CoatHanger, Comb, ConcretePumpTruck, Crack, Crane, ' - 'Cup, DentalChair, Desk, DeskChair, Diagram, DishRack, DoorHandle, Dragonfish, Dragonfly, Drum, Earphone, Easel, ElectricIron, Excavator, Eyeglasses, ' - 'Fan, Fence, Fencing, FerrisWheel, FireExtinguisher, Fishing, Flag, FloorLamp, Forklift, GasStation, Gate, Gear, Goal, Golf, GymEquipment, Hammock, ' - 'Handcart, Handcraft, Handrail, HangGlider, Harp, Harvester, Headset, Helicopter, Helmet, Hook, HorizontalBar, Hydrovalve, IroningTable, Jewelry, Key, ' - 'KidsPlayground, Kitchenware, Kite, Knife, Ladder, LaundryRack, Lightning, Lobster, Locust, Machine, MachineGun, MagazineRack, Mantis, Medal, MemorialArchway, ' - 'Microphone, Missile, MobileHolder, Monitor, Mosquito, Motorcycle, MovingTrolley, Mower, MusicPlayer, MusicStand, ObservationTower, Octopus, OilWell, ' - 'OlympicLogo, OperatingTable, OutdoorFitnessEquipment, Parachute, Pavilion, Piano, Pipe, PlowHarrow, PoleVault, Punchbag, Rack, Racket, Rifle, Ring, Robot, ' - 'RockClimbing, Rope, Sailboat, Satellite, Scaffold, Scale, Scissor, Scooter, Sculpture, Seadragon, Seahorse, Seal, SewingMachine, Ship, Shoe, ShoppingCart, ' - 'ShoppingTrolley, Shower, Shrimp, Signboard, Skateboarding, Skeleton, Skiing, Spade, SpeedBoat, Spider, Spoon, Stair, Stand, Stationary, SteeringWheel, ' - 'Stethoscope, Stool, Stove, StreetLamp, SweetStand, Swing, Sword, TV, Table, TableChair, TableLamp, TableTennis, Tank, Tapeline, Teapot, Telescope, Tent, ' - 'TobaccoPipe, Toy, Tractor, TrafficLight, TrafficSign, Trampoline, TransmissionTower, Tree, Tricycle, TrimmerCover, Tripod, Trombone, Truck, Trumpet, Tuba, ' - 'UAV, Umbrella, UnevenBars, UtilityPole, VacuumCleaner, Violin, Wakesurfing, Watch, WaterTower, WateringPot, Well, WellLid, Wheel, Wheelchair, WindTurbine, Windmill, WineGlass, WireWhisk, Yacht' -) -class_labels_TR_sorted = _class_labels_TR_sorted.split(', ') - - -### models/backbones/build_backbones.py - -import torch -import torch.nn as nn -from collections import OrderedDict -from torchvision.models import vgg16, vgg16_bn, VGG16_Weights, VGG16_BN_Weights, resnet50, ResNet50_Weights -# from models.pvt_v2 import pvt_v2_b0, pvt_v2_b1, pvt_v2_b2, pvt_v2_b5 -# from models.swin_v1 import swin_v1_t, swin_v1_s, swin_v1_b, swin_v1_l -# from config import Config - - -config = Config() - -def build_backbone(bb_name, pretrained=True, params_settings=''): - if bb_name == 'vgg16': - bb_net = list(vgg16(pretrained=VGG16_Weights.DEFAULT if pretrained else None).children())[0] - bb = nn.Sequential(OrderedDict({'conv1': bb_net[:4], 'conv2': bb_net[4:9], 'conv3': bb_net[9:16], 'conv4': bb_net[16:23]})) - elif bb_name == 'vgg16bn': - bb_net = list(vgg16_bn(pretrained=VGG16_BN_Weights.DEFAULT if pretrained else None).children())[0] - bb = nn.Sequential(OrderedDict({'conv1': bb_net[:6], 'conv2': bb_net[6:13], 'conv3': bb_net[13:23], 'conv4': bb_net[23:33]})) - elif bb_name == 'resnet50': - bb_net = list(resnet50(pretrained=ResNet50_Weights.DEFAULT if pretrained else None).children()) - bb = nn.Sequential(OrderedDict({'conv1': nn.Sequential(*bb_net[0:3]), 'conv2': bb_net[4], 'conv3': bb_net[5], 'conv4': bb_net[6]})) - else: - bb = eval('{}({})'.format(bb_name, params_settings)) - if pretrained: - bb = load_weights(bb, bb_name) - return bb - -def load_weights(model, model_name): - save_model = torch.load(config.weights[model_name], map_location='cpu') - model_dict = model.state_dict() - state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model.items() if k in model_dict.keys()} - # to ignore the weights with mismatched size when I modify the backbone itself. - if not state_dict: - save_model_keys = list(save_model.keys()) - sub_item = save_model_keys[0] if len(save_model_keys) == 1 else None - state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model[sub_item].items() if k in model_dict.keys()} - if not state_dict or not sub_item: - print('Weights are not successully loaded. Check the state dict of weights file.') - return None - else: - print('Found correct weights in the "{}" item of loaded state_dict.'.format(sub_item)) - model_dict.update(state_dict) - model.load_state_dict(model_dict) - return model - - - -### models/modules/decoder_blocks.py - -import torch -import torch.nn as nn -# from models.aspp import ASPP, ASPPDeformable -# from config import Config - - -# config = Config() - - -class BasicDecBlk(nn.Module): - def __init__(self, in_channels=64, out_channels=64, inter_channels=64): - super(BasicDecBlk, self).__init__() - inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64 - self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1) - self.relu_in = nn.ReLU(inplace=True) - if config.dec_att == 'ASPP': - self.dec_att = ASPP(in_channels=inter_channels) - elif config.dec_att == 'ASPPDeformable': - self.dec_att = ASPPDeformable(in_channels=inter_channels) - self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1) - self.bn_in = nn.BatchNorm2d(inter_channels) if config.batch_size > 1 else nn.Identity() - self.bn_out = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity() - - def forward(self, x): - x = self.conv_in(x) - x = self.bn_in(x) - x = self.relu_in(x) - if hasattr(self, 'dec_att'): - x = self.dec_att(x) - x = self.conv_out(x) - x = self.bn_out(x) - return x - - -class ResBlk(nn.Module): - def __init__(self, in_channels=64, out_channels=None, inter_channels=64): - super(ResBlk, self).__init__() - if out_channels is None: - out_channels = in_channels - inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64 - - self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1) - self.bn_in = nn.BatchNorm2d(inter_channels) if config.batch_size > 1 else nn.Identity() - self.relu_in = nn.ReLU(inplace=True) - - if config.dec_att == 'ASPP': - self.dec_att = ASPP(in_channels=inter_channels) - elif config.dec_att == 'ASPPDeformable': - self.dec_att = ASPPDeformable(in_channels=inter_channels) - - self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1) - self.bn_out = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity() - - self.conv_resi = nn.Conv2d(in_channels, out_channels, 1, 1, 0) - - def forward(self, x): - _x = self.conv_resi(x) - x = self.conv_in(x) - x = self.bn_in(x) - x = self.relu_in(x) - if hasattr(self, 'dec_att'): - x = self.dec_att(x) - x = self.conv_out(x) - x = self.bn_out(x) - return x + _x - - - -### models/modules/lateral_blocks.py - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from functools import partial - -# from config import Config - - -# config = Config() - - -class BasicLatBlk(nn.Module): - def __init__(self, in_channels=64, out_channels=64, inter_channels=64): - super(BasicLatBlk, self).__init__() - inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64 - self.conv = nn.Conv2d(in_channels, out_channels, 1, 1, 0) - - def forward(self, x): - x = self.conv(x) - return x - - - -### models/modules/aspp.py - -import torch -import torch.nn as nn -import torch.nn.functional as F -# from models.deform_conv import DeformableConv2d -# from config import Config - - -# config = Config() - - -class _ASPPModule(nn.Module): - def __init__(self, in_channels, planes, kernel_size, padding, dilation): - super(_ASPPModule, self).__init__() - self.atrous_conv = nn.Conv2d(in_channels, planes, kernel_size=kernel_size, - stride=1, padding=padding, dilation=dilation, bias=False) - self.bn = nn.BatchNorm2d(planes) if config.batch_size > 1 else nn.Identity() - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - x = self.atrous_conv(x) - x = self.bn(x) - - return self.relu(x) - - -class ASPP(nn.Module): - def __init__(self, in_channels=64, out_channels=None, output_stride=16): - super(ASPP, self).__init__() - self.down_scale = 1 - if out_channels is None: - out_channels = in_channels - self.in_channelster = 256 // self.down_scale - if output_stride == 16: - dilations = [1, 6, 12, 18] - elif output_stride == 8: - dilations = [1, 12, 24, 36] - else: - raise NotImplementedError - - self.aspp1 = _ASPPModule(in_channels, self.in_channelster, 1, padding=0, dilation=dilations[0]) - self.aspp2 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[1], dilation=dilations[1]) - self.aspp3 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[2], dilation=dilations[2]) - self.aspp4 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[3], dilation=dilations[3]) - - self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)), - nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False), - nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(), - nn.ReLU(inplace=True)) - self.conv1 = nn.Conv2d(self.in_channelster * 5, out_channels, 1, bias=False) - self.bn1 = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity() - self.relu = nn.ReLU(inplace=True) - self.dropout = nn.Dropout(0.5) - - def forward(self, x): - x1 = self.aspp1(x) - x2 = self.aspp2(x) - x3 = self.aspp3(x) - x4 = self.aspp4(x) - x5 = self.global_avg_pool(x) - x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True) - x = torch.cat((x1, x2, x3, x4, x5), dim=1) - - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - - return self.dropout(x) - - -##################### Deformable -class _ASPPModuleDeformable(nn.Module): - def __init__(self, in_channels, planes, kernel_size, padding): - super(_ASPPModuleDeformable, self).__init__() - self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size, - stride=1, padding=padding, bias=False) - self.bn = nn.BatchNorm2d(planes) if config.batch_size > 1 else nn.Identity() - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - x = self.atrous_conv(x) - x = self.bn(x) - - return self.relu(x) - - -class ASPPDeformable(nn.Module): - def __init__(self, in_channels, out_channels=None, parallel_block_sizes=[1, 3, 7]): - super(ASPPDeformable, self).__init__() - self.down_scale = 1 - if out_channels is None: - out_channels = in_channels - self.in_channelster = 256 // self.down_scale - - self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0) - self.aspp_deforms = nn.ModuleList([ - _ASPPModuleDeformable(in_channels, self.in_channelster, conv_size, padding=int(conv_size//2)) for conv_size in parallel_block_sizes - ]) - - self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)), - nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False), - nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(), - nn.ReLU(inplace=True)) - self.conv1 = nn.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False) - self.bn1 = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity() - self.relu = nn.ReLU(inplace=True) - self.dropout = nn.Dropout(0.5) - - def forward(self, x): - x1 = self.aspp1(x) - x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms] - x5 = self.global_avg_pool(x) - x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True) - x = torch.cat((x1, *x_aspp_deforms, x5), dim=1) - - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - - return self.dropout(x) - - - -### models/refinement/refiner.py - -import torch -import torch.nn as nn -from collections import OrderedDict -import torch -import torch.nn as nn -import torch.nn.functional as F -from torchvision.models import vgg16, vgg16_bn -from torchvision.models import resnet50 - -# from config import Config -# from dataset import class_labels_TR_sorted -# from models.build_backbone import build_backbone -# from models.decoder_blocks import BasicDecBlk -# from models.lateral_blocks import BasicLatBlk -# from models.ing import * -# from models.stem_layer import StemLayer - - -class RefinerPVTInChannels4(nn.Module): - def __init__(self, in_channels=3+1): - super(RefinerPVTInChannels4, self).__init__() - self.config = Config() - self.epoch = 1 - self.bb = build_backbone(self.config.bb, params_settings='in_channels=4') - - lateral_channels_in_collection = { - 'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64], - 'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64], - 'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192], - } - channels = lateral_channels_in_collection[self.config.bb] - self.squeeze_module = BasicDecBlk(channels[0], channels[0]) - - self.decoder = Decoder(channels) - - if 0: - for key, value in self.named_parameters(): - if 'bb.' in key: - value.requires_grad = False - - def forward(self, x): - if isinstance(x, list): - x = torch.cat(x, dim=1) - ########## Encoder ########## - if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']: - x1 = self.bb.conv1(x) - x2 = self.bb.conv2(x1) - x3 = self.bb.conv3(x2) - x4 = self.bb.conv4(x3) - else: - x1, x2, x3, x4 = self.bb(x) - - x4 = self.squeeze_module(x4) - - ########## Decoder ########## - - features = [x, x1, x2, x3, x4] - scaled_preds = self.decoder(features) - - return scaled_preds - - -class Refiner(nn.Module): - def __init__(self, in_channels=3+1): - super(Refiner, self).__init__() - self.config = Config() - self.epoch = 1 - self.stem_layer = StemLayer(in_channels=in_channels, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN') - self.bb = build_backbone(self.config.bb) - - lateral_channels_in_collection = { - 'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64], - 'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64], - 'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192], - } - channels = lateral_channels_in_collection[self.config.bb] - self.squeeze_module = BasicDecBlk(channels[0], channels[0]) - - self.decoder = Decoder(channels) - - if 0: - for key, value in self.named_parameters(): - if 'bb.' in key: - value.requires_grad = False - - def forward(self, x): - if isinstance(x, list): - x = torch.cat(x, dim=1) - x = self.stem_layer(x) - ########## Encoder ########## - if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']: - x1 = self.bb.conv1(x) - x2 = self.bb.conv2(x1) - x3 = self.bb.conv3(x2) - x4 = self.bb.conv4(x3) - else: - x1, x2, x3, x4 = self.bb(x) - - x4 = self.squeeze_module(x4) - - ########## Decoder ########## - - features = [x, x1, x2, x3, x4] - scaled_preds = self.decoder(features) - - return scaled_preds - - -class Decoder(nn.Module): - def __init__(self, channels): - super(Decoder, self).__init__() - self.config = Config() - DecoderBlock = eval('BasicDecBlk') - LateralBlock = eval('BasicLatBlk') - - self.decoder_block4 = DecoderBlock(channels[0], channels[1]) - self.decoder_block3 = DecoderBlock(channels[1], channels[2]) - self.decoder_block2 = DecoderBlock(channels[2], channels[3]) - self.decoder_block1 = DecoderBlock(channels[3], channels[3]//2) - - self.lateral_block4 = LateralBlock(channels[1], channels[1]) - self.lateral_block3 = LateralBlock(channels[2], channels[2]) - self.lateral_block2 = LateralBlock(channels[3], channels[3]) - - if self.config.ms_supervision: - self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0) - self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0) - self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0) - self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2, 1, 1, 1, 0)) - - def forward(self, features): - x, x1, x2, x3, x4 = features - outs = [] - p4 = self.decoder_block4(x4) - _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True) - _p3 = _p4 + self.lateral_block4(x3) - - p3 = self.decoder_block3(_p3) - _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True) - _p2 = _p3 + self.lateral_block3(x2) - - p2 = self.decoder_block2(_p2) - _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True) - _p1 = _p2 + self.lateral_block2(x1) - - _p1 = self.decoder_block1(_p1) - _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True) - p1_out = self.conv_out1(_p1) - - if self.config.ms_supervision: - outs.append(self.conv_ms_spvn_4(p4)) - outs.append(self.conv_ms_spvn_3(p3)) - outs.append(self.conv_ms_spvn_2(p2)) - outs.append(p1_out) - return outs - - -class RefUNet(nn.Module): - # Refinement - def __init__(self, in_channels=3+1): - super(RefUNet, self).__init__() - self.encoder_1 = nn.Sequential( - nn.Conv2d(in_channels, 64, 3, 1, 1), - nn.Conv2d(64, 64, 3, 1, 1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True) - ) - - self.encoder_2 = nn.Sequential( - nn.MaxPool2d(2, 2, ceil_mode=True), - nn.Conv2d(64, 64, 3, 1, 1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True) - ) - - self.encoder_3 = nn.Sequential( - nn.MaxPool2d(2, 2, ceil_mode=True), - nn.Conv2d(64, 64, 3, 1, 1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True) - ) - - self.encoder_4 = nn.Sequential( - nn.MaxPool2d(2, 2, ceil_mode=True), - nn.Conv2d(64, 64, 3, 1, 1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True) - ) - - self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True) - ##### - self.decoder_5 = nn.Sequential( - nn.Conv2d(64, 64, 3, 1, 1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True) - ) - ##### - self.decoder_4 = nn.Sequential( - nn.Conv2d(128, 64, 3, 1, 1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True) - ) - - self.decoder_3 = nn.Sequential( - nn.Conv2d(128, 64, 3, 1, 1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True) - ) - - self.decoder_2 = nn.Sequential( - nn.Conv2d(128, 64, 3, 1, 1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True) - ) - - self.decoder_1 = nn.Sequential( - nn.Conv2d(128, 64, 3, 1, 1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True) - ) - - self.conv_d0 = nn.Conv2d(64, 1, 3, 1, 1) - - self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) - - def forward(self, x): - outs = [] - if isinstance(x, list): - x = torch.cat(x, dim=1) - hx = x - - hx1 = self.encoder_1(hx) - hx2 = self.encoder_2(hx1) - hx3 = self.encoder_3(hx2) - hx4 = self.encoder_4(hx3) - - hx = self.decoder_5(self.pool4(hx4)) - hx = torch.cat((self.upscore2(hx), hx4), 1) - - d4 = self.decoder_4(hx) - hx = torch.cat((self.upscore2(d4), hx3), 1) - - d3 = self.decoder_3(hx) - hx = torch.cat((self.upscore2(d3), hx2), 1) - - d2 = self.decoder_2(hx) - hx = torch.cat((self.upscore2(d2), hx1), 1) - - d1 = self.decoder_1(hx) - - x = self.conv_d0(d1) - outs.append(x) - return outs - - - -### models/stem_layer.py - -import torch.nn as nn -# from utils import build_act_layer, build_norm_layer - - -class StemLayer(nn.Module): - r""" Stem layer of InternImage - Args: - in_channels (int): number of input channels - out_channels (int): number of output channels - act_layer (str): activation layer - norm_layer (str): normalization layer - """ - - def __init__(self, - in_channels=3+1, - inter_channels=48, - out_channels=96, - act_layer='GELU', - norm_layer='BN'): - super().__init__() - self.conv1 = nn.Conv2d(in_channels, - inter_channels, - kernel_size=3, - stride=1, - padding=1) - self.norm1 = build_norm_layer( - inter_channels, norm_layer, 'channels_first', 'channels_first' - ) - self.act = build_act_layer(act_layer) - self.conv2 = nn.Conv2d(inter_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1) - self.norm2 = build_norm_layer( - out_channels, norm_layer, 'channels_first', 'channels_first' - ) - - def forward(self, x): - x = self.conv1(x) - x = self.norm1(x) - x = self.act(x) - x = self.conv2(x) - x = self.norm2(x) - return x - - -### models/birefnet.py - -import torch -import torch.nn as nn -import torch.nn.functional as F -from kornia.filters import laplacian -from transformers import PreTrainedModel -from einops import rearrange - -# from config import Config -# from dataset import class_labels_TR_sorted -# from models.build_backbone import build_backbone -# from models.decoder_blocks import BasicDecBlk, ResBlk, HierarAttDecBlk -# from models.lateral_blocks import BasicLatBlk -# from models.aspp import ASPP, ASPPDeformable -# from models.ing import * -# from models.refiner import Refiner, RefinerPVTInChannels4, RefUNet -# from models.stem_layer import StemLayer -from .BiRefNet_config import BiRefNetConfig - - -def image2patches(image, grid_h=2, grid_w=2, patch_ref=None, transformation='b c (hg h) (wg w) -> (b hg wg) c h w'): - if patch_ref is not None: - grid_h, grid_w = image.shape[-2] // patch_ref.shape[-2], image.shape[-1] // patch_ref.shape[-1] - patches = rearrange(image, transformation, hg=grid_h, wg=grid_w) - return patches - -def patches2image(patches, grid_h=2, grid_w=2, patch_ref=None, transformation='(b hg wg) c h w -> b c (hg h) (wg w)'): - if patch_ref is not None: - grid_h, grid_w = patch_ref.shape[-2] // patches[0].shape[-2], patch_ref.shape[-1] // patches[0].shape[-1] - image = rearrange(patches, transformation, hg=grid_h, wg=grid_w) - return image - -class BiRefNet( - PreTrainedModel -): - config_class = BiRefNetConfig - def __init__(self, bb_pretrained=True, config=BiRefNetConfig()): - super(BiRefNet, self).__init__(config) - bb_pretrained = config.bb_pretrained - self.config = Config() - self.epoch = 1 - self.bb = build_backbone(self.config.bb, pretrained=bb_pretrained) - - channels = self.config.lateral_channels_in_collection - - if self.config.auxiliary_classification: - self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) - self.cls_head = nn.Sequential( - nn.Linear(channels[0], len(class_labels_TR_sorted)) - ) - - if self.config.squeeze_block: - self.squeeze_module = nn.Sequential(*[ - eval(self.config.squeeze_block.split('_x')[0])(channels[0]+sum(self.config.cxt), channels[0]) - for _ in range(eval(self.config.squeeze_block.split('_x')[1])) - ]) - - self.decoder = Decoder(channels) - - if self.config.ender: - self.dec_end = nn.Sequential( - nn.Conv2d(1, 16, 3, 1, 1), - nn.Conv2d(16, 1, 3, 1, 1), - nn.ReLU(inplace=True), - ) - - # refine patch-level segmentation - if self.config.refine: - if self.config.refine == 'itself': - self.stem_layer = StemLayer(in_channels=3+1, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN') - else: - self.refiner = eval('{}({})'.format(self.config.refine, 'in_channels=3+1')) - - if self.config.freeze_bb: - # Freeze the backbone... - print(self.named_parameters()) - for key, value in self.named_parameters(): - if 'bb.' in key and 'refiner.' not in key: - value.requires_grad = False - - def forward_enc(self, x): - if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']: - x1 = self.bb.conv1(x); x2 = self.bb.conv2(x1); x3 = self.bb.conv3(x2); x4 = self.bb.conv4(x3) - else: - x1, x2, x3, x4 = self.bb(x) - if self.config.mul_scl_ipt == 'cat': - B, C, H, W = x.shape - x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True)) - x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1) - x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1) - x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1) - x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1) - elif self.config.mul_scl_ipt == 'add': - B, C, H, W = x.shape - x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True)) - x1 = x1 + F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True) - x2 = x2 + F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True) - x3 = x3 + F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True) - x4 = x4 + F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True) - class_preds = self.cls_head(self.avgpool(x4).view(x4.shape[0], -1)) if self.training and self.config.auxiliary_classification else None - if self.config.cxt: - x4 = torch.cat( - ( - *[ - F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True), - F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True), - F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True), - ][-len(self.config.cxt):], - x4 - ), - dim=1 - ) - return (x1, x2, x3, x4), class_preds - - def forward_ori(self, x): - ########## Encoder ########## - (x1, x2, x3, x4), class_preds = self.forward_enc(x) - if self.config.squeeze_block: - x4 = self.squeeze_module(x4) - ########## Decoder ########## - features = [x, x1, x2, x3, x4] - if self.training and self.config.out_ref: - features.append(laplacian(torch.mean(x, dim=1).unsqueeze(1), kernel_size=5)) - scaled_preds = self.decoder(features) - return scaled_preds, class_preds - - def forward(self, x): - scaled_preds, class_preds = self.forward_ori(x) - class_preds_lst = [class_preds] - return [scaled_preds, class_preds_lst] if self.training else scaled_preds - - -class Decoder(nn.Module): - def __init__(self, channels): - super(Decoder, self).__init__() - self.config = Config() - DecoderBlock = eval(self.config.dec_blk) - LateralBlock = eval(self.config.lat_blk) - - if self.config.dec_ipt: - self.split = self.config.dec_ipt_split - N_dec_ipt = 64 - DBlock = SimpleConvs - ic = 64 - ipt_cha_opt = 1 - self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic) - self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic) - self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic) - self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic) - self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic) - else: - self.split = None - - self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[1]) - self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[2]) - self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3]) - self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3]//2) - self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt] if self.config.dec_ipt else 0), 1, 1, 1, 0)) - - self.lateral_block4 = LateralBlock(channels[1], channels[1]) - self.lateral_block3 = LateralBlock(channels[2], channels[2]) - self.lateral_block2 = LateralBlock(channels[3], channels[3]) - - if self.config.ms_supervision: - self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0) - self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0) - self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0) - - if self.config.out_ref: - _N = 16 - self.gdt_convs_4 = nn.Sequential(nn.Conv2d(channels[1], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True)) - self.gdt_convs_3 = nn.Sequential(nn.Conv2d(channels[2], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True)) - self.gdt_convs_2 = nn.Sequential(nn.Conv2d(channels[3], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True)) - - self.gdt_convs_pred_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) - self.gdt_convs_pred_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) - self.gdt_convs_pred_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) - - self.gdt_convs_attn_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) - self.gdt_convs_attn_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) - self.gdt_convs_attn_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) - - def forward(self, features): - if self.training and self.config.out_ref: - outs_gdt_pred = [] - outs_gdt_label = [] - x, x1, x2, x3, x4, gdt_gt = features - else: - x, x1, x2, x3, x4 = features - outs = [] - - if self.config.dec_ipt: - patches_batch = image2patches(x, patch_ref=x4, transformation='b c (hg h) (wg w) -> b (c hg wg) h w') if self.split else x - x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1) - p4 = self.decoder_block4(x4) - m4 = self.conv_ms_spvn_4(p4) if self.config.ms_supervision and self.training else None - if self.config.out_ref: - p4_gdt = self.gdt_convs_4(p4) - if self.training: - # >> GT: - m4_dia = m4 - gdt_label_main_4 = gdt_gt * F.interpolate(m4_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True) - outs_gdt_label.append(gdt_label_main_4) - # >> Pred: - gdt_pred_4 = self.gdt_convs_pred_4(p4_gdt) - outs_gdt_pred.append(gdt_pred_4) - gdt_attn_4 = self.gdt_convs_attn_4(p4_gdt).sigmoid() - # >> Finally: - p4 = p4 * gdt_attn_4 - _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True) - _p3 = _p4 + self.lateral_block4(x3) - - if self.config.dec_ipt: - patches_batch = image2patches(x, patch_ref=_p3, transformation='b c (hg h) (wg w) -> b (c hg wg) h w') if self.split else x - _p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1) - p3 = self.decoder_block3(_p3) - m3 = self.conv_ms_spvn_3(p3) if self.config.ms_supervision and self.training else None - if self.config.out_ref: - p3_gdt = self.gdt_convs_3(p3) - if self.training: - # >> GT: - # m3 --dilation--> m3_dia - # G_3^gt * m3_dia --> G_3^m, which is the label of gradient - m3_dia = m3 - gdt_label_main_3 = gdt_gt * F.interpolate(m3_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True) - outs_gdt_label.append(gdt_label_main_3) - # >> Pred: - # p3 --conv--BN--> F_3^G, where F_3^G predicts the \hat{G_3} with xx - # F_3^G --sigmoid--> A_3^G - gdt_pred_3 = self.gdt_convs_pred_3(p3_gdt) - outs_gdt_pred.append(gdt_pred_3) - gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid() - # >> Finally: - # p3 = p3 * A_3^G - p3 = p3 * gdt_attn_3 - _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True) - _p2 = _p3 + self.lateral_block3(x2) - - if self.config.dec_ipt: - patches_batch = image2patches(x, patch_ref=_p2, transformation='b c (hg h) (wg w) -> b (c hg wg) h w') if self.split else x - _p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1) - p2 = self.decoder_block2(_p2) - m2 = self.conv_ms_spvn_2(p2) if self.config.ms_supervision and self.training else None - if self.config.out_ref: - p2_gdt = self.gdt_convs_2(p2) - if self.training: - # >> GT: - m2_dia = m2 - gdt_label_main_2 = gdt_gt * F.interpolate(m2_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True) - outs_gdt_label.append(gdt_label_main_2) - # >> Pred: - gdt_pred_2 = self.gdt_convs_pred_2(p2_gdt) - outs_gdt_pred.append(gdt_pred_2) - gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid() - # >> Finally: - p2 = p2 * gdt_attn_2 - _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True) - _p1 = _p2 + self.lateral_block2(x1) - - if self.config.dec_ipt: - patches_batch = image2patches(x, patch_ref=_p1, transformation='b c (hg h) (wg w) -> b (c hg wg) h w') if self.split else x - _p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1) - _p1 = self.decoder_block1(_p1) - _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True) - - if self.config.dec_ipt: - patches_batch = image2patches(x, patch_ref=_p1, transformation='b c (hg h) (wg w) -> b (c hg wg) h w') if self.split else x - _p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1) - p1_out = self.conv_out1(_p1) - - if self.config.ms_supervision and self.training: - outs.append(m4) - outs.append(m3) - outs.append(m2) - outs.append(p1_out) - return outs if not (self.config.out_ref and self.training) else ([outs_gdt_pred, outs_gdt_label], outs) - - -class SimpleConvs(nn.Module): - def __init__( - self, in_channels: int, out_channels: int, inter_channels=64 - ) -> None: - super().__init__() - self.conv1 = nn.Conv2d(in_channels, inter_channels, 3, 1, 1) - self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1) - - def forward(self, x): - return self.conv_out(self.conv1(x)) diff --git a/src/garmentiq/segmentation/model_definition/birefnet/config.json b/src/garmentiq/segmentation/model_definition/birefnet/config.json deleted file mode 100644 index 06d8fa9..0000000 --- a/src/garmentiq/segmentation/model_definition/birefnet/config.json +++ /dev/null @@ -1,20 +0,0 @@ -{ - "_name_or_path": "ZhengPeng7/BiRefNet", - "architectures": [ - "BiRefNet" - ], - "auto_map": { - "AutoConfig": "BiRefNet_config.BiRefNetConfig", - "AutoModelForImageSegmentation": "birefnet.BiRefNet" - }, - "custom_pipelines": { - "image-segmentation": { - "pt": [ - "AutoModelForImageSegmentation" - ], - "tf": [], - "type": "image" - } - }, - "bb_pretrained": false -} \ No newline at end of file diff --git a/src/garmentiq/segmentation/model_definition/birefnet/load_birefnet_config.py b/src/garmentiq/segmentation/model_definition/birefnet/load_birefnet_config.py deleted file mode 100644 index 54fa784..0000000 --- a/src/garmentiq/segmentation/model_definition/birefnet/load_birefnet_config.py +++ /dev/null @@ -1,33 +0,0 @@ -import os -import json -from .BiRefNet_config import BiRefNetConfig - -def load_birefnet_config(): - """ - Reads and loads the bundled configuration for the BiRefNet model. - - This function facilitates strictly offline initialization by dynamically locating the - `config.json` file associated with BiRefNet within the local package directory. It - securely reads the local JSON file and unpacks its contents directly into a Hugging Face - `BiRefNetConfig` object, ensuring the package remains completely air-gapped and independent - of external network requests. - - Raises: - FileNotFoundError: If the corresponding offline `config.json` file cannot be located - in the module directory. - - Returns: - BiRefNetConfig: The loaded configuration object ready to be passed into the model loader. - """ - # Locate the directory this specific python file lives in - current_dir = os.path.dirname(os.path.abspath(__file__)) - config_path = os.path.join(current_dir, "config.json") - - # Read the local json securely - with open(config_path, 'r') as f: - config_dict = json.load(f) - - # Unpack the dictionary directly into the Config class - return BiRefNetConfig(**config_dict) - -__all__ = ["load_birefnet_config"] diff --git a/src/garmentiq/segmentation/model_definition/sam/__init__.py b/src/garmentiq/segmentation/model_definition/sam/__init__.py deleted file mode 100644 index acadab1..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/__init__.py +++ /dev/null @@ -1,91 +0,0 @@ -# garmentiq/segmentation/model_definition/sam/__init__.py -"""The Segment Anything model family. - -Covers SAM 1, SAM 2.1, and SAM 3 behind one registry, together with their bundled -configurations and processors. The model classes are imported lazily, so GarmentIQ -still imports on a `transformers` release that predates a given generation, and asking -for a model your release does not carry raises a clear `ImportError`. - -The model classes themselves are re-exported from `transformers` and documented -upstream, so they are not repeated here. They can be imported from this module -directly: - - from garmentiq.segmentation.model_definition.sam import ( - SamModel, Sam2Model, Sam3Model, - ) - -The full set is `SamModel`, `SamConfig`, `SamProcessor`, `Sam2Model`, `Sam2Config`, -`Sam2Processor`, `Sam2ImageProcessor`, `Sam3Model`, `Sam3Config`, `Sam3Processor`, -`Sam3ImageProcessor`, `Sam3TrackerModel`, `Sam3TrackerConfig`, and -`Sam3TrackerProcessor`. -""" -from .sam import ( - load_sam_config, - load_sam_processor, - load_sam3_tracker, - load_sam3_tracker_config, - load_sam3_tracker_processor, - sam_family, - sam_capabilities, - sam_model_class, - SAM1_MODELS, - SAM2_MODELS, - SAM3_MODELS, - VALID_SAM_MODELS, - ALL_SAM_MODELS, - SAM_CAPABILITIES, - SAM_PROMPT_DEPTHS, -) - -# Model classes come from transformers and are resolved on first access, so that -# importing GarmentIQ does not require a transformers release new enough to -# provide every SAM generation. -_LAZY = { - "Sam3TrackerModel", - "Sam3TrackerConfig", - "Sam3TrackerProcessor", - "Sam3ImageProcessor", - "SamModel", - "SamConfig", - "SamProcessor", - "Sam2Model", - "Sam2Config", - "Sam2Processor", - "Sam2ImageProcessor", - "Sam3Model", - "Sam3Config", - "Sam3Processor", -} - - -def __getattr__(name): - """Defer transformers imports until a SAM class is actually requested.""" - if name in _LAZY: - from . import sam - - return getattr(sam, name) - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") - - -__all__ = [ - "load_sam_config", - "load_sam_processor", - "load_sam3_tracker", - "load_sam3_tracker_config", - "load_sam3_tracker_processor", - "sam_family", - "sam_capabilities", - "sam_model_class", - "SAM1_MODELS", - "SAM2_MODELS", - "SAM3_MODELS", - "VALID_SAM_MODELS", - "ALL_SAM_MODELS", - "SAM_CAPABILITIES", - "SAM_PROMPT_DEPTHS", -] - - -def __dir__(): - """List the lazily resolved model classes alongside the eager exports.""" - return sorted(set(__all__) | _LAZY) diff --git a/src/garmentiq/segmentation/model_definition/sam/sam-vit-b/config.json b/src/garmentiq/segmentation/model_definition/sam/sam-vit-b/config.json deleted file mode 100644 index 5880e4f..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam-vit-b/config.json +++ /dev/null @@ -1,249 +0,0 @@ -{ - "_commit_hash": null, - "_name_or_path": "/tmp/facebook/sam-vit-base", - "architectures": [ - "SamModel" - ], - "initializer_range": 0.02, - "mask_decoder_config": { - "_name_or_path": "", - "add_cross_attention": false, - "architectures": null, - "attention_downsample_rate": 2, - "bad_words_ids": null, - "begin_suppress_tokens": null, - "bos_token_id": null, - "chunk_size_feed_forward": 0, - "cross_attention_hidden_size": null, - "decoder_start_token_id": null, - "diversity_penalty": 0.0, - "do_sample": false, - "early_stopping": false, - "encoder_no_repeat_ngram_size": 0, - "eos_token_id": null, - "exponential_decay_length_penalty": null, - "finetuning_task": null, - "forced_bos_token_id": null, - "forced_eos_token_id": null, - "hidden_act": "relu", - "hidden_size": 256, - "id2label": { - "0": "LABEL_0", - "1": "LABEL_1" - }, - "iou_head_depth": 3, - "iou_head_hidden_dim": 256, - "is_decoder": false, - "is_encoder_decoder": false, - "label2id": { - "LABEL_0": 0, - "LABEL_1": 1 - }, - "layer_norm_eps": 1e-06, - "length_penalty": 1.0, - "max_length": 20, - "min_length": 0, - "mlp_dim": 2048, - "model_type": "", - "no_repeat_ngram_size": 0, - "num_attention_heads": 8, - "num_beam_groups": 1, - "num_beams": 1, - "num_hidden_layers": 2, - "num_multimask_outputs": 3, - "num_return_sequences": 1, - "output_attentions": false, - "output_hidden_states": false, - "output_scores": false, - "pad_token_id": null, - "prefix": null, - "problem_type": null, - "pruned_heads": {}, - "remove_invalid_values": false, - "repetition_penalty": 1.0, - "return_dict": true, - "return_dict_in_generate": false, - "sep_token_id": null, - "suppress_tokens": null, - "task_specific_params": null, - "temperature": 1.0, - "tf_legacy_loss": false, - "tie_encoder_decoder": false, - "tie_word_embeddings": true, - "tokenizer_class": null, - "top_k": 50, - "top_p": 1.0, - "torch_dtype": null, - "torchscript": false, - "transformers_version": "4.29.0.dev0", - "typical_p": 1.0, - "use_bfloat16": false - }, - "model_type": "sam", - "prompt_encoder_config": { - "_name_or_path": "", - "add_cross_attention": false, - "architectures": null, - "bad_words_ids": null, - "begin_suppress_tokens": null, - "bos_token_id": null, - "chunk_size_feed_forward": 0, - "cross_attention_hidden_size": null, - "decoder_start_token_id": null, - "diversity_penalty": 0.0, - "do_sample": false, - "early_stopping": false, - "encoder_no_repeat_ngram_size": 0, - "eos_token_id": null, - "exponential_decay_length_penalty": null, - "finetuning_task": null, - "forced_bos_token_id": null, - "forced_eos_token_id": null, - "hidden_act": "gelu", - "hidden_size": 256, - "id2label": { - "0": "LABEL_0", - "1": "LABEL_1" - }, - "image_embedding_size": 64, - "image_size": 1024, - "is_decoder": false, - "is_encoder_decoder": false, - "label2id": { - "LABEL_0": 0, - "LABEL_1": 1 - }, - "layer_norm_eps": 1e-06, - "length_penalty": 1.0, - "mask_input_channels": 16, - "max_length": 20, - "min_length": 0, - "model_type": "", - "no_repeat_ngram_size": 0, - "num_beam_groups": 1, - "num_beams": 1, - "num_point_embeddings": 4, - "num_return_sequences": 1, - "output_attentions": false, - "output_hidden_states": false, - "output_scores": false, - "pad_token_id": null, - "patch_size": 16, - "prefix": null, - "problem_type": null, - "pruned_heads": {}, - "remove_invalid_values": false, - "repetition_penalty": 1.0, - "return_dict": true, - "return_dict_in_generate": false, - "sep_token_id": null, - "suppress_tokens": null, - "task_specific_params": null, - "temperature": 1.0, - "tf_legacy_loss": false, - "tie_encoder_decoder": false, - "tie_word_embeddings": true, - "tokenizer_class": null, - "top_k": 50, - "top_p": 1.0, - "torch_dtype": null, - "torchscript": false, - "transformers_version": "4.29.0.dev0", - "typical_p": 1.0, - "use_bfloat16": false - }, - "torch_dtype": "float32", - "transformers_version": null, - "vision_config": { - "_name_or_path": "", - "add_cross_attention": false, - "architectures": null, - "attention_dropout": 0.0, - "bad_words_ids": null, - "begin_suppress_tokens": null, - "bos_token_id": null, - "chunk_size_feed_forward": 0, - "cross_attention_hidden_size": null, - "decoder_start_token_id": null, - "diversity_penalty": 0.0, - "do_sample": false, - "dropout": 0.0, - "early_stopping": false, - "encoder_no_repeat_ngram_size": 0, - "eos_token_id": null, - "exponential_decay_length_penalty": null, - "finetuning_task": null, - "forced_bos_token_id": null, - "forced_eos_token_id": null, - "global_attn_indexes": [ - 2, - 5, - 8, - 11 - ], - "hidden_act": "gelu", - "hidden_size": 768, - "id2label": { - "0": "LABEL_0", - "1": "LABEL_1" - }, - "image_size": 1024, - "initializer_factor": 1.0, - "initializer_range": 1e-10, - "intermediate_size": 6144, - "is_decoder": false, - "is_encoder_decoder": false, - "label2id": { - "LABEL_0": 0, - "LABEL_1": 1 - }, - "layer_norm_eps": 1e-06, - "length_penalty": 1.0, - "max_length": 20, - "min_length": 0, - "mlp_dim": 3072, - "mlp_ratio": 4.0, - "model_type": "", - "no_repeat_ngram_size": 0, - "num_attention_heads": 12, - "num_beam_groups": 1, - "num_beams": 1, - "num_channels": 3, - "num_hidden_layers": 12, - "num_pos_feats": 128, - "num_return_sequences": 1, - "output_attentions": false, - "output_channels": 256, - "output_hidden_states": false, - "output_scores": false, - "pad_token_id": null, - "patch_size": 16, - "prefix": null, - "problem_type": null, - "projection_dim": 512, - "pruned_heads": {}, - "qkv_bias": true, - "remove_invalid_values": false, - "repetition_penalty": 1.0, - "return_dict": true, - "return_dict_in_generate": false, - "sep_token_id": null, - "suppress_tokens": null, - "task_specific_params": null, - "temperature": 1.0, - "tf_legacy_loss": false, - "tie_encoder_decoder": false, - "tie_word_embeddings": true, - "tokenizer_class": null, - "top_k": 50, - "top_p": 1.0, - "torch_dtype": null, - "torchscript": false, - "transformers_version": "4.29.0.dev0", - "typical_p": 1.0, - "use_abs_pos": true, - "use_bfloat16": false, - "use_rel_pos": true, - "window_size": 14 - } -} diff --git a/src/garmentiq/segmentation/model_definition/sam/sam-vit-b/preprocessor_config.json b/src/garmentiq/segmentation/model_definition/sam/sam-vit-b/preprocessor_config.json deleted file mode 100644 index 732fbaf..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam-vit-b/preprocessor_config.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "do_convert_rgb": true, - "do_normalize": true, - "do_pad": true, - "do_rescale": true, - "do_resize": true, - "image_mean": [ - 0.485, - 0.456, - 0.406 - ], - "image_processor_type": "SamImageProcessor", - "image_std": [ - 0.229, - 0.224, - 0.225 - ], - "pad_size": { - "height": 1024, - "width": 1024 - }, - "processor_class": "SamProcessor", - "resample": 2, - "rescale_factor": 0.00392156862745098, - "size": { - "longest_edge": 1024 - } -} diff --git a/src/garmentiq/segmentation/model_definition/sam/sam-vit-h/config.json b/src/garmentiq/segmentation/model_definition/sam/sam-vit-h/config.json deleted file mode 100644 index 9729017..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam-vit-h/config.json +++ /dev/null @@ -1,249 +0,0 @@ -{ - "_commit_hash": null, - "_name_or_path": "/tmp/facebook/sam-vit-huge", - "architectures": [ - "SamModel" - ], - "initializer_range": 0.02, - "mask_decoder_config": { - "_name_or_path": "", - "add_cross_attention": false, - "architectures": null, - "attention_downsample_rate": 2, - "bad_words_ids": null, - "begin_suppress_tokens": null, - "bos_token_id": null, - "chunk_size_feed_forward": 0, - "cross_attention_hidden_size": null, - "decoder_start_token_id": null, - "diversity_penalty": 0.0, - "do_sample": false, - "early_stopping": false, - "encoder_no_repeat_ngram_size": 0, - "eos_token_id": null, - "exponential_decay_length_penalty": null, - "finetuning_task": null, - "forced_bos_token_id": null, - "forced_eos_token_id": null, - "hidden_act": "relu", - "hidden_size": 256, - "id2label": { - "0": "LABEL_0", - "1": "LABEL_1" - }, - "iou_head_depth": 3, - "iou_head_hidden_dim": 256, - "is_decoder": false, - "is_encoder_decoder": false, - "label2id": { - "LABEL_0": 0, - "LABEL_1": 1 - }, - 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"model_type": "sam", - "prompt_encoder_config": { - "_name_or_path": "", - "add_cross_attention": false, - "architectures": null, - "bad_words_ids": null, - "begin_suppress_tokens": null, - "bos_token_id": null, - "chunk_size_feed_forward": 0, - "cross_attention_hidden_size": null, - "decoder_start_token_id": null, - "diversity_penalty": 0.0, - "do_sample": false, - "early_stopping": false, - "encoder_no_repeat_ngram_size": 0, - "eos_token_id": null, - "exponential_decay_length_penalty": null, - "finetuning_task": null, - "forced_bos_token_id": null, - "forced_eos_token_id": null, - "hidden_act": "gelu", - "hidden_size": 256, - "id2label": { - "0": "LABEL_0", - "1": "LABEL_1" - }, - "image_embedding_size": 64, - "image_size": 1024, - "is_decoder": false, - "is_encoder_decoder": false, - "label2id": { - "LABEL_0": 0, - "LABEL_1": 1 - }, - "layer_norm_eps": 1e-06, - "length_penalty": 1.0, - "mask_input_channels": 16, - "max_length": 20, - "min_length": 0, - "model_type": "", - "no_repeat_ngram_size": 0, - 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} -} diff --git a/src/garmentiq/segmentation/model_definition/sam/sam-vit-h/preprocessor_config.json b/src/garmentiq/segmentation/model_definition/sam/sam-vit-h/preprocessor_config.json deleted file mode 100644 index 732fbaf..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam-vit-h/preprocessor_config.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "do_convert_rgb": true, - "do_normalize": true, - "do_pad": true, - "do_rescale": true, - "do_resize": true, - "image_mean": [ - 0.485, - 0.456, - 0.406 - ], - "image_processor_type": "SamImageProcessor", - "image_std": [ - 0.229, - 0.224, - 0.225 - ], - "pad_size": { - "height": 1024, - "width": 1024 - }, - "processor_class": "SamProcessor", - "resample": 2, - "rescale_factor": 0.00392156862745098, - "size": { - "longest_edge": 1024 - } -} diff --git a/src/garmentiq/segmentation/model_definition/sam/sam-vit-l/config.json b/src/garmentiq/segmentation/model_definition/sam/sam-vit-l/config.json deleted file mode 100644 index dfcc462..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam-vit-l/config.json +++ /dev/null @@ -1,249 +0,0 @@ -{ - "_commit_hash": null, - "_name_or_path": "/tmp/facebook/sam-vit-large", - "architectures": [ - "SamModel" - ], - "initializer_range": 0.02, - "mask_decoder_config": { - "_name_or_path": "", - "add_cross_attention": false, - "architectures": null, - "attention_downsample_rate": 2, - "bad_words_ids": null, - "begin_suppress_tokens": null, - "bos_token_id": null, - "chunk_size_feed_forward": 0, - "cross_attention_hidden_size": null, - "decoder_start_token_id": null, - "diversity_penalty": 0.0, - "do_sample": false, - "early_stopping": false, - "encoder_no_repeat_ngram_size": 0, - "eos_token_id": null, - "exponential_decay_length_penalty": null, - "finetuning_task": null, - "forced_bos_token_id": null, - "forced_eos_token_id": null, - "hidden_act": "relu", - "hidden_size": 256, - "id2label": { - "0": "LABEL_0", - "1": "LABEL_1" - }, - "iou_head_depth": 3, - "iou_head_hidden_dim": 256, - "is_decoder": false, - "is_encoder_decoder": false, - "label2id": { - "LABEL_0": 0, - "LABEL_1": 1 - }, - 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} -} diff --git a/src/garmentiq/segmentation/model_definition/sam/sam-vit-l/preprocessor_config.json b/src/garmentiq/segmentation/model_definition/sam/sam-vit-l/preprocessor_config.json deleted file mode 100644 index 732fbaf..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam-vit-l/preprocessor_config.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "do_convert_rgb": true, - "do_normalize": true, - "do_pad": true, - "do_rescale": true, - "do_resize": true, - "image_mean": [ - 0.485, - 0.456, - 0.406 - ], - "image_processor_type": "SamImageProcessor", - "image_std": [ - 0.229, - 0.224, - 0.225 - ], - "pad_size": { - "height": 1024, - "width": 1024 - }, - "processor_class": "SamProcessor", - "resample": 2, - "rescale_factor": 0.00392156862745098, - "size": { - "longest_edge": 1024 - } -} diff --git a/src/garmentiq/segmentation/model_definition/sam/sam.py b/src/garmentiq/segmentation/model_definition/sam/sam.py deleted file mode 100644 index 2102e7d..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam.py +++ /dev/null @@ -1,571 +0,0 @@ -"""The SAM registry, loaders, and capability table. - -Each supported variant is registered with the family it belongs to, the prompt types it -accepts, and the nesting depth its processor expects. SAM 3 is a special case: one -checkpoint contains both an open-vocabulary **detector**, prompted by text or boxes, and -a **tracker** carrying the classic SAM prompt encoder, which accepts points and labels. -`load_sam3_tracker` reassembles the tracker from the same weights file. -""" -import os -import json -import re -from typing import TYPE_CHECKING, Optional, Union - -import torch - -if TYPE_CHECKING: # pragma: no cover - import only for type checkers - from transformers import ( - SamModel, - SamConfig, - SamProcessor, - Sam2Model, - Sam2Config, - Sam2Processor, - Sam2ImageProcessor, - Sam3Model, - Sam3Config, - Sam3Processor, - ) - -# Which transformers release each class needs. Resolved lazily so that importing -# GarmentIQ works on older transformers, and only using a newer SAM family raises. -_LAZY_CLASSES = { - "SamModel": "SAM 1", - "SamConfig": "SAM 1", - "SamProcessor": "SAM 1", - "Sam2Model": "SAM 2", - "Sam2Config": "SAM 2", - "Sam2Processor": "SAM 2", - "Sam2ImageProcessor": "SAM 2", - "Sam3Model": "SAM 3", - "Sam3Config": "SAM 3", - "Sam3Processor": "SAM 3", - "Sam3TrackerModel": "SAM 3", - "Sam3TrackerConfig": "SAM 3", - "Sam3TrackerProcessor": "SAM 3", - "Sam3ImageProcessor": "SAM 3", -} - - -def _resolve(name: str): - """Fetches a transformers class on demand with an actionable error message.""" - import transformers - - try: - return getattr(transformers, name) - except AttributeError as e: - raise ImportError( - f"'{name}' is not available in transformers " - f"{getattr(transformers, '__version__', 'unknown')}. " - f"{_LAZY_CLASSES.get(name, 'This feature')} support requires a newer " - f"transformers release; upgrade with `pip install -U transformers`." - ) from e - - -def __getattr__(name): - """Module-level lazy attribute access (PEP 562).""" - if name in _LAZY_CLASSES: - return _resolve(name) - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") - -# Segment Anything 1 variants. Configs are bundled with the package. -SAM1_MODELS = ["sam-vit-b", "sam-vit-l", "sam-vit-h"] - -# Segment Anything 2.1 variants. Configs are bundled with the package. -SAM2_MODELS = [ - "sam2.1-hiera-tiny", - "sam2.1-hiera-small", - "sam2.1-hiera-base-plus", - "sam2.1-hiera-large", -] - -# Segment Anything 3. Meta distributes SAM 3 under a gated licence, so the weights must be -# downloaded manually after accepting it, but the configuration files are bundled here so -# that no Hugging Face access is needed once you have the checkpoint. -SAM3_MODELS = ["sam3"] - -# Backwards-compatible alias: prior releases only supported SAM 1. -VALID_SAM_MODELS = SAM1_MODELS - -SAM_FAMILIES = { - "sam1": { - "models": SAM1_MODELS, - "model_class": "SamModel", - "config_class": "SamConfig", - "processor_class": "SamProcessor", - "bundled": True, - }, - "sam2": { - "models": SAM2_MODELS, - "model_class": "Sam2Model", - "config_class": "Sam2Config", - "processor_class": "Sam2Processor", - "bundled": True, - }, - "sam3": { - "models": SAM3_MODELS, - "model_class": "Sam3Model", - "config_class": "Sam3Config", - "processor_class": "Sam3Processor", - "bundled": True, - }, -} - -# Which prompt types each family accepts natively. -# points / labels / boxes : geometric prompts understood by the prompt encoder -# text : natural-language prompt understood by the model itself -# SAM 1 and SAM 2 have no text encoder at all, so a text prompt for those -# families is resolved into boxes by a separate grounding model before inference. -SAM_CAPABILITIES = { - "sam1": {"points": True, "labels": True, "boxes": True, "text": False}, - "sam2": {"points": True, "labels": True, "boxes": True, "text": False}, - "sam3": {"points": False, "labels": False, "boxes": True, "text": True}, - # SAM 3's tracker carries the SAM-style prompt encoder, so it accepts clicks and - # boxes on still images. It has no text encoder of its own. - "sam3-tracker": {"points": True, "labels": True, "boxes": True, "text": False}, -} - -# Nesting depth each family's processor expects for geometric prompts. -# SAM 2 strictly requires one extra level over SAM 1 and raises otherwise. -SAM_PROMPT_DEPTHS = { - "sam1": {"points": 3, "labels": 2, "boxes": 3}, - "sam2": {"points": 4, "labels": 3, "boxes": 3}, - "sam3": {"boxes": 3}, - # Verified against Sam3TrackerProcessor: it rejects depth-3 points like SAM 2 does. - "sam3-tracker": {"points": 4, "labels": 3, "boxes": 3}, -} - -ALL_SAM_MODELS = SAM1_MODELS + SAM2_MODELS + SAM3_MODELS - - -def sam_family(model_type: str) -> str: - """ - Returns the Segment Anything family a variant belongs to. - - Args: - model_type (str): A SAM variant identifier, e.g. `"sam-vit-b"`, - `"sam2.1-hiera-tiny"`, or `"sam3"`. - - Raises: - ValueError: If `model_type` is not a recognised SAM variant. - - Returns: - str: One of `"sam1"`, `"sam2"`, or `"sam3"`. - """ - for family, spec in SAM_FAMILIES.items(): - if model_type in spec["models"]: - return family - raise ValueError( - f"Invalid model_type '{model_type}'. Choose from: {ALL_SAM_MODELS}" - ) - - -def sam_capabilities(model_type: str) -> dict: - """ - Reports which prompt types a SAM variant accepts natively. - - Args: - model_type (str): A SAM variant identifier, e.g. `"sam-vit-b"` or `"sam3"`. - - Raises: - ValueError: If `model_type` is not a recognised SAM variant. - - Returns: - dict: Mapping of `"points"`, `"labels"`, `"boxes"`, and `"text"` to booleans. - SAM 1 and SAM 2 report `text=False` because they contain no text encoder, - and SAM 3 reports `points=False` because it accepts only text and box prompts. - """ - return dict(SAM_CAPABILITIES[sam_family(model_type)]) - - -def sam_model_class(model_type: str): - """ - Returns the Hugging Face model class matching a SAM variant. - - Args: - model_type (str): A SAM variant identifier, e.g. `"sam2.1-hiera-tiny"`. - - Raises: - ValueError: If `model_type` is not a recognised SAM variant. - - Returns: - type: `SamModel`, `Sam2Model`, or `Sam3Model`. - """ - return _resolve(SAM_FAMILIES[sam_family(model_type)]["model_class"]) - - -# Prefix carried by SAM 3 image-model weights inside the released video checkpoint. -SAM3_DETECTOR_PREFIX = "detector_model." - -# Prefix carried by SAM 3 tracker weights inside the same checkpoint. The tracker shares -# the detector's vision encoder backbone, which is stored only once under the detector -# prefix, but it has its own FPN neck stored under `tracker_neck.`. -SAM3_TRACKER_PREFIX = "tracker_model." -SAM3_TRACKER_NECK_PREFIX = "tracker_neck." - - -def _unwrap_detector_config(config_dict: dict) -> dict: - """ - Returns the image-level SAM 3 configuration from a released SAM 3 config. - - Meta distributes SAM 3 as a video model whose `config.json` describes `sam3_video` - and nests the image model under `detector_config`. GarmentIQ segments still images, - so the nested detector configuration is the one that matches `Sam3Model`. Configs - that already describe the image model are returned unchanged. - - Args: - config_dict (dict): The raw configuration dictionary read from `config.json`. - - Returns: - dict: The configuration describing the image-level model. - """ - detector = config_dict.get("detector_config") - if isinstance(detector, dict) and detector.get("model_type") == "sam3": - return detector - return config_dict - - -def _variant_dir(model_type: str, model_dir: Optional[str]) -> str: - """Resolves the directory holding a variant's offline configuration files.""" - if model_dir is not None: - if not os.path.isdir(model_dir): - raise FileNotFoundError( - f"Provided model_dir '{model_dir}' is not an existing directory." - ) - return model_dir - - family = sam_family(model_type) - if not SAM_FAMILIES[family]["bundled"]: - raise FileNotFoundError( - f"'{model_type}' is distributed under a gated licence, so its configuration " - f"is not bundled with GarmentIQ. Accept the licence, download the model files " - f"once (config.json, processor/tokenizer files and the weights), then pass " - f"model_dir='/path/to/{model_type}' to load it fully offline." - ) - - current_dir = os.path.dirname(os.path.abspath(__file__)) - return os.path.join(current_dir, model_type) - - -def load_sam_config(model_type: str = "sam-vit-b", model_dir: Optional[str] = None): - """ - Reads and loads the configuration for a specified Segment Anything Model variant. - - This function facilitates strictly offline initialization by dynamically locating the - `config.json` file associated with the chosen SAM variant within the local package - directory. It reads the JSON file securely and converts it into the Hugging Face - configuration object matching the variant's family, entirely bypassing external - network requests. - - Configurations for SAM 1 (`sam-vit-*`), SAM 2.1 (`sam2.1-hiera-*`) and SAM 3 (`sam3`) - all ship with the package, so no Hugging Face access is needed. SAM 3 weights are still - gated, so download the checkpoint once and point `model_path` at it when loading the - model; `model_dir` here only overrides the bundled configuration. - - Released SAM 3 configurations describe the video model and nest the image model under - `detector_config`. GarmentIQ segments still images, so that nested section is unwrapped - automatically. - - Args: - model_type (str, optional): The identifier for the desired SAM variant. Must be one - of `["sam-vit-b", "sam-vit-l", "sam-vit-h", - "sam2.1-hiera-tiny", "sam2.1-hiera-small", - "sam2.1-hiera-base-plus", "sam2.1-hiera-large", "sam3"]`. - Default is `"sam-vit-b"`. - model_dir (str, optional): Path to a local directory containing the variant's - `config.json`, used to override the bundled configuration. - Default is None. - - Raises: - ValueError: If the provided `model_type` is not within the supported variants list. - FileNotFoundError: If the corresponding offline `config.json` file cannot be located. - - Returns: - Union[SamConfig, Sam2Config, Sam3Config]: The loaded configuration object ready to be - passed into the matching SAM model class. - """ - family = sam_family(model_type) - config_path = os.path.join(_variant_dir(model_type, model_dir), "config.json") - - if not os.path.exists(config_path): - raise FileNotFoundError(f"Offline config missing at {config_path}.") - - with open(config_path, "r") as f: - config_dict = json.load(f) - - config_dict = _unwrap_detector_config(config_dict) - - return _resolve(SAM_FAMILIES[family]["config_class"]).from_dict(config_dict) - - -def _supports_backend_kwarg() -> bool: - """ - Reports whether the installed transformers accepts the `backend` image-processor kwarg. - - `backend` replaced the deprecated `use_fast` flag in transformers 5.4.0. Older releases - do not recognise it, so the two must be selected by version. - """ - import transformers - - version = getattr(transformers, "__version__", "0") - parts = re.findall(r"\d+", version)[:2] - try: - return tuple(int(p) for p in parts) >= (5, 4) - except ValueError: # pragma: no cover - unparseable version string - return False - - -def load_sam_processor( - model_type: str = "sam-vit-b", - use_fast: Optional[bool] = None, - model_dir: Optional[str] = None, - backend: Optional[str] = None, -): - """ - Loads the offline processor for a specified Segment Anything Model variant. - - This function instantiates the processor matching the variant's family by reading - bundled preprocessor (and, for SAM 3, tokenizer) configuration files from the local - variant directory. Loading from a local path ensures the package remains completely - air-gapped. The image resizing backend can be selected to balance speed against - strict backward compatibility. - - Args: - model_type (str, optional): The identifier for the desired SAM variant. - Default is `"sam-vit-b"`. - use_fast (bool, optional): Deprecated alias for `backend`, kept for backwards - compatibility. `True` maps to `"torchvision"` and `False` - maps to `"pil"`. Only applies to SAM 1. Default is None. - model_dir (str, optional): Path to a local directory containing the variant's - processor files, used to override the bundled ones. - Default is None. - backend (str, optional): Image resizing backend, either `"pil"` or `"torchvision"`. - `"torchvision"` is faster, while `"pil"` matches the - historical GarmentIQ default. Only applies to SAM 1. - Default is None, which resolves to `"pil"`. - - Raises: - ValueError: If the provided `model_type` is not within the supported variants list, - or if `backend` is neither `"pil"` nor `"torchvision"`. - FileNotFoundError: If the corresponding offline processor configuration cannot be - located. - - Returns: - Union[SamProcessor, Sam2Processor, Sam3Processor]: The instantiated processor ready - for image and prompt transformations. - """ - family = sam_family(model_type) - variant_dir = _variant_dir(model_type, model_dir) - - if backend is None and use_fast is not None: - backend = "torchvision" if use_fast else "pil" - if backend is None: - # Historical GarmentIQ default. Selected explicitly so results stay identical - # across transformers releases, which have shifted their own default over time. - backend = "pil" - if backend not in ("pil", "torchvision"): - raise ValueError( - f"Invalid backend {backend!r}. Choose either 'pil' or 'torchvision'." - ) - - if family == "sam1": - processor_path = os.path.join(variant_dir, "preprocessor_config.json") - if not os.path.exists(processor_path): - raise FileNotFoundError( - f"Offline processor config missing at {processor_path}." - ) - # transformers 5.4 replaced `use_fast` with `backend`; passing the deprecated - # flag to a newer release emits a warning, so select the kwarg by version. - backend_kwargs = ( - {"backend": backend} - if _supports_backend_kwarg() - else {"use_fast": backend == "torchvision"} - ) - # Loading from a local directory disables Hugging Face network calls - return _resolve("SamProcessor").from_pretrained( - processor_path, **backend_kwargs - ) - - if family == "sam2": - processor_path = os.path.join(variant_dir, "preprocessor_config.json") - if not os.path.exists(processor_path): - raise FileNotFoundError( - f"Offline processor config missing at {processor_path}." - ) - with open(processor_path, "r") as f: - image_processor_dict = json.load(f) - # The bundled config names the deprecated `*Fast` class; build the image - # processor explicitly so loading stays offline and warning-free. - image_processor_dict.pop("image_processor_type", None) - image_processor = _resolve("Sam2ImageProcessor")(**image_processor_dict) - return _resolve("Sam2Processor")(image_processor=image_processor) - - # SAM 3 bundles a tokenizer alongside the image processor, so the whole - # directory is handed to the processor loader. - return _resolve("Sam3Processor").from_pretrained(variant_dir) - - -def load_sam3_tracker_config(model_dir: Optional[str] = None): - """ - Reads and loads the bundled configuration for SAM 3's point-promptable tracker. - - SAM 3's image model is an open-vocabulary detector prompted by text or boxes, and does - not accept points. The release also contains a tracker, whose prompt encoder is the - SAM-style one that understands points, boxes and masks. This function loads that - tracker's configuration so still images can be segmented from a click. - - Args: - model_dir (str, optional): Path to a local directory containing - `tracker_config.json`, used to override the bundled - configuration. Default is None. - - Raises: - FileNotFoundError: If the offline `tracker_config.json` cannot be located. - ImportError: If the installed transformers release does not provide SAM 3. - - Returns: - Sam3TrackerConfig: The configuration for `Sam3TrackerModel`. - """ - variant_dir = _variant_dir("sam3", model_dir) - config_path = os.path.join(variant_dir, "tracker_config.json") - - if not os.path.exists(config_path): - # A directly downloaded SAM 3 release nests it inside the video config. - fallback = os.path.join(variant_dir, "config.json") - if os.path.exists(fallback): - with open(fallback, "r") as f: - nested = json.load(f).get("tracker_config") - if isinstance(nested, dict): - return _resolve("Sam3TrackerConfig").from_dict(nested) - raise FileNotFoundError(f"Offline tracker config missing at {config_path}.") - - with open(config_path, "r") as f: - config_dict = json.load(f) - - return _resolve("Sam3TrackerConfig").from_dict(config_dict) - - -def load_sam3_tracker_processor(): - """ - Builds the processor for SAM 3's point-promptable tracker. - - The tracker takes only geometric prompts, so unlike `load_sam_processor("sam3")` no - tokenizer is involved. The image processor is constructed from its defaults, which - keeps loading fully offline. - - Raises: - ImportError: If the installed transformers release does not provide SAM 3. - - Returns: - Sam3TrackerProcessor: The processor for `Sam3TrackerModel`. - """ - image_processor = _resolve("Sam3ImageProcessor")() - return _resolve("Sam3TrackerProcessor")(image_processor=image_processor) - - -def load_sam3_tracker( - model_path: str, - model_dir: Optional[str] = None, - device: Union[str, torch.device] = "cpu", -): - """ - Loads SAM 3's point-promptable tracker from a released SAM 3 checkpoint. - - SAM 3 ships as a single checkpoint holding both the detector and the tracker, and the - two share one vision encoder that is stored only once under the detector's prefix. This - function reassembles the tracker by pairing `tracker_model.*` weights with the shared - `detector_model.vision_encoder.*` backbone, then verifies every expected tensor was - found so a mismatched checkpoint fails loudly instead of yielding a random model. - - Use this when you want to prompt SAM 3 with points on a still image. For text or box - prompts use the detector via `Sam3Model` and `load_sam_config("sam3")` instead. - - Args: - model_path (str): Local path to the SAM 3 `model.safetensors` checkpoint. - model_dir (str, optional): Directory holding `tracker_config.json`, used to - override the bundled configuration. Default is None. - device (Union[str, torch.device], optional): The device to load onto, e.g. `"cpu"`, - `"cuda"`, or `"mps"`. Default is `"cpu"`. - - Raises: - FileNotFoundError: If `model_path` does not exist. - ValueError: If the checkpoint does not contain the tensors the tracker expects, or - if the requested `device` is unavailable. - ImportError: If the installed transformers release does not provide SAM 3. - - Returns: - Sam3TrackerModel: The assembled tracker in evaluation mode, placed on `device`. - """ - from safetensors.torch import load_file - - from garmentiq.utils.device import resolve_device - - if not os.path.exists(model_path): - raise FileNotFoundError(f"SAM 3 checkpoint not found at {model_path}.") - - device = resolve_device(device) - - model = _resolve("Sam3TrackerModel")(load_sam3_tracker_config(model_dir)) - expected = model.state_dict() - - if model_path.endswith(".safetensors"): - state_dict = load_file(model_path, device="cpu") - else: - state_dict = torch.load(model_path, map_location="cpu", weights_only=True) - - shared_prefix = SAM3_DETECTOR_PREFIX + "vision_encoder." - neck_prefix = shared_prefix + "neck." - assembled = {} - for key, value in state_dict.items(): - if key.startswith(neck_prefix): - # The detector has its own neck with the same layer names as the tracker's. - # Skip it: the tracker's neck is stored separately under `tracker_neck.`, - # and using the detector's would silently produce a garbage mask. - continue - if key.startswith(shared_prefix): - # The vision encoder backbone is shared, and stored once under the detector. - assembled[key[len(SAM3_DETECTOR_PREFIX) :]] = value - elif key.startswith(SAM3_TRACKER_NECK_PREFIX): - assembled[ - "vision_encoder.neck." + key[len(SAM3_TRACKER_NECK_PREFIX) :] - ] = value - elif key.startswith(SAM3_TRACKER_PREFIX): - stripped = key[len(SAM3_TRACKER_PREFIX) :] - if stripped in expected: - assembled[stripped] = value - - missing = set(expected) - set(assembled) - if missing: - raise ValueError( - f"SAM 3 tracker weights are incomplete: {len(missing)} of {len(expected)} " - f"tensors were not found in {model_path!r} " - f"(first missing: {sorted(missing)[:3]}). Check that the checkpoint is a " - f"full SAM 3 release." - ) - - model.load_state_dict(assembled, strict=True) - model = model.to(device) - model.eval() - return model - - -__all__ = [ - "SamModel", - "Sam2Model", - "Sam3Model", - "load_sam_config", - "load_sam_processor", - "load_sam3_tracker", - "load_sam3_tracker_config", - "load_sam3_tracker_processor", - "sam_family", - "sam_capabilities", - "sam_model_class", - "SAM1_MODELS", - "SAM2_MODELS", - "SAM3_MODELS", - "VALID_SAM_MODELS", - "ALL_SAM_MODELS", - "SAM_CAPABILITIES", - "SAM_PROMPT_DEPTHS", -] diff --git a/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-base-plus/config.json b/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-base-plus/config.json deleted file mode 100644 index d533fd5..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-base-plus/config.json +++ /dev/null @@ -1,233 +0,0 @@ -{ - "architectures": [ - "Sam2VideoModel" - ], - "enable_occlusion_spatial_embedding": true, - "enable_temporal_pos_encoding_for_object_pointers": true, - "image_size": 1024, - "initializer_range": 0.02, - "mask_decoder_config": { - "attention_downsample_rate": 2, - "dynamic_multimask_stability_delta": 0.05, - "dynamic_multimask_stability_thresh": 0.98, - "dynamic_multimask_via_stability": true, - "hidden_act": "gelu", - "hidden_size": 256, - "iou_head_depth": 3, - "iou_head_hidden_dim": 256, - "mlp_dim": 2048, - "model_type": "", - "num_attention_heads": 8, - "num_hidden_layers": 2, - "num_multimask_outputs": 3 - }, - "mask_downsampler_embed_dim": 256, - "mask_downsampler_hidden_act": "gelu", - "mask_downsampler_kernel_size": 3, - "mask_downsampler_padding": 1, - "mask_downsampler_stride": 2, - "mask_downsampler_total_stride": 16, - "max_object_pointers_in_encoder": 16, - "memory_attention_downsample_rate": 1, - "memory_attention_dropout": 0.1, - "memory_attention_feed_forward_hidden_act": "relu", - "memory_attention_feed_forward_hidden_size": 2048, - "memory_attention_hidden_size": 256, - "memory_attention_num_attention_heads": 1, - "memory_attention_num_layers": 4, - "memory_attention_rope_dropout": 0.1, - "memory_attention_rope_feat_sizes": [ - 64, - 64 - ], - "memory_attention_rope_theta": 10000, - "memory_encoder_hidden_size": 256, - "memory_encoder_output_channels": 64, - "memory_fuser_embed_dim": 256, - "memory_fuser_hidden_act": "gelu", - "memory_fuser_intermediate_dim": 1024, - "memory_fuser_kernel_size": 7, - "memory_fuser_layer_scale_init_value": 1e-06, - "memory_fuser_num_layers": 2, - "memory_fuser_padding": 3, - "model_type": "sam2_video", - "multimask_max_pt_num": 1, - "multimask_min_pt_num": 0, - "multimask_output_for_tracking": true, - "multimask_output_in_sam": true, - "num_maskmem": 7, - "prompt_encoder_config": { - "hidden_act": "gelu", - "hidden_size": 256, - "image_size": 1024, - "layer_norm_eps": 1e-06, - "mask_input_channels": 16, - "model_type": "", - "num_point_embeddings": 4, - "patch_size": 16, - "scale": 1 - }, - "sigmoid_bias_for_mem_enc": -10.0, - "sigmoid_scale_for_mem_enc": 20.0, - "torch_dtype": "float32", - "transformers_version": "4.56.0.dev0", - "vision_config": { - "backbone_channel_list": [ - 896, - 448, - 224, - 112 - ], - "backbone_config": { - "_name_or_path": "", - "add_cross_attention": false, - "architectures": null, - "bad_words_ids": null, - "begin_suppress_tokens": null, - "blocks_per_stage": [ - 2, - 3, - 16, - 3 - ], - "bos_token_id": null, - "chunk_size_feed_forward": 0, - "cross_attention_hidden_size": null, - "decoder_start_token_id": null, - "diversity_penalty": 0.0, - "do_sample": false, - "early_stopping": false, - "embed_dim_per_stage": [ - 112, - 224, - 448, - 896 - ], - "encoder_no_repeat_ngram_size": 0, - "eos_token_id": null, - "exponential_decay_length_penalty": null, - "finetuning_task": null, - "forced_bos_token_id": null, - "forced_eos_token_id": null, - "global_attention_blocks": [ - 12, - 16, - 20 - ], - "hidden_act": "gelu", - "hidden_size": 112, - "id2label": { - "0": "LABEL_0", - "1": "LABEL_1" - }, - "image_size": [ - 1024, - 1024 - ], - "initializer_range": 0.02, - "is_decoder": false, - "is_encoder_decoder": false, - "label2id": { - "LABEL_0": 0, - "LABEL_1": 1 - }, - "layer_norm_eps": 1e-06, - "length_penalty": 1.0, - "max_length": 20, - "min_length": 0, - "mlp_ratio": 4.0, - "model_type": "sam2_hiera_det_model", - "no_repeat_ngram_size": 0, - "num_attention_heads": 1, - "num_attention_heads_per_stage": [ - 2, - 4, - 8, - 16 - ], - "num_beam_groups": 1, - "num_beams": 1, - "num_channels": 3, - "num_query_pool_stages": 3, - "num_return_sequences": 1, - "output_attentions": false, - "output_hidden_states": false, - "output_scores": false, - "pad_token_id": null, - "patch_kernel_size": [ - 7, - 7 - ], - "patch_padding": [ - 3, - 3 - ], - "patch_stride": [ - 4, - 4 - ], - "prefix": null, - "problem_type": null, - "pruned_heads": {}, - "query_stride": [ - 2, - 2 - ], - "remove_invalid_values": false, - "repetition_penalty": 1.0, - "return_dict": true, - "return_dict_in_generate": false, - "sep_token_id": null, - "suppress_tokens": null, - "task_specific_params": null, - "temperature": 1.0, - "tf_legacy_loss": false, - "tie_encoder_decoder": false, - "tie_word_embeddings": true, - "tokenizer_class": null, - "top_k": 50, - "top_p": 1.0, - "torch_dtype": null, - "torchscript": false, - "typical_p": 1.0, - "use_bfloat16": false, - "window_positional_embedding_background_size": [ - 14, - 14 - ], - "window_size_per_stage": [ - 8, - 4, - 14, - 7 - ] - }, - "backbone_feature_sizes": [ - [ - 256, - 256 - ], - [ - 128, - 128 - ], - [ - 64, - 64 - ] - ], - "fpn_hidden_size": 256, - "fpn_kernel_size": 1, - "fpn_padding": 0, - "fpn_stride": 1, - "fpn_top_down_levels": [ - 2, - 3 - ], - "hidden_act": "gelu", - "initializer_range": 0.02, - "layer_norm_eps": 1e-06, - "model_type": "sam2_vision_model", - "num_feature_levels": 3 - } -} diff --git a/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-base-plus/preprocessor_config.json b/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-base-plus/preprocessor_config.json deleted file mode 100644 index 8499e53..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-base-plus/preprocessor_config.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "crop_size": null, - "data_format": "channels_first", - "default_to_square": true, - "device": null, - "disable_grouping": null, - "do_center_crop": null, - "do_convert_rgb": true, - "do_normalize": true, - "do_rescale": true, - "do_resize": true, - "image_mean": [ - 0.485, - 0.456, - 0.406 - ], - "image_processor_type": "Sam2ImageProcessorFast", - "image_std": [ - 0.229, - 0.224, - 0.225 - ], - "input_data_format": null, - "mask_size": { - "height": 256, - "width": 256 - }, - "processor_class": "Sam2VideoProcessor", - "resample": 2, - "rescale_factor": 0.00392156862745098, - "return_tensors": null, - "size": { - "height": 1024, - "width": 1024 - } -} diff --git a/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-large/config.json b/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-large/config.json deleted file mode 100644 index 62a1dd9..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-large/config.json +++ /dev/null @@ -1,233 +0,0 @@ -{ - "architectures": [ - "Sam2VideoModel" - ], - "enable_occlusion_spatial_embedding": true, - "enable_temporal_pos_encoding_for_object_pointers": true, - "image_size": 1024, - "initializer_range": 0.02, - "mask_decoder_config": { - "attention_downsample_rate": 2, - "dynamic_multimask_stability_delta": 0.05, - "dynamic_multimask_stability_thresh": 0.98, - "dynamic_multimask_via_stability": true, - "hidden_act": "gelu", - "hidden_size": 256, - "iou_head_depth": 3, - "iou_head_hidden_dim": 256, - "mlp_dim": 2048, - "model_type": "", - "num_attention_heads": 8, - "num_hidden_layers": 2, - "num_multimask_outputs": 3 - }, - "mask_downsampler_embed_dim": 256, - "mask_downsampler_hidden_act": "gelu", - "mask_downsampler_kernel_size": 3, - "mask_downsampler_padding": 1, - "mask_downsampler_stride": 2, - "mask_downsampler_total_stride": 16, - "max_object_pointers_in_encoder": 16, - "memory_attention_downsample_rate": 1, - "memory_attention_dropout": 0.1, - "memory_attention_feed_forward_hidden_act": "relu", - "memory_attention_feed_forward_hidden_size": 2048, - "memory_attention_hidden_size": 256, - "memory_attention_num_attention_heads": 1, - "memory_attention_num_layers": 4, - "memory_attention_rope_dropout": 0.1, - "memory_attention_rope_feat_sizes": [ - 64, - 64 - ], - "memory_attention_rope_theta": 10000, - "memory_encoder_hidden_size": 256, - "memory_encoder_output_channels": 64, - "memory_fuser_embed_dim": 256, - "memory_fuser_hidden_act": "gelu", - "memory_fuser_intermediate_dim": 1024, - "memory_fuser_kernel_size": 7, - "memory_fuser_layer_scale_init_value": 1e-06, - "memory_fuser_num_layers": 2, - "memory_fuser_padding": 3, - "model_type": "sam2_video", - "multimask_max_pt_num": 1, - "multimask_min_pt_num": 0, - "multimask_output_for_tracking": true, - "multimask_output_in_sam": true, - "num_maskmem": 7, - "prompt_encoder_config": { - "hidden_act": "gelu", - "hidden_size": 256, - "image_size": 1024, - "layer_norm_eps": 1e-06, - "mask_input_channels": 16, - "model_type": "", - "num_point_embeddings": 4, - "patch_size": 16, - "scale": 1 - }, - "sigmoid_bias_for_mem_enc": -10.0, - "sigmoid_scale_for_mem_enc": 20.0, - "torch_dtype": "float32", - "transformers_version": "4.56.0.dev0", - "vision_config": { - "backbone_channel_list": [ - 1152, - 576, - 288, - 144 - ], - "backbone_config": { - "_name_or_path": "", - "add_cross_attention": false, - "architectures": null, - "bad_words_ids": null, - "begin_suppress_tokens": null, - "blocks_per_stage": [ - 2, - 6, - 36, - 4 - ], - "bos_token_id": null, - "chunk_size_feed_forward": 0, - "cross_attention_hidden_size": null, - "decoder_start_token_id": null, - "diversity_penalty": 0.0, - "do_sample": false, - "early_stopping": false, - "embed_dim_per_stage": [ - 144, - 288, - 576, - 1152 - ], - "encoder_no_repeat_ngram_size": 0, - "eos_token_id": null, - "exponential_decay_length_penalty": null, - "finetuning_task": null, - "forced_bos_token_id": null, - "forced_eos_token_id": null, - "global_attention_blocks": [ - 23, - 33, - 43 - ], - "hidden_act": "gelu", - "hidden_size": 144, - "id2label": { - "0": "LABEL_0", - "1": "LABEL_1" - }, - "image_size": [ - 1024, - 1024 - ], - "initializer_range": 0.02, - "is_decoder": false, - "is_encoder_decoder": false, - "label2id": { - "LABEL_0": 0, - "LABEL_1": 1 - }, - "layer_norm_eps": 1e-06, - "length_penalty": 1.0, - "max_length": 20, - "min_length": 0, - "mlp_ratio": 4.0, - "model_type": "sam2_hiera_det_model", - "no_repeat_ngram_size": 0, - "num_attention_heads": 1, - "num_attention_heads_per_stage": [ - 2, - 4, - 8, - 16 - ], - "num_beam_groups": 1, - "num_beams": 1, - "num_channels": 3, - "num_query_pool_stages": 3, - "num_return_sequences": 1, - "output_attentions": false, - "output_hidden_states": false, - "output_scores": false, - "pad_token_id": null, - "patch_kernel_size": [ - 7, - 7 - ], - "patch_padding": [ - 3, - 3 - ], - "patch_stride": [ - 4, - 4 - ], - "prefix": null, - "problem_type": null, - "pruned_heads": {}, - "query_stride": [ - 2, - 2 - ], - "remove_invalid_values": false, - "repetition_penalty": 1.0, - "return_dict": true, - "return_dict_in_generate": false, - "sep_token_id": null, - "suppress_tokens": null, - "task_specific_params": null, - "temperature": 1.0, - "tf_legacy_loss": false, - "tie_encoder_decoder": false, - "tie_word_embeddings": true, - "tokenizer_class": null, - "top_k": 50, - "top_p": 1.0, - "torch_dtype": null, - "torchscript": false, - "typical_p": 1.0, - "use_bfloat16": false, - "window_positional_embedding_background_size": [ - 7, - 7 - ], - "window_size_per_stage": [ - 8, - 4, - 16, - 8 - ] - }, - "backbone_feature_sizes": [ - [ - 256, - 256 - ], - [ - 128, - 128 - ], - [ - 64, - 64 - ] - ], - "fpn_hidden_size": 256, - "fpn_kernel_size": 1, - "fpn_padding": 0, - "fpn_stride": 1, - "fpn_top_down_levels": [ - 2, - 3 - ], - "hidden_act": "gelu", - "initializer_range": 0.02, - "layer_norm_eps": 1e-06, - "model_type": "sam2_vision_model", - "num_feature_levels": 3 - } -} diff --git a/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-large/preprocessor_config.json b/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-large/preprocessor_config.json deleted file mode 100644 index 8499e53..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-large/preprocessor_config.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "crop_size": null, - "data_format": "channels_first", - "default_to_square": true, - "device": null, - "disable_grouping": null, - "do_center_crop": null, - "do_convert_rgb": true, - "do_normalize": true, - "do_rescale": true, - "do_resize": true, - "image_mean": [ - 0.485, - 0.456, - 0.406 - ], - "image_processor_type": "Sam2ImageProcessorFast", - "image_std": [ - 0.229, - 0.224, - 0.225 - ], - "input_data_format": null, - "mask_size": { - "height": 256, - "width": 256 - }, - "processor_class": "Sam2VideoProcessor", - "resample": 2, - "rescale_factor": 0.00392156862745098, - "return_tensors": null, - "size": { - "height": 1024, - "width": 1024 - } -} diff --git a/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-small/config.json b/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-small/config.json deleted file mode 100644 index be32655..0000000 --- a/src/garmentiq/segmentation/model_definition/sam/sam2.1-hiera-small/config.json +++ /dev/null @@ -1,233 +0,0 @@ -{ - "architectures": [ - "Sam2VideoModel" - ], - "enable_occlusion_spatial_embedding": true, - "enable_temporal_pos_encoding_for_object_pointers": true, - "image_size": 1024, - "initializer_range": 0.02, - "mask_decoder_config": { - "attention_downsample_rate": 2, - "dynamic_multimask_stability_delta": 0.05, - "dynamic_multimask_stability_thresh": 0.98, - "dynamic_multimask_via_stability": true, - "hidden_act": "gelu", - "hidden_size": 256, - "iou_head_depth": 3, - "iou_head_hidden_dim": 256, - "mlp_dim": 2048, - "model_type": "", - "num_attention_heads": 8, - "num_hidden_layers": 2, - "num_multimask_outputs": 3 - }, - "mask_downsampler_embed_dim": 256, - "mask_downsampler_hidden_act": "gelu", - "mask_downsampler_kernel_size": 3, - "mask_downsampler_padding": 1, - "mask_downsampler_stride": 2, - "mask_downsampler_total_stride": 16, - "max_object_pointers_in_encoder": 16, - "memory_attention_downsample_rate": 1, - "memory_attention_dropout": 0.1, - "memory_attention_feed_forward_hidden_act": "relu", - "memory_attention_feed_forward_hidden_size": 2048, - "memory_attention_hidden_size": 256, - "memory_attention_num_attention_heads": 1, - "memory_attention_num_layers": 4, - "memory_attention_rope_dropout": 0.1, - "memory_attention_rope_feat_sizes": [ - 64, - 64 - ], - "memory_attention_rope_theta": 10000, - "memory_encoder_hidden_size": 256, - "memory_encoder_output_channels": 64, - "memory_fuser_embed_dim": 256, - "memory_fuser_hidden_act": "gelu", - "memory_fuser_intermediate_dim": 1024, - "memory_fuser_kernel_size": 7, - "memory_fuser_layer_scale_init_value": 1e-06, - "memory_fuser_num_layers": 2, - "memory_fuser_padding": 3, - "model_type": "sam2_video", - "multimask_max_pt_num": 1, - "multimask_min_pt_num": 0, - "multimask_output_for_tracking": true, - "multimask_output_in_sam": true, - "num_maskmem": 7, - "prompt_encoder_config": { - "hidden_act": "gelu", - "hidden_size": 256, - "image_size": 1024, - "layer_norm_eps": 1e-06, - "mask_input_channels": 16, - "model_type": "", - "num_point_embeddings": 4, - "patch_size": 16, - "scale": 1 - }, - "sigmoid_bias_for_mem_enc": -10.0, - "sigmoid_scale_for_mem_enc": 20.0, - "torch_dtype": "float32", - "transformers_version": "4.56.0.dev0", - "vision_config": { - "backbone_channel_list": [ - 768, - 384, - 192, - 96 - ], - "backbone_config": { - "_name_or_path": "", - "add_cross_attention": false, - "architectures": null, - "bad_words_ids": null, - "begin_suppress_tokens": null, - "blocks_per_stage": [ - 1, - 2, - 11, - 2 - ], - "bos_token_id": null, - "chunk_size_feed_forward": 0, - "cross_attention_hidden_size": null, - "decoder_start_token_id": null, - "diversity_penalty": 0.0, - "do_sample": false, - "early_stopping": false, - "embed_dim_per_stage": [ - 96, - 192, - 384, - 768 - ], - "encoder_no_repeat_ngram_size": 0, - "eos_token_id": null, - "exponential_decay_length_penalty": null, - "finetuning_task": null, - "forced_bos_token_id": null, - "forced_eos_token_id": null, - "global_attention_blocks": [ - 7, - 10, - 13 - ], - "hidden_act": "gelu", - 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\ No newline at end of file diff --git a/src/garmentiq/segmentation/plot.py b/src/garmentiq/segmentation/plot.py deleted file mode 100644 index fb4290e..0000000 --- a/src/garmentiq/segmentation/plot.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Displaying an image, mask, or background-modified result.""" -import matplotlib.pyplot as plt -import numpy as np - - -def plot(image_np: np.ndarray, figsize: tuple = (6, 6)): - """ - Displays an image using matplotlib, with optional customization of the figure size. - - This function takes an image in the form of a NumPy array and displays it using matplotlib. - If the image is 2D (grayscale), it will use a grayscale colormap for visualization. - It also provides an option to adjust the figure size via the `figsize` parameter. - - Args: - image_np (numpy.ndarray): The image to be displayed. Can be either 2D (grayscale) or 3D (color) numpy array. - figsize (tuple, optional): The size of the figure (width, height). Default is (6, 6). - - Raises: - ValueError: If the image provided is not a numpy array. - - Returns: - None. The function directly displays the image. - """ - plt.figure(figsize=figsize) - - # If the image is 2D (grayscale), use 'gray' colormap for a black & white mask - if image_np.ndim == 2: - plt.imshow(image_np, cmap="gray") - else: - plt.imshow(image_np) - - # Remove axis and padding around the plot - plt.axis("off") - plt.subplots_adjust(left=0, right=1, top=1, bottom=0) - plt.show() diff --git a/src/garmentiq/segmentation/process_and_save_images.py b/src/garmentiq/segmentation/process_and_save_images.py deleted file mode 100644 index ecbfec0..0000000 --- a/src/garmentiq/segmentation/process_and_save_images.py +++ /dev/null @@ -1,107 +0,0 @@ -"""Batch segmentation of a directory of images.""" -import os -import torch -from transformers import AutoModelForImageSegmentation -from tqdm.auto import tqdm -from PIL import Image -import numpy as np -from typing import Union -from garmentiq.segmentation.extract import extract -from garmentiq.segmentation.change_background_color import change_background_color - - -def process_and_save_images( - image_dir: str, - output_dir: str, - model: AutoModelForImageSegmentation, - resize_dim: tuple[int, int], - normalize_mean: list[float, float, float], - normalize_std: list[float, float, float], - background_color: tuple[int, int, int] = None, - high_precision: bool = True, - device: Union[str, torch.device] = "cpu", -): - """ - Processes images from a directory by extracting segmentation masks and optionally modifying - the background color, then saving the masks and modified images to specified output directories. - - This function applies the `extract` function to segment each image, generating a mask, and - optionally modifies the background of each image using the `change_background_color` function - before saving both the masks and the modified images to disk. - - Args: - image_dir (str): The directory containing the input images to process. - output_dir (str): The directory where the processed masks and modified images will be saved. - model (transformers.AutoModelForImageSegmentation): The pre-trained model used for image segmentation. - resize_dim (tuple[int, int]): The target dimensions to resize the images before processing (width, height). - normalize_mean (list[float, float, float]): The mean values used for image normalization. - normalize_std (list[float, float, float]): The standard deviation values used for image normalization. - background_color (tuple[int, int, int], optional): The background color to apply to the image (RGB tuple), - or None to skip the modification. Default is None. - high_precision (bool, optional): Whether to use high precision (32-bit) for image processing. Default is True. - device (Union[str, torch.device], optional): The device to run inference on for every image, e.g. - `"cpu"`, `"cuda"`, `"cuda:0"`, or `"mps"`. Hardware - acceleration is opt-in; pass it explicitly to use a - GPU or Apple Silicon. Default is `"cpu"`. - - Raises: - FileNotFoundError: If the input image directory does not exist. - ValueError: If the requested `device` is invalid or unavailable on this machine, or if the - `model` provided does not work correctly for image segmentation. - - Returns: - None. The processed masks and modified images are saved to the specified output directory. - """ - # Create output directories for masks and modified images - mask_dir = os.path.join(output_dir, "masks") - os.makedirs(mask_dir, exist_ok=True) - - if background_color is not None: - modified_image_dir = os.path.join(output_dir, "bg_modified") - os.makedirs(modified_image_dir, exist_ok=True) - - image_files = [ - f - for f in os.listdir(image_dir) - if f.lower().endswith((".png", ".jpg", ".jpeg")) - ] - - # Loop through all image files in the provided directory - with tqdm(total=len(image_files), desc="Processing Images", unit="image") as pbar: - for filename in os.listdir(image_dir): - if filename.lower().endswith((".png", ".jpg", ".jpeg")): - image_path = os.path.join(image_dir, filename) - - # Extract image and mask using the extract function - image_np, mask_np = extract( - model=model, - image_path=image_path, - resize_dim=resize_dim, - normalize_mean=normalize_mean, - normalize_std=normalize_std, - high_precision=high_precision, - device=device, - ) - - # Save the mask image - mask_pil = Image.fromarray(mask_np) - mask_pil.save( - os.path.join(mask_dir, f"mask_{os.path.splitext(filename)[0]}.png") - ) - - if background_color is not None: - # Change the background color if provided - modified_image = change_background_color( - image_np, mask_np, background_color - ) - - # Save the modified image with the new background color - modified_image_pil = Image.fromarray(modified_image) - modified_image_pil.save( - os.path.join( - modified_image_dir, - f"bg_modified_{os.path.splitext(filename)[0]}.png", - ) - ) - - pbar.update(1) diff --git a/src/garmentiq/tailor.py b/src/garmentiq/tailor.py deleted file mode 100644 index 86626ba..0000000 --- a/src/garmentiq/tailor.py +++ /dev/null @@ -1,1030 +0,0 @@ -"""The tailor agent, which runs the whole GarmentIQ pipeline. - -Processes a folder of images end to end: classification, segmentation, optional alpha -matting, landmark detection, refinement, derivation, and measurement. Results are -written to an output directory and summarised in a metadata table. -""" -import os -from typing import List, Dict, Type, Any, Optional, Union -import torch -import torch.nn as nn -import numpy as np -from pathlib import Path -import pandas as pd -from tqdm.auto import tqdm -import textwrap -from PIL import Image, ImageDraw, ImageFont -from . import classification -from . import segmentation -from . import landmark -from . import matting -from . import utils - - -# Background colour used to composite the alpha matte for landmark detection when neither -# the matting nor the segmentation stage specifies one. Detection needs an RGB image, and a -# plain neutral background measurably helps the pose model compared with the raw photo. -DEFAULT_MATTE_DETECTION_BACKGROUND = (255, 255, 255) - - -class tailor: - """ - The `tailor` class acts as a central agent for the GarmentIQ pipeline, - orchestrating garment measurement from classification to landmark derivation. - - It integrates functionalities from other modules (classification, segmentation, landmark) - to provide a smooth end-to-end process for automated garment measurement from images. - - Attributes: - input_dir (str): Directory containing input images. - model_dir (str): Directory where models are stored. - output_dir (str): Directory to save processed outputs. - class_dict (dict): Dictionary defining garment classes and their properties. - do_derive (bool): Flag to enable landmark derivation. - do_refine (bool): Flag to enable landmark refinement. - classification_model_path (str): Path to the classification model. - classification_model_class (Type[nn.Module]): Class definition for the classification model. - classification_model_args (Dict): Arguments for the classification model. - segmentation_model_path (str): Name or path for the segmentation model. - segmentation_model_class (Type[nn.Module]): Class definition for the segmentation model. - segmentation_model_args (Dict): Arguments for the segmentation model. - landmark_detection_model_path (str): Path to the landmark detection model. - landmark_detection_model_class (Type[nn.Module]): Class definition for the landmark detection model. - landmark_detection_model_args (Dict): Arguments for the landmark detection model. - refinement_args (Optional[Dict]): Arguments for landmark refinement. - derivation_dict (Optional[Dict]): Dictionary for landmark derivation rules. - device (torch.device): The device all models are loaded onto and run on. - do_matte (bool): Flag to enable the alpha matting stage. - matting_model_args (Optional[Dict]): Arguments for the matting model. - """ - - def __init__( - self, - input_dir: str, - model_dir: str, - output_dir: str, - class_dict: dict, - do_derive: bool, - do_refine: bool, - classification_model_path: str, - classification_model_class: Type[nn.Module], - classification_model_args: Dict, - segmentation_model_path: str, - segmentation_model_class: Type[nn.Module], - segmentation_model_args: Dict, - landmark_detection_model_path: str, - landmark_detection_model_class: Type[nn.Module], - landmark_detection_model_args: Dict, - refinement_args: Optional[Dict] = None, - derivation_dict: Optional[Dict] = None, - device: Union[str, torch.device] = "cpu", - do_matte: bool = False, - matting_model_path: Optional[str] = None, - matting_model_class: Optional[Type[nn.Module]] = None, - matting_model_args: Optional[Dict] = None, - ): - """ - Initializes the `tailor` agent with paths, model configurations, and processing flags. - - Args: - input_dir (str): Path to the directory containing input images. - model_dir (str): Path to the directory where all required models are stored. - output_dir (str): Path to the directory where all processed outputs will be saved. - class_dict (dict): A dictionary defining the garment classes, their predefined points, - index ranges, and instruction JSON file paths. - do_derive (bool): If True, enables the landmark derivation step. - do_refine (bool): If True, enables the landmark refinement step. - classification_model_path (str): The filename or relative path to the classification model. - classification_model_class (Type[nn.Module]): The Python class of the classification model. - classification_model_args (Dict): A dictionary of arguments to initialize the classification model. - segmentation_model_path (str): The filename or relative path of the segmentation model. - segmentation_model_class (Type[nn.Module]): The Python class of the segmentation model. - segmentation_model_args (Dict): A dictionary of arguments for the segmentation model. - For SAM this typically holds `model_config`, `processor`, - and `prompt` (with `"points"`, `"labels"`, `"boxes"`, - and/or `"text"`), plus optional `grounding_model` and - `grounding_processor` when using a text prompt with - SAM 1 or SAM 2. An optional `background_color` triggers - background replacement. - landmark_detection_model_path (str): The filename or relative path to the landmark detection model. - landmark_detection_model_class (Type[nn.Module]): The Python class of the landmark detection model. - landmark_detection_model_args (Dict): A dictionary of arguments for the landmark detection model. - refinement_args (Optional[Dict]): Optional arguments for the refinement process, - e.g., `window_size`, `ksize`, `sigmaX`. Defaults to None. - derivation_dict (Optional[Dict]): A dictionary defining derivation rules for non-predefined landmarks. - Required if `do_derive` is True. - device (Union[str, torch.device], optional): The device that every model in the pipeline - is loaded onto and run on, e.g. `"cpu"`, - `"cuda"`, `"cuda:0"`, or `"mps"`. Hardware - acceleration is opt-in; pass it explicitly - to use a GPU or Apple Silicon. - Defaults to `"cpu"`. - do_matte (bool, optional): If True, enables the alpha matting stage, which refines the - hard segmentation mask into a soft alpha matte. Matting in - the pipeline is deliberately built on top of segmentation: - the segmentation mask supplies the trimap (ViTMatte) or the - guidance mask (Matting Anything), so enabling this forces the - segmentation stage to run. Defaults to False. - matting_model_path (str, optional): The filename or relative path to the matting model - weights, relative to `model_dir`. Required when - `do_matte` is True and the model is loaded by - GarmentIQ. Defaults to None. - matting_model_class (Type[nn.Module], optional): The Python class of the matting model, - e.g. `VitMatteForImageMatting`. - Required when `do_matte` is True. - Defaults to None. - matting_model_args (Dict, optional): Arguments for the matting model. For ViTMatte this - holds `model_config` (e.g. - `{"config": load_vitmatte_config(...)}`), - `processor`, and optional `trimap_args` and - `background_color`. Alternatively pass a - preconstructed model as `model`, which is how - Matting Anything is supplied since it pairs a - decoder with a SAM instance. Defaults to None. - - Raises: - ValueError: If `do_derive` is True but `derivation_dict` is None, if `do_matte` is True - but no matting model is provided, or if the requested `device` is invalid - or unavailable on this machine. - """ - # Device (resolved once and reused by every stage of the pipeline) - self.device = utils.resolve_device(device) - - # Directories - self.input_dir = input_dir - self.model_dir = model_dir - self.output_dir = output_dir - - # Classes - self.class_dict = class_dict - self.classes = sorted(list(class_dict.keys())) - - # Derivation - self.do_derive = do_derive - if self.do_derive: - if derivation_dict is None: - raise ValueError( - "`derivation_dict` must be provided if `do_derive=True`." - ) - self.derivation_dict = derivation_dict - else: - self.derivation_dict = None - - # Refinement setup - self.do_refine = do_refine - - if self.do_refine: - if refinement_args is None: - self.refinement_args = {} - self.refinement_args = refinement_args - else: - self.refinement_args = None - - # Classification model setup - self.classification_model_path = classification_model_path - self.classification_model_args = classification_model_args - self.classification_model_class = classification_model_class - filtered_model_args = { - k: v - for k, v in self.classification_model_args.items() - if k not in ("pretrained", "resize_dim", "normalize_mean", "normalize_std") - } - - # Load the model using the filtered arguments - self.classification_model = classification.load_model( - model_path=f"{self.model_dir}/{self.classification_model_path}", - model_class=self.classification_model_class, - model_args=filtered_model_args, - device=self.device, - ) - - # Segmentation model setup - self.segmentation_model_path = segmentation_model_path - self.segmentation_model_class = segmentation_model_class - self.segmentation_model_args = segmentation_model_args - self.segmentation_has_bg_color = "background_color" in segmentation_model_args - self.segmentation_model = segmentation.load_model( - model_path=f"{self.model_dir}/{self.segmentation_model_path}", - model_class=self.segmentation_model_class, - model_args=self.segmentation_model_args.get("model_config"), - device=self.device, - ) - - # Landmark detection model setup - self.landmark_detection_model_path = landmark_detection_model_path - self.landmark_detection_model_class = landmark_detection_model_class - self.landmark_detection_model_args = landmark_detection_model_args - self.landmark_detection_model = landmark.detection.load_model( - model_path=f"{self.model_dir}/{self.landmark_detection_model_path}", - model_class=self.landmark_detection_model_class, - device=self.device, - ) - - # Matting setup (optional, and always layered on top of segmentation) - self.do_matte = do_matte - self.matting_model_path = matting_model_path - self.matting_model_class = matting_model_class - self.matting_model_args = matting_model_args or {} - self.matting_model = None - - if self.do_matte: - # Matting refines a segmentation mask, so the pipeline cannot run it - # without a working segmentation stage. - if self.segmentation_model is None: - raise ValueError( - "`do_matte=True` requires segmentation, because the segmentation mask " - "supplies the trimap (ViTMatte) or guidance mask (Matting Anything). " - "Configure the segmentation model, or set `do_matte=False`." - ) - - preloaded = self.matting_model_args.get("model") - if preloaded is not None: - # Matting Anything is assembled by the caller because it pairs a - # decoder with an existing SAM instance. - self.matting_model = preloaded.to(self.device) - elif matting_model_class is not None and matting_model_path is not None: - self.matting_model = matting.load_model( - model_class=self.matting_model_class, - model_path=f"{self.model_dir}/{self.matting_model_path}", - model_args=self.matting_model_args.get("model_config"), - device=self.device, - ) - else: - missing = [] - if matting_model_class is None: - missing.append("`matting_model_class`") - if matting_model_path is None: - missing.append("`matting_model_path`") - raise ValueError( - f"`do_matte=True` requires a matting model, but {' and '.join(missing)} " - f"{'was' if len(missing) == 1 else 'were'} not provided. Either pass " - f"`matting_model_class` and `matting_model_path` (for ViTMatte), or pass " - f"an already constructed model as `matting_model_args={{'model': ...}}` " - f"(for Matting Anything)." - ) - - # ViTMatte consumes a stacked image+trimap tensor built by its processor, - # so a missing processor would only fail deep inside inference. - model_names = {c.__name__ for c in type(self.matting_model).__mro__} - is_mam = "MattingAnything" in model_names - if not is_mam and self.matting_model_args.get("processor") is None: - raise ValueError( - "`do_matte=True` with a trimap-based model such as ViTMatte requires a " - "processor. Pass it as " - "`matting_model_args={'processor': load_vitmatte_processor(...)}`." - ) - if is_mam and not self.matting_model_args.get("prompt"): - raise ValueError( - "`do_matte=True` with Matting Anything requires a prompt for its internal " - "SAM. Pass it as " - "`matting_model_args={'prompt': {'boxes': [[[x0, y0, x1, y1]]]}}`." - ) - - def summary(self): - """ - Prints a summary of the `tailor` agent's configuration, including directory paths, - defined classes, processing options (refine, derive, device), and loaded models. - """ - width = 80 - sep = "=" * width - - print(sep) - print("TAILOR AGENT SUMMARY".center(width)) - print(sep) - - # Directories - print("DIRECTORY PATHS".center(width, "-")) - print(f"{'Input directory:':25} {self.input_dir}") - print(f"{'Model directory:':25} {self.model_dir}") - print(f"{'Output directory:':25} {self.output_dir}") - print() - - # Classes - print("CLASSES".center(width, "-")) - print(f"{'Class Index':<11} | Class Name") - print(f"{'-'*11} | {'-'*66}") - for i, cls in enumerate(self.classes): - print(f"{i:<11} | {cls}") - print() - - # Flags - print("OPTIONS".center(width, "-")) - print(f"{'Do refine?:':25} {self.do_refine}") - print(f"{'Do derive?:':25} {self.do_derive}") - print(f"{'Do matte?:':25} {self.do_matte}") - print(f"{'Device:':25} {self.device}") - print() - - # Models - print("MODELS".center(width, "-")) - print( - f"{'Classification Model:':25} {self.classification_model_class.__name__}" - ) - print(f"{'Segmentation Model:':25} {self.segmentation_model_class.__name__}") - print(f"{' └─ Change BG color?:':25} {self.segmentation_has_bg_color}") - print( - f"{'Landmark Detection Model:':25} {self.landmark_detection_model_class.__class__.__name__}" - ) - if self.do_matte: - print(f"{'Matting Model:':25} {type(self.matting_model).__name__}") - matte_bg = self.matting_model_args.get("background_color") - print(f"{' └─ Composite BG color?:':25} {matte_bg is not None}") - print(sep) - - def classify(self, image: str, verbose=False): - """ - Classifies a single garment image using the configured classification model. - - Args: - image (str): The filename of the image to classify, located in `self.input_dir`. - verbose (bool): If True, prints detailed classification output. Defaults to False. - - Returns: - tuple: - - label (str): The predicted class label of the garment. - - probabilities (List[float]): A list of probabilities for each class. - """ - label, probablities = classification.predict( - model=self.classification_model, - image_path=f"{self.input_dir}/{image}", - classes=self.classes, - resize_dim=self.classification_model_args.get("resize_dim"), - normalize_mean=self.classification_model_args.get("normalize_mean"), - normalize_std=self.classification_model_args.get("normalize_std"), - device=self.device, - verbose=verbose, - ) - return label, probablities - - def segment(self, image: str): - """ - Segments a single garment image to extract its mask and optionally modifies the background color. - - This method acts as an intelligent router for your segmentation arguments. It automatically - filters out initialization keys (e.g., `model_config`) and post-processing keys - (e.g., `background_color`) from `self.segmentation_model_args`. The remaining arguments - (such as `processor` and `prompt` for SAM or `resize_dim` for standard models such as BiRefNet) - are dynamically passed into the extraction pipeline. - - For Segment Anything models the prompt is supplied via the `prompt` dictionary, which accepts - `"points"`, `"labels"`, `"boxes"`, and/or `"text"`. When a text prompt is used with SAM 1 or - SAM 2, also provide `grounding_model` and `grounding_processor` in the segmentation arguments, - because those families have no text encoder and need the phrase grounded into boxes first. - - Args: - image (str): The filename of the image to segment, located in `self.input_dir`. - - Raises: - ValueError: If a SAM model is configured without any prompt, or if a text prompt is used - with SAM 1 or SAM 2 without a grounding model. - - Returns: - tuple: - - original_img (np.ndarray): The original input image converted to a numpy array. - - mask (np.ndarray): The extracted binary segmentation mask as a numpy array. - - bg_modified_img (np.ndarray, optional): The image with the background color replaced. - This third element is only returned if - `background_color` is provided in the - segmentation arguments. - """ - # 1. Filter out initialization and post-processing arguments - extraction_kwargs = { - k: v for k, v in self.segmentation_model_args.items() - if k not in ["model_config", "background_color"] - } - - # 2. Extract using the unified function and unpacked kwargs - original_img, mask = segmentation.extract( - model=self.segmentation_model, - image_path=f"{self.input_dir}/{image}", - device=self.device, - **extraction_kwargs - ) - - # 3. Handle optional background color modification - background_color = self.segmentation_model_args.get("background_color") - - if background_color is None: - return original_img, mask - else: - bg_modified_img = segmentation.change_background_color( - image_np=original_img, mask_np=mask, background_color=background_color - ) - return original_img, mask, bg_modified_img - - def matte(self, image: str, mask: np.ndarray): - """ - Refines a segmentation mask into a soft alpha matte for a single image. - - Matting in the pipeline is intentionally built on top of segmentation rather than - run standalone: the segmentation mask is what supplies the trimap for ViTMatte or - the guidance mask for Matting Anything. Calling this without a mask therefore - raises, which is why `do_matte=True` forces the segmentation stage to run. - - Standalone matting has no such requirement; call `garmentiq.matting.matte` directly - with whatever image and trimap you already have. - - Args: - image (str): The filename of the image to matte, located in `self.input_dir`. - mask (numpy.ndarray): The segmentation mask produced for the same image. - - Raises: - ValueError: If matting is not configured, or if `mask` is None. - - Returns: - numpy.ndarray: The alpha matte as `uint8` in `[0, 255]`, matching the image size. - """ - if not self.do_matte or self.matting_model is None: - raise ValueError( - "Matting is not configured on this tailor agent. Construct it with " - "`do_matte=True` and a matting model." - ) - if mask is None: - raise ValueError( - "Matting in the tailor pipeline requires a segmentation mask. Run the " - "segmentation stage first, then pass its mask here." - ) - - _, alpha = matting.matte( - model=self.matting_model, - image_path=f"{self.input_dir}/{image}", - processor=self.matting_model_args.get("processor"), - mask=mask, - prompt=self.matting_model_args.get("prompt"), - trimap_args=self.matting_model_args.get("trimap_args"), - device=self.device, - ) - return alpha - - def detect(self, class_name: str, image: Union[str, np.ndarray]): - """ - Detects predefined landmarks on a garment image based on its classified class. - - Args: - class_name (str): The classified name of the garment. - image (Union[str, np.ndarray]): The path to the image file or a NumPy array of the image. - - Returns: - tuple: - - coords (np.array): Detected landmark coordinates. - - maxval (np.array): Confidence scores for detected landmarks. - - detection_dict (dict): A dictionary containing detailed landmark detection data. - """ - if isinstance(image, str): - image = f"{self.input_dir}/{image}" - - coords, maxval, detection_dict = landmark.detect( - class_name=class_name, - class_dict=self.class_dict, - image_path=image, - model=self.landmark_detection_model, - scale_std=self.landmark_detection_model_args.get("scale_std"), - resize_dim=self.landmark_detection_model_args.get("resize_dim"), - normalize_mean=self.landmark_detection_model_args.get("normalize_mean"), - normalize_std=self.landmark_detection_model_args.get("normalize_std"), - device=self.device, - ) - return coords, maxval, detection_dict - - def derive( - self, - class_name: str, - detection_dict: dict, - derivation_dict: dict, - landmark_coords: np.array, - np_mask: np.array, - ): - """ - Derives non-predefined landmark coordinates based on predefined landmarks and a mask. - - Args: - class_name (str): The name of the garment class. - detection_dict (dict): The dictionary containing detected landmarks. - derivation_dict (dict): The dictionary defining derivation rules. - landmark_coords (np.array): NumPy array of initial landmark coordinates. - np_mask (np.array): NumPy array of the segmentation mask. - - Returns: - tuple: - - derived_coords (dict): A dictionary of the newly derived landmark coordinates. - - updated_detection_dict (dict): The detection dictionary updated with derived landmarks. - """ - derived_coords, updated_detection_dict = landmark.derive( - class_name=class_name, - detection_dict=detection_dict, - derivation_dict=derivation_dict, - landmark_coords=landmark_coords, - np_mask=np_mask, - ) - return derived_coords, updated_detection_dict - - def refine( - self, - class_name: str, - detection_np: np.array, - detection_conf: np.array, - detection_dict: dict, - mask: np.array, - window_size: int = 5, - ksize: tuple = (11, 11), - sigmaX: float = 0.0, - ): - """ - Refines detected landmark coordinates using a blurred segmentation mask. - - Args: - class_name (str): The name of the garment class. - detection_np (np.array): NumPy array of initial landmark predictions. - detection_conf (np.array): NumPy array of confidence scores for each predicted landmark. - detection_dict (dict): Dictionary containing landmark data for each class. - mask (np.array): Grayscale mask image used to guide refinement. - window_size (int, optional): Size of the window used in the refinement algorithm. Defaults to 5. - ksize (tuple, optional): Kernel size for Gaussian blur. Must be odd integers. Defaults to (11, 11). - sigmaX (float, optional): Gaussian kernel standard deviation in the X direction. Defaults to 0.0. - - Returns: - tuple: - - refined_detection_np (np.array): Array of the same shape as `detection_np` with refined coordinates. - - detection_dict (dict): Updated detection dictionary with refined landmark coordinates. - """ - if self.refinement_args: - if self.refinement_args.get("window_size") is not None: - window_size = self.refinement_args["window_size"] - if self.refinement_args.get("ksize") is not None: - ksize = self.refinement_args["ksize"] - if self.refinement_args.get("sigmaX") is not None: - sigmaX = self.refinement_args["sigmaX"] - - refined_detection_np, refined_detection_dict = landmark.refine( - class_name=class_name, - detection_np=detection_np, - detection_conf=detection_conf, - detection_dict=detection_dict, - mask=mask, - window_size=window_size, - ksize=ksize, - sigmaX=sigmaX, - ) - - return refined_detection_np, refined_detection_dict - - def measure( - self, - save_segmentation_image: bool = False, - save_measurement_image: bool = False, - save_matting_image: bool = False, - ): - """ - Executes the full garment measurement pipeline for all images in the input directory. - - This method processes each image through a multi-stage pipeline that includes garment classification, - segmentation, landmark detection, optional refinement, and measurement derivation. During classification, - the system identifies the type of garment (e.g., shirt, dress, pants). Segmentation follows, producing - binary or instance masks that separate the garment from the background. When matting is - enabled, the mask is then refined into a soft alpha matte, and the resulting alpha-composited - image replaces the hard background-modified image as the input to landmark detection. - Landmark detection is then - performed to locate anatomical or garment-specific keypoints such as shoulders or waist positions. If - enabled, an optional refinement step applies post-processing or model-based corrections to improve the - accuracy of detected keypoints. Finally, the system calculates key garment dimensions - such as chest width, - waist width, and full length - based on the detected landmarks. In addition to this processing pipeline, - the method also manages data and visual output exports. For each input image, a cleaned JSON file is - generated containing the predicted garment class, landmark coordinates, and the resulting measurements. - Optionally, visual outputs such as segmentation masks and images annotated with landmarks and measurements - can be saved to assist in inspection or debugging. - - Args: - save_segmentation_image (bool): If True, saves segmentation masks and background-modified images. - Defaults to False. - save_measurement_image (bool): If True, saves images overlaid with detected landmarks and measurements. - Defaults to False. - save_matting_image (bool): If True, saves the alpha mattes produced by the matting stage, - and the alpha-composited images when a `background_color` is - set in `matting_model_args`. Only has an effect when the agent - was constructed with `do_matte=True`. Defaults to False. - - Raises: - ValueError: If `save_matting_image` is True but the agent was not constructed with - `do_matte=True`, or if the matting stage cannot find a segmentation mask. - - Returns: - tuple: - - metadata (pd.DataFrame): A DataFrame containing metadata for each processed image, such as: - - Original image path - - Paths to any saved segmentation or annotated images - - Class and measurement results - - outputs (dict): A dictionary mapping image filenames to their detailed processing results, including: - - Predicted class - - Detected landmarks with coordinates and confidence scores - - Calculated measurements - - File paths to any saved images (if applicable) - - Example of exported JSON: - ``` - { - "cloth_3.jpg": { - "class": "vest dress", - "landmarks": { - "10": { - "conf": 0.7269417643547058, - "x": 611.0, - "y": 861.0 - }, - "16": { - "conf": 0.6769524812698364, - "x": 1226.0, - "y": 838.0 - }, - "17": { - "conf": 0.7472652196884155, - "x": 1213.0, - "y": 726.0 - }, - "18": { - "conf": 0.7360446453094482, - "x": 1238.0, - "y": 613.0 - }, - "2": { - "conf": 0.9256571531295776, - "x": 703.0, - "y": 264.0 - }, - "20": { - "x": 700.936, - "y": 2070.0 - }, - "8": { - "conf": 0.7129100561141968, - "x": 563.0, - "y": 613.0 - }, - "9": { - "conf": 0.8203497529029846, - "x": 598.0, - "y": 726.0 - } - }, - "measurements": { - "chest": { - "distance": 675.0, - "landmarks": { - "end": "18", - "start": "8" - } - }, - "full length": { - "distance": 1806.0011794281863, - "landmarks": { - "end": "20", - "start": "2" - } - }, - "hips": { - "distance": 615.4299310238331, - "landmarks": { - "end": "16", - "start": "10" - } - }, - "waist": { - "distance": 615.0, - "landmarks": { - "end": "17", - "start": "9" - } - } - } - } - } - ``` - """ - # Some helper variables - use_bg_color = self.segmentation_model_args.get("background_color") is not None - use_matte_bg = ( - self.do_matte - and self.matting_model_args.get("background_color") is not None - ) - outputs = {} - - if save_matting_image and not self.do_matte: - raise ValueError( - "`save_matting_image=True` but this tailor agent was not constructed with " - "`do_matte=True`, so there is no matting stage to save output from." - ) - - # Step 1: Create the output directory - Path(self.output_dir).mkdir(parents=True, exist_ok=True) - Path(f"{self.output_dir}/measurement_json").mkdir(parents=True, exist_ok=True) - - if save_segmentation_image and ( - use_bg_color or self.do_derive or self.do_refine - ): - Path(f"{self.output_dir}/mask_image").mkdir(parents=True, exist_ok=True) - if use_bg_color: - Path(f"{self.output_dir}/bg_modified_image").mkdir( - parents=True, exist_ok=True - ) - - if save_measurement_image: - Path(f"{self.output_dir}/measurement_image").mkdir( - parents=True, exist_ok=True - ) - - if save_matting_image and self.do_matte: - Path(f"{self.output_dir}/matte_image").mkdir(parents=True, exist_ok=True) - if use_matte_bg: - Path(f"{self.output_dir}/matte_composite_image").mkdir( - parents=True, exist_ok=True - ) - - # Step 2: Collect image filenames from input_dir - image_extensions = ["*.jpg", "*.jpeg", "*.png", "*.bmp", "*.tiff"] - input_path = Path(self.input_dir) - - image_files = [] - for ext in image_extensions: - image_files.extend(input_path.glob(ext)) - - # Step 3: Determine column structure - columns = [ - "filename", - "class", - "mask_image" if use_bg_color or self.do_derive or self.do_refine else None, - "bg_modified_image" if use_bg_color else None, - "matte_image" if save_matting_image and self.do_matte else None, - "matte_composite_image" - if save_matting_image and use_matte_bg - else None, - "measurement_image", - "measurement_json", - ] - columns = [col for col in columns if col is not None] - - metadata = pd.DataFrame(columns=columns) - metadata["filename"] = [img.name for img in image_files] - - # Step 4: Print start message and information - print(f"Start measuring {len(metadata['filename'])} garment images ...") - - # Build the step list dynamically so every enabled stage is reported. - steps = ["classification"] - if use_bg_color or self.do_derive or self.do_refine or self.do_matte: - steps.append("segmentation") - if self.do_matte: - steps.append("matting") - steps.append("landmark detection") - if self.do_refine: - steps.append("landmark refinement") - if self.do_derive: - steps.append("landmark derivation") - - if len(steps) == 1: - listed = steps[0] - elif len(steps) == 2: - listed = f"{steps[0]} and {steps[1]}" - else: - listed = ", ".join(steps[:-1]) + f", and {steps[-1]}" - message = f"There are {len(steps)} measurement steps: {listed}." - - print(textwrap.fill(message, width=80)) - - # Step 5: Classification - for idx, image in tqdm( - enumerate(metadata["filename"]), total=len(metadata), desc="Classification" - ): - label, _ = self.classify(image=image, verbose=False) - metadata.at[idx, "class"] = label - outputs[image] = {} - - # Step 6: Segmentation - # Matting consumes the segmentation mask, so enabling it forces this stage. - if use_bg_color or self.do_derive or self.do_refine or self.do_matte: - for idx, image in tqdm( - enumerate(metadata["filename"]), - total=len(metadata), - desc="Segmentation", - ): - if use_bg_color: - original_img, mask, bg_modified_image = self.segment(image=image) - outputs[image] = { - "mask": mask, - "bg_modified_image": bg_modified_image, - } - else: - original_img, mask = self.segment(image=image) - outputs[image] = { - "mask": mask, - } - - # Step 6b: Matting (always after segmentation, using its mask as guidance) - if self.do_matte: - matte_bg_color = self.matting_model_args.get("background_color") - # Landmark detection needs an RGB image, so the alpha matte is always - # composited onto some background. The colour is chosen in order of - # specificity: the matting colour, then the segmentation colour, then a - # neutral default. Compositing always happens when matting is enabled, so - # that the matte genuinely drives detection even when neither stage was - # asked to replace the background in its saved output. - detect_bg_color = matte_bg_color - if detect_bg_color is None: - detect_bg_color = self.segmentation_model_args.get("background_color") - if detect_bg_color is None: - detect_bg_color = DEFAULT_MATTE_DETECTION_BACKGROUND - - for idx, image in tqdm( - enumerate(metadata["filename"]), - total=len(metadata), - desc="Matting", - ): - if outputs[image].get("mask") is None: - raise ValueError( - f"Matting requires a segmentation mask, but none was produced for " - f"{image!r}. The segmentation stage must run before matting." - ) - alpha = self.matte(image=image, mask=outputs[image]["mask"]) - outputs[image]["alpha"] = alpha - - composited = matting.composite( - image_np=np.array( - Image.open(f"{self.input_dir}/{image}").convert("RGB") - ), - alpha_np=alpha, - background_color=detect_bg_color, - ) - outputs[image]["matte_detect_image"] = composited - # The composited image is only offered as a saved output when the user - # explicitly asked for a matting background colour, keeping that output - # optional in the same way segmentation's background replacement is. - if matte_bg_color is not None: - outputs[image]["matte_composite"] = composited - - # Step 7: Landmark detection - # Detection runs on a background-replaced image when one was requested, because - # a clean background helps the pose model. When matting is enabled its softer, - # more accurate composite is used in place of the hard segmentation composite. - for idx, image in tqdm( - enumerate(metadata["filename"]), - total=len(metadata), - desc="Landmark detection", - ): - label = metadata.loc[metadata["filename"] == image, "class"].values[0] - - if self.do_matte and outputs[image].get("matte_detect_image") is not None: - detect_input = outputs[image]["matte_detect_image"] - elif use_bg_color: - detect_input = outputs[image]["bg_modified_image"] - else: - detect_input = image - - coords, maxvals, detection_dict = self.detect( - class_name=label, image=detect_input - ) - outputs[image]["detection_dict"] = detection_dict - if self.do_derive or self.do_refine: - outputs[image]["coords"] = coords - outputs[image]["maxvals"] = maxvals - - # Step 8: Landmark refinement - if self.do_refine: - for idx, image in tqdm( - enumerate(metadata["filename"]), - total=len(metadata), - desc="Landmark refinement", - ): - label = metadata.loc[metadata["filename"] == image, "class"].values[0] - updated_coords, updated_detection_dict = self.refine( - class_name=label, - detection_np=outputs[image]["coords"], - detection_conf=outputs[image]["maxvals"], - detection_dict=outputs[image]["detection_dict"], - mask=outputs[image]["mask"], - ) - outputs[image]["coords"] = updated_coords - outputs[image]["detection_dict"] = updated_detection_dict - - # Step 9: Landmark derivation - if self.do_derive: - for idx, image in tqdm( - enumerate(metadata["filename"]), - total=len(metadata), - desc="Landmark derivation", - ): - label = metadata.loc[metadata["filename"] == image, "class"].values[0] - derived_coords, updated_detection_dict = self.derive( - class_name=label, - detection_dict=outputs[image]["detection_dict"], - derivation_dict=self.derivation_dict, - landmark_coords=outputs[image]["coords"], - np_mask=outputs[image]["mask"], - ) - outputs[image]["detection_dict"] = updated_detection_dict - - # Step 10: Save segmentation image - if save_segmentation_image and ( - use_bg_color or self.do_derive or self.do_refine - ): - for idx, image in tqdm( - enumerate(metadata["filename"]), - total=len(metadata), - desc="Save segmentation image", - ): - transformed_name = os.path.splitext(image)[0] - Image.fromarray(outputs[image]["mask"]).save( - f"{self.output_dir}/mask_image/{transformed_name}_mask.png" - ) - metadata.at[ - idx, "mask_image" - ] = f"{self.output_dir}/mask_image/{transformed_name}_mask.png" - if use_bg_color: - Image.fromarray(outputs[image]["bg_modified_image"]).save( - f"{self.output_dir}/bg_modified_image/{transformed_name}_bg_modified.png" - ) - metadata.at[ - idx, "bg_modified_image" - ] = f"{self.output_dir}/bg_modified_image/{transformed_name}_bg_modified.png" - - # Step 10b: Save matting image - if save_matting_image and self.do_matte: - for idx, image in tqdm( - enumerate(metadata["filename"]), - total=len(metadata), - desc="Save matting image", - ): - transformed_name = os.path.splitext(image)[0] - alpha_path = ( - f"{self.output_dir}/matte_image/{transformed_name}_matte.png" - ) - Image.fromarray(outputs[image]["alpha"]).save(alpha_path) - metadata.at[idx, "matte_image"] = alpha_path - - if use_matte_bg: - composite_path = ( - f"{self.output_dir}/matte_composite_image/" - f"{transformed_name}_matte_composite.png" - ) - Image.fromarray(outputs[image]["matte_composite"]).save( - composite_path - ) - metadata.at[idx, "matte_composite_image"] = composite_path - - # Step 11: Save measurement image - if save_measurement_image: - for idx, image in tqdm( - enumerate(metadata["filename"]), - total=len(metadata), - desc="Save measurement image", - ): - label = metadata.loc[metadata["filename"] == image, "class"].values[0] - transformed_name = os.path.splitext(image)[0] - - image_to_save = Image.open(f"{self.input_dir}/{image}").convert("RGB") - draw = ImageDraw.Draw(image_to_save) - font = ImageFont.load_default() - landmarks = outputs[image]["detection_dict"][label]["landmarks"] - - for lm_id, lm_data in landmarks.items(): - x, y = lm_data["x"], lm_data["y"] - radius = 5 - draw.ellipse( - (x - radius, y - radius, x + radius, y + radius), fill="green" - ) - draw.text((x + 8, y - 8), lm_id, fill="green", font=font) - - image_to_save.save( - f"{self.output_dir}/measurement_image/{transformed_name}_measurement.png" - ) - metadata.at[ - idx, "measurement_image" - ] = f"{self.output_dir}/measurement_image/{transformed_name}_measurement.png" - - # Step 12: Save measurement json - for idx, image in tqdm( - enumerate(metadata["filename"]), - total=len(metadata), - desc="Save measurement json", - ): - label = metadata.loc[metadata["filename"] == image, "class"].values[0] - transformed_name = os.path.splitext(image)[0] - - # Clean the detection dictionary - final_dict = utils.clean_detection_dict( - class_name=label, - image_name=image, - detection_dict=outputs[image]["detection_dict"], - ) - - # Export JSON - utils.export_dict_to_json( - data=final_dict, - filename=f"{self.output_dir}/measurement_json/{transformed_name}_measurement.json", - ) - - metadata.at[ - idx, "measurement_json" - ] = f"{self.output_dir}/measurement_json/{transformed_name}_measurement.json" - - # Step 13: Save metadata as a CSV - metadata.to_csv(f"{self.output_dir}/metadata.csv", index=False) - - return metadata, outputs diff --git a/src/garmentiq/utils/__init__.py b/src/garmentiq/utils/__init__.py deleted file mode 100644 index 8dbb586..0000000 --- a/src/garmentiq/utils/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# garmentiq/utils/__init__.py -"""Shared helpers used across the GarmentIQ modules. - -Small utilities that several modules depend on: archive handling and dataset -validation, device resolution and accelerator cache management, checkpoint loading -with a mismatch guard, measurement distance computation, and JSON export. -""" -from .unzip import unzip -from .check_unzipped_dir import check_unzipped_dir -from .check_filenames_metadata import check_filenames_metadata -from .validate_garment_class_dict import validate_garment_class_dict -from .compute_measurement_distances import compute_measurement_distances -from .export_dict_to_json import export_dict_to_json -from .clean_detection_dict import clean_detection_dict -from .device import resolve_device, empty_cache, inputs_to_device -from .checkpoint import load_state_dict_checked diff --git a/src/garmentiq/utils/check_filenames_metadata.py b/src/garmentiq/utils/check_filenames_metadata.py deleted file mode 100644 index 49bdd7c..0000000 --- a/src/garmentiq/utils/check_filenames_metadata.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Checking that dataset filenames agree with the metadata table.""" -import os -import shutil - - -def check_filenames_metadata(output_dir, file_dir, metadata_df): - """ - Validates that the filenames in a directory match those listed in a metadata DataFrame. - - This function compares the filenames found in the specified directory with the filenames - listed in the provided metadata DataFrame. If the filenames do not match exactly (after sorting), - the output directory is deleted and a `ValueError` is raised. If they match, a confirmation - message is printed. - - Args: - output_dir (str): Path to the output directory that will be deleted if filenames do not match. - file_dir (str): Path to the directory containing the files to check. - metadata_df (pandas.DataFrame): A pandas DataFrame containing a 'filename' column with expected filenames. - - Raises: - ValueError: If the filenames in the directory do not match those in the metadata. - - Returns: - None - """ - file_list = os.listdir(file_dir) - metadata_filenames = metadata_df["filename"].tolist() - file_list.sort() - metadata_filenames.sort() - - if file_list != metadata_filenames: - shutil.rmtree(output_dir) - raise ValueError( - f"Mismatch between directory filenames and metadata filenames. Maybe try again." - ) - else: - print(f"\nAll filenames in {file_dir} match the metadata.\n") diff --git a/src/garmentiq/utils/check_unzipped_dir.py b/src/garmentiq/utils/check_unzipped_dir.py deleted file mode 100644 index 2fdada8..0000000 --- a/src/garmentiq/utils/check_unzipped_dir.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Validating the structure of an extracted dataset.""" -import os - - -def check_unzipped_dir(folder): - """ - Verifies the structure and contents of an unzipped dataset directory. - - This function checks that the specified folder contains: - - A subdirectory named 'images' with at least one `.jpg`, `.png`, `.jpeg` file. - - A file named 'metadata.csv'. - - If any of these components are missing, a `FileNotFoundError` is raised. - - Args: - folder (str): Path to the root folder of the unzipped dataset. - - Raises: - FileNotFoundError: If the 'images' folder is missing, - if no `.jpg`, `.png`, `.jpeg` files are found in the 'images' folder, - or if 'metadata.csv' is missing. - - Returns: - None - """ - image_dir = os.path.join(folder, "images") - metadata_path = os.path.join(folder, "metadata.csv") - - if not os.path.isdir(image_dir): - raise FileNotFoundError(f"Missing 'images' folder in: {folder}") - - jpg_files = [f for f in os.listdir(image_dir) if f.lower().endswith(".jpg") or f.lower().endswith(".png") or f.lower().endswith(".jpeg")] - if not jpg_files: - raise FileNotFoundError(f"No .jpg, .png, or .jpeg file found in: {image_dir}") - - if not os.path.isfile(metadata_path): - raise FileNotFoundError(f"Missing 'metadata.csv' in: {folder}") diff --git a/src/garmentiq/utils/checkpoint.py b/src/garmentiq/utils/checkpoint.py deleted file mode 100644 index 0e53eeb..0000000 --- a/src/garmentiq/utils/checkpoint.py +++ /dev/null @@ -1,64 +0,0 @@ -"""Checkpoint loading with a mismatch guard. - -Weights are loaded leniently so that partial checkpoints keep working, but a checkpoint -that does not match the model class would otherwise load nothing and leave a silently -random model. This module warns when few or none of the expected tensors are found. -""" -import warnings - - -def load_state_dict_checked(model, state_dict, model_path: str = ""): - """ - Loads weights into a model and warns when the checkpoint does not match it. - - GarmentIQ loads every checkpoint with `strict=False`, because legitimate checkpoints - routinely carry extra tensors (a bundled tracker, an optimiser state, a frozen - backbone) or omit a head that is re-initialised. The cost of that leniency is that a - checkpoint meant for a different architecture loads *nothing* and silently yields a - randomly initialised model, which then produces plausible-looking but meaningless - output. - - This helper keeps the lenient behaviour but makes that failure visible: if no tensor - in the checkpoint matches the model, or almost none do, a warning is raised naming the - file. It never blocks a load, so existing code keeps working. - - Args: - model (torch.nn.Module): The model to load weights into. - state_dict (dict): The checkpoint tensors, already stripped of any prefixes. - model_path (str, optional): Path to the checkpoint, used in the warning message. - Default is "". - - Returns: - torch.nn.modules.module._IncompatibleKeys: The result of `load_state_dict`, listing - missing and unexpected keys. - """ - expected = set(model.state_dict().keys()) - provided = set(state_dict.keys()) - matched = expected & provided - - where = f" from {model_path!r}" if model_path else "" - - if expected and not matched: - warnings.warn( - f"No weights{where} matched {type(model).__name__}: none of the " - f"{len(provided)} tensor(s) in the checkpoint correspond to the " - f"{len(expected)} the model expects. The model is left randomly initialised " - f"and its output will be meaningless. Check that the checkpoint matches the " - f"model class.", - RuntimeWarning, - stacklevel=3, - ) - elif expected and len(matched) < 0.5 * len(expected): - warnings.warn( - f"Only {len(matched)} of {len(expected)} weights expected by " - f"{type(model).__name__} were found{where}. The remaining " - f"{len(expected) - len(matched)} are randomly initialised, which usually " - f"means the checkpoint does not match the model class.", - RuntimeWarning, - stacklevel=3, - ) - - return model.load_state_dict(state_dict, strict=False) - - -__all__ = ["load_state_dict_checked"] diff --git a/src/garmentiq/utils/clean_detection_dict.py b/src/garmentiq/utils/clean_detection_dict.py deleted file mode 100644 index 5f3fddf..0000000 --- a/src/garmentiq/utils/clean_detection_dict.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Trimming a detection record down to its reportable fields.""" -import os -from garmentiq.utils import compute_measurement_distances - - -def clean_detection_dict(class_name: str, image_name: str, detection_dict: dict): - """ - Cleans and reformats a detection dictionary, computes measurement distances, - and nests the cleaned data under the image name, with class name stored inside. - - Args: - class_name (str): The name of the garment class. - image_name (str): The original filename of the image. - detection_dict (dict): The raw detection dictionary containing landmark and measurement data. - - Returns: - dict: A dictionary structured as {image_name: {..., "class": class_name}}. - """ - - transformed_name = os.path.splitext(image_name)[0] - - # Compute distances and get a fresh copy of detection_dict - _, clean_dict = compute_measurement_distances(detection_dict) - - # Safely extract and clean the content under the class_name - original_data = clean_dict.get(class_name, {}) - - # Clean landmarks - if "landmarks" in original_data: - for lm_id in list(original_data["landmarks"].keys()): - lm = original_data["landmarks"][lm_id] - original_data["landmarks"][lm_id] = { - k: lm[k] for k in ("x", "y", "conf") if k in lm - } - - # Clean measurements - if "measurements" in original_data: - for m_id in list(original_data["measurements"].keys()): - m = original_data["measurements"][m_id] - original_data["measurements"][m_id] = { - k: m[k] for k in ("landmarks", "distance") if k in m - } - - # Insert the class name as metadata - original_data["class"] = class_name - - # Return dict keyed by image name - final_dict = {image_name: original_data} - return final_dict diff --git a/src/garmentiq/utils/compute_measurement_distances.py b/src/garmentiq/utils/compute_measurement_distances.py deleted file mode 100644 index 84ddfbf..0000000 --- a/src/garmentiq/utils/compute_measurement_distances.py +++ /dev/null @@ -1,50 +0,0 @@ -"""Computing measurement distances between landmark pairs.""" -import math - - -def compute_measurement_distances(garment_dict): - """ - Computes Euclidean distances between specified landmark points for each garment - in a dictionary and updates the dictionary with these distances. - - This function iterates through the `garment_dict`, accesses the 'landmarks' - and 'measurements' for each garment, calculates the Euclidean distance - between the 'start' and 'end' landmarks defined for each measurement, - and stores the rounded distance in both a new dictionary and within the - original `garment_dict`. - - Args: - garment_dict (dict): A dictionary where keys are garment names and values - contain 'landmarks' (with 'x' and 'y' coordinates) - and 'measurements' (with 'start' and 'end' landmark IDs). - - Returns: - tuple: - - distances (dict): A dictionary mapping measurement names to their computed distances. - - garment_dict (dict): The original `garment_dict` updated with a 'distance' field - for each measurement. - """ - - def euclidean(p1, p2): - return math.sqrt((p1["x"] - p2["x"]) ** 2 + (p1["y"] - p2["y"]) ** 2) - - distances = {} - for garment_name, garment_data in garment_dict.items(): - landmarks = garment_data["landmarks"] - for measurement_name, measurement_data in garment_data["measurements"].items(): - start_id = measurement_data["landmarks"]["start"] - end_id = measurement_data["landmarks"]["end"] - - point1 = landmarks[start_id] - point2 = landmarks[end_id] - distance = euclidean(point1, point2) - - # Store the distance in the return dict - distances[measurement_name] = round(distance, 16) - - # Update the original dictionary - garment_dict[garment_name]["measurements"][measurement_name][ - "distance" - ] = round(distance, 16) - - return distances, garment_dict diff --git a/src/garmentiq/utils/device.py b/src/garmentiq/utils/device.py deleted file mode 100644 index 5e7c136..0000000 --- a/src/garmentiq/utils/device.py +++ /dev/null @@ -1,134 +0,0 @@ -"""Device resolution and accelerator memory management. - -GarmentIQ runs on CPU by default and never auto-detects an accelerator. These helpers -validate a requested device, fail fast with an actionable message when it is -unavailable, move processor output onto it, and release its cached memory afterwards. -""" -import torch -from typing import Union - -DeviceLike = Union[str, torch.device, None] - - -def resolve_device(device: DeviceLike = "cpu") -> torch.device: - """ - Resolves and validates a user supplied device specification. - - GarmentIQ runs on CPU by default. Hardware acceleration is opt-in: the caller - explicitly requests it by passing `device="cuda"`, `device="cuda:1"`, or - `device="mps"` (Apple Silicon). This function normalises the request into a - `torch.device` and fails fast with an actionable message when the requested - backend is unavailable, instead of silently degrading to CPU. - - Args: - device (Union[str, torch.device], optional): The requested device, e.g. `"cpu"`, - `"cuda"`, `"cuda:0"`, or `"mps"`. - `None` is treated as `"cpu"`. - Default is `"cpu"`. - - Raises: - ValueError: If `device` is not a valid device specification, or if the requested - accelerator (CUDA or MPS) is not available on the current machine. - - Returns: - torch.device: The validated device to run computation on. - """ - if device is None: - return torch.device("cpu") - - if isinstance(device, torch.device): - resolved = device - else: - try: - resolved = torch.device(device) - except (RuntimeError, TypeError, ValueError) as e: - raise ValueError( - f"Invalid device specification {device!r}. Expected values such as " - f'"cpu", "cuda", "cuda:0", or "mps". Original error: {e}' - ) - - if resolved.type == "cuda" and not torch.cuda.is_available(): - raise ValueError( - f"Requested device {str(resolved)!r} but CUDA is not available in this " - f"PyTorch installation. Use device='cpu', or device='mps' on Apple Silicon." - ) - - if resolved.type == "mps" and not ( - hasattr(torch.backends, "mps") and torch.backends.mps.is_available() - ): - raise ValueError( - f"Requested device {str(resolved)!r} but the MPS backend is not available. " - f"MPS requires macOS on Apple Silicon with a compatible PyTorch build. " - f"Use device='cpu' instead." - ) - - return resolved - - -def inputs_to_device(inputs, device: DeviceLike = "cpu"): - """ - Moves a processor's batched output onto `device`, narrowing dtypes MPS cannot hold. - - Hugging Face image processors emit some tensors as float64 — SAM's `input_points` - and `input_boxes` are built from plain Python floats, for example. The MPS backend - has no float64 support at all, so moving such a batch onto an Apple Silicon GPU - raises `TypeError: Cannot convert a MPS Tensor to float64 dtype`. Narrowing those - tensors to float32 is lossless in practice, because they carry pixel coordinates - rather than values that need double precision. - - The narrowing is applied **only** when the target is MPS. CPU and CUDA keep the - processor's original dtypes, so their numerical results are unchanged. - - Args: - inputs (BatchFeature | BatchEncoding | dict): The processor output to move. - device (Union[str, torch.device], optional): The device to move onto, e.g. - `"cpu"`, `"cuda"`, or `"mps"`. - Default is `"cpu"`. - - Raises: - ValueError: If the requested `device` is invalid or unavailable on this machine. - - Returns: - The same mapping, with its tensors placed on `device`. - """ - resolved = resolve_device(device) - - if resolved.type == "mps": - for key, value in list(inputs.items()): - if isinstance(value, torch.Tensor) and value.dtype == torch.float64: - inputs[key] = value.to(torch.float32) - - return inputs.to(resolved) - - -def empty_cache(device: DeviceLike = "cpu") -> None: - """ - Releases cached accelerator memory held by the allocator after inference or training. - - PyTorch caching allocators keep freed blocks reserved for reuse, which inflates - reported memory usage and can starve other processes on the same accelerator. - This helper clears that cache for the backend actually in use, and is a no-op on - CPU (where no such cache exists) and for accelerators that are not available. - - Args: - device (Union[str, torch.device], optional): The device whose cache should be - released. Default is `"cpu"`. - - Returns: - None - """ - if device is None: - return - - if isinstance(device, torch.device): - resolved = device - else: - try: - resolved = torch.device(device) - except (RuntimeError, TypeError, ValueError): - return - - if resolved.type == "cuda" and torch.cuda.is_available(): - torch.cuda.empty_cache() - elif resolved.type == "mps" and hasattr(torch, "mps"): - torch.mps.empty_cache() diff --git a/src/garmentiq/utils/export_dict_to_json.py b/src/garmentiq/utils/export_dict_to_json.py deleted file mode 100644 index d8333c3..0000000 --- a/src/garmentiq/utils/export_dict_to_json.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Writing a measurement result to a JSON file.""" -import json - - -def export_dict_to_json(data: dict, filename: str): - """ - Export a dictionary to a JSON file. - - Args: - data (dict): The dictionary to export. - filename (str): The path to the JSON file (e.g., "output.json"). - """ - try: - with open(filename, "w", encoding="utf-8") as f: - json.dump(data, f, indent=2, ensure_ascii=False) - except Exception as e: - print(f"Failed to export dictionary: {e}") diff --git a/src/garmentiq/utils/unzip.py b/src/garmentiq/utils/unzip.py deleted file mode 100644 index 1f98214..0000000 --- a/src/garmentiq/utils/unzip.py +++ /dev/null @@ -1,23 +0,0 @@ -"""Extracting a dataset archive.""" -import zipfile -from tqdm.auto import tqdm - - -def unzip(zip_path, extract_to="."): - """ - Extracts the contents of a ZIP file with a progress bar. - - This function unzips the contents of the specified ZIP file to the given directory. - A progress bar is displayed during the extraction using `tqdm`. - - Args: - zip_path (str): Path to the ZIP file to be extracted. - extract_to (str): Destination directory where the contents will be extracted. Defaults to the current directory. - - Returns: - None - """ - with zipfile.ZipFile(zip_path, "r") as zip_ref: - members = zip_ref.infolist() - for member in tqdm(members, desc="Extracting"): - zip_ref.extract(member, extract_to) diff --git a/src/garmentiq/utils/validate_garment_class_dict.py b/src/garmentiq/utils/validate_garment_class_dict.py deleted file mode 100644 index 4162fc3..0000000 --- a/src/garmentiq/utils/validate_garment_class_dict.py +++ /dev/null @@ -1,51 +0,0 @@ -"""Validating a garment class dictionary.""" -def validate_garment_class_dict(class_dict: dict) -> bool: - """ - Validates the structure and content of a garment class dictionary. - - Ensures that the dictionary adheres to the expected format, including the presence - of required keys ("num_predefined_points", "index_range", "instruction") and - correct data types and logical consistency for their values. - - Args: - class_dict (dict): The dictionary to be validated. - - Returns: - bool: True if the dictionary is valid, False otherwise. - """ - required_keys = {"num_predefined_points", "index_range", "instruction"} - - if not isinstance(class_dict, dict): - return False - - for class_name, class_info in class_dict.items(): - if not isinstance(class_name, str): - return False - if not isinstance(class_info, dict): - return False - if not required_keys.issubset(class_info.keys()): - return False - - num_points = class_info["num_predefined_points"] - index_range = class_info["index_range"] - instruction = class_info["instruction"] - - # Check types - if not isinstance(num_points, int): - return False - if ( - not isinstance(index_range, tuple) - or len(index_range) != 2 - or not all(isinstance(i, int) for i in index_range) - ): - return False - - # Check logical consistency - if index_range[1] - index_range[0] != num_points: - return False - - # Validate instruction field (must be a string ending with .json) - if not (isinstance(instruction, str) and instruction.endswith(".json")): - return False - - return True diff --git a/src/requirements.txt b/src/requirements.txt deleted file mode 100644 index 93e57ae..0000000 --- a/src/requirements.txt +++ /dev/null @@ -1,16 +0,0 @@ -requests>=2.30.0 -tqdm>=4.67.1 -pandas>=2.2.2 -numpy>=2.0.0,<3.0.0 -torch>=2.7.0 -torchvision>=0.22.0 -scikit-learn>=1.6.1 -Pillow>=11.1.0 -matplotlib>=3.10.0 -transformers>=5.0.0 -kornia>=0.8.0 -timm>=1.0.15 -einops>=0.8.1 -safetensors>=0.7.0 -shapely>=2.1.1 -opencv-python>=4.11.0.86 diff --git a/test/adv_usage_classification_model_fine_tuning.ipynb b/working/examples/adv_usage_classification_model_fine_tuning/adv_usage_classification_model_fine_tuning.ipynb similarity index 100% rename from test/adv_usage_classification_model_fine_tuning.ipynb rename to working/examples/adv_usage_classification_model_fine_tuning/adv_usage_classification_model_fine_tuning.ipynb diff --git a/test/adv_usage_classification_model_training_evaluation.ipynb b/working/examples/adv_usage_classification_model_training_evaluation/adv_usage_classification_model_training_evaluation.ipynb similarity index 100% rename from test/adv_usage_classification_model_training_evaluation.ipynb rename to working/examples/adv_usage_classification_model_training_evaluation/adv_usage_classification_model_training_evaluation.ipynb diff --git a/test/adv_usage_custom_measurement_instruction.ipynb b/working/examples/adv_usage_custom_measurement_instruction/adv_usage_custom_measurement_instruction.ipynb similarity index 100% rename from test/adv_usage_custom_measurement_instruction.ipynb rename to working/examples/adv_usage_custom_measurement_instruction/adv_usage_custom_measurement_instruction.ipynb diff --git a/test/tutorial_classification.ipynb b/working/examples/tutorial_classification/tutorial_classification.ipynb similarity index 100% rename from test/tutorial_classification.ipynb rename to working/examples/tutorial_classification/tutorial_classification.ipynb diff --git a/test/tutorial_grounding.ipynb b/working/examples/tutorial_grounding/tutorial_grounding.ipynb similarity index 100% rename from test/tutorial_grounding.ipynb rename to working/examples/tutorial_grounding/tutorial_grounding.ipynb diff --git a/test/tutorial_landmark_detection.ipynb b/working/examples/tutorial_landmark_detection/tutorial_landmark_detection.ipynb similarity index 100% rename from test/tutorial_landmark_detection.ipynb rename to working/examples/tutorial_landmark_detection/tutorial_landmark_detection.ipynb diff --git a/test/tutorial_landmark_refinement_and_derivation.ipynb b/working/examples/tutorial_landmark_refinement_and_derivation/tutorial_landmark_refinement_and_derivation.ipynb similarity index 100% rename from test/tutorial_landmark_refinement_and_derivation.ipynb rename to working/examples/tutorial_landmark_refinement_and_derivation/tutorial_landmark_refinement_and_derivation.ipynb diff --git a/test/tutorial_matting.ipynb b/working/examples/tutorial_matting/tutorial_matting.ipynb similarity index 100% rename from test/tutorial_matting.ipynb rename to working/examples/tutorial_matting/tutorial_matting.ipynb diff --git a/test/tutorial_segmentation.ipynb b/working/examples/tutorial_segmentation/tutorial_segmentation.ipynb similarity index 100% rename from test/tutorial_segmentation.ipynb rename to working/examples/tutorial_segmentation/tutorial_segmentation.ipynb diff --git a/test/tutorial_tailor.ipynb b/working/examples/tutorial_tailor/tutorial_tailor.ipynb similarity index 100% rename from test/tutorial_tailor.ipynb rename to working/examples/tutorial_tailor/tutorial_tailor.ipynb diff --git a/working/examples/web_landmark_detection/main.ipynb b/working/examples/web_landmark_detection/main.ipynb new file mode 100644 index 0000000..6b08114 --- /dev/null +++ b/working/examples/web_landmark_detection/main.ipynb @@ -0,0 +1,163 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "835e74a1-b705-4742-9199-f45e48336cf6", + "metadata": {}, + "outputs": [], + "source": [ + "import garmentiq as giq\n", + "from garmentiq.landmark.detection.model_definition import PoseHighResolutionNet\n", + "from garmentiq.garment_classes import garment_classes\n", + "from flask_cors import CORS\n", + "from flask import Flask, request, jsonify\n", + "from PIL import Image\n", + "import numpy as np\n", + "import io\n", + "import cv2\n", + "import base64" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd59761d-ea54-4486-a960-95d1fb020350", + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "mkdir -p ./models\n", + "if [ ! -f ./models/hrnet.pth ]; then\n", + " wget -q -O ./models/hrnet.pth \\\n", + " https://huggingface.co/lygitdata/garmentiq/resolve/main/hrnet.pth\n", + "else\n", + " echo \"Model file already exists, skipping download.\"\n", + "fi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6cec29b-d894-4207-ab07-fe8fcc0a659c", + "metadata": {}, + "outputs": [], + "source": [ + "app = Flask(__name__)\n", + "CORS(app)\n", + "\n", + "# load model once\n", + "print(\"Loading HRNet model…\")\n", + "HRNet = giq.landmark.detection.load_model(\n", + " model_path=\"./models/hrnet.pth\",\n", + " model_class=PoseHighResolutionNet()\n", + ")\n", + "print(\"Model loaded.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9392663-bd9b-4071-a500-ad4ac046d54f", + "metadata": {}, + "outputs": [], + "source": [ + "@app.route('/health')\n", + "def health_check():\n", + " return jsonify({\n", + " \"status\": \"active\",\n", + " \"token\": \"a3f7d2c14e65bb2e8f01a9dc4f6c9823d279f1e05b3a6d74c0987b1c2fae3c65\",\n", + " \"model\": \"web_landmark_detection\",\n", + " \"version\": \"1.0.0\"\n", + " })\n", + "\n", + "@app.route('/landmark_detection', methods=['POST'])\n", + "def landmark_detection():\n", + " files = request.files.getlist('images')\n", + " garment_class = request.form.get('garment_class')\n", + "\n", + " images_np = []\n", + " images_coords = []\n", + "\n", + " # Convert uploaded images to numpy arrays\n", + " for file in files:\n", + " image = Image.open(file.stream).convert('RGB')\n", + " image_np = np.array(image)\n", + " images_np.append(image_np)\n", + "\n", + " # Run detection and collect coordinates\n", + " for img in images_np:\n", + " coords, _, _ = giq.landmark.detect(\n", + " class_name=garment_class,\n", + " class_dict=garment_classes,\n", + " image_path=img,\n", + " model=HRNet,\n", + " scale_std=200.0,\n", + " resize_dim=[288, 384],\n", + " normalize_mean=[0.485, 0.456, 0.406],\n", + " normalize_std=[0.229, 0.224, 0.225]\n", + " )\n", + " images_coords.append(coords)\n", + "\n", + " # Annotate images and encode to base64\n", + " base64_images = []\n", + " for img, coords in zip(images_np, images_coords):\n", + " # Convert RGB numpy array to BGR for OpenCV drawing\n", + " img_annotated = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n", + " height, width = img_annotated.shape[:2]\n", + " \n", + " # Circle radius relative to image size\n", + " radius = max(2, int(round(min(height, width) * 0.0125))) # at least radius 2\n", + " \n", + " for point in coords[0]: # coords is (1, N, 2)\n", + " x, y = int(round(point[0])), int(round(point[1]))\n", + " cv2.circle(img_annotated, (x, y), radius, (0, 255, 0), -1) # green filled circle in BGR\n", + " \n", + " # Convert back to RGB for PIL\n", + " img_annotated_rgb = cv2.cvtColor(img_annotated, cv2.COLOR_BGR2RGB)\n", + " pil_img = Image.fromarray(img_annotated_rgb)\n", + " \n", + " buffered = io.BytesIO()\n", + " pil_img.save(buffered, format=\"PNG\")\n", + " img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')\n", + " base64_images.append(img_str)\n", + "\n", + " return jsonify({\n", + " \"results\": base64_images\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "460f8ce6-82dd-4393-a588-bfbf7466cbe3", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == '__main__':\n", + " app.run(host='0.0.0.0', port=5001)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/working/examples/web_segmentation/main.ipynb b/working/examples/web_segmentation/main.ipynb new file mode 100644 index 0000000..5daf9f6 --- /dev/null +++ b/working/examples/web_segmentation/main.ipynb @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "835e74a1-b705-4742-9199-f45e48336cf6", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import base64\n", + "from flask import Flask, request, jsonify\n", + "from flask_cors import CORS\n", + "import garmentiq as giq\n", + "from garmentiq.segmentation.model_definition.birefnet import BiRefNet, load_birefnet_config\n", + "import shutil" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a549833b", + "metadata": {}, + "outputs": [], + "source": [ + "# Download BiRefNet model\n", + "!mkdir -p ./models/birefnet\n", + "\n", + "!wget -q -O ./models/birefnet/model.safetensors \\\n", + " https://huggingface.co/lygitdata/BiRefNet_garmentiq_backup/resolve/main/model.safetensors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6cec29b-d894-4207-ab07-fe8fcc0a659c", + "metadata": {}, + "outputs": [], + "source": [ + "app = Flask(__name__)\n", + "CORS(app)\n", + "\n", + "# where we put uploads & outputs\n", + "BASE_DIR = 'images'\n", + "INPUT_DIR = os.path.join(BASE_DIR, 'input')\n", + "OUTPUT_DIR = os.path.join(BASE_DIR, 'output')\n", + "os.makedirs(INPUT_DIR, exist_ok=True)\n", + "os.makedirs(OUTPUT_DIR, exist_ok=True)\n", + "\n", + "# load model once\n", + "print(\"Loading BiRefNet model…\")\n", + "birefnet = giq.segmentation.load_model(\n", + " model_class=BiRefNet,\n", + " model_path=\"./models/birefnet/model.safetensors\",\n", + " model_args=load_birefnet_config()\n", + ")\n", + "print(\"Model loaded.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9392663-bd9b-4071-a500-ad4ac046d54f", + "metadata": {}, + "outputs": [], + "source": [ + "@app.route('/health')\n", + "def health_check():\n", + " return jsonify({\n", + " \"status\": \"active\",\n", + " \"token\": \"fe9def684914b305540615b3bba461fbf8ea58f460c72b1fe128d3cef93fe4f8\",\n", + " \"model\": \"web_segmentation\",\n", + " \"version\": \"1.0.0\"\n", + " })\n", + "\n", + "@app.route('/segment', methods=['POST'])\n", + "def segment():\n", + " files = request.files.getlist('images')\n", + " if not files:\n", + " return jsonify(error='No images uploaded'), 400\n", + "\n", + " # Ensure input/output directories are fresh\n", + " for d in (INPUT_DIR, OUTPUT_DIR):\n", + " if os.path.exists(d):\n", + " shutil.rmtree(d)\n", + " os.makedirs(d, exist_ok=True)\n", + "\n", + " # Save uploaded images\n", + " for f in files:\n", + " f.save(os.path.join(INPUT_DIR, f.filename))\n", + "\n", + " # Parse optional RGB values\n", + " r = request.form.get('red', '')\n", + " g = request.form.get('green', '')\n", + " b = request.form.get('blue', '')\n", + " background_color = None\n", + " if r != '' and g != '' and b != '':\n", + " try:\n", + " rgb = [int(r), int(g), int(b)]\n", + " if any(c < 0 or c > 255 for c in rgb):\n", + " raise ValueError\n", + " background_color = rgb\n", + " except ValueError:\n", + " return jsonify(error='Invalid RGB values; must be integers 0–255'), 400\n", + "\n", + " # Prepare processing arguments\n", + " kwargs = {\n", + " 'image_dir': INPUT_DIR,\n", + " 'output_dir': OUTPUT_DIR,\n", + " 'model': birefnet,\n", + " 'resize_dim': (1024, 1024),\n", + " 'normalize_mean': [0.485, 0.456, 0.406],\n", + " 'normalize_std': [0.229, 0.224, 0.225]\n", + " }\n", + " if background_color:\n", + " kwargs['background_color'] = background_color\n", + "\n", + " # Run segmentation\n", + " giq.segmentation.process_and_save_images(**kwargs)\n", + "\n", + " # Collect masks\n", + " masks = []\n", + " masks_folder = os.path.join(OUTPUT_DIR, 'masks')\n", + " if os.path.isdir(masks_folder):\n", + " for fn in sorted(os.listdir(masks_folder)):\n", + " path = os.path.join(masks_folder, fn)\n", + " if os.path.isfile(path):\n", + " with open(path, 'rb') as imgf:\n", + " b64 = base64.b64encode(imgf.read()).decode('utf-8')\n", + " masks.append({'filename': fn, 'base64': b64})\n", + "\n", + " # Collect background-modified images if any\n", + " modified = []\n", + " if background_color:\n", + " mod_folder = os.path.join(OUTPUT_DIR, 'bg_modified')\n", + " if os.path.isdir(mod_folder):\n", + " for fn in sorted(os.listdir(mod_folder)):\n", + " path = os.path.join(mod_folder, fn)\n", + " if os.path.isfile(path):\n", + " with open(path, 'rb') as imgf:\n", + " b64 = base64.b64encode(imgf.read()).decode('utf-8')\n", + " modified.append({'filename': fn, 'base64': b64})\n", + "\n", + " # Return results\n", + " return jsonify(masks=masks, bg_modified=modified)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "460f8ce6-82dd-4393-a588-bfbf7466cbe3", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == '__main__':\n", + " app.run(host='0.0.0.0', port=5000)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/working/examples/web_tailor/main.ipynb b/working/examples/web_tailor/main.ipynb new file mode 100644 index 0000000..e39e738 --- /dev/null +++ b/working/examples/web_tailor/main.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "6d1331b2-f8b8-4ed0-a7c3-1d28785922a9", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import base64\n", + "from flask import Flask, request, jsonify\n", + "from flask_cors import CORS\n", + "import shutil\n", + "import urllib.request\n", + "import pandas as pd\n", + "import garmentiq as giq\n", + "from garmentiq.classification.model_definition import tinyViT\n", + "from garmentiq.landmark.detection.model_definition import PoseHighResolutionNet\n", + "from garmentiq.garment_classes import garment_classes\n", + "from garmentiq.landmark.derivation.derivation_dict import derivation_dict\n", + "from garmentiq.segmentation.model_definition.birefnet import BiRefNet, load_birefnet_config\n", + "\n", + "# Initialize the empty tailer object\n", + "tailor = None\n", + "do_refine = None\n", + "do_derive = None\n", + "background_color = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "286a359e-1014-4113-9d71-24208724573d", + "metadata": {}, + "outputs": [], + "source": [ + "app = Flask(__name__)\n", + "CORS(app)\n", + "\n", + "# where we put uploads & outputs\n", + "BASE_DIR = 'tailor_files'\n", + "INPUT_DIR = os.path.join(BASE_DIR, 'input')\n", + "OUTPUT_DIR = os.path.join(BASE_DIR, 'output')\n", + "MODELS_DIR = os.path.join(BASE_DIR, 'models')\n", + "os.makedirs(INPUT_DIR, exist_ok=True)\n", + "os.makedirs(OUTPUT_DIR, exist_ok=True)\n", + "os.makedirs(MODELS_DIR, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59917ee5-62f3-47f0-a953-d4bec64db55e", + "metadata": {}, + "outputs": [], + "source": [ + "# Download the classification model\n", + "# Define model URLs\n", + "classification_model_url = \"https://huggingface.co/lygitdata/garmentiq/resolve/main/tiny_vit_inditex_finetuned.pt\"\n", + "landmark_model_url = \"https://huggingface.co/lygitdata/garmentiq/resolve/main/hrnet.pth\"\n", + "segmentation_model_url = \"https://huggingface.co/lygitdata/BiRefNet_garmentiq_backup/resolve/main/model.safetensors\"\n", + "\n", + "# Define local file paths using MODELS_DIR\n", + "classification_model_path = os.path.join(MODELS_DIR, \"tiny_vit_inditex_finetuned.pt\")\n", + "landmark_model_path = os.path.join(MODELS_DIR, \"hrnet.pth\")\n", + "segmentation_model_path = os.path.join(MODELS_DIR, \"model.safetensors\")\n", + "\n", + "# Function to download only if file doesn't exist\n", + "def download_if_missing(url, destination_path):\n", + " if not os.path.exists(destination_path):\n", + " print(f\"Downloading to {destination_path}...\")\n", + " urllib.request.urlretrieve(url, destination_path)\n", + " print(\"Download complete.\")\n", + " else:\n", + " print(f\"File already exists: {destination_path}. Skipping download.\")\n", + "\n", + "# Perform conditional downloads\n", + "download_if_missing(classification_model_url, classification_model_path)\n", + "download_if_missing(landmark_model_url, landmark_model_path)\n", + "download_if_missing(segmentation_model_url, segmentation_model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8877cadd-1ffd-4ce4-9017-c2f77a8dc750", + "metadata": {}, + "outputs": [], + "source": [ + "def encode_image_to_base64(path):\n", + " with open(path, \"rb\") as img_file:\n", + " return base64.b64encode(img_file.read()).decode('utf-8')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4232f4e-3b49-4ee2-b2d9-ad754c292e24", + "metadata": {}, + "outputs": [], + "source": [ + "@app.route('/health')\n", + "def health_check():\n", + " return jsonify({\n", + " \"status\": \"active\",\n", + " \"token\": \"31f21a4323cf148339669c736f522cf89fa570a2bffc80ba874078477b76f81b\",\n", + " \"model\": \"web_tailor\",\n", + " \"version\": \"1.0.0\"\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd94aa86-b570-4809-9078-0499750c40bb", + "metadata": {}, + "outputs": [], + "source": [ + "@app.route('/setup', methods=['POST'])\n", + "def setup():\n", + " global tailor\n", + " global do_refine\n", + " global do_derive\n", + " global background_color\n", + "\n", + " do_refine = request.form.get('do_refine') == 'true'\n", + " do_derive = request.form.get('do_derive') == 'true'\n", + " r = request.form.get('red', '')\n", + " g = request.form.get('green', '')\n", + " b = request.form.get('blue', '')\n", + " background_color = None\n", + "\n", + " if r and g and b:\n", + " try:\n", + " rgb = [int(r), int(g), int(b)]\n", + " if any(c < 0 or c > 255 for c in rgb):\n", + " raise ValueError\n", + " background_color = rgb\n", + " except ValueError:\n", + " return jsonify(error='Invalid RGB values; must be integers 0–255'), 400\n", + "\n", + " # Setup the tailor agent\n", + " tailor = giq.tailor(\n", + " input_dir=INPUT_DIR,\n", + " model_dir=MODELS_DIR,\n", + " output_dir=OUTPUT_DIR,\n", + " class_dict=garment_classes,\n", + " do_derive=do_derive,\n", + " derivation_dict=derivation_dict,\n", + " do_refine=do_refine,\n", + " classification_model_path=\"tiny_vit_inditex_finetuned.pt\",\n", + " classification_model_class=tinyViT,\n", + " classification_model_args={\n", + " \"num_classes\": len(garment_classes),\n", + " \"img_size\": (120, 184),\n", + " \"patch_size\": 6,\n", + " \"resize_dim\": (120, 184),\n", + " \"normalize_mean\": [0.8047, 0.7808, 0.7769],\n", + " \"normalize_std\": [0.2957, 0.3077, 0.3081],\n", + " },\n", + " segmentation_model_path=\"model.safetensors\",\n", + " segmentation_model_class=BiRefNet,\n", + " segmentation_model_args={\n", + " \"model_config\": load_birefnet_config(),\n", + " \"resize_dim\": (1024, 1024),\n", + " \"normalize_mean\": [0.485, 0.456, 0.406],\n", + " \"normalize_std\": [0.229, 0.224, 0.225],\n", + " \"background_color\": [102, 255, 102]\n", + " },\n", + " landmark_detection_model_path=\"hrnet.pth\",\n", + " landmark_detection_model_class=PoseHighResolutionNet(),\n", + " landmark_detection_model_args={\n", + " \"scale_std\": 200.0,\n", + " \"resize_dim\": [288, 384],\n", + " \"normalize_mean\": [0.485, 0.456, 0.406],\n", + " \"normalize_std\": [0.229, 0.224, 0.225],\n", + " },\n", + " )\n", + "\n", + " return jsonify(message='Tailor setup complete'), 200" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "233bec33-c6a0-4542-819c-72fe4f873f0c", + "metadata": {}, + "outputs": [], + "source": [ + "@app.route('/measure', methods=['POST'])\n", + "def measure():\n", + " global tailor, do_refine, do_derive, background_color\n", + " \n", + " files = request.files.getlist('images')\n", + " if not files:\n", + " return jsonify(error='No images uploaded'), 400\n", + "\n", + " # Ensure input/output directories are fresh\n", + " for d in (INPUT_DIR, OUTPUT_DIR):\n", + " if os.path.exists(d):\n", + " shutil.rmtree(d)\n", + " os.makedirs(d, exist_ok=True)\n", + "\n", + " # Save uploaded images\n", + " for f in files:\n", + " f.save(os.path.join(INPUT_DIR, f.filename))\n", + "\n", + " # Run segmentation & measurement\n", + " metadata, _ = tailor.measure(save_segmentation_image=True, save_measurement_image=True)\n", + " metadata = metadata.sort_values(by='filename', ascending=True)\n", + "\n", + " # Structure results\n", + " results = []\n", + " for idx, row in metadata.iterrows():\n", + " filename = row['filename']\n", + " \n", + " # Read & encode the JSON contents\n", + " json_b64 = None\n", + " json_path = row.get(\"measurement_json\")\n", + " if json_path:\n", + " with open(json_path, 'r') as jf:\n", + " text = jf.read()\n", + " json_b64 = base64.b64encode(text.encode('utf-8')).decode('utf-8')\n", + " \n", + " entry = {\n", + " \"Image name\": filename,\n", + " \"Class\": row.get(\"class\", None),\n", + " \"Measurement image\": encode_image_to_base64(row[\"measurement_image\"]),\n", + " \"Measurement JSON (base64)\": json_b64,\n", + " }\n", + " \n", + " if row.get(\"mask_image\"):\n", + " entry[\"Mask\"] = encode_image_to_base64(row[\"mask_image\"])\n", + " \n", + " if row.get(\"bg_modified_image\"):\n", + " entry[\"Background modified\"] = encode_image_to_base64(row[\"bg_modified_image\"])\n", + " \n", + " results.append(entry)\n", + " \n", + " return jsonify(results), 200" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "677539e0-33bb-4dcd-b976-5937730e4b9b", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == '__main__':\n", + " app.run(host='0.0.0.0', port=5002)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}